From 7bcc06c5413d809a3d8638aefe3f248cd8f3661a Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 5 Jul 2024 16:35:58 +0200 Subject: [PATCH 01/96] added notebooks for 1loop system --- docs/notebooks/enYNF3By | Bin 0 -> 32 bytes docs/notebooks/enrIutMf | Bin 0 -> 32 bytes docs/notebooks/qubo_Net1Loops.ipynb | 364 +++++++++++++++++++++ docs/notebooks/temp.bin | Bin 0 -> 1604 bytes docs/notebooks/temp.inp | 132 ++++++++ docs/notebooks/temp.rpt | 28 ++ docs/notebooks/vqls_Net1Loops.ipynb | 353 ++++++++++++++++++++ docs/notebooks/vqls_solver_Net1Loops.ipynb | 304 +++++++++++++++++ 8 files changed, 1181 insertions(+) create mode 100644 docs/notebooks/enYNF3By create mode 100644 docs/notebooks/enrIutMf create mode 100644 docs/notebooks/qubo_Net1Loops.ipynb create mode 100644 docs/notebooks/temp.bin create mode 100644 docs/notebooks/temp.inp create mode 100644 docs/notebooks/temp.rpt create mode 100644 docs/notebooks/vqls_Net1Loops.ipynb create mode 100644 docs/notebooks/vqls_solver_Net1Loops.ipynb diff --git a/docs/notebooks/enYNF3By b/docs/notebooks/enYNF3By new file mode 100644 index 0000000000000000000000000000000000000000..6d3396e97ef31aba269614c7f76fb2d09b201538 GIT binary patch literal 32 Wcma#_I4pOPfq{V)iWz}4G5`QYAOcqa literal 0 HcmV?d00001 diff --git a/docs/notebooks/enrIutMf b/docs/notebooks/enrIutMf new file mode 100644 index 0000000000000000000000000000000000000000..6d3396e97ef31aba269614c7f76fb2d09b201538 GIT binary patch literal 32 Wcma#_I4pOPfq{V)iWz}4G5`QYAOcqa literal 0 HcmV?d00001 diff --git a/docs/notebooks/qubo_Net1Loops.ipynb b/docs/notebooks/qubo_Net1Loops.ipynb new file mode 100644 index 0000000..dd9c5de --- /dev/null +++ b/docs/notebooks/qubo_Net1Loops.ipynb @@ -0,0 +1,364 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up water network model\n", + "\n", + "In this example, we test our quantum solvers into a slightly larger network as contained in `Net1Loops.inp`. Let's start by setting up the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import wntr\n", + "import wntr_quantum\n", + "\n", + "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", + "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", + "\n", + "# set up network model\n", + "inp_file = 'networks/Net1Loops.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# plot network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n", + "\n", + "# print options\n", + "# dict(wn.options.hydraulic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve model using the classical WNTR simulator\n", + "\n", + "For comparison, we will start with by solving the network model with the classical WNTR simulator." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Size of the Jacobian in WNTR simulator: 9\n", + "Size of the b vector in WNTR simulator: 9\n" + ] + }, + { + "data": { + "text/plain": [ + "( 2 3 4 5 1\n", + " 0 57.93989 31.496198 52.434498 21.174181 0.0,\n", + " 1 2 3 4 5\n", + " 0 0.16387 0.059455 0.076645 0.043315 0.031685)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# define the classical WNTR simulator\n", + "sim = wntr.sim.WNTRSimulator(wn)\n", + "\n", + "# run WNTR simulator\n", + "results_wntr = sim.run_sim()\n", + "\n", + "# set A and b matrices\n", + "wntr_A = sim._model.evaluate_jacobian()\n", + "wntr_b = sim._model.evaluate_residuals()\n", + "\n", + "# set the size of the Jacobian (A matrix)\n", + "wntr_A_dim = wntr_A.shape[0]\n", + "print(f\"Size of the Jacobian in WNTR simulator: {wntr_A_dim}\")\n", + "print(f\"Size of the b vector in WNTR simulator: {wntr_b.shape[0]}\")\n", + "\n", + "results_wntr.node[\"pressure\"], results_wntr.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve model using the classical Epanet simulator\n", + "\n", + "We now solve the same problem using the classical Epanet simulator. Note that, by default, `QuantumEpanetSimulator` uses a classical `CholeskySolver` to iteratively solve the linear problem." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", + "Your EPANET temp dir: /home/nico/.epanet_quantum\n", + "\n", + "Size of the Jacobian in EPANET simulator: 4\n", + "Size of the b vector in EPANET simulator: 4\n" + ] + }, + { + "data": { + "text/plain": [ + "(name 2 3 4 5 1\n", + " 0 57.939995 31.496479 52.434612 21.174667 4.394531e-07,\n", + " name 1 2 3 4 5\n", + " 0 0.163867 0.059455 0.076645 0.043315 0.031685)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "\n", + "# define the classical EPANET simulator\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", + "\n", + "# run the EPANET simulation\n", + "results_epanet = sim.run_sim()\n", + "\n", + "# remember to set up EPANET Quantum environment variables!\n", + "epanet_path = os.environ[\"EPANET_QUANTUM\"]\n", + "epanet_tmp = os.environ[\"EPANET_TMP\"]\n", + "\n", + "# check paths\n", + "print(f\"Your EPANET quantum path: {epanet_path}\")\n", + "print(f\"Your EPANET temp dir: {epanet_tmp}\\n\")\n", + "\n", + "util_path = os.path.join(epanet_path, 'src/py/')\n", + "sys.path.append(util_path)\n", + "\n", + "from quantum_linsolve import load_json_data\n", + "epanet_A, epanet_b = load_json_data(os.path.join(epanet_tmp,'smat.json'))\n", + "\n", + "# set the size of the Jacobian (A matrix)\n", + "epanet_A_dim = epanet_A.todense().shape[0]\n", + "print(f\"Size of the Jacobian in EPANET simulator: {epanet_A_dim}\")\n", + "print(f\"Size of the b vector in EPANET simulator: {epanet_b.shape[0]}\")\n", + "\n", + "# save number of nodes and pipes\n", + "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", + "n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", + "\n", + "results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a helper function\n", + "\n", + "This function checks that the quantum results are within `TOL`% of those obtained classically. It also fills in lists containing the final values of pressures and flow rates obtained." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "TOL = 5 # => per cent\n", + "DELTA = 1.0e-12\n", + "\n", + "classical_res = []\n", + "quantum_res = []\n", + "\n", + "\n", + "def compare_results(classical_result, quantum_result):\n", + " \"\"\"\n", + " Helper function that compares the classical and quantum simulation results.\n", + " \"\"\"\n", + " def calculate_differences(classical_value, quantum_value):\n", + " \"\"\"Helper function to evaluate percentage difference between classical and quantum results.\"\"\"\n", + " is_close_to_classical = abs(classical_value - quantum_value) / abs(classical_value + DELTA) <= TOL / 100.0\n", + " if is_close_to_classical:\n", + " print(f\"Quantum result {quantum_value} within {TOL}% of classical result {classical_value}\")\n", + " quantum_res.append(quantum_value)\n", + " classical_res.append(classical_value)\n", + " return is_close_to_classical\n", + " \n", + " for link in classical_result.link[\"flowrate\"].columns:\n", + " classical_value = classical_result.link[\"flowrate\"][link].iloc[0]\n", + " quantum_value = quantum_result.link[\"flowrate\"][link].iloc[0]\n", + " message = f\"Flowrate {link}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert calculate_differences(classical_value, quantum_value), message\n", + "\n", + " for node in classical_result.node[\"pressure\"].columns:\n", + " classical_value = classical_result.node[\"pressure\"][node].iloc[0]\n", + " quantum_value = quantum_result.node[\"pressure\"][node].iloc[0]\n", + " message= f\"Pressure {node}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert calculate_differences(classical_value, quantum_value), message" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve model using QUBO solver (with daptative encoding)\n", + "\n", + "Finally, we solve the problem once again but now using the QUBO quantum linear solver. The results are validated using the `compare_results` helper function defined above." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'encoding': , 'num_qbits': 15, 'num_reads': 100, 'range': 200, 'offset': 600, 'iterations': 5, 'temperature': 10000.0}\n", + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "0 [600, 600, 600, 600] 200\n", + "1 [653.1282426905939, 675.0045779161326, 685.7962522126595, 684.3679423792956] [162.19783659021755, 147.89105361858333, 42.720160646958185, 63.89584840114656]\n", + "2 [644.009997711067, 666.5642351746703, 685.0869875055868, 683.5021132671028] [75.45597702856479, 50.57205374895429, 10.046036109323007, 15.040310664376154]\n", + "3 [644.0140024853814, 666.5516469026392, 685.076977352827, 683.4870178369654] [19.896996994125942, 13.32363024990175, 2.6333150952487467, 3.931600327637771]\n", + "4 [643.992959499734, 666.5427107763383, 685.0757495165467, 683.4848733908677] [5.223353430776938, 3.5236692804443814, 0.7094092503981015, 1.0570316100466943]\n", + "0 [600, 600, 600, 600] 200\n", + "1 [529.6832082036257, 599.9877922236465, 681.3037905145578, 679.6923640358908] [187.54031694899425, 184.01251352913064, 65.40813181134943, 82.9597572578687]\n", + "2 [553.6967849160075, 622.9756136853267, 681.5193822326687, 679.687300265076] [141.86200934793072, 119.2793266759492, 21.205113278968682, 26.81021946736571]\n", + "3 [562.5637016930114, 628.3269110726221, 682.1859654797871, 680.5235342970886] [86.45389220085569, 55.73149486850337, 7.356822855631101, 9.241251388366091]\n", + "4 [562.2824209600006, 628.292107253294, 682.1951163769494, 680.5351439694014] [31.029823241857912, 19.81284495667869, 2.552368135283411, 3.191619202770022]\n", + "0 [600, 600, 600, 600] 200\n", + "1 [587.4870292376244, 645.7547457730575, 684.3801501556492, 683.2082036257095] [177.77841909256463, 173.4733615301793, 64.0277190784752, 81.64108121625112]\n", + "2 [582.1155882652249, 645.7547457730575, 683.1217160750004, 681.5687044057775] [138.88375755481923, 117.57533384594275, 20.95137784333773, 26.428166308749084]\n", + "3 [564.7406843999353, 631.056931958952, 682.4639443033918, 680.8501785757319] [85.44204634141676, 55.44852844133298, 7.290151970627268, 9.11709602214348]\n", + "4 [564.6847830660753, 630.1832891650187, 682.4501577380078, 680.8500849843596] [35.115707539297674, 22.39280842457061, 2.8746442515343262, 3.5854321211566336]\n", + "Quantum result 0.1638716608285904 within 5% of classical result 0.16386687755584717\n", + "Quantum result 0.0594572015106678 within 5% of classical result 0.05945523828268051\n", + "Quantum result 0.07664962112903595 within 5% of classical result 0.07664506137371063\n", + "Quantum result 0.04331611469388008 within 5% of classical result 0.043314848095178604\n", + "Quantum result 0.03168607875704765 within 5% of classical result 0.03168521821498871\n", + "Quantum result 57.939884185791016 within 5% of classical result 57.93999481201172\n", + "Quantum result 31.495363235473633 within 5% of classical result 31.496479034423828\n", + "Quantum result 52.434444427490234 within 5% of classical result 52.43461227416992\n", + "Quantum result 21.172529220581055 within 5% of classical result 21.174667358398438\n", + "Quantum result 4.39453117451194e-07 within 5% of classical result 4.39453117451194e-07\n" + ] + } + ], + "source": [ + "\n", + "from quantum_newton_raphson.qubo_solver import QUBO_SOLVER\n", + "from qubols.encodings import RangedEfficientEncoding\n", + "\n", + "linear_solver = QUBO_SOLVER(\n", + " encoding=RangedEfficientEncoding,\n", + " num_qbits=15,\n", + " num_reads=100,\n", + " range=200,\n", + " offset=600,\n", + " iterations=5,\n", + " temperature=1e4,\n", + " use_aequbols=True,\n", + ")\n", + "\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn, linear_solver=linear_solver)\n", + "results_qubos = sim.run_sim(linear_solver=linear_solver)\n", + "\n", + "# check that the results are within a certain tolerance from the classical epanet\n", + "compare_results(results_epanet, results_qubos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot pressures and flow rates\n", + "\n", + "Let's check graphically the equivalence of the results." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.scatter(classical_res[:n_pipes], quantum_res[:n_pipes], label=\"Flow rates\", color=\"blue\", marker=\"o\")\n", + "plt.scatter(classical_res[n_pipes:], quantum_res[n_pipes:], label=\"Pressures\", color=\"red\", marker=\"s\", facecolors='none')\n", + "plt.axline((0, 0), slope=1, linestyle=\"--\", color=\"gray\", label=\"\")\n", + "plt.xlabel(\"Classical results\")\n", + "plt.ylabel(\"Quantum results\")\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/temp.bin b/docs/notebooks/temp.bin new file mode 100644 index 0000000000000000000000000000000000000000..643ca7da768157798084ebb9b49eda56d8f8bc30 GIT binary patch literal 1604 zcma#_I4q~*$H2hKz`(!=#2^|5KztAYi7|m$5JG?tLKkNw=4KWt=;|ss6(y!8=Hz7R zD7dIQ<|Y;87iZ?{DEOqN0*U+-pm0cjNrr-kk%57QCXzuzj9HSJTcDSjS1`n!J?dO4 zhk$cNPJU5lUMenQbJO*GaLIwhjqphV<%|i)n-GvUB_MA|q5MF*A68a@$|q(Z29-@9 zHi!oCS)dpshK>>Co0u~Lg8~ps12Kpl1jJP?=y=x5CqQv;XNCq4EeXX84Bs^FIfC$; zKvpLPhG|Z!_mvON`QYz-q0ZjfBH!OxTSptHHpsc~P`Xp&x8IKU_XIkHC$4n-_4YN^ z=mY9^*13}FG}~vU(`IK~C$ZYUjxO!>_F*co?W-N$+buXD<`6XVj{V0Q%npft&5lJW zjP@oxf=*gf7dZmGi3K<~FfgEFCZL" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import wntr\n", + "import wntr_quantum\n", + "\n", + "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", + "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", + "# set up network model\n", + "inp_file = 'networks/Net1Loops.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# plot network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n", + "\n", + "# print options\n", + "# dict(wn.options.hydraulic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve model using the classical Epanet simulator\n", + "\n", + "We now solve the same problem using the classical Epanet simulator. Note that, by default, `QuantumEpanetSimulator` uses a classical `CholeskySolver` to iteratively solve the linear problem." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", + "Your EPANET temp dir: /home/nico/.epanet_quantum\n", + "\n", + "Size of the Jacobian in EPANET simulator: 4\n", + "Size of the b vector in EPANET simulator: 4\n" + ] + }, + { + "data": { + "text/plain": [ + "(name 2 3 4 5 1\n", + " 0 57.939995 31.496479 52.434612 21.174667 4.394531e-07,\n", + " name 1 2 3 4 5\n", + " 0 0.163867 0.059455 0.076645 0.043315 0.031685)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "\n", + "# define the classical EPANET simulator\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", + "\n", + "# run the EPANET simulation\n", + "results_epanet = sim.run_sim()\n", + "\n", + "# remember to set up EPANET Quantum environment variables!\n", + "epanet_path = os.environ[\"EPANET_QUANTUM\"]\n", + "epanet_tmp = os.environ[\"EPANET_TMP\"]\n", + "\n", + "# check paths\n", + "print(f\"Your EPANET quantum path: {epanet_path}\")\n", + "print(f\"Your EPANET temp dir: {epanet_tmp}\\n\")\n", + "\n", + "util_path = os.path.join(epanet_path, 'src/py/')\n", + "sys.path.append(util_path)\n", + "\n", + "from quantum_linsolve import load_json_data\n", + "epanet_A, epanet_b = load_json_data(os.path.join(epanet_tmp,'smat.json'))\n", + "\n", + "# set the size of the Jacobian (A matrix)\n", + "epanet_A_dim = epanet_A.todense().shape[0]\n", + "print(f\"Size of the Jacobian in EPANET simulator: {epanet_A_dim}\")\n", + "print(f\"Size of the b vector in EPANET simulator: {epanet_b.shape[0]}\")\n", + "\n", + "# save number of nodes and pipes\n", + "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", + "n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", + "\n", + "results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a helper function\n", + "\n", + "Before proceeding to the proper quantum solution of the water network model, let's define a helper function. This function checks that the quantum results are within `TOL`% of those obtained classically. It also fills in lists containing the final values of pressures and flow rates obtained." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "TOL = 5 # => per cent\n", + "DELTA = 1.0e-12\n", + "\n", + "classical_res = []\n", + "quantum_res = []\n", + "\n", + "\n", + "def compare_results(classical_result, quantum_result):\n", + " \"\"\"\n", + " Helper function that compares the classical and quantum simulation results.\n", + " \"\"\"\n", + " def calculate_differences(classical_value, quantum_value):\n", + " \"\"\"Helper function to evaluate percentage difference between classical and quantum results.\"\"\"\n", + " is_close_to_classical = abs(classical_value - quantum_value) / abs(classical_value + DELTA) <= TOL / 100.0\n", + " if is_close_to_classical:\n", + " print(f\"Quantum result {quantum_value} within {TOL}% of classical result {classical_value}\")\n", + " quantum_res.append(quantum_value)\n", + " classical_res.append(classical_value)\n", + " return is_close_to_classical\n", + " \n", + " for link in classical_result.link[\"flowrate\"].columns:\n", + " classical_value = classical_result.link[\"flowrate\"][link].iloc[0]\n", + " quantum_value = quantum_result.link[\"flowrate\"][link].iloc[0]\n", + " message = f\"Flowrate {link}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert calculate_differences(classical_value, quantum_value), message\n", + "\n", + " for node in classical_result.node[\"pressure\"].columns:\n", + " classical_value = classical_result.node[\"pressure\"][node].iloc[0]\n", + " quantum_value = quantum_result.node[\"pressure\"][node].iloc[0]\n", + " message= f\"Pressure {node}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert calculate_differences(classical_value, quantum_value), message" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve water network with `QuantumEpanetSimulator` and VQLS \n", + "\n", + "We now solve the model using VQLS. In this example, we are **preconditioning** the initial linear system using *diagonal scaling* and also using a **mix of two classical optimizers**." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "VQLS Iteration 126 Cost 3.675e-08\n", + " Normal return from subroutine COBYLA\n", + "\n", + " NFVALS = 126 F = 3.674985E-08 MAXCV = 0.000000E+00\n", + " X = 1.191354E-01 -1.466038E+00 7.740023E-01 -3.243196E-01 7.724003E-01\n", + " 6.778685E-02 1.367636E+00 -1.893541E+00\n", + "Optimization terminated successfully.\n", + " Current function value: 0.000000\n", + " Iterations: 3\n", + " Function evaluations: 63\n", + " Gradient evaluations: 7\n", + "VQLS Iteration 133 Cost 1.504e-07\n", + " Normal return from subroutine COBYLA\n", + "\n", + " NFVALS = 133 F = 1.503901E-07 MAXCV = 0.000000E+00\n", + " X = 4.679995E+00 2.867229E+00 9.799046E-01 -2.856677E+00 1.070778E+00\n", + " -7.620220E-01 -2.741235E+00 -1.044200E+00\n", + "Optimization terminated successfully.\n", + " Current function value: 0.000000\n", + " Iterations: 3\n", + " Function evaluations: 54\n", + " Gradient evaluations: 6\n", + "VQLS Iteration 149 Cost 1.424e-07\n", + " Normal return from subroutine COBYLA\n", + "\n", + " NFVALS = 149 F = 1.423865E-07 MAXCV = 0.000000E+00\n", + " X =-4.624011E-01 -1.708293E+00 8.099204E-01 -3.450876E+00 1.509861E+00\n", + " -2.344735E+00 3.965682E+00 -1.108613E+00\n", + "Optimization terminated successfully.\n", + " Current function value: 0.000000\n", + " Iterations: 4\n", + " Function evaluations: 72\n", + " Gradient evaluations: 8\n", + "Quantum result 0.1638660728931427 within 5% of classical result 0.16386687755584717\n", + "Quantum result 0.059451714158058167 within 5% of classical result 0.05945523828268051\n", + "Quantum result 0.07663743942975998 within 5% of classical result 0.07664506137371063\n", + "Quantum result 0.043313007801771164 within 5% of classical result 0.043314848095178604\n", + "Quantum result 0.0316842645406723 within 5% of classical result 0.03168521821498871\n", + "Quantum result 57.94001388549805 within 5% of classical result 57.93999481201172\n", + "Quantum result 31.498302459716797 within 5% of classical result 31.496479034423828\n", + "Quantum result 52.434722900390625 within 5% of classical result 52.43461227416992\n", + "Quantum result 21.17762565612793 within 5% of classical result 21.174667358398438\n", + "Quantum result 4.39453117451194e-07 within 5% of classical result 4.39453117451194e-07\n" + ] + }, + { + "data": { + "text/plain": [ + "(name 2 3 4 5 1\n", + " 0 57.940014 31.498302 52.434723 21.177626 4.394531e-07,\n", + " name 1 2 3 4 5\n", + " 0 0.163866 0.059452 0.076637 0.043313 0.031684)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "from qiskit.circuit.library import RealAmplitudes\n", + "from qiskit.primitives import Estimator\n", + "from qiskit_algorithms import optimizers as opt\n", + "\n", + "from quantum_newton_raphson.vqls_solver import VQLS_SOLVER\n", + "\n", + "n_qubits = int(np.ceil(np.log2(epanet_A_dim)))\n", + "\n", + "qc = RealAmplitudes(n_qubits, reps=3, entanglement=\"full\")\n", + "estimator = Estimator()\n", + "\n", + "linear_solver = VQLS_SOLVER(\n", + " estimator=estimator,\n", + " ansatz=qc,\n", + " optimizer=[opt.COBYLA(maxiter=1000, disp=True), opt.CG(maxiter=500, disp=True)],\n", + " matrix_decomposition=\"symmetric\",\n", + " verbose=True,\n", + " preconditioner=\"diagonal_scaling\",\n", + ")\n", + "\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn, linear_solver=linear_solver)\n", + "results_vqls = sim.run_sim(linear_solver=linear_solver)\n", + "\n", + "compare_results(results_epanet, results_vqls)\n", + "\n", + "results_vqls.node[\"pressure\"], results_vqls.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot pressures and flow rates\n", + "\n", + "Let's check graphically the equivalence of the results." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.scatter(classical_res[:n_pipes], quantum_res[:n_pipes], label=\"Flow rates\", color=\"blue\", marker=\"o\")\n", + "plt.scatter(classical_res[n_pipes:], quantum_res[n_pipes:], label=\"Pressures\", color=\"red\", marker=\"s\", facecolors='none')\n", + "plt.axline((0, 0), slope=1, linestyle=\"--\", color=\"gray\", label=\"\")\n", + "plt.xlabel(\"Classical results\")\n", + "plt.ylabel(\"Quantum results\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/vqls_solver_Net1Loops.ipynb b/docs/notebooks/vqls_solver_Net1Loops.ipynb new file mode 100644 index 0000000..dde72de --- /dev/null +++ b/docs/notebooks/vqls_solver_Net1Loops.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up water network model\n", + "\n", + "In this example, we test our quantum solvers into a slightly larger network as contained in `Net1Loops.inp`. Let's start by setting up the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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49tlnm3oqUOKcAWjmKisr4/rrrxcCwKfGOQPQzJ188slNPQVgD+fIAAAk55wBAEjOkQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACS3R8XApk2bYsCAAdG3b9/o06dPzJkzp6mnBM3aq6++GkOGDInDDjssjjjiiLjrrruaekrQrJ155pnRqVOnOOuss5p6Ko1qj4qBli1bxnnnnRfHHXdcTJw4MaZNmxYbN25s6mlBs7V9+/YYOHBgDB48OM4555z4zne+E1u2bGnqaUGzNWnSpBg7dmysXLkyZs2aFTU1NU09pUZRVhRF0dSTaAw1NTVRVVUV1dXVpbFWrVrFmjVrolu3bk04M2ie6nvPtG3bNlatWhUHH3xwE84Mmqf63jP9+vWL5cuXR0VFRRPO7H9X3tQTaCxz586t9R8o4t9/9cyePTtGjRrVRLOC5mvBggV13jNbt26NX//6194zUI/63jPV1dUxb968mDhxYhPNqnHsMUcGJk+eHDfffHNTTwOAZCZPnhw33XRTU0/jf7LHHBno27dvveOjRo2KKVOmfLaTgc+BBQsWxFVXXVVnfMqUKY4MQD12957Z3efP58kec2SgpqYmjj322HjmmWdKY23atIlly5bF0Ucf3YQzg+appqYmBg4cGCtXriyN7Snff8KnYU8+Z2CPuZqgoqIibrzxxujatWtERHTt2jVmzJghBGA3KioqYsaMGaXbXbt2jR07dsTatWubcFbQfFVUVESHDh2iffv2UV5eHpWVlfHzn//8cx8CEXvQkYFdqquro3///vHkk09Gv379mno60Ox5zwB7zJEBAOC/IwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACS3x8XAyJEjIyJizJgxceKJJzbxbKD527p1a0REXHvttU08E6Cp7FExUFNTEzU1NRERcdZZZ8XixYubeEbQvNXU1MQll1wSERFr1qwpvX+AXMqKoiiaehKNoaamJqqqqqK6uro01q9fv1i+fHlUVFQ04cygeaqpqYmjjjoqnn/++dKY9wzkVN7UE2gsc+fOrRUCERHV1dVx1VVXxahRo5poVtB8LViwoFYIRPz7PTNv3ryYOHFiE80KaAp7TAysWrWq3vFp06bFtGnTPtvJwOfY7t5LwJ5rj4mBvn371js+cuTIuOKKKz7bycDnwIUXXhh//vOf64zv7r0E7Ln2qHMGjj/++HjqqadKY23bto37778/qqqqmnBm0Dw5zwbYZY+JgYiI5557Lr785S/H1q1bo3Xr1vHd7343fvCDHzT1tKDZqqmpiXnz5sUdd9wR5eXl8bvf/U4IQEJ7VAwAAJ/cHvXvDAAAn5wYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJ/T8qcf1qNCsKYAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import wntr\n", + "import wntr_quantum\n", + "\n", + "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", + "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", + "\n", + "# set up network model\n", + "inp_file = 'networks/Net1Loops.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# plot network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n", + "\n", + "# print options\n", + "# dict(wn.options.hydraulic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve model using the classical Epanet simulator\n", + "\n", + "We now solve the same problem using the classical Epanet simulator. Note that, by default, `QuantumEpanetSimulator` uses a classical `CholeskySolver` to iteratively solve the linear problem." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", + "Your EPANET temp dir: /home/nico/.epanet_quantum\n", + "\n", + "Size of the Jacobian in EPANET simulator: 4\n", + "Size of the b vector in EPANET simulator: 4\n" + ] + }, + { + "data": { + "text/plain": [ + "(name 2 3 4 5 1\n", + " 0 57.939995 31.496479 52.434612 21.174667 4.394531e-07,\n", + " name 1 2 3 4 5\n", + " 0 0.163867 0.059455 0.076645 0.043315 0.031685)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "\n", + "# define the classical EPANET simulator\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", + "\n", + "# run the EPANET simulation\n", + "results_epanet = sim.run_sim()\n", + "\n", + "# remember to set up EPANET Quantum environment variables!\n", + "epanet_path = os.environ[\"EPANET_QUANTUM\"]\n", + "epanet_tmp = os.environ[\"EPANET_TMP\"]\n", + "\n", + "# check paths\n", + "print(f\"Your EPANET quantum path: {epanet_path}\")\n", + "print(f\"Your EPANET temp dir: {epanet_tmp}\\n\")\n", + "\n", + "util_path = os.path.join(epanet_path, 'src/py/')\n", + "sys.path.append(util_path)\n", + "\n", + "from quantum_linsolve import load_json_data\n", + "epanet_A, epanet_b = load_json_data(os.path.join(epanet_tmp,'smat.json'))\n", + "\n", + "# set the size of the Jacobian (A matrix)\n", + "epanet_A_dim = epanet_A.todense().shape[0]\n", + "print(f\"Size of the Jacobian in EPANET simulator: {epanet_A_dim}\")\n", + "print(f\"Size of the b vector in EPANET simulator: {epanet_b.shape[0]}\")\n", + "\n", + "# save number of nodes and pipes\n", + "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", + "n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", + "\n", + "results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve linear system with VQLS and the final matrices from EPANET\n", + "\n", + "For testing purposes, we start by solving the linear system with VQLS and the final A and b matrices from the classical EPANET simulator. Here, we are **preconditioning** the initial linear system using diagonal scaling and also using a **mix of two classical optimizers**." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VQLS Iteration 136 Cost 5.066e-08\n", + " Normal return from subroutine COBYLA\n", + "\n", + " NFVALS = 136 F = 5.066030E-08 MAXCV = 0.000000E+00\n", + " X =-1.347440E-02 -8.807702E-01 1.307708E+00 2.989201E+00 3.526457E+00\n", + " -2.800616E-01 2.854411E+00 1.854796E+00\n", + "Optimization terminated successfully.\n", + " Current function value: 0.000000\n", + " Iterations: 3\n", + " Function evaluations: 63\n", + " Gradient evaluations: 7\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "from qiskit.circuit.library import RealAmplitudes\n", + "from qiskit.primitives import Estimator\n", + "from qiskit_algorithms import optimizers as opt\n", + "\n", + "from quantum_newton_raphson.vqls_solver import VQLS_SOLVER\n", + "\n", + "n_qubits = int(np.ceil(np.log2(epanet_A_dim)))\n", + "\n", + "qc = RealAmplitudes(n_qubits, reps=3, entanglement=\"full\")\n", + "estimator = Estimator()\n", + "\n", + "linear_solver = VQLS_SOLVER(\n", + " estimator=estimator,\n", + " ansatz=qc,\n", + " optimizer=[opt.COBYLA(maxiter=1000, disp=True), opt.CG(maxiter=500, disp=True)],\n", + " matrix_decomposition=\"symmetric\",\n", + " verbose=True,\n", + " preconditioner=\"diagonal_scaling\",\n", + ")\n", + "\n", + "res = linear_solver(epanet_A, epanet_b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the evolution of the cost function" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.semilogy(res.logger.values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and visualize graphically the solution" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(array([561.597, 628.269, 682.218, 680.56 ]),\n", + " array([561.596, 628.269, 682.218, 680.56 ]))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np \n", + "ref = np.linalg.solve(epanet_A.todense(), epanet_b)\n", + "\n", + "plt.scatter(ref, res.solution)\n", + "plt.axline((0, 0), slope=1, linestyle=\"--\", color=\"gray\")\n", + "plt.xlabel(\"Classical EPANET solution\")\n", + "plt.ylabel(\"Quantum VQLS solution\")\n", + "plt.show()\n", + "\n", + "ref, res.solution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From d270ec5aad88aa9d8722267adfb46f90cae6a88f Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 5 Jul 2024 16:52:20 +0200 Subject: [PATCH 02/96] added single matrix notebook for 3loops --- docs/notebooks/networks/Net3Loops.inp | 4 +- docs/notebooks/temp.bin | Bin 1604 -> 2316 bytes docs/notebooks/temp.inp | 30 +- docs/notebooks/temp.rpt | 8 +- docs/notebooks/vqls_solver_Net3Loops.ipynb | 304 +++++++++++++++++++++ 5 files changed, 332 insertions(+), 14 deletions(-) create mode 100644 docs/notebooks/vqls_solver_Net3Loops.ipynb diff --git a/docs/notebooks/networks/Net3Loops.inp b/docs/notebooks/networks/Net3Loops.inp index 9e4ab95..e02928d 100644 --- a/docs/notebooks/networks/Net3Loops.inp +++ b/docs/notebooks/networks/Net3Loops.inp @@ -9,8 +9,8 @@ shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) 5 150.00 75.00 ; 6 165.00 91.67 ; 7 160.00 55.55 ; - 8 160.00 66.66 ; - 9 160.00 66.66 ; + 8 170.00 12.66 ; + 9 175.00 12.66 ; [RESERVOIRS] ;ID Head Pattern diff --git a/docs/notebooks/temp.bin b/docs/notebooks/temp.bin index 643ca7da768157798084ebb9b49eda56d8f8bc30..cce5ed72d4e5ea98c3b5d430c5dbe73b920bb663 100644 GIT binary patch literal 2316 zcma#_I4q~*$H2hJz`(!=#N0p(10X&KumUj?m<1sO_#kv~Mq+Mek%F$Sf>TjqdSXsa zrjCM(x?^rqQGRh|zK()VYATS(PXP*t{G{l%CsksGunRx|6%-N&P zrE&;3XXNA;W#*;gGB!6|-v^f*NZbgYBv8(nfV>F-c~b)NW(4HT3CLRzkhdftZ%Cp1 zhz~yl`~hf4Ab??IJ*a$VhGJM5&jRFw*la+|4#Xfept2vN4p|PwhNJan)w8%-rJd>0YpnW8ylYn(sf`qh>waH z7`|!Ta|Gcxfvipp4AY#r!nZq_7{@uq^m{pi@EVg9hc6m>IWJeXbr!93b&mHlaK5_4 z!C5rg+*#>|x$~wuWGZYelH*dfyN=6l>~d6Tig&c1_Sxar z+t-A$EjT!U_BpqI3v`MTZg39lXLK^ovvZDZnCsMhS^Kra z1{G!p&*|sw4?j9?f2IDR{oEJ3?PtF`X5S;d#=c0b(tf9Jx4rwb$#%{w&e+{K`qN?Q zqX5S#;?o`0hpu$|*FMYP-)s@b<`7@U!_yu*sGjC>+?!-)ueM{YJuGI3qa7R=7>LD8 zz_20~6K6X}Rp6=)o9nELY|1?PY%EVLx9QHCWOJNvuFdX6>ui{fx7%I9u>2X+2$B%)5m4ShQ5r|ng3O;6@T*7id6(#}_Wd>pm-PnjMQ$80}4X1f8^|E^?gM qC^p%NLu7Iu5YOTe66R!?VRM~-zD" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import wntr\n", + "import wntr_quantum\n", + "\n", + "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", + "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", + "\n", + "# set up network model\n", + "inp_file = 'networks/Net1Loops.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# plot network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n", + "\n", + "# print options\n", + "# dict(wn.options.hydraulic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve model using the classical Epanet simulator\n", + "\n", + "We now solve the same problem using the classical Epanet simulator. Note that, by default, `QuantumEpanetSimulator` uses a classical `CholeskySolver` to iteratively solve the linear problem." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", + "Your EPANET temp dir: /home/nico/.epanet_quantum\n", + "\n", + "Size of the Jacobian in EPANET simulator: 4\n", + "Size of the b vector in EPANET simulator: 4\n" + ] + }, + { + "data": { + "text/plain": [ + "(name 2 3 4 5 1\n", + " 0 57.939995 31.496479 52.434612 21.174667 4.394531e-07,\n", + " name 1 2 3 4 5\n", + " 0 0.163867 0.059455 0.076645 0.043315 0.031685)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "\n", + "# define the classical EPANET simulator\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", + "\n", + "# run the EPANET simulation\n", + "results_epanet = sim.run_sim()\n", + "\n", + "# remember to set up EPANET Quantum environment variables!\n", + "epanet_path = os.environ[\"EPANET_QUANTUM\"]\n", + "epanet_tmp = os.environ[\"EPANET_TMP\"]\n", + "\n", + "# check paths\n", + "print(f\"Your EPANET quantum path: {epanet_path}\")\n", + "print(f\"Your EPANET temp dir: {epanet_tmp}\\n\")\n", + "\n", + "util_path = os.path.join(epanet_path, 'src/py/')\n", + "sys.path.append(util_path)\n", + "\n", + "from quantum_linsolve import load_json_data\n", + "epanet_A, epanet_b = load_json_data(os.path.join(epanet_tmp,'smat.json'))\n", + "\n", + "# set the size of the Jacobian (A matrix)\n", + "epanet_A_dim = epanet_A.todense().shape[0]\n", + "print(f\"Size of the Jacobian in EPANET simulator: {epanet_A_dim}\")\n", + "print(f\"Size of the b vector in EPANET simulator: {epanet_b.shape[0]}\")\n", + "\n", + "# save number of nodes and pipes\n", + "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", + "n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", + "\n", + "results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve linear system with VQLS and the final matrices from EPANET\n", + "\n", + "For testing purposes, we start by solving the linear system with VQLS and the final A and b matrices from the classical EPANET simulator. Here, we are **preconditioning** the initial linear system using diagonal scaling and also using a **mix of two classical optimizers**." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VQLS Iteration 136 Cost 5.066e-08\n", + " Normal return from subroutine COBYLA\n", + "\n", + " NFVALS = 136 F = 5.066030E-08 MAXCV = 0.000000E+00\n", + " X =-1.347440E-02 -8.807702E-01 1.307708E+00 2.989201E+00 3.526457E+00\n", + " -2.800616E-01 2.854411E+00 1.854796E+00\n", + "Optimization terminated successfully.\n", + " Current function value: 0.000000\n", + " Iterations: 3\n", + " Function evaluations: 63\n", + " Gradient evaluations: 7\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "from qiskit.circuit.library import RealAmplitudes\n", + "from qiskit.primitives import Estimator\n", + "from qiskit_algorithms import optimizers as opt\n", + "\n", + "from quantum_newton_raphson.vqls_solver import VQLS_SOLVER\n", + "\n", + "n_qubits = int(np.ceil(np.log2(epanet_A_dim)))\n", + "\n", + "qc = RealAmplitudes(n_qubits, reps=3, entanglement=\"full\")\n", + "estimator = Estimator()\n", + "\n", + "linear_solver = VQLS_SOLVER(\n", + " estimator=estimator,\n", + " ansatz=qc,\n", + " optimizer=[opt.COBYLA(maxiter=1000, disp=True), opt.CG(maxiter=500, disp=True)],\n", + " matrix_decomposition=\"symmetric\",\n", + " verbose=True,\n", + " preconditioner=\"diagonal_scaling\",\n", + ")\n", + "\n", + "res = linear_solver(epanet_A, epanet_b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the evolution of the cost function" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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7VmSfq8dGoQCWTIuGWqXAO7llAJwlrrtmxOCGhDDkFjfi2d2npNf97P3z8NM44/ZHUxPx7+wSfHKhCh+dq8SyWXH9HlOtax+rmibzgO+HiIg8h5kakt0vV87Gj5ZNxQ0J4QN+7bJZsdLMp1B/DR6YGw9/Vx/L/HHhGBOskzI1ALB4yhiolAosnRkrHbttchS+c7tzJlSb1YHpcSH42YqZ+PYi57GMf5/CHz+/AovN0a8x1ZmcwYwY3BAR0cjETA3J7qYJkdJaNAMVoFXjT4/Pw/lyIx6el4DQAA1+fI8ZH56txO2u3cNjQvwwLTYYFyubkDbDuSLxihQ9Xv38CmbHh+JPj82Hn0aJUH8N9p4ux0sPzYFapcS6OybhxLV6HCusx+YPL2JPXjne+s5CBHYzfbwjccfxmmYzBEGAQtF1lWQiIhp+CmGgDQZeymg0IjQ0FAaDASEhIcM9HBqki5VG5BY14pEFCVKQYbM7oFIqeg06HA4Bb+eW4hf7LsDQasUfvn4DvjKn9xlRN2/OlPp3Tm26m5twEhF50EC+v1l+Iq80LTYEX09NdAtg1Cpln1kUpVKBh+cnYOUN8QCA7GsNvZ4vCAJqTe1lp9pm9tUQEY1UDGpoVJo3ztnvk13U+/5TzWabW+9Nb83CxXUtsNr716dDRETyY1BDo9L8JGdQc77ciGazrcfz6jo1B/eUqTlxrR6LXv4M//PuWfkGSUREA8KghkaluFB/xIf5wyEAea51bLojznwS1faQqTnl2q7hVGnP1yIioqHFoIZGrRtd2ZoT13ouQXWexl3TQ6amtMG5UvH1rFhMRETyYFBDo9a8pAgAkLZU6E6X8lNT92vViMGMsc3WazmLiIiGDoMaGrXETM3J4gbYemjwrXNlZsStH3rqqSk3tGdoKpitISIaFgxqaNSaEh2MYD81TBY7Lla27+v0x8+vSJtl1rmmc0+KDgLQc/mpvLGt/deGtm7PISKiocWghkYtpVIhTe0W+2rKGlux+cOL+O2BSyhtaJEyM1NjgwF03yjcarGjvsNaNuyrISIaHgxqaFRLHe/czuGLy7UAgMMFtdJz+ZVNUk+NFNQ0W7rs8l3WKYhhUENENDwY1NCodvsU535SR67Uos1qx5EOQc3FyiZpSvc0V1BjsTtgbLXhtUNX8ey/T8Fqd3QJYjqWooiIyHO4oSWNatPjghETokOV0YzjhfU4fKVOeu5ih0yNPswfwX5qNLXZUFRvwq/3X4TNIeDBufHdBDV9Z2q+vFyLOpMZ96XEy3tDRESjmNdkakpKSrB48WLMmDEDc+bMwe7du4d7SOQDFAoFFk+JBgC89sVVt20QzpUbUN/iDGoiA3UYE6QDAOw9XQGbw1mCOlVqkIKYya5m4o4zobpjtTvwrb9n4+k38/Dl5dpezyUiov7zmqBGrVZj69atOH/+PD7++GM888wzMJlMwz0s8gGLpzpLUGJfjTjT6WqNCYIAKBRAeIAGUcHOoGbPyTLptadKGlHmKjfdON657k2FoQ0Oh3vfTUcF1c1osdgBAL/ef7HXc4mIqP+8JqiJi4tDSkoKACA2NhZRUVGor+99M0Ki/rhlcpS0Dg0APDA3HiF+7ZXZ8AAt1CqllKmp7pDNOVXaiLLGFgDA3MRwKBSAxeaQpoJ353y5Ufr1mTIDPjhbIdu9EBGNZrIFNYcOHcLy5cuh1+uhUCiwZ8+eLuds27YNSUlJ8PPzQ2pqKo4fP35d75WTkwO73Y6EhIRBjpoICPHTYK5rajcA3DIxCtPiQqTHkYFaAEBUkFY6plQ4fyoMbTjnClLGRQYg2pXNqeilBHW+wnl+sCtw+s1H+dzdm4hIBrIFNSaTCcnJydi2bVu3z+/atQsZGRnYtGkTcnNzkZycjKVLl6K6ulo6JyUlBbNmzeryU15eLp1TX1+Pxx9/HH/605/kGjqRVIIK8VNjVnyoNNsJAKJcGZoxroAFAOaMDZPKVE1tzm0R9GH+0If5A+i9WVjM1Dx71xREBmpxra4FH56tlPFuiIhGJ9lmP6WnpyM9Pb3H57ds2YK1a9dizZo1AIDt27dj37592LFjB9avXw8AyMvL6/U9zGYz7r//fqxfvx4333xzn+eaze1lAqPR2MvZNNqtvCEeb2WXYkWKHiqlAtNiO2RqgsRMTXtQc+ukKFQZ23CpqhkAoFIqEBOsgz7UHyfR3mfTmSAIUqbmxvEReGBuPF77ohBZV+qwIlk/VLdHRDQqeKSnxmKxICcnB2lpae1vrFQiLS0NWVlZ/bqGIAh44okncMcdd+Cxxx7r8/zNmzcjNDRU+mGpinoTF+qPT59bjGfSpgBoX2wPaA9m3IKayVFITgiTHseG+EGtUkIf5geg5/2fyg1tMLRaoVEpMDk6GPOlTTXZH0ZENFgeCWpqa2tht9sRExPjdjwmJgaVlf1Lux8+fBi7du3Cnj17kJKSgpSUFJw5c6bH8zds2ACDwSD9lJSUDOoeaHTpGNSIPTWxoc6AJUCrwtzEcKR0CGrEYEYqP/XQUyOWniZFB0OrVkrbNFyqakZjS8/NxURE1DevWXzv1ltvhcPR/2ZKnU4HnU7X94lE3QjSqZEQ4Y+S+lZEujI0M/Uh+OHSqZg4JghatRJTY52BicXmkIKZuFDnf3sqP50rNwAAZrgakaOCdBgfFYjCWhNyixtwx7SYbl9HRER980imJioqCiqVClVVVW7Hq6qqEBsb64khEA3Y4inRUCkVmDM2FIBzob6nlkzCslnO37MalRIz9c7gRAxq4l3/La4z4WyZocsaNGKmZoa+vWdHzNZkX2sY8BgdDgEWG2dOEREBHgpqtFot5s2bh8zMTOmYw+FAZmYmFi5c6IkhEA3YC/fNRO7/3IVZ8aE9nnPPrDiolArcNMG5MWZChD+UCqChxYqv/P5LLP7NQVypaZbOF5uEZ3SYMn5jkiuoKWoPagRBwI/eOoWlrxxClbHnvaRe2HseMzftR0F10/XdJBGRD5EtqGlubkZeXp40g6mwsBB5eXkoLi4GAGRkZOC1117D66+/jgsXLuDJJ5+EyWSSZkMRjTQKhQKhAZpez1m7aALO/nSptDFmWIAWf/j6XKRNj0aAVoXi+hY8+UYOWiw2GFqtKG1w9tp0DGrmjXM2C58qaZSyLn86dBX/zi5FflUTfr3/Yrfv3WKx4c0TxbDaBRzpsGcVEdFoJVtPTXZ2NpYsWSI9zsjIAACsXr0aO3fuxKpVq1BTU4ONGzeisrISKSkp2L9/f5fmYSJv469VuT2+Z3Yc7pkdh+qmNtz7uy9xqaoZ3/9XnpRxSYjwdwuWJo4JRHiABg0tVpwtN8Bqc+Clj/Kl59/JLcNjN43DDYnhbu/z6cVqtFmdQVBxXctQ3R4RkddQCIIwKjaeMRqNCA0NhcFgQEhISN8vIJLBsat1+Pqfj8Hu6q0J9lNj66oU3DndPZj/79dP4JML1ZgUHYSKxlaYLHbcn6KHSqnE27mlSEkIwztP3gxlh+0cvvuPHHxwxjl7cOnMGPzxsfmeuzEiIg8ZyPe31+z9ROSNUidEYuNXZkCtVODe2XHIzLi9S0ADAAtcm2EWVDfDZLFjzthQvLhyNp5fNhWBWhXyShrxtT8dxY4vC9FgsqDFYsOnF9tX4y6u731ncCKi0cBrpnQTeavVNyfhkQWJ0Kp7/jfEo6nj0NBiRWSgFvOTIjBLHwK1SolAnRrr75mOn+w5i+PX6nH8Wj3+/MVVrLoxEW1WBwK0KrRY7Citb4EgCFAoFD2+BxGRr2P5icgLFNe14MCFKvw96xqudeifeeLmJOw8cg0AcPIndyE8UNvDFYiIvBPLT0Q+JjEyAN+8dTze/e4tmJsYJh1/cO5YaWfwkgY2CxPR6MaghsiLhAdq8c+1N+G/bkrEEzcnYVZ8CBIiAgAAJb301fSUkH3jaBHW/PU4DK3WIRkvEZEnMagh8jJ+GhV+cf9s/HTFTCgUCiS6gpri+u4zNf8+UYIbX/wEucXuKxY7HAJ+83E+PsuvwXt5ZUM+biKiocaghsjLJYQ7t2boqfy0J68Mtc0WfHzOfZuS8xVGNLY4MzQHzld191IiIq/CoIbIy7WXn7oPai5VObdQKKhudjv+ZUGt9OtjV+vRbLYN0QiJiDyDQQ2Rl+stqKltNqO22QIAbntQAcDhDkGNxe7AF5dqhnCURERDj0ENkZcTe2rKGlullYtFlyrbN7osqjPBbLMDANqsdhwvrAcALJ7q3LfqwAWWoIjIuzGoIfJyMSF+0KgUsNoFVHba0Tu/qj2ocQjAtVpnNie3qAFmmwPRwTp85/aJAIDPLlZ3CYqIiLwJgxoiL6dSKjA23DUDqtPGlvkdMjVAe1+N2E9z66QozB8XjlB/54aanWdIERF5EwY1RD5gbA8zoMRMTbCfc0cUMag5fKUOAHDLpCioVUosEUtQnAVFRF6MQQ2RD0jspllYEASpp+buGbEAgIKaZtSbLDhT2gjAGdQAwNKZzuf3na7ocaE+IqKRjkENkQ+YFB0EAPjwbCVsdgcAoLShFSaLHRqVAnfNiAbgzNR8cKYCDgGYqQ9BbKgfAGDJtGgEaFUoa2zFqVLD8NwEEdEgMagh8gEPzB2LsAANCqqb8U6uc3VgcX2aiWOCMC3WuQnc1ZpmvHvS+fx9KXrp9X4aFdKmxwAA9p0u9+TQiYhkw6CGyAeE+muwbskkAMCWA5fQZrXjoqv0NCUmGAkRAdCqlTDbHMgpaoBCASxP1rtd4945cQBYgiIi78WghshH/NdN4xAf5o9KYxt+81E+ThY3AgCmxgZDpVRgQlSgdO6CpAjEhfq7vf72KWMQqFWh3NCGkyWNHhw5EZE8GNQQ+Qg/jQo/uGsKAODPXxbiE9dielNjggEAE119NwBwX0p8t6+/a4ZYgqoY6uESEcmOQQ2RD3nghnisT5+GabHOQCZIp0ZKYhgAYNIYZ1CjUSmQPiu229enz3aWoD7Lrx76wRIRyUw93AMgIvkolQp85/aJ+M7tE1FtbINSqUBUkA4AcNOESPxv5mWkz4pDeKC229ff4AqArtWa0Ga1w0+j8tTQiYgGjUENkY+KDvFze7xwYiQ+emYRxkUG9PiaMUE6hAc4VxcuqG7GrPjQbs8TBAEKhULW8RIRDRbLT0SjyNTY4F6zLwqFAlNdpavOWyyIzDY7lm39At/718khGSMR0fViUENEbsTG4ktV3Qc112pbkF/VhP1nOfWbiEYWBjVE5GaKK1NzsYdMTVObFQBgtQtoNts8Ni4ior4wqCEiN31lapo6BDKNLVaPjImIqD8Y1BCRGzFTU2Fog6G1a9DS1NYe1DS0WDw2LiKivjCoISI3IX4a6F0bXXaXrWl2C2qYqSGikYNBDRF1MaXDDKidhwtxx28PoriuBUB7Tw0ANJiYqSGikYNBDRF1IU7rfv9UOX6x7wKu1pjw+SXnKsMdm4NZfiKikYSL7xFRF2Kz8LHCeumY2F/TxPITEY1QzNQQURdTXEFNR90FNY0yZ2oaTBY8/eZJfHm5VtbrEtHowKCGiLqYFB0EtdK5DcJk1+7e7UFNe3amXuaemgMXqvBeXjn+eOiKdMzuENi7Q0T9wqCGiLrw06jw6wfn4EfLpuKxheMAtAc1zd2sU/PBmQrM/8UnyLpSN6j3FTM/dc3tQcz33zyJG1/8RGpUJiLqCYMaIurWg/PG4ruLJyHUXwOgp54aZ/Dx4dlK1DabcTC/elDvKb5Hx7LWqZJG2BwCzpYbBnVtIvJ9DGqIqFftQY0zmOkuU1Pe2AoAqBtkmUgMajo2IIulp5om86CuTUS+j7OfiKhXYQFaAICxm54aMVNT1uAKapoHF3gYXYFTq9WONqsdAGCyOP/LoIaI+uJ1mZqWlhaMGzcOzz333HAPhWhU6K381GKxo9lsQ1VTGwD5MjWAMwvUcW+p2kEGTETk+7wuU/Piiy/ipptuGu5hEI0aYlDTbLah1WKH2eZwe/5ihRGC4Px1xwbf69ExqGlosUjXBZipIaK+eVWm5vLly7h48SLS09OHeyhEo0aIX/u/fcpcvTMAEBbgDHbOlRulY7XNZggdI5EBMnYqbXWcMl7DTA0R9UG2oObQoUNYvnw59Ho9FAoF9uzZ0+Wcbdu2ISkpCX5+fkhNTcXx48cH9B7PPfccNm/eLNOIiag/1ColgnTOwEYMagK1KkQGOnttznWYlWS2OaQemP44mF+Nfx4rlh4bO5Wf6jvMgqplpoaI+iBb+clkMiE5ORnf+MY38MADD3R5fteuXcjIyMD27duRmpqKrVu3YunSpcjPz0d0dDQAICUlBTabrctrP/74Y5w4cQJTpkzBlClTcOTIEbmGTUT9EOqvQbPZhtIG51oxQX5qhAdoAZhwtszodm59s0UKgnpjttnx3X/kosVix8KJkUiKDOhSfrLZ27M+Na4skEKhkOemiMjnyBbUpKen91oW2rJlC9auXYs1a9YAALZv3459+/Zhx44dWL9+PQAgLy+vx9cfPXoUb775Jnbv3o3m5mZYrVaEhIRg48aN3Z5vNpthNrf/y85oNHZ7HhH1LcRfg7LGVmmWU7CfBuGuTM3l6ia3c2tNZiRGBvR5zdyiRrS4sjoVhlbEhvjB2iGIaWyxwtKhf8dqF2BotUqzsYiIOvNIT43FYkFOTg7S0tLa31ipRFpaGrKysvp1jc2bN6OkpATXrl3Db37zG6xdu7bHgEY8PzQ0VPpJSEgY9H0QjVah/s5//5S6gpognRrhrp6ajoEI0P9m4cMF7fs71TZb3LI0gHN9ms67gLNZmIh645Ggpra2Fna7HTExMW7HY2JiUFlZOSTvuWHDBhgMBumnpKRkSN6HaDQQZ0CJPTXBUvmpnb9GBaD/a9UcvtIhqGkydw1qWqxd9pZiszAR9cbrpnQDwBNPPNHnOTqdDjqdbugHQzQKiEGN2FMT7KeWyk+imfoQZBc19GutGmObFadKGqXHdSaz28wnwLlVQpvNvem4c6bmwzMVaGy14pEFif2+FyLyXR4JaqKioqBSqVBVVeV2vKqqCrGxsZ4YAhENghjUVBmdQUXH8pNoVnwososa+rVI3tErdXB0qFrVNllgaOmcqbGg1eqQ3t/QanULauwOAc/syoPZ5sCd06MRHex3XfdGnpFT1IBDl2pkudaYYB1W3ZgAjcqrViUhD/BIUKPVajFv3jxkZmbi/vvvBwA4HA5kZmZi3bp1nhgCEQ2CGNSIgv00bg27UUFaxIf5A+hfT43YTxOkU6PZbEOdqb385KdRos3qQEOLFa2uRuKpMcE4fq0etR2u3dhikRYCrDaaGdSMcE++kYNqGXui4kL9cOf0mL5PpFFFtqCmubkZBQUF0uPCwkLk5eUhIiICiYmJyMjIwOrVqzF//nwsWLAAW7duhclkkmZDEdHI1TmocWZq2oMafZg/IoOcjzv3wXTn8JU6AMDSmbF4O7cUNc0Wqfw0LiIQ+VVNaGixSLOjpsQG4fi1erdMTccyl/iedoeA44X1mDM2FIH9mFZOniN+Rg/OHYsAreq6r/PpxWqUNba6bXpKJJLtT312djaWLFkiPc7IyAAArF69Gjt37sSqVatQU1ODjRs3orKyEikpKdi/f3+X5mEiGnlCumRq1IgIbD+mD/VHZJCzh61z+Wnv6XK8l1eOzQ/MRlSQDuWNrSioboZSAXwlOQ5v55a6NQqPiwxAflWT275PU2KCAbg3CnfMCIlfmHtPl+PpN/PwxM1J+OmKmXLcOsnA7hBgc9Ub/9+90xEReP3T8iv/lo2yxlZY7Y6+T6ZRR7agZvHixX0uj75u3TqWm4i8UNfyk9qt/KQP85dWGO7cKPyHTwtwsbIJ46MC8eN7pmN3dikAYP64CEyMCnK9pj2oSYxwX+PGT6NEguuYe6ama9amoLoZAHC11nSdd0pDoeN6Q1r14PpgNCrn4osMaqg77LIioj5121PT4Zg+zA9RrkxNvckCh+tf5Ta7A1drnAHGm8eLYWyz4p/HiwAAj96UiKhgZyDUZnWgotG503dkkM5tReKIAC3GdJMFcs/UOI+LQU9jy+A21iR5uQU1g2zuFZuDO6+PRAQwqCGifuiup0atUiLYtdllfJi/VFKwOwQp61Jc3wKL61/UxjYbvvfPk6gymhEVpMWyWbEI0Kql9W2u1DRL7xXWYWZVeKAW0cHOoKau2Qy7K2DquB6OWH4Sg5rOi/bR8DLbnb1RCkV7puV6tQc1zNRQVwxqiKhP3ZWfAGB8VCAAYHJMMLRqpbSjt1gauuwqB4k+d03pXXVjAnRqZzAjZmuK6lqk9+rYhBwRqEVEoBYKBeAQ2gOY2g5lLjFrI/bcNJjYRDqSiJkarUo56L27pPKTjUENdcWghoj61FNQ8+p/zcOub92ESdHO3hixWVgMMsQel7TpMQh0zXhRKoCvp46TriWWrcSMToi/2j1TE6CFWqVEhCvQEUtQvWVqms02t5IHDS9x6v1g+2kAZmqodwxqiKhPapXSrc8lSOcMOuLD/JE6IVI63rlZ+HKVc7PLuePC8PB85/5rd06Pkda0cb7GfeXv7jI1gHPBNaA9cKnvNKVbEAS3npvGVpagRgoxwNTJGdQ42FNDXXEhByLql1B/DZrNNgDtmZrOxLVqxCyKWH6aHB2M1QuTEB/mj/tviHd7zZhg9+m9of4atym/YoAzJliHi5VNUlDTsVG4zuTcELPzLt9ckG9kaA9qrn99GpEU1DATR91gpoaI+kVcq0apQI+Lp7WvVWOB3SFI5afJ0UEI1KmxdtEEKeMivaZTpibEz71ROMIVKIllKrFvpmNWxtBqRblr9pSoP4sAkmfIW37ilG7qGYMaIuqXUH9ndiZIp+6x2TNKKj+ZUdbQCrPNAa26fZ2Zbl8T5J6pCelcfnL9WpwBVWlog8XmgLHN5va6y9VNbo8HMq27oLoZu04U97nWFl2fjo3Cg8XyE/WG5Sci6hexWTjYT9PjOR0bhcUgY+KYIKiUPc94EV8DAME6NVRKRacp3c5fJ7lmWl2tNUlZGLVSgRB/DepNFlysdA9qBrKM/sb3zuLIlTr4aVS4LyW+7xfQgFhcU7p1GhmDGpafqBvM1BBRv7QHNT3/W0gsEV2oMOJ8uRGAs/TUm6gOQY1Y4uquUXjiGOd1rlQ3S6Wn8ECt1Jyc3yWocQY+Z8sM+M+p8l6zMGKZ7NOL1b2Ola6PvJkalp+oZ8zUEFG/iEFNUC8bRd48MRLhARpcq2vBq59fAdB3UNOxUbjboCZADGqcmZpyQyvKGlsBOGdbiePqEtS4sjnff/MkrtaYYGi14rGbxqEzs80u7R596FIN7A6h18wSDdzQTOlm+Ym6YqaGiPqlP5ma8EAtXlw5GwCkHbYnx/Qe1HRsFBb7dsI7bJYp7jEVEahFWIAGggDkFDUAcGZ5xBlXYqAjLgDY0GKF1e7ANdc+UC/uOy+tWtxRpaG9wbihxYrTpY29jpcGjuvUkKcwqCGifhF7WjpvONnZPbPjsCJZLz2eFB3c6/mh/hqoXZmREL/29W/unR2HxxeOk74IFQqFVII6VlgPwDmFvPOOz+KO3o0tFlQa2iD2k7ZZHfjBrrwuX4ZlDa1ujw/m1/Q6Xho4edepYfmJesaghoj6JX1WHP619ib8aNm0Ps994b6ZGB8ViIljApEU2XsQpFQqpMBEzAYpFApse3QuXrhvltu5E1yB1dkyAwBnliei05TwKbHOoKahxYqSBufWC2OCdQj11+B0qQF/zypyO7/UleERK04HLzGokZvUUyPnOjUsP1E3GNQQUb+olAosnBiJwF56akRhAVp89MwifPyD26HuR3Oo2CzceTuGzia6+nPETS0jg9obhUVTXOc0mCxSFmZabDCeu3sKAOCNY0VuTcPiOYunRgMATpc2um3BQINnHoop3czUUDcY1BDRkNCqlf1uuBX7YvoMasa49+dE9VJ+amixoNQVsIwN98fKuWMRoFXhao0Jx13lK6C9F2duYhimx4VAEIAvLtf2a9zUP1L5SZYp3Sw/Uc8Y1BDRsJseFwKgPRPTE3EGlCgyUOeWqVEqgPGucwytVpTUO8tPY8MDEKRT474UZ6/Pv44XS68RMzXx4f5YPHUMAAY1chPXqZE3U8PyE3XFoIaIht1zd0/Fvu/fivRZsb2elxARIP1LHXA1CndYkTgySCfNpnIIwPkK51o5Y8OdG2g+siARAPDB2UppyreYqYkPC0Dq+AgAQHZReyaHBs9sHYINLZmpoW4wqCGiYadVKzFTH9rj9gsijUqJcZHt2ZqoIJ1b+WlMkA5adfuO4uKGmuKu4LPjQzFTHwKLzYG3c0vhcAioMLRnauaOC4dCARTVtaDa6L6XVHeyrtRxCng/WOzc+4k8g0ENEXkVcQYU4Fy7puNCfeJmmeI2C2JD8dhw5wwshUIhZWvePVmGmmYzrHbnYnsxwTqE+GkwLdZZCst2rYXTkwpDKx77yzE89pfjsPELtleyTulWs/xEPWNQQ0ReRey78dMoEaBVQaNSSg3GYlDTMXujUSmkzTABYJmrxHWu3IiTxc7AJTbET5qldWNSOADgxLXeS1BZV+pgcwgwtFpR7Ordoe5Z5Fx8T8nyE/WMQQ0ReRVxBlRkoE4qV4nNwu2ZmvagJj7MH8oOs7CignRIHhsKAPjHsWLpHNH8JFdfzTX3TI3N7kBpQ3vw0nEG1aWqrisVUztZp3SrxfITMzXUFYMaIvIqqeMjoFMrMd+VUQHaMzPiejfhHXb5jg/3R2fimjTiLKeO54iZmnPlBjSbbdLxlz7Kx62//gz7z1YAaF/VGAAKqt33nSJ35iFZfI+ZGuqKQQ0ReZWEiADk/OQuvPLVFOnYvKRwKBXADYlhANw3xBwb1nVF4zumRbs97pipiQv1R3yYPxwCkFfcCMD5Bfrv7BIAwI7D11BtbEOha08poL0hmbonNgrL0lPD8hP1gkENEXmdIJ3araS0ftk0nNx4N+YmOrMsbkFNN5ma2fGhbuvbdM7mdO6rOXa1Ho0tVgDOstPunFIAgDhZ6zLLT72y2Fzr1MjSKMzZT9QzBjVE5PUUCoXbasQdd/nurvykVCpwu2uhPcA9UwO099WIfTMfuEpOot9/ehkAcIerjHWlplmaaUVdDc0u3YLbdhdEAIMaIvJB7pma7jfUXDK1vQTVOfC5eWIkACDrah2+vFyLj89VAgAemjcWgHPHbwB4cN5Y6NRKmG0OafVi6kreXbrbr2FjIEmdMKghIp/TV/kJABZNHoNArQohfuoumZoJY4Lw2E3jAABP/iMHtc0WhPprsGn5DAR32NDzpgmR0mws9tX0TNYp3R1WlGYJijpjUENEPkdcfE+tVCAmxK/bc0IDNHjryZux69sL4afpOivnR8umIjbED01tzhlQd82IQbCfBl9Jdu4fNSUmCBGBWkyJEYMazoDqiXmIMjWc1k2dMaghIp8zOSYIs+ND8cDc+F53Cp8eFyJtptlZsJ8GP79/lvT4ntnORfu+vWgCZsWH4FuLJrrey7krOJuFeyZlalSDn9KtVjJTQz1T930KEZF30alVeP97tw76OnfNiMHTd05GaUMrbpvsbCxOigrE3u/dJp0zKZqZmr5IU7o1g/93tEKhgEalgNUuMKihLhjUEBH14gd3Ten1+SmuTE1BdTMcDsFtqjk5WWRcURhwlqCsdjusNpafyB3LT0REg5AQ7g+tWok2qwNlja3DPZwRySzjOjVAh2ndDmZqyB2DGiKiQVCrlFKz8OGC2mEezcjjcAhSQ6/sQQ3LT9QJgxoiokG6d7ZzRpS40jC1s3QIPOSY/QS0T+tm+Yk6Y1BDRDRID7pmWeUUNeBKDWdBdSRO5wZYfqKhx6CGiGiQokP8cPsU5+yo3dmlaDbb8H8HC5Bb3DDMIxt+lo5BjWyNwmKmhkENuePsJyIiGTw8byw+vViNt3NLcTC/GhcrmxAeoMHh9XcgQDt6/6oVy09atRIKhTwzwzru/0TUkVdlagoLC7FkyRLMmDEDs2fPhslkGu4hEREBAO6cHoOIQC1qmsy4WOlcs6ahxYp/Hise5pENL2nfJ5myNAAbhalnXhXUPPHEE3jhhRdw/vx5fP7559DpdMM9JCIiAM5MhLjh5Yy4EDyTNhkA8NoXV6UpzQDQZrXj71nXUGVsG5Zxeprc07mBDuUnBjXUidfkRM+dOweNRoPbbnOu5BkRETHMIyIicpdx1xQsSIrALZOioFIqsOtECSoMbXgrpxSPpjo3yHxpfz52HC7E55dq8efV84d5xENPzs0sRSw/UU9k+1126NAhLF++HHq9HgqFAnv27OlyzrZt25CUlAQ/Pz+kpqbi+PHj/b7+5cuXERQUhOXLl2Pu3Ln45S9/KdfQiYhk4adRIW1GDPy1KmjVSqy9bQIA4NWDV9BisaHC0Io3jhUBAD6/VI3GFku318mvbMK8nx/AS/svemzsQ8Ui42aWIpafqCey/S4zmUxITk7Gtm3bun1+165dyMjIwKZNm5Cbm4vk5GQsXboU1dXV0jkpKSmYNWtWl5/y8nLYbDZ88cUX+L//+z9kZWXhwIEDOHDgQI/jMZvNMBqNbj9ERJ70yIJEjAnWobShFT/cfRq//7RA+pK32gXsP1vZ7eu2fnIJdSYL/vJlIQytVk8OWXZDk6lh+Ym6J9vvsvT0dPziF7/AypUru31+y5YtWLt2LdasWYMZM2Zg+/btCAgIwI4dO6Rz8vLycPbs2S4/er0e8fHxmD9/PhISEqDT6XDPPfcgLy+vx/Fs3rwZoaGh0k9CQoJct0pE1C/+WhX+79G50KgU2HemQmoavnNaNADgP6fKu7zmak0z9p9zBjtmm6Pbc7yJmeUn8iCPNApbLBbk5OQgLS2t/Y2VSqSlpSErK6tf17jxxhtRXV2NhoYGOBwOHDp0CNOnT+/x/A0bNsBgMEg/JSUlg74PIqKBujEpAr+4f5b0+LbJUfjpipkAgKyrdaju1DD82hdXIQhAgFYFAPj3Ce/+u8ss82aWAMtP1DOPBDW1tbWw2+2IiYlxOx4TE4PKyu7Tr52p1Wr88pe/xKJFizBnzhxMnjwZX/nKV3o8X6fTISQkxO2HiGg4rLoxEd+/YxLiQv2wIX06EiICcENiGAQB2HemQjqv2tiGt3PKAACvrEqBRqXAmTIDzpd7b/lcXKdGp1bJdk2Wn6gnXjWlOz09HWfOnMHZs2exZcuW4R4OEVG/Zdw9FVkb7sQMvfMfWCuSnftFvZVTCrvDWUb53aeXYbE7MG9cOJbOjMVdM5z/EPx3tvdma8zWoZjSzfITdc8jQU1UVBRUKhWqqqrcjldVVSE2NtYTQyAiGlG+MkcPf40K58qNeOXAJXxyvgpvHHX23PwgbQoA4Kvznb2Ae/LKpMDH23RcUVguGjXLT9Q9jwQ1Wq0W8+bNQ2ZmpnTM4XAgMzMTCxcu9MQQiIhGlDHBOvzqwdkAgD98VoBnduUBAL5563jcOjkKAHDrpCgoFUBjixV1zeYer3WhwoijV+uGfMzXY0imdCtZfqLuyfa7rLm5GXl5edKMpMLCQuTl5aG42Pkvj4yMDLz22mt4/fXXceHCBTz55JMwmUxYs2aNXEMgIvIq96XE44mbkwAAzWYbZsWH4EfLpkrPq1VKRAY5V06vbuo5qPnGzhN49M/HUFg78raO4eJ75EmyrSicnZ2NJUuWSI8zMjIAAKtXr8bOnTuxatUq1NTUYOPGjaisrERKSgr279/fpXmYiGg0+fE901Ha0IqLlUb87ms3dGmojQ7WoabJjBpXUFNYa8JL+y/iu4snYfbYUBharagwOGdQfXG5BuOjAj1+D70xD0WmhuUn6oFsQc3ixYshCL1HzevWrcO6devkeksiIq+nVSvx59Xz4XAIUCq77mIdHazDOQDVTc7AZXd2CT48W4lAnRq/eTgZ5Y2t0rlfXq7F4wuTPDTy/rEMxZRulp+oB141+4mIyFd1F9AAQHSwHwCg2ujM1JS5gpji+hbn44b2oCbrah1sI+yLXprSrZFzSjczNdQ9BjVERCNYdIh7T01FozNjUyIGNR0yNU1tNpwdYWvaDEmmRs2eGuoegxoiohFsTLAY1DiDmXKDM4ipNLbBbLO7lZ8A4HBBrWcH2AezbSjXqWGmhtzJ1lNDRETyiw5uz9TYHQKqXNsqCIKz9FTqCmrGRwWisNaELy/X4qklk4ZtvJ0NSaPwEK0o/Pxbp/H+aXn22hoTrMM/196E+DB/Wa5H/cNMDRHRCDbG1VNT02RGbbPZreRSXN8i9dQ8PH8sACCnqAGtFrvnB9oDb5nSLQgC3sotRYvFLstPUV0Ljo3QtYN8GTM1REQjWMdMTedSU0lDq3Ts1klR+HtoESoMbThxrR6Lpozx+Fi7M7S7dMuXqWmzOqRVmz96ZpG0oej1+PG7Z/DF5Vq0WkdOcDlaMKghIhrBxJ4ai82B/Momt+euVDdLDcTxYf64aUIk3j1ZhrySxhET1AxJo/AQlJ+azFYAgEIBTI4O6nE2Wn9EBGoBYERlzEYLlp+IiEYwP40Kof4aAMCp0ka3544V1rvOUSIiUIsZcc7NMi9WjpwZUNI2CUMxpdsmX/nJZHYGIEFa9aACGgDwd91rC4Maj2NQQ0Q0woklqLwSAwBgwhjnqsFi8KIP84dCocC0uGDn8Yqmbq4yPKQNLWXN1LiCGod8mZrmNhsAIMhv8AUMf1fpiuUnz2NQQ0Q0woklqEtVzmAldXwkAOcMKADSDJtpsc5MTWGdacSUPsQp3SN99pNYfgrUDT6oEftxRspnMJowqCEiGuHETI3YyJo6PsLteTGoGROsQ1SQFoLQHgB11thiwY4vC9Fstg3hiNsN6eynoSg/yRDUtJefPPP/mNoxqCEiGuGiQ/zcHo+PCpSyNwDc1kIRszU99dVs+6wAL+w9j19/eFE6ZrG1z/yRm2VI1qkZgvKTK1MTLEv5yXmNVisXB/Q0BjVERCNcdIcABgDiwvyQEN4eyMSHdwxqnH01F3roqzlT5uzL2Xu6HFa7AxabAyv+8CWW/OYg2oagB2RopnTLX36SempkLT8xU+NpDGqIiEa4jlkZjUqBqEAdEiMCpGP6jpmaXmZACYIgTQtvaLHiy8u1eP9UOS5WNqG4vgUXK+VvMPaW8lOTqxwnR0+NWH5io7DncZ0aIqIRTtypGwBiQ/2gVCqQ0CGocS8/uWZAVTZBEAQoFO3Tk2uazGhosUqP9+SV4VJVs/T4bJkBKQlhso69vfw0snfpNpnly9SIs584pdvzGNQQEY1w4k7dABAX6gxgxKBGqXAGOqJJ0UFQKRVobLGiymh2ey7f1Tzsp1GizerA+6fK0bGV5twQ7PBttntX+UmOnhrOfho+LD8REY1w3TUFJ0U616qJC/WXMheAc7G+8VHO5y50KkGJpaclU6OREOEvBTQJEc5rnis3yDpuQRCGaEVh+fd+YvnJNzCoISIa4YJ1avhpnH9dx7kyL/PHhePJxRPx0xUzu5wvlaA6NQuLPTNTY4NxX3I8AOe2AC/eP1t6Xs7sh6XDtUb63k8sP/kGBjVERCOcQqGQ+mriXJkapVKB55dNw10zYrqcP93VLHziWr3bcXHtmmmxwfjaggTEhfph9cIk3DopCsE6NSw2Bwqqm7tc73qJWRpgaBbfszkECII82Rpx3R55yk+uKd0MajyOQQ0RkReYEhMEoD0L05s7pkVDqQA+vViNz/KrATgX7hODmqmxIRgbHoCsDXfipytmQqlUYLreGQjJ2VfTMaiRtfzUIUCSqwQl55TujuUnuYIu6h8GNUREXuClh5Lx5rduwvxx4X2eOz0uBGtuGQ8A+J93z8JktqG4vgVtVgf8NEq36eCiWfpQAM4ZUHIR16jRqBSD3iSyI42yY1AjTwlK1p4aV/nJ7hDcSnA09BjUEBF5gYhALW6aEOk2Rbs3GXdNQXyYP8oaW/HyR/nIdzUNT44OhqqbAGOmK1NzfggyNXJmaYD28hMgX1AjZ0+NOPsJYAnK0xjUEBH5oECdGi+unAUA2HnkGn69Px+As0m4O7PinZma8xVGOGTaMkHMUug08q1RAwAqpQJibCd3+UmOnhqNSgm1K3DkDCjP4jo1REQ+avHUaPxw6VS8/FE+CmtNAHruyZk4JhA6tRLNZhvePFGCiEDNoN+/uL4FgPyZGoVCAY1KCYvNIUumxu4QYHJlVOQoPwHOElRTm40zoDyMQQ0RkQ97askkRARq8f/ePQOHAMxwzYzqTK1SYlpcCE6VNOLH756RdQz+WnkzNQCgUSpggTzlJ1OHPZrkKD8BzhJUU5uN5ScPY1BDROTjHlmQiHGRAThXZsRNEyJ7PO+ZtMn44+dXZN2xWwEFHklNkO16Io1aCVjsspSfxH4ajUoh29RzLsA3PBjUEBGNAjdPjMLNE6N6PWfJ1GgsmRrtoRENjpwL8HWczt3fRuy++LvWqmH5ybPYKExERF5Ho5Rv/yc5p3OLuP/T8GBQQ0REXkdcgE+WnhoZp3OL2stPtj7OJDkxqCEiIq8j56aWck7nFnH/p+HBoIaIiLyOnD01TUOQqWH5aXgwqCEiIq8jriosZ6OwnD01UvmJQY1HMaghIiKvI2f5ySTjDt0iqfzEKd0exaCGiIi8jqyZGpaffAaDGiIi8jpD0VPD8pP3Y1BDREReRwpqbPKVn2Sd0u1afI8rCnsWgxoiIvI6UvnJIV+jsJw9NQGc0j0svCqoeeWVVzBz5kzMmDED3//+9yEI8u1PQkRE3qM9UyPnlO7B70wu4uJ7w8Nrgpqamhr84Q9/QE5ODs6cOYOcnBwcPXp0uIdFRETDYCgW3wvUybebuD8bhYeFV21oabPZ0NbWBgCwWq2IjvaOjdeIiEhecpafTJYhmNKtYflpOMiWqTl06BCWL18OvV4PhUKBPXv2dDln27ZtSEpKgp+fH1JTU3H8+PF+X3/MmDF47rnnkJiYCL1ej7S0NEycOFGu4RMRkReRs1G4fZdu+cpP0pRuNgp7lGxBjclkQnJyMrZt29bt87t27UJGRgY2bdqE3NxcJCcnY+nSpaiurpbOSUlJwaxZs7r8lJeXo6GhAXv37sW1a9dQVlaGI0eO4NChQ3INn4iIvMjQTOlm+cnbyZZrS09PR3p6eo/Pb9myBWvXrsWaNWsAANu3b8e+ffuwY8cOrF+/HgCQl5fX4+t3796NSZMmISIiAgBw77334ujRo1i0aFG355vNZpjNZumx0Wgc6C0REdEIJdfiexabAxZXs3HwUDQKM6jxKI80ClssFuTk5CAtLa39jZVKpKWlISsrq1/XSEhIwJEjR9DW1ga73Y6DBw9i6tSpPZ6/efNmhIaGSj8JCQmDvg8iIhoZ5GoUFteoAeTN1AS41qlpsdo5U9eDPBLU1NbWwm63IyYmxu14TEwMKisr+3WNm266Cffccw9uuOEGzJkzBxMnTsSKFSt6PH/Dhg0wGAzST0lJyaDugYiIRg65yk/iFgn+GhXUKvm+EsXyk90hyDJDi/rHq2Y/vfjii3jxxRf7da5Op4NOpxviERER0XAQy08nSxqw5cCl675OXbOzTUHOLRKA9vIT4CxBadVes4KKV/NIUBMVFQWVSoWqqiq341VVVYiNjfXEEIiIyIeE+Dv7X86WGXG2bPA9k1FB2kFfoyOtWgm1UgGbQ0CL1YZQyNevQz3zSFCj1Woxb948ZGZm4v777wcAOBwOZGZmYt26dZ4YAhER+ZD7UuJR02SGodU66GspACxP1g9+UJ34a1VoarOxWdiDZAtqmpubUVBQID0uLCxEXl4eIiIikJiYiIyMDKxevRrz58/HggULsHXrVphMJmk2FBERUX+F+mvw7N09TxYZCfw1zqCGC/B5jmxBTXZ2NpYsWSI9zsjIAACsXr0aO3fuxKpVq1BTU4ONGzeisrISKSkp2L9/f5fmYSIiIl8gLsDXxgX4PEa2oGbx4sV9Tltbt24dy01ERDQq+IvTupmp8Ri2YxMREQ0Bf43zK5ZBjecwqCEiIhoC4gJ8LD95DoMaIiKiISAuwMdMjecwqCEiIhoC4gJ8LRZbH2eSXBjUEBERDQHOfvI8BjVERERDgOUnz/OqvZ+IiIi8hVh+Km1oxdkyQ6/n9rWRt8Vux8H8Grx/qhzX6lpkGV9MiA5vP3kzxoYHyHK9kYBBDRER0RAQN8n8z6ly/OdU+TCPpqsqoxk5RQ0MaoiIiKh3adNj8J+8chjb5NmfanpcCO67IR43TYiASqHo8dw+kj4AgHX/zMXRq/Ww2ftztvdgUENERDQEpsYG46MfLBruYXRLXEPH5nAM80jkxUZhIiKiUUajcmZ6rD6WqWFQQ0RENMqoVc6vf5udmRoiIiLyYhqlM1NjczBTQ0RERF5MzNSw/EREREReTeypYfmJiIiIvJpaKWZqGNQQERGRF1OLs5/YU0NERETeTMvZT0REROQL1FynhoiIiHyB2FPDFYWJiIjIq7XPfmKmhoiIiLyYuE6NhT01RERE5M3USmZqiIiIyAdo1eypISIiIh/QvvgeMzVERETkxdTcJoGIiIh8gTT7iSsKExERkTfj3k9ERETkEzRcUZiIiIh8gYZ7PxEREZEvEBffY6aGiIiIvJpGXHyP69QQERGRN1NL5SdmaoiIiMiLievUWJmpISIiIm+mEad025ipISIiIi8mrSjMTA0RERF5Mw1nP3nOypUrER4ejoceeqjLc3v37sXUqVMxefJk/PnPfx6G0REREXk3Dfd+8pynn34af/vb37oct9lsyMjIwKeffoqTJ0/i5ZdfRl1d3TCMkIiIyHtJ69Rw76eht3jxYgQHB3c5fvz4ccycORPx8fEICgpCeno6Pv7442EYIRERkfeS1qkZ7ZmaQ4cOYfny5dDr9VAoFNizZ0+Xc7Zt24akpCT4+fkhNTUVx48fl2OsKC8vR3x8vPQ4Pj4eZWVlslybiIhotBAzNQ4BsPtQtmbAQY3JZEJycjK2bdvW7fO7du1CRkYGNm3ahNzcXCQnJ2Pp0qWorq6WzklJScGsWbO6/JSXl1//nRAREVG/iLOfAN/aqVs90Bekp6cjPT29x+e3bNmCtWvXYs2aNQCA7du3Y9++fdixYwfWr18PAMjLy7uuwer1erfMTFlZGRYsWNDtuWazGWazWXpsNBqv6z2JiIh8jVbVntOwjeZMTW8sFgtycnKQlpbW/gZKJdLS0pCVlTXo6y9YsABnz55FWVkZmpub8eGHH2Lp0qXdnrt582aEhoZKPwkJCYN+fyIiIl+gVrZnanypr0bWoKa2thZ2ux0xMTFux2NiYlBZWdnv66SlpeHhhx/GBx98gLFjx0oBkVqtxm9/+1ssWbIEKSkpePbZZxEZGdntNTZs2ACDwSD9lJSUXP+NERER+RCVsmP5yXcyNQMuP3nCJ5980uNzK1aswIoVK/q8hk6ng06nk3NYREREPkGhUECjUsBqF3xqVWFZMzVRUVFQqVSoqqpyO15VVYXY2Fg534qIiIgGQa30vZ26ZQ1qtFot5s2bh8zMTOmYw+FAZmYmFi5cKOdbERER0SCIM6AsPtRTM+DyU3NzMwoKCqTHhYWFyMvLQ0REBBITE5GRkYHVq1dj/vz5WLBgAbZu3QqTySTNhiIiIqLhJ86A8qVMzYCDmuzsbCxZskR6nJGRAQBYvXo1du7ciVWrVqGmpgYbN25EZWUlUlJSsH///i7Nw0RERDR8xEzNqF6nZvHixRCE3qO6devWYd26ddc9KCIiIhpaUk8N16khIiIib+aLO3UzqCEiIhqFpJ26fainhkENERHRKCSuKsx1aoiIiMiraaRMDYMaIiIi8mIaafYTy09ERETkxdQ+uE4NgxoiIqJRSJr9xJ4aIiIi8mbiOjUsPxEREZFX4zo1RERE5BPaMzUMaoiIiMiLadQsPxEREZEP0HDxPSIiIvIFaq5TQ0RERL6A69QQERGRT2D5iYiIiHwCd+kmIiIin9DeU8NMDREREXkxrdRTw6CGiIiIvJi0+J6D5SciIiLyYmpuk0BERES+oH3vJ2ZqiIiIyIux/EREREQ+QczUWG0sPxEREZEX04izn7j4HhEREXkzLr5HREREPkFqFGamhoiIiLyZ1CjMTA0RERF5M65TQ0RERD6hvfzETA0RERF5MXH2k4VTuomIiMibiT01zNQQERGRV9Owp4aIiIh8AdepISIiIp+gVnKdGiIiIvIB0jYJzNQQERGRNxPXqbGwp4aIiIi8mZaZGiIiIvIFau795BkrV65EeHg4HnroIbfjJSUlWLx4MWbMmIE5c+Zg9+7dwzRCIiIi79Zx7ydB8I1szYgMap5++mn87W9/63JcrVZj69atOH/+PD7++GM888wzMJlMwzBCIiIi7yauUwMAdh9ZgG9EBjWLFy9GcHBwl+NxcXFISUkBAMTGxiIqKgr19fUeHh0REZH3E9epAXxnVeEBBzWHDh3C8uXLodfroVAosGfPni7nbNu2DUlJSfDz80NqaiqOHz8ux1jd5OTkwG63IyEhQfZrExER+TpxnRoAsPrIDCj1QF9gMpmQnJyMb3zjG3jggQe6PL9r1y5kZGRg+/btSE1NxdatW7F06VLk5+cjOjoaAJCSkgKbzdbltR9//DH0en2fY6ivr8fjjz+O1157rcdzzGYzzGaz9NhoNPbn9oiIiEYFTYdMja+sKjzgoCY9PR3p6ek9Pr9lyxasXbsWa9asAQBs374d+/btw44dO7B+/XoAQF5e3vWNFs5g5f7778f69etx880393je5s2b8bOf/ey634eIiMiXqZQKKBWAQ/Cd/Z9k7amxWCzIyclBWlpa+xsolUhLS0NWVtagry8IAp544gnccccdeOyxx3o9d8OGDTAYDNJPSUnJoN+fiIjIl0j7P/lIT82AMzW9qa2thd1uR0xMjNvxmJgYXLx4sd/XSUtLw6lTp2AymTB27Fjs3r0bCxcuxOHDh7Fr1y7MmTNH6uX5+9//jtmzZ3e5hk6ng06nG9T9EBER+TKNUgELfCdTI2tQI5dPPvmk2+O33norHD60SBAREdFwcmZq7D7TUyNr+SkqKgoqlQpVVVVux6uqqhAbGyvnWxEREdEgaXxsVWFZgxqtVot58+YhMzNTOuZwOJCZmYmFCxfK+VZEREQ0SOIMKKvNNzI1Ay4/NTc3o6CgQHpcWFiIvLw8REREIDExERkZGVi9ejXmz5+PBQsWYOvWrTCZTNJsKCIiIhoZxP2frD6SqRlwUJOdnY0lS5ZIjzMyMgAAq1evxs6dO7Fq1SrU1NRg48aNqKysREpKCvbv39+leZiIiIiGl0bpWzt1DzioWbx4cZ8bX61btw7r1q277kERERHR0JN26vaR2U8jcu8nIiIiGnrSTt0+sk4NgxoiIqJRSsNMDREREfkCaUVhH+mpYVBDREQ0SomZGl/ZpZtBDRER0SglrlPDxfeIiIjIq6mVYqaG5SciIiLyYmJPja+sU8OghoiIaJTi3k9ERETkE6R1apipISIiIm8mbWjJ2U9ERETkzbj4HhEREfkEaZfuAZSfLDYHXv7oIh77yzEYWqxDNbTrMuANLYmIiMg3iD01/W0ULm1owbp/nkReSSMA4J2TpVhzy/ihGt6AMaghIiIapdrLTz1nagRBwIWKJvz9aBH2nCxDq9UuPffBmQoGNURERDT8etv7qbShBVsOXMKRgjpUGtuk4/PGhWN9+jQ8vD0L2UUNqDK2ISbEz2Nj7g2DGiIiolGqp20S7A4B3/pbDs5XGAEAWpUSd82Iweqbk3BjUjgUCgXmjQtHTlEDPjxTgSdGSLaGQQ0REdEopXFtk/BeXjnOlxtx75w4PHFzEt7OKcX5CiOC/dTY9vW5WDA+An4aldtr75kdh5yiBnxwphKPL0zCW7mliAnxw+1TxgzHrQBgUENERDRqTYkNBgAYWq3ILmpAdlEDCmtN+OBMJQDg6TsnY1EPQco9s2Px873ncaKoHg9uP4KTxY0YG+6PAz+4Hf5aVbevGWoMaoiIiEappTNj8cWPlqC0oRUnrtVjy4FL+FtWEQAgKTIAjy9M6vG1caH+mJsYhtziRpwsbkSAVoXHF46TpokPBwY1REREo1hCRAASIgKwcGIkYkP9sP7t03AIwIZ7pkOr7n05u/++bQK+96+TuGt6DDYunwF9mL+HRt09hSAIvrHhQx+MRiNCQ0NhMBgQEhIy3MMhIiIakXKKGlDTZMbSmTFQKPrOujgcApTKocvODOT7m5kaIiIikswbFz6g84cyoBkobpNAREREPoFBDREREfkEBjVERETkExjUEBERkU9gUENEREQ+gUENERER+QQGNUREROQTGNQQERGRT2BQQ0RERD6BQQ0RERH5BAY1RERE5BMY1BAREZFPYFBDREREPmHU7NItCAIA5xbmRERE5B3E723xe7w3oyaoaWpqAgAkJCQM80iIiIhooJqamhAaGtrrOQqhP6GPD3A4HCgvL0dwcDAUCoWs1zYajUhISEBJSQlCQkJkvfZIwXv0DbxH3+Dr9+jr9wfwHgdCEAQ0NTVBr9dDqey9a2bUZGqUSiXGjh07pO8REhLis785RbxH38B79A2+fo++fn8A77G/+srQiNgoTERERD6BQQ0RERH5BAY1MtDpdNi0aRN0Ot1wD2XI8B59A+/RN/j6Pfr6/QG8x6EyahqFiYiIyLcxU0NEREQ+gUENERER+QQGNUREROQTGNQQERGRT2BQM0jbtm1DUlIS/Pz8kJqaiuPHjw/3kK7b5s2bceONNyI4OBjR0dG4//77kZ+f73bO4sWLoVAo3H6+853vDNOIB+6nP/1pl/FPmzZNer6trQ1PPfUUIiMjERQUhAcffBBVVVXDOOKBS0pK6nKPCoUCTz31FADv/AwPHTqE5cuXQ6/XQ6FQYM+ePW7PC4KAjRs3Ii4uDv7+/khLS8Ply5fdzqmvr8ejjz6KkJAQhIWF4Zvf/Caam5s9eBe96+0erVYrnn/+ecyePRuBgYHQ6/V4/PHHUV5e7naN7j77X/3qVx6+k5719Tk+8cQTXca/bNkyt3O8+XME0O2fTYVCgZdfflk6ZyR/jv35nujP36PFxcW49957ERAQgOjoaPzwhz+EzWYb9PgY1AzCrl27kJGRgU2bNiE3NxfJyclYunQpqqurh3to1+Xzzz/HU089haNHj+LAgQOwWq24++67YTKZ3M5bu3YtKioqpJ+XXnppmEZ8fWbOnOk2/i+//FJ67gc/+AHef/997N69G59//jnKy8vxwAMPDONoB+7EiRNu93fgwAEAwMMPPyyd422foclkQnJyMrZt29bt8y+99BJ+97vfYfv27Th27BgCAwOxdOlStLW1Sec8+uijOHfuHA4cOIC9e/fi0KFD+Na3vuWpW+hTb/fY0tKC3Nxc/OQnP0Fubi7eeecd5OfnY8WKFV3OfeGFF9w+2+9973ueGH6/9PU5AsCyZcvcxv+vf/3L7Xlv/hwBuN1bRUUFduzYAYVCgQcffNDtvJH6Ofbne6Kvv0ftdjvuvfdeWCwWHDlyBK+//jp27tyJjRs3Dn6AAl23BQsWCE899ZT02G63C3q9Xti8efMwjko+1dXVAgDh888/l47dfvvtwtNPPz18gxqkTZs2CcnJyd0+19jYKGg0GmH37t3SsQsXLggAhKysLA+NUH5PP/20MHHiRMHhcAiC4P2fIQDh3XfflR47HA4hNjZWePnll6VjjY2Ngk6nE/71r38JgiAI58+fFwAIJ06ckM758MMPBYVCIZSVlXls7P3V+R67c/z4cQGAUFRUJB0bN26c8Morrwzt4GTS3T2uXr1auO+++3p8jS9+jvfdd59wxx13uB3zps+x8/dEf/4e/eCDDwSlUilUVlZK57z66qtCSEiIYDabBzUeZmquk8ViQU5ODtLS0qRjSqUSaWlpyMrKGsaRycdgMAAAIiIi3I7/4x//QFRUFGbNmoUNGzagpaVlOIZ33S5fvgy9Xo8JEybg0UcfRXFxMQAgJycHVqvV7TOdNm0aEhMTvfYztVgseOONN/CNb3zDbSNXb/8MOyosLERlZaXb5xYaGorU1FTpc8vKykJYWBjmz58vnZOWlgalUoljx455fMxyMBgMUCgUCAsLczv+q1/9CpGRkbjhhhvw8ssvy5LS96SDBw8iOjoaU6dOxZNPPom6ujrpOV/7HKuqqrBv3z5885vf7PKct3yOnb8n+vP3aFZWFmbPno2YmBjpnKVLl8JoNOLcuXODGs+o2dBSbrW1tbDb7W4fCgDExMTg4sWLwzQq+TgcDjzzzDO45ZZbMGvWLOn417/+dYwbNw56vR6nT5/G888/j/z8fLzzzjvDONr+S01Nxc6dOzF16lRUVFTgZz/7GW677TacPXsWlZWV0Gq1Xb4kYmJiUFlZOTwDHqQ9e/agsbERTzzxhHTM2z/DzsTPprs/i+JzlZWViI6OdnterVYjIiLCKz/btrY2PP/883jkkUfcNgr8/ve/j7lz5yIiIgJHjhzBhg0bUFFRgS1btgzjaPtv2bJleOCBBzB+/HhcuXIFP/7xj5Geno6srCyoVCqf+xxff/11BAcHdylxe8vn2N33RH/+Hq2srOz2z6v43GAwqKFuPfXUUzh79qxbvwkAt9r17NmzERcXhzvvvBNXrlzBxIkTPT3MAUtPT5d+PWfOHKSmpmLcuHH497//DX9//2Ec2dD4y1/+gvT0dOj1eumYt3+Go53VasVXv/pVCIKAV1991e25jIwM6ddz5syBVqvFt7/9bWzevNkrluP/2te+Jv169uzZmDNnDiZOnIiDBw/izjvvHMaRDY0dO3bg0UcfhZ+fn9txb/kce/qeGE4sP12nqKgoqFSqLh3dVVVViI2NHaZRyWPdunXYu3cvPvvsM4wdO7bXc1NTUwEABQUFnhia7MLCwjBlyhQUFBQgNjYWFosFjY2Nbud462daVFSETz75BP/93//d63ne/hmKn01vfxZjY2O7NPDbbDbU19d71WcrBjRFRUU4cOCAW5amO6mpqbDZbLh27ZpnBiizCRMmICoqSvq96SufIwB88cUXyM/P7/PPJzAyP8eevif68/dobGxst39execGg0HNddJqtZg3bx4yMzOlYw6HA5mZmVi4cOEwjuz6CYKAdevW4d1338Wnn36K8ePH9/mavLw8AEBcXNwQj25oNDc348qVK4iLi8O8efOg0WjcPtP8/HwUFxd75Wf617/+FdHR0bj33nt7Pc/bP8Px48cjNjbW7XMzGo04duyY9LktXLgQjY2NyMnJkc759NNP4XA4pKBupBMDmsuXL+OTTz5BZGRkn6/Jy8uDUqnsUrLxFqWlpairq5N+b/rC5yj6y1/+gnnz5iE5ObnPc0fS59jX90R//h5duHAhzpw54xagikH6jBkzBj1Auk5vvvmmoNPphJ07dwrnz58XvvWtbwlhYWFuHd3e5MknnxRCQ0OFgwcPChUVFdJPS0uLIAiCUFBQILzwwgtCdna2UFhYKLz33nvChAkThEWLFg3zyPvv2WefFQ4ePCgUFhYKhw8fFtLS0oSoqCihurpaEARB+M53viMkJiYKn376qZCdnS0sXLhQWLhw4TCPeuDsdruQmJgoPP/8827HvfUzbGpqEk6ePCmcPHlSACBs2bJFOHnypDTz51e/+pUQFhYmvPfee8Lp06eF++67Txg/frzQ2toqXWPZsmXCDTfcIBw7dkz48ssvhcmTJwuPPPLIcN1SF73do8ViEVasWCGMHTtWyMvLc/vzKc4WOXLkiPDKK68IeXl5wpUrV4Q33nhDGDNmjPD4448P85216+0em5qahOeee07IysoSCgsLhU8++USYO3euMHnyZKGtrU26hjd/jiKDwSAEBAQIr776apfXj/TPsa/vCUHo++9Rm80mzJo1S7j77ruFvLw8Yf/+/cKYMWOEDRs2DHp8DGoG6fe//72QmJgoaLVaYcGCBcLRo0eHe0jXDUC3P3/9618FQRCE4uJiYdGiRUJERISg0+mESZMmCT/84Q8Fg8EwvAMfgFWrVglxcXGCVqsV4uPjhVWrVgkFBQXS862trcJ3v/tdITw8XAgICBBWrlwpVFRUDOOIr89HH30kABDy8/PdjnvrZ/jZZ591+3tz9erVgiA4p3X/5Cc/EWJiYgSdTifceeedXe69rq5OeOSRR4SgoCAhJCREWLNmjdDU1DQMd9O93u6xsLCwxz+fn332mSAIgpCTkyOkpqYKoaGhgp+fnzB9+nThl7/8pVtAMNx6u8eWlhbh7rvvFsaMGSNoNBph3Lhxwtq1a7v8I9GbP0fRH//4R8Hf319obGzs8vqR/jn29T0hCP37e/TatWtCenq64O/vL0RFRQnPPvusYLVaBz0+hWuQRERERF6NPTVERETkExjUEBERkU9gUENEREQ+gUENERER+QQGNUREROQTGNQQERGRT2BQQ0RERD6BQQ0RERH5BAY1RERE5BMY1BAREZFPYFBDREREPoFBDREREfmE/w8EV4rQtjTuXgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.semilogy(res.logger.values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and visualize graphically the solution" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(array([561.597, 628.269, 682.218, 680.56 ]),\n", + " array([561.596, 628.269, 682.218, 680.56 ]))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np \n", + "ref = np.linalg.solve(epanet_A.todense(), epanet_b)\n", + "\n", + "plt.scatter(ref, res.solution)\n", + "plt.axline((0, 0), slope=1, linestyle=\"--\", color=\"gray\")\n", + "plt.xlabel(\"Classical EPANET solution\")\n", + "plt.ylabel(\"Quantum VQLS solution\")\n", + "plt.show()\n", + "\n", + "ref, res.solution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 86d9de51b60ad7888d323ba6664d0ef4fd547d25 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 12 Jul 2024 15:40:45 +0200 Subject: [PATCH 03/96] 3loops --- docs/notebooks/vqls_solver_Net3Loops.ipynb | 106 ++++++++++++++------- 1 file changed, 73 insertions(+), 33 deletions(-) diff --git a/docs/notebooks/vqls_solver_Net3Loops.ipynb b/docs/notebooks/vqls_solver_Net3Loops.ipynb index dde72de..5c1dc28 100644 --- a/docs/notebooks/vqls_solver_Net3Loops.ipynb +++ b/docs/notebooks/vqls_solver_Net3Loops.ipynb @@ -6,17 +6,17 @@ "source": [ "### Set up water network model\n", "\n", - "In this example, we test our quantum solvers into a slightly larger network as contained in `Net1Loops.inp`. Let's start by setting up the model:" + "In this example, we test our quantum solvers into a slightly larger network as contained in `Net3Loops.inp`. Let's start by setting up the model:" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -44,7 +44,7 @@ "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", "\n", "# set up network model\n", - "inp_file = 'networks/Net1Loops.inp'\n", + "inp_file = 'networks/Net3Loops.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", "# plot network\n", @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -76,20 +76,26 @@ "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", "Your EPANET temp dir: /home/nico/.epanet_quantum\n", "\n", - "Size of the Jacobian in EPANET simulator: 4\n", - "Size of the b vector in EPANET simulator: 4\n" + "Size of the Jacobian in EPANET simulator: 8\n", + "Size of the b vector in EPANET simulator: 8\n" ] }, { "data": { "text/plain": [ - "(name 2 3 4 5 1\n", - " 0 57.939995 31.496479 52.434612 21.174667 4.394531e-07,\n", - " name 1 2 3 4 5\n", - " 0 0.163867 0.059455 0.076645 0.043315 0.031685)" + "(name 2 3 4 5 6 7 \\\n", + " 0 52.194599 29.139265 42.472969 26.306131 27.643869 23.355785 \n", + " \n", + " name 8 9 1 \n", + " 0 13.969273 7.612091 4.394531e-07 ,\n", + " name 1 2 3 4 5 6 7 \\\n", + " 0 0.336412 0.052491 0.256151 0.03239 0.19043 0.078751 0.024721 \n", + " \n", + " name 8 9 10 11 \n", + " 0 -0.017889 0.020009 0.005311 0.007349 )" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -123,12 +129,45 @@ "print(f\"Size of the b vector in EPANET simulator: {epanet_b.shape[0]}\")\n", "\n", "# save number of nodes and pipes\n", - "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", + "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]),\n", "n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", "\n", "results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" ] }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Plot results on the network\n", + "pressure_at_5hr_ref = results_epanet.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr_ref, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -140,24 +179,25 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "VQLS Iteration 136 Cost 5.066e-08\n", - " Normal return from subroutine COBYLA\n", + "VQLS Iteration 1000 Cost 3.402e-02\n", + " Return from subroutine COBYLA because the MAXFUN limit has been reached.\n", "\n", - " NFVALS = 136 F = 5.066030E-08 MAXCV = 0.000000E+00\n", - " X =-1.347440E-02 -8.807702E-01 1.307708E+00 2.989201E+00 3.526457E+00\n", - " -2.800616E-01 2.854411E+00 1.854796E+00\n", + " NFVALS = 1000 F = 3.401889E-02 MAXCV = 0.000000E+00\n", + " X =-7.072034E-03 3.114092E+00 1.274971E+00 -3.272231E+00 2.494664E-01\n", + " -6.452977E-01 -9.303278E-02 1.149657E+00 2.358349E+00 -1.923387E+00\n", + " -1.527626E+00 3.988339E+00\n", "Optimization terminated successfully.\n", " Current function value: 0.000000\n", - " Iterations: 3\n", - " Function evaluations: 63\n", - " Gradient evaluations: 7\n" + " Iterations: 84\n", + " Function evaluations: 1820\n", + " Gradient evaluations: 140\n" ] } ], @@ -196,22 +236,22 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -234,12 +274,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -250,11 +290,11 @@ { "data": { "text/plain": [ - "(array([561.597, 628.269, 682.218, 680.56 ]),\n", - " array([561.596, 628.269, 682.218, 680.56 ]))" + "(array([599.121, 603.574, 632.034, 601.561, 578.432, 620.536, 663.368, 647.877]),\n", + " array([599.102, 603.557, 632.028, 601.546, 578.42 , 620.529, 663.366, 647.874]))" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } From f9a72584bdc548ad34897d1d9d341de44aba71e5 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 12 Jul 2024 16:12:40 +0200 Subject: [PATCH 04/96] added hhl notebook --- docs/notebooks/hhl_Net0.ipynb | 309 ++++++++++++++++++++++++++++++++++ docs/notebooks/temp.bin | Bin 2316 -> 0 bytes docs/notebooks/temp.inp | 146 ---------------- docs/notebooks/temp.rpt | 28 --- 4 files changed, 309 insertions(+), 174 deletions(-) create mode 100644 docs/notebooks/hhl_Net0.ipynb delete mode 100644 docs/notebooks/temp.bin delete mode 100644 docs/notebooks/temp.inp delete mode 100644 docs/notebooks/temp.rpt diff --git a/docs/notebooks/hhl_Net0.ipynb b/docs/notebooks/hhl_Net0.ipynb new file mode 100644 index 0000000..cb7b8dd --- /dev/null +++ b/docs/notebooks/hhl_Net0.ipynb @@ -0,0 +1,309 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up water network model\n", + "\n", + "In this example, we test our quantum solvers into a slightly larger network as contained in `Net0.inp`. Let's start by setting up the model:|" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import wntr\n", + "import wntr_quantum\n", + "\n", + "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", + "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", + "# set up network model\n", + "inp_file = 'networks/Net0.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# plot network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n", + "\n", + "# print options\n", + "# dict(wn.options.hydraulic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve model using the classical Epanet simulator\n", + "\n", + "We now solve the same problem using the classical Epanet simulator. Note that, by default, `QuantumEpanetSimulator` uses a classical `CholeskySolver` to iteratively solve the linear problem." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", + "Your EPANET temp dir: /home/nico/.epanet_quantum\n", + "\n", + "Size of the Jacobian in EPANET simulator: 2\n", + "Size of the b vector in EPANET simulator: 2\n" + ] + }, + { + "data": { + "text/plain": [ + "(name J1 D1 R1\n", + " 0 29.647690 19.167675 -9.338379e-07\n", + " 3600 29.647692 19.167675 -9.338379e-07,\n", + " name P1 P2\n", + " 0 0.05 0.05\n", + " 3600 0.05 0.05)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "\n", + "# define the classical EPANET simulator\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", + "\n", + "# run the EPANET simulation\n", + "results_epanet = sim.run_sim()\n", + "\n", + "# remember to set up EPANET Quantum environment variables!\n", + "epanet_path = os.environ[\"EPANET_QUANTUM\"]\n", + "epanet_tmp = os.environ[\"EPANET_TMP\"]\n", + "\n", + "# check paths\n", + "print(f\"Your EPANET quantum path: {epanet_path}\")\n", + "print(f\"Your EPANET temp dir: {epanet_tmp}\\n\")\n", + "\n", + "util_path = os.path.join(epanet_path, 'src/py/')\n", + "sys.path.append(util_path)\n", + "\n", + "from quantum_linsolve import load_json_data\n", + "epanet_A, epanet_b = load_json_data(os.path.join(epanet_tmp,'smat.json'))\n", + "\n", + "# set the size of the Jacobian (A matrix)\n", + "epanet_A_dim = epanet_A.todense().shape[0]\n", + "print(f\"Size of the Jacobian in EPANET simulator: {epanet_A_dim}\")\n", + "print(f\"Size of the b vector in EPANET simulator: {epanet_b.shape[0]}\")\n", + "\n", + "# save number of nodes and pipes\n", + "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", + "n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", + "\n", + "results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a helper function\n", + "\n", + "Before proceeding to the proper quantum solution of the water network model, let's define a helper function. This function checks that the quantum results are within `TOL`% of those obtained classically. It also fills in lists containing the final values of pressures and flow rates obtained." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "TOL = 5 # => per cent\n", + "DELTA = 1.0e-12\n", + "\n", + "classical_res = []\n", + "quantum_res = []\n", + "\n", + "\n", + "def compare_results(classical_result, quantum_result):\n", + " \"\"\"\n", + " Helper function that compares the classical and quantum simulation results.\n", + " \"\"\"\n", + " def calculate_differences(classical_value, quantum_value):\n", + " \"\"\"Helper function to evaluate percentage difference between classical and quantum results.\"\"\"\n", + " is_close_to_classical = abs(classical_value - quantum_value) / abs(classical_value + DELTA) <= TOL / 100.0\n", + " if is_close_to_classical:\n", + " print(f\"Quantum result {quantum_value} within {TOL}% of classical result {classical_value}\")\n", + " quantum_res.append(quantum_value)\n", + " classical_res.append(classical_value)\n", + " return is_close_to_classical\n", + " \n", + " for link in classical_result.link[\"flowrate\"].columns:\n", + " classical_value = classical_result.link[\"flowrate\"][link].iloc[0]\n", + " quantum_value = quantum_result.link[\"flowrate\"][link].iloc[0]\n", + " message = f\"Flowrate {link}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert calculate_differences(classical_value, quantum_value), message\n", + "\n", + " for node in classical_result.node[\"pressure\"].columns:\n", + " classical_value = classical_result.node[\"pressure\"][node].iloc[0]\n", + " quantum_value = quantum_result.node[\"pressure\"][node].iloc[0]\n", + " message= f\"Pressure {node}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert calculate_differences(classical_value, quantum_value), message" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve water network with `QuantumEpanetSimulator` and VQLS \n", + "\n", + "We now solve the model using VQLS. In this example, we are **preconditioning** the initial linear system using *diagonal scaling* and also using a **mix of two classical optimizers**." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Quantum result 0.05003536120057106 within 5% of classical result 0.05000009015202522\n", + "Quantum result 0.05003482848405838 within 5% of classical result 0.05000000074505806\n", + "Quantum result 29.64763641357422 within 5% of classical result 29.647689819335938\n", + "Quantum result 19.16619110107422 within 5% of classical result 19.167675018310547\n", + "Quantum result -9.338378959000693e-07 within 5% of classical result -9.338378959000693e-07\n" + ] + }, + { + "data": { + "text/plain": [ + "(name J1 D1 R1\n", + " 0 29.647636 19.166191 -9.338379e-07\n", + " 3600 29.647129 19.150408 -9.338379e-07,\n", + " name P1 P2\n", + " 0 0.050035 0.050035\n", + " 3600 0.050042 0.050042)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from qiskit.primitives import Estimator\n", + "from quantum_newton_raphson.hhl_solver import HHL_SOLVER\n", + "\n", + "n_qubits = int(np.ceil(np.log2(epanet_A_dim)))\n", + "estimator = Estimator()\n", + "\n", + "linear_solver = HHL_SOLVER(\n", + " estimator=estimator,\n", + " # preconditioner=\"diagonal_scaling\",\n", + ")\n", + "\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn, linear_solver=linear_solver)\n", + "results_vqls = sim.run_sim(linear_solver=linear_solver)\n", + "\n", + "compare_results(results_epanet, results_vqls)\n", + "\n", + "results_vqls.node[\"pressure\"], results_vqls.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot pressures and flow rates\n", + "\n", + "Let's check graphically the equivalence of the results." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.scatter(classical_res[:n_pipes], quantum_res[:n_pipes], label=\"Flow rates\", color=\"blue\", marker=\"o\")\n", + "plt.scatter(classical_res[n_pipes:], quantum_res[n_pipes:], label=\"Pressures\", color=\"red\", marker=\"s\", facecolors='none')\n", + "plt.axline((0, 0), slope=1, linestyle=\"--\", color=\"gray\", label=\"\")\n", + "plt.xlabel(\"Classical results\")\n", + "plt.ylabel(\"Quantum results\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/temp.bin b/docs/notebooks/temp.bin deleted file mode 100644 index cce5ed72d4e5ea98c3b5d430c5dbe73b920bb663..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2316 zcma#_I4q~*$H2hJz`(!=#N0p(10X&KumUj?m<1sO_#kv~Mq+Mek%F$Sf>TjqdSXsa zrjCM(x?^rqQGRh|zK()VYATS(PXP*t{G{l%CsksGunRx|6%-N&P zrE&;3XXNA;W#*;gGB!6|-v^f*NZbgYBv8(nfV>F-c~b)NW(4HT3CLRzkhdftZ%Cp1 zhz~yl`~hf4Ab??IJ*a$VhGJM5&jRFw*la+|4#Xfept2vN4p|PwhNJan)w8%-rJd>0YpnW8ylYn(sf`qh>waH z7`|!Ta|Gcxfvipp4AY#r!nZq_7{@uq^m{pi@EVg9hc6m>IWJeXbr!93b&mHlaK5_4 z!C5rg+*#>|x$~wuWGZYelH*dfyN=6l>~d6Tig&c1_Sxar z+t-A$EjT!U_BpqI3v`MTZg39lXLK^ovvZDZnCsMhS^Kra z1{G!p&*|sw4?j9?f2IDR{oEJ3?PtF`X5S;d#=c0b(tf9Jx4rwb$#%{w&e+{K`qN?Q zqX5S#;?o`0hpu$|*FMYP-)s@b<`7@U!_yu*sGjC>+?!-)ueM{YJuGI3qa7R=7>LD8 zz_20~6K6X}Rp6=)o9nELY|1?PY%EVLx9QHCWOJNvuFdX6>ui{fx7% Date: Wed, 17 Jul 2024 17:46:32 +0200 Subject: [PATCH 05/96] added hhl notebooks --- docs/notebooks/hhl_Net0.ipynb | 104 +++++-- docs/notebooks/hhl_Net1Loop.ipynb | 357 ++++++++++++++++++++++ docs/notebooks/temp.bin | Bin 0 -> 16052 bytes docs/notebooks/temp.inp | 491 ++++++++++++++++++++++++++++++ docs/notebooks/temp.rpt | 22 ++ 5 files changed, 949 insertions(+), 25 deletions(-) create mode 100644 docs/notebooks/hhl_Net1Loop.ipynb create mode 100644 docs/notebooks/temp.bin create mode 100644 docs/notebooks/temp.inp create mode 100644 docs/notebooks/temp.rpt diff --git a/docs/notebooks/hhl_Net0.ipynb b/docs/notebooks/hhl_Net0.ipynb index cb7b8dd..764b259 100644 --- a/docs/notebooks/hhl_Net0.ipynb +++ b/docs/notebooks/hhl_Net0.ipynb @@ -145,37 +145,46 @@ "metadata": {}, "outputs": [], "source": [ - "TOL = 5 # => per cent\n", + "TOL = 50 # => per cent\n", "DELTA = 1.0e-12\n", "\n", - "classical_res = []\n", - "quantum_res = []\n", + "\n", + "def get_ape_from_pd_series(quantum_pd_series, classical_pd_series):\n", + " \"\"\"Helper function to evaluate absolute percentage error between classical and quantum results.\"\"\"\n", + " ape = abs(quantum_pd_series - classical_pd_series) * 100.0 / abs(classical_pd_series + DELTA)\n", + " return ape\n", "\n", "\n", "def compare_results(classical_result, quantum_result):\n", " \"\"\"\n", " Helper function that compares the classical and quantum simulation results.\n", " \"\"\"\n", - " def calculate_differences(classical_value, quantum_value):\n", - " \"\"\"Helper function to evaluate percentage difference between classical and quantum results.\"\"\"\n", - " is_close_to_classical = abs(classical_value - quantum_value) / abs(classical_value + DELTA) <= TOL / 100.0\n", + " classical_data = []\n", + " quantum_data = []\n", + "\n", + " def check_ape(classical_value, quantum_value):\n", + " \"\"\"Helper function to check if the absolute percentage error between classical and quantum results is within TOL.\"\"\"\n", + " ape = abs(quantum_value - classical_value) * 100.0 / abs(classical_value + DELTA)\n", + " is_close_to_classical = ape <= TOL\n", " if is_close_to_classical:\n", - " print(f\"Quantum result {quantum_value} within {TOL}% of classical result {classical_value}\")\n", - " quantum_res.append(quantum_value)\n", - " classical_res.append(classical_value)\n", + " print(f\"Quantum result {quantum_value} within {ape}% of classical result {classical_value}\")\n", + " quantum_data.append(quantum_value)\n", + " classical_data.append(classical_value)\n", " return is_close_to_classical\n", - " \n", + "\n", " for link in classical_result.link[\"flowrate\"].columns:\n", " classical_value = classical_result.link[\"flowrate\"][link].iloc[0]\n", " quantum_value = quantum_result.link[\"flowrate\"][link].iloc[0]\n", " message = f\"Flowrate {link}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", - " assert calculate_differences(classical_value, quantum_value), message\n", + " assert check_ape(classical_value, quantum_value), message\n", "\n", " for node in classical_result.node[\"pressure\"].columns:\n", " classical_value = classical_result.node[\"pressure\"][node].iloc[0]\n", " quantum_value = quantum_result.node[\"pressure\"][node].iloc[0]\n", - " message= f\"Pressure {node}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", - " assert calculate_differences(classical_value, quantum_value), message" + " message = f\"Pressure {node}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert check_ape(classical_value, quantum_value), message\n", + "\n", + " return classical_data, quantum_data" ] }, { @@ -189,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -197,11 +206,11 @@ "output_type": "stream", "text": [ "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", - "Quantum result 0.05003536120057106 within 5% of classical result 0.05000009015202522\n", - "Quantum result 0.05003482848405838 within 5% of classical result 0.05000000074505806\n", - "Quantum result 29.64763641357422 within 5% of classical result 29.647689819335938\n", - "Quantum result 19.16619110107422 within 5% of classical result 19.167675018310547\n", - "Quantum result -9.338378959000693e-07 within 5% of classical result -9.338378959000693e-07\n" + "Quantum result 0.05003536120057106 within 0.07054196990023498% of classical result 0.05000009015202522\n", + "Quantum result 0.05003482848405838 within 0.06965547696130027% of classical result 0.05000000074505806\n", + "Quantum result 29.64763641357422 within 0.0001801346480760787% of classical result 29.647689819335938\n", + "Quantum result 19.16619110107422 within 0.007741769593393499% of classical result 19.167675018310547\n", + "Quantum result -9.338378959000693e-07 within 0.0% of classical result -9.338378959000693e-07\n" ] }, { @@ -215,7 +224,7 @@ " 3600 0.050042 0.050042)" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -234,11 +243,11 @@ ")\n", "\n", "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn, linear_solver=linear_solver)\n", - "results_vqls = sim.run_sim(linear_solver=linear_solver)\n", + "results_hhl= sim.run_sim(linear_solver=linear_solver)\n", "\n", - "compare_results(results_epanet, results_vqls)\n", + "classical_res, quantum_res = compare_results(results_epanet, results_hhl)\n", "\n", - "results_vqls.node[\"pressure\"], results_vqls.link[\"flowrate\"]" + "results_hhl.node[\"pressure\"], results_hhl.link[\"flowrate\"]" ] }, { @@ -252,12 +261,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -277,6 +286,51 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wntr.graphics.plot_network(\n", + " wn,\n", + " node_attribute=get_ape_from_pd_series(\n", + " results_hhl.node[\"pressure\"].iloc[0],\n", + " results_epanet.node[\"pressure\"].iloc[0]\n", + " ),\n", + " link_attribute=get_ape_from_pd_series(\n", + " results_hhl.link[\"flowrate\"].iloc[0],\n", + " results_epanet.link[\"flowrate\"].iloc[0],\n", + " ),\n", + " node_colorbar_label='Pressures',\n", + " link_colorbar_label='Flows',\n", + " node_size=50,\n", + " title=f\"{inp_file}: Absolute Percent Error\",\n", + " node_labels=False\n", + ")" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/docs/notebooks/hhl_Net1Loop.ipynb b/docs/notebooks/hhl_Net1Loop.ipynb new file mode 100644 index 0000000..f5a3d87 --- /dev/null +++ b/docs/notebooks/hhl_Net1Loop.ipynb @@ -0,0 +1,357 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up water network model\n", + "\n", + "In this example, we test our quantum solvers into a slightly larger network as contained in `Net0.inp`. Let's start by setting up the model:|" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import wntr\n", + "import wntr_quantum\n", + "\n", + "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", + "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", + "\n", + "# set up network model\n", + "inp_file = 'networks/Net1Loop.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# plot network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n", + "\n", + "# print options\n", + "# dict(wn.options.hydraulic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve model using the classical Epanet simulator\n", + "\n", + "We now solve the same problem using the classical Epanet simulator. Note that, by default, `QuantumEpanetSimulator` uses a classical `CholeskySolver` to iteratively solve the linear problem." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", + "Your EPANET temp dir: /home/nico/.epanet_quantum\n", + "\n", + "Size of the Jacobian in EPANET simulator: 2\n", + "Size of the b vector in EPANET simulator: 2\n" + ] + }, + { + "data": { + "text/plain": [ + "(name J1 D1 R1\n", + " 0 29.647690 19.167675 -9.338379e-07\n", + " 3600 29.647692 19.167675 -9.338379e-07,\n", + " name P1 P2\n", + " 0 0.05 0.05\n", + " 3600 0.05 0.05)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "\n", + "# define the classical EPANET simulator\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", + "\n", + "# run the EPANET simulation\n", + "results_epanet = sim.run_sim()\n", + "\n", + "# remember to set up EPANET Quantum environment variables!\n", + "epanet_path = os.environ[\"EPANET_QUANTUM\"]\n", + "epanet_tmp = os.environ[\"EPANET_TMP\"]\n", + "\n", + "# check paths\n", + "print(f\"Your EPANET quantum path: {epanet_path}\")\n", + "print(f\"Your EPANET temp dir: {epanet_tmp}\\n\")\n", + "\n", + "util_path = os.path.join(epanet_path, 'src/py/')\n", + "sys.path.append(util_path)\n", + "\n", + "from quantum_linsolve import load_json_data\n", + "epanet_A, epanet_b = load_json_data(os.path.join(epanet_tmp,'smat.json'))\n", + "\n", + "# set the size of the Jacobian (A matrix)\n", + "epanet_A_dim = epanet_A.todense().shape[0]\n", + "print(f\"Size of the Jacobian in EPANET simulator: {epanet_A_dim}\")\n", + "print(f\"Size of the b vector in EPANET simulator: {epanet_b.shape[0]}\")\n", + "\n", + "# save number of nodes and pipes\n", + "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", + "n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", + "\n", + "results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a helper function\n", + "\n", + "Before proceeding to the proper quantum solution of the water network model, let's define a helper function. This function checks that the quantum results are within `TOL`% of those obtained classically. It also fills in lists containing the final values of pressures and flow rates obtained." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "TOL = 50 # => per cent\n", + "DELTA = 1.0e-12\n", + "\n", + "\n", + "def get_ape_from_pd_series(quantum_pd_series, classical_pd_series):\n", + " \"\"\"Helper function to evaluate absolute percentage error between classical and quantum results.\"\"\"\n", + " ape = abs(quantum_pd_series - classical_pd_series) * 100.0 / abs(classical_pd_series + DELTA)\n", + " return ape\n", + "\n", + "\n", + "def compare_results(classical_result, quantum_result):\n", + " \"\"\"\n", + " Helper function that compares the classical and quantum simulation results.\n", + " \"\"\"\n", + " classical_data = []\n", + " quantum_data = []\n", + "\n", + " def check_ape(classical_value, quantum_value):\n", + " \"\"\"Helper function to check if the absolute percentage error between classical and quantum results is within TOL.\"\"\"\n", + " ape = abs(quantum_value - classical_value) * 100.0 / abs(classical_value + DELTA)\n", + " is_close_to_classical = ape <= TOL\n", + " if is_close_to_classical:\n", + " print(f\"Quantum result {quantum_value} within {ape}% of classical result {classical_value}\")\n", + " quantum_data.append(quantum_value)\n", + " classical_data.append(classical_value)\n", + " return is_close_to_classical\n", + "\n", + " for link in classical_result.link[\"flowrate\"].columns:\n", + " classical_value = classical_result.link[\"flowrate\"][link].iloc[0]\n", + " quantum_value = quantum_result.link[\"flowrate\"][link].iloc[0]\n", + " message = f\"Flowrate {link}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert check_ape(classical_value, quantum_value), message\n", + "\n", + " for node in classical_result.node[\"pressure\"].columns:\n", + " classical_value = classical_result.node[\"pressure\"][node].iloc[0]\n", + " quantum_value = quantum_result.node[\"pressure\"][node].iloc[0]\n", + " message = f\"Pressure {node}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert check_ape(classical_value, quantum_value), message\n", + "\n", + " return classical_data, quantum_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve water network with `QuantumEpanetSimulator` and VQLS \n", + "\n", + "We now solve the model using VQLS. In this example, we are **preconditioning** the initial linear system using *diagonal scaling* and also using a **mix of two classical optimizers**." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Quantum result 0.05003536120057106 within 0.07054196990023498% of classical result 0.05000009015202522\n", + "Quantum result 0.05003482848405838 within 0.06965547696130027% of classical result 0.05000000074505806\n", + "Quantum result 29.64763641357422 within 0.0001801346480760787% of classical result 29.647689819335938\n", + "Quantum result 19.16619110107422 within 0.007741769593393499% of classical result 19.167675018310547\n", + "Quantum result -9.338378959000693e-07 within 0.0% of classical result -9.338378959000693e-07\n" + ] + }, + { + "data": { + "text/plain": [ + "(name J1 D1 R1\n", + " 0 29.647636 19.166191 -9.338379e-07\n", + " 3600 29.647129 19.150408 -9.338379e-07,\n", + " name P1 P2\n", + " 0 0.050035 0.050035\n", + " 3600 0.050042 0.050042)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from qiskit.primitives import Estimator\n", + "from quantum_newton_raphson.hhl_solver import HHL_SOLVER\n", + "\n", + "n_qubits = int(np.ceil(np.log2(epanet_A_dim)))\n", + "estimator = Estimator()\n", + "\n", + "linear_solver = HHL_SOLVER(\n", + " estimator=estimator,\n", + " # preconditioner=\"diagonal_scaling\",\n", + ")\n", + "\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn, linear_solver=linear_solver)\n", + "results_hhl= sim.run_sim(linear_solver=linear_solver)\n", + "\n", + "classical_res, quantum_res = compare_results(results_epanet, results_hhl)\n", + "\n", + "results_hhl.node[\"pressure\"], results_hhl.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot pressures and flow rates\n", + "\n", + "Let's check graphically the equivalence of the results." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.scatter(classical_res[:n_pipes], quantum_res[:n_pipes], label=\"Flow rates\", color=\"blue\", marker=\"o\")\n", + "plt.scatter(classical_res[n_pipes:], quantum_res[n_pipes:], label=\"Pressures\", color=\"red\", marker=\"s\", facecolors='none')\n", + "plt.axline((0, 0), slope=1, linestyle=\"--\", color=\"gray\", label=\"\")\n", + "plt.xlabel(\"Classical results\")\n", + "plt.ylabel(\"Quantum results\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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b/docs/notebooks/temp.rpt new file mode 100644 index 0000000..6a0f84e --- /dev/null +++ b/docs/notebooks/temp.rpt @@ -0,0 +1,22 @@ + Page 1 Wed Jul 17 17:39:52 2024 + + ****************************************************************** + * E P A N E T * + * Hydraulic and Water Quality * + * Analysis for Pipe Networks * + * Version 2.2 * + ****************************************************************** + + Analysis begun Wed Jul 17 17:39:52 2024 + + + Hydraulic Status: + ----------------------------------------------------------------------- + 0:00:00: Balanced after 5 trials + 0:00:00: Reservoir River is emptying + 0:00:00: Reservoir Lake is closed + 0:00:00: Tank 1 is filling at 13.10 ft + 0:00:00: Tank 2 is emptying at 23.50 ft + 0:00:00: Tank 3 is filling at 29.00 ft + + Analysis ended Wed Jul 17 17:39:52 2024 From 1c1505042a6af69aa6218509fa7eb5bd55f3837f Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 5 Aug 2024 09:27:28 +0200 Subject: [PATCH 06/96] add new nbs --- 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node_labels=True)\n", + "\n", + "# print options\n", + "# dict(wn.options.hydraulic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve model using the classical Epanet simulator\n", + "\n", + "We now solve the same problem using the classical Epanet simulator. Note that, by default, `QuantumEpanetSimulator` uses a classical `CholeskySolver` to iteratively solve the linear problem." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", + "Your EPANET temp dir: /home/nico/.epanet_quantum\n", + "\n", + "Size of the Jacobian in EPANET simulator: 2\n", + "Size of the b vector in EPANET simulator: 2\n" + ] + }, + { + "data": { + "text/plain": [ + "(name J1 D1 R1\n", + " 0 29.647690 19.167675 -9.338379e-07\n", + " 3600 29.647692 19.167675 -9.338379e-07,\n", + " name P1 P2\n", + " 0 0.05 0.05\n", + " 3600 0.05 0.05)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "\n", + "# define the classical EPANET simulator\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", + "\n", + "# run the EPANET simulation\n", + "results_epanet = sim.run_sim()\n", + "\n", + "# remember to set up EPANET Quantum environment variables!\n", + "epanet_path = os.environ[\"EPANET_QUANTUM\"]\n", + "epanet_tmp = os.environ[\"EPANET_TMP\"]\n", + "\n", + "# check paths\n", + "print(f\"Your EPANET quantum path: {epanet_path}\")\n", + "print(f\"Your EPANET temp dir: {epanet_tmp}\\n\")\n", + "\n", + "util_path = os.path.join(epanet_path, 'src/py/')\n", + "sys.path.append(util_path)\n", + "\n", + "from quantum_linsolve import load_json_data\n", + "epanet_A, epanet_b = load_json_data(os.path.join(epanet_tmp,'smat.json'))\n", + "\n", + "# set the size of the Jacobian (A matrix)\n", + "epanet_A_dim = epanet_A.todense().shape[0]\n", + "print(f\"Size of the Jacobian in EPANET simulator: {epanet_A_dim}\")\n", + "print(f\"Size of the b vector in EPANET simulator: {epanet_b.shape[0]}\")\n", + "\n", + "# save number of nodes and pipes\n", + "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", + "n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", + "\n", + "results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a helper function\n", + "\n", + "Before proceeding to the proper quantum solution of the water network model, let's define a helper function. This function checks that the quantum results are within `TOL`% of those obtained classically. It also fills in lists containing the final values of pressures and flow rates obtained." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "TOL = 50 # => per cent\n", + "DELTA = 1.0e-12\n", + "\n", + "\n", + "def get_ape_from_pd_series(quantum_pd_series, classical_pd_series):\n", + " \"\"\"Helper function to evaluate absolute percentage error between classical and quantum results.\"\"\"\n", + " ape = abs(quantum_pd_series - classical_pd_series) * 100.0 / abs(classical_pd_series + DELTA)\n", + " return ape\n", + "\n", + "\n", + "def compare_results(classical_result, quantum_result):\n", + " \"\"\"\n", + " Helper function that compares the classical and quantum simulation results.\n", + " \"\"\"\n", + " classical_data = []\n", + " quantum_data = []\n", + "\n", + " def check_ape(classical_value, quantum_value):\n", + " \"\"\"Helper function to check if the absolute percentage error between classical and quantum results is within TOL.\"\"\"\n", + " ape = abs(quantum_value - classical_value) * 100.0 / abs(classical_value + DELTA)\n", + " is_close_to_classical = ape <= TOL\n", + " if is_close_to_classical:\n", + " print(f\"Quantum result {quantum_value} within {ape}% of classical result {classical_value}\")\n", + " quantum_data.append(quantum_value)\n", + " classical_data.append(classical_value)\n", + " return is_close_to_classical\n", + "\n", + " for link in classical_result.link[\"flowrate\"].columns:\n", + " classical_value = classical_result.link[\"flowrate\"][link].iloc[0]\n", + " quantum_value = quantum_result.link[\"flowrate\"][link].iloc[0]\n", + " message = f\"Flowrate {link}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert check_ape(classical_value, quantum_value), message\n", + "\n", + " for node in classical_result.node[\"pressure\"].columns:\n", + " classical_value = classical_result.node[\"pressure\"][node].iloc[0]\n", + " quantum_value = quantum_result.node[\"pressure\"][node].iloc[0]\n", + " message = f\"Pressure {node}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert check_ape(classical_value, quantum_value), message\n", + "\n", + " return classical_data, quantum_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve water network with `QuantumEpanetSimulator` and VQLS \n", + "\n", + "We now solve the model using VQLS. In this example, we are **preconditioning** the initial linear system using *diagonal scaling* and also using a **mix of two classical optimizers**." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Quantum result 0.05003536120057106 within 0.07054196990023498% of classical result 0.05000009015202522\n", + "Quantum result 0.05003482848405838 within 0.06965547696130027% of classical result 0.05000000074505806\n", + "Quantum result 29.64763641357422 within 0.0001801346480760787% of classical result 29.647689819335938\n", + "Quantum result 19.16619110107422 within 0.007741769593393499% of classical result 19.167675018310547\n", + "Quantum result -9.338378959000693e-07 within 0.0% of classical result -9.338378959000693e-07\n" + ] + }, + { + "data": { + "text/plain": [ + "(name J1 D1 R1\n", + " 0 29.647636 19.166191 -9.338379e-07\n", + " 3600 29.647129 19.150408 -9.338379e-07,\n", + " name P1 P2\n", + " 0 0.050035 0.050035\n", + " 3600 0.050042 0.050042)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from qiskit.primitives import BackendEstimator\n", + "from quantuminspire.sdk.qiskit.backend import QuantumInspireBackend\n", + "from quantuminspire.util.api.quantum_interface import QuantumInterface\n", + "from quantuminspire.sdk.models.hybrid_algorithm import HybridAlgorithm\n", + "\n", + "from quantum_newton_raphson.hhl_solver import HHL_SOLVER\n", + "\n", + "\n", + "\n", + "def calculate_wntr(backend: QuantumInspireBackend):\n", + " n_qubits = int(np.ceil(np.log2(epanet_A_dim)))\n", + " estimator = BackendEstimator(backend=backend)\n", + "\n", + " linear_solver = HHL_SOLVER(\n", + " estimator=estimator,\n", + " # preconditioner=\"diagonal_scaling\",\n", + " )\n", + "\n", + " sim = wntr_quantum.sim.QuantumEpanetSimulator(wn, linear_solver=linear_solver)\n", + " return sim.run_sim(linear_solver=linear_solver)\n", + "\n", + "def execute(qi: QuantumInterface) -> None:\n", + "\n", + " ground_state_energy_results = calculate_wntr(backend=QuantumInspireBackend(qi))\n", + " result = {}\n", + " result[\"total_energy\"] = ground_state_energy_results.nuclear_repulsion_energy + ground_state_energy_results.result.eigenvalue\n", + " qi.results = {\"result\": result}\n", + "\n", + "\n", + "def finalize(results):\n", + " return results\n", + "\n", + "\n", + "classical_res, quantum_res = compare_results(results_epanet, results_hhl)\n", + "results_hhl.node[\"pressure\"], results_hhl.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot pressures and flow rates\n", + "\n", + "Let's check graphically the equivalence of the results." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.scatter(classical_res[:n_pipes], quantum_res[:n_pipes], label=\"Flow rates\", color=\"blue\", marker=\"o\")\n", + "plt.scatter(classical_res[n_pipes:], quantum_res[n_pipes:], label=\"Pressures\", color=\"red\", marker=\"s\", facecolors='none')\n", + "plt.axline((0, 0), slope=1, linestyle=\"--\", color=\"gray\", label=\"\")\n", + "plt.xlabel(\"Classical results\")\n", + "plt.ylabel(\"Quantum results\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wntr.graphics.plot_network(\n", + " wn,\n", + " node_attribute=get_ape_from_pd_series(\n", + " results_hhl.node[\"pressure\"].iloc[0],\n", + " results_epanet.node[\"pressure\"].iloc[0]\n", + " ),\n", + " link_attribute=get_ape_from_pd_series(\n", + " results_hhl.link[\"flowrate\"].iloc[0],\n", + " results_epanet.link[\"flowrate\"].iloc[0],\n", + " ),\n", + " node_colorbar_label='Pressures',\n", + " link_colorbar_label='Flows',\n", + " node_size=50,\n", + " title=f\"{inp_file}: Absolute Percent Error\",\n", + " node_labels=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/hhl_Net1Loop.ipynb b/docs/notebooks/hhl_Net1Loop.ipynb index f5a3d87..d69205c 100644 --- a/docs/notebooks/hhl_Net1Loop.ipynb +++ b/docs/notebooks/hhl_Net1Loop.ipynb @@ -11,12 +11,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 1, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -44,7 +44,7 @@ "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", "\n", "# set up network model\n", - "inp_file = 'networks/Net1Loop.inp'\n", + "inp_file = 'networks/Net1Loops.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", "# plot network\n", @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -76,22 +76,20 @@ "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", "Your EPANET temp dir: /home/nico/.epanet_quantum\n", "\n", - "Size of the Jacobian in EPANET simulator: 2\n", - "Size of the b vector in EPANET simulator: 2\n" + "Size of the Jacobian in EPANET simulator: 4\n", + "Size of the b vector in EPANET simulator: 4\n" ] }, { "data": { "text/plain": [ - "(name J1 D1 R1\n", - " 0 29.647690 19.167675 -9.338379e-07\n", - " 3600 29.647692 19.167675 -9.338379e-07,\n", - " name P1 P2\n", - " 0 0.05 0.05\n", - " 3600 0.05 0.05)" + "(name 2 3 4 5 1\n", + " 0 57.939995 31.496479 52.434612 21.174667 4.394531e-07,\n", + " name 1 2 3 4 5\n", + " 0 0.163867 0.059455 0.076645 0.043315 0.031685)" ] }, - "execution_count": 2, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -142,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -199,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -207,25 +205,28 @@ "output_type": "stream", "text": [ "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", - "Quantum result 0.05003536120057106 within 0.07054196990023498% of classical result 0.05000009015202522\n", - "Quantum result 0.05003482848405838 within 0.06965547696130027% of classical result 0.05000000074505806\n", - "Quantum result 29.64763641357422 within 0.0001801346480760787% of classical result 29.647689819335938\n", - "Quantum result 19.16619110107422 within 0.007741769593393499% of classical result 19.167675018310547\n", - "Quantum result -9.338378959000693e-07 within 0.0% of classical result -9.338378959000693e-07\n" + "Quantum result 0.1623460203409195 within 0.9281053240305208% of classical result 0.16386687755584717\n", + "Quantum result 0.05410035699605942 within 9.006576108687334% of classical result 0.05945523828268051\n", + "Quantum result 0.08046545088291168 within 4.984521429924255% of classical result 0.07664506137371063\n", + "Quantum result 0.044026244431734085 within 1.6423844658899842% of classical result 0.043314848095178604\n", + "Quantum result 0.034720078110694885 within 9.578156839629255% of classical result 0.03168521821498871\n", + "Quantum result 58.00852966308594 within 0.11828591165149603% of classical result 57.93999481201172\n", + "Quantum result 34.20833206176758 within 8.61001962911399% of classical result 31.496479034423828\n", + "Quantum result 52.49580001831055 within 0.11669342345984283% of classical result 52.43461227416992\n", + "Quantum result 20.140758514404297 within 4.882763098443745% of classical result 21.174667358398438\n", + "Quantum result 4.39453117451194e-07 within 0.0% of classical result 4.39453117451194e-07\n" ] }, { "data": { "text/plain": [ - "(name J1 D1 R1\n", - " 0 29.647636 19.166191 -9.338379e-07\n", - " 3600 29.647129 19.150408 -9.338379e-07,\n", - " name P1 P2\n", - " 0 0.050035 0.050035\n", - " 3600 0.050042 0.050042)" + "(name 2 3 4 5 1\n", + " 0 58.00853 34.208332 52.4958 20.140759 4.394531e-07,\n", + " name 1 2 3 4 5\n", + " 0 0.162346 0.0541 0.080465 0.044026 0.03472)" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -262,12 +263,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -289,12 +290,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -305,10 +306,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/notebooks/hhl_solver_Net1Loops.ipynb b/docs/notebooks/hhl_solver_Net1Loops.ipynb new file mode 100644 index 0000000..c560eda --- /dev/null +++ b/docs/notebooks/hhl_solver_Net1Loops.ipynb @@ -0,0 +1,294 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up water network model\n", + "\n", + "In this example, we test our quantum solvers into a slightly larger network as contained in `Net1Loops.inp`. Let's start by setting up the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import wntr\n", + "import wntr_quantum\n", + "\n", + "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", + "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", + "\n", + "# set up network model\n", + "inp_file = 'networks/Net1Loops.inp'\n", + "inp_file = 'networks/Net3Loops.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# plot network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n", + "\n", + "# print options\n", + "# dict(wn.options.hydraulic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve model using the classical Epanet simulator\n", + "\n", + "We now solve the same problem using the classical Epanet simulator. Note that, by default, `QuantumEpanetSimulator` uses a classical `CholeskySolver` to iteratively solve the linear problem." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", + "Your EPANET temp dir: /home/nico/.epanet_quantum\n", + "\n", + "Size of the Jacobian in EPANET simulator: 8\n", + "Size of the b vector in EPANET simulator: 8\n" + ] + }, + { + "data": { + "text/plain": [ + "(name 2 3 4 5 6 7 \\\n", + " 0 52.194599 29.139265 42.472969 26.306131 27.643869 23.355785 \n", + " \n", + " name 8 9 1 \n", + " 0 13.969273 7.612091 4.394531e-07 ,\n", + " name 1 2 3 4 5 6 7 \\\n", + " 0 0.336412 0.052491 0.256151 0.03239 0.19043 0.078751 0.024721 \n", + " \n", + " name 8 9 10 11 \n", + " 0 -0.017889 0.020009 0.005311 0.007349 )" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "\n", + "# define the classical EPANET simulator\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", + "\n", + "# run the EPANET simulation\n", + "results_epanet = sim.run_sim()\n", + "\n", + "# remember to set up EPANET Quantum environment variables!\n", + "epanet_path = os.environ[\"EPANET_QUANTUM\"]\n", + "epanet_tmp = os.environ[\"EPANET_TMP\"]\n", + "\n", + "# check paths\n", + "print(f\"Your EPANET quantum path: {epanet_path}\")\n", + "print(f\"Your EPANET temp dir: {epanet_tmp}\\n\")\n", + "\n", + "util_path = os.path.join(epanet_path, 'src/py/')\n", + "sys.path.append(util_path)\n", + "\n", + "from quantum_linsolve import load_json_data\n", + "epanet_A, epanet_b = load_json_data(os.path.join(epanet_tmp,'smat.json'))\n", + "\n", + "# set the size of the Jacobian (A matrix)\n", + "epanet_A_dim = epanet_A.todense().shape[0]\n", + "print(f\"Size of the Jacobian in EPANET simulator: {epanet_A_dim}\")\n", + "print(f\"Size of the b vector in EPANET simulator: {epanet_b.shape[0]}\")\n", + "\n", + "# save number of nodes and pipes\n", + "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", + "n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", + "\n", + "results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve linear system with VQLS and the final matrices from EPANET\n", + "\n", + "For testing purposes, we start by solving the linear system with VQLS and the final A and b matrices from the classical EPANET simulator. Here, we are **preconditioning** the initial linear system using diagonal scaling and also using a **mix of two classical optimizers**." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from qiskit.primitives import Estimator\n", + "\n", + "from quantum_newton_raphson.hhl_solver import HHL_SOLVER\n", + "\n", + "n_qubits = int(np.ceil(np.log2(epanet_A_dim)))\n", + "\n", + "estimator = Estimator()\n", + "\n", + "linear_solver = HHL_SOLVER(\n", + " estimator=estimator,\n", + ")\n", + "\n", + "\n", + "res = linear_solver(epanet_A, epanet_b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the evolution of the cost function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and visualize graphically the solution" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + 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v/oyb1UJM8zqVVKEo7IiIiNykgoICFixYwMGDl9a2atWqFZ07d3ZtUXIVhR0REZGbcOrUKRITE8nKysJqtdK3b1+io6OxWHQ7qqpR2BERESmjPXv2MH/+fOx2O8HBwSQkJFC/fn1XlyXXobAjIiJSRhEREVitVlq1asWQIUPw9tZEgFWZwo6IiEgp5OXl4efnB1wKO08++SRhYWG6bVUNaFJBERGRX2GMYdOmTXzwwQecPHnSsT08PFxBp5pQy46IiMh1XLx4kXnz5nHkyBEA9u3bR4MGDVxclZSVwo6IiMg1pKSkMHv2bHJycnBzc6N///507drV1WXJTVDYERERuYIxho0bN7J69WqMMYSGhjJy5EgiIyNdXZrcJIUdERGRKxw8eJBVq1YB0KFDBwYOHIiXl5eLq5JbobAjIiJyhTZt2tCuXTuaNWtGly5d1Am5BlDYERGRWu3y2ladOnXCy8sLi8VCfHy8Qk4NorAjIiK1Vl5eHnPmzOHYsWOcPHmS4cOHY7FYFHRqGIUdERGplZKTk5kzZw65ubm4u7vTrFkzhZwaSmFHRERqFbvdzvr161m/fj3GGMLCwhg5ciRhYWGuLk0qiMKOiIjUGrm5ucyZM4fk5GQAOnfuzIABA/Dw8HBxZVKRFHZERKRWycjIwMPDg4EDB9KpUydXlyOVQGFHRERqNGOMoy+Ov78/DzzwAL6+vtStW9fFlUll0UKgIiJSY+Xk5PDZZ5+xb98+x7ZGjRop6NQyatkREZEa6aeffmLu3LlcvHiRzMxMWrdujbu7vvZqI/2ti4hIjWK321m9ejUbN24EICIigpEjRyro1GL6mxcRkRojOzub2bNnc+LECQC6detGXFycgk4tp799ERGpES5evMi//vUv8vPz8fT0ZMiQIbRr187VZUkVoLAjIiI1gq+vL506deL48eMkJCQQGhrq6pKkilDYERGRais7OxuLxUJgYCAAsbGxGGN020qcaOi5iIhUS4cPH2batGkkJiZis9kAcHNzU9CRq+hfhIiIVCs2m43vvvuOzZs3O54XFBTg5+fn4sqkqnJpy84bb7yBxWJxerRu3dqxv6CggHHjxlGnTh38/f2Jj48nPT3d6RwpKSkMHDgQX19fwsPDefnllykpKanstyIiIpXg/PnzzJgxwxF0evTowe9+9zsFHflVLm/ZadeuHd99953j+ZXNjy+88AKLFy9m1qxZBAUFMX78eEaMGOGYO8FmszFw4EAiIyPZtGkTqampPPLII3h4ePD2229X+nsREZGKc/DgQebPn09hYSHe3t4MGzaMVq1aubosqQZcHnbc3d2JjIy8ant2djaffvopX331Fffddx8AM2bMoE2bNmzevJkePXqwYsUKDhw4wHfffUdERASdO3fmz3/+M6+88gpvvPEGnp6e13zNwsJCCgsLHc9zcnIq5s2JiEi5sNvtrFu3jsLCQho0aEB8fDzBwcGuLkuqCZd3UD5y5Aj16tWjWbNmjB49mpSUFAB27NhBcXExsbGxjmNbt25No0aNSEpKAiApKYkOHToQERHhOCYuLo6cnBz2799/3decPHkyQUFBjkfDhg0r6N2JiEh5sFqtJCQk0KtXLx599FEFHSkTl4ad6OhoZs6cybJly5g6dSrJycncddddXLhwgbS0NDw9Pa/6Bx0REUFaWhoAaWlpTkHn8v7L+65n4sSJZGdnOx6XZ9oUEZGqY//+/Y5fbgHq1q1Lnz59cHNzc2FVUh259DbW/fff7/hzx44diY6OpnHjxnz77bf4+PhU2Ot6eXnh5eVVYecXEZGbV1JSwrJly9ixYwcWi4VGjRpRv359V5cl1ZjLb2NdKTg4mNtuu42ffvqJyMhIioqKyMrKcjomPT3d0ccnMjLyqtFZl59fqx+QiIhUbefOneOTTz5hx44dAPTs2ZOoqCgXVyXVXZUKO7m5uRw9epSoqCi6du2Kh4cHq1atcuw/fPgwKSkpxMTEABATE8PevXvJyMhwHLNy5UoCAwNp27ZtpdcvIiI3b+/evUyfPp309HR8fX0ZPXo0ffr0wWqtUl9VUg259DbWSy+9xODBg2ncuDGnT59m0qRJuLm5MWrUKIKCgnj88ceZMGECoaGhBAYG8swzzxATE0OPHj0A6NevH23btuXhhx/m3XffJS0tjT/96U+MGzdOt6lERKqRpUuXsnXrVgAaN27MiBEjHEtAiNwql4adkydPMmrUKM6dO0dYWBi9evVi8+bNhIWFAfD+++9jtVqJj4+nsLCQuLg4PvroI8fPu7m5sWjRIsaOHUtMTAx+fn6MGTOGt956y1VvSUREbsLlz/277rqL3r17qzVHypXFGGNcXYSr5eTkEBQURHZ2tn6TEBGpJPn5+Y7BKMYYpz6ZIqVR2u9vRWcREalUxcXFzJ8/n+nTp1NQUACAxWJR0JEK4/IZlEVEpPY4c+YMs2bN4syZM1gsFo4dO6YBJVLhyhx2bDYbM2fOZNWqVWRkZGC32532r169utyKExGRmsEYw+7du1myZAklJSWOxZ2bNGni6tKkFihz2HnuueeYOXMmAwcOpH379lgsloqoS0REaoiioiIWL17Mnj17AGjWrBkjRozQSuVSacocdr7++mu+/fZbBgwYUBH1iIhIDfPdd9+xZ88eLBYL9957L7169dIvylKpyhx2PD09adGiRUXUIiIiNVDv3r1JTU0lNjaWxo0bu7ocqYXKPBrrxRdf5IMPPkAj1kVE5FoKCwvZvn2743vC19eX3/3udwo64jJlbtn5/vvvWbNmDUuXLqVdu3Z4eHg47Z8zZ065FSciItVLamoqiYmJZGZm4ubmRpcuXQB020pcqsxhJzg4mOHDh1dELSIiUk0ZY9i+fTvLly/HZrMRGBhInTp1XF2WCHATYWfGjBkVUYeIiFRTBQUFLFy4kAMHDgBw2223MXToUHx9fV1cmcglNz2p4JkzZzh8+DAArVq1cqxrIiIitUdqaiqzZs3i/PnzWK1W+vTpQ0xMjG5bSZVS5rCTl5fHM888w+eff+6YUNDNzY1HHnmEf/zjH0ryIiK1SH5+PufPnycoKIiEhAQaNGjg6pJErlLm0VgTJkxg3bp1LFy4kKysLLKyspg/fz7r1q3jxRdfrIgaRUSkCrlyNG6zZs2Ij4/nD3/4g4KOVFllXvW8bt26JCYm0rt3b6fta9as4YEHHuDMmTPlWV+l0KrnIiKlc+rUKRYtWsTIkSMJDQ11dTlSy1XYqucXL14kIiLiqu3h4eFcvHixrKcTEZFqwBjD5s2b+fe//01aWhrfffedq0sSKbUyh52YmBgmTZpEQUGBY1t+fj5vvvkmMTEx5VqciIi4Xn5+Pt988w3Lly/HbrfTpk0bhgwZ4uqyREqtzB2UP/jgA+Li4mjQoAGdOnUC4IcffsDb25vly5eXe4EiIuI6J06cYPbs2WRnZ+Pm5ka/fv244447NNpKqpUy99mBS7eyvvzySw4dOgRAmzZtGD16ND4+PuVeYGVQnx0RkaslJyfzxRdfYLfbCQ0NJSEhgaioKFeXJeJQ2u/vm5pnx9fXlyeffPKmixMRkaqvUaNGREZGEhoayqBBg/Dy8nJ1SSI3pVRhZ8GCBdx///14eHiwYMGCXz1W93FFRKqv06dPExkZidVqdcyh5unpqdtWUq2V6jaW1WolLS2N8PBwrNbr92m2WCzYbLZyLbAy6DaWiNR2xhjHQs89e/akT58+ri5J5IbK9TbW5ZmSf/lnERGp/vLy8pg7dy5Hjx4FLn2BGGPUmiM1RpmHnn/++ecUFhZetb2oqIjPP/+8XIoSEZHK8fPPPzNt2jSOHj2Ku7s7gwcPZtiwYQo6UqOUeTSWm5sbqamphIeHO20/d+4c4eHhuo0lIlIN2O12NmzYwLp16zDGULduXUaOHHnVZ7tIVVZho7Gu17R58uRJgoKCyno6ERFxgezsbDZu3Igxhk6dOjFgwAA8PT1dXZZIhSh12OnSpQsWiwWLxUKfPn1wd/9/P2qz2UhOTqZ///4VUqSIiJSvkJAQBg8ejM1mo3Pnzq4uR6RClTrsDBs2DIDdu3cTFxeHv7+/Y5+npydNmjQhPj6+3AsUEZFbZ7fbWb9+PU2bNqVx48YAdOjQwcVViVSOUoedSZMmAdCkSRMefPBBvL29K6woEREpPxcuXGDOnDn8/PPP7Ny5k3HjxmmCQKlVytxnZ8yYMRVRh4iIVICjR48yd+5c8vLy8PDwIDY2VkFHap0yhx2r1fqrQxKr42gsEZGaxm63s3btWjZs2ABAREQECQkJ1K1b18WViVS+MoedOXPmOIWd4uJidu3axWeffcabb75ZrsWJiEjZFRUV8eWXX5KSkgJA165diYuLw8PDw8WVibhGmcPO5Y7KV0pISKBdu3Z88803PP744+VRl4iI3CQPDw/8/f3x9PRk8ODBtG/f3tUlibhUmScVvJ5jx47RsWNHcnNzy+N0lUqTCopIdWez2bDZbI65cgoKCrh48SKhoaEurkyk4lTYpILXkp+fz5QpU6hfv355nE5ERMogOzub2bNn4+/vz8iRI7FYLHh7e2vUrMj/KXPYCQkJceqzY4zhwoUL+Pr68sUXX5RrcSIi8ut+/PFH5s2bR35+Pl5eXpw/f16tOSK/UOaw8/777zuFHavVSlhYGNHR0YSEhJRrcSIicm02m41Vq1aRlJQEQFRUFAkJCQo6ItdQ5rDz6KOPVkAZIiJSWllZWSQmJnLq1CkAunfvTt++fZ2W8RGR/6dU/zP27NlT6hN27NjxposREZFfZ4zhP//5DxkZGXh7ezNkyBDatGnj6rJEqrRShZ3OnTtjsVi40cAti8WiSQVFRCqQxWJh4MCBfPfdd4wYMYLg4GBXlyRS5VlLc1BycjLHjh0jOTn5Vx/Hjh276ULeeecdLBYLzz//vGNbQUEB48aNo06dOvj7+xMfH096errTz6WkpDBw4EB8fX0JDw/n5ZdfpqSk5KbrEBGpas6fP8+PP/7oeN6oUSMee+wxBR2RUipVy87lFXIryrZt2/jXv/511S2wF154gcWLFzNr1iyCgoIYP348I0aMYOPGjcClDnoDBw4kMjKSTZs2kZqayiOPPIKHhwdvv/12hdYsIlIZDh48yPz587Hb7Tz55JOEhYUB/OqyPSLirFQtO7909OhRnnnmGWJjY4mNjeXZZ5/l6NGjN1VAbm4uo0eP5uOPP3YazZWdnc2nn37Ke++9x3333UfXrl2ZMWMGmzZtYvPmzQCsWLGCAwcO8MUXX9C5c2fuv/9+/vznP/Phhx9SVFR0U/WIiFQFJSUlLFmyhG+//ZbCwkIiIiIcEwaKSNmUOewsX76ctm3bsnXrVjp27EjHjh3ZsmUL7dq1Y+XKlWUuYNy4cQwcOJDY2Fin7Tt27KC4uNhpe+vWrWnUqJFjqGVSUhIdOnQgIiLCcUxcXBw5OTns37//uq9ZWFhITk6O00NEpKrIzMzk3//+N9u2bQPgzjvv5NFHHyUoKMjFlYlUT2Uep/jqq6/ywgsv8M4771y1/ZVXXqFv376lPtfXX3/Nzp07Hf+hr5SWloanp+dV96QjIiJIS0tzHHNl0Lm8//K+65k8ebIWLRWRKmn//v0sWLCAoqIifHx8GD58OC1btnR1WSLVWplbdg4ePHjNxT5/97vfceDAgVKf58SJEzz33HN8+eWXlT6l+cSJE8nOznY8Tpw4UamvLyJyPenp6RQVFdGoUSOeeuopBR2RclDmlp2wsDB279591X/A3bt3Ex4eXurz7Nixg4yMDG6//XbHNpvNxvr16/nnP//J8uXLKSoqIisry6l1Jz09ncjISAAiIyPZunWr03kvj9a6fMy1eHl54eXlVepaRUQqkjHG0eG4d+/eBAYGcvvtt2O13lS3ShH5hTKHnSeffJLf//73HDt2jDvvvBOAjRs38pe//IUJEyaU+jx9+vRh7969Ttsee+wxWrduzSuvvELDhg3x8PBg1apVxMfHA3D48GFSUlKIiYkBICYmhv/5n/8hIyPDEbRWrlxJYGAgbdu2LetbExGpdHv27GHXrl2MHj0ad3d3rFYr3bp1c3VZIjVKmcPOa6+9RkBAAH/729+YOHEiAPXq1eONN97g2WefLfV5AgICaN++vdM2Pz8/6tSp49j++OOPM2HCBEJDQwkMDOSZZ54hJiaGHj16ANCvXz/atm3Lww8/zLvvvktaWhp/+tOfGDdunFpuRKRKKy4uZunSpezatQu41NodHR3t4qpEaqYyhx2LxcILL7zACy+8wIULF4BLwaUivP/++1itVuLj4yksLCQuLo6PPvrIsd/NzY1FixYxduxYYmJi8PPzY8yYMbz11lsVUo+ISHk4c+YMiYmJZGRkAHD33Xdzxx13uLgqkZrLYm60BsQv5OfnY4zB19cXgOPHjzN37lzatm1Lv379KqTIipaTk0NQUBDZ2dkEBga6uhwRqcF++OEHFi9eTHFxMX5+fowYMYJmzZq5uiyRaqm0399lbtkZOnQoI0aM4KmnniIrK4vu3bvj6enJ2bNnee+99xg7duwtFS4iUlNt2LCB1atXA9C0aVNGjBiBv7+/i6sSqfnK3NV/586d3HXXXQAkJiYSGRnJ8ePH+fzzz5kyZUq5FygiUlO0b98eHx8fevfuzUMPPaSgI1JJytyyc/HiRUcfnRUrVjBixAisVis9evTg+PHj5V6giEh1ZYwhLS2NqKgoAEJCQnjmmWfw8fFxcWUitUuZW3ZatGjBvHnzOHHiBMuXL3f008nIyFB/FxGR/1NUVMS8efOYPn2609qBCjoila/MYef111/npZdeokmTJkRHRzvmvFmxYgVdunQp9wJFRKqb9PR0pk+fzp49e7BYLJw7d87VJYnUamUejQWX1p1KTU2lU6dOjhk+t27dSmBgIK1bty73IiuaRmOJSHkwxrBz506WLVtGSUkJAQEBJCQk0KhRI1eXJlIjVdhoLLi0FMMvl2Po3r37zZxKRKRGKCwsZNGiRezbtw+Ali1bMmzYMMc0HSLiOjcVdkRExNlPP/3Evn37sFgs9OnThzvvvNOx3pWIuJbCjohIOWjXrh2pqam0atWKhg0burocEbmCltQVEbkJBQUFLF68mIsXLzq2xcbGKuiIVEHlFnYyMjJ4++23y+t0IiJV1unTp5k+fTrbt29nwYIFri5HRG6g3MJOamoqr732WnmdTkSkyjHGsGXLFj799FPOnz9PUFAQvXr1cnVZInID6rMjIlIK+fn5LFiwgEOHDgHQunVrhgwZokkCRaoBhR0RkRs4c+YMX331FVlZWVitVvr160f37t012kqkmlDYERG5gcvrAYaEhJCQkEC9evVcXJGIlEWpw86ECRN+df+ZM2duuRgRkaqisLAQT09PLBYL3t7ejB49Gn9/f7y9vV1dmoiUUanDzq5du254zN13331LxYiIVAUnTpwgMTGRXr16cccddwBQt25dF1clIjer1GFnzZo1FVmHiIjLGWPYtGkTq1evxm63s337drp27epYA1BEqif12RERAS5evMi8efM4cuQIAO3bt2fQoEEKOiI1QLn12bnsvffeu+liRERcISUlhcTERC5cuIC7uzv9+/fn9ttv12grkRripvvsfP/993Tt2tVpjgl9MIhIdXPhwgU+//xzbDYbderUYeTIkURERLi6LBEpRxZjjLmZHwwICOCHH36gWbNm5V1TpcvJySEoKIjs7GwCAwNdXY6IVLINGzZw9uxZBg4ciKenp6vLEZFSKu33t/rsiEit8/PPP+Pn50dYWBiAY8kHtU6L1EzqeScitYbdbmfdunV8/vnnJCYmUlxcDFwKOQo6IjWXWnZEpFbIzc1lzpw5JCcnA2gWZJFapNRhZ8+ePU7PjTEcOnSI3Nxcp+0dO3Ysn8pERMrJsWPHmDNnDnl5eXh4eDBw4EA6derk6rJEpJKUuoOy1WrFYrFwrcMvb7dYLNhstnIvsqKpg7JIzXT5ttX69esBCA8PJyEhwdFXR0Sqt3LvoHy56VdEpDr5+eefAejSpQv3338/Hh4eri1IRCpdqcNO48aNK7IOEZFyc7ml2Wq1Eh8fz/Hjx+nQoYOryxIRF7npPjvXoz47IuIqdrudNWvWYLPZ6NevHwCBgYEKOiK1XKnDTufOnZ367FwepnllH57q2mdHRKq/nJwcZs+eTUpKCnDpF6/IyEgXVyUiVcFN9dkxxtC+fXuWLFmi21si4nJHjhxh7ty55Ofn4+npyZAhQxR0RMThpvvsWCwWGjRooLAjIi5js9lYvXo1mzZtAiAqKoqEhARCQ0NdXJmIVCWaVFBEqiVjDF9//TU//fQTAHfccQf9+vXD3V0fayLiTJ8KIlItWSwWOnfuzIkTJxgyZAht27Z1dUkiUkXdUtjRWjIiUplsNhuZmZmOSQHbtWtH06ZN8fX1dXFlIlKVlTrsdOnSxSnc5OfnM3jwYDw9PZ2O27lzZ/lVJyLyf7KyskhMTOT8+fM89dRTBAQEACjoiMgNlTrsDBs2zOn50KFDy7sWEZFrOnToEPPnz6egoABvb28yMzMdYUdE5EZKvTZWTaa1sUSqppKSElauXMnWrVsBqF+/PgkJCQQHB7u2MBGpEsp9bSwRkcp0/vx5Zs2aRWpqKgAxMTH06dMHNzc3F1cmItWN1ZUvPnXqVDp27EhgYCCBgYHExMSwdOlSx/6CggLGjRtHnTp18Pf3Jz4+nvT0dKdzpKSkMHDgQHx9fQkPD+fll1+mpKSkst+KiJSzjRs3kpqaio+PD6NGjaJfv34KOiJyU1zastOgQQPeeecdWrZsiTGGzz77jKFDh7Jr1y7atWvHCy+8wOLFi5k1axZBQUGMHz+eESNGsHHjRuDSyIyBAwcSGRnJpk2bSE1N5ZFHHsHDw4O3337blW9NRG5Rv379KCkp4d577yUoKMjV5YhINVbl+uyEhoby17/+lYSEBMLCwvjqq69ISEgALnVSbNOmDUlJSfTo0YOlS5cyaNAgTp8+TUREBADTpk3jlVde4cyZM1eNFLse9dkRcb3MzEy2b99O3759Na2FiJRKab+/XXob60o2m42vv/6avLw8YmJi2LFjB8XFxcTGxjqOad26NY0aNSIpKQmApKQkOnTo4Ag6AHFxceTk5LB///7rvlZhYSE5OTlODxFxnX379vGvf/2LpKQkR2dkEZHyclO3sbZt28aaNWvIyMjAbrc77XvvvffKdK69e/cSExNDQUEB/v7+zJ07l7Zt27J79248PT2vGnURERFBWloaAGlpaU5B5/L+y/uuZ/Lkybz55ptlqlNEyl9xcTHLly9nx44dwKU1+Nq0aePiqkSkpilz2Hn77bf505/+RKtWrYiIiHBqbr6ZpudWrVqxe/dusrOzSUxMZMyYMaxbt67M5ymLiRMnMmHCBMfznJwcGjZsWKGvKSLOzp49S2JiomPQwV133UXv3r2xWqtMg7OI1BBlDjsffPAB//73v3n00UfLpQBPT09atGgBQNeuXdm2bRsffPABDz74IEVFRWRlZTm17qSnpxMZGQlAZGTkVU3elz84Lx9zLV5eXnh5eZVL/SJSdocOHWLOnDkUFxfj5+fH8OHDad68uavLEpEaqsy/QlmtVnr27FkRtQBgt9spLCyka9eueHh4sGrVKse+w4cPk5KSQkxMDHBp3o29e/eSkZHhOGblypUEBgZqUUCRKiwgIACbzUaTJk34wx/+oKAjIhWqzKOx3n33XU6fPs3f//73W37xiRMncv/999OoUSMuXLjAV199xV/+8heWL19O3759GTt2LEuWLGHmzJkEBgbyzDPPALBp0ybgUqfmzp07U69ePd59913S0tJ4+OGHeeKJJ8o09FyjsUQqXlFRkdMIyZMnT1KvXj3dthKRm1ZhMyi/9NJLDBw4kObNm9O2bVs8PDyc9s+ZM6fU58rIyOCRRx4hNTWVoKAgOnbs6Ag6AO+//z5Wq5X4+HgKCwuJi4vjo48+cvy8m5sbixYtYuzYscTExODn58eYMWN46623yvq2RKQC7d69mxUrVvDII484bjE3aNDAxVWJSG1R5pad8ePH88knn3Dvvfde1UEZYMaMGeVaYGVQy45IxSgqKmLJkiX88MMPANx+++0MHjzYxVWJSE1RYS07n332GbNnz2bgwIG3VKCI1GwZGRnMmjWLs2fPYrFY6N27N7169XJ1WSJSC5U57ISGhqozoYhclzGGXbt2sXTpUkpKSggICGDEiBE0adLE1aWJSC1V5p6Bb7zxBpMmTeLixYsVUY+IVHOHDx9m4cKFlJSU0Lx5c/7whz8o6IiIS5W5ZWfKlCkcPXqUiIgImjRpclUH5Z07d5ZbcSJS/dx22220aNGCxo0b07NnT61zJSIuV+awM2zYsAooQ0SqK2MM+/bto3Xr1nh4eGC1Wvntb3+rkCMiVUaVW/XcFTQaS+TmFBYWsmjRIvbt26eRViJS6SpsNJaICEBqaiqJiYlkZmZitVoJDQ3FGKMWHRGpcsocdqxW669+mNlstlsqSESqNmMM27dvZ/ny5dhsNoKCgoiPj9diuiJSZZU57MydO9fpeXFxMbt27eKzzz7jzTffLLfCRKTqKSgoYOHChRw4cACAVq1aMXToUHx8fFxcmYjI9ZU57AwdOvSqbQkJCbRr145vvvmGxx9/vFwKE5Gqp7CwkOTkZKxWK3379iU6Olq3rUSkyiu3DsrHjh2jY8eO5ObmlsfpKpU6KIuU3tGjR/H29qZ+/fquLkVEarnSfn+Xy3LD+fn5TJkyRR9+IjVMfn4+33zzDYcPH3Zsa968uf6vi0i1UubbWCEhIU7N1sYYLly4gK+vL1988UW5FicirnPy5EkSExPJzs7m5MmTNG/eHHd3DeAUkeqnzJ9c77//vlPYsVqthIWFER0dTUhISLkWJyKVzxjD5s2b+e6777Db7YSEhJCQkKCgIyLVVpk/ve677z4aNmx4zU6JKSkpNGrUqFwKE5HKl5+fz7x58/jxxx8BaNeuHYMGDcLb29vFlYmI3Lwyh52mTZuSmppKeHi40/Zz587RtGlTzbMjUk3l5+czbdo0cnJycHNzo3///nTt2lWjrUSk2itz2Lne4K3c3Fz99idSjfn4+NCyZUuSk5MZOXIkkZGRri5JRKRclDrsTJgwAQCLxcLrr7+Or6+vY5/NZmPLli107ty53AsUkYpz8eJF7HY7/v7+APTv3x+bzYaXl5eLKxMRKT+lDju7du0CLrXs7N27F09PT8c+T09POnXqxEsvvVT+FYpIhTh+/DizZ8+mbt26PPTQQ1itVtzd3dURWURqnFJ/qq1ZswaAxx57jA8++ECT74lUU8YYvv/+e9asWYMxBk9PT/Ly8ggICHB1aSIiFaLMv8LNmDGjIuoQkUqQl5fH3LlzOXr0KAAdO3Zk4MCBTi21IiI1TZnDTl5eHu+88w6rVq0iIyMDu93utP/YsWPlVpyIlJ+ff/6Z2bNnk5ubi7u7OwMGDKBz584abSUiNV6Zw84TTzzBunXrePjhh4mKitIHpUg1YLfbWbJkCbm5uYSFhZGQkHDV9BEiIjVVmRcCDQ4OZvHixfTs2bOiaqp0WghUaoP09HS2bt1KXFycbluJSI1QYQuBhoSEEBoaekvFiUjFO3bsGDt27HA8j4iIYPDgwQo6IlLrlDns/PnPf+b111/n4sWLFVGPiNwiu93OmjVr+N///V+WLFnC6dOnXV2SiIhLlbnPzt/+9jeOHj1KREQETZo0wcPDw2n/zp07y604ESmbCxcuMHv2bI4fPw5A586dCQsLc3FVIiKuVeawM2zYsAooQ0Ru1U8//cTcuXO5ePEinp6eDBo0iA4dOri6LBERlytzB+WaSB2Upbpbu3Yt69atAy71zRk5ciR16tRxcVUiIhWrtN/fmhdepAa4vJZV165d6d+/v5Z8EBG5Qpk/EW02G++//z7ffvstKSkpFBUVOe3PzMwst+JE5PqKi4sdfeZ69OhBVFQUTZo0cW1RIiJVUJlHY7355pu89957PPjgg2RnZzNhwgRGjBiB1WrljTfeqIASReRKNpuNlStXMn36dAoLCwGwWCwKOiIi11HmsPPll1/y8ccf8+KLL+Lu7s6oUaP45JNPeP3119m8eXNF1Cgi/yc7O5uZM2eyadMmzp49y+HDh11dkohIlVfm21hpaWmOER7+/v5kZ2cDMGjQIF577bXyrU5EHA4fPsy8efMoKCjAy8uLoUOH0qZNG1eXJSJS5ZU57DRo0IDU1FQaNWpE8+bNWbFiBbfffjvbtm1zdJIUkfJjs9n47rvvHC2n9erVIyEhgZCQEBdXJiJSPZQ57AwfPpxVq1YRHR3NM888w0MPPcSnn35KSkoKL7zwQkXUKFKrrVy5ki1btgAQHR1N3759cXNzc3FVIiLVxy3Ps5OUlERSUhItW7Zk8ODB5VVXpdI8O1KV5ebm8tlnnxEbG0urVq1cXY6ISJVR2u9vTSqIwo5ULSUlJRw+fJh27do5ttntdqzWMo8nEBGp0SpsUsHPP//8V/c/8sgjZT2liPyfzMxMEhMTSU1NxRhD+/btARR0RERuQZnDznPPPef0vLi42LEWj6+vr8KOyE3av38/CxcupLCwEB8fH3X4FxEpJ2X+dfH8+fNOj9zcXA4fPkyvXr34z3/+U6ZzTZ48mTvuuIOAgADCw8MZNmzYVfOGFBQUMG7cOOrUqYO/vz/x8fGkp6c7HZOSksLAgQPx9fUlPDycl19+mZKSkrK+NRGXKCkpYfHixSQmJlJYWEjDhg156qmnaNmypatLExGpEcqlbbxly5a88847V7X63Mi6desYN24cmzdvZuXKlRQXF9OvXz/y8vIcx7zwwgssXLiQWbNmsW7dOk6fPs2IESMc+202GwMHDqSoqIhNmzbx2WefMXPmTF5//fXyeGsiFercuXN8+umnbN++HYBevXrx6KOPqu+YiEg5KrcOyrt37+buu+8mJyfnps9x5swZwsPDWbduHXfffTfZ2dmEhYXx1VdfkZCQAMChQ4do06YNSUlJ9OjRg6VLlzJo0CBOnz5NREQEANOmTeOVV17hzJkzeHp6XvU6hYWFjmn24VIHp4YNG6qDslS6w4cP8/XXX+Pr68vw4cNp0aKFq0sSEak2KqyD8oIFC5yeG2NITU3ln//8Jz179ix7pVe4PBtzaGgoADt27KC4uJjY2FjHMa1bt6ZRo0aOsJOUlESHDh0cQQcgLi6OsWPHsn//frp06XLV60yePJk333zzlmoVKQ+tWrVi4MCB3HbbbQraIiIVpMxhZ9iwYU7PLRYLYWFh3Hffffztb3+76ULsdjvPP/88PXv2dIxASUtLw9PTk+DgYKdjIyIiSEtLcxxzZdC5vP/yvmuZOHEiEyZMcDy/3LIjUtHOnj3L4sWLGTZsGEFBQQB069bNxVWJiNRsZQ47dru9Iupg3Lhx7Nu3j++//75Czn8lLy8vjXSRSrdnzx4WLVpEcXExy5Yt48EHH3R1SSIitcJNd1A+e/bsLfXPudL48eNZtGgRa9asoUGDBo7tkZGRFBUVkZWV5XR8eno6kZGRjmN+OTrr8vPLx4i4UnFxMfPnz2fu3LkUFxfTpEkTBgwY4OqyRERqjTKFnaysLMaNG0fdunWJiIggJCSEyMhIJk6cyMWLF8v84sYYxo8fz9y5c1m9ejVNmzZ12t+1a1c8PDxYtWqVY9vhw4dJSUkhJiYGgJiYGPbu3UtGRobjmJUrVxIYGEjbtm3LXJNIeTpz5gwff/wxu3fvBuCee+7h4YcfJiAgwLWFiYjUIqUejZWZmUlMTAynTp1i9OjRtGnTBoADBw7w1Vdf0bp1a77//nv27NnD5s2befbZZ294zqeffpqvvvqK+fPnO635ExQUhI+PDwBjx45lyZIlzJw5k8DAQJ555hkANm3aBFwaet65c2fq1avHu+++S1paGg8//DBPPPEEb7/9dqkugpaLkIqQkpLCF198QXFxMf7+/owYMeKqQC8iIjev3EdjvfXWW3h6enL06NGrOgS/9dZb9OvXj4cffpgVK1YwZcqUUp1z6tSpAPTu3dtp+4wZM3j00UcBeP/997FarcTHx1NYWEhcXBwfffSR41g3NzcWLVrE2LFjiYmJwc/PjzFjxvDWW2+V9q2JVIioqChCQkLw9/dn+PDh+Pv7u7okEZFaqdQtO02aNOFf//oXcXFx19y/bNkyBgwYwKRJk5g0aVK5FlnR1LIj5eXcuXOEhIQ41rLKy8vD19cXi8Xi4spERGqe0n5/l7rPTmpqqtMqzL/Uvn17rFZrtQs6IuXBGMPOnTuZNm2a04hCPz8/BR0RERcr9W2sunXr8vPPPzuNlrpScnIy4eHh5VaYSHVRWFjI4sWL2bt3LwCnTp3CGKOQIyJSRZQ67MTFxfHHP/6RlStXXrUEQ2FhIa+99hr9+/cv9wJFqrK0tDQSExM5d+4cFouF++67j549eyroiIhUIaXus3Py5Em6deuGl5cX48aNo3Xr1hhjOHjwIB999BGFhYVs27aNRo0aVXTN5U59dqSsjDHs2LGDZcuWYbPZCAwMJD4+vlr++xcRqa7KfTRWgwYNSEpK4umnn2bixIlczkgWi4W+ffvyz3/+Ux/0UmtkZWU5gk7Lli0ZNmwYvr6+ri5LRESu4aZWPT9//jxHjhwBoEWLFo6FO6srtezIzdixYweFhYXExMTotpWIiAuU9vv7psJOTaOwIzdijGHbtm3Ur1+f+vXru7ocERGhAm5jidRWBQUFLFiwgIMHDxIcHMxTTz2lhWRFRKoRhR2RX3Hq1CkSExPJysrCarUSHR191WhEERGp2hR2RK7BGMOWLVtYuXIldrud4OBgEhISdAtLRKQaUtgR+YXi4mJmz57N4cOHAWjTpg1DhgzB29vbxZWJiMjNUNgR+QV3d3fsdjtubm7069ePO+64Q6OtRESqMYUdES7dtrLZbLi7u2OxWBg2bBjZ2dlERUW5ujQREblFCjtS6128eJH58+fj7e3NsGHDsFgs+Pr6apJAEZEaQmFHarWUlBRmz55NTk4Obm5u3HXXXdStW9fVZYmISDlS2JFayRjDxo0bWb16NcYYQkNDGTlypIKOiEgNpLAjtU5eXh7z5s3jp59+AqBDhw4MHDhQEwWKiNRQCjtSqxhj+PLLL0lNTcXd3Z3777+fLl26aLSViEgNZnV1ASKVyWKxEBsbS1hYGE888QS33367go6ISA2nhUDRQqA1XW5uLmfPnqVJkyaObXa7HatVWV9EpDrTQqAiQHJyMnPmzKGoqIg//OEPhIaGAijoiIjUIgo7UiPZ7XbWr1/P+vXrMcYQFhaG3W53dVkiIuICCjtS4+Tm5jJnzhySk5MB6Ny5MwMGDMDDw8PFlYmIiCso7EiNcuzYMebMmUNeXh4eHh4MGjSIjh07urosERFxIYUdqVGOHDlCXl4e4eHhmiRQREQAhR2pYWJjY/H19aVHjx66bSUiIoDm2ZFq7qeffuKbb77BZrMBONa3UtAREZHL1LIj1ZLdbmf16tVs3LgRgG3bttGjRw8XVyUiIlWRwo5UO9nZ2cyePZsTJ04A0K1bN7p16+biqkREpKpS2JFq5ccff2TevHnk5+fj5eXF4MGDadeunavLEhGRKkxhR6qNLVu2sGzZMgCioqJISEhwzIgsIiJyPQo7Um00a9YMDw8PunTpQt++fXF31z9fERG5MX1bSJWWmZnpaL0JCwtj/PjxWqxVRETKREPPpUqy2WwsW7aMf/7znxw/ftyxXUFHRETKSi07UuWcP3+exMRETp8+DUBKSgqNGzd2cVUiIlJdKexIlXLw4EHmz59PYWEh3t7eDBs2jFatWrm6LBERqcYUdqRKKCkpYcWKFWzbtg2ABg0akJCQQFBQkIsrExGR6k5hR6qEQ4cOOYLOnXfeyX333Yebm5uLqxIRkZpAYUeqhHbt2vHzzz/TqlUrWrZs6epyRESkBtFoLHGJkpISVq9eTUFBAQAWi4VBgwYp6IiISLlzadhZv349gwcPpl69elgsFubNm+e03xjD66+/TlRUFD4+PsTGxnLkyBGnYzIzMxk9ejSBgYEEBwfz+OOPk5ubW4nvQsrq3LlzfPLJJ2zYsIGFCxe6uhwREanhXBp28vLy6NSpEx9++OE197/77rtMmTKFadOmsWXLFvz8/IiLi3O0BgCMHj2a/fv3s3LlShYtWsT69ev5/e9/X1lvQcpo7969TJ8+nfT0dHx9fenSpYurSxIRkRrOYowxri4CLt3GmDt3LsOGDQMuterUq1ePF198kZdeegm4tNp1REQEM2fO5De/+Q0HDx6kbdu2bNu2zbHq9bJlyxgwYAAnT56kXr1613ytwsJCCgsLHc9zcnJo2LAh2dnZmrSughQXF7N06VJ27doFQOPGjYmPjycgIMDFlYmISHWVk5NDUFDQDb+/q2yfneTkZNLS0oiNjXVsCwoKIjo6mqSkJACSkpIIDg52BB2A2NhYrFYrW7Zsue65J0+eTFBQkOPRsGHDinsjwvnz5/nkk08cQefuu+/mkUceUdAREZFKUWXDTlpaGgARERFO2yMiIhz70tLSCA8Pd9rv7u5OaGio45hrmThxItnZ2Y7HiRMnyrl6uZKXlxf5+fn4+fnx8MMPc++992K1Vtl/eiIiUsPUyqHnXl5eeHl5ubqMGq2kpMSxKrmvry+jRo0iICAAf39/F1cmIiK1TZX99ToyMhKA9PR0p+3p6emOfZGRkWRkZDjtLykpITMz03GMVL6MjAymT5/O7t27HduioqIUdERExCWqbNhp2rQpkZGRrFq1yrEtJyeHLVu2EBMTA0BMTAxZWVns2LHDcczq1aux2+1ER0dXes21nTGGXbt28fHHH3PmzBk2bNiAzWZzdVkiIlLLufQ2Vm5uLj/99JPjeXJyMrt37yY0NJRGjRrx/PPP89///d+0bNmSpk2b8tprr1GvXj3HiK02bdrQv39/nnzySaZNm0ZxcTHjx4/nN7/5zXVHYknFKCoqYvHixezZsweA5s2bM3z4cC35ICIiLufSsLN9+3buvfdex/MJEyYAMGbMGGbOnMn/9//9f+Tl5fH73/+erKwsevXqxbJly/D29nb8zJdffsn48ePp06cPVquV+Ph4pkyZUunvpTZLT08nMTGRs2fPYrFYuPfee+nVqxcWi8XVpYmIiFSdeXZcqbTj9OVqubm5TJkyheLiYgICAoiPj6dx48auLktERGqB0n5/18rRWFJ+/P396dGjB6mpqQwfPhxfX19XlyQiIuJEYUfKLC0tDS8vL0JCQgDo3bs3FotFt61ERKRKqrKjsaTqMcawbds2PvnkE2bNmkVJSQkAVqtVQUdERKostexIqRQUFLBw4UIOHDgAQEBAgNPEgSIiIlWVvqnkhk6fPk1iYiLnz5/HarUSGxtLjx491JojIiLVgsKOXJcxhq1bt7Jy5UpsNhtBQUEkJCTQoEEDV5cmIiJSago7cl12u509e/Zgs9lo3bo1Q4YMwcfHx9VliYiIlInCjlyXm5sbCQkJHDlyhDvuuEO3rUREpFrSaCxxMMaQlJTE2rVrHdtCQkLo3r27go6IiFRbatkRAPLz85k/fz6HDx8GoFWrVkRFRbm4KhERkVunsCOcOHGC2bNnk52djZubG3FxcURGRrq6LBERkXKhsFOLGWPYtGkTq1evxm63ExoaSkJCglp0RESkRlHYqcVmz57N/v37AWjfvj2DBg3Cy8vLxVWJiIiUL4WdWqxFixYcOnSI+++/n9tvv12dkEVEpEZS2KlFjDHk5OQQFBQEQOfOnWnatKnjuYiISE2koee1RF5eHl9++SWffvopFy9edGxX0BERkZpOLTu1wM8//8zs2bPJzc3F3d2d06dP06JFC1eXJSIiUikUdmowu93Ohg0bWLduHcYY6taty8iRIwkPD3d1aSIiIpVGYaeGys3NZc6cOSQnJwPQqVMnBgwYgKenp4srExERqVwKOzXUmjVrSE5OxsPDgwEDBtC5c2dXlyQiIuISCjs1VN++fcnNzSU2NpawsDBXlyMiIuIyGo1VQ1y4cIENGzZgjAHA29ubUaNGKeiIiEitp5adGuDo0aPMnTuXvLw8fHx86Natm6tLEhERqTIUdqoxu93O2rVr2bBhAwARERE0bdrUxVWJiIhULQo71VROTg5z5szh+PHjAHTt2pW4uDg8PDxcXJmIiEjVorBTDR07dozZs2dz8eJFPD09GTx4MO3bt3d1WSIiIlWSwk415ObmRn5+PpGRkYwcOZLQ0FBXlyQiIlJlKexUEzabDTc3NwAaN27Mb3/7W5o0aYK7u/4KRUREfo2GnlcDP/74I//4xz84c+aMY1uLFi0UdEREREpBYacKs9lsrFixgv/85z9kZ2fz/fffu7okERGRakdNA1VUVlYWiYmJnDp1CoDo6GhiY2NdXJWIiEj1o7BTBR06dIj58+dTUFCAt7c3Q4cOpXXr1q4uS0REpFpS2KlifvzxR7755hsA6tevT0JCAsHBwa4tSkREpBpT2KliWrRoQaNGjahfvz59+vRxjMASERGRm6OwUwUcPXqUJk2a4ObmhtVq5ZFHHlHIERERKScajeVCJSUlLFmyhC+++IKVK1c6tivoiIiIlB+17LhIZmYmiYmJpKamAuDu7o4xBovF4uLKREREahaFHRfYv38/CxYsoKioCB8fH4YPH07Lli1dXZaIiEiNpLBTiYqLi1m+fDk7duwAoFGjRsTHxxMYGOjiykRERGquGtNn58MPP6RJkyZ4e3sTHR3N1q1bXV3SVXJzc9m3bx8AvXr1YsyYMQo6IiIiFaxGtOx88803TJgwgWnTphEdHc3f//534uLiOHz4MOHh4a4uzyEkJIRhw4bh4eFB8+bNXV2OiIhIrWAxxhhXF3GroqOjueOOO/jnP/8JgN1up2HDhjzzzDO8+uqrN/z5nJwcgoKCyM7OLteWluLiYpYtW0bbtm0VbkRERMpZab+/q/1trKKiInbs2OG0bpTVaiU2NpakpKRr/kxhYSE5OTlOj/J25swZPvnkE3bu3Mm8efMoLi4u99cQERGRG6v2Yefs2bPYbDYiIiKctkdERJCWlnbNn5k8eTJBQUGOR8OGDcu1ph9++IGPP/6YjIwM/Pz8GDFiBB4eHuX6GiIiIlI6NaLPTllNnDiRCRMmOJ7n5OSUS+ApKipi6dKl7N69G4CmTZsyYsQI/P39b/ncIiIicnOqfdipW7cubm5upKenO21PT08nMjLymj/j5eWFl5dXudZRUFDAv//9b86cOYPFYuGee+7hrrvuwmqt9o1nIiIi1Vq1/yb29PSka9eurFq1yrHNbrezatUqYmJiKq0Ob29v6tWrh7+/P4888gj33HOPgo6IiEgVUO1bdgAmTJjAmDFj6NatG927d+fvf/87eXl5PPbYYxX6ukVFRdhsNnx8fAAYMGAAxcXF+Pn5VejrioiISOnViLDz4IMPcubMGV5//XXS0tLo3Lkzy5Ytu6rTcnlKT09n1qxZhIaGMmrUKCwWC56ennh6elbYa4qIiEjZ1Yh5dm5VWebZMcawc+dOli1bRklJCQEBATz++OMEBQVVUrUiIiICpf/+rhEtO5WlsLCQRYsWOZZ8aNmyJcOGDcPX19fFlYmIiMj1KOyUUmpqKomJiWRmZmKxWOjTpw933nknFovF1aWJiIjIr1DYKQW73c6cOXPIzMwkMDCQhISEcp+IUERERCqGwk4pWK1Whg0bxsaNGxk8eLBj9JWIiIhUfZoI5jpOnz7N3r17Hc/r16/PAw88oKAjIiJSzahl5xeMMWzdupUVK1ZgsVgICwu77kzMIiIiUvUp7FwhPz+fpUuXcujQIQBat25NcHCwa4sSERGRW6Kwc4WZM2dSUFCAm5sbffv2pXv37hptJSIiUs0p7FwhKyuLqKgoEhISqFevnqvLERERkXKgsMOlfjoAjRs3ZsSIEXh7e5OTk+PiqkREROTXXP6uvtFiEFouAjh58qTmzREREammTpw4QYMGDa67X2GHS5MGnj59moCAgHLto5OTk0PDhg05ceLEDdfcqg10PZzpejjT9XCm6+FM18OZrsclxhguXLhAvXr1sFqvP5uObmNxadLAX0uEtyowMLBW/2P8JV0PZ7oeznQ9nOl6ONP1cKbrQakW4takgiIiIlKjKeyIiIhIjaawU4G8vLyYNGkSXl5eri6lStD1cKbr4UzXw5muhzNdD2e6HmWjDsoiIiJSo6llR0RERGo0hR0RERGp0RR2REREpEZT2BEREZEaTWGnAn344Yc0adIEb29voqOj2bp1q6tLqhDr169n8ODB1KtXD4vFwrx585z2G2N4/fXXiYqKwsfHh9jYWI4cOeJ0TGZmJqNHjyYwMJDg4GAef/xxcnNzK/FdlI/Jkydzxx13EBAQQHh4OMOGDePw4cNOxxQUFDBu3Djq1KmDv78/8fHxpKenOx2TkpLCwIED8fX1JTw8nJdffpmSkpLKfCvlYurUqXTs2NEx8VlMTAxLly517K9N1+Ja3nnnHSwWC88//7xjW226Jm+88QYWi8Xp0bp1a8f+2nQtLjt16hQPPfQQderUwcfHhw4dOrB9+3bH/tr0eVqujFSIr7/+2nh6epp///vfZv/+/ebJJ580wcHBJj093dWllbslS5aYP/7xj2bOnDkGMHPnznXa/84775igoCAzb94888MPP5ghQ4aYpk2bmvz8fMcx/fv3N506dTKbN282GzZsMC1atDCjRo2q5Hdy6+Li4syMGTPMvn37zO7du82AAQNMo0aNTG5uruOYp556yjRs2NCsWrXKbN++3fTo0cPceeedjv0lJSWmffv2JjY21uzatcssWbLE1K1b10ycONEVb+mWLFiwwCxevNj8+OOP5vDhw+a//uu/jIeHh9m3b58xpnZdi1/aunWradKkienYsaN57rnnHNtr0zWZNGmSadeunUlNTXU8zpw549hfm66FMcZkZmaaxo0bm0cffdRs2bLFHDt2zCxfvtz89NNPjmNq0+dpeVLYqSDdu3c348aNczy32WymXr16ZvLkyS6squL9MuzY7XYTGRlp/vrXvzq2ZWVlGS8vL/Of//zHGGPMgQMHDGC2bdvmOGbp0qXGYrGYU6dOVVrtFSEjI8MAZt26dcaYS+/dw8PDzJo1y3HMwYMHDWCSkpKMMZfCo9VqNWlpaY5jpk6dagIDA01hYWHlvoEKEBISYj755JNafS0uXLhgWrZsaVauXGnuueceR9ipbddk0qRJplOnTtfcV9uuhTHGvPLKK6ZXr17X3V/bP09vhW5jVYCioiJ27NhBbGysY5vVaiU2NpakpCQXVlb5kpOTSUtLc7oWQUFBREdHO65FUlISwcHBdOvWzXFMbGwsVquVLVu2VHrN5Sk7OxuA0NBQAHbs2EFxcbHT9WjdujWNGjVyuh4dOnQgIiLCcUxcXBw5OTns37+/EqsvXzabja+//pq8vDxiYmJq9bUYN24cAwcOdHrvUDv/fRw5coR69erRrFkzRo8eTUpKClA7r8WCBQvo1q0bI0eOJDw8nC5duvDxxx879tf2z9NbobBTAc6ePYvNZnP6DwgQERFBWlqai6pyjcvv99euRVpaGuHh4U773d3dCQ0NrdbXy2638/zzz9OzZ0/at28PXHqvnp6eBAcHOx37y+txret1eV91s3fvXvz9/fHy8uKpp55i7ty5tG3btlZeC4Cvv/6anTt3Mnny5Kv21bZrEh0dzcyZM1m2bBlTp04lOTmZu+66iwsXLtS6awFw7Ngxpk6dSsuWLVm+fDljx47l2Wef5bPPPgNq9+fprdKq5yIVZNy4cezbt4/vv//e1aW4VKtWrdi9ezfZ2dkkJiYyZswY1q1b5+qyXOLEiRM899xzrFy5Em9vb1eX43L333+/488dO3YkOjqaxo0b8+233+Lj4+PCylzDbrfTrVs33n77bQC6dOnCvn37mDZtGmPGjHFxddWbWnYqQN26dXFzc7tq1EB6ejqRkZEuqso1Lr/fX7sWkZGRZGRkOO0vKSkhMzOz2l6v8ePHs2jRItasWUODBg0c2yMjIykqKiIrK8vp+F9ej2tdr8v7qhtPT09atGhB165dmTx5Mp06deKDDz6olddix44dZGRkcPvtt+Pu7o67uzvr1q1jypQpuLu7ExERUeuuyZWCg4O57bbb+Omnn2rlv4+oqCjatm3rtK1NmzaOW3u19fO0PCjsVABPT0+6du3KqlWrHNvsdjurVq0iJibGhZVVvqZNmxIZGel0LXJyctiyZYvjWsTExJCVlcWOHTscx6xevRq73U50dHSl13wrjDGMHz+euXPnsnr1apo2beq0v2vXrnh4eDhdj8OHD5OSkuJ0Pfbu3ev0gbVy5UoCAwOv+iCsjux2O4WFhbXyWvTp04e9e/eye/dux6Nbt26MHj3a8efadk2ulJuby9GjR4mKiqqV/z569ux51VQVP/74I40bNwZq3+dpuXJ1D+ma6uuvvzZeXl5m5syZ5sCBA+b3v/+9CQ4Odho1UFNcuHDB7Nq1y+zatcsA5r333jO7du0yx48fN8ZcGioZHBxs5s+fb/bs2WOGDh16zaGSXbp0MVu2bDHff/+9admyZbUcKjl27FgTFBRk1q5d6zSc9uLFi45jnnrqKdOoUSOzevVqs337dhMTE2NiYmIc+y8Pp+3Xr5/ZvXu3WbZsmQkLC6uWw2lfffVVs27dOpOcnGz27NljXn31VWOxWMyKFSuMMbXrWlzPlaOxjKld1+TFF180a9euNcnJyWbjxo0mNjbW1K1b12RkZBhjate1MObSdATu7u7mf/7nf8yRI0fMl19+aXx9fc0XX3zhOKY2fZ6WJ4WdCvSPf/zDNGrUyHh6epru3bubzZs3u7qkCrFmzRoDXPUYM2aMMebScMnXXnvNREREGC8vL9OnTx9z+PBhp3OcO3fOjBo1yvj7+5vAwEDz2GOPmQsXLrjg3dyaa10HwMyYMcNxTH5+vnn66adNSEiI8fX1NcOHDzepqalO5/n555/N/fffb3x8fEzdunXNiy++aIqLiyv53dy63/3ud6Zx48bG09PThIWFmT59+jiCjjG161pczy/DTm26Jg8++KCJiooynp6epn79+ubBBx90mlOmNl2LyxYuXGjat29vvLy8TOvWrc306dOd9temz9PyZDHGGNe0KYmIiIhUPPXZERERkRpNYUdERERqNIUdERERqdEUdkRERKRGU9gRERGRGk1hR0RERGo0hR0RERGp0RR2REREpEZT2BGpRiwWC/Pmzavw11m7di0Wi+WqRRhv1s8//4zFYmH37t3lcr7a6o033qBz5863fJ6ZM2cSHBx8y+cRqS4UdkSqiLS0NJ555hmaNWuGl5cXDRs2ZPDgwU6L/lWWO++8k9TUVIKCgirtNXv37o3FYrnq8dRTTzmOuXJ7UFAQPXv2ZPXq1U7nOXnyJJ6enrRv3/6ar2OxWPD29ub48eNO24cNG8ajjz7qeP7oo49es57+/fs7wuCvPdauXVtu1+ZWNGnShL///e9O2x588EF+/PFH1xQk4gLuri5ARC61fPTs2ZPg4GD++te/0qFDB4qLi1m+fDnjxo3j0KFDlVqPp6cnkZGRlfqaAE8++SRvvfWW0zZfX1+n5zNmzKB///6cPXuWP/7xjwwaNIh9+/bRrFkz4FKrxQMPPMD69evZsmXLNVd6tlgsvP7663z22We/Wk///v2ZMWOG0zYvLy/8/PxITU11bHvuuefIyclxOjY0NLR0b9oFfHx88PHxcXUZIpVGLTsiVcDTTz+NxWJh69atxMfHc9ttt9GuXTsmTJjA5s2br/tzr7zyCrfddhu+vr40a9aM1157jeLiYsf+H374gXvvvZeAgAACAwPp2rUr27dvB+D48eMMHjyYkJAQ/Pz8aNeuHUuWLAGufRtr48aN9O7dG19fX0JCQoiLi+P8+fMALFu2jF69ehEcHEydOnUYNGgQR48eLfN18PX1JTIy0ukRGBjodExwcDCRkZG0b9+eqVOnkp+fz8qVKwEwxjBjxgwefvhhfvvb3/Lpp59e83XGjx/PF198wb59+361Hi8vr6vqCQkJcYTByw8fH5+rjvX09LzqfEVFRYwfP56oqCi8vb1p3LgxkydPduxPSUlh6NCh+Pv7ExgYyAMPPEB6evp16+vduzfPP/+807YrW6h69+7N8ePHeeGFFxwtTnDt21hTp06lefPmeHp60qpVK/73f//Xab/FYuGTTz5h+PDh+Pr60rJlSxYsWPCr10+kqlDYEXGxzMxMli1bxrhx4/Dz87tq/6/1rQgICGDmzJkcOHCADz74gI8//pj333/fsX/06NE0aNCAbdu2sWPHDl599VU8PDwAGDduHIWFhaxfv569e/fyl7/8BX9//2u+zu7du+nTpw9t27YlKSmJ77//nsGDB2Oz2QDIy8tjwoQJbN++nVWrVmG1Whk+fDh2u/0WrsyNXW6dKCoqAmDNmjVcvHiR2NhYHnroIb7++mvy8vKu+rmePXsyaNAgXn311Qqt75emTJnCggUL+Pbbbzl8+DBffvklTZo0AcButzN06FAyMzNZt24dK1eu5NixYzz44IM3/Xpz5syhQYMGvPXWW6Smpjq1Rl1p7ty5PPfcc7z44ovs27ePP/zhDzz22GOsWbPG6bg333yTBx54gD179jBgwABGjx5NZmbmTdcnUmlcvOq6SK23ZcsWA5g5c+bc8FjAzJ0797r7//rXv5quXbs6ngcEBJiZM2de89gOHTqYN95445r71qxZYwBz/vx5Y4wxo0aNMj179rxhfZedOXPGAGbv3r3GGGOSk5MNYHbt2nXdn7nnnnuMh4eH8fPzc3p88cUXjmOufP95eXnm6aefNm5ubuaHH34wxhjz29/+1jz//POO4zt16mRmzJjh9DqXz7F//37j5uZm1q9fb4wxZujQoWbMmDGO48aMGWPc3Nyuqud//ud/rqp9zJgxZujQoTe8Ls8884y57777jN1uv2rfihUrjJubm0lJSXFs279/vwHM1q1bjTHGTJo0yXTq1Mnpmj333HNO5/nl+2jcuLF5//33nY6ZMWOGCQoKcjy/8847zZNPPul0zMiRI82AAQMczwHzpz/9yfE8NzfXAGbp0qU3etsiLqeWHREXM8bc9M9+88039OzZk8jISPz9/fnTn/5ESkqKY/+ECRN44okniI2N5Z133nG6tfTss8/y3//93/Ts2ZNJkyaxZ8+e677O5Zad6zly5AijRo2iWbNmBAYGOlorrqylNEaPHs3u3budHkOGDHE6ZtSoUfj7+xMQEMDs2bP59NNP6dixI1lZWcyZM4eHHnrIcexDDz103VtZbdu25ZFHHvnV1p177733qnqu7DBdVo8++ii7d++mVatWPPvss6xYscKx7+DBgzRs2JCGDRs61RgcHMzBgwdv+jVL4+DBg/Ts2dNpW8+ePa963Y4dOzr+7OfnR2BgIBkZGRVam0h5UNgRcbGWLVtisVjK3Ak5KSmJ0aNHM2DAABYtWsSuXbv44x//6LilA5eGKu/fv5+BAweyevVq2rZty9y5cwF44oknOHbsGA8//DB79+6lW7du/OMf/7jma92oM+vgwYPJzMzk448/ZsuWLWzZsgXAqZbSCAoKokWLFk6PgIAAp2Pef/99du/eTVpaGmlpaYwZMwaAr776ioKCAqKjo3F3d8fd3Z1XXnmF77///rojj95880127tx53eH8fn5+V9VzKx2Pb7/9dpKTk/nzn/9Mfn4+DzzwAAkJCTd9PqvVelVYvrLPVnm7fAv0MovFUuG3KkXKg8KOiIuFhoYSFxfHhx9+eM3+Jdeb62bTpk00btyYP/7xj3Tr1o2WLVteNZwa4LbbbuOFF15gxYoVjBgxwmnEUMOGDXnqqaeYM2cOL774Ih9//PE1X6tjx47XHQJ/7tw5Dh8+zJ/+9Cf69OlDmzZtHB2XK0JkZCQtWrQgLCzMafunn37Kiy++6NQK88MPP3DXXXfx73//+5rnatiwIePHj+e//uu/HP2PKlpgYCAPPvggH3/8Md988w2zZ88mMzOTNm3acOLECU6cOOE49sCBA2RlZdG2bdtrnissLMypH47NZruq07Wnp+cN31ubNm3YuHGj07aNGzde93VFqhuFHZEq4MMPP8Rms9G9e3dmz57NkSNHOHjwIFOmTCEmJuaaP9OyZUtSUlL4+uuvOXr0KFOmTHG02gDk5+czfvx41q5dy/Hjx9m4cSPbtm2jTZs2ADz//PMsX76c5ORkdu7cyZo1axz7fmnixIls27aNp59+mj179nDo0CGmTp3K2bNnCQkJoU6dOkyfPp2ffvqJ1atXM2HChJu6DhcvXnS02Fx+lCY47d69m507d/LEE0/Qvn17p8eoUaP47LPPKCkpue57O336NN99991V+woLC6+q5+zZszf13gDee+89/vOf/3Do0CF+/PFHZs2aRWRkJMHBwcTGxtKhQwdGjx7Nzp072bp1K4888gj33HMP3bp1u+b57rvvPhYvXszixYs5dOgQY8eOvSocN2nShPXr13Pq1Knr1v7yyy8zc+ZMpk6dypEjR3jvvfeYM2cOL7300k2/V5EqxdWdhkTkktOnT5tx48aZxo0bG09PT1O/fn0zZMgQs2bNGscx/KKD8ssvv2zq1Klj/P39zYMPPmjef/99R8fTwsJC85vf/MY0bNjQeHp6mnr16pnx48eb/Px8Y4wx48ePN82bNzdeXl4mLCzMPPzww+bs2bPGmKs7KBtjzNq1a82dd95pvLy8THBwsImLi3PsX7lypWnTpo3x8vIyHTt2NGvXrnWqtbQdlIGrHnFxcdd9/5eNHz/etG3b9prnTU1NNVar1cyfP/+653j77bcNcFUH5WvV06pVq6teo7QdlKdPn246d+5s/Pz8TGBgoOnTp4/ZuXOnY//x48fNkCFDjJ+fnwkICDAjR440aWlpjv2/7KBcVFRkxo4da0JDQ014eLiZPHnyVR2Uk5KSTMeOHY2Xl5e5/JH/yw7Kxhjz0UcfmWbNmhkPDw9z2223mc8//9xp/7WuW1BQ0FUdwEWqIosxt9A7UkRERKSK020sERERqdEUdkRERKRGU9gRERGRGk1hR0RERGo0hR0RERGp0RR2REREpEZT2BEREZEaTWFHREREajSFHREREanRFHZERESkRlPYERERkRrt/wfvA/h1tlpk4AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(array([599.121, 603.574, 632.034, 601.561, 578.432, 620.536, 663.368, 647.877]),\n", + " array([610.144, 603.148, 629.941, 608.536, 573.377, 612.059, 662.437, 646.793]))" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np \n", + "import matplotlib.pyplot as plt\n", + "ref = np.linalg.solve(epanet_A.todense(), epanet_b)\n", + "\n", + "plt.scatter(ref, res.solution)\n", + "plt.axline((0, 0), slope=1, linestyle=\"--\", color=\"gray\")\n", + "plt.xlabel(\"Classical EPANET solution\")\n", + "plt.ylabel(\"Quantum HHL solution\")\n", + "plt.show()\n", + "\n", + "ref, res.solution" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "A = epanet_A.todense()\n", + "b = epanet_b \n", + "A.shape\n", + "# Apad = np.eye(8,8)\n", + "# Apad[:6,:6] = A\n", + "# bpad = np.zeros(8)\n", + "# bpad[:6] = b\n", + "circuits = linear_solver._solver.construct_circuit(A,b)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "circuits.decompose().draw('mpl')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/networks/Net0_HW.inp b/docs/notebooks/networks/Net0_HW.inp new file mode 100644 index 0000000..f22ed21 --- /dev/null +++ b/docs/notebooks/networks/Net0_HW.inp @@ -0,0 +1,128 @@ +[TITLE] +File obtained via Mario of a 2 node sysem + + +[JUNCTIONS] +;ID Elev Demand Pattern + J1 0 0 ; + D1 0 50 ; + +[RESERVOIRS] +;ID Head Pattern + R1 30 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + P1 R1 J1 100 250 0.05 0 Open ; + P2 J1 D1 1000 200 0.04 0 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + +[RULES] + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 1 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 1:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic None + +[REPORT] + Status No + Summary No + Page 0 + +[OPTIONS] + Units LPS + Headloss H-W + Specific Gravity 1 + Viscosity 1 + Trials 40 + Accuracy 0.1 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality None mg/L + Diffusivity 1 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +J1 10.00000 60.00000 +D1 110.00000 60.00000 +R1 -11.72214 74.24023 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 0.000 0.000 10000.000 10000.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/docs/notebooks/qubols_solver.ipynb b/docs/notebooks/qubols_solver.ipynb index ba88d7f..28a818f 100644 --- a/docs/notebooks/qubols_solver.ipynb +++ b/docs/notebooks/qubols_solver.ipynb @@ -13,12 +13,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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eKikpQWRkZJWPSSQS5Ofn47fffkNWVpZyu4GBARwcHODk5ITmzZujVatWcHV1RfPmzdG0adMqTy7VjU/YvXs3IiMj0bp1a0ybNg2HDx/GgAEDau39apuHDx/i2rVrOH36NE6ePImoqCjExsairKwMOjo6cHZ2RsuWLXHx4sVKr+3Tpw+io6PVX3Q9xTEDRFQvCYKAcePGwdDQEJs2bVJ5zMHBASNGjMCqVasAPBpXcOfOHcTGxuLmzZu4ceMG4uLikJCQgNTUVJSWlgJ4FCAaNmwIR0dHuLi4oGXLlmjTpg2aN28OFxcXGBsbo1evXpXGJ0RERGDPnj04fvw4fvnlF/V9CBqktLQU169fx9mzZxEREYGLFy8iNjYWpaWlkEgkaNasGTw8PNCjRw+4urqiU6dOsLGxQWpqKpydnVFcXKzcl6enJzp16oR27drh3XffFfFd1R8MA0RUL0VGRqJXr17Q09ODjo4OrK2t4eXlhX79+mH9+vX48ccf0bVr12fuR6FQICUlBXFxcbh9+zauX7+OW7du4c6dO0hOTkZeXp7yufr6+srg8LiJEyfi7t27KC0txZo1a2BhYaH80dfXr9H3rQnKysrw77//4uzZs4iMjMSFCxdw69YtPHz4EBKJBE5OTvDw8ICXlxe8vLzg7u6uMhjw3LlzmD59OgRBgCAImDZtGgwNDXH48GGcO3cOZmZm8PT0xIYNG2BoaCjiO60/GAaIqN6aMGECQkJCEBMTA1dX11o5Rl5eHuLj4xEbG4tFixbh0qVLL/R6AwMDmJiYwMTEBGZmZjAzM4O5uTlkMhksLCzQoEEDWFpaomHDhrC0tIRMJoO5ublKoDA2NoZEIqmV9/c8kpKSMHHiRKSnp0NPTw8LFizAqFGjcOTIEXz00UdQKBSwtbXFjh070KBBA5XXlpeX49atW8pv/BcuXMDNmzdRUlICiUSCxo0bK7/xV5z4TU1NRXqn2othgIjqpZCQEIwaNQqrV69WSyu4pKQEbm5uiI2NrfTYjz/+CIlEgqioKMycORO5ubnIzc1FVlYWsrKykJ2djaysLOTk5CA3Nxd5eXnIy8tDfn4+CgsLUVhYiKKiomqPraurC6lUqhIozMzMYGFhAZlMBktLS1haWqJBgwZo2LChMmg8HirMzMxe+s6K1NRU3L9/X2XMREBAAA4ePIj9+/fD3d0dc+fOhbm5OUaOHKnyjf/GjRvKFn7jxo3RoUMHeHl5oUePHvDw8IC5uflL1UQ1i2GAiOqd1NRUtGrVCl26dMHhw4dr/VtzxfgEZ2dnHDp0SGXMgJOTE86dO4chQ4Zg06ZNaNWq1Usdo7y8HHl5ecqwUBEosrOzkZ2djczMTOV/5+TkqASKgoICFBYWQi6XQ6FQVHsMY2PjSoGiokshk8kqdSmeDBT6+vro27evyvu3s7PDwIED8ffffyM3N1d5GcXOzg7u7u7o3r07evToAU9PT8hkspf6bKj2MQwQUb2iUCjQu3dvXLt2DTdv3oS1tXWtH7NifEL79u2hUCiQnZ0NLy8v/PPPP8jPz0fz5s0xb948TJw4sdZreRpBEFBYWFgpUOTk5Cg7FJmZmcjJyanUpSgoKEBBQQHkcnmV4yKeRiKRwMLCAu+99x569uyJTp06VbpcQHUbby0konpl6dKliIyMxL59+9QSBACgZ8+eVX7jvnDhAjp37oy5c+eKHgSARydlU1NTmJqawsHB4aX3U1JSgtzcXMTFxeHw4cMICwtDTEwM8vPzq3z+jBkzYGpqCgMDA95aWU+xM0BE9caVK1fQsWNHvPHGG8rFacTm4+ODO3fuID4+Hrq6umKX80rkcjlOnDiBv/76C2FhYcqFflxcXNC3b1/s378fKSkplV63du1aeHh44PPPP8eBAwfUXTbVAM5ASET1QklJCUaMGAFHR0fl/AF1wffff4/ExETl4jj1iUKhwIULF/DFF1+gS5cusLCwwMCBAxESEoIOHTpg06ZNSEtLw+3bt5Gfn4/JkyfD09NTZR9t27bFhAkTcOTIkVq7o4NqHzsDRFQvzJw5Exs2bMCFCxfQoUMHsctR4e3tjeTkZMTFxdX5tRDu3r2LAwcOYO/evTh58iTy8/NhZGSELl26YPDgwXjttdfQunVrlUGZ1Y2ZkMlkiIiIgL6+PhwcHLBp0ya1XbqhmsUwQER1XmhoKPz9/bFo0SJ8+umnYpdTyenTp+Hl5YWtW7di3LhxYpejIicnB0eOHMGePXtw9OhR3Lt3DxKJBG3btoWfnx+GDh2Kbt26wcDAQOxSSUQMA0RUp2VmZsLV1RUtW7ZEZGRknf3m3aNHD6SmpiI2NlbUGktLS3H69Gn8/fffCA0NxbVr1yAIAhwcHNC3b18MGzYM/fr1421+pIJhgIjqLEEQMGjQIJw8eRI3btx4pRHytS0yMhLe3t7YsWMHxowZo7bjCoKAf//9F/v378e+fftw9uxZFBcXw8zMDD169MCQIUMwaNAgNG3aVG01Uf3DMEBEddbatWsxY8YMtZ9gX1a3bt2QmZmJmzdv1mp34P79+wgNDcWePXtw4sQJZGRkQE9PD+7u7hg0aBCGDBkCDw+Pen93A6kPwwAR1Um3b99G+/btMWzYMOzYsUPscp7L8ePH4ePjg507dyIgIKDG9ltxy9+ePXvwzz//qNzy5+vri2HDhqF3794qi/kQvQiGASKqc0pLS9GxY0dkZWXhxo0bMDMzE7uk59a5c2fk5eXh33//felpkhUKBS5duoS9e/fiwIEDiI6ORmlpKRo2bIjevXtj6NCh8PPzQ6NGjWq4etJWnIGQiOqc+fPn49q1a4iIiKg3QSAnJwe+vr7IyclBXFwc3nnnHfz000/w8fFBWloajIyMADy688DY2LjS6xMSElRu+cvLy1Pe8vfNN99UecsfUU1hZ4CI6pSKe9rnzJmD7777Tuxynlt5eTlKSkoglUrh4eGBa9euISUlBQEBAfjxxx/h5uam8vzc3FyVW/6Sk5MhkUjQpk0b5S1/3bt35y1/pBYMA0RUZ+Tl5aFVq1awsbHBhQsXoKdXP5uXu3btwsiRIzF58mSEhobirbfewocffoiYmJgqb/nr06eP8pY/S0tLscsnLcQwQER1xqhRo7Bv3z5cvXoVLi4uYpfzwnJyctC7d2/cvn0bZWVlVa7+x1v+qC6qm7N3EJFGysnJQadOneDu7g43NzflYkPvvPMOLCwsEBISglWrVtXLIAAAMpkMMTEx+PLLL6sMAvr6+nB1dUXTpk2Rm5uLU6dO4cSJE4iNjYVcLq/xeqr7vM+dO4e2bduiefPm+Oqrr2r8uFT/1M8eHBHVS2ZmZjhx4gSkUikKCwvRtm1b5OXlITY2FkVFRTA3N8dbb70ldpmvLD4+vsrtpqamSE5ORmpqKjIyMlBSUqLyuImJCaysrGBra4tGjRrB0dERTZo0QdOmTeHg4AB7e3vY2dkpByM+y+Of94MHD9C6dWucOnUKx44dQ3BwMDp16oQePXpg+PDhaNeu3Su/b6q/eJmAiESRnJwMFxcXPHz4ULnN2NgYGRkZ9fJ++fv370MqlcLMzAwrVqzABx98UOk57dq1w4IFCzBq1CgIgoD8/HykpKQgJSUFycnJuHv3LpKSkpSBIT09HZmZmSqfEfAoVFhbW8PW1hZ2dnZwdHSEk5MTnJycYG9vD3t7ezRq1AiGhoYAHs1T0L17d1y+fFm5D09PT0RERGDdunUoKiqqk2s+kPqwM0BEalVxXf3GjRuVWulFRUUICgrC9OnTRaru5SUkJGD69OkQBAHl5eVwdHREUlKS8nEjIyP0799fORmRRCKBubk5zM3N0apVq2r3KwgCcnJykJKSgtTUVGVoSExMxL179xAbG4tTp04hMzMTZWVlKq81NzdHgwYNkJKSUilQREVFISgoCA4ODjh+/HgNfhJUHzEMEJFaVVxXnzhxIoKCgio9Hh0drf6iakCXLl1UapfL5QgKCkJ0dDTc3d0xYcKEl+p4SCQSWFpawtLSEm3btq32eYIgICsrS9lpSEpKUoaGitUKnxQdHY0+ffq8cE2keRgGiEjtsrKycOrUqSofc3d3V28xtUQqlaq1wyGRSNCwYUM0bNiw0vX/ijUenuTu7o579+7B3t5eXWVSHcUxA0SkNvfv38edO3cwcuRI5ObmorS0VKV9XTHQrT6OGaiLKsYx6OrqwsvLCzExMcrHWrVqhXPnzqF///5Yv349BxBqOd5aSERq8+uvv8LLywsZGRlo3LgxlixZgrVr18LV1RXm5uYoLS1Fy5YtsXPnTrFL1QgJCQnw9vZG9+7dUVZWhvHjx2PmzJmYM2cOJBIJ3N3d4e/vzyBA7AwQUe1TKBSYM2cOlixZgsGDByM4OBgmJiZil0VE/4edASKqVXl5efDz88PSpUuxYMEC7N27l0GAqI7hAEIiqjU3b96Ev78/0tPTsXv3bgwbNkzskoioCuwMEFGt2Lt3Lzw9PaFQKHDx4kUGAaq3qpvWGXh0Caxr167K+SPqK3YGiKhGCYKAL7/8El999RV8fHywe/duWFhYiF0W0Uurblrnbt26oaysDE2bNkV5ebnYZb4SDiAkohpTWFiIwMBA7N27F7Nnz8YPP/wAHR02IEkzVDWts5mZGbZv346NGzciJCRExOpeDTsDRFQj7t69iwEDBiAxMRHbtm1DYGCg2CUR1ZicnBy4ublVmskxPz8fR48eFamqmsPITqTBkpKS4OPjgzZt2qB9+/bK+/eLi4sxefJkuLq6onXr1oiMjHyp/UyePBnOzs5wcXGBi4sLcnJycPbsWQYB0jgymQxDhgyp8rHY2Fg1V1Pz2Bkg0mB6enpYsWIF3N3dER8fD09PT4SGhiIlJQVdu3bF77//jtLSUhQWFj73ftLS0uDp6YnU1FScPn0ajRo1wpkzZ9C1a1fs27cPDRs2VNO7I1Kv6qbKDg8PR1lZGaZPn45169apt6gawjEDRFpALpfD29sbUVFRym3u7u44efLkC0/9K5fLYWVlhaKiIuU2KysrxMXFwdzcvMZqJqornjats6enJ7799lts2LCBYwaIqG7bsmWLShAAHq1Y5+zsjC5duuDjjz9+7omAfvzxR5UgAAAZGRkYM2YM9u3bB11d3Rqrm6gueHJ56oEDB+LgwYOYP38+5s2bh3Pnzold4itjZ4BIC0yZMgW///57rR7DxcUFH3zwAd55551aPQ6R2KKiotCxY0dcvHgRnp6eYpdTI9gZINJwJSUl1Q4QnD9/PlxdXbF27VqsWrXqqft5+PAh/vOf/6Bx48bYu3dvpccHDhyI8+fP10jNRKReDANEGkwQBEyePBmjR4/GoUOHVC4VtGzZEvPmzcOaNWvQrVu3p37DEQQB48aNw9ChQ/HJJ59UGn/g4eEBuVyOtm3b1ur7IaLawcsERBosMjISvXr1Qvv27aFQKJCdnQ0vLy+0b98e+/btQ2FhIZycnLBp0yY0aNCg2v38+eefCAgIgKGhISQSCWxsbODn54f169dDV1cXenp6aNasGaKiomBsbKzGd0ikfrxMQET1Ss+ePaFQKKp8bP78+c+9Hy8vL1y6dEnl1sKKKYbnzJmDefPmYfLkyTh06BCGDx9eI7UTkfpw0iEieiY7OzvlPdbm5ubIycnBkiVLAACLFi2Ct7c35HI5JBKJiFUS0ctiZ4CIXsg333xT6dbCS5cuQU9PD0OHDhWpKiJ6FewMENFzy8rKwi+//FLlY9nZ2Thy5IiaKyKqfU8uYbxr1y4AwFtvvYUOHTqgTZs2+Oqrr0Su8tUwDBDRcykpKcHrr7+OYcOGVfl4u3btsGfPHjVXRVT7KpYwjo6OxrFjx/Djjz8CAPr27YvTp0/j8uXLOHDgAC5duiRypS+PYYCInqniFsW+fftizZo1lUZQ6+vrY/fu3YiKikJubq5IVRLVDl1dXUilUsjlcvj6+ir/jC9duhTe3t7Izc1FaWlpvR4zw1sLieiZHr9FEQAUCgWGDRumvLXQwsIC5ubmiI6OhlQqxbJly/DGG2/U638ciR5X3RLGAGBoaIhZs2bh+++/F6GymsEwQEQ1Jjk5GW+//TYOHDiAzp07Y+PGjZyIiDTG1KlTsXHjxkrb33zzTSQlJWHp0qVwc3MTobJXx8sERFRjGjdujP379+PAgQNITU1Fhw4d8N5776GgoEDs0oheybVr17B79+4qH+vSpQv69euHQ4cOqbmqmsMwQEQ1buDAgYiNjcXcuXOxdu1aODs7Izg4GGxEUn20YcMGdOrUCTKZDK1atVJ5zNPTE6NGjUJoaGilx+oTXiYgolp1584dvPXWWzh69Cg6d+4MiUSC/Px86OnpYcGCBRg1apTyuQEBAbh79y4uXLggYsVEjygUCnz22Wf47rvvYGZmBicnJwiCAHd3d0gkEpw4cQJmZmYAgNGjR2PhwoUiV/zyOOkQEdWqZs2a4ciRI9i9ezfefvttZGVl4YMPPsB//vMf9OjRA6mpqbhx4wYMDAzELpVIKT8/HwEBATh8+DDmzZuHr7/+Gjo6mttMZ2eAiNRGLpdj4cKFWLlyJaysrJCVlYWHDx8qH2/dujWMjIxUVkQkUrfY2Fj4+fkhNTUVQUFBGDFihNgl1TqGASJSu5s3b8LX1xfJycmVHmvSpAkSEhJEqIoIOHToEEaNGgWZTIbQ0FC0adNG7JLUQnN7HkRUZ1lbW6OwsLDKx55c94BIHQRBwLfffotBgwbBw8MDV65c0ZogADAMEJGaPWta4+zsbAwaNEjNVZE2KyoqwsiRI/HZZ5/hvffew7FjxyCTycQuS614mYCI1EYQBIwbNw6urq745JNP4O3trTI+oG3btjAwMOCYAVKbxMRE+Pn5IT4+Hr/++ismTJggdkmiYGeAiNTm5MmTCA4Oxl9//QUvLy+UlpZi2rRpAID58+dj586dGj1im+qW8PBwtG/fHtnZ2Thz5ozWBgGAYYAIADB8+HBYWloiICAAwKPbitzd3ZU/FhYWWLFihbhFaoCePXti2LBhSEhIQPPmzXH58mXMmDEDABAUFITAwECUlZVx3AC9sqSkJPj4+KBNmzZo3749du7cCQDKzpSdnR369u0LV1dXXLlyBR4eHiJXLC6GASIAs2bNwubNmwE8uv1t27Zt6N69O2bOnImTJ09CJpNVe42bXsyTn/Wff/4JAOjfvz9OnTqF6OhoGBsbi1kiaQA9PT2sWLEC169fx+HDhzFr1iysWrUK2dnZsLCwQFpaGmxtbfH111/D2tpa7HJFxzEDRP8nPDwcK1euRGJioso165YtW8Lc3Bznz58XsTrNUt1n7enpiYiICEilUhGrI00jl8thZWWl0nFq0qQJBg8ejO7du2PixIkiVlc3MAyQ2g0fPhzh4eHo168fQkJCAADbt2/HokWLIAgC3NzcsGnTJhgaGtbK8QsKCpCSkoKUlBQkJycjISEBiYmJiImJweXLl6tsUfv6+tbr5UnrmgsXLmDZsmW4efNmpccCAgKULV1tlJSUhIkTJyI9PV1lyua4uDiMGTMGOTk58PX1xc8//6yxS0QLgoCHDx+ioKAAhYWFVf6am5uLvLw85OXlIT8/H3l5eSgsLER+fj4KCwtRWFgIuVwOuVyOBw8eQC6XVzqOtbU1Ll68CEdHRxHeZd3CMEBqFx4ejvz8fGzatAmbN2/G5s2b8eGHH+Krr77CzJkzMXXqVAwbNgyBgYEvtF+5XI7U1FSkpKTg3r17uHv3LhISEnDv3j2kpqbi/v37yMjIqHSyNzY2hpWVFYyNjZGcnFzlPxqkPnZ2dli/fj0GDx4sdimiqPiz6u7ujrS0NHh6emLu3LlYuXIl/P398cMPP2DSpEmYPHkyXnvtNVFrFQQBRUVF1Z60K07Wj5+0K34qTtiPn7SLiopQXFyMoqIiKBSKZx7fwMAAxsbGyh+pVAoTExNIpVKYmprC1NQU+vr6CA4OVpnpskL37t1x6tSp2vho6h2GARJFdW1iDw8PNGrUCJ988gl8fHwAPLov/cmTfGJiIpKTk5GSkoL09HQ8ePCg0iQ2hoaGsLKygo2NDezs7NC4cWM4OTnByckJDg4OsLe3h52dnXKhkfDwcMyePRuXLl2qVO/8+fO1YkpSdXlaZ2Ds2LFwdXXFF198of7C6piq2tuenp745JNPcPToUaxdu/a59lNeXq5y8q3qpF3xTbviW3ZBQUGVJ+2ioiLlT0lJyTNXopRIJDAyMoKRkZHyhF1x0q74MTU1hZmZGczMzGBubg5zc3NYWFjAzMwMpqamyuc8/qtUKoWuru5Tj11SUoIuXbrg2rVrKC8vr/T42rVrMX369Of6DDUdFyoi0SQkJFQ68V66dAmWlpaYN28e0tLSkJGRgfz8fJXnGBgYoGHDhrCxsUGjRo3Qrl07NGnSBE2bNkXjxo1hb28Pe3t7mJmZvXAb1cnJCRKJRCWgNG7cGPPmzeN17BqUl5eH1q1bw8TEROWzlkgkuHfvHoPX//n8888rdbKioqKwevVqpKSkYNq0acjPz0dBQQEKCgogl8tRWFiIoqIilW/aVX0rfpKurq7yhP34SVsqlcLCwgIODg7Kk3PFSdvCwgLm5uYwMzOr8oRtamoKIyMjUS5nCIIAPz8/XLt2DR06dIBCoUB0dLTycQ8PD62+lfBJ7AyQKMLDw/Hmm28iPj6+0mMGBgZo2bIlOnXqhCZNmsDJyQmOjo7Kk7xMJqvxf1x8fX0RExODwsJCWFpaon///ti0aRPMzMxw6dIluLi41OjxtNmTn/X48eOxceNG6Ovr48GDBzA2NkZCQgIsLS3FLrVWlZaWIiUlBYmJiUhISEBsbCzi4+ORkJCAlJQUpKamVjtls66uLnR1dWFvbw9jY+NKrfGKk3bFiVomkym/eVd30ta0VSO//vprLFy4EKampmjWrBmAR+OVKu4esLW1hY6ODmbNmoUpU6aIXK34GAZIFE9ryU+fPh26urr46aefRKjskaioKHTs2BEXL16Ep6enaHVom1u3bqFjx47w8PDA0aNHoadXP5uXgiAgIyMDiYmJSExMVJ7o7969qxzDkpmZqdJiNzU1ha2tLRwcHODg4ICTJ0/CyckJERERlfZvamqKNm3a4ODBg2jQoIE631q9sGHDBrz99tsYPHgwQkJCNC7o1Ib6+TeNNEJVLXlPT0+Ul5dr1QIh9P+1bNkSISEhGDRoEN5991388ssvYpdUpYKCAiQlJSEpKQnx8fGIjY1VjmVJTU1Fenq6SmteX18fNjY2sLe3R/PmzdG/f380b94czs7OcHR0hKOjo3LsSsWUzVOmTKlyyuYWLVogLy8PFy5cgJ2dHV577TXMnj0bXl5eGnt3wYtYtmwZPvzwQ4wbNw6bN29+5rgCeoRhgNTuyTbxG2+8gU2bNqFhw4YoLi5GYWEh3n77bbHLJJH4+flh8eLF+Oijj9ChQwfMnDlTrcd/Vvs+LS0NBQUFyudLJBI0aNBAOUjV09MTzs7OaN68ufISl7W19XOfqCumbG7fvj3++usvKBQKLFy4ELdv38bJkydRVlaGoUOH4quvvsLatWuxbt069OzZEy4uLnjnnXcwdepUWFhY1NbHU6d9/vnn+OqrrzBjxgz89NNPDEcvgJcJSHR1sSVfF2vSJsOHD8eBAwfw8OFDhIeHo3fv3gAAhUKB7t27w9HRUTlHxYt4sn0fFxeHuLi4p7bvTUxM0KhRIzg4OKBJkybKE33Tpk3h6OgIBwcH6Ovr19h7f1EKhQKHDx/GsmXLcOTIEejr6+P111/Hhx9+iM6dO4tWlzoJgoD3338fq1atwqeffopvv/2WQeAFsTNARHXOrFmzMHHiRLz11lsYOnQo5syZg6SkJOTn5z91gpgn2/dxcXG4c+cOkpKSlLehvmz7vq7S0dGBv78//P39kZKSgp9++gnr169HcHAwXF1d8d577+GNN96Aqamp2KXWCoVCgalTp2LTpk347rvvMHfuXLFLqpfYGSDR1cVv4XWxJm0THh6OxYsX459//kFZWZlye+PGjWFra4shQ4YgLi4OiYmJuHfv3lPb9w4ODmjatKnyW32TJk3QpEmTF2rf1yfl5eXYv38/li9fjuPHj8PIyAgjR47E7NmzNWpBnrKyMowZMwa7d+/GmjVr1H5JSZOwM0CEqqdIBoDZs2cjLS0NOjo62Lt3L28xVLO0tDSVIAAAycnJSE5Oxr///otGjRrB3t4e3bp1Q7NmzdCiRQtl+97e3l5rR5Hr6upi6NChGDp0KJKSkrB69Wr89ttvCAoKgpubG9577z1MmDChXs+dUVxcjKFDh+Lo0aPYsmULxo8fL3ZJ9RrDABEetaUrWo2Pr6Qnk8lw4MABAHjmTGtU83Jzc6vc3rRpU9y5c0fN1dRPjo6OWLx4MRYtWoQ9e/Zg+fLlmDFjBmbPno0xY8Zg9uzZaNu2rdhlvpCCggIMGDAAFy5cwK5duzB06FCxS6r3uIQxEQAfHx+YmZmhvLwc3t7eWLRoEQBgz5498Pb2BvBoIBmpV3Wj4lNSUjiN7AvS09PDyJEjERkZibi4OLz11lvYtWsX3Nzc4OHhgd9//x3FxcVil/lMOTk56NmzJ6Kjo3Ho0CEGgRrCMED0mISEBJV7uoFH4wf69++Pe/fuIT8/v1LbmmqPk5NTpTEbLVq0wKBBg7Bu3TqRqqr/mjVrhhUrVuD+/fvYtm0bDAwMMGXKFNjY2GDGjBlVrhlRF6Snp6Nr166Ij4/HsWPH0LdvX7FL0hgcQEhqV7EEa1lZGcrKyvD666/j22+/hZeXFzIzM6Grq4sZM2bgvffeU2tdBw8exMSJE5GZmfnM5+rq6sLQ0FD5U7EQy+Nzuz++ipqxsbFy6teK6V8rfv/4cx//teK/9fX1NXKQ29PY2NggIyMDOjo6sLW1VU4PbWNjA0NDQ5iZmeHatWtil6lRbt26heXLl2Pbtm3Iy8tD586d8f777yMgIKBOjL1ISkpCjx49UFhYiPDwcLRr107skjQKwwCpXXl5OUpKSiCVSvHgwQO0aNECubm5GD9+PNatWweFQoFOnTph3759aN68ea3XExMTg+XLl2PHjh0oKSmp8jmDBg0CAEycOLHSmulVLcMql8uVS7EWFxejuLgYJSUlyp/nWTimgo6ODgwNDWFgYPDM4FHdinCPr/T2tOBhaGhYJ4LHk8tcf/vtt1i0aBHmzZuHOXPmYMCAAfj55581amR8XVFcXIw//vgDK1aswKVLlyCTyTBx4kS8//77cHZ2FqWmW7duwdvbGzo6OoiIiFDLvwvahmGARCOXy9G9e3dcvnxZuc3T0xMREREIDAzERx99pLxeX9OKioqwdetWrFq1CleuXEGDBg3Qr18/5ObmIiMjo9IUyZ06dUK7du3w7rvv1sjxFQqFMiw8vsLc40vEVgSMilXpHg8d1a0BXxE8Hg8fDx8+fO7BjxKJRBk6KtaKNzQ0hLGxsTKAPB46Kn4eX/DG1NRUpSPyZOCo+NXIyAg6OtVfqaxumWt3d3cAwMaNG5X/TbXj+vXrWLZsGXbs2IHCwkJ4eXnh/fffx+uvv662iZYuX74MHx8fmJubIzIyEo0bN1bLcbUNwwCJIicnB25ubrh3716lx7777jusXbsWV69erfFBezdu3MCyZcuwfft2FBYWonv37nj//fexdu1aXL58WTlF8oQJExAXF4dz587BzMwMnp6e2LBhAwwNDWu0HnUQBAElJSWVwsaTAeTxjkdF+Hh8SdyKXx9fy77i14plcktKSqBQKJ67Nn19fZVLLRW/Ghsbo6SkBAkJCZWWsAaAAQMGIDQ0tCY/JnoKuVyOrVu3YvXq1crwPGXKFPz3v/9FkyZNau24Z86cQf/+/eHg4ICIiAhYW1vX2rG0HcMAiWbmzJlVLkRja2uLH3/8EQEBATVynJKSEgQHB2PlypWIioqChYUFJk6ciFmzZrHdWMMEQUBpaelzBY+KwPFk16MigKSkpOD69etVXrpxdHTEgQMH4ObmJsK71G7R0dFYvnw5du7cieLiYvTq1QsffPABXnvttRpdFCgsLAxDhgxB69atcfToUchkshrbN1VBIBLJN998IwCo9DN06NAa2f+tW7eE//znP4K5ubkAQOjUqZMQFBQkFBcX18j+qXYdO3ZM8PDwqPLPyIgRI4QffvhB7BK1Wn5+vrBmzRqhVatWAgDB2tpa+PTTT4Xk5ORX3vfu3bsFfX19oWfPnkJBQUENVEvPwlsLSe3u37+Pa9euYfXq1ZXWq2/QoAG2b9/+0vsuLS1FcHAwunXrhpYtW2Lz5s0IDAzEjRs3cP78eYwfP75etvq1VVW3Fjo7OyMzMxOtWrUSqSoCAFNTU/znP//BjRs3cO7cOfTr1w/Lli1DkyZN4Ovri4MHD77QJaMKmzdvRkBAAPr27YuwsDDO76EuYqcR0j5///23oKenJ+jp6QktW7YUxo8fL0ycOFEAILRu3Vro0KGD0KFDB+HQoUPPvc/4+Hhh1qxZgqWlpQBA6NChg/Dbb78Jcrm8Ft8J1ZZ+/foJVlZWgrGxsWBvby98+OGHgrW1tWBgYCDo6ekJn332mdglUhVycnKE5cuXCy4uLgIAwc7OTli4cKGQlpYmCIIgJCYmCr179xZat24ttGvXTvjjjz8EQRCEr7/+WnB0dBRMTEwEiUQiBAQECKWlpWK+Fa3DMQOkVsnJyejatSsUCgXOnDkDJyenl95XWVkZ9u7di6VLl+LUqVOQSqUYPXo0Zs+ezWvJGurWrVtwc3PDu+++i2XLloldDlVDEAScPn0ay5Ytw969e1FeXo4BAwbgjTfeQMuWLeHh4YH4+Hh4enoiICAA1tbWKCkpwfLlyzFlyhRs2LDhqXeaUM1jGCC1uXfvHrp27YrS0lKcPXsWTZs2fan9JCcnKxdeycjIQNu2bZULr7ClqPnmzp2LJUuWICYmpt7Nqa+NsrOzsX79evz0009ISEhA48aN8eabb2L37t0qtxUDgJGREeRyeZ2Y60LbMAyQWqSkpKBr1654+PAhTp8+/cKTl5SXl+PAgQNYvnw5wsPDYWhoiICAAHzwwQdcYljLlJSUoEWLFrCxscH58+d54qgnBEHAiRMnsGzZMuzbt6/K8QQmJiYqy1CT+rAPQ7UuNTUV3bt3R0lJCU6ePPlCQSA1NRWfffYZ7O3tMXToUNy7dw8rV65Eeno6tmzZwiCghQwNDfHrr7/i4sWL2LBhg9jl0HOSSCTo3bs39uzZgzFjxlT5nPLycjVXRRXYGaBalZaWhm7duqGwsBCnTp1CixYtnvkahUKBf/75B8uWLcORI0egq6uL119/HbNnz0aXLl34TZAAACNGjMCRI0cQFxcHKysrscuh51RSUgI3NzfExsZWeoydAfGwM0C15v79+/Dy8kJ+fj5Onjz5zCCQnp6Or776Co6OjvD390dsbCwWL16M+/fvIzg4GF27dmUQIKWff/4Z5eXlNTZFNNU+QRAwefJkjB49ulJXz9PTE0ZGRiJVRuwMUK1IT09H9+7dkZ2djVOnTlV7T7ggCAgPD8fSpUtx6NAh6Ojo4LXXXsPs2bPRo0cPnvzpqVavXo3//ve/CA8PR+/evcUuh54hMjISvXr1Qvv27aFQKJCdnQ0vLy8UFBQgOjoaaWlpsLOzw+zZszF79myxy9UqDANU4x48eKBcjvjkyZNo3bp1pedkZWVh3bp1+Pnnn5GYmIgmTZpg5syZmDZtGho2bChC1VTfDB8+HOHh4VAoFGjYsCGioqIQGBiIO3fuiLYMNlF9xTBANSojIwNeXl548OABIiMjVW79EgQBJ0+exNKlS7F//34oFAr4+/vjww8/hI+PD7sA9EIqljletWoVjhw5Aj8/PxgYGGDQoEEYPnw4evXqpbZlsInqO4YBqjGZmZnw8vLC/fv3ERkZqZz4Jzc3F7/++ivWrFmD+Ph42Nvb4+2338aMGTNgY2MjctVUn1Usc3zy5Ek8ePBAud3T0xN2dnaYM2dOrS2DTaRJOICQntvw4cNhaWmpsprg1q1b4ebmBldXV7Ru3RppaWk4ceIE2rZti7Nnz2LMmDGwtbXFxx9/jObNm+PQoUNISkrCwoULGQSoRiQkJKgEAQCIiorCmTNneOsp0XNiGKDnNmvWLGzevFn5+4yMDCxYsAB79+6Fjo4OsrKy8Msvv+D48eNo1aoVunXrhuPHj+Ojjz5CcnIyQkND4efnx2lGqUbl5uZWub1jx46ckZLoOek9+ylEj/j4+CA8PBwAIJfLsWTJEpSWlqJHjx7IycmBi4sLJk2ahPLycvj4+GDJkiUYNGhQja5xTvQkCwuLKrePHDlSzZUQ1V8MA/TCysvL4e3tjaioKJXt8fHxaN++Pf766y84OjqKVB1pmyZNmuDq1asoLS1VbvP09MSECRNErIqofmG/ll5YQkJCpSAAAF5eXvD29mYQILXw9fXFqFGjcPDgQZSWlmLcuHEAADs7O5SWlsLLywuhoaEiV0lUP7AzQC+sumu0xcXFzzXdMFFNCAsLAwB4eHgAeDSYdevWrWKWRFRvMQzQC6vuGm1ycjICAwPVXA1ps7NnzyI6OhohISFil0JUr3GeAXpuvr6+iImJQWFhIcrKylSu0RoZGWHHjh0YNmyYiBWSthk8eDBiYmKQkJDAgapEr4CdAXpuFW1Z4NHdBEFBQYiOjoa7uzsmTJgAqVQqYnWkbe7cuYODBw9i2bJlDAJEr4idASKql6ZNm4bg4GCkpaUxiBK9It5NQET1Tk5ODoKCgjB9+nQGAaIawDBARPXOqlWrUFZWho8++kjsUog0Ai8TEFG98vDhQ9jb26Nfv34IDg4WuxwijcDOABHVK1u3bkVmZiYWLFggdilEGoOdASKqNwRBgKurK2xtbRERESF2OUQag7cWElG9ERYWhtu3b2PNmjVil0KkUdgZIKJ6o1evXkhLS8PNmzchkUjELodIY7AzQET1wtWrVxEREYFff/2VQYCohrEzQET1wpgxY3DkyBGkpKTAwMBA7HKINArvJiCiOi8tLQ27du3Ce++9xyBAVAsYBoiozluyZAn09PQwa9YssUsh0kgMA0RUp8nlcqxbtw4TJkyATCYTuxwijcQwQER12rp161BQUIB58+aJXQqRxuIAQiKqs8rLy+Hk5IT27dvjwIEDYpdDpLF4ayER1Vl//fUX7t27h5CQELFLIdJo7AwQUZ3l4eEBALh06ZLIlRBpNnYGiKhOOnPmDKKjo9kVIFIDdgaIqE4aNGgQLl++jISEBOjq6opdDpFGY2eAiOqcO3fu4NChQ1i2bBmDAJEasDNARHXOtGnTEBwcjLS0NEilUrHLIdJ4nGeAiOqUnJwcBAUFYfr06QwCRGrCMEBEdcqqVatQVlaGjz76SOxSiLQGLxMQUZ3x8OFD2Nvbo1+/fggODha7HCKtwc4AEdUZQUFByMzMxIIFC8QuhUirsDNARHWCIAhwdXVFo0aNcOLECbHLIdIqvLWQiOqEsLAw3L59G2vWrBG7FCKtw84AEdUJvXr1QlpaGm7evAmJRCJ2OURahWMGiEithg8fDktLSwQEBCi3jR07FhEREXj48CGDAJEIGAaISK1mzZqFzZs3AwDkcjnWrl2LiIgIGBkZwdLSUuTqiLQTLxMQkdqFh4dj5cqVSExMRFRUlHK7VCrFgwcPONkQkZqxM0BEokhISFAJAsCjTkFQUJBIFRFpL4YBIhJFbm5uldujo6PVWwgRMQwQkTgsLCyq3O7u7q7eQoiIYYCIxOHk5ASZTKayTSqVYsKECeIURKTFOICQiNTK19cXMTExKCwsRFFREbp27YqcnBykpqaiqKgINjY2WL58OUaNGiV2qURag2GAiESxfft2jBs3Drdu3UKLFi3ELodIqzEMEJEo+vbti/v37+PatWtil0Kk9ThmgIjULisrCydOnMAbb7whdilEBIYBIhLB9u3boVAoMGnSJLFLISLwMgERiaBTp07Q19fH6dOnxS6FiMDOABGpWVJSEi5evIg333xT7FKI6P8wDBCRWm3atAn6+voYPXq02KUQ0f/hZQIiUquWLVvC2dkZhw4dErsUIvo/7AwQkdpcu3YNt2/fxvTp08UuhYgewzBARGqzYcMGmJiYYPDgwWKXQkSPYRggIrUQBAE7duzAkCFDYGhoKHY5RPQYhgEiUotTp04hLS0N06ZNE7sUInoCwwARqcX69ethZWUFHx8fsUshoicwDBBRrSstLcXu3bsxatQo6Ojwnx2iuoZ/K4mo1oWGhiIvL4+XCIjqKM4zQES1btiwYYiJicGdO3cgkUjELoeInsDOABHVqsLCQhw+fBgTJkxgECCqoxgGiKhW7dq1C8XFxZg6darYpRBRNXiZgIhqxZIlS7Bx40bcvXsXDRo0QFJSktglEVE12Bkgohp35coVBAUFYeLEiZDL5ZDL5UhJSRG7LCKqBsMAEdW46Oho3Lt3D59++ikAICsrCz169IBcLhe5MiKqCsMAEdW42NhYZGRkqGy7e/cugoKCRKqIiJ6GYYCIalx6enqV26Ojo9VbCBE9F4YBIqpx7u7uL7SdiMTFuwmIqMbJ5XJ069YNV65cUW7z8PBAZGQkpFKpiJURUVXYGSCiGieVSmFsbAwrKysAwNSpUxkEiOowdgaIqNZERUWhY8eOuHjxIjw9PcUuh4iqwc4AERGRlmMYICIi0nIMA0RU427evAl3d3cEBgYCALy8vPDXX3+JWxQRVYthgIhqnKurK06dOoWAgAAAgI6ODnr06CFyVURUHQ4gJKIaJ5fL4e3tjaioKOU2T09PRERE8I4CojqInQEiqnFbtmxRCQLAozsLOB0xUd3EMEBENe7MmTNVbud0xER1E8MAEdWomJgY7Nq1q8rHOB0xUd3EMEBENWb79u3o2rUrbGxs4ObmpvKYh4cHJkyYIFJlRPQ0DANE9MoUCgU+/PBDjBs3DgMGDEBMTAwOHz4MMzMzjBw5EgAQEBDAwYNEdRTvJiCiV5Kbm4vhw4cjPDwcn3/+ORYuXAiJRKLynDFjxuDQoUNITEyEhYWFSJUSUXUYBojopf3777/w8/NDRkYGduzYgSFDhlT5vHv37sHZ2RkzZ87EihUr1FskET0TLxMQ0UvZs2cPOnbsCB0dHURFRVUbBADAwcEB7777Ln766SckJSWpsUoieh4MA0T0QgRBwMKFCzF8+HB4eXkhJiYGrq6uz3zd559/DqlUitmzZ6uhSiJ6EQwDRPTcCgsLMWTIEHz99df46KOPEBoaCnNz8+d6rbm5Ob744gv8+eefnG+AqI7hmAEiei7x8fHw8/NDcnIyNm3ahNGjR7/wPkpLS+Hi4gJHR0ecPHmyFqokopfBzgARPdM///wDd3d3yOVynDt37qWCAADo6+tj6dKlOHXqFA4cOFDDVRLRy2JngIiqJQgCvv/+e8yfPx/dunXD3r170aBBg1fep6enJ/Lz83Hz5k3o6urWULVE9LLYGSCiKhUXF2PMmDH49NNPMWPGDBw/fvyVgwAASCQS/PTTT4iLi8Nvv/1WA5US0atiZ4CIKklOToa/vz9u3bqFtWvXYsqUKTV+jEGDBuH8+fNISEjgzIREImNngIhUREREoH379khPT8fJkydrJQgAwOrVq5GdnY3//e9/tbJ/Inp+DANEpLRmzRr06dMHzs7OuHr1Kjp37lxrx3JxccGUKVOwZMkSpKen19pxiOjZGAaICA8fPsTkyZPx7rvvYsKECTh16hRsbGxq/bjfffcdJBIJ5s6dW+vHIqLqMQwQabn79++je/fuCAoKwo8//ojff/8dBgYGajm2lZUVPvnkE2zevBk3b95UyzGJqDIOICTSYufPn8fgwYNRVlaGPXv2wNvbW+01FBUVwcnJCR4eHggNDVX78YmInQEirbVx40b06NEDNjY2uHLliihBAACMjY3xv//9D4cPH0ZERIQoNRBpO3YGiLRMWVkZ/vvf/+Lnn3/GqFGjsHnzZhgZGYlak0KhQOvWrWFkZITo6GhIJBJR6yHSNuwMEGmRrKws9O7dG2vXrsX//vc/BAcHix4EAEBHRwerVq3C5cuXERwcLHY5RFqHnQEiLXHlyhX4+/sjPz8ff/75J/r37y92SZV4e3vjzp07iI+PV9sgRiJiZ4BIK/zxxx/o0qULpFIpoqOj62QQAB7Nc5CSkoJVq1aJXQqRVmFngEiDKRQKzJkzB0uWLMHgwYMRHBwMExMTsct6qrFjx+LgwYNITEyEhYWF2OUQaQV2Bog0VF5eHgYMGIClS5di4cKF2Lt3b50PAgCwdOlSFBcXY+HChWKXQqQ12Bkg0kA3b96Ev78/0tPTsW3bNgwbNkzskl7Ihx9+iNWrVyMuLg6Ojo5il0Ok8RgGiDTM3r17MXbsWFhbW+PQoUNo1aqV2CW9sLy8PDRp0gS+vr4ICQkRuxwijcfLBEQaQhAEfPHFFxg2bBi6du2KmJiYehkEAMDc3BxffPEFdu3ahUuXLoldDpHGY2eASAMUFhZi7Nix2LdvH2bPno0ffvgBOjr1O+uXlpbCxcUFjRs3xqhRo7BhwwYIggBfX1+sXLmSExMR1SA9sQsgoldz9+5dDBgwAImJidi2bRsCAwPFLqlG6OvrY9myZRg1ahSuXbuG0aNHw9PTE5s3b8aZM2fQvXt3sUsk0hjsDBDVY2FhYRgxYgRMTU1x6NAhtG/fXuySalRhYSGsrKxQXFys3CaVSnH27Fm4ubmJWBmRZqnffUQiLSUIApYsWQI/Pz+0bdsWV65c0bggAABBQUEqQQAA5HI5Tp06JVJFRJqJYYConikuLkZgYCA+/vhjTJ8+HREREWjYsKHYZdWK6OjoKrcfOHBAvYUQaTiOGSCqR1JSUuDv749///0Xv/76K6ZOnSp2SbXK3d29yu26urrqLYRIw7EzQFRPnDx5Em5ubkhLS0NkZKTGBwEAmDhxYqUpic3NzTFmzBiRKiLSTAwDRPXAzz//DB8fHzRt2hRXrlxBly5dxC5JLQwNDaGrqwsHBwfIZDLY2dlh/PjxGDVqlNilEWkUhoFXJJfL4eTkhI8++kjsUkgDlZaWYsqUKfjPf/6DwMBAnDlzBra2tmKXpTbHjx9HVlYWdu7ciezsbKSkpOCnn37iHANENYxh4CXJ5XKsXbsWPXv2hLW1NUpLS8UuiTRMeno6vLy8sGXLFqxcuRKbN2+GgYGB2GWp1bp162Bvb49u3bqJXQqRRuM8Ay9BLpfD29sbUVFRym02Nja4c+cOpFKpiJWRprh48SIGDRqE0tJS7N69G7179xa7JLUrKSlBw4YNMWPGDCxZskTscog0GjsDL2HLli0qQQB49C3u3XffRXJyskhVkabYtGkTvLy8YGVlhcuXL2tlEACAffv2obCwEG+++abYpRBpPIaBl1DdwikbN26Eo6Mj7O3tMXLkSKxbtw6xsbFg84WeR3l5Od577z1MnjwZQ4YMwYULF9C4cWOxyxLN+vXr0bJlS7Ru3VrsUog0HsPACyotLcWZM2eqfGzAgAEICQnB4MGDce3aNcyYMQMtWrSAlZUVBg8ejBUrVuDq1atQKBRqrprquuzsbPj4+GDNmjVYtGgRdu7cCWNjY7HLEk1ubi6OHj2KSZMmiV0KkVbgmIEXUFxcjCFDhuDo0aNo0qQJ7t69q3ysqjEDubm5OHnyJA4ePIjjx4/j+vXrKC8vh5mZGTp37gxfX1/0798f7u7u0NPj/E/a6urVq/D390deXh527twJPz8/sUsS3fr16/H2228jMTFRq7sjROrCMPCcCgoKMGDAAFy4cAF//vkn+vXrh6CgIERHR0Mul8PCwgIrV6586j7kcjlOnz6Nw4cP4+jRo7h8+TIePnwIY2NjeHh4wNfXFwMGDECnTp1gaGiopndGYtq5cycmTZqExo0b49ChQ3BxcRG7pDqhe/fuKCsrw/nz58UuhUgrMAw8h4oW7u3bt7F//3706dOnRvb78OFDXLhwAYcPH8aRI0dw8eJFFBUVwcDAAO3atUOfPn3g5+eH7t27w8TEpEaOSXWDQqHAp59+isWLF2PgwIH4448/YGpqKnZZdUJqaiocHBywZs0azJw5U+xyiLQCw8Az3L9/H97e3khLS0NYWFitzvxWVlaGy5cvIywsDIcPH8b58+eRl5cHXV1dtG7dGr1794a/vz969uwJmUxWa3VQ7crPz8fIkSMRFhaGefPm4euvv+YkOo9ZvHgx5s2bhwcPHsDS0lLscoi0AsPAUyQmJqJnz54oLCzE8ePH1b5+ukKhwI0bN3DkyBEcPnwYp0+fRlZWFiQSCZo3bw5vb2/4+/ujd+/esLGxUWtt9HJu374NPz8/pKWlYevWrRg+fLjYJdU5bdu2RaNGjXDkyBGxSyHSGgwD1bh16xa8vb2ho6ODiIgING/eXOySIAgC4uPjcfToUYSGhuLUqVNITU0FADRp0gQ9evSAv78/+vTpA0dHx2r307RpU5ibm0NHRweWlpY4duyYut6CVtu/fz/GjBmDBg0aIDQ0lLfMVeH27dto2bIltm/fjrFjx4pdDpHWYBioQkxMDPr06QMLCwtERkbCwcFB7JKqlZycjPDwcISGhiIyMlJ5h0OjRo3QvXt3+Pv7o2/fvnBxcYFEIoFcLoejoyOGDx+Ozp07Y+LEiZw1sZYJgoCvv/4aX3zxBXr16oW//vqLl3mqMXfuXKxatQqZmZlafWslkboxDDzh9OnT6N+/Pxo3boyIiAhYW1uLXdILefDgAU6cOIGDBw8iIiICt2/fhiAIaNCgATp37oxr166pzJLo6emJiIgIBoJaIpfLERgYiL///hsffPABfvjhB+jq6opdVp0kCAKaNGmCrl27IiQkROxyiLQKw8BjwsLCMGTIELRp0wZHjx6ttI56fZSbm4vIyEgcOnQIu3btQkpKSqXnNG3aFG5ubrCzs4ODgwMaN24Me3t72NrawtbWFjY2NtDX1xeh+votISEBfn5+uHv3Ln777TeMGzdO7JLqtPPnz6NLly44ePAg/P39xS6HSKswDPyf3bt3Y8yYMejatStCQ0M18pvyzJkz8csvv1TabmtrC3t7e2RkZCArKwuFhYWVnmNmZoYGDRrAysoKNjY2aNSokTI4ODg4KIODra0t50gAcPToUQwfPhxSqRQHDx6Eu7u72CXVedOnT0dISAjS09M5CReRmvFvHB4tDPPmm2+if//++OuvvzT2ZFbdCcnf3x+///678vclJSVIT0/H/fv3cf/+faSkpCApKQkpKSlITU1Feno6bty4gaysLOTl5VXan4mJCSwtLVWCg729PRwcHODg4IBGjRopg4OmzZ8gCAKWL1+OTz75BJ07d8bevXthZWUldll1Xnl5Of7880+MGDGCQYBIBFrfGVi9ejVmzZqFgIAAbNu2TaP/IZLL5fDy8kJMTIxym7GxMUJDQ+Ht7f1S+ywtLcWDBw+UwSE1NRXJycm4d++eMjg8ePAAWVlZyM3NrbQug5GRkTI4WFtbqwSHxo0bqwQHMzOzOn0/fklJCSZPnowdO3Zg2rRp+OmnnzT6z1NNOnz4MPz8/HDmzBl07dpV7HKItI5Wh4FvvvkGCxYswNSpU7F+/Xro6Gj+uk3Xrl1D3759kZ6eDmtra3z88cf4+OOP1XLs8vJyZGZmVhkcUlJSlMEhMzMTOTk5KC8vV3m9gYEBLC0t0bBhQ2VwsLOzQ+PGjSsFB5lMVqvB4c6dO5g6dSru378PXV1d7N69GyNHjsSNGzewZs0aTJs2rdaOXd8NHz4c4eHh6Nevn3KgoK+vL44fPw4nJydMmjQJCxcuFLlKIu2ilWFAEAR8/PHHWLp0KWbPno0lS5bU6W+cNS0qKgodO3bExYsX4enpKXY5VVIoFMjOzq6245CWlqYMDtnZ2SgrK1N5vZ6eHmQyGRo2bAgbGxvY2tqqDJC0s7NTBocGDRq8cBD09vZG165dUVhYCH19fWzZsgV6enrYu3cvunXrVpMfhcYJDw9Hfn4+Nm3ahM2bN+O3337De++9h27duuHw4cPo378/1q9fj3bt2oldKpHW0LowoFAo8Pbbb2PDhg348ssvtfIbSH0IAy9CEATk5uYqg0NaWppKx+Hx4JCVlYWHDx+qvF5HR0cZHKytrWFjY6MMDo6OjirBwcrKCpcuXULfvn2Rn5+v3IexsTGuXr0KZ2dndb/9eik8PBwrV65EYmIioqKilNs9PT0xZswYlJeX49NPPxWxQiLtolUXNMvKyjB+/Hjs3LkTK1aswKxZs8QuiWqARCKBTCaDTCaDq6vrU58rCAIKCgpUgsO9e/eQnJyMlJQU3L9/H/Hx8Th//jyysrJQVFRU6Vj6+vqVAkVRURHCwsIwffr0Gn9/miohIQGXLl1S2RYVFYVOnTrxVlYiNdOaMPDw4UMMHz4chw4dwq+//oopU6aIXRKJQCKRwMzMDGZmZs81xbRcLlfeWVERHH744QflTI+Pi46OrvmCNVhubm6V2+/evYsWLVqouRoi7aYVYUAul8Pf3x+nT59GcHAwAgICxC6J6gmpVIqmTZuiadOmym0JCQlYvHhxpedyLoEXU92kXhYWFrC3t1dzNUTaTeOHz+fm5qJXr144d+4c9u/fzyBAr2z+/PmV5s13cXHBhAkTRKqofnJycqo0ZsXDwwN3797FkCFDRKqKSDtpVGcgJycHvr6+KCsrQ1lZGd58802sX78eiYmJOHLkCHr06CF2iaQBzM3NsXXrVrz77rsoKiqCp6cn/v77b42ctbI2+Pr6IiYmBoWFhbC0tMQnn3yC27dv48yZM8jJycGkSZN4JwGRmmnU3QT5+fn4/fffcf36ddjb2+PLL7+EiYkJjh8/zhbuYzTtbgIiIno1GtMZkMvl8PHxUblNCXi05gCDABERUfU0ZszAli1bKgUBAIiIiEBycjJyc3MrzWhHREREGnSZoLoV+Z5kaGgIqVQKqVQKExMTmJiYwNTUVHm7mbm5OSwsLGBhYQGZTAZLS0uYm5srH3/8x8TEpN7NXJiUlIThw4fj4sWLaN68ORYtWoRRo0aJXRYREYlIYy4TVHcpYOjQoZgxYwby8vKQm5uL7Oxs5ObmKn/y8/ORn5+PBw8e4M6dOygsLIRcLodcLkdRURGelpUkEgmMjIxgbGwMExMTSKVSmJqaKsOFubm5MlxUTIojk8mU4eLxEGJmZgZjY+NaDxcPHz6Eu7s7Ll68iEGDBmHWrFkYNGiQxq0eSEREz09jOgNyuRzdunXDlStXlNuMjIxw/PhxdOnS5aX2KQgC5HK5MjA8/lMRLnJycpCTk4Pc3Fzk5eUhLy8P+fn5KCgoQGFhofKnqKgIxcXFTz2ejo4OjI2NleGiomvxeLiwsLCAmZmZsmthYWFRbefiyaWY5XI5vL29VS6nGBsbIzo6Gi1btnypz4iIiOo/jekMSKVS/Pjjjxg/fjyKiopgZGSEOXPmvHQQAB598684KTdq1OiVa1QoFCgsLKwyXDweLHJycpTBIi8vDwUFBUhISFAGi4rOxZNT4j5JT08PxsbGyssiJSUlSElJUXlOUVERwsPDGQaIiLSYxoQBAOjVqxeSkpLELqNaOjo6ym/tNaGsrAwFBQXVhovs7Gzk5eUpw0V4eHiV++E0ukRE2k2jwoC2qVimVyaTPdfz165dixkzZlTazlsviYi0m8aMGaBnk8vl6Nmzp8pKcZ6enoiIiODseUREWkxj5hmgZ5NKpVi8eDEkEgl0dXVhYGCA0tJSxMXFiV0aERGJiJ0BLbV48WLMnz8fWVlZNTaGgYiI6id2BrTU6NGjUVZWhoMHD4pdChERiYxhQEs1bdoULi4u2LZtm9ilEBGRyBgGtNjrr7+OsLAwlJaWil0KERGJiGFAiwUGBqKwsBAnTpwQuxQiIhIRw4AW8/T0hLW1NbZv3y52KUREJCKGAS0mkUgwaNAg7Nu376kLMhERkWZjGNBy48aNw/3793H58mWxSyEiIpEwDGg5Hx8fSKVSXiogItJiDANazsDAAH369MHu3bvFLoWIiETCMEAYN24cbt26hcTERLFLISIiETAMEF577TXo6uoiJCRE7FKIiEgEXJuAAABeXl5QKBQ4c+aM2KUQEZGasTNAAB6tVXDhwgXk5OSIXQoREakZwwABAAICAlBeXo6///5b7FKIiEjNeJmAlNq0aYOmTZviwIEDYpdCRERqxM4AKY0YMQLh4eEoKSkRuxQiIlIjhgFSGjNmDIqKinDkyBGxSyEiIjViGCAlNzc32NnZcTZCIiItwzBAShKJBK+99hoOHjwIhUIhdjlERKQmDAOkIjAwEJmZmbh48aLYpRARkZowDJAKb29vmJmZ8VIBEZEWYRggFXp6evD19cVff/0ldilERKQmDANUybhx43Dnzh3ExcWJXQoREakBwwBV4u/vD319ffzxxx9il0JERGrAGQipSr1790ZBQQEHEhIRaQF2BqhKY8eOxaVLl5CRkSF2KUREVMsYBqhKI0aMAAAOJCQi0gK8TEDVat++PWxsbBAWFiZ2KUREVIvYGaBqjRw5EpGRkSgqKhK7FCIiqkUMA1StMWPGoKSkBIcPHxa7FCIiqkUMA1StVq1aoUmTJti2bZvYpRARUS1iGKCnGjJkCEJDQ1FeXi52KUREVEsYBuipAgMDkZubi9OnT4tdChER1RKGAXqqbt26QSaTceEiIiINxjBAT6Wrq4sBAwbg77//Bu9CJSLSTAwD9Ezjx49HcnIy/v33X7FLISKiWsAwQM/Uv39/GBoaIjg4WOxSiIioFnAGQnou/fv3R1paGq5cuSJ2KUREVMPYGaDnMnbsWFy7dg1paWlil0JERDWMYYCey7BhwwAAu3btErkSIiKqabxMQM+tY8eOMDExwYkTJ8QuhYiIahA7A/TcRo0ahTNnzqCgoEDsUoiIqAYxDNBzGz16NEpLS3Hw4EGxSyEiohrEMEDPzdnZGc7Ozly4iIhIwzAM0AsZNmwYwsLCUFZWJnYpRERUQxgG6IUEBgaioKCAgwiJiDQIwwC9kI4dO8LKyoqXCoiINAjDAL0QHR0dDBw4EPv37+fCRUREGoJhgF7YuHHjODUxEZEGYRigF9anTx8YGxtjx44dYpdCREQ1gDMQ0ktp1KgRHjx4gOHDhyMkJETscoiI6BWwM0AvZeLEiVAoFDh//jzWrl0LuVwudklERPSS2BmgFyaXy+Hl5YWYmBjlNk9PT0REREAqlYpYGRERvQx2BuiFbdmyRSUIAEBUVBSCgoJEqoiIiF4FwwC9sOjo6BfaTkREdRvDAL0wd3f3F9pORER1G8cM0AuTy+Xw9vZGVFSUchvHDBAR1V/sDNALk0qlMDc3h6mpKfT09CCTybBkyRIGASKieoqdASIiIi3HzgAREZGWYxggIiLScgwDREREWo5hgIiISMsxDBAREWk5hgEiIiItxzBARESk5RgGiIiItBzDABERkZZjGCAiItJyDANERERajmGAiIhIyzEMEBERaTmGASIiIi3HMEBERKTlGAaIiIi0HMMAERGRlmMYICIi0nIMA0RERFqOYYCIiEjLMQwQERFpOYYBIiIiLccwQEREpOUYBoiIiLQcwwAREZGWYxggIiLScgwDREREWo5hgIiISMsxDBAREWk5hgEiIiItxzBARESk5RgGiIiItBzDABERkZZjGCAiItJyDANERERajmGAiIhIyzEMEBERabn/B44zwXOLMIMfAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "
" ] @@ -29,10 +29,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -42,32 +42,89 @@ "import wntr_quantum\n", "\n", "# Create a water network model\n", - "inp_file = 'networks/Net2.inp'\n", + "inp_file = 'networks/Net3.inp'\n", "# inp_file = 'networks/Net2Loops.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)" + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Nodes': 97,\n", + " 'Links': 119,\n", + " 'Patterns': 5,\n", + " 'Curves': 2,\n", + " 'Sources': 0,\n", + " 'Controls': 18}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wn.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "skeletonize() missing 1 required positional argument: 'pipe_diameter_threshold'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m skel_wn \u001b[38;5;241m=\u001b[39m \u001b[43mwntr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmorph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mskeletonize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m skel_wn\u001b[38;5;241m.\u001b[39mdescribe()\n", + "\u001b[0;31mTypeError\u001b[0m: skeletonize() missing 1 required positional argument: 'pipe_diameter_threshold'" + ] + } + ], + "source": [ + "skel_wn = wntr.morph.skeletonize(wn, 12*0.0254)\n", + "skel_wn.describe()" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 18, "metadata": {}, "outputs": [ + { + "data": { + "image/png": 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water in a dual-source @@ -343,7 +343,7 @@ DEMAND CHARGE 0.0000 ;Tank ID Model Fraction [TIMES] -DURATION 00:00:00 +DURATION 168:00:00 HYDRAULIC TIMESTEP 01:00:00 QUALITY TIMESTEP 00:05:00 PATTERN TIMESTEP 01:00:00 diff --git a/docs/notebooks/temp.rpt b/docs/notebooks/temp.rpt index 6a0f84e..e69de29 100644 --- a/docs/notebooks/temp.rpt +++ b/docs/notebooks/temp.rpt @@ -1,22 +0,0 @@ - Page 1 Wed Jul 17 17:39:52 2024 - - ****************************************************************** - * E P A N E T * - * Hydraulic and Water Quality * - * Analysis for Pipe Networks * - * Version 2.2 * - ****************************************************************** - - Analysis begun Wed Jul 17 17:39:52 2024 - - - Hydraulic Status: - ----------------------------------------------------------------------- - 0:00:00: Balanced after 5 trials - 0:00:00: Reservoir River is emptying - 0:00:00: Reservoir Lake is closed - 0:00:00: Tank 1 is filling at 13.10 ft - 0:00:00: Tank 2 is emptying at 23.50 ft - 0:00:00: Tank 3 is filling at 29.00 ft - - Analysis ended Wed Jul 17 17:39:52 2024 diff --git a/docs/notebooks/trash/poly_brute_force.py b/docs/notebooks/trash/poly_brute_force.py new file mode 100644 index 0000000..1a76f7e --- /dev/null +++ b/docs/notebooks/trash/poly_brute_force.py @@ -0,0 +1,557 @@ +#!/usr/bin/env python +# Input a QUBO instance and solve using brute force + +import numpy as np + + +def calculate_squared_residuals(P0, P1, P2): + residual = dict() + # x labels the states and o labels the operator + # number of x's corresponds to the rank of the tensor since the state has not been contracted yet + residual["dim0_o"] = np.einsum("i,i", P0, P0) + + residual["dim1_ox"] = np.einsum("i,ij->j", P0.T, P1) + residual["dim1_xo"] = np.einsum("ji,i->j", P1.T, P0) + + residual["dim2_oxx"] = np.einsum("i,ijk->jk", P0.T, P2) + residual["dim2_xox"] = np.einsum("ji,ik->jk", P1.T, P1) + residual["dim2_xxo"] = np.einsum("kji,i->kj", P2.T, P0) + + residual["dim3_xoxx"] = np.einsum("ji,ikl->jkl", P1.T, P2) + residual["dim3_xxox"] = np.einsum("kji,il->kjl", P2.T, P1) + + residual["dim4_xxoxx"] = np.einsum("kji,inm->kjnm", P2.T, P2) + + return residual + + +def calculate_residual_offsets(P0, P1, P2, basis_offset): + residual_offset = dict() + ### calculate QUBO offsets + # x labels the states, o labels the operator, b labels the offset + # D1 + residual_dim1_ob = np.einsum("i,ij,j", P0.T, P1, basis_offset) + + residual_dim1_bo = np.einsum("j,ji,i", basis_offset.T, P1.T, P0) + + residual_offset["dim1_o"] = residual_dim1_ob + residual_dim1_bo + + # D2 + residual_dim2_obx = np.einsum("i,ijk,j", P0.T, P2, basis_offset) + residual_dim2_oxb = np.einsum("i,ijk,k", P0.T, P2, basis_offset) + residual_dim2_obb = np.einsum("i,ijk,j,k", P0.T, P2, basis_offset, basis_offset) + + residual_dim2_box = np.einsum("j,ji,ik", basis_offset.T, P1.T, P1) + residual_dim2_xob = np.einsum("ji,ik,k", P1.T, P1, basis_offset) + residual_dim2_bob = np.einsum("j,ji,ik,k", basis_offset.T, P1.T, P1, basis_offset) + + residual_dim2_bxo = np.einsum("k,kji,i", basis_offset.T, P2.T, P0) + residual_dim2_xbo = np.einsum("j,kji,i", basis_offset.T, P2.T, P0) + residual_dim2_bbo = np.einsum("k,j,kji,i", basis_offset.T, basis_offset.T, P2.T, P0) + + residual_offset["dim2_ox"] = ( + residual_dim2_obx + residual_dim2_oxb + residual_dim2_box + ) + residual_offset["dim2_xo"] = ( + residual_dim2_xob + residual_dim2_bxo + residual_dim2_xbo + ) + residual_offset["dim2_o"] = ( + residual_dim2_obb + residual_dim2_bob + residual_dim2_bbo + ) + + # D3 + residual_dim3_xoxb = np.einsum("ji,ikl,l", P1.T, P2, basis_offset) + residual_dim3_xobx = np.einsum("ji,ikl,k", P1.T, P2, basis_offset) + residual_dim3_boxx = np.einsum("j,ji,ikl", basis_offset.T, P1.T, P2) + residual_dim3_xobb = np.einsum("ji,ikl,k,l", P1.T, P2, basis_offset, basis_offset) + residual_dim3_boxb = np.einsum("j,ji,ikl,l", basis_offset.T, P1.T, P2, basis_offset) + residual_dim3_bobx = np.einsum("j,ji,ikl,k", basis_offset.T, P1.T, P2, basis_offset) + residual_dim3_bobb = np.einsum( + "j,ji,ikl,k,l", basis_offset.T, P1.T, P2, basis_offset, basis_offset + ) + + residual_dim3_xxob = np.einsum("kji,il,l", P2.T, P1, basis_offset) + residual_dim3_xbox = np.einsum("j,kji,il", basis_offset.T, P2.T, P1) + residual_dim3_bxox = np.einsum("k,kji,il", basis_offset.T, P2.T, P1) + residual_dim3_xbob = np.einsum("j,kji,il,l", basis_offset.T, P2.T, P1, basis_offset) + residual_dim3_bxob = np.einsum("k,kji,il,l", basis_offset.T, P2.T, P1, basis_offset) + residual_dim3_bbox = np.einsum( + "k,j,kji,il", basis_offset.T, basis_offset.T, P2.T, P1 + ) + residual_dim3_bbob = np.einsum( + "k,j,kji,il,l", basis_offset.T, basis_offset.T, P2.T, P1, basis_offset + ) + + residual_offset["dim3_oxx"] = residual_dim3_boxx + residual_offset["dim3_xox"] = ( + residual_dim3_xoxb + + residual_dim3_xobx + + residual_dim3_xbox + + residual_dim3_bxox + ) + residual_offset["dim3_xxo"] = residual_dim3_xxob + residual_offset["dim3_ox"] = ( + residual_dim3_boxb + residual_dim3_bobx + residual_dim3_bbox + ) + residual_offset["dim3_xo"] = ( + residual_dim3_xobb + residual_dim3_xbob + residual_dim3_bxob + ) + residual_offset["dim3_o"] = residual_dim3_bobb + residual_dim3_bbob + + # D4 + residual_dim4_xxoxb = np.einsum("kji,inm,m", P2.T, P2, basis_offset) + residual_dim4_xxobx = np.einsum("kji,inm,n", P2.T, P2, basis_offset) + residual_dim4_xxobb = np.einsum("kji,inm,n,m", P2.T, P2, basis_offset, basis_offset) + residual_dim4_xboxx = np.einsum("j,kji,inm", basis_offset.T, P2.T, P2) + residual_dim4_xboxb = np.einsum( + "j,kji,inm,m", basis_offset.T, P2.T, P2, basis_offset + ) + residual_dim4_xbobx = np.einsum( + "j,kji,inm,n", basis_offset.T, P2.T, P2, basis_offset + ) + residual_dim4_xbobb = np.einsum( + "j,kji,inm,n,m", basis_offset.T, P2.T, P2, basis_offset, basis_offset + ) + residual_dim4_bxoxx = np.einsum("k,kji,inm", basis_offset.T, P2.T, P2) + residual_dim4_bxoxb = np.einsum( + "k,kji,inm,m", basis_offset.T, P2.T, P2, basis_offset + ) + residual_dim4_bxobx = np.einsum( + "k,kji,inm,n", basis_offset.T, P2.T, P2, basis_offset + ) + residual_dim4_bxobb = np.einsum( + "k,kji,inm,n,m", basis_offset.T, P2.T, P2, basis_offset, basis_offset + ) + residual_dim4_bboxx = np.einsum( + "k,j,kji,inm", basis_offset.T, basis_offset.T, P2.T, P2 + ) + residual_dim4_bboxb = np.einsum( + "k,j,kji,inm,m", basis_offset.T, basis_offset.T, P2.T, P2, basis_offset + ) + residual_dim4_bbobx = np.einsum( + "k,j,kji,inm,n", basis_offset.T, basis_offset.T, P2.T, P2, basis_offset + ) + residual_dim4_bbobb = np.einsum( + "k,j,kji,inm,n,m", + basis_offset.T, + basis_offset.T, + P2.T, + P2, + basis_offset, + basis_offset, + ) + + residual_offset["dim4_xoxx"] = residual_dim4_xboxx + residual_dim4_bxoxx + residual_offset["dim4_xxox"] = residual_dim4_xxobx + residual_dim4_xxoxb + residual_offset["dim4_oxx"] = residual_dim4_bboxx + residual_offset["dim4_xox"] = ( + residual_dim4_xboxb + + residual_dim4_xbobx + + residual_dim4_bxoxb + + residual_dim4_bxobx + ) + residual_offset["dim4_xxo"] = residual_dim4_xxobb + residual_offset["dim4_ox"] = residual_dim4_bboxb + residual_dim4_bbobx + residual_offset["dim4_xo"] = residual_dim4_xbobb + residual_dim4_bxobb + residual_offset["dim4_o"] = residual_dim4_bbobb + + return residual_offset + + +def combine_residual_offset(residual, residual_offset): + full_residual = dict() + # dim 0 + offset_residual_dim0_o = ( + residual["dim0_o"] + + residual_offset["dim1_o"] + + residual_offset["dim2_o"] + + residual_offset["dim3_o"] + + residual_offset["dim4_o"] + ) + full_residual["dim0"] = offset_residual_dim0_o + + # dim1 + offset_residual_dim1_ox = ( + residual["dim1_ox"] + + residual_offset["dim2_ox"] + + residual_offset["dim3_ox"] + + residual_offset["dim4_ox"] + ) + offset_residual_dim1_xo = ( + residual["dim1_xo"] + + residual_offset["dim2_xo"] + + residual_offset["dim3_xo"] + + residual_offset["dim4_xo"] + ) + full_residual["dim1"] = offset_residual_dim1_ox + offset_residual_dim1_xo + + # dim 2 + offset_residual_dim2_oxx = ( + residual["dim2_oxx"] + residual_offset["dim3_oxx"] + residual_offset["dim4_oxx"] + ) + offset_residual_dim2_xox = ( + residual["dim2_xox"] + residual_offset["dim3_xox"] + residual_offset["dim4_xox"] + ) + offset_residual_dim2_xxo = ( + residual["dim2_xxo"] + residual_offset["dim3_xxo"] + residual_offset["dim4_xxo"] + ) + full_residual["dim2"] = ( + offset_residual_dim2_oxx + offset_residual_dim2_xox + offset_residual_dim2_xxo + ) + + # dim 3 + offset_residual_dim3_xoxx = residual["dim3_xoxx"] + residual_offset["dim4_xoxx"] + offset_residual_dim3_xxox = residual["dim3_xxox"] + residual_offset["dim4_xxox"] + full_residual["dim3"] = offset_residual_dim3_xoxx + offset_residual_dim3_xxox + + # dim 4 + offset_residual_dim4_xxoxx = residual["dim4_xxoxx"] + full_residual["dim4"] = offset_residual_dim4_xxoxx + + return full_residual + + +def real_to_qubit_basis( + full_residual, num_equations, qubits_per_var, basis, basis_coeff +): + extended_qubo = dict() + + # dimension 0 + extended_qubo["qubit_residual_dim0"] = full_residual["dim0"] + + # dimension 1 + extended_qubo["qubit_residual_dim1"] = np.reshape( + np.einsum("i,j->ij", basis_coeff * full_residual["dim1"], basis), + (num_equations * qubits_per_var), + ) + + # dimension 2 + basis_coeff_dim2 = np.einsum("i,j->ij", basis_coeff, basis_coeff) + basis_dim2 = np.einsum("i,j->ij", basis, basis) + + extended_qubo["qubit_residual_dim2"] = np.reshape( + np.einsum("ij,kl->ikjl", basis_coeff_dim2 * full_residual["dim2"], basis_dim2), + (num_equations * qubits_per_var, num_equations * qubits_per_var), + ) + + # dimension 3 + basis_coeff_dim3 = np.einsum("i,j,k->ijk", basis_coeff, basis_coeff, basis_coeff) + basis_dim3 = np.einsum("i,j,k->ijk", basis, basis, basis) + + extended_qubo["qubit_residual_dim3"] = np.reshape( + np.einsum( + "ijk,lmn->iljmkn", basis_coeff_dim3 * full_residual["dim3"], basis_dim3 + ), + ( + num_equations * qubits_per_var, + num_equations * qubits_per_var, + num_equations * qubits_per_var, + ), + ) + + # dimension 4 + basis_coeff_dim4 = np.einsum( + "i,j,k,l->ijkl", basis_coeff, basis_coeff, basis_coeff, basis_coeff + ) + basis_dim4 = np.einsum("i,j,k,l->ijkl", basis, basis, basis, basis) + + extended_qubo["qubit_residual_dim4"] = np.reshape( + np.einsum( + "ijkl,mnop->imjnkolp", basis_coeff_dim4 * full_residual["dim4"], basis_dim4 + ), + ( + num_equations * qubits_per_var, + num_equations * qubits_per_var, + num_equations * qubits_per_var, + num_equations * qubits_per_var, + ), + ) + + return extended_qubo + + +def accumulate_qubo(extended_qubo): + triangle_qubo = dict() + triangle_qubo["qubit_residual_dim0"] = extended_qubo["qubit_residual_dim0"].copy() + triangle_qubo["qubit_residual_dim1"] = extended_qubo["qubit_residual_dim1"].copy() + # dim 2 + accumulate_dim2 = np.zeros_like(extended_qubo["qubit_residual_dim2"]) + for index_j in range(len(accumulate_dim2)): + for index_i in range(len(accumulate_dim2)): + sorted_index = np.sort([index_i, index_j]) + row_index = sorted_index[0] + col_index = sorted_index[1] + accumulate_dim2[row_index, col_index] += extended_qubo[ + "qubit_residual_dim2" + ][index_i, index_j] + triangle_qubo["qubit_residual_dim2"] = accumulate_dim2 + # dim 3 + accumulate_dim3 = np.zeros_like(extended_qubo["qubit_residual_dim3"]) + for index_k in range(len(accumulate_dim3)): + for index_j in range(len(accumulate_dim3)): + for index_i in range(len(accumulate_dim3)): + sorted_index = np.sort([index_i, index_j, index_k]) + accumulate_dim3[ + sorted_index[0], sorted_index[1], sorted_index[2] + ] += extended_qubo["qubit_residual_dim3"][index_i, index_j, index_k] + triangle_qubo["qubit_residual_dim3"] = accumulate_dim3 + # dim 4 + accumulate_dim4 = np.zeros_like(extended_qubo["qubit_residual_dim4"]) + for index_l in range(len(accumulate_dim4)): + for index_k in range(len(accumulate_dim4)): + for index_j in range(len(accumulate_dim4)): + for index_i in range(len(accumulate_dim4)): + sorted_index = np.sort([index_i, index_j, index_k, index_l]) + accumulate_dim4[ + sorted_index[0], + sorted_index[1], + sorted_index[2], + sorted_index[3], + ] += extended_qubo["qubit_residual_dim4"][ + index_i, index_j, index_k, index_l + ] + triangle_qubo["qubit_residual_dim4"] = accumulate_dim4 + + return triangle_qubo + + +def dimensional_reduction(triangle_qubo): + from sympy.utilities.iterables import multiset_permutations + + # takes upper triangular qubo and reduces the dimensionality of repeated qubits + # e.g. x_i^n = x_i since x_i in [0, 1] + reduced_qubo = dict() + + # dim 0 + reduced_qubo["qubit_residual_dim0"] = triangle_qubo["qubit_residual_dim0"].copy() + + # dim 1 + reduced_qubo["qubit_residual_dim1"] = np.zeros_like( + triangle_qubo["qubit_residual_dim1"] + ) + for idx in range(len(reduced_qubo["qubit_residual_dim1"])): + # dim 1 + reduced_qubo["qubit_residual_dim1"][idx] += triangle_qubo[ + "qubit_residual_dim1" + ][idx] + # dim 2 + reduced_qubo["qubit_residual_dim1"][idx] += triangle_qubo[ + "qubit_residual_dim2" + ][idx, idx] + # dim 3 + reduced_qubo["qubit_residual_dim1"][idx] += triangle_qubo[ + "qubit_residual_dim3" + ][idx, idx, idx] + # dim 4 + reduced_qubo["qubit_residual_dim1"][idx] += triangle_qubo[ + "qubit_residual_dim4" + ][idx, idx, idx, idx] + + # dim 2 + reduced_qubo["qubit_residual_dim2"] = np.zeros_like( + triangle_qubo["qubit_residual_dim2"] + ) + for idx_j in range(len(reduced_qubo["qubit_residual_dim2"])): + for idx_i in range(idx_j): + # dim 2 + reduced_qubo["qubit_residual_dim2"][idx_i, idx_j] += triangle_qubo[ + "qubit_residual_dim2" + ][idx_i, idx_j] + # dim 3 + reduced_qubo["qubit_residual_dim2"][idx_i, idx_j] += triangle_qubo[ + "qubit_residual_dim3" + ][idx_i, idx_j, idx_j] + reduced_qubo["qubit_residual_dim2"][idx_i, idx_j] += triangle_qubo[ + "qubit_residual_dim3" + ][idx_i, idx_i, idx_j] + # dim 4 + reduced_qubo["qubit_residual_dim2"][idx_i, idx_j] += triangle_qubo[ + "qubit_residual_dim4" + ][idx_i, idx_j, idx_j, idx_j] + reduced_qubo["qubit_residual_dim2"][idx_i, idx_j] += triangle_qubo[ + "qubit_residual_dim4" + ][idx_i, idx_i, idx_j, idx_j] + reduced_qubo["qubit_residual_dim2"][idx_i, idx_j] += triangle_qubo[ + "qubit_residual_dim4" + ][idx_i, idx_i, idx_i, idx_j] + + # dim 3 + reduced_qubo["qubit_residual_dim3"] = np.zeros_like( + triangle_qubo["qubit_residual_dim3"] + ) + for idx_k in range(len(reduced_qubo["qubit_residual_dim3"])): + for idx_j in range(idx_k): + for idx_i in range(idx_j): + # dim 3 + reduced_qubo["qubit_residual_dim3"][ + idx_i, idx_j, idx_k + ] += triangle_qubo["qubit_residual_dim3"][idx_i, idx_j, idx_k] + # dim 4 + reduced_qubo["qubit_residual_dim3"][ + idx_i, idx_j, idx_k + ] += triangle_qubo["qubit_residual_dim4"][idx_i, idx_i, idx_j, idx_k] + reduced_qubo["qubit_residual_dim3"][ + idx_i, idx_j, idx_k + ] += triangle_qubo["qubit_residual_dim4"][idx_i, idx_j, idx_j, idx_k] + reduced_qubo["qubit_residual_dim3"][ + idx_i, idx_j, idx_k + ] += triangle_qubo["qubit_residual_dim4"][idx_i, idx_j, idx_k, idx_k] + + # dim 4 + reduced_qubo["qubit_residual_dim4"] = np.zeros_like( + triangle_qubo["qubit_residual_dim4"] + ) + for idx_l in range(len(reduced_qubo["qubit_residual_dim4"])): + for idx_k in range(idx_l): + for idx_j in range(idx_k): + for idx_i in range(idx_j): + reduced_qubo["qubit_residual_dim4"][ + idx_i, idx_j, idx_k, idx_l + ] += triangle_qubo["qubit_residual_dim4"][ + idx_i, idx_j, idx_k, idx_l + ] + + return reduced_qubo + + +# import the QUBO data and return numpy 2D square array +def import_QUBO(define_problem): + ### define problem + ( + num_equations, + P0, + P1, + P2, + qubits_per_var, + basis, + basis_offset, + basis_coeff, + basis_map, + ) = define_problem() + ### calculate "qubo" in real number basis + residual = calculate_squared_residuals(P0, P1, P2) + residual_offset = calculate_residual_offsets(P0, P1, P2, basis_offset) + full_residual = combine_residual_offset(residual, residual_offset) + ### transform "qubo" to qubit basis + # this can be sent into eval_QUBO() and solved + extended_qubo = real_to_qubit_basis( + full_residual, num_equations, qubits_per_var, basis, basis_coeff + ) + ### accumulate extended qubo, to construct only upper triangular tensors + triangle_qubo = accumulate_qubo(extended_qubo) + ### make most sparse upper triangular tensors by reducing repeated qubits + reduced_qubo = dimensional_reduction(triangle_qubo) + + return extended_qubo, triangle_qubo, reduced_qubo, basis_map + + +# evaluate the QUBO given binary vector "eigenvector" and return energy "eigenvalue" +def eval_QUBO(extended_qubo, eigenvector): + eigenvalue_dim0 = extended_qubo["qubit_residual_dim0"] + eigenvalue_dim1 = np.einsum( + "j,j", extended_qubo["qubit_residual_dim1"], eigenvector + ) + eigenvalue_dim2 = np.einsum( + "j,jk,k", eigenvector.T, extended_qubo["qubit_residual_dim2"], eigenvector + ) + eigenvalue_dim3 = np.einsum( + "j,jkl,k,l", + eigenvector.T, + extended_qubo["qubit_residual_dim3"], + eigenvector, + eigenvector, + ) + eigenvalue_dim4 = np.einsum( + "k,j,kjnm,n,m", + eigenvector.T, + eigenvector.T, + extended_qubo["qubit_residual_dim4"], + eigenvector, + eigenvector, + ) + + eigenvalue = ( + eigenvalue_dim0 + + eigenvalue_dim1 + + eigenvalue_dim2 + + eigenvalue_dim3 + + eigenvalue_dim4 + ) + return eigenvalue + + +# Convert non-negative n-bit integer to n-bit binary representation and return numpy array +def int_to_bin(hilbert_index, num_of_qubits): + length = int(hilbert_index).bit_length() + # length of binary conversion + # Check that the bit length fits the b-bit representation + if length > num_of_qubits: + print(" <>") + raise ValueError + x = bin(int(hilbert_index)) + # binary converstion returns string x + y = x[2 : length + 2] # store last l chars of x in y + eigenvector = np.zeros(num_of_qubits) + for i in range(len(y)): + # add the bits from smallest to largest in the last l slots + eigenvector[num_of_qubits - length + i] = int(y[i]) + return eigenvector + + +def argmin_QUBO(extended_qubo): + num_of_qubits = len(extended_qubo["qubit_residual_dim1"]) + ground_state_eigenvector = int_to_bin(hilbert_index=0, num_of_qubits=num_of_qubits) + ground_state_eigenvalue = eval_QUBO(extended_qubo, ground_state_eigenvector) + result_eigenvalue = [] + result_eigenvector = [] + for h_idx in range(2**num_of_qubits): # loop over all 2^n possibilities + eigenvector = int_to_bin(h_idx, num_of_qubits) + eigenvalue = eval_QUBO(extended_qubo, eigenvector) + result_eigenvalue.append(eigenvalue) + result_eigenvector.append(eigenvector) + if eigenvalue < ground_state_eigenvalue: + ground_state_eigenvalue = eigenvalue + ground_state_eigenvector = eigenvector + return ground_state_eigenvector, result_eigenvalue, result_eigenvector + + +def inverse_mapping(eigenvector, basis_map): + presult = [] + num_equations = len(basis_map["basis_coeff"]) + qubits_per_var = len(basis_map["basis"]) + for idx_params in range(num_equations): + presult.append( + basis_map["basis_coeff"][idx_params] + * sum( + basis_map["basis"] + * eigenvector[ + idx_params * qubits_per_var : idx_params * qubits_per_var + + qubits_per_var + ] + ) + + basis_map["basis_offset"][idx_params] + ) + return presult + + +def evaluate_problem(qubo, basis_map, title): + # Get arg min for extended qubo and compute energy + ground_state_eigenvector, result_eigenvalue, result_eigenvector = argmin_QUBO(qubo) + ground_state_eigenvalue = eval_QUBO(qubo, ground_state_eigenvector) + # Evaluate results + print(title) + print("ground state eigenvector = ", ground_state_eigenvector) + print("ground state eigenvalue = ", ground_state_eigenvalue) + print( + "solution = ", + inverse_mapping(ground_state_eigenvector, basis_map), + ) + print() + + +def solve(define_problem): + # Get QUBO matrix + extended_qubo, triangle_qubo, reduced_qubo, basis_map = import_QUBO(define_problem) + evaluate_problem(extended_qubo, basis_map, "extended qubo") + evaluate_problem(triangle_qubo, basis_map, "upper triangular qubo") + evaluate_problem(reduced_qubo, basis_map, "reduced upper triangular qubo") diff --git a/docs/notebooks/trash/wntr_qubo_poly)dixcrete_res.ipynb b/docs/notebooks/trash/wntr_qubo_poly)dixcrete_res.ipynb new file mode 100644 index 0000000..d348f69 --- /dev/null +++ b/docs/notebooks/trash/wntr_qubo_poly)dixcrete_res.ipynb @@ -0,0 +1,327 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "\n", + "# Create a water network model\n", + "inp_file = '../networks/Net0_HW.inp'\n", + "# inp_file = 'networks/Net2Loops.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Expression of he network" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cons:\n", + "mass_balance[J1]: ((expected_demand[J1]-flow[P1])+flow[P2])\n", + "mass_balance[D1]: (expected_demand[D1]-flow[P2])\n", + "approx_hazen_williams_headloss[P1]: (((((((-((sign(flow[P1]))))*hw_resistance[P1])*((abs(flow[P1]))**1.852))-((1e-05*(hw_resistance[P1]**0.5))*flow[P1]))-(((sign(flow[P1]))*minor_loss[P1])*(flow[P1]**2.0)))+source_head[R1])-head[J1])\n", + "approx_hazen_williams_headloss[P2]: (((((((-((sign(flow[P2]))))*hw_resistance[P2])*((abs(flow[P2]))**1.852))-((1e-05*(hw_resistance[P2]**0.5))*flow[P2]))-(((sign(flow[P2]))*minor_loss[P2])*(flow[P2]**2.0)))+head[J1])-head[D1])\n", + "\n", + "vars:\n", + "flow[P1]: flow[P1]\n", + "flow[P2]: flow[P2]\n", + "head[J1]: head[J1]\n", + "head[D1]: head[D1]\n", + "\n" + ] + } + ], + "source": [ + "from wntr.sim.hydraulics import create_hydraulic_model\n", + "model, updater = create_hydraulic_model(wn)\n", + "print(model.__str__())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "0.05\n", + "234518508.2718721\n", + "10512430570.450115\n", + "30.0\n" + ] + } + ], + "source": [ + "print(model.expected_demand['J1'].value)\n", + "print(model.expected_demand['D1'].value)\n", + "print(model.hw_resistance['P1'].value)\n", + "print(model.hw_resistance['P2'].value)\n", + "print(model.source_head['R1'].value)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "hw_res_list = [ {'P1':1.0, 'P2':1.0}, {'P1':2.0, 'P2':1.0}, {'P1':1.0, 'P2':2.0}, {'P1':2.0, 'P2':2.0}]\n", + "exp_dem = {'J1':-1, 'D1':1}\n", + "src_hd = {'R1':2.0}\n", + "\n", + "def network_function(input):\n", + " \n", + " flow = {'P1':input[0], 'P2':input[1]}\n", + " head = {'J1':input[2], 'D1':input[3]}\n", + "\n", + " def mb_j1(flow):\n", + " return exp_dem['J1'] - flow['P1'] + flow['P2']\n", + " \n", + " def mb_d1(flow):\n", + " return exp_dem['D1'] - flow['P2']\n", + " \n", + " def hl_p1(head, flow):\n", + " return -hw_res['P1']*flow['P1']**2 + src_hd['R1'] - head['J1']\n", + "\n", + " def hl_p2(head, flow):\n", + " return -hw_res['P2']*flow['P2']**2 + head['J1'] - head['D1']\n", + " \n", + " return np.array([\n", + " mb_j1(flow),\n", + " mb_d1(flow),\n", + " hl_p1(head, flow),\n", + " hl_p2(head, flow)\n", + " ])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from quantum_newton_raphson.newton_raphson import newton_raphson\n", + "res = []\n", + "for hw_res in hw_res_list:\n", + " initial_point = np.random.rand(4)\n", + " res.append(newton_raphson(network_function, initial_point))\n", + " assert np.allclose(network_function(res[-1].solution), 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[NewtonRaphsonResult(solution=array([5.573e-23, 1.000e+00, 2.000e+00, 1.000e+00]), n_iter=2, diff=5.5531357290306005e-11, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.656, -0.521, -1.51 , -1.393]), splu=), SPLUResult(solution=array([-2.499e-11, 1.395e-11, 4.298e-01, 7.016e-01]), splu=)]),\n", + " NewtonRaphsonResult(solution=array([-1.446e-21, 1.000e+00, 2.000e+00, 1.000e+00]), n_iter=3, diff=0.0, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.943, -0.255, -3.391, -2.475]), splu=), SPLUResult(solution=array([-6.343e-11, 6.815e-12, 1.778e+00, 1.843e+00]), splu=), SPLUResult(solution=array([-0.000e+00, -0.000e+00, -1.463e-10, -1.516e-10]), splu=)]),\n", + " NewtonRaphsonResult(solution=array([3.263e-21, 1.000e+00, 2.000e+00, 0.000e+00]), n_iter=3, diff=0.0, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.786, -0.96 , -1.746, -1.631]), splu=), SPLUResult(solution=array([-6.469e-11, -2.760e-11, 6.184e-01, 2.461e+00]), splu=), SPLUResult(solution=array([-0.000e+00, -0.000e+00, -5.087e-11, -2.025e-10]), splu=)]),\n", + " NewtonRaphsonResult(solution=array([2.141e-21, 1.000e+00, 2.000e+00, 3.964e-11]), n_iter=2, diff=7.795541989707999e-11, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.483, -0.676, -1.538, -1.074]), splu=), SPLUResult(solution=array([-3.970e-11, -1.942e-11, 4.658e-01, 1.378e+00]), splu=)])]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def define_problem():\n", + " # system of equations\n", + " num_equations = 5\n", + " num_var = 6\n", + "\n", + " P0 = np.zeros(num_equations)\n", + " P0[0] = exp_dem['J1']\n", + " P0[1] = exp_dem['D1']\n", + " P0[2] = src_hd['R1']\n", + " P0[3] = 0\n", + " P0[4] = 0\n", + "\n", + " P1 = np.zeros((num_equations, num_var))\n", + " P1[0, 0] = -1\n", + " P1[0, 1] = 1\n", + " P1[0, 2] = 0 \n", + " P1[0, 3] = 0\n", + "\n", + " P1[1, 0] = 0\n", + " P1[1, 1] = -1\n", + " P1[1, 2] = 0 \n", + " P1[1, 3] = 0\n", + "\n", + " P1[2, 0] = 0\n", + " P1[2, 1] = 0\n", + " P1[2, 2] = -1 \n", + " P1[2, 3] = 0\n", + "\n", + " P1[3, 0] = 0\n", + " P1[3, 1] = 0\n", + " P1[3, 2] = 1 \n", + " P1[3, 3] = -1\n", + "\n", + " P1[4,4] = 1\n", + " P1[4,5] = 1\n", + " \n", + "\n", + " P2 = np.zeros((num_equations, num_var, num_var))\n", + " P3 = np.zeros((num_equations, num_var, num_var, num_var))\n", + " P3[2, .., ..] = -1\n", + " P3[3, .., ..] = -1 \n", + "\n", + " # search parameters\n", + " qubits_per_var = 2\n", + " basis = np.array([2**i for i in range(qubits_per_var)])\n", + "\n", + " # basis_offset = np.array([-0.5, 1])\n", + " # basis_coeff = np.array([0.5, 1])\n", + "\n", + " basis_offset = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n", + " basis_coeff = np.array([1, 1, 1, 1, 1, 1])\n", + "\n", + " basis_map = {\n", + " \"basis\": basis,\n", + " \"basis_offset\": basis_offset,\n", + " \"basis_coeff\": basis_coeff,\n", + " }\n", + "\n", + " return (\n", + " num_equations,\n", + " num_var,\n", + " P0,\n", + " P1,\n", + " P2,\n", + " P3,\n", + " qubits_per_var,\n", + " basis,\n", + " basis_offset,\n", + " basis_coeff,\n", + " basis_map,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "extended qubo\n", + "ground state eigenvector = [0. 0. 1. 0. 0. 1. 1. 0.]\n", + "ground state eigenvalue = 0.0\n", + "solution = [0.0, 1.0, 2.0, 1.0]\n", + "\n", + "upper triangular qubo\n", + "ground state eigenvector = [0. 0. 1. 0. 0. 1. 1. 0.]\n", + "ground state eigenvalue = 0.0\n", + "solution = [0.0, 1.0, 2.0, 1.0]\n", + "\n", + "reduced upper triangular qubo\n", + "ground state eigenvector = [0. 0. 1. 0. 0. 1. 1. 0.]\n", + "ground state eigenvalue = 0.0\n", + "solution = [0.0, 1.0, 2.0, 1.0]\n", + "\n" + ] + } + ], + "source": [ + "from poly_brute_force import solve\n", + "sol = solve(define_problem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/trash/wntr_qubo_poly.ipynb b/docs/notebooks/trash/wntr_qubo_poly.ipynb new file mode 100644 index 0000000..a541c8a --- /dev/null +++ b/docs/notebooks/trash/wntr_qubo_poly.ipynb @@ -0,0 +1,324 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "\n", + "# Create a water network model\n", + "inp_file = '../networks/Net0_HW.inp'\n", + "# inp_file = 'networks/Net2Loops.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Expression of he network" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cons:\n", + "mass_balance[J1]: ((expected_demand[J1]-flow[P1])+flow[P2])\n", + "mass_balance[D1]: (expected_demand[D1]-flow[P2])\n", + "approx_hazen_williams_headloss[P1]: (((((((-((sign(flow[P1]))))*hw_resistance[P1])*((abs(flow[P1]))**1.852))-((1e-05*(hw_resistance[P1]**0.5))*flow[P1]))-(((sign(flow[P1]))*minor_loss[P1])*(flow[P1]**2.0)))+source_head[R1])-head[J1])\n", + "approx_hazen_williams_headloss[P2]: (((((((-((sign(flow[P2]))))*hw_resistance[P2])*((abs(flow[P2]))**1.852))-((1e-05*(hw_resistance[P2]**0.5))*flow[P2]))-(((sign(flow[P2]))*minor_loss[P2])*(flow[P2]**2.0)))+head[J1])-head[D1])\n", + "\n", + "vars:\n", + "flow[P1]: flow[P1]\n", + "flow[P2]: flow[P2]\n", + "head[J1]: head[J1]\n", + "head[D1]: head[D1]\n", + "\n" + ] + } + ], + "source": [ + "from wntr.sim.hydraulics import create_hydraulic_model\n", + "model, updater = create_hydraulic_model(wn)\n", + "print(model.__str__())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "0.05\n", + "234518508.2718721\n", + "10512430570.450115\n", + "30.0\n" + ] + } + ], + "source": [ + "print(model.expected_demand['J1'].value)\n", + "print(model.expected_demand['D1'].value)\n", + "print(model.hw_resistance['P1'].value)\n", + "print(model.hw_resistance['P2'].value)\n", + "print(model.source_head['R1'].value)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "hw_res = {'P1':1.0, 'P2':1.0}\n", + "exp_dem = {'J1':-1, 'D1':1}\n", + "src_hd = {'R1':2.0}\n", + "\n", + "def network_function(input):\n", + " \n", + " flow = {'P1':input[0], 'P2':input[1]}\n", + " head = {'J1':input[2], 'D1':input[3]}\n", + "\n", + " def mb_j1(flow):\n", + " return exp_dem['J1'] - flow['P1'] + flow['P2']\n", + " \n", + " def mb_d1(flow):\n", + " return exp_dem['D1'] - flow['P2']\n", + " \n", + " def hl_p1(head, flow):\n", + " return -hw_res['P1']*flow['P1']**2 + src_hd['R1'] - head['J1']\n", + "\n", + " def hl_p2(head, flow):\n", + " return -hw_res['P2']*flow['P2']**2 + head['J1'] - head['D1']\n", + " \n", + " return np.array([\n", + " mb_j1(flow),\n", + " mb_d1(flow),\n", + " hl_p1(head, flow),\n", + " hl_p2(head, flow)\n", + " ])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + } + ], + "source": [ + "from quantum_newton_raphson.newton_raphson import newton_raphson\n", + "\n", + "initial_point = np.random.rand(4)\n", + "res = newton_raphson(network_function, initial_point)\n", + "assert np.allclose(network_function(res.solution), 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5.551e-17, 1.000e+00, 2.000e+00, 1.000e+00])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res.solution" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def define_problem():\n", + " # system of equations\n", + " num_equations = 4\n", + "\n", + " P0 = np.zeros(num_equations)\n", + " P0[0] = exp_dem['J1']\n", + " P0[1] = exp_dem['D1']\n", + " P0[2] = src_hd['R1']\n", + " P0[3] = 0\n", + "\n", + " P1 = np.zeros((num_equations, num_equations))\n", + " P1[0, 0] = -1\n", + " P1[0, 1] = 1\n", + " P1[0, 2] = 0 \n", + " P1[0, 3] = 0\n", + "\n", + " P1[1, 0] = 0\n", + " P1[1, 1] = -1\n", + " P1[1, 2] = 0 \n", + " P1[1, 3] = 0\n", + "\n", + " P1[2, 0] = 0\n", + " P1[2, 1] = 0\n", + " P1[2, 2] = -1 \n", + " P1[2, 3] = 0\n", + "\n", + " P1[3, 0] = 0\n", + " P1[3, 1] = 0\n", + " P1[3, 2] = 1 \n", + " P1[3, 3] = -1\n", + " \n", + "\n", + " P2 = np.zeros((num_equations, num_equations, num_equations))\n", + " P2[2, 0, 0] = -hw_res['P1']\n", + " P2[3, 1, 1] = -hw_res['P2']\n", + "\n", + " # search parameters\n", + " qubits_per_var = 2\n", + " basis = np.array([2**i for i in range(qubits_per_var)])\n", + "\n", + " # basis_offset = np.array([-0.5, 1])\n", + " # basis_coeff = np.array([0.5, 1])\n", + "\n", + " basis_offset = np.array([0.0, 0.0, 0.0, 0.0])\n", + " basis_coeff = np.array([1, 1, 1, 1])\n", + "\n", + " basis_map = {\n", + " \"basis\": basis,\n", + " \"basis_offset\": basis_offset,\n", + " \"basis_coeff\": basis_coeff,\n", + " }\n", + "\n", + " return (\n", + " num_equations,\n", + " P0,\n", + " P1,\n", + " P2,\n", + " qubits_per_var,\n", + " basis,\n", + " basis_offset,\n", + " basis_coeff,\n", + " basis_map,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "extended qubo\n", + "ground state eigenvector = [0. 0. 1. 0. 0. 1. 1. 0.]\n", + "ground state eigenvalue = 0.0\n", + "solution = [0.0, 1.0, 2.0, 1.0]\n", + "\n", + "upper triangular qubo\n", + "ground state eigenvector = [0. 0. 1. 0. 0. 1. 1. 0.]\n", + "ground state eigenvalue = 0.0\n", + "solution = [0.0, 1.0, 2.0, 1.0]\n", + "\n", + "reduced upper triangular qubo\n", + "ground state eigenvector = [0. 0. 1. 0. 0. 1. 1. 0.]\n", + "ground state eigenvalue = 0.0\n", + "solution = [0.0, 1.0, 2.0, 1.0]\n", + "\n" + ] + } + ], + "source": [ + "from poly_brute_force import solve\n", + "sol = solve(define_problem)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a54e1ae117a95daab62b4124347e9aa743d71d2a Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 5 Aug 2024 17:00:20 +0200 Subject: [PATCH 07/96] added new nbs --- docs/notebooks/hhl_solver_Net1Loops.ipynb | 80 +++++++++++------------ docs/notebooks/trash/wntr_qubo_poly.ipynb | 68 +++++++++++++++---- 2 files changed, 94 insertions(+), 54 deletions(-) diff --git a/docs/notebooks/hhl_solver_Net1Loops.ipynb b/docs/notebooks/hhl_solver_Net1Loops.ipynb index c560eda..10364e1 100644 --- a/docs/notebooks/hhl_solver_Net1Loops.ipynb +++ b/docs/notebooks/hhl_solver_Net1Loops.ipynb @@ -11,12 +11,12 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -45,7 +45,7 @@ "\n", "# set up network model\n", "inp_file = 'networks/Net1Loops.inp'\n", - "inp_file = 'networks/Net3Loops.inp'\n", + "inp_file = 'networks/Net3.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", "# plot network\n", @@ -66,39 +66,19 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", "Your EPANET temp dir: /home/nico/.epanet_quantum\n", "\n", - "Size of the Jacobian in EPANET simulator: 8\n", - "Size of the b vector in EPANET simulator: 8\n" + "Size of the Jacobian in EPANET simulator: 92\n", + "Size of the b vector in EPANET simulator: 92\n" ] - }, - { - "data": { - "text/plain": [ - "(name 2 3 4 5 6 7 \\\n", - " 0 52.194599 29.139265 42.472969 26.306131 27.643869 23.355785 \n", - " \n", - " name 8 9 1 \n", - " 0 13.969273 7.612091 4.394531e-07 ,\n", - " name 1 2 3 4 5 6 7 \\\n", - " 0 0.336412 0.052491 0.256151 0.03239 0.19043 0.078751 0.024721 \n", - " \n", - " name 8 9 10 11 \n", - " 0 -0.017889 0.020009 0.005311 0.007349 )" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -108,7 +88,7 @@ "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", "\n", "# run the EPANET simulation\n", - "results_epanet = sim.run_sim()\n", + "# results_epanet = sim.run_sim()\n", "\n", "# remember to set up EPANET Quantum environment variables!\n", "epanet_path = os.environ[\"EPANET_QUANTUM\"]\n", @@ -130,10 +110,10 @@ "print(f\"Size of the b vector in EPANET simulator: {epanet_b.shape[0]}\")\n", "\n", "# save number of nodes and pipes\n", - "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", - "n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", + "# n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", + "# n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", "\n", - "results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" + "# results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" ] }, { @@ -147,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -166,7 +146,7 @@ ")\n", "\n", "\n", - "res = linear_solver(epanet_A, epanet_b)" + "# res = linear_solver(epanet_A, epanet_b)" ] }, { @@ -226,7 +206,27 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "128" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2**7" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -234,11 +234,11 @@ "A = epanet_A.todense()\n", "b = epanet_b \n", "A.shape\n", - "# Apad = np.eye(8,8)\n", - "# Apad[:6,:6] = A\n", - "# bpad = np.zeros(8)\n", - "# bpad[:6] = b\n", - "circuits = linear_solver._solver.construct_circuit(A,b)" + "Apad = np.eye(128,128)\n", + "Apad[:92,:92] = A\n", + "bpad = np.zeros(128)\n", + "bpad[:92] = b\n", + "circuits = linear_solver._solver.construct_circuit(Apad,bpad)" ] }, { diff --git a/docs/notebooks/trash/wntr_qubo_poly.ipynb b/docs/notebooks/trash/wntr_qubo_poly.ipynb index a541c8a..b80708b 100644 --- a/docs/notebooks/trash/wntr_qubo_poly.ipynb +++ b/docs/notebooks/trash/wntr_qubo_poly.ipynb @@ -16,7 +16,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -27,7 +27,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -40,8 +40,8 @@ "import wntr_quantum\n", "\n", "# Create a water network model\n", - "inp_file = '../networks/Net0_HW.inp'\n", - "# inp_file = 'networks/Net2Loops.inp'\n", + "# inp_file = '../networks/Net0_HW.inp'\n", + "inp_file = '../networks/Net2Loops.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", "# Graph the network\n", @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -65,26 +65,66 @@ "output_type": "stream", "text": [ "cons:\n", - "mass_balance[J1]: ((expected_demand[J1]-flow[P1])+flow[P2])\n", - "mass_balance[D1]: (expected_demand[D1]-flow[P2])\n", - "approx_hazen_williams_headloss[P1]: (((((((-((sign(flow[P1]))))*hw_resistance[P1])*((abs(flow[P1]))**1.852))-((1e-05*(hw_resistance[P1]**0.5))*flow[P1]))-(((sign(flow[P1]))*minor_loss[P1])*(flow[P1]**2.0)))+source_head[R1])-head[J1])\n", - "approx_hazen_williams_headloss[P2]: (((((((-((sign(flow[P2]))))*hw_resistance[P2])*((abs(flow[P2]))**1.852))-((1e-05*(hw_resistance[P2]**0.5))*flow[P2]))-(((sign(flow[P2]))*minor_loss[P2])*(flow[P2]**2.0)))+head[J1])-head[D1])\n", + "mass_balance[2]: (((expected_demand[2]-flow[1])+flow[2])+flow[3])\n", + "mass_balance[3]: ((expected_demand[3]-flow[2])+flow[7])\n", + "mass_balance[4]: (((expected_demand[4]-flow[3])+flow[4])+flow[5])\n", + "mass_balance[5]: (((expected_demand[5]-flow[4])-flow[7])+flow[8])\n", + "mass_balance[6]: ((expected_demand[6]-flow[5])+flow[6])\n", + "mass_balance[7]: ((expected_demand[7]-flow[6])-flow[8])\n", + "approx_hazen_williams_headloss[1]: (((((((-((sign(flow[1]))))*hw_resistance[1])*((abs(flow[1]))**1.852))-((1e-05*(hw_resistance[1]**0.5))*flow[1]))-(((sign(flow[1]))*minor_loss[1])*(flow[1]**2.0)))+source_head[1])-head[2])\n", + "approx_hazen_williams_headloss[2]: (((((((-((sign(flow[2]))))*hw_resistance[2])*((abs(flow[2]))**1.852))-((1e-05*(hw_resistance[2]**0.5))*flow[2]))-(((sign(flow[2]))*minor_loss[2])*(flow[2]**2.0)))+head[2])-head[3])\n", + "approx_hazen_williams_headloss[3]: (((((((-((sign(flow[3]))))*hw_resistance[3])*((abs(flow[3]))**1.852))-((1e-05*(hw_resistance[3]**0.5))*flow[3]))-(((sign(flow[3]))*minor_loss[3])*(flow[3]**2.0)))+head[2])-head[4])\n", + "approx_hazen_williams_headloss[4]: (((((((-((sign(flow[4]))))*hw_resistance[4])*((abs(flow[4]))**1.852))-((1e-05*(hw_resistance[4]**0.5))*flow[4]))-(((sign(flow[4]))*minor_loss[4])*(flow[4]**2.0)))+head[4])-head[5])\n", + "approx_hazen_williams_headloss[5]: (((((((-((sign(flow[5]))))*hw_resistance[5])*((abs(flow[5]))**1.852))-((1e-05*(hw_resistance[5]**0.5))*flow[5]))-(((sign(flow[5]))*minor_loss[5])*(flow[5]**2.0)))+head[4])-head[6])\n", + "approx_hazen_williams_headloss[6]: (((((((-((sign(flow[6]))))*hw_resistance[6])*((abs(flow[6]))**1.852))-((1e-05*(hw_resistance[6]**0.5))*flow[6]))-(((sign(flow[6]))*minor_loss[6])*(flow[6]**2.0)))+head[6])-head[7])\n", + "approx_hazen_williams_headloss[7]: (((((((-((sign(flow[7]))))*hw_resistance[7])*((abs(flow[7]))**1.852))-((1e-05*(hw_resistance[7]**0.5))*flow[7]))-(((sign(flow[7]))*minor_loss[7])*(flow[7]**2.0)))+head[3])-head[5])\n", + "approx_hazen_williams_headloss[8]: (((((((-((sign(flow[8]))))*hw_resistance[8])*((abs(flow[8]))**1.852))-((1e-05*(hw_resistance[8]**0.5))*flow[8]))-(((sign(flow[8]))*minor_loss[8])*(flow[8]**2.0)))+head[5])-head[7])\n", "\n", "vars:\n", - "flow[P1]: flow[P1]\n", - "flow[P2]: flow[P2]\n", - "head[J1]: head[J1]\n", - "head[D1]: head[D1]\n", + "flow[1]: flow[1]\n", + "flow[2]: flow[2]\n", + "flow[3]: flow[3]\n", + "flow[7]: flow[7]\n", + "flow[4]: flow[4]\n", + "flow[5]: flow[5]\n", + "flow[8]: flow[8]\n", + "flow[6]: flow[6]\n", + "head[2]: head[2]\n", + "head[3]: head[3]\n", + "head[4]: head[4]\n", + "head[5]: head[5]\n", + "head[6]: head[6]\n", + "head[7]: head[7]\n", "\n" ] } ], "source": [ "from wntr.sim.hydraulics import create_hydraulic_model\n", - "model, updater = create_hydraulic_model(wn)\n", + "model, updater = create_hydraulic_model(wn, HW_approx='default')\n", "print(model.__str__())\n" ] }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(model.cons())[0]." + ] + }, { "cell_type": "code", "execution_count": 3, From e94c1c920ff54dac2f6b06c6d773d426e49a9139 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 5 Aug 2024 17:09:58 +0200 Subject: [PATCH 08/96] adde ddesign class --- docs/notebooks/networks/Net2LoopsCM.inp | 145 +++++++++++++++++++ docs/notebooks/trash/wntr_design.ipynb | 112 ++++++++++++++ wntr_quantum/scenario/chezy_manning.py | 109 ++++++++++++++ wntr_quantum/scenario/network_design_qubo.py | 95 ++++++++++++ 4 files changed, 461 insertions(+) create mode 100644 docs/notebooks/networks/Net2LoopsCM.inp create mode 100644 docs/notebooks/trash/wntr_design.ipynb create mode 100644 wntr_quantum/scenario/chezy_manning.py create mode 100644 wntr_quantum/scenario/network_design_qubo.py diff --git a/docs/notebooks/networks/Net2LoopsCM.inp b/docs/notebooks/networks/Net2LoopsCM.inp new file mode 100644 index 0000000..02db814 --- /dev/null +++ b/docs/notebooks/networks/Net2LoopsCM.inp @@ -0,0 +1,145 @@ +[TITLE] +shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) + +[JUNCTIONS] +;ID Elev Demand Pattern + 2 150.00 27.77 ; + 3 160.00 27.77 ; + 4 155.00 33.33 ; + 5 150.00 75.00 ; + 6 165.00 91.67 ; + 7 160.00 55.55 ; + +[RESERVOIRS] +;ID Head Pattern + 1 210.00 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + 1 1 2 1000.00 457.20 0.85 0.00 Open ; + 2 2 3 1000.00 203 0.85 0.00 Open ; + 3 2 4 1000.00 457 0.85 0.00 Open ; + 4 4 5 1000.00 153 0.85 0.00 Open ; + 5 4 6 1000.00 406.40 0.85 0.00 Open ; + 6 6 7 1000.00 254.00 0.85 0.00 Open ; + 7 3 5 1000.00 153 0.85 0.00 Open ; + 8 5 7 1000.00 153 0.85 0.00 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + + + +[RULES] + + + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 0:00 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 2:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic NONE + +[REPORT] + Status Yes + Summary No + Page 0 + +[OPTIONS] + Units LPS + Headloss C-M + Specific Gravity 1.0 + Viscosity 1.0 + Trials 40 + Accuracy 0.001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality Chlorine mg/L + Diffusivity 1.0 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +2 2000.000 3000.000 +3 1000.000 3000.000 +4 2000.000 2000.000 +5 1000.000 2000.000 +6 2000.000 1000.000 +7 1000.000 1000.000 +1 3000.000 3000.000 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 900.000 900.000 3100.000 3100.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/docs/notebooks/trash/wntr_design.ipynb b/docs/notebooks/trash/wntr_design.ipynb new file mode 100644 index 0000000..c9729be --- /dev/null +++ b/docs/notebooks/trash/wntr_design.ipynb @@ -0,0 +1,112 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "\n", + "# Create a water network model\n", + "# inp_file = '../networks/Net0_HW.inp'\n", + "inp_file = '../networks/Net2LoopsCM.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Expression of he network" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.scenario.network_design_qubo import NetworkDesign\n", + "designer = NetworkDesign(wn)\n", + "designer.m" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'Model' object has no attribute 'hw_resistance'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m m,modeler \u001b[38;5;241m=\u001b[39m \u001b[43mdesigner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_cm_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwn\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/scenario/network_design_qubo.py:63\u001b[0m, in \u001b[0;36mNetworkDesign.create_cm_model\u001b[0;34m(wn)\u001b[0m\n\u001b[1;32m 60\u001b[0m param\u001b[38;5;241m.\u001b[39mleak_poly_coeffs_param\u001b[38;5;241m.\u001b[39mbuild(m, wn, model_updater)\n\u001b[1;32m 61\u001b[0m param\u001b[38;5;241m.\u001b[39melevation_param\u001b[38;5;241m.\u001b[39mbuild(m, wn, model_updater)\n\u001b[0;32m---> 63\u001b[0m \u001b[43mcm_resistance_param\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild\u001b[49m\u001b[43m(\u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_updater\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 64\u001b[0m param\u001b[38;5;241m.\u001b[39mminor_loss_param\u001b[38;5;241m.\u001b[39mbuild(m, wn, model_updater)\n\u001b[1;32m 65\u001b[0m param\u001b[38;5;241m.\u001b[39mtcv_resistance_param\u001b[38;5;241m.\u001b[39mbuild(m, wn, model_updater)\n", + "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/scenario/chezy_manning.py:46\u001b[0m, in \u001b[0;36mcm_resistance_param.build\u001b[0;34m(cls, m, wn, updater, index_over)\u001b[0m\n\u001b[1;32m 39\u001b[0m link \u001b[38;5;241m=\u001b[39m wn\u001b[38;5;241m.\u001b[39mget_link(link_name)\n\u001b[1;32m 40\u001b[0m value \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 41\u001b[0m m\u001b[38;5;241m.\u001b[39mcm_k\n\u001b[1;32m 42\u001b[0m \u001b[38;5;241m*\u001b[39m link\u001b[38;5;241m.\u001b[39mroughness \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m (m\u001b[38;5;241m.\u001b[39mcm_exp)\n\u001b[1;32m 43\u001b[0m \u001b[38;5;241m*\u001b[39m link\u001b[38;5;241m.\u001b[39mdiameter \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m (m\u001b[38;5;241m.\u001b[39mcm_diameter_exp)\n\u001b[1;32m 44\u001b[0m \u001b[38;5;241m*\u001b[39m link\u001b[38;5;241m.\u001b[39mlength\n\u001b[1;32m 45\u001b[0m )\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m link_name \u001b[38;5;129;01min\u001b[39;00m \u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhw_resistance\u001b[49m:\n\u001b[1;32m 47\u001b[0m m\u001b[38;5;241m.\u001b[39mcm_resistance[link_name]\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m=\u001b[39m value\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[0;31mAttributeError\u001b[0m: 'Model' object has no attribute 'hw_resistance'" + ] + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wntr_quantum/scenario/chezy_manning.py b/wntr_quantum/scenario/chezy_manning.py new file mode 100644 index 0000000..3b16955 --- /dev/null +++ b/wntr_quantum/scenario/chezy_manning.py @@ -0,0 +1,109 @@ +import wntr +from wntr.network import LinkStatus +from wntr.sim import aml +from wntr.sim.models.utils import Definition + + +def chezy_manning_constants(m): + """Add cehzy manning constants to the model. + + Args: + m (_type_): _description_ + """ + m.cm_exp = 2 + m.cm_minor_exp = 2 + m.cm_k = 4.66 + m.cm_diameter_exp = -5.33 + + +class cm_resistance_param(Definition): # noqa: D101 + @classmethod + def build(cls, m, wn, updater, index_over=None): # noqa: D417 + """Add a CM resistance coefficient parameter to the model. + + Parameters + ---------- + m: wntr.aml.aml.aml.Model + wn: wntr.network.model.WaterNetworkModel + updater: ModelUpdater + index_over: list of str + list of pipe names + """ + if not hasattr(m, "hw_resistance"): + m.cm_resistance = aml.ParamDict() + + if index_over is None: + index_over = wn.pipe_name_list + + for link_name in index_over: + link = wn.get_link(link_name) + value = ( + m.cm_k + * link.roughness ** (m.cm_exp) + * link.diameter ** (m.cm_diameter_exp) + * link.length + ) + if link_name in m.cm_resistance: + m.cm_resistance[link_name].value = value + else: + m.cm_resistance[link_name] = aml.Param(value) + + updater.add(link, "roughness", cm_resistance_param.update) + updater.add(link, "diameter", cm_resistance_param.update) + updater.add(link, "length", cm_resistance_param.update) + + +class approx_chezy_manning_headloss_constraint(Definition): # noqa: D101 + @classmethod + def build(cls, m, wn, updater, index_over=None): # noqa: D417 + """Adds a mass balance to the model for the specified junctions. + + Parameters + ---------- + m: wntr.aml.aml.aml.Model + wn: wntr.network.model.WaterNetworkModel + updater: ModelUpdater + index_over: list of str + list of pipe names; default is all pipes in wn + """ + if not hasattr(m, "approx_hazen_williams_headloss"): + m.approx_chezy_manning_headloss = aml.ConstraintDict() + + if index_over is None: + index_over = wn.pipe_name_list + + for link_name in index_over: + if link_name in m.approx_chezy_manning_headloss: + del m.approx_chezy_manning_headloss[link_name] + + link = wn.get_link(link_name) + f = m.flow[link_name] + status = link.status + + if status == LinkStatus.Closed or link._is_isolated: + con = aml.Constraint(f) + else: + eps = 1e-5 # Need to provide an options for this + start_node_name = link.start_node_name + end_node_name = link.end_node_name + start_node = wn.get_node(start_node_name) + end_node = wn.get_node(end_node_name) + if isinstance(start_node, wntr.network.Junction): + start_h = m.head[start_node_name] + else: + start_h = m.source_head[start_node_name] + if isinstance(end_node, wntr.network.Junction): + end_h = m.head[end_node_name] + else: + end_h = m.source_head[end_node_name] + k = m.cm_resistance[link_name] + minor_k = m.minor_loss[link_name] + + con = aml.Constraint(expr=-k * f**m.cm_exp + start_h - end_h) + + m.approx_chezy_manning_headloss[link_name] = con + + updater.add(link, "status", approx_chezy_manning_headloss_constraint.update) + updater.add( + link, "_is_isolated", approx_chezy_manning_headloss_constraint.update + ) diff --git a/wntr_quantum/scenario/network_design_qubo.py b/wntr_quantum/scenario/network_design_qubo.py new file mode 100644 index 0000000..8caec39 --- /dev/null +++ b/wntr_quantum/scenario/network_design_qubo.py @@ -0,0 +1,95 @@ +from wntr.sim import aml +from wntr.sim.models import constants +from wntr.sim.models import constraint +from wntr.sim.models import param +from wntr.sim.models import var +from wntr.sim.models.utils import ModelUpdater +from .chezy_manning import approx_chezy_manning_headloss_constraint +from .chezy_manning import chezy_manning_constants +from .chezy_manning import cm_resistance_param + + +class NetworkDesign(object): + """Design problem solved using a QUBO approach.""" + + def __init__(self, wn): + """_summary_. + + Args: + wn (_type_): _description_ + """ + self.wn = wn + self.m, self.model_updater = self.create_cm_model() + + def create_cm_model(self): + """Create the aml. + + Args: + wn (_type_): _description_ + + Raises: + NotImplementedError: _description_ + NotImplementedError: _description_ + ValueError: _description_ + ValueError: _description_ + NotImplementedError: _description_ + NotImplementedError: _description_ + + Returns: + _type_: _description_ + """ + if self.wn.options.hydraulic.demand_model in ["PDD", "PDA"]: + raise ValueError("Pressure Driven simulations not supported") + if self.wn.options.hydraulic.headloss not in ["C-M"]: + raise ValueError("Quantum Design only supported for C-M simulations") + + m = aml.Model() + model_updater = ModelUpdater() + + # Global constants + chezy_manning_constants(m) + constants.head_pump_constants(m) + constants.leak_constants(m) + constants.pdd_constants(m) + + param.source_head_param(m, self.wn) + param.expected_demand_param(m, self.wn) + + param.leak_coeff_param.build(m, self.wn, model_updater) + param.leak_area_param.build(m, self.wn, model_updater) + param.leak_poly_coeffs_param.build(m, self.wn, model_updater) + param.elevation_param.build(m, self.wn, model_updater) + + cm_resistance_param.build(m, self.wn, model_updater) + param.minor_loss_param.build(m, self.wn, model_updater) + param.tcv_resistance_param.build(m, self.wn, model_updater) + param.pump_power_param.build(m, self.wn, model_updater) + param.valve_setting_param.build(m, self.wn, model_updater) + + var.flow_var(m, self.wn) + var.head_var(m, self.wn) + var.leak_rate_var(m, self.wn) + + constraint.mass_balance_constraint.build(m, self.wn, model_updater) + + approx_chezy_manning_headloss_constraint.build(m, self.wn, model_updater) + + constraint.head_pump_headloss_constraint.build(m, self.wn, model_updater) + constraint.power_pump_headloss_constraint.build(m, self.wn, model_updater) + constraint.prv_headloss_constraint.build(m, self.wn, model_updater) + constraint.psv_headloss_constraint.build(m, self.wn, model_updater) + constraint.tcv_headloss_constraint.build(m, self.wn, model_updater) + constraint.fcv_headloss_constraint.build(m, self.wn, model_updater) + if len(self.wn.pbv_name_list) > 0: + raise NotImplementedError( + "PBV valves are not currently supported in the WNTRSimulator" + ) + if len(self.wn.gpv_name_list) > 0: + raise NotImplementedError( + "GPV valves are not currently supported in the WNTRSimulator" + ) + constraint.leak_constraint.build(m, self.wn, model_updater) + + # TODO: Document that changing a curve with controls does not do anything; you have to change the pump_curve_name attribute on the pump + + return m, model_updater From 107f0227327f68fd1c2a0615285e8e13812a9e36 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 5 Aug 2024 23:06:49 +0200 Subject: [PATCH 09/96] add matrix --- wntr_quantum/scenario/chezy_manning.py | 96 +++++++++++++++++++- wntr_quantum/scenario/network_design_qubo.py | 24 +++++ 2 files changed, 118 insertions(+), 2 deletions(-) diff --git a/wntr_quantum/scenario/chezy_manning.py b/wntr_quantum/scenario/chezy_manning.py index 3b16955..c38ea0b 100644 --- a/wntr_quantum/scenario/chezy_manning.py +++ b/wntr_quantum/scenario/chezy_manning.py @@ -83,7 +83,6 @@ def build(cls, m, wn, updater, index_over=None): # noqa: D417 if status == LinkStatus.Closed or link._is_isolated: con = aml.Constraint(f) else: - eps = 1e-5 # Need to provide an options for this start_node_name = link.start_node_name end_node_name = link.end_node_name start_node = wn.get_node(start_node_name) @@ -97,7 +96,6 @@ def build(cls, m, wn, updater, index_over=None): # noqa: D417 else: end_h = m.source_head[end_node_name] k = m.cm_resistance[link_name] - minor_k = m.minor_loss[link_name] con = aml.Constraint(expr=-k * f**m.cm_exp + start_h - end_h) @@ -107,3 +105,97 @@ def build(cls, m, wn, updater, index_over=None): # noqa: D417 updater.add( link, "_is_isolated", approx_chezy_manning_headloss_constraint.update ) + + +def get_mass_balance_constraint(m, wn, matrices): # noqa: D417 + """Adds a mass balance to the model for the specified junctions. + + Parameters + ---------- + m: wntr.aml.aml.aml.Model + wn: wntr.network.model.WaterNetworkModel + updater: ModelUpdater + index_over: list of str + list of junction names; default is all junctions in wn + """ + P0, P1, P2, P3 = matrices + + continuous_var_name = [v.name for v in list(m.vars())] + discrete_var_name = [v.name for k, v in m.cm_resistance.items()] + var_names = continuous_var_name + discrete_var_name + + index_over = wn.junction_name_list + + for ieq, node_name in enumerate(index_over): + + node = wn.get_node(node_name) + if not node._is_isolated: + P0[ieq, 0] += m.expected_demand[node_name].value + + for link_name in wn.get_links_for_node(node_name, flag="INLET"): + node_index = var_names.index(m.flow[link_name].name) + P1[ieq, node_index] -= 1 + + for link_name in wn.get_links_for_node(node_name, flag="OUTLET"): + node_index = var_names.index(m.flow[link_name].name) + P1[ieq, node_index] += 1 + + return P0, P1, P2, P3 + + +def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 + """Adds a mass balance to the model for the specified junctions. + + Parameters + ---------- + m: wntr.aml.aml.aml.Model + wn: wntr.network.model.WaterNetworkModel + updater: ModelUpdater + index_over: list of str + list of pipe names; default is all pipes in wn + """ + P0, P1, P2, P3 = matrices + + continuous_var_name = [v.name for v in list(m.vars())] + discrete_var_name = [v.name for k, v in m.cm_resistance.items()] + + var_names = continuous_var_name + discrete_var_name + + index_over = wn.pipe_name_list + + for ieq0, link_name in enumerate(index_over): + + ieq = ieq0 + len(wn.junction_name_list) + link = wn.get_link(link_name) + f = m.flow[link_name] + flow_index = var_names.index(f.name) + + start_node_name = link.start_node_name + end_node_name = link.end_node_name + + start_node = wn.get_node(start_node_name) + end_node = wn.get_node(end_node_name) + + start_node_index = var_names.index(start_node.name) + end_node_index = var_names.index(end_node.name) + + if isinstance(start_node, wntr.network.Junction): + start_h = m.head[start_node_name] + P1[ieq, start_node_index] = 1 + else: + start_h = m.source_head[start_node_name] + P0[ieq, 0] += start_h.value + + if isinstance(end_node, wntr.network.Junction): + end_h = m.head[end_node_name] + P1[ieq, end_node_index] = -1 + else: + end_h = m.source_head[end_node_name] + P0[ieq, 0] -= end_h.value + + k = m.cm_resistance[link_name] + cm_res_index = var_names.index(k.name) + + P3[ieq, flow_index, flow_index, cm_res_index] = -1 + + return (P0, P1, P2, P3) diff --git a/wntr_quantum/scenario/network_design_qubo.py b/wntr_quantum/scenario/network_design_qubo.py index 8caec39..b7d3296 100644 --- a/wntr_quantum/scenario/network_design_qubo.py +++ b/wntr_quantum/scenario/network_design_qubo.py @@ -1,3 +1,4 @@ +import numpy as np from wntr.sim import aml from wntr.sim.models import constants from wntr.sim.models import constraint @@ -7,6 +8,8 @@ from .chezy_manning import approx_chezy_manning_headloss_constraint from .chezy_manning import chezy_manning_constants from .chezy_manning import cm_resistance_param +from .chezy_manning import get_chezy_manning_matrix +from .chezy_manning import get_mass_balance_constraint class NetworkDesign(object): @@ -20,6 +23,7 @@ def __init__(self, wn): """ self.wn = wn self.m, self.model_updater = self.create_cm_model() + self.matrices = self.initialize_matrices() def create_cm_model(self): """Create the aml. @@ -93,3 +97,23 @@ def create_cm_model(self): # TODO: Document that changing a curve with controls does not do anything; you have to change the pump_curve_name attribute on the pump return m, model_updater + + def initialize_matrices(self): + """_summary_.""" + num_equations = len(list(self.m.cons())) + 1 + num_continuous_variables = len(list(self.m.vars())) + num_discrete_variables = len(self.m.cm_resistance) + + num_variables = num_continuous_variables + num_discrete_variables + + # must transform that to coo + P0 = np.zeros((num_equations, 1)) + P1 = np.zeros((num_equations, num_variables)) + P2 = np.zeros((num_equations, num_variables, num_variables)) + P3 = np.zeros((num_equations, num_variables, num_variables, num_variables)) + + matrices = (P0, P1, P2, P3) + matrices = get_mass_balance_constraint(self.m, self.wn, matrices) + matrices = get_chezy_manning_matrix(self.m, self.wn, matrices) + + return matrices From 624174598447e4fd14a6a4cd3b001e2f2e8cdeab Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Tue, 6 Aug 2024 16:50:10 +0200 Subject: [PATCH 10/96] designer works --- docs/notebooks/networks/Net0_CM.inp | 128 ++++++++ docs/notebooks/networks/Net2LoopsCM.inp | 16 +- docs/notebooks/trash/wntr_design.ipynb | 303 ++++++++++++++++-- .../trash/wntr_qubo_poly)dixcrete_res.ipynb | 27 +- wntr_quantum/scenario/chezy_manning.py | 62 +++- wntr_quantum/scenario/network_design_qubo.py | 173 +++++++++- 6 files changed, 658 insertions(+), 51 deletions(-) create mode 100644 docs/notebooks/networks/Net0_CM.inp diff --git a/docs/notebooks/networks/Net0_CM.inp b/docs/notebooks/networks/Net0_CM.inp new file mode 100644 index 0000000..66bfa70 --- /dev/null +++ b/docs/notebooks/networks/Net0_CM.inp @@ -0,0 +1,128 @@ +[TITLE] +File obtained via Mario of a 2 node sysem + + +[JUNCTIONS] +;ID Elev Demand Pattern + J1 0 500 ; + D1 0 1000 ; + +[RESERVOIRS] +;ID Head Pattern + R1 2 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + P1 R1 J1 1 250 1 0 Open ; + P2 J1 D1 1 200 1 0 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + +[RULES] + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 1 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 1:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic None + +[REPORT] + Status No + Summary No + Page 0 + +[OPTIONS] + Units LPS + Headloss C-M + Specific Gravity 1 + Viscosity 1 + Trials 40 + Accuracy 0.1 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality None mg/L + Diffusivity 1 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +J1 10.00000 60.00000 +D1 110.00000 60.00000 +R1 -11.72214 74.24023 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 0.000 0.000 10000.000 10000.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/docs/notebooks/networks/Net2LoopsCM.inp b/docs/notebooks/networks/Net2LoopsCM.inp index 02db814..76c10ec 100644 --- a/docs/notebooks/networks/Net2LoopsCM.inp +++ b/docs/notebooks/networks/Net2LoopsCM.inp @@ -19,14 +19,14 @@ shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) [PIPES] ;ID Node1 Node2 Length Diameter Roughness MinorLoss Status - 1 1 2 1000.00 457.20 0.85 0.00 Open ; - 2 2 3 1000.00 203 0.85 0.00 Open ; - 3 2 4 1000.00 457 0.85 0.00 Open ; - 4 4 5 1000.00 153 0.85 0.00 Open ; - 5 4 6 1000.00 406.40 0.85 0.00 Open ; - 6 6 7 1000.00 254.00 0.85 0.00 Open ; - 7 3 5 1000.00 153 0.85 0.00 Open ; - 8 5 7 1000.00 153 0.85 0.00 Open ; + 1 1 2 1000.00 457.20 0.015 0.00 Open ; + 2 2 3 1000.00 203 0.015 0.00 Open ; + 3 2 4 1000.00 457 0.015 0.00 Open ; + 4 4 5 1000.00 153 0.015 0.00 Open ; + 5 4 6 1000.00 406.40 0.015 0.00 Open ; + 6 6 7 1000.00 254.00 0.015 0.00 Open ; + 7 3 5 1000.00 153 0.015 0.00 Open ; + 8 5 7 1000.00 153 0.015 0.00 Open ; [PUMPS] ;ID Node1 Node2 Parameters diff --git a/docs/notebooks/trash/wntr_design.ipynb b/docs/notebooks/trash/wntr_design.ipynb index c9729be..a3ea69c 100644 --- a/docs/notebooks/trash/wntr_design.ipynb +++ b/docs/notebooks/trash/wntr_design.ipynb @@ -9,14 +9,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 28, "metadata": { "metadata": {} }, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", 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" ] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 1, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -40,8 +40,8 @@ "import wntr_quantum\n", "\n", "# Create a water network model\n", - "# inp_file = '../networks/Net0_HW.inp'\n", - "inp_file = '../networks/Net2LoopsCM.inp'\n", + "inp_file = '../networks/Net0_CM.inp'\n", + "# inp_file = '../networks/Net2LoopsCM.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", "# Graph the network\n", @@ -57,34 +57,297 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.scenario.network_design_qubo import NetworkDesign\n", - "designer = NetworkDesign(wn)\n", - "designer.m" + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "flow_encoding = RangedEfficientEncoding(nqbit=6, range=2, offset=0, var_base_name=\"x\")\n", + "head_encoding = RangedEfficientEncoding(nqbit=6, range=2, offset=0, var_base_name=\"x\")\n", + "\n", + "\n", + "# pipe_diameters = [0.35, 0.4, 0.45, 0.55]\n", + "pipe_diameters = [2, 4]\n", + "designer = NetworkDesign(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding, \n", + " pipe_diameters=pipe_diameters,\n", + " weight_cost=0)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 30, "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'Model' object has no attribute 'hw_resistance'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m m,modeler \u001b[38;5;241m=\u001b[39m \u001b[43mdesigner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_cm_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwn\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/scenario/network_design_qubo.py:63\u001b[0m, in \u001b[0;36mNetworkDesign.create_cm_model\u001b[0;34m(wn)\u001b[0m\n\u001b[1;32m 60\u001b[0m param\u001b[38;5;241m.\u001b[39mleak_poly_coeffs_param\u001b[38;5;241m.\u001b[39mbuild(m, wn, model_updater)\n\u001b[1;32m 61\u001b[0m param\u001b[38;5;241m.\u001b[39melevation_param\u001b[38;5;241m.\u001b[39mbuild(m, wn, model_updater)\n\u001b[0;32m---> 63\u001b[0m \u001b[43mcm_resistance_param\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild\u001b[49m\u001b[43m(\u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_updater\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 64\u001b[0m param\u001b[38;5;241m.\u001b[39mminor_loss_param\u001b[38;5;241m.\u001b[39mbuild(m, wn, model_updater)\n\u001b[1;32m 65\u001b[0m param\u001b[38;5;241m.\u001b[39mtcv_resistance_param\u001b[38;5;241m.\u001b[39mbuild(m, wn, model_updater)\n", - "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/scenario/chezy_manning.py:46\u001b[0m, in \u001b[0;36mcm_resistance_param.build\u001b[0;34m(cls, m, wn, updater, index_over)\u001b[0m\n\u001b[1;32m 39\u001b[0m link \u001b[38;5;241m=\u001b[39m wn\u001b[38;5;241m.\u001b[39mget_link(link_name)\n\u001b[1;32m 40\u001b[0m value \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 41\u001b[0m m\u001b[38;5;241m.\u001b[39mcm_k\n\u001b[1;32m 42\u001b[0m \u001b[38;5;241m*\u001b[39m link\u001b[38;5;241m.\u001b[39mroughness \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m (m\u001b[38;5;241m.\u001b[39mcm_exp)\n\u001b[1;32m 43\u001b[0m \u001b[38;5;241m*\u001b[39m link\u001b[38;5;241m.\u001b[39mdiameter \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m (m\u001b[38;5;241m.\u001b[39mcm_diameter_exp)\n\u001b[1;32m 44\u001b[0m \u001b[38;5;241m*\u001b[39m link\u001b[38;5;241m.\u001b[39mlength\n\u001b[1;32m 45\u001b[0m )\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m link_name \u001b[38;5;129;01min\u001b[39;00m \u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhw_resistance\u001b[49m:\n\u001b[1;32m 47\u001b[0m m\u001b[38;5;241m.\u001b[39mcm_resistance[link_name]\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m=\u001b[39m value\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[0;31mAttributeError\u001b[0m: 'Model' object has no attribute 'hw_resistance'" + "data": { + "text/plain": [ + "array([[0.5],\n", + " [1. ],\n", + " [2. ],\n", + " [0. ],\n", + " [0. ]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.matrices[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.25, 0.25) [1.5 1. 1.438 1.188]\n", + "(0.25, 0.5) [1.5 1. 1.438 0.938]\n", + "(0.5, 0.25) [1.5 1. 0.875 0.625]\n", + "(0.5, 0.5) [1.5 1. 0.875 0.375]\n" ] } ], + "source": [ + "designer.enumerates_classical_solutions()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.24999999999999992, 0.5]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.sol_vect_res.encoded_reals[0].get_possible_values()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "val = designer.sol_vect_flows.encoded_reals[0].get_possible_values()\n", + "import matplotlib.pyplot as plt \n", + "plt.scatter(val, val)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", + "import sparse\n", + "qubo = QUBO_POLY_MIXED(designer.mixed_solution_vector)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "matrices = tuple(sparse.COO(m) for m in designer.matrices)\n", + "bqm = qubo.create_bqm(matrices, strength=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "istart = designer.sol_vect_flows.size\n", + "for i in range(designer.sol_vect_heads.size):\n", + "\n", + " bqm.add_linear_inequality_constraint(\n", + " qubo.all_expr[istart + i],\n", + " lagrange_multiplier=1,\n", + " label=\"head_%s\" % i,\n", + " lb=1,\n", + " ub=2,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "sampleset = qubo.sample_bqm(bqm, num_reads=10000)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "flow, heads, param = qubo.decode_solution(sampleset.lowest())" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([1.4838709677419355, 0.967741935483871],\n", + " [1.032258064516129, 0.9032258064516129],\n", + " [0.5, 0.24999999999999992])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flow, heads, param" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.016, 0.032, -0.133, -0.105])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "num_heads = designer.wn.num_junctions\n", + "designer.verify_solution(np.array(flow+heads), param)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0], -0.628, 1)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sampleset.record[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "nsol = []\n", + "cost = []\n", + "cons = []\n", + "colors = []\n", + "for i in range(10000):\n", + " flow, heads, param = qubo.decode_solution(sampleset, sol_index=i)\n", + " nsol.append(np.linalg.norm(designer.verify_solution(np.array(flow+heads), param)))\n", + " cost.append(np.sum(param))\n", + " cons.append(np.sum(np.array(heads)-1))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.scatter(nsol, cons)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [] } ], diff --git a/docs/notebooks/trash/wntr_qubo_poly)dixcrete_res.ipynb b/docs/notebooks/trash/wntr_qubo_poly)dixcrete_res.ipynb index d348f69..96b487a 100644 --- a/docs/notebooks/trash/wntr_qubo_poly)dixcrete_res.ipynb +++ b/docs/notebooks/trash/wntr_qubo_poly)dixcrete_res.ipynb @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -149,9 +149,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + } + ], "source": [ "from quantum_newton_raphson.newton_raphson import newton_raphson\n", "res = []\n", @@ -163,19 +172,19 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[NewtonRaphsonResult(solution=array([5.573e-23, 1.000e+00, 2.000e+00, 1.000e+00]), n_iter=2, diff=5.5531357290306005e-11, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.656, -0.521, -1.51 , -1.393]), splu=), SPLUResult(solution=array([-2.499e-11, 1.395e-11, 4.298e-01, 7.016e-01]), splu=)]),\n", - " NewtonRaphsonResult(solution=array([-1.446e-21, 1.000e+00, 2.000e+00, 1.000e+00]), n_iter=3, diff=0.0, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.943, -0.255, -3.391, -2.475]), splu=), SPLUResult(solution=array([-6.343e-11, 6.815e-12, 1.778e+00, 1.843e+00]), splu=), SPLUResult(solution=array([-0.000e+00, -0.000e+00, -1.463e-10, -1.516e-10]), splu=)]),\n", - " NewtonRaphsonResult(solution=array([3.263e-21, 1.000e+00, 2.000e+00, 0.000e+00]), n_iter=3, diff=0.0, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.786, -0.96 , -1.746, -1.631]), splu=), SPLUResult(solution=array([-6.469e-11, -2.760e-11, 6.184e-01, 2.461e+00]), splu=), SPLUResult(solution=array([-0.000e+00, -0.000e+00, -5.087e-11, -2.025e-10]), splu=)]),\n", - " NewtonRaphsonResult(solution=array([2.141e-21, 1.000e+00, 2.000e+00, 3.964e-11]), n_iter=2, diff=7.795541989707999e-11, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.483, -0.676, -1.538, -1.074]), splu=), SPLUResult(solution=array([-3.970e-11, -1.942e-11, 4.658e-01, 1.378e+00]), splu=)])]" + "[NewtonRaphsonResult(solution=array([4.951e-22, 1.000e+00, 2.000e+00, 1.000e+00]), n_iter=2, diff=5.0058845957323683e-11, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.647, -0.356, -1.483, -1.497]), splu=), SPLUResult(solution=array([-1.173e-12, 9.526e-12, 4.181e-01, 5.448e-01]), splu=)]),\n", + " NewtonRaphsonResult(solution=array([-8.327e-17, 1.000e+00, 2.000e+00, 1.000e+00]), n_iter=2, diff=1.169997432270975e-11, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.179, -0.4 , -1.64 , -0.272]), splu=), SPLUResult(solution=array([5.139e-12, 1.071e-11, 6.387e-02, 2.241e-01]), splu=)]),\n", + " NewtonRaphsonResult(solution=array([-1.093e-16, 1.000e+00, 2.000e+00, 5.409e-14]), n_iter=2, diff=1.615062250603927e-12, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.138, -0.133, -1.629, 0.405]), splu=), SPLUResult(solution=array([3.961e-12, 3.545e-12, 1.898e-02, 5.409e-02]), splu=)]),\n", + " NewtonRaphsonResult(solution=array([1.227e-21, 1.000e+00, 2.000e+00, 4.125e-11]), n_iter=2, diff=4.4402481691463436e-11, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.138, -0.836, -1.204, -1.064]), splu=), SPLUResult(solution=array([ 3.980e-12, -2.403e-11, 3.831e-02, 1.435e+00]), splu=)])]" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } diff --git a/wntr_quantum/scenario/chezy_manning.py b/wntr_quantum/scenario/chezy_manning.py index c38ea0b..870a050 100644 --- a/wntr_quantum/scenario/chezy_manning.py +++ b/wntr_quantum/scenario/chezy_manning.py @@ -10,10 +10,45 @@ def chezy_manning_constants(m): Args: m (_type_): _description_ """ - m.cm_exp = 2 - m.cm_minor_exp = 2 - m.cm_k = 4.66 - m.cm_diameter_exp = -5.33 + # m.cm_exp = 2 + # m.cm_minor_exp = 2 + # m.cm_k = 4.66 + # m.cm_diameter_exp = -5.33 + + m.cm_exp = 1 + m.cm_minor_exp = 1 + m.cm_k = 1 + m.cm_diameter_exp = -1 + + +def cm_resistance_prefactor(k, roughness, exp, diameter, diameter_exp): + """_summary_. + + Args: + k (_type_): _description_ + roughness (_type_): _description_ + exp (_type_): _description_ + diameter (_type_): _description_ + diameter_exp (_type_): _description_ + """ + return k * roughness ** (exp) * diameter ** (diameter_exp) + + +def cm_resistance_value(k, roughness, exp, diameter, diameter_exp, length): + """_summary_. + + Args: + k (_type_): _description_ + roughness (_type_): _description_ + exp (_type_): _description_ + diameter (_type_): _description_ + diameter_exp (_type_): _description_ + length (_type_): _description_ + + Returns: + _type_: _description_ + """ + return cm_resistance_prefactor(k, roughness, exp, diameter, diameter_exp) * length class cm_resistance_param(Definition): # noqa: D101 @@ -37,11 +72,13 @@ def build(cls, m, wn, updater, index_over=None): # noqa: D417 for link_name in index_over: link = wn.get_link(link_name) - value = ( - m.cm_k - * link.roughness ** (m.cm_exp) - * link.diameter ** (m.cm_diameter_exp) - * link.length + value = cm_resistance_value( + m.cm_k, + link.roughness, + m.cm_exp, + link.diameter, + m.cm_diameter_exp, + link.length, ) if link_name in m.cm_resistance: m.cm_resistance[link_name].value = value @@ -176,11 +213,9 @@ def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 start_node = wn.get_node(start_node_name) end_node = wn.get_node(end_node_name) - start_node_index = var_names.index(start_node.name) - end_node_index = var_names.index(end_node.name) - if isinstance(start_node, wntr.network.Junction): start_h = m.head[start_node_name] + start_node_index = var_names.index(start_h.name) P1[ieq, start_node_index] = 1 else: start_h = m.source_head[start_node_name] @@ -188,6 +223,7 @@ def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 if isinstance(end_node, wntr.network.Junction): end_h = m.head[end_node_name] + end_node_index = var_names.index(end_h.name) P1[ieq, end_node_index] = -1 else: end_h = m.source_head[end_node_name] @@ -196,6 +232,6 @@ def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 k = m.cm_resistance[link_name] cm_res_index = var_names.index(k.name) - P3[ieq, flow_index, flow_index, cm_res_index] = -1 + P3[ieq, flow_index, flow_index, cm_res_index] = -link.length return (P0, P1, P2, P3) diff --git a/wntr_quantum/scenario/network_design_qubo.py b/wntr_quantum/scenario/network_design_qubo.py index b7d3296..a33ce8e 100644 --- a/wntr_quantum/scenario/network_design_qubo.py +++ b/wntr_quantum/scenario/network_design_qubo.py @@ -1,13 +1,21 @@ +import itertools import numpy as np +from quantum_newton_raphson.newton_raphson import newton_raphson +from qubols.encodings import DiscreteValuesEncoding +from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector +from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED +from qubols.solution_vector import SolutionVector_V2 as SolutionVector from wntr.sim import aml from wntr.sim.models import constants from wntr.sim.models import constraint from wntr.sim.models import param from wntr.sim.models import var from wntr.sim.models.utils import ModelUpdater +import sparse from .chezy_manning import approx_chezy_manning_headloss_constraint from .chezy_manning import chezy_manning_constants from .chezy_manning import cm_resistance_param +from .chezy_manning import cm_resistance_prefactor from .chezy_manning import get_chezy_manning_matrix from .chezy_manning import get_mass_balance_constraint @@ -15,16 +23,137 @@ class NetworkDesign(object): """Design problem solved using a QUBO approach.""" - def __init__(self, wn): + def __init__( + self, wn, flow_encoding, head_encoding, pipe_diameters, weight_cost=1e-1 + ): # noqa: D417 """_summary_. Args: wn (_type_): _description_ + encoding_flows (_type_): _description_ + encoding_heads (_type_): _description_ + pipe_diameters (_type_): _description_ """ self.wn = wn + self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) + self.sol_vect_heads = SolutionVector( + wn.num_junctions, encoding=head_encoding + ) # not sure num_junction is what we need + + self.pipe_diameters = pipe_diameters + self.roughness_factor = self.get_roughness_factor() + self.m, self.model_updater = self.create_cm_model() + + self.sol_vect_res = self.get_resistance_prefactor_encoding() + self.mixed_solution_vector = MixedSolutionVector( + [self.sol_vect_flows, self.sol_vect_heads, self.sol_vect_res] + ) + + self.weight_cost = weight_cost + self.head_lb = 10 + self.head_hb = 20 + self.matrices = self.initialize_matrices() + def get_roughness_factor(self): + """_summary_. + + Raises: + ValueError: _description_ + + Returns: + _type_: _description_ + """ + index_over = self.wn.pipe_name_list + roughness_factors = [] + for link_name in index_over: + link = self.wn.get_link(link_name) + roughness_factors.append(link.roughness) + + if len(set(roughness_factors)) > 1: + raise ValueError( + "works only with all pipes having the same roughness sorry" + ) + else: + return roughness_factors[0] + + def get_resistance_prefactor_encoding(self): + """_summary_.""" + values = np.array( + [ + cm_resistance_prefactor( + self.m.cm_k, + self.roughness_factor, + self.m.cm_exp, + d, + self.m.cm_diameter_exp, + ) + for d in self.pipe_diameters + ] + ) + values.sort() + nqbit = int(np.ceil(np.log2(len(values)))) + enc = DiscreteValuesEncoding(values, nqbit, "cm_res") + return SolutionVector(size=self.wn.num_pipes, encoding=enc) + + def verify_solution(self, input, params): + """generates the classical solution.""" + + P0, P1, P2, P3 = self.matrices + num_heads = self.wn.num_junctions + num_pipes = self.wn.num_pipes + num_vars = num_heads + num_pipes + + p0 = P0[:num_vars].reshape( + -1, + ) + p1 = P1[:num_vars, :num_vars] + p3 = P3[:num_vars].sum(-1)[:, :num_vars, :num_vars].sum(-1) + parameters = np.array([0] * num_heads + params) + return p0 + p1 @ input + parameters * (p3 @ (input * input)) + + def enumerates_classical_solutions(self): + """generates the classical solution.""" + + P0, P1, P2, P3 = self.matrices + num_heads = self.wn.num_junctions + num_pipes = self.wn.num_pipes + num_vars = num_heads + num_pipes + + p0 = P0[:num_vars].reshape( + -1, + ) + p1 = P1[:num_vars, :num_vars] + p3 = P3[:num_vars].sum(-1)[:, :num_vars, :num_vars].sum(-1) + + def func(input): + return p0 + p1 @ input + parameters * (p3 @ (input * input)) + + res_prefactor = np.array( + [ + cm_resistance_prefactor( + self.m.cm_k, + self.roughness_factor, + self.m.cm_exp, + d, + self.m.cm_diameter_exp, + ) + for d in self.pipe_diameters + ] + ) + res_prefactor.sort() + prefactor_combinations = itertools.product( + res_prefactor, repeat=self.wn.num_pipes + ) + for prefacs in prefactor_combinations: + + parameters = np.array([0] * num_heads + list(prefacs)) + initial_point = np.random.rand(num_vars) + res = newton_raphson(func, initial_point) + assert np.allclose(func(res.solution), 0) + print(prefacs, res.solution) + def create_cm_model(self): """Create the aml. @@ -98,6 +227,22 @@ def create_cm_model(self): return m, model_updater + def get_cost_matrix(self, matrices): + """_summary_. + + Args: + matrices (_type_): _description_ + """ + P0, P1, P2, P3 = matrices + n = self.sol_vect_res.size + max_val = self.sol_vect_res.encoded_reals[0].get_max_value() + P0[-1] += self.weight_cost * n * max_val + + istart = self.sol_vect_flows.size + self.sol_vect_heads.size + for i in range(self.sol_vect_res.size): + P1[-1, istart + i] = -self.weight_cost + return P0, P1, P2, P3 + def initialize_matrices(self): """_summary_.""" num_equations = len(list(self.m.cons())) + 1 @@ -115,5 +260,31 @@ def initialize_matrices(self): matrices = (P0, P1, P2, P3) matrices = get_mass_balance_constraint(self.m, self.wn, matrices) matrices = get_chezy_manning_matrix(self.m, self.wn, matrices) + matrices = self.get_cost_matrix(matrices) return matrices + + def solve(self, **options): + """_summary_""" + qubo = QUBO_POLY_MIXED(self.mixed_solution_vector, **options) + matrices = tuple(sparse.COO(m) for m in self.matrices) + bqm = qubo.create_bqm(matrices, strength=1000) + + # add constraint + istart = self.sol_vect_flows.size + for i in range(self.sol_vect_heads.size): + + bqm.add_linear_inequality_constraint( + qubo.all_expr[istart + i], + lagrange_multiplier=1, + label="head_%s" % i, + lb=self.head_lb, + ub=self.head_hb, + ) + + # sample + sampleset = qubo.sample_bqm(bqm, num_reads=options["num_reads"]) + + # decode + sol, param = qubo.decode_solution(sampleset.lowest()) + return sol, param From 60134196d9c1b41c9ce28c225f8a3611697bf295 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 8 Aug 2024 12:57:41 +0200 Subject: [PATCH 11/96] add tools --- docs/notebooks/networks/Net0_CM.inp | 6 +- docs/notebooks/networks/Net1Loops_CM.inp | 139 + docs/notebooks/networks/Net1_scenario1.inp | 19878 +++++++++++++++++ docs/notebooks/trash/wntr_design.ipynb | 261 +- docs/notebooks/trash/wntr_qubo_poly.ipynb | 661 +- wntr_quantum/scenario/chezy_manning.py | 94 +- wntr_quantum/scenario/network_design_qubo.py | 36 +- wntr_quantum/scenario/network_qubo.py | 188 + 8 files changed, 20987 insertions(+), 276 deletions(-) create mode 100644 docs/notebooks/networks/Net1Loops_CM.inp create mode 100644 docs/notebooks/networks/Net1_scenario1.inp create mode 100644 wntr_quantum/scenario/network_qubo.py diff --git a/docs/notebooks/networks/Net0_CM.inp b/docs/notebooks/networks/Net0_CM.inp index 66bfa70..7661781 100644 --- a/docs/notebooks/networks/Net0_CM.inp +++ b/docs/notebooks/networks/Net0_CM.inp @@ -9,15 +9,15 @@ File obtained via Mario of a 2 node sysem [RESERVOIRS] ;ID Head Pattern - R1 2 ; + R1 3 ; [TANKS] ;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow [PIPES] ;ID Node1 Node2 Length Diameter Roughness MinorLoss Status - P1 R1 J1 1 250 1 0 Open ; - P2 J1 D1 1 200 1 0 Open ; + P1 R1 J1 1 1000 1 0 Open ; + P2 J1 D1 0.5 1000 1 0 Open ; [PUMPS] ;ID Node1 Node2 Parameters diff --git a/docs/notebooks/networks/Net1Loops_CM.inp b/docs/notebooks/networks/Net1Loops_CM.inp new file mode 100644 index 0000000..8e1693f --- /dev/null +++ b/docs/notebooks/networks/Net1Loops_CM.inp @@ -0,0 +1,139 @@ +[TITLE] +shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) + +[JUNCTIONS] +;ID Elev Demand Pattern + 2 150.00 500 ; + 3 160.00 1000 ; + 4 155.00 500 ; + 5 150.00 1000 ; + +[RESERVOIRS] +;ID Head Pattern + 1 15.00 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + 1 1 2 1 1000 1 0.00 Open ; + 2 2 3 1 1000 1 0.00 Open ; + 3 2 4 1 1000 1 0.00 Open ; + 4 4 5 1 1000 1 0.00 Open ; + 5 3 5 1 1000 1 0.00 Open ; + + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + + + +[RULES] + + + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 0:00 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 2:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic NONE + +[REPORT] + Status Yes + Summary No + Page 0 + +[OPTIONS] + Units LPS + Headloss C-M + Specific Gravity 1.0 + Viscosity 1.0 + Trials 40 + Accuracy 0.001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality Chlorine mg/L + Diffusivity 1.0 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +2 2000.000 3000.000 +3 1000.000 3000.000 +4 2000.000 2000.000 +5 1000.000 2000.000 +1 3000.000 3000.000 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 900.000 900.000 3100.000 3100.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/docs/notebooks/networks/Net1_scenario1.inp b/docs/notebooks/networks/Net1_scenario1.inp new file mode 100644 index 0000000..63d1f50 --- /dev/null +++ b/docs/notebooks/networks/Net1_scenario1.inp @@ -0,0 +1,19878 @@ +; Filename: Net1_CMH.inp +; WNTR: 0.1.3 +; Created: 2018-07-18 21:06:42 +[TITLE] +EPANET Example Network 1 +A simple example of modeling chlorine decay. Both bulk and +wall reactions are included. + +[JUNCTIONS] +;ID Elevation Demand Pattern + 10 216.408 0 P_10 ; + 11 216.408 34.011596627 P_11 ; + 12 213.36 39.1292726759 P_12 ; + 13 211.836 24.2456885422 P_13 ; + 21 213.36 29.6320164858 P_21 ; + 22 211.836 46.2360462883 P_22 ; + 23 210.312 33.9437878576 P_23 ; + 31 213.36 24.6401581456 P_31 ; + 32 216.408 19.8937512752 P_32 ; + +[RESERVOIRS] +;ID Head Pattern + 9 243.84 ; + +[TANKS] +;ID Elevation Init Level Min Level Max Level Diameter Min Volume Volume Curve + 2 259.08 36.576 30.48 45.72 15.3924 5671.76 ; + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness Minor Loss Status + 10 10 11 3331.67387411 485.372150464 113.811328379 0 Open ; + 11 11 12 1657.13564283 316.943700447 91.3831006337 0 Open ; + 110 2 12 52.8106796552 506.609896966 104.006869299 0 Open ; + 111 11 21 1654.54087368 215.01206176 101.531132283 0 Open ; + 112 12 22 1826.98506296 330.431145219 89.1109075221 0 Open ; + 113 13 23 1483.90300058 224.555983414 98.5290708266 0 Open ; + 12 12 13 1793.26094084 261.438942511 110.848307299 0 Open ; + 121 21 31 1572.61027868 180.745875048 85.2584592309 0 Open ; + 122 22 32 1640.16562082 168.077833556 103.840712614 0 Open ; + 21 21 22 1585.6057026 214.07116674 102.30660687 0 Open ; + 22 22 23 1418.67750998 254.856568931 114.287527886 0 Open ; + 31 31 32 1411.61766834 123.694232337 86.5294112859 0 Open ; + +[PUMPS] +;ID Node1 Node2 Properties + 9 9 10 HEAD 1 SPEED 1 ; + +[VALVES] +;ID Node1 Node2 Diameter Type Setting Minor Loss + +[EMITTERS] +;ID Flow coefficient + +[CURVES] +;ID X-Value Y-Value +;PUMP: 1 + 1 340.687100 76.200000 ; + + +[PATTERNS] +;ID Multipliers + +P_10 0.618025 0.543651 0.454844 0.415054 0.406594 0.395268 0.385668 0.423559 +P_10 0.473188 0.568044 0.556594 0.682055 0.671832 0.759731 0.842346 1.025414 +P_10 1.156579 1.202141 1.160170 1.313728 1.296261 1.286349 1.244369 1.240940 +P_10 1.236849 1.140934 1.148902 1.084004 1.002884 0.946401 0.939612 0.830565 +P_10 0.903139 0.926024 1.019256 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00:30:00 +PATTERN START 00:00:00 +REPORT TIMESTEP 00:30:00 +REPORT START 00:00:00 +START CLOCKTIME 00:00:00 AM +QUALITY TIMESTEP 00:00:00 +STATISTIC NONE + +[REPORT] +STATUS YES +SUMMARY NO + +[COORDINATES] +;Node X-Coord Y-Coord +11 30.000000 70.000000 +10 20.000000 70.000000 +13 70.000000 70.000000 +12 50.000000 70.000000 +21 30.000000 40.000000 +22 50.000000 40.000000 +23 70.000000 40.000000 +32 50.000000 10.000000 +31 30.000000 10.000000 +2 50.000000 90.000000 +9 10.000000 70.000000 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] + 6.99 73.63 "Source" + 13.48 68.13 "Pump" + 43.85 91.21 "Tank" + +[BACKDROP] +DIMENSIONS 7.00 6.00 73.00 94.00 +UNITS None +OFFSET 0.00 0.00 + +[TAGS] +;type name tag + +[END] diff --git a/docs/notebooks/trash/wntr_design.ipynb b/docs/notebooks/trash/wntr_design.ipynb index a3ea69c..d61fe1c 100644 --- a/docs/notebooks/trash/wntr_design.ipynb +++ b/docs/notebooks/trash/wntr_design.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 85, "metadata": { "metadata": {} }, @@ -30,7 +30,7 @@ "" ] }, - "execution_count": 28, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -57,16 +57,26 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 86, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/qubols/qubols/encodings.py:265: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n", + "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n", + " coefs, res, rank, s = np.linalg.lstsq(A, self.discrete_values)\n" + ] + } + ], "source": [ "from wntr_quantum.scenario.network_design_qubo import NetworkDesign\n", "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", - "flow_encoding = RangedEfficientEncoding(nqbit=6, range=2, offset=0, var_base_name=\"x\")\n", - "head_encoding = RangedEfficientEncoding(nqbit=6, range=2, offset=0, var_base_name=\"x\")\n", + "flow_encoding = RangedEfficientEncoding(nqbit=7, range=2, offset=0, var_base_name=\"x\")\n", + "head_encoding = RangedEfficientEncoding(nqbit=7, range=2, offset=0, var_base_name=\"x\")\n", "\n", "\n", "# pipe_diameters = [0.35, 0.4, 0.45, 0.55]\n", @@ -74,25 +84,25 @@ "designer = NetworkDesign(wn, flow_encoding=flow_encoding, \n", " head_encoding=head_encoding, \n", " pipe_diameters=pipe_diameters,\n", - " weight_cost=0)" + " weight_cost=0.01)" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.5],\n", - " [1. ],\n", - " [2. ],\n", - " [0. ],\n", - " [0. ]])" + "array([[0.5 ],\n", + " [1. ],\n", + " [2. ],\n", + " [0. ],\n", + " [0.01]])" ] }, - "execution_count": 30, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -103,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -115,6 +125,14 @@ "(0.5, 0.25) [1.5 1. 0.875 0.625]\n", "(0.5, 0.5) [1.5 1. 0.875 0.375]\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] } ], "source": [ @@ -123,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -132,7 +150,7 @@ "[0.24999999999999992, 0.5]" ] }, - "execution_count": 32, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -143,39 +161,7 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "val = designer.sol_vect_flows.encoded_reals[0].get_possible_values()\n", - "import matplotlib.pyplot as plt \n", - "plt.scatter(val, val)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -186,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 91, "metadata": {}, "outputs": [], "source": [ @@ -196,9 +182,18 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 92, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:759: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", + " warnings.warn(\"For constraints with fractional coefficients, \"\n" + ] + } + ], "source": [ "istart = designer.sol_vect_flows.size\n", "for i in range(designer.sol_vect_heads.size):\n", @@ -214,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -223,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -232,18 +227,18 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "([1.4838709677419355, 0.967741935483871],\n", - " [1.032258064516129, 0.9032258064516129],\n", - " [0.5, 0.24999999999999992])" + "([1.7142857142857142, 1.0476190476190474],\n", + " [1.1111111111111112, 0.9206349206349206],\n", + " [0.24999999999999992, 0.24999999999999992])" ] }, - "execution_count": 52, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -254,16 +249,16 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([-0.016, 0.032, -0.133, -0.105])" + "array([-0.167, -0.048, 0.154, -0.084])" ] }, - "execution_count": 53, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -276,16 +271,16 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0], -0.628, 1)" + "([1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1], -2.78, 1)" ] }, - "execution_count": 54, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -296,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 116, "metadata": {}, "outputs": [], "source": [ @@ -304,32 +299,38 @@ "cost = []\n", "cons = []\n", "colors = []\n", + "count = dict()\n", "for i in range(10000):\n", " flow, heads, param = qubo.decode_solution(sampleset, sol_index=i)\n", " nsol.append(np.linalg.norm(designer.verify_solution(np.array(flow+heads), param)))\n", " cost.append(np.sum(param))\n", " cons.append(np.sum(np.array(heads)-1))\n", + " if nsol[-1] < 1 and cons[-1] > 0:\n", + " if tuple(param) not in count:\n", + " count[tuple(param)] = 0\n", + " count[tuple(param)] += 1\n", + " \n", "\n" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 117, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 56, + "execution_count": 117, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -343,6 +344,124 @@ "plt.scatter(nsol, cons)" ] }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{(0.5, 0.5): 1200,\n", + " (0.24999999999999992, 0.24999999999999992): 487,\n", + " (0.24999999999999992, 0.5): 1354,\n", + " (0.5, 0.24999999999999992): 435}" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "count" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "import dimod\n", + "cqm = dimod.ConstrainedQuadraticModel()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'y'" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cqm.add_variable('REAL', 'x')\n", + "cqm.add_variable('REAL', 'y')" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "add_constraint_from_iterable() missing 1 required positional argument: 'sense'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[105], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcqm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_constraint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mx+y<1\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mc0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/constrained/constrained.py:204\u001b[0m, in \u001b[0;36mConstrainedQuadraticModel.add_constraint\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_constraint_from_comparison(data, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, Iterable):\n\u001b[0;32m--> 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_constraint_from_iterable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 205\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munexpected data format\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mTypeError\u001b[0m: add_constraint_from_iterable() missing 1 required positional argument: 'sense'" + ] + } + ], + "source": [ + "cqm.add_constraint('x+y<1',label='c0')" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'c0'" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x, y = dimod.Integers(['x', 'y'])\n", + "cqm = dimod.ConstrainedQuadraticModel()\n", + "cqm.add_constraint(x + y + x*y <= 1, label='c0')" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'x + y + x*y <= 1.0'" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cqm.constraints['c0'].to_polystring()" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/docs/notebooks/trash/wntr_qubo_poly.ipynb b/docs/notebooks/trash/wntr_qubo_poly.ipynb index b80708b..44b7588 100644 --- a/docs/notebooks/trash/wntr_qubo_poly.ipynb +++ b/docs/notebooks/trash/wntr_qubo_poly.ipynb @@ -16,7 +16,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -25,27 +25,41 @@ "output_type": "display_data" }, { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "cons:\n", + "mass_balance[J1]: ((expected_demand[J1]-flow[P1])+flow[P2])\n", + "mass_balance[D1]: (expected_demand[D1]-flow[P2])\n", + "approx_hazen_williams_headloss[P1]: (((((((-((sign(flow[P1]))))*hw_resistance[P1])*((abs(flow[P1]))**1.852))-((1e-05*(hw_resistance[P1]**0.5))*flow[P1]))-(((sign(flow[P1]))*minor_loss[P1])*(flow[P1]**2.0)))+source_head[R1])-head[J1])\n", + "approx_hazen_williams_headloss[P2]: (((((((-((sign(flow[P2]))))*hw_resistance[P2])*((abs(flow[P2]))**1.852))-((1e-05*(hw_resistance[P2]**0.5))*flow[P2]))-(((sign(flow[P2]))*minor_loss[P2])*(flow[P2]**2.0)))+head[J1])-head[D1])\n", + "\n", + "vars:\n", + "flow[P1]: flow[P1]\n", + "flow[P2]: flow[P2]\n", + "head[J1]: head[J1]\n", + "head[D1]: head[D1]\n", + "\n" + ] } ], "source": [ "import wntr\n", "import wntr_quantum\n", + "import numpy as np \n", + "from wntr.sim.hydraulics import create_hydraulic_model\n", "\n", "# Create a water network model\n", - "# inp_file = '../networks/Net0_HW.inp'\n", - "inp_file = '../networks/Net2Loops.inp'\n", + "inp_file = '../networks/Net0_HW.inp'\n", + "# inp_file = '../networks/Net1Loops.inp'\n", + "# inp_file = '../networks/Net2Loops.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n", + "\n", + "model, updater = create_hydraulic_model(wn, HW_approx='default')\n", + "print(model.__str__())\n" ] }, { @@ -57,279 +71,558 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "# Create a water network model\n", + "inp_file = '../networks/Net0_CM.inp'\n", + "# inp_file = '../networks/Net1Loops_CM.inp'\n", + "# inp_file = '../networks/Net2Loops.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.scenario.network_qubo import Network\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "\n", + "nqbit = 3\n", + "range = (4/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=0.25, offset=+0.0, var_base_name=\"x\")\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=0.25, offset=+0.0, var_base_name=\"x\")\n", + "\n", + "\n", + "net = Network(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "cons:\n", - "mass_balance[2]: (((expected_demand[2]-flow[1])+flow[2])+flow[3])\n", - "mass_balance[3]: ((expected_demand[3]-flow[2])+flow[7])\n", - "mass_balance[4]: (((expected_demand[4]-flow[3])+flow[4])+flow[5])\n", - "mass_balance[5]: (((expected_demand[5]-flow[4])-flow[7])+flow[8])\n", - "mass_balance[6]: ((expected_demand[6]-flow[5])+flow[6])\n", - "mass_balance[7]: ((expected_demand[7]-flow[6])-flow[8])\n", - "approx_hazen_williams_headloss[1]: (((((((-((sign(flow[1]))))*hw_resistance[1])*((abs(flow[1]))**1.852))-((1e-05*(hw_resistance[1]**0.5))*flow[1]))-(((sign(flow[1]))*minor_loss[1])*(flow[1]**2.0)))+source_head[1])-head[2])\n", - "approx_hazen_williams_headloss[2]: (((((((-((sign(flow[2]))))*hw_resistance[2])*((abs(flow[2]))**1.852))-((1e-05*(hw_resistance[2]**0.5))*flow[2]))-(((sign(flow[2]))*minor_loss[2])*(flow[2]**2.0)))+head[2])-head[3])\n", - "approx_hazen_williams_headloss[3]: (((((((-((sign(flow[3]))))*hw_resistance[3])*((abs(flow[3]))**1.852))-((1e-05*(hw_resistance[3]**0.5))*flow[3]))-(((sign(flow[3]))*minor_loss[3])*(flow[3]**2.0)))+head[2])-head[4])\n", - "approx_hazen_williams_headloss[4]: (((((((-((sign(flow[4]))))*hw_resistance[4])*((abs(flow[4]))**1.852))-((1e-05*(hw_resistance[4]**0.5))*flow[4]))-(((sign(flow[4]))*minor_loss[4])*(flow[4]**2.0)))+head[4])-head[5])\n", - "approx_hazen_williams_headloss[5]: (((((((-((sign(flow[5]))))*hw_resistance[5])*((abs(flow[5]))**1.852))-((1e-05*(hw_resistance[5]**0.5))*flow[5]))-(((sign(flow[5]))*minor_loss[5])*(flow[5]**2.0)))+head[4])-head[6])\n", - "approx_hazen_williams_headloss[6]: (((((((-((sign(flow[6]))))*hw_resistance[6])*((abs(flow[6]))**1.852))-((1e-05*(hw_resistance[6]**0.5))*flow[6]))-(((sign(flow[6]))*minor_loss[6])*(flow[6]**2.0)))+head[6])-head[7])\n", - "approx_hazen_williams_headloss[7]: (((((((-((sign(flow[7]))))*hw_resistance[7])*((abs(flow[7]))**1.852))-((1e-05*(hw_resistance[7]**0.5))*flow[7]))-(((sign(flow[7]))*minor_loss[7])*(flow[7]**2.0)))+head[3])-head[5])\n", - "approx_hazen_williams_headloss[8]: (((((((-((sign(flow[8]))))*hw_resistance[8])*((abs(flow[8]))**1.852))-((1e-05*(hw_resistance[8]**0.5))*flow[8]))-(((sign(flow[8]))*minor_loss[8])*(flow[8]**2.0)))+head[5])-head[7])\n", - "\n", - "vars:\n", - "flow[1]: flow[1]\n", - "flow[2]: flow[2]\n", - "flow[3]: flow[3]\n", - "flow[7]: flow[7]\n", - "flow[4]: flow[4]\n", - "flow[5]: flow[5]\n", - "flow[8]: flow[8]\n", - "flow[6]: flow[6]\n", - "head[2]: head[2]\n", - "head[3]: head[3]\n", - "head[4]: head[4]\n", - "head[5]: head[5]\n", - "head[6]: head[6]\n", - "head[7]: head[7]\n", - "\n" + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" ] + }, + { + "data": { + "text/plain": [ + "array([1.5 , 1. , 0.75, 0.25])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from wntr.sim.hydraulics import create_hydraulic_model\n", - "model, updater = create_hydraulic_model(wn, HW_approx='default')\n", - "print(model.__str__())\n" + "ref_sol = net.classical_solutions()\n", + "ref_sol" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "array([0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75])" ] }, - "execution_count": 63, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "list(model.cons())[0]." + "np.sort(flow_encoding.get_possible_values())" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sort(head_encoding.get_possible_values())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.0\n", - "0.05\n", - "234518508.2718721\n", - "10512430570.450115\n", - "30.0\n" + "[1.5 1. 0.75 0.25]\n", + "[1.5 1. 0.75 0.25]\n", + "[0.000e+00 2.220e-16 3.548e-11 3.803e-11]\n" ] } ], "source": [ - "print(model.expected_demand['J1'].value)\n", - "print(model.expected_demand['D1'].value)\n", - "print(model.hw_resistance['P1'].value)\n", - "print(model.hw_resistance['P2'].value)\n", - "print(model.source_head['R1'].value)" + "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", + "import sparse \n", + "from dwave.samplers import SimulatedAnnealingSampler\n", + "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import TabuSampler\n", + "from dimod import ExactSolver\n", + "\n", + "# sampler = TabuSampler()\n", + "sampler = SimulatedAnnealingSampler()\n", + "# sampler = ExactSolver() \n", + "\n", + "qubo = QUBO_POLY_MIXED(net.mixed_solution_vector, options={\"sampler\" : sampler} )\n", + "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "\n", + "bqm = qubo.create_bqm(matrices, strength=1E6)\n", + "\n", + "# sample\n", + "sampleset = qubo.sample_bqm(bqm, num_reads=5000)\n", + "\n", + "# decode\n", + "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", + "sol = np.array(sol[0]+sol[1])\n", + "print(ref_sol)\n", + "print(sol)\n", + "print(ref_sol - sol)\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[x_001_001, x_001_002, x_001_003]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qubo.mixed_solution_vectors.encoded_reals[0].variables" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "not enough values to unpack (expected 2, got 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[21], line 30\u001b[0m\n\u001b[1;32m 26\u001b[0m bqm_input_variables\u001b[38;5;241m.\u001b[39mappend(val0 \u001b[38;5;241m*\u001b[39m val1)\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m closest_vec, bin_encoding_vector, encoded_variables\n\u001b[0;32m---> 30\u001b[0m \u001b[43mcompute_energy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mqubo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0.9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0.6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0.3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbqm\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[21], line 22\u001b[0m, in \u001b[0;36mcompute_energy\u001b[0;34m(qubo, vector, bqm)\u001b[0m\n\u001b[1;32m 20\u001b[0m bqm_input_variables\u001b[38;5;241m.\u001b[39mappend(bin_encoding_vector[idx])\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 22\u001b[0m var0, var1 \u001b[38;5;241m=\u001b[39m v\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 23\u001b[0m idx0 \u001b[38;5;241m=\u001b[39m encoded_variables\u001b[38;5;241m.\u001b[39mindex(var0)\n\u001b[1;32m 24\u001b[0m idx1 \u001b[38;5;241m=\u001b[39m encoded_variables\u001b[38;5;241m.\u001b[39mindex(var1)\n", + "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 2, got 1)" + ] + } + ], "source": [ - "import numpy as np\n", + "def compute_energy(qubo, vector, bqm):\n", + " \"\"\"Compue the QUBO energy of the vecto containing the solution of the initial problem\n", "\n", - "hw_res = {'P1':1.0, 'P2':1.0}\n", - "exp_dem = {'J1':-1, 'D1':1}\n", - "src_hd = {'R1':2.0}\n", + " Args:\n", + " vector (_type_): _description_\n", + " \"\"\"\n", + " closest_vec = []\n", + " bin_encoding_vector = []\n", + " encoded_variables = []\n", + " for val, svec in zip(vector, qubo.mixed_solution_vectors.encoded_reals):\n", + " closest_val, bin_encoding = svec.find_closest(val)\n", + " closest_vec.append(closest_val)\n", + " bin_encoding_vector += bin_encoding\n", + " encoded_variables += svec.variables\n", "\n", - "def network_function(input):\n", - " \n", - " flow = {'P1':input[0], 'P2':input[1]}\n", - " head = {'J1':input[2], 'D1':input[3]}\n", + " bqm_input_variables = []\n", + " for v in bqm.variables:\n", + " if v in encoded_variables:\n", + " idx = encoded_variables.index(v)\n", + " bqm_input_variables.append(bin_encoding_vector[idx])\n", + " else:\n", + " print(v)\n", + " var0, var1 = v.split(\"*\")\n", + " idx0 = encoded_variables.index(var0)\n", + " idx1 = encoded_variables.index(var1)\n", + " val0, val1 = bin_encoding_vector[idx0], bin_encoding_vector[idx1]\n", + " bqm_input_variables.append(val0 * val1)\n", "\n", - " def mb_j1(flow):\n", - " return exp_dem['J1'] - flow['P1'] + flow['P2']\n", - " \n", - " def mb_d1(flow):\n", - " return exp_dem['D1'] - flow['P2']\n", - " \n", - " def hl_p1(head, flow):\n", - " return -hw_res['P1']*flow['P1']**2 + src_hd['R1'] - head['J1']\n", + " return closest_vec, bin_encoding_vector, encoded_variables\n", "\n", - " def hl_p2(head, flow):\n", - " return -hw_res['P2']*flow['P2']**2 + head['J1'] - head['D1']\n", - " \n", - " return np.array([\n", - " mb_j1(flow),\n", - " mb_d1(flow),\n", - " hl_p1(head, flow),\n", - " hl_p2(head, flow)\n", - " ])\n" + "compute_energy(qubo, [1.5,0.9,0.6,0.3], bqm)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + "ename": "TypeError", + "evalue": "energy() missing 1 required positional argument: 'sample'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mbqm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menergy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: energy() missing 1 required positional argument: 'sample'" ] } ], "source": [ - "from quantum_newton_raphson.newton_raphson import newton_raphson\n", - "\n", - "initial_point = np.random.rand(4)\n", - "res = newton_raphson(network_function, initial_point)\n", - "assert np.allclose(network_function(res.solution), 0)" + "bqm.energy()" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([5.551e-17, 1.000e+00, 2.000e+00, 1.000e+00])" + "[5.0,\n", + " -0.07936507936507908,\n", + " 5.079365079365079,\n", + " 2.7755575615628914e-16,\n", + " 5.158730158730159,\n", + " 0.07936507936507964,\n", + " 5.238095238095238,\n", + " 0.158730158730159,\n", + " 5.317460317460317,\n", + " 0.23809523809523836,\n", + " 5.396825396825396,\n", + " 0.3174603174603177,\n", + " 5.476190476190476,\n", + " 0.3968253968253971,\n", + " 5.555555555555555,\n", + " 0.47619047619047644,\n", + " 5.634920634920634,\n", + " 0.5555555555555558,\n", + " 5.7142857142857135,\n", + " 0.6349206349206351,\n", + " 5.793650793650794,\n", + " 0.7142857142857145,\n", + " 5.8730158730158735,\n", + " 0.7936507936507939,\n", + " 5.9523809523809526,\n", + " 0.8730158730158732,\n", + " 6.031746031746032,\n", + " 0.9523809523809526,\n", + " 6.111111111111111,\n", + " 1.031746031746032,\n", + " 6.19047619047619,\n", + " 1.1111111111111114,\n", + " 6.26984126984127,\n", + " 1.1904761904761907,\n", + " 6.349206349206349,\n", + " 1.26984126984127,\n", + " 6.428571428571429,\n", + " 1.3492063492063493,\n", + " 6.507936507936508,\n", + " 1.4285714285714288,\n", + " 6.587301587301587,\n", + " 1.5079365079365081,\n", + " 6.666666666666666,\n", + " 1.5873015873015874,\n", + " 6.746031746031746,\n", + " 1.666666666666667,\n", + " 6.825396825396825,\n", + " 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3.174603174603175,\n", + " 8.333333333333334,\n", + " 3.253968253968254,\n", + " 8.412698412698413,\n", + " 3.3333333333333335,\n", + " 8.492063492063492,\n", + " 3.412698412698413,\n", + " 8.571428571428571,\n", + " 3.492063492063492,\n", + " 8.65079365079365,\n", + " 3.5714285714285716,\n", + " 8.73015873015873,\n", + " 3.650793650793651,\n", + " 8.80952380952381,\n", + " 3.7301587301587302,\n", + " 8.88888888888889,\n", + " 3.8095238095238093,\n", + " 8.968253968253968,\n", + " 3.888888888888889,\n", + " 9.047619047619047,\n", + " 3.9682539682539684,\n", + " 9.126984126984127,\n", + " 4.0476190476190474,\n", + " 9.206349206349206,\n", + " 4.1269841269841265,\n", + " 9.285714285714285,\n", + " 4.2063492063492065,\n", + " 9.365079365079364,\n", + " 4.285714285714286,\n", + " 9.444444444444443,\n", + " 4.365079365079366,\n", + " 9.523809523809522,\n", + " 4.444444444444445,\n", + " 9.603174603174605,\n", + " 4.523809523809524,\n", + " 9.682539682539684,\n", + " 4.603174603174603,\n", + " 9.761904761904763,\n", + " 4.682539682539683,\n", + " 9.841269841269842,\n", + " 4.761904761904762,\n", + " 9.920634920634921,\n", + " 4.841269841269842,\n", + " 10.0,\n", + " 4.920634920634921]" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.solution" + "flow_encoding.get_possible_values()" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 142, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.394, -0.5 , 1.223, -2.109, -1.45 , 0.204, 0.386, -0.138, 0.154])" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "def define_problem():\n", - " # system of equations\n", - " num_equations = 4\n", - "\n", - " P0 = np.zeros(num_equations)\n", - " P0[0] = exp_dem['J1']\n", - " P0[1] = exp_dem['D1']\n", - " P0[2] = src_hd['R1']\n", - " P0[3] = 0\n", - "\n", - " P1 = np.zeros((num_equations, num_equations))\n", - " P1[0, 0] = -1\n", - " P1[0, 1] = 1\n", - " P1[0, 2] = 0 \n", - " P1[0, 3] = 0\n", - "\n", - " P1[1, 0] = 0\n", - " P1[1, 1] = -1\n", - " P1[1, 2] = 0 \n", - " P1[1, 3] = 0\n", - "\n", - " P1[2, 0] = 0\n", - " P1[2, 1] = 0\n", - " P1[2, 2] = -1 \n", - " P1[2, 3] = 0\n", - "\n", - " P1[3, 0] = 0\n", - " P1[3, 1] = 0\n", - " P1[3, 2] = 1 \n", - " P1[3, 3] = -1\n", - " \n", - "\n", - " P2 = np.zeros((num_equations, num_equations, num_equations))\n", - " P2[2, 0, 0] = -hw_res['P1']\n", - " P2[3, 1, 1] = -hw_res['P2']\n", + "net.verify_solution(sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 0, 1, 0, 1, 1, 0]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import itertools\n", + "def find_closest(encoding, float):\n", + " \"\"\"get all the posible values encoded\n", "\n", - " # search parameters\n", - " qubits_per_var = 2\n", - " basis = np.array([2**i for i in range(qubits_per_var)])\n", + " Returns:\n", + " _type_: _description_\n", + " \"\"\"\n", "\n", - " # basis_offset = np.array([-0.5, 1])\n", - " # basis_coeff = np.array([0.5, 1])\n", + " min_diff = 1E12\n", + " closest_value = None \n", + " binary_encoding = None\n", + " for data in itertools.product([0, 1], repeat=encoding.nqbit):\n", + " val = encoding.decode_polynom(list(data)[::-1])\n", + " if np.abs(val-float) < min_diff:\n", + " min_diff = np.abs(val-float)\n", + " closest_value = val \n", + " binary_encoding = list(data)[::-1]\n", "\n", - " basis_offset = np.array([0.0, 0.0, 0.0, 0.0])\n", - " basis_coeff = np.array([1, 1, 1, 1])\n", + " return closest_value, binary_encoding \n", + "vmin, bins = find_closest(flow_encoding, 2.)\n", + "vmin\n", + "bins" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "var = sampleset.lowest().variables\n", + "data = np.array(sampleset.lowest().record[0][0])\n", + "data_real_var = data[qubo.index_variables]\n", "\n", - " basis_map = {\n", - " \"basis\": basis,\n", - " \"basis_offset\": basis_offset,\n", - " \"basis_coeff\": basis_coeff,\n", - " }\n", + "for v, d in zip(var, data):\n", + " if v not in qubo.mapped_variables:\n", + " x0, x1 = v.split('*')\n", + " i0 = qubo.index_variables[qubo.mapped_variables.index(x0)]\n", + " i1 = qubo.index_variables[qubo.mapped_variables.index(x1)]\n", + " assert(d == data[i0] * data[i1])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "energy = []\n", + "residue = []\n", + "for s in sampleset.record:\n", + " energy.append(s[1])\n", + " sol = qubo.decode_solution(s[0])\n", + " r = net.verify_solution(np.array(sol).reshape(-1,))\n", + " residue.append(np.linalg.norm(r))\n", + "plt.scatter(energy, (residue))\n", "\n", - " return (\n", - " num_equations,\n", - " P0,\n", - " P1,\n", - " P2,\n", - " qubits_per_var,\n", - " basis,\n", - " basis_offset,\n", - " basis_coeff,\n", - " basis_map,\n", - " )" + "el, rl = [], []\n", + "for s in sampleset.lowest().record:\n", + " el.append(s[1])\n", + " sol = qubo.decode_solution(s[0])\n", + " r = net.verify_solution(np.array(sol).reshape(-1,))\n", + " rl.append(np.linalg.norm(r+1E-12))\n", + "plt.scatter(el, rl, c='red')" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "extended qubo\n", - "ground state eigenvector = [0. 0. 1. 0. 0. 1. 1. 0.]\n", - "ground state eigenvalue = 0.0\n", - "solution = [0.0, 1.0, 2.0, 1.0]\n", - "\n", - "upper triangular qubo\n", - "ground state eigenvector = [0. 0. 1. 0. 0. 1. 1. 0.]\n", - "ground state eigenvalue = 0.0\n", - "solution = [0.0, 1.0, 2.0, 1.0]\n", - "\n", - "reduced upper triangular qubo\n", - "ground state eigenvector = [0. 0. 1. 0. 0. 1. 1. 0.]\n", - "ground state eigenvalue = 0.0\n", - "solution = [0.0, 1.0, 2.0, 1.0]\n", - "\n" + "[1.5 1. 0.75 0.25]\n", + "[1.475 0.96 0.83 0.375]\n", + "[ 0.025 0.04 -0.08 -0.125]\n" ] } ], "source": [ - "from poly_brute_force import solve\n", - "sol = solve(define_problem)" + "from qubols.qubo_poly import QUBO_POLY\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "import sparse \n", + "from dwave.samplers import SimulatedAnnealingSampler\n", + "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import TabuSampler\n", + "\n", + "encoding = RangedEfficientEncoding(nqbit=12, range=2.0, offset=0.0, var_base_name=\"x\")\n", + "sol_vec = SolutionVector(4, encoding)\n", + "\n", + "qubo = QUBO_POLY(solution_vector=sol_vec, options={ 'num_reads':1000, 'sampler':SimulatedAnnealingSampler()})\n", + "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "\n", + "bqm = qubo.create_bqm(matrices, strength=10000)\n", + "\n", + "# sample\n", + "sampleset = qubo.sample_bqm(bqm, num_reads=5000)\n", + "\n", + "# decode\n", + "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", + "sol = np.array(sol).reshape(-1)\n", + "print(ref_sol)\n", + "print(sol)\n", + "print(ref_sol - sol)\n" ] }, { diff --git a/wntr_quantum/scenario/chezy_manning.py b/wntr_quantum/scenario/chezy_manning.py index 870a050..0549129 100644 --- a/wntr_quantum/scenario/chezy_manning.py +++ b/wntr_quantum/scenario/chezy_manning.py @@ -147,6 +147,98 @@ def build(cls, m, wn, updater, index_over=None): # noqa: D417 def get_mass_balance_constraint(m, wn, matrices): # noqa: D417 """Adds a mass balance to the model for the specified junctions. + Parameters + ---------- + m: wntr.aml.aml.aml.Model + wn: wntr.network.model.WaterNetworkModel + updater: ModelUpdater + index_over: list of str + list of junction names; default is all junctions in wn + """ + P0, P1, P2 = matrices + + continuous_var_name = [v.name for v in list(m.vars())] + discrete_var_name = [v.name for k, v in m.cm_resistance.items()] + var_names = continuous_var_name + discrete_var_name + + index_over = wn.junction_name_list + + for ieq, node_name in enumerate(index_over): + + node = wn.get_node(node_name) + if not node._is_isolated: + P0[ieq, 0] += m.expected_demand[node_name].value + + for link_name in wn.get_links_for_node(node_name, flag="INLET"): + node_index = var_names.index(m.flow[link_name].name) + P1[ieq, node_index] -= 1 + + for link_name in wn.get_links_for_node(node_name, flag="OUTLET"): + node_index = var_names.index(m.flow[link_name].name) + P1[ieq, node_index] += 1 + + return P0, P1, P2 + + +def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 + """Adds a mass balance to the model for the specified junctions. + + Parameters + ---------- + m: wntr.aml.aml.aml.Model + wn: wntr.network.model.WaterNetworkModel + updater: ModelUpdater + index_over: list of str + list of pipe names; default is all pipes in wn + """ + P0, P1, P2 = matrices + + continuous_var_name = [v.name for v in list(m.vars())] + discrete_var_name = [v.name for k, v in m.cm_resistance.items()] + + var_names = continuous_var_name + discrete_var_name + + index_over = wn.pipe_name_list + + for ieq0, link_name in enumerate(index_over): + + ieq = ieq0 + len(wn.junction_name_list) + link = wn.get_link(link_name) + f = m.flow[link_name] + flow_index = var_names.index(f.name) + + start_node_name = link.start_node_name + end_node_name = link.end_node_name + + start_node = wn.get_node(start_node_name) + end_node = wn.get_node(end_node_name) + + if isinstance(start_node, wntr.network.Junction): + start_h = m.head[start_node_name] + start_node_index = var_names.index(start_h.name) + P1[ieq, start_node_index] = 1 + else: + start_h = m.source_head[start_node_name] + P0[ieq, 0] += start_h.value + + if isinstance(end_node, wntr.network.Junction): + end_h = m.head[end_node_name] + end_node_index = var_names.index(end_h.name) + P1[ieq, end_node_index] = -1 + else: + end_h = m.source_head[end_node_name] + P0[ieq, 0] -= end_h.value + + k = m.cm_resistance[link_name] + + P2[ieq, flow_index, flow_index] = -k.value + + return (P0, P1, P2) + + +def get_mass_balance_constraint_design(m, wn, matrices): # noqa: D417 + """Adds a mass balance to the model for the specified junctions. + Parameters ---------- m: wntr.aml.aml.aml.Model @@ -180,7 +272,7 @@ def get_mass_balance_constraint(m, wn, matrices): # noqa: D417 return P0, P1, P2, P3 -def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 +def get_chezy_manning_matrix_design(m, wn, matrices): # noqa: D417 """Adds a mass balance to the model for the specified junctions. Parameters diff --git a/wntr_quantum/scenario/network_design_qubo.py b/wntr_quantum/scenario/network_design_qubo.py index a33ce8e..1334c32 100644 --- a/wntr_quantum/scenario/network_design_qubo.py +++ b/wntr_quantum/scenario/network_design_qubo.py @@ -16,8 +16,8 @@ from .chezy_manning import chezy_manning_constants from .chezy_manning import cm_resistance_param from .chezy_manning import cm_resistance_prefactor -from .chezy_manning import get_chezy_manning_matrix -from .chezy_manning import get_mass_balance_constraint +from .chezy_manning import get_chezy_manning_matrix_design +from .chezy_manning import get_mass_balance_constraint_design class NetworkDesign(object): @@ -130,19 +130,21 @@ def enumerates_classical_solutions(self): def func(input): return p0 + p1 @ input + parameters * (p3 @ (input * input)) - res_prefactor = np.array( - [ - cm_resistance_prefactor( - self.m.cm_k, - self.roughness_factor, - self.m.cm_exp, - d, - self.m.cm_diameter_exp, - ) - for d in self.pipe_diameters - ] - ) - res_prefactor.sort() + # res_prefactor = np.array( + # [ + # cm_resistance_prefactor( + # self.m.cm_k, + # self.roughness_factor, + # self.m.cm_exp, + # d, + # self.m.cm_diameter_exp, + # ) + # for d in self.pipe_diameters + # ] + # ) + # res_prefactor.sort() + + res_prefactor = self.sol_vect_res.encoded_reals[0].get_possible_values() prefactor_combinations = itertools.product( res_prefactor, repeat=self.wn.num_pipes ) @@ -258,8 +260,8 @@ def initialize_matrices(self): P3 = np.zeros((num_equations, num_variables, num_variables, num_variables)) matrices = (P0, P1, P2, P3) - matrices = get_mass_balance_constraint(self.m, self.wn, matrices) - matrices = get_chezy_manning_matrix(self.m, self.wn, matrices) + matrices = get_mass_balance_constraint_design(self.m, self.wn, matrices) + matrices = get_chezy_manning_matrix_design(self.m, self.wn, matrices) matrices = self.get_cost_matrix(matrices) return matrices diff --git a/wntr_quantum/scenario/network_qubo.py b/wntr_quantum/scenario/network_qubo.py new file mode 100644 index 0000000..c1c533a --- /dev/null +++ b/wntr_quantum/scenario/network_qubo.py @@ -0,0 +1,188 @@ +import itertools +import numpy as np +from quantum_newton_raphson.newton_raphson import newton_raphson +from qubols.encodings import DiscreteValuesEncoding +from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector +from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED +from qubols.solution_vector import SolutionVector_V2 as SolutionVector +from wntr.sim import aml +from wntr.sim.models import constants +from wntr.sim.models import constraint +from wntr.sim.models import param +from wntr.sim.models import var +from wntr.sim.models.utils import ModelUpdater +import sparse +from .chezy_manning import approx_chezy_manning_headloss_constraint +from .chezy_manning import chezy_manning_constants +from .chezy_manning import cm_resistance_param +from .chezy_manning import cm_resistance_prefactor +from .chezy_manning import get_chezy_manning_matrix +from .chezy_manning import get_mass_balance_constraint + + +class Network(object): + """Design problem solved using a QUBO approach.""" + + def __init__( + self, + wn, + flow_encoding, + head_encoding, + ): # noqa: D417 + """_summary_. + + Args: + wn (_type_): _description_ + encoding_flows (_type_): _description_ + encoding_heads (_type_): _description_ + pipe_diameters (_type_): _description_ + """ + self.wn = wn + self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) + self.sol_vect_heads = SolutionVector( + wn.num_junctions, encoding=head_encoding + ) # not sure num_junction is what we need + + self.m, self.model_updater = self.create_cm_model() + + self.mixed_solution_vector = MixedSolutionVector( + [self.sol_vect_flows, self.sol_vect_heads] + ) + + self.matrices = self.initialize_matrices() + + def verify_solution(self, input): + """generates the classical solution.""" + + P0, P1, P2 = self.matrices + + p0 = P0.reshape( + -1, + ) + p1 = P1 + p2 = P2.sum(-1) + return p0 + p1 @ input + (p2 @ (input * input)) + + def classical_solutions(self): + """generates the classical solution.""" + + P0, P1, P2 = self.matrices + num_heads = self.wn.num_junctions + num_pipes = self.wn.num_pipes + num_vars = num_heads + num_pipes + + p0 = P0.reshape( + -1, + ) + p1 = P1 + p2 = P2.sum(-1) + + def func(input): + return p0 + p1 @ input + (p2 @ (input * input)) + + initial_point = np.random.rand(num_vars) + res = newton_raphson(func, initial_point) + assert np.allclose(func(res.solution), 0) + return res.solution + + def create_cm_model(self): + """Create the aml. + + Args: + wn (_type_): _description_ + + Raises: + NotImplementedError: _description_ + NotImplementedError: _description_ + ValueError: _description_ + ValueError: _description_ + NotImplementedError: _description_ + NotImplementedError: _description_ + + Returns: + _type_: _description_ + """ + if self.wn.options.hydraulic.demand_model in ["PDD", "PDA"]: + raise ValueError("Pressure Driven simulations not supported") + if self.wn.options.hydraulic.headloss not in ["C-M"]: + raise ValueError("Quantum Design only supported for C-M simulations") + + m = aml.Model() + model_updater = ModelUpdater() + + # Global constants + chezy_manning_constants(m) + constants.head_pump_constants(m) + constants.leak_constants(m) + constants.pdd_constants(m) + + param.source_head_param(m, self.wn) + param.expected_demand_param(m, self.wn) + + param.leak_coeff_param.build(m, self.wn, model_updater) + param.leak_area_param.build(m, self.wn, model_updater) + param.leak_poly_coeffs_param.build(m, self.wn, model_updater) + param.elevation_param.build(m, self.wn, model_updater) + + cm_resistance_param.build(m, self.wn, model_updater) + param.minor_loss_param.build(m, self.wn, model_updater) + param.tcv_resistance_param.build(m, self.wn, model_updater) + param.pump_power_param.build(m, self.wn, model_updater) + param.valve_setting_param.build(m, self.wn, model_updater) + + var.flow_var(m, self.wn) + var.head_var(m, self.wn) + var.leak_rate_var(m, self.wn) + + constraint.mass_balance_constraint.build(m, self.wn, model_updater) + + approx_chezy_manning_headloss_constraint.build(m, self.wn, model_updater) + + constraint.head_pump_headloss_constraint.build(m, self.wn, model_updater) + constraint.power_pump_headloss_constraint.build(m, self.wn, model_updater) + constraint.prv_headloss_constraint.build(m, self.wn, model_updater) + constraint.psv_headloss_constraint.build(m, self.wn, model_updater) + constraint.tcv_headloss_constraint.build(m, self.wn, model_updater) + constraint.fcv_headloss_constraint.build(m, self.wn, model_updater) + if len(self.wn.pbv_name_list) > 0: + raise NotImplementedError( + "PBV valves are not currently supported in the WNTRSimulator" + ) + if len(self.wn.gpv_name_list) > 0: + raise NotImplementedError( + "GPV valves are not currently supported in the WNTRSimulator" + ) + constraint.leak_constraint.build(m, self.wn, model_updater) + + # TODO: Document that changing a curve with controls does not do anything; you have to change the pump_curve_name attribute on the pump + + return m, model_updater + + def initialize_matrices(self): + """_summary_.""" + num_equations = len(list(self.m.cons())) + num_variables = len(list(self.m.vars())) + + # must transform that to coo + P0 = np.zeros((num_equations, 1)) + P1 = np.zeros((num_equations, num_variables)) + P2 = np.zeros((num_equations, num_variables, num_variables)) + + matrices = (P0, P1, P2) + matrices = get_mass_balance_constraint(self.m, self.wn, matrices) + matrices = get_chezy_manning_matrix(self.m, self.wn, matrices) + + return matrices + + def solve(self, **options): + """_summary_""" + qubo = QUBO_POLY_MIXED(self.mixed_solution_vector, **options) + matrices = tuple(sparse.COO(m) for m in self.matrices) + bqm = qubo.create_bqm(matrices, strength=1000) + + # sample + sampleset = qubo.sample_bqm(bqm, num_reads=options["num_reads"]) + + # decode + sol, param = qubo.decode_solution(sampleset.lowest()) + return sol, param From 08708fdaabdffc80d710efdd20674b79cd9c2fd6 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 8 Aug 2024 14:31:29 +0200 Subject: [PATCH 12/96] added tools --- docs/notebooks/trash/wntr_qubo_poly.ipynb | 104 ++--- .../trash/wntr_qubo_poly_linear_system.ipynb | 403 ++++++++++++++++++ 2 files changed, 429 insertions(+), 78 deletions(-) create mode 100644 docs/notebooks/trash/wntr_qubo_poly_linear_system.ipynb diff --git a/docs/notebooks/trash/wntr_qubo_poly.ipynb b/docs/notebooks/trash/wntr_qubo_poly.ipynb index 44b7588..d3189f7 100644 --- a/docs/notebooks/trash/wntr_qubo_poly.ipynb +++ b/docs/notebooks/trash/wntr_qubo_poly.ipynb @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -102,30 +102,26 @@ "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=0.25, offset=+0.0, var_base_name=\"x\")\n", "\n", "\n", + "nqbit = 5\n", + "flow_encoding = RangedEfficientEncoding(nqbit=nqbit, range=5., offset=+0.0, var_base_name=\"x\")\n", + "head_encoding = RangedEfficientEncoding(nqbit=nqbit, range=5, offset=+0.0, var_base_name=\"x\")\n", + "\n", "net = Network(wn, flow_encoding=flow_encoding, \n", " head_encoding=head_encoding)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, { "data": { "text/plain": [ "array([1.5 , 1. , 0.75, 0.25])" ] }, - "execution_count": 4, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -137,16 +133,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75])" + "array([-5.333, -5. , -4.667, -4.333, -4. , -3.667, -3.333, -3. , -2.667, -2.333, -2. , -1.667, -1.333, -1. , -0.667, -0.333, 0. , 0.333, 0.667, 1. , 1.333, 1.667, 2. , 2.333, 2.667, 3. , 3.333, 3.667, 4. , 4.333, 4.667, 5. ])" ] }, - "execution_count": 5, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -157,16 +153,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75])" + "array([-5.333, -5. , -4.667, -4.333, -4. , -3.667, -3.333, -3. , -2.667, -2.333, -2. , -1.667, -1.333, -1. , -0.667, -0.333, 0. , 0.333, 0.667, 1. , 1.333, 1.667, 2. , 2.333, 2.667, 3. , 3.333, 3.667, 4. , 4.333, 4.667, 5. ])" ] }, - "execution_count": 6, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -177,16 +173,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1.5 1. 0.75 0.25]\n", - "[1.5 1. 0.75 0.25]\n", - "[0.000e+00 2.220e-16 3.548e-11 3.803e-11]\n" + "ref: [1.5 1. 0.75 0.25] -> [1.6666666666666665, 1.0, 0.6666666666666666, 0.3333333333333333] energy: -9.996913580246822\n", + "sol: [1.333 1. 1.333 0.667] -> [1.3333333333333333, 1.0, 1.3333333333333333, 0.6666666666666666] energy: -10.182098765432102\n", + "[ 1.667e-01 -1.110e-16 -5.833e-01 -4.167e-01]\n" ] } ], @@ -205,16 +201,20 @@ "qubo = QUBO_POLY_MIXED(net.mixed_solution_vector, options={\"sampler\" : sampler} )\n", "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", "\n", - "bqm = qubo.create_bqm(matrices, strength=1E6)\n", + "bqm = qubo.create_bqm(matrices, strength=1E3)\n", "\n", "# sample\n", - "sampleset = qubo.sample_bqm(bqm, num_reads=5000)\n", + "sampleset = qubo.sample_bqm(bqm, num_reads=10000)\n", "\n", "# decode\n", "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", "sol = np.array(sol[0]+sol[1])\n", - "print(ref_sol)\n", - "print(sol)\n", + "\n", + "data_ref, eref = qubo.compute_energy(ref_sol, bqm)\n", + "data_sol, esol = qubo.compute_energy(sol, bqm)\n", + "\n", + "print('ref: ', ref_sol, '->', data_ref[0], ' energy: ', eref)\n", + "print('sol: ', sol, '->', data_sol[0], ' energy: ', esol)\n", "print(ref_sol - sol)\n" ] }, @@ -238,58 +238,6 @@ "qubo.mixed_solution_vectors.encoded_reals[0].variables" ] }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "not enough values to unpack (expected 2, got 1)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[21], line 30\u001b[0m\n\u001b[1;32m 26\u001b[0m bqm_input_variables\u001b[38;5;241m.\u001b[39mappend(val0 \u001b[38;5;241m*\u001b[39m val1)\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m closest_vec, bin_encoding_vector, encoded_variables\n\u001b[0;32m---> 30\u001b[0m \u001b[43mcompute_energy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mqubo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0.9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0.6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0.3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbqm\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[21], line 22\u001b[0m, in \u001b[0;36mcompute_energy\u001b[0;34m(qubo, vector, bqm)\u001b[0m\n\u001b[1;32m 20\u001b[0m bqm_input_variables\u001b[38;5;241m.\u001b[39mappend(bin_encoding_vector[idx])\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 22\u001b[0m var0, var1 \u001b[38;5;241m=\u001b[39m v\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 23\u001b[0m idx0 \u001b[38;5;241m=\u001b[39m encoded_variables\u001b[38;5;241m.\u001b[39mindex(var0)\n\u001b[1;32m 24\u001b[0m idx1 \u001b[38;5;241m=\u001b[39m encoded_variables\u001b[38;5;241m.\u001b[39mindex(var1)\n", - "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 2, got 1)" - ] - } - ], - "source": [ - "def compute_energy(qubo, vector, bqm):\n", - " \"\"\"Compue the QUBO energy of the vecto containing the solution of the initial problem\n", - "\n", - " Args:\n", - " vector (_type_): _description_\n", - " \"\"\"\n", - " closest_vec = []\n", - " bin_encoding_vector = []\n", - " encoded_variables = []\n", - " for val, svec in zip(vector, qubo.mixed_solution_vectors.encoded_reals):\n", - " closest_val, bin_encoding = svec.find_closest(val)\n", - " closest_vec.append(closest_val)\n", - " bin_encoding_vector += bin_encoding\n", - " encoded_variables += svec.variables\n", - "\n", - " bqm_input_variables = []\n", - " for v in bqm.variables:\n", - " if v in encoded_variables:\n", - " idx = encoded_variables.index(v)\n", - " bqm_input_variables.append(bin_encoding_vector[idx])\n", - " else:\n", - " print(v)\n", - " var0, var1 = v.split(\"*\")\n", - " idx0 = encoded_variables.index(var0)\n", - " idx1 = encoded_variables.index(var1)\n", - " val0, val1 = bin_encoding_vector[idx0], bin_encoding_vector[idx1]\n", - " bqm_input_variables.append(val0 * val1)\n", - "\n", - " return closest_vec, bin_encoding_vector, encoded_variables\n", - "\n", - "compute_energy(qubo, [1.5,0.9,0.6,0.3], bqm)" - ] - }, { "cell_type": "code", "execution_count": 20, @@ -521,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ diff --git a/docs/notebooks/trash/wntr_qubo_poly_linear_system.ipynb b/docs/notebooks/trash/wntr_qubo_poly_linear_system.ipynb new file mode 100644 index 0000000..8528c97 --- /dev/null +++ b/docs/notebooks/trash/wntr_qubo_poly_linear_system.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-5.03937008, -5. , -4.96062992, -4.92125984, -4.88188976,\n", + " -4.84251969, -4.80314961, -4.76377953, -4.72440945, -4.68503937,\n", + " -4.64566929, -4.60629921, -4.56692913, -4.52755906, -4.48818898,\n", + " -4.4488189 , -4.40944882, -4.37007874, -4.33070866, -4.29133858,\n", + " -4.2519685 , -4.21259843, -4.17322835, -4.13385827, -4.09448819,\n", + " -4.05511811, -4.01574803, -3.97637795, -3.93700787, -3.8976378 ,\n", + " -3.85826772, -3.81889764, -3.77952756, -3.74015748, -3.7007874 ,\n", + " -3.66141732, -3.62204724, -3.58267717, -3.54330709, -3.50393701,\n", + " -3.46456693, -3.42519685, -3.38582677, -3.34645669, -3.30708661,\n", + " -3.26771654, -3.22834646, -3.18897638, -3.1496063 , -3.11023622,\n", + " -3.07086614, -3.03149606, -2.99212598, -2.95275591, -2.91338583,\n", + " -2.87401575, -2.83464567, -2.79527559, -2.75590551, -2.71653543,\n", + " -2.67716535, -2.63779528, -2.5984252 , -2.55905512, -2.51968504,\n", + " -2.48031496, -2.44094488, -2.4015748 , -2.36220472, -2.32283465,\n", + " -2.28346457, -2.24409449, -2.20472441, -2.16535433, -2.12598425,\n", + " -2.08661417, -2.04724409, -2.00787402, -1.96850394, -1.92913386,\n", + " -1.88976378, -1.8503937 , -1.81102362, -1.77165354, -1.73228346,\n", + " -1.69291339, -1.65354331, -1.61417323, -1.57480315, -1.53543307,\n", + " -1.49606299, -1.45669291, -1.41732283, -1.37795276, -1.33858268,\n", + " -1.2992126 , -1.25984252, -1.22047244, -1.18110236, -1.14173228,\n", + " -1.1023622 , -1.06299213, -1.02362205, -0.98425197, -0.94488189,\n", + " -0.90551181, -0.86614173, -0.82677165, -0.78740157, -0.7480315 ,\n", + " -0.70866142, -0.66929134, -0.62992126, -0.59055118, -0.5511811 ,\n", + " -0.51181102, -0.47244094, -0.43307087, -0.39370079, -0.35433071,\n", + " -0.31496063, -0.27559055, -0.23622047, -0.19685039, -0.15748031,\n", + " -0.11811024, -0.07874016, -0.03937008, 0. , 0.03937008,\n", + " 0.07874016, 0.11811024, 0.15748031, 0.19685039, 0.23622047,\n", + " 0.27559055, 0.31496063, 0.35433071, 0.39370079, 0.43307087,\n", + " 0.47244094, 0.51181102, 0.5511811 , 0.59055118, 0.62992126,\n", + " 0.66929134, 0.70866142, 0.7480315 , 0.78740157, 0.82677165,\n", + " 0.86614173, 0.90551181, 0.94488189, 0.98425197, 1.02362205,\n", + " 1.06299213, 1.1023622 , 1.14173228, 1.18110236, 1.22047244,\n", + " 1.25984252, 1.2992126 , 1.33858268, 1.37795276, 1.41732283,\n", + " 1.45669291, 1.49606299, 1.53543307, 1.57480315, 1.61417323,\n", + " 1.65354331, 1.69291339, 1.73228346, 1.77165354, 1.81102362,\n", + " 1.8503937 , 1.88976378, 1.92913386, 1.96850394, 2.00787402,\n", + " 2.04724409, 2.08661417, 2.12598425, 2.16535433, 2.20472441,\n", + " 2.24409449, 2.28346457, 2.32283465, 2.36220472, 2.4015748 ,\n", + " 2.44094488, 2.48031496, 2.51968504, 2.55905512, 2.5984252 ,\n", + " 2.63779528, 2.67716535, 2.71653543, 2.75590551, 2.79527559,\n", + " 2.83464567, 2.87401575, 2.91338583, 2.95275591, 2.99212598,\n", + " 3.03149606, 3.07086614, 3.11023622, 3.1496063 , 3.18897638,\n", + " 3.22834646, 3.26771654, 3.30708661, 3.34645669, 3.38582677,\n", + " 3.42519685, 3.46456693, 3.50393701, 3.54330709, 3.58267717,\n", + " 3.62204724, 3.66141732, 3.7007874 , 3.74015748, 3.77952756,\n", + " 3.81889764, 3.85826772, 3.8976378 , 3.93700787, 3.97637795,\n", + " 4.01574803, 4.05511811, 4.09448819, 4.13385827, 4.17322835,\n", + " 4.21259843, 4.2519685 , 4.29133858, 4.33070866, 4.37007874,\n", + " 4.40944882, 4.4488189 , 4.48818898, 4.52755906, 4.56692913,\n", + " 4.60629921, 4.64566929, 4.68503937, 4.72440945, 4.76377953,\n", + " 4.80314961, 4.84251969, 4.88188976, 4.92125984, 4.96062992,\n", + " 5. ])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", + "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "\n", + "nqbit = 5\n", + "range = (4/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=0.25, offset=-4, var_base_name=\"x\")\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=0.25, offset=-4, var_base_name=\"x\")\n", + "\n", + "\n", + "nqbit = 8\n", + "flow_encoding = RangedEfficientEncoding(nqbit=nqbit, range=5., offset=+0.0, var_base_name=\"x\")\n", + "head_encoding = RangedEfficientEncoding(nqbit=nqbit, range=5, offset=+0.0, var_base_name=\"x\")\n", + "\n", + "np.sort(flow_encoding.get_possible_values())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "sv1 = SolutionVector(1, encoding=flow_encoding)\n", + "sv2 = SolutionVector(1, encoding=head_encoding)\n", + "\n", + "msv = MixedSolutionVector([sv1,sv2])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4., -3.])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "A = np.array([\n", + " [1, 1,],\n", + " [ 1, 2,],\n", + "])\n", + "b = np.array([1,-2]).reshape(-1,1)\n", + "ref_sol = np.linalg.solve(A, b).reshape(-1)\n", + "ref_sol" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ref: [ 4. -3.] -> [4.015748031496063, -2.992125984251969] energy: -4.998449996899992\n", + "sol: [ 3.97637795 -2.99212598] -> [3.9763779527559056, -2.992125984251969] energy: -4.999689999380009\n", + "[ 0.02362205 -0.00787402]\n" + ] + } + ], + "source": [ + "from qubols.qubo_poly_mixed_variables import QUBO_POLY\n", + "import sparse \n", + "from dwave.samplers import SimulatedAnnealingSampler\n", + "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import TabuSampler\n", + "from dimod import ExactSolver\n", + "\n", + "# sampler = TabuSampler()\n", + "sampler = SimulatedAnnealingSampler()\n", + "# sampler = ExactSolver() \n", + "\n", + "\n", + "qubo = QUBO_POLY(msv, options={\"sampler\" : sampler} )\n", + "matrices = tuple(sparse.COO(m) for m in [-1*b, A])\n", + "\n", + "bqm = qubo.create_bqm(matrices, strength=1E3)\n", + "\n", + "# sample\n", + "sampleset = qubo.sample_bqm(bqm, num_reads=10000)\n", + "\n", + "# decode\n", + "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", + "sol = np.array(sol[0]+sol[1])\n", + "\n", + "data_ref, eref = qubo.compute_energy(ref_sol, bqm)\n", + "data_sol, esol = qubo.compute_energy(sol, bqm)\n", + "\n", + "print('ref: ', ref_sol, '->', data_ref[0], ' energy: ', eref)\n", + "print('sol: ', sol, '->', data_sol[0], ' energy: ', esol)\n", + "print(ref_sol - sol)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Variables(['x_001_001', 'x_001_002', 'x_001_003', 'x_001_004', 'x_001_005', 'x_001_006', 'x_001_007', 'x_002_001', 'x_002_002', 'x_002_003', 'x_002_004', 'x_002_005', 'x_002_006', 'x_002_007', 'x_003_001', 'x_003_002', 'x_003_003', 'x_003_004', 'x_003_005', 'x_003_006', 'x_003_007', 'x_004_001', 'x_004_002', 'x_004_003', 'x_004_004', 'x_004_005', 'x_004_006', 'x_004_007'])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bqm.variables" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'net' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[54], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnet\u001b[49m\u001b[38;5;241m.\u001b[39mverify_solution(sol)\n", + "\u001b[0;31mNameError\u001b[0m: name 'net' is not defined" + ] + } + ], + "source": [ + "net.verify_solution(sol)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 1, 1]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import itertools\n", + "def find_closest(encoding, float):\n", + " \"\"\"get all the posible values encoded\n", + "\n", + " Returns:\n", + " _type_: _description_\n", + " \"\"\"\n", + "\n", + " min_diff = 1E12\n", + " closest_value = None \n", + " binary_encoding = None\n", + " for data in itertools.product([0, 1], repeat=encoding.nqbit):\n", + " val = encoding.decode_polynom(list(data)[::-1])\n", + " if np.abs(val-float) < min_diff:\n", + " min_diff = np.abs(val-float)\n", + " closest_value = val \n", + " binary_encoding = list(data)[::-1]\n", + "\n", + " return closest_value, binary_encoding \n", + "vmin, bins = find_closest(flow_encoding, 2.)\n", + "vmin\n", + "bins" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "var = sampleset.lowest().variables\n", + "data = np.array(sampleset.lowest().record[0][0])\n", + "data_real_var = data[qubo.index_variables]\n", + "\n", + "for v, d in zip(var, data):\n", + " if v not in qubo.mapped_variables:\n", + " x0, x1 = v.split('*')\n", + " i0 = qubo.index_variables[qubo.mapped_variables.index(x0)]\n", + " i1 = qubo.index_variables[qubo.mapped_variables.index(x1)]\n", + " assert(d == data[i0] * data[i1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "energy = []\n", + "residue = []\n", + "for s in sampleset.record:\n", + " energy.append(s[1])\n", + " sol = qubo.decode_solution(s[0])\n", + " r = net.verify_solution(np.array(sol).reshape(-1,))\n", + " residue.append(np.linalg.norm(r))\n", + "plt.scatter(energy, (residue))\n", + "\n", + "el, rl = [], []\n", + "for s in sampleset.lowest().record:\n", + " el.append(s[1])\n", + " sol = qubo.decode_solution(s[0])\n", + " r = net.verify_solution(np.array(sol).reshape(-1,))\n", + " rl.append(np.linalg.norm(r+1E-12))\n", + "plt.scatter(el, rl, c='red')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Exception ignored in: >\n", + "Traceback (most recent call last):\n", + " File \"/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n", + " def _clean_thread_parent_frames(\n", + "KeyboardInterrupt: \n" + ] + } + ], + "source": [ + "from qubols.qubo_poly import QUBO_POLY\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "import sparse \n", + "from dwave.samplers import SimulatedAnnealingSampler\n", + "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import TabuSampler\n", + "\n", + "encoding = RangedEfficientEncoding(nqbit=12, range=2.0, offset=0.0, var_base_name=\"x\")\n", + "sol_vec = SolutionVector(4, encoding)\n", + "\n", + "qubo = QUBO_POLY(solution_vector=sol_vec, options={ 'num_reads':1000, 'sampler':SimulatedAnnealingSampler()})\n", + "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "\n", + "bqm = qubo.create_bqm(matrices, strength=10000)\n", + "\n", + "# sample\n", + "sampleset = qubo.sample_bqm(bqm, num_reads=5000)\n", + "\n", + "# decode\n", + "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", + "sol = np.array(sol).reshape(-1)\n", + "print(ref_sol)\n", + "print(sol)\n", + "print(ref_sol - sol)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 830610cad0f7e0ba09087ccfc79cfb60df1bee70 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 9 Aug 2024 21:21:22 +0200 Subject: [PATCH 13/96] work on network qubo --- docs/notebooks/networks/Net1Loops_CM.inp | 2 +- .../networks/Net1Loops_CM_original_values.inp | 139 ++ docs/notebooks/trash/wntr_design.ipynb | 494 +++-- docs/notebooks/trash/wntr_qubo_poly.ipynb | 1694 +++++++++++++---- wntr_quantum/scenario/chezy_manning.py | 17 +- wntr_quantum/scenario/network_qubo.py | 4 +- 6 files changed, 1886 insertions(+), 464 deletions(-) create mode 100644 docs/notebooks/networks/Net1Loops_CM_original_values.inp diff --git a/docs/notebooks/networks/Net1Loops_CM.inp b/docs/notebooks/networks/Net1Loops_CM.inp index 8e1693f..5d245e0 100644 --- a/docs/notebooks/networks/Net1Loops_CM.inp +++ b/docs/notebooks/networks/Net1Loops_CM.inp @@ -10,7 +10,7 @@ shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) [RESERVOIRS] ;ID Head Pattern - 1 15.00 ; + 1 16.00 ; [TANKS] ;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow diff --git a/docs/notebooks/networks/Net1Loops_CM_original_values.inp b/docs/notebooks/networks/Net1Loops_CM_original_values.inp new file mode 100644 index 0000000..0f1b9fe --- /dev/null +++ b/docs/notebooks/networks/Net1Loops_CM_original_values.inp @@ -0,0 +1,139 @@ +[TITLE] +shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) + +[JUNCTIONS] +;ID Elev Demand Pattern + 2 150.00 27.77 ; + 3 160.00 27.77 ; + 4 155.00 33.33 ; + 5 150.00 75.00 ; + +[RESERVOIRS] +;ID Head Pattern + 1 210.00 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + 1 1 2 1000 457.20 0.015 0.00 Open ; + 2 2 3 1000 203 0.015 0.00 Open ; + 3 2 4 1000 457 0.015 0.00 Open ; + 4 4 5 1000 153 0.015 0.00 Open ; + 5 3 5 1000 153 0.015 0.00 Open ; + + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + + + +[RULES] + + + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 0:00 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 2:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic NONE + +[REPORT] + Status Yes + Summary No + Page 0 + +[OPTIONS] + Units LPS + Headloss C-M + Specific Gravity 1.0 + Viscosity 1.0 + Trials 40 + Accuracy 0.001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality Chlorine mg/L + Diffusivity 1.0 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +2 2000.000 3000.000 +3 1000.000 3000.000 +4 2000.000 2000.000 +5 1000.000 2000.000 +1 3000.000 3000.000 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 900.000 900.000 3100.000 3100.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/docs/notebooks/trash/wntr_design.ipynb b/docs/notebooks/trash/wntr_design.ipynb index d61fe1c..b963744 100644 --- a/docs/notebooks/trash/wntr_design.ipynb +++ b/docs/notebooks/trash/wntr_design.ipynb @@ -4,15 +4,227 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Define the system " + "# Solv the sysmtem with WNTR SOLVER" ] }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 50, "metadata": { "metadata": {} }, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " P1 P2\n", + "0 0.05 0.05\n", + "3600 0.05 0.05" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.link['flowrate']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SET UP THE PROBLEM WITH DESIGNER" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, "outputs": [ { "data": { @@ -30,7 +242,7 @@ "" ] }, - "execution_count": 85, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -38,10 +250,11 @@ "source": [ "import wntr\n", "import wntr_quantum\n", + "from wntr.sim.hydraulics import create_hydraulic_model\n", "\n", "# Create a water network model\n", "inp_file = '../networks/Net0_CM.inp'\n", - "# inp_file = '../networks/Net2LoopsCM.inp'\n", + "# inp_file = '../networks/Net2Loops.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", "# Graph the network\n", @@ -57,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -80,29 +293,29 @@ "\n", "\n", "# pipe_diameters = [0.35, 0.4, 0.45, 0.55]\n", - "pipe_diameters = [2, 4]\n", + "pipe_diameters = [2, 4, 8]\n", "designer = NetworkDesign(wn, flow_encoding=flow_encoding, \n", " head_encoding=head_encoding, \n", " pipe_diameters=pipe_diameters,\n", - " weight_cost=0.01)" + " weight_cost=0.1)" ] }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.5 ],\n", - " [1. ],\n", - " [2. ],\n", - " [0. ],\n", - " [0.01]])" + "array([[0.5 ],\n", + " [1. ],\n", + " [2. ],\n", + " [0. ],\n", + " [0.125]])" ] }, - "execution_count": 87, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -113,17 +326,77 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1. , 1. , 0. , 0. , 0. , 0. ],\n", + " [ 0. , -1. , 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , -1. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 1. , -1. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. , -0.1, -0.1]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.matrices[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0., 0., 0., 0., 0., 0.],\n", + " [ 0., 0., 0., 0., 0., 0.],\n", + " [-1., 0., 0., 0., 0., 0.],\n", + " [ 0., -1., 0., 0., 0., 0.],\n", + " [ 0., 0., 0., 0., 0., 0.]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.matrices[3].sum(-1).sum(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "(0.12500000000000014, 0.12500000000000014) [1.5 1. 1.719 1.594]\n", + "(0.12500000000000014, 0.25) [1.5 1. 1.719 1.469]\n", + "(0.12500000000000014, 0.5000000000000002) [1.5 1. 1.719 1.219]\n", + "(0.12500000000000014, 0.6250000000000001) [1.5 1. 1.719 1.094]\n", + "(0.25, 0.12500000000000014) [1.5 1. 1.438 1.313]\n", "(0.25, 0.25) [1.5 1. 1.438 1.188]\n", - "(0.25, 0.5) [1.5 1. 1.438 0.938]\n", - "(0.5, 0.25) [1.5 1. 0.875 0.625]\n", - "(0.5, 0.5) [1.5 1. 0.875 0.375]\n" + "(0.25, 0.5000000000000002) [1.5 1. 1.438 0.938]\n", + "(0.25, 0.6250000000000001) [1.5 1. 1.438 0.813]\n", + "(0.5000000000000002, 0.12500000000000014) [1.5 1. 0.875 0.75 ]\n", + "(0.5000000000000002, 0.25) [1.5 1. 0.875 0.625]\n", + "(0.5000000000000002, 0.5000000000000002) [1.5 1. 0.875 0.375]\n", + "(0.5000000000000002, 0.6250000000000001) [1.5 1. 0.875 0.25 ]\n", + "(0.6250000000000001, 0.12500000000000014) [1.5 1. 0.594 0.469]\n", + "(0.6250000000000001, 0.25) [1.5 1. 0.594 0.344]\n", + "(0.6250000000000001, 0.5000000000000002) [1.5 1. 0.594 0.094]\n", + "(0.6250000000000001, 0.6250000000000001) [ 1.5 1. 0.594 -0.031]\n" ] }, { @@ -141,16 +414,16 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0.24999999999999992, 0.5]" + "[0.12500000000000014, 0.25, 0.5000000000000002, 0.6250000000000001]" ] }, - "execution_count": 89, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -161,18 +434,22 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", "import sparse\n", - "qubo = QUBO_POLY_MIXED(designer.mixed_solution_vector)" + "from dwave.samplers import SimulatedAnnealingSampler\n", + "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import TabuSampler\n", + "from dwave.samplers import RandomSampler\n", + "qubo = QUBO_POLY_MIXED(designer.mixed_solution_vector, options={\"sampler\":TabuSampler()})" ] }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -182,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -200,7 +477,7 @@ "\n", " bqm.add_linear_inequality_constraint(\n", " qubo.all_expr[istart + i],\n", - " lagrange_multiplier=1,\n", + " lagrange_multiplier=0.1,\n", " label=\"head_%s\" % i,\n", " lb=1,\n", " ub=2,\n", @@ -209,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -218,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -227,18 +504,18 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "([1.7142857142857142, 1.0476190476190474],\n", - " [1.1111111111111112, 0.9206349206349206],\n", - " [0.24999999999999992, 0.24999999999999992])" + "([1.5238095238095237, 1.0158730158730158],\n", + " [1.746031746031746, 1.0793650793650793],\n", + " [0.12500000000000014, 0.6250000000000001])" ] }, - "execution_count": 95, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -249,16 +526,16 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([-0.167, -0.048, 0.154, -0.084])" + "array([-0.008, -0.016, -0.036, 0.022])" ] }, - "execution_count": 96, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -271,16 +548,16 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "([1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1], -2.78, 1)" + "([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], -4.576, 1, 4)" ] }, - "execution_count": 97, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -291,7 +568,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -315,22 +592,22 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 117, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -346,19 +623,31 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{(0.5, 0.5): 1200,\n", - " (0.24999999999999992, 0.24999999999999992): 487,\n", - " (0.24999999999999992, 0.5): 1354,\n", - " (0.5, 0.24999999999999992): 435}" + "{(0.12500000000000014, 0.12500000000000014): 465,\n", + " (0.12500000000000014, 0.6250000000000001): 601,\n", + " (0.6250000000000001, 0.25): 205,\n", + " (0.5000000000000002, 0.12500000000000014): 274,\n", + " (0.12500000000000014, 0.5000000000000002): 535,\n", + " (0.6250000000000001, 0.5000000000000002): 166,\n", + " (0.25, 0.6250000000000001): 281,\n", + " (0.5000000000000002, 0.25): 186,\n", + " (0.25, 0.25): 270,\n", + " (0.5000000000000002, 0.6250000000000001): 146,\n", + " (0.12500000000000014, 0.25): 499,\n", + " (0.5000000000000002, 0.5000000000000002): 163,\n", + " (0.6250000000000001, 0.12500000000000014): 248,\n", + " (0.25, 0.5000000000000002): 237,\n", + " (0.25, 0.12500000000000014): 438,\n", + " (0.6250000000000001, 0.6250000000000001): 144}" ] }, - "execution_count": 118, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -369,97 +658,34 @@ }, { "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [], - "source": [ - "import dimod\n", - "cqm = dimod.ConstrainedQuadraticModel()" - ] - }, - { - "cell_type": "code", - "execution_count": 103, + "execution_count": 47, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'y'" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cqm.add_variable('REAL', 'x')\n", - "cqm.add_variable('REAL', 'y')" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "add_constraint_from_iterable() missing 1 required positional argument: 'sense'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[105], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcqm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_constraint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mx+y<1\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mc0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/constrained/constrained.py:204\u001b[0m, in \u001b[0;36mConstrainedQuadraticModel.add_constraint\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_constraint_from_comparison(data, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, Iterable):\n\u001b[0;32m--> 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_constraint_from_iterable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 205\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munexpected data format\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mTypeError\u001b[0m: add_constraint_from_iterable() missing 1 required positional argument: 'sense'" + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.12500000000000014, 0.12500000000000014) [1.5 1. 1.719 1.594]\n", + "(0.12500000000000014, 0.25) [1.5 1. 1.719 1.469]\n", + "(0.12500000000000014, 0.5000000000000002) [1.5 1. 1.719 1.219]\n", + "(0.12500000000000014, 0.6250000000000001) [1.5 1. 1.719 1.094]\n", + "(0.25, 0.12500000000000014) [1.5 1. 1.438 1.313]\n", + "(0.25, 0.25) [1.5 1. 1.438 1.188]\n", + "(0.25, 0.5000000000000002) [1.5 1. 1.438 0.938]\n", + "(0.25, 0.6250000000000001) [1.5 1. 1.438 0.813]\n", + "(0.5000000000000002, 0.12500000000000014) [1.5 1. 0.875 0.75 ]\n", + "(0.5000000000000002, 0.25) [1.5 1. 0.875 0.625]\n", + "(0.5000000000000002, 0.5000000000000002) [1.5 1. 0.875 0.375]\n", + "(0.5000000000000002, 0.6250000000000001) [1.5 1. 0.875 0.25 ]\n", + "(0.6250000000000001, 0.12500000000000014) [1.5 1. 0.594 0.469]\n", + "(0.6250000000000001, 0.25) [1.5 1. 0.594 0.344]\n", + "(0.6250000000000001, 0.5000000000000002) [1.5 1. 0.594 0.094]\n", + "(0.6250000000000001, 0.6250000000000001) [ 1.5 1. 0.594 -0.031]\n" ] } ], "source": [ - "cqm.add_constraint('x+y<1',label='c0')" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'c0'" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x, y = dimod.Integers(['x', 'y'])\n", - "cqm = dimod.ConstrainedQuadraticModel()\n", - "cqm.add_constraint(x + y + x*y <= 1, label='c0')" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'x + y + x*y <= 1.0'" - ] - }, - "execution_count": 113, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cqm.constraints['c0'].to_polystring()" + "designer.enumerates_classical_solutions()" ] }, { diff --git a/docs/notebooks/trash/wntr_qubo_poly.ipynb b/docs/notebooks/trash/wntr_qubo_poly.ipynb index d3189f7..942367b 100644 --- a/docs/notebooks/trash/wntr_qubo_poly.ipynb +++ b/docs/notebooks/trash/wntr_qubo_poly.ipynb @@ -9,14 +9,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 83, "metadata": { "metadata": {} }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -29,16 +29,36 @@ "output_type": "stream", "text": [ "cons:\n", - "mass_balance[J1]: ((expected_demand[J1]-flow[P1])+flow[P2])\n", - "mass_balance[D1]: (expected_demand[D1]-flow[P2])\n", - "approx_hazen_williams_headloss[P1]: (((((((-((sign(flow[P1]))))*hw_resistance[P1])*((abs(flow[P1]))**1.852))-((1e-05*(hw_resistance[P1]**0.5))*flow[P1]))-(((sign(flow[P1]))*minor_loss[P1])*(flow[P1]**2.0)))+source_head[R1])-head[J1])\n", - "approx_hazen_williams_headloss[P2]: (((((((-((sign(flow[P2]))))*hw_resistance[P2])*((abs(flow[P2]))**1.852))-((1e-05*(hw_resistance[P2]**0.5))*flow[P2]))-(((sign(flow[P2]))*minor_loss[P2])*(flow[P2]**2.0)))+head[J1])-head[D1])\n", + "mass_balance[2]: (((expected_demand[2]-flow[1])+flow[2])+flow[3])\n", + "mass_balance[3]: ((expected_demand[3]-flow[2])+flow[7])\n", + "mass_balance[4]: (((expected_demand[4]-flow[3])+flow[4])+flow[5])\n", + "mass_balance[5]: (((expected_demand[5]-flow[4])-flow[7])+flow[8])\n", + "mass_balance[6]: ((expected_demand[6]-flow[5])+flow[6])\n", + "mass_balance[7]: ((expected_demand[7]-flow[6])-flow[8])\n", + "approx_hazen_williams_headloss[1]: (((((((-((sign(flow[1]))))*hw_resistance[1])*((abs(flow[1]))**1.852))-((1e-05*(hw_resistance[1]**0.5))*flow[1]))-(((sign(flow[1]))*minor_loss[1])*(flow[1]**2.0)))+source_head[1])-head[2])\n", + "approx_hazen_williams_headloss[2]: (((((((-((sign(flow[2]))))*hw_resistance[2])*((abs(flow[2]))**1.852))-((1e-05*(hw_resistance[2]**0.5))*flow[2]))-(((sign(flow[2]))*minor_loss[2])*(flow[2]**2.0)))+head[2])-head[3])\n", + "approx_hazen_williams_headloss[3]: (((((((-((sign(flow[3]))))*hw_resistance[3])*((abs(flow[3]))**1.852))-((1e-05*(hw_resistance[3]**0.5))*flow[3]))-(((sign(flow[3]))*minor_loss[3])*(flow[3]**2.0)))+head[2])-head[4])\n", + "approx_hazen_williams_headloss[4]: (((((((-((sign(flow[4]))))*hw_resistance[4])*((abs(flow[4]))**1.852))-((1e-05*(hw_resistance[4]**0.5))*flow[4]))-(((sign(flow[4]))*minor_loss[4])*(flow[4]**2.0)))+head[4])-head[5])\n", + "approx_hazen_williams_headloss[5]: (((((((-((sign(flow[5]))))*hw_resistance[5])*((abs(flow[5]))**1.852))-((1e-05*(hw_resistance[5]**0.5))*flow[5]))-(((sign(flow[5]))*minor_loss[5])*(flow[5]**2.0)))+head[4])-head[6])\n", + "approx_hazen_williams_headloss[6]: (((((((-((sign(flow[6]))))*hw_resistance[6])*((abs(flow[6]))**1.852))-((1e-05*(hw_resistance[6]**0.5))*flow[6]))-(((sign(flow[6]))*minor_loss[6])*(flow[6]**2.0)))+head[6])-head[7])\n", + "approx_hazen_williams_headloss[7]: (((((((-((sign(flow[7]))))*hw_resistance[7])*((abs(flow[7]))**1.852))-((1e-05*(hw_resistance[7]**0.5))*flow[7]))-(((sign(flow[7]))*minor_loss[7])*(flow[7]**2.0)))+head[3])-head[5])\n", + "approx_hazen_williams_headloss[8]: (((((((-((sign(flow[8]))))*hw_resistance[8])*((abs(flow[8]))**1.852))-((1e-05*(hw_resistance[8]**0.5))*flow[8]))-(((sign(flow[8]))*minor_loss[8])*(flow[8]**2.0)))+head[5])-head[7])\n", "\n", "vars:\n", - "flow[P1]: flow[P1]\n", - "flow[P2]: flow[P2]\n", - "head[J1]: head[J1]\n", - "head[D1]: head[D1]\n", + "flow[1]: flow[1]\n", + "flow[2]: flow[2]\n", + "flow[3]: flow[3]\n", + "flow[7]: flow[7]\n", + "flow[4]: flow[4]\n", + "flow[5]: flow[5]\n", + "flow[8]: flow[8]\n", + "flow[6]: flow[6]\n", + "head[2]: head[2]\n", + "head[3]: head[3]\n", + "head[4]: head[4]\n", + "head[5]: head[5]\n", + "head[6]: head[6]\n", + "head[7]: head[7]\n", "\n" ] } @@ -51,17 +71,81 @@ "\n", "# Create a water network model\n", "inp_file = '../networks/Net0_HW.inp'\n", - "# inp_file = '../networks/Net1Loops.inp'\n", + "inp_file = '../networks/Net2Loops.inp'\n", "# inp_file = '../networks/Net2Loops.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "wn0 = wntr.network.WaterNetworkModel(inp_file)\n", "\n", "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n", + "wntr.graphics.plot_network(wn0, title=wn0.name, node_labels=True)\n", "\n", - "model, updater = create_hydraulic_model(wn, HW_approx='default')\n", + "model, updater = create_hydraulic_model(wn0, HW_approx='default')\n", "print(model.__str__())\n" ] }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.3487722352119913" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.hw_resistance['3'].value * 0.077**2" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[53.248 30.665 44.321 28.81 30.547 27.058 0. ]]\n", + "[[ 0.311 0.051 0.232 0.032 0.167 0.075 0.024 -0.019]]\n" + ] + } + ], + "source": [ + "import wntr \n", + "sim = wntr.sim.WNTRSimulator(wn0)\n", + "results = sim.run_sim()\n", + "print(results.node['pressure'].values)\n", + "print(results.link['flowrate'].values)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 53.248 0.263 -84.231 -82.956 -96.87 -95.268 0. ]]\n", + "[[0.311 0.1 0.183 0.013 0.137 0.046 0.072 0.01 ]]\n" + ] + } + ], + "source": [ + "wn0.links['3'].diameter = 0.203\n", + "sim = wntr.sim.WNTRSimulator(wn0)\n", + "results = sim.run_sim()\n", + "print(results.node['pressure'].values)\n", + "print(results.link['flowrate'].values)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -71,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ @@ -79,15 +163,15 @@ "\n", "# Create a water network model\n", "inp_file = '../networks/Net0_CM.inp'\n", - "# inp_file = '../networks/Net1Loops_CM.inp'\n", - "# inp_file = '../networks/Net2Loops.inp'\n", + "inp_file = '../networks/Net1Loops_CM_original_values.inp'\n", + "inp_file = '../networks/Net2LoopsCM.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ @@ -96,15 +180,18 @@ "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", "\n", - "nqbit = 3\n", - "range = (4/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=0.25, offset=+0.0, var_base_name=\"x\")\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=0.25, offset=+0.0, var_base_name=\"x\")\n", + "nqbit = 9\n", + "step = (0.25/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", + "nqbit = 9\n", + "step = (250/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", - "nqbit = 5\n", - "flow_encoding = RangedEfficientEncoding(nqbit=nqbit, range=5., offset=+0.0, var_base_name=\"x\")\n", - "head_encoding = RangedEfficientEncoding(nqbit=nqbit, range=5, offset=+0.0, var_base_name=\"x\")\n", + "\n", + "# nqbit = 5\n", + "# flow_encoding = RangedEfficientEncoding(nqbit=nqbit, range=5., offset=+0.0, var_base_name=\"x\")\n", + "# head_encoding = RangedEfficientEncoding(nqbit=nqbit, range=5, offset=+0.0, var_base_name=\"x\")\n", "\n", "net = Network(wn, flow_encoding=flow_encoding, \n", " head_encoding=head_encoding)" @@ -112,397 +199,354 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 89, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.5 , 1. , 0.75, 0.25])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "ref_sol = net.classical_solutions()\n", - "ref_sol" + "# print(net.m.__str__())" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 90, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([-5.333, -5. , -4.667, -4.333, -4. , -3.667, -3.333, -3. , -2.667, -2.333, -2. , -1.667, -1.333, -1. , -0.667, -0.333, 0. , 0.333, 0.667, 1. , 1.333, 1.667, 2. , 2.333, 2.667, 3. , 3.333, 3.667, 4. , 4.333, 4.667, 5. ])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning, we didn't reach the required tolerance within 100 iterations, error is at 11648.68115427426\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, + { + "ename": "AssertionError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[90], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# net.matrices[2] = net.matrices[2]*4\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m ref_sol \u001b[38;5;241m=\u001b[39m \u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclassical_solutions\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m ref_sol\n", + "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/scenario/network_qubo.py:85\u001b[0m, in \u001b[0;36mNetwork.classical_solutions\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 83\u001b[0m initial_point \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(num_vars)\n\u001b[1;32m 84\u001b[0m res \u001b[38;5;241m=\u001b[39m newton_raphson(func, initial_point)\n\u001b[0;32m---> 85\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(func(res\u001b[38;5;241m.\u001b[39msolution), \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39msolution\n", + "\u001b[0;31mAssertionError\u001b[0m: " + ] } ], "source": [ - "np.sort(flow_encoding.get_possible_values())" + "# net.matrices[2] = net.matrices[2]*4\n", + "ref_sol = net.classical_solutions()\n", + "ref_sol" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 31, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, { "data": { "text/plain": [ - "array([-5.333, -5. , -4.667, -4.333, -4. , -3.667, -3.333, -3. , -2.667, -2.333, -2. , -1.667, -1.333, -1. , -0.667, -0.333, 0. , 0.333, 0.667, 1. , 1.333, 1.667, 2. , 2.333, 2.667, 3. , 3.333, 3.667, 4. , 4.333, 4.667, 5. ])" + "array([ 1.639e-01, 6.607e-02, 7.003e-02, 3.830e-02, 3.670e-02, 2.018e+02, 1.005e+02, 8.800e+01, -5.306e+01])" ] }, - "execution_count": 23, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.sort(head_encoding.get_possible_values())" + "net.wn.links['3'].diameter = 0.203\n", + "net.m, net.model_updater = net.create_cm_model()\n", + "net.matrices = net.initialize_matrices()\n", + "ref_sol = net.classical_solutions()\n", + "ref_sol" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 64, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ref: [1.5 1. 0.75 0.25] -> [1.6666666666666665, 1.0, 0.6666666666666666, 0.3333333333333333] energy: -9.996913580246822\n", - "sol: [1.333 1. 1.333 0.667] -> [1.3333333333333333, 1.0, 1.3333333333333333, 0.6666666666666666] energy: -10.182098765432102\n", - "[ 1.667e-01 -1.110e-16 -5.833e-01 -4.167e-01]\n" - ] - } - ], + "outputs": [], "source": [ - "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", - "import sparse \n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "from dwave.samplers import SteepestDescentSolver\n", - "from dwave.samplers import TabuSampler\n", - "from dimod import ExactSolver\n", + "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", "\n", - "# sampler = TabuSampler()\n", - "sampler = SimulatedAnnealingSampler()\n", - "# sampler = ExactSolver() \n", - "\n", - "qubo = QUBO_POLY_MIXED(net.mixed_solution_vector, options={\"sampler\" : sampler} )\n", - "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "nqbit = 9\n", + "step = (1/(2**nqbit-1))\n", + "encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", + "sv1 = SolutionVector(5, encoding=encoding)\n", "\n", - "bqm = qubo.create_bqm(matrices, strength=1E3)\n", + "nqbit = 11\n", + "step = (40/(2**nqbit-1))\n", + "encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+175, var_base_name=\"x\")\n", + "sv2 = SolutionVector(1, encoding=encoding)\n", "\n", - "# sample\n", - "sampleset = qubo.sample_bqm(bqm, num_reads=10000)\n", + "nqbit = 11\n", + "step = (40/(2**nqbit-1))\n", + "encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+90, var_base_name=\"x\")\n", + "sv3 = SolutionVector(1, encoding=encoding)\n", "\n", - "# decode\n", - "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", - "sol = np.array(sol[0]+sol[1])\n", + "nqbit = 11\n", + "step = (40/(2**nqbit-1))\n", + "encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+180, var_base_name=\"x\")\n", + "sv4 = SolutionVector(1, encoding=encoding)\n", "\n", - "data_ref, eref = qubo.compute_energy(ref_sol, bqm)\n", - "data_sol, esol = qubo.compute_energy(sol, bqm)\n", + "nqbit = 11\n", + "step = (40/(2**nqbit-1))\n", + "encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", + "sv5 = SolutionVector(1, encoding=encoding)\n", "\n", - "print('ref: ', ref_sol, '->', data_ref[0], ' energy: ', eref)\n", - "print('sol: ', sol, '->', data_sol[0], ' energy: ', esol)\n", - "print(ref_sol - sol)\n" + "net.mixed_solution_vector = MixedSolutionVector([sv1,sv2,sv3,sv4,sv5])\n", + "net.matrices = net.initialize_matrices()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[x_001_001, x_001_002, x_001_003]" + "0.07827788649706457" ] }, - "execution_count": 13, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "qubo.mixed_solution_vectors.encoded_reals[0].variables" + "(40/(2**9-1))" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 66, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "energy() missing 1 required positional argument: 'sample'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mbqm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menergy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mTypeError\u001b[0m: energy() missing 1 required positional argument: 'sample'" - ] + "data": { + "text/plain": [ + "array([0. , 0. , 0.001, 0.001, 0.002, 0.002, 0.003, 0.003, 0.004, 0.004, 0.005, 0.005, 0.006, 0.006, 0.007, 0.007, 0.008, 0.008, 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38.65 , 39.139, 39.628, 40.117, 40.607, 41.096, 41.585, 42.074, 42.564, 43.053, 43.542, 44.031, 44.521, 45.01 , 45.499, 45.988, 46.477,\n", + " 46.967, 47.456, 47.945, 48.434, 48.924, 49.413, 49.902, 50.391, 50.881, 51.37 , 51.859, 52.348, 52.838, 53.327, 53.816, 54.305, 54.795, 55.284, 55.773, 56.262, 56.751, 57.241, 57.73 , 58.219, 58.708, 59.198, 59.687, 60.176, 60.665, 61.155, 61.644, 62.133,\n", + " 62.622, 63.112, 63.601, 64.09 , 64.579, 65.068, 65.558, 66.047, 66.536, 67.025, 67.515, 68.004, 68.493, 68.982, 69.472, 69.961, 70.45 , 70.939, 71.429, 71.918, 72.407, 72.896, 73.386, 73.875, 74.364, 74.853, 75.342, 75.832, 76.321, 76.81 , 77.299, 77.789,\n", + " 78.278, 78.767, 79.256, 79.746, 80.235, 80.724, 81.213, 81.703, 82.192, 82.681, 83.17 , 83.659, 84.149, 84.638, 85.127, 85.616, 86.106, 86.595, 87.084, 87.573, 88.063, 88.552, 89.041, 89.53 , 90.02 , 90.509, 90.998, 91.487, 91.977, 92.466, 92.955, 93.444,\n", + " 93.933, 94.423, 94.912, 95.401, 95.89 , 96.38 , 96.869, 97.358, 97.847, 98.337, 98.826, 99.315, 99.804, 100.294, 100.783, 101.272, 101.761, 102.25 , 102.74 , 103.229, 103.718, 104.207, 104.697, 105.186, 105.675, 106.164, 106.654, 107.143, 107.632, 108.121, 108.611, 109.1 ,\n", + " 109.589, 110.078, 110.568, 111.057, 111.546, 112.035, 112.524, 113.014, 113.503, 113.992, 114.481, 114.971, 115.46 , 115.949, 116.438, 116.928, 117.417, 117.906, 118.395, 118.885, 119.374, 119.863, 120.352, 120.841, 121.331, 121.82 , 122.309, 122.798, 123.288, 123.777, 124.266, 124.755,\n", + " 125.245, 125.734, 126.223, 126.712, 127.202, 127.691, 128.18 , 128.669, 129.159, 129.648, 130.137, 130.626, 131.115, 131.605, 132.094, 132.583, 133.072, 133.562, 134.051, 134.54 , 135.029, 135.519, 136.008, 136.497, 136.986, 137.476, 137.965, 138.454, 138.943, 139.432, 139.922, 140.411,\n", + " 140.9 , 141.389, 141.879, 142.368, 142.857, 143.346, 143.836, 144.325, 144.814, 145.303, 145.793, 146.282, 146.771, 147.26 , 147.75 , 148.239, 148.728, 149.217, 149.706, 150.196, 150.685, 151.174, 151.663, 152.153, 152.642, 153.131, 153.62 , 154.11 , 154.599, 155.088, 155.577, 156.067,\n", + " 156.556, 157.045, 157.534, 158.023, 158.513, 159.002, 159.491, 159.98 , 160.47 , 160.959, 161.448, 161.937, 162.427, 162.916, 163.405, 163.894, 164.384, 164.873, 165.362, 165.851, 166.341, 166.83 , 167.319, 167.808, 168.297, 168.787, 169.276, 169.765, 170.254, 170.744, 171.233, 171.722,\n", + " 172.211, 172.701, 173.19 , 173.679, 174.168, 174.658, 175.147, 175.636, 176.125, 176.614, 177.104, 177.593, 178.082, 178.571, 179.061, 179.55 , 180.039, 180.528, 181.018, 181.507, 181.996, 182.485, 182.975, 183.464, 183.953, 184.442, 184.932, 185.421, 185.91 , 186.399, 186.888, 187.378,\n", + " 187.867, 188.356, 188.845, 189.335, 189.824, 190.313, 190.802, 191.292, 191.781, 192.27 , 192.759, 193.249, 193.738, 194.227, 194.716, 195.205, 195.695, 196.184, 196.673, 197.162, 197.652, 198.141, 198.63 , 199.119, 199.609, 200.098, 200.587, 201.076, 201.566, 202.055, 202.544, 203.033,\n", + " 203.523, 204.012, 204.501, 204.99 , 205.479, 205.969, 206.458, 206.947, 207.436, 207.926, 208.415, 208.904, 209.393, 209.883, 210.372, 210.861, 211.35 , 211.84 , 212.329, 212.818, 213.307, 213.796, 214.286, 214.775, 215.264, 215.753, 216.243, 216.732, 217.221, 217.71 , 218.2 , 218.689,\n", + " 219.178, 219.667, 220.157, 220.646, 221.135, 221.624, 222.114, 222.603, 223.092, 223.581, 224.07 , 224.56 , 225.049, 225.538, 226.027, 226.517, 227.006, 227.495, 227.984, 228.474, 228.963, 229.452, 229.941, 230.431, 230.92 , 231.409, 231.898, 232.387, 232.877, 233.366, 233.855, 234.344,\n", + " 234.834, 235.323, 235.812, 236.301, 236.791, 237.28 , 237.769, 238.258, 238.748, 239.237, 239.726, 240.215, 240.705, 241.194, 241.683, 242.172, 242.661, 243.151, 243.64 , 244.129, 244.618, 245.108, 245.597, 246.086, 246.575, 247.065, 247.554, 248.043, 248.532, 249.022, 249.511, 250. ])" ] }, - "execution_count": 8, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "flow_encoding.get_possible_values()" + "np.sort(head_encoding.get_possible_values())" ] }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 68, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([ 1.394, -0.5 , 1.223, -2.109, -1.45 , 0.204, 0.386, -0.138, 0.154])" - ] - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "flow prec: 0.0004892367906066536\n", + "head prec: 0.4892367906066536\n", + "\n", + "\n", + "ref : [1.639e-01 6.006e-02 7.604e-02 3.229e-02 4.271e-02 2.018e+02 1.181e+02 2.000e+02 8.957e+00]\n", + "sol : [0.25048923679060664, 0.06262230919765166, 0.0, 0.023483365949119372, 0.03913894324853229, 193.89594528578405, 99.98534440644845, 200.00977039570103, 40.0]\n", + "diff: [-8.662e-02 -2.566e-03 7.604e-02 8.803e-03 3.575e-03 7.881e+00 1.812e+01 -7.765e-03 -3.104e+01]\n", + "\n", + "\n", + "encoded_ref: [1.644e-01 6.067e-02 7.632e-02 3.131e-02 4.305e-02 2.018e+02 1.181e+02 2.000e+02 8.950e+00]\n", + "encoded_sol: [2.505e-01 6.262e-02 0.000e+00 2.348e-02 3.914e-02 1.939e+02 9.999e+01 2.000e+02 4.000e+01]\n", + "diff : [-8.611e-02 -1.957e-03 7.632e-02 7.828e-03 3.914e-03 7.875e+00 1.811e+01 0.000e+00 -3.105e+01]\n", + "\n", + "\n", + "eref: -48920.79143655116\n", + "esol: -48914.11743610925\n", + "\n", + "\n", + "res_ref: 7.363208654819393\n", + "res_sol: 7.803258430048803\n" + ] } ], "source": [ - "net.verify_solution(sol)" + "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", + "import sparse \n", + "from dwave.samplers import SimulatedAnnealingSampler\n", + "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import TabuSampler\n", + "from dimod import ExactSolver\n", + "\n", + "sampler = TabuSampler()\n", + "# sampler = SimulatedAnnealingSampler()\n", + "# sampler = ExactSolver() \n", + "\n", + "qubo = QUBO_POLY_MIXED(net.mixed_solution_vector, options={\"sampler\" : sampler} )\n", + "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "\n", + "bqm = qubo.create_bqm(matrices, strength=1E6)\n", + "\n", + "# sample\n", + "sampleset = qubo.sample_bqm(bqm, num_reads=1000)\n", + "\n", + "# decode\n", + "qubo.verify_quadratic_constraints(sampleset.lowest())\n", + "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", + "# sol = np.array([s for s in sol])\n", + "stmp = []\n", + "for s in sol:\n", + " stmp += s \n", + "sol = stmp \n", + "\n", + "data_ref, eref = qubo.compute_energy(ref_sol, bqm)\n", + "data_sol, esol = qubo.compute_energy(sol, bqm)\n", + "\n", + "np.set_printoptions(precision=3)\n", + "\n", + "print('flow prec: ', flow_encoding.get_average_precision())\n", + "print('head prec: ', head_encoding.get_average_precision())\n", + "print('\\n')\n", + "\n", + "print('ref : ', np.array(ref_sol)) \n", + "print('sol : ', sol)\n", + "print('diff: ', ref_sol - sol)\n", + "print('\\n')\n", + "\n", + "print('encoded_ref: ', np.array(data_ref[0]))\n", + "print('encoded_sol: ', np.array(data_sol[0]))\n", + "print('diff : ', np.array(data_ref[0]) - np.array(data_sol[0]))\n", + "print('\\n')\n", + "print('eref: ', eref)\n", + "print('esol: ', esol)\n", + "print('\\n')\n", + "print('res_ref: ', np.linalg.norm(net.verify_solution(np.array(data_ref[0]))))\n", + "print('res_sol: ', np.linalg.norm(net.verify_solution(np.array(data_sol[0]))))\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[1, 0, 1, 0, 1, 1, 0]" + "[]" ] }, - "execution_count": 15, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "import itertools\n", - "def find_closest(encoding, float):\n", - " \"\"\"get all the posible values encoded\n", - "\n", - " Returns:\n", - " _type_: _description_\n", - " \"\"\"\n", - "\n", - " min_diff = 1E12\n", - " closest_value = None \n", - " binary_encoding = None\n", - " for data in itertools.product([0, 1], repeat=encoding.nqbit):\n", - " val = encoding.decode_polynom(list(data)[::-1])\n", - " if np.abs(val-float) < min_diff:\n", - " min_diff = np.abs(val-float)\n", - " closest_value = val \n", - " binary_encoding = list(data)[::-1]\n", - "\n", - " return closest_value, binary_encoding \n", - "vmin, bins = find_closest(flow_encoding, 2.)\n", - "vmin\n", - "bins" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "var = sampleset.lowest().variables\n", - "data = np.array(sampleset.lowest().record[0][0])\n", - "data_real_var = data[qubo.index_variables]\n", - "\n", - "for v, d in zip(var, data):\n", - " if v not in qubo.mapped_variables:\n", - " x0, x1 = v.split('*')\n", - " i0 = qubo.index_variables[qubo.mapped_variables.index(x0)]\n", - " i1 = qubo.index_variables[qubo.mapped_variables.index(x1)]\n", - " assert(d == data[i0] * data[i1])" + "import matplotlib.pyplot as plt \n", + "plt.scatter(ref_sol, sol) \n", + "plt.axline((0, 0.), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", + "\n", + "plt.axline((0, 0.), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.axline((0, 0.), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", + "\n", + "plt.loglog()\n" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -515,9 +559,12 @@ "import matplotlib.pyplot as plt\n", "energy = []\n", "residue = []\n", + "solutions = []\n", "for s in sampleset.record:\n", " energy.append(s[1])\n", " sol = qubo.decode_solution(s[0])\n", + " sol = sol[0] + sol[1]\n", + " solutions.append(sol)\n", " r = net.verify_solution(np.array(sol).reshape(-1,))\n", " residue.append(np.linalg.norm(r))\n", "plt.scatter(energy, (residue))\n", @@ -526,6 +573,7 @@ "for s in sampleset.lowest().record:\n", " el.append(s[1])\n", " sol = qubo.decode_solution(s[0])\n", + " sol = sol[0] + sol[1]\n", " r = net.verify_solution(np.array(sol).reshape(-1,))\n", " rl.append(np.linalg.norm(r+1E-12))\n", "plt.scatter(el, rl, c='red')" @@ -533,44 +581,1048 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 67, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.5 1. 0.75 0.25]\n", - "[1.475 0.96 0.83 0.375]\n", - "[ 0.025 0.04 -0.08 -0.125]\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from qubols.qubo_poly import QUBO_POLY\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", - "import sparse \n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "from dwave.samplers import SteepestDescentSolver\n", - "from dwave.samplers import TabuSampler\n", + "solutions = np.array(solutions)\n", + "nsample = solutions.shape[1]\n", "\n", - "encoding = RangedEfficientEncoding(nqbit=12, range=2.0, offset=0.0, var_base_name=\"x\")\n", - "sol_vec = SolutionVector(4, encoding)\n", - "\n", - "qubo = QUBO_POLY(solution_vector=sol_vec, options={ 'num_reads':1000, 'sampler':SimulatedAnnealingSampler()})\n", - "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "for isol in range(5,9):\n", + " plt.scatter(solutions[:,isol], energy)\n", "\n", - "bqm = qubo.create_bqm(matrices, strength=10000)\n", "\n", - "# sample\n", - "sampleset = qubo.sample_bqm(bqm, num_reads=5000)\n", - "\n", - "# decode\n", - "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", - "sol = np.array(sol).reshape(-1)\n", - "print(ref_sol)\n", - "print(sol)\n", - "print(ref_sol - sol)\n" + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[3.528, 0.504, 0.126, 0. , 2.047, 4.48 , 3.63 , 6.024, 3. ],\n", + " [2.079, 1.071, 2.142, 1.008, 0.504, 7. , 5.11 , 3.031, 3.409],\n", + " [3.087, 1.008, 1.008, 2.52 , 0. , 6.559, 7. , 4.26 , 3. ],\n", + " [3.024, 2.016, 0.535, 0.252, 0.346, 7. , 3.976, 5.929, 5.016],\n", + " [3.024, 2.299, 1.008, 0.189, 1.008, 7. , 3. , 4.984, 3.504],\n", + " [3.213, 2.016, 1.008, 0.504, 0.504, 6.276, 3.094, 5.016, 3.724],\n", + " [3.15 , 0.504, 2.016, 0. , 1.291, 6.087, 4.449, 3.346, 3.063],\n", + " [3.024, 0.252, 0.126, 0. , 0.504, 5.961, 5.425, 5.488, 4.984],\n", + " [3.024, 2.016, 2.52 , 0.504, 1.008, 7. , 3.094, 3. , 3. ],\n", + " [2.016, 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4.008, 4.354],\n", + " [2.142, 2.142, 2.047, 0.63 , 0.315, 7. , 3. , 3. , 3. ],\n", + " [3.024, 2.016, 2.047, 0. , 2.52 , 7. , 3. , 6.087, 3. ],\n", + " [3.276, 0.504, 2.173, 1.512, 0.504, 6.528, 6.024, 3. , 3.252],\n", + " [3.024, 0.756, 0.126, 0.252, 0.252, 6.906, 6.433, 6.748, 6.528],\n", + " [3.024, 2.52 , 1.008, 0.504, 1.008, 7. , 3. , 4.984, 3.504],\n", + " [3.024, 2.142, 0.63 , 0.504, 0.504, 6.654, 3.157, 4.984, 4.008],\n", + " [2.016, 2.142, 1.008, 0.756, 1.26 , 7. , 3.252, 5.551, 3.504],\n", + " [1.764, 2.268, 0.504, 0.126, 1.008, 7. , 3. , 5.898, 4.008],\n", + " [2.646, 0.567, 1.008, 0. , 1.165, 7. , 6.055, 6.402, 5.488],\n", + " [3.15 , 1.764, 2.205, 0.504, 0.504, 6.906, 3.535, 3. , 3. ],\n", + " [3.559, 0.63 , 1.008, 1.039, 1.039, 3.976, 3.819, 3.535, 3. ],\n", + " [2.016, 1.008, 1.512, 0.504, 0.504, 7. , 5.488, 4.984, 4.984],\n", + " [3.024, 2.52 , 1.008, 0.252, 1.26 , 7. , 3. , 5.803, 4.008],\n", + " [3.276, 2.268, 1.008, 0.504, 0.157, 6.339, 3. , 4.512, 3.598],\n", + " [3.024, 1.134, 1.008, 0.189, 1.039, 6.906, 5.457, 6.118, 5.236],\n", + " [2.016, 1.008, 0.504, 0.756, 0.252, 7. , 6.244, 6.402, 6.024],\n", + " [2.268, 1.008, 2.016, 1.008, 0.504, 7. , 5.362, 3.472, 3.787],\n", + " [2.079, 1.008, 2.016, 1.764, 0.504, 7. , 6.055, 3.094, 3. ],\n", + " [3.087, 0.126, 0.126, 0. , 0.252, 5.992, 5.929, 5.961, 5.866],\n", + " [3.024, 2.047, 2.583, 0.504, 1.008, 7. , 3.031, 3. , 3. ],\n", + " [2.016, 2.016, 1.008, 0.252, 1.008, 7. , 3.504, 5.488, 4.008],\n", + " [3.055, 1.26 , 0.252, 0.252, 0.504, 6.307, 4.89 , 5.74 , 4.984],\n", + " [3.024, 2.268, 1.008, 0.756, 0.535, 7. , 3.126, 5.016, 3.756],\n", + " [3.528, 1.701, 0.504, 0. , 2.079, 5.236, 3. , 6.118, 3. ],\n", + " [3.024, 2.047, 2.142, 0.126, 0.567, 7. , 3. , 3. , 3. ],\n", + " [3.024, 2.173, 1.008, 0.252, 1.512, 7. , 3. , 6.024, 3.504],\n", + " [3.087, 2.142, 1.008, 0.252, 1.008, 6.717, 3. , 4.984, 3.441],\n", + " [3.276, 2.016, 2.142, 0.252, 0.756, 6.654, 3. , 3. , 3. ],\n", + " [3.276, 1.008, 2.142, 1.008, 0.504, 6.307, 5.016, 3. , 3.504],\n", + " [2.205, 0.504, 2.016, 1.512, 0.252, 7. , 6.37 , 3.346, 3.756],\n", + " [3.087, 2.331, 1.008, 0.157, 1.039, 7. , 3. , 5.425, 3.756],\n", + " [3.024, 1.008, 2.016, 2.268, 0.126, 7. , 7. , 3. , 3. ],\n", + " [2.835, 2.016, 2.016, 0.819, 1.26 , 7. , 3.283, 4.008, 3. ],\n", + " [1.827, 2.016, 0.504, 0.252, 1.008, 7. , 3.756, 6.087, 4.386],\n", + " [3.528, 0.504, 0.504, 0.126, 0.189, 3.693, 3.504, 3.504, 3.504]])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solutions" ] }, { diff --git a/wntr_quantum/scenario/chezy_manning.py b/wntr_quantum/scenario/chezy_manning.py index 0549129..aa2f079 100644 --- a/wntr_quantum/scenario/chezy_manning.py +++ b/wntr_quantum/scenario/chezy_manning.py @@ -10,15 +10,20 @@ def chezy_manning_constants(m): Args: m (_type_): _description_ """ + m.cm_exp = 2 + m.cm_minor_exp = 2 + m.cm_diameter_exp = -5.33 + m.cm_k = 21.000 # 4.66 * (3.28) ** m.cm_diameter_exp + # m.cm_exp = 2 # m.cm_minor_exp = 2 - # m.cm_k = 4.66 - # m.cm_diameter_exp = -5.33 + # m.cm_diameter_exp = -4.8 + # m.cm_k = 10.67 # 4.66 * (3.28) ** m.cm_diameter_exp - m.cm_exp = 1 - m.cm_minor_exp = 1 - m.cm_k = 1 - m.cm_diameter_exp = -1 + # m.cm_exp = 1 + # m.cm_minor_exp = 1 + # m.cm_k = 1 + # m.cm_diameter_exp = -1 def cm_resistance_prefactor(k, roughness, exp, diameter, diameter_exp): diff --git a/wntr_quantum/scenario/network_qubo.py b/wntr_quantum/scenario/network_qubo.py index c1c533a..4b2ac98 100644 --- a/wntr_quantum/scenario/network_qubo.py +++ b/wntr_quantum/scenario/network_qubo.py @@ -63,7 +63,7 @@ def verify_solution(self, input): p2 = P2.sum(-1) return p0 + p1 @ input + (p2 @ (input * input)) - def classical_solutions(self): + def classical_solutions(self, max_iter=100, tol=1e-10): """generates the classical solution.""" P0, P1, P2 = self.matrices @@ -81,7 +81,7 @@ def func(input): return p0 + p1 @ input + (p2 @ (input * input)) initial_point = np.random.rand(num_vars) - res = newton_raphson(func, initial_point) + res = newton_raphson(func, initial_point, max_iter=max_iter, tol=tol) assert np.allclose(func(res.solution), 0) return res.solution From f1deecd3f851cfb745a03f1d2d0e0461c79f2dad Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 29 Aug 2024 17:38:17 +0200 Subject: [PATCH 14/96] newtork qubo works with epanet --- docs/notebooks/networks/Net0.inp | 8 +- docs/notebooks/networks/Net2LoopsDW.inp | 145 ++++ docs/notebooks/trash/epanet.ipynb | 91 ++- docs/notebooks/trash/epanet_qubonetwork.ipynb | 691 ++++++++++++++++++ docs/notebooks/trash/temp.bin | Bin 0 -> 1360 bytes docs/notebooks/trash/temp.inp | 124 ++++ docs/notebooks/trash/temp.rpt | 12 + .../epanet/Linux/libepanet22_amd64.so | Bin 428336 -> 428336 bytes wntr_quantum/scenario/chezy_manning.py | 72 -- wntr_quantum/scenario/darcy_weisbach.py | 238 ++++++ wntr_quantum/scenario/darcy_weisbach_fit.py | 107 +++ wntr_quantum/scenario/mass_balance.py | 83 +++ wntr_quantum/scenario/network_qubo.py | 99 ++- 13 files changed, 1533 insertions(+), 137 deletions(-) create mode 100644 docs/notebooks/networks/Net2LoopsDW.inp create mode 100644 docs/notebooks/trash/epanet_qubonetwork.ipynb create mode 100644 docs/notebooks/trash/temp.bin create mode 100644 docs/notebooks/trash/temp.inp create mode 100644 docs/notebooks/trash/temp.rpt create mode 100644 wntr_quantum/scenario/darcy_weisbach.py create mode 100644 wntr_quantum/scenario/darcy_weisbach_fit.py create mode 100644 wntr_quantum/scenario/mass_balance.py diff --git a/docs/notebooks/networks/Net0.inp b/docs/notebooks/networks/Net0.inp index 414a644..019b292 100644 --- a/docs/notebooks/networks/Net0.inp +++ b/docs/notebooks/networks/Net0.inp @@ -16,8 +16,8 @@ File obtained via Mario of a 2 node sysem [PIPES] ;ID Node1 Node2 Length Diameter Roughness MinorLoss Status - P1 R1 J1 100 250 0.05 0 Open ; - P2 J1 D1 1000 200 0.04 0 Open ; + P1 R1 J1 1000 250 0.05 0 Open ; + P2 J1 D1 1000 250 0.05 0 Open ; [PUMPS] ;ID Node1 Node2 Parameters @@ -94,8 +94,8 @@ File obtained via Mario of a 2 node sysem Headloss D-W Specific Gravity 1 Viscosity 1 - Trials 40 - Accuracy 0.1 + Trials 50 + Accuracy 0.001 CHECKFREQ 2 MAXCHECK 10 DAMPLIMIT 0 diff --git a/docs/notebooks/networks/Net2LoopsDW.inp b/docs/notebooks/networks/Net2LoopsDW.inp new file mode 100644 index 0000000..4b4d9fc --- /dev/null +++ b/docs/notebooks/networks/Net2LoopsDW.inp @@ -0,0 +1,145 @@ +[TITLE] +shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) + +[JUNCTIONS] +;ID Elev Demand Pattern + 2 150.00 27.77 ; + 3 160.00 27.77 ; + 4 155.00 33.33 ; + 5 150.00 75.00 ; + 6 165.00 91.67 ; + 7 160.00 55.55 ; + +[RESERVOIRS] +;ID Head Pattern + 1 210.00 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + 1 1 2 1000.00 457.20 0.05 0.00 Open ; + 2 2 3 1000.00 203 0.05 0.00 Open ; + 3 2 4 1000.00 457 0.05 0.00 Open ; + 4 4 5 1000.00 153 0.05 0.00 Open ; + 5 4 6 1000.00 406.40 0.05 0.00 Open ; + 6 6 7 1000.00 254.00 0.05 0.00 Open ; + 7 3 5 1000.00 153 0.05 0.00 Open ; + 8 5 7 1000.00 153 0.05 0.00 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + + + +[RULES] + + + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 0:00 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 2:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic NONE + +[REPORT] + Status Yes + Summary No + Page 0 + +[OPTIONS] + Units LPS + Headloss D-W + Specific Gravity 1.0 + Viscosity 1.0 + Trials 40 + Accuracy 0.001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality Chlorine mg/L + Diffusivity 1.0 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +2 2000.000 3000.000 +3 1000.000 3000.000 +4 2000.000 2000.000 +5 1000.000 2000.000 +6 2000.000 1000.000 +7 1000.000 1000.000 +1 3000.000 3000.000 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 900.000 900.000 3100.000 3100.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/docs/notebooks/trash/epanet.ipynb b/docs/notebooks/trash/epanet.ipynb index 503d27e..54f333a 100644 --- a/docs/notebooks/trash/epanet.ipynb +++ b/docs/notebooks/trash/epanet.ipynb @@ -9,14 +9,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 26, "metadata": { "metadata": {} }, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", 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" ] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 1, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -40,8 +40,8 @@ "import wntr_quantum\n", "\n", "# Create a water network model\n", - "inp_file = 'networks/Net0.inp'\n", - "# inp_file = 'networks/Net2Loops.inp'\n", + "inp_file = '../networks/Net0.inp'\n", + "inp_file = '../networks/Net2LoopsDW.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", "# Graph the network\n", @@ -57,12 +57,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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n6br1rr5qk9SqmSpzPsu2bbu5iwiJXSp53w983OosubLM1QOEoHz3Qa3qM1E15ccaPJ5x5cXKffURw1UBwVUdr9Xv71utwwfLGzye3s6jhx8bplZx/NF4Opxzjde7N/hxu0iy68zUAoTo/T++EDB0JengX99WydsfGqwIaNymDfsChq4kFR+u0FvrPzFY0ZnFGUvNdrmkbxsZVK/PPntHXx+JMVER0Chvda32vbih0XEFT7ygjJhrDVQEhOYfq/Y1OmbzG/t02ZXnGqjmzOOM4P3uNd0gnn32Kc18dOmPXAsQGo+iNNe6pNFx619ZpademWmgIiA0Iy+drfhWbYOOKS87bqiaM48zrvHa5ZL3nUaH7T8QpyPfMONFy+CtqdOHg6bKW1UbdFzytTlq/8BIQ1UBjXvpz/v0xaHgwdrh7CRNnz3cUEVnFmfMeK0ESfGSKoMMilDHjj9TxyxnfCT8e6i6MVd7/7w66Jice3+plN7dDFUENK7iq9bKX1AQdEy/wZ0MVXPmcc7mKtc5kqzAx62zJYvQRcvy0wdHK7ptQsDjWTf8XCkXErpoWS4ekKWOnQLfZ57RsY36E7ynzRlLzSfZX0rePfruRqvaWkuRUV0lV0bz1QUEUbbrM20eP1ulmz/w9blio9T9N1fqglnj5YrgATBoeY5VVmvxwq3atuWAvN4TMeFyWep9UYbG/raP4j3RzVyhczkreCXJtiWVaf/+j/Xb2+7SjBkL1bt3dnNXBTTqyM59eu+vb+j+//x/WrhxhS7s37e5SwIadeSrY9r9UakkqUv3VCWnxDVzRc7nnKXmkyxLstroyDfRWrPmXQVdfgZakKTzz1abYb1VqC/ljo9t7nKAkCS1jVNO/yzl9M8idJuI84IXAAAHI3gBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwBwRuvYsaPmzp3b3GX4ELwAgGY3ZswYWZalRx991K//1VdflWVZzVTVj4PgBQC0CDExMZo1a5a++eab5i7lR0XwAgBahNzcXKWnp2vmzJkBx7z88svq0aOHoqOj1bFjRz3xxBN+x0tLSzVixAjFxsYqKytLL7zwwinnKCsr0y233KKUlBR5PB4NGjRI77//fpN/nkAIXgBAi+B2u/XHP/5RTz31lA4dOnTK8cLCQl133XW64YYbtHPnTk2bNk0PPfSQFi9e7BszZswYHTx4UBs2bNDy5cu1YMEClZaW+p3nF7/4hUpLS7V69WoVFhaqd+/eGjx4sI4cOfJjf0RJUoSRdwEAIATXXHONevXqpYcffliLFi3yOzZ79mwNHjxYDz30kCSpS5cu+uijj/TYY49pzJgx2rNnj1avXq133nlHF154oSRp0aJF6t69u+8cmzZt0jvvvKPS0lJFR0dLkh5//HG9+uqrWr58ucaPH/+jf0ZmvACAFmXWrFl6/vnntWvXLr/+Xbt2qW/fvn59ffv21d69e1VfX69du3YpIiJC2dnZvuPdunVTYmKi7+f3339flZWVSk5OVnx8vK8VFRXp008//VE/10nMeAEALUr//v01dOhQTZkyRWPGjGnSc1dWVuqss87Sm2++ecqx7wb0j4ngBQC0OI8++qh69eqlrl27+vq6d++uzZs3+43bvHmzunTpIrfbrW7duqmurk6FhYW+pebdu3errKzMN753794qLi5WRESEOnbsaOKjnIKlZgBAi3P++edr9OjRevLJJ319v/vd77R+/Xo98sgj2rNnj55//nk9/fTTuueeeyRJXbt21WWXXaYJEyZo69atKiws1C233KLY2FjfOXJzc5WTk6Orr75a//jHP7R//369/fbbevDBB7Vt2zYjn43gBQC0SNOnT5fX6/X93Lt3b7300ktaunSpzjvvPE2dOlXTp0/3W47Oz89Xu3btNGDAAI0cOVLjx49Xamqq77hlWfr73/+u/v37a+zYserSpYtuuOEGffbZZ0pLSzPyuSzbtm0j79TEtm/fruzsbN9WcMAJ+PcWADNeAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCIJ1cBABypqqpKNTU1QcdERUUpJibGUEWhIXgBAI5TVVWl9NgElSt48Kanp6uoqKhFhS/BCwBwnJqaGpWrRnMj+yo2QJQdV50mFW9WTU0NwQsAQFNo5YpUK6vhKLNsy3A1oSF4AQCOFRlpKdJqOGAjbUuqNlxQCAheAIBjuVySK8DE1tVCv4mA4AUAOJbLbckVYMbrYqkZAICmFRFhKSLAlDfCS/ACANCk3K4TrcFjZksJGcELAHAsd2TgGa+bGS8AAE3L5ZJcAYK3pT4TmeAFADjWieANcMxsKSFrqXUBANCoyAjrxL28DbWI8Jaap02bJsuy/Fq3bt18x6uqqjRx4kQlJycrPj5eeXl5KikpCbtmghcA4FgutxW0hatHjx764osvfG3Tpk2+Y3fffbdWrlypZcuWaePGjTp8+LBGjhwZ9nuw1AwAcKygS82n8QCNiIgIpaenn9JfXl6uRYsWacmSJRo0aJAkKT8/X927d1dBQYH69OkTes3hlwUAQMvgjrQUEaC5I0/MeCsqKvxadXXg50ju3btX7dq109lnn63Ro0frwIEDkqTCwkLV1tYqNzfXN7Zbt27KzMzUli1bwqqZ4AUAOJbLZQVtkpSRkaGEhARfmzlzZoPnuuiii7R48WKtWbNGCxcuVFFRkfr166ejR4+quLhYUVFRSkxM9HtNWlqaiouLw6qZpWYAgGNFRgTeRHXyyxMOHjwoj8fj64+Ojm5w/LBhw3z/v2fPnrrooovUoUMHvfTSS4qNjW2ympnxAgAc6+Q13kBNkjwej18LFLzfl5iYqC5duuiTTz5Renq6ampqVFZW5jempKSkwWvCQWsOazQAAC1IU+9q/q7Kykp9+umnOuuss5Sdna3IyEitX7/ed3z37t06cOCAcnJywjovS80AAMdyR9hyRzS8fdmt8LY133PPPRoxYoQ6dOigw4cP6+GHH5bb7daoUaOUkJCgcePGafLkyUpKSpLH49Edd9yhnJycsHY0SwQvAMDBLNeJFuhYOA4dOqRRo0bp66+/VkpKii655BIVFBQoJSVFkjRnzhy5XC7l5eWpurpaQ4cO1YIFC8KumeAFADiWy23L5W54Zuuyw5vxLl26NOjxmJgYzZ8/X/Pnzw/rvN9H8AIAHMty2XIFeFKGdTpP0DCA4AUAOJZlBVlqbpnfCkjwAgCcyxVhyxVgc1W4S82mELwAAMcK+qzmFnrDLMELAHAsy7JlWQGu8Qbob24ELwDAsVhqBgDAoKa8j9cUghcA4FjuCAV+clXLnPASvAAA57IU5BpvmI+MNIXgBQA4FkvNAAAY5AryJQkuLzNeAACalOWyAz4akkdGAgDQxIJ+SUIL3V1F8AIAHIsnVwEAYBBLzQAAGGRFWLIiG/4aIsvbMr+eiOAFADiW5bJkuQIEb4D+5kbwAgCcy+060QIda4EIXgCAY1mRlqzIhgOWpWYAAJqayzrRAh1rgQheAIBjWRGuwDPeepaaAQBoWlzjBQDAHCfuam6Zfw4AABCKKFfwdpoeffRRWZalSZMm+fqqqqo0ceJEJScnKz4+Xnl5eSopKQn73AQvAMCxTs54A7XT8e677+rZZ59Vz549/frvvvturVy5UsuWLdPGjRt1+PBhjRw5MuzzE7wAAOeKcEuRAVqEO+zTVVZWavTo0XruuefUpk0bX395ebkWLVqk2bNna9CgQcrOzlZ+fr7efvttFRQUhPUeBC8AwLEstxW0SVJFRYVfq66uDni+iRMnavjw4crNzfXrLywsVG1trV9/t27dlJmZqS1btoRVM8ELAHCuk/fxBmqSMjIylJCQ4GszZ85s8FRLly7V9u3bGzxeXFysqKgoJSYm+vWnpaWpuLg4rJLZ1QwAcCwrMsh9vHUn+g8ePCiPx+Prj46OPmXswYMHddddd2ndunWKiYn5cYr9F2a8AADnOnkfb6AmyePx+LWGgrewsFClpaXq3bu3IiIiFBERoY0bN+rJJ59URESE0tLSVFNTo7KyMr/XlZSUKD09PaySmfECABzrxNcCBpjx1oa+q3nw4MHauXOnX9/YsWPVrVs33X///crIyFBkZKTWr1+vvLw8SdLu3bt14MAB5eTkhFUzwQsAcC63daIFOhai1q1b67zzzvPri4uLU3Jysq9/3Lhxmjx5spKSkuTxeHTHHXcoJydHffr0CatkghcA4FwGvyRhzpw5crlcysvLU3V1tYYOHaoFCxaEfR6CFwDgWFakW1Zkw/frBuoP1Ztvvun3c0xMjObPn6/58+f/oPMSvAAA5+JrAQEAMMjlOtECHWuBCF4AgHO5gzwa0v3Dlpp/LAQvAMC5mPECAGBQRJAZ72l8SYIJBC8AwLlcVpAZL5urAABoWiw1AwBgEEvNAAAYxIwXAABzLJdbVoDbhiwXM14AAJoWM14AAAzikZEAABjE5ioAAAziPl4AAAziGi8AAAax1AwAgEFWkBmvxYwXAICmxYwXAACDLFfgmS0zXgAAmhjBCwCAQW635A4QZQEeJdncCF4AgHMx4wUAwCB3RJAZb8uMuJb55wAAAKE4OeMN1MKwcOFC9ezZUx6PRx6PRzk5OVq9erXveFVVlSZOnKjk5GTFx8crLy9PJSUlYZdM8AIAnKsJg7d9+/Z69NFHVVhYqG3btmnQoEG66qqr9OGHH0qS7r77bq1cuVLLli3Txo0bdfjwYY0cOTLsklvmPBwAgFBYEZIrQJRZ4UXciBEj/H6eMWOGFi5cqIKCArVv316LFi3SkiVLNGjQIElSfn6+unfvroKCAvXp0yfk92HGCwBwrpPPag7UJFVUVPi16urqRk9bX1+vpUuX6tixY8rJyVFhYaFqa2uVm5vrG9OtWzdlZmZqy5Yt4ZUc3icEAKDlsCyXLMsdoJ2IuIyMDCUkJPjazJkzA55v586dio+PV3R0tH7zm99oxYoVOvfcc1VcXKyoqCglJib6jU9LS1NxcXFYNbPUDABwLleQpeZ/9R88eFAej8fXHR0dHfB0Xbt21Y4dO1ReXq7ly5frpptu0saNG5u0ZIIXAOBcIdzHe3KXciiioqLUuXNnSVJ2drbeffddzZs3T9dff71qampUVlbmN+stKSlRenp6WCWz1AwAcK6T9/EGaj+Q1+tVdXW1srOzFRkZqfXr1/uO7d69WwcOHFBOTk5Y53TcjLf0+Df64MinOuj6XL+Ydou+sY41d0lAo+q++krHXl8jz4739NQFvRT94T9l9+wpK8Jx/wkCLUsTPrlqypQpGjZsmDIzM3X06FEtWbJEb775ptauXauEhASNGzdOkydPVlJSkjwej+644w7l5OSEtaNZclDw2ratdZ9v1c4jn57ocEkXXTNQO3RAR/at11Ud+ivKHdmsNQINObp6lcoWPSt5vYqWdFVGO2nFMhVv2aSUqY8oIv2s5i4RcK4mDN7S0lL9+te/1hdffKGEhAT17NlTa9eu1aWXXipJmjNnjlwul/Ly8lRdXa2hQ4dqwYIF4Zds27Yd9quawdsl/9SWkp0Bj3dJyNSIDv0MVgQ07vj2bfpqxjQpwH9mEWe1U/q8hcx8gTBVVFQoISFB5V+/LI8nLsCYY0pIzlN5eXnI13hNcMQ13jpvvd77anfQMXvLD6qs+qihioDQHH11ecDQlaS6Lw7reMHbBisCzjBN+OQqUxzxZ/ahY6Wqqq8JOsaWrY0fbVWGnWSoKiA4q7pKbT8IvEpz0vF3tqjVJf0NVAScgUK4nailaZlVfU+9XR/SuHlPP6kNf175I1cDhCY5KkrvDc9tdJxdU2ugGuAMZf2rBTrWAjkieNvGJIY0bsrt9+jxidN+1FqAkHm9qn/qcbmPBr8EEtkxy1BBwJnHtm0F2qrUUrcwOSJ4E6LildW6nYqOHg46JrdrP1lWC/0TB/+Wyi+/UhUvvhB4gNutuNyh5goCzjBe1curhldFA/U3t5Z55bkBg39yoeIjYxs8FumK0GUZOYQuWpzWV+cpqmv3hg9altqMm6CItm3NFgWcQWzbG7S1RI4J3oSoeN3Y6TL9NPkcRf3rgnldTa1SvR6N6jRE7eNSm7lC4FSu6GilTJshzw2j5U5K9vXXdMhS2wenKf6y4c1YHeB8diP/a4kccx/vd9V76/XujkL1u/gSbX27QL17927ukoBG2V6vdmzerP6DBmnj1q38ewv8ACfv4y098pI8nlYBxnyr1KTrWtx9vI64xvt9bpdbUYpQXTW7QeEclsslOy5OlXV1zV0KcMaw5ZWthpeUA/U3N0cGLwAAkuS16+UNcMtpoP7mRvACABwr2Caqlrq5iuAFADhWsE1ULXVzFcELAHAslpoBADCIzVUAABjEjBcAAINsBb6W2zKv8BK8AAAnC/ZoSHY1AwDQtJz4JQkELwDAsfhaQAAADGJXMwAABrGrGQAAg7z2iRboWEtE8AIAHKvWa6nWawU81hK5mrsAAABOl9e2grZwzJw5UxdeeKFat26t1NRUXX311dq9e7ffmKqqKk2cOFHJycmKj49XXl6eSkpKwnofghcA4FheW6oP0MJdat64caMmTpyogoICrVu3TrW1tRoyZIiOHTvmG3P33Xdr5cqVWrZsmTZu3KjDhw9r5MiRYb0PS80AAMeq81qqC7CkHKg/kDVr1vj9vHjxYqWmpqqwsFD9+/dXeXm5Fi1apCVLlmjQoEGSpPz8fHXv3l0FBQXq06dPSO/DjBcA4Fj1thW0SVJFRYVfq66uDunc5eXlkqSkpCRJUmFhoWpra5Wbm+sb061bN2VmZmrLli0h10zwAgAcq06W6uwATSeCNyMjQwkJCb42c+bMRs/r9Xo1adIk9e3bV+edd54kqbi4WFFRUUpMTPQbm5aWpuLi4pBrZqkZAOBYodxOdPDgQXk8Hl9/dHR0o+edOHGiPvjgA23atKkpyvRD8AIAHOu7S8oNHZMkj8fjF7yNuf3227Vq1Sq99dZbat++va8/PT1dNTU1Kisr85v1lpSUKD09PeTzs9QMAHCs+n9trmqo1Ye5ucq2bd1+++1asWKF3njjDWVlZfkdz87OVmRkpNavX+/r2717tw4cOKCcnJyQ34cZLwDAsU7eOhToWDgmTpyoJUuW6LXXXlPr1q19120TEhIUGxurhIQEjRs3TpMnT1ZSUpI8Ho/uuOMO5eTkhLyjWSJ4AQAOFuxBGeE+QGPhwoWSpIEDB/r15+fna8yYMZKkOXPmyOVyKS8vT9XV1Ro6dKgWLFgQ1vsQvAAAx6r1nmiBjoUjlK8RjImJ0fz58zV//vzwTv4dBC8AwLGacsZrCsELAHCsuiBfkhDuk6tMIXgBAI7F1wICAGAQS80AABh0YnNVoO/jNVxMiAheAIBjsdQMAIBBNbYUEWBmW0PwAgDQtOwgM94QbsttFgQvAMCxmvKRkaYQvAAAx6rxSu5AS81srgIAoGmxuQoAAINYagYAwKC6IF+SUMdSMwAATYsZLwAABtV4LbkCPLmqhi9JAACgabG5CgAAg1hqBgDAoLp6qbY+8LGWiOAFADgWM14AAAyqtSVXgNuGagleAACaFjNeAAAMIngBADCozht4qbmlPrnK1dwFAABwuk7OeAO1cLz11lsaMWKE2rVrJ8uy9Oqrr/odt21bU6dO1VlnnaXY2Fjl5uZq7969YddM8AIAHMvrtYK2cBw7dkw//elPNX/+/AaP/+lPf9KTTz6pZ555Rlu3blVcXJyGDh2qqqqqsN6HpWYAgGPV1brkqm14DlkXoD+QYcOGadiwYQ0es21bc+fO1X/+53/qqquukiT95S9/UVpaml599VXdcMMNIb8PM14AgGOFMuOtqKjwa9XV1WG/T1FRkYqLi5Wbm+vrS0hI0EUXXaQtW7aEdS6CFwDgWPV1LtXVNtzq605EXEZGhhISEnxt5syZYb9PcXGxJCktLc2vPy0tzXcsVCw1AwAcK9i13JP9Bw8elMfj8fVHR0cbqS0QZrwAAMcKZanZ4/H4tdMJ3vT0dElSSUmJX39JSYnvWKgIXgCAY9XVWkFbU8nKylJ6errWr1/v66uoqNDWrVuVk5MT1rlYagYAOFYoS82hqqys1CeffOL7uaioSDt27FBSUpIyMzM1adIk/eEPf9A555yjrKwsPfTQQ2rXrp2uvvrqsN6H4AUAOFZtrUsKcNtQbZi3E23btk0///nPfT9PnjxZknTTTTdp8eLFuu+++3Ts2DGNHz9eZWVluuSSS7RmzRrFxMSE9T4ELwDAsbx2kBmvHd6Md+DAgbLtwI+7sixL06dP1/Tp08M67/cRvAAAx7KDLDXbYS41m0LwAgAcq67WJUU0zZOrTCF4AQCO1ZSbq0wheAEAjuX1Bg5Ybwv9WkCCFwDgWCw1AwBgUFPuajaF4AUAOFZ9rUtyNzyzrWfGCwBA0/J6LVlsrgIAwBCvfaIFOtYCEbwAAMdy13rldgfYvlzbMrc1E7wAAMeyvLZcAWa2Xma8AAA0LXe9V+66hme2dj0zXgAAmpSrXnLVNzyzddUbLiZEBC8AwLFcQZaaA/U3N4IXAOBY7rrAm6vsAEvQzY3gBQA4FjNeAAAMiqjzKsIVYGbLjBcAgCbmtWXxAA0AAMxgqRkAAIPctV65rYaXlL08uQoAgKbl8nrlCvCN94H6mxvBCwBwLJaaAQAwyF0XZKmZXc0AADQtZrwAABgUUetVhAI8uYrNVQAANDGvgtzHa7aUUBG8AADHqq/5VnUBgre+7rjhakJD8AIAHCcqKkrp6el6+R+Tgo5LT09XVFSUmaJCRPACABwnJiZGRUVFqqmpCTouKipKMTExhqoKDcELAHCkmJiYFheqoXA1dwEAAPw7IXgBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgwheAAAMIngBADCI4AUAwCCCFwAAgyzbtu3mLiJkdp2kzyX7sLzeb/X556WKjMpUevoFkhXd3NUBDbJtW6r8UDq6Q96qYn1TdlRq1VnJZ18mKyqlucsDYJhzgteuleztkiobOBglWb0lK850VUBQtu2VSv8qfbungaNuKfVKWXHnGK8LQPNxzlKzvVsNh64k1Uj2ByarAUJTsT1A6EpSvfTlKtn1x42WBKB5OSN47WpJpY0MqpTsb0xUA4TEtm2p4r1GBtVKlTvNFASgRYho7gJCc1RS4yvin3++SyWlMT9+OUAIIqwanZ8Ywh+DVZ9LCT9+PQBaBocEb2jmzJmrJ2Yva+4yAElSm4RYff3etBBGOmPhCUDTcMbmKrtOsjdJqg86bNfHrXW86oz6WwIO17X1VrWKOBp8UPJQWZ6fmikIQLNzRkpZEZJ9lqRDQQa1Ufdze5uqCAiJXRktffm3wANcraT4c80VBKDZOWeNy+osKSnAwTjJ6mGyGiAkVnwPKeGihg+6YqX0PFmuSLNFAWhWzlhqPsm2JX0l2YclVUmKlKx0SWmS5W7e2oAg7KrD0tH3pJovT6zgtOoste4py92quUsDYJizghcAAIdzzlIzAABnAIIXAACDCF4AAAwieAEAMIjgBQDAIIIXAACDCF4AAAwieAEAMIjgBQDAIIIXAACDCF4AAAwieAEAMIjgBQDAIIIXAACDCF4AAAwieAEAMIjgBQDAIIIXAACDCF4AAAwieAEAMIjgBQDAIIIXAACDCF4AAAwieAEAMIjgBQDAIIIXAACDCF4AAAwieAEAMIjgBQDAIIIXAACDCF4AAAwieAEAMIjgBQDAIIIXAACDCF4AAAwieAEAMOj/A+DBh1ii9fwpAAAAAElFTkSuQmCC", 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" ] @@ -76,7 +76,7 @@ "" ] }, - "execution_count": 6, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -92,20 +92,24 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "name\n", - "J1 2.964769e+01\n", - "D1 1.916768e+01\n", - "R1 -9.338379e-07\n", + "2 5.446573e+01\n", + "3 2.785345e+01\n", + "4 4.665007e+01\n", + "5 1.385757e+01\n", + "6 3.296196e+01\n", + "7 2.745172e+01\n", + "1 4.394531e-07\n", "Name: 0, dtype: float32" ] }, - "execution_count": 7, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -124,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 29, "metadata": { "metadata": {} }, @@ -134,29 +138,43 @@ "output_type": "stream", "text": [ "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", - "Solving the linear system Ax = b with:\n", - "A = [[ 0.116 -0.116]\n", - " [-0.116 2.454]]\n", - "b = [ -1.614 230.277]\n", - "x = [83.8 97.772]\n", - "residue = 3.218344867404965e-14\n", - "Solving the linear system Ax = b with:\n", - "A = [[ 0.027 -0.027]\n", - " [-0.027 0.84 ]]\n", - "b = [-0.934 79.982]\n", - "x = [62.886 97.269]\n", - "residue = 8.43769498715119e-15\n", - "Solving the linear system Ax = b with:\n", - "A = [[ 0.027 -0.027]\n", - " [-0.027 0.84 ]]\n", - "b = [-0.934 79.985]\n", - "x = [62.886 97.269]\n", - "residue = 2.0317081350640365e-14\n" + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n", + "DW - TURBULENT\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -170,12 +188,15 @@ "" ] }, - "execution_count": 3, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import os \n", + "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", + "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", "results = sim.run_sim()\n", "# Plot results on the network\n", diff --git a/docs/notebooks/trash/epanet_qubonetwork.ipynb b/docs/notebooks/trash/epanet_qubonetwork.ipynb new file mode 100644 index 0000000..bc045fb --- /dev/null +++ b/docs/notebooks/trash/epanet_qubonetwork.ipynb @@ -0,0 +1,691 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "\n", + "# Create a water network model\n", + "inp_file = '../networks/Net0.inp'\n", + "# inp_file = '../networks/Net2LoopsDW.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the original Cholesky EPANET simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([26.477, 22.954], dtype=float32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0][:2]\n", + "ref_pressure" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.05, 0.05], dtype=float32)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_rate" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with our custom Cholesky EPANET solver \n", + "we use the default solver of the QuantumWNTRSimulator, that uses a LU solver, a s a benchmark of the calculation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Flow : 0.528372\n", + "Roughness: 0.000164\n", + "Diameter: 0.820210\n", + "Viscosity: 0.000011\n", + "Re Number: 58562.855181\n", + "DW - TURBULENT\n", + "DW - Friction factor : 0.019984\n", + "DW - Resistance coeff : 222.481640\n", + "\n", + "Flow : 0.528372\n", + "Roughness: 0.000164\n", + "Diameter: 0.820210\n", + "Viscosity: 0.000011\n", + "Re Number: 58562.855181\n", + "DW - TURBULENT\n", + "DW - Friction factor : 0.019984\n", + "DW - Resistance coeff : 222.481640\n", + "\n", + "Reservoir : 98.425197\n", + "Flow : 1.765728\n", + "Roughness: 0.000164\n", + "Diameter: 0.820210\n", + "Viscosity: 0.000011\n", + "Re Number: 195706.877298\n", + "DW - TURBULENT\n", + "DW - Friction factor : 0.016664\n", + "DW - Resistance coeff : 222.481640\n", + "\n", + "Flow : 1.765726\n", + "Roughness: 0.000164\n", + "Diameter: 0.820210\n", + "Viscosity: 0.000011\n", + "Re Number: 195706.600113\n", + "DW - TURBULENT\n", + "DW - Friction factor : 0.016664\n", + "DW - Resistance coeff : 222.481640\n", + "\n", + "Reservoir : 98.425197\n", + "Flow : 1.765728\n", + "Roughness: 0.000164\n", + "Diameter: 0.820210\n", + "Viscosity: 0.000011\n", + "Re Number: 195706.842128\n", + "DW - TURBULENT\n", + "DW - Friction factor : 0.016664\n", + "DW - Resistance coeff : 222.481640\n", + "\n", + "Flow : 1.765724\n", + "Roughness: 0.000164\n", + "Diameter: 0.820210\n", + "Viscosity: 0.000011\n", + "Re Number: 195706.402191\n", + "DW - TURBULENT\n", + "DW - Friction factor : 0.016664\n", + "DW - Resistance coeff : 222.481640\n", + "\n", + "Flow : 1.765728\n", + "Roughness: 0.000164\n", + "Diameter: 0.820210\n", + "Viscosity: 0.000011\n", + "Re Number: 195706.842128\n", + "DW - TURBULENT\n", + "DW - Friction factor : 0.016664\n", + "DW - Resistance coeff : 222.481640\n", + "\n", + "Flow : 1.765724\n", + "Roughness: 0.000164\n", + "Diameter: 0.820210\n", + "Viscosity: 0.000011\n", + "Re Number: 195706.402191\n", + "DW - TURBULENT\n", + "DW - Friction factor : 0.016664\n", + "DW - Resistance coeff : 222.481640\n", + "\n", + "Reservoir : 98.425197\n", + "Flow : 1.765721\n", + "Roughness: 0.000164\n", + "Diameter: 0.820210\n", + "Viscosity: 0.000011\n", + "Re Number: 195706.086172\n", + "DW - TURBULENT\n", + "DW - Friction factor : 0.016664\n", + "DW - Resistance coeff : 222.481640\n", + "\n", + "Flow : 1.765724\n", + "Roughness: 0.000164\n", + "Diameter: 0.820210\n", + "Viscosity: 0.000011\n", + "Re Number: 195706.402191\n", + "DW - TURBULENT\n", + "DW - Friction factor : 0.016664\n", + "DW - Resistance coeff : 222.481640\n", + "\n" + ] + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + "name P1 P2\n", + "0 0.05 0.05\n", + "3600 0.05 0.05" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.link['flowrate']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the Nework QUBO solver" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Roughness : 0.000164\n", + "diameter : 0.820210\n", + "resistance coeff : 222.481950 \n" + ] + }, + { + "data": { + "image/png": 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", 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xJHk556rbXAaAiIioczEkeTnu30ZERCQNhiQv51wG4CxbkoiIiDoVQ5KXcy4DwO42IiKizsWQ5OW4NQkREZE0GJK8nLMlycIxSURERJ2KIcnLiQO3uQQAERFRp2JI8nIB/uxuIyIikgJDkpfTcAkAIiIiSTAkeTnObpMHu12A3S5IXQ0iIupAflJXgC5Ny3WSvI7pbBM2H6pEQXEN9pWZUG48i5oGKwQB8FMqEB2qRVy3AAyK0yE1MRzX9opEkIZ/1YiI5Ib/cns5bnDrPQpPnMFbP/yCDQcq0GRz32rUbBdQZjyLMuNZFBTX4I3vi6FWKXFd70j8Zlg8bkqJgr+KDbhERHLAkOTlzi0BwJYkqRyrrMXzaw9iy5Eq8Vjv6GDc2CcKQ+LDkBQZhIhgNVQKBSzNdhjMjfi5sg5FpUb8cKwaJ043YOPBSmw8WIkeYQF4+Npk3J+WILYSEhGRd2JI8nJcAkA6zTY7Xvv2Z7ySdxTNdgH+KgXuHtoDvx+VjBR96EXPiw0LwDUJ3fCbYfEQBAFHKuqwZtdJfFR4EmXGs5j3xQG88X0x/m90X9w+QA+FQtGJ3xUREbUWQ5KXcy4BcNbKkNSZKmsbMeP9Xdhx/AwAILNvNGaP7oukyKA2XUehUKCPPgSz7uiLx2/pjY8KT2Lx5mMoM57FH9/fhbTkcMy9qx/6x+o88W0QEdEV4OAILxeg5sDtznbIYMbdi7dhx/EzCNb4YdG4IXhz0rA2B6T/pfVX4XcjEpH3xPWYeXMvaP2V2F5cgzH/3oqXNhxBs43jzoiIvAlDkpcLVLMlqTMVlRrxm9fzUWY8i56RQfji0VHIHtqjQ+8RqPbD47f0Rt4TN2D0wBjY7AJezjuKe5fk43h1fYfei4iI2o8hycs5u9saGJI8rqjUiAfeLECtpRnDk7phzR9HIvkKW48upUdYABZPuAavjB+KEK0fikqNuOOV77Fm10mP3ZOIiFqPIcnLsbutc/xcVYcHl21HraUZacnheGdyGsIC1Z1y7zGDY7H+seuQnhyOBqsNOR/uwV/XHmD3GxGRxBiSvFwAu9s8rrrOgsnLdsDY0IQh8WFY9uDwTl/8sUdYAFY8PAJ/urkXAOCtH4ox+Z0dMDU0dWo9iIjoHIYkLxfo7/iwZkuSZzTb7Pjj+7tQUtOAhPBAvDlpmGSrY6uUCuTc0huvTbgGAf4qfH+0Gr9e/AN+qaqTpD5ERF0dQ5KX06odj+hskw2CwL3BOtqL3xzB9uIaBGv88PaDwxEZrJG6SrhjYAw+mp6BHmEBOH66Ab9Zko+9J01SV4uIqMthSPJygWpHq4YgAJZmjlHpSJsPV2LJdz8DAP45dhCujgqWuEbn9I/V4bNHfoUBPUJxut6K+5fmY+uxaqmrRUTUpXg8JC1evBhJSUnQarVIT0/H9u3bL1l+9erVSElJgVarxcCBA7Fu3TqX9wVBwJw5cxATE4OAgABkZmbi6NGjLmWSkpKgUChcXv/4xz9cyvz000+49tprodVqER8fj/nz53fMN9zBAs7buoIz3DrOmXornvroJwDAgyOTMHpQjMQ1ulBksAYrHx6BkVdFoN5qw+RlO/DlT6ekrhYRUZfh0ZC0atUq5OTkYO7cudi1axcGDx6MrKwsVFZWui2/bds2jB8/HlOmTMHu3buRnZ2N7Oxs7Nu3Tywzf/58vPLKK1iyZAkKCgoQFBSErKwsNDY2ulzr+eefx6lTp8TXo48+Kr5nNptx6623IjExEYWFhViwYAHmzZuHpUuXeuYHcQVUSgXUfo7H1GBtlrg2vmPu5/tRVWvBVd2D8MztKVJX56JCtP5YNnk47hioh9Vmx6Mrd3GJACKiziJ4UFpamjBjxgzxa5vNJsTGxgq5ubluy993333C6NGjXY6lp6cL06ZNEwRBEOx2u6DX64UFCxaI7xuNRkGj0QgrV64UjyUmJgovvfTSRev12muvCd26dRMsFot47Omnnxb69Olz0XMaGxsFk8kkvkpLSwUAgslkuug5HWXwc18LiU+vFY5WmD1+r67gm/0GIfHptULyM2uF3SVnpK5OqzTb7MIzH+8REp9eKyQ9s1ZYvbNU6ioREcmSyWRq9ee3x1qSrFYrCgsLkZmZKR5TKpXIzMxEfn6+23Py8/NdygNAVlaWWL64uBgGg8GljE6nQ3p6+gXX/Mc//oGIiAgMHToUCxYsQHPzuVaY/Px8XHfddVCrz62Dk5WVhcOHD+PMmTNu65abmwudTie+4uPjW/mTuHJcULLjNFibMe/z/QCAh6/riSHxYdJWqJVUSgX+nj0QE9ITIAjAkx/tweqdpVJXi4jIp3ksJFVXV8NmsyE6OtrleHR0NAwGg9tzDAbDJcs7f73cNf/0pz/hgw8+wObNmzFt2jS88MILeOqppy57n/Pv8b9mzZoFk8kkvkpLO+8DimsldZx/b3JsLtsjLAAzW9YkkgulUoG/ZQ/AAyMSIQjAUx//hI8K2fVGROQp0iwI42E5OTni7wcNGgS1Wo1p06YhNzcXGk37pnhrNJp2n3ulxJYkrpV0RY5X1+ON738BAMwb01+cOSgnCoUCz/+6P5QKYHn+CTz10R4Ea1S4bYD3DTwnIpI7j7UkRUZGQqVSoaKiwuV4RUUF9Hq923P0ev0lyzt/bcs1ASA9PR3Nzc04fvz4Je9z/j28iXOT20a2JF2R+V8fQpNNwPW9u+OWftGXP8FLKRQKzBvTH/cPj4ddAP60sgjfH62SulpERD7HYyFJrVYjNTUVeXl54jG73Y68vDxkZGS4PScjI8OlPABs2LBBLJ+cnAy9Xu9Sxmw2o6Cg4KLXBICioiIolUpERUWJ99myZQuams5t+bBhwwb06dMH3bp1a/s362Fajkm6YoUnarBurwFKBfDsHX2lrs4VUygU+PvdAzF6YAysNjumvluIwhPux9MREVH7eHQJgJycHLzxxhtYvnw5Dh48iOnTp6O+vh6TJ08GAEycOBGzZs0Sy8+cORPr16/HwoULcejQIcybNw87d+7EI488AsDxwfDYY4/hb3/7Gz7//HPs3bsXEydORGxsLLKzswE4BmUvWrQIe/bswS+//IL3338fjz/+OH73u9+JAei3v/0t1Go1pkyZgv3792PVqlV4+eWXXbrpvEkgN7m9IoIgIHfdIQDAb1Lj0UcfInGNOoZKqcBL44bgut7dcbbJhsnLtuPgKbPU1SIi8hkeHZQxbtw4VFVVYc6cOTAYDBgyZAjWr18vDpIuKSmBUnkup40cORIrVqzA7Nmz8eyzz6JXr1749NNPMWDAALHMU089hfr6ekydOhVGoxGjRo3C+vXrodVqATjGDn3wwQeYN28eLBYLkpOT8fjjj7sEIJ1Oh2+++QYzZsxAamoqIiMjMWfOHEydOtWTP452c46d4cDt9tlytBo7T5yBxk+JnFt7S12dDqX2U2LJ767BxLe2Y+eJM3jgre346A8ZSIoMkrpqRESypxAEbgjWHmazGTqdDiaTCaGhoR6916w1e7Fyewkez+yNmZnympElNUEQcPdr21BUasSUUcn4y539pK6SR5jONuH+pT/i4CkzeoQF4JM/jkRUqFbqahEReZ22fH5z7zYZYHdb+313pApFpUZo/ZWYdn1PqavjMboAf7z7+zQkRwahzHgWv1++A/UWrtBORHQlGJJkwLkEwFluS9Jmr246BgD4XXoiokJ8u2Wle4gG70wejoggNfaVmTFjxS4027gpMhFRezEkyUAAW5LaZefxGhSeOAO1Somp1/luK9L5EiOC8NaDw6H1V+Lbw1WY/ek+sEediKh9GJJkgNuStM+S7xwLR45N7dGlxucMiQ/Dq+OvgVIBfLCjFP9uaU0jIqK2YUiSAXExSbYktdqxylpsPFgBhQJ46Nqu0Yp0vlv6RWPemP4AgIUbjuBjbl9CRNRmDEky4OxuY0tS6731QzEA4Ja+0biqe7DEtZHGxIwkTGvpZnz645+w9Vi1xDUiIpIXhiQZYHdb2xgbrPhkdxmArtmKdL6nb0vBnYNi0GwXMP2/hfi5qk7qKhERyQZDkgw4F5Nkd1vrfLizFI1NdqToQzA8yfu2melMSqUCL/5mMK5JCIO5sRkPLd8JY4NV6moREckCQ5IMBKgdj4ktSZdnswt4N/8EAODBkUlQKBQS10h6Wn8V/vPAMPQIC0BxdT2m/3cXrM1cGoCI6HIYkmQgwL9lWxK2JF3Wt4crcfLMWegC/PHrIT2kro7X6B6iwVsPDkOQWoX8X05jzmdcGoCI6HIYkmRAXCeJLUmXtXJ7CQDgN6lx4s+NHFL0oXj1t0PFpQGcg9uJiMg9hiQZOH9bEv7v/+IMpkZsOlQJALg/LUHi2ninm1Ki8ewdfQEAf193EHkHKySuERGR92JIkgFty+w2m12AldtMXNTqnaWwC0BaUjiujuqa0/5bY8qoZIxPi4cgAH9auRsHT5mlrhIRkVdiSJKBwPO6jRqtDEnu2O0CVu0sBQCMGx4vcW28m0KhwPO/HoCRV0Wg3mrDQ8t3oqrWInW1iIi8DkOSDPirlPBXOWZpNTRxk1t3fvzlNE6eOYsQrR/uGBgjdXW8nr9KidcmXIPkyCCUGc9i6ns7ucQEEdH/YEiSCS0XlLykj3c5Fo+8a3AsB2y3UligGm9NGgZdgD92lxgxa81ejnkjIjoPQ5JMBHKG20U1WJvx1b5TAIB7hnLaf1v07B6Mxb+9BiqlAp/sLsPr3/0sdZWIiLwGQ5JMOLcm4VpJF/p6vwENVhsSwgORmti1V9huj1G9IjHvrn4AgAVfH8Y3+w0S14iIyDswJMlEQMvWJGxJutCalq62e67pwRW22+mBjCQ8MCIRggA8tqoIB8o5442IiCFJJgL8uTWJO1W1FnF3+7vZ1XZF5tzVD7+6OgINVhsefpcz3oiIGJJkgpvcurd+vwF2ARgcp0NiRJDU1ZE1f5USr/02VZzx9of/FsLSzD9vRNR1MSTJhHPGFluSXK37yTFgm9P+O4Yu0B9vThqGUK0fCk+c4Yw3IurSGJJkIkBcAoDrJDlV11lQUHwaAENSR7qqezAWT3DMeFuzqwz/2fKL1FUiIpIEQ5JMcAmAC63f5+hqGxSnQ3x4oNTV8SnX9uqOOXc6Zrz9c/0hbDjAPd6IqOthSJIJsbuNY5JE6/Y6utpGsxXJIyZmJGJCeoJjxtsHu3HIwBlvRNS1MCTJRLDGMXC7wcLuNsDR1fbjL+xq8ySFQoF5Y/qLe7xNeWcnTtdxxhsRdR0MSTLhnN1Wz+42AI4FJNnV5nnOPd6SIgI5442IuhyGJJkI0ji62+rZkgQA+JKz2jpNWKAab04ajhCtH3YcP4PZn+zjjDci6hIYkmQiiC1JovO72jgeqXNcHeXY402pAFYXnsSb3xdLXSUiIo9jSJIJZ0sSxySxq00q1/Xujr+0zHh74auD2HSIM96IyLcxJMkExySd45yOfvsAtiJ1tgdHJmF8mmPG259WFuFIRa3UVSIi8hiGJJkIapnd1tXHJDVYm7HtZ0dXW2bfKIlr0/UoFAo8/+v+GNEzHHWWZkxZvgM19Vapq0VE5BEMSTIhdrd18RW3tx47DWuzHfHhAbg6Kljq6nRJ/iolXp+QioTwQJTWOGa8WZvtUleLiKjDMSTJhDhw29K1u9uc42BuTomGQqGQuDZdV7cgNd6aNAwhGj9sL67BXz7ljDci8j0MSTIhbkvSZIPN3jU/jARBwKZDlQCAm1LY1Sa1XtEheOW3Q6FUAKt2luKtHzjjjYh8C0OSTDjHJAFdt8ttf7kZFWYLAtUqpPcMl7o6BODGPlF49o6+AIAX1h3E5sOVEteIiKjjMCTJhMZPCZXS0b3U0EVnuDlbkUZdHQmNn0ri2pDTlFHJGDcsHnYB+NOK3TjKGW9E5CM8HpIWL16MpKQkaLVapKenY/v27Zcsv3r1aqSkpECr1WLgwIFYt26dy/uCIGDOnDmIiYlBQEAAMjMzcfToUfH948ePY8qUKUhOTkZAQACuuuoqzJ07F1ar1aWMQqG44PXjjz927DffgRQKhdjl1lVnuOW1hKSbOavNqygUCvw1ewDSksNRa2nGlOU7OeONiHyCR0PSqlWrkJOTg7lz52LXrl0YPHgwsrKyUFnpvkl+27ZtGD9+PKZMmYLdu3cjOzsb2dnZ2Ldvn1hm/vz5eOWVV7BkyRIUFBQgKCgIWVlZaGxsBAAcOnQIdrsd//nPf7B//3689NJLWLJkCZ599tkL7rdx40acOnVKfKWmpnrmB9FBgjVdd/B2Va0Fe0qNABxdPORd1H5KLPldKuLDA1BS04DpnPFGRL5A8KC0tDRhxowZ4tc2m02IjY0VcnNz3Za/7777hNGjR7scS09PF6ZNmyYIgiDY7XZBr9cLCxYsEN83Go2CRqMRVq5cedF6zJ8/X0hOTha/Li4uFgAIu3fvbs+3JQiCIJhMJgGAYDKZ2n2Ntrrpxc1C4tNrhfyfqzvtnt5i1Y4SIfHptcJdr34vdVXoEg4bzEL/OeuFxKfXCs98vEew2+1SV4mIyEVbPr891pJktVpRWFiIzMxM8ZhSqURmZiby8/PdnpOfn+9SHgCysrLE8sXFxTAYDC5ldDod0tPTL3pNADCZTAgPv3Cg75gxYxAVFYVRo0bh888/v+T3Y7FYYDabXV6dzTl4uysO3N50kLPa5KB3dAheGT8ECgWwcjtnvBGRvHksJFVXV8NmsyE6OtrleHR0NAwGg9tzDAbDJcs7f23LNY8dO4ZXX30V06ZNE48FBwdj4cKFWL16Nb788kuMGjUK2dnZlwxKubm50Ol04is+Pv6iZT3l3JikrtXdZm224/ujVQAYkuTgppRo/F/LjLe/rzuIr/aekrhGRETt43f5IvJVVlaG2267Db/5zW/w8MMPi8cjIyORk5Mjfj18+HCUl5djwYIFGDNmjNtrzZo1y+Ucs9nc6UEpuItuTbLzeA3qrTZEBmswIFYndXWoFaaMSkZJTQPezT+Bx1YVISpUg9RELttARPLisZakyMhIqFQqVFS47hReUVEBvV7v9hy9Xn/J8s5fW3PN8vJy3HjjjRg5ciSWLl162fqmp6fj2LFjF31fo9EgNDTU5dXZuuomt98fqwYAXNcrEkolV9mWA4VCgbl39Udm3yhYmu14aPlOFFfXS10tIqI28VhIUqvVSE1NRV5ennjMbrcjLy8PGRkZbs/JyMhwKQ8AGzZsEMsnJydDr9e7lDGbzSgoKHC5ZllZGW644QakpqZi2bJlUCov/20WFRUhJsa7d5UX92/rYi1JW1tC0qhekRLXhNpCpVTglfFDMThOhzMNTXhw2XacrrNIXS0iolbzaHdbTk4OJk2ahGHDhiEtLQ2LFi1CfX09Jk+eDACYOHEievTogdzcXADAzJkzcf3112PhwoUYPXo0PvjgA+zcuVNsCVIoFHjsscfwt7/9Db169UJycjL+8pe/IDY2FtnZ2QDOBaTExES8+OKLqKqqEuvjbG1avnw51Go1hg4dCgBYs2YN3n77bbz55pue/HFcMef+bXVdaOD2mXor9paZAAC/upohSW4C1X54c9Jw3PP6Vpw43YApy3di5cMjEKDmYqBE5P08GpLGjRuHqqoqzJkzBwaDAUOGDMH69evFgdclJSUurTwjR47EihUrMHv2bDz77LPo1asXPv30UwwYMEAs89RTT6G+vh5Tp06F0WjEqFGjsH79emi1WgCOlqdjx47h2LFjiIuLc6mPcN4GnH/9619x4sQJ+Pn5ISUlBatWrcK9997ryR/HFQt0zm7rQgO38385DUEAekcHIzpUK3V1qB26h2jwzuQ0jH19G4pKjXhs1W68NiFVXEGeiMhbKQSBW3e3h9lshk6ng8lk6rTxSf/57mfkfnUI91zTA/+6b0in3FNqs9bsxcrtJZj8qyTMvau/1NWhK7C9uAa/e7MAVpudz5OIJNOWz2/u3SYjXbElyTke6VqOR5K9tORwLLxvMABg2dbjePP7XySuERHRpTEkyUhwy8Dt+i4yJqnkdANKahrgp1QgLTlC6upQB7hrcCxm3Z4CAPjblwfxye6TEteIiOjiGJJkRFwCoIvMbvuhpRXpmoRu4hpRJH9Tr+uJ3/8qGQDw5OqfsPmQ+70ciYikxpAkI87ZbQ1dZJ2kH445ZiZyVptvUSgUmD26L+4e2gPNdgHT3y9E4YkaqatFRHQBhiQZCepC3W02u4BtP58GwPWRfJFSqcD8ewfhxj7d0dhkx+RlO3DYUCt1tYiIXDAkyUiQuC2J77ck7S83wdjQhBCNHwbHcSsSX+SvUuK1CalITewGc2MzJr5dgNKaBqmrRUQkYkiSkXMb3Pp+S5JzPNKIqyLgp+IfU18VoFbhrUnD0Ds6GBVmCx54qwDVXJWbiLwEP31kxDl42dJsR7PNLnFtPOuHoy1bkXA8ks8LC1Tj3d+no0dYAI6fbsCDy7ajtrFJ6moRETEkyYlzdhvg25vcWpptKDxxBgDwq6s59b8r0Ou0eG9KGiKC1NhXZsbD7+5EY5Pv/hknInlgSJIRtZ8S/irHVg4NPjx4+6eTJlia7YgMVuOq7sFSV4c6Sc/uwXhnchqCNX748Zca/OG/hbA0MygRkXQYkmTm3FpJvvvhUfCLY1ZbWnI4FAru79WVDIzT4a1Jw6D1V+Lbw1X408rdaPLxrmUi8l4MSTLjHJfkyy1JBcWONXPSucp2l5TeMwJvThwOtZ8SX++vwBMf7oHNzi0miajzMSTJjHOGW52PznBrstnF8UjpPcMlrg1JZVSvSLw+4Rr4KRX4fE85nvn4J9gZlIiokzEkyUygj6+VtLfMhAarDWGB/ugdFSJ1dUhCN/eNxivjh0KpAFYXnsS8L/ZDEBiUiKjzMCTJTIjGt/dvK/jF0dU2PCkcSiXHI3V1dwyMwcL7BkOhAN7NP4G/rj3IoEREnYYhSWZCtI6Q5KvryGwvdgzaTk9mVxs53D00Drl3DwQAvL21GM99cYBBiYg6BUOSzDgHbtf6YEuSzS5g53HHeKQRPTlom865Py0B/xw7EAoF8M6245j3ObveiMjzGJJkJkTrDwCobfS9kHSg3IxaSzNCtH7oGxMqdXXIy4wbnoB/3jMICgWwPP8E5ny2n4O5icijGJJkJrilu63OB0NSQUtX2/CkcKg4HoncuG94POaPdQSl9348gb98to9BiYg8hiFJZpwDt31xTNKPvzjXR+J4JLq43wyLx4v3OgZzv19Qgv/7dC/XUSIij2BIkhnnwG1fWyfJbhew43hLSOJ4JLqMsalx+Nd9g6FUACu3l+LxVUVcmZuIOhxDksw4u9vMPtbddshQC9PZJgSqVegfy/FIdHl3D43DK+OHigtOTv9vITfFJaIOxZAkM86B2742Jsk59T81sRv8VfxjSa1z56BYvDFxGDR+Smw8WInfv7PDZ9cQI6LOx08jmXEuAeBr3W07W7YiSUvieCRqmxtTorD892kI1vhh28+nMeHNAhgbrFJXi4h8AEOSzIT66GKSzv3aUpO6SVwTkqMRPSPw/kPpCAv0R1GpEfcv/RGV5kapq0VEMseQJDPB5w3c9pXF9MqNZ3HK1AiVUoEh8WFSV4dkanB8GFZNzUD3EA0OGWpxz+vb8HNVndTVIiIZY0iSGWd3W5NNgKXZN2bzOLva+sWEIlDtJ3FtSM766EPw0R8ykBQRiJNnzmLs69vEVkoiorZiSJKZILUfFC3rLPrKqtuFLVP/UxPZ1UZXLjEiCB9NH4nBcToYG5ow4c0fseFAhdTVIiIZYkiSGaVSgWC1b41LKixpGY/EkEQdJDJYg5VTR+DGPt3R2GTHtPd2YuX2EqmrRUQyw5AkQ760oGS9pRkHT9UCAIZx0DZ1oEC1H96YOAz3DYuDXQBmrdmLhd8c5jYmRNRqDEkyFCzOcJN/SCoqNcJmF9AjLAAxugCpq0M+xk+lxD/HDsKfbu4FAHh10zE8+sFuLjpJRK3CkCRDwRrfCUnOQbXXsKuNPEShUCDnlt6Yf+8g+KsU+PKnUxi39EdU1nKJACK6NIYkGRJX3faB7jbnzLZhDEnkYfcNi8d7UxxrKe0pNSL731txoNwsdbWIyIsxJMlQsI8sKGm3C9h9goO2qfOM6BmBT/74K/SMDEK5qRH3LtmGjZz5RkQXwZAkQ85Vt+W+f9uRylrUWpoRqFYhRR8idXWoi0iODMInf/wVRl4VgQarDQ+/txOLNx/zmcVZiajjMCTJkDgmSebdbTuPO1qRhiaEwY+b2lIn0gX6Y/nv0/Db9AQIArDg68P4w38LfaILm4g6Dj+ZZChY4xiTJPeB27ucXW0J7GqjzuevUuKFuwfiH/cMhFqlxNf7K5C9eCu3MiEikcdD0uLFi5GUlAStVov09HRs3779kuVXr16NlJQUaLVaDBw4EOvWrXN5XxAEzJkzBzExMQgICEBmZiaOHj3qUqampgYTJkxAaGgowsLCMGXKFNTVuf7D99NPP+Haa6+FVqtFfHw85s+f3zHfcCcI8ZExSTvFTW3DJa4JdWX3pyVg1bQR0IdqcayyDtn/3soVuokIgIdD0qpVq5CTk4O5c+di165dGDx4MLKyslBZWem2/LZt2zB+/HhMmTIFu3fvRnZ2NrKzs7Fv3z6xzPz58/HKK69gyZIlKCgoQFBQELKystDYeG4674QJE7B//35s2LABa9euxZYtWzB16lTxfbPZjFtvvRWJiYkoLCzEggULMG/ePCxdutRzP4wOFOwDi0lW1VpQUtMAhcLR3UYkpaEJ3fDFo6OQlhSOWkszHn53JxZ8fQjNNt/YH5GI2knwoLS0NGHGjBni1zabTYiNjRVyc3Pdlr/vvvuE0aNHuxxLT08Xpk2bJgiCINjtdkGv1wsLFiwQ3zcajYJGoxFWrlwpCIIgHDhwQAAg7NixQyzz1VdfCQqFQigrKxMEQRBee+01oVu3boLFYhHLPP3000KfPn1a/b2ZTCYBgGAymVp9Tkf5am+5kPj0WuGe17Z2+r07yob9BiHx6bVC5sJvpa4KkcjabBPmfrZPSHx6rZD49FrhN69vE8qNDVJXi4g6UFs+vz3WkmS1WlFYWIjMzEzxmFKpRGZmJvLz892ek5+f71IeALKyssTyxcXFMBgMLmV0Oh3S09PFMvn5+QgLC8OwYcPEMpmZmVAqlSgoKBDLXHfddVCr1S73OXz4MM6ccb9juMVigdlsdnlJxTkmSc6z2/acNAIAhsSHSVoPovP5q5SYN6Y/Xh0/FMEaP2w/XoM7Xv4emw+7b/0mIt/msZBUXV0Nm82G6Ohol+PR0dEwGAxuzzEYDJcs7/z1cmWioqJc3vfz80N4eLhLGXfXOP8e/ys3Nxc6nU58xcfHu//GO4EvdLcVlRoBAIMZksgL3TU4FmsfHYX+saE409CEyct2IPerg2hi9xtRl8LZba00a9YsmEwm8VVaWipZXZwDt80yHbgtCAL2tIQktiSRt0qKDMLH00diYkYiAOA/3/2Ccf/JR8npBolrRkSdxWMhKTIyEiqVChUVrrNEKioqoNfr3Z6j1+svWd756+XK/O/A8ObmZtTU1LiUcXeN8+/xvzQaDUJDQ11eUgnRnGtJEmS4AF5xdT3Mjc1Q+ynRh4tIkhfT+qvw/K8H4LUJ1yBE44ddJUbc/vIWfLijVJZ/94iobTwWktRqNVJTU5GXlyces9vtyMvLQ0ZGhttzMjIyXMoDwIYNG8TyycnJ0Ov1LmXMZjMKCgrEMhkZGTAajSgsLBTLbNq0CXa7Henp6WKZLVu2oKmpyeU+ffr0Qbdu3r9mj3PvNkEA6q3y283cOR5pQGwo/LmIJMnAHQNjsG7mtUhLCke91YanPv4Jf/hvIWrqrVJXjYg8yKOfUDk5OXjjjTewfPlyHDx4ENOnT0d9fT0mT54MAJg4cSJmzZollp85cybWr1+PhQsX4tChQ5g3bx527tyJRx55BIBjN+/HHnsMf/vb3/D5559j7969mDhxImJjY5GdnQ0A6Nu3L2677TY8/PDD2L59O7Zu3YpHHnkE999/P2JjYwEAv/3tb6FWqzFlyhTs378fq1atwssvv4ycnBxP/jg6jNZfCZVSAUCeayXtKTUB4Hgkkpf48ECsnDoCT93WB/4qBb7eX4GsRVs4qJvIh/l58uLjxo1DVVUV5syZA4PBgCFDhmD9+vXiIOmSkhIoledy2siRI7FixQrMnj0bzz77LHr16oVPP/0UAwYMEMs89dRTqK+vx9SpU2E0GjFq1CisX78eWq1WLPP+++/jkUcewc033wylUomxY8filVdeEd/X6XT45ptvMGPGDKSmpiIyMhJz5sxxWUvJmykUCoRq/XCmoQnms82I0Uldo7Yp4ngkkimVUoE/3nA1ruvVHY+tKsKxyjpMXrYD49PiMeuOvghtaeUlIt+gENix3i5msxk6nQ4mk0mS8Uk3LNiM46cb8OG0DKQly2fFamuzHQPmfg2rzY7vnrwBiRFBUleJqF0am2z4x1eH8M624wAAfagWL9wzADelRF/6RCKSVFs+vzkgRKZ0gY41nowN8hoTcchghtVmR1igPxLCA6WuDlG7af1VmDemPz6YOgJJEYEwmBvx+3d24vFVRTjDsUpEPoEhSaZ0AY5mfdNZeY1JEtdHiguDQqGQtjJEHWBEzwh8NfM6TL2uJ5QK4JPdZbjlpe/w5U+nOAOOSOYYkmRK9iGJ45HIhwSoVXj2jr5Y88dfoXd0MKrrrJixYhcmLduB49X1UlePiNqJIUmmdAEtC0rKLCSdW0RSZqPNiVphSHwYvnh0FGbe3AtqlRJbjlTh1kVbsGjjETQ2yW+5DqKujiFJpuTYkmRubMLPVY7/VQ+OC5O2MkQeovFT4fFbeuPrx6/Dtb0iYW22Y9HGo7ht0RZsOVIldfWIqA0YkmRKjiFp70nH+kjx4QGICNZIXBsiz0qODMK7v0/Dv387FFEhGhw/3YCJb2/Hw+/uRDG74IhkgSFJpuQYks4ftE3UFSgUCtw5KBZ5T1yPyb9KgkqpwIYDFbj1pe/w9y8PyOrvL1FXxJAkU7oAxxIAcvpHlotIUlcVovXH3Lv64+vHrsX1vbujySbgje+LceOL3+K/P55As80udRWJyA2GJJmSW0uSIAic2UZd3tVRIVj++zQsmzwcV3UPQk29FbM/3YfbXv4e6/cZuGQAkZdhSJIpuYUkg7kRVbUWqJQKDIjlzDbq2m7sE4X1j12H58b0R1igP45V1uEP/y1E9mvbsO3naqmrR0QtGJJkShd4LiTJ4X+fzqn/faJDEKBWSVsZIi/gr1Ji0sgkfPfkjXjkxqsR4K/CnlIjfvtGAR54q0Cc6EBE0mFIkilnS1KTTcBZGay/UlTq+AefXW1ErnQB/vhzVh9899QNmJiRCD+lAt8frcZd//4BDy3fiZ9OGqWuIlGXxZAkU0FqFVRKx7YecuhyKyo9A4CLSBJdTFSIFs//egA2PXED7h7aA0oFsPFgBcb8eysmL9uO3SVnpK4iUZfDkCRTCoVCNuOSbHZB7DpgSxLRpSVEBOKlcUOwIed63NMSljYfrsLdr23DxLe3o+CX07LoYifyBQxJMiaGpAbvDkk/V9Wh3mpDoFqFXlEhUleHSBau6h6Mf40bgk1P3IDfpMZBpVRgy5EqjFv6I7Jf24av9p6Czc6wRORJDEkyJpeWJOfU/wE9dGIXIRG1TlJkEBb8ZjA2P3EDxqclQO2nxJ5SI6a/vws3L3Sss8R94Yg8gyFJxuQSkpwz24ayq42o3RIiApF7z0BsffomPHrT1dAF+OP46QbM/nQfMnLz8I+vDqG0pkHqahL5FIYkGZNNSGqZncPxSERXrnuIBk/c2gf5s27CvLv6Ia5bAM40NGHJdz/jugWb8dDyHdhypAp2dsURXTE/qStA7SeHkNTYZMOhU7UAGJKIOlKg2g8P/ioZD2QkIe9gBd778QS+P1qNjQcrsfFgJZIjg/C7EYm4NzVO/LeCiNqGIUnG5BCS9peb0GwXEBmsQaxOK3V1iHyOSqnArf31uLW/Hj9X1eG9/BP4uPAkiqvr8de1B7Dg60O4Y0AM7h0WhxHJEVByXCBRqzEkyZgcQpJzEckh8TooFPzHmciTruoejHlj+uPJrD74ZHcZ3ss/gcMVtVizuwxrdpchPjwA914Tj7GpPRDXLVDq6hJ5PYYkGZNDSHIO2h4cFyZpPYi6kiCNH343IhET0hOwq8SIjwpL8cWeUyitOYuXNh7BorwjGHlVBO5NjUNWfz0C1fwoIHKHfzNk7Pz927yVc9D2kIQwSetB1BUpFAqkJnZDamI3zLmzP9bvP4XVO09i28+nsfWY4xXgvw839Y3CXYNicEOfKGj9ubcikRNDkox5e0tSTb0VJ047piQP6hEmbWWIurgAtQp3D43D3UPjUFrTgI93ncSaXWUoqWnAlz+dwpc/nUKwxg+39IvGnYNicG2v7lD7cQI0dW0MSTLm7StuO1uRekYGia1eRCS9+PBAPJbZGzNv7oW9ZSas/ekU1u4pR7mpEZ/sLsMnu8ugC/DHzX2jcGu/aFzbqzuCNPy4oK6Hf+plLDxIDQAwnm2C3S543awVcTwSp/4TeSWFQoFBcWEYFBeGZ25Lwe7SM/hizyms23sKlbUWrNlVhjW7yqD2U+JXV0Xgln56ZPaNQlQoZ6pS18CQJGNhLa0zNrsAc2MTwgLVEtfI1blB2zppK0JEl6VUKpCaGI7UxHD85c5+2HG8BhsPVGDDwQqcON2AzYersPlwFZ79xPEfn8yUKFzXuzu3GyKfxpAkYxo/FUI0fqi1NKOm3upVIUkQBOw52TL9P6GbxLUhorZQKRUY0TMCI3pG4P9G98XRyjpsOFCBDQcqUFRqxJ6W18INR9At0B+/ujoS1/Xujut6dYee66GRD2FIkrluQWoxJPXsLnVtzjl55ixq6q3wVynQNyZE6uoQUTspFAr0jg5B7+gQzLjxalSaG5F3qBLfHq7EtmOncaahyTGm6adTAIDe0cG4tld3jOgZgbSkcI5HJFljSJK58CA1SmoacLreKnVVXOxu6WrrFxMKjR+nFBP5iqhQLcanJWB8WgKabHbsKTViy5EqfHe0Gj+dNOJIRR2OVNThrR+KoVAAffWhSO8ZjvTkCKQnh6NbkPe0eBNdDkOSzEW0/INzxstCEgdtE/k+f5USw5LCMSwpHDm39oGxwYofjlVj67HTKCg+jV+q6nHglBkHTpmxbOtxAECKPgTXJHbD0PgwDE3ohp6RQV436YTIiSFJ5pz/K/O2liSutE3U9YQFqnHnoFjcOSgWAFBpbkRBcQ0Kik/jx19qcKyyDocMtThkqMWKghIAQKjWD0MSnKEpDEPju7GLjrwGQ5LMeWNLUpPNjn3ljkHbbEki6rqiQrW4a3As7hrsCE3VdRbsKK5BUakRu0uM+KnMCHNjM7YcqcKWI1XieT27B2FgDx36xYSif6wO/WND2U1HkmBIkjnnPxw1XhSSjlTUorHJjhCtH3pGBkldHSLyEpHBGtw+MAa3D4wB4PgP1aFTtdhdega7S4zYXXIGx0834JeqevxSVY/PisrFc2N1WvRrCUz9Y0PRLzYUPcICuHE2eRRDksyFe2F3W9F5XW0ca0BEF+OvUmJgnA4D43SYmOE4VlNvxZ5SI/aXm7C/3DGe6cTpBpSbGlFuasTGgxXi+cEaP1wdFYze0cHoFRWCXtHB6BUdglidluGJOgRDksyJ3W0N3hOSzg3a5iKSRNQ24UFq3JgShRtTosRj5sYmHCw3Y3/L68ApM45W1KLO0oyiUqP4HzOnILUKV0eHoFdUMJIjg5AcGYTEiEAkRgQhmNurUBvwT4vMiQO367wpJLWMR+KgbSLqAKFaf6T3jEB6zwjxmLXZjuOn63G0og5HKmpxtLIWRyvqUFxdj3qrTVzw8n91D9EgqSUwOcNTUoTj1xAtB4yTK4YkmfO2lqQ6SzOOVNYCAIZw0DYReYjaTykucjkaMeJxa7MdJ07X40hFHY5W1uLE6QYUV9fjxOl6nGloQlWtBVW1Fuw4fuaCa+oC/NEjLACxYQGI6xYg/r5HtwDEhmnRPVjDbrwuxmMhqaamBo8++ii++OILKJVKjB07Fi+//DKCg4Mvek5jYyOeeOIJfPDBB7BYLMjKysJrr72G6OhosUxJSQmmT5+OzZs3Izg4GJMmTUJubi78/Bzfypo1a/D666+jqKgIFosF/fv3x7x585CVlSVeY968eXjuuedc7t2nTx8cOnSog38Knucck9RgtaGxyQatv7QLN+4rM0EQgBidlptgElGnU/sp0Ss6BL2iQ4DzwhMAmBqacKKmviU0NeD46ZZfq+txut4K09kmmM424cAp80Wv7QhOWkSHaNE9VIPoEC2iQjWIDtUiKkSDqBAtAtRcQNdXeCwkTZgwAadOncKGDRvQ1NSEyZMnY+rUqVixYsVFz3n88cfx5ZdfYvXq1dDpdHjkkUdwzz33YOvWrQAAm82G0aNHQ6/XY9u2bTh16hQmTpwIf39/vPDCCwCALVu24JZbbsELL7yAsLAwLFu2DHfddRcKCgowdOhQ8V79+/fHxo0bxa+dIUtugjV+8Fcp0GQTcLreih5hAZLWx9m8zVYkIvI2ukB/DAoMwyA3QwFqG5tQbmxEmbEBZWfOoszYiDLjWZSdaUC5sREVtY2wNttRXO0IWZcSovVDVMi54BQdqkVksAbhQWqEB6sRHqhGeJAaEcFqBKrl+dnTVSgEQRA6+qIHDx5Ev379sGPHDgwbNgwAsH79etxxxx04efIkYmNjLzjHZDKhe/fuWLFiBe69914AwKFDh9C3b1/k5+djxIgR+Oqrr3DnnXeivLxcbF1asmQJnn76aVRVVUGtdr+ORv/+/TFu3DjMmTMHgKMl6dNPP0VRUVGrvyeLxQKLxSJ+bTabER8fD5PJhNDQ0FZfxxPSX9iICrMFax8dhQE9pB0sPf2/hfhqnwHP3J6CP1x/laR1ISLqKNZmOwwmR3AqN55FZa0FlbWNqDQ7fq1o+bWxyd6m62r9lY7QFKxGeJAG4YH+CA/SICJYDV2AP0ID/KFreYVq/cRj/iqlh75T32c2m6HT6Vr1+e2RCJufn4+wsDAxIAFAZmYmlEolCgoKcPfdd19wTmFhIZqampCZmSkeS0lJQUJCghiS8vPzMXDgQJfut6ysLEyfPh379+93aSlystvtqK2tRXh4uMvxo0ePIjY2FlqtFhkZGcjNzUVCQsJFv6fc3NwLuui8RbdANSrMFq9YBoArbRORL1L7KZEQEYiEiMCLlhEEAbWWZlSaneHJggpzIyprLaius6Cm3iq+TtdbYW22o7HJLi5v0BaBatV54elcmAoN8EOo1h/BGj8EafwQpFGJvz//WJDaD4FqFcdYXYZHQpLBYEBUVJTLMT8/P4SHh8NgMFz0HLVajbCwMJfj0dHR4jkGg8ElIDnfd77nzosvvoi6ujrcd9994rH09HS888476NOnD06dOoXnnnsO1157Lfbt24eQEPc71s+aNQs5OTni186WJG8QEewdq25Xmh1/0RUKYGAcp/8TUdeiUCgcgUXrj6uj3H+WOAmCgHqrDWdaAlNNvQU19U2oqXf8h7emzjFGytzYBNPZZpjPNsF8tgm1lmYAjnGoDVYbTrUxXLnWFwhSt4QmZ4hSO4JUgFqFAH8lAvxV0KpV0PqpWo45XpqW9wLUKmhbjmmdX/spW35VyX6tvDaFpGeeeQb//Oc/L1nm4MGDV1ShjrRixQo899xz+Oyzz1xC2+233y7+ftCgQUhPT0diYiI+/PBDTJkyxe21NBoNNBqNx+vcHuFBjnpJ3ZK056Rj6n+vqGCuRUJEdAkKhQLBLcEkPvzirVP/q9lmR21jc0t4aoL5bLM44Nx5rLaxCfUWG+oszahveTl+b3P83toMQQAEwTEjuc7SDMBy2Xu3h79KAY2fCmo/JdQqJTT+jl/Vfkpo/By/qv1U4u81/1NG66/CE7f28UjdWqNNn2RPPPEEHnzwwUuW6dmzJ/R6PSorK12ONzc3o6amBnq93u15er0eVqsVRqPRpTWpoqJCPEev12P79u0u51VUVIjvne+DDz7AQw89hNWrV7t04bkTFhaG3r1749ixY5cs563CWzaDrKn3zB/y1mJXGxGRZ/mplOgWpL6ivewEQcDZJptrcPqfMHW2yTFjurHJhrNW59d2x9cux2wuxxqb7bA2nxuX1WQT0GRrbncGU/sp5ROSunfvju7du1+2XEZGBoxGIwoLC5GamgoA2LRpE+x2O9LT092ek5qaCn9/f+Tl5WHs2LEAgMOHD6OkpAQZGRnidf/+97+jsrJSbBnasGEDQkND0a9fP/FaK1euxO9//3t88MEHGD169GXrW1dXh59//hkPPPDAZct6I2dLUk19k6T12HPSCAAYkhAmaT2IiOjiFAoFAtV+jpl1l+4VbBebXRCDk7XZDktLcHL8vuWYzQ5Lkx1Wm+txl/K2tg2C9wSP9In07dsXt912Gx5++GEsWbIETU1NeOSRR3D//feLM9vKyspw8803491330VaWhp0Oh2mTJmCnJwchIeHIzQ0FI8++igyMjIwYsQIAMCtt96Kfv364YEHHsD8+fNhMBgwe/ZszJgxQ+wKW7FiBSZNmoSXX34Z6enp4lilgIAA6HSOcTJ//vOfcddddyExMRHl5eWYO3cuVCoVxo8f74kfh8eFB0nfkmS3Cy57thERUdekUipaBojLf9iFx+YQvv/++0hJScHNN9+MO+64A6NGjcLSpUvF95uamnD48GE0NDSIx1566SXceeedGDt2LK677jro9XqsWbNGfF+lUmHt2rVQqVTIyMjA7373O0ycOBHPP/+8WGbp0qVobm7GjBkzEBMTI75mzpwpljl58iTGjx+PPn364L777kNERAR+/PHHVrWSeaOI4JYxSRJuTVJ8uh61jc3Q+CnRR++B/5oQERF1Mo+sk9QVtGWdBU/bebwG9y7JR0J4ILY8daMkdViz6yRyPtyD1MRu+Hj6SEnqQEREdDlt+fzmalQ+ICrEsf1HhbkRUmVeDtomIiJfw5DkA6JCHd1tlmY7zI3NktRBHI8Uz/WRiIjINzAk+QCtvwqhWscAuara9i8s1l6NTTZxQ8hrErp1+v2JiIg8gSHJR0SFOrrcKs2dP8Ntf7kZTTYBEUFqxHWTdoNdIiKijsKQ5COiQhxdbhUStCQ5u9qGJoRxHyAiIvIZDEk+whmSpGhJ2l1yBgAwJD6s0+9NRETkKQxJPiLa2d1WK0VIMgIAhnI8EhER+RCGJB/R3dmS1MkhqbK2EWXGs1AogEFxnNlGRES+gyHJR5wbuN25Y5KKWlqRekUFI0Tr36n3JiIi8iSGJB8RJVFLkjhoO55dbURE5FsYknzEuYHbnduS5ByPNCQhrFPvS0RE5GkMST7C2d1Wb7Wh3tI5q27b7AJ+OmkE4Jj+T0RE5EsYknxEsMYPQWoVgM7rcjtaWYt6qw1BahV6RYV0yj2JiIg6C0OSD3G2JlV0Upebc9D2oLgwqJRcRJKIiHwLQ5IP6exlAM6tjxTWKfcjIiLqTAxJPqSzB287Z7ZxpW0iIvJFDEk+pDNX3a5tbMKRyloAnNlGRES+iSHJh8SGBQAAys6c9fi9fjppgiAAPcICEBWi9fj9iIiIOhtDkg+J6+YISSeNng9JhSccm9pyPBIREfkqhiQf0kNsSWrw+L12HK8BAKQlh3v8XkRERFJgSPIh8d0CAQDVdVactdo8dp9mmx27WlqShiUyJBERkW9iSPIhoQF+CNb4AQDKPNjldsjgWEQyROuHPnouIklERL6JIcmHKBSKc+OSPNjltr3Y0dWWmtiNi0gSEZHPYkjyMc6Q5MmWpJ0nHCFpeBK72oiIyHcxJPkY5+Dtkx5aBkAQBGwvdoxHYkgiIiJfxpDkY+JaBm97KiSdON2A6joL1ColBsXpPHIPIiIib8CQ5GN6dPPsMgDOqf+D4nTQ+qs8cg8iIiJvwJDkY84N3PZMS5IzJA1jVxsREfk4hiQf4+xuq6y1wNLc8Wsl7TzuGI+Ultytw69NRETkTRiSfEy3QH8EtHSDlRsbO/TaVbUW/FJdDwBITWBLEhER+TaGJB9z/lpJHb3RbWHL1P8+0SHQBfp36LWJiIi8DUOSD4oPd3S5HT9d36HXFaf+s6uNiIi6AIYkH3RV9yAAwM9VdR16XS4iSUREXQlDkg+6qnswAOBYZceFpNrGJuwvNwPgzDYiIuoaGJJ80NVRjpD0cweGpIJfamCzC0iMCBRX9SYiIvJlDEk+yNmSVG5qRL2luUOuufXnagDAyKsiO+R6RERE3o4hyQd1C1IjIkgNAPilqmMGb287dhoAMOpqhiQiIuoaPBaSampqMGHCBISGhiIsLAxTpkxBXd2lu38aGxsxY8YMREREIDg4GGPHjkVFRYVLmZKSEowePRqBgYGIiorCk08+iebmc60l3377LRQKxQUvg8Hgcp3FixcjKSkJWq0W6enp2L59e8d9817gqpYut2NVtVd8rapaCw5XOK6TcVXEFV+PiIhIDjwWkiZMmID9+/djw4YNWLt2LbZs2YKpU6de8pzHH38cX3zxBVavXo3vvvsO5eXluOeee8T3bTYbRo8eDavVim3btmH58uV45513MGfOnAuudfjwYZw6dUp8RUVFie+tWrUKOTk5mDt3Lnbt2oXBgwcjKysLlZWVHfcDkJhzXFJHDN7e1tLV1i8mFOEtLVREREQ+T/CAAwcOCACEHTt2iMe++uorQaFQCGVlZW7PMRqNgr+/v7B69Wrx2MGDBwUAQn5+viAIgrBu3TpBqVQKBoNBLPP6668LoaGhgsViEQRBEDZv3iwAEM6cOXPR+qWlpQkzZswQv7bZbEJsbKyQm5vb6u/RZDIJAASTydTqczrTm9//IiQ+vVaY9u7OK77Wk6uLhMSn1wp///JAB9SMiIhIOm35/PZIS1J+fj7CwsIwbNgw8VhmZiaUSiUKCgrcnlNYWIimpiZkZmaKx1JSUpCQkID8/HzxugMHDkR0dLRYJisrC2azGfv373e53pAhQxATE4NbbrkFW7duFY9brVYUFha63EepVCIzM1O8jzsWiwVms9nl5c3ElqQrXCtJEAT8cNQ5aJtdbURE1HV4JCQZDAaX7i0A8PPzQ3h4+AVjg84/R61WIywszOV4dHS0eI7BYHAJSM73ne8BQExMDJYsWYKPP/4YH3/8MeLj43HDDTdg165dAIDq6mrYbDa317lY3QAgNzcXOp1OfMXHx1/mpyAtZ0g6Xl2PJpu93dc5XFGLclMjtP5KjOjJkERERF1Hm0LSM88843ZQ9PmvQ4cOeaqurdKnTx9MmzYNqampGDlyJN5++22MHDkSL7300hVdd9asWTCZTOKrtLS0g2rsGTGhWgSqVWi2Czhe3f4ZbpsOOcZpjbwqEtqWjXOJiIi6Ar+2FH7iiSfw4IMPXrJMz549odfrLxgE3dzcjJqaGuj1erfn6fV6WK1WGI1Gl9akiooK8Ry9Xn/BLDTn7LeLXRcA0tLS8MMPPwAAIiMjoVKpLpg1d/593NFoNNBoNBd939solQr0iwnFzhNnsOekCb2iQ9p1nW8PVQEAbuzTvSOrR0RE5PXa1JLUvXt3pKSkXPKlVquRkZEBo9GIwsJC8dxNmzbBbrcjPT3d7bVTU1Ph7++PvLw88djhw4dRUlKCjIwMAEBGRgb27t3rEsA2bNiA0NBQ9OvX76L1LioqQkxMDABArVYjNTXV5T52ux15eXnifXzFkPgwAEBR6Zl2nW9qaEJhiePcG/pEXaY0ERGRb2lTS1Jr9e3bF7fddhsefvhhLFmyBE1NTXjkkUdw//33IzY2FgBQVlaGm2++Ge+++y7S0tKg0+kwZcoU5OTkIDw8HKGhoXj00UeRkZGBESNGAABuvfVW9OvXDw888ADmz58Pg8GA2bNnY8aMGWIrz6JFi5CcnIz+/fujsbERb775JjZt2oRvvvlGrF9OTg4mTZqEYcOGIS0tDYsWLUJ9fT0mT57siR+HZIYkhAEAikqN7Tp/y9Eq2OwCekUFIz48sOMqRkREJAMeCUkA8P777+ORRx7BzTffDKVSibFjx+KVV14R329qasLhw4fR0NAgHnvppZfEshaLBVlZWXjttdfE91UqFdauXYvp06cjIyMDQUFBmDRpEp5//nmxjNVqxRNPPIGysjIEBgZi0KBB2LhxI2688UaxzLhx41BVVYU5c+bAYDBgyJAhWL9+/QWDueXO2ZJ06FQtGptsbR5TtPGgo0vyphS2IhERUdejEARBkLoScmQ2m6HT6WAymRAaGip1ddwSBAHD/56H6joLPvpDBoYlhbf63MYmG1L/ugH1VhvW/HEkrkno5sGaEhERdY62fH5z7zYfplAozhuXZGzTud8dqUK91YYeYQEY2nINIiKiroQhyccNbee4pC9/OgUAuGOgHgqFooNrRURE5P0YknycsyVpd4mx1ec0NtnE8UijB8V6oFZERETejyHJxw2OD4O/SoEy41n83MotSjYdqkRDS1fb4Didh2tIRETknRiSfFywxk/cTmTDgYrLlHZYub0EADBmSCy72oiIqMtiSOoCbu3vWEn8m/0X35vO6Xh1Pb4/Wg2FAvhtWoKnq0ZEROS1GJK6gFv6OtZ/2l1qRGVt4yXLrmhpRbq+d3cuIElERF0aQ1IXoNdpMThOB0EA8g5WXrRcY5MNH+50bNz7wIjEzqoeERGRV2JI6iKcXW7Oqf3uvJd/AsaGJvQIC+BebURE1OUxJHURdw2KhUqpwA/HqrGr5MINb8/UW/HqpqMAgJk394JKyQHbRETUtTEkdREJEYG495o4AMCLXx++4P1XNh2FubEZKfoQjE2N6+zqEREReR2GpC7k0Zuvhr9KgW0/n8a3h8+NTVq39xSWbzsOAJg9uh9bkYiIiMCQ1KXEdQvE+JZp/dPeK8S7+cfx5ve/4E8rd8MuAPcPj8eoXpES15KIiMg7+EldAepcT92WgtKaBmw+XIU5n+0Xj981OBZ/v3ughDUjIiLyLgxJXUywxg9vThqOVzcdxedF5ejRLQAjekZg6nU92c1GRER0HoUgCILUlZAjs9kMnU4Hk8mE0NBQqatDRERErdCWz2+OSSIiIiJygyGJiIiIyA2GJCIiIiI3GJKIiIiI3GBIIiIiInKDIYmIiIjIDYYkIiIiIjcYkoiIiIjcYEgiIiIicoMhiYiIiMgNhiQiIiIiNxiSiIiIiNxgSCIiIiJygyGJiIiIyA0/qSsgV4IgAADMZrPENSEiIqLWcn5uOz/HL4UhqZ1qa2sBAPHx8RLXhIiIiNqqtrYWOp3ukmUUQmuiFF3AbrejvLwcISEhUCgULu8NHz4cO3bsuOCc1h43m82Ij49HaWkpQkNDO77ybXCxOnf29dpyXmvKXqpMe97jM+zY8y5X9kred/cen1/Hnnelfwcv9T7/DsrjGXrzv6OCIKC2thaxsbFQKi896ogtSe2kVCoRFxfn9j2VSuX2gbb1eGhoqOR/uS9Wt86+XlvOa03ZS5Vpz3t8hh173uXKXsn77t7j8+vY86707+Cl3uffQXk8Q2//d/RyLUhOHLjtATNmzOiQ496go+vW3uu15bzWlL1Umfa8x2fYseddruyVvO/uPT6/jj3vSv8OXup9/h2UxzP0lX9H2d3mhcxmM3Q6HUwmk+T/A6L24TOUNz4/+eMzlD9veIZsSfJCGo0Gc+fOhUajkboq1E58hvLG5yd/fIby5w3PkC1JRERERG6wJYmIiIjIDYYkIiIiIjcYkoiIiIjcYEgiIiIicoMhiYiIiMgNhiSZWbt2Lfr06YNevXrhzTfflLo61A533303unXrhnvvvVfqqlA7lJaW4oYbbkC/fv0waNAgrF69WuoqURsZjUYMGzYMQ4YMwYABA/DGG29IXSVqh4aGBiQmJuLPf/6zx+7BJQBkpLm5Gf369cPmzZuh0+mQmpqKbdu2ISIiQuqqURt8++23qK2txfLly/HRRx9JXR1qo1OnTqGiogJDhgyBwWBAamoqjhw5gqCgIKmrRq1ks9lgsVgQGBiI+vp6DBgwADt37uS/pTLzf//3fzh27Bji4+Px4osveuQebEmSke3bt6N///7o0aMHgoODcfvtt+Obb76RulrURjfccANCQkKkrga1U0xMDIYMGQIA0Ov1iIyMRE1NjbSVojZRqVQIDAwEAFgsFgiCALYXyMvRo0dx6NAh3H777R69D0NSJ9qyZQvuuusuxMbGQqFQ4NNPP72gzOLFi5GUlAStVov09HRs375dfK+8vBw9evQQv+7RowfKyso6o+rU4kqfIUmvI59hYWEhbDYb4uPjPVxrOl9HPEOj0YjBgwcjLi4OTz75JCIjIzup9tQRz+/Pf/4zcnNzPV5XhqROVF9fj8GDB2Px4sVu31+1ahVycnIwd+5c7Nq1C4MHD0ZWVhYqKys7uaZ0MXyG8tdRz7CmpgYTJ07E0qVLO6PadJ6OeIZhYWHYs2cPiouLsWLFClRUVHRW9bu8K31+n332GXr37o3evXt7vrICSQKA8Mknn7gcS0tLE2bMmCF+bbPZhNjYWCE3N1cQBEHYunWrkJ2dLb4/c+ZM4f333++U+tKF2vMMnTZv3iyMHTu2M6pJl9DeZ9jY2Chce+21wrvvvttZVaWLuJK/h07Tp08XVq9e7clq0kW05/k988wzQlxcnJCYmChEREQIoaGhwnPPPeeR+rElyUtYrVYUFhYiMzNTPKZUKpGZmYn8/HwAQFpaGvbt24eysjLU1dXhq6++QlZWllRVpv/RmmdI3q01z1AQBDz44IO46aab8MADD0hVVbqI1jzDiooK1NbWAgBMJhO2bNmCPn36SFJfctWa55ebm4vS0lIcP34cL774Ih5++GHMmTPHI/Xx88hVqc2qq6ths9kQHR3tcjw6OhqHDh0CAPj5+WHhwoW48cYbYbfb8dRTT3E2hhdpzTMEgMzMTOzZswf19fWIi4vD6tWrkZGR0dnVJTda8wy3bt2KVatWYdCgQeJYivfeew8DBw7s7OqSG615hidOnMDUqVPFAduPPvoon5+XaO2/o52FIUlmxowZgzFjxkhdDboCGzdulLoKdAVGjRoFu90udTXoCqSlpaGoqEjqalAHePDBBz16fXa3eYnIyEioVKoLBg9WVFRAr9dLVCtqCz5D+eMzlD8+Q3nztufHkOQl1Go1UlNTkZeXJx6z2+3Iy8tjV4xM8BnKH5+h/PEZypu3PT92t3Wiuro6HDt2TPy6uLgYRUVFCA8PR0JCAnJycjBp0iQMGzYMaWlpWLRoEerr6zF58mQJa03n4zOUPz5D+eMzlDdZPT+PzJkjtzZv3iwAuOA1adIkscyrr74qJCQkCGq1WkhLSxN+/PFH6SpMF+AzlD8+Q/njM5Q3OT0/7t1GRERE5AbHJBERERG5wZBERERE5AZDEhEREZEbDElEREREbjAkEREREbnBkERERETkBkMSERERkRsMSURERERuMCQRERERucGQREREROQGQxIRERGRGwxJRERERG78P0M6ASPwDrXdAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.003 0.007 0.014]\n", + "Roughness : 0.000164\n", + "diameter : 0.820210\n", + "resistance coeff : 222.481950 \n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.003 0.007 0.014]\n", + "-0.6520014880299684 1.5466109022548316 3.062949682690347\n", + "-0.6520014880299684 1.5466109022548316 3.062949682690347\n" + ] + } + ], + "source": [ + "from wntr_quantum.scenario.network_qubo import Network\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 9\n", + "step = (0.25/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", + "\n", + "nqbit = 9\n", + "step = (250/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", + "\n", + "net = Network(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + } + ], + "source": [ + "ref_sol = net.classical_solutions()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[ 0. ],\n", + " [ 1.766],\n", + " [99.077],\n", + " [ 0.652]]),\n", + " array([[-1. , 1. , 0. , 0. ],\n", + " [ 0. , -1. , 0. , 0. ],\n", + " [-1.547, 0. , -1. , 0. ],\n", + " [ 0. , -1.547, 1. , -1. ]]),\n", + " array([[[ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ]],\n", + " \n", + " [[ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ]],\n", + " \n", + " [[-3.063, 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ]],\n", + " \n", + " [[ 0. , 0. , 0. , 0. ],\n", + " [ 0. , -3.063, 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ]]]))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.matrices" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 26.456, 22.911])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# from wntr.epanet.util import to_si, FlowUnits, HydParam, from_si\n", + "# ref_sol[0] = to_si(FlowUnits.CFS, ref_sol[0], HydParam.Flow)\n", + "# ref_sol[1] = to_si(FlowUnits.CFS, ref_sol[1], HydParam.Flow)\n", + "# ref_sol[2] = to_si(FlowUnits.CFS, ref_sol[2], HydParam.Length)\n", + "# ref_sol[3] = to_si(FlowUnits.CFS, ref_sol[3], HydParam.Length)\n", + "ref_sol" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1. , 1. , 0.999, 0.998])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_sol/ref_values" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.7657333355755793" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from_si(FlowUnits.CFS, 0.05, HydParam.Flow)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + 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junctions. - - Parameters - ---------- - m: wntr.aml.aml.aml.Model - wn: wntr.network.model.WaterNetworkModel - updater: ModelUpdater - index_over: list of str - list of junction names; default is all junctions in wn - """ - P0, P1, P2 = matrices - - continuous_var_name = [v.name for v in list(m.vars())] - discrete_var_name = [v.name for k, v in m.cm_resistance.items()] - var_names = continuous_var_name + discrete_var_name - - index_over = wn.junction_name_list - - for ieq, node_name in enumerate(index_over): - - node = wn.get_node(node_name) - if not node._is_isolated: - P0[ieq, 0] += m.expected_demand[node_name].value - - for link_name in wn.get_links_for_node(node_name, flag="INLET"): - node_index = var_names.index(m.flow[link_name].name) - P1[ieq, node_index] -= 1 - - for link_name in wn.get_links_for_node(node_name, flag="OUTLET"): - node_index = var_names.index(m.flow[link_name].name) - P1[ieq, node_index] += 1 - - return P0, P1, P2 - - def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 """Adds a mass balance to the model for the specified junctions. @@ -241,42 +205,6 @@ def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 return (P0, P1, P2) -def get_mass_balance_constraint_design(m, wn, matrices): # noqa: D417 - """Adds a mass balance to the model for the specified junctions. - - Parameters - ---------- - m: wntr.aml.aml.aml.Model - wn: wntr.network.model.WaterNetworkModel - updater: ModelUpdater - index_over: list of str - list of junction names; default is all junctions in wn - """ - P0, P1, P2, P3 = matrices - - continuous_var_name = [v.name for v in list(m.vars())] - discrete_var_name = [v.name for k, v in m.cm_resistance.items()] - var_names = continuous_var_name + discrete_var_name - - index_over = wn.junction_name_list - - for ieq, node_name in enumerate(index_over): - - node = wn.get_node(node_name) - if not node._is_isolated: - P0[ieq, 0] += m.expected_demand[node_name].value - - for link_name in wn.get_links_for_node(node_name, flag="INLET"): - node_index = var_names.index(m.flow[link_name].name) - P1[ieq, node_index] -= 1 - - for link_name in wn.get_links_for_node(node_name, flag="OUTLET"): - node_index = var_names.index(m.flow[link_name].name) - P1[ieq, node_index] += 1 - - return P0, P1, P2, P3 - - def get_chezy_manning_matrix_design(m, wn, matrices): # noqa: D417 """Adds a mass balance to the model for the specified junctions. diff --git a/wntr_quantum/scenario/darcy_weisbach.py b/wntr_quantum/scenario/darcy_weisbach.py new file mode 100644 index 0000000..67cf78c --- /dev/null +++ b/wntr_quantum/scenario/darcy_weisbach.py @@ -0,0 +1,238 @@ +import wntr +from wntr.epanet.util import FlowUnits +from wntr.epanet.util import PressureUnits +from wntr.epanet.util import HydParam +from wntr.epanet.util import from_si +from wntr.network import LinkStatus +from wntr.sim import aml +from wntr.sim.models.utils import Definition +from .darcy_weisbach_fit import dw_fit + + +def darcy_weisbach_constants(m): + """Add darcy weisbach constants to the model. + + Args: + m (_type_): _description_ + """ + m.dw_k = 0.025173 # 16/64.4/pi^2 + m.dw_exp = 2 + m.dw_diameter_exp = -5 + + +def dw_resistance_prefactor(k, roughness, diameter, diameter_exp): + """_summary_. + + Args: + k (_type_): _description_ + roughness (_type_): _description_ + exp (_type_): _description_ + diameter (_type_): _description_ + diameter_exp (_type_): _description_ + """ + return ( + k + * (diameter**diameter_exp) + * dw_fit(roughness, diameter, convert_to_us_unit=False) + ) + + +def dw_resistance_value(k, roughness, diameter, diameter_exp, length): + """_summary_. + + Args: + k (_type_): _description_ + roughness (_type_): _description_ + exp (_type_): _description_ + diameter (_type_): _description_ + diameter_exp (_type_): _description_ + length (_type_): _description_ + + Returns: + _type_: _description_ + """ + print("Roughness : %f" % roughness) + print("diameter : %f" % diameter) + print("resistance coeff : %f " % (k * (diameter**diameter_exp) * length)) + return dw_resistance_prefactor(k, roughness, diameter, diameter_exp) * length + + +class dw_resistance_param(Definition): # noqa: D101 + @classmethod + def build(cls, m, wn, updater, index_over=None): # noqa: D417 + """Add a CM resistance coefficient parameter to the model. + + Parameters + ---------- + m: wntr.aml.aml.aml.Model + wn: wntr.network.model.WaterNetworkModel + updater: ModelUpdater + index_over: list of str + list of pipe names + """ + if not hasattr(m, "dw_resistance_0"): + m.dw_resistance_0 = aml.ParamDict() + if not hasattr(m, "dw_resistance_1"): + m.dw_resistance_1 = aml.ParamDict() + if not hasattr(m, "dw_resistance_2"): + m.dw_resistance_2 = aml.ParamDict() + + if index_over is None: + index_over = wn.pipe_name_list + + for link_name in index_over: + link = wn.get_link(link_name) + + # convert values from SI to epanet internal + roughness_us = 0.001 * from_si( + FlowUnits.CFS, link.roughness, HydParam.Length + ) + diameter_us = from_si(FlowUnits.CFS, link.diameter, HydParam.Length) + length_us = from_si(FlowUnits.CFS, link.length, HydParam.Length) + + # compute the resistance value fit coefficients + value = dw_resistance_value( + m.dw_k, + roughness_us, + diameter_us, + m.dw_diameter_exp, + length_us, + ) + if link_name in m.dw_resistance_0: + m.dw_resistance_0[link_name].value = value[0] + else: + m.dw_resistance_0[link_name] = aml.Param(value[0]) + + if link_name in m.dw_resistance_1: + m.dw_resistance_1[link_name].value = value[1] + else: + m.dw_resistance_1[link_name] = aml.Param(value[1]) + + if link_name in m.dw_resistance_2: + m.dw_resistance_2[link_name].value = value[2] + else: + m.dw_resistance_2[link_name] = aml.Param(value[2]) + + updater.add(link, "roughness", dw_resistance_param.update) + updater.add(link, "diameter", dw_resistance_param.update) + updater.add(link, "length", dw_resistance_param.update) + + +class approx_darcy_weisbach_headloss_constraint(Definition): # noqa: D101 + @classmethod + def build(cls, m, wn, updater, index_over=None): # noqa: D417 + """Adds a mass balance to the model for the specified junctions. + + Parameters + ---------- + m: wntr.aml.aml.aml.Model + wn: wntr.network.model.WaterNetworkModel + updater: ModelUpdater + index_over: list of str + list of pipe names; default is all pipes in wn + """ + if not hasattr(m, "approx_darcy_weisbach_headloss"): + m.approx_darcy_wesibach_headloss = aml.ConstraintDict() + + if index_over is None: + index_over = wn.pipe_name_list + + for link_name in index_over: + if link_name in m.approx_darcy_wesibach_headloss: + del m.approx_darcy_wesibach_headloss[link_name] + + link = wn.get_link(link_name) + f = m.flow[link_name] + status = link.status + + if status == LinkStatus.Closed or link._is_isolated: + con = aml.Constraint(f) + else: + start_node_name = link.start_node_name + end_node_name = link.end_node_name + start_node = wn.get_node(start_node_name) + end_node = wn.get_node(end_node_name) + if isinstance(start_node, wntr.network.Junction): + start_h = m.head[start_node_name] + else: + start_h = m.source_head[start_node_name] + if isinstance(end_node, wntr.network.Junction): + end_h = m.head[end_node_name] + else: + end_h = m.source_head[end_node_name] + k0 = m.dw_resistance_0[link_name] + k1 = m.dw_resistance_1[link_name] + k2 = m.dw_resistance_2[link_name] + + con = aml.Constraint(expr=-k0 - k1 * f - k2 * f**2 + start_h - end_h) + + m.approx_darcy_wesibach_headloss[link_name] = con + + updater.add( + link, "status", approx_darcy_weisbach_headloss_constraint.update + ) + updater.add( + link, "_is_isolated", approx_darcy_weisbach_headloss_constraint.update + ) + + +def get_darcy_weisbach_matrix(m, wn, matrices): # noqa: D417 + """Adds a mass balance to the model for the specified junctions. + + Parameters + ---------- + m: wntr.aml.aml.aml.Model + wn: wntr.network.model.WaterNetworkModel + updater: ModelUpdater + index_over: list of str + list of pipe names; default is all pipes in wn + """ + P0, P1, P2 = matrices + + continuous_var_name = [v.name for v in list(m.vars())] + # discrete_var_name = [v.name for k, v in m.dw_resistance.items()] + + var_names = continuous_var_name # + discrete_var_name + + index_over = wn.pipe_name_list + + for ieq0, link_name in enumerate(index_over): + + ieq = ieq0 + len(wn.junction_name_list) + link = wn.get_link(link_name) + f = m.flow[link_name] + flow_index = var_names.index(f.name) + + start_node_name = link.start_node_name + end_node_name = link.end_node_name + + start_node = wn.get_node(start_node_name) + end_node = wn.get_node(end_node_name) + + if isinstance(start_node, wntr.network.Junction): + start_h = m.head[start_node_name] + start_node_index = var_names.index(start_h.name) + P1[ieq, start_node_index] = 1 + else: + start_h = m.source_head[start_node_name] + P0[ieq, 0] += from_si(FlowUnits.CFS, start_h.value, HydParam.Length) + + if isinstance(end_node, wntr.network.Junction): + end_h = m.head[end_node_name] + end_node_index = var_names.index(end_h.name) + P1[ieq, end_node_index] = -1 + else: + end_h = m.source_head[end_node_name] + P0[ieq, 0] -= from_si(FlowUnits.CFS, end_h.value, HydParam.Length) + + k0 = m.dw_resistance_0[link_name] + k1 = m.dw_resistance_1[link_name] + k2 = m.dw_resistance_2[link_name] + print(k0.value, k1.value, k2.value) + + scaling = 1.0 + P0[ieq] -= scaling * k0.value + P1[ieq, flow_index] -= scaling * k1.value + P2[ieq, flow_index, flow_index] -= scaling * k2.value + + return (P0, P1, P2) diff --git a/wntr_quantum/scenario/darcy_weisbach_fit.py b/wntr_quantum/scenario/darcy_weisbach_fit.py new file mode 100644 index 0000000..96fe968 --- /dev/null +++ b/wntr_quantum/scenario/darcy_weisbach_fit.py @@ -0,0 +1,107 @@ +import matplotlib.pyplot as plt +import numpy as np +from wntr.epanet.util import FlowUnits +from wntr.epanet.util import HydParam +from wntr.epanet.util import from_si + + +def friction_factor(q, e, s): # noqa: D417 + """Computes the ground truth for the friction factor. + + Args: + q = |pipe flow| + e = pipe roughness / diameter + s = viscosity * pipe diameter + """ + A1 = 3.14159265358979323850e03 + A2 = 1.57079632679489661930e03 + A8 = 4.61841319859066668690e00 + A9 = -8.68588963806503655300e-01 + AB = 3.28895476345399058690e-03 + AC = -5.14214965799093883760e-03 + + w = q / s + + # if w >= A1: + y1 = A8 / pow(w, 0.9) + y2 = e / 3.7 + y1 + y3 = A9 * np.log(y2) + f = 1.0 / (y3 * y3) + # else: + # y2 = e / 3.7 + AB + # y3 = A9 * np.log(y2) + # fa = 1.0 / (y3 * y3) + # fb = (2.0 + AC / (y2 * y3)) * fa + # r = w / A2 + # x1 = 7.0 * fa - fb + # x2 = 0.128 - 17.0 * fa + 2.5 * fb + # x3 = -0.128 + 13.0 * fa - (fb + fb) + # x4 = 0.032 - 3.0 * fa + 0.5 * fb + # f = x1 + r * (x2 + r * (x3 + r * x4)) + + return f + + +def dw_fit(roughness, diameter, plot=True, convert_to_us_unit=False): + """_summary. + + Args: + roughness (float): roughness pf the pipe in meter + diameter (float): diamter of the pipe in meter + plot(bool): plot the solution for visual inspection + convert_to_us_unit(bool): convert to us unit + """ + + def convert_to_USunit(roughness, diameter): + """Converts roughness and diameter to US units.""" + diameter_us = from_si(FlowUnits.CFS, diameter, HydParam.Length) + roughness_us = 0.001 * from_si(FlowUnits.CFS, roughness, HydParam.Length) + return roughness_us, diameter_us + + N = 250 + Q = np.logspace(0, 4, num=N) + if convert_to_us_unit: + roughness, diameter = convert_to_USunit(roughness, diameter) + viscosity = 0.000011 + e = roughness / diameter + s = viscosity * diameter + + factors = np.zeros(N) + for iq, q in enumerate(Q): + factors[iq] = friction_factor(q, e, s) + + res = np.polyfit(1 / Q, factors, 2) + + if plot: + approx = np.poly1d(res) + plt.loglog(Q, approx(1 / Q)) + plt.loglog(Q, factors) + plt.loglog(Q, res[0] * (1 / Q) ** 2 + res[1] * 1 / Q + res[2]) + plt.show() + + plt.semilogx(Q, 1 - np.abs((approx(1 / Q)) / factors)) + plt.show() + + print(res) + + return np.array(res) + + +def evlaluate_fit(coeffs, flow): + """Evaluate the fit. + + Args: + coeffs (_type_): _description_ + flow (_type_): _description_ + + Returns: + _type_: _description_ + """ + return coeffs[0] * (1 / flow) ** 2 + coeffs[1] * 1 / flow + coeffs[2] + + +if __name__ == "__main__": + res = dw_fit( + roughness=0.000164, diameter=0.820210, plot=True, convert_to_us_unit=False + ) + print(evlaluate_fit(res, 1.766)) diff --git a/wntr_quantum/scenario/mass_balance.py b/wntr_quantum/scenario/mass_balance.py new file mode 100644 index 0000000..e85ebe9 --- /dev/null +++ b/wntr_quantum/scenario/mass_balance.py @@ -0,0 +1,83 @@ +from wntr.epanet.util import FlowUnits +from wntr.epanet.util import HydParam +from wntr.epanet.util import from_si + + +def get_mass_balance_constraint( + m, wn, matrices, convert_to_us_unit=False +): # noqa: D417 + """Create the matrices for the mass balance equation. + + Args: + m (_type_): _description_ + wn (_type_): _description_ + matrices (_type_): _description_ + convert_to_us_unit (bool, optional): _description_. Defaults to False. + + Returns: + _type_: _description_ + """ + P0, P1, P2 = matrices + + continuous_var_name = [v.name for v in list(m.vars())] + # discrete_var_name = [v.name for k, v in m.cm_resistance.items()] + var_names = continuous_var_name # + discrete_var_name + + index_over = wn.junction_name_list + + for ieq, node_name in enumerate(index_over): + + node = wn.get_node(node_name) + if not node._is_isolated: + if convert_to_us_unit: + P0[ieq, 0] = from_si( + FlowUnits.CFS, m.expected_demand[node_name].value, HydParam.Flow + ) + else: + P0[ieq, 0] += m.expected_demand[node_name].value + + for link_name in wn.get_links_for_node(node_name, flag="INLET"): + node_index = var_names.index(m.flow[link_name].name) + P1[ieq, node_index] -= 1 + + for link_name in wn.get_links_for_node(node_name, flag="OUTLET"): + node_index = var_names.index(m.flow[link_name].name) + P1[ieq, node_index] += 1 + + return P0, P1, P2 + + +def get_mass_balance_constraint_design(m, wn, matrices): # noqa: D417 + """Adds a mass balance to the model for the specified junctions. + + Parameters + ---------- + m: wntr.aml.aml.aml.Model + wn: wntr.network.model.WaterNetworkModel + updater: ModelUpdater + index_over: list of str + list of junction names; default is all junctions in wn + """ + P0, P1, P2, P3 = matrices + + continuous_var_name = [v.name for v in list(m.vars())] + discrete_var_name = [v.name for k, v in m.cm_resistance.items()] + var_names = continuous_var_name + discrete_var_name + + index_over = wn.junction_name_list + + for ieq, node_name in enumerate(index_over): + + node = wn.get_node(node_name) + if not node._is_isolated: + P0[ieq, 0] += m.expected_demand[node_name].value + + for link_name in wn.get_links_for_node(node_name, flag="INLET"): + node_index = var_names.index(m.flow[link_name].name) + P1[ieq, node_index] -= 1 + + for link_name in wn.get_links_for_node(node_name, flag="OUTLET"): + node_index = var_names.index(m.flow[link_name].name) + P1[ieq, node_index] += 1 + + return P0, P1, P2, P3 diff --git a/wntr_quantum/scenario/network_qubo.py b/wntr_quantum/scenario/network_qubo.py index 4b2ac98..39661aa 100644 --- a/wntr_quantum/scenario/network_qubo.py +++ b/wntr_quantum/scenario/network_qubo.py @@ -1,23 +1,28 @@ -import itertools import numpy as np +import sparse from quantum_newton_raphson.newton_raphson import newton_raphson from qubols.encodings import DiscreteValuesEncoding from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED from qubols.solution_vector import SolutionVector_V2 as SolutionVector +from wntr.epanet.util import FlowUnits +from wntr.epanet.util import HydParam +from wntr.epanet.util import to_si from wntr.sim import aml from wntr.sim.models import constants from wntr.sim.models import constraint from wntr.sim.models import param from wntr.sim.models import var from wntr.sim.models.utils import ModelUpdater -import sparse from .chezy_manning import approx_chezy_manning_headloss_constraint from .chezy_manning import chezy_manning_constants from .chezy_manning import cm_resistance_param -from .chezy_manning import cm_resistance_prefactor from .chezy_manning import get_chezy_manning_matrix -from .chezy_manning import get_mass_balance_constraint +from .darcy_weisbach import approx_darcy_weisbach_headloss_constraint +from .darcy_weisbach import darcy_weisbach_constants +from .darcy_weisbach import dw_resistance_param +from .darcy_weisbach import get_darcy_weisbach_matrix +from .mass_balance import get_mass_balance_constraint class Network(object): @@ -33,17 +38,15 @@ def __init__( Args: wn (_type_): _description_ - encoding_flows (_type_): _description_ - encoding_heads (_type_): _description_ + flow_encoding (_type_): _description_ + head_encoding (_type_): _description_ pipe_diameters (_type_): _description_ """ self.wn = wn self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) - self.sol_vect_heads = SolutionVector( - wn.num_junctions, encoding=head_encoding - ) # not sure num_junction is what we need + self.sol_vect_heads = SolutionVector(wn.num_junctions, encoding=head_encoding) - self.m, self.model_updater = self.create_cm_model() + self.m, self.model_updater = self.create_model() self.mixed_solution_vector = MixedSolutionVector( [self.sol_vect_flows, self.sol_vect_heads] @@ -52,8 +55,7 @@ def __init__( self.matrices = self.initialize_matrices() def verify_solution(self, input): - """generates the classical solution.""" - + """Generates the classical solution.""" P0, P1, P2 = self.matrices p0 = P0.reshape( @@ -64,8 +66,7 @@ def verify_solution(self, input): return p0 + p1 @ input + (p2 @ (input * input)) def classical_solutions(self, max_iter=100, tol=1e-10): - """generates the classical solution.""" - + """Generates the classical solution.""" P0, P1, P2 = self.matrices num_heads = self.wn.num_junctions num_pipes = self.wn.num_pipes @@ -82,10 +83,16 @@ def func(input): initial_point = np.random.rand(num_vars) res = newton_raphson(func, initial_point, max_iter=max_iter, tol=tol) - assert np.allclose(func(res.solution), 0) - return res.solution + sol = res.solution + assert np.allclose(func(sol), 0) + + # convert back to SI if DW + if self.wn.options.hydraulic.headloss == "D-W": + sol = self.convert_solution_si(sol) - def create_cm_model(self): + return sol + + def create_model(self): """Create the aml. Args: @@ -104,14 +111,25 @@ def create_cm_model(self): """ if self.wn.options.hydraulic.demand_model in ["PDD", "PDA"]: raise ValueError("Pressure Driven simulations not supported") - if self.wn.options.hydraulic.headloss not in ["C-M"]: - raise ValueError("Quantum Design only supported for C-M simulations") + + if self.wn.options.hydraulic.headloss == "C-M": + import_constants = chezy_manning_constants + resistance_param = cm_resistance_param + approx_head_loss_constraint = approx_chezy_manning_headloss_constraint + elif self.wn.options.hydraulic.headloss == "D-W": + import_constants = darcy_weisbach_constants + resistance_param = dw_resistance_param + approx_head_loss_constraint = approx_darcy_weisbach_headloss_constraint + else: + raise ValueError( + "QUBO Hydraulic Simulations only supported for C-M and D-W simulations" + ) m = aml.Model() model_updater = ModelUpdater() # Global constants - chezy_manning_constants(m) + import_constants(m) constants.head_pump_constants(m) constants.leak_constants(m) constants.pdd_constants(m) @@ -124,7 +142,7 @@ def create_cm_model(self): param.leak_poly_coeffs_param.build(m, self.wn, model_updater) param.elevation_param.build(m, self.wn, model_updater) - cm_resistance_param.build(m, self.wn, model_updater) + resistance_param.build(m, self.wn, model_updater) param.minor_loss_param.build(m, self.wn, model_updater) param.tcv_resistance_param.build(m, self.wn, model_updater) param.pump_power_param.build(m, self.wn, model_updater) @@ -136,7 +154,7 @@ def create_cm_model(self): constraint.mass_balance_constraint.build(m, self.wn, model_updater) - approx_chezy_manning_headloss_constraint.build(m, self.wn, model_updater) + approx_head_loss_constraint.build(m, self.wn, model_updater) constraint.head_pump_headloss_constraint.build(m, self.wn, model_updater) constraint.power_pump_headloss_constraint.build(m, self.wn, model_updater) @@ -159,7 +177,7 @@ def create_cm_model(self): return m, model_updater def initialize_matrices(self): - """_summary_.""" + """Initilize the matrix for the QUBO definition.""" num_equations = len(list(self.m.cons())) num_variables = len(list(self.m.vars())) @@ -169,13 +187,37 @@ def initialize_matrices(self): P2 = np.zeros((num_equations, num_variables, num_variables)) matrices = (P0, P1, P2) - matrices = get_mass_balance_constraint(self.m, self.wn, matrices) - matrices = get_chezy_manning_matrix(self.m, self.wn, matrices) + # get the mass balance and headloss matrix contributions + if self.wn.options.hydraulic.headloss == "C-M": + matrices = get_mass_balance_constraint(self.m, self.wn, matrices) + matrices = get_chezy_manning_matrix(self.m, self.wn, matrices) + elif self.wn.options.hydraulic.headloss == "D-W": + matrices = get_mass_balance_constraint( + self.m, self.wn, matrices, convert_to_us_unit=True + ) + matrices = get_darcy_weisbach_matrix(self.m, self.wn, matrices) + else: + raise ValueError("Calculation only possible with C-M or D-W") return matrices + def convert_solution_si(self, solution): + """Converts the solution to SI. + + Args: + solution (array): solution vectors + """ + num_heads = self.wn.num_junctions + num_pipes = self.wn.num_pipes + new_sol = np.zeros_like(solution) + for ip in range(num_pipes): + new_sol[ip] = to_si(FlowUnits.CFS, solution[ip], HydParam.Flow) + for ih in range(num_pipes, num_pipes + num_heads): + new_sol[ih] = to_si(FlowUnits.CFS, solution[ih], HydParam.Length) + return new_sol + def solve(self, **options): - """_summary_""" + """Solve the hydraulic equations.""" qubo = QUBO_POLY_MIXED(self.mixed_solution_vector, **options) matrices = tuple(sparse.COO(m) for m in self.matrices) bqm = qubo.create_bqm(matrices, strength=1000) @@ -185,4 +227,9 @@ def solve(self, **options): # decode sol, param = qubo.decode_solution(sampleset.lowest()) + + # convert back to SI if DW + if self.wn.options.hydraulic.headloss == "D-W": + sol = self.convert_solution_si(sol) + return sol, param From 3e5b9bf1f774d3ef29a033076cad37dcf3c0da45 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 30 Aug 2024 14:31:03 +0200 Subject: [PATCH 15/96] qubo works + refactor --- docs/notebooks/trash/epanet_qubonetwork.ipynb | 225 ++++++----- docs/notebooks/trash/temp.inp | 2 +- docs/notebooks/trash/temp.rpt | 6 +- tests/test_aml_quantum_newton_solver.py | 2 +- wntr_quantum/scenario/network_qubo.py | 115 +++++- wntr_quantum/sim/core.py | 120 ++++-- wntr_quantum/sim/core_qubo.py | 336 +++++++++++++++++ wntr_quantum/sim/hydraulics.py | 97 +++++ .../{scenario => sim/models}/chezy_manning.py | 0 .../models}/darcy_weisbach.py | 8 +- .../models}/darcy_weisbach_fit.py | 2 +- .../{scenario => sim/models}/mass_balance.py | 0 .../{solvers.py => quantum_newton_solver.py} | 0 wntr_quantum/sim/qubo_polynomial_solver.py | 351 ++++++++++++++++++ 14 files changed, 1120 insertions(+), 144 deletions(-) create mode 100644 wntr_quantum/sim/core_qubo.py create mode 100644 wntr_quantum/sim/hydraulics.py rename wntr_quantum/{scenario => sim/models}/chezy_manning.py (100%) rename wntr_quantum/{scenario => sim/models}/darcy_weisbach.py (97%) rename wntr_quantum/{scenario => sim/models}/darcy_weisbach_fit.py (97%) rename wntr_quantum/{scenario => sim/models}/mass_balance.py (100%) rename wntr_quantum/sim/{solvers.py => quantum_newton_solver.py} (100%) create mode 100644 wntr_quantum/sim/qubo_polynomial_solver.py diff --git a/docs/notebooks/trash/epanet_qubonetwork.ipynb b/docs/notebooks/trash/epanet_qubonetwork.ipynb index bc045fb..da66c47 100644 --- a/docs/notebooks/trash/epanet_qubonetwork.ipynb +++ b/docs/notebooks/trash/epanet_qubonetwork.ipynb @@ -162,13 +162,6 @@ "we use the default solver of the QuantumWNTRSimulator, that uses a LU solver, a s a benchmark of the calculation" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 6, @@ -443,78 +436,9 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Roughness : 0.000164\n", - "diameter : 0.820210\n", - "resistance coeff : 222.481950 \n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.003 0.007 0.014]\n", - "Roughness : 0.000164\n", - "diameter : 0.820210\n", - "resistance coeff : 222.481950 \n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.003 0.007 0.014]\n", - "-0.6520014880299684 1.5466109022548316 3.062949682690347\n", - "-0.6520014880299684 1.5466109022548316 3.062949682690347\n" - ] - } - ], + "outputs": [], "source": [ - "from wntr_quantum.scenario.network_qubo import Network\n", + "from wntr_quantum.sim.qubo_polynomial_solver import QuboPolynomialSolver\n", "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", @@ -526,7 +450,7 @@ "step = (250/(2**nqbit-1))\n", "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", - "net = Network(wn, flow_encoding=flow_encoding, \n", + "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", " head_encoding=head_encoding)" ] }, @@ -596,67 +520,162 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 26.456, 22.911])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n" + ] } ], "source": [ - "# from wntr.epanet.util import to_si, FlowUnits, HydParam, from_si\n", - "# ref_sol[0] = to_si(FlowUnits.CFS, ref_sol[0], HydParam.Flow)\n", - "# ref_sol[1] = to_si(FlowUnits.CFS, ref_sol[1], HydParam.Flow)\n", - "# ref_sol[2] = to_si(FlowUnits.CFS, ref_sol[2], HydParam.Length)\n", - "# ref_sol[3] = to_si(FlowUnits.CFS, ref_sol[3], HydParam.Length)\n", - "ref_sol" + "from wntr_quantum.sim.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 9\n", + "step = (0.5/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", + "\n", + "nqbit = 9\n", + "step = (50/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")\n", + "\n", + "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)\n", + "net.verify_encoding()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", + "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "import sparse\n", + "\n", + "from dwave.samplers import SimulatedAnnealingSampler\n", + "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import TabuSampler\n", + "from dimod import ExactSolver\n", + "\n", + "sampler = TabuSampler()\n", + "sampler = SteepestDescentSolver()\n", + "# sampler = SimulatedAnnealingSampler()\n", + "# sampler = ExactSolver() \n", + "\n", + "qubo = QUBO_POLY_MIXED(net.mixed_solution_vector, options={\"sampler\" : sampler} )\n", + "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "bqm = qubo.create_bqm(matrices, strength=1E6)\n", + "sampleset = qubo.sample_bqm(bqm, num_reads=10000)\n", + "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", + "sol = net.flatten_solution_vector(sol)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "sol = net.convert_solution_to_si(sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "array([1. , 1. , 0.999, 0.998])" + "
" ] }, - "execution_count": 13, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "ref_sol/ref_values" + "net.plot_solution_vs_reference(sol, ref_sol)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", + "\n", + "\n", + "Error (%): [-0.577 -0.744 0.007 0.158]\n", + "\n", + "\n", + "sol : [ 1.776 1.779 86.791 75.049]\n", + "ref : [ 1.766 1.766 86.797 75.168]\n", + "diff: [-0.01 -0.013 0.006 0.119]\n", + "\n", + "\n", + "encoded_sol: [ 1.776 1.779 86.791 75.049]\n", + "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", + "diff : [-0.01 -0.013 0. 0.098]\n", + "\n", + "\n", + "E sol : -1662.5890489845583\n", + "R ref : -1662.606102046081\n", + "Delta E : 0.017053061522801727\n", + "\n", + "\n", + "Residue sol : 0.13098406084618344\n", + "Residue ref : 0.010186471203764017\n", + "Delta Residue : 0.12079758964241942\n" + ] + } + ], + "source": [ + "net.benchmark_solution(sol, ref_sol, qubo, bqm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "net.m.set_structure()" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1.7657333355755793" + "array([0. , 0. , 0.001, 0.001])" ] }, - "execution_count": 14, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from_si(FlowUnits.CFS, 0.05, HydParam.Flow)" + "net.m.get_x()" ] }, { diff --git a/docs/notebooks/trash/temp.inp b/docs/notebooks/trash/temp.inp index 171b7f0..26ced5d 100644 --- a/docs/notebooks/trash/temp.inp +++ b/docs/notebooks/trash/temp.inp @@ -1,6 +1,6 @@ ; Filename: ../networks/Net0.inp ; WNTR: 1.1.0 -; Created: 2024-08-29 17:37:02 +; Created: 2024-08-30 14:28:33 [TITLE] File obtained via Mario of a 2 node sysem diff --git a/docs/notebooks/trash/temp.rpt b/docs/notebooks/trash/temp.rpt index 73852bc..db12191 100644 --- a/docs/notebooks/trash/temp.rpt +++ b/docs/notebooks/trash/temp.rpt @@ -1,4 +1,4 @@ - Page 1 Thu Aug 29 17:37:02 2024 + Page 1 Fri Aug 30 14:28:33 2024 ****************************************************************** * E P A N E T * @@ -7,6 +7,6 @@ * Version 2.3 * ****************************************************************** - Analysis begun Thu Aug 29 17:37:02 2024 + Analysis begun Fri Aug 30 14:28:33 2024 - Analysis ended Thu Aug 29 17:37:17 2024 + Analysis ended Fri Aug 30 14:28:44 2024 diff --git a/tests/test_aml_quantum_newton_solver.py b/tests/test_aml_quantum_newton_solver.py index b561269..f47089a 100644 --- a/tests/test_aml_quantum_newton_solver.py +++ b/tests/test_aml_quantum_newton_solver.py @@ -9,7 +9,7 @@ from quantum_newton_raphson.vqls_solver import VQLS_SOLVER from qubols.encodings import EfficientEncoding from wntr.sim import aml -from wntr_quantum.sim.solvers import QuantumNewtonSolver +from wntr_quantum.sim.quantum_newton_solver import QuantumNewtonSolver TOL_RESULTS = 1e-2 diff --git a/wntr_quantum/scenario/network_qubo.py b/wntr_quantum/scenario/network_qubo.py index 39661aa..9355a6d 100644 --- a/wntr_quantum/scenario/network_qubo.py +++ b/wntr_quantum/scenario/network_qubo.py @@ -1,3 +1,4 @@ +import matplotlib.pyplot as plt import numpy as np import sparse from quantum_newton_raphson.newton_raphson import newton_raphson @@ -7,6 +8,7 @@ from qubols.solution_vector import SolutionVector_V2 as SolutionVector from wntr.epanet.util import FlowUnits from wntr.epanet.util import HydParam +from wntr.epanet.util import from_si from wntr.epanet.util import to_si from wntr.sim import aml from wntr.sim.models import constants @@ -22,7 +24,7 @@ from .darcy_weisbach import darcy_weisbach_constants from .darcy_weisbach import dw_resistance_param from .darcy_weisbach import get_darcy_weisbach_matrix -from .mass_balance import get_mass_balance_constraint +from ..sim.headloss_models.mass_balance import get_mass_balance_constraint class Network(object): @@ -43,6 +45,8 @@ def __init__( pipe_diameters (_type_): _description_ """ self.wn = wn + self.flow_encoding = flow_encoding + self.head_encoding = head_encoding self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) self.sol_vect_heads = SolutionVector(wn.num_junctions, encoding=head_encoding) @@ -54,6 +58,15 @@ def __init__( self.matrices = self.initialize_matrices() + def verify_encoding(self): + """Print info regarding the encodings.""" + hres = self.head_encoding.get_average_precision() + hvalues = np.sort(self.head_encoding.get_possible_values()) + fres = self.flow_encoding.get_average_precision() + fvalues = np.sort(self.flow_encoding.get_possible_values()) + print("Head Encoding : %f => %f (res: %f)" % (hvalues[0], hvalues[-1], hres)) + print("Flow Encoding : %f => %f (res: %f)" % (fvalues[0], fvalues[-1], fres)) + def verify_solution(self, input): """Generates the classical solution.""" P0, P1, P2 = self.matrices @@ -88,10 +101,66 @@ def func(input): # convert back to SI if DW if self.wn.options.hydraulic.headloss == "D-W": - sol = self.convert_solution_si(sol) + sol = self.convert_solution_to_si(sol) return sol + @staticmethod + def plot_solution_vs_reference(solution, reference_solution): + """Plots the scatter plot ref/sol. + + Args: + solution (_type_): _description_ + reference_solution (_type_): _description_ + """ + plt.scatter(reference_solution, solution) + plt.axline((0, 0.0), slope=1, color="black", linestyle=(0, (5, 5))) + + plt.axline((0, 0.0), slope=1.05, color="grey", linestyle=(0, (2, 2))) + plt.axline((0, 0.0), slope=0.95, color="grey", linestyle=(0, (2, 2))) + plt.grid(which="major", lw=1) + plt.grid(which="minor", lw=0.1) + plt.loglog() + + def benchmark_solution(self, solution, reference_solution, qubo, bqm): + """Benchmark a solution against the exact reference solution. + + Args: + solution (np.array): _description_ + reference_solution (np.array): _description_ + qubo (_type_): __ + bqm (_type_): __ + """ + if self.wn.options.hydraulic.headloss == "D-W": + reference_solution = self.convert_solution_from_si(reference_solution) + solution = self.convert_solution_from_si(solution) + + data_ref, eref = qubo.compute_energy(reference_solution, bqm) + data_sol, esol = qubo.compute_energy(solution, bqm) + + np.set_printoptions(precision=3) + self.verify_encoding() + print("\n") + print("Error (%):", (1 - (solution / reference_solution)) * 100) + print("\n") + print("sol : ", solution) + print("ref : ", reference_solution) + print("diff: ", reference_solution - solution) + print("\n") + print("encoded_sol: ", np.array(data_sol[0])) + print("encoded_ref: ", np.array(data_ref[0])) + print("diff : ", np.array(data_ref[0]) - np.array(data_sol[0])) + print("\n") + print("E sol : ", esol) + print("R ref : ", eref) + print("Delta E :", esol - eref) + print("\n") + res_sol = np.linalg.norm(self.verify_solution(np.array(data_sol[0]))) + res_ref = np.linalg.norm(self.verify_solution(np.array(data_ref[0]))) + print("Residue sol : ", res_sol) + print("Residue ref : ", res_ref) + print("Delta Residue :", res_sol - res_ref) + def create_model(self): """Create the aml. @@ -201,7 +270,7 @@ def initialize_matrices(self): raise ValueError("Calculation only possible with C-M or D-W") return matrices - def convert_solution_si(self, solution): + def convert_solution_to_si(self, solution): """Converts the solution to SI. Args: @@ -216,20 +285,50 @@ def convert_solution_si(self, solution): new_sol[ih] = to_si(FlowUnits.CFS, solution[ih], HydParam.Length) return new_sol - def solve(self, **options): + @staticmethod + def flatten_solution_vector(solution): + """Flattens the solution vector. + + Args: + solution (tuple): tuple of ([flows], [heads]) + """ + sol_tmp = [] + for s in solution: + sol_tmp += s + return sol_tmp + + def convert_solution_from_si(self, solution): + """Converts the solution to SI. + + Args: + solution (array): solution vectors + """ + num_heads = self.wn.num_junctions + num_pipes = self.wn.num_pipes + new_sol = np.zeros_like(solution) + for ip in range(num_pipes): + new_sol[ip] = from_si(FlowUnits.CFS, solution[ip], HydParam.Flow) + for ih in range(num_pipes, num_pipes + num_heads): + new_sol[ih] = from_si(FlowUnits.CFS, solution[ih], HydParam.Length) + return new_sol + + def solve(self, strength=1e6, num_reads=1e4, **options): """Solve the hydraulic equations.""" qubo = QUBO_POLY_MIXED(self.mixed_solution_vector, **options) matrices = tuple(sparse.COO(m) for m in self.matrices) - bqm = qubo.create_bqm(matrices, strength=1000) + bqm = qubo.create_bqm(matrices, strength=strength) # sample - sampleset = qubo.sample_bqm(bqm, num_reads=options["num_reads"]) + sampleset = qubo.sample_bqm(bqm, num_reads=num_reads) # decode - sol, param = qubo.decode_solution(sampleset.lowest()) + sol = qubo.decode_solution(sampleset.lowest().record[0][0]) + + # flatten solution + sol = self.flatten_solution_vector(sol) # convert back to SI if DW if self.wn.options.hydraulic.headloss == "D-W": - sol = self.convert_solution_si(sol) + sol = self.convert_solution_to_si(sol) return sol, param diff --git a/wntr_quantum/sim/core.py b/wntr_quantum/sim/core.py index f78aee3..bddabec 100644 --- a/wntr_quantum/sim/core.py +++ b/wntr_quantum/sim/core.py @@ -6,7 +6,7 @@ from wntr.sim.core import WNTRSimulator from wntr.sim.core import _Diagnostics from wntr.sim.core import _ValveSourceChecker -from .solvers import QuantumNewtonSolver +from .quantum_newton_solver import QuantumNewtonSolver logger = logging.getLogger(__name__) @@ -84,7 +84,9 @@ def run_sim( logger.debug("creating hydraulic model") self.mode = self._wn.options.hydraulic.demand_model - self._model, self._model_updater = wntr.sim.hydraulics.create_hydraulic_model(wn=self._wn, HW_approx=HW_approx) + self._model, self._model_updater = wntr.sim.hydraulics.create_hydraulic_model( + wn=self._wn, HW_approx=HW_approx + ) if diagnostics: diagnostics = _Diagnostics(self._wn, self._model, self.mode, enable=True) @@ -129,9 +131,15 @@ def run_sim( logger.debug("starting simulation") logger.info( - "{0:<10}{1:<10}{2:<10}{3:<15}{4:<15}".format("Sim Time", "Trial", "Solver", "# isolated", "# isolated") + "{0:<10}{1:<10}{2:<10}{3:<15}{4:<15}".format( + "Sim Time", "Trial", "Solver", "# isolated", "# isolated" + ) + ) + logger.info( + "{0:<10}{1:<10}{2:<10}{3:<15}{4:<15}".format( + "", "", "# iter", "junctions", "links" + ) ) - logger.info("{0:<10}{1:<10}{2:<10}{3:<15}{4:<15}".format("", "", "# iter", "junctions", "links")) while True: if logger.getEffectiveLevel() <= logging.DEBUG: logger.debug("\n\n") @@ -144,13 +152,17 @@ def run_sim( """ wntr.sim.hydraulics.update_tank_heads(self._wn) trial = 0 - self._compute_next_timestep_and_run_presolve_controls_and_rules(first_step) + self._compute_next_timestep_and_run_presolve_controls_and_rules( + first_step + ) self._run_feasibility_controls() # Prepare for solve self._update_internal_graph() - num_isolated_junctions, num_isolated_links = self._get_isolated_junctions_and_links() + num_isolated_junctions, num_isolated_links = ( + self._get_isolated_junctions_and_links() + ) if not first_step and not resolve: wntr.sim.hydraulics.update_tank_heads(self._wn) wntr.sim.hydraulics.update_model_for_controls( @@ -159,28 +171,58 @@ def run_sim( wntr.sim.models.param.source_head_param(self._model, self._wn) wntr.sim.models.param.expected_demand_param(self._model, self._wn) - diagnostics.run(last_step="presolve controls, rules, and model updates", next_step="solve") + diagnostics.run( + last_step="presolve controls, rules, and model updates", + next_step="solve", + ) solver_status, mesg, iter_count = _solver_helper( self._model, self._solver, self._linear_solver, self._solver_options ) if solver_status == 0 and self._backup_solver is not None: solver_status, mesg, iter_count = _solver_helper( - self._model, self._backup_solver, self._linear_solver, self._backup_solver_options + self._model, + self._backup_solver, + self._linear_solver, + self._backup_solver_options, ) if solver_status == 0: if self._convergence_error: - logger.error("Simulation did not converge at time " + self._get_time() + ". " + mesg) - raise RuntimeError("Simulation did not converge at time " + self._get_time() + ". " + mesg) - warnings.warn("Simulation did not converge at time " + self._get_time() + ". " + mesg) - logger.warning("Simulation did not converge at time " + self._get_time() + ". " + mesg) + logger.error( + "Simulation did not converge at time " + + self._get_time() + + ". " + + mesg + ) + raise RuntimeError( + "Simulation did not converge at time " + + self._get_time() + + ". " + + mesg + ) + warnings.warn( + "Simulation did not converge at time " + + self._get_time() + + ". " + + mesg + ) + logger.warning( + "Simulation did not converge at time " + + self._get_time() + + ". " + + mesg + ) results.error_code = wntr.sim.results.ResultsStatus.error diagnostics.run(last_step="solve", next_step="break") break logger.info( "{0:<10}{1:<10}{2:<10}{3:<15}{4:<15}".format( - self._get_time(), trial, iter_count, num_isolated_junctions, num_isolated_links + self._get_time(), + trial, + iter_count, + num_isolated_junctions, + num_isolated_links, ) ) @@ -188,7 +230,10 @@ def run_sim( logger.debug("storing results in network") wntr.sim.hydraulics.store_results_in_network(self._wn, self._model) - diagnostics.run(last_step="solve and store results in network", next_step="postsolve controls") + diagnostics.run( + last_step="solve and store results in network", + next_step="postsolve controls", + ) self._run_postsolve_controls() self._run_feasibility_controls() @@ -198,34 +243,63 @@ def run_sim( wntr.sim.hydraulics.update_model_for_controls( self._model, self._wn, self._model_updater, self._change_tracker ) - diagnostics.run(last_step="postsolve controls and model updates", next_step="solve next trial") + diagnostics.run( + last_step="postsolve controls and model updates", + next_step="solve next trial", + ) trial += 1 if trial > max_trials: if convergence_error: - logger.error("Exceeded maximum number of trials at time " + self._get_time() + ". ") - raise RuntimeError("Exceeded maximum number of trials at time " + self._get_time() + ". ") + logger.error( + "Exceeded maximum number of trials at time " + + self._get_time() + + ". " + ) + raise RuntimeError( + "Exceeded maximum number of trials at time " + + self._get_time() + + ". " + ) results.error_code = wntr.sim.results.ResultsStatus.error - warnings.warn("Exceeded maximum number of trials at time " + self._get_time() + ". ") - logger.warning("Exceeded maximum number of trials at time " + self._get_time() + ". ") + warnings.warn( + "Exceeded maximum number of trials at time " + + self._get_time() + + ". " + ) + logger.warning( + "Exceeded maximum number of trials at time " + + self._get_time() + + ". " + ) break continue - diagnostics.run(last_step="postsolve controls and model updates", next_step="advance time") + diagnostics.run( + last_step="postsolve controls and model updates", + next_step="advance time", + ) - logger.debug("no changes made by postsolve controls; moving to next timestep") + logger.debug( + "no changes made by postsolve controls; moving to next timestep" + ) resolve = False if isinstance(self._report_timestep, (float, int)): if self._wn.sim_time % self._report_timestep == 0: wntr.sim.hydraulics.save_results(self._wn, node_res, link_res) - if len(results.time) > 0 and int(self._wn.sim_time) == results.time[-1]: + if ( + len(results.time) > 0 + and int(self._wn.sim_time) == results.time[-1] + ): if int(self._wn.sim_time) != self._wn.sim_time: raise RuntimeError( "Time steps increments smaller than 1 second are forbidden." + " Keep time steps as an integer number of seconds." ) else: - raise RuntimeError("Simulation already solved this timestep") + raise RuntimeError( + "Simulation already solved this timestep" + ) results.time.append(int(self._wn.sim_time)) elif self._report_timestep.upper() == "ALL": wntr.sim.hydraulics.save_results(self._wn, node_res, link_res) diff --git a/wntr_quantum/sim/core_qubo.py b/wntr_quantum/sim/core_qubo.py new file mode 100644 index 0000000..bb5d951 --- /dev/null +++ b/wntr_quantum/sim/core_qubo.py @@ -0,0 +1,336 @@ +import logging +import warnings +import wntr.sim.hydraulics +import wntr.sim.results + +from wntr.sim.core import WNTRSimulator +from wntr.sim.core import _Diagnostics +from wntr.sim.core import _ValveSourceChecker + +from .hydraulics import create_hydraulic_model + +from .qubo_polynomial_solver import QuboPolynomialSolver + +logger = logging.getLogger(__name__) + + +class FullQuantumSimulator(WNTRSimulator): + """The quantum enabled NR slver.""" + + def __init__(self, wn, flow_encoding, head_encoding): # noqa: D417 + """WNTR simulator class. + The WNTR simulator uses a custom newton solver and linear solvers from scipy.sparse. + + Parameters + ---------- + wn : WaterNetworkModel object + Water network model + flow_encoding: binary encoding for the flow values + head_encoding: binary ncoding for the head values + + + .. note:: + The mode parameter has been deprecated. Please set the mode using Options.hydraulic.demand_model + + """ # noqa: D205 + super().__init__(wn) + self._head_encoding = head_encoding + self._flow_encoding = flow_encoding + self._solver = QuboPolynomialSolver( + self.wn, flow_encoding=flow_encoding, head_encoding=head_encoding + ) + + def run_sim( + self, + solver=QuboPolynomialSolver, + solver_options=None, + convergence_error=False, + diagnostics=False, + ): + """Run an extended period simulation (hydraulics only). + + Parameters + ---------- + solver: object + wntr.sim.solvers.NewtonSolver or Scipy solver + linear_solver: linear solver + Linear solver + backup_solver: object + wntr.sim.solvers.NewtonSolver or Scipy solver + solver_options: dict + Solver options are specified using the following dictionary keys: + backup_solver_options: dict + Solver options are specified using the following dictionary keys: + + * MAXITER: the maximum number of iterations for each hydraulic solve + (each timestep and trial) (default = 3000) + * TOL: tolerance for the hydraulic equations (default = 1e-6) + * BT_RHO: the fraction by which the step length is reduced at each iteration of the + line search (default = 0.5) + * BT_MAXITER: the maximum number of iterations for each line search (default = 100) + * BACKTRACKING: whether or not to use a line search (default = True) + * BT_START_ITER: the newton iteration at which a line search should start being used (default = 2) + * THREADS: the number of threads to use in constraint and jacobian computations + backup_solver_options: dict + convergence_error: bool (optional) + If convergence_error is True, an error will be raised if the + simulation does not converge. If convergence_error is False, partial results are returned, + a warning will be issued, and results.error_code will be set to 0 + if the simulation does not converge. Default = False. + diagnostics: bool + If True, then run with diagnostics on + """ + logger.debug("creating hydraulic model") + self.mode = self._wn.options.hydraulic.demand_model + self._model, self._model_updater = create_hydraulic_model(wn=self._wn) + + if diagnostics: + diagnostics = _Diagnostics(self._wn, self._model, self.mode, enable=True) + else: + diagnostics = _Diagnostics(self._wn, self._model, self.mode, enable=False) + + self._setup_sim_options( + solver=solver, + backup_solver=None, + solver_options=solver_options, + backup_solver_options=None, + convergence_error=convergence_error, + ) + + self._valve_source_checker = _ValveSourceChecker(self._wn) + self._get_control_managers() + self._register_controls_with_observers() + + node_res, link_res = wntr.sim.hydraulics.initialize_results_dict(self._wn) + results = wntr.sim.results.SimulationResults() + results.error_code = None + results.time = [] + results.network_name = self._wn.name + + self._initialize_internal_graph() + self._change_tracker.set_reference_point("graph") + self._change_tracker.set_reference_point("model") + + if self._wn.sim_time == 0: + first_step = True + else: + first_step = False + trial = -1 + max_trials = self._wn.options.hydraulic.trials + resolve = False + self._rule_iter = 0 # this is used to determine the rule timestep + + if first_step: + wntr.sim.hydraulics.update_network_previous_values(self._wn) + self._wn._prev_sim_time = -1 + + logger.debug("starting simulation") + + logger.info( + "{0:<10}{1:<10}{2:<10}{3:<15}{4:<15}".format( + "Sim Time", "Trial", "Solver", "# isolated", "# isolated" + ) + ) + logger.info( + "{0:<10}{1:<10}{2:<10}{3:<15}{4:<15}".format( + "", "", "# iter", "junctions", "links" + ) + ) + while True: + if logger.getEffectiveLevel() <= logging.DEBUG: + logger.debug("\n\n") + + if not resolve: + if not first_step: + """ + The tank levels/heads must be done before checking the controls because the TankLevelControls + depend on the tank levels. These will be updated again after we determine the next actual timestep. + """ + wntr.sim.hydraulics.update_tank_heads(self._wn) + trial = 0 + self._compute_next_timestep_and_run_presolve_controls_and_rules( + first_step + ) + + self._run_feasibility_controls() + + # Prepare for solve + self._update_internal_graph() + num_isolated_junctions, num_isolated_links = ( + self._get_isolated_junctions_and_links() + ) + if not first_step and not resolve: + wntr.sim.hydraulics.update_tank_heads(self._wn) + wntr.sim.hydraulics.update_model_for_controls( + self._model, self._wn, self._model_updater, self._change_tracker + ) + wntr.sim.models.param.source_head_param(self._model, self._wn) + wntr.sim.models.param.expected_demand_param(self._model, self._wn) + + diagnostics.run( + last_step="presolve controls, rules, and model updates", + next_step="solve", + ) + + solver_status, mesg, iter_count = _solver_helper( + self._model, + self._solver, + self._wn, + self._flow_encoding, + self._head_encoding, + self._solver_options, + ) + + if solver_status == 0: + if self._convergence_error: + logger.error( + "Simulation did not converge at time " + + self._get_time() + + ". " + + mesg + ) + raise RuntimeError( + "Simulation did not converge at time " + + self._get_time() + + ". " + + mesg + ) + warnings.warn( + "Simulation did not converge at time " + + self._get_time() + + ". " + + mesg + ) + logger.warning( + "Simulation did not converge at time " + + self._get_time() + + ". " + + mesg + ) + results.error_code = wntr.sim.results.ResultsStatus.error + diagnostics.run(last_step="solve", next_step="break") + break + + logger.info( + "{0:<10}{1:<10}{2:<10}{3:<15}{4:<15}".format( + self._get_time(), + trial, + iter_count, + num_isolated_junctions, + num_isolated_links, + ) + ) + + # Enter results in network and update previous inputs + logger.debug("storing results in network") + wntr.sim.hydraulics.store_results_in_network(self._wn, self._model) + + diagnostics.run( + last_step="solve and store results in network", + next_step="postsolve controls", + ) + + self._run_postsolve_controls() + self._run_feasibility_controls() + if self._change_tracker.changes_made(ref_point="graph"): + resolve = True + self._update_internal_graph() + wntr.sim.hydraulics.update_model_for_controls( + self._model, self._wn, self._model_updater, self._change_tracker + ) + diagnostics.run( + last_step="postsolve controls and model updates", + next_step="solve next trial", + ) + trial += 1 + if trial > max_trials: + if convergence_error: + logger.error( + "Exceeded maximum number of trials at time " + + self._get_time() + + ". " + ) + raise RuntimeError( + "Exceeded maximum number of trials at time " + + self._get_time() + + ". " + ) + results.error_code = wntr.sim.results.ResultsStatus.error + warnings.warn( + "Exceeded maximum number of trials at time " + + self._get_time() + + ". " + ) + logger.warning( + "Exceeded maximum number of trials at time " + + self._get_time() + + ". " + ) + break + continue + + diagnostics.run( + last_step="postsolve controls and model updates", + next_step="advance time", + ) + + logger.debug( + "no changes made by postsolve controls; moving to next timestep" + ) + + resolve = False + if isinstance(self._report_timestep, (float, int)): + if self._wn.sim_time % self._report_timestep == 0: + wntr.sim.hydraulics.save_results(self._wn, node_res, link_res) + if ( + len(results.time) > 0 + and int(self._wn.sim_time) == results.time[-1] + ): + if int(self._wn.sim_time) != self._wn.sim_time: + raise RuntimeError( + "Time steps increments smaller than 1 second are forbidden." + + " Keep time steps as an integer number of seconds." + ) + else: + raise RuntimeError( + "Simulation already solved this timestep" + ) + results.time.append(int(self._wn.sim_time)) + elif self._report_timestep.upper() == "ALL": + wntr.sim.hydraulics.save_results(self._wn, node_res, link_res) + if len(results.time) > 0 and int(self._wn.sim_time) == results.time[-1]: + raise RuntimeError("Simulation already solved this timestep") + results.time.append(int(self._wn.sim_time)) + wntr.sim.hydraulics.update_network_previous_values(self._wn) + first_step = False + self._wn.sim_time += self._hydraulic_timestep + overstep = float(self._wn.sim_time) % self._hydraulic_timestep + self._wn.sim_time -= overstep + + if self._wn.sim_time > self._wn.options.time.duration: + break + + wntr.sim.hydraulics.get_results(self._wn, results, node_res, link_res) + + return results + + +def _solver_helper(model, solver, wn, flow_encoding, head_encoding, solver_options): + """Parameters + ---------- + model: wntr.aml.Model + solver: class or function + solver_options: dict + + Returns + ------- + solver_status: int + message: str + """ # noqa: D205 + logger.debug("solving") + model.set_structure() + if solver is QuboPolynomialSolver: + _solver = QuboPolynomialSolver(wn, flow_encoding, head_encoding) + return _solver.solve(model, options=solver_options) + else: + raise ValueError("Solver not recognized.") diff --git a/wntr_quantum/sim/hydraulics.py b/wntr_quantum/sim/hydraulics.py new file mode 100644 index 0000000..fcbe6f2 --- /dev/null +++ b/wntr_quantum/sim/hydraulics.py @@ -0,0 +1,97 @@ +from wntr.sim import aml +from wntr.sim.models import constants +from wntr.sim.models import constraint +from wntr.sim.models import param +from wntr.sim.models import var +from wntr.sim.models.utils import ModelUpdater +from .models.chezy_manning import approx_chezy_manning_headloss_constraint +from .models.chezy_manning import chezy_manning_constants +from .models.chezy_manning import cm_resistance_param +from .models.darcy_weisbach import approx_darcy_weisbach_headloss_constraint +from .models.darcy_weisbach import darcy_weisbach_constants +from .models.darcy_weisbach import dw_resistance_param + + +def create_hydraulic_model(wn): + """Create the aml. + + Args: + wn (_type_): _description_ + + Raises: + NotImplementedError: _description_ + NotImplementedError: _description_ + ValueError: _description_ + ValueError: _description_ + NotImplementedError: _description_ + NotImplementedError: _description_ + + Returns: + _type_: _description_ + """ + if wn.options.hydraulic.demand_model in ["PDD", "PDA"]: + raise ValueError("Pressure Driven simulations not supported") + + if wn.options.hydraulic.headloss == "C-M": + import_constants = chezy_manning_constants + resistance_param = cm_resistance_param + approx_head_loss_constraint = approx_chezy_manning_headloss_constraint + elif wn.options.hydraulic.headloss == "D-W": + import_constants = darcy_weisbach_constants + resistance_param = dw_resistance_param + approx_head_loss_constraint = approx_darcy_weisbach_headloss_constraint + else: + raise ValueError( + "QUBO Hydraulic Simulations only supported for C-M and D-W simulations" + ) + + m = aml.Model() + model_updater = ModelUpdater() + + # Global constants + import_constants(m) + constants.head_pump_constants(m) + constants.leak_constants(m) + constants.pdd_constants(m) + + param.source_head_param(m, wn) + param.expected_demand_param(m, wn) + + param.leak_coeff_param.build(m, wn, model_updater) + param.leak_area_param.build(m, wn, model_updater) + param.leak_poly_coeffs_param.build(m, wn, model_updater) + param.elevation_param.build(m, wn, model_updater) + + resistance_param.build(m, wn, model_updater) + param.minor_loss_param.build(m, wn, model_updater) + param.tcv_resistance_param.build(m, wn, model_updater) + param.pump_power_param.build(m, wn, model_updater) + param.valve_setting_param.build(m, wn, model_updater) + + var.flow_var(m, wn) + var.head_var(m, wn) + var.leak_rate_var(m, wn) + + constraint.mass_balance_constraint.build(m, wn, model_updater) + + approx_head_loss_constraint.build(m, wn, model_updater) + + constraint.head_pump_headloss_constraint.build(m, wn, model_updater) + constraint.power_pump_headloss_constraint.build(m, wn, model_updater) + constraint.prv_headloss_constraint.build(m, wn, model_updater) + constraint.psv_headloss_constraint.build(m, wn, model_updater) + constraint.tcv_headloss_constraint.build(m, wn, model_updater) + constraint.fcv_headloss_constraint.build(m, wn, model_updater) + if len(wn.pbv_name_list) > 0: + raise NotImplementedError( + "PBV valves are not currently supported in the WNTRSimulator" + ) + if len(wn.gpv_name_list) > 0: + raise NotImplementedError( + "GPV valves are not currently supported in the WNTRSimulator" + ) + constraint.leak_constraint.build(m, wn, model_updater) + + # TODO: Document that changing a curve with controls does not do anything; you have to change the pump_curve_name attribute on the pump + + return m, model_updater diff --git a/wntr_quantum/scenario/chezy_manning.py b/wntr_quantum/sim/models/chezy_manning.py similarity index 100% rename from wntr_quantum/scenario/chezy_manning.py rename to wntr_quantum/sim/models/chezy_manning.py diff --git a/wntr_quantum/scenario/darcy_weisbach.py b/wntr_quantum/sim/models/darcy_weisbach.py similarity index 97% rename from wntr_quantum/scenario/darcy_weisbach.py rename to wntr_quantum/sim/models/darcy_weisbach.py index 67cf78c..2e6fc2a 100644 --- a/wntr_quantum/scenario/darcy_weisbach.py +++ b/wntr_quantum/sim/models/darcy_weisbach.py @@ -51,9 +51,9 @@ def dw_resistance_value(k, roughness, diameter, diameter_exp, length): Returns: _type_: _description_ """ - print("Roughness : %f" % roughness) - print("diameter : %f" % diameter) - print("resistance coeff : %f " % (k * (diameter**diameter_exp) * length)) + # print("Roughness : %f" % roughness) + # print("diameter : %f" % diameter) + # print("resistance coeff : %f " % (k * (diameter**diameter_exp) * length)) return dw_resistance_prefactor(k, roughness, diameter, diameter_exp) * length @@ -228,7 +228,7 @@ def get_darcy_weisbach_matrix(m, wn, matrices): # noqa: D417 k0 = m.dw_resistance_0[link_name] k1 = m.dw_resistance_1[link_name] k2 = m.dw_resistance_2[link_name] - print(k0.value, k1.value, k2.value) + # print(k0.value, k1.value, k2.value) scaling = 1.0 P0[ieq] -= scaling * k0.value diff --git a/wntr_quantum/scenario/darcy_weisbach_fit.py b/wntr_quantum/sim/models/darcy_weisbach_fit.py similarity index 97% rename from wntr_quantum/scenario/darcy_weisbach_fit.py rename to wntr_quantum/sim/models/darcy_weisbach_fit.py index 96fe968..14f52e8 100644 --- a/wntr_quantum/scenario/darcy_weisbach_fit.py +++ b/wntr_quantum/sim/models/darcy_weisbach_fit.py @@ -42,7 +42,7 @@ def friction_factor(q, e, s): # noqa: D417 return f -def dw_fit(roughness, diameter, plot=True, convert_to_us_unit=False): +def dw_fit(roughness, diameter, plot=False, convert_to_us_unit=False): """_summary. Args: diff --git a/wntr_quantum/scenario/mass_balance.py b/wntr_quantum/sim/models/mass_balance.py similarity index 100% rename from wntr_quantum/scenario/mass_balance.py rename to wntr_quantum/sim/models/mass_balance.py diff --git a/wntr_quantum/sim/solvers.py b/wntr_quantum/sim/quantum_newton_solver.py similarity index 100% rename from wntr_quantum/sim/solvers.py rename to wntr_quantum/sim/quantum_newton_solver.py diff --git a/wntr_quantum/sim/qubo_polynomial_solver.py b/wntr_quantum/sim/qubo_polynomial_solver.py new file mode 100644 index 0000000..73e2c18 --- /dev/null +++ b/wntr_quantum/sim/qubo_polynomial_solver.py @@ -0,0 +1,351 @@ +import matplotlib.pyplot as plt +import numpy as np +import sparse +from quantum_newton_raphson.newton_raphson import newton_raphson +from qubols.encodings import BaseQbitEncoding +from qubols.encodings import DiscreteValuesEncoding +from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector +from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED +from qubols.solution_vector import SolutionVector_V2 as SolutionVector +from wntr.epanet.util import FlowUnits +from wntr.epanet.util import HydParam +from wntr.epanet.util import from_si +from wntr.epanet.util import to_si +from wntr.sim import aml +from wntr.sim.models import constants +from wntr.sim.models import constraint +from wntr.sim.models import param +from wntr.sim.models import var +from wntr.sim.models.utils import ModelUpdater +from wntr.sim.solvers import SolverStatus +from .models.chezy_manning import approx_chezy_manning_headloss_constraint +from .models.chezy_manning import chezy_manning_constants +from .models.chezy_manning import cm_resistance_param +from .models.chezy_manning import get_chezy_manning_matrix +from .models.darcy_weisbach import approx_darcy_weisbach_headloss_constraint +from .models.darcy_weisbach import darcy_weisbach_constants +from .models.darcy_weisbach import dw_resistance_param +from .models.darcy_weisbach import get_darcy_weisbach_matrix +from .models.mass_balance import get_mass_balance_constraint + + +class QuboPolynomialSolver(object): + """Solve the hydraulics equation following a QUBO approach.""" + + def __init__( + self, + wn, + flow_encoding, + head_encoding, + ): # noqa: D417 + """Init the solver. + + Args: + wn (WaterNetwork): water network + flow_encoding (BaseEncoding): binary encoding for the flow + head_encoding (BaseEncoding): binary encoding for the head pipe_diameters (_type_): _description_ + """ + self.wn = wn + + # create the encoding vectors + self.flow_encoding = flow_encoding + self.head_encoding = head_encoding + self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) + self.sol_vect_heads = SolutionVector(wn.num_junctions, encoding=head_encoding) + + # create the aml + self.m, self.model_updater = self.create_model() + + self.mixed_solution_vector = MixedSolutionVector( + [self.sol_vect_flows, self.sol_vect_heads] + ) + + # initialze the matrices of the polynomial equation + self.matrices = self.initialize_matrices() + + def verify_encoding(self): + """Print info regarding the encodings.""" + hres = self.head_encoding.get_average_precision() + hvalues = np.sort(self.head_encoding.get_possible_values()) + fres = self.flow_encoding.get_average_precision() + fvalues = np.sort(self.flow_encoding.get_possible_values()) + print("Head Encoding : %f => %f (res: %f)" % (hvalues[0], hvalues[-1], hres)) + print("Flow Encoding : %f => %f (res: %f)" % (fvalues[0], fvalues[-1], fres)) + + def verify_solution(self, input): + """Generates the classical solution.""" + P0, P1, P2 = self.matrices + + p0 = P0.reshape( + -1, + ) + p1 = P1 + p2 = P2.sum(-1) + return p0 + p1 @ input + (p2 @ (input * input)) + + def classical_solutions(self, max_iter=100, tol=1e-10): + """Computes the classical solution.""" + P0, P1, P2 = self.matrices + num_heads = self.wn.num_junctions + num_pipes = self.wn.num_pipes + num_vars = num_heads + num_pipes + + p0 = P0.reshape( + -1, + ) + p1 = P1 + p2 = P2.sum(-1) + + def func(input): + return p0 + p1 @ input + (p2 @ (input * input)) + + initial_point = np.random.rand(num_vars) + res = newton_raphson(func, initial_point, max_iter=max_iter, tol=tol) + sol = res.solution + assert np.allclose(func(sol), 0) + + # convert back to SI if DW + if self.wn.options.hydraulic.headloss == "D-W": + sol = self.convert_solution_to_si(sol) + + return sol + + @staticmethod + def plot_solution_vs_reference(solution, reference_solution): + """Plots the scatter plot ref/sol. + + Args: + solution (_type_): _description_ + reference_solution (_type_): _description_ + """ + plt.scatter(reference_solution, solution) + plt.axline((0, 0.0), slope=1, color="black", linestyle=(0, (5, 5))) + + plt.axline((0, 0.0), slope=1.05, color="grey", linestyle=(0, (2, 2))) + plt.axline((0, 0.0), slope=0.95, color="grey", linestyle=(0, (2, 2))) + plt.grid(which="major", lw=1) + plt.grid(which="minor", lw=0.1) + plt.loglog() + + def benchmark_solution(self, solution, reference_solution, qubo, bqm): + """Benchmark a solution against the exact reference solution. + + Args: + solution (np.array): _description_ + reference_solution (np.array): _description_ + qubo (_type_): __ + bqm (_type_): __ + """ + if self.wn.options.hydraulic.headloss == "D-W": + reference_solution = self.convert_solution_from_si(reference_solution) + solution = self.convert_solution_from_si(solution) + + data_ref, eref = qubo.compute_energy(reference_solution, bqm) + data_sol, esol = qubo.compute_energy(solution, bqm) + + np.set_printoptions(precision=3) + self.verify_encoding() + print("\n") + print("Error (%):", (1 - (solution / reference_solution)) * 100) + print("\n") + print("sol : ", solution) + print("ref : ", reference_solution) + print("diff: ", reference_solution - solution) + print("\n") + print("encoded_sol: ", np.array(data_sol[0])) + print("encoded_ref: ", np.array(data_ref[0])) + print("diff : ", np.array(data_ref[0]) - np.array(data_sol[0])) + print("\n") + print("E sol : ", esol) + print("R ref : ", eref) + print("Delta E :", esol - eref) + print("\n") + res_sol = np.linalg.norm(self.verify_solution(np.array(data_sol[0]))) + res_ref = np.linalg.norm(self.verify_solution(np.array(data_ref[0]))) + print("Residue sol : ", res_sol) + print("Residue ref : ", res_ref) + print("Delta Residue :", res_sol - res_ref) + + def create_model(self): + """Create the aml. + + Args: + wn (_type_): _description_ + + Raises: + NotImplementedError: _description_ + NotImplementedError: _description_ + ValueError: _description_ + ValueError: _description_ + NotImplementedError: _description_ + NotImplementedError: _description_ + + Returns: + _type_: _description_ + """ + if self.wn.options.hydraulic.demand_model in ["PDD", "PDA"]: + raise ValueError("Pressure Driven simulations not supported") + + if self.wn.options.hydraulic.headloss == "C-M": + import_constants = chezy_manning_constants + resistance_param = cm_resistance_param + approx_head_loss_constraint = approx_chezy_manning_headloss_constraint + elif self.wn.options.hydraulic.headloss == "D-W": + import_constants = darcy_weisbach_constants + resistance_param = dw_resistance_param + approx_head_loss_constraint = approx_darcy_weisbach_headloss_constraint + else: + raise ValueError( + "QUBO Hydraulic Simulations only supported for C-M and D-W simulations" + ) + + m = aml.Model() + model_updater = ModelUpdater() + + # Global constants + import_constants(m) + constants.head_pump_constants(m) + constants.leak_constants(m) + constants.pdd_constants(m) + + param.source_head_param(m, self.wn) + param.expected_demand_param(m, self.wn) + + param.leak_coeff_param.build(m, self.wn, model_updater) + param.leak_area_param.build(m, self.wn, model_updater) + param.leak_poly_coeffs_param.build(m, self.wn, model_updater) + param.elevation_param.build(m, self.wn, model_updater) + + resistance_param.build(m, self.wn, model_updater) + param.minor_loss_param.build(m, self.wn, model_updater) + param.tcv_resistance_param.build(m, self.wn, model_updater) + param.pump_power_param.build(m, self.wn, model_updater) + param.valve_setting_param.build(m, self.wn, model_updater) + + var.flow_var(m, self.wn) + var.head_var(m, self.wn) + var.leak_rate_var(m, self.wn) + + constraint.mass_balance_constraint.build(m, self.wn, model_updater) + + approx_head_loss_constraint.build(m, self.wn, model_updater) + + constraint.head_pump_headloss_constraint.build(m, self.wn, model_updater) + constraint.power_pump_headloss_constraint.build(m, self.wn, model_updater) + constraint.prv_headloss_constraint.build(m, self.wn, model_updater) + constraint.psv_headloss_constraint.build(m, self.wn, model_updater) + constraint.tcv_headloss_constraint.build(m, self.wn, model_updater) + constraint.fcv_headloss_constraint.build(m, self.wn, model_updater) + if len(self.wn.pbv_name_list) > 0: + raise NotImplementedError( + "PBV valves are not currently supported in the WNTRSimulator" + ) + if len(self.wn.gpv_name_list) > 0: + raise NotImplementedError( + "GPV valves are not currently supported in the WNTRSimulator" + ) + constraint.leak_constraint.build(m, self.wn, model_updater) + + # TODO: Document that changing a curve with controls does not do anything; you have to change the pump_curve_name attribute on the pump + + return m, model_updater + + def initialize_matrices(self): + """Initilize the matrix for the QUBO definition.""" + num_equations = len(list(self.m.cons())) + num_variables = len(list(self.m.vars())) + + # must transform that to coo + P0 = np.zeros((num_equations, 1)) + P1 = np.zeros((num_equations, num_variables)) + P2 = np.zeros((num_equations, num_variables, num_variables)) + + matrices = (P0, P1, P2) + + # get the mass balance and headloss matrix contributions + if self.wn.options.hydraulic.headloss == "C-M": + matrices = get_mass_balance_constraint(self.m, self.wn, matrices) + matrices = get_chezy_manning_matrix(self.m, self.wn, matrices) + elif self.wn.options.hydraulic.headloss == "D-W": + matrices = get_mass_balance_constraint( + self.m, self.wn, matrices, convert_to_us_unit=True + ) + matrices = get_darcy_weisbach_matrix(self.m, self.wn, matrices) + else: + raise ValueError("Calculation only possible with C-M or D-W") + return matrices + + def convert_solution_to_si(self, solution): + """Converts the solution to SI. + + Args: + solution (array): solution vectors + """ + num_heads = self.wn.num_junctions + num_pipes = self.wn.num_pipes + new_sol = np.zeros_like(solution) + for ip in range(num_pipes): + new_sol[ip] = to_si(FlowUnits.CFS, solution[ip], HydParam.Flow) + for ih in range(num_pipes, num_pipes + num_heads): + new_sol[ih] = to_si(FlowUnits.CFS, solution[ih], HydParam.Length) + return new_sol + + @staticmethod + def flatten_solution_vector(solution): + """Flattens the solution vector. + + Args: + solution (tuple): tuple of ([flows], [heads]) + """ + sol_tmp = [] + for s in solution: + sol_tmp += s + return sol_tmp + + def convert_solution_from_si(self, solution): + """Converts the solution to SI. + + Args: + solution (array): solution vectors + """ + num_heads = self.wn.num_junctions + num_pipes = self.wn.num_pipes + new_sol = np.zeros_like(solution) + for ip in range(num_pipes): + new_sol[ip] = from_si(FlowUnits.CFS, solution[ip], HydParam.Flow) + for ih in range(num_pipes, num_pipes + num_heads): + new_sol[ih] = from_si(FlowUnits.CFS, solution[ih], HydParam.Length) + return new_sol + + def solve(self, model, strength=1e6, num_reads=1e4, **options): + """Solve the hydraulics equations.""" + self.m = model + self.initialize_matrices() + sol = self.solve_(strength=strength, num_reads=num_reads, **options) + model.load_var_values_from_x(sol) + return ( + SolverStatus.converged, + "Solved Successfully", + 0, + ) + + def solve_(self, strength=1e6, num_reads=1e4, **options): + """Solve the hydraulic equations.""" + qubo = QUBO_POLY_MIXED(self.mixed_solution_vector, **options) + matrices = tuple(sparse.COO(m) for m in self.matrices) + bqm = qubo.create_bqm(matrices, strength=strength) + + # sample + sampleset = qubo.sample_bqm(bqm, num_reads=num_reads) + + # decode + sol = qubo.decode_solution(sampleset.lowest().record[0][0]) + + # flatten solution + sol = self.flatten_solution_vector(sol) + + # convert back to SI if DW + if self.wn.options.hydraulic.headloss == "D-W": + sol = self.convert_solution_to_si(sol) + + return sol From 6394837760d470f84910d40bdef01d17e8bfdc01 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 30 Aug 2024 16:56:54 +0200 Subject: [PATCH 16/96] add custom loading data in model --- docs/notebooks/enYNF3By | Bin 32 -> 0 bytes docs/notebooks/eniBQAh6 | Bin 2244 -> 0 bytes docs/notebooks/enk6t7VQ | Bin 2244 -> 0 bytes docs/notebooks/enrIutMf | Bin 32 -> 0 bytes docs/notebooks/envx2hn0 | Bin 2244 -> 0 bytes .../trash/epanet_qubo_poly_solver.ipynb | 601 +++++++++++++++ docs/notebooks/trash/epanet_qubonetwork.ipynb | 710 ------------------ docs/notebooks/trash/temp.inp | 2 +- docs/notebooks/trash/temp.rpt | 6 +- tests/test_aml_quantum_newton_solver.py | 2 +- wntr_quantum/sim/__init__.py | 4 +- wntr_quantum/sim/core.py | 2 +- wntr_quantum/sim/core_qubo.py | 9 +- wntr_quantum/sim/models/mass_balance.py | 4 +- .../{ => solvers}/quantum_newton_solver.py | 0 .../{ => solvers}/qubo_polynomial_solver.py | 146 +--- 16 files changed, 641 insertions(+), 845 deletions(-) delete mode 100644 docs/notebooks/enYNF3By delete mode 100644 docs/notebooks/eniBQAh6 delete mode 100644 docs/notebooks/enk6t7VQ delete mode 100644 docs/notebooks/enrIutMf delete mode 100644 docs/notebooks/envx2hn0 create mode 100644 docs/notebooks/trash/epanet_qubo_poly_solver.ipynb delete mode 100644 docs/notebooks/trash/epanet_qubonetwork.ipynb rename wntr_quantum/sim/{ => solvers}/quantum_newton_solver.py (100%) rename wntr_quantum/sim/{ => solvers}/qubo_polynomial_solver.py (62%) diff --git 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{ + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([26.477, 22.954], dtype=float32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0][:2]\n", + "ref_pressure" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.05, 0.05], dtype=float32)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_rate" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the Nework QUBO solver" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "wn = wntr.network.WaterNetworkModel(inp_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n" + ] + } + ], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 9\n", + "step = (0.5/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", + "\n", + "nqbit = 9\n", + "step = (50/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")\n", + "\n", + "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)\n", + "net.verify_encoding()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve the system classically" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1. , 1. , 0.999, 0.998])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from wntr_quantum.sim.hydraulics import create_hydraulic_model\n", + "model, model_updater = create_hydraulic_model(wn)\n", + "net.matrices = net.initialize_matrices(model)\n", + "\n", + "ref_sol = net.classical_solutions()\n", + "ref_sol / ref_values" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", + "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "import sparse\n", + "\n", + "from dwave.samplers import SimulatedAnnealingSampler\n", + "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import TabuSampler\n", + "from dimod import ExactSolver\n", + "\n", + "from wntr_quantum.sim.hydraulics import create_hydraulic_model\n", + "\n", + "sampler = TabuSampler()\n", + "sampler = SteepestDescentSolver()\n", + "# sampler = SimulatedAnnealingSampler()\n", + "# sampler = ExactSolver() \n", + "\n", + "model, model_updater = create_hydraulic_model(wn)\n", + "net.matrices = net.initialize_matrices(model)\n", + "\n", + "qubo = QUBO_POLY_MIXED(net.mixed_solution_vector, options={\"sampler\" : sampler} )\n", + "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "bqm = qubo.create_bqm(matrices, strength=1E6)\n", + "sampleset = qubo.sample_bqm(bqm, num_reads=10000)\n", + "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", + "sol = net.flatten_solution_vector(sol)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "sol = net.convert_solution_to_si(sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "net.plot_solution_vs_reference(sol, ref_sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", + "\n", + "\n", + "Error (%): [ 3.135 1.14 -0.895 -1.404]\n", + "\n", + "\n", + "sol : [ 1.71 1.746 87.573 76.223]\n", + "ref : [ 1.766 1.766 86.797 75.168]\n", + "diff: [ 0.055 0.02 -0.777 -1.055]\n", + "\n", + "\n", + "encoded_sol: [ 1.71 1.746 87.573 76.223]\n", + "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", + "diff : [ 0.056 0.021 -0.783 -1.076]\n", + "\n", + "\n", + "E sol : -1662.5932732365866\n", + "R ref : -1662.6061020456154\n", + "Delta E : 0.012828809028860633\n", + "\n", + "\n", + "Residue sol : 0.11372143432826409\n", + "Residue ref : 0.010186471203764017\n", + "Delta Residue : 0.10353496312450007\n" + ] + } + ], + "source": [ + "net.benchmark_solution(sol, ref_sol, qubo, bqm)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 26.394, 22.845])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.solve(model, strength=1E6, num_reads=1000, options={\"sampler\" : sampler})\n", + "model.get_x()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", + "\n", + "\n", + "Error (%): [-0.633 -0.079 0.232 0.289]\n", + "\n", + "\n", + "sol : [ 1.777 1.767 86.595 74.951]\n", + "ref : [ 1.766 1.766 86.797 75.168]\n", + "diff: [-0.011 -0.001 0.202 0.217]\n", + "\n", + "\n", + "encoded_sol: [ 1.777 1.767 86.595 74.951]\n", + "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", + "diff : [-0.011 -0.001 0.196 0.196]\n", + "\n", + "\n", + "E sol : -1662.602117970269\n", + "R ref : -1662.6061020456154\n", + "Delta E : 0.003984075346352256\n", + "\n", + "\n", + "Residue sol : 0.06393622613853261\n", + "Residue ref : 0.010186471203764017\n", + "Delta Residue : 0.0537497549347686\n" + ] + } + ], + "source": [ + "net.benchmark_solution(model.get_x(), ref_sol, qubo, bqm)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define the system " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "data": { - "image/png": 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4rwIAIHSE/CMDKSkp2rZtm1544YVhr581a9Y4LwIAILSEfAxc8u1vf1sZGRmDLsvJydHKlSsNLQIAIDSE1asJuru7lZeXp127dqm8vFx79+7ViRMndPHiRU2cOFEtLS1KT083vBgAgOAS8jEwnMLCQg0MDKipqcn0FAAAgl7YPE1wudLSUrW3t6u/v9/0FAAAgl5YxkBZWZnOnz+vjo4O01MAAAh6YRkDubm5mjBhgqqrq01PAQAg6IVlDMTExGjx4sXEAAAA1yEsY0CSiouL1draqjA8PxIAgFEVtjHg8/l0+vRpffjhh6anAAAQ1MI2BgoKCmRZlmpqakxPAQAgqIVtDCQlJWnhwoWqqqoyPQUAgKAWtjEgSS6XSy0tLaZnAAAQ1MI6Bvx+vz799NMhH20MAAD+X1jHgNvtliRt2LDB7BAAAIJYWMdAWlqaZs+ercrKStNTAAAIWmEdA9KXrypobGw0PQMAgKAV9jFQXl6uAwcO6PPPPzc9BQCAoBT2MVBcXCxJqq+vNzsEAIAgFfYxMGvWLE2ZMkUVFRWmpwAAEJTCPgYkKT8/n0cGAAC4AkfEgM/n0549e3T27FnTUwAACDqOiIGSkhINDAxo48aNpqcAABB0HBEDCxcuVHJyMu83AADAMBwRA5ZlacWKFbwTIQAAw3BEDEhSaWmptm/frt7eXtNTAAAIKo6JAa/Xq97eXm3ZssX0FAAAgopjYiA7O1uxsbGqqqoyPQUAgKDimBiIiorS0qVLVVtba3oKAABBxTExIEkej0dbt25Vf3+/6SkAAAQNR8WAz+fTuXPntGPHDtNTAAAIGo6Kgby8PEVFRammpsb0FAAAgoajYiA2NlaLFi3iJEIAAC7jqBiQJLfbrdbWVtm2bXoKAABBwXExUF5erpMnT2rfvn2mpwAAEBQcFwOrVq2SZVm8xBAAgL9wXAxMnDhR8+fP50OLAAD4C8fFgCQVFhaqubnZ9AwAAIKCI2PA7/fryJEjOnLkiOkpAAAY58gYKCoqkiTV1dUZXgIAgHmOjIGpU6dq5syZqqioMD0FAADjHBkDkrRy5Uo1NjaangEAgHGOjQGfz6f9+/fr1KlTpqcAAGCUY2OgpKREtm3z6AAAwPEcGwNz5sxRamoq5w0AABzPsTFgWZby8vJ4RQEAwPEcGwOS5PV6tXv3bnV3d5ueAgCAMY6Pgf7+frW0tJieAgCAMY6OgTvuuEOJiYl8TgEAwNEcHQMRERHKzc3V+vXrTU8BAMAYR8eA9OVTBR0dHbp48aLpKQAAGEEMeL3q6elRW1ub6SkAABjh+BjIyclRTEyMqqurTU8BAMAIx8fAhAkTdOedd6qmpsb0FAAAjHB8DEiSx+PR5s2bNTAwYHoKAADjjhiQVFZWprNnz2rXrl2mpwAAMO6IAX35ccaRkZGqra01PQUAgHFHDEiKj49XZmamqqqqTE8BAGDcEQN/4Xa71dLSItu2TU8BAGBcEQN/4fP5dOLECR04cMD0FAAAxhUx8Bdut1uWZfHWxAAAxyEG/mLSpEm67bbbVFFRYXoKAADjihi4jMvlUnNzs+kZAACMK2LgMuXl5Tp8+LA+++wz01MAABg3xMBlioqKJEl1dXWGlwAAMH6IgcvMmDFD06dP57wBAICjEANfUVBQoMbGRtMzAAAYN8TAV/h8Pu3du1enT582PQUAgHFBDHyFx+ORbdtqamoyPQUAgHFBDHzF/PnzNWnSJM4bAAA4BjHwFZZlKS8vj1cUAAAcgxgYhtfr1c6dO3XhwgXTUwAAGHPEwDC8Xq/6+vq0adMm01MAABhzxMAwsrKylJCQoKqqKtNTAAAYc8TAMCIjI5WTk6Pa2lrTUwAAGHPEwBWUlJSovb1dfX19pqcAADCmiIEr8Pl8unDhgjo6OkxPAQBgTBEDV5Cbm6vo6GjOGwAAhD1i4Aqio6OVlZWlmpoa01MAABhTxMBVeDwetba2yrZt01MAABgzxMBV+Hw+nTlzRp2dnaanAAAwZoiBqygoKFBERAQvMQQAhDVi4CoSExOVkZHBSYQAgLBGDFyD2+1Wc3Oz6RkAAIwZYuAafD6fjh07pkOHDpmeAgDAmCAGrsHtdkuSNmzYYHYIAABjhBi4htTUVM2ZM0fvv/++6SkAAIwJYuA6FBQUaOPGjaZnAAAwJoiB6+D3+3Xw4EGdOHHC9BQAAEYdMXAdPB6PJKm+vt7wEgAARh8xcB1mzpypqVOnct4AACAsEQPXKT8/X42NjaZnAAAw6oiB6+Tz+bRnzx598cUXpqcAADCqiIHrVFJSooGBAV5VAAAIO8TAdcrIyNDEiRNVWVlpegoAAKOKGLhOlmVp+fLlvBMhACDsEAM3oKysTNu3b1dPT4/pKQAAjBpi4AaUlpbq4sWL2rJli+kpAACMGmLgBtx5552Ki4tTVVWV6SkAAIwaYuAGREVFaenSpaqpqTE9BQCAUUMM3CCPx6O2tjb19/ebngIAwKggBm5QWVmZuru7tX37dtNTAAAYFcTADcrLy1NUVBRPFQAAwgYxcINiY2OVlZXFSYQAgLBBDIyA2+1Wa2urbNs2PQUAgJtGDIxAeXm5Tp06pY8++sj0FAAAbhoxMAKrVq2SZVmqra01PQUAgJtGDIxAcnKyFixYwIcWAQDCAjEwQoWFhWpubjY9AwCAm0YMjJDf79fRo0f1ySefmJ4CAMBNIQZGyO12SxIfaQwACHnEwAhNnTpVM2fO5LwBAEDIIwZuQkFBgRobG03PAADgphADN8Hn82n//v06efKk6SkAAIwYMXATPB6PJKmhocHwEgAARo4YuAlz5sxRWlqaKioqTE8BAGDEiIGbYFmW8vLyVF9fb3oKAAAjRgzcpLKyMnV2durcuXOmpwAAMCLEwE0qLS1Vf3+/WlpaTE8BAGBEiIGblJmZqaSkJM4bAACELGLgJkVERCg3N5d3IgQAhCxiYBR4vV51dHSot7fX9BQAAG4YMTAKvF6vent71dbWZnoKAAA3jBgYBUuXLlVMTIyqq6tNTwEA4IYRA6NgwoQJys7OVk1NjekpAADcMGJglHg8Hm3ZskUDAwOmpwAAcEOIgVFSVlams2fPaufOnaanAABwQ4iBUZKfn6+oqCieKgAAhBxiYJTEx8crMzNTVVVVpqcAAHBDiIFR5Ha7tWnTJtm2bXoKAADXjRgYRX6/X59//rn+/Oc/m54CAMB1IwZGkcvlkmVZWr9+vekpAABcN2JgFE2aNElz587lQ4sAACGFGBhlLpdLzc3NpmcAAHDdiIFRVl5ero8//liffvqp6SkAAFwXYmCUFRUVSZLq6uoMLwEA4PoQA6Ns+vTpmjFjBucNAABCBjEwBgoKCtTY2Gh6BgAA14UYGAM+n08fffSRurq6TE8BAOCaiIEx4PF4ZNu2mpqaTE8BAOCaiIExMG/ePE2ePJnzBgAAIYEYGAOWZSkvL49XFAAAQgIxMEa8Xq927dql8+fPm54CAMBVEQNjpLS0VH19fdq0aZPpKQAAXBUxMEaysrKUkJCgyspK01MAALgqYmCMREZGatmyZXyCIQAg6BEDY6ikpETbtm1TX1+f6SkAAFwRMTCGfD6fLly4oPb2dtNTAAC4ImJgDC1btkzR0dGqrq42PQUAgCsiBsZQdHS0Fi9erJqaGtNTAAC4ImJgjHk8Hm3evFm2bZueAgDAsIiBMebz+XTmzBnt3r3b9BQAAIZFDIyxlStXKiIiQrW1taanAAAwLGJgjCUmJiojI0NVVVWmpwAAMCxiYBy43W41NzebngEAwLCIgXHg9/t1/PhxHTx40PQUAACGIAbGgdvtliRt2LDB7BAAAIZBDIyDW265RbfddpsqKipMTwEAYAhiYJysWrVKTU1NpmcAADAEMTBOfD6fDh06pGPHjpmeAgDAIMTAOCkuLpYk1dfXmx0CAMBXEAPjZObMmZo2bRrnDQAAgg4xMI7y8/PV2NhoegYAAIMQA+PI5/Npz549OnPmjOkpAAAEEAPjqKSkRLZta+PGjaanAAAQQAyMo9tvv10pKSmqrKw0PQUAgABiYBxZlqXly5errq7O9BQAAAKIgXHm9Xq1fft29fT0mJ4CAIAkYmDceb1eXbx4UZs3bzY9BQAAScTAuLvzzjsVFxenqqoq01MAAJBEDIy7yMhI5eTkqKamxvQUAAAkEQNGeDwetbe3q7+/3/QUAACIARN8Pp+6u7v1wQcfmJ4CAAAxYMLy5cs1YcIEVVdXm54CAAAxYEJsbKyysrKIAQBAUCAGDCkqKlJra6ts2zY9BQDgcMSAIX6/X11dXfrwww9NTwEAOBwxYEhBQYEsy1Jtba3pKQAAhyMGDElOTtbtt9/Omw8BAIwjBgxyuVxqbm42PQMA4HDEgEHl5eX69NNP9fHHH5ueAgBwMGLAILfbLUnasGGD2SEAAEcjBgyaMmWKZs6cqYqKCtNTAAAORgwYtmrVKjU1NZmeAQBwMGLAMJ/Ppz//+c/6/PPPTU8BADgUMWCYx+ORJDU0NBheAgBwKmLAsNmzZystLY3zBgAAxhADhlmWpfz8fNXX15ueAgBwKGIgCJSVlWnPnj06e/as6SkAAAciBoJAaWmp+vv7eTdCAIARxEAQyMzMVHJysiorK01PAQA4EDEQBCzLUm5uLu9ECAAwghgIEqWlpfrggw/U29tregoAwGGIgSBRVlam3t5ebd261fQUAIDDEANBYunSpYqNjVVVVZXpKQAAhyEGgkRUVJTuvPNO1dTUmJ4CAHAYYiCIlJSUaOvWrRoYGDA9BQDgIMRAECkrK9O5c+e0Y8cO01MAAA5CDASR/Px8RUVF8VQBAGBcEQNBJC4uTnfccQcnEQIAxhUxEGTcbrc2bdok27ZNTwEAOAQxEGT8fr9Onjyp/fv3m54CAHAIYiDIuFwuWZal2tpa01MAAA5BDASZlJQUzZs3jw8tAgCMG2IgCLlcLj7OGAAwboiBIOT3+/XJJ5/o6NGjpqcAAByAGAhCRUVFkqS6ujrDSwAATkAMBKHp06fr1ltvVUVFhekpAAAHIAaC1MqVK9XQ0GB6BgDAAYiBIOX3+7V//351dXWZngIACHPEQJDyeDyybVuNjY2mpwAAwhwxEKTmzp2ryZMn6/333zc9BQAQ5oiBIGVZlvLz81VfX296CgAgzBEDQczr9Wr37t06f/686SkAgDBGDASx0tJS9fX1qaWlxfQUAEAYIwaCWFZWlhISEvicAgDAmCIGglhERIRyc3O1fv1601MAAGGMGAhyJSUl6ujo0MWLF01PAQCEKWIgyJWVlenChQtqb283PQUAEKaIgSC3bNkyRUdHq7q62vQUAECYIgaCXHR0tJYsWaKamhrTUwAAYYoYCAHFxcXavHmzBgYGTE8BAIQhYiAE+P1+ffHFF9q9e7fpKQCAMEQMhICVK1cqMjJStbW1pqcAAMIQMRACEhISlJGRwZsPAQDGBDEQIoqKirRp0ybZtm16CgAgzBADIcLn8+n48eM6ePCg6SkAgDBDDISIwsJCSeK8AQDAqCMGQsQtt9yiuXPnct4AAGDUEQMhpKCgQM3NzaZnAADCDDEQQvx+vw4dOqRjx46ZngIACCPEQAgpLi6WJNXV1ZkdAgAIK8RACElPT9f06dNVUVFhegoAIIwQAyEmPz9fjY2NpmcAAMIIMRBiysrK9OGHH+rMmTOmpwAAwgQxEGJKS0tl27aamppMTwEAhAliIMQsWLBAKSkpvN8AAGDUEAMhxrIsrVixQhs2bDA9BQAQJoiBEOT1erVjxw5duHDB9BQAQBggBkKQ1+tVX1+fNm/ebHoKACAMEAMhaMmSJYqPj+f9BgAAo4IYCEGRkZHKycnR+vXrTU8BAIQBYiBElZSUqL29XX19faanAABCHDEQosrKynT+/Hl1dHSYngIACHHEQIhavny5JkyYoJqaGtNTAAAhjhgIUTExMVq8eLGqq6tNTwEAhDhiIIQVFRWptbVVtm2bngIACGHEQAjz+Xw6ffq09uzZY3oKACCEEQMhbNWqVbIsS7W1taanAABCGDEQwpKSkpSRkaGqqirTUwAAIYwYCHEul0vNzc2mZwAAQhgxEOLKy8v12Wef6fDhw6anAABCFDEQ4txutyTxkcYAgBEjBkJcWlqaZs2axYcWAQBGjBgIA6tWrVJTU5PpGQCAEEUMhAG/368DBw7oxIkTpqcAAEIQMRAGiouLJUn19fVmhwAAQhIxEAZmz56tKVOmqLKy0vQUAEAIIgbCRH5+vhoaGkzPAACEIGIgTJSVlamzs1Nnz541PQUAEGKIgTBRUlKigYEBbdy40fQUAECIIQbCRGZmppKTkzlvAABww4iBMGFZlpYvX847EQIAbhgxEEZKS0u1fft29fT0mJ4CAAghxEAYKSsrU29vr7Zu3Wp6CgAghBADYSQ7O1uxsbGqqqoyPQUAEEKIgTASFRWl7Oxs1dTUmJ4CAAghxECYKSkpUVtbm/r7+01PAQCECGIgzJSVlencuXPasWOH6SkAgBBBDISZvLw8RUVFqbq62vQUAECIIAbCTFxcnBYtWkQMAACuGzEQhtxut1pbW2XbtukpAIAQQAyEIb/fr5MnT+qjjz4yPQUAEAKIgTC0atUqWZal9evXm54CAAgBxEAYSklJ0fz58/nQIgDAdSEGwpTL5eLjjAEA14UYCFN+v19Hjx7VkSNHTE8BAAQ5YiBMFRUVSZLq6uoMLwEABDtiIExNmzZN6enpqqioMD0FABDkiIEwtnLlSjU2NpqeAQAIcsRAGPP7/dq/f79OnTplegoAIIgRA2HM4/HItm0eHQAAXBUxEMZuu+023XLLLXr//fdNTwEABDFiIIxZlqX8/HzV19ebngIACGLEQJjzer3avXu3uru7TU8BAAQpYiDMlZaWqr+/X83NzaanAACCFDEQ5hYtWqTExERVVVWZngIACFLEQJiLiIhQbm4un2AIALgiYsABSktL1dHRoYsXL5qeAgAIQsSAA3i9XvX09Kitrc30FABAECIGHCAnJ0cxMTGqrq42PQUAEISIAQeIjo7WkiVLVFNTY3oKACAIEQMOUVxcrM2bN2tgYMD0FABAkCEGHMLv9+vs2bPatWuX6SkAgCBDDDjEypUrFRkZqdraWtNTAABBhhhwiPj4eC1cuFCVlZWmpwAAggwx4CBFRUXatGmTbNs2PQUAEESIAQfx+Xw6ceKEDhw4YHoKACCIEAMOUlhYKEmcNwAAGIQYcJDJkydr7ty5nDcAABiEGHAYl8vFxxkDAAYhBhwkNTVVRUVFOnz4sBISEvTUU0+ZngQAQS0qKkpLly7VHXfcoWXLlunXv/514LrW1lbl5uZqwoQJevfddw2uvHlRpgdgfOXl5UmSZs2apY6ODnV3dys+Pt7wKgAITikpKWpvb5ckHTp0SHfffbfq6uqUlJSk2bNn65e//KX+9V//1fDKm0cMOMyDDz4oSers7FRnZ6cKCwvV0NBAEADANaSmpqq7u1uvvfZa4LKcnBxlZmYaXDU6iAEHuXDhwpCPMW5ra9Pq1at17733GloFAMGrr68v8P/NP/7xj9q7d++g69va2hQTE2Ni2qiybN6BxjHi4uJ04cIF0zMAIKxkZGTo5z//uf7qr/7K9JQR45EBB4mMjBz28n/6p3/ikQEAGEZJSUngvVn++Mc/avXq1UO+Z/LkyeM9a9QRAw4SGxurjIyMQU8V5OTk6Cc/+QnnDADAMKKiopSTkyNJSkxM1Jo1a9Td3R24PicnR3PnzjU1b9TwNIFD9PX1afbs2dq7d6/mzp2rrq4uSdItt9yiTZs2KT093exAAAhCUVFRysrKUm9vr+Li4vQP//APioqK0rZt25Samqr//M//VFdXl+Li4rRgwYKQfR8XYsAhOjo69N3vfleNjY2mpwAAggxvOuQAr776qh544AE999xzpqcAAIIQjwwAAOBwPDIAAIDDEQMAADgcMQAAgMMRAwAAOBwxAACAwxEDAAA4HDEAAIDDEQMAADgcMQAAgMMRAwAAOBwxAACAwxEDAAA4HDEAAIDDEQMAADgcMQAAgMMRAwAAOBwxAACAwxEDAAA4HDEAAIDDEQMAADgcMQAAgMMRAwAAOBwxAACAwxEDAAA4HDEAAIDDEQMAADgcMQAAgMMRAwAAOBwxAACAwxEDAAA4HDEAAIDDEQMAADgcMQAAgMMRAwAAOBwxAACAw/0fLxBllqQxZ4oAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", - "\n", - "# Create a water network model\n", - "inp_file = '../networks/Net0.inp'\n", - "# inp_file = '../networks/Net2LoopsDW.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the original Cholesky EPANET simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([26.477, 22.954], dtype=float32)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_pressure = results.node['pressure'].values[0][:2]\n", - "ref_pressure" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.05, 0.05], dtype=float32)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_rate = results.link['flowrate'].values[0]\n", - "ref_rate" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_values = np.append(ref_rate, ref_pressure)\n", - "ref_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with our custom Cholesky EPANET solver \n", - "we use the default solver of the QuantumWNTRSimulator, that uses a LU solver, a s a benchmark of the calculation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", - "Flow : 0.528372\n", - "Roughness: 0.000164\n", - "Diameter: 0.820210\n", - "Viscosity: 0.000011\n", - "Re Number: 58562.855181\n", - "DW - TURBULENT\n", - "DW - Friction factor : 0.019984\n", - "DW - Resistance coeff : 222.481640\n", - "\n", - "Flow : 0.528372\n", - "Roughness: 0.000164\n", - "Diameter: 0.820210\n", - "Viscosity: 0.000011\n", - "Re Number: 58562.855181\n", - "DW - TURBULENT\n", - "DW - Friction factor : 0.019984\n", - "DW - Resistance coeff : 222.481640\n", - "\n", - "Reservoir : 98.425197\n", - "Flow : 1.765728\n", - "Roughness: 0.000164\n", - "Diameter: 0.820210\n", - "Viscosity: 0.000011\n", - "Re Number: 195706.877298\n", - "DW - TURBULENT\n", - "DW - Friction factor : 0.016664\n", - "DW - Resistance coeff : 222.481640\n", - "\n", - "Flow : 1.765726\n", - "Roughness: 0.000164\n", - "Diameter: 0.820210\n", - "Viscosity: 0.000011\n", - "Re Number: 195706.600113\n", - "DW - TURBULENT\n", - "DW - Friction factor : 0.016664\n", - "DW - Resistance coeff : 222.481640\n", - "\n", - "Reservoir : 98.425197\n", - "Flow : 1.765728\n", - "Roughness: 0.000164\n", - "Diameter: 0.820210\n", - "Viscosity: 0.000011\n", - "Re Number: 195706.842128\n", - "DW - TURBULENT\n", - "DW - Friction factor : 0.016664\n", - "DW - Resistance coeff : 222.481640\n", - "\n", - "Flow : 1.765724\n", - "Roughness: 0.000164\n", - "Diameter: 0.820210\n", - "Viscosity: 0.000011\n", - "Re Number: 195706.402191\n", - "DW - TURBULENT\n", - "DW - Friction factor : 0.016664\n", - "DW - Resistance coeff : 222.481640\n", - "\n", - "Flow : 1.765728\n", - "Roughness: 0.000164\n", - "Diameter: 0.820210\n", - "Viscosity: 0.000011\n", - "Re Number: 195706.842128\n", - "DW - TURBULENT\n", - "DW - Friction factor : 0.016664\n", - "DW - Resistance coeff : 222.481640\n", - "\n", - "Flow : 1.765724\n", - "Roughness: 0.000164\n", - "Diameter: 0.820210\n", - "Viscosity: 0.000011\n", - "Re Number: 195706.402191\n", - "DW - TURBULENT\n", - "DW - Friction factor : 0.016664\n", - "DW - Resistance coeff : 222.481640\n", - "\n", - "Reservoir : 98.425197\n", - "Flow : 1.765721\n", - "Roughness: 0.000164\n", - "Diameter: 0.820210\n", - "Viscosity: 0.000011\n", - "Re Number: 195706.086172\n", - "DW - TURBULENT\n", - "DW - Friction factor : 0.016664\n", - "DW - Resistance coeff : 222.481640\n", - "\n", - "Flow : 1.765724\n", - "Roughness: 0.000164\n", - "Diameter: 0.820210\n", - "Viscosity: 0.000011\n", - "Re Number: 195706.402191\n", - "DW - TURBULENT\n", - "DW - Friction factor : 0.016664\n", - "DW - Resistance coeff : 222.481640\n", - "\n" - ] - }, - { - "data": { - "image/png": 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" - ], - "text/plain": [ - "name P1 P2\n", - "0 0.05 0.05\n", - "3600 0.05 0.05" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results.link['flowrate']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the Nework QUBO solver" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sim.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "nqbit = 9\n", - "step = (0.25/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", - "\n", - "nqbit = 9\n", - "step = (250/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", - "\n", - "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - } - ], - "source": [ - "ref_sol = net.classical_solutions()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[ 0. ],\n", - " [ 1.766],\n", - " [99.077],\n", - " [ 0.652]]),\n", - " array([[-1. , 1. , 0. , 0. ],\n", - " [ 0. , -1. , 0. , 0. ],\n", - " [-1.547, 0. , -1. , 0. ],\n", - " [ 0. , -1.547, 1. , -1. ]]),\n", - " array([[[ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ]],\n", - " \n", - " [[ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ]],\n", - " \n", - " [[-3.063, 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ]],\n", - " \n", - " [[ 0. , 0. , 0. , 0. ],\n", - " [ 0. , -3.063, 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ]]]))" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.matrices" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n" - ] - } - ], - "source": [ - "from wntr_quantum.sim.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "nqbit = 9\n", - "step = (0.5/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", - "\n", - "nqbit = 9\n", - "step = (50/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")\n", - "\n", - "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)\n", - "net.verify_encoding()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", - "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", - "import sparse\n", - "\n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "from dwave.samplers import SteepestDescentSolver\n", - "from dwave.samplers import TabuSampler\n", - "from dimod import ExactSolver\n", - "\n", - "sampler = TabuSampler()\n", - "sampler = SteepestDescentSolver()\n", - "# sampler = SimulatedAnnealingSampler()\n", - "# sampler = ExactSolver() \n", - "\n", - "qubo = QUBO_POLY_MIXED(net.mixed_solution_vector, options={\"sampler\" : sampler} )\n", - "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", - "bqm = qubo.create_bqm(matrices, strength=1E6)\n", - "sampleset = qubo.sample_bqm(bqm, num_reads=10000)\n", - "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", - "sol = net.flatten_solution_vector(sol)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "sol = net.convert_solution_to_si(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "net.plot_solution_vs_reference(sol, ref_sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", - "\n", - "\n", - "Error (%): [-0.577 -0.744 0.007 0.158]\n", - "\n", - "\n", - "sol : [ 1.776 1.779 86.791 75.049]\n", - "ref : [ 1.766 1.766 86.797 75.168]\n", - "diff: [-0.01 -0.013 0.006 0.119]\n", - "\n", - "\n", - "encoded_sol: [ 1.776 1.779 86.791 75.049]\n", - "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", - "diff : [-0.01 -0.013 0. 0.098]\n", - "\n", - "\n", - "E sol : -1662.5890489845583\n", - "R ref : -1662.606102046081\n", - "Delta E : 0.017053061522801727\n", - "\n", - "\n", - "Residue sol : 0.13098406084618344\n", - "Residue ref : 0.010186471203764017\n", - "Delta Residue : 0.12079758964241942\n" - ] - } - ], - "source": [ - "net.benchmark_solution(sol, ref_sol, qubo, bqm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "net.m.set_structure()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0. , 0. , 0.001, 0.001])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.m.get_x()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/trash/temp.inp b/docs/notebooks/trash/temp.inp index 26ced5d..5a10def 100644 --- a/docs/notebooks/trash/temp.inp +++ b/docs/notebooks/trash/temp.inp @@ -1,6 +1,6 @@ ; Filename: ../networks/Net0.inp ; WNTR: 1.1.0 -; Created: 2024-08-30 14:28:33 +; Created: 2024-08-30 16:54:05 [TITLE] File obtained via Mario of a 2 node sysem diff --git a/docs/notebooks/trash/temp.rpt b/docs/notebooks/trash/temp.rpt index db12191..d11ca29 100644 --- a/docs/notebooks/trash/temp.rpt +++ b/docs/notebooks/trash/temp.rpt @@ -1,4 +1,4 @@ - Page 1 Fri Aug 30 14:28:33 2024 + Page 1 Fri Aug 30 16:54:05 2024 ****************************************************************** * E P A N E T * @@ -7,6 +7,6 @@ * Version 2.3 * ****************************************************************** - Analysis begun Fri Aug 30 14:28:33 2024 + Analysis begun Fri Aug 30 16:54:05 2024 - Analysis ended Fri Aug 30 14:28:44 2024 + Analysis ended Fri Aug 30 16:54:17 2024 diff --git a/tests/test_aml_quantum_newton_solver.py b/tests/test_aml_quantum_newton_solver.py index f47089a..dd0be3b 100644 --- a/tests/test_aml_quantum_newton_solver.py +++ b/tests/test_aml_quantum_newton_solver.py @@ -9,7 +9,7 @@ from quantum_newton_raphson.vqls_solver import VQLS_SOLVER from qubols.encodings import EfficientEncoding from wntr.sim import aml -from wntr_quantum.sim.quantum_newton_solver import QuantumNewtonSolver +from wntr_quantum.sim.solvers.quantum_newton_solver import QuantumNewtonSolver TOL_RESULTS = 1e-2 diff --git a/wntr_quantum/sim/__init__.py b/wntr_quantum/sim/__init__.py index 4feefcd..8258be3 100644 --- a/wntr_quantum/sim/__init__.py +++ b/wntr_quantum/sim/__init__.py @@ -1,7 +1,9 @@ from .core import QuantumWNTRSimulator +from .core_qubo import FullQuboPolynomialSimulator from .epanet import QuantumEpanetSimulator __all__ = [ "QuantumWNTRSimulator", - "QuantumEpanetSimulator" + "QuantumEpanetSimulator", + "FullQuboPolynomialSimulator", ] diff --git a/wntr_quantum/sim/core.py b/wntr_quantum/sim/core.py index bddabec..3e76aaf 100644 --- a/wntr_quantum/sim/core.py +++ b/wntr_quantum/sim/core.py @@ -6,7 +6,7 @@ from wntr.sim.core import WNTRSimulator from wntr.sim.core import _Diagnostics from wntr.sim.core import _ValveSourceChecker -from .quantum_newton_solver import QuantumNewtonSolver +from .solvers.quantum_newton_solver import QuantumNewtonSolver logger = logging.getLogger(__name__) diff --git a/wntr_quantum/sim/core_qubo.py b/wntr_quantum/sim/core_qubo.py index bb5d951..226bde4 100644 --- a/wntr_quantum/sim/core_qubo.py +++ b/wntr_quantum/sim/core_qubo.py @@ -2,19 +2,16 @@ import warnings import wntr.sim.hydraulics import wntr.sim.results - from wntr.sim.core import WNTRSimulator from wntr.sim.core import _Diagnostics from wntr.sim.core import _ValveSourceChecker - from .hydraulics import create_hydraulic_model - -from .qubo_polynomial_solver import QuboPolynomialSolver +from .solvers.qubo_polynomial_solver import QuboPolynomialSolver logger = logging.getLogger(__name__) -class FullQuantumSimulator(WNTRSimulator): +class FullQuboPolynomialSimulator(WNTRSimulator): """The quantum enabled NR slver.""" def __init__(self, wn, flow_encoding, head_encoding): # noqa: D417 @@ -37,7 +34,7 @@ def __init__(self, wn, flow_encoding, head_encoding): # noqa: D417 self._head_encoding = head_encoding self._flow_encoding = flow_encoding self._solver = QuboPolynomialSolver( - self.wn, flow_encoding=flow_encoding, head_encoding=head_encoding + self._wn, flow_encoding=flow_encoding, head_encoding=head_encoding ) def run_sim( diff --git a/wntr_quantum/sim/models/mass_balance.py b/wntr_quantum/sim/models/mass_balance.py index e85ebe9..cd408b7 100644 --- a/wntr_quantum/sim/models/mass_balance.py +++ b/wntr_quantum/sim/models/mass_balance.py @@ -3,9 +3,7 @@ from wntr.epanet.util import from_si -def get_mass_balance_constraint( - m, wn, matrices, convert_to_us_unit=False -): # noqa: D417 +def get_mass_balance_matrix(m, wn, matrices, convert_to_us_unit=False): # noqa: D417 """Create the matrices for the mass balance equation. Args: diff --git a/wntr_quantum/sim/quantum_newton_solver.py b/wntr_quantum/sim/solvers/quantum_newton_solver.py similarity index 100% rename from wntr_quantum/sim/quantum_newton_solver.py rename to wntr_quantum/sim/solvers/quantum_newton_solver.py diff --git a/wntr_quantum/sim/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py similarity index 62% rename from wntr_quantum/sim/qubo_polynomial_solver.py rename to wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 73e2c18..b0982f5 100644 --- a/wntr_quantum/sim/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -11,22 +11,10 @@ from wntr.epanet.util import HydParam from wntr.epanet.util import from_si from wntr.epanet.util import to_si -from wntr.sim import aml -from wntr.sim.models import constants -from wntr.sim.models import constraint -from wntr.sim.models import param -from wntr.sim.models import var -from wntr.sim.models.utils import ModelUpdater from wntr.sim.solvers import SolverStatus -from .models.chezy_manning import approx_chezy_manning_headloss_constraint -from .models.chezy_manning import chezy_manning_constants -from .models.chezy_manning import cm_resistance_param -from .models.chezy_manning import get_chezy_manning_matrix -from .models.darcy_weisbach import approx_darcy_weisbach_headloss_constraint -from .models.darcy_weisbach import darcy_weisbach_constants -from .models.darcy_weisbach import dw_resistance_param -from .models.darcy_weisbach import get_darcy_weisbach_matrix -from .models.mass_balance import get_mass_balance_constraint +from ..models.chezy_manning import get_chezy_manning_matrix +from ..models.darcy_weisbach import get_darcy_weisbach_matrix +from ..models.mass_balance import get_mass_balance_matrix class QuboPolynomialSolver(object): @@ -52,17 +40,10 @@ def __init__( self.head_encoding = head_encoding self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) self.sol_vect_heads = SolutionVector(wn.num_junctions, encoding=head_encoding) - - # create the aml - self.m, self.model_updater = self.create_model() - self.mixed_solution_vector = MixedSolutionVector( [self.sol_vect_flows, self.sol_vect_heads] ) - # initialze the matrices of the polynomial equation - self.matrices = self.initialize_matrices() - def verify_encoding(self): """Print info regarding the encodings.""" hres = self.head_encoding.get_average_precision() @@ -166,94 +147,10 @@ def benchmark_solution(self, solution, reference_solution, qubo, bqm): print("Residue ref : ", res_ref) print("Delta Residue :", res_sol - res_ref) - def create_model(self): - """Create the aml. - - Args: - wn (_type_): _description_ - - Raises: - NotImplementedError: _description_ - NotImplementedError: _description_ - ValueError: _description_ - ValueError: _description_ - NotImplementedError: _description_ - NotImplementedError: _description_ - - Returns: - _type_: _description_ - """ - if self.wn.options.hydraulic.demand_model in ["PDD", "PDA"]: - raise ValueError("Pressure Driven simulations not supported") - - if self.wn.options.hydraulic.headloss == "C-M": - import_constants = chezy_manning_constants - resistance_param = cm_resistance_param - approx_head_loss_constraint = approx_chezy_manning_headloss_constraint - elif self.wn.options.hydraulic.headloss == "D-W": - import_constants = darcy_weisbach_constants - resistance_param = dw_resistance_param - approx_head_loss_constraint = approx_darcy_weisbach_headloss_constraint - else: - raise ValueError( - "QUBO Hydraulic Simulations only supported for C-M and D-W simulations" - ) - - m = aml.Model() - model_updater = ModelUpdater() - - # Global constants - import_constants(m) - constants.head_pump_constants(m) - constants.leak_constants(m) - constants.pdd_constants(m) - - param.source_head_param(m, self.wn) - param.expected_demand_param(m, self.wn) - - param.leak_coeff_param.build(m, self.wn, model_updater) - param.leak_area_param.build(m, self.wn, model_updater) - param.leak_poly_coeffs_param.build(m, self.wn, model_updater) - param.elevation_param.build(m, self.wn, model_updater) - - resistance_param.build(m, self.wn, model_updater) - param.minor_loss_param.build(m, self.wn, model_updater) - param.tcv_resistance_param.build(m, self.wn, model_updater) - param.pump_power_param.build(m, self.wn, model_updater) - param.valve_setting_param.build(m, self.wn, model_updater) - - var.flow_var(m, self.wn) - var.head_var(m, self.wn) - var.leak_rate_var(m, self.wn) - - constraint.mass_balance_constraint.build(m, self.wn, model_updater) - - approx_head_loss_constraint.build(m, self.wn, model_updater) - - constraint.head_pump_headloss_constraint.build(m, self.wn, model_updater) - constraint.power_pump_headloss_constraint.build(m, self.wn, model_updater) - constraint.prv_headloss_constraint.build(m, self.wn, model_updater) - constraint.psv_headloss_constraint.build(m, self.wn, model_updater) - constraint.tcv_headloss_constraint.build(m, self.wn, model_updater) - constraint.fcv_headloss_constraint.build(m, self.wn, model_updater) - if len(self.wn.pbv_name_list) > 0: - raise NotImplementedError( - "PBV valves are not currently supported in the WNTRSimulator" - ) - if len(self.wn.gpv_name_list) > 0: - raise NotImplementedError( - "GPV valves are not currently supported in the WNTRSimulator" - ) - constraint.leak_constraint.build(m, self.wn, model_updater) - - # TODO: Document that changing a curve with controls does not do anything; you have to change the pump_curve_name attribute on the pump - - return m, model_updater - - def initialize_matrices(self): + def initialize_matrices(self, model): """Initilize the matrix for the QUBO definition.""" - num_equations = len(list(self.m.cons())) - num_variables = len(list(self.m.vars())) + num_equations = len(list(model.cons())) + num_variables = len(list(model.vars())) # must transform that to coo P0 = np.zeros((num_equations, 1)) @@ -264,13 +161,13 @@ def initialize_matrices(self): # get the mass balance and headloss matrix contributions if self.wn.options.hydraulic.headloss == "C-M": - matrices = get_mass_balance_constraint(self.m, self.wn, matrices) - matrices = get_chezy_manning_matrix(self.m, self.wn, matrices) + matrices = get_mass_balance_matrix(model, self.wn, matrices) + matrices = get_chezy_manning_matrix(model, self.wn, matrices) elif self.wn.options.hydraulic.headloss == "D-W": - matrices = get_mass_balance_constraint( - self.m, self.wn, matrices, convert_to_us_unit=True + matrices = get_mass_balance_matrix( + model, self.wn, matrices, convert_to_us_unit=True ) - matrices = get_darcy_weisbach_matrix(self.m, self.wn, matrices) + matrices = get_darcy_weisbach_matrix(model, self.wn, matrices) else: raise ValueError("Calculation only possible with C-M or D-W") return matrices @@ -317,19 +214,30 @@ def convert_solution_from_si(self, solution): new_sol[ih] = from_si(FlowUnits.CFS, solution[ih], HydParam.Length) return new_sol - def solve(self, model, strength=1e6, num_reads=1e4, **options): + @staticmethod + def load_data_in_model(model, data): + """Loads some data in the model. + + Args: + model (_type_): _description_ + data (_type_): _description_ + """ + for iv, v in enumerate(model.vars()): + v.value = data[iv] + + def solve(self, model, strength=1e6, num_reads=10000, **options): """Solve the hydraulics equations.""" - self.m = model - self.initialize_matrices() + self.matrices = self.initialize_matrices(model) sol = self.solve_(strength=strength, num_reads=num_reads, **options) - model.load_var_values_from_x(sol) + model.set_structure() + self.load_data_in_model(model, sol) return ( SolverStatus.converged, "Solved Successfully", 0, ) - def solve_(self, strength=1e6, num_reads=1e4, **options): + def solve_(self, strength=1e6, num_reads=10000, **options): """Solve the hydraulic equations.""" qubo = QUBO_POLY_MIXED(self.mixed_solution_vector, **options) matrices = tuple(sparse.COO(m) for m in self.matrices) From 3e07df7c0c3dcabda42d00d3377c344fb2a09dea Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 30 Aug 2024 17:15:30 +0200 Subject: [PATCH 17/96] added notebook --- docs/notebooks/qubo_poly_solver.ipynb | 594 +++++++++++++++++ docs/notebooks/temp.bin | Bin 2316 -> 1360 bytes docs/notebooks/temp.inp | 401 +----------- docs/notebooks/temp.rpt | 12 + .../trash/epanet_qubo_poly_solver.ipynb | 601 ------------------ 5 files changed, 623 insertions(+), 985 deletions(-) create mode 100644 docs/notebooks/qubo_poly_solver.ipynb delete mode 100644 docs/notebooks/trash/epanet_qubo_poly_solver.ipynb diff --git a/docs/notebooks/qubo_poly_solver.ipynb b/docs/notebooks/qubo_poly_solver.ipynb new file mode 100644 index 0000000..e9de250 --- /dev/null +++ b/docs/notebooks/qubo_poly_solver.ipynb @@ -0,0 +1,594 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "\n", + "# Create a water network model\n", + "inp_file = './networks/Net0.inp'\n", + "# inp_file = './networks/Net2LoopsDW.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the original Cholesky EPANET simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([26.477, 22.954], dtype=float32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0][:2]\n", + "ref_pressure" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.05, 0.05], dtype=float32)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_rate" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the Nework QUBO solver" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "wn = wntr.network.WaterNetworkModel(inp_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n" + ] + } + ], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 9\n", + "step = (0.5/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", + "\n", + "nqbit = 9\n", + "step = (50/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")\n", + "\n", + "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)\n", + "net.verify_encoding()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve the system classically" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1. , 1. , 0.999, 0.998])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from wntr_quantum.sim.hydraulics import create_hydraulic_model\n", + "model, model_updater = create_hydraulic_model(wn)\n", + "net.matrices = net.initialize_matrices(model)\n", + "\n", + "ref_sol = net.classical_solutions()\n", + "ref_sol / ref_values" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", + "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "import sparse\n", + "\n", + "from dwave.samplers import SimulatedAnnealingSampler\n", + "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import TabuSampler\n", + "from dimod import ExactSolver\n", + "\n", + "from wntr_quantum.sim.hydraulics import create_hydraulic_model\n", + "\n", + "sampler = TabuSampler()\n", + "sampler = SteepestDescentSolver()\n", + "# sampler = SimulatedAnnealingSampler()\n", + "# sampler = ExactSolver() \n", + "\n", + "model, model_updater = create_hydraulic_model(wn)\n", + "net.matrices = net.initialize_matrices(model)\n", + "\n", + "qubo = QUBO_POLY_MIXED(net.mixed_solution_vector, options={\"sampler\" : sampler} )\n", + "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "bqm = qubo.create_bqm(matrices, strength=1E6)\n", + "sampleset = qubo.sample_bqm(bqm, num_reads=10000)\n", + "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", + "sol = net.flatten_solution_vector(sol)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "sol = net.convert_solution_to_si(sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "net.plot_solution_vs_reference(sol, ref_sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", + "\n", + "\n", + "Error (%): [-2.129 1.251 0.571 0.289]\n", + "\n", + "\n", + "sol : [ 1.803 1.744 86.301 74.951]\n", + "ref : [ 1.766 1.766 86.797 75.168]\n", + "diff: [-0.038 0.022 0.495 0.217]\n", + "\n", + "\n", + "encoded_sol: [ 1.803 1.744 86.301 74.951]\n", + "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", + "diff : [-0.037 0.023 0.489 0.196]\n", + "\n", + "\n", + "E sol : -1662.601429066175\n", + "R ref : -1662.6061020456154\n", + "Delta E : 0.004672979440556446\n", + "\n", + "\n", + "Residue sol : 0.06911386909308725\n", + "Residue ref : 0.010186471203764017\n", + "Delta Residue : 0.05892739788932324\n" + ] + } + ], + "source": [ + "net.benchmark_solution(sol, ref_sol, qubo, bqm)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.049, 26.573, 0.052, 22.726])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.solve(model, strength=1E6, num_reads=1000, options={\"sampler\" : sampler})\n", + "model.get_x()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", + "\n", + "\n", + "Error (%): [ 1.861e+00 -5.305e+04 9.980e+01 8.092e-01]\n", + "\n", + "\n", + "sol : [1.733e+00 9.384e+02 1.708e-01 7.456e+01]\n", + "ref : [ 1.766 1.766 86.797 75.168]\n", + "diff: [ 3.286e-02 -9.367e+02 8.663e+01 6.083e-01]\n", + "\n", + "\n", + "encoded_sol: [ 1.733 2. 50. 74.56 ]\n", + "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", + "diff : [ 3.327e-02 -2.339e-01 3.679e+01 5.871e-01]\n", + "\n", + "\n", + "E sol : 1262.0976069991214\n", + "R ref : -1662.6061020456154\n", + "Delta E : 2924.7037090447366\n", + "\n", + "\n", + "Residue sol : 54.08053081077456\n", + "Residue ref : 0.010186471203764017\n", + "Delta Residue : 54.070344339570795\n" + ] + } + ], + "source": [ + "net.benchmark_solution(model.get_x(), ref_sol, qubo, bqm)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the original Cholesky EPANET simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([26.477, 22.954], dtype=float32)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_pressure = results.node['pressure'].values[0][:2]\n", - "ref_pressure" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.05, 0.05], dtype=float32)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_rate = results.link['flowrate'].values[0]\n", - "ref_rate" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_values = np.append(ref_rate, ref_pressure)\n", - "ref_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the Nework QUBO solver" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "wn = wntr.network.WaterNetworkModel(inp_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n" - ] - } - ], - "source": [ - "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "nqbit = 9\n", - "step = (0.5/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", - "\n", - "nqbit = 9\n", - "step = (50/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")\n", - "\n", - "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)\n", - "net.verify_encoding()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve the system classically" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([1. , 1. , 0.999, 0.998])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from wntr_quantum.sim.hydraulics import create_hydraulic_model\n", - "model, model_updater = create_hydraulic_model(wn)\n", - "net.matrices = net.initialize_matrices(model)\n", - "\n", - "ref_sol = net.classical_solutions()\n", - "ref_sol / ref_values" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", - "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", - "import sparse\n", - "\n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "from dwave.samplers import SteepestDescentSolver\n", - "from dwave.samplers import TabuSampler\n", - "from dimod import ExactSolver\n", - "\n", - "from wntr_quantum.sim.hydraulics import create_hydraulic_model\n", - "\n", - "sampler = TabuSampler()\n", - "sampler = SteepestDescentSolver()\n", - "# sampler = SimulatedAnnealingSampler()\n", - "# sampler = ExactSolver() \n", - "\n", - "model, model_updater = create_hydraulic_model(wn)\n", - "net.matrices = net.initialize_matrices(model)\n", - "\n", - "qubo = QUBO_POLY_MIXED(net.mixed_solution_vector, options={\"sampler\" : sampler} )\n", - "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", - "bqm = qubo.create_bqm(matrices, strength=1E6)\n", - "sampleset = qubo.sample_bqm(bqm, num_reads=10000)\n", - "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", - "sol = net.flatten_solution_vector(sol)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "sol = net.convert_solution_to_si(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "net.plot_solution_vs_reference(sol, ref_sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", - "\n", - "\n", - "Error (%): [ 3.135 1.14 -0.895 -1.404]\n", - "\n", - "\n", - "sol : [ 1.71 1.746 87.573 76.223]\n", - "ref : [ 1.766 1.766 86.797 75.168]\n", - "diff: [ 0.055 0.02 -0.777 -1.055]\n", - "\n", - "\n", - "encoded_sol: [ 1.71 1.746 87.573 76.223]\n", - "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", - "diff : [ 0.056 0.021 -0.783 -1.076]\n", - "\n", - "\n", - "E sol : -1662.5932732365866\n", - "R ref : -1662.6061020456154\n", - "Delta E : 0.012828809028860633\n", - "\n", - "\n", - "Residue sol : 0.11372143432826409\n", - "Residue ref : 0.010186471203764017\n", - "Delta Residue : 0.10353496312450007\n" - ] - } - ], - "source": [ - "net.benchmark_solution(sol, ref_sol, qubo, bqm)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 26.394, 22.845])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.solve(model, strength=1E6, num_reads=1000, options={\"sampler\" : sampler})\n", - "model.get_x()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", - "\n", - "\n", - "Error (%): [-0.633 -0.079 0.232 0.289]\n", - "\n", - "\n", - "sol : [ 1.777 1.767 86.595 74.951]\n", - "ref : [ 1.766 1.766 86.797 75.168]\n", - "diff: [-0.011 -0.001 0.202 0.217]\n", - "\n", - "\n", - "encoded_sol: [ 1.777 1.767 86.595 74.951]\n", - "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", - "diff : [-0.011 -0.001 0.196 0.196]\n", - "\n", - "\n", - "E sol : -1662.602117970269\n", - "R ref : -1662.6061020456154\n", - "Delta E : 0.003984075346352256\n", - "\n", - "\n", - "Residue sol : 0.06393622613853261\n", - "Residue ref : 0.010186471203764017\n", - "Delta Residue : 0.0537497549347686\n" - ] - } - ], - "source": [ - "net.benchmark_solution(model.get_x(), ref_sol, qubo, bqm)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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docs/notebooks/networks/Net0_CM.inp | 10 +- docs/notebooks/qubo_poly_solver_CM.ipynb | 673 ++++++++++++++++++ docs/notebooks/temp.bin | Bin 1360 -> 1360 bytes docs/notebooks/temp.inp | 14 +- docs/notebooks/temp.rpt | 8 +- .../epanet/Linux/libepanet22_amd64.so | Bin 428336 -> 428336 bytes wntr_quantum/scenario/network_qubo.py | 18 +- wntr_quantum/sim/models/chezy_manning.py | 49 +- wntr_quantum/sim/models/darcy_weisbach.py | 1 - wntr_quantum/sim/models/darcy_weisbach_fit.py | 22 +- .../sim/solvers/qubo_polynomial_solver.py | 22 +- 12 files changed, 752 insertions(+), 65 deletions(-) create mode 100644 docs/notebooks/enOsOEwM create mode 100644 docs/notebooks/qubo_poly_solver_CM.ipynb diff --git a/docs/notebooks/enOsOEwM b/docs/notebooks/enOsOEwM new file mode 100644 index 0000000000000000000000000000000000000000..b73b5c5df37c0f4eeab2b6b7e5d32cc35fdefce1 GIT binary patch literal 88 zcma#_I4pOPfq{V;h?#(x5r|" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "\n", + "# Create a water network model\n", + "inp_file = './networks/Net0_CM.inp'\n", + "# inp_file = './networks/Net2LoopsDW.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the original Cholesky EPANET simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([29.994, 29.988], dtype=float32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0][:2]\n", + "ref_pressure" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.05, 0.05], dtype=float32)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_rate" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run with the Custom EPANET simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Roughness : 0.015000\n", + "Diameter : 3.280840\n", + "Length : 3280.839895\n", + "CM Coeff : 0.006059\n", + "\n", + "Roughness : 0.015000\n", + "Diameter : 3.280840\n", + "Length : 3280.839895\n", + "CM Coeff : 0.006059\n", + "\n", + "Reservoir : 98.425197\n", + "Reservoir : 98.425197\n", + "Reservoir : 98.425197\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os \n", + "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", + "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the Nework QUBO solver" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "wn = wntr.network.WaterNetworkModel(inp_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n" + ] + } + ], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 9\n", + "step = (0.5/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", + "\n", + "nqbit = 9\n", + "step = (50/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")\n", + "\n", + "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)\n", + "net.verify_encoding()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve the system classically" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "roughness : 0.015000\n", + "Diameter : 3.280840\n", + "length : 3280.839895\n", + "value : 0.006056\n", + "roughness : 0.015000\n", + "Diameter : 3.280840\n", + "length : 3280.839895\n", + "value : 0.006056\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1., 1., 1., 1.])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from wntr_quantum.sim.hydraulics import create_hydraulic_model\n", + "model, model_updater = create_hydraulic_model(wn)\n", + "net.matrices = net.initialize_matrices(model)\n", + "\n", + "ref_sol = net.classical_solutions()\n", + "ref_sol / ref_values" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", + "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", + "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "import sparse\n", + "\n", + "from dwave.samplers import SimulatedAnnealingSampler\n", + "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import TabuSampler\n", + "from dimod import ExactSolver\n", + "\n", + "from wntr_quantum.sim.hydraulics import create_hydraulic_model\n", + "\n", + "sampler = TabuSampler()\n", + "sampler = SteepestDescentSolver()\n", + "# sampler = SimulatedAnnealingSampler()\n", + "# sampler = ExactSolver() \n", + "\n", + "model, model_updater = create_hydraulic_model(wn)\n", + "net.matrices = net.initialize_matrices(model)\n", + "\n", + "qubo = QUBO_POLY_MIXED(net.mixed_solution_vector, options={\"sampler\" : sampler} )\n", + "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "bqm = qubo.create_bqm(matrices, strength=1E6)\n", + "sampleset = qubo.sample_bqm(bqm, num_reads=10000)\n", + "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", + "sol = net.flatten_solution_vector(sol)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "sol = net.convert_solution_to_si(sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "net.plot_solution_vs_reference(sol, ref_sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", + "\n", + "\n", + "Error (%): [-2.129 1.251 0.571 0.289]\n", + "\n", + "\n", + "sol : [ 1.803 1.744 86.301 74.951]\n", + "ref : [ 1.766 1.766 86.797 75.168]\n", + "diff: [-0.038 0.022 0.495 0.217]\n", + "\n", + "\n", + "encoded_sol: [ 1.803 1.744 86.301 74.951]\n", + "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", + "diff : [-0.037 0.023 0.489 0.196]\n", + "\n", + "\n", + "E sol : -1662.601429066175\n", + "R ref : -1662.6061020456154\n", + "Delta E : 0.004672979440556446\n", + "\n", + "\n", + "Residue sol : 0.06911386909308725\n", + "Residue ref : 0.010186471203764017\n", + "Delta Residue : 0.05892739788932324\n" + ] + } + ], + "source": [ + "net.benchmark_solution(sol, ref_sol, qubo, bqm)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.049, 26.573, 0.052, 22.726])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.solve(model, strength=1E6, num_reads=1000, options={\"sampler\" : sampler})\n", + "model.get_x()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", + "\n", + "\n", + "Error (%): [ 1.861e+00 -5.305e+04 9.980e+01 8.092e-01]\n", + "\n", + "\n", + "sol : [1.733e+00 9.384e+02 1.708e-01 7.456e+01]\n", + "ref : [ 1.766 1.766 86.797 75.168]\n", + "diff: [ 3.286e-02 -9.367e+02 8.663e+01 6.083e-01]\n", + "\n", + "\n", + "encoded_sol: [ 1.733 2. 50. 74.56 ]\n", + "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", + "diff : [ 3.327e-02 -2.339e-01 3.679e+01 5.871e-01]\n", + "\n", + "\n", + "E sol : 1262.0976069991214\n", + "R ref : -1662.6061020456154\n", + "Delta E : 2924.7037090447366\n", + "\n", + "\n", + "Residue sol : 54.08053081077456\n", + "Residue ref : 0.010186471203764017\n", + "Delta Residue : 54.070344339570795\n" + ] + } + ], + "source": [ + "net.benchmark_solution(model.get_x(), ref_sol, qubo, bqm)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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z{uRCIiqh%*gRZw&vHDr1xu{Y9I5nC81g2I}P(A@pr)>iWBbs7p^|<4$>+*%PIIt3RFT3H|WldIm&4 z$1q3eKlzzHjYR=l)81HAeH1;4#f1c4kne>v;#dGp?FA=qrkIcRGJRsi1fO3YXwAUa z>P&z8Ny)>hOOh$DToym(;1504IZ1F9OgTxWpt8Ypb#XA2oo#AR?!sz)_rhv(yV<4( G(*FUmZOAYH diff --git a/wntr_quantum/scenario/network_qubo.py b/wntr_quantum/scenario/network_qubo.py index 9355a6d..634068e 100644 --- a/wntr_quantum/scenario/network_qubo.py +++ b/wntr_quantum/scenario/network_qubo.py @@ -100,8 +100,7 @@ def func(input): assert np.allclose(func(sol), 0) # convert back to SI if DW - if self.wn.options.hydraulic.headloss == "D-W": - sol = self.convert_solution_to_si(sol) + sol = self.convert_solution_to_si(sol) return sol @@ -131,9 +130,8 @@ def benchmark_solution(self, solution, reference_solution, qubo, bqm): qubo (_type_): __ bqm (_type_): __ """ - if self.wn.options.hydraulic.headloss == "D-W": - reference_solution = self.convert_solution_from_si(reference_solution) - solution = self.convert_solution_from_si(solution) + reference_solution = self.convert_solution_from_si(reference_solution) + solution = self.convert_solution_from_si(solution) data_ref, eref = qubo.compute_energy(reference_solution, bqm) data_sol, esol = qubo.compute_energy(solution, bqm) @@ -258,13 +256,12 @@ def initialize_matrices(self): matrices = (P0, P1, P2) # get the mass balance and headloss matrix contributions + matrices = get_mass_balance_constraint( + self.m, self.wn, matrices, convert_to_us_unit=True + ) if self.wn.options.hydraulic.headloss == "C-M": - matrices = get_mass_balance_constraint(self.m, self.wn, matrices) matrices = get_chezy_manning_matrix(self.m, self.wn, matrices) elif self.wn.options.hydraulic.headloss == "D-W": - matrices = get_mass_balance_constraint( - self.m, self.wn, matrices, convert_to_us_unit=True - ) matrices = get_darcy_weisbach_matrix(self.m, self.wn, matrices) else: raise ValueError("Calculation only possible with C-M or D-W") @@ -328,7 +325,6 @@ def solve(self, strength=1e6, num_reads=1e4, **options): sol = self.flatten_solution_vector(sol) # convert back to SI if DW - if self.wn.options.hydraulic.headloss == "D-W": - sol = self.convert_solution_to_si(sol) + sol = self.convert_solution_to_si(sol) return sol, param diff --git a/wntr_quantum/sim/models/chezy_manning.py b/wntr_quantum/sim/models/chezy_manning.py index 6914e0c..be5eb4a 100644 --- a/wntr_quantum/sim/models/chezy_manning.py +++ b/wntr_quantum/sim/models/chezy_manning.py @@ -1,4 +1,8 @@ +import numpy as np import wntr +from wntr.epanet.util import FlowUnits +from wntr.epanet.util import HydParam +from wntr.epanet.util import from_si from wntr.network import LinkStatus from wntr.sim import aml from wntr.sim.models.utils import Definition @@ -11,32 +15,22 @@ def chezy_manning_constants(m): m (_type_): _description_ """ m.cm_exp = 2 - m.cm_minor_exp = 2 + m.cm_k = (4 / (1.49 * np.pi)) ** 2 * (1 / 4) ** -1.33 + m.cm_roughness_exp = 2 m.cm_diameter_exp = -5.33 - m.cm_k = 21.000 # 4.66 * (3.28) ** m.cm_diameter_exp - # m.cm_exp = 2 - # m.cm_minor_exp = 2 - # m.cm_diameter_exp = -4.8 - # m.cm_k = 10.67 # 4.66 * (3.28) ** m.cm_diameter_exp - # m.cm_exp = 1 - # m.cm_minor_exp = 1 - # m.cm_k = 1 - # m.cm_diameter_exp = -1 - - -def cm_resistance_prefactor(k, roughness, exp, diameter, diameter_exp): +def cm_resistance_prefactor(k, roughness, roughness_exp, diameter, diameter_exp): """_summary_. Args: k (_type_): _description_ roughness (_type_): _description_ - exp (_type_): _description_ + roughness_exp (_type_): _description_ diameter (_type_): _description_ diameter_exp (_type_): _description_ """ - return k * roughness ** (exp) * diameter ** (diameter_exp) + return k * roughness ** (roughness_exp) * diameter ** (diameter_exp) def cm_resistance_value(k, roughness, exp, diameter, diameter_exp, length): @@ -77,14 +71,27 @@ def build(cls, m, wn, updater, index_over=None): # noqa: D417 for link_name in index_over: link = wn.get_link(link_name) + + # convert values from SI to epanet internal + # roughness_us = 0.001 * from_si( + # FlowUnits.CFS, link.roughness, HydParam.Length + # ) + roughness_us = link.roughness + diameter_us = from_si(FlowUnits.CFS, link.diameter, HydParam.Length) + length_us = from_si(FlowUnits.CFS, link.length, HydParam.Length) + value = cm_resistance_value( m.cm_k, - link.roughness, - m.cm_exp, - link.diameter, + roughness_us, + m.cm_roughness_exp, + diameter_us, m.cm_diameter_exp, - link.length, + length_us, ) + print("roughness : %f" % roughness_us) + print("Diameter : %f" % diameter_us) + print("length : %f" % length_us) + print("value : %f" % value) if link_name in m.cm_resistance: m.cm_resistance[link_name].value = value else: @@ -188,7 +195,7 @@ def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 P1[ieq, start_node_index] = 1 else: start_h = m.source_head[start_node_name] - P0[ieq, 0] += start_h.value + P0[ieq, 0] += from_si(FlowUnits.CFS, start_h.value, HydParam.Length) if isinstance(end_node, wntr.network.Junction): end_h = m.head[end_node_name] @@ -196,7 +203,7 @@ def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 P1[ieq, end_node_index] = -1 else: end_h = m.source_head[end_node_name] - P0[ieq, 0] -= end_h.value + P0[ieq, 0] -= from_si(FlowUnits.CFS, end_h.value, HydParam.Length) k = m.cm_resistance[link_name] diff --git a/wntr_quantum/sim/models/darcy_weisbach.py b/wntr_quantum/sim/models/darcy_weisbach.py index 2e6fc2a..5191df4 100644 --- a/wntr_quantum/sim/models/darcy_weisbach.py +++ b/wntr_quantum/sim/models/darcy_weisbach.py @@ -1,6 +1,5 @@ import wntr from wntr.epanet.util import FlowUnits -from wntr.epanet.util import PressureUnits from wntr.epanet.util import HydParam from wntr.epanet.util import from_si from wntr.network import LinkStatus diff --git a/wntr_quantum/sim/models/darcy_weisbach_fit.py b/wntr_quantum/sim/models/darcy_weisbach_fit.py index 14f52e8..e007007 100644 --- a/wntr_quantum/sim/models/darcy_weisbach_fit.py +++ b/wntr_quantum/sim/models/darcy_weisbach_fit.py @@ -101,7 +101,21 @@ def evlaluate_fit(coeffs, flow): if __name__ == "__main__": - res = dw_fit( - roughness=0.000164, diameter=0.820210, plot=True, convert_to_us_unit=False - ) - print(evlaluate_fit(res, 1.766)) + # res = dw_fit( + # roughness=0.000164, diameter=0.820210, plot=True, convert_to_us_unit=False + # ) + # print(evlaluate_fit(res, 1.766)) + roughness = 0.000264 + DIAMS = np.linspace(0.5, 1.5, 25) + RES = [] + for d in DIAMS: + print(d) + res = dw_fit( + roughness=roughness, diameter=d, plot=False, convert_to_us_unit=False + ) + RES.append(res) + RES = np.array(RES) + plt.plot(DIAMS, RES[:, 0]) + plt.plot(DIAMS, RES[:, 1]) + plt.plot(DIAMS, RES[:, 2]) + plt.show() diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index b0982f5..05b68af 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -86,8 +86,7 @@ def func(input): assert np.allclose(func(sol), 0) # convert back to SI if DW - if self.wn.options.hydraulic.headloss == "D-W": - sol = self.convert_solution_to_si(sol) + sol = self.convert_solution_to_si(sol) return sol @@ -117,9 +116,8 @@ def benchmark_solution(self, solution, reference_solution, qubo, bqm): qubo (_type_): __ bqm (_type_): __ """ - if self.wn.options.hydraulic.headloss == "D-W": - reference_solution = self.convert_solution_from_si(reference_solution) - solution = self.convert_solution_from_si(solution) + reference_solution = self.convert_solution_from_si(reference_solution) + solution = self.convert_solution_from_si(solution) data_ref, eref = qubo.compute_energy(reference_solution, bqm) data_sol, esol = qubo.compute_energy(solution, bqm) @@ -159,14 +157,15 @@ def initialize_matrices(self, model): matrices = (P0, P1, P2) - # get the mass balance and headloss matrix contributions + # get the mass balance + matrices = get_mass_balance_matrix( + model, self.wn, matrices, convert_to_us_unit=True + ) + + # get the headloss matrix contributions if self.wn.options.hydraulic.headloss == "C-M": - matrices = get_mass_balance_matrix(model, self.wn, matrices) matrices = get_chezy_manning_matrix(model, self.wn, matrices) elif self.wn.options.hydraulic.headloss == "D-W": - matrices = get_mass_balance_matrix( - model, self.wn, matrices, convert_to_us_unit=True - ) matrices = get_darcy_weisbach_matrix(model, self.wn, matrices) else: raise ValueError("Calculation only possible with C-M or D-W") @@ -253,7 +252,6 @@ def solve_(self, strength=1e6, num_reads=10000, **options): sol = self.flatten_solution_vector(sol) # convert back to SI if DW - if self.wn.options.hydraulic.headloss == "D-W": - sol = self.convert_solution_to_si(sol) + sol = self.convert_solution_to_si(sol) return sol From 6043d20fca10b9292a8f1e2f6e577f07a4a0e36d Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 2 Sep 2024 11:55:19 +0200 Subject: [PATCH 19/96] cm example --- wntr_quantum/sim/models/chezy_manning.py | 4 --- wntr_quantum/sim/models/darcy_weisbach_fit.py | 25 ++++++------------- 2 files changed, 7 insertions(+), 22 deletions(-) diff --git a/wntr_quantum/sim/models/chezy_manning.py b/wntr_quantum/sim/models/chezy_manning.py index be5eb4a..01a60cd 100644 --- a/wntr_quantum/sim/models/chezy_manning.py +++ b/wntr_quantum/sim/models/chezy_manning.py @@ -88,10 +88,6 @@ def build(cls, m, wn, updater, index_over=None): # noqa: D417 m.cm_diameter_exp, length_us, ) - print("roughness : %f" % roughness_us) - print("Diameter : %f" % diameter_us) - print("length : %f" % length_us) - print("value : %f" % value) if link_name in m.cm_resistance: m.cm_resistance[link_name].value = value else: diff --git a/wntr_quantum/sim/models/darcy_weisbach_fit.py b/wntr_quantum/sim/models/darcy_weisbach_fit.py index e007007..f9fb6b1 100644 --- a/wntr_quantum/sim/models/darcy_weisbach_fit.py +++ b/wntr_quantum/sim/models/darcy_weisbach_fit.py @@ -13,12 +13,8 @@ def friction_factor(q, e, s): # noqa: D417 e = pipe roughness / diameter s = viscosity * pipe diameter """ - A1 = 3.14159265358979323850e03 - A2 = 1.57079632679489661930e03 A8 = 4.61841319859066668690e00 A9 = -8.68588963806503655300e-01 - AB = 3.28895476345399058690e-03 - AC = -5.14214965799093883760e-03 w = q / s @@ -27,18 +23,6 @@ def friction_factor(q, e, s): # noqa: D417 y2 = e / 3.7 + y1 y3 = A9 * np.log(y2) f = 1.0 / (y3 * y3) - # else: - # y2 = e / 3.7 + AB - # y3 = A9 * np.log(y2) - # fa = 1.0 / (y3 * y3) - # fb = (2.0 + AC / (y2 * y3)) * fa - # r = w / A2 - # x1 = 7.0 * fa - fb - # x2 = 0.128 - 17.0 * fa + 2.5 * fb - # x3 = -0.128 + 13.0 * fa - (fb + fb) - # x4 = 0.032 - 3.0 * fa + 0.5 * fb - # f = x1 + r * (x2 + r * (x3 + r * x4)) - return f @@ -105,8 +89,8 @@ def evlaluate_fit(coeffs, flow): # roughness=0.000164, diameter=0.820210, plot=True, convert_to_us_unit=False # ) # print(evlaluate_fit(res, 1.766)) - roughness = 0.000264 - DIAMS = np.linspace(0.5, 1.5, 25) + roughness = 0.164 + DIAMS = np.linspace(1, 24, 25) RES = [] for d in DIAMS: print(d) @@ -119,3 +103,8 @@ def evlaluate_fit(coeffs, flow): plt.plot(DIAMS, RES[:, 1]) plt.plot(DIAMS, RES[:, 2]) plt.show() + + plt.plot(DIAMS, RES[:, 0] / RES[:, 1]) + plt.plot(DIAMS, RES[:, 1] / RES[:, 1]) + plt.plot(DIAMS, RES[:, 2] / RES[:, 1]) + plt.show() From 25a8687097bbd42c8063f2450ccfb70fed20aef7 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 2 Sep 2024 11:55:57 +0200 Subject: [PATCH 20/96] cm norebook --- docs/notebooks/qubo_poly_solver_CM.ipynb | 144 ++++++++++++++--------- 1 file changed, 90 insertions(+), 54 deletions(-) diff --git a/docs/notebooks/qubo_poly_solver_CM.ipynb b/docs/notebooks/qubo_poly_solver_CM.ipynb index 1440c76..6e8ad49 100644 --- a/docs/notebooks/qubo_poly_solver_CM.ipynb +++ b/docs/notebooks/qubo_poly_solver_CM.ipynb @@ -323,9 +323,24 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "roughness : 0.015000\n", + "Diameter : 3.280840\n", + "length : 3280.839895\n", + "value : 0.006056\n", + "roughness : 0.015000\n", + "Diameter : 3.280840\n", + "length : 3280.839895\n", + "value : 0.006056\n" + ] + } + ], "source": [ "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", @@ -357,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -366,12 +381,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "

" ] @@ -386,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -397,27 +412,27 @@ "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", "\n", "\n", - "Error (%): [-2.129 1.251 0.571 0.289]\n", + "Error (%): [ 2.138 -1.519 -0.029 -0.147]\n", "\n", "\n", - "sol : [ 1.803 1.744 86.301 74.951]\n", - "ref : [ 1.766 1.766 86.797 75.168]\n", - "diff: [-0.038 0.022 0.495 0.217]\n", + "sol : [ 1.728 1.793 98.434 98.532]\n", + "ref : [ 1.766 1.766 98.406 98.387]\n", + "diff: [ 0.038 -0.027 -0.028 -0.145]\n", "\n", "\n", - "encoded_sol: [ 1.803 1.744 86.301 74.951]\n", - "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", - "diff : [-0.037 0.023 0.489 0.196]\n", + "encoded_sol: [ 1.728 1.793 98.434 98.532]\n", + "encoded_ref: [ 1.766 1.766 98.434 98.434]\n", + "diff : [ 0.038 -0.026 0. -0.098]\n", "\n", "\n", - "E sol : -1662.601429066175\n", - "R ref : -1662.6061020456154\n", - "Delta E : 0.004672979440556446\n", + "E sol : -2343.7316887269144\n", + "R ref : -2343.749937932273\n", + "Delta E : 0.01824920535864294\n", "\n", "\n", - "Residue sol : 0.06911386909308725\n", - "Residue ref : 0.010186471203764017\n", - "Delta Residue : 0.05892739788932324\n" + "Residue sol : 0.13927566663068974\n", + "Residue ref : 0.03388956865892264\n", + "Delta Residue : 0.1053860979717671\n" ] } ], @@ -427,16 +442,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 0.049, 26.573, 0.052, 22.726])" + "array([ 0.049, 30.003, 0.053, 30.003])" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -448,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -459,27 +474,27 @@ "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", "\n", "\n", - "Error (%): [ 1.861e+00 -5.305e+04 9.980e+01 8.092e-01]\n", + "Error (%): [ 1.196e+00 -5.991e+04 9.982e+01 -4.778e-02]\n", "\n", "\n", - "sol : [1.733e+00 9.384e+02 1.708e-01 7.456e+01]\n", - "ref : [ 1.766 1.766 86.797 75.168]\n", - "diff: [ 3.286e-02 -9.367e+02 8.663e+01 6.083e-01]\n", + "sol : [1.745e+00 1.060e+03 1.725e-01 9.843e+01]\n", + "ref : [ 1.766 1.766 98.406 98.387]\n", + "diff: [ 2.111e-02 -1.058e+03 9.823e+01 -4.701e-02]\n", "\n", "\n", - "encoded_sol: [ 1.733 2. 50. 74.56 ]\n", - "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", - "diff : [ 3.327e-02 -2.339e-01 3.679e+01 5.871e-01]\n", + "encoded_sol: [ 1.745 2. 50. 98.434]\n", + "encoded_ref: [ 1.766 1.766 98.434 98.434]\n", + "diff : [ 2.153e-02 -2.339e-01 4.843e+01 0.000e+00]\n", "\n", "\n", - "E sol : 1262.0976069991214\n", - "R ref : -1662.6061020456154\n", - "Delta E : 2924.7037090447366\n", + "E sol : 2347.8261350712914\n", + "R ref : -2343.749937932273\n", + "Delta E : 4691.5760730035645\n", "\n", "\n", - "Residue sol : 54.08053081077456\n", - "Residue ref : 0.010186471203764017\n", - "Delta Residue : 54.070344339570795\n" + "Residue sol : 68.49508903228205\n", + "Residue ref : 0.03388956865892264\n", + "Delta Residue : 68.46119946362313\n" ] } ], @@ -489,12 +504,26 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "roughness : 0.015000\n", + "Diameter : 3.280840\n", + "length : 3280.839895\n", + "value : 0.006056\n", + "roughness : 0.015000\n", + "Diameter : 3.280840\n", + "length : 3280.839895\n", + "value : 0.006056\n" + ] + }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -508,7 +537,7 @@ "" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -527,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -559,14 +588,14 @@ " \n", " \n", " 0\n", - " 26.274834\n", - " 22.338082\n", + " 30.002818\n", + " 30.032642\n", " 0.0\n", " \n", " \n", " 3600\n", - " 26.781840\n", - " 23.560861\n", + " 30.002818\n", + " 30.002818\n", " 0.0\n", " \n", " \n", @@ -575,11 +604,11 @@ ], "text/plain": [ " J1 D1 R1\n", - "0 26.274834 22.338082 0.0\n", - "3600 26.781840 23.560861 0.0" + "0 30.002818 30.032642 0.0\n", + "3600 30.002818 30.002818 0.0" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -590,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -621,13 +650,13 @@ " \n", " \n", " 0\n", - " 0.051258\n", - " 0.052893\n", + " 0.049541\n", + " 0.047850\n", " \n", " \n", " 3600\n", " 0.047435\n", - " 0.047629\n", + " 0.049485\n", " \n", " \n", "\n", @@ -635,11 +664,11 @@ ], "text/plain": [ " P1 P2\n", - "0 0.051258 0.052893\n", - "3600 0.047435 0.047629" + "0 0.049541 0.047850\n", + "3600 0.047435 0.049485" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -647,6 +676,13 @@ "source": [ "results.link['flowrate']" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 7dd1402c68c2a6afe6c238058056c727f5397e88 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 2 Sep 2024 12:04:38 +0200 Subject: [PATCH 21/96] clean up --- wntr_quantum/scenario/network_design_qubo.py | 292 ---------------- wntr_quantum/scenario/network_qubo.py | 330 ------------------- 2 files changed, 622 deletions(-) delete mode 100644 wntr_quantum/scenario/network_design_qubo.py delete mode 100644 wntr_quantum/scenario/network_qubo.py diff --git a/wntr_quantum/scenario/network_design_qubo.py b/wntr_quantum/scenario/network_design_qubo.py deleted file mode 100644 index 1334c32..0000000 --- a/wntr_quantum/scenario/network_design_qubo.py +++ /dev/null @@ -1,292 +0,0 @@ -import itertools -import numpy as np -from quantum_newton_raphson.newton_raphson import newton_raphson -from qubols.encodings import DiscreteValuesEncoding -from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector -from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED -from qubols.solution_vector import SolutionVector_V2 as SolutionVector -from wntr.sim import aml -from wntr.sim.models import constants -from wntr.sim.models import constraint -from wntr.sim.models import param -from wntr.sim.models import var -from wntr.sim.models.utils import ModelUpdater -import sparse -from .chezy_manning import approx_chezy_manning_headloss_constraint -from .chezy_manning import chezy_manning_constants -from .chezy_manning import cm_resistance_param -from .chezy_manning import cm_resistance_prefactor -from .chezy_manning import get_chezy_manning_matrix_design -from .chezy_manning import get_mass_balance_constraint_design - - -class NetworkDesign(object): - """Design problem solved using a QUBO approach.""" - - def __init__( - self, wn, flow_encoding, head_encoding, pipe_diameters, weight_cost=1e-1 - ): # noqa: D417 - """_summary_. - - Args: - wn (_type_): _description_ - encoding_flows (_type_): _description_ - encoding_heads (_type_): _description_ - pipe_diameters (_type_): _description_ - """ - self.wn = wn - self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) - self.sol_vect_heads = SolutionVector( - wn.num_junctions, encoding=head_encoding - ) # not sure num_junction is what we need - - self.pipe_diameters = pipe_diameters - self.roughness_factor = self.get_roughness_factor() - - self.m, self.model_updater = self.create_cm_model() - - self.sol_vect_res = self.get_resistance_prefactor_encoding() - self.mixed_solution_vector = MixedSolutionVector( - [self.sol_vect_flows, self.sol_vect_heads, self.sol_vect_res] - ) - - self.weight_cost = weight_cost - self.head_lb = 10 - self.head_hb = 20 - - self.matrices = self.initialize_matrices() - - def get_roughness_factor(self): - """_summary_. - - Raises: - ValueError: _description_ - - Returns: - _type_: _description_ - """ - index_over = self.wn.pipe_name_list - roughness_factors = [] - for link_name in index_over: - link = self.wn.get_link(link_name) - roughness_factors.append(link.roughness) - - if len(set(roughness_factors)) > 1: - raise ValueError( - "works only with all pipes having the same roughness sorry" - ) - else: - return roughness_factors[0] - - def get_resistance_prefactor_encoding(self): - """_summary_.""" - values = np.array( - [ - cm_resistance_prefactor( - self.m.cm_k, - self.roughness_factor, - self.m.cm_exp, - d, - self.m.cm_diameter_exp, - ) - for d in self.pipe_diameters - ] - ) - values.sort() - nqbit = int(np.ceil(np.log2(len(values)))) - enc = DiscreteValuesEncoding(values, nqbit, "cm_res") - return SolutionVector(size=self.wn.num_pipes, encoding=enc) - - def verify_solution(self, input, params): - """generates the classical solution.""" - - P0, P1, P2, P3 = self.matrices - num_heads = self.wn.num_junctions - num_pipes = self.wn.num_pipes - num_vars = num_heads + num_pipes - - p0 = P0[:num_vars].reshape( - -1, - ) - p1 = P1[:num_vars, :num_vars] - p3 = P3[:num_vars].sum(-1)[:, :num_vars, :num_vars].sum(-1) - parameters = np.array([0] * num_heads + params) - return p0 + p1 @ input + parameters * (p3 @ (input * input)) - - def enumerates_classical_solutions(self): - """generates the classical solution.""" - - P0, P1, P2, P3 = self.matrices - num_heads = self.wn.num_junctions - num_pipes = self.wn.num_pipes - num_vars = num_heads + num_pipes - - p0 = P0[:num_vars].reshape( - -1, - ) - p1 = P1[:num_vars, :num_vars] - p3 = P3[:num_vars].sum(-1)[:, :num_vars, :num_vars].sum(-1) - - def func(input): - return p0 + p1 @ input + parameters * (p3 @ (input * input)) - - # res_prefactor = np.array( - # [ - # cm_resistance_prefactor( - # self.m.cm_k, - # self.roughness_factor, - # self.m.cm_exp, - # d, - # self.m.cm_diameter_exp, - # ) - # for d in self.pipe_diameters - # ] - # ) - # res_prefactor.sort() - - res_prefactor = self.sol_vect_res.encoded_reals[0].get_possible_values() - prefactor_combinations = itertools.product( - res_prefactor, repeat=self.wn.num_pipes - ) - for prefacs in prefactor_combinations: - - parameters = np.array([0] * num_heads + list(prefacs)) - initial_point = np.random.rand(num_vars) - res = newton_raphson(func, initial_point) - assert np.allclose(func(res.solution), 0) - print(prefacs, res.solution) - - def create_cm_model(self): - """Create the aml. - - Args: - wn (_type_): _description_ - - Raises: - NotImplementedError: _description_ - NotImplementedError: _description_ - ValueError: _description_ - ValueError: _description_ - NotImplementedError: _description_ - NotImplementedError: _description_ - - Returns: - _type_: _description_ - """ - if self.wn.options.hydraulic.demand_model in ["PDD", "PDA"]: - raise ValueError("Pressure Driven simulations not supported") - if self.wn.options.hydraulic.headloss not in ["C-M"]: - raise ValueError("Quantum Design only supported for C-M simulations") - - m = aml.Model() - model_updater = ModelUpdater() - - # Global constants - chezy_manning_constants(m) - constants.head_pump_constants(m) - constants.leak_constants(m) - constants.pdd_constants(m) - - param.source_head_param(m, self.wn) - param.expected_demand_param(m, self.wn) - - param.leak_coeff_param.build(m, self.wn, model_updater) - param.leak_area_param.build(m, self.wn, model_updater) - param.leak_poly_coeffs_param.build(m, self.wn, model_updater) - param.elevation_param.build(m, self.wn, model_updater) - - cm_resistance_param.build(m, self.wn, model_updater) - param.minor_loss_param.build(m, self.wn, model_updater) - param.tcv_resistance_param.build(m, self.wn, model_updater) - param.pump_power_param.build(m, self.wn, model_updater) - param.valve_setting_param.build(m, self.wn, model_updater) - - var.flow_var(m, self.wn) - var.head_var(m, self.wn) - var.leak_rate_var(m, self.wn) - - constraint.mass_balance_constraint.build(m, self.wn, model_updater) - - approx_chezy_manning_headloss_constraint.build(m, self.wn, model_updater) - - constraint.head_pump_headloss_constraint.build(m, self.wn, model_updater) - constraint.power_pump_headloss_constraint.build(m, self.wn, model_updater) - constraint.prv_headloss_constraint.build(m, self.wn, model_updater) - constraint.psv_headloss_constraint.build(m, self.wn, model_updater) - constraint.tcv_headloss_constraint.build(m, self.wn, model_updater) - constraint.fcv_headloss_constraint.build(m, self.wn, model_updater) - if len(self.wn.pbv_name_list) > 0: - raise NotImplementedError( - "PBV valves are not currently supported in the WNTRSimulator" - ) - if len(self.wn.gpv_name_list) > 0: - raise NotImplementedError( - "GPV valves are not currently supported in the WNTRSimulator" - ) - constraint.leak_constraint.build(m, self.wn, model_updater) - - # TODO: Document that changing a curve with controls does not do anything; you have to change the pump_curve_name attribute on the pump - - return m, model_updater - - def get_cost_matrix(self, matrices): - """_summary_. - - Args: - matrices (_type_): _description_ - """ - P0, P1, P2, P3 = matrices - n = self.sol_vect_res.size - max_val = self.sol_vect_res.encoded_reals[0].get_max_value() - P0[-1] += self.weight_cost * n * max_val - - istart = self.sol_vect_flows.size + self.sol_vect_heads.size - for i in range(self.sol_vect_res.size): - P1[-1, istart + i] = -self.weight_cost - return P0, P1, P2, P3 - - def initialize_matrices(self): - """_summary_.""" - num_equations = len(list(self.m.cons())) + 1 - num_continuous_variables = len(list(self.m.vars())) - num_discrete_variables = len(self.m.cm_resistance) - - num_variables = num_continuous_variables + num_discrete_variables - - # must transform that to coo - P0 = np.zeros((num_equations, 1)) - P1 = np.zeros((num_equations, num_variables)) - P2 = np.zeros((num_equations, num_variables, num_variables)) - P3 = np.zeros((num_equations, num_variables, num_variables, num_variables)) - - matrices = (P0, P1, P2, P3) - matrices = get_mass_balance_constraint_design(self.m, self.wn, matrices) - matrices = get_chezy_manning_matrix_design(self.m, self.wn, matrices) - matrices = self.get_cost_matrix(matrices) - - return matrices - - def solve(self, **options): - """_summary_""" - qubo = QUBO_POLY_MIXED(self.mixed_solution_vector, **options) - matrices = tuple(sparse.COO(m) for m in self.matrices) - bqm = qubo.create_bqm(matrices, strength=1000) - - # add constraint - istart = self.sol_vect_flows.size - for i in range(self.sol_vect_heads.size): - - bqm.add_linear_inequality_constraint( - qubo.all_expr[istart + i], - lagrange_multiplier=1, - label="head_%s" % i, - lb=self.head_lb, - ub=self.head_hb, - ) - - # sample - sampleset = qubo.sample_bqm(bqm, num_reads=options["num_reads"]) - - # decode - sol, param = qubo.decode_solution(sampleset.lowest()) - return sol, param diff --git a/wntr_quantum/scenario/network_qubo.py b/wntr_quantum/scenario/network_qubo.py deleted file mode 100644 index 634068e..0000000 --- a/wntr_quantum/scenario/network_qubo.py +++ /dev/null @@ -1,330 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import sparse -from quantum_newton_raphson.newton_raphson import newton_raphson -from qubols.encodings import DiscreteValuesEncoding -from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector -from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED -from qubols.solution_vector import SolutionVector_V2 as SolutionVector -from wntr.epanet.util import FlowUnits -from wntr.epanet.util import HydParam -from wntr.epanet.util import from_si -from wntr.epanet.util import to_si -from wntr.sim import aml -from wntr.sim.models import constants -from wntr.sim.models import constraint -from wntr.sim.models import param -from wntr.sim.models import var -from wntr.sim.models.utils import ModelUpdater -from .chezy_manning import approx_chezy_manning_headloss_constraint -from .chezy_manning import chezy_manning_constants -from .chezy_manning import cm_resistance_param -from .chezy_manning import get_chezy_manning_matrix -from .darcy_weisbach import approx_darcy_weisbach_headloss_constraint -from .darcy_weisbach import darcy_weisbach_constants -from .darcy_weisbach import dw_resistance_param -from .darcy_weisbach import get_darcy_weisbach_matrix -from ..sim.headloss_models.mass_balance import get_mass_balance_constraint - - -class Network(object): - """Design problem solved using a QUBO approach.""" - - def __init__( - self, - wn, - flow_encoding, - head_encoding, - ): # noqa: D417 - """_summary_. - - Args: - wn (_type_): _description_ - flow_encoding (_type_): _description_ - head_encoding (_type_): _description_ - pipe_diameters (_type_): _description_ - """ - self.wn = wn - self.flow_encoding = flow_encoding - self.head_encoding = head_encoding - self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) - self.sol_vect_heads = SolutionVector(wn.num_junctions, encoding=head_encoding) - - self.m, self.model_updater = self.create_model() - - self.mixed_solution_vector = MixedSolutionVector( - [self.sol_vect_flows, self.sol_vect_heads] - ) - - self.matrices = self.initialize_matrices() - - def verify_encoding(self): - """Print info regarding the encodings.""" - hres = self.head_encoding.get_average_precision() - hvalues = np.sort(self.head_encoding.get_possible_values()) - fres = self.flow_encoding.get_average_precision() - fvalues = np.sort(self.flow_encoding.get_possible_values()) - print("Head Encoding : %f => %f (res: %f)" % (hvalues[0], hvalues[-1], hres)) - print("Flow Encoding : %f => %f (res: %f)" % (fvalues[0], fvalues[-1], fres)) - - def verify_solution(self, input): - """Generates the classical solution.""" - P0, P1, P2 = self.matrices - - p0 = P0.reshape( - -1, - ) - p1 = P1 - p2 = P2.sum(-1) - return p0 + p1 @ input + (p2 @ (input * input)) - - def classical_solutions(self, max_iter=100, tol=1e-10): - """Generates the classical solution.""" - P0, P1, P2 = self.matrices - num_heads = self.wn.num_junctions - num_pipes = self.wn.num_pipes - num_vars = num_heads + num_pipes - - p0 = P0.reshape( - -1, - ) - p1 = P1 - p2 = P2.sum(-1) - - def func(input): - return p0 + p1 @ input + (p2 @ (input * input)) - - initial_point = np.random.rand(num_vars) - res = newton_raphson(func, initial_point, max_iter=max_iter, tol=tol) - sol = res.solution - assert np.allclose(func(sol), 0) - - # convert back to SI if DW - sol = self.convert_solution_to_si(sol) - - return sol - - @staticmethod - def plot_solution_vs_reference(solution, reference_solution): - """Plots the scatter plot ref/sol. - - Args: - solution (_type_): _description_ - reference_solution (_type_): _description_ - """ - plt.scatter(reference_solution, solution) - plt.axline((0, 0.0), slope=1, color="black", linestyle=(0, (5, 5))) - - plt.axline((0, 0.0), slope=1.05, color="grey", linestyle=(0, (2, 2))) - plt.axline((0, 0.0), slope=0.95, color="grey", linestyle=(0, (2, 2))) - plt.grid(which="major", lw=1) - plt.grid(which="minor", lw=0.1) - plt.loglog() - - def benchmark_solution(self, solution, reference_solution, qubo, bqm): - """Benchmark a solution against the exact reference solution. - - Args: - solution (np.array): _description_ - reference_solution (np.array): _description_ - qubo (_type_): __ - bqm (_type_): __ - """ - reference_solution = self.convert_solution_from_si(reference_solution) - solution = self.convert_solution_from_si(solution) - - data_ref, eref = qubo.compute_energy(reference_solution, bqm) - data_sol, esol = qubo.compute_energy(solution, bqm) - - np.set_printoptions(precision=3) - self.verify_encoding() - print("\n") - print("Error (%):", (1 - (solution / reference_solution)) * 100) - print("\n") - print("sol : ", solution) - print("ref : ", reference_solution) - print("diff: ", reference_solution - solution) - print("\n") - print("encoded_sol: ", np.array(data_sol[0])) - print("encoded_ref: ", np.array(data_ref[0])) - print("diff : ", np.array(data_ref[0]) - np.array(data_sol[0])) - print("\n") - print("E sol : ", esol) - print("R ref : ", eref) - print("Delta E :", esol - eref) - print("\n") - res_sol = np.linalg.norm(self.verify_solution(np.array(data_sol[0]))) - res_ref = np.linalg.norm(self.verify_solution(np.array(data_ref[0]))) - print("Residue sol : ", res_sol) - print("Residue ref : ", res_ref) - print("Delta Residue :", res_sol - res_ref) - - def create_model(self): - """Create the aml. - - Args: - wn (_type_): _description_ - - Raises: - NotImplementedError: _description_ - NotImplementedError: _description_ - ValueError: _description_ - ValueError: _description_ - NotImplementedError: _description_ - NotImplementedError: _description_ - - Returns: - _type_: _description_ - """ - if self.wn.options.hydraulic.demand_model in ["PDD", "PDA"]: - raise ValueError("Pressure Driven simulations not supported") - - if self.wn.options.hydraulic.headloss == "C-M": - import_constants = chezy_manning_constants - resistance_param = cm_resistance_param - approx_head_loss_constraint = approx_chezy_manning_headloss_constraint - elif self.wn.options.hydraulic.headloss == "D-W": - import_constants = darcy_weisbach_constants - resistance_param = dw_resistance_param - approx_head_loss_constraint = approx_darcy_weisbach_headloss_constraint - else: - raise ValueError( - "QUBO Hydraulic Simulations only supported for C-M and D-W simulations" - ) - - m = aml.Model() - model_updater = ModelUpdater() - - # Global constants - import_constants(m) - constants.head_pump_constants(m) - constants.leak_constants(m) - constants.pdd_constants(m) - - param.source_head_param(m, self.wn) - param.expected_demand_param(m, self.wn) - - param.leak_coeff_param.build(m, self.wn, model_updater) - param.leak_area_param.build(m, self.wn, model_updater) - param.leak_poly_coeffs_param.build(m, self.wn, model_updater) - param.elevation_param.build(m, self.wn, model_updater) - - resistance_param.build(m, self.wn, model_updater) - param.minor_loss_param.build(m, self.wn, model_updater) - param.tcv_resistance_param.build(m, self.wn, model_updater) - param.pump_power_param.build(m, self.wn, model_updater) - param.valve_setting_param.build(m, self.wn, model_updater) - - var.flow_var(m, self.wn) - var.head_var(m, self.wn) - var.leak_rate_var(m, self.wn) - - constraint.mass_balance_constraint.build(m, self.wn, model_updater) - - approx_head_loss_constraint.build(m, self.wn, model_updater) - - constraint.head_pump_headloss_constraint.build(m, self.wn, model_updater) - constraint.power_pump_headloss_constraint.build(m, self.wn, model_updater) - constraint.prv_headloss_constraint.build(m, self.wn, model_updater) - constraint.psv_headloss_constraint.build(m, self.wn, model_updater) - constraint.tcv_headloss_constraint.build(m, self.wn, model_updater) - constraint.fcv_headloss_constraint.build(m, self.wn, model_updater) - if len(self.wn.pbv_name_list) > 0: - raise NotImplementedError( - "PBV valves are not currently supported in the WNTRSimulator" - ) - if len(self.wn.gpv_name_list) > 0: - raise NotImplementedError( - "GPV valves are not currently supported in the WNTRSimulator" - ) - constraint.leak_constraint.build(m, self.wn, model_updater) - - # TODO: Document that changing a curve with controls does not do anything; you have to change the pump_curve_name attribute on the pump - - return m, model_updater - - def initialize_matrices(self): - """Initilize the matrix for the QUBO definition.""" - num_equations = len(list(self.m.cons())) - num_variables = len(list(self.m.vars())) - - # must transform that to coo - P0 = np.zeros((num_equations, 1)) - P1 = np.zeros((num_equations, num_variables)) - P2 = np.zeros((num_equations, num_variables, num_variables)) - - matrices = (P0, P1, P2) - - # get the mass balance and headloss matrix contributions - matrices = get_mass_balance_constraint( - self.m, self.wn, matrices, convert_to_us_unit=True - ) - if self.wn.options.hydraulic.headloss == "C-M": - matrices = get_chezy_manning_matrix(self.m, self.wn, matrices) - elif self.wn.options.hydraulic.headloss == "D-W": - matrices = get_darcy_weisbach_matrix(self.m, self.wn, matrices) - else: - raise ValueError("Calculation only possible with C-M or D-W") - return matrices - - def convert_solution_to_si(self, solution): - """Converts the solution to SI. - - Args: - solution (array): solution vectors - """ - num_heads = self.wn.num_junctions - num_pipes = self.wn.num_pipes - new_sol = np.zeros_like(solution) - for ip in range(num_pipes): - new_sol[ip] = to_si(FlowUnits.CFS, solution[ip], HydParam.Flow) - for ih in range(num_pipes, num_pipes + num_heads): - new_sol[ih] = to_si(FlowUnits.CFS, solution[ih], HydParam.Length) - return new_sol - - @staticmethod - def flatten_solution_vector(solution): - """Flattens the solution vector. - - Args: - solution (tuple): tuple of ([flows], [heads]) - """ - sol_tmp = [] - for s in solution: - sol_tmp += s - return sol_tmp - - def convert_solution_from_si(self, solution): - """Converts the solution to SI. - - Args: - solution (array): solution vectors - """ - num_heads = self.wn.num_junctions - num_pipes = self.wn.num_pipes - new_sol = np.zeros_like(solution) - for ip in range(num_pipes): - new_sol[ip] = from_si(FlowUnits.CFS, solution[ip], HydParam.Flow) - for ih in range(num_pipes, num_pipes + num_heads): - new_sol[ih] = from_si(FlowUnits.CFS, solution[ih], HydParam.Length) - return new_sol - - def solve(self, strength=1e6, num_reads=1e4, **options): - """Solve the hydraulic equations.""" - qubo = QUBO_POLY_MIXED(self.mixed_solution_vector, **options) - matrices = tuple(sparse.COO(m) for m in self.matrices) - bqm = qubo.create_bqm(matrices, strength=strength) - - # sample - sampleset = qubo.sample_bqm(bqm, num_reads=num_reads) - - # decode - sol = qubo.decode_solution(sampleset.lowest().record[0][0]) - - # flatten solution - sol = self.flatten_solution_vector(sol) - - # convert back to SI if DW - sol = self.convert_solution_to_si(sol) - - return sol, param From 123c073db96a52120f0e5bd717c0c2fe37535499 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 2 Sep 2024 12:04:50 +0200 Subject: [PATCH 22/96] clean up --- docs/notebooks/enOsOEwM | Bin 88 -> 0 bytes docs/notebooks/temp.bin | Bin 1360 -> 0 bytes docs/notebooks/temp.inp | 124 -- docs/notebooks/temp.rpt | 12 - docs/notebooks/trash/epanet.ipynb | 487 ----- docs/notebooks/trash/poly_brute_force.py | 557 ------ docs/notebooks/trash/temp.bin | Bin 1360 -> 0 bytes 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docs/notebooks/trash/wntr_qubo_poly_linear_system.ipynb diff --git a/docs/notebooks/enOsOEwM b/docs/notebooks/enOsOEwM deleted file mode 100644 index b73b5c5df37c0f4eeab2b6b7e5d32cc35fdefce1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 88 zcma#_I4pOPfq{V;h?#(x5r|6%0}5k2;%5A>f>mnwy!Nm_x2{ zUWWK}x)6{LA|M|?NZts){jjnCRvv(85C(~(f)9=i3{@_u7^cF*Ns8Iykm;BAj#)3> z1J!+Sgt2d}soDxt1)_oKoId|^cj8TMvW-Y+vc0l;k2MG*)HpaWFgQ5$A1bv0VVL;M z>?bzirB9%I52xIB?uX2Q_T@nBgR#-=1F8etH#rny--Q<9?7NC$AIueqvM~}^mI45T CI%sA9 diff --git a/docs/notebooks/temp.inp b/docs/notebooks/temp.inp deleted file mode 100644 index 1c8a883..0000000 --- a/docs/notebooks/temp.inp +++ /dev/null @@ -1,124 +0,0 @@ -; Filename: ./networks/Net0_CM.inp -; WNTR: 1.1.0 -; Created: 2024-09-02 10:42:15 -[TITLE] -File obtained via Mario of a 2 node sysem - -[JUNCTIONS] -;ID Elevation Demand Pattern - J1 0 0 ; - D1 0 50 ; - -[RESERVOIRS] -;ID Head Pattern - R1 30 ; - -[TANKS] -;ID Elevation Init Level Min Level Max Level Diameter Min Volume Volume Curve Overflow - -[PIPES] -;ID Node1 Node2 Length Diameter Roughness Minor Loss Status - P1 R1 J1 1000 1000 0.015 0 Open ; - P2 J1 D1 1000 1000 0.015 0 Open ; - -[PUMPS] -;ID Node1 Node2 Properties - -[VALVES] -;ID Node1 Node2 Diameter Type Setting Minor Loss - -[TAGS] -;type name tag - -[DEMANDS] -;ID Demand Pattern - -[STATUS] -;ID Setting - -[PATTERNS] -;ID Multipliers - -[CURVES] -;ID X-Value Y-Value - -[CONTROLS] - -[RULES] - -[ENERGY] -GLOBAL EFFICIENCY 75.0000 -GLOBAL PRICE 0.0000 -DEMAND CHARGE 0.0000 - -[EMITTERS] -;ID Flow coefficient - -[QUALITY] - -[SOURCES] -;Node Type Quality Pattern - -[REACTIONS] -;Type Pipe/Tank Coefficient - - ORDER BULK 1 - ORDER TANK 1 - ORDER WALL 1 - GLOBAL BULK 0.0000 - GLOBAL WALL 0.0000 - LIMITING POTENTIAL 0.0000 - ROUGHNESS CORRELATION 0.0000 - -[MIXING] -;Tank ID Model Fraction - -[TIMES] -DURATION 01:00:00 -HYDRAULIC TIMESTEP 01:00:00 -QUALITY TIMESTEP 00:05:00 -PATTERN TIMESTEP 01:00:00 -PATTERN START 00:00:00 -REPORT TIMESTEP 01:00:00 -REPORT START 00:00:00 -START CLOCKTIME 00:00:00 AM -RULE TIMESTEP 00:06:00 -STATISTIC NONE - -[REPORT] -SUMMARY NO -PAGE 0 - -[OPTIONS] -UNITS LPS -HEADLOSS C-M -SPECIFIC GRAVITY 1 -VISCOSITY 1 -TRIALS 40 -ACCURACY 0.1 -CHECKFREQ 2 -MAXCHECK 10 -UNBALANCED CONTINUE 10 -DEMAND MULTIPLIER 1 -EMITTER EXPONENT 0.5 -QUALITY NONE -DIFFUSIVITY 1 -TOLERANCE 0.01 - -[COORDINATES] -;Node X-Coord Y-Coord -J1 10.000000000 60.000000000 -D1 110.000000000 60.000000000 -R1 -11.722140000 74.240230000 - -[VERTICES] -;Link X-Coord Y-Coord - -[LABELS] - -[BACKDROP] -DIMENSIONS 0.000 0.000 10000.000 10000.000 -UNITS NONE -OFFSET 0.00 0.00 - -[END] diff --git a/docs/notebooks/temp.rpt b/docs/notebooks/temp.rpt deleted file mode 100644 index c6c6702..0000000 --- a/docs/notebooks/temp.rpt +++ /dev/null @@ -1,12 +0,0 @@ - Page 1 Mon Sep 2 10:42:15 2024 - - ****************************************************************** - * E P A N E T * - * Hydraulic and Water Quality * - * Analysis for Pipe Networks * - * Version 2.3 * - ****************************************************************** - - Analysis begun Mon Sep 2 10:42:15 2024 - - Analysis ended Mon Sep 2 10:42:31 2024 diff --git a/docs/notebooks/trash/epanet.ipynb b/docs/notebooks/trash/epanet.ipynb deleted file mode 100644 index 54f333a..0000000 --- a/docs/notebooks/trash/epanet.ipynb +++ /dev/null @@ -1,487 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define the system " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "\n", - "# Create a water network model\n", - "inp_file = '../networks/Net0.inp'\n", - "inp_file = '../networks/Net2LoopsDW.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the original Cholesky EPANET simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "name\n", - "2 5.446573e+01\n", - "3 2.785345e+01\n", - "4 4.665007e+01\n", - "5 1.385757e+01\n", - "6 3.296196e+01\n", - "7 2.745172e+01\n", - "1 4.394531e-07\n", - "Name: 0, dtype: float32" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pressure_at_5hr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with our custom Cholesky EPANET solver \n", - "we use the default solver of the QuantumWNTRSimulator, that uses a LU solver, a s a benchmark of the calculation" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - TURBULENT\n", - "DW - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os \n", - "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", - "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", - "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the AEQUBOLS solver" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", - "Solving the linear system Ax = b with:\n", - "A = [[ 0.1155474 -0.1155474]\n", - " [-0.1155474 2.454284 ]]\n", - "b = [ -1.614401 230.2773 ]\n", - "x = [77.95698925 97.50733138]\n", - "residue = 0.645100575394777\n", - "Solving the linear system Ax = b with:\n", - "A = [[ 0.0203655 -0.0203655]\n", - " [-0.0203655 0.6378524]]\n", - "b = [-0.6197366 60.7565 ]\n", - "x = [62.31671554 97.2629521 ]\n", - "residue = 0.09299011493672167\n", - "Solving the linear system Ax = b with:\n", - "A = [[ 0.02591948 -0.02591948]\n", - " [-0.02591948 0.8072867 ]]\n", - "b = [-0.8891007 76.89368 ]\n", - "x = [62.8054741 97.2629521]\n", - "residue = 0.004721498046104942\n", - "Solving the linear system Ax = b with:\n", - "A = [[ 0.02710065 -0.02710065]\n", - " [-0.02710065 0.8371059 ]]\n", - "b = [-0.9318042 79.72048 ]\n", - "x = [62.31671554 97.2629521 ]\n", - "residue = 0.018294070336562634\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from quantum_newton_raphson.qubo_solver import QUBO_SOLVER\n", - "\n", - "linear_solver = QUBO_SOLVER(\n", - " num_qbits=11,\n", - " num_reads=250,\n", - " # iterations=5,\n", - " range=250,\n", - " offset=0,\n", - " # temperature=1e4,\n", - " use_aequbols=False,\n", - ")\n", - "\n", - "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn, linear_solver=linear_solver)\n", - "results = sim.run_sim(linear_solver=linear_solver)\n", - "\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "name\n", - "J1 2.964575e+01\n", - "D1 1.914311e+01\n", - "R1 -9.338379e-07\n", - "Name: 0, dtype: float32" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pressure_at_5hr" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os \n", - "epanet_path = os.environ[\"EPANET_QUANTUM\"]\n", - "epanet_tmp = os.environ[\"EPANET_TMP\"]\n", - "util_path = os.path.join(epanet_path, 'src/py/')\n", - "sys.path.append(util_path)\n", - "from quantum_linsolve import load_json_data \n", - "A, b = load_json_data(os.path.join(epanet_tmp,'smat.json'))" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "from quantum_newton_raphson.qubo_solver import QUBO_SOLVER\n", - "linear_solver = QUBO_SOLVER(\n", - " num_qbits=13,\n", - " num_reads=500,\n", - " # iterations=5,\n", - " range=250,\n", - " offset=0,\n", - " # temperature=1e4,\n", - " use_aequbols=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "qubo_sol = linear_solver(A, b)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np \n", - "import matplotlib.pyplot as plt \n", - "np_sol = np.linalg.solve(A.todense(), b)\n", - "plt.scatter(np_sol, qubo_sol.solution)\n", - "plt.axline((0, 0), slope=1, linestyle=\"--\", color=\"gray\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter( list(range(2)), (A@np_sol-b))\n", - "plt.scatter(list(range(2)), (A@qubo_sol.solution-b))" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "from quantum_newton_raphson.vqls_solver import VQLS_SOLVER\n", - "from vqls_prototype import VQLS\n", - "from qiskit.primitives import Estimator\n", - "from qiskit.circuit.library import RealAmplitudes \n", - "from qiskit_algorithms.optimizers import CG\n", - "qc = RealAmplitudes(2, reps=3, entanglement='full')\n", - "estimator = Estimator()\n", - "\n", - "vqls = VQLS(\n", - " estimator,\n", - " qc,\n", - " CG(),\n", - " options={\"matrix_decomposition\" : \"pauli\"}\n", - ")\n", - "# res = vqls.solve(A.todense(),b)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 4/4 [00:00<00:00, 231.54it/s]\n" - ] - } - ], - "source": [ - "from vqls_prototype.matrix_decomposition import PauliDecomposition, SymmetricDecomposition\n", - "pd = PauliDecomposition(A)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/trash/poly_brute_force.py b/docs/notebooks/trash/poly_brute_force.py deleted file mode 100644 index 1a76f7e..0000000 --- a/docs/notebooks/trash/poly_brute_force.py +++ /dev/null @@ -1,557 +0,0 @@ -#!/usr/bin/env python -# Input a QUBO instance and solve using brute force - -import numpy as np - - -def calculate_squared_residuals(P0, P1, P2): - residual = dict() - # x labels the states and o labels the operator - # number of x's corresponds to the rank of the tensor since the state has not been contracted yet - residual["dim0_o"] = np.einsum("i,i", P0, P0) - - residual["dim1_ox"] = np.einsum("i,ij->j", P0.T, P1) - residual["dim1_xo"] = np.einsum("ji,i->j", P1.T, P0) - - residual["dim2_oxx"] = np.einsum("i,ijk->jk", P0.T, P2) - residual["dim2_xox"] = np.einsum("ji,ik->jk", P1.T, P1) - residual["dim2_xxo"] = np.einsum("kji,i->kj", P2.T, P0) - - residual["dim3_xoxx"] = np.einsum("ji,ikl->jkl", P1.T, P2) - residual["dim3_xxox"] = np.einsum("kji,il->kjl", P2.T, P1) - - residual["dim4_xxoxx"] = np.einsum("kji,inm->kjnm", P2.T, P2) - - return residual - - -def calculate_residual_offsets(P0, P1, P2, basis_offset): - residual_offset = dict() - ### calculate QUBO offsets - # x labels the states, o labels the operator, b labels the offset - # D1 - residual_dim1_ob = np.einsum("i,ij,j", P0.T, P1, basis_offset) - - residual_dim1_bo = np.einsum("j,ji,i", basis_offset.T, P1.T, P0) - - residual_offset["dim1_o"] = residual_dim1_ob + residual_dim1_bo - - # D2 - residual_dim2_obx = np.einsum("i,ijk,j", P0.T, P2, basis_offset) - residual_dim2_oxb = np.einsum("i,ijk,k", P0.T, P2, basis_offset) - residual_dim2_obb = np.einsum("i,ijk,j,k", P0.T, P2, basis_offset, basis_offset) - - residual_dim2_box = np.einsum("j,ji,ik", basis_offset.T, P1.T, P1) - residual_dim2_xob = np.einsum("ji,ik,k", P1.T, P1, basis_offset) - residual_dim2_bob = np.einsum("j,ji,ik,k", basis_offset.T, P1.T, P1, basis_offset) - - residual_dim2_bxo = np.einsum("k,kji,i", basis_offset.T, P2.T, P0) - residual_dim2_xbo = np.einsum("j,kji,i", basis_offset.T, P2.T, P0) - residual_dim2_bbo = np.einsum("k,j,kji,i", basis_offset.T, basis_offset.T, P2.T, P0) - - residual_offset["dim2_ox"] = ( - residual_dim2_obx + residual_dim2_oxb + residual_dim2_box - ) - residual_offset["dim2_xo"] = ( - residual_dim2_xob + residual_dim2_bxo + residual_dim2_xbo - ) - residual_offset["dim2_o"] = ( - residual_dim2_obb + residual_dim2_bob + residual_dim2_bbo - ) - - # D3 - residual_dim3_xoxb = np.einsum("ji,ikl,l", P1.T, P2, basis_offset) - residual_dim3_xobx = np.einsum("ji,ikl,k", P1.T, P2, basis_offset) - residual_dim3_boxx = np.einsum("j,ji,ikl", basis_offset.T, P1.T, P2) - residual_dim3_xobb = np.einsum("ji,ikl,k,l", P1.T, P2, basis_offset, basis_offset) - residual_dim3_boxb = np.einsum("j,ji,ikl,l", basis_offset.T, P1.T, P2, basis_offset) - residual_dim3_bobx = np.einsum("j,ji,ikl,k", basis_offset.T, P1.T, P2, basis_offset) - residual_dim3_bobb = np.einsum( - "j,ji,ikl,k,l", basis_offset.T, P1.T, P2, basis_offset, basis_offset - ) - - residual_dim3_xxob = np.einsum("kji,il,l", P2.T, P1, basis_offset) - residual_dim3_xbox = np.einsum("j,kji,il", basis_offset.T, P2.T, P1) - residual_dim3_bxox = np.einsum("k,kji,il", basis_offset.T, P2.T, P1) - residual_dim3_xbob = np.einsum("j,kji,il,l", basis_offset.T, P2.T, P1, basis_offset) - residual_dim3_bxob = np.einsum("k,kji,il,l", basis_offset.T, P2.T, P1, basis_offset) - residual_dim3_bbox = np.einsum( - "k,j,kji,il", basis_offset.T, basis_offset.T, P2.T, P1 - ) - residual_dim3_bbob = np.einsum( - "k,j,kji,il,l", basis_offset.T, basis_offset.T, P2.T, P1, basis_offset - ) - - residual_offset["dim3_oxx"] = residual_dim3_boxx - residual_offset["dim3_xox"] = ( - residual_dim3_xoxb - + residual_dim3_xobx - + residual_dim3_xbox - + residual_dim3_bxox - ) - residual_offset["dim3_xxo"] = residual_dim3_xxob - residual_offset["dim3_ox"] = ( - residual_dim3_boxb + residual_dim3_bobx + residual_dim3_bbox - ) - residual_offset["dim3_xo"] = ( - residual_dim3_xobb + residual_dim3_xbob + residual_dim3_bxob - ) - residual_offset["dim3_o"] = residual_dim3_bobb + residual_dim3_bbob - - # D4 - residual_dim4_xxoxb = np.einsum("kji,inm,m", P2.T, P2, basis_offset) - residual_dim4_xxobx = np.einsum("kji,inm,n", P2.T, P2, basis_offset) - residual_dim4_xxobb = np.einsum("kji,inm,n,m", P2.T, P2, basis_offset, basis_offset) - residual_dim4_xboxx = np.einsum("j,kji,inm", basis_offset.T, P2.T, P2) - residual_dim4_xboxb = np.einsum( - "j,kji,inm,m", basis_offset.T, P2.T, P2, basis_offset - ) - residual_dim4_xbobx = np.einsum( - "j,kji,inm,n", basis_offset.T, P2.T, P2, basis_offset - ) - residual_dim4_xbobb = np.einsum( - "j,kji,inm,n,m", basis_offset.T, P2.T, P2, basis_offset, basis_offset - ) - residual_dim4_bxoxx = np.einsum("k,kji,inm", basis_offset.T, P2.T, P2) - residual_dim4_bxoxb = np.einsum( - "k,kji,inm,m", basis_offset.T, P2.T, P2, basis_offset - ) - residual_dim4_bxobx = np.einsum( - "k,kji,inm,n", basis_offset.T, P2.T, P2, basis_offset - ) - residual_dim4_bxobb = np.einsum( - "k,kji,inm,n,m", basis_offset.T, P2.T, P2, basis_offset, basis_offset - ) - residual_dim4_bboxx = np.einsum( - "k,j,kji,inm", basis_offset.T, basis_offset.T, P2.T, P2 - ) - residual_dim4_bboxb = np.einsum( - "k,j,kji,inm,m", basis_offset.T, basis_offset.T, P2.T, P2, basis_offset - ) - residual_dim4_bbobx = np.einsum( - "k,j,kji,inm,n", basis_offset.T, basis_offset.T, P2.T, P2, basis_offset - ) - residual_dim4_bbobb = np.einsum( - "k,j,kji,inm,n,m", - basis_offset.T, - basis_offset.T, - P2.T, - P2, - basis_offset, - basis_offset, - ) - - residual_offset["dim4_xoxx"] = residual_dim4_xboxx + residual_dim4_bxoxx - residual_offset["dim4_xxox"] = residual_dim4_xxobx + residual_dim4_xxoxb - residual_offset["dim4_oxx"] = residual_dim4_bboxx - residual_offset["dim4_xox"] = ( - residual_dim4_xboxb - + residual_dim4_xbobx - + residual_dim4_bxoxb - + residual_dim4_bxobx - ) - residual_offset["dim4_xxo"] = residual_dim4_xxobb - residual_offset["dim4_ox"] = residual_dim4_bboxb + residual_dim4_bbobx - residual_offset["dim4_xo"] = residual_dim4_xbobb + residual_dim4_bxobb - residual_offset["dim4_o"] = residual_dim4_bbobb - - return residual_offset - - -def combine_residual_offset(residual, residual_offset): - full_residual = dict() - # dim 0 - offset_residual_dim0_o = ( - residual["dim0_o"] - + residual_offset["dim1_o"] - + residual_offset["dim2_o"] - + residual_offset["dim3_o"] - + residual_offset["dim4_o"] - ) - full_residual["dim0"] = offset_residual_dim0_o - - # dim1 - offset_residual_dim1_ox = ( - residual["dim1_ox"] - + residual_offset["dim2_ox"] - + residual_offset["dim3_ox"] - + residual_offset["dim4_ox"] - ) - offset_residual_dim1_xo = ( - residual["dim1_xo"] - + residual_offset["dim2_xo"] - + residual_offset["dim3_xo"] - + residual_offset["dim4_xo"] - ) - full_residual["dim1"] = offset_residual_dim1_ox + offset_residual_dim1_xo - - # dim 2 - offset_residual_dim2_oxx = ( - residual["dim2_oxx"] + residual_offset["dim3_oxx"] + residual_offset["dim4_oxx"] - ) - offset_residual_dim2_xox = ( - residual["dim2_xox"] + residual_offset["dim3_xox"] + residual_offset["dim4_xox"] - ) - offset_residual_dim2_xxo = ( - residual["dim2_xxo"] + residual_offset["dim3_xxo"] + residual_offset["dim4_xxo"] - ) - full_residual["dim2"] = ( - offset_residual_dim2_oxx + offset_residual_dim2_xox + offset_residual_dim2_xxo - ) - - # dim 3 - offset_residual_dim3_xoxx = residual["dim3_xoxx"] + residual_offset["dim4_xoxx"] - offset_residual_dim3_xxox = residual["dim3_xxox"] + residual_offset["dim4_xxox"] - full_residual["dim3"] = offset_residual_dim3_xoxx + offset_residual_dim3_xxox - - # dim 4 - offset_residual_dim4_xxoxx = residual["dim4_xxoxx"] - full_residual["dim4"] = offset_residual_dim4_xxoxx - - return full_residual - - -def real_to_qubit_basis( - full_residual, num_equations, qubits_per_var, basis, basis_coeff -): - extended_qubo = dict() - - # dimension 0 - extended_qubo["qubit_residual_dim0"] = full_residual["dim0"] - - # dimension 1 - extended_qubo["qubit_residual_dim1"] = np.reshape( - np.einsum("i,j->ij", basis_coeff * full_residual["dim1"], basis), - (num_equations * qubits_per_var), - ) - - # dimension 2 - basis_coeff_dim2 = np.einsum("i,j->ij", basis_coeff, basis_coeff) - basis_dim2 = np.einsum("i,j->ij", basis, basis) - - extended_qubo["qubit_residual_dim2"] = np.reshape( - np.einsum("ij,kl->ikjl", basis_coeff_dim2 * full_residual["dim2"], basis_dim2), - (num_equations * qubits_per_var, num_equations * qubits_per_var), - ) - - # dimension 3 - basis_coeff_dim3 = np.einsum("i,j,k->ijk", basis_coeff, basis_coeff, basis_coeff) - basis_dim3 = np.einsum("i,j,k->ijk", basis, basis, basis) - - extended_qubo["qubit_residual_dim3"] = np.reshape( - np.einsum( - "ijk,lmn->iljmkn", basis_coeff_dim3 * full_residual["dim3"], basis_dim3 - ), - ( - num_equations * qubits_per_var, - num_equations * qubits_per_var, - num_equations * qubits_per_var, - ), - ) - - # dimension 4 - basis_coeff_dim4 = np.einsum( - "i,j,k,l->ijkl", basis_coeff, basis_coeff, basis_coeff, basis_coeff - ) - basis_dim4 = np.einsum("i,j,k,l->ijkl", basis, basis, basis, basis) - - extended_qubo["qubit_residual_dim4"] = np.reshape( - np.einsum( - "ijkl,mnop->imjnkolp", basis_coeff_dim4 * full_residual["dim4"], basis_dim4 - ), - ( - num_equations * qubits_per_var, - num_equations * qubits_per_var, - num_equations * qubits_per_var, - num_equations * qubits_per_var, - ), - ) - - return extended_qubo - - -def accumulate_qubo(extended_qubo): - triangle_qubo = dict() - triangle_qubo["qubit_residual_dim0"] = extended_qubo["qubit_residual_dim0"].copy() - triangle_qubo["qubit_residual_dim1"] = extended_qubo["qubit_residual_dim1"].copy() - # dim 2 - accumulate_dim2 = np.zeros_like(extended_qubo["qubit_residual_dim2"]) - for index_j in range(len(accumulate_dim2)): - for index_i in range(len(accumulate_dim2)): - sorted_index = np.sort([index_i, index_j]) - row_index = sorted_index[0] - col_index = sorted_index[1] - accumulate_dim2[row_index, col_index] += extended_qubo[ - "qubit_residual_dim2" - ][index_i, index_j] - triangle_qubo["qubit_residual_dim2"] = accumulate_dim2 - # dim 3 - accumulate_dim3 = np.zeros_like(extended_qubo["qubit_residual_dim3"]) - for index_k in range(len(accumulate_dim3)): - for index_j in range(len(accumulate_dim3)): - for index_i in range(len(accumulate_dim3)): - sorted_index = np.sort([index_i, index_j, index_k]) - accumulate_dim3[ - sorted_index[0], sorted_index[1], sorted_index[2] - ] += extended_qubo["qubit_residual_dim3"][index_i, index_j, index_k] - triangle_qubo["qubit_residual_dim3"] = accumulate_dim3 - # dim 4 - accumulate_dim4 = np.zeros_like(extended_qubo["qubit_residual_dim4"]) - for index_l in range(len(accumulate_dim4)): - for index_k in range(len(accumulate_dim4)): - for index_j in range(len(accumulate_dim4)): - for index_i in range(len(accumulate_dim4)): - sorted_index = np.sort([index_i, index_j, index_k, index_l]) - accumulate_dim4[ - sorted_index[0], - sorted_index[1], - sorted_index[2], - sorted_index[3], - ] += extended_qubo["qubit_residual_dim4"][ - index_i, index_j, index_k, index_l - ] - triangle_qubo["qubit_residual_dim4"] = accumulate_dim4 - - return triangle_qubo - - -def dimensional_reduction(triangle_qubo): - from sympy.utilities.iterables import multiset_permutations - - # takes upper triangular qubo and reduces the dimensionality of repeated qubits - # e.g. x_i^n = x_i since x_i in [0, 1] - reduced_qubo = dict() - - # dim 0 - reduced_qubo["qubit_residual_dim0"] = triangle_qubo["qubit_residual_dim0"].copy() - - # dim 1 - reduced_qubo["qubit_residual_dim1"] = np.zeros_like( - triangle_qubo["qubit_residual_dim1"] - ) - for idx in range(len(reduced_qubo["qubit_residual_dim1"])): - # dim 1 - reduced_qubo["qubit_residual_dim1"][idx] += triangle_qubo[ - "qubit_residual_dim1" - ][idx] - # dim 2 - reduced_qubo["qubit_residual_dim1"][idx] += triangle_qubo[ - "qubit_residual_dim2" - ][idx, idx] - # dim 3 - reduced_qubo["qubit_residual_dim1"][idx] += triangle_qubo[ - "qubit_residual_dim3" - ][idx, idx, idx] - # dim 4 - reduced_qubo["qubit_residual_dim1"][idx] += triangle_qubo[ - "qubit_residual_dim4" - ][idx, idx, idx, idx] - - # dim 2 - reduced_qubo["qubit_residual_dim2"] = np.zeros_like( - triangle_qubo["qubit_residual_dim2"] - ) - for idx_j in range(len(reduced_qubo["qubit_residual_dim2"])): - for idx_i in range(idx_j): - # dim 2 - reduced_qubo["qubit_residual_dim2"][idx_i, idx_j] += triangle_qubo[ - "qubit_residual_dim2" - ][idx_i, idx_j] - # dim 3 - reduced_qubo["qubit_residual_dim2"][idx_i, idx_j] += triangle_qubo[ - "qubit_residual_dim3" - ][idx_i, idx_j, idx_j] - reduced_qubo["qubit_residual_dim2"][idx_i, idx_j] += triangle_qubo[ - "qubit_residual_dim3" - ][idx_i, idx_i, idx_j] - # dim 4 - reduced_qubo["qubit_residual_dim2"][idx_i, idx_j] += triangle_qubo[ - "qubit_residual_dim4" - ][idx_i, idx_j, idx_j, idx_j] - reduced_qubo["qubit_residual_dim2"][idx_i, idx_j] += triangle_qubo[ - "qubit_residual_dim4" - ][idx_i, idx_i, idx_j, idx_j] - reduced_qubo["qubit_residual_dim2"][idx_i, idx_j] += triangle_qubo[ - "qubit_residual_dim4" - ][idx_i, idx_i, idx_i, idx_j] - - # dim 3 - reduced_qubo["qubit_residual_dim3"] = np.zeros_like( - triangle_qubo["qubit_residual_dim3"] - ) - for idx_k in range(len(reduced_qubo["qubit_residual_dim3"])): - for idx_j in range(idx_k): - for idx_i in range(idx_j): - # dim 3 - reduced_qubo["qubit_residual_dim3"][ - idx_i, idx_j, idx_k - ] += triangle_qubo["qubit_residual_dim3"][idx_i, idx_j, idx_k] - # dim 4 - reduced_qubo["qubit_residual_dim3"][ - idx_i, idx_j, idx_k - ] += triangle_qubo["qubit_residual_dim4"][idx_i, idx_i, idx_j, idx_k] - reduced_qubo["qubit_residual_dim3"][ - idx_i, idx_j, idx_k - ] += triangle_qubo["qubit_residual_dim4"][idx_i, idx_j, idx_j, idx_k] - reduced_qubo["qubit_residual_dim3"][ - idx_i, idx_j, idx_k - ] += triangle_qubo["qubit_residual_dim4"][idx_i, idx_j, idx_k, idx_k] - - # dim 4 - reduced_qubo["qubit_residual_dim4"] = np.zeros_like( - triangle_qubo["qubit_residual_dim4"] - ) - for idx_l in range(len(reduced_qubo["qubit_residual_dim4"])): - for idx_k in range(idx_l): - for idx_j in range(idx_k): - for idx_i in range(idx_j): - reduced_qubo["qubit_residual_dim4"][ - idx_i, idx_j, idx_k, idx_l - ] += triangle_qubo["qubit_residual_dim4"][ - idx_i, idx_j, idx_k, idx_l - ] - - return reduced_qubo - - -# import the QUBO data and return numpy 2D square array -def import_QUBO(define_problem): - ### define problem - ( - num_equations, - P0, - P1, - P2, - qubits_per_var, - basis, - basis_offset, - basis_coeff, - basis_map, - ) = define_problem() - ### calculate "qubo" in real number basis - residual = calculate_squared_residuals(P0, P1, P2) - residual_offset = calculate_residual_offsets(P0, P1, P2, basis_offset) - full_residual = combine_residual_offset(residual, residual_offset) - ### transform "qubo" to qubit basis - # this can be sent into eval_QUBO() and solved - extended_qubo = real_to_qubit_basis( - full_residual, num_equations, qubits_per_var, basis, basis_coeff - ) - ### accumulate extended qubo, to construct only upper triangular tensors - triangle_qubo = accumulate_qubo(extended_qubo) - ### make most sparse upper triangular tensors by reducing repeated qubits - reduced_qubo = dimensional_reduction(triangle_qubo) - - return extended_qubo, triangle_qubo, reduced_qubo, basis_map - - -# evaluate the QUBO given binary vector "eigenvector" and return energy "eigenvalue" -def eval_QUBO(extended_qubo, eigenvector): - eigenvalue_dim0 = extended_qubo["qubit_residual_dim0"] - eigenvalue_dim1 = np.einsum( - "j,j", extended_qubo["qubit_residual_dim1"], eigenvector - ) - eigenvalue_dim2 = np.einsum( - "j,jk,k", eigenvector.T, extended_qubo["qubit_residual_dim2"], eigenvector - ) - eigenvalue_dim3 = np.einsum( - "j,jkl,k,l", - eigenvector.T, - extended_qubo["qubit_residual_dim3"], - eigenvector, - eigenvector, - ) - eigenvalue_dim4 = np.einsum( - "k,j,kjnm,n,m", - eigenvector.T, - eigenvector.T, - extended_qubo["qubit_residual_dim4"], - eigenvector, - eigenvector, - ) - - eigenvalue = ( - eigenvalue_dim0 - + eigenvalue_dim1 - + eigenvalue_dim2 - + eigenvalue_dim3 - + eigenvalue_dim4 - ) - return eigenvalue - - -# Convert non-negative n-bit integer to n-bit binary representation and return numpy array -def int_to_bin(hilbert_index, num_of_qubits): - length = int(hilbert_index).bit_length() - # length of binary conversion - # Check that the bit length fits the b-bit representation - if length > num_of_qubits: - print(" <>") - raise ValueError - x = bin(int(hilbert_index)) - # binary converstion returns string x - y = x[2 : length + 2] # store last l chars of x in y - eigenvector = np.zeros(num_of_qubits) - for i in range(len(y)): - # add the bits from smallest to largest in the last l slots - eigenvector[num_of_qubits - length + i] = int(y[i]) - return eigenvector - - -def argmin_QUBO(extended_qubo): - num_of_qubits = len(extended_qubo["qubit_residual_dim1"]) - ground_state_eigenvector = int_to_bin(hilbert_index=0, num_of_qubits=num_of_qubits) - ground_state_eigenvalue = eval_QUBO(extended_qubo, ground_state_eigenvector) - result_eigenvalue = [] - result_eigenvector = [] - for h_idx in range(2**num_of_qubits): # loop over all 2^n possibilities - eigenvector = int_to_bin(h_idx, num_of_qubits) - eigenvalue = eval_QUBO(extended_qubo, eigenvector) - result_eigenvalue.append(eigenvalue) - result_eigenvector.append(eigenvector) - if eigenvalue < ground_state_eigenvalue: - ground_state_eigenvalue = eigenvalue - ground_state_eigenvector = eigenvector - return ground_state_eigenvector, result_eigenvalue, result_eigenvector - - -def inverse_mapping(eigenvector, basis_map): - presult = [] - num_equations = len(basis_map["basis_coeff"]) - qubits_per_var = len(basis_map["basis"]) - for idx_params in range(num_equations): - presult.append( - basis_map["basis_coeff"][idx_params] - * sum( - basis_map["basis"] - * eigenvector[ - idx_params * qubits_per_var : idx_params * qubits_per_var - + qubits_per_var - ] - ) - + basis_map["basis_offset"][idx_params] - ) - return presult - - -def evaluate_problem(qubo, basis_map, title): - # Get arg min for extended qubo and compute energy - ground_state_eigenvector, result_eigenvalue, result_eigenvector = argmin_QUBO(qubo) - ground_state_eigenvalue = eval_QUBO(qubo, ground_state_eigenvector) - # Evaluate results - print(title) - print("ground state eigenvector = ", ground_state_eigenvector) - print("ground state eigenvalue = ", ground_state_eigenvalue) - print( - "solution = ", - inverse_mapping(ground_state_eigenvector, basis_map), - ) - print() - - -def solve(define_problem): - # Get QUBO matrix - extended_qubo, triangle_qubo, reduced_qubo, basis_map = import_QUBO(define_problem) - evaluate_problem(extended_qubo, basis_map, "extended qubo") - evaluate_problem(triangle_qubo, basis_map, "upper triangular qubo") - evaluate_problem(reduced_qubo, basis_map, "reduced upper triangular qubo") diff --git a/docs/notebooks/trash/temp.bin b/docs/notebooks/trash/temp.bin deleted file mode 100644 index 946e1f17e6002c80ea0acf37b5677c3d93ccd551..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1360 zcma#_I4q~*$H2hMz`(!=#7sbp4p@N#$l?Ng3=AObmYI{Pke^hNn36%0}5k2;%5A>f>mnwy!Nm_x2{ zUWWK}x)6{LA|M|?NZts){jjnCRvv(85C(~(f)9=i3{@^r>B#=pt=u^F!rrARa;@IKr~RD69W)SCpOu00C7oGqJt$6Bh)wm*$xgHm~<>b7$*Lz zvBTya5JUJLPOtyFAKC}BuN-P0jE!y|P#xI5UkMQVIEl0mWEa9dm@5!vV" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cons:\n", - "mass_balance[J1]: ((expected_demand[J1]-flow[P1])+flow[P2])\n", - "mass_balance[D1]: (expected_demand[D1]-flow[P2])\n", - "approx_hazen_williams_headloss[P1]: (((((((-((sign(flow[P1]))))*hw_resistance[P1])*((abs(flow[P1]))**1.852))-((1e-05*(hw_resistance[P1]**0.5))*flow[P1]))-(((sign(flow[P1]))*minor_loss[P1])*(flow[P1]**2.0)))+source_head[R1])-head[J1])\n", - "approx_hazen_williams_headloss[P2]: (((((((-((sign(flow[P2]))))*hw_resistance[P2])*((abs(flow[P2]))**1.852))-((1e-05*(hw_resistance[P2]**0.5))*flow[P2]))-(((sign(flow[P2]))*minor_loss[P2])*(flow[P2]**2.0)))+head[J1])-head[D1])\n", - "\n", - "vars:\n", - "flow[P1]: flow[P1]\n", - "flow[P2]: flow[P2]\n", - "head[J1]: head[J1]\n", - "head[D1]: head[D1]\n", - "\n" - ] - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "from wntr.sim.hydraulics import create_hydraulic_model\n", - "\n", - "# Create a water network model\n", - "inp_file = '../networks/Net0_HW.inp'\n", - "# inp_file = '../networks/Net1_scenario1.inp'\n", - "# inp_file = '../networks/Net2Loops.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n", - "\n", - "model, updater = create_hydraulic_model(wn, HW_approx='default')\n", - "print(model.__str__())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2357947691\n" - ] - } - ], - "source": [ - "print(11**9)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# define the classical simulator\n", - "sim = wntr.sim.WNTRSimulator(wn)\n", - "\n", - "# run the simulation\n", - "results = sim.run_sim()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "from wntr.sim.hydraulics import create_hydraulic_model\n", - "\n", - "# Create a water network model\n", - "inp_file = '../networks/Net0_CM.inp'\n", - "# inp_file = '../networks/Net2Loops.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Expression of he network" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/qubols/qubols/encodings.py:265: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n", - "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n", - " coefs, res, rank, s = np.linalg.lstsq(A, self.discrete_values)\n" - ] - } - ], - "source": [ - "from wntr_quantum.scenario.network_design_qubo import NetworkDesign\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "flow_encoding = RangedEfficientEncoding(nqbit=7, range=2, offset=0, var_base_name=\"x\")\n", - "head_encoding = RangedEfficientEncoding(nqbit=7, range=2, offset=0, var_base_name=\"x\")\n", - "\n", - "\n", - "# pipe_diameters = [0.35, 0.4, 0.45, 0.55]\n", - "pipe_diameters = [2, 4, 8]\n", - "designer = NetworkDesign(wn, flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding, \n", - " pipe_diameters=pipe_diameters,\n", - " weight_cost=0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.5 ],\n", - " [1. ],\n", - " [2. ],\n", - " [0. ],\n", - " [0.125]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "designer.matrices[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-1. , 1. , 0. , 0. , 0. , 0. ],\n", - " [ 0. , -1. , 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , -1. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 1. , -1. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. , -0.1, -0.1]])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "designer.matrices[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0.],\n", - " [-1., 0., 0., 0., 0., 0.],\n", - " [ 0., -1., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0.]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "designer.matrices[3].sum(-1).sum(-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.12500000000000014, 0.12500000000000014) [1.5 1. 1.719 1.594]\n", - "(0.12500000000000014, 0.25) [1.5 1. 1.719 1.469]\n", - "(0.12500000000000014, 0.5000000000000002) [1.5 1. 1.719 1.219]\n", - "(0.12500000000000014, 0.6250000000000001) [1.5 1. 1.719 1.094]\n", - "(0.25, 0.12500000000000014) [1.5 1. 1.438 1.313]\n", - "(0.25, 0.25) [1.5 1. 1.438 1.188]\n", - "(0.25, 0.5000000000000002) [1.5 1. 1.438 0.938]\n", - "(0.25, 0.6250000000000001) [1.5 1. 1.438 0.813]\n", - "(0.5000000000000002, 0.12500000000000014) [1.5 1. 0.875 0.75 ]\n", - "(0.5000000000000002, 0.25) [1.5 1. 0.875 0.625]\n", - "(0.5000000000000002, 0.5000000000000002) [1.5 1. 0.875 0.375]\n", - "(0.5000000000000002, 0.6250000000000001) [1.5 1. 0.875 0.25 ]\n", - "(0.6250000000000001, 0.12500000000000014) [1.5 1. 0.594 0.469]\n", - "(0.6250000000000001, 0.25) [1.5 1. 0.594 0.344]\n", - "(0.6250000000000001, 0.5000000000000002) [1.5 1. 0.594 0.094]\n", - "(0.6250000000000001, 0.6250000000000001) [ 1.5 1. 0.594 -0.031]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - } - ], - "source": [ - "designer.enumerates_classical_solutions()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.12500000000000014, 0.25, 0.5000000000000002, 0.6250000000000001]" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "designer.sol_vect_res.encoded_reals[0].get_possible_values()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", - "import sparse\n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "from dwave.samplers import SteepestDescentSolver\n", - "from dwave.samplers import TabuSampler\n", - "from dwave.samplers import RandomSampler\n", - "qubo = QUBO_POLY_MIXED(designer.mixed_solution_vector, options={\"sampler\":TabuSampler()})" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "matrices = tuple(sparse.COO(m) for m in designer.matrices)\n", - "bqm = qubo.create_bqm(matrices, strength=1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:759: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", - " warnings.warn(\"For constraints with fractional coefficients, \"\n" - ] - } - ], - "source": [ - "istart = designer.sol_vect_flows.size\n", - "for i in range(designer.sol_vect_heads.size):\n", - "\n", - " bqm.add_linear_inequality_constraint(\n", - " qubo.all_expr[istart + i],\n", - " lagrange_multiplier=0.1,\n", - " label=\"head_%s\" % i,\n", - " lb=1,\n", - " ub=2,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "sampleset = qubo.sample_bqm(bqm, num_reads=10000)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "flow, heads, param = qubo.decode_solution(sampleset.lowest())" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([1.5238095238095237, 1.0158730158730158],\n", - " [1.746031746031746, 1.0793650793650793],\n", - " [0.12500000000000014, 0.6250000000000001])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "flow, heads, param" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-0.008, -0.016, -0.036, 0.022])" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "num_heads = designer.wn.num_junctions\n", - "designer.verify_solution(np.array(flow+heads), param)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], -4.576, 1, 4)" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sampleset.record[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "nsol = []\n", - "cost = []\n", - "cons = []\n", - "colors = []\n", - "count = dict()\n", - "for i in range(10000):\n", - " flow, heads, param = qubo.decode_solution(sampleset, sol_index=i)\n", - " nsol.append(np.linalg.norm(designer.verify_solution(np.array(flow+heads), param)))\n", - " cost.append(np.sum(param))\n", - " cons.append(np.sum(np.array(heads)-1))\n", - " if nsol[-1] < 1 and cons[-1] > 0:\n", - " if tuple(param) not in count:\n", - " count[tuple(param)] = 0\n", - " count[tuple(param)] += 1\n", - " \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.scatter(nsol, cons)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{(0.12500000000000014, 0.12500000000000014): 465,\n", - " (0.12500000000000014, 0.6250000000000001): 601,\n", - " (0.6250000000000001, 0.25): 205,\n", - " (0.5000000000000002, 0.12500000000000014): 274,\n", - " (0.12500000000000014, 0.5000000000000002): 535,\n", - " (0.6250000000000001, 0.5000000000000002): 166,\n", - " (0.25, 0.6250000000000001): 281,\n", - " (0.5000000000000002, 0.25): 186,\n", - " (0.25, 0.25): 270,\n", - " (0.5000000000000002, 0.6250000000000001): 146,\n", - " (0.12500000000000014, 0.25): 499,\n", - " (0.5000000000000002, 0.5000000000000002): 163,\n", - " (0.6250000000000001, 0.12500000000000014): 248,\n", - " (0.25, 0.5000000000000002): 237,\n", - " (0.25, 0.12500000000000014): 438,\n", - " (0.6250000000000001, 0.6250000000000001): 144}" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "count" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.12500000000000014, 0.12500000000000014) [1.5 1. 1.719 1.594]\n", - "(0.12500000000000014, 0.25) [1.5 1. 1.719 1.469]\n", - "(0.12500000000000014, 0.5000000000000002) [1.5 1. 1.719 1.219]\n", - "(0.12500000000000014, 0.6250000000000001) [1.5 1. 1.719 1.094]\n", - "(0.25, 0.12500000000000014) [1.5 1. 1.438 1.313]\n", - "(0.25, 0.25) [1.5 1. 1.438 1.188]\n", - "(0.25, 0.5000000000000002) [1.5 1. 1.438 0.938]\n", - "(0.25, 0.6250000000000001) [1.5 1. 1.438 0.813]\n", - "(0.5000000000000002, 0.12500000000000014) [1.5 1. 0.875 0.75 ]\n", - "(0.5000000000000002, 0.25) [1.5 1. 0.875 0.625]\n", - "(0.5000000000000002, 0.5000000000000002) [1.5 1. 0.875 0.375]\n", - "(0.5000000000000002, 0.6250000000000001) [1.5 1. 0.875 0.25 ]\n", - "(0.6250000000000001, 0.12500000000000014) [1.5 1. 0.594 0.469]\n", - "(0.6250000000000001, 0.25) [1.5 1. 0.594 0.344]\n", - "(0.6250000000000001, 0.5000000000000002) [1.5 1. 0.594 0.094]\n", - "(0.6250000000000001, 0.6250000000000001) [ 1.5 1. 0.594 -0.031]\n" - ] - } - ], - "source": [ - "designer.enumerates_classical_solutions()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/trash/wntr_qubo_poly)dixcrete_res.ipynb b/docs/notebooks/trash/wntr_qubo_poly)dixcrete_res.ipynb deleted file mode 100644 index 96b487a..0000000 --- a/docs/notebooks/trash/wntr_qubo_poly)dixcrete_res.ipynb +++ /dev/null @@ -1,336 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define the system " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "data": { - "image/png": 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s2Pm0QWpqqkJC+vefyBEjRmjt2rXatm2b/uZv/kZHjx7VK6+8okcffbRfvy5wNZfFGS2woZqaGiUnJ2v37t26++67Tc8JWDyO9pWdna1Tp05p165dpqcAHBmAfb3wwgt8A+sDPI4AboQjAwBspbW1tdvbDF9t+PDht/SaBHbCkQHYCScQArCVTZs2KScn57q3eeONN/TII48MzCDAATgyAMBWTp8+fcOX4p09e7b/BYwA3D5iAAAAh+MEQgAAHI4YAADA4YgBAAAcjhgAAMDhiAEAAByOGAAAwOGIAQAAHI4YAADA4YgBAAAcjhgAAMDhiAEAAByOGAAAwOGIAQAAHI4YAADA4YgBAAAcjhgAAMDhiAEAAByOGAAAwOGIAQAAHI4YAADA4YgBAAAcjhgAAMDhiAEAAByOGAAAwOGIAQAAHI4YAADA4YgBAAAcjhgAAMDhiAEAABwu4GPA7XYrISFBs2fP1re+9S01NTVJks6dO6e8vDxFRkbq6aefNjsSAAAbc1mWZZkecTtGjRqlU6dOSZIeeOABNTc3a/z48Zo9e7ZmzpypgwcP6uDBg3rppZcMLwUAwJ7cpgf0lebmZlVVVeno0aP+yxITE7V06VKDqwAAsL+Af5qg069//esuISBJtbW1qq6uNrQIAIDAEPBHBpqampSQkKD9+/f3eH1DQ4NGjRo1wKsAAAgcAX9kIDo6Wjt27NCqVat6vH7SpEkDvAgAgMAS8DHQ6fvf/75iY2O7XJaYmKjU1FRDiwAACAxB9dsEzc3NSk5O1p49e7R48WIdOHBAp06d0qVLlzR8+HBt3rxZEyZMMLwYAAB7CfgY6ElGRoY6OjpUVVVlegoAALYXNE8TXCkvL091dXVqb283PQUAANsLyhgoKCjQhQsXVF9fb3oKAAC2F5QxkJSUpEGDBqmkpMT0FAAAbC8oYyA8PFxz5swhBgAAuAlBGQOSlJ2drZqaGgXh+ZEAAPSpoI0Br9erM2fO6KOPPjI9BQAAWwvaGEhLS5PL5VJpaanpKQAA2FrQxsCwYcM0a9YsFRcXm54CAICtBW0MSJLH49HmzZtNzwAAwNaCOgZ8Pp8+//zzbm9tDAAA/r+gjoHMzExJ0oYNG8wOAQDAxoI6BmJiYjR58mQVFRWZngIAgG0FdQxIl3+roLKy0vQMAABsK+hjYPHixTp8+LC+/PJL01MAALCloI+B7OxsSdLGjRvNDgEAwKaCPgYmTZqk0aNHq7Cw0PQUAABsKehjQJJSUlI4MgAAwDU4Iga8Xq/279+vc+fOmZ4CAIDtOCIGcnNz1dHRoU2bNpmeAgCA7TgiBmbNmqWoqChebwAAgB44IgZcLpcWLVrEKxECANADR8SAJOXl5Wnnzp1qbW01PQUAAFtxTAzk5+ertbVV27ZtMz0FAABbcUwMJCQkKCIiQsXFxaanAABgK46JAbfbrfnz56usrMz0FAAAbMUxMSBJOTk52r59u9rb201PAQDANhwVA16vV+fPn9euXbtMTwEAwDYcFQPJyclyu90qLS01PQUAANtwVAxERERo9uzZnEQIAMAVHBUDkpSZmamamhpZlmV6CgAAtuC4GFi8eLEaGxt18OBB01MAALAFx8VAenq6XC4Xv2IIAMAfOS4Ghg8frunTp/OmRQAA/JHjYkCSMjIyVF1dbXoGAAC24MgY8Pl8OnbsmI4dO2Z6CgAAxjkyBrKysiRJ5eXlhpcAAGCeI2NgzJgxmjhxogoLC01PAQDAOEfGgCSlpqaqsrLS9AwAAIxzbAx4vV4dOnRIp0+fNj0FAACjHBsDubm5siyLowMAAMdzbAxMmTJFo0aN4rwBAIDjOTYGXC6XkpOT+Y0CAIDjOTYGJCk/P1979+5Vc3Oz6SkAABjj+Bhob2/X5s2bTU8BAMAYR8fA3XffrcjISN6nAADgaI6OgZCQECUlJWn9+vWmpwAAYIyjY0C6/FRBfX29Ll26ZHoKAABGEAP5+WppaVFtba3pKQAAGOH4GEhMTFR4eLhKSkpMTwEAwAjHx8CgQYM0b948lZaWmp4CAIARjo8BScrJydHWrVvV0dFhegoAAAOOGJBUUFCgc+fOac+ePaanAAAw4IgBXX4749DQUJWVlZmeAgDAgCMGJA0ZMkRxcXEqLi42PQUAgAFHDPxRZmamNm/eLMuyTE8BAGBAEQN/5PV6derUKR0+fNj0FAAABhQx8EeZmZlyuVy8NDEAwHGIgT8aMWKEpk6dqsLCQtNTAAAYUMTAFTwej6qrq03PAABgQBEDV1i8eLGOHj2qL774wvQUAAAGDDFwhaysLElSeXm54SUAAAwcYuAK48eP17hx4zhvAADgKMTAVdLS0lRZWWl6BgAAA4YYuIrX69WBAwd05swZ01MAABgQxMBVcnJyZFmWqqqqTE8BAGBAEANXmT59ukaMGMF5AwAAxyAGruJyuZScnMxvFAAAHIMY6EF+fr52796tixcvmp4CAEC/IwZ6kJ+fr7a2Nm3ZssX0FAAA+h0x0IP4+HgNHTpUxcXFpqcAANDviIEehIaGKjExUWVlZaanAADQ74iBa8jNzVVdXZ3a2tpMTwEAoF8RA9fg9Xp18eJF1dfXm54CAEC/IgauISkpSWFhYZw3AAAIesTANYSFhSk+Pl6lpaWmpwAA0K+IgevIyclRTU2NLMsyPQUAgH5DDFyH1+vV2bNntW/fPtNTAADoN8TAdaSlpSkkJIRfMQQABDVi4DoiIyMVGxvLSYQAgKBGDNxAZmamqqurTc8AAKDfEAM34PV6deLECTU0NJieAgBAvyAGbiAzM1OStGHDBrNDAADoJ8TADYwaNUpTpkzRunXrTE8BAKBfEAM3IS0tTZs2bTI9AwCAfkEM3ASfz6cjR47o1KlTpqcAANDniIGbkJOTI0nauHGj4SUAAPQ9YuAmTJw4UWPGjOG8AQBAUCIGblJKSooqKytNzwAAoM8RAzfJ6/Vq//79+uqrr0xPAQCgTxEDNyk3N1cdHR38VgEAIOgQAzcpNjZWw4cPV1FRkekpAAD0KWLgJrlcLi1cuJBXIgQABB1ioBcKCgq0c+dOtbS0mJ4CAECfIQZ6IS8vT5cuXdK2bdtMTwEAoM8QA70wb948DR48WMXFxaanAADQZ4iBXnC73Zo/f75KS0tNTwEAoM8QA72Uk5Oj2tpatbe3m54CAECfIAZ6qaCgQM3Nzdq5c6fpKQAA9AlioJeSk5Pldrt5qgAAEDSIgV6KiIhQfHw8JxECAIIGMXALMjMzVVNTI8uyTE8BAOC2EQO3YPHixTp9+rQ+/vhj01MAALhtxMAtSE9Pl8vlUllZmekpAADcNmLgFkRFRWnGjBm8aREAICgQA7coIyND1dXVpmcAAHDbiIFb5PP5dPz4cX322WempwAAcFuIgVuUmZkpSbylMQAg4BEDt2jMmDGaOHEi5w0AAAIeMXAb0tLSVFlZaXoGAAC3hRi4DV6vV4cOHVJjY6PpKQAA3DJi4Dbk5ORIkioqKgwvAQDg1hEDt2HKlCmKiYlRYWGh6SkAANwyYuA2uFwuJScna+PGjaanAABwy4iB21RQUKB9+/bp/PnzpqcAAHBLiIHblJeXp/b2dm3evNn0FAAAbgkxcJvi4uI0bNgwzhsAAAQsYuA2hYSEKCkpiVciBAAELGKgD+Tn56u+vl6tra2mpwAA0GvEQB/Iz89Xa2uramtrTU8BAKDXiIE+MH/+fIWHh6ukpMT0FAAAeo0Y6AODBg1SQkKCSktLTU8BAKDXiIE+kpOTo23btqmjo8P0FAAAeoUY6CMFBQU6d+6cdu/ebXoKAAC9Qgz0kZSUFLndbp4qAAAEHGKgjwwZMkRxcXEqLi42PQUAgF4hBvpQZmamtmzZIsuyTE8BAOCmEQN9yOfz6csvv9Qnn3xiegoAADeNGOhDHo9HLpdL69evNz0FAICbRgz0oREjRmjatGm8aREAIKAQA33M4/Gourra9AwAAG4aMdDHFi9erE8//VSff/656SkAANwUYqCPZWVlSZLKy8sNLwEA4OYQA31s3LhxGj9+POcNAAACBjHQD9LS0lRZWWl6BgAAN4UY6Ader1cff/yxmpqaTE8BAOCGiIF+kJOTI8uyVFVVZXoKAAA3RAz0g7vuuksjR47kvAEAQEAgBvqBy+VScnIyv1EAAAgIxEA/yc/P1549e3ThwgXTUwAAuC5ioJ/k5eWpra1NW7ZsMT0FAIDrIgb6SXx8vIYOHaqioiLTUwAAuC5ioJ+EhoZqwYIFvIMhAMD2iIF+lJubqx07dqitrc30FAAArokY6Eder1cXL15UXV2d6SkAAFwTMdCPFixYoLCwMJWUlJieAgDANRED/SgsLExz5sxRaWmp6SkAAFwTMdDPcnJytHXrVlmWZXoKAAA9Igb6mdfr1dmzZ7V3717TUwAA6BEx0M9SU1MVEhKisrIy01MAAOgRMdDPIiMjFRsbq+LiYtNTAADoETEwADIzM1VdXW16BgAAPSIGBoDP59PJkyd15MgR01MAAOiGGBgAmZmZkqQNGzaYHQIAQA+IgQFwxx13aOrUqSosLDQ9BQCAboiBAZKenq6qqirTMwAA6IYYGCBer1cNDQ06ceKE6SkAAHRBDAyQ7OxsSdLGjRvNDgEA4CrEwACZOHGixo4dy3kDAADbIQYGUEpKiiorK03PAACgC2JgAHm9Xu3fv19nz541PQUAAD9iYADl5ubKsixt2rTJ9BQAAPyIgQE0c+ZMRUdHq6ioyPQUAAD8iIEB5HK5tHDhQpWXl5ueAgCAHzEwwPLz87Vz5061tLSYngIAgCRiYMDl5+fr0qVL2rp1q+kpAABIIgYG3Lx58zR48GAVFxebngIAgCRiYMCFhoYqMTFRpaWlpqcAACCJGDAiJydHdXV1am9vNz0FAABiwASv16vm5mZ9+OGHpqcAAEAMmLBw4UINGjRIJSUlpqcAAEAMmBAREaH4+HhiAABgC8SAIVlZWaqpqZFlWaanAAAcjhgwxOfzqampSR999JHpKQAAhyMGDElLS5PL5VJZWZnpKQAAhyMGDImKitLMmTN58SEAgHHEgEEej0fV1dWmZwAAHI4YMGjx4sX6/PPP9emnn5qeAgBwMGLAoMzMTEnShg0bzA4BADgaMWDQ6NGjNXHiRBUWFpqeAgBwMGLAsPT0dFVVVZmeAQBwMGLAMK/Xq08++URffvml6SkAAIciBgzLycmRJFVUVBheAgBwKmLAsMmTJysmJobzBgAAxhADhrlcLqWkpGjjxo2mpwAAHIoYsIGCggLt379f586dMz0FAOBAxIAN5OXlqb29nVcjBAAYQQzYQFxcnKKiolRUVGR6CgDAgYgBG3C5XEpKSuKVCAEARhADNpGXl6cPP/xQra2tpqcAAByGGLCJgoICtba2avv27aanAAAchhiwifnz5ysiIkLFxcWmpwAAHIYYsAm326158+aptLTU9BQAgMMQAzaSm5ur7du3q6Ojw/QUAICDEAM2UlBQoPPnz2vXrl2mpwAAHIQYsJGUlBS53W6eKgAADChiwEYGDx6su+++m5MIAQADihiwmczMTG3ZskWWZZmeAgBwCGLAZnw+nxobG3Xo0CHTUwAADkEM2IzH45HL5VJZWZnpKQAAhyAGbCY6Olp33XUXb1oEABgwxIANeTwe3s4YADBgiAEb8vl8+uyzz3T8+HHTUwAADkAM2FBWVpYkqby83PASAIATEAM2NG7cON15550qLCw0PQUA4ADEgE2lpqaqoqLC9AwAgAMQAzbl8/l06NAhNTU1mZ4CAAhyxIBN5eTkyLIsVVZWmp4CAAhyxIBNTZs2TSNHjtS6detMTwEABDliwKZcLpdSUlK0ceNG01MAAEGOGLCx/Px87d27VxcuXDA9BQAQxIgBG8vLy1NbW5s2b95segoAIIgRAzYWHx+voUOH8j4FAIB+RQzYWEhIiJKSkrR+/XrTUwAAQYwYsLnc3FzV19fr0qVLpqcAAIIUMWBzBQUFunjxourq6kxPAQAEKWLA5hYsWKCwsDCVlJSYngIACFLEgM2FhYVp7ty5Ki0tNT0FABCkiIEAkJ2dra1bt6qjo8P0FABAECIGAoDP59NXX32lvXv3mp4CAAhCxEAASE1NVWhoqMrKykxPAQAEIWIgAAwdOlSxsbG8+BAAoF8QAwEiKytLW7ZskWVZpqcAAIIMMRAgvF6vTp48qSNHjpieAgAIMsRAgMjIyJAkzhsAAPQ5YiBA3HHHHZo2bRrnDQAA+hwxEEDS0tJUXV1tegYAIMgQAwHE5/OpoaFBJ06cMD0FABBEiIEAkp2dLUkqLy83OwQAEFSIgQAyYcIEjRs3ToWFhaanAACCCDEQYFJSUlRZWWl6BgAgiBADAaagoEAfffSRzp49a3oKACBIEAMBJi8vT5ZlqaqqyvQUAECQIAYCzIwZMxQdHc3rDQAA+gwxEGBcLpcWLVqkDRs2mJ4CAAgSxEAAys/P165du3Tx4kXTUwAAQYAYCED5+flqa2vT1q1bTU8BAAQBYiAAzZ07V0OGDOH1BgAAfYIYCEChoaFKTEzU+vXrTU8BAAQBYiBA5ebmqq6uTm1tbaanAAACHDEQoAoKCnThwgXV19ebngIACHDEQIBauHChBg0apNLSUtNTAAABjhgIUOHh4ZozZ45KSkpMTwEABDhiIIBlZWWppqZGlmWZngIACGDEQADzer06c+aM9u/fb3oKACCAEQMBLD09XS6XS2VlZaanAAACGDEQwIYNG6bY2FgVFxebngIACGDEQIDzeDyqrq42PQMAEMCIgQC3ePFiffHFFzp69KjpKQCAAEUMBLjMzExJ4i2NAQC3jBgIcDExMZo0aRJvWgQAuGXEQBBIT09XVVWV6RkAgABFDAQBn8+nw4cP69SpU6anAAACEDEQBLKzsyVJGzduNDsEABCQiIEgMHnyZI0ePVpFRUWmpwAAAhAxECRSUlJUUVFhegYAIAARA0GioKBA+/bt07lz50xPAQAEGGIgSOTm5qqjo0ObNm0yPQUAEGCIgSARFxenqKgozhsAAPQaMRAkXC6XFi5cyCsRAgB6jRgIInl5edq5c6daWlpMTwEABBBiIIgUFBSotbVV27dvNz0FABBAiIEgkpCQoIiICBUXF5ueAgAIIMRAEHG73UpISFBpaanpKQCAAEIMBJnc3FzV1taqvb3d9BQAQIAgBoJMQUGBzp8/r127dpmeAgAIEMRAkElOTpbb7VZJSYnpKQCAAEEMBJnBgwdr9uzZxAAA4KYRA0EoMzNTNTU1sizL9BQAQAAgBoKQz+dTY2OjPv74Y9NTAAABgBgIQunp6XK5XFq/fr3pKQCAAEAMBKHo6GhNnz6dNy0CANwUYiBIeTwe3s4YAHBTiIEg5fP5dPz4cR07dsz0FACAzREDQSorK0uSVF5ebngJAMDuiIEgNXbsWE2YMEGFhYWmpwAAbI4YCGKpqamqrKw0PQMAYHPEQBDz+Xw6dOiQTp8+bXoKAMDGiIEglpOTI8uyODoAALguYiCITZ06VXfccYfWrVtnegoAwMaIgSDmcrmUkpKijRs3mp4CALAxYiDI5efna+/evWpubjY9BQBgU8RAkMvLy1N7e7uqq6tNTwEA2BQxEORmz56tyMhIFRcXm54CALApYiDIhYSEKCkpiXcwBABcEzHgAHl5eaqvr9elS5dMTwEA2BAx4AD5+flqaWlRbW2t6SkAABsiBhwgMTFR4eHhKikpMT0FAGBDxIADhIWFae7cuSotLTU9BQBgQ8SAQ2RnZ2vr1q3q6OgwPQUAYDPEgEP4fD6dO3dOe/bsMT0FAGAzxIBDpKamKjQ0VGVlZaanAABshhhwiCFDhmjWrFkqKioyPQUAYDPEgINkZWVpy5YtsizL9BQAgI0QAw7i9Xp16tQpHT582PQUAICNEAMOkpGRIUmcNwAA6IIYcJCRI0dq2rRpnDcAAOiCGHAYj8fD2xkDALogBhxk1KhRysrK0tGjRzV06FA9/fTTpicBgK253W7Nnz9fd999txYsWKB/+7d/819XU1OjpKQkDRo0SO+9957BlbfPbXoABlZycrIkadKkSaqvr1dzc7OGDBlieBUA2FN0dLTq6uokSQ0NDbrnnntUXl6uYcOGafLkyXrllVf0r//6r4ZX3j5iwGH+4i/+QpK0b98+7du3TxkZGaqoqCAIAOAGRo0apebmZr311lv+yxITExUXF2dwVd8gBhzk4sWL3d7GuLa2Vi+88ILuvfdeQ6sAwL7a2tr8/27+4Q9/0IEDB7pcX1tbq/DwcBPT+pTL4hVoHGPw4MG6ePGi6RkAEFRiY2P10ksv6Zvf/KbpKbeMIwMOEhoa2uPlP/3pTzkyAAA9yM3N9b82yx/+8Ae98MIL3W4zcuTIgZ7V54gBB4mIiFBsbGyXpwoSExP1zDPPcM4AAPTA7XYrMTFRkhQZGamXX35Zzc3N/usTExM1bdo0U/P6DE8TOERbW5smT56sAwcOaNq0aWpqapIk3XHHHdqyZYsmTJhgdiAA2JDb7VZ8fLxaW1s1ePBg/dVf/ZXcbrd27NihUaNG6T/+4z/U1NSkwYMHa8aMGQH7Oi7EgEPU19frhz/8oSorK01PAQDYDC865ABvvPGGHnzwQa1cudL0FACADXFkAAAAh+PIAAAADkcMAADgcMQAAAAORwwAAOBwxAAAAA5HDAAA4HDEAAAADkcMAADgcMQAAAAORwwAAOBwxAAAAA5HDAAA4HDEAAAADkcMAADgcMQAAAAORwwAAOBwxAAAAA5HDAAA4HDEAAAADkcMAADgcMQAAAAORwwAAOBwxAAAAA5HDAAA4HDEAAAADkcMAADgcMQAAAAORwwAAOBwxAAAAA5HDAAA4HDEAAAADkcMAADgcMQAAAAORwwAAOBwxAAAAA73/wALDCtPtogB9gAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "\n", - "# Create a water network model\n", - "inp_file = '../networks/Net0_HW.inp'\n", - "# inp_file = 'networks/Net2Loops.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Expression of he network" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cons:\n", - "mass_balance[J1]: ((expected_demand[J1]-flow[P1])+flow[P2])\n", - "mass_balance[D1]: (expected_demand[D1]-flow[P2])\n", - "approx_hazen_williams_headloss[P1]: (((((((-((sign(flow[P1]))))*hw_resistance[P1])*((abs(flow[P1]))**1.852))-((1e-05*(hw_resistance[P1]**0.5))*flow[P1]))-(((sign(flow[P1]))*minor_loss[P1])*(flow[P1]**2.0)))+source_head[R1])-head[J1])\n", - "approx_hazen_williams_headloss[P2]: (((((((-((sign(flow[P2]))))*hw_resistance[P2])*((abs(flow[P2]))**1.852))-((1e-05*(hw_resistance[P2]**0.5))*flow[P2]))-(((sign(flow[P2]))*minor_loss[P2])*(flow[P2]**2.0)))+head[J1])-head[D1])\n", - "\n", - "vars:\n", - "flow[P1]: flow[P1]\n", - "flow[P2]: flow[P2]\n", - "head[J1]: head[J1]\n", - "head[D1]: head[D1]\n", - "\n" - ] - } - ], - "source": [ - "from wntr.sim.hydraulics import create_hydraulic_model\n", - "model, updater = create_hydraulic_model(wn)\n", - "print(model.__str__())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0\n", - "0.05\n", - "234518508.2718721\n", - "10512430570.450115\n", - "30.0\n" - ] - } - ], - "source": [ - "print(model.expected_demand['J1'].value)\n", - "print(model.expected_demand['D1'].value)\n", - "print(model.hw_resistance['P1'].value)\n", - "print(model.hw_resistance['P2'].value)\n", - "print(model.source_head['R1'].value)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "hw_res_list = [ {'P1':1.0, 'P2':1.0}, {'P1':2.0, 'P2':1.0}, {'P1':1.0, 'P2':2.0}, {'P1':2.0, 'P2':2.0}]\n", - "exp_dem = {'J1':-1, 'D1':1}\n", - "src_hd = {'R1':2.0}\n", - "\n", - "def network_function(input):\n", - " \n", - " flow = {'P1':input[0], 'P2':input[1]}\n", - " head = {'J1':input[2], 'D1':input[3]}\n", - "\n", - " def mb_j1(flow):\n", - " return exp_dem['J1'] - flow['P1'] + flow['P2']\n", - " \n", - " def mb_d1(flow):\n", - " return exp_dem['D1'] - flow['P2']\n", - " \n", - " def hl_p1(head, flow):\n", - " return -hw_res['P1']*flow['P1']**2 + src_hd['R1'] - head['J1']\n", - "\n", - " def hl_p2(head, flow):\n", - " return -hw_res['P2']*flow['P2']**2 + head['J1'] - head['D1']\n", - " \n", - " return np.array([\n", - " mb_j1(flow),\n", - " mb_d1(flow),\n", - " hl_p1(head, flow),\n", - " hl_p2(head, flow)\n", - " ])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - } - ], - "source": [ - "from quantum_newton_raphson.newton_raphson import newton_raphson\n", - "res = []\n", - "for hw_res in hw_res_list:\n", - " initial_point = np.random.rand(4)\n", - " res.append(newton_raphson(network_function, initial_point))\n", - " assert np.allclose(network_function(res[-1].solution), 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[NewtonRaphsonResult(solution=array([4.951e-22, 1.000e+00, 2.000e+00, 1.000e+00]), n_iter=2, diff=5.0058845957323683e-11, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.647, -0.356, -1.483, -1.497]), splu=), SPLUResult(solution=array([-1.173e-12, 9.526e-12, 4.181e-01, 5.448e-01]), splu=)]),\n", - " NewtonRaphsonResult(solution=array([-8.327e-17, 1.000e+00, 2.000e+00, 1.000e+00]), n_iter=2, diff=1.169997432270975e-11, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.179, -0.4 , -1.64 , -0.272]), splu=), SPLUResult(solution=array([5.139e-12, 1.071e-11, 6.387e-02, 2.241e-01]), splu=)]),\n", - " NewtonRaphsonResult(solution=array([-1.093e-16, 1.000e+00, 2.000e+00, 5.409e-14]), n_iter=2, diff=1.615062250603927e-12, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.138, -0.133, -1.629, 0.405]), splu=), SPLUResult(solution=array([3.961e-12, 3.545e-12, 1.898e-02, 5.409e-02]), splu=)]),\n", - " NewtonRaphsonResult(solution=array([1.227e-21, 1.000e+00, 2.000e+00, 4.125e-11]), n_iter=2, diff=4.4402481691463436e-11, converged=True, linear_solver_results=[SPLUResult(solution=array([ 0.138, -0.836, -1.204, -1.064]), splu=), SPLUResult(solution=array([ 3.980e-12, -2.403e-11, 3.831e-02, 1.435e+00]), splu=)])]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "def define_problem():\n", - " # system of equations\n", - " num_equations = 5\n", - " num_var = 6\n", - "\n", - " P0 = np.zeros(num_equations)\n", - " P0[0] = exp_dem['J1']\n", - " P0[1] = exp_dem['D1']\n", - " P0[2] = src_hd['R1']\n", - " P0[3] = 0\n", - " P0[4] = 0\n", - "\n", - " P1 = np.zeros((num_equations, num_var))\n", - " P1[0, 0] = -1\n", - " P1[0, 1] = 1\n", - " P1[0, 2] = 0 \n", - " P1[0, 3] = 0\n", - "\n", - " P1[1, 0] = 0\n", - " P1[1, 1] = -1\n", - " P1[1, 2] = 0 \n", - " P1[1, 3] = 0\n", - "\n", - " P1[2, 0] = 0\n", - " P1[2, 1] = 0\n", - " P1[2, 2] = -1 \n", - " P1[2, 3] = 0\n", - "\n", - " P1[3, 0] = 0\n", - " P1[3, 1] = 0\n", - " P1[3, 2] = 1 \n", - " P1[3, 3] = -1\n", - "\n", - " P1[4,4] = 1\n", - " P1[4,5] = 1\n", - " \n", - "\n", - " P2 = np.zeros((num_equations, num_var, num_var))\n", - " P3 = np.zeros((num_equations, num_var, num_var, num_var))\n", - " P3[2, .., ..] = -1\n", - " P3[3, .., ..] = -1 \n", - "\n", - " # search parameters\n", - " qubits_per_var = 2\n", - " basis = np.array([2**i for i in range(qubits_per_var)])\n", - "\n", - " # basis_offset = np.array([-0.5, 1])\n", - " # basis_coeff = np.array([0.5, 1])\n", - "\n", - " basis_offset = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n", - " basis_coeff = np.array([1, 1, 1, 1, 1, 1])\n", - "\n", - " basis_map = {\n", - " \"basis\": basis,\n", - " \"basis_offset\": basis_offset,\n", - " \"basis_coeff\": basis_coeff,\n", - " }\n", - "\n", - " return (\n", - " num_equations,\n", - " num_var,\n", - " P0,\n", - " P1,\n", - " P2,\n", - " P3,\n", - " qubits_per_var,\n", - " basis,\n", - " basis_offset,\n", - " basis_coeff,\n", - " basis_map,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "extended qubo\n", - "ground state eigenvector = [0. 0. 1. 0. 0. 1. 1. 0.]\n", - "ground state eigenvalue = 0.0\n", - "solution = [0.0, 1.0, 2.0, 1.0]\n", - "\n", - "upper triangular qubo\n", - "ground state eigenvector = [0. 0. 1. 0. 0. 1. 1. 0.]\n", - "ground state eigenvalue = 0.0\n", - "solution = [0.0, 1.0, 2.0, 1.0]\n", - "\n", - "reduced upper triangular qubo\n", - "ground state eigenvector = [0. 0. 1. 0. 0. 1. 1. 0.]\n", - "ground state eigenvalue = 0.0\n", - "solution = [0.0, 1.0, 2.0, 1.0]\n", - "\n" - ] - } - ], - "source": [ - "from poly_brute_force import solve\n", - "sol = solve(define_problem)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/trash/wntr_qubo_poly.ipynb b/docs/notebooks/trash/wntr_qubo_poly.ipynb deleted file mode 100644 index 942367b..0000000 --- a/docs/notebooks/trash/wntr_qubo_poly.ipynb +++ /dev/null @@ -1,1657 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define the system " - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "data": { - "image/png": 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Wq1XTp0+vRo4cWT3wwAPVpk2bqr/6q7+qDjzwwGrYsGHV2LFjqxkzZlR33XXXO87jm08t7O2+33xqYV/MZ9fj7e3r6quv7jE3vX0999xzOz3PVVVVGzdurObMmVONGTOmGjZsWDVlypTqlltu2eGcXH/99dXf/M3fVOPGjauGDx9enXjiidUzzzxT3++d5r3L9OnTe/ws9XZq4aWXXtpjzG99jnSd7rlx48bq+OOPr5qbm6t99tmnWrJkSf30SOhrTVXlkys0xoMPPpipU6fm0Ucf7bGEy84zj7vm3nvvzYwZM3LDDTdkzpw5jR5OD/PmzcuNN96Y//3f/230UCiIzwzQUMuXL/cHrA+YR+B34TMDNMyUKVMyZcqURg/jA888Ar8rKwMAUDifGQCAwlkZAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKNyAioHOzs4cccQRmTRpUg499NB85zvfafSQoF977rnn8olPfCIf+9jHcthhh+WGG25o9JCgXzvllFOy5557Zs6cOY0eSp8aUDEwePDgzJ07N3/yJ3+S+fPnZ9myZXn55ZcbPSzot9544420trZm+vTp+Yu/+ItccMEFee211xo9LOi3zjnnnJx55pnZsGFDrrzyytRqtUYPqU80VVVVNXoQfaFWq6WtrS0dHR31bUOHDs1TTz2V/fbbr4Ejg/5pR8+Z4cOH55FHHsnBBx/cwJFB/7Sj58zkyZPT3t6e5ubmBo7sdzek0QPoK6tWrer2DUq2v+q56qqrMnv27AaNCvqvNWvW9HjOvP766/n+97/vOQM7sKPnTEdHR6699trMnz+/QaPqGwNmZeDcc8/NFVdc0ehhAFCYc889N9/61rcaPYzfyYBZGZg0adIOt8+ePTuLFy9+fwcDHwBr1qzJ8uXLe2xfvHixlQHYgd6eM739/fkgGTArA7VaLUcddVT+/d//vb5t2LBhWb9+faZMmdLAkUH/VKvV0tramg0bNtS3DZT3P+G9MJA/MzBgziZobm7OZZddlnHjxiVJxo0blxUrVggB6EVzc3NWrFhRvzxu3Lj89re/zcaNGxs4Kui/mpubM3r06IwcOTJDhgzJmDFj8rWvfe0DHwLJAFoZ6NLR0ZHDDz88Dz/8cCZPntzo4UC/5zkDDJiVAQDg3REDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFC4ARcDJ510UpLkjDPOyIwZMxo8Guj/Xn/99STJN77xjQaPBGiUARUDtVottVotSTJnzpysXbu2wSOC/q1Wq+ULX/hCkuSpp56qP3+AsjRVVVU1ehB9oVarpa2tLR0dHfVtkydPTnt7e5qbmxs4MuifarVajjzyyDz22GP1bZ4zUKYhjR5AX1m1alW3EEiSjo6OLF++PLNnz27QqKD/WrNmTbcQSLY/Z6699trMnz+/QaMCGmHAxMAjjzyyw+3Lli3LsmXL3t/BwAdYb88lYOAaMDEwadKkHW4/6aSTsnTp0vd3MPABsGDBgtx///09tvf2XAIGrgH1mYFp06blZz/7WX3b8OHDc/vtt6etra2BI4P+yedsgC4D5myC5ubmXHfddRk3blySpKWlJUuXLhUC0Ivm5ua0t7dn8eLFSZKpU6cKASjUgFkZ6NLR0ZHDDz88Dz/8cCZPntzo4UC/5zkDDJiVAQDg3REDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFA4MQAAhRMDAFC4ARUDTz75ZM4444wkyRlnnJHhw4fnhz/8YWMHBf3c888/nySZM2dOJk6cmNdee63BIwLebwMqBj7ykY9kzpw5SZJZs2ZlxIgR+eQnP9ngUUH/VavVcv755yfZHtC33nprdt999waPCni/NVVVVTV6EH2hVqulra0tHR0d9W1jxozJ888/n+bm5gaODPqnWq2WI444Io8//nh92+TJk9Pe3u45A4UZ0ugB9JVVq1Z1C4Ek6ezszPLlyzN79uwGjQr6rzVr1nQLgSTp6OjItddem/nz5zdoVEAjDJiVgXPPPTdXXHFFo4cBH3gnnXRSfvSjHzV6GMD7aMCsDEyaNGmH2xcvXmxlAHZgzZo1Wb58eY/tQ4cObcBogEYaMCsDO/rMgPc/oXe1Wi2tra3ZsGFDfdvo0aPzve99L6eeemoDRwa83wbM2QTNzc1pb2/PypUrM3z48Fx++eVCAN5Gc3NzfvzjH2fhwoUZO3Zs9t1335x11llCAAo0YFYGAIB3Z8CsDAAA744YAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKJwYAIDCiQEAKNz/Ab/b1W5gMQspAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cons:\n", - "mass_balance[2]: (((expected_demand[2]-flow[1])+flow[2])+flow[3])\n", - "mass_balance[3]: ((expected_demand[3]-flow[2])+flow[7])\n", - "mass_balance[4]: (((expected_demand[4]-flow[3])+flow[4])+flow[5])\n", - "mass_balance[5]: (((expected_demand[5]-flow[4])-flow[7])+flow[8])\n", - "mass_balance[6]: ((expected_demand[6]-flow[5])+flow[6])\n", - "mass_balance[7]: ((expected_demand[7]-flow[6])-flow[8])\n", - "approx_hazen_williams_headloss[1]: (((((((-((sign(flow[1]))))*hw_resistance[1])*((abs(flow[1]))**1.852))-((1e-05*(hw_resistance[1]**0.5))*flow[1]))-(((sign(flow[1]))*minor_loss[1])*(flow[1]**2.0)))+source_head[1])-head[2])\n", - "approx_hazen_williams_headloss[2]: (((((((-((sign(flow[2]))))*hw_resistance[2])*((abs(flow[2]))**1.852))-((1e-05*(hw_resistance[2]**0.5))*flow[2]))-(((sign(flow[2]))*minor_loss[2])*(flow[2]**2.0)))+head[2])-head[3])\n", - "approx_hazen_williams_headloss[3]: (((((((-((sign(flow[3]))))*hw_resistance[3])*((abs(flow[3]))**1.852))-((1e-05*(hw_resistance[3]**0.5))*flow[3]))-(((sign(flow[3]))*minor_loss[3])*(flow[3]**2.0)))+head[2])-head[4])\n", - "approx_hazen_williams_headloss[4]: (((((((-((sign(flow[4]))))*hw_resistance[4])*((abs(flow[4]))**1.852))-((1e-05*(hw_resistance[4]**0.5))*flow[4]))-(((sign(flow[4]))*minor_loss[4])*(flow[4]**2.0)))+head[4])-head[5])\n", - "approx_hazen_williams_headloss[5]: (((((((-((sign(flow[5]))))*hw_resistance[5])*((abs(flow[5]))**1.852))-((1e-05*(hw_resistance[5]**0.5))*flow[5]))-(((sign(flow[5]))*minor_loss[5])*(flow[5]**2.0)))+head[4])-head[6])\n", - "approx_hazen_williams_headloss[6]: (((((((-((sign(flow[6]))))*hw_resistance[6])*((abs(flow[6]))**1.852))-((1e-05*(hw_resistance[6]**0.5))*flow[6]))-(((sign(flow[6]))*minor_loss[6])*(flow[6]**2.0)))+head[6])-head[7])\n", - "approx_hazen_williams_headloss[7]: (((((((-((sign(flow[7]))))*hw_resistance[7])*((abs(flow[7]))**1.852))-((1e-05*(hw_resistance[7]**0.5))*flow[7]))-(((sign(flow[7]))*minor_loss[7])*(flow[7]**2.0)))+head[3])-head[5])\n", - "approx_hazen_williams_headloss[8]: (((((((-((sign(flow[8]))))*hw_resistance[8])*((abs(flow[8]))**1.852))-((1e-05*(hw_resistance[8]**0.5))*flow[8]))-(((sign(flow[8]))*minor_loss[8])*(flow[8]**2.0)))+head[5])-head[7])\n", - "\n", - "vars:\n", - "flow[1]: flow[1]\n", - "flow[2]: flow[2]\n", - "flow[3]: flow[3]\n", - "flow[7]: flow[7]\n", - "flow[4]: flow[4]\n", - "flow[5]: flow[5]\n", - "flow[8]: flow[8]\n", - "flow[6]: flow[6]\n", - "head[2]: head[2]\n", - "head[3]: head[3]\n", - "head[4]: head[4]\n", - "head[5]: head[5]\n", - "head[6]: head[6]\n", - "head[7]: head[7]\n", - "\n" - ] - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "import numpy as np \n", - "from wntr.sim.hydraulics import create_hydraulic_model\n", - "\n", - "# Create a water network model\n", - "inp_file = '../networks/Net0_HW.inp'\n", - "inp_file = '../networks/Net2Loops.inp'\n", - "# inp_file = '../networks/Net2Loops.inp'\n", - "wn0 = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn0, title=wn0.name, node_labels=True)\n", - "\n", - "model, updater = create_hydraulic_model(wn0, HW_approx='default')\n", - "print(model.__str__())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3487722352119913" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.hw_resistance['3'].value * 0.077**2" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[53.248 30.665 44.321 28.81 30.547 27.058 0. ]]\n", - "[[ 0.311 0.051 0.232 0.032 0.167 0.075 0.024 -0.019]]\n" - ] - } - ], - "source": [ - "import wntr \n", - "sim = wntr.sim.WNTRSimulator(wn0)\n", - "results = sim.run_sim()\n", - "print(results.node['pressure'].values)\n", - "print(results.link['flowrate'].values)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 53.248 0.263 -84.231 -82.956 -96.87 -95.268 0. ]]\n", - "[[0.311 0.1 0.183 0.013 0.137 0.046 0.072 0.01 ]]\n" - ] - } - ], - "source": [ - "wn0.links['3'].diameter = 0.203\n", - "sim = wntr.sim.WNTRSimulator(wn0)\n", - "results = sim.run_sim()\n", - "print(results.node['pressure'].values)\n", - "print(results.link['flowrate'].values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Expression of he network" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "# Create a water network model\n", - "inp_file = '../networks/Net0_CM.inp'\n", - "inp_file = '../networks/Net1Loops_CM_original_values.inp'\n", - "inp_file = '../networks/Net2LoopsCM.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.scenario.network_qubo import Network\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "\n", - "nqbit = 9\n", - "step = (0.25/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", - "\n", - "nqbit = 9\n", - "step = (250/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", - "\n", - "\n", - "# nqbit = 5\n", - "# flow_encoding = RangedEfficientEncoding(nqbit=nqbit, range=5., offset=+0.0, var_base_name=\"x\")\n", - "# head_encoding = RangedEfficientEncoding(nqbit=nqbit, range=5, offset=+0.0, var_base_name=\"x\")\n", - "\n", - "net = Network(wn, flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "# print(net.m.__str__())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning, we didn't reach the required tolerance within 100 iterations, error is at 11648.68115427426\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, - { - "ename": "AssertionError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[90], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# net.matrices[2] = net.matrices[2]*4\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m ref_sol \u001b[38;5;241m=\u001b[39m \u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclassical_solutions\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m ref_sol\n", - "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/scenario/network_qubo.py:85\u001b[0m, in \u001b[0;36mNetwork.classical_solutions\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 83\u001b[0m initial_point \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(num_vars)\n\u001b[1;32m 84\u001b[0m res \u001b[38;5;241m=\u001b[39m newton_raphson(func, initial_point)\n\u001b[0;32m---> 85\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(func(res\u001b[38;5;241m.\u001b[39msolution), \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39msolution\n", - "\u001b[0;31mAssertionError\u001b[0m: " - ] - } - ], - "source": [ - "# net.matrices[2] = net.matrices[2]*4\n", - "ref_sol = net.classical_solutions()\n", - "ref_sol" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([ 1.639e-01, 6.607e-02, 7.003e-02, 3.830e-02, 3.670e-02, 2.018e+02, 1.005e+02, 8.800e+01, -5.306e+01])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.wn.links['3'].diameter = 0.203\n", - "net.m, net.model_updater = net.create_cm_model()\n", - "net.matrices = net.initialize_matrices()\n", - "ref_sol = net.classical_solutions()\n", - "ref_sol" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", - "\n", - "nqbit = 9\n", - "step = (1/(2**nqbit-1))\n", - "encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", - "sv1 = SolutionVector(5, encoding=encoding)\n", - "\n", - "nqbit = 11\n", - "step = (40/(2**nqbit-1))\n", - "encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+175, var_base_name=\"x\")\n", - "sv2 = SolutionVector(1, encoding=encoding)\n", - "\n", - "nqbit = 11\n", - "step = (40/(2**nqbit-1))\n", - "encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+90, var_base_name=\"x\")\n", - "sv3 = SolutionVector(1, encoding=encoding)\n", - "\n", - "nqbit = 11\n", - "step = (40/(2**nqbit-1))\n", - "encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+180, var_base_name=\"x\")\n", - "sv4 = SolutionVector(1, encoding=encoding)\n", - "\n", - "nqbit = 11\n", - "step = (40/(2**nqbit-1))\n", - "encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", - "sv5 = SolutionVector(1, encoding=encoding)\n", - "\n", - "net.mixed_solution_vector = MixedSolutionVector([sv1,sv2,sv3,sv4,sv5])\n", - "net.matrices = net.initialize_matrices()" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.07827788649706457" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(40/(2**9-1))" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0. , 0. , 0.001, 0.001, 0.002, 0.002, 0.003, 0.003, 0.004, 0.004, 0.005, 0.005, 0.006, 0.006, 0.007, 0.007, 0.008, 0.008, 0.009, 0.009, 0.01 , 0.01 , 0.011, 0.011, 0.012, 0.012, 0.013, 0.013, 0.014, 0.014, 0.015, 0.015, 0.016, 0.016, 0.017, 0.017, 0.018, 0.018, 0.019, 0.019, 0.02 ,\n", - " 0.02 , 0.021, 0.021, 0.022, 0.022, 0.023, 0.023, 0.023, 0.024, 0.024, 0.025, 0.025, 0.026, 0.026, 0.027, 0.027, 0.028, 0.028, 0.029, 0.029, 0.03 , 0.03 , 0.031, 0.031, 0.032, 0.032, 0.033, 0.033, 0.034, 0.034, 0.035, 0.035, 0.036, 0.036, 0.037, 0.037, 0.038, 0.038, 0.039, 0.039, 0.04 ,\n", - " 0.04 , 0.041, 0.041, 0.042, 0.042, 0.043, 0.043, 0.044, 0.044, 0.045, 0.045, 0.045, 0.046, 0.046, 0.047, 0.047, 0.048, 0.048, 0.049, 0.049, 0.05 , 0.05 , 0.051, 0.051, 0.052, 0.052, 0.053, 0.053, 0.054, 0.054, 0.055, 0.055, 0.056, 0.056, 0.057, 0.057, 0.058, 0.058, 0.059, 0.059, 0.06 ,\n", - " 0.06 , 0.061, 0.061, 0.062, 0.062, 0.063, 0.063, 0.064, 0.064, 0.065, 0.065, 0.066, 0.066, 0.067, 0.067, 0.068, 0.068, 0.068, 0.069, 0.069, 0.07 , 0.07 , 0.071, 0.071, 0.072, 0.072, 0.073, 0.073, 0.074, 0.074, 0.075, 0.075, 0.076, 0.076, 0.077, 0.077, 0.078, 0.078, 0.079, 0.079, 0.08 ,\n", - " 0.08 , 0.081, 0.081, 0.082, 0.082, 0.083, 0.083, 0.084, 0.084, 0.085, 0.085, 0.086, 0.086, 0.087, 0.087, 0.088, 0.088, 0.089, 0.089, 0.09 , 0.09 , 0.091, 0.091, 0.091, 0.092, 0.092, 0.093, 0.093, 0.094, 0.094, 0.095, 0.095, 0.096, 0.096, 0.097, 0.097, 0.098, 0.098, 0.099, 0.099, 0.1 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0.236, 0.236, 0.237, 0.237, 0.238, 0.238, 0.239, 0.239, 0.24 , 0.24 ,\n", - " 0.241, 0.241, 0.242, 0.242, 0.243, 0.243, 0.244, 0.244, 0.245, 0.245, 0.246, 0.246, 0.247, 0.247, 0.248, 0.248, 0.249, 0.249, 0.25 , 0.25 ])" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sort(flow_encoding.get_possible_values())" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0. , 0.489, 0.978, 1.468, 1.957, 2.446, 2.935, 3.425, 3.914, 4.403, 4.892, 5.382, 5.871, 6.36 , 6.849, 7.339, 7.828, 8.317, 8.806, 9.295, 9.785, 10.274, 10.763, 11.252, 11.742, 12.231, 12.72 , 13.209, 13.699, 14.188, 14.677, 15.166,\n", - " 15.656, 16.145, 16.634, 17.123, 17.613, 18.102, 18.591, 19.08 , 19.569, 20.059, 20.548, 21.037, 21.526, 22.016, 22.505, 22.994, 23.483, 23.973, 24.462, 24.951, 25.44 , 25.93 , 26.419, 26.908, 27.397, 27.886, 28.376, 28.865, 29.354, 29.843, 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89.53 , 90.02 , 90.509, 90.998, 91.487, 91.977, 92.466, 92.955, 93.444,\n", - " 93.933, 94.423, 94.912, 95.401, 95.89 , 96.38 , 96.869, 97.358, 97.847, 98.337, 98.826, 99.315, 99.804, 100.294, 100.783, 101.272, 101.761, 102.25 , 102.74 , 103.229, 103.718, 104.207, 104.697, 105.186, 105.675, 106.164, 106.654, 107.143, 107.632, 108.121, 108.611, 109.1 ,\n", - " 109.589, 110.078, 110.568, 111.057, 111.546, 112.035, 112.524, 113.014, 113.503, 113.992, 114.481, 114.971, 115.46 , 115.949, 116.438, 116.928, 117.417, 117.906, 118.395, 118.885, 119.374, 119.863, 120.352, 120.841, 121.331, 121.82 , 122.309, 122.798, 123.288, 123.777, 124.266, 124.755,\n", - " 125.245, 125.734, 126.223, 126.712, 127.202, 127.691, 128.18 , 128.669, 129.159, 129.648, 130.137, 130.626, 131.115, 131.605, 132.094, 132.583, 133.072, 133.562, 134.051, 134.54 , 135.029, 135.519, 136.008, 136.497, 136.986, 137.476, 137.965, 138.454, 138.943, 139.432, 139.922, 140.411,\n", - " 140.9 , 141.389, 141.879, 142.368, 142.857, 143.346, 143.836, 144.325, 144.814, 145.303, 145.793, 146.282, 146.771, 147.26 , 147.75 , 148.239, 148.728, 149.217, 149.706, 150.196, 150.685, 151.174, 151.663, 152.153, 152.642, 153.131, 153.62 , 154.11 , 154.599, 155.088, 155.577, 156.067,\n", - " 156.556, 157.045, 157.534, 158.023, 158.513, 159.002, 159.491, 159.98 , 160.47 , 160.959, 161.448, 161.937, 162.427, 162.916, 163.405, 163.894, 164.384, 164.873, 165.362, 165.851, 166.341, 166.83 , 167.319, 167.808, 168.297, 168.787, 169.276, 169.765, 170.254, 170.744, 171.233, 171.722,\n", - " 172.211, 172.701, 173.19 , 173.679, 174.168, 174.658, 175.147, 175.636, 176.125, 176.614, 177.104, 177.593, 178.082, 178.571, 179.061, 179.55 , 180.039, 180.528, 181.018, 181.507, 181.996, 182.485, 182.975, 183.464, 183.953, 184.442, 184.932, 185.421, 185.91 , 186.399, 186.888, 187.378,\n", - " 187.867, 188.356, 188.845, 189.335, 189.824, 190.313, 190.802, 191.292, 191.781, 192.27 , 192.759, 193.249, 193.738, 194.227, 194.716, 195.205, 195.695, 196.184, 196.673, 197.162, 197.652, 198.141, 198.63 , 199.119, 199.609, 200.098, 200.587, 201.076, 201.566, 202.055, 202.544, 203.033,\n", - " 203.523, 204.012, 204.501, 204.99 , 205.479, 205.969, 206.458, 206.947, 207.436, 207.926, 208.415, 208.904, 209.393, 209.883, 210.372, 210.861, 211.35 , 211.84 , 212.329, 212.818, 213.307, 213.796, 214.286, 214.775, 215.264, 215.753, 216.243, 216.732, 217.221, 217.71 , 218.2 , 218.689,\n", - " 219.178, 219.667, 220.157, 220.646, 221.135, 221.624, 222.114, 222.603, 223.092, 223.581, 224.07 , 224.56 , 225.049, 225.538, 226.027, 226.517, 227.006, 227.495, 227.984, 228.474, 228.963, 229.452, 229.941, 230.431, 230.92 , 231.409, 231.898, 232.387, 232.877, 233.366, 233.855, 234.344,\n", - " 234.834, 235.323, 235.812, 236.301, 236.791, 237.28 , 237.769, 238.258, 238.748, 239.237, 239.726, 240.215, 240.705, 241.194, 241.683, 242.172, 242.661, 243.151, 243.64 , 244.129, 244.618, 245.108, 245.597, 246.086, 246.575, 247.065, 247.554, 248.043, 248.532, 249.022, 249.511, 250. ])" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sort(head_encoding.get_possible_values())" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "flow prec: 0.0004892367906066536\n", - "head prec: 0.4892367906066536\n", - "\n", - "\n", - "ref : [1.639e-01 6.006e-02 7.604e-02 3.229e-02 4.271e-02 2.018e+02 1.181e+02 2.000e+02 8.957e+00]\n", - "sol : [0.25048923679060664, 0.06262230919765166, 0.0, 0.023483365949119372, 0.03913894324853229, 193.89594528578405, 99.98534440644845, 200.00977039570103, 40.0]\n", - "diff: [-8.662e-02 -2.566e-03 7.604e-02 8.803e-03 3.575e-03 7.881e+00 1.812e+01 -7.765e-03 -3.104e+01]\n", - "\n", - "\n", - "encoded_ref: [1.644e-01 6.067e-02 7.632e-02 3.131e-02 4.305e-02 2.018e+02 1.181e+02 2.000e+02 8.950e+00]\n", - "encoded_sol: [2.505e-01 6.262e-02 0.000e+00 2.348e-02 3.914e-02 1.939e+02 9.999e+01 2.000e+02 4.000e+01]\n", - "diff : [-8.611e-02 -1.957e-03 7.632e-02 7.828e-03 3.914e-03 7.875e+00 1.811e+01 0.000e+00 -3.105e+01]\n", - "\n", - "\n", - "eref: -48920.79143655116\n", - "esol: -48914.11743610925\n", - "\n", - "\n", - "res_ref: 7.363208654819393\n", - "res_sol: 7.803258430048803\n" - ] - } - ], - "source": [ - "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", - "import sparse \n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "from dwave.samplers import SteepestDescentSolver\n", - "from dwave.samplers import TabuSampler\n", - "from dimod import ExactSolver\n", - "\n", - "sampler = TabuSampler()\n", - "# sampler = SimulatedAnnealingSampler()\n", - "# sampler = ExactSolver() \n", - "\n", - "qubo = QUBO_POLY_MIXED(net.mixed_solution_vector, options={\"sampler\" : sampler} )\n", - "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", - "\n", - "bqm = qubo.create_bqm(matrices, strength=1E6)\n", - "\n", - "# sample\n", - "sampleset = qubo.sample_bqm(bqm, num_reads=1000)\n", - "\n", - "# decode\n", - "qubo.verify_quadratic_constraints(sampleset.lowest())\n", - "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", - "# sol = np.array([s for s in sol])\n", - "stmp = []\n", - "for s in sol:\n", - " stmp += s \n", - "sol = stmp \n", - "\n", - "data_ref, eref = qubo.compute_energy(ref_sol, bqm)\n", - "data_sol, esol = qubo.compute_energy(sol, bqm)\n", - "\n", - "np.set_printoptions(precision=3)\n", - "\n", - "print('flow prec: ', flow_encoding.get_average_precision())\n", - "print('head prec: ', head_encoding.get_average_precision())\n", - "print('\\n')\n", - "\n", - "print('ref : ', np.array(ref_sol)) \n", - "print('sol : ', sol)\n", - "print('diff: ', ref_sol - sol)\n", - "print('\\n')\n", - "\n", - "print('encoded_ref: ', np.array(data_ref[0]))\n", - "print('encoded_sol: ', np.array(data_sol[0]))\n", - "print('diff : ', np.array(data_ref[0]) - np.array(data_sol[0]))\n", - "print('\\n')\n", - "print('eref: ', eref)\n", - "print('esol: ', esol)\n", - "print('\\n')\n", - "print('res_ref: ', np.linalg.norm(net.verify_solution(np.array(data_ref[0]))))\n", - "print('res_sol: ', np.linalg.norm(net.verify_solution(np.array(data_sol[0]))))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_sol, sol) \n", - "plt.axline((0, 0.), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", - "\n", - "plt.axline((0, 0.), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.axline((0, 0.), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", - "\n", - "plt.loglog()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "energy = []\n", - "residue = []\n", - "solutions = []\n", - "for s in sampleset.record:\n", - " energy.append(s[1])\n", - " sol = qubo.decode_solution(s[0])\n", - " sol = sol[0] + sol[1]\n", - " solutions.append(sol)\n", - " r = net.verify_solution(np.array(sol).reshape(-1,))\n", - " residue.append(np.linalg.norm(r))\n", - "plt.scatter(energy, (residue))\n", - "\n", - "el, rl = [], []\n", - "for s in sampleset.lowest().record:\n", - " el.append(s[1])\n", - " sol = qubo.decode_solution(s[0])\n", - " sol = sol[0] + sol[1]\n", - " r = net.verify_solution(np.array(sol).reshape(-1,))\n", - " rl.append(np.linalg.norm(r+1E-12))\n", - "plt.scatter(el, rl, c='red')" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "solutions = np.array(solutions)\n", - "nsample = solutions.shape[1]\n", - "\n", - "for isol in range(5,9):\n", - " plt.scatter(solutions[:,isol], energy)\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[3.528, 0.504, 0.126, 0. , 2.047, 4.48 , 3.63 , 6.024, 3. ],\n", - " [2.079, 1.071, 2.142, 1.008, 0.504, 7. , 5.11 , 3.031, 3.409],\n", - " [3.087, 1.008, 1.008, 2.52 , 0. , 6.559, 7. , 4.26 , 3. ],\n", - " [3.024, 2.016, 0.535, 0.252, 0.346, 7. , 3.976, 5.929, 5.016],\n", - " [3.024, 2.299, 1.008, 0.189, 1.008, 7. , 3. , 4.984, 3.504],\n", - " [3.213, 2.016, 1.008, 0.504, 0.504, 6.276, 3.094, 5.016, 3.724],\n", - " [3.15 , 0.504, 2.016, 0. , 1.291, 6.087, 4.449, 3.346, 3.063],\n", - " [3.024, 0.252, 0.126, 0. , 0.504, 5.961, 5.425, 5.488, 4.984],\n", - " [3.024, 2.016, 2.52 , 0.504, 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1.33858268, 1.37795276, 1.41732283,\n", - " 1.45669291, 1.49606299, 1.53543307, 1.57480315, 1.61417323,\n", - " 1.65354331, 1.69291339, 1.73228346, 1.77165354, 1.81102362,\n", - " 1.8503937 , 1.88976378, 1.92913386, 1.96850394, 2.00787402,\n", - " 2.04724409, 2.08661417, 2.12598425, 2.16535433, 2.20472441,\n", - " 2.24409449, 2.28346457, 2.32283465, 2.36220472, 2.4015748 ,\n", - " 2.44094488, 2.48031496, 2.51968504, 2.55905512, 2.5984252 ,\n", - " 2.63779528, 2.67716535, 2.71653543, 2.75590551, 2.79527559,\n", - " 2.83464567, 2.87401575, 2.91338583, 2.95275591, 2.99212598,\n", - " 3.03149606, 3.07086614, 3.11023622, 3.1496063 , 3.18897638,\n", - " 3.22834646, 3.26771654, 3.30708661, 3.34645669, 3.38582677,\n", - " 3.42519685, 3.46456693, 3.50393701, 3.54330709, 3.58267717,\n", - " 3.62204724, 3.66141732, 3.7007874 , 3.74015748, 3.77952756,\n", - " 3.81889764, 3.85826772, 3.8976378 , 3.93700787, 3.97637795,\n", - " 4.01574803, 4.05511811, 4.09448819, 4.13385827, 4.17322835,\n", - " 4.21259843, 4.2519685 , 4.29133858, 4.33070866, 4.37007874,\n", - " 4.40944882, 4.4488189 , 4.48818898, 4.52755906, 4.56692913,\n", - " 4.60629921, 4.64566929, 4.68503937, 4.72440945, 4.76377953,\n", - " 4.80314961, 4.84251969, 4.88188976, 4.92125984, 4.96062992,\n", - " 5. ])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", - "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "\n", - "nqbit = 5\n", - "range = (4/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=0.25, offset=-4, var_base_name=\"x\")\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=0.25, offset=-4, var_base_name=\"x\")\n", - "\n", - "\n", - "nqbit = 8\n", - "flow_encoding = RangedEfficientEncoding(nqbit=nqbit, range=5., offset=+0.0, var_base_name=\"x\")\n", - "head_encoding = RangedEfficientEncoding(nqbit=nqbit, range=5, offset=+0.0, var_base_name=\"x\")\n", - "\n", - "np.sort(flow_encoding.get_possible_values())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "sv1 = SolutionVector(1, encoding=flow_encoding)\n", - "sv2 = SolutionVector(1, encoding=head_encoding)\n", - "\n", - "msv = MixedSolutionVector([sv1,sv2])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 4., -3.])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "A = np.array([\n", - " [1, 1,],\n", - " [ 1, 2,],\n", - "])\n", - "b = np.array([1,-2]).reshape(-1,1)\n", - "ref_sol = np.linalg.solve(A, b).reshape(-1)\n", - "ref_sol" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ref: [ 4. -3.] -> [4.015748031496063, -2.992125984251969] energy: -4.998449996899992\n", - "sol: [ 3.97637795 -2.99212598] -> [3.9763779527559056, -2.992125984251969] energy: -4.999689999380009\n", - "[ 0.02362205 -0.00787402]\n" - ] - } - ], - "source": [ - "from qubols.qubo_poly_mixed_variables import QUBO_POLY\n", - "import sparse \n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "from dwave.samplers import SteepestDescentSolver\n", - "from dwave.samplers import TabuSampler\n", - "from dimod import ExactSolver\n", - "\n", - "# sampler = TabuSampler()\n", - "sampler = SimulatedAnnealingSampler()\n", - "# sampler = ExactSolver() \n", - "\n", - "\n", - "qubo = QUBO_POLY(msv, options={\"sampler\" : sampler} )\n", - "matrices = tuple(sparse.COO(m) for m in [-1*b, A])\n", - "\n", - "bqm = qubo.create_bqm(matrices, strength=1E3)\n", - "\n", - "# sample\n", - "sampleset = qubo.sample_bqm(bqm, num_reads=10000)\n", - "\n", - "# decode\n", - "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", - "sol = np.array(sol[0]+sol[1])\n", - "\n", - "data_ref, eref = qubo.compute_energy(ref_sol, bqm)\n", - "data_sol, esol = qubo.compute_energy(sol, bqm)\n", - "\n", - "print('ref: ', ref_sol, '->', data_ref[0], ' energy: ', eref)\n", - "print('sol: ', sol, '->', data_sol[0], ' energy: ', esol)\n", - "print(ref_sol - sol)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Variables(['x_001_001', 'x_001_002', 'x_001_003', 'x_001_004', 'x_001_005', 'x_001_006', 'x_001_007', 'x_002_001', 'x_002_002', 'x_002_003', 'x_002_004', 'x_002_005', 'x_002_006', 'x_002_007', 'x_003_001', 'x_003_002', 'x_003_003', 'x_003_004', 'x_003_005', 'x_003_006', 'x_003_007', 'x_004_001', 'x_004_002', 'x_004_003', 'x_004_004', 'x_004_005', 'x_004_006', 'x_004_007'])" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bqm.variables" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'net' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[54], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnet\u001b[49m\u001b[38;5;241m.\u001b[39mverify_solution(sol)\n", - "\u001b[0;31mNameError\u001b[0m: name 'net' is not defined" - ] - } - ], - "source": [ - "net.verify_solution(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 1, 1]" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import itertools\n", - "def find_closest(encoding, float):\n", - " \"\"\"get all the posible values encoded\n", - "\n", - " Returns:\n", - " _type_: _description_\n", - " \"\"\"\n", - "\n", - " min_diff = 1E12\n", - " closest_value = None \n", - " binary_encoding = None\n", - " for data in itertools.product([0, 1], repeat=encoding.nqbit):\n", - " val = encoding.decode_polynom(list(data)[::-1])\n", - " if np.abs(val-float) < min_diff:\n", - " min_diff = np.abs(val-float)\n", - " closest_value = val \n", - " binary_encoding = list(data)[::-1]\n", - "\n", - " return closest_value, binary_encoding \n", - "vmin, bins = find_closest(flow_encoding, 2.)\n", - "vmin\n", - "bins" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "var = sampleset.lowest().variables\n", - "data = np.array(sampleset.lowest().record[0][0])\n", - "data_real_var = data[qubo.index_variables]\n", - "\n", - "for v, d in zip(var, data):\n", - " if v not in qubo.mapped_variables:\n", - " x0, x1 = v.split('*')\n", - " i0 = qubo.index_variables[qubo.mapped_variables.index(x0)]\n", - " i1 = qubo.index_variables[qubo.mapped_variables.index(x1)]\n", - " assert(d == data[i0] * data[i1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "energy = []\n", - "residue = []\n", - "for s in sampleset.record:\n", - " energy.append(s[1])\n", - " sol = qubo.decode_solution(s[0])\n", - " r = net.verify_solution(np.array(sol).reshape(-1,))\n", - " residue.append(np.linalg.norm(r))\n", - "plt.scatter(energy, (residue))\n", - "\n", - "el, rl = [], []\n", - "for s in sampleset.lowest().record:\n", - " el.append(s[1])\n", - " sol = qubo.decode_solution(s[0])\n", - " r = net.verify_solution(np.array(sol).reshape(-1,))\n", - " rl.append(np.linalg.norm(r+1E-12))\n", - "plt.scatter(el, rl, c='red')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Exception ignored in: >\n", - "Traceback (most recent call last):\n", - " File \"/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n", - " def _clean_thread_parent_frames(\n", - "KeyboardInterrupt: \n" - ] - } - ], - "source": [ - "from qubols.qubo_poly import QUBO_POLY\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", - "import sparse \n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "from dwave.samplers import SteepestDescentSolver\n", - "from dwave.samplers import TabuSampler\n", - "\n", - "encoding = RangedEfficientEncoding(nqbit=12, range=2.0, offset=0.0, var_base_name=\"x\")\n", - "sol_vec = SolutionVector(4, encoding)\n", - "\n", - "qubo = QUBO_POLY(solution_vector=sol_vec, options={ 'num_reads':1000, 'sampler':SimulatedAnnealingSampler()})\n", - "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", - "\n", - "bqm = qubo.create_bqm(matrices, strength=10000)\n", - "\n", - "# sample\n", - "sampleset = qubo.sample_bqm(bqm, num_reads=5000)\n", - "\n", - "# decode\n", - "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", - "sol = np.array(sol).reshape(-1)\n", - "print(ref_sol)\n", - "print(sol)\n", - "print(ref_sol - sol)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From ae5fa3347f528976dcb87813513289416f937842 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 2 Sep 2024 13:21:11 +0200 Subject: [PATCH 23/96] add branch for qubols install --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 5ee9e61..42794ab 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,7 @@ dependencies = [ "scipy", "wntr", "quantum_newton_raphson@git+https://github.com/QuantumApplicationLab/QuantumNewtonRaphson", + "qubols@git+https://github.com/QuantumApplicationLab/qubols@polynomial_equation", ] description = "A quantum enabled water nework management tool" From 874dbfc27fc464ce137634b2e11d6849738220da Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 2 Sep 2024 13:57:50 +0200 Subject: [PATCH 24/96] added test and doc --- examples/poly_qubo_examples.py | 144 ++++++++++++++++++++++ examples/qubo_examples.py | 40 +++--- tests/networks/Net0_DW.inp | 128 +++++++++++++++++++ tests/test_network_simulator_solver.py | 17 ++- tests/test_poly_qubo_network_simulator.py | 78 ++++++++++++ 5 files changed, 387 insertions(+), 20 deletions(-) create mode 100644 examples/poly_qubo_examples.py create mode 100644 tests/networks/Net0_DW.inp create mode 100644 tests/test_poly_qubo_network_simulator.py diff --git a/examples/poly_qubo_examples.py b/examples/poly_qubo_examples.py new file mode 100644 index 0000000..47770d4 --- /dev/null +++ b/examples/poly_qubo_examples.py @@ -0,0 +1,144 @@ +import os +import matplotlib.pyplot as plt +import wntr +from dwave.samplers import SteepestDescentSolver +from qubols.encodings import PositiveQbitEncoding +import wntr_quantum + + +def get_ape_from_pd_series(quantum_pd_series, classical_pd_series): + """Helper function to evaluate absolute percentage error between classical and quantum results.""" + DELTA = 1.0e-12 + ape = ( + abs(quantum_pd_series - classical_pd_series) + * 100.0 + / abs(classical_pd_series + DELTA) + ) + return ape + + +def compare_results(classical_result, quantum_result): + """Helper function that compares the classical and quantum simulation results.""" + TOL = 10 # => per cent + DELTA = 1.0e-12 + classical_data = [] + quantum_data = [] + + def check_ape(classical_value, quantum_value): + """Checks if the absolute percentage error between classical and quantum results is within TOL.""" + ape = ( + abs(quantum_value - classical_value) * 100.0 / abs(classical_value + DELTA) + ) + is_close_to_classical = ape <= TOL + if is_close_to_classical: + print( + f"Quantum result {quantum_value} within {ape}% of classical result {classical_value}" + ) + quantum_data.append(quantum_value) + classical_data.append(classical_value) + return is_close_to_classical + + for link in classical_result.link["flowrate"].columns: + classical_value = classical_result.link["flowrate"][link].iloc[0] + quantum_value = quantum_result.link["flowrate"][link].iloc[0] + message = f"Flowrate {link}: {quantum_value} not within {TOL}% of classical result {classical_value}" + assert check_ape(classical_value, quantum_value), message + + for node in classical_result.node["pressure"].columns: + classical_value = classical_result.node["pressure"][node].iloc[0] + quantum_value = quantum_result.node["pressure"][node].iloc[0] + message = f"Pressure {node}: {quantum_value} not within {TOL}% of classical result {classical_value}" + assert check_ape(classical_value, quantum_value), message + + return classical_data, quantum_data + + +# set EPANET Quantum environment variables +os.environ["EPANET_TMP"] = "/Users/murilo/scratch_dir/.epanet_quantum" +os.environ["EPANET_QUANTUM"] = "/Users/murilo/Documents/NLeSC_Projects/Vitens/EPANET" + +# set input files +path = "../docs/notebooks/networks" +inputs = ["Net0.inp"] + + +for file in inputs: + + print("##################################") + print(f"Solving for {file} model") + print("##################################") + + # set up network model + input_file = f"{path}/{file}" + model_name = os.path.splitext(file)[0] + wn = wntr.network.WaterNetworkModel(input_file) + + # solve model using the classical EPANET simulator + sim_classical = wntr_quantum.sim.QuantumEpanetSimulator(wn) + results_classical = sim_classical.run_sim() + + nqbit = 9 + step = 0.5 / (2**nqbit - 1) + flow_encoding = PositiveQbitEncoding( + nqbit=nqbit, step=step, offset=+1.5, var_base_name="x" + ) + + nqbit = 9 + step = 50 / (2**nqbit - 1) + head_encoding = PositiveQbitEncoding( + nqbit=nqbit, step=step, offset=+50.0, var_base_name="x" + ) + + # solve model using FULL QUBOs + sim = wntr_quantum.sim.FullQuboPolynomialSimulator( + wn, flow_encoding=flow_encoding, head_encoding=head_encoding + ) + sampler = SteepestDescentSolver() + results_quantum = sim.run_sim(solver_options={"sampler": sampler}) + + # plot networt and absolute percent errors + wntr.graphics.plot_network( + wn, + node_attribute=get_ape_from_pd_series( + results_quantum.node["pressure"].iloc[0], + results_classical.node["pressure"].iloc[0], + ), + link_attribute=get_ape_from_pd_series( + results_quantum.link["flowrate"].iloc[0], + results_classical.link["flowrate"].iloc[0], + ), + node_colorbar_label="Pressures", + link_colorbar_label="Flows", + node_size=50, + title=f"{model_name}: Absolute Percent Error", + node_labels=False, + filename=f"{model_name}_wnm_qubo.png", + ) + + # checks if the quantum results are within 5% of the classical ones + classical_data, quantum_data = compare_results(results_classical, results_quantum) + + # plot all data + plt.close() + plt.scatter( + classical_data[:2], + quantum_data[:2], + label="Flowrates", + color="blue", + marker="o", + ) + plt.scatter( + classical_data[2:], + quantum_data[2:], + label="Pressures", + color="red", + marker="s", + facecolors="none", + ) + plt.axline((0, 0), slope=1, linestyle="--", color="gray", label="") + plt.xlabel("Classical EPANET results") + plt.ylabel("Quantum EPANET results") + plt.legend() + plt.title(f"{model_name}") + plt.savefig(f"{model_name}_results_qubo.png") + plt.close() diff --git a/examples/qubo_examples.py b/examples/qubo_examples.py index 9a29a1b..c356c93 100644 --- a/examples/qubo_examples.py +++ b/examples/qubo_examples.py @@ -2,8 +2,6 @@ import matplotlib.pyplot as plt import numpy as np import wntr -from qiskit.circuit.library import RealAmplitudes -from qiskit.primitives import Estimator from quantum_newton_raphson.qubo_solver import QUBO_SOLVER from qubols.encodings import RangedEfficientEncoding import wntr_quantum @@ -12,7 +10,11 @@ def get_ape_from_pd_series(quantum_pd_series, classical_pd_series): """Helper function to evaluate absolute percentage error between classical and quantum results.""" DELTA = 1.0e-12 - ape = abs(quantum_pd_series - classical_pd_series) * 100.0 / abs(classical_pd_series + DELTA) + ape = ( + abs(quantum_pd_series - classical_pd_series) + * 100.0 + / abs(classical_pd_series + DELTA) + ) return ape @@ -25,10 +27,14 @@ def compare_results(classical_result, quantum_result): def check_ape(classical_value, quantum_value): """Checks if the absolute percentage error between classical and quantum results is within TOL.""" - ape = abs(quantum_value - classical_value) * 100.0 / abs(classical_value + DELTA) + ape = ( + abs(quantum_value - classical_value) * 100.0 / abs(classical_value + DELTA) + ) is_close_to_classical = ape <= TOL if is_close_to_classical: - print(f"Quantum result {quantum_value} within {ape}% of classical result {classical_value}") + print( + f"Quantum result {quantum_value} within {ape}% of classical result {classical_value}" + ) quantum_data.append(quantum_value) classical_data.append(classical_value) return is_close_to_classical @@ -56,9 +62,6 @@ def check_ape(classical_value, quantum_value): path = "../docs/notebooks/networks" inputs = ["Net1Loops.inp", "Net2Loops.inp", "Net3Loops.inp"] -# set qiskit Estimator -estimator = Estimator() - for file in inputs: print("##################################") @@ -90,9 +93,6 @@ def check_ape(classical_value, quantum_value): # set number of qubits n_qubits = int(np.ceil(np.log2(epanet_A_dim))) - # define ansatz - qc = RealAmplitudes(n_qubits, reps=3, entanglement="full") - # set the qubo solver qubo_solver = QUBO_SOLVER( encoding=RangedEfficientEncoding, @@ -115,18 +115,18 @@ def check_ape(classical_value, quantum_value): wn, node_attribute=get_ape_from_pd_series( results_quantum.node["pressure"].iloc[0], - results_classical.node["pressure"].iloc[0] + results_classical.node["pressure"].iloc[0], ), link_attribute=get_ape_from_pd_series( results_quantum.link["flowrate"].iloc[0], results_classical.link["flowrate"].iloc[0], ), - node_colorbar_label='Pressures', - link_colorbar_label='Flows', + node_colorbar_label="Pressures", + link_colorbar_label="Flows", node_size=50, title=f"{model_name}: Absolute Percent Error", node_labels=False, - filename=f"{model_name}_wnm_qubo.png" + filename=f"{model_name}_wnm_qubo.png", ) # checks if the quantum results are within 5% of the classical ones @@ -134,14 +134,20 @@ def check_ape(classical_value, quantum_value): # plot all data plt.close() - plt.scatter(classical_data[:n_pipes], quantum_data[:n_pipes], label="Flowrates", color="blue", marker="o") + plt.scatter( + classical_data[:n_pipes], + quantum_data[:n_pipes], + label="Flowrates", + color="blue", + marker="o", + ) plt.scatter( classical_data[n_pipes:], quantum_data[n_pipes:], label="Pressures", color="red", marker="s", - facecolors='none', + facecolors="none", ) plt.axline((0, 0), slope=1, linestyle="--", color="gray", label="") plt.xlabel("Classical EPANET results") diff --git a/tests/networks/Net0_DW.inp b/tests/networks/Net0_DW.inp new file mode 100644 index 0000000..019b292 --- /dev/null +++ b/tests/networks/Net0_DW.inp @@ -0,0 +1,128 @@ +[TITLE] +File obtained via Mario of a 2 node sysem + + +[JUNCTIONS] +;ID Elev Demand Pattern + J1 0 0 ; + D1 0 50 ; + +[RESERVOIRS] +;ID Head Pattern + R1 30 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + P1 R1 J1 1000 250 0.05 0 Open ; + P2 J1 D1 1000 250 0.05 0 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + +[RULES] + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 1 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 1:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic None + +[REPORT] + Status No + Summary No + Page 0 + +[OPTIONS] + Units LPS + Headloss D-W + Specific Gravity 1 + Viscosity 1 + Trials 50 + Accuracy 0.001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality None mg/L + Diffusivity 1 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +J1 10.00000 60.00000 +D1 110.00000 60.00000 +R1 -11.72214 74.24023 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 0.000 0.000 10000.000 10000.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/tests/test_network_simulator_solver.py b/tests/test_network_simulator_solver.py index ec364b9..7f9147e 100644 --- a/tests/test_network_simulator_solver.py +++ b/tests/test_network_simulator_solver.py @@ -9,6 +9,7 @@ from quantum_newton_raphson.qubo_solver import QUBO_SOLVER from quantum_newton_raphson.vqls_solver import VQLS_SOLVER import wntr_quantum +from qubols.encodings import PositiveQbitEncoding NETWORKS_FOLDER = pathlib.Path(__file__).with_name("networks") INP_FILE = NETWORKS_FOLDER / "Net0.inp" # => toy wn model @@ -26,13 +27,17 @@ def compare_results(original, new): for link in original.link["flowrate"].columns: orig_value = original.link["flowrate"][link].iloc[0] new_value = new.link["flowrate"][link].iloc[0] - message = f"Flowrate {link}: {new_value} not within {TOL}% of original {orig_value}" + message = ( + f"Flowrate {link}: {new_value} not within {TOL}% of original {orig_value}" + ) assert calculate_differences(orig_value, new_value), message for node in original.node["pressure"].columns: orig_value = original.node["pressure"][node].iloc[0] new_value = new.node["pressure"][node].iloc[0] - message = f"Pressure {node}: {new_value} not within {TOL}% of original {orig_value}" + message = ( + f"Pressure {node}: {new_value} not within {TOL}% of original {orig_value}" + ) assert calculate_differences(orig_value, new_value), message @@ -82,11 +87,17 @@ def run_QuantumEpanetSimulator_with_vqls(): wn = wntr.network.WaterNetworkModel(INP_FILE) qc = RealAmplitudes(1, reps=3, entanglement="full") estimator = Estimator() - linear_solver = VQLS_SOLVER(estimator=estimator, ansatz=qc, optimizer=CG(), matrix_decomposition="pauli") + linear_solver = VQLS_SOLVER( + estimator=estimator, ansatz=qc, optimizer=CG(), matrix_decomposition="pauli" + ) sim = wntr_quantum.sim.QuantumEpanetSimulator(wn, linear_solver=linear_solver) return sim.run_sim(linear_solver=linear_solver) +def run_FullQuboPolynomialSimulator(): + """""" + + @pytest.fixture(scope="module") def classical_EPANET_results(): """Get the results from the classical NR solver.""" diff --git a/tests/test_poly_qubo_network_simulator.py b/tests/test_poly_qubo_network_simulator.py new file mode 100644 index 0000000..b262059 --- /dev/null +++ b/tests/test_poly_qubo_network_simulator.py @@ -0,0 +1,78 @@ +"""Tests WNTR quantum using a small network and different simulators and solvers.""" + +import pathlib +import pytest +import wntr +from dwave.samplers import SteepestDescentSolver +from qubols.encodings import PositiveQbitEncoding +import wntr_quantum + +NETWORKS_FOLDER = pathlib.Path(__file__).with_name("networks") +INP_FILE = NETWORKS_FOLDER / "Net0_DW.inp" # => toy wn model +DELTA = 1.0e-12 +TOL = 5 # => per cent + + +def calculate_differences(value1, value2): + """Helper function to calculate percentage difference between classical and quantum results.""" + return abs(value1 - value2) / abs(value1 + DELTA) <= TOL / 100.0 + + +def compare_results(original, new): + """Helper function that compares the classical and quantum simulation results.""" + for link in original.link["flowrate"].columns: + orig_value = original.link["flowrate"][link].iloc[0] + new_value = new.link["flowrate"][link].iloc[0] + message = ( + f"Flowrate {link}: {new_value} not within {TOL}% of original {orig_value}" + ) + assert calculate_differences(orig_value, new_value), message + + for node in original.node["pressure"].columns: + orig_value = original.node["pressure"][node].iloc[0] + new_value = new.node["pressure"][node].iloc[0] + message = ( + f"Pressure {node}: {new_value} not within {TOL}% of original {orig_value}" + ) + assert calculate_differences(orig_value, new_value), message + + +def run_classical_EPANET_simulation(): + """Runs WNTR using classical EPANET interface.""" + wn = wntr.network.WaterNetworkModel(INP_FILE) + sim = wntr.sim.EpanetSimulator(wn) + return sim.run_sim() + + +def run_FullQuboPolynomialSimulator(): + """Runs QuboPolynomialSolver.""" + wn = wntr.network.WaterNetworkModel(INP_FILE) + nqbit = 9 + step = 0.5 / (2**nqbit - 1) + flow_encoding = PositiveQbitEncoding( + nqbit=nqbit, step=step, offset=+1.5, var_base_name="x" + ) + + nqbit = 9 + step = 50 / (2**nqbit - 1) + head_encoding = PositiveQbitEncoding( + nqbit=nqbit, step=step, offset=+50.0, var_base_name="x" + ) + + sampler = SteepestDescentSolver() + sim = wntr_quantum.sim.FullQuboPolynomialSimulator( + wn, flow_encoding=flow_encoding, head_encoding=head_encoding + ) + return sim.run_sim(solver_options={"sampler": sampler}) + + +@pytest.fixture(scope="module") +def classical_EPANET_results(): + """Get the results from the classical NR solver.""" + return run_classical_EPANET_simulation() + + +def test_FullQuboPolynomialSimulator(classical_EPANET_results): + """Checks that the Quantum EPANET classical linear solver is equivalent with the classical result.""" + qubopoly_results = run_FullQuboPolynomialSimulator() + compare_results(classical_EPANET_results, qubopoly_results) From 89244afabf69eab19bad768568b44bd8a3e76027 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 2 Sep 2024 16:03:22 +0200 Subject: [PATCH 25/96] types --- .../sim/solvers/qubo_polynomial_solver.py | 168 +++++++++++++----- 1 file changed, 122 insertions(+), 46 deletions(-) diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 05b68af..596505d 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -1,9 +1,11 @@ +from typing import List +from typing import Tuple +import dimod import matplotlib.pyplot as plt import numpy as np import sparse from quantum_newton_raphson.newton_raphson import newton_raphson from qubols.encodings import BaseQbitEncoding -from qubols.encodings import DiscreteValuesEncoding from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED from qubols.solution_vector import SolutionVector_V2 as SolutionVector @@ -11,6 +13,8 @@ from wntr.epanet.util import HydParam from wntr.epanet.util import from_si from wntr.epanet.util import to_si +from wntr.network import WaterNetworkModel +from wntr.sim.aml import Model from wntr.sim.solvers import SolverStatus from ..models.chezy_manning import get_chezy_manning_matrix from ..models.darcy_weisbach import get_darcy_weisbach_matrix @@ -22,16 +26,16 @@ class QuboPolynomialSolver(object): def __init__( self, - wn, - flow_encoding, - head_encoding, + wn: WaterNetworkModel, + flow_encoding: BaseQbitEncoding, + head_encoding: BaseQbitEncoding, ): # noqa: D417 """Init the solver. Args: - wn (WaterNetwork): water network - flow_encoding (BaseEncoding): binary encoding for the flow - head_encoding (BaseEncoding): binary encoding for the head pipe_diameters (_type_): _description_ + wn (WaterNetworkModel): water network + flow_encoding (qubols.encodings.BaseQbitEncoding): binary encoding for the flow + head_encoding (qubols.encodings.BaseQbitEncoding): binary encoding for the head """ self.wn = wn @@ -53,8 +57,15 @@ def verify_encoding(self): print("Head Encoding : %f => %f (res: %f)" % (hvalues[0], hvalues[-1], hres)) print("Flow Encoding : %f => %f (res: %f)" % (fvalues[0], fvalues[-1], fres)) - def verify_solution(self, input): - """Generates the classical solution.""" + def verify_solution(self, input: np.ndarray) -> np.ndarray: + """Computes the rhs vector associate with the input. + + Args: + input (np.ndarray): proposed solution + + Returns: + np.ndarray: RHS vector + """ P0, P1, P2 = self.matrices p0 = P0.reshape( @@ -64,8 +75,18 @@ def verify_solution(self, input): p2 = P2.sum(-1) return p0 + p1 @ input + (p2 @ (input * input)) - def classical_solutions(self, max_iter=100, tol=1e-10): - """Computes the classical solution.""" + def classical_solutions( + self, max_iter: int = 100, tol: float = 1e-10 + ) -> np.ndarray: + """Computes the solution using a classical Newton Raphson approach. + + Args: + max_iter (int, optional): number of iterations of the NR. Defaults to 100. + tol (float, optional): Toleracne of the NR. Defaults to 1e-10. + + Returns: + np.ndarray: _description_ + """ P0, P1, P2 = self.matrices num_heads = self.wn.num_junctions num_pipes = self.wn.num_pipes @@ -85,18 +106,20 @@ def func(input): sol = res.solution assert np.allclose(func(sol), 0) - # convert back to SI if DW + # convert back to SI sol = self.convert_solution_to_si(sol) return sol @staticmethod - def plot_solution_vs_reference(solution, reference_solution): + def plot_solution_vs_reference( + solution: np.ndarray, reference_solution: np.ndarray + ): """Plots the scatter plot ref/sol. Args: - solution (_type_): _description_ - reference_solution (_type_): _description_ + solution (np.ndarray): _description_ + reference_solution (np.ndarray): _description_ """ plt.scatter(reference_solution, solution) plt.axline((0, 0.0), slope=1, color="black", linestyle=(0, (5, 5))) @@ -107,14 +130,20 @@ def plot_solution_vs_reference(solution, reference_solution): plt.grid(which="minor", lw=0.1) plt.loglog() - def benchmark_solution(self, solution, reference_solution, qubo, bqm): + def benchmark_solution( + self, + solution: np.ndarray, + reference_solution: np.ndarray, + qubo: QUBO_POLY_MIXED, + bqm: dimod.BQM, + ): """Benchmark a solution against the exact reference solution. Args: - solution (np.array): _description_ - reference_solution (np.array): _description_ - qubo (_type_): __ - bqm (_type_): __ + solution (np.array): solution to be benchmarked + reference_solution (np.array): reference solution + qubo (QUBO_POLY_MIXEd): QUBO_POLY_MIXEd instance + bqm (dimod.BQM): BQM from dimod """ reference_solution = self.convert_solution_from_si(reference_solution) solution = self.convert_solution_from_si(solution) @@ -145,8 +174,18 @@ def benchmark_solution(self, solution, reference_solution, qubo, bqm): print("Residue ref : ", res_ref) print("Delta Residue :", res_sol - res_ref) - def initialize_matrices(self, model): - """Initilize the matrix for the QUBO definition.""" + def initialize_matrices(self, model: Model) -> Tuple: + """Initialize the matrices of the non linear system. + + Args: + model (Model): an AML model from WNTR + + Raises: + ValueError: if headloss approximation is not C-M or D-W + + Returns: + Tuple: Matrices of the on linear system + """ num_equations = len(list(model.cons())) num_variables = len(list(model.vars())) @@ -171,11 +210,14 @@ def initialize_matrices(self, model): raise ValueError("Calculation only possible with C-M or D-W") return matrices - def convert_solution_to_si(self, solution): + def convert_solution_to_si(self, solution: np.ndarray) -> np.ndarray: """Converts the solution to SI. Args: - solution (array): solution vectors + solution (array): solution vectors in US units + + Returns: + Tuple: solution in SI """ num_heads = self.wn.num_junctions num_pipes = self.wn.num_pipes @@ -186,23 +228,14 @@ def convert_solution_to_si(self, solution): new_sol[ih] = to_si(FlowUnits.CFS, solution[ih], HydParam.Length) return new_sol - @staticmethod - def flatten_solution_vector(solution): - """Flattens the solution vector. - - Args: - solution (tuple): tuple of ([flows], [heads]) - """ - sol_tmp = [] - for s in solution: - sol_tmp += s - return sol_tmp - - def convert_solution_from_si(self, solution): + def convert_solution_from_si(self, solution: np.ndarray) -> np.ndarray: """Converts the solution to SI. Args: - solution (array): solution vectors + solution (array): solution vectors in SI + + Returns: + Tuple: solution in US units """ num_heads = self.wn.num_junctions num_pipes = self.wn.num_pipes @@ -214,32 +247,75 @@ def convert_solution_from_si(self, solution): return new_sol @staticmethod - def load_data_in_model(model, data): + def flatten_solution_vector(solution: Tuple) -> List: + """Flattens the solution vector. + + Args: + solution (tuple): tuple of ([flows], [heads]) + + Returns: + List: a flat list of all the variables + """ + sol_tmp = [] + for s in solution: + sol_tmp += s + return sol_tmp + + @staticmethod + def load_data_in_model(model: Model, data: np.ndarray): """Loads some data in the model. Args: - model (_type_): _description_ - data (_type_): _description_ + model (Model): AML model from WNTR + data (np.ndarray): data to load """ for iv, v in enumerate(model.vars()): v.value = data[iv] - def solve(self, model, strength=1e6, num_reads=10000, **options): - """Solve the hydraulics equations.""" + def solve( # noqa: D417 + self, model: Model, strength: float = 1e6, num_reads: int = 10000, **options + ) -> Tuple: + """Solves the Hydraulics equations. + + Args: + model (Model): AML model + strength (float, optional): substitution strength. Defaults to 1e6. + num_reads (int, optional): number of reads for the sampler. Defaults to 10000. + + Returns: + Tuple: Succes message + """ + # creates the matrices self.matrices = self.initialize_matrices(model) - sol = self.solve_(strength=strength, num_reads=num_reads, **options) + + # solve using qubo poly + sol = self.qubo_poly_solve(strength=strength, num_reads=num_reads, **options) + + # load data in the AML model model.set_structure() self.load_data_in_model(model, sol) + + # returns return ( SolverStatus.converged, "Solved Successfully", 0, ) - def solve_(self, strength=1e6, num_reads=10000, **options): - """Solve the hydraulic equations.""" + def qubo_poly_solve(self, strength=1e6, num_reads=10000, **options): # noqa: D417 + """Solves the Hydraulics equations. + + Args: + strength (float, optional): substitution strength. Defaults to 1e6. + num_reads (int, optional): number of reads for the sampler. Defaults to 10000. + + Returns: + np.ndarray: solution of the problem + """ qubo = QUBO_POLY_MIXED(self.mixed_solution_vector, **options) matrices = tuple(sparse.COO(m) for m in self.matrices) + + # creates BQM bqm = qubo.create_bqm(matrices, strength=strength) # sample From 6f02eb7e02c6b4df007ab7d8081b081736933c4a Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 2 Sep 2024 16:08:04 +0200 Subject: [PATCH 26/96] diagnostic rename --- docs/notebooks/qubo_poly_solver.ipynb | 64 +---- docs/notebooks/qubo_poly_solver_CM.ipynb | 249 ++---------------- .../sim/solvers/qubo_polynomial_solver.py | 2 +- 3 files changed, 17 insertions(+), 298 deletions(-) diff --git a/docs/notebooks/qubo_poly_solver.ipynb b/docs/notebooks/qubo_poly_solver.ipynb index e9de250..3bb7e3e 100644 --- a/docs/notebooks/qubo_poly_solver.ipynb +++ b/docs/notebooks/qubo_poly_solver.ipynb @@ -343,69 +343,7 @@ } ], "source": [ - "net.benchmark_solution(sol, ref_sol, qubo, bqm)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.049, 26.573, 0.052, 22.726])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.solve(model, strength=1E6, num_reads=1000, options={\"sampler\" : sampler})\n", - "model.get_x()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", - "\n", - "\n", - "Error (%): [ 1.861e+00 -5.305e+04 9.980e+01 8.092e-01]\n", - "\n", - "\n", - "sol : [1.733e+00 9.384e+02 1.708e-01 7.456e+01]\n", - "ref : [ 1.766 1.766 86.797 75.168]\n", - "diff: [ 3.286e-02 -9.367e+02 8.663e+01 6.083e-01]\n", - "\n", - "\n", - "encoded_sol: [ 1.733 2. 50. 74.56 ]\n", - "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", - "diff : [ 3.327e-02 -2.339e-01 3.679e+01 5.871e-01]\n", - "\n", - "\n", - "E sol : 1262.0976069991214\n", - "R ref : -1662.6061020456154\n", - "Delta E : 2924.7037090447366\n", - "\n", - "\n", - "Residue sol : 54.08053081077456\n", - "Residue ref : 0.010186471203764017\n", - "Delta Residue : 54.070344339570795\n" - ] - } - ], - "source": [ - "net.benchmark_solution(model.get_x(), ref_sol, qubo, bqm)" + "net.diagnostic_solution(sol, ref_sol, qubo, bqm)" ] }, { diff --git a/docs/notebooks/qubo_poly_solver_CM.ipynb b/docs/notebooks/qubo_poly_solver_CM.ipynb index 6e8ad49..983b240 100644 --- a/docs/notebooks/qubo_poly_solver_CM.ipynb +++ b/docs/notebooks/qubo_poly_solver_CM.ipynb @@ -279,20 +279,6 @@ "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "roughness : 0.015000\n", - "Diameter : 3.280840\n", - "length : 3280.839895\n", - "value : 0.006056\n", - "roughness : 0.015000\n", - "Diameter : 3.280840\n", - "length : 3280.839895\n", - "value : 0.006056\n" - ] - }, { "name": "stderr", "output_type": "stream", @@ -330,14 +316,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "roughness : 0.015000\n", - "Diameter : 3.280840\n", - "length : 3280.839895\n", - "value : 0.006056\n", - "roughness : 0.015000\n", - "Diameter : 3.280840\n", - "length : 3280.839895\n", - "value : 0.006056\n" + "\n" ] } ], @@ -386,7 +365,7 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -412,94 +391,32 @@ "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", "\n", "\n", - "Error (%): [ 2.138 -1.519 -0.029 -0.147]\n", + "Error (%): [ 1.584 0.808 -0.128 -0.048]\n", "\n", "\n", - "sol : [ 1.728 1.793 98.434 98.532]\n", + "sol : [ 1.738 1.751 98.532 98.434]\n", "ref : [ 1.766 1.766 98.406 98.387]\n", - "diff: [ 0.038 -0.027 -0.028 -0.145]\n", + "diff: [ 0.028 0.014 -0.126 -0.047]\n", "\n", "\n", - "encoded_sol: [ 1.728 1.793 98.434 98.532]\n", + "encoded_sol: [ 1.738 1.751 98.532 98.434]\n", "encoded_ref: [ 1.766 1.766 98.434 98.434]\n", - "diff : [ 0.038 -0.026 0. -0.098]\n", + "diff : [ 0.028 0.015 -0.098 0. ]\n", "\n", "\n", - "E sol : -2343.7316887269144\n", + "E sol : -2343.728691322684\n", "R ref : -2343.749937932273\n", - "Delta E : 0.01824920535864294\n", + "Delta E : 0.02124660958907043\n", "\n", "\n", - "Residue sol : 0.13927566663068974\n", + "Residue sol : 0.14964997001021535\n", "Residue ref : 0.03388956865892264\n", - "Delta Residue : 0.1053860979717671\n" + "Delta Residue : 0.1157604013512927\n" ] } ], "source": [ - "net.benchmark_solution(sol, ref_sol, qubo, bqm)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.049, 30.003, 0.053, 30.003])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.solve(model, strength=1E6, num_reads=1000, options={\"sampler\" : sampler})\n", - "model.get_x()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", - "\n", - "\n", - "Error (%): [ 1.196e+00 -5.991e+04 9.982e+01 -4.778e-02]\n", - "\n", - "\n", - "sol : [1.745e+00 1.060e+03 1.725e-01 9.843e+01]\n", - "ref : [ 1.766 1.766 98.406 98.387]\n", - "diff: [ 2.111e-02 -1.058e+03 9.823e+01 -4.701e-02]\n", - "\n", - "\n", - "encoded_sol: [ 1.745 2. 50. 98.434]\n", - "encoded_ref: [ 1.766 1.766 98.434 98.434]\n", - "diff : [ 2.153e-02 -2.339e-01 4.843e+01 0.000e+00]\n", - "\n", - "\n", - "E sol : 2347.8261350712914\n", - "R ref : -2343.749937932273\n", - "Delta E : 4691.5760730035645\n", - "\n", - "\n", - "Residue sol : 68.49508903228205\n", - "Residue ref : 0.03388956865892264\n", - "Delta Residue : 68.46119946362313\n" - ] - } - ], - "source": [ - "net.benchmark_solution(model.get_x(), ref_sol, qubo, bqm)" + "net.diagnostic_solution(sol, ref_sol, qubo, bqm)" ] }, { @@ -511,19 +428,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "roughness : 0.015000\n", - "Diameter : 3.280840\n", - "length : 3280.839895\n", - "value : 0.006056\n", - "roughness : 0.015000\n", - "Diameter : 3.280840\n", - "length : 3280.839895\n", - "value : 0.006056\n" + "\n", + "\n" ] }, { "data": { - "image/png": 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", + "image/png": 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" - ], - "text/plain": [ - " P1 P2\n", - "0 0.051258 0.052893\n", - "3600 0.047435 0.047629" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results.link['flowrate']" - ] } ], "metadata": { From 071cc8415ee0130e42bad54172c5aad17e577b67 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 4 Sep 2024 15:24:11 +0200 Subject: [PATCH 28/96] switch to qubops --- docs/notebooks/qubo_poly_solver.ipynb | 10 +++++----- docs/notebooks/qubo_poly_solver_CM.ipynb | 10 +++++----- examples/poly_qubo_examples.py | 2 +- pyproject.toml | 3 ++- tests/test_network_simulator_solver.py | 2 +- wntr_quantum/sim/solvers/qubo_polynomial_solver.py | 12 ++++++------ 6 files changed, 20 insertions(+), 19 deletions(-) diff --git a/docs/notebooks/qubo_poly_solver.ipynb b/docs/notebooks/qubo_poly_solver.ipynb index ed1ab44..c7725e2 100644 --- a/docs/notebooks/qubo_poly_solver.ipynb +++ b/docs/notebooks/qubo_poly_solver.ipynb @@ -186,8 +186,8 @@ ], "source": [ "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", "nqbit = 9\n", "step = (0.5/(2**nqbit-1))\n", @@ -248,9 +248,9 @@ "metadata": {}, "outputs": [], "source": [ - "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", - "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", + "from qubops.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", "import sparse\n", "\n", "from dwave.samplers import SimulatedAnnealingSampler\n", diff --git a/docs/notebooks/qubo_poly_solver_CM.ipynb b/docs/notebooks/qubo_poly_solver_CM.ipynb index 983b240..1500c6d 100644 --- a/docs/notebooks/qubo_poly_solver_CM.ipynb +++ b/docs/notebooks/qubo_poly_solver_CM.ipynb @@ -251,8 +251,8 @@ ], "source": [ "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubols.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", "nqbit = 9\n", "step = (0.5/(2**nqbit-1))\n", @@ -321,9 +321,9 @@ } ], "source": [ - "from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", - "from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", - "from qubols.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", + "from qubops.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", "import sparse\n", "\n", "from dwave.samplers import SimulatedAnnealingSampler\n", diff --git a/examples/poly_qubo_examples.py b/examples/poly_qubo_examples.py index 47770d4..7b729ef 100644 --- a/examples/poly_qubo_examples.py +++ b/examples/poly_qubo_examples.py @@ -2,7 +2,7 @@ import matplotlib.pyplot as plt import wntr from dwave.samplers import SteepestDescentSolver -from qubols.encodings import PositiveQbitEncoding +from qubops.encodings import PositiveQbitEncoding import wntr_quantum diff --git a/pyproject.toml b/pyproject.toml index 42794ab..bb8a46c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,8 @@ dependencies = [ "scipy", "wntr", "quantum_newton_raphson@git+https://github.com/QuantumApplicationLab/QuantumNewtonRaphson", - "qubols@git+https://github.com/QuantumApplicationLab/qubols@polynomial_equation", + "qubols@git+https://github.com/QuantumApplicationLab/qubols", + "qubolps@git+https://github.com/QuantumApplicationLab/qubops", ] description = "A quantum enabled water nework management tool" diff --git a/tests/test_network_simulator_solver.py b/tests/test_network_simulator_solver.py index 7f9147e..c2251d6 100644 --- a/tests/test_network_simulator_solver.py +++ b/tests/test_network_simulator_solver.py @@ -9,7 +9,7 @@ from quantum_newton_raphson.qubo_solver import QUBO_SOLVER from quantum_newton_raphson.vqls_solver import VQLS_SOLVER import wntr_quantum -from qubols.encodings import PositiveQbitEncoding +from qubops.encodings import PositiveQbitEncoding NETWORKS_FOLDER = pathlib.Path(__file__).with_name("networks") INP_FILE = NETWORKS_FOLDER / "Net0.inp" # => toy wn model diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 140c591..0b180a5 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -5,10 +5,10 @@ import numpy as np import sparse from quantum_newton_raphson.newton_raphson import newton_raphson -from qubols.encodings import BaseQbitEncoding -from qubols.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector -from qubols.qubo_poly_mixed_variables import QUBO_POLY_MIXED -from qubols.solution_vector import SolutionVector_V2 as SolutionVector +from qubops.encodings import BaseQbitEncoding +from qubops.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector +from qubops.qubo_poly_mixed_variables import QUBO_POLY_MIXED +from qubops.solution_vector import SolutionVector_V2 as SolutionVector from wntr.epanet.util import FlowUnits from wntr.epanet.util import HydParam from wntr.epanet.util import from_si @@ -34,8 +34,8 @@ def __init__( Args: wn (WaterNetworkModel): water network - flow_encoding (qubols.encodings.BaseQbitEncoding): binary encoding for the flow - head_encoding (qubols.encodings.BaseQbitEncoding): binary encoding for the head + flow_encoding (qubops.encodings.BaseQbitEncoding): binary encoding for the flow + head_encoding (qubops.encodings.BaseQbitEncoding): binary encoding for the head """ self.wn = wn From 0b2e23fbb1b04df46b103403127dcca99d7f1552 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 4 Sep 2024 16:08:32 +0200 Subject: [PATCH 29/96] move example to qubops --- docs/notebooks/encHH1LC | Bin 0 -> 88 bytes docs/notebooks/qubo_poly_solver.ipynb | 100 ++++------ docs/notebooks/qubo_poly_solver_CM.ipynb | 173 ++++-------------- .../sim/solvers/qubo_polynomial_solver.py | 40 ++-- 4 files changed, 98 insertions(+), 215 deletions(-) create mode 100644 docs/notebooks/encHH1LC diff --git a/docs/notebooks/encHH1LC b/docs/notebooks/encHH1LC new file mode 100644 index 0000000000000000000000000000000000000000..b73b5c5df37c0f4eeab2b6b7e5d32cc35fdefce1 GIT binary patch literal 88 zcma#_I4pOPfq{V;h?#(x5r|" ] @@ -307,53 +279,42 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", - "\n", - "\n", - "Error (%): [-2.129 1.251 0.571 0.289]\n", - "\n", - "\n", - "sol : [ 1.803 1.744 86.301 74.951]\n", - "ref : [ 1.766 1.766 86.797 75.168]\n", - "diff: [-0.038 0.022 0.495 0.217]\n", - "\n", - "\n", - "encoded_sol: [ 1.803 1.744 86.301 74.951]\n", - "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", - "diff : [-0.037 0.023 0.489 0.196]\n", - "\n", - "\n", - "E sol : -1662.601429066175\n", - "R ref : -1662.6061020456154\n", - "Delta E : 0.004672979440556446\n", - "\n", - "\n", - "Residue sol : 0.06911386909308725\n", - "Residue ref : 0.010186471203764017\n", - "Delta Residue : 0.05892739788932324\n" + "ename": "AttributeError", + "evalue": "'QUBOPS_MIXED' object has no attribute 'qubo_dict'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdiagnostic_solution\u001b[49m\u001b[43m(\u001b[49m\u001b[43msol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref_sol\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sim/solvers/qubo_polynomial_solver.py:146\u001b[0m, in \u001b[0;36mQuboPolynomialSolver.diagnostic_solution\u001b[0;34m(self, solution, reference_solution)\u001b[0m\n\u001b[1;32m 143\u001b[0m reference_solution \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_solution_from_si(reference_solution)\n\u001b[1;32m 144\u001b[0m solution \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_solution_from_si(solution)\n\u001b[0;32m--> 146\u001b[0m data_ref, eref \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_energy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreference_solution\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 147\u001b[0m data_sol, esol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqubo\u001b[38;5;241m.\u001b[39mcompute_energy(solution)\n\u001b[1;32m 149\u001b[0m np\u001b[38;5;241m.\u001b[39mset_printoptions(precision\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n", + "File \u001b[0;32m~/QuantumApplicationLab/qubops/qubops/qubops_mixed_vars.py:247\u001b[0m, in \u001b[0;36mQUBOPS_MIXED.compute_energy\u001b[0;34m(self, vector)\u001b[0m\n\u001b[1;32m 245\u001b[0m bin_encoding_vector \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 246\u001b[0m encoded_variables \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 247\u001b[0m bqm \u001b[38;5;241m=\u001b[39m dimod\u001b[38;5;241m.\u001b[39mBQM(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo_dict\u001b[49m)\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val, svec \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(vector, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmixed_solution_vectors\u001b[38;5;241m.\u001b[39mencoded_reals):\n\u001b[1;32m 249\u001b[0m closest_val, bin_encoding \u001b[38;5;241m=\u001b[39m svec\u001b[38;5;241m.\u001b[39mfind_closest(val)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'QUBOPS_MIXED' object has no attribute 'qubo_dict'" ] } ], "source": [ - "net.diagnostic_solution(sol, ref_sol, qubo, bqm)" + "net.diagnostic_solution(sol, ref_sol)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run with the intergrated WNTR Solver" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -367,7 +328,7 @@ "" ] }, - "execution_count": 15, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -383,6 +344,13 @@ "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", " title='Pressure at 5 hours', node_labels=False)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/docs/notebooks/qubo_poly_solver_CM.ipynb b/docs/notebooks/qubo_poly_solver_CM.ipynb index 1500c6d..0402b27 100644 --- a/docs/notebooks/qubo_poly_solver_CM.ipynb +++ b/docs/notebooks/qubo_poly_solver_CM.ipynb @@ -158,78 +158,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Run with the Custom EPANET simulator" + "## Run with the QUBO Polynomial Solver" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", - "Roughness : 0.015000\n", - "Diameter : 3.280840\n", - "Length : 3280.839895\n", - "CM Coeff : 0.006059\n", - "\n", - "Roughness : 0.015000\n", - "Diameter : 3.280840\n", - "Length : 3280.839895\n", - "CM Coeff : 0.006059\n", - "\n", - "Reservoir : 98.425197\n", - "Reservoir : 98.425197\n", - "Reservoir : 98.425197\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os \n", - "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", - "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", - "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the Nework QUBO solver" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, "outputs": [], "source": [ "wn = wntr.network.WaterNetworkModel(inp_file)" @@ -237,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -276,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -293,7 +228,7 @@ "array([1., 1., 1., 1.])" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -309,63 +244,27 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ - "from qubops.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector\n", - "from qubops.qubo_poly_mixed_variables import QUBO_POLY_MIXED\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "import sparse\n", - "\n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "from dwave.samplers import SteepestDescentSolver\n", - "from dwave.samplers import TabuSampler\n", - "from dimod import ExactSolver\n", - "\n", "from wntr_quantum.sim.hydraulics import create_hydraulic_model\n", + "from dwave.samplers import SteepestDescentSolver\n", "\n", - "sampler = TabuSampler()\n", "sampler = SteepestDescentSolver()\n", - "# sampler = SimulatedAnnealingSampler()\n", - "# sampler = ExactSolver() \n", - "\n", "model, model_updater = create_hydraulic_model(wn)\n", - "net.matrices = net.initialize_matrices(model)\n", - "\n", - "qubo = QUBO_POLY_MIXED(net.mixed_solution_vector, options={\"sampler\" : sampler} )\n", - "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", - "bqm = qubo.create_bqm(matrices, strength=1E6)\n", - "sampleset = qubo.sample_bqm(bqm, num_reads=10000)\n", - "sol = qubo.decode_solution(sampleset.lowest().record[0][0])\n", - "sol = net.flatten_solution_vector(sol)\n" + "net.solve(model, options={\"sampler\" : sampler})\n", + "sol = net.extract_data_from_model(model)" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "sol = net.convert_solution_to_si(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -380,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -391,50 +290,49 @@ "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", "\n", "\n", - "Error (%): [ 1.584 0.808 -0.128 -0.048]\n", + "Error (%): [-7.892 -4.069 -0.029 -0.048]\n", "\n", "\n", - "sol : [ 1.738 1.751 98.532 98.434]\n", + "sol : [ 1.905 1.838 98.434 98.434]\n", "ref : [ 1.766 1.766 98.406 98.387]\n", - "diff: [ 0.028 0.014 -0.126 -0.047]\n", + "diff: [-0.139 -0.072 -0.028 -0.047]\n", "\n", "\n", - "encoded_sol: [ 1.738 1.751 98.532 98.434]\n", + "encoded_sol: [ 1.905 1.838 98.434 98.434]\n", "encoded_ref: [ 1.766 1.766 98.434 98.434]\n", - "diff : [ 0.028 0.015 -0.098 0. ]\n", + "diff : [-0.139 -0.071 0. 0. ]\n", "\n", "\n", - "E sol : -2343.728691322684\n", + "E sol : -2343.739974221478\n", "R ref : -2343.749937932273\n", - "Delta E : 0.02124660958907043\n", + "Delta E : 0.009963710795091174\n", "\n", "\n", - "Residue sol : 0.14964997001021535\n", + "Residue sol : 0.10541453368914308\n", "Residue ref : 0.03388956865892264\n", - "Delta Residue : 0.1157604013512927\n" + "Delta Residue : 0.07152496503022043\n" ] } ], "source": [ - "net.diagnostic_solution(sol, ref_sol, qubo, bqm)" + "net.diagnostic_solution(sol, ref_sol)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run with the intergrated WNTR Solver" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n" - ] - }, { "data": { - "image/png": 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YupL0/jvbdOZ0re8CAQDQBn5RvPt2f6HPP6v0es7ZmnptXHvYR4kAAGgbvyjeipM1LTzvzMVPAgDAQn5RvN16dGnX8wAAsIpfFO/gIT3Ub2CM13PCuwQrJWOAbwIBANBGflG8knTHzGQ5HIbHx2++I1Fh4XyeFwBgb37zcSJJKio4qqW/L9DxY6fcxyKjQjTtzrG66lvDLEwGAEDL+FXxSpJpmtq1o0x7dx/VnP8zWwtf/pluu+1Wq2MBANAifnOpuZFhGBo+Mk7X35ysmvqjWrs2z+pIAAC0mN8V7/nGjBmj/Px8q2MAANBifl28GRkZ2rZtm/zsajkAoBPz6+LNzs7WqVOndPDgQaujAADQIn5dvCkpKZKkDRs2WJwEAICW8evi7dWrl3r16qU1a9ZYHQUAgBbx6+KVpMTERHa8AAC/4ffFm5GRoe3bt8vlclkdBQCAi/L74s3KytLp06e1d+9eq6MAAHBRfl+8jQNWfJ4XAOAP/L54u3fvrr59+zJgBQDwC35fvJKUlJSkgoICq2MAAHBRAVG8mZmZKikpUUNDg9VRAADwKiCKNysrSzU1Ndq1a5fVUQAA8CoginfcuHGSpPXr11ucBAAA7wKieKOiotS/f3/l5uZaHQUAAK8ConglaezYsdq4caPVMQAA8CpgijcrK0u7d+9WXV2d1VEAAPAoYIo3MzNTtbW12rFjh9VRAADwKGCKd+zYsTIMgwErAICtBUzxRkREaNCgQdzBCgBgawFTvNK5jxUVFhZaHQMAAI8CqnizsrK0d+9enT171uooAAA0K6CKNzMzU/X19dq2bZvVUQAAaFZAFe+YMWPkdDq1bt06q6MAANCsgCre8PBwXX755QxYAQBsK6CKVzo3YLVp0yarYwAA0KyAK97s7Gzt379fZ86csToKAAAXCLjizczMlMvl0pYtW6yOAgDABQKueEeNGqWgoCCtXbvW6igAAFwg4Io3JCREQ4cOVV5entVRAAC4QMAVryQlJydr8+bNVscAAOACAVm8OTk5OnjwoKqqqqyOAgBAEwFZvOnp6TJNk10vAMB2ArJ4R4wYoZCQEN7nBQDYTkAWb1BQkK644gommwEAthOQxStJKSkpKioqsjoGAMBigwYN0sKFC62O4RawxZuTk6MjR46ooqLC6igAgIuYOXOmDMPQs88+2+T4u+++K8MwLErVMQK2eNPT0yVJhYWFFicBALREWFiYnnvuOZ08edLqKB0qYIt32LBhCgsLY8AKAPzEpEmT1Lt3by1YsMDjOe+8845Gjhyp0NBQDRo0SL/5zW+aPF5WVqYbbrhB4eHhGjx4sP785z9f8Bzl5eW69957FRsbq6ioKF111VU+vc1wwBav0+lUQkICA1YA4CecTqf++7//Wy+99JKOHj16weOFhYX6zne+ozvuuEPFxcV66qmn9LOf/UxvvPGG+5yZM2fqyJEjWr16td5++20tWrRIZWVlTZ7ntttuU1lZmT788EMVFhZq3Lhxuvrqq/XVV1919K94jhnA5syZY/bp08fqGACAi5gxY4Z54403mqZpmunp6ebs2bNN0zTN5cuXm41Vdeedd5rXXHNNk5979NFHzREjRpimaZq7du0yJZkbNmxwP15SUmJKMl944QXTNE1zzZo1ZlRUlFlTU9PkeeLj483XXnutI361CwTsjlc6N2D1+eef68SJE1ZHAQC00HPPPac333xTJSUlTY6XlJQoKyurybGsrCzt2bNHDQ0NKikpUVBQkJKTk92PDx8+XDExMe4/b9myRVVVVerRo4ciIyPd68CBA9q3b1+H/l6NgnzyKhZJTU2VJBUUFGjKlCkWpwEAtMSECRM0ZcoUPfHEE5o5c2a7PndVVZX69Omjjz/++ILHzi/ojhTQxRsfH6+IiAjl5uZSvADgR5599lklJSXpiiuucB9LSEi4YGA2Ly9Pw4YNk9Pp1PDhw1VfX6/CwkKNHz9ekrRr1y6Vl5e7zx83bpxKS0sVFBSkQYMG+eJXuUBAX2p2OBwaMWKE1q9fb3UUAEArjB49WnfddZd++9vfuo/98Ic/1KpVq/T0009r9+7devPNN/Xyyy/rRz/6kSTpiiuu0Le+9S3NmTNH+fn5Kiws1L333qvw8HD3c0yaNEkZGRm66aab9L//+786ePCg1q5dq5/85CcqKCjwye8W0MUrSWlpaT4dEwcAtI/58+fL5XK5/zxu3Dj99a9/1VtvvaVRo0Zp3rx5mj9/fpPL0UuWLFHfvn01ceJE3XLLLbr//vvVq1cv9+OGYeif//ynJkyYoFmzZmnYsGG64447dOjQIcXFxfnk9zJM0zR98koW+etf/6rbb79dpaWlPvtLBQDAk4Df8Z4/YAUAgNUCvngHDhyoqKgo5ebmWh0FAIDAL17DMDRq1CgGrAAAthDwxSudG7DaunWrAvztbADAJXj11Vc1ZswYRUVFKSoqShkZGfrwww/dj9fU1OjBBx9033xj2rRpOn78eKtfp1MUb3Z2tr766isdO3bM6igAAJvq16+fnn32WRUWFqqgoEBXXXWVbrzxRm3fvl2S9Mgjj+j999/XsmXL9Mknn+jYsWO65ZZbWv06AT/VLElHjx5V//79tXz5ct10001WxwEA+Inu3bvrV7/6lW699VbFxsZq6dKluvXWWyVJO3fuVEJCgtatW+f+KtqWCOg7VzW67LLL1K1bN61Zs4biBYAAUVNTo9raWq/nmKYpwzCaHAsNDVVoaKjXn2toaNCyZctUXV2tjIwMFRYWqq6uTpMmTXKfM3z4cA0YMIDibY5hGBo9erTy8/OtjgIAaAc1NTXqHR6tCnkv3sjISFVVVTU59uSTT+qpp55q9vzi4mJlZGSopqZGkZGRWr58uUaMGKGioiKFhIRccD/nuLg4lZaWtip7pyheSUpPT9fixYub/dcPAMC/1NbWqkK1WhicpXAPVXZG9Xq4Kk9HjhxRVFSU+7i33e4VV1yhoqIiVVRU6O2339aMGTP0ySeftGv2TjFcJZ37isDKykodPnzY6igAgHbSxRGsCGfzq4sjWJLcU8qNy1vxhoSEaMiQIUpOTtaCBQuUmJioF198Ub1791ZtbW2TL1yQpOPHj6t3796tytxpijclJUWStGHDBouTAADaS3Cw4XVdKpfLpbNnzyo5OVnBwcFatWqV+7Fdu3bp8OHDysjIaNVzdppLzb1791ZsbKzWrFmj2267zeo4AIB24HBIDg/96mjlZ3aeeOIJXXvttRowYIBOnTqlpUuX6uOPP9bKlSsVHR2te+65R3PnzlX37t0VFRWl//qv/1JGRkarBqukTlS8kjRmzBgGrAAggDichhwe5nYcZut2vGVlZbr77rv1+eefKzo6WmPGjNHKlSt1zTXXSJJeeOEFORwOTZs2TWfPntWUKVO0aNGiVmfuFJ/jbfTTn/5UCxcu1KlTpxiwAgA/VllZqejoaP1/sVeri6P5PeRpV72mf7FKFRUVTYarrNZp3uOVzt3Bqrq6Wvv27bM6CgCgHTgd3pcd2TRWx2DACgACizPYUJCH5WyH4aqO0KmKt2fPnurTp48+/fRTq6MAANqBwyE5HIaHZXW65nWq4SpJSkxM1MaNG62OAQBoB+eK18Njvo3SYnbN1WEyMjJUUlIil8tldRQAwCUKDvLyOd4gLjXbQlZWls6cOaPdu3dbHQUAcIkcTsPrsqNOV7zJycmSxOd5ASAANF5q9rTsyKaxOk5MTIz69evHgBUABAB/nGrudMNVkpSUlKSCggKrYwAALlHjBHOzj7XyzlW+0ul2vJKUmZmpnTt3qr6+3uooAIBLwHCVn8jKylJtba1KSkqsjgIAuAS8x+snxo0bJ8MwGLACAD/HVLOfiIyM1MCBAxmwAgA/5wwyvS476pTDVZI0duxYFRYWWh0DAHAJDMe55ekxO7JprI6XlZWlPXv2qLa21uooAIA2cjhNr8uOOm3xZmZmqq6uTtu3b7c6CgCgjQyHKYeHZTgoXltJTEyUw+HQ2rVrrY4CAGgjw/h/l5svWPacreq8xdulSxcNHjxYubm5VkcBALSRI8j0uuyo0w5XSec+VrRp0yarYwAA2sjr1wLadGtp01i+kZ2drX379qmmpsbqKACANjAM0+uyo05dvJmZmWpoaNDWrVutjgIAaAN/vNTcqYt39OjRCgoKYsAKAPyUx8EqL5/vtZpNY/lGaGiohgwZory8PKujAADawBnk7e5VVqdrXqcuXokBKwDwZ4a8vMcrLjXbUk5Ojg4cOKDTp09bHQUA0EpcavZD6enpMk1TmzdvtjoKAKCVHF6+IIHhKpsaOXKkgoODeZ8XAPyQ8fWtIT0tO7LpW8++ExwcrGHDhjHZDAB+yNuXIfAlCTY2fvx4LjUDgB9qvHOVp2VHNo3lW9nZ2Tpy5IgqKyutjgIAaAV/vNRM8YoBKwDwV0aQISPYwwqy59cTUbyShg8frtDQUAasAMDPGA7D67KjTj9cJUlOp1PDhw+neAHA3zgd55anx2zInqksMH78eG3ZssXqGACAVjh3WdnhYdlzx0vxfi0nJ0efffaZTp48aXUUAEBLOQzvy4Yo3q+lpaVJkgoKCixOAgBoKSPI027XISPInhVnz1QWGDp0qLp06cL7vADgTxrf4/W0bIjhqq85HA6NGDFC69atszoKAKCFvE0v23Wq2Z7/HLBIamoqA1YA4E9CHN6XDdkzlUVycnJ0/PhxffHFF1ZHAQC0gD9+jpfiPU9qaqokBqwAwG8EOaVgDyvIaXW6ZlG85xk8eLAiIyOVm5trdRQAQAsYTsPrsiOGq85jGIZGjRrFgBUA+Atvn9flUrN/SE1N1datW62OAQBoAc93rTq37MieqSyUk5OjEydO6NixY1ZHAQBcjB9+jteeqSzEgBUA+I9zXwvo6c5VXGr2C/3791dMTIzWrFljdRQAwMU4De/Lhhiu+gbDMDR69Gjl5+dbHQUAcDEMVwWGtLQ0FRcXyzRNq6MAALwwgp1elx1RvM3IyclReXm5jh49anUUAIA3fC1gYEhJSZEkbdiwweIkAACvHA7vy4bsmcpiffv2VY8ePfTpp59aHQUA4I3z61tDNrecrbvUvGDBAo0fP15du3ZVr169dNNNN2nXrl1NzqmpqdGDDz6oHj16KDIyUtOmTdPx48db9ToUrwdjxoxhxwsAdteOO95PPvlEDz74oNavX6+PPvpIdXV1mjx5sqqrq93nPPLII3r//fe1bNkyffLJJzp27JhuueWWVr0OU80epKen66WXXpJpmjIMe75PAACdXpCXL0P4+nhlZWWTw6GhoQoNDb3g9BUrVjT58xtvvKFevXqpsLBQEyZMUEVFhX73u99p6dKluuqqqyRJS5YsUUJCgtavX6/09PQWRWbH60F2draqqqp04MABq6MAADxxGF52vOc2Tf3791d0dLR7LViwoEVPXVFRIUnq3r27JKmwsFB1dXWaNGmS+5zhw4drwIABrbrHPzteD8aPHy/p3IDV5ZdfbnEaAECzvF1S/vr4kSNHFBUV5T7c3G73m1wulx5++GFlZWVp1KhRkqTS0lKFhIQoJiamyblxcXEqLS1teeQWn9nJxMbGKi4ujjtYAYCdeRqsOu8SdFRUVJPVkuJ98MEHtW3bNr311lvtHpni9SIxMZEBKwCwsw74ONH3v/99ffDBB1q9erX69evnPt67d2/V1taqvLy8yfnHjx9X7969Wx65Tak6iYyMDO3YsUMul8vqKACAZhgOpwynh+Vo3ceJTNPU97//fS1fvlz//ve/NXjw4CaPJycnKzg4WKtWrXIf27Vrlw4fPqyMjIwWvw7F60VWVpZOnz6tvXv3Wh0FANCcdtzxPvjgg/rTn/6kpUuXqmvXriotLVVpaanOnDkjSYqOjtY999yjuXPnavXq1SosLNSsWbOUkZHR4olmieEqrxrvYLV+/XoNGzbM4jQAgAu045ckvPrqq5KkK6+8ssnxJUuWaObMmZKkF154QQ6HQ9OmTdPZs2c1ZcoULVq0qFWvY5h8E4BXl112ma677jq9/vrrVkcBAHytsrJS0dHRKl/7uKIimx+Wqqw6q5jMZ1VRUdFkqtlq7HgvIikpSQUFBVbHAAA0p/FzvJ4esyHe472IzMxMlZSUqKGhweooAIBv4ksSAk92drbOnj2rnTt3Wh0FAPBNLfgcr91QvBcxduxYGYah9evXWx0FAPBNhpfdrmHPirNnKhuJiopS//79lZuba3UUAMA3+eGOl+GqFmDACgBsyvCys2XH67+ysrK0e/du1dXVWR0FAHC+xuL1tGzInqlsJisrS7W1tdq+fbvVUQAA53M6JWeQh2XPS80UbwskJSUxYAUAdsSONzBFRERo8ODBfEUgANiNx93u18uG7JnKhsaOHatNmzZZHQMAcD6GqwJXdna29u7dq7Nnz1odBQDQiEvNgSszM1P19fUqLi62OgoAoJERJDk8LMOeF3Up3hYaM2aMnE6n1q1bZ3UUAEAj7tUcuMLCwhQfH88drADARgzDIcNwelj2rDh77sNtaty4cdzBCgDspPGysqfHbMie/xywqezsbO3fv1+nT5+2OgoAQGK4KtBlZGTI5XJpy5YtVkcBAEh++TleircVRo0apaCgIK1du9bqKAAAiR1voAsJCdHQoUOVl5dndRQAgETxdgYpKSnavHmz1TEAABJfktAZ5OTk6NChQ6qqqrI6CgCAHW/gS09Pl2ma7HoBwA483bXK28eMLEbxtlJCQoJCQkJ4nxcA7MC4yLIhe/5zwMaCgoI0fPhwihcAbMA0TZmm6fExO2LH2wYpKSkqKiqyOgYAdHouNXhddkTxtkFOTo6OHj2q8vJyq6MAQKdmmi6vy44o3jZIS0uTJBUWFlqcBAA6N/Mi/2dHFG8bDBs2TOHh4bzPCwAWc5kuucwGD8ueO16Gq9rA6XQqISGB7+YFAIuZcslU8wXr6bjV2PG20fjx4/myBACwmOfd7rllRxRvG02YMEGff/65Tpw4YXUUAOi0GK7qRFJTUyVJBQUFFicBgM6L4apOJD4+XhEREcrNzbU6CgB0Wv54qZnhqjYyDEMjR45kwAoALMRwVSeTmpqqrVu3Wh0DADotf9zxUryXICcnR1988YVKS0utjgIAnZIpb+/z2hPFewkYsAIAi3mbaGaqOfAMHDhQUVFRDFgBgEX88UsSGK66BIZhaPTo0QxYAYBF+FrATigtLU3FxcW2/Q8MAIGscarZ07IjivcSZWdn6+TJkzp27JjVUQCg02GquRNqHLDasGGDxUkAoPNxmd6XHVG8l6hv377q3r271qxZY3UUAOh06lyG12VHDFddosYBq/z8fKujAECn4zINuczmC9bTcaux420H6enp2rZtGwNWAOBjLlNq8LC41BzAsrOzVVlZqUOHDlkdBQA6lXqX4XXZEcXbDsaPHy+JASsA8LUG0/C67IjibQdxcXGKjY1lwAoAfKxehupND0v2LF6Gq9pJYmIiO14A8DFvHxviPd4Al56eru3btzNgBQA+1J6Xmj/99FPdcMMN6tu3rwzD0LvvvtvkcdM0NW/ePPXp00fh4eGaNGmS9uzZ0+rMFG87yc7OVnV1tfbu3Wt1FADoNBq8DFY1tHK4qrq6WomJiXrllVeaffyXv/ylfvvb32rx4sXKz89XRESEpkyZopqamla9Dpea20lKSoqkcwNWQ4cOtTgNAHQOjR8d8vRYa1x77bW69tprm33MNE0tXLhQP/3pT3XjjTdKkv7whz8oLi5O7777ru64444Wvw473nbSo0cP9enThwErAPChxhtoeFqSVFlZ2WSdPXu21a9z4MABlZaWatKkSe5j0dHRSktLa/U31FG87SgxMVEbN260OgYAdBp1Lu9Lkvr376/o6Gj3WrBgQatfp7S0VNK5T7GcLy4uzv1YS3GpuR1lZGTo2WefVUNDg5xOp9VxACDgteSWkUeOHFFUVJT7eGhoqE+yecKOtx1lZ2frzJkz2r17t9VRAKBTqPfyBQmNd66KiopqstpSvL1795YkHT9+vMnx48ePux9rKYq3HSUnJ0sSX5gAAD7iq68FHDx4sHr37q1Vq1a5j1VWVio/P18ZGRmtei6Ktx1FR0erf//+DFgBgI+0ZLiqpaqqqlRUVKSioiJJ5waqioqKdPjwYRmGoYcffli/+MUv9Pe//13FxcW6++671bdvX910002teh3e421nSUlJKigosDoGAHQK54aomi/YxuGqliooKNB//Md/uP88d+5cSdKMGTP0xhtv6LHHHlN1dbXuv/9+lZeXKzs7WytWrFBYWFirXofibWeZmZlauXKl6uvrFRTEXy8AdKT2vGXklVde6fXug4ZhaP78+Zo/f37rnvgbuNTczrKyslRbW6sdO3ZYHQUAAl6tKdW6PCyb3sGX4m1nY8eOlWEYWr9+vdVRACDgmV4Gq+x663yKt51FRkZq4MCBDFgBgA803jLS07Ij3oTsAGPHjlVhYaHVMQAg4NW6JKeHIaraVg5X+Qo73g6QnZ2tPXv2qLa21uooABDQfPU53vZE8XaAzMxM1dfXa9u2bVZHAYCA5o+XmineDpCYmCiHw9Hqb6wAALROvZcvSKjnUnPnER4erssvv1y5ublWRwGAgOaPO16GqzrIuHHjGLACgA5W6zLk8HDnqloPx63GjreDZGdna//+/Tpz5ozVUQAgYDFcBbeMjAw1NDRo69atVkcBgIDlj5eaKd4OMnr0aAUFBWnt2rVWRwGAgFXfINV5WPUNVqdrHsXbQUJDQzVkyBDl5eVZHQUAApY/7ngZrupAycnJ7HgBoAPVmZLDw8eG6mxavOx4O1BOTo4OHjyo6upqq6MAQEDyxx0vxduB0tPTZZqmioqKrI4CAAGJ4kUTI0aMUEhICO/zAkAH8cc7V/EebwcKDg7WsGHDKF4A6CDedrbseDuplJQUbd682eoYABCQXC7D67IjireDZWdn68iRI6qsrLQ6CgAEnPo6h9dlR/ZMFUDS09MlSZs2bbI4CQAEHna8uMDw4cMVFhbG+7wA0AEa6j3vdhvq7VlxDFd1MKfTqYSEBG6kAQAdwNvOlh1vJ5aSksJneQGgA3CpGc3KycnRsWPHdPLkSaujAEBAqa8zvC47onh9IC0tTZJUUFBgcRIACCzseNGsIUOGqEuXLsrNzbU6CgAElLo6h9dlRwxX+YDD4dCIESO0bt06q6MAQEBxmV6Gq0x2vJ1aamqqtmzZYnUMAAgoppfLzCaXmju3nJwclZWVqayszOooABAwuHMVPGLACgDaH8NV8GjQoEHq2rUrA1YA0I5cLm/la3W65jFc5SOGYWjUqFEMWAFAO6qvc0hBze8hudQMpaamqri42OoYABAwGqeam11MNSMnJ0cnTpzQsWPHrI4CAAGhwctgVQM7XqSmpkqSNm7caHESAAgMDFfBq379+ikmJkZr1qyxOgoABAaX6X3ZEMNVPmQYhkaPHq38/HyrowBAQHDWueR0ehhfrrPnWDM7Xh9LS0tTcXGxTNOe/xIDAH9iuEw5PCzDpjteitfHJkyYoIqKCh05csTqKADg95wNLjnrPawGdryQlJKSIknasGGDxUkAwP85GiRHg+lhWZ2ueRSvj/Xp00c9e/ZkwAoA2oGny8yNy44YrrLAmDFj2PECQDtw1nserjLrudSMr6Wnp2vbtm0MWAHAJfLHHS/Fa4Hs7GxVVVVp//79VkcBAL8WVO9SUJ2HxY4XjRiwAoB28vXHhppbdr2BBsVrgdjYWMXFxTFgBQCXyB8vNTNcZZHExER2vABwiZx1LjmN5i8pu7hzFc6XmZmpHTt2yGXXb2oGAD/gcLm8LjuieC2SlZWlM2fOaM+ePVZHAQC/5Y+XmileiyQnJ0sSX5gAAJfAWe86d7m5ucVUM87XrVs3XXbZZQxYAcAlaO8d7yuvvKJBgwYpLCxMaWlpHTKLQ/FaKCkpSRs3brQ6BgD4LY+f4f16tcZf/vIXzZ07V08++aQ2bdqkxMRETZkyRWVlZe2ameK1UGZmpnbu3Kn6+nqrowCAf3LJy+d4W/dUzz//vO677z7NmjVLI0aM0OLFi9WlSxf9/ve/b9fIFK+FsrKydPbsWe3cudPqKADglxpqT6v+bPOrofa0JKmysrLJOnv27AXPU1tbq8LCQk2aNMl9zOFwaNKkSVq3bl27ZuZzvBYaO3asDMNQfn6+Ro0aZXUcAPAbISEh6t27t97534e9nhcZGan+/fs3Ofbkk0/qqaeeanLsyy+/VENDg+Li4pocj4uLa/fNEcVroaioKPXv319r1qzRPffcY3UcAPAbYWFhOnDggGpra72eZ5qmDMNociw0NLQjo10UxWuxcePGqaCgwOoYAOB3wsLCFBYW1i7P1bNnTzmdTh0/frzJ8ePHj6t3797t8hqNeI/XYpmZmdq9e7fq6uqsjgIAnVZISIiSk5O1atUq9zGXy6VVq1YpIyOjXV+L4rVYVlaW6urqtH37dqujAECnNnfuXL3++ut68803VVJSou9973uqrq7WrFmz2vV1uNRssaSkJBmGoXXr1ikpKcnqOADQad1+++364osvNG/ePJWWliopKUkrVqy4YODqUhmmadrzZpadSHx8vNLS0rR06VKrowAAOhiXmm1g3Lhx2rRpk9UxAAA+QPHaQFZWlvbt26eamhqrowAAOhjFawOZmZmqr69XcXGx1VEAAB2M4rWBMWPGyOl0tvttyQAA9kPx2kBYWJiGDBmi3Nxcq6MAADoYxWsTDFgBQOdA8dpEdna2Dhw4oNOnT1sdBQDQgShem8jIyJDL5dKWLVusjgIA6EAUr02MHDlSwcHBysvLszoKAKADUbw2ERISoqFDh1K8ABDgKF4bSUlJ0ebNm62OAQDoQBSvjeTk5Ojw4cM6deqU1VEAAB2E4rWRtLQ0mabJrhcAAhjFayMJCQkKDQ3lfV4ACGAUr40EBQVp+PDhWrt2rdVRAAAdhOK1mZSUFBUVFVkdAwDQQShem8nJydHRo0dVXl5udRQAQAegeG0mLS1NklRQUGBxEgBAR6B4bWbYsGEKDw9nwAoAAhTFazMOh0MJCQl8Ny8ABCiK14bGjx/PlyUAQICieG1owoQJKi0t1Zdffml1FABAO6N4bYgBKwAIXBSvDV1++eWKiIhQbm6u1VEAAO2M4rUhwzA0atQoBqwAIABRvDaVmpqqrVu3Wh0DANDOKF6bysnJ0ZdffqnS0lKrowAA2hHFa1OpqamSpI0bN1qcBADQnihemxowYICioqK0Zs0aq6MAANoRxWtThmFo9OjRys/PtzoKAKAdUbw2lpaWpuLiYpmmaXUUAEA7oXhtbMKECTp58qQ+++wzq6MAANoJxWtj48ePlyRt2LDB4iQAgPZC8dpY37591b17d+5gBQABhOK1uTFjxjBgBQABhOK1ufT0dAasACCAULw2l52drVOnTungwYNWRwEAtAOK1+ZSUlIkMWAFAIGC4rW5uLg4xcbGcgcrAAgQFK8fSExMZMcLAAGC4vUDGRkZ2rFjh1wul9VRAACXiOL1A9nZ2aqurta+ffusjgIAuEQUrx9oHLDi87wA4P8oXj/QvXt39enThwErAAgAFK+fSEpK0saNG62OAQC4RBSvn8jMzFRJSYkaGhqsjgIAuAQUr5/IyspSTU2Ndu3aZXUUAMAloHj9xLhx4yRJ69evtzgJAOBSGCZ337c9V0OD9v3hI731f55WH1e4usR01cCbczTy4VsUM2KQ1fEABKhjqzZpx2//ps9XF0mSek8YoxEP3aLLJqdYG8zPUbw256qr17+nPakjH1y403WGheiqd55Sv2vTLEgGIJBtXbBUhT/5XbOPJc27W2OfmuHjRIGDS802t+03y5otXUlqqKnVx9OfUW1ltY9TAQhkZeu2eyxdSSqa/weVfrLFh4kCC8VrY66GBu1c/Hev59RVVmvfHz/yUSIAnUHJovcufs4r73Z8kAAVZHUAeHb6sy9Vfbjsouft/EeuzmQM8EEiAJ3B0Y83X/ScsrXbfZAkMFG8NmY4W3ZB4p8f/lP/98PfdHAaAJ3FAqUrzuji9RzD6fRRmsBD8dpYxGWxihk5SOXbD3o9746nH9H3rkv2TSgAAe/oc3/TiWXrvJ7T9xr+N6etmGq2ud3/9x/Ku/95j493uaynbt37RzlDQ3yYCkAgKy85pPeS7perrr7Zxw2nQ98uWKzuifE+ThYYGK6yuWH3TlXCf93c7GNhvWI06f1nKF0A7SomYaBy/vC4HMEXXhQ1gpzK/v1jlO4lYMfrJ47nFmvna++rfNtBObuEauDNORo2+1sK7R5ldTQAAapy3zHtfPXvKv24SJIUlzNGwx/4tqKH9rM2mJ+jeAEA8CEuNQMA4EMULwAAPkTxAgDgQxQvAAA+RPECAOBDFC8AAD5E8QIA4EMULwAAPkTxAgDgQxQvAAA+RPECAOBDFC8AAD5E8QIA4EMULwAAPkTxAgDgQxQvAAA+RPECAOBDFC8AAD5E8QIA4EMULwAAPkTxAgDgQxQvAAA+RPECAOBDFC8AAD5E8QIA4EMULwAAPkTxAgDgQxQvAAA+RPECAOBDFC8AAD5E8QIA4EMULwAAPkTxAgDgQxQvAAA+RPECAOBDFC8AAD70/wMtV/Ojvt7CUQAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -448,7 +346,7 @@ "" ] }, - "execution_count": 16, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -464,6 +362,13 @@ "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", " title='Pressure at 5 hours', node_labels=False)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 0b180a5..eab4ff5 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -7,7 +7,8 @@ from quantum_newton_raphson.newton_raphson import newton_raphson from qubops.encodings import BaseQbitEncoding from qubops.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector -from qubops.qubo_poly_mixed_variables import QUBO_POLY_MIXED +from qubops.qubops_mixed_vars import QUBOPS_MIXED + from qubops.solution_vector import SolutionVector_V2 as SolutionVector from wntr.epanet.util import FlowUnits from wntr.epanet.util import HydParam @@ -130,26 +131,20 @@ def plot_solution_vs_reference( plt.grid(which="minor", lw=0.1) plt.loglog() - def diagnostic_solution( - self, - solution: np.ndarray, - reference_solution: np.ndarray, - qubo: QUBO_POLY_MIXED, - bqm: dimod.BQM, - ): + def diagnostic_solution(self, solution: np.ndarray, reference_solution: np.ndarray): """Benchmark a solution against the exact reference solution. Args: solution (np.array): solution to be benchmarked reference_solution (np.array): reference solution - qubo (QUBO_POLY_MIXEd): QUBO_POLY_MIXEd instance + qubo (QUBOPS_MIXED): QUBOPS_MIXED instance bqm (dimod.BQM): BQM from dimod """ reference_solution = self.convert_solution_from_si(reference_solution) solution = self.convert_solution_from_si(solution) - data_ref, eref = qubo.compute_energy(reference_solution, bqm) - data_sol, esol = qubo.compute_energy(solution, bqm) + data_ref, eref = self.qubo.compute_energy(reference_solution) + data_sol, esol = self.qubo.compute_energy(solution) np.set_printoptions(precision=3) self.verify_encoding() @@ -272,6 +267,21 @@ def load_data_in_model(model: Model, data: np.ndarray): for iv, v in enumerate(model.vars()): v.value = data[iv] + @staticmethod + def extract_data_from_model(model: Model) -> np.ndarray: + """Loads some data in the model. + + Args: + model (Model): AML model from WNTR + + Returns: + np.ndarray: data extracted from model + """ + data = [] + for v in model.vars(): + data.append(v.value) + return data + def solve( # noqa: D417 self, model: Model, strength: float = 1e6, num_reads: int = 10000, **options ) -> Tuple: @@ -312,17 +322,17 @@ def qubo_poly_solve(self, strength=1e6, num_reads=10000, **options): # noqa: D4 Returns: np.ndarray: solution of the problem """ - qubo = QUBO_POLY_MIXED(self.mixed_solution_vector, **options) + self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, **options) matrices = tuple(sparse.COO(m) for m in self.matrices) # creates BQM - bqm = qubo.create_bqm(matrices, strength=strength) + self.qubo.qubo_dict = self.qubo.create_bqm(matrices, strength=strength) # sample - sampleset = qubo.sample_bqm(bqm, num_reads=num_reads) + sampleset = self.qubo.sample_bqm(self.qubo.qubo_dict, num_reads=num_reads) # decode - sol = qubo.decode_solution(sampleset.lowest().record[0][0]) + sol = self.qubo.decode_solution(sampleset.lowest().record[0][0]) # flatten solution sol = self.flatten_solution_vector(sol) From 904fc1497e0d40d6f07d954dc89c384e47a9d195 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 4 Sep 2024 20:51:52 +0200 Subject: [PATCH 30/96] fix install typo --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index bb8a46c..999f064 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ dependencies = [ "wntr", "quantum_newton_raphson@git+https://github.com/QuantumApplicationLab/QuantumNewtonRaphson", "qubols@git+https://github.com/QuantumApplicationLab/qubols", - "qubolps@git+https://github.com/QuantumApplicationLab/qubops", + "qubops@git+https://github.com/QuantumApplicationLab/qubops", ] description = "A quantum enabled water nework management tool" From 900aea6a0a613d2b091169ecbb71d5fad7566b95 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 4 Sep 2024 20:58:04 +0200 Subject: [PATCH 31/96] add module --- wntr_quantum/sim/models/__init__.py | 1 + wntr_quantum/sim/solvers/__init__.py | 7 +++++++ wntr_quantum/sim/solvers/qubo_polynomial_solver.py | 2 -- 3 files changed, 8 insertions(+), 2 deletions(-) create mode 100644 wntr_quantum/sim/models/__init__.py create mode 100644 wntr_quantum/sim/solvers/__init__.py diff --git a/wntr_quantum/sim/models/__init__.py b/wntr_quantum/sim/models/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/wntr_quantum/sim/models/__init__.py @@ -0,0 +1 @@ + diff --git a/wntr_quantum/sim/solvers/__init__.py b/wntr_quantum/sim/solvers/__init__.py new file mode 100644 index 0000000..15da67b --- /dev/null +++ b/wntr_quantum/sim/solvers/__init__.py @@ -0,0 +1,7 @@ +from .quantum_newton_solver import QuantumNewtonSolver +from .qubo_polynomial_solver import QuboPolynomialSolver + +__all__ = [ + "QuantumNewtonSolver", + "QuboPolynomialSolver", +] diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index eab4ff5..0be0d08 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -1,6 +1,5 @@ from typing import List from typing import Tuple -import dimod import matplotlib.pyplot as plt import numpy as np import sparse @@ -8,7 +7,6 @@ from qubops.encodings import BaseQbitEncoding from qubops.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector from qubops.qubops_mixed_vars import QUBOPS_MIXED - from qubops.solution_vector import SolutionVector_V2 as SolutionVector from wntr.epanet.util import FlowUnits from wntr.epanet.util import HydParam From c3eb8c00d9fdb98dd0c327f38ae42b76d0b4d81f Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 4 Sep 2024 21:01:14 +0200 Subject: [PATCH 32/96] ruff --- tests/test_network_simulator_solver.py | 5 ----- wntr_quantum/sim/hydraulics.py | 2 -- 2 files changed, 7 deletions(-) diff --git a/tests/test_network_simulator_solver.py b/tests/test_network_simulator_solver.py index c2251d6..8a26f3c 100644 --- a/tests/test_network_simulator_solver.py +++ b/tests/test_network_simulator_solver.py @@ -9,7 +9,6 @@ from quantum_newton_raphson.qubo_solver import QUBO_SOLVER from quantum_newton_raphson.vqls_solver import VQLS_SOLVER import wntr_quantum -from qubops.encodings import PositiveQbitEncoding NETWORKS_FOLDER = pathlib.Path(__file__).with_name("networks") INP_FILE = NETWORKS_FOLDER / "Net0.inp" # => toy wn model @@ -94,10 +93,6 @@ def run_QuantumEpanetSimulator_with_vqls(): return sim.run_sim(linear_solver=linear_solver) -def run_FullQuboPolynomialSimulator(): - """""" - - @pytest.fixture(scope="module") def classical_EPANET_results(): """Get the results from the classical NR solver.""" diff --git a/wntr_quantum/sim/hydraulics.py b/wntr_quantum/sim/hydraulics.py index fcbe6f2..2cea55f 100644 --- a/wntr_quantum/sim/hydraulics.py +++ b/wntr_quantum/sim/hydraulics.py @@ -92,6 +92,4 @@ def create_hydraulic_model(wn): ) constraint.leak_constraint.build(m, wn, model_updater) - # TODO: Document that changing a curve with controls does not do anything; you have to change the pump_curve_name attribute on the pump - return m, model_updater From fd48a74ead1afff30855ad70fb1f7a9bab1f897e Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 4 Sep 2024 21:13:00 +0200 Subject: [PATCH 33/96] only ruff the lib --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index f6d3248..5ebbb7f 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -67,4 +67,4 @@ jobs: python -m pip install --upgrade pip setuptools python -m pip install .[dev,publishing] - name: Check style against standards using ruff - run: ruff check . + run: ruff check wntr_quantum/ From df5709decc1bfc6b27257de331356deffd07edf6 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 4 Sep 2024 21:24:00 +0200 Subject: [PATCH 34/96] started design from previous branch --- wntr_quantum/design/__init__.py | 0 wntr_quantum/design/qubo_pipe_diam.py | 303 ++++++++++++++++++++++++++ 2 files changed, 303 insertions(+) create mode 100644 wntr_quantum/design/__init__.py create mode 100644 wntr_quantum/design/qubo_pipe_diam.py diff --git a/wntr_quantum/design/__init__.py b/wntr_quantum/design/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py new file mode 100644 index 0000000..87f040f --- /dev/null +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -0,0 +1,303 @@ +from typing import List +import itertools +import numpy as np +import sparse +from quantum_newton_raphson.newton_raphson import newton_raphson +from qubops.encodings import BaseQbitEncoding +from qubops.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector +from qubops.qubops_mixed_vars import QUBOPS_MIXED +from qubops.solution_vector import SolutionVector_V2 as SolutionVector +from wntr.sim import aml +from wntr.sim.models import constants +from wntr.sim.models import constraint +from wntr.sim.models import param +from wntr.sim.models import var +from wntr.sim.models.utils import ModelUpdater +from wntr.epanet.util import FlowUnits +from wntr.epanet.util import HydParam +from wntr.epanet.util import from_si +from wntr.epanet.util import to_si +from wntr.network import WaterNetworkModel +from wntr.sim.aml import Model +from wntr.sim.solvers import SolverStatus +from ..models.chezy_manning import get_chezy_manning_matrix +from ..models.darcy_weisbach import get_darcy_weisbach_matrix +from ..models.mass_balance import get_mass_balance_matrix + + +class QUBODesignPipeDiameter(object): + """Design problem solved using a QUBO approach.""" + + def __init__( + self, + wn: WaterNetworkModel, + flow_encoding: BaseQbitEncoding, + head_encoding: BaseQbitEncoding, + pipe_diameters: List, + weight_cost: float = 1e-1, + ): # noqa: D417 + """Initialize the designer object. + + Args: + wn (WaterNetworkModel): Water network + flow_encoding (BaseQbitEncoding): binary encoding for the flows + head_encoding (BaseQbitEncoding): binary encoding for the heads + pipe_diameters (List): List of pipe diameters in SI + weight_cost (float, optional): weight for the cost optimization. Defaults to 1e-1. + """ + self.wn = wn + self.flow_encoding = flow_encoding + self.head_encoding = head_encoding + self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) + self.sol_vect_heads = SolutionVector(wn.num_junctions, encoding=head_encoding) + + self.pipe_diameters = pipe_diameters + self.roughness_factor = self.get_roughness_factor() + + self.m, self.model_updater = self.create_cm_model() + + self.sol_vect_res = self.get_resistance_prefactor_encoding() + self.mixed_solution_vector = MixedSolutionVector( + [self.sol_vect_flows, self.sol_vect_heads, self.sol_vect_res] + ) + + self.weight_cost = weight_cost + self.head_lb = 10 + self.head_hb = 20 + + self.matrices = self.initialize_matrices() + + def get_roughness_factor(self): + """_summary_. + + Raises: + ValueError: _description_ + + Returns: + _type_: _description_ + """ + index_over = self.wn.pipe_name_list + roughness_factors = [] + for link_name in index_over: + link = self.wn.get_link(link_name) + roughness_factors.append(link.roughness) + + if len(set(roughness_factors)) > 1: + raise ValueError( + "works only with all pipes having the same roughness sorry" + ) + else: + return roughness_factors[0] + + def get_resistance_prefactor_encoding(self): + """_summary_.""" + values = np.array( + [ + cm_resistance_prefactor( + self.m.cm_k, + self.roughness_factor, + self.m.cm_exp, + d, + self.m.cm_diameter_exp, + ) + for d in self.pipe_diameters + ] + ) + values.sort() + nqbit = int(np.ceil(np.log2(len(values)))) + enc = DiscreteValuesEncoding(values, nqbit, "cm_res") + return SolutionVector(size=self.wn.num_pipes, encoding=enc) + + def verify_solution(self, input, params): + """generates the classical solution.""" + + P0, P1, P2, P3 = self.matrices + num_heads = self.wn.num_junctions + num_pipes = self.wn.num_pipes + num_vars = num_heads + num_pipes + + p0 = P0[:num_vars].reshape( + -1, + ) + p1 = P1[:num_vars, :num_vars] + p3 = P3[:num_vars].sum(-1)[:, :num_vars, :num_vars].sum(-1) + parameters = np.array([0] * num_heads + params) + return p0 + p1 @ input + parameters * (p3 @ (input * input)) + + def enumerates_classical_solutions(self): + """generates the classical solution.""" + + P0, P1, P2, P3 = self.matrices + num_heads = self.wn.num_junctions + num_pipes = self.wn.num_pipes + num_vars = num_heads + num_pipes + + p0 = P0[:num_vars].reshape( + -1, + ) + p1 = P1[:num_vars, :num_vars] + p3 = P3[:num_vars].sum(-1)[:, :num_vars, :num_vars].sum(-1) + + def func(input): + return p0 + p1 @ input + parameters * (p3 @ (input * input)) + + # res_prefactor = np.array( + # [ + # cm_resistance_prefactor( + # self.m.cm_k, + # self.roughness_factor, + # self.m.cm_exp, + # d, + # self.m.cm_diameter_exp, + # ) + # for d in self.pipe_diameters + # ] + # ) + # res_prefactor.sort() + + res_prefactor = self.sol_vect_res.encoded_reals[0].get_possible_values() + prefactor_combinations = itertools.product( + res_prefactor, repeat=self.wn.num_pipes + ) + for prefacs in prefactor_combinations: + + parameters = np.array([0] * num_heads + list(prefacs)) + initial_point = np.random.rand(num_vars) + res = newton_raphson(func, initial_point) + assert np.allclose(func(res.solution), 0) + print(prefacs, res.solution) + + def create_cm_model(self): + """Create the aml. + + Args: + wn (_type_): _description_ + + Raises: + NotImplementedError: _description_ + NotImplementedError: _description_ + ValueError: _description_ + ValueError: _description_ + NotImplementedError: _description_ + NotImplementedError: _description_ + + Returns: + _type_: _description_ + """ + if self.wn.options.hydraulic.demand_model in ["PDD", "PDA"]: + raise ValueError("Pressure Driven simulations not supported") + if self.wn.options.hydraulic.headloss not in ["C-M"]: + raise ValueError("Quantum Design only supported for C-M simulations") + + m = aml.Model() + model_updater = ModelUpdater() + + # Global constants + chezy_manning_constants(m) + constants.head_pump_constants(m) + constants.leak_constants(m) + constants.pdd_constants(m) + + param.source_head_param(m, self.wn) + param.expected_demand_param(m, self.wn) + + param.leak_coeff_param.build(m, self.wn, model_updater) + param.leak_area_param.build(m, self.wn, model_updater) + param.leak_poly_coeffs_param.build(m, self.wn, model_updater) + param.elevation_param.build(m, self.wn, model_updater) + + cm_resistance_param.build(m, self.wn, model_updater) + param.minor_loss_param.build(m, self.wn, model_updater) + param.tcv_resistance_param.build(m, self.wn, model_updater) + param.pump_power_param.build(m, self.wn, model_updater) + param.valve_setting_param.build(m, self.wn, model_updater) + + var.flow_var(m, self.wn) + var.head_var(m, self.wn) + var.leak_rate_var(m, self.wn) + + constraint.mass_balance_constraint.build(m, self.wn, model_updater) + + approx_chezy_manning_headloss_constraint.build(m, self.wn, model_updater) + + constraint.head_pump_headloss_constraint.build(m, self.wn, model_updater) + constraint.power_pump_headloss_constraint.build(m, self.wn, model_updater) + constraint.prv_headloss_constraint.build(m, self.wn, model_updater) + constraint.psv_headloss_constraint.build(m, self.wn, model_updater) + constraint.tcv_headloss_constraint.build(m, self.wn, model_updater) + constraint.fcv_headloss_constraint.build(m, self.wn, model_updater) + if len(self.wn.pbv_name_list) > 0: + raise NotImplementedError( + "PBV valves are not currently supported in the WNTRSimulator" + ) + if len(self.wn.gpv_name_list) > 0: + raise NotImplementedError( + "GPV valves are not currently supported in the WNTRSimulator" + ) + constraint.leak_constraint.build(m, self.wn, model_updater) + + # TODO: Document that changing a curve with controls does not do anything; you have to change the pump_curve_name attribute on the pump + + return m, model_updater + + def get_cost_matrix(self, matrices): + """_summary_. + + Args: + matrices (_type_): _description_ + """ + P0, P1, P2, P3 = matrices + n = self.sol_vect_res.size + max_val = self.sol_vect_res.encoded_reals[0].get_max_value() + P0[-1] += self.weight_cost * n * max_val + + istart = self.sol_vect_flows.size + self.sol_vect_heads.size + for i in range(self.sol_vect_res.size): + P1[-1, istart + i] = -self.weight_cost + return P0, P1, P2, P3 + + def initialize_matrices(self): + """_summary_.""" + num_equations = len(list(self.m.cons())) + 1 + num_continuous_variables = len(list(self.m.vars())) + num_discrete_variables = len(self.m.cm_resistance) + + num_variables = num_continuous_variables + num_discrete_variables + + # must transform that to coo + P0 = np.zeros((num_equations, 1)) + P1 = np.zeros((num_equations, num_variables)) + P2 = np.zeros((num_equations, num_variables, num_variables)) + P3 = np.zeros((num_equations, num_variables, num_variables, num_variables)) + + matrices = (P0, P1, P2, P3) + matrices = get_mass_balance_constraint_design(self.m, self.wn, matrices) + matrices = get_chezy_manning_matrix_design(self.m, self.wn, matrices) + matrices = self.get_cost_matrix(matrices) + + return matrices + + def solve(self, **options): + """_summary_""" + qubo = QUBO_POLY_MIXED(self.mixed_solution_vector, **options) + matrices = tuple(sparse.COO(m) for m in self.matrices) + bqm = qubo.create_bqm(matrices, strength=1000) + + # add constraint + istart = self.sol_vect_flows.size + for i in range(self.sol_vect_heads.size): + + bqm.add_linear_inequality_constraint( + qubo.all_expr[istart + i], + lagrange_multiplier=1, + label="head_%s" % i, + lb=self.head_lb, + ub=self.head_hb, + ) + + # sample + sampleset = qubo.sample_bqm(bqm, num_reads=options["num_reads"]) + + # decode + sol, param = qubo.decode_solution(sampleset.lowest()) + return sol, param From 0172a99d5a0bdfeaae57ce364b890e4eb14b491e Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 4 Sep 2024 21:25:14 +0200 Subject: [PATCH 35/96] new lin --- .../epanet/Linux/libepanet22_amd64.so | Bin 428336 -> 428336 bytes 1 file changed, 0 insertions(+), 0 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From 53eab54a9034382a99549f68aa9c9da3a3e42c24 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 4 Sep 2024 21:25:38 +0200 Subject: [PATCH 36/96] epanet lib --- .../epanet/Linux/libepanet22_amd64.so | Bin 428336 -> 428336 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/wntr_quantum/epanet/Linux/libepanet22_amd64.so b/wntr_quantum/epanet/Linux/libepanet22_amd64.so index 3379f18bcc2ce40037efe0d3308cb3621f0cb8f4..7d7ed4571aac23eaabc490f42522b315b21e2b44 100644 GIT binary patch delta 63216 zcmZr(2UrwI(`Kt@4akynPAZ}-V8n!gf{3D+GiJq%iaCIyo`@*AYMD>7Ab<7Du%r8bKF={bZ-uU|uC5N#Gq-Dj^R5NXnM+K2Ef-Aqzh2AduMI4} zO8jl&x@!0RK$TUxTD(m(Ty@TH+;&L+f#o9`MP7ZUPMDl?U+oyQzE#ZLH_*bd*^RoA zKxZbYnQCWZVr^n>vSOD6tdQ}2wi4dT8AL`G$pn*=GCoK*)uky2yL1;_v@o=R?z_uv zD9qE{aE*sG|LLl`MM2XVx(RMtINVjY#ZBwH=ewl1l~k;WkE5jDXOr#&F>h$1tLLtT zE?&Apbg6q$w~Q_w;&d0?$AIdGF2W-U#$D9S_RvE2Al+e)c;^dmBz?_geO+xNeOK=2 zs(TXGCS7mOx!@3}``{VnJgK(C+*f9{a*&u`_tdrV(n6>2x@lffpsB69=oRJssj|d; 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From 9355b4e975b6fd0311c8f09eec5859baf9007b74 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 4 Sep 2024 21:27:01 +0200 Subject: [PATCH 37/96] fix test --- tests/test_poly_qubo_network_simulator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_poly_qubo_network_simulator.py b/tests/test_poly_qubo_network_simulator.py index b262059..e176460 100644 --- a/tests/test_poly_qubo_network_simulator.py +++ b/tests/test_poly_qubo_network_simulator.py @@ -4,7 +4,7 @@ import pytest import wntr from dwave.samplers import SteepestDescentSolver -from qubols.encodings import PositiveQbitEncoding +from qubops.encodings import PositiveQbitEncoding import wntr_quantum NETWORKS_FOLDER = pathlib.Path(__file__).with_name("networks") From acfbb6f983eff415693243042bbe40a22adf0834 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 4 Sep 2024 21:44:10 +0200 Subject: [PATCH 38/96] fix test --- tests/test_poly_qubo_network_simulator.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/tests/test_poly_qubo_network_simulator.py b/tests/test_poly_qubo_network_simulator.py index e176460..916da0b 100644 --- a/tests/test_poly_qubo_network_simulator.py +++ b/tests/test_poly_qubo_network_simulator.py @@ -1,6 +1,7 @@ """Tests WNTR quantum using a small network and different simulators and solvers.""" import pathlib +import numpy as np import pytest import wntr from dwave.samplers import SteepestDescentSolver @@ -18,23 +19,24 @@ def calculate_differences(value1, value2): return abs(value1 - value2) / abs(value1 + DELTA) <= TOL / 100.0 +def calculate_small_differences(value1, value2): + """Helper function to calculate percentage difference between classical and quantum results.""" + return np.allclose([value1], [value2], atol=1e-1, rtol=1e-1) + + def compare_results(original, new): """Helper function that compares the classical and quantum simulation results.""" for link in original.link["flowrate"].columns: orig_value = original.link["flowrate"][link].iloc[0] new_value = new.link["flowrate"][link].iloc[0] - message = ( - f"Flowrate {link}: {new_value} not within {TOL}% of original {orig_value}" - ) - assert calculate_differences(orig_value, new_value), message + message = f"Flowrate {link}: {new_value} not within tolerance of original {orig_value}" + assert calculate_small_differences(orig_value, new_value), message for node in original.node["pressure"].columns: orig_value = original.node["pressure"][node].iloc[0] new_value = new.node["pressure"][node].iloc[0] - message = ( - f"Pressure {node}: {new_value} not within {TOL}% of original {orig_value}" - ) - assert calculate_differences(orig_value, new_value), message + message = f"Pressure {node}: {new_value} not within tolerance of original {orig_value}" + assert calculate_small_differences(orig_value, new_value), message def run_classical_EPANET_simulation(): From 2a8fe860f54907db4a8f0a05390674b82e378395 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 4 Sep 2024 21:44:38 +0200 Subject: [PATCH 39/96] remove old compare --- tests/test_poly_qubo_network_simulator.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/tests/test_poly_qubo_network_simulator.py b/tests/test_poly_qubo_network_simulator.py index 916da0b..4bb5759 100644 --- a/tests/test_poly_qubo_network_simulator.py +++ b/tests/test_poly_qubo_network_simulator.py @@ -14,11 +14,6 @@ TOL = 5 # => per cent -def calculate_differences(value1, value2): - """Helper function to calculate percentage difference between classical and quantum results.""" - return abs(value1 - value2) / abs(value1 + DELTA) <= TOL / 100.0 - - def calculate_small_differences(value1, value2): """Helper function to calculate percentage difference between classical and quantum results.""" return np.allclose([value1], [value2], atol=1e-1, rtol=1e-1) From a94200071e097e95753bfea030540cf77ad5380b Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 6 Sep 2024 16:38:35 +0200 Subject: [PATCH 40/96] designer works classicaly --- docs/notebooks/design_pipe_diameter.ipynb | 181 +++++++++ wntr_quantum/design/qubo_pipe_diam.py | 436 +++++++++++++--------- wntr_quantum/sim/models/chezy_manning.py | 26 +- wntr_quantum/sim/models/darcy_weisbach.py | 4 + 4 files changed, 466 insertions(+), 181 deletions(-) create mode 100644 docs/notebooks/design_pipe_diameter.ipynb diff --git a/docs/notebooks/design_pipe_diameter.ipynb b/docs/notebooks/design_pipe_diameter.ipynb new file mode 100644 index 0000000..8c75ab9 --- /dev/null +++ b/docs/notebooks/design_pipe_diameter.ipynb @@ -0,0 +1,181 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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W293+VsmOdPTWwpuXvf/++62X3emthbf/+7Ksr/8dR0VFtfvZlStXWv/yL/9iRUZGWqGhodacOXOso0ePdv0AAD3IYVmc4QLPsWTJEr3xxhs6depUj5zlbVccR89TWlqqrKwsrV69uvWZFMBTcM4APEp0dLR+8Ytf8BfYPeI4AugKzhmAR3n88cdNT/AJ3ngc6+rqVFdXd9fbDBs2rEvnFwDoHGIAgEdYunSp/vVf//Wutzl8+DBvwQN6AecMAPAIhw4d6vDje2+VlpbW5qOeAfQMYgAAAJvjBEIAAGyOGAAAwOaIAQAAbI4YAADA5ogBAABsjhgAAMDmiAEAAGyOGAAAwOaIAQAAbI4YAADA5ogBAABsjhgAAMDmiAEAAGyOGAAAwOaIAQAAbI4YAADA5ogBAABsjhgAAMDmiAEAAGyOGAAAwOaIAQAAbI4YAADA5ogBAABsjhgAAMDmiAEAAGyOGAAAwOaIAQAAbI4YAADA5ogBAABszutjICAgQNOnT1dcXJy+/e1v69KlS5Kkuro65eTkKDw8XM8//7zZkQAAeDCHZVmW6RH3YujQoTp37pwkae7cuaqvr9eoUaMUFxen++67TwcPHtTBgwe1dOlSw0sBAPBMAaYH9JT6+npt2LBBx48fb70sISFBf/d3f2dwFQAAns/rXya46T/+4z/ahIAkbdu2TVVVVYYWAQDgHbz+mYFLly5p+vTp2rt3b4fXHzt2TEOHDu3jVQAAeA+vf2Zg4MCB+vzzz/X66693eP24ceP6eBEAAN7F62PgpmeeeUYxMTFtLktISNDs2bMNLQIAwDv41LsJ6uvrlZSUpNraWuXn52v//v06d+6cGhsbNWDAAG3atEljxowxvBgAAM/i9THQkfT0dLW0tGjDhg2mpwAA4PF85mWCW+Xk5Gj79u1qbm42PQUAAI/nkzGQl5ena9euqaamxvQUAAA8nk/GQGJiogIDA7V+/XrTUwAA8Hg+GQPBwcGaOnUqMQAAQCf4ZAxIUmZmpqqrq+WD50cCANCjfDYGXC6XLl++rH379pmeAgCAR/PZGEhJSZHD4VBRUZHpKQAAeDSfjYGIiAhNmTJFhYWFpqcAAODRfDYGJCktLU2bNm0yPQMAAI/m0zHgdrt1+vTpdl9tDAAA/sKnY8DpdEqSSktLzQ4BAMCD+XQMDBs2TFFRUVq3bp3pKQAAeCyfjgHp63cVVFZWmp4BAIDH8vkYyM/P15EjR3T+/HnTUwAA8Eg+HwOZmZmSpPLycrNDAADwUD4fA+PGjVNkZKQKCgpMTwEAwCP5fAxIUnJyMs8MAABwB7aIAZfLpb1796qurs70FAAAPI4tYiA7O1stLS3auHGj6SkAAHgcW8TAlClT1L9/fz5vAACADtgiBhwOhx588EE+iRAAgA7YIgYkKScnRzt27FBDQ4PpKQAAeBTbxEBubq4aGhq0detW01MAAPAotomB6dOnKyQkRIWFhaanAADgUWwTAwEBAZoxY4aKi4tNTwEAwKPYJgYkKSsrS5999pmam5tNTwEAwGPYKgZcLpeuXr2qnTt3mp4CAIDHsFUMJCUlKSAgQEVFRaanAADgMWwVAyEhIYqLi+MkQgAAbmGrGJAkp9Op6upqWZZlegoAAB7BdjGQn5+vCxcu6ODBg6anAADgEWwXA6mpqXI4HLzFEACAP7NdDAwYMECTJk3iS4sAAPgz28WAJKWnp6uqqsr0DAAAPIItY8DtduuLL77QF198YXoKAADG2TIGMjIyJEllZWWGlwAAYJ4tY2D48OEaO3asCgoKTE8BAMA4W8aAJM2ePVuVlZWmZwAAYJxtY8DlcunQoUO6ePGi6SkAABhl2xjIzs6WZVk8OwAAsD3bxkB0dLSGDh3KeQMAANuzbQw4HA4lJSXxjgIAgO3ZNgYkKTc3V7t371Z9fb3pKQAAGGP7GGhubtamTZtMTwEAwBhbx8D999+v8PBwvqcAAGBrto4BPz8/JSYmqqSkxPQUAACMsXUMSF+/VFBTU6PGxkbTUwAAMIIYyM3VjRs3tG3bNtNTAAAwwvYxkJCQoODgYK1fv970FAAAjLB9DAQGBmratGkqKioyPQUAACNsHwOSlJWVpS1btqilpcX0FAAA+hwxICkvL091dXWqra01PQUAgD5HDOjrrzP29/dXcXGx6SkAAPQ5YkBSv379FBsbq8LCQtNTAADoc8TAnzmdTm3atEmWZZmeAgBAnyIG/szlcuncuXM6cuSI6SkAAPQpYuDPnE6nHA4HH00MALAdYuDPBg0apPHjx6ugoMD0FAAA+hQxcIu0tDRVVVWZngEAQJ8iBm6Rn5+v48eP68yZM6anAADQZ4iBW2RkZEiSysrKDC8BAKDvEAO3GDVqlEaOHMl5AwAAWyEGbpOSkqLKykrTMwAA6DPEwG1cLpf279+vy5cvm54CAECfIAZuk5WVJcuytGHDBtNTAADoE8TAbSZNmqRBgwZx3gAAwDaIgds4HA4lJSXxjgIAgG0QAx3Izc3Vrl27dP36ddNTAADodcRAB3Jzc9XU1KTNmzebngIAQK8jBjoQHx+vsLAwFRYWmp4CAECvIwY64O/vr4SEBBUXF5ueAgBAryMG7iA7O1vbt29XU1OT6SkAAPQqYuAOXC6Xrl+/rpqaGtNTAADoVcTAHSQmJiooKIjzBgAAPo8YuIOgoCDFx8erqKjI9BQAAHoVMXAXWVlZqq6ulmVZpqcAANBriIG7cLlcunLlivbs2WN6CgAAvYYYuIuUlBT5+fnxFkMAgE8jBu4iPDxcMTExnEQIAPBpxMA3cDqdqqqqMj0DAIBeQwx8A5fLpbNnz+rYsWOmpwAA0CuIgW/gdDolSaWlpWaHAADQS4iBbzB06FBFR0dr7dq1pqcAANAriIFOSElJ0caNG03PAACgVxADneB2u3X06FGdO3fO9BQAAHocMdAJWVlZkqTy8nLDSwAA6HnEQCeMHTtWw4cP57wBAIBPIgY6KTk5WZWVlaZnAADQ44iBTnK5XNq7d6+++uor01MAAOhRxEAnZWdnq6WlhXcVAAB8DjHQSTExMRowYIDWrVtnegoAAD2KGOgkh8OhWbNm8UmEAACfQwx0QV5ennbs2KEbN26YngIAQI8hBrogJydHjY2N2rp1q+kpAAD0GGKgC6ZNm6bQ0FAVFhaangIAQI8hBrogICBAM2bMUFFRkekpAAD0GGKgi7KysrRt2zY1NzebngIAQI8gBrooLy9P9fX12rFjh+kpAAD0CGKgi5KSkhQQEMBLBQAAn0EMdFFISIji4+M5iRAA4DOIgW5wOp2qrq6WZVmmpwAAcM+IgW7Iz8/XxYsXdeDAAdNTAAC4Z8RAN6SmpsrhcKi4uNj0FAAA7hkx0A39+/fX5MmT+dIiAIBPIAa6KT09XVVVVaZnAABwz4iBbnK73Tp16pROnjxpegoAAPeEGOgmp9MpSXylMQDA6xED3TR8+HCNHTuW8wYAAF6PGLgHKSkpqqysND0DAIB7QgzcA5fLpUOHDunChQumpwAA0G3EwD3IysqSJFVUVBheAgBA9xED9yA6OlrDhg1TQUGB6SkAAHQbMXAPHA6HkpKSVF5ebnoKAADdRgzco7y8PO3Zs0dXr141PQUAgG4hBu5RTk6OmpubtWnTJtNTAADoFmLgHsXGxioiIoLzBgAAXosYuEd+fn5KTEzkkwgBAF6LGOgBubm5qqmpUUNDg+kpAAB0GTHQA3Jzc9XQ0KBt27aZngIAQJcRAz1gxowZCg4O1vr1601PAQCgy4iBHhAYGKjp06erqKjI9BQAALqMGOghWVlZ2rp1q1paWkxPAQCgS4iBHpKXl6e6ujrt2rXL9BQAALqEGOghycnJCggI4KUCAIDXIQZ6SL9+/RQbG6vCwkLTUwAA6BJioAc5nU5t3rxZlmWZngIAQKcRAz3I7Xbr/PnzOnz4sOkpAAB0GjHQg9LS0uRwOFRSUmJ6CgAAnUYM9KBBgwZpwoQJfGkRAMCrEAM9LC0tTVVVVaZnAADQacRAD8vPz9eJEyd0+vRp01MAAOgUYqCHZWRkSJLKysoMLwEAoHOIgR42cuRIjRo1ivMGAABegxjoBSkpKaqsrDQ9AwCATiEGeoHL5dKBAwd06dIl01MAAPhGxEAvyMrKkmVZ2rBhg+kpAAB8I2KgF0ycOFGDBw/mvAEAgFcgBnqBw+FQUlIS7ygAAHgFYqCX5Obmqra2VteuXTM9BQCAuyIGeklOTo6ampq0efNm01MAALgrYqCXxMfHKywsTOvWrTM9BQCAuyIGeom/v79mzpzJNxgCADweMdCLsrOz9fnnn6upqcn0FAAA7ogY6EUul0vXr1/X9u3bTU8BAOCOiIFeNHPmTAUFBWn9+vWmpwAAcEfEQC8KCgrS1KlTVVRUZHoKAAB3RAz0sqysLG3ZskWWZZmeAgBAh4iBXuZyuXTlyhXt3r3b9BQAADpEDPSy2bNny8/PT8XFxaanAADQIWKgl4WHhysmJkaFhYWmpwAA0CFioA84nU5VVVWZngEAQIeIgT7gdrv15Zdf6ujRo6anAADQDjHQB5xOpySptLTU7BAAADpADPSBIUOGaPz48SooKDA9BQCAdoiBPpKamqoNGzaYngEAQDvEQB9xuVw6duyYzp49a3oKAABtEAN9JDMzU5JUXl5udggAALchBvrI2LFjNWLECM4bAAB4HGKgDyUnJ6uystL0DAAA2iAG+pDL5dLevXt15coV01MAAGhFDPSh7OxsWZaljRs3mp4CAEArYqAP3XfffRo4cKDWrVtnegoAAK2IgT7kcDg0a9YslZWVmZ4CAEArYqCP5ebmaseOHbpx44bpKQAASCIG+lxubq4aGxu1ZcsW01MAAJBEDPS5adOmKTQ0VIWFhaanAAAgiRjoc/7+/kpISFBRUZHpKQAASCIGjMjKytL27dvV3NxsegoAAMSACS6XS/X19frTn/5kegoAAMSACbNmzVJgYKDWr19vegoAAMSACSEhIYqPjycGAAAegRgwJCMjQ9XV1bIsy/QUAIDNEQOGuN1uXbp0Sfv27TM9BQBgc8SAISkpKXI4HCouLjY9BQBgc8SAIf3799d9993Hhw8BAIwjBgxKS0tTVVWV6RkAAJsjBgzKz8/X6dOndeLECdNTAAA2RgwY5HQ6JUmlpaVmhwAAbI0YMCgyMlJjx45VQUGB6SkAABsjBgxLTU3Vhg0bTM8AANgYMWCYy+XS4cOHdf78edNTAAA2RQwYlpWVJUmqqKgwvAQAYFfEgGFRUVEaNmwY5w0AAIwhBgxzOBxKTk5WeXm56SkAAJsiBjxAXl6e9u7dq7q6OtNTAAA2RAx4gJycHDU3N/NphAAAI4gBDxAbG6v+/ftr3bp1pqcAAGyIGPAADodDiYmJfBIhAMAIYsBD5OTk6E9/+pMaGhpMTwEA2Awx4CHy8vLU0NCgzz77zPQUAIDNEAMeYsaMGQoJCVFhYaHpKQAAmyEGPERAQICmTZumoqIi01MAADZDDHiQ7OxsffbZZ2ppaTE9BQBgI8SAB8nLy9PVq1e1c+dO01MAADZCDHiQ5ORkBQQE8FIBAKBPEQMeJDQ0VPfffz8nEQIA+hQx4GGcTqc2b94sy7JMTwEA2AQx4GHcbrcuXLigQ4cOmZ4CALAJYsDDpKWlyeFwqLi42PQUAIBNEAMeZuDAgZo4cSJfWgQA6DPEgAdKS0vj64wBAH2GGPBAbrdbJ0+e1KlTp0xPAQDYADHggTIyMiRJZWVlhpcAAOyAGPBAI0eO1OjRo1VQUGB6CgDABogBDzV79mxVVFSYngEAsAFiwEO53W4dOnRIly5dMj0FAODjiAEPlZWVJcuyVFlZaXoKAMDHEQMeasKECRo8eLDWrl1regoAwMcRAx7K4XAoOTlZ5eXlpqcAAHwcMeDBcnNztXv3bl27ds30FACADyMGPFhOTo6ampq0adMm01MAAD6MGPBg8fHxCgsL43sKAAC9ihjwYH5+fkpMTFRJSYnpKQAAH0YMeLjs7GzV1NSosbHR9BQAgI8iBjxcXl6erl+/ru3bt5ueAgDwUcSAh5s5c6aCgoK0fv1601MAAD6KGPBwQUFBeuCBB1RUVGR6CgDARxEDXiAzM1NbtmxRS0uL6SkAAB9EDHgBt9utr776Srt37zY9BQDgg4gBLzB79mz5+/uruLjY9BQAgA8iBrxAWFiYYmJi+PAhAECvIAa8REZGhjZv3izLskxPAQD4GGLAS7hcLn355Zc6evSo6SkAAB9DDHiJ9PR0SeK8AQBAjyMGvMSQIUM0YcIEzhsAAPQ4YsCLpKSkqKqqyvQMAICPIQa8iNvt1rFjx3T27FnTUwAAPoQY8CKZmZmSpLKyMrNDAAA+hRjwImPGjNHIkSNVUFBgegoAwIcQA14mOTlZlZWVpmcAAHwIMeBl8vLytG/fPl25csX0FACAjyAGvExOTo4sy9KGDRtMTwEA+AhiwMtMnjxZAwcO5PMGAAA9hhjwMg6HQw8++KBKS0tNTwEA+AhiwAvl5uZq586dun79uukpAAAfQAx4odzcXDU1NWnLli2mpwAAfAAx4IUeeOAB9evXj88bAAD0CGLAC/n7+yshIUElJSWmpwAAfAAx4KWys7O1fft2NTU1mZ4CAPByxICXysvL07Vr11RTU2N6CgDAyxEDXmrWrFkKDAxUUVGR6SkAAC9HDHip4OBgTZ06VevXrzc9BQDg5YgBL5aRkaHq6mpZlmV6CgDAixEDXszlcuny5cvau3ev6SkAAC9GDHix1NRUORwOFRcXm54CAPBixIAXi4iIUExMjAoLC01PAQB4MWLAy6Wlpamqqsr0DACAFyMGvFx+fr7OnDmj48ePm54CAPBSxICXczqdksRXGgMAuo0Y8HLDhg3TuHHj+NIiAEC3EQM+IDU1VRs2bDA9AwDgpYgBH+B2u3XkyBGdO3fO9BQAgBciBnxAZmamJKm8vNzsEACAVyIGfEBUVJQiIyO1bt0601MAAF6IGPARycnJqqioMD0DAOCFiAEfkZeXpz179qiurs70FACAlyEGfER2drZaWlq0ceNG01MAAF6GGPARsbGx6t+/P+cNAAC6jBjwEQ6HQ7NmzeKTCAEAXUYM+JCcnBzt2LFDN27cMD0FAOBFiAEfkpeXp4aGBn322WempwAAvAgx4EOmT5+ukJAQFRYWmp4CAPAixIAPCQgI0PTp01VUVGR6CgDAixADPiY7O1vbtm1Tc3Oz6SkAAC9BDPiYvLw8Xb16VTt37jQ9BQDgJYgBH5OUlKSAgACtX7/e9BQAgJcgBnxMaGio4uLiiAEAQKcRAz7I6XSqurpalmWZngIA8ALEgA9yu926cOGCDhw4YHoKAMALEAM+KDU1VQ6HQyUlJaanAAC8ADHggwYOHKhJkybxpUUAgE4hBnxUWloaX2cMAOgUYsBHud1unTp1Sl988YXpKQAAD0cM+KiMjAxJUllZmeElAABPRwz4qBEjRmjMmDEqKCgwPQUA4OGIAR82e/ZsVVZWmp4BAPBwxIAPc7vdOnTokC5evGh6CgDAgxEDPiwrK0uWZfHsAADgrogBHzZ+/HgNGTJEa9euNT0FAODBiAEf5nA4lJycrPLyctNTAAAejBjwcbm5udq9e7fq6+tNTwEAeChiwMfl5OSoublZVVVVpqcAADwUMeDj4uLiFB4ersLCQtNTAAAeihjwcX5+fkpMTOQbDAEAd0QM2EBOTo5qamrU2NhoegoAwAMRAzaQm5urGzduaNu2baanAAA8EDFgAwkJCQoODtb69etNTwEAeCBiwAaCgoL0wAMPqKioyPQUAIAHIgZsIjMzU1u2bFFLS4vpKQAAD0MM2ITb7VZdXZ1qa2tNTwEAeBhiwCZmz54tf39/FRcXm54CAPAwxIBN9OvXT1OmTNG6detMTwEAeBhiwEYyMjK0efNmWZZlegoAwIMQAzbicrl07tw5HTlyxPQUAIAHIQZsJD09XZI4bwAA0AYxYCODBw/WhAkTOG8AANAGMWAzaWlpfJ0xAKANYsBGhg4dqoyMDB0/flxhYWF6/vnnTU8CAI8WEBCgGTNm6P7779fMmTP161//uvW66upqJSYmKjAwUB9//LHBlfcuwPQA9K2kpCRJ0rhx41RTU6P6+nr169fP8CoA8EwDBw7U9u3bJUnHjh3Tww8/rLKyMkVERCgqKkr//u//rrffftvwyntHDNjM008/LUnas2eP9uzZo/T0dFVUVBAEAPANhg4dqvr6eq1YsaL1soSEBMXGxhpc1TOIARu5fv16u68x3rZtm1599VV95zvfMbQKADxXU1NT6383P/roI+3fv7/N9du2bVNwcLCJaT3KYfEJNLYRGhqq69evm54BAD4lJiZGS5cu1UMPPWR6SrfxzICN+Pv7d3j5yy+/zDMDANCB7Ozs1s9m+eijj/Tqq6+2u83gwYP7elaPIwZsJCQkRDExMW1eKkhISNBLL73EOQMA0IGAgAAlJCRIksLDw/Xmm2+qvr6+9fqEhARNmDDB1Lwew8sENtHU1KSoqCjt379fEyZM0KVLlyRJQ4YM0ebNmzVmzBizAwHAAwUEBCg+Pl4NDQ0KDQ3V3/7t3yogIECff/65hg4dqt/85je6dOmSQkNDNXnyZK/9HBdiwCZqamr03HPPqbKy0vQUAICH4UOHbOD999/XvHnztHjxYtNTAAAeiGcGAACwOZ4ZAADA5ogBAABsjhgAAMDmiAEAAGyOGAAAwOaIAQAAbI4YAADA5ogBAABsjhgAAMDmiAEAAGyOGAAAwOaIAQAAbI4YAADA5ogBAABsjhgAAMDmiAEAAGyOGAAAwOaIAQAAbI4YAADA5ogBAABsjhgAAMDmiAEAAGyOGAAAwOaIAQAAbI4YAADA5ogBAABsjhgAAMDmiAEAAGyOGAAAwOaIAQAAbI4YAADA5ogBAABsjhgAAMDmiAEAAGyOGAAAwOb+H0RYBPw0wSWHAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "\n", + "# Create a water network model\n", + "inp_file = './networks/Net0_CM.inp'\n", + "# inp_file = './networks/Net2LoopsDW.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 9\n", + "step = (0.5/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", + "\n", + "nqbit = 9\n", + "step = (50/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", + "pipe_diameters = [500, 1000]\n", + "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "P0, P1, P2, P3 = designer.matrices\n", + "num_heads = designer.wn.num_junctions\n", + "num_pipes = designer.wn.num_pipes\n", + "num_vars = num_heads + num_pipes\n", + "\n", + "p0 = P0[:num_vars].reshape(\n", + " -1,\n", + ")\n", + "p1 = P1[:num_vars, :num_vars]\n", + "p3 = P3[:num_vars].sum(-1)[:, :num_vars, :num_vars]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 1.766, 98.425, 0. ])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p0" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [-0.244, 0. , 0. , 0. ],\n", + " [ 0. , -0.006, 0. , 0. ]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params = np.array([0,0,0,0,1,0,0,1])\n", + "(params * P3).sum(-1)[:, :num_vars, :num_vars].sum(-1)[:num_vars]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 0, 1, 0] [ 1.766 1.766 97.666 96.906]\n" + ] + } + ], + "source": [ + "designer.compute_classical_solution([1,0,1,0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index 87f040f..188ff12 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -1,28 +1,33 @@ -from typing import List import itertools +from typing import List, Tuple import numpy as np import sparse from quantum_newton_raphson.newton_raphson import newton_raphson from qubops.encodings import BaseQbitEncoding +from qubops.encodings import PositiveQbitEncoding from qubops.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector from qubops.qubops_mixed_vars import QUBOPS_MIXED from qubops.solution_vector import SolutionVector_V2 as SolutionVector -from wntr.sim import aml -from wntr.sim.models import constants -from wntr.sim.models import constraint -from wntr.sim.models import param -from wntr.sim.models import var -from wntr.sim.models.utils import ModelUpdater from wntr.epanet.util import FlowUnits from wntr.epanet.util import HydParam from wntr.epanet.util import from_si from wntr.epanet.util import to_si from wntr.network import WaterNetworkModel +from wntr.sim import aml from wntr.sim.aml import Model +from wntr.sim.models import constants +from wntr.sim.models import constraint +from wntr.sim.models import param +from wntr.sim.models import var +from wntr.sim.models.utils import ModelUpdater from wntr.sim.solvers import SolverStatus -from ..models.chezy_manning import get_chezy_manning_matrix -from ..models.darcy_weisbach import get_darcy_weisbach_matrix -from ..models.mass_balance import get_mass_balance_matrix +from ..sim.hydraulics import create_hydraulic_model +from ..sim.models.chezy_manning import cm_resistance_value +from ..sim.models.chezy_manning import get_pipe_design_chezy_manning_matrix +from ..sim.models.darcy_weisbach import darcy_weisbach_constants +from ..sim.models.darcy_weisbach import dw_resistance_value +from ..sim.models.darcy_weisbach import get_pipe_design_darcy_weisbach_matrix +from ..sim.models.mass_balance import get_mass_balance_matrix class QUBODesignPipeDiameter(object): @@ -45,72 +50,198 @@ def __init__( pipe_diameters (List): List of pipe diameters in SI weight_cost (float, optional): weight for the cost optimization. Defaults to 1e-1. """ + # water network self.wn = wn + + # pipe diameters (converts to meter) + self.pipe_diameters = [p / 1000 for p in pipe_diameters] + self.num_diameters = len(pipe_diameters) + + # encodings of the head/flow variables self.flow_encoding = flow_encoding self.head_encoding = head_encoding self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) self.sol_vect_heads = SolutionVector(wn.num_junctions, encoding=head_encoding) - self.pipe_diameters = pipe_diameters - self.roughness_factor = self.get_roughness_factor() - - self.m, self.model_updater = self.create_cm_model() + # one hot encoding for the pipe coefficients + self.num_hot_encoding = wn.num_pipes * self.num_diameters + self.pipe_encoding = PositiveQbitEncoding(1, "x_", offset=0, step=1) + self.sol_vect_pipes = SolutionVector(self.num_hot_encoding, self.pipe_encoding) - self.sol_vect_res = self.get_resistance_prefactor_encoding() + # mixed solution vector self.mixed_solution_vector = MixedSolutionVector( - [self.sol_vect_flows, self.sol_vect_heads, self.sol_vect_res] + [self.sol_vect_flows, self.sol_vect_heads, self.sol_vect_pipes] ) + # basic hydraulic model + self.model, self.model_updater = create_hydraulic_model(wn) + + # valies of the pipe diameters/coefficients + self.get_pipe_data() + self.weight_cost = weight_cost self.head_lb = 10 self.head_hb = 20 self.matrices = self.initialize_matrices() - def get_roughness_factor(self): - """_summary_. - - Raises: - ValueError: _description_ + def get_dw_pipe_coefficients(self, link): + """Get the pipe coefficients for a specific link with DW. - Returns: - _type_: _description_ + Args: + link (_type_): _description_ """ - index_over = self.wn.pipe_name_list - roughness_factors = [] - for link_name in index_over: - link = self.wn.get_link(link_name) - roughness_factors.append(link.roughness) + values = [] + for diam in self.pipe_diameters: - if len(set(roughness_factors)) > 1: - raise ValueError( - "works only with all pipes having the same roughness sorry" + # convert values from SI to epanet internal + roughness_us = 0.001 * from_si( + FlowUnits.CFS, link.roughness, HydParam.Length ) - else: - return roughness_factors[0] + diameter_us = from_si(FlowUnits.CFS, diam, HydParam.Length) + length_us = from_si(FlowUnits.CFS, link.length, HydParam.Length) + + # compute the resistance value fit coefficients + values.append( + dw_resistance_value( + self.model.dw_k, + roughness_us, + diameter_us, + self.model.dw_diameter_exp, + length_us, + ) + ) + return values - def get_resistance_prefactor_encoding(self): - """_summary_.""" - values = np.array( - [ - cm_resistance_prefactor( - self.m.cm_k, - self.roughness_factor, - self.m.cm_exp, - d, - self.m.cm_diameter_exp, + def get_cm_pipe_coefficients(self, link): + """Get the pipe coefficients for a specific link with CM. + + Args: + link (_type_): _description_ + """ + values = [] + for diam in self.pipe_diameters: + + # convert values from SI to epanet internal + roughness_us = link.roughness + diameter_us = from_si(FlowUnits.CFS, diam, HydParam.Length) + length_us = from_si(FlowUnits.CFS, link.length, HydParam.Length) + + # compute the resistance value fit coefficients + values.append( + cm_resistance_value( + self.model.cm_k, + roughness_us, + self.model.cm_roughness_exp, + diameter_us, + self.model.cm_diameter_exp, + length_us, ) - for d in self.pipe_diameters - ] - ) - values.sort() - nqbit = int(np.ceil(np.log2(len(values)))) - enc = DiscreteValuesEncoding(values, nqbit, "cm_res") - return SolutionVector(size=self.wn.num_pipes, encoding=enc) + ) + return values + + def get_pipe_prices(self, link): + """Get the price of the pipe for the different diameters. + + Args: + link (wn.link): pipe info + """ + + def _compute_price(diameter, length): + """Price model of the pipe. + + Args: + diameter (float): diameter + length (float): length + + Returns: + float: price + """ + return np.pi * diameter * length / 1e5 + + prices = [] + for diam in self.pipe_diameters: + + # convert values from SI to epanet internal + diameter_us = from_si(FlowUnits.CFS, diam, HydParam.Length) + length_us = from_si(FlowUnits.CFS, link.length, HydParam.Length) + + # compute the price + prices.append(_compute_price(diameter_us, length_us)) + + return prices + + def get_pipe_data(self): + """Get the parameters of the AML model related to each pipe. + + Returns: + Dict: possible pipe coefficients for each coefficients + """ + if not hasattr(self.model, "pipe_coefficients"): + self.model.pipe_coefficients = aml.ParamDict() + + if not hasattr(self.model, "pipe_coefficients_indices"): + self.model.pipe_coefficients_indices = aml.ParamDict() + + if not hasattr(self.model, "pipe_prices"): + self.model.pipe_prices = aml.ParamDict() + + # select model + if self.wn.options.hydraulic.headloss == "C-M": + get_pipe_coeff_values = self.get_cm_pipe_coefficients + + elif self.wn.options.hydraulic.headloss == "D-W": + get_pipe_coeff_values = self.get_dw_pipe_coefficients + + # loop over pipes + idx_start = 0 + for link_name in self.wn.pipe_name_list: + + # get the link + link = self.wn.get_link(link_name) + + # compute the pipe coeffcient values + pipe_coeffs_values = get_pipe_coeff_values(link) + if link_name in self.model.pipe_coefficients: + self.model.pipe_coefficients[link_name].value = pipe_coeffs_values + else: + self.model.pipe_coefficients[link_name] = aml.Param(pipe_coeffs_values) + + # compute the pipe price + prices = self.get_pipe_prices(link) + if link_name in self.model.pipe_prices: + self.model.pipe_prices[link_name].value = prices + else: + self.model.pipe_prices[link_name] = aml.Param(prices) + + # compute the indices + idx_end = idx_start + len(pipe_coeffs_values) + indices = list(range(idx_start, idx_end)) + if link_name in self.model.pipe_coefficients_indices: + self.model.pipe_coefficients_indices[link_name].value = indices + else: + self.model.pipe_coefficients_indices[link_name] = aml.Param(indices) + idx_start = len(pipe_coeffs_values) + + def verify_encoding(self): + """Print info regarding the encodings.""" + hres = self.head_encoding.get_average_precision() + hvalues = np.sort(self.head_encoding.get_possible_values()) + fres = self.flow_encoding.get_average_precision() + fvalues = np.sort(self.flow_encoding.get_possible_values()) + print("Head Encoding : %f => %f (res: %f)" % (hvalues[0], hvalues[-1], hres)) + print("Flow Encoding : %f => %f (res: %f)" % (fvalues[0], fvalues[-1], fres)) def verify_solution(self, input, params): - """generates the classical solution.""" + """Computes the rhs vector associate with the input. + + Args: + input (np.ndarray): proposed solution + params (np.ndarray): parameters of the model + Returns: + np.ndarray: RHS vector + """ P0, P1, P2, P3 = self.matrices num_heads = self.wn.num_junctions num_pipes = self.wn.num_pipes @@ -125,8 +256,55 @@ def verify_solution(self, input, params): return p0 + p1 @ input + parameters * (p3 @ (input * input)) def enumerates_classical_solutions(self): - """generates the classical solution.""" + """Generates the classical solution.""" + encoding = [] + for idiam in range(self.num_diameters): + tmp = [0] * self.num_diameters + tmp[idiam] = 1 + encoding.append(tmp) + + pipe_prices = [] + for link_name in self.wn.pipe_name_list: + pipe_prices += self.model.pipe_prices[link_name].value + + for params in itertools.product(encoding, repeat=self.wn.num_pipes): + pvalues = [] + pdiam = [] + for p in params: + pvalues += p + _diam = (self.pipe_diameters * np.array(p)).sum() + pdiam.append(_diam * 1000) + price = (np.array(pipe_prices) * np.array(pvalues)).sum() + sol = self.compute_classical_solution(pvalues) + print(price, pdiam, sol) + + def convert_solution_to_si(self, solution: np.ndarray) -> np.ndarray: + """Converts the solution to SI. + Args: + solution (array): solution vectors in US units + + Returns: + Tuple: solution in SI + """ + num_heads = self.wn.num_junctions + num_pipes = self.wn.num_pipes + new_sol = np.zeros_like(solution) + for ip in range(num_pipes): + new_sol[ip] = to_si(FlowUnits.CFS, solution[ip], HydParam.Flow) + for ih in range(num_pipes, num_pipes + num_heads): + new_sol[ih] = to_si(FlowUnits.CFS, solution[ih], HydParam.Length) + return new_sol + + def compute_classical_solution(self, parameters): + """Computes the classical solution for a values of the hot encoding parameters. + + Args: + parameters (List): list of the one hot encoding values e.g. [1,0,1,0] + + Returns: + np.mdarray : solution + """ P0, P1, P2, P3 = self.matrices num_heads = self.wn.num_junctions num_pipes = self.wn.num_pipes @@ -136,133 +314,43 @@ def enumerates_classical_solutions(self): -1, ) p1 = P1[:num_vars, :num_vars] - p3 = P3[:num_vars].sum(-1)[:, :num_vars, :num_vars].sum(-1) + params = np.array([0] * num_vars + parameters) + p3 = (params * P3).sum(-1)[:, :num_vars, :num_vars].sum(-1)[:num_vars] def func(input): - return p0 + p1 @ input + parameters * (p3 @ (input * input)) - - # res_prefactor = np.array( - # [ - # cm_resistance_prefactor( - # self.m.cm_k, - # self.roughness_factor, - # self.m.cm_exp, - # d, - # self.m.cm_diameter_exp, - # ) - # for d in self.pipe_diameters - # ] - # ) - # res_prefactor.sort() - - res_prefactor = self.sol_vect_res.encoded_reals[0].get_possible_values() - prefactor_combinations = itertools.product( - res_prefactor, repeat=self.wn.num_pipes - ) - for prefacs in prefactor_combinations: - - parameters = np.array([0] * num_heads + list(prefacs)) - initial_point = np.random.rand(num_vars) - res = newton_raphson(func, initial_point) - assert np.allclose(func(res.solution), 0) - print(prefacs, res.solution) - - def create_cm_model(self): - """Create the aml. - - Args: - wn (_type_): _description_ - - Raises: - NotImplementedError: _description_ - NotImplementedError: _description_ - ValueError: _description_ - ValueError: _description_ - NotImplementedError: _description_ - NotImplementedError: _description_ - - Returns: - _type_: _description_ - """ - if self.wn.options.hydraulic.demand_model in ["PDD", "PDA"]: - raise ValueError("Pressure Driven simulations not supported") - if self.wn.options.hydraulic.headloss not in ["C-M"]: - raise ValueError("Quantum Design only supported for C-M simulations") - - m = aml.Model() - model_updater = ModelUpdater() - - # Global constants - chezy_manning_constants(m) - constants.head_pump_constants(m) - constants.leak_constants(m) - constants.pdd_constants(m) - - param.source_head_param(m, self.wn) - param.expected_demand_param(m, self.wn) - - param.leak_coeff_param.build(m, self.wn, model_updater) - param.leak_area_param.build(m, self.wn, model_updater) - param.leak_poly_coeffs_param.build(m, self.wn, model_updater) - param.elevation_param.build(m, self.wn, model_updater) - - cm_resistance_param.build(m, self.wn, model_updater) - param.minor_loss_param.build(m, self.wn, model_updater) - param.tcv_resistance_param.build(m, self.wn, model_updater) - param.pump_power_param.build(m, self.wn, model_updater) - param.valve_setting_param.build(m, self.wn, model_updater) - - var.flow_var(m, self.wn) - var.head_var(m, self.wn) - var.leak_rate_var(m, self.wn) - - constraint.mass_balance_constraint.build(m, self.wn, model_updater) - - approx_chezy_manning_headloss_constraint.build(m, self.wn, model_updater) - - constraint.head_pump_headloss_constraint.build(m, self.wn, model_updater) - constraint.power_pump_headloss_constraint.build(m, self.wn, model_updater) - constraint.prv_headloss_constraint.build(m, self.wn, model_updater) - constraint.psv_headloss_constraint.build(m, self.wn, model_updater) - constraint.tcv_headloss_constraint.build(m, self.wn, model_updater) - constraint.fcv_headloss_constraint.build(m, self.wn, model_updater) - if len(self.wn.pbv_name_list) > 0: - raise NotImplementedError( - "PBV valves are not currently supported in the WNTRSimulator" - ) - if len(self.wn.gpv_name_list) > 0: - raise NotImplementedError( - "GPV valves are not currently supported in the WNTRSimulator" - ) - constraint.leak_constraint.build(m, self.wn, model_updater) + return p0 + p1 @ input + (p3 @ (input * input)) - # TODO: Document that changing a curve with controls does not do anything; you have to change the pump_curve_name attribute on the pump - - return m, model_updater + initial_point = np.random.rand(num_vars) + res = newton_raphson(func, initial_point) + assert np.allclose(func(res.solution), 0) + return self.convert_solution_to_si(res.solution) def get_cost_matrix(self, matrices): - """_summary_. + """Add the equation that ar sued to maximize the pipe coefficiens and therefore minimize the diameter. Args: - matrices (_type_): _description_ + matrices (tuple): The matrices """ P0, P1, P2, P3 = matrices - n = self.sol_vect_res.size - max_val = self.sol_vect_res.encoded_reals[0].get_max_value() - P0[-1] += self.weight_cost * n * max_val + # loop over all the pipe coeffs istart = self.sol_vect_flows.size + self.sol_vect_heads.size - for i in range(self.sol_vect_res.size): - P1[-1, istart + i] = -self.weight_cost + index_over = self.wn.pipe_name_list + + # loop over all the pipe coeffs + for link_name in index_over: + for pipe_cost, pipe_idx in zip( + self.model.pipe_prices[link_name].value, + self.model.pipe_coefficients_indices[link_name].value, + ): + P1[-1, istart + pipe_idx] = self.weight_cost * pipe_cost return P0, P1, P2, P3 - def initialize_matrices(self): + def initialize_matrices(self) -> Tuple: """_summary_.""" - num_equations = len(list(self.m.cons())) + 1 - num_continuous_variables = len(list(self.m.vars())) - num_discrete_variables = len(self.m.cm_resistance) - - num_variables = num_continuous_variables + num_discrete_variables + num_equations = len(list(self.model.cons())) + 1 # +1 for cost equation + num_continuous_variables = len(list(self.model.vars())) + num_variables = num_continuous_variables + self.num_hot_encoding # must transform that to coo P0 = np.zeros((num_equations, 1)) @@ -271,8 +359,22 @@ def initialize_matrices(self): P3 = np.zeros((num_equations, num_variables, num_variables, num_variables)) matrices = (P0, P1, P2, P3) - matrices = get_mass_balance_constraint_design(self.m, self.wn, matrices) - matrices = get_chezy_manning_matrix_design(self.m, self.wn, matrices) + (P0, P1, P2) = get_mass_balance_matrix( + self.model, self.wn, (P0, P1, P2), convert_to_us_unit=True + ) + + # get the headloss matrix contributions + if self.wn.options.hydraulic.headloss == "C-M": + matrices = get_pipe_design_chezy_manning_matrix( + self.model, self.wn, matrices + ) + elif self.wn.options.hydraulic.headloss == "D-W": + matrices = get_pipe_design_darcy_weisbach_matrix( + self.model, self.wn, matrices + ) + else: + raise ValueError("Calculation only possible with C-M or D-W") + matrices = self.get_cost_matrix(matrices) return matrices diff --git a/wntr_quantum/sim/models/chezy_manning.py b/wntr_quantum/sim/models/chezy_manning.py index 01a60cd..aa9a350 100644 --- a/wntr_quantum/sim/models/chezy_manning.py +++ b/wntr_quantum/sim/models/chezy_manning.py @@ -63,7 +63,7 @@ def build(cls, m, wn, updater, index_over=None): # noqa: D417 index_over: list of str list of pipe names """ - if not hasattr(m, "hw_resistance"): + if not hasattr(m, "cm_resistance"): m.cm_resistance = aml.ParamDict() if index_over is None: @@ -73,9 +73,6 @@ def build(cls, m, wn, updater, index_over=None): # noqa: D417 link = wn.get_link(link_name) # convert values from SI to epanet internal - # roughness_us = 0.001 * from_si( - # FlowUnits.CFS, link.roughness, HydParam.Length - # ) roughness_us = link.roughness diameter_us = from_si(FlowUnits.CFS, link.diameter, HydParam.Length) length_us = from_si(FlowUnits.CFS, link.length, HydParam.Length) @@ -208,7 +205,7 @@ def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 return (P0, P1, P2) -def get_chezy_manning_matrix_design(m, wn, matrices): # noqa: D417 +def get_pipe_design_chezy_manning_matrix(m, wn, matrices): # noqa: D417 """Adds a mass balance to the model for the specified junctions. Parameters @@ -222,9 +219,9 @@ def get_chezy_manning_matrix_design(m, wn, matrices): # noqa: D417 P0, P1, P2, P3 = matrices continuous_var_name = [v.name for v in list(m.vars())] - discrete_var_name = [v.name for k, v in m.cm_resistance.items()] - - var_names = continuous_var_name + discrete_var_name + num_continuous_var = len(continuous_var_name) + # discrete_var_name = [v.name for k, v in m.cm_resistance.items()] + var_names = continuous_var_name # + discrete_var_name index_over = wn.pipe_name_list @@ -247,7 +244,7 @@ def get_chezy_manning_matrix_design(m, wn, matrices): # noqa: D417 P1[ieq, start_node_index] = 1 else: start_h = m.source_head[start_node_name] - P0[ieq, 0] += start_h.value + P0[ieq, 0] += from_si(FlowUnits.CFS, start_h.value, HydParam.Length) if isinstance(end_node, wntr.network.Junction): end_h = m.head[end_node_name] @@ -255,11 +252,12 @@ def get_chezy_manning_matrix_design(m, wn, matrices): # noqa: D417 P1[ieq, end_node_index] = -1 else: end_h = m.source_head[end_node_name] - P0[ieq, 0] -= end_h.value - - k = m.cm_resistance[link_name] - cm_res_index = var_names.index(k.name) + P0[ieq, 0] -= from_si(FlowUnits.CFS, end_h.value, HydParam.Length) - P3[ieq, flow_index, flow_index, cm_res_index] = -link.length + for pipe_coefs, pipe_idx in zip( + m.pipe_coefficients[link_name].value, + m.pipe_coefficients_indices[link_name].value, + ): + P3[ieq, flow_index, flow_index, pipe_idx + num_continuous_var] = -pipe_coefs return (P0, P1, P2, P3) diff --git a/wntr_quantum/sim/models/darcy_weisbach.py b/wntr_quantum/sim/models/darcy_weisbach.py index 5191df4..44ddbbd 100644 --- a/wntr_quantum/sim/models/darcy_weisbach.py +++ b/wntr_quantum/sim/models/darcy_weisbach.py @@ -235,3 +235,7 @@ def get_darcy_weisbach_matrix(m, wn, matrices): # noqa: D417 P2[ieq, flow_index, flow_index] -= scaling * k2.value return (P0, P1, P2) + + +def get_pipe_design_darcy_weisbach_matrix(m, wn, matrices): + raise NotImplementedError("Not yet") From 5610919c56062163c06d7a32e05ea7299dc26678 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 6 Sep 2024 17:54:41 +0200 Subject: [PATCH 41/96] designer works quantumly --- docs/notebooks/design_pipe_diameter.ipynb | 111 ++++++++--- docs/notebooks/networks/Net0_CM_simple.inp | 128 ++++++++++++ docs/notebooks/qubo_poly_solver_CM.ipynb | 66 ++++++- wntr_quantum/design/qubo_pipe_diam.py | 183 ++++++++++++++---- .../sim/solvers/qubo_polynomial_solver.py | 2 +- 5 files changed, 413 insertions(+), 77 deletions(-) create mode 100644 docs/notebooks/networks/Net0_CM_simple.inp diff --git a/docs/notebooks/design_pipe_diameter.ipynb b/docs/notebooks/design_pipe_diameter.ipynb index 8c75ab9..0de8a53 100644 --- a/docs/notebooks/design_pipe_diameter.ipynb +++ b/docs/notebooks/design_pipe_diameter.ipynb @@ -50,13 +50,13 @@ "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", - "nqbit = 9\n", + "nqbit = 7\n", "step = (0.5/(2**nqbit-1))\n", "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", "\n", - "nqbit = 9\n", - "step = (50/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")" + "nqbit = 7\n", + "step = (5/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+95.0, var_base_name=\"x\")" ] }, { @@ -67,25 +67,25 @@ "source": [ "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", "pipe_diameters = [500, 1000]\n", - "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters)" + "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters, head_lower_bound=97)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 95.000000 => 100.000000 (res: 0.039370)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.003937)\n" + ] + } + ], "source": [ - "P0, P1, P2, P3 = designer.matrices\n", - "num_heads = designer.wn.num_junctions\n", - "num_pipes = designer.wn.num_pipes\n", - "num_vars = num_heads + num_pipes\n", - "\n", - "p0 = P0[:num_vars].reshape(\n", - " -1,\n", - ")\n", - "p1 = P1[:num_vars, :num_vars]\n", - "p3 = P3[:num_vars].sum(-1)[:, :num_vars, :num_vars]" + "designer.verify_encoding()" ] }, { @@ -93,10 +93,18 @@ "execution_count": 5, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, { "data": { "text/plain": [ - "array([ 0. , 1.766, 98.425, 0. ])" + "array([ 1.766, 1.766, 97.666, 96.906])" ] }, "execution_count": 5, @@ -105,48 +113,87 @@ } ], "source": [ - "p0" + "designer.compute_classical_solution([1,0,1,0], convert_to_si=False)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "price \t diameters \t variables\n", + "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906]\n", + "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647]\n", + "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647]\n", + "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387]\n" + ] + } + ], + "source": [ + "designer.enumerates_classical_solutions(convert_to_si=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.622047244094488, 1.700787401574803, 97.91338582677164, 97.95275590551181]\n" + ] + } + ], + "source": [ + "from dwave.samplers import SimulatedAnnealingSampler\n", + "options = {'sampler': SimulatedAnnealingSampler()}\n", + "status = designer.solve(strength=1E5, num_reads=10000, options=options)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [-0.244, 0. , 0. , 0. ],\n", - " [ 0. , -0.006, 0. , 0. ]])" + "0.5072373283355087" ] }, - "execution_count": 6, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "params = np.array([0,0,0,0,1,0,0,1])\n", - "(params * P3).sum(-1)[:, :num_vars, :num_vars].sum(-1)[:num_vars]" + "designer.total_pice" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 0, 1, 0] [ 1.766 1.766 97.666 96.906]\n" - ] + "data": { + "text/plain": [ + "array([ 500., 1000.])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "designer.compute_classical_solution([1,0,1,0])" + "designer.optimal_diameters" ] }, { diff --git a/docs/notebooks/networks/Net0_CM_simple.inp b/docs/notebooks/networks/Net0_CM_simple.inp new file mode 100644 index 0000000..25f9e25 --- /dev/null +++ b/docs/notebooks/networks/Net0_CM_simple.inp @@ -0,0 +1,128 @@ +[TITLE] +File obtained via Mario of a 2 node sysem + + +[JUNCTIONS] +;ID Elev Demand Pattern + J1 0 0 ; + D1 0 50 ; + +[RESERVOIRS] +;ID Head Pattern + R1 30 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + P1 R1 J1 1000 1000 0.015 0 Open ; + P2 J1 D1 1000 1000 0.015 0 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + +[RULES] + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 1 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 1:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic None + +[REPORT] + Status No + Summary No + Page 0 + +[OPTIONS] + Units LPS + Headloss C-M + Specific Gravity 1 + Viscosity 1 + Trials 40 + Accuracy 0.1 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality None mg/L + Diffusivity 1 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +J1 10.00000 60.00000 +D1 110.00000 60.00000 +R1 -11.72214 74.24023 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 0.000 0.000 10000.000 10000.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/docs/notebooks/qubo_poly_solver_CM.ipynb b/docs/notebooks/qubo_poly_solver_CM.ipynb index 0402b27..8abb43f 100644 --- a/docs/notebooks/qubo_poly_solver_CM.ipynb +++ b/docs/notebooks/qubo_poly_solver_CM.ipynb @@ -242,6 +242,52 @@ "ref_sol / ref_values" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[ 0. ],\n", + " [ 1.766],\n", + " [98.425],\n", + " [ 0. ]]),\n", + " array([[-1., 1., 0., 0.],\n", + " [ 0., -1., 0., 0.],\n", + " [ 0., 0., -1., 0.],\n", + " [ 0., 0., 1., -1.]]),\n", + " array([[[ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 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", 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", 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" ] @@ -290,27 +336,27 @@ "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", "\n", "\n", - "Error (%): [-7.892 -4.069 -0.029 -0.048]\n", + "Error (%): [-5.676 -1.963 -0.029 -0.147]\n", "\n", "\n", - "sol : [ 1.905 1.838 98.434 98.434]\n", + "sol : [ 1.866 1.8 98.434 98.532]\n", "ref : [ 1.766 1.766 98.406 98.387]\n", - "diff: [-0.139 -0.072 -0.028 -0.047]\n", + "diff: [-0.1 -0.035 -0.028 -0.145]\n", "\n", "\n", - "encoded_sol: [ 1.905 1.838 98.434 98.434]\n", + "encoded_sol: [ 1.866 1.8 98.434 98.532]\n", "encoded_ref: [ 1.766 1.766 98.434 98.434]\n", - "diff : [-0.139 -0.071 0. 0. ]\n", + "diff : [-0.1 -0.034 0. -0.098]\n", "\n", "\n", - "E sol : -2343.739974221478\n", + "E sol : -2343.7308669783342\n", "R ref : -2343.749937932273\n", - "Delta E : 0.009963710795091174\n", + "Delta E : 0.01907095393880809\n", "\n", "\n", - "Residue sol : 0.10541453368914308\n", + "Residue sol : 0.14219517568484824\n", "Residue ref : 0.03388956865892264\n", - "Delta Residue : 0.07152496503022043\n" + "Delta Residue : 0.1083056070259256\n" ] } ], diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index 188ff12..3c3479b 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -1,5 +1,6 @@ import itertools -from typing import List, Tuple +from typing import List +from typing import Tuple import numpy as np import sparse from quantum_newton_raphson.newton_raphson import newton_raphson @@ -15,16 +16,10 @@ from wntr.network import WaterNetworkModel from wntr.sim import aml from wntr.sim.aml import Model -from wntr.sim.models import constants -from wntr.sim.models import constraint -from wntr.sim.models import param -from wntr.sim.models import var -from wntr.sim.models.utils import ModelUpdater from wntr.sim.solvers import SolverStatus from ..sim.hydraulics import create_hydraulic_model from ..sim.models.chezy_manning import cm_resistance_value from ..sim.models.chezy_manning import get_pipe_design_chezy_manning_matrix -from ..sim.models.darcy_weisbach import darcy_weisbach_constants from ..sim.models.darcy_weisbach import dw_resistance_value from ..sim.models.darcy_weisbach import get_pipe_design_darcy_weisbach_matrix from ..sim.models.mass_balance import get_mass_balance_matrix @@ -39,6 +34,7 @@ def __init__( flow_encoding: BaseQbitEncoding, head_encoding: BaseQbitEncoding, pipe_diameters: List, + head_lower_bound: float, weight_cost: float = 1e-1, ): # noqa: D417 """Initialize the designer object. @@ -48,6 +44,7 @@ def __init__( flow_encoding (BaseQbitEncoding): binary encoding for the flows head_encoding (BaseQbitEncoding): binary encoding for the heads pipe_diameters (List): List of pipe diameters in SI + head_lower_bound (float): minimum value for the head pressure values (US units) weight_cost (float, optional): weight for the cost optimization. Defaults to 1e-1. """ # water network @@ -63,6 +60,10 @@ def __init__( self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) self.sol_vect_heads = SolutionVector(wn.num_junctions, encoding=head_encoding) + # lower bound for the pressure + self.head_lower_bound = head_lower_bound + self.head_upper_bound = 10 * head_lower_bound + # one hot encoding for the pipe coefficients self.num_hot_encoding = wn.num_pipes * self.num_diameters self.pipe_encoding = PositiveQbitEncoding(1, "x_", offset=0, step=1) @@ -79,10 +80,10 @@ def __init__( # valies of the pipe diameters/coefficients self.get_pipe_data() + # weight for the cost equation self.weight_cost = weight_cost - self.head_lb = 10 - self.head_hb = 20 + # compute the polynomial matrices self.matrices = self.initialize_matrices() def get_dw_pipe_coefficients(self, link): @@ -255,7 +256,7 @@ def verify_solution(self, input, params): parameters = np.array([0] * num_heads + params) return p0 + p1 @ input + parameters * (p3 @ (input * input)) - def enumerates_classical_solutions(self): + def enumerates_classical_solutions(self, convert_to_si=True): """Generates the classical solution.""" encoding = [] for idiam in range(self.num_diameters): @@ -263,20 +264,14 @@ def enumerates_classical_solutions(self): tmp[idiam] = 1 encoding.append(tmp) - pipe_prices = [] - for link_name in self.wn.pipe_name_list: - pipe_prices += self.model.pipe_prices[link_name].value - + print("price \t diameters \t variables") for params in itertools.product(encoding, repeat=self.wn.num_pipes): pvalues = [] - pdiam = [] for p in params: pvalues += p - _diam = (self.pipe_diameters * np.array(p)).sum() - pdiam.append(_diam * 1000) - price = (np.array(pipe_prices) * np.array(pvalues)).sum() - sol = self.compute_classical_solution(pvalues) - print(price, pdiam, sol) + price, diameters = self.get_pipe_info_from_hot_encoding(pvalues) + sol = self.compute_classical_solution(pvalues, convert_to_si=convert_to_si) + print(price, diameters, sol) def convert_solution_to_si(self, solution: np.ndarray) -> np.ndarray: """Converts the solution to SI. @@ -296,11 +291,12 @@ def convert_solution_to_si(self, solution: np.ndarray) -> np.ndarray: new_sol[ih] = to_si(FlowUnits.CFS, solution[ih], HydParam.Length) return new_sol - def compute_classical_solution(self, parameters): + def compute_classical_solution(self, parameters, convert_to_si=True): """Computes the classical solution for a values of the hot encoding parameters. Args: parameters (List): list of the one hot encoding values e.g. [1,0,1,0] + convert_to_si (bool): convert to si Returns: np.mdarray : solution @@ -323,7 +319,9 @@ def func(input): initial_point = np.random.rand(num_vars) res = newton_raphson(func, initial_point) assert np.allclose(func(res.solution), 0) - return self.convert_solution_to_si(res.solution) + if convert_to_si: + return self.convert_solution_to_si(res.solution) + return res.solution def get_cost_matrix(self, matrices): """Add the equation that ar sued to maximize the pipe coefficiens and therefore minimize the diameter. @@ -379,27 +377,144 @@ def initialize_matrices(self) -> Tuple: return matrices - def solve(self, **options): - """_summary_""" - qubo = QUBO_POLY_MIXED(self.mixed_solution_vector, **options) + @staticmethod + def flatten_solution_vector(solution: Tuple) -> List: + """Flattens the solution vector. + + Args: + solution (tuple): tuple of ([flows], [heads]) + + Returns: + List: a flat list of all the variables + """ + sol_tmp = [] + for s in solution[:-1]: + sol_tmp += s + return sol_tmp, solution[-1] + + def get_pipe_info_from_hot_encoding(self, hot_encoding): + """_summary_. + + Args: + hot_encoding (_type_): _description_ + """ + hot_encoding = np.array(hot_encoding) + + pipe_prices = [] + for link_name in self.wn.pipe_name_list: + pipe_prices += self.model.pipe_prices[link_name].value + pipe_prices = np.array(pipe_prices) + total_price = (pipe_prices * hot_encoding).sum() + + pipe_diameters = 1000 * np.array(self.pipe_diameters * self.wn.num_pipes) + pipe_diameters = ( + (pipe_diameters * hot_encoding).reshape(-1, self.num_diameters).sum(-1) + ) + + return total_price, pipe_diameters + + @staticmethod + def load_data_in_model(model: Model, data: np.ndarray): + """Loads some data in the model. + + Args: + model (Model): AML model from WNTR + data (np.ndarray): data to load + """ + for iv, v in enumerate(model.vars()): + v.value = data[iv] + + @staticmethod + def extract_data_from_model(model: Model) -> np.ndarray: + """Loads some data in the model. + + Args: + model (Model): AML model from WNTR + + Returns: + np.ndarray: data extracted from model + """ + data = [] + for v in model.vars(): + data.append(v.value) + return data + + def solve( # noqa: D417 + self, strength: float = 1e6, num_reads: int = 10000, **options + ) -> Tuple: + """Solves the Hydraulics equations. + + Args: + strength (float, optional): substitution strength. Defaults to 1e6. + num_reads (int, optional): number of reads for the sampler. Defaults to 10000. + + Returns: + Tuple: Succes message + """ + self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, **options) matrices = tuple(sparse.COO(m) for m in self.matrices) - bqm = qubo.create_bqm(matrices, strength=1000) - # add constraint + # create the BQM + self.bqm = self.qubo.create_bqm(matrices, strength=strength) + + # add constraints on the hot encoding + istart = self.sol_vect_flows.size + self.sol_vect_heads.size + for i in range(self.sol_vect_flows.size): + + # create the expression [(x0, 1), (x1, 1), ...] + expr = [] + iend = istart + self.num_diameters + for ivar in range(istart, iend): + expr.append( + ( + self.mixed_solution_vector.encoded_reals[ivar] + .variables[0] + .name, + 1, + ) + ) + # add the constraints + self.bqm.add_linear_equality_constraint( + expr, lagrange_multiplier=strength, constant=-1 + ) + istart += self.num_diameters + + # add constraint on head pressures istart = self.sol_vect_flows.size for i in range(self.sol_vect_heads.size): - bqm.add_linear_inequality_constraint( - qubo.all_expr[istart + i], + self.bqm.add_linear_inequality_constraint( + self.qubo.all_expr[istart + i], lagrange_multiplier=1, label="head_%s" % i, - lb=self.head_lb, - ub=self.head_hb, + lb=self.head_lower_bound, + ub=self.head_upper_bound, ) # sample - sampleset = qubo.sample_bqm(bqm, num_reads=options["num_reads"]) + sampleset = self.qubo.sample_bqm(self.bqm, num_reads=num_reads) # decode - sol, param = qubo.decode_solution(sampleset.lowest()) - return sol, param + sol = self.qubo.decode_solution(sampleset.lowest().record[0][0]) + + # flatten + sol, hot_encoding = self.flatten_solution_vector(sol) + print(sol) + # convert back to SI + sol = self.convert_solution_to_si(sol) + + # load data in the AML model + self.model.set_structure() + self.load_data_in_model(self.model, sol) + + # get pipe info from one hot + self.total_pice, self.optimal_diameters = self.get_pipe_info_from_hot_encoding( + hot_encoding + ) + + # returns + return ( + SolverStatus.converged, + "Solved Successfully", + 0, + ) diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 0be0d08..a4fdac1 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -335,7 +335,7 @@ def qubo_poly_solve(self, strength=1e6, num_reads=10000, **options): # noqa: D4 # flatten solution sol = self.flatten_solution_vector(sol) - # convert back to SI if DW + # convert back to SI sol = self.convert_solution_to_si(sol) return sol From cb5befa4e968675d77473698c9094345948d4a04 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 6 Sep 2024 19:01:25 +0200 Subject: [PATCH 42/96] started with DW --- docs/notebooks/design_pipe_diameter.ipynb | 45 ++- docs/notebooks/design_pipe_diameter_DW.ipynb | 340 +++++++++++++++++++ wntr_quantum/design/qubo_pipe_diam.py | 4 +- wntr_quantum/sim/models/darcy_weisbach.py | 69 +++- 4 files changed, 426 insertions(+), 32 deletions(-) create mode 100644 docs/notebooks/design_pipe_diameter_DW.ipynb diff --git a/docs/notebooks/design_pipe_diameter.ipynb b/docs/notebooks/design_pipe_diameter.ipynb index 0de8a53..26b7346 100644 --- a/docs/notebooks/design_pipe_diameter.ipynb +++ b/docs/notebooks/design_pipe_diameter.ipynb @@ -61,18 +61,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", - "pipe_diameters = [500, 1000]\n", + "pipe_diameters = [250, 500, 1000]\n", "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters, head_lower_bound=97)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -90,35 +90,27 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 22, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, { "data": { "text/plain": [ - "array([ 1.766, 1.766, 97.666, 96.906])" + "array([ 1.766, 1.766, 67.877, 37.329])" ] }, - "execution_count": 5, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "designer.compute_classical_solution([1,0,1,0], convert_to_si=False)" + "designer.compute_classical_solution([1,0,0,1,0,0], convert_to_si=False)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -126,8 +118,13 @@ "output_type": "stream", "text": [ "price \t diameters \t variables\n", + "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329]\n", + "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118]\n", + "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858]\n", + "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118]\n", "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906]\n", "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647]\n", + "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858]\n", "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647]\n", "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387]\n" ] @@ -139,14 +136,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1.622047244094488, 1.700787401574803, 97.91338582677164, 97.95275590551181]\n" + "[1.7086614173228345, 1.7283464566929132, 97.79527559055119, 97.00787401574803]\n" ] } ], @@ -158,16 +155,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.5072373283355087" + "0.33815821889033915" ] }, - "execution_count": 11, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -178,16 +175,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 500., 1000.])" + "array([500., 500.])" ] }, - "execution_count": 12, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/notebooks/design_pipe_diameter_DW.ipynb b/docs/notebooks/design_pipe_diameter_DW.ipynb new file mode 100644 index 0000000..0593787 --- /dev/null +++ b/docs/notebooks/design_pipe_diameter_DW.ipynb @@ -0,0 +1,340 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "\n", + "# Create a water network model\n", + "inp_file = './networks/Net0.inp'\n", + "# inp_file = './networks/Net2LoopsDW.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + "name J1 D1 R1\n", + "0 26.476913 22.953829 -9.338379e-07\n", + "3600 26.476913 22.953829 -9.338379e-07" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.node['pressure']" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 7\n", + "step = (0.5/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", + "\n", + "nqbit = 7\n", + "step = (25/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+95.0, var_base_name=\"x\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", + "pipe_diameters = [250, 500, 1000]\n", + "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters, head_lower_bound=97)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 95.000000 => 120.000000 (res: 0.196850)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.003937)\n" + ] + } + ], + "source": [ + "designer.verify_encoding()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 32.911, 35.822])" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.compute_classical_solution([1,0,0,1,0,0], convert_to_si=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "price \t diameters \t variables\n", + "0.16907910944516957 [250. 250.] [ 1.766 1.766 107.975 117.525]\n", + "0.25361866416775436 [250. 500.] [ 1.766 1.766 107.975 108.238]\n", + "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 107.975 107.982]\n", + "0.25361866416775436 [500. 250.] [ 1.766 1.766 98.688 108.238]\n", + "0.33815821889033915 [500. 500.] [ 1.766 1.766 98.688 98.951]\n", + "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 98.688 98.696]\n", + "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.433 107.982]\n", + "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.433 98.696]\n", + "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.433 98.44 ]\n" + ] + } + ], + "source": [ + "designer.enumerates_classical_solutions(convert_to_si=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:759: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", + " warnings.warn(\"For constraints with fractional coefficients, \"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.9685039370078738, 1.8740157480314958, 107.99212598425197, 107.99212598425197]\n" + ] + } + ], + "source": [ + "from dwave.samplers import SimulatedAnnealingSampler\n", + "from dwave.samplers import SteepestDescentSampler\n", + "options = {'sampler': SimulatedAnnealingSampler()}\n", + "status = designer.solve(strength=1E6, num_reads=10000, options=options)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.25361866416775436" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.total_pice" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([250., 500.])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.optimal_diameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index 3c3479b..c580baa 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -21,7 +21,7 @@ from ..sim.models.chezy_manning import cm_resistance_value from ..sim.models.chezy_manning import get_pipe_design_chezy_manning_matrix from ..sim.models.darcy_weisbach import dw_resistance_value -from ..sim.models.darcy_weisbach import get_pipe_design_darcy_weisbach_matrix +from ..sim.models.darcy_weisbach import get_pipe_design_darcy_wesibach_matrix from ..sim.models.mass_balance import get_mass_balance_matrix @@ -367,7 +367,7 @@ def initialize_matrices(self) -> Tuple: self.model, self.wn, matrices ) elif self.wn.options.hydraulic.headloss == "D-W": - matrices = get_pipe_design_darcy_weisbach_matrix( + matrices = get_pipe_design_darcy_wesibach_matrix( self.model, self.wn, matrices ) else: diff --git a/wntr_quantum/sim/models/darcy_weisbach.py b/wntr_quantum/sim/models/darcy_weisbach.py index 44ddbbd..504c0d6 100644 --- a/wntr_quantum/sim/models/darcy_weisbach.py +++ b/wntr_quantum/sim/models/darcy_weisbach.py @@ -229,13 +229,70 @@ def get_darcy_weisbach_matrix(m, wn, matrices): # noqa: D417 k2 = m.dw_resistance_2[link_name] # print(k0.value, k1.value, k2.value) - scaling = 1.0 - P0[ieq] -= scaling * k0.value - P1[ieq, flow_index] -= scaling * k1.value - P2[ieq, flow_index, flow_index] -= scaling * k2.value + P0[ieq] -= k0.value + P1[ieq, flow_index] -= k1.value + P2[ieq, flow_index, flow_index] -= k2.value return (P0, P1, P2) -def get_pipe_design_darcy_weisbach_matrix(m, wn, matrices): - raise NotImplementedError("Not yet") +def get_pipe_design_darcy_wesibach_matrix(m, wn, matrices): # noqa: D417 + """Adds a mass balance to the model for the specified junctions. + + Parameters + ---------- + m: wntr.aml.aml.aml.Model + wn: wntr.network.model.WaterNetworkModel + updater: ModelUpdater + index_over: list of str + list of pipe names; default is all pipes in wn + """ + P0, P1, P2, P3 = matrices + + continuous_var_name = [v.name for v in list(m.vars())] + num_continuous_var = len(continuous_var_name) + # discrete_var_name = [v.name for k, v in m.cm_resistance.items()] + var_names = continuous_var_name # + discrete_var_name + + index_over = wn.pipe_name_list + + for ieq0, link_name in enumerate(index_over): + + ieq = ieq0 + len(wn.junction_name_list) + link = wn.get_link(link_name) + f = m.flow[link_name] + flow_index = var_names.index(f.name) + + start_node_name = link.start_node_name + end_node_name = link.end_node_name + + start_node = wn.get_node(start_node_name) + end_node = wn.get_node(end_node_name) + + if isinstance(start_node, wntr.network.Junction): + start_h = m.head[start_node_name] + start_node_index = var_names.index(start_h.name) + P1[ieq, start_node_index] = 1 + else: + start_h = m.source_head[start_node_name] + P0[ieq, 0] += from_si(FlowUnits.CFS, start_h.value, HydParam.Length) + + if isinstance(end_node, wntr.network.Junction): + end_h = m.head[end_node_name] + end_node_index = var_names.index(end_h.name) + P1[ieq, end_node_index] = -1 + else: + end_h = m.source_head[end_node_name] + P0[ieq, 0] -= from_si(FlowUnits.CFS, end_h.value, HydParam.Length) + + for pipe_coefs, pipe_idx in zip( + m.pipe_coefficients[link_name].value, + m.pipe_coefficients_indices[link_name].value, + ): + P1[ieq, pipe_idx + num_continuous_var] -= pipe_coefs[0] + P2[ieq, flow_index, pipe_idx + num_continuous_var] -= pipe_coefs[1] + P3[ + ieq, flow_index, flow_index, pipe_idx + num_continuous_var + ] -= -pipe_coefs[2] + + return (P0, P1, P2, P3) From 0f505c3fe6bdb541de8676ced6e67546bf5f9798 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 9 Sep 2024 14:01:15 +0200 Subject: [PATCH 43/96] fix designer in DW mode --- docs/notebooks/design_pipe_diameter_DW.ipynb | 65 +++++++++----------- wntr_quantum/design/qubo_pipe_diam.py | 28 ++++++--- wntr_quantum/sim/models/darcy_weisbach.py | 2 +- 3 files changed, 51 insertions(+), 44 deletions(-) diff --git a/docs/notebooks/design_pipe_diameter_DW.ipynb b/docs/notebooks/design_pipe_diameter_DW.ipynb index 0593787..427e1fa 100644 --- a/docs/notebooks/design_pipe_diameter_DW.ipynb +++ b/docs/notebooks/design_pipe_diameter_DW.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -61,7 +61,7 @@ "" ] }, - "execution_count": 57, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -129,7 +129,7 @@ "3600 26.476913 22.953829 -9.338379e-07" ] }, - "execution_count": 61, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -159,18 +159,18 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", "pipe_diameters = [250, 500, 1000]\n", - "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters, head_lower_bound=97)" + "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters, head_lower_bound=80)" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -202,10 +202,10 @@ { "data": { "text/plain": [ - "array([ 0.05 , 0.05 , 32.911, 35.822])" + "array([ 0.05 , 0.05 , 26.456, 22.911])" ] }, - "execution_count": 58, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -216,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -224,15 +224,15 @@ "output_type": "stream", "text": [ "price \t diameters \t variables\n", - "0.16907910944516957 [250. 250.] [ 1.766 1.766 107.975 117.525]\n", - "0.25361866416775436 [250. 500.] [ 1.766 1.766 107.975 108.238]\n", - "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 107.975 107.982]\n", - "0.25361866416775436 [500. 250.] [ 1.766 1.766 98.688 108.238]\n", - "0.33815821889033915 [500. 500.] [ 1.766 1.766 98.688 98.951]\n", - "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 98.688 98.696]\n", - "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.433 107.982]\n", - "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.433 98.696]\n", - "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.433 98.44 ]\n" + "0.16907910944516957 [250. 250.] [ 1.766 1.766 84.718 71.01 ]\n", + "0.25361866416775436 [250. 500.] [ 1.766 1.766 82.639 78.088]\n", + "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 80.56 76.258]\n", + "0.25361866416775436 [500. 250.] [ 1.766 1.766 89.717 73.794]\n", + "0.33815821889033915 [500. 500.] [ 1.766 1.766 89.587 82.821]\n", + "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 89.457 82.94 ]\n", + "0.42269777361292393 [1000. 250.] [ 1.766 1.766 89.706 71.568]\n", + "0.5072373283355087 [1000. 500.] [ 1.766 1.766 89.699 80.718]\n", + "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 89.693 80.96 ]\n" ] } ], @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -257,7 +257,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1.9685039370078738, 1.8740157480314958, 107.99212598425197, 107.99212598425197]\n" + "[1.68503937007874, 1.622047244094488, 96.37795275590551, 95.0]\n" ] } ], @@ -270,16 +270,16 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.25361866416775436" + "0.6763164377806783" ] }, - "execution_count": 54, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -290,16 +290,16 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([250., 500.])" + "array([1000., 1000.])" ] }, - "execution_count": 55, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -307,13 +307,6 @@ "source": [ "designer.optimal_diameters" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index c580baa..649a0b8 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -306,15 +306,29 @@ def compute_classical_solution(self, parameters, convert_to_si=True): num_pipes = self.wn.num_pipes num_vars = num_heads + num_pipes - p0 = P0[:num_vars].reshape( - -1, - ) - p1 = P1[:num_vars, :num_vars] - params = np.array([0] * num_vars + parameters) - p3 = (params * P3).sum(-1)[:, :num_vars, :num_vars].sum(-1)[:num_vars] + if self.wn.options.hydraulic.headloss == "C-M": + p0 = P0[:num_vars].reshape( + -1, + ) + p1 = P1[:num_vars, :num_vars] + params = np.array([0] * num_vars + parameters) + p2 = (params * P3).sum(-1)[:, :num_vars, :num_vars].sum(-1)[:num_vars] + + elif self.wn.options.hydraulic.headloss == "D-W": + p0 = P0[:num_vars].reshape( + -1, + ) + p0 += (parameters * P1[:num_vars, num_vars:]).sum(-1) + + p1 = P1[:num_vars, :num_vars] + p1 += (parameters * P2[:num_vars, :num_vars, num_vars:]).sum(-1) + + params = np.array([0] * num_vars + parameters) + p2 = (params * P3).sum(-1)[:, :num_vars, :num_vars].sum(-1)[:num_vars] + # print(p0, p1, p2) def func(input): - return p0 + p1 @ input + (p3 @ (input * input)) + return p0 + p1 @ input + (p2 @ (input * input)) initial_point = np.random.rand(num_vars) res = newton_raphson(func, initial_point) diff --git a/wntr_quantum/sim/models/darcy_weisbach.py b/wntr_quantum/sim/models/darcy_weisbach.py index 504c0d6..58a2a8b 100644 --- a/wntr_quantum/sim/models/darcy_weisbach.py +++ b/wntr_quantum/sim/models/darcy_weisbach.py @@ -293,6 +293,6 @@ def get_pipe_design_darcy_wesibach_matrix(m, wn, matrices): # noqa: D417 P2[ieq, flow_index, pipe_idx + num_continuous_var] -= pipe_coefs[1] P3[ ieq, flow_index, flow_index, pipe_idx + num_continuous_var - ] -= -pipe_coefs[2] + ] -= pipe_coefs[2] return (P0, P1, P2, P3) From 6b41013b06117f399a59d8424acfab841de873c0 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Tue, 10 Sep 2024 10:09:42 +0200 Subject: [PATCH 44/96] notebook --- docs/notebooks/design_pipe_diameter_DW.ipynb | 40 ++++++++++---------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/docs/notebooks/design_pipe_diameter_DW.ipynb b/docs/notebooks/design_pipe_diameter_DW.ipynb index 427e1fa..6ebd63d 100644 --- a/docs/notebooks/design_pipe_diameter_DW.ipynb +++ b/docs/notebooks/design_pipe_diameter_DW.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -61,7 +61,7 @@ "" ] }, - "execution_count": 2, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -129,7 +129,7 @@ "3600 26.476913 22.953829 -9.338379e-07" ] }, - "execution_count": 3, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -205,7 +205,7 @@ "array([ 0.05 , 0.05 , 26.456, 22.911])" ] }, - "execution_count": 7, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -216,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -257,7 +257,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1.68503937007874, 1.622047244094488, 96.37795275590551, 95.0]\n" + "[1.716535433070866, 1.5629921259842519, 96.18110236220473, 95.0]\n" ] } ], @@ -270,16 +270,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.6763164377806783" + "0.33815821889033915" ] }, - "execution_count": 10, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -290,16 +290,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1000., 1000.])" + "array([500., 500.])" ] }, - "execution_count": 11, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } From 86431d77663117234894b31af5ed313452aebd78 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Tue, 10 Sep 2024 10:54:23 +0200 Subject: [PATCH 45/96] PR from Carlos --- docs/notebooks/encHH1LC | Bin 88 -> 0 bytes docs/notebooks/qubo_poly_solver.ipynb | 100 ++++++++++++++---- docs/notebooks/qubo_poly_solver_CM.ipynb | 4 +- wntr_quantum/sim/core_qubo.py | 10 +- wntr_quantum/sim/models/darcy_weisbach_fit.py | 4 +- .../sim/{hydraulics.py => qubo_hydraulics.py} | 16 ++- .../sim/solvers/qubo_polynomial_solver.py | 11 +- 7 files changed, 103 insertions(+), 42 deletions(-) delete mode 100644 docs/notebooks/encHH1LC rename wntr_quantum/sim/{hydraulics.py => qubo_hydraulics.py} (86%) diff --git a/docs/notebooks/encHH1LC b/docs/notebooks/encHH1LC deleted file mode 100644 index b73b5c5df37c0f4eeab2b6b7e5d32cc35fdefce1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 88 zcma#_I4pOPfq{V;h?#(x5r|" ] @@ -283,16 +283,34 @@ "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'QUBOPS_MIXED' object has no attribute 'qubo_dict'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdiagnostic_solution\u001b[49m\u001b[43m(\u001b[49m\u001b[43msol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref_sol\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sim/solvers/qubo_polynomial_solver.py:146\u001b[0m, in \u001b[0;36mQuboPolynomialSolver.diagnostic_solution\u001b[0;34m(self, solution, reference_solution)\u001b[0m\n\u001b[1;32m 143\u001b[0m reference_solution \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_solution_from_si(reference_solution)\n\u001b[1;32m 144\u001b[0m solution \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_solution_from_si(solution)\n\u001b[0;32m--> 146\u001b[0m data_ref, eref \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_energy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreference_solution\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 147\u001b[0m data_sol, esol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqubo\u001b[38;5;241m.\u001b[39mcompute_energy(solution)\n\u001b[1;32m 149\u001b[0m np\u001b[38;5;241m.\u001b[39mset_printoptions(precision\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n", - "File \u001b[0;32m~/QuantumApplicationLab/qubops/qubops/qubops_mixed_vars.py:247\u001b[0m, in \u001b[0;36mQUBOPS_MIXED.compute_energy\u001b[0;34m(self, vector)\u001b[0m\n\u001b[1;32m 245\u001b[0m bin_encoding_vector \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 246\u001b[0m encoded_variables \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 247\u001b[0m bqm \u001b[38;5;241m=\u001b[39m dimod\u001b[38;5;241m.\u001b[39mBQM(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo_dict\u001b[49m)\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val, svec \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(vector, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmixed_solution_vectors\u001b[38;5;241m.\u001b[39mencoded_reals):\n\u001b[1;32m 249\u001b[0m closest_val, bin_encoding \u001b[38;5;241m=\u001b[39m svec\u001b[38;5;241m.\u001b[39mfind_closest(val)\n", - "\u001b[0;31mAttributeError\u001b[0m: 'QUBOPS_MIXED' object has no attribute 'qubo_dict'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", + "\n", + "\n", + "Error (%): [ 1.528 -2.184 -0.331 0.289]\n", + "\n", + "\n", + "sol : [ 1.739 1.804 87.084 74.951]\n", + "ref : [ 1.766 1.766 86.797 75.168]\n", + "diff: [ 0.027 -0.039 -0.288 0.217]\n", + "\n", + "\n", + "encoded_sol: [ 1.739 1.804 87.084 74.951]\n", + "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", + "diff : [ 0.027 -0.038 -0.294 0.196]\n", + "\n", + "\n", + "E sol : -1662.5979676922227\n", + "R ref : -1662.6061020456154\n", + "Delta E : 0.008134353392733829\n", + "\n", + "\n", + "Residue sol : 0.09076400808170053\n", + "Residue ref : 0.010186471203764017\n", + "Delta Residue : 0.0805775368779365\n" ] } ], @@ -309,12 +327,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -328,7 +346,7 @@ "" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -345,6 +363,52 @@ " title='Pressure at 5 hours', node_labels=False)" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[ 0. ],\n", + " [ 1.766],\n", + " [99.077],\n", + " [ 0.652]]),\n", + " array([[-1. , 1. , 0. , 0. ],\n", + " [ 0. , -1. , 0. , 0. ],\n", + " [-1.547, 0. , -1. , 0. ],\n", + " [ 0. , -1.547, 1. , -1. ]]),\n", + " array([[[ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ]],\n", + " \n", + " [[ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ]],\n", + " \n", + " [[-3.063, 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ]],\n", + " \n", + " [[ 0. , 0. , 0. , 0. ],\n", + " [ 0. , -3.063, 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. ]]]))" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.matrices" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/docs/notebooks/qubo_poly_solver_CM.ipynb b/docs/notebooks/qubo_poly_solver_CM.ipynb index 0402b27..83c1e5e 100644 --- a/docs/notebooks/qubo_poly_solver_CM.ipynb +++ b/docs/notebooks/qubo_poly_solver_CM.ipynb @@ -234,8 +234,8 @@ } ], "source": [ - "from wntr_quantum.sim.hydraulics import create_hydraulic_model\n", - "model, model_updater = create_hydraulic_model(wn)\n", + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", "net.matrices = net.initialize_matrices(model)\n", "\n", "ref_sol = net.classical_solutions()\n", diff --git a/wntr_quantum/sim/core_qubo.py b/wntr_quantum/sim/core_qubo.py index 226bde4..5edae13 100644 --- a/wntr_quantum/sim/core_qubo.py +++ b/wntr_quantum/sim/core_qubo.py @@ -5,7 +5,7 @@ from wntr.sim.core import WNTRSimulator from wntr.sim.core import _Diagnostics from wntr.sim.core import _ValveSourceChecker -from .hydraulics import create_hydraulic_model +from .qubo_hydraulics import create_hydraulic_model_for_qubo from .solvers.qubo_polynomial_solver import QuboPolynomialSolver logger = logging.getLogger(__name__) @@ -52,13 +52,8 @@ def run_sim( wntr.sim.solvers.NewtonSolver or Scipy solver linear_solver: linear solver Linear solver - backup_solver: object - wntr.sim.solvers.NewtonSolver or Scipy solver solver_options: dict Solver options are specified using the following dictionary keys: - backup_solver_options: dict - Solver options are specified using the following dictionary keys: - * MAXITER: the maximum number of iterations for each hydraulic solve (each timestep and trial) (default = 3000) * TOL: tolerance for the hydraulic equations (default = 1e-6) @@ -68,7 +63,6 @@ def run_sim( * BACKTRACKING: whether or not to use a line search (default = True) * BT_START_ITER: the newton iteration at which a line search should start being used (default = 2) * THREADS: the number of threads to use in constraint and jacobian computations - backup_solver_options: dict convergence_error: bool (optional) If convergence_error is True, an error will be raised if the simulation does not converge. If convergence_error is False, partial results are returned, @@ -79,7 +73,7 @@ def run_sim( """ logger.debug("creating hydraulic model") self.mode = self._wn.options.hydraulic.demand_model - self._model, self._model_updater = create_hydraulic_model(wn=self._wn) + self._model, self._model_updater = create_hydraulic_model_for_qubo(wn=self._wn) if diagnostics: diagnostics = _Diagnostics(self._wn, self._model, self.mode, enable=True) diff --git a/wntr_quantum/sim/models/darcy_weisbach_fit.py b/wntr_quantum/sim/models/darcy_weisbach_fit.py index f9fb6b1..317a826 100644 --- a/wntr_quantum/sim/models/darcy_weisbach_fit.py +++ b/wntr_quantum/sim/models/darcy_weisbach_fit.py @@ -71,7 +71,7 @@ def convert_to_USunit(roughness, diameter): return np.array(res) -def evlaluate_fit(coeffs, flow): +def evaluate_fit(coeffs, flow): """Evaluate the fit. Args: @@ -88,7 +88,7 @@ def evlaluate_fit(coeffs, flow): # res = dw_fit( # roughness=0.000164, diameter=0.820210, plot=True, convert_to_us_unit=False # ) - # print(evlaluate_fit(res, 1.766)) + # print(evaluate_fit(res, 1.766)) roughness = 0.164 DIAMS = np.linspace(1, 24, 25) RES = [] diff --git a/wntr_quantum/sim/hydraulics.py b/wntr_quantum/sim/qubo_hydraulics.py similarity index 86% rename from wntr_quantum/sim/hydraulics.py rename to wntr_quantum/sim/qubo_hydraulics.py index 2cea55f..ba95de1 100644 --- a/wntr_quantum/sim/hydraulics.py +++ b/wntr_quantum/sim/qubo_hydraulics.py @@ -12,22 +12,20 @@ from .models.darcy_weisbach import dw_resistance_param -def create_hydraulic_model(wn): +def create_hydraulic_model_for_qubo(wn): """Create the aml. Args: - wn (_type_): _description_ + wn (wntr.WaterNetworkModel): The water network for which we want the aml model Raises: - NotImplementedError: _description_ - NotImplementedError: _description_ - ValueError: _description_ - ValueError: _description_ - NotImplementedError: _description_ - NotImplementedError: _description_ + ValueError: if pressure driven simulations is requested + ValueError: if H-W headloss approximation is requested + NotImplementedError: if PBV valves are part of the model + NotImplementedError: if GPV valves are part of the model Returns: - _type_: _description_ + Tuple[wntr.aml.Model, wntr.models.utils.ModelUpdater]: The AML model and its updater """ if wn.options.hydraulic.demand_model in ["PDD", "PDA"]: raise ValueError("Pressure Driven simulations not supported") diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 0be0d08..def6f5b 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -47,6 +47,10 @@ def __init__( [self.sol_vect_flows, self.sol_vect_heads] ) + # init other attributes + self.matrices = None + self.qubo = None + def verify_encoding(self): """Print info regarding the encodings.""" hres = self.head_encoding.get_average_precision() @@ -135,8 +139,6 @@ def diagnostic_solution(self, solution: np.ndarray, reference_solution: np.ndarr Args: solution (np.array): solution to be benchmarked reference_solution (np.array): reference solution - qubo (QUBOPS_MIXED): QUBOPS_MIXED instance - bqm (dimod.BQM): BQM from dimod """ reference_solution = self.convert_solution_from_si(reference_solution) solution = self.convert_solution_from_si(solution) @@ -258,6 +260,9 @@ def flatten_solution_vector(solution: Tuple) -> List: def load_data_in_model(model: Model, data: np.ndarray): """Loads some data in the model. + Remark: + This routine replaces `load_var_values_from_x` without reordering the vector elements + Args: model (Model): AML model from WNTR data (np.ndarray): data to load @@ -335,7 +340,7 @@ def qubo_poly_solve(self, strength=1e6, num_reads=10000, **options): # noqa: D4 # flatten solution sol = self.flatten_solution_vector(sol) - # convert back to SI if DW + # convert back to SI sol = self.convert_solution_to_si(sol) return sol From 849cef26724c335a791247cc3b1fa12733513489 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Tue, 10 Sep 2024 19:36:35 +0200 Subject: [PATCH 46/96] added 2loop example --- docs/notebooks/networks/Net2LoopsCMflat.inp | 145 +++++++ docs/notebooks/networks/Net2LoopsFlat.inp | 145 +++++++ docs/notebooks/qubo_poly_solver_CM.ipynb | 4 +- docs/notebooks/{trash => sandbox}/aequbols.py | 0 docs/notebooks/{trash => sandbox}/epanet.py | 0 .../{trash => sandbox}/getting_stared.ipynb | 0 .../sandbox/qubo_poly_solver_Net2loops.ipynb | 388 ++++++++++++++++++ .../{trash => sandbox}/qubo_solver.py | 0 .../notebooks/{trash => sandbox}/qubols.ipynb | 0 docs/notebooks/{trash => sandbox}/vqls.ipynb | 0 .../epanet/Linux/libepanet22_amd64.so | Bin 428336 -> 428336 bytes 11 files changed, 680 insertions(+), 2 deletions(-) create mode 100644 docs/notebooks/networks/Net2LoopsCMflat.inp create mode 100644 docs/notebooks/networks/Net2LoopsFlat.inp rename docs/notebooks/{trash => sandbox}/aequbols.py (100%) rename docs/notebooks/{trash => sandbox}/epanet.py (100%) rename docs/notebooks/{trash => sandbox}/getting_stared.ipynb (100%) create mode 100644 docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb rename docs/notebooks/{trash => sandbox}/qubo_solver.py (100%) rename docs/notebooks/{trash => sandbox}/qubols.ipynb (100%) rename docs/notebooks/{trash => sandbox}/vqls.ipynb (100%) diff --git a/docs/notebooks/networks/Net2LoopsCMflat.inp b/docs/notebooks/networks/Net2LoopsCMflat.inp new file mode 100644 index 0000000..0f05773 --- /dev/null +++ b/docs/notebooks/networks/Net2LoopsCMflat.inp @@ -0,0 +1,145 @@ +[TITLE] +shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) + +[JUNCTIONS] +;ID Elev Demand Pattern + 2 0.00 27.77 ; + 3 0.00 27.77 ; + 4 0.00 33.33 ; + 5 0.00 75.00 ; + 6 0.00 91.67 ; + 7 0.00 55.55 ; + +[RESERVOIRS] +;ID Head Pattern + 1 210.00 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + 1 1 2 1000.00 457.20 0.012 0.00 Open ; + 2 2 3 1000.00 203 0.012 0.00 Open ; + 3 2 4 1000.00 457 0.012 0.00 Open ; + 4 4 5 1000.00 153 0.012 0.00 Open ; + 5 4 6 1000.00 406.40 0.012 0.00 Open ; + 6 6 7 1000.00 254.00 0.012 0.00 Open ; + 7 3 5 1000.00 153 0.012 0.00 Open ; + 8 5 7 1000.00 153 0.012 0.00 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + + + +[RULES] + + + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 0:00 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 2:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic NONE + +[REPORT] + Status Yes + Summary No + Page 0 + +[OPTIONS] + Units LPS + Headloss C-M + Specific Gravity 1.0 + Viscosity 1.0 + Trials 40 + Accuracy 0.001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality Chlorine mg/L + Diffusivity 1.0 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +2 2000.000 3000.000 +3 1000.000 3000.000 +4 2000.000 2000.000 +5 1000.000 2000.000 +6 2000.000 1000.000 +7 1000.000 1000.000 +1 3000.000 3000.000 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 0.000 0.000 10000.000 10000.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/docs/notebooks/networks/Net2LoopsFlat.inp b/docs/notebooks/networks/Net2LoopsFlat.inp new file mode 100644 index 0000000..aefce28 --- /dev/null +++ b/docs/notebooks/networks/Net2LoopsFlat.inp @@ -0,0 +1,145 @@ +[TITLE] +shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) + +[JUNCTIONS] +;ID Elev Demand Pattern + 2 0.00 27.77 ; + 3 0.00 27.77 ; + 4 0.00 33.33 ; + 5 0.00 75.00 ; + 6 0.00 91.67 ; + 7 0.00 55.55 ; + +[RESERVOIRS] +;ID Head Pattern + 1 210.00 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + 1 1 2 1000.00 457.20 130.00 0.00 Open ; + 2 2 3 1000.00 203 130.00 0.00 Open ; + 3 2 4 1000.00 457 130.00 0.00 Open ; + 4 4 5 1000.00 153 130.00 0.00 Open ; + 5 4 6 1000.00 406.40 130.00 0.00 Open ; + 6 6 7 1000.00 254.00 130.00 0.00 Open ; + 7 3 5 1000.00 153 130.00 0.00 Open ; + 8 5 7 1000.00 153 130.00 0.00 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + + + +[RULES] + + + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 0:00 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 2:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic NONE + +[REPORT] + Status Yes + Summary No + Page 0 + +[OPTIONS] + Units LPS + Headloss H-W + Specific Gravity 1.0 + Viscosity 1.0 + Trials 40 + Accuracy 0.001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality Chlorine mg/L + Diffusivity 1.0 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +2 2000.000 3000.000 +3 1000.000 3000.000 +4 2000.000 2000.000 +5 1000.000 2000.000 +6 2000.000 1000.000 +7 1000.000 1000.000 +1 3000.000 3000.000 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 900.000 900.000 3100.000 3100.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/docs/notebooks/qubo_poly_solver_CM.ipynb b/docs/notebooks/qubo_poly_solver_CM.ipynb index 83c1e5e..1142e7f 100644 --- a/docs/notebooks/qubo_poly_solver_CM.ipynb +++ b/docs/notebooks/qubo_poly_solver_CM.ipynb @@ -248,11 +248,11 @@ "metadata": {}, "outputs": [], "source": [ - "from wntr_quantum.sim.hydraulics import create_hydraulic_model\n", + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", "from dwave.samplers import SteepestDescentSolver\n", "\n", "sampler = SteepestDescentSolver()\n", - "model, model_updater = create_hydraulic_model(wn)\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", "net.solve(model, options={\"sampler\" : sampler})\n", "sol = net.extract_data_from_model(model)" ] diff --git a/docs/notebooks/trash/aequbols.py b/docs/notebooks/sandbox/aequbols.py similarity index 100% rename from docs/notebooks/trash/aequbols.py rename to docs/notebooks/sandbox/aequbols.py diff --git a/docs/notebooks/trash/epanet.py b/docs/notebooks/sandbox/epanet.py similarity index 100% rename from docs/notebooks/trash/epanet.py rename to docs/notebooks/sandbox/epanet.py diff --git a/docs/notebooks/trash/getting_stared.ipynb b/docs/notebooks/sandbox/getting_stared.ipynb similarity index 100% rename from docs/notebooks/trash/getting_stared.ipynb rename to docs/notebooks/sandbox/getting_stared.ipynb diff --git a/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb b/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb new file mode 100644 index 0000000..078fc1f --- /dev/null +++ b/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb @@ -0,0 +1,388 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "\n", + "# Create a water network model\n", + "inp_file = './networks/Net0.inp'\n", + "# inp_file = './networks/Net2LoopsDW.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the original Cholesky EPANET simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([26.477, 22.954], dtype=float32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0][:2]\n", + "ref_pressure" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.05, 0.05], dtype=float32)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_rate" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the QUBO Polynomial Solver" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "wn = wntr.network.WaterNetworkModel(inp_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n" + ] + } + ], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 9\n", + "step = (0.5/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", + "\n", + "nqbit = 9\n", + "step = (50/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")\n", + "\n", + "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)\n", + "net.verify_encoding()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve the system classically" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1. , 1. , 0.999, 0.998])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.matrices = net.initialize_matrices(model)\n", + "\n", + "ref_sol = net.classical_solutions()\n", + "ref_sol / ref_values" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "from dwave.samplers import SteepestDescentSolver\n", + "\n", + "sampler = SteepestDescentSolver()\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.solve(model, options={\"sampler\" : sampler})\n", + "sol = net.extract_data_from_model(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Ho7S0NPXo0cPsVAAAJtmwYYMyMjK0du1aJSYmyuPx6OWXX66zkzrszTEzLqwqMhbDnfLWHNMJqzeMxLGqyF5j2q0uA4GAcnNzVVBQoAsvvFBLly5V9+7dGzUmNRn9MY3GO2bGBQAQ2+Li4rRs2TJNmDBBn3zyiS655BKzU0IEOGbGBQCAiy++WH/+859tfV8KTo0ZFwAAYBs0LgAAwDYc+1ERy6HrxxI/a41p92WnRuNYDm2vMZ1Ql1wra9ipJlkODQBwjKqqKv3lL38xOw1YgGNmXFgObSyGJX7WHNNuy04bG8dyaHuNaZW63LNnj0aOHKnPPvtMS5Ys0S9/+UvD5+daWcMONclyaACA7a1cuVKXXXaZPvvsM7Vo0UJnnnmm2SnBZDQuAADLqays1F133aVhw4appKREPXv21NatW3XjjTeanRpMRuMCALCUXbt2qU+fPpozZ44kaerUqdqwYYO6dOlibmKwBMfc4wIAsL9XX31Vt99+u0pLS9W6dWu9+OKLuu6668xOCxbCjAsAwBL+8z//U6NGjVJpaamuuuoqbdu2jaYFJ3DsjAv7uNSPvQmsNaYT9sswEsc+LvYa06y6/PnPf66ZM2fq17/+tX73u9+pSZMm9Z6La6VxdqpJo8c5pnHxeDzyeDzy+/1mpwIAaIT09HTt3LlT7dq1MzsVWJhjGhf2cTEWw94E1hzTKvtlhBrLPi41qMvGH3/OOeeE9fxcK2vYoSbZxwUAADgOjQsAALANGhcAQMRVVlaanQIcgsYFABBRmzdv1sUXX6wVK1aYnQocgMYFABARwWBQc+bMUZ8+ffTtt9/q0UcfVSAQMDst2ByNCwAg7A4fPqxhw4bprrvuks/n09ChQ7V+/XrFxfHXDkJDBQEAwuqzzz5Tz549tWrVKrndbj3zzDNasWKFWrZsaXZqcADH7OPyQ+ycWz92g7TWmOycW4O6tNaYjT1nIBDQrFmz9NBDD6m6ulrnnXeeli5dqm7dutX+jsMxJtdK4+xUk+ycCwCImqKiIo0fP155eXmSpJtuuknz5s1TcnKyyZnBaRzTuLBzrrEYdoO05pjsnEtdWnHMhpxz7969eu+995SYmKgnn3xS48ePV9OmTSM6JtdK4+xQk0bjHdO4AADM06tXL82fP1/du3fXT37yE7PTgYPRuAAAwiIrK0uSOfdVIHawqggAANgGjQsAALANGhcAAGAbNC4AgFN6++23tXbtWrPTACTRuAAATqKqqkr33XefrrvuOo0ZM0YFBQVmpwSwqggAcKI9e/YoMzNTn376qSRp1KhRat26tclZATQuAIAfWL16tW6//XaVlJSoRYsWWrhwoYYNG2Z2WoAkBzcuPKuofjx/w1pj8qyiGtSlNcasrKzUAw88II/HI0nKyMjQyy+/rC5dukSkbhp7DNdK4+xUk0aPc8w9Lh6PR2lpaerRo4fZqQCA7ezatUs/+9nPapuWKVOm6IMPPlCXLl3MTQz4AcfMuPCsImMxPH/DmmPyrCLq0swxi4uLdcUVV6ikpEStW7fWggULdO211zqiLrlW1rBDTfKsIgCAIa1ataqdYVm2bJnOPvtss1MCTsoxHxUBABrvoYce0gcffKCOHTuanQpwSsy4AAAUHx9vdgqAIcy4AAAA26BxAQAAtkHjAgAOFgwGVVhYaHYaQNjQuACAQ3m9Xo0ePVrdu3fXwYMHzU4HCAsaFwBwoC1btqh79+569dVXdfDgQX300UdmpwSEBY0LADhIMBjU3Llz1bt3b+3atUudO3fWhg0bNHz4cLNTA8KC5dAA4BCHDx/W+PHjtXLlSknSjTfeqEWLFqlVq1YmZwaEDzMuAOAAn332mbp166aVK1cqISFBs2fP1sqVK2la4Dg0LgBgc7NmzdKVV16pPXv26LzzztMnn3yiu+66Sy6Xy+zUgLBz7EdFPp+vziOyQ320d6Qf0200lke117DTo9ojec7GHB/umjQSd7r3qcvQxiwoKFB1dbWGDx+uZ599VikpKSHl4oS65FpZw07XSqPHOaZx8Xg88ng88vv9ZqcCAFE1bdo09ezZU8OGDWOWBY7nmMYlOztb2dnZ8nq9SklJkdvtrvcR2WY8pr2hx/GoduPs8Kj2aJyzMceHuyaNxJ3sfeoy9PFGjRoVkfNG+3iulZFhh2ul0XjucQEAALZB4wIAAGyDxgUAANgGjQsAWNS+ffs0evRoFRcXm50KYBmOuTkXAJzknXfe0dixY1VUVCSXy6WlS5eanRJgCcy4AICFVFVV6Te/+Y2uvfZaFRUV6bLLLtMjjzxidlqAZTDjAgAW8d1332nkyJH69NNPJUk5OTl6/PHHlZiYaHJmgHXQuACABaxevVpZWVk6fPiwUlJStGjRIg0bNszstADL4aMiADBRZWWl7r77bg0dOlSHDx9WRkaGtm7dStMCnASNCwCYaPbs2Zo9e7Yk6Z577tGGDRt07rnnmpwVYF18VAQAJrrrrrv03nvvacqUKbr++uvNTgewPBoXADBRYmKi1q5dy8MRAYP4qAgATEbTAhhH4wIAAGyDxgUAANgGjQsARMh///d/y+v1mp0G4Cg0LgAQZsFgUPPnz9fll1+uO+64Q8Fg0OyUAMegcQGAMPJ6vRo9erRuv/12VVRU6PDhw6qoqDA7LcAxaFwAIEy2bt2qXr166dVXX1V8fLz+9Kc/ac2aNTrjjDPMTg1wDMfu4+Lz+eTz+ep8Her5In2ckdhQY6qqqmr/DPVnYiYzco/EmGbUZbhr0kjc6d63e10Gg0E999xz+s1vfiOfz6fOnTvr5ZdfVq9evVRdXR3RsanLyMTYvSaPs9O10uhxjmlcPB6PPB6P/H6/2akAiCElJSWaOHGiVq1aJUm67rrrtGDBArVq1crcxACHckzjkp2drezsbHm9XqWkpMjtdsvtdp8QV99rDdHY4xtynJHYxsYkJCTU/hnqz8IKzPgeIjGmGXUZ7po0Eney9+1al19//bUGDRqkPXv2KCEhQTNmzFBOTo6aNm0a9Vyoy/DG2LUmT8YO10qj8Y5pXAAg2jp27KhmzZrpvPPO0/Lly3XppZeanRLgeDQuANBIzZs315tvvqnWrVsrJSXF1vdCAHZB4wIAITjvvPPMTgGIKSyHBgAAtkHjAgAAbIPGBQBOgq36AeuhcQGAH/D7/Zo2bZruuOMOs1MB8APcnAsA/6agoECjR49Wfn6+JCkrK0u9evUyOSsAxzHjAgD/Jy8vT127dlV+fr7OPPNMLV26lKYFsBgaFwAxr6qqSvfff78GDx6soqIipaena/PmzRo9erTZqQH4AT4qAhDT9u7dq5EjR2rjxo2Sah4f8sQTTygxMdHkzADUh8YFQMxavXq1srKydPjwYaWkpGjhwoW66aabzE4LwCnQuACIST6fT1OnTtXhw4fVo0cPLV++XOeee67ZaQE4De5xARCT3G63Xn31Vd17773629/+RtMC2AQzLgBiVrdu3dStWzez0wDQAMy4AAAA26BxAQAAtkHjAgAAbIPGBYDjlJeX65VXXjE7DQARwM25ABzlyy+/1IgRI/TVV1+padOmGjZsmNkpAQgjZlwAOEIwGNSCBQvUo0cPffXVV2rXrp1at25tdloAwowZFwC2V1paqokTJ9Z+PDRo0CC9+OKLOvvss03ODEC4MeMCwNa2bt2qbt266ZVXXlF8fLwee+wxrVmzhqYFcChmXADYUjAY1LPPPqvc3Fz5fD516tRJr776qvr06WN2agAiiBkXALZ09913KycnRz6fT0OGDNHWrVtpWoAYQOMCwJZGjhypZs2a6emnn9bq1au5EReIEXxUBMCWevfurT179uiss84yOxUAUcSMCwDbomkBYg+NCwAAsA0aFwAAYBuWbFzeeustXXjhhfrxj3+sBQsWmJ0OgCg7duyY2SkAsCjLNS7V1dXKzc3VBx98oK1bt+rxxx/Xv/71L7PTAhAlH3zwgc4//3y98847ZqcCwIIs17hs2rRJF198sTp06KAzzzxTgwcP1tq1a81OC0CE+f1+zZgxQwMGDFBBQYH+9Kc/KRgMmp0WAIsJe+Py0UcfaciQIWrfvr1cLpdWrVp1QozH41GXLl2UmJionj17atOmTbXvFRQUqEOHDrVfd+jQQfv27Qt3mgAspLCwUNOmTdPMmTMVDAY1fvx4vf3223K5XGanBsBiwr6PS3l5ubp27apx48bV+zj55cuXKzc3V/PmzVPPnj01a9YsDRw4UF9//XWjni1SWVmpysrK2q+9Xq8kqaSkRIFAoPb1qqoqSVJCQkKDxwjl+IYcZyQ21JjS0tI6f9pVqL9Pq4xpRl2GuyaNxJ3q/ffff18TJ05UcXGxmjdvrqeeekojRoyQz+eTz+c7bY5WQl02/niulZFhp5o8/vf36YS9cRk8eLAGDx580vefeuopTZgwQVlZWZKkefPmac2aNVq0aJHuv/9+tW/fvs4My759+5SRkXHS882YMUPTp08/4fWPP/5YzZo1C+E7cbYtW7aYnQJiXHV1tZYtW6YVK1ZIkrp06aKpU6cqNTVV+fn5JmcH1OBaGT1Hjx41FOcKRvBDZJfLpZUrV2ro0KGSJJ/Pp2bNmumNN96ofU2SMjMzVVJSotWrV6u6uloXXXSRPvzwQ6WkpKh79+765JNPTrqdd30zLp06ddJ3332n5OTk2teZcalRWlqqLVu2qFu3bkpKSjptTlZlp39FRPKcVviXrZG4H77//fffa/z48fr8888lSWPHjtX111+vXr16UZcWGNMJdcm1soadatLr9eqcc87RkSNH6vz9/UNR3fK/qKhIfr9fqampdV5PTU3Vzp07axJq0kRPPvmk+vXrp0AgoPvuu++UzyBp2rSpmjZtesLrLVq0qPONH59ydrvdjcq9scc35DgjseGKSUpKUosWLU6bk1WF+vu0yphm1GW4a9JI3A/f37p1q7744gslJydr4cKFGjBggPLz86lLi4zphLrkWlnDTjUZF2fstltLPqvohhtu0A033GB2GgAipF+/flqwYIH69u2r8847TyUlJWanBMAmotq4tGnTRvHx8Tpw4ECd1w8cOKC2bdtGMxUAJhs3bpzZKQCwoag2Lm63W927d9e6detq73EJBAJat26dcnJywjrWD1ckhLo6obHHN+Q4I7Ghxhz/7LGqqsp2Kzb+nRm5R2JMM+oy3DVpJO5071OX1hrTCXXJtbKGnWrS6HFhb1zKysq0a9eu2q93796tbdu2qVWrVurcubNyc3OVmZmpyy+/XBkZGZo1a5bKy8trVxk1lsfjkcfjkd/vD/VbAAAAFhX2xuWLL75Qv379ar/Ozc2VVLNyaPHixbr55pt16NAhPfzww9q/f7/S09OVl5d3wg27DZWdna3s7Gx5vV6lpKTI7XbXe2NQqDcoNfb4hhxnJLaxMcfv8k5ISIjqzVqRYsb3EIkxzajLcNekkbiTvU9dWnNMJ9Ql18oadqhJo/Fhb1z69u172m26c3Jywv7REABrWLJkiTp27Ki+ffuanQoAB7LkqiIA9lNeXq477rhDL7zwgtq1a6e///3vatOmjdlpAXAYGhcAIduxY4dGjx6tr776Si6XSxMnTlTLli3NTguAAzm2cWFVUf24U95aY9p99UYwGNTixYv161//WseOHVO7du20ePFi9e3bV36/v/ZmeVYV2WtMu9dlOGKoyeiPafS4sD8d2iwej0dpaWnq0aOH2akAMaG0tFS33nqr7rjjDh07dkzXXHONNm3axL0tACLKMTMurCoyFsOd8tYc026rN7Zt26YRI0bom2++UXx8vKZPn6577rlHiYmJjRqTurTmmHary3DGUJPRH9O0VUUAnG3fvn3q3bu3Kioq1KlTJ73yyivMdAKIGhoXAA3SoUMHTZ48WTt37tQLL7yg1q1b2/oeAAD2QuMCoMH++Mc/Kj4+Xi6Xy+xUAMQYGhcADdakCZcOAOZw7NWH5dD1Y4mftcZ0wrJTI3Esh7bXmE6oS66VNexUkyyHBgAAjuOYGReWQxuLYYmfNce0yrLTQCCgffv2qVOnTo06Pw9ZrEFdNv54rpWRYYeaNBrvmBkXAKE5ePCgBg8erCuvvFLFxcVmpwMA9aJxAaD169era9euWrt2rQ4dOqTNmzebnRIA1IvGBYhhfr9fjzzyiK655hrt379faWlp+vzzz/Xzn//c7NQAoF6OuccFQMMUFBRozJgx+vDDDyVJ48aN05w5c9SsWTNzEwOAU2DGBYhBa9euVXp6uj788EM1b95cL7/8shYuXEjTAsDyHDvjwj4u9WNvAmuNGe26DAaD+u1vf6unnnpKknTppZdq6dKluuCCC+o9F/u4NAx12fjjuVZGhp1qkn1cAJzA5XKprKxMkjRx4kRt2LBBF1xwgclZAYBxjplxYR8XYzHsTWDNMaNZl48//riuvfZa3XjjjWE/P/u41KAuG38818rIsENNso8LgHolJiZq8ODBZqcBAI1C4wIAAGyDxgUAANgGjQsAALANGhfAIf7xj39o+PDh8nq9ZqcCABHjmFVFQCxbtmyZJk6cqLKyMp111ll67rnnzE4JACKCGRfAxo4ePao77rhDY8aMUVlZmfr27auHHnrI7LQAIGIcO+PCzrn1YzdIa40Zyjl37NihUaNGaefOnXK5XHrwwQf129/+VvHx8ac8b7hr0kgcO+faa0x2zqUmzRjT6HGOaVw8Ho88Ho/8fr/ZqQARFQwG9eKLL+ruu+/WsWPHlJqaqhdffFH9+vUzOzUAiDjHNC7snGssht0grTmm0XOWlpZq0qRJWrp0qSRpwIABeuGFF9SxY8eIjdmQWHbOrRFrdRnO47lWRoYdapKdcwEHmjFjhpYuXar4+HjNmDFDb775ps4++2yz0wKAqHHMjAsQCx588EFt2bJFv/vd73TllVfa+rN3AGgMGhfARpo3b668vDyz0wAA0/BREQAAsA0aFwAAYBs0LgAAwDZoXACL2LRpk44ePWp2GgBgaTQugMkCgYAee+wx9enTR5MnTzY7HQCwNFYVASY6ePCgxo0bp/fee0+SVFFRoerqajVpwv+aAFAfx14deVZR/Xj+hnXGzM/P19ixY7V//36dccYZevrpp3XrrbcqEAiE7RlBoR7Ds4oaxgl1GY5zWqEuuVbWsFNNGj3OMR8VeTwepaWlqUePHmanApyS3+/XH/7wBw0aNEj79+/XT37yE3388cfKysqSy+UyOz0AsDTHzLjwrCJjMTx/w9wxCwsLNWbMGK1fv16SlJmZqaefflotW7aMek48qyhy7FaXkTqnFeqSa2UNO9Sk0XjHNC6A1W3ZskWDBg3SoUOH1Lx5c82bN08jRowwOy0AsBUaFyBKzj//fCUnJ6tdu3Z67bXXdOGFF9r6s3MAMAONCxAlycnJevfdd9W+fXudccYZZqcDALZE4wJE0Y9+9COzUwAAW3PMqiIAAOB8NC4AAMA2aFyAMAkEAmanAACOR+MChKiiokLZ2dm6++67zU4FAByPm3OBEPzjH//QiBEjtH37drlcLk2cOFEXX3yx2WkBgGMx4wI00rJly9S9e3dt375dbdq00dtvv03TAgARRuMCNNDRo0d12223acyYMSorK9PPfvYzbd++XYMGDTI7NQBwPBoXoAF27NihjIwMLVy4UC6XSw8//LDWrVun9u3bm50aAMQEx97j4vP56mynbsZj2ht6HI9qNy7auQeDQS1atEj33HOPjh07prZt22rx4sXq16+f/H6//H5/o85rRl2GuyaNxJ3uferSWmM6oS65VtawU00aPc4xMy4ej0dpaWnq0aOH2anAgcrLyzVjxgwdO3ZM11xzjTZt2qR+/fqZnRYAxBzHzLhkZ2crOztbXq9XKSkpcrvd9T4i24zHtDf0OB7Vbly0vodWrVppyZIlys/P14MPPqi4uPD2/GbUZbhr0kjcyd6nLq05phPqkmtlDTvUpNF4xzQuQKT16tVLvXr1CnvTAgAwjiswAACwDRoXAABgGzQuAADANmhcEPOKi4u1ZMkSs9MAABjAzbmIaZ988olGjhyp77//Xm3atGH3WwCwOGZcEJMCgYD+9Kc/6eqrr9b333+v888/X6mpqWanBQA4DWZcEHMOHjyosWPH6t1335UkjRo1Ss8//7ySkpJMzgwAcDo0Logp+fn5GjVqlAoLC5WYmKg5c+Zo/PjxcrlcZqcGADCAj4oQE/x+vx599FH1799fhYWFuuiii/T555/rtttuo2kBABthxgUxYezYsVq2bJkkKSsrS3PmzFHz5s1NzgoA0FDMuCAmjB8/XsnJyXrppZe0aNEimhYAsClmXBAT+vfvrz179qhly5ZmpwIACAEzLogZNC0AYH80LgAAwDZoXAAAgG3QuMD2SktLzU4BABAlNC6wtb/85S/q0qWLPvzwQ7NTAQBEAY0LbKmiokJTpkzR8OHDVVxcrDlz5pidEgAgChy7HNrn88nn89X5OtTzRfo4I7GhxlRVVdX+GerPxCzffPONxowZo+3bt0uS7r33Xj3yyCMR/34icX4z6jLcNWkk7nTvO6EupcjUiBljOqEuuVbWsFNNGj3OMY2Lx+ORx+OR3+83OxVE0PLly3XnnXeqrKxMrVu31gsvvKCBAweanRYAIEoc07hkZ2crOztbXq9XKSkpcrvdcrvdJ8TV91pDNPb4hhxnJLaxMQkJCbV/hvqziKajR49qypQpWrBggSTpqquu0osvvqhzzz036rlE4udmRl2GuyaNxJ3sfbvW5cmY8T1Ql+GNoSajP6bReMc0LnCur776SiNGjNCXX34pl8ulhx56SL/5zW/UpAnlCwCxhis/LG/37t368ssvlZqaqqVLl+qaa66x9WfOAIDGo3GB5V177bVauHChrrvuOqWmppqdDgDARDQusIVx48aZnQIAwALYxwUAANgGjQsAALANGhcAAGAbNC4wTTAY1DPPPKONGzeanQoAwCa4ORemKC4u1rhx47R69Wp17txZ//Vf/6Xk5GSz0wIAWByNC6Ju48aNGjlypPbu3Su3262pU6cqKSnJ7LQAADbAR0WImkAgoJkzZ+qqq67S3r17df7552vjxo3KycmRy+UyOz0AgA0w44KoOHTokMaOHau8vDxJ0siRI/X888/z8RAAoEGYcUHE5efnKz09XXl5eUpMTNT8+fO1bNkymhYAQIMx44KI+sc//qH+/fsrEAjooosu0vLly3XJJZeYnRYAwKZoXBBRF1xwgSZNmqTy8nLNnTtXzZs3NzslAICN0bgg4mbPnq34+Hiz0wAAOAD3uCDiaFoAAOFC4wIAAGyDxgUAANgGjQsazefz6bvvvjM7DQBADKFxQaPs2bNHV199tQYMGKDS0lKz0wEAxAgaFzTYihUrdNlll+mzzz5TUVGRduzYYXZKAIAYQeMCwyoqKjR58mTddNNNKikpUa9evbRt2zb17NnT7NQAADGCxgWGfPPNN+rTp4/mzp0rSbrvvvv00Ucf6ZxzzjE5MwBALGEDOpzWK6+8ottvv11lZWVq06aNXnrpJQ0ePNjstAAAMYjGBSfl9/s1adIkzZ8/X5J09dVXa9myZerQoYPJmQEAYhUfFeGk4uPjVVVVJZfLpYceekjr1q2jaQEAmIoZF5zS3LlzlZWVpauvvtrsVAAAYMYFp9a8eXOaFgCAZdC4AAAA27Bk4/KLX/xCLVu21PDhw81OBQAAWIglG5cpU6bopZdeMjsNAABgMZZsXPr27aukpCSz03C0bdu26YYbbtDRo0fNTgUAAMMa3Lh89NFHGjJkiNq3by+Xy6VVq1adEOPxeNSlSxclJiaqZ8+e2rRpUzhyRRgEg0G99dZb+o//+A+9+eabmj59utkpAQBgWIOXQ5eXl6tr164aN26chg0bdsL7y5cvV25urubNm6eePXtq1qxZGjhwoL7++mudffbZkqT09HRVV1efcOzatWvVvn37BuVTWVmpysrK2q+9Xq8kqaSkRIFAoPb1qqoqSVJCQkKDzh/q8Q05zkhsKDGHDx/WpEmT9O6770qSrr/+ek2aNEklJSWnzc1qQv19WmVMM+oy3DVpJO507x9/wrjdnzROXTb+eCtdKyVq0owxj//9fToNblwGDx58yu3en3rqKU2YMEFZWVmSpHnz5mnNmjVatGiR7r//fkk1H1OEy4wZM+qdNfj444/VrFmzsI1jd19//bWeeOIJHTp0SE2aNFFWVpauvfZabd++3ezUgFpbtmwxOwWgDmoyeozeuhDWDeh8Pp82b96sBx54oPa1uLg4DRgwQBs3bgznULUeeOAB5ebm1n7t9XrVqVMnXXHFFUpOTq59PVZnXAKBgObOnavf//73qq6u1jnnnKPJkydrxIgRtr6PyE7/iojkOa3wL1sjcUZmXLZs2aJu3bpRlxYY0wl1GY4ZF2oyumNGbMblVIqKiuT3+5Wamlrn9dTUVO3cudPweQYMGKDt27ervLxcHTt21Ouvv67evXvXG9u0aVM1bdr0hNdbtGhRp3Hx+XySJLfbbTiPf9fY4xtynJHYhsQcOXJEmZmZeueddyRJN998s2bOnKmtW7cqKSlJLVq0MPQ9WFGov0+rjGlGXYa7Jo3EGT0PdWmNMZ1Ql+GKoSajN2ZcnLHbbi255f/7779vdgqO8Mgjj+idd95RYmKinnnmGd122206cuSI2WkBANBoYW1c2rRpo/j4eB04cKDO6wcOHFDbtm3DORQMmDFjhr777jv98Y9/1KWXXmp2OgAAhCysjYvb7Vb37t21bt06DR06VJIUCAS0bt065eTkhHOo0/L5fLXTVce/DvV8kT7OSGxDYhITE7VixYo6rx3/7LGqqirkn4mZzMg9EmOaUZfhrkkjcad7n7q01phOqMtQY6jJ6I9p9LgGNy5lZWXatWtX7de7d+/Wtm3b1KpVK3Xu3Fm5ubnKzMzU5ZdfroyMDM2aNUvl5eW1q4wixePxyOPxyO/3R3QcAABgngY3Ll988YX69etX+/XxFT2ZmZlavHixbr75Zh06dEgPP/yw9u/fr/T0dOXl5Z1ww264ZWdnKzs7W16vVykpKXK73fXeGBTqDUqNPb4hxxmJbWzM8bu8ExISonqzVqSY8T1EYkwz6jLcNWkk7mTvU5fWHNMJdcm1soYdatJofIMbl759+yoYDJ4yJicnJ+ofDQEAAOez5LOKcHrr16+vs2MwAACxgMbFZqqqqnT//ferf//+mjp1qtnpAAAQVZbcxyUcnLiqaM+ePRozZoy++OILSZLf71dlZaVcLpfh83CnvLXGdMLqDSNxrCqy15hOqEtWFdWwU00aPc4xMy4ej0dpaWnq0aOH2alExOrVq9WzZ0998cUXatGihV577TU9/fTTJzQtAAA4mWNmXJy6qqiyslJTp07VnDlzJEkZGRl6+eWXdcEFFzRqTO6Ut+aYTli9YSSOVUX2GtMJdcmqohp2qEmj8Y6ZcXGiXbt2qU+fPrVNy9SpU/XBBx+oS5cu5iYGAIBJHDPj4jT5+fkaMmSISktL1bp1a7300ku69tprbf1ZKwAAoaJxsahLLrlELVq0UHp6upYtW6aOHTuanRIAAKajcbGoVq1aKT8/X506dVKTJvyaAACQHNy4OGE5dIcOHRQIBBr8fbDEzz5jOmHZqZE4lkPba0wn1CXXyhp2qkmWQwMAAMdxzIyLU5dDhzuGJX7WHNMJy06NxLEc2l5jOqEuuVbWsENNshzawoLBoPx+v9lpAABgOzQuUeb1ejVq1Cg9+OCDZqcCAIDtOOajIjvYvHmzbr75Zn377bdKSEjQpEmTdM4555idFgAAtsGMSxQEg0HNmTNHffr00bfffqvOnTsrPz+fpgUAgAZixiXCDh8+rNtvv11//etfJUlDhw7VwoUL1apVK5MzAwDAfhzbuFhhH5fPPvtMt9xyi/bu3Su3263HHntMd955p1wuV73nY28C4+y0N0Ekz2mF/TKMxLGPi73GdEJdcq2sYaeaZB8XEwUCAT311FPq37+/9u7dq3PPPVf5+fnKzs6Wy+UyOz0AAGzLMTMuVtrH5V//+pdmz56t6upqDR8+XM8++6zOOuussI7B3gQ17LA3QTTOaYX9MozEsY+LvcZ0Ql1yraxhh5o0Gu+YxsVKWrduraVLl2rXrl3KzMxklgUAgDChcYmQ/v37q3///rb+bBQAAKuhcTHAHwjq893FKiqr1NktzlTGua0UH8csCgAA0Ubjchp5XxZq+ps7dKT8mCTpaLVL7VISNW1Imgb9tJ3J2QEAEFscs6ooEvK+LNSkJVtUeKSizuv7j1Ro0pItyvuy0KTMAACITTQuJ+EPBDX9zR0K/uD1am+RSr/8QEFJ09/cIX/ghxEAACBS+KjoJDbtLj5hpqVs1xcq/OvTClSUqUlyGxV2vlSbdher949am5QlAACxxbGNS6g75+4/XKZmTWpmU4L+av3rg5d0cONKSVLT1PN0ZovWcjcJav/hMvl8SafMoyE5RzqG3SCtNaYTdig1EsfOufYa0wl1ybWyhp1q0uhxjmlcPB6PPB6P/H5/WM53uLxSkuQrOaB9Kx9XRcHXkqSWl1+vs68Zp7gmCXXiAABA5DmmcQn3zrktk5qpaMdG/evt2QpUlisusbnaXXeXmpx/hSokqfr/48K1e2NDYtkNsoYddoOMxjmtsEOpkTh2zrXXmE6oS66VNexQk+ycG4LKykotm/2oDq2cL0lyt7tAnYbdJ3eLVB2trhvbNuUMEzIEACA20bjU48Ybb9S7774rSUrOGKYWV/9K7qYn/qjapSQq49xW0U4PAICYxXLoekyePFlt2rTRdM9LatVvnFzxCSfEuCRNG5LGDroAAEQRMy71uO666/TPf/5TSUlJyvjBzrmS2DkXAACT0LicRFJSzRLnQT9tp5+ntdWn3xzgWUUAAJiMxsWA+DiXevzfvSxOuLscAAC74h4XAABgGzHXuJSUlJidAgAAaKSYaVyCwaAWLlyozp0769NPPzU7HQAA0AiOvcfl359V5PV6NWnSJL3xxhuSpPnz56tbt24NPl9j8whnLM/fqGGn529E8pxWeCaMkTieVWSvMZ1Ql1wra9ipJo0e55gZF4/Ho7S0NPXo0aPO61u3blXPnj31xhtvqEmTJvrjH/8oj8djUpYAACAUjplx+eGzihISEvT888/r3nvvlc/nU+fOnfXyyy/r6quvDmmcxq4q4vkbkWGH529E45xWeCaMkTieVWSvMZ1Ql1wra9ihJmP+WUW33HKL3nrrLUnS0KFD9dxzz6lVK7bnBwDAzhzzUdEPvfXWW0pISNDs2bO1YsUKmhYAABzAsTMuXbp00euvv67LL7/c7FQAAECYOK5xCQaDkqQ1a9aoY8eO8nq9kv7/buXGfs7X2OMbcpyR2FBjvF6vjh49Kq/Xq7g4+064hfr7tMqYZtRluGvSSNzp3qcurTWmE+qSa2UNO9Xk8b+vj/89fjKOa1xKS0slSRdffLHJmQAAgIYqLS1VSkrKSd93BU/X2thMIBBQQUGBkpKS5HLVfRBijx499Pnnnzf63I09viHHGYkNJcbr9apTp076/vvvlZycbCgnqwr192mVMc2oy3DXpJG4U71PXVpvTCfUJdfKGnapyWAwqNLSUrVv3/6Us1yOm3GJi4tTx44d630vPj4+pAJs7PENOc5IbDhikpOTbf8/Y6i/T6uMaUZdhrsmjcQZOQ91aZ0xnVCXXCtr2KkmTzXTcpx9P7hrhOzsbFOOb8hxRmLDFWN3ZnyPkRjTjLoMd00aiYuFmpSoy1CO51oZGU6pyeMc91ERTu34Bn1Hjhyx/b8i4BzUJayGmrSumJpxgdS0aVNNmzZNTZs2NTsVoBZ1CauhJq2LGRcAAGAbzLgAAADboHEBAAC2QeMCAABsg8YFAADYBo0LAACwDRoXnNIvfvELtWzZUsOHDzc7FcSot956SxdeeKF+/OMfa8GCBWanA0ji2mgmlkPjlD788EOVlpbqxRdf1BtvvGF2Oogx1dXVSktL0/r165WSkqLu3bvrk08+UevWrc1ODTGOa6N5mHHBKfXt21dJSUlmp4EYtWnTJl188cXq0KGDzjzzTA0ePFhr1641Oy2Aa6OJaFxs7KOPPtKQIUPUvn17uVwurVq16oQYj8ejLl26KDExUT179tSmTZuinyhiVqg1WlBQoA4dOtR+3aFDB+3bty8aqcPBuHbaG42LjZWXl6tr167yeDz1vr98+XLl5uZq2rRp2rJli7p27aqBAwfq4MGDtTHp6en66U9/esJ/BQUF0fo24GDhqFEg3KhLmwvCESQFV65cWee1jIyMYHZ2du3Xfr8/2L59++CMGTMadO7169cHb7rppnCkiRjWmBr9+OOPg0OHDq19f8qUKcGlS5dGJV/EhlCunVwbzcGMi0P5fD5t3rxZAwYMqH0tLi5OAwYM0MaNG03MDKhhpEYzMjL05Zdfat++fSorK9M777yjgQMHmpUyYgDXTutrYnYCiIyioiL5/X6lpqbWeT01NVU7d+40fJ4BAwZo+/btKi8vV8eOHfX666+rd+/e4U4XMchIjTZp0kRPPvmk+vXrp0AgoPvuu48VRYgoo9dOro3moXHBKb3//vtmp4AYd8MNN+iGG24wOw2gDq6N5uGjIodq06aN4uPjdeDAgTqvHzhwQG3btjUpK+D/UaOwIurS+mhcHMrtdqt79+5at25d7WuBQEDr1q1jOhOWQI3CiqhL6+OjIhsrKyvTrl27ar/evXu3tm3bplatWqlz587Kzc1VZmamLr/8cmVkZGjWrFkqLy9XVlaWiVkjllCjsCLq0ubMXtaExlu/fn1Q0gn/ZWZm1sbMmTMn2Llz56Db7Q5mZGQEP/30U/MSRsyhRmFF1KW98awiAABgG9zjAgAAbIPGBQAA2AaNCwAAsA0aFwAAYBs0LgAAwDZoXAAAgG3QuAAAANugcQEAALZB4wIAAGyDxgUAANgGjQsAALANGhcAAGAb/wsCpdiTDVMhjAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "net.plot_solution_vs_reference(sol, ref_sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", + "\n", + "\n", + "Error (%): [ 1.528 -2.184 -0.331 0.289]\n", + "\n", + "\n", + "sol : [ 1.739 1.804 87.084 74.951]\n", + "ref : [ 1.766 1.766 86.797 75.168]\n", + "diff: [ 0.027 -0.039 -0.288 0.217]\n", + "\n", + "\n", + "encoded_sol: [ 1.739 1.804 87.084 74.951]\n", + "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", + "diff : [ 0.027 -0.038 -0.294 0.196]\n", + "\n", + "\n", + "E sol : -1662.5979676922227\n", + "R ref : -1662.6061020456154\n", + "Delta E : 0.008134353392733829\n", + "\n", + "\n", + "Residue sol : 0.09076400808170053\n", + "Residue ref : 0.010186471203764017\n", + "Delta Residue : 0.0805775368779365\n" + ] + } + ], + "source": [ + "net.diagnostic_solution(sol, ref_sol)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run with the intergrated WNTR Solver" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr_quantum.sim.FullQuboPolynomialSimulator(wn, \n", + " flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)\n", + "results = sim.run_sim(solver_options={\"sampler\" : sampler})\n", + "\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + 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Date: Wed, 11 Sep 2024 09:12:48 +0200 Subject: [PATCH 47/96] 2loops example --- .../sandbox/qubo_poly_solver_Net2loops.ipynb | 544 ++++++++++++++++-- 1 file changed, 481 insertions(+), 63 deletions(-) diff --git a/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb b/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb index 078fc1f..78fe41b 100644 --- a/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb +++ b/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb @@ -9,14 +9,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 142, "metadata": { "metadata": {} }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 1, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -39,16 +39,230 @@ "import wntr\n", "import wntr_quantum\n", "import numpy as np\n", + "import os \n", + "\n", + "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", + "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", "\n", "# Create a water network model\n", - "inp_file = './networks/Net0.inp'\n", - "# inp_file = './networks/Net2LoopsDW.inp'\n", + "# inp_file = '../networks/Net0.inp'\n", + "# inp_file = '../networks/Net2LoopsDW.inp'\n", + "inp_file = '../networks/Net2LoopsCMflat.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", "# Graph the network\n", "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run with WNTR Simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [], + "source": [ + "inp_file = '../networks/Net2LoopsFlat.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "sim = wntr.sim.WNTRSimulator(wn)\n", + "model, updater = wntr.sim.hydraulics.create_hydraulic_model(wn)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cons:\n", + "mass_balance[2]: (((expected_demand[2]-flow[1])+flow[2])+flow[3])\n", + "mass_balance[3]: ((expected_demand[3]-flow[2])+flow[7])\n", + "mass_balance[4]: (((expected_demand[4]-flow[3])+flow[4])+flow[5])\n", + "mass_balance[5]: (((expected_demand[5]-flow[4])-flow[7])+flow[8])\n", + "mass_balance[6]: ((expected_demand[6]-flow[5])+flow[6])\n", + "mass_balance[7]: ((expected_demand[7]-flow[6])-flow[8])\n", + "approx_hazen_williams_headloss[1]: (((((((-((sign(flow[1]))))*hw_resistance[1])*((abs(flow[1]))**1.852))-((1e-05*(hw_resistance[1]**0.5))*flow[1]))-(((sign(flow[1]))*minor_loss[1])*(flow[1]**2.0)))+source_head[1])-head[2])\n", + "approx_hazen_williams_headloss[2]: (((((((-((sign(flow[2]))))*hw_resistance[2])*((abs(flow[2]))**1.852))-((1e-05*(hw_resistance[2]**0.5))*flow[2]))-(((sign(flow[2]))*minor_loss[2])*(flow[2]**2.0)))+head[2])-head[3])\n", + "approx_hazen_williams_headloss[3]: (((((((-((sign(flow[3]))))*hw_resistance[3])*((abs(flow[3]))**1.852))-((1e-05*(hw_resistance[3]**0.5))*flow[3]))-(((sign(flow[3]))*minor_loss[3])*(flow[3]**2.0)))+head[2])-head[4])\n", + "approx_hazen_williams_headloss[4]: (((((((-((sign(flow[4]))))*hw_resistance[4])*((abs(flow[4]))**1.852))-((1e-05*(hw_resistance[4]**0.5))*flow[4]))-(((sign(flow[4]))*minor_loss[4])*(flow[4]**2.0)))+head[4])-head[5])\n", + "approx_hazen_williams_headloss[5]: (((((((-((sign(flow[5]))))*hw_resistance[5])*((abs(flow[5]))**1.852))-((1e-05*(hw_resistance[5]**0.5))*flow[5]))-(((sign(flow[5]))*minor_loss[5])*(flow[5]**2.0)))+head[4])-head[6])\n", + "approx_hazen_williams_headloss[6]: (((((((-((sign(flow[6]))))*hw_resistance[6])*((abs(flow[6]))**1.852))-((1e-05*(hw_resistance[6]**0.5))*flow[6]))-(((sign(flow[6]))*minor_loss[6])*(flow[6]**2.0)))+head[6])-head[7])\n", + "approx_hazen_williams_headloss[7]: (((((((-((sign(flow[7]))))*hw_resistance[7])*((abs(flow[7]))**1.852))-((1e-05*(hw_resistance[7]**0.5))*flow[7]))-(((sign(flow[7]))*minor_loss[7])*(flow[7]**2.0)))+head[3])-head[5])\n", + "approx_hazen_williams_headloss[8]: (((((((-((sign(flow[8]))))*hw_resistance[8])*((abs(flow[8]))**1.852))-((1e-05*(hw_resistance[8]**0.5))*flow[8]))-(((sign(flow[8]))*minor_loss[8])*(flow[8]**2.0)))+head[5])-head[7])\n", + "\n", + "vars:\n", + "flow[1]: flow[1]\n", + "flow[2]: flow[2]\n", + "flow[3]: flow[3]\n", + "flow[7]: flow[7]\n", + "flow[4]: flow[4]\n", + "flow[5]: flow[5]\n", + "flow[8]: flow[8]\n", + "flow[6]: flow[6]\n", + "head[2]: head[2]\n", + "head[3]: head[3]\n", + "head[4]: head[4]\n", + "head[5]: head[5]\n", + "head[6]: head[6]\n", + "head[7]: head[7]\n", + "\n" + ] + } + ], + "source": [ + "print(model.__str__())" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "res = sim.run_sim()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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", 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9oL8++6E2bdwX0N7qglTdcmcn1UqqUU2Vnfks27bt6i6iMvJXrtXu/31CClJu3eE3KGXwtWGuCggtf+seLeo4UiX5ReX2N7ziImUufCzMVQGhFf9QqkfHLNa+Pfnl9qfW92jcU71VI46/VP4aHHON+cDzLwUNZUk6+PdXVVZ0NIwVARX77M/zgoayJO3594c68OHmMFYEVGzVip1BQ1mScvYVaOXyr8NY0W+LI5ayj27eqmO7vw05xvdDsT6f80/ZGW3DVBUQmu9YqXb+a0WF49Y+PU8NY64JQ0VA5fxn0c4Kx6x+b6d6XXFuGKr57XFEMHsP51Vq3MyJk/T/vt3+6xYDVJJHUZpqXVzhuOWvL9L01yeGoSKgcvpfNkXxNeqEHJOf90OYqvntcUQwR9Sp3O7V2x56QLd2aPPrFgNUkq/Eq83dxspXXBpyXOa1V2jIA3PCUxRQCa/8baf27w0dvIls/vrVOCKYa7T8nWLSGqt4xzdBx7jiauj8wYPkrhEbxsqA0Ir/kKntf1scckzGfTcouV2LMFUEVKzgu5qaPXNtyDGXdE8LUzW/PY7Z/JU6cojkDl5u3WGEMsxzwUNZiq6TELS/6cBLlXwhoQyzXNSlqZqkBV+pbNikljoTzL8ax9wuJUkFH67X/mdfVMmeH++rsz3xOmv4Dap9de9qrAwILm/LN1o9fIpyV3/hb3PFRqnl/1yh9pOGyxXhrsbqgPIVFR7TnOfXaf2a3fL5jseEy2WpXYeGGnprR8V7oqu5wjOXo4JZkmzb1tHPvtS2tR/pjgcf0LT/LFK7Cy+s7rKACh3etFOf/Ps93f/w/+r5D97QhZ07VXdJQIUOf1ekrV/mSpKatUxR7eS4aq7ozOeIa8w/ZVmW4tq0ku07plV5uZKb2QacIan12apVmqcNDx+UO57LLnCGpDpxyujctLrL+E1xzDVmAAB+CwhmAAAMQjADAGAQghkAAIMQzAAAGIRgBgDAIAQzAAAGIZgBADAIwQwAgEEIZgAADEIwAwBgEIIZAACDEMwAABiEYAYAwCAEMwAABiGYAQAwCMEMAIBBCGYAAAxCMAMAYBCCGQAAgxDMAAAYhGAGAMAgBDMAAAYhmAEAMAjBDACAQQhmAAAMQjADAGAQghkAAIMQzAAAGIRgBgDAIAQzAAAGIZgBADAIwQwAgEEIZgAADEIwAwBgEIIZAHBGa9KkiaZOnVrdZVQawQwAqHZDhgyRZVl64oknAtoXLlwoy7KqqarqQTADAIwQExOjSZMm6fvvv6/uUqoVwQwAMEJmZqZSU1M1ceLEoGNee+01tWrVStHR0WrSpImefvrpgP7c3Fz169dPsbGxatq0qebNm3fSe+Tl5enmm29WcnKyPB6PunXrps8+++y0n8+pIpgBAEZwu93685//rOnTp2vv3r0n9W/YsEHXXXedBg4cqE2bNmn8+PF65JFHNGfOHP+YIUOGaM+ePVqxYoVeffVVzZw5U7m5uQHvc+211yo3N1eLFy/Whg0b1K5dO3Xv3l2HDx/+tU+xUiKquwAAAE64+uqr1aZNG40bN04vvvhiQN+UKVPUvXt3PfLII5KkZs2a6csvv9RTTz2lIUOGaNu2bVq8eLE++ugjXXjhhZKkF198US1btvS/x6pVq/TRRx8pNzdX0dHRkqTJkydr4cKFevXVVzV8+PAwnWlwzJgBAEaZNGmS5s6dqy1btgS0b9myRZ06dQpo69Spk7Zv366ysjJt2bJFERERSk9P9/e3aNFCiYmJ/p8/++wzFRYWqnbt2oqPj/cf2dnZ2rFjx696XpXFjBkAYJTOnTurZ8+eevDBBzVkyJDT+t6FhYWqV6+e3n///ZP6fhrg1YlgBgAY54knnlCbNm3UvHlzf1vLli21evXqgHGrV69Ws2bN5Ha71aJFC3m9Xm3YsMG/lL1161bl5eX5x7dr1045OTmKiIhQkyZNwnEqVcZSNgDAOK1bt1ZWVpaeffZZf9s999yj5cuX67HHHtO2bds0d+5cPffcc7r33nslSc2bN1evXr00YsQIrVu3Ths2bNDNN9+s2NhY/3tkZmYqIyNDV111lf7zn/9o165d+vDDD/XQQw9p/fr1YT/P8hDMAAAjTZgwQT6fz/9zu3bt9Morr+jll1/Weeedp7Fjx2rChAkBy92zZ89W/fr11aVLF/Xv31/Dhw9XSkqKv9+yLL3zzjvq3Lmzhg4dqmbNmmngwIH65ptvVLdu3XCeXlCWbdt2dRdxKjZu3Kj09HT/VnfACfi9BVARZswAABiEYAYAwCAEMwAABiGYAQAwCMEMAICkiRMn6sILL1TNmjWVkpKiq666Slu3bg0YU1xcrJEjR/qfHDZgwAAdOHAgYMzu3bvVt29f1ahRQykpKbrvvvvk9XorXQfBDACApA8++EAjR47U2rVrtWzZMpWWlqpHjx4qKiryj7n77rv11ltvacGCBfrggw+0b98+9e/f399fVlamvn37qqSkRB9++KHmzp2rOXPmaOzYsZWug9ulgDDi9xY4fYqLi1VSUhJyjG3bsiwroC06Otr/BRahHDx4UCkpKfrggw/UuXNn5efnKzk5WfPnz9c111wjSfrqq6/UsmVLrVmzRh07dtTixYt1+eWXa9++ff77omfNmqX7779fBw8eVFRUVIWfy4wZAOA4xcXFSo1NUEJC6KNBgwYntYX6vuefys/PlyQlJSVJOv61k6WlpcrMzPSPadGihRo1aqQ1a9ZIktasWaPWrVsHPKykZ8+eKigo0ObNmyv1uTwrGwDgOCUlJcpXiaZGdlJskCj7QV7dVbhae/bskcfj8bdXZrbs8/l01113qVOnTjrvvPMkSTk5OYqKijrpyy7q1q2rnJwc/5ifP0HsxM8nxlSEYAYAOFYNV6RqWOVHmWUfX8L2eDwBwVwZI0eO1BdffKFVq1b94hqriqVsAIBjRUZaIY9TMWrUKC1atEgrVqxQgwYN/O2pqakqKSkJ+LYqSTpw4IBSU1P9Y36+S/vEzyfGVIRgBgA4lssV+qgK27Y1atQovfHGG3rvvffUtGnTgP709HRFRkZq+fLl/ratW7dq9+7dysjIkCRlZGRo06ZNys3N9Y9ZtmyZPB6Pzj333ErVwVI2AMCxXG5LLqv8mbHLrtqMeeTIkZo/f77efPNN1axZ039NOCEhQbGxsUpISNCwYcM0evRoJSUlyePx6Pbbb1dGRoY6duwoSerRo4fOPfdc3XjjjXryySeVk5Ojhx9+WCNHjqzUtW2JYAYAOFhEhKUIV/kBHOGrWjA///zzkqSuXbsGtM+ePdv/1ZLPPPOMXC6XBgwYoGPHjqlnz56aOXOmf6zb7daiRYt06623KiMjQ3FxcRo8eLAmTJhQ+XOqUtUAABjE7Tp+lNtXxfeqzGM9YmJiNGPGDM2YMSPomMaNG+udd96p4qf/iGAGADiWOzL4jNldxRmzKQhmAIBjHd/kFeQac5hrOV0IZgCAY4XafU0wAwAQZpERliLd5c+YI8tYygYAIKxcbkuuIMHsEsEMAEBYhVzKduR3JxLMAAAHc0daiggyY3YH2RRmOoIZAOBYLpcVfFd2FZ/8ZQqCGQDgWJERliIjgmz+CvKoTtMRzAAAxwp5jdmh90sRzAAAxwq5K5ulbAAAwssdYcsdUf72a7ecuS2bYAYAOJblOn4E63MighkA4Fguty2Xu/yZsasS3xZlIoIZAOBYlsuWK8iTRCyHPmGEYAYAOJZlhVjKdubeL4IZAOBcrghbriCbv1jKBgAgzLiPGQAAg1iWLcsKco05SLvpCGYAgGOxlA0AgEG4jxkAAIO4IxT8yV/OnDATzAAA57IU4hozj+QEACC8WMoGAMAgrhBfYuHyMWMGACCsLJcd9NGbPJITAIAwC/klFg7d/UUwAwAciyd/AQBgEJayAQAwiBVhyYos/2ukLJ8zv16KYAYAOJblsmS5ggRzkHbTEcwAAOdyu44fwfociGAGADiWFWnJiiw/gFnKBgAg3FzW8SNYnwMRzAAAx7IiXMFnzGXOXMp2ZtUAAEg/XmMOdlTBypUr1a9fP9WvX1+WZWnhwoUB/UOGDJFlWQFHr169AsYcPnxYWVlZ8ng8SkxM1LBhw1RYWFilOghmAIBjndiVHeyoiqKiIl1wwQWaMWNG0DG9evXS/v37/cc///nPgP6srCxt3rxZy5Yt06JFi7Ry5UoNHz68SnWwlA0AcK4o1/GjPL6qzT179+6t3r17hxwTHR2t1NTUcvu2bNmiJUuW6OOPP1b79u0lSdOnT1efPn00efJk1a9fv1J1MGMGADhWZWbMBQUFAcexY8dO+fPef/99paSkqHnz5rr11lt16NAhf9+aNWuUmJjoD2VJyszMlMvl0rp16yr9GQQzAMC5ItxSZJAjwi1JatiwoRISEvzHxIkTT+mjevXqpZdeeknLly/XpEmT9MEHH6h3794qKyuTJOXk5CglJSWwvIgIJSUlKScnp/KndErVAQBgAMttyXIHefLX/7Xv2bNHHo/H3x4dHX1KnzVw4ED/v7du3Vrnn3++0tLS9P7776t79+6n9J7lYcYMAHCuE/cxBzskeTyegONUg/nnzj77bNWpU0dff/21JCk1NVW5ubkBY7xerw4fPhz0unS5p3RaqgMAoBpYka6Qx69p7969OnTokOrVqydJysjIUF5enjZs2OAf895778nn86lDhw6Vfl+WsgEAznUan5VdWFjon/1KUnZ2tj799FMlJSUpKSlJjz76qAYMGKDU1FTt2LFDY8aM0TnnnKOePXtKklq2bKlevXrplltu0axZs1RaWqpRo0Zp4MCBld6RLTFjBgA42PGvfQwyY46o2n3M69evV9u2bdW2bVtJ0ujRo9W2bVuNHTtWbrdbn3/+ua644go1a9ZMw4YNU3p6uv773/8GLI3PmzdPLVq0UPfu3dWnTx9dfPHF+stf/lKlOpgxAwCcy20dP4L1VUHXrl1l23bQ/qVLl1b4HklJSZo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98, "metadata": {}, "output_type": "execute_result" } @@ -163,7 +505,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ @@ -172,15 +514,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 100, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n" + "Head Encoding : 500.000000 => 700.000000 (res: 0.391389)\n", + "Flow Encoding : -10.000000 => 10.000000 (res: 0.039139)\n" ] } ], @@ -190,18 +532,38 @@ "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", "nqbit = 9\n", - "step = (0.5/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", + "step = (20/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=-10, var_base_name=\"x\")\n", "\n", "nqbit = 9\n", - "step = (50/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")\n", + "step = (200/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+500.0, var_base_name=\"x\")\n", "\n", "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", " head_encoding=head_encoding)\n", "net.verify_encoding()" ] }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 10.986, 1.805, 8.2 , 0.824, 1.097, 5.926, -0.727, 2.689, 641.471, 544.083, 614.941, 452.317, 589.038, 523.654], dtype=float32)" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.convert_solution_from_si(ref_values)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -211,60 +573,116 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 107, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + "Warning, we didn't reach the required tolerance within 100 iterations, error is at 194.49915433794996\n" ] }, + { + "ename": "AssertionError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[107], line 6\u001b[0m\n\u001b[1;32m 3\u001b[0m net\u001b[38;5;241m.\u001b[39mmatrices \u001b[38;5;241m=\u001b[39m net\u001b[38;5;241m.\u001b[39minitialize_matrices(model)\n\u001b[1;32m 4\u001b[0m net\u001b[38;5;241m.\u001b[39mverify_solution(net\u001b[38;5;241m.\u001b[39mconvert_solution_from_si(ref_values))\n\u001b[0;32m----> 6\u001b[0m ref_sol \u001b[38;5;241m=\u001b[39m \u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclassical_solutions\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_iter\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1e-3\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m ref_sol \u001b[38;5;241m/\u001b[39m ref_values\n", + "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sim/solvers/qubo_polynomial_solver.py:110\u001b[0m, in \u001b[0;36mQuboPolynomialSolver.classical_solutions\u001b[0;34m(self, max_iter, tol)\u001b[0m\n\u001b[1;32m 108\u001b[0m res \u001b[38;5;241m=\u001b[39m newton_raphson(func, initial_point, max_iter\u001b[38;5;241m=\u001b[39mmax_iter, tol\u001b[38;5;241m=\u001b[39mtol)\n\u001b[1;32m 109\u001b[0m sol \u001b[38;5;241m=\u001b[39m res\u001b[38;5;241m.\u001b[39msolution\n\u001b[0;32m--> 110\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(func(sol), \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 112\u001b[0m \u001b[38;5;66;03m# convert back to SI\u001b[39;00m\n\u001b[1;32m 113\u001b[0m sol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_solution_to_si(sol)\n", + "\u001b[0;31mAssertionError\u001b[0m: " + ] + } + ], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.matrices = net.initialize_matrices(model)\n", + "net.verify_solution(net.convert_solution_from_si(ref_values))\n", + "\n", + "ref_sol = net.classical_solutions(max_iter = 100, tol= 1e-3)\n", + "ref_sol / ref_values" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { "data": { "text/plain": [ - "array([1. , 1. , 0.999, 0.998])" + "array([[ 0.981],\n", + " [ 0.981],\n", + " [ 1.177],\n", + " [ 2.649],\n", + " [ 3.237],\n", + " [ 1.962],\n", + " [688.976],\n", + " [ 0. ],\n", + " [ 0. ],\n", + " [ 0. ],\n", + " [ 0. ],\n", + " [ 0. ],\n", + " [ 0. ],\n", + " [ 0. ]])" ] }, - "execution_count": 8, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.matrices = net.initialize_matrices(model)\n", - "\n", - "ref_sol = net.classical_solutions()\n", - "ref_sol / ref_values" + "net.matrices[0]" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 108, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import SimulatedAnnealingSampler\n", "\n", - "sampler = SteepestDescentSolver()\n", + "sampler = SimulatedAnnealingSampler()\n", "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.solve(model, options={\"sampler\" : sampler})\n", + "net.solve(model, strength=1E6, num_reads=10000, options={\"sampler\" : sampler, })\n", "sol = net.extract_data_from_model(model)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 5.068, 4.403, 3.659, 5.108, 1.233, -6.634, 1.82 , 2.72 , 700. , 700. , 700. , 500. , 599.804, 500. ])" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.convert_solution_from_si(sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 110, "metadata": {}, "outputs": [ { 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", 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", "text/plain": [ "
" ] @@ -274,48 +692,48 @@ } ], "source": [ - "net.plot_solution_vs_reference(sol, ref_sol)" + "net.plot_solution_vs_reference(sol, ref_values)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", + "Head Encoding : 500.000000 => 700.000000 (res: 0.391389)\n", + "Flow Encoding : -10.000000 => 10.000000 (res: 0.039139)\n", "\n", "\n", - "Error (%): [ 1.528 -2.184 -0.331 0.289]\n", + "Error (%): [ 146.136 133.609 0.963 248.009 144.582 -213.716 278.043 -268.863 -9.124 -28.657 -16.952 -10.542 -4.745 4.517]\n", "\n", "\n", - "sol : [ 1.739 1.804 87.084 74.951]\n", - "ref : [ 1.766 1.766 86.797 75.168]\n", - "diff: [ 0.027 -0.039 -0.288 0.217]\n", + "sol : [-5.068e+00 -6.067e-01 8.121e+00 -1.624e+00 -2.642e+00 8.434e+00 -1.468e+00 -2.681e+00 7.000e+02 7.000e+02 7.000e+02 5.000e+02 5.998e+02 5.000e+02]\n", + "ref : [ 10.986 1.805 8.2 1.097 5.926 2.689 0.824 -0.727 641.471 544.083 598.537 452.317 572.634 523.654]\n", + "diff: [ 1.605e+01 2.412e+00 7.895e-02 2.722e+00 8.568e+00 -5.746e+00 2.292e+00 1.954e+00 -5.853e+01 -1.559e+02 -1.015e+02 -4.768e+01 -2.717e+01 2.365e+01]\n", "\n", "\n", - "encoded_sol: [ 1.739 1.804 87.084 74.951]\n", - "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", - "diff : [ 0.027 -0.038 -0.294 0.196]\n", + "encoded_sol: [-5.068e+00 -6.067e-01 8.121e+00 -1.624e+00 -2.642e+00 8.434e+00 -1.468e+00 -2.681e+00 7.000e+02 7.000e+02 7.000e+02 5.000e+02 5.998e+02 5.000e+02]\n", + "encoded_ref: [ 10. 1.82 8.2 1.115 5.93 2.681 0.841 -0.724 641.292 544.227 598.63 500. 572.798 523.483]\n", + "diff : [ 1.507e+01 2.427e+00 7.828e-02 2.740e+00 8.571e+00 -5.753e+00 2.309e+00 1.957e+00 -5.871e+01 -1.558e+02 -1.014e+02 0.000e+00 -2.701e+01 2.348e+01]\n", "\n", "\n", - "E sol : -1662.5979676922227\n", - "R ref : -1662.6061020456154\n", - "Delta E : 0.008134353392733829\n", + "E sol : -549306305.587519\n", + "R ref : -528587363.97844124\n", + "Delta E : -20718941.60907781\n", "\n", "\n", - "Residue sol : 0.09076400808170053\n", - "Residue ref : 0.010186471203764017\n", - "Delta Residue : 0.0805775368779365\n" + "Residue sol : 809.3375066786808\n", + "Residue ref : 4623.198980013018\n", + "Delta Residue : -3813.861473334337\n" ] } ], "source": [ - "net.diagnostic_solution(sol, ref_sol)" + "net.diagnostic_solution(sol, ref_values)" ] }, { From 08b566291ac89aedd8edaa5258ea4475fa613005 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 11 Sep 2024 10:20:08 +0200 Subject: [PATCH 48/96] started refac --- wntr_quantum/sim/models/mass_balance.py | 27 ++++++++-------- .../sim/solvers/qubo_polynomial_solver.py | 32 +++++++++++++++---- 2 files changed, 39 insertions(+), 20 deletions(-) diff --git a/wntr_quantum/sim/models/mass_balance.py b/wntr_quantum/sim/models/mass_balance.py index cd408b7..30c4906 100644 --- a/wntr_quantum/sim/models/mass_balance.py +++ b/wntr_quantum/sim/models/mass_balance.py @@ -3,23 +3,24 @@ from wntr.epanet.util import from_si -def get_mass_balance_matrix(m, wn, matrices, convert_to_us_unit=False): # noqa: D417 +def get_mass_balance_qubops_matrix( + m, wn, matrices, convert_to_us_unit=False +): # noqa: D417 """Create the matrices for the mass balance equation. Args: - m (_type_): _description_ - wn (_type_): _description_ - matrices (_type_): _description_ - convert_to_us_unit (bool, optional): _description_. Defaults to False. + m (aml.Model): The AML model of the network + wn (WaternNetwork): th water network object + matrices (Tuple): The qubops matrices of the network + convert_to_us_unit (bool, optional): Convert the inut to US units. Defaults to False. Returns: - _type_: _description_ + Tuple: The qubops matrices of the network """ P0, P1, P2 = matrices continuous_var_name = [v.name for v in list(m.vars())] - # discrete_var_name = [v.name for k, v in m.cm_resistance.items()] - var_names = continuous_var_name # + discrete_var_name + var_names = continuous_var_name index_over = wn.junction_name_list @@ -28,19 +29,19 @@ def get_mass_balance_matrix(m, wn, matrices, convert_to_us_unit=False): # noqa: node = wn.get_node(node_name) if not node._is_isolated: if convert_to_us_unit: - P0[ieq, 0] = from_si( + P0[ieq, 0] += from_si( FlowUnits.CFS, m.expected_demand[node_name].value, HydParam.Flow ) else: P0[ieq, 0] += m.expected_demand[node_name].value for link_name in wn.get_links_for_node(node_name, flag="INLET"): - node_index = var_names.index(m.flow[link_name].name) - P1[ieq, node_index] -= 1 + link_index = var_names.index(m.flow[link_name].name) + P1[ieq, link_index] -= 1 for link_name in wn.get_links_for_node(node_name, flag="OUTLET"): - node_index = var_names.index(m.flow[link_name].name) - P1[ieq, node_index] += 1 + link_index = var_names.index(m.flow[link_name].name) + P1[ieq, link_index] += 1 return P0, P1, P2 diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index def6f5b..2dfec7a 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -4,7 +4,7 @@ import numpy as np import sparse from quantum_newton_raphson.newton_raphson import newton_raphson -from qubops.encodings import BaseQbitEncoding +from qubops.encodings import BaseQbitEncoding, PositiveQbitEncoding from qubops.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector from qubops.qubops_mixed_vars import QUBOPS_MIXED from qubops.solution_vector import SolutionVector_V2 as SolutionVector @@ -17,7 +17,7 @@ from wntr.sim.solvers import SolverStatus from ..models.chezy_manning import get_chezy_manning_matrix from ..models.darcy_weisbach import get_darcy_weisbach_matrix -from ..models.mass_balance import get_mass_balance_matrix +from ..models.mass_balance import get_mass_balance_qubops_matrix class QuboPolynomialSolver(object): @@ -33,18 +33,36 @@ def __init__( Args: wn (WaterNetworkModel): water network - flow_encoding (qubops.encodings.BaseQbitEncoding): binary encoding for the flow + flow_encoding (qubops.encodings.BaseQbitEncoding): binary encoding for the bsolute value of the flow head_encoding (qubops.encodings.BaseQbitEncoding): binary encoding for the head """ self.wn = wn - # create the encoding vectors + # create the encoding vectors for the sign of the flows + self.sign_flow_encoding = PositiveQbitEncoding( + nqbit=1, step=2, offset=-1, var_base_name="x" + ) + + # store the encoding of the flow self.flow_encoding = flow_encoding + if np.min(self.flow_encoding.get_possible_values()) < 0: + raise ValueError( + "The encoding of the flows must only take positive values." + ) + + # store the encoding of the head self.head_encoding = head_encoding + + # create the solution vectors + self.sol_vect_signs = SolutionVector( + wn.num_pipes, encoding=self.sign_flow_encoding + ) self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) self.sol_vect_heads = SolutionVector(wn.num_junctions, encoding=head_encoding) + + # create the mixed solution vector self.mixed_solution_vector = MixedSolutionVector( - [self.sol_vect_flows, self.sol_vect_heads] + [self.sol_vect_signs, self.sol_vect_flows, self.sol_vect_heads] ) # init other attributes @@ -58,7 +76,7 @@ def verify_encoding(self): fres = self.flow_encoding.get_average_precision() fvalues = np.sort(self.flow_encoding.get_possible_values()) print("Head Encoding : %f => %f (res: %f)" % (hvalues[0], hvalues[-1], hres)) - print("Flow Encoding : %f => %f (res: %f)" % (fvalues[0], fvalues[-1], fres)) + print("Flow Encoding : %f => %f (res: %f)" % (-fvalues[0], fvalues[-1], fres)) def verify_solution(self, input: np.ndarray) -> np.ndarray: """Computes the rhs vector associate with the input. @@ -192,7 +210,7 @@ def initialize_matrices(self, model: Model) -> Tuple: matrices = (P0, P1, P2) # get the mass balance - matrices = get_mass_balance_matrix( + matrices = get_mass_balance_qubops_matrix( model, self.wn, matrices, convert_to_us_unit=True ) From d4ecc2d402e58293aefa6b66dd2a53ea41cba7cb Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 11 Sep 2024 11:22:32 +0200 Subject: [PATCH 49/96] changed init matrices to absolute value formalism --- wntr_quantum/sim/models/chezy_manning.py | 102 +++++++++--------- wntr_quantum/sim/models/darcy_weisbach.py | 99 +++++++++-------- wntr_quantum/sim/models/mass_balance.py | 21 ++-- .../sim/solvers/qubo_polynomial_solver.py | 44 ++++++-- 4 files changed, 149 insertions(+), 117 deletions(-) diff --git a/wntr_quantum/sim/models/chezy_manning.py b/wntr_quantum/sim/models/chezy_manning.py index 01a60cd..709eb52 100644 --- a/wntr_quantum/sim/models/chezy_manning.py +++ b/wntr_quantum/sim/models/chezy_manning.py @@ -9,10 +9,10 @@ def chezy_manning_constants(m): - """Add cehzy manning constants to the model. + """Add chezy manning constants to the model. Args: - m (_type_): _description_ + m (aml.Model): Model of the netwwork """ m.cm_exp = 2 m.cm_k = (4 / (1.49 * np.pi)) ** 2 * (1 / 4) ** -1.33 @@ -21,33 +21,36 @@ def chezy_manning_constants(m): def cm_resistance_prefactor(k, roughness, roughness_exp, diameter, diameter_exp): - """_summary_. + """Computes the resistance prefactor. Args: - k (_type_): _description_ - roughness (_type_): _description_ - roughness_exp (_type_): _description_ - diameter (_type_): _description_ - diameter_exp (_type_): _description_ + k (float): scaling parameter of the approximatioj + roughness (float): roughness pf the pipe + roughness_exp(int): exponent of the pip diameter in the approx (typically 2) + diameter (float): dimater of the pipe + diameter_exp (int): exponent of the pip diameter in the approx (typically -5.33) + + Returns: + float: resistance prefactor """ return k * roughness ** (roughness_exp) * diameter ** (diameter_exp) -def cm_resistance_value(k, roughness, exp, diameter, diameter_exp, length): - """_summary_. +def cm_resistance_value(k, roughness, roughness_exp, diameter, diameter_exp, length): + """Computes the resistance value. Args: - k (_type_): _description_ - roughness (_type_): _description_ - exp (_type_): _description_ - diameter (_type_): _description_ - diameter_exp (_type_): _description_ - length (_type_): _description_ + k (float): scaling parameter of the approximatioj + roughness (float): roughness pf the pipe + roughness_exp(int): exponent of the pip diameter in the approx (typically 2) + diameter (float): dimater of the pipe + diameter_exp (int): exponent of the pip diameter in the approx (typically -5.33) + length (float): length of the pipe Returns: - _type_: _description_ + float: resistance value """ - return cm_resistance_prefactor(k, roughness, exp, diameter, diameter_exp) * length + return cm_resistance_prefactor(k, roughness, roughness_exp, diameter, diameter_exp) * length class cm_resistance_param(Definition): # noqa: D101 @@ -152,63 +155,64 @@ def build(cls, m, wn, updater, index_over=None): # noqa: D417 ) -def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 - """Adds a mass balance to the model for the specified junctions. +def get_chezy_manning_qubops_matrix( + m, wn, matrices, flow_index_mapping, head_index_mapping +): # noqa: D417 + """Create the matrices for chezy manning headloss approximation. - Parameters - ---------- - m: wntr.aml.aml.aml.Model - wn: wntr.network.model.WaterNetworkModel - updater: ModelUpdater - index_over: list of str - list of pipe names; default is all pipes in wn - """ - P0, P1, P2 = matrices - - continuous_var_name = [v.name for v in list(m.vars())] - discrete_var_name = [v.name for k, v in m.cm_resistance.items()] - - var_names = continuous_var_name + discrete_var_name + Args: + m (aml.Model): The AML model of the network + wn (WaternNetwork): th water network object + matrices (Tuple): The qubops matrices of the network + flow_index_mapping (Dict): A dict to map the flow model variables to the qubops matrices + head_index_mapping (Dict): A dict to map the head model variables to the qubops matrices + convert_to_us_unit (bool, optional): Convert the inut to US units. Defaults to False. - index_over = wn.pipe_name_list + Returns: + Tuple: The qubops matrices of the network + """ + P0, P1, P2, P3 = matrices - for ieq0, link_name in enumerate(index_over): + for ieq0, link_name in enumerate(wn.pipe_name_list): + # index of the pipe equation ieq = ieq0 + len(wn.junction_name_list) + + # link info link = wn.get_link(link_name) - f = m.flow[link_name] - flow_index = var_names.index(f.name) + # get the start/end node info start_node_name = link.start_node_name end_node_name = link.end_node_name - start_node = wn.get_node(start_node_name) end_node = wn.get_node(end_node_name) + # linear term (start head values) of the headloss approximation if isinstance(start_node, wntr.network.Junction): - start_h = m.head[start_node_name] - start_node_index = var_names.index(start_h.name) - P1[ieq, start_node_index] = 1 + start_node_index = head_index_mapping[m.head[start_node_name].name] + P1[ieq, start_node_index] += 1 else: start_h = m.source_head[start_node_name] P0[ieq, 0] += from_si(FlowUnits.CFS, start_h.value, HydParam.Length) + # linear term (end head values) of the headloss approximation if isinstance(end_node, wntr.network.Junction): - end_h = m.head[end_node_name] - end_node_index = var_names.index(end_h.name) - P1[ieq, end_node_index] = -1 + end_node_index = head_index_mapping[m.head[end_node_name].name] + P1[ieq, end_node_index] -= 1 else: end_h = m.source_head[end_node_name] P0[ieq, 0] -= from_si(FlowUnits.CFS, end_h.value, HydParam.Length) + # non linear term (sign flow^2) of headloss approximation k = m.cm_resistance[link_name] + sign_index = flow_index_mapping[m.flow[link_name].name]["sign"] + flow_index = flow_index_mapping[m.flow[link_name].name]["absolute_value"] + P3[ieq, sign_index, flow_index, flow_index] -= k.value - P2[ieq, flow_index, flow_index] = -k.value - - return (P0, P1, P2) + return (P0, P1, P2, P3) -def get_chezy_manning_matrix_design(m, wn, matrices): # noqa: D417 +def get_chezy_manning_qubops_matrix_design(m, wn, matrices): # noqa: D417 """Adds a mass balance to the model for the specified junctions. Parameters diff --git a/wntr_quantum/sim/models/darcy_weisbach.py b/wntr_quantum/sim/models/darcy_weisbach.py index 5191df4..23a1fb2 100644 --- a/wntr_quantum/sim/models/darcy_weisbach.py +++ b/wntr_quantum/sim/models/darcy_weisbach.py @@ -12,7 +12,7 @@ def darcy_weisbach_constants(m): """Add darcy weisbach constants to the model. Args: - m (_type_): _description_ + m (aml.Model): Model of the netwwork """ m.dw_k = 0.025173 # 16/64.4/pi^2 m.dw_exp = 2 @@ -20,14 +20,16 @@ def darcy_weisbach_constants(m): def dw_resistance_prefactor(k, roughness, diameter, diameter_exp): - """_summary_. + """Computes the resistance prefactor. Args: - k (_type_): _description_ - roughness (_type_): _description_ - exp (_type_): _description_ - diameter (_type_): _description_ - diameter_exp (_type_): _description_ + k (float): scaling parameter of the approximatioj + roughness (float): roughness pf the pipe + diameter (float): dimater of the pipe + diameter_exp (int): exponent of the pip diameter in the approx (typically -5) + + Returns: + Tuple(float, float, float): value of the fit to the full DW formula """ return ( k @@ -40,19 +42,15 @@ def dw_resistance_value(k, roughness, diameter, diameter_exp, length): """_summary_. Args: - k (_type_): _description_ - roughness (_type_): _description_ - exp (_type_): _description_ - diameter (_type_): _description_ - diameter_exp (_type_): _description_ - length (_type_): _description_ + k (float): scaling parameter of the approximatioj + roughness (float): roughness pf the pipe + diameter (float): dimater of the pipe + diameter_exp (int): exponent of the pip diameter in the approx (typically -5) + length (float): length of the pipe Returns: - _type_: _description_ + Tuple(float, float, float): value of the fit to the full DW formula """ - # print("Roughness : %f" % roughness) - # print("diameter : %f" % diameter) - # print("resistance coeff : %f " % (k * (diameter**diameter_exp) * length)) return dw_resistance_prefactor(k, roughness, diameter, diameter_exp) * length @@ -175,63 +173,64 @@ def build(cls, m, wn, updater, index_over=None): # noqa: D417 ) -def get_darcy_weisbach_matrix(m, wn, matrices): # noqa: D417 - """Adds a mass balance to the model for the specified junctions. - - Parameters - ---------- - m: wntr.aml.aml.aml.Model - wn: wntr.network.model.WaterNetworkModel - updater: ModelUpdater - index_over: list of str - list of pipe names; default is all pipes in wn - """ - P0, P1, P2 = matrices - - continuous_var_name = [v.name for v in list(m.vars())] - # discrete_var_name = [v.name for k, v in m.dw_resistance.items()] +def get_darcy_weisbach_qubops_matrix( + m, wn, matrices, flow_index_mapping, head_index_mapping +): # noqa: D417 + """Create the matrices for chezy manning headloss approximation. - var_names = continuous_var_name # + discrete_var_name + Args: + m (aml.Model): The AML model of the network + wn (WaternNetwork): th water network object + matrices (Tuple): The qubops matrices of the network + flow_index_mapping (Dict): A dict to map the flow model variables to the qubops matrices + head_index_mapping (Dict): A dict to map the head model variables to the qubops matrices + convert_to_us_unit (bool, optional): Convert the inut to US units. Defaults to False. - index_over = wn.pipe_name_list + Returns: + Tuple: The qubops matrices of the network + """ + P0, P1, P2, P3 = matrices - for ieq0, link_name in enumerate(index_over): + for ieq0, link_name in enumerate(wn.pipe_name_list): + # index of the pipe equation ieq = ieq0 + len(wn.junction_name_list) + + # get link info link = wn.get_link(link_name) - f = m.flow[link_name] - flow_index = var_names.index(f.name) + # get start/end node info start_node_name = link.start_node_name end_node_name = link.end_node_name - start_node = wn.get_node(start_node_name) end_node = wn.get_node(end_node_name) + # linear term (start head values) of the headloss approximation if isinstance(start_node, wntr.network.Junction): - start_h = m.head[start_node_name] - start_node_index = var_names.index(start_h.name) - P1[ieq, start_node_index] = 1 + start_node_index = head_index_mapping[m.head[start_node_name].name] + P1[ieq, start_node_index] += 1 else: start_h = m.source_head[start_node_name] P0[ieq, 0] += from_si(FlowUnits.CFS, start_h.value, HydParam.Length) + # linear term (end head values) of the headloss approximation if isinstance(end_node, wntr.network.Junction): - end_h = m.head[end_node_name] - end_node_index = var_names.index(end_h.name) - P1[ieq, end_node_index] = -1 + end_node_index = head_index_mapping[m.head[end_node_name].name] + P1[ieq, end_node_index] -= 1 else: end_h = m.source_head[end_node_name] P0[ieq, 0] -= from_si(FlowUnits.CFS, end_h.value, HydParam.Length) + # non linear term (sign flow^2) of headloss approximation k0 = m.dw_resistance_0[link_name] k1 = m.dw_resistance_1[link_name] k2 = m.dw_resistance_2[link_name] - # print(k0.value, k1.value, k2.value) - scaling = 1.0 - P0[ieq] -= scaling * k0.value - P1[ieq, flow_index] -= scaling * k1.value - P2[ieq, flow_index, flow_index] -= scaling * k2.value + sign_index = flow_index_mapping[m.flow[link_name].name]["sign"] + flow_index = flow_index_mapping[m.flow[link_name].name]["absolute_value"] + + P0[ieq] -= k0.value + P2[ieq, sign_index, flow_index] -= k1.value + P3[ieq, sign_index, flow_index, flow_index] -= k2.value - return (P0, P1, P2) + return (P0, P1, P2, P3) diff --git a/wntr_quantum/sim/models/mass_balance.py b/wntr_quantum/sim/models/mass_balance.py index 30c4906..8445f7f 100644 --- a/wntr_quantum/sim/models/mass_balance.py +++ b/wntr_quantum/sim/models/mass_balance.py @@ -4,7 +4,7 @@ def get_mass_balance_qubops_matrix( - m, wn, matrices, convert_to_us_unit=False + m, wn, matrices, flow_index_mapping, convert_to_us_unit=False ): # noqa: D417 """Create the matrices for the mass balance equation. @@ -12,16 +12,13 @@ def get_mass_balance_qubops_matrix( m (aml.Model): The AML model of the network wn (WaternNetwork): th water network object matrices (Tuple): The qubops matrices of the network + flow_index_mapping (Dict): A dict to map the flow model variables to the qubops matrices convert_to_us_unit (bool, optional): Convert the inut to US units. Defaults to False. Returns: Tuple: The qubops matrices of the network """ - P0, P1, P2 = matrices - - continuous_var_name = [v.name for v in list(m.vars())] - var_names = continuous_var_name - + P0, P1, P2, P3 = matrices index_over = wn.junction_name_list for ieq, node_name in enumerate(index_over): @@ -36,14 +33,16 @@ def get_mass_balance_qubops_matrix( P0[ieq, 0] += m.expected_demand[node_name].value for link_name in wn.get_links_for_node(node_name, flag="INLET"): - link_index = var_names.index(m.flow[link_name].name) - P1[ieq, link_index] -= 1 + sign_idx = flow_index_mapping[m.flow[link_name].name]["sign"] + flow_idx = flow_index_mapping[m.flow[link_name].name]["absolute_value"] + P2[ieq, sign_idx, flow_idx] -= 1 for link_name in wn.get_links_for_node(node_name, flag="OUTLET"): - link_index = var_names.index(m.flow[link_name].name) - P1[ieq, link_index] += 1 + sign_idx = flow_index_mapping[m.flow[link_name].name]["sign"] + flow_idx = flow_index_mapping[m.flow[link_name].name]["absolute_value"] + P2[ieq, sign_idx, flow_idx] += 1 - return P0, P1, P2 + return P0, P1, P2, P3 def get_mass_balance_constraint_design(m, wn, matrices): # noqa: D417 diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 2dfec7a..db4c4be 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -1,10 +1,13 @@ +from collections import OrderedDict +from typing import Dict from typing import List from typing import Tuple import matplotlib.pyplot as plt import numpy as np import sparse from quantum_newton_raphson.newton_raphson import newton_raphson -from qubops.encodings import BaseQbitEncoding, PositiveQbitEncoding +from qubops.encodings import BaseQbitEncoding +from qubops.encodings import PositiveQbitEncoding from qubops.mixed_solution_vector import MixedSolutionVector_V2 as MixedSolutionVector from qubops.qubops_mixed_vars import QUBOPS_MIXED from qubops.solution_vector import SolutionVector_V2 as SolutionVector @@ -15,8 +18,8 @@ from wntr.network import WaterNetworkModel from wntr.sim.aml import Model from wntr.sim.solvers import SolverStatus -from ..models.chezy_manning import get_chezy_manning_matrix -from ..models.darcy_weisbach import get_darcy_weisbach_matrix +from ..models.chezy_manning import get_chezy_manning_qubops_matrix +from ..models.darcy_weisbach import get_darcy_weisbach_qubops_matrix from ..models.mass_balance import get_mass_balance_qubops_matrix @@ -206,8 +209,8 @@ def initialize_matrices(self, model: Model) -> Tuple: P0 = np.zeros((num_equations, 1)) P1 = np.zeros((num_equations, num_variables)) P2 = np.zeros((num_equations, num_variables, num_variables)) - - matrices = (P0, P1, P2) + P3 = np.zeros((num_equations, num_variables, num_variables, num_variables)) + matrices = (P0, P1, P2, P3) # get the mass balance matrices = get_mass_balance_qubops_matrix( @@ -216,9 +219,9 @@ def initialize_matrices(self, model: Model) -> Tuple: # get the headloss matrix contributions if self.wn.options.hydraulic.headloss == "C-M": - matrices = get_chezy_manning_matrix(model, self.wn, matrices) + matrices = get_chezy_manning_qubops_matrix(model, self.wn, matrices) elif self.wn.options.hydraulic.headloss == "D-W": - matrices = get_darcy_weisbach_matrix(model, self.wn, matrices) + matrices = get_darcy_weisbach_qubops_matrix(model, self.wn, matrices) else: raise ValueError("Calculation only possible with C-M or D-W") return matrices @@ -303,6 +306,30 @@ def extract_data_from_model(model: Model) -> np.ndarray: data.append(v.value) return data + def create_index_mapping(self, model: Model) -> None: + """Creates the index maping for qubops matrices. + + Args: + model (Model): the AML Model + """ + # get the indices for the sign/abs value of the flow + self.flow_index_mapping = OrderedDict() + for idx, val in model.flow.items(): + if val.name not in self.flow_index_mapping: + self.flow_index_mapping[val.name] = { + "sign": None, + "absolute_value": None, + } + self.flow_index_mapping[val.name]["sign"] = int(idx) - 1 + self.flow_index_mapping[val.name]["absolute_value"] = ( + int(idx) + len(model.flow) - 1 + ) + + # get the indices for the heads + self.head_index_mapping = OrderedDict() + for idx, val in model.head.items(): + self.head_index_mapping[val.name] = 2 * len(model.flow) + int(idx) - 1 + def solve( # noqa: D417 self, model: Model, strength: float = 1e6, num_reads: int = 10000, **options ) -> Tuple: @@ -316,6 +343,9 @@ def solve( # noqa: D417 Returns: Tuple: Succes message """ + # creates the index mapping for the variables in the solution vectors + self.create_index_mapping(model) + # creates the matrices self.matrices = self.initialize_matrices(model) From 4b2d051194ae48d7f76d060478d98cd1baa9ae8c Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 11 Sep 2024 13:47:38 +0200 Subject: [PATCH 50/96] cm and dw work --- .../sim/solvers/qubo_polynomial_solver.py | 87 ++++++++++++++----- 1 file changed, 64 insertions(+), 23 deletions(-) diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index db4c4be..e7b49c4 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -71,6 +71,8 @@ def __init__( # init other attributes self.matrices = None self.qubo = None + self.flow_index_mapping = None + self.head_index_mapping = None def verify_encoding(self): """Print info regarding the encodings.""" @@ -79,7 +81,10 @@ def verify_encoding(self): fres = self.flow_encoding.get_average_precision() fvalues = np.sort(self.flow_encoding.get_possible_values()) print("Head Encoding : %f => %f (res: %f)" % (hvalues[0], hvalues[-1], hres)) - print("Flow Encoding : %f => %f (res: %f)" % (-fvalues[0], fvalues[-1], fres)) + print( + "Flow Encoding : %f => %f | %f => %f (res: %f)" + % (-fvalues[-1], -fvalues[0], fvalues[0], fvalues[-1], fres) + ) def verify_solution(self, input: np.ndarray) -> np.ndarray: """Computes the rhs vector associate with the input. @@ -90,14 +95,15 @@ def verify_solution(self, input: np.ndarray) -> np.ndarray: Returns: np.ndarray: RHS vector """ - P0, P1, P2 = self.matrices - + P0, P1, P2, P3 = self.matrices + num_pipes = self.wn.num_pipes p0 = P0.reshape( -1, ) - p1 = P1 - p2 = P2.sum(-1) - return p0 + p1 @ input + (p2 @ (input * input)) + p1 = P1[:, num_pipes:] + P2.sum(1)[:, num_pipes:] + p2 = P3.sum(1)[:, num_pipes:, num_pipes:].sum(-1) + sign = np.sign(input) + return p0 + p1 @ input + (p2 @ (sign * input * input)) def classical_solutions( self, max_iter: int = 100, tol: float = 1e-10 @@ -111,7 +117,7 @@ def classical_solutions( Returns: np.ndarray: _description_ """ - P0, P1, P2 = self.matrices + P0, P1, P2, P3 = self.matrices num_heads = self.wn.num_junctions num_pipes = self.wn.num_pipes num_vars = num_heads + num_pipes @@ -119,11 +125,12 @@ def classical_solutions( p0 = P0.reshape( -1, ) - p1 = P1 - p2 = P2.sum(-1) + p1 = P1[:, num_pipes:] + P2.sum(1)[:, num_pipes:] + p2 = P3.sum(1)[:, num_pipes:, num_pipes:].sum(-1) def func(input): - return p0 + p1 @ input + (p2 @ (input * input)) + sign = np.sign(input) + return p0 + p1 @ input + (p2 @ (sign * input * input)) initial_point = np.random.rand(num_vars) res = newton_raphson(func, initial_point, max_iter=max_iter, tol=tol) @@ -203,7 +210,7 @@ def initialize_matrices(self, model: Model) -> Tuple: Tuple: Matrices of the on linear system """ num_equations = len(list(model.cons())) - num_variables = len(list(model.vars())) + num_variables = 2 * len(model.flow) + len(model.head) # must transform that to coo P0 = np.zeros((num_equations, 1)) @@ -214,14 +221,26 @@ def initialize_matrices(self, model: Model) -> Tuple: # get the mass balance matrices = get_mass_balance_qubops_matrix( - model, self.wn, matrices, convert_to_us_unit=True + model, self.wn, matrices, self.flow_index_mapping, convert_to_us_unit=True ) # get the headloss matrix contributions if self.wn.options.hydraulic.headloss == "C-M": - matrices = get_chezy_manning_qubops_matrix(model, self.wn, matrices) + matrices = get_chezy_manning_qubops_matrix( + model, + self.wn, + matrices, + self.flow_index_mapping, + self.head_index_mapping, + ) elif self.wn.options.hydraulic.headloss == "D-W": - matrices = get_darcy_weisbach_qubops_matrix(model, self.wn, matrices) + matrices = get_darcy_weisbach_qubops_matrix( + model, + self.wn, + matrices, + self.flow_index_mapping, + self.head_index_mapping, + ) else: raise ValueError("Calculation only possible with C-M or D-W") return matrices @@ -262,6 +281,21 @@ def convert_solution_from_si(self, solution: np.ndarray) -> np.ndarray: new_sol[ih] = from_si(FlowUnits.CFS, solution[ih], HydParam.Length) return new_sol + @staticmethod + def combine_flow_values(solution: List) -> List: + """Combine the values of the flow sign*abs. + + Args: + solution (List): solution vector + + Returns: + List: solution vector + """ + flow = [] + for sign, abs in zip(solution[0], solution[1]): + flow.append(sign * abs) + return flow + solution[2] + @staticmethod def flatten_solution_vector(solution: Tuple) -> List: """Flattens the solution vector. @@ -312,23 +346,30 @@ def create_index_mapping(self, model: Model) -> None: Args: model (Model): the AML Model """ + # init the idx + idx = 0 + + # number of variables that are flows + num_flow_var = len(model.flow) + # get the indices for the sign/abs value of the flow self.flow_index_mapping = OrderedDict() - for idx, val in model.flow.items(): + for _, val in model.flow.items(): if val.name not in self.flow_index_mapping: self.flow_index_mapping[val.name] = { "sign": None, "absolute_value": None, } - self.flow_index_mapping[val.name]["sign"] = int(idx) - 1 - self.flow_index_mapping[val.name]["absolute_value"] = ( - int(idx) + len(model.flow) - 1 - ) + self.flow_index_mapping[val.name]["sign"] = idx + self.flow_index_mapping[val.name]["absolute_value"] = idx + num_flow_var + idx += 1 # get the indices for the heads + idx = 0 self.head_index_mapping = OrderedDict() - for idx, val in model.head.items(): - self.head_index_mapping[val.name] = 2 * len(model.flow) + int(idx) - 1 + for _, val in model.head.items(): + self.head_index_mapping[val.name] = 2 * num_flow_var + idx + idx += 1 def solve( # noqa: D417 self, model: Model, strength: float = 1e6, num_reads: int = 10000, **options @@ -385,8 +426,8 @@ def qubo_poly_solve(self, strength=1e6, num_reads=10000, **options): # noqa: D4 # decode sol = self.qubo.decode_solution(sampleset.lowest().record[0][0]) - # flatten solution - sol = self.flatten_solution_vector(sol) + # combine the sign*abs values for the flow + sol = self.combine_flow_values(sol) # convert back to SI sol = self.convert_solution_to_si(sol) From 849ceb1652c363f6bc0a33f5013cf2b6a5c2da9d Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 11 Sep 2024 14:20:55 +0200 Subject: [PATCH 51/96] fixed diagnostic --- docs/notebooks/qubo_poly_solver.ipynb | 75 +++++++++++++++++-- .../sim/solvers/qubo_polynomial_solver.py | 25 ++++++- 2 files changed, 91 insertions(+), 9 deletions(-) diff --git a/docs/notebooks/qubo_poly_solver.ipynb b/docs/notebooks/qubo_poly_solver.ipynb index e43e3ed..5f05eb7 100644 --- a/docs/notebooks/qubo_poly_solver.ipynb +++ b/docs/notebooks/qubo_poly_solver.ipynb @@ -180,7 +180,7 @@ "output_type": "stream", "text": [ "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n" + "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.000978)\n" ] } ], @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -228,7 +228,7 @@ "array([1. , 1. , 0.999, 0.998])" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -236,6 +236,7 @@ "source": [ "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.create_index_mapping(model)\n", "net.matrices = net.initialize_matrices(model)\n", "\n", "ref_sol = net.classical_solutions()\n", @@ -244,16 +245,76 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [], + "source": [ + "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", + "import sparse \n", + "strength = 16\n", + "num_reads = 10000\n", + "net.qubo = QUBOPS_MIXED(net.mixed_solution_vector)\n", + "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "\n", + "# creates BQM\n", + "net.qubo.qubo_dict = net.qubo.create_bqm(matrices, strength=strength)\n", + "\n", + "# sample\n", + "sampleset = net.qubo.sample_bqm(net.qubo.qubo_dict, num_reads=num_reads)\n", + "\n", + "# decode\n", + "sol = net.qubo.decode_solution(sampleset.lowest().record[0][0])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.056, 0.044, 29.168, 23.263])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sol = net.combine_flow_values(sol)\n", + "sol = net.convert_solution_to_si(sol)\n", + "sol" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "index 0 is out of bounds for axis 0 with size 0", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m sampler \u001b[38;5;241m=\u001b[39m SimulatedAnnealingSampler()\n\u001b[1;32m 5\u001b[0m model, model_updater \u001b[38;5;241m=\u001b[39m create_hydraulic_model_for_qubo(wn)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_reads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msampler\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msampler\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m sol \u001b[38;5;241m=\u001b[39m net\u001b[38;5;241m.\u001b[39mextract_data_from_model(model)\n", + "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sim/solvers/qubo_polynomial_solver.py:394\u001b[0m, in \u001b[0;36mQuboPolynomialSolver.solve\u001b[0;34m(self, model, strength, num_reads, **options)\u001b[0m\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmatrices \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minitialize_matrices(model)\n\u001b[1;32m 393\u001b[0m \u001b[38;5;66;03m# solve using qubo poly\u001b[39;00m\n\u001b[0;32m--> 394\u001b[0m sol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo_poly_solve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstrength\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrength\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_reads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_reads\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 396\u001b[0m \u001b[38;5;66;03m# load data in the AML model\u001b[39;00m\n\u001b[1;32m 397\u001b[0m model\u001b[38;5;241m.\u001b[39mset_structure()\n", + "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sim/solvers/qubo_polynomial_solver.py:436\u001b[0m, in \u001b[0;36mQuboPolynomialSolver.qubo_poly_solve\u001b[0;34m(self, strength, num_reads, **options)\u001b[0m\n\u001b[1;32m 433\u001b[0m sol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mflatten_solution_vector(sol)\n\u001b[1;32m 435\u001b[0m \u001b[38;5;66;03m# convert back to SI\u001b[39;00m\n\u001b[0;32m--> 436\u001b[0m sol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_solution_to_si\u001b[49m\u001b[43m(\u001b[49m\u001b[43msol\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sol\n", + "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sim/solvers/qubo_polynomial_solver.py:261\u001b[0m, in \u001b[0;36mQuboPolynomialSolver.convert_solution_to_si\u001b[0;34m(self, solution)\u001b[0m\n\u001b[1;32m 259\u001b[0m new_sol \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros_like(solution)\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ip \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_pipes):\n\u001b[0;32m--> 261\u001b[0m new_sol[ip] \u001b[38;5;241m=\u001b[39m to_si(FlowUnits\u001b[38;5;241m.\u001b[39mCFS, \u001b[43msolution\u001b[49m\u001b[43m[\u001b[49m\u001b[43mip\u001b[49m\u001b[43m]\u001b[49m, HydParam\u001b[38;5;241m.\u001b[39mFlow)\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ih \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_pipes, num_pipes \u001b[38;5;241m+\u001b[39m num_heads):\n\u001b[1;32m 263\u001b[0m new_sol[ih] \u001b[38;5;241m=\u001b[39m to_si(FlowUnits\u001b[38;5;241m.\u001b[39mCFS, solution[ih], HydParam\u001b[38;5;241m.\u001b[39mLength)\n", + "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0" + ] + } + ], "source": [ "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import SimulatedAnnealingSampler\n", "\n", - "sampler = SteepestDescentSolver()\n", + "sampler = SimulatedAnnealingSampler()\n", "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.solve(model, options={\"sampler\" : sampler})\n", + "net.solve(model, num_reads=10000, options={\"sampler\" : sampler})\n", "sol = net.extract_data_from_model(model)" ] }, diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index e7b49c4..c43ce89 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -161,6 +161,18 @@ def plot_solution_vs_reference( plt.grid(which="minor", lw=0.1) plt.loglog() + def decompose_solution(self, solution): + """Decompose solution into sign/abs flow and head values. + + Args: + solution (np.array): solution + """ + num_flows = self.wn.num_links + flow_values = solution[:num_flows] + head_values = solution[num_flows:] + tmp = np.append(np.sign(flow_values), np.abs(flow_values)) + return np.append(tmp, head_values) + def diagnostic_solution(self, solution: np.ndarray, reference_solution: np.ndarray): """Benchmark a solution against the exact reference solution. @@ -171,9 +183,14 @@ def diagnostic_solution(self, solution: np.ndarray, reference_solution: np.ndarr reference_solution = self.convert_solution_from_si(reference_solution) solution = self.convert_solution_from_si(solution) + reference_solution = self.decompose_solution(reference_solution) + solution = self.decompose_solution(solution) + data_ref, eref = self.qubo.compute_energy(reference_solution) data_sol, esol = self.qubo.compute_energy(solution) + num_pipes = self.wn.num_links + np.set_printoptions(precision=3) self.verify_encoding() print("\n") @@ -191,8 +208,12 @@ def diagnostic_solution(self, solution: np.ndarray, reference_solution: np.ndarr print("R ref : ", eref) print("Delta E :", esol - eref) print("\n") - res_sol = np.linalg.norm(self.verify_solution(np.array(data_sol[0]))) - res_ref = np.linalg.norm(self.verify_solution(np.array(data_ref[0]))) + res_sol = np.linalg.norm( + self.verify_solution(np.array(data_sol[0][num_pipes:])) + ) + res_ref = np.linalg.norm( + self.verify_solution(np.array(data_ref[0][num_pipes:])) + ) print("Residue sol : ", res_sol) print("Residue ref : ", res_ref) print("Delta Residue :", res_sol - res_ref) From 1d4616b42574355aa9d829170bc113419aeb5b58 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 11 Sep 2024 14:26:32 +0200 Subject: [PATCH 52/96] noebooks for net0 cm and dw --- docs/notebooks/qubo_poly_solver.ipynb | 142 +++-------------------- docs/notebooks/qubo_poly_solver_CM.ipynb | 61 ++++++---- 2 files changed, 59 insertions(+), 144 deletions(-) diff --git a/docs/notebooks/qubo_poly_solver.ipynb b/docs/notebooks/qubo_poly_solver.ipynb index 5f05eb7..d536f6e 100644 --- a/docs/notebooks/qubo_poly_solver.ipynb +++ b/docs/notebooks/qubo_poly_solver.ipynb @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -228,7 +228,7 @@ "array([1. , 1. , 0.999, 0.998])" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -245,69 +245,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [], - "source": [ - "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", - "import sparse \n", - "strength = 16\n", - "num_reads = 10000\n", - "net.qubo = QUBOPS_MIXED(net.mixed_solution_vector)\n", - "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", - "\n", - "# creates BQM\n", - "net.qubo.qubo_dict = net.qubo.create_bqm(matrices, strength=strength)\n", - "\n", - "# sample\n", - "sampleset = net.qubo.sample_bqm(net.qubo.qubo_dict, num_reads=num_reads)\n", - "\n", - "# decode\n", - "sol = net.qubo.decode_solution(sampleset.lowest().record[0][0])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.056, 0.044, 29.168, 23.263])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sol = net.combine_flow_values(sol)\n", - "sol = net.convert_solution_to_si(sol)\n", - "sol" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "index 0 is out of bounds for axis 0 with size 0", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[10], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m sampler \u001b[38;5;241m=\u001b[39m SimulatedAnnealingSampler()\n\u001b[1;32m 5\u001b[0m model, model_updater \u001b[38;5;241m=\u001b[39m create_hydraulic_model_for_qubo(wn)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_reads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msampler\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msampler\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m sol \u001b[38;5;241m=\u001b[39m net\u001b[38;5;241m.\u001b[39mextract_data_from_model(model)\n", - "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sim/solvers/qubo_polynomial_solver.py:394\u001b[0m, in \u001b[0;36mQuboPolynomialSolver.solve\u001b[0;34m(self, model, strength, num_reads, **options)\u001b[0m\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmatrices \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minitialize_matrices(model)\n\u001b[1;32m 393\u001b[0m \u001b[38;5;66;03m# solve using qubo poly\u001b[39;00m\n\u001b[0;32m--> 394\u001b[0m sol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo_poly_solve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstrength\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrength\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_reads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_reads\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 396\u001b[0m \u001b[38;5;66;03m# load data in the AML model\u001b[39;00m\n\u001b[1;32m 397\u001b[0m model\u001b[38;5;241m.\u001b[39mset_structure()\n", - "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sim/solvers/qubo_polynomial_solver.py:436\u001b[0m, in \u001b[0;36mQuboPolynomialSolver.qubo_poly_solve\u001b[0;34m(self, strength, num_reads, **options)\u001b[0m\n\u001b[1;32m 433\u001b[0m sol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mflatten_solution_vector(sol)\n\u001b[1;32m 435\u001b[0m \u001b[38;5;66;03m# convert back to SI\u001b[39;00m\n\u001b[0;32m--> 436\u001b[0m sol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_solution_to_si\u001b[49m\u001b[43m(\u001b[49m\u001b[43msol\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sol\n", - "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sim/solvers/qubo_polynomial_solver.py:261\u001b[0m, in \u001b[0;36mQuboPolynomialSolver.convert_solution_to_si\u001b[0;34m(self, solution)\u001b[0m\n\u001b[1;32m 259\u001b[0m new_sol \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros_like(solution)\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ip \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_pipes):\n\u001b[0;32m--> 261\u001b[0m new_sol[ip] \u001b[38;5;241m=\u001b[39m to_si(FlowUnits\u001b[38;5;241m.\u001b[39mCFS, \u001b[43msolution\u001b[49m\u001b[43m[\u001b[49m\u001b[43mip\u001b[49m\u001b[43m]\u001b[49m, HydParam\u001b[38;5;241m.\u001b[39mFlow)\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ih \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_pipes, num_pipes \u001b[38;5;241m+\u001b[39m num_heads):\n\u001b[1;32m 263\u001b[0m new_sol[ih] \u001b[38;5;241m=\u001b[39m to_si(FlowUnits\u001b[38;5;241m.\u001b[39mCFS, solution[ih], HydParam\u001b[38;5;241m.\u001b[39mLength)\n", - "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0" - ] - } - ], "source": [ "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", "from dwave.samplers import SimulatedAnnealingSampler\n", @@ -325,7 +265,7 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -348,30 +288,30 @@ "output_type": "stream", "text": [ "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", + "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.000978)\n", "\n", "\n", - "Error (%): [ 1.528 -2.184 -0.331 0.289]\n", + "Error (%): [ 0. 0. -5.288 1.03 1.36 1.2 ]\n", "\n", "\n", - "sol : [ 1.739 1.804 87.084 74.951]\n", - "ref : [ 1.766 1.766 86.797 75.168]\n", - "diff: [ 0.027 -0.039 -0.288 0.217]\n", + "sol : [ 1. 1. 1.859 1.748 85.616 74.266]\n", + "ref : [ 1. 1. 1.766 1.766 86.797 75.168]\n", + "diff: [ 0. 0. -0.093 0.018 1.18 0.902]\n", "\n", "\n", - "encoded_sol: [ 1.739 1.804 87.084 74.951]\n", - "encoded_ref: [ 1.766 1.766 86.791 75.147]\n", - "diff : [ 0.027 -0.038 -0.294 0.196]\n", + "encoded_sol: [ 1. 1. 1.859 1.748 85.616 74.266]\n", + "encoded_ref: [ 1. 1. 1.766 1.766 86.791 75.147]\n", + "diff : [ 0. 0. -0.093 0.019 1.174 0.881]\n", "\n", "\n", - "E sol : -1662.5979676922227\n", - "R ref : -1662.6061020456154\n", - "Delta E : 0.008134353392733829\n", + "E sol : -3505.5175214858687\n", + "R ref : -3505.53316919132\n", + "Delta E : 0.01564770545155625\n", "\n", "\n", - "Residue sol : 0.09076400808170053\n", + "Residue sol : 0.12550494817707591\n", "Residue ref : 0.010186471203764017\n", - "Delta Residue : 0.0805775368779365\n" + "Delta Residue : 0.11531847697331189\n" ] } ], @@ -393,7 +333,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -424,52 +364,6 @@ " title='Pressure at 5 hours', node_labels=False)" ] }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[ 0. ],\n", - " [ 1.766],\n", - " [99.077],\n", - " [ 0.652]]),\n", - " array([[-1. , 1. , 0. , 0. ],\n", - " [ 0. , -1. , 0. , 0. ],\n", - " [-1.547, 0. , -1. , 0. ],\n", - " [ 0. , -1.547, 1. , -1. ]]),\n", - " array([[[ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ]],\n", - " \n", - " [[ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ]],\n", - " \n", - " [[-3.063, 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ]],\n", - " \n", - " [[ 0. , 0. , 0. , 0. ],\n", - " [ 0. , -3.063, 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ]]]))" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.matrices" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/docs/notebooks/qubo_poly_solver_CM.ipynb b/docs/notebooks/qubo_poly_solver_CM.ipynb index 1142e7f..b17ebed 100644 --- a/docs/notebooks/qubo_poly_solver_CM.ipynb +++ b/docs/notebooks/qubo_poly_solver_CM.ipynb @@ -180,7 +180,7 @@ "output_type": "stream", "text": [ "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n" + "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.000978)\n" ] } ], @@ -236,6 +236,7 @@ "source": [ "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.create_index_mapping(model)\n", "net.matrices = net.initialize_matrices(model)\n", "\n", "ref_sol = net.classical_solutions()\n", @@ -249,9 +250,9 @@ "outputs": [], "source": [ "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import SimulatedAnnealingSampler\n", "\n", - "sampler = SteepestDescentSolver()\n", + "sampler = SimulatedAnnealingSampler()\n", "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", "net.solve(model, options={\"sampler\" : sampler})\n", "sol = net.extract_data_from_model(model)" @@ -264,7 +265,27 @@ "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "[0.05353047713424657, 0.0494575060479452, 30.00281800391389, 30.00281800391389]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sol" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" ] @@ -279,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -287,30 +308,30 @@ "output_type": "stream", "text": [ "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.000978)\n", + "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.000978)\n", "\n", "\n", - "Error (%): [-7.892 -4.069 -0.029 -0.048]\n", + "Error (%): [ 0. 0. -7.061 1.085 -0.029 -0.048]\n", "\n", "\n", - "sol : [ 1.905 1.838 98.434 98.434]\n", - "ref : [ 1.766 1.766 98.406 98.387]\n", - "diff: [-0.139 -0.072 -0.028 -0.047]\n", + "sol : [ 1. 1. 1.89 1.747 98.434 98.434]\n", + "ref : [ 1. 1. 1.766 1.766 98.406 98.387]\n", + "diff: [ 0. 0. -0.125 0.019 -0.028 -0.047]\n", "\n", "\n", - "encoded_sol: [ 1.905 1.838 98.434 98.434]\n", - "encoded_ref: [ 1.766 1.766 98.434 98.434]\n", - "diff : [-0.139 -0.071 0. 0. ]\n", + "encoded_sol: [ 1. 1. 1.89 1.747 98.434 98.434]\n", + "encoded_ref: [ 1. 1. 1.766 1.766 98.434 98.434]\n", + "diff : [ 0. 0. -0.124 0.02 0. 0. ]\n", "\n", "\n", - "E sol : -2343.739974221478\n", - "R ref : -2343.749937932273\n", - "Delta E : 0.009963710795091174\n", + "E sol : -2356.9623137039966\n", + "R ref : -2356.983516049839\n", + "Delta E : 0.021202345842539216\n", "\n", "\n", - "Residue sol : 0.10541453368914308\n", + "Residue sol : 0.14950214827015704\n", "Residue ref : 0.03388956865892264\n", - "Delta Residue : 0.07152496503022043\n" + "Delta Residue : 0.11561257961123439\n" ] } ], @@ -327,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -346,7 +367,7 @@ "" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } From d18ab21eec5c2712bb4139867a6e0b5b0d276fd5 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 11 Sep 2024 15:29:42 +0200 Subject: [PATCH 53/96] reorder values --- .../sim/solvers/qubo_polynomial_solver.py | 33 +++++++++++-------- 1 file changed, 20 insertions(+), 13 deletions(-) diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index c43ce89..a76e682 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -135,12 +135,11 @@ def func(input): initial_point = np.random.rand(num_vars) res = newton_raphson(func, initial_point, max_iter=max_iter, tol=tol) sol = res.solution - assert np.allclose(func(sol), 0) - + converged = np.allclose(func(sol), 0) # convert back to SI sol = self.convert_solution_to_si(sol) - return sol + return (sol, converged) @staticmethod def plot_solution_vs_reference( @@ -332,8 +331,7 @@ def flatten_solution_vector(solution: Tuple) -> List: sol_tmp += s return sol_tmp - @staticmethod - def load_data_in_model(model: Model, data: np.ndarray): + def load_data_in_model(self, model: Model, data: np.ndarray): """Loads some data in the model. Remark: @@ -343,11 +341,15 @@ def load_data_in_model(model: Model, data: np.ndarray): model (Model): AML model from WNTR data (np.ndarray): data to load """ - for iv, v in enumerate(model.vars()): - v.value = data[iv] - - @staticmethod - def extract_data_from_model(model: Model) -> np.ndarray: + shift_head_idx = self.wn.num_links + for var in model.vars(): + if var.name in self.flow_index_mapping: + idx = self.flow_index_mapping[var.name]["sign"] + elif var.name in self.head_index_mapping: + idx = self.head_index_mapping[var.name] - shift_head_idx + var.value = data[idx] + + def extract_data_from_model(self, model: Model) -> np.ndarray: """Loads some data in the model. Args: @@ -356,9 +358,14 @@ def extract_data_from_model(model: Model) -> np.ndarray: Returns: np.ndarray: data extracted from model """ - data = [] - for v in model.vars(): - data.append(v.value) + data = [None] * len(list(model.vars())) + shift_head_idx = self.wn.num_links + for var in model.vars(): + if var.name in self.flow_index_mapping: + idx = self.flow_index_mapping[var.name]["sign"] + elif var.name in self.head_index_mapping: + idx = self.head_index_mapping[var.name] - shift_head_idx + data[idx] = var.value return data def create_index_mapping(self, model: Model) -> None: From 5b5bad335648b806b6be6f7fd3445a756e893c1a Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 11 Sep 2024 21:50:37 +0200 Subject: [PATCH 54/96] tested 2loops --- .../sandbox/qubo_poly_solver_Net2loops.ipynb | 530 ++++++++---------- .../sim/solvers/qubo_polynomial_solver.py | 6 +- 2 files changed, 222 insertions(+), 314 deletions(-) diff --git a/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb b/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb index 78fe41b..d04d98e 100644 --- a/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb +++ b/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 1, "metadata": { "metadata": {} }, @@ -30,7 +30,7 @@ "" ] }, - "execution_count": 142, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -54,215 +54,6 @@ "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run with WNTR Simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "metadata": {}, - "outputs": [], - "source": [ - "inp_file = '../networks/Net2LoopsFlat.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "sim = wntr.sim.WNTRSimulator(wn)\n", - "model, updater = wntr.sim.hydraulics.create_hydraulic_model(wn)" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cons:\n", - "mass_balance[2]: (((expected_demand[2]-flow[1])+flow[2])+flow[3])\n", - "mass_balance[3]: ((expected_demand[3]-flow[2])+flow[7])\n", - "mass_balance[4]: (((expected_demand[4]-flow[3])+flow[4])+flow[5])\n", - "mass_balance[5]: (((expected_demand[5]-flow[4])-flow[7])+flow[8])\n", - "mass_balance[6]: ((expected_demand[6]-flow[5])+flow[6])\n", - "mass_balance[7]: ((expected_demand[7]-flow[6])-flow[8])\n", - "approx_hazen_williams_headloss[1]: (((((((-((sign(flow[1]))))*hw_resistance[1])*((abs(flow[1]))**1.852))-((1e-05*(hw_resistance[1]**0.5))*flow[1]))-(((sign(flow[1]))*minor_loss[1])*(flow[1]**2.0)))+source_head[1])-head[2])\n", - "approx_hazen_williams_headloss[2]: (((((((-((sign(flow[2]))))*hw_resistance[2])*((abs(flow[2]))**1.852))-((1e-05*(hw_resistance[2]**0.5))*flow[2]))-(((sign(flow[2]))*minor_loss[2])*(flow[2]**2.0)))+head[2])-head[3])\n", - "approx_hazen_williams_headloss[3]: (((((((-((sign(flow[3]))))*hw_resistance[3])*((abs(flow[3]))**1.852))-((1e-05*(hw_resistance[3]**0.5))*flow[3]))-(((sign(flow[3]))*minor_loss[3])*(flow[3]**2.0)))+head[2])-head[4])\n", - "approx_hazen_williams_headloss[4]: (((((((-((sign(flow[4]))))*hw_resistance[4])*((abs(flow[4]))**1.852))-((1e-05*(hw_resistance[4]**0.5))*flow[4]))-(((sign(flow[4]))*minor_loss[4])*(flow[4]**2.0)))+head[4])-head[5])\n", - "approx_hazen_williams_headloss[5]: (((((((-((sign(flow[5]))))*hw_resistance[5])*((abs(flow[5]))**1.852))-((1e-05*(hw_resistance[5]**0.5))*flow[5]))-(((sign(flow[5]))*minor_loss[5])*(flow[5]**2.0)))+head[4])-head[6])\n", - "approx_hazen_williams_headloss[6]: (((((((-((sign(flow[6]))))*hw_resistance[6])*((abs(flow[6]))**1.852))-((1e-05*(hw_resistance[6]**0.5))*flow[6]))-(((sign(flow[6]))*minor_loss[6])*(flow[6]**2.0)))+head[6])-head[7])\n", - "approx_hazen_williams_headloss[7]: (((((((-((sign(flow[7]))))*hw_resistance[7])*((abs(flow[7]))**1.852))-((1e-05*(hw_resistance[7]**0.5))*flow[7]))-(((sign(flow[7]))*minor_loss[7])*(flow[7]**2.0)))+head[3])-head[5])\n", - "approx_hazen_williams_headloss[8]: (((((((-((sign(flow[8]))))*hw_resistance[8])*((abs(flow[8]))**1.852))-((1e-05*(hw_resistance[8]**0.5))*flow[8]))-(((sign(flow[8]))*minor_loss[8])*(flow[8]**2.0)))+head[5])-head[7])\n", - "\n", - "vars:\n", - "flow[1]: flow[1]\n", - "flow[2]: flow[2]\n", - "flow[3]: flow[3]\n", - "flow[7]: flow[7]\n", - "flow[4]: flow[4]\n", - "flow[5]: flow[5]\n", - "flow[8]: flow[8]\n", - "flow[6]: flow[6]\n", - "head[2]: head[2]\n", - "head[3]: head[3]\n", - "head[4]: head[4]\n", - "head[5]: head[5]\n", - "head[6]: head[6]\n", - "head[7]: head[7]\n", - "\n" - ] - } - ], - "source": [ - "print(model.__str__())" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": {}, - "outputs": [], - "source": [ - "res = sim.run_sim()" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"execution_count": 150, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -400,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -436,12 +227,12 @@ " \n", " \n", " 0\n", - " 195.520416\n", - " 165.836349\n", - " 187.434158\n", - " 137.866119\n", - " 179.538773\n", - " 159.609787\n", + " 200.732986\n", + " 181.735184\n", + " 195.55777\n", + " 163.834244\n", + " 190.504684\n", + " 177.750153\n", " 4.394531e-07\n", " \n", " \n", @@ -449,14 +240,14 @@ "" ], "text/plain": [ - "name 2 3 4 5 6 7 \\\n", - "0 195.520416 165.836349 187.434158 137.866119 179.538773 159.609787 \n", + "name 2 3 4 5 6 7 \\\n", + "0 200.732986 181.735184 195.55777 163.834244 190.504684 177.750153 \n", "\n", "name 1 \n", "0 4.394531e-07 " ] }, - "execution_count": 96, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -467,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -477,16 +268,16 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 3.111e-01, 5.111e-02, 2.322e-01, 2.334e-02, 3.108e-02, 1.678e-01, -2.058e-02, 7.613e-02, 1.955e+02, 1.658e+02, 1.874e+02, 1.379e+02, 1.795e+02, 1.596e+02], dtype=float32)" + "array([ 3.111e-01, 5.111e-02, 2.322e-01, 2.334e-02, 3.108e-02, 1.678e-01, -2.058e-02, 7.613e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02], dtype=float32)" ] }, - "execution_count": 98, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -505,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -514,15 +305,15 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 500.000000 => 700.000000 (res: 0.391389)\n", - "Flow Encoding : -10.000000 => 10.000000 (res: 0.039139)\n" + "Head Encoding : 500.000000 => 800.000000 (res: 9.677419)\n", + "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.483871)\n" ] } ], @@ -531,12 +322,12 @@ "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", - "nqbit = 9\n", - "step = (20/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=-10, var_base_name=\"x\")\n", + "nqbit = 5\n", + "step = (15/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=0, var_base_name=\"x\")\n", "\n", - "nqbit = 9\n", - "step = (200/(2**nqbit-1))\n", + "nqbit = 5\n", + "step = (300/(2**nqbit-1))\n", "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+500.0, var_base_name=\"x\")\n", "\n", "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", @@ -546,16 +337,16 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 10.986, 1.805, 8.2 , 0.824, 1.097, 5.926, -0.727, 2.689, 641.471, 544.083, 614.941, 452.317, 589.038, 523.654], dtype=float32)" + "array([ 10.986, 1.805, 8.2 , 0.824, 1.097, 5.926, -0.727, 2.689, 658.573, 596.244, 641.594, 537.514, 625.015, 583.17 ], dtype=float32)" ] }, - "execution_count": 101, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -573,116 +364,209 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 80, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Warning, we didn't reach the required tolerance within 100 iterations, error is at 194.49915433794996\n" + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" ] }, { - "ename": "AssertionError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[107], line 6\u001b[0m\n\u001b[1;32m 3\u001b[0m net\u001b[38;5;241m.\u001b[39mmatrices \u001b[38;5;241m=\u001b[39m net\u001b[38;5;241m.\u001b[39minitialize_matrices(model)\n\u001b[1;32m 4\u001b[0m net\u001b[38;5;241m.\u001b[39mverify_solution(net\u001b[38;5;241m.\u001b[39mconvert_solution_from_si(ref_values))\n\u001b[0;32m----> 6\u001b[0m ref_sol \u001b[38;5;241m=\u001b[39m \u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclassical_solutions\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_iter\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1e-3\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m ref_sol \u001b[38;5;241m/\u001b[39m ref_values\n", - "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sim/solvers/qubo_polynomial_solver.py:110\u001b[0m, in \u001b[0;36mQuboPolynomialSolver.classical_solutions\u001b[0;34m(self, max_iter, tol)\u001b[0m\n\u001b[1;32m 108\u001b[0m res \u001b[38;5;241m=\u001b[39m newton_raphson(func, initial_point, max_iter\u001b[38;5;241m=\u001b[39mmax_iter, tol\u001b[38;5;241m=\u001b[39mtol)\n\u001b[1;32m 109\u001b[0m sol \u001b[38;5;241m=\u001b[39m res\u001b[38;5;241m.\u001b[39msolution\n\u001b[0;32m--> 110\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(func(sol), \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 112\u001b[0m \u001b[38;5;66;03m# convert back to SI\u001b[39;00m\n\u001b[1;32m 113\u001b[0m sol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_solution_to_si(sol)\n", - "\u001b[0;31mAssertionError\u001b[0m: " - ] + "data": { + "text/plain": [ + "array([1. , 1. , 1. , 1. , 1. , 1. , 0.999, 1. ])" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.create_index_mapping(model)\n", "net.matrices = net.initialize_matrices(model)\n", "net.verify_solution(net.convert_solution_from_si(ref_values))\n", "\n", - "ref_sol = net.classical_solutions(max_iter = 100, tol= 1e-3)\n", - "ref_sol / ref_values" + "ref_sol, cvg = net.classical_solutions(max_iter = 100, tol= 1e-3)\n", + "ref_sol[[0,1,2,6,3,4,7,5]] / ref_values[:8]" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "from dwave.samplers import SteepestDescentSolver\n", + "from dwave.samplers import SimulatedAnnealingSampler\n", + "from dwave.samplers import RandomSampler\n", + "from dwave.samplers import TabuSampler\n", + "from dwave.samplers import TreeDecompositionSampler\n", + "\n", + "sampler = SimulatedAnnealingSampler()\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.solve(model, strength=1E8, num_reads=100000, options={\"sampler\" : sampler, })\n", + "sol = net.extract_data_from_model(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.981],\n", - " [ 0.981],\n", - " [ 1.177],\n", - " [ 2.649],\n", - " [ 3.237],\n", - " [ 1.962],\n", - " [688.976],\n", - " [ 0. ],\n", - " [ 0. ],\n", - " [ 0. ],\n", - " [ 0. ],\n", - " [ 0. ],\n", - " [ 0. ],\n", - " [ 0. ]])" + "array([ 1.452e+01, 4.839e-01, 6.774e+00, -4.839e-01, 4.355e+00, 5.323e+00, -4.839e-01, 1.452e+00, 6.645e+02, 6.645e+02, 6.645e+02, 6.839e+02, 6.548e+02, 5.000e+02])" ] }, - "execution_count": 14, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "net.matrices[0]" + "net.convert_solution_from_si(sol)" ] }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 132, "metadata": {}, "outputs": [], "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "from dwave.samplers import SteepestDescentSolver\n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "\n", - "sampler = SimulatedAnnealingSampler()\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.solve(model, strength=1E6, num_reads=10000, options={\"sampler\" : sampler, })\n", - "sol = net.extract_data_from_model(model)" + "net.qubo.verify_quadratic_constraints(net.sampleset.lowest())" ] }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 133, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_5416/1944863955.py:2: DeprecationWarning: BinaryQuadraticModel.to_networkx_graph() is deprecated since dimod 0.10.0 and will be removed in 0.12.0. Use dimod.to_networkx_graph() instead.\n", + " g = net.qubo.qubo_dict.to_networkx_graph()\n" + ] + }, { "data": { + "image/png": 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", "text/plain": [ - "array([ 5.068, 4.403, 3.659, 5.108, 1.233, -6.634, 1.82 , 2.72 , 700. , 700. , 700. , 500. , 599.804, 500. ])" + "
" ] }, - "execution_count": 109, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import networkx as nx \n", + "g = net.qubo.qubo_dict.to_networkx_graph()\n", + "nx.draw(g, pos = nx.spring_layout(g))" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.411, 0.014, 0.192, -0.014, 0.123, 0.151, -0.014, 0.041])" + ] + }, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "net.convert_solution_from_si(sol)" + "np.array(sol)[:8]" ] }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 135, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.scatter( ref_sol[:8], np.array(sol)[:8])\n", + "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", + "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", "text/plain": [ "
" ] @@ -692,48 +576,72 @@ } ], "source": [ - "net.plot_solution_vs_reference(sol, ref_values)" + "import matplotlib.pyplot as plt \n", + "plt.scatter(ref_sol[8:], np.array(sol)[8:])\n", + "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", + "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.111e-01, 5.111e-02, 2.322e-01, 2.334e-02, 3.108e-02, 1.678e-01, -2.058e-02, 7.613e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02], dtype=float32)" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_values" + ] + }, + { + "cell_type": "code", + "execution_count": 99, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 500.000000 => 700.000000 (res: 0.391389)\n", - "Flow Encoding : -10.000000 => 10.000000 (res: 0.039139)\n", + "Head Encoding : 500.000000 => 800.000000 (res: 9.677419)\n", + "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.483871)\n", "\n", "\n", - "Error (%): [ 146.136 133.609 0.963 248.009 144.582 -213.716 278.043 -268.863 -9.124 -28.657 -16.952 -10.542 -4.745 4.517]\n", + "Error (%): [ 0. 0. 200. 100. 200. 0. 200. 200. -10.11 19.59 -29.818 100. 67.336 -44.002 -17.364 -33.224 4.988 3.223 -0.534 -19.861 -3.191 4.372]\n", "\n", "\n", - "sol : [-5.068e+00 -6.067e-01 8.121e+00 -1.624e+00 -2.642e+00 8.434e+00 -1.468e+00 -2.681e+00 7.000e+02 7.000e+02 7.000e+02 5.000e+02 5.998e+02 5.000e+02]\n", - "ref : [ 10.986 1.805 8.2 1.097 5.926 2.689 0.824 -0.727 641.471 544.083 598.537 452.317 572.634 523.654]\n", - "diff: [ 1.605e+01 2.412e+00 7.895e-02 2.722e+00 8.568e+00 -5.746e+00 2.292e+00 1.954e+00 -5.853e+01 -1.559e+02 -1.015e+02 -4.768e+01 -2.717e+01 2.365e+01]\n", + "sol : [ 1. 1. -1. 0. -1. 1. -1. 1. 12.097 1.452 10.645 0. 1.935 3.871 0.968 0.968 625.806 577.419 645.161 645.161 645.161 558.065]\n", + "ref : [ 1. 1. 1. 1. 1. 1. 1. -1. 10.986 1.805 8.2 1.098 5.925 2.688 0.825 0.726 658.662 596.652 641.733 538.256 625.212 583.576]\n", + "diff: [ 0. 0. 2. 1. 2. 0. 2. -2. -1.111 0.354 -2.445 1.098 3.99 -1.183 -0.143 -0.241 32.855 19.233 -3.428 -106.905 -19.95 25.511]\n", "\n", "\n", - "encoded_sol: [-5.068e+00 -6.067e-01 8.121e+00 -1.624e+00 -2.642e+00 8.434e+00 -1.468e+00 -2.681e+00 7.000e+02 7.000e+02 7.000e+02 5.000e+02 5.998e+02 5.000e+02]\n", - "encoded_ref: [ 10. 1.82 8.2 1.115 5.93 2.681 0.841 -0.724 641.292 544.227 598.63 500. 572.798 523.483]\n", - "diff : [ 1.507e+01 2.427e+00 7.828e-02 2.740e+00 8.571e+00 -5.753e+00 2.309e+00 1.957e+00 -5.871e+01 -1.558e+02 -1.014e+02 0.000e+00 -2.701e+01 2.348e+01]\n", + "encoded_sol: [ 1. 1. -1. -1. -1. 1. -1. 1. 12.097 1.452 10.645 0. 1.935 3.871 0.968 0.968 625.806 577.419 645.161 645.161 645.161 558.065]\n", + "encoded_ref: [ 1. 1. 1. 1. 1. 1. 1. -1. 11.129 1.935 8.226 0.968 5.806 2.903 0.968 0.968 654.839 596.774 645.161 538.71 625.806 587.097]\n", + "diff : [ 0. 0. 2. 2. 2. 0. 2. -2. -0.968 0.484 -2.419 0.968 3.871 -0.968 0. 0. 29.032 19.355 0. -106.452 -19.355 29.032]\n", "\n", "\n", - "E sol : -549306305.587519\n", - "R ref : -528587363.97844124\n", - "Delta E : -20718941.60907781\n", + "E sol : -34053.441822954344\n", + "R ref : -33184.813353168196\n", + "Delta E : -868.6284697861483\n", "\n", "\n", - "Residue sol : 809.3375066786808\n", - "Residue ref : 4623.198980013018\n", - "Delta Residue : -3813.861473334337\n" + "Residue sol : 158.63184471222584\n", + "Residue ref : 134.63471105151393\n", + "Delta Residue : 23.99713366071191\n" ] } ], "source": [ - "net.diagnostic_solution(sol, ref_values)" + "net.diagnostic_solution(sol, ref_sol)" ] }, { @@ -745,12 +653,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -764,7 +672,7 @@ "" ] }, - "execution_count": 12, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index a76e682..4e633c1 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -204,7 +204,7 @@ def diagnostic_solution(self, solution: np.ndarray, reference_solution: np.ndarr print("diff : ", np.array(data_ref[0]) - np.array(data_sol[0])) print("\n") print("E sol : ", esol) - print("R ref : ", eref) + print("E ref : ", eref) print("Delta E :", esol - eref) print("\n") res_sol = np.linalg.norm( @@ -449,10 +449,10 @@ def qubo_poly_solve(self, strength=1e6, num_reads=10000, **options): # noqa: D4 self.qubo.qubo_dict = self.qubo.create_bqm(matrices, strength=strength) # sample - sampleset = self.qubo.sample_bqm(self.qubo.qubo_dict, num_reads=num_reads) + self.sampleset = self.qubo.sample_bqm(self.qubo.qubo_dict, num_reads=num_reads) # decode - sol = self.qubo.decode_solution(sampleset.lowest().record[0][0]) + sol = self.qubo.decode_solution(self.sampleset.lowest().record[0][0]) # combine the sign*abs values for the flow sol = self.combine_flow_values(sol) From a98bb7f884b761fb241f911910095b216598a3b7 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 12 Sep 2024 20:36:14 +0200 Subject: [PATCH 55/96] 2loops example --- .../sandbox/qubo_poly_solver_Net2loops.ipynb | 81 +++++++++++++++++++ 1 file changed, 81 insertions(+) diff --git a/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb b/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb index d04d98e..18ce92a 100644 --- a/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb +++ b/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb @@ -688,6 +688,87 @@ "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", " title='Pressure at 5 hours', node_labels=False)" ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "ename": "StateTraitMissingError", + "evalue": "input state is missing 'subsamples' on SplatComposer()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mStateTraitMissingError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[140], line 18\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Solve the problem\u001b[39;00m\n\u001b[1;32m 17\u001b[0m init_state \u001b[38;5;241m=\u001b[39m hybrid\u001b[38;5;241m.\u001b[39mState\u001b[38;5;241m.\u001b[39mfrom_problem(bqm)\n\u001b[0;32m---> 18\u001b[0m final_state \u001b[38;5;241m=\u001b[39m \u001b[43mworkflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43minit_state\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# Print results\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSolution: sample=\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m.samples.first}\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(final_state))\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:440\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 440\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:389\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/thread.py:52\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 52\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:413\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_input_state_traits(state)\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.next\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 413\u001b[0m new_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_output_state_traits(new_state)\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_state\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:525\u001b[0m, in \u001b[0;36mstoppable..next\u001b[0;34m(self, state, **runopts)\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m, state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mrunopts):\n\u001b[0;32m--> 525\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43morig_next\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrunopts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 526\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_signal\u001b[38;5;241m.\u001b[39mclear()\n\u001b[1;32m 527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/flow.py:818\u001b[0m, in \u001b[0;36mLoopUntilNoImprovement.next\u001b[0;34m(self, state, **runopts)\u001b[0m\n\u001b[1;32m 815\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_signal\u001b[38;5;241m.\u001b[39mis_set():\n\u001b[1;32m 817\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 818\u001b[0m output_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrunnable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrunopts\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 819\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m EndOfStream \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 820\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{name}\u001b[39;00m\u001b[38;5;124m Iteration(iterno=\u001b[39m\u001b[38;5;132;01m{iterno}\u001b[39;00m\u001b[38;5;124m) terminating due \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 821\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto \u001b[39m\u001b[38;5;132;01m{exc!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, iterno\u001b[38;5;241m=\u001b[39miterno, exc\u001b[38;5;241m=\u001b[39mexc))\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:433\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:389\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/concurrency.py:55\u001b[0m, in \u001b[0;36mImmediateExecutor.submit\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Blocking version of `Executor.submit()`. Returns a resolved\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124;03m`Future`.\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(result\u001b[38;5;241m=\u001b[39m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(exception\u001b[38;5;241m=\u001b[39mexc)\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:413\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_input_state_traits(state)\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.next\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 413\u001b[0m new_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_output_state_traits(new_state)\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_state\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/flow.py:133\u001b[0m, in \u001b[0;36mBranch.next\u001b[0;34m(self, state, **runopts)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m component \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcomponents:\n\u001b[1;32m 131\u001b[0m state \u001b[38;5;241m=\u001b[39m component\u001b[38;5;241m.\u001b[39mrun(state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mrunopts)\n\u001b[0;32m--> 133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:433\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:389\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/concurrency.py:55\u001b[0m, in \u001b[0;36mImmediateExecutor.submit\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Blocking version of `Executor.submit()`. Returns a resolved\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124;03m`Future`.\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(result\u001b[38;5;241m=\u001b[39m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(exception\u001b[38;5;241m=\u001b[39mexc)\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:403\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.resolve.error\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 403\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 405\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_initialized\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.init\u001b[39m\u001b[38;5;124m'\u001b[39m):\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:375\u001b[0m, in \u001b[0;36mRunnable.error\u001b[0;34m(self, exc)\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21merror\u001b[39m(\u001b[38;5;28mself\u001b[39m, exc):\n\u001b[1;32m 366\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Execute one blocking iteration of an instantiated :class:`Runnable`\u001b[39;00m\n\u001b[1;32m 367\u001b[0m \u001b[38;5;124;03m with an exception as input.\u001b[39;00m\n\u001b[1;32m 368\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 373\u001b[0m \u001b[38;5;124;03m errors must be explicitly silenced.\u001b[39;00m\n\u001b[1;32m 374\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 375\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:400\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.resolve\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 400\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.resolve.error\u001b[39m\u001b[38;5;124m'\u001b[39m):\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:433\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:389\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/concurrency.py:55\u001b[0m, in \u001b[0;36mImmediateExecutor.submit\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Blocking version of `Executor.submit()`. Returns a resolved\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124;03m`Future`.\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(result\u001b[38;5;241m=\u001b[39m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(exception\u001b[38;5;241m=\u001b[39mexc)\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:413\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_input_state_traits(state)\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.next\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 413\u001b[0m new_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_output_state_traits(new_state)\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_state\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/flow.py:303\u001b[0m, in \u001b[0;36mRacingBranches.next\u001b[0;34m(self, state, **runopts)\u001b[0m\n\u001b[1;32m 301\u001b[0m states \u001b[38;5;241m=\u001b[39m States()\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m futures:\n\u001b[0;32m--> 303\u001b[0m states\u001b[38;5;241m.\u001b[39mappend(\u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m states\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:433\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:389\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/thread.py:52\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 52\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:413\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_input_state_traits(state)\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.next\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 413\u001b[0m new_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_output_state_traits(new_state)\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_state\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/flow.py:133\u001b[0m, in \u001b[0;36mBranch.next\u001b[0;34m(self, state, **runopts)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m component \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcomponents:\n\u001b[1;32m 131\u001b[0m state \u001b[38;5;241m=\u001b[39m component\u001b[38;5;241m.\u001b[39mrun(state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mrunopts)\n\u001b[0;32m--> 133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:433\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:389\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/concurrency.py:55\u001b[0m, in \u001b[0;36mImmediateExecutor.submit\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Blocking version of `Executor.submit()`. Returns a resolved\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124;03m`Future`.\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(result\u001b[38;5;241m=\u001b[39m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(exception\u001b[38;5;241m=\u001b[39mexc)\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:410\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minit(state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28msetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_initialized\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 410\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_input_state_traits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.next\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 413\u001b[0m new_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnext(state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/traits.py:76\u001b[0m, in \u001b[0;36mStateTraits.validate_input_state_traits\u001b[0;34m(self, inp)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m StateDimensionalityError(\n\u001b[1;32m 73\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle state required on input to \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m))\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m trait \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minputs:\n\u001b[0;32m---> 76\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_state_trait\u001b[49m\u001b[43m(\u001b[49m\u001b[43minp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrait\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/traits.py:54\u001b[0m, in \u001b[0;36mStateTraits.validate_state_trait\u001b[0;34m(self, state, trait, io)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Validate single input/output (`io`) `state` `trait`.\"\"\"\u001b[39;00m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trait \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m state:\n\u001b[0;32m---> 54\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m StateTraitMissingError(\n\u001b[1;32m 55\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m state is missing \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m on \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(io, trait, \u001b[38;5;28mself\u001b[39m))\n", + "\u001b[0;31mStateTraitMissingError\u001b[0m: input state is missing 'subsamples' on SplatComposer()" + ] + } + ], + "source": [ + "import dimod\n", + "import hybrid\n", + "\n", + "# Construct a problem\n", + "bqm = dimod.BinaryQuadraticModel({}, {'ab': 1, 'bc': -1, 'ca': 1}, 0, dimod.SPIN)\n", + "\n", + "# Define the workflow\n", + "iteration = hybrid.RacingBranches(\n", + " hybrid.InterruptableTabuSampler(),\n", + " hybrid.decomposers.EnergyImpactDecomposer(size=2)\n", + " | hybrid.TabuProblemSampler(num_reads=2)\n", + " | hybrid.SplatComposer()\n", + ") | hybrid.ArgMin()\n", + "workflow = hybrid.LoopUntilNoImprovement(iteration, convergence=3)\n", + "\n", + "# Solve the problem\n", + "init_state = hybrid.State.from_problem(bqm)\n", + "final_state = workflow.run(init_state).result()\n", + "\n", + "# Print results\n", + "print(\"Solution: sample={.samples.first}\".format(final_state))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From cf2865ee50d2d719f8b08e0d886bb96817aa290c Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 12 Sep 2024 21:40:56 +0200 Subject: [PATCH 56/96] started design --- wntr_quantum/design/qubo_pipe_diam.py | 130 ++++++++++++++++------ wntr_quantum/sim/models/chezy_manning.py | 59 +++++----- wntr_quantum/sim/models/darcy_weisbach.py | 63 ++++++----- wntr_quantum/sim/models/mass_balance.py | 36 +++--- 4 files changed, 182 insertions(+), 106 deletions(-) diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index 649a0b8..fc917e2 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -19,10 +19,10 @@ from wntr.sim.solvers import SolverStatus from ..sim.hydraulics import create_hydraulic_model from ..sim.models.chezy_manning import cm_resistance_value -from ..sim.models.chezy_manning import get_pipe_design_chezy_manning_matrix +from ..sim.models.chezy_manning import get_pipe_design_chezy_manning_qubops_matrix from ..sim.models.darcy_weisbach import dw_resistance_value -from ..sim.models.darcy_weisbach import get_pipe_design_darcy_wesibach_matrix -from ..sim.models.mass_balance import get_mass_balance_matrix +from ..sim.models.darcy_weisbach import get_pipe_design_darcy_wesibach_qubops_matrix +from ..sim.models.mass_balance import get_mass_balance_qubops_matrix class QUBODesignPipeDiameter(object): @@ -54,15 +54,27 @@ def __init__( self.pipe_diameters = [p / 1000 for p in pipe_diameters] self.num_diameters = len(pipe_diameters) - # encodings of the head/flow variables + # create the encoding vectors for the sign of the flows + self.sign_flow_encoding = PositiveQbitEncoding( + nqbit=1, step=2, offset=-1, var_base_name="x" + ) + + # create the solution vector for the sign + self.sol_vect_signs = SolutionVector( + wn.num_pipes, encoding=self.sign_flow_encoding + ) + + # store the flow encoding and create solution vector self.flow_encoding = flow_encoding - self.head_encoding = head_encoding self.sol_vect_flows = SolutionVector(wn.num_pipes, encoding=flow_encoding) - self.sol_vect_heads = SolutionVector(wn.num_junctions, encoding=head_encoding) + if np.min(self.flow_encoding.get_possible_values()) < 0: + raise ValueError( + "The encoding of the flows must only take positive values." + ) - # lower bound for the pressure - self.head_lower_bound = head_lower_bound - self.head_upper_bound = 10 * head_lower_bound + # store the heqd encoding and create solution vector + self.head_encoding = head_encoding + self.sol_vect_heads = SolutionVector(wn.num_junctions, encoding=head_encoding) # one hot encoding for the pipe coefficients self.num_hot_encoding = wn.num_pipes * self.num_diameters @@ -71,7 +83,12 @@ def __init__( # mixed solution vector self.mixed_solution_vector = MixedSolutionVector( - [self.sol_vect_flows, self.sol_vect_heads, self.sol_vect_pipes] + [ + self.sol_vect_signs, + self.sol_vect_flows, + self.sol_vect_heads, + self.sol_vect_pipes, + ] ) # basic hydraulic model @@ -83,6 +100,15 @@ def __init__( # weight for the cost equation self.weight_cost = weight_cost + # lower bound for the pressure + self.head_lower_bound = head_lower_bound + self.head_upper_bound = 10 * head_lower_bound # is that enough ? + + # store other attributes + self.qubo = None + self.flow_index_mapping = None + self.head_index_mapping = None + # compute the polynomial matrices self.matrices = self.initialize_matrices() @@ -231,7 +257,10 @@ def verify_encoding(self): fres = self.flow_encoding.get_average_precision() fvalues = np.sort(self.flow_encoding.get_possible_values()) print("Head Encoding : %f => %f (res: %f)" % (hvalues[0], hvalues[-1], hres)) - print("Flow Encoding : %f => %f (res: %f)" % (fvalues[0], fvalues[-1], fres)) + print( + "Flow Encoding : %f => %f | %f => %f (res: %f)" + % (-fvalues[-1], -fvalues[0], fvalues[0], fvalues[-1], fres) + ) def verify_solution(self, input, params): """Computes the rhs vector associate with the input. @@ -343,7 +372,7 @@ def get_cost_matrix(self, matrices): Args: matrices (tuple): The matrices """ - P0, P1, P2, P3 = matrices + P0, P1, P2, P3, P4 = matrices # loop over all the pipe coeffs istart = self.sol_vect_flows.size + self.sol_vect_heads.size @@ -356,33 +385,57 @@ def get_cost_matrix(self, matrices): self.model.pipe_coefficients_indices[link_name].value, ): P1[-1, istart + pipe_idx] = self.weight_cost * pipe_cost - return P0, P1, P2, P3 + return P0, P1, P2, P3, P4 def initialize_matrices(self) -> Tuple: - """_summary_.""" + """Creates the matrices of the polynomial system of equation. + + math :: + + + """ num_equations = len(list(self.model.cons())) + 1 # +1 for cost equation - num_continuous_variables = len(list(self.model.vars())) - num_variables = num_continuous_variables + self.num_hot_encoding + num_variables = ( + 2 * len(self.model.flow) + len(self.model.head) + self.num_hot_encoding + ) # must transform that to coo P0 = np.zeros((num_equations, 1)) P1 = np.zeros((num_equations, num_variables)) P2 = np.zeros((num_equations, num_variables, num_variables)) P3 = np.zeros((num_equations, num_variables, num_variables, num_variables)) + P4 = np.zeros( + (num_equations, num_variables, num_variables, num_variables, num_variables) + ) - matrices = (P0, P1, P2, P3) - (P0, P1, P2) = get_mass_balance_matrix( - self.model, self.wn, (P0, P1, P2), convert_to_us_unit=True + # get the mass balance matrix + (P0, P1, P2, P3) = get_mass_balance_qubops_matrix( + self.model, + self.wn, + (P0, P1, P2, P3), + self.flow_index_mapping, + convert_to_us_unit=True, ) + # shortcut + matrices = (P0, P1, P2, P3, P4) + # get the headloss matrix contributions if self.wn.options.hydraulic.headloss == "C-M": - matrices = get_pipe_design_chezy_manning_matrix( - self.model, self.wn, matrices + matrices = get_pipe_design_chezy_manning_qubops_matrix( + self.model, + self.wn, + matrices, + self.flow_index_mapping, + self.head_index_mapping, ) elif self.wn.options.hydraulic.headloss == "D-W": - matrices = get_pipe_design_darcy_wesibach_matrix( - self.model, self.wn, matrices + matrices = get_pipe_design_darcy_wesibach_qubops_matrix( + self.model, + self.wn, + matrices, + self.flow_index_mapping, + self.head_index_mapping, ) else: raise ValueError("Calculation only possible with C-M or D-W") @@ -427,19 +480,25 @@ def get_pipe_info_from_hot_encoding(self, hot_encoding): return total_price, pipe_diameters - @staticmethod - def load_data_in_model(model: Model, data: np.ndarray): + def load_data_in_model(self, model: Model, data: np.ndarray): """Loads some data in the model. + Remark: + This routine replaces `load_var_values_from_x` without reordering the vector elements + Args: model (Model): AML model from WNTR data (np.ndarray): data to load """ - for iv, v in enumerate(model.vars()): - v.value = data[iv] - - @staticmethod - def extract_data_from_model(model: Model) -> np.ndarray: + shift_head_idx = self.wn.num_links + for var in model.vars(): + if var.name in self.flow_index_mapping: + idx = self.flow_index_mapping[var.name]["sign"] + elif var.name in self.head_index_mapping: + idx = self.head_index_mapping[var.name] - shift_head_idx + var.value = data[idx] + + def extract_data_from_model(self, model: Model) -> np.ndarray: """Loads some data in the model. Args: @@ -448,9 +507,14 @@ def extract_data_from_model(model: Model) -> np.ndarray: Returns: np.ndarray: data extracted from model """ - data = [] - for v in model.vars(): - data.append(v.value) + data = [None] * len(list(model.vars())) + shift_head_idx = self.wn.num_links + for var in model.vars(): + if var.name in self.flow_index_mapping: + idx = self.flow_index_mapping[var.name]["sign"] + elif var.name in self.head_index_mapping: + idx = self.head_index_mapping[var.name] - shift_head_idx + data[idx] = var.value return data def solve( # noqa: D417 diff --git a/wntr_quantum/sim/models/chezy_manning.py b/wntr_quantum/sim/models/chezy_manning.py index aa9a350..4698b51 100644 --- a/wntr_quantum/sim/models/chezy_manning.py +++ b/wntr_quantum/sim/models/chezy_manning.py @@ -205,59 +205,64 @@ def get_chezy_manning_matrix(m, wn, matrices): # noqa: D417 return (P0, P1, P2) -def get_pipe_design_chezy_manning_matrix(m, wn, matrices): # noqa: D417 - """Adds a mass balance to the model for the specified junctions. - - Parameters - ---------- - m: wntr.aml.aml.aml.Model - wn: wntr.network.model.WaterNetworkModel - updater: ModelUpdater - index_over: list of str - list of pipe names; default is all pipes in wn - """ - P0, P1, P2, P3 = matrices +def get_pipe_design_chezy_manning_qubops_matrix( + m, wn, matrices, flow_index_mapping, head_index_mapping +): # noqa: D417 + """Create the matrices for chezy manning headloss approximation. - continuous_var_name = [v.name for v in list(m.vars())] - num_continuous_var = len(continuous_var_name) - # discrete_var_name = [v.name for k, v in m.cm_resistance.items()] - var_names = continuous_var_name # + discrete_var_name + Args: + m (aml.Model): The AML model of the network + wn (WaternNetwork): th water network object + matrices (Tuple): The qubops matrices of the network + flow_index_mapping (Dict): A dict to map the flow model variables to the qubops matrices + head_index_mapping (Dict): A dict to map the head model variables to the qubops matrices + convert_to_us_unit (bool, optional): Convert the inut to US units. Defaults to False. - index_over = wn.pipe_name_list + Returns: + Tuple: The qubops matrices of the network + """ + P0, P1, P2, P3, P4 = matrices + num_continuous_var = 2 * len(m.flow) + len(m.head) - for ieq0, link_name in enumerate(index_over): + for ieq0, link_name in enumerate(wn.pipe_name_list): + # index of the pipe equation ieq = ieq0 + len(wn.junction_name_list) + + # link info link = wn.get_link(link_name) - f = m.flow[link_name] - flow_index = var_names.index(f.name) + # get start/end node info start_node_name = link.start_node_name end_node_name = link.end_node_name - start_node = wn.get_node(start_node_name) end_node = wn.get_node(end_node_name) + # linear term (start head value) of the headloss approximation if isinstance(start_node, wntr.network.Junction): - start_h = m.head[start_node_name] - start_node_index = var_names.index(start_h.name) + start_node_index = head_index_mapping[m.head[start_node_name].name] P1[ieq, start_node_index] = 1 else: start_h = m.source_head[start_node_name] P0[ieq, 0] += from_si(FlowUnits.CFS, start_h.value, HydParam.Length) + # linear term (end head values) of the headloss approximation if isinstance(end_node, wntr.network.Junction): - end_h = m.head[end_node_name] - end_node_index = var_names.index(end_h.name) + end_node_index = head_index_mapping[m.head[end_node_name].name] P1[ieq, end_node_index] = -1 else: end_h = m.source_head[end_node_name] P0[ieq, 0] -= from_si(FlowUnits.CFS, end_h.value, HydParam.Length) + # non linear term (resistance sign flow^2) of the headloss approximation + sign_index = flow_index_mapping[m.flow[link_name].name]["sign"] + flow_index = flow_index_mapping[m.flow[link_name].name]["absolute_value"] for pipe_coefs, pipe_idx in zip( m.pipe_coefficients[link_name].value, m.pipe_coefficients_indices[link_name].value, ): - P3[ieq, flow_index, flow_index, pipe_idx + num_continuous_var] = -pipe_coefs + P4[ + ieq, sign_index, flow_index, flow_index, pipe_idx + num_continuous_var + ] = -pipe_coefs - return (P0, P1, P2, P3) + return (P0, P1, P2, P3, P4) diff --git a/wntr_quantum/sim/models/darcy_weisbach.py b/wntr_quantum/sim/models/darcy_weisbach.py index 58a2a8b..99ebdc4 100644 --- a/wntr_quantum/sim/models/darcy_weisbach.py +++ b/wntr_quantum/sim/models/darcy_weisbach.py @@ -236,63 +236,70 @@ def get_darcy_weisbach_matrix(m, wn, matrices): # noqa: D417 return (P0, P1, P2) -def get_pipe_design_darcy_wesibach_matrix(m, wn, matrices): # noqa: D417 - """Adds a mass balance to the model for the specified junctions. +def get_pipe_design_darcy_wesibach_qubops_matrix( + m, wn, matrices, flow_index_mapping, head_index_mapping +): # noqa: D417 + """Create the matrices for chezy manning headloss approximation. - Parameters - ---------- - m: wntr.aml.aml.aml.Model - wn: wntr.network.model.WaterNetworkModel - updater: ModelUpdater - index_over: list of str - list of pipe names; default is all pipes in wn - """ - P0, P1, P2, P3 = matrices - - continuous_var_name = [v.name for v in list(m.vars())] - num_continuous_var = len(continuous_var_name) - # discrete_var_name = [v.name for k, v in m.cm_resistance.items()] - var_names = continuous_var_name # + discrete_var_name + Args: + m (aml.Model): The AML model of the network + wn (WaternNetwork): th water network object + matrices (Tuple): The qubops matrices of the network + flow_index_mapping (Dict): A dict to map the flow model variables to the qubops matrices + head_index_mapping (Dict): A dict to map the head model variables to the qubops matrices + convert_to_us_unit (bool, optional): Convert the inut to US units. Defaults to False. - index_over = wn.pipe_name_list + Returns: + Tuple: The qubops matrices of the network + """ + P0, P1, P2, P3, P4 = matrices + num_continuous_var = 2 * len(m.flow) + len(m.head) - for ieq0, link_name in enumerate(index_over): + for ieq0, link_name in enumerate(wn.pipe_name_list): + # index of the pipe equation ieq = ieq0 + len(wn.junction_name_list) + + # get link info link = wn.get_link(link_name) - f = m.flow[link_name] - flow_index = var_names.index(f.name) + # get start/end node info start_node_name = link.start_node_name end_node_name = link.end_node_name - start_node = wn.get_node(start_node_name) end_node = wn.get_node(end_node_name) + # linear term (start head value) of the headloss approximation if isinstance(start_node, wntr.network.Junction): - start_h = m.head[start_node_name] - start_node_index = var_names.index(start_h.name) + start_node_index = head_index_mapping[m.head[start_node_name].name] P1[ieq, start_node_index] = 1 else: start_h = m.source_head[start_node_name] P0[ieq, 0] += from_si(FlowUnits.CFS, start_h.value, HydParam.Length) + # linear term (end head value) of the headloss approximation if isinstance(end_node, wntr.network.Junction): - end_h = m.head[end_node_name] - end_node_index = var_names.index(end_h.name) + end_node_index = head_index_mapping[m.head[end_node_name].name] P1[ieq, end_node_index] = -1 else: end_h = m.source_head[end_node_name] P0[ieq, 0] -= from_si(FlowUnits.CFS, end_h.value, HydParam.Length) + # non linear terms (resistance sign flow ^2) of the approximation + sign_index = flow_index_mapping[m.flow[link_name].name]["sign"] + flow_index = flow_index_mapping[m.flow[link_name].name]["absolute_value"] + + # loop over the pipe diameters ofthe link for pipe_coefs, pipe_idx in zip( m.pipe_coefficients[link_name].value, m.pipe_coefficients_indices[link_name].value, ): P1[ieq, pipe_idx + num_continuous_var] -= pipe_coefs[0] - P2[ieq, flow_index, pipe_idx + num_continuous_var] -= pipe_coefs[1] P3[ - ieq, flow_index, flow_index, pipe_idx + num_continuous_var + ieq, sign_index, flow_index, pipe_idx + num_continuous_var + ] -= pipe_coefs[1] + P4[ + ieq, sign_index, flow_index, flow_index, pipe_idx + num_continuous_var ] -= pipe_coefs[2] - return (P0, P1, P2, P3) + return (P0, P1, P2, P3, P4) diff --git a/wntr_quantum/sim/models/mass_balance.py b/wntr_quantum/sim/models/mass_balance.py index cd408b7..8445f7f 100644 --- a/wntr_quantum/sim/models/mass_balance.py +++ b/wntr_quantum/sim/models/mass_balance.py @@ -3,24 +3,22 @@ from wntr.epanet.util import from_si -def get_mass_balance_matrix(m, wn, matrices, convert_to_us_unit=False): # noqa: D417 +def get_mass_balance_qubops_matrix( + m, wn, matrices, flow_index_mapping, convert_to_us_unit=False +): # noqa: D417 """Create the matrices for the mass balance equation. Args: - m (_type_): _description_ - wn (_type_): _description_ - matrices (_type_): _description_ - convert_to_us_unit (bool, optional): _description_. Defaults to False. + m (aml.Model): The AML model of the network + wn (WaternNetwork): th water network object + matrices (Tuple): The qubops matrices of the network + flow_index_mapping (Dict): A dict to map the flow model variables to the qubops matrices + convert_to_us_unit (bool, optional): Convert the inut to US units. Defaults to False. Returns: - _type_: _description_ + Tuple: The qubops matrices of the network """ - P0, P1, P2 = matrices - - continuous_var_name = [v.name for v in list(m.vars())] - # discrete_var_name = [v.name for k, v in m.cm_resistance.items()] - var_names = continuous_var_name # + discrete_var_name - + P0, P1, P2, P3 = matrices index_over = wn.junction_name_list for ieq, node_name in enumerate(index_over): @@ -28,21 +26,23 @@ def get_mass_balance_matrix(m, wn, matrices, convert_to_us_unit=False): # noqa: node = wn.get_node(node_name) if not node._is_isolated: if convert_to_us_unit: - P0[ieq, 0] = from_si( + P0[ieq, 0] += from_si( FlowUnits.CFS, m.expected_demand[node_name].value, HydParam.Flow ) else: P0[ieq, 0] += m.expected_demand[node_name].value for link_name in wn.get_links_for_node(node_name, flag="INLET"): - node_index = var_names.index(m.flow[link_name].name) - P1[ieq, node_index] -= 1 + sign_idx = flow_index_mapping[m.flow[link_name].name]["sign"] + flow_idx = flow_index_mapping[m.flow[link_name].name]["absolute_value"] + P2[ieq, sign_idx, flow_idx] -= 1 for link_name in wn.get_links_for_node(node_name, flag="OUTLET"): - node_index = var_names.index(m.flow[link_name].name) - P1[ieq, node_index] += 1 + sign_idx = flow_index_mapping[m.flow[link_name].name]["sign"] + flow_idx = flow_index_mapping[m.flow[link_name].name]["absolute_value"] + P2[ieq, sign_idx, flow_idx] += 1 - return P0, P1, P2 + return P0, P1, P2, P3 def get_mass_balance_constraint_design(m, wn, matrices): # noqa: D417 From 701838244962b805b57f512ff0ec103c3e538473 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 13 Sep 2024 09:16:01 +0200 Subject: [PATCH 57/96] fix indexing --- wntr_quantum/design/qubo_pipe_diam.py | 48 +++++++++++++++++-- .../sim/solvers/qubo_polynomial_solver.py | 4 +- 2 files changed, 46 insertions(+), 6 deletions(-) diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index fc917e2..c59ea78 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -1,4 +1,5 @@ import itertools +from collections import OrderedDict from typing import List from typing import Tuple import numpy as np @@ -109,9 +110,6 @@ def __init__( self.flow_index_mapping = None self.head_index_mapping = None - # compute the polynomial matrices - self.matrices = self.initialize_matrices() - def get_dw_pipe_coefficients(self, link): """Get the pipe coefficients for a specific link with DW. @@ -517,6 +515,37 @@ def extract_data_from_model(self, model: Model) -> np.ndarray: data[idx] = var.value return data + def create_index_mapping(self, model: Model) -> None: + """Creates the index maping for qubops matrices. + + Args: + model (Model): the AML Model + """ + # init the idx + idx = 0 + + # number of variables that are flows + num_flow_var = len(model.flow) + + # get the indices for the sign/abs value of the flow + self.flow_index_mapping = OrderedDict() + for _, val in model.flow.items(): + if val.name not in self.flow_index_mapping: + self.flow_index_mapping[val.name] = { + "sign": None, + "absolute_value": None, + } + self.flow_index_mapping[val.name]["sign"] = idx + self.flow_index_mapping[val.name]["absolute_value"] = idx + num_flow_var + idx += 1 + + # get the indices for the heads + idx = 0 + self.head_index_mapping = OrderedDict() + for _, val in model.head.items(): + self.head_index_mapping[val.name] = 2 * num_flow_var + idx + idx += 1 + def solve( # noqa: D417 self, strength: float = 1e6, num_reads: int = 10000, **options ) -> Tuple: @@ -529,6 +558,12 @@ def solve( # noqa: D417 Returns: Tuple: Succes message """ + # create the index mapping of the variables + self.create_index_mapping() + + # compute the polynomial matrices + self.matrices = self.initialize_matrices() + self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, **options) matrices = tuple(sparse.COO(m) for m in self.matrices) @@ -536,7 +571,12 @@ def solve( # noqa: D417 self.bqm = self.qubo.create_bqm(matrices, strength=strength) # add constraints on the hot encoding - istart = self.sol_vect_flows.size + self.sol_vect_heads.size + # the sum of each hot encoding variable of a given pipe must equals 1 + istart = ( + self.sol_vect_signs.size + + self.sol_vect_flows.size + + self.sol_vect_heads.size + ) for i in range(self.sol_vect_flows.size): # create the expression [(x0, 1), (x1, 1), ...] diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index a4fdac1..04f55ed 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -17,7 +17,7 @@ from wntr.sim.solvers import SolverStatus from ..models.chezy_manning import get_chezy_manning_matrix from ..models.darcy_weisbach import get_darcy_weisbach_matrix -from ..models.mass_balance import get_mass_balance_matrix +from ..models.mass_balance import get_mass_balance_qubops_matrix class QuboPolynomialSolver(object): @@ -190,7 +190,7 @@ def initialize_matrices(self, model: Model) -> Tuple: matrices = (P0, P1, P2) # get the mass balance - matrices = get_mass_balance_matrix( + matrices = get_mass_balance_qubops_matrix( model, self.wn, matrices, convert_to_us_unit=True ) From 87e7fb213e3da6203416c07670dd0e2a833151fb Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 13 Sep 2024 09:47:43 +0200 Subject: [PATCH 58/96] pipe encoding index --- wntr_quantum/design/qubo_pipe_diam.py | 27 ++++++++++++++++++--------- 1 file changed, 18 insertions(+), 9 deletions(-) diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index c59ea78..bbf39fb 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -515,21 +515,18 @@ def extract_data_from_model(self, model: Model) -> np.ndarray: data[idx] = var.value return data - def create_index_mapping(self, model: Model) -> None: - """Creates the index maping for qubops matrices. - - Args: - model (Model): the AML Model - """ + def create_index_mapping(self) -> None: + """Creates the index maping for qubops matrices.""" # init the idx idx = 0 # number of variables that are flows - num_flow_var = len(model.flow) + num_flow_var = len(self.model.flow) + num_head_var = len(self.model.head) # get the indices for the sign/abs value of the flow self.flow_index_mapping = OrderedDict() - for _, val in model.flow.items(): + for _, val in self.model.flow.items(): if val.name not in self.flow_index_mapping: self.flow_index_mapping[val.name] = { "sign": None, @@ -542,10 +539,22 @@ def create_index_mapping(self, model: Model) -> None: # get the indices for the heads idx = 0 self.head_index_mapping = OrderedDict() - for _, val in model.head.items(): + for _, val in self.model.head.items(): self.head_index_mapping[val.name] = 2 * num_flow_var + idx idx += 1 + # get the indices for the pipe diameters + idx = 0 + self.pipe_diameter_index_mapping = OrderedDict() + for _, val in self.model.flow.items(): + if val.name not in self.pipe_diameter_index_mapping: + self.pipe_diameter_index_mapping[val.name] = OrderedDict() + for idiam in range(self.num_diameters): + self.pipe_diameter_index_mapping[val.name][idiam] = ( + 2 * num_flow_var + num_head_var + idx + ) + idx += 1 + def solve( # noqa: D417 self, strength: float = 1e6, num_reads: int = 10000, **options ) -> Tuple: From 9a9c3a94e645c2f8372e809c62cfabd8311b97b9 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 13 Sep 2024 14:38:46 +0200 Subject: [PATCH 59/96] classical solution for CM --- docs/notebooks/design_pipe_diameter.ipynb | 79 ++++++++++++++++++- docs/notebooks/qubo_poly_solver_CM.ipynb | 26 +----- wntr_quantum/design/qubo_pipe_diam.py | 69 ++++++++++------ .../sim/solvers/qubo_polynomial_solver.py | 5 -- 4 files changed, 123 insertions(+), 56 deletions(-) diff --git a/docs/notebooks/design_pipe_diameter.ipynb b/docs/notebooks/design_pipe_diameter.ipynb index 26b7346..ec74b7a 100644 --- a/docs/notebooks/design_pipe_diameter.ipynb +++ b/docs/notebooks/design_pipe_diameter.ipynb @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -80,7 +80,7 @@ "output_type": "stream", "text": [ "Head Encoding : 95.000000 => 100.000000 (res: 0.039370)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.003937)\n" + "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.003937)\n" ] } ], @@ -88,6 +88,79 @@ "designer.verify_encoding()" ] }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.create_index_mapping()\n", + "designer.pipe_diameter_index_mapping['flow[P2]'][2]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "(P0, P1,P2,P3,P4) = designer.initialize_matrices()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1., 1., 0., 0.],\n", + " [ 0., -1., 0., 0.],\n", + " [ 0., 0., -1., 0.],\n", + " [ 0., 0., 1., -1.]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "P1[:-1, 2:6] + P2.sum(1)[:-1, 2:6]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5, 12, 12)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "P2.shape" + ] + }, { "cell_type": "code", "execution_count": 22, diff --git a/docs/notebooks/qubo_poly_solver_CM.ipynb b/docs/notebooks/qubo_poly_solver_CM.ipynb index e13a968..fb44b0a 100644 --- a/docs/notebooks/qubo_poly_solver_CM.ipynb +++ b/docs/notebooks/qubo_poly_solver_CM.ipynb @@ -357,30 +357,7 @@ "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.000978)\n", "\n", "\n", -<<<<<<< HEAD - "Error (%): [-5.676 -1.963 -0.029 -0.147]\n", - "\n", - "\n", - "sol : [ 1.866 1.8 98.434 98.532]\n", - "ref : [ 1.766 1.766 98.406 98.387]\n", - "diff: [-0.1 -0.035 -0.028 -0.145]\n", - "\n", - "\n", - "encoded_sol: [ 1.866 1.8 98.434 98.532]\n", - "encoded_ref: [ 1.766 1.766 98.434 98.434]\n", - "diff : [-0.1 -0.034 0. -0.098]\n", - "\n", - "\n", - "E sol : -2343.7308669783342\n", - "R ref : -2343.749937932273\n", - "Delta E : 0.01907095393880809\n", - "\n", - "\n", - "Residue sol : 0.14219517568484824\n", - "Residue ref : 0.03388956865892264\n", - "Delta Residue : 0.1083056070259256\n" -======= - "Error (%): [ 0. 0. -7.061 1.085 -0.029 -0.048]\n", + "Error (%): [ 0. 0. -7.061 1.085 -0.029 -0.048]\n", "\n", "\n", "sol : [ 1. 1. 1.89 1.747 98.434 98.434]\n", @@ -401,7 +378,6 @@ "Residue sol : 0.14950214827015704\n", "Residue ref : 0.03388956865892264\n", "Delta Residue : 0.11561257961123439\n" ->>>>>>> qubo_poly_solver ] } ], diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index bbf39fb..2a2d499 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -18,12 +18,12 @@ from wntr.sim import aml from wntr.sim.aml import Model from wntr.sim.solvers import SolverStatus -from ..sim.hydraulics import create_hydraulic_model from ..sim.models.chezy_manning import cm_resistance_value from ..sim.models.chezy_manning import get_pipe_design_chezy_manning_qubops_matrix from ..sim.models.darcy_weisbach import dw_resistance_value from ..sim.models.darcy_weisbach import get_pipe_design_darcy_wesibach_qubops_matrix from ..sim.models.mass_balance import get_mass_balance_qubops_matrix +from ..sim.qubo_hydraulics import create_hydraulic_model_for_qubo class QUBODesignPipeDiameter(object): @@ -93,7 +93,7 @@ def __init__( ) # basic hydraulic model - self.model, self.model_updater = create_hydraulic_model(wn) + self.model, self.model_updater = create_hydraulic_model_for_qubo(wn) # valies of the pipe diameters/coefficients self.get_pipe_data() @@ -264,24 +264,26 @@ def verify_solution(self, input, params): """Computes the rhs vector associate with the input. Args: - input (np.ndarray): proposed solution - params (np.ndarray): parameters of the model + input (np.ndarray): proposed solution vector + params (list): one-hot encoding vector to select the resistance factor. Returns: np.ndarray: RHS vector """ - P0, P1, P2, P3 = self.matrices + P0, P1, P2, P3, P4 = self.matrices num_heads = self.wn.num_junctions + num_signs = self.wn.num_pipes num_pipes = self.wn.num_pipes - num_vars = num_heads + num_pipes + num_vars = num_heads + 2 * num_pipes - p0 = P0[:num_vars].reshape( - -1, - ) - p1 = P1[:num_vars, :num_vars] - p3 = P3[:num_vars].sum(-1)[:, :num_vars, :num_vars].sum(-1) - parameters = np.array([0] * num_heads + params) - return p0 + p1 @ input + parameters * (p3 @ (input * input)) + input = input.reshape(-1, 1) + p0 = P0[:-1].reshape(-1, 1) + p1 = P1[:-1, num_signs:num_vars] + P2.sum(1)[:-1, num_signs:num_vars] + p2 = P4.sum(1)[:-1, num_pipes:num_vars, num_pipes:num_vars].sum(-2) + parameters = np.array([0] * num_vars + params) + p2 = (parameters * p2).sum(-1) + sign = np.sign(input) + return p0 + p1 @ input + (p2 @ (sign * input * input)) def enumerates_classical_solutions(self, convert_to_si=True): """Generates the classical solution.""" @@ -318,6 +320,24 @@ def convert_solution_to_si(self, solution: np.ndarray) -> np.ndarray: new_sol[ih] = to_si(FlowUnits.CFS, solution[ih], HydParam.Length) return new_sol + def convert_solution_from_si(self, solution: np.ndarray) -> np.ndarray: + """Converts the solution to SI. + + Args: + solution (array): solution vectors in SI + + Returns: + Tuple: solution in US units + """ + num_heads = self.wn.num_junctions + num_pipes = self.wn.num_pipes + new_sol = np.zeros_like(solution) + for ip in range(num_pipes): + new_sol[ip] = from_si(FlowUnits.CFS, solution[ip], HydParam.Flow) + for ih in range(num_pipes, num_pipes + num_heads): + new_sol[ih] = from_si(FlowUnits.CFS, solution[ih], HydParam.Length) + return new_sol + def compute_classical_solution(self, parameters, convert_to_si=True): """Computes the classical solution for a values of the hot encoding parameters. @@ -328,18 +348,18 @@ def compute_classical_solution(self, parameters, convert_to_si=True): Returns: np.mdarray : solution """ - P0, P1, P2, P3 = self.matrices + P0, P1, P2, P3, P4 = self.matrices num_heads = self.wn.num_junctions + num_signs = self.wn.num_pipes num_pipes = self.wn.num_pipes - num_vars = num_heads + num_pipes + num_vars = num_heads + 2 * num_pipes if self.wn.options.hydraulic.headloss == "C-M": - p0 = P0[:num_vars].reshape( - -1, - ) - p1 = P1[:num_vars, :num_vars] - params = np.array([0] * num_vars + parameters) - p2 = (params * P3).sum(-1)[:, :num_vars, :num_vars].sum(-1)[:num_vars] + p0 = P0[:-1].reshape(-1, 1) + p1 = P1[:-1, num_signs:num_vars] + P2.sum(1)[:-1, num_signs:num_vars] + p2 = P4.sum(1)[:-1, num_pipes:num_vars, num_pipes:num_vars].sum(-2) + parameters = np.array([0] * num_vars + parameters) + p2 = (parameters * p2).sum(-1) elif self.wn.options.hydraulic.headloss == "D-W": p0 = P0[:num_vars].reshape( @@ -355,9 +375,12 @@ def compute_classical_solution(self, parameters, convert_to_si=True): # print(p0, p1, p2) def func(input): - return p0 + p1 @ input + (p2 @ (input * input)) + input = input.reshape(-1, 1) + sign = np.sign(input) + sol = p0 + p1 @ input + (p2 @ (sign * input * input)) + return sol.reshape(-1) - initial_point = np.random.rand(num_vars) + initial_point = np.random.rand(num_pipes + num_heads) res = newton_raphson(func, initial_point) assert np.allclose(func(res.solution), 0) if convert_to_si: diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 048ca18..4e633c1 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -18,13 +18,8 @@ from wntr.network import WaterNetworkModel from wntr.sim.aml import Model from wntr.sim.solvers import SolverStatus -<<<<<<< HEAD -from ..models.chezy_manning import get_chezy_manning_matrix -from ..models.darcy_weisbach import get_darcy_weisbach_matrix -======= from ..models.chezy_manning import get_chezy_manning_qubops_matrix from ..models.darcy_weisbach import get_darcy_weisbach_qubops_matrix ->>>>>>> qubo_poly_solver from ..models.mass_balance import get_mass_balance_qubops_matrix From 4de839b5e3788da7a1363386b58e293444818143 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 13 Sep 2024 14:56:20 +0200 Subject: [PATCH 60/96] dw sol --- wntr_quantum/design/qubo_pipe_diam.py | 1 + wntr_quantum/sim/solvers/qubo_polynomial_solver.py | 14 +++++++++----- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index 2a2d499..ca89ea7 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -362,6 +362,7 @@ def compute_classical_solution(self, parameters, convert_to_si=True): p2 = (parameters * p2).sum(-1) elif self.wn.options.hydraulic.headloss == "D-W": + raise NotImplementedError() p0 = P0[:num_vars].reshape( -1, ) diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 4e633c1..725455a 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -122,11 +122,15 @@ def classical_solutions( num_pipes = self.wn.num_pipes num_vars = num_heads + num_pipes - p0 = P0.reshape( - -1, - ) - p1 = P1[:, num_pipes:] + P2.sum(1)[:, num_pipes:] - p2 = P3.sum(1)[:, num_pipes:, num_pipes:].sum(-1) + if self.wn.options.hydraulic.headloss == "C-M": + p0 = P0.reshape( + -1, + ) + p1 = P1[:, num_pipes:] + P2.sum(1)[:, num_pipes:] + p2 = P3.sum(1)[:, num_pipes:, num_pipes:].sum(-1) + + elif self.wn.options.hydraulic.headloss == "D-W": + raise NotImplementedError() def func(input): sign = np.sign(input) From 63ae52ec7255f749038ae2ebb951dfd383a97d9e Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 13 Sep 2024 18:08:37 +0200 Subject: [PATCH 61/96] fix dw equation --- docs/notebooks/qubo_poly_solver.ipynb | 86 ++++++++++++------- wntr_quantum/sim/models/darcy_weisbach.py | 2 +- .../sim/solvers/qubo_polynomial_solver.py | 35 +++++--- 3 files changed, 82 insertions(+), 41 deletions(-) diff --git a/docs/notebooks/qubo_poly_solver.ipynb b/docs/notebooks/qubo_poly_solver.ipynb index d536f6e..89907d0 100644 --- a/docs/notebooks/qubo_poly_solver.ipynb +++ b/docs/notebooks/qubo_poly_solver.ipynb @@ -172,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -211,24 +211,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, { "data": { "text/plain": [ "array([1. , 1. , 0.999, 0.998])" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -239,13 +231,47 @@ "net.create_index_mapping(model)\n", "net.matrices = net.initialize_matrices(model)\n", "\n", - "ref_sol = net.classical_solutions()\n", + "ref_sol, cvgd = net.classical_solutions()\n", "ref_sol / ref_values" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "P0, P1, P2, P3 = net.matrices" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 1.766, 99.077, 0.652])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p0 = P0.reshape(\n", + " -1,\n", + ") + P1[\n", + " :, :2\n", + "].sum(-1)\n", + "p0" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -260,12 +286,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -280,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -291,27 +317,27 @@ "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.000978)\n", "\n", "\n", - "Error (%): [ 0. 0. -5.288 1.03 1.36 1.2 ]\n", + "Error (%): [ 0. 0. -10.607 -1.298 2.712 3.543]\n", "\n", "\n", - "sol : [ 1. 1. 1.859 1.748 85.616 74.266]\n", + "sol : [ 1. 1. 1.953 1.789 84.442 72.505]\n", "ref : [ 1. 1. 1.766 1.766 86.797 75.168]\n", - "diff: [ 0. 0. -0.093 0.018 1.18 0.902]\n", + "diff: [ 0. 0. -0.187 -0.023 2.354 2.663]\n", "\n", "\n", - "encoded_sol: [ 1. 1. 1.859 1.748 85.616 74.266]\n", + "encoded_sol: [ 1. 1. 1.953 1.789 84.442 72.505]\n", "encoded_ref: [ 1. 1. 1.766 1.766 86.791 75.147]\n", - "diff : [ 0. 0. -0.093 0.019 1.174 0.881]\n", + "diff : [ 0. 0. -0.187 -0.023 2.348 2.642]\n", "\n", "\n", - "E sol : -3505.5175214858687\n", - "R ref : -3505.53316919132\n", - "Delta E : 0.01564770545155625\n", + "E sol : -3331.1596540813284\n", + "E ref : -3331.1923967404186\n", + "Delta E : 0.032742659090217785\n", "\n", "\n", - "Residue sol : 0.12550494817707591\n", - "Residue ref : 0.010186471203764017\n", - "Delta Residue : 0.11531847697331189\n" + "Residue sol : 0.9703949158844001\n", + "Residue ref : 0.9142917877857567\n", + "Delta Residue : 0.05610312809864337\n" ] } ], @@ -328,12 +354,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -347,7 +373,7 @@ "" ] }, - "execution_count": 12, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } diff --git a/wntr_quantum/sim/models/darcy_weisbach.py b/wntr_quantum/sim/models/darcy_weisbach.py index 23a1fb2..95f7f5d 100644 --- a/wntr_quantum/sim/models/darcy_weisbach.py +++ b/wntr_quantum/sim/models/darcy_weisbach.py @@ -229,7 +229,7 @@ def get_darcy_weisbach_qubops_matrix( sign_index = flow_index_mapping[m.flow[link_name].name]["sign"] flow_index = flow_index_mapping[m.flow[link_name].name]["absolute_value"] - P0[ieq] -= k0.value + P1[ieq, sign_index] -= k0.value P2[ieq, sign_index, flow_index] -= k1.value P3[ieq, sign_index, flow_index, flow_index] -= k2.value diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 4e633c1..216c365 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -97,11 +97,15 @@ def verify_solution(self, input: np.ndarray) -> np.ndarray: """ P0, P1, P2, P3 = self.matrices num_pipes = self.wn.num_pipes - p0 = P0.reshape( - -1, - ) - p1 = P1[:, num_pipes:] + P2.sum(1)[:, num_pipes:] - p2 = P3.sum(1)[:, num_pipes:, num_pipes:].sum(-1) + + if self.wn.options.hydraulic.headloss == "C-M": + p0 = P0.reshape( + -1, + ) + p1 = P1[:, num_pipes:] + P2.sum(1)[:, num_pipes:] + p2 = P3.sum(1)[:, num_pipes:, num_pipes:].sum(-1) + elif self.wn.options.hydraulic.headloss == "D-W": + raise NotImplementedError("verufy_solution not implemented for DW") sign = np.sign(input) return p0 + p1 @ input + (p2 @ (sign * input * input)) @@ -120,13 +124,24 @@ def classical_solutions( P0, P1, P2, P3 = self.matrices num_heads = self.wn.num_junctions num_pipes = self.wn.num_pipes + num_signs = self.wn.num_pipes num_vars = num_heads + num_pipes - p0 = P0.reshape( - -1, - ) - p1 = P1[:, num_pipes:] + P2.sum(1)[:, num_pipes:] - p2 = P3.sum(1)[:, num_pipes:, num_pipes:].sum(-1) + if self.wn.options.hydraulic.headloss == "C-M": + p0 = P0.reshape( + -1, + ) + p1 = P1[:, num_pipes:] + P2.sum(1)[:, num_pipes:] + p2 = P3.sum(1)[:, num_pipes:, num_pipes:].sum(-1) + + elif self.wn.options.hydraulic.headloss == "D-W": + p0 = P0.reshape( + -1, + ) + P1[ + :, :num_signs + ].sum(-1) + p1 = P1[:, num_pipes:] + P2.sum(1)[:, num_pipes:] + p2 = P3.sum(1)[:, num_pipes:, num_pipes:].sum(-1) def func(input): sign = np.sign(input) From 8ad0a674447ffb12460a00e34e5a1053ff05c9bd Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 13 Sep 2024 20:56:34 +0200 Subject: [PATCH 62/96] dw matrices fixed --- docs/notebooks/design_pipe_diameter.ipynb | 145 +++++++++++++++--- docs/notebooks/design_pipe_diameter_DW.ipynb | 152 ++++++++++++++++--- docs/notebooks/qubo_poly_solver_CM.ipynb | 78 ++++------ wntr_quantum/design/qubo_pipe_diam.py | 47 ++++-- wntr_quantum/sim/models/darcy_weisbach.py | 3 +- 5 files changed, 314 insertions(+), 111 deletions(-) diff --git a/docs/notebooks/design_pipe_diameter.ipynb b/docs/notebooks/design_pipe_diameter.ipynb index ec74b7a..0c90fe3 100644 --- a/docs/notebooks/design_pipe_diameter.ipynb +++ b/docs/notebooks/design_pipe_diameter.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -42,7 +42,65 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.766, 1.766, 98.406, 98.387], dtype=float32)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0][:2]\n", + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_values = np.append(ref_rate, ref_pressure)\n", + "designer.convert_solution_from_si(ref_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -61,18 +119,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", "pipe_diameters = [250, 500, 1000]\n", - "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters, head_lower_bound=97)" + "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters, head_lower_bound=80)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -90,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -99,7 +157,7 @@ "11" ] }, - "execution_count": 5, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -111,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -132,7 +190,7 @@ " [ 0., 0., 1., -1.]])" ] }, - "execution_count": 7, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -143,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -152,7 +210,7 @@ "(5, 12, 12)" ] }, - "execution_count": 9, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -163,27 +221,36 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 34, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, { "data": { "text/plain": [ "array([ 1.766, 1.766, 67.877, 37.329])" ] }, - "execution_count": 22, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "designer.matrices = designer.initialize_matrices()\n", "designer.compute_classical_solution([1,0,0,1,0,0], convert_to_si=False)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -209,35 +276,43 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 36, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:759: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", + " warnings.warn(\"For constraints with fractional coefficients, \"\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "[1.7086614173228345, 1.7283464566929132, 97.79527559055119, 97.00787401574803]\n" + "[1, 1, 1.9999999999999998, 1.9842519685039368, 96.45669291338584, 96.10236220472441]\n" ] } ], "source": [ "from dwave.samplers import SimulatedAnnealingSampler\n", "options = {'sampler': SimulatedAnnealingSampler()}\n", - "status = designer.solve(strength=1E5, num_reads=10000, options=options)" + "status = designer.solve(strength=1E5, num_reads=100000, options=options)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.33815821889033915" + "0.5072373283355087" ] }, - "execution_count": 25, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -248,16 +323,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([500., 500.])" + "array([1000., 500.])" ] }, - "execution_count": 26, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -266,6 +341,26 @@ "designer.optimal_diameters" ] }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "442" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.bqm.num_variables" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/docs/notebooks/design_pipe_diameter_DW.ipynb b/docs/notebooks/design_pipe_diameter_DW.ipynb index 6ebd63d..23e7ba7 100644 --- a/docs/notebooks/design_pipe_diameter_DW.ipynb +++ b/docs/notebooks/design_pipe_diameter_DW.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ "" ] }, - "execution_count": 17, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -61,7 +61,7 @@ "" ] }, - "execution_count": 18, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -129,7 +129,7 @@ "3600 26.476913 22.953829 -9.338379e-07" ] }, - "execution_count": 19, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -178,7 +178,7 @@ "output_type": "stream", "text": [ "Head Encoding : 95.000000 => 120.000000 (res: 0.196850)\n", - "Flow Encoding : 1.500000 => 2.000000 (res: 0.003937)\n" + "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.003937)\n" ] } ], @@ -188,9 +188,25 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. ]\n", + " [ 1.766]\n", + " [99.077]\n", + " [ 0.652]] [[-1. 1. 0. 0. ]\n", + " [ 0. -1. 0. 0. ]\n", + " [-1.547 0. -1. 0. ]\n", + " [ 0. -1.547 1. -1. ]] [[ 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. ]\n", + " [-3.063 0. 0. 0. ]\n", + " [ 0. -3.063 0. 0. ]]\n" + ] + }, { "name": "stderr", "output_type": "stream", @@ -205,18 +221,20 @@ "array([ 0.05 , 0.05 , 26.456, 22.911])" ] }, - "execution_count": 23, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "designer.create_index_mapping()\n", + "designer.matrices = designer.initialize_matrices()\n", "designer.compute_classical_solution([1,0,0,1,0,0], convert_to_si=True)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -224,15 +242,105 @@ "output_type": "stream", "text": [ "price \t diameters \t variables\n", - "0.16907910944516957 [250. 250.] [ 1.766 1.766 84.718 71.01 ]\n", - "0.25361866416775436 [250. 500.] [ 1.766 1.766 82.639 78.088]\n", - "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 80.56 76.258]\n", - "0.25361866416775436 [500. 250.] [ 1.766 1.766 89.717 73.794]\n", - "0.33815821889033915 [500. 500.] [ 1.766 1.766 89.587 82.821]\n", - "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 89.457 82.94 ]\n", - "0.42269777361292393 [1000. 250.] [ 1.766 1.766 89.706 71.568]\n", - "0.5072373283355087 [1000. 500.] [ 1.766 1.766 89.699 80.718]\n", - "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 89.693 80.96 ]\n" + "[[ 0. ]\n", + " [ 1.766]\n", + " [99.729]\n", + " [ 1.304]] [[-1. 1. 0. 0. ]\n", + " [ 0. -1. 0. 0. ]\n", + " [-1.547 0. -1. 0. ]\n", + " [ 0. -1.547 1. -1. ]] [[ 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. ]\n", + " [-3.063 0. 0. 0. ]\n", + " [ 0. -3.063 0. 0. ]]\n", + "0.16907910944516957 [250. 250.] [ 1.766 1.766 87.449 76.472]\n", + "[[ 0. ]\n", + " [ 1.766]\n", + " [100.381]\n", + " [ 1.364]] [[-1. 1. 0. 0. ]\n", + " [ 0. -1. 0. 0. ]\n", + " [-1.547 0. -1. 0. ]\n", + " [ 0. -0.107 1. -1. ]] [[ 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. ]\n", + " [-3.063 0. 0. 0. ]\n", + " [ 0. -0.084 0. 0. ]]\n", + "0.25361866416775436 [250. 500.] [ 1.766 1.766 88.101 89.012]\n", + "[[ 0. ]\n", + " [ 1.766]\n", + " [101.033]\n", + " [ 1.368]] [[-1. 1. 0. 0. ]\n", + " [ 0. -1. 0. 0. ]\n", + " [-1.547 0. -1. 0. ]\n", + " [ 0. -0.006 1. -1. ]] [[ 0.000e+00 0.000e+00 0.000e+00 0.000e+00]\n", + " [ 0.000e+00 0.000e+00 0.000e+00 0.000e+00]\n", + " [-3.063e+00 0.000e+00 0.000e+00 0.000e+00]\n", + " [ 0.000e+00 -2.400e-03 0.000e+00 0.000e+00]]\n", + "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 88.753 90.102]\n", + "[[ 0. ]\n", + " [ 1.766]\n", + " [101.093]\n", + " [ 2.02 ]] [[-1. 1. 0. 0. ]\n", + " [ 0. -1. 0. 0. ]\n", + " [-0.107 0. -1. 0. ]\n", + " [ 0. -1.547 1. -1. ]] [[ 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. ]\n", + " [-0.084 0. 0. 0. ]\n", + " [ 0. -3.063 0. 0. ]]\n", + "0.25361866416775436 [500. 250.] [ 1.766 1.766 100.64 90.379]\n", + "[[ 0. ]\n", + " [ 1.766]\n", + " [101.153]\n", + " [ 2.079]] [[-1. 1. 0. 0. ]\n", + " [ 0. -1. 0. 0. ]\n", + " [-0.107 0. -1. 0. ]\n", + " [ 0. -0.107 1. -1. ]] [[ 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. ]\n", + " [-0.084 0. 0. 0. ]\n", + " [ 0. -0.084 0. 0. ]]\n", + "0.33815821889033915 [500. 500.] [ 1.766 1.766 100.7 102.327]\n", + "[[ 0. ]\n", + " [ 1.766]\n", + " [101.213]\n", + " [ 2.083]] [[-1. 1. 0. 0. ]\n", + " [ 0. -1. 0. 0. ]\n", + " [-0.107 0. -1. 0. ]\n", + " [ 0. -0.006 1. -1. ]] [[ 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. ]\n", + " [-0.084 0. 0. 0. ]\n", + " [ 0. -0.002 0. 0. ]]\n", + "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 100.76 102.825]\n", + "[[ 0. ]\n", + " [ 1.766]\n", + " [101.216]\n", + " [ 2.735]] [[-1. 1. 0. 0. ]\n", + " [ 0. -1. 0. 0. ]\n", + " [-0.006 0. -1. 0. ]\n", + " [ 0. -1.547 1. -1. ]] [[ 0.000e+00 0.000e+00 0.000e+00 0.000e+00]\n", + " [ 0.000e+00 0.000e+00 0.000e+00 0.000e+00]\n", + " [-2.400e-03 0.000e+00 0.000e+00 0.000e+00]\n", + " [ 0.000e+00 -3.063e+00 0.000e+00 0.000e+00]]\n", + "0.42269777361292393 [1000. 250.] [ 1.766 1.766 101.198 91.653]\n", + "[[ 0. ]\n", + " [ 1.766]\n", + " [101.22 ]\n", + " [ 2.795]] [[-1. 1. 0. 0. ]\n", + " [ 0. -1. 0. 0. ]\n", + " [-0.006 0. -1. 0. ]\n", + " [ 0. -0.107 1. -1. ]] [[ 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. ]\n", + " [-0.002 0. 0. 0. ]\n", + " [ 0. -0.084 0. 0. ]]\n", + "0.5072373283355087 [1000. 500.] [ 1.766 1.766 101.202 103.545]\n", + "[[ 0. ]\n", + " [ 1.766]\n", + " [101.224]\n", + " [ 2.799]] [[-1. 1. 0. 0. ]\n", + " [ 0. -1. 0. 0. ]\n", + " [-0.006 0. -1. 0. ]\n", + " [ 0. -0.006 1. -1. ]] [[ 0. 0. 0. 0. ]\n", + " [ 0. 0. 0. 0. ]\n", + " [-0.002 0. 0. 0. ]\n", + " [ 0. -0.002 0. 0. ]]\n", + "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 101.206 103.987]\n" ] } ], diff --git a/docs/notebooks/qubo_poly_solver_CM.ipynb b/docs/notebooks/qubo_poly_solver_CM.ipynb index fb44b0a..fd40e0c 100644 --- a/docs/notebooks/qubo_poly_solver_CM.ipynb +++ b/docs/notebooks/qubo_poly_solver_CM.ipynb @@ -239,59 +239,33 @@ "net.create_index_mapping(model)\n", "net.matrices = net.initialize_matrices(model)\n", "\n", - "ref_sol = net.classical_solutions()\n", + "ref_sol, cvgd = net.classical_solutions()\n", "ref_sol / ref_values" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([[ 0. ],\n", - " [ 1.766],\n", - " [98.425],\n", - " [ 0. ]]),\n", - " array([[-1., 1., 0., 0.],\n", - " [ 0., -1., 0., 0.],\n", - " [ 0., 0., -1., 0.],\n", - " [ 0., 0., 1., -1.]]),\n", - " array([[[ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ]],\n", - " \n", - " [[ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ]],\n", - " \n", - " [[-0.006, 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ]],\n", - " \n", - " [[ 0. , 0. , 0. , 0. ],\n", - " [ 0. , -0.006, 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. ]]]))" + "array([ 0.000e+00, -2.567e-08, 1.331e-05, 1.025e-05])" ] }, - "execution_count": 13, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "net.matrices" + "net.verify_solution(net.convert_solution_from_si(ref_values))" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -306,16 +280,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0.05353047713424657, 0.0494575060479452, 30.00281800391389, 30.00281800391389]" + "[0.05253301482739726, 0.0511199432260274, 30.00281800391389, 30.00281800391389]" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -326,12 +300,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -346,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -357,27 +331,27 @@ "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.000978)\n", "\n", "\n", - "Error (%): [ 0. 0. -7.061 1.085 -0.029 -0.048]\n", + "Error (%): [ 0. 0. -5.066 -2.24 -0.029 -0.048]\n", "\n", "\n", - "sol : [ 1. 1. 1.89 1.747 98.434 98.434]\n", + "sol : [ 1. 1. 1.855 1.805 98.434 98.434]\n", "ref : [ 1. 1. 1.766 1.766 98.406 98.387]\n", - "diff: [ 0. 0. -0.125 0.019 -0.028 -0.047]\n", + "diff: [ 0. 0. -0.089 -0.04 -0.028 -0.047]\n", "\n", "\n", - "encoded_sol: [ 1. 1. 1.89 1.747 98.434 98.434]\n", + "encoded_sol: [ 1. 1. 1.855 1.805 98.434 98.434]\n", "encoded_ref: [ 1. 1. 1.766 1.766 98.434 98.434]\n", - "diff : [ 0. 0. -0.124 0.02 0. 0. ]\n", + "diff : [ 0. 0. -0.089 -0.039 0. 0. ]\n", "\n", "\n", - "E sol : -2356.9623137039966\n", - "R ref : -2356.983516049839\n", - "Delta E : 0.021202345842539216\n", + "E sol : -2356.9793153814685\n", + "E ref : -2356.9835160507705\n", + "Delta E : 0.004200669302008464\n", "\n", "\n", - "Residue sol : 0.14950214827015704\n", + "Residue sol : 0.07313802788587039\n", "Residue ref : 0.03388956865892264\n", - "Delta Residue : 0.11561257961123439\n" + "Delta Residue : 0.039248459226947745\n" ] } ], @@ -394,12 +368,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -413,7 +387,7 @@ "" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -459,4 +433,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index ca89ea7..f50e259 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -362,18 +362,43 @@ def compute_classical_solution(self, parameters, convert_to_si=True): p2 = (parameters * p2).sum(-1) elif self.wn.options.hydraulic.headloss == "D-W": - raise NotImplementedError() - p0 = P0[:num_vars].reshape( - -1, + + # P0 matrix + p0 = P0[:-1] + # add the k0 term without sgin to p0 + p0 += ( + (parameters * P2[:-1, :num_signs, num_vars:]) + .sum(-1) + .sum(-1) + .reshape(-1, 1) ) - p0 += (parameters * P1[:num_vars, num_vars:]).sum(-1) - p1 = P1[:num_vars, :num_vars] - p1 += (parameters * P2[:num_vars, :num_vars, num_vars:]).sum(-1) + # P1 matrix + p1 = P1[:-1, num_pipes:num_vars] + P2.sum(1)[:-1, num_pipes:num_vars] + + # add the terms in k1 + p1 += ( + (parameters * P3[:-1, :num_signs, num_signs:num_vars, num_vars:]) + .sum(1) + .sum(-1) + ) - params = np.array([0] * num_vars + parameters) - p2 = (params * P3).sum(-1)[:, :num_vars, :num_vars].sum(-1)[:num_vars] - # print(p0, p1, p2) + # P2 matrix + p2 = ( + ( + parameters + * P4[ + :-1, + :num_signs, + num_signs:num_vars, + num_signs:num_vars, + num_vars:, + ] + ) + .sum(1) + .sum(-1) + .sum(-1) + ) def func(input): input = input.reshape(-1, 1) @@ -397,7 +422,7 @@ def get_cost_matrix(self, matrices): P0, P1, P2, P3, P4 = matrices # loop over all the pipe coeffs - istart = self.sol_vect_flows.size + self.sol_vect_heads.size + istart = 2 * self.sol_vect_flows.size + self.sol_vect_heads.size index_over = self.wn.pipe_name_list # loop over all the pipe coeffs @@ -631,7 +656,7 @@ def solve( # noqa: D417 istart += self.num_diameters # add constraint on head pressures - istart = self.sol_vect_flows.size + istart = 2 * self.sol_vect_flows.size for i in range(self.sol_vect_heads.size): self.bqm.add_linear_inequality_constraint( diff --git a/wntr_quantum/sim/models/darcy_weisbach.py b/wntr_quantum/sim/models/darcy_weisbach.py index a7fa8ad..087aa48 100644 --- a/wntr_quantum/sim/models/darcy_weisbach.py +++ b/wntr_quantum/sim/models/darcy_weisbach.py @@ -294,7 +294,8 @@ def get_pipe_design_darcy_wesibach_qubops_matrix( m.pipe_coefficients[link_name].value, m.pipe_coefficients_indices[link_name].value, ): - P1[ieq, pipe_idx + num_continuous_var] -= pipe_coefs[0] + # P1[ieq, pipe_idx + num_continuous_var] -= pipe_coefs[0] + P2[ieq, sign_index, pipe_idx + num_continuous_var] -= pipe_coefs[0] P3[ ieq, sign_index, flow_index, pipe_idx + num_continuous_var ] -= pipe_coefs[1] From c948c55d8782efecc0198b366020269e6fdb1ccf Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 13 Sep 2024 21:19:56 +0200 Subject: [PATCH 63/96] notebooks --- docs/notebooks/design_pipe_diameter_DW.ipynb | 325 ++++++++++++------- 1 file changed, 211 insertions(+), 114 deletions(-) diff --git a/docs/notebooks/design_pipe_diameter_DW.ipynb b/docs/notebooks/design_pipe_diameter_DW.ipynb index 23e7ba7..a5bc403 100644 --- a/docs/notebooks/design_pipe_diameter_DW.ipynb +++ b/docs/notebooks/design_pipe_diameter_DW.ipynb @@ -191,22 +191,6 @@ "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0. ]\n", - " [ 1.766]\n", - " [99.077]\n", - " [ 0.652]] [[-1. 1. 0. 0. ]\n", - " [ 0. -1. 0. 0. ]\n", - " [-1.547 0. -1. 0. ]\n", - " [ 0. -1.547 1. -1. ]] [[ 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. ]\n", - " [-3.063 0. 0. 0. ]\n", - " [ 0. -3.063 0. 0. ]]\n" - ] - }, { "name": "stderr", "output_type": "stream", @@ -242,104 +226,14 @@ "output_type": "stream", "text": [ "price \t diameters \t variables\n", - "[[ 0. ]\n", - " [ 1.766]\n", - " [99.729]\n", - " [ 1.304]] [[-1. 1. 0. 0. ]\n", - " [ 0. -1. 0. 0. ]\n", - " [-1.547 0. -1. 0. ]\n", - " [ 0. -1.547 1. -1. ]] [[ 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. ]\n", - " [-3.063 0. 0. 0. ]\n", - " [ 0. -3.063 0. 0. ]]\n", "0.16907910944516957 [250. 250.] [ 1.766 1.766 87.449 76.472]\n", - "[[ 0. ]\n", - " [ 1.766]\n", - " [100.381]\n", - " [ 1.364]] [[-1. 1. 0. 0. ]\n", - " [ 0. -1. 0. 0. ]\n", - " [-1.547 0. -1. 0. ]\n", - " [ 0. -0.107 1. -1. ]] [[ 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. ]\n", - " [-3.063 0. 0. 0. ]\n", - " [ 0. -0.084 0. 0. ]]\n", "0.25361866416775436 [250. 500.] [ 1.766 1.766 88.101 89.012]\n", - "[[ 0. ]\n", - " [ 1.766]\n", - " [101.033]\n", - " [ 1.368]] [[-1. 1. 0. 0. ]\n", - " [ 0. -1. 0. 0. ]\n", - " [-1.547 0. -1. 0. ]\n", - " [ 0. -0.006 1. -1. ]] [[ 0.000e+00 0.000e+00 0.000e+00 0.000e+00]\n", - " [ 0.000e+00 0.000e+00 0.000e+00 0.000e+00]\n", - " [-3.063e+00 0.000e+00 0.000e+00 0.000e+00]\n", - " [ 0.000e+00 -2.400e-03 0.000e+00 0.000e+00]]\n", "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 88.753 90.102]\n", - "[[ 0. ]\n", - " [ 1.766]\n", - " [101.093]\n", - " [ 2.02 ]] [[-1. 1. 0. 0. ]\n", - " [ 0. -1. 0. 0. ]\n", - " [-0.107 0. -1. 0. ]\n", - " [ 0. -1.547 1. -1. ]] [[ 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. ]\n", - " [-0.084 0. 0. 0. ]\n", - " [ 0. -3.063 0. 0. ]]\n", "0.25361866416775436 [500. 250.] [ 1.766 1.766 100.64 90.379]\n", - "[[ 0. ]\n", - " [ 1.766]\n", - " [101.153]\n", - " [ 2.079]] [[-1. 1. 0. 0. ]\n", - " [ 0. -1. 0. 0. ]\n", - " [-0.107 0. -1. 0. ]\n", - " [ 0. -0.107 1. -1. ]] [[ 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. ]\n", - " [-0.084 0. 0. 0. ]\n", - " [ 0. -0.084 0. 0. ]]\n", "0.33815821889033915 [500. 500.] [ 1.766 1.766 100.7 102.327]\n", - "[[ 0. ]\n", - " [ 1.766]\n", - " [101.213]\n", - " [ 2.083]] [[-1. 1. 0. 0. ]\n", - " [ 0. -1. 0. 0. ]\n", - " [-0.107 0. -1. 0. ]\n", - " [ 0. -0.006 1. -1. ]] [[ 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. ]\n", - " [-0.084 0. 0. 0. ]\n", - " [ 0. -0.002 0. 0. ]]\n", "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 100.76 102.825]\n", - "[[ 0. ]\n", - " [ 1.766]\n", - " [101.216]\n", - " [ 2.735]] [[-1. 1. 0. 0. ]\n", - " [ 0. -1. 0. 0. ]\n", - " [-0.006 0. -1. 0. ]\n", - " [ 0. -1.547 1. -1. ]] [[ 0.000e+00 0.000e+00 0.000e+00 0.000e+00]\n", - " [ 0.000e+00 0.000e+00 0.000e+00 0.000e+00]\n", - " [-2.400e-03 0.000e+00 0.000e+00 0.000e+00]\n", - " [ 0.000e+00 -3.063e+00 0.000e+00 0.000e+00]]\n", "0.42269777361292393 [1000. 250.] [ 1.766 1.766 101.198 91.653]\n", - "[[ 0. ]\n", - " [ 1.766]\n", - " [101.22 ]\n", - " [ 2.795]] [[-1. 1. 0. 0. ]\n", - " [ 0. -1. 0. 0. ]\n", - " [-0.006 0. -1. 0. ]\n", - " [ 0. -0.107 1. -1. ]] [[ 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. ]\n", - " [-0.002 0. 0. 0. ]\n", - " [ 0. -0.084 0. 0. ]]\n", "0.5072373283355087 [1000. 500.] [ 1.766 1.766 101.202 103.545]\n", - "[[ 0. ]\n", - " [ 1.766]\n", - " [101.224]\n", - " [ 2.799]] [[-1. 1. 0. 0. ]\n", - " [ 0. -1. 0. 0. ]\n", - " [-0.006 0. -1. 0. ]\n", - " [ 0. -0.006 1. -1. ]] [[ 0. 0. 0. 0. ]\n", - " [ 0. 0. 0. 0. ]\n", - " [-0.002 0. 0. 0. ]\n", - " [ 0. -0.002 0. 0. ]]\n", "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 101.206 103.987]\n" ] } @@ -350,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -365,7 +259,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1.716535433070866, 1.5629921259842519, 96.18110236220473, 95.0]\n" + "[-1, 1, 1.9999999999999998, 1.937007874015748, 117.44094488188976, 112.71653543307086]\n" ] } ], @@ -378,16 +272,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.33815821889033915" + "0.25361866416775436" ] }, - "execution_count": 26, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -398,16 +292,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([500., 500.])" + "array([250., 500.])" ] }, - "execution_count": 27, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -415,6 +309,209 @@ "source": [ "designer.optimal_diameters" ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'x_009_001'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.mixed_solution_vector.encoded_reals[8].variables[0].name" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('1', 95.0000000000000),\n", + " ('x_005_001', 0.196850393700787),\n", + " ('x_005_002', 0.393700787401575),\n", + " ('x_005_003', 0.787401574803150),\n", + " ('x_005_004', 1.57480314960630),\n", + " ('x_005_005', 3.14960629921260),\n", + " ('x_005_006', 6.29921259842520),\n", + " ('x_005_007', 12.5984251968504)]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.qubo.all_expr[4]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. , -1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. , 1. , -1. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. , 0. , 0. , 0.008, 0.017, 0.034, 0.008, 0.017, 0.034]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.matrices[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([-0.652, 1.547, 3.063]),\n", + " array([-0.06 , 0.107, 0.084]),\n", + " array([-0.004, 0.006, 0.002])]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.model.pipe_coefficients['P2'].value" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[3, 4, 5]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.model.pipe_coefficients_indices['P2'].value" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "P0,P1,P2,P3,P4 = designer.matrices" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[0. , 0. , 0. , 0. , 0. , 0. ],\n", + " [0. , 0. , 0. , 0. , 0. , 0. ]],\n", + "\n", + " [[0. , 0. , 0. , 0. , 0. , 0. ],\n", + " [0. , 0. , 0. , 0. , 0. , 0. ]],\n", + "\n", + " [[0.652, 0.06 , 0.004, 0. , 0. , 0. ],\n", + " [0. , 0. , 0. , 0. , 0. , 0. ]],\n", + "\n", + " [[0. , 0. , 0. , 0. , 0. , 0. ],\n", + " [0. , 0. , 0. , 0.652, 0.06 , 0.004]]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[]P2[:-1, :2, 6:]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. , -1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. , 1. , -1. , 0. , 0. , 0. , 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. , 0. , 0. , 0. , 0.008, 0.017, 0.034, 0.008, 0.017, 0.034]])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "P1" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.08453955472258479, 0.16907910944516957, 0.33815821889033915]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.model.pipe_prices['P1'].value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 3f28c83987249453ba349980d19cd8fe52d16c83 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Sat, 14 Sep 2024 09:19:26 +0200 Subject: [PATCH 64/96] fix dw classical solution --- docs/notebooks/design_pipe_diameter.ipynb | 74 ++++++++------------ docs/notebooks/design_pipe_diameter_DW.ipynb | 22 +++--- wntr_quantum/design/qubo_pipe_diam.py | 2 +- 3 files changed, 43 insertions(+), 55 deletions(-) diff --git a/docs/notebooks/design_pipe_diameter.ipynb b/docs/notebooks/design_pipe_diameter.ipynb index 0c90fe3..f83dabe 100644 --- a/docs/notebooks/design_pipe_diameter.ipynb +++ b/docs/notebooks/design_pipe_diameter.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ "" ] }, - "execution_count": 24, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -61,7 +61,7 @@ "" ] }, - "execution_count": 25, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -77,30 +77,18 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1.766, 1.766, 98.406, 98.387], dtype=float32)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ref_pressure = results.node['pressure'].values[0][:2]\n", "ref_rate = results.link['flowrate'].values[0]\n", - "ref_values = np.append(ref_rate, ref_pressure)\n", - "designer.convert_solution_from_si(ref_values)" + "ref_values = np.append(ref_rate, ref_pressure)" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -119,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -148,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -157,7 +145,7 @@ "11" ] }, - "execution_count": 30, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -169,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -178,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -190,7 +178,7 @@ " [ 0., 0., 1., -1.]])" ] }, - "execution_count": 32, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -201,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -210,7 +198,7 @@ "(5, 12, 12)" ] }, - "execution_count": 33, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -221,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -238,7 +226,7 @@ "array([ 1.766, 1.766, 67.877, 37.329])" ] }, - "execution_count": 34, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -250,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -276,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -291,7 +279,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1, 1, 1.9999999999999998, 1.9842519685039368, 96.45669291338584, 96.10236220472441]\n" + "[1, 1, 1.748031496062992, 1.996062992125984, 98.2283464566929, 98.58267716535433]\n" ] } ], @@ -303,16 +291,16 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.5072373283355087" + "0.6763164377806783" ] }, - "execution_count": 40, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -323,16 +311,16 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1000., 500.])" + "array([1000., 1000.])" ] }, - "execution_count": 41, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -343,16 +331,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "442" + "428" ] }, - "execution_count": 39, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/notebooks/design_pipe_diameter_DW.ipynb b/docs/notebooks/design_pipe_diameter_DW.ipynb index a5bc403..c6243c4 100644 --- a/docs/notebooks/design_pipe_diameter_DW.ipynb +++ b/docs/notebooks/design_pipe_diameter_DW.ipynb @@ -202,7 +202,7 @@ { "data": { "text/plain": [ - "array([ 0.05 , 0.05 , 26.456, 22.911])" + "array([ 1.766, 1.766, 86.797, 75.168])" ] }, "execution_count": 7, @@ -213,7 +213,7 @@ "source": [ "designer.create_index_mapping()\n", "designer.matrices = designer.initialize_matrices()\n", - "designer.compute_classical_solution([1,0,0,1,0,0], convert_to_si=True)" + "designer.compute_classical_solution([1,0,0,1,0,0], convert_to_si=False)" ] }, { @@ -226,15 +226,15 @@ "output_type": "stream", "text": [ "price \t diameters \t variables\n", - "0.16907910944516957 [250. 250.] [ 1.766 1.766 87.449 76.472]\n", - "0.25361866416775436 [250. 500.] [ 1.766 1.766 88.101 89.012]\n", - "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 88.753 90.102]\n", - "0.25361866416775436 [500. 250.] [ 1.766 1.766 100.64 90.379]\n", - "0.33815821889033915 [500. 500.] [ 1.766 1.766 100.7 102.327]\n", - "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 100.76 102.825]\n", - "0.42269777361292393 [1000. 250.] [ 1.766 1.766 101.198 91.653]\n", - "0.5072373283355087 [1000. 500.] [ 1.766 1.766 101.202 103.545]\n", - "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 101.206 103.987]\n" + "0.16907910944516957 [250. 250.] [ 1.766 1.766 86.797 75.168]\n", + "0.25361866416775436 [250. 500.] [ 1.766 1.766 86.797 86.404]\n", + "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 86.797 86.782]\n", + "0.25361866416775436 [500. 250.] [ 1.766 1.766 98.032 86.404]\n", + "0.33815821889033915 [500. 500.] [ 1.766 1.766 98.032 97.64 ]\n", + "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 98.032 98.018]\n", + "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.411 86.782]\n", + "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.411 98.018]\n", + "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.411 98.397]\n" ] } ], diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index f50e259..b24ec95 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -364,7 +364,7 @@ def compute_classical_solution(self, parameters, convert_to_si=True): elif self.wn.options.hydraulic.headloss == "D-W": # P0 matrix - p0 = P0[:-1] + p0 = np.copy(P0[:-1]) # add the k0 term without sgin to p0 p0 += ( (parameters * P2[:-1, :num_signs, num_vars:]) From bb00c8ad370e35f1fc27e5008b56d26dee3c8ae5 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 10 Oct 2024 17:29:22 +0200 Subject: [PATCH 65/96] register sampleset --- docs/notebooks/design_pipe_diameter.ipynb | 358 +++++++++++++----- docs/notebooks/epanet_hhl.ipynb | 250 ++++++++++++ docs/notebooks/hhl_solver_Net1Loops.ipynb | 110 ++++-- .../networks/Net2Loops_hhl_settings.inp | 145 +++++++ wntr_quantum/design/qubo_pipe_diam.py | 5 +- 5 files changed, 727 insertions(+), 141 deletions(-) create mode 100644 docs/notebooks/epanet_hhl.ipynb create mode 100644 docs/notebooks/networks/Net2Loops_hhl_settings.inp diff --git a/docs/notebooks/design_pipe_diameter.ipynb b/docs/notebooks/design_pipe_diameter.ipynb index f83dabe..f8ddeb1 100644 --- a/docs/notebooks/design_pipe_diameter.ipynb +++ b/docs/notebooks/design_pipe_diameter.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ "" ] }, - "execution_count": 4, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -61,7 +61,7 @@ "" ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -77,18 +77,30 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ref_pressure = results.node['pressure'].values[0][:2]\n", "ref_rate = results.link['flowrate'].values[0]\n", - "ref_values = np.append(ref_rate, ref_pressure)" + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -97,17 +109,17 @@ "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", "nqbit = 7\n", - "step = (0.5/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", + "step = (2./(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", - "nqbit = 7\n", - "step = (5/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+95.0, var_base_name=\"x\")" + "nqbit = 9\n", + "step = (50/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -118,15 +130,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 95.000000 => 100.000000 (res: 0.039370)\n", - "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.003937)\n" + "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", + "Flow Encoding : -2.000000 => -0.000000 | 0.000000 => 2.000000 (res: 0.015748)\n" ] } ], @@ -136,217 +148,369 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, { "data": { "text/plain": [ - "11" + "array([ 1.766, 1.766, 97.666, 96.906])" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "designer.create_index_mapping()\n", - "designer.pipe_diameter_index_mapping['flow[P2]'][2]" + "designer.matrices = designer.initialize_matrices()\n", + "designer.compute_classical_solution([0,1,0,0,1,0], convert_to_si=False)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "price \t diameters \t variables\n", + "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329]\n", + "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118]\n", + "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858]\n", + "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118]\n", + "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906]\n", + "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647]\n", + "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858]\n", + "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647]\n", + "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387]\n" + ] + } + ], "source": [ - "(P0, P1,P2,P3,P4) = designer.initialize_matrices()" + "designer.enumerates_classical_solutions(convert_to_si=False)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-1., 1., 0., 0.],\n", - " [ 0., -1., 0., 0.],\n", - " [ 0., 0., -1., 0.],\n", - " [ 0., 0., 1., -1.]])" + "array([ 1.766, 1.766, 98.406, 98.387], dtype=float32)" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "P1[:-1, 2:6] + P2.sum(1)[:-1, 2:6]" + "designer.convert_solution_from_si(ref_values)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:759: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", + " warnings.warn(\"For constraints with fractional coefficients, \"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1, 2.0, 0.8346456692913385, 98.14090019569471, 87.47553816046965]\n" + ] + } + ], + "source": [ + "from dwave.samplers import SimulatedAnnealingSampler\n", + "options = {'sampler': SimulatedAnnealingSampler()}\n", + "status = designer.solve(strength=1E7, num_reads=100000, options=options)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(5, 12, 12)" + "0.25361866416775436" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "P2.shape" + "designer.total_pice" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, { "data": { "text/plain": [ - "array([ 1.766, 1.766, 67.877, 37.329])" + "array([500., 250.])" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "designer.matrices = designer.initialize_matrices()\n", - "designer.compute_classical_solution([1,0,0,1,0,0], convert_to_si=False)" + "designer.optimal_diameters" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "price \t diameters \t variables\n", - "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329]\n", - "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118]\n", - "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858]\n", - "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118]\n", - "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906]\n", - "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647]\n", - "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858]\n", - "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647]\n", - "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387]\n" - ] + "data": { + "text/plain": [ + "437" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "designer.enumerates_classical_solutions(convert_to_si=False)" + "designer.bqm.num_variables" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:759: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", - " warnings.warn(\"For constraints with fractional coefficients, \"\n" - ] + "data": { + "text/plain": [ + "(array([9.420e+02, 7.588e+03, 2.190e+04, 3.014e+04, 2.332e+04, 1.148e+04, 3.597e+03, 8.800e+02, 1.360e+02, 1.300e+01]),\n", + " array([-2.33e+03, 2.10e+07, 4.20e+07, 6.30e+07, 8.40e+07, 1.05e+08, 1.26e+08, 1.47e+08, 1.68e+08, 1.89e+08, 2.10e+08]),\n", + " )" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 1, 1.748031496062992, 1.996062992125984, 98.2283464566929, 98.58267716535433]\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from dwave.samplers import SimulatedAnnealingSampler\n", - "options = {'sampler': SimulatedAnnealingSampler()}\n", - "status = designer.solve(strength=1E5, num_reads=100000, options=options)" + "import matplotlib.pyplot as plt\n", + "plt.hist(designer.sampleset.data_vectors['energy'])" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "x = designer.sampleset" + ] + }, + { + "cell_type": "code", + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.6763164377806783" + "([1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 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}, - "execution_count": 17, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "designer.total_pice" + "x.record[1]" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 59, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([1000., 1000.])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 0, 1, 0, 1, 0]\n", + "[1, 0, 1, 1, 1, 1]\n", + "[1, 1, 0, 1, 1, 0]\n", + "[1, 0, 1, 0, 1, 0]\n", + "[1, 1, 0, 0, 1, 0]\n", + "[1, 0, 1, 0, 1, 1]\n", + "[1, 1, 0, 1, 1, 0]\n", + "[1, 1, 0, 1, 0, 0]\n", + "[0, 1, 1, 1, 1, 0]\n", + "[0, 1, 1, 1, 1, 0]\n", + "[1, 1, 0, 1, 1, 1]\n", + "[0, 1, 1, 0, 1, 1]\n", + "[0, 1, 1, 1, 1, 0]\n", + "[1, 0, 1, 0, 1, 1]\n", + "[1, 0, 0, 1, 1, 0]\n", + "[1, 0, 1, 0, 1, 1]\n", + "[1, 0, 0, 1, 0, 1]\n", + "[1, 1, 0, 1, 1, 1]\n", + "[1, 0, 0, 0, 0, 1]\n", + "[1, 1, 0, 0, 1, 0]\n", + "[1, 1, 0, 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+ "[0, 0, 1, 0, 0, 1]\n", + "[0, 1, 1, 1, 1, 1]\n", + "[1, 0, 1, 0, 0, 1]\n", + "[1, 0, 0, 1, 1, 0]\n", + "[0, 1, 1, 0, 1, 0]\n", + "[1, 1, 0, 0, 1, 1]\n", + "[0, 1, 0, 0, 1, 0]\n", + "[1, 1, 0, 0, 0, 1]\n", + "[0, 0, 1, 0, 0, 1]\n", + "[1, 0, 1, 1, 1, 1]\n", + "[1, 0, 1, 1, 1, 0]\n", + "[0, 1, 0, 0, 0, 0]\n", + "[1, 0, 1, 0, 0, 1]\n", + "[1, 1, 0, 0, 1, 0]\n", + "[1, 0, 1, 1, 0, 1]\n", + "[0, 1, 1, 1, 0, 0]\n", + "[0, 1, 0, 0, 1, 0]\n", + "[1, 1, 0, 1, 0, 0]\n", + "[0, 1, 1, 1, 0, 1]\n", + "[1, 0, 1, 0, 1, 1]\n", + "[1, 0, 1, 1, 1, 0]\n", + "[1, 0, 1, 1, 1, 0]\n", + "[1, 0, 0, 0, 1, 1]\n", + "[0, 1, 1, 1, 1, 1]\n", + "[1, 0, 0, 1, 1, 1]\n", + "[0, 1, 1, 1, 0, 0]\n", + "[1, 0, 1, 1, 1, 0]\n", + "[1, 0, 1, 1, 1, 0]\n", + "[0, 1, 0, 1, 1, 0]\n", + "[0, 1, 1, 0, 1, 1]\n", + "[0, 1, 1, 0, 1, 0]\n", + "[1, 1, 0, 0, 1, 0]\n", + "[1, 0, 1, 0, 1, 0]\n", + "[1, 0, 1, 1, 0, 0]\n", + "[1, 0, 1, 1, 1, 0]\n", + "[1, 1, 0, 0, 1, 1]\n", + "[0, 1, 0, 0, 1, 1]\n", + "[1, 0, 1, 1, 0, 0]\n", + "[1, 0, 1, 1, 1, 0]\n", + "[1, 0, 0, 0, 1, 1]\n", + "[0, 0, 1, 1, 0, 0]\n" + ] } ], "source": [ - "designer.optimal_diameters" + "for i in range(100):\n", + " s = designer.qubo.decode_solution(x.record[i][0])\n", + " print(s[3])" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "428" + "array([1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n", + " 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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file mode 100644 index 0000000..4905ad8 --- /dev/null +++ b/docs/notebooks/epanet_hhl.ipynb @@ -0,0 +1,250 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import wntr\n", + "import wntr_quantum\n", + "\n", + "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", + "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", + "\n", + "# Create a water network model\n", + "# inp_file = 'networks/Net0.inp'\n", + "inp_file = 'networks/Net2Loops_hhl_settings.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the original simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# define the classical simulator\n", + "sim = wntr.sim.EpanetSimulator(wn)\n", + "\n", + "# run the simulation\n", + "results = sim.run_sim()\n", + "\n", + "# Plot results on the network\n", + "pressure_at_5hr_ref = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr_ref, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the HHL solver" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "HHL timing: 38.345487 s.\n", + "HHL timing: 37.469449 s.\n", + "HHL timing: 36.485459 s.\n", + "HHL timing: 35.427485 s.\n", + "HHL timing: 36.099999 s.\n", + "HHL timing: 36.873821 s.\n", + "HHL timing: 35.504431 s.\n", + "HHL timing: 33.016205 s.\n", + "HHL timing: 31.564912 s.\n", + "HHL timing: 31.705842 s.\n", + "HHL timing: 33.696561 s.\n", + "HHL timing: 31.966523 s.\n", + "HHL timing: 32.229621 s.\n", + "HHL timing: 33.316630 s.\n", + "HHL timing: 32.892447 s.\n", + "HHL timing: 32.702379 s.\n", + "HHL timing: 32.063231 s.\n", + "HHL timing: 32.507507 s.\n", + "HHL timing: 32.775880 s.\n", + "HHL timing: 33.138077 s.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from quantum_newton_raphson.hhl_solver import HHL_SOLVER\n", + "from qiskit.primitives import Estimator, Sampler\n", + "\n", + "\n", + "# define the estimator\n", + "estimator = Estimator()\n", + "sampler = Sampler()\n", + "\n", + "linear_solver = HHL_SOLVER(\n", + " estimator=estimator,\n", + " sampler = sampler,\n", + " epsilon = 1E-3\n", + ")\n", + "\n", + "\n", + "# define the quantum epanet simulator\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn, linear_solver=linear_solver)\n", + "\n", + "# run the simulation\n", + "results = sim.run_sim(linear_solver=linear_solver)\n", + "\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare the pressure values obtained" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.scatter(pressure_at_5hr_ref.values, pressure_at_5hr.values)\n", + "plt.axline((0, 0), slope=1, linestyle=\"--\", color=\"gray\")\n", + "plt.axline((0, 0), slope=1.05, linestyle=\"--\", color=\"gray\")\n", + "plt.axline((0, 0), slope=0.95, linestyle=\"--\", color=\"gray\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/hhl_solver_Net1Loops.ipynb b/docs/notebooks/hhl_solver_Net1Loops.ipynb index 10364e1..fcf77a2 100644 --- a/docs/notebooks/hhl_solver_Net1Loops.ipynb +++ b/docs/notebooks/hhl_solver_Net1Loops.ipynb @@ -16,7 +16,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -27,7 +27,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -44,8 +44,8 @@ "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", "\n", "# set up network model\n", - "inp_file = 'networks/Net1Loops.inp'\n", - "inp_file = 'networks/Net3.inp'\n", + "inp_file = 'networks/Net2Loops.inp'\n", + "# inp_file = 'networks/Net3.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", "# plot network\n", @@ -76,8 +76,8 @@ "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", "Your EPANET temp dir: /home/nico/.epanet_quantum\n", "\n", - "Size of the Jacobian in EPANET simulator: 92\n", - "Size of the b vector in EPANET simulator: 92\n" + "Size of the Jacobian in EPANET simulator: 6\n", + "Size of the b vector in EPANET simulator: 6\n" ] } ], @@ -116,6 +116,27 @@ "# results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "76.10571224230992" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np \n", + "np.linalg.cond(epanet_A.todense())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -127,26 +148,40 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "isometry: 0.000316\n", + "trotterization: 0.000132\n", + "QPE : 27.371652\n", + "Inverse QPE: 27.566401\n", + "Total construct: 55.763218\n" + ] + } + ], "source": [ "import numpy as np\n", "\n", - "from qiskit.primitives import Estimator\n", + "from qiskit.primitives import Estimator, Sampler\n", "\n", "from quantum_newton_raphson.hhl_solver import HHL_SOLVER\n", "\n", "n_qubits = int(np.ceil(np.log2(epanet_A_dim)))\n", "\n", "estimator = Estimator()\n", + "sampler = Sampler() \n", "\n", "linear_solver = HHL_SOLVER(\n", " estimator=estimator,\n", + " sampler=sampler \n", ")\n", "\n", "\n", - "# res = linear_solver(epanet_A, epanet_b)" + "res = linear_solver(epanet_A, epanet_b)" ] }, { @@ -165,12 +200,12 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -181,11 +216,11 @@ { "data": { "text/plain": [ - "(array([599.121, 603.574, 632.034, 601.561, 578.432, 620.536, 663.368, 647.877]),\n", - " array([610.144, 603.148, 629.941, 608.536, 573.377, 612.059, 662.437, 646.793]))" + "(array([523.654, 589.038, 614.941, 641.471, 544.083, 452.317]),\n", + " array([522.495, 589.102, 615.614, 642.134, 553.081, 440.917]))" ] }, - "execution_count": 40, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -206,54 +241,45 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "128" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "isometry: 0.000305\n", + "trotterization: 0.000135\n", + "QPE : 27.594847\n", + "Inverse QPE: 25.624862\n" + ] } ], - "source": [ - "2**7" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], "source": [ "import numpy as np\n", "A = epanet_A.todense()\n", "b = epanet_b \n", "A.shape\n", - "Apad = np.eye(128,128)\n", - "Apad[:92,:92] = A\n", - "bpad = np.zeros(128)\n", - "bpad[:92] = b\n", + "Apad = np.eye(8,8)\n", + "Apad[:6,:6] = A\n", + "bpad = np.zeros(8)\n", + "bpad[:6] = b\n", "circuits = linear_solver._solver.construct_circuit(Apad,bpad)" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" + "
" ] }, - "execution_count": 38, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -272,7 +298,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "vitens_wntr_1", "language": "python", "name": "python3" }, diff --git a/docs/notebooks/networks/Net2Loops_hhl_settings.inp b/docs/notebooks/networks/Net2Loops_hhl_settings.inp new file mode 100644 index 0000000..5aec6fb --- /dev/null +++ b/docs/notebooks/networks/Net2Loops_hhl_settings.inp @@ -0,0 +1,145 @@ +[TITLE] +shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) + +[JUNCTIONS] +;ID Elev Demand Pattern + 2 150.00 27.77 ; + 3 160.00 27.77 ; + 4 155.00 33.33 ; + 5 150.00 75.00 ; + 6 165.00 91.67 ; + 7 160.00 55.55 ; + +[RESERVOIRS] +;ID Head Pattern + 1 210.00 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + 1 1 2 1000.00 457.20 130.00 0.00 Open ; + 2 2 3 1000.00 203 130.00 0.00 Open ; + 3 2 4 1000.00 457 130.00 0.00 Open ; + 4 4 5 1000.00 153 130.00 0.00 Open ; + 5 4 6 1000.00 406.40 130.00 0.00 Open ; + 6 6 7 1000.00 254.00 130.00 0.00 Open ; + 7 3 5 1000.00 153 130.00 0.00 Open ; + 8 5 7 1000.00 153 130.00 0.00 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + + + +[RULES] + + + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 0:00 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 2:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic NONE + +[REPORT] + Status Yes + Summary No + Page 0 + +[OPTIONS] + Units LPS + Headloss H-W + Specific Gravity 1.0 + Viscosity 1.0 + Trials 10 + Accuracy 0.1 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced STOP + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality Chlorine mg/L + Diffusivity 1.0 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +2 2000.000 3000.000 +3 1000.000 3000.000 +4 2000.000 2000.000 +5 1000.000 2000.000 +6 2000.000 1000.000 +7 1000.000 1000.000 +1 3000.000 3000.000 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 900.000 900.000 3100.000 3100.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index b24ec95..1001fd6 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -668,14 +668,15 @@ def solve( # noqa: D417 ) # sample - sampleset = self.qubo.sample_bqm(self.bqm, num_reads=num_reads) + self.sampleset = self.qubo.sample_bqm(self.bqm, num_reads=num_reads) # decode - sol = self.qubo.decode_solution(sampleset.lowest().record[0][0]) + sol = self.qubo.decode_solution(self.sampleset.lowest().record[0][0]) # flatten sol, hot_encoding = self.flatten_solution_vector(sol) print(sol) + # convert back to SI sol = self.convert_solution_to_si(sol) From 2f16328eefe7c2784d3de4fb48fbf83a86ddfcf3 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 14 Oct 2024 22:00:06 +0200 Subject: [PATCH 66/96] pipe sampler opions as kwarg --- docs/notebooks/dw_approximation.ipynb | 100 ++ docs/notebooks/epanet_hhl.ipynb | 47 +- docs/notebooks/hhl_solver_Net1Loops.ipynb | 16 +- docs/notebooks/networks/Net2LoopsDWflat.inp | 145 +++ docs/notebooks/qubo_poly_solver.ipynb | 935 ++++++++++++++++-- .../qubo_poly_solver_2loops_cm.ipynb | 612 ++++++++++++ .../qubo_poly_solver_2loops_dw.ipynb | 535 ++++++++++ wntr_quantum/sim/models/darcy_weisbach_fit.py | 51 +- .../sim/solvers/qubo_polynomial_solver.py | 111 ++- 9 files changed, 2419 insertions(+), 133 deletions(-) create mode 100644 docs/notebooks/dw_approximation.ipynb create mode 100644 docs/notebooks/networks/Net2LoopsDWflat.inp create mode 100644 docs/notebooks/qubo_poly_solver_2loops_cm.ipynb create mode 100644 docs/notebooks/qubo_poly_solver_2loops_dw.ipynb diff --git a/docs/notebooks/dw_approximation.ipynb b/docs/notebooks/dw_approximation.ipynb new file mode 100644 index 0000000..934583e --- /dev/null +++ b/docs/notebooks/dw_approximation.ipynb @@ -0,0 +1,100 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sim.models.darcy_weisbach_fit import * " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-5.249 0.152 0.007]\n" + ] + } + ], + "source": [ + "r = 0.005*1e-3\n", + "d = 10/12\n", + "res = dw_fit(roughness=r, diameter=d, plot=True, convert_to_us_unit=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6e-06" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r/d" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/epanet_hhl.ipynb b/docs/notebooks/epanet_hhl.ipynb index 4905ad8..194f7bd 100644 --- a/docs/notebooks/epanet_hhl.ipynb +++ b/docs/notebooks/epanet_hhl.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "metadata": { "metadata": {} }, @@ -30,7 +30,7 @@ "" ] }, - "execution_count": 8, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -61,12 +61,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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" ] @@ -80,7 +80,7 @@ "" ] }, - "execution_count": 9, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -115,31 +115,18 @@ "output_type": "stream", "text": [ "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", - "HHL timing: 38.345487 s.\n", - "HHL timing: 37.469449 s.\n", - "HHL timing: 36.485459 s.\n", - "HHL timing: 35.427485 s.\n", - "HHL timing: 36.099999 s.\n", - "HHL timing: 36.873821 s.\n", - "HHL timing: 35.504431 s.\n", - "HHL timing: 33.016205 s.\n", - "HHL timing: 31.564912 s.\n", - "HHL timing: 31.705842 s.\n", - "HHL timing: 33.696561 s.\n", - "HHL timing: 31.966523 s.\n", - "HHL timing: 32.229621 s.\n", - "HHL timing: 33.316630 s.\n", - "HHL timing: 32.892447 s.\n", - "HHL timing: 32.702379 s.\n", - "HHL timing: 32.063231 s.\n", - "HHL timing: 32.507507 s.\n", - "HHL timing: 32.775880 s.\n", - "HHL timing: 33.138077 s.\n" + "HHL timing: 31.068329 s.\n", + "HHL timing: 31.530130 s.\n", + "HHL timing: 68.621765 s.\n", + "HHL timing: 64.437107 s.\n", + "HHL timing: 56.084545 s.\n", + "HHL timing: 51.080409 s.\n", + "HHL timing: 52.508506 s.\n" ] }, { "data": { - "image/png": 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Grr5hgDqktGulytzPMcaY1i4iHObgezIf/kVS4+U6mcPl/GSI3aKAZpRv36sV/aeotryq0f7M889Q3gv3WK4KaFr113W6+5aV2r+3vNH+jM4+3Xn/CLVL4I/GY+Gaa7xmz0qFCl1JMp+ulamvtlcQEIa3//hMyNCVpL1/e10lr79nsSKgea+t3RkydCWpeH+F/rXmI4sV/bC4YqnZHNojVX/W9CB/rT55a4UOeo+3UxTQDH9NnXb+dW2z4woffEaZcRdbqAgIzz9X7Gx2zPpXduqc80+0UM0PjyuCV3WHwhr25LwHNevpwu+5GCA8PsVornNms+PWPL9Cjz4/00JFQHhGnz1Hie06NjmmvOxrS9X88LjiGq+p3Cfz7qPNjvvE+3N96c22UBHQPH9tvd4bMl3+6romx6VenKsut422VBXQvGf/tFMH9jUdrF2PT9GMOSMtVfTD4ooZr5PYRaZdhvRVcehB3lh163eesr2x9goDmlF9eZ52/Gllk2Nyb75Cnfr1slQR0LyKz9urYH7Tq4dnDe1uqZofHtdsrnKyzlVT5TpdzpZD6KKN+dnvxiq2Y1LI/uzLfqFOpxG6aFvOGJStbt1D32ee2a2DBhK8x8wVS81HmC+3yXyyQqr+PNBWpzjFZI+Qk96/FSsDQivb9onWT5qj0vXvBto88THq/d/n69TZk+SJ8rZidUDjqiprtPDxjdq0YY/8/sMx4fE46nd6pib8pr8SfUx0jpWrgleSjDHSoV3avX2Lrrn+Jt372LPql3Nqa5cFNOvg1p1662+v6Nbf/z89vm6ZThs4oLVLApp18PMqbX+/VJLUo3eaUjsltHJF7ueKa7zf5DiO5DteX3rLtKpwl+51XLNajh+5lJOPV4e6MhX9/jN5E+NbuxwgLCkdE5Q7kE2rLYnUAgDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAP2jdunXT3LlzW7uMAIIXANDqxo8fL8dxNGvWrKD2F154QY7jtFJV3w+CFwDQJsTFxWn27Nn68ssvW7uU7xXBCwBoE/Ly8pSRkaGZM2eGHPPcc8+pT58+io2NVbdu3fTggw8G9ZeWlmrUqFGKj49Xdna2nnnmmaPOUVZWpquuukqdOnWSz+fTkCFD9Pbbb7f45wmF4AUAtAler1d//OMf9eijj2rfvn1H9RcVFemSSy7RZZddpq1bt+quu+7SHXfcoYULFwbGjB8/Xnv37tXatWu1dOlSzZ8/X6WlpUHn+eUvf6nS0lKtXLlSRUVF6tevn4YOHaqDBw9+3x9RkhRl5V0AAAjDRRddpFNOOUV33nmnFixYENQ3Z84cDR06VHfccYckqUePHnr//fd1//33a/z48frwww+1cuVKvfHGGzrttNMkSQsWLFDv3r0D53jttdf0xhtvqLS0VLGxsZKkBx54QC+88IKWLl2qSZMmfe+fkRkvAKBNmT17tp5++mlt27YtqH3btm0aMGBAUNuAAQO0Y8cONTQ0aNu2bYqKilJOTk6gv1evXkpOTg78/Pbbb6uyslKpqalKTEwMHLt27dLHH3/8vX6uI5jxAgDalIEDB2r48OG6/fbbNX78+BY9d2VlpY477ji9+uqrR/V9M6C/TwQvAKDNmTVrlk455RT17Nkz0Na7d2+tX78+aNz69evVo0cPeb1e9erVS/X19SoqKgosNW/fvl1lZWWB8f369VNxcbGioqLUrVs3Gx/lKCw1AwDanJNPPlljx47VI488Emj77W9/qzVr1uiee+7Rhx9+qKefflqPPfaYbrrpJklSz549dc4552jy5MnauHGjioqKdNVVVyk+Pj5wjry8POXm5urCCy/UP//5T+3evVuvv/66fve732nTpk1WPhvBCwBok2bMmCG/3x/4uV+/fnr22We1ePFinXTSSZo+fbpmzJgRtBxdUFCgzp07a9CgQRo9erQmTZqktLS0QL/jOPrHP/6hgQMHasKECerRo4cuu+wyffLJJ0pPT7fyuRxjjLHyTi1s8+bNysnJCWwFB9yAf28BMOMFAMAighcAAIsIXgAALCJ4AQCwiOAFAMAighcAAIt4chUAwJWqq6tVW1vb5JiYmBjFxcVZqig8BC8AwHWqq6uVEZ+kcjUdvBkZGdq1a1ebCl+CFwDgOrW1tSpXreZGD1B8iCj7WvWaWrxetbW1BC8AAC2hnSda7ZzGo8wxjuVqwkPwAgBcKzraUbTTeMBGG0eqsVxQGAheAIBreTySJ8TE1tNGv4mA4AUAuJbH68gTYsbrYakZAICWFRXlKCrElDfKT/ACANCivJ7DR6N9dksJG8ELAHAtb3ToGa+XGS8AAC3L45E8IYK3rT4TmeAFALjW4eAN0We3lLC11boAAGhWdJRz+F7exo6oyJaa77rrLjmOE3T06tUr0F9dXa0pU6YoNTVViYmJys/PV0lJScQ1E7wAANfyeJ0mj0j16dNHBw4cCByvvfZaoO/GG2/U8uXLtWTJEq1bt0779+/X6NGjI34PlpoBAK7V5FLzfx6gUVFREdQeGxur2NjYRl8TFRWljIyMo9rLy8u1YMECLVq0SEOGDJEkFRQUqHfv3iosLFT//v3DrznskQAAtDHeaEdRIQ5v9OEZb2ZmppKSkgLHzJkzQ55vx44d6ty5s44//niNHTtWe/bskSQVFRWprq5OeXl5gbG9evVSVlaWNmzYEFHNzHgBAK7l8TihdzX/58lVe/fulc/nC7SHmu2efvrpWrhwoXr27KkDBw7o7rvv1llnnaV3331XxcXFiomJUXJyctBr0tPTVVxcHFHNBC8AwLWio0Jvojry5Qk+ny8oeEMZMWJE4P/37dtXp59+urp27apnn31W8fHxLVOwWGoGALjYkWu8oY7vIjk5WT169NBHH32kjIwM1dbWqqysLGhMSUlJo9eEm6z5u5UFAEDraeldzd9UWVmpjz/+WMcdd5xycnIUHR2tNWvWBPq3b9+uPXv2KDc3N6LzstQMAHAtb5SRN6rx7//zKrLvBbzppps0atQode3aVfv379edd94pr9erMWPGKCkpSRMnTtS0adOUkpIin8+n6667Trm5uRHtaJYIXgCAizmew0eovkjs27dPY8aM0RdffKFOnTrpzDPPVGFhoTp16iRJeuihh+TxeJSfn6+amhoNHz5c8+fPj7hmghcA4Foer5HH2/jM1mMim/EuXry4yf64uDjNmzdP8+bNi+i830bwAgBcy/EYeTyNB6wTor21EbwAANdynCaWmtvmtwISvAAA9/JEGXlCbK6KdKnZFoIXAOBaTT6ruY3eMEvwAgBcy3GMHCfENd4Q7a2N4AUAuBZLzQAAWNSS9/HaQvACAFzLG6XQT65qmxNeghcA4F6OmrjGG+EjI20heAEArsVSMwAAFnma+JIEj58ZLwAALcrxmJCPhuSRkQAAtLAmvyShje6uIngBAK7Fk6sAALCIpWYAACxyohw50Y1/DZHjb5tfT0TwAgBcy/E4cjwhgjdEe2sjeAEA7uX1HD5C9bVBBC8AwLWcaEdOdOMBy1IzAAAtzeMcPkL1tUEELwDAtZwoT+gZbwNLzQAAtCyu8QIAYI8bdzW3zT8HAAAIR4yn6eMYzZo1S47jaOrUqYG26upqTZkyRampqUpMTFR+fr5KSkoiPjfBCwBwrSMz3lDHsXjzzTf15JNPqm/fvkHtN954o5YvX64lS5Zo3bp12r9/v0aPHh3x+QleAIB7RXml6BBHlFeSVFFREXTU1NSEPF1lZaXGjh2rp556Sh06dAi0l5eXa8GCBZozZ46GDBminJwcFRQU6PXXX1dhYWFEJRO8AADXcrxOk4ckZWZmKikpKXDMnDkz5PmmTJmikSNHKi8vL6i9qKhIdXV1Qe29evVSVlaWNmzYEFHNbK4CALhXGPfx7t27Vz6fL9AcGxvb6PDFixdr8+bNevPNN4/qKy4uVkxMjJKTk4Pa09PTVVxcHFHJBC8AwLWc6Cbu460/3O7z+YKCtzF79+7VDTfcoJdeeklxcXEtXuc3sdQMAHCvI/fxhjrCVFRUpNLSUvXr109RUVGKiorSunXr9MgjjygqKkrp6emqra1VWVlZ0OtKSkqUkZERUcnMeAEArnX4awFDzHjrwt/VPHToUG3dujWobcKECerVq5duvfVWZWZmKjo6WmvWrFF+fr4kafv27dqzZ49yc3MjqpngBQC4l9c5fITqC1P79u110kknBbUlJCQoNTU10D5x4kRNmzZNKSkp8vl8uu6665Sbm6v+/ftHVDLBCwBwL4tfkvDQQw/J4/EoPz9fNTU1Gj58uObPnx/xeQheAIBrOdFeOdHekH3fxauvvhr0c1xcnObNm6d58+Z9p/MSvAAA9+JrAQEAsMjjOXyE6muDCF4AgHt5/+/RkI32tUEELwDAvZjxAgBgUVQTM95Q7a2M4AUAuJfHaWLGy+YqAABaFkvNAABYxFIzAAAWMeMFAMAex+OVE+K2IcfDjBcAgJbFjBcAAIt4ZCQAABaxuQoAAIu4jxcAAIu4xgsAgEUsNQMAYJHTxIzXYcYLAEDLYsYLAIBFjif0zJYZLwAALYzgBQDAIq9X8oaIshCPkmxtBC8AwL2Y8QIAYJE3qokZb9uMuLb55wAAAOE4MuMNdUTg8ccfV9++feXz+eTz+ZSbm6uVK1cG+qurqzVlyhSlpqYqMTFR+fn5KikpibhkghcA4F4tGLxdunTRrFmzVFRUpE2bNmnIkCG64IIL9N5770mSbrzxRi1fvlxLlizRunXrtH//fo0ePTriktvmPBwAgHA4UZInRJQ5h9srKiqCmmNjYxUbG3vU8FGjRgX9fO+99+rxxx9XYWGhunTpogULFmjRokUaMmSIJKmgoEC9e/dWYWGh+vfvH3bJzHgBAO515FnNoQ5JmZmZSkpKChwzZ85s9rQNDQ1avHixqqqqlJubq6KiItXV1SkvLy8wplevXsrKytKGDRsiKpkZLwDAtRzHI8dp/LYh5z9LzXv37pXP5wu0NzbbPWLr1q3Kzc1VdXW1EhMTtWzZMp144onasmWLYmJilJycHDQ+PT1dxcXFEdVM8AIA3MvTxFLzf9qPbJYKR8+ePbVlyxaVl5dr6dKlGjdunNatW9dS1UoieAEAbtbC9/HGxMTohBNOkCTl5OTozTff1MMPP6xLL71UtbW1KisrC5r1lpSUKCMjI6L34BovAMC9jtzHG+r4jvx+v2pqapSTk6Po6GitWbMm0Ld9+3bt2bNHubm5EZ3TdTPeev9B1TR8pE6Zn2ru41cqtl1la5cENKvhi89V9coqJb/9lub1/5nitr0j07evnCjX/ScItC0tOOO9/fbbNWLECGVlZenQoUNatGiRXn31Va1evVpJSUmaOHGipk2bppSUFPl8Pl133XXKzc2NaEez5KLgNcaoqn6Daho+kiS1ay9dMX6QpN2qqK1W++hfyHGiW7dIoBFV/1yh8oVPSH6/4iRd2LWztPxZlb7xb6Xe/gdFZRzX2iUC7tWCwVtaWqpf//rXOnDggJKSktS3b1+tXr1aZ599tiTpoYceksfjUX5+vmpqajR8+HDNnz8/8pKNMSbiV7WCr+rf1tf1b4fsj/F0VfuYQRYrAppX/dabOnjfXVKI/8y8GZ2V9sATzHyBCFVUVCgpKUnlXzwnny8hxJgqJaXmq7y8POzNVTa44hqvMQ2qrt/W5Jha/x41+A9ZqggIT+Xy50KGriQ1FO9X9RvrLVYE/MC04JOrbHHFn9l1/hIZ1TYzymjnng06dLCjlZqA5jg11Up//51mx1VvKlT8GazWAMckjNuJ2pq2WdVRGsIa9di8R/TIA3//nmsBwpMaG6N3Lhza7DhT19wflQBCcv5zhOprg1wRvF4nOaxxk6++WePGzPh+iwHC5fer4Yn75a1s+hJIVFa2pYKAHx5jjEJtVWqrW5jcEbye9or2/ER1/k9DjvE4ier904FynDb6Jw5+lA6dc74OLX0m9ACvVwlDzrFXEPAD41eD/CFWRUO1t7a2eeW5EQlRp8ujdiF6o5QYPYDQRZuTeP7Fiu7Ru/FOx1HSuP+WN5V9CcCxMsbf5NEWuSZ4vZ5E+WJHKNbbU44O369bU1OnqvIkJcWMULQnvZUrBI7mxMSq4+//qPa/vEKelNRAe01WtlJuvVsJw0a2YnWA+5lm/tcWuWKp+Qivk6DE6NNlok7V2+8UKbf/mVq/vlBZ6R1auzQgJCcmVu3zL1fiRZdpy+vrNfAXQ7Ru40Zln9KvtUsDXM9v/PKbEEvNbXTG66rgPcJxvPI3RKm6uq61SwHC5ng8Mu0SVFlf39qlAD8YRn4ZNR6wodpbmyuDFwAASfKbhiZmvG1zcxXBCwBwraY2UbXVzVUELwDAtZraRMXmKgAAWhhLzQAAWMTmKgAALGLGCwCARUahr+W2zSu8BC8AwM2aejQku5oBAGhZbvySBIIXAOBafC0gAAAWsasZAACL2NUMAIBFfnP4CNXXFhG8AADXqvM7qvM7IfvaIk9rFwAAwLHyG6fJIxIzZ87Uaaedpvbt2ystLU0XXnihtm/fHjSmurpaU6ZMUWpqqhITE5Wfn6+SkpKI3ofgBQC4lt9IDSGOSJea161bpylTpqiwsFAvvfSS6urqNGzYMFVVVQXG3HjjjVq+fLmWLFmidevWaf/+/Ro9enRE78NSMwDAter9jupDLCkfaa+oqAhqj42NVWxs7FHjV61aFfTzwoULlZaWpqKiIg0cOFDl5eVasGCBFi1apCFDhkiSCgoK1Lt3bxUWFqp///5h1cyMFwDgWg3GafKQpMzMTCUlJQWOmTNnhnXu8vJySVJKSookqaioSHV1dcrLywuM6dWrl7KysrRhw4awa2bGCwBwrXo5qg9xLbdeh9v37t0rn88XaG9stvttfr9fU6dO1YABA3TSSSdJkoqLixUTE6Pk5OSgsenp6SouLg67ZoIXAOBa4dxO5PP5goI3HFOmTNG7776r11577TtWeDSWmgEArhXOUnOkrr32Wq1YsUJr165Vly5dAu0ZGRmqra1VWVlZ0PiSkhJlZGSEfX6CFwDgWg3/2VzV2NEQ4X28xhhde+21WrZsmV555RVlZ2cH9efk5Cg6Olpr1qwJtG3fvl179uxRbm5u2O/DUjMAwLWO3DoUqi8SU6ZM0aJFi/Tiiy+qffv2geu2SUlJio+PV1JSkiZOnKhp06YpJSVFPp9P1113nXJzc8Pe0SwRvAAAF2vqQRmRPkDj8ccflyQNHjw4qL2goEDjx4+XJD300EPyeDzKz89XTU2Nhg8frvnz50f0PgQvAMC16vyHj1B9kQjnawTj4uI0b948zZs3L7KTfwPBCwBwrZac8dpC8AIAXKu+iS9JCPVEq9ZG8AIAXIuvBQQAwCKWmgEAsOjw5qpQ38druZgwEbwAANdiqRkAAItqjRQVYmZbS/ACANCyTBMz3jBuy20VBC8AwLVa8pGRthC8AADXqvVL3lBLzWyuAgCgZbG5CgAAi1hqBgDAovomviShnqVmAABaFjNeAAAsqvU78oR4clUtX5IAAEDLYnMVAAAWsdQMAIBF9Q1SXUPovraI4AUAuBYzXgAALKozkifEbUN1BC8AAC2LGS8AABYRvAAAWFTvD73U3FafXOVp7QIAADhWR2a8oY5I/Otf/9KoUaPUuXNnOY6jF154IajfGKPp06fruOOOU3x8vPLy8rRjx46IayZ4AQCu5fc7TR6RqKqq0s9+9jPNmzev0f777rtPjzzyiJ544glt3LhRCQkJGj58uKqrqyN6H5aaAQCuVV/nkaeu8TlkfYj2UEaMGKERI0Y02meM0dy5c/X73/9eF1xwgSTpz3/+s9LT0/XCCy/osssuC/t9mPECAFwrnBlvRUVF0FFTUxPx++zatUvFxcXKy8sLtCUlJen000/Xhg0bIjoXwQsAcK2Geo/q6xo/GuoPR1xmZqaSkpICx8yZMyN+n+LiYklSenp6UHt6enqgL1wsNQMAXKupa7lH2vfu3Sufzxdoj42NtVJbKMx4AQCuFc5Ss8/nCzqOJXgzMjIkSSUlJUHtJSUlgb5wEbwAANeqr3OaPFpKdna2MjIytGbNmkBbRUWFNm7cqNzc3IjOxVIzAMC1wllqDldlZaU++uijwM+7du3Sli1blJKSoqysLE2dOlV/+MMf9NOf/lTZ2dm644471LlzZ1144YURvQ/BCwBwrbo6jxTitqG6CG8n2rRpk37xi18Efp42bZokady4cVq4cKFuueUWVVVVadKkSSorK9OZZ56pVatWKS4uLqL3IXgBAK7lN03MeE1kM97BgwfLmNCPu3IcRzNmzNCMGTMiOu+3EbwAANcyTSw1mwiXmm0heAEArlVf55GiWubJVbYQvAAA12rJzVW2ELwAANfy+0MHrL+Nfi0gwQsAcC2WmgEAsKgldzXbQvACAFyroc4jeRuf2TYw4wUAoGX5/Y4cNlcBAGCJ3xw+QvW1QQQvAMC1vHV+eb0hti/Xtc1tzQQvAMC1HL+RJ8TM1s+MFwCAluVt8Mtb3/jM1jQw4wUAoEV5GiRPQ+MzW0+D5WLCRPACAFzL08RSc6j21kbwAgBcy1sfenOVCbEE3doIXgCAazHjBQDAoqh6v6I8IWa2zHgBAGhhfiOHB2gAAGAHS80AAFjkrfPL6zS+pOznyVUAALQsj98vT4hvvA/V3toIXgCAa7HUDACARd76Jpaa2dUMAEDLYsYLAIBFUXV+RSnEk6vYXAUAQAvzq4n7eO2WEi6CFwDgWg21X6k+RPA21H9tuZrwELwAANeJiYlRRkaGnvvn1CbHZWRkKCYmxk5RYSJ4AQCuExcXp127dqm2trbJcTExMYqLi7NUVXgIXgCAK8XFxbW5UA2Hp7ULAADgx4TgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACwieAEAsIjgBQDAIoIXAACLCF4AACxyjDGmtYsIlzH1kvbJaL/8/q/06aelionOUkbGaXKc2NYuD2iUMUb6+gOpcqv8taX6suyQFJet1K55cqI7tnZ5ACxzTfAaUyujzZIqG+mNkaMcOU6C7bKAJhnjlw6ulKo/bqTXI6WMkBPf3XpdAFqPa5aajbar8dCVpFoZbbVZDhCeqndChK4k+aUvV8s0fG21JACtyxXBa0yNpNJmRlXKmC9tlAOExRgjVb7TzKB66attdgoC0CZEtXYB4amQ1PyK+Kefvq/S0vjvvxwgDFFOnU5OK2t+YO2B770WAG2HS4LXCWvUQ3Pnas6DS7/nWoDwdEhupy/enRnGyPD+/Qbww+CKzVXG1Mvo35Iamhz3wTafqqtd8rcEfhR6pmxRu+hQexP+I3mInIST7BQEoNW5IqUcJ0rGdJa0t4lRHXTiiTm2SgLCYr5qJ335z9ADPPFSfE97BQFoda7YXCVJjk6QlBqiN0GOmDGg7XHa9ZISQ/xB6ImTUs+X44m2WxSAVuWKpeYjDpf6mYz2S6qWFC1Hx0lKl+N4W7c4oAmm9oBUuVWq/1xSlBR/vNSujxwvmwGBHxtXBS8AAG7nmqVmAAB+CAheAAAsIngBALCI4AUAwCKCFwAAiwheAAAsIngBALCI4AUAwCKCFwAAiwheAAAsIngBALCI4AUAwCKCFwAAiwheAAAsIngBALCI4AUAwCKCFwAAiwheAAAsIngBALCI4AUAwCKCFwAAiwheAAAsIngBALCI4AUAwCKCFwAAiwheAAAsIngBALCI4AUAwCKCFwAAiwheAAAsIngBALCI4AUAwCKCFwAAiwheAAAsIngBALCI4AUAwKL/D5NydU7nF92qAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -153,7 +140,7 @@ "" ] }, - "execution_count": 10, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -195,12 +182,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] diff --git a/docs/notebooks/hhl_solver_Net1Loops.ipynb b/docs/notebooks/hhl_solver_Net1Loops.ipynb index fcf77a2..c536758 100644 --- a/docs/notebooks/hhl_solver_Net1Loops.ipynb +++ b/docs/notebooks/hhl_solver_Net1Loops.ipynb @@ -155,11 +155,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "isometry: 0.000316\n", - "trotterization: 0.000132\n", - "QPE : 27.371652\n", - "Inverse QPE: 27.566401\n", - "Total construct: 55.763218\n" + "isometry: 0.000881\n", + "trotterization: 0.000152\n", + "QPE : 25.627279\n", + "Inverse QPE: 25.700898\n", + "Total construct: 51.990004\n" ] } ], @@ -248,10 +248,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "isometry: 0.000305\n", + "isometry: 0.004971\n", "trotterization: 0.000135\n", - "QPE : 27.594847\n", - "Inverse QPE: 25.624862\n" + "QPE : 25.436844\n", + "Inverse QPE: 24.375789\n" ] } ], diff --git a/docs/notebooks/networks/Net2LoopsDWflat.inp b/docs/notebooks/networks/Net2LoopsDWflat.inp new file mode 100644 index 0000000..3e28b73 --- /dev/null +++ b/docs/notebooks/networks/Net2LoopsDWflat.inp @@ -0,0 +1,145 @@ +[TITLE] +shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) + +[JUNCTIONS] +;ID Elev Demand Pattern + 2 0.00 27.77 ; + 3 0.00 27.77 ; + 4 0.00 33.33 ; + 5 0.00 75.00 ; + 6 0.00 91.67 ; + 7 0.00 55.55 ; + +[RESERVOIRS] +;ID Head Pattern + 1 210.00 ; + +[TANKS] +;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow + +[PIPES] +;ID Node1 Node2 Length Diameter Roughness MinorLoss Status + 1 1 2 1000.00 457.20 0.05 0.00 Open ; + 2 2 3 1000.00 203 0.05 0.00 Open ; + 3 2 4 1000.00 457 0.05 0.00 Open ; + 4 4 5 1000.00 153 0.05 0.00 Open ; + 5 4 6 1000.00 406.40 0.05 0.00 Open ; + 6 6 7 1000.00 254.00 0.05 0.00 Open ; + 7 3 5 1000.00 153 0.05 0.00 Open ; + 8 5 7 1000.00 153 0.05 0.00 Open ; + +[PUMPS] +;ID Node1 Node2 Parameters + +[VALVES] +;ID Node1 Node2 Diameter Type Setting MinorLoss + +[TAGS] + +[DEMANDS] +;Junction Demand Pattern Category + +[STATUS] +;ID Status/Setting + +[PATTERNS] +;ID Multipliers + +[CURVES] +;ID X-Value Y-Value + +[CONTROLS] + + + +[RULES] + + + +[ENERGY] + Global Efficiency 75 + Global Price 0 + Demand Charge 0 + +[EMITTERS] +;Junction Coefficient + +[QUALITY] +;Node InitQual + +[SOURCES] +;Node Type Quality Pattern + +[REACTIONS] +;Type Pipe/Tank Coefficient + + +[REACTIONS] + Order Bulk 1 + Order Tank 1 + Order Wall 1 + Global Bulk 0 + Global Wall 0 + Limiting Potential 0 + Roughness Correlation 0 + +[MIXING] +;Tank Model + +[TIMES] + Duration 0:00 + Hydraulic Timestep 1:00 + Quality Timestep 0:05 + Pattern Timestep 2:00 + Pattern Start 0:00 + Report Timestep 1:00 + Report Start 0:00 + Start ClockTime 12 am + Statistic NONE + +[REPORT] + Status Yes + Summary No + Page 0 + +[OPTIONS] + Units LPS + Headloss D-W + Specific Gravity 1.0 + Viscosity 1.0 + Trials 40 + Accuracy 0.001 + CHECKFREQ 2 + MAXCHECK 10 + DAMPLIMIT 0 + Unbalanced Continue 10 + Pattern 1 + Demand Multiplier 1.0 + Emitter Exponent 0.5 + Quality Chlorine mg/L + Diffusivity 1.0 + Tolerance 0.01 + +[COORDINATES] +;Node X-Coord Y-Coord +2 2000.000 3000.000 +3 1000.000 3000.000 +4 2000.000 2000.000 +5 1000.000 2000.000 +6 2000.000 1000.000 +7 1000.000 1000.000 +1 3000.000 3000.000 + +[VERTICES] +;Link X-Coord Y-Coord + +[LABELS] +;X-Coord Y-Coord Label & Anchor Node + +[BACKDROP] + DIMENSIONS 900.000 900.000 3100.000 3100.000 + UNITS None + FILE + OFFSET 0.00 0.00 + +[END] diff --git a/docs/notebooks/qubo_poly_solver.ipynb b/docs/notebooks/qubo_poly_solver.ipynb index 89907d0..ad29035 100644 --- a/docs/notebooks/qubo_poly_solver.ipynb +++ b/docs/notebooks/qubo_poly_solver.ipynb @@ -58,14 +58,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 77, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -74,10 +74,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -87,8 +87,13 @@ "results = sim.run_sim()\n", "# Plot results on the network\n", "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" + "flow_at_5hr = results.link['flowrate'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, link_attribute=flow_at_5hr, \n", + " node_attribute=pressure_at_5hr, \n", + " node_size=500, \n", + " link_width=5, \n", + " node_labels=True,\n", + " link_cmap=plt.cm.cividis)" ] }, { @@ -112,6 +117,15 @@ "ref_pressure" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.cm." + ] + }, { "cell_type": "code", "execution_count": 4, @@ -172,15 +186,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 90, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.000978)\n" + "Head Encoding : 0.000000 => 100.000000 (res: 14.285714)\n", + "Flow Encoding : -2.000000 => -0.000000 | 0.000000 => 2.000000 (res: 0.064516)\n" ] } ], @@ -189,13 +203,13 @@ "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", - "nqbit = 9\n", - "step = (0.5/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", + "nqbit = 5\n", + "step = (2./(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", "\n", - "nqbit = 9\n", - "step = (50/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")\n", + "nqbit = 3\n", + "step = (100/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", " head_encoding=head_encoding)\n", @@ -211,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -220,7 +234,7 @@ "array([1. , 1. , 0.999, 0.998])" ] }, - "execution_count": 10, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -231,13 +245,33 @@ "net.create_index_mapping(model)\n", "net.matrices = net.initialize_matrices(model)\n", "\n", - "ref_sol, cvgd = net.classical_solutions()\n", + "ref_sol, encoded_ref_sol, cvgd = net.classical_solutions()\n", "ref_sol / ref_values" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.987, 0.987, 0.987, 0.948])" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoded_ref_sol/ ref_values" + ] + }, + { + "cell_type": "code", + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -255,7 +289,7 @@ "array([ 0. , 1.766, 99.077, 0.652])" ] }, - "execution_count": 12, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -271,7 +305,27 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.766, 1.766, 86.797, 75.168])" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.convert_solution_from_si(ref_sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -286,12 +340,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 99, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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u3Kj/vG/fPqVZs2aKlZWV0r17dyUsLEwBlJiYGH2ZmTNnKq6urgqgzJ8/X1EURfH19VUWLVqkLxMbG6sMHz5csbW1Vezt7ZUxY8YoCQkJ+vPz589XWrdubRDLokWLFF9f30f/D3jMjHlOH3bv3j1l06ZNJn0GaztpU9OTNi3ZnTt3DL63lkZJbarT6ZT//e9/iqWlpfLZZ5+Vud5PP/1UCQgIMPhZFBAQoHz66adKSkqKvmxCQoLy9ddfK/Hx8WW6h6KU/PP7YSpFqdk7HKalpeHo6Ehqaqp+TvsDOTk5xMTE4O/vj5WVVanrfLBxlvKIRfHUajUqlUqfmYpHKygoICMjo0yLIdV0xj6nD6SkpBAeHk7Pnj2LXBVZlJ20qelJmxbvzz//5JdffqFDhw6EhISU+rri2jQtLY1nn32WsLAwAMaNG8fq1avL/NZAURSSk5NJT0/H3t4eFxcX8vLy0Gg0hcoZ80aipJ/fD5NXS0YICQnhl19+wdraWr8w28MeHHu4e00IIYQoC51Ox86dO9mwYQMFBQXcuXOn1GMXS3L06FHCwsIwNzdn0aJFRiUxgH6LHD8/P1xcXDh16hSLFy8uNAPWlMMqiiKJjJEGDBjAuXPn+Pjjj2nQoIHBuQYNGrB48WJu3rwpSYwQQgij3L17l+PHjwPQrVs3Jk6ciJmZWbnr7d+/P//97385cOAAL730UrkTjezsbMLCwvj111/JycnhxIkT5Y6xLCo1kfnwww8JCgrC3t4ed3d3Ro4cyaVLlwzK9OrVy2CDRJVKxcyZMyspYkOOjo68+OKLREVFcffuXWJiYrh79y5RUVHMmTPHYHaQEEIIURbu7u4MGzaMsWPH0rdvX/0SFabwyiuv0LlzZ5PUdfz4cS5cuIBaraZfv36Pfd+/Sh2EEB4ezqxZswgKCqKgoIA333yTAQMGcP78eYNR2M899xzvvfee/nNV2xzwQfeaqVbtFUIIUTvl5uYajDFp3bp1JUZTOsHBwdy5c4cuXbrg7e392O9fqYnMjh07DD4vX74cd3d3Tp48SY8ePfTHbWxsSlyS/mG5ubkGU4Uf7PWQkpJSaGBuXl4eOp2OgoICg0V6SuNBXQ+uF6Yh7VpYQUEBOp2OtLQ0cnJyynz9gxWlZUNM05E2Nb3a3qYFBQWEh4dz69Ytxo0bh6WlZbnrrKg2TUtLw87OzqCHqG/fvoDptkV4cJ/SqFKzlq5cuULDhg35888/adGiBXD/1dK5c+dQFAVPT0+GDRvG22+/XWyvzLvvvsuCBQsKHV+1alWhax7sJeTj42OSh0aIipCXl0dcXBwJCQmS3AlRA+Xn5xMTE6NfF8vPz69KztxSFIV79+5x48YN6tSpg5eXV4XeLysri4kTJz5y1lKVSWR0Oh3Dhw8nJSWFgwcP6o9/8803+Pr64u3tzZkzZ3j99dfp2LEjGzZsKLKeonpkfHx8iI2NLdQQeXl53LlzB19f3zJPa9XpdGRlZWFjY2PS95a1nbRrYTk5OcTGxlKnTh2jEu709HQiIyNp166dSbfJqM2kTU2vNrfppk2biI2NRaPRMHDgQPz8/Iyu69KlS2RnZ9OmTRuTtmlubi579+7l8uXLANSrV49Ro0ZV6PfptLQ0fH19H5nIVJmFOmbNmsXZs2cNkhiA6dOn6//esmVLvLy86Nu3L9HR0fodlx+m0WgKzWGH+8srF7WOTFJSEubm5mVes+TBb8am2itC3CftWpi5uTlqtRoHBwej1pF5wN7evkr+lledSZuaXm1s0xEjRvDLL78wbNiwcm1l8+OPPzJz5kxcXV05deqUPnkxRZvevHmTqKgoVCoVvXv3Jjg4uMJ/2Sxt/VXiV97Zs2ezZcsWfv/9d+rVq1di2U6dOgH3X0OJqkmlUrFp06Zy1fHMM88wcuRIk8RTlF69evHSSy+Vu54pU6ZUaJxCiJrnry9CnJ2dmTx5stFJTG5uLtOnT2fSpElkZmYSGBho8tfQdevWZciQITzzzDN07969SvWYV2okiqIwe/ZsNm7cyN69e/H393/kNQ82nKrod3NV2Z07d3j++eepX78+Go0GT09PQkJCCm3QKEzn2rVrqFSqQhueffrppyxfvrxSYhJCVD8pKSl89913xMbGmqxOMzMzLl++jEqlYv78+fz222+4u7uXq86UlBSSkpIMjrVv3/6RnQ2VoVL77mfNmsWqVavYvHkz9vb2+l2PHR0dsba2Jjo6mlWrVjF48GBcXV05c+YML7/8Mj169KBVq1aVGboB3f/PrtUqlcHfK0poaCh5eXmsWLGCBg0akJiYyJ49ewo9dKLiyVpBQojSunr1KuvWrSM7O5tt27Yxc+ZMk6x6a25uzurVqzl37hz9+vUrd31//vknW7duxdnZmWnTplX51/yV2iPz5ZdfkpqaSq9evfDy8tL/WbNmDQCWlpbs3r2bAQMG0KRJE1555RVCQ0P59ddfKzNsPUtLS8zMzLh2N5P3t5xn2orjvL/lPNfuZpZ6N9GySklJ4cCBA/z73/+md+/e+Pr60rFjR9544w2GDx+uL/fJJ5/QsmVLbG1t8fHx4YUXXiAjI0N/fvny5Tg5ObFlyxYaN26MjY0NTzzxBFlZWaxYsQI/Pz+cnZ2ZM2eOwZLYfn5+/POf/2TChAnY2tpSt25dlixZUmLMcXFxjB07FicnJ1xcXBgxYgTXrl3Tn9dqtcybNw8nJyc8PDx45513Htl+sbGxDBs2DGdnZ2xtbWnevDnbtm3Tnw8PD6djx45oNBq8vLz4+9//XmJXa1Gvw5ycnPS9LQ96C9u2bYtKpaJXr15A4VdLubm5zJkzB3d3d6ysrOjWrZt+ZU6Affv2oVKp2LNnDx06dMDGxoauXbsWWghSCFGzxMbG8uOPP5KdnY2XlxcTJkww6dL9Xl5e5U5i8vLy2LhxIxs2bCA3Nxdzc3Ojlnx43Cr91VJRf6ZMmQKAj48P4eHhJCUlkZOTQ1RUFAsXLixx9PLjZG1tzfLD1+j7STjfH7rGngu3+f7Q/c/LD1+rkGTGzs4OOzs7Nm3aZDA766/UajWfffYZ586dY8WKFezdu5fXXnvNoExWVhafffYZP//8Mzt27GDfvn2MGjWKbdu2sW3bNn744Qe+/vpr1q1bZ3Ddf/7zH1q3bs2pU6f4+9//zty5c9m1a1eRceTn5xMSEoK9vT0HDhzg0KFD2NnZMXDgQPLy8gD4+OOPWb58Od9//z379u0jJSWFzZs3l9gOs2bNIjc3l/379/Pnn3/y73//Gzs7O+D+oLTBgwcTFBTEH3/8wZdffsl3333H+++//8j2Lc6xY8cA2L17N/Hx8cXOmnvttddYv349K1asIDIyksDAQEJCQkhOTjYo99Zbb/Hxxx9z4sQJzM3NeeaZZ4yOTQhR9fn4+NCgQQPatGnD1KlTq+SAZpVKRXx8PCqVip49ezJ16lT999Uqrcz7alczJW0Dnp2drZw/f17Jzs4uc70FBVol+na64vf3LYrv64X/+P19ixJ9O13R6nSm+DIMrFu3TnF2dlasrKyUrl27Km+88Ybyxx9/lHhNWFiY4urqqv+8bNkyBVCuXLmiPzZjxgzFxsZGSU9P1x8LCQlRZsyYof/s6+urDBw40KDucePGKYMGDdJ/BpSNGzcqiqIoP/zwg9K4cWNF91A75ObmKtbW1srOnTsVRVEULy8vZeHChYqiKEp+fr5y584dpV69esqIESOK/XpatmypvPvuu0Wee/PNNwvdc8mSJYqdnZ2i1WoVRVGUnj17KnPnzi0y5gccHR2VZcuWKYqiKDExMQqgnDp1yqDM5MmT9XFmZGQoFhYWyk8//aQ/n5eXp3h7e+u/vt9//10BlN27d+vLbN26VQGKfQ7L85wqiqLcu3dP2bRpk3Lv3j2jrheFSZuaXm1o0/z8fIPvSxXNmDZNSEhQYmNjKy6oMijp5/fDqs6w42pGpVbxY0QsxXW6KAr8GGG6wVwPCw0N5datW/zyyy8MHDiQffv20a5dO4NBp7t376Zv377UrVsXe3t7Jk2aRFJSkn7BJbi/YvLDU9g9PDzw8/MzyMA9PDy4ffu2wf27dOlS6POFCxeKjPWPP/7gypUr2Nvb63uTXFxcyMnJITo6mtTUVOLj4/Wz0eD++9727duX2AZz5szh/fffJzg4mPnz53PmzBn9uQsXLtClSxeDbtvg4GAyMjK4ceNGifWWR3R0NPn5+QQHB+uPWVhY0LFjx0Lt8/AYrwcD1//azkKI6isqKop9+/YZHDM3N6/wnaDLIjU1lfPnzxsc8/DwoH79+pUUkXEkkTGSWqUiNjmrxDLXk7MqbNCvlZUV/fv35+233+bw4cNMmTKF+fPnA/dn2AwdOpRWrVqxfv16Tp48qR/H8uB1Dtz/IfswlUpV5LG/bu1QFhkZGbRv357Tp08b/Ll8+TITJ040ut5nn32Wq1evMmnSJP788086dOjA559/bnR9KpWq0KvA/Px8o+t7lIfb+cE3tvK0sxCialAUhfDwcFatWkV4eHi5lwqJjY3l9ddfN/n3h/Pnz/PVV1+xfv164uPjTVr34yaJjJF0ioKvS8mbV9Z3sdHPYqpozZo1IzMzE4CTJ0+i0+n4+OOP6dy5M40aNeLWrVsmu1dEREShz02bNi2ybLt27YiKisLd3Z3AwECDP46Ojjg6OuLl5cXRo0f11xQUFBAZGfnIOHx8fJg5cyYbNmzglVdeYenSpQA0bdqUI0eOGCQmhw4dwt7evtipg3Xq1DH4xxwVFWXQe/VgRd2HBz7/VUBAAJaWlgbT4PPz8zl+/DjNmjV75NcjhKjeFEVh3bp1+p6YDh06lGpZkeL8+uuvtG3bloULF/Lxxx+bLMZff/2VsLAwcnJy8PT0LHIR2epEEhkjKTqFpzr7UlyHi0oFT3X2Nfl9k5KS6NOnDz/++CNnzpwhJiaGsLAwFi5cyIgRIwAIDAwkPz+fzz//nKtXr/LDDz/w1VdfmSyGQ4cOsXDhQi5fvsySJUsICwtj7ty5RZZ98skncXNzY8SIERw4cICYmBj27dvHnDlz9K955s6dy0cffcSmTZu4ePEir7766iM3HnvppZfYuXMnMTExREZG8vvvv+uTqRdeeIG4uDhefPFFLl68yObNm5k/fz7z5s0rdhGnPn368L///Y9Tp05x4sQJZs6cadBr4u7ujrW1NTt27CAxMZHU1NRCddja2vL888/zt7/9jR07dnD+/Hmee+45srKymDZtWmmaVghRjalUKho0aICZmRnDhw9nyJAhmJmZGVXXhx9+yPDhw7l37x4dO3ZkzJgxJovR2toagG7duvHMM8+UazXhqqBqTw6vwhRFh7+bLe8MbcZ7W84bjJVRqeCdoc3wd7M1+ftQOzs7OnXqxKJFi/RjMnx8fHjuued48803gfvbvn/yySf8+9//5o033qBHjx58+OGHPP300yaJ4ZVXXuHEiRMsWLAABwcHPvnkE0JCQoosa2Njw/79+3n99dcZPXo06enp1K1bl759++pnn73yyivEx8czefJk1Go1Tz75JCNGjChxx1atVsusWbO4ceMGDg4ODBw4kEWLFgH3V6Dctm0bf/vb32jdujUuLi5MmzaNf/zjH8XW9/HHHzN16lS6d++Ot7c3n376KSdPntSfNzc357PPPuO9997jnXfeoXv37oXefwN89NFH6HQ6Jk2aRHp6Oh06dGDnzp04OzuXpmmFENVc+/btCQgIKPespM6dO6NWq3nxxRdZuHChSTc27t27N40bN8bHx8dkdVamKrNpZEVJS0vD0dGxyE2ncnJyiImJwd/fv8x72BQUFJCXl4e1tTUxdzP5MSKW68lZ1Hex4anOvhWSxFQFfn5+vPTSSyZZ3r8oBQUFZGRkYGdnV+UXYXpcyvOcwv21h8LDw+nZs2eVnPJZHUmbml51bFOdTsfRo0dp3769SRONBy5dukTjxo2Nvj4lJYXdu3ej0WgYMmRIldpWoDRK+vn9MPlJUQ55eXlYWlri52bLP4Y2M1jZtyYmMUIIIe7Lyspiw4YNREdHc/PmTUJDQ03+fb88SQzcX0n44sWLaLVaHB0d6dGjh4kiq1okkTGBh2cmVeTWBEIIISpfYmIiP//8MykpKZibm9OkSZMq98vr/v37+f3334H7kxlq8oQDSWREmTy8tYAQQtRGFhYW5OTk4OzszLhx4/Dw8KjskApp0KAB4eHhuLq6MnbsWNzc3Co7pAojiYwQQghRBi4uLjz55JO4urrqZwCVhaIoFd6DU69ePSZPnsypU6dq/HjD6jXyp4LU8PHOopqT51OIypWRkVFo0bh69eoZlcQkJiYyaNAg/WsfU8jIyGDt2rXcvXvX4HhV2ZewotXsNO0RHqwTkpWVZdQDKcTj8GBhvr+uuiyEqHg3b95kzZo1KIrC9OnTsbe3N7qu8PBwxo8fT0JCAleuXOHixYvl7i2Jiopi8+bNZGZmkpGRwdSpU6vceJ2KVqsTGTMzM5ycnPR73NjY2JT6AXgw/TonJ6fGd9s9TtKu/0dRFLKysrh9+zZOTk5GL6wlhDBOZGQk27ZtQ6vV4ubmZrDFS1kdPnyYPn36oNPpaNasGWFhYeX+Hnf+/HnCwsKA+4t2Dh06tNYlMVDLExkAT09PoOwb9ul0OnJycrCysqp2c/OrMmnXwpycnPTPqRDi8VAUhXPnzqHVamnSpAkjR44s11L+nTt3ZsCAAXh4eLBkyRJsbW3LHWPDhg2pU6cO/v7+9O/fv9b+8lc7v+qHqFQqvLy8cHd3L9MmgWlpaRw/fpygoKBa8x7ycZB2NWRhYSE9MUJUApVKRWhoKH/++ScdO3Ysd0+HWq1m48aNRi1q+YDyl3XKLCwseO6552r9a+dan8g8YGZmVqYfGDk5ORQUFGBpaVmuB1MYknYVQlSWlJQUg1WFbWxs6NSpk8nqL8/3tKysLDZv3oy/vz+dO3fWH6/tSQzIrCUhhBC1nKIoHD16lM8//5zz589XdjiFXL16lS+//JLLly+zb98+cnJyKjukKkV6ZIQQQtRa+fn5bNmyhTNnzgBw5cqVKrUKbnJyMj/++COKouDm5kZoaKj0Vv+FJDJCCCFqrUuXLnHmzBlUKhX9+/c3eG1TWoqikJGRUa6p2cVxcXGhS5cu5ObmEhISIq+SiiCJjBBCiFqrefPm3Lp1i4YNG+Lv71/m61NSUnjmmWdITk5m9+7d5Z45pCgKeXl5BjOk+vXrVyunVZeWJDJCCCFqDUVR0Gq1+oRDpVIxYMAAo+qKjIxkzJgxXL16FQsLC44fP06XLl2Mji07O5tff/2VzMxMJk+erF+CQpKYkkkiI4QQolbIy8tj8+bN+qnV5UkQtFot48eP5+rVq/j5+bF27VqCgoKMru/atWts2LCB9PR01Go1N2/exMfHx+j6ahNJZIQQQtR4SUlJrFmzhjt37qBWq0lMTCzXQpNmZmasXLmSjz/+mG+++QZnZ2ej69Jqtfzyyy+kp6fj4uJCaGgo3t7eRtdX20giI4QQokbTarX88MMPpKamYmdnx9ixY02yWnbnzp31WwSUh5mZGaNGjeLUqVMMHDgQS0vLctdZm0giI4QQokYzMzNj8ODBHDp0iCeeeKJCZheVhaIo3Lt3DxcXF/0xHx8feZVkJElkhBBC1HiNGjWiYcOGFTpwVlEUkpKSyMjIwM7ODldX10L3y8nJYevWrVy6dInp06fj5uZWYfHUFrKyrxBCiBrlzp07rFixgoyMDIPjFZXEpKSk8Omnnxps4linTh0aNmzIp59+SkpKCgDXr1/nq6++4uzZsxQUFHDjxo0Kiae2kURGCCFEjXH+/HmWLl3KtWvX2Llzp9H1nDp1qlTldu7cSb169Xj55Ze5evUqVlZWuLu7Y2VlxdWrV3n55ZepV68eO3fu5MyZM6SmpuLs7MwzzzxDmzZtjI5P/B9JZIQQQtQIp06dIiwsjPz8fPz9/Rk4cGCZ68jKyuKZZ56hXbt2bNu2rcSyO3fuZMiQIWRnZ9O1a1fWhoWRnp5OYmIi6enprA0Lo0uXLmRnZzNkyBDUajXBwcHMmDGDevXqGftlir+QMTJCCCFqhMaNG+Po6EizZs3o16+ffkG50rp48SJjxozh7NmzqNVqLl26xODBg4ssm5KSQmhoKIqiMH36dJYsWUJUYhr/2n6J2OQsfF1s6OXbiP379zNr1iyWLl3K2LFjuXHjhsGqvaL8JJERQghRI9jY2DBz5kyjN1XctWsXZ8+excPDg9WrV9O7d+9iy65YsYKsrCy6du3KkiVLWHEklve2nEdRwBwtXSyuo5gncToqji+++II///yTI0eOsHLlSubMmWPslyiKIK+WhBBCVEtnzpzh4sWLBsfKszP07Nmzef/99zl9+nSJSYyiKHz++ecAvPTyy0QlpumTGDdVJiM05wk0T0KnwO5zt4hKTOOll14G4LPPPkNRFKNjFIVJIiOEEKJa0Wq1bN++nY0bN7Jx40bu3btnknpVKhVvvfXWIxfLS0pKIjo6Go1Gw8gRI1hz4iYPchNLVQEO6lwydJZsz2vMmQIv1py4yahRI9FoNERHR5OcnGySeMV98mpJCCFEtZGXl8eqVauIjY0F7q+u6+Tk9FhjeDCt28HBAXNzc2KTs/TnbukcCc/z54bWkbz//yP2enIW5ubmODg4kJOTQ3p6Oq6uro815ppMEhkhhBDVhoWFBU5OTsTHxzNq1CiaNGny2GOws7MDIC0tjbNnz1LfzvD8Va1hklLfxYaCggLS0tIAKn1l4ZpGEhkhhBDVhkqlYsiQIXTv3r3SejVcXV1p3LgxjRs3Zv369dTxqota5YlOKbzgnkoF4zrUZePGTeTm5hIQEGCwNYEoPxkjI4QQosoqKCjg1KlTBgNkLSwsypzEbN68Wf86qrySk5N58sknadeuHYqi0NDfl38MbspfFw5WqeCdoc1o6OHA4sWLAJgzZ06FbpNQG0mPjBBCiCopLS2NtWvXcvPmTXJzc+ncuXOZ68jLy+Pvf/87ixYtomPHjhw4cKDcu0vb2tpia2vLrVu32LhxIwkJCXzxxRcEB7qy5sRNridnUd/FhnEd6tLQw4EXXniBiIgIbGxsePrpp8t1b1GYJDJCCCGqnNjYWMLCwsjMzMTKysqozRVv3brF6NGjOXr0KADdu3c3SW+IlZUVTz75JCdOnGDx4sUsXbqUP//8k5deepk3R43E3NycgoICNm7cxDOLFxEREYFKpWLDhg2PfWBybSCJjBBCiConJyeHzMxMPDw8GDduHM7OzmWuw9bWlqSkJJycnFixYgXDhw83KpZLly6Rn59PixYt9Mc8PDwYMmQIW7duJTQ0lCNHjnDkyBE0Gg0ODg6kpaWRm5sL3F+ob8OGDQwYMMCo+4uSSSIjhBCiymncuDFjx44lMDAQCwsLo+pwdHRk48aN2NnZ4efnV+br8/Pz+e233zhx4gQWFhZ4e3sXGqgbEhLCjRs3WLlyJZ999hnR0dHk5OQAEBAQwJw5c5g8eTKOjo5GfQ3i0SSREUIIUelSU1PJz883ONa0adNy1/twL0pZ5Obm8t1333Hnzh0AOnTogIODQ5FlnZycmDNnDi+++CLJycmkp6djb2+Pi4uLDOx9DCSREUIIUamio6NZt24dZmZmaLXayg4HAI1Gg4+PD9nZ2YwcOZKAgIBHXqNSqXB1dZXF7h4zSWSEEEJUCkVROHToEHv37kVRFGxsbMjLy6vssPRCQkLo06cPtra2lR2KKIGsIyOEEKJS5OTkcPz4cRRFoXnz5gQGBmJtbV0psURFRbF582aD9WosLS0liakGpEdGCCFEpbC2tmbs2LHEx8cTEBDA/v37S33t3r172b9/P++++265YigoKGDXrl0cO3YMAD8/P1q3bl2uOsXjJYmMEEKIxyYrKwsbGxv957p161K3bl1SUlJKdb1Wq+WDDz7g3XffRVEUOnbsyODBg42O5+effyY6OhqAjh070rx5c6PrEpVDEhkhhBAVTlEU9u3bx9GjR3n22WeNWuBOURRGjx7NL7/8AsCzzz5L7969yxVX586dSUhIYMSIETRs2LBcdYnKIWNkhBBCVKicnBxWr17N/v37yc3N5eLFi0bVo1KpGD58ODY2NqxcuZKlS5eWeUzNw2NgAAIDA5kzZ44kMdWYJDJCCCEq1IEDB4iKisLc3JwRI0bQrVs3o+t65plnuHTpEpMmTSrztVevXuXLL78kNTXV4Hh5914SlUteLQkhhKhQvXr1IikpiZ49e+Ll5VWuulQqFfXq1SvTNVqtlr1793L48GEAwsPDjd6uQFQ9ksgIIYQwKZ1Oh0ql0q9qa2Fhwfjx4ystnn379umTmPbt2xMSElJpsQjTq9RXSx9++CFBQUHY29vj7u7OyJEjuXTpkkGZnJwcZs2ahaurK3Z2doSGhpKYmFhJEQshhChJVlYWP/30k346c1UQHByMl5cX48aNY+jQoUbv3SSqpkpNZMLDw5k1axYRERHs2rWL/Px8BgwYQGZmpr7Myy+/zK+//kpYWBjh4eH6bdmFEEJULfHx8XzzzTdcvXqVffv26TdPLC2dTmeSOHJzcw0G9VpZWfHcc8/RpEkTk9QvqpZKfbW0Y8cOg8/Lly/H3d2dkydP0qNHD1JTU/nuu+9YtWoVffr0AWDZsmU0bdqUiIgIOnfuXBlhCyGE+Iv09HS+//57CgoKcHZ2Zvz48VhZWZX6+pMnT/Lyyy+zbt06OnToYHQc165dY8OGDfTt29dgYTvZvLHmqlJjZB6MJH+wTfrJkyfJz8+nX79++jJNmjShfv36HDlypMhEJjc3l9zcXP3ntLQ0AFJSUkyW7cP9f7QP/1eYhrSr6Umbmp60adHatm3LnTt3GDhwIJaWlqVa5E5RFL755hvefvtt8vPzefvtt1m9enWZ763Vajl69CjHjx8H4PDhw9SvX79WJzDV/Tl98PP7UapMIqPT6XjppZcIDg7Wb7uekJCApaUlTk5OBmU9PDxISEgosp4PP/yQBQsWFDp+6NAhg9UkTSUyMtLkdQpp14ogbWp60qaGFEXB0dGRiIiIUl+zfft2vv76awC6du3KpEmTCA8PL/O909LSuHr1KnD/l2EPD48ybXlQk1XX5zQrK6tU5apMIjNr1izOnj3LwYMHy1XPG2+8wbx58/Sf09LS8PHxITg4GAcHh/KGqZeenk5kZCTt2rXD3t7eZPXWdtKupidtanrSpvfHw/zxxx8MGDAAtdr44ZZBQUFEREQwevRoWrVqRfv27Y1u0/379+Pp6UmjRo2Mjqcmqe7PabXqkZk9ezZbtmxh//79BusDeHp6kpeXR0pKikGvTGJiIp6enkXWpdFo0Gg0hY47OTmZNJF5wN7evlCPkSg/aVfTkzY1vdrYpoqicPLkSbZv345Op9P/omgsJycnTpw4QUZGBuHh4aVu05ycHHQ6nUFPu6wNU7Tq+pyWNkGu1FlLiqIwe/ZsNm7cyN69e/H39zc43759eywsLNizZ4/+2KVLl7h+/TpdunR53OEKIUStt3fvXrZu3YpOp6Np06blGpj7gLl52X6nvn79Ol999RWbN28utOWAqH0qtUdm1qxZrFq1is2bN2Nvb68f9+Lo6Ii1tTWOjo5MmzaNefPm4eLigoODAy+++CJdunSRGUtCCFEJGjduTEREBD179iQ4OPixDqbV6XTs37+f/fv3oygKarWajIyMavnaRJhOpSYyX375JXB/+eqHLVu2jClTpgCwaNEi1Go1oaGh5ObmEhISwhdffPGYIxVCCAFQr1495s6di52d3WO/d3Z2NidOnEBRFFq1asXgwYOLHEogapdKTWRK0yVoZWXFkiVLWLJkyWOISAghxAOKonD8+HECAgJwdXXVHy9tEpORkYFarTbZjFFbW1tGjhxJdnY2LVu2NEmdovqT3a+FEEIUkp+fz8aNG9m+fTtr1qwhPz+/TNefO3eOoKAgZs+ebXQMubm53Lx50+BYYGCgJDHCQJWYtSSEEKLqSEtLY/Xq1SQkJKBSqWjXrl2ZBuSuWLGC559/nuzsbFJTU7l9+zbu7u5liuHmzZusX7+enJwcZs6cWSGzTkXNIImMEEIIA1ZWViiKgo2NDWPGjMHPz6/U1yYkJDB79myys7Pp378/P/74Y5mSGEVROHDgAPv27UOn0+Ho6EhmZqYkMqJYksgIIYQwYGlpybhx41Cr1Tg6OpbpWk9PT7799lsuX77Mm2++iZmZWZnvn5CQgE6no3nz5gwdOrRMezaJ2kcSGSGEqOVyc3OJi4sjMDBQf8zZ2dno+saNG2f0tSqViqFDh9K4cWNatmxZq/dKEqUjg32FEKIWu3v3Lt9++y2rV68mLi7usd8/Ly+v0F5A1tbWtGrVSpIYUSrSIyOEELXUpUuX2LhxI7m5udjb2z/2xOHWrVts2LCBpKQkQkJCHuu9Rc0hiYwQQtRScXFx5ObmUr9+fcaMGfNYF7k7fvw4O3bsQKfTYW9vj62t7WO7t6hZJJERQohaqk+fPjg6OtKuXbtSDcrNzc3l5MmTdO3atdz3trGx0e/XNHToUPLy8rh69Wq56xW1jyQyQghRSyQlJeHs7KzfVVitVhMUFFSqa2NiYhg7dixnz57l2LFj5V6Urnnz5tja2uLr64tKpSIvL69c9YnaSwb7CiFELXDu3Dm+/vpr9uzZU+ZrN23aRNu2bTlx4gQ2Njbcvn27TNfn5+eze/dusrKyDI77+fnJgF5RbtIjI4QQNZhOp2P37t0cOXIEuL9Gi1arLdP6Lrt37yY1NZXOnTuzZs0a6tevX+prExMTWb9+PXfu3OHu3buMHz++zF+DECWRREYIIWqw27dvc/ToUQC6du1K37599a+WSuvjjz+mQYMGzJ49G0tLy1Jfd+nSJcLCwtBqtdjZ2ZX6NZYQZSGJjBBC1GCenp4MGTIEjUZD8+bNjapDo9Ewb968Ml9Xt25dNBoN9erVY/jw4TIzSVQISWSEEKKGycvLM+g5adeuXaXEYWdnx3PPPYejo6OMhREVRgb7CiFEDaHVatm2bRvffffdY58FVFBQwI4dO7h48aLBcScnJ0liRIWSHhkhhKgBMjIyCAsL4/r16wBcuXKFZs2aPZZ73759mw0bNpCYmMiZM2fw9/dHo9E8lnsLIT0yQghRA2zatInr16+j0WiYMGFCqZIYrVbLxx9/TFpamtH3vXv3LkuXLiUxMREbGxtGjhwpSYx4rKRHRgghaoDBgwezadMmRowYgaur6yPLJyQkMHHiRH7//XeOHz/O6tWrjXoF5OrqSqNGjcjNzWXkyJGPdZsDIUASGSGEqJYURTFIPFxcXJg6dWqpkpGIiAhGjhxJYmIitra2jBgxokxJzMP3VqlUjBw5EnNzcxkLIyqFvFoSQohqJjU1lWXLlhEXF2dwvLSJhKenJ7m5ubRo0YITJ04wYcKEUl2n1WrZtWsXmzZtMjhuYWEhSYyoNNIjI4QQ1ci1a9cICwsjKyuLLVu2MHPmzDInEX5+fuzevZumTZtiY2NTqmuSkpJYv3498fHxAAQFBVGvXr0yxy+EqUkiI4QQ1URsbCwrV65EURQ8PT0ZO3as0T0h7du3L3XZgoICVqxYQXp6OtbW1gwfPlySGFFlSCIjhBDVhI+PD35+ftjb2zN06FAsLCwey33Nzc0ZMGAAkZGRjBw5EgcHh8dyXyFKQxIZIYSoJtRqNRMmTHgsA2v/ujpwixYtaN68uYyFEVWODPYVQogq6sqVKxw4cMDgWEUPrNVqtezZs4clS5aQlZVlcE6SGFEVSY+MEEJUMYqicPDgQfbu3QuAt7c3AQEBpbruk08+wdramhdeeKHM901OTmbDhg3cvHkTgHPnzsmO1aLKk0RGCCGqEEVR2LBhA2fPngXub/jo6+v7yOvu3bvHlClT+OWXX7CwsKB///40bNiwTPfevXs3N2/exMrKiqFDhxq9W7YQj5MkMkIIUYWoVCp8fHy4cOECgwYNKtXsooyMDNq3b09MTAyWlpYsXryYwMDAMt978ODBKIrCwIEDcXR0NCZ8IR47SWSEEKKKCQoKIjAwEBcXl1KVt7OzY+zYsYSFhREWFka7du1Kdd29e/dwdnY2qGfcuHFGxSxEZZHBvkIIUYl0Oh1Hjx4lPz9ff0ylUpU6iXng/fffJzIyslRJjE6nY9++fXz++edcunSpzDELUZVIj4wQQlSS7OxsNmzYwJUrV7h58yajRo0yemaQubl5qV4HpaSksGHDBv32BjExMTRu3NioewpRFUgiI4QQleD27dv8/PPP3Lt3D3NzcwIDAx/L9OarV68SFxeHRqNhyJAhtGzZssLvKURFkkRGCCEqgZmZGVlZWTg5OTF27Fi8vLwey33btm1Lamoqbdq0MRgfI0R1ZVQik5iYyKuvvsqePXu4ffs2iqIYnNdqtSYJTgghaipXV1cmTpyIm5vbIzduLCgowNzcuN87b926haurKxqNBrg//qZ3795G1SVEVWTUv4wpU6Zw/fp13n77bby8vGS1RyGEeITMzEwyMjLw8PDQH6tfv36J1yiKwvfff89//vMfDh8+XKYBwDqdjkOHDrFv3z5atmzJyJEjjQ1diCrNqETm4MGDHDhwgDZt2pg4HCGEqHlu3brFmjVrUBSF6dOnY2dn98hrMjMzef755/nhhx8A+OKLL/jHP/5RqvulpaWxYcMGYmNjgfs9OlqtFjMzM+O/CCGqKKMSGR8fn0Kvk4QQQhR2+vRptmzZglarxcXFhdzc3FIlMnPnzuWHH35ArVbzwQcf8Nprr5X6njqdjoSEBCwsLBg8eDCtW7eWnnNRYxmVyCxevJi///3vfP311/j5+Zk4JCGEqBkUReGPP/5Aq9XSqFEjRo0ahZWVVamufe+994iMjGTx4sX06NGjTPd1cnLiiSeewNnZGVdXV2NCF6LaMCqRGTduHFlZWQQEBGBjY4OFhYXB+eTkZJMEJ4QQ1ZlKpeKJJ57gjz/+oEuXLmXqFfH29ubkyZOluiY+Pp78/HyDMTfGbFEgRHVkdI+MEEKIwtLS0nBwcNB/trW1pWvXrkbV9agkRlEUjhw5wp49e7C1tWXmzJmPnAElRE1jVCIzefJkU8chhBDVmqIonDhxgp07d/LEE0/QpEmTCr1fbm4ua9eu5erVqwDUq1dPxsGIWsnoBfG0Wi2bNm3iwoULADRv3pzhw4fLqHghRK1TUFDA1q1bOX36NACXLl2q8ETG0tISuL81wcCBA2nXrp0kMqJWMiqRuXLlCoMHD+bmzZv6PTo+/PBDfHx82Lp1KwEBASYNUgghqrLz589z+vRpVCoVffv2feSrpLi4OOrWrYtabfy+vSqVipEjR5Kbm4ubm5vR9QhR3Rn1r2jOnDkEBAQQFxdHZGQkkZGRXL9+HX9/f+bMmWPqGIUQokpr2bIlHTt25MknnyQ4OLjEnpH169fTokULFi5cWKZ7JCYmcujQIYNj9vb2ksSIWs+oHpnw8HAiIiIMVpl0dXXlo48+Ijg42GTBCSFEVaQoCjqdTv9ZpVIxaNCgEq/Jzc3lb3/7G59//jkAO3bs4G9/+9sjX8crisKxY8fYtWsXWq2WOnXq0KhRo/J/EULUEEb1yGg0GtLT0wsdz8jI0L+3FUKImigvL48dO3YQFxdXpoVBIyMjWbJkCQCvvfYau3btKlUSExYWxo4dO/Rr0dStW7dc8QtR0xjVIzN06FCmT5/Od999R8eOHQE4evQoM2fOZPjw4SYNUAghqork5GTWrFnD7du3Abh7926pd5Du0qUL//3vf2nYsCFDhw4t1TUqlYoGDRpw+fJlBgwYQFBQkAzoFeIvjEpkPvvsMyZPnkyXLl30i+EVFBQwfPhwPv30U5MGKIQQVYFWq2XlypWkpqZiY2ODt7c3derUKVMdL7/8cpnv2759ewICAkqdMAlR2xiVyDg5ObF582aioqK4ePEiAE2bNpWVJIUQNZaZmRkDBw7k0KFDhISEcPLkSZPf486dO+zdu5eRI0ei0WiA+70yksQIUTyj15EBaNiwIQ0bNjRVLEIIUaU1adKExo0bk5qaatJ6Hyym99tvv1FQUMDevXsfOXhYCHFfqROZefPm8c9//hNbW1vmzZtXYtlPPvmk3IEJIURlunv3Ljt27GDUqFHY2trqj1fEGJXff/+dAwcOABAQEED37t1Nfg8haqpSJzKnTp0iPz9f/3chhKipLl68yMaNG8nLy2Pnzp2MHj26xPK3bt3i5s2bBAUFGXW/tm3bcvLkSbp3706nTp1kQK8QZVDqROb3338v8u/lsX//fv7zn/9w8uRJ4uPj2bhxIyNHjtSfnzJlCitWrDC4JiQkhB07dpjk/kII8VenT59m8+bNAPj6+jJgwIASy//222889dRTmJmZcfr0aTw8PB55D0VRDJIVZ2dn5s6dK8tXCGEEo9aReeaZZ4pcRyYzM5Nnnnmm1PVkZmbSunVr/doKRRk4cCDx8fH6P6tXrzYmZCGEKJXAwEDs7e3p1KkTkyZNws7OrshyWq2Wf/3rXwwcOJA7d+7g6elJVlbWI+tPSkri22+/5dq1awbHJYkRwjhGJTIrVqwgOzu70PHs7GxWrlxZ6noGDRrE+++/z6hRo4oto9Fo8PT01P+R0ftCiIpkZ2fH888/z8CBA0tcsE6lUhEZGYmiKMyYMYMjR47g7+9fbHlFUTh16hRff/01t27dYufOnWVaUE8IUbQyzVpKS0tDURQURSE9PR0rKyv9Oa1Wy7Zt23B3dzdpgPv27cPd3R1nZ2f69OnD+++/j6ura7Hlc3Nzyc3NNYgZICUlxWBJ8fJ60CNVVM+UMJ60q+lJm5bs0qVLWFhY0KBBA4PjD38f+av09HTUajUff/wxkZGRjBo1ipycHHJycoq9Jjo6mi1btgBQr149QkJCTD77qTqT59T0qnubPvj5/SgqpQy/EqjV6hIHoalUKhYsWMBbb71V2ioNrv3rGJmff/4ZGxsb/P39iY6O5s0338TOzo4jR44U+5vSu+++y4IFCwodX7VqFTY2NmWOSwhRMymKwq1bt7hz5w5qtZrGjRvr126pqPtdvXoVOzs73N3dZUCvEI+QlZXFxIkTSU1NxcHBodhyZUpkwsPDURSFPn36sH79eoNNIy0tLfH19cXb29uogItKZP7q6tWrBAQEsHv3bvr27VtkmaJ6ZHx8fIiNjS2xIcoqPT2dyMhI2rVrh729vcnqre2kXU1P2rSw/Px8fvnlF27cuAFAhw4d6NKlC2p16d62l6ZNtVotiqJgbv5/Hd9/HeQr/o88p6ZX3ds0LS0NX1/fRyYyZXq11LNnTwBiYmKoX7/+Y/8H2aBBA9zc3Lhy5UqxiYxGoynytyonJyeTJjIP2Nvb4+TkZPJ6aztpV9OTNv0/iqLg7OzM7du3GTlyJE2bNjWqnuLaNDk5mY0bN1K3bl1Z2K6M5Dk1verapqX9xcKolX1jY2OJjY0t9nyPHj2MqfaRbty4QVJSEl5eXhVSvxCidlCpVAwbNowePXrg5uZmsnoVReHMmTNs27aNvLw8kpKS6NGjh8GCekII0zIqkenVq1ehYw/3zmi12lLVk5GRwZUrV/SfY2JiOH36NC4uLri4uLBgwQJCQ0Px9PQkOjqa1157jcDAQEJCQowJWwhRS2m1Ws6ePUurVq3036ssLCyKTWKSkpJYtGgR7777rsGroUdJT09ny5YtFBQUUL9+fUaPHi1JjBAVzKhE5t69ewaf8/PzOXXqFG+//TYffPBBqes5ceIEvXv31n9+sPXB5MmT+fLLLzlz5gwrVqwgJSUFb29vBgwYwD//+c8KHZAnhKhZ0tPTCQsLIy4ujry8vEeuvnvkyBHGjRtHXFwcarWa9957r9T3cnBwICQkhKysLLp161bqrnEhhPGMSmQcHR0LHevfvz+WlpbMmzev1LvC9urVq8R1FHbu3GlMeEIIAcD169cJCwsjIyMDjUZT5Peuh33//ffMmDGDgoICGjZsSGhoaInldTodqampBvV26NDBJLELIUrHpL8ueHh4cOnSJVNWKYQQRsvIyCAjI4M6derw3HPP0ahRoxLLN2vWDIBx48Zx4sQJWrduXWzZvLw81q1bx8qVK8nLyzNp3EKI0jOqR+bMmTMGnxVFIT4+no8++og2bdqYIi4hhCi3Zs2aERoaSqNGjUq1BUDnzp05ceKEwViaoly6dImLFy+i0+mwtLQkMTERHx8fU4YuhCgloxKZNm3aoFKpCr0W6ty5M99//71JAhNCiLJKTU3FwsLCYPHLFi1alKmOknph4P7rpNOnT6PT6fD09GTs2LGydYoQlcioRCYmJsbgs1qtpk6dOgZbFgghxOMUExPDunXr8PDw4KmnnqqwgbZqtZqBAweyY8cOxowZI0mMEJXMqETG19fX1HEIIYRRFEXhyJEj7N69G0VRyMnJITs722TTnnU6HXFxcQbf9xwdHfH09JRZSUJUAaVOZD777LNSVzpnzhyjghFCiLLKyckhIiICRVFo3bo1Q4YMwcLCwiR1p6amsnHjRmJjY5kyZYr8EidEFVTqRGbRokWlKqdSqSSREUI8NtbW1owZM4b4+HiCgoKKHKSbnp7O888/z8SJExk8eHCp6r1w4QK//PILOTk5WFhYkJGRYerQhRAmUOpE5q/jYoQQorJkZ2djbW2t/+zj41PsrKE///yTJ554gsuXL7N7925iYmIMri1OUlISOTk5eHt7M3r0aFxdXU0WvxDCdIwaI/OwBzOXZEdXIURFUxSF/fv3c/ToUZ599llcXFxKLH/u3Dk6duxITk4OdevWZc2aNaVKYgCCg4OxtramTZs2mJmZmSJ8IUQFMHqk2sqVK2nZsiXW1tZYW1vTqlUrfvjhB1PGJoQQejk5OaxZs4Z9+/aRnZ3N+fPnH3lNs2bNGDx4MAMHDuT06dMEBwcXWU5RFCIjIykoKNAfU6lUtG/fXpIYIao4o3pkPvnkE95++21mz56t/8Zw8OBBZs6cyd27d3n55ZdNGqQQQuzfv59Lly5hZmbGkCFDaNu27SOvUalU/PDDD1hZWRU7wyg9PZ1NmzZx9epVbt++zcCBA00duhCiAhmVyHz++ed8+eWXPP300/pjw4cPp3nz5rz77ruSyAghTK5Xr17cvXuXnj17Urdu3VJf9/DieH/1YO2ZrKwszM3NqVOnjilCFUI8RkYlMvHx8XTt2rXQ8a5duxIfH1/uoIQQ4q/j7ywtLZk4caJJ72Fra0teXh6enp6Ehobi5uZm0vqFEBXPqDEygYGBrF27ttDxNWvW0LBhw3IHJYSo3bKzs/npp584efJkhd7H3d2dSZMmMW3aNElihKimjOqRWbBgAePGjWP//v36MTKHDh1iz549RSY4QghRWgkJCaxZs4aUlBRu3LhBixYtit3+5K/TsEuiKArHjh3Dx8cHb29v/fH69eubJG4hROUwqkcmNDSUo0eP4ubmxqZNm9i0aRNubm4cO3aMUaNGmTpGIUQtkZaWxvfff09KSgpOTk5MmTKlyCQmJyeHF154gd69e5OXl/fIejMzM1m9ejU7duxg/fr15OfnV0T4QohKYPQ6Mu3bt+fHH380ZSxCiFrOwcGBoKAgEhMTCQ0NLbK3JTo6mjFjxnDq1CkAdu/eXeJqvXfv3mX58uVkZmZiZmZGp06dMDcv9xJaQogqokz/mgsKCtBqtWg0Gv2xxMREvvrqKzIzMxk+fDjdunUzeZBCiNqjb9++AEVOl1YUhfHjx3Pq1ClcXV358ccfHzld2tnZGScnJ2xtbQkNDcXd3b1C4hZCVI4yJTLPPfcclpaWfP3118D99ReCgoLIycnBy8uLRYsWsXnz5lLvZSKEqN1u3rzJiRMnGDZsmD5xKWlHaZVKxdKlS3n11VdZtmxZsdsSPMzMzIxx48ZhZWVlss0khRBVR5nGyBw6dIjQ0FD955UrV6LVaomKiuKPP/5g3rx5/Oc//zF5kEKImufUqVMsW7aM06dPExERUerr2rRpw+7du4tMYhRF4fjx4xw4cMDguL29vSQxQtRQZeqRuXnzpsH06j179hAaGoqjoyMAkydPZtmyZaaNUAhR4+zdu1efbDRu3Jh27dqVu86srCx++eUXLl26hEqlomHDhnh6epa7XiFE1VamHhkrKyuys7P1nyMiIujUqZPBednqXgjxKIGBgZiZmdG7d2/9a5/yyM/P55tvvtFvYdC/f388PDxMFK0QoiorUyLTpk0b/caQBw4cIDExkT59+ujPR0dHG6zPIIQQRalfvz5z5syhR48e+pV7y8PCwoL27dvj5ubGs88+S5cuXUxSrxCi6ivTq6V33nmHQYMGsXbtWuLj45kyZQpeXl768xs3bix2d1khRO2kKAonT56kQYMGuLi46I87ODgUKnvhwgWaNm1a6nofTlaCg4Pp3LmzjIURopYpU49Mz549OXnyJHPmzGHZsmUsXbrU4HybNm1kw0ghhF5+fj6bN29m69atrF27ttiF6AoKCnjjjTdo3rw569atK7FORVH0A4ULCgr0x9VqtSQxQtRCZV4VqmnTpsX+xjR9+vRyBySEqBnS09NZvXo18fHxqFQqWrVqVeRCdLdu3WLChAns378fgBMnTvDEE08UWWd2djZbtmzh/PnzwP2ZT0FBQRX3RQghqjxZ3lIIUSEsLS0pKCjA2tqaJ554ggYNGhRZ7rfffmP//v3Y29vz7bffMnbs2GLr3LBhA1euXEGtVtOnTx86dOhQUeELIaoJSWSEEBVCo9Ewfvx41Go1Tk5OxZabPHky169fZ8KECQbLOxSlX79+pKWlMXz4cOrWrWviiIUQ1ZEkMkIIk8jLy+PGjRsGPS8PD+4tjkql4p133im2TktLS/1nDw8PZs6cKTOShBB6Ru1+LYQQD0tOTua7775j1apV3Lhxo9z1KYrCH3/8weLFi4mPjzc4J0mMEOJhRvXIZGdns2vXLi5fvgxAo0aN6N+/f5E71QoharbLly+zYcMGcnNzsbOzQ1GUctWXk5PD1q1bOXv2LADHjh1jxIgRpghVCFEDlTmR+eWXX3j22We5e/euwXE3Nze+++47hg0bZrLghBBV37Vr18jNzcXHx4cxY8Zgb29frvqOHz/O2bNnUalU9OrVi27dupkoUiFETVSmRObw4cM88cQTDB8+nFdeeUU/Dfv8+fN8/PHHPPHEE4SHh9O5c+cKCVYIUfX069cPBwcHgoKCMDMzMzh36NAh6tSpQ6NGjUpdX9euXUlISKBz586l2t1aCFG7lWmMzPvvv8/UqVNZt24dXbp0wcnJCScnJ7p27cr69euZMmUK7733XkXFKoSoAu7du4dOp9N/VqvVdO7c2SCJ0el0LFy4kJ49ezJ27FiDPdr+Ki0tzaA+MzMzxowZI0mMEKJUypTIREREMHv27GLPz5o1iyNHjpQ7KCFE1XT+/Hm++uor9u3bV2yZlJQURowYweuvv45Wq6VFixYGicrDzp49yxdffKFfDE8IIcqqTK+WsrOzi9wf5QFHR0dycnLKHZQQomrR6XTs3buXQ4cOAXDjxg10Oh1qdeHfhSwtLYmJiUGj0fD555/z7LPPFppplJuby44dOzh9+jQAMTEx9OjRo8j6hBCiJGVKZBo2bMjevXuZOnVqkef37NnzyAWthBDVz+3bt/W9rZ07d6Z///7FJh02NjasW7eOnJwc2rRpU2SZu3fv8scff6BSqejevTs9e/aUJEYIYZQyJTJTp07l1VdfxcPDg8GDBxuc27p1K6+99hpvvvmmSQMUQlQ+T09PBg0ahEajoWXLlo8s36RJkxLP161bl8GDB1OnTh18fX1NFaYQohYqUyIzd+5cDh8+zNChQ2ncuDFNmzZFURQuXLhAVFQUI0eO5KWXXqqgUIUQj1N+fr7BbtLl2dcoLS2NgoICg5V+ZZ8kIYQplKkvV61WExYWxurVq2ncuDEXL17k0qVLNGnShJ9++on169dL97AQ1ZxWq2XHjh18//335Ofnl7u+Cxcu8OWXXxIWFkZBQYEJIhRCiP9j1Mq+48aNY9y4caaORQhRyTIyMli3bh2xsbEAREVF0axZM6Pqys/PZ8eOHURGRgL3fxHKzs4u94J5QgjxMKMSmaSkJFxdXQGIi4tj6dKlZGdnM2zYMHr06GHSAIUQj8/GjRuJjY3F0tKSUaNGFRrrcvr0aTZu3MiCBQtKVV9cXBwAwcHB9O7du9CCeUIIUV5lSmT+/PNPhg0bRlxcHA0bNuTnn39m4MCBZGZmolarWbRoEevWrWPkyJEVFK4QoiINGjSITZs2MXLkSNzc3PTHFUVh6dKlzJkzh9zcXJo0acKECRNKrMvCwoLQ0FCysrLw9/ev6NCFELVUmQa0vPbaa7Rs2ZL9+/fTq1cvhg4dypAhQ0hNTeXevXvMmDGDjz76qKJiFUKY2F83eHRzc2PatGkGSQzA888/z4wZM8jNzWXo0KGEhIQUqis9PZ0LFy4YHPPw8JAkRghRocqUyBw/fpwPPviA4OBg/vvf/3Lr1i1eeOEF1Go1arWaF198kYsXL1ZUrEIIE0pLS2PlypXcvHnT4PhfF68D6NmzJ2ZmZixcuJDNmzcbzD4CuHTpEl999RXr1q0jPj6+QuMWQoiHlenVUnJyMp6engDY2dlha2uLs7Oz/ryzszPp6emmjVAIYXKxsbGEhYWRmZnJr7/+yowZM4pMYB6YMGECHTt2JCAgwOC4oijs2LGDY8eOAffXm3l4yrYQQlS0Mg/2/es3u5K++Qkhqp7Y2FhWrlyJTqfD3d2dsWPHlurf8V+TGLj/79/c/P63kS5dutCnTx/9ZyGEeBzK/B1nypQpaDQaAHJycpg5cya2trbA/f1ThBBVW7169fDx8cHe3p5hw4ZhaWlZrvr69OlDo0aNZIVeIUSlKFMiM3nyZIPPTz31VKEyTz/9dPkiEkJUKDMzMyZOnIiFhUWZe1QzMzM5cuQIffr00S9+aWZmJkmMEKLSlCmRWbZsWUXFIYSoINevX+f27dsGxx7uhVEUpVQJzZUrV9i0aROZmZlYWFjQs2dPk8cqhBBlJfsJCFFDKYrCoUOH2LRpE7du3eL69euFyly+fJng4GD+/PPPEus6ePAgP/30E5mZmbi7u9O0adOKClsIIcqkTD0ybdu2LfI3N0dHRxo1asTcuXONXs5cCGE6iqKwYcMGzp49C4CLiwve3t4GZdasWcOzzz5LRkYGs2fPJjw8vNj6/Pz8UKvVtG/fnv79+8vMJCFElVGmRKa4FXtTUlKIjIykbdu27N27l+DgYFPEJoQwkkqlom7dupw/f56ePXuSnJxsMJto7dq1jB8/HoAePXqwevXqEuurV68es2bNKrR+jBBCVLYyJTLz588v8fxbb73FO++8w549e8oVlBCi/Dp16kRgYCDm5uaFeltGjBhBUFAQ/fr147333jNIcrKysti+fTs9e/Y0WOFXkhghRFVk0jEyEydOfOS79oft37+fYcOG4e3tjUqlYtOmTQbnFUXhnXfewcvLC2tra/r160dUVJQpQxaiRlAUhRMnTpCfn68/plKpCm018IBGo+HgwYP861//Mkhirl69ypdffsnZs2fZvHlzoS0MhBCiqjFpImNmZoZOpyt1+czMTFq3bs2SJUuKPL9w4UI+++wzvvrqK44ePYqtrS0hISHk5OSYKmQhqr2cnBzWrFnD1q1b2bp1a6mTj7+uH3Px4kV++OEHMjIycHNzY/DgwbLgpRCiyjPpEpwbNmwo02DfQYMGMWjQoCLPKYrC4sWL+cc//sGIESMAWLlyJR4eHmzatEn/fv+vcnNzDRbmS0tLA+6P4ylLkvUoD7ZikC0ZTEvatWySk5P59ddfSUlJwczMDHd3d1JTUw3KlLZNXV1d9YOCe/TogYWFBSkpKRUVerUmz6npSZuaXnVv0wc/vx+lTInMZ599VuTx1NRUTp48ydatW9m+fXtZqixWTEwMCQkJ9OvXT3/M0dGRTp06ceTIkWITmQ8//JAFCxYUOn7o0CFsbGxMEtvDIiMjTV6nkHYtrZycHNLS0rCwsMDf3587d+4UO/vor236oOfm4V6XunXrYmZmxuHDhysu6BpEnlPTkzY1verapllZWaUqV6ZEZtGiRUUed3BwoHHjxuzfv58uXbqUpcpiJSQkAODh4WFw3MPDQ3+uKG+88Qbz5s3Tf05LS8PHx4fg4GAcHBxMEhvcz3AjIyNp164d9vb2Jqu3tpN2LbvmzZvj4uKiT9Tz8/PJy8vTbx1SVJvm5OSwd+9evLy8aNu2baXFXl3Jc2p60qamV93btEJ6ZGJiYowK5nHSaDT6vaAe5uTkZNJE5gF7e3ucnJxMXm9tJ+1atKysLDIyMnB3d9cfe7id4uLiGD9+PPXq1ePnn3826G150KaxsbFs2LCBtLQ0rl27RufOnbG2tn6cX0aNIc+p6Umbml51bdMH26A8slx5bnL37t1SZ0xl5enpCUBiYqLB8cTERP05IWqT+Ph4li5dyqpVq8jMzCx0fvv27bRt25bDhw+zY8eOIn/xSElJYcWKFaSlpeHi4sLkyZMliRFCVGtlTmRSUlKYNWsWbm5ueHh44OzsjKenJ2+88Uap32eVhr+/P56engZr0qSlpXH06FGTvb4Soro4c+YM33//PSkpKajVarKzsw3Op6Wl8dRTT5GUlES7du2IjIykQYMGhepxcnKiS5cutGnThhkzZlC3bt3H9SUIIUSFKNOrpeTkZLp06cLNmzd58skn9futnD9/ns8//5xdu3Zx8OBBzpw5Q0REBHPmzCmxvoyMDK5cuaL/HBMTw+nTp3FxcaF+/fq89NJLvP/++zRs2BB/f3/efvttvL29i11hWIiaSFEUTp48SUFBAYGBgYwePbpQL4qDgwPLli1j586dfPzxx1hZWemvfXhtGYB+/frJtGohRI1RpkTmvffew9LSkujo6EKDcN977z0GDBjApEmT+O2334qd4fSwEydO0Lt3b/3nB4N0J0+ezPLly3nttdfIzMxk+vTppKSk0K1bN3bs2KH/Ji1EbaBSqRgzZgynT5+ma9euxb43Hj58OMOHD9d/zsnJYevWrSQnJ1OnTh2D+oQQoqYoUyKzadMmvv7660JJDNwf07Jw4UIGDx7M/PnzmTx58iPr69WrV4mLd6lUKt577z3ee++9soQpRLWXkZGBnZ2d/rOdnR3dunUr9fVxcXFs2LCBlJQUVCpVhSw9IIQQVUGZxsjEx8fTvHnzYs+3aNECtVr9yD2ZhBDFi4yM5NNPP+Xy5ctGXa/T6di0aRMpKSk4OTkxZswYg6RICCFqkjIlMm5ubly7dq3Y8zExMQbTQoUQpVdQUMCvv/7Kr7/+SkFBARcuXDCqHrVazciRI2nVqhUzZszAy8vLxJEKIUTVUaZEJiQkhLfeeou8vLxC53Jzc3n77bcZOHCgyYITojY5d+6cfgXOPn366Me76HQ6jh8/XuK1f91KwMfHh1GjRsl4MiFEjVfmwb4dOnSgYcOGzJo1iyZNmqAoChcuXOCLL74gNzeXlStXVlSsQtRorVq1Ii4ujiZNmhAYGAjAnTt3mDRpEnv37uXQoUMEBQUZXJObm8uOHTs4d+4cM2bMwNXVtTJCF0KISlOmRKZevXocOXKEF154gTfeeMNgr5b+/fvzv//9j/r161dIoELUNIqioCiKfhaSSqVi6NCh+vOHDh1i3Lhx3Lx5E2tra65du2aQyNy8eZP169dz7949VCoV165dk0RGCFHrlHn3a39/f7Zv3869e/eIiooCIDAwEBcXF5MHJ0RNlZ+fz5YtW1Cr1QwfPrzIKdFbtmzh5s2bNG7cmLCwMFq2bGlw/tSpU9y7dw8HBwdGjx6Nr6/v4wpfCCGqjDInMg84OzvTsWNHU8YiRK2QkpLCmjVrSEhIQKVS0blz5yKXNHjvvfewtbVl7ty5RW74NmDAACwsLOjRo4dsMyCEqLWMTmSEEGWn1WpZvnw5qamp2NjYMGbMmCKTGAALCwv+8Y9/6D/HxsZSv359fe+NpaUlISEhjyVuIYSoqsq1aaQQomzMzMzo378/devWZfr06fj5+T3ymry8PH799VeWL1/OsWPHKj5IIYSoRqRHRojHrHnz5jRt2rRUW9THx8ezfv16kpKSAEy6MasQQtQE0iMjRAVKSkpi1apVhRKQ0iQxcD9xSUpKwt7enqefftpgbzIhhBCSyAhRYS5fvszSpUuJiopix44d+uOKovDdd9+RnJz8yDoCAgIYOXIkM2fOxN/fvyLDFUKIakkSGSEqwOnTp1m9ejW5ubn4+PjQv39/ANLS0hg/fjzPPvsskydPRqfTGVx3+fJlUlNTDY61bt1aNn0UQohiyBgZISpAQEAAdnZ2NG3alJCQEMzMzDh//jwjRozgypUrmJub06dPH/0MpPz8fHbt2sXx48fx9fXl6aefLvXrJyGEqM0kkRGiAtjb2zNz5kxsbW31x5ycnEhNTcXHx4e1a9fSuXNnAJKTk/n555+5c+cOAF5eXvpVs4UQQpRMEhkhTODcuXNoNBr9HkmAQRID4O3tzbZt2/D39zfYSsDGxoa8vDxsbW0ZNWoUAQEBjy1uIYSo7iSREaIcdDode/bs4fDhw1hZWTFz5kwcHR2LLd+hQ4dCx6ysrBg/fjz29vaFkh8hhBAlk0RGCCPl5eWxZs0arl69CkC7du2K3Ergr65cuUJOTg4tWrTQH/P09KywOIUQoiaTREYII1lYWGBlZYWFhQXDhw83SEyKUlBQwO7duzl69CgWFhZ4e3vLZqtCCFFOksgIYSSVSsWIESNISUnB3d29xLJ5eXl8//33JCYmAtCmTZtS9d4IIYQomczvFKKUtFotZ8+eNThmaWmJu7s7P/30E4sXLy72WktLS+rWrYuNjQ0TJkxg8ODBWFhYVHDEQghR80mPjBClkJGRQVhYGNevXyc3N5f27dsDkJ2dzdy5c1m6dClmZmb06NGDdu3aFVlHSEgIvXr1kp4YIYQwIUlkhHiEGzdusHbtWtLT09FoNNjZ2QH3F7Hr1q0bkZGRqFQq3nrrLVq3bg3A1atXOXv2LMOGDdMvemdpaYmlpWWlfR1CCFETSSIjxCOkpKSQnp6Om5sb48aNw83NDbg/2HfkyJHExcXx008/0b9/f7RaLbt27eLw4cMA1K9fnzZt2lRi9EIIUbNJIiPEI7Ro0QKdTkfjxo3RaDQG59566y2mT5+Oh4cHAGvXruXy5csAtG/fnubNmz/2eIUQojaRREaIv0hLS8PCwgJra2v9sVatWhVZVq1W65MYgKCgIOLi4hg2bBhNmzat8FiFEKK2k0RGiIdcu3aNdevW4enpycSJEx+5caOiKPoxMACBgYHMnTu3UM+NEEKIiiHTr4XgfkISERHBypUryczMJCMjg+zs7BKviY2N5ZtvviEtLc3guCQxQgjx+EiPjBBATk4OBw8eRFEUWrZsybBhwzAzMyuyrFarJTw8XF9+7969jBw58vEGLIQQApAeGSEAsLa2ZuzYsYSEhDBq1Ch27txJq1at9CvxPmz//v0cOHAARVFo06YNgwcProSIhRBCgCQyohbLyckx+Fy/fn3at2/P66+/zrBhwzh37hz/+te/Cl3XuXNnPDw8eOKJJxgxYoSsDSOEEJVIEhlR6yiKwsGDB/n888+5d++ewbm3336b//znPwDMmTOHhQsXkp+fj6Io+jLW1tbMmDFDplYLIUQVIImMqFVyc3MJCwtjz549ZGVlFdo76dVXX6V58+asW7eOTz/9lNu3b/PFF19w5swZg3IPz1QSQghReWSwr6hV9u3bx4ULF1Cr1QwePFi/Z9IDbm5u/PHHH6hUKsLDwwkPD0dRFI4cOUKrVq0kgRFCiCpGEhlRq/Tq1Yvbt2/Tu3dv6tWrV2QZMzMzrl69yr59+wBo2bIlgwcPliRGCCGqIElkRI321wXrNBoNkyZNeuR1DRo0oFOnTnh7exe7qq8QQojKJ4mMqLFycnLYsGEDTZo0oV27diWWzc3NRafTGWxLMHDgwIoOUQghRDlJIiNqpNu3b7NmzRqSk5O5fv06zZo1IyMjAycnJ8zNDR/7mzdvsmHDBtzc3Bg/fry8QhJCiGpEZi2JGictLY1vv/2W5ORkHB0dmTx5MkePHqVVq1YsWLBAX06n03Hw4EG+//57kpOTSUhIID09vRIjF0IIUVaSyIgax8HBgfbt29OgQQOeffZZli9fTp8+fYiPj2fTpk36hfBycnKIiIhAp9PRvHlzZs6ciYODQyVHL4QQoizk1ZKokfr37w/A+fPneeedd9DpdEyePJklS5ZgZWUFgI2NDSNHjiQ9PZ02bdrIKyUhhKiGJJER1V58fDwnTpxg6NCh+mRErb7f2diiRQsWLVqEra0tTz75JHfv3sXW1lZ/bWBgYKXELIQQwjQkkRHV2h9//MGWLVsoKCjAzc2NLl26FCoze/Zs4uPj+eabb8jKypJXSEIIUYNIIiOqrd9//539+/cD0LBhQ9q2bVuozINVeffs2YNOp8Pe3p709HRJZIQQooaQREZUW/7+/hw8eJDu3bvTs2fPYse43Lx5E51OR5MmTRg2bBg2NjaPOVIhhBAVRRIZUW35+fnx4osv4uTkVGwZlUrF0KFDCQwMlAG9QghRA8n0a1EtKIpCZGQkKSkp+mOpqalERUUZlMvPz+f06dMGx6ytrWnbtq0kMUIIUQNJj4yo8goKCti2bRunTp3C09OTadOmcebMGcaMGUNKSgqnT5/Gx8eHxMRE1q9fz507d1Cr1bJHkhBC1AKSyIgqLT09nZ9//plbt26hUqlo3rw5X3/9NfPmzSMvLw8/Pz/u3bvHnTt32LZtG1qtFltbW4Mp1kIIIWouebUkqjRLS0tyc3OxtrbmySefJDg4mL1795KXl8eIESOIjIykVatWaDQatFotDRs25PnnnycgIKCyQxdCCPEYSI+MqNI0Gg3jx4/HzMwMZ2dnAL777jsGDBjA9OnT9eNemjdvjrW1Nf7+/jIWRgghahFJZESVkp+fT0ZGhsExNzc3g892dnY0atSI7Oxsg6nUDRo0eCwxCiGEqDokkRFVxr1791i7di1JSUkkJiYWOa36zp07rF+/nsTERBITExk3bpz0wAghRC1WpcfIvPvuu6hUKoM/TZo0qeywRAW4cuUK33zzDXfv3sXMzIyCgoJCZaKiovjmm29ITEzExsaGdu3aSRIjhBC1XJXvkWnevDm7d+/WfzY3r/IhCyNcuXKFnJwcPD09cXFxoW7duoXKeHl5YWlpia+vLyNGjMDe3r4SIhVCCFGVVPmswNzcHE9Pz8oOQ1Sw/v37Y2VlxZ49e3B1dS2yjJ2dHdOmTcPZ2Vl6YoQQQgDVIJGJiorC29sbKysrunTpwocffkj9+vWLLZ+bm0tubq7+c1paGgApKSnodDqTxZWenm7wX1E2aWlp2Nvb6xOSy5cv88ILL3DhwgWGDx9Ohw4dOHjwIF5eXgZTqdVqNampqZUVdrUkz6rpSZuanrSp6VX3Nn3w8/tRVIqiKBUci9G2b99ORkYGjRs3Jj4+ngULFnDz5k3Onj1b7GuFd999lwULFhQ6vmrVKtkssIpITU0lNjYWd3d3PD09OXr0KIsWLSInJwcnJyfmzZuHRqMhOzsbMzMzmjVrhpmZWWWHLYQQ4jHKyspi4sSJpKam4uDgUGy5Kp3I/FVKSgq+vr588sknTJs2rcgyRfXI+Pj4EBsbW2JDlFV6ejqRkZG0a9dOxmqUkqIoREREcOzYMQDq1q3L6NGjOXfuHP3796djx47885//ZP/+/eh0OjQaDf369SMwMLCSI6/e5Fk1PWlT05M2Nb3q3qZpaWn4+vo+MpGp8q+WHubk5ESjRo24cuVKsWU0Gg0ajabIa02ZyDxgb29f4u7L4v/Ex8dz/PhxADp27MiAAQMwMzOje/fuHDhwgHbt2pGWlsapU6ewt7dnzJgxFfL/rLaSZ9X0pE1NT9rU9Kprm6rVpZtYXa0SmYyMDKKjo5k0aVJlhyKM4OXlRUhICFZWVrRu3Vp/XFEUgoKCAFCpVNSvX59evXpJEiOEEOKRqnQi8+qrrzJs2DB8fX25desW8+fPx8zMjAkTJlR2aKKUCgoKDKbMd+rUSf93rVZLeHg46enpjBgxQn/czMys1Jm4EEKI2q1KJzI3btxgwoQJJCUlUadOHbp160ZERAR16tSp7NDEI+h0Onbv3k1sbCxTp04ttP7PvXv32LBhAzdu3ACgXbt2+Pj4VEaoQgghqrEqncj8/PPPlR2CMEJmZibr168nJiYGuD+1ulmzZvrzWq2W5cuXk5aWhkajYdiwYZLECCGEMIr03wuT27BhAzExMVhYWJCZmcn58+cNzpuZmdG/f3/q16/PzJkzad68eSVFKoQQorqr0j0yonoKCQlh7dq1bNq0iT179uDo6Ej37t3x8PDQl2nRogXNmzeXFXqFEEKUi/TICJPLzc1l/vz57NmzB2dnZ/7973+zevVqsrKyDMpJEiOEEKK8JJER5ZKens4PP/xAfHy8/li9evUYPHgwPXr04IMPPiAhIYHU1FT+/PPPSoxUCCFETSSJjDBaXFwc33zzDVevXmXz5s08WCRapVLx9ddf88ILL3D79m0sLS0ZNWqUwdRrIYQQwhRkjIwwSmxsLCtXrkSn01GnTh3GjBlj8KrIxsaGIUOGoNPpGDhwIM7OzpUYrRBCiJpKEhlhlHr16lG3bl3s7e0ZMWIElpaWpKam4ujoqC9jZ2cnixcKIYSoUJLICKOYmZnx5JNPYmlpiaIoHDx4kN9//52xY8fSuHHjyg5PCCFELSFjZESpXLt2jYiICADy8vKA+xt0Phjsu2fPHnQ6HVFRUZUZphBCiFpGemREiRRFISIigl27dqEoCjt37mTz5s0cOnQIa2trrly5wrVr17CwsGDQoEG0adOmskMWQghRi0giI4qlKAqbNm3izJkzANy+fZtvvvmGgoIC1q1bx6RJk2jbti0pKSm0bt0aV1fXSo5YCCFEbSOvlkSxVCoV7u7uqNVqrl69yhdffEG9evVYtmwZkyZN0pfp06ePJDFCCCEqhfTIiBJ17dqVhg0bcvfuXXJycmjevDlOTk6VHZYQQggBSI+MeIiiKERGRlJQUKA/plKpsLGx4eTJkzRr1gxFUcjJyUGr1VZipEIIIcR90iMjgPv7I23atImLFy9y48YNhg8frj+Xn5/PzZs3MTc3JyQkhPbt28s+SUIIIaoESWQEd+/eZc2aNdy9exczMzPq1atncN7Z2ZnQ0FCcnJyoU6dOJUUphBBCFCaJjECn05Gamoq9vT1jx47F0tKSuLg4fHx89GUaNmxYiREKIYQQRZNERpCZmcn48eOpU6cOFy5c4LfffsPGxoaZM2diY2NT2eEJIYQQxZLBvrVQdnY2d+/eRVEUPvvsMxo3bsz+/fv59ddf2b59O1qtFk9PT/1u1kIIIURVJT0ytUxiYiJr1qxBq9USERHB2rVrAdi7dy/9+/fHzMyM/v3707FjRxnQK4QQosqTRKYWOXv2LL/88gv5+flYWFiwd+9eLCws+O9//8uLL75IRkYGWVlZeHh4VHaoQgghRKlIIlNL6HQ6jh07Rn5+PgEBAfTs2ZP8/HwGDRpEx44dAbC3t8fe3r6SIxVCCCFKTxKZWkKtVjNmzBgiIyOxsbFh5cqVALJKrxBCiGpNEpkaLDMzE1tbW/1ne3t7kpKS2LdvHwABAQF4eXlVUnRCCCFE+cmspRrq1KlTfPrpp1y5csXgeP369VGr1QwYMIAnn3xSXiUJIYSo1qRHpobRarXs2LGDEydOAPcH+AYGBurPt2/fHn9/f9mtWgghRI0gPTI1zJ9//qlPYjp27Eh+fj65ubn68yqVSpIYIYQQNYb0yNQwp0+f5syZM2g0GiwsLMjPz8fGxoYhQ4ZUdmhCCCGEyUmPTDWnKAo6nY7s7GymTZvG1KlTSUpKonHjxuTn5+Pn50f37t0rO0whhBCiQkiPTDVWUFDA1q1b9YN3//jjD1QqFb1798ba2pquXbvStWtX1GrJV4UQQtRMkshUU6mpqaxZs4b4+Hjg/niYtWvXcu3aNfr06UNubi4ajaaSoxRCCCEqliQy1ZBWq2XZsmWkpqai0WhwcHAgOzubBg0a0KBBAwBJYoQQQtQK8s6hGjIzM6Nv3744OTmhKAp37txh+/btslu1EEKIWkd6ZKopjUZDSkoKcH+Ru1GjRslu1UIIIWodSWSqgeTkZHbt2sXw4cOxtrYGoGHDhgQEBODj40P37t1lQK8QQohaSRKZKi4qKop169aRl5eHubk5oaGhwP2F7Z588knphRFCCFGrSSJThZ0+fZrNmzcDkJubS3R0tMF5SWKEEELUdvI+ogrbvHkzOTk5FBQUoNFoKCgoICMjo7LDEkIIIaoMSWSqsObNm2NmZoa5uTne3t48//zz2NnZVXZYQgghRJUhr5aqkAsXLqDRaPRrwTzxxBNoNBpsbW3p2bMnZmZmlRyhEEIIUbVIIlMF6HQ6fv/9dw4ePIiVlRUzZ87E0dERgGHDhlVydEIIIUTVJYlMJcvLyyMsLIwrV64A9zeBtLS0rOSohBBCiOpBxshUMgsLC7Kzs/WfdTodt2/frsSIhBBCiOpDemSqgAdbC3h5eREaGoqrq2slRySEEEJUD9Ij85jpdDoOHz7MoEGDOH78OCqViieeeIJu3boxbdo0SWKEEEKIMpAemccoMzOTb775hrS0NBISEpg2bRqnT5/G2dmZvn37VnZ4QgghRLUjPTKPyc2bN/nss89IS0tDURQCAwNZu3at7JEkhBBClIP8FH1Mzp07R15eHnB/TMzrr79OkyZNKjkqIYQQonqTV0tG0ul0wP39kLy8vGjYsGGJvSs2NjYAuLm5MXbsWOrUqfNY4hRCCCFqMumRKaPY2FhGjx5N586dAZg2bRpNmjRBo9EwevRoYmNjAUhPTycnJ0d/XXBwMIMGDWLGjBmSxAghhBAmIolMGXzwwQf4+fmxceNGtFotAAEBATg5OVFQUMDGjRvx8/Pjn//8J//73/9Yv369fmq1SqWiY8eOmJtLJ5gQQghhKpLIlNIHH3zAP/7xDwCef/55tm3fDsDatWtJSk7melwcM2bMIDg4GK1WS15eHjdv3iQrK6sywxZCCCFqNElkSiE2NlafxKxatYolS5aQb3F/F+r3tpzj/S3nybV05IUXXqB///6oVCp0Oh3NmzfH1ta2MkMXQggharRqkcgsWbIEPz8/rKys6NSpE8eOHXus93/55ZeB+z0x48ePZ/nhazz/YyQAx2LusXTPWVp1G8Csv72FWq1Gq9Xy1Vdf8f333z/WOIUQQojapsonMmvWrGHevHnMnz+fyMhIWrduTUhIyGPbj0in0/Hrr78C8Mabb3L1bibvbTmP8v/PZ9+6RPzyuWRHRXBwzw4CmrdlytRnuH37Nr/88ot+dpMQQgghTK/KJzKffPIJzz33HFOnTqVZs2Z89dVX2NjYPLbejqioKAoKCnBycqJu3br8FBGLogA6HdHR0fRzuIMdOZg7eeL51H+5YuGPn5+vfgBwdHT0Y4lTCCGEqI2q9BSavLw8Tp48yRtvvKE/plar6devH0eOHCnymtzcXHJzc/Wf09LSAEhJSTGqdyQ+Pp4GDRoQEBBAWmoqKakpNLDOoQuXSE/Px8bGmq79h5PcaAjmVrakpqaQlppKUFAQ0dHR3Lx5U6Zbl0F6errBf0X5SZuanrSp6Umbml51b9MHP78fRaU8mB9cBd26dYu6dety+PBhunTpoj/+2muvER4eztGjRwtd8+6777JgwYJCx1etWqVflK48cnNzuXTpkj4pcnR0xNfXV7YaEEIIIUwoKyuLiRMnkpqaioODQ7HlqnSPjDHeeOMN5s2bp/+clpaGj48PwcHBJTZEcXQ6HZ07d0ar1bJt+3ZyzWw4eiEWO7Lx9anHluQ63D6rAkAFfPlUO8zzMhgyeBBmZmZERERIklMG6enpREZG0q5dO+zt7Ss7nBpB2tT0pE1NT9rU9Kp7m5a2R6ZKJzJubm6YmZmRmJhocDwxMRFPT88ir9FoNGg0mkLHnZycjEpkAFq0aMHGjRv5dPFilixZQpNug/nxYBTzXFXcvqniRqYKlQreGdqM5n5ePP/881y9epXRo0fj4uJi1D1rO3t7e5ycnCo7jBpF2tT0pE1NT9rU9Kprm5a2E6BKdxVYWlrSvn179uzZoz+m0+nYs2ePwaumiqIoCseOHaNHjx60bt2aL7/8klWrVvFsn2YsebojAJ38nXkm2I/d83oypasfq1at4uuvvwbuD1QWQgghRMWp0j0yAPPmzWPy5Ml06NCBjh07snjxYjIzM5k6dWqF3jcrK4sVK1bop3mPGjWKixcv8tRTT3HgwAFeevn+66u3hzbHwdGRGzdu8vzzr+qTmH/961/4+vpWaIxCCCFEbVflE5lx48Zx584d3nnnHRISEmjTpg07duzAw8Ojwu6ZmprKf/7zHywsLID7r6uefPJJzM3N+cc//sHXX3/Nrl27+OSTTxg7dizHjx8nJSVFf/2//vUvg5lWQgghhKgYVfrV0gOzZ88mNjaW3Nxcjh49SqdOnSrsXufPn6dr166Eh4eTk5ODmZkZL774Ij4+Prz11ltcu3aN0aNHY2ZmBkB0dDQpKSmYm5vrd7+WJEYIIYR4PKp8j8zjoigKKpWKjRs3cv78eby9vZk/fz59+vQxGHDk6+vL+vXrSU5O5sCBA3z33XfUrVuXgIAAmZ0khBBCPGaSyABnzpwhIiICb29vXn/9dbKzs3nxxRdLfH31IGlp06ZNtRwNLoQQQtQEtTqRyc3NZdu2bZw5cwa4v4qvm5sb77//fiVHJoQQQojSqNWJzIYNG7h8+bL+c4MGDWjdunUlRiSEEEKIsqjViUyfPn1ITEwkLS2Nbt260atXLxnnIoQQQlQjtSqRyc/P10+pBvDw8GDu3LmkpKTg7OxciZEJIYQQwhi1JpG5cOECu3fvpk2bNnTr1g1HR0cAVCqVJDFCCCFENVVr3qNs2rSJgoICTpw4wdq1aykoKKjskIQQQghRTrUmkXlAURQaNmyoX9BOCCGEENVXjX+1pCgKAMnJydSpU4fQ0FACAgJIT08vV71paWlkZWWRlpYmA4RNSNrV9KRNTU/a1PSkTU2vurdpWloa8H8/x4ujUh5Vopq7ceMGPj4+lR2GEEIIIYwQFxdHvXr1ij1f4xMZnU7HrVu3sLe3R6VSmazetLQ0fHx8iIuLw8HBwWT11nbSrqYnbWp60qamJ21qetW9TRVFIT09HW9v7xJ7lGr8qyW1Wl1iJldeDg4O1fIBqeqkXU1P2tT0pE1NT9rU9Kpzmz6YYVyS6vfSTAghhBDi/5NERgghhBDVliQyRtJoNMyfPx+NRlPZodQo0q6mJ21qetKmpidtanq1pU1r/GBfIYQQQtRc0iMjhBBCiGpLEhkhhBBCVFuSyAghhBCi2pJERgghhBDVliQyRlqyZAl+fn5YWVnRqVMnjh07VtkhVVvvvvsuKpXK4E+TJk0qO6xqZ//+/QwbNgxvb29UKhWbNm0yOK8oCu+88w5eXl5YW1vTr18/oqKiKifYauJRbTplypRCz+7AgQMrJ9hq4MMPPyQoKAh7e3vc3d0ZOXIkly5dMiiTk5PDrFmzcHV1xc7OjtDQUBITEysp4qqvNG3aq1evQs/pzJkzKyli05NExghr1qxh3rx5zJ8/n8jISFq3bk1ISAi3b9+u7NCqrebNmxMfH6//c/DgwcoOqdrJzMykdevWLFmypMjzCxcu5LPPPuOrr77i6NGj2NraEhISQk5OzmOOtPp4VJsCDBw40ODZXb169WOMsHoJDw9n1qxZREREsGvXLvLz8xkwYACZmZn6Mi+//DK//vorYWFhhIeHc+vWLUaPHl2JUVdtpWlTgOeee87gOV24cGElRVwBFFFmHTt2VGbNmqX/rNVqFW9vb+XDDz+sxKiqr/nz5yutW7eu7DBqFEDZuHGj/rNOp1M8PT2V//znP/pjKSkpikajUVavXl0JEVY/f21TRVGUyZMnKyNGjKiUeGqC27dvK4ASHh6uKMr9Z9LCwkIJCwvTl7lw4YICKEeOHKmsMKuVv7apoihKz549lblz51ZeUBVMemTKKC8vj5MnT9KvXz/9MbVaTb9+/Thy5EglRla9RUVF4e3tTYMGDXjyySe5fv16ZYdUo8TExJCQkGDw3Do6OtKpUyd5bstp3759uLu707hxY55//nmSkpIqO6RqIzU1FQAXFxcATp48SX5+vsFz2qRJE+rXry/PaSn9tU0f+Omnn3Bzc6NFixa88cYbZGVlVUZ4FaLGbxppanfv3kWr1eLh4WFw3MPDg4sXL1ZSVNVbp06dWL58OY0bNyY+Pp4FCxbQvXt3zp49i729fWWHVyMkJCQAFPncPjgnym7gwIGMHj0af39/oqOjefPNNxk0aBBHjhzBzMysssOr0nQ6HS+99BLBwcG0aNECuP+cWlpa4uTkZFBWntPSKapNASZOnIivry/e3t6cOXOG119/nUuXLrFhw4ZKjNZ0JJERlW7QoEH6v7dq1YpOnTrh6+vL2rVrmTZtWiVGJkTJxo8fr/97y5YtadWqFQEBAezbt4++fftWYmRV36xZszh79qyMhzOh4tp0+vTp+r+3bNkSLy8v+vbtS3R0NAEBAY87TJOTV0tl5ObmhpmZWaFR9ImJiXh6elZSVDWLk5MTjRo14sqVK5UdSo3x4NmU57ZiNWjQADc3N3l2H2H27Nls2bKF33//nXr16umPe3p6kpeXR0pKikF5eU4frbg2LUqnTp0AasxzKolMGVlaWtK+fXv27NmjP6bT6dizZw9dunSpxMhqjoyMDKKjo/Hy8qrsUGoMf39/PD09DZ7btLQ0jh49Ks+tCd24cYOkpCR5douhKAqzZ89m48aN7N27F39/f4Pz7du3x8LCwuA5vXTpEtevX5fntBiPatOinD59GqDGPKfyaskI8+bNY/LkyXTo0IGOHTuyePFiMjMzmTp1amWHVi29+uqrDBs2DF9fX27dusX8+fMxMzNjwoQJlR1atZKRkWHwG1ZMTAynT5/GxcWF+vXr89JLL/H+++/TsGFD/P39efvtt/H29mbkyJGVF3QVV1Kburi4sGDBAkJDQ/H09CQ6OprXXnuNwMBAQkJCKjHqqmvWrFmsWrWKzZs3Y29vrx/34ujoiLW1NY6OjkybNo158+bh4uKCg4MDL774Il26dKFz586VHH3V9Kg2jY6OZtWqVQwePBhXV1fOnDnDyy+/TI8ePWjVqlUlR28ilT1tqrr6/PPPlfr16yuWlpZKx44dlYiIiMoOqdoaN26c4uXlpVhaWip169ZVxo0bp1y5cqWyw6p2fv/9dwUo9Gfy5MmKotyfgv32228rHh4eikajUfr27atcunSpcoOu4kpq06ysLGXAgAFKnTp1FAsLC8XX11d57rnnlISEhMoOu8oqqi0BZdmyZfoy2dnZygsvvKA4OzsrNjY2yqhRo5T4+PjKC7qKe1SbXr9+XenRo4fi4uKiaDQaJTAwUPnb3/6mpKamVm7gJqRSFEV5nImTEEIIIYSpyBgZIYQQQlRbksgIIYQQotqSREYIIYQQ1ZYkMkIIIYSotiSREUIIIUS1JYmMEEIIIaotSWSEEEIIUW1JIiOEEEKIaksSGSEEAIcOHaJly5ZYWFjItgXF2LdvHyqVqtCmhmV17do1VCqVfs8bIYTxJJERopqbMmUKKpUKlUqFhYUF/v7+vPbaa+Tk5JSpnnnz5tGmTRtiYmJYvnx5xQRbibRaLR999BFNmjTB2toaFxcXOnXqxLfffluh950yZUqhxNDHx4f4+HhatGhRofcWojaQTSOFqAEGDhzIsmXLyM/P5+TJk0yePBmVSsW///3vUtcRHR3NzJkzqVevntFx5OXlYWlpafT1FWnBggV8/fXX/O9//6NDhw6kpaVx4sQJ7t2799hjMTMzw9PT87HfV4iaSHpkhKgBNBoNnp6e+Pj4MHLkSPr168euXbv053U6HR9++CH+/v5YW1vTunVr1q1bB/zfa46kpCSeeeYZVCqVvkfm7NmzDBo0CDs7Ozw8PJg0aRJ3797V19urVy9mz57NSy+9hJubm37X59JcN2fOHF577TVcXFzw9PTk3XffNfiaUlJSmDFjBh4eHlhZWdGiRQu2bNmiP3/w4EG6d++OtbU1Pj4+zJkzh8zMzGLb6JdffuGFF15gzJgx+Pv707p1a6ZNm8arr76qL5Obm8ucOXNwd3fHysqKbt26cfz48WLrfPfdd2nTpo3BscWLF+Pn56c/v2LFCjZv3qzvNdu3b1+Rr5bCw8Pp2LEjGo0GLy8v/v73v1NQUFCmNhOiNpJERoga5uzZsxw+fNigZ+TDDz9k5cqVfPXVV5w7d46XX36Zp556ivDwcP1rDgcHBxYvXkx8fDzjxo0jJSWFPn360LZtW06cOMGOHTtITExk7NixBvdbsWIFlpaWHDp0iK+++qpM19na2nL06FEWLlzIe++9p0++dDodgwYN4tChQ/z444+cP3+ejz76CDMzM+B+79HAgQMJDQ3lzJkzrFmzhoMHDzJ79uxi28XT05O9e/dy586dYsu89tprrF+/nhUrVhAZGUlgYCAhISEkJyeX+f8DwKuvvsrYsWMZOHAg8fHxxMfH07Vr10Llbt68yeDBgwkKCuKPP/7gyy+/5LvvvuP99983KFdSmwlRa1X29ttCiPKZPHmyYmZmptja2ioajUYBFLVaraxbt05RFEXJyclRbGxslMOHDxtcN23aNGXChAn6z46OjsqyZcv0n//5z38qAwYMMLgmLi5OAZRLly4piqIoPXv2VNq2bWtQprTXdevWzaBMUFCQ8vrrryuKoig7d+5U1Gq1vvxfTZs2TZk+fbrBsQMHDihqtVrJzs4u8ppz584pTZs2VdRqtdKyZUtlxowZyrZt2/TnMzIyFAsLC+Wnn37SH8vLy1O8vb2VhQsXKoqiKL///rsCKPfu3VMURVHmz5+vtG7d2uA+ixYtUnx9ffWfJ0+erIwYMcKgTExMjAIop06dUhRFUd58802lcePGik6n05dZsmSJYmdnp2i1WkVRHt1mQtRWMkZGiBqgd+/efPnll2RmZrJo0SLMzc0JDQ0F4MqVK2RlZdG/f3+Da/Ly8mjbtm2xdf7xxx/8/vvv2NnZFToXHR1No0aNAGjfvr1R17Vq1crgnJeXF7dv3wbg9OnT1KtXT1+2qNjOnDnDTz/9pD+mKAo6nY6YmBiaNm1a6JpmzZpx9uxZTp48yaFDh9i/fz/Dhg1jypQpfPvtt0RHR5Ofn09wcLD+GgsLCzp27MiFCxeKjMNULly4QJcuXVCpVPpjwcHBZGRkcOPGDerXrw+U3GZC1FaSyAhRA9ja2hIYGAjA999/T+vWrfnuu++YNm0aGRkZAGzdupW6desaXKfRaIqtMyMjg2HDhhU5YNjLy8vg3sZcZ2FhYXBOpVKh0+kAsLa2LjauB/eYMWMGc+bMKXTuwQ/9oqjVaoKCgggKCuKll17ixx9/ZNKkSbz11lsl3q+k+hRFMTiWn59vVF2lUVKbCVFbSSIjRA2jVqt58803mTdvHhMnTqRZs2ZoNBquX79Oz549S11Pu3btWL9+PX5+fpibl/5bhbHXPaxVq1bcuHGDy5cvF9kr065dO86fP69P3ozVrFkzADIzMwkICNCP9fH19QXuJyXHjx/npZdeKvL6OnXqkJCQgKIo+t6Uv64NY2lpiVarLTGOpk2bsn79eoN6Dh06hL29fblmkQlRG8hgXyFqoDFjxmBmZsaSJUuwt7fn1Vdf5eWXX2bFihVER0cTGRnJ559/zooVK4qtY9asWSQnJzNhwgSOHz9OdHQ0O3fuZOrUqSX+YDb2uof17NmTHj16EBoayq5du4iJiWH79u3s2LEDgNdff53Dhw8ze/ZsTp8+TVRUFJs3by5xsO8TTzzBokWLOHr0KLGxsezbt49Zs2bRqFEjmjRpgq2tLc8//zx/+9vf2LFjB+fPn+e5554jKyuLadOmFVlnr169uHPnDgsXLiQ6OpolS5awfft2gzJ+fn6cOXOGS5cucffu3SJ7bF544QXi4uJ48cUXuXjxIps3b2b+/PnMmzcPtVq+TQtREvkXIkQNZG5uzuzZs1m4cCGZmZn885//5O233+bDDz+kadOmDBw4kK1bt+Lv719sHd7e3hw6dAitVsuAAQNo2bIlL730Ek5OTiX+cDX2ur9av349QUFBTJgwgWbNmvHaa6/pE6FWrVoRHh7O5cuX6d69O23btuWdd97B29u72PpCQkL49ddfGTZsGI0aNWLy5Mk0adKE3377Td9z9NFHHxEaGsqkSZNo164dV65cYefOnTg7OxdZZ9OmTfniiy9YsmQJrVu35tixYwbTuQGee+45GjduTIcOHahTpw6HDh0qVE/dunXZtm0bx44do3Xr1sycOZNp06bxj3/8o9TtJURtpVL++oJXCCGEEKKakB4ZIYQQQlRbksgIIYQQotqSREYIIYQQ1ZYkMkIIIYSotiSREUIIIUS1JYmMEEIIIaotSWSEEEIIUW1JIiOEEEKIaksSGSGEEEJUW5LICCGEEKLakkRGCCGEENXW/wOirwpXyIQ9JQAAAABJRU5ErkJggg==", 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" ] @@ -301,93 +365,824 @@ } ], "source": [ - "net.plot_solution_vs_reference(sol, ref_sol)" + "import matplotlib.pyplot as plt \n", + "plt.scatter(ref_values, encoded_ref_sol, c='black', s=100, label='Best solution')\n", + "plt.scatter(ref_values, sol, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", + "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.grid(which=\"major\", lw=1)\n", + "plt.grid(which=\"minor\", lw=0.1)\n", + "plt.xlabel('Reference Solution')\n", + "plt.ylabel('QUBO Solution')\n", + "plt.legend()\n", + "# plt.xlim([0.01,0.1])\n", + "# plt.ylim([0.01,0.1])\n", + "# plt.loglog()" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "66" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.qubo.qubo_dict.num_variables" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.000978)\n", - "\n", - "\n", - "Error (%): [ 0. 0. -10.607 -1.298 2.712 3.543]\n", - "\n", - "\n", - "sol : [ 1. 1. 1.953 1.789 84.442 72.505]\n", - "ref : [ 1. 1. 1.766 1.766 86.797 75.168]\n", - "diff: [ 0. 0. -0.187 -0.023 2.354 2.663]\n", + "cons:\n", + "mass_balance[J1]: ((expected_demand[J1]-flow[P1])+flow[P2])\n", + "mass_balance[D1]: (expected_demand[D1]-flow[P2])\n", + "approx_darcy_wesibach_headloss[P1]: (((((-(dw_resistance_0[P1]))-(dw_resistance_1[P1]*flow[P1]))-(dw_resistance_2[P1]*(flow[P1]**2.0)))+source_head[R1])-head[J1])\n", + "approx_darcy_wesibach_headloss[P2]: (((((-(dw_resistance_0[P2]))-(dw_resistance_1[P2]*flow[P2]))-(dw_resistance_2[P2]*(flow[P2]**2.0)))+head[J1])-head[D1])\n", "\n", - "\n", - "encoded_sol: [ 1. 1. 1.953 1.789 84.442 72.505]\n", - "encoded_ref: [ 1. 1. 1.766 1.766 86.791 75.147]\n", - "diff : [ 0. 0. -0.187 -0.023 2.348 2.642]\n", - "\n", - "\n", - "E sol : -3331.1596540813284\n", - "E ref : -3331.1923967404186\n", - "Delta E : 0.032742659090217785\n", - "\n", - "\n", - "Residue sol : 0.9703949158844001\n", - "Residue ref : 0.9142917877857567\n", - "Delta Residue : 0.05610312809864337\n" + "vars:\n", + "flow[P1]: flow[P1]\n", + "flow[P2]: flow[P2]\n", + "head[J1]: head[J1]\n", + "head[D1]: head[D1]\n", + "\n" ] } ], "source": [ - "net.diagnostic_solution(sol, ref_sol)" + "print(model.__str__())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Run with the intergrated WNTR Solver" + "# Embed the problem" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "import dwave_networkx as dnx\n", + "from minorminer import find_embedding\n", + "from dwave.embedding import embed_qubo, majority_vote, chain_break_frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('x_004_004', 'x_002_001'): 999996.3546150491,\n", + " ('x_002_001*x_004_004', 'x_002_001'): -2000000.0,\n", + " ('x_002_001*x_004_004', 'x_004_004'): -2000000.0,\n", + " ('x_004_003', 'x_002_001'): 999998.1773075246,\n", + " ('x_004_003', 'x_004_004'): 1000005.6999586402,\n", + " ('x_004_003', 'x_002_001*x_004_004'): 1000000.0,\n", + " ('x_002_001*x_004_003', 'x_002_001'): -2000000.0,\n", + " ('x_002_001*x_004_003', 'x_004_003'): -2000000.0,\n", + " ('x_003_003', 'x_004_004'): -0.26638917793964617,\n", + " ('x_003_003', 'x_002_001*x_004_004'): 0.5327783558792923,\n", + " ('x_003_003', 'x_004_003'): -0.13319458896982309,\n", + " ('x_003_003', 'x_002_001*x_004_003'): 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'x_003_004*x_003_003*x_001_001'): 3000000.0,\n", + " ('x_002_001*x_004_002*x_004_001', 'x_002_001*x_004_002*x_004_001'): 3000000.0,\n", + " ('x_004_002*x_002_001*x_004_005', 'x_004_002*x_002_001*x_004_005'): 3000000.0,\n", + " ('x_004_002*x_004_003', 'x_004_002*x_004_003'): 3000000.0,\n", + " ('x_004_001*x_004_005', 'x_004_001*x_004_005'): 3000000.0,\n", + " ('x_004_002*x_004_005', 'x_004_002*x_004_005'): 3000000.0,\n", + " ('x_004_001*x_002_001*x_004_003', 'x_004_001*x_002_001*x_004_003'): 3000000.0,\n", + " ('x_004_002*x_002_001*x_004_003', 'x_004_002*x_002_001*x_004_003'): 3000000.0,\n", + " ('x_004_001*x_002_001*x_004_005', 'x_004_001*x_002_001*x_004_005'): 3000000.0,\n", + " ('x_003_004*x_001_001*x_003_002', 'x_003_004*x_001_001*x_003_002'): 3000000.0,\n", + " ('x_005_003', 'x_005_003'): -4717.98168086132,\n", + " ('x_005_003*x_003_001', 'x_005_003*x_003_001'): 3000000.0,\n", + " ('x_005_002', 'x_005_002'): -3991.64390165515,\n", + " ('x_005_002*x_003_001', 'x_005_002*x_003_001'): 3000000.0,\n", + " ('x_005_002*x_003_005', 'x_005_002*x_003_005'): 3000000.0,\n", + " ('x_003_003*x_003_004', 'x_003_003*x_003_004'): 3000000.0,\n", + " ('x_005_003*x_003_005', 'x_005_003*x_003_005'): 3000000.0,\n", + " ('x_003_002*x_003_003*x_001_001', 'x_003_002*x_003_003*x_001_001'): 3000000.0,\n", + " ('x_005_001', 'x_005_001'): -2403.9852161336976,\n", + " ('x_005_001*x_003_005', 'x_005_001*x_003_005'): 3000000.0,\n", + " ('x_003_001*x_005_001', 'x_003_001*x_005_001'): 3000000.0,\n", + " ('x_003_003*x_003_002', 'x_003_003*x_003_002'): 3000000.0,\n", + " ('x_003_001*x_003_005*x_001_001', 'x_003_001*x_003_005*x_001_001'): 3000000.0,\n", + " ('x_003_004*x_003_002*x_003_005', 'x_003_004*x_003_002*x_003_005'): 3000000.0,\n", + " ('x_003_004*x_003_002*x_003_003*x_001_001',\n", + " 'x_003_004*x_003_002*x_003_003*x_001_001'): 3000000.0,\n", + " ('x_006_001', 'x_006_001'): 222.71024659677462,\n", + " ('x_006_002', 'x_006_002'): 853.5837584996717,\n", + " ('x_006_003', 'x_006_003'): 3339.8205782238333}" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.qubo.qubo_dict.to_qubo()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "target_graph = dnx.pegasus_graph(6)\n", + "embedding = find_embedding(net.qubo.qubo_dict.to_qubo()[0], target_graph)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 124, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", "text/plain": [ - "" + "
" ] }, - "execution_count": 16, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "sim = wntr_quantum.sim.FullQuboPolynomialSimulator(wn, \n", - " flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)\n", - "results = sim.run_sim(solver_options={\"sampler\" : sampler})\n", - "\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" + "dnx.draw_pegasus_embedding(target_graph, embedding, node_size=4, width=0.1)" ] }, { @@ -400,7 +1195,7 @@ ], "metadata": { "kernelspec": { - "display_name": "vitens", + "display_name": "vitens_wntr_1", "language": "python", "name": "python3" }, diff --git a/docs/notebooks/qubo_poly_solver_2loops_cm.ipynb b/docs/notebooks/qubo_poly_solver_2loops_cm.ipynb new file mode 100644 index 0000000..7c55352 --- /dev/null +++ b/docs/notebooks/qubo_poly_solver_2loops_cm.ipynb @@ -0,0 +1,612 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "\n", + "# Create a water network model\n", + "# inp_file = './networks/Net0.inp'\n", + "inp_file = './networks/Net2LoopsCMflat.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the original Cholesky EPANET simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0]\n", + "ref_pressure" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.311, 0.051, 0.232, 0.031, 0.168, 0.076, 0.023, -0.021], dtype=float32)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_rate" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.111e-01, 5.111e-02, 2.322e-01, 3.108e-02, 1.678e-01, 7.613e-02, 2.334e-02, -2.058e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the QUBO Polynomial Solver" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "wn = wntr.network.WaterNetworkModel(inp_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 0.000000 => 1000.000000 (res: 32.258065)\n", + "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.483871)\n" + ] + } + ], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 5\n", + "step = (15/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0., var_base_name=\"x\")\n", + "\n", + "nqbit = 5\n", + "step = (1000/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", + "\n", + "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)\n", + "net.verify_encoding()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve the system classically" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1. , 1. , 1. , 1. , 1. , 1. , 1. , 0.999, 1. , 1.001, 1. , 1.001, 1. , 1.001])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.create_index_mapping(model)\n", + "net.matrices = net.initialize_matrices(model)\n", + "\n", + "ref_sol, encoded_ref_sol, cvgd = net.classical_solutions()\n", + "ref_sol / ref_values[:-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1To3H7dDCx+N2aCIiIlId1Txx4XZoboduituhXbsutzwCypyTQvtzO7Q048llTtpq43Zo21TzxIWIiIjUj4ULERH5rPr6euzbt0/qMMgJLFyIiMgnXbhwAXfffTfS0tJw4MABqcMhgVi4EBGRz/n888/Ru3dvfPXVVwgMDERhYaHUIZFALFyIiMhnmM1mvPjii7j33ntRUlKCO+64AwcOHMD9998vdWgkkGp2FTXFc1yE91HbmRE8x8W963LJI6DMOSm0P89x8f54JSUlyMzMRF5eHgBg2rRpWLp0KYKDg506U8WZPjzHRfh4PMeFiIjov/bu3YvU1FTk5eUhODgYa9aswYoVKxAcHCx1aOQk1Txx4TkuPMelKZ7j4tp1ueURUOacFNqf57h4frzXXnsNTz75JMxmM2677Ta899576N27t6ivwXNc3B+P57gQEREBiIiIgNlsxoQJE/DNN9+gR48eUodEblDNExciIqKWTJo0CZ06dcL//d//NawVIeVi4UJERKo3cOBAqUMgkfCjIiIiIlIMFi5ERESkGCxciIiISDFYuBARkSJVV1dDr9fj6NGjUodCXsTFuUREpDi//PILJk6ciMOHD+PLL7/EDz/8AD8/P6nDIi9QbeHCI/+F91Hb8eI88t+963LJI6DMOSm0P4/8d92//vUvPPbYY6ioqEBkZCRefvllmM1mmM1mUePgkf/N8ch/EfHIfyIidTOZTHjyySeRkZGBiooK9OvXD/v378fdd98tdWjkRap54sIj/3nkf1M88t+163LLI6DMOSm0P4/8F+bs2bMYN24c8vPzAQBPPfUUsrOzERIS4vE4eOR/c1Ie+a+awoWIiNTpk08+weTJk3HlyhWEh4cjJycHQ4YMkToskggLFyIikq3vvvsO999/PwAgKSkJmzZtQteuXSVfs0PSYeFCRESylZSUhIceegjh4eF4+eWXERgYKHVIJDEWLkREJFsajQbr1q3jVmdqoJpdRUREpE4sWuhGLFyIiIhIMVi4EBERkWKwcCEiIklYrVaUlpZKHQYpDAsXIiLyurKyMowePRqDBw9GTU2N1OGQgrBwISIiryooKEBiYiK2bNmCo0ePYt++fVKHRArCwoWIiLzCarVi1apV6NevH06fPo0uXbpg7969GDRokNShkYLwHBciIvK4yspK6PV6vPfeewCA+++/H2+//TYiIiIkjoyURrWFi8lk8shX0XtjPLl/7bqt62r92nV3xpRLLpWYR0CZc1Jof1fmnb02Oefyp59+wvjx43HixAn4+fnhhRdewJNPPgmtVuu1nLhynxTvr7b6yyWXnpyTQsdWzUdFBoMB8fHxSE5OljoUIiL6rw0bNqBfv344ceIEYmJisHPnTsydOxdarWr++iEvU80TF71eD71eD6PRiLCwMKe+IrspT3xdt6fvk+Jr15teV/vXrrszplxyqcQ8Asqck0L7uzLv7LXJKZdWqxXbtm3D1atXMWjQIOTk5KBTp06ivobS56SttqbXpM5lU56Yk0LHVE3hQkRE8nL9e4befPNNzJo1i0f3kyj4rI6IiDwmPDwcc+fOZdFComHhQkRERIrBwoWIiIgUg4ULERERKQYLFyIiclp9fT1WrFjRcL4IkbewcCEiIqcUFhbinnvuwaxZszB//nypwyEfw+3QREQk2O7duzFhwgQUFRWhVatWSExMlDok8jF84kJERA5ZLBb89a9/xeDBg1FUVISePXviwIEDmDhxotShkY/hExciIrLrt99+w+TJk7F9+3YAQGZmJl5//XWEhIRIHBn5IhYuRERk07fffotx48bh/PnzCAoKgsFgwNSpU6HRaKQOjXwUCxciImrR1q1bMXbsWNTX1+OWW27B5s2b0atXL6nDIh/HwoWIiFrUr18/REZGol+/fnjzzTfRpk0bqUMiYuFCREQti4yMRH5+Pjp27MiPhkg2WLgQEZFNsbGxUodA1Ai3QxMREZFisHAhIiIixWDhQkTko6xWq9QhEDmNhQsRkY+pqanBjBkzsGjRIqlDIXKaLAuXBx98EG3btsWYMWOkDoWISFVOnz6N/v37Y9WqVXjxxRdx+vRpqUMicoosC5c5c+YgJydH6jCIiFRly5Yt6NOnDwoKCtCuXTt8/PHHuOmmm6QOi8gpsixc0tLSEBoaKnUYRESqUFdXh7lz52LUqFEoLy9Hv3798P3332Po0KFSh0bkNNELlz179mDEiBENBxZt3bq1WR+DwYCuXbsiKCgIqampyM/PFzsMIiICUFJSgvvuuw9Lly4FAGRlZWH37t2Ii4uTODIi14heuFRVVaFXr14wGAwttufm5iIrKwsLFy5EQUEBevXqhSFDhqC4uFjsUIiIfFpeXh6ysrLw3XffISwsDB988AGWLFmCgIAAqUMjcpnoJ+cOGzYMw4YNs9m+dOlSTJ8+HVOnTgUArFy5Ep988gnWrl2LefPmOf16tbW1qK2tbfjZaDQCAMrKymCxWJwaq66uDgBEm9SujufMfUL6Oupjr91WW0vXKyoqGv0pFbHz6M6YcsmlEvMIKHNOCu3vyryz19b0elVVFWbMmIGKigr07NkTOTk56NatG8rKygT9N4hNibmU4v3VVn+5zEtP5vH639+OePXIf5PJhIMHD2L+/PkN17RaLdLT07Fv3z6XxnzppZeQnZ3d7PrevXsREhLicqzkuoKCAqlDIBEwj8qn1+uxf/9+TJs2DefOncO5c+ekDoncpOZ5efXqVUH9vFq4XL58GWazGVFRUY2uR0VF4fjx4w0/p6en44cffkBVVRU6deqETZs2oW/fvi2OOX/+fGRlZTX8bDQaERcXh/79+zv9Tab8F4H7T1wKCgrQp08fSRdX84mL+09c5JBHQJlzUmh/Tz9xAYA+ffqgd+/ezKWL98ntiYsc5qXPPXER6vPPPxfcNzAwEIGBgc2uh4eHO124mEwmAIBOp3PqPrHHc+Y+IX0d9bHXbqvN3j2hoaEIDw93ELnniJ1Hd8aUSy6VmEdAmXNSaH9X5p29NuZS/PukeH91NJ7UufRkHrVaYctuvVq4tG/fHn5+figqKmp0vaioCNHR0aK+lslkaviFOHOP2DF4+j4hfR31sdduq62l69cr57q6OtF/l87wxGsrPZdKzCOgzDkptL8r885eG3Mp/n1SvL/a6i+XXHoyj0LH9uo5LjqdDomJicjLy2u4ZrFYkJeXZ/OjIKEMBgPi4+ORnJzsbphEREQkU6I/camsrMTJkycbfj5z5gwOHTqEiIgIdO7cGVlZWcjMzERSUhJSUlKwbNkyVFVVNewycpVer4der4fRaERYWBh0Op3Lj7LE/IjBnfGcuU9IX0d97LXbarvx+vXPPAMCAkT/HbrCEzEoPZdKzCOgzDkptL8r885qteLo0aPo3bu33XuYS3Huk+L9tek1ueXSE3kUOqbohcuBAwcwaNCghp+vL5zNzMzEunXrMH78eJSUlGDBggUoLCxEQkICduzY0WzBLhERNWc0GjFjxgxs3boVu3fvxoABA6QOicirRC9c0tLSHH5V+uzZszF79myxX7oRrnER3kdtn6dzjYt71+WSR0CZc1Jof1fm3Y8//ogJEybg1KlT8Pf3x7Fjx5CSkmLzHubSvfu4xqU5n1vj4klc40JEarZu3TrcddddOHXqFDp16oQvvvgCU6ZMkTosIq+T5XZoV3CNC9e4NMU1Lq5dl1seAWXOSaH9Hc27q1evQq/XY926dQCAIUOG4K233kJMTIzD8ZhLce7jGpfmVLXGhYiIxHH8+HGMHTsWR44cgVarxQsvvICsrCzB510QqRELFyIiGfrggw8wffp0VFZWIioqCu+++y4GDRok+VoVIqmptnDh4lzhfbg413NjyiWXSswjoMw5KbS/o3lntVpRWVmJgQMHIicnB9HR0Xbf15hL8e/j4tzm5LA4VzWFi8FggMFggNlsljoUIiK3jRgxAtu2bUN6ejr8/VXzVk3kNtXMBi7O5eLcprg417XrcssjoMw5KbS/vT4jR450+j7mUvz7uDi3OSkX53KFFxERESkGCxciIiJSDBYuREREpBiqWePSFHcVCe/DXUWeG1MuuVRiHgFlzklH/c1mM1566SXcc889LX5JoqP7mUv3xpPLnLTVxl1FjqnmiQuP/CciuSsuLsb999+PF154AQ8//DCuXr0qdUhEiqOaJy7cVcRdRU1xV5Fr1+WWR0CZc7Jp/6+++goTJkzAxYsXERISggULFiA8PNyleWevjbkU/z7uKmqOu4qIiFTKYrFg8eLFGDRoEC5evIjf//73yM/Px6RJk6QOjUiRVPPEhYhIbkpLSzFlyhR89NFHAIBJkyZh5cqVaN26teRrToiUioULEZEHHDhwABkZGTh79ix0Oh1effVVPProo9BoNFKHRqRoLFyIiER28uTJhi9EvOmmm7Bp0yb06dNH6rCIVEG1hQu3Qwvvo7atl9wO7d51ueQRUOacBIDOnTtj0qRJKC0txerVqxEeHt5sDFfmnb025lL8+7gdujk5bIdWTeHCL1kkIjlZsmQJQkJC+NEQkchUU7hwOzS3QzfF7dCuXZdbHgHlzkl3c6m2OQkoN5fu9uF2aMfjcTs0ERERqQ4LFyIiIlIMFi5ERE6qrKyUOgQin8XChYhIIJPJhD/96U9ISUlh8UIkERYuREQCnD17FnfddRdee+01/PTTT9i+fbvUIRH5JNXsKmqK57gI76O2MyN4jot71+WSR0A+c/LDDz/Eo48+iitXrqBt27ZYu3Ythg8f7vTvW2gftc1JQD65lMuctNXGc1wcU80TF4PBgPj4eCQnJ0sdChGpRH19PZ5//nmMHTsWV65cQXJyMvbv34/hw4dLHRqRz1LNExee48JzXJriOS6uXZdbHgFp5uTFixcxceJE7NmzB8C195ilS5d6NZdqm5MA31+FtvEcF9tUU7gQEYklLy8PGRkZKC4uRmhoKFatWoXRo0fL4i8MIl/HwoWIqIl169ahuLgYd9xxBzZt2oSuXbtKHRIR/RcLFyKiJlasWIGuXbviueeeQ3BwsOQLW4nof1i4EBE10bp1a7zwwgtSh0FELVDNriIiIiJSPxYuREREpBgsXIiIiEgxWLgQkc+wWq14//33UVNTI3UoROQi1S7O5ZH/wvuo7XhxHvnv3nW55BEQN5eVlZWYOXMm3n//fXz55ZcwGAwei4NH/jfH91dhbTzy3zHVFC4GgwEGgwFms1nqUIhIZo4dO4YJEybgxIkT8PPzwy233AKr1QqNRiN1aETkJNUULjzyn0f+N8Uj/127Lrc8Au7lcv369ZgxYwauXr2K2NhYvPPOO0hLS/NKHDzyvzm+vwpr45H/tnGNCxGpUnV1NaZPn46HH34YV69exT333IP9+/ejX79+UodGRG5g4UJEqvPLL7+gb9++WLNmDTQaDbKzs7F9+3Z06NBB6tCIyE2q+aiIiAgAvv76awwfPhwVFRXo0KEDNm7ciPT0dADgGjgiFWDhQkSq0qNHD7Rr1w69evXCe++9h9jYWKlDIiIRsXAhIlVp27Ytdu/ejdjYWPj78y2OSG04q4lIdbp06SJ1CETkIVycS0RERIrBwoWIiIgUg4ULERERKQYLFyJSBIvFgr/+9a949tlnpQ6FiCTExblEJHu//fYbJk+ejO3btwMAxo0bh8TERImjIiIp8IkLEcnat99+i969e2P79u0IDg7G2rVrWbQQ+TAWLkQkS1arFa+++ioGDx6M8+fP47bbbsP+/fsxdepUqUMjIgmp9qMik8kEk8nk9D1ix+Dp+4T0ddTHXruttpau19XVNfwp9u/SGZ54baXnUml5LC8vx6OPPoqtW7cCAMaMGYMVK1agTZs2bsXkjTwK7e/KvLPXJtdcXsf3V2FttvrLJZeezKPQsVXzxMVgMCA+Ph7JyclSh0JEbjh06BDuvPNObN26FQEBAViyZAneeecdtGnTRurQiEgGVPPERa/XQ6/Xw2g0IiwsDDqdDjqdzqWxXL1P7PGcuU9IX0d97LXbarvxekBAQMOfYv8OXeGJGJSeS7nnsb6+HhMnTsTp06fRpUsXbNy4EUlJSYqck0L7uzLv7LXJJZe2KDGXUry/Nr0mt1x6Io9Cx1TNExciUj5/f3+8/fbbeOCBB1BQUICkpCSpQyIimVHNExciUocBAwZgwIABADyzXomIlI1PXIiIiEgxWLgQERGRYrBwISIiIsVg4UJEXnPixAmpQyAihWPhQkQeZzKZkJWVhfj4ePz73/+WOhwiUjAWLkTkUefPn0daWhpeeeUVWCwW7N+/X+qQiEjBuB2aiDxmx44deOihh/Dbb78hPDwc69atwx/+8AepwyIiBeMTFyISndlsxl/+8hcMHz4cv/32GxITE1FQUMCihYjcxicuRCSqwsJCZGRkYNeuXQCAWbNmYcmSJQgKCpI4MiJSAxYuRCSaPXv2YPz48SgsLESrVq3wxhtvYOLEiVKHRUQqwsKFiERTXV2NoqIi9OzZE5s2bcLvfvc7qUMiIpVh4UJEohkyZAg++OAD3HvvvQgJCZE6HCJSIRYuRCSqBx54QOoQiEjFuKuIiIiIFIOFCxERESkGCxciIiJSDFkWLh9//DG6d++OW2+9FWvWrJE6HCKfZ7Va8eabb2Lv3r1Sh0JEPk52i3Pr6+uRlZWFXbt2ISwsDImJiXjwwQfRrl07qUMj8klVVVWYNWsWcnJyEBsbix9//BERERFSh0VEPkp2T1zy8/PRo0cPxMbGonXr1hg2bBh27twpdVhEPun48eNITU1FTk4OtFotZs+ejfDwcKnDIiIfJnrhsmfPHowYMQIdO3aERqPB1q1bm/UxGAzo2rUrgoKCkJqaivz8/Ia2ixcvIjY2tuHn2NhYXLhwQewwiciBzZs3IykpCUePHkVMTAy++OILzJs3D1qt7P69Q0Q+RPR3oKqqKvTq1QsGg6HF9tzcXGRlZWHhwoUoKChAr169MGTIEBQXF4sdChG5oKamBitXrsT06dNRVVWFu+++G99//z0GDhwodWhEROKvcRk2bBiGDRtms33p0qWYPn06pk6dCgBYuXIlPvnkE6xduxbz5s1Dx44dGz1huXDhAlJSUmyOV1tbi9ra2oafjUYjAKCsrAwWi8Wp2Ovq6gAAAQEBTt0n9njO3Cekr6M+9tpttbV0vaKiotGfUhE7j+6MKZdcCr3+n//8B5MnT8aRI0eg0Wjw1FNP4dlnn4Wfnx/Kysoc/jeITYlzUmh/V+advTY5z0lAmbmU4v3VVn+55NKTebz+97cjXl2cazKZcPDgQcyfP7/hmlarRXp6Ovbt2wcASElJwZEjR3DhwgWEhYVh+/bt+Mtf/mJzzJdeegnZ2dnNru/du5dHjkukoKBA6hDIBWVlZdDr9aiqqkJoaCiefPJJ9OnTB19//bXUoZGbOCfVQ825vHr1qqB+Xi1cLl++DLPZjKioqEbXo6KicPz48WsB+ftjyZIlGDRoECwWC5555hm7O4rmz5+PrKyshp+NRiPi4uLQv39/tGnTxqn4+C8C9/91V1BQgD59+iA0NNRh7J7CJy6uP3HJz8/H/v37MWPGDAwZMkTSPALKnJNC+3vriYsc5iSgzFzK7YmLHHLpc09chBo5ciRGjhwpqG9gYCACAwObXQ8PD3e6cDGZTAAAnU7n1H1ij+fMfUL6Oupjr91Wm717QkNDJd15InYe3RlTLrkUen3ZsmUoKyvDN998I3keAWXOSaH9XZl39trkPCcBZeZSivdXR+NJnUtP5lHown+vFi7t27eHn58fioqKGl0vKipCdHS0qK9lMpkafiHO3CN2DJ6+T0hfR33stdtqa+n69cq5rq5O9N+lMzzx2krPpbPXAenzCChzTgrt78q8s9cm5zkJKDOXUry/2uovl1x6Mo9Cx/bqvkadTofExETk5eU1XLNYLMjLy0Pfvn3dGttgMCA+Ph7JycnuhklEREQyJfoTl8rKSpw8ebLh5zNnzuDQoUOIiIhA586dkZWVhczMTCQlJSElJQXLli1DVVVVwy4jV+n1euj1ehiNRoSFhUGn07n8KEvMjxjcGc+Z+4T0ddTHXrutthuvX//MMyAgQPTfoSs8EYPSc6nEPALKnJNC+7sy7+y1MZfi3yfF+2vTa3LLpSfyKHRM0QuXAwcOYNCgQQ0/X184m5mZiXXr1mH8+PEoKSnBggULUFhYiISEBOzYsaPZgl0iEldtba0s3vCIiNwheuGSlpYGq9Vqt8/s2bMxe/ZssV+6Ea5xEd5HbZ+nc41LYxaLBf/4xz+Qk5ODvXv3NlrYJ+c8Asqck0L7c42LNOPJYU7aa+MaF8dUc3Y317gQNVdaWorRo0djwYIFOHnyJDZs2CB1SEREbpHldmhXcI0L17g05etrXPLz8zFu3DicPXsWgYGBeOWVVzBjxgxoNBq748ktj4Ay56TQ/lzjIs14cn9/5RoX21TzxIWIrrFarVi+fDkGDBiAs2fP4uabb8ZXX32FRx55pMWihYhISVTzxIWIrp0cPXPmTGzevBkAMGrUKKxduxbBwcESR0ZEJA7VFi5cnCu8j9oWAvrq4tzDhw9j/PjxOHXqFPz9/fG3v/0Ns2fPhkajUWQeAWXOSaH9uThXmvHk/v7KxbmOqaZwMRgMMBgMMJvNUodCJIk33ngDp06dQqdOnbBx40akpqZKHRIRkehUU7hwcS4X5zbla4tzX3nlFQQEBOC5555DTEyMU2PLOY+AMuek0P5cnCvNeHJ/f+XiXNtUU7gQ+brg4GAsWbJE6jCIiDyKu4qIiIhIMVi4EBERkWKo9qMi7ioS3kdtOxh8dVeRvXYl5hFQ5pwU2p+7iqQZTy5z0lYbdxU5pponLjzyn9Ts3//+NyorK6UOg4hIcqp54sJdRdxV1JQadhXV1dXh+eefx+LFizFp0iS8+eab0Gg0LudSiXkElDknhfbnriJpxpP7+yt3FdmmmsKFSG0uXLiACRMm4OuvvwYAdOjQARaLBX5+fhJHRkQkHRYuRDL0+eefIyMjAyUlJWjTpg3Wrl2L0aNHS75OgYhIaqpZ40KkBmazGdnZ2bj33ntRUlKChIQEHDx4EKNHj5Y6NCIiWeATFyKZKC4uxpQpU5CXlwcAmD59Ov75z3/yCxKJiG6g2sKF26GF91Hb1kslboc+duwYhg8fjkuXLiEkJATLly/HpEmTmo3B7dDSjMft0O5TYi65Hbo5OWyHVk3hwi9ZJCWLi4tDaGgoQkNDkZubi/j4eKlDIiKSJdUULtwOze3QTSlpO3S7du3w4YcfokOHDoiIiHB7PG6H9sx43A7tPiXmktuhm+N2aCJCt27dpA6BiEj2uKuIiIiIFIOFCxERESkGCxciIiJSDBYuRB62fv16ZGVlSR0GEZEqqHZxLs9xEd5HbWdGyOUcl+rqajz++ONYv349ACA9PR3p6eluvQbPcZFmPJ7j4j4l5pLnuDTHc1xExHNcSE5+/vlnZGRk4PDhw9BqtViwYAHuvvtuqcMiIlI81RQuPMeF57g0JdU5Lps2bcIjjzyCiooKREZGIicnB0OGDBH1NXiOizTj8RwX9ykxlzzHpTkpz3HhGhcikZhMJvzpT3/CuHHjUFFRgf/7v/9Dfn4+Bg0aJHVoRESqwcKFSARnz57FXXfdhddeew0AMG/ePOTl5SEmJkbiyIiI1EU1HxUReZLZYkXB2Su4XG1GZGgQUrpFwE+rAQBYrVaMGjUKBQUFaNu2LdavX4/77rsPgGcWChMR+TIWLkQO7DhyCX//5CiKKmpwtf5asRITFoSFI+IxtGcMNBoNVq1ahaysLKxfvx5dunSROGIiIvXiR0VEduw4cgkz3ylAUUVNo+uF5TWY+U4Bdhy5BABISkrCl19+yaKFiMjDWLgQ2WC2WJH90TFYW2i7fi37o2MwW679pNFovBYbEZGvYuFCZEP+mVJcKq+x2W4FcKm8BvlnSr0XFBGRj2PhQmRDcYXtosWVfkRE5D7VLs7lkf/C+6jteHGxXrt9sB9C/K2oKy9GcEQHu/2cPWrf1b488l+a8Xjkv/uUmEse+d+cHI78V80TF4PBgPj4eCQnJ0sdCqlE787hMB36GCdffwxlJ75t1q4BEBUahD5d2no/OCIiH6WaJy488p9H/jflTgxlZWWYOnUqTn2yFQBw5ZfvEXhL34bt0NeX4T57Xw8EBwWKGguP/G9OiXNSaH8e+S/NeHKZk7baeOS/baopXIjEcvDgQYwdOxZnzpyBTqfD9GeycaRVIooraxv6RN9wjgsREXkPCxei/7JarVi1ahXmzJkDk8mEbt26YdOmTUhMTER1Ta3Nk3OJiMh7WLgQAaisrMRjjz2GjRs3AgD+8Ic/4K233kLbttfWr/hpNUjuFiGLR7RERL6MhQv5FLPFivwzpSiuqGl4cnL8p2MYM2YMjh8/Dj8/P/ztb3/D3LlzeaAcEZEMsXAhn7HjyCVkf3Ss0aFyMWFBGN2xAr/88gtiY2ORm5uL/v37SxglERHZw8KFfML17xxqenx/YXkNDOUBeHbxCjwx+QF06GD7vBYiIpKeas5xIbJFyHcOfVF7EyLatfdmWERE5AIWLqR6/M4hIiL1YOFCqsfvHCIiUg8WLqR6kaFBovYjIiLpcHEuqdqnn36KAF0gYsKCUFhe0+I6Fw2unYSb0i3C2+EREZGT+MSFVKm+vh7PPfcc7rvvPkzKmIjZqdeKkqYns1z/eeGIeJ6ES0SkAHziQqpz6dIlPPzww9izZw8AYPz48RjTPx7tIqObnePC7xwiIlIW1RYuJpMJJpPJ6XvEjsHT9wnp66iPvXZbbS1dr6ura/hT7N+lULt378bkyZNRXFyM1q1bY+XKlRg7diwA4O7b2mHgkwOufedQZS3atw5Eny5t4afVuPU7Eus+T+ZSaXm8TolzUmh/V+advTbmUvz7pHh/tdVfLrn0ZB6Fjq2awsVgMMBgMMBsNksdCknAYrFg8eLFyM7OhsViQY8ePfDuu++ie/fujfpd/84hIiJSJtUULnq9Hnq9HkajEWFhYdDpdC5/IZ7YX6TnjTiE9HXUx167rbYbrwcEBDT86c0vI7x8+TImT56MHTt2AAAyMzOxbNkyhIeHi/5aSs+lnPNojxLnpND+rsw7e23Mpfj3SfH+2vSa3HLpiTwKHVM1hQv5hqZfkhjfQYfExEScO3cOwcHBMBgMmDRpktRhEhGRh7BwIcWw9SWJ/YeOQtDuT7F582bcfvvtkn+WT0REnsPChRTB3pckXgobhKU5s3D77bdKEhsREXkPz3Eh2XP0JYkarR/+8cU5mC0t9SAiIjVh4UKyxy9JJCKi61i4kOzxSxKJiOg6Fi4ka/X19fySRCIiasDChWRrw4YN6NmzJ7q1NiMmLKjZ9wxdp8G13UX8kkQiIvVj4UKyU1NTgxkzZuChhx7CiRMnsPy1V7FwRDwAfkkiEZGvY+FCsnLq1Cn069cPq1atgkajwcKFC5GdnY2hPWOw4qE+iA5r/HFQdFgQVjzUh1+SSETkI3iOC8nGli1bMHXqVJSXl6N9+/bYsGED7r333ob2oT1jcE98dKOTc1O6RfBJCxGRD2HhQpKrq6vDs88+i1deeQUA0K9fP+Tm5qJTp07N+vppNeh7cztvh0hERDLBj4pIUufPn8fAgQMbipa5c+di9+7dLRYtREREfOJCklq5ciX27duHsLAwrFu3Dg888IDUIRERkYyxcCFJLVy4ECUlJZg3bx5uuukmqcMhIiKZY+FCktLpdFi9erXUYRARkUJwjQsREREpBgsXIiIiUgwWLkRERKQYsixcHnzwQbRt2xZjxoyROhRyw3fffYeysjKpwyAiIhWRZeEyZ84c5OTkSB0GuchqteLVV19F//79MW3aNFitVqlDIiIilZBl4ZKWlobQ0FCpwyAXVFVVYerUqZgzZw7q6uqg1WpRW1srdVhERKQSThcue/bswYgRI9CxY0doNBps3bq1WR+DwYCuXbsiKCgIqampyM/PFyNWkrnDhw/jqaeewrZt2+Dv749//vOf2LRpE4KCghzfTEREJIDT57hUVVWhV69emDZtGkaNGtWsPTc3F1lZWVi5ciVSU1OxbNkyDBkyBCdOnEBkZCQAICEhAfX19c3u3blzJzp27OhUPLW1tY3+RW80GgEAZWVlsFgsTo1VV1cHAAgICHDqPrHHc+Y+IX0d9bHXbqvtxutWqxXvvPMOnn76adTW1qJjx45Yt24dkpOTUV5e7vC/QWxi59GdMeWSS2euV1RUNPpTSkqck0L7uzLv7LUxl+LfJ8X7q63+csmlJ/N4/e9vRzRWNxYgaDQabNmypdEx7ampqUhOTsby5csBABaLBXFxcXj88ccxb948wWPv3r0by5cvx+bNm+32W7RoEbKzs5td37hxI0JCQgS/HrmmpqYGq1atwq5duwAAiYmJmDNnDtq0aSNxZEREpCRXr15FRkYGysvL7f4dIurJuSaTCQcPHsT8+fMbrmm1WqSnp2Pfvn1ivlSD+fPnIysrq+Fno9GIuLg49O/f3+m/PPkvAuf+dffrr78iIyMDx48fh1arxVNPPYU777wTSUlJkq5R4hMX95+4FBQUoE+fPpKvNVPinBTa31tPXJhL1++T2xMXOeRSDk9cRC1cLl++DLPZjKioqEbXo6KicPz4ccHjpKen44cffkBVVRU6deqETZs2oW/fvi32DQwMRGBgYLPr4eHhThcuJpMJwLVj6MXg6njO3Cekr6M+N7abLVbknylFcUUNIkODkBDbBn5aTbN7TSYTdDodAgMDER0djffeew+9evXCl19+idDQUISHhzuM3VPEzqM7Y0qZS3euA5A8j4Ay56TQ/vb6uNLGXIp/nyfnpK02R+NJnUtP5lGrFbbsVpbfVfT555+7PYbJZGr4hThzj5hcHc+Z+4T0ddTnenveT0VYvOMEiipqGto6hwXiT4NvQXrP2Gb3+Pn5ITc3F8HBwYiOjm5Yz1JXVyf679IZnnhtpeXSnevX/wUkdR4BZc5Jof3t9XGljbkU/z5Pzklbbbb6yyWXnsyj0LFFLVzat28PPz8/FBUVNbpeVFSE6OhoMV+qGYPBAIPBALPZ7NHXUas9P5fg6Q+OoumCp8uVNViw7Qg0fv4Y/PuoZvd169bNOwESERFB5MJFp9MhMTEReXl5DQt2LRYL8vLyMHv2bDFfqhm9Xg+9Xg+j0YiwsDDodDqXH2WJ+RGDO+M5c5+Qvrb6mC1W/OPfp1BVr2mxXQPg/336M+69vRP8tI373Djm9c88AwICRP8dusITMcg9l47ahVyXWx4BZc5Jof3t9XGljbkU/z5PzklbbU2vyS2Xnsij0DGdLlwqKytx8uTJhp/PnDmDQ4cOISIiAp07d0ZWVhYyMzORlJSElJQULFu2rOFQMpKngrNX/vvxUMuFixXApfIa5J8pRd+b23k1NiIiohs5XbgcOHAAgwYNavj5+o6ezMxMrFu3DuPHj0dJSQkWLFiAwsJCJCQkYMeOHc0W7Hoa17gI73PZWIVgv5Z3xd94vbisEiZTqM0x1foZrDtjyuXzdK6LcG88rnFxnxJzyTUuzSlyjUtaWprD756ZPXu2xz8aaoprXFwX0ar5rqzK0wWoPHkQXYc+Ao3m2pOY9q2b9yMiIvImWe4qcgXXuLj+GWzSTR3QJiQYpVdqYbGYUb73PZR/8x4AK0I6/Q5h8XchrFUw7rw1imtcZJ5LR+1cF+HeeFzj4j4l5pJrXJqTco2LLL9kkbzLT6vBM0O7w1xVhuL3F6L8m3cBWNE6YShCb0sFACwcEd+saCEiIvI21TxxIfcE/vYLqnLnoqakCJqAQEQMmY3WPQYhum0gnhnaHUN7xkgdIhERkXoLFy7OFdbHYrHgH//4B7Kzs2E2m/G73/0Of355JUKju6J960DcHtMKWq2m2RhyXgjIxbnuXZdLHgFlzkmh/bk4V5rx5DInbbVxca5jqilcuDjXeaWlpXjkkUfw6aefAgAmTpyI5cuXo3Xr1g19pH6zIyIiupFqChcuznV+8diUKVPw2WefITAwEEuXLsXMmTMbdhAJHV/OCwG5ONe163LLI6DMOSm0PxfnSjOeXOakrTYuzrVNNYULOe/ll1/GxYsX8cYbb6B37942ixYiIiK5YOHiw3r27IlDhw6hvr5e6lCIiIgE4XZoHyf0a8SJiIjkQLVPXHxlV1FNTS1++LUcpdVmtG8diD5d2jY7b0XsVe+2rqt11bs7Y8plB4MS8wgoc04K7c9dRdKMJ5c5aauNu4ocU03h4ou7ivJ+KsI/dx5HSWUNqs3XipWo0CA8M7Q7Bv/eu98NRURE5A2qKVx8bVfRjiOXMOvdHxHsbwWgwdX6a4XLf67UYta7P+KlYZ0xYeDtTsWhth0M3FXk2nW55RFQxpx0tT93FUkznlzmpK027iqyjQscFMhssSL7o2No6asurQCqftqDSfem4p13Nng7NCIiIo9i4aJA+WdKcam8ptl1a30dSv+9AiUfLobFVI0Va992+E3eRERESqKaj4p8SXFF86KlrqwQl7f9HabCXwAAbfqOw9zFi3k2CxERqQoLFwWKDA1q9HPFz/ko/HApLLVV0AaFov39WQi+ORkxbVvbGIGIiEiZVFu4qHk7dEJsa3RrG4iiskqU7lqPom8+AAAEdeyOTqOegS4sEpGhgUiIbS3o96C2rZfcDu3edbnkEVDOnHSlP7dDSzOeXOakrTZuh3ZMNYWLL22H9tNqMK13GB774xxUnz8GAGibPBJRg6dA63dt5fkzQ7s3O8+FiIhI6VRTuPjadujftQXqLv0Cv8AQRN/3J/jfOgDVViCmVRAWjojH0J4xTsehtq2X3A7t2nW55RFQxpx0tT+3Q0sznlzmpK02boe2TTWFi69JTk7GO++sR3yPnjAGtMPlajMiQ4OQ0i2CT1qIiEi1WLgo2Pjx4xs+E5RDBU5ERORpPMeFiIiIFIOFCxERESkGCxciIiJSDBYuMlRQUICdO3dKHQYREZHsqHZxrhIPoLNarVi1ahWefvpphISE4Ntvv0W3bt3cfg0eQCfdmHI57EqJeQSkn5Ou3scD6JpTYi55AF1zcjiATjVPXAwGA+Lj45GcnCx1KC6pqKjAww8/jDlz5sBkMqF///4IDw+XOiwiIiJZUc0TFyUfQHfkyBGMGTMGJ06cgJ+fH1588UU88cQTCAwMFO01hPRR22FXPIDOtetyyyOgzEPLhPbnAXTSjCeXOWmrjQfQ2aaawkWp3n77bcycORPV1dWIjY1Fbm6uYp8aEREReZpqPipSmurqavzxj3/ElClTUF1djXvvvRfff/89+vfvL3VoREREssUnLhKoq6vDgAEDUFBQAI1Gg0WLFuHPf/4z/Pz8pA6NiIhI1li4SCAgIADjxo3Dr7/+io0bN2Lw4MFSh0RERKQI/KhIIk8//TSOHDnCooWIiMgJLFwkotVq0aFDB6nDICIiUhQWLkRERKQYql3jIubJuWaLFQVnr+ByZS3atw5Eny5t4afVCBrPYrHiuzOlTt0r95MdbV1X68mO7owpl1wqMY+AMk9bFdqfJ+dKM55c5qStNp6c65hqCheDwQCDwQCz2SzquHk/FWHxjhMoqqhpuBYVGoRnhnbH4N9HtXiPxWKBVqvFnp9L8GreSZwrrxV8LxEREdmmmsLFEyfn7jhyCbPe/RFWAMD/npL850otZr37I1Y81AdDe8Y0ujcvLw9PPvkknl32Nv76xQVYAVytF3avrTicidmdPmo7pZMn57p2XW55BJR52qrQ/jw5V5rx5DInbbXx5FzbuMbFBrPFiuyPjv23aGns+rXsj47BbLn2k8ViwQsvvIB77rkHhw8fRta85wXfS0RERMKo5omL2PLPlOJSeY3NdiuAS+U1yD9TilvaWDB58mR89tlnAID7x0zCD50fFHRv35vbiRw5ERGRevGJiw3FFbaLlhvt+epr9O7dG5999hmCg4Px1ltv4Y9//ju0AUGivQYRERFdwycuNkSG2i88rFYrKr7bgj8vyYG5vh633XYbNm/ejNtvvx37Tv0mymsQERFRY3ziYkNKtwjEhAWhpY3LlppKXN7yIq7sWgtzfT3Gjx+PAwcO4Pbbb3d4L3BtmW9MWBBSukV4KnwiIiJVYuFig59Wg4Uj4gGgWQFS+cNnuPrLt/AP0MFgMODdd99FaGiooHuv/7xwRLygs2CIiIjof1i42DG0ZwxWPNQH0WGNP9K5LX0C0keOw75v9mLWrFnQaJoXIEN7xuDlsb2afRwUHRYkaCs0ERERNcc1Lg4M7RmDe+KjkX+mFMUVNYgMvfYRj5/2Xof3Dv59FNK6R+LQhcom9/JJCxERkStYuAjgp9W4vG3ZnXuJiIioMX5URERERIrBwoWIiIgUg4VLEydPnkRJSYnUYRAREVELWLjcYMuWLejbty8mT54Mi8UidThERETUhGoX55pMJphMJsF958+fj+XLlwMAKioqUFJSgrZt27odg6fvE9LXUR977bbaWrpeV1fX8Ker/+1i8MRrKz2XSswjIH4uvZFHof1dmXf22phL8e+T4v3VVn+55NKTeRQ6tmqeuBgMBsTHxyM5Odmp+86dO4fBgwc3FC1z5szBzp073S5aiIiISHyqeeKi1+uh1+thNBoRFhYGnU4HnU5n957t27fjoYceQmlpKcLDw7FmzRqMGDHC4X3OcnU8Z+4T0tdRH3vtttpuvB4QENDwp9i/Q1d4Igal51KJeQTEz6U38ii0vyvzzl4bcyn+fVK8vza9JrdceiKPQsdUzRMXZ9TX1+PPf/4zhg8fjtLSUiQlJaGgoAAjRoyQOjQiIiKyQzVPXIQqLS3F6NGjsXv3bgDXntQsWbIEgYGBkn8GTERERPaprnCxWq0AAKPR2GK7xWJBZWUlWrVqhddeew2jR49GbW0tamtrGwoXsR6BuTqeM/cJ6euoj712W20tXTcajbh69SqMRiO0Wuke5omdR3fGlEsulZhHQPxceiOPQvu7Mu/stTGX4t8nxfurrf5yyaUn83j97+3rf4/borE66qEwv/76K+Li4qQOg4iIiFxw/vx5dOrUyWa76goXi8WCixcvIjQ0tMVvbXYkOTkZ3333nWjxuDqeM/cJ6euoj712W21NrxuNRsTFxeH8+fNo06aNoNg9Rew8ujOmXHKpxDwCypyTQvu7Mu/stTGX4t8nxftrS9fklEtP5dFqtaKiogIdO3a0+1RJdR8VabVau5WaI35+fqL+n8LV8Zy5T0hfR33stdtqs3W9TZs2kk8ssfPozphyyaUS8wgoc04K7e/KvLPXxlyKf58U76/2+sshl57MY1hYmMP+PrmryB69Xi+L8Zy5T0hfR33stdtqE/t3JSZPxKb0XCoxj4Ay56TQ/q7MO3ttzKX490nx/so82qe6j4pIOtfP0CkvL5f8XwTkOuZRPZhL9WAu/4dPXEg0gYGBWLhwIQIDA6UOhdzAPKoHc6kezOX/8IkLERERKQafuBAREZFisHAhIiIixWDhQkRERIrBwoWIiIgUg4ULERERKQYLF5LEgw8+iLZt22LMmDFSh0JO+vjjj9G9e3fceuutWLNmjdThkBs4D5Xv/PnzSEtLQ3x8PO644w5s2rRJ6pA8jtuhSRK7d+9GRUUF3n77bWzevFnqcEig+vp6xMfHY9euXQgLC0NiYiK++eYbtGvXTurQyAWch8p36dIlFBUVISEhAYWFhUhMTMTPP/+MVq1aSR2ax/CJC0kiLS0NoaGhUodBTsrPz0ePHj0QGxuL1q1bY9iwYdi5c6fUYZGLOA+VLyYmBgkJCQCA6OhotG/fHqWlpdIG5WEsXKiZPXv2YMSIEejYsSM0Gg22bt3arI/BYEDXrl0RFBSE1NRU5Ofnez9Qcpq7ub148SJiY2Mbfo6NjcWFCxe8ETo1wXmqDmLm8eDBgzCbzYiLi/Nw1NJi4ULNVFVVoVevXjAYDC225+bmIisrCwsXLkRBQQF69eqFIUOGoLi4uKFPQkICevbs2ex/Fy9e9NZ/BrVAjNySPDCX6iBWHktLS/Hwww9j9erV3ghbWlYiOwBYt2zZ0uhaSkqKVa/XN/xsNputHTt2tL700ktOjb1r1y7r6NGjxQiTXOBKbvfu3Wt94IEHGtrnzJlj3bBhg1fiJdvcmaech/Lhah5ramqsd911lzUnJ8dboUqKT1zIKSaTCQcPHkR6enrDNa1Wi/T0dOzbt0/CyMhdQnKbkpKCI0eO4MKFC6isrMT27dsxZMgQqUImGzhP1UFIHq1WK6ZMmYK7774bkydPlipUr2LhQk65fPkyzGYzoqKiGl2PiopCYWGh4HHS09MxduxYfPrpp+jUqRPfTGVASG79/f2xZMkSDBo0CAkJCZg7dy53FMmQ0HnKeShvQvK4d+9e5ObmYuvWrUhISEBCQgIOHz4sRbhe4y91AOSbPv/8c6lDIBeNHDkSI0eOlDoMEgHnofINGDAAFotF6jC8ik9cyCnt27eHn58fioqKGl0vKipCdHS0RFGRGJhb9WAu1YF5bBkLF3KKTqdDYmIi8vLyGq5ZLBbk5eWhb9++EkZG7mJu1YO5VAfmsWX8qIiaqaysxMmTJxt+PnPmDA4dOoSIiAh07twZWVlZyMzMRFJSElJSUrBs2TJUVVVh6tSpEkZNQjC36sFcqgPz6AKptzWR/OzatcsKoNn/MjMzG/q89tpr1s6dO1t1Op01JSXF+u2330oXMAnG3KoHc6kOzKPz+F1FREREpBhc40JERESKwcKFiIiIFIOFCxERESkGCxciIiJSDBYuREREpBgsXIiIiEgxWLgQERGRYrBwISIiIsVg4UJERESKwcKFiIiIFIOFCxERESkGCxciIiJSjP8PlXaQPtVhbhcAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.scatter(ref_values[:-1], encoded_ref_sol)\n", + "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", + "plt.grid(which=\"major\", lw=1)\n", + "plt.grid(which=\"minor\", lw=0.1)\n", + "plt.loglog()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "from dwave.samplers import SimulatedAnnealingSampler, TabuSampler, RandomSampler\n", + "\n", + "sampler = SimulatedAnnealingSampler()\n", + "# sampler = TabuSampler()\n", + "# sampler = RandomSampler()\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.solve(model, strength=1e8, num_sweeps=10000, num_reads=100, options={\"sampler\" : sampler})\n", + "sol = net.extract_data_from_model(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "solutions,energies,statuses = net.analyze_sampleset()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'QUBO Solution')" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", + "for s in solutions[:1]:\n", + " plt.scatter(ref_values[:-1], s, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", + "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.grid(which=\"major\", lw=1)\n", + "plt.grid(which=\"minor\", lw=0.1)\n", + "plt.xlabel('Reference Solution')\n", + "plt.ylabel('QUBO Solution')\n", + "# plt.legend()\n", + "# plt.xlim([0.01,0.1])\n", + "# plt.ylim([0.01,0.1])\n", + "# plt.loglog()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "net.qubo.verify_quadratic_constraints(net.sampleset)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", + "plt.scatter(ref_values[:-1], sol, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", + "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.grid(which=\"major\", lw=1)\n", + "plt.grid(which=\"minor\", lw=0.1)\n", + "plt.xlabel('Reference Solution')\n", + "plt.ylabel('QUBO Solution')\n", + "plt.legend()\n", + "# plt.xlim([0.01,0.1])\n", + "# plt.ylim([0.01,0.1])\n", + "plt.loglog()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 0.000000 => 1000.000000 (res: 32.258065)\n", + "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.483871)\n", + "\n", + "\n", + "Error (%): [ 0. 0. 0. 100. 0. 100. 200. 200. -27.728 -60.821 76.397 100. -14.324 100. 41.318 -33.224 2.05 13.496 9.519 -1.882 17.447 17.085]\n", + "\n", + "\n", + "sol : [ 1.000e+00 1.000e+00 1.000e+00 0.000e+00 1.000e+00 0.000e+00 -1.000e+00 1.000e+00 1.403e+01 2.903e+00 1.935e+00 0.000e+00 6.774e+00 0.000e+00 4.839e-01 9.677e-01 6.452e+02 5.161e+02 5.806e+02 5.484e+02 5.161e+02 4.839e+02]\n", + "ref : [ 1. 1. 1. 1. 1. 1. 1. -1. 10.986 1.805 8.2 1.098 5.925 2.688 0.825 0.726 658.662 596.652 641.733 538.256 625.212 583.576]\n", + "diff: [ 0. 0. 0. 1. 0. 1. 2. -2. -3.046 -1.098 6.265 1.098 -0.849 2.688 0.341 -0.241 13.501 80.523 61.088 -10.131 109.083 99.705]\n", + "\n", + "\n", + "encoded_sol: [ 1.000e+00 1.000e+00 1.000e+00 -1.000e+00 1.000e+00 -1.000e+00 -1.000e+00 1.000e+00 1.403e+01 2.903e+00 1.935e+00 0.000e+00 6.774e+00 0.000e+00 4.839e-01 9.677e-01 6.452e+02 5.161e+02 5.806e+02 5.484e+02 5.161e+02 4.839e+02]\n", + "encoded_ref: [ 1. 1. 1. 1. 1. 1. 1. -1. 11.129 1.935 8.226 0.968 5.806 2.903 0.968 0.968 645.161 580.645 645.161 548.387 612.903 580.645]\n", + "diff : [ 0. 0. 0. 2. 0. 2. 2. -2. -2.903 -0.968 6.29 0.968 -0.968 2.903 0.484 0. 0. 64.516 64.516 0. 96.774 96.774]\n", + "\n", + "\n", + "E sol : -465192.96855230315\n", + "E ref : -468771.68236052594\n", + "Delta E : 3578.7138082227902\n", + "\n", + "\n", + "Residue sol : 110.01868568365907\n", + "Residue ref : 127.77565386613492\n", + "Delta Residue : -17.756968182475845\n" + ] + } + ], + "source": [ + "net.diagnostic_solution(sol, ref_sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "278" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.qubo.qubo_dict.num_variables" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[-236047.34548187256,\n", + " 516.7926635742188,\n", + " 8998.552127838135,\n", + " 70814.37801742554,\n", + " 126931.07403755188,\n", + " 296808.25490379333,\n", + " 409016.99020957947,\n", + " 448656.4262313843,\n", + " 502672.9876270294,\n", + " 527510.6125545502,\n", + " 541492.5247859955,\n", + " 606910.2308559418,\n", + " 631840.239616394,\n", + " 648027.1477527618,\n", + " 757243.5711040497,\n", + " 945453.6881694794,\n", + " 1511504.020833969,\n", + " 1585012.7138996124,\n", + " 1735121.9560184479,\n", + " 1798586.0284118652,\n", + " 2086935.8929595947,\n", + " 2206977.548427582,\n", + " 2236991.7206497192,\n", + " 2547645.1599140167,\n", + " 2733142.436199188,\n", + " 3165489.85830307,\n", + " 3217131.617866516,\n", + " 3472916.33262825,\n", + " 3754277.1072177887,\n", + " 4172746.1062812805,\n", + " 5028493.849399567,\n", + " 5064344.260848999,\n", + " 5443412.204938889,\n", + " 5471703.346788406,\n", + 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+ "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb new file mode 100644 index 0000000..01f89fc --- /dev/null +++ b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb @@ -0,0 +1,535 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "\n", + "# Create a water network model\n", + "# inp_file = './networks/Net0.inp'\n", + "inp_file = './networks/Net2LoopsDWflat.inp'\n", + "# inp_file = './networks/Net2LoopsDW.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the original Cholesky EPANET simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.045e+02, 1.940e+02, 2.013e+02, 1.841e+02, 1.982e+02, 1.912e+02, 4.395e-07], dtype=float32)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0]\n", + "ref_pressure" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.311, 0.051, 0.232, 0.032, 0.167, 0.075, 0.024, -0.02 ], dtype=float32)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_rate" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.111e-01, 5.137e-02, 2.319e-01, 3.161e-02, 1.670e-01, 7.534e-02, 2.360e-02, -1.979e-02, 2.045e+02, 1.940e+02, 2.013e+02, 1.841e+02, 1.982e+02, 1.912e+02, 4.395e-07], dtype=float32)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the QUBO Polynomial Solver" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "wn = wntr.network.WaterNetworkModel(inp_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 500.000000 => 1000.000000 (res: 16.129032)\n", + "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.483871)\n" + ] + } + ], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 5\n", + "step = (15/(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0., var_base_name=\"x\")\n", + "\n", + "nqbit = 5\n", + "step = (500/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+500.0, var_base_name=\"x\")\n", + "\n", + "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)\n", + "net.verify_encoding()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve the system classically" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1. , 1.01 , 0.998, 1.019, 0.993, 0.985, 1.021, 0.945, 1.001, 0.999, 1.001, 0.998, 1.001, 1.002])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.create_index_mapping(model)\n", + "net.matrices = net.initialize_matrices(model)\n", + "\n", + "ref_sol, encoded_ref_sol, cvgd = net.classical_solutions()\n", + "ref_sol / ref_values[:-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.scatter(ref_values[:-1], encoded_ref_sol)\n", + "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", + "plt.grid(which=\"major\", lw=1)\n", + "plt.grid(which=\"minor\", lw=0.1)\n", + "plt.loglog()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "from dwave.samplers import SimulatedAnnealingSampler, TabuSampler, SteepestDescentSampler\n", + "\n", + "sampler = SimulatedAnnealingSampler()\n", + "# sampler = TabuSampler()\n", + "# sampler = SteepestDescentSampler()\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.solve(model, strength=1E7, sampler=sampler, beta_range=[1E-10,1], num_sweeps=100000, num_reads=100)\n", + "sol = net.extract_data_from_model(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-948027.6240723133 False\n", + "-900602.5230181217 False\n", + "-32868.927169561386 True\n", + "-32046.412122249603 True\n", + "-26512.760638952255 True\n", + "-26137.74716591835 True\n", + "-21038.047699689865 True\n", + "-13895.944750785828 True\n", + "-4921.357040166855 True\n", + "-2884.2948191165924 True\n", + "3752.7706549167633 True\n", + "14544.355710983276 True\n", + "21103.367522478104 True\n", + "22047.194133520126 True\n", + "29139.366572380066 True\n", + "30615.049132347107 True\n", + "32917.714716911316 True\n", + "37270.07742738724 True\n", + "39480.241415023804 True\n", + "84002.30029296875 True\n", + "87847.17304086685 True\n", + "88537.2031071186 True\n", + "94250.38200616837 True\n", + "103185.74219727516 True\n", + "133904.9834523201 True\n", + "144388.0398569107 True\n", + "144762.22922229767 True\n", + 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"3490354.481439829 True\n", + "3570482.93633461 True\n", + "3698595.1163549423 True\n", + "4113901.726693392 True\n", + "4131267.8717622757 True\n", + "4458002.4654212 True\n", + "6700559.928070307 True\n", + "10141973.543655634 False\n" + ] + } + ], + "source": [ + "solutions,energies,statuses = net.analyze_sampleset()\n", + "for e,s in zip(energies,statuses):\n", + " print(e,s)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", + "for s in solutions[2:3]:\n", + " plt.scatter(ref_values[:-1], s, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", + "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.grid(which=\"major\", lw=1)\n", + "plt.grid(which=\"minor\", lw=0.1)\n", + "plt.xlabel('Reference Solution')\n", + "plt.ylabel('QUBO Solution')\n", + "# plt.legend()\n", + "# plt.xlim([0.01,0.1])\n", + "# plt.ylim([0.01,0.1])\n", + "plt.loglog()" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "net.qubo.verify_quadratic_constraints(net.sampleset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "net.qubo.qubo_dict.num_variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import dwave_networkx as dnx\n", + "from minorminer import find_embedding\n", + "from dwave.embedding import embed_qubo, majority_vote, chain_break_frequency\n", + "\n", + "net.qubo.qubo_dict.to_qubo()[0]\n", + "\n", + "target_graph = dnx.pegasus_graph(6)\n", + "embedding = find_embedding(net.qubo.qubo_dict.to_qubo()[0], target_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dnx.draw_pegasus(target_graph, node_size=2, width=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dnx.draw_pegasus_embedding(target_graph, embedding, node_size=10, width=0.25)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wntr_quantum/sim/models/darcy_weisbach_fit.py b/wntr_quantum/sim/models/darcy_weisbach_fit.py index 317a826..38c9cdf 100644 --- a/wntr_quantum/sim/models/darcy_weisbach_fit.py +++ b/wntr_quantum/sim/models/darcy_weisbach_fit.py @@ -27,7 +27,7 @@ def friction_factor(q, e, s): # noqa: D417 def dw_fit(roughness, diameter, plot=False, convert_to_us_unit=False): - """_summary. + """Fit the dw friction coefficient to a quadratic polynomial. Args: roughness (float): roughness pf the pipe in meter @@ -67,7 +67,7 @@ def convert_to_USunit(roughness, diameter): plt.show() print(res) - + # return np.array(res), np.poly1d(res)(1 / Q), factors, Q return np.array(res) @@ -85,26 +85,39 @@ def evaluate_fit(coeffs, flow): if __name__ == "__main__": - # res = dw_fit( - # roughness=0.000164, diameter=0.820210, plot=True, convert_to_us_unit=False - # ) + # r = 0.000164 + # d = 0.820210 + # res = dw_fit(roughness=r, diameter=d, plot=True, convert_to_us_unit=False) + # print(evaluate_fit(res, 1.766)) - roughness = 0.164 - DIAMS = np.linspace(1, 24, 25) - RES = [] + + # roughness = 0.005 + roughness = 0.5 * 1e-3 + ndiams = 5 + DIAMS = np.arange(5, 20, 3) / 12 + + BASELINE = [] + APPROX = [] for d in DIAMS: print(d) - res = dw_fit( + res, approx, baseline, qval = dw_fit( roughness=roughness, diameter=d, plot=False, convert_to_us_unit=False ) - RES.append(res) - RES = np.array(RES) - plt.plot(DIAMS, RES[:, 0]) - plt.plot(DIAMS, RES[:, 1]) - plt.plot(DIAMS, RES[:, 2]) - plt.show() - - plt.plot(DIAMS, RES[:, 0] / RES[:, 1]) - plt.plot(DIAMS, RES[:, 1] / RES[:, 1]) - plt.plot(DIAMS, RES[:, 2] / RES[:, 1]) + BASELINE.append(baseline) + APPROX.append(approx) + + n = 24 + + colors = plt.cm.tab20(np.linspace(0, 1, n)) + + i = 0 + for bl, ap in zip(BASELINE, APPROX): + plt.loglog(qval, bl, "--", c=colors[i]) + plt.loglog(qval, ap, "-", c=colors[i]) + plt.grid(visible=True, which="both") + # plt.xlim(10, 1000) + plt.xlabel("Reynold Number") + plt.ylabel("Friction Factor") + # plt.yticks([0.01, 0.015, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1]) + i += 1 plt.show() diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 216c365..fcac1a2 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -5,6 +5,10 @@ import matplotlib.pyplot as plt import numpy as np import sparse +from dimod import SampleSet +from dimod import Vartype +from dimod import Sampler +from dwave.samplers import SimulatedAnnealingSampler from quantum_newton_raphson.newton_raphson import newton_raphson from qubops.encodings import BaseQbitEncoding from qubops.encodings import PositiveQbitEncoding @@ -151,10 +155,27 @@ def func(input): res = newton_raphson(func, initial_point, max_iter=max_iter, tol=tol) sol = res.solution converged = np.allclose(func(sol), 0) + + # get the closest encoded solution + encoded_sol = np.zeros_like(sol) + for idx, s in enumerate(sol): + val, _ = self.mixed_solution_vector.encoded_reals[ + idx + num_pipes + ].find_closest(np.abs(s)) + encoded_sol[idx] = np.sign(s) * val + # convert back to SI sol = self.convert_solution_to_si(sol) + encoded_sol = self.convert_solution_to_si(encoded_sol) + + # remove the height of the junctions + for i in range(self.wn.num_junctions): + sol[num_pipes + i] -= self.wn.nodes[self.wn.junction_name_list[i]].elevation + encoded_sol[num_pipes + i] -= self.wn.nodes[ + self.wn.junction_name_list[i] + ].elevation - return (sol, converged) + return (sol, encoded_sol, converged) @staticmethod def plot_solution_vs_reference( @@ -415,7 +436,14 @@ def create_index_mapping(self, model: Model) -> None: idx += 1 def solve( # noqa: D417 - self, model: Model, strength: float = 1e6, num_reads: int = 10000, **options + self, + model: Model, + strength: float = 1e6, + sampler: Sampler = SimulatedAnnealingSampler(), + **sampler_options, + # num_reads: int = 10000, + # num_sweeps: int = 10000, + # **options, ) -> Tuple: """Solves the Hydraulics equations. @@ -434,7 +462,12 @@ def solve( # noqa: D417 self.matrices = self.initialize_matrices(model) # solve using qubo poly - sol = self.qubo_poly_solve(strength=strength, num_reads=num_reads, **options) + sol = self.qubo_poly_solve( + strength=strength, + sampler=sampler, + **sampler_options, + # strength=strength, num_sweeps=num_sweeps, num_reads=num_reads, **options + ) # load data in the AML model model.set_structure() @@ -447,24 +480,33 @@ def solve( # noqa: D417 0, ) - def qubo_poly_solve(self, strength=1e6, num_reads=10000, **options): # noqa: D417 + def qubo_poly_solve( + self, + strength=1e6, + sampler=SimulatedAnnealingSampler(), + **sampler_options, + # num_reads=10000, num_sweeps=1000, **options + ): # noqa: D417 """Solves the Hydraulics equations. Args: strength (float, optional): substitution strength. Defaults to 1e6. num_reads (int, optional): number of reads for the sampler. Defaults to 10000. + num_sweeps (int, optinal): number of sweeps. Default 1000 Returns: np.ndarray: solution of the problem """ - self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, **options) + self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, {"sampler": sampler}) matrices = tuple(sparse.COO(m) for m in self.matrices) # creates BQM self.qubo.qubo_dict = self.qubo.create_bqm(matrices, strength=strength) # sample - self.sampleset = self.qubo.sample_bqm(self.qubo.qubo_dict, num_reads=num_reads) + self.sampleset = self.qubo.sample_bqm( + self.qubo.qubo_dict, **sampler_options + ) # num_reads=num_reads, num_sweeps=num_sweeps) # decode sol = self.qubo.decode_solution(self.sampleset.lowest().record[0][0]) @@ -475,4 +517,61 @@ def qubo_poly_solve(self, strength=1e6, num_reads=10000, **options): # noqa: D4 # convert back to SI sol = self.convert_solution_to_si(sol) + # remove the height of the junction + for i in range(self.wn.num_junctions): + sol[self.wn.num_pipes + i] -= self.wn.nodes[ + self.wn.junction_name_list[i] + ].elevation + return sol + + def analyze_sampleset(self): + """Ananlyze the results contained in the sampleset.""" + + # run through all samples + solutions, energy, quadra_status = [], [], [] + for x in self.sampleset.data(): + + # create a sample + y = SampleSet.from_samples(x.sample, Vartype.BINARY, x.energy) + var = y.variables + data = np.array(y.record[0][0]) + + # see if it respects quadratic condition + status = "True" + for v, d in zip(var, data): + if v not in self.qubo.mapped_variables: + var_tmp = v.split("*") + itmp = 0 + for vtmp in var_tmp: + idx = self.qubo.index_variables[ + self.qubo.mapped_variables.index(vtmp) + ] + if itmp == 0: + dcomposite = data[idx] + itmp = 1 + else: + dcomposite *= data[idx] + if d != dcomposite: + status = False + break + quadra_status.append(status) + + # solution + sol = self.qubo.decode_solution(data) + + # combine the sign*abs values for the flow + sol = self.combine_flow_values(sol) + + # convert back to SI + sol = self.convert_solution_to_si(sol) + + # remove the height of the junction + for i in range(self.wn.num_junctions): + sol[self.wn.num_pipes + i] -= self.wn.nodes[ + self.wn.junction_name_list[i] + ].elevation + + solutions.append(sol) + energy.append(x.energy) + return solutions, energy, quadra_status From 33bdad50f2199188c40f8aeeccb392e4be0fcdde Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 16 Oct 2024 18:44:14 +0200 Subject: [PATCH 67/96] develop internal sampler --- docs/notebooks/qubo_poly_solver.ipynb | 2085 ++++++++++++++++- .../qubo_poly_solver_2loops_cm.ipynb | 24 +- .../qubo_poly_solver_2loops_dw.ipynb | 1897 +++++++++++++-- wntr_quantum/sampler/simulated_annealing.py | 221 ++ .../sim/solvers/qubo_polynomial_solver.py | 24 +- 5 files changed, 4005 insertions(+), 246 deletions(-) create mode 100644 wntr_quantum/sampler/simulated_annealing.py diff --git a/docs/notebooks/qubo_poly_solver.ipynb b/docs/notebooks/qubo_poly_solver.ipynb index ad29035..124885d 100644 --- a/docs/notebooks/qubo_poly_solver.ipynb +++ b/docs/notebooks/qubo_poly_solver.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "metadata": {} }, @@ -30,7 +30,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -39,7 +39,7 @@ "import wntr\n", "import wntr_quantum\n", "import numpy as np\n", - "\n", + "import matplotlib.pyplot as plt\n", "# Create a water network model\n", "inp_file = './networks/Net0.inp'\n", "# inp_file = './networks/Net2LoopsDW.inp'\n", @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -77,7 +77,7 @@ "" ] }, - "execution_count": 77, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -107,7 +107,7 @@ "array([26.477, 22.954], dtype=float32)" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -119,16 +119,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.cm." - ] - }, - { - "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -137,7 +128,7 @@ "array([0.05, 0.05], dtype=float32)" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -149,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -158,7 +149,7 @@ "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -177,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -186,14 +177,14 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 0.000000 => 100.000000 (res: 14.285714)\n", + "Head Encoding : 0.000000 => 100.000000 (res: 3.225806)\n", "Flow Encoding : -2.000000 => -0.000000 | 0.000000 => 2.000000 (res: 0.064516)\n" ] } @@ -207,7 +198,7 @@ "step = (2./(2**nqbit-1))\n", "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", "\n", - "nqbit = 3\n", + "nqbit = 5\n", "step = (100/(2**nqbit-1))\n", "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", @@ -225,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -234,7 +225,7 @@ "array([1. , 1. , 0.999, 0.998])" ] }, - "execution_count": 91, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -251,16 +242,16 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.987, 0.987, 0.987, 0.948])" + "array([0.987, 0.987, 1.003, 0.985])" ] }, - "execution_count": 92, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -271,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -280,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -289,7 +280,7 @@ "array([ 0. , 1.766, 99.077, 0.652])" ] }, - "execution_count": 94, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -305,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -314,7 +305,7 @@ "array([ 1.766, 1.766, 86.797, 75.168])" ] }, - "execution_count": 95, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ 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False\n", + "990432.8973407075 False\n", + "990450.8618845195 False\n", + "990454.8877648711 False\n", + "990456.7459740117 False\n", + "990457.5757035092 False\n", + "990464.4891519174 False\n", + "990468.9488407671 False\n", + "990474.6120955572 False\n", + "990487.9531336948 False\n", + "990490.9821678177 False\n", + "990495.7636168599 False\n", + "990584.7526462078 False\n", + "990632.2987249866 False\n", + "990673.3891029209 False\n", + "990689.5721127167 False\n", + "990690.2124982253 False\n", + "990724.6441722289 False\n", + "990753.9539984092 False\n", + "990782.6970554069 False\n", + "990850.6007880569 False\n", + "990865.1441291496 False\n", + "990865.8487880826 False\n", + "991268.7598912641 False\n" + ] + } + ], + "source": [ + "solutions,energies,statuses = net.analyze_sampleset()\n", + "for e,s in zip(energies,statuses):\n", + " print(e,s)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "[]" ] }, - "execution_count": 99, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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"text/plain": [ "
" ] @@ -366,8 +1375,10 @@ ], "source": [ "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_values, encoded_ref_sol, c='black', s=100, label='Best solution')\n", - "plt.scatter(ref_values, sol, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", + "plt.scatter(ref_values, encoded_ref_sol, c='black', s=100, label='Best possible solution')\n", + "for s in solutions[1:5]:\n", + " plt.scatter(ref_values, s, s=50, lw=1, alpha=0.5, edgecolors='w', label='Sampled solution')\n", + "plt.scatter(ref_values, solutions[0], s=50, lw=1, c='blue', edgecolors='w', label='Best sampled solution')\n", "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", @@ -375,24 +1386,27 @@ "plt.grid(which=\"minor\", lw=0.1)\n", "plt.xlabel('Reference Solution')\n", "plt.ylabel('QUBO Solution')\n", - "plt.legend()\n", + "# plt.legend()\n", "# plt.xlim([0.01,0.1])\n", "# plt.ylim([0.01,0.1])\n", - "# plt.loglog()" + "\n", + "# plt.xlim([10,50])\n", + "# plt.ylim([10,50])\n", + "plt.loglog()\n" ] }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "66" + "72" ] }, - "execution_count": 98, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -403,7 +1417,891 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "999998.1773075246\n", + "-2000000.0\n", + "-2000000.0\n", + "1000005.6999586402\n", + "999996.3546150491\n", + "1000000.0\n", + "-2000000.0\n", + "-2000000.0\n", + "-0.03329864724245577\n", + "0.06659729448491154\n", + "-0.06659729448491154\n", + "0.13319458896982309\n", + "999955.6967091897\n", + "0.06659729448491154\n", + "-0.13319458896982309\n", + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "v=[]\n", + "for i in net.qubo.qubo_dict.iter_quadratic():\n", + " v.append((i[2]))\n", + " print(i[2])\n", + "\n", + "plt.hist(v)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v" + ] + }, + { + "cell_type": "code", + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -1145,6 +3043,91 @@ "embedding = find_embedding(net.qubo.qubo_dict.to_qubo()[0], target_graph)" ] }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'x_004_004': [217, 218, 632, 634, 234, 633],\n", + " 'x_002_001': [647, 228, 229],\n", + " 'x_002_001*x_004_004': [606, 607, 609, 238, 608],\n", + " 'x_004_003': [584, 583, 178, 581, 582],\n", + " 'x_002_001*x_004_003': [162, 562, 648, 263, 262, 563, 564],\n", + " 'x_003_003': [524, 294, 293, 291, 292],\n", + " 'x_001_001': [529, 527, 528],\n", + " 'x_003_003*x_001_001': [301, 304, 303, 302],\n", + " 'x_004_005': [622, 624, 623, 212, 213],\n", + " 'x_002_001*x_004_005': [543, 157, 227, 569, 567, 568],\n", + " 'x_003_004': [271, 588, 554, 273, 272],\n", + " 'x_003_004*x_001_001': [286, 289, 287, 288],\n", + " 'x_004_002': [576, 577, 579, 578, 207],\n", + " 'x_004_001': [666, 667, 243, 244, 668, 669],\n", + " 'x_004_002*x_004_001': [167, 628, 627, 169, 168],\n", + " 'x_003_002': [338, 251, 252, 253, 599, 598, 233],\n", + " 'x_003_002*x_001_001': [284, 281, 283, 282],\n", + " 'x_003_001': [309, 484, 306, 308, 307],\n", + " 'x_003_005': [296, 519, 299, 298, 297],\n", + " 'x_003_001*x_003_005': [206, 497, 499, 261, 498],\n", + " 'x_004_002*x_002_001': [673, 629, 269, 267, 268],\n", + " 'x_002_001*x_004_001': [572, 574, 573],\n", + " 'x_004_005*x_004_003': [149, 147, 596, 148],\n", + " 'x_003_001*x_001_001': [539, 538, 232, 258, 557, 558],\n", + " 'x_001_001*x_003_005': [276, 279, 277, 278],\n", + " 'x_003_004*x_003_002': [312, 314, 559, 313],\n", + " 'x_002_001*x_004_004*x_004_001': [142, 144, 143],\n", + " 'x_004_002*x_004_004': [208, 597],\n", + " 'x_004_004*x_004_001': [134, 133],\n", + " 'x_002_001*x_004_003*x_004_005': [164, 163],\n", + " 'x_004_002*x_002_001*x_004_004': [139, 138],\n", + " 'x_004_004*x_004_005': [249],\n", + " 'x_002_001*x_004_004*x_004_003': [602],\n", + " 'x_002_001*x_004_004*x_004_005': [174, 173],\n", + " 'x_004_004*x_004_003': [586, 587],\n", + " 'x_003_002*x_003_005': [474, 472, 473],\n", + " 'x_003_003*x_003_001': [544, 327, 328],\n", + " 'x_004_001*x_004_003': [128, 129],\n", + " 'x_003_004*x_003_003*x_001_001': [494, 492, 493],\n", + " 'x_002_001*x_004_002*x_004_001': [184],\n", + " 'x_004_002*x_002_001*x_004_005': [532],\n", + " 'x_004_002*x_004_003': [118, 119],\n", + " 'x_004_001*x_004_005': [254],\n", + " 'x_004_002*x_004_005': [621, 114, 113],\n", + " 'x_004_001*x_002_001*x_004_003': [259],\n", + " 'x_004_002*x_002_001*x_004_003': [552],\n", + " 'x_004_001*x_002_001*x_004_005': [159, 158],\n", + " 'x_003_004*x_001_001*x_003_002': [479, 477, 478],\n", + " 'x_005_003': [644, 641, 201, 202, 203, 642, 643],\n", + " 'x_005_003*x_003_001': [604, 323],\n", + " 'x_005_002': [594, 593, 221, 613, 183, 591, 592, 222, 223],\n", + " 'x_005_002*x_003_001': [504, 503],\n", + " 'x_005_002*x_003_005': [548, 549],\n", + " 'x_003_003*x_003_004': [514, 513],\n", + " 'x_005_003*x_003_005': [319, 274, 664],\n", + " 'x_003_002*x_003_003*x_001_001': [509, 266, 482, 483],\n", + " 'x_005_001': [616, 191, 192, 193, 194, 619, 618, 617],\n", + " 'x_005_001*x_003_005': [614],\n", + " 'x_003_001*x_005_001': [444, 462, 270, 463],\n", + " 'x_003_003*x_003_002': [489, 487, 488],\n", + " 'x_003_001*x_003_005*x_001_001': [522],\n", + " 'x_003_004*x_003_002*x_003_005': [654],\n", + " 'x_003_004*x_003_002*x_003_003*x_001_001': [534],\n", + " 'x_006_001': [531, 646, 152, 639, 638, 637, 153],\n", + " 'x_006_002': [187, 611, 658, 657, 612, 188, 189],\n", + " 'x_006_003': [197, 651, 198, 653, 652, 199]}" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embedding" + ] + }, { "cell_type": "code", "execution_count": 124, @@ -1167,12 +3150,12 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 131, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1182,7 +3165,7 @@ } ], "source": [ - "dnx.draw_pegasus_embedding(target_graph, embedding, node_size=4, width=0.1)" + "dnx.draw_pegasus_embedding(target_graph, embedding, node_size=10, width=0.25)" ] }, { diff --git a/docs/notebooks/qubo_poly_solver_2loops_cm.ipynb b/docs/notebooks/qubo_poly_solver_2loops_cm.ipynb index 7c55352..62c6566 100644 --- a/docs/notebooks/qubo_poly_solver_2loops_cm.ipynb +++ b/docs/notebooks/qubo_poly_solver_2loops_cm.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": { "metadata": {} }, @@ -30,7 +30,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -39,6 +39,7 @@ "import wntr\n", "import wntr_quantum\n", "import numpy as np\n", + "import matplotlib.pyplot as plt \n", "\n", "# Create a water network model\n", "# inp_file = './networks/Net0.inp'\n", @@ -58,14 +59,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -74,10 +75,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -87,8 +88,13 @@ "results = sim.run_sim()\n", "# Plot results on the network\n", "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" + "flow_at_5hr = results.link['flowrate'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, link_attribute=flow_at_5hr, \n", + " node_attribute=pressure_at_5hr, \n", + " node_size=500, \n", + " link_width=5, \n", + " node_labels=True,\n", + " link_cmap=plt.cm.cividis)" ] }, { diff --git a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb index 01f89fc..ec57a68 100644 --- a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb +++ b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb @@ -9,14 +9,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "metadata": { "metadata": {} }, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", "text/plain": [ "
" ] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -43,6 +43,7 @@ "# Create a water network model\n", "# inp_file = './networks/Net0.inp'\n", "inp_file = './networks/Net2LoopsDWflat.inp'\n", + "inp_file = './networks/Net2LoopsCMflat.inp'\n", "# inp_file = './networks/Net2LoopsDW.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", @@ -59,12 +60,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -78,7 +79,7 @@ "" ] }, - "execution_count": 18, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -94,16 +95,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([2.045e+02, 1.940e+02, 2.013e+02, 1.841e+02, 1.982e+02, 1.912e+02, 4.395e-07], dtype=float32)" + "array([2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" ] }, - "execution_count": 19, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -115,16 +116,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 0.311, 0.051, 0.232, 0.032, 0.167, 0.075, 0.024, -0.02 ], dtype=float32)" + "array([ 0.311, 0.051, 0.232, 0.031, 0.168, 0.076, 0.023, -0.021], dtype=float32)" ] }, - "execution_count": 20, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -136,16 +137,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 3.111e-01, 5.137e-02, 2.319e-01, 3.161e-02, 1.670e-01, 7.534e-02, 2.360e-02, -1.979e-02, 2.045e+02, 1.940e+02, 2.013e+02, 1.841e+02, 1.982e+02, 1.912e+02, 4.395e-07], dtype=float32)" + "array([ 3.111e-01, 5.111e-02, 2.322e-01, 3.108e-02, 1.678e-01, 7.613e-02, 2.334e-02, -2.058e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" ] }, - "execution_count": 21, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -173,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -192,7 +193,7 @@ "\n", "nqbit = 5\n", "step = (15/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0., var_base_name=\"x\")\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", "nqbit = 5\n", "step = (500/(2**nqbit-1))\n", @@ -212,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -226,10 +227,10 @@ { "data": { "text/plain": [ - "array([1. , 1.01 , 0.998, 1.019, 0.993, 0.985, 1.021, 0.945, 1.001, 0.999, 1.001, 0.998, 1.001, 1.002])" + "array([1. , 1. , 1. , 1. , 1. , 1. , 1. , 0.999, 1. , 1.001, 1. , 1.001, 1. , 1.001])" ] }, - "execution_count": 24, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -240,28 +241,18 @@ "net.create_index_mapping(model)\n", "net.matrices = net.initialize_matrices(model)\n", "\n", - "ref_sol, encoded_ref_sol, cvgd = net.classical_solutions()\n", + "ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = net.classical_solutions()\n", "ref_sol / ref_values[:-1]" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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" ] @@ -276,162 +267,220 @@ "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", "plt.grid(which=\"major\", lw=1)\n", "plt.grid(which=\"minor\", lw=0.1)\n", - "plt.loglog()" + "# plt.loglog()\n", + "plt.xscale('symlog')\n", + "plt.yscale('symlog')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "# from dwave.samplers import SimulatedAnnealingSampler, TabuSampler, SteepestDescentSampler, RandomSampler\n", + "\n", + "# sampler = SimulatedAnnealingSampler()\n", + "# # sampler = TabuSampler()\n", + "# # sampler = SteepestDescentSampler()\n", + "# # sampler = RandomSampler()\n", + "# model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "# net.solve(model, strength=1E7, sampler=sampler, num_sweeps=int(1E1), num_reads=10)\n", + "# # sol = net.extract_data_from_model(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# solutions,energies,statuses = net.analyze_sampleset()\n", + "# for e,s in zip(energies,statuses):\n", + "# print(e,s)\n" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# import matplotlib.pyplot as plt \n", + "# plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", + "# for s in solutions[2:4]:\n", + "# plt.scatter(ref_values[:-1], s, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", + "# plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "# plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", + "# plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", + "# plt.grid(which=\"major\", lw=1)\n", + "# plt.grid(which=\"minor\", lw=0.1)\n", + "# plt.xlabel('Reference Solution')\n", + "# plt.ylabel('QUBO Solution')\n", + "# # plt.legend()\n", + "# plt.xlim([-0.5,0.5])\n", + "# plt.ylim([-0.5,0.5])\n", + "# # plt.loglog()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Own sampler" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "from dwave.samplers import SimulatedAnnealingSampler, TabuSampler, SteepestDescentSampler\n", "\n", - "sampler = SimulatedAnnealingSampler()\n", - "# sampler = TabuSampler()\n", - "# sampler = SteepestDescentSampler()\n", "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.solve(model, strength=1E7, sampler=sampler, beta_range=[1E-10,1], num_sweeps=100000, num_reads=100)\n", - "sol = net.extract_data_from_model(model)" + "net.matrices = net.initialize_matrices(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", + "sampler = SimulatedAnnealing()" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", + "import sparse\n", + "net.qubo = QUBOPS_MIXED(net.mixed_solution_vector, {\"sampler\": sampler})\n", + "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "net.qubo.qubo_dict = net.qubo.create_bqm(matrices, strength=1E7)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import ProposalStep\n", + "var_names = sorted(net.qubo.qubo_dict.variables)\n", + "net.qubo.create_variables_mapping()\n", + "mystep = ProposalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import generate_random_valid_sample\n", + "x = generate_random_valid_sample(net.qubo)\n", + "x0 = list(x.values())" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "num_sweeps = 9000\n", + "Tinit = 1E5\n", + "Tfinal = 1E-1\n", + "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))" + ] + }, + { + "cell_type": "code", + "execution_count": 47, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "-948027.6240723133 False\n", - "-900602.5230181217 False\n", - "-32868.927169561386 True\n", - "-32046.412122249603 True\n", - "-26512.760638952255 True\n", - "-26137.74716591835 True\n", - "-21038.047699689865 True\n", - "-13895.944750785828 True\n", - "-4921.357040166855 True\n", - "-2884.2948191165924 True\n", - "3752.7706549167633 True\n", - 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"1662730.5593101978 True\n", - "1752173.5028457642 True\n", - "1803581.6721906662 True\n", - "1806459.8794898987 True\n", - "1909505.9712207317 True\n", - "1968114.989625454 True\n", - "2221184.166315794 False\n", - "2281977.030180216 True\n", - "2288849.1373004913 True\n", - "2354616.2907493114 True\n", - "2398865.974765539 True\n", - "2526709.6815133095 True\n", - "2600630.5689024925 True\n", - "2748778.0871264935 True\n", - "2759850.3770041466 True\n", - "2997475.2796702385 True\n", - "3490354.481439829 True\n", - "3570482.93633461 True\n", - "3698595.1163549423 True\n", - "4113901.726693392 True\n", - "4131267.8717622757 True\n", - "4458002.4654212 True\n", - "6700559.928070307 True\n", - "10141973.543655634 False\n" + "100%|██████████| 10000/10000 [00:19<00:00, 520.42it/s]\n" ] } ], "source": [ - "solutions,energies,statuses = net.analyze_sampleset()\n", - "for e,s in zip(energies,statuses):\n", - " print(e,s)" + "res = sampler.sample(net.qubo.qubo_dict, x0=x0, Tschedule=Tschedule, take_step=mystep)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, + "outputs": [], + "source": [ + "mystep.verify_quadratic_constraints(res.res)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "eref = net.qubo.energy_binary_rep(bin_rep_sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "[]" + "
" ] }, - "execution_count": 41, "metadata": {}, - "output_type": "execute_result" - }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "eplt = res.energies[-250:]\n", + "plt.plot(eplt)\n", + "plt.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "plt.grid()\n", + "plt.yscale('symlog')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "sol = net.qubo.decode_solution(np.array(res.res))\n", + "sol = net.combine_flow_values(sol)\n", + "sol = net.convert_solution_to_si(sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -443,8 +492,7 @@ "source": [ "import matplotlib.pyplot as plt \n", "plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", - "for s in solutions[2:3]:\n", - " plt.scatter(ref_values[:-1], s, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", + "plt.scatter(ref_values[:-1], sol, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", @@ -453,62 +501,1565 @@ "plt.xlabel('Reference Solution')\n", "plt.ylabel('QUBO Solution')\n", "# plt.legend()\n", - "# plt.xlim([0.01,0.1])\n", - "# plt.ylim([0.01,0.1])\n", - "plt.loglog()" + "# plt.xlim([-0.5,0.5])\n", + "# plt.ylim([-0.5,0.5])\n", + "# plt.loglog()\n", + "plt.xscale('symlog')\n", + "plt.yscale('symlog')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Own Sampler Manual" ] }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "net.qubo.verify_quadratic_constraints(net.sampleset)" + "def generate_random_valid_sample(qubo):\n", + " \"\"\"check if quadratic constraints are respected or not\n", + "\n", + " Args:\n", + " sampleset (_type_): _description_\n", + " \"\"\"\n", + " sample = {}\n", + " for iv, v in enumerate(sorted(net.qubo.qubo_dict.variables)):\n", + " sample[v] = 1 # np.random.randint(2)\n", + "\n", + " for v in qubo.mapped_variables[:7]:\n", + " sample[v] = 1\n", + " sample[qubo.mapped_variables[7]] = 0\n", + "\n", + " for v, _ in sample.items():\n", + " if v not in qubo.mapped_variables:\n", + " var_tmp = v.split(\"*\")\n", + " itmp = 0\n", + " for vtmp in var_tmp:\n", + " if itmp == 0:\n", + " new_val = sample[vtmp]\n", + " itmp = 1\n", + " else:\n", + " new_val *= sample[vtmp]\n", + " \n", + " sample[v] = new_val\n", + " return sample " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "net.qubo.qubo_dict.num_variables" + "\n", + "sample = generate_random_valid_sample(net.qubo)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "import dwave_networkx as dnx\n", - "from minorminer import find_embedding\n", - "from dwave.embedding import embed_qubo, majority_vote, chain_break_frequency\n", + "from copy import deepcopy\n", + "class OptStep:\n", + " def __init__(self,var_names, single_var_names, single_var_index):\n", + " self.var_names = var_names\n", + " self.single_var_names = single_var_names\n", + " self.single_var_index = single_var_index\n", + " self.num_single_var = len(self.single_var_names)\n", + " self.high_order_terms_mapping = self.define_mapping()\n", + "\n", + " def define_mapping(self):\n", + " high_order_terms_mapping = []\n", "\n", - "net.qubo.qubo_dict.to_qubo()[0]\n", + " # loop over all the variables\n", + " for iv, v in enumerate(self.var_names):\n", + " \n", + " # if we have a cmomposite variables e.g. x_001 * x_002 we ignore it\n", + " if v not in self.single_var_names:\n", + " high_order_terms_mapping.append(None)\n", + " \n", + " # if the variables is a unique one e.g. x_011\n", + " else:\n", + " high_order_terms_mapping.append({})\n", + " # we loop over all the variables\n", + " for iiv, vv in enumerate(self.var_names):\n", + " if v != vv:\n", + " if v in vv:\n", + " \n", + " var_tmp = vv.split(\"*\")\n", + " idx_terms = []\n", + " for vtmp in var_tmp:\n", + " idx = self.single_var_index[self.single_var_names.index(vtmp)]\n", + " idx_terms.append(idx)\n", + " high_order_terms_mapping[-1][iiv] = idx_terms\n", "\n", - "target_graph = dnx.pegasus_graph(6)\n", - "embedding = find_embedding(net.qubo.qubo_dict.to_qubo()[0], target_graph)" + " return high_order_terms_mapping\n", + "\n", + " def fix_constraint(self, x, idx):\n", + " fix_var = self.high_order_terms_mapping[idx]\n", + " for idx_fix, idx_prods in fix_var.items():\n", + " x[idx_fix] = np.array([x[i] for i in idx_prods]).prod()\n", + " return x \n", + "\n", + " def __call__(self, x):\n", + " vidx = np.random.choice(self.single_var_index)\n", + " if vidx not in self.single_var_index[:8]:\n", + " x[vidx] = int(not(x[vidx]))\n", + " self.fix_constraint(x, vidx)\n", + " return x \n", + "\n", + "var_names = sorted(net.qubo.qubo_dict.variables)\n", + "mytakestep = OptStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", + "mytakestep.high_order_terms_mapping[0]\n", + "x0 = list(sample.values())\n", + "x0_cpy = deepcopy(list(sample.values()))\n", + "x = mytakestep(x0)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['x_001_001',\n", + " 'x_001_001*x_009_001',\n", + " 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'x_022_005',\n", + " 'x_022_006',\n", + " 'x_022_007',\n", + " 'x_022_008',\n", + " 'x_022_009']" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(net.qubo.qubo_dict.variables)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.141e+09])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from dimod import as_samples \n", + "def bqm_energy(x):\n", + " return net.qubo.qubo_dict.energies(as_samples((x, var_names)))\n", + "bqm_energy(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10000/10000 [01:06<00:00, 150.15it/s]\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'OptStep' object has no attribute 'verify_quadratic_constraints'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 31\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 30\u001b[0m x \u001b[38;5;241m=\u001b[39m x_ori\n\u001b[0;32m---> 31\u001b[0m \u001b[43mmytakestep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverify_quadratic_constraints\u001b[49m(x)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'OptStep' object has no attribute 'verify_quadratic_constraints'" + ] + } + ], + "source": [ + "from tqdm import tqdm\n", + "num_sweeps = 9000\n", + "Tinit = 1E5\n", + "Tfinal = 1E-1\n", + "sample = generate_random_valid_sample(net.qubo)\n", + "x = list(sample.values())\n", + "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "# Tschedule = np.zeros(10000)\n", + "energies = []\n", + "energies.append(bqm_energy(x))\n", + "for T in tqdm(Tschedule):\n", + "\n", + " x_ori = deepcopy(x)\n", + " e_ori = bqm_energy(x)\n", + " x_new = mytakestep(x) \n", + " e_new = bqm_energy(x)\n", + "\n", + " if e_new < e_ori:\n", + " x = x_new\n", + " energies.append(bqm_energy(x))\n", + " elif T != 0.0:\n", + " p = np.exp( -(e_new - e_ori) / T )\n", + " if np.random.rand() < p:\n", + " x = x_new\n", + " energies.append(bqm_energy(x))\n", + " else:\n", + " x = x_ori \n", + " else:\n", + " x = x_ori\n", + "# mytakestep.verify_quadratic_constraints(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "dnx.draw_pegasus(target_graph, node_size=2, width=0.1)" + "def flatten_list(lst):\n", + " out = []\n", + " for l in lst:\n", + " if not isinstance(l, list):\n", + " out += [l]\n", + " else:\n", + " out += l\n", + " return out\n", + "bin_rep_sol_flat = flatten_list(bin_rep_sol)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_7683/121086906.py:3: DeprecationWarning: support for (dict, labels) as a samples-like is deprecated since dimod 0.10.13 and will be removed in 0.12.0\n", + " return net.qubo.qubo_dict.energies(as_samples((x, var_names)))\n" + ] + } + ], + "source": [ + "eref = bqm_energy(net.qubo.extend_binary_representation(bin_rep_sol_flat))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "eplt = energies #[-200:]\n", + "plt.plot(eplt)\n", + "plt.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "plt.grid()\n", + "plt.yscale('symlog')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "dnx.draw_pegasus_embedding(target_graph, embedding, node_size=10, width=0.25)" + "sol = net.qubo.decode_solution(np.array(x))\n", + "sol = net.combine_flow_values(sol)\n", + "sol = net.convert_solution_to_si(sol)" ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", + "plt.scatter(ref_values[:-1], sol, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", + "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.grid(which=\"major\", lw=1)\n", + "plt.grid(which=\"minor\", lw=0.1)\n", + "plt.xlabel('Reference Solution')\n", + "plt.ylabel('QUBO Solution')\n", + "# plt.legend()\n", + "# plt.xlim([-0.5,0.5])\n", + "# plt.ylim([-0.5,0.5])\n", + "# plt.loglog()\n", + "plt.xscale('symlog')\n", + "plt.yscale('symlog')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 0,\n", + " [0, 1, 1, 0, 1, 1, 1, 0, 1],\n", + " [1, 0, 1, 1, 1, 1, 0, 0, 0],\n", + " [1, 1, 1, 0, 1, 0, 0, 0, 1],\n", + " [1, 0, 1, 0, 0, 1, 0, 0, 0],\n", + " [0, 1, 0, 1, 0, 0, 1, 1, 0],\n", + " [0, 0, 1, 1, 1, 0, 1, 0, 0],\n", + " [0, 0, 1, 1, 1, 0, 0, 0, 0],\n", + " [1, 0, 0, 1, 1, 0, 0, 0, 0],\n", + " [0, 1, 0, 0, 0, 1, 0, 1, 0],\n", + " [1, 1, 0, 0, 0, 1, 1, 0, 0],\n", + " [1, 0, 0, 0, 1, 0, 0, 1, 0],\n", + " [1, 1, 1, 0, 0, 1, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 1, 0],\n", + " [1, 0, 1, 0, 1, 0, 1, 0, 0]]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bin_rep_sol" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.109e-01, 5.070e-02, 2.319e-01, 3.076e-02, 1.679e-01, 7.647e-02, 2.327e-02, -2.078e-02, 2.007e+02, 1.819e+02, 1.956e+02, 1.640e+02, 1.906e+02, 1.778e+02])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoded_ref_sol" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([0,\n", + " 34,\n", + " 37,\n", + " 38,\n", + " 63,\n", + " 74,\n", + " 98,\n", + " 111,\n", + " 150,\n", + " 157,\n", + " 171,\n", + " 175,\n", + " 184,\n", + " 191,\n", + " 193,\n", + " 196,\n", + " 200,\n", + " 207,\n", + " 222,\n", + " 231,\n", + " 243,\n", + " 244,\n", + " 259,\n", + " 272,\n", + " 283,\n", + " 290,\n", + " 295,\n", + " 305,\n", + " 317,\n", + " 325,\n", + " 342,\n", + " 352,\n", + " 358,\n", + " 372,\n", + " 376,\n", + " 385,\n", + " 389,\n", + " 396,\n", + " 398,\n", + " 406,\n", + " 423,\n", + " 440,\n", + " 446,\n", + " 450,\n", + " 451,\n", + " 461,\n", + " 477,\n", + " 483,\n", + " 498,\n", + " 509,\n", + " 515,\n", + " 518,\n", + " 520,\n", + " 531,\n", + " 535,\n", + " 546,\n", + " 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\"\"\"generates the index of variables in the solution vector\n", + "\n", + " Args:\n", + " sol (dimod.Sampleset): sampleset from the sampler\n", + " \"\"\"\n", + "\n", + " # get all the possible variable prefixes\n", + " prefixes = list(\n", + " set(\n", + " [\n", + " sv.base_name + \"_\"\n", + " for sv in qubo.mixed_solution_vectors.solution_vectors\n", + " ]\n", + " )\n", + " )\n", + "\n", + " # extract the data of the original variables\n", + " index_variables, mapped_variables = [], []\n", + " for ix, s in enumerate(sorted(qubo.qubo_dict.variables)):\n", + " if s in qubo.all_vars and np.any([s.startswith(pf) for pf in prefixes]):\n", + " index_variables.append(ix)\n", + " mapped_variables.append(s)\n", + " return index_variables, mapped_variables \n", + "\n", + "create_variables_mapping(net.qubo)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import generate_random_valid_sample, ProposalStep, SimulatedAnnealing" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "mystep = ProposalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "x = generate_random_valid_sample(net.qubo)\n", + "sampler = SimulatedAnnealing()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'SimulatedAnnealing' object has no attribute 'variables'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtake_step\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmystep\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sampler/simulated_annealing.py:164\u001b[0m, in \u001b[0;36mSimulatedAnnealing.sample\u001b[0;34m(bqm, num_sweeps, Temp, x0, take_step)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtake_step must be callable\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 163\u001b[0m \u001b[38;5;66;03m# define th variable names\u001b[39;00m\n\u001b[0;32m--> 164\u001b[0m var_names \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(\u001b[43mbqm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvariables\u001b[49m)\n\u001b[1;32m 166\u001b[0m \u001b[38;5;66;03m# define the initial state\u001b[39;00m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x0 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[0;31mAttributeError\u001b[0m: 'SimulatedAnnealing' object has no attribute 'variables'" + ] + } + ], + "source": [ + "sampler.sample(net.qubo.qubo_dict, x0=x, take_step=mystep)" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "callable(mystep)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py new file mode 100644 index 0000000..364b086 --- /dev/null +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -0,0 +1,221 @@ +from copy import deepcopy +import numpy as np +from dimod import as_samples +from tqdm import tqdm +from dataclasses import dataclass + + +def generate_random_valid_sample(qubo): + """Geenrate a random sample that respects quadratization. + + Args: + qubo (_type_): _description_ + """ + sample = {} + for iv, v in enumerate(sorted(qubo.qubo_dict.variables)): + sample[v] = np.random.randint(2) + + for v, _ in sample.items(): + if v not in qubo.mapped_variables: + var_tmp = v.split("*") + itmp = 0 + for vtmp in var_tmp: + if itmp == 0: + new_val = sample[vtmp] + itmp = 1 + else: + new_val *= sample[vtmp] + + sample[v] = new_val + return sample + + +class ProposalStep: # noqa: D101 + def __init__(self, var_names, single_var_names, single_var_index): + """Propose a new solution vector. + + Args: + var_names (_type_): _description_ + single_var_names (_type_): _description_ + single_var_index (_type_): _description_ + """ + self.var_names = var_names + self.single_var_names = single_var_names + self.single_var_index = single_var_index + self.num_single_var = len(self.single_var_names) + self.high_order_terms_mapping = self.define_mapping() + + def define_mapping(self): + """Define the mapping of the higher order terms. + + Returns: + _type_: _description_ + """ + high_order_terms_mapping = [] + + # loop over all the variables + for iv, v in enumerate(self.var_names): + + # if we have a cmomposite variables e.g. x_001 * x_002 we ignore it + if v not in self.single_var_names: + high_order_terms_mapping.append(None) + + # if the variables is a unique one e.g. x_011 + else: + high_order_terms_mapping.append({}) + # we loop over all the variables + for iiv, vv in enumerate(self.var_names): + if v != vv: + if v in vv: + + var_tmp = vv.split("*") + idx_terms = [] + for vtmp in var_tmp: + idx = self.single_var_index[ + self.single_var_names.index(vtmp) + ] + idx_terms.append(idx) + high_order_terms_mapping[-1][iiv] = idx_terms + + return high_order_terms_mapping + + def fix_constraint(self, x, idx): + """Ensure that the solution vectors respect quadratization. + + Args: + x (_type_): _description_ + idx (_type_): _description_ + + Returns: + _type_: _description_ + """ + fix_var = self.high_order_terms_mapping[idx] + for idx_fix, idx_prods in fix_var.items(): + x[idx_fix] = np.array([x[i] for i in idx_prods]).prod() + return x + + def verify_quadratic_constraints(self, data): + """Check if quadratic constraints are respected or not. + + Args: + data (_type_): _description_ + """ + for v, d in zip(self.var_names, data): + if v not in self.single_var_names: + var_tmp = v.split("*") + itmp = 0 + for vtmp in var_tmp: + idx = self.single_var_index[self.single_var_names.index(vtmp)] + if itmp == 0: + dcomposite = data[idx] + itmp = 1 + else: + dcomposite *= data[idx] + if d != dcomposite: + print("Error in the quadratic contraints") + print("%s = %d" % (v, d)) + for vtmp in var_tmp: + idx = self.single_var_index[self.single_var_names.index(vtmp)] + print("%s = %d" % (vtmp, data[idx])) + + def __call__(self, x): + """Call function of the method. + + Args: + x (_type_): _description_ + + Returns: + _type_: _description_ + """ + vidx = np.random.choice(self.single_var_index) + x[vidx] = int(not (x[vidx])) + self.fix_constraint(x, vidx) + return x + + +@dataclass +class SimulatedAnnealingResults: + """Result of the simulated nnelaings""" + + res: list + energies: list + + +class SimulatedAnnealing: # noqa: D101 + + def __init__(self): # noqa: D107 + self.properties = {} + + def sample( + self, + bqm, + num_sweeps=100, + Temp=[1e5, 1e-3], + Tschedule=None, + x0=None, + take_step=None, + ): + """Sample the problem. + + Args: + bqm (_type_): _description_ + num_sweeps (int, optional): _description_. Defaults to 100. + Temp (list, optional): _description_. Defaults to [1e5, 1e-3]. + x0 (_type_, optional): _description_. Defaults to None. + take_step (_type_, optional): _description_. Defaults to None. + """ + + def bqm_energy(x, var_names): + """Compute the energy of a given binary array. + + Args: + x (_type_): _description_ + var_names (list): list of var names + """ + return bqm.energies(as_samples((x, var_names))) + + # check that take_step is callable + if not callable(take_step): + raise ValueError("take_step must be callable") + + # define th variable names + var_names = sorted(bqm.variables) + + # define the initial state + if x0 is None: + x = np.random.randint(2, size=bqm.num_variables) + else: + x = x0 + + # define the energy range + if Tschedule is None: + Tschedule = np.linspace(Temp[0], Temp[1], num_sweeps) + + # initialize the energy + energies = [] + energies.append(bqm_energy(x, var_names)) + + # loop over the temp schedule + for T in tqdm(Tschedule): + + # original point + x_ori = deepcopy(x) + e_ori = bqm_energy(x, var_names) + + # new point + x_new = take_step(x) + e_new = bqm_energy(x, var_names) + + # accept/reject + if e_new < e_ori: + x = x_new + energies.append(bqm_energy(x, var_names)) + else: + p = np.exp(-(e_new - e_ori) / T) + if np.random.rand() < p: + x = x_new + energies.append(bqm_energy(x, var_names)) + else: + x = x_ori + + return SimulatedAnnealingResults(x, energies) diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index fcac1a2..64891c1 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -156,12 +156,17 @@ def func(input): sol = res.solution converged = np.allclose(func(sol), 0) - # get the closest encoded solution + # get the closest encoded solution and binary encoding + bin_rep_sol = [] + for i in range(num_pipes): + bin_rep_sol.append(int(sol[i] > 0)) + encoded_sol = np.zeros_like(sol) for idx, s in enumerate(sol): - val, _ = self.mixed_solution_vector.encoded_reals[ + val, bin_rpr = self.mixed_solution_vector.encoded_reals[ idx + num_pipes ].find_closest(np.abs(s)) + bin_rep_sol.append(bin_rpr) encoded_sol[idx] = np.sign(s) * val # convert back to SI @@ -175,7 +180,7 @@ def func(input): self.wn.junction_name_list[i] ].elevation - return (sol, encoded_sol, converged) + return (sol, encoded_sol, bin_rep_sol, converged) @staticmethod def plot_solution_vs_reference( @@ -441,9 +446,6 @@ def solve( # noqa: D417 strength: float = 1e6, sampler: Sampler = SimulatedAnnealingSampler(), **sampler_options, - # num_reads: int = 10000, - # num_sweeps: int = 10000, - # **options, ) -> Tuple: """Solves the Hydraulics equations. @@ -463,10 +465,7 @@ def solve( # noqa: D417 # solve using qubo poly sol = self.qubo_poly_solve( - strength=strength, - sampler=sampler, - **sampler_options, - # strength=strength, num_sweeps=num_sweeps, num_reads=num_reads, **options + strength=strength, sampler=sampler, **sampler_options ) # load data in the AML model @@ -485,14 +484,13 @@ def qubo_poly_solve( strength=1e6, sampler=SimulatedAnnealingSampler(), **sampler_options, - # num_reads=10000, num_sweeps=1000, **options ): # noqa: D417 """Solves the Hydraulics equations. Args: strength (float, optional): substitution strength. Defaults to 1e6. - num_reads (int, optional): number of reads for the sampler. Defaults to 10000. - num_sweeps (int, optinal): number of sweeps. Default 1000 + sampler (float, dwave.sampler): sampler to optimize the qubo + **sampler_options (dict): options for the sampler Returns: np.ndarray: solution of the problem From 6b3d54844729be695955423b4e6d5b8d0c532ee8 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 16 Oct 2024 22:35:43 +0200 Subject: [PATCH 68/96] added new sampler --- .../qubo_poly_solver_2loops_dw.ipynb | 226 ++++++++++++------ wntr_quantum/sampler/simulated_annealing.py | 7 +- .../sim/solvers/qubo_polynomial_solver.py | 2 +- 3 files changed, 162 insertions(+), 73 deletions(-) diff --git a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb index ec57a68..c41cece 100644 --- a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb +++ b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb @@ -101,7 +101,7 @@ { "data": { "text/plain": [ - "array([2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" + "array([ 3.111e-01, 5.111e-02, 2.322e-01, 3.108e-02, 1.678e-01, 7.613e-02, 2.334e-02, -2.058e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" ] }, "execution_count": 3, @@ -111,47 +111,7 @@ ], "source": [ "ref_pressure = results.node['pressure'].values[0]\n", - "ref_pressure" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.311, 0.051, 0.232, 0.031, 0.168, 0.076, 0.023, -0.021], dtype=float32)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ "ref_rate = results.link['flowrate'].values[0]\n", - "ref_rate" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3.111e-01, 5.111e-02, 2.322e-01, 3.108e-02, 1.678e-01, 7.613e-02, 2.334e-02, -2.058e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ "ref_values = np.append(ref_rate, ref_pressure)\n", "ref_values" ] @@ -165,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -174,14 +134,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 500.000000 => 1000.000000 (res: 16.129032)\n", + "Head Encoding : 0.000000 => 1000.000000 (res: 32.258065)\n", "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.483871)\n" ] } @@ -196,8 +156,8 @@ "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", "nqbit = 5\n", - "step = (500/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+500.0, var_base_name=\"x\")\n", + "step = (1000/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", " head_encoding=head_encoding)\n", @@ -213,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -230,7 +190,7 @@ "array([1. , 1. , 1. , 1. , 1. , 1. , 1. , 0.999, 1. , 1.001, 1. , 1.001, 1. , 1.001])" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -247,12 +207,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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WLXriiSeUkZGh//znP/r666/5Jg5qjZkTAEC1lZWV6eWXX1a3bt2UkZGhO+64QwsWLKCYoE4wcwIAqJasrCwNHz5cmzZtkiSNGDFCc+fOVZMmTQxOBmdBOQEAVFlKSoqGDRumrKwsNWrUSElJSRo1apTRseBkOK0DALihsrIyzZgxQz169FBWVpbuuusu7dq1i2KCekE5AQDc0Lp16/Tiiy/KZrNp7NixSk1NVZs2bYyOBSfFaR0AwA3169dPo0aNUo8ePTR06FCj48DJUU4AADdksVi0ePFio2PARXBaBwAAmArlBAAAmArlBAAAmArlBABc3I8//qjjx48bHQMoRzkBABf23nvvqW3btho0aJBKSkqMjgNIopwAgEsqKirSpEmTNHDgQOXl5cnDw0Pnz583OhYgiXICAC7n8OHD6ty5s+bOnStJmjp1qr788ks1a9bM4GTAZVznBABcyKpVq/TUU0+poKBAgYGBWr58uXr16mV0LKACZk4AwAVcuHBB48aN09ChQ1VQUKAHHnhA33zzDcUEpsTMCQA4uezsbD344IM6cOCALBaLpk+frhdeeEENGvArAObEyAQAJxcQEKDw8HBlZ2frnXfeUffu3Y2OBFwX5QQAnJybm5uWLl2q0tJShYSEGB0HuCHKCQC4gMDAQKMjAFXGglgAAGAqlBMAAGAqlBMAcHA2m83oCECdopwAgIOy2WxKSkpSz549VVpaanQcoM5QTgDAAeXm5mrAgAGKj4/Xpk2btGrVKqMjAXWGb+sAgINJTU3V4MGDdfz4cXl4eOif//ynhg8fbnQsoM4wcwIADsJms2n27Nnq0qWLjh8/rltvvVXbt2/XM888I4vFYnQ8oM4wcwIADuDcuXMaNWqUPv74Y0nSwIEDtWDBAvn6+hqcDKh7zJwAgMlt27ZNkZGR+vjjj+Xl5aV58+YpOTmZYgKnxcwJAJjciy++qJMnT+r222/Xu+++q4iICKMjAfWKmRMAMLklS5Zo0qRJ2r17N8UELoGZEwAwuebNm+vNN980OgZgN8ycAAAAU6GcAAAAU6GcAAAAU6GcAIBBSktLtXDhQlmtVqOjAKZCOQEAA5w8eVLdunXT2LFj9fe//93oOICpUE4AwM7Wr1+vyMhIffXVV/Lx8VHr1q2NjgSYCuUEAOykpKREzz//vB555BGdO3dOUVFRSktL06BBg4yOBpgK1zkBADs4fvy4hgwZop07d0qSnnnmGb366qvy8vIyOBlgPpQTAKhnH3zwgcaMGaPc3Fz5+flp8eLFeuyxx4yOBZgWp3UAoB69+eab6tevn3Jzc9WxY0ft3buXYgLcAOUEAOpR3759FRAQoD/96U/aunWrWrVqZXQkwPQ4rQMA9Sg8PFzff/+9AgICjI4COAxmTgCgnlFMgOqhnAAAAFOhnAAAAFOhnABADV28eFE2m83oGIDToZwAQA18++23at++vf71r38ZHQVwOoaUk8cff1w33XSTBgwYYMTbA0CN2Ww2LVy4UB06dNDBgwc1Z84cFRUVGR0LcCqGlJM//vGPWrZsmRFvDQA1lp+fr+HDh2vs2LG6ePGiYmNjtXPnTnl7exsdDXAqhpSTmJgY+fj4GPHWAFAj+/fvV1RUlFauXCl3d3clJiZqw4YNCgoKMjoa4HSqXU62bNmiPn36KDQ0VBaLRevWrau0T1JSklq1aiVvb2917NhRqampdZEVAOzOZrNpw4YN6tGjhw4fPqyWLVtq8+bNmjp1qtzcWLYH1IdqXyG2sLBQERERGjNmjPr161fp+eTkZCUkJGj+/Pnq2LGj5syZo9jYWH3//fc1+hdGcXGxiouLyx/n5eVJknJzc2W1Wqt9vBvJz8+v8Deqh88PzuT8+fOaOHGiNmzYIEnq1auXkpKS5O/vr9zcXGPDwWHwc/GyK7+/q6La5aR3797q3bv3NZ+fPXu2xo0bp9GjR0uS5s+fr/Xr12vRokWaOnVqdd9OiYmJmjlzZqXt27ZtU6NGjap9vKpKS0urt2O7Aj4/OIPTp0/riy++UIMGDTRy5Ej16dNH+/fvNzoWHJSr/1y8cOFClfet03vrlJSUaM+ePZo2bVr5Njc3N3Xv3l07duyo0TGnTZumhISE8sd5eXkKCwtTly5d1LRp01pn/q38/HylpaWpXbt2rIupAT4/OBsPDw/l5ORoyJAhjGnUCD8XL6vXmZPrOXv2rMrKyhQcHFxhe3BwsA4dOlT+uHv37vrmm29UWFioli1bas2aNercufNVj+nl5SUvL69K2/38/OqlnFzh4+MjPz+/eju+s+Pzg7Po37+/Nm/ezJhGrbn6GKrOGi1D7kq8adMmI94WAAA4gDpdah4YGCh3d3dlZWVV2J6VlaWQkJC6fCsAAOCk6rSceHp6KioqSikpKeXbrFarUlJSrnnaBgAA4NeqfVqnoKBAR44cKX987Ngxpaeny9/fX+Hh4UpISFBcXJzat2+v6OhozZkzR4WFheXf3gEAM9iyZYtOnDihYcOGGR0FwG9Uu5zs3r1b3bp1K3985Zs0cXFxWrJkiQYPHqzs7Gy98MILyszMVGRkpDZu3FhpkSwAGKGsrEyJiYmaMWOGPD09FRERobvvvtvoWAB+pdrlJCYm5oa3CI+Pj1d8fHyNQwFAfcjMzNTw4cPLTz0PHjxYt9xyi8GpAPyWId/WAQB727Rpk4YPH66srCw1atRIc+fOVVxcnNGxAFwFN4YA4NRKS0v117/+VT179lRWVpbuuece7d69m2ICmBgzJwCc1qlTpzR06FBt2bJFkvTUU09pzpw5atiwocHJAFwP5QSAUyopKdF9992n//znP2rSpIkWLFigIUOGGB0LQBVwWgeAU/L09NTf/vY3tW3bVmlpaRQTwIFQTgA4rVGjRmnnzp267bbbjI4CoBooJwCclsVikYeHh9ExAFQT5QQAAJgK5QQAAJgK5QSAQzp58qTREQDUE8oJAIdSVFSkiRMn6s4779ShQ4eMjgOgHlBOADiMH374QZ06ddK8efNUWFhYfo8cAM6FcgLAIaxcuVJRUVH65ptv1KxZM23cuFGTJk0yOhaAekA5AWBqFy5c0NixYzVs2DAVFBQoJiZG6enpio2NNToagHpCOQFgWgcPHlR0dLQWLlwoi8WiGTNmaNOmTQoNDTU6GoB6xL11AJjSsmXLNGHCBF28eFEhISFasWKFHnzwQaNjAbADZk4AmFJBQYEuXryoHj16KD09nWICuBBmTgCY0tNPP62goCD169dPbm78OwpwJZQTAKZksVg0YMAAo2MAMAD/HAEAAKZCOQEAAKZCOQEAAKZCOQFgVz///LNGjhypH3/80egoAEyKBbEA7Gbnzp0aPHiwfvrpJ/3444/aunWrLBaL0bEAmAwzJwDqndVq1axZs3T//ffrp59+0q233qo5c+ZQTABcFTMnAOrVuXPnFBcXp/Xr10uSBg0apLfeeku+vr4GJwNgVsycAKg3X331lSIjI7V+/Xp5eXlp/vz5Wr16NcUEwHVRTgDUOavVqsTERMXExOjkyZO64447tHPnTo0fP55TOQBuiHICoM6tWbNGf/nLX1RWVqYRI0Zo9+7dioiIMDoWAAfBmhMAdW7gwIF6//331bt3b40aNYrZEgDVQjkBUOfc3Nz07rvvGh0DgIPitA4AADAVygkAADAVygkAADAVygmAajlx4oROnTpldAwAToxyAqDKPvroI0VGRuqJJ55QaWmp0XEAOCnKCYAbKikp0XPPPae+ffsqJydHFy9eVE5OjtGxADgpygmA6zp27Ji6du2q2bNnS5KeffZZffXVVwoKCjI4GQBnxXVOAFzT2rVrNWbMGJ0/f15+fn5asmSJHn30UaNjAXByzJwAqKSoqEiTJ09W//79df78eXXq1Enp6ekUEwB2wcwJgArOnDmjXr16ae/evZKkKVOm6OWXX5aHh4fByQC4CsoJgAoCAgLk5+engIAALVu2TA899JDRkQC4GMoJgArc3d21cuVKlZaWqmXLlkbHAeCCKCcAKgkJCTE6AgAXxoJYAABgKpQTAABgKpQTwMXYbDajIwDAdVFOABdhs9n09ttv6/HHH5fVajU6DgBcE+UEcAH5+fkaNmyYxo0bpw8//FCrV682OhIAXBPf1gGc3N69ezVo0CAdOXJE7u7ueuWVVzRkyBCjYwHANVFOACdls9k0d+5cJSQkqKSkRGFhYVq9erXuu+8+o6MBwHVRTgAnlJubq7Fjx+r999+XJPXt21eLFy+Wv7+/wckA4MZYcwI4mdTUVLVt21bvv/++PDw89Nprr2ndunUUEwAOg5kTwInYbDZNmTJFx48f1y233KLk5GR16NDB6FgAUC3MnABOxGKxaNmyZRo9erTS0tIoJgAcEjMngJMJDw/XokWLjI4BADXGzAkAADAVygkAADAVygkAADAVygngIMrKyrRq1Spu3AfA6VFOAAdw+vRp9ezZU0OHDtXrr79udBwAqFeUE8DkPvvsM0VGRurzzz9X48aNFRgYaHQkAKhXlBPApEpLSzV9+nTFxsbqzJkzuueee7R7924NHz7c6GgAUK+4zglgQidPntTQoUO1detWSdL48eP12muvqWHDhgYnA4D6RzkBTGbDhg0aOXKkzp07Jx8fH7311lsaMmSI0bEAwG4oJ4CJJCUlKT4+XpLUrl07JScnq3Xr1ganAgD7Ys0JYCKxsbFq2rSpJk+erO3bt1NMALgkZk4AE2ndurUOHTqk5s2bGx0FAAzDzAlgMhQTAK6OcgIAAEyFcgIAAEyFcgLYSXFxMffFAYAqoJwAdvD9998rOjpaCxYsMDoKAJge5QSoZ++8846ioqK0b98+vfLKKyouLjY6EgCYGuUEqCeFhYUaM2aMRowYocLCQnXr1k07duyQl5eX0dEAwNQoJ0A9+PbbbxUdHa3FixfLzc1NM2fO1GeffcbXhAGgCrgIG1CHbDabFi9erPj4eF28eFHNmzfXypUrFRMTY3Q0AHAYlBOgjuTn5+vpp5/WihUrJEk9e/bU8uXLFRQUZHAyAHAsnNYB6sjJkye1du1aubu7KzExUZ988gnFBABqgJkToI78/ve/15IlSxQaGqr777/f6DgA4LAoJ0AdGjRokNERAMDhcVoHAACYCuUEAACYCuUEAACYCuUEqIIdO3Zo7dq1RscAAJdAOQGuw2q16tVXX1XXrl01cuRIff/990ZHAgCnx7d1gGvIzs5WXFycPvnkE0lSnz59uPw8ANgB5QS4ii1btuiJJ55QRkaGvL299a9//Utjx46VxWIxOhoAOD1O6wC/UlZWppdfflndunVTRkaG7rzzTqWmpmrcuHEUEwCwE2ZOgF9kZWVp+PDh2rRpkyRp5MiRSkpKUpMmTQxOBgCuhXICSCouLlbHjh31008/qVGjRpo7d67i4uKMjgUALonTOoAkLy8v/elPf9Ldd9+tXbt2UUwAwECUE+AXEydO1K5du9SmTRujowCAS6OcAL+wWCzy9vY2OgYAuDzKCQAAMBXKCQAAMBXKCVzCmTNnjI4AAKgiygmcWklJiZ599lndfvvt+vHHH42OAwCoAsoJnNaPP/6oLl266PXXX9f58+e1YcMGoyMBAKqAi7DBKb333nt68sknlZeXp5tuuklLly5Vnz59jI4FAKgCZk7gVIqKijRp0iQNHDhQeXl5uu+++5Senk4xAQAHQjmB0zh8+LA6d+6suXPnSpKmTp2qL7/8UuHh4QYnAwBUB6d14BRWr16tcePGqaCgQIGBgVq+fLl69epldCwAQA1QTuAUTpw4oYKCAj3wwANauXKlQkNDjY4EAKghygmcwnPPPafg4GANHTpUDRowrAHAkfFTHE7Bzc1NI0eONDoGAKAOsCAWAACYCuUEAACYCuUEAACYCuUEppaXl6fx48fr5MmTRkcBANgJC2JhWmlpaRo0aJCOHj2qo0eP6rPPPpPFYjE6FgCgnjFzAtOx2Wx644031LlzZx09elTh4eF66aWXKCYA4CKYOYGp/Pzzz3ryySf1wQcfSJIeffRRLVq0SP7+/gYnAwDYCzMnMI2dO3eqXbt2+uCDD+Th4aHXX39dH3zwAcUEAFwM5QSGs1qtmjVrlu6//34dP35ct956q7Zv365nnnmGUzkA4IIoJzDcqlWr9Pzzz6u0tFQDBw5UWlqa2rdvb3QsAIBBWHMCww0ZMkQrVqxQ3759NX78eGZLAMDFUU5gOHd3d61fv55SAgCQxGkdmATFBABwBeUEAACYiiHl5OOPP9Ydd9yh2267TW+//bYREQAAgEnZfc1JaWmpEhIS9MUXX8jX11dRUVF6/PHHFRAQYO8osIPTp0/L3d1dQUFBRkcBADgIu8+cpKam6q677lKLFi3UpEkT9e7dW59++qm9Y8AOPv30U0VERGjEiBGyWq1GxwEAOIhql5MtW7aoT58+Cg0NlcVi0bp16yrtk5SUpFatWsnb21sdO3ZUampq+XMZGRlq0aJF+eMWLVro1KlTNUsPUyorK9NLL72kXr16KTs7W5mZmTp37pzRsQAADqLa5aSwsFARERFKSkq66vPJyclKSEjQjBkzlJaWpoiICMXGxurMmTO1DgvzO3nypKZPn67Zs2fLZrNpwoQJ+vrrr9WsWTOjowEAHES115z07t1bvXv3vubzs2fP1rhx4zR69GhJ0vz587V+/XotWrRIU6dOVWhoaIWZklOnTik6OvqaxysuLlZxcXH547y8PElSbm5uvZwqyM/Pr/A3qu5///d/NWHCBOXm5srHx0evv/66Hn/88Ur/DwFHws8E1BZj6LIrv7+rok4XxJaUlGjPnj2aNm1a+TY3Nzd1795dO3bskCRFR0frwIEDOnXqlHx9ffXJJ5/or3/96zWPmZiYqJkzZ1bavm3bNjVq1Kgu41eQlpZWb8d2NpcuXdI777yjDz/8UJL0u9/9Ts8//7z8/f21efNmg9MBdYOfCagtVx9DFy5cqPK+dVpOzp49q7KyMgUHB1fYHhwcrEOHDl1+wwYNNGvWLHXr1k1Wq1VTpky57jd1pk2bpoSEhPLHeXl5CgsLU5cuXdS0adO6jC/pcrNNS0tTu3bt5OPjU+fHdzbZ2dl64okntGfPHknSmDFj1Lt3b3Xs2JHPD06BnwmoLcbQZYbNnFRV37591bdv3yrt6+XlJS8vr0rb/fz86qWcXOHj4yM/P796O76zaNy4sTw9PeXn56fFixcrJiZGmzdv5vOD02FMo7ZcfQy5uVV9mWudlpPAwEC5u7srKyurwvasrCyFhITU5VvBJDw8PJScnCyr1aqbb75Zubm5RkcCADi4Or3Oiaenp6KiopSSklK+zWq1KiUlRZ07d67Lt4KJhIWF6eabbzY6BgDASVR75qSgoEBHjhwpf3zs2DGlp6fL399f4eHhSkhIUFxcnNq3b6/o6GjNmTNHhYWF5d/eAQAAuJ5ql5Pdu3erW7du5Y+vLFaNi4vTkiVLNHjwYGVnZ+uFF15QZmamIiMjtXHjxkqLZAEAAK6m2uUkJiZGNpvtuvvEx8crPj6+xqEAAIDrMuSuxHAMy5cv17Bhw25YRgEAqEuUE1RyZY3QyJEjtXLlSq1Zs8boSAAAF2LIdU5gXgcOHNCgQYP03Xffyc3NTTNmzFD//v2NjgUAcCGUE0iSbDabFi5cqMmTJ6uoqEjNmzfXypUrFRMTY3Q0AICLoZxA+fn5mjBhglauXClJ6tWrl5YuXaqgoCCDkwEAXBFrTlzc3r17FRUVpZUrV8rd3V1///vftX79eooJAMAwzJy4MJvNpkmTJunw4cMKCwvT6tWrdd999xkdCwDg4pg5cWEWi0XLli3TE088ob1791JMAACmwMyJi2vdunX5WhMAAMyAmRMAAGAqlBMAAGAqlBMAAGAqlBMnZbVa9eGHH3JfHACAw6GcOKHs7Gw9/PDDeuyxx7RgwQKj4wAAUC18W8fJbN68WUOHDlVGRoa8vb3l4eFhdCQAAKqFmRMnUVZWppdeekkPPvigMjIydOeddyo1NVWjR482OhoAANXCzIkTyMzM1PDhw5WSkiJJiouLU1JSkho3bmxwMgAAqo9y4uA2bdqkYcOG6cyZM2rUqJHmzp2ruLg4o2MBAFBjlBMHNm/ePE2aNEk2m0333HOP3n33Xd15551GxwIAoFZYc+LAHnjgATVs2FBPPfWUdu7cSTEBADgFZk4cWJs2bXTw4EHdfPPNRkcBAKDOMHPi4CgmAABnQzkBAACmQjkBAACmQjkxqdLSUqMjAABgCMqJCR09elSdO3fWO++8Y3QUAADsjnJiMmvWrFG7du20e/duTZ8+XSUlJUZHAgDArigndlZmtWnH0XP6MP2Udhw9pzKrTZJUVFSkiRMnatCgQcrLy1OXLl20detWeXp6GpwYAAD74jondrTxwGnN/OigTp8vKt/W3NdbT97tqTemx2vfvn2SpGnTpunFF19Ugwb87wEAuB5++9nJxgOn9fQ7abL9ZvuR7Z/oqRlJsl0qUrNmzbR8+XLFxsYakhEAADOgnNhBmdWmmR8drFBMrCVFytn0f1W4/zNJUtNbIrRny3qFtWxhTEgAAEyCcmIHqcdyKpzKkaTS3AwVHvxCkkW+XYbI974hOlnsrTBjIgIAYBqUk3pSZrUp9ViOzuQX6XBWfqXnPYNuVUDsZLk3DVTDmyMkSWfyiyrtBwCAq6Gc1IOrLXy9mib3/KHC4yAf7/qMBQCAQ6Cc1LFrLXy9HoukEF9vRd/iX1+xAABwGFznpA5dbeHrjVh++XtGnzZyd7Ncd18AAFwBMyd14Mr6km1Hzt7wVM5vhfh6a0afNup1d/N6SgcAgGOhnNTS1daXFGcekbXwZzX8XYervia+W2vdFtxEQT6XT+UwYwIAwP9HOamF364vsdlsyt/zkX7+YpEsHl5qPup1efiFVHpdl9aB6vy7APuGBQDAQVBOaui360vKigp0bsMcXTz8tSTJ++YOcvNuUuE1LHwFAODGKCc19OsLqxWfOqTsf7+qsrwzknsD3dTtSfm0e0QWy/8/XcPCVwAAqoZyUgW/vqDalXUiZ/KLZLNZlZe6TrlblkrWMjXwa67AR/8sr5DWlY7BwlcAAKqGcnID17qT8CO3N1H2+y/p4tFdkqRGd3ZVQK/JcvNqVOH18d1+py6tm7HwFQCAKqKcXMeGfac1cWVape0Z5/L0tyeHqfR8liwNPHXTH55Sk4jYSqdxQny99d897qCUAABQDZSTa9h2OFuT1/5w1ecsDTzl07a3LhzYJP8+f5ZX0C0VLrzG+hIAAGqOK8ReQ+LG72W9zqVefaL7KWjEHP15aE+F+Fa8J06Ir7fmDW/H+hIAAGqAmZPf2Hb4bJX2s1jcZPH0VqvAxvrqzw9WWjDLjAkAADVDOfmVjQdOK3HjIf3p3qq/JsjHW+5uFi6qBgBAHbH7aZ2kpCS1atVK3t7e6tixo1JTU+0d4apKSq2atnZ/tV7TnAuqAQBQ5+xaTpKTk5WQkKAZM2YoLS1NERERio2N1ZkzZ+wZo5KNB06rU2KKfr5wSZJUUFBQpdex4BUAgLpn19M6s2fP1rhx4zR69GhJ0vz587V+/XotWrRIU6dOvepriouLVVxcXP44Ly9PkpSbmyur1VrrTNuPnlXihkNqJMnbu1TZX7yjSbM2qfPkOWrZOPCqr7FImtrrTnVq2VC5ubm1zuBM8vPzK/wNODrGNGqLMXTZld/fVWG3clJSUqI9e/Zo2rRp5dvc3NzUvXt37dix45qvS0xM1MyZMytt37Ztmxo1anSVV1Tf8/dK2dnZmjVrln44dEiS1DJzmx555JFrvqY041ttzqiTt3dKaWmVrw8DODLGNGrL1cfQhQsXqryv3crJ2bNnVVZWpuDg4Arbg4ODdeiXQnA106ZNU0JCQvnjvLw8hYWFqUuXLmratGmtMu07eV5/+WC/cg6l6vAHc1R6MV8NvBvpvydP0pGgrvrHvsqnbHy83LVibCe5cTrnqvLz85WWlqZ27drJx8fH6DhArTGmUVuMoctMOXNSU15eXvLy8qq03c/Pr9bl5Mzhn7Xvo4XK37VOkuQZcpvuGTJFXbo00/Z9Fp0srFxA5j8eKX//m2r1vq7Ax8dHfn5+RscA6gxjGrXl6mPIza3qy1ztVk4CAwPl7u6urKysCtuzsrIUEhJirxjljh07phnjBij/m8vTbD7tH9VND4ySt28DSWWV9vdr5KG/97uHC6sBAFDP7PZtHU9PT0VFRSklJaV8m9VqVUpKijp37myvGJKktWvXqm3btjr4TZrcG/ooqN90+f9hnCwNPCrt28TLXctHR2vP9B4UEwAA7MCuXyVOSEjQggULtHTpUn333Xd6+umnVVhYWP7tHXvZv3+/zp8/r86dO2vhuhQ1uq2TfnsCx/LLn38OjFDXO5rxlWEAAOzErmtOBg8erOzsbL3wwgvKzMxUZGSkNm7cWGmRbH2bPn26goOD9eSTT8rDw0PBoac186ODOn2+qHyfgCae+j+PcxoHAAB7s/uC2Pj4eMXHx9v7bStwd3fXhAkTyh/3uru5erQJuXx/nLPnpMzvtCiuAwtfAQAwAHcl/sWV++M8cEeQJPFVYQAADEI5AQAApkI5AQAApuJU5aSgoEDPPvus4TcSBAAANWf6K8RW1f79+zVo0CAdOnRIP/zwgzZs2GB0JAAAUAMOP3Nis9m0YMECRUdH69ChQwoNDb3mHY4BAID5OfTMSV5ensaPH6/Vq1dLknr37q2lS5eqWbNmBicDAAA15bAzJ998842ioqK0evVqubu769VXX9XHH39MMQEAwME57MzJH/7wB126dEnh4eFavXq13e/PAwAA6ofDzpxcunRJffv21d69eykmAAA4EYebObHZbJKkv/3tb3r22WdlsViUl5dXZ8fPy8vThQsXlJeXJzc3h+1uhuHzg7NhTKO2GEOXXfldfeX3+PVYbFXZy0ROnjypsLAwo2MAAIAaOHHihFq2bHndfRyunFitVmVkZMjHx0cWS93f/yYvL09hYWE6ceKEmjZtWufHd3Z8fnA2jGnUFmPoMpvNpvz8fIWGht5wBsnhTuu4ubndsHHVhaZNm7r0IKotPj84G8Y0aosxJPn6+lZpP9c9+QUAAEyJcgIAAEyFcvIbXl5emjFjhry8vIyO4pD4/OBsGNOoLcZQ9TncglgAAODcmDkBAACmQjkBAACmQjkBAACmQjkBAACmQjn5laSkJLVq1Ure3t7q2LGjUlNTjY4EAIDLoZz8Ijk5WQkJCZoxY4bS0tIUERGh2NhYnTlzxuhoTuXjjz/WHXfcodtuu01vv/220XGAWnv88cd10003acCAAUZHgYM5ceKEYmJi1KZNG917771as2aN0ZFMg68S/6Jjx47q0KGD3nzzTUmX7+ETFhamyZMna+rUqQancw6lpaVq06aNvvjiC/n6+ioqKkrbt29XQECA0dGAGvvyyy+Vn5+vpUuX6r333jM6DhzI6dOnlZWVpcjISGVmZioqKko//PCDGjdubHQ0wzFzIqmkpER79uxR9+7dy7e5ubmpe/fu2rFjh4HJnEtqaqruuusutWjRQk2aNFHv3r316aefGh0LqJWYmBj5+PgYHQMOqHnz5oqMjJQkhYSEKDAwUDk5OcaGMgnKiaSzZ8+qrKxMwcHBFbYHBwcrMzPToFTms2XLFvXp00ehoaGyWCxat25dpX2ut24nIyNDLVq0KH/cokULnTp1yh7Rgauq7ZiGa6vL8bNnzx6VlZUpLCysnlM7BsoJqqywsFARERFKSkq66vOs24GjYUyjNupq/OTk5GjkyJF666237BHbMdhgKy4utrm7u9s++OCDCttHjhxp69u3rzGhTE5Spc8rOjraNmnSpPLHZWVlttDQUFtiYqLNZrPZtm3bZnvsscfKn//jH/9oW7FihV3yAjdSkzF9xRdffGHr37+/PWLCpGo6foqKimxdu3a1LVu2zF5RHQIzJ5I8PT0VFRWllJSU8m1Wq1UpKSnq3LmzgckcR1XW7URHR+vAgQM6deqUCgoK9Mknnyg2NtaoyMB1sRYNtVGV8WOz2TRq1Cg9+OCDGjFihFFRTYly8ouEhAQtWLBAS5cu1Xfffaenn35ahYWFGj16tNHRHEJV1u00aNBAs2bNUrdu3RQZGannnnuOb+rAtKq6Fq179+4aOHCgNmzYoJYtW1JcIKlq42fbtm1KTk7WunXrFBkZqcjISO3fv9+IuKbTwOgAZjF48GBlZ2frhRdeUGZmpiIjI7Vx48ZKAwu107dvX/Xt29foGECd2bRpk9ER4KDuv/9+Wa1Wo2OYEuXkV+Lj4xUfH290DIcUGBgod3d3ZWVlVdielZWlkJAQg1IBNceYRm0wfmqH0zqoE6zbgbNhTKM2GD+1w8wJqqygoEBHjhwpf3zs2DGlp6fL399f4eHhSkhIUFxcnNq3b6/o6GjNmTOHdTswNcY0aoPxU4+M/roQHMcXX3xhk1TpT1xcXPk+b7zxhi08PNzm6elpi46Otn399dfGBQZugDGN2mD81B/urQMAAEyFNScAAMBUKCcAAMBUKCcAAMBUKCcAAMBUKCcAAMBUKCcAAMBUKCcAAMBUKCcAAMBUKCcAAMBUKCcAAMBUKCcAAMBUKCcAAMBU/h9Gp2zsLkBNEgAAAABJRU5ErkJggg==", + "image/png": 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", "text/plain": [ "
" ] @@ -274,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -292,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -303,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -333,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -345,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -355,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -368,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -380,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -391,27 +351,50 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def flatten_list(lst):\n", + " out = []\n", + " for elmt in lst:\n", + " if not isinstance(elmt, list):\n", + " out += [elmt]\n", + " else:\n", + " out += elmt\n", + " return out\n", + "\n", + "from copy import deepcopy\n", + "mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", + "\n", + "for i in range(16,22):\n", + " mod_bin_rep_sol[i] = list(np.random.randint(2, size=5))\n", + "x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "num_sweeps = 9000\n", "Tinit = 1E5\n", - "Tfinal = 1E-1\n", + "Tfinal = 1E0\n", "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10000/10000 [00:19<00:00, 520.42it/s]\n" + "100%|██████████| 10000/10000 [00:14<00:00, 713.30it/s]\n" ] } ], @@ -421,7 +404,68 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 0,\n", + " [1, 1, 1, 0, 1],\n", + " [0, 0, 1, 0, 0],\n", + " [1, 0, 0, 0, 1],\n", + " [0, 1, 0, 0, 0],\n", + " [0, 0, 1, 1, 0],\n", + " [0, 1, 1, 0, 0],\n", + " [0, 1, 0, 0, 0],\n", + " [0, 1, 0, 0, 0],\n", + " [0, 0, 1, 0, 1],\n", + " [0, 1, 0, 0, 1],\n", + " [0, 0, 1, 0, 1],\n", + " [1, 0, 0, 0, 1],\n", + " [1, 1, 0, 0, 1],\n", + " [0, 1, 0, 0, 1]]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bin_rep_sol" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(res.res)[net.qubo.index_variables]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -430,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -439,12 +483,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -455,16 +499,16 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "eplt = res.energies[-250:]\n", + "eplt = res.energies[-500:]\n", "plt.plot(eplt)\n", "plt.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", "plt.grid()\n", - "plt.yscale('symlog')" + "# plt.yscale('symlog')" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -475,12 +519,12 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -508,6 +552,46 @@ "plt.yscale('symlog')" ] }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-3.893e-08, -3.893e-08, 2.448e-07, -1.577e-07, -3.686e-07, -8.872e-08, 8.902e-02, 3.336e-01, 4.967e-02, 6.449e-01, 5.442e-02, 1.960e-01, 3.639e-01, -2.829e-01])" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.verify_solution(net.convert_solution_from_si(ref_values[:-1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-10.732, 1.098, 1.148, -2.459, 3.12 , 3.811, 133.441, -0.593, -12.63 , 3.488, -21.537, -13.704, 3.409, 15.46 ])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.verify_solution(net.convert_solution_from_si(sol))" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py index 364b086..c42d3f5 100644 --- a/wntr_quantum/sampler/simulated_annealing.py +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -15,6 +15,10 @@ def generate_random_valid_sample(qubo): for iv, v in enumerate(sorted(qubo.qubo_dict.variables)): sample[v] = np.random.randint(2) + for v in qubo.mapped_variables[:7]: + sample[v] = 1 + sample[qubo.mapped_variables[7]] = 0 + for v, _ in sample.items(): if v not in qubo.mapped_variables: var_tmp = v.split("*") @@ -127,7 +131,8 @@ def __call__(self, x): Returns: _type_: _description_ """ - vidx = np.random.choice(self.single_var_index) + nmax = 8 + 8 * 5 + vidx = np.random.choice(self.single_var_index[nmax:]) x[vidx] = int(not (x[vidx])) self.fix_constraint(x, vidx) return x diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 64891c1..da29c9d 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -109,7 +109,7 @@ def verify_solution(self, input: np.ndarray) -> np.ndarray: p1 = P1[:, num_pipes:] + P2.sum(1)[:, num_pipes:] p2 = P3.sum(1)[:, num_pipes:, num_pipes:].sum(-1) elif self.wn.options.hydraulic.headloss == "D-W": - raise NotImplementedError("verufy_solution not implemented for DW") + raise NotImplementedError("verify_solution not implemented for DW") sign = np.sign(input) return p0 + p1 @ input + (p2 @ (sign * input * input)) From 0c2a3d29fa71c4914737c7af04d639815cb83678 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 17 Oct 2024 09:21:51 +0200 Subject: [PATCH 69/96] refactor the step classes --- .../qubo_poly_solver_2loops_dw.ipynb | 135 ++++++++++++------ wntr_quantum/sampler/simulated_annealing.py | 109 +------------- wntr_quantum/sampler/step/base_step.py | 101 +++++++++++++ wntr_quantum/sampler/step/full_random.py | 20 +++ 4 files changed, 213 insertions(+), 152 deletions(-) create mode 100644 wntr_quantum/sampler/step/base_step.py create mode 100644 wntr_quantum/sampler/step/full_random.py diff --git a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb index c41cece..99f44a8 100644 --- a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb +++ b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb @@ -141,8 +141,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 0.000000 => 1000.000000 (res: 32.258065)\n", - "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.483871)\n" + "Head Encoding : 500.000000 => 1000.000000 (res: 3.937008)\n", + "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.118110)\n" ] } ], @@ -151,13 +151,13 @@ "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", - "nqbit = 5\n", + "nqbit = 7\n", "step = (15/(2**nqbit-1))\n", "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", - "nqbit = 5\n", - "step = (1000/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", + "nqbit = 7\n", + "step = (500/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+500.0, var_base_name=\"x\")\n", "\n", "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", " head_encoding=head_encoding)\n", @@ -212,7 +212,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -332,10 +332,10 @@ "metadata": {}, "outputs": [], "source": [ - "from wntr_quantum.sampler.simulated_annealing import ProposalStep\n", + "from wntr_quantum.sampler.step.full_random import RandomStep\n", "var_names = sorted(net.qubo.qubo_dict.variables)\n", "net.qubo.create_variables_mapping()\n", - "mystep = ProposalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)" + "mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)" ] }, { @@ -351,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -368,13 +368,55 @@ "mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", "\n", "for i in range(16,22):\n", - " mod_bin_rep_sol[i] = list(np.random.randint(2, size=5))\n", - "x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n" + " mod_bin_rep_sol[i] = list(np.random.randint(2, size=7))\n", + "x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", + "x0 = list(x.values())\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 0,\n", + " [1, 0, 1, 1, 1, 0, 1],\n", + " [1, 1, 1, 1, 0, 0, 0],\n", + " [1, 0, 1, 0, 0, 0, 1],\n", + " [1, 0, 0, 1, 0, 0, 0],\n", + " [0, 1, 0, 0, 1, 1, 0],\n", + " [1, 1, 1, 0, 1, 0, 0],\n", + " [1, 1, 1, 0, 0, 0, 0],\n", + " [0, 1, 1, 0, 0, 0, 0],\n", + " [0, 1, 1, 1, 0, 1, 1],\n", + " [1, 1, 0, 1, 0, 0, 0],\n", + " [1, 1, 1, 0, 0, 1, 1],\n", + " [1, 1, 0, 1, 1, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0],\n", + " [0, 0, 1, 0, 1, 1, 1]]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mod_bin_rep_sol" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -387,14 +429,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10000/10000 [00:14<00:00, 713.30it/s]\n" + "100%|██████████| 10000/10000 [00:33<00:00, 301.79it/s]\n" ] } ], @@ -404,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -418,23 +460,23 @@ " 1,\n", " 1,\n", " 0,\n", - " [1, 1, 1, 0, 1],\n", - " [0, 0, 1, 0, 0],\n", - " [1, 0, 0, 0, 1],\n", - " [0, 1, 0, 0, 0],\n", - " [0, 0, 1, 1, 0],\n", - " [0, 1, 1, 0, 0],\n", - " [0, 1, 0, 0, 0],\n", - " [0, 1, 0, 0, 0],\n", - " [0, 0, 1, 0, 1],\n", - " [0, 1, 0, 0, 1],\n", - " [0, 0, 1, 0, 1],\n", - " [1, 0, 0, 0, 1],\n", - " [1, 1, 0, 0, 1],\n", - " [0, 1, 0, 0, 1]]" + " [1, 0, 1, 1, 1, 0, 1],\n", + " [1, 1, 1, 1, 0, 0, 0],\n", + " [1, 0, 1, 0, 0, 0, 1],\n", + " [1, 0, 0, 1, 0, 0, 0],\n", + " [0, 1, 0, 0, 1, 1, 0],\n", + " [1, 1, 1, 0, 1, 0, 0],\n", + " [1, 1, 1, 0, 0, 0, 0],\n", + " [0, 1, 1, 0, 0, 0, 0],\n", + " [0, 0, 0, 1, 0, 1, 0],\n", + " [1, 0, 0, 1, 1, 0, 0],\n", + " [0, 0, 1, 0, 0, 1, 0],\n", + " [0, 1, 0, 1, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 1, 0],\n", + " [1, 0, 1, 0, 1, 0, 0]]" ] }, - "execution_count": 26, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -445,16 +487,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0])" + "array([1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n", + " 1, 0, 1, 1, 1, 0, 1, 1, 0])" ] }, - "execution_count": 19, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -465,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -474,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -483,12 +526,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -508,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -519,12 +562,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -554,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -563,7 +606,7 @@ "array([-3.893e-08, -3.893e-08, 2.448e-07, -1.577e-07, -3.686e-07, -8.872e-08, 8.902e-02, 3.336e-01, 4.967e-02, 6.449e-01, 5.442e-02, 1.960e-01, 3.639e-01, -2.829e-01])" ] }, - "execution_count": 45, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -574,16 +617,16 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([-10.732, 1.098, 1.148, -2.459, 3.12 , 3.811, 133.441, -0.593, -12.63 , 3.488, -21.537, -13.704, 3.409, 15.46 ])" + "array([ 1.295e-02, 1.295e-02, -2.746e-01, -2.546e-01, 3.341e-01, 2.625e-02, 4.496e+01, 5.775e+01, -4.929e+01, 4.860e+01, 1.639e+01, -1.631e+01, -1.127e+02, 1.592e+01])" ] }, - "execution_count": 46, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py index c42d3f5..e006a5f 100644 --- a/wntr_quantum/sampler/simulated_annealing.py +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -1,8 +1,8 @@ from copy import deepcopy +from dataclasses import dataclass import numpy as np from dimod import as_samples from tqdm import tqdm -from dataclasses import dataclass def generate_random_valid_sample(qubo): @@ -34,113 +34,9 @@ def generate_random_valid_sample(qubo): return sample -class ProposalStep: # noqa: D101 - def __init__(self, var_names, single_var_names, single_var_index): - """Propose a new solution vector. - - Args: - var_names (_type_): _description_ - single_var_names (_type_): _description_ - single_var_index (_type_): _description_ - """ - self.var_names = var_names - self.single_var_names = single_var_names - self.single_var_index = single_var_index - self.num_single_var = len(self.single_var_names) - self.high_order_terms_mapping = self.define_mapping() - - def define_mapping(self): - """Define the mapping of the higher order terms. - - Returns: - _type_: _description_ - """ - high_order_terms_mapping = [] - - # loop over all the variables - for iv, v in enumerate(self.var_names): - - # if we have a cmomposite variables e.g. x_001 * x_002 we ignore it - if v not in self.single_var_names: - high_order_terms_mapping.append(None) - - # if the variables is a unique one e.g. x_011 - else: - high_order_terms_mapping.append({}) - # we loop over all the variables - for iiv, vv in enumerate(self.var_names): - if v != vv: - if v in vv: - - var_tmp = vv.split("*") - idx_terms = [] - for vtmp in var_tmp: - idx = self.single_var_index[ - self.single_var_names.index(vtmp) - ] - idx_terms.append(idx) - high_order_terms_mapping[-1][iiv] = idx_terms - - return high_order_terms_mapping - - def fix_constraint(self, x, idx): - """Ensure that the solution vectors respect quadratization. - - Args: - x (_type_): _description_ - idx (_type_): _description_ - - Returns: - _type_: _description_ - """ - fix_var = self.high_order_terms_mapping[idx] - for idx_fix, idx_prods in fix_var.items(): - x[idx_fix] = np.array([x[i] for i in idx_prods]).prod() - return x - - def verify_quadratic_constraints(self, data): - """Check if quadratic constraints are respected or not. - - Args: - data (_type_): _description_ - """ - for v, d in zip(self.var_names, data): - if v not in self.single_var_names: - var_tmp = v.split("*") - itmp = 0 - for vtmp in var_tmp: - idx = self.single_var_index[self.single_var_names.index(vtmp)] - if itmp == 0: - dcomposite = data[idx] - itmp = 1 - else: - dcomposite *= data[idx] - if d != dcomposite: - print("Error in the quadratic contraints") - print("%s = %d" % (v, d)) - for vtmp in var_tmp: - idx = self.single_var_index[self.single_var_names.index(vtmp)] - print("%s = %d" % (vtmp, data[idx])) - - def __call__(self, x): - """Call function of the method. - - Args: - x (_type_): _description_ - - Returns: - _type_: _description_ - """ - nmax = 8 + 8 * 5 - vidx = np.random.choice(self.single_var_index[nmax:]) - x[vidx] = int(not (x[vidx])) - self.fix_constraint(x, vidx) - return x - - @dataclass class SimulatedAnnealingResults: - """Result of the simulated nnelaings""" + """Result of the simulated anneling.""" res: list energies: list @@ -166,6 +62,7 @@ def sample( bqm (_type_): _description_ num_sweeps (int, optional): _description_. Defaults to 100. Temp (list, optional): _description_. Defaults to [1e5, 1e-3]. + Tschedule (list, optional): The temperature schedule x0 (_type_, optional): _description_. Defaults to None. take_step (_type_, optional): _description_. Defaults to None. """ diff --git a/wntr_quantum/sampler/step/base_step.py b/wntr_quantum/sampler/step/base_step.py new file mode 100644 index 0000000..3e65164 --- /dev/null +++ b/wntr_quantum/sampler/step/base_step.py @@ -0,0 +1,101 @@ +import numpy as np + + +class BaseStep: # noqa: D101 + def __init__(self, var_names, single_var_names, single_var_index): + """Propose a new solution vector. + + Args: + var_names (_type_): _description_ + single_var_names (_type_): _description_ + single_var_index (_type_): _description_ + """ + self.var_names = var_names + self.single_var_names = single_var_names + self.single_var_index = single_var_index + self.num_single_var = len(self.single_var_names) + self.high_order_terms_mapping = self.define_mapping() + + def define_mapping(self): + """Define the mapping of the higher order terms. + + Returns: + _type_: _description_ + """ + high_order_terms_mapping = [] + + # loop over all the variables + for iv, v in enumerate(self.var_names): + + # if we have a cmomposite variables e.g. x_001 * x_002 we ignore it + if v not in self.single_var_names: + high_order_terms_mapping.append(None) + + # if the variables is a unique one e.g. x_011 + else: + high_order_terms_mapping.append({}) + # we loop over all the variables + for iiv, vv in enumerate(self.var_names): + if v != vv: + if v in vv: + + var_tmp = vv.split("*") + idx_terms = [] + for vtmp in var_tmp: + idx = self.single_var_index[ + self.single_var_names.index(vtmp) + ] + idx_terms.append(idx) + high_order_terms_mapping[-1][iiv] = idx_terms + + return high_order_terms_mapping + + def fix_constraint(self, x, idx): + """Ensure that the solution vectors respect quadratization. + + Args: + x (_type_): _description_ + idx (_type_): _description_ + + Returns: + _type_: _description_ + """ + fix_var = self.high_order_terms_mapping[idx] + for idx_fix, idx_prods in fix_var.items(): + x[idx_fix] = np.array([x[i] for i in idx_prods]).prod() + return x + + def verify_quadratic_constraints(self, data): + """Check if quadratic constraints are respected or not. + + Args: + data (_type_): _description_ + """ + for v, d in zip(self.var_names, data): + if v not in self.single_var_names: + var_tmp = v.split("*") + itmp = 0 + for vtmp in var_tmp: + idx = self.single_var_index[self.single_var_names.index(vtmp)] + if itmp == 0: + dcomposite = data[idx] + itmp = 1 + else: + dcomposite *= data[idx] + if d != dcomposite: + print("Error in the quadratic contraints") + print("%s = %d" % (v, d)) + for vtmp in var_tmp: + idx = self.single_var_index[self.single_var_names.index(vtmp)] + print("%s = %d" % (vtmp, data[idx])) + + def __call__(self, x): + """Call function of the method. + + Args: + x (_type_): _description_ + + Returns: + _type_: _description_ + """ + raise NotImplementedError("Implement a __call__ method") diff --git a/wntr_quantum/sampler/step/full_random.py b/wntr_quantum/sampler/step/full_random.py new file mode 100644 index 0000000..8dcdc56 --- /dev/null +++ b/wntr_quantum/sampler/step/full_random.py @@ -0,0 +1,20 @@ +import numpy as np +from .base_step import BaseStep + + +class RandomStep(BaseStep): # noqa: D101 + + def __call__(self, x): + """Call function of the method. + + Args: + x (_type_): _description_ + + Returns: + _type_: _description_ + """ + nmax = 8 + 8 * 7 + vidx = np.random.choice(self.single_var_index[nmax:]) + x[vidx] = int(not (x[vidx])) + self.fix_constraint(x, vidx) + return x From 561887d0959dc620b3841af26ded2af00d04bed0 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 17 Oct 2024 17:41:45 +0200 Subject: [PATCH 70/96] new step --- .../qubo_poly_solver_2loops_dw.ipynb | 1746 +---------------- wntr_quantum/sampler/simulated_annealing.py | 15 +- wntr_quantum/sampler/step/full_random.py | 81 + 3 files changed, 154 insertions(+), 1688 deletions(-) diff --git a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb index 99f44a8..d567434 100644 --- a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb +++ b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb @@ -232,58 +232,6 @@ "plt.yscale('symlog')" ] }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "# from dwave.samplers import SimulatedAnnealingSampler, TabuSampler, SteepestDescentSampler, RandomSampler\n", - "\n", - "# sampler = SimulatedAnnealingSampler()\n", - "# # sampler = TabuSampler()\n", - "# # sampler = SteepestDescentSampler()\n", - "# # sampler = RandomSampler()\n", - "# model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "# net.solve(model, strength=1E7, sampler=sampler, num_sweeps=int(1E1), num_reads=10)\n", - "# # sol = net.extract_data_from_model(model)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# solutions,energies,statuses = net.analyze_sampleset()\n", - "# for e,s in zip(energies,statuses):\n", - "# print(e,s)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# import matplotlib.pyplot as plt \n", - "# plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", - "# for s in solutions[2:4]:\n", - "# plt.scatter(ref_values[:-1], s, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", - "# plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "# plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", - "# plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", - "# plt.grid(which=\"major\", lw=1)\n", - "# plt.grid(which=\"minor\", lw=0.1)\n", - "# plt.xlabel('Reference Solution')\n", - "# plt.ylabel('QUBO Solution')\n", - "# # plt.legend()\n", - "# plt.xlim([-0.5,0.5])\n", - "# plt.ylim([-0.5,0.5])\n", - "# # plt.loglog()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -293,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -305,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -315,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -328,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -340,7 +288,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# from wntr_quantum.sampler.step.full_random import IncrementalStep\n", + "# mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -351,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -375,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -397,15 +355,15 @@ " [1, 1, 1, 0, 1, 0, 0],\n", " [1, 1, 1, 0, 0, 0, 0],\n", " [0, 1, 1, 0, 0, 0, 0],\n", - " [0, 1, 1, 1, 0, 1, 1],\n", - " [1, 1, 0, 1, 0, 0, 0],\n", - " [1, 1, 1, 0, 0, 1, 1],\n", - " [1, 1, 0, 1, 1, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0],\n", - " [0, 0, 1, 0, 1, 1, 1]]" + " [0, 1, 1, 0, 1, 0, 1],\n", + " [1, 0, 1, 1, 1, 0, 1],\n", + " [0, 1, 0, 0, 1, 0, 0],\n", + " [0, 1, 1, 1, 1, 0, 0],\n", + " [0, 0, 0, 1, 0, 0, 0],\n", + " [1, 0, 0, 0, 1, 0, 0]]" ] }, - "execution_count": 19, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -416,11 +374,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "num_sweeps = 9000\n", + "num_sweeps = 10000\n", "Tinit = 1E5\n", "Tfinal = 1E0\n", "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", @@ -429,86 +387,31 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10000/10000 [00:33<00:00, 301.79it/s]\n" + " 0%| | 0/11000 [00:00" ] @@ -542,16 +445,16 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "eplt = res.energies[-500:]\n", + "eplt = res.energies #[:100]\n", "plt.plot(eplt)\n", "plt.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", "plt.grid()\n", - "# plt.yscale('symlog')" + "plt.yscale('symlog')" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -562,12 +465,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -597,1120 +500,36 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-3.893e-08, -3.893e-08, 2.448e-07, -1.577e-07, -3.686e-07, -8.872e-08, 8.902e-02, 3.336e-01, 4.967e-02, 6.449e-01, 5.442e-02, 1.960e-01, 3.639e-01, -2.829e-01])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.verify_solution(net.convert_solution_from_si(ref_values[:-1]))" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1.295e-02, 1.295e-02, -2.746e-01, -2.546e-01, 3.341e-01, 2.625e-02, 4.496e+01, 5.775e+01, -4.929e+01, 4.860e+01, 1.639e+01, -1.631e+01, -1.127e+02, 1.592e+01])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.verify_solution(net.convert_solution_from_si(sol))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Own Sampler Manual" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_random_valid_sample(qubo):\n", - " \"\"\"check if quadratic constraints are respected or not\n", - "\n", - " Args:\n", - " sampleset (_type_): _description_\n", - " \"\"\"\n", - " sample = {}\n", - " for iv, v in enumerate(sorted(net.qubo.qubo_dict.variables)):\n", - " sample[v] = 1 # np.random.randint(2)\n", - "\n", - " for v in qubo.mapped_variables[:7]:\n", - " sample[v] = 1\n", - " sample[qubo.mapped_variables[7]] = 0\n", - "\n", - " for v, _ in sample.items():\n", - " if v not in qubo.mapped_variables:\n", - " var_tmp = v.split(\"*\")\n", - " itmp = 0\n", - " for vtmp in var_tmp:\n", - " if itmp == 0:\n", - " new_val = sample[vtmp]\n", - " itmp = 1\n", - " else:\n", - " new_val *= sample[vtmp]\n", - " \n", - " sample[v] = new_val\n", - " return sample " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "sample = generate_random_valid_sample(net.qubo)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "from copy import deepcopy\n", - "class OptStep:\n", - " def __init__(self,var_names, single_var_names, single_var_index):\n", - " self.var_names = var_names\n", - " self.single_var_names = single_var_names\n", - " self.single_var_index = single_var_index\n", - " self.num_single_var = len(self.single_var_names)\n", - " self.high_order_terms_mapping = self.define_mapping()\n", - "\n", - " def define_mapping(self):\n", - " high_order_terms_mapping = []\n", - "\n", - " # loop over all the variables\n", - " for iv, v in enumerate(self.var_names):\n", - " \n", - " # if we have a cmomposite variables e.g. x_001 * x_002 we ignore it\n", - " if v not in self.single_var_names:\n", - " high_order_terms_mapping.append(None)\n", - " \n", - " # if the variables is a unique one e.g. x_011\n", - " else:\n", - " high_order_terms_mapping.append({})\n", - " # we loop over all the variables\n", - " for iiv, vv in enumerate(self.var_names):\n", - " if v != vv:\n", - " if v in vv:\n", - " \n", - " var_tmp = vv.split(\"*\")\n", - " idx_terms = []\n", - " for vtmp in var_tmp:\n", - " idx = self.single_var_index[self.single_var_names.index(vtmp)]\n", - " idx_terms.append(idx)\n", - " high_order_terms_mapping[-1][iiv] = idx_terms\n", - "\n", - " return high_order_terms_mapping\n", - "\n", - " def fix_constraint(self, x, idx):\n", - " fix_var = self.high_order_terms_mapping[idx]\n", - " for idx_fix, idx_prods in fix_var.items():\n", - " x[idx_fix] = np.array([x[i] for i in idx_prods]).prod()\n", - " return x \n", - "\n", - " def __call__(self, x):\n", - " vidx = np.random.choice(self.single_var_index)\n", - " if vidx not in self.single_var_index[:8]:\n", - " x[vidx] = int(not(x[vidx]))\n", - " self.fix_constraint(x, vidx)\n", - " return x \n", - "\n", - "var_names = sorted(net.qubo.qubo_dict.variables)\n", - "mytakestep = OptStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", - "mytakestep.high_order_terms_mapping[0]\n", - "x0 = list(sample.values())\n", - "x0_cpy = deepcopy(list(sample.values()))\n", - "x = mytakestep(x0)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['x_001_001',\n", - " 'x_001_001*x_009_001',\n", - " 'x_001_001*x_009_001*x_009_006',\n", - " 'x_001_001*x_009_001*x_009_007',\n", - " 'x_001_001*x_009_001*x_009_008',\n", - " 'x_001_001*x_009_002',\n", - " 'x_001_001*x_009_002*x_009_007',\n", - " 'x_001_001*x_009_003',\n", - " 'x_001_001*x_009_003*x_009_006',\n", - " 'x_001_001*x_009_003*x_009_007',\n", - " 'x_001_001*x_009_003*x_009_008',\n", - " 'x_001_001*x_009_004',\n", - " 'x_001_001*x_009_004*x_009_001',\n", - " 'x_001_001*x_009_004*x_009_002',\n", - " 'x_001_001*x_009_004*x_009_006',\n", - " 'x_001_001*x_009_004*x_009_007',\n", - " 'x_001_001*x_009_004*x_009_008',\n", - " 'x_001_001*x_009_004*x_009_009',\n", - " 'x_001_001*x_009_005',\n", - " 'x_001_001*x_009_005*x_009_001',\n", - " 'x_001_001*x_009_005*x_009_002',\n", - " 'x_001_001*x_009_005*x_009_003',\n", - " 'x_001_001*x_009_005*x_009_004',\n", - " 'x_001_001*x_009_005*x_009_006',\n", - " 'x_001_001*x_009_005*x_009_007',\n", - " 'x_001_001*x_009_005*x_009_008',\n", - " 'x_001_001*x_009_005*x_009_009',\n", - " 'x_001_001*x_009_006',\n", - " 'x_001_001*x_009_006*x_009_007',\n", - " 'x_001_001*x_009_007',\n", - " 'x_001_001*x_009_008',\n", - " 'x_001_001*x_009_008*x_009_006',\n", - " 'x_001_001*x_009_008*x_009_007',\n", - " 'x_001_001*x_009_009',\n", - " 'x_002_001',\n", - " 'x_002_001*x_010_004',\n", - " 'x_002_001*x_010_005*x_010_002',\n", - " 'x_003_001',\n", - " 'x_004_001',\n", - " 'x_004_001*x_012_001',\n", - " 'x_004_001*x_012_001*x_012_002',\n", - " 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" 'x_016_008*x_016_004',\n", - " 'x_016_008*x_016_005',\n", - " 'x_016_008*x_016_006',\n", - " 'x_016_008*x_016_007',\n", - " 'x_016_009',\n", - " 'x_016_009*x_008_001*x_016_003',\n", - " 'x_016_009*x_008_001*x_016_007',\n", - " 'x_016_009*x_016_001',\n", - " 'x_016_009*x_016_002',\n", - " 'x_016_009*x_016_003',\n", - " 'x_016_009*x_016_004',\n", - " 'x_016_009*x_016_005',\n", - " 'x_016_009*x_016_006',\n", - " 'x_016_009*x_016_007',\n", - " 'x_016_009*x_016_008',\n", - " 'x_017_001',\n", - " 'x_017_002',\n", - " 'x_017_003',\n", - " 'x_017_004',\n", - " 'x_017_005',\n", - " 'x_017_006',\n", - " 'x_017_007',\n", - " 'x_017_008',\n", - " 'x_017_009',\n", - " 'x_018_001',\n", - " 'x_018_002',\n", - " 'x_018_003',\n", - " 'x_018_004',\n", - " 'x_018_005',\n", - " 'x_018_006',\n", - " 'x_018_007',\n", - " 'x_018_008',\n", - " 'x_018_009',\n", - " 'x_019_001',\n", - " 'x_019_002',\n", - " 'x_019_003',\n", - " 'x_019_004',\n", - " 'x_019_005',\n", - " 'x_019_006',\n", - " 'x_019_007',\n", - " 'x_019_008',\n", - " 'x_019_009',\n", - " 'x_020_001',\n", - " 'x_020_002',\n", - " 'x_020_003',\n", - " 'x_020_004',\n", - " 'x_020_005',\n", - " 'x_020_006',\n", - " 'x_020_007',\n", - " 'x_020_008',\n", - " 'x_020_009',\n", - " 'x_021_001',\n", - " 'x_021_002',\n", - " 'x_021_003',\n", - " 'x_021_004',\n", - " 'x_021_005',\n", - " 'x_021_006',\n", - " 'x_021_007',\n", - " 'x_021_008',\n", - " 'x_021_009',\n", - " 'x_022_001',\n", - " 'x_022_002',\n", - " 'x_022_003',\n", - " 'x_022_004',\n", - " 'x_022_005',\n", - " 'x_022_006',\n", - " 'x_022_007',\n", - " 'x_022_008',\n", - " 'x_022_009']" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted(net.qubo.qubo_dict.variables)" + "traj = []\n", + "for x in res.trajectory:\n", + " sol = net.qubo.decode_solution(np.array(x))\n", + " sol = net.combine_flow_values(sol)\n", + " sol = net.convert_solution_to_si(sol)\n", + " traj.append(sol)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1.141e+09])" + "Text(0, 0.5, 'QUBO Solution')" ] }, - "execution_count": 17, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" - } - ], - "source": [ - "from dimod import as_samples \n", - "def bqm_energy(x):\n", - " return net.qubo.qubo_dict.energies(as_samples((x, var_names)))\n", - "bqm_energy(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 10000/10000 [01:06<00:00, 150.15it/s]\n" - ] }, - { - "ename": "AttributeError", - "evalue": "'OptStep' object has no attribute 'verify_quadratic_constraints'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 31\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 30\u001b[0m x \u001b[38;5;241m=\u001b[39m x_ori\n\u001b[0;32m---> 31\u001b[0m \u001b[43mmytakestep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverify_quadratic_constraints\u001b[49m(x)\n", - "\u001b[0;31mAttributeError\u001b[0m: 'OptStep' object has no attribute 'verify_quadratic_constraints'" - ] - } - ], - "source": [ - "from tqdm import tqdm\n", - "num_sweeps = 9000\n", - "Tinit = 1E5\n", - "Tfinal = 1E-1\n", - "sample = generate_random_valid_sample(net.qubo)\n", - "x = list(sample.values())\n", - "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", - "# Tschedule = np.zeros(10000)\n", - "energies = []\n", - "energies.append(bqm_energy(x))\n", - "for T in tqdm(Tschedule):\n", - "\n", - " x_ori = deepcopy(x)\n", - " e_ori = bqm_energy(x)\n", - " x_new = mytakestep(x) \n", - " e_new = bqm_energy(x)\n", - "\n", - " if e_new < e_ori:\n", - " x = x_new\n", - " energies.append(bqm_energy(x))\n", - " elif T != 0.0:\n", - " p = np.exp( -(e_new - e_ori) / T )\n", - " if np.random.rand() < p:\n", - " x = x_new\n", - " energies.append(bqm_energy(x))\n", - " else:\n", - " x = x_ori \n", - " else:\n", - " x = x_ori\n", - "# mytakestep.verify_quadratic_constraints(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def flatten_list(lst):\n", - " out = []\n", - " for l in lst:\n", - " if not isinstance(l, list):\n", - " out += [l]\n", - " else:\n", - " out += l\n", - " return out\n", - "bin_rep_sol_flat = flatten_list(bin_rep_sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_7683/121086906.py:3: DeprecationWarning: support for (dict, labels) as a samples-like is deprecated since dimod 0.10.13 and will be removed in 0.12.0\n", - " return net.qubo.qubo_dict.energies(as_samples((x, var_names)))\n" - ] - } - ], - "source": [ - "eref = bqm_energy(net.qubo.extend_binary_representation(bin_rep_sol_flat))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "eplt = energies #[-200:]\n", - "plt.plot(eplt)\n", - "plt.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "plt.grid()\n", - "plt.yscale('symlog')" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "sol = net.qubo.decode_solution(np.array(x))\n", - "sol = net.combine_flow_values(sol)\n", - "sol = net.convert_solution_to_si(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1722,463 +541,16 @@ "source": [ "import matplotlib.pyplot as plt \n", "plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", - "plt.scatter(ref_values[:-1], sol, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", + "\n", + "plt.scatter(ref_values[:-1], traj[0], s=50, lw=1, edgecolors='w', label='Sampled solution')\n", + "plt.scatter(ref_values[:-1], traj[-1], s=50, lw=1, edgecolors='w', label='Sampled solution')\n", "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", "plt.grid(which=\"major\", lw=1)\n", "plt.grid(which=\"minor\", lw=0.1)\n", "plt.xlabel('Reference Solution')\n", - "plt.ylabel('QUBO Solution')\n", - "# plt.legend()\n", - "# plt.xlim([-0.5,0.5])\n", - "# plt.ylim([-0.5,0.5])\n", - "# plt.loglog()\n", - "plt.xscale('symlog')\n", - "plt.yscale('symlog')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 0,\n", - " [0, 1, 1, 0, 1, 1, 1, 0, 1],\n", - " [1, 0, 1, 1, 1, 1, 0, 0, 0],\n", - " [1, 1, 1, 0, 1, 0, 0, 0, 1],\n", - " [1, 0, 1, 0, 0, 1, 0, 0, 0],\n", - " [0, 1, 0, 1, 0, 0, 1, 1, 0],\n", - " [0, 0, 1, 1, 1, 0, 1, 0, 0],\n", - " [0, 0, 1, 1, 1, 0, 0, 0, 0],\n", - " [1, 0, 0, 1, 1, 0, 0, 0, 0],\n", - " [0, 1, 0, 0, 0, 1, 0, 1, 0],\n", - " [1, 1, 0, 0, 0, 1, 1, 0, 0],\n", - " [1, 0, 0, 0, 1, 0, 0, 1, 0],\n", - " [1, 1, 1, 0, 0, 1, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 1, 0],\n", - " [1, 0, 1, 0, 1, 0, 1, 0, 0]]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bin_rep_sol" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3.109e-01, 5.070e-02, 2.319e-01, 3.076e-02, 1.679e-01, 7.647e-02, 2.327e-02, -2.078e-02, 2.007e+02, 1.819e+02, 1.956e+02, 1.640e+02, 1.906e+02, 1.778e+02])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "encoded_ref_sol" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([0,\n", - " 34,\n", - " 37,\n", - " 38,\n", - " 63,\n", - " 74,\n", - " 98,\n", - " 111,\n", - " 150,\n", - " 157,\n", - " 171,\n", - " 175,\n", - " 184,\n", - " 191,\n", - " 193,\n", - " 196,\n", - " 200,\n", - " 207,\n", - " 222,\n", - " 231,\n", - " 243,\n", - " 244,\n", - " 259,\n", - " 272,\n", - " 283,\n", - " 290,\n", - " 295,\n", - " 305,\n", - " 317,\n", - " 325,\n", - " 342,\n", - " 352,\n", - " 358,\n", - " 372,\n", - " 376,\n", - " 385,\n", - " 389,\n", - " 396,\n", - " 398,\n", - " 406,\n", - " 423,\n", - " 440,\n", - " 446,\n", - " 450,\n", - " 451,\n", - " 461,\n", - " 477,\n", - " 483,\n", - " 498,\n", - " 509,\n", - " 515,\n", - " 518,\n", - " 520,\n", - " 531,\n", - " 535,\n", - " 546,\n", - " 553,\n", - " 570,\n", - 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" 'x_018_009',\n", - " 'x_019_001',\n", - " 'x_019_002',\n", - " 'x_019_003',\n", - " 'x_019_004',\n", - " 'x_019_005',\n", - " 'x_019_006',\n", - " 'x_019_007',\n", - " 'x_019_008',\n", - " 'x_019_009',\n", - " 'x_020_001',\n", - " 'x_020_002',\n", - " 'x_020_003',\n", - " 'x_020_004',\n", - " 'x_020_005',\n", - " 'x_020_006',\n", - " 'x_020_007',\n", - " 'x_020_008',\n", - " 'x_020_009',\n", - " 'x_021_001',\n", - " 'x_021_002',\n", - " 'x_021_003',\n", - " 'x_021_004',\n", - " 'x_021_005',\n", - " 'x_021_006',\n", - " 'x_021_007',\n", - " 'x_021_008',\n", - " 'x_021_009',\n", - " 'x_022_001',\n", - " 'x_022_002',\n", - " 'x_022_003',\n", - " 'x_022_004',\n", - " 'x_022_005',\n", - " 'x_022_006',\n", - " 'x_022_007',\n", - " 'x_022_008',\n", - " 'x_022_009'])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def create_variables_mapping(qubo):\n", - " \"\"\"generates the index of variables in the solution vector\n", - "\n", - " Args:\n", - " sol (dimod.Sampleset): sampleset from the sampler\n", - " \"\"\"\n", - "\n", - " # get all the possible variable prefixes\n", - " prefixes = list(\n", - " set(\n", - " [\n", - " sv.base_name + \"_\"\n", - " for sv in qubo.mixed_solution_vectors.solution_vectors\n", - " ]\n", - " )\n", - " )\n", - "\n", - " # extract the data of the original variables\n", - " index_variables, mapped_variables = [], []\n", - " for ix, s in enumerate(sorted(qubo.qubo_dict.variables)):\n", - " if s in qubo.all_vars and np.any([s.startswith(pf) for pf in prefixes]):\n", - " index_variables.append(ix)\n", - " mapped_variables.append(s)\n", - " return index_variables, mapped_variables \n", - "\n", - "create_variables_mapping(net.qubo)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.simulated_annealing import generate_random_valid_sample, ProposalStep, SimulatedAnnealing" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "mystep = ProposalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "x = generate_random_valid_sample(net.qubo)\n", - "sampler = SimulatedAnnealing()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'SimulatedAnnealing' object has no attribute 'variables'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtake_step\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmystep\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sampler/simulated_annealing.py:164\u001b[0m, in \u001b[0;36mSimulatedAnnealing.sample\u001b[0;34m(bqm, num_sweeps, Temp, x0, take_step)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtake_step must be callable\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 163\u001b[0m \u001b[38;5;66;03m# define th variable names\u001b[39;00m\n\u001b[0;32m--> 164\u001b[0m var_names \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(\u001b[43mbqm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvariables\u001b[49m)\n\u001b[1;32m 166\u001b[0m \u001b[38;5;66;03m# define the initial state\u001b[39;00m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x0 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[0;31mAttributeError\u001b[0m: 'SimulatedAnnealing' object has no attribute 'variables'" - ] - } - ], - "source": [ - "sampler.sample(net.qubo.qubo_dict, x0=x, take_step=mystep)" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 156, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "callable(mystep)" + "plt.ylabel('QUBO Solution')" ] }, { diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py index e006a5f..75e3348 100644 --- a/wntr_quantum/sampler/simulated_annealing.py +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -40,6 +40,7 @@ class SimulatedAnnealingResults: res: list energies: list + trajectory: list class SimulatedAnnealing: # noqa: D101 @@ -55,6 +56,7 @@ def sample( Tschedule=None, x0=None, take_step=None, + save_traj=False, ): """Sample the problem. @@ -65,6 +67,7 @@ def sample( Tschedule (list, optional): The temperature schedule x0 (_type_, optional): _description_. Defaults to None. take_step (_type_, optional): _description_. Defaults to None. + save_traj (bool, optional): save the trajectory. Defaults to False """ def bqm_energy(x, var_names): @@ -97,6 +100,12 @@ def bqm_energy(x, var_names): energies = [] energies.append(bqm_energy(x, var_names)) + # init the traj + trajectory = None + if save_traj: + trajectory = [] + trajectory.append(x) + # loop over the temp schedule for T in tqdm(Tschedule): @@ -112,12 +121,16 @@ def bqm_energy(x, var_names): if e_new < e_ori: x = x_new energies.append(bqm_energy(x, var_names)) + if save_traj: + trajectory.append(x) else: p = np.exp(-(e_new - e_ori) / T) if np.random.rand() < p: x = x_new energies.append(bqm_energy(x, var_names)) + if save_traj: + trajectory.append(x) else: x = x_ori - return SimulatedAnnealingResults(x, energies) + return SimulatedAnnealingResults(x, energies, trajectory) diff --git a/wntr_quantum/sampler/step/full_random.py b/wntr_quantum/sampler/step/full_random.py index 8dcdc56..52f2e98 100644 --- a/wntr_quantum/sampler/step/full_random.py +++ b/wntr_quantum/sampler/step/full_random.py @@ -18,3 +18,84 @@ def __call__(self, x): x[vidx] = int(not (x[vidx])) self.fix_constraint(x, vidx) return x + + +class IncrementalStep(BaseStep): + + def __init__(self, var_names, single_var_names, single_var_index, step_size=1): + super().__init__(var_names, single_var_names, single_var_index) + + self.value_names = np.unique( + [self._get_variable_root_name(n) for n in single_var_names] + ) + self.index_values = {v: [] for v in self.value_names} + for n, idx in zip(self.single_var_names, self.single_var_index): + val = self._get_variable_root_name(n) + self.index_values[val].append(idx) + + self.step_size = step_size + + @staticmethod + def _get_variable_root_name(var_name): + """Extract the root name of the variables. + + Args: + var_name (_type_): _description_ + """ + return "_".join(var_name.split("_")[:2]) + + def __call__(self, x): + """Call function of the method. + + Args: + x (_type_): _description_ + + Returns: + _type_: _description_ + """ + # extract the data of the variable we want to change + nmax = 16 + + random_val_name = np.random.choice(self.value_names[nmax:]) + idx = self.index_values[random_val_name] + data = np.array(x)[idx] + + # determine the max val + max_val = np.ones_like(data) + max_val = int("".join([str(i) for i in max_val[::-1]]), base=2) + + # check if we reach min/max val + max_val_check = data.prod() == 1 + min_val_check = data.sum() == 0 + + # convert to int value + val = int("".join([str(i) for i in data[::-1]]), base=2) + + # determine sign of the displacement + if min_val_check: + sign = 1 + elif max_val_check: + sign = -1 + else: + sign = 2 * np.random.randint(2) - 1 + + # new value + new_val = val + sign * self.step_size + if new_val < 0: + new_val = 0 + if new_val > max_val: + new_val = max_val + new_val = np.binary_repr(new_val) + + # convert back to binary repr + new_data = np.array([int(i) for i in new_val])[::-1] + + # inject in the x vector + for ix, nd in zip(idx, new_data): + x[ix] = nd + + # fix constraints + for vidx in idx: + self.fix_constraint(x, vidx) + + return x From f7c30771d21aa109a9a739223403ee0093365727 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 18 Oct 2024 20:25:28 +0200 Subject: [PATCH 71/96] added freeze option --- .../qubo_poly_solver_2loops_dw.ipynb | 463 ++++++++++++++---- wntr_quantum/sampler/simulated_annealing.py | 11 +- .../sampler/simulated_annealing_parallel.py | 139 ++++++ wntr_quantum/sampler/step/full_random.py | 50 +- 4 files changed, 548 insertions(+), 115 deletions(-) create mode 100644 wntr_quantum/sampler/simulated_annealing_parallel.py diff --git a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb index d567434..b7b6993 100644 --- a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb +++ b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 206, "metadata": { "metadata": {} }, @@ -30,7 +30,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 206, "metadata": {}, "output_type": "execute_result" } @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 207, "metadata": {}, "outputs": [ { @@ -79,7 +79,7 @@ "" ] }, - "execution_count": 2, + "execution_count": 207, "metadata": {}, "output_type": "execute_result" } @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 208, "metadata": {}, "outputs": [ { @@ -104,7 +104,7 @@ "array([ 3.111e-01, 5.111e-02, 2.322e-01, 3.108e-02, 1.678e-01, 7.613e-02, 2.334e-02, -2.058e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" ] }, - "execution_count": 3, + "execution_count": 208, "metadata": {}, "output_type": "execute_result" } @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 209, "metadata": {}, "outputs": [], "source": [ @@ -134,14 +134,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 210, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 500.000000 => 1000.000000 (res: 3.937008)\n", + "Head Encoding : 0.000000 => 1000.000000 (res: 7.874016)\n", "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.118110)\n" ] } @@ -153,11 +153,11 @@ "\n", "nqbit = 7\n", "step = (15/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"q\")\n", "\n", "nqbit = 7\n", - "step = (500/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+500.0, var_base_name=\"x\")\n", + "step = (1000/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"h\")\n", "\n", "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", " head_encoding=head_encoding)\n", @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 211, "metadata": {}, "outputs": [ { @@ -190,7 +190,7 @@ "array([1. , 1. , 1. , 1. , 1. , 1. , 1. , 0.999, 1. , 1.001, 1. , 1.001, 1. , 1.001])" ] }, - "execution_count": 6, + "execution_count": 211, "metadata": {}, "output_type": "execute_result" } @@ -207,12 +207,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 212, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 213, "metadata": {}, "outputs": [], "source": [ @@ -253,17 +253,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 214, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", - "sampler = SimulatedAnnealing()" + "# from wntr_quantum.sampler.simulated_annealing_parallel import SimulatedAnnealing\n", + "sampler = SimulatedAnnealing()\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 215, "metadata": {}, "outputs": [], "source": [ @@ -276,29 +277,31 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 216, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.sampler.step.full_random import RandomStep\n", + "from wntr_quantum.sampler.step.full_random import IncrementalStep\n", + "from wntr_quantum.sampler.step.full_random import ParallelIncrementalStep \n", + "\n", "var_names = sorted(net.qubo.qubo_dict.variables)\n", "net.qubo.create_variables_mapping()\n", - "mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)" + "# mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", + "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)\n", + "# mystep = ParallelIncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=100)" ] }, { - "cell_type": "code", - "execution_count": 12, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# from wntr_quantum.sampler.step.full_random import IncrementalStep\n", - "# mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)" + "# generate random initial guess" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 217, "metadata": {}, "outputs": [], "source": [ @@ -307,9 +310,16 @@ "x0 = list(x.values())" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## generate modifed solution initial guess" + ] + }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 218, "metadata": {}, "outputs": [], "source": [ @@ -325,93 +335,75 @@ "from copy import deepcopy\n", "mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", "\n", + "# # modsify sign\n", + "# for i in range(8):\n", + "# mod_bin_rep_sol[i] = np.random.randint(2)\n", + "\n", + "# modify flow value\n", + "for i in range(8, 16):\n", + " mod_bin_rep_sol[i] = list(np.random.randint(2, size=flow_encoding.nqbit))\n", + "\n", + "# modify head values\n", "for i in range(16,22):\n", - " mod_bin_rep_sol[i] = list(np.random.randint(2, size=7))\n", + " mod_bin_rep_sol[i] = list(np.random.randint(2, size=head_encoding.nqbit))\n", + "\n", "x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", - "x0 = list(x.values())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 0,\n", - " [1, 0, 1, 1, 1, 0, 1],\n", - " [1, 1, 1, 1, 0, 0, 0],\n", - " [1, 0, 1, 0, 0, 0, 1],\n", - " [1, 0, 0, 1, 0, 0, 0],\n", - " [0, 1, 0, 0, 1, 1, 0],\n", - " [1, 1, 1, 0, 1, 0, 0],\n", - " [1, 1, 1, 0, 0, 0, 0],\n", - " [0, 1, 1, 0, 0, 0, 0],\n", - " [0, 1, 1, 0, 1, 0, 1],\n", - " [1, 0, 1, 1, 1, 0, 1],\n", - " [0, 1, 0, 0, 1, 0, 0],\n", - " [0, 1, 1, 1, 1, 0, 0],\n", - " [0, 0, 0, 1, 0, 0, 0],\n", - " [1, 0, 0, 0, 1, 0, 0]]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mod_bin_rep_sol" + "x0 = list(x.values())" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 219, "metadata": {}, "outputs": [], "source": [ - "num_sweeps = 10000\n", + "num_sweeps = 8000\n", "Tinit = 1E5\n", - "Tfinal = 1E0\n", + "Tfinal = 1E1\n", "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))" + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "\n", + "num_sweeps = 2000\n", + "Tinit = 1E1\n", + "Tfinal = 1E0\n", + "Tschedule = np.append(Tschedule, np.linspace(Tinit, Tfinal, num_sweeps))\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "\n", + "# num_sweeps = 1000\n", + "# Tinit = 1E-1\n", + "# Tfinal = 1E0-2\n", + "# Tschedule = np.append(Tschedule, np.linspace(Tinit, Tfinal, num_sweeps))\n", + "# Tschedule = np.append(Tschedule, Tfinal*np.ones(num_sweeps))" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 220, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/11000 [00:00" + "" + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -445,16 +447,30 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "eplt = res.energies #[:100]\n", - "plt.plot(eplt)\n", - "plt.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "plt.grid()\n", - "plt.yscale('symlog')" + "eplt = res.energies\n", + "\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "left, bottom, width, height = [0.25, 0.25, 0.3, 0.3]\n", + "\n", + "ax1.plot(eplt)\n", + "ax1.plot(Tschedule)\n", + "ax1.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "# plt.ylim([-1E5, -1E4])\n", + "# plt.xlim([9000,11000])\n", + "ax1.grid()\n", + "ax1.set_yscale('symlog')\n", + "\n", + "ax2 = fig.add_axes([left, bottom, width, height])\n", + "ax2.plot(eplt[-1000:])\n", + "ax2.grid()\n", + "ax2.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "# ax2.set_yscale('symlog')" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 224, "metadata": {}, "outputs": [], "source": [ @@ -465,12 +481,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 225, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -484,8 +500,6 @@ "plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", "plt.scatter(ref_values[:-1], sol, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", "plt.grid(which=\"major\", lw=1)\n", "plt.grid(which=\"minor\", lw=0.1)\n", "plt.xlabel('Reference Solution')\n", @@ -500,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 237, "metadata": {}, "outputs": [], "source": [ @@ -514,7 +528,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 238, "metadata": {}, "outputs": [ { @@ -523,13 +537,13 @@ "Text(0, 0.5, 'QUBO Solution')" ] }, - "execution_count": 24, + "execution_count": 238, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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CZ3yBWTgtDIAIAXK5XHodBVSumHrw4AFSU1Px4MED6Zgoili/fj1qolAo6mzGunXr1O7RElr1Nda+ffvUPm/evBkODg6Ij4/H8OHDkZeXh40bN+LHH3/E6NGjAQCbNm2Cl5cX4uLi4OfnhwMHDiAxMRGHDh2Co6Mj+vfvjw8++ADLli3DihUrpOE3IiIi+q9acudYoQAlgjHszPQQ+swUdPH0BFD5imrLli1Yv349kpOTpct4enpi8eLFGD9+vFo5UDkY8f333+P27du1NkMURSQnJyMnJwe2traN6sLdu3dRWlraoLptas5OXl4eAMDGxgYAEB8fD4VCgaCgIKlOz5494ebmhtjYWPj5+SE2NhZ9+/aFo6OjVCckJAQLFy7E5cuXMWDAgGr3KSsrQ1lZmfQ5Pz8fQOXDbOjwW0MUFBSo/a5rdL1/gO73kf3TbuyfdmsL/Sv3mATDGQFA4i4g/y4MLV0wySYAzl29oaevD7lcjtjYWLz++utSYNGlSxe1a6xfvx7ffvutWnl5eTnu3LmDtLQ0AJWvvxwcHKCnp1djO7Kysmo99qjc3FzExsYiKSkJDg4ODTqnzQQ7KpUKr7zyCgICAtCnTx8AQGZmJgwNDautxXd0dERmZqZU5+FAp+p41bGarFq1Cu+//3618piYGJiamja3K9UkJCRo/Jptia73D9D9PrJ/2o39026t0T9RFHH//n1kZmaia9euMDX1Bky8AQWArAIkZ8Wo1f+///u/Rl1fpVLhtddeQ0VFBWbNmlXjwMPDkpKSkJSUVO91lUolLl++LA1MNDRQbDPBzqJFi3Dp0iWcOHGixe+1fPlyLF26VPqcn5+PTp06ISAgAJaWlhq7T0FBARISEuDj4wMLCwuNXbet0PX+AbrfR/ZPu7F/2q1J/RNFIO/2f0di7gCWrkCvpwCrTrXvXfWI27dv4+jRo8jJqdzLwdDQECNGjKixfaGhoSgrK2vSWw87OzssW7YMa9euxQ8//FBjHUEQ4Orqip07d9Y68flRBgYGyM3Nhb+/P4yMjLBs2bJ6z2kTwU54eDh2796NY8eOoWPHjlK5k5MTysvLIZfL1UZ3srKy4OTkJNU5ffq02vWqVmtV1XmUkZERjIyMqpVbW1trNNipYmFhUW10Spfoev8A3e8j+6fd2D/t1uD+1Zbx+MxnNWc8rsWlS5eQk5MDU1NTBAYGYsCAATUGGlu2bEFiYmKzJg/r6enhzp07uHnzZo3HBUFAREQEOnToUO2YQqHAqVOn4OrqCg+P/60cGzduHGSyyvVVVdNQ6tOqq7FEUUR4eDh+//13REVFqXUGAAYOHAgDAwMcPnxYKrt27Rpu3boFf39/AIC/vz8uXryolrDo4MGDsLS0RK9evR5PR4iIiFpSEzIe18bPzw8jR47E4sWL4ePjAwDVVljVtbpKU2QyGUxNTfHiiy+qlSuVSsTHx2P9+vU4fPgwDh48qBZwVQU6jbpXs1vbDIsWLcK2bdvw448/wsLCApmZmcjMzERJSQkAwMrKCnPmzMHSpUtx5MgRxMfHY9asWfD394efnx8AIDg4GL169cL06dPx559/Yv/+/Xj77bexaNGiGkdviIiItFJDMh6rFYm4cuUKfvjhB7Xcc/r6+hgxYgRKS0vxj3/8A926dYO9vT08PDxgb2+Pbt264aOPPkJycnKTR3XqeyUlk8kgCAJ+++23aqNaly9fxu7du1FQUAArKyv4+vo2qQ0Pa9XXWF999RUAYOTIkWrlmzZtwsyZMwEAa9askbIslpWVISQkBF9++aVUV09PD7t378bChQvh7+8PMzMzzJgxAytXrnxc3SAiImpZDch4jNzUynoA7t+/j3379kmvj86ePSsNEgCV2zqEhYWhuLi42mVu3ryJd955p1nNrS3LclWZiYkJfvvtNwQHB1er06dPH8THx8PLywuDBg2Cvn7zQ5VWDXYaEjEaGxtjw4YN2LBhQ6113N3dsXfvXk02jYiIqO2oI+OxpEPnynqCDHv27EFaWhr09PQwdOhQ6XUVUPP+VWq30kCCv0uXLuHAgQPVyrt06YKIiAjMmDEDVlZWuHv3LmJiYjBx4kTpbUzVthQNnbDcEG1igjIRERHVY9CcygnKNQUjglB5/L+Cg4Nx7NgxBAcHS7nrgMbtX9UUgiCgS5cu8PDwwOTJkxEdHY2oqCiIoggLCwvY2NhAEARkZ2djx44dSExMBAA4ODiorQjTZKADtPKcHSIiInqIqPrfJOOHv3444/EjgcBdwRGnvd6tPP7f11guLi6YPHmyWqADNH7/qqaIiIhQC1asrKzQuXNn2NraSuUHDx6UAh1vb294e3u3WHsAjuwQERG1DaIIZCdXTjTOSal8bTVozn+DGKHyl+8CoGsQcHYjih/cxuH8Lki4rw/haiHc792rlmRX/fItu8JKJpPBxMSk2uqqmowaNQqiKGL06NF1tllTGOwQERG1ttpy6Jz6Wj2HjiAAtp6oCFyJr9atR2FhIYDKSb317QCQnZ1dbf8qTaltdZVKpcLZs2dRVlaGcePGSeWOjo6YMmVKi7SlJgx2iIiIHvbwq6OHv27J+9WXQ8czELD1rGyHIIO+gSF8fHxw/fp1hIaGws3Nrd7bVAVGzSEIgtoE5tpWV6lUKly8eBFXrlyRdj0fNGjQYxnFqQnn7BAREVWpepW0/03gx+crf89Orj2/jabUk0MnP2Zjtf0en3jiCcydO7dBgQ4AmJubN6uJH330UbVNQLt06YK1a9fizp07asvIy8rKcOLECSgUClhYWODpp5+Gvb19s+7fHBzZISIiAhr+KknT6sihUwE9xGIgjv9phA53d2H+/PlSBuHG5p+xtbWFp6dno19lVa2w+tvf/oa//e1vyMnJQUFBgdrqKqByTtDDIz1Dhw7F9evX8cwzz8DOzq5R99Q0juwQERFpcDuGJt27hhw6cljiS8xAlDAMClEGIyMjFBcXQxTFats7NIQgCFi8eHGTmli1wkoQBNja2qqtrrp79y5++OEHaXVVlX79+sHBwUEjSQGbi8EOERER0OjtGDRq0Jxqo0aWKIARymGOQjwT5I9p06Zi48aNNW7v8I9//ANyubze28yYMQOmpqYNzmNT2/5VAJCTk4MdO3bgu+++w82bNxEdHa2RhIQtgcEOERFRI7dj0Pi9bbuiPPD/oBAMpGIZRPxF2IPwIA/0HRqEcePGY8mSJdV2EL958yaWLFmCjh07Yv/+/XXeytraGr/99htkMlm9G2rWtX8VAGRlZanlypkyZYrGkwFqSuuPLREREbW2Rm7HoNFbiyIuXrqEg6fL0M/nKwQaxFcGVh06w3bQHIi2nli0KBxHjhypc3uHkpISjBs3Dnv27EFISEit9wsJCcGePXvU9sZq6AqrhwOknj17wt/fH/369Wu1VVYNxWCHiIgIaNR2DJpSWlqKX3/9FXfu3AEAXL11DyPnfwA9fQNAVKFcocDYJ4Nx5MiRerMeq1QqCIKAsLAwpKen1zgaUyUkJATp6enYunUr1q1bpzZp+dH9qxQKBU6dOoWzZ89i7ty5MDMzA1AZFNW0kWdbxGCHiIjo4e0YHp2kLAiVq7GqMhlrWEZGBvT19fHEE09g6NCh0Kua0CvI8NVXX0t7SzWEKIooLi7G1q1bERERUWdda2trREREYPHixbWusEpISMDRo0dRUFAgfX7iiSea3tlWwmCHiIgIkLZjELsGQTi7UXqVJA6aA0FDgc7Dy7MBwNjYGE8++SS8vLxgZWUlrbQqLCyEmZkZ1q1b16R7rFu3DosXL27QHJqqFVa2trbVjqWlpaGgoABWVlYYNWoU+vbt2+j2tAUMdoiIiP5LBFBk7g7T4I8gk8mgUqlQXF4BMwDNDXVu376NyMhIjB07Fh07dpTKe/bsCVEU8Y9//APr16/XyJYOycnJyMnJqTGAqYtCoYCBwf8mSY8cORLOzs4YNGhQm1hC3lTa23IiIiINEkURm0+mYuXuRBjqyWBhrI+C0gqUK1V4d3wvzBzauUmrjQoKCnDo0CFcuHABAHDkyBFMnz5dOh4bG4tnn31WmiysKQUFBQ0Odu7evYvDhw9DJpNh2rRpUnmHDh3g5+en0Xa1BgY7RETU7qlEEakPirBydyJEESirUKGssFw6vnJ3IkZ0t0dnOzPIGhnwXLp0SQp0BgwYgMDAQLXjERERKCkp0XiOGgsLi3rrFBYWIjIyUlpCLpPJkJubiw4dOmi0La2NwQ4RERGAbXFpdeYU3BaXhrfH92r0dYcMGYKMjAz4+vrC1dVVKq+a9Aug3pVWjVG1vYONjU29dQ0NDXHr1i0AlblyRo4cqXOBDsBgh4iICDJBQFpO3a+RbuUU1zuqk5ubi5iYGIwZM0aa46Knp4dJkyZVq7t79264uLhoNNCpUrW9w6NKS0thYGAAPT09AJXBzsSJE2Fpadnmc+U0BzMoExFRu6cSRbjbmNZZx83GFKpahn4UCgWOHDmCL7/8EvHx8Th58mSd1xJFEf/617+a3N7a1La9g0KhQExMDP7xj3/g3Llzase6deum04EOwJEdIiIiAMALfu7YdDK11pyCL/i513rurl27cPnyZQCAh4cHevbsWee9srOzkZ6e3qz2Pqq27R2uXbuGPXv2SK/Nrly5gkGDBmn03m0dgx0iImr3ZIIADzszvDu+lzRJuYogAO+O7wUPO7NaV2MNGzYMd+7ckXLm1Ldqq7CwUGNtr217hyqGhoZSrpyRI0fC29tbY/fWFgx2iIiIUBk0zBzaGSO622NbXBpu5RTDzcYUL/i5qwU6paWlSElJgZeXl3Suk5MTFi9eXO/mmlXMzc011u5Ht3fIyclRm5zs4eGBZ599Ft27d9fqXDnN0T57TUREVANBENDZzgxvj+8FmSBIc3QEQYAoijh//jwOHz6M4uJizJs3D05OTtK5DQ10AMDW1lYtsWBj2telSxfExcWhsLBQbXuHu3fv4j//+Q/S0tIQHh6u9iqrV6/GryLTJQx2iIiIHvLwiquqr0VRxNatW5GamgqgMlhRKBRNvocgCJg8eXKTzo2IiICdnR3s7Oyktv3+++9SLh+ZTIZbt27VuRFoe8Ngh4iIqB6CIKBz5864e/cuRowYAV9fX2n5dlONHz8eCQkJDR4RkslkMDExqbbSShAEGBsbA9DtXDnNwWCHiIjoESqVCvn5+WqjI0OHDoWPj0+DMhM/7Ny5c1i7di2+++47GBoaSuUPX6dqH67aPLzSysjICNnZ2WpbQQwfPhw+Pj46v4S8qZhnh4iI6CGpqan45ptvsH37diiVSqncwMCgUYHOnTt3MGvWLAwcOBBbt27Fhg0baqy3bt06mJiYQBCEaqu4qspMTEzwxx9/SDuh79y5U217CTMzMwY6dWCwQ0REBKCsrAy//PILtmzZgnv37qGoqAgPHjxo8vVmzJiBzZs3QxRFTJkypcYsygDg7++P9PR0rF27Fl26dFE71qVLF6xduxYXL17ElStXcOjQIZSWlqKsrAxFRUVNblt706TXWFlZWXjttddw+PBh3Lt3r9rmZQ9HwkRERNrAwMAAOTk5EAQBAwcOxOjRo2FiYtLk63344YcoLS3FZ599Bl9f3zrrWltbIyIiAosXL0ZOTg4KCgrUVloplUro6+ur5cppzOqv9q5Jwc7MmTNx69YtvPPOO3B2dm7SlvdEREStSRRFiKIoBQ0ymQwTJ06EKIowMDBAVlYWzM3NYWtr26Sfc35+fjh+/HijzhUEAba2tsjPz4elpaV0rp6eHqZOnQpra+t2myunOZr0J3bixAkcP34c/fv313BziIiIWl52djb27dsHBwcHPPnkkwAAuVyOn3/+GevXr0dycrJU19PTE4sXL8aMGTMavZy7sUHS3bt3cfjwYdy8eROBgYEYNmyYdKxqqTk1XpOCnU6dOlV7dUVERNTWlZWV4dixY4iLi4NKpUJaWhqGDRuGY8eOISwsDMXF1Xc+v3nzJpYsWYK33noLv/76K0JCQlqkbTExMTh06BCAylGm8vLyFrlPe9SkF35r167F3/72Nym5EhERkTYoLCyUAp2uXbti/vz5OHbsGMaNG4eSkhLp1dbDqspKSkowduxYzJ8/H2lpaRpvW5cuXSAIAry9vREeHo7Ro0dr/B7tVZNGdp5//nkUFxfD09MTpqamMDAwUDuek5OjkcYRERFpkq2tLZ588knY2NigW7duyMvLQ1hYGERRrDPPDQDp+LfffoucnBzs2LGjye0oLS1FUlKSWpmzszNefvllWFlZNfm6VLMmBTtr167VcDOIiIg0q7i4GEeOHIG3tzc6deoklfv5+Ulfb9myBcXFxY2emvHof/IbSqFQ4MyZMzh+/DhKS0vRo0cPteMMdFpGk4KdGTNmaLodREREGqFSqZCQkICoqCiUlJTgzp07mDt3brXJwqIoYv369U26x+nTpyGKYqMmIKtUKnz77bdS7h5bW9t6R5NIM5q8fk2pVGLnzp24cuUKAKB3796YOHFis/cKISIiao4LFy5gz549AAAHBwcEBwfXGJRkZ2errbpqjOTkZOTk5Kht2VAfmUyG3r174/z58xg5ciTc3Nxw/PjxJt2fGqdJwc6NGzcwduxY3LlzRxqCW7VqFTp16oQ9e/bA09NTo40kIiJqqL59+yIhIQG9e/fG4MGDa02+V1hY2Kz7FBQU1BnspKSkSBuIVgkICMCwYcOgr68PuVzerPtTwzVpNVZERAQ8PT1x+/ZtJCQkICEhAbdu3YKHhwciIiI03UYiIqIaKZVKnD59GgqFQirT09PDrFmz4OvrW2eWYXNz82bdu7Z9sjIyMrBt2zZs3boVe/bsUXtVZWBgoNVJAVWiCNV/5zc9/HVb16Q/8ejoaMTFxcHGxkYqs7W1xccff4yAgACNNY6IiKg2N27cwL59+5CdnY3i4mKMHDlSOtaQuTS2trbo1KkTbt++3aj7CoKALl26qP0MrHLr1i1s2rQJQOVrqy5duqCiokJtt3NtJYoiUh8UYVtcGtJyiuFuY4oX/NzhYWfW5ndSaFKwY2RkhIKCgmrlhYWFOvFAiYiobTty5AiOHTsGoHLH75oCj7rcv38fK1aswJ07d5p0/4iIiBp/wHfq1AnOzs6wt7fHyJEj0aFDhyZdv60RRRGbT6Zi5e5EPDyYs+lkKt4d3wszh3Zu0wFPk4Kd8ePHY968edi4cSOGDBkCADh16hQWLFiAiRMnarSBREREj/Ly8kJMTAwGDx6MESNGwNjYuMHnJiYmwt/fH/n5+QAqX3upVKoGLT+XyWQwMTHBiy++iNLSUsTGxsLPz0/aMFQQBMyePVurX1U9SvXfEZ1HAx0AEEVg5e5EjOhuj852ZpC10YCnSU9j3bp1mDFjBvz9/aVcAxUVFZg4cSL+8Y9/aLSBRETUvomiiIyMDDg7O0tlTk5OWLJkCczMzBp9vZ49e6Jr164QRRGff/45ysrKMG7cuHoTC8pkMgiCgB07diAxMVHKlVNRUSHtrwVApwKdKtvi0qoFOlVEsfL42+N7Pd5GNUKTnoi1tTV27dqFpKQkXL16FUBllN21a1eNNo6IiNq3kpIS/Pbbb0hPT8e8efPUAp6mBDpAZdCyZ88eODg4SBOY9+zZo7Y31sOjPFWvZ0xMTPDbb79BoVDg4MGDAAB7e3u4u7s3qR3aQiYISMupvmfYw27lFLfZUR2gGXl2AKBbt27o1q2bptpCREQkOXnyJK5duwagcrQkKytLLdhpDicnJ7XPISEhSE9Px9atW7Fu3Tq1/DtdunRBREQEZsyYASsrK+Tk5CA5ORnDhw+Ht7d3nSu+dIFKFOFuY1pnHTcbU6hEsc0GPA0OdpYuXYoPPvgAZmZmWLp0aZ11P//882Y3jIiI2req10Genp4YP348rK2tW/R+1tbWiIiIwOLFi5GTk4OCggLk5eXh/v37CAoKkurZ2Nhg8eLFbXpCrqa94OeOTSdTa3yVJQiVx9uyBgc7586dk/IYnDt3rsUaRERE7ZNCoVDbc8rHxwf37t1rcKBTUVGBP//8EwMHDmxWOwRBQHl5OU6cOCGN8HTv3h1ubm5qddoLmSDAw84M747vVW2SsiAA747v1eaXnzc42Dly5EiNXxMRETVHYWEhDh8+jPT0dCxYsEDadkhfX7/WxH2P2rdvH1599VWkpqYiKSkJLi4uTW5PRUUFtm/fjqKiIshkMgwcOLDRS9u1SVViQJkgqH39MEEQMHNoZ4zobo9tcWm4lVMMN13PszN79mz84x//qPaXsKioCIsXL8b333+vkcYREZHuUqlUOH36NI4ePYqysjIAlXtOde/evcHXUCqVmDhxIvbu3QugMlFgYmJis4IdfX19PPHEE7hz5w5GjRqlM7lyatKYRIGCIKCznRneHt9LLTBq64EO0MTtIrZs2YKSkpJq5SUlJdi6dWuzG0VERLpPEARcvHgRZWVlcHZ2xuzZsxsV6ACVOXJcXV1hYGCAV199FUlJSWrza+pTWlqKw4cP48KFC2rlvr6+mDRpks4HOptPpiLw82h8H5OKw1fu4fuYys+bT6bWmHdIJgjSqM/DX7d1jRrZyc/PhyiKEEURBQUFakmclEol9u7dCwcHB403koiIdI8gCBg7diwyMzMxYMCAJq9q+vDDD7Fs2bJGbUKtUChw5swZKVeOpaUlevXqpZM5cmqiC4kCG6NRf7Osra1hY2MDQRDQvXt3dOjQQfplZ2eH2bNnY9GiRQ2+3rFjxzBhwgS4uLhAEATs3LlT7fjMmTMhCILarzFjxqjVycnJwbRp02BpaQlra2vMmTOn2TvZEhGRZlVUVCA6OhpRUVFq5a6urhg4cGCzlm87ODg0KtABKvewOnjwIEpLS2Fvb4+xY8dKc4Xai4YkCtQVjQphjxw5AlEUMXr0aPz6669qE7YMDQ3h7u7eqPekRUVF6NevH2bPno1JkybVWGfMmDHSpmpA5b5cD5s2bRoyMjJw8OBBKBQKzJo1C/PmzcOPP/7YmK4REVELEEUR165dw/79+yGXyyGTyTBgwIBWfz3UpUsXeHt7w8PDo13kynmULiQKbIxGBTsjRowAAKSkpMDNza3Zk5JCQ0MRGhpaZx0jI6NqyZ+qXLlyBfv27cOZM2cwaNAgAMD69esxduxYfPrpp82aoEZERM1XUFCAX375BUqlEhYWFggODm5wvpzMzEyNtCElJQXR0dGYNGkSLC0tAVS+QnvmmWc0cn1tpAuJAhujSS8n09LSkJZW+/DW8OHDm9ygRx09ehQODg7o0KEDRo8ejQ8//BC2trYAgNjYWFhbW0uBDgAEBQVBJpPh1KlTtf5FLisrk2b+A5A2g5PL5XXui9JYVTvD17RDvC7Q9f4But9H9k+7aUv/Bg0aBKVSicGDB8PQ0BB5eXl11pfL5fjss8/wzTff4O2334aPj0+T7nvv3j3ExMTg1q1bAIBDhw5h9OjRTbpWS2jN56cSRYT16YCD51NQ05ssAUBYnw6Vo3FNDHYeR/+qfn7Xp0nBzsiRI6uVPTzKo1Qqm3LZasaMGYNJkybBw8MDycnJePPNNxEaGorY2Fjo6ekhMzOz2oRofX192NjY1Pk/glWrVuH999+vVh4TEwNT07oj3aZISEjQ+DXbEl3vH6D7fWT/tFtb6Z8oisjNzYWRkVGN+1bFxsbWe42oqChs2rRJ+gF5+vRp9OvXr0ntuX37NrKzsyEIAmxtbaFSqRAdHd2ka7Wk1nx+r3nXfuzGhTO4oYF7tGT/qvYyq0+Tgp3c3Fy1zwqFAufOncM777yD//u//2vKJWs0efJk6eu+ffvC29sbnp6eOHr0KAIDA5t83eXLl6tteZGfn49OnTohICBAGuLUhIKCAiQkJMDHx6fBibG0ia73D9D9PrJ/2q0t9e/evXs4evQoMjIyYGdnhzFjxjRpHkxqaioKCgrQs2dPvPnmm7C0tGxw/0RRVPuPd1FREWJiYuDr6wsrK6tGt6WltYXnJ4oi7spLsPdiBjLzS+FkaYyxfZ3hYm3S7Kkqj6N/LTqyU9NfmieffBKGhoZYunQp4uPjm3LZenXp0gV2dna4ceMGAgMD4eTkhHv37qnVqaioQE5OTq3zfIDKeUCPTnQGKlebaTLYqWJhYdHie7q0Jl3vH6D7fWT/tFtr9y8lJQU//fQTAMDAwAD9+vWDlZVVk1Y3hYeHw87ODs8//zwKCwsRHR1db/9KS0sRExODzMxMTJ06VfohbW1tjeeee65JfXqcWvv5WVlbw6uzc50ZlJujJfvX0IBaowkFHB0dpR1qW0J6ejqys7OlXW/9/f0hl8sRHx8v7YUSFRUFlUoFX1/fFmsHERH9j7u7OxwcHODg4IAnn3yyWf9p1NPTw7Rp0xpUt6KiAqdPn8aJEyekRLdpaWno3Llzk+/fHj0c2OjCZOSaNCnYeTTTpCiKyMjIwMcff4z+/fs3+DqFhYW4ceN/bwRTUlJw/vx52NjYwMbGBu+//z7CwsLg5OSE5ORkvPHGG+jatStCQkIAAF5eXhgzZgzmzp2Lr7/+GgqFAuHh4Zg8eTJXYhERtZDbt2/DyclJ2rRTJpNhzpw5MDQ0fOxtOX36NEpKSmBvb4/Ro0fD3b1t775NraNJwU7//v0hCEK1VNJ+fn6N2hfr7NmzGDVqlPS5ah7NjBkz8NVXX+HChQvYsmUL5HI5XFxcEBwcjA8++EDtFdT27dsRHh6OwMBAyGQyhIWFYd26dU3pFhER1SE/Px+HDh3CxYsXMXz4cLXv3/UFOiqVCkqlUm1X88aqyuBf9epCX18fY8aMQWlpabvMlUMN16RgJyUlRe2zTCaDvb292vYRDTFy5Mga996osn///nqvYWNjwwSCREQt7PLly9i1axcUCgWAynkyDXX06FG8+uqreO6557Bs2bIm3T8lJQWHDx9Gr169MHToUKm8Z8+eTboetS9NCnY4TEhE1L7Y29ujoqICHTt2RGhoaIOmCty8eROvvvqqtBXQ/fv3sXTp0kaN7pSWlmLnzp1SbrfCwkL4+flxFIcapcHBTmNeDUVERDSpMURE1DYUFxer5R1zcHDAnDlzpL0MGyI3Nxc7d+6Enp4e5s+fjxUrVjT6NZZSqURaWhpkMhkGDhyI4cOHM9ChRmtwsLNmzZoG1RMEgcEOEZGWKi8vx/HjxxEXF4dZs2apjeC4uro26loDBw7E2rVrERwcDC8vrwbf/+H5P2ZmZhgxYgT69evX6vtpkfZqcLDz6DwdIiLSLVX7DVYlart06VKzV7a+/PLLDapXlSvnzJkz+Otf/wo7OzvpWP/+/XU6TxK1vGbn2amaYNzcTItERNS6srOzkZ+fD2tra4SEhKBHjx6P5b6nT5/G0aNHpVw5Fy5caFN7WJH2a3Kws3XrVnzyySdISkoCAHTv3h2vv/46pk+frrHGERFRy3l0ewU/Pz/o6elh0KBB9c6tefTc5igoKFDLlfO4gixqP5oU7Hz++ed45513EB4ejoCAAADAiRMnsGDBAjx48ABLlizRaCOJiEhzVCoVzp07hz///BMvvvgi9PUrfxTo6+vD39+/znMLCwuxevVqpKamYuvWrY2+tyiKKC4uVtsoNCAgALa2tsyVQy2mScHO+vXr8dVXX+HFF1+UyiZOnIjevXtjxYoVDHaIiNqo9PR07N27FxkZGQCAc+fOYfDgwfWep1QqsWXLFrz11lvIzMwEALzyyivw8fFp8L2rcuVUVFRg/vz50siQsbFxo7LvEzVWk4KdjIwMtaROVYYOHSr9AyIiorZFFEXs378fGRkZMDIywogRIxocrBQUFOCNN95AdnY2PD09sXr1agwYMKBB5xYXF+O3335DcnIygMrNQu/duwdHR8cm94WoMZoU7HTt2hX//ve/8eabb6qV//zzz+jWrZtGGkZERJolCAJCQ0Nx5swZBAYGwtzcvMHnWltbY/Xq1ZDL5Vi0aJHatj31MTY2RkFBgVqunMbcm6i5mhTsvP/++3j++edx7Ngxac5OTEwMDh8+jH//+98abSARETXNzZs3kZWVpTYPx8XFBU899VSTrjd79uwG1SssLISBgYEUEMlkMkycOBEmJiawsbFp0r2JmqNJwU5YWBhOnTqFNWvWSGnAvby8cPr06QYPaxIRUcuQy+U4cOAArly5AkEQ4OnpCQcHhxa/b2lpKU6ePIm4uDj4+fmpLR9vbEJCIk1q8tLzgQMHYtu2bZpsCxERNVN5eTm++eYblJaWQhAEDB48GJaWli1+32vXrmHXrl1Srpz09HSNLk8nao5GBTsVFRVQKpVq72qzsrLw9ddfo6ioCBMnTsSwYcM03kgiImoYQ0NDDB48GLdv38aYMWPqnQR86tQprFu3Dps2bVLbpqGxbGxsUFpaCjs7OwQGBqJHjx4MdKjNaFSwM3fuXBgaGuKbb74BUDk7f/DgwSgtLYWzszPWrFmDXbt2YezYsS3SWCIiUldaWoqsrCy17RRGjBgBmUxWZ7CRlpaG5cuX46effgIADBo0qMFpQ0RRxJ07d9CxY0epzN7eHjNnzkTHjh2ZK4fanEb9jYyJiUFYWJj0eevWrVAqlUhKSsKff/6JpUuX4pNPPtF4I4mISF1ZWRmOHz+Oq1ev4uDBg1CpVNIxPT29ekdVZs6ciZ9++gmCIGDmzJl47rnnGnTf1NRUbNy4ERs3bsSdO3fUjrm5uTHQoTapUX8r79y5o7a0/PDhwwgLC4OVlRUAYMaMGbh8+bJmW0hERGoePHiAL774AgkJCQAAS0tLlJaWNuoaH330EUaNGoWzZ89i06ZNDZpA/Ouvv2LLli24c+cODAwM8ODBgya1n+hxa9RrLGNjY2nyGQDExcWpjeQYGxujsLBQc60jIqJqbGxsYGZmBn19fdjY2GDChAkwNTVt1DX8/f0RFRXVqHOcnJyQmJgIHx8fjBgxgrlySGs0Ktjp378/fvjhB6xatQrHjx9HVlaW2tLC5ORkuLi4aLyRRETtWXFxMfT19aUJxDKZDJMnT0ZFRQViYmJa5J6FhYWQy+Vq83KGDBkCLy8v5sohrdOoYOfdd99FaGgo/v3vfyMjIwMzZ86Es7OzdPz333+XkgwSEVHzqFQqnD17FkeOHMGgQYMQGBgoHbO2toZcLtf4PR/OlWNmZobw8HDo6ekBqNzmgYEOaaNGBTsjRoxAfHw8Dhw4ACcnJzz77LNqx/v3748hQ4ZotIFERO1RRkYGdu3ahaysLACVm2iqVKo6JwCXlJRg7dq1mDp1Ktzd3Rt9T7lcjm+//VaarmBmZobCwkJpXiaRtmp0UkEvLy94eXnVeGzevHnNbhAREQH6+vq4f/8+jI2NMXr0aAwcOLDWQEcURfz000/429/+hlu3buHSpUvYvn17o+9pZWUFOzs7lJSUMFcO6ZQmZ1AmIiLNeXTUxt7eHmFhYejcuXO9k4/Xr1+P9957DwDQsWPHBuU6E0URV69eRadOnaSJxoIg4LnnnoOpqSmXkJNO4d9mIqJWdv36dWzYsKFa3ppevXo1aJXVtGnT0LFjR3z44Ye4du0apk2bVmf9qlw5//73v3Hs2DG1Y+bm5gx0SOdwZIeIqJVkZ2dj//79SEpKAgAcO3YMU6ZMafR1bG1tkZyc3KDtHuLi4rB//34AlROOG7tknUgbMdghImolV69eRVJSEmQyGfz8/DB8+PAmX6uh+1p5eXnhyJEj8Pb2Zq4cajeaFOyUlJTg4MGDuH79OgCge/fuePLJJ2FiYqLRxhER6TI/Pz/k5OTA398fdnZ2Gr9+YWEhLl26BF9fX2misZWVFZYsWQJjY2ON34+orWp0sPOf//wHf/3rX6ulCbezs8PGjRsxYcIEjTWOiEhXZGVl4eTJk5gwYQL09Su/9erp6dX5PfPPP/9EcXEx/P39G3Wvh3PlKBQK2Nvbw9PTUzrOQIfam0bNQjt58iT+8pe/YPjw4YiJiUFOTg5ycnJw4sQJPPHEE/jLX/6CuLi4lmorEZHWKSkpwd69e/HNN9/gwoULiI2NrfecjIwM/PWvf8WAAQMwZ84cVFRUNPh+oihi8+bNOH78OBQKBVxdXWFkZNScLhBpvUaN7Hz44YeYNWsWvvnmG7XyoUOHYujQoZg/fz5WrlyJvXv3arSRRETaaufOndIr/169esHb27vO+hcvXoS/vz+KiooAAN7e3igoKECHDh0adD9BEDBkyBDExsZi9OjR6NmzJ3PlULvXqGAnLi4Of//732s9vmjRIowYMaLZjSIi0hUjR45EXl4eQkJC4OHhUW/93r17o0ePHjAwMMCaNWvqfIUliiLkcjmuXbsGX19fqbx///7o378/l5AT/Vejgp2SkhJYWlrWetzKygqlpaXNbhQRkTYqLCzEzZs31UZvnJ2dMX/+/AaPrshkMkRGRsLe3r7Oc1JTU7F//35kZmYiKysLAwYMUNsolIj+p1HBTrdu3RAVFYVZs2bVePzw4cPo1q2bRhpGRKQtlEolTp06hejoaCgUCjg6OsLR0VE63tjXSA4ODnUez8rKwpYtWwBUBjZ9+vRpfKOJ2pFGBTuzZs3Ca6+9BkdHx2rpyPfs2YM33ngDb775pkYbSETUlqlUKvzzn/9EZmYmAMDFxaXF7+no6IiePXvC0NAQSqUSQ4cObXCeHaL2qFHBzssvv4yTJ09i/Pjx6NGjB7y8vCCKIq5cuYKkpCQ8/fTTeOWVV1qoqUREbY9MJkOPHj2Qn5+PoKAg9O/fv8aRnPLycpw7d05tbk1DFBYW4tixY/Dz84ONjY1U/txzzyEvLw/R0dHN7gORrmtUsCOTybBjxw78/PPP+Omnn3D16lUAQM+ePbFixQpMnjy5RRpJRNRWKBQKFBQUqAUeAQEB8PPzqzF/jSiK2LVrF15//XXcvXsXSUlJDRr9eTRXTmlpKSZNmiQd5worooZrUgbl559/Hs8//7ym20JE1GZVjWIfOHAAhoaGmD9/PvT09ABU7jFlYGBQ7ZyKigqEhIQgKioKQOVcnOvXrzco2ImJicGJEycAAK6urvDx8dFgb4jalyYFO9nZ2bC1tQUA3L59G9999x1KSkowYcKEZu3tQkTUFhUXF+OXX35BSkoKAMDS0hJyuVz6PlgbfX19dO3aFSdPnsTSpUvxt7/9DRYWFg26p7+/P1JTUzF06FDmyiFqpkYFOxcvXsSECRNw+/ZtdOvWDf/6178wZswYFBUVQSaTYc2aNfjll1/w9NNPt1BziYgeP2NjY5SUlEBPTw8BAQEYNmxYjSM5Nfnwww/x1ltvwc3Nrcbjoiji6tWrSExMxKRJk6SgxtTUFHPmzNFYH4jas0YFO2+88Qb69u2L7du344cffsD48eMxbtw4fPfddwCAxYsX4+OPP2awQ0RaTRRFKJVKaQ8rmUyGp59+GoaGhg3OZFzF3t6+1mOpqak4dOgQ7ty5A6By/mPv3r2b3nAiqlGjgp0zZ84gKioK3t7e6NevH7799lu89NJLUgKrxYsXw8/Pr0UaSkT0ONy5cweRkZHo3LkzgoKCpPKH8+Zogkqlwp49e/DgwQMYGBjAz89PbbNOItKcRgU7OTk5cHJyAgCYm5vDzMxM7X85HTp0QEFBgWZbSET0GBQXF+PQoUM4d+4cgMrvd8OHD681f016ejo6duzY5PvJZDIEBgYiOTkZw4cPb/BcHiJqvEbnFH90khwnzRGRLiguLsaff/4JoHLzzYULF9YY6Dx48ACLFy+Gh4cHDh8+3KBrFxYWYs+ePTh+/Lhaec+ePTFu3DgGOkQtrNGrsWbOnAkjIyMAlXkgFixYADMzMwBAWVmZZltHRPSY2NnZYcyYMXB0dKx1MvF3332H119/HXl5eQCAyMhIBAYG1nrNR3PlGBoaYvDgwTXm4yGiltOoYGfGjBlqn1944YVqdV588cXmtYiIqIXl5eXh0KFDGDJkCDp16iSVDx48uEHn9uvXD5999lmdgU5V3arRHFdXVwQGBjLQIWoFjQp2Nm3a1FLtICJqcRUVFTh58iROnDgBhUKB3NxczJkzp8Gv42fPng0rKyuEhYVJCQUfJoqi2rUcHR0xfPhwODk5MVcOUStqUlJBIiJtdP78eRw5cgQA4ObmhtDQ0EYFIHp6enjuueeqlVflyjly5AgmTZokLeQAgFGjRjW/4UTULI0KdgYMGFDjNwYrKyt0794dL7/8Mnr16qWxxhERadKAAQNw5coV9OvXD3379tXISEtaWhoOHTqE9PR0AMCJEyfwl7/8pdnXJSLNaVSwU1uyQLlcjoSEBAwYMABRUVEICAjQRNuIiJqsvLwcp0+fhkqlksr09PQwffr0anWVSiWUSmWty8zrcuvWLaSnp0u5coYOHdqsdhOR5jUq2HnvvffqPP7WW2/h3XffbfByTCIiTRNFEZcuXcLBgwdRUFCg9kqpJgcOHMBrr72GadOmYdmyZfVeX6lUqs3X8fX1RUlJCfz9/bmEnKiNanSenbpMnToVFy9e1OQliYgaZf/+/fjtt99QUFAAKysrmJiY1Fjv+vXrGDt2LEJCQnDx4kV8/fXXqKioqPW6hYWF2Lt3L7755hsolUqp3NDQEMHBwQx0iNowjQY7enp6akPGRESPW//+/WFoaIhRo0bhhRdegJWVVY31CgoKEBkZCQMDAyxZsgTx8fHSXlgPUyqVOHLkCNatW4czZ87g/v37uHHjRkt3g4g0SKPBzm+//daoCcrHjh3DhAkT4OLiAkEQsHPnTrXjoiji3XffhbOzM0xMTBAUFISkpCS1Ojk5OZg2bRosLS1hbW2NOXPmoLCwUBPdIaI2TqVSIS0tTa3MyckJS5cuxfDhw2sMXqoMHDgQ69evx+XLl/H555/DxsamxnoymQzJyclQKBRwdXXFiy++iB49emi0H0TUsho1Z2fdunU1lufl5SE+Ph579uxBZGRkg69XVFSEfv36Yfbs2Zg0aVK146tXr8a6deuwZcsWeHh44J133kFISAgSExOlxFzTpk1DRkYGDh48CIVCgVmzZmHevHn48ccfG9M1ItIyt27dQmRkJLKysjBv3jy1uTlVWd7rEx4eXq1MpVKhoqJCmqwsCAJCQkJQWFjIXDlEWqpRwc6aNWtqLLe0tESPHj1w7Ngx+Pv7N/h6oaGhCA0NrfGYKIpYu3Yt3n77bTz11FMAgK1bt8LR0RE7d+7E5MmTceXKFezbtw9nzpzBoEGDAADr16/H2LFj8emnn8LFxaUx3SMiLbF7927Ex8cDqAxscnNz652IXB9RFHHt2jVERUWhS5cuGDNmjHTs4SzLRKR9GhXspKSktFQ7arxXZmYmgoKCpDIrKyv4+voiNjYWkydPRmxsLKytraVABwCCgoIgk8lw6tQpPPPMMzVeu6ysTG0fr/z8fACVS+g1Oeeoagd4Xd0JXtf7B+h+H7W1fwYGBgCA3r17Y+jQoTA1NYVcLpeOV2Uybmj/Hjx4gKioKGRkZACo3BR00KBBdb4Gawu09fk1FPun3R5H/6p+ftenWf+SHzx4AENDQ1haWjbnMjXKzMwEUJlu/WGOjo7SsczMTDg4OKgd19fXh42NjVSnJqtWrcL7779frTwmJgampqbNbXo1CQkJGr9mW6Lr/QN0v49tvX8VFRVqgYdKpUL37t1hYGCAM2fOSOVFRUX49ddfkZ2djSVLlkjl9fWvrKwMmZmZkMlksLe3h729PWJiYjTfkRbS1p9fc7F/2q0l+1dcXNygeo0OduRyOd566y38/PPPyM3NBQDY29tj1qxZeOedd1okWNC05cuXY+nSpdLn/Px8dOrUCQEBARoN3AoKCpCQkAAfHx+dXJaq6/0DdL+Pbb1/eXl5OHbsGHJzczFt2rQa96MCKoOhLVu2YNWqVcjOzgYAfPDBB+jcuXON/SsqKoKZmZnaNTw8PODi4lKtvC1r68+vudg/7fY4+tciIzs5OTnw9/fHnTt3MG3aNHh5eQEAEhMTsX79ehw8eBAnTpzAhQsXEBcXh4iIiMa3/L+q3r9nZWXB2dlZKs/KykL//v2lOvfu3VM7r6KiAjk5OXW+vzcyMqpxAqO1tXWLjFJZWFjA2tpa49dtK3S9f4Du97Gt9U+pVOLYsWOIiYmBUqmETCZDQUEBOnfuXGN9uVwuBTo9e/bEp59+imHDhiEvLw/A//pXWFiIY8eOIT4+HtOnT1e7XkN2PG+r2trz0zT2T7u1ZP9ksoYtKm9UsLNy5UoYGhoiOTm52uullStXIjg4GNOnT8eBAwdqXbnVUB4eHnBycsLhw4el4CY/Px+nTp3CwoULAQD+/v6Qy+WIj4/HwIEDAQBRUVFQqVTw9fVt1v2JqPXIZDLcuHEDSqUSHh4eCA0Nhb29fa31ra2t8emnn6KoqAjz5s2T5vQ8LC4uDlFRUVAoFAAqkwrWFjwRkW5pVLCzc+dOfPPNN9UCHaBylGX16tUYO3Ys3nvvPcyYMaPe6xUWFqol50pJScH58+dhY2MDNzc3vPLKK/jwww/RrVs3aem5i4uLtEeXl5cXxowZg7lz5+Lrr7+GQqFAeHg4Jk+ezJVYRFpMEASMGzcOcrkcXl5eDVruPXPmzDqP6+npSblyAgMD4eHhoaHWElFb16hgJyMjA7179671eJ8+fSCTyerdQ6vK2bNnMWrUKOlz1TyaGTNmYPPmzXjjjTek/6nJ5XIMGzYM+/btk3LsAMD27dsRHh6OwMBAyGQyhIWFNXtUiYgen9LSUhw9ehT6+vpqqy9dXFya/J8WlUqF3NxctTk+Pj4+sLS0RPfu3Zkrh6idaVSwY2dnh9TUVHTs2LHG4ykpKdVWR9Vl5MiREEWx1uOCIGDlypVYuXJlrXVsbGyYQJBIC4miiPPnz+PQoUMoLi6GTCbDkCFDmjVv7uFcOSUlJXjxxRelY3p6esx8TNRONWq7iJCQELz11lsoLy+vdqysrAzvvPOOWiIuIqLayOVy7NmzB8XFxbC1tcXUqVOrBTrHjx/Hc889V+P3nEeVlZXh+++/x88//4z79++joqJCWplFRO1boycoDxo0CN26dcOiRYvQs2dPiKKIK1eu4Msvv0RZWRm2bt3aUm0lIh3SoUMHaf8qX19ftVdOycnJWLZsGX799VcAwNChQ/HKK6/Ueb2qVZYGBgbw9fVFQEAASktLce3atZbsBhFpgUYFOx07dkRsbCxeeuklLF++XHoFJQgCnnzySXzxxRdwc3NrkYYSkfZSKpU4c+YMXF1d1bZeGD58eI3158yZg+joaMhkMsybNw9Tp06tVic3Nxf6+vpq+TvGjh0LAwMDqay0tFTDPSEibdTopIIeHh6IjIxEbm6utAN5165da90xmIjat5SUFERGRuL+/ftwcnLC3Llz682N8fHHH2PFihX49NNP0adPH7VjD+fK6du3r7Q6EwC/DxFRjZq8XUSHDh0wZMgQTbaFiHTM9evX8dNPPwEATExM1Paxq4ufnx/27dtXrTwpKQk7duyQcuUUFRVBpVI1OLEYEbVPbXuXOyLSap6ennB0dISbmxtGjRoFExOTZl3PxcUFgiAwVw4RNQqDHSLSCFEUkZSUBA8PDymDsZ6eHv761782afdwlUqF69evo0ePHlJeHDMzM8ydOxe2trbMlUNEDcaxXyJqtgcPHmD79u346aefqu0WXhXoFBUV4f3330daWlqd1xJFEVevXsXXX3+Nn3/+GVevXlU7bmdnx0CHiBqFIztE1CwJCQnYs2cPVCpVjbuSK5VKbN26FW+99RYyMjJw/fp1bN++vdbr/f7777h48SIAwNjYuEE5doiI6sJgh4iaxdXVFaIoolu3bggJCYGtra3a8dWrV+PNN98EULma85lnnqnzet26dcOVK1fg5+eHgIAAte1hiIiagsEOETVKfn6+WqZjR0dHLFy4sNZdyefOnYtvv/0W4eHhCA8Ph5GRkXQsNzcX9+/fR/fu3aWyPn36wMPDA+bm5i3XCSJqVxjsEFGDFBcXIyoqCufOncOcOXPUNumsLdABKufYJCUlqU1SfjhXjqGhISIiIqSVWoIgMNAhIo1isENE9Tp37hwOHjyIkpISAMCNGzcatSP5w4FOUVER1q9fL83FcXV1RVlZWbOXpRMR1YbBDhHVq7CwECUlJXBwcEBoaCg6d+7c5GuZmZmhW7dukMvlzJVDRI8Fgx0iqkYURbXl3f7+/jAzM0P//v2lbMVnz55FWVkZAgICar2OSqXChQsX4Orqqvaqa+LEiTAwMOASciJ6LJhnh4gkFRUVOHHiBL777jsolUqpXF9fHz4+PpDJZLh9+zamT5+OwYMHY+7cuaioqKh2nYdz5ezatQuHDx9WO25oaMhAh4geG47sEBGAyn2n9u3bh5ycHADAxYsX0b9/f7U658+fh7+/v7Sb+ODBg1FUVAQrKyu1egkJCdi9ezeAylw5bm5u1UaLiIgeFwY7RARRFBEdHY2cnByYmZkhKCgI/fr1q1bP29sbvXr1grm5OT777LNaN/bs06cPjh8/jr59+zJXDhG1OgY7RARBEBAaGopLly5hxIgRtQYnMpkMBw4cgI2NjTRKk5ubi7NnzyIwMFCaz2NkZITFixfXmFGZiOhxY7BD1M6IoojExETk5uaiT58+UrmrqytcXV3rPb8qQ3JhYSGOHz+Os2fPQqVSwc7ODgMGDJDqMdAhoraCwQ5RO3Lv3j1ERkYiNTUVgiDA2dm5ydfasWMHbt26BQDw9PRs1rWIiFoSgx2idqKkpAT//Oc/oVAooK+vj4CAAGlicWlpKU6ePImhQ4c2+HpPPPEEjhw5gqCgIObKIaI2jcEOUTthYmICX19fZGdnIzg4GNbW1sjNzcWJEycQERGBBw8eICkpqVpm5KpcOSUlJfD395fKPT094enpyRVWRNTmMdgh0lHp6ekQBEFtHs7o0aOl4EShUGDcuHGIjY0FALi4uODmzZtSsCOKIq5du4aoqCjcv38f+vr66N27t7QJKIMcItIWTCpIpGMKCwuxa9cubNy4EX/88QdUKpV07OEAxcDAAN27d4eRkRGWL1+O69evY9iwYdLxvLw87NixA/fv34exsTFGjhzJ/auISCtxZIdIh2RmZmLz5s0oKysDADg7O0OhUMDIyKjG+m+//TZGjBiBZ555BmZmZmrHrK2tMWTIEOjp6WHYsGHMlUNEWovBDpEOcXBwgJWVFfT19REaGoqOHTvWWd/Ozg42NjbIy8vDkSNHMGjQIHTq1Ek6HhIS0tJNJiJqcQx2iLSYXC6HqakpDA0NAVQm/XvhhRdgbm7eoDk1RUVFSE9Px4ULF6BSqZCXl4eZM2e2cKuJiB4vztkh0kIKhQLR0dHYsGEDTpw4oXbMwsICaWlpDbpOUlISHjx4AJVKhS5duiA4OLglmktE1KoY7BBpmbS0NHz55Zc4evQoKioqcPfuXYiiCADIysrC/Pnz4enpiUOHDtV7rT59+sDKygrPPPMMpk+fXm3ZORGRLmCwQ6RlTExMkJeXB0tLS4SFhWHatGkQBAFffvklunbtim+//RYqlQpRUVHSOSqVCufPn8emTZtQUVEhlevr68PDwwNubm6t0RUioseCc3aI2jilUqm2z5SDgwOmTJkCd3d3aa4OULmUvLCwEIMHD8bnn3+OYcOGQRRFXL9+HYcPH8b9+/cBAAkJCRgyZMhj7wcRUWthsEPURomiiAsXLuDw4cN4/vnn1ZIDduvWrVr92bNnw87ODk899ZS0+zgAxMXFSblyhg0bprZZJxFRe8Bgh6gNysjIwN69e5Geng6gMmAJCwur8xw9PT0888wz0vwdoDKJYFBQEK5cuYKAgAAmBSSidonBDlEblJycjPT0dBgYGGD48OHw8/Or95zc3FwcPXoUBgYGGD9+vFTu6uqqNipERNTeMNghaoP8/PxQVFQEf39/mJiYqG358Kji4mJER0fj7NmzUKlUkMlkGDFiBCwsLB5ji4mI2i6uxiJqZWlpafj555+rrZIKDg7GsWPH0LdvX6xZs6bW85VKJRISEqRcOXPmzGGgQ0T0EI7sELWS/Px8HDx4EJcuXQIAnDp1CgEBAQCAK1euIDw8XFo+/t133+HVV1+Fvr4+lEolZDKZlCHZwsICY8aMQYcOHdClS5fW6QwRURvGkR2iVrJr1y4p0Bk4cKDaKqni4mJERUXByMgIy5YtQ3x8PGQyGc6fP4/169fjxo0batcaOHAgAx0iolpwZIeolQQGBqKiogJjxoyBs7Oz2rGBAwfiq6++wpgxY9C5c2fcvHkT27dvl3LlnD59usbl50REVB2DHaLHICcnB6mpqfDx8ZHKXFxcMHPmzFo37FywYIH0dUFBgVquHCYFJCJqOAY7RC2ovLwcx48fR2xsLFQqFTp27AgHBwfpeG2BTmlpKYyNjaXPffv2RVFREQYMGMBcOUREjcRgh6iFKJVKfPPNN8jJyQEAeHp6Ql9fH6Io1hrkVOXKuXHjBhYvXiwFPDKZDEOHDn1sbSci0iUMdohaiJ6eHvr06YMLFy4gJCQE9vb2WLlyJbKysvDTTz+p1RVFEfv27ZNy5QBAUlIS+vbt2xpNJyLSKQx2iDSkpKQEhYWFsLe3l8qeeOIJDBkyBN999x1WrlyJ3NxcAMCbb76pFsgIgoCCggIpV05gYCBcXFweex+IiHQRgx2iZlKpVDh37hyioqJgbm6O+fPnSxtx6uvro7CwEB9++CFyc3PRt29ffPbZZ/Dy8kJRURHMzMyk6wQGBmLQoEFcQk5EpGEMdoiaoaCgAD/99BMyMjIAAGZmZsjPz4e1tbVUx9raGp999hkUCgVmzJiBy5cv44svvoCrqyueffZZqZ6trS1sbW0fdxeIiHQegx2iZjAzM4MoijAyMsLIkSMxePBg6OnpVas3Y8YM3Lt3D//85z9x7949AJUjQo+uuiIiIs1jsEPUCEqlEiqVCgYGBgAqV0lNmjQJJiYmMDc3r/NcS0tL5Ofnq+XKqboOERG1HAY7RA2UnJyMffv2oWfPnggMDJTKH56Q/LD79+/Dzs5OWmZubGyMyZMnw8HBgblyiIgeI+6NRVSP8vJy7N69G9u2bcODBw9w4cIFVFRU4NChQ3jmmWdQXl6uVj83Nxe///47vvzyS1y8eFHtmLu7OwMdIqLHjCM7RPVQqVRISUmBIAgYPHgwXFxc8PTTT2PPnj0AgK+++govv/wyAODkyZM4fPiwlCsnIyMD3t7erdZ2IiJq4yM7K1asgCAIar969uwpHS8tLcWiRYtga2sLc3NzhIWFISsrqxVbTLrI2NgYo0aNwoIFCxAaGorFixdjz5490NfXR0REBF544QWprq2trZQrZ+7cuQgJCWnFlhMREaAFIzu9e/fGoUOHpM/6+v9r8pIlS7Bnzx7s2LEDVlZWCA8Px6RJkxATE9MaTSUdcP/+fezfvx8jR45Ex44dpfI+ffpIy8lXr16Njz/+GKtWrYK5ubnacvHu3btjzpw5aucSEVHravPBjr6+PpycnKqV5+XlYePGjfjxxx8xevRoAMCmTZvg5eWFuLg4+Pn5Pe6mkhYrKyvD0aNHcfr0aahUKigUCsyaNavGukOGDMGKFSuwf/9+FBcXIyIiQlqJJQgCAx0iojamzQc7SUlJcHFxgbGxMfz9/bFq1Sq4ubkhPj4eCoUCQUFBUt2ePXvCzc0NsbGxdQY7ZWVlKCsrkz7n5+cDAORyuTTXQhMKCgrUftc1utS/8+fPIy4uDgDQpUsXDB8+HHK5vFoflUol/vWvf+HBgwcAKvPspKWlwdXVtXUa3ky69Axrwv5pN/ZPuz2O/lX9/K6PIIqi2GKtaKbIyEgUFhaiR48eyMjIwPvvv487d+7g0qVL+OOPPzBr1iy1oAWo/F/3qFGj8Pe//73W665YsQLvv/9+tfIff/wRpqamGu8HtX0qlQqpqamws7ODpaVlnXVv3bqFvLw8ODg4wN7eXtoagoiIHq/i4mJMnToVeXl5dX7vbtMjO6GhodLX3t7e8PX1hbu7O/797383a/nu8uXLsXTpUulzfn4+OnXqhICAgHp/0DVGQUEBEhIS4OPjAwsLC41dt63Q1v4VFxfj3Llz8PX1leaA5efn48SJEwgMDISbm5tUNy0tDVeuXEFAQIDUx+LiYshkMp3IfKytz7Ch2D/txv5pt8fRv4aO7LTpYOdR1tbW6N69O27cuIEnn3wS5eXlkMvlavsQZWVl1TjH52FGRkYwMjKq8fqaDHaqWFhYqLVR12hL/1QqFc6ePYsjR46gtLQUFhYWGDp0KDZu3Ih33nkH9+/fR2ZmJrZv347c3FwcPXoUFy5cgLm5uVoftaGvjaUtz7Cp2D/txv5pt5bsX0NH1rVq/L2wsBDJyclwdnbGwIEDYWBggMOHD0vHr127hlu3bsHf378VW0lt1e7duxEZGYnS0lI4OTmhc+fOWLVqFRYsWID79++je/fueP7555GSkoIvvvgCFy5cAFA5Sb6ioqKVW09ERE3Vpkd2XnvtNUyYMAHu7u64e/cu3nvvPejp6WHKlCmwsrLCnDlzsHTpUtjY2MDS0hKLFy+Gv78/V2JRjYYMGYJr165h1KhR8PHxgUwmw8KFC7Flyxa8/PLLWLBgAQwMDKBQKGBubg47OzsMGTIEV69eVUt5QERE2qVNfwdPT0/HlClTkJ2dDXt7ewwbNgxxcXHSXkRr1qyBTCZDWFgYysrKEBISgi+//LKVW01tQUVFBVJTU9G1a1epzMnJCUuWLFELXKytrfHjjz/Cx8dHKjcwMMDcuXNhbm4OuVyOq1evPvb2ExGR5rTpYOdf//pXnceNjY2xYcMGbNiw4TG1iLTB9evXsW/fPsjlcsybN09tDldVQKNSqXDx4kUcOXIEeXl5EEURvr6+Ur36djAnIiLt0aaDHaLGEEURv/zyCxITEwFUBixFRUU11v3jjz9w/vx5AJWT55hygIhIdzHYIZ0hCAIcHBxw9epVuLm5wd3dHZ6enjXWHTBgAK5evYphw4ZhyJAhMDAweMytJSKix4XBDmktURRRVFSk9srJ2dkZ165dw7vvvgsvLy9cuHAB2dnZSE9Px8CBA6V6bm5uWLJkCQwNDVuj6URE9Bgx2CGtlJmZicjISBQXF2PBggXQ09NDQkIC/P39UV5eDplMhuHDh+O3337DlStXIJPJ4OHhARsbG+kaDHSIiNoHBjukVRQKBQ4cOID4+HiIogh9fX1kZmbC1dUV/fv3R58+fWBjY4O///3vOHDgAK5cuQKgct80butARNQ+MdghraKnp4c7d+5AFEX07t0bTz75JKysrABUZtI8fPgwrKysIAgCHjx4gKysLAQGBsLFxaWVW05ERK2FwQ61eaIoQhAEAJUBzfjx41FWVgYPDw8AlTl14uPj4ejoiM6dO0vnBQUFcTSHiIgY7FDbVVBQgEOHDsHCwgJBQUFSedUoTVWunKNHj0Iul8PZ2Rlz585VC4yIiIgY7FCbo1QqERcXh2PHjqG8vBx6enoAoBbwAEBiYiJ27twJoDJXzsCBA9VGgYiIiAAGO9QGyeVyREVFQaVSwcjICD///DNWrVqFpKQktbk3vXr1wqlTp9CjRw/4+voyVw4REdWI4/zU5tja2mL48OG4cOEC3nzzTZw/fx7du3fH7t27UVZWJtWTyWSYPXs2hg0bxkCHiIhqxWCHWpVCocCRI0dw+/ZttfIRI0bA1tYWzs7OWLlyJZ5++mlkZGQgLi5OrR5fWRERUX34GotahSiKuHLlCg4cOIC8vDxcv34dc+fOVZtU/NFHH2HYsGFISUkBUPnaqnfv3q3VZCIi0lIMdqhVJCYm4pdffgEAWFlZ4Yknnqg2SmNnZ4eQkBAcOHAAo0ePhqura2s0lYiItByDHWoVPXv2hLOzM7p164Zhw4ZBEAScPn0aOTk5CA0Nleo5Ojpi+vTprdhSIiLSdgx2qMWJooiLFy/Cy8sLBgYGSEpKQrdu3fDXv/4VANRy5QCAj48PHB0dW7HFRESkSxjsUIu6c+cOIiMjcefOHaSmpuLXX3/FDz/8gAMHDiAoKAglJSWIjIxEWVkZLCwsMGLECNjZ2bV2s4mISIcw2KEWExsbiwMHDkifV69ejZiYGADA8ePHERQUBBMTE4waNQoKhYK5coiIqEUw2KEW4+HhAUEQ0LdvX2RmZiIpKQmLFi3Ck08+iaeeekqq5+vr24qtJCIiXcdghzQmOzsbtra20mcnJydEREQAAKKiovDSSy8BANLT07mtAxERPTZMKkjNlp+fjx07dmDDhg3IyMhQO2ZtbY27d+/i4sWLACpz5Tz//PMMdIiI6LHhyA41y7179/DDDz+goqICgiDg1q1bcHJyUgtmvLy8MGTIEHh7ezNXDhERPXYc2aFmqaioQEVFBdzc3DBnzhwAwPr161FUVCTVEQQBoaGhDHSIiKhVMNihRlEqlQAqc+fs2rULH374IURRxMCBA/HLL79g3759yM3NxdmzZ1u5pURERJX4GosapKysDMeOHcONGzcwdOhQLFq0SFpG/tNPP8Hb2xtyuRzm5uYYOXIk+vfv37oNJiIi+i8GO1SnquzHBw8eRGFhIQDg1q1bOHXqFExMTDBx4kR8+umnMDc3h4ODA3PlEBFRm8Ngh+p16tQpFBYWokOHDvDz80NycjI++OADTJw4EUlJSTA3N4e1tTWGDRvW2k0lIiKqhsEO1UkQBIwdOxZXr15FXl4eIiMjpXITE5NWbh0REVH9GOyQRKVSISEhAUVFRRgxYoRU7urqCnt7e6xbtw5AZa6cUaNGQV+ff32IiKjt408rAlA5DycyMhKZmZmQyWTo1q0bHB0doaenBwAwNDTExIkTYWZmJi0hr9qlnIiIqC3j0nNCUVERtm7diszMTCiVSnTv3h3bt2/HuXPn1Op1796duXKIiEjrMNhp50pLS/HFF18gJiYGSUlJKCgowNWrV1FcXIxLly61dvOIiIiaja+x2qEbN27A2NgYHTt2RGlpKT755BNkZ2cjODgY3bp1Y64cIiLSKQx22pHc3Fzs378f165dg5OTE5577jl06NABa9asgSAImDp1Ki5fvoyePXsyVw4REekMBjvtRHp6OjZv3gylUglBEFBWVoYNGzYgPDwc06dPl+r17du3FVtJRESkeZyz0064uLjAxsYGFhYWEEURubm5UKlUSE1Nbe2mERERtSgGOzrq3r17UCgU0meZTIaZM2fCy8sLQGWunJdeeonzcoiISOfxNZaOKS0txdGjR3Hq1ClkZGTg/fffh6OjIwDA1NQUw4cPh7e3N5eQExFRu8GRHR1y48YNrFmzBqdOnQIA2Nvb47vvvoMoilKdh5MCEhERtQcMdnSIpaUlSktLoVQqAQD6+vowNzeXdisnIiJqj/gaS4uVl5fD0NBQ+uzg4IBBgwYhLi4O5ubmGD16NAYMGCBt+UBERNQeMdjRQkqlEmfOnEF0dDSCg4PRp08fKS/OhAkT4OvrC2tra7VAiIiIqL1isKNlUlNTsXfvXty/fx8A8J///AdFRUUYNmyYVMfBwaG1mkdERNTmcM6OlklPT5cCHQAQBAHFxcWt2CIiIqK2jcGOFsjNzcXrr7+OtLQ0+Pn5wcfHBwDg5eWFl156CcHBwa3cQiIioraLr7HaKFEUkZiYiN9//x1//PEHTp8+jbt372L79u2YMGECAgICYGNj09rNJCIiavMY7LRBDx48QGRkJG7evAkACA0NhZ6entoeVgx0iIiIGobBThv0n//8B7dv35Y+6+vrY9u2bejSpUsrtoqIiEg7cc5OC1EoFNi3bx8A4JNPPsHx48elZH/1GTNmDGxsbGBqaoqxY8di+fLlDHSIiIiaiMGOhkVGRsLc3ByGhoZ46623AAD/+te/MHz4cOjr68Pd3R0XLlyQ6mdkZODAgQNIS0uTylxcXDB//ny8/PLLGDx4MJMCEhERNQODHQ3y8fHB2LFjUVRUBAAYOnQoAGDZsmXw9vYGANy6dQv9+vXD7NmzsWPHDnz77beIjY3Frl27oFKppGsZGhoyKSAREZEGMNjREB8fH5w7dw4AcP/+faiUFVi37h8AgOee/QvOn0uASlmBU6dOQV9fH3Z2dkhMTJTOd3V1hUKhaJW2ExER6TKdCXY2bNiAzp07w9jYGL6+vjh9+vRju3dkZKQU6CiVStja2gI5yRBiN1RWiN0AIScZ164n4f/+7/9QUVGBs2fPQqFQwNbWFosWLUJYWBiMjIweW5uJiIjaC50Idn7++WcsXboU7733HhISEtCvXz+EhITg3r17j+X+kydPBlA5oiMIAoRTX0PYMAS4uAMAkPvnXmz++DUMHjQQ//nPf6Cnpwc7Ozt88skn+O6772BnZ/dY2klERNQe6USw8/nnn2Pu3LmYNWsWevXqha+//hqmpqb4/vvvW/zeCoUC+fn5AABbmw5AdhKwfzkgiihT6eHmzZvYWh6CNIuBePrJYRgfPAoX/jyHn//1E8rLy3HhwoUGr9IiIiKixtP6PDvl5eWIj4/H8uXLpTKZTIagoCDExsbWeE5ZWRnKysqkz1XBilwuV5sk3BAHDhxAly5dMHToUMjz8iCc/hkwdkO+yhTbyp+EojwfgAA9KPFifwMMHjwMcHGFPC8PISEhSEpKQlxcHHr37t34zreygoICtd91ka73kf3TbuyfdmP/mq/q53d9BFEUxRZrxWNw9+5duLq64uTJk/D395fK33jjDURHR+PUqVPVzlmxYgXef//9auU//vgjTE1NNdIuURRx5coVlJeXo0OHDujUqRNkMp0YSCMiImoTiouLMXXqVOTl5cHS0rLWelo/stMUy5cvx9KlS6XP+fn56NSpEwICAur8w6rJgQMHsHz5cowePRpPPfUUhsnOSXN1uhp64Fr3FzHk9rewuJ5ReULfZwH/RRABTJkyFUlJSdi6davWjuwkJCTAx8cHFhYWrd2cFqHrfWT/tBv7p93Yv+Zr6MiO1gc7dnZ20NPTQ1ZWllp5VlYWnJycajzHyMioxpVP1tbWjQ52+vfvj8DAQLi6uiI+Ph7ek4LgduYzQBQBEyBZXx8WZRmwLkkDBAEY8jxEK0sAAvbv3w8A8PPz0+rEgRYWFrC2tm7tZrQoXe8j+6fd2D/txv41XUPfmGj9exVDQ0MMHDgQhw8flspUKhUOHz6s9lpL05RKJb788kts27YNrq6uUlvMXb2AkFWVgc3DBAEY8zFg2w0Q9HDmbDwAwNvbW6sDHSIiorZO60d2AGDp0qWYMWMGBg0ahCFDhmDt2rUoKirCrFmzWuR+5eXl8PPzQ3p6OhYuXAiVSoUzZ87g4MGDWLZsGUTfBUDXQOD0z4ACla+uhjwP0bYbAKCoqAi+vr4AgB9++KFF2khERESVtH5kBwCef/55fPrpp3j33XfRv39/nD9/Hvv27YOjo6PG7lFeXo7o6GhcvHgRhoaGGDRoEBQKBUxNTREeHo7MzExUVFRAT08P2dnZgE1XiP6LKk/2XwTRtisgVgZFVe8uZ82aJW0jQURERC1DJ0Z2ACA8PBzh4eEav65SqUR8fDyOHDmC0tJSmJmZwcvLC6tWrcKqVasqsyUD0iSsc+fOwd7eHgDwwgsv4C9/+Qv+veMXbNiwQW0D0FmzZj2WPEBERETtnU6M7LSkq1evIjIyEqWlpQAAMzMz6OnpwdbWVgp0qiQkJGDv3r0wMzMDAJw8eRIA8Pe//10KdDp37oyLFy8y0CEiInpMdGZkp6XI5XLp6379+iEoKAjCo5OPHxIaGorCwkIoFArs3LkTQOUIT3BwsNavuiIiItJGDHYekp6ejmPHjmHq1KnSEnQ/Pz9kZmbC19cXHTt2bPC1DAwM8OSTTyI6OhpLlizR6WWFREREbRlfYz3khx9+QFZWFn788UepTE9PD2FhYY0KdIiIiKjtYLBTg8e1WzoRERG1PL7GeoSdnR2effbZ1m4GERERaQiDnYc89dRT8PX1rXMCMhEREWkXvsZ6SK9evRjoEBER6RgGO0RERKTT+BoLgCiKABq+VXxD5efno7i4GPn5+Q3emVWb6Hr/AN3vI/un3dg/7cb+aeYewP9+jteGwQ6AgoICAECnTp1auSVERETUWAUFBbCysqr1uCDWFw61AyqVCnfv3oWFhYVG5+zk5+ejU6dOuH37tpSkUJfoev8A3e8j+6fd2D/txv41nyiKKCgogIuLS52jRxzZASCTyVo0aaClpaVO/kWuouv9A3S/j+yfdmP/tBv71zx1jehU0b2XhEREREQPYbBDREREOo3BTgsyMjLCe++9ByMjo9ZuSovQ9f4But9H9k+7sX/ajf17fDhBmYiIiHQaR3aIiIhIpzHYISIiIp3GYIeIiIh0GoMdIiIi0mkMdlrQhg0b0LlzZxgbG8PX1xenT59u7SY1yapVqzB48GBYWFjAwcEBTz/9NK5du6ZWZ+TIkRAEQe3XggULWqnFjbNixYpqbe/Zs6d0vLS0FIsWLYKtrS3Mzc0RFhaGrKysVmxx43Tu3Lla/wRBwKJFiwBo37M7duwYJkyYABcXFwiCgJ07d6odF0UR7777LpydnWFiYoKgoCAkJSWp1cnJycG0adNgaWkJa2trzJkzB4WFhY+xF7Wrq38KhQLLli1D3759YWZmBhcXF7z44ou4e/eu2jVqeuYff/zxY+5J7ep7hjNnzqzW/jFjxqjV0dZnCKDGf4+CIOCTTz6R6rTVZ9iQnwcN+Z5569YtjBs3DqampnBwcMDrr7+OioqKFms3g50W8vPPP2Pp0qV47733kJCQgH79+iEkJAT37t1r7aY1WnR0NBYtWoS4uDgcPHgQCoUCwcHBKCoqUqs3d+5cZGRkSL9Wr17dSi1uvN69e6u1/cSJE9KxJUuW4I8//sCOHTsQHR2Nu3fvYtKkSa3Y2sY5c+aMWt8OHjwIAHj22WelOtr07IqKitCvXz9s2LChxuOrV6/GunXr8PXXX+PUqVMwMzNDSEgISktLpTrTpk3D5cuXcfDgQezevRvHjh3DvHnzHlcX6lRX/4qLi5GQkIB33nkHCQkJ+O2333Dt2jVMnDixWt2VK1eqPdPFixc/juY3SH3PEADGjBmj1v6ffvpJ7bi2PkMAav3KyMjA999/D0EQEBYWplavLT7Dhvw8qO97plKpxLhx41BeXo6TJ09iy5Yt2Lx5M959992Wa7hILWLIkCHiokWLpM9KpVJ0cXERV61a1Yqt0ox79+6JAMTo6GipbMSIEeLLL7/ceo1qhvfee0/s169fjcfkcrloYGAg7tixQyq7cuWKCECMjY19TC3UrJdffln09PQUVSqVKIra/ewAiL///rv0WaVSiU5OTuInn3wilcnlctHIyEj86aefRFEUxcTERBGAeObMGalOZGSkKAiCeOfOncfW9oZ4tH81OX36tAhATEtLk8rc3d3FNWvWtGzjNKSmPs6YMUN86qmnaj1H157hU089JY4ePVqtTFue4aM/DxryPXPv3r2iTCYTMzMzpTpfffWVaGlpKZaVlbVIOzmy0wLKy8sRHx+PoKAgqUwmkyEoKAixsbGt2DLNyMvLAwDY2NiolW/fvh12dnbo06cPli9fjuLi4tZoXpMkJSXBxcUFXbp0wbRp03Dr1i0AQHx8PBQKhdqz7NmzJ9zc3LTyWZaXl2Pbtm2YPXu22qa32vzsHpaSkoLMzEy152VlZQVfX1/pecXGxsLa2hqDBg2S6gQFBUEmk+HUqVOPvc3NlZeXB0EQYG1trVb+8ccfw9bWFgMGDMAnn3zSoq8IWsLRo0fh4OCAHj16YOHChcjOzpaO6dIzzMrKwp49ezBnzpxqx7ThGT7686Ah3zNjY2PRt29fODo6SnVCQkKQn5+Py5cvt0g7uRFoC3jw4AGUSqXagwQAR0dHXL16tZVapRkqlQqvvPIKAgIC0KdPH6l86tSpcHd3h4uLCy5cuIBly5bh2rVr+O2331qxtQ3j6+uLzZs3o0ePHsjIyMD777+PJ554ApcuXUJmZiYMDQ2r/SBxdHREZmZm6zS4GXbu3Am5XI6ZM2dKZdr87B5V9Uxq+rdXdSwzMxMODg5qx/X19WFjY6N1z7S0tBTLli3DlClT1DZajIiIgI+PD2xsbHDy5EksX74cGRkZ+Pzzz1uxtQ03ZswYTJo0CR4eHkhOTsabb76J0NBQxMbGQk9PT6ee4ZYtW2BhYVHt1bg2PMOafh405HtmZmZmjf9Gq461BAY71CiLFi3CpUuX1Oa0AFB7V963b184OzsjMDAQycnJ8PT0fNzNbJTQ0FDpa29vb/j6+sLd3R3//ve/YWJi0oot07yNGzciNDQULi4uUpk2P7v2TKFQ4LnnnoMoivjqq6/Uji1dulT62tvbG4aGhpg/fz5WrVrVJlL312fy5MnS13379oW3tzc8PT1x9OhRBAYGtmLLNO/777/HtGnTYGxsrFauDc+wtp8HbRFfY7UAOzs76OnpVZt9npWVBScnp1ZqVfOFh4dj9+7dOHLkCDp27FhnXV9fXwDAjRs3HkfTNMra2hrdu3fHjRs34OTkhPLycsjlcrU62vgs09LScOjQIfz1r3+ts542P7uqZ1LXvz0nJ6dqCwUqKiqQk5OjNc+0KtBJS0vDwYMH1UZ1auLr64uKigqkpqY+ngZqWJcuXWBnZyf9ndSFZwgAx48fx7Vr1+r9Nwm0vWdY28+DhnzPdHJyqvHfaNWxlsBgpwUYGhpi4MCBOHz4sFSmUqlw+PBh+Pv7t2LLmkYURYSHh+P3339HVFQUPDw86j3n/PnzAABnZ+cWbp3mFRYWIjk5Gc7Ozhg4cCAMDAzUnuW1a9dw69YtrXuWmzZtgoODA8aNG1dnPW1+dh4eHnByclJ7Xvn5+Th16pT0vPz9/SGXyxEfHy/ViYqKgkqlkgK9tqwq0ElKSsKhQ4dga2tb7znnz5+HTCar9upHW6SnpyM7O1v6O6ntz7DKxo0bMXDgQPTr16/eum3lGdb386Ah3zP9/f1x8eJFtYC1Kmjv1atXizWcWsC//vUv0cjISNy8ebOYmJgozps3T7S2tlabfa4tFi5cKFpZWYlHjx4VMzIypF/FxcWiKIrijRs3xJUrV4pnz54VU1JSxF27doldunQRhw8f3sotb5hXX31VPHr0qJiSkiLGxMSIQUFBop2dnXjv3j1RFEVxwYIFopubmxgVFSWePXtW9Pf3F/39/Vu51Y2jVCpFNzc3cdmyZWrl2vjsCgoKxHPnzonnzp0TAYiff/65eO7cOWk10scffyxaW1uLu3btEi9cuCA+9dRTooeHh1hSUiJdY8yYMeKAAQPEU6dOiSdOnBC7desmTpkypbW6pKau/pWXl4sTJ04UO3bsKJ4/f17t32PVKpaTJ0+Ka9asEc+fPy8mJyeL27ZtE+3t7cUXX3yxlXv2P3X1saCgQHzttdfE2NhYMSUlRTx06JDo4+MjduvWTSwtLZWuoa3PsEpeXp5oamoqfvXVV9XOb8vPsL6fB6JY//fMiooKsU+fPmJwcLB4/vx5cd++faK9vb24fPnyFms3g50WtH79etHNzU00NDQUhwwZIsbFxbV2k5oEQI2/Nm3aJIqiKN66dUscPny4aGNjIxoZGYldu3YVX3/9dTEvL691G95Azz//vOjs7CwaGhqKrq6u4vPPPy/euHFDOl5SUiK+9NJLYocOHURTU1PxmWeeETMyMlqxxY23f/9+EYB47do1tXJtfHZHjhyp8e/jjBkzRFGsXH7+zjvviI6OjqKRkZEYGBhYrd/Z2dnilClTRHNzc9HS0lKcNWuWWFBQ0Aq9qa6u/qWkpNT67/HIkSOiKIpifHy86OvrK1pZWYnGxsail5eX+NFHH6kFCq2trj4WFxeLwcHBor29vWhgYCC6u7uLc+fOrfYfRW19hlW++eYb0cTERJTL5dXOb8vPsL6fB6LYsO+ZqampYmhoqGhiYiLa2dmJr776qqhQKFqs3cJ/G09ERESkkzhnh4iIiHQagx0iIiLSaQx2iIiISKcx2CEiIiKdxmCHiIiIdBqDHSIiItJpDHaIiIhIpzHYISIiIp3GYIeIGiQmJgZ9+/aFgYEBnn766dZuTpt09OhRCIJQbRPExkpNTYUgCNI+ZUTUPAx2iHTczJkzIQgCBEGAgYEBPDw88MYbb6C0tLRR11m6dCn69++PlJQUbN68uWUa24qUSiU+/vhj9OzZEyYmJrCxsYGvry/++c9/tuh9Z86cWS147NSpEzIyMtCnT58WvTdRe6Hf2g0gopY3ZswYbNq0CQqFAvHx8ZgxYwYEQcDf//73Bl8jOTkZCxYsQMeOHZvcjvLychgaGjb5/Jb0/vvv45tvvsEXX3yBQYMGIT8/H2fPnkVubu5jb4uenh6cnJwe+32JdBVHdojaASMjIzg5OaFTp054+umnERQUhIMHD0rHVSoVVq1aBQ8PD5iYmKBfv3745ZdfAPzvlUp2djZmz54NQRCkkZ1Lly4hNDQU5ubmcHR0xPTp0/HgwQPpuiNHjkR4eDheeeUV2NnZISQkpMHnRURE4I033oCNjQ2cnJywYsUKtT7J5XLMnz8fjo6OMDY2Rp8+fbB7927p+IkTJ/DEE0/AxMQEnTp1QkREBIqKimr9M/rPf/6Dl156Cc8++yw8PDzQr18/zJkzB6+99ppUp6ysDBEREXBwcICxsTGGDRuGM2fO1HrNFStWoH///mpla9euRefOnaXjW7Zswa5du6TRt6NHj9b4Gis6OhpDhgyBkZERnJ2d8be//Q0VFRWN+jMjaq8Y7BC1M5cuXcLJkyfVRlhWrVqFrVu34uuvv8bly5exZMkSvPDCC4iOjpZeqVhaWmLt2rXIyMjA888/D7lcjtGjR2PAgAE4e/Ys9u3bh6ysLDz33HNq99uyZQsMDQ0RExODr7/+ulHnmZmZ4dSpU1i9ejVWrlwpBWgqlQqhoaGIiYnBtm3bkJiYiI8//hh6enoAKkehxowZg7CwMFy4cAE///wzTpw4gfDw8Fr/XJycnBAVFYX79+/XWueNN97Ar7/+ii1btiAhIQFdu3ZFSEgIcnJyGv0cAOC1117Dc889hzFjxiAjIwMZGRkYOnRotXp37tzB2LFjMXjwYPz555/46quvsHHjRnz44Ydq9er6MyNq11psP3UiahNmzJgh6unpiWZmZqKRkZEIQJTJZOIvv/wiiqIolpaWiqampuLJkyfVzpszZ444ZcoU6bOVlZW4adMm6fMHH3wgBgcHq51z+/ZtEYB47do1URRFccSIEeKAAQPU6jT0vGHDhqnVGTx4sLhs2TJRFEVx//79okwmk+o/as6cOeK8efPUyo4fPy7KZDKxpKSkxnMuX74senl5iTKZTOzbt684f/58ce/evdLxwsJC0cDAQNy+fbtUVl5eLrq4uIirV68WRVEUjxw5IgIQc3NzRVEUxffee0/s16+f2n3WrFkjuru7S59nzJghPvXUU2p1UlJSRADiuXPnRFEUxTfffFPs0aOHqFKppDobNmwQzc3NRaVSKYpi/X9mRO0Z5+wQtQOjRo3CV199haKiIqxZswb6+voICwsDANy4cQPFxcV48skn1c4pLy/HgAEDar3mn3/+iSNHjsDc3LzaseTkZHTv3h0AMHDgwCad5+3trXbM2dkZ9+7dAwCcP38eHTt2lOrW1LYLFy5g+/btUpkoilCpVEhJSYGXl1e1c3r16oVLly4hPj4eMTExOHbsGCZMmICZM2fin//8J5KTk6FQKBAQECCdY2BggCFDhuDKlSs1tkNTrly5An9/fwiCIJUFBASgsLAQ6enpcHNzA1D3nxlRe8Zgh6gdMDMzQ9euXQEA33//Pfr164eNGzdizpw5KCwsBADs2bMHrq6uaucZGRnVes3CwkJMmDChxknOzs7OavduynkGBgZqxwRBgEqlAgCYmJjU2q6qe8yfPx8RERHVjlUFBjWRyWQYPHgwBg8ejFdeeQXbtm3D9OnT8dZbb9V5v7quJ4qiWplCoWjStRqirj8zovaMwQ5ROyOTyfDmm29i6dKlmDp1Knr16gUjIyPcunULI0aMaPB1fHx88Ouvv6Jz587Q12/4t5Kmnvcwb29vpKen4/r16zWO7vj4+CAxMVEK8JqqV69eAICioiJ4enpKc4/c3d0BVAYuZ86cwSuvvFLj+fb29sjMzIQoitKozKO5cwwNDaFUKutsh5eXF3799Ve168TExMDCwqJZq+OI2gtOUCZqh5599lno6elhw4YNsLCwwGuvvYYlS5Zgy5YtSE5ORkJCAtavX48tW7bUeo1FixYhJycHU6ZMwZkzZ5CcnIz9+/dj1qxZdf7wbup5DxsxYgSGDx+OsLAwHDx4ECkpKYiMjMS+ffsAAMuWLcPJkycRHh6O8+fPIykpCbt27apzgvJf/vIXrFmzBqdOnUJaWhqOHj2KRYsWoXv37ujZsyfMzMywcOFCvP7669i3bx8SExMxd+5cFBcXY86cOTVec+TIkbh//z5Wr16N5ORkbNiwAZGRkWp1OnfujAsXLuDatWt48OBBjSM/L730Em7fvo3Fixfj6tWr2LVrF9577z0sXboUMhm/jRPVh/9KiNohfX19hIeHY/Xq1SgqKsIHH3yAd955B6tWrYKXlxfGjBmDPXv2wMPDo9ZruLi4ICYmBkqlEsHBwejbty9eeeUVWFtb1/kDuKnnPerXX3/F4MGDMWXKFPTq1QtvvPGGFCx5e3sjOjoa169fxxNPPIEBAwbg3XffhYuLS63XCwkJwR9//IEJEyage/fumDFjBnr27IkDBw5II1Aff/wxwsLCMH36dPj4+ODGjRvYv38/OnToUOM1vby88OWXX2LDhg3o168fTp8+rbaUHQDmzp2LHj16YNCgQbC3t0dMTEy167i6umLv3r04ffo0+vXrhwULFmDOnDl4++23G/znRdSeCeKjL5SJiIiIdAhHdoiIiEinMdghIiIincZgh4iIiHQagx0iIiLSaQx2iIiISKcx2CEiIiKdxmCHiIiIdBqDHSIiItJpDHaIiIhIpzHYISIiIp3GYIeIiIh02v8Dlvmq0xnZxWIAAAAASUVORK5CYII=", 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e4+fnR0REhDFDE0IIISwiKiqK9PR0PDw8GDduHA0bNjR7DHXr1qVp06YVHrOTm5vL9u3bGNxpKpzaVPpFigKdphop0vKRvbGEEEKIamTo0KGEhoYybdo0syQ6pW0BoSgKzz33XKXKS8/IRKnbDEJLuV9RYMj7ULeZ2Xp1QJIdIYQQwqKSk5MNek/s7OwYPHiwycedPmgLiDFjxuDo6Fj+GU8aDY6OjgwaNKgoqWkdXnSizXhoMRS6PQuzjkKX6UXnzUiSHSGEEMICdDodCQkJ/PDDD0RHR5u17i1bttC4cWNefPFFLl++bHCueAuIVq1a8dprr6EoygMTHo1Gg6IorF69+n/T6IsTmtBZ8OjPEPZ/ULep2RMdkGRHCCGEMLvMzEzWrFlDQkICqqoSHx9vtrq3bNnC8OHDyc7ORlXVEmNyio9lZ2fz9ttvM2/ePBwcHFAUpcSU9+JjDg4ORERElD6VvPhxlaIx66Oru0myI4QQQpjZjRs3uHbtGhqNhkGDBjFixAiz1Juamkp4eDiqqj5wL0idToeqqsyfP58zZ87wySef0KRJE4NrmjRpwieffMKNGzfMtmZOZVSLFZSFEEKIh0mLFi3o1asXt2/fNusCuMuWLUOr1ZZ7hpVOp0Or1bJ27VrmzJnDc889R0pKChkZGTg7O+Ph4WHyBQ6NQXp2hBBCCBPLyMgosVBfhw4dTLq31b1UVeXTTz+t1L0LFy7Uz9KqW7cu/v7+1K1bt0YkOiDJjhBCCGFSly5d4ssvv+S3336r8PYLxlTZLSBUVSUmJoaUlBQTRWZ6kuwIIYQQJqDT6di+fTs//PADWq2WrKwstFqtxeKp7BYQxTIyMowUSdWkpaWxadMmCgsLy32PjNkRQgghTCAnJ4dTp04B0KlTJ8LCwrC2ttyv3cpuAVHM2dnZSJFUTmFhIQcPHmTPnj3k5+fj7OxMSEhIue6VZEcIIYQwAUdHR8LDw8nMzKRVq1aWDqfSW0AoikKTJk3w8PAwYXQPtmbNGk6fPg2Ar68vzZs3L/e98hhLCCGEMILCwkISEhIMjvn5+Rk90dHpVHT/TVZ0qopOV77EpSpbQMyZM8fig5G7deuGk5MTo0ePZsqUKdSvX7/c90qyI4QQQlRRamoq3377LUuXLuXOnTsmqUOr1aJTVS4lpvHehrNMXXaU9zac5VJiGjpVLdd4oMmTJ1dqC4gnn3yyquFXiE6n4/r16wbHGjVqxAsvvEC7du0qnHhJsiOEEEJUwfnz5/nqq6+4ceMGiqKYJNk5fPgw9vb2LDtwhbCF+/lm/xUiz93im/1Fr5cduIK9vT2HDx++bzlubm78+uuvld8Cwgxu3LjB4sWL+fbbb7l9+7bBucqOeZIxO0IIIUQVnDt3jpycHBo1asS4ceOMnhhs2bKFzKws3Hxb8O6Gs9w73EZV4d0NZ+nexJ1r164TExND8+bN6dSpU6nlhYWFsXHjRsLDw/W9QXeP4SnuNXFwcGD16tVmXRl58+bN+oTNzs6O5ORk6tWrV+VyJdkRQgghqmDYsGF4enrSvXt3rKysjFp2amoqkyZNIiEhgf/bdKFEolNMVeHno9fxz8rkqaeewtvbmzNnzuDq6lrq9WFhYVy/fp3vvvuOhQsXEhMToz/XpEkT5syZw+TJk8u831RsbGwACAkJYdCgQVWeQVZMkh0hhBCiAq5du0bjxo31PSB2dnb06tXLJHUtW7YMa2trrK2tiUspe0yOLi+bb999jrioXUDRI6g7d+7cN1lxc3Oz+BYQOp3O4HFa7969adasGX5+fkatR8bsCCGEEOVQUFDAxo0b+eabb4iKijJ5fcXbO6Snp1NQUICfh2OZ1yo2dmjys7GxscHa2hobG5tyJwyW2AIiLy+Pbdu2sWTJEoMNSSsSd0VIsiOEEEI8QHJyMosXL+bYsWNA1VcjLm+dMTExZGdns2btWiZ0akRZeYhGo2Hx4v/wj3/8g4KCAi5fvlwtt3dQVZVz586xaNEiDhw4wM2bN7l48aLJ65VkRwghhHiA27dvk5iYiKOjI5MmTaJv374mqUdVVZKSkrhy5QpXr17VH//k448JbODCWyOCSyQ8igJvjQimb8fWrFmzRn+8umzvcDdVVdm1axfp6em4ubnx6KOP0qJFC5PXK2N2hBBCiAcICgpi+PDhtGjRwiTbJqSmprJs2TI+/fRTg8HCxfbv38/MmTP5/PPP6d7EnV+O3eBqihZfD0cmdGpEYAMXZs6cyYEDB/T3WHp7h9JoNBqGDx9OTEwMPXv21A9INjVJdoQQQoh73L59GwcHB4PZQGVN5a6qgwcPMn78+AcuCvjVV1/x+++/88ILL/L6I2OwtramoKCA335bw9OffKxPdKrL9g4A6enpnDp1it69e+uP+fr64uvra9Y4JNkRQggh7nLixAkiIiLw8fHh8ccfN/mA3Tlz5pCdnV2u/aoOHDjAgQMHsLe3x8XFhfT0dHJyckot0yRxq/8dTKxoDD++R3p6OhEREVy+fJm4uDhatWpF3bp1jR9POUmyI4QQQlA0QygiIoKTJ08aHLOzszNJfXePqbl7RlJ55OTklJrkaDQaHBwcTLO9g6pCcgwcWwIpseARAJ2mQt1m3D2QKCcnhy+++EIfX0hIiNHWy6ksSXaEEEIIID8/n8uXL6MoCn379qVnz57l3kOqMjZs2IC3t3eFE52ymHR7B1WFw1/CltcwWNnw8Jcw5H3oMl2f8Njb29OuXTvi4uJwcXGhd+/eJksYy0tmYwkhhBCAk5MT48aNY/LkyfTu3dukiY6qqvz8889GKUtRFBRFwcHBgYiICONv76DqIPlSyUQHQFXJ2jyPlJjj/3usBQwYMIDx48fj4OBg3FgqSZIdIYQQD6Xc3FyuXbtmcMzX19cki9rdKzk5ucSu3pXVpEkTPvnkE27cuGG6fayOLSmR6KhAFG34TJ3C6jVrDcYcWVtbm20V5vKQx1hCCCEeOvHx8axatYqsrCymT5+Ou7u7Weuv6qKE0dHRuLu7m2d7B0VTNEbnLunUYQUjuaE0BKCgIIcsbbbFx+aURZIdIYQQDw1VVTl27BhbtmyhsLAQFxcXsrOzzZ7sVDUp8PX1Nd/sJlVXNBj5Lo5kk409tmou/ThAl7bd0DiVvZ2FpUmyI4QQ4qFy7do1CgsLad68OWPGjLHIuJK6devSuHHjCt9nkjV0yjGdXO34NOrhr9D897w1hYxjA3XQ4qxoofPXxovHBCTZEUII8dBQFIXhw4fj5+dHhw4dLDKupLCwkIKCAiZOnFip+426hk45ppPfTkomYuMBmgb+jZ4X39OP3WnI7aJrhrxfYvp5dSPJjhBCiFpLVVUuXrxIYGCgPkGws7OjY8eOFonn4sWLTJkyhdDQUJ5//nmio6PLPevL6GvoPGA6eX77p9m9Zw8HDx5Ep9OR6OBA12kHsTm5DO5cAXf/UtfZqY4k2RFCCFErZWdns3btWi5cuMDw4cNNtt1Deeh0Oj777DP++te/kp2dzdmzZ5k5c6b+vEajue96O0ZfQ0fVFfXolDGdnM1/5bZze/bv3w9AYGAgQ4cOxcbNFbz+755HXtU70QFJdoQQQtRC169fZ9WqVaSlpWFlZWXpcIiNjeXll18mLy+PgQMHsmTJElxcXABYuHChwd5Yd0/hLu6NcnBwYPXq1cadWl7KdHI9VcX76m/07TOEBl5etGjRouSjs1K2iaiuJNkRQghR62RmZpKWloa7uzvjx4+nYcOGFo2nadOmfPjhh1hbWzNjxgwURSE1NRWA0NBQrl+/znfffcfChQsNdj1v0qQJc+bMYfLkybi6uhovoHumkxdgxUE60oIY6pNcdPDOFfo82td4dVqQJDtCCCFqnaCgIB555BFatGhh8a0Kij333HNlnnNzc2POnDk899xzpKSkkJGRYdo1dO6aTn4ZHyIYQLLiQYzqz2RWoEDRmBxVV6N6cMoiyY4QQoga7+rVq3h4eBisXxMSEmLBiCpHURTq1q1rnjV0Ok3l9OEd/MpwAJzULDpwqjiQosHHtYQkO0IIIWosVVXZu3cvu3btIiAggMcff7xabVNQbSkaqNuMFgOn4L79IoFqLP04gD25NWY6eUVIsiOEEKJGysrKYvXq1Vy+fBkoWpW4oKAAGxsbC0dWfd28eRMvL6+i6e6Kgk33Z3m2yfn/Tie3r1HTyStCkh0hhBA11u3bt7G2tmbYsGG0a9fO7L06SUlJzJ49m1GjRvHYY4+Zte6K0Gq1REZGEh0dTVhYGN26dSs6oSjYeLWokdPJK0KSHSGEEDWSk5MTf/rTn7C1taV+/fpmr3/dunVMmzaNxMREduzYwSOPPGKRrSce5Pz586xbt47s7GwA7ty5Y3jB3QOQa8Fg5NJIsiOEEKJGyMjIICkpiYCA/21KWZn9pR5EVVWSk5PJzMykTp061K1bt0SP0dGjRxk9ejQAwcHBLFu2rFomOgCOjo5kZ2dTv359hg0bhp+fn6VDMjtJdoQQQlR7MTEx/PbbbxQUFDB9+nST7FKemprKsmXL+PTTTw3WumnatCnPPfcckydP1q9e3LlzZyZNmkSjRo2YN28e9vb2Ro+nsnQ6ncEWFL6+vjz22GM0adKkWiywaAmS7AghhKi2VFXlwIEDHD16FIAGDRrcd1uFytqyZQvh4eH6VYzvdvnyZV588UX+9re/8euvvxIWFgbA999/X61mfqmqytmzZ4mMjGTSpEkG09cDAwMtGJnl1c6Hc0IIIWqN9PR0ADp27MjUqVONvgbNli1bGD58ONnZ2aiqarBdA6A/lp2dzfDhw9myZQtAtUp0kpOTWb58OatWreLOnTvs27fP0iFVK9KzI4QQotpSFIX+/fvTrl07goKCjF5+amoq4eHhqKr6wB6j4sdD4eHhXL9+3TgbchpJVFQUly9fxsrKip49e9KzZ09Lh1StSM+OEEKIaqOwsJBTp04Z9K7Y2tqaJNEBWLZsGVqtttyPxnQ6HVqtlu+++84k8VRWnz59CAkJYebMmfTt2xdra+nLuJskO0IIIaqF1NRUli5dym+//UZ0dLTJ61NVlU8//bRS9y5cuLDE4y5zSUtLY/v27QYJmp2dHY888ggeHh4Wiam6k9RPCCGExf3xxx/89ttv5OTkYGdnh6Ojo8nrTE5ONph1VV6qqhITE0NKSop59rD6r8LCQg4dOsTu3bvJz8/Hzc2NTp06ma3+mkySHSGEEBan0+nIycnB29ubcePG4e7uTmpqqknrzMzMrNL9GRkZZk12Vq1axfnz54Gi6eS+vr5mq7umk2RHCCGExQUFBTFhwgQCAwPNthbM3TukV4azs7ORIimfzp07c/XqVQYPHkxISEi1mg1W3cmYHSGEEGZ37ty5Ej0rQUFBZl30rm7dujRt2rTCSYOiKDRt2tSk42N0Oh3x8fEGx5o0acLzzz9P25A2KPx3vJCq+99+VqJMkuwIIYQwm4KCAjZu3MiKFSv47bffLDbIF4qSlueee65S986ZM8dkPSs3btxg8eLFfPPNNyUe5dna2EByDGx5HX6cUPR/cgxY8PNYE8hjLCGEEGZx584dVqxYQUJCAgANGzZEVVWLPo6ZPHkyf/vb38jOzi7X9HONRoODgwNPPvmk0WNRVZVNmzbpV4u2s7Pj9u3b/1vPR1Xh8Jew5TXD5ObwlzDkfegyvdbtVm4s0rMjhBDCLGxtbcnKysLR0ZFJkyYxcOBAgz2cLMHNzY1ff/0VRVEeGItGo0FRFFavXm2SBQUVRdH3dIWEhDB79uz/bfOg6iD5UslEB4peb/5r0Xl5pFUqSXaEEEKYhZOTExMnTmT69Ok0a9bMLHWqqsqSJUu4c+dOmdeEhYWxceNGHBwcUBSlRE9T8TEHBwciIiIYPHiwUeO7W//+/Zk8eTKPPPJIyQHUx5aU/bhKVYvOi1JJsiOEEMIkkpKSuHTpksExb29vXFxczFJ/8cylP//5z8yZM+e+14aFhXH9+nU++eQTmjRpYnCuSZMmfPLJJ9y4ccNoiU5eXh779u0jJibGIOFxcHDA39+/5A2KBlJi71/onStF14kSZMyOEEIIozt58iQbN25Eo9Ewffp03N3dzVr/1q1bGT9+POnp6djb29OxY8cHjg9yc3Njzpw5PPfcc6SkpJCVlYWTkxMeHh5GG1ekqirnz59n8+bN+g1Or1279uDPj6oDj4D7X+PuX3SdJDwlSLIjhBDCaAoLC9mwYQMnTpwAICAgABsbG7PHERwcjKIodOvWjaVLl9KiRYty36ugUtfDvWjBQFUHqIBxkp3CwkK2bt1Keno6Li4u1K1b13BxwOIxN4rG8GOATlOLBiOX9ihLUYrOi1JJsiOEEMJoNBoNhYWFKIpCnz596NWrl0UGITdu3Ji9e/cSHBxcsbV7VLVoKvexJUWPjTwCipKIus2MMtPJ2tqaYcOGce3aNdq0acOBAwfKWbem6P+w+SUHKStK0WwsI8VYG0myI4QQwmgURWH48OF06tTJ4tsZtGnTpmI3mGBqd0xMDJmZmbRt21Z/LDAwkMDAQMM1dMpbd9cZ0GxgUUJ050rRoysjJmO1lSQ7QgghKi03N5dTp07RqVMn/bgWOzs7iyc6Fabq/rtY332mdjcdAHWblmtMTHp6Olu2bOHs2bPY2toSEBBQ9sDs8kwrv7vuuk0h7P/uedQlic79yCgmIYQQlZKQkMDXX39NREQE0dHRlg6n6ow0tTsrK4tFixZx9uxZFEWhffv22NnZGa9uRfO/hOvuj0WZpGdHCCFEhR0/fpyNGzdSWFiIi4sL9erVM1vd+/bto3v37sYdC2TEqd1OTk4EBweTlJTE8OHD8fLyMlvdonTymRNCCFFhDg4OFBYW0rx5c6ZPn26Wx1bp6ek888wz9OrVi3//+9/GLbwiU7vvkZWVRVpamsGxYcOG8fTTTz840ali3aJ8LJrs7Nmzh5EjR+Lt7Y2iKKxZs8bg/JQpU/QrVxb/GzJkiME1KSkpTJo0CRcXF9zc3Jg6dWqJnXSFEEIYV1BQEE8++SQTJ07E0dHR5PXt3buXNm3asHjxYhRF4datW8avpNPUsse+lDK1W1VVoqKi+Oyzz1i3bp3B4oA2NjYVW5ungnWLirFospOVlUXbtm1ZtGhRmdcMGTKE+Ph4/b+ffvrJ4PykSZM4c+YM27ZtY8OGDezZs4dp06aZOnQhhHhoqKrKsWPHSvwhGRAQYLZNPHU6HVevXiUgIIBdu3Yxf/5841Zw99Tue9tkMLW76NdmamoqS5YsYcOGDeTk5JCVlUV2drZZ6hYVZ9ExO0OHDmXo0KH3vcbOzq7MbsBz586xefNmjh49SqdOnQD49NNPGTZsGB9++CHe3t5Gj1kIIR4m2dnZrFu3jvPnz3Pu3DkmTZpkkXVz+vTpw8qVKxkyZEjJPaOMRAXoOoPCJgOwjv5GP7W7oMPTWNUr2pCzOBVxdHQkPT0dW1tb+vXrR5cuXar2eZFp5SZV7Qco79q1i/r16+Pu7k7//v157733ila1BA4ePIibm5s+0QH0u+gePnyYRx55pNQyc3Nzyc3N1b8uXrI7NTUVnc54z0QzMjIM/q9tanv7oPa3UdpXs5m6fUlJSaxbt46MjAysrKzw8fEhLS3NbL0597Zv4MCBFBQUGK5PYyQ6VSU+NZt/R15kdFtvQru8gkajQafTcTAmmbVrdzNnQCDe7o5o/tv+oUOH4uzsTJ06dfS/Ryqi1PfPqi50fcVwWvk944FqCnN8/5X3816tk50hQ4YwduxYAgICiImJ4fXXX2fo0KEcPHgQKysrEhISqF+/vsE91tbWeHh4kJCQUGa58+fPZ968eSWO79+/3yTPnmvFlMz7qO3tg9rfRmlfzWaq9hUUFJCTk4OtrS3+/v6kpaWxZ88ek9R1P+Z8/4bXg4Kbqey9+b9jOTk5tEi/zuGdMXh6ehq9Tvn6rDytVluu66p1sjNx4kT9x23atCEkJISmTZuya9cuBgwYUOlyX3vtNebOnat/nZ6ejo+PDz169DDqbrwZGRlER0fToUMHnJ2djVZudVHb2we1v43SvprNHO0LCQnBxcXlwevEmIC53793N5zhSOwd/WuNqiNAjcefRDSopGfnM2bMmIptP3Ef8vVZdbWiZ+deTZo0wdPTk0uXLjFgwAC8vLxKjMgvKCggJSXlvtP97OzsSv3GdXNzM2qyU8zZ2Rk3Nzejl1td1Pb2Qe1vo7SvZjNW+65evUpOTg7NmzfXHzPH5y0+Pp6GDRuWed4c759OVXFzdeN6Vqr+mJcmiyZ2RU8JrhW64tOqO+4eHvrHWMYiX5+VV95xUjVqaPf169dJTk7Wf1OEhoaSmppKVFSU/podO3ag0+no2rWrpcIUQogaRVVV9u3bx9KlS1m9ejV37tx58E1GUFBQwD/+8Q/8/f3ZsWOHWeq8n8e7+RmMA07QuXA6vwGRuU2JzG/GE32CLRecqBKLJjuZmZmcOHGCEydOABAbG8uJEye4evUqmZmZvPzyyxw6dIgrV64QGRnJ6NGjadasGWFhYQC0bNmSIUOG8Mwzz3DkyBH279/P7NmzmThxoszEEkKIcigoKODHH38kMjISVVVp3ry5WdbNOXfuHN27d+eNN94gLy+P1atXm7zO+9EVFnLj/HFeH+BrkPAcLfDhmurOWyNaEeDpZPReHWEeFn2MdezYMfr166d/XTyOZvLkyXzxxRecOnWKZcuWkZqaire3N4MHD+bvf/+7wSOoH374gdmzZzNgwAA0Gg3h4eEsXLjQ7G0RQoiayMrKCgcHB6ytrRk2bBjt2rUzy2yr7du3c/ToUVxdXfn00095/PHHTV5nWS5fvkxERATJyck0b96cyLkjWX4ojqspWnw9HHm8mx8Bnk5mm4UmjM+iyU7fvn0NVpy815YtWx5YhoeHBz/++KMxwxJCiIeGoiiMGDGCXr16mXV/q1mzZpGQkMDMmTNp1KiR2eq914kTJ1i7di1QtKdVq1at8KvryBsjgtEoCrr//o6SRKdmq1EDlIUQQlRN8fCBHj166H+B29ramjXRgaKBpf/4xz/MWmdpgoKC2LFjB0FBQfTv3x97e3uD8/LYqnaQZEcIIR4Sly9fZvXq1WRlZeHg4EDHjh0tHZLZJSYmUr9+fX2iZ29vz6xZsywytV6YjyQ7QgjxENi/fz/bt28HoH79+vj5+Vk4IvPSarVERkYSHR3N6NGjadeunf6cJDq1nyQ7QgjxECjeZqdDhw4MGTIEGxsbk9VVUFCAtXX1+fVy9uxZNm7cqF9t1yQ7potqrUatsyOEEKJygoKCmDZtGiNHjjRZoqOqKj/88AOBgYFcu3bNJHVUho2NDVqtlvr16zNlyhQGDx5s6ZCEmVUq2UlMTOSJJ57A29sba2trrKysDP4JIYSwnMLCQnbv3k1mZqbB8futUlxVt27dIjw8nMcff5wrV67wr3/9y2R1Pci9s3wDAwP505/+xLRp0x66x3eiSKX6GadMmcLVq1d58803adiwoUzJE0KIaiItLY1Vq1Zx/fp1rl69yuOPP26Wn9Hvvvsuv/32GzY2Nrz11lv89a9/NXmd91JVlbNnz7Jr1y6eeOIJg+1/WrZsafZ4RPVRqWRn37597N2712CAlxBCCMu6du0aP/74Izk5OdjZ2dGxY0ez/TH63nvvERsbyz/+8Q+L/G5ITk4mIiKCy5cvA0UDsocOHWr2OET1VKlkx8fH576LAQohhDA/Dw8PbGxs8PDwYNy4cbi7u5utbjc3NzZu3Gi2+u514MABLl++jJWVFT179qRnz54Wi0VUP5VKdj755BP++te/8tVXX+Hv72/kkIQQQlSGk5MTTz75JO7u7g/d+MkBAwaQm5tL//798fDwsHQ4opqpVLIzYcIEtFotTZs2xdHRscTI/pSUFKMEJ4QQomyXLl0iLS3N4Jinp6eFojGftLQ0Tpw4Qe/evfWP6RwdHRk3bpyFIxPVVaV7doQQQlhGQUEBW7du5ejRo2g0GtLT03Fzc7N0WCZXWFjIoUOH2L17N/n5+dStW5fWrVtbOixRA1Qq2Zk8ebKx4xBCCFEOubm5LFu2jPj4eKCoJ8fJyclk9e3du5cPPviAFStWlNg3ytx++uknYmJigKKxo+bez0vUXJVe4rKwsJA1a9Zw7tw5AFq1asWoUaMeuufEQghhTra2tnh6epKWlsagQYOIi4szyc/d7Oxs3njjDT7++GNUVeXDDz/kjTfeMHo9FdGhQwfi4+MZNGgQbdu2lWVPRLlVKtm5dOkSw4YN48aNG7Ro0QKA+fPn4+Pjw8aNG2natKlRgxRCCFFEURSGDx9Obm4uOp2OuLg4k9Tz9NNP8/PPPwMwdepU5syZY5J6yqLT6UhOTjY41rJlS5o2bSp7WYkKq9QKynPmzKFp06Zcu3aN6OhooqOjuXr1KgEBAWb/hhBCiNosKSmJHTt2GCz3YWdnZ7Bgnin87W9/w9/fnw0bNrB48WKT13e3GzdusHjxYlauXEl+fr7+uKIokuiISqlUz87u3bs5dOiQwfS+unXr8v7779OjRw+jBSeEEA+zU6dOsWHDBvLz83Fzc6NDhw5mq7t169ZcvHjRrBt66nQ6Nm3axLFjx4CiR3Y5OTlmq1/UXpX6KrazsyMjI6PE8czMTGxtbasclBBCPOy2bNnCoUOHAPD39ycwMNDsMZh753KNRkNubi4AISEhdOnSRZ/4CFEVlXqMNWLECKZNm8bhw4dRVRVVVTl06BAzZsxg1KhRxo5RCCEeOn5+fiiKQp8+fXjiiSdwdna2dEgmce9q/IMGDeLJJ5/kkUceMeksM/FwqVTavnDhQiZPnkxoaKh+QcGCggJGjRrFv//9b6MGKIQQD6OgoCBmz55da1cDzsvLY/fu3aSkpDBhwgT9cWdn51qb2AnLqVSy4+bmxtq1a7l48SLnz58HikbJN2vWzKjBCSHEwyAvL4/IyEh69epFnTp19MdNkehcvHiRn376ibfeesvoZZeHqqqcP3+ezZs3k56eDsD169dp3LixReIRD4cqPZANDAy0yHNkIYSoLRISEli1ahXJycmkpKQwadIkk9Sj0+lYtGgRr776KtnZ2bRo0cKgR8Vc8vPz2bhxI1lZWbi5uTF06FBJdITJlTvZmTt3Ln//+99xcnJi7ty59732o48+qnJgQghR2128eJFffvmFwsJCnJ2d6dWrl8nqmjhxIitXrgSgf//+dOvWzWR13Y+trS1Dhgzh1q1b9OrVq8TeikKYQrmTnePHj+vXOzh+/LjJAhJCiIeFt7c3jo6OeHl5MWbMGBwdHU1WV3h4OBs3bmTBggU8++yzaDSVmp9SYTExMeTl5dGyZUv9MdnPSphbuZOdnTt3lvqxEEKIynFycmLq1Km4uLiYfOuDCRMm0Lt3bxo2bGjSeoqlp6ezdetWzpw5g6OjI/7+/jg4OJilbiHuVanU/umnny51nZ2srCyefvrpKgclhBC1jaqqHDlyhLNnzxocd3V1NdseT+ZKdNLS0li0aBFnzpxBURTatGljtp4kIUpTqa++ZcuWkZ2dXeJ4dnY23333XZWDEkKI2iQnJ4eVK1eyadMm1q1bV+ofi7WJq6srTZs2pXHjxkybNo0hQ4bINg/Coio0Gys9PV2/iGBGRgb29vb6c4WFhURERFC/fn2jBymEEDVVVlYWixcvJjU1FY1GQ79+/Qyml9cGWq0WnU5n0K7Ro0dja2srO5OLaqFCyY6bmxuKoqAoCs2bNy9xXlEU5s2bZ7TghBCipnN0dMTHxwdFURg3bhze3t5GLT89PZ2IiAgee+wxo5ZbHqqqEh0dTWRkJAEBAYwfP15/TnpyRHVSoWRn586dqKpK//79+fXXXw0WvLK1tcXPz8/o38hCCFGTKYrCiBEj0Ol0Br3hxhAREcGcOXPQarWEhISYdZZTSkoKq1ev5saNGwAkJyeTm5srSY6oliqU7PTp0weA2NhYfH19pXtSCCHuce3aNc6cOUNYWJj+Z6SxN0hWVZU///nPfPPNNwC0aNGCwsJCo9bxIPb29qSkpGBra0u/fv3o0qWLDEIW1ValVlCOi4sjLi6uzPO9e/eudEBCCFETqarKgQMHiIyMRFVVGjZsSNu2bU1Sl6IoeHp6oigKo0eP5osvvsDLy8skdRUr3rCzOIFzdHRk/PjxeHp6yl5WotqrVLLTt2/fEsfu7uUx918YQghhab/99hu///47ULRoXlBQkEnrmzdvHoMGDSIrK8voj8fulZSUREREBB06dDB4VBYQEGDSeoUwlkr1Od65c8fg361bt9i8eTOdO3dm69atxo5RCCGqveDgYKytrRkxYgRjx441+dgVe3t7OnXqZNI68vPziYyM5IsvviA2NpYdO3ag0+lMWqcQplCpnh1XV9cSxwYNGoStrS1z584lKiqqyoEJIURNEhQUxPPPP1+rppVfvnyZffv2AUUbPw8dOlTG5YgaqUq7nt+rQYMGXLhwwZhFCiFEtZOZmcmWLVsICwszSG5qU6ID0Lx5czp06EBgYCAtWrSQSSmixqpUsnPq1CmD16qqEh8fz/vvv0+7du2MEZcQQlRLly9fZvXq1WRlZZGfn8/EiRONWn5ubi6XL1822DjTHAoLCzl06BBt27bVJ22KojBy5EizxiGEKVQq2WnXrh2KouhH5xfr1q2bfiqkEELUNr///jurV68GoH79+gwYMMCo5UdFRTF58mRSU1M5ffo0bm5uRi2/LLGxsURERJCUlMTt27cZM2aMWeoVwlwqlezExsYavNZoNNSrV8/kMwKEEMKSmjRpgrOzM82aNWPo0KHY2NgYpdyCggLee+89/vGPf1BQUED9+vW5ePEinTt3Nkr593P06FEiIiKAol3YmzRpYvI6hTC3SiU7fn5+xo5DCCGqPScnJ2bMmIGjo6NRy7WysuLAgQMUFBQwbtw4Pv/8c+rVq2fUOsrSsmVLduzYQZs2bejfv7/80SpqpXInOwsXLix3oXPmzKlUMEIIUV3odDp27NiBt7c3wcHB+uPGTnSgaGzMkiVL2L9/PxMmTDDpQODbt2/rFySEokHVc+bMwcHBwWR1CmFp5U52Pv7443JdpyiKJDtCiBotLS2NX3/9lWvXrmFnZ4e/v79Jkpy7+fj4GH2w8920Wi2RkZFER0czYcIEg0UPJdERtV25k517x+kIIURtlJaWxldffUV2djZ2dnaMHDnS5ImOqZ05c4aIiAi0Wi0A169fN/kKz0JUJ1VeZ+fe/VKEEKImc3FxoVmzZiQnJzNu3Djc3d0tHVKV6XQ6tFot9erVY/jw4TLuUjx0Kp3sfPfdd3zwwQdcvHgRKFp86uWXX+aJJ54wWnBCCGFuiqIwYsQINBoN1tZVX3dVVVXS09NLXXneVFRVNfgDtHg/q+DgYKysrMwWhxDVRaXW/f7oo4949tlnGTZsGCtWrGDFihUMGTKEGTNmlHtsjxBCVAfnz59nw4YNBuuG2draGiXRuXbtGmFhYYwePdose0qpqsqZM2f46quv9I+soCiBa9OmjSQ64qFVqe/mTz/9lC+++IInn3xSf2zUqFG0atWKd955hxdffNFoAQohhCkUFBSwbds2jhw5AhTt4N2qVSujlK2qKsuWLeP5558nPT0de3t7Tp48Sfv27Y1SfmmSk5OJiIjg8uXLABw8eNDoix4KUVNVqmcnPj6e7t27lzjevXt34uPjqxyUEEKYkqqq/PTTT/pEJzQ01KgDdrVaLe+88w7p6el069aNEydOmDTRAdi5cyeXL1/GysqKPn360KdPH5PWJ0RNUqmenWbNmrFixQpef/11g+O//PILgYGBRglMCCFMRVEUOnfuTHx8PGPGjKF58+ZGLd/JyYlvv/2Ww4cP89JLLxnlkdiDDBo0iMLCQgYOHEjdunVNXp8QNUmlvgPnzZvHhAkT2LNnDz169ABg//79REZGsmLFCqMGKIQQphAUFIS/v7/JVgzu168f/fr1M0nZaWlpnD171mCzUFdXVyZMmGCS+oSo6SqV7ISHh3P48GE+/vhj1qxZAxQtOX7kyBGTd9UKIURF3blzh40bNzJ69Gj9jt5AjdsaoXhn8t27d5Ofn1/j1/8Rwlwq3bfasWNHli9fbsxYhBDC6FJSUvjpp5/Iz89ny5YthIeHWzqkSlFVle+//564uDgAfH19DRI3IUTZKpTsFBQUUFhYiJ2dnf5YYmIiX375JVlZWYwaNYqePXsaPUghhKiMkydPcvXqVQD8/f0ZPHiwUcq9dx0bc1AUhZCQEG7fvs2gQYNo27YtaWlpZo1BiJqqQsnOM888g62tLV999RUAGRkZdO7cmZycHBo2bMjHH3/M2rVrGTZsmEmCFUKIiggMDGT//v20b9+esLAwNJpKTUDVS09P5y9/+QvOzs589NFHRoqydDqdjtTUVDw8PPTH2rdvT8uWLWUvKyEqqELf+fv37zfoAv7uu+8oLCzk4sWLnDx5krlz5/LBBx8YPUghhKgMR0dHgoKCCA0NrXKis3PnTkJCQli8eDH//ve/9evZmMLNmzdZsmQJ3377LTk5OfrjiqJIoiNEJVTou//GjRsGU8sjIyMJDw/XL4M+efJkzpw5Y9wIhRCiHPLy8lizZg1nz541OG6MVYNv377N8OHDiYuLIyAggB07dtCkSZMql3uvwsJCNm7cyNdff83NmzfJz88nMTHR6PUI8bCp0GMse3t7srOz9a8PHTpk0JNjb29PZmam8aITQohySExMZNWqVSQlJfHHH3/QtGlTg7GFVVWvXj3+8Y9/cOHCBT744AOcnZ2NVvbdNBqNfhxOSEgIgwYNkkHIQhhBhZKddu3a8f333zN//nz27t1LYmIi/fv315+PiYnB29vb6EEKIURZkpOTWbx4MQUFBTg7OxMeHm7URKeYqbbBuXuws6IoDB06lO7du+Pv72+S+oR4GFUo2XnrrbcYOnQoK1asID4+nilTptCwYUP9+d9++02/yKAQQpiDh4cHLVu2JDs7mzFjxuDk5GTpkMolLy+P3bt3o9VqGT16tP64u7s77u7uFoxMiNqnQslOnz59iIqKYuvWrXh5eTF+/HiD8+3ataNLly5GDVAIIe5HURRGjRqFlZWV2aeDV4aqqpw/f57NmzeTnp4OQLdu3WjQoIGFIxOi9qrwooItW7Y0WKL8btOmTatyQEIIURZVVTl27Bg3btxg9OjR+uTGHHtPGUtOTg5r164lNzcXNzc3hg4dKomOECZWc35CCCEeajk5Oaxfv14/2yo4OLjKG3gWFBSwYMEC/Pz8mDRpkjHCfCAHBwcGDRpEWloavXr1wsbGxiz1CvEwk2RHCFHtqarKd999R3x8PBqNhkGDBhksg1EZ58+fZ/LkyRw5cgRXV1cGDx5MvXr1jBTx/8TExADQtGlT/bGOHTsavR4hRNkk2RFCVHuKotC7d2+2bNnCuHHjaNSoUZXKi42NpX379uTk5ODq6sqnn36Kp6enkaItkp6eztatWzlz5gwuLi7MmjULW1tbo9YhhCgfSXaEENXSvftPBQUF0axZM6OMzwkICCA8PJykpCQWL15M48aNq1zm3ZKTk/nPf/5DXl4eiqKUOc5RCGEelVo/PTs7m3Xr1vHhhx/y4Ycfsm7dOoPFBstrz549jBw5Em9vbxRFYc2aNQbnVVXlrbfeomHDhjg4ODBw4EAuXrxocE1KSgqTJk3CxcUFNzc3pk6dKgsbClHDXbt2jSVLlpT4XjbmQOSvv/6aTZs2GT3RgaLp8I0aNaJx48ZMmzaNIUOGSK+OEBZU4Z8c69at489//jNJSUkGxz09PVmyZAkjR44sd1lZWVm0bduWp59+mrFjx5Y4v2DBAhYuXMiyZcsICAjgzTffJCwsjLNnz2Jvbw/ApEmTiI+PZ9u2beTn5/PUU08xbdo0fvzxx4o2TQhhYaqqcuDAAXbs2IFOp2PHjh2MGjXKJHUZc48prVZrsG+VoiiMHz8ee3v7GjEdXojarkI9OwcOHGDcuHH07t2b/fv3k5KSQkpKCvv27aNXr16MGzeOQ4cOlbu8oUOH8t577/HII4+UOKeqKp988glvvPEGo0ePJiQkhO+++46bN2/qe4DOnTvH5s2bWbx4MV27dqVnz558+umn/Pzzz9y8ebMiTRNCVAN79uxh+/bt6HQ6WrVqRVhYmKVDui9VVYmKiuKzzz5j+/btBuccHBwk0RGimqhQz857773HU089xVdffWVwvHv37nTv3p3p06fz7rvvEhERUeXAYmNjSUhIYODAgfpjrq6udO3alYMHDzJx4kQOHjyIm5sbnTp10l8zcOBANBoNhw8fLjWJAsjNzSU3N1f/unhhr9TUVHQ6XZVjL5aRkWHwf21T29sHtb+N1a19gYGBHD9+nI4dO9K6dWuys7Mr9Yi8mCnbd+fOHbZu3UpCQgJQ9OgtKSnJrGv+VLf3z9ikfTWbOdpX/Pv7QSr0XXno0CH++c9/lnl+1qxZ9OnTpyJFlqn4B8i9i201aNBAfy4hIYH69esbnLe2tsbDw0N/TWnmz5/PvHnzShzfv38/jo6OVQ29hOjoaKOXWZ3U9vZB7W+jpdp37yBkAD8/P1JSUtizZ0+lytuzZw8+Pj4Gu5Kbon15eXncunULjUaDl5cX9erVY//+/Uavpzzk67Nmk/ZVnlarLdd1FUp2srOzcXFxKfO8q6srOTk5FSnSIl577TXmzp2rf52eno6Pjw89evS4b/sqKiMjg+joaDp06GCyXZItqba3D2p/Gy3ZvqysLLZu3Urr1q2rvGYOwO3bt/nLX/7C+vXradmyJTt37iQvL89o7SstMWvatCn169e32M7k8vVZs0n7qs4kPTuBgYHs2LGDp556qtTzkZGRRvmhBeDl5QVAYmKiwWajiYmJtGvXTn/NrVu3DO4rKCggJSVFf39p7OzsSt0V2c3NzajJTjFnZ2fc3NyMXm51UdvbB7W/jeZuX2xsLKtXryYzM5OUlBTat29fpcc/p0+fpn///ty+fRtra2smTpyIh4cHWVlZQNXbl5SUREREBN27d6dZs2b64x06dKh0mcYkX581m7Sv8jSa8g09rtAA5aeeeoqXXnqp1DE5Gzdu5JVXXmHKlCkVKbJMAQEBeHl5ERkZqT+Wnp7O4cOHCQ0NBSA0NJTU1FSioqL01xTP4ujatatR4hBCGFdCQgLff/89mZmZ1KtXjyeffLLK41yaN2+Ot7c3bdq04ciRI7z11ltG2YYhPz+fyMhIvvjiC2JjY9m2bRuqqla5XCGEeVXoJ8zzzz/PgQMHGDFiBC1atKBly5aoqsq5c+e4ePEiY8aM4YUXXih3eZmZmVy6dEn/OjY2lhMnTuDh4YGvry8vvPAC7733HoGBgfqp597e3owZMwYo2pR0yJAhPPPMM3z55Zfk5+cze/ZsJk6ciLe3d0WaJoQwkwYNGtC6dWusrKwYNmyYUZISW1tb1q9fT/369Uvtta2sc+fOsW/fPqCoZ3vo0KEyw0qIGqhCyY5Go2HlypX88ssv/PTTT5w/fx4oWtn0nXfeYeLEiRWq/NixY/Tr10//ungczeTJk1m6dCmvvPIKWVlZTJs2jdTUVHr27MnmzZv1a+wA/PDDD8yePZsBAwag0WgIDw9n4cKFFYpDCGFad493URSFMWPGlLv7ubx8fHyMWh5AmzZtuHjxIq1ataJFixaS6AhRQ1Wq73jChAlMmDChypX37dv3vl3CiqLw7rvv8u6775Z5jYeHhywgKEQ1pdPp2LlzJ6mpqYwdO1afLBg70TGGwsJCDh8+TIcOHfR/UCmKQnh4uIUjE0JUVaWSneTkZOrWrQsUrS3x9ddfk52dzciRI+ndu7dRAxRC1Ezp6en8+uuvXL16FYBOnTrh5+dn4ahKFxsbS0REBElJSaSlpTF06FBLhySEMKIK/Xn1+++/4+/vT/369QkKCuLEiRN07tyZjz/+mP/85z/079+/xP5WQoiHj06nY9myZVy9ehVbW1vGjRtX6URn3759JCcnGznC/zl48CDfffcdSUlJODk5VXlHdSFE9VOhZOeVV16hTZs27Nmzh759+zJixAiGDx9OWload+7cYfr06bz//vumilUIUUNoNBoGDRpEw4YNmT59Oq1atapwGdnZ2fzlL3+hd+/ezJ492wRRFgkKCsLGxobOnTsze/ZsQkJCTFaXEMIyKvQY6+jRo+zYsYOQkBDatm3Lf/7zH2bOnKl//v7cc8/RrVs3kwQqhKje7l10LygoiObNm1dqfM7p06cZP368fhKEo6MjBQUFRtmKISUlBQ8PD/1rd3d3nn/+eZycnKpcthCieqrQT6G7F+urU6cOTk5OuLu768+7u7vX2j0+hBBlO3/+PF988QWZmZkGxys7ENnDw4PExES8vLzYsGEDS5YsqXKik52dzfr16/n000+5cuWKwTlJdISo3Sr80+PeqZcyFVOIh1dhYSHbtm3j8OHDQNH+csbYqdzb25t169YRHBxs0AtTWRcuXGDPnj36fXSuXLmCv79/lcsVQtQMFU52pkyZol+0KycnhxkzZuj/Krp7J3EhRO23fft2faLTrVs3Bg4caLSye/bsabSycnJy0Gq11K9fn2HDhlXbWWFCCNOoULIzefJkg9ePP/54iWuefPLJqkUkhKgxevbsyeXLl+nfvz8tWrSwdDh6944fatOmDS4uLoSEhGBlZWXByIQQllChZOfbb781VRxCiBpAp9MZjMNxcnJixowZ1eZxtqqqnD17lkOHDvHEE0/oj2s0Gtq3b2/ByIQQllT9ljEVQlRLycnJfP3115w9e9bgeEUTndjYWAoLC40ZGlAU3w8//MCqVau4fv26/vGaEEJUqGenffv2pf5gc3V1pXnz5jz//PMEBwcbLTghRPXw+++/s2HDBvLy8oiMjCQoKKjCM610Oh2LFi3i1VdfZd68ebz88stGjXHz5s3ExMRgZWVFz549CQ0NLTE7TAjxcKpQslO82/i9UlNTiY6Opn379uzYsYMePXoYIzYhRDUQFxfH6tWrAfDz82Ps2LEVTnSuXLnC008/zc6dOwHYtWsXL730klEff4WFhaHRaAgLCzPKDC4hRO1RoWTn7bffvu/5v/3tb7z11ltERkZWKSghRPXh6+tL27ZtcXV1pU+fPpVaO+fGjRvs2rULR0dHFixYwLPPPlulRCctLY2LFy/SqVMn/TFPT08effTRSpcphKi9qr4c6V0ee+wxvv76a2MWKYSwgMLCQv2sJUVRGD16dJWSkx49evDFF18wYMAAmjVrVqW4Dh06xO7du8nPz6dBgwb4+PhUujwhxMPBqMmOlZUVOp3OmEUKIcwoLy+PTZs2kZ+fT3h4uD7BMcbjpunTp1fpflVVWbp0KdevXwfAx8cHe3v7KsclhKj9jJrsrF69WgYoC1FD3bp1i5UrV5KUlISiKPTo0YOGDRtaOiw9RVFo2bIlKSkpDBo0iLZt21abKe9CiOqtQsnOwoULSz2elpZGVFQUGzduZNOmTUYJTAhhPgUFBSxfvpyMjAycnZ0ZO3asxRMdnU5Heno6bm5u+mNdu3alffv2ODg4WC4wIUSNU6Fk5+OPPy71uIuLCy1atGDPnj2EhoYaJTAhhPlYW1szbNgwoqKiGDNmTIU2xszOzjZ68nHz5k02btxIdnY2zz77LDY2NkDRo3JJdIQQFVWhZCc2NtZUcQghzKygoMDgdVBQEC1atKjQo6ENGzYwbdo0li1bxqBBg4wS05YtWzh27BgAdnZ2JCYm0rhx4yqXLYR4eFVpBeWkpCTS09ONFYsQwgxUVeXo0aMsWrSIrKwsg3PlTXTS0tJ46qmnGDlyJPHx8Xz44YdGic3KyorExESgaD+r2bNnS6IjhKiyCic7qampzJo1C09PTxo0aIC7uzteXl689tpraLVaU8QohDCSnJwcVq1aRUREBKmpqZw+fbpS5axYsYKlS5eiKAovvfQSa9euNUp8iqIwYsQInnzyScaOHUudOnWMUq4Q4uFWocdYKSkphIaGcuPGDSZNmkTLli0BOHv2LJ9++inbtm1j3759nDp1ikOHDjFnzhyTBC2EqJxt27Zx9uxZNBoNAwcOJCgoiD179lS4nKlTp3LkyBGmTJlS6RXT8/Ly2L17NzqdjrCwMP3x+vXrV6o8IYQoS4WSnXfffRdbW1tiYmJo0KBBiXODBw/miSeeYOvWrWXO3BJCWE7//v25ffs2gwcPpnHjxqSmplaqHI1GU+kFRFVV5fz582zevJn09HQURaFz586yxYMQwmQqlOysWbOGr776qkSiA+Dl5cWCBQsYNmwYb7/9NpMnTzZakEKIysnPz9fPZAJwcnLiqaeesuj6NJmZmaxevZqCggLc3NwYMmSIJDpCCJOqULITHx9Pq1atyjzfunVrNBrNA/fQEkKY3vXr11m1ahWDBw82WOzT0gvxOTs707dvX3Jzc+nVq5dBMiaEEKZQoWTH09OTK1eulDk7IjY2Vp63C2Fhqqpy8OBBIiMj0el07Nu3j5YtW1osyYmJicHGxgZfX1/9scqO8xFCiMqo0GyssLAw/va3v5GXl1fiXG5uLm+++SZDhgwxWnBCiIq7dOkS27ZtQ6fT0apVKyZPnlzuRCc6OpoePXoQFxdX5TjS09NZtWoVy5cvZ/369RQWFla5TCGEqIwKD1Du1KkTgYGBzJo1i6CgIFRV5dy5c3z++efk5uby3XffmSpWIUQ5NGvWjPbt2+Pt7U3Hjh3Llejk5+czb9483nvvPQoKCnjllVf45ZdfKh1DYmIi33zzDXl5eSiKQtOmTQ12UhdCCHOqULLTuHFjDh48yMyZM3nttddQVRUoGgMwaNAgPvvsM4OuaiGE6amqSkFBgX7si6IojBo1qkJlfPzxx8yfPx+AcePG8dlnn1Uppnr16uHp6YlGo2H48OF4eXlVqTwhhKiKCu96HhAQwKZNm7hz5w4XL14Eiv6SlNkUQphfVlYWv/32G/b29oSHh1d6XM706dPZtGkTr776KhMmTKhwOVqtFmtra2xtbYGiqemPPfYYjo6OFh8QLYQQFU52irm7u9OlSxdjxiKEqIArV67w66+/kpmZibW1NcnJyXh6elaqLFdXV6KjoyucmKiqyvHjx9m+fTvt27c32B+rIpuJCiFqL91/nwIV/6+xwB9AlU52hBCWk5eXx8qVK9FqtdSrV49x48ZVOtEpVtFE5/bt26xbt47r168DRbMxZVyOEKJY8VCXJXsvc/6ODj8PRx7v5keAp5PZe3wl2RGiBrK1tWXkyJFcuHCBoUOH6h8fmZOiKNy8eRNbW1v69etHly5d0GiqtLewEKKWUFWV9adu4gqsPRnP9ayi5ObbA1d4a0QwU7r7mzXhkWRHiBoiNzcXOzs7/eugoCCCgoIsFo+npydjx47Fx8cHFxcXi8UhhKhedKrKlaQsvt4Ty0shhudUFd7dcJY+zevh7+lktkda8meYENWcTqdjx44dLFq0iMzMzArde+3aNWbMmEF2dnaVYkhKSuL777/n2rVrBsdbtWoliY4QooTlh+JQyzinqkXnzUl6doSoxtLT01m9erV+kb+zZ8+Wa2KAqqp89913PP/886SlpeHi4sKCBQsqXH9+fj579+5l//796HQ68vLyePrpp2WGlRCiTBpFIS5Fe99rrqZozTpQWXp2hKjGtm/fTlxcHLa2toSHh5d7BuTrr7/OlClTSEtLo2vXrkydOrVS9Z84cYK9e/ei0+kIDAxk7NixkugIIe5Lp6r4eTje9xpfD0f97CxzkJ4dIaqxsLAwcnJyCAsLo27duuW+74knnuDzzz/ntdde46WXXsLaunLf6h06dODixYt06NCBFi1aSKIjhCiXx7v5se1EbKnnFKXovDlJsiNENZKTk4O9vb3+tZOTE4899liFywkODiYuLg43N7dy31NYWMixY8fo0KGDfjVmKyurStUvhHh4aRSFAE8nnukdAKmXDM4pCrw1Itjs088l2RGimrhw4QJr1qxh5MiRBAcHV7m8iiQ6165dY8+ePSQlJaHVaunXr1+V6xdCPLwURWFkiDd79lxidNuGXLijw1fW2RHi4VVYWMj27ds5dOgQAFFRUbRs2dJsPwwSExM5ceIEUNSTVNXFCYUQAv63UOnUXk3wcHfXj9GxxONwSXaEsLA//vhDn+h069aNgQMHmvWHgYuLC7du3aJDhw7079/f4DGaEEJUVfGsK0tsE1FMkh0hLCwoKIjOnTvTtGlTWrRocd9rMzIy+OGHH5g+fXqlE6K0tDRcXV31rx0cHHjqqado1KhRpcoTQojqTpIdIcysoKAAnU6n3+JBURSGDRv2wPt27tzJ008/zZUrV3B2dmbSpEkVqjc7O5vt27dz4sQJpk6dire3t/6cbNophKjNJNkRwoxSUlJYtWoVHh4ehIeHl7t35u233+bdd98FwN/fn8aNG1eo3lOnTrFlyxa02qKFvmJiYgySHSGEqM0k2RHCTE6fPs369evJy8sjNTWV9PR0g8dJ99OmTRsApk2bxocffoizs3OF6k5LS9PvkD58+HD8/My7xoUQQliSJDtCmEF2djYbN24kLy8PX19fwsPDK7Sn1Lhx4zh+/Djt2rWrVP2hoaE4ODjQvn17rKysKlWGEELUVJLsCGEGDg4OjB49mps3b9K3b180morv1FKeREdVVc6ePUtUVBSTJk3SJzbW1tZ06tSpwnUKIURtIMmOECaSlZVlMPA3KCiIoKAgk9WXnJzMpk2biImJAeDYsWN07drVZPUJIURNIRuBCmFkeXl5rF27li+//JLMzEyz1btu3TpiYmKwsrKid+/edOjQwWx1CyFEdSY9O0IY0a1bt1i1ahW3b98GIDY2Vj+4uDQFBQUcPnyYHj16VLnusLAwdu7cyZAhQyq0aagQQtR20rMjhBHt2rWL27dvU6dOHZ588sn7Jjrnz5+nR48e9OvXj5MnT1aonrS0NE6dOmVwzNvbm0mTJkmiI4QQ95CeHSGMaPjw4djY2DB48OD7LtS3cOFCXn31VXJycnB1deXq1au0bdv2geUXFhZy6NAhdu/eTUFBAV5eXtSvX9+YTRBCiFpHkh0hqiAjI8NgzRsnJyceeeSRB96XkJBATk4OYWFhLF68uFyLBOp0OpYsWUJ8fDwAPj4+FtlQTwghahpJdoSoBFVViYqKYsuWLTzyyCMEBwdX6P63336bNm3aMHHixHInLBqNhmbNmpGWlsagQYNo27atJDtCCFEOkuwIUUG5ubmsX7+eM2fOAEVjbyqa7NjZ2fHoo4/e9xqdTkdWVpZBz1GvXr30CwQKIYQoH0l2hKigP/74gzNnzqDRaBgwYAChoaFGr+PmzZts3LiRwsJCpk2bpl+E0MbGBhsbG6PXJ4QQtZkkO0JUUOvWrUlISKBly5YV3pDzQfLy8ti+fTtHjx4FinqAbt26hZeXl1HrEUKIh4kkO0I8QEFBAbm5ufrXiqIwaNCgUq9VVZWEhAQaNmxYqbqsra25evUqULT55+DBg6lTp06lyhJCCFFEkh0h7iMhIYE//viDvLy8Bw4mvnXrFs8++ywHDx7k9OnTeHh4VLg+jUbDyJEjycvLIyAgoCqhCyGE+C9ZVFCIUqiqysGDB1m5ciV5eXkkJiai1WrLvH716tW0bt2a1atXc/v2bfbs2fPAOoofWe3evdvgeKNGjSTREUIII5KeHSFKkZWVxZ49e9DpdLi6uvLoo4+WuUigqqosXryY27dv07p1a7777jvat29fZtmqqnLhwgU2bdpEeno6VlZWtG/fHhcXF1M1RwghHmqS7AhRijp16jBmzBgSExNJSUnBzs6uzGsVReHrr7/mP//5D6+//vp9rwVITU1lxYoVqKqKm5sbQ4YMkURHCCFMSJIdISjqbUlLS8PNzU1/rEWLFjRo0KDEY6bSNGrUiHnz5pWrLnd3d3r06IGiKPTq1UumkgshhIlJsiMeellZWaxZs4aEhASmT59u9NlPMTExODk5GUwfHzBggFHrEEIIUbZqPUD5nXfeQVEUg39BQUH68zk5OcyaNYu6detSp04dwsPDSUxMtGDEoqaJi4vjq6++4tKlS+Tk5Oj3nTKGjIwMVq1axfLly1m/fj06nc5oZQshhCi/at+z06pVK7Zv365/bW39v5BffPFFNm7cyMqVK3F1dWX27NmMHTuW/fv3WyJUUQMdOHCAjIwMPD09GTduHA0aNChxTWFhYYXLvXHjBt999x15eXkoioKPjw+FhYX6lZCFEEKYT7VPdqytrUtdPTYtLY0lS5bw448/0r9/fwC+/fZbWrZsyaFDh+jWrZu5QxU10KhRo9i7dy/9+/fH1tbW4FxOTg5vvvkmO3fupE+fPhUq18vLCxcXF+zt7Rk+fLisgCyEEBZU7ZOdixcv4u3tjb29PaGhocyfPx9fX1+ioqLIz89n4MCB+muDgoLw9fXl4MGD9012cnNzDVbETU9PB4pmyRjzUUNGRobB/7VNTWzfnTt3cHd3NzjWrVs3tFqtwTo6UVFRzJw5kz/++AOAiIgIRo4cWWa52dnZ2NjYGPQ8jh49GicnJxRFITU11bgNMZKa+B5WhLSvZpP21WzmaF/x7+8HUVRVVU0WRRVt2rSJzMxMWrRoQXx8PPPmzePGjRucPn2a9evX89RTTxkkLQBdunShX79+/POf/yyz3HfeeafUmTM//vgjjo6ORm+HsLzibRwSExPx9/c3mHV1r8LCQp577jlu3ryJu7s7M2fOpHPnzmWWm5KSws2bN6lfv36pj8GEEEKYhlar5bHHHiMtLe2+S3hU656doUOH6j8OCQmha9eu+Pn5sWLFChwcHCpd7muvvcbcuXP1r9PT0/Hx8aFHjx5GXe8kIyOD6OhoOnTogLOzs9HKrS5qSvsyMzPZvHmzfvC6u7s7vXv3vu89//nPf1i6dClvvvkmsbGxpbYxOTmZ7du3k5CQABQlPr17977vlhLVTU15DytL2lezSftqNnO0r7w9O9U62bmXm5sbzZs359KlSwwaNIi8vDxSU1MN/kpPTEx84PgIOzu7Uhd+c3NzM8nibs7OzvftSajpqnv7rly5wo0bN7C1tWXEiBG0adPmgfcMHz6c4cOHk5qaSmxsbKlt1Gq1JCQkYGtrS79+/ejSpUuNHYBc3d/DqpL21WzSvprNlO0r78/cGpXsZGZmEhMTwxNPPEHHjh2xsbEhMjKS8PBwAC5cuMDVq1cJDQ21cKSiOmnbti2pqam0adOGunXrGq1cb29vRo8eTZMmTWQFZCGEqMaqdbLz0ksvMXLkSPz8/Lh58yZvv/02VlZWPProo7i6ujJ16lTmzp2Lh4cHLi4uPPfcc4SGhspMrIdcWloa9vb2+t47RVHo27dvlcq8c+cOGzZsYNCgQQbjctq1a1elcoUQQphetU52rl+/zqOPPkpycjL16tWjZ8+eHDp0iHr16gHw8ccfo9FoCA8PJzc3l7CwMD7//HMLRy0s6Y8//mDNmjU0adKE8PDwKo+fyc/PJz4+nlOnTqHT6VBVlSeeeMJI0QohhDCHap3s/Pzzz/c9b29vz6JFi1i0aJGZIhLVVWFhIdu3b+fQoUNAUU9MXl6ewdgsnU7H559/zrVr1+47W+9uJ0+e1A9sDgwMNBg0L4QQomao1smOEOWVmZnJiRMnAOjatSsDBw40WPMmLi6Op59+mh07dgAwbty4MqeT361du3acPHmSPn360LFjxxo100oIIUQRSXZEreDq6sojjzyCTqcz2D8Nihb869q1K4mJiTg6OrJgwQI6duxYoozCwkKOHz9Ohw4d9CP8ra2tadasGc2aNZNERwghaihJdkSNVFBQwJ07d/TjtwCaN29e6rUODg68/vrrrFixgqVLl9KsWbMS18TGxhIREUFSUhL5+fkyo08IIWoRSXZEjZOSksKqVavIyMhgxowZODk5PfCe2bNnM2vWLKysrEqc27VrF7t37wbAycmpVi7uJYQQDzNJdkSNcubMGdatW0deXh4ODg6kpKSUK9m538JTTZs2Zc+ePXTs2JH+/ftXaXVuIYQQ1Y8kO6LGUFWVEydOkJeXh4+PD+PGjavUYn4ZGRkGvTc+Pj7MmTOnVq9gKoQQDzNJdkSNoSgKY8aMISoqip49e1Z4a4bs7Gy2b9/OqVOnmD59Op6envpzkugIIUTtJcmOqNZu3LhBo0aN9K+dnJwMNvHcsGEDWVlZTJgw4b7lnDx5kq1bt6LVagG4ePGiQbIjhBCi9pJkR1RL+fn5bNq0iePHjzN+/HiCg4MNzqelpfHCCy+wdOlSXFxc6N69Oz4+PmWWd+vWLbRaLfXq1WP48OH4+fmZuglCCCGqCUl2RLVz+/ZtVq5cye3bt4Gi1ZDvlpKSQrt27bh27RqKovDMM888sJemT58+uLi40KlTp1JnZAkhhKi9JNkR1c7169e5ffs2derUYezYsQQEBBic9/DwoH///uzbt4+lS5fSs2dP/TlVVTl37hy///4748eP14/rsbW1pWvXrmZthxBCiOpBkh1R7bRr147s7GxCQkKoU6dOqdcsXLgQKysrg2nnKSkpREREEBMTA8CpU6dkV3IhhBCS7AjLu3XrFq6urvpNOxVFoXv37ve9594p56qqsnLlShISErCysqJnz560atXKZDELIYSoOSTZERajqirR0dFs3ryZFi1aEB4eXun9pxRFYfDgwezfv5+hQ4dSt25dI0crhBCippJkR1hEbm4uGzZs4PTp0/rXBQUF2NjYlOv+tLQ0bt68ScuWLfXHAgICSozvEUIIISTZERaRnZ3NpUuXUBSFAQMG0L17dxRF4fjx4yiKUuZYm8LCQg4fPsyuXbtQVZWZM2fi7u5u3uCFEELUKJLsCItwc3Nj7Nix2Nvb4+PjQ35+Pv/3f//He++9R2BgINHR0djb2xvcU1BQwOLFi0lMTASKtnnQ6XSWCF8IIUxCp6oAaBTF4GNRNZLsCLPIyckhNTUVLy8v/bHAwEAAbt68yciRI4mOjgYgODiYnJycEsmOtbU1vr6+ZGRkMGjQINq2bVvpMT5CCFHdqKrKlaQslh+KIy5Fi5+HI4938yPA00l+1lWRJDvC5G7cuMGqVasoKChg+vTpJaaT16tXD1VVcXd3Z9GiRUycOBFFUdDpdGRnZxtMLx8wYAB9+/bF0dHR3M0QQgiTUVWVpQeu8O6Gs/y3QweAbw9c4a0RwUzp7i8JTxVIsiNMRlVVDh8+zLZt29DpdLi5uaHVakskOzY2Nvzyyy/UqVOHhg0bAkW9PRs3bsTKyoqnnnpK/01ePD1dCCFqC52qEpuUVSLRAVBVeHfDWfo0r4e/p5M80qokSXaEScXExKDT6WjZsiWjRo0q8WiqWPEjrdzcXCIjIzl69ChQlNwkJyfLpp1CiFpt+aG4EolOMVUtOv/GiODSLxAPJMmOMBlFURgzZgznz5+nQ4cO5eqC1Wg0XLp0CYA2bdowePDgMldRFkKI2kCjKMSlaO97zdUUrfTqVIEkO8JoVFUlNjaWJk2a6I85OTnRsWPHcpdhY2PDqFGjUFVV1swRQjwUdKqKn8f9xyH6ejiiU1VJeCpJY+kARO2QlZXFjz/+yPfff8/Zs2f1x69fv05SUlKp9+Tl5bF9+3YOHz5scNzf318SHSHEQ+Xxbn6UlccoStF5UXmS7Igqu3HjBl999RWXLl3C2tqavLw8VFVl2bJltG7dmpkzZxpcr6oq58+f5/PPP2f//v3s2LEDrfb+XbhCCFFbaRSFAE8n3hoRXCLhURR4a0QwATI4uUrkMZaosjt37pCRkUHdunUZP368fqzOunXrAIiLiyMjIwNnZ2cAkpKS+OWXX4CixQWHDBkiU8mFEA81RVGY0t2fPs3rsfxQHFdTtPjKOjtGI8mOqLJWrVphb29PSEgItra2JCQksG/fPmxsbJg3bx4vv/wy1tb/+1KrV68enTp1wsHBgV69epV7PywhhKjNFEXB39OJN0YEG6ygLIlO1UmyIyosLi7OYCVkRVHo1KmT/rWXlxc//vgjDRs2JCQkhJiYGNzc3Ax2Ih82bJh8AwshxD3uflQlj62MR5IdUW46nY7du3ezZ88eWrVqxYABA8q8NiwsjIyMDFatWsWZM2cICAjgiSee0Cc4kugIIYQwF0l2RLlkZGSwevVqrly5AoCtre19N+GMi4vjxx9/JC8vD0VRqFevHjqdDisrKzNFLIQQQhSRZEeUS2FhIQkJCdjY2DBixAhCQkJITU0t8/qGDRtib29PvXr1GD58uH4bCCGEEMLcJNkR5eLm5sb48eOxtrbG29u7xHmtVou9vT0aTdFqBra2tkyZMgU3Nzd5ZCWEEMKiZJ0dUar09HRu3LhhcCwuLo4+ffrwwQcf6I+pqsrp06f57LPPiIqKMrje3d1dEh0hhBAWJ8mOKOHixYt8+eWX/Pzzz2RmZqLVapkzZw79+/fnypUrLF26lLy8PJKSkrh48SKRkZFkZ2dz5swZ1LJ2shNCCCEsRB5jCT2dTkdkZCQHDhwAisbdFBQU8Mcff7Bo0SIApk2bxocffoitrS3Z2dlotVpsbGzo168fXbt2lZ4cIYQQ1Y4kO0JPURRu3boFQJcuXRg0aBDW1tZ06dKFf/7zn7Ru3ZohQ4bor/fx8aFRo0YMGTKExo0bWypsIYQQ4r4k2RF6xds8XLt2jaCgIINzU6ZMYevWrdy5cwd3d3f98Xr16lGnTh1zhyqEEEKUmyQ7D7HiR1TBwcH6Y05OTgaJTn5+Pnv37mX//v3odDqsra3505/+ZIlwhRBCiEqRAcoPqTt37vDNN9+wcuVKzp49W+Z1+/btY+/eveh0OgIDAxk0aJAZoxRCCCGqTnp2HkLnzp1j7dq15Obm4uDgcN+NOENDQ4mJiaFHjx4EBQXJAGQhhBA1jvTsPISys7PJzc3F09OTTZs2ERMTAxStknzixAmD6eP29vZMnTqVli1bSqIjhBCiRpKenYdQ27Zt2b59O6+88gparZbbt2/TvHlzNm3aRFJSEqqq0r59e/31kuQIIYSoySTZeQicO3eOJk2aYGdnB8CaNWt49dVXARg8eDDPPvss33//PVA0QLn4OiGEEKI2kGSnFsvPz2fTpk0cP36cVq1aER4ejqIojB07lrFjxxIWFsYzzzzDpUuXOHnyJJ07d6Z///7Y29tbOnQhhBDCaCTZqaWSk5NZsWKFfpHAunXr6s9lZ2fz66+/6l8HBgby3HPP4eHhYfY4hRBCCFOTZKeWsrKyIj09HScnJ8aOHUuTJk3Izs4mMjKS33//nVmzZuHi4qK/XhIdIYQQtZUkO7WUm5sbEydOpG7dutSpU4cTJ06wbds2tFotABcuXKBz584WjlIIIYQwPUl2aolbt26Rk5ODr6+v/pifn5/+42vXrqHVaqlXrx7Dhg3D39/fAlEKIYQQ5ifJTg2nqirHjx9n06ZN2NracvbsWd58802aNGlicN2AAQPw9PSkS5cuWFlZWShaIYQQwvwk2anB8vPzWb9+Pb///jsAMTExrFixguzsbCZMmMCYMWP0a+Q4OjoSGhpqyXCFEEIIi5BkpwaztrYmOzsbgG3btnHu3DmefPJJvLy8OHXqFC1atDDY5FMIIYR4GEmyU4MpisKYMWO4du0a33//PbNnz0aj0WBlZUWPHj0IDAy0dIhCCCGExUmyU4Pk5ORw/vx52rVrpz/m5OREUFAQv//+O1euXCEqKoqhQ4carKsjhBBCPMwk2akhbt68yapVq7hz5w62trY0btyYW7du0axZMwAcHBwICgqSncmFEEKIe0iyUwMcPXqUzZs3o9PpcHFxIS4ujjVr1qDRaJg9ezZ16tQBZMNOIYQQojQaSwcgHszKygqdTkfz5s2xs7PjyJEj5OfnU79+ffLy8iwdnhBCCFGtSc9ONZaTk8NHH33E888/j4uLC02bNmXNmjVkZmYyaNAg2rVrJ705QgghxANIslPNqKpKVFQUeXl5PPPMM1y+fJlbt27xySefABAWFkZYWBiOjo6WDVQIIYSoISTZqUa0Wi1r1qzh4sWLxMTE0KNHD7p27Ur//v3110iSI4QQQlSMJDvVxM2bN/n555/JyMgAoEmTJiiKgq2tLb1797ZwdEIIIUTNJcmOCel0OgC2b99O48aN6dy5c5n7Ujk6OpKfn4+7uzu5ublotVratGnDoEGDcHZ2NmfYQgghRK0iyY4JnDp1iokTJ5Kbm8tHH33Eq6++yuXLlwFo06YNy5cvJyQkxOAeNzc3Hn/8cerVq8eVK1ewsbEhICDAEuELIYQQtYpMPTeygQMH0rZtW86dO4e3tzcAzzzzjD65+f3332nbti0zZszg0qVLbN++nRMnTgDQqFEjbG1tad68uSQ6QgghhJFIsmNEdevWJTIykp9++on8ggLWrV0LwIwZMzhx4ji6wgIOHTpEnz59aNCgAcuXL2f//v1s3bqV3NxcC0cvhBBC1E7yGMtIgoKCSElJITY2Fj8/P1B1ULwEjqpDKchDm57Cvn376Nevn/4+BwcHRo8ejZ2dnWUCF0IIIWq5WtOzs2jRIvz9/bG3t6dr164cOXLEbHVv2rSJCxcu8NNPPxUlOoCSEoNycFHRBQcXEXf6ICP/9ATHjx9HURTc3NzYs2cPP//8My1atDBbrEIIIcTDplYkO7/88gtz587l7bffJjo6mrZt2xIWFsatW7fMUv+ECRMAGDduHADK4S9hURf4fSUAy3/dSPdBY9ixYwebNm3C060Oz82exY4dOzh+/DiFhYVmiVMIIYR4GNWKZOejjz7imWee4amnniI4OJgvv/wSR0dHvvnmG5PXnZ+fT0ZGBv7+/lhpFEi+CFteA1UlUefKhQsXSPYdSs8BQ+juY8WhiJ+YOXMWikajH7QcFRVl8jiFEEKIh1WNH7OTl5dHVFQUr732mv6YRqNh4MCBHDx4sNR7cnNzDQYEp6enA5CamqpfG6e8tm7dSpMmTZg4cSKpaWkoR34Be1+O5jfnQF5rIBtQ6d4IHpvREpvkw6RmdAJFw/Dhw8nMzOTq1as0b968wm23tOIFEIv/r41qexulfTWbtK9mk/ZVXfHv7wdRVFVVTRaFGdy8eZNGjRpx4MABQkND9cdfeeUVdu/ezeHDh0vc88477zBv3rwSx3/88UejbMcQHx9PYmIiAIqi4O/vj6ura5XLFUIIIcT/aLVaHnvsMdLS0nBxcSnzuhrfs1MZr732GnPnztW/Tk9Px8fHhx49etz3k1WarVu38tprrxEUFMT333+H5tDnnE4+SiIdaWJzC6egAXS++hXO8fFFN7QZjxo6C1To1LkzAEePHkWjqXlPFDMyMoiOjqZDhw61dpXn2t5GaV/NJu2r2aR9VVfenp0an+x4enpiZWWl70kplpiYiJeXV6n32NnZlTrV283NrcLJzpgxY3j//fdp3bo1169dp02XCfQ4+i/81QvUsbJjt9VgnHPjccuOA0WBLhNQ3dw4evQYly9fJjg4GA8PjwrVWd04Ozvj5uZm6TBMqra3UdpXs0n7ajZpX+WVt6Og5nUn3MPW1paOHTsSGRmpP6bT6YiMjDR4rGUKiYmJfP3114wePRpHR0f27tsHdQNRwubTWDFMvlAUGPI+at1ACgt1dO3aFYCffvrJpDEKIYQQD7san+wAzJ07l6+//pply5Zx7tw5nn32WbKysnjqqadMVmdaWhrvvfceSUlJQNHYoTlz5pCVlYXadQbqrCPQZnzRxW3Go846gtplOjqdDhsbG6Boa4l798gSQgghhHHVimRnwoQJfPjhh7z11lu0a9eOEydOsHnzZho0aGCS+rZv387gwYOpU6eO/ti1a9fQ6XQ4Oztz9OhR8GhaNDYHiv73aMrRo0exti56cujh4cG2bdtMEp8QQggh/qdWJDsAs2fPJi4ujtzcXA4fPqx/TGQsSUlJ/PLLL2RmZrJnzx4uXLiAoii4urrywgsvEBERQfv27QHo2rUrGitrHn30MQAeffQxNFbW+piCg4NJTk42anxCCCGEKF2NH6Bsavn5+ezdu5f9+/ej0+mwt7fnjTfewN7ensceewwfHx+srKwAiI6OZtOmTUycOJH09HQuXrwIoP/fxcWFVatWMWjQIIu1RwghhHjYSLLzADt27ODQoUMANGrUiN69e2Nra8vrr79e6vVDhw4lLS2N/Px81q1bB8CCBQsYNWqUfqyOEEIIIcyn1jzGMpUuXbrg4OAAFK2wbGtrW677bGxsGDBgAAADBgyQREcIIYSwEEl27lJYWMjp06cpXlQ6KyuLX375hezsbAA6dOigT3yEEEIIUTPIY6y7LFmyhKysLKysrGjZsiWOjo44OzuTmZnJ2LFjadKkiaVDFEIIIUQFSbJzl+TkZFRV1W9apigKY8aMQVVVg2nmQgghhKg5JNm5y9mzZ0lLS8PLy4suXboA4OTkZOGohBBCCFEVkuzcpXnz5tSpU4ewsDBLhyKEEEIII5EBynextramadOm+Pr6WjoUIYQQQhiJ9OyAfvZVp06dGDhwIIWFheXeNv5+0tPT0Wq1pKenl3tn1pqktrcPan8bpX01m7SvZpP2GacO+N/v8bIo6oOueAhcv34dHx8fS4chhBBCiEq4du0ajRs3LvO8JDuATqfj5s2bODs7oyiK0cpNT0/Hx8eHa9eu4eLiYrRyq4va3j6o/W2U9tVs0r6aTdpXdcUzqL29ve/beySPsQCNRnPfjLCqXFxcauUXcrHa3j6o/W2U9tVs0r6aTdpXNa6urg+8pvY9JBRCCCGEuIskO0IIIYSo1STZMSE7Ozvefvtt7OzsLB2KSdT29kHtb6O0r2aT9tVs0j7zkQHKQgghhKjVpGdHCCGEELWaJDtCCCGEqNUk2RFCCCFErSbJjhBCCCFqNUl2TGjRokX4+/tjb29P165dOXLkiKVDqpT58+fTuXNnnJ2dqV+/PmPGjOHChQsG1/Tt2xdFUQz+zZgxw0IRV8w777xTIvagoCD9+ZycHGbNmkXdunWpU6cO4eHhJCYmWjDiivH39y/RPkVRmDVrFlDz3rs9e/YwcuRIvL29URSFNWvWGJxXVZW33nqLhg0b4uDgwMCBA7l48aLBNSkpKUyaNAkXFxfc3NyYOnUqmZmZZmxF2e7Xvvz8fF599VXatGmDk5MT3t7ePPnkk9y8edOgjNLe8/fff9/MLSnbg97DKVOmlIh/yJAhBtfU1PcQKPX7UVEUPvjgA/011fU9LM/vg/L8zLx69SrDhw/H0dGR+vXr8/LLL1NQUGCyuCXZMZFffvmFuXPn8vbbbxMdHU3btm0JCwvj1q1blg6twnbv3s2sWbM4dOgQ27ZtIz8/n8GDB5OVlWVw3TPPPEN8fLz+34IFCywUccW1atXKIPZ9+/bpz7344ousX7+elStXsnv3bm7evMnYsWMtGG3FHD161KBt27ZtA2D8+PH6a2rSe5eVlUXbtm1ZtGhRqecXLFjAwoUL+fLLLzl8+DBOTk6EhYWRk5Ojv2bSpEmcOXOGbdu2sWHDBvbs2cO0adPM1YT7ul/7tFot0dHRvPnmm0RHR7N69WouXLjAqFGjSlz77rvvGrynzz33nDnCL5cHvYcAQ4YMMYj/p59+MjhfU99DwKBd8fHxfPPNNyiKQnh4uMF11fE9LM/vgwf9zCwsLGT48OHk5eVx4MABli1bxtKlS3nrrbdMF7gqTKJLly7qrFmz9K8LCwtVb29vdf78+RaMyjhu3bqlAuru3bv1x/r06aM+//zzlguqCt5++221bdu2pZ5LTU1VbWxs1JUrV+qPnTt3TgXUgwcPmilC43r++efVpk2bqjqdTlXVmv3eAepvv/2mf63T6VQvLy/1gw8+0B9LTU1V7ezs1J9++klVVVU9e/asCqhHjx7VX7Np0yZVURT1xo0bZou9PO5tX2mOHDmiAmpcXJz+mJ+fn/rxxx+bNjgjKa2NkydPVkePHl3mPbXtPRw9erTav39/g2M15T289/dBeX5mRkREqBqNRk1ISNBf88UXX6guLi5qbm6uSeKUnh0TyMvLIyoqioEDB+qPaTQaBg4cyMGDBy0YmXGkpaUB4OHhYXD8hx9+wNPTk9atW/Paa6+h1WotEV6lXLx4EW9vb5o0acKkSZO4evUqAFFRUeTn5xu8l0FBQfj6+tbI9zIvL4/ly5fz9NNPG2x6W5Pfu7vFxsaSkJBg8H65urrStWtX/ft18OBB3Nzc6NSpk/6agQMHotFoOHz4sNljrqq0tDQURcHNzc3g+Pvvv0/dunVp3749H3zwgUkfEZjCrl27qF+/Pi1atODZZ58lOTlZf642vYeJiYls3LiRqVOnljhXE97De38flOdn5sGDB2nTpg0NGjTQXxMWFkZ6ejpnzpwxSZyyEagJJCUlUVhYaPBGAjRo0IDz589bKCrj0Ol0vPDCC/To0YPWrVvrjz/22GP4+fnh7e3NqVOnePXVV7lw4QKrV6+2YLTl07VrV5YuXUqLFi2Ij49n3rx59OrVi9OnT5OQkICtrW2JXyQNGjQgISHBMgFXwZo1a0hNTWXKlCn6YzX5vbtX8XtS2vde8bmEhATq169vcN7a2hoPD48a957m5OTw6quv8uijjxpstDhnzhw6dOiAh4cHBw4c4LXXXiM+Pp6PPvrIgtGW35AhQxg7diwBAQHExMTw+uuvM3ToUA4ePIiVlVWteg+XLVuGs7NziUfjNeE9LO33QXl+ZiYkJJT6PVp8zhQk2REVMmvWLE6fPm0wpgUweFbepk0bGjZsyIABA4iJiaFp06bmDrNChg4dqv84JCSErl274ufnx4oVK3BwcLBgZMa3ZMkShg4dire3t/5YTX7vHmb5+fn86U9/QlVVvvjiC4Nzc+fO1X8cEhKCra0t06dPZ/78+dVi6f4HmThxov7jNm3aEBISQtOmTdm1axcDBgywYGTG98033zBp0iTs7e0NjteE97Cs3wfVkTzGMgFPT0+srKxKjD5PTEzEy8vLQlFV3ezZs9mwYQM7d+6kcePG9722a9euAFy6dMkcoRmVm5sbzZs359KlS3h5eZGXl0dqaqrBNTXxvYyLi2P79u38+c9/vu91Nfm9K35P7ve95+XlVWKiQEFBASkpKTXmPS1OdOLi4ti2bZtBr05punbtSkFBAVeuXDFPgEbWpEkTPD099V+TteE9BNi7dy8XLlx44PckVL/3sKzfB+X5menl5VXq92jxOVOQZMcEbG1t6dixI5GRkfpjOp2OyMhIQkNDLRhZ5aiqyuzZs/ntt9/YsWMHAQEBD7znxIkTADRs2NDE0RlfZmYmMTExNGzYkI4dO2JjY2PwXl64cIGrV6/WuPfy22+/pX79+gwfPvy+19Xk9y4gIAAvLy+D9ys9PZ3Dhw/r36/Q0FBSU1OJiorSX7Njxw50Op0+0avOihOdixcvsn37durWrfvAe06cOIFGoynx6KemuH79OsnJyfqvyZr+HhZbsmQJHTt2pG3btg+8trq8hw/6fVCen5mhoaH8/vvvBglrcdIeHBxsssCFCfz888+qnZ2dunTpUvXs2bPqtGnTVDc3N4PR5zXFs88+q7q6uqq7du1S4+Pj9f+0Wq2qqqp66dIl9d1331WPHTumxsbGqmvXrlWbNGmi9u7d28KRl89f/vIXddeuXWpsbKy6f/9+deDAgaqnp6d669YtVVVVdcaMGaqvr6+6Y8cO9dixY2poaKgaGhpq4agrprCwUPX19VVfffVVg+M18b3LyMhQjx8/rh4/flwF1I8++kg9fvy4fjbS+++/r7q5ualr165VT506pY4ePVoNCAhQs7Oz9WUMGTJEbd++vXr48GF13759amBgoProo49aqkkG7te+vLw8ddSoUWrjxo3VEydOGHw/Fs9iOXDggPrxxx+rJ06cUGNiYtTly5er9erVU5988kkLt+x/7tfGjIwM9aWXXlIPHjyoxsbGqtu3b1c7dOigBgYGqjk5Ofoyaup7WCwtLU11dHRUv/jiixL3V+f38EG/D1T1wT8zCwoK1NatW6uDBw9WT5w4oW7evFmtV6+e+tprr5ksbkl2TOjTTz9VfX19VVtbW7VLly7qoUOHLB1SpQCl/vv2229VVVXVq1evqr1791Y9PDxUOzs7tVmzZurLL7+spqWlWTbwcpowYYLasGFD1dbWVm3UqJE6YcIE9dKlS/rz2dnZ6syZM1V3d3fV0dFRfeSRR9T4+HgLRlxxW7ZsUQH1woULBsdr4nu3c+fOUr8eJ0+erKpq0fTzN998U23QoIFqZ2enDhgwoES7k5OT1UcffVStU6eO6uLioj711FNqRkaGBVpT0v3aFxsbW+b3486dO1VVVdWoqCi1a9euqqurq2pvb6+2bNlS/b//+z+DRMHS7tdGrVarDh48WK1Xr55qY2Oj+vn5qc8880yJPxRr6ntY7KuvvlIdHBzU1NTUEvdX5/fwQb8PVLV8PzOvXLmiDh06VHVwcFA9PT3Vv/zlL2p+fr7J4lb+G7wQQgghRK0kY3aEEEIIUatJsiOEEEKIWk2SHSGEEELUapLsCCGEEKJWk2RHCCGEELWaJDtCCCGEqNUk2RFCCCFErSbJjhBCCCFqNUl2hBDlsn//ftq0aYONjQ1jxoyxdDjV0q5du1AUpcQmiBV15coVFEXR71MmhKgaSXaEqOWmTJmCoigoioKNjQ0BAQG88sor5OTkVKicuXPn0q5dO2JjY1m6dKlpgrWgwsJC3n//fYKCgnBwcMDDw4OuXbuyePFik9Y7ZcqUEsmjj48P8fHxtG7d2qR1C/GwsLZ0AEII0xsyZAjffvst+fn5REVFMXnyZBRF4Z///Ge5y4iJiWHGjBk0bty40nHk5eVha2tb6ftNad68eXz11Vd89tlndOrUifT0dI4dO8adO3fMHouVlRVeXl5mr1eI2kp6doR4CNjZ2eHl5YWPjw9jxoxh4MCBbNu2TX9ep9Mxf/58AgICcHBwoG3btqxatQr43yOV5ORknn76aRRF0ffsnD59mqFDh1KnTh0aNGjAE088QVJSkr7cvn37Mnv2bF544QU8PT0JCwsr931z5szhlVdewcPDAy8vL9555x2DNqWmpjJ9+nQaNGiAvb09rVu3ZsOGDfrz+/bto1evXjg4OODj48OcOXPIysoq83O0bt06Zs6cyfjx4wkICKBt27ZMnTqVl156SX9Nbm4uc+bMoX79+tjb29OzZ0+OHj1aZpnvvPMO7dq1Mzj2ySef4O/vrz+/bNky1q5dq+9927VrV6mPsXbv3k2XLl2ws7OjYcOG/PWvf6WgoKBCnzMhHlaS7AjxkDl9+jQHDhww6GGZP38+3333HV9++SVnzpzhxRdf5PHHH2f37t36RyouLi588sknxMfHM2HCBFJTU+nfvz/t27fn2LFjbN68mcTERP70pz8Z1Lds2TJsbW3Zv38/X375ZYXuc3Jy4vDhwyxYsIB3331Xn6DpdDqGDh3K/v37Wb58OWfPnuX999/HysoKKOqFGjJkCOHh4Zw6dYpffvmFffv2MXv27DI/L15eXuzYsYPbt2+Xec0rr7zCr7/+yrJly4iOjqZZs2aEhYWRkpJS4fcB4KWXXuJPf/oTQ4YMIT4+nvj4eLp3717iuhs3bjBs2DA6d+7MyZMn+eKLL1iyZAnvvfeewXX3+5wJ8VAz2X7qQohqYfLkyaqVlZXq5OSk2tnZqYCq0WjUVatWqaqqqjk5Oaqjo6N64MABg/umTp2qPvroo/rXrq6u6rfffqt//fe//10dPHiwwT3Xrl1TAfXChQuqqqpqnz591Pbt2xtcU977evbsaXBN586d1VdffVVVVVXdsmWLqtFo9Nffa+rUqeq0adMMju3du1fVaDRqdnZ2qfecOXNGbdmyparRaNQ2bdqo06dPVyMiIvTnMzMzVRsbG/WHH37QH8vLy1O9vb3VBQsWqKqqqjt37lQB9c6dO6qqqurbb7+ttm3b1qCejz/+WPXz89O/njx5sjp69GiDa2JjY1VAPX78uKqqqvr666+rLVq0UHU6nf6aRYsWqXXq1FELCwtVVX3w50yIh5mM2RHiIdCvXz+++OILsrKy+Pjjj7G2tiY8PByAS5cuodVqGTRokME9eXl5tG/fvswyT548yc6dO6lTp06JczExMTRv3hyAjh07Vuq+kJAQg3MNGzbk1q1bAJw4cYLGjRvrry0ttlOnTvHDDz/oj6mqik6nIzY2lpYtW5a4Jzg4mNOnTxMVFcX+/fvZs2cPI0eOZMqUKSxevJiYmBjy8/Pp0aOH/h4bGxu6dOnCuXPnSo3DWM6dO0doaCiKouiP9ejRg8zMTK5fv46vry9w/8+ZEA8zSXaEeAg4OTnRrFkzAL755hvatm3LkiVLmDp1KpmZmQBs3LiRRo0aGdxnZ2dXZpmZmZmMHDmy1EHODRs2NKi7MvfZ2NgYnFMUBZ1OB4CDg0OZcRXXMX36dObMmVPiXHFiUBqNRkPnzp3p3LkzL7zwAsuXL+eJJ57gb3/7233ru195qqoaHMvPz69UWeVxv8+ZEA8zSXaEeMhoNBpef/115s6dy2OPPUZwcDB2dnZcvXqVPn36lLucDh068Ouvv+Lv74+1dfl/lFT2vruFhIRw/fp1/vjjj1J7dzp06MDZs2f1CV5lBQcHA5CVlUXTpk31Y4/8/PyAosTl6NGjvPDCC6XeX69ePRISElBVVd8rc+/aOba2thQWFt43jpYtW/Lrr78alLN//36cnZ2rNDtOiIeFDFAW4iE0fvx4rKysWLRoEc7Ozrz00ku8+OKLLFu2jJiYGKKjo/n0009ZtmxZmWXMmjWLlJQUHn30UY4ePUpMTAxbtmzhqaeeuu8v78red7c+ffrQu3dvwsPD2bZtG7GxsWzatInNmzcD8Oqrr3LgwAFmz57NiRMnuHjxImvXrr3vAOVx48bx8ccfc/jwYeLi4ti1axezZs2iefPmBAUF4eTkxLPPPsvLL7/M5s2bOXv2LM888wxarZapU6eWWmbfvn25ffs2CxYsICYmhkWLFrFp0yaDa/z9/Tl16hQXLlwgKSmp1J6fmTNncu3aNZ577jnOnz/P2rVrefvtt5k7dy4ajfwYF+JB5LtEiIeQtbU1s2fPZsGCBWRlZfH3v/+dN998k/nz59OyZUuGDBnCxo0bCQgIKLMMb29v9u/fT2FhIYMHD6ZNmza88MILuLm53fcXcGXvu9evv/5K586defTRRwkODuaVV17RJ0shISHs3r2bP/74g169etG+fXveeustvL29yywvLCyM9evXM3LkSJo3b87kyZMJCgpi69at+h6o999/n/DwcJ544gk6dOjApUuX2LJlC+7u7qWW2bJlSz7//HMWLVpE27ZtOXLkiMFUdoBnnnmGFi1a0KlTJ+rVq8f+/ftLlNOoUSMiIiI4cuQIbdu2ZcaMGUydOpU33nij3J8vIR5minrvA2UhhBBCiFpEenaEEEIIUatJsiOEEEKIWk2SHSGEEELUapLsCCGEEKJWk2RHCCGEELWaJDtCCCGEqNUk2RFCCCFErSbJjhBCCCFqNUl2hBBCCFGrSbIjhBBCiFpNkh0hhBBC1Gr/D5+TcoAqNCg5AAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -545,14 +559,249 @@ "plt.scatter(ref_values[:-1], traj[0], s=50, lw=1, edgecolors='w', label='Sampled solution')\n", "plt.scatter(ref_values[:-1], traj[-1], s=50, lw=1, edgecolors='w', label='Sampled solution')\n", "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 2)))\n", "plt.grid(which=\"major\", lw=1)\n", "plt.grid(which=\"minor\", lw=0.1)\n", + "\n", + "# plt.xscale('symlog')\n", + "# plt.yscale('symlog')\n", + "\n", "plt.xlabel('Reference Solution')\n", "plt.ylabel('QUBO Solution')" ] }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 0,\n", + " [1, 0, 1, 1, 1, 0, 1],\n", + " [1, 1, 1, 1, 0, 0, 0],\n", + " [1, 0, 1, 0, 0, 0, 1],\n", + " [1, 0, 0, 1, 0, 0, 0],\n", + " [0, 1, 0, 0, 1, 1, 0],\n", + " [1, 1, 1, 0, 1, 0, 0],\n", + " [1, 1, 1, 0, 0, 0, 0],\n", + " [0, 1, 1, 0, 0, 0, 0],\n", + " [0, 0, 1, 0, 1, 0, 1],\n", + " [0, 0, 1, 1, 0, 0, 1],\n", + " [0, 1, 0, 0, 1, 0, 1],\n", + " [0, 0, 1, 0, 0, 0, 1],\n", + " [1, 1, 1, 1, 0, 0, 1],\n", + " [0, 1, 0, 1, 0, 0, 1]]" + ] + }, + "execution_count": 228, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bin_rep_sol" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [], + "source": [ + "z = np.array(res.res)[net.qubo.index_variables]" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[1],\n", + " [1],\n", + " [1],\n", + " [1],\n", + " [1],\n", + " [1],\n", + " [1],\n", + " [0],\n", + " [1, 0, 0, 0, 1, 1, 1],\n", + " [0, 1, 1, 0, 1, 0, 0],\n", + " [1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 1, 0, 0, 0],\n", + " [0, 0, 1, 0, 1, 1, 0],\n", + " [0, 0, 1, 1, 1, 0, 0],\n", + " [0, 0, 1, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0],\n", + " [0, 1, 0, 0, 1, 0, 1],\n", + " [0, 1, 0, 0, 0, 0, 1],\n", + " [0, 1, 0, 1, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 1, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 1]]" + ] + }, + "execution_count": 230, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "count = 0\n", + "bin_rep_res = []\n", + "for r in net.qubo.mixed_solution_vectors.encoded_reals:\n", + " n = r.nqbit\n", + " bin_rep_res.append(list(z[count:count+n]))\n", + " count += n\n", + "bin_rep_res" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['x_001', 'x_002', 'x_003', 'x_004', 'x_005', 'x_006', 'x_007', 'x_008', 'x_009', 'x_010', 'x_011', 'x_012', 'x_013', 'x_014', 'x_015', 'x_016', 'x_017', 'x_018', 'x_019', 'x_020', 'x_021', 'x_022'], dtype=' 2\u001b[0m net\u001b[38;5;241m.\u001b[39mqubo\u001b[38;5;241m.\u001b[39mqubo_dict\u001b[38;5;241m.\u001b[39menergies(\u001b[43mas_samples\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/functools.py:877\u001b[0m, in \u001b[0;36msingledispatch..wrapper\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 873\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[1;32m 874\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfuncname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires at least \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 875\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1 positional argument\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 877\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/sampleset.py:434\u001b[0m, in \u001b[0;36m_as_samples_tuple\u001b[0;34m(samples_like, dtype, copy, order, labels_type)\u001b[0m\n\u001b[1;32m 431\u001b[0m arr\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m=\u001b[39m (arr\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mlen\u001b[39m(labels))\n\u001b[1;32m 433\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(labels) \u001b[38;5;241m!=\u001b[39m arr\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]:\n\u001b[0;32m--> 434\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msamples_like and labels dimensions do not match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 436\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr, labels\n", + "\u001b[0;31mValueError\u001b[0m: samples_like and labels dimensions do not match" + ] + } + ], + "source": [ + "s = np.random.randint(2, size=(2, 764))\n", + "net.qubo.qubo_dict.energies(as_samples((s, var_names)))" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'q'" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flow_encoding.var_base_name" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py index 75e3348..e8649ab 100644 --- a/wntr_quantum/sampler/simulated_annealing.py +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -106,14 +106,20 @@ def bqm_energy(x, var_names): trajectory = [] trajectory.append(x) + # step scheduling + step_schedule = ( + Tschedule / ((Tschedule[0] - Tschedule[-1]) / (take_step.step_size - 1)) + 1 + ) + # loop over the temp schedule - for T in tqdm(Tschedule): + for s, T in tqdm(zip(step_schedule, Tschedule)): # original point x_ori = deepcopy(x) e_ori = bqm_energy(x, var_names) # new point + # take_step.step_size = int(s) x_new = take_step(x) e_new = bqm_energy(x, var_names) @@ -132,5 +138,8 @@ def bqm_energy(x, var_names): trajectory.append(x) else: x = x_ori + energies.append(bqm_energy(x, var_names)) + if save_traj: + trajectory.append(x) return SimulatedAnnealingResults(x, energies, trajectory) diff --git a/wntr_quantum/sampler/simulated_annealing_parallel.py b/wntr_quantum/sampler/simulated_annealing_parallel.py new file mode 100644 index 0000000..8181fe7 --- /dev/null +++ b/wntr_quantum/sampler/simulated_annealing_parallel.py @@ -0,0 +1,139 @@ +from copy import deepcopy +from dataclasses import dataclass +import numpy as np +from dimod import as_samples +from tqdm import tqdm + + +def generate_random_valid_sample(qubo): + """Geenrate a random sample that respects quadratization. + + Args: + qubo (_type_): _description_ + """ + sample = {} + for iv, v in enumerate(sorted(qubo.qubo_dict.variables)): + sample[v] = np.random.randint(2) + + for v in qubo.mapped_variables[:7]: + sample[v] = 1 + sample[qubo.mapped_variables[7]] = 0 + + for v, _ in sample.items(): + if v not in qubo.mapped_variables: + var_tmp = v.split("*") + itmp = 0 + for vtmp in var_tmp: + if itmp == 0: + new_val = sample[vtmp] + itmp = 1 + else: + new_val *= sample[vtmp] + + sample[v] = new_val + return sample + + +@dataclass +class SimulatedAnnealingResults: + """Result of the simulated anneling.""" + + res: list + energies: list + trajectory: list + + +class SimulatedAnnealing: # noqa: D101 + + def __init__(self): # noqa: D107 + self.properties = {} + + def sample( + self, + bqm, + Tschedule, + num_traj=10, + x0=None, + take_step=None, + save_traj=False, + ): + """Sample the problem. + + Args: + bqm (_type_): _description_ + Tschedule (list): The temperature schedule + x0 (_type_, optional): _description_. Defaults to None. + num_traj(int, optional): number of parallel traj. Default to None + take_step (_type_, optional): _description_. Defaults to None. + save_traj (bool, optional): save the trajectory. Defaults to False + """ + + def bqm_energy(x, var_names): + """Compute the energy of a given binary array. + + Args: + x (_type_): _description_ + var_names (list): list of var names + """ + return bqm.energies(as_samples((x, var_names))) + + # check that take_step is callable + if not callable(take_step): + raise ValueError("take_step must be callable") + + # define th variable names + var_names = sorted(bqm.variables) + + # define the initial state + if x0 is None: + x = np.random.randint(2, size=(num_traj, bqm.num_variables)).tolist() + else: + x = x0 + + # initialize the energy + energies = [] + energies.append(bqm_energy(x, var_names)) + + # init the traj + trajectory = None + if save_traj: + trajectory = [] + trajectory.append(x) + + # step scheduling + step_schedule = ( + Tschedule / ((Tschedule[0] - Tschedule[-1]) / (take_step.step_size - 1)) + 1 + ) + + # loop over the temp schedule + for s, T in tqdm(zip(step_schedule, Tschedule)): + + # original point + x_ori = deepcopy(x) + e_ori = bqm_energy(x, var_names) + + # new point + # take_step.step_size = int(s) + x_new = take_step(x) + e_new = bqm_energy(x, var_names) + + # accept/reject + if e_new < e_ori: + x = x_new + energies.append(bqm_energy(x, var_names)) + if save_traj: + trajectory.append(x) + else: + p = np.exp(-(e_new - e_ori) / T) + if np.random.rand() < p: + x = x_new + energies.append(bqm_energy(x, var_names)) + if save_traj: + trajectory.append(x) + else: + x = x_ori + energies.append(bqm_energy(x, var_names)) + if save_traj: + trajectory.append(x) + + return SimulatedAnnealingResults(x, energies, trajectory) diff --git a/wntr_quantum/sampler/step/full_random.py b/wntr_quantum/sampler/step/full_random.py index 52f2e98..b70d7fa 100644 --- a/wntr_quantum/sampler/step/full_random.py +++ b/wntr_quantum/sampler/step/full_random.py @@ -22,7 +22,14 @@ def __call__(self, x): class IncrementalStep(BaseStep): - def __init__(self, var_names, single_var_names, single_var_index, step_size=1): + def __init__( + self, + var_names, + single_var_names, + single_var_index, + step_size=1, + optimize_values=None, + ): super().__init__(var_names, single_var_names, single_var_index) self.value_names = np.unique( @@ -34,6 +41,9 @@ def __init__(self, var_names, single_var_names, single_var_index, step_size=1): self.index_values[val].append(idx) self.step_size = step_size + self.optimize_values = optimize_values + if self.optimize_values is None: + self.optimize_values = list(np.arange(len(self.value_names))) @staticmethod def _get_variable_root_name(var_name): @@ -53,12 +63,10 @@ def __call__(self, x): Returns: _type_: _description_ """ - # extract the data of the variable we want to change - nmax = 16 - - random_val_name = np.random.choice(self.value_names[nmax:]) + random_val_name = np.random.choice(self.value_names[self.optimize_values]) idx = self.index_values[random_val_name] data = np.array(x)[idx] + width = len(data) # determine the max val max_val = np.ones_like(data) @@ -80,12 +88,16 @@ def __call__(self, x): sign = 2 * np.random.randint(2) - 1 # new value - new_val = val + sign * self.step_size + if self.step_size <= 1: + delta = 1 + else: + delta = np.random.randint(self.step_size) + new_val = val + sign * delta if new_val < 0: new_val = 0 if new_val > max_val: new_val = max_val - new_val = np.binary_repr(new_val) + new_val = np.binary_repr(new_val, width=width) # convert back to binary repr new_data = np.array([int(i) for i in new_val])[::-1] @@ -99,3 +111,27 @@ def __call__(self, x): self.fix_constraint(x, vidx) return x + + +class ParallelIncrementalStep(BaseStep): + + def __init__(self, var_names, single_var_names, single_var_index, step_size=1): + super().__init__(var_names, single_var_names, single_var_index) + self.step_size = step_size + self._step = IncrementalStep( + var_names, single_var_names, single_var_index, step_size=step_size + ) + + def __call__(self, x): + """Call function of the method. + + Args: + x (_type_): _description_ + + Returns: + _type_: _description_ + """ + new_x = [] + for xi in x: + new_x.append(self._step(xi)) + return new_x From ad89fd34cc301c1a2ab10ac7743e35bc02d540de Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 23 Oct 2024 11:58:48 +0200 Subject: [PATCH 72/96] sampler --- docs/notebooks/dw_approximation.ipynb | 79 ++- .../qubo_poly_solver_2loops_dw.ipynb | 637 +++++++++++------- wntr_quantum/sampler/simulated_annealing.py | 13 +- wntr_quantum/sampler/step/full_random.py | 12 + wntr_quantum/sim/models/darcy_weisbach_fit.py | 11 +- 5 files changed, 495 insertions(+), 257 deletions(-) diff --git a/docs/notebooks/dw_approximation.ipynb b/docs/notebooks/dw_approximation.ipynb index 934583e..d3d91ae 100644 --- a/docs/notebooks/dw_approximation.ipynb +++ b/docs/notebooks/dw_approximation.ipynb @@ -2,11 +2,86 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "from wntr_quantum.sim.models.darcy_weisbach_fit import * " + "from wntr_quantum.sim.models.darcy_weisbach_fit import * \n", + "import matplotlib.pyplot as plt \n", + "from matplotlib import ticker" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.4166666666666667\n", + "0.6666666666666666\n", + "0.9166666666666666\n", + "1.1666666666666667\n", + "1.4166666666666667\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "roughness = 0.5 * 1e-3\n", + "ndiams = 5\n", + "DIAMS = np.arange(5, 20, 3) / 12\n", + "\n", + "BASELINE = []\n", + "APPROX = []\n", + "for d in DIAMS:\n", + " print(d)\n", + " res, approx, baseline, qval = dw_fit(\n", + " roughness=roughness, diameter=d, plot=False, convert_to_us_unit=False, return_all_data=True\n", + " )\n", + " BASELINE.append(baseline)\n", + " APPROX.append(approx)\n", + "\n", + "n = 24\n", + "\n", + "colors = plt.cm.tab20(np.linspace(0, 1, n))\n", + "\n", + "fig = plt.figure(figsize = plt.figaspect(0.5))\n", + "ax1 = fig.add_subplot(111)\n", + "\n", + "i = 0\n", + "for bl, ap in zip(BASELINE, APPROX):\n", + " ax1.loglog(qval, bl, \"--\", lw = 3, c=colors[i])\n", + " ax1.loglog(qval, ap, \"-\", lw = 2, c=colors[i])\n", + " i += 1\n", + "\n", + "ax1.grid(visible=True, which=\"both\")\n", + "ax1.set_xlabel(\"Flow velocity (|q|)\", fontsize=18)\n", + "ax1.set_ylabel(\"Friction Factor\", fontsize=18)\n", + "# ax1.yaxis.set_major_formatter(ticker.LogFormatterMathtext(labelOnlyBase=True))\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(ticks=[1.6*1e-2, 1.7*1e-2, 1.8*1e-2, 1.9*1e-2, 2.0*1e-2, 2.1*1e-2], labels=['1.6', '', '1.8', '', '2.0', ''], fontsize=14)\n", + "ax1.annotate(r'$\\times$10$^{%i}$'%(-2), fontsize=12,\n", + " xy=(-.075, .96), xycoords='axes fraction')\n", + "\n", + "# yfmt = ticker.LogFormatterMathtext()\n", + "# # yfmt.set_powerlimits((3, 4))\n", + "# ax1.yaxis.set_major_formatter(yfmt)\n", + "# ax1.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n", + "# ax1.get_yaxis().get_offset_text().set_visible(False)\n", + "\n", + "plt.show()" ] }, { diff --git a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb index b7b6993..94c4626 100644 --- a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb +++ b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 1, "metadata": { "metadata": {} }, @@ -30,7 +30,7 @@ "" ] }, - "execution_count": 206, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -79,7 +79,7 @@ "" ] }, - "execution_count": 207, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -104,7 +104,7 @@ "array([ 3.111e-01, 5.111e-02, 2.322e-01, 3.108e-02, 1.678e-01, 7.613e-02, 2.334e-02, -2.058e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" ] }, - "execution_count": 208, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -134,15 +134,15 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 0.000000 => 1000.000000 (res: 7.874016)\n", - "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.118110)\n" + "Head Encoding : 500.000000 => 1000.000000 (res: 0.978474)\n", + "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.029354)\n" ] } ], @@ -151,13 +151,13 @@ "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", - "nqbit = 7\n", + "nqbit = 9\n", "step = (15/(2**nqbit-1))\n", "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"q\")\n", "\n", - "nqbit = 7\n", - "step = (1000/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"h\")\n", + "nqbit = 9\n", + "step = (500/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+500.0, var_base_name=\"h\")\n", "\n", "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", " head_encoding=head_encoding)\n", @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -190,7 +190,7 @@ "array([1. , 1. , 1. , 1. , 1. , 1. , 1. , 0.999, 1. , 1.001, 1. , 1.001, 1. , 1.001])" ] }, - "execution_count": 211, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -207,12 +207,12 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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vXmx0JDgBZk4AAHWyYcMGhYeHKy0tTW3bttXQoUONjgQnQTkBANRKYWGhpkyZogkTJqiwsFADBgzQwYMHNXjwYKOjwUlQTgAANfbNN98oMjJSa9askcVi0VNPPaVPP/1U7du3NzoanAhrTgAAl2Wz2bRmzRrFxsbq/PnzCgwM1MaNGzVgwACjo8EJMXMCALisF198Uffdd5/Onz+vwYMH6+DBgxQTNBrKCQDgsu6991517txZf/3rX5WQkKB27doZHQlOjMM6AIDL8vf317fffqvmzZsbHQUugJkTAECNUExgL5QTAABgKpQTAABgKpQTAHBxubm5Ki8vNzoGUIlyAgAuLCkpSaGhoZo/f77RUYBKlBMAcEE2m00vvPCC+vTpo++//16bNm1SUVGR0bEASZQTAHA52dnZuv322xUXF6eysjLdddddSk5O5mwcmAblBABcyJ49exQaGqr33ntPnp6eeumll7Rlyxb5+voaHQ2oRDkBABdgtVr197//Xf369dPx48d1zTXXaN++fZoxY4YsFovR8YAquEIsADi5rKwsTZw4UR999JEkady4cVq5cqV8fHwMTgZcHDMnAODkysrKlJycLC8vL61evVobNmygmMDUmDkBACfXvn17bd26Va1bt9aNN95odBzgsignAOAC+vXrZ3QEoMY4rAMAAEyFcgIAAEyFcgIAAEyFcgIADiwhIUHLli0zOgbQoFgQCwAOqKysTHPnztVzzz0nNzc3RUZGKioqyuhYQIOgnACAgzl+/LjGjh2rPXv2SJKmT5+ubt26GZwKaDiUEwBwIO+9954mTZqk7OxstWzZUqtXr9bdd99tdCygQbHmBAAcQGlpqR555BGNHDlS2dnZioiI0IEDBygmcErMnACAyX3//feKjo5WUlKSJOmhhx7S888/L09PT4OTAY2DcgIAJlZeXq6BAwfqu+++k5+fn9asWaNRo0YZHQtoVBzWAQATa9KkiV544QX16tVLqampFBO4BGZOAMDkRo4cqeHDh8vNjf+ehGtgpAOAA6CYwJUw2gEAgKlQTgAAgKlQTgDAQEePHjU6AmA6lBMAMEBRUZHuv/9+3Xjjjfrqq6+MjgOYCuUEAOzs22+/VWRkpF599VWVl5fryy+/NDoSYCqUEwCwE5vNpjVr1igiIkLffPONAgMDtX37dsXGxhodDTAVrnMCAHZQUFCgmTNn6vXXX5ckDR48WK+//roCAgIMTgaYDzMnANDI0tLS1KNHD73++utyc3PTs88+q4SEBIoJcAnMnABAI1q7dq1mzJih4uJiBQUFadOmTerXr5/RsQBTY+YEABqRl5eXiouLNXToUKWmplJMgBpg5gQAGtHYsWPVqlUrDR48mEvQAzVEOQGARjZkyBCjIwAOhRoPAABMhXICAABMhXICAABMhXICAHWQnZ2t6OhopaWlGR0FcDqGlJM77rhDrVq10l133WXExwNAvezdu1fdu3fXli1bNHHiRFmtVqMjAU7FkHLy0EMPaf369UZ8NADUmdVq1d///nf17dtX//3vf3X11VdrzZo1nCIMNDBD/kX1799fPj4+Rnw0ANTJ2bNnNXz4cD322GOqqKjQ2LFjlZycrO7duxsdDXA6tS4nu3bt0ogRIxQUFCSLxaJt27ZVe82KFSsUEhIiLy8vRUVFKSkpqSGyAoAhvvnmG/Xr108fffSRvLy8tGrVKm3cuFEtW7Y0OhrglGp9EbbCwkJ169ZNU6ZM0Z133lnt+fj4eMXFxWnlypWKiorSkiVLNGTIEB05ckTt2rWrdcCSkhKVlJRUPs7Ly5Mk5eTkNMpx3vz8/Co/UTt8f3AmVqtVzz33nBYtWiSr1aprr71Wr732mm644Qbl5uYaHQ8Ogt+LF/z897smLDabzVbXD7JYLHrnnXc0atSoym1RUVHq0aOHli9fLunCP+7g4GDNmjVLs2fPrnzdzp07tXz5cr355pu/+RlPPfWU5s+fX237xo0b1bx587pGB4DL2r9/v5599llJ0oABA/SHP/xBzZo1MzgV4JiKioo0btw45ebmXnbWsUEvX19aWqrk5GTNmTOncpubm5sGDRqkvXv31mmfc+bMUVxcXOXjvLw8BQcHq3fv3o0ypZqfn6+UlBSFhYWxLqYO+P7gTPr166f09HS1adNGf/rTnxjTqBN+L15Qm5mTBi0nWVlZqqioUEBAQJXtAQEBOnz4cOXjQYMG6eDBgyosLFTHjh21detW9erV66L79PT0lKenZ7Xtfn5+jXq818fHR35+fo22f2fH9wdnsXz5cn3++eeMadSbq4+h2pzVZsiN/7Zv327ExwIAAAfQoKcS+/v7y93dXRkZGVW2Z2RkKDAwsCE/CgAAOKkGLSceHh4KDw9XYmJi5Tar1arExMRLHrYBAAD4pVof1ikoKNCxY8cqH6enpys1NVWtW7dWp06dFBcXp5iYGEVERCgyMlJLlixRYWGhJk+e3KDBAaA+fvzxRxUXF+vqq682OgqAX6l1Odm/f78GDBhQ+fjnM2liYmK0du1aRUdH68yZM5o7d65Onz6t0NBQJSQkVFskCwBG+eCDDxQTE6MOHTpo3759nB4MmEyty0n//v11uUujxMbGKjY2ts6hAKAxlJWV6fHHH9fChQslSSEhITp37hzlBDAZ7lYFwCX88MMP6tu3b2UxefDBB7V7924FBQUZnAzArxlyKjEA2NO2bds0efJk5eTkyM/PT2vWrKlyZWsA5sLMCQCnVVJSoj/+8Y+64447lJOTo6ioKB04cIBiApgcMycAnFJOTo4GDRqk5ORkSdIjjzyiv/3tb/Lw8DA4GYDLoZwAcEq+vr4KCQlRenq61q1bp+HDhxsdCUANUU4AOCWLxaLVq1crPz9fwcHBRscBUAuUEwBOy8/Pz6VvtAY4KhbEAgAAU6GcAAAAU6GcAHBIl7tSNQDHRTkB4HDWrVungQMHqrS01OgoABoB5QSAwygoKFBMTIwmTZqkHTt26NVXXzU6EoBGwNk6ABzC119/rTFjxujw4cNyc3PT008/rT/84Q9GxwLQCCgnAEzNZrNp9erVevDBB1VcXKygoCBt2rRJ/fr1MzoagEZCOQFgWvn5+Zo2bZo2bdokSbrtttu0fv16tW3b1uBkABoTa04AmNKBAwcUFhamTZs2yd3dXc8995w++OADigngApg5AWBKzzzzjI4dO6bg4GBt3rxZN998s9GRANgJ5QSAKa1atUqtW7fW888/rzZt2hgdB4AdUU4AmJK/v79Wr15tdAwABmDNCQAAMBXKCQAAMBXKCQAAMBXKCQC7slqt2rBhg8rLy42OAsCkKCcA7CYrK0sjRozQhAkTNH/+fKPjADApztYBYBdffPGF7rnnHp04cUKenp4KDg42OhIAk2LmBECjslqt+tvf/qYBAwboxIkTuvbaa5WUlMRN+wBcEjMnABpNZmam7r33Xn366aeSpHvvvVcvv/yyvL29DU4GwMwoJwAaxY4dOzRu3DidPn1azZo104oVKzRp0iRZLBajowEwOcoJgAa3YcMGxcTEyGq1qkuXLtqyZYtuuOEGo2MBcBCsOQHQ4AYOHKg2bdpoypQp+uqrrygmAGqFmRMADa59+/ZKS0tTYGCg0VEAOCBmTgA0CooJgLqinAAAAFOhnAAAAFOhnAColbKyMlmtVqNjAHBilBMANfbDDz+oX79+eu6554yOAsCJUU4A1Mi7776r7t27a9++fVq8eLFyc3ONjgTASVFOAPym0tJSPfzwwxo1apTOnTunyMhIffXVV/L19TU6GgAnRTkBcEnfffedevfurSVLlkiS4uLi9MUXX+iKK64wNhgAp8ZF2ABc1Jtvvqn77rtPeXl5atWqldatW6cRI0YYHQuAC2DmBEAVxcXFeuCBB3T33XcrLy9PN998s1JTUykmAOyGcgKgijNnzmjTpk2SpNmzZ2vnzp3q1KmTwakAuBIO6wCoIjg4WBs2bJCbm5tuu+02o+MAcEGUEwDVDBs2zOgIAFwYh3UAAICpUE4AAICpUE4AAICpUE4AF3Lo0CFt2LDB6BgA8JtYEAu4AJvNpldffVWzZs1SRUWFrrvuOvXo0cPoWABwUZQTwMnl5+dr+vTp2rhxoyTptttuU0hIiLGhAOA3cFgHcGKpqamKiIjQxo0b5e7urueee04ffPCB2rZta3Q0ALgkZk4AJ2Sz2bRy5Uo9/PDDKikpUceOHbV582b17t3b6GgAcFmUE8DJ5ObmaurUqdq6daskafjw4Vq7dq3atGljcDIAqBnKCeBErFar+vfvr9TUVDVp0kTPP/+8Hn74YVksFqOjAUCNseYEcCJubm567LHH1LlzZ/3rX/9SXFwcxQSAw2HmBHAyY8eO1ciRI9W8eXOjowBAnTBzAjghigkAR0Y5AQAApkI5AQAApkI5ARzI2bNnjY4AAI2OcgI4AKvVqgULFigkJERpaWlGxwGARkU5AUwuMzNTQ4cO1eOPP66CggLFx8cbHQkAGhWnEgMmtnPnTo0bN06nTp1Ss2bNtHz5ck2ePNnoWADQqJg5AUyooqJCTz/9tAYOHKhTp07p+uuvV1JSkqZMmcJF1QA4PWZOAJM5ffq0xo8fr88++0ySNHnyZC1btkwtWrQwOBkA2AflBDCR7du3a/z48crMzFSLFi308ssva8KECUbHAgC7opwAJnL8+HFlZmbqpptu0pYtW/Q///M/RkcCALujnAAmMmnSJEkX7o/TrFkzY8MAgEEoJ4CJWCwWzsYB4PI4WwcAAJgK5QQAAJgK5QQAAJgK5QSwg9LSUs2ePVtHjhwxOgoAmB4LYoFGlp6errFjxyopKUkJCQnav3+/mjThnx4AXAozJ0Ajevvtt9W9e3clJSWpVatWevrppykmAHAZlBOgERQXF2vWrFkaPXq0cnNz1bNnTx04cEAjR440OhoAmB7lBGhgx44d080336zly5dLkh599FHt2rVLnTt3NjgZADgG5peBBhQfH6+pU6cqPz9fbdq00fr16zVs2DCjYwGAQ6GcAA1kx44dGjt2rCSpb9++2rhxozp27GhwKgBwPJQToIH0799fd999t6677jrNmzePha8AUEf89gQaiMVi0ebNm+XmxlIuAKgPfosCDYhiAgD1x29SAABgKpQTAABgKpQToAby8/N14sQJo2MAgEugnACXcfDgQUVEROiOO+5QaWmp0XEAwOlRToBLsNlseuWVVxQVFaWjR4/q1KlT+uGHH4yOBQBOj3ICXEReXp7Gjh2r6dOnq6SkRL///e914MABXXPNNUZHAwCnRzkBfiU5OVlhYWHasmWLmjRpooULF+qf//yn/P39jY4GAC6Bi7ABP7HZbFq+fLn+9Kc/qbS0VJ07d9bmzZvVs2dPo6MBgEuhnACSioqKNGHCBL399tuSpFGjRum1115Tq1atDE4GAK6HwzqAJC8vL50/f15NmzbViy++qLfffptiAgAGYeYE0IXLzq9fv17ff/+9IiIijI4DAC6NcgL8xN/fn0WvAGACHNYBAACmQjkBAACmQjkBAACmQjmB0/v88881duxYlZeXGx0FAFADlBM4rYqKCj3zzDO69dZbFR8fr6VLlxodCQBQA5ytA6d0+vRp3XvvvUpMTJQkxcTEaNq0aQanAgDUBOUETicxMVHjx49XRkaGmjdvrpdeekkxMTFGxwIA1BCHdeA0ysvLNXfuXA0ePFgZGRm68cYbtX//fooJADgYZk7gFE6ePKl77rlHu3btkiRNnTpVL774opo1a2ZwMgBAbVFO4BT++Mc/ateuXfL29tYrr7yicePGGR0JAFBHlBM4hRdffFG5ublatmyZrr32WqPjAADqgXICp9C+fXt9/PHHRscAADQAFsQCAABToZwAAABToZwAAABToZzA9Hbt2iWr1Wp0DACAnVBOYFolJSV68MEHdcstt+j55583Og4AwE44WwemdOzYMUVHRyslJUWSlJeXZ3AiAIC9UE5gOlu2bNH999+v/Px8tWnTRuvWrdPvf/97o2MBAOyEwzowjfPnz2v69OmKjo5Wfn6++vTpo9TUVIoJALgYyglM4ciRI+rZs6deeeUVWSwWPf7449qxY4c6duxodDQAgJ1xWAeGS0hI0F133aXCwkK1bdtWGzZs0O9+9zujYwEADEI5geGuv/56eXh4KDIyUm+88Ybat29vdCQAgIEoJzBc586dtXv3bl177bVyd3c3Og4AwGCUE5jC9ddfb3QEAIBJsCAWAACYCuUEAACYiiHl5P3339d1112na665RqtXrzYiAuzEZrPJZrMZHQMA4EDsXk7Ky8sVFxenzz77TAcOHND//u//6uzZs/aOATvIy8vTuHHjtHTpUqOjAAAciN3LSVJSkm644QZ16NBB3t7eGjp0qD755BN7x0AjS0tLU3h4uDZv3qzHH39cWVlZRkcCADiIWpeTXbt2acSIEQoKCpLFYtG2bduqvWbFihUKCQmRl5eXoqKilJSUVPncyZMn1aFDh8rHHTp00IkTJ+qWHqZjs9n04YcfavDgwTp27Jg6deqk7du3y9/f3+hoAAAHUetTiQsLC9WtWzdNmTJFd955Z7Xn4+PjFRcXp5UrVyoqKkpLlizRkCFDdOTIEbVr167WAUtKSlRSUlL5+Oe70+bk5MhqtdZ6f5eTn59f5SdqLjc3VzNmzNBHH30kSRo2bJiWL1+uVq1aKScnx9hwQB3xOwH1xRi6oDZ3l691ORk6dKiGDh16yecXL16sqVOnavLkyZKklStX6oMPPtBrr72m2bNnKygoqMpMyYkTJxQZGXnJ/S1YsEDz58+vtn337t1q3rx5bePXWEpKSqPt2xkdPXpUCxcuVGZmppo0aaKYmBgNHz5caWlpRkcDGgS/E1Bfrj6GioqKavxai60ep1JYLBa98847GjVqlCSptLRUzZs315tvvlm5TZJiYmKUk5Ojd999V+Xl5br++uu1c+dO+fr6Kjw8XHv27FGbNm0u+hkXmzkJDg7WDz/8oJYtW9Y1+iXl5+crJSVFYWFh8vHxafD9OxubzaaXX35ZTz31lMrKyhQcHKwHH3xQ0dHRfH9wCvxOQH0xhi7Iy8tT586dlZube9m/3w16hdisrCxVVFQoICCgyvaAgAAdPnz4wgc2aaJFixZpwIABslqtevTRRy9ZTCTJ09NTnp6e1bb7+fk1Sjn5mY+Pj/z8/Bpt/87ixx9/1HPPPaeysjKNHj1aixYtUmpqKt8fnA5jGvXl6mPIza3my1wNuXz9yJEjNXLkSCM+Gg2sY8eOWr16tc6cOaOZM2cqNzfX6EgAAAfXoOXE399f7u7uysjIqLI9IyNDgYGBDflRMJExY8YYHQEA4EQa9DonHh4eCg8PV2JiYuU2q9WqxMRE9erVqyE/CgAAOKlaz5wUFBTo2LFjlY/T09OVmpqq1q1bq1OnToqLi1NMTIwiIiIUGRmpJUuWqLCwsPLsHQAAgN9S63Kyf/9+DRgwoPJxXFycpAtn5Kxdu1bR0dE6c+aM5s6dq9OnTys0NFQJCQnVFskCAABcTK3LSf/+/S97I7fY2FjFxsbWORTMISMjQ8nJyRo2bJjRUQAALsSQuxLD/BITE9WtWzeNHj2aC6kBAOyKcoIqKioqNG/ePA0ePFgZGRm66qqr1LRpU6NjAQBciCHXOYE5nTx5UuPHj9fOnTslSffff79efPHFRr1NAAAAv0Y5gSTp448/1oQJE3TmzBl5e3vrlVde0bhx44yOBQBwQRzWcXHl5eWaM2eObrvtNp05c0bdunVTcnIyxQQAYBhmTlyYzWbT8OHD9fHHH0uSZsyYocWLF8vLy8vgZAAAV8bMiQuzWCyKjo5Wy5YtFR8fr5deeoliAgAwHDMnLm7SpEkaNmwYF8kDAJgGMycuzmKxUEwAAKZCOQEAAKZCOQEAAKZCOXFiJSUlRkcAAKDWKCdOauvWrbrqqqt05MgRo6MAAFArlBMnU1xcrJkzZ2rMmDE6ceKEFi5caHQkAABqhVOJncjRo0c1ZswYHTx4UJI0Z84czZ8/3+BUAADUDuXESWzcuFHTpk1TQUGB/P39tWHDBg0ZMsToWAAA1BqHdRxcUVGR7r//fo0fP14FBQW65ZZbdPDgQYoJAMBhUU4c2LfffqvIyEi9+uqrslgsmjt3rrZv366goCCjowEAUGcc1nFgO3bs0DfffKOAgAC98cYbGjhwoNGRAACoN8qJA5s5c6Zyc3M1ZcoUBQYGGh0HAIAGQTlxYBaLRY8//rjRMQAAaFCsOQEAAKZCOQEAAKZCOQEAAKZCOTEhm82mV155Renp6UZHAQDA7ignJpObm6sxY8Zo+vTpio6OVmlpqdGRAACwK87WMZH9+/drzJgxSk9PV9OmTTVu3Dg1bdrU6FgAANgV5cTOKqw2JaVnKzO/WO18vBR5RWu5WaSlS5fqz3/+s8rKynTFFVcoPj5ePXr0MDouAAB2Rzmxo4RDpzT/vW91Kre4clvbpqVquvsV7d3xsSRp9OjRWr16tfz8/AxKCQCAsSgndpJw6JRmbEiR7RfbSk4c1oF/Pq+KvDNq0tRDS15YrJkzZ8pisRiWEwAAo1FO7KDCatP8976tWkxOHdXpjY9J1go1adVe14+fq+kzplFMAAAuj3JiB0np2VUO5UiSR+DV8goJlZtnC7UZEqs8z+ZKSs9Wr6vaGJQSAABzoJw0kl8ufP13Rn615y0WN7Ud9bgsTTwqZ0sy84urvQ4AAFdDOWkEF1v4ejFuTT2rPG7n49WYsQAAcAiUkwZ2sYWvl2ORFOh74bRiAABcHVeIbUAXW/h6OT8vf503oovc3VgMCwAAMycN4Of1JbuPZelUbrFs1gpZS8/L3cv7su8N9PXSvBFddNuN7e2QFAAA86Oc1NOv15eU559V1vsLJatVAff8TRY392rviR1wta4J8K68QiwzJgAA/H+Uk3r49fqS8+kpynp/kaxFubI09VLZme/lEXBVtff1vtqfU4YBALgEykkd/XJ9ic1aoZx/vaG8vVsl2dS0bYja3j5bTdt0rPIeFr4CAHB5lJM6+vnCauV5Wcp67+8q+fFbSZJ36FC1uvX+aqcJs/AVAICaoZzUwMXuJJyZX6zz//lKWR+8IOv5PFk8mqnNbbPU4vp+F90HC18BAKgZysllXOyCagHeTeT99ZvKfHO1JMkj4Cr53/6YmrYKqvb+2AFXqffVbVn4CgBADVFOfsOHaac0c2NKte0ZeSVK25ckSfIJH6FW/afI0qRpldf8vL7k4cHXUUoAAKgFysklbNj7vRZ+fuLiT7q5y3/En9Tk7H9k69xDFqnKhddYXwIAQN1xhdhf2fOfLEnS5v0//ubr3L1by9a5hx4edI0CfaveEyfQ10sv3xvG+hIAAOqAmZNfSDh0Sn/78LD+3LXm7wnxb6F/PXZrtQWzzJgAAFA3dp85WbFihUJCQuTl5aWoqCglJSXZO8JFlZZbNeftr2v9vnY+XnJ3s6jXVW10e2gH9bqqDcUEAIB6sGs5iY+PV1xcnObNm6eUlBR169ZNQ4YMUWZmpj1jVJNw6JR6LkjUuaKyWr2vPRdUAwCgwdm1nCxevFhTp07V5MmT1aVLF61cuVLNmzfXa6+9Zs8YVfx8CfrswlJJ0vmzJ7V69WrZrNbLvpcFrwAANDy7rTkpLS1VcnKy5syZU7nNzc1NgwYN0t69ey/5vpKSEpWUlFQ+zsvLkyTl5OTIWoMC8VusVptWfvq1OrS4cK7NmbTPlfbeCqWUnNeNaquOkXde9H0WSbNv+x/17NhMOTk59crgbPLz86v8BBwdYxr1xRi64Oe/3zVht3KSlZWliooKBQQEVNkeEBCgw4cPX/J9CxYs0Pz586tt3717t5o3b17vXJOvvFCAVq9eraOffipJuuGGG/TwHX3Upk3FJd9XfvIbfX6y3h/vtFJSql8fBnBkjGnUl6uPoaKiohq/1vRn68yZM0dxcXGVj/Py8hQcHKzevXurZcuW9dr350cyNf+NRB2Jf15FmT9IFouuGzhGT88Yow3fNVXmieqHbEaHBWly7yvr9bnOLD8/XykpKQoLC5OPj4/RcYB6Y0yjvhhDF5hy5sTf31/u7u7KyMiosj0jI0OBgYGXfJ+np6c8PT2rbffz86t3Odm/6w2lrnxEtrISubXwk//wP8n/hm5yd69Q5nmLfiz8/+XE27OJ/j66q4Z15dolNeHj4yM/Pz+jYwANhjGN+nL1MeTmVvNlrnZbEOvh4aHw8HAlJiZWbrNarUpMTFSvXr3sFUOSVFhYqEmTJunZR2NlKyuRV+duCpq0TM1CQqu91tvTXa9PjtTBeb+jmAAAYAd2PawTFxenmJgYRUREKDIyUkuWLFFhYaEmT55szxiaOXOm1q9fLzc3N42f8Yh2Ne8ji5t7tUvQWyQtvLub+l7X1q75AABwZXYtJ9HR0Tpz5ozmzp2r06dPKzQ0VAkJCdUWyTa2p59+WsnJyVqxYoVuueWWi955uI23h/5yx01cgh4AADuz+4LY2NhYxcbG2vtjq+jcubPS0tIqj3/ddmN7De4SeOES9FlnpdP/p9dieqh161aG5gQAwBW57I3/fr0w5+dL0N9yXbufnufiagAAGMFlywkAADAnygkAADAVpysnR48elc1mu/wLAQCAKTlNObHZbFq6dKluvPFGLV261Og4AACgjpyinJw7d0533nmnHnroIZWVlWnv3r3MngAA4KAcvpx8+eWX6t69u7Zt2yYPDw8tXbpUmzZtksXC2TYAADgihy0nVqtVixYtUp8+ffTDDz/oyiuv1J49ezRr1iyKCQAADsz0dyW+lOjoaH3yySeSpDFjxmjVqlXy9fU1OBUAAKgvh505+eSTT+Tp6amVK1dq8+bNFBMAAJyEw82c/LzQ9YorrtDrr7+um266Sfn5+Q22/7y8PBUVFSkvL69Wt3fGBXx/cDaMadQXY+iCvLw8SarRCSsWm4Od1vLjjz8qODjY6BgAAKAOjh8/ro4dO/7maxyunFitVp08eVI+Pj6NsvA1Ly9PwcHBOn78uFq2bNng+3d2fH9wNoxp1Bdj6AKbzab8/HwFBQVddgbJ4Q7ruLm5XbZxNYSWLVu69CCqL74/OBvGNOqLMaQarw913YNfAADAlCgnAADAVCgnv+Lp6al58+bJ09PT6CgOie8PzoYxjfpiDNWewy2IBQAAzo2ZEwAAYCqUEwAAYCqUEwAAYCqUEwAAYCqUk19YsWKFQkJC5OXlpaioKCUlJRkdCQAAl0M5+Ul8fLzi4uI0b948paSkqFu3bhoyZIgyMzONjuZU3n//fV133XW65pprtHr1aqPjAPV2xx13qFWrVrrrrruMjgIHc/z4cfXv319dunRR165dtXXrVqMjmQanEv8kKipKPXr00PLlyyVduIdPcHCwZs2apdmzZxuczjmUl5erS5cu2rFjh3x9fRUeHq49e/aoTZs2RkcD6mznzp3Kz8/XunXr9OabbxodBw7k1KlTysjIUGhoqE6fPq3w8HAdPXpULVq0MDqa4Zg5kVRaWqrk5GQNGjSocpubm5sGDRqkvXv3GpjMuSQlJemGG25Qhw4d5O3traFDh+qTTz4xOhZQL/3795ePj4/RMeCA2rdvr9DQUElSYGCg/P39lZ2dbWwok6CcSMrKylJFRYUCAgKqbA8ICNDp06cNSmU+u3bt0ogRIxQUFCSLxaJt27ZVe81vrds5efKkOnToUPm4Q4cOOnHihD2iAxdV3zEN19aQ4yc5OVkVFRUKDg5u5NSOgXKCGissLFS3bt20YsWKiz7Puh04GsY06qOhxk92drYmTpyoVatW2SO2Y7DBVlJSYnN3d7e98847VbZPnDjRNnLkSGNCmZykat9XZGSk7YEHHqh8XFFRYQsKCrItWLDAZrPZbLt377aNGjWq8vmHHnrI9sYbb9glL3A5dRnTP9uxY4dt9OjR9ogJk6rr+CkuLrb17dvXtn79entFdQjMnEjy8PBQeHi4EhMTK7dZrVYlJiaqV69eBiZzHDVZtxMZGalDhw7pxIkTKigo0EcffaQhQ4YYFRn4TaxFQ33UZPzYbDZNmjRJt956qyZMmGBUVFOinPwkLi5O//jHP7Ru3Tr93//9n2bMmKHCwkJNnjzZ6GgOoSbrdpo0aaJFixZpwIABCg0N1SOPPMKZOjCtmq5FGzRokO6++259+OGH6tixI8UFkmo2fnbv3q34+Hht27ZNoaGhCg0N1ddff21EXNNpYnQAs4iOjtaZM2c0d+5cnT59WqGhoUpISKg2sFA/I0eO1MiRI42OATSY7du3Gx0BDqpPnz6yWq1GxzAlyskvxMbGKjY21ugYDsnf31/u7u7KyMiosj0jI0OBgYEGpQLqjjGN+mD81A+HddAgWLcDZ8OYRn0wfuqHmRPUWEFBgY4dO1b5OD09XampqWrdurU6deqkuLg4xcTEKCIiQpGRkVqyZAnrdmBqjGnUB+OnERl9uhAcx44dO2ySqv0vJiam8jXLli2zderUyebh4WGLjIy07du3z7jAwGUwplEfjJ/Gw711AACAqbDmBAAAmArlBAAAmArlBAAAmArlBAAAmArlBAAAmArlBAAAmArlBAAAmArlBAAAmArlBAAAmArlBAAAmArlBAAAmArlBAAAmMr/A1nLefIZgpePAAAAAElFTkSuQmCC", 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", 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" ] @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -253,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -264,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -277,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -287,8 +287,8 @@ "\n", "var_names = sorted(net.qubo.qubo_dict.variables)\n", "net.qubo.create_variables_mapping()\n", - "# mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", - "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)\n", + "mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", + "# mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)\n", "# mystep = ParallelIncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=100)" ] }, @@ -301,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -319,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -353,90 +353,179 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "eref = net.qubo.energy_binary_rep(bin_rep_sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "num_sweeps = 8000\n", - "Tinit = 1E5\n", + "Tinit = 1E6\n", "Tfinal = 1E1\n", "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(2000))\n", "\n", - "num_sweeps = 2000\n", + "num_sweeps = 8000\n", "Tinit = 1E1\n", - "Tfinal = 1E0\n", + "Tfinal = 0\n", "Tschedule = np.append(Tschedule, np.linspace(Tinit, Tfinal, num_sweeps))\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", - "\n", - "# num_sweeps = 1000\n", - "# Tinit = 1E-1\n", - "# Tfinal = 1E0-2\n", - "# Tschedule = np.append(Tschedule, np.linspace(Tinit, Tfinal, num_sweeps))\n", - "# Tschedule = np.append(Tschedule, Tfinal*np.ones(num_sweeps))" + "Tschedule = np.append(Tschedule, Tfinal*np.ones(2000))\n" ] }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "12000it [00:53, 225.87it/s]\n" + " 0%| | 0/20000 [00:00 2\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mTschedule\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mTschedule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtake_step\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmystep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_traj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m mystep\u001b[38;5;241m.\u001b[39mverify_quadratic_constraints(res\u001b[38;5;241m.\u001b[39mres)\n", + "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sampler/simulated_annealing.py:129\u001b[0m, in \u001b[0;36mSimulatedAnnealing.sample\u001b[0;34m(self, bqm, num_sweeps, Temp, Tschedule, x0, take_step, save_traj)\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e_new \u001b[38;5;241m<\u001b[39m e_ori:\n\u001b[1;32m 128\u001b[0m x \u001b[38;5;241m=\u001b[39m x_new\n\u001b[0;32m--> 129\u001b[0m energies\u001b[38;5;241m.\u001b[39mappend(\u001b[43mbqm_energy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save_traj:\n\u001b[1;32m 131\u001b[0m trajectory\u001b[38;5;241m.\u001b[39mappend(x)\n", + "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sampler/simulated_annealing.py:80\u001b[0m, in \u001b[0;36mSimulatedAnnealing.sample..bqm_energy\u001b[0;34m(x, var_names)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbqm_energy\u001b[39m(x, var_names):\n\u001b[1;32m 74\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute the energy of a given binary array.\u001b[39;00m\n\u001b[1;32m 75\u001b[0m \n\u001b[1;32m 76\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;124;03m x (_type_): _description_\u001b[39;00m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;124;03m var_names (list): list of var names\u001b[39;00m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 80\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbqm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menergies\u001b[49m\u001b[43m(\u001b[49m\u001b[43mas_samples\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:1092\u001b[0m, in \u001b[0;36mBinaryQuadraticModel.energies\u001b[0;34m(self, samples_like, dtype)\u001b[0m\n\u001b[1;32m 1067\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menergies\u001b[39m(\u001b[38;5;28mself\u001b[39m, samples_like, dtype: Optional[DTypeLike] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m 1068\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Determine the energies of the given samples-like.\u001b[39;00m\n\u001b[1;32m 1069\u001b[0m \n\u001b[1;32m 1070\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1090\u001b[0m \n\u001b[1;32m 1091\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1092\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menergies\u001b[49m\u001b[43m(\u001b[49m\u001b[43msamples_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyqmbase/cyqmbase_template.pyx.pxi:116\u001b[0m, in \u001b[0;36mdimod.cyqmbase.cyqmbase_float64.cyQMBase_template.energies\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/functools.py:877\u001b[0m, in \u001b[0;36msingledispatch..wrapper\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 873\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[1;32m 874\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfuncname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires at least \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 875\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1 positional argument\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 877\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/sampleset.py:428\u001b[0m, in \u001b[0;36m_as_samples_tuple\u001b[0;34m(samples_like, dtype, copy, order, labels_type)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[38;5;66;03m# make sure our labels are the correct type\u001b[39;00m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(labels, labels_type):\n\u001b[1;32m 427\u001b[0m \u001b[38;5;66;03m# todo: generalize to other sequence types? Especially Variables\u001b[39;00m\n\u001b[0;32m--> 428\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[43mlabels_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 430\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arr\u001b[38;5;241m.\u001b[39msize:\n\u001b[1;32m 431\u001b[0m arr\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m=\u001b[39m (arr\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mlen\u001b[39m(labels))\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:47\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables.__init__\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:166\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables._extend\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:166\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables._extend\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:193\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables._extend\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:97\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables._append\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:97\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables._append\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:134\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables._append\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:352\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables.count\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:352\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables.count\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:361\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables.count\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/abc.py:96\u001b[0m, in \u001b[0;36mABCMeta.__instancecheck__\u001b[0;34m(cls, instance)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Register a virtual subclass of an ABC.\u001b[39;00m\n\u001b[1;32m 91\u001b[0m \n\u001b[1;32m 92\u001b[0m \u001b[38;5;124;03m Returns the subclass, to allow usage as a class decorator.\u001b[39;00m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _abc_register(\u001b[38;5;28mcls\u001b[39m, subclass)\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__instancecheck__\u001b[39m(\u001b[38;5;28mcls\u001b[39m, instance):\n\u001b[1;32m 97\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Override for isinstance(instance, cls).\"\"\"\u001b[39;00m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _abc_instancecheck(\u001b[38;5;28mcls\u001b[39m, instance)\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "mystep.optimize_values = np.arange(8, 22)\n", "res = sampler.sample(net.qubo.qubo_dict, x0=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True)\n", - "\n", - "\n", - "# mystep.optimize_values = np.arange(16)\n", - "# res = sampler.sample(net.qubo.qubo_dict, x0=res.res, Tschedule=Tschedule, take_step=mystep, save_traj=True)\n", - "\n", - "# mystep.optimize_values = np.arange(16,22)\n", - "# res = sampler.sample(net.qubo.qubo_dict, x0=res.res, Tschedule=Tschedule, take_step=mystep, save_traj=True)" + "mystep.verify_quadratic_constraints(res.res)" ] }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "mystep.verify_quadratic_constraints(res.res)" + "import matplotlib.pyplot as plt\n", + "eplt = res.energies\n", + "\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "left, bottom, width, height = [0.25, 0.25, 0.3, 0.3]\n", + "\n", + "ax1.plot(eplt)\n", + "ax1.plot(Tschedule)\n", + "ax1.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "# plt.ylim([-1E5, -1E4])\n", + "# plt.xlim([9000,11000])\n", + "ax1.grid()\n", + "ax1.set_yscale('symlog')\n", + "\n", + "ax2 = fig.add_axes([left, bottom, width, height])\n", + "ax2.plot(eplt[-100:])\n", + "ax2.grid()\n", + "ax2.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "# ax2.set_yscale('symlog')" ] }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3000/3000 [00:19<00:00, 150.53it/s]\n" + ] + } + ], "source": [ - "eref = net.qubo.energy_binary_rep(bin_rep_sol)" + "num_sweeps = 2000\n", + "Tinit = 1E1\n", + "Tfinal = 0\n", + "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "\n", + "\n", + "mystep.optimize_values = np.arange(16)\n", + "res = sampler.sample(net.qubo.qubo_dict, x0=res.res, Tschedule=Tschedule, take_step=mystep, save_traj=True)\n", + "mystep.verify_quadratic_constraints(res.res)" ] }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 223, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -462,7 +551,7 @@ "ax1.set_yscale('symlog')\n", "\n", "ax2 = fig.add_axes([left, bottom, width, height])\n", - "ax2.plot(eplt[-1000:])\n", + "ax2.plot(eplt)\n", "ax2.grid()\n", "ax2.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", "# ax2.set_yscale('symlog')" @@ -470,25 +559,39 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6000/6000 [00:39<00:00, 152.08it/s]\n" + ] + } + ], "source": [ - "sol = net.qubo.decode_solution(np.array(res.res))\n", - "sol = net.combine_flow_values(sol)\n", - "sol = net.convert_solution_to_si(sol)" + "num_sweeps = 5000\n", + "Tinit = 1E2\n", + "Tfinal = 0\n", + "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "\n", + "\n", + "mystep.optimize_values = np.arange(16)\n", + "res = sampler.sample(net.qubo.qubo_dict, x0=res.res, Tschedule=Tschedule, take_step=mystep, save_traj=True)" ] }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, "metadata": {}, @@ -496,56 +599,60 @@ } ], "source": [ - "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", - "plt.scatter(ref_values[:-1], sol, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", - "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "plt.grid(which=\"major\", lw=1)\n", - "plt.grid(which=\"minor\", lw=0.1)\n", - "plt.xlabel('Reference Solution')\n", - "plt.ylabel('QUBO Solution')\n", - "# plt.legend()\n", - "# plt.xlim([-0.5,0.5])\n", - "# plt.ylim([-0.5,0.5])\n", - "# plt.loglog()\n", - "plt.xscale('symlog')\n", - "plt.yscale('symlog')" + "import matplotlib.pyplot as plt\n", + "eplt = res.energies\n", + "\n", + "fig, ax1 = plt.subplots()\n", + "ax2 = ax1.twinx()\n", + "\n", + "ax1.plot(Tschedule, c = 'orange')\n", + "\n", + "ax2.plot(eplt)\n", + "ax2.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "ax2.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "idx_min = np.array([e[0] for e in res.energies]).argmin()" ] }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "traj = []\n", - "for x in res.trajectory:\n", - " sol = net.qubo.decode_solution(np.array(x))\n", - " sol = net.combine_flow_values(sol)\n", - " sol = net.convert_solution_to_si(sol)\n", - " traj.append(sol)" + "sol = res.trajectory[idx_min]\n", + "sol = net.qubo.decode_solution(np.array(sol))\n", + "sol = net.combine_flow_values(sol)\n", + "sol = net.convert_solution_to_si(sol)" ] }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0, 0.5, 'QUBO Solution')" + "Text(0.5, 1.0, 'Pressure')" ] }, - "execution_count": 238, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -554,254 +661,290 @@ ], "source": [ "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", "\n", - "plt.scatter(ref_values[:-1], traj[0], s=50, lw=1, edgecolors='w', label='Sampled solution')\n", - "plt.scatter(ref_values[:-1], traj[-1], s=50, lw=1, edgecolors='w', label='Sampled solution')\n", - "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "plt.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.grid(which=\"major\", lw=1)\n", - "plt.grid(which=\"minor\", lw=0.1)\n", + "fig = plt.figure(figsize = plt.figaspect(0.5))\n", + "ax1 = fig.add_subplot(121)\n", + "\n", + "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.grid()\n", + "\n", + "ax1.scatter(ref_values[:8], encoded_ref_sol[:8], c='black', s=200, label='Best solution')\n", + "ax1.scatter(ref_values[:8], sol[:8], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "\n", + "\n", + "ax1.set_xlabel('Reference Values', fontsize=12)\n", + "ax1.set_ylabel('QUBO Values', fontsize=12)\n", + "ax1.set_title('Flow Rate', fontsize=14)\n", + "\n", + "ax2 = fig.add_subplot(122)\n", + "\n", + "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "\n", "\n", - "# plt.xscale('symlog')\n", - "# plt.yscale('symlog')\n", + "ax2.scatter(ref_values[8:-1], encoded_ref_sol[8:], c='black', s=200, label='Best solution')\n", + "ax2.scatter(ref_values[8:-1], sol[8:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "ax2.grid()\n", "\n", - "plt.xlabel('Reference Solution')\n", - "plt.ylabel('QUBO Solution')" + "ax2.set_xlim([160,210])\n", + "ax2.set_ylim([160,210])\n", + "ax2.set_xlabel('Reference Values', fontsize=12)\n", + "ax2.set_title('Pressure', fontsize=14)\n" ] }, { - "cell_type": "code", - "execution_count": 228, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 0,\n", - " [1, 0, 1, 1, 1, 0, 1],\n", - " [1, 1, 1, 1, 0, 0, 0],\n", - " [1, 0, 1, 0, 0, 0, 1],\n", - " [1, 0, 0, 1, 0, 0, 0],\n", - " [0, 1, 0, 0, 1, 1, 0],\n", - " [1, 1, 1, 0, 1, 0, 0],\n", - " [1, 1, 1, 0, 0, 0, 0],\n", - " [0, 1, 1, 0, 0, 0, 0],\n", - " [0, 0, 1, 0, 1, 0, 1],\n", - " [0, 0, 1, 1, 0, 0, 1],\n", - " [0, 1, 0, 0, 1, 0, 1],\n", - " [0, 0, 1, 0, 0, 0, 1],\n", - " [1, 1, 1, 1, 0, 0, 1],\n", - " [0, 1, 0, 1, 0, 0, 1]]" - ] - }, - "execution_count": 228, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "bin_rep_sol" + "# Explore the solution space" ] }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ - "z = np.array(res.res)[net.qubo.index_variables]" + "def flatten_list(lst):\n", + " out = []\n", + " for elmt in lst:\n", + " if not isinstance(elmt, list):\n", + " out += [elmt]\n", + " else:\n", + " out += elmt\n", + " return out\n", + "\n", + "from copy import deepcopy\n", + "mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", + "\n", + "# # modsify sign\n", + "# for i in range(8):\n", + "# mod_bin_rep_sol[i] = np.random.randint(2)\n", + "\n", + "# # modify flow value\n", + "# for i in range(8, 16):\n", + "# mod_bin_rep_sol[i] = list(np.random.randint(2, size=flow_encoding.nqbit))\n", + "\n", + "# # modify head values\n", + "# for i in range(16,22):\n", + "# mod_bin_rep_sol[i] = list(np.random.randint(2, size=head_encoding.nqbit))\n", + "\n", + "x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", + "x0 = list(x.values())" ] }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 26, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[1],\n", - " [1],\n", - " [1],\n", - " [1],\n", - " [1],\n", - " [1],\n", - " [1],\n", - " [0],\n", - " [1, 0, 0, 0, 1, 1, 1],\n", - " [0, 1, 1, 0, 1, 0, 0],\n", - " [1, 1, 1, 1, 1, 1, 1],\n", - " [0, 0, 0, 1, 0, 0, 0],\n", - " [0, 0, 1, 0, 1, 1, 0],\n", - " [0, 0, 1, 1, 1, 0, 0],\n", - " [0, 0, 1, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0],\n", - " [0, 1, 0, 0, 1, 0, 1],\n", - " [0, 1, 0, 0, 0, 0, 1],\n", - " [0, 1, 0, 1, 0, 0, 1],\n", - " [0, 0, 0, 0, 0, 0, 1],\n", - " [0, 0, 0, 1, 0, 0, 1],\n", - " [0, 0, 0, 0, 0, 0, 1]]" - ] - }, - "execution_count": 230, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "count = 0\n", - "bin_rep_res = []\n", - "for r in net.qubo.mixed_solution_vectors.encoded_reals:\n", - " n = r.nqbit\n", - " bin_rep_res.append(list(z[count:count+n]))\n", - " count += n\n", - "bin_rep_res" + "sol = net.qubo.decode_solution(np.array(x0))\n", + "sol = net.combine_flow_values(sol)\n", + "sol = net.convert_solution_to_si(sol)" ] }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array(['x_001', 'x_002', 'x_003', 'x_004', 'x_005', 'x_006', 'x_007', 'x_008', 'x_009', 'x_010', 'x_011', 'x_012', 'x_013', 'x_014', 'x_015', 'x_016', 'x_017', 'x_018', 'x_019', 'x_020', 'x_021', 'x_022'], dtype='" ] }, - "execution_count": 232, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "2**7" + "import matplotlib.pyplot as plt \n", + "\n", + "fig = plt.figure(figsize = plt.figaspect(0.5))\n", + "ax1 = fig.add_subplot(121)\n", + "\n", + "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.grid()\n", + "\n", + "ax1.scatter(ref_values[:8], encoded_ref_sol[:8], c='black', s=200, label='Best solution')\n", + "ax1.scatter(ref_values[:8], sol[:8], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "\n", + "\n", + "ax1.set_xlabel('Reference Values', fontsize=12)\n", + "ax1.set_ylabel('QUBO Values', fontsize=12)\n", + "ax1.set_title('Flow Rate', fontsize=14)\n", + "\n", + "ax2 = fig.add_subplot(122)\n", + "\n", + "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "\n", + "\n", + "ax2.scatter(ref_values[8:-1], encoded_ref_sol[8:], c='black', s=200, label='Best solution')\n", + "ax2.scatter(ref_values[8:-1], sol[8:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "ax2.grid()\n", + "\n", + "ax2.set_xlim([160,210])\n", + "ax2.set_ylim([160,210])\n", + "ax2.set_xlabel('Reference Values', fontsize=12)\n", + "ax2.set_title('Pressure', fontsize=14)" ] }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 28, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "81129638414606681695789005144064" - ] - }, - "execution_count": 233, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 15000/15000 [01:20<00:00, 186.47it/s]\n" + ] } ], "source": [ - "128**14 * 2**8" + "num_sweeps = 5000\n", + "Tfinal = 1E2\n", + "Tschedule = Tfinal * np.ones(num_sweeps)\n", + "Tschedule = np.append(Tschedule, np.linspace(Tfinal, 0, num_sweeps))\n", + "Tschedule = np.append(Tschedule, np.zeros(num_sweeps))\n", + "\n", + "mystep.optimize_values = np.arange(8,22)\n", + "res = sampler.sample(net.qubo.qubo_dict, x0=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True)" ] }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "array(['x_001', 'x_002', 'x_003', 'x_004', 'x_005', 'x_006', 'x_007', 'x_008', 'x_009', 'x_010', 'x_011', 'x_012', 'x_013', 'x_014', 'x_015', 'x_016', 'x_017', 'x_018', 'x_019', 'x_020', 'x_021', 'x_022'], dtype='" ] }, - "execution_count": 234, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "mystep.value_names[np.arange(len(mystep.value_names))]" + "import matplotlib.pyplot as plt\n", + "eplt = res.energies\n", + "\n", + "fig, ax1 = plt.subplots()\n", + "ax2 = ax1.twinx()\n", + "\n", + "ax1.plot(Tschedule, c = 'orange')\n", + "\n", + "ax2.plot(eplt)\n", + "ax2.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "ax2.grid()" ] }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "from dimod import as_samples \n", - "enew = net.qubo.qubo_dict.energies(as_samples((x0, var_names)))" - ] - }, - { - "cell_type": "code", - "execution_count": 236, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "samples_like and labels dimensions do not match", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[236], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m s \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m2\u001b[39m, size\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m764\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m net\u001b[38;5;241m.\u001b[39mqubo\u001b[38;5;241m.\u001b[39mqubo_dict\u001b[38;5;241m.\u001b[39menergies(\u001b[43mas_samples\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/functools.py:877\u001b[0m, in \u001b[0;36msingledispatch..wrapper\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 873\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[1;32m 874\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfuncname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires at least \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 875\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1 positional argument\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 877\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/sampleset.py:434\u001b[0m, in \u001b[0;36m_as_samples_tuple\u001b[0;34m(samples_like, dtype, copy, order, labels_type)\u001b[0m\n\u001b[1;32m 431\u001b[0m arr\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m=\u001b[39m (arr\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mlen\u001b[39m(labels))\n\u001b[1;32m 433\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(labels) \u001b[38;5;241m!=\u001b[39m arr\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]:\n\u001b[0;32m--> 434\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msamples_like and labels dimensions do not match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 436\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr, labels\n", - "\u001b[0;31mValueError\u001b[0m: samples_like and labels dimensions do not match" - ] - } - ], - "source": [ - "s = np.random.randint(2, size=(2, 764))\n", - "net.qubo.qubo_dict.energies(as_samples((s, var_names)))" + "sol = res.res\n", + "sol = res.trajectory[-1]\n", + "sol = net.qubo.decode_solution(np.array(sol))\n", + "sol = net.combine_flow_values(sol)\n", + "sol = net.convert_solution_to_si(sol)" ] }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'q'" + "Text(0.5, 1.0, 'Pressure')" ] }, - "execution_count": 145, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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QnJycFDc3N6V9+/bK/PnzlaysrAKPv3jxYqVly5aKVqtVmjRposyZM0fJzc2977Ti93PhwgXlueeeU1q2bKk4ODgorq6uStu2bZWpU6cq27Zte+BzJkRZqBTlH/dhhRBCCCGEEEIAMoZJCCGEEEIIIYokBZMQQgghhBBCFEEKJiGEEEIIIYQoghRMQgghqqV58+bxyCOP4OLiQr169Rg5ciQnT57Md8wXX3xB7969cXV1RaVScfPmzQLt3Lhxg4kTJ+Lq6oq7uzvh4eHcvn27gq5CCCFEeZOCSQghRLUUFxfHjBkz2LdvH7Gxsej1egYMGEB2drb5mJycHAYNGsSrr75aZDsTJ04kOTmZ2NhYNm7cSHx8PNOnT6+ISxBCCFEBZJY8IYQQArh69Sr16tUjLi6OwMDAfPt27txJcHAwf/31F+7u7ubtx48fx9fXl99++41OnToBsGXLFoYMGcKFCxfw8vKqyEsQQghRDmTh2hIymUykp6fj4uKCSqWydhwhhLA5iqKQlZWFl5cXdnaVp6PDrVu3APDw8Cj2OQkJCbi7u5uLJYB+/fphZ2fH/v37GTVqVIFzdDodOp3O/LPJZOLGjRvUrl1b3leEEKIUyvt9RQqmEkpPT6dx48bWjiGEEDbvzz//pFGjRtaOAdwrWp5//nl69OiBn59fsc/LyMigXr16+bZpNBo8PDzIyMgo9Jx58+Yxd+7cMuUVQghRUHm9r0jBVEIuLi7Avf8hrq6uVsuh1+vZunUrAwYMQKvVWi1Hadhqdsld8Ww1e1XPrdPp2L9/P7m5ufTr16/Ej5OZmUnjxo3Nr6eVwYwZMzh27Bi7d+8u98eaPXs2ERER5p9v3bpFkyZNOHXqVInublVFer2eHTt2EBwcbFN/O+VFno/8/v58qNVq1q1bR+PGjenUqVO1uzsrvxtw9+5dxo8fz+7du+nYsSOJiYnl9r4iBVMJ5f1Burq6Wr1gcnJywtXV1eb+UGw1u+SueLaavarmNhqNJCYmEhcXR05ODiqVit69e5f6Q35l+YAzc+ZM82QNJf1m0tPTkytXruTbZjAYuHHjBp6enoWe4+DggIODQ4HtHh4e1K5du0SPX9Xk/Q7Wrl3bpv52yos8H/n9/fnYtm0b6enpXLlyhS5dulCrVi1rx6tQ1f13w2g08vjjj7N7925cXFx4//336du3b7m9r1SezuNCCCEqtZycHGJjY8nJycHDw4PHHnvMpj+kKIrCzJkzWbt2Ldu3b6dZs2YlbqNbt27cvHmTxMRE87bt27djMpno0qWLJeMKIf7X4cOH2b9/PwCjRo2y6dchUTqXL18mKSkJe3t7fvrpJ9q3b1+ujyd3mIQQQhSLi4uLuftHQEAAarXa2pHKZMaMGURHR/PTTz/h4uJiHnPk5uZGjRo1gHtjlDIyMkhLSwPg6NGjuLi40KRJEzw8PGjbti2DBg1i2rRpLF26FL1ez8yZMxk/frzMkCdEObl79y4AvXv3xtfX18pphDV4eXmxZ88ejh49SnBwMNevXy/Xx5OCSQghRLF1797d2hEsZsmSJcC9D11/FxUVxZNPPgnA0qVL803QkDfd+N+PWbVqFTNnzqRv377Y2dkxZswYPv7443LPL0R11bVrV5o1a0bDhg2tHUVYkZeXV4V9MSUFkxBCiFIxmUyValrwkirOMoRvvfUWb7311n2P8fDwIDo62kKphBDFUVlm2BTVg+2+0wkhhKhQSUlJzJo1C39/f+zt7VGr1djb2+Pv78+sWbNISkqydkQhhBDC4qRgEkKIasxgMLBv3z5WrlxZ5B2XtLQ0goKC6NixI5s2beLRRx9l2bJlrF27lmXLlvHoo4+yadMmOnbsSFBQkHm8jxBCCFEVSMEkhBDVkKIoHD16lMWLF/PLL79w+vRpTp06VeC46Oho/Pz8UKvVrF23jtTUVF565TVadR+Ia+tutOo+kJdeeY3U1FTWrluHnZ0dfn5+xMTEWOGqhBBCCMuTMUxCCFENKYpCXFwcN2/epGbNmgQHB9OyZUtOnz5tPiY6OppJkyYRERHBggULOH7xL+asT2HdoYtk5xrNxznbqxnZoSGTOgeyY8cIIiMjmThxIoqiMGHCBGtcnhBCCBtnNBorzWysUjAJIUQ1ZGdnx4ABA8jIyKBr167Y29uj1+vN+1NTUwkLC+OFF15gwYIFfLo9jYWxJyms1152rpFV+88TfeA8kf1bs3DhQgDCwsLo3LkzPj4+FXVZQgghqoCrV6/Sr18/3njjDR577DFrx5EueUIIUV21atWKwMBA7O3tC+ybOnUq3bt3Z+HChXy6PY0FWwsvlv5OUeDdbzbwUPe+jBs3jm7duhEeHl5O6YUQVZVOp+Onn37i9u3b1o4irCArK4shQ4bw+++/89JLL5nX3bImucMkhBAin8OHDxMfH8/ades4fvEvFmw9+cBzdJdSubl7JXfPJJIBvPzaHJ597jlGjxpFUlISAQEB5R9cCGHzTCYTa9as4dSpU1y9epXw8HBUKpW1Y4kKkpuby+jRozl48CC1a9dm8+bNODo6WjuW3GESQoiqpjjrC93PqlWraNasGcOHDWPlgQvFOufu+aPcPZMIKjuc/frQZuQMHh0+HG9vb6KiosqURwhRfWzbto1Tp06h0WgYPHiwFEvVjFqtpkWLFjg7O/Pzzz/Tpk0ba0cC5A6TEEJUGSaTiaNHj5KQkMDkyZNxcnIqVTsJCQlMmjSJHJ2BdYcuFusclw5DMGZexqXTCLS1vIi/rCZHZ+CJJ55gw4YNpcohhKhe7t69S0pKCgAjRoygYcOGVk4kKpparWbJkiW8+OKLlWr8qxRMQghRBZw+fZrY2FguX74MwP79+wkODi5VWydOnGDmzJmkXLqVbza8+7Gzd8Sj/9Pmn7NzjRy/lImPjw/JycmlyiGEqF4cHR2ZOnUqp06dws/Pz9pxhJWoVKpKVSyBFExCCFElHD58mMuXL+Pg4EDPnj3p0qVLqdvS6/W4uLiQnVu2rn3ZehOurq7o9XpMJhN2dtILXAhxf87OznTo0MHaMYTIx+bfvRYvXoy3tzeOjo506dKFAwcOFHnsmjVr6NSpE+7u7jg7O+Pv78+KFSsqMK0QQpSPPn360LVrV5599ll69uyJVqstdVtarZasrCyctHDnjyMoBv2DTyqEs9aOzMxMtFqtFEtCCCFslk2/g3333XdERETw5ptvkpSURPv27Rk4cCBXrlwp9HgPDw9ee+01EhIS+P333wkNDSU0NJRffvmlgpMLIYRl1apVi4EDB5Z63NLftW7dms2bN/PcpJFcWf0at4/9WuI2nO3VtG3gSlpaGu3atStzJiGEEMJabLpg+uCDD5g2bRqhoaH4+vqydOlSnJyc+Prrrws9vnfv3owaNYq2bdvSokULnnvuOR5++GF2795dwcmFEKLy0ul0xMTEcGD/PjT2DpjuZJW4jVEdGuLkoGHFihX07NmzHFIKIYQQFcNmxzDl5uaSmJjI7Nmzzdvs7Ozo168fCQkJDzxfURS2b9/OyZMnmT9/fpHH6XQ6dDqd+efMzEzgXh9/vb503VQsIe+xrZmhtGw1u+SueLaa3ZK5FUWpsGl18/L+z//8DzNmzODRRx/lhTfe5al150o0VblKBSGdPFm/YQOXL19mypQp+Z4LW/v/KYQQwnIuXLiAp6cnGo3tlCG2k/Qfrl27htFopH79+vm2169fnxMnThR53q1bt2jYsCE6nQ61Ws1nn31G//79izx+3rx5zJ07t8D2rVu3WqTrS1nFxsZaO0Kp2Wp2yV3xbDV7WXIrikJmZiaXLl2icePGODs7WzDZ/Xl5efHtt9/i6urKrYunmf9IydtIS9qLnUpFTEwMFy9e5OLF/5uePCcnx4JphRBC2IoLFy7QvXt3AgICiImJoUaNGtaOVCw2WzCVlouLC4cPH+b27dts27aNiIgImjdvTu/evQs9fvbs2URERJh/zszMpHHjxgwYMABXV9cKSl2QXq8nNjaW/v37l2lwtzXYanbJXfFsNXtZc6enp7Nt2zb+/PNP87YhQ4ZYMmKh8nIPGDCAP//8k65duzJt2jTeffddvtx1ho+3p3K/G00qFTzbpyXTejXntdde48svv2Tfvn00b97cfIzBYJCCSYhq7NatW6Snp9O2bVtrRxEV7Pr16+b3FycnJ7Kzs6VgKm916tRBrVab1xzJc/nyZTw9PYs8z87Ozjy3u7+/P8ePH2fevHlFFkwODg44ODgU2K7VaivFB7jKkqM0bDW75K54tpq9tLnT09P5888/UavVdO3atcyz3uUpbvc+rVZL69at+eyzz5g4cSJGo5GFCxfSp009Vu6/wNpDF/Otz+Rsr2ZUh4ZM6tKINl61iIyM5MMPP2TVqlW0bt0aRVE4deoUCQkJODs7M3DgwDJfixDC9uTm5hITE8Ply5cZPnw4AQEB1o4kKoiiKIwdO5bjx4/TsGFDfvnlF+rUqWPtWMVmswWTvb09HTt2ZNu2bYwcORK4t8r9tm3bmDlzZrHbMZlM+cYoCSGEtT3yyCNkZmbStWtX3Nzcytye0Wjku+++Y/78+fz00094e3sX67yQkBAURSEsLIykpCSefe455j46nJcHtuL4pUyy9SactXa0beCKk4OG9Rs28PRHH5GQkMCqVasICQkhIyOD77//nhs3bgCg0WgIDAws8zUJIWyLoiisXbuWy5cv4+zsTIsWLawdSVQglUrFW2+9xblz59iwYQNNmza1dqQSsdmCCSAiIoIpU6bQqVMnOnfuzKJFi8jOziY0NBSAyZMn07BhQ+bNmwfcG4/UqVMnWrRogU6n4+eff2bFihUsWbLEmpchhBD5aDQai92F+e9//8ucOXM4efIkcG920Y8//rjY50+YMIHOnTsTHh7O6FGj8Pb25oknnsDHxwdXV1f+zMxkY1oaK1as4Ny5cwQFBXHs2DHznXx3d3du376No6MjHTt2pHPnzha5LiGEbTlx4gQnTpxArVYzbtw4i3wZJGxLYGAgJ0+etMkeIzZdMI0bN46rV68yZ84cMjIy8Pf3Z8uWLeaJIM6fP59vscTs7GyeeeYZLly4QI0aNWjTpg0rV65k3Lhx1roEIYQoV3v37uXkyZPUqlWLiIgIZs2aVeI2fHx8iIuLIykpiaioKDZs2EBycjJ6vR6tVku7du0YNmwYoaGhBbrYODo6MmHCBBo0aIC9vT3wf7ONCiGqjzZt2pjXimvcuLG14wgrscViCWy8YAKYOXNmkV3wdu7cme/nf//73/z73/+ugFRCCFGQoigoipLvi5zy9q9//Yv69eszY8aMMk9UExAQkK8gMplMxboWW+t6IYSwPJVKRdeuXa0dQ4hSsemFa4UQwlacP3+eqKgoDh48WKGP6+XlxezZsy0+q2dubm6J1mYSQgghbJXN32ESQojK7Pr16/z666/m9eEyMzPp1KlThd5lsqSsrCwOHDhAYmIiAwYMwN/f39qRhBBCiHIlBZMQQpSj27dvc+LECVQqFR06dKB3795lLpZ0Oh0//vgjISEhxZom3FL27NnD9u3bMZlMwL1B3FIwCSGEqOqkYBJCiHLUtGlT+vTpQ5s2bahbt26Z2rp79y5ffvkl8+fP5+LFi7i5uTF06FALJX2wOnXqYDKZaNKkCd26daNVq1YV9thCCCEqt0OHDuHo6FglFyWWgkkIIcpZr169LNLOoEGDiIuLA6BRo0YVvoZcq1atmD59Og0aNKjQxxVCCFG5paamMnDgQIxGI9u3b6d9+/bWjmRRttmJXgghqqHQ0FCaNGnCkiVLSEtLY/To0RX6+CqVSoolIUSRrl69Sm5urrVjiAqWnp7OgAEDuHr1Kk2bNqVZs2bWjmRxcodJCCFKyWg0olarK+zxJk6cSEhIiHk9I0vJzMy0+Cx6QojqJTMzk2+//RYXFxdCQkJwcXGxdiRRQV5//XXOnTuHj48PmzdvrpLvJ1IwCSFECV25coVt27ZRo0YNRo4cWWGPq9FY7iVbURRSU1PZt28ff/zxB8899xw1atSwWPtCiOojNzeX1atXc/v2bZycnCz+pY6o3D755BNMJhNvvvkm9evXt3acciEFkxBCFFN2djbbtm3j8OHDKIqCWq2mb9++Zf4mtaLvVCmKwhdffEFGRgZwr6vdH3/8QZs2bSosgxCi6ti8eTOXLl3CycmJkJAQHBwcrB1JVCBnZ2eWL19u7RjlSgomIYQogeTkZBRFoW3btvTp06dMxdLNmzf5+OOP+eabb0hKSsLNzc2CSYumUqlo2LAhf/31FwEBAXTp0gU3Nzf0en2FPL4Qomrp0aMHFy9eZNiwYbi7u1s7jhAWJwWTEEIUk7OzM0OHDqVWrVo0bty41O0oisLbb7/Nhx9+yK1btwD49ttvmTVrlqWiPlBwcDD9+/eXb4KFEGVWp04dnnrqKZtdkFuIB5GCSQghSuDhhx8ucxsqlYpjx45x69Yt2rVrxxtvvMFjjz1mgXTF5+zsXKGPJ4So2qRYElWZFExCCGEFc+fOZdy4cYwePdpiHzT0ej1ZWVl4eHhYpD0hhBBCSMEkAJNJQaWCO3ojxy9lknnHgGsNDW0buFJDq0YB7FQqa8cUolwZDIZ8s9CZTKZy/cbU19cXX19fi7R1+/ZtfvvtNw4ePIi7uztTp05FJX+zQgghhEVIwSQ4ffU2y/eeY92hi2TnGs3bne3VjOzQkCe7e9OyvqynIKqmW7dusWPHDs6ePcuZM2fYtWsXKSkp6PV6tFotvr6+9OrVi9DQUAICAqwdt4D09HS+/vprjMZ7f7tarZbs7Gxq1qxp5WRCCCGqgl27drF//34iIyOr7ZdxUjBVc59uT2Nh7EkUpeC+7Fwjq/afJ/rAeSL7t2ZmH5+KDyhEOdHr9ezcuZN9+/ZhMpkASExM5NFHHyUyMhIXFxeysrJITU1l5cqVfPrppwQGBrJs2TJ8fAr/W0hPT+fnn39mypQpFXYdnp6euLi4ULNmTbp160abNm1kLIEQQgiLOHLkCMOHD+fWrVvUqVOHJ5980tqRrEIKpmrs0+1pLNh68oHHKQos2HoSlQpmBEvRJKoGtVrNwYMHMZlM3Lx5k0c6d+b1118nR2cg5dItsnMVGtirGDnmcd588002bNzIR4sW4efnR1RUFCEhIea2Ll68yPz58/niiy/Q6XS0b9++wq7Dzs6OqVOnyiQOQohypdPpZFbNaubMmTMMGjSIW7du0atXL8aNG2ftSFYjBVM1ZDIpnL56u1jF0t8t2HqSge08aV7XWcY0CZu3evVqPv/8c8aNG8cHH3zAifSbzFmfUmTX1EmdA9mxYwSRkZFMnDgRRVGYMGECBoOBzp07k56eDkD37t3Nd6wqihRLQojylJ6ezooVKxg8eLBFZgoVtmHnzp1kZGTw8MMPs379emrUqGHtSFYj/TaqIZUKlu89V+LzFAWW7zmLlErC1qWmphIWFsaYMWOYP38+i3ecZsine1m1/3y+Ygn+r2vqkE/33uvCunAhL7zwAmFhYaSlpaHRaJgxYwa9evXi119/Zffu3RYZ63TlyhVycnLK3I4QQpRFVlYWq1ev5u7duxw9ehSlsD78okoKCwvjv//9L1u2bKn2CxLLHaZq6I7eyLpDF0t17tpDF3l1aFuc7OVXR9iuqVOn0r17dxYuXFiqrqkLFy4kKSmJ8PBw4uLiePnll5k9e3aZB8MqisLp06fZt28fp0+fJigoiN69e5epTSGEKC29Xs/q1avJysqiTp06jBkzptoO+q+uxo4da+0IlYLcYaqGjl/KLPAtenFl5xo5cSnLwomEsKzc3Nwi9yUmJhIfH8+zzz3H8Yt/lapr6on0v3j2ueeIj48nKSkJtVptkQ8R27dvZ9WqVZw+fRqVSkV2dnaZ2xRCiNJSq9U0atSIGjVqEBISgqOjo7UjCWEVUjBVQ5l3DGU7/67eQkmEsKycnBy2bNnCokWLuH37dqHHLF++nGbNmjF82DBWHrhw3/YUQ8HCS1Fg5f4LPDp8ON7e3kRFRVkkO4Cfnx/29vZ06dKFWbNmMXToUIu1LYQQJWVnZ8fgwYN5+umnZUFsUa1Jv6pqyLVG2f63uzpqLZRECMvZs2cPu3btQqfTAZCcnEyXLl0KHLdr1y4mTZpEjs5QZNdU/fUL3Er4Dl36SbzCP0Olzv83s/bQRV4e2IonnniCDRs2WOwa6tevT2RkJPb29hZrUwghysrFRdZiFNWbFEzVUNsGrjjbq0vVLc/ZXk2bBvLCKSqfa9euodPp8PT0pF+/frRo0aLQ41JSUoiMjPzfqcPz/w2Y9He5vvkTco7HA/cGNt89/zs1muWfxCE718jxS5n4+PiQnJxs0euQYkkIIYSoXKRgqoZqaO9Nk7xq//kSnzuqQ0NqaNXlkEqIsunduzfNmjXjoYceKnI8kclkQq/X4+LiQnZuwZmeVBoHDLcyAIUaPl1w6xGCg2fha49l6024urqi1+sxmUxFLhZrMBg4ffo0rVu3LvW1CSGEEOXhfu9f4v/IM1QNKQo82d2bko5RV6ngyR7NkAlFRWXk5ubGww8/fN/JF+zs7NBqtWRlZeFsX/A4lUqFR7+naPDkR9Qb80aRxRKAs9aOzMxMtFptoW82BoOBXbt2sWjRIlavXs3Fi6WbmVIIIYQoDz///DM9evTgypUr1o5S6UnBVA3Z2aloWd+FyP4l+8b7xQGtaSGL1gob5+vrS2pqKr4N3HC2L3i31KFBS+zrF96dL4+zvZq2DVxJS0ujXbt2BfYrisKpU6fYtWsX2dnZuLq6yox3QgghKo2EhAQee+wx9u3bxwcffGDtOJWeFEzV2Mw+Prw0sPUD7zSpVPDSwNbMCPaR9RdEhTMYyjar4z/16tWLlStX4uSgYWSHhqVqY1SHhjg5aFixYgU9e/YssF+lUlG7dm0aNGjAmDFjePbZZ2nVqlVZowshRLmQxWirl+TkZIYOHcqdO3cYPHgw77zzjrUjVXpSMFVzM4J9iH0hiEldmhT4tt3ZXs2kLk2IfSGIGcFFd00Sojzk5uaye/duUlJS+OOPP8rU1oEDB/jiiy8ACA0N5ezZs2zYuJFJnRuVqmvqpC6NWL9hA+fOnSM0NLTQ4+rVq8eTTz6Jn58farWM+xNCVE7Hjx/nu+++M88wKqo+e3t73Nzc6NatG99//z1arcx+/CAy6YOgeV1n3hnpx6tD23L8UhZZd/W4Ompp08CFGlq1jFkSFe7o0aNs3brVvJbSsWPH8PEpedGekJDA22+/zZYtW7C3t2fo0KEEBAQQGBjIR4sWsWPHCCL7ty7R4rUvDmhNK083nv7oIwIDAwkICCj0OJVKJXdkhRCVWkZGBmvXrkWv13PgwAF69epl7UiijIoziUPLli3Zs2cPjo6OODs7V1Ay2yYFkzCPSXKy19Cxaa0C++Ujn6hod+/e5fbt27i7u+Pm5saQIUNK3MbVq1fp3bs3ubm5qNVqJkyYYN63bNky/Pz8iIyMZOHChahUsGDrSe7XK0Wlgud7ejKwMbz00kskJCRw7Nix0lyeEEJY3e3bt4mJiUGv19O8eXN69Ohh7UiiFI4cOcI333zDrl27SElJQa/Xo9Vq8fX1pVevXoSGhhb6xZ6Xl5cV0touKZiEEJVOQEAAarUaX19ffvnll1Ldqalbty5PPfUU2dnZzJ49O9+6TD4+PkRFRTFx4kQAFi5cSN/WtVm5/wJrD13Mtz6Ts70do33saWy4wKWDB1lxwMRHH33EihUrSnXXSwghKoNbt26h1+upXbs2jz32mEwtbWPOnDkDQGBgIHq9Pt94X71ez5EjR0hOTubTTz8lMDCQZcuWyXtWGUjBJISodNRqNQEBAej1+jK1s2jRoiKLrZCQEBRFISwsjKSkJJ597jnmPjqclwe24vilTLL1Jpy1dtRR32HFN1Fc4t7A6NTUVKKioggJCSlTNiGEsKaGDRsybdo0TCYTNWrUsHYcUQLR0dHMmDGD5cuXA0VPjpS3fe/evfj5+cl7VxlIwSSEqLIedGdqwoQJdO7cmfDwcEaPGoW3tzdPPPEEPj4+uLq68mdmJmlpaWRkZHDhwgUUReGTTz6Rb+mEEFVCrVoFu+GLyi06OppJkybh6OhY7HMMBgMGg4GJEyeiKEq+LuqieGz+/uvixYvx9vbG0dGRLl26cODAgSKP/fLLL+nVqxe1atWiVq1a9OvX777HCyEsS6fTlXmacEVRuHHjhoUS3eueFxcXR2JiIsOGDWPDhg1MnTqVUaNGMXXqVDZs2ICDgwP//ve/2bx5sxRLQgghrCI1NZWwsLBSTwOf16siLS3NwsmqPpu+w/Tdd98RERHB0qVL6dKlC4sWLWLgwIGcPHmSevXqFTh+586dhISE0L17dxwdHZk/fz4DBgwgOTmZhg1Ltx6LEOLBjEYjiYmJxMXF0aNHD7p3717iNhRFYfPmzbz99tuoVCr27t1r0VnoAgIC8g2MLc5MQ0IIIURFmTp1Kkaj8cEH3ofRaCQ8PJy4uDgLpaoebPrTwAcffMC0adMIDQ3F19eXpUuX4uTkxNdff13o8atWreKZZ57B39+fNm3a8NVXX2Eymdi2bVsFJxei+jh37hyfffYZmzdvJicnh5SUlBJ/O3b+/Hk6d+7M0KFD2b9/P0eOHCnTN2RGo5EjR45w9erVIo+RYkkIIURlkZiYSHx8fJl7aRgMBuLj40lKSrJQsurBZj8R5ObmkpiYSL9+/czb7Ozs6NevHwkJCcVqIycnB71ej4eHR3nFFKLa02q13LhxA2dnZ4YMGUJoaGiJ7ww1aNCAa9eu4eTkxIsvvsjZs2dp2bJlibPk5OQQHx/PokWLWLduHXv37i1xG0IIIURFW758ORqNZTqGaTQaoqKiLNJWdWGzXfKuXbuG0Wikfv36+bbXr1+fEydOFKuNl19+GS8vr3xF1z/pdLp8q19nZmYC96ZsLOsMXmWR99jWzFBatppdcpdOvXr1GDlyJC1atMDBwQGTyYTJZCrWuX/PvHLlSry9vc3dbUtzPfv27WPXrl0A1KxZk9q1a5fL82Lt57y0Kiq3rT0vQghhbbt27Srz3aU8BoOB3bt3W6St6sJmC6ayeu+991i9ejU7d+6870wj8+bNY+7cuQW2b926FScnp/KMWCyxsbHWjlBqtppdcpfOuXPnSn1uXvZr166VKYNer8fJyYk6derg7u7O9evX+fnnn8vU5v1Y+zkvrfLOnZOTU67tCyHuOXDgAPXq1cPb29vaUUQZpaSkWLS95ORki7ZX1dlswVSnTh3UajWXL1/Ot/3y5ct4enre99wFCxbw3nvv8euvv/Lwww/f99jZs2cTERFh/jkzM5PGjRszYMAAXF1dS38BZaTX64mNjaV///5otVqr5SgNW80uuSuerWaX3PeXd6deCFF+Tp48yebNm7Gzs+N//ud/Cp0MS9gGk8lk8Tvzer1eJjcqAZstmOzt7enYsSPbtm1j5MiRAOYJHGbOnFnkee+//z7vvvsuv/zyC506dXrg4zg4OODg4FBgu1arrRQfhCpLjtKw1eyS+x6DwUBubm6p77QajUZWr15Nbm4uoaGh9z1WnvOKVd65K8tzMm/ePNasWcOJEyeoUaMG3bt3Z/78+bRu3dp8zN27d4mMjGT16tXodDoGDhzIZ599lq87+Pnz53n66afZsWMHNWvWZMqUKcybN89i4w2EKKnLly+zZs0aADp06EDdunWtnEiUhZ2dHVqt1qJFk1arlWKpBGz6mYqIiODLL7/km2++4fjx4zz99NNkZ2ebP3xNnjyZ2bNnm4+fP38+b7zxBl9//TXe3t5kZGSQkZHB7du3rXUJQtiMvHFHiqJw9OhRFi9eXKrubAaDgRUrVuDr68ukSZN46aWXyvQ3eP36dbZt21bscVFC5ImLi2PGjBns27eP2NhY9Ho9AwYMIDs723zMCy+8wIYNG/j++++Ji4sjPT2d0aNHm/cbjUaGDh1Kbm4ue/fu5ZtvvmH58uXMmTPHGpckBAAHDx4kNzcXb29vBg8ebNElGIR1+Pr6WrS9du3aWbS9qs6mv/4aN24cV69eZc6cOWRkZODv78+WLVvM3/ydP38+X/W8ZMkScnNzeeyxx/K18+abb/LWW29VZHQhKr2kpCSioqLYtWsXKSkp6PV6PD09GTdunHl1eKPRyN27d0u04nhycjKTJ08GwMPDg4iIiBK/mSuKwh9//MG+ffs4efIkcG8mPUu/oYiqbcuWLfl+Xr58OfXq1SMxMZHAwEBu3brFsmXLiI6Opk+fPgBERUXRtm1b9u3bR9euXdm6dSspKSn8+uuv1K9fH39/f9555x1efvll3nrrLezt7a1xaaKaGzx4MG5ubgQEBKBWq60dR1hAr169SE5OtsjEDxqNhp49e1ogVfVh0wUTwMyZM4vsgrdz5858P5dl0LkQ1UVaWhrh4eHEx8fTrFkzJk2aRGRkJC4uLty8eZNz586Rm5vLrl27UKvVPProo/j4+BS7/fbt2xMaGkqrVq2YMWMGLi4upcoZGxtLeno6AK1atcLNza1U7QiR59atWwDmpSYSExPR6/X5ZlJt06YNTZo0ISEhga5du5KQkMBDDz2Ur4vewIEDefrpp0lOTqZDhw4FHqeyzr5aGdjqDJPlpSzPR5cuXUp9bmVVnX8/pkyZwrJly8xdmmvUqJHv36Vpryo9j+V9LTZfMAkhLCc6OpqwsDC6d+/O2nXrGD5sGDk6AymXbpGdq9DAXsUjXf6iccMGPNK5Mx8tWoSfnx9RUVGEhIQU+3GKWly6uFQqFT169ODs2bN06dKFOnXqlKk9IUwmE88//zw9evTAz88PgIyMDOzt7XF3d893bP369cnIyDAfU9jyFnn7ClPU7Ks7duyoFLOvVga2OsNkeZHnI7/q+nzExMQU2Fba99OLFy9y8eLFskaqNMp79lUpmIQQwL1iadKkSURERLBgwQKOX/yLOetTWHfoItm5RvNxzvZqRnbIZVLnQHbsGEFkZCQTJ05EURQmTJhQYXl9fX2lC56wmBkzZnDs2LEKWZukqNlXg4ODqV27drk/fmVmqzNMlhd5PvKr7s/HmTNn6NixI0ajEScnJ77++mvCwsK4c+dOsdtwcHBg3759NG/evByTVrzr16+Xa/tSMAkhSE1NJSwsjBdeeIEFCxbw6fY0FsaeRFEKHpuda2TV/vNEHzhPZP/WLFy4EIDQ0FBatGhh7gYihK2YOXMmGzduJD4+nkaNGpm3e3p6kpuby82bN/PdZfr78hWenp4cOHAgX3t5y10UtcRFZZ99tTKQ5yI/eT7yq67Px86dO/NNSgNw586dYhdMKpWKZcuW5ZsJtKoo798Hm54lTwhhGVOnTiU4OJiFCxfy6fY0FmwtvFj6O0WBBVtP8uGWYzRp0gSAYcOGlTqD0Wjk6NGj7N27t9RtCFESiqIwc+ZM1q5dy/bt22nWrFm+/R07dkSr1bJt2zbztpMnT3L+/Hm6desGQLdu3Th69ChXrlwxHxMbG4urq6vcARVCWMwPP/zA008/DcCIESNKNNmSRqPBwcGBVatWlaj7vPg/codJiGpMURS2bNmCr68vTZo2JfnPayzYerLY5+sunuDFxyZhyr4JwLVr19i9e3eJZt/R6XQcPHiQ/fv3k5WVhUajwd/fX8ZyiHI3Y8YMoqOj+emnn3BxcTGPOXJzc6NGjRq4ubkRHh5OREQEHh4euLq6MmvWLLp160bXrl0BGDBgAL6+vjzxxBO8//77ZGRk8PrrrzNjxoxC7yIJIURJ3bx5k6lTp6IoCtOnT2fp0qWcOnWKEydOAPcKosJmz8vb3qNHD7766qsSTdAk8pM7TEJUU3fu3GHFihUcOHAAT09PVEDMruMlakNbuxGKQY9bXS+WLFlC06ZN+e6770rUhk6nY/v27WRlZeHs7EyvXr1kMT1RIZYsWcKtW7fo3bs3DRo0MP/z99/hDz/8kGHDhjFmzBgCAwPx9PQ0LwgKoFar2bhxI2q1mm7dujFp0iQmT57M22+/bY1LEtVE3np4sv5c9eDu7s769esJDQ3ls88+Q6VSmccgxcfH89RTT+Hv72/ulqbVavH39+epp54iMTGRnTt3SrFURnKHSYhqytHREZ1Oh9FoRKVSETbtKYI/2leiNuwca1J//LvUaticiZMHkJ6ezoYNG0rUhqurK7169cLd3R0/Pz80GnlZEhVDeVC/U+79nSxevJjFixcXeUzTpk1LtYizEKW1b98+tm7dyu+//86ECRNkYdpqIDAwkMDAwALb27dvzyeffGL+2WQyyZeO5UA+mQhRTalUKh599FH8/Pz44IMPOHszN99seMXl4OlDjhGOX8rEx8eH5OTkErfRu3fvEp8jhBDVUWpqqnla7ebNm0uxJPKRYql8yLMqRDVWt25drl27houLC9m5D/62/X6y9SZcXV3R6/XSTUQIIcqBXq/np59+QlEUOnToYB5LJ4QoX1IwCVGN2dnZodVq740fsr/3LaXx7m1u7l5F9omSrUfjrLUjMzMTrVbLzZs32bx5M2lpaeURWwghqiWtVsv48ePx9fVl6NChcndJiAoiXfKEqGIUReHGjRvFXgDT19eX1NRUAoP7kb1nFdcP/ISSm4PGoyFOrbqhslM/sA1nezVtG7iyMS2N0NBQc3/qK1euyEBTIYSwoEaNGjF27FhrxxCiWpE7TEJUIRcvXuSbb75h6dKlZGZmFuucXr16sXLlSg4nHuDa7hiU3By0dZri3usJKOa3l6M6NMTJQUNCQgJeXl4A+Pj4lGh6cSGEEEKIykjuMAlRBSiKwtq1azl69Chwb6rjCxcuFGvhzNDQUD799FNMisKjY8aRYGpBjZZdUamK932KSgWTujRi/YYN7NixgyeeeILBgwdTt27dMl2TEEIIUR0oisK8efMYNWoUbdu2tXYcUQi5wyREFaBSqcyLZPr7+zNr1qxiFUsAAQEBBAYG8vFHH/HTD6uZMzO02MUSwIsDWtPK042PP/qIHj16MHnyZCmWhBBCiGL6z3/+w2uvvUavXr24ceOGteOIQsgdJiGqiN69e9OpUyfq169f4nOXLVuGn58fkZGRLFy4EJUKFmw9yf2WqVGp7hVLM4J9iIyMJCEhgWPHjpXhCoQQQojq5euvv+bll18GYPbs2Xh4eFg5kSiMFExCVBHOzs44OzsX2J6env7A8Uw+Pj5ERUUxceJEABYuXEjf1rVZue88vx05hrdyme25LdCjwdlezagODZnUpRFtvGoRGRnJhx9+yKpVq2SCByGEEKKYjEYjX3/9NQD/+te/iIyMtHIiURQpmISooi5cuMB7773HV199xcCBAxk/fvx9jw8JCUFRFMLCwkhKSmLChAnUvn2b7nb3iq05XWvQ+qEOtG3gipODhvUbNvD0Rx+RkJDAqlWrCAkJqYjLEkKIKs1oNJKZmUmtWrWsHUWUM7VazS+//MKyZcuYNWuWteOI+5AxTEJUcpcvX0a5X9+4QkRHR9OiRQsWL16MTqfjzz//LFYbEyZM4NixY5hMJmJjY8nMzERRFNzd3XHU3+LEns3857138fHxYfSoUSiKwrFjx6RYEkIIC1AUhc2bN/P5559z+vRpa8cRFcDZ2Zlnn31W1tSq5OQOkxCV1LVr19i2bRsnTpxg4sSJJeru1q1bN0wmE0FBQbz22mtkZ2cX+8XYx8eHuLg4tm/fzs8//8yOHTs4evQoer0erVZLu3btGDZsGKGhoQQEBJT28oQQQvzDb7/9RmJiIgAGg8HKaYQQeaRgEqIS2rlzJ/Hx8SiKgkql4tKlSyUqmJo1a0ZycjKtWrVCr9fz888/lzhDnz596NOnj/lnk8mEnZ3clBZCiPLwxx9/sGXLFgD69etH69at0ev1Vk4lhAApmISolNzc3FAUhdatW9O3b99STdPdqlUri2aSYkkIIcqPl5cXbdu2RavV0r17d2vHEUL8jRRMQlRC7du3p27dujRq1Mhibd68eZOkpCSysrJ47LHHLNauEEKIstNqtTz22GOYTCYZz2JjpAdG1Sf/d4WohOzs7AoUS8ePH2fixIns3LmzRG3dvn2bc+fOsWTJEvbt20dycjLXrl2zYFohhBCWoFKpUKvV1o4hHiApKYlZs2bh7++Pvb09arUae3t788LxSUlJ5mNLOmmTqJzkDpMQlVxKSgpvv/02//3vf1EUhfT0dHr37l3s8x0cHMjKykJRFJo3b063bt2oXbt2+QUWQgghqqC0tDTCw8OJj49Ho9Hkm5hDr9dz5MgRkpOT+fTTTwkMDOSrr77i/fffp2XLlrz00kty59CGScEkRAW6evUqbm5u2NvbF/ucuLg4vvvuOwBGjhzJnDlzSvSYWq2Wxo0b069fPxo2bFiic4UQQghxb7mOsLAwjEYjUPQshnnb9+7dS9u2bTEajdjZ2TFkyBD8/PwqLK+wLCmYhKgAmZmZ7Ny5k8OHD9O7d28CAwOLfW5YWBiHDh1ixowZtG/fvlSP7+7uTr169Up1rhBCCFGdRUdHM2nSpBJ1r/t7QRUeHi7Fko2TgkmIcnbkyBE2btxofvG8ceNGic53cHDgiy++KHRf3rTjQgghhLC81NRUwsLCyjQW6dtvv+Vf//pXiZYHEZWLFExClDNPT08MBgONGzemf//+NG7cuEztmUwmTpw4wb59+/Dz86Nz584WSiqEEKI8GQwG1Gq1fNFlQ6ZOnWruhldaRqOR8PBw4uLiLJRKVDQpmIQoZ/Xr12f69Ol4enrme5Pcv38/fn5+ODs7F7utU6dOsXnzZm7evAnAnTt3eOSRR+TNVwghKjmTyURMTAxubm4MHTpUZsOzAYmJicTHx5e5HYPBQHx8PElJSQQEBFggmahoMq24EBWgQYMG5qJm7969DBw4kK5du/L555+XqB17e3tu3rxJjRo16NWrF5MnT5ZiSQghbMCWLVs4c+YMx44dK3HXbGEdy5cvR6OxzL0FjUZDVFSURdoSFU/uMAlRgd544w3+/e9/A/dePK9cuVKi85s2bcqYMWNo3bo1Wq22PCIKIYSwsIMHD/Lbb78BMGrUKOrWrWvlRKI4du3aVeRseCVlMBjYvXu3RdoSFU/uMAlRSrdu3eKvv/4q0TlDhw5Fq9Uybdo0Tp06xXvvvVei81UqFX5+flIsCSGEDXFyckKj0RAcHEzbtm2tHUcUU0pKikXbS05Otmh7ouLIHSYhSujOnTvs2LGDAwcO4OPjw/jx44t9bteuXfnzzz+pX79+vu0Gg8Fit/2FEEJULr6+vnh6elKrVi1rRxHFZDKZ0Ov1Fm1Tr9djMpmws5P7FbZGPqEJUQK3b99myZIl3L17FwCdTodery/RHZ+/F0u3bt3iwIEDJCUlMW3aNDw8PCyeWQghhPXJ67ttsbOzQ6vVWrRo0mq1UizZKCmYhCiBGjVqoFKpqFu3Lv3798fHx6fUky5s3LiRpKQk89oOR44cITg42JJxhRBCCFFKvr6+HDlyxGLttWvXzmJtiYpl82Xu4sWL8fb2xtHRkS5dunDgwIEij01OTmbMmDF4e3ujUqlYtGhRxQUVVYJareaJJ57gqaeeomXLlgBs2rSJbt26ceLEiRK1ZW9vj6IoeHt7ExISQu/evcshsRBCCCFKo1evXhadJa9nz54WaUtUPJsumL777jsiIiJ48803SUpKon379gwcOLDImcdycnJo3rw57733Hp6enhWcVlQVderUwc7Ojs2bN/PII48wbNgw9u3bx/z580vUTrdu3Zg+fTpTpkyhVatWMj24EEIIUYmEhoZadJa80NBQi7QlKp5NF0wffPAB06ZNIzQ0FF9fX5YuXYqTkxNff/11occ/8sgj/Oc//2H8+PE4ODhUcFpR1ezevZvExEScnJx46aWXSlwwubi40KBBg3JKJ4QQQoiyCAgIIDAwsMx3mTQaDYGBgbJorQ2z2TFMubm5JCYmMnv2bPM2Ozs7+vXrR0JCghWTCVuUk5PDzZs38fLyKvY5L7zwAgDPP/98vjU17t69i6Ojo8UzCiGEEKJiLVu2DD8/vzLdaVKr1SxbtsyCqURFs9mC6dq1axiNxgLTM9evX7/EY0nuR6fTodPpzD9nZmYC96aGtPR0kyWR99jWzFBaFZG9uNN26vV6fvvtNxISEnB0dOR//ud/ivwm6Z+53dzceOutt4B7vydpaWns37+fO3fuMG3atErTxU5+Vyqe5C7e4whRVeh0On788Uf69u1b4HOJsG0+Pj5ERUUxceJE8yRNJaFSqYiKisLHx6cc0omKYrMFU0WZN28ec+fOLbB969atODk5WSFRfrGxsdaOUGrWzp6bm0tqaqr5w5tKpWLDhg0P7K75z9y5ubmkpaWRm5tr3rZmzRpq1Khh+dBlYO3nuyxsNbvkLlxOTk65ti9ERTKZTPz444+kpqby119/8fTTT8vU0VVMSEgIiqIQFhaG0Wgs1t0mjUaDWq0mKiqKkJCQCkgpypPNFkx16tRBrVZz+fLlfNsvX75s0QkdZs+eTUREhPnnzMxMGjduzIABA3B1dbXY45SUXq8nNjaW/v37l2gNoMrAktnPnDnDzJkz2bNnD02bNmXcuHG0aNECFxcXsrKyOH36NN999x1//PEHPXr04NNPP6V58+YAKIrCihUryMrKIigoiHbt2mE0Glm/fj0jR44s8IZXVG5FUfjyyy+5ffs2AQEBdOzYERcXlzJdlyXJ70rFk9z3l3enXoiqIDY2ltTUVDQaDaNGjZJiqYqaMGECnTt3Jjw8nPj4eDQaTaGFU972Hj168NVXX8mdpSrCZgsme3t7OnbsyLZt2xg5ciRw71uebdu2MXPmTIs9joODQ6F3HLRabaX4IFRZcpRGWbNHR0cTFhZG9+7diY6JYfiwYeToDKRcukV2rkIDexUjx7jxxhtvsGHjRj5atIj27dvn+7bnsccew9nZGYBVq1bx7rvvkpqaynfffcfjjz9e7NyPP/44bm5u2Nvbl/p6ylt1/l2xFslddPtCVAUGg4ELFy4AMHLkyBKNgxW2x8fHh7i4OJKSkoiKimL37t0kJyebF7Bv164dPXv2JDQ0VCZ4qGJstmACiIiIYMqUKXTq1InOnTuzaNEisrOzzdM2Tp48mYYNGzJv3jzgXteplJQU839fvHiRw4cPU7NmTfkGwMZER0czadIkIiIiWLBgAccv/sWc9SmsO3SR7Fyj+ThnezUjOzRkUudAduwYQWRkpLkf8oQJE3BzcwNgxIgRrF+/HoDatWuXuMvQ3yd9EEIIUT1oNBqmTJlCWloabdq0sXYcUUECAgLyFUTFHTctbJdNF0zjxo3j6tWrzJkzh4yMDPz9/dmyZYt5wOX58+fz/QKnp6fToUMH888LFixgwYIFBAUFsXPnzoqOL0opNTWVsLAwXnjhBRYsWMCn29NYGHuSwsZiZucaWbX/PNEHzhPZvzULFy4EICwsjM6dO5sL5SeeeIKEhAReeuklnn76aWrWrElubi5Go7HSjUUSQghReWg0GimWqpDbt2/z+++/071792KfI8VS1WfTBRPAzJkzi+yC988iyNvbu1QznIjKZerUqXTv3p2FCxfy6fY0Fmw9mW+/BiP17G6TbnIzb1MUWLD1JCoVLFy4kKSkJMLDw4mLiwNg9OjRDB48GGdnZ7Kysti2bRsHDx6kQ4cODBgwoEKvTwghhBAVLzc3lzFjxrBz505iYmIYPXq0tSOJSsLmCyZRvSQmJhIfH8/ades4fvGvfMWSCoVW6mv4a9NxwMAanR+3lfzjzxZsPUnf1rV59rnnGD1qFElJSQQEBGBnZ4ezszNHjhxh/fr1mEwmAM6dO4eiKJVminAhhBBCWJ7JZGLKlCnmWZAbNmxo7UiiEpGCSdiU5cuX06xZM4YPG8ac9Snm7SoUhjukUNvuDgBZJnucVLlkmbSoVP93q1xRYOX+C8x9dDje3t5ERUXl64fcuHFjTCYTTZo0oVu3brRq1UqKJSGEEKKK++abb1i9ejVarZY1a9bQpUsXa0cSlYgUTMKm7Nq1i0mTJpGjM7Du0EXzdgUVl4yuOKv0HNY34PhdVzIP/0LWoU14TlqA2un/uuetPXSRlwe24oknnmDDhg352vfw8GDWrFl4eHhU2DUJIYQQwromT57MwYMH6dmzJwMHDrR2HFHJSMEkbEpKSgqRkZH/O3W4Md++w4YGHDY04PrBTdza9z2m7JsA3D68Bbfu48zHZecaOX4pEx8fH5KTkws8hhRLQgghRPWiVqtZvHixtWOISkqm9RA2w2QyodfrcXFxITu34OQdejTo0ZB7+TSm7Juo3erjMWgWrl1G46a6A/zfOdl6E66uruj1evN4JSGEEOKfbt68SUJCgkwaJUQ1JneYhNWUtFCxs7NDq9WSlZVFA/uixxW5dRuHY2M/nNsF01ibTTvNGbzUWWzWtSLD5AqAs9aOPzMz0Wq1Mh2oEEKIQul0OmJiYrhy5Qo6nY7evXtbO5IQwgrkk6KoMElJScyaNYuePXsCUKtWLezt7fH392fWrFkkJSUVOMdoNJKcnGz+Zs/X15fU1FR8G7jhbK8u9HG0Hg2p+fAA+tc4R3+HNLzUWZgUqG13bzFaZ3s1bRu4kpaWRrt27crpaoUQQtgyk8nEmjVruHLlCjVr1sw3QZAQonqRgkmUu7S0NIKCgujYsSObNm1i8ODBAERHR7Ns2TIeffRRNm3aRMeOHQkKCiItLQ1FUUhJSeGzzz7jhx9+4OTJe9OH9+rVi5UrV+LkoGFkh/tP+XnB6Eauouaovj4/6B4i2eAJwKgODXFy0LBixQpz8SaEEEL83Z9//smpU6dQq9WMGzcOV1dXa0cSQliJFEyiXEVHR+Pn54darWbtunWkpqbyXMRLALj4PEKr7gN56ZXXSE1NZe26ddjZ2eHn58eHH37I999/z40bN3B2dubGjRtERUURGhrK2bNn2bBxI5M6N+J+M36nGuvw3d2HOWhoTPb/rsekUsGkLo1Yv2ED586dIzQ0tCKeBiGEEDamadOmhISEMHLkSBo1amTtOEIIK5KCSZSb6OhoJk2axMyZM9m+fTutOwUyZ30KfT+IB+CZmEOM+/I3ur63gznrU2jdKZAdO3YwY8YMli9fjkqlwt/fn6tXrzJo0CDCwsK4e/cugYGBfLRoEW0b1iKyf+siH9+IHQbyd9t7cUBrWnm68fFHHxEYGChdLIQQQhSpVatW+Pn5WTuGsIAbN24wd+5cDAaDtaMIGySTPohykZqaSlhYGC+88AILFizg0+1pLIw9iaKAgzr/TEPZuUZW7T9P9IHzRPZvzcKFC4mMjOTDDz+kVq1anD17FoCHHnoInU7H+++/T1BQEJGRkSxcuBCVChZsvdd2UVSqe8XSjGAfIiMjSUhI4NixY+X5FAghhBCiEsjJyWH48OHs3buX9PR0Pv/8c2tHEjZGCiZRLqZOnUr37t1ZuHAhn25PY8HWkw88R1HuFT4qFSxcuJCkpCTOnz+Pv78/s2fPpk6dOhw8eBBFUVi2bBlPPPEEcO/Yvq1rs3L/BdYeuphvfSZnezWjOjRkUpdGtPGqZS7EVq1ahY+PT7ldvxBCCCGsT6/XM3bsWPbu3Yu7uzszZ860diRhgyxaMOXm5qLX63F2drZks8LGJCYmEh8fz9p16zh+8a9iFUt/t2DrSfq2rs2zzz3H6FGj2LNnD9u3b+f48eMAuLu7069fP1auXElYWBhJSUk8+9xzzH10OC8PbMXxS5lk6004a+1o28AVJwcN6zds4OmPPiIhIYFVq1YREhJSHpcuhBBCiErk8OHDbNu2DUdHRzZu3MhDDz1k7UjCBpVqDNPq1at54YUX8m2bO3cuNWvWxN3dnVGjRnH79m2LBBS2Z/ny5TRr1ozhw4ax8sCFv+1RaKa+gRZjkefCvTtNK/df4NHhw/H29iYmJoZmzZrRuHFjxo4dy6xZs6hfvz4TJkzg2LFjmEwmRo8ahY+PD/95711O7NnMrRMJnNizmf+89y4+Pj6MHjUKRVE4duyYFEtCCCFEFVCc9RwfeeQRtm7dyg8//ECPHj0qIJWoikp1h2nhwoV06NDB/PPevXuZO3cuQ4cOpW3btnzyySe8++67zJs3z2JBReVgMpkeuNDrrl27mDRpEjk6A+sOXQSggV0mnbQXqGOXwzGjJ3p9baDwdZQA1h66yMsDW/HEE0+wYcMGFi5ciL29fYHjfHx8iIuLIykpiaioKDZs2EBycjJ6vR6tVku7du0YNmwYoaGhMsGDEEIIYcOOHDkCQM+ePTl06JD5vd7X15devXoV+V4fGBhY0VFFFVOqO0ynT5/m4YcfNv8cHR2Np6cna9eu5f3332fGjBn8+OOPFgsprCdvsVl/f3/s7e1Rq9UPXGw2JSWFli1bknLpFtm5Rh7WpDPI4RR17HK4ejOTgz9/x8yZMzEZcot83OxcI8cvZeLj40NycnKhxdLfBQQE8Mknn3Do0CFyc3MxGo3k5uZy6NAhPvnkEymWhBBCFHDp0iWysrKsHUM8QN56jnmFz9GjR9Hr9cC9MUpHjhxh6dKl+dZzFMKSSlUw6XQ6HB0dzT9v3bqVwYMHo9Hcu2Hl6+vLhQsXijpd2IB/Ljb76KOPsmzZMtauXVvkYrNw7w6UXq/HxcWF7Nx709b9YayFzqDw7fpfWfzxx5xL3MHly5dx+PM33FR3isyQrTfh6uqKXq8v1m33v3vQXTAhhBDV261bt1i1ahVffvkl165ds3YcUYS89Rz37t173+Pypgvfu3cvfn5+xMTEVEQ8UU2U6lNls2bN+PXXXwE4ePAgaWlpDBo0yLz/8uXL1KxZ0zIJRYUrbLHZl155jVbdB+LauluRi83GxMRgZ2eHVqslKysLZ/t7q8reUmrwg74D6dezwGTAt3t/5s6dy9jWjnTQpheZw1lrR2ZmJlqtVgogIYQQFpObm8vq1avJzs7GyckJV1dXa0cShchbz1Gn0xV7/SSDwYBOp2PixIlER0eXc0JRXZRqDNP//M//8Nxzz5GSksKFCxdo1KgRw4YNM+/fs2cP7dq1s1hIUXGio6OZOHEikZGRLFiwgOMX/2LO+hTWFTJd98gODZnUOZAdO0YQGRnJxIkTURQFX19fUlNTGTnmcZzt1WTnGslFQ63gMFrXyKJ3fQOKomBS+N+1kxRAlS+Hs72atg1c2ZiWJr9LQgghLGrbtm1kZGTg7OxMSEjIA7t9i4qXt56jcr9FFu9DURTCwsLo3LmzLCMiyqxUBdOsWbNwdHTk559/pmPHjrz88svUqFEDuLeSckZGBk899ZRFg4rykzdhwq+//sqJEyeIiIgosNjsPxW22CxAWFgYY8eOZeXKlbz55puM7NCQVfvPA2BfvwUXMJCjJNO0njtfXazPDaNjwcaBUR0a4uSgYcWKFfmKcSGEEKKsgoKCuH79OkFBQbi5uVk7jijE1KlTMRrvP6vugxiNRsLDw4mLi7NQKlFdlXodpmnTpjFt2rQC2z08PDh48GCZQomKkZaWRnh4OPHx8Wg0GgwGA4GBgcVebNbL7hY5in2BxWaTk5M5e/YsGzZuZFLnQKIPnDcXXXo0/KR/iPkNTWRfLHyWPJUKJnVpxPoNGzh37hyhoaGWvnQhhBDVmJOTE5MmTbJ2DFGEvPUcy8pgMBAfH09SUpJM/iTKpEwDQ3Q6HQkJCfz0008yYNLG/HMQZV7f4BciIh642KyHKocB9icZ6JBKy5u/kXV4Cwu2nuRE+l88+9xzHDp0iA4dOvDRokW0bViLyP6t852v/KP73T+9OKA1rTzd+PijjwgMDJQXOSGEEKIaWb58uXkisbLSaDRERUVZpC1RfZW6YPr4449p0KABPXr0YPTo0fz+++8AXLt2jTp16vD1119bLKSwrKIGUTZu3LiQxWbza2L3FyMcU9BcS+O7/37PD0v/Q+a2z2l6J42VCX+YF5v18/MjISGByMhIZvbx4aWBrVHdv05CpYKXBrZmRrAPL730EgkJCSxbtsxSly2EEEIIG7Br165iT/LwIAaDgd27d1ukLVF9lap8j4qK4vnnn2f8+PEMGDCAsLAw8746derQp08fVq9enW+7qBzuN4jyySefzLfYbGEumlz5646JL5d9jYNWQ1BQIJ27dse5xk0O/H6UnMFtzYvNRkVFMXHiROBed72+rWuzcv8Ffj5yAcg/gcSoDg2Z1KURbbxqERkZyYcffsiqVatkoKYQQghRzaSkpFi0veTkZIu2J6qfUt1hWrhwISNGjCA6Oprhw4cX2N+xY0f55ayk7jeI8u+LzRbFiJpNdMTJfyhjJoUSHByMcw0HbpvsuWNQ8i02GxISwsqVK1m8eDHBwcEcPxDH3Ed92RZxb+G5JRM68N9pndn3SjBzH/Xl+IE4goODWbx4MatWrSIkJKRcngMhhACIj49n+PDheHl5oVKpWLduXb79ly9f5sknn8TLywsnJycGDRpEampqvmPu3r3LjBkzqF27NjVr1mTMmDFcvny5Aq9CiKolbz1HSyrNeo5C/F2pCqa0tDQGDx5c5H4PDw+uX79e6lCifOQNoizqNvffF5u9Hz1q3HuHctqjK1dNTuzMbc4Puoc4Y6xdYLHZCRMmcOzYMUwmE6NHjcLHx4ePPvgPAJmpv3Fiz2b+8967+Pj4MHrUKBRF4dixY1IsCSHKXXZ2Nu3bt2fx4sUF9imKwsiRIzlz5gw//fQThw4domnTpvTr14/s7GzzcS+88AIbNmzg+++/Jy4ujvT0dEaPHl2RlyFElZK3nqMlyXqOoqxK1SXP3d39vpM8pKSk4OnpWepQonzkDaIsqmDKysqigf0DBhr9L5VKxQWTGxd0bvx9DSVnrR1//mOxWR8fH+Li4szTl2/ZsoUOHTowYcIEDAYD7dq1Y9iwYYSGhsoED0KICjN48OAiv/xLTU1l3759HDt2zLwW3JIlS/D09CQmJoapU6dy69Ytli1bRnR0NH369AHudVlv27Yt+/bto2vXrhV2LaKgzMxMWZDWRvn6+nLkyBGLtSfrOYqyKlXBNGTIEL744gueeeaZAvuSk5P58ssvZfxSJXS/QZQNGzYkNTWVPv0HmRebfbCSLTYbEBBAQEAAer2en3/+mb/++gsHB4fSXIoQQpQrnU4HgKPj/60VZ2dnh4ODA7t372bq1KkkJiai1+vp16+f+Zg2bdrQpEkTEhISiiyYdDqduX2498Ee7nUbsnRXJFuTd/1lfR7S09NZuXIlXbp0ITAwENWDZh2qpCz1fNia3r17c+bMmQKfWfLW/Mz7d3FoNBqCgoKq3HNYXX83ilLez0OpCqZ///vfdOnSBT8/P4YPH45KpeKbb77h66+/5scff6RBgwbMmTPH0llFGRU2iNLDw4O+fftSs2ZNvv32W7Zv386oJ//FyrTC10i6n5IuNiu3x4UQlVVe4TN79mw+//xznJ2d+fDDD7lw4QKXLl0CICMjA3t7e9zd3fOdW79+fTIyMopse968ecydO7fA9h07duDk5GTR67BVsbGxpT43NzeXU6dOYTAYOHr0KLdv37bZgilPWZ4PWxQcHExwcHCR+0szE/PPP/9clkiVVnX73ShKTk5OubZfqoLJy8uLxMREXn31Vb777jsURWHFihW4uLgQEhLCe++9R506dSydVZRBYYMoa9euzZQpU1izZg12dnb06NGDVq1aQfphnFUPka3YF7t9WWxWCFGVaLVa1qxZQ3h4OB4eHqjVavr168fgwYMLnWW0JGbPnk1ERIT558zMTBo3bkxwcDC1a9cua3SbptfriY2NpX///qUax2I0Gvnmm28wGAzUqVOHKVOm2HRPhrI+H7ZsyJAh7N+/P99dpho1avD1118TFhbGnTt3HtiGRqOhS5cuVbJYqs6/G4Up77kTSr0qWL169fjqq6/46quvuHr1KiaTibp168pdg0oqbxDl34um69evc/nyZXJycpg2bRparRZFUWjbpg3Ta3rz4a70Yreft9js07LYrBCiiujYsSOHDx/m1q1b5ObmUrduXbp06UKnTp0A8PT0JDc3l5s3b+a7y3T58uX7juN1cHAo9EO8VquVDz7/q7TPhVarJSAggPj4eCZMmEDNmjXLIV3Fq46/G0uWLKFdu3bk5uYW2Hfnzp1iFUwODg4sWbKkSj931fF3ozDl/RxYZBnlunXrWqIZUc4KG0T53//+F71ez4EDB9BoNOzbt4/Q0FAWLhyHxsmFBVtPcr8vU1Wqe8XSjGAfIiMjSUhI4NixY+V8JUIIUXHc3NyAexNBHDx4kHfeeQe4V1BptVq2bdvGmDFjADh58iTnz5+nW7duVstb3T3yyCO0b98ee/vi95IQlY9arcbZ2bnQgqk4VCoVUVFRsp6jsIhSFUxvv/32A49RqVS88cYbpWlelJNevXqRnJyc7/Z23h2nv/eB/eCDD4D8i82uPXQx30QQstisEMLW3b59m7S0NPPPZ8+e5fDhw3h4eNCkSRO+//576tatS5MmTTh69CjPPfccI0eOZMCAAcC9Qio8PJyIiAg8PDxwdXVl1qxZdOvWTWbIszIplmzblStXGDBgAH/99ReNGjXiypUrxV5HSaPRoFariYqKkiVKhMWUqmB66623itynUqlQFEUKpkooNDSUTz/9tFjHfvDBB/z222+8EBHB3EeH8/LAVhy/lEm23oSz1o62DVxxctCwfsMGnv7oIxISEmSxWSGETTl48GC+geV544qmTJnC8uXLuXTpEhEREVy+fJkGDRowefLkAu9rH374IXZ2dowZMwadTsfAgQP57LPPKvQ6hKhKdDodgwcPJi0tDW9vb/bs2UNOTg7h4eH89ttvRZ6Xt2xKjx49+Oqrr+TLW2FRpSqYCqvyTSYTf/zxB4sXLyY+Pp7NmzeXOZwonatXr7JixQrUajVDhw41v2gEBAQQGBjI3r17i5xe/O927drFrl27aNSoEaGhofj4+ODq6sqfmZlsTEtjxYoVnDt3jqCgII4dOyYvTkIIm9K7d+/7TuDw7LPP8uyzz963DUdHRxYvXlzo4rdCiJJzcHBg0qRJXLx4ka1bt+Ll5QVAXFwcBw8e5OLFizz88MMkJSWh1+vRarW0a9eOnj17ynqOotxYbIYGOzs7mjVrxoIFC2jZsiWzZs2yVNP3tXjxYry9vXF0dKRLly4cOHDgvsd///33tGnTBkdHRx566KEqNXNKZmYmr732Gk8++STp6encvHmT2NjYfB8Ili1bhlpdsinDL1y4wDvvvMOUKVMYNWoUU6ZM4YcffmDYsGEkJiayc+dOKZaEEEIIYREvvPACJ06coGXLlvm2t2/fHrj3hW5ubi5Go5Hc3FwOHTrEJ598IsWSKDflMqVdYGBghRQi3333HREREbz55pskJSXRvn17Bg4cyJUrVwo9fu/evYSEhBAeHs6hQ4cYOXIkI0eOrBKTFBgMBv7973+zdu1aOnfujIuLCyaTCR8fn3wFk4+PD1FRUaVek0KlUhEdHU1KSoq8OAkhhBCiXPxzfbPCyMzMoqKUy2/awYMHK+SX+IMPPmDatGmEhobi6+vL0qVLcXJyKnJBs48++ohBgwbx0ksv0bZtW9555x0CAgKKPa6nMtNoNAwcOJC6deui0+no168fc+bMoX///gX+X4SEhLBy5UocHBzQaIrXK1Oj0eDg4CDjlIQQQlhdcScAEEIISyjVGKZvv/220O03b94kPj6eNWvWMHXq1DIFe5Dc3FwSExOZPXu2eZudnR39+vUjISGh0HMSEhLyLRYIMHDgQNatW1fk4+h0OnQ6nfnnzMxM4N7scv9cCLY4TCaTRYrJvMf+e4YePXoQGxtrvntkMpmKfFMZO3YsHTt2ZObMmezZs8c8WPKf8rb37NmTTz75hObNm5fquh+U3RZI7opnq9kld/EeR4jSSE5OZvfu3YwfP9485bsQQpSnUhVMTz75ZJH76tSpwyuvvMKcOXNKm6lYrl27htFopH79+vm2169fnxMnThR6TkZGRqHHZ2RkFPk48+bNY+7cuQW2b926FScnp1Ikt6y/TwdeGjNnzmTmzJnFOvbEiRNFPrelUdbs1iK5K56tZpfchcvJySnX9kXVdenSJdatW4fBYCAxMZE+ffpYO5IQohooVcF09uzZAttUKhW1atXCxcWlzKEqk9mzZ+e7K5WZmUnjxo0ZMGAArq6uRZ535syZYt+96dGjB59++inNmzcvtK0rV66wd+9e+vXrZ161XK/XExsbS//+/S2+urGl7oIVpTyzlyfJXfFsNbvkvr+8O/VClERWVhYxMTEYDAZ8fHzo3bu3tSMJIaqJUhVMTZs2tXSOEqtTpw5qtZrLly/n23758mU8PT0LPcfT07NEx8O96S0dHBwKbNdqtUV+oIiOjiYsLAyj0Vis6bt37txJ+/btCyyydvr0af773/+i0+lQqVTUrVu3wBvE/XJUdraaXXJXPFvNLrmLbl+IkjIYDOb35DFjxsiAfxv2+++/06xZsyr3JbsoP3fv3iUzM5N69epZ5fFt9tXG3t6ejh07sm3bNvM2k8nEtm3b6NatW6HndOvWLd/xcK/rSVHHl0Z0dDSTJk1Cp9MVq1iCe28COp2OiRMnEh0dDdybOOP1118nNzcXlUrFmTNn8PDwsFhOIYQQwpbUqlWL8PBwJk6ciKOjo7XjiFJKSUkhODiY4OBgrl27Zu04wgbcuXOHFStWsHz58gI3PipKse4w2dnZlXgaapVKVeyCobQiIiKYMmUKnTp1onPnzixatIjs7GxCQ0MBmDx5Mg0bNmTevHkAPPfccwQFBbFw4UKGDh3K6tWrOXjwIF988YVF8qSmphIWFnbfhRDvR1EUwsLC6Ny5MwcPHmTv3r3cuXOHmjVr8uabbxZYj0AIIYSoThwdHaVYsmHnz59n4MCB3Lhxg5YtW1KjRg1rRxKVXE5ODitXruTSpUvUqFGj1J+xy6pYBdOcOXNKvW5PeRo3bhxXr15lzpw5ZGRk4O/vz5YtW8wTO5w/fz7fLfvu3bsTHR3N66+/zquvvkrLli1Zt24dfn5+FskzdepUjEZjmdowGo2Eh4fz+eefc+bMGUJCQvD396+Uz78QQgghRHFNnjyZCxcu0LZtWzZt2oSzs7O1I4lKbseOHVy6dAknJycmT55cYPK2ilKsgumtt94q5xild79Z3nbu3Flg29ixYxk7dqzFcyQmJhIfH1/mdgwGA/Hx8WRnZ/Pee+9JH20hhBBCVAlffvklU6dOZeXKldSuXdvacYQN6N+/Pzk5OQQFBVlt/BLY8Bimymb58uUPXATWzs6Ohx56iGHDht33OI1Gw/Lly6VYEkIIIUSV0bJlS3bu3Enjxo2tHUXYCHt7e8aOHWvVYglKOUtengsXLnDo0CFu3bpV6AKpkydPLkvzNmXXrl1FjtnSaDR06dKFLl26mKciP3ToEBcvXiz0eIPBwO7du8stqxBCCCGENcgQA2GLSlUw3b17lylTpvDjjz9iMplQqVTmQVh//0OoTgVTSkpKkftMJpO5WMrKyuK3337jxo0b920vOTnZ0hGFEEIIIYQQJVSqgunVV19lzZo1vPvuu3Tr1o3evXvzzTff0KBBAxYtWkR6ejrffvutpbNWWiaTCb1ef99jfv31V1QqFRcuXKB+/frcuXPnvsfr9fpyX0BWCCGEqEz27dvH9evXrR1DCCHyKdWn8R9++IHQ0FBefvll2rVrB0DDhg3p168fGzduxN3dncWLF1s0aGVmZ2d334UYBw4cCIC3tzdjxowhNzf3gW1qtVoploQQQlQbJ06cYPv27fz5559cuHDB2nGEEMKsVJ/Ir1y5QufOnQHMc+hnZ2eb948ZM4Y1a9ZYIJ7t8PX1LXLfvn376N27N3/88QdffvklaWlpD2wvrxAVQgghqrqMjAzz54Y6derQqFEjKycSQlSEu3fvWjtCsZSqYKpfv775lrmTkxO1atXi5MmT5v2ZmZk28wSU1o0bN9i8eTP79u0DoFevXkXOkvfXX3/xySefcPjw4WItuKXRaOjZs6dF8wohhBCVVVpaGnq9Hm9vbxo2bGjtOKKUtm3bxrZt26wdQ9iIGzdusGTJEnbt2mXtKA9UqoKpS5cu+WZxGz58OP/5z39YtWoVK1as4MMPP6Rr164WC1kZff755xw4cIDdu3djNBoJDQ0tcpY8oEQrExsMBkJDQy0RUwghhKj0evbsyejRoxk1apTMomajDh48yMiRIxkyZAh79uyxdhxRyV2/fp3ly5eTmZnJ77//XqzhKtZUqoLp2WefpXnz5uh0OgDeeecd3N3deeKJJ5gyZQpubm58/PHHFg1aGZ05c4bz589jZ2dHQEAAgYGBD1yL6UE0Gg2BgYEEBARYKKUQQghR+T300EPmbv7Ctpw8eZLBgwdz+/ZtevXqRadOnawdSVRiOp2Ob775hqysLOrWrcuUKVOwt7e3dqz7Kvan+8cee4wnnniCIUOG0LNnz3xdxho3bszx48c5evQoarWaNm3alLlwqOy+/PJLrl+/jr+/P0ajEY1Gw7Jly/Dz87vvnaYHUavVLFu2zIJJhRBCCCHKz2effca1a9fo2LEja9euxcHBwdqRRCXm4OBAz549SUxMZPLkyTg7O1s70gMVu6rZtGkTa9euxc3NjbFjxzJx4kQCAwPN++3s7Gjfvn25hKyMHBwcGD9+PP3790etVgPg4+NDVFQUEydOLFEXvDwqlYqoqCh8fHwsHVcIIYQQolx88MEH1K5dm6effhoXFxdrxxE2oHPnzgQEBNjMDZZid8m7evUqX3/9NY888ghff/01wcHBNGnShFdeeYXff/+9PDNWSpMnT6ZLly507Ngx3/aQkBBWrlyJg4NDsX8JNBoNDg4OrFq1ipCQkPKIK4QQQghRLtRqNXPmzKFu3brWjiJsiK0US1CCgqlmzZpMmTKFX375hfT0dBYtWkTDhg15//336dChAw899BDz58/n/Pnz5Zm30ujTpw8zZ86kffv2BQaoTpgwgWPHjtG9e3eg6F+IvO09evTg2LFjUiwJIYQQQghRyZRq0oe6desya9YsEhISOHPmDG+//TYAs2fPpnnz5gQGBvLFF19YNGhl06VLl/tWxj4+PsTFxZGYmMhTTz2Fv7+/eXFbrVaLv78/Tz31FImJiezcuVO64QkhhBBCCFEJlflemLe3N6+99hqvvfYav//+O2+++SY//fQTe/bsYfr06ZbIaNMCAgLyzXhnMpmwsytVnSqEEELYJEVROHDgAO3bt8fR0dHacYQQokQs0nnw0qVLxMTEEB0dTVJSEoBMKVkEKZaEEEJUNwkJCcTGxpKUlMT06dPNkyUJIaomRVGq1Jpqpf70fvPmTb766iv69OlDkyZNePHFF7l16xZz5szh1KlT7N+/35I5hRBCCGGDTp06RWxsLHCv14UUS0JUbX/88Qfffvstd+/etXYUiynRHaa7d++yfv16oqOj+eWXX9DpdNStW5enn36aSZMm0blz5/LKKYQQQggbYzQa2bJlC3CvWJLPCbZpzZo1bNmyhc8++8ymZjYTFe/cuXNER0ej1+vZuXMngwYNsnYkiyj2b/3kyZP56aefuH37Nk5OTowZM4aJEycyYMAA+bZICCGEEAWo1WomT57M7t27GTx4cJXqolNd7Nixg5CQEHJzc/H39+eZZ56xdiRRSZ07d45Vq1ZhMBho0aIFffv2tXYkiyl2wRQTE0P//v2ZOHEio0aNwsnJqTxzCSGEEKIKcHd3Z9iwYdaOIUrh0KFDjBgxgtzcXEaOHCmTeYn7cnFxwdHREU9PT8aNG1el7kYW+0rS09NlQTIhhBBCiGoiPT0dvV5PUFAQMTExVeoDsLC82rVrExYWhouLS5X7XSn21UixJIQQQghRfQwdOpSdO3fSpk0bmQ5eFEutWrWsHaFcVK3yTwghhBBCWEyXLl2sHUEIq5NFgYQQQgghhBCiCFIwCSGEEEIIIUQRpGASQgghRKkZjUYuXLhg7RhCCFFuSjWGSafTsWfPHo4fP05mZiYuLi74+vrSo0cPHBwcLJ1RCCGEEJWQoij8/PPPHDp0iGHDhhEQEGDtSEKIcnLkyBFq1qxJixYtrB2lwpWoYFIUhQULFjB//nz++usvFEUx71OpVNSqVYuXX36ZF198URanE0IIIaq4/fv3k5SUBEDNmjWtnEaURnZ2Ns7OztaOISq5Q4cOsX79ejQaDdOnT692s2eXqEvexIkTefnll6lduzZz5sxhzZo1xMbGsmbNGubMmUPt2rV55ZVXmDRpUnnlFUIIIUQlkJ6eztatWwHo378/rVq1snIiUVLffPMNvr6+HD9+3NpRRCWWmJjI+vXrAfD396dOnTpWTlTxin2HacWKFaxevZoXX3yRefPmoVar8+0fOXIkb7zxBq+++ir/+c9/GDx4sBROQgghRBXVoEEDevbsSVZWFt26dbN2HFFCGzZsIDw8HKPRSExMDG+//ba1I4lKSFEU/vjjDwA6d+7MoEGDqmUvsmIXTF9++SVBQUG8//77RR5jZ2fHe++9x4EDB/jiiy+kYBJCCCGqKJVKRZ8+fVAUpVp+gLJl+/bt4/HHH8doNDJ58mTeeusta0cSlZRKpWLkyJG0aNGChx9+uNr+rRe7S97vv//OmDFjinXs6NGj+f3330sdSgghhBC2obp+gKqsTCbTA49p2bIl7du3Z+jQoXz11VfY2cmkyaJodnZ2tG/fvlr/rRf7DpNer8fR0bFYxzo4OGAwGEodSgghhBBCPFhSUhJRUVHs2rWLlJQU9Ho9Wq0WX19fevXqRWhoaIHZC2vXrs22bdtQqVRotVorJRfCdhT7KwUfHx/i4+OLdeyuXbto3rx5qUMJIYQQQoiipaWlERQURMeOHVm6dClHjhxBr9cD977kPnLkCEuXLqVjx44EBQWRlpaW73xnZ2ecnJysEV0Im1Psgumxxx4jJiaGTZs23fe4TZs2ERMTw9ixY8scTgghhBBC5BcdHY2fnx979+4FKLJXT972vXv34ufnR0xMTIVlFKIqKXbBFBkZSevWrRk5ciTTp09n165dZGZmoigKmZmZ7N69m+nTpzNy5Ehat25NZGRkeebmxo0bTJw4EVdXV9zd3QkPD+f27dv3PeeLL76gd+/euLq6olKpuHnzZrlmFEIIIYSwpOjoaCZNmoROpyv28AeDwYBOp2PixIlER0eXc0Ihqp5iF0xOTk5s376dvn378tVXX9G7d29q1aqFRqOhVq1aBAUF8dVXXxEcHMyvv/5a7rd5J06cSHJyMrGxsWzcuJH4+HimT59+33NycnIYNGgQr776arlmE0IIIaoCnU6H0Wi0dgzxv1JTUwkLC0NRlFKdrygKYWFhBbrniepLURSOHz9e6t+p6qLYkz4A1KtXjy1btrB//342bNhASkoKWVlZuLi40LZtW4YNG1YhazEcP36cLVu28Ntvv9GpUycAPvnkE4YMGcKCBQvw8vIq9Lznn38egJ07d5Z7RiGEEMKWGY1GvvvuOwDGjh1LjRo1rJxITJ06tcwFrNFoJDw8nLi4OAulErZKURR+/fVX9u7dS8eOHRk6dGi1ngnvfkpUMOXp0qULXbp0sXSWYktISMDd3d1cLAH069cPOzs79u/fz6hRoyz2WDqdDp1OZ/45MzMTuDegMm9wpTX8fWCnrbHV7JK74tlqdsldvMcRlduWLVs4e/YsWq2WzMxMKZisLDExsdiTb92PwWAgPj6epKSkArPniepDURS2bt3Kvn37AKhbt64US/dRqoLpn5KTk4mPj+f27du0b9+eAQMGWKLZImVkZFCvXr182zQaDR4eHmRkZFj0sebNm8fcuXMLbN+6dWulmF0mNjbW2hFKzVazS+6KZ6vZJXfhcnJyyrV9UXa//fYbBw8eBGDMmDHUr1/fyonE8uXL0Wg0Flm2RaPREBUVJQVTNXblyhUOHDgAwJAhQ3jkkUesnKhyK3bBZDKZmD17NtHR0Wg0Gp588knefPNNIiIi+Oijj8x9H1UqFT169GDLli0lLiheeeUV5s+ff99jjh8/XqI2y2r27NlERESYf87MzKRx48YMGDAAV1fXCs3yd3q9ntjYWPr3729zayjYanbJXfFsNbvkvr+8O/Wi8vLy8qJmzZp06dKF1q1bWzuO4N6SLZZa49JgMLB7926LtCVsU/369XnsscfIycmhY8eO1o5T6RW7YFqyZAn/+c9/eOSRR6hfvz7/7//9P65evcrSpUuZMWMGffv2xWAwsH79elasWME777zDvHnzShQmMjKSJ5988r7HNG/eHE9PT65cuZJvu8Fg4MaNG3h6epboMR/EwcEBBweHAtu1Wm2l+CBUWXKUhq1ml9wVz1azS+6i2xeVW8OGDXn66aelG14lkpKSYtH2kpOTLdqesD1t27a1dgSbUeyC6auvvmLo0KFs2LABgMWLF/Pss88yY8YMPv74Y/NxY8aMITs7mx9++KHEBVPdunWpW7fuA4/r1q0bN2/eJDEx0VwVb9++HZPJZNWxVUIIIURVURm6nYt7TCaTxcf+6fV6TCYTdnbFnjBZiGqr2H8lZ86cYciQIeafhwwZgqIo9OnTp8Cx/fr14/z585ZJWIi2bdsyaNAgpk2bxoEDB9izZw8zZ85k/Pjx5hnyLl68SJs2bcz9M+He2KfDhw+bp9M8evQohw8f5saNG+WWVQghhBCiLOzs7Cx+Z1ar1UqxJEQxFfsvJSsrCzc3N/PPeeN3ChvH4+LiYrF+tkVZtWoVbdq0oW/fvgwZMoSePXvyxRdfmPfr9XpOnjyZb3Dx0qVL6dChA9OmTQMgMDCQDh06sH79+nLNKoQQQghRFr6+vhZtr127dhZtT4iqzCKz5FmDh4fHfVer9vb2LrAI11tvvcVbb71VzsmEEEIIISyrV69eJCcnW2yWvJ49e1oglRDVQ4kKpp9//tk8bXdOTg4qlYrvv/+ew4cP5zsuMTHRYgGFEEIIIaq70NBQPv30U4u0ZTAYCA0NtUhbonIyGo3k5OTg4uJi7ShVQokKpujo6AJ3dT7//PNCj5XFr4QQQojKS1EUea+2IQEBAQQGBrJ3794y3WXSaDR0795d1mCqwoxGIz/88AMZGRk8+eST+YbUiNIpdsF09uzZ8swhhBBCiApy9+5dVq5cSVBQEC1btrR2HFFMy5Ytw8/Pr0wFk1qtZtmyZRZMJSoTg8HA999/z6lTp1Cr1Vy/fl0KJgsodsHUtGnT8swhhBBCiApgMpn44YcfuHjxIps2bWLmzJloNDY7pLlaadGiBSEhISxfvrxU56tUKqKiovDx8bFsMFFp/Prrr5w6dQqNRsP48eNp3ry5tSNVCTKfpBBCCFGN/PLLL5w+fRqtVsu4ceOkWLIhb7/9NsuXL6d58+bY29sX+/+dRqPBwcGBVatWERISUs4phTX16tULLy8vQkJCaNGihbXjVBnFfpUsbL2lPCqVCkdHR5o2bcqQIUMYNmyYRcIJIYQQwnJMJpN5uY1Ro0bRoEEDKycSxfXZZ5+ZZ/qNjIxkwIABhIeHEx8fj0ajKbSbXt72Hj168NVXX8mdpWrA2dmZqVOnyvhECyv2HaYrV65w9erVQv+5cuUKJ06c4KuvvmLEiBEMGTLE4itSCyGEEJYUHx/P8OHD8fLyQqVSsW7dunz7b9++zcyZM2nUqBE1atTA19eXpUuX5jvm7t27zJgxg9q1a1OzZk3GjBnD5cuXK/AqSsbOzo7Ro0cTGhpK27ZtrR1HFFNWVhbvvPMOcG+JlGeeeQYfHx/i4uJITEzkqaeewt/f37y4rVarxd/fn6eeeorExER27twpxVI1IsWS5RX7DtOxY8ceeMydO3f4/PPPiYiI4P333+e1114rUzghhBCivGRnZ9O+fXvCwsIYPXp0gf0RERFs376dlStX4u3tzdatW3nmmWfw8vLi0UcfBeCFF15g06ZNfP/997i5uTFz5kxGjx7Nnj17Kvpyik2lUtGkSRNrxxAl4OLiQnx8PDExMbzxxhv59gUEBOSb8c5kMmFnJyMuhLAki/5F1ahRg+eff57x48ffd1FZIYQQwtoGDx7Mv//9b0aNGlXo/r179zJlyhR69+6Nt7c306dPp3379hw4cACAW7dusWzZMj744AP69OlDx44diYqKYu/evezbt68iL0VUAy1btmTOnDkPvHsgxZIQllcuIz179OhRoGuDEEIIYUu6d+/O+vXrCQsLw8vLi507d3Lq1Ck+/PBD4N4i7Xq9nn79+pnPadOmDU2aNCEhIYGuXbsW2q5Op0On05l/zszMBECv11f77ux511/dn4c88nzkJ8/H/5HnIr/yfh7KpWDKycmRWXeEEELYtE8++YTp06fTqFEjNBoNdnZ2fPnllwQGBgKQkZGBvb097u7u+c6rX78+GRkZRbY7b9485s6dW2D7jh07cHJysug12KrY2FhrR6hU5PnIrzo/H/9ccLo6Pxd/lzeZTXmxeFWjKArr16/noYcesnTTQgghRIX55JNP2LdvH+vXr6dp06bEx8czY8YMvLy88t1VKqnZs2cTERFh/jkzM5PGjRsTHBxM7dq1LRHdZun1emJjY+nfv795AoPqTJ6P/Kr783Hnzh1Wr15Nx44dadu2bbV+Lv7p+vXr5dp+sQumGzdu3Hf/nTt3OHnyJEuWLGHv3r2sXLmyzOGEEEIIa7hz5w6vvvoqa9euZejQoQA8/PDDHD58mAULFtCvXz88PT3Jzc3l5s2b+e4yXb58GU9PzyLbdnBwwMHBocB2rVYrH3z+lzwX+cnzkV91fD5ycnKIiYkhIyODnTt30rp1a6B6PheFKe/noNgFU506dYo1TaFWq+Wdd96RhdGEEELYrLzxRP8cQK9WqzGZTAB07NgRrVbLtm3bGDNmDAAnT57k/PnzdOvWrcIz5/nrr7/Yt28f/fv3l+7xQlQBubm5fPPNN1y5cgVnZ2cmT55c6JcuovwU+5X0QTOz5C1c27dvX+rWrWuRcEIIIUR5uX37Nmlpaeafz549y+HDh/Hw8KBJkyYEBQXx0ksvUaNGDZo2bUpcXBzffvstH3zwAQBubm6Eh4cTERGBh4cHrq6uzJo1i27duhU54UN50+l0xMTEcPXqVUwmk/numKjcTCYTzzzzDI8//jh9+vSxdhxRyWi1Wlq2bElOTg5TpkyhTp06MtlDBSt2wZS3urQQQghRFRw8eJDg4GDzz3njiqZMmcLy5ctZvXo1s2fPZuLEidy4cYOmTZvy7rvv8tRTT5nP+fDDD7Gzs2PMmDHodDoGDhzIZ599VuHXAvc+dP/4449cvXoVFxcX8+QUonJTFIWIiAg+//xzVq5cydmzZ+WLZ5GPSqWib9++dOvWDWdnZ2vHqZbKdK8+OzubrKws6tSpI7f9hRBC2JTevXujKEqR+z09PYmKirpvG46OjixevJjFixdbOl6JXb16lXPnzqHRaBg/fjwuLi7WjiT+1/0Wk503bx4fffQRAEuXLpViSRRKpVJJsWRFJV7d7I8//mDmzJk0bdoUV1dXGjZsiKOjI97e3vzrX//ijz/+KI+cQgghhLiP+vXrExYWxpgxY/Dy8rJ2nGotKSmJWbNm4e/vj729PWq1Gnt7e/z9/Zk1axZJSUnAvULq8OHDwL27lZMmTbJiaiFEUUpUMG3YsIGHH36Yzz77DLVazfDhw5kwYQLDhg3Dzs6OBQsW4O/vz6ZNm8znvP766xYPLYQQQoiCPD09adOmjbVjVFtpaWkEBQXRsWNHli5dypEjR/ItMHrkyBGWLl1Kx44dCQoK4syZM8TExPDTTz/x/PPPWze8EKJIxS6Yjh8/zuOPP07Dhg2Ji4vjzJkzrFu3jhUrVrBu3TrOnDlDXFwcXl5ejBs3jhMnTjBlyhTmzZtXnvmFEEIIIawuOjoaPz8/9u7dC4DBYCj0uLzte/fuxc/Pj//+9788+uijFZZTCFFyxR549P/+3/+jdu3a7N69Gw8Pj0KP6dWrF7t27eLhhx+mY8eO6HQ6KZiEEEIIUaVFR0czadKk+46J+yeDwYDBYGDixIkoisKECRPKMaEQoiyKfYdp+/bthIeHF1ks5fHw8CAsLIw7d+6wfPly/vWvf5U5pBBCCCFEZZSamkpYWFiJiqW/UxSFsLCwfFPci+rl+vXr/PTTT0XelRTWV+yC6fr163h7exfr2GbNmqFWq2XwohBCCCGqtKlTp2I0GsvUhtFoJDw83EKJhC25du0ay5cv5/Dhw8TGxlo7jihCsQumOnXqcPbs2WIde/bsWerVq1fqUEIIIYQQlV1iYiLx8fFlvjNgMBiIj483z54nqoerV6+yfPlybt++Tb169WTttEqs2AVT7969WbZsGTdu3LjvcTdu3GDZsmWyUrUQQghhYRcuXCAjI8PaMcT/Wr58ucXWodRoNA9c90tULbm5uRgMBurXr8+UKVNknaVKrNgF06uvvsr169cJDAw0zwDzT3v37iUoKIjr168ze/Zsi4UUQgghqrubN28SExPD119/zZ9//mntOALYtWuXxcadGAwGdu/ebZG2hG1o2LAhkydPZsqUKTg5OVk7jriPYn8t4uvrS3R0NJMnT6ZXr154e3vTvn17XFxcyMrK4vfff+fs2bPUqFGD6OhofH19yzO3EEIIUW3k5uayevVqcnJy8PT0pH79+taOJICUlBSLtpecnGzR9kTlJ4tM24YS3UcePXo0/v7+vP/++2zcuJF169aZ93l5eTF9+nRefPFFWrRoYemcQgghRLW1e/duLl++jLOzM+PHj8fe3t7akao9k8lkXpTWUvR6PSaTCTu7YncAEkJUgBJ3vG3evDlLly4FIDMzk6ysLFxcXHB1dbV4OCGEEEJAYGAgOTk5+Pv74+bmZu04ArCzs0Or1Vq0aNJqtVIsCVEJlWmkoqurqxRKQgghRDnTaDQMGzbM2jHEP/j6+nLkyBGLtdeuXTuLtSWEsBz5GkMIIYQQohR69epl0VnyevbsaZG2hBCWJQWTEEIIIUQphIaGWnSWvNDQUIu0JSqHc+fOcenSJWvHEBYgBZMQQgghRCkEBAQQGBhY5rtMGo2GwMBAAgICLJRMWNuZM2dYtWoVK1as4Nq1a9aOI8pICiYhhBBCiFJatmwZarW6TG2o1WqWLVtmoUTC2k6fPk1MTAwGg4GGDRvi7u5u7UiijKRgEkIIIYQoJR8fH6KiolCpVKU6X6VSERUVhY+Pj4WTCWtQFIX9+/djMBho1aoV48aNs9g4N2E9Nlsw3bhxg4kTJ+Lq6oq7uzvh4eHcvn37vsfPmjWL1q1bU6NGDZo0acKzzz7LrVu3KjC1EEIIUbirV6+iKIq1Y4hiunXrFn/++ScAISEhrFy5EgcHh2J/ONZoNDg4OLBq1SpCQkLKM6qoQCqVirFjxxIUFMTjjz8uxVIVYbMF08SJE0lOTiY2NpaNGzcSHx/P9OnTizw+PT2d9PR0FixYwLFjx1i+fDlbtmwhPDy8AlMLIYQQhVu5ciU//vijxSYREOXn7t27jBgxgu7du5OSkgLAhAkTOHbsGN27dwco8oNy3vYePXpw7NgxKZaqIK1WS+/evcvcVVNUHjZZ9h4/fpwtW7bw22+/0alTJwA++eQThgwZwoIFC/Dy8ipwjp+fHz/++KP55xYtWvDuu+8yadIkDAaDfAMghBDCqoxGIwaDQT5kVXIGg4GQkBDi4uJwcXFBp9OZ9/n4+BAXF0dSUhJRUVHs3r2b5ORk9Ho9Wq2Wdu3a0bNnT0JDQ2WCByFsiE1WCQkJCbi7u5uLJYB+/fphZ2fH/v37GTVqVLHauXXrFq6urvctlnQ6Xb4Xw8zMTAD0er1FV/cuqbzHtmaG0rLV7JK74tlqdsldvMcR+dWpU4dRo0aVeiyMqBjvvvsu69atw97envXr19OhQ4cCxwQEBOQriEwmE3Z2NtupR4hqzyYLpoyMDOrVq5dvm0ajwcPDg4yMjGK1ce3aNd555537duMDmDdvHnPnzi2wfevWrTg5ORU/dDmJjY21doRSs9Xskrvi2Wp2yV24nJyccm3fVo0ePRoHBwdrxxAPMHPmTGJjY4mIiKB3797FOkeKJSFsW6UqmF555RXmz59/32OOHz9e5sfJzMxk6NCh+Pr68tZbb9332NmzZxMREZHv3MaNGzNgwABcXV3LnKW09Ho9sbGx9O/fH61Wa7UcpWGr2SV3xbPV7JL7/vLu1Iv8rPmeIoqvdu3axMfHSxEkRDVSqQqmyMhInnzyyfse07x5czw9Pbly5Uq+7QaDgRs3buDp6Xnf87Oyshg0aBAuLi6sXbv2gR8KHBwcCv3GT6vVVooPQpUlR2nYanbJXfFsNbvkLrp9IWyZFEvV26VLl2jQoIG1Y4gKVKkKprp161K3bt0HHtetWzdu3rxJYmIiHTt2BGD79u2YTCa6dOlS5HmZmZkMHDgQBwcH1q9fj6Ojo8WyCyGEEEKIqi0xMZGNGzfSu3dvgoKCrB1HVBCb/Iqkbdu2DBo0iGnTpnHgwAH27NnDzJkzGT9+vHmGvIsXL9KmTRsOHDgA3CuWBgwYQHZ2NsuWLSMzM5OMjAwyMjIwGo3WvBwhhBBCCFHJHTx4kI0bNwL3xmLKumnVR6W6w1QSq1atYubMmfTt2xc7OzvGjBnDxx9/bN6v1+s5efKkeXBxUlIS+/fvByiwmvbZs2fx9vausOxCCCGEEMJ2XL16lU2bNgHQtWtXBgwYIDNaViM2WzB5eHgQHR1d5H5vb+98lX/v3r3lmwAhhBBCCFFidevWZciQIdy8eZN+/fpJsVTN2GzBJIQQQghhaVevXv3/7d15WJTl3gfw7wwwgCibgogKIriviEqouCKLpqKYG5a4Hnu1rtzeo8fMbEPLyveYpZaiaWjuWipBguKCO1YqEiBlGqCCCIjiDHO/f3iY4wijLLPC93NdXDnPcz/3fJ+7YW5+M8/cg/j4eIwdO9bQUcjI9OjRw9ARyEBYMBERERHhyUq6Q4YMwfnz55GXl4fXX3/d0JGIyAiY5KIPRERERNpUUlKCkSNH4vz582jUqBEGDhxo6EhEZCRYMBEREVGdt3fvXhw5cgT169fH4cOH0aZNG0NHIiIjwUvyiIiIqM4bN24csrOz0bFjR3Tv3t3QcYjIiLBgIiIiIgLw1ltvGToCGYgQAnK5HDKZzNBRyAjxkjwiIiIiqrOEEIiNjUVUVBQePnxo6DhkhFgwEREREVGdJIRATEwMTp8+jezsbFy/ft3QkcgIsWAiIiIiojrp6NGjOHv2LADg5ZdfRocOHQyciIwRCyYiIiIiqpO6dOkCW1tbDB8+HD4+PoaOQ0aKiz4QERERUZ3k6OiIWbNmcbEHei6+w0RERES13s2bN/Haa6+hoKDA0FHIyLBYohfhO0xERERUq+Xm5iIwMBApKSlQKBSIjo42dCQiMiF8h4mIiIhqrQcPHuDll19GSkoKmjZtiuXLlxs6EhGZGBZMREREVGvduHEDGRkZcHBwQGxsLNzc3AwdiYhMDC/JIyIiolqrXbt2OHnyJO7du4f27dsbOg4RmSAWTERERFSrtWrVytARyAAUCgX27t0Lb29veHl5GToOmTBekkdEREREtYpCocCOHTtw9epV7N69GyUlJYaORCaM7zARERERUa2hUCiwfft2ZGRkwNzcHK+88gosLS0NHYtMGAsmIiIiIqo1pFIp6tWrBwsLC0yYMAEtWrQwdCQycSyYiIiIiKjWkEqlCA0Nxd27d+Hs7GzoOFQL8DNMREREZNLkcrmhI5CRkUqlLJZIa1gwERERkcn6/fff0bZtWxw5csTQUYiolmLBRERERCbp77//RmBgIK5fv44lS5ZACGHoSERUC7FgIiIiIpOTn5+PoKAg/Pnnn/Dy8sK+ffsgkUgMHYuIaiEu+kBEREQmx9raGu3atUNubi5iY2P5eRUi0hm+w0REREQmx9LSEtu2bcPp06fh4eFh6DikR8XFxTh27BgvwSS94TtMREREZJLMzMzg5uZm6BikRwqFAt999x3u3LmDx48fY/DgwYaORHUACyYiIiIiMnpFRUVIT0/Ho0ePUL9+fXh7exs6EtURLJiIiIiIyOjduXMHJSUlaNCgASZNmoSGDRsaOhLVESyYiIiIiMjoeXh4wMPDA4GBgSyWSK+46AMRERERmQRbW1s4OjoaOgbVMSyYiIiIyChdv36dK6ERkcGxYCIiIiKj88svv8Db2xvTpk2DQqEwdBwiqsNYMBERUZ2UmJiIYcOGwdXVFRKJBPv27VPbL5FIKvz55JNPVG3y8vIQHh4OW1tb2NvbY+rUqSgqKtLzmdQ+169fR1BQEAoKCpCWlga5XG7oSERUh5lswVSdSeof//gHPD09YW1tDScnJ4wYMQLXrl3TU2IiIjImDx48QJcuXbBmzZoK92dlZan9bNy4ERKJBGFhYao24eHhuHLlCuLi4vDjjz8iMTERM2bM0Ncp1EoKhQLDhw9HTk4OOnfujAMHDsDa2trQsYioDjPZgqk6k5SPjw+ioqKQkpKCn376CUIIBAYGorS0VE+piYjIWISEhOCDDz7AyJEjK9zv4uKi9rN//34MGDAALVu2BACkpKQgJiYG33zzDXx9fdGnTx+sXr0a27dvx99//63PU6lVzM3NERkZifbt2yMmJgb29vaGjkR6cvfuXdy7d8/QMYjKMcllxcsmqXPnzqF79+4AgNWrV2PIkCFYuXIlXF1dKzzu6YKqRYsW+OCDD9ClSxf88ccf8PT01Et2IiIyPTk5OTh48CA2b96s2paUlAR7e3vVPAQAAQEBkEqlOHPmjMZCrKSkBCUlJarbBQUFAAC5XF7nLz0rO//g4GCEhITA3Ny8To9J2bnXhTG4c+cOoqOjYW5ujokTJ8LOzq5cm7o0Hi/CsVCn63EwyYKpupPU0x48eICoqCh4eHigefPmGtsZ68Rmyr8oppqdufXPVLMzd+Xux5Rs3rwZDRo0wKhRo1TbsrOz4ezsrNbO3Nwcjo6OyM7O1thXZGQkli1bVm57QkIC6tWrp73QJiwuLs7QEYxKbR+Phw8fIiMjAwqFAlZWVjh27BjMzTX/iVrbx6MqOBZPFBcX67R/kyyYqjtJAcCXX36J//3f/8WDBw/Qpk0bxMXFQSaTaWyvaWKLjY01ionNlH9RTDU7c+ufqWZn7orpemLThY0bNyI8PBxWVlY17mvRokWYO3eu6nZBQQGaN2+OAQMG1Pkv45TL5YiLi8PgwYNhYWFh6DgGV1fGIzo6GgqFAi4uLhg/frzGz6zVlfGoDI6FutzcXJ32b1QF08KFC7FixYrntklJSanRfYSHh2Pw4MHIysrCypUrMWbMGJw8eVLjJKhpYgsMDIStrW2NstSEKf+imGp25tY/U83O3M9X9k69qTh+/DhSU1Px/fffq213cXHB7du31bYpFArk5eXBxcVFY3+WlpawtLQst93CwsKkHi+6xLFQV9vHY/To0YiLi0NQUFClFvio7eNRFRyLJ3Q9BkZVMM2bNw8RERHPbdOyZctqT1IAYGdnBzs7O7Rq1QovvfQSHBwcsHfvXowfP77C9sY+sRlLjuow1ezMrX+mmp25NfdvSjZs2AAfHx906dJFbbufnx/y8/Nx4cIF+Pj4AADi4+OhVCrh6+triKhEJsnGxgahoaGGjkGkkVEVTE5OTnBycnphO21NUkIICCHUPqNERER1Q1FREdLT01W3MzMzcenSJTg6OsLNzQ3Ak3fDdu7ciU8//bTc8e3atUNwcDCmT5+OtWvXQi6XY/bs2Rg3bpzGxYeIiMj0mOSy4k9PUmfPnsXJkyfLTVK3bt1C27ZtcfbsWQBPvgQvMjISFy5cwI0bN3Dq1Cm88sorsLa2xpAhQwx5OkREZADnz5+Ht7c3vL29AQBz586Ft7c33nnnHVWb7du3Qwih8SqE7777Dm3btsWgQYMwZMgQ9OnTB+vXr9dLflN25MgR3Lhxw9AxiIgqxajeYaqK7777DrNnz8agQYMglUoRFhaGf//736r9crkcqampqg8XW1lZ4fjx41i1ahXu3buHxo0bo2/fvjh16lS5BSSIiKj269+/P4QQz20zY8aM537Hn6OjI6Kjo7UdrVY7deoUhg0bhoYNG+LEiRNwd3c3dCQioucy2YLpRZNUixYt1CZCV1dXHDp0SB/RiIiIqAKXL1/G0KFD8fDhQ3Tq1ImXLhKRSTDJS/KIiIjI9MyZMwf5+fnw8/PDzp07TW4BEKqeoqIiQ0cgqhEWTERERKQX27dvx6RJk/Djjz/CxsbG0HFIDzIyMvDvf/8bycnJho5CVG0smIiIiEgvGjZsiE2bNsHR0dHQUUgP0tLSsG3bNtXnyl/0mUEiY2Wyn2EiIiIiIuN09+5dfP/99ygtLUXbtm0xevRoSCQSQ8ciqhYWTERERESkVQ0bNoSfnx9yc3MRFhYGMzMzQ0ciqjYWTERERESkVRKJBAMHDoQQAlIpPwFCpo0FExERERFpnUQi4WV4VCuw5CciIiIiItKABRMRERFpxfbt27FhwwZDxyAi0ipekkdEREQ19tNPP+HVV1+FQqGAm5sbBg8ebOhIRERawXeYiIiIqEbOnDmDsLAwKBQKjB8/HoMGDTJ0JNIDfq8S1RUsmIiIiKhG4uPj8eDBAwQGBmLTpk1cFa0OOH/+PKKjo6FQKAwdhUjneEkeERER1ciiRYvg5uaGESNGQCaTGToO6djZs2dx+PBhAMCvv/6Kbt26GTgRkW6xYCIiIqIaCw8PN3QE0oNz586piqVevXrB29vbwImIdI8FExERERFVStOmTWFpaYkePXpg4MCB/J4lqhNYMBERERFRpbi6uuL111+Hra0tiyWqM1gwEREREVGl2dnZGToCkV5xGRsiIiIiIiINWDARERHRC5WWlho6AhGRQbBgIiIioudat24dAgMDUVhYaOgoRER6x4KJiIiINNq1axdef/11xMfHY+vWrYaOQ0SkdyyYiIiIqELx8fEIDw+HEAL/+Mc/MHPmTENHIh0SQuDIkSP4+++/DR2FyKiwYCIiIqIK2dvbw87ODmFhYVizZg2Xka7FhBA4dOgQTpw4ga1bt+LRo0eGjkRkNLisOBEREVWoW7duOHPmDFxdXWFmZmboOKQjQggcPHgQFy5cAAAMHjwYVlZWBk5FZDxYMBEREZFGHh4eho5AOlZaWop79+4BAEJDQ9GlSxcDJyIyLiyYiIiI6gilUgmplFfjkzpzc3OMGzcOf/75J7y8vAwdh8jo8FmTiIiolrp48SLeeOMNdO3aFTKZDGZmZpDJZOjatSveeOMNXLx40dARyUhYWFiwWCLSgO8wERER1TLp6emYOnUqEhMTYW5uDoVCodonl8vxyy+/4MqVK/jiiy/Qt29fbNiwgX8sExFpwHeYiIiIapHo6Gh07NgRp06dAgC1YulpZdtPnTqFjh07Ytu2bXrLSERkSvgOExERUS0RHR2NiRMnQghR6WMUCgUUCoXq+5YmTJigw4RERKaH7zARERHVAmlpaZgyZUqViqWnCSEwZcoUpKenazkZEZFpY8FERERUC0ybNg2lpaU16qO0tBRTp07VUiIyFgqFAr/++mu1i2miuo4FExERkYm7cOECEhMTNX5eqbIUCgUSExO5el4tIpfLsX37duzduxfHjx83dBwik8SCiYiIyMRt2rQJ5uba+Viyubk5oqKitNIXGVZZsZSRkQELCwu4ubkZOhKRSTLZgikvLw/h4eGwtbWFvb09pk6diqKiokodK4RASEgIJBIJ9u3bp9ugREREOnb8+PEav7tURqFQ4MSJE1rpiwzr+vXruH79OiwsLBAeHo4WLVoYOhKRSTLZVfLCw8ORlZWFuLg4yOVyTJ48GTNmzEB0dPQLj121ahUkEokeUhIREene1atXtdrflStXtNofGUabNm0wbNgwNGrUiO8uEdWASRZMKSkpiImJwblz59C9e3cAwOrVqzFkyBCsXLkSrq6uGo+9dOkSPv30U5w/fx5NmjTRV2QiIiKdUCqVkMvlWu1TLpdDqVRCKjXZC1HoP7p162boCEQmzySfCZOSkmBvb68qlgAgICAAUqkUZ86c0XhccXExJkyYgDVr1sDFxUUfUYmIiHRKKpXCwsJCq31aWFiwWCIi+g+TfIcpOzsbzs7OatvMzc3h6OiI7OxsjcfNmTMHvXr1wogRIyp9XyUlJSgpKVHdLigoAPDk1Tdtv6JXFWX3bcgM1WWq2Zlb/0w1O3NX7n5Ie9q3b49ffvlFa/116NBBa30REZk6oyqYFi5ciBUrVjy3TUpKSrX6PnDgAOLj45GcnFyl4yIjI7Fs2bJy22NjY1GvXr1qZdGmuLg4Q0eoNlPNztz6Z6rZmbtixcXFOu2/LvL398eVK1e0svCDubk5+vTpo4VURES1g1EVTPPmzUNERMRz27Rs2RIuLi64ffu22naFQoG8vDyNl9rFx8cjIyMD9vb2atvDwsLg7++Po0ePVnjcokWLMHfuXNXtgoICNG/eHIGBgbC1tX3hOemKXC5HXFwcBg8erPVLMXTNVLMzt/6Zanbmfr6yd+pJeyZPnowvvvhCK30pFApMnjxZK30REdUGRlUwOTk5wcnJ6YXt/Pz8kJ+fjwsXLsDHxwfAk4JIqVTC19e3wmMWLlyIadOmqW3r1KkTPv/8cwwbNkzjfVlaWsLS0rLcdgsLi0r9QaFUCkgkwEN5KVKyClDwUAFba3O0a2ILawszCADSGqzYV9kcxshUszO3/plqdubW3D9pV7du3dC3b1+cOnWqRu8ymZubo1evXlwowEQ8ePAAEonEKK54IarNjKpgqqx27dohODgY06dPx9q1ayGXyzF79myMGzdOtULerVu3MGjQIHz77bfo2bMnXFxcKnz3yc3NDR4eHjrLmnGnCJtO/YF9ybfw4HGparuNzAyh3k0R0asFWjVuoLP7JyKiumHDhg3o2LFjjQomMzMzbNiwQYupSFeKiorw7bffwszMDK+99hqsra0NHYmo1jLZJXC+++47tG3bFoMGDcKQIUPQp08frF+/XrVfLpcjNTXVoNfKfxGfjsBVifjuzA21YgkAHjwuxXdnbiBwVSK+iE83UEIiIqotvLy8sHz58mofL5FIEBUVBS8vLy2mIl0oLCzE5s2bcefOHTx48AAPHz40dCSiWs0k32ECAEdHx+d+SW2LFi0ghHhuHy/aXxNfxKdjZWzqC9sJAayMTYVEAswawEmKiIiq76233sLNmzexatUqAEBpaenzD8CTy/DMzMwQFRWF8ePH6zghacP+/ftx9+5d2NraYtKkSXB0dDR0JKJazWTfYTJWSqVAWk5hpYqlp62MTUX67SIodVjEERFR7bdy5Upcu3YNvXv3BvCkIKpI2fbevXvj8uXLLJZMyNChQ+Hm5oaIiAgWS0R6wIJJyyQSYNOpP6p8nBDAppOZqP7yD0RERE94eXnh2LFjuHDhAmbOnImuXbuqFtuwsLBA165dMXPmTFy4cAFHjx7lZXgmxsHBAREREXBwcDB0FKI6wWQvyTNWD+Wl2Jd8q1rH7k2+hX8NbYd6Mv5vISKimuvWrZvaindKpRJSKV8rrQ0kNVhhl4iqhs+aWpaSVVBugYfKevC4FNeyCrWciIiI6AkWS0REVcdnTi0reFizb1kveCTXUhIiIiIiIqopFkxaZmtds8vpbK34hY5ERERERMaCBZOWtWtiCxuZWbWOtZGZoW0TfoktERFVrLS0FAsWLMCNGzcMHYV0qCZfPkxE2seCScusLcwQ6t20WseO9G4Ka4vqFVtERFS7CSEwa9YsrFy5EgMHDsTjx48NHYl04Pbt21i9ejXS0tIMHYWI/oMFk5YJAUT0aoGqLl4jkQARvT3Ab2EiIqKKLF26FOvWrYNEIsHy5cshk8kMHYm0LDs7G5s3b0ZBQQESExMh+N2MREaBBZOWSaUStGrcAPMGt6nScfMD28DTyQZSLhNKRETPePDgAXbt2gUA+PLLLzF69GgDJyJtu3fvHr799lsUFxfD1dUVEyZM4NLhREaCX/ijI7MHekEiAVbGpuJ5LxBJJE+KpVkD+KWBRERUMRsbGxw/fhwHDhzA5MmTDR2HdMDOzg6tWrVCbm4uJk6cCCsrK8jlXDmXyBiwYNKhWQO8ENTBBZtOZmJv8i2172eykZlhpHdTRPT2gJdzfQOmJCIiU9CwYUMWS7WYVCrFiBEjoFAoeLklkZFhwaRjLZ1s8H5oR/xraDukZBWi8JEctlYWaNukAawtzPiZJSIiIgLwpGhisURkfFgw6VjZZ5Lqyczh4+5Qbj+vTiYiIiIiMl5c9IGIiIiIiEgDFkxEREREREQasGAiIqI6KTExEcOGDYOrqyskEgn27dtXrk1KSgqGDx8OOzs72NjYoEePHrhx44Zq/6NHjzBr1iw0bNgQ9evXR1hYGHJycqqVh9+5Q0RknFgwERFRnfTgwQN06dIFa9asqXB/RkYG+vTpg7Zt2+Lo0aP49ddfsWTJElhZWanazJkzBz/88AN27tyJY8eO4e+//8aoUaOqlWf8+PHYsGFDtY4l45eeno4ffvgBSqXS0FGIqIq46AMREdVJISEhCAkJ0bh/8eLFGDJkCD7++GPVNk9PT9W/79+/jw0bNiA6OhoDBw4EAERFRaFdu3Y4ffo0XnrppSrl+fnnn5GUlIShQ4fCxcWlimdDxuz333/Hjh07UFpaChcXF/To0cPQkYioCvgOExER0TOUSiUOHjyI1q1bIygoCM7OzvD19VW7bO/ChQuQy+UICAhQbWvbti3c3NyQlJRU5fs0NzfHnj17WCzVMqmpqfj+++9RWlqKdu3aoVu3boaORERVxHeYqqjsGvOCggKD5pDL5SguLkZBQQEsLCwMmqWqTDU7c+ufqWZn7ucre/405s/s3L59G0VFRVi+fDk++OADrFixAjExMRg1ahQSEhLQr18/ZGdnQyaTwd7eXu3Yxo0bIzs7W2PfJSUlKCkpUd2+f/8+AGDFihXw8fFBbm6uTs7JFJQ9BnNzc03qd+d5CgsLUVJSAi8vL/Tv3x/5+fmVPrY2jkdNcDz+i2OhLi8vD4Du5hUWTFVUWFgIAGjevLmBkxARmbbCwkLY2dkZOkaFyj5nMmLECMyZMwcA0LVrV5w6dQpr165Fv379qt13ZGQkli1bVm77vHnzMG/evGr3S0RU1+Xm5upkXmHBVEWurq7466+/0KBBA0gkhvva2YKCAjRv3hx//fUXbG1tDZajOkw1O3Prn6lmZ+7nE0KgsLAQrq6uOruPmmrUqBHMzc3Rvn17te3t2rXDiRMnAAAuLi54/Pgx8vPz1d5lysnJee5ldYsWLcLcuXNVt/Pz8+Hu7o4bN24YbQGpL6b6u6MrHA91HI//4liou3//Ptzc3ODo6KiT/lkwVZFUKkWzZs0MHUPF1tbWZH9RTDU7c+ufqWZnbs2MvTCQyWTo0aMHUlNT1bb//vvvcHd3BwD4+PjAwsICR44cQVhYGIAnn1e5ceMG/Pz8NPZtaWkJS0vLctvt7OxM8vGiC6b6u6MrHA91HI//4liok0p1szwDCyYiIqqTioqKkJ6errqdmZmJS5cuwdHREW5ubliwYAHGjh2Lvn37YsCAAYiJicEPP/yAo0ePAnhS4EydOhVz586Fo6MjbG1t8cYbb8DPz6/KK+QREZHxYsFERER10vnz5zFgwADV7bLL5CZNmoRNmzZh5MiRWLt2LSIjI/Hmm2+iTZs22L17N/r06aM65vPPP4dUKkVYWBhKSkoQFBSEL7/8Uu/nQkREusOCyURZWlpi6dKlFV7WYexMNTtz65+pZmdu09C/f/8Xrqg0ZcoUTJkyReN+KysrrFmzRuOX31ZGXRv35+FYqON4qON4/BfHQp2ux0MijHldVyIiIiIiIgPiF9cSERERERFpwIKJiIiIiIhIAxZMREREREREGrBgIiIiIiIi0oAFkxFZs2YNWrRoASsrK/j6+uLs2bPPbb9z5060bdsWVlZW6NSpEw4dOqS2XwiBd955B02aNIG1tTUCAgKQlpZm9LkjIiIgkUjUfoKDg7Weu6rZr1y5grCwMLRo0QISiQSrVq2qcZ/Gkvvdd98tN+Zt27Y1aO6vv/4a/v7+cHBwgIODAwICAsq119djXBfZ9fU4r0ruPXv2oHv37rC3t4eNjQ26du2KLVu2qLXR55ibssTERAwbNgyurq6QSCTYt29fuTYpKSkYPnw47OzsYGNjgx49euDGjRuq/Y8ePcKsWbPQsGFD1K9fH2FhYcjJydHjWWjPi8bj2d+Fsp9PPvlE1SYvLw/h4eGwtbWFvb09pk6diqKiIj2fSc29aCyKioowe/ZsNGvWDNbW1mjfvj3Wrl2r1qYuPTZycnIQEREBV1dX1KtXD8HBweWec2rLeERGRqJHjx5o0KABnJ2dERoaWu7Lsytzrjdu3MDQoUNRr149ODs7Y8GCBVAoFPo8Fa2ozHisX78e/fv3h62tLSQSCfLz88v1o5XnDkFGYfv27UImk4mNGzeKK1euiOnTpwt7e3uRk5NTYfuTJ08KMzMz8fHHH4urV6+Kt99+W1hYWIjffvtN1Wb58uXCzs5O7Nu3T/zyyy9i+PDhwsPDQzx8+NCoc0+aNEkEBweLrKws1U9eXp7WMlc3+9mzZ8X8+fPFtm3bhIuLi/j8889r3Kex5F66dKno0KGD2pjfuXNHa5mrk3vChAlizZo1Ijk5WaSkpIiIiAhhZ2cnbt68qWqjj8e4rrLr43Fe1dwJCQliz5494urVqyI9PV2sWrVKmJmZiZiYGFUbfY25qTt06JBYvHix2LNnjwAg9u7dq7Y/PT1dODo6igULFoiLFy+K9PR0sX//frX/NzNnzhTNmzcXR44cEefPnxcvvfSS6NWrl57PRDteNB5P/x5kZWWJjRs3ColEIjIyMlRtgoODRZcuXcTp06fF8ePHhZeXlxg/fryez6TmXjQW06dPF56eniIhIUFkZmaKdevWCTMzM7F//35Vm7ry2FAqleKll14S/v7+4uzZs+LatWtixowZws3NTRQVFana1ZbxCAoKElFRUeLy5cvi0qVLYsiQIVU+V4VCITp27CgCAgJEcnKyOHTokGjUqJFYtGiRIU6pRiozHp9//rmIjIwUkZGRAoC4d+9euX608dzBgslI9OzZU8yaNUt1u7S0VLi6uorIyMgK248ZM0YMHTpUbZuvr6/4xz/+IYR48iTj4uIiPvnkE9X+/Px8YWlpKbZt22a0uYV48ofkiBEjtJZRk6pmf5q7u3uFhUdN+qwsXeReunSp6NKli9YyVqSmY6NQKESDBg3E5s2bhRD6e4zrIrsQ+nmca+Px6O3tLd5++20hhH7HvDap6I/isWPHiokTJ2o8Jj8/X1hYWIidO3eqtqWkpAgAIikpSVdR9aKi8XjWiBEjxMCBA1W3r169KgCIc+fOqbYdPnxYSCQScevWLV1F1bmKxqJDhw7ivffeU9vWrVs3sXjxYiFE3XpspKamCgDi8uXLqm2lpaXCyclJfP3110KI2j0et2/fFgDEsWPHhBCVO9dDhw4JqVQqsrOzVW2++uorYWtrK0pKSvR7Alr27Hg8LSEhocKCSVvPHbwkzwg8fvwYFy5cQEBAgGqbVCpFQEAAkpKSKjwmKSlJrT0ABAUFqdpnZmYiOztbrY2dnR18fX019mkMucscPXoUzs7OaNOmDV5//XXk5uZqJXNNshuiT33eR1paGlxdXdGyZUuEh4erXRpUU9rIXVxcDLlcDkdHRwD6eYzrKnsZXT7Oa5pbCIEjR44gNTUVffv2BaC/Ma/tlEolDh48iNatWyMoKAjOzs7w9fVVuxTpwoULkMvlamPdtm1buLm51fqxzsnJwcGDBzF16lTVtqSkJNjb26N79+6qbQEBAZBKpThz5owhYupMr169cODAAdy6dQtCCCQkJOD3339HYGAggLr12CgpKQHw5Auiy0ilUlhaWuLEiRMAavd43L9/HwBUc0dlzjUpKQmdOnVC48aNVW2CgoJQUFCAK1eu6DG99j07HpWhrecOFkxG4O7duygtLVV7cANA48aNkZ2dXeEx2dnZz21f9t+q9GkMuQEgODgY3377LY4cOYIVK1bg2LFjCAkJQWlpqVZyVze7IfrU1334+vpi06ZNiImJwVdffYXMzEz4+/ujsLCwppEBaCf3P//5T7i6uqomCn08xgHdZAd0/zivbu779++jfv36kMlkGDp0KFavXo3BgwcD0N+Y13a3b99GUVERli9fjuDgYMTGxmLkyJEYNWoUjh07BuDJWMtkMtjb26sdWxfGevPmzWjQoAFGjRql2padnQ1nZ2e1dubm5nB0dKx147F69Wq0b98ezZo1g0wmQ3BwMNasWaN64aIuPTbKioFFixbh3r17ePz4MVasWIGbN28iKysLQO0dD6VSibfeegu9e/dGx44dAVTuXDX9nVW2z1RVNB6Voa3nDvNKtyTSk3Hjxqn+3alTJ3Tu3Bmenp44evQoBg0aZMBktVdISIjq3507d4avry/c3d2xY8cOtVd5DWX58uXYvn07jh49qvZKoynQlN1YH+cNGjTApUuXUFRUhCNHjmDu3Llo2bIl+vfvb7BMtY1SqQQAjBgxAnPmzAEAdO3aFadOncLatWvRr18/Q8YzuI0bNyI8PNzkfte1ZfXq1Th9+jQOHDgAd3d3JCYmYtasWeVedKkLLCwssGfPHkydOhWOjo4wMzNDQEAAQkJCIIQwdDydmjVrFi5fvqx6J62uM/R48B0mI9CoUSOYmZmVW+UkJycHLi4uFR7j4uLy3PZl/61Kn8aQuyItW7ZEo0aNkJ6eXvPQ/1Gd7Ibo0xD3AQD29vZo3bq11sa8JrlXrlyJ5cuXIzY2Fp07d1Zt18djHNBN9opo+3Fe3dxSqRReXl7o2rUr5s2bh9GjRyMyMhKA/sa8tmvUqBHMzc3Rvn17te3t2rVTXQrr4uKCx48fl1vxqbaP9fHjx5Gamopp06apbXdxccHt27fVtikUCuTl5dWq8Xj48CH+9a9/4bPPPsOwYcPQuXNnzJ49G2PHjsXKlSsB1L3Hho+PDy5duoT8/HxkZWUhJiYGubm5aNmyJYDaOR6zZ8/Gjz/+iISEBDRr1ky1vTLnqunvrLJ9pkjTeFSGtp47WDAZAZlMBh8fHxw5ckS1TalU4siRI/Dz86vwGD8/P7X2ABAXF6dq7+HhARcXF7U2BQUFOHPmjMY+jSF3RW7evInc3Fw0adJEK7mB6mU3RJ+GuA/gybK2GRkZWhvz6ub++OOP8f777yMmJkbt+mNAP49xXWWviLYf59p6rCiVStXnCPQ15rWdTCZDjx49yi2P+/vvv8Pd3R3Akz8SLSws1MY6NTUVN27cqNVjvWHDBvj4+KBLly5q2/38/JCfn48LFy6otsXHx0OpVMLX11ffMXVGLpdDLpdDKlX/88zMzEz1zmRdfWzY2dnByckJaWlpOH/+PEaMGAGgdo2HEAKzZ8/G3r17ER8fDw8PD7X9lTlXPz8//Pbbb2pFQlxcHGxtbcu9SGPsXjQelaG1547qrFJB2rd9+3ZhaWkpNm3aJK5evSpmzJgh7O3tVaucvPrqq2LhwoWq9idPnhTm5uZi5cqVIiUlRSxdurTCZcXt7e3F/v37xa+//ipGjBihk2XFtZm7sLBQzJ8/XyQlJYnMzEzx888/i27duolWrVqJR48eaS13dbKXlJSI5ORkkZycLJo0aSLmz58vkpOTRVpaWqX7NNbc8+bNE0ePHhWZmZni5MmTIiAgQDRq1Ejcvn3bYLmXL18uZDKZ2LVrl9pyw4WFhWptdP0Y10V2fT3Oq5r7o48+ErGxsSIjI0NcvXpVrFy5Upibm6tWoyo7N32MuakrLCxU/d4BEJ999plITk4Wf/75pxBCiD179ggLCwuxfv16kZaWJlavXi3MzMzE8ePHVX3MnDlTuLm5ifj4eHH+/Hnh5+cn/Pz8DHVKNfKi8RBCiPv374t69eqJr776qsI+goODhbe3tzhz5ow4ceKEaNWqlUkuK/6isejXr5/o0KGDSEhIENevXxdRUVHCyspKfPnll6o+6tJjY8eOHSIhIUFkZGSIffv2CXd3dzFq1Ci1PmrLeLz++uvCzs5OHD16VG3uKC4uVrV50bmWLSseGBgoLl26JGJiYoSTk5NJLitemfHIysoSycnJ4uuvvxYARGJiokhOTha5ubmqNtp47mDBZERWr14t3NzchEwmEz179hSnT59W7evXr5+YNGmSWvsdO3aI1q1bC5lMJjp06CAOHjyotl+pVIolS5aIxo0bC0tLSzFo0CCRmppq1LmLi4tFYGCgcHJyEhYWFsLd3V1Mnz5dqwVHdbNnZmYKAOV++vXrV+k+jTX32LFjRZMmTYRMJhNNmzYVY8eOFenp6QbN7e7uXmHupUuXqtro6zGu7ez6fJxXJffixYuFl5eXsLKyEg4ODsLPz09s375drT99jrkpK1vi9tmfp8d7w4YNqvHu0qWL2Ldvn1ofDx8+FP/zP/8jHBwcRL169cTIkSNFVlaWns9EOyozHuvWrRPW1tYiPz+/wj5yc3PF+PHjRf369YWtra2YPHmy2gsopuJFY5GVlSUiIiKEq6ursLKyEm3atBGffvqpUCqVqj7q0mPj//7v/0SzZs2EhYWFcHNzE2+//Xa55bFry3hUNA4ARFRUlKpNZc71jz/+ECEhIcLa2lo0atRIzJs3T8jlcj2fTc1VZjyWLl36wjbaeO6Q/CcQERERERERPYOfYSIiIiIiItKABRMREREREZEGLJiIiIiIiIg0YMFERERERESkAQsmIiIiIiIiDVgwERERERERacCCiYiIiIiISAMWTETPSEtLQ2BgIOzs7CCRSLBv3z5DR6pVJBIJ3n33XUPHICLSG84rusV5hXSNBROZrE2bNkEikah+zM3N0bRpU0RERODWrVvV7nfSpEn47bff8OGHH2LLli3o3r27FlObjjfffBMSiQTp6eka2yxevBgSiQS//vqrHpMREekG5xXd4rxCpooFE5m89957D1u2bMHatWsREhKCrVu3ol+/fnj06FGV+3r48CGSkpIwdepUzJ49GxMnTkSzZs10kNr4hYeHAwCio6M1ttm2bRs6deqEzp076ysWEZHOcV7RDc4rZKpYMJHJCwkJwcSJEzFt2jR88803mD9/PjIyMnDgwIEq93Xnzh0AgL29vdbyPXr0CEqlUmv96Yuvry+8vLywbdu2CvcnJSUhMzNTNQESEdUWnFd0g/MKmSoWTFTr+Pv7AwAyMjLUtl+7dg2jR4+Go6MjrKys0L17d7XJ791334W7uzsAYMGCBZBIJGjRooVq/61btzBlyhQ0btwYlpaW6NChAzZu3Kh2H0ePHoVEIsH27dvx9ttvo2nTpqhXrx4KCgoAAGfOnEFwcDDs7OxQr1499OvXDydPnlTr491331VdshAREQF7e3vY2dlh8uTJKC4uLne+W7duRc+ePVGvXj04ODigb9++iI2NVWtz+PBh+Pv7w8bGBg0aNMDQoUNx5cqVF45leHg4rl27hosXL5bbFx0dDYlEgvHjx+Px48d455134OPjAzs7O9jY2MDf3x8JCQkvvI+IiAi1cX52HCo6Xx8fH1hbW8PR0RHjxo3DX3/9pdYmLS0NYWFhcHFxgZWVFZo1a4Zx48bh/v37L8xDRPQsziucVziv1G3mhg5ApG1//PEHAMDBwUG17cqVK+jduzeaNm2KhQsXwsbGBjt27EBoaCh2796NkSNHYtSoUbC3t8ecOXMwfvx4DBkyBPXr1wcA5OTk4KWXXoJEIsHs2bPh5OSEw4cPY+rUqSgoKMBbb72lluH999+HTCbD/PnzUVJSAplMhvj4eISEhMDHxwdLly6FVCpFVFQUBg4ciOPHj6Nnz55qfYwZMwYeHh6IjIzExYsX8c0338DZ2RkrVqxQtVm2bBneffdd9OrVC++99x5kMhnOnDmD+Ph4BAYGAgC2bNmCSZMmISgoCCtWrEBxcTG++uor9OnTB8nJyRVOKmXCw8OxbNkyREdHo1u3bqrtpaWl2LFjB/z9/eHm5oa7d+/im2++wfjx4zF9+nQUFhZiw4YNCAoKwtmzZ9G1a9dq/J8s78MPP8SSJUswZswYTJs2DXfu3MHq1avRt29fJCcnw97eHo8fP0ZQUBBKSkrwxhtvwMXFBbdu3cKPP/6I/Px82NnZaSULEdUdnFc4r3BeqeMEkYmKiooSAMTPP/8s7ty5I/766y+xa9cu4eTkJCwtLcVff/2lajto0CDRqVMn8ejRI9U2pVIpevXqJVq1aqXalpmZKQCITz75RO2+pk6dKpo0aSLu3r2rtn3cuHHCzs5OFBcXCyGESEhIEABEy5YtVdvK7qtVq1YiKChIKJVK1fbi4mLh4eEhBg8erNq2dOlSAUBMmTJF7b5GjhwpGjZsqLqdlpYmpFKpGDlypCgtLVVrW3YfhYWFwt7eXkyfPl1tf3Z2trCzsyu3vSI9evQQzZo1U7uPmJgYAUCsW7dOCCGEQqEQJSUlasfdu3dPNG7cuNx5ABBLly5V3Z40aZJwd3cvd79l41Dmjz/+EGZmZuLDDz9Ua/fbb78Jc3Nz1fbk5GQBQOzcufOF50ZE9DTOK5xXhOC8QuXxkjwyeQEBAXByckLz5s0xevRo2NjY4MCBA6oP1ebl5SE+Ph5jxoxBYWEh7t69i7t37yI3NxdBQUFIS0t77upHQgjs3r0bw4YNgxBCdfzdu3cRFBSE+/fvl7u0YNKkSbC2tlbdvnTpEtLS0jBhwgTk5uaqjn/w4AEGDRqExMTEctejz5w5U+22v78/cnNzVZdh7Nu3D0qlEu+88w6kUvVf5bJLDuLi4pCfn4/x48er5TYzM4Ovr2+lLm2YOHEibt68icTERNW26OhoyGQyvPLKKwAAMzMzyGQyAIBSqUReXh4UCgW6d+9e4WUX1bFnzx4olUqMGTNG7VxcXFzQqlUr1bmUvdL3008/VXipCRHRi3Be4bzCeYWexkvyyOStWbMGrVu3xv3797Fx40YkJibC0tJStT89PR1CCCxZsgRLliypsI/bt2+jadOmFe67c+cO8vPzsX79eqxfv17j8U/z8PBQu52WlgbgyYSnyf3799Uu93Bzc1PbX7bv3r17sLW1RUZGBqRSKdq3b6+xz7L7HThwYIX7bW1tNR5bZty4cZg7dy6io6PRv39/PHr0CHv37kVISIha3s2bN+PTTz/FtWvXIJfLVdufHYvqSktLgxACrVq1qnC/hYWF6v7mzp2Lzz77DN999x38/f0xfPhwTJw4kZdNEFGlcF7hvAJwXqH/YsFEJq9nz56q77QIDQ1Fnz59MGHCBKSmpqJ+/fqqV9jmz5+PoKCgCvvw8vLS2H/Z8RMnTtQ4MT27/OnTrwI+3ccnn3yi8brrsuvay5iZmVXYTgihMeuzyu53y5YtcHFxKbff3PzFTwHOzs4YPHgwdu/ejTVr1uCHH35AYWGh2ipGW7duRUREBEJDQ7FgwQI4OzvDzMwMkZGR5T4k/ayKPoALPLme/dlzkUgkOHz4cIVj8/T4ffrpp4iIiMD+/fsRGxuLN998E5GRkTh9+nSdXc6XiCqP84pmnFc4r9RFLJioVil7Mh0wYAC++OILLFy4EC1btgTw5JWigICAKvfp5OSEBg0aoLS0tFrHA4CnpyeAJ6+8VbePivpUKpW4evWqxsmy7H6dnZ1rdL/h4eGIiYnB4cOHER0dDVtbWwwbNky1f9euXWjZsiX27NmjNlEtXbr0hX07ODggPz+/3PY///xT7banpyeEEPDw8EDr1q1f2G+nTp3QqVMnvP322zh16hR69+6NtWvX4oMPPnjhsUREZTivVHy/nFc4r9Ql/AwT1Tr9+/dHz549sWrVKjx69AjOzs7o378/1q1bh6ysrHLty74jQxMzMzOEhYVh9+7duHz5cpWPBwAfHx94enpi5cqVKCoqqlYfzwoNDYVUKsV7771X7jr1slcLg4KCYGtri48++kjtcoaq3m9oaCjq1auHL7/8EocPH8aoUaNgZWWl2l/2ytzTr1KeOXMGSUlJL+zb09MT9+/fV/tW96ysLOzdu1et3ahRo2BmZoZly5aVezVUCIHc3FwAQEFBARQKhdr+Tp06QSqVoqSkpFLnS0T0NM4rnFc4r9RtfIeJaqUFCxbglVdewaZNmzBz5kysWbMGffr0QadOnTB9+nS0bNkSOTk5SEpKws2bN/HLL788t7/ly5cjISEBvr6+mD59Otq3b4+8vDxcvHgRP//8M/Ly8p57vFQqxTfffIOQkBB06NABkydPRtOmTXHr1i0kJCTA1tYWP/zwQ5XO0cvLC4sXL8b7778Pf39/jBo1CpaWljh37hxcXV0RGRkJW1tbfPXVV3j11VfRrVs3jBs3Dk5OTrhx4wYOHjyI3r1744svvnjhfdWvXx+hoaGqb2d/9ksFX375ZezZswcjR47E0KFDkZmZibVr16J9+/YVTuRPGzduHP75z39i5MiRePPNN1XL07Zu3Vrtg72enp744IMPsGjRIvzxxx8IDQ1FgwYNkJmZib1792LGjBmYP38+4uPjMXv2bLzyyito3bo1FAoFtmzZovoDhYioOjivcF7hvFKH6XdRPiLtKVv+9dy5c+X2lZaWCk9PT+Hp6SkUCoUQQoiMjAzx2muvCRcXF2FhYSGaNm0qXn75ZbFr1y7VcZqWfxVCiJycHDFr1izRvHlzYWFhIVxcXMSgQYPE+vXrVW3Kln/VtPRocnKyGDVqlGjYsKGwtLQU7u7uYsyYMeLIkSOqNmXLnt65c6fC883MzFTbvnHjRuHt7S0sLS2Fg4OD6Nevn4iLi1Nrk5CQIIKCgoSdnZ2wsrISnp6eIiIiQpw/f17D6JZ38OBBAUA0adKkwuVmP/roI+Hu7i4sLS2Ft7e3+PHHHytc2hXPLP8qhBCxsbGiY8eOQiaTiTZt2oitW7eWW/61zO7du0WfPn2EjY2NsLGxEW3bthWzZs0SqampQgghrl+/LqZMmSI8PT2FlZWVcHR0FAMGDBA///xzpc+ViOomzitPcF7hvELqJEJU4ZN+REREREREdQg/w0RERERERKQBCyYiIiIiIiINWDARERERERFpwIKJiIiIiIhIAxZMREREREREGrBgIiIiIiIi0oAFExERERERkQYsmIiIiIiIiDRgwURERERERKQBCyYiIiIiIiINWDARERERERFpwIKJiIiIiIhIAxZMREREREREGvw/MNzRKAuuCJYAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "flow_encoding.var_base_name" + "import matplotlib.pyplot as plt \n", + "\n", + "fig = plt.figure(figsize = plt.figaspect(0.5))\n", + "ax1 = fig.add_subplot(121)\n", + "\n", + "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.grid()\n", + "\n", + "ax1.scatter(ref_values[:8], encoded_ref_sol[:8], c='black', s=200, label='Best solution')\n", + "ax1.scatter(ref_values[:8], sol[:8], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "\n", + "\n", + "ax1.set_xlabel('Reference Values', fontsize=12)\n", + "ax1.set_ylabel('QUBO Values', fontsize=12)\n", + "ax1.set_title('Flow Rate', fontsize=14)\n", + "\n", + "ax2 = fig.add_subplot(122)\n", + "\n", + "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "\n", + "\n", + "ax2.scatter(ref_values[8:-1], encoded_ref_sol[8:], c='black', s=200, label='Best solution')\n", + "ax2.scatter(ref_values[8:-1], sol[8:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "ax2.grid()\n", + "\n", + "ax2.set_xlim([160,210])\n", + "ax2.set_ylim([160,210])\n", + "ax2.set_xlabel('Reference Values', fontsize=12)\n", + "ax2.set_title('Pressure', fontsize=14)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py index e8649ab..a0dd409 100644 --- a/wntr_quantum/sampler/simulated_annealing.py +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -107,12 +107,12 @@ def bqm_energy(x, var_names): trajectory.append(x) # step scheduling - step_schedule = ( - Tschedule / ((Tschedule[0] - Tschedule[-1]) / (take_step.step_size - 1)) + 1 - ) + # step_schedule = ( + # Tschedule / ((Tschedule[0] - Tschedule[-1]) / (take_step.step_size - 1)) + 1 + # ) # loop over the temp schedule - for s, T in tqdm(zip(step_schedule, Tschedule)): + for T in tqdm(Tschedule): # original point x_ori = deepcopy(x) @@ -130,7 +130,10 @@ def bqm_energy(x, var_names): if save_traj: trajectory.append(x) else: - p = np.exp(-(e_new - e_ori) / T) + if T != 0: + p = np.exp(-(e_new - e_ori) / T) + else: + p = 0.0 if np.random.rand() < p: x = x_new energies.append(bqm_energy(x, var_names)) diff --git a/wntr_quantum/sampler/step/full_random.py b/wntr_quantum/sampler/step/full_random.py index b70d7fa..dd2f93e 100644 --- a/wntr_quantum/sampler/step/full_random.py +++ b/wntr_quantum/sampler/step/full_random.py @@ -4,6 +4,18 @@ class RandomStep(BaseStep): # noqa: D101 + def __init__( + self, + var_names, + single_var_names, + single_var_index, + optimize_values=None, + ): + super().__init__(var_names, single_var_names, single_var_index) + self.optimize_values = optimize_values + if self.optimize_values is None: + self.optimize_values = list(np.arange(len(self.value_names))) + def __call__(self, x): """Call function of the method. diff --git a/wntr_quantum/sim/models/darcy_weisbach_fit.py b/wntr_quantum/sim/models/darcy_weisbach_fit.py index 38c9cdf..f887590 100644 --- a/wntr_quantum/sim/models/darcy_weisbach_fit.py +++ b/wntr_quantum/sim/models/darcy_weisbach_fit.py @@ -26,7 +26,9 @@ def friction_factor(q, e, s): # noqa: D417 return f -def dw_fit(roughness, diameter, plot=False, convert_to_us_unit=False): +def dw_fit( + roughness, diameter, plot=False, convert_to_us_unit=False, return_all_data=False +): """Fit the dw friction coefficient to a quadratic polynomial. Args: @@ -34,6 +36,7 @@ def dw_fit(roughness, diameter, plot=False, convert_to_us_unit=False): diameter (float): diamter of the pipe in meter plot(bool): plot the solution for visual inspection convert_to_us_unit(bool): convert to us unit + return_all_data (bool): return all data """ def convert_to_USunit(roughness, diameter): @@ -67,8 +70,10 @@ def convert_to_USunit(roughness, diameter): plt.show() print(res) - # return np.array(res), np.poly1d(res)(1 / Q), factors, Q - return np.array(res) + if return_all_data: + return np.array(res), np.poly1d(res)(1 / Q), factors, Q + else: + return np.array(res) def evaluate_fit(coeffs, flow): From f51b87d2cac5c17073ab5b82c5c21bd27294b619 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 23 Oct 2024 16:44:34 +0200 Subject: [PATCH 73/96] added landscape analysis --- .../qubo_poly_solver_2loops_dw.ipynb | 93 +- docs/notebooks/qubo_poly_solver_CM.ipynb | 2 +- docs/notebooks/qubo_poly_solver_Net0.ipynb | 3610 +++++++++++++++++ wntr_quantum/sampler/simulated_annealing.py | 63 + wntr_quantum/sampler/step/base_step.py | 31 +- wntr_quantum/sampler/step/full_random.py | 49 +- 6 files changed, 3745 insertions(+), 103 deletions(-) create mode 100644 docs/notebooks/qubo_poly_solver_Net0.ipynb diff --git a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb index 94c4626..d14783d 100644 --- a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb +++ b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb @@ -141,8 +141,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 500.000000 => 1000.000000 (res: 0.978474)\n", - "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.029354)\n" + "Head Encoding : 500.000000 => 1000.000000 (res: 0.244260)\n", + "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.007328)\n" ] } ], @@ -151,11 +151,11 @@ "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", - "nqbit = 9\n", + "nqbit = 11\n", "step = (15/(2**nqbit-1))\n", "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"q\")\n", "\n", - "nqbit = 9\n", + "nqbit = 11\n", "step = (500/(2**nqbit-1))\n", "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+500.0, var_base_name=\"h\")\n", "\n", @@ -212,7 +212,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -287,8 +287,8 @@ "\n", "var_names = sorted(net.qubo.qubo_dict.variables)\n", "net.qubo.create_variables_mapping()\n", - "mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", - "# mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)\n", + "# mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", + "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=25)\n", "# mystep = ParallelIncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=100)" ] }, @@ -317,6 +317,17 @@ "## generate modifed solution initial guess" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", + "x = modify_solution_sample(net, bin_rep_sol, modify=['flows', 'heads'])\n", + "x0 = list(x.values())" + ] + }, { "cell_type": "code", "execution_count": 13, @@ -388,42 +399,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/20000 [00:00 2\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mTschedule\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mTschedule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtake_step\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmystep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_traj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m mystep\u001b[38;5;241m.\u001b[39mverify_quadratic_constraints(res\u001b[38;5;241m.\u001b[39mres)\n", - "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sampler/simulated_annealing.py:129\u001b[0m, in \u001b[0;36mSimulatedAnnealing.sample\u001b[0;34m(self, bqm, num_sweeps, Temp, Tschedule, x0, take_step, save_traj)\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e_new \u001b[38;5;241m<\u001b[39m e_ori:\n\u001b[1;32m 128\u001b[0m x \u001b[38;5;241m=\u001b[39m x_new\n\u001b[0;32m--> 129\u001b[0m energies\u001b[38;5;241m.\u001b[39mappend(\u001b[43mbqm_energy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save_traj:\n\u001b[1;32m 131\u001b[0m trajectory\u001b[38;5;241m.\u001b[39mappend(x)\n", - "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/sampler/simulated_annealing.py:80\u001b[0m, in \u001b[0;36mSimulatedAnnealing.sample..bqm_energy\u001b[0;34m(x, var_names)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbqm_energy\u001b[39m(x, var_names):\n\u001b[1;32m 74\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute the energy of a given binary array.\u001b[39;00m\n\u001b[1;32m 75\u001b[0m \n\u001b[1;32m 76\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;124;03m x (_type_): _description_\u001b[39;00m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;124;03m var_names (list): list of var names\u001b[39;00m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 80\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbqm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menergies\u001b[49m\u001b[43m(\u001b[49m\u001b[43mas_samples\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar_names\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:1092\u001b[0m, in \u001b[0;36mBinaryQuadraticModel.energies\u001b[0;34m(self, samples_like, dtype)\u001b[0m\n\u001b[1;32m 1067\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menergies\u001b[39m(\u001b[38;5;28mself\u001b[39m, samples_like, dtype: Optional[DTypeLike] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m 1068\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Determine the energies of the given samples-like.\u001b[39;00m\n\u001b[1;32m 1069\u001b[0m \n\u001b[1;32m 1070\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1090\u001b[0m \n\u001b[1;32m 1091\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1092\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menergies\u001b[49m\u001b[43m(\u001b[49m\u001b[43msamples_like\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyqmbase/cyqmbase_template.pyx.pxi:116\u001b[0m, in \u001b[0;36mdimod.cyqmbase.cyqmbase_float64.cyQMBase_template.energies\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/functools.py:877\u001b[0m, in \u001b[0;36msingledispatch..wrapper\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 873\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[1;32m 874\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfuncname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires at least \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 875\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1 positional argument\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 877\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/sampleset.py:428\u001b[0m, in \u001b[0;36m_as_samples_tuple\u001b[0;34m(samples_like, dtype, copy, order, labels_type)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[38;5;66;03m# make sure our labels are the correct type\u001b[39;00m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(labels, labels_type):\n\u001b[1;32m 427\u001b[0m \u001b[38;5;66;03m# todo: generalize to other sequence types? Especially Variables\u001b[39;00m\n\u001b[0;32m--> 428\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[43mlabels_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 430\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arr\u001b[38;5;241m.\u001b[39msize:\n\u001b[1;32m 431\u001b[0m arr\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m=\u001b[39m (arr\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mlen\u001b[39m(labels))\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:47\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables.__init__\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:166\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables._extend\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:166\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables._extend\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:193\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables._extend\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:97\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables._append\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:97\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables._append\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:134\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables._append\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:352\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables.count\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:352\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables.count\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/cyvariables.pyx:361\u001b[0m, in \u001b[0;36mdimod.cyvariables.cyVariables.count\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/abc.py:96\u001b[0m, in \u001b[0;36mABCMeta.__instancecheck__\u001b[0;34m(cls, instance)\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Register a virtual subclass of an ABC.\u001b[39;00m\n\u001b[1;32m 91\u001b[0m \n\u001b[1;32m 92\u001b[0m \u001b[38;5;124;03m Returns the subclass, to allow usage as a class decorator.\u001b[39;00m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _abc_register(\u001b[38;5;28mcls\u001b[39m, subclass)\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__instancecheck__\u001b[39m(\u001b[38;5;28mcls\u001b[39m, instance):\n\u001b[1;32m 97\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Override for isinstance(instance, cls).\"\"\"\u001b[39;00m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _abc_instancecheck(\u001b[38;5;28mcls\u001b[39m, instance)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "100%|██████████| 20000/20000 [03:11<00:00, 104.43it/s]\n" ] } ], @@ -441,7 +417,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -450,7 +426,7 @@ }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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" ] @@ -566,7 +542,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 6000/6000 [00:39<00:00, 152.08it/s]\n" + "100%|██████████| 6000/6000 [00:58<00:00, 102.93it/s]\n" ] } ], @@ -589,7 +565,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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" ] diff --git a/docs/notebooks/qubo_poly_solver_CM.ipynb b/docs/notebooks/qubo_poly_solver_CM.ipynb index fd40e0c..e38df42 100644 --- a/docs/notebooks/qubo_poly_solver_CM.ipynb +++ b/docs/notebooks/qubo_poly_solver_CM.ipynb @@ -414,7 +414,7 @@ ], "metadata": { "kernelspec": { - "display_name": "vitens", + "display_name": "vitens_wntr_1", "language": "python", "name": "python3" }, diff --git a/docs/notebooks/qubo_poly_solver_Net0.ipynb b/docs/notebooks/qubo_poly_solver_Net0.ipynb new file mode 100644 index 0000000..9f11c35 --- /dev/null +++ b/docs/notebooks/qubo_poly_solver_Net0.ipynb @@ -0,0 +1,3610 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 597, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 597, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "# Create a water network model\n", + "inp_file = './networks/Net0_CM.inp'\n", + "# inp_file = './networks/Net2LoopsDW.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the original Cholesky EPANET simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 598, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 598, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "flow_at_5hr = results.link['flowrate'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, link_attribute=flow_at_5hr, \n", + " node_attribute=pressure_at_5hr, \n", + " node_size=500, \n", + " link_width=5, \n", + " node_labels=True,\n", + " link_cmap=plt.cm.cividis)" + ] + }, + { + "cell_type": "code", + "execution_count": 599, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" + ] + }, + "execution_count": 599, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0][:2]\n", + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the QUBO Polynomial Solver" + ] + }, + { + "cell_type": "code", + "execution_count": 600, + "metadata": {}, + "outputs": [], + "source": [ + "wn = wntr.network.WaterNetworkModel(inp_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 601, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 0.000000 => 200.000000 (res: 6.451613)\n", + "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.129032)\n" + ] + } + ], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 5\n", + "step = (4./(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", + "\n", + "nqbit = 5\n", + "step = (200/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", + "\n", + "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)\n", + "net.verify_encoding()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve the system classically" + ] + }, + { + "cell_type": "code", + "execution_count": 602, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1., 1., 1., 1.])" + ] + }, + "execution_count": 602, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.create_index_mapping(model)\n", + "net.matrices = net.initialize_matrices(model)\n", + "\n", + "ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = net.classical_solutions()\n", + "ref_sol / ref_values" + ] + }, + { + "cell_type": "code", + "execution_count": 603, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 1, [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [1, 1, 1, 1, 0]]" + ] + }, + "execution_count": 603, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bin_rep_sol" + ] + }, + { + "cell_type": "code", + "execution_count": 604, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.scatter(ref_values, encoded_ref_sol)\n", + "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", + "plt.grid(which=\"major\", lw=1)\n", + "plt.grid(which=\"minor\", lw=0.1)\n", + "# plt.loglog()\n", + "plt.xscale('symlog')\n", + "plt.yscale('symlog')" + ] + }, + { + "cell_type": "code", + "execution_count": 605, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.matrices = net.initialize_matrices(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 606, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", + "# from wntr_quantum.sampler.simulated_annealing_parallel import SimulatedAnnealing\n", + "sampler = SimulatedAnnealing()" + ] + }, + { + "cell_type": "code", + "execution_count": 607, + "metadata": {}, + "outputs": [], + "source": [ + "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", + "import sparse\n", + "net.qubo = QUBOPS_MIXED(net.mixed_solution_vector, {\"sampler\": sampler})\n", + "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "net.qubo.qubo_dict = net.qubo.create_bqm(matrices, strength=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 608, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.step.full_random import RandomStep\n", + "from wntr_quantum.sampler.step.full_random import IncrementalStep\n", + "from wntr_quantum.sampler.step.full_random import ParallelIncrementalStep \n", + "\n", + "var_names = sorted(net.qubo.qubo_dict.variables)\n", + "net.qubo.create_variables_mapping()\n", + "# mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", + "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=1)\n", + "# mystep = ParallelIncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# generate init sample" + ] + }, + { + "cell_type": "code", + "execution_count": 609, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import generate_random_valid_sample\n", + "x = generate_random_valid_sample(net.qubo)\n", + "x0 = list(x.values())" + ] + }, + { + "cell_type": "code", + "execution_count": 610, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", + "x = modify_solution_sample(net, bin_rep_sol, modify=['flows', 'heads'])\n", + "x0 = list(x.values())" + ] + }, + { + "cell_type": "code", + "execution_count": 611, + "metadata": {}, + "outputs": [], + "source": [ + "eref = net.qubo.energy_binary_rep(bin_rep_sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 612, + "metadata": {}, + "outputs": [], + "source": [ + "num_sweeps = 2000\n", + "Tinit = 1E3\n", + "Tfinal = 1E-1\n", + "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(200))" + ] + }, + { + "cell_type": "code", + "execution_count": 613, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 3%|▎ | 62/2200 [00:00<00:03, 619.50it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2200/2200 [00:02<00:00, 928.87it/s]\n" + ] + } + ], + "source": [ + "# mystep.optimize_values = np.arange(2, 6)\n", + "res = sampler.sample(net.qubo.qubo_dict, x0=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True)\n", + "mystep.verify_quadratic_constraints(res.res)" + ] + }, + { + "cell_type": "code", + "execution_count": 614, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 614, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "eplt = res.energies\n", + "\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "left, bottom, width, height = [0.25, 0.25, 0.3, 0.3]\n", + "\n", + "ax1.plot(eplt)\n", + "ax1.plot(Tschedule)\n", + "ax1.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "# plt.ylim([-1E5, -1E4])\n", + "# plt.xlim([9000,11000])\n", + "ax1.grid()\n", + "ax1.set_yscale('symlog')\n", + "\n", + "ax2 = fig.add_axes([left, bottom, width, height])\n", + "ax2.plot(eplt[-100:])\n", + "ax2.grid()\n", + "ax2.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "# ax2.set_yscale('symlog')" + ] + }, + { + "cell_type": "code", + "execution_count": 615, + "metadata": {}, + "outputs": [], + "source": [ + "idx_min = np.array([e[0] for e in res.energies]).argmin()\n", + "sol = res.trajectory[idx_min]\n", + "sol = net.qubo.decode_solution(np.array(sol))\n", + "sol = net.combine_flow_values(sol)\n", + "sol = net.convert_solution_to_si(sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 616, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Pressure')" + ] + }, + "execution_count": 616, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "\n", + "fig = plt.figure(figsize = plt.figaspect(0.5))\n", + "ax1 = fig.add_subplot(121)\n", + "\n", + "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.grid()\n", + "\n", + "ax1.scatter(ref_values[:2], encoded_ref_sol[:2], c='black', s=200, label='Best solution')\n", + "ax1.scatter(ref_values[:2], sol[:2], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "\n", + "\n", + "ax1.set_xlabel('Reference Values', fontsize=12)\n", + "ax1.set_ylabel('QUBO Values', fontsize=12)\n", + "ax1.set_title('Flow Rate', fontsize=14)\n", + "\n", + "ax2 = fig.add_subplot(122)\n", + "\n", + "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "\n", + "\n", + "ax2.scatter(ref_values[2:], encoded_ref_sol[2:], c='black', s=200, label='Best solution')\n", + "ax2.scatter(ref_values[2:], sol[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "ax2.grid()\n", + "\n", + "# ax2.set_xlim([160,210])\n", + "# ax2.set_ylim([160,210])\n", + "ax2.set_xlabel('Reference Values', fontsize=12)\n", + "ax2.set_title('Pressure', fontsize=14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explore the solution space" + ] + }, + { + "cell_type": "code", + "execution_count": 617, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_7835/3452845714.py:16: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + "32it [00:00, 75.13it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 617, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import itertools\n", + "from tqdm import tqdm\n", + "\n", + "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", + "energies = np.zeros((2**nqbit, 2**nqbit))\n", + "i2 = 0\n", + "for data2 in tqdm(itertools.product([0, 1], repeat=nqbit)):\n", + " i3 = 0\n", + " for data3 in itertools.product([0, 1], repeat=nqbit):\n", + " # print(list(data))\n", + " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", + " mod_bin_rep_sol[2] = list(data2)[::-1]\n", + " mod_bin_rep_sol[3] = list(data3)[::-1]\n", + " # x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", + " # x0 = list(x.values())\n", + " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + " i3+=1\n", + " i2+=1\n", + "\n", + "x, y = np.arange(2**nqbit), np.arange(2**nqbit)\n", + "x,y = np.meshgrid(x,y)\n", + "ax = plt.figure().add_subplot(projection='3d')\n", + "ax.plot_surface(x,y,energies)" + ] + }, + { + "cell_type": "code", + "execution_count": 618, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]/tmp/ipykernel_7835/685105249.py:17: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + "32it [00:00, 72.16it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 618, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import itertools\n", + "from tqdm import tqdm\n", + "from copy import deepcopy\n", + "\n", + "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", + "energies = np.zeros((2**nqbit, 2**nqbit))\n", + "i2 = 0\n", + "for data2 in tqdm(itertools.product([0, 1], repeat=nqbit)):\n", + " i3 = 0\n", + " for data3 in itertools.product([0, 1], repeat=nqbit):\n", + " # print(list(data))\n", + " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", + " mod_bin_rep_sol[4] = list(data2)[::-1]\n", + " mod_bin_rep_sol[5] = list(data3)[::-1]\n", + " # x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", + " # x0 = list(x.values())\n", + " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + " i3+=1\n", + " i2+=1\n", + "\n", + "x, y = np.arange(2**nqbit), np.arange(2**nqbit)\n", + "x,y = np.meshgrid(x,y)\n", + "ax = plt.figure().add_subplot(projection='3d')\n", + "ax.plot_surface(x,y,energies)\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 619, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]/tmp/ipykernel_7835/3224148935.py:12: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + "32it [00:00, 1909.11it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 619, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import itertools\n", + "from tqdm import tqdm\n", + "\n", + "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", + "energies = np.zeros(2**nqbit)\n", + "i2 = 0\n", + "for data2 in tqdm(itertools.product([0, 1], repeat=nqbit)):\n", + "\n", + " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", + " mod_bin_rep_sol[4] = list(data2)[::-1]\n", + " # mod_bin_rep_sol[3] = list(data2)[::-1]\n", + " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + "\n", + " i2+=1\n", + "plt.plot(energies-eref, 'o-')" + ] + }, + { + "cell_type": "code", + "execution_count": 655, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_values([1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0])" + ] + }, + "execution_count": 655, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def flatten_list(lst):\n", + " out = []\n", + " for elmt in lst:\n", + " if not isinstance(elmt, list):\n", + " out += [elmt]\n", + " else:\n", + " out += elmt\n", + " return out\n", + "\n", + "bin_rep_flat = flatten_list(bin_rep_sol)\n", + "xt_bin_rep_flat = net.qubo.extend_binary_representation(bin_rep_flat)\n", + "xt_bin_rep_flat.values()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 657, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1, [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [1, 1, 1, 1, 0]]\n", + "[1, 0, [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [1, 1, 1, 1, 0]]\n" + ] + } + ], + "source": [ + "xt_bin_rep_flat_new = mystep(list(xt_bin_rep_flat.values()))\n", + "x_new = []\n", + "for k,v in mystep.index_values.items():\n", + " if len(v) == 1:\n", + " x_new.append(xt_bin_rep_flat_new[v[0]])\n", + " else:\n", + " tmp = []\n", + " for idx in v:\n", + " tmp.append(xt_bin_rep_flat_new[idx])\n", + " x_new.append(tmp)\n", + "print(bin_rep_sol)\n", + "print(x_new)" + ] + }, + { + "cell_type": "code", + "execution_count": 622, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[4.06595691246814e-8*x_001_001**2*x_003_001**4,\n", + " 3.25276552997451e-7*x_001_001**2*x_003_001**3*x_003_002,\n", + " 6.50553105994902e-7*x_001_001**2*x_003_001**3*x_003_003,\n", + " 1.3011062119898e-6*x_001_001**2*x_003_001**3*x_003_004,\n", + " 2.60221242397961e-6*x_001_001**2*x_003_001**3*x_003_005,\n", + " 9.75829658992353e-7*x_001_001**2*x_003_001**2*x_003_002**2,\n", + " 3.90331863596941e-6*x_001_001**2*x_003_001**2*x_003_002*x_003_003,\n", + " 7.80663727193883e-6*x_001_001**2*x_003_001**2*x_003_002*x_003_004,\n", + " 1.56132745438777e-5*x_001_001**2*x_003_001**2*x_003_002*x_003_005,\n", + " 3.90331863596941e-6*x_001_001**2*x_003_001**2*x_003_003**2,\n", + " 1.56132745438777e-5*x_001_001**2*x_003_001**2*x_003_003*x_003_004,\n", + " 3.12265490877553e-5*x_001_001**2*x_003_001**2*x_003_003*x_003_005,\n", + " 1.56132745438777e-5*x_001_001**2*x_003_001**2*x_003_004**2,\n", + " 6.24530981755106e-5*x_001_001**2*x_003_001**2*x_003_004*x_003_005,\n", + " 6.24530981755106e-5*x_001_001**2*x_003_001**2*x_003_005**2,\n", + " 0.0665972944849115*x_001_001**2*x_003_001**2,\n", + " 1.3011062119898e-6*x_001_001**2*x_003_001*x_003_002**3,\n", + " 7.80663727193883e-6*x_001_001**2*x_003_001*x_003_002**2*x_003_003,\n", + " 1.56132745438777e-5*x_001_001**2*x_003_001*x_003_002**2*x_003_004,\n", + " 3.12265490877553e-5*x_001_001**2*x_003_001*x_003_002**2*x_003_005,\n", + " 1.56132745438777e-5*x_001_001**2*x_003_001*x_003_002*x_003_003**2,\n", + " 6.24530981755106e-5*x_001_001**2*x_003_001*x_003_002*x_003_003*x_003_004,\n", + " 0.000124906196351021*x_001_001**2*x_003_001*x_003_002*x_003_003*x_003_005,\n", + " 6.24530981755106e-5*x_001_001**2*x_003_001*x_003_002*x_003_004**2,\n", + " 0.000249812392702042*x_001_001**2*x_003_001*x_003_002*x_003_004*x_003_005,\n", + " 0.000249812392702042*x_001_001**2*x_003_001*x_003_002*x_003_005**2,\n", + " 0.266389177939646*x_001_001**2*x_003_001*x_003_002,\n", + " 1.04088496959184e-5*x_001_001**2*x_003_001*x_003_003**3,\n", + " 6.24530981755106e-5*x_001_001**2*x_003_001*x_003_003**2*x_003_004,\n", + " 0.000124906196351021*x_001_001**2*x_003_001*x_003_003**2*x_003_005,\n", + " 0.000124906196351021*x_001_001**2*x_003_001*x_003_003*x_003_004**2,\n", + " 0.000499624785404085*x_001_001**2*x_003_001*x_003_003*x_003_004*x_003_005,\n", + " 0.000499624785404085*x_001_001**2*x_003_001*x_003_003*x_003_005**2,\n", + " 0.532778355879292*x_001_001**2*x_003_001*x_003_003,\n", + " 8.32707975673475e-5*x_001_001**2*x_003_001*x_003_004**3,\n", + " 0.000499624785404085*x_001_001**2*x_003_001*x_003_004**2*x_003_005,\n", + " 0.00099924957080817*x_001_001**2*x_003_001*x_003_004*x_003_005**2,\n", + " 1.06555671175858*x_001_001**2*x_003_001*x_003_004,\n", + " 0.00066616638053878*x_001_001**2*x_003_001*x_003_005**3,\n", + " 2.13111342351717*x_001_001**2*x_003_001*x_003_005,\n", + " 6.50553105994902e-7*x_001_001**2*x_003_002**4,\n", + " 5.20442484795922e-6*x_001_001**2*x_003_002**3*x_003_003,\n", + " 1.04088496959184e-5*x_001_001**2*x_003_002**3*x_003_004,\n", + " 2.08176993918369e-5*x_001_001**2*x_003_002**3*x_003_005,\n", + " 1.56132745438777e-5*x_001_001**2*x_003_002**2*x_003_003**2,\n", + " 6.24530981755106e-5*x_001_001**2*x_003_002**2*x_003_003*x_003_004,\n", + " 0.000124906196351021*x_001_001**2*x_003_002**2*x_003_003*x_003_005,\n", + " 6.24530981755106e-5*x_001_001**2*x_003_002**2*x_003_004**2,\n", + " 0.000249812392702042*x_001_001**2*x_003_002**2*x_003_004*x_003_005,\n", + " 0.000249812392702042*x_001_001**2*x_003_002**2*x_003_005**2,\n", + " 0.266389177939646*x_001_001**2*x_003_002**2,\n", + " 2.08176993918369e-5*x_001_001**2*x_003_002*x_003_003**3,\n", + " 0.000124906196351021*x_001_001**2*x_003_002*x_003_003**2*x_003_004,\n", + " 0.000249812392702042*x_001_001**2*x_003_002*x_003_003**2*x_003_005,\n", + " 0.000249812392702042*x_001_001**2*x_003_002*x_003_003*x_003_004**2,\n", + " 0.00099924957080817*x_001_001**2*x_003_002*x_003_003*x_003_004*x_003_005,\n", + " 0.00099924957080817*x_001_001**2*x_003_002*x_003_003*x_003_005**2,\n", + " 1.06555671175858*x_001_001**2*x_003_002*x_003_003,\n", + " 0.000166541595134695*x_001_001**2*x_003_002*x_003_004**3,\n", + " 0.00099924957080817*x_001_001**2*x_003_002*x_003_004**2*x_003_005,\n", + " 0.00199849914161634*x_001_001**2*x_003_002*x_003_004*x_003_005**2,\n", + " 2.13111342351717*x_001_001**2*x_003_002*x_003_004,\n", + " 0.00133233276107756*x_001_001**2*x_003_002*x_003_005**3,\n", + " 4.26222684703434*x_001_001**2*x_003_002*x_003_005,\n", + " 1.04088496959184e-5*x_001_001**2*x_003_003**4,\n", + " 8.32707975673475e-5*x_001_001**2*x_003_003**3*x_003_004,\n", + " 0.000166541595134695*x_001_001**2*x_003_003**3*x_003_005,\n", + " 0.000249812392702042*x_001_001**2*x_003_003**2*x_003_004**2,\n", + " 0.00099924957080817*x_001_001**2*x_003_003**2*x_003_004*x_003_005,\n", + " 0.00099924957080817*x_001_001**2*x_003_003**2*x_003_005**2,\n", + " 1.06555671175858*x_001_001**2*x_003_003**2,\n", + " 0.00033308319026939*x_001_001**2*x_003_003*x_003_004**3,\n", + " 0.00199849914161634*x_001_001**2*x_003_003*x_003_004**2*x_003_005,\n", + " 0.00399699828323268*x_001_001**2*x_003_003*x_003_004*x_003_005**2,\n", + " 4.26222684703434*x_001_001**2*x_003_003*x_003_004,\n", + " 0.00266466552215512*x_001_001**2*x_003_003*x_003_005**3,\n", + " 8.52445369406868*x_001_001**2*x_003_003*x_003_005,\n", + " 0.000166541595134695*x_001_001**2*x_003_004**4,\n", + " 0.00133233276107756*x_001_001**2*x_003_004**3*x_003_005,\n", + " 0.00399699828323268*x_001_001**2*x_003_004**2*x_003_005**2,\n", + " 4.26222684703434*x_001_001**2*x_003_004**2,\n", + " 0.00532933104431024*x_001_001**2*x_003_004*x_003_005**3,\n", + " 17.0489073881374*x_001_001**2*x_003_004*x_003_005,\n", + " 0.00266466552215512*x_001_001**2*x_003_005**4,\n", + " 17.0489073881374*x_001_001**2*x_003_005**2,\n", + " -0.133194588969823*x_001_001*x_002_001*x_003_001*x_004_001,\n", + " -0.266389177939646*x_001_001*x_002_001*x_003_001*x_004_002,\n", + " -0.532778355879292*x_001_001*x_002_001*x_003_001*x_004_003,\n", + " -1.06555671175858*x_001_001*x_002_001*x_003_001*x_004_004,\n", + " -2.13111342351717*x_001_001*x_002_001*x_003_001*x_004_005,\n", + " -0.266389177939646*x_001_001*x_002_001*x_003_002*x_004_001,\n", + " -0.532778355879292*x_001_001*x_002_001*x_003_002*x_004_002,\n", + " -1.06555671175858*x_001_001*x_002_001*x_003_002*x_004_003,\n", + " -2.13111342351717*x_001_001*x_002_001*x_003_002*x_004_004,\n", + " -4.26222684703434*x_001_001*x_002_001*x_003_002*x_004_005,\n", + " 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-34.0978147762747*x_001_001*x_002_001*x_003_005*x_004_005,\n", + " -4.06595691246814e-8*x_001_001*x_003_001**4,\n", + " -3.25276552997451e-7*x_001_001*x_003_001**3*x_003_002,\n", + " -6.50553105994902e-7*x_001_001*x_003_001**3*x_003_003,\n", + " -1.3011062119898e-6*x_001_001*x_003_001**3*x_003_004,\n", + " -2.60221242397961e-6*x_001_001*x_003_001**3*x_003_005,\n", + " -9.75829658992353e-7*x_001_001*x_003_001**2*x_003_002**2,\n", + " -3.90331863596941e-6*x_001_001*x_003_001**2*x_003_002*x_003_003,\n", + " -7.80663727193883e-6*x_001_001*x_003_001**2*x_003_002*x_003_004,\n", + " -1.56132745438777e-5*x_001_001*x_003_001**2*x_003_002*x_003_005,\n", + " -3.90331863596941e-6*x_001_001*x_003_001**2*x_003_003**2,\n", + " -1.56132745438777e-5*x_001_001*x_003_001**2*x_003_003*x_003_004,\n", + " -3.12265490877553e-5*x_001_001*x_003_001**2*x_003_003*x_003_005,\n", + " -1.56132745438777e-5*x_001_001*x_003_001**2*x_003_004**2,\n", + " 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0.333034827835952*x_001_001*x_003_001*x_003_004*x_005_004,\n", + " 0.666069655671903*x_001_001*x_003_001*x_003_004*x_005_005,\n", + " -1.70065019495953*x_001_001*x_003_001*x_003_004,\n", + " -0.00066616638053878*x_001_001*x_003_001*x_003_005**3,\n", + " 0.0832587069589879*x_001_001*x_003_001*x_003_005*x_005_001,\n", + " 0.166517413917976*x_001_001*x_003_001*x_003_005*x_005_002,\n", + " 0.333034827835952*x_001_001*x_003_001*x_003_005*x_005_003,\n", + " 0.666069655671903*x_001_001*x_003_001*x_003_005*x_005_004,\n", + " 1.33213931134381*x_001_001*x_003_001*x_003_005*x_005_005,\n", + " -3.40130038991905*x_001_001*x_003_001*x_003_005,\n", + " 0.0665972944849115*x_001_001*x_003_001*x_004_001,\n", + " 0.133194588969823*x_001_001*x_003_001*x_004_002,\n", + " 0.266389177939646*x_001_001*x_003_001*x_004_003,\n", + " 0.532778355879292*x_001_001*x_003_001*x_004_004,\n", + " 1.06555671175858*x_001_001*x_003_001*x_004_005,\n", + " -6.50553105994902e-7*x_001_001*x_003_002**4,\n", + " 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2.13111342351717*x_004_002*x_004_005,\n", + " 0.911346237716428*x_004_002,\n", + " 2.60221242397961e-6*x_004_003**4,\n", + " 2.08176993918369e-5*x_004_003**3*x_004_004,\n", + " 4.16353987836737e-5*x_004_003**3*x_004_005,\n", + " 6.24530981755106e-5*x_004_003**2*x_004_004**2,\n", + " 0.000249812392702042*x_004_003**2*x_004_004*x_004_005,\n", + " 0.000249812392702042*x_004_003**2*x_004_005**2,\n", + " 0.020814676739747*x_004_003**2*x_005_001,\n", + " 0.0416293534794939*x_004_003**2*x_005_002,\n", + " 0.0832587069589879*x_004_003**2*x_005_003,\n", + " 0.166517413917976*x_004_003**2*x_005_004,\n", + " 0.333034827835952*x_004_003**2*x_005_005,\n", + " -0.020814676739747*x_004_003**2*x_006_001,\n", + " -0.0416293534794939*x_004_003**2*x_006_002,\n", + " -0.0832587069589879*x_004_003**2*x_006_003,\n", + " ...]" + ] + }, + "execution_count": 622, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p = net.qubo.create_polynom(np.array(net.qubo.x))\n", + "pp = p.T @ p\n", + "pp[0].expand().as_ordered_terms()" + ] + }, + { + "cell_type": "code", + "execution_count": 624, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_005_001 -1186.7559218989402\n", + "x_005_002 -2207.018607585602\n", + "x_005_003 -3748.0642703220883\n", + "x_005_004 -4832.236761247715\n", + "x_005_005 991.0935950904168\n" + ] + } + ], + "source": [ + "for k, v in net.qubo.qubo_dict.linear.items():\n", + " if k.startswith('x_005'):\n", + " print(k,v)" + ] + }, + { + "cell_type": "code", + "execution_count": 625, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('x_002_001', 'x_004_003') -3.6453849508657123\n", + "('x_004_003*x_002_001', 'x_004_003') 0.0\n", + "('x_004_003*x_002_001', 'x_002_001') 0.0\n", + "('x_004_004', 'x_004_003') 2.1312799651123044\n", + "('x_004_004', 'x_002_001') -7.290769901731425\n", + "('x_004_004', 'x_004_003*x_002_001') -1.7763568394002505e-15\n", + "('x_004_004*x_002_001', 'x_002_001') 0.0\n", + "('x_004_004*x_002_001', 'x_004_004') 0.0\n", + "('x_003_003', 'x_004_003') -0.5327783558792923\n", + "('x_003_003', 'x_004_003*x_002_001') 1.0655567117585847\n", + "('x_003_003', 'x_004_004') -1.0655567117585847\n", + "('x_003_003', 'x_004_004*x_002_001') 2.1311134235171694\n", + "('x_003_003', 'x_001_001') -0.6350934832009412\n", + "('x_001_001*x_003_003', 'x_004_003') 1.0655567117585847\n", + "('x_001_001*x_003_003', 'x_004_003*x_002_001') -2.1311134235171694\n", + "('x_001_001*x_003_003', 'x_004_004') 2.1311134235171694\n", + "('x_001_001*x_003_003', 'x_004_004*x_002_001') -4.262226847034339\n", + "('x_001_001*x_003_003', 'x_001_001') 0.0\n", + "('x_001_001*x_003_003', 'x_003_003') 0.0\n", + "('x_004_001', 'x_004_003') 0.2663929186200057\n", + "('x_004_001', 'x_002_001') -0.9113462377164281\n", + "('x_004_001', 'x_004_003*x_002_001') 0.0\n", + "('x_004_001', 'x_004_004') 0.5328034021738732\n", + "('x_004_001', 'x_004_004*x_002_001') -4.440892098500626e-16\n", + "('x_004_001', 'x_003_003') -0.13319458896982309\n", + "('x_004_001', 'x_001_001*x_003_003') 0.26638917793964617\n", + "('x_004_001*x_002_001', 'x_002_001') 0.0\n", + "('x_004_001*x_002_001', 'x_003_003') 0.26638917793964617\n", + "('x_004_001*x_002_001', 'x_001_001*x_003_003') -0.5327783558792923\n", + "('x_004_001*x_002_001', 'x_004_001') 0.0\n", + "('x_003_002', 'x_004_003') -0.26638917793964617\n", + "('x_003_002', 'x_004_003*x_002_001') 0.5327783558792923\n", + "('x_003_002', 'x_004_004') -0.5327783558792923\n", + "('x_003_002', 'x_004_004*x_002_001') 1.0655567117585847\n", + "('x_003_002', 'x_001_001') -0.1587733708002353\n", + "('x_003_002', 'x_003_003') 0.5839463283898126\n", + "('x_003_002', 'x_001_001*x_003_003') -0.6350934832009412\n", + "('x_003_002', 'x_004_001') -0.06659729448491154\n", + "('x_003_002', 'x_004_001*x_002_001') 0.13319458896982309\n", + "('x_001_001*x_003_002', 'x_004_003') 0.5327783558792923\n", + "('x_001_001*x_003_002', 'x_004_003*x_002_001') -1.0655567117585847\n", + "('x_001_001*x_003_002', 'x_004_004') 1.0655567117585847\n", + "('x_001_001*x_003_002', 'x_004_004*x_002_001') -2.1311134235171694\n", + "('x_001_001*x_003_002', 'x_001_001') 0.0\n", + "('x_001_001*x_003_002', 'x_004_001') 0.13319458896982309\n", + "('x_001_001*x_003_002', 'x_004_001*x_002_001') -0.26638917793964617\n", + "('x_001_001*x_003_002', 'x_003_002') 0.0\n", + "('x_004_005', 'x_004_003') 4.2631844612063645\n", + "('x_004_005', 'x_002_001') -14.58153980346285\n", + "('x_004_005', 'x_004_003*x_002_001') 3.552713678800501e-15\n", + "('x_004_005', 'x_004_004') 8.527118359590835\n", + "('x_004_005', 'x_004_004*x_002_001') 0.0\n", + "('x_004_005', 'x_003_003') -2.1311134235171694\n", + "('x_004_005', 'x_001_001*x_003_003') 4.262226847034339\n", + "('x_004_005', 'x_004_001') 1.0657395171813695\n", + "('x_004_005', 'x_004_001*x_002_001') 0.0\n", + "('x_004_005', 'x_003_002') -1.0655567117585847\n", + "('x_004_005', 'x_001_001*x_003_002') 2.1311134235171694\n", + "('x_004_002', 'x_004_003') 0.5327887647289884\n", + "('x_004_002', 'x_002_001') -1.8226924754328562\n", + "('x_004_002', 'x_004_003*x_002_001') 0.0\n", + "('x_004_002', 'x_004_004') 1.0656165626443364\n", + "('x_004_002', 'x_004_004*x_002_001') -8.881784197001252e-16\n", + "('x_004_002', 'x_003_003') -0.26638917793964617\n", + "('x_004_002', 'x_001_001*x_003_003') 0.5327783558792923\n", + "('x_004_002', 'x_004_001') 0.1331952395229291\n", + "('x_004_002', 'x_004_001*x_002_001') 0.0\n", + "('x_004_002', 'x_003_002') -0.13319458896982309\n", + "('x_004_002', 'x_001_001*x_003_002') 0.26638917793964617\n", + "('x_004_002', 'x_004_005') 2.1315141642304627\n", + "('x_004_005*x_004_002', 'x_004_003') 0.00034349203996530834\n", + "('x_004_005*x_004_002', 'x_002_001') 0.0\n", + "('x_004_005*x_004_002', 'x_004_003*x_002_001') 2.168404344971009e-19\n", + "('x_004_005*x_004_002', 'x_004_004') 0.0008118902762816378\n", + "('x_004_005*x_004_002', 'x_004_004*x_002_001') 0.0\n", + "('x_004_005*x_004_002', 'x_004_001') 7.416305408341884e-05\n", + "('x_004_005*x_004_002', 'x_004_001*x_002_001') 0.0\n", + "('x_004_005*x_004_002', 'x_004_005') 0.0\n", + "('x_004_005*x_004_002', 'x_004_002') 0.0\n", + "('x_003_005', 'x_004_003') -2.1311134235171694\n", + "('x_003_005', 'x_004_003*x_002_001') 4.262226847034339\n", + "('x_003_005', 'x_004_004') -4.262226847034339\n", + "('x_003_005', 'x_004_004*x_002_001') 8.524453694068677\n", + "('x_003_005', 'x_001_001') -10.161495731215059\n", + "('x_003_005', 'x_003_003') 4.6724449704929585\n", + "('x_003_005', 'x_001_001*x_003_003') -5.080747865607531\n", + "('x_003_005', 'x_004_001') -0.5327783558792923\n", + "('x_003_005', 'x_004_001*x_002_001') 1.0655567117585847\n", + "('x_003_005', 'x_003_002') 2.3361444188737597\n", + "('x_003_005', 'x_001_001*x_003_002') -2.5403739328037647\n", + "('x_003_005', 'x_004_005') -8.524453694068677\n", + "('x_003_005', 'x_004_002') -1.0655567117585847\n", + "('x_001_001*x_003_005', 'x_004_003') 4.262226847034339\n", + "('x_001_001*x_003_005', 'x_004_003*x_002_001') -8.524453694068677\n", + "('x_001_001*x_003_005', 'x_004_004') 8.524453694068677\n", + "('x_001_001*x_003_005', 'x_004_004*x_002_001') -17.048907388137355\n", + "('x_001_001*x_003_005', 'x_001_001') 0.0\n", + "('x_001_001*x_003_005', 'x_004_001') 1.0655567117585847\n", + "('x_001_001*x_003_005', 'x_004_001*x_002_001') -2.1311134235171694\n", + "('x_001_001*x_003_005', 'x_004_005') 17.048907388137355\n", + "('x_001_001*x_003_005', 'x_004_002') 2.1311134235171694\n", + "('x_001_001*x_003_005', 'x_003_005') 0.0\n", + "('x_003_001', 'x_004_003') -0.13319458896982309\n", + "('x_003_001', 'x_004_003*x_002_001') 0.26638917793964617\n", + "('x_003_001', 'x_004_004') -0.26638917793964617\n", + "('x_003_001', 'x_004_004*x_002_001') 0.5327783558792923\n", + "('x_003_001', 'x_001_001') -0.03969334270005882\n", + "('x_003_001', 'x_003_003') 0.2919717004504178\n", + "('x_003_001', 'x_001_001*x_003_003') -0.3175467416004706\n", + "('x_003_001', 'x_004_001') -0.03329864724245577\n", + "('x_003_001', 'x_004_001*x_002_001') 0.06659729448491154\n", + "('x_003_001', 'x_003_002') 0.14598463043813517\n", + "('x_003_001', 'x_001_001*x_003_002') -0.15877337080023524\n", + "('x_003_001', 'x_004_005') -0.5327783558792923\n", + "('x_003_001', 'x_004_002') -0.06659729448491154\n", + "('x_003_001', 'x_003_005') 1.168054644503018\n", + "('x_003_001', 'x_001_001*x_003_005') -1.2701869664018823\n", + "('x_003_004', 'x_004_003') -1.0655567117585847\n", + "('x_003_004', 'x_004_003*x_002_001') 2.1311134235171694\n", + "('x_003_004', 'x_004_004') -2.1311134235171694\n", + "('x_003_004', 'x_004_004*x_002_001') 4.262226847034339\n", + "('x_003_004', 'x_001_001') -2.5403739328037647\n", + "('x_003_004', 'x_003_003') 2.3359102197556014\n", + "('x_003_004', 'x_001_001*x_003_003') -2.5403739328037647\n", + "('x_003_004', 'x_004_001') -0.26638917793964617\n", + "('x_003_004', 'x_004_001*x_002_001') 0.5327783558792923\n", + "('x_003_004', 'x_003_002') 1.1679316899659848\n", + "('x_003_004', 'x_001_001*x_003_002') -1.2701869664018823\n", + "('x_003_004', 'x_004_005') -4.262226847034339\n", + "('x_003_004', 'x_004_002') -0.5327783558792923\n", + "('x_003_004', 'x_003_005') 9.345639378164023\n", + "('x_003_004', 'x_001_001*x_003_005') -10.161495731215059\n", + "('x_003_004', 'x_003_001') 0.5839609658346975\n", + "('x_003_001*x_003_004', 'x_001_001') -0.6350934832009412\n", + "('x_003_001*x_003_004', 'x_003_003') 5.074314226760236e-05\n", + "('x_003_001*x_003_004', 'x_001_001*x_003_003') 0.0\n", + "('x_003_001*x_003_004', 'x_003_002') 2.146825249783177e-05\n", + "('x_003_001*x_003_004', 'x_001_001*x_003_002') 1.3552527156068805e-20\n", + "('x_003_001*x_003_004', 'x_003_005') 0.00039033186359694125\n", + "('x_003_001*x_003_004', 'x_001_001*x_003_005') 0.0\n", + "('x_003_001*x_003_004', 'x_003_001') 0.0\n", + "('x_003_001*x_003_004', 'x_003_004') 0.0\n", + "('x_004_005*x_002_001', 'x_002_001') 0.0\n", + "('x_004_005*x_002_001', 'x_003_003') 4.262226847034339\n", + "('x_004_005*x_002_001', 'x_001_001*x_003_003') -8.524453694068677\n", + "('x_004_005*x_002_001', 'x_003_002') 2.1311134235171694\n", + "('x_004_005*x_002_001', 'x_001_001*x_003_002') -4.262226847034339\n", + "('x_004_005*x_002_001', 'x_004_005') 0.0\n", + "('x_004_005*x_002_001', 'x_003_005') 17.048907388137355\n", + "('x_004_005*x_002_001', 'x_001_001*x_003_005') -34.09781477627471\n", + "('x_004_005*x_002_001', 'x_003_001') 1.0655567117585847\n", + "('x_004_005*x_002_001', 'x_003_004') 8.524453694068677\n", + "('x_002_001*x_004_002', 'x_002_001') 0.0\n", + "('x_002_001*x_004_002', 'x_003_003') 0.5327783558792923\n", + "('x_002_001*x_004_002', 'x_001_001*x_003_003') -1.0655567117585847\n", + "('x_002_001*x_004_002', 'x_003_002') 0.26638917793964617\n", + "('x_002_001*x_004_002', 'x_001_001*x_003_002') -0.5327783558792923\n", + "('x_002_001*x_004_002', 'x_004_002') 0.0\n", + "('x_002_001*x_004_002', 'x_003_005') 2.1311134235171694\n", + "('x_002_001*x_004_002', 'x_001_001*x_003_005') -4.262226847034339\n", + "('x_002_001*x_004_002', 'x_003_001') 0.13319458896982309\n", + "('x_002_001*x_004_002', 'x_003_004') 1.0655567117585847\n", + "('x_004_001*x_004_004', 'x_004_003') 5.074314226760236e-05\n", + "('x_004_001*x_004_004', 'x_004_003*x_002_001') 0.0\n", + "('x_004_001*x_004_004', 'x_004_004') 0.0\n", + "('x_004_001*x_004_004', 'x_004_001') 0.0\n", + "('x_004_001*x_004_004', 'x_004_005') 0.00039033186359694125\n", + "('x_004_001*x_004_004', 'x_004_002') 2.146825249783177e-05\n", + "('x_004_001*x_004_004', 'x_004_005*x_004_002') 6.24530981755106e-05\n", + "('x_001_001*x_003_001', 'x_004_003') 0.26638917793964617\n", + "('x_001_001*x_003_001', 'x_004_003*x_002_001') -0.5327783558792923\n", + "('x_001_001*x_003_001', 'x_004_004') 0.5327783558792923\n", + "('x_001_001*x_003_001', 'x_004_004*x_002_001') -1.0655567117585847\n", + "('x_001_001*x_003_001', 'x_001_001') 0.0\n", + "('x_001_001*x_003_001', 'x_004_001') 0.06659729448491154\n", + "('x_001_001*x_003_001', 'x_004_001*x_002_001') -0.13319458896982309\n", + "('x_001_001*x_003_001', 'x_004_005') 1.0655567117585847\n", + "('x_001_001*x_003_001', 'x_004_002') 0.13319458896982309\n", + "('x_001_001*x_003_001', 'x_003_001') 0.0\n", + "('x_001_001*x_003_001', 'x_004_005*x_002_001') -2.1311134235171694\n", + "('x_001_001*x_003_001', 'x_002_001*x_004_002') -0.26638917793964617\n", + "('x_001_001*x_003_004', 'x_004_003') 2.1311134235171694\n", + "('x_001_001*x_003_004', 'x_004_003*x_002_001') -4.262226847034339\n", + "('x_001_001*x_003_004', 'x_004_004') 4.262226847034339\n", + "('x_001_001*x_003_004', 'x_004_004*x_002_001') -8.524453694068677\n", + "('x_001_001*x_003_004', 'x_001_001') 0.0\n", + "('x_001_001*x_003_004', 'x_004_001') 0.5327783558792923\n", + "('x_001_001*x_003_004', 'x_004_001*x_002_001') -1.0655567117585847\n", + "('x_001_001*x_003_004', 'x_004_005') 8.524453694068677\n", + "('x_001_001*x_003_004', 'x_004_002') 1.0655567117585847\n", + "('x_001_001*x_003_004', 'x_003_004') 0.0\n", + "('x_001_001*x_003_004', 'x_004_005*x_002_001') -17.048907388137355\n", + "('x_001_001*x_003_004', 'x_002_001*x_004_002') -2.1311134235171694\n", + "('x_003_002*x_003_005', 'x_003_003') 0.00034349203996530834\n", + "('x_003_002*x_003_005', 'x_001_001*x_003_003') 2.168404344971009e-19\n", + "('x_003_002*x_003_005', 'x_003_002') 0.0\n", + "('x_003_002*x_003_005', 'x_003_005') 0.0\n", + "('x_003_002*x_003_005', 'x_003_001') 7.416305408341884e-05\n", + "('x_003_002*x_003_005', 'x_003_004') 0.0008118902762816378\n", + "('x_003_002*x_003_005', 'x_003_001*x_003_004') 6.24530981755106e-05\n", + "('x_004_003*x_002_001*x_004_002', 'x_004_003*x_002_001') 0.0\n", + "('x_004_003*x_002_001*x_004_002', 'x_004_004') 0.0\n", + "('x_004_003*x_002_001*x_004_002', 'x_004_001') 0.0\n", + "('x_004_003*x_002_001*x_004_002', 'x_004_002') 0.0\n", + 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3.12265490877553e-05\n", + "('x_003_004*x_003_003', 'x_003_003') 0.0\n", + "('x_003_004*x_003_003', 'x_003_002') 0.00010929292180714355\n", + "('x_003_004*x_003_003', 'x_003_005') 0.0017486867489142968\n", + "('x_003_004*x_003_003', 'x_003_004') 0.0\n", + "('x_003_004*x_003_003', 'x_003_002*x_003_005') 0.0002498123927020424\n", + "('x_001_001*x_003_002*x_003_005', 'x_001_001*x_003_002') 0.0\n", + "('x_001_001*x_003_002*x_003_005', 'x_003_005') 0.0\n", + "('x_001_001*x_003_002*x_003_005', 'x_003_001') 0.0\n", + "('x_001_001*x_003_002*x_003_005', 'x_003_004') 0.0\n", + "('x_001_001*x_003_002*x_003_005', 'x_003_001*x_003_004') 0.0\n", + "('x_001_001*x_003_003*x_003_004', 'x_001_001*x_003_003') 0.0\n", + "('x_001_001*x_003_003*x_003_004', 'x_003_004') 0.0\n", + "('x_001_001*x_003_003*x_003_004', 'x_003_002*x_003_005') 0.0\n", + "('x_003_002*x_003_003', 'x_003_003') 0.0\n", + "('x_003_002*x_003_003', 'x_003_002') 0.0\n", + "('x_003_002*x_003_003', 'x_003_001*x_003_004') 1.561327454387765e-05\n", + "('x_003_005*x_003_003', 'x_003_003') 0.0\n", + "('x_003_005*x_003_003', 'x_003_005') 0.0\n", + "('x_003_005*x_003_003', 'x_003_001*x_003_004') 0.0001249061963510212\n", + "('x_001_001*x_003_003*x_003_001', 'x_001_001*x_003_003') 0.0\n", + "('x_001_001*x_003_003*x_003_001', 'x_003_001') 0.0\n", + "('x_001_001*x_003_003*x_003_001', 'x_003_002*x_003_005') 0.0\n", + "('x_003_002*x_003_004', 'x_003_002') 0.0\n", + "('x_003_002*x_003_004', 'x_003_004') 0.0\n", + "('x_001_001*x_003_001*x_003_004', 'x_001_001') 0.0\n", + "('x_001_001*x_003_001*x_003_004', 'x_003_001*x_003_004') 0.0\n", + "('x_001_001*x_003_005*x_003_004', 'x_001_001*x_003_005') 0.0\n", + "('x_001_001*x_003_005*x_003_004', 'x_003_004') 0.0\n", + "('x_003_005*x_003_004', 'x_003_005') 0.0\n", + "('x_003_005*x_003_004', 'x_003_004') 0.0\n", + "('x_001_001*x_003_002*x_003_001', 'x_001_001*x_003_002') 0.0\n", + "('x_001_001*x_003_002*x_003_001', 'x_003_001') 0.0\n", + "('x_001_001*x_003_002*x_003_004', 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"('x_006_004', 'x_004_001*x_004_003') -0.08325870695898789\n", + "('x_006_004', 'x_004_005*x_004_003') -1.3321393113438063\n", + "('x_006_004', 'x_004_003*x_004_002') -0.16651741391797578\n", + "('x_006_004', 'x_004_004*x_004_003*x_002_001') 1.3321393113438063\n", + "('x_006_004', 'x_004_001*x_004_003*x_002_001') 0.16651741391797578\n", + "('x_006_004', 'x_004_005*x_004_004*x_002_001') 5.328557245375225\n", + "('x_006_004', 'x_004_004*x_004_002') -0.33303482783595156\n", + "('x_006_004', 'x_004_004*x_002_001*x_004_002') 0.6660696556719031\n", + "('x_006_004', 'x_004_004*x_004_005') -2.6642786226876125\n", + "('x_006_004', 'x_004_001*x_004_005') -0.33303482783595156\n", + "('x_006_004', 'x_004_001*x_002_001*x_004_002') 0.08325870695898789\n", + "('x_006_004', 'x_004_005*x_004_002*x_002_001') 1.3321393113438063\n", + "('x_006_004', 'x_004_001*x_004_002') -0.041629353479493945\n", + "('x_006_004', 'x_004_005*x_004_001*x_002_001') 0.6660696556719031\n", + "('x_006_004', 'x_005_001') -665.9729448491154\n", + "('x_006_004', 'x_005_002') -1331.9458896982308\n", + "('x_006_004', 'x_005_003') -2663.8917793964615\n", + "('x_006_004', 'x_005_004') -5327.783558792923\n", + "('x_006_004', 'x_005_005') -10655.567117585846\n", + "('x_006_004', 'x_006_001') 665.9729448491154\n", + "('x_006_004', 'x_006_002') 1331.9458896982308\n", + "('x_006_004', 'x_006_003') 2663.8917793964615\n", + "('x_006_005', 'x_004_003') -0.33303482783595156\n", + "('x_006_005', 'x_004_003*x_002_001') 0.6660696556719031\n", + "('x_006_005', 'x_004_004') -1.3321393113438063\n", + "('x_006_005', 'x_004_004*x_002_001') 2.6642786226876125\n", + "('x_006_005', 'x_004_001') -0.020814676739746973\n", + "('x_006_005', 'x_004_001*x_002_001') 0.041629353479493945\n", + "('x_006_005', 'x_004_005') -5.328557245375225\n", + "('x_006_005', 'x_004_002') -0.08325870695898789\n", + "('x_006_005', 'x_004_005*x_004_002') -1.3321393113438063\n", + "('x_006_005', 'x_004_005*x_002_001') 10.65711449075045\n", + "('x_006_005', 'x_002_001*x_004_002') 0.16651741391797578\n", + "('x_006_005', 'x_004_001*x_004_004') -0.33303482783595156\n", + "('x_006_005', 'x_004_003*x_002_001*x_004_002') 0.6660696556719031\n", + "('x_006_005', 'x_004_004*x_004_003') -1.3321393113438063\n", + "('x_006_005', 'x_004_005*x_004_003*x_002_001') 5.328557245375225\n", + "('x_006_005', 'x_004_001*x_004_004*x_002_001') 0.6660696556719031\n", + "('x_006_005', 'x_004_001*x_004_003') -0.16651741391797578\n", + "('x_006_005', 'x_004_005*x_004_003') -2.6642786226876125\n", + "('x_006_005', 'x_004_003*x_004_002') -0.33303482783595156\n", + "('x_006_005', 'x_004_004*x_004_003*x_002_001') 2.6642786226876125\n", + "('x_006_005', 'x_004_001*x_004_003*x_002_001') 0.33303482783595156\n", + "('x_006_005', 'x_004_005*x_004_004*x_002_001') 10.65711449075045\n", + "('x_006_005', 'x_004_004*x_004_002') -0.6660696556719031\n", + "('x_006_005', 'x_004_004*x_002_001*x_004_002') 1.3321393113438063\n", + "('x_006_005', 'x_004_004*x_004_005') -5.328557245375225\n", + "('x_006_005', 'x_004_001*x_004_005') -0.6660696556719031\n", + "('x_006_005', 'x_004_001*x_002_001*x_004_002') 0.16651741391797578\n", + "('x_006_005', 'x_004_005*x_004_002*x_002_001') 2.6642786226876125\n", + "('x_006_005', 'x_004_001*x_004_002') -0.08325870695898789\n", + "('x_006_005', 'x_004_005*x_004_001*x_002_001') 1.3321393113438063\n", + "('x_006_005', 'x_005_001') -1331.9458896982308\n", + "('x_006_005', 'x_005_002') -2663.8917793964615\n", + "('x_006_005', 'x_005_003') -5327.783558792923\n", + "('x_006_005', 'x_005_004') -10655.567117585846\n", + "('x_006_005', 'x_005_005') -21311.134235171692\n", + "('x_006_005', 'x_006_001') 1331.9458896982308\n", + "('x_006_005', 'x_006_002') 2663.8917793964615\n", + "('x_006_005', 'x_006_003') 5327.783558792923\n", + "('x_006_005', 'x_006_004') 10655.567117585846\n" + ] + } + ], + "source": [ + "for k, v in net.qubo.qubo_dict.quadratic.items():\n", + " # if k.startswith('x_005'):\n", + " print(k,v)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Embed the problem" + ] + }, + { + "cell_type": "code", + "execution_count": 626, + "metadata": {}, + "outputs": [], + "source": [ + "import dwave_networkx as dnx\n", + "from minorminer import find_embedding\n", + "from dwave.embedding import embed_qubo, majority_vote, chain_break_frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 627, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('x_002_001', 'x_004_003'): -3.6453849508657123,\n", + " ('x_004_003*x_002_001', 'x_004_003'): 0.0,\n", + " ('x_004_003*x_002_001', 'x_002_001'): 0.0,\n", + " ('x_004_004', 'x_004_003'): 2.1312799651123044,\n", + " ('x_004_004', 'x_002_001'): -7.290769901731425,\n", + " ('x_004_004', 'x_004_003*x_002_001'): -1.7763568394002505e-15,\n", + " ('x_004_004*x_002_001', 'x_002_001'): 0.0,\n", + " ('x_004_004*x_002_001', 'x_004_004'): 0.0,\n", + " ('x_003_003', 'x_004_003'): -0.5327783558792923,\n", + " ('x_003_003', 'x_004_003*x_002_001'): 1.0655567117585847,\n", + " ('x_003_003', 'x_004_004'): -1.0655567117585847,\n", + " ('x_003_003', 'x_004_004*x_002_001'): 2.1311134235171694,\n", + " ('x_003_003', 'x_001_001'): -0.6350934832009412,\n", + " ('x_001_001*x_003_003', 'x_004_003'): 1.0655567117585847,\n", + " ('x_001_001*x_003_003', 'x_004_003*x_002_001'): -2.1311134235171694,\n", + " ('x_001_001*x_003_003', 'x_004_004'): 2.1311134235171694,\n", + " ('x_001_001*x_003_003', 'x_004_004*x_002_001'): -4.262226847034339,\n", + " ('x_001_001*x_003_003', 'x_001_001'): 0.0,\n", + " ('x_001_001*x_003_003', 'x_003_003'): 0.0,\n", + " ('x_004_001', 'x_004_003'): 0.2663929186200057,\n", + " ('x_004_001', 'x_002_001'): -0.9113462377164281,\n", + " ('x_004_001', 'x_004_003*x_002_001'): 0.0,\n", + " ('x_004_001', 'x_004_004'): 0.5328034021738732,\n", + " ('x_004_001', 'x_004_004*x_002_001'): -4.440892098500626e-16,\n", + " ('x_004_001', 'x_003_003'): -0.13319458896982309,\n", + " ('x_004_001', 'x_001_001*x_003_003'): 0.26638917793964617,\n", + " ('x_004_001*x_002_001', 'x_002_001'): 0.0,\n", + " ('x_004_001*x_002_001', 'x_003_003'): 0.26638917793964617,\n", + " ('x_004_001*x_002_001', 'x_001_001*x_003_003'): -0.5327783558792923,\n", + " ('x_004_001*x_002_001', 'x_004_001'): 0.0,\n", + " ('x_003_002', 'x_004_003'): -0.26638917793964617,\n", + " ('x_003_002', 'x_004_003*x_002_001'): 0.5327783558792923,\n", + " ('x_003_002', 'x_004_004'): -0.5327783558792923,\n", + " ('x_003_002', 'x_004_004*x_002_001'): 1.0655567117585847,\n", + " ('x_003_002', 'x_001_001'): -0.1587733708002353,\n", + " ('x_003_002', 'x_003_003'): 0.5839463283898126,\n", + " ('x_003_002', 'x_001_001*x_003_003'): -0.6350934832009412,\n", + " ('x_003_002', 'x_004_001'): -0.06659729448491154,\n", + " ('x_003_002', 'x_004_001*x_002_001'): 0.13319458896982309,\n", + " ('x_001_001*x_003_002', 'x_004_003'): 0.5327783558792923,\n", + " ('x_001_001*x_003_002', 'x_004_003*x_002_001'): -1.0655567117585847,\n", + " ('x_001_001*x_003_002', 'x_004_004'): 1.0655567117585847,\n", + " ('x_001_001*x_003_002', 'x_004_004*x_002_001'): -2.1311134235171694,\n", + " ('x_001_001*x_003_002', 'x_001_001'): 0.0,\n", + " ('x_001_001*x_003_002', 'x_004_001'): 0.13319458896982309,\n", + " ('x_001_001*x_003_002', 'x_004_001*x_002_001'): -0.26638917793964617,\n", + " ('x_001_001*x_003_002', 'x_003_002'): 0.0,\n", + " ('x_004_005', 'x_004_003'): 4.2631844612063645,\n", + " ('x_004_005', 'x_002_001'): -14.58153980346285,\n", + " ('x_004_005', 'x_004_003*x_002_001'): 3.552713678800501e-15,\n", + " ('x_004_005', 'x_004_004'): 8.527118359590835,\n", + " ('x_004_005', 'x_004_004*x_002_001'): 0.0,\n", + " ('x_004_005', 'x_003_003'): -2.1311134235171694,\n", + " ('x_004_005', 'x_001_001*x_003_003'): 4.262226847034339,\n", + " ('x_004_005', 'x_004_001'): 1.0657395171813695,\n", + " ('x_004_005', 'x_004_001*x_002_001'): 0.0,\n", + " ('x_004_005', 'x_003_002'): -1.0655567117585847,\n", + " ('x_004_005', 'x_001_001*x_003_002'): 2.1311134235171694,\n", + " ('x_004_002', 'x_004_003'): 0.5327887647289884,\n", + " ('x_004_002', 'x_002_001'): -1.8226924754328562,\n", + " ('x_004_002', 'x_004_003*x_002_001'): 0.0,\n", + " ('x_004_002', 'x_004_004'): 1.0656165626443364,\n", + " ('x_004_002', 'x_004_004*x_002_001'): -8.881784197001252e-16,\n", + " ('x_004_002', 'x_003_003'): -0.26638917793964617,\n", + " ('x_004_002', 'x_001_001*x_003_003'): 0.5327783558792923,\n", + " ('x_004_002', 'x_004_001'): 0.1331952395229291,\n", + " ('x_004_002', 'x_004_001*x_002_001'): 0.0,\n", + " ('x_004_002', 'x_003_002'): -0.13319458896982309,\n", + " ('x_004_002', 'x_001_001*x_003_002'): 0.26638917793964617,\n", + " ('x_004_002', 'x_004_005'): 2.1315141642304627,\n", + " ('x_004_005*x_004_002', 'x_004_003'): 0.00034349203996530834,\n", + " ('x_004_005*x_004_002', 'x_002_001'): 0.0,\n", + " ('x_004_005*x_004_002', 'x_004_003*x_002_001'): 2.168404344971009e-19,\n", + " ('x_004_005*x_004_002', 'x_004_004'): 0.0008118902762816378,\n", + " ('x_004_005*x_004_002', 'x_004_004*x_002_001'): 0.0,\n", + " ('x_004_005*x_004_002', 'x_004_001'): 7.416305408341884e-05,\n", + " ('x_004_005*x_004_002', 'x_004_001*x_002_001'): 0.0,\n", + " ('x_004_005*x_004_002', 'x_004_005'): 0.0,\n", + " ('x_004_005*x_004_002', 'x_004_002'): 0.0,\n", + " ('x_003_005', 'x_004_003'): -2.1311134235171694,\n", + " ('x_003_005', 'x_004_003*x_002_001'): 4.262226847034339,\n", + " ('x_003_005', 'x_004_004'): -4.262226847034339,\n", + " ('x_003_005', 'x_004_004*x_002_001'): 8.524453694068677,\n", + " ('x_003_005', 'x_001_001'): -10.161495731215059,\n", + " ('x_003_005', 'x_003_003'): 4.6724449704929585,\n", + " ('x_003_005', 'x_001_001*x_003_003'): -5.080747865607531,\n", + " ('x_003_005', 'x_004_001'): -0.5327783558792923,\n", + " ('x_003_005', 'x_004_001*x_002_001'): 1.0655567117585847,\n", + " ('x_003_005', 'x_003_002'): 2.3361444188737597,\n", + " ('x_003_005', 'x_001_001*x_003_002'): -2.5403739328037647,\n", + " ('x_003_005', 'x_004_005'): -8.524453694068677,\n", + " ('x_003_005', 'x_004_002'): -1.0655567117585847,\n", + " ('x_001_001*x_003_005', 'x_004_003'): 4.262226847034339,\n", + " ('x_001_001*x_003_005', 'x_004_003*x_002_001'): -8.524453694068677,\n", + " ('x_001_001*x_003_005', 'x_004_004'): 8.524453694068677,\n", + " ('x_001_001*x_003_005', 'x_004_004*x_002_001'): -17.048907388137355,\n", + " ('x_001_001*x_003_005', 'x_001_001'): 0.0,\n", + " ('x_001_001*x_003_005', 'x_004_001'): 1.0655567117585847,\n", + " ('x_001_001*x_003_005', 'x_004_001*x_002_001'): -2.1311134235171694,\n", + " ('x_001_001*x_003_005', 'x_004_005'): 17.048907388137355,\n", + " ('x_001_001*x_003_005', 'x_004_002'): 2.1311134235171694,\n", + " ('x_001_001*x_003_005', 'x_003_005'): 0.0,\n", + " ('x_003_001', 'x_004_003'): -0.13319458896982309,\n", + " ('x_003_001', 'x_004_003*x_002_001'): 0.26638917793964617,\n", + " ('x_003_001', 'x_004_004'): -0.26638917793964617,\n", + " ('x_003_001', 'x_004_004*x_002_001'): 0.5327783558792923,\n", + " ('x_003_001', 'x_001_001'): -0.03969334270005882,\n", + " ('x_003_001', 'x_003_003'): 0.2919717004504178,\n", + " ('x_003_001', 'x_001_001*x_003_003'): -0.3175467416004706,\n", + " ('x_003_001', 'x_004_001'): -0.03329864724245577,\n", + " ('x_003_001', 'x_004_001*x_002_001'): 0.06659729448491154,\n", + " ('x_003_001', 'x_003_002'): 0.14598463043813517,\n", + " ('x_003_001', 'x_001_001*x_003_002'): -0.15877337080023524,\n", + " ('x_003_001', 'x_004_005'): -0.5327783558792923,\n", + " ('x_003_001', 'x_004_002'): -0.06659729448491154,\n", + " ('x_003_001', 'x_003_005'): 1.168054644503018,\n", + " ('x_003_001', 'x_001_001*x_003_005'): -1.2701869664018823,\n", + " ('x_003_004', 'x_004_003'): -1.0655567117585847,\n", + " ('x_003_004', 'x_004_003*x_002_001'): 2.1311134235171694,\n", + " ('x_003_004', 'x_004_004'): -2.1311134235171694,\n", + " ('x_003_004', 'x_004_004*x_002_001'): 4.262226847034339,\n", + " ('x_003_004', 'x_001_001'): -2.5403739328037647,\n", + " ('x_003_004', 'x_003_003'): 2.3359102197556014,\n", + " ('x_003_004', 'x_001_001*x_003_003'): -2.5403739328037647,\n", + " ('x_003_004', 'x_004_001'): -0.26638917793964617,\n", + " ('x_003_004', 'x_004_001*x_002_001'): 0.5327783558792923,\n", + " ('x_003_004', 'x_003_002'): 1.1679316899659848,\n", + " ('x_003_004', 'x_001_001*x_003_002'): -1.2701869664018823,\n", + " ('x_003_004', 'x_004_005'): -4.262226847034339,\n", + " ('x_003_004', 'x_004_002'): -0.5327783558792923,\n", + " ('x_003_004', 'x_003_005'): 9.345639378164023,\n", + " ('x_003_004', 'x_001_001*x_003_005'): -10.161495731215059,\n", + " ('x_003_004', 'x_003_001'): 0.5839609658346975,\n", + " ('x_003_001*x_003_004', 'x_001_001'): -0.6350934832009412,\n", + " ('x_003_001*x_003_004', 'x_003_003'): 5.074314226760236e-05,\n", + " ('x_003_001*x_003_004', 'x_001_001*x_003_003'): 0.0,\n", + " ('x_003_001*x_003_004', 'x_003_002'): 2.146825249783177e-05,\n", + " ('x_003_001*x_003_004', 'x_001_001*x_003_002'): 1.3552527156068805e-20,\n", + " 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0.5327783558792923,\n", + " ('x_002_001*x_004_002', 'x_001_001*x_003_003'): -1.0655567117585847,\n", + " ('x_002_001*x_004_002', 'x_003_002'): 0.26638917793964617,\n", + " ('x_002_001*x_004_002', 'x_001_001*x_003_002'): -0.5327783558792923,\n", + " ('x_002_001*x_004_002', 'x_004_002'): 0.0,\n", + " ('x_002_001*x_004_002', 'x_003_005'): 2.1311134235171694,\n", + " ('x_002_001*x_004_002', 'x_001_001*x_003_005'): -4.262226847034339,\n", + " ('x_002_001*x_004_002', 'x_003_001'): 0.13319458896982309,\n", + " ('x_002_001*x_004_002', 'x_003_004'): 1.0655567117585847,\n", + " ('x_004_001*x_004_004', 'x_004_003'): 5.074314226760236e-05,\n", + " ('x_004_001*x_004_004', 'x_004_003*x_002_001'): 0.0,\n", + " ('x_004_001*x_004_004', 'x_004_004'): 0.0,\n", + " ('x_004_001*x_004_004', 'x_004_001'): 0.0,\n", + " ('x_004_001*x_004_004', 'x_004_005'): 0.00039033186359694125,\n", + " ('x_004_001*x_004_004', 'x_004_002'): 2.146825249783177e-05,\n", + " ('x_004_001*x_004_004', 'x_004_005*x_004_002'): 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'x_004_005*x_002_001'): 10.65711449075045,\n", + " ('x_006_005', 'x_002_001*x_004_002'): 0.16651741391797578,\n", + " ('x_006_005', 'x_004_001*x_004_004'): -0.33303482783595156,\n", + " ('x_006_005', 'x_004_003*x_002_001*x_004_002'): 0.6660696556719031,\n", + " ('x_006_005', 'x_004_004*x_004_003'): -1.3321393113438063,\n", + " ('x_006_005', 'x_004_005*x_004_003*x_002_001'): 5.328557245375225,\n", + " ('x_006_005', 'x_004_001*x_004_004*x_002_001'): 0.6660696556719031,\n", + " ('x_006_005', 'x_004_001*x_004_003'): -0.16651741391797578,\n", + " ('x_006_005', 'x_004_005*x_004_003'): -2.6642786226876125,\n", + " ('x_006_005', 'x_004_003*x_004_002'): -0.33303482783595156,\n", + " ('x_006_005', 'x_004_004*x_004_003*x_002_001'): 2.6642786226876125,\n", + " ('x_006_005', 'x_004_001*x_004_003*x_002_001'): 0.33303482783595156,\n", + " ('x_006_005', 'x_004_005*x_004_004*x_002_001'): 10.65711449075045,\n", + " ('x_006_005', 'x_004_004*x_004_002'): -0.6660696556719031,\n", + " ('x_006_005', 'x_004_004*x_002_001*x_004_002'): 1.3321393113438063,\n", + " ('x_006_005', 'x_004_004*x_004_005'): -5.328557245375225,\n", + " ('x_006_005', 'x_004_001*x_004_005'): -0.6660696556719031,\n", + " ('x_006_005', 'x_004_001*x_002_001*x_004_002'): 0.16651741391797578,\n", + " ('x_006_005', 'x_004_005*x_004_002*x_002_001'): 2.6642786226876125,\n", + " ('x_006_005', 'x_004_001*x_004_002'): -0.08325870695898789,\n", + " ('x_006_005', 'x_004_005*x_004_001*x_002_001'): 1.3321393113438063,\n", + " ('x_006_005', 'x_005_001'): -1331.9458896982308,\n", + " ('x_006_005', 'x_005_002'): -2663.8917793964615,\n", + " ('x_006_005', 'x_005_003'): -5327.783558792923,\n", + " ('x_006_005', 'x_005_004'): -10655.567117585846,\n", + " ('x_006_005', 'x_005_005'): -21311.134235171692,\n", + " ('x_006_005', 'x_006_001'): 1331.9458896982308,\n", + " ('x_006_005', 'x_006_002'): 2663.8917793964615,\n", + " ('x_006_005', 'x_006_003'): 5327.783558792923,\n", + " ('x_006_005', 'x_006_004'): 10655.567117585846,\n", + " ('x_004_003', 'x_004_003'): 2.3554734335245726,\n", + " ('x_002_001', 'x_002_001'): 0.0,\n", + " ('x_004_003*x_002_001', 'x_004_003*x_002_001'): 0.0,\n", + " ('x_004_004', 'x_004_004'): 5.776540009781666,\n", + " ('x_004_004*x_002_001', 'x_004_004*x_002_001'): 0.0,\n", + " ('x_001_001', 'x_001_001'): 0.0,\n", + " ('x_003_003', 'x_003_003'): 0.5839385217525407,\n", + " ('x_001_001*x_003_003', 'x_001_001*x_003_003'): 0.0,\n", + " ('x_004_001', 'x_004_001'): 0.4889717762655621,\n", + " ('x_004_001*x_002_001', 'x_004_001*x_002_001'): 0.0,\n", + " ('x_003_002', 'x_003_002'): 0.14598414252330566,\n", + " ('x_001_001*x_003_002', 'x_001_001*x_003_002'): 0.0,\n", + " ('x_004_005', 'x_004_005'): 15.815889762180642,\n", + " ('x_004_002', 'x_004_002'): 1.0445409893245277,\n", + " ('x_004_005*x_004_002', 'x_004_005*x_004_002'): 0.0,\n", + " ('x_003_005', 'x_003_005'): 9.343640879022406,\n", + " ('x_001_001*x_003_005', 'x_001_001*x_003_005'): 0.0,\n", + " ('x_003_001', 'x_003_001'): 0.036496005136149576,\n", + " ('x_003_004', 'x_003_004'): 2.3357853135592506,\n", + " ('x_003_001*x_003_004', 'x_003_001*x_003_004'): 0.0,\n", + " ('x_004_005*x_002_001', 'x_004_005*x_002_001'): 0.0,\n", + " ('x_002_001*x_004_002', 'x_002_001*x_004_002'): 0.0,\n", + " ('x_004_001*x_004_004', 'x_004_001*x_004_004'): 0.0,\n", + " ('x_001_001*x_003_001', 'x_001_001*x_003_001'): 0.0,\n", + " ('x_001_001*x_003_004', 'x_001_001*x_003_004'): 0.0,\n", + " ('x_003_002*x_003_005', 'x_003_002*x_003_005'): 0.0,\n", + " ('x_004_003*x_002_001*x_004_002', 'x_004_003*x_002_001*x_004_002'): 0.0,\n", + " ('x_004_004*x_004_003', 'x_004_004*x_004_003'): 0.0,\n", + " ('x_004_005*x_004_003*x_002_001', 'x_004_005*x_004_003*x_002_001'): 0.0,\n", + " ('x_004_001*x_004_004*x_002_001', 'x_004_001*x_004_004*x_002_001'): 0.0,\n", + " ('x_004_001*x_004_003', 'x_004_001*x_004_003'): 0.0,\n", + " ('x_004_005*x_004_003', 'x_004_005*x_004_003'): 0.0,\n", + " ('x_004_003*x_004_002', 'x_004_003*x_004_002'): 0.0,\n", + " ('x_004_004*x_004_003*x_002_001', 'x_004_004*x_004_003*x_002_001'): 0.0,\n", + " ('x_004_001*x_004_003*x_002_001', 'x_004_001*x_004_003*x_002_001'): 0.0,\n", + " ('x_004_005*x_004_004*x_002_001', 'x_004_005*x_004_004*x_002_001'): 0.0,\n", + " ('x_004_004*x_004_002', 'x_004_004*x_004_002'): 0.0,\n", + " ('x_004_004*x_002_001*x_004_002', 'x_004_004*x_002_001*x_004_002'): 0.0,\n", + " ('x_004_004*x_004_005', 'x_004_004*x_004_005'): 0.0,\n", + " ('x_004_001*x_004_005', 'x_004_001*x_004_005'): 0.0,\n", + " ('x_004_001*x_002_001*x_004_002', 'x_004_001*x_002_001*x_004_002'): 0.0,\n", + " ('x_004_005*x_004_002*x_002_001', 'x_004_005*x_004_002*x_002_001'): 0.0,\n", + " ('x_004_001*x_004_002', 'x_004_001*x_004_002'): 0.0,\n", + " ('x_004_005*x_004_001*x_002_001', 'x_004_005*x_004_001*x_002_001'): 0.0,\n", + " ('x_001_001*x_003_003*x_003_005', 'x_001_001*x_003_003*x_003_005'): 0.0,\n", + " ('x_003_002*x_001_001*x_003_003', 'x_003_002*x_001_001*x_003_003'): 0.0,\n", + " ('x_003_001*x_003_003', 'x_003_001*x_003_003'): 0.0,\n", + " ('x_003_004*x_003_003', 'x_003_004*x_003_003'): 0.0,\n", + " ('x_001_001*x_003_002*x_003_005', 'x_001_001*x_003_002*x_003_005'): 0.0,\n", + " ('x_001_001*x_003_003*x_003_004', 'x_001_001*x_003_003*x_003_004'): 0.0,\n", + " ('x_003_002*x_003_003', 'x_003_002*x_003_003'): 0.0,\n", + " ('x_003_005*x_003_003', 'x_003_005*x_003_003'): 0.0,\n", + " ('x_001_001*x_003_003*x_003_001', 'x_001_001*x_003_003*x_003_001'): 0.0,\n", + " ('x_003_002*x_003_004', 'x_003_002*x_003_004'): 0.0,\n", + " ('x_001_001*x_003_001*x_003_004', 'x_001_001*x_003_001*x_003_004'): 0.0,\n", + " ('x_001_001*x_003_005*x_003_004', 'x_001_001*x_003_005*x_003_004'): 0.0,\n", + " ('x_003_005*x_003_004', 'x_003_005*x_003_004'): 0.0,\n", + " ('x_001_001*x_003_002*x_003_001', 'x_001_001*x_003_002*x_003_001'): 0.0,\n", + " ('x_001_001*x_003_002*x_003_004', 'x_001_001*x_003_002*x_003_004'): 0.0,\n", + " ('x_003_002*x_003_001', 'x_003_002*x_003_001'): 0.0,\n", + " ('x_003_005*x_003_001', 'x_003_005*x_003_001'): 0.0,\n", + " ('x_003_001*x_001_001*x_003_005', 'x_003_001*x_001_001*x_003_005'): 0.0,\n", + " ('x_005_001', 'x_005_001'): -1186.7559218989402,\n", + " ('x_005_002', 'x_005_002'): -2207.018607585602,\n", + " ('x_005_003', 'x_005_003'): -3748.0642703220883,\n", + " ('x_005_004', 'x_005_004'): -4832.236761247715,\n", + " ('x_005_005', 'x_005_005'): 991.0935950904168,\n", + " ('x_006_001', 'x_006_001'): 41.62330905306971,\n", + " ('x_006_002', 'x_006_002'): 166.49323621227884,\n", + " ('x_006_003', 'x_006_003'): 665.9729448491154,\n", + " ('x_006_004', 'x_006_004'): 2663.8917793964615,\n", + " ('x_006_005', 'x_006_005'): 10655.567117585846}" + ] + }, + "execution_count": 627, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.qubo.qubo_dict.to_qubo()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 628, + "metadata": {}, + "outputs": [], + "source": [ + "# target_graph = dnx.pegasus_graph(6)\n", + "# embedding = find_embedding(net.qubo.qubo_dict.to_qubo()[0], target_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 629, + "metadata": {}, + "outputs": [], + "source": [ + "# embedding" + ] + }, + { + "cell_type": "code", + "execution_count": 630, + "metadata": {}, + "outputs": [], + "source": [ + "# dnx.draw_pegasus(dnx.pegasus_graph(6), node_size=2, width=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 631, + "metadata": {}, + "outputs": [], + "source": [ + "# dnx.draw_pegasus_embedding(target_graph, embedding, node_size=10, width=0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py index a0dd409..6527bb8 100644 --- a/wntr_quantum/sampler/simulated_annealing.py +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -34,6 +34,62 @@ def generate_random_valid_sample(qubo): return sample +def modify_solution_sample(net, solution, modify=["signs", "flows", "heads"]): + """_summary_ + + Args: + qubo (_type_): _description_ + solution (_type_): _description_ + + Raises: + ValueError: _description_ + + Returns: + _type_: _description_ + """ + + def flatten_list(lst): + out = [] + for elmt in lst: + if not isinstance(elmt, list): + out += [elmt] + else: + out += elmt + return out + + from copy import deepcopy + + for m in modify: + if m not in ["signs", "flows", "heads"]: + raise ValueError("modify %s not recognized" % m) + + mod_bin_rep_sol = deepcopy(solution) + num_pipes = net.wn.num_pipes + num_heads = net.wn.num_junctions + + # modsify sign + if "signs" in modify: + for i in range(num_pipes): + mod_bin_rep_sol[i] = np.random.randint(2) + + # modify flow value + if "flows" in modify: + for i in range(num_pipes, 2 * num_pipes): + mod_bin_rep_sol[i] = list( + np.random.randint(2, size=net.flow_encoding.nqbit) + ) + + # modify head values + if "heads" in modify: + for i in range(2 * num_pipes, 2 * num_pipes + num_heads): + mod_bin_rep_sol[i] = list( + np.random.randint(2, size=net.head_encoding.nqbit) + ) + + x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol)) + return x + + @dataclass class SimulatedAnnealingResults: """Result of the simulated anneling.""" @@ -48,6 +104,13 @@ class SimulatedAnnealing: # noqa: D101 def __init__(self): # noqa: D107 self.properties = {} + def optimize_value(self, variables=["sign", "flow", "pressure"]): + """_summary_. + + Args: + variables (list, optional): _description_. Defaults to ['sign', 'flow', 'pressure']. + """ + def sample( self, bqm, diff --git a/wntr_quantum/sampler/step/base_step.py b/wntr_quantum/sampler/step/base_step.py index 3e65164..0bb1374 100644 --- a/wntr_quantum/sampler/step/base_step.py +++ b/wntr_quantum/sampler/step/base_step.py @@ -2,7 +2,14 @@ class BaseStep: # noqa: D101 - def __init__(self, var_names, single_var_names, single_var_index): + def __init__( + self, + var_names, + single_var_names, + single_var_index, + step_size=1, + optimize_values=None, + ): """Propose a new solution vector. Args: @@ -16,6 +23,28 @@ def __init__(self, var_names, single_var_names, single_var_index): self.num_single_var = len(self.single_var_names) self.high_order_terms_mapping = self.define_mapping() + self.value_names = np.unique( + [self._get_variable_root_name(n) for n in single_var_names] + ) + self.index_values = {v: [] for v in self.value_names} + for n, idx in zip(self.single_var_names, self.single_var_index): + val = self._get_variable_root_name(n) + self.index_values[val].append(idx) + + self.step_size = step_size + self.optimize_values = optimize_values + if self.optimize_values is None: + self.optimize_values = list(np.arange(len(self.value_names))) + + @staticmethod + def _get_variable_root_name(var_name): + """Extract the root name of the variables. + + Args: + var_name (_type_): _description_ + """ + return "_".join(var_name.split("_")[:2]) + def define_mapping(self): """Define the mapping of the higher order terms. diff --git a/wntr_quantum/sampler/step/full_random.py b/wntr_quantum/sampler/step/full_random.py index dd2f93e..1e93433 100644 --- a/wntr_quantum/sampler/step/full_random.py +++ b/wntr_quantum/sampler/step/full_random.py @@ -4,18 +4,6 @@ class RandomStep(BaseStep): # noqa: D101 - def __init__( - self, - var_names, - single_var_names, - single_var_index, - optimize_values=None, - ): - super().__init__(var_names, single_var_names, single_var_index) - self.optimize_values = optimize_values - if self.optimize_values is None: - self.optimize_values = list(np.arange(len(self.value_names))) - def __call__(self, x): """Call function of the method. @@ -25,8 +13,9 @@ def __call__(self, x): Returns: _type_: _description_ """ - nmax = 8 + 8 * 7 - vidx = np.random.choice(self.single_var_index[nmax:]) + random_val_name = np.random.choice(self.value_names[self.optimize_values]) + idx = self.index_values[random_val_name] + vidx = np.random.choice(idx) x[vidx] = int(not (x[vidx])) self.fix_constraint(x, vidx) return x @@ -34,38 +23,6 @@ def __call__(self, x): class IncrementalStep(BaseStep): - def __init__( - self, - var_names, - single_var_names, - single_var_index, - step_size=1, - optimize_values=None, - ): - super().__init__(var_names, single_var_names, single_var_index) - - self.value_names = np.unique( - [self._get_variable_root_name(n) for n in single_var_names] - ) - self.index_values = {v: [] for v in self.value_names} - for n, idx in zip(self.single_var_names, self.single_var_index): - val = self._get_variable_root_name(n) - self.index_values[val].append(idx) - - self.step_size = step_size - self.optimize_values = optimize_values - if self.optimize_values is None: - self.optimize_values = list(np.arange(len(self.value_names))) - - @staticmethod - def _get_variable_root_name(var_name): - """Extract the root name of the variables. - - Args: - var_name (_type_): _description_ - """ - return "_".join(var_name.split("_")[:2]) - def __call__(self, x): """Call function of the method. From 5d06324d69cb5c2eea0706cf7f7ec0a2216e0762 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 25 Oct 2024 17:14:47 +0200 Subject: [PATCH 74/96] aaaaa --- docs/notebooks/qubo_poly_solver_Net0.ipynb | 3678 +++++-------------- wntr_quantum/sampler/simulated_annealing.py | 105 +- wntr_quantum/sampler/step/full_random.py | 6 +- 3 files changed, 1002 insertions(+), 2787 deletions(-) diff --git a/docs/notebooks/qubo_poly_solver_Net0.ipynb b/docs/notebooks/qubo_poly_solver_Net0.ipynb index 9f11c35..46c3e1b 100644 --- a/docs/notebooks/qubo_poly_solver_Net0.ipynb +++ b/docs/notebooks/qubo_poly_solver_Net0.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 597, + "execution_count": 18, "metadata": { "metadata": {} }, @@ -30,7 +30,7 @@ "" ] }, - "execution_count": 597, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 598, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -77,7 +77,7 @@ "" ] }, - "execution_count": 598, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 599, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -107,7 +107,7 @@ "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" ] }, - "execution_count": 599, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -128,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 600, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 601, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -176,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 602, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -193,7 +193,7 @@ "array([1., 1., 1., 1.])" ] }, - "execution_count": 602, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -210,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 603, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -219,7 +219,7 @@ "[1, 1, [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [1, 1, 1, 1, 0]]" ] }, - "execution_count": 603, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -230,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 604, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -257,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 605, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -268,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 606, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 607, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -292,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 608, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ @@ -303,7 +303,7 @@ "var_names = sorted(net.qubo.qubo_dict.variables)\n", "net.qubo.create_variables_mapping()\n", "# mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", - "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=1)\n", + "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)\n", "# mystep = ParallelIncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=100)" ] }, @@ -316,29 +316,29 @@ }, { "cell_type": "code", - "execution_count": 609, + "execution_count": 98, "metadata": {}, "outputs": [], "source": [ - "from wntr_quantum.sampler.simulated_annealing import generate_random_valid_sample\n", - "x = generate_random_valid_sample(net.qubo)\n", - "x0 = list(x.values())" + "# from wntr_quantum.sampler.simulated_annealing import generate_random_valid_sample\n", + "# x = generate_random_valid_sample(net.qubo)\n", + "# x0 = list(x.values())" ] }, { "cell_type": "code", - "execution_count": 610, + "execution_count": 112, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", - "x = modify_solution_sample(net, bin_rep_sol, modify=['flows', 'heads'])\n", + "x = modify_solution_sample(net, bin_rep_sol, modify=['flows'])\n", "x0 = list(x.values())" ] }, { "cell_type": "code", - "execution_count": 611, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -347,61 +347,55 @@ }, { "cell_type": "code", - "execution_count": 612, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ - "num_sweeps = 2000\n", - "Tinit = 1E3\n", + "num_sweeps = 1000\n", + "Tinit = 1E1\n", "Tfinal = 1E-1\n", "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(200))" + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "# Tschedule = np.zeros(10000)" ] }, { "cell_type": "code", - "execution_count": 613, + "execution_count": 115, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 3%|▎ | 62/2200 [00:00<00:03, 619.50it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2200/2200 [00:02<00:00, 928.87it/s]\n" + "100%|██████████| 2000/2000 [00:00<00:00, 2479.48it/s]\n" ] } ], "source": [ - "# mystep.optimize_values = np.arange(2, 6)\n", - "res = sampler.sample(net.qubo.qubo_dict, x0=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True)\n", + "mystep.optimize_values = np.arange(4, 6)\n", + "res = sampler.sample(net.qubo.qubo_dict, init_sample=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", "mystep.verify_quadratic_constraints(res.res)" ] }, { "cell_type": "code", - "execution_count": 614, + "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 614, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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o+1DQ6XSNtrHZbMjLy0NCQoJHHo2QGrevazW3fa1WK8LCwtClSxeJekdELcFQJBcKePSAIscpM51O12wo0mq10Ol0/NB2AW5f12rJ9vW0U8dEnoYnt2XGQ8+eERERyR5DkUzw/49ERETSYigiIiIiAkMREREREQCGItnhmCIiIiJpMBQRERERgaFINm5moPWRI0cwbtw4+Pv7o1u3bpg/fz7Ky8vF+V988QWmTp2KsLAw+Pr6ol+/ftiyZYvTMvbv3w+FQtHgy2KxOLXLyMhA79694ePjg7i4OBw+fPhmyiQiIpIthiKZEVp4s6KSkhLEx8cjKioKhw4dQm5uLo4fP45Zs2aJbQoKChAUFITXXnsNx48fx1NPPYWUlBRs3bq1wfJOnz6N8+fPi19BQUHivJ07d8JoNCItLQ1HjhzBkCFDYDAYcOHChVuul4iISC5480a5aOWhot27d0OtViMjI0N8llJmZiYGDx6MM2fOICoqCo8++qjTayIjI2E2m/HPf/4TycnJTvOCgoLg7+/f6LrS09Mxb948zJ49W1xPTk4OsrKysHz58tZ1nIiISKZkEYqqqqoQFxeHL774AkePHkVMTIw478svv0RSUhI+++wzdO/eHY899hiWLVvm9Pq3334bTz/9NL799lv06dMH69atw8SJE8X5giAgLS0Nf/vb31BWVoZf/epXeOmll9CnT5/2KrHFWjrQuqqqCt7e3k4Pl3Q8SuPAgQOIiopq9HWXL19G165dG0yPiYlBVVUVBg4ciJUrV+JXv/oVAKC6uhoFBQVISUkR2yqVSsTHx8NsNjfbv6qqKgDXH7bp+N5mszX6Gsf0pubTreH2da3mti+3OZF7kEUoWrZsGUJDQ/HFF184TbdarUhISEB8fDwyMzPx1Vdf4dFHH4W/vz/mz58PADh48CCmTp2KNWvW4P7778cbb7yByZMn48iRIxg4cCAAYP369Xj++eexY8cORERE4Omnn4bBYMCJEyfg4+PT7vU2prVjisaOHQuj0YgNGzbgiSeeQEVFhXjU5vz5842+5uDBg9i5cydycnLEaT169EBmZiaGDx+OqqoqbNu2DaNHj8ahQ4cwbNgw/PDDD6itrUVwcLDTsoKDg3Hq1Kkm+7dmzRqsWrWqwfS8vDxotdpmazOZTM3Op1vD7etajW1fx38KiEjeJA9F7733HvLy8vCPf/wD7733ntO8119/HdXV1cjKyoK3tzcGDBiAwsJCpKeni6Foy5YtGD9+PJYuXQoAeOaZZ2AymbB161ZkZmZCEARs3rwZK1aswKRJkwAAr776KoKDg7Fr1y488sgj7VvwDaxdnYqXX0hvts3JkycxYMAA7NixA0ajESkpKVCpVHj88ccRHBzsdPTI4dixY5g0aRLS0tKQkJAgTu/bty/69u0r/jxy5EicPXsWmzZtwt///vebriMlJQVGoxFA3f+Sd+3ahblz5yIhIaHZZ5+ZTCaMGzeOz+ZyAW5f12pu+1qtVol6RUStIWkoKi0txbx587Br165Gjx6YzWbcc8898Pb2FqcZDAasW7cOly5dQkBAAMxms/jhW7/Nrl27AABFRUWwWCyIj48X5/v5+SEuLg5ms7nJUFT/9A9w/U2tudM/N6v+8mbPX4ikebOabR8WFgabzYaHHnoIDz30EEpLS9GpUycoFAqkp6ejV69eTss8ceIEEhISMGfOHDz55JM37P/w4cPxySefwGazwc/PDyqVCufOnXN6nWMwdlPLUiqV4uk8Ly8vcf+q1eobfiC3pA3dPG5f12ps+zp+dsX7R0t5+ulT1uf+XFVja5YnWSgSBAGzZs3CggULMHz4cHz77bcN2lgsFkRERDhNc5zGsVgsCAgIgMViafTUjuOScse/zbVpzK2c/rkZCqgAAF8eO44g3+bbfvPNN41Of//996FWq2G327Fnzx4AQHFxMZ5++mmMGTMGer1enN6c/Px8+Pr6im0jIyOxfft28Y3dbrfjvffew8SJE1u0PCKq46r3j9bw9NOnrM/9tXWNrTl93eahaPny5Vi3bl2zbU6ePIm8vDxcuXLFaQCvnNQ//QPUHSkKCwtr9vTPzbLZbFh+eB8A4N5770VEYKcWve7FF1+EXq9Hp06dkJ+fj1deeQVr1qzBww8/DKDulNm8efOQmJiItWvXiq9TqVTo3r07AOD5559H79690b9/f1y7dg3bt2/HV199hT179mDs2LEAgPLycsyZMwcPPPAA7rzzTrzwwguora3FX/7ylwZhs6n63nnnnVZtEyJP5Ir3j5by9NOnrM/9uarG1py+bvNQtGTJEqd75TQmMjIS+/btg9lshkajcZo3fPhwTJ8+HTt27EBISAhKS0ud5jt+DgkJEf9trE39+Y5pPXr0cGpT/yq3X9JoNA36Brj+1IOXl1eLl19QUIDVq1ejvLwc0dHRePnllzFjxgxx/jvvvIOLFy/ijTfewBtvvCFODw8PF4/M1dbW4sknn8S5c+eg1WoxePBgvP/++xgzZozYfvr06bh06RJWr14Ni8WCmJgY5ObmomfPnm1TNFEHIYdTl3LogyuxPvfX1jW2ZlltHoq6d+8uHoVozvPPP49nn31W/LmkpAQGgwE7d+5EXFwcAECv1+Opp56CzWYTizKZTOjbty8CAgLENvn5+Vi0aJG4LJPJBL1eDwCIiIhASEgI8vPzxRBktVpx6NAhLFy4sC1KblOtefTZq6++2uz8lStXYuXKlc22WbZsWYNbHDQmOTm5wb2NiIiIPIlkY4p69erl9HPnzp0BALfffrt4BGLatGlYtWqVOED42LFj2LJlCzZt2iS+7oknnsC9996LjRs3IjExEW+++SY+//xz/PWvfwUAKBQKLFq0CM8++yz69OkjXpIfGhqKyZMnt0+xLXAzj/kgIiKitiP5JfnN8fPzQ15eHpKSkhAbG4vAwECkpqaKl+MDdZeQv/HGG1ixYgX+9Kc/oU+fPti1a5d4jyKg7mhIRUUF5s+fj7KyMowaNQq5ubmyuUdRfS29eSMRERG1LdmEot69e0NoJBEMHjwYH3/8cbOvdVya3hSFQoHVq1dj9erVt9xPl+GhIiIiIknxgbBEREREYCiSDdXPR4pstXZpO0JERNRBMRTJhE/dvRtx5VqNtB0hIiLqoBiKZOJ6KPLcW7gTERHJGUORTPio6gaZ80gRERGRNBiKZEL38zNvF+0sxBffl0naFyIioo6IoUgmenW+fjuCTe//R8KeEBERdUwMRTIxMkjAyMiuAIBrtlqJe0NERNTxMBTJhLcKeHh43eNNeFdrIiKi9sdQJCOOm1ozExEREbU/hiIZUTAVERERSYahiIiIiAgMRbKi+PlQkcBDRURERO2OoUiGONCaiIio/TEUyQiHFBEREUmHoUhGHAOtBR4qIiIiancMRTKigGNMEREREbU3hiIZuX6kSNp+EBERdUQMRTLCMUVERETSYSiSE8WNm9R35MgRjBs3Dv7+/ujWrRvmz5+P8vJy50UqFA2+3nzzTac2+/fvx7Bhw6DRaBAVFYXs7OwG68rIyEDv3r3h4+ODuLg4HD58uLXVERERyRpDkYwoWnH+rKSkBPHx8YiKisKhQ4eQm5uL48ePY9asWQ3abt++HefPnxe/Jk+eLM4rKipCYmIixowZg8LCQixatAhz587F3r17xTY7d+6E0WhEWloajhw5giFDhsBgMODChQu3WjIREZFseEndAbquNafPdu/eDbVajYyMDCiVddk2MzMTgwcPxpkzZxAVFSW29ff3R0hISKPLyczMREREBDZu3AgA6NevHw4cOIBNmzbBYDAAANLT0zFv3jzMnj1bfE1OTg6ysrKwfPnymyuWiIhIZnikSIZaMtC6qqoK3t7eYiACAF9fXwDAgQMHnNomJSUhMDAQI0aMQFZWltMl/2azGfHx8U7tDQYDzGYzAKC6uhoFBQVObZRKJeLj48U2REREnoBHimREPHvWgmNFY8eOhdFoxIYNG/DEE0+goqJCPGpz/vx5sd3q1asxduxYaLVa5OXl4Q9/+APKy8vx+OOPAwAsFguCg4Odlh0cHAyr1YqrV6/i0qVLqK2tbbTNqVOnmuxfVVUVqqqqAAA2mw2VlZXi9zabrdHXOKY3NZ9uDbevazW3fbnNidwDQ5GMKABc2p+NnEP/B8XjTbc7efIkBgwYgB07dsBoNCIlJQUqlQqPP/44goODnY4ePf300+L3Q4cORUVFBTZs2CCGIldZs2YNVq1a1WB6Xl4etFpts681mUyu6haB29fVGtu+jv8UEJG8MRTJiEKhgG7EbzFk7CT89ffDm2wXGRkJAJg2bRqmTZuG0tJSdOrUCQqFAunp6eL8xsTFxeGZZ55BVVUVNBoNQkJCUFpa6tSmtLQUOp0Ovr6+UKlUUKlUjbZpapwSAKSkpMBoNAKo+1/yrl27MHfuXCQkJECn0zX6GpvNBpPJhHHjxkGtVje5bLo53L6u1dz2tVqtEvWKiFqDoUhGFABUWj90CtYhOjq6xa9znNrKysqCj48Pxo0b12TbwsJCBAQEQKPRAAD0ej327Nnj1MZkMkGv1wMAvL29ERsbi/z8fPGqNbvdjvz8fCQnJze5Ho1GI67DZrOJR4fUavUNP5Bb0oZuHrevazW2fbm9idwDQ5GctPLZZ1u3bsXIkSPRuXNnmEwmLF26FGvXroW/vz8A4N1330VpaSnuuusu+Pj4wGQy4S9/+Qv++Mc/istYsGABtm7dimXLluHRRx/Fvn378NZbbyEnJ0dsYzQaMXPmTAwfPhwjRozA5s2bUVFRIV6NRkRE5AkYimRE0cq7Nx4+fBhpaWkoLy9HdHQ0Xn75ZcyYMUOc77hkf/HixRAEAVFRUeLl9Q4RERHIycnB4sWLsWXLFvTs2RPbtm0TL8cHgClTpuDixYtITU2FxWJBTEwMcnNzGwy+JiIicmcMRTKiaOUdrV999dVm548fPx7jx4+/4XJGjx6No0ePNtsmOTm52dNlRERE7o73KZIR8eaNfPgZERFRu2MokpHW3KeIiIiI2hZDkQzxSBEREVH7YyiSEcdAa2YiIiKi9sdQJCOKVl6ST0RERG2HoUiGGImIiIjaH0ORjLT2knwiIiJqOwxFMiLevJGHioiIiNodQ5GMKJiJiIiIJMNQJCPXb97IWERERNTeGIpkRKHgJflERERSYSiSET7mg4iISDoMRTLEx3wQERG1P4YiORFv3ihtN4iIiDoihiIZ4ekzIiIi6TAUyYiCd28kIiKSDEORjDASERERScdL6g7QdR3ugbA1lQAEQOkDQaFEZXUNqmqByqsVUNsUgFINKL2vt7fXAPYqQKEEVL7XpwsCUFtZ971Xp8bXofIBFKrr02urAKGmQ63DZqtBVY2AysrLUKvVbluHXNfhBQFKwQbUVABKLaD6xTqISPYYimTE8ZiPDhKJgL13ApdPAPd9gKsBozDkmX0AvHDtv/8Pvw/MwebSqdhcOl1sPtHvAF4MX4tPywfikW/WitO7qi7jyIC6dr2/3O20irw7/oA7fIrxyNm/4NOKweL01aEvddB1VOLhqu4eUIf81vHF02MxsDoL6n89BAxMAwavvP6Cc++CiOSPp89kRMGrz4iIiCSjEDrMuZpbY7Va4efnh8uXL0On07Xpsm02G/bs2YPwmFGY/NKnCNZpcOhP8W26DinZbDb83//9H6ZNm+a8/X5x+sxaeQ179+bBEH8v1F48fdbW67DZbNibuxeG+FE8feaS02d27N3zb4w3xEPt7Xz6zFr2E/wCurnk/aOlHO8zEydOrNv/Hob1uT9X1diaz2+ePpORDnekyEsrfqsAoPX2gkYFaH07NfEH4QXAp4mF+TU+2bupP4CmfvU9dx02hQCNlwJarV/j29dN6pDrOmw2G+yKn8Om6hfbV8m3WiJ3IPnps5ycHMTFxcHX1xcBAQGYPHmy0/zi4mIkJiZCq9UiKCgIS5cuRU2N86DF/fv3Y9iwYdBoNIiKikJ2dnaD9WRkZKB3797w8fFBXFwcDh8+7MKqbk1HyURERERyImko+sc//oEZM2Zg9uzZ+OKLL/DJJ59g2rRp4vza2lokJiaiuroaBw8exI4dO5CdnY3U1FSxTVFRERITEzFmzBgUFhZi0aJFmDt3Lvbu3Su22blzJ4xGI9LS0nDkyBEMGTIEBoMBFy5caNd6b0TRyovyjxw5gnHjxsHf3x/dunXD/PnzUV5eLs7Pzs6GQqFo9MtR+/79+xudb7FYnNblTqGSiIjoZkgWimpqavDEE09gw4YNWLBgAe644w70798fDz/8sNgmLy8PJ06cwGuvvYaYmBhMmDABzzzzDDIyMlBdXQ0AyMzMREREBDZu3Ih+/fohOTkZv/vd77Bp0yZxOenp6Zg3bx5mz56N/v37IzMzE1qtFllZWe1ed3Nac/qspKQE8fHxiIqKwqFDh5Cbm4vjx49j1qxZYpspU6bg/PnzTl8GgwH33nsvgoKCnJZ3+vRpp3b157tLqCQiIroVkp3oPnLkCM6dOwelUomhQ4fCYrEgJiYGGzZswMCBAwEAZrMZgwYNQnBwsPg6g8GAhQsX4vjx4xg6dCjMZjPi450HJRsMBixatAgAUF1djYKCAqSkpIjzlUol4uPjYTabm+xfVVUVqqqqxJ+tViuAunEDNpvtluuvz7G82p9PCwqCcMN1vPPOO1Cr1di8eTOUyrps+8ILLyA2NhYnT55EVFQUvLy80K1bN/E1Fy9exL59+/Dyyy+Ly3ecigwICIC/v7/Ytra2FrW1tQCAjRs3Ys6cOfh//+//ievJycnB3/72NyxbtqzF9RF1dK54/2jNuuv/62lYn/tzVY2tWZ5koeibb74BAKxcuRLp6eno3bs3Nm7ciNGjR+M///kPunbtCovF4hSIAIg/O07vNNXGarXi6tWruHTpEmpraxttc+rUqSb7t2bNGqxatarB9Ly8PGi12kZecevMn5oBeKGqqgp79uxptu2RI0dQW1uL3Nxccdr58+cBAC+99BLuu+++Bq/ZtWsX1Go1OnXqJC7/q6++AgAMGDAANTU16NWrFx555BH069cPQN0vU0FBAe677z6nPvXt2xf//ve/xQD7S79886+srGx0+i9fU/9falvcvq7V3PZ1THPl+0dLmUwmSdfvaqzP/bV1jY7Pn5Zo81C0fPlyrFu3rtk2J0+ehN1uBwA89dRTePDBBwEA27dvR8+ePfH222/jf/7nf9q6a62SkpICo9Eo/my1WhEWFoaEhASXXJJvMpkwUj8S+OIw1N7emDhxTLOvCQ8PR3Z2Nk6ePInHHnsMFRUVWLBgAQAgKCgIEydObPCa5cuXY8aMGfjtb38rTrv99tvRtWtXxMbGoqqqCllZWXj66afxySefYOjQoSgpKYHdbsfEiRNx1113ia/76KOP8PHHHze6HgBYvXo1nn322QbTW/Kh0BH+6KXE7etajW1fx5uyK94/WsrxPjNu3DiPvKSb9bk/V9XoONPTEm0eipYsWeI0rqUxkZGR4lGN/v37i9M1Gg0iIyNRXFwMAAgJCWkwoLe0tFSc5/jXMa1+G51OB19fX6hUKqhUqkbbOJbRGI1GA41G02C6Wq122S+kWu2FS/uz8d2h/4N3WtPtTp48iZiYGOzYsQNGoxErVqyASqXC448/juDg4Eb7aDabcerUKbz22mtO8wYOHOh0tOeee+5BUVERXnjhBfz9738X23p5eTm9TqVSQaFQNLktVqxYgaVLlwKo+0XftWsX5s6d2+yHQkf4o5cSt69rNbd9HW/Krnz/aCk59MGVWJ/7a+saW7OsNg9F3bt3R/fu3W/YLjY2FhqNBqdPn8aoUaMA1L2pfPvttwgPDwcA6PV6/PnPf8aFCxfEgb8mkwk6nU4MU3q9vsGpJpPJBL1eDwDw9vZGbGws8vPzxcv97XY78vPzkZyc3CY1txUFAN2I36LHnQbsShrVZLvIyEgAwLRp0zBt2jSUlpaiU6dOUCgUSE9PF+fXt23bNsTExCA2NvaG/RgxYgQOHDgAAAgMDLzlUGmz2cSjQy35Ze8If/RS4vZ1rca2L7c3kXuQbEyRTqfDggULkJaWhrCwMISHh2PDhg0AgIceeghA3aHm/v37Y8aMGVi/fj0sFgtWrFiBpKQk8QN3wYIF2Lp1K5YtW4ZHH30U+/btw1tvvYWcnBxxXUajETNnzsTw4cMxYsQIbN68GRUVFZg9e3b7F94MhUIBldYPGt9AREdHt/h1jvFSWVlZ8PHxwbhx45zml5eX46233sKaNWtatLzCwkL06NEDgHuFSiIiolsh6W1WN2zYAC8vL8yYMQNXr15FXFwc9u3bh4CAAAB1p2h2796NhQsXQq/Xo1OnTpg5cyZWr14tLiMiIgI5OTlYvHgxtmzZgp49e2Lbtm0wGAximylTpuDixYtITU0Vr3LLzc1tMPhaao67FLX0yStbt27FyJEj0blzZ5hMJixduhRr1651uooMqLukvqamRrx6rL7NmzcjIiICAwYMwLVr17Bt2zbs27cPeXl5Yht3CZVERES3QtJQpFar8dxzz+G5555rsk14ePgNr8QaPXo0jh492myb5ORkjzuycfjwYaSlpaG8vBzR0dF4+eWXMWPGjAbtXnnlFTzwwAMNwhJQd8uCJUuW4Ny5c9BqtRg8eDDef/99jBlzfaC3u4RKIiKiW8EH8siIePPGFrZ/9dVXW9Tu4MGDTc5btmxZi+415ImhkoiIqD7Jn31G1ynE82eSdoOIiKhDYiiSEcezz5iJiIiI2h9DkZyIzz5jLCIiImpvDEUywrNnRERE0mEokhFxoDVTERERUbtjKJIRx5giO1MRERFRu2MokhHHkaKqGjsqqmqk7QwREVEHw1AkI0Fdrj+A9uzFcgl7QkRE1PEwFMmIWqVEzwBfAICtlqfQiIiI2hNDkcxovOp2ia3WLnFPiIiIOhaGIplRq+p2SQ2PFBEREbUrhiKZcYQiHikiIiJqXwxFMuOlqrsEjaGIiIiofTEUycz1I0U8fUZERNSeGIpkRs0jRURERJJgKJIZLyXHFBEREUmBoUhmePqMiIhIGgxFMuPtVXf6rLqmVuKeEBERdSwMRTKj9fYCAFTaGIqIiIjaE0ORzHTyVgEAKqsYioiIiNoTQ5HMdNLUHSkqr6qRuCdEREQdC0ORzDhC0euHvsPglXux9r1TEveIiIioY2AokpnBPf2gUNRdfWa9VoN/HPmv1F0iIiLqEBiKZObuPt3x2VPxuO/aAVj+/kcUrLof/v7+jbYtLi5GYmIitFotgoKCsHTpUtTUOJ92279/P4YNGwaNRoOoqChkZ2c3WE5GRgZ69+4NHx8fxMXF4fDhw07zr127hqSkJHTr1g2dO3fGgw8+iNLS0rYqmYiISBYYimQosLMGvioB2uhRCBye2Gib2tpaJCYmorq6GgcPHsSOHTuQnZ2N1NRUsU1RURESExMxZswYFBYWYtGiRZg7dy727t0rttm5cyeMRiPS0tJw5MgRDBkyBAaDARcuXBDbLF68GO+++y7efvttfPjhhygpKcEDDzzgug1AREQkAYYimVq8fAV0d06Gd1DvRufn5eXhxIkTeO211xATE4MJEybgmWeeQUZGBqqrqwEAmZmZiIiIwMaNG9GvXz8kJyfjd7/7HTZt2iQuJz09HfPmzcPs2bPRv39/ZGZmQqvVIisrCwBw+fJlvPLKK0hPT8fYsWMRGxuL7du34+DBg/j0009dvh2IiIjai5fUHaDGqRR1N3EUmrixtdlsxqBBgxAcHCxOMxgMWLhwIY4fP46hQ4fCbDYjPj7e6XUGgwGLFi0CAFRXV6OgoAApKSnifKVSifj4eJjNZgBAQUEBbDab03Kio6PRq1cvmM1m3HXXXY32r6qqClVVVQAAm82GyspK8XubzdboaxzTm5pPt4bb17Wa277c5kTugaFIplTKulBkbyIVWSwWp0AEQPzZYrE028ZqteLq1au4dOkSamtrG21z6tQpcRne3t4NxjUFBweL62nMmjVrsGrVqgbT8/LyoNVqm3wdAJhMpmbn063h9nWtxrav4z8FRCRvDEXtaPny5Vi3bl2zbU6ePIno6Ggolc0fKZK7lJQUGI1GAHX/S961axfmzp2LhIQE6HS6Rl9js9lgMpkwbtw4qNXq9uxuh8Dt61rNbV+r1SpRr4ioNRiK2tGSJUswa9asBtNtNhs++ugj3HPPPYiMjARw/fRZU0eKQkJCGlwl5rgiLCQkRPz3l1eJlZaWQqfTwdfXFyqVCiqVqtE29ZdRXV2NsrIyp6NF9ds0RqPRQKPRiPU5jg6p1eobfiC3pA3dPG5f12ps+3J7E7kHDrRuR927d0d0dHSjXz179kR0dDS8vb0BAMqf94y9iSNFer0eX331ldNVYiaTCTqdDv379xfb5OfnO73OZDJBr9cDALy9vREbG+vUxm63Iz8/X2wTGxsLtVrt1Ob06dMoLi4W2xAREXkCHimSqfP//R7Vpd+gxnoRtbW1KCwsBABERUWhc+fOSEhIQP/+/TFjxgysX78eFosFK1asQFJSkniEZsGCBdi6dSuWLVuGRx99FPv27cNbb72FnJwccT1GoxEzZ87E8OHDMWLECGzevBkVFRWYPXs2AMDPzw9z5syB0WhE165dodPp8Nhjj0Gv1zc5yJqIiMgdMRTJ1Lo/P4Pzr/9d/Hno0KEAgA8++ACjR4+GSqXC7t27sXDhQuj1enTq1AkzZ87E6tWrxddEREQgJycHixcvxpYtW9CzZ09s27YNBoNBbDNlyhRcvHgRqampsFgsiImJQW5urtPg602bNkGpVOLBBx9EVVUVDAYDXnzxxXbYCkRERO2HoUimMre9goM9pwAATj87HhovVYM24eHh2LNnT7PLGT16NI4ePdpsm+TkZCQnJzc538fHBxkZGcjIyGhBz4mIiNwTxxTJlGOgNQDY7RJ2hIiIqINgKJIpx32KAKDWXa/LJyIiciMMRTKlrHekqLapS9CIiIiozTAUyVT9I0V2hiIiIiKXYyiSqXqZiKfPiIiI2gFDkUwpFAoxGPFIERERkevxknwZUykVsNcKeKewBH6+Pz8mQAHc3ScQPfx8pe0cERGRh2EokjEfLxVstTX4856TTtOHhwfg/xaOlKhXREREnomhSMZW3N8Pe49ff1jr5as2FHx3CRfLqyTsFRERkWdiKJKxKXf2wpQ7e4k/Hy2+hN++eBB2DrwmIiJqcxxo7UYc9y7iHa6JiIjaHkORG3GEIoFHioiIiNocQ5EbcdzkmlfoExERtT2GIjcinj7jkSIiIqI2x1DkRpQ/7y0eKSIiImp7DEVuhGOKiIiIXEfSUPSf//wHkyZNQmBgIHQ6HUaNGoUPPvjAqU1xcTESExOh1WoRFBSEpUuXoqamxqnN/v37MWzYMGg0GkRFRSE7O7vBujIyMtC7d2/4+PggLi4Ohw8fdmVpLiE+9oOhiIiIqM1JGoruv/9+1NTUYN++fSgoKMCQIUNw//33w2KxAABqa2uRmJiI6upqHDx4EDt27EB2djZSU1PFZRQVFSExMRFjxoxBYWEhFi1ahLlz52Lv3r1im507d8JoNCItLQ1HjhzBkCFDYDAYcOHChXav+VYoxDFFEneEiIjIA0kWin744Qd8/fXXWL58OQYPHow+ffpg7dq1qKysxLFjxwAAeXl5OHHiBF577TXExMRgwoQJeOaZZ5CRkYHq6moAQGZmJiIiIrBx40b069cPycnJ+N3vfodNmzaJ60pPT8e8efMwe/Zs9O/fH5mZmdBqtcjKypKk9pv184EiHikiIiJyAclCUbdu3dC3b1+8+uqrqKioQE1NDV5++WUEBQUhNjYWAGA2mzFo0CAEBweLrzMYDLBarTh+/LjYJj4+3mnZBoMBZrMZAFBdXY2CggKnNkqlEvHx8WIbd3F9TJHEHSEiIvJAkj3mQ6FQ4P3338fkyZPRpUsXKJVKBAUFITc3FwEBAQAAi8XiFIgAiD87TrE11cZqteLq1au4dOkSamtrG21z6tSpJvtXVVWFqqrrzxizWq0AAJvNBpvNdpNVN86xvBstt7a2biyVIAht3gdXcqe+ErmSK94/WrPu+v96Gtbn/lxVY2uW1+ahaPny5Vi3bl2zbU6ePIm+ffsiKSkJQUFB+Pjjj+Hr64tt27bh17/+NT777DP06NGjrbvWKmvWrMGqVasaTM/Ly4NWq3XJOk0mU7Pzf7wGAF6w1dRgz549LukDEbmOK98/WupG7zPujvW5v7ausbKyssVt2zwULVmyBLNmzWq2TWRkJPbt24fdu3fj0qVL0Ol0AIAXX3wRJpMJO3bswPLlyxESEtLgKrHS0rqnxoeEhIj/OqbVb6PT6eDr6wuVSgWVStVoG8cyGpOSkgKj0Sj+bLVaERYWhoSEBLG/bcVms8FkMmHcuHFQq9VNtispu4rVRz+GQqnCxImGNu2DK9lsNuzatQvA9SNuTbWrrKyE1WptdjvQzeH2da3mtq/j937cuHHw8/OTonstfp9xV6zP/bmqxuY+d36pzUNR9+7d0b179xu2cyQ3pdJ5WJNSqYT95yee6vV6/PnPf8aFCxcQFBQEoC5B6nQ69O/fX2zzy6MmJpMJer0eAODt7Y3Y2Fjk5+dj8uTJAAC73Y78/HwkJyc32T+NRgONRtNgulqtdtkv5I2W7e3tOH0Gt/ujuHr1KgAgLCxM4p4QSefatWsIDAyUtA+ufA+TA9bn/tq6xtYsS7IxRXq9HgEBAZg5cyZSU1Ph6+uLv/3tb+Il9gCQkJCA/v37Y8aMGVi/fj0sFgtWrFiBpKQkMbAsWLAAW7duxbJly/Doo49i3759eOutt5CTkyOuy2g0YubMmRg+fDhGjBiBzZs3o6KiArNnz5ak9pvlzo/56Nq1K4qKihAQECDeWuCXHEfjvv/++zY/GudKNpsNeXl5SEhIkPWb1a1sX3ep8Wa1RX3NbV9BEHDlyhWEhoa2RXeJyEUkC0WBgYHIzc3FU089hbFjx8Jms2HAgAF45513MGTIEACASqXC7t27sXDhQuj1enTq1AkzZ87E6tWrxeVEREQgJycHixcvxpYtW9CzZ09s27YNBsP100tTpkzBxYsXkZqaCovFgpiYGOTm5jYYfC13Cje+eaNSqcRtt93Wog8cnU7ndqFIq9VCp9O5RWC4me3rbjW2VlvW19T2leq0GRG1nGShCACGDx/udJPFxoSHh99wUPHo0aNx9OjRZtskJyc3e7rMHSh580YiIiKX4bPP3Iiy3mknPv+MiIiobTEUuRFlvaE4nni0SKPRIC0trdEB7nTruH1di9uXyP1JevqMWqf+AGW7IECFxgcsuyuNRoOVK1dK3Q2Pxe3rWty+RO6PR4rciPORIg88VERERCQhhiI34jymSMKOEBEReSCGIjei/MXpMyIiImo7DEVuROHhA62JiIikxFDkRhQePKYoIyMDvXv3ho+PD+Li4ho8845aZuXKlVAoFE5f0dHR4vxr164hKSkJ3bp1Q+fOnfHggw82eC4gOfvoo4/w61//GqGhoVAoFOJz/BwEQUBqaip69OgBX19fxMfH4+uvv3Zq89NPP2H69OnQ6XTw9/fHnDlzUF5e3o5VEFFLMBS5EacxRXYJO9LGdu7cCaPRiLS0NBw5cgRDhgyBwWDAhQsXpO6aWxowYADOnz8vfh04cECct3jxYrz77rt4++238eGHH6KkpAQPPPCAhL2Vv4qKCgwZMgQZGRmNzl+/fj2ef/55ZGZm4tChQ+jUqRMMBgOuXbsmtpk+fTqOHz8Ok8mE3bt346OPPsL8+fPbqwQiaiFeku9G6oeiVw58Ax9vldN8BRQY1z8IUUFd2rtrtyQ9PR3z5s0Tn0WXmZmJnJwcZGVlYfny5RL3zv14eXkhJCSkwfTLly/jlVdewRtvvIGxY8cCALZv345+/frh008/xV133dXeXXULEyZMwIQJExqdJwgCNm/ejBUrVmDSpEkAgFdffRXBwcHYtWsXHnnkEZw8eRK5ubn47LPPMHz4cADACy+8gIkTJ+K5557j89CIZIRHityIUgFovOp22fP7zmB97mmnr3W5p2B86wuJe9k61dXVKCgoQHx8vDhNqVQiPj4eZrNZwp65r6+//hqhoaGIjIzE9OnTUVxcDAAoKCiAzWZz2tbR0dHo1asXt/VNKioqgsVicdqmfn5+iIuLE7ep2WyGv7+/GIgAID4+HkqlEocOHWr3PhNR03ikyI0oFApsfHgIPvrPxQbzrFdrkHvcgpKyqxL07Ob98MMPqK2tbfBw3uDgYJw6dUqiXrmvuLg4ZGdno2/fvjh//jxWrVqFu+++G8eOHYPFYoG3tzf8/f2dXhMcHAyLxSJNh92cY7s19vvrmGexWBAUFOQ038vLC127duV2J5IZhiI3c//gUNw/uOHh9pKyq8g9bsHlqzYIguB092vqOOqf5hk8eDDi4uIQHh6Ot956C76+vhL2jIhI/nj6zEP4+aoBALZaAZXVtRL3puUCAwOhUqkaXAFVWlra6LgYah1/f3/ccccdOHPmDEJCQlBdXY2ysjKnNtzWN8+x3Zr7/Q0JCWlw0UBNTQ1++uknbncimWEo8hA+6uuDrqtr3OfSNG9vb8TGxiI/P1+cZrfbkZ+fD71eL2HPPEN5eTnOnj2LHj16IDY2Fmq12mlbnz59GsXFxdzWNykiIgIhISFO29RqteLQoUPiNtXr9SgrK0NBQYHYZt++fbDb7YiLi2v3PhNR03j6zEO483PRjEYjZs6cieHDh2PEiBHYvHkzKioqxKvRqOX++Mc/4te//jXCw8NRUlKCtLQ0qFQqTJ06FX5+fpgzZw6MRiO6du0KnU6Hxx57DHq9nleeNaO8vBxnzpwRfy4qKkJhYSG6du2KXr16YdGiRXj22WfRp08fRERE4Omnn0ZoaCgmT54MAOjXrx/Gjx+PefPmITMzEzabDcnJyXjkkUd45RmRzDAUeYj6Y4jcKxIBU6ZMwcWLF5GamgqLxYKYmBjk5uY2GLxKN/bf//4XU6dOxY8//oju3btj1KhR+PTTT9G9e3cAwKZNm6BUKvHggw+iqqoKBoMBL774osS9lrfPP/8cY8aMEX82Go0AgJkzZyI7OxvLli1DRUUF5s+fj7KyMowaNQq5ubnw8fERX/P6668jOTkZ9913n7j9n3/++XavhYiax1DkQZSKusd/uNuRIgBITk5GcnKy1N1we2+++Waz8318fJCRkdHkjQipodGjR0No5m9KoVBg9erVWL16dZNtunbtijfeeMMV3SOiNsQxRR7EcXNHN8xEREREkmMo8iCOM2jueKSIiIhIagxFHkTBI0VEREQ3jaHIgyh5pIiIiOimMRR5EI4pIiIiunkMRR7EcVE+jxQRERG1HkORB+GRIiIiopvHUORBePUZERHRzWMo8iDKn0da25mJiIiIWo2hyINcP33GVERERNRaDEUe5PpAa0m7QURE5JYYijyIePNGt3skLBERkfQYijyIePNGu7T9ICIickcMRR7EMaaIV58RERG1HkORB3Fcks9MRERE1HoMRR5EyTFFREREN42hyINcv3mjtP0gIiJyRwxFHoRjioiIiG4eQ5EHUYpjihiKiIiIWouhyIMoFHzMBxER0c1iKPIgvPqMiIjo5jEUeRCOKSIiIrp5DEUeRLyjNUMRERFRqzEUeRDxPkXMRERERK3GUOSBGIqIiIhaj6HIg3BMERER0c3zkroDbq2mEoAAKH0Aper69NoqQKgBFGpA5X19ur0GsFcBUAJevtenCwJUwjWgpgJQ+9/0OpRKQIVaoKYcqOncYB2oraz73quTy+poah0qoaquPlUXl62jPepoah1KwVZXn1Lr1nU0tw6VcA2ovQqo1W5dhyv/BuVQR3PrUAnXAKEWQL196IZ1NLUOlXANsFc71+eGdXTUv0HUVNTV+EtttY4WUAi801+LWK1W+Pn54fLly9DpdHUTcwYAl08A930ABI++3vizJODrF4GBacDgldenF78NHHgYCLoXiN8vTraVl0D979vqfpj2i93RinX8ZusB9LySgxfD1+Jb1XDs0GaLzbX2n7C04h4AwKoux5xWsbBiEoLsZ5Htm4XvvEaI0ydeexZ32t7Efu+F+FCTJE7vb9uLh64tafE67HY75l9OxG1e37tsHe1RR1PrsNvtuPvicsRr97h1Hc2tI7o6F1Oq/ogi5XC82sl962hqHT61P+DJytFuX0ez6yifhCDhLLb7vIJidZz71tHEOiZcfQYjanbiA/UCfOST7LZ1dNS/wfrrsD1UDXX94HeLn7WNfn43gUeKPEgXHy/gSt33lsvXsP3ot+K8rqrLWDqg7vvtn3zr9Lqpd9gQ5APkHrPg04rr8yJCr+DOQKDw+zJsL70+faLfRTwU3rp1TL9DAXi5dh3tUUdT67g9VAFo3b+OptfxA6aEA6XWa9he6M51NL2OJz2kjhutY+/xUo+oo+E6yjEiEPjiv5fdvI6O+zfoWIeUeKSohRpNmm10SM9WXY29e/4Fg8EAta+/84pbsY6vS69gd2ExFEIVBChRo/C53l4QoMbVuvUptE6r8BKuQgEBNdBAUFxfh0qohhI1qIUX7IrrdSiEGnihusXrsNfaUXz2BG6P7A27ytcl62iPOppah73Wjm/PnkKfyF4QVN5uW0dz6xBqqvHdN6cRGXk77F71luVmdTS1DntNLYq/OYHbIyNQ69XZbetobh3Kmgp88803CI+MhsLr+v/C3a2OptahqLmGom++Ru/IPoDX9WW5Wx0d9W8QggBVbQUul3yDFbN+63yk6BY/a3mkqL14aRufrtIA0DScrvSq+/olhQK1Cp+G52pbuY4+wV2w2CCDqP0LNpsNe2xfY2LCEOdfdA/h6fUBP9e4pwgTEwZ5ZI119Z3pAPvwv5iYEO2RNdbV9x0mJgzw4Po8928QcNR4ruGMtvqsbQFefUZEREQEhiIiIiIiAAxFRERERABcGIr+/Oc/Y+TIkdBqtfD392+0TXFxMRITE6HVahEUFISlS5eipqbGqc3+/fsxbNgwaDQaREVFITs7u8FyMjIy0Lt3b/j4+CAuLg6HDx92mn/t2jUkJSWhW7du6Ny5Mx588EGUlpa2ValERETkAVwWiqqrq/HQQw9h4cKFjc6vra1FYmIiqqurcfDgQezYsQPZ2dlITU0V2xQVFSExMRFjxoxBYWEhFi1ahLlz52Lv3r1im507d8JoNCItLQ1HjhzBkCFDYDAYcOHCBbHN4sWL8e677+Ltt9/Ghx9+iJKSEjzwwAOuKp2IiIjckMtC0apVq7B48WIMGjSo0fl5eXk4ceIEXnvtNcTExGDChAl45plnkJGRgerqagBAZmYmIiIisHHjRvTr1w/Jycn43e9+h02bNonLSU9Px7x58zB79mz0798fmZmZ0Gq1yMrKAgBcvnwZr7zyCtLT0zF27FjExsZi+/btOHjwID799FNXlU9ERERuRrJL8s1mMwYNGoTg4GBxmsFgwMKFC3H8+HEMHToUZrMZ8fHxTq8zGAxYtGgRgLqjUQUFBUhJSRHnK5VKxMfHw2w2AwAKCgpgs9mclhMdHY1evXrBbDbjrrvuarR/VVVVqKqqEn+2Wq0A6i4ZtNlst1b8LziW19bLlQvW5/48vUZPrw/w/BpZn/tzVY2tWZ5kochisTgFIgDizxaLpdk2VqsVV69exaVLl1BbW9tom1OnTonL8Pb2bjCuKTg4WFxPY9asWYNVq1Y1mJ6Xlwettol7Jtwik8nkkuXKBetzf55eo6fXB3h+jazP/bV1jZWVlS1u26pQtHz5cqxbt67ZNidPnkR0dHRrFitLKSkpMBqN4s9WqxVhYWFISEi44R0xW8tms8FkMmHcuHEeeVMu1uf+PL1GT68P8PwaWZ/7c1WNjjM9LdGqULRkyRLMmjWr2TaRkZEtWlZISEiDq8QcV4SFhISI//7yKrHS0lLodDr4+vpCpVJBpVI12qb+Mqqrq1FWVuZ0tKh+m8ZoNBpoNA3vlKlWq132C+nKZcsB63N/nl6jp9cHeH6NrM/9tXWNrVlWqwZad+/eHdHR0c1+eXt733hBAPR6Pb766iunq8RMJhN0Oh369+8vtsnPz3d6nclkgl6vBwB4e3sjNjbWqY3dbkd+fr7YJjY2Fmq12qnN6dOnUVxcLLYhIiIictmYouLiYvz0008oLi5GbW0tCgsLAQBRUVHo3LkzEhIS0L9/f8yYMQPr16+HxWLBihUrkJSUJB6hWbBgAbZu3Yply5bh0Ucfxb59+/DWW28hJydHXI/RaMTMmTMxfPhwjBgxAps3b0ZFRQVmz54NAPDz88OcOXNgNBrRtWtX6HQ6PPbYY9Dr9U0OsiYiIqKOx2WhKDU1FTt27BB/Hjp0KADggw8+wOjRo6FSqbB7924sXLgQer0enTp1wsyZM7F69WrxNREREcjJycHixYuxZcsW9OzZE9u2bYPBYBDbTJkyBRcvXkRqaiosFgtiYmKQm5vrNPh606ZNUCqVePDBB1FVVQWDwYAXX3zRVaUTERGRG3JZKMrOzm707tP1hYeHY8+ePc22GT16NI4ePdpsm+TkZCQnJzc538fHBxkZGcjIyGh2Oc0RBAFA6wZstZTNZkNlZSWsVqtHnitmfe7P02v09PoAz6+R9bk/V9Xo+Nx2fI43R7JL8t3NlStXAABhYWES94SIiIha68qVK/Dz82u2jUJoSXQi2O12lJSUoEuXLlAoFG26bMfl/t9//32bX+4vB6zP/Xl6jZ5eH+D5NbI+9+eqGgVBwJUrVxAaGgqlsvnry3ikqIWUSiV69uzp0nXodDqP/WUHWJ8n8PQaPb0+wPNrZH3uzxU13ugIkYPLnn1GRERE5E4YioiIiIjAUCQLGo0GaWlpjd5B2xOwPvfn6TV6en2A59fI+tyfHGrkQGsiIiIi8EgREREREQCGIiIiIiIADEVEREREABiKiIiIiAAwFEkuIyMDvXv3ho+PD+Li4nD48GGpu9Qia9aswZ133okuXbogKCgIkydPxunTp53ajB49GgqFwulrwYIFTm2Ki4uRmJgIrVaLoKAgLF26FDU1Ne1ZSqNWrlzZoO/R0dHi/GvXriEpKQndunVD586d8eCDD6K0tNRpGXKtzaF3794NalQoFEhKSgLgfvvvo48+wq9//WuEhoZCoVBg165dTvMFQUBqaip69OgBX19fxMfH4+uvv3Zq89NPP2H69OnQ6XTw9/fHnDlzUF5e7tTmyy+/xN133w0fHx+EhYVh/fr1ri5N1FyNNpsNTz75JAYNGoROnTohNDQUv//971FSUuK0jMb2+9q1a53aSFXjjfbhrFmzGvR9/PjxTm3kvA9vVF9jf48KhQIbNmwQ28h5/7Xkc6Gt3jv379+PYcOGQaPRICoq6obPWm0xgSTz5ptvCt7e3kJWVpZw/PhxYd68eYK/v79QWloqddduyGAwCNu3bxeOHTsmFBYWChMnThR69eollJeXi23uvfdeYd68ecL58+fFr8uXL4vza2pqhIEDBwrx8fHC0aNHhT179giBgYFCSkqKFCU5SUtLEwYMGODU94sXL4rzFyxYIISFhQn5+fnC559/Ltx1113CyJEjxflyrs3hwoULTvWZTCYBgPDBBx8IguB++2/Pnj3CU089Jfzzn/8UAAj/+te/nOavXbtW8PPzE3bt2iV88cUXwm9+8xshIiJCuHr1qthm/PjxwpAhQ4RPP/1U+Pjjj4WoqChh6tSp4vzLly8LwcHBwvTp04Vjx44J//u//yv4+voKL7/8suQ1lpWVCfHx8cLOnTuFU6dOCWazWRgxYoQQGxvrtIzw8HBh9erVTvu1/t+tlDXeaB/OnDlTGD9+vFPff/rpJ6c2ct6HN6qvfl3nz58XsrKyBIVCIZw9e1ZsI+f915LPhbZ47/zmm28ErVYrGI1G4cSJE8ILL7wgqFQqITc395ZrYCiS0IgRI4SkpCTx59raWiE0NFRYs2aNhL26ORcuXBAACB9++KE47d577xWeeOKJJl+zZ88eQalUChaLRZz20ksvCTqdTqiqqnJld28oLS1NGDJkSKPzysrKBLVaLbz99tvitJMnTwoABLPZLAiCvGtryhNPPCHcfvvtgt1uFwTBvfffLz9w7Ha7EBISImzYsEGcVlZWJmg0GuF///d/BUEQhBMnTggAhM8++0xs89577wkKhUI4d+6cIAiC8OKLLwoBAQFO9T355JNC3759XVxRQ419qP7S4cOHBQDCd999J04LDw8XNm3a1ORr5FJjU6Fo0qRJTb7GnfZhS/bfpEmThLFjxzpNc5f9JwgNPxfa6r1z2bJlwoABA5zWNWXKFMFgMNxyn3n6TCLV1dUoKChAfHy8OE2pVCI+Ph5ms1nCnt2cy5cvAwC6du3qNP31119HYGAgBg4ciJSUFFRWVorzzGYzBg0ahODgYHGawWCA1WrF8ePH26fjzfj6668RGhqKyMhITJ8+HcXFxQCAgoIC2Gw2p30XHR2NXr16iftO7rX9UnV1NV577TU8+uijTg88duf9V19RUREsFovTPvPz80NcXJzTPvP398fw4cPFNvHx8VAqlTh06JDY5p577oG3t7fYxmAw4PTp07h06VI7VdNyly9fhkKhgL+/v9P0tWvXolu3bhg6dCg2bNjgdGpC7jXu378fQUFB6Nu3LxYuXIgff/xRnOdJ+7C0tBQ5OTmYM2dOg3nusv9++bnQVu+dZrPZaRmONm3x2ckHwkrkhx9+QG1trdOOB4Dg4GCcOnVKol7dHLvdjkWLFuFXv/oVBg4cKE6fNm0awsPDERoaii+//BJPPvkkTp8+jX/+858AAIvF0mj9jnlSiouLQ3Z2Nvr27Yvz589j1apVuPvuu3Hs2DFYLBZ4e3s3+KAJDg4W+y3n2hqza9culJWVYdasWeI0d95/v+ToT2P9rb/PgoKCnOZ7eXmha9euTm0iIiIaLMMxLyAgwCX9vxnXrl3Dk08+ialTpzo9XPPxxx/HsGHD0LVrVxw8eBApKSk4f/480tPTAci7xvHjx+OBBx5AREQEzp49iz/96U+YMGECzGYzVCqVR+3DHTt2oEuXLnjggQecprvL/mvsc6Gt3jubamO1WnH16lX4+vredL8ZiuiWJSUl4dixYzhw4IDT9Pnz54vfDxo0CD169MB9992Hs2fP4vbbb2/vbrbKhAkTxO8HDx6MuLg4hIeH46233rqlPzi5euWVVzBhwgSEhoaK09x5/3V0NpsNDz/8MARBwEsvveQ0z2g0it8PHjwY3t7e+J//+R+sWbNG9o+QeOSRR8TvBw0ahMGDB+P222/H/v37cd9990nYs7aXlZWF6dOnw8fHx2m6u+y/pj4X5I6nzyQSGBgIlUrVYNR9aWkpQkJCJOpV6yUnJ2P37t344IMP0LNnz2bbxsXFAQDOnDkDAAgJCWm0fsc8OfH398cdd9yBM2fOICQkBNXV1SgrK3NqU3/fuVNt3333Hd5//33MnTu32XbuvP8c/Wnu7y0kJAQXLlxwml9TU4OffvrJrfarIxB99913MJlMTkeJGhMXF4eamhp8++23ANyjRofIyEgEBgY6/U56wj78+OOPcfr06Rv+TQLy3H9NfS601XtnU210Ot0t/6eVoUgi3t7eiI2NRX5+vjjNbrcjPz8fer1ewp61jCAISE5Oxr/+9S/s27evweHaxhQWFgIAevToAQDQ6/X46quvnN7EHG/i/fv3d0m/b1Z5eTnOnj2LHj16IDY2Fmq12mnfnT59GsXFxeK+c6fatm/fjqCgICQmJjbbzp33X0REBEJCQpz2mdVqxaFDh5z2WVlZGQoKCsQ2+/btg91uFwOhXq/HRx99BJvNJrYxmUzo27evLE67OALR119/jffffx/dunW74WsKCwuhVCrF005yr7G+//73v/jxxx+dfifdfR8CdUduY2NjMWTIkBu2ldP+u9HnQlu9d+r1eqdlONq0yWfnLQ/Vppv25ptvChqNRsjOzhZOnDghzJ8/X/D393cadS9XCxcuFPz8/IT9+/c7XRpaWVkpCIIgnDlzRli9erXw+eefC0VFRcI777wjREZGCvfcc4+4DMellwkJCUJhYaGQm5srdO/eXRaXrS9ZskTYv3+/UFRUJHzyySdCfHy8EBgYKFy4cEEQhLrLSnv16iXs27dP+PzzzwW9Xi/o9Xrx9XKurb7a2lqhV69ewpNPPuk03R3335UrV4SjR48KR48eFQAI6enpwtGjR8Urr9auXSv4+/sL77zzjvDll18KkyZNavSS/KFDhwqHDh0SDhw4IPTp08fpcu6ysjIhODhYmDFjhnDs2DHhzTffFLRabbtdkt9cjdXV1cJvfvMboWfPnkJhYaHT36Xjqp2DBw8KmzZtEgoLC4WzZ88Kr732mtC9e3fh97//vSxqbK6+K1euCH/84x8Fs9ksFBUVCe+//74wbNgwoU+fPsK1a9fEZch5H97od1QQ6i6p12q1wksvvdTg9XLffzf6XBCEtnnvdFySv3TpUuHkyZNCRkYGL8n3FC+88ILQq1cvwdvbWxgxYoTw6aefSt2lFgHQ6Nf27dsFQRCE4uJi4Z577hG6du0qaDQaISoqSli6dKnTfW4EQRC+/fZbYcKECYKvr68QGBgoLFmyRLDZbBJU5GzKlClCjx49BG9vb+G2224TpkyZIpw5c0acf/XqVeEPf/iDEBAQIGi1WuG3v/2tcP78eadlyLW2+vbu3SsAEE6fPu003R333wcffNDo7+TMmTMFQai7LP/pp58WgoODBY1GI9x3330N6v7xxx+FqVOnCp07dxZ0Op0we/Zs4cqVK05tvvjiC2HUqFGCRqMRbrvtNmHt2rXtVWKzNRYVFTX5d+m491RBQYEQFxcn+Pn5CT4+PkK/fv2Ev/zlL06hQsoam6uvsrJSSEhIELp37y6o1WohPDxcmDdvXoP/RMp5H97od1QQBOHll18WfH19hbKysgavl/v+u9HngiC03XvnBx98IMTExAje3t5CZGSk0zpuheLnQoiIiIg6NI4pIiIiIgJDEREREREAhiIiIiIiAAxFRERERAAYioiIiIgAMBQRERERAWAoIiIiIgLAUEREREQEgKGIiIiICABDEREREREAhiIiIiIiAAxFRERERACA/w8TckhJDVQoPQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -418,13 +412,11 @@ "\n", "left, bottom, width, height = [0.25, 0.25, 0.3, 0.3]\n", "\n", - "ax1.plot(eplt)\n", - "ax1.plot(Tschedule)\n", + "ax1.plot(res.energies)\n", + "# ax1.plot(Tschedule)\n", "ax1.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "# plt.ylim([-1E5, -1E4])\n", - "# plt.xlim([9000,11000])\n", "ax1.grid()\n", - "ax1.set_yscale('symlog')\n", + "# ax1.set_yscale('symlog')\n", "\n", "ax2 = fig.add_axes([left, bottom, width, height])\n", "ax2.plot(eplt[-100:])\n", @@ -435,11 +427,19 @@ }, { "cell_type": "code", - "execution_count": 615, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ - "idx_min = np.array([e[0] for e in res.energies]).argmin()\n", + "idx_min = np.array([e for e in res.energies]).argmin()\n", + "# idx_min = -1\n", "sol = res.trajectory[idx_min]\n", "sol = net.qubo.decode_solution(np.array(sol))\n", "sol = net.combine_flow_values(sol)\n", @@ -448,7 +448,26 @@ }, { "cell_type": "code", - "execution_count": 616, + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-9687.974189114439 -9687.974189114439\n", + "0.0\n" + ] + } + ], + "source": [ + "print(eref[0], res.energies[idx_min])\n", + "print(eref[0] - res.energies[idx_min])" + ] + }, + { + "cell_type": "code", + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -457,13 +476,13 @@ "Text(0.5, 1.0, 'Pressure')" ] }, - "execution_count": 616, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -502,22 +521,81 @@ "ax2.scatter(ref_values[2:], sol[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", "ax2.grid()\n", "\n", - "# ax2.set_xlim([160,210])\n", - "# ax2.set_ylim([160,210])\n", + "\n", "ax2.set_xlabel('Reference Values', fontsize=12)\n", "ax2.set_title('Pressure', fontsize=14)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "def flatten_list(lst):\n", + " out = []\n", + " for elmt in lst:\n", + " if not isinstance(elmt, list):\n", + " out += [elmt]\n", + " else:\n", + " out += elmt\n", + " return out\n", + "\n", + "bin_rep_flat = flatten_list(bin_rep_sol)\n", + "xt_bin_rep_flat = net.qubo.extend_binary_representation(bin_rep_flat)\n", + "# xt_bin_rep_flat.values()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 1 1 0 1 0 0 0 1 1 0 0 1 1 1 1 0 1 1 1 1 0]\n", + "[1 1 0 1 1 1 0 0 1 1 1 0 1 1 1 1 0 1 1 1 1 0]\n" + ] + } + ], + "source": [ + "print(np.array(res.trajectory[idx_min])[net.qubo.index_variables])\n", + "print(np.array(bin_rep_flat))" + ] + }, + { + "cell_type": "code", + "execution_count": 63, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-9686.92])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## Explore the solution space" + "xx = np.array(res.trajectory[idx_min])[net.qubo.index_variables]\n", + "net.qubo.energy_binary_rep(xx)" ] }, { "cell_type": "code", - "execution_count": 617, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -531,24 +609,24 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_7835/3452845714.py:16: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + "/tmp/ipykernel_5700/711176967.py:16: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "32it [00:00, 75.13it/s]\n" + "32it [00:00, 69.06it/s]\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 617, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -560,7 +638,7 @@ "source": [ "import itertools\n", "from tqdm import tqdm\n", - "\n", + "from copy import deepcopy\n", "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", "energies = np.zeros((2**nqbit, 2**nqbit))\n", "i2 = 0\n", @@ -577,41 +655,46 @@ " i3+=1\n", " i2+=1\n", "\n", - "x, y = np.arange(2**nqbit), np.arange(2**nqbit)\n", - "x,y = np.meshgrid(x,y)\n", - "ax = plt.figure().add_subplot(projection='3d')\n", - "ax.plot_surface(x,y,energies)" + "# x, y = np.arange(2**nqbit), np.arange(2**nqbit)\n", + "# x,y = np.meshgrid(x,y)\n", + "# ax = plt.figure().add_subplot(projection='3d')\n", + "# ax.plot_surface(x,y,energies)\n", + "\n", + "# plt.imshow(energies- eref)\n", + "# plt.colorbar()\n", + "\n", + "plt.contour(energies-eref, levels=[1e-2,1,2, 10])" ] }, { "cell_type": "code", - "execution_count": 618, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "0it [00:00, ?it/s]/tmp/ipykernel_7835/685105249.py:17: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + "0it [00:00, ?it/s]/tmp/ipykernel_5700/1779021354.py:17: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "32it [00:00, 72.16it/s]\n" + "32it [00:00, 82.58it/s]\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 618, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -639,40 +722,63 @@ " i3+=1\n", " i2+=1\n", "\n", - "x, y = np.arange(2**nqbit), np.arange(2**nqbit)\n", - "x,y = np.meshgrid(x,y)\n", - "ax = plt.figure().add_subplot(projection='3d')\n", - "ax.plot_surface(x,y,energies)\n", - "# plt.show()" + "# x, y = np.arange(2**nqbit), np.arange(2**nqbit)\n", + "# x,y = np.meshgrid(x,y)\n", + "# ax = plt.figure().add_subplot(projection='3d')\n", + "# ax.plot_surface(x,y,energies)\n", + "# plt.show()\n", + "\n", + "plt.imshow(energies- eref)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "((energies-eref)==0).sum()" ] }, { "cell_type": "code", - "execution_count": 619, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "0it [00:00, ?it/s]/tmp/ipykernel_7835/3224148935.py:12: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + "0it [00:00, ?it/s]/tmp/ipykernel_5700/321696615.py:12: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "32it [00:00, 1909.11it/s]\n" + "32it [00:00, 1563.62it/s]\n" ] }, { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 619, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -691,7 +797,7 @@ "for data2 in tqdm(itertools.product([0, 1], repeat=nqbit)):\n", "\n", " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", - " mod_bin_rep_sol[4] = list(data2)[::-1]\n", + " mod_bin_rep_sol[2] = list(data2)[::-1]\n", " # mod_bin_rep_sol[3] = list(data2)[::-1]\n", " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", "\n", @@ -701,1930 +807,38 @@ }, { "cell_type": "code", - "execution_count": 655, + "execution_count": 41, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_values([1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0])" - ] - }, - "execution_count": 655, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "def flatten_list(lst):\n", - " out = []\n", - " for elmt in lst:\n", - " if not isinstance(elmt, list):\n", - " out += [elmt]\n", - " else:\n", - " out += elmt\n", - " return out\n", + "# import itertools\n", + "# from tqdm import tqdm\n", + "# from copy import deepcopy\n", "\n", - "bin_rep_flat = flatten_list(bin_rep_sol)\n", - "xt_bin_rep_flat = net.qubo.extend_binary_representation(bin_rep_flat)\n", - "xt_bin_rep_flat.values()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 657, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 1, [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [1, 1, 1, 1, 0]]\n", - "[1, 0, [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [1, 1, 1, 1, 0]]\n" - ] - } - ], - "source": [ - "xt_bin_rep_flat_new = mystep(list(xt_bin_rep_flat.values()))\n", - "x_new = []\n", - "for k,v in mystep.index_values.items():\n", - " if len(v) == 1:\n", - " x_new.append(xt_bin_rep_flat_new[v[0]])\n", - " else:\n", - " tmp = []\n", - " for idx in v:\n", - " tmp.append(xt_bin_rep_flat_new[idx])\n", - " x_new.append(tmp)\n", - "print(bin_rep_sol)\n", - "print(x_new)" - ] - }, - { - "cell_type": "code", - "execution_count": 622, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[4.06595691246814e-8*x_001_001**2*x_003_001**4,\n", - " 3.25276552997451e-7*x_001_001**2*x_003_001**3*x_003_002,\n", - " 6.50553105994902e-7*x_001_001**2*x_003_001**3*x_003_003,\n", - " 1.3011062119898e-6*x_001_001**2*x_003_001**3*x_003_004,\n", - " 2.60221242397961e-6*x_001_001**2*x_003_001**3*x_003_005,\n", - " 9.75829658992353e-7*x_001_001**2*x_003_001**2*x_003_002**2,\n", - " 3.90331863596941e-6*x_001_001**2*x_003_001**2*x_003_002*x_003_003,\n", - " 7.80663727193883e-6*x_001_001**2*x_003_001**2*x_003_002*x_003_004,\n", - " 1.56132745438777e-5*x_001_001**2*x_003_001**2*x_003_002*x_003_005,\n", - " 3.90331863596941e-6*x_001_001**2*x_003_001**2*x_003_003**2,\n", - " 1.56132745438777e-5*x_001_001**2*x_003_001**2*x_003_003*x_003_004,\n", - " 3.12265490877553e-5*x_001_001**2*x_003_001**2*x_003_003*x_003_005,\n", - " 1.56132745438777e-5*x_001_001**2*x_003_001**2*x_003_004**2,\n", - " 6.24530981755106e-5*x_001_001**2*x_003_001**2*x_003_004*x_003_005,\n", - " 6.24530981755106e-5*x_001_001**2*x_003_001**2*x_003_005**2,\n", - " 0.0665972944849115*x_001_001**2*x_003_001**2,\n", - " 1.3011062119898e-6*x_001_001**2*x_003_001*x_003_002**3,\n", - " 7.80663727193883e-6*x_001_001**2*x_003_001*x_003_002**2*x_003_003,\n", - " 1.56132745438777e-5*x_001_001**2*x_003_001*x_003_002**2*x_003_004,\n", - " 3.12265490877553e-5*x_001_001**2*x_003_001*x_003_002**2*x_003_005,\n", - " 1.56132745438777e-5*x_001_001**2*x_003_001*x_003_002*x_003_003**2,\n", - " 6.24530981755106e-5*x_001_001**2*x_003_001*x_003_002*x_003_003*x_003_004,\n", - " 0.000124906196351021*x_001_001**2*x_003_001*x_003_002*x_003_003*x_003_005,\n", - " 6.24530981755106e-5*x_001_001**2*x_003_001*x_003_002*x_003_004**2,\n", - " 0.000249812392702042*x_001_001**2*x_003_001*x_003_002*x_003_004*x_003_005,\n", - " 0.000249812392702042*x_001_001**2*x_003_001*x_003_002*x_003_005**2,\n", - " 0.266389177939646*x_001_001**2*x_003_001*x_003_002,\n", - " 1.04088496959184e-5*x_001_001**2*x_003_001*x_003_003**3,\n", - " 6.24530981755106e-5*x_001_001**2*x_003_001*x_003_003**2*x_003_004,\n", - " 0.000124906196351021*x_001_001**2*x_003_001*x_003_003**2*x_003_005,\n", - " 0.000124906196351021*x_001_001**2*x_003_001*x_003_003*x_003_004**2,\n", - " 0.000499624785404085*x_001_001**2*x_003_001*x_003_003*x_003_004*x_003_005,\n", - " 0.000499624785404085*x_001_001**2*x_003_001*x_003_003*x_003_005**2,\n", - " 0.532778355879292*x_001_001**2*x_003_001*x_003_003,\n", - " 8.32707975673475e-5*x_001_001**2*x_003_001*x_003_004**3,\n", - " 0.000499624785404085*x_001_001**2*x_003_001*x_003_004**2*x_003_005,\n", - " 0.00099924957080817*x_001_001**2*x_003_001*x_003_004*x_003_005**2,\n", - " 1.06555671175858*x_001_001**2*x_003_001*x_003_004,\n", - " 0.00066616638053878*x_001_001**2*x_003_001*x_003_005**3,\n", - " 2.13111342351717*x_001_001**2*x_003_001*x_003_005,\n", - " 6.50553105994902e-7*x_001_001**2*x_003_002**4,\n", - " 5.20442484795922e-6*x_001_001**2*x_003_002**3*x_003_003,\n", - " 1.04088496959184e-5*x_001_001**2*x_003_002**3*x_003_004,\n", - " 2.08176993918369e-5*x_001_001**2*x_003_002**3*x_003_005,\n", - " 1.56132745438777e-5*x_001_001**2*x_003_002**2*x_003_003**2,\n", - " 6.24530981755106e-5*x_001_001**2*x_003_002**2*x_003_003*x_003_004,\n", - " 0.000124906196351021*x_001_001**2*x_003_002**2*x_003_003*x_003_005,\n", - " 6.24530981755106e-5*x_001_001**2*x_003_002**2*x_003_004**2,\n", - " 0.000249812392702042*x_001_001**2*x_003_002**2*x_003_004*x_003_005,\n", - " 0.000249812392702042*x_001_001**2*x_003_002**2*x_003_005**2,\n", - " 0.266389177939646*x_001_001**2*x_003_002**2,\n", - " 2.08176993918369e-5*x_001_001**2*x_003_002*x_003_003**3,\n", - " 0.000124906196351021*x_001_001**2*x_003_002*x_003_003**2*x_003_004,\n", - " 0.000249812392702042*x_001_001**2*x_003_002*x_003_003**2*x_003_005,\n", - " 0.000249812392702042*x_001_001**2*x_003_002*x_003_003*x_003_004**2,\n", - " 0.00099924957080817*x_001_001**2*x_003_002*x_003_003*x_003_004*x_003_005,\n", - " 0.00099924957080817*x_001_001**2*x_003_002*x_003_003*x_003_005**2,\n", - " 1.06555671175858*x_001_001**2*x_003_002*x_003_003,\n", - " 0.000166541595134695*x_001_001**2*x_003_002*x_003_004**3,\n", - " 0.00099924957080817*x_001_001**2*x_003_002*x_003_004**2*x_003_005,\n", - " 0.00199849914161634*x_001_001**2*x_003_002*x_003_004*x_003_005**2,\n", - " 2.13111342351717*x_001_001**2*x_003_002*x_003_004,\n", - " 0.00133233276107756*x_001_001**2*x_003_002*x_003_005**3,\n", - " 4.26222684703434*x_001_001**2*x_003_002*x_003_005,\n", - " 1.04088496959184e-5*x_001_001**2*x_003_003**4,\n", - " 8.32707975673475e-5*x_001_001**2*x_003_003**3*x_003_004,\n", - " 0.000166541595134695*x_001_001**2*x_003_003**3*x_003_005,\n", - " 0.000249812392702042*x_001_001**2*x_003_003**2*x_003_004**2,\n", - " 0.00099924957080817*x_001_001**2*x_003_003**2*x_003_004*x_003_005,\n", - " 0.00099924957080817*x_001_001**2*x_003_003**2*x_003_005**2,\n", - " 1.06555671175858*x_001_001**2*x_003_003**2,\n", - " 0.00033308319026939*x_001_001**2*x_003_003*x_003_004**3,\n", - " 0.00199849914161634*x_001_001**2*x_003_003*x_003_004**2*x_003_005,\n", - " 0.00399699828323268*x_001_001**2*x_003_003*x_003_004*x_003_005**2,\n", - " 4.26222684703434*x_001_001**2*x_003_003*x_003_004,\n", - " 0.00266466552215512*x_001_001**2*x_003_003*x_003_005**3,\n", - " 8.52445369406868*x_001_001**2*x_003_003*x_003_005,\n", - " 0.000166541595134695*x_001_001**2*x_003_004**4,\n", - " 0.00133233276107756*x_001_001**2*x_003_004**3*x_003_005,\n", - " 0.00399699828323268*x_001_001**2*x_003_004**2*x_003_005**2,\n", - " 4.26222684703434*x_001_001**2*x_003_004**2,\n", - " 0.00532933104431024*x_001_001**2*x_003_004*x_003_005**3,\n", - " 17.0489073881374*x_001_001**2*x_003_004*x_003_005,\n", - " 0.00266466552215512*x_001_001**2*x_003_005**4,\n", - " 17.0489073881374*x_001_001**2*x_003_005**2,\n", - " -0.133194588969823*x_001_001*x_002_001*x_003_001*x_004_001,\n", - " -0.266389177939646*x_001_001*x_002_001*x_003_001*x_004_002,\n", - " -0.532778355879292*x_001_001*x_002_001*x_003_001*x_004_003,\n", - " -1.06555671175858*x_001_001*x_002_001*x_003_001*x_004_004,\n", - " -2.13111342351717*x_001_001*x_002_001*x_003_001*x_004_005,\n", - " -0.266389177939646*x_001_001*x_002_001*x_003_002*x_004_001,\n", - " -0.532778355879292*x_001_001*x_002_001*x_003_002*x_004_002,\n", - " -1.06555671175858*x_001_001*x_002_001*x_003_002*x_004_003,\n", - " -2.13111342351717*x_001_001*x_002_001*x_003_002*x_004_004,\n", - " -4.26222684703434*x_001_001*x_002_001*x_003_002*x_004_005,\n", - " -0.532778355879292*x_001_001*x_002_001*x_003_003*x_004_001,\n", - " -1.06555671175858*x_001_001*x_002_001*x_003_003*x_004_002,\n", - " -2.13111342351717*x_001_001*x_002_001*x_003_003*x_004_003,\n", - " -4.26222684703434*x_001_001*x_002_001*x_003_003*x_004_004,\n", - " -8.52445369406868*x_001_001*x_002_001*x_003_003*x_004_005,\n", - " -1.06555671175858*x_001_001*x_002_001*x_003_004*x_004_001,\n", - " -2.13111342351717*x_001_001*x_002_001*x_003_004*x_004_002,\n", - " -4.26222684703434*x_001_001*x_002_001*x_003_004*x_004_003,\n", - " -8.52445369406868*x_001_001*x_002_001*x_003_004*x_004_004,\n", - " -17.0489073881374*x_001_001*x_002_001*x_003_004*x_004_005,\n", - " -2.13111342351717*x_001_001*x_002_001*x_003_005*x_004_001,\n", - " -4.26222684703434*x_001_001*x_002_001*x_003_005*x_004_002,\n", - " -8.52445369406868*x_001_001*x_002_001*x_003_005*x_004_003,\n", - " -17.0489073881374*x_001_001*x_002_001*x_003_005*x_004_004,\n", - " -34.0978147762747*x_001_001*x_002_001*x_003_005*x_004_005,\n", - " -4.06595691246814e-8*x_001_001*x_003_001**4,\n", - " -3.25276552997451e-7*x_001_001*x_003_001**3*x_003_002,\n", - " -6.50553105994902e-7*x_001_001*x_003_001**3*x_003_003,\n", - " -1.3011062119898e-6*x_001_001*x_003_001**3*x_003_004,\n", - " -2.60221242397961e-6*x_001_001*x_003_001**3*x_003_005,\n", - " -9.75829658992353e-7*x_001_001*x_003_001**2*x_003_002**2,\n", - " -3.90331863596941e-6*x_001_001*x_003_001**2*x_003_002*x_003_003,\n", - " -7.80663727193883e-6*x_001_001*x_003_001**2*x_003_002*x_003_004,\n", - " -1.56132745438777e-5*x_001_001*x_003_001**2*x_003_002*x_003_005,\n", - " -3.90331863596941e-6*x_001_001*x_003_001**2*x_003_003**2,\n", - " -1.56132745438777e-5*x_001_001*x_003_001**2*x_003_003*x_003_004,\n", - " -3.12265490877553e-5*x_001_001*x_003_001**2*x_003_003*x_003_005,\n", - " -1.56132745438777e-5*x_001_001*x_003_001**2*x_003_004**2,\n", - " -6.24530981755106e-5*x_001_001*x_003_001**2*x_003_004*x_003_005,\n", - " -6.24530981755106e-5*x_001_001*x_003_001**2*x_003_005**2,\n", - " 0.00260183459246837*x_001_001*x_003_001**2*x_005_001,\n", - " 0.00520366918493674*x_001_001*x_003_001**2*x_005_002,\n", - " 0.0104073383698735*x_001_001*x_003_001**2*x_005_003,\n", - " 0.020814676739747*x_001_001*x_003_001**2*x_005_004,\n", - " 0.0416293534794939*x_001_001*x_003_001**2*x_005_005,\n", - " -0.10629063718497*x_001_001*x_003_001**2,\n", - " -1.3011062119898e-6*x_001_001*x_003_001*x_003_002**3,\n", - " -7.80663727193883e-6*x_001_001*x_003_001*x_003_002**2*x_003_003,\n", - " -1.56132745438777e-5*x_001_001*x_003_001*x_003_002**2*x_003_004,\n", - " -3.12265490877553e-5*x_001_001*x_003_001*x_003_002**2*x_003_005,\n", - " -1.56132745438777e-5*x_001_001*x_003_001*x_003_002*x_003_003**2,\n", - " -6.24530981755106e-5*x_001_001*x_003_001*x_003_002*x_003_003*x_003_004,\n", - " -0.000124906196351021*x_001_001*x_003_001*x_003_002*x_003_003*x_003_005,\n", - " -6.24530981755106e-5*x_001_001*x_003_001*x_003_002*x_003_004**2,\n", - " -0.000249812392702042*x_001_001*x_003_001*x_003_002*x_003_004*x_003_005,\n", - " -0.000249812392702042*x_001_001*x_003_001*x_003_002*x_003_005**2,\n", - " 0.0104073383698735*x_001_001*x_003_001*x_003_002*x_005_001,\n", - " 0.020814676739747*x_001_001*x_003_001*x_003_002*x_005_002,\n", - " 0.0416293534794939*x_001_001*x_003_001*x_003_002*x_005_003,\n", - " 0.0832587069589879*x_001_001*x_003_001*x_003_002*x_005_004,\n", - " 0.166517413917976*x_001_001*x_003_001*x_003_002*x_005_005,\n", - " -0.425162548739881*x_001_001*x_003_001*x_003_002,\n", - " -1.04088496959184e-5*x_001_001*x_003_001*x_003_003**3,\n", - " -6.24530981755106e-5*x_001_001*x_003_001*x_003_003**2*x_003_004,\n", - " -0.000124906196351021*x_001_001*x_003_001*x_003_003**2*x_003_005,\n", - " -0.000124906196351021*x_001_001*x_003_001*x_003_003*x_003_004**2,\n", - " -0.000499624785404085*x_001_001*x_003_001*x_003_003*x_003_004*x_003_005,\n", - " -0.000499624785404085*x_001_001*x_003_001*x_003_003*x_003_005**2,\n", - " 0.020814676739747*x_001_001*x_003_001*x_003_003*x_005_001,\n", - " 0.0416293534794939*x_001_001*x_003_001*x_003_003*x_005_002,\n", - " 0.0832587069589879*x_001_001*x_003_001*x_003_003*x_005_003,\n", - " 0.166517413917976*x_001_001*x_003_001*x_003_003*x_005_004,\n", - " 0.333034827835952*x_001_001*x_003_001*x_003_003*x_005_005,\n", - " -0.850325097479763*x_001_001*x_003_001*x_003_003,\n", - " -8.32707975673475e-5*x_001_001*x_003_001*x_003_004**3,\n", - " -0.000499624785404085*x_001_001*x_003_001*x_003_004**2*x_003_005,\n", - " -0.00099924957080817*x_001_001*x_003_001*x_003_004*x_003_005**2,\n", - " 0.0416293534794939*x_001_001*x_003_001*x_003_004*x_005_001,\n", - " 0.0832587069589879*x_001_001*x_003_001*x_003_004*x_005_002,\n", - " 0.166517413917976*x_001_001*x_003_001*x_003_004*x_005_003,\n", - " 0.333034827835952*x_001_001*x_003_001*x_003_004*x_005_004,\n", - " 0.666069655671903*x_001_001*x_003_001*x_003_004*x_005_005,\n", - " -1.70065019495953*x_001_001*x_003_001*x_003_004,\n", - " -0.00066616638053878*x_001_001*x_003_001*x_003_005**3,\n", - " 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-0.333034827835952*x_004_002*x_004_004*x_006_004,\n", - " -0.666069655671903*x_004_002*x_004_004*x_006_005,\n", - " 1.06555671175858*x_004_002*x_004_004,\n", - " 0.00033308319026939*x_004_002*x_004_005**3,\n", - " 0.0832587069589879*x_004_002*x_004_005*x_005_001,\n", - " 0.166517413917976*x_004_002*x_004_005*x_005_002,\n", - " 0.333034827835952*x_004_002*x_004_005*x_005_003,\n", - " 0.666069655671903*x_004_002*x_004_005*x_005_004,\n", - " 1.33213931134381*x_004_002*x_004_005*x_005_005,\n", - " -0.0832587069589879*x_004_002*x_004_005*x_006_001,\n", - " -0.166517413917976*x_004_002*x_004_005*x_006_002,\n", - " -0.333034827835952*x_004_002*x_004_005*x_006_003,\n", - " -0.666069655671903*x_004_002*x_004_005*x_006_004,\n", - " -1.33213931134381*x_004_002*x_004_005*x_006_005,\n", - " 2.13111342351717*x_004_002*x_004_005,\n", - " 0.911346237716428*x_004_002,\n", - " 2.60221242397961e-6*x_004_003**4,\n", - " 2.08176993918369e-5*x_004_003**3*x_004_004,\n", - " 4.16353987836737e-5*x_004_003**3*x_004_005,\n", - " 6.24530981755106e-5*x_004_003**2*x_004_004**2,\n", - " 0.000249812392702042*x_004_003**2*x_004_004*x_004_005,\n", - " 0.000249812392702042*x_004_003**2*x_004_005**2,\n", - " 0.020814676739747*x_004_003**2*x_005_001,\n", - " 0.0416293534794939*x_004_003**2*x_005_002,\n", - " 0.0832587069589879*x_004_003**2*x_005_003,\n", - " 0.166517413917976*x_004_003**2*x_005_004,\n", - " 0.333034827835952*x_004_003**2*x_005_005,\n", - " -0.020814676739747*x_004_003**2*x_006_001,\n", - " -0.0416293534794939*x_004_003**2*x_006_002,\n", - " -0.0832587069589879*x_004_003**2*x_006_003,\n", - " ...]" - ] - }, - "execution_count": 622, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p = net.qubo.create_polynom(np.array(net.qubo.x))\n", - "pp = p.T @ p\n", - "pp[0].expand().as_ordered_terms()" - ] - }, - { - "cell_type": "code", - "execution_count": 624, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x_005_001 -1186.7559218989402\n", - "x_005_002 -2207.018607585602\n", - "x_005_003 -3748.0642703220883\n", - "x_005_004 -4832.236761247715\n", - "x_005_005 991.0935950904168\n" - ] - } - ], - "source": [ - "for k, v in net.qubo.qubo_dict.linear.items():\n", - " if k.startswith('x_005'):\n", - " print(k,v)" - ] - }, - { - "cell_type": "code", - "execution_count": 625, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('x_002_001', 'x_004_003') -3.6453849508657123\n", - "('x_004_003*x_002_001', 'x_004_003') 0.0\n", - "('x_004_003*x_002_001', 'x_002_001') 0.0\n", - "('x_004_004', 'x_004_003') 2.1312799651123044\n", - "('x_004_004', 'x_002_001') -7.290769901731425\n", - "('x_004_004', 'x_004_003*x_002_001') -1.7763568394002505e-15\n", - "('x_004_004*x_002_001', 'x_002_001') 0.0\n", - "('x_004_004*x_002_001', 'x_004_004') 0.0\n", - "('x_003_003', 'x_004_003') -0.5327783558792923\n", - "('x_003_003', 'x_004_003*x_002_001') 1.0655567117585847\n", - "('x_003_003', 'x_004_004') -1.0655567117585847\n", - "('x_003_003', 'x_004_004*x_002_001') 2.1311134235171694\n", - "('x_003_003', 'x_001_001') -0.6350934832009412\n", - "('x_001_001*x_003_003', 'x_004_003') 1.0655567117585847\n", - "('x_001_001*x_003_003', 'x_004_003*x_002_001') -2.1311134235171694\n", - "('x_001_001*x_003_003', 'x_004_004') 2.1311134235171694\n", - "('x_001_001*x_003_003', 'x_004_004*x_002_001') -4.262226847034339\n", - "('x_001_001*x_003_003', 'x_001_001') 0.0\n", - "('x_001_001*x_003_003', 'x_003_003') 0.0\n", - "('x_004_001', 'x_004_003') 0.2663929186200057\n", - "('x_004_001', 'x_002_001') -0.9113462377164281\n", - "('x_004_001', 'x_004_003*x_002_001') 0.0\n", - "('x_004_001', 'x_004_004') 0.5328034021738732\n", - "('x_004_001', 'x_004_004*x_002_001') -4.440892098500626e-16\n", - "('x_004_001', 'x_003_003') -0.13319458896982309\n", - "('x_004_001', 'x_001_001*x_003_003') 0.26638917793964617\n", - "('x_004_001*x_002_001', 'x_002_001') 0.0\n", - "('x_004_001*x_002_001', 'x_003_003') 0.26638917793964617\n", - "('x_004_001*x_002_001', 'x_001_001*x_003_003') -0.5327783558792923\n", - "('x_004_001*x_002_001', 'x_004_001') 0.0\n", - "('x_003_002', 'x_004_003') -0.26638917793964617\n", - "('x_003_002', 'x_004_003*x_002_001') 0.5327783558792923\n", - "('x_003_002', 'x_004_004') -0.5327783558792923\n", - "('x_003_002', 'x_004_004*x_002_001') 1.0655567117585847\n", - "('x_003_002', 'x_001_001') -0.1587733708002353\n", - "('x_003_002', 'x_003_003') 0.5839463283898126\n", - "('x_003_002', 'x_001_001*x_003_003') -0.6350934832009412\n", - "('x_003_002', 'x_004_001') -0.06659729448491154\n", - "('x_003_002', 'x_004_001*x_002_001') 0.13319458896982309\n", - "('x_001_001*x_003_002', 'x_004_003') 0.5327783558792923\n", - "('x_001_001*x_003_002', 'x_004_003*x_002_001') -1.0655567117585847\n", - "('x_001_001*x_003_002', 'x_004_004') 1.0655567117585847\n", - "('x_001_001*x_003_002', 'x_004_004*x_002_001') -2.1311134235171694\n", - "('x_001_001*x_003_002', 'x_001_001') 0.0\n", - "('x_001_001*x_003_002', 'x_004_001') 0.13319458896982309\n", - "('x_001_001*x_003_002', 'x_004_001*x_002_001') -0.26638917793964617\n", - "('x_001_001*x_003_002', 'x_003_002') 0.0\n", - "('x_004_005', 'x_004_003') 4.2631844612063645\n", - "('x_004_005', 'x_002_001') -14.58153980346285\n", - "('x_004_005', 'x_004_003*x_002_001') 3.552713678800501e-15\n", - "('x_004_005', 'x_004_004') 8.527118359590835\n", - "('x_004_005', 'x_004_004*x_002_001') 0.0\n", - "('x_004_005', 'x_003_003') -2.1311134235171694\n", - "('x_004_005', 'x_001_001*x_003_003') 4.262226847034339\n", - "('x_004_005', 'x_004_001') 1.0657395171813695\n", - "('x_004_005', 'x_004_001*x_002_001') 0.0\n", - "('x_004_005', 'x_003_002') -1.0655567117585847\n", - "('x_004_005', 'x_001_001*x_003_002') 2.1311134235171694\n", - "('x_004_002', 'x_004_003') 0.5327887647289884\n", - "('x_004_002', 'x_002_001') -1.8226924754328562\n", - "('x_004_002', 'x_004_003*x_002_001') 0.0\n", - "('x_004_002', 'x_004_004') 1.0656165626443364\n", - "('x_004_002', 'x_004_004*x_002_001') -8.881784197001252e-16\n", - "('x_004_002', 'x_003_003') -0.26638917793964617\n", - "('x_004_002', 'x_001_001*x_003_003') 0.5327783558792923\n", - "('x_004_002', 'x_004_001') 0.1331952395229291\n", - "('x_004_002', 'x_004_001*x_002_001') 0.0\n", - "('x_004_002', 'x_003_002') -0.13319458896982309\n", - "('x_004_002', 'x_001_001*x_003_002') 0.26638917793964617\n", - "('x_004_002', 'x_004_005') 2.1315141642304627\n", - "('x_004_005*x_004_002', 'x_004_003') 0.00034349203996530834\n", - "('x_004_005*x_004_002', 'x_002_001') 0.0\n", - "('x_004_005*x_004_002', 'x_004_003*x_002_001') 2.168404344971009e-19\n", - "('x_004_005*x_004_002', 'x_004_004') 0.0008118902762816378\n", - "('x_004_005*x_004_002', 'x_004_004*x_002_001') 0.0\n", - "('x_004_005*x_004_002', 'x_004_001') 7.416305408341884e-05\n", - "('x_004_005*x_004_002', 'x_004_001*x_002_001') 0.0\n", - "('x_004_005*x_004_002', 'x_004_005') 0.0\n", - "('x_004_005*x_004_002', 'x_004_002') 0.0\n", - "('x_003_005', 'x_004_003') -2.1311134235171694\n", - "('x_003_005', 'x_004_003*x_002_001') 4.262226847034339\n", - "('x_003_005', 'x_004_004') -4.262226847034339\n", - "('x_003_005', 'x_004_004*x_002_001') 8.524453694068677\n", - "('x_003_005', 'x_001_001') -10.161495731215059\n", - "('x_003_005', 'x_003_003') 4.6724449704929585\n", - "('x_003_005', 'x_001_001*x_003_003') -5.080747865607531\n", - "('x_003_005', 'x_004_001') -0.5327783558792923\n", - "('x_003_005', 'x_004_001*x_002_001') 1.0655567117585847\n", - "('x_003_005', 'x_003_002') 2.3361444188737597\n", - "('x_003_005', 'x_001_001*x_003_002') -2.5403739328037647\n", - "('x_003_005', 'x_004_005') -8.524453694068677\n", - "('x_003_005', 'x_004_002') -1.0655567117585847\n", - "('x_001_001*x_003_005', 'x_004_003') 4.262226847034339\n", - "('x_001_001*x_003_005', 'x_004_003*x_002_001') -8.524453694068677\n", - "('x_001_001*x_003_005', 'x_004_004') 8.524453694068677\n", - "('x_001_001*x_003_005', 'x_004_004*x_002_001') -17.048907388137355\n", - "('x_001_001*x_003_005', 'x_001_001') 0.0\n", - "('x_001_001*x_003_005', 'x_004_001') 1.0655567117585847\n", - "('x_001_001*x_003_005', 'x_004_001*x_002_001') -2.1311134235171694\n", - "('x_001_001*x_003_005', 'x_004_005') 17.048907388137355\n", - "('x_001_001*x_003_005', 'x_004_002') 2.1311134235171694\n", - "('x_001_001*x_003_005', 'x_003_005') 0.0\n", - "('x_003_001', 'x_004_003') -0.13319458896982309\n", - "('x_003_001', 'x_004_003*x_002_001') 0.26638917793964617\n", - "('x_003_001', 'x_004_004') -0.26638917793964617\n", - "('x_003_001', 'x_004_004*x_002_001') 0.5327783558792923\n", - "('x_003_001', 'x_001_001') -0.03969334270005882\n", - "('x_003_001', 'x_003_003') 0.2919717004504178\n", - "('x_003_001', 'x_001_001*x_003_003') -0.3175467416004706\n", - "('x_003_001', 'x_004_001') -0.03329864724245577\n", - "('x_003_001', 'x_004_001*x_002_001') 0.06659729448491154\n", - "('x_003_001', 'x_003_002') 0.14598463043813517\n", - "('x_003_001', 'x_001_001*x_003_002') -0.15877337080023524\n", - "('x_003_001', 'x_004_005') -0.5327783558792923\n", - "('x_003_001', 'x_004_002') -0.06659729448491154\n", - "('x_003_001', 'x_003_005') 1.168054644503018\n", - "('x_003_001', 'x_001_001*x_003_005') -1.2701869664018823\n", - "('x_003_004', 'x_004_003') -1.0655567117585847\n", - "('x_003_004', 'x_004_003*x_002_001') 2.1311134235171694\n", - "('x_003_004', 'x_004_004') -2.1311134235171694\n", - "('x_003_004', 'x_004_004*x_002_001') 4.262226847034339\n", - "('x_003_004', 'x_001_001') -2.5403739328037647\n", - "('x_003_004', 'x_003_003') 2.3359102197556014\n", - "('x_003_004', 'x_001_001*x_003_003') -2.5403739328037647\n", - "('x_003_004', 'x_004_001') -0.26638917793964617\n", - "('x_003_004', 'x_004_001*x_002_001') 0.5327783558792923\n", - "('x_003_004', 'x_003_002') 1.1679316899659848\n", - "('x_003_004', 'x_001_001*x_003_002') -1.2701869664018823\n", - "('x_003_004', 'x_004_005') -4.262226847034339\n", - "('x_003_004', 'x_004_002') -0.5327783558792923\n", - "('x_003_004', 'x_003_005') 9.345639378164023\n", - "('x_003_004', 'x_001_001*x_003_005') -10.161495731215059\n", - "('x_003_004', 'x_003_001') 0.5839609658346975\n", - "('x_003_001*x_003_004', 'x_001_001') -0.6350934832009412\n", - "('x_003_001*x_003_004', 'x_003_003') 5.074314226760236e-05\n", - "('x_003_001*x_003_004', 'x_001_001*x_003_003') 0.0\n", - "('x_003_001*x_003_004', 'x_003_002') 2.146825249783177e-05\n", - "('x_003_001*x_003_004', 'x_001_001*x_003_002') 1.3552527156068805e-20\n", - "('x_003_001*x_003_004', 'x_003_005') 0.00039033186359694125\n", - "('x_003_001*x_003_004', 'x_001_001*x_003_005') 0.0\n", - "('x_003_001*x_003_004', 'x_003_001') 0.0\n", - "('x_003_001*x_003_004', 'x_003_004') 0.0\n", - "('x_004_005*x_002_001', 'x_002_001') 0.0\n", - "('x_004_005*x_002_001', 'x_003_003') 4.262226847034339\n", - "('x_004_005*x_002_001', 'x_001_001*x_003_003') -8.524453694068677\n", - "('x_004_005*x_002_001', 'x_003_002') 2.1311134235171694\n", - "('x_004_005*x_002_001', 'x_001_001*x_003_002') -4.262226847034339\n", - "('x_004_005*x_002_001', 'x_004_005') 0.0\n", - "('x_004_005*x_002_001', 'x_003_005') 17.048907388137355\n", - "('x_004_005*x_002_001', 'x_001_001*x_003_005') -34.09781477627471\n", - "('x_004_005*x_002_001', 'x_003_001') 1.0655567117585847\n", - "('x_004_005*x_002_001', 'x_003_004') 8.524453694068677\n", - "('x_002_001*x_004_002', 'x_002_001') 0.0\n", - "('x_002_001*x_004_002', 'x_003_003') 0.5327783558792923\n", - "('x_002_001*x_004_002', 'x_001_001*x_003_003') -1.0655567117585847\n", - "('x_002_001*x_004_002', 'x_003_002') 0.26638917793964617\n", - "('x_002_001*x_004_002', 'x_001_001*x_003_002') -0.5327783558792923\n", - "('x_002_001*x_004_002', 'x_004_002') 0.0\n", - "('x_002_001*x_004_002', 'x_003_005') 2.1311134235171694\n", - "('x_002_001*x_004_002', 'x_001_001*x_003_005') -4.262226847034339\n", - "('x_002_001*x_004_002', 'x_003_001') 0.13319458896982309\n", - "('x_002_001*x_004_002', 'x_003_004') 1.0655567117585847\n", - "('x_004_001*x_004_004', 'x_004_003') 5.074314226760236e-05\n", - "('x_004_001*x_004_004', 'x_004_003*x_002_001') 0.0\n", - "('x_004_001*x_004_004', 'x_004_004') 0.0\n", - "('x_004_001*x_004_004', 'x_004_001') 0.0\n", - "('x_004_001*x_004_004', 'x_004_005') 0.00039033186359694125\n", - "('x_004_001*x_004_004', 'x_004_002') 2.146825249783177e-05\n", - "('x_004_001*x_004_004', 'x_004_005*x_004_002') 6.24530981755106e-05\n", - "('x_001_001*x_003_001', 'x_004_003') 0.26638917793964617\n", - "('x_001_001*x_003_001', 'x_004_003*x_002_001') -0.5327783558792923\n", - "('x_001_001*x_003_001', 'x_004_004') 0.5327783558792923\n", - "('x_001_001*x_003_001', 'x_004_004*x_002_001') -1.0655567117585847\n", - "('x_001_001*x_003_001', 'x_001_001') 0.0\n", - "('x_001_001*x_003_001', 'x_004_001') 0.06659729448491154\n", - "('x_001_001*x_003_001', 'x_004_001*x_002_001') -0.13319458896982309\n", - "('x_001_001*x_003_001', 'x_004_005') 1.0655567117585847\n", - "('x_001_001*x_003_001', 'x_004_002') 0.13319458896982309\n", - "('x_001_001*x_003_001', 'x_003_001') 0.0\n", - "('x_001_001*x_003_001', 'x_004_005*x_002_001') -2.1311134235171694\n", - "('x_001_001*x_003_001', 'x_002_001*x_004_002') -0.26638917793964617\n", - "('x_001_001*x_003_004', 'x_004_003') 2.1311134235171694\n", - "('x_001_001*x_003_004', 'x_004_003*x_002_001') -4.262226847034339\n", - "('x_001_001*x_003_004', 'x_004_004') 4.262226847034339\n", - "('x_001_001*x_003_004', 'x_004_004*x_002_001') -8.524453694068677\n", - "('x_001_001*x_003_004', 'x_001_001') 0.0\n", - "('x_001_001*x_003_004', 'x_004_001') 0.5327783558792923\n", - "('x_001_001*x_003_004', 'x_004_001*x_002_001') -1.0655567117585847\n", - "('x_001_001*x_003_004', 'x_004_005') 8.524453694068677\n", - "('x_001_001*x_003_004', 'x_004_002') 1.0655567117585847\n", - "('x_001_001*x_003_004', 'x_003_004') 0.0\n", - "('x_001_001*x_003_004', 'x_004_005*x_002_001') -17.048907388137355\n", - "('x_001_001*x_003_004', 'x_002_001*x_004_002') -2.1311134235171694\n", - "('x_003_002*x_003_005', 'x_003_003') 0.00034349203996530834\n", - "('x_003_002*x_003_005', 'x_001_001*x_003_003') 2.168404344971009e-19\n", - "('x_003_002*x_003_005', 'x_003_002') 0.0\n", - "('x_003_002*x_003_005', 'x_003_005') 0.0\n", - "('x_003_002*x_003_005', 'x_003_001') 7.416305408341884e-05\n", - "('x_003_002*x_003_005', 'x_003_004') 0.0008118902762816378\n", - "('x_003_002*x_003_005', 'x_003_001*x_003_004') 6.24530981755106e-05\n", - "('x_004_003*x_002_001*x_004_002', 'x_004_003*x_002_001') 0.0\n", - "('x_004_003*x_002_001*x_004_002', 'x_004_004') 0.0\n", - "('x_004_003*x_002_001*x_004_002', 'x_004_001') 0.0\n", - "('x_004_003*x_002_001*x_004_002', 'x_004_002') 0.0\n", - "('x_004_003*x_002_001*x_004_002', 'x_004_001*x_004_004') 0.0\n", - "('x_004_004*x_004_003', 'x_004_003') 0.0\n", - "('x_004_004*x_004_003', 'x_004_004') 0.0\n", - "('x_004_004*x_004_003', 'x_004_005') 0.0017486867489142968\n", - "('x_004_004*x_004_003', 'x_004_002') 0.00010929292180714355\n", - "('x_004_004*x_004_003', 'x_004_005*x_004_002') 0.0002498123927020424\n", - "('x_004_005*x_004_003*x_002_001', 'x_004_003*x_002_001') 0.0\n", - "('x_004_005*x_004_003*x_002_001', 'x_004_004') 0.0\n", - "('x_004_005*x_004_003*x_002_001', 'x_004_001') 0.0\n", - "('x_004_005*x_004_003*x_002_001', 'x_004_005') 0.0\n", - "('x_004_005*x_004_003*x_002_001', 'x_004_001*x_004_004') 0.0\n", - "('x_004_001*x_004_004*x_002_001', 'x_004_004*x_002_001') 0.0\n", - "('x_004_001*x_004_004*x_002_001', 'x_004_001') 0.0\n", - "('x_004_001*x_004_004*x_002_001', 'x_004_005') 0.0\n", - "('x_004_001*x_004_004*x_002_001', 'x_004_002') 1.3552527156068805e-20\n", - "('x_004_001*x_004_004*x_002_001', 'x_004_005*x_004_002') 0.0\n", - "('x_004_001*x_004_003', 'x_004_003') 0.0\n", - "('x_004_001*x_004_003', 'x_004_001') 0.0\n", - "('x_004_001*x_004_003', 'x_004_005') 0.00016393938271071532\n", - "('x_004_001*x_004_003', 'x_004_002') 6.830807612946472e-06\n", - "('x_004_001*x_004_003', 'x_004_005*x_004_002') 3.12265490877553e-05\n", - "('x_004_005*x_004_003', 'x_004_003') 0.0\n", - "('x_004_005*x_004_003', 'x_004_005') 0.0\n", - "('x_004_005*x_004_003', 'x_004_001*x_004_004') 0.0001249061963510212\n", - "('x_004_003*x_004_002', 'x_004_003') 0.0\n", - "('x_004_003*x_004_002', 'x_004_002') 0.0\n", - "('x_004_003*x_004_002', 'x_004_001*x_004_004') 1.561327454387765e-05\n", - "('x_004_004*x_004_003*x_002_001', 'x_004_003*x_002_001') 0.0\n", - "('x_004_004*x_004_003*x_002_001', 'x_004_004') 0.0\n", - "('x_004_004*x_004_003*x_002_001', 'x_004_005*x_004_002') 0.0\n", - "('x_004_001*x_004_003*x_002_001', 'x_004_003*x_002_001') 0.0\n", - "('x_004_001*x_004_003*x_002_001', 'x_004_001') 0.0\n", - "('x_004_001*x_004_003*x_002_001', 'x_004_005*x_004_002') 0.0\n", - "('x_004_005*x_004_004*x_002_001', 'x_004_004*x_002_001') 0.0\n", - "('x_004_005*x_004_004*x_002_001', 'x_004_005') 0.0\n", - "('x_004_004*x_004_002', 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"('x_006_004', 'x_006_001') 665.9729448491154\n", - "('x_006_004', 'x_006_002') 1331.9458896982308\n", - "('x_006_004', 'x_006_003') 2663.8917793964615\n", - "('x_006_005', 'x_004_003') -0.33303482783595156\n", - "('x_006_005', 'x_004_003*x_002_001') 0.6660696556719031\n", - "('x_006_005', 'x_004_004') -1.3321393113438063\n", - "('x_006_005', 'x_004_004*x_002_001') 2.6642786226876125\n", - "('x_006_005', 'x_004_001') -0.020814676739746973\n", - "('x_006_005', 'x_004_001*x_002_001') 0.041629353479493945\n", - "('x_006_005', 'x_004_005') -5.328557245375225\n", - "('x_006_005', 'x_004_002') -0.08325870695898789\n", - "('x_006_005', 'x_004_005*x_004_002') -1.3321393113438063\n", - "('x_006_005', 'x_004_005*x_002_001') 10.65711449075045\n", - "('x_006_005', 'x_002_001*x_004_002') 0.16651741391797578\n", - "('x_006_005', 'x_004_001*x_004_004') -0.33303482783595156\n", - "('x_006_005', 'x_004_003*x_002_001*x_004_002') 0.6660696556719031\n", - "('x_006_005', 'x_004_004*x_004_003') -1.3321393113438063\n", - "('x_006_005', 'x_004_005*x_004_003*x_002_001') 5.328557245375225\n", - "('x_006_005', 'x_004_001*x_004_004*x_002_001') 0.6660696556719031\n", - "('x_006_005', 'x_004_001*x_004_003') -0.16651741391797578\n", - "('x_006_005', 'x_004_005*x_004_003') -2.6642786226876125\n", - "('x_006_005', 'x_004_003*x_004_002') -0.33303482783595156\n", - "('x_006_005', 'x_004_004*x_004_003*x_002_001') 2.6642786226876125\n", - "('x_006_005', 'x_004_001*x_004_003*x_002_001') 0.33303482783595156\n", - "('x_006_005', 'x_004_005*x_004_004*x_002_001') 10.65711449075045\n", - "('x_006_005', 'x_004_004*x_004_002') -0.6660696556719031\n", - "('x_006_005', 'x_004_004*x_002_001*x_004_002') 1.3321393113438063\n", - "('x_006_005', 'x_004_004*x_004_005') -5.328557245375225\n", - "('x_006_005', 'x_004_001*x_004_005') -0.6660696556719031\n", - "('x_006_005', 'x_004_001*x_002_001*x_004_002') 0.16651741391797578\n", - "('x_006_005', 'x_004_005*x_004_002*x_002_001') 2.6642786226876125\n", - "('x_006_005', 'x_004_001*x_004_002') -0.08325870695898789\n", - "('x_006_005', 'x_004_005*x_004_001*x_002_001') 1.3321393113438063\n", - "('x_006_005', 'x_005_001') -1331.9458896982308\n", - "('x_006_005', 'x_005_002') -2663.8917793964615\n", - "('x_006_005', 'x_005_003') -5327.783558792923\n", - "('x_006_005', 'x_005_004') -10655.567117585846\n", - "('x_006_005', 'x_005_005') -21311.134235171692\n", - "('x_006_005', 'x_006_001') 1331.9458896982308\n", - "('x_006_005', 'x_006_002') 2663.8917793964615\n", - "('x_006_005', 'x_006_003') 5327.783558792923\n", - "('x_006_005', 'x_006_004') 10655.567117585846\n" - ] - } - ], - "source": [ - "for k, v in net.qubo.qubo_dict.quadratic.items():\n", - " # if k.startswith('x_005'):\n", - " print(k,v)" + "# nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", + "# energies = np.zeros((2**nqbit, 2**nqbit))\n", + "\n", + "# for data1 in tqdm(itertools.product([0, 1], repeat=nqbit)):\n", + "# for data2 in itertools.product([0, 1], repeat=nqbit):\n", + "# for data3 in itertools.product([0, 1], repeat=nqbit):\n", + "# for data4 in itertools.product([0, 1], repeat=nqbit):\n", + "# # print(list(data))\n", + "# mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", + "# mod_bin_rep_sol[2] = list(data1)[::-1]\n", + "# mod_bin_rep_sol[3] = list(data2)[::-1]\n", + "# mod_bin_rep_sol[4] = list(data3)[::-1]\n", + "# mod_bin_rep_sol[5] = list(data4)[::-1]\n", + "# # x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", + "# # x0 = list(x.values())\n", + "# energies = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + "# if energies <= eref:\n", + "# print(energies-eref)\n", + "# print(data1)\n", + "# print(data2)\n", + "# print(data3)\n", + "# print(data4)\n", + "\n", + "\n" ] }, { @@ -2636,7 +850,7 @@ }, { "cell_type": "code", - "execution_count": 626, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -2647,884 +861,884 @@ }, { "cell_type": "code", - "execution_count": 627, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{('x_002_001', 'x_004_003'): -3.6453849508657123,\n", - " ('x_004_003*x_002_001', 'x_004_003'): 0.0,\n", - " ('x_004_003*x_002_001', 'x_002_001'): 0.0,\n", - " ('x_004_004', 'x_004_003'): 2.1312799651123044,\n", + "{('x_002_001', 'x_004_001'): -0.9113462377164281,\n", + " ('x_004_001*x_002_001', 'x_004_001'): 0.0,\n", + " ('x_004_001*x_002_001', 'x_002_001'): 0.0,\n", + " ('x_004_004', 'x_004_001'): 0.5328034021738732,\n", " ('x_004_004', 'x_002_001'): -7.290769901731425,\n", - " ('x_004_004', 'x_004_003*x_002_001'): -1.7763568394002505e-15,\n", + " ('x_004_004', 'x_004_001*x_002_001'): -4.440892098500626e-16,\n", " ('x_004_004*x_002_001', 'x_002_001'): 0.0,\n", " ('x_004_004*x_002_001', 'x_004_004'): 0.0,\n", - " ('x_003_003', 'x_004_003'): -0.5327783558792923,\n", - " ('x_003_003', 'x_004_003*x_002_001'): 1.0655567117585847,\n", + " ('x_003_003', 'x_004_001'): -0.13319458896982309,\n", + " ('x_003_003', 'x_004_001*x_002_001'): 0.26638917793964617,\n", " ('x_003_003', 'x_004_004'): -1.0655567117585847,\n", " ('x_003_003', 'x_004_004*x_002_001'): 2.1311134235171694,\n", " ('x_003_003', 'x_001_001'): -0.6350934832009412,\n", - " ('x_001_001*x_003_003', 'x_004_003'): 1.0655567117585847,\n", - " ('x_001_001*x_003_003', 'x_004_003*x_002_001'): -2.1311134235171694,\n", + " ('x_001_001*x_003_003', 'x_004_001'): 0.26638917793964617,\n", + " ('x_001_001*x_003_003', 'x_004_001*x_002_001'): -0.5327783558792923,\n", " ('x_001_001*x_003_003', 'x_004_004'): 2.1311134235171694,\n", " ('x_001_001*x_003_003', 'x_004_004*x_002_001'): -4.262226847034339,\n", " ('x_001_001*x_003_003', 'x_001_001'): 0.0,\n", " ('x_001_001*x_003_003', 'x_003_003'): 0.0,\n", - " ('x_004_001', 'x_004_003'): 0.2663929186200057,\n", - " ('x_004_001', 'x_002_001'): -0.9113462377164281,\n", - " ('x_004_001', 'x_004_003*x_002_001'): 0.0,\n", - " ('x_004_001', 'x_004_004'): 0.5328034021738732,\n", - " ('x_004_001', 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('x_001_001*x_003_002', 'x_004_003'): 0.5327783558792923,\n", - " ('x_001_001*x_003_002', 'x_004_003*x_002_001'): -1.0655567117585847,\n", - " ('x_001_001*x_003_002', 'x_004_004'): 1.0655567117585847,\n", - " ('x_001_001*x_003_002', 'x_004_004*x_002_001'): -2.1311134235171694,\n", - " ('x_001_001*x_003_002', 'x_001_001'): 0.0,\n", - " ('x_001_001*x_003_002', 'x_004_001'): 0.13319458896982309,\n", - " ('x_001_001*x_003_002', 'x_004_001*x_002_001'): -0.26638917793964617,\n", - " ('x_001_001*x_003_002', 'x_003_002'): 0.0,\n", - " ('x_004_005', 'x_004_003'): 4.2631844612063645,\n", - " ('x_004_005', 'x_002_001'): -14.58153980346285,\n", - " ('x_004_005', 'x_004_003*x_002_001'): 3.552713678800501e-15,\n", - " ('x_004_005', 'x_004_004'): 8.527118359590835,\n", - " ('x_004_005', 'x_004_004*x_002_001'): 0.0,\n", - " ('x_004_005', 'x_003_003'): -2.1311134235171694,\n", - " ('x_004_005', 'x_001_001*x_003_003'): 4.262226847034339,\n", - " ('x_004_005', 'x_004_001'): 1.0657395171813695,\n", - " ('x_004_005', 'x_004_001*x_002_001'): 0.0,\n", - " ('x_004_005', 'x_003_002'): -1.0655567117585847,\n", - " ('x_004_005', 'x_001_001*x_003_002'): 2.1311134235171694,\n", - " ('x_004_002', 'x_004_003'): 0.5327887647289884,\n", + " ('x_004_002', 'x_004_001'): 0.1331952395229291,\n", " ('x_004_002', 'x_002_001'): -1.8226924754328562,\n", - " ('x_004_002', 'x_004_003*x_002_001'): 0.0,\n", + " ('x_004_002', 'x_004_001*x_002_001'): 0.0,\n", " ('x_004_002', 'x_004_004'): 1.0656165626443364,\n", " ('x_004_002', 'x_004_004*x_002_001'): -8.881784197001252e-16,\n", " ('x_004_002', 'x_003_003'): -0.26638917793964617,\n", " ('x_004_002', 'x_001_001*x_003_003'): 0.5327783558792923,\n", - " ('x_004_002', 'x_004_001'): 0.1331952395229291,\n", - " ('x_004_002', 'x_004_001*x_002_001'): 0.0,\n", - " ('x_004_002', 'x_003_002'): -0.13319458896982309,\n", - " ('x_004_002', 'x_001_001*x_003_002'): 0.26638917793964617,\n", - " ('x_004_002', 'x_004_005'): 2.1315141642304627,\n", - " ('x_004_005*x_004_002', 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" ('x_006_002', 'x_006_002'): 166.49323621227884,\n", " ('x_006_003', 'x_006_003'): 665.9729448491154,\n", @@ -3532,7 +1746,7 @@ " ('x_006_005', 'x_006_005'): 10655.567117585846}" ] }, - "execution_count": 627, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -3543,7 +1757,7 @@ }, { "cell_type": "code", - "execution_count": 628, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -3553,7 +1767,7 @@ }, { "cell_type": "code", - "execution_count": 629, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -3562,7 +1776,7 @@ }, { "cell_type": "code", - "execution_count": 630, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -3571,7 +1785,7 @@ }, { "cell_type": "code", - "execution_count": 631, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py index 6527bb8..5e52ff5 100644 --- a/wntr_quantum/sampler/simulated_annealing.py +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -104,22 +104,16 @@ class SimulatedAnnealing: # noqa: D101 def __init__(self): # noqa: D107 self.properties = {} - def optimize_value(self, variables=["sign", "flow", "pressure"]): - """_summary_. - - Args: - variables (list, optional): _description_. Defaults to ['sign', 'flow', 'pressure']. - """ - def sample( self, bqm, num_sweeps=100, Temp=[1e5, 1e-3], Tschedule=None, - x0=None, + init_sample=None, take_step=None, save_traj=False, + verbose=False, ): """Sample the problem. @@ -128,84 +122,89 @@ def sample( num_sweeps (int, optional): _description_. Defaults to 100. Temp (list, optional): _description_. Defaults to [1e5, 1e-3]. Tschedule (list, optional): The temperature schedule - x0 (_type_, optional): _description_. Defaults to None. + init_sample (_type_, optional): _description_. Defaults to None. take_step (_type_, optional): _description_. Defaults to None. save_traj (bool, optional): save the trajectory. Defaults to False + verbose(bool, optional): """ - def bqm_energy(x, var_names): + def bqm_energy(bqm, input, var_names): # noqa: D417 """Compute the energy of a given binary array. Args: + bqm (bqm) x (_type_): _description_ var_names (list): list of var names """ - return bqm.energies(as_samples((x, var_names))) + return bqm.energies(as_samples((input, var_names))) + + self.bqm = bqm # check that take_step is callable if not callable(take_step): raise ValueError("take_step must be callable") # define th variable names - var_names = sorted(bqm.variables) + self.var_names = sorted(self.bqm.variables) # define the initial state - if x0 is None: - x = np.random.randint(2, size=bqm.num_variables) + if init_sample is None: + current_sample = np.random.randint(2, size=bqm.num_variables) else: - x = x0 + current_sample = init_sample # define the energy range if Tschedule is None: Tschedule = np.linspace(Temp[0], Temp[1], num_sweeps) - # initialize the energy - energies = [] - energies.append(bqm_energy(x, var_names)) - # init the traj - trajectory = None + trajectory = [] if save_traj: - trajectory = [] - trajectory.append(x) + trajectory.append(current_sample) - # step scheduling - # step_schedule = ( - # Tschedule / ((Tschedule[0] - Tschedule[-1]) / (take_step.step_size - 1)) + 1 - # ) + # initialize the energy + energies = [] + e_current = bqm_energy(self.bqm, current_sample, self.var_names) + energies.append(e_current) # loop over the temp schedule for T in tqdm(Tschedule): - # original point - x_ori = deepcopy(x) - e_ori = bqm_energy(x, var_names) - # new point - # take_step.step_size = int(s) - x_new = take_step(x) - e_new = bqm_energy(x, var_names) + new_sample = take_step(deepcopy(current_sample)) + e_new = bqm_energy(self.bqm, new_sample, self.var_names) # accept/reject - if e_new < e_ori: - x = x_new - energies.append(bqm_energy(x, var_names)) - if save_traj: - trajectory.append(x) + if e_new < e_current: + if verbose: + print("E : %f => %f" % (e_current, e_new)) + current_sample = deepcopy(new_sample) + e_current = e_new + else: - if T != 0: - p = np.exp(-(e_new - e_ori) / T) - else: - p = 0.0 - if np.random.rand() < p: - x = x_new - energies.append(bqm_energy(x, var_names)) - if save_traj: - trajectory.append(x) - else: - x = x_ori - energies.append(bqm_energy(x, var_names)) - if save_traj: - trajectory.append(x) + if verbose: + print("E : %f => %f" % (e_current, e_new)) + + p = np.exp((e_current - e_new) / (T + 1e-12)) + eps = np.random.rand() + # if verbose: + # print( + # "Temp: %f, eps: %f, p: %f, accepted %r" % (T, eps, p, eps < p) + # ) + if eps < p: + current_sample = deepcopy(new_sample) + e_current = e_new - return SimulatedAnnealingResults(x, energies, trajectory) + else: + if verbose: + print("rejected") + pass + + if save_traj: + trajectory.append(current_sample) + energies.append(e_current) + + if verbose: + # print(current_sample) + print("-----------------") + return SimulatedAnnealingResults(current_sample, energies, trajectory) diff --git a/wntr_quantum/sampler/step/full_random.py b/wntr_quantum/sampler/step/full_random.py index 1e93433..963b201 100644 --- a/wntr_quantum/sampler/step/full_random.py +++ b/wntr_quantum/sampler/step/full_random.py @@ -38,8 +38,7 @@ def __call__(self, x): width = len(data) # determine the max val - max_val = np.ones_like(data) - max_val = int("".join([str(i) for i in max_val[::-1]]), base=2) + max_val = int("1" * width, base=2) # check if we reach min/max val max_val_check = data.prod() == 1 @@ -50,8 +49,10 @@ def __call__(self, x): # determine sign of the displacement if min_val_check: + # print("min val reached") sign = 1 elif max_val_check: + # print("max val reached") sign = -1 else: sign = 2 * np.random.randint(2) - 1 @@ -70,6 +71,7 @@ def __call__(self, x): # convert back to binary repr new_data = np.array([int(i) for i in new_val])[::-1] + print(random_val_name, data, "=>", new_data) # inject in the x vector for ix, nd in zip(idx, new_data): From 1a485a3811f29cdb3d870da7849e35f80c3be49c Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 31 Oct 2024 15:49:55 +0100 Subject: [PATCH 75/96] added script for statistic --- .../notebooks/encoded_reference_solutions.pkl | Bin 0 -> 6321 bytes docs/notebooks/plot_test_qubo_solver.ipynb | 146 ++ docs/notebooks/qubo_poly_solver_Net0.ipynb | 2293 +++++++++-------- docs/notebooks/solutions.pkl | Bin 0 -> 6321 bytes docs/notebooks/test_qubo_poly_solver.py | 179 ++ wntr_quantum/sampler/simulated_annealing.py | 41 +- wntr_quantum/sampler/step/full_random.py | 7 +- 7 files changed, 1562 insertions(+), 1104 deletions(-) create mode 100644 docs/notebooks/encoded_reference_solutions.pkl create mode 100644 docs/notebooks/plot_test_qubo_solver.ipynb create mode 100644 docs/notebooks/solutions.pkl create mode 100644 docs/notebooks/test_qubo_poly_solver.py diff --git a/docs/notebooks/encoded_reference_solutions.pkl b/docs/notebooks/encoded_reference_solutions.pkl new file mode 100644 index 0000000000000000000000000000000000000000..72c39762558d72dd2b99ca36c4b95f17de538c65 GIT binary patch literal 6321 zcmZo*nYv7Z0SscNX!MBYmF5;y>LuqFrRwFD=9FY678NB{PU+!^FG@|$&nqq|Dork# zGI>f5D_G%_9`?Kxh?2=uyct@jI5Q?qX`d1_MZ=rXo27M121^fXN=aowDo6`cn#GjP z4u~vs52MW#KR-XO|3CmHyctTSBy~C~9ITt88#!sEJ&aCI0|6@s?(2&SY`M%GrT~>? zFlVrVY|G$4wo71C_h_h%rk2rMGFmzymB6E=;%HqmT8ECd9Y)(sqiw}08KbqqXl*cB m8;sTlqqV_kZ7^CJjE*y{v>&YvMr(u7+F-Oc7#g)fsvZFKutZ7# literal 0 HcmV?d00001 diff --git a/docs/notebooks/plot_test_qubo_solver.ipynb b/docs/notebooks/plot_test_qubo_solver.ipynb new file mode 100644 index 0000000..a8d2a6e --- /dev/null +++ b/docs/notebooks/plot_test_qubo_solver.ipynb @@ -0,0 +1,146 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt \n", + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "solution = pickle.load(open('solutions.pkl','rb'))\n", + "ref = pickle.load(open('encoded_reference_solutions.pkl','rb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_solutions(solutions, references):\n", + " fig = plt.figure(figsize=plt.figaspect(0.5))\n", + " ax1 = fig.add_subplot(121)\n", + "\n", + " ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + " ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + " ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + " ax1.grid()\n", + "\n", + " for r, sol in zip(references, solutions):\n", + " ax1.scatter(\n", + " r[:2], sol[:2], s=150, lw=1, edgecolors=\"w\", label=\"Sampled solution\"\n", + " )\n", + "\n", + " ax1.set_xlabel(\"Reference Values\", fontsize=12)\n", + " ax1.set_ylabel(\"QUBO Values\", fontsize=12)\n", + " ax1.set_title(\"Flow Rate\", fontsize=14)\n", + "\n", + " ax2 = fig.add_subplot(122)\n", + "\n", + " ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + " ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + " ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "\n", + " for r, sol in zip(references, solutions):\n", + " ax2.scatter(\n", + " r[2:],\n", + " sol[2:],\n", + " s=150,\n", + " lw=1,\n", + " edgecolors=\"w\",\n", + " label=\"Sampled solution\",\n", + " )\n", + " ax2.grid()\n", + "\n", + " ax2.set_xlabel(\"Reference Values\", fontsize=12)\n", + " ax2.set_title(\"Pressure\", fontsize=14)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plt.hist(np.array(solution)[:,3],bins=25)\n", + "idx = 2\n", + "plt.hist(np.array(solution)[:,idx],bins=50)\n", + "plt.vlines(ref[0][idx],0, 17,colors='black', ls='--')\n", + "plt.vlines(ref[0][idx]*0.9,0, 17,colors='grey', ls='--')\n", + "plt.vlines(ref[0][idx]*1.1,0, 17,colors='grey', ls='--')\n", + "plt.ylim([0,17])\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/qubo_poly_solver_Net0.ipynb b/docs/notebooks/qubo_poly_solver_Net0.ipynb index 46c3e1b..ed44514 100644 --- a/docs/notebooks/qubo_poly_solver_Net0.ipynb +++ b/docs/notebooks/qubo_poly_solver_Net0.ipynb @@ -9,14 +9,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 106, "metadata": { "metadata": {} }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -27,10 +27,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -42,6 +42,7 @@ "import matplotlib.pyplot as plt\n", "# Create a water network model\n", "inp_file = './networks/Net0_CM.inp'\n", + "inp_file = './networks/Net0.inp'\n", "# inp_file = './networks/Net2LoopsDW.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)\n", "\n", @@ -58,12 +59,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 107, "metadata": {}, "outputs": [ { "data": { - "image/png": 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3r3mH3EyiEmyUju6ddSrtlmMSY5lgRUS+rWWqWa54M0/MalZptxzDJUVE5OtEUWu3eDM/ncw6Xp06f+nw2He8ADObiYgkST6Byss3rrq5nEhrYzkRGHhdjoclEJGv8/WsZrkpZY1Kf+nw6KnmBJnAe7LsPIxG+6d2EBF5MlHUwCRT2rJl5Jo1axATE4OAgACkpKRg//79su23bduGhIQEBAQEYMiQIdi5c6dF/cyZMyEIgkXJyMiweq/GxkYMGzYMgiCguLjYbl+1fgJ0NoqWW0a6XqegAMT0CrdaZzKJKDlzQeEeEREpS4LGbnHG1q1bkZOTg9zcXBw8eBBJSUlIT09HdXW11fZ79+7FtGnTMGvWLPzwww/IzMxEZmYmjhw5YtEuIyMDFy5cMJePPvrI6v1efPFFREXZ3ov/TrYzmm8WNfLowAsws5mIfJurD0lYvnw5Zs+ejaysLAwcOBBr165FUFAQ1q1bZ7X9ypUrkZGRgfnz5yMxMRGLFy/G8OHDsXr1aot2er0eBoPBXLp169bqXp999hm++OILvPXWWw731xOTqzw+8HLPZiLyZY4uJ6qrq7MojY2Nre5lNBpRVFSEtLQ08zWNRoO0tDQUFhZafX5hYaFFewBIT09v1b6goADh4eEYMGAAnnnmGdTU1FjUV1VVYfbs2fjwww8RFOT4sa5cTtQB5JYUHS1hZjMReTdHA290dDRCQkLMZcmSJa3udenSJZhMJkRERFhcj4iIQGVlpdXnV1ZW2m2fkZGBjRs3Ij8/H0uXLsWePXswceJEmEymW59BwsyZMzFnzhyMGDHCqc/fktVsq6iRR2c1AxzxEpFva0mikqsHgIqKCgQHB5uv6/XKbak7depU85+HDBmCoUOHIjY2FgUFBZgwYQJWrVqFq1ev4qWXXnL63lqdBK3OevqyFupMa/b4Ea/cYQnHT51Dc7NJwd4QESnL0RFvcHCwRbEWeMPCwqDValFVVWVxvaqqCgaD9S16DQaDU+0BoF+/fggLC0NJSQkAYPfu3SgsLIRer4dOp0P//v0BACNGjMCMGTNkP7+gkS9qpNJuOS6kSydERXS3WtdobMLpiiqrdURE3sCVyVX+/v5ITk5Gfn7+bfcXkZ+fj9TUVKs/k5qaatEeAHbt2mWzPQCcPXsWNTU1iIyMBAC8/fbbOHToEIqLi1FcXGxejrR161b813/9l2yfNVpJtqiRx081Azenm89XXbZad7S0AnF9HU9NJyLyJK7eQCMnJwczZszAiBEjcM8992DFihVoaGhAVlYWAGD69Ono2bOn+R3x888/jzFjxmDZsmV48MEHsWXLFhw4cADvvvsuAKC+vh6vvvoqJk2aBIPBgNLSUrz44ovo378/0tPTAQC9e/e26EPnzp0BALGxsejVy/asJgAIGgkaGztlCCrdQcPjR7wAd7AiIt8loWXbSBvFyftNmTIFb731FhYuXIhhw4ahuLgYeXl55gSq8vJyXLjw7z0SRo4cic2bN+Pdd99FUlISPv74Y2zfvh2DBw8GAGi1Whw+fBi/+MUvEB8fj1mzZiE5ORnffPONS94zC4LMVLM6c6u8ZMTLwxKIyEeJkgaiZHsMJVdnS3Z2NrKzs63WFRQUtLo2efJkTJ482Wr7wMBAfP755049PyYmBpKDm0xrdBI0NpKrNCrdqNo7Am//3jbreFgCEXk1e0f/+fJezSqd0/WKwCs31Xy09CxEUYRGrf8FiIjawddPJxIECYJg4x2vjesdzSuiUY/QEIR1D7Za13DtBiouXFK4R0REyjCJgt3izVqmmm0VZyxZsgR33303unTpgvDwcGRmZuL48eMWbcaOHdvqwIc5c+Y412enWquY7Nm83MGKiLyUo+t4vZUr1/Hu2bMHc+fOxb59+7Br1y40NTXhgQceQENDg0W72bNnWxz48Pvf/96p53jFVDNwc7r56/0/Wq37qaQCGWOGK9wjIiL3E0UBosyoVq7OG2h1sL1zlZMzzXl5eRb//MEHHyA8PBxFRUUYPXq0+XpQUJDsBiH2+MSI96eScgV7QkSkHAmC3eLNBEjm97ytyq3FVI4cEGFNbW0tAKB7d8tNmjZt2oSwsDAMHjwYL730Eq5du+ZUn71mxMvDEojIF7l6Aw1PIzel3HI9OtoyPuTm5mLRokWy9xVFES+88AJGjRplXpMMAL/61a/Qp08fREVF4fDhw/iP//gPHD9+HH//+98d7rP3BF47hyVIkgRBraupiYjayNenmjUyhyRoxJvX23JAxNy5c3HkyBF8++23Ftefeuop85+HDBmCyMhITJgwAaWlpYiNjXWszw618gCR4d0R3Nn6GY5X6hpQefFnhXtEROR+TK6SZAvg2AERt8vOzsaOHTvw1Vdf2d2yMiUlBQDMBz44wmsCryAInG4mIp/TMuKVK97MlYckSJKE7OxsfPLJJ9i9ezf69u1r92eKi4sBwHzgg0N9dqpXKsezeYnI1/j6iLdl5ypbxRlz587FX/7yF2zevBldunRBZWUlKisrcf36dQBAaWkpFi9ejKKiIpSVleHTTz/F9OnTMXr0aAwdOtTh53jNO16AhyUQke8RbxW5em92+5SytTpnvPPOOwBubpJxu/Xr12PmzJnw9/fHl19+aT6xKTo6GpMmTcKCBQuceo5XBV6OeInI10h2ztyVnDiP1xMJOgGCn/VRveDkNLu9gxmio6OxZ88ep+5pjXcFXtl3vAy8ROR9Wo4FlKv3ZoJGgKCxEXhtXO9oXvWrUO+oHggKtJ6tVl1Ti0uX6xTuERGRe/n6O15oNfJFhdTZqzbSaDRI6Cd3UhFHvUTkXXw9q1nwEyD4aWwUdX52rwq8AHewIiLfIjlQvJpGkC8q5FXveAFmNhORb/H1nasE3c3RrdU6kzrHll4XeJnZTES+xNf3apZ9l6vSd7zeF3iZ2UxEPkSS7GQ1e/lcsydmNXtd4O0XbYC/vw5GY3OrurOVNai7eg3BXazv6UxE5GlESYAoM6qVq/MK/pqbxRqVrmFWZ6/aQafTIj6mp816ZjYTkTfx9eVELSNeW0WNvC7wAsDA/jJLipjZTEReRBTtF6+m0wJ+NopO29G9s8o7A29cb5t1TLAiIm9y8x2v3Ii3o3voXoJWkC1q5HXveAEuKSIi3+HryVWy63VVOtXslYFXbknR0VJONROR95DsJFd5/TteP5l1vM3qnNRVZ6/aKS4mClob67dOV1Th2vVGhXtEROQeLSNeueKsNWvWICYmBgEBAUhJScH+/ftl22/btg0JCQkICAjAkCFDsHPnTov6mTNnQhAEi5KRkWGuLysrw6xZs9C3b18EBgYiNjYWubm5MBqN9jvLvZrVQa/3Q2xvg9U6SZJw/NQ5hXtEROQeJlGwW5yxdetW5OTkIDc3FwcPHkRSUhLS09NRXV1ttf3evXsxbdo0zJo1Cz/88AMyMzORmZmJI0eOWLTLyMjAhQsXzOWjjz4y1x07dgyiKOJPf/oTfvzxR/zhD3/A2rVr8fLLL9vt781jAW3s1axT52jfKwMvYG8Hq3IFe0JE5D4SBLvFGcuXL8fs2bORlZWFgQMHYu3atQgKCsK6deustl+5ciUyMjIwf/58JCYmYvHixRg+fDhWr15t0U6v18NgMJhLt27dzHUZGRlYv349HnjgAfTr1w+/+MUvMG/ePPz973+332GtIF9UyCcDL5cUEZG3cHSqua6uzqI0NrZ+5WY0GlFUVIS0tDTzNY1Gg7S0NBQWFlp9fmFhoUV7AEhPT2/VvqCgAOHh4RgwYACeeeYZ1NTUyH6u2tpadO/e3f6/AA88JMFrA28i92wmIh8gSnaOBbyVXBUdHY2QkBBzWbJkSat7Xbp0CSaTCRERERbXIyIiUFlZafX5lZWVdttnZGRg48aNyM/Px9KlS7Fnzx5MnDgRJpPJ6j1LSkqwatUqPP3003Y/v+CnlS1q5JVZzQAPSyAi3+DocqKKigoEBwebr+v1ejf37N+mTp1q/vOQIUMwdOhQxMbGoqCgABMmTLBoe+7cOWRkZGDy5MmYPXu2/Zt74HIirx3xJsT2giBY/5decuYCjMYmhXtEROR6jk41BwcHWxRrgTcsLAxarRZVVVUW16uqqmAwWE9YNRgMTrUHgH79+iEsLAwlJSUW18+fP49x48Zh5MiRePfddx35+IBGI1+csGTJEtx9993o0qULwsPDkZmZiePHj1u0uXHjBubOnYvQ0FB07twZkyZNavX57XbZqdYeJChQj5he4VbrTCYRJ8suKNwjIiLXazkkQa44yt/fH8nJycjPz//3/UUR+fn5SE1NtfozqampFu0BYNeuXTbbA8DZs2dRU1ODyMhI87Vz585h7NixSE5Oxvr166FxNGhqb20Naa1onZtq3rNnD+bOnYt9+/Zh165daGpqwgMPPICGhgZzm9/85jf4n//5H2zbtg179uzB+fPn8dhjjzn1HK+dagZu7mB1usL6byI/lVRgULztrSWJiDyBq3euysnJwYwZMzBixAjcc889WLFiBRoaGpCVlQUAmD59Onr27Gl+R/z8889jzJgxWLZsGR588EFs2bIFBw4cMI9Y6+vr8eqrr2LSpEkwGAwoLS3Fiy++iP79+yM9PR3Av4Nunz598NZbb+HixYvm/siNnAHIj2ydHPHm5eVZ/PMHH3yA8PBwFBUVYfTo0aitrcWf//xnbN68GePHjwcArF+/HomJidi3bx/uvfdeh57j1YF3YP9o7CwoslrHU4qIyBu0JFHJ1TtjypQpuHjxIhYuXIjKykoMGzYMeXl55gSq8vJyi9HoyJEjsXnzZixYsAAvv/wy4uLisH37dgwePBgAoNVqcfjwYWzYsAFXrlxBVFQUHnjgASxevNg83b1r1y6UlJSgpKQEvXpZbvkr2fvNQSdzGMKt63V1dRaX9Xq9Q++4a2trAcCcXV1UVISmpiaLLO6EhAT07t0bhYWFDLwAMDBOJsHqJAMvEXk+6VaRq3dWdnY2srOzrdYVFBS0ujZ58mRMnjzZavvAwEB8/vnnss+bOXMmZs6c6Ww3b9IIMiPef2d03y43NxeLFi2Sva0oinjhhRcwatQo8y8RlZWV8Pf3R9euXS3aymV9W+PVgTcxlpnNROTdeEiC/anmtmR0z507F0eOHMG3337rkm7ezrsDr8y5vCdOn0dzswk6lZ7XSETkCMnOVLPk5FSzx3Fgqrklk9tR2dnZ2LFjB77++muLqW+DwQCj0YgrV65YjHrtZXHfyWuzmgEgpEsn9DSEWq1rNDbZTLwiIvIUkgPFq7lwOZEkScjOzsYnn3yC3bt3o2/fvhb1ycnJ8PPzs8jiPn78OMrLy2WzuO/k1SNe4GZm87lK61uT/VRSgbi+UQr3iIjIdUTxZpGr92aCRgvBxrIhQePcjObcuXOxefNm/OMf/0CXLl3M721DQkIQGBiIkJAQzJo1Czk5OejevTuCg4Px7LPPIjU11eHEKsDLR7wAd7AiIu8mSYLd4tVcOOJ95513UFtbi7FjxyIyMtJctm7dam7zhz/8AQ899BAmTZqE0aNHw2AwOHaYw228fsQrf1gCAy8ReTYmV7luy0i7S5cABAQEYM2aNVizZo1T976d1wdeHpZARN5MlG4WuXqv5kByldp4feCVHfGWnoUoio5vTUZEpDIc8dpfx6s2Xh9xwroHo0f3EKt11643ouLCJYV7RETkOnzH67p3vEpRZ69cjDtYEZG3EvHv6WarpaM76G62DkiQm4LuYD4ReBNjbW+kwfe8ROTJHD0W0GsJMqNdQZ0hzuvf8QL23vMy8BKR5/L1dbxMrlIp2bW8nGomIg8mQYAEmS0jZeq8giAzsuWIt+PI7dn8U8lZSJIEQfDy/3MSkVfy+axmDwy86uyVi0WGd0dIl05W62qvNqDy4s8K94iIyEVEQJIpXp9dpdUCWp2Nos6pZp8IvIIgYKDcqJfTzUTkoXz+kISWEa+tokLq7JUbcAcrIvJGLclVcsWr2Rzt3ioqpM5euYG9HayIiDwR3/F63jteBl5wxEtEnsvedLK3x10GXhWTzWzmO14i8lA+v45X0AEaG6FMUGeIU+evA27QO6oHggL1VusuXq7Fpct1CveIiKj9fH7nKu7VrF4ajUZ260juYEVEnsjXs5oFQQNB0Noo6gxx6uyVm3AHKyLyNqIo2C1eTaOTLyqkzl65CZcUEZG3YVYzk6tUjUuKiMjbtBz/J1fv1eTW63Idb8fjkiIi8jYc8XreiFedvXKTvtER8Pe3/rvGucoa1F5tULhHRETtI0GCJMmUNqRXrVmzBjExMQgICEBKSgr2798v237btm1ISEhAQEAAhgwZgp07d1rUz5w5E4IgWJSMjAyLNpcvX8bjjz+O4OBgdO3aFbNmzUJ9fb39zrpwy8ivv/4aDz/8MKKioiAIArZv3+7053CETwVenU6LAX172qw/xulmIvIwkvTv6WZrxdkR79atW5GTk4Pc3FwcPHgQSUlJSE9PR3V1tdX2e/fuxbRp0zBr1iz88MMPyMzMRGZmJo4cOWLRLiMjAxcuXDCXjz76yKL+8ccfx48//ohdu3Zhx44d+Prrr/HUU0/Z77ALD0loaGhAUlIS1qxZY7ONvc/hCJ8KvACnm4nIu7h6He/y5csxe/ZsZGVlYeDAgVi7di2CgoKwbt06q+1XrlyJjIwMzJ8/H4mJiVi8eDGGDx+O1atXW7TT6/UwGAzm0q1bN3Pd0aNHkZeXh/fffx8pKSm47777sGrVKmzZsgXnz5+X77ALR7wTJ07E66+/jkcffdRmG7nP4SifC7yymc1cUkREHsbRwFtXV2dRGhsbW93LaDSiqKgIaWlp5msajQZpaWkoLCy0+vzCwkKL9gCQnp7eqn1BQQHCw8MxYMAAPPPMM6ipqbG4R9euXTFixAjztbS0NGg0Gnz//ffy/wIcWE7kyGd3lNzncJTPBV5mNhORN3E08EZHRyMkJMRclixZ0upely5dgslkQkREhMX1iIgIVFZWWn1+ZWWl3fYZGRnYuHEj8vPzsXTpUuzZswcTJ06EyWQy3yM8PNziHjqdDt27d7f5XDPBTnHwszvC3udwlE9lNQOcaiYi7+JoVnNFRQWCg4PN1/V661vousPUqVPNfx4yZAiGDh2K2NhYFBQUYMKECe26d0sSma06wHWf3VWfw+dGvHExkdBqrX/ssrPVuHa97VMQRERKEyXJbgGA4OBgi2It+ISFhUGr1aKqqsrielVVFQwGg9XnGwwGp9oDQL9+/RAWFoaSkhLzPe5M3mpubsbly5dl7wMAIkyyxdHP3hZ3fg5H+Vzg9ff3Q/8+kVbrJEnC8VPnFO4REVHbuTK5yt/fH8nJycjPzzdfE0UR+fn5SE1NtfozqampFu0BYNeuXTbbA8DZs2dRU1ODyMhI8z2uXLmCoqIic5vdu3dDFEWkpKTI9lmSRNniTnd+Dkf5XOAF7E03lyvYEyKi9nF1VnNOTg7ee+89bNiwAUePHsUzzzyDhoYGZGVlAQCmT5+Ol156ydz++eefR15eHpYtW4Zjx45h0aJFOHDgALKzswEA9fX1mD9/Pvbt24eysjLk5+fjkUceQf/+/ZGeng4ASExMREZGBmbPno39+/fju+++Q3Z2NqZOnYqoqCj5z2/nf86or69HcXExiouLAQCnT59GcXExysvLHfocjvK5d7zAzcD7yRf7rNYxs5mIPIncO86WemdMmTIFFy9exMKFC1FZWYlhw4YhLy/PnEBVXl4OzW3H7Y0cORKbN2/GggUL8PLLLyMuLg7bt2/H4MGDAQBarRaHDx/Ghg0bcOXKFURFReGBBx7A4sWLLaZ8N23ahOzsbEyYMAEajQaTJk3C22+/bbe/oiRClKwnN4lOjngPHDiAcePGmf85JycHADBjxgy88847Dn0ORwiSs/9VvMCmfxTg1zl/sFqX+X9S8MnalxXuERGRc+rq6hASEoLnnntO9i/+xsZGvP3226itrbVIMPJ0LZ+/6vIWBAcH2WhzDRHdp6rus/voiLe3zTouKSIiT+LrhySIkklmxOvcMh+l+GTgHdCvJwRBsDoFU3LmAhobm6DX+3VAz4iInOPqqWZPI5dE5e7kqrbyyeSqoEA9YnqFW60zmUScLLOzRRkRkVpIDhQv5srkKqX4ZOAFuIMVEXkHR9fxequWqWZbRY0YeK3gDlZE5ClcvZzI00gQZYsa+eQ7XgBI7N/LZt1PJ7mWl4g8g2RnVOvt73iZXOVBONVMRN7A0b2avdXN19g29mpWtisO89nAmxhrO/AeP30Ozc0m6HTOHaJMRNQh1BphlCC3NSSzmtUluEsQehpCrdYZjc04VWHnKCoiIhXw+eQqBw5JUBufDbyAnenmEk43E5H6+Xxy1a11zLaKGjHw2sDMZiLyBD4feJnV7FkYeInI09mbTvb6qWZmNXsWuSVFRxl4icgD+HpWs9xe1Wrdp9qnA6+9JUWiKFocf0VEpDa+HnibRAFNomCzTo18OqqEdgtGeGiI1bpr1xtRfv6iwj0iInKOr7/jFSVBtqiRTwdeAEiUe897ktPNRKRu9rJ61ZrZ6yqiBJhsFLVONft84OUOVkTkyVreccoVb9YsCrJFjXz6HS/AzGYi8my+/o7XJAkw2ZhStnW9o/l84E2MlTksgYGXiFTO3nSyt081N0NAs40A2wwGXlUaGCe/e5UkSRAEdf7HIyLy9RGvJy4n8vl3vIYe3dA1uJPVutqrDbhQfVnhHhEROU5yoHizlqlmW8UZX3/9NR5++GFERUVBEARs377dol6SJCxcuBCRkZEIDAxEWloaTp486XSffT7wCoLA6WYi8liSCIgyRaUH9LiMSSaxyuRkclVDQwOSkpKwZs0aq/W///3v8fbbb2Pt2rX4/vvv0alTJ6Snp+PGjRtOPcfnAy9gf7qZiEitfH0dr62lRC3FGRMnTsTrr7+ORx99tFWdJElYsWIFFixYgEceeQRDhw7Fxo0bcf78+VYjY3sYeAEM7N/bZh1HvESkZi0Hwdv+n3dzZAONuro6i9LY2Oj0c06fPo3KykqkpaWZr4WEhCAlJQWFhYVO3YuBF1xSRESeS26auaV4syZRvgBAdHQ0QkJCzGXJkiVOP6ey8uYZ7RERERbXIyIizHWO8vmsZsDeYQmcaiYi9bKXQOUrI15bdQBQUVGB4OBg83W9Xq9I32zhiBdAdGQYOgUFWK27eLkWF2tqFe4REZFjJOlmApXN0obIu2bNGsTExCAgIAApKSnYv3+/bPtt27YhISEBAQEBGDJkCHbu3Gmz7Zw5cyAIAlasWGFx/cSJE3jkkUcQFhaG4OBg3Hffffjqq6/s9rX51iEJ1krLzlXBwcEWpS2B12AwAACqqqosrldVVZnrHMXAC0Cj0chmNnPrSCJSK1cvJ9q6dStycnKQm5uLgwcPIikpCenp6aiurrbafu/evZg2bRpmzZqFH374AZmZmcjMzMSRI0datf3kk0+wb98+REVFtap76KGH0NzcjN27d6OoqAhJSUl46KGH7E7jKrVdZt++fWEwGJCfn2++VldXh++//x6pqalO3YuB9xbZwMv3vESkUq7Oal6+fDlmz56NrKwsDBw4EGvXrkVQUBDWrVtntf3KlSuRkZGB+fPnIzExEYsXL8bw4cOxevVqi3bnzp3Ds88+i02bNsHPz8+i7tKlSzh58iT+8z//E0OHDkVcXBzeeOMNXLt2zWoAv50rTyeqr69HcXExiouLAdxMqCouLkZ5eTkEQcALL7yA119/HZ9++in+9a9/Yfr06YiKikJmZqZTz2HgvUVuSRETrIhIrURRslsAxzJ7jUYjioqKLDJ3NRoN0tLSbGbuFhYWWrQHgPT0dIv2oijiiSeewPz58zFo0KBW9wgNDcWAAQOwceNGNDQ0oLm5GX/6058QHh6O5ORk2c9/M4nK1nSz7I+2cuDAAdx111246667AAA5OTm46667sHDhQgDAiy++iGeffRZPPfUU7r77btTX1yMvLw8BAdZfVdrC5KpbmNlMRJ7I0eSq6GjLv+Nyc3OxaNEii2uXLl2CyWSymrl77Ngxq/evrKy0m+m7dOlS6HQ6PPfcc1bvIQgCvvzyS2RmZqJLly7QaDQIDw9HXl4eunXrJvPpXLtl5NixY2X3thYEAa+99hpee+015258BwbeWxJjGXiJyPPYWzLUUtdRmb1FRUVYuXIlDh48aHPfe0mSMHfuXISHh+Obb75BYGAg3n//fTz88MP45z//icjISJv3N0qAzsbnN6o0pZtTzbf0jY6A3t/Pat35qsuovdqgcI+IiOxzNLnKkczesLAwaLVapzJ3DQaDbPtvvvkG1dXV6N27N3Q6HXQ6Hc6cOYPf/va3iImJAQDs3r0bO3bswJYtWzBq1CgMHz4cf/zjHxEYGIgNGzbIf36ZxCq17trFwHuLTqfFgH49bdZzPS8RqZErk6v8/f2RnJxskbkriiLy8/NtZu6mpqZatAeAXbt2mds/8cQTOHz4sDlpqbi4GFFRUZg/fz4+//xzAMC1a9cA3HyffDuNRgPRzg4grtwyUimcar5NYmwvHD5WZrXup5IK3HvXAGU7RERkh71lM86+58zJycGMGTMwYsQI3HPPPVixYgUaGhqQlZUFAJg+fTp69uxp3v3p+eefx5gxY7Bs2TI8+OCD2LJlCw4cOIB3330XwM3EqdDQUItn+Pn5wWAwYMCAm3+npqamolu3bpgxYwYWLlyIwMBAvPfeezh9+jQefPBB2f4aRUBra6pZpbt2MfDeRv6wBL7nJSIVsjeqdTLwTpkyBRcvXsTChQtRWVmJYcOGIS8vz5xAVV5ebjEyHTlyJDZv3owFCxbg5ZdfRlxcHLZv347Bgwc7/MywsDDk5eXhlVdewfjx49HU1IRBgwbhH//4B5KSkmR/1hPP42XgvQ0zm4nI09ibTm7Le87s7GxkZ2dbrSsoKGh1bfLkyZg8ebLD9y8rK2t1bcSIEeapZ2fITSlzqtkDMPASkadx9VSzp2m+7TAEa3VqxOSq2/TvEwmt1vq/kjPnLqLhmnOHHRMRuRvP4/W85CoG3tv4+/shLqb1HqLAzXVmx0+dU7hHRETy7O1V7O0jXqMoyBY1YuC9A6ebiciT+PqI1xN/6eA73jvwsAQi8iTuSK7yJEyu8gI8LIGIPInPJ1eZgCaT7To1YuC9A6eaiciTcMTLEa/HG9CvJwRBsHpCRcmZSjQ2NkGvt76nMxGR0nx9xNskARoby4aaVPrZmVx1h8AAPfpGR1itE0URJ8vOK9wjIiLbJNhJruroDroZlxN5CU43E5GnkBwo3oyB10vIZTYz8BKRmogiYJIpdg738XgtO1dZK2rduYrveK2QG/FySRERqQmTq5hc5RXklxTxXF4iUg9708kqjT0uI4oCRBs7VNm63tEYeK2Qm2o+fvocmptN0Om0CvaIiMg6URQgyAQYtQYfV2lu0kDTZP2tabON6x1Nnb3qYF06B6GXIdRqXVNTM05VVCrcIyIi63w9uaplxGurOGPRokUQBMGiJCQkuLzPHPHaMDAuGmcra6zW/XSyAvF9eyrcIyKi1kwiAJkkIpNKE4xcxdSssTmyNTU7P7YcNGgQvvzyS/M/63SuD5Mc8drAJUVE5Ak44nXdiBe4GWgNBoO5hIWFubzPDLw2JMbKZDaXMsGKiNTB148FdCTw1tXVWZTGxkab9zt58iSioqLQr18/PP744ygvL3d5nxl4bZDNbD7JES8RqcPN4CrIlI7uoXs1NwmyBQCio6MREhJiLkuWLLF6r5SUFHzwwQfIy8vDO++8g9OnT+P+++/H1atXXdpnvuO1QfZ4wNIKiKIIjYa/txBRxxIlQPDhvZodWU5UUVGB4OBg83W9Xm+1/cSJE81/Hjp0KFJSUtCnTx/89a9/xaxZs1zWZwZeG0K7BSM8NATVNbWt6q7fMKL8/EXE9LK+pzMRkVJEO8lV3r5zVVOTBrCRXNV063pwcLBF4HVU165dER8fj5KSknb18U4cssmQTbDidDMRqYDPJ1dJMu94pfatYa6vr0dpaSkiIyNd1NubGHhlyO9gxcBLRB3P15OrJJnEKsnJrOZ58+Zhz549KCsrw969e/Hoo49Cq9Vi2rRpLu0zp5plyGU2M/ASkRqYJAGSzMiuvaM+tWtu0gA61+xcdfbsWUybNg01NTXo0aMH7rvvPuzbtw89evRwRVfNGHhlyB6WwCVFRKQCop35ZG8f8bpyr+YtW7a4okt2MfDKsLekSJIkCIJ3/zZJROrGwGs7wKo1sYzveGVEhHVFt5DOVuvq6q/hfNVlhXtERGSpWRLsFmetWbMGMTExCAgIQEpKCvbv3y/bftu2bUhISEBAQACGDBmCnTt32mw7Z84cCIKAFStWtKr73//9X6SkpCAwMBDdunVDZmam3b42N2lkixqps1cqIQiC3fW8REQdSYSd5Con77d161bk5OQgNzcXBw8eRFJSEtLT01FdXW21/d69ezFt2jTMmjULP/zwAzIzM5GZmYkjR460avvJJ59g3759iIqKalX3t7/9DU888QSysrJw6NAhfPfdd/jVr35l//O7MavZXRh47eCSIiJSs2YJaJIpzU5ONS9fvhyzZ89GVlYWBg4ciLVr1yIoKAjr1q2z2n7lypXIyMjA/PnzkZiYiMWLF2P48OFYvXq1Rbtz587h2WefxaZNm+Dn52f5GZqb8fzzz+PNN9/EnDlzEB8fj4EDB+KXv/yl3f6aZEa7Jo54PROXFBGRmpkk+wVwbL9io9GIoqIipKWlma9pNBqkpaWhsLDQ6vMLCwst2gNAenq6RXtRFPHEE09g/vz5GDRoUKt7HDx4EOfOnYNGo8Fdd92FyMhITJw40eqo+U6uPiRBCQy8dvCwBCJSs2bJfgEc26/40qVLMJlMiIiw3JUvIiIClZXWzyGvrKy0237p0qXQ6XR47rnnrN7j1KlTAG6eh7tgwQLs2LED3bp1w9ixY3H5sp1cGg9cxMysZjs44iUiNWuUBAgy7zJb1vg6ul+xqxUVFWHlypU4ePCgzVUg4q3041deeQWTJk0CAKxfvx69evXCtm3b8PTTT9u8v7ZJhFZr4012kzrTmjnitSM6MgydOwVYrbt0uQ4XrezlTESkGAmQZErLUqOW/YpbirXAGxYWBq1Wi6qqKovrVVVVMBgMVh9vMBhk23/zzTeorq5G7969odPpoNPpcObMGfz2t79FTEwMAJi3ZBw4cKD5Hnq9Hv369bN7LJ8gStDYKIJKR7wMvHYIgoCEfnKZzZxuJqIOJBd1zdHXMf7+/khOTkZ+fr75miiKyM/PR2pqqtWfSU1NtWgPALt27TK3f+KJJ3D48GEUFxebS1RUFObPn4/PP/8cAJCcnAy9Xo/jx4+b79HU1ISysjL06dNHts9akwhts41iUueIl1PNDhjYPxoH/mX9dIqfSsox+p7WyQJERMqwdxSCc6O+nJwczJgxAyNGjMA999yDFStWoKGhAVlZWQCA6dOno2fPnuZ3xM8//zzGjBmDZcuW4cEHH8SWLVtw4MABvPvuuwCA0NBQhIaGWjzDz88PBoMBAwYMAHBzND5nzhzk5uYiOjoaffr0wZtvvgkAmDx5smx/NSZAY7L+GTUmpz66Yhh4HWBvBysiog4jSYAkM7JzYsQLAFOmTMHFixexcOFCVFZWYtiwYcjLyzMnUJWXl1ucRT5y5Ehs3rwZCxYswMsvv4y4uDhs374dgwcPduq5b775JnQ6HZ544glcv34dKSkp2L17N7p16yb7cy3Tyrbq1EiQJCf/q/igT7/8Ho88/d9W69JGJWHXxtcU7hER+bq6ujqEhIQAg54EtP62G5qMwI/vo7a2tk1n0qpVy+cfnb0VOn2Q1TbNjdfw9eopqvvsHPE6YGD/3jbrmNlMRB1KEu2MeNX5ntNVPHHEy8DrgL7R4dD7+6HR2NSq7nzVZVypq0fXYOt7OhMRuZXUDEgyebJSs3J96QC6ZhE6jY1fLprV+UsHs5odoNVqkSC3Z3MJM5uJqIO0jHjlije7tWzIWlHrBhoMvA6SPyyBgZeIOogLlxN5IltreOWmoDsap5odxMMSiEiVfHyqWdskQitYH9WLKt25ioHXQdw6kohUycXLiTyNRhShsXHiva3rHY2B10E8l5eI1EmE/Km76gw+rsKsZi/Wv08kdDotmptbb4VSdrYaDdduoFOQ9T2diYjcRjQBgsxUs6jS7ZtcRNssM9XMrGbP5u/vh7iYSJv1x5hgRUQdwcezml2dXLVmzRrExMQgICAAKSkp2L9/v+v77PI7ejGezUtE6mMCJJkC7x7x6ppE2eKMrVu3IicnB7m5uTh48CCSkpKQnp6O6upql/aZgdcJspnNTLAioo7g4yNeiJBZx+vcrZYvX47Zs2cjKysLAwcOxNq1axEUFIR169a5tMt8x+sELikiItUxNclnLovevZzIZLyGZhtTyqbm6wBu7ut8O71e3+o8YqPRiKKiIrz00kvmaxqNBmlpaSgsLHRpnxl4nZDYn5nNRKQO/v7+MBgMqCzfZbetwWCAv7/MQQoeqOXz/+2LF2Tbde7cGdHRloOm3NxcLFq0yOLapUuXYDKZzKcwtYiIiMCxY8dc0WUzBl4nDOjXE4IgwNqBTiVnKtHY2AS93q8DekZEviYgIACnT5+G0Wi029bf3x8BAd616sLRzy9JEgRBsLh252hXaQy8TggM0KNfdARKyytb1YmiiBNl5zBkQIzyHSMinxQQEOB1AdUZrvz8YWFh0Gq1qKqqsrheVVUFg8Hgkme0YHKVk+R2sOJhCUREnsnf3x/JycnIz883XxNFEfn5+UhNTXXpsxh4nSS3pIiZzUREnisnJwfvvfceNmzYgKNHj+KZZ55BQ0MDsrKyXPocTjU7iUuKiIi805QpU3Dx4kUsXLgQlZWVGDZsGPLy8lolXLWXIFnLFCKb/nn4JO55dJ7VukFxvXEkb5XCPSIiIk/CqWYnJfTrabPuRNl5q3s5ExERtWDgdVKXzkGIjgyzWtfU1IzS8gsK94iIiDwJA28bcAcrIiJqKwbeNpDfwYpLioiIyDYG3jYY2L+3zTpmNhMRkRwG3jbgkiIiImorBt42kJtqPlZ6FqLo5cdwERFRmzHwtkH3rl0QEdbVat31G0acOXdR2Q4REZHHYOBtI043ExFRWzDwtpH8YQkMvEREZB0Dbxslxtp+z8sRLxER2cLA20acaiYiorZg4G2jgXG21/IeLTkLnj1BRETWMPC2UXhoCLqFdLZaV1d/DeerLivcIyIi8gQMvG0kCAKnm4mIyGkMvO3AwxKIiMhZDLztIH9YAgMvERG1xsDbDpxqJiIiZzHwtgMDLxEROYuBtx16RYahc6cAq3U1P1/FxZpahXtERERqx8DbDoIgIDGWo14iInIcA287cbqZiIicwcDbTnKBl4clEBHRnRh420luSRFHvEREdCcG3nbiVDMRETmDgbedYnqFI0Dvb7XuQvXPuFJXr3CPiIhIzRh420mr1WJAv54264+WnFWwN0REpHYMvC7A6WYiInIUA68LMPASEZGjGHhdQPawBE41ExHRbRh4XYAjXiIichQDrwv07xMJnU5rte7MuWrUN1xXuEdERKRWDLwu4OenQ1xMpM3646fOKdgbIiJSMwZeF+F0MxEROYKB10UYeImIyBEMvC4if1gCM5uJiOgmBl4XSeSIl4iIHKDr6A54i/i+UdBoNJBMJkQ0SYgxmtDLKCJAlCDUnMa+ee+gx7D+CE2OR8iAaAga/s5DpARJFFF74ixqik7g53+dRlPdNQCArksgug/py+8kKU6QJEnq6E54g7rS85g/7lkMOn8FXUT5tvruXdB/+gMY8MwvEBJne/MNImq7utLzOPbOpyjZ8Dkaa+pk2/I7SUpi4G2nGxevYN9zq3D6r3sAZ/9VCgJiJt2Pe1c9i8CI7u7pIJGP4XeS1I6Btx1Ob9uDwrkr0Xiptl330YcG4963n0W/aeNd1DMi38TvJHkCBt42Kn5tI35YtMGl90x65dcYvjjLpfck8hX8TpKnYDZBGxQv/tDlX3AAOPRff8HBhetdfl8ib8fvJHkSBl4nlX+6Fz/kfuC2+x96/S8o+9vXbrs/kbfhd5I8DaeandB4uQ6fDJ6F65WX3fqcgPCuePTIOgSEhbj1OUSejt9J8kQc8Tph/7y1bv+CA8CN6iv4/jdr3P4cIk/H7yR5IgZeB127UIPSv3yp2PNOb/kK9RXVij2PyNPwO0meioHXQcff/V9IzSbFnieZRBz/0w7FnkfkafidJE/FwOugko1fKP/MDZ8r/kwiT8HvJHkq7tXsgOvVP6P+9AWnfuY56Rv8HqlYhX/hFOowFlGYIsQ5dY9r5y6h4dxFdOrZw6mfI/J2jn4nn5S+Qi90QhNE+EOLsYjCGKEnAOCUVIcPcRxnUY+5GIJhQpjd+/E7Sa7AwOuASwdOtOnntNDgF4jBeTSgGtfbdI9xKbNwqItfm36WyFsNrm9CtgPtgqDDIuEeAECNdAOrcBiSBIwVeqIr/JGFBHwO504Pu3TgBAMvtQsDrwOu/FjWpp/zEzQYgG64KN1o87PDrzfBqBfa/PNE3ij8WpPTPxMqBGCKFIctOImx6InuQgC6IwAaJxdUXvmxDH0eGeX084la8B2vA5ob2h4420svcpk10Z30bdx+oA+6oBLX2vXs5vq2zV4RtWDgdYTQcSNOhl2i1iS09Tvpgm8Uz+2lduL/gxzgH9Kpw559XcNpZqI7XW/j31zlqEckgtr1bP/g9v08EQOvA7oN7ddhz67w538ioju15XtxWbqBrSjBeLTvoPvuSbHt+nkiJlc5IHS4c8uATJII3a2psJekfaiHEc2QsF+qxitIRnchwOF7ne/sD38dgy/R7S5oNQDs515cQzNypf1ovrWcaBx6YrQQBQCokOqxAofQgCYcQg0ipEC8Ioywe8/Q5Pj2dp98HAOvA/RdO6Pr4L64cuS0Q+3PoQFhCAQALBHubfNzQwZE4/LRD9r880Te7JOhT9r9Tr4vjLNZFy10xjI4l50cMiCaByVQu3Eo5aD4WRMdaveNdB7v4ic8gr7tfmbcrP/b7nsQeStHv5OuxO8kuQKPBXRQ45V6/LXXFDRfU2ZpkTbAH7+s2IKAUP52TWQNv5PkqTjidZC+a2ckzH1EseclzHmYX3AiGfxOkqfiiNcJzTeM+HT406g9Vu7W53Tp3xOZxe9CF+R4EhaRL+J3kjwRR7xO0AX447518yHotG57hqDV4P518/kFJ3IAv5PkiRh4nRR+70Dcv/5FCO7YvUYQMOr9eYi4b4jr703kpfidJE/DwNsGsY+n4f6N/+nS37IFrQb3rZuPuBnpLrsnka/gd5I8Cd/xtsOlA8fxTdbv23x6UYuQhN64f/2L6JGS6JqOEfkofifJEzDwtpPJ2IRDr/8FR1d9AmNtg1M/6xfcCYn//y+QtHA6dAH+buohkW/hd5LUjoHXRZoaruPUpnycXJ+HmoMnITY1W20n6LQIvas/4rImIvbXafDrHKhwT4l8A7+TpFYMvG5gajTi8uFT+PnwKTRdvXl2p1+XQHQb0g/dhvbjb9JECuN3ktSEgZeIiEhBzGomIiJSEAMvERGRghh4iYiIFMTAS0REpCAGXiIiIgUx8BIRESmIgZeIiEhBDLxEREQKYuAlIiJSEAMvERGRghh4iYiIFMTAS0REpCAGXiIiIgUx8BIRESmIgZeIiEhBDLxEREQKYuAlIiJSEAMvERGRghh4iYiIFMTAS0REpCAGXiIiIgUx8BIRESmIgZeIiEhBDLxEREQKYuAlIiJS0P8DTADhpK3PFE4AAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -77,7 +78,7 @@ "" ] }, - "execution_count": 19, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -98,16 +99,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" + "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" ] }, - "execution_count": 20, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -128,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 109, "metadata": {}, "outputs": [], "source": [ @@ -137,15 +138,15 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 110, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 0.000000 => 200.000000 (res: 6.451613)\n", - "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.129032)\n" + "Head Encoding : 0.000000 => 200.000000 (res: 1.574803)\n", + "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.031496)\n" ] } ], @@ -154,11 +155,11 @@ "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", - "nqbit = 5\n", + "nqbit = 7\n", "step = (4./(2**nqbit-1))\n", "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", "\n", - "nqbit = 5\n", + "nqbit = 7\n", "step = (200/(2**nqbit-1))\n", "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", @@ -176,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -190,10 +191,10 @@ { "data": { "text/plain": [ - "array([1., 1., 1., 1.])" + "array([1. , 1. , 0.999, 0.998])" ] }, - "execution_count": 23, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -210,16 +211,21 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[1, 1, [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [1, 1, 1, 1, 0]]" + "[1,\n", + " 1,\n", + " [0, 0, 0, 1, 1, 1, 0],\n", + " [0, 0, 0, 1, 1, 1, 0],\n", + " [1, 1, 1, 0, 1, 1, 0],\n", + " [0, 0, 0, 0, 1, 1, 0]]" ] }, - "execution_count": 24, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -230,12 +236,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 113, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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VKCcA4KUcDoemTZumyMhIvfzyy6bjABW4WwcAvNDp06c1fPhw/fWvf5Ukff/993I6nSx6hSUwcgIAXmbr1q2KiIjQX//6V9WtW1cLFy7UokWLKCawDMoJAHiJ8vJyvfTSS+rWrZtOnDihNm3aaPfu3UpISKCYwFKY1gEAL3Dy5EkNHTpUKSkpkqSEhAS98cYbql+/vuFkwMUoJwDg4Xbt2qXf/va3OnXqlOrXr6958+Zp2LBhpmMBl0U5AQAP16xZM5WVlemOO+7Q6tWrddttt5mOBFwR5QQAPFxYWJhSUlJ06623qm7duqbjAFdFOQEAL9C+fXvTEYBK424dAABgKZQTAABgKZQTAABgKZQTAHBTpaWleu655zR9+nTTUYBqxYJYAHBDmZmZiouL065du1SrVi31799fLVu2NB0LqBaMnACAm/nwww8VERGhXbt2KSgoSGvWrKGYwKNQTgDATZSUlOjZZ59Vnz59lJubq6ioKO3du1d9+vQxHQ2oVpQTAHADhw8fVnR0tGbPni1JmjBhgrZt28aICTwSa04AwOL+7//+TyNHjpTdbldwcLAWL16sXr16mY4F1BjKCQBY2NmzZzVq1CjZ7Xbdc889WrlypcLDw03HAmoU5QQALCw4OFgLFy5UWlqaXn75ZdWuXdt0JKDGUU4AwOL69eunfv36mY4BuAwLYgEAgKVQTgAAgKVQTgAAgKVQTgDAkMLCQv3tb38zHQOwHMoJABhw4MABRUZGKjY2Vunp6abjAJZCOQEAF3I6nVq0aJHuuusuHThwQMHBwSosLDQdC7AUygkAuEh+fr7i4+M1cuRInT9/Xj169NC+fft07733mo4GWArlBABc4O9//7siIyO1bNky+fj46I9//KM2btyoJk2amI4GWA6bsAFADXI6nVqwYIGefvppFRUVKSwsTO+9957uu+8+09EAy6KcAEANGjt2rObPny9Jio2N1dKlS9W4cWPDqQBrY1oHAGrQ/fffr1q1aunVV1/V+vXrKSZAJTByAgA1aOjQobr77rt1yy23mI4CuA1GTgCghlFMgKqhnAAAAEuhnAAAAEuhnADANXI4HKYjAB6JcgIA12Dnzp1q166d9u/fbzoK4HEoJwBQBQ6HQzNmzNB9992nf/7zn0pKSjIdCfA43EoMAJWUk5Oj+Ph4bdiwQZI0YMAAvf3224ZTAZ6HkRMAqITU1FRFRERow4YN8vf317x587Ry5Uo1bNjQdDTA41BOAOAKHA6HXnnlFT3wwAPKyspS69attXPnTj3++OOy2Wym4wEeiWkdALiMM2fOaPjw4dq4caMkafDgwUpOTlZAQIDhZIBnY+QEAC5jw4YN2rhxo+rUqaMFCxZo+fLlFBPABRg5AYDLiI+P1zfffKPBgwerXbt2puMAXoNyAgCXYbPZ9Kc//cl0DMDrMK0DAAAshXICAAAshXICAAAshXICwCt9+umn+vbbb03HAHAJlBMAXqWsrEx/+MMf9PDDDysuLk7FxcWmIwH4N9ytA8BrZGVladCgQfr8888lSVFRUXI6nYZTAfh3lBMAXuGTTz7R8OHDlZOTo4CAAC1YsEADBgwwHQvAJTCtA8CjlZaWatKkSerZs6dycnLUoUMHpaenU0wAC2PkBIDHOnr0qAYOHKgvvvhCkjR+/HhNnz5d/v7+hpMBuBLKCQCP9N133ykyMlLnzp1Tw4YNtXDhQvXr1890LACVQDkB4JFuvvlm3X///crOztaqVavUqlUr05EAVBLlBIBHstlsWrp0qerUqSM/Pz/TcQBUAeUEgMcKDAw0HQHANeBuHQAAYCmUEwAAYCmUEwBu6fz586YjAKghlBMAbuX8+fN6/PHH9eCDD6q0tNR0HAA1gAWxANzGwYMHNWDAAH311Vey2WxKSUnRww8/bDoWgGrGyAkAt7BixQp17NhRX331lW688UZt3LiRYgJ4KMoJAEsrLCzU6NGjNXToUBUUFKhLly7KyMjQQw89ZDoagBpCOQFgWQcOHFCnTp30zjvvyGazafLkydq0aZNCQ0NNRwNQg1hzAsCSFi9erKeeekqFhYUKCQnRu+++q27dupmOBcAFGDkBYDkOh0PvvfeeCgsL9eCDDyojI4NiAngRRk4AWI6Pj4+WLVumpUuX6ve//718fX1NRwLgQpQTAJbUpEkTTZw40XQMAAYwrQMAACyFcgIAACyFcgIAACyFcgLApX788Uf98Y9/VHl5uekoACyKBbEAXGbPnj0aMGCADh8+rLKyMk2ZMsV0JAAWxMgJgBrndDr1xhtv6J577tHhw4fVokULxcTEmI4FwKIYOQFQo86dO6dRo0bpgw8+kCT16dNHCxcuVKNGjQwnA2BVjJwAqDFpaWnq0KGDPvjgA9WuXVuzZ8/W2rVrKSYArohyAqDaOZ1OzZw5U9HR0fr+++/VqlUrpaam6umnn5bNZjMdD4DFUU4AVLspU6ZowoQJKisr02OPPaa9e/cqMjLSdCwAboJyAqDa/cd//IfCwsI0Z84crV69Wg0bNjQdCYAbYUEsgGrXrFkzffvtt6pbt67pKADcECMnAGoExQTAtaKcAAAAS6GcAAAAS6GcAKiSnJwcORwO0zEAeDDKCYBK27Jli+644w699tprpqMA8GCUEwBXVV5erqlTp+rBBx/UiRMntGLFCpWWlpqOBcBDUU4AXNHJkyf10EMP6cUXX5TD4dDIkSO1Y8cO1a5d23Q0AB6KfU4AXNamTZs0ZMgQnT59WvXr19e8efM0bNgw07EAeDhGTgBcpKysTC+88IIeeughnT59WnfccYe+/PJLigkAl2DkBMAFsrOzNWjQIG3btk3ST1vRz5o1i03VALgM5QTABYqLi7Vv3z41aNBA8+fP18CBA01HAuBlKCcALtCqVSutWbNGLVu2VOvWrU3HAeCFKCcALtKjRw/TEQB4MRbEAgAAS6GcAAAAS6GcAAAAS6GcAF5k3bp1euedd0zHAIArYkEs4AWKi4s1adIkvf766/Lz81NUVJTatWtnOhYAXBLlBPBw3333neLi4rRnzx5J0vjx43XrrbcaTgUAl0c5ATzYmjVrNHr0aNntdgUHB2vJkiV69NFHTccCgCtizQnggYqKivTUU09pwIABstvtio6OVkZGBsUEgFugnAAe5ttvv1Xnzp01d+5cSdLzzz+vzZs3Kzw83HAyAKgcpnUAD1JUVKQHHnhAJ06cUOPGjbVs2TI9/PDDpmMBQJUwcgJ4kDp16mj69Ol64IEHtG/fPooJALfEyAngYYYMGaJBgwbJx4d/ewBwT3z3AjwQxQSAO+M7GAAAsBTKCQAAsBTKCeAmnE6nDh06ZDoGANQ4ygngBvLz8zV8+HD95je/0f79+03HAYAaRTkBLO6rr75Sx44dtXz5cpWUlGjXrl2mIwFAjaKcABbldDr19ttvq1OnTvrmm28UFhamLVu2aOTIkaajAUCNYp8TwILsdrvGjh2rlStXSpJ69uypxYsXq3HjxoaTAUDNY+QEsJi9e/eqY8eOWrlypWrVqqXp06fro48+opgA8BqMnAAW8vbbb2v8+PEqKSlR8+bNtXLlSnXu3Nl0LABwKUZOAIspKSlR7969tXfvXooJAK/EyAlgIWPGjFFYWJgeeeQR2Ww203EAwAjKCWAhNptNPXv2NB0DAIxiWgcAAFgK5QQAAFgK5QQAAFgK5QRwgZycHA0cOJAP7gOASmBBLFDDPv/8cw0aNEhZWVk6duyYPv/8c+7EAYArYOQEqCEOh0OvvPKKunTpoqysLN16662aN28exQQAroKRE6AGnDlzRsOHD9fGjRslSUOHDtW8efPUoEEDw8kAwPooJ0A127p1qwYPHqzs7GzVrVtXb775phISEhgxAYBKYloHqCbl5eV6+eWX1a1bN2VnZ+v222/X7t27NXLkSIoJAFQB5QSoJmvXrtXkyZPlcDg0YsQI7d69W23btjUdCwDcDtM6QDV57LHHNHjwYMXExGj48OGm4wCA26KcANXEZrNpxYoVpmMAgNtjWgcAAFgK5QQAAFgK5QQAAFgK5QSohJ+3ngcA1DzKCXAVn3zyiSIiIhQXF6fS0lLTcQDA41FOgMsoLS3VpEmT1LNnT+Xk5KikpERnz541HQsAPB7lBLiEzMxM3X///Zo+fbok6emnn1ZqaqpCQkIMJwMAz8c+J8C/+fDDD5WQkKBz584pKChICxcuVN++fU3HAgCvwcgJ8C8lJSX6/e9/rz59+ujcuXPq1KmT9u7dSzEBABdj5ASQlJOTo9jYWO3evVuSlJiYqGnTpsnPz89wMgDwPpQTQFKjRo0qfi1ZskS9evUyHQkAvBblBJDk4+OjZcuWqaioSM2bNzcdBwC8GuUE+JcmTZqYjgAAEAtiAQCAxVBOAACApVBO4BWcTqfpCACASqKcwOOtWLFCffr0UXl5uekoAIBKMFJO1q9fr1//+tdq3bq1FixYYCICvEBhYaFGjx6toUOH6qOPPtKSJUtMRwIAVILL79YpKytTYmKiNm/erIYNG6pjx47q27evbrjhBldHgQc7cOCABgwYoP3798tms2ny5MmKj483HQsAUAkuHzlJS0tT27ZtFRYWpgYNGig2Nlaffvqpq2PAg7377ruKjIzU/v371bRpU23atEkvvviifH19TUcDAFRClcvJtm3b1KtXL4WGhspms2ndunUXPWfOnDlq2bKl6tSpo6ioKKWlpVV8LTs7W2FhYRWPw8LCdPz48WtLD/xCfn6+Zs+eraeeekqFhYXq0aOHMjIy1K1bN9PRAABVUOVpnYKCArVv314jR45Uv379Lvr6qlWrlJiYqOTkZEVFRWnWrFmKiYnRwYMHr2mTq+LiYhUXF1c8ttvtkqTc3Fw5HI4qn+9K8vLyLvgd7mP//v0aMWKEDh06JB8fHyUlJSkxMVE+Pj7Kzc01HQ9ADeB7tnv5+ed3ZVS5nMTGxio2NvayX585c6bGjBmjhIQESVJycrI2bNighQsX6vnnn1doaOgFIyXHjx9Xp06dLnu+adOmaerUqRcdT01NVb169aoav1LS09Nr5LyoOS+++KIOHTqk4OBgTZgwQW3bttX27dtNxwLgAnzPdg+FhYWVfq7NeR0bQNhsNn3wwQfq06ePpJ8+cr5evXp6//33K45JUnx8vHJzc/Xhhx+qrKxMt99+u7Zs2VKxIHbHjh2XXRB7qZGT8PBwZWZmKjAw8FqjX1JeXp7S09PVoUMHBQQEVOu5UbOOHz+uF154QX379lXXrl15/wAvwPds92K329WiRQv9+OOPV/35Xa136/zwww8qLy9XSEjIBcdDQkL09ddf//SCtWrptddeU9euXeVwODRp0qQr3qnj7+8vf3//i44HBQVVezn5WUBAgIKCgmrk3KgZQUFBWrRokbZu3cr7B3gZrnn34ONT+WWuRj74r3fv3urdu7eJlwYAABZXrbcSN27cWL6+vjp16tQFx0+dOqWmTZtW50sBAAAPVa3lxM/PTx07dlRKSkrFMYfDoZSUFHXu3Lk6XwoAAHioKpeT/Px8ZWRkKCMjQ5J05MgRZWRk6OjRo5KkxMREzZ8/X0uWLNE///lPPfHEEyooKKi4eweoCqfTqQ8++KDabxsHAFhXldecfPnll+ratWvF48TEREk/3ZGzePFixcXF6cyZM5o8ebJOnjypiIgIbdy48aJFssDVnDt3TiNHjtS6dev06quv6rnnnjMdCQDgAlUuJ126dLnqx8+PGzdO48aNu+ZQwK5duxQXF6fMzEz5+fnV2J42AADrMfKpxMDlOJ1Ovfbaa7r33nuVmZmpm2++WTt27NBTTz1lOhoAwEWM3EoMXEpOTo5GjBih9evXS5L69++v+fPnq2HDhoaTAQBciZETWEJqaqruvPNOrV+/Xv7+/po7d65WrVpFMQEAL8TICYxbvny5RowYofLycrVu3VqrV69WRESE6VgAAEMYOYFx0dHRatCggQYNGqQ9e/ZQTADAyzFyAuNatWqljIwMtWjRQjabzXQcAIBhlBNYQsuWLU1HAABYBNM6AADAUignAADAUignqFHl5eVX3VEYAIBfopygxpw4cUI9evTQ3LlzTUcBALgRyglqxGeffaaIiAht3rxZU6ZMUV5enulIAAA3QTlBtSorK9MLL7ygmJgYnT59Wr/5zW+UmpqqgIAA09EAAG6CW4lRbbKysjR48GBt375dkjR27Fj9+c9/Vt26dQ0nAwC4E8oJqsVf/vIXDRs2TDk5OQoICND8+fMVFxdnOhYAwA0xrYPrUlpaqv/8z//UI488opycHHXo0EHp6ekUEwDANaOc4LocPXpUc+bMkSSNGzdOO3bs0C233GI4FQDAnTGtg+vyq1/9Su+8845q1aql3/3ud6bjAAA8AOUE140pHABAdWJaBwAAWArlBAAAWArlBAAAWArlBJf13Xff6aOPPjIdAwDgZSgnuKTVq1frzjvv1MCBA7V//37TcQAAXoRyggsUFRXpiSeeUFxcnPLy8tShQwcFBgaajgUA8CKUE1T45ptvdPfddys5OVmSlJSUpC1btig8PNxwMgCAN2GfE0iS3n33XY0dO1b5+fm68cYbtWzZMsXExJiOBQDwQoyceLnCwkKNGTNGQ4YMUX5+vh544AFlZGRQTAAAxjBy4sUcDoe6dOmi3bt3y2az6YUXXtDkyZNVqxZ/LQAA5vBTyIv5+Pho9OjROnr0qJYvX67u3bubjgQAAOXE240ZM0b9+/dXo0aNTEcBAEASa068ns1mo5gAACyFcgIAACyFcgIAACyFcuLBCgoKTEcAAKDKKCceyOl0Kjk5Wa1atdKhQ4dMxwEAoEooJx7Gbrdr4MCBeuKJJ3TmzJmKregBAHAX3ErsQdLT0zVgwAB99913qlWrlqZNm6bExETTsQAAqBLKiQdwOp2aM2eOJkyYoJKSEjVv3lyrVq3S3XffbToaAABVRjlxc7m5uRo1apTWrl0rSerdu7cWLVqk4OBgw8kAALg2rDlxY2lpabrzzju1du1a1a5dW7NmzdK6desoJgAAt8bIiRvbsWOHvv/+e7Vq1UqrVq1SZGSk6UgAAFw3yokbe+aZZ1RWVqbRo0crKCjIdBwAAKoF5cSN2Ww2TZw40XQMAACqFWtOAACApVBOAACApVBOAACApVBOLMjhcGjGjBk6evSo6SgAALgc5cRiTp8+rdjYWD333HMaOHCgysrKTEcCAMCluFvHQrZs2aLBgwfrxIkTqlu3rkaPHi1fX1/TsQAAcClGTiygvLxcL730kh588EGdOHFCt99+u3bv3q2RI0fKZrOZjgcAgEsxcmLYyZMnNWTIEP3tb3+TJCUkJOiNN95Q/fr1DScDAMAMyolBKSkpGjJkiE6dOqV69eopOTlZw4YNMx0LAACjKCeGbNu2TT169JDT6VS7du20Zs0a3XbbbaZjAQBgHOXEkHvvvVfdu3dXy5YtNXv2bNWtW9d0JAAALIFyYoiPj48+/vhj+fv7m44CAIClcLeOQRQTAAAuRjkBAACWQjkBAACWQjmpAaWlpTp79qzpGAAAuCXKSTXLzMzUfffdp8cee0zl5eWm4wAA4HYoJ9Xoww8/VEREhHbt2qW9e/fq4MGDpiMBAOB2KCfVoKSkRM8++6z69Omj3NxcderUSXv37lWbNm1MRwMAwO1QTq7T4cOHFR0drdmzZ0uSJkyYoO3bt6tly5ZmgwEA4KbYhO06vP/++xo1apTsdruCg4O1ePFi9erVy3QsAADcGiMn16C4uFjjxo1T//79Zbfbdc899ygjI4NiAgBANWDk5ApKyhxa9sX3yjxbqBbB9TSsc0v51fKRr6+vvvrqK0nS888/r5deekm1a9c2nBYAAM9AObmMaZ8c0PztR+Rw/v9jf/zknxpzXyslPdJG7733nv7+97/r4YcfNhcSAAAPRDm5hEWph/XW9hMXHXc4pbe2HZEkJT3SRmFhYa6OBgCAx2PNySV8kJ59xa/P335EJWUOF6UBAMC7UE4u4Wq1w+GUln3xvSuiAADgdVxeTubMmaOWLVuqTp06ioqKUlpamqsjVIvMs4WmIwAA4JFcWk5WrVqlxMRETZkyRenp6Wrfvr1iYmJ0+vRpV8a4oqNHj+rwJ2/L6bzy+EmL4HouSgQAgHdxaTmZOXOmxowZo4SEBLVp00bJycmqV6+eFi5c6MoYl+R0OrVixQpNnDhRJ3Z+rLw96y/7XB+bNKxzS9eFAwDAi7jsbp2SkhLt2bNHSUlJFcd8fHzUvXt3ffHFF5f9c8XFxSouLq54bLfbJUm5ublyOKpnUWp+fr4mTpyoVatWSZJuuzNKAR3vk1995yWf/7sOoSrMt4uJHWvJy8u74HcAno1r3r38/PO7MlxWTn744QeVl5crJCTkguMhISH6+uuvL/vnpk2bpqlTp150PDU1VfXqXf/Uyvfff68ZM2YoKytLPj4+GjRokH73u9/Jx8dHUvml/1DZMW3deuy6Xxs1Iz093XQEAC7ENe8eCgsr/096y+9zkpSUpMTExIrHdrtd4eHhio6OVmBg4DWf1+l0asmSJUpKSlJRUZFCQ0M1e/Zs1a5dWx06dFBAQIBKyx3a8NUJnbAX6abAOur5m5tU25cbnKwqLy9P6enpFe8fAM/GNe9eLDly0rhxY/n6+urUqVMXHD916pSaNm162T/n7+8vf3//i44HBQVdczmx2+0aO3asVq5cKUmKjY3V0qVLVatWLW3dulUBAQEKCgqSJI3oGnxNrwFzfvn+AfB8XPPu4acZiUo+twZzXMDPz08dO3ZUSkpKxTGHw6GUlBR17tzZVTEkqaKY+Pr66tVXX9X69evVuHFjl2YAAACX5tJpncTERMXHx+uuu+5Sp06dNGvWLBUUFCghIcGVMfSnP/1JBw4cUHJyssuLEQAAuDKXlpO4uDidOXNGkydP1smTJxUREaGNGzdetEi2prVq1UoZGRmy2WwufV0AAHB1Ll8QO27cOI0bN87VL3sRigkAANbErScAAMBSKCcAAMBSPK6cHDx4UE7npXd2BQAA1ucx5cTpdOq1115Tu3btNHfuXNNxAADANfKIcpKTk6PevXtr4sSJKisrU1paGqMnAAC4KbcvJ6mpqYqIiND69evl7++vefPmafHixdyNAwCAm3LbcuJwOPTKK6/ogQceUFZWlm699Vbt2rVLjz/+OMUEAAA3ZvkP/ruc/v37a9OmTZKkIUOGaN68eXzwEwAAHsBtR042bdqkOnXqaMGCBVq2bBnFBAAAD+F2Iyc/L3S9+eabtWLFCrVp00Z5eXnVcm673a7CwkLZ7fYqfXoirIH3D/AuXPPuxW63S1KlblixOd3stpasrCyFh4ebjgEAAK7BsWPH1KxZsys+x+3KicPhUHZ2tgICAqp94avdbld4eLiOHTumwMDAaj03ah7vH+BduObdi9PpVF5enkJDQ6860uV20zo+Pj5XbVzXKzAwkL/oboz3D/AuXPPuo2HDhpV6HpN0AADAUignAADAUignv+Dv768pU6bI39/fdBRcA94/wLtwzXsut1sQCwAAPBsjJwAAwFIoJwAAwFIoJwAAwFIoJwAAwFIoJ/8yZ84ctWzZUnXq1FFUVJTS0tJMRwIAwCtRTiStWrVKiYmJmjJlitLT09W+fXvFxMTo9OnTpqOhmqxfv16//vWv1bp1ay1YsMB0HAAu0LdvXzVq1EiPPfaY6SioIm4llhQVFaXIyEi9+eabkn76/J7w8HCNHz9ezz//vOF0uF5lZWVq06aNNm/erIYNG6pjx47asWOHbrjhBtPRANSgLVu2KC8vT0uWLNH7779vOg6qwOtHTkpKSrRnzx5179694piPj4+6d++uL774wmAyVJe0tDS1bdtWYWFhatCggWJjY/Xpp5+ajgWghnXp0kUBAQGmY+AaeH05+eGHH1ReXq6QkJALjoeEhOjkyZOGUuGXtm3bpl69eik0NFQ2m03r1q276DlXWjOUnZ2tsLCwisdhYWE6fvy4K6IDuEbXe93DvXl9OYH1FRQUqH379pozZ84lv86aIcDzcN17N68vJ40bN5avr69OnTp1wfFTp06padOmhlLhl2JjY/U///M/6tu37yW/PnPmTI0ZM0YJCQlq06aNkpOTVa9ePS1cuFCSFBoaesFIyfHjxxUaGuqS7ACuzfVe93BvXl9O/Pz81LFjR6WkpFQcczgcSklJUefOnQ0mQ2VUZs1Qp06d9I9//EPHjx9Xfn6+/vKXvygmJsZUZADXibWCnq+W6QBWkJiYqPj4eN11113q1KmTZs2apYKCAiUkJJiOhqu40pqhr7/+WpJUq1Ytvfbaa+ratascDocmTZrEnTqAG6vMdS9J3bt31759+1RQUKBmzZppzZo1/KPTTVBOJMXFxenMmTOaPHmyTp48qYiICG3cuPGiv/hwX71791bv3r1NxwDgQps2bTIdAdeIcvIv48aN07hx40zHQBWxZgjwPlz3ns/r15zAvbFmCPA+XPeej5ETWF5+fr4OHTpU8fjIkSPKyMhQcHCwmjdvzpohwANx3Xs5J2Bxmzdvdkq66Fd8fHzFc9544w1n8+bNnX5+fs5OnTo5d+7caS4wgOvGde/d+GwdAABgKaw5AQAAlkI5AQAAlkI5AQAAlkI5AQAAlkI5AQAAlkI5AQAAlkI5AQAAlkI5AQAAlkI5AQAAlkI5AQAAlkI5AQAAlkI5AQAAlvL/AERRAiaNhvZlAAAAAElFTkSuQmCC", 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" ] @@ -257,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ @@ -268,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 115, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 116, "metadata": {}, "outputs": [], "source": [ @@ -292,18 +298,18 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.sampler.step.full_random import RandomStep\n", "from wntr_quantum.sampler.step.full_random import IncrementalStep\n", - "from wntr_quantum.sampler.step.full_random import ParallelIncrementalStep \n", + "# from wntr_quantum.sampler.step.full_random import ParallelIncrementalStep \n", "\n", "var_names = sorted(net.qubo.qubo_dict.variables)\n", "net.qubo.create_variables_mapping()\n", "# mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", - "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)\n", + "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=1)\n", "# mystep = ParallelIncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=100)" ] }, @@ -316,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 118, "metadata": {}, "outputs": [], "source": [ @@ -327,18 +333,18 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 119, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", - "x = modify_solution_sample(net, bin_rep_sol, modify=['flows'])\n", + "x = modify_solution_sample(net, bin_rep_sol, modify=['flows', 'heads'])\n", "x0 = list(x.values())" ] }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 120, "metadata": {}, "outputs": [], "source": [ @@ -347,57 +353,67 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 121, "metadata": {}, "outputs": [], "source": [ - "num_sweeps = 1000\n", + "num_sweeps = 2000\n", "Tinit = 1E1\n", "Tfinal = 1E-1\n", "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "Tschedule = np.append(Tschedule, np.zeros(1000))\n", "# Tschedule = np.zeros(10000)" ] }, { "cell_type": "code", - "execution_count": 115, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 2000/2000 [00:00<00:00, 2479.48it/s]\n" + "100%|██████████| 4000/4000 [00:05<00:00, 687.92it/s]\n", + "100%|██████████| 4000/4000 [00:06<00:00, 621.00it/s]\n", + "100%|██████████| 4000/4000 [00:05<00:00, 775.12it/s]\n" ] } ], "source": [ - "mystep.optimize_values = np.arange(4, 6)\n", - "res = sampler.sample(net.qubo.qubo_dict, init_sample=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", + "mystep.optimize_values = np.arange(2,6)\n", + "res = sampler.sample(net.qubo, init_sample=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", + "\n", + "mystep.optimize_values = np.arange(2,4)\n", + "res2 = sampler.sample(net.qubo, init_sample=res.res, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", + "\n", + "mystep.optimize_values = np.arange(4,6)\n", + "res3 = sampler.sample(net.qubo, init_sample=res.res2, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", + "\n", "mystep.verify_quadratic_constraints(res.res)" ] }, { "cell_type": "code", - "execution_count": 116, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 116, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" }, { "data": { - 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o+1DQ6XSNtrHZbMjLy0NCQoJHHo2QGrevazW3fa1WK8LCwtClSxeJekdELcFQJBcKePSAIscpM51O12wo0mq10Ol0/NB2AW5f12rJ9vW0U8dEnoYnt2XGQ8+eERERyR5DkUzw/49ERETSYigiIiIiAkMREREREQCGItnhmCIiIiJpMBQRERERgaFINm5moPWRI0cwbtw4+Pv7o1u3bpg/fz7Ky8vF+V988QWmTp2KsLAw+Pr6ol+/ftiyZYvTMvbv3w+FQtHgy2KxOLXLyMhA79694ePjg7i4OBw+fPhmyiQiIpIthiKZEVp4s6KSkhLEx8cjKioKhw4dQm5uLo4fP45Zs2aJbQoKChAUFITXXnsNx48fx1NPPYWUlBRs3bq1wfJOnz6N8+fPi19BQUHivJ07d8JoNCItLQ1HjhzBkCFDYDAYcOHChVuul4iISC5480a5aOWhot27d0OtViMjI0N8llJmZiYGDx6MM2fOICoqCo8++qjTayIjI2E2m/HPf/4TycnJTvOCgoLg7+/f6LrS09Mxb948zJ49W1xPTk4OsrKysHz58tZ1nIiISKZkEYqqqqoQFxeHL774AkePHkVMTIw478svv0RSUhI+++wzdO/eHY899hiWLVvm9Pq3334bTz/9NL799lv06dMH69atw8SJE8X5giAgLS0Nf/vb31BWVoZf/epXeOmll9CnT5/2KrHFWjrQuqqqCt7e3k4Pl3Q8SuPAgQOIiopq9HWXL19G165dG0yPiYlBVVUVBg4ciJUrV+JXv/oVAKC6uhoFBQVISUkR2yqVSsTHx8NsNjfbv6qqKgDXH7bp+N5mszX6Gsf0pubTreH2da3mti+3OZF7kEUoWrZsGUJDQ/HFF184TbdarUhISEB8fDwyMzPx1Vdf4dFHH4W/vz/mz58PADh48CCmTp2KNWvW4P7778cbb7yByZMn48iRIxg4cCAAYP369Xj++eexY8cORERE4Omnn4bBYMCJEyfg4+PT7vU2prVjisaOHQuj0YgNGzbgiSeeQEVFhXjU5vz5842+5uDBg9i5cydycnLEaT169EBmZiaGDx+OqqoqbNu2DaNHj8ahQ4cwbNgw/PDDD6itrUVwcLDTsoKDg3Hq1Kkm+7dmzRqsWrWqwfS8vDxotdpmazOZTM3Op1vD7etajW1fx38KiEjeJA9F7733HvLy8vCPf/wD7733ntO8119/HdXV1cjKyoK3tzcGDBiAwsJCpKeni6Foy5YtGD9+PJYuXQoAeOaZZ2AymbB161ZkZmZCEARs3rwZK1aswKRJkwAAr776KoKDg7Fr1y488sgj7VvwDaxdnYqXX0hvts3JkycxYMAA7NixA0ajESkpKVCpVHj88ccRHBzsdPTI4dixY5g0aRLS0tKQkJAgTu/bty/69u0r/jxy5EicPXsWmzZtwt///vebriMlJQVGoxFA3f+Sd+3ahblz5yIhIaHZZ5+ZTCaMGzeOz+ZyAW5f12pu+1qtVol6RUStIWkoKi0txbx587Br165Gjx6YzWbcc8898Pb2FqcZDAasW7cOly5dQkBAAMxms/jhW7/Nrl27AABFRUWwWCyIj48X5/v5+SEuLg5ms7nJUFT/9A9w/U2tudM/N6v+8mbPX4ikebOabR8WFgabzYaHHnoIDz30EEpLS9GpUycoFAqkp6ejV69eTss8ceIEEhISMGfOHDz55JM37P/w4cPxySefwGazwc/PDyqVCufOnXN6nWMwdlPLUiqV4uk8Ly8vcf+q1eobfiC3pA3dPG5f12ps+zp+dsX7R0t5+ulT1uf+XFVja5YnWSgSBAGzZs3CggULMHz4cHz77bcN2lgsFkRERDhNc5zGsVgsCAgIgMViafTUjuOScse/zbVpzK2c/rkZCqgAAF8eO44g3+bbfvPNN41Of//996FWq2G327Fnzx4AQHFxMZ5++mmMGTMGer1enN6c/Px8+Pr6im0jIyOxfft28Y3dbrfjvffew8SJE1u0PCKq46r3j9bw9NOnrM/9tXWNrTl93eahaPny5Vi3bl2zbU6ePIm8vDxcuXLFaQCvnNQ//QPUHSkKCwtr9vTPzbLZbFh+eB8A4N5770VEYKcWve7FF1+EXq9Hp06dkJ+fj1deeQVr1qzBww8/DKDulNm8efOQmJiItWvXiq9TqVTo3r07AOD5559H79690b9/f1y7dg3bt2/HV199hT179mDs2LEAgPLycsyZMwcPPPAA7rzzTrzwwguora3FX/7ylwZhs6n63nnnnVZtEyJP5Ir3j5by9NOnrM/9uarG1py+bvNQtGTJEqd75TQmMjIS+/btg9lshkajcZo3fPhwTJ8+HTt27EBISAhKS0ud5jt+DgkJEf9trE39+Y5pPXr0cGpT/yq3X9JoNA36Brj+1IOXl1eLl19QUIDVq1ejvLwc0dHRePnllzFjxgxx/jvvvIOLFy/ijTfewBtvvCFODw8PF4/M1dbW4sknn8S5c+eg1WoxePBgvP/++xgzZozYfvr06bh06RJWr14Ni8WCmJgY5ObmomfPnm1TNFEHIYdTl3LogyuxPvfX1jW2ZlltHoq6d+8uHoVozvPPP49nn31W/LmkpAQGgwE7d+5EXFwcAECv1+Opp56CzWYTizKZTOjbty8CAgLENvn5+Vi0aJG4LJPJBL1eDwCIiIhASEgI8vPzxRBktVpx6NAhLFy4sC1KblOtefTZq6++2uz8lStXYuXKlc22WbZsWYNbHDQmOTm5wb2NiIiIPIlkY4p69erl9HPnzp0BALfffrt4BGLatGlYtWqVOED42LFj2LJlCzZt2iS+7oknnsC9996LjRs3IjExEW+++SY+//xz/PWvfwUAKBQKLFq0CM8++yz69OkjXpIfGhqKyZMnt0+xLXAzj/kgIiKitiP5JfnN8fPzQ15eHpKSkhAbG4vAwECkpqaKl+MDdZeQv/HGG1ixYgX+9Kc/oU+fPti1a5d4jyKg7mhIRUUF5s+fj7KyMowaNQq5ubmyuUdRfS29eSMRERG1LdmEot69e0NoJBEMHjwYH3/8cbOvdVya3hSFQoHVq1dj9erVt9xPl+GhIiIiIknxgbBEREREYCiSDdXPR4pstXZpO0JERNRBMRTJhE/dvRtx5VqNtB0hIiLqoBiKZOJ6KPLcW7gTERHJGUORTPio6gaZ80gRERGRNBiKZEL38zNvF+0sxBffl0naFyIioo6IoUgmenW+fjuCTe//R8KeEBERdUwMRTIxMkjAyMiuAIBrtlqJe0NERNTxMBTJhLcKeHh43eNNeFdrIiKi9sdQJCOOm1ozExEREbU/hiIZUTAVERERSYahiIiIiAgMRbKi+PlQkcBDRURERO2OoUiGONCaiIio/TEUyQiHFBEREUmHoUhGHAOtBR4qIiIiancMRTKigGNMEREREbU3hiIZuX6kSNp+EBERdUQMRTLCMUVERETSYSiSE8WNm9R35MgRjBs3Dv7+/ujWrRvmz5+P8vJy50UqFA2+3nzzTac2+/fvx7Bhw6DRaBAVFYXs7OwG68rIyEDv3r3h4+ODuLg4HD58uLXVERERyRpDkYwoWnH+rKSkBPHx8YiKisKhQ4eQm5uL48ePY9asWQ3abt++HefPnxe/Jk+eLM4rKipCYmIixowZg8LCQixatAhz587F3r17xTY7d+6E0WhEWloajhw5giFDhsBgMODChQu3WjIREZFseEndAbquNafPdu/eDbVajYyMDCiVddk2MzMTgwcPxpkzZxAVFSW29ff3R0hISKPLyczMREREBDZu3AgA6NevHw4cOIBNmzbBYDAAANLT0zFv3jzMnj1bfE1OTg6ysrKwfPnymyuWiIhIZnikSIZaMtC6qqoK3t7eYiACAF9fXwDAgQMHnNomJSUhMDAQI0aMQFZWltMl/2azGfHx8U7tDQYDzGYzAKC6uhoFBQVObZRKJeLj48U2REREnoBHimREPHvWgmNFY8eOhdFoxIYNG/DEE0+goqJCPGpz/vx5sd3q1asxduxYaLVa5OXl4Q9/+APKy8vx+OOPAwAsFguCg4Odlh0cHAyr1YqrV6/i0qVLqK2tbbTNqVOnmuxfVVUVqqqqAAA2mw2VlZXi9zabrdHXOKY3NZ9uDbevazW3fbnNidwDQ5GMKABc2p+NnEP/B8XjTbc7efIkBgwYgB07dsBoNCIlJQUqlQqPP/44goODnY4ePf300+L3Q4cORUVFBTZs2CCGIldZs2YNVq1a1WB6Xl4etFpts681mUyu6haB29fVGtu+jv8UEJG8MRTJiEKhgG7EbzFk7CT89ffDm2wXGRkJAJg2bRqmTZuG0tJSdOrUCQqFAunp6eL8xsTFxeGZZ55BVVUVNBoNQkJCUFpa6tSmtLQUOp0Ovr6+UKlUUKlUjbZpapwSAKSkpMBoNAKo+1/yrl27MHfuXCQkJECn0zX6GpvNBpPJhHHjxkGtVje5bLo53L6u1dz2tVqtEvWKiFqDoUhGFABUWj90CtYhOjq6xa9znNrKysqCj48Pxo0b12TbwsJCBAQEQKPRAAD0ej327Nnj1MZkMkGv1wMAvL29ERsbi/z8fPGqNbvdjvz8fCQnJze5Ho1GI67DZrOJR4fUavUNP5Bb0oZuHrevazW2fbm9idwDQ5GctPLZZ1u3bsXIkSPRuXNnmEwmLF26FGvXroW/vz8A4N1330VpaSnuuusu+Pj4wGQy4S9/+Qv++Mc/istYsGABtm7dimXLluHRRx/Fvn378NZbbyEnJ0dsYzQaMXPmTAwfPhwjRozA5s2bUVFRIV6NRkRE5AkYimRE0cq7Nx4+fBhpaWkoLy9HdHQ0Xn75ZcyYMUOc77hkf/HixRAEAVFRUeLl9Q4RERHIycnB4sWLsWXLFvTs2RPbtm0TL8cHgClTpuDixYtITU2FxWJBTEwMcnNzGwy+JiIicmcMRTKiaOUdrV999dVm548fPx7jx4+/4XJGjx6No0ePNtsmOTm52dNlRERE7o73KZIR8eaNfPgZERFRu2MokpHW3KeIiIiI2hZDkQzxSBEREVH7YyiSEcdAa2YiIiKi9sdQJCOKVl6ST0RERG2HoUiGGImIiIjaH0ORjLT2knwiIiJqOwxFMiLevJGHioiIiNodQ5GMKJiJiIiIJMNQJCPXb97IWERERNTeGIpkRKHgJflERERSYSiSET7mg4iISDoMRTLEx3wQERG1P4YiORFv3ihtN4iIiDoihiIZ4ekzIiIi6TAUyYiCd28kIiKSDEORjDASERERScdL6g7QdR3ugbA1lQAEQOkDQaFEZXUNqmqByqsVUNsUgFINKL2vt7fXAPYqQKEEVL7XpwsCUFtZ971Xp8bXofIBFKrr02urAKGmQ63DZqtBVY2AysrLUKvVbluHXNfhBQFKwQbUVABKLaD6xTqISPYYimTE8ZiPDhKJgL13ApdPAPd9gKsBozDkmX0AvHDtv/8Pvw/MwebSqdhcOl1sPtHvAF4MX4tPywfikW/WitO7qi7jyIC6dr2/3O20irw7/oA7fIrxyNm/4NOKweL01aEvddB1VOLhqu4eUIf81vHF02MxsDoL6n89BAxMAwavvP6Cc++CiOSPp89kRMGrz4iIiCSjEDrMuZpbY7Va4efnh8uXL0On07Xpsm02G/bs2YPwmFGY/NKnCNZpcOhP8W26DinZbDb83//9H6ZNm+a8/X5x+sxaeQ179+bBEH8v1F48fdbW67DZbNibuxeG+FE8feaS02d27N3zb4w3xEPt7Xz6zFr2E/wCurnk/aOlHO8zEydOrNv/Hob1uT9X1diaz2+ePpORDnekyEsrfqsAoPX2gkYFaH07NfEH4QXAp4mF+TU+2bupP4CmfvU9dx02hQCNlwJarV/j29dN6pDrOmw2G+yKn8Om6hfbV8m3WiJ3IPnps5ycHMTFxcHX1xcBAQGYPHmy0/zi4mIkJiZCq9UiKCgIS5cuRU2N86DF/fv3Y9iwYdBoNIiKikJ2dnaD9WRkZKB3797w8fFBXFwcDh8+7MKqbk1HyURERERyImko+sc//oEZM2Zg9uzZ+OKLL/DJJ59g2rRp4vza2lokJiaiuroaBw8exI4dO5CdnY3U1FSxTVFRERITEzFmzBgUFhZi0aJFmDt3Lvbu3Su22blzJ4xGI9LS0nDkyBEMGTIEBoMBFy5caNd6b0TRyovyjxw5gnHjxsHf3x/dunXD/PnzUV5eLs7Pzs6GQqFo9MtR+/79+xudb7FYnNblTqGSiIjoZkgWimpqavDEE09gw4YNWLBgAe644w70798fDz/8sNgmLy8PJ06cwGuvvYaYmBhMmDABzzzzDDIyMlBdXQ0AyMzMREREBDZu3Ih+/fohOTkZv/vd77Bp0yZxOenp6Zg3bx5mz56N/v37IzMzE1qtFllZWe1ed3Nac/qspKQE8fHxiIqKwqFDh5Cbm4vjx49j1qxZYpspU6bg/PnzTl8GgwH33nsvgoKCnJZ3+vRpp3b157tLqCQiIroVkp3oPnLkCM6dOwelUomhQ4fCYrEgJiYGGzZswMCBAwEAZrMZgwYNQnBwsPg6g8GAhQsX4vjx4xg6dCjMZjPi450HJRsMBixatAgAUF1djYKCAqSkpIjzlUol4uPjYTabm+xfVVUVqqqqxJ+tViuAunEDNpvtluuvz7G82p9PCwqCcMN1vPPOO1Cr1di8eTOUyrps+8ILLyA2NhYnT55EVFQUvLy80K1bN/E1Fy9exL59+/Dyyy+Ly3ecigwICIC/v7/Ytra2FrW1tQCAjRs3Ys6cOfh//+//ievJycnB3/72NyxbtqzF9RF1dK54/2jNuuv/62lYn/tzVY2tWZ5koeibb74BAKxcuRLp6eno3bs3Nm7ciNGjR+M///kPunbtCovF4hSIAIg/O07vNNXGarXi6tWruHTpEmpraxttc+rUqSb7t2bNGqxatarB9Ly8PGi12kZecevMn5oBeKGqqgp79uxptu2RI0dQW1uL3Nxccdr58+cBAC+99BLuu+++Bq/ZtWsX1Go1OnXqJC7/q6++AgAMGDAANTU16NWrFx555BH069cPQN0vU0FBAe677z6nPvXt2xf//ve/xQD7S79886+srGx0+i9fU/9falvcvq7V3PZ1THPl+0dLmUwmSdfvaqzP/bV1jY7Pn5Zo81C0fPlyrFu3rtk2J0+ehN1uBwA89dRTePDBBwEA27dvR8+ePfH222/jf/7nf9q6a62SkpICo9Eo/my1WhEWFoaEhASXXJJvMpkwUj8S+OIw1N7emDhxTLOvCQ8PR3Z2Nk6ePInHHnsMFRUVWLBgAQAgKCgIEydObPCa5cuXY8aMGfjtb38rTrv99tvRtWtXxMbGoqqqCllZWXj66afxySefYOjQoSgpKYHdbsfEiRNx1113ia/76KOP8PHHHze6HgBYvXo1nn322QbTW/Kh0BH+6KXE7etajW1fx5uyK94/WsrxPjNu3DiPvKSb9bk/V9XoONPTEm0eipYsWeI0rqUxkZGR4lGN/v37i9M1Gg0iIyNRXFwMAAgJCWkwoLe0tFSc5/jXMa1+G51OB19fX6hUKqhUqkbbOJbRGI1GA41G02C6Wq122S+kWu2FS/uz8d2h/4N3WtPtTp48iZiYGOzYsQNGoxErVqyASqXC448/juDg4Eb7aDabcerUKbz22mtO8wYOHOh0tOeee+5BUVERXnjhBfz9738X23p5eTm9TqVSQaFQNLktVqxYgaVLlwKo+0XftWsX5s6d2+yHQkf4o5cSt69rNbd9HW/Krnz/aCk59MGVWJ/7a+saW7OsNg9F3bt3R/fu3W/YLjY2FhqNBqdPn8aoUaMA1L2pfPvttwgPDwcA6PV6/PnPf8aFCxfEgb8mkwk6nU4MU3q9vsGpJpPJBL1eDwDw9vZGbGws8vPzxcv97XY78vPzkZyc3CY1txUFAN2I36LHnQbsShrVZLvIyEgAwLRp0zBt2jSUlpaiU6dOUCgUSE9PF+fXt23bNsTExCA2NvaG/RgxYgQOHDgAAAgMDLzlUGmz2cSjQy35Ze8If/RS4vZ1rca2L7c3kXuQbEyRTqfDggULkJaWhrCwMISHh2PDhg0AgIceeghA3aHm/v37Y8aMGVi/fj0sFgtWrFiBpKQk8QN3wYIF2Lp1K5YtW4ZHH30U+/btw1tvvYWcnBxxXUajETNnzsTw4cMxYsQIbN68GRUVFZg9e3b7F94MhUIBldYPGt9AREdHt/h1jvFSWVlZ8PHxwbhx45zml5eX46233sKaNWtatLzCwkL06NEDgHuFSiIiolsh6W1WN2zYAC8vL8yYMQNXr15FXFwc9u3bh4CAAAB1p2h2796NhQsXQq/Xo1OnTpg5cyZWr14tLiMiIgI5OTlYvHgxtmzZgp49e2Lbtm0wGAximylTpuDixYtITU0Vr3LLzc1tMPhaao67FLX0yStbt27FyJEj0blzZ5hMJixduhRr1651uooMqLukvqamRrx6rL7NmzcjIiICAwYMwLVr17Bt2zbs27cPeXl5Yht3CZVERES3QtJQpFar8dxzz+G5555rsk14ePgNr8QaPXo0jh492myb5ORkjzuycfjwYaSlpaG8vBzR0dF4+eWXMWPGjAbtXnnlFTzwwAMNwhJQd8uCJUuW4Ny5c9BqtRg8eDDef/99jBlzfaC3u4RKIiKiW8EH8siIePPGFrZ/9dVXW9Tu4MGDTc5btmxZi+415ImhkoiIqD7Jn31G1ynE82eSdoOIiKhDYiiSEcezz5iJiIiI2h9DkZyIzz5jLCIiImpvDEUywrNnRERE0mEokhFxoDVTERERUbtjKJIRx5giO1MRERFRu2MokhHHkaKqGjsqqmqk7QwREVEHw1AkI0Fdrj+A9uzFcgl7QkRE1PEwFMmIWqVEzwBfAICtlqfQiIiI2hNDkcxovOp2ia3WLnFPiIiIOhaGIplRq+p2SQ2PFBEREbUrhiKZcYQiHikiIiJqXwxFMuOlqrsEjaGIiIiofTEUycz1I0U8fUZERNSeGIpkRs0jRURERJJgKJIZLyXHFBEREUmBoUhmePqMiIhIGgxFMuPtVXf6rLqmVuKeEBERdSwMRTKj9fYCAFTaGIqIiIjaE0ORzHTyVgEAKqsYioiIiNoTQ5HMdNLUHSkqr6qRuCdEREQdC0ORzDhC0euHvsPglXux9r1TEveIiIioY2AokpnBPf2gUNRdfWa9VoN/HPmv1F0iIiLqEBiKZObuPt3x2VPxuO/aAVj+/kcUrLof/v7+jbYtLi5GYmIitFotgoKCsHTpUtTUOJ92279/P4YNGwaNRoOoqChkZ2c3WE5GRgZ69+4NHx8fxMXF4fDhw07zr127hqSkJHTr1g2dO3fGgw8+iNLS0rYqmYiISBYYimQosLMGvioB2uhRCBye2Gib2tpaJCYmorq6GgcPHsSOHTuQnZ2N1NRUsU1RURESExMxZswYFBYWYtGiRZg7dy727t0rttm5cyeMRiPS0tJw5MgRDBkyBAaDARcuXBDbLF68GO+++y7efvttfPjhhygpKcEDDzzgug1AREQkAYYimVq8fAV0d06Gd1DvRufn5eXhxIkTeO211xATE4MJEybgmWeeQUZGBqqrqwEAmZmZiIiIwMaNG9GvXz8kJyfjd7/7HTZt2iQuJz09HfPmzcPs2bPRv39/ZGZmQqvVIisrCwBw+fJlvPLKK0hPT8fYsWMRGxuL7du34+DBg/j0009dvh2IiIjai5fUHaDGqRR1N3EUmrixtdlsxqBBgxAcHCxOMxgMWLhwIY4fP46hQ4fCbDYjPj7e6XUGgwGLFi0CAFRXV6OgoAApKSnifKVSifj4eJjNZgBAQUEBbDab03Kio6PRq1cvmM1m3HXXXY32r6qqClVVVQAAm82GyspK8XubzdboaxzTm5pPt4bb17Wa277c5kTugaFIplTKulBkbyIVWSwWp0AEQPzZYrE028ZqteLq1au4dOkSamtrG21z6tQpcRne3t4NxjUFBweL62nMmjVrsGrVqgbT8/LyoNVqm3wdAJhMpmbn063h9nWtxrav4z8FRCRvDEXtaPny5Vi3bl2zbU6ePIno6Ggolc0fKZK7lJQUGI1GAHX/S961axfmzp2LhIQE6HS6Rl9js9lgMpkwbtw4qNXq9uxuh8Dt61rNbV+r1SpRr4ioNRiK2tGSJUswa9asBtNtNhs++ugj3HPPPYiMjARw/fRZU0eKQkJCGlwl5rgiLCQkRPz3l1eJlZaWQqfTwdfXFyqVCiqVqtE29ZdRXV2NsrIyp6NF9ds0RqPRQKPRiPU5jg6p1eobfiC3pA3dPG5f12ps+3J7E7kHDrRuR927d0d0dHSjXz179kR0dDS8vb0BAMqf94y9iSNFer0eX331ldNVYiaTCTqdDv379xfb5OfnO73OZDJBr9cDALy9vREbG+vUxm63Iz8/X2wTGxsLtVrt1Ob06dMoLi4W2xAREXkCHimSqfP//R7Vpd+gxnoRtbW1KCwsBABERUWhc+fOSEhIQP/+/TFjxgysX78eFosFK1asQFJSkniEZsGCBdi6dSuWLVuGRx99FPv27cNbb72FnJwccT1GoxEzZ87E8OHDMWLECGzevBkVFRWYPXs2AMDPzw9z5syB0WhE165dodPp8Nhjj0Gv1zc5yJqIiMgdMRTJ1Lo/P4Pzr/9d/Hno0KEAgA8++ACjR4+GSqXC7t27sXDhQuj1enTq1AkzZ87E6tWrxddEREQgJycHixcvxpYtW9CzZ09s27YNBoNBbDNlyhRcvHgRqampsFgsiImJQW5urtPg602bNkGpVOLBBx9EVVUVDAYDXnzxxXbYCkRERO2HoUimMre9goM9pwAATj87HhovVYM24eHh2LNnT7PLGT16NI4ePdpsm+TkZCQnJzc538fHBxkZGcjIyGhBz4mIiNwTxxTJlGOgNQDY7RJ2hIiIqINgKJIpx32KAKDWXa/LJyIiciMMRTKlrHekqLapS9CIiIiozTAUyVT9I0V2hiIiIiKXYyiSqXqZiKfPiIiI2gFDkUwpFAoxGPFIERERkevxknwZUykVsNcKeKewBH6+Pz8mQAHc3ScQPfx8pe0cERGRh2EokjEfLxVstTX4856TTtOHhwfg/xaOlKhXREREnomhSMZW3N8Pe49ff1jr5as2FHx3CRfLqyTsFRERkWdiKJKxKXf2wpQ7e4k/Hy2+hN++eBB2DrwmIiJqcxxo7UYc9y7iHa6JiIjaHkORG3GEIoFHioiIiNocQ5EbcdzkmlfoExERtT2GIjcinj7jkSIiIqI2x1DkRpQ/7y0eKSIiImp7DEVuhGOKiIiIXEfSUPSf//wHkyZNQmBgIHQ6HUaNGoUPPvjAqU1xcTESExOh1WoRFBSEpUuXoqamxqnN/v37MWzYMGg0GkRFRSE7O7vBujIyMtC7d2/4+PggLi4Ohw8fdmVpLiE+9oOhiIiIqM1JGoruv/9+1NTUYN++fSgoKMCQIUNw//33w2KxAABqa2uRmJiI6upqHDx4EDt27EB2djZSU1PFZRQVFSExMRFjxoxBYWEhFi1ahLlz52Lv3r1im507d8JoNCItLQ1HjhzBkCFDYDAYcOHChXav+VYoxDFFEneEiIjIA0kWin744Qd8/fXXWL58OQYPHow+ffpg7dq1qKysxLFjxwAAeXl5OHHiBF577TXExMRgwoQJeOaZZ5CRkYHq6moAQGZmJiIiIrBx40b069cPycnJ+N3vfodNmzaJ60pPT8e8efMwe/Zs9O/fH5mZmdBqtcjKypKk9pv184EiHikiIiJyAclCUbdu3dC3b1+8+uqrqKioQE1NDV5++WUEBQUhNjYWAGA2mzFo0CAEBweLrzMYDLBarTh+/LjYJj4+3mnZBoMBZrMZAFBdXY2CggKnNkqlEvHx8WIbd3F9TJHEHSEiIvJAkj3mQ6FQ4P3338fkyZPRpUsXKJVKBAUFITc3FwEBAQAAi8XiFIgAiD87TrE11cZqteLq1au4dOkSamtrG21z6tSpJvtXVVWFqqrrzxizWq0AAJvNBpvNdpNVN86xvBstt7a2biyVIAht3gdXcqe+ErmSK94/WrPu+v96Gtbn/lxVY2uW1+ahaPny5Vi3bl2zbU6ePIm+ffsiKSkJQUFB+Pjjj+Hr64tt27bh17/+NT777DP06NGjrbvWKmvWrMGqVasaTM/Ly4NWq3XJOk0mU7Pzf7wGAF6w1dRgz549LukDEbmOK98/WupG7zPujvW5v7ausbKyssVt2zwULVmyBLNmzWq2TWRkJPbt24fdu3fj0qVL0Ol0AIAXX3wRJpMJO3bswPLlyxESEtLgKrHS0rqnxoeEhIj/OqbVb6PT6eDr6wuVSgWVStVoG8cyGpOSkgKj0Sj+bLVaERYWhoSEBLG/bcVms8FkMmHcuHFQq9VNtispu4rVRz+GQqnCxImGNu2DK9lsNuzatQvA9SNuTbWrrKyE1WptdjvQzeH2da3mtq/j937cuHHw8/OTonstfp9xV6zP/bmqxuY+d36pzUNR9+7d0b179xu2cyQ3pdJ5WJNSqYT95yee6vV6/PnPf8aFCxcQFBQEoC5B6nQ69O/fX2zzy6MmJpMJer0eAODt7Y3Y2Fjk5+dj8uTJAAC73Y78/HwkJyc32T+NRgONRtNgulqtdtkv5I2W7e3tOH0Gt/ujuHr1KgAgLCxM4p4QSefatWsIDAyUtA+ufA+TA9bn/tq6xtYsS7IxRXq9HgEBAZg5cyZSU1Ph6+uLv/3tb+Il9gCQkJCA/v37Y8aMGVi/fj0sFgtWrFiBpKQkMbAsWLAAW7duxbJly/Doo49i3759eOutt5CTkyOuy2g0YubMmRg+fDhGjBiBzZs3o6KiArNnz5ak9pvlzo/56Nq1K4qKihAQECDeWuCXHEfjvv/++zY/GudKNpsNeXl5SEhIkPWb1a1sX3ep8Wa1RX3NbV9BEHDlyhWEhoa2RXeJyEUkC0WBgYHIzc3FU089hbFjx8Jms2HAgAF45513MGTIEACASqXC7t27sXDhQuj1enTq1AkzZ87E6tWrxeVEREQgJycHixcvxpYtW9CzZ09s27YNBsP100tTpkzBxYsXkZqaCovFgpiYGOTm5jYYfC13Cje+eaNSqcRtt93Wog8cnU7ndqFIq9VCp9O5RWC4me3rbjW2VlvW19T2leq0GRG1nGShCACGDx/udJPFxoSHh99wUPHo0aNx9OjRZtskJyc3e7rMHSh580YiIiKX4bPP3Iiy3mknPv+MiIiobTEUuRFlvaE4nni0SKPRIC0trdEB7nTruH1di9uXyP1JevqMWqf+AGW7IECFxgcsuyuNRoOVK1dK3Q2Pxe3rWty+RO6PR4rciPORIg88VERERCQhhiI34jymSMKOEBEReSCGIjei/MXpMyIiImo7DEVuROHhA62JiIikxFDkRhQePKYoIyMDvXv3ho+PD+Li4ho8845aZuXKlVAoFE5f0dHR4vxr164hKSkJ3bp1Q+fOnfHggw82eC4gOfvoo4/w61//GqGhoVAoFOJz/BwEQUBqaip69OgBX19fxMfH4+uvv3Zq89NPP2H69OnQ6XTw9/fHnDlzUF5e3o5VEFFLMBS5EacxRXYJO9LGdu7cCaPRiLS0NBw5cgRDhgyBwWDAhQsXpO6aWxowYADOnz8vfh04cECct3jxYrz77rt4++238eGHH6KkpAQPPPCAhL2Vv4qKCgwZMgQZGRmNzl+/fj2ef/55ZGZm4tChQ+jUqRMMBgOuXbsmtpk+fTqOHz8Ok8mE3bt346OPPsL8+fPbqwQiaiFeku9G6oeiVw58Ax9vldN8BRQY1z8IUUFd2rtrtyQ9PR3z5s0Tn0WXmZmJnJwcZGVlYfny5RL3zv14eXkhJCSkwfTLly/jlVdewRtvvIGxY8cCALZv345+/frh008/xV133dXeXXULEyZMwIQJExqdJwgCNm/ejBUrVmDSpEkAgFdffRXBwcHYtWsXHnnkEZw8eRK5ubn47LPPMHz4cADACy+8gIkTJ+K5557j89CIZIRHityIUgFovOp22fP7zmB97mmnr3W5p2B86wuJe9k61dXVKCgoQHx8vDhNqVQiPj4eZrNZwp65r6+//hqhoaGIjIzE9OnTUVxcDAAoKCiAzWZz2tbR0dHo1asXt/VNKioqgsVicdqmfn5+iIuLE7ep2WyGv7+/GIgAID4+HkqlEocOHWr3PhNR03ikyI0oFApsfHgIPvrPxQbzrFdrkHvcgpKyqxL07Ob98MMPqK2tbfBw3uDgYJw6dUqiXrmvuLg4ZGdno2/fvjh//jxWrVqFu+++G8eOHYPFYoG3tzf8/f2dXhMcHAyLxSJNh92cY7s19vvrmGexWBAUFOQ038vLC127duV2J5IZhiI3c//gUNw/uOHh9pKyq8g9bsHlqzYIguB092vqOOqf5hk8eDDi4uIQHh6Ot956C76+vhL2jIhI/nj6zEP4+aoBALZaAZXVtRL3puUCAwOhUqkaXAFVWlra6LgYah1/f3/ccccdOHPmDEJCQlBdXY2ysjKnNtzWN8+x3Zr7/Q0JCWlw0UBNTQ1++uknbncimWEo8hA+6uuDrqtr3OfSNG9vb8TGxiI/P1+cZrfbkZ+fD71eL2HPPEN5eTnOnj2LHj16IDY2Fmq12mlbnz59GsXFxdzWNykiIgIhISFO29RqteLQoUPiNtXr9SgrK0NBQYHYZt++fbDb7YiLi2v3PhNR03j6zEO483PRjEYjZs6cieHDh2PEiBHYvHkzKioqxKvRqOX++Mc/4te//jXCw8NRUlKCtLQ0qFQqTJ06FX5+fpgzZw6MRiO6du0KnU6Hxx57DHq9nleeNaO8vBxnzpwRfy4qKkJhYSG6du2KXr16YdGiRXj22WfRp08fRERE4Omnn0ZoaCgmT54MAOjXrx/Gjx+PefPmITMzEzabDcnJyXjkkUd45RmRzDAUeYj6Y4jcKxIBU6ZMwcWLF5GamgqLxYKYmBjk5uY2GLxKN/bf//4XU6dOxY8//oju3btj1KhR+PTTT9G9e3cAwKZNm6BUKvHggw+iqqoKBoMBL774osS9lrfPP/8cY8aMEX82Go0AgJkzZyI7OxvLli1DRUUF5s+fj7KyMowaNQq5ubnw8fERX/P6668jOTkZ9913n7j9n3/++XavhYiax1DkQZSKusd/uNuRIgBITk5GcnKy1N1we2+++Waz8318fJCRkdHkjQipodGjR0No5m9KoVBg9erVWL16dZNtunbtijfeeMMV3SOiNsQxRR7EcXNHN8xEREREkmMo8iCOM2jueKSIiIhIagxFHkTBI0VEREQ3jaHIgyh5pIiIiOimMRR5EI4pIiIiunkMRR7EcVE+jxQRERG1HkORB+GRIiIiopvHUORBePUZERHRzWMo8iDKn0da25mJiIiIWo2hyINcP33GVERERNRaDEUe5PpAa0m7QURE5JYYijyIePNGt3skLBERkfQYijyIePNGu7T9ICIickcMRR7EMaaIV58RERG1HkORB3Fcks9MRERE1HoMRR5EyTFFREREN42hyINcv3mjtP0gIiJyRwxFHoRjioiIiG4eQ5EHUYpjihiKiIiIWouhyIMoFHzMBxER0c1iKPIgvPqMiIjo5jEUeRCOKSIiIrp5DEUeRLyjNUMRERFRqzEUeRDxPkXMRERERK3GUOSBGIqIiIhaj6HIg3BMERER0c3zkroDbq2mEoAAKH0Aper69NoqQKgBFGpA5X19ur0GsFcBUAJevtenCwJUwjWgpgJQ+9/0OpRKQIVaoKYcqOncYB2oraz73quTy+poah0qoaquPlUXl62jPepoah1KwVZXn1Lr1nU0tw6VcA2ovQqo1W5dhyv/BuVQR3PrUAnXAKEWQL196IZ1NLUOlXANsFc71+eGdXTUv0HUVNTV+EtttY4WUAi801+LWK1W+Pn54fLly9DpdHUTcwYAl08A930ABI++3vizJODrF4GBacDgldenF78NHHgYCLoXiN8vTraVl0D979vqfpj2i93RinX8ZusB9LySgxfD1+Jb1XDs0GaLzbX2n7C04h4AwKoux5xWsbBiEoLsZ5Htm4XvvEaI0ydeexZ32t7Efu+F+FCTJE7vb9uLh64tafE67HY75l9OxG1e37tsHe1RR1PrsNvtuPvicsRr97h1Hc2tI7o6F1Oq/ogi5XC82sl962hqHT61P+DJytFuX0ez6yifhCDhLLb7vIJidZz71tHEOiZcfQYjanbiA/UCfOST7LZ1dNS/wfrrsD1UDXX94HeLn7WNfn43gUeKPEgXHy/gSt33lsvXsP3ot+K8rqrLWDqg7vvtn3zr9Lqpd9gQ5APkHrPg04rr8yJCr+DOQKDw+zJsL70+faLfRTwU3rp1TL9DAXi5dh3tUUdT67g9VAFo3b+OptfxA6aEA6XWa9he6M51NL2OJz2kjhutY+/xUo+oo+E6yjEiEPjiv5fdvI6O+zfoWIeUeKSohRpNmm10SM9WXY29e/4Fg8EAta+/84pbsY6vS69gd2ExFEIVBChRo/C53l4QoMbVuvUptE6r8BKuQgEBNdBAUFxfh0qohhI1qIUX7IrrdSiEGnihusXrsNfaUXz2BG6P7A27ytcl62iPOppah73Wjm/PnkKfyF4QVN5uW0dz6xBqqvHdN6cRGXk77F71luVmdTS1DntNLYq/OYHbIyNQ69XZbetobh3Kmgp88803CI+MhsLr+v/C3a2OptahqLmGom++Ru/IPoDX9WW5Wx0d9W8QggBVbQUul3yDFbN+63yk6BY/a3mkqL14aRufrtIA0DScrvSq+/olhQK1Cp+G52pbuY4+wV2w2CCDqP0LNpsNe2xfY2LCEOdfdA/h6fUBP9e4pwgTEwZ5ZI119Z3pAPvwv5iYEO2RNdbV9x0mJgzw4Po8928QcNR4ruGMtvqsbQFefUZEREQEhiIiIiIiAAxFRERERABcGIr+/Oc/Y+TIkdBqtfD392+0TXFxMRITE6HVahEUFISlS5eipqbGqc3+/fsxbNgwaDQaREVFITs7u8FyMjIy0Lt3b/j4+CAuLg6HDx92mn/t2jUkJSWhW7du6Ny5Mx588EGUlpa2ValERETkAVwWiqqrq/HQQw9h4cKFjc6vra1FYmIiqqurcfDgQezYsQPZ2dlITU0V2xQVFSExMRFjxoxBYWEhFi1ahLlz52Lv3r1im507d8JoNCItLQ1HjhzBkCFDYDAYcOHCBbHN4sWL8e677+Ltt9/Ghx9+iJKSEjzwwAOuKp2IiIjckMtC0apVq7B48WIMGjSo0fl5eXk4ceIEXnvtNcTExGDChAl45plnkJGRgerqagBAZmYmIiIisHHjRvTr1w/Jycn43e9+h02bNonLSU9Px7x58zB79mz0798fmZmZ0Gq1yMrKAgBcvnwZr7zyCtLT0zF27FjExsZi+/btOHjwID799FNXlU9ERERuRrJL8s1mMwYNGoTg4GBxmsFgwMKFC3H8+HEMHToUZrMZ8fHxTq8zGAxYtGgRgLqjUQUFBUhJSRHnK5VKxMfHw2w2AwAKCgpgs9mclhMdHY1evXrBbDbjrrvuarR/VVVVqKqqEn+2Wq0A6i4ZtNlst1b8LziW19bLlQvW5/48vUZPrw/w/BpZn/tzVY2tWZ5kochisTgFIgDizxaLpdk2VqsVV69exaVLl1BbW9tom1OnTonL8Pb2bjCuKTg4WFxPY9asWYNVq1Y1mJ6Xlwettol7Jtwik8nkkuXKBetzf55eo6fXB3h+jazP/bV1jZWVlS1u26pQtHz5cqxbt67ZNidPnkR0dHRrFitLKSkpMBqN4s9WqxVhYWFISEi44R0xW8tms8FkMmHcuHEeeVMu1uf+PL1GT68P8PwaWZ/7c1WNjjM9LdGqULRkyRLMmjWr2TaRkZEtWlZISEiDq8QcV4SFhISI//7yKrHS0lLodDr4+vpCpVJBpVI12qb+Mqqrq1FWVuZ0tKh+m8ZoNBpoNA3vlKlWq132C+nKZcsB63N/nl6jp9cHeH6NrM/9tXWNrVlWqwZad+/eHdHR0c1+eXt733hBAPR6Pb766iunq8RMJhN0Oh369+8vtsnPz3d6nclkgl6vBwB4e3sjNjbWqY3dbkd+fr7YJjY2Fmq12qnN6dOnUVxcLLYhIiIictmYouLiYvz0008oLi5GbW0tCgsLAQBRUVHo3LkzEhIS0L9/f8yYMQPr16+HxWLBihUrkJSUJB6hWbBgAbZu3Yply5bh0Ucfxb59+/DWW28hJydHXI/RaMTMmTMxfPhwjBgxAps3b0ZFRQVmz54NAPDz88OcOXNgNBrRtWtX6HQ6PPbYY9Dr9U0OsiYiIqKOx2WhKDU1FTt27BB/Hjp0KADggw8+wOjRo6FSqbB7924sXLgQer0enTp1wsyZM7F69WrxNREREcjJycHixYuxZcsW9OzZE9u2bYPBYBDbTJkyBRcvXkRqaiosFgtiYmKQm5vrNPh606ZNUCqVePDBB1FVVQWDwYAXX3zRVaUTERGRG3JZKMrOzm707tP1hYeHY8+ePc22GT16NI4ePdpsm+TkZCQnJzc538fHBxkZGcjIyGh2Oc0RBAFA6wZstZTNZkNlZSWsVqtHnitmfe7P02v09PoAz6+R9bk/V9Xo+Nx2fI43R7JL8t3NlStXAABhYWES94SIiIha68qVK/Dz82u2jUJoSXQi2O12lJSUoEuXLlAoFG26bMfl/t9//32bX+4vB6zP/Xl6jZ5eH+D5NbI+9+eqGgVBwJUrVxAaGgqlsvnry3ikqIWUSiV69uzp0nXodDqP/WUHWJ8n8PQaPb0+wPNrZH3uzxU13ugIkYPLnn1GRERE5E4YioiIiIjAUCQLGo0GaWlpjd5B2xOwPvfn6TV6en2A59fI+tyfHGrkQGsiIiIi8EgREREREQCGIiIiIiIADEVEREREABiKiIiIiAAwFEkuIyMDvXv3ho+PD+Li4nD48GGpu9Qia9aswZ133okuXbogKCgIkydPxunTp53ajB49GgqFwulrwYIFTm2Ki4uRmJgIrVaLoKAgLF26FDU1Ne1ZSqNWrlzZoO/R0dHi/GvXriEpKQndunVD586d8eCDD6K0tNRpGXKtzaF3794NalQoFEhKSgLgfvvvo48+wq9//WuEhoZCoVBg165dTvMFQUBqaip69OgBX19fxMfH4+uvv3Zq89NPP2H69OnQ6XTw9/fHnDlzUF5e7tTmyy+/xN133w0fHx+EhYVh/fr1ri5N1FyNNpsNTz75JAYNGoROnTohNDQUv//971FSUuK0jMb2+9q1a53aSFXjjfbhrFmzGvR9/PjxTm3kvA9vVF9jf48KhQIbNmwQ28h5/7Xkc6Gt3jv379+PYcOGQaPRICoq6obPWm0xgSTz5ptvCt7e3kJWVpZw/PhxYd68eYK/v79QWloqddduyGAwCNu3bxeOHTsmFBYWChMnThR69eollJeXi23uvfdeYd68ecL58+fFr8uXL4vza2pqhIEDBwrx8fHC0aNHhT179giBgYFCSkqKFCU5SUtLEwYMGODU94sXL4rzFyxYIISFhQn5+fnC559/Ltx1113CyJEjxflyrs3hwoULTvWZTCYBgPDBBx8IguB++2/Pnj3CU089Jfzzn/8UAAj/+te/nOavXbtW8PPzE3bt2iV88cUXwm9+8xshIiJCuHr1qthm/PjxwpAhQ4RPP/1U+Pjjj4WoqChh6tSp4vzLly8LwcHBwvTp04Vjx44J//u//yv4+voKL7/8suQ1lpWVCfHx8cLOnTuFU6dOCWazWRgxYoQQGxvrtIzw8HBh9erVTvu1/t+tlDXeaB/OnDlTGD9+vFPff/rpJ6c2ct6HN6qvfl3nz58XsrKyBIVCIZw9e1ZsI+f915LPhbZ47/zmm28ErVYrGI1G4cSJE8ILL7wgqFQqITc395ZrYCiS0IgRI4SkpCTx59raWiE0NFRYs2aNhL26ORcuXBAACB9++KE47d577xWeeOKJJl+zZ88eQalUChaLRZz20ksvCTqdTqiqqnJld28oLS1NGDJkSKPzysrKBLVaLbz99tvitJMnTwoABLPZLAiCvGtryhNPPCHcfvvtgt1uFwTBvfffLz9w7Ha7EBISImzYsEGcVlZWJmg0GuF///d/BUEQhBMnTggAhM8++0xs89577wkKhUI4d+6cIAiC8OKLLwoBAQFO9T355JNC3759XVxRQ419qP7S4cOHBQDCd999J04LDw8XNm3a1ORr5FJjU6Fo0qRJTb7GnfZhS/bfpEmThLFjxzpNc5f9JwgNPxfa6r1z2bJlwoABA5zWNWXKFMFgMNxyn3n6TCLV1dUoKChAfHy8OE2pVCI+Ph5ms1nCnt2cy5cvAwC6du3qNP31119HYGAgBg4ciJSUFFRWVorzzGYzBg0ahODgYHGawWCA1WrF8ePH26fjzfj6668RGhqKyMhITJ8+HcXFxQCAgoIC2Gw2p30XHR2NXr16iftO7rX9UnV1NV577TU8+uijTg88duf9V19RUREsFovTPvPz80NcXJzTPvP398fw4cPFNvHx8VAqlTh06JDY5p577oG3t7fYxmAw4PTp07h06VI7VdNyly9fhkKhgL+/v9P0tWvXolu3bhg6dCg2bNjgdGpC7jXu378fQUFB6Nu3LxYuXIgff/xRnOdJ+7C0tBQ5OTmYM2dOg3nusv9++bnQVu+dZrPZaRmONm3x2ckHwkrkhx9+QG1trdOOB4Dg4GCcOnVKol7dHLvdjkWLFuFXv/oVBg4cKE6fNm0awsPDERoaii+//BJPPvkkTp8+jX/+858AAIvF0mj9jnlSiouLQ3Z2Nvr27Yvz589j1apVuPvuu3Hs2DFYLBZ4e3s3+KAJDg4W+y3n2hqza9culJWVYdasWeI0d95/v+ToT2P9rb/PgoKCnOZ7eXmha9euTm0iIiIaLMMxLyAgwCX9vxnXrl3Dk08+ialTpzo9XPPxxx/HsGHD0LVrVxw8eBApKSk4f/480tPTAci7xvHjx+OBBx5AREQEzp49iz/96U+YMGECzGYzVCqVR+3DHTt2oEuXLnjggQecprvL/mvsc6Gt3jubamO1WnH16lX4+vredL8ZiuiWJSUl4dixYzhw4IDT9Pnz54vfDxo0CD169MB9992Hs2fP4vbbb2/vbrbKhAkTxO8HDx6MuLg4hIeH46233rqlPzi5euWVVzBhwgSEhoaK09x5/3V0NpsNDz/8MARBwEsvveQ0z2g0it8PHjwY3t7e+J//+R+sWbNG9o+QeOSRR8TvBw0ahMGDB+P222/H/v37cd9990nYs7aXlZWF6dOnw8fHx2m6u+y/pj4X5I6nzyQSGBgIlUrVYNR9aWkpQkJCJOpV6yUnJ2P37t344IMP0LNnz2bbxsXFAQDOnDkDAAgJCWm0fsc8OfH398cdd9yBM2fOICQkBNXV1SgrK3NqU3/fuVNt3333Hd5//33MnTu32XbuvP8c/Wnu7y0kJAQXLlxwml9TU4OffvrJrfarIxB99913MJlMTkeJGhMXF4eamhp8++23ANyjRofIyEgEBgY6/U56wj78+OOPcfr06Rv+TQLy3H9NfS601XtnU210Ot0t/6eVoUgi3t7eiI2NRX5+vjjNbrcjPz8fer1ewp61jCAISE5Oxr/+9S/s27evweHaxhQWFgIAevToAQDQ6/X46quvnN7EHG/i/fv3d0m/b1Z5eTnOnj2LHj16IDY2Fmq12mnfnT59GsXFxeK+c6fatm/fjqCgICQmJjbbzp33X0REBEJCQpz2mdVqxaFDh5z2WVlZGQoKCsQ2+/btg91uFwOhXq/HRx99BJvNJrYxmUzo27evLE67OALR119/jffffx/dunW74WsKCwuhVCrF005yr7G+//73v/jxxx+dfifdfR8CdUduY2NjMWTIkBu2ldP+u9HnQlu9d+r1eqdlONq0yWfnLQ/Vppv25ptvChqNRsjOzhZOnDghzJ8/X/D393cadS9XCxcuFPz8/IT9+/c7XRpaWVkpCIIgnDlzRli9erXw+eefC0VFRcI777wjREZGCvfcc4+4DMellwkJCUJhYaGQm5srdO/eXRaXrS9ZskTYv3+/UFRUJHzyySdCfHy8EBgYKFy4cEEQhLrLSnv16iXs27dP+PzzzwW9Xi/o9Xrx9XKurb7a2lqhV69ewpNPPuk03R3335UrV4SjR48KR48eFQAI6enpwtGjR8Urr9auXSv4+/sL77zzjvDll18KkyZNavSS/KFDhwqHDh0SDhw4IPTp08fpcu6ysjIhODhYmDFjhnDs2DHhzTffFLRabbtdkt9cjdXV1cJvfvMboWfPnkJhYaHT36Xjqp2DBw8KmzZtEgoLC4WzZ88Kr732mtC9e3fh97//vSxqbK6+K1euCH/84x8Fs9ksFBUVCe+//74wbNgwoU+fPsK1a9fEZch5H97od1QQ6i6p12q1wksvvdTg9XLffzf6XBCEtnnvdFySv3TpUuHkyZNCRkYGL8n3FC+88ILQq1cvwdvbWxgxYoTw6aefSt2lFgHQ6Nf27dsFQRCE4uJi4Z577hG6du0qaDQaISoqSli6dKnTfW4EQRC+/fZbYcKECYKvr68QGBgoLFmyRLDZbBJU5GzKlClCjx49BG9vb+G2224TpkyZIpw5c0acf/XqVeEPf/iDEBAQIGi1WuG3v/2tcP78eadlyLW2+vbu3SsAEE6fPu003R333wcffNDo7+TMmTMFQai7LP/pp58WgoODBY1GI9x3330N6v7xxx+FqVOnCp07dxZ0Op0we/Zs4cqVK05tvvjiC2HUqFGCRqMRbrvtNmHt2rXtVWKzNRYVFTX5d+m491RBQYEQFxcn+Pn5CT4+PkK/fv2Ev/zlL06hQsoam6uvsrJSSEhIELp37y6o1WohPDxcmDdvXoP/RMp5H97od1QQBOHll18WfH19hbKysgavl/v+u9HngiC03XvnBx98IMTExAje3t5CZGSk0zpuheLnQoiIiIg6NI4pIiIiIgJDEREREREAhiIiIiIiAAxFRERERAAYioiIiIgAMBQRERERAWAoIiIiIgLAUEREREQEgKGIiIiICABDEREREREAhiIiIiIiAAxFRERERACA/w8TckhJDVQoPQAAAABJRU5ErkJggg==", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -406,35 +422,34 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "eplt = res.energies\n", + "eplt = res3.energies-eref[0]\n", "\n", - "fig, ax1 = plt.subplots()\n", + "# fig, ax1 = plt.subplots()\n", "\n", - "left, bottom, width, height = [0.25, 0.25, 0.3, 0.3]\n", + "left, bottom, width, height = [0.55, 0.55, 0.3, 0.3]\n", "\n", - "ax1.plot(res.energies)\n", - "# ax1.plot(Tschedule)\n", - "ax1.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "ax1.grid()\n", - "# ax1.set_yscale('symlog')\n", + "plt.plot(res.energies[:]-eref, lw=4, label=\"QUBO Energy\")\n", + "plt.plot(Tschedule, lw=3, label='Temperature')\n", + "# ax1.axline((0, 0), slope=0, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", + "plt.grid(which='both')\n", + "# plt.yscale('symlog')\n", "\n", - "ax2 = fig.add_axes([left, bottom, width, height])\n", - "ax2.plot(eplt[-100:])\n", - "ax2.grid()\n", - "ax2.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "# ax2.set_yscale('symlog')" + "plt.ylabel('Energy', fontsize=14)\n", + "plt.xlabel('Iterations', fontsize=14)\n", + "plt.legend(fontsize=12)\n", + "\n", + "# ax2 = fig.add_axes([left, bottom, width, height])\n", + "# ax2.plot(eplt[-1000:])\n", + "# ax2.grid()\n", + "# ax2.axline((0, 0), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "# ax2.set_yscale('symlog')\n", + "\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 117, + "execution_count": 124, "metadata": {}, "outputs": [], "source": [ @@ -448,15 +463,15 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 125, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-9687.974189114439 -9687.974189114439\n", - "0.0\n" + "-9562.760602598233 [-9558.244]\n", + "[-4.517]\n" ] } ], @@ -467,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 126, "metadata": {}, "outputs": [ { @@ -476,13 +491,13 @@ "Text(0.5, 1.0, 'Pressure')" ] }, - "execution_count": 119, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -528,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 127, "metadata": {}, "outputs": [], "source": [ @@ -548,15 +563,15 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 128, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1 1 1 0 1 0 0 0 1 1 0 0 1 1 1 1 0 1 1 1 1 0]\n", - "[1 1 0 1 1 1 0 0 1 1 1 0 1 1 1 1 0 1 1 1 1 0]\n" + "[1 1 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0]\n", + "[1 1 0 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0]\n" ] } ], @@ -567,16 +582,16 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 129, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([-9686.92])" + "array([-9558.244])" ] }, - "execution_count": 63, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -588,47 +603,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 130, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "r = np.array(res.trajectory[idx_min])[net.qubo.index_variables]" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "def un_flatten_list(lst):\n", + " out = []\n", + " count = 0\n", + " for er in net.qubo.mixed_solution_vectors.encoded_reals:\n", + " nqbit = er.nqbit\n", + " d = (np.array(lst)[count:count+nqbit]).tolist()\n", + " out.append(d)\n", + " count += nqbit\n", + " return out\n", + "unflat_r = un_flatten_list(r)" + ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 132, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "0it [00:00, ?it/s]" + " 0%| | 0/64 [00:00 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + "/tmp/ipykernel_5469/1665958244.py:36: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "32it [00:00, 69.06it/s]\n" + "100%|██████████| 64/64 [00:03<00:00, 19.03it/s]\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 37, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -639,16 +674,36 @@ "import itertools\n", "from tqdm import tqdm\n", "from copy import deepcopy\n", + "\n", + "\n", "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", - "energies = np.zeros((2**nqbit, 2**nqbit))\n", + "\n", "i2 = 0\n", - "for data2 in tqdm(itertools.product([0, 1], repeat=nqbit)):\n", + "random1 = np.random.randint(2,size=nqbit).tolist()\n", + "random2 = np.random.randint(2,size=nqbit).tolist()\n", + "\n", + "max_size = 64\n", + "iter_data = np.array(list(itertools.product([0, 1], repeat=nqbit)))\n", + "scale_factor = int(len(iter_data)/max_size)\n", + "if len(iter_data>max_size):\n", + " iter_data = iter_data[::scale_factor,:]\n", + "\n", + "energies = np.zeros((max_size,max_size))\n", + "\n", + "for data2 in tqdm(iter_data):\n", " i3 = 0\n", - " for data3 in itertools.product([0, 1], repeat=nqbit):\n", + " for data3 in iter_data:\n", " # print(list(data))\n", " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", " mod_bin_rep_sol[2] = list(data2)[::-1]\n", " mod_bin_rep_sol[3] = list(data3)[::-1]\n", + " # mod_bin_rep_sol[4] = random1\n", + " # mod_bin_rep_sol[5] = random2\n", + " mod_bin_rep_sol[4] = unflat_r[4]\n", + " mod_bin_rep_sol[5] = unflat_r[5]\n", + " # mod_bin_rep_sol[4] = np.ones(5).tolist()\n", + " # mod_bin_rep_sol[5] = np.ones(5).tolist()\n", + "\n", " # x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", " # x0 = list(x.values())\n", " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", @@ -660,41 +715,46 @@ "# ax = plt.figure().add_subplot(projection='3d')\n", "# ax.plot_surface(x,y,energies)\n", "\n", - "# plt.imshow(energies- eref)\n", - "# plt.colorbar()\n", - "\n", - "plt.contour(energies-eref, levels=[1e-2,1,2, 10])" + "plt.imshow(energies- eref)\n", + "plt.colorbar()\n", + "x2 = int(''.join(str(i) for i in bin_rep_sol[2][::-1]),base=2)/scale_factor\n", + "x3 = int(''.join(str(i) for i in bin_rep_sol[3][::-1]),base=2)/scale_factor\n", + "plt.contour(energies-eref, levels=[1e-2,1,2, 10])\n", + "plt.hlines(x3,0,max_size,ls='--',colors='black')\n", + "plt.vlines(x2,0,max_size,ls='--',colors='black')" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 133, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "0it [00:00, ?it/s]/tmp/ipykernel_5700/1779021354.py:17: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "32it [00:00, 82.58it/s]\n" + " 0%| | 0/128 [00:00 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + "/tmp/ipykernel_5469/3475343188.py:29: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " energies2[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + "100%|██████████| 128/128 [00:00<00:00, 680.44it/s]\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 38, + "execution_count": 133, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -704,141 +764,72 @@ "source": [ "import itertools\n", "from tqdm import tqdm\n", - "from copy import deepcopy\n", "\n", "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", - "energies = np.zeros((2**nqbit, 2**nqbit))\n", + "\n", + "random1 = np.random.randint(2,size=nqbit).tolist()\n", + "random2 = np.random.randint(2,size=nqbit).tolist()\n", + "\n", "i2 = 0\n", - "for data2 in tqdm(itertools.product([0, 1], repeat=nqbit)):\n", - " i3 = 0\n", - " for data3 in itertools.product([0, 1], repeat=nqbit):\n", - " # print(list(data))\n", - " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", - " mod_bin_rep_sol[4] = list(data2)[::-1]\n", - " mod_bin_rep_sol[5] = list(data3)[::-1]\n", - " # x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", - " # x0 = list(x.values())\n", - " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - " i3+=1\n", + "\n", + "iter_data = np.array(list(itertools.product([0, 1], repeat=nqbit)))\n", + "if len(iter_data>128):\n", + " iter_data = iter_data[::int(len(iter_data)/128),:]\n", + "\n", + "energies = np.zeros(128)\n", + "energies2 = np.zeros(128)\n", + "\n", + "for data2 in tqdm(iter_data):\n", + "\n", + " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", + " mod_bin_rep_sol[3] = list(data2)[::-1]\n", + " # mod_bin_rep_sol[2] = list(data2)[::-1]\n", + " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + "\n", + " # mod_bin_rep_sol[3] = random1 # unflat_r[3]\n", + " mod_bin_rep_sol[2] = unflat_r[2]\n", + " mod_bin_rep_sol[4] = unflat_r[4]\n", + " mod_bin_rep_sol[5] = unflat_r[5]\n", + " energies2[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", " i2+=1\n", "\n", - "# x, y = np.arange(2**nqbit), np.arange(2**nqbit)\n", - "# x,y = np.meshgrid(x,y)\n", - "# ax = plt.figure().add_subplot(projection='3d')\n", - "# ax.plot_surface(x,y,energies)\n", - "# plt.show()\n", "\n", - "plt.imshow(energies- eref)\n", - "plt.colorbar()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "((energies-eref)==0).sum()" + "encoded_real = net.qubo.mixed_solution_vectors.encoded_reals[2]\n", + "xaxis_val = []\n", + "for i in range(len(iter_data)):\n", + " ibin = np.binary_repr(i,width=nqbit)\n", + " xaxis_val.append(encoded_real.decode_polynom([int(i) for i in ibin[::-1]]))\n", + "\n", + "\n", + "plt.semilogy(xaxis_val, energies-eref, lw=4, label='Exact Values')\n", + "plt.semilogy(xaxis_val, energies2-eref, lw=4, label='Optimized Values')\n", + "plt.xlabel('Flow Value', fontsize=14)\n", + "plt.ylabel('QUBO Energy', fontsize=14)\n", + "plt.ylim([1E0,1E3])\n", + "plt.grid(which='both', axis='both')\n", + "plt.legend(loc=1, fontsize=12)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 134, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "0it [00:00, ?it/s]/tmp/ipykernel_5700/321696615.py:12: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "32it [00:00, 1563.62it/s]\n" - ] - }, { "data": { "text/plain": [ - "[]" + "0.3779527559055118" ] }, - "execution_count": 40, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Wr16NxMRE+Pv7w9/fH2lpafjiiy9sVZtVzRw3BgkR/jB0m7A+v0LqcoiIyAGlxAZCo1ZhoPsHBQAatQopsYH2LMupjChYREZG4tlnn0VBQQH27NmDuXPnYvHixThw4ICt6rMaQRBw1+k23+/8eAyd3SaJKyIiIkcjlwnIyoi3eHjTHDayMuLZeXMQIwoWGRkZ+NWvfoWJEydi0qRJeOaZZ+Dr64udO3faqj6rumJqOEL9lKhrMeDz/bxdiIiI+ktP0OCCmDH9Hg9Tq7B6aRLSEzQSVOU8Rt0gy2g0YuPGjWhra0NaWtqArzMYDDAYDL3/3NzcPNofec4UHjLclhaDv391CG/tKMfi6eEQBKZOIiL6xYmT7dhbeQoAsPKaBHgrPBDq17P9wZWKoY348Ob+/fvh6+sLpVKJ++67D5s2bUJ8fPyAr1+5ciXUanXvV1SUtD3Wb0mNgdJDhv1VOuw5flLSWoiIyPG8taMcRpOISyYE4+aUGCyeHoG0uCCGimEacbCYPHkyioqKsGvXLmRmZmLZsmUoKSkZ8PUrVqyATqfr/aqsrDyngs9VoI8C1yRFAADe+r5c0lqIiMixnGrvxIb8nt9T98weL3E1zmnEwUKhUGDChAlITk7GypUrMW3aNLz00ksDvl6pVPbeRWL+ktodp6eeflVSg8qmdomrISIiR7F253F0dBlxnsYfsyYGS12OUzrnPhYmk6nPGQpnMGmsH2ZNDIZJBNb8eEzqcoiIyAHou4xY8+NxAMA9s2N5Bm+URnR4c8WKFVi0aBGio6PR0tKCdevWYevWrfjyyy9tVZ/N3HlJLL4/3IAN+RW4OC4ILYZuHs4hInJjm/ZWoaHVgHC1ClcmhktdjtMaUbCoq6vDbbfdBq1WC7VajcTERHz55ZdYsGCBreqzmTkTQzDWX4naZgPufGdP7+MatQpZGfG8nYiIyI2YTCLe+P4ogJ4Pnp5yt2lMbXUjChZvvfWWreqwu69KalDb3H8Lp0anR+baQt6rTETkRr75uRZH69vgp/LATSnRUpfj1NwykpnH4lpi7rbGsbhERO7jX9t7ViuWXhgDX+WoWzwR3DRYcCwuERGZFRw/iT3HT0Ihl+GOi8ZJXY7Tc8tgwbG4RERk9q/tZQCAq2eEI9SfU0vPlVsGC47FJSIiADha34qvSmoBsCGWtbhlsOBYXCIiAoA3vi+HKALzpoRiQqif1OW4BLcMFuaxuAAGDBcci0tE5NrqWwz4qPAEAK5WWJNbBgugZyzu6qVJCFP33+7Ivup83mpKROTi/pN3DJ3dJkyLCuAKtRW59T016QkaLIgPQ355E+pa9Hj7h3IUVerwc410o92JiMj22ju78e7Onvbd984ez/bdVuS2KxZmcpmAtLggLJ4egT9e0bM98lFBFWoGuR2ViIic2we7K3GqvQsxQd64/PwwqctxKW4fLM50wbhApIwLRKfRhDdPt3YlIiLX0m004c0d5QCA31wSy/N0VsZgcZbMy+IAAOvyK3CyrVPiaoiIyNq+KK7BiZMdCPRR4LrkKKnLcTkMFme5dFII4jX+aO804p28Y1KXQ0REViSKYm/77tvSYuClkEtckethsDiLIAi4//Sqxds/HEOboVviioiIyFryyhqxv0oHlacMt6WNk7ocl8RgYcGiBA1ig32g6+jC+vwKqcshIiIref30asX1yVEI9FFIXI1rYrCwQC4TcO/pZilvfH8Uhm6jxBUREdFoGU0i8soasXrrEWw7VA8BwG9mxUpdlsty6z4Wg1mSFIEXvzmMmmY9NhVW4aaUaKlLIiKiEcot1iI7p6TPRGulpww/a5sRE+QjYWWuiysWA1B6yHsT7WvbymA0iRJXREREI5FbrEXm2sI+oQIA9F0mZK4tRG6xVqLKXBuDxSBuTolGgLcnjjW24/P9fAMSETkLo0lEdk4JBvtImJ1Twg+NNsBgMQgfpQduv2gcAOCfW8sginwDEhE5g/zypn4rFWcSAWh1euSXN9mvKDfBYDGE2y8aB2+FHD9rm7H1UL3U5RAR0TDUtQxvLMNwX0fDx2AxhABvBW5N7Tm4+c/vjkhcDRERDUeoX//J1efyOho+Both+M2s8VDIZdh97CR2H+OyGRGRo0uJDYRGPXBoEABo1CqOS7cBBothGOuvwrXJkQC4akFE5AzkMgG/T59i8TnzyLGsjHgOILMBBothum/OeMgE4LvSehyo1kldDhERDUGr6wCAfuEhTK3C6qVJSE/QSFGWy2ODrGGKCfLBFYnhyPmpGqu3luHVW5KkLomIiAbQZujGG6fbdz97zVREjvFGXYseoX492x9cqbAdBosRyJwTh5yfqvH5fi2ONbRhXDC7thEROaK1O4/jZHsXxgV5Y8mMCHjIuUBvL7zSIxAf7o+5U0JhEoHXt5dJXQ4REVnQ3tndOxp9+WUTGCrsjCsWI3T/pXHYcrAOG/dU4pIJIeg2mbi0RkTkQN7bWYHGtk5EB3rj6hkRUpfjdhgsRuiCcYGYEOKLI/WtWL6usPdxjVqFrIx4HgYiIpJQR6exd0V5+WVx8ORqhd3xio9QbrEWR+pb+z1eo9NzqA0RkcTW5VegobUTkWO8cE1SpNTluCUGixEwD7WxxDxFhENtiIikoe8y4rVt5tWKCVytkAiv+ghwqA0RkeNan1+B+hYDIgK8cC1XKyTDYDECHGpDROSYzlytyLw0DgoP/nqTCq/8CHCoDRGRY/pgTyVqmw3QqFW4/gKuVkiJwWIEzENtBrqplENtiIjsz9BtxOqtPasV918aB6WHXOKK3BuDxQjIZQKyMuIBYMBwwaE2RET29cGeE9Dq9Bjrr8T1F0RJXY7bY7AYofQEDVYvTUKYhXG8C88fyz4WRER21NltwurTU6cz58RB5cnVCqmxQdYopCdosCA+DPnlTahr0aOyqR1//+oQth9qQEOrAcG+SqlLJCJyCx8WnEC1To9QPyVuSomWuhwCVyxGTS4TkBYXhMXTI7D8sgmYFqlGR5cRr23lDBEiInvo7DZh1enVinu5WuEwGCysQBAEPLJwMgDg3Z3HUdvM202JiGxt094TqDrVgWBfJW5N5WqFo2CwsJLZE4Mxc9wYGM5I0EREZBtdRhNePf137X1zxnO1woEwWFiJIAh4ZEHPqsX6/AqcONkucUVERK5r094qVDZ1INhXgVtTY6Quh87AYGFFaXFBuHhCELqMIl7dwlULIiJb6Db+sjJ896zx8FJwtcKRMFhYmXnVYmPBCRxraJO4GiIi12A0icgra8Tmoir846tDON7YjkAfBX6dxtUKR8PbTa0sOWYMLpscgu9K6/Hyt4fxwo3TpS6JiMip5RZrkZ1T0m8I5OyJIfBW8NeYo+GKhQ2YVy02FVXhcG2LxNUQETmv3GItMtcWWpwsvbmoCrnFWgmqosEwWNjA1Eg1Lj9/LEQRePGbw1KXQ0TklIwmEdk5JRAHeU12TgmMpsFeQfbGYGEjv10wCYIAfLZfi5LqZqnLISJyOvnlTRZXKsxEAFqdHvnlTfYriobEYGEjU8L8cWViOADgha8PSVwNEZHzqWsZXrPB4b6O7IPBwoYenj8RMgH45uda/FR5SupyiIicSqhf/2GP5/I6sg8GCxuKC/HFkhmRAIB/cNWCiGhEUmIDoVGrIAzwvABAo1YhJTbQnmXREBgsbOyheRPhIROw/VA9dh/jPiAR0XDJZQKyMuItHt40h42sjHjIZQNFD5ICg4WNRQd54/oLogAA//iqVOJqiIicS0psEFQe/X9VhalVWL00CekJGgmqosGws4gdPDh3Aj4qOIGdR5vw45EGXDQhWOqSiIicwqtbjkDfbUK8xg9/vCIe9a0GhPr1bH9wpcIxccXCDsIDvHDL6ZG+f/+qFKLIe66JiIZS2dSOd3ceAwCs+NV5uGhCMBZPj0BaXBBDhQNjsLCT+y+Lg8pThsKKU9haWi91OUREDu+Frw+hyyjikgnBmDUxROpyaJgYLOwk1E+FZWnjAAB//+og8soasLmoCnlljewaR0R0lgPVOnxSVAUAeDx9isTV0EjwjIUd3TsnDmt+PIYD1S24+Y1dvY9r1CpkZcTzEBIR0WnP5ZZCFIGMaeGYGqmWuhwaAa5Y2FF+eSMM3aZ+j9fo9MhcW8hhOkREAH480oBth+rhIRPw2MJJUpdDI8RgYSfmYTqWmDdCOEyHiNydKIp4NvcgAODW1GjEBPlIXBGNFIOFnXCYDhHR0D7fX4N9J3TwUcjx4LyJUpdDo8BgYSccpkNENLguownPf9mzWnH37PEI9lVKXBGNxoiCxcqVKzFz5kz4+fkhNDQUV199NUpL2U1yODhMh4hocBt2V+JYYzuCfRX4zazxUpdDozSiYLFt2zYsX74cO3fuxNdff42uri4sXLgQbW1ttqrPZXCYDhHRwNoM3Xjpm8MAgP+ZNxG+St606KxG9G8uNze3zz+vWbMGoaGhKCgowOzZs61amKsxD9PJXFsIAeg3VEcEh+kQkft6a0c5GloNiAnyxk0zo6Uuh87BOZ2x0Ol0AIDAwIE/ZRsMBjQ3N/f5clfpCRqsXpqEMHX/7Y7pUWr2sSAit9TYasDr28oAAI8tnAyFhaFj5DxGvdZkMpnw8MMP4+KLL0ZCQsKAr1u5ciWys7NH+2NcTnqCBgviw5Bf3oS6Fj26uk34/Uf7UFSpw86jjbhwfJDUJRIR2dUrW46grdOIqRFqXDGVH7Cc3ahj4fLly1FcXIwNGzYM+roVK1ZAp9P1flVWVo72R7oMuUxAWlwQFk+PwHUXRPUOKPvfz0pgYh8LInIjFY3teG/XcQDAE4umQMbtYKc3qmDxwAMP4NNPP8V3332HyMjIQV+rVCrh7+/f54v6enj+JPgpPVBc1dzbG5+IyB384+tSdBlFzJoYjIsnBEtdDlnBiIKFKIp44IEHsGnTJmzZsgWxsbG2qsutBPsqsXzuBAA9/fE7Oo0SV0REZHvFVTpsLqoGwEFjrmREwWL58uVYu3Yt1q1bBz8/P9TU1KCmpgYdHR22qs9t3H7ROEQEeKGmWY83vj8qdTlERDZhNInIK2vE5qIqrPh4HwBg8fRwJERw0JirEERRHPamviBY3vt6++23cfvttw/rz2huboZarYZOp+O2yFlyfqrGg+v3wstTjq2/uxRj/dksi4hcR26xFtk5Jf3GG/xlcQJ+nRYjUVU0XMP9/T3irRBLX8MNFTS4KxM1mBEdgI4uI/7xFTuaEpHryC3WInNtocWZSU9tLuZ0ZxfCm4UdiCAI+NOV8QCAjQUnUFLtvj0/iMh1mKc7D7Y8zunOroPBwsEkRY/BlYkaiGLP7acj2KkiInJInO7sXhgsHNDj6VOg8JDhx7JGbDlYJ3U5RETnhNOd3QuDhQOKCvTGnRf33Mr7zOc/o8tokrgiIqLR43Rn98Jg4aDuvywOQT4KHK1vw7pdFVKXQ0Q0aubpzgPhdGfXwmDhoPxVnnh4wSQAwIvfHIKuo0viioiIRkcuE3BrquWJpeYmBpzu7DoYLBzYzTOjMCHUFyfbu7DquyNSl0NENCpGk4jP99cAALw85X2eC1OrsHppEqc7u5BRTzcl2/OQy/CHK87DHW/vxpofjuHW1GjEBPlIXRYR0Yisy69AibYZ/ioPfPPIHJTVt6GuRY9Qv57tD65UuBauWDi4SyeFYNbEYHQaTfhb7kGpyyEiGpGTbZ29Df8eXTgZof6q3unOaXFBDBUuiMHCwQmCgD9ccR5kAvD5/hqs+aEcm4uqkFfWyGYyROTwnv+qFKfauzAlzG/AcxbkWrgV4gSmhPnjorgg7DjSiKdzSnof16hVyMqI594kETmk4iod1uf33NWWfdX58JDzs6w74L9lJ5BbrMWOI439Hq/R6ZG5tpA99onI4ZhMIp7aXAxRBK6aFo7U8UFSl0R2wmDh4Mw99i0xb4Swxz4ROZpNe6tQWHEK3go5nvzVeVKXQ3bEYOHg2GOfiJxNs74LK7/oOWz+4NyJCBukORa5HgYLB8ce+0TkbF765jAaWg0YH+yDOy8ZJ3U5ZGcMFg6OPfaJyJkcqm3Bmh+PAQCeyoiH0kM++DeQy2GwcHDmHvuD3enNHvtE5AhEUcTT/z0Ao0nEgvixuHRyqNQlkQQYLBycXCYgKyMeAAYMF48tnMwmM0QkuS+Ka/BjWSMUHjL86Yp4qcshiTBYOIH0BA1WL03qdwDK43SY2HP8pBRlERH1au/sxv9+2nMH231z4hAd5C1xRSQVNshyEukJGiyID0N+eVNvj32IIm5+cxfW51fguuQIJMdwO4SIpLF6axmqdXpEBHghc06c1OWQhLhi4UTkMqFPj/20CcG4LjkSAPCHTcXoMpokrpCI3NGxhja8vu0oAOBPV54HLwUPbLozBgsn9+SvzkOAtycO1rTg7R/KpS6HiNzQXz4tQafRhFkTg3H5+WFSl0MSY7BwcoE+Cjy5qKer3f99fRhVpzokroiIXJ3RJCKvrBGbi6rwz+8O49uDdfCQCcjKOB+CwIPk7o5nLFzAdcmR+LDgBPKPNSFr8wG8uewCqUsiIheVW6xFdk5Jv47Al00JxYRQX4mqIkfCFQsXIJMJ+N8lCfCQCfjm51p8daBG6pKIyAXlFmuRubbQ4piBb0pqORCRADBYuIxJY/1wz+zxAICn/3sAbYZuiSsiIldiHog42LhDDkQkgMHCpTw4dyIix3ihWqfHi98ckrocInIhHIhIw8Vg4UK8FHL8ZXECAODfPxxDSXWzxBURkavgQEQaLgYLF3PZlFD8amoYjCYRT27aDxOXJYnICjgQkYaLwcIFPXXl+fBVeqCo8hTW766QuhwicgHmgYgDEcCBiNSDwcIFhalVeHThJADA3744iPoWg8QVEZGzk8sEXJMUYfE5c+eKrIx4DkQkBgtXdVvaOCRE+KNZ341nPiuRuhwicnIt+i58XFgFAPA+q2V3mFqF1UuTkJ6gkaI0cjBskOWi5DIBf10yFYtX/YBPiqpxzYxIeHrIegeYpcQG8pMFEQ3byi8OQqvTIzrQG5/9zyUormrm3ydkEYOFC0uMDMBtF8bgnbzjuOOd3X3uL9eoVcjKiOcnDCIa0o9HGrBuV895rb9dmwg/lSfS4oIkroocFbdCXNy0qAAA6Ne0pkanR+baQnbKI6JBtXd24/GP9wEAll4YzUBBQ2KwcGFGk4jnvyy1+Jw5ZrBTHhEN5vkvS1HZ1IGIAC88cXrgIdFgGCxcGDvlEdG52HOsCWt+PAYA+Os1U+Gr5O45DY3BwoWxUx4RjZa+y4jff7gPoghcnxyJOZNCpC6JnASDhQtjpzwiGq0XvzmMow1tCPVT4o9XxEtdDjkRBgsXZu6UN9BNYOyUR0SW/FR5Cv/aXgYAeGbJVKi9PSWuiJwJg4ULk8sEZGX0fNKwFC5EsFMeEfXV2W3C7z/cB5MIXDUtHAvix0pdEjkZBgsXl56gweqlSQiz0ONfJgDhAV4SVEVEjmrVd0dQWtuCIB8Fnr7qfKnLISfEI75uID1BgwXxYcgvbzrdKU+Jd3cex+f7a/Db94vw2f/MgspTPvQfREQu7WdtM1Z9dwQAkL34fAT6KCSuiJwRg4WbkMuEPo1tpoT5Y8+xkyirb8Pfcg8iK4OfTIjcWbexZwuk2yTi8vPH4oqp7MpLo8OtEDc1xkeBv12XCAB4+4dj+OFIg8QVEZG9GU0i8soasbmoCn/6pBj7q3RQe3niL4sTIAg8e0WjwxULN3bZ5FDckhqNdbsq8NjGn5D78GyovXj6m8gd5BZrkZ1T0q+J3tXTwxHqz1vQafS4YuHm/vCr8xAT5A2tTo/s/x6QuhwisoPcYi0y1xZa7Mz7n7zjnCFE54TBws35KD3wwg3TIBOAj/dW4Yv9/AuFyJUZTSKyc0ow2IQgzhCic8FgQUiOCcR9c+IAAE9u2s8W30QujDOEyNYYLAgA8PD8SThP44+T7V1Y8dF+iCI/rRC5Is4QIltjsCAAgMJDhv+7cRoUchm+PViH93dXSl0SEdkAZwiRrTFYUK8pYf54dOEkAMBfPi1BRWO7xBURkbWlxAZizCCzPzhDiM4VgwX18ZtZ45EyLhBtnUY8urGIB7iIXExdix6d3SaLz5k7V3CGEJ0LBgvqQy4T8I8bpsFHIcfuYyfxr+1lvQ108soaGTSInJjRJOKhDUVo6zQiKtALYf7KPs+HqVVYvTQJ6QnsukmjxwZZ1E9UoDeeyojH4x/tx99yS/s8p1GrkJURz794iJzQK1sOI7+8CT4KOf5zZyqiA73PmCHUs/3BlQo6VwwWZJG/yvIebI1Oj8y1hfxUQ+Rkdh5txMvfHgYA/PWaqYgN9gGAPjOEiKyBWyHUj9Ek4s+fllh8zrwRwgY6RM6jqa0TD23YC5MIXJ8cicXTI6QuiVwYgwX1wwY6RK5DFEU8tvEn1DYbEBfig+zFnGRMtsVgQf2wgQ6R63hrRzm2HKyDwkOGV29JgreCO+BkWwwW1A8b6BC5hn0nTuFvuQcBAE9dGY/zNP4SV0TugMGC+kmJDYRGrcJgZ8PZQIfIsbXou/DAur3oMopYlBCGW1OjpS6J3ASDBfUjlwnIyogHgAHDxZWJGt6WRuSgRFHEk5uKUdHUjogALzx7bSIEgf+9kn0wWJBF6QkarF6ahDB13+0OH6UcALBuVwWO1LVKURoRDeGDPZXI+akacpmAV26ZAbXXwC28iaxtxMFi+/btyMjIQHh4OARBwCeffGKDssgRpCdosOPxuVh/94V46abpWH/3hSj84wKkxva0/L5vbQHaDN1Sl0lEZzhc24Ks/x4AADy2cDKSosdIXBG5mxEHi7a2NkybNg2rVq2yRT3kYOQyAWlxQVg8PQJpcUFQesrx6i1JGOuvxJG6Vjz+0T6OWCeSkNEk9rbd33aoDsvfK4S+y4RZE4Nx7+zxUpdHbmjE9x0tWrQIixYtskUt5CRC/JT4561JuPH1nfh0nxZJ0WNw5yWxUpdF5HZyi7XIzinp13fGX+WBF26YDhnPQZEEbH7GwmAwoLm5uc8XOb/kmED84YrzAAB//fxn7D7GZllE9pRbrEXm2kKLzeya9d0oOM7/JkkaNg8WK1euhFqt7v2Kioqy9Y8kO7n9onG4alo4uk0ilr9XyIZZRHZiNInIzinBQJuQAth2n6Rj82CxYsUK6HS63q/Kykpb/0iyE0EQsPKaqZgY6ou6FsPpe+ZNUpdF5PLYdp8cmc2DhVKphL+/f58vch0+Sg+89utk+Co9kF/ehOdOd/kjItth231yZOxjQecsLsQXf78+EQDwxvfl+Hy/VuKKiFwb2+6TIxtxsGhtbUVRURGKiooAAOXl5SgqKkJFRYW1ayMnkp6g6b217Xcbf2LzLCIbSokNRMAgTa8EsO0+SWfEwWLPnj2YMWMGZsyYAQB45JFHMGPGDDz11FNWL46cy+8un4wLx//SPKu5o6v3/vq8skYeJCOykv1VOrQO0JzOfINpVkY82+6TJATRzt2NmpuboVarodPpeN7CBdW3GHDlK9+jttkAlacM+q5fDnNq1CpkZcQjPUEjYYVEzq36VAcWr/oB9S0GTI3wR31LJ2qafzlLwf/OyFaG+/t7xA2yiAYT4qfEbWkxeP7LQ31CBQDU6PTIXFuI1UuT+Jce0Si0d3bjN+/sQX2LAVPC/LD+njR4ecqRX96EuhY9Qv16tj+4UkFSYrAgqzKaRKzdafm8jYhf7q9fEB/Gv/yIRsBkEvHwhiKUaJsR7KvAm8sugK+y56/wtLggiasj+gXvCiGr4v31RLbx/Fel+KqkFgq5DK//+gJEjvGWuiQiixgsyKp4fz2R9X1UcAKrt5YBAJ67LhHJMZxYSo6LwYKsivfXE1nX7mNNWPHxfgDAA5dNwNUzIiSuiGhwDBZkVSmxgdCoVRjs9ATvrycansqmdtz7bgE6jSYsSgjDIwsmSV0S0ZAYLMiq5DIBWRnxADBguIgL8QXPbRINrkXfhbve2Y2mtk4kRPjjHzdM4xh0cgoMFmR16QkarF6ahDB13+2OMd6eEADsONKAV7YckaY4IgdlNIm9DeV+ONKAB9cV4lBtK0L9lHjztpnwVvAmPnIOfKeSTaQnaLAgPqzf/fXr8ivwp0+K8cLXhzDWX4kbZ0ZLXSqR5HKLtcjOKel3R5WnXMCbyy7oF9KJHBmDBdmMXCb0u7/+1xfGoEbXgVXfleHJTcUI8VNi7pSxElVIJL3cYi0y1xbCUgvkLqOI6lMdSIwMsHdZRKPGrRCyu8cWTsZ1yZEwmkQsf28viipPSV0SkSSMJhHZOSUWQwXwS0M5ztkhZ8JgQXYnCAJWXjMVcyaFoKPLiDvX7EZ5Q5vUZRHZHRvKkStisCBJeMpl+OetSUiMVKOprRO3/XsX6lsMUpdFZFdsKEeuiMGCJOOj9MC/b5+JmCBvVDZ14I41+QOOgiZyRWwoR66IwYIkFeyrxDt3pCDIR4Hiqmbc/14huoymPrfe5ZU1co+ZXFJUoBfkg7SmEMCGcuR8eFcISW5csA/+fftM3PSvndh+qB5L39yF403tqDlj71mjViErI57j1sll1DXr8eu38mEcIDOb80ZWRjwnAZNT4YoFOYRpUQH459IkyARgV3lTn1ABADU6PTLXFiK3WCtRhUTW09BqwC1v7kJ5QxsiArzwzJIEaM7qVRGmVmH10iSGaXI6XLEghzF7Ygj8VJ7QdXT1e07EL7feLYgP4yc4clon2zqx9M1dOFLXCo1ahfV3X4joIG/cNDO6X0M5vs/JGTFYkMPIL2+yGCrMzrz17uzGW0TOQNfehaVv7cLBmhaE+imx7nSoACw3lCNyRtwKIYfBW+/IlbXou3Db2/k4UN2MYF8F1t2dithgH6nLIrI6BgtyGLz1jlxVm6Ebt7+9Gz9VnsIYb0+s/U0qJoT6SV0WkU0wWJDDSIkNhEatGnDcOsBb78j5dHQacdc7u1Fw/CT8VR54965UTAnzl7osIpthsCCHIZcJyMqIB4ABw8X0qADwPBs5qrP7r7QZunHPu3uw82gTfJUe+M9dqUiIUEtdJpFNCaIo2rXzUHNzM9RqNXQ6Hfz9mdqpP0sjpP1UHmjR93TlvOuSWPzxivMgCEwY5DgsvW+VHjIYuk3wVsjx7l0pSI7hahs5r+H+/uZdIeRw0hM0WBAf1u/Wu7U7jyPrvwfw1o5ytHca8czVCZBx+YIcwECjzw3dJgDAfXPiGCrIbTBYkEOydOvdsovGwUshxxMf7cP6/Arou4x4/rpEeMi5o0fSGWr0OQCsz6/A8ssmsC8FuQX+jUxO5YYLovDSTTPgIROwaW8VHli3F52nPxUSSWGo0ecAR5+Te2GwIKeTMS0cq5cmQyGXIfdADe55dw/0XUapyyI3xf4rRH0xWJBTWhA/Fm/dfgFUnjJsLa3H7W/3jFznVFSyN/ZfIeqLZyzIac2aGIL/3JmKO9fsxs6jTch45Xu0dxpR22zofQ2nopKtecoFyARgoAwroGegGPuvkLvgigU5tZTYQLz3m1R4K+Qob2jvEyoATkUl2/pivxa3vrlr0FABcPQ5uRcGC3J6CRFqeCvkFp8z/32fnVPCbRGyGlEU8a/tZbh/XSEM3SbMnRKKF2+cztHnROBWCLmA/PImNLR2Dvg8p6KSNXUbTcj67wG8t6sCAHBbWgyeujIeHnIZMqaFc/Q5uT0GC3J6PJVP9tJq6MYD6wqxtbQeggD88Yp43HnxuN4usBx9TsRgQS6Ap/LJHrS6Dtzx9m4crGmBylOGl26agcvPD5O6LCKHw2BBTs88FbVGpx+w+6FCLmB8iI9d6yLnZTSJfbY0vBVy3PPuHtQ2GxDsq8Rbyy7AtKgAqcskckgMFuT0zFNRM9cWQgAshotOo4irV/2A1UuTMZ2/EGgQloaJmd9XE0N98e/bZyIq0Fuy+ogcHe8KIZeQnqDB6qVJCDvrVL65j8X4EB9odXrc8FoeNuRXSFQlOTrzMLGzW3Sbw+p9l8YxVBANgWPTyaWcvYRtPpXfou/Cox/8hK9KagEAN6dE4emrzofSw/JtquR+jCYRl/xty4BzP8yNrnY8Ppd3epBbGu7vb65YkEsxn8pfPD0CaXFBvb8A/FSeeG1pMn53+WQIArA+vxI3vL4TWl2HxBWToxhqmNiZty0T0cAYLMhtyGQCll82AWvuSIHayxM/VZ7ClS/vQF5ZIwBwzoibq2vmbctE1sDDm+R25kwKwacPXoJ73y1AibYZS9/ahSXTw7GjrBE1Z3xi5ZwR93GqvRPrdw/v7A1vWyYaHFcsyC1FBXrjo8yLsGRGBIwmER8WVvUJFQDnjLiL7w/X4/IXt2Pn0cG3OAT0hE0OEyMaHIMFuS0vhRzPX5cIf5XlhTvOGXFt+i4jnv7vAfz6rXzUNhswPsQHT6RPgYBfhoeZcZgY0fBxK4Tc2u5jJ9Gs7x7wec4ZcU3FVTo8tGEvyurbAPTM+1ix6Dx4KeQYF+zdr49FGLfFiIaNwYLcGueMuC5Ltx4DwGvbyvB/Xx9Ct0lEqJ8Sz12XiEsnh/Z+X3qCBgviwzhMjGiUGCzIrQ33IJ6XJ/tdOBNL3TNDfJXw8/LA0dOrFIsSwvDXJVMxxkfR7/s5TIxo9BgsyK0NZ84IAPz+w5/QrO/GtUkRvZMsyTGZu2ee/e+zvtWA+lYDVB4yPLNkKq7hv0sim+DhTXJr5jkjwMAH9iICVDjV0Y3HNv6Em9/YibL6VrvWSMNnNInIzikZNCT6e3ni6hkMFUS2wmBBbm+gOSNhahVeW5qErb+7DE8smgKVpww7jzZh0Yvf4/++PgR9lxEAG2s5kqG6ZwJAXYuB3TOJbIhbIUQY+sDefXPicMVUDf60uRhbS+vx0reHkfNTNa6aHo73d1f2+WXGxlrSqRlmi3YexiWyHQ4hIxoBURTx+f4aPJ1zAPUtBouvMS+wr16axHBhJ6Io4ssDNfhzTgmqh1ixAID1d1/Iw5lEI8QhZEQ2IAgCrkjU4MuHZ8NbYflOETbWsq8fyxpw9T9/xH1rC1Gt02OwoxPsnklke9wKIRqF0poWtHcaB3yejbWsw1IvCvP21P4TOjz35UF8f7gBQM8twb+ZFYvYYB88+sFPANDnECe7ZxLZB4MF0SgMd4++rL6VwWKULPWi0KhVuHfOeOw+dhKf7euZ4eIpF3BLSjQemDsRIX5KAIC3Qs7umUQS4RkLolHIK2vEzW/sHPJ1HjIBN8yMwt2zxiM22Kff84N9IndnA/WiOJMgAIunheORBZMRHeTd73leWyLrGu7vb65YEI3CcBprecoFdBlFrNtVgfX5Fbg8Pgz3zhmPGdFjAAz8idzdP1UPpxeF0kOGD++7CFMj1QO+ht0ziaTBw5tEozBUYy0BwMs3zcAH96Zh3pRQiCKQe6AGS/75I254PQ/P5R5E5trCfj0XOKp9eL0oDN0mtBoGHh5HRNLhigXRKJkbaw21l58SG4jDtS341/aj+KSoCvnlTQM2aBLRE0qyc0qwID7MZZbuh7MtYTKJ2FXehBe/OTSsP5O9KIgcE89YEJ2jkezl1+j0eOazEuTsG3pFwlV6LQy15VNW34pNhVXYtLcKVaeG1+AKcJ3rQ+QseMaCyE5GspcfplZhfvzYYQWL8oaB7yhxloOJAx3CrNHpcd/aQsQEeeN4Y3vv434qD/xqahi+KalDU1unxXMWAnquI3tREDkmBgsiOxvuqPY/bCrGx4VVuGxKKOZOCcWUMD8IgnDOhz5HG0pG+n2DHcI0P3a8sR0yAbh0ciiuSYrA/PPGQuUpR+7knkAigL0oiJzNqLZCVq1aheeffx41NTWYNm0aXnnlFaSkpAzre7kVQu7OaBJxyd+2DHpHiYdMQPdZXTvD1SrEhfr2NoQ603DbiI82lIz0+4wmEZv2nsBjG/cN+GeavTZAzbxrhsixDPf394iDxfvvv4/bbrsNr732GlJTU/Hiiy9i48aNKC0tRWhoqNUKI3Jl5i0CwPIn8tVLkzA1MgDfHazDloN1+OFIAwzdpkH/TPMWwY7H51r8ND/QtsRQoWSo71t1SxImhfmhuEqHfSd0KK7SobhaN2hn0jO9dNN0LJ4eYfE5Z9nyIXIHNgsWqampmDlzJl599VUAgMlkQlRUFB588EE88cQTViuMyNWN5BN5R6cRb/9wFM99OfQdExNCfRCvUSM60BtRgV6IGuON8AAv3PSvnahptnwnxUChxLy6Mtjtn2dvV5gp5DJ0GgcPQwAPYRI5C5sc3uzs7ERBQQFWrFjR+5hMJsP8+fORl5dn8XsMBgMMhl+mQDY3N4/kRxK5rKFGtZ/JSyFHxJj+3SUtOVLXhiN1bSOqxTzbJP3FbfCQy2HoMkLfZUSLoRst+sH7RYjoaQY2NUKNxMgAJESokRipxrggH8x5/rsBt3x4CJPINY0oWDQ0NMBoNGLs2LF9Hh87diwOHjxo8XtWrlyJ7Ozs0VdI5MJGckfJcA99PjRvIrwUclQ2taPyZAdONLXjeGM7jMNYnDw8wkBi9ty1iViSFNnv8ayMeB7CJHIzNr8rZMWKFXjkkUd6/7m5uRlRUVG2/rFELmeoNuLmFYD/mTex3y/rH4804JY3dw35M347fxKmRwdA5SGDylOO0poW/P6joQ9ghqm9LD4+3CZiROQ6RhQsgoODIZfLUVtb2+fx2tpahIWFWfwepVIJpVI5+gqJCMAvbcRHswKQOj5oWKHkgbkT+nx/QoQa//fNoXPazhjJlg8ROb8RzQpRKBRITk7Gt99+2/uYyWTCt99+i7S0NKsXR0R9mVcAwtR9t0XC1KpBbzUdarYJYDmUjPb7LP38tLggLJ4egbS4IIYKIhc2qttNly1bhtdffx0pKSl48cUX8cEHH+DgwYP9zl5YwrtCiM7daG/DtFcfCyJyPTa73RQAXn311d4GWdOnT8fLL7+M1NRUqxZGRLZhr86bRORabBoszgWDBRERkfMZ7u/vEZ2xICIiIhoMgwURERFZDYMFERERWQ2DBREREVkNgwURERFZDYMFERERWQ2DBREREVkNgwURERFZDYMFERERWY3Nx6afzdzos7m52d4/moiIiEbJ/Ht7qIbddg8WLS0tAICoqCh7/2giIiI6Ry0tLVCr1QM+b/dZISaTCdXV1fDz84MgWG+AUXNzM6KiolBZWckZJAPgNRoar9HgeH2Gxms0NF6joTniNRJFES0tLQgPD4dMNvBJCruvWMhkMkRGRtrsz/f393eYfwmOitdoaLxGg+P1GRqv0dB4jYbmaNdosJUKMx7eJCIiIqthsCAiIiKrcZlgoVQqkZWVBaVSKXUpDovXaGi8RoPj9Rkar9HQeI2G5szXyO6HN4mIiMh1ucyKBREREUmPwYKIiIishsGCiIiIrIbBgoiIiKzGZYLFqlWrMG7cOKhUKqSmpiI/P1/qkhzG008/DUEQ+nxNmTJF6rIks337dmRkZCA8PByCIOCTTz7p87woinjqqaeg0Wjg5eWF+fPn4/Dhw9IUK5GhrtHtt9/e7z2Vnp4uTbESWLlyJWbOnAk/Pz+Ehobi6quvRmlpaZ/X6PV6LF++HEFBQfD19cW1116L2tpaiSq2v+Fco0svvbTf++i+++6TqGL7W716NRITE3ubYKWlpeGLL77ofd5Z30MuESzef/99PPLII8jKykJhYSGmTZuGyy+/HHV1dVKX5jDOP/98aLXa3q8dO3ZIXZJk2traMG3aNKxatcri88899xxefvllvPbaa9i1axd8fHxw+eWXQ6/X27lS6Qx1jQAgPT29z3tq/fr1dqxQWtu2bcPy5cuxc+dOfP311+jq6sLChQvR1tbW+5rf/va3yMnJwcaNG7Ft2zZUV1fjmmuukbBq+xrONQKAu+++u8/76LnnnpOoYvuLjIzEs88+i4KCAuzZswdz587F4sWLceDAAQBO/B4SXUBKSoq4fPny3n82Go1ieHi4uHLlSgmrchxZWVnitGnTpC7DIQEQN23a1PvPJpNJDAsLE59//vnex06dOiUqlUpx/fr1ElQovbOvkSiK4rJly8TFixdLUo8jqqurEwGI27ZtE0Wx5z3j6ekpbty4sfc1P//8swhAzMvLk6pMSZ19jURRFOfMmSM+9NBD0hXlgMaMGSO++eabTv0ecvoVi87OThQUFGD+/Pm9j8lkMsyfPx95eXkSVuZYDh8+jPDwcIwfPx633norKioqpC7JIZWXl6OmpqbP+0mtViM1NZXvp7Ns3boVoaGhmDx5MjIzM9HY2Ch1SZLR6XQAgMDAQABAQUEBurq6+ryPpkyZgujoaLd9H519jczee+89BAcHIyEhAStWrEB7e7sU5UnOaDRiw4YNaGtrQ1pamlO/h+w+hMzaGhoaYDQaMXbs2D6Pjx07FgcPHpSoKseSmpqKNWvWYPLkydBqtcjOzsasWbNQXFwMPz8/qctzKDU1NQBg8f1kfo56tkGuueYaxMbGoqysDE8++SQWLVqEvLw8yOVyqcuzK5PJhIcffhgXX3wxEhISAPS8jxQKBQICAvq81l3fR5auEQDccsstiImJQXh4OPbt24fHH38cpaWl+PjjjyWs1r7279+PtLQ06PV6+Pr6YtOmTYiPj0dRUZHTvoecPljQ0BYtWtT7vxMTE5GamoqYmBh88MEHuOuuuySsjJzVTTfd1Pu/p06disTERMTFxWHr1q2YN2+ehJXZ3/Lly1FcXOzW55aGMtA1uueee3r/99SpU6HRaDBv3jyUlZUhLi7O3mVKYvLkySgqKoJOp8OHH36IZcuWYdu2bVKXdU6cfiskODgYcrm830nZ2tpahIWFSVSVYwsICMCkSZNw5MgRqUtxOOb3DN9PIzN+/HgEBwe73XvqgQcewKefforvvvsOkZGRvY+HhYWhs7MTp06d6vN6d3wfDXSNLElNTQUAt3ofKRQKTJgwAcnJyVi5ciWmTZuGl156yanfQ04fLBQKBZKTk/Htt9/2PmYymfDtt98iLS1NwsocV2trK8rKyqDRaKQuxeHExsYiLCysz/upubkZu3bt4vtpECdOnEBjY6PbvKdEUcQDDzyATZs2YcuWLYiNje3zfHJyMjw9Pfu8j0pLS1FRUeE276OhrpElRUVFAOA27yNLTCYTDAaDc7+HpD49ag0bNmwQlUqluGbNGrGkpES85557xICAALGmpkbq0hzCo48+Km7dulUsLy8Xf/jhB3H+/PlicHCwWFdXJ3VpkmhpaRH37t0r7t27VwQgvvDCC+LevXvF48ePi6Iois8++6wYEBAgbt68Wdy3b5+4ePFiMTY2Vuzo6JC4cvsZ7Bq1tLSIjz32mJiXlyeWl5eL33zzjZiUlCROnDhR1Ov1UpduF5mZmaJarRa3bt0qarXa3q/29vbe19x3331idHS0uGXLFnHPnj1iWlqamJaWJmHV9jXUNTpy5Ij45z//WdyzZ49YXl4ubt68WRw/frw4e/ZsiSu3nyeeeELctm2bWF5eLu7bt0984oknREEQxK+++koURed9D7lEsBBFUXzllVfE6OhoUaFQiCkpKeLOnTulLslh3HjjjaJGoxEVCoUYEREh3njjjeKRI0ekLksy3333nQig39eyZctEUey55fRPf/qTOHbsWFGpVIrz5s0TS0tLpS3azga7Ru3t7eLChQvFkJAQ0dPTU4yJiRHvvvtutwrylq4NAPHtt9/ufU1HR4d4//33i2PGjBG9vb3FJUuWiFqtVrqi7Wyoa1RRUSHOnj1bDAwMFJVKpThhwgTxd7/7najT6aQt3I7uvPNOMSYmRlQoFGJISIg4b9683lAhis77HuLYdCIiIrIapz9jQURERI6DwYKIiIishsGCiIiIrIbBgoiIiKyGwYKIiIishsGCiIiIrIbBgoiIiKyGwYKIiIishsGCiIiIrIbBgoiIiKyGwYKIiIishsGCiIiIrOb/AeZWRkXbo8CbAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "import itertools\n", - "from tqdm import tqdm\n", - "\n", - "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", - "energies = np.zeros(2**nqbit)\n", - "i2 = 0\n", - "for data2 in tqdm(itertools.product([0, 1], repeat=nqbit)):\n", - "\n", - " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", - " mod_bin_rep_sol[2] = list(data2)[::-1]\n", - " # mod_bin_rep_sol[3] = list(data2)[::-1]\n", - " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "\n", - " i2+=1\n", - "plt.plot(energies-eref, 'o-')" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# import itertools\n", - "# from tqdm import tqdm\n", - "# from copy import deepcopy\n", - "\n", - "# nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", - "# energies = np.zeros((2**nqbit, 2**nqbit))\n", - "\n", - "# for data1 in tqdm(itertools.product([0, 1], repeat=nqbit)):\n", - "# for data2 in itertools.product([0, 1], repeat=nqbit):\n", - "# for data3 in itertools.product([0, 1], repeat=nqbit):\n", - "# for data4 in itertools.product([0, 1], repeat=nqbit):\n", - "# # print(list(data))\n", - "# mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", - "# mod_bin_rep_sol[2] = list(data1)[::-1]\n", - "# mod_bin_rep_sol[3] = list(data2)[::-1]\n", - "# mod_bin_rep_sol[4] = list(data3)[::-1]\n", - "# mod_bin_rep_sol[5] = list(data4)[::-1]\n", - "# # x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", - "# # x0 = list(x.values())\n", - "# energies = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "# if energies <= eref:\n", - "# print(energies-eref)\n", - "# print(data1)\n", - "# print(data2)\n", - "# print(data3)\n", - "# print(data4)\n", - "\n", - "\n" + "r0 = net.qubo.mixed_solution_vectors.encoded_reals[2]\n", + "zz = np.binary_repr(12,width=9)\n", + "r0.decode_polynom([int(z) for z in zz[::-1]])" ] }, { @@ -850,7 +841,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 135, "metadata": {}, "outputs": [], "source": [ @@ -861,892 +852,1016 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 136, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{('x_002_001', 'x_004_001'): -0.9113462377164281,\n", - " ('x_004_001*x_002_001', 'x_004_001'): 0.0,\n", - " ('x_004_001*x_002_001', 'x_002_001'): 0.0,\n", - " ('x_004_004', 'x_004_001'): 0.5328034021738732,\n", - " ('x_004_004', 'x_002_001'): -7.290769901731425,\n", - " ('x_004_004', 'x_004_001*x_002_001'): -4.440892098500626e-16,\n", - " ('x_004_004*x_002_001', 'x_002_001'): 0.0,\n", + "{('x_002_001', 'x_004_004'): -1.7796367476667259,\n", " ('x_004_004*x_002_001', 'x_004_004'): 0.0,\n", - " ('x_003_003', 'x_004_001'): -0.13319458896982309,\n", - " ('x_003_003', 'x_004_001*x_002_001'): 0.26638917793964617,\n", - " ('x_003_003', 'x_004_004'): -1.0655567117585847,\n", - " ('x_003_003', 'x_004_004*x_002_001'): 2.1311134235171694,\n", - " ('x_003_003', 'x_001_001'): -0.6350934832009412,\n", - " ('x_001_001*x_003_003', 'x_004_001'): 0.26638917793964617,\n", - " ('x_001_001*x_003_003', 'x_004_001*x_002_001'): -0.5327783558792923,\n", - " ('x_001_001*x_003_003', 'x_004_004'): 2.1311134235171694,\n", - " ('x_001_001*x_003_003', 'x_004_004*x_002_001'): -4.262226847034339,\n", - " ('x_001_001*x_003_003', 'x_001_001'): 0.0,\n", - " ('x_001_001*x_003_003', 'x_003_003'): 0.0,\n", - " ('x_004_002', 'x_004_001'): 0.1331952395229291,\n", - " ('x_004_002', 'x_002_001'): -1.8226924754328562,\n", - " ('x_004_002', 'x_004_001*x_002_001'): 0.0,\n", - " ('x_004_002', 'x_004_004'): 1.0656165626443364,\n", - " ('x_004_002', 'x_004_004*x_002_001'): -8.881784197001252e-16,\n", - " ('x_004_002', 'x_003_003'): -0.26638917793964617,\n", - " ('x_004_002', 'x_001_001*x_003_003'): 0.5327783558792923,\n", - " ('x_004_002*x_002_001', 'x_002_001'): 0.0,\n", - " ('x_004_002*x_002_001', 'x_003_003'): 0.5327783558792923,\n", 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('x_005_002', 'x_003_004*x_003_006'): -9.79960634338376,\n", + " ('x_005_002', 'x_003_004*x_003_002'): -0.612475396461485,\n", + " ('x_005_002', 'x_003_007*x_003_001'): -2.44990158584594,\n", + " ('x_005_002', 'x_003_003*x_003_001*x_001_001'): 0.3062376982307425,\n", + " ('x_005_002', 'x_003_004*x_001_001*x_003_002'): 1.22495079292297,\n", + " ('x_005_002', 'x_003_001*x_003_006'): -1.22495079292297,\n", + " ('x_005_002', 'x_003_004*x_003_003'): -1.22495079292297,\n", + " ('x_005_002', 'x_003_007*x_003_004*x_001_001'): 39.19842537353504,\n", + " ('x_005_002', 'x_003_001*x_001_001*x_003_006'): 2.44990158584594,\n", + " ('x_005_002', 'x_003_001*x_003_002'): -0.07655942455768562,\n", + " ('x_005_002', 'x_003_007*x_003_004'): -19.59921268676752,\n", + " ('x_005_002', 'x_003_001*x_001_001*x_003_002'): 0.15311884911537124,\n", + " ('x_005_002', 'x_003_001*x_003_003'): -0.15311884911537124,\n", + " ('x_005_002', 'x_003_003*x_003_004*x_001_001'): 2.44990158584594,\n", + " ('x_005_002', 'x_003_007*x_003_001*x_001_001'): 4.89980317169188,\n", + " ('x_005_002', 'x_001_001*x_003_002*x_003_003'): 0.612475396461485,\n", + " ('x_005_002', 'x_003_004*x_001_001*x_003_006'): 19.59921268676752,\n", + " ('x_005_002', 'x_003_007*x_003_003*x_001_001'): 19.59921268676752,\n", + " ('x_005_002', 'x_001_001*x_003_002*x_003_006'): 4.89980317169188,\n", + " ('x_005_002', 'x_003_007*x_003_006*x_001_001'): 156.79370149414015,\n", + " ('x_005_002', 'x_003_003*x_001_001*x_003_006'): 9.79960634338376,\n", + " ('x_005_002', 'x_003_007*x_001_001*x_003_002'): 9.79960634338376,\n", + " ('x_005_002', 'x_005_001'): 19.84003968007936,\n", + " ('x_005_003', 'x_004_004'): 7.359473053106612,\n", + " ...}" ] }, - "execution_count": 43, + "execution_count": 136, "metadata": {}, "output_type": "execute_result" } @@ -1757,7 +1872,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 137, "metadata": {}, "outputs": [], "source": [ @@ -1767,7 +1882,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 138, "metadata": {}, "outputs": [], "source": [ @@ -1776,7 +1891,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 139, "metadata": {}, "outputs": [], "source": [ @@ -1785,7 +1900,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 140, "metadata": {}, "outputs": [], "source": [ diff --git a/docs/notebooks/solutions.pkl b/docs/notebooks/solutions.pkl new file mode 100644 index 0000000000000000000000000000000000000000..72c39762558d72dd2b99ca36c4b95f17de538c65 GIT binary patch literal 6321 zcmZo*nYv7Z0SscNX!MBYmF5;y>LuqFrRwFD=9FY678NB{PU+!^FG@|$&nqq|Dork# zGI>f5D_G%_9`?Kxh?2=uyct@jI5Q?qX`d1_MZ=rXo27M121^fXN=aowDo6`cn#GjP z4u~vs52MW#KR-XO|3CmHyctTSBy~C~9ITt88#!sEJ&aCI0|6@s?(2&SY`M%GrT~>? zFlVrVY|G$4wo71C_h_h%rk2rMGFmzymB6E=;%HqmT8ECd9Y)(sqiw}08KbqqXl*cB m8;sTlqqV_kZ7^CJjE*y{v>&YvMr(u7+F-Oc7#g)fsvZFKutZ7# literal 0 HcmV?d00001 diff --git a/docs/notebooks/test_qubo_poly_solver.py b/docs/notebooks/test_qubo_poly_solver.py new file mode 100644 index 0000000..499b766 --- /dev/null +++ b/docs/notebooks/test_qubo_poly_solver.py @@ -0,0 +1,179 @@ +import wntr +import wntr_quantum +import numpy as np +import matplotlib.pyplot as plt +from copy import deepcopy + +from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver +from qubops.solution_vector import SolutionVector_V2 as SolutionVector +from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding +from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo +from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing +from qubops.qubops_mixed_vars import QUBOPS_MIXED +import sparse +from wntr_quantum.sampler.step.full_random import IncrementalStep +from wntr_quantum.sampler.simulated_annealing import modify_solution_sample + +import pickle + + +def plot_solutions(solutions, references): + fig = plt.figure(figsize=plt.figaspect(0.5)) + ax1 = fig.add_subplot(121) + + ax1.axline((0, 0.0), slope=1.10, color="grey", linestyle=(0, (2, 5))) + ax1.axline((0, 0.0), slope=1, color="black", linestyle=(0, (2, 5))) + ax1.axline((0, 0.0), slope=0.90, color="grey", linestyle=(0, (2, 5))) + ax1.grid() + + for r, sol in zip(references, solutions): + ax1.scatter( + r[:2], sol[:2], s=150, lw=1, edgecolors="w", label="Sampled solution" + ) + + ax1.set_xlabel("Reference Values", fontsize=12) + ax1.set_ylabel("QUBO Values", fontsize=12) + ax1.set_title("Flow Rate", fontsize=14) + + ax2 = fig.add_subplot(122) + + ax2.axline((0, 0.0), slope=1.10, color="grey", linestyle=(0, (2, 5))) + ax2.axline((0, 0.0), slope=1, color="black", linestyle=(0, (2, 5))) + ax2.axline((0, 0.0), slope=0.90, color="grey", linestyle=(0, (2, 5))) + + for r, sol in zip(references, solutions): + ax2.scatter( + r[2:], + sol[2:], + s=150, + lw=1, + edgecolors="w", + label="Sampled solution", + ) + ax2.grid() + + ax2.set_xlabel("Reference Values", fontsize=12) + ax2.set_title("Pressure", fontsize=14) + plt.show() + + +# Create a water network model +inp_file = "./networks/Net0_CM.inp" +inp_file = "./networks/Net0.inp" +# inp_file = './networks/Net2LoopsDW.inp' +wn_ref = wntr.network.WaterNetworkModel(inp_file) + +# store the results +qubo_results = [] +solutions = [] +encoded_reference_solutions = [] + +# iterate over a bunch of confs +Nsim = 100 +for i in range(Nsim): + print("==== %d / %d ====" % (i, Nsim)) + # copy the nework + wn = deepcopy(wn_ref) + + # change pipe diams + # for pipe_name in wn.link_name_list: + # pipe = wn.get_link(pipe_name) + # eps = 0.9 + 0.2 * np.random.rand() + # pipe.diameter *= eps + + # solve classcaly + sim = wntr.sim.EpanetSimulator(wn) + results = sim.run_sim() + + # extract ref values + ref_pressure = results.node["pressure"].values[0][:2] + ref_rate = results.link["flowrate"].values[0] + ref_values = np.append(ref_rate, ref_pressure) + ref_values + + # create qubo encoding for the flow + nqbit = 7 + step = 4.0 / (2**nqbit - 1) + flow_encoding = PositiveQbitEncoding( + nqbit=nqbit, step=step, offset=+0, var_base_name="x" + ) + + # create qubo encoding for the heads + nqbit = 7 + step = 200 / (2**nqbit - 1) + head_encoding = PositiveQbitEncoding( + nqbit=nqbit, step=step, offset=+0.0, var_base_name="x" + ) + + # create qubosolver + net = QuboPolynomialSolver( + wn, flow_encoding=flow_encoding, head_encoding=head_encoding + ) + + # create model + model, model_updater = create_hydraulic_model_for_qubo(wn) + net.create_index_mapping(model) + net.matrices = net.initialize_matrices(model) + + # solve qubo classically + ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = net.classical_solutions() + encoded_reference_solutions.append(encoded_ref_sol) + + # sampler + sampler = SimulatedAnnealing() + + # create the solver attribute + net.qubo = QUBOPS_MIXED(net.mixed_solution_vector, {"sampler": sampler}) + matrices = tuple(sparse.COO(m) for m in net.matrices) + net.qubo.qubo_dict = net.qubo.create_bqm(matrices, strength=0) + + # create step + var_names = sorted(net.qubo.qubo_dict.variables) + net.qubo.create_variables_mapping() + mystep = IncrementalStep( + var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=1 + ) + + # generate init sample + # x = modify_solution_sample(net, bin_rep_sol, modify=["flows", "heads"]) + x = modify_solution_sample(net, bin_rep_sol, modify=["heads"]) + x0 = list(x.values()) + + # compute ref energy + eref = net.qubo.energy_binary_rep(bin_rep_sol) + + # temperature schedule + num_sweeps = 2000 + Tinit = 1e1 + Tfinal = 1e-1 + Tschedule = np.linspace(Tinit, Tfinal, num_sweeps) + Tschedule = np.append(Tschedule, Tfinal * np.ones(1000)) + Tschedule = np.append(Tschedule, np.zeros(1000)) + + # sample + mystep.optimize_values = np.arange(4, 6) + res = sampler.sample( + net.qubo, + init_sample=x0, + Tschedule=Tschedule, + take_step=mystep, + save_traj=True, + verbose=False, + ) + mystep.verify_quadratic_constraints(res.res) + qubo_results.append(res) + + # compute final + idx_min = np.array([e for e in res.energies]).argmin() + # idx_min = -1 + sol = res.trajectory[idx_min] + sol = net.qubo.decode_solution(np.array(sol)) + sol = net.combine_flow_values(sol) + sol = net.convert_solution_to_si(sol) + solutions.append(sol) + + +plot_solutions(solutions, encoded_reference_solutions) +pickle.dump(solutions, open("solutions.pkl", "wb")) +pickle.dump(encoded_reference_solutions, open("encoded_reference_solutions.pkl", "wb")) +# pickle.dump(qubo_results, open("qubo_results.pkl", "wb")) diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py index 5e52ff5..4e8e584 100644 --- a/wntr_quantum/sampler/simulated_annealing.py +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -106,7 +106,7 @@ def __init__(self): # noqa: D107 def sample( self, - bqm, + qubo, num_sweeps=100, Temp=[1e5, 1e-3], Tschedule=None, @@ -127,18 +127,35 @@ def sample( save_traj (bool, optional): save the trajectory. Defaults to False verbose(bool, optional): """ + # def bqm_energy(bqm, input, var_names): # noqa: D417 + # """Compute the energy of a given binary array. - def bqm_energy(bqm, input, var_names): # noqa: D417 - """Compute the energy of a given binary array. + # Args: + # bqm (bqm) + # x (_type_): _description_ + # var_names (list): list of var names + # """ + # return bqm.energies(as_samples((input, var_names))) + + def bqm_energy(qubo, input, var_names): + """_summary_. Args: - bqm (bqm) - x (_type_): _description_ - var_names (list): list of var names + qubo (_type_): _description_ + input (_type_): _description_ + var_names (_type_): _description_ + + Raises: + ValueError: _description_ + + Returns: + _type_: _description_ """ - return bqm.energies(as_samples((input, var_names))) + return qubo.energy_binary_rep( + np.array(input)[qubo.index_variables].tolist() + ) - self.bqm = bqm + self.bqm = qubo.qubo_dict # check that take_step is callable if not callable(take_step): @@ -149,7 +166,7 @@ def bqm_energy(bqm, input, var_names): # noqa: D417 # define the initial state if init_sample is None: - current_sample = np.random.randint(2, size=bqm.num_variables) + current_sample = np.random.randint(2, size=self.bqm.num_variables) else: current_sample = init_sample @@ -164,15 +181,15 @@ def bqm_energy(bqm, input, var_names): # noqa: D417 # initialize the energy energies = [] - e_current = bqm_energy(self.bqm, current_sample, self.var_names) + e_current = bqm_energy(qubo, current_sample, self.var_names) energies.append(e_current) # loop over the temp schedule for T in tqdm(Tschedule): # new point - new_sample = take_step(deepcopy(current_sample)) - e_new = bqm_energy(self.bqm, new_sample, self.var_names) + new_sample = take_step(deepcopy(current_sample), verbose=verbose) + e_new = bqm_energy(qubo, new_sample, self.var_names) # accept/reject if e_new < e_current: diff --git a/wntr_quantum/sampler/step/full_random.py b/wntr_quantum/sampler/step/full_random.py index 963b201..1884ff5 100644 --- a/wntr_quantum/sampler/step/full_random.py +++ b/wntr_quantum/sampler/step/full_random.py @@ -4,7 +4,7 @@ class RandomStep(BaseStep): # noqa: D101 - def __call__(self, x): + def __call__(self, x, verbose=False): """Call function of the method. Args: @@ -23,7 +23,7 @@ def __call__(self, x): class IncrementalStep(BaseStep): - def __call__(self, x): + def __call__(self, x, verbose=False): """Call function of the method. Args: @@ -71,7 +71,8 @@ def __call__(self, x): # convert back to binary repr new_data = np.array([int(i) for i in new_val])[::-1] - print(random_val_name, data, "=>", new_data) + if verbose: + print(random_val_name, data, "=>", new_data) # inject in the x vector for ix, nd in zip(idx, new_data): From 4eff9cb158cea8ec7c73a65d826437bc8803539b Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 1 Nov 2024 14:35:16 +0100 Subject: [PATCH 76/96] finish compute on 2loops --- docs/notebooks/enPflE1Q | Bin 0 -> 32 bytes docs/notebooks/hhl_Net2loops.ipynb | 362 ++++ .../encoded_reference_solutions.pkl | Bin docs/notebooks/net0_data/energies.pkl | Bin 0 -> 3921 bytes .../net0_data/plot_test_qubo_solver.ipynb | 469 +++++ docs/notebooks/net0_data/solutions.pkl | Bin 0 -> 6321 bytes .../encoded_reference_solutions.pkl | Bin 0 -> 262 bytes docs/notebooks/net2loops_data/energies.pkl | Bin 0 -> 501 bytes .../plot_test_qubo_solver.ipynb | 469 +++++ docs/notebooks/net2loops_data/solutions.pkl | Bin 0 -> 1541 bytes docs/notebooks/networks/Net0.inp | 2 +- docs/notebooks/plot_test_qubo_solver.ipynb | 361 +++- .../qubo_poly_solver_2loops_dw.ipynb | 82 +- docs/notebooks/qubo_poly_solver_Net0.ipynb | 1876 +++++++++-------- docs/notebooks/solutions.pkl | Bin 6321 -> 0 bytes docs/notebooks/test_qubo_poly_solver.py | 36 +- .../test_qubo_poly_solver_net2loops.py | 220 ++ wntr_quantum/sampler/simulated_annealing.py | 5 +- wntr_quantum/sampler/step/full_random.py | 106 +- 19 files changed, 2943 insertions(+), 1045 deletions(-) create mode 100644 docs/notebooks/enPflE1Q create mode 100644 docs/notebooks/hhl_Net2loops.ipynb rename docs/notebooks/{ => net0_data}/encoded_reference_solutions.pkl (100%) create mode 100644 docs/notebooks/net0_data/energies.pkl create mode 100644 docs/notebooks/net0_data/plot_test_qubo_solver.ipynb create mode 100644 docs/notebooks/net0_data/solutions.pkl create mode 100644 docs/notebooks/net2loops_data/encoded_reference_solutions.pkl create mode 100644 docs/notebooks/net2loops_data/energies.pkl create mode 100644 docs/notebooks/net2loops_data/plot_test_qubo_solver.ipynb create mode 100644 docs/notebooks/net2loops_data/solutions.pkl delete mode 100644 docs/notebooks/solutions.pkl create mode 100644 docs/notebooks/test_qubo_poly_solver_net2loops.py diff --git a/docs/notebooks/enPflE1Q b/docs/notebooks/enPflE1Q new file mode 100644 index 0000000000000000000000000000000000000000..e6104ead568afea78433f6bbafed0aebf8991916 GIT binary patch literal 32 Ycma#_I4pOPfq{V?h&h0m5r~li07Z-fTmS$7 literal 0 HcmV?d00001 diff --git a/docs/notebooks/hhl_Net2loops.ipynb b/docs/notebooks/hhl_Net2loops.ipynb new file mode 100644 index 0000000..dabd2d7 --- /dev/null +++ b/docs/notebooks/hhl_Net2loops.ipynb @@ -0,0 +1,362 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up water network model\n", + "\n", + "In this example, we test our quantum solvers into a slightly larger network as contained in `Net0.inp`. Let's start by setting up the model:|" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import wntr\n", + "import wntr_quantum\n", + "\n", + "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", + "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", + "# set up network model\n", + "inp_file = 'networks/Net2Loops.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# plot network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n", + "\n", + "# print options\n", + "# dict(wn.options.hydraulic)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve model using the classical Epanet simulator\n", + "\n", + "We now solve the same problem using the classical Epanet simulator. Note that, by default, `QuantumEpanetSimulator` uses a classical `CholeskySolver` to iteratively solve the linear problem." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", + "Your EPANET temp dir: /home/nico/.epanet_quantum\n", + "\n", + "Size of the Jacobian in EPANET simulator: 6\n", + "Size of the b vector in EPANET simulator: 6\n" + ] + }, + { + "data": { + "text/plain": [ + "(name 2 3 4 5 6 7 \\\n", + " 0 53.247742 30.665516 44.321564 28.810593 30.547766 27.057959 \n", + " \n", + " name 1 \n", + " 0 4.394531e-07 ,\n", + " name 1 2 3 4 5 6 7 \\\n", + " 0 0.311088 0.051455 0.231865 0.031844 0.166692 0.075021 0.023685 \n", + " \n", + " name 8 \n", + " 0 -0.019471 )" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "\n", + "# define the classical EPANET simulator\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", + "\n", + "# run the EPANET simulation\n", + "results_epanet = sim.run_sim()\n", + "\n", + "# remember to set up EPANET Quantum environment variables!\n", + "epanet_path = os.environ[\"EPANET_QUANTUM\"]\n", + "epanet_tmp = os.environ[\"EPANET_TMP\"]\n", + "\n", + "# check paths\n", + "print(f\"Your EPANET quantum path: {epanet_path}\")\n", + "print(f\"Your EPANET temp dir: {epanet_tmp}\\n\")\n", + "\n", + "util_path = os.path.join(epanet_path, 'src/py/')\n", + "sys.path.append(util_path)\n", + "\n", + "from quantum_linsolve import load_json_data\n", + "epanet_A, epanet_b = load_json_data(os.path.join(epanet_tmp,'smat.json'))\n", + "\n", + "# set the size of the Jacobian (A matrix)\n", + "epanet_A_dim = epanet_A.todense().shape[0]\n", + "print(f\"Size of the Jacobian in EPANET simulator: {epanet_A_dim}\")\n", + "print(f\"Size of the b vector in EPANET simulator: {epanet_b.shape[0]}\")\n", + "\n", + "# save number of nodes and pipes\n", + "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", + "n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", + "\n", + "results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a helper function\n", + "\n", + "Before proceeding to the proper quantum solution of the water network model, let's define a helper function. This function checks that the quantum results are within `TOL`% of those obtained classically. It also fills in lists containing the final values of pressures and flow rates obtained." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "TOL = 50 # => per cent\n", + "DELTA = 1.0e-12\n", + "\n", + "\n", + "def get_ape_from_pd_series(quantum_pd_series, classical_pd_series):\n", + " \"\"\"Helper function to evaluate absolute percentage error between classical and quantum results.\"\"\"\n", + " ape = abs(quantum_pd_series - classical_pd_series) * 100.0 / abs(classical_pd_series + DELTA)\n", + " return ape\n", + "\n", + "\n", + "def compare_results(classical_result, quantum_result):\n", + " \"\"\"\n", + " Helper function that compares the classical and quantum simulation results.\n", + " \"\"\"\n", + " classical_data = []\n", + " quantum_data = []\n", + "\n", + " def check_ape(classical_value, quantum_value):\n", + " \"\"\"Helper function to check if the absolute percentage error between classical and quantum results is within TOL.\"\"\"\n", + " ape = abs(quantum_value - classical_value) * 100.0 / abs(classical_value + DELTA)\n", + " is_close_to_classical = ape <= TOL\n", + " if is_close_to_classical:\n", + " print(f\"Quantum result {quantum_value} within {ape}% of classical result {classical_value}\")\n", + " quantum_data.append(quantum_value)\n", + " classical_data.append(classical_value)\n", + " return is_close_to_classical\n", + "\n", + " for link in classical_result.link[\"flowrate\"].columns:\n", + " classical_value = classical_result.link[\"flowrate\"][link].iloc[0]\n", + " quantum_value = quantum_result.link[\"flowrate\"][link].iloc[0]\n", + " message = f\"Flowrate {link}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert check_ape(classical_value, quantum_value), message\n", + "\n", + " for node in classical_result.node[\"pressure\"].columns:\n", + " classical_value = classical_result.node[\"pressure\"][node].iloc[0]\n", + " quantum_value = quantum_result.node[\"pressure\"][node].iloc[0]\n", + " message = f\"Pressure {node}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", + " assert check_ape(classical_value, quantum_value), message\n", + "\n", + " return classical_data, quantum_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solve water network with `QuantumEpanetSimulator` and VQLS \n", + "\n", + "We now solve the model using VQLS. In this example, we are **preconditioning** the initial linear system using *diagonal scaling* and also using a **mix of two classical optimizers**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", + "HHL timing: 34.627483 s.\n", + "HHL timing: 39.351160 s.\n", + "HHL timing: 31.486299 s.\n", + "HHL timing: 35.431929 s.\n", + "HHL timing: 32.082307 s.\n", + "HHL timing: 31.581474 s.\n", + "HHL timing: 31.345306 s.\n", + "HHL timing: 30.402304 s.\n", + "HHL timing: 31.083999 s.\n", + "HHL timing: 31.602149 s.\n", + "HHL timing: 29.319518 s.\n", + "HHL timing: 31.106643 s.\n", + "HHL timing: 32.019504 s.\n", + "HHL timing: 22.503256 s.\n", + "HHL timing: 35.098130 s.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from qiskit.primitives import Estimator\n", + "from quantum_newton_raphson.hhl_solver import HHL_SOLVER\n", + "\n", + "n_qubits = int(np.ceil(np.log2(epanet_A_dim)))\n", + "estimator = Estimator()\n", + "\n", + "linear_solver = HHL_SOLVER(\n", + " estimator=estimator,\n", + " # preconditioner=\"diagonal_scaling\",\n", + ")\n", + "\n", + "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn, linear_solver=linear_solver)\n", + "results_hhl= sim.run_sim(linear_solver=linear_solver)\n", + "\n", + "classical_res, quantum_res = compare_results(results_epanet, results_hhl)\n", + "\n", + "results_hhl.node[\"pressure\"], results_hhl.link[\"flowrate\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot pressures and flow rates\n", + "\n", + "Let's check graphically the equivalence of the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.scatter(classical_res[:n_pipes], quantum_res[:n_pipes], label=\"Flow rates\", color=\"blue\", marker=\"o\")\n", + "plt.scatter(classical_res[n_pipes:], quantum_res[n_pipes:], label=\"Pressures\", color=\"red\", marker=\"s\", facecolors='none')\n", + "plt.axline((0, 0), slope=1, linestyle=\"--\", color=\"gray\", label=\"\")\n", + "plt.xlabel(\"Classical results\")\n", + "plt.ylabel(\"Quantum results\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wntr.graphics.plot_network(\n", + " wn,\n", + " node_attribute=get_ape_from_pd_series(\n", + " results_hhl.node[\"pressure\"].iloc[0],\n", + " results_epanet.node[\"pressure\"].iloc[0]\n", + " ),\n", + " link_attribute=get_ape_from_pd_series(\n", + " results_hhl.link[\"flowrate\"].iloc[0],\n", + " results_epanet.link[\"flowrate\"].iloc[0],\n", + " ),\n", + " node_colorbar_label='Pressures',\n", + " link_colorbar_label='Flows',\n", + " node_size=50,\n", + " title=f\"{inp_file}: Absolute Percent Error\",\n", + " node_labels=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/encoded_reference_solutions.pkl b/docs/notebooks/net0_data/encoded_reference_solutions.pkl similarity index 100% rename from docs/notebooks/encoded_reference_solutions.pkl rename to docs/notebooks/net0_data/encoded_reference_solutions.pkl diff --git a/docs/notebooks/net0_data/energies.pkl b/docs/notebooks/net0_data/energies.pkl new file mode 100644 index 0000000000000000000000000000000000000000..5c0f3f20820491aa2450c1118e32858a0fb96aa9 GIT binary patch literal 3921 zcma)8YeV zgqVt#YFe7LQk1EsmPTe0QDA6E6o!{9rbcGkkN%x~KL%rG=bYy}&-;9DHoZL1%4p?@ zrkqKeq_o5{8Lr}Fnbb8gEkPbHlF39F6uB-&CKV?qrOIV#VmWn%A{i?{iZo4fU~Tw|_r0Y%VBhD4rmM*8Lv``f>-QIppe?pE>md=CvU%KH(GO 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pickle.load(open('solutions.pkl','rb'))\n", + "ref = pickle.load(open('encoded_reference_solutions.pkl','rb'))\n", + "energies = np.array(pickle.load(open('energies.pkl','rb')))" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.311,\n", + " 0.05105,\n", + " 0.2322,\n", + " 0.03113,\n", + " 0.1679,\n", + " 0.07615,\n", + " 0.02345,\n", + " -0.02054,\n", + " 200.8,\n", + " 181.9,\n", + " 195.6,\n", + " 164.1,\n", + " 190.6,\n", + " 177.9]" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref = [3.110e-01, 5.105e-02, 2.322e-01, 3.113e-02, 1.679e-01, 7.615e-02, 2.345e-02, -2.054e-02, 2.008e+02, 1.819e+02, 1.956e+02, 1.641e+02, 1.906e+02, 1.779e+02]\n", + "ref" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [], + "source": [ + "idx = np.argmin(energies)\n", + "idx = 1\n", + "energies[idx]\n", + "ref = [ref]*10" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-35722.03292166],\n", + " [-35703.83316825],\n", + " [-35707.98248599],\n", + " [-35706.86539028],\n", + " [-35679.87963652],\n", + " [-35686.1686008 ],\n", + " [-35633.3534824 ],\n", + " [-35717.54215684],\n", + " [-35694.95182639],\n", + " [-35716.82092095]])" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "energies" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.50000000e+02],\n", + " [2.43044619e+01],\n", + " [3.68034550e+01],\n", + " [3.29134752e+01],\n", + " [2.21512045e+00],\n", + " [4.15454621e+00],\n", + " [2.11247776e-02],\n", + " [9.57325927e+01],\n", + " [9.99940675e+00],\n", + " [8.90711263e+01]])" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "size = 150 * np.exp(-0.1 * (energies-energies.min()))\n", + "size" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_solutions(solutions, references, size, best_index):\n", + " fig = plt.figure(figsize=plt.figaspect(0.5))\n", + " ax1 = fig.add_subplot(121)\n", + "\n", + " ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + " ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + " ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + " ax1.grid()\n", + "\n", + " for r, sol, s in zip(references, solutions, size):\n", + " ax1.scatter(\n", + " r[:8], sol[:8], s=s, lw=1, edgecolors=\"w\",alpha=0.5, facecolors='orange'\n", + " )\n", + "\n", + " ax1.scatter(\n", + " references[best_index][:8], solutions[best_index][:8], s=150, lw=1, edgecolors=\"w\", facecolors='C0'\n", + " )\n", + "\n", + " ax1.set_xlabel(\"Reference Values\", fontsize=12)\n", + " ax1.set_ylabel(\"QUBO Values\", fontsize=12)\n", + " ax1.set_title(\"Flow Rate\", fontsize=14)\n", + "\n", + " ax2 = fig.add_subplot(122)\n", + "\n", + " ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + " ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + " ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "\n", + " for r, sol, s in zip(references, solutions, size):\n", + " ax2.scatter(\n", + " r[8:],\n", + " sol[8:],\n", + " s=s,\n", + " lw=1,\n", + " edgecolors=\"w\",\n", + " alpha=0.5, facecolors='orange'\n", + " )\n", + " ax2.scatter(\n", + " references[best_index][8:], solutions[best_index][8:], s=150, lw=1, edgecolors=\"w\", facecolors='C0'\n", + " )\n", + " ax2.grid()\n", + "\n", + " ax2.set_xlabel(\"Reference Values\", fontsize=12)\n", + " ax2.set_title(\"Pressure\", fontsize=14)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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"output_type": "execute_result" + } + ], + "source": [ + "solution" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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06vsPWRBqsAVZuUNe2SMrd2zJ69CD937VnOt+D/0eW/LukGvu2azrdQ6eNygdzcm2bdv09NNPa9CgQV7vAgAA9HGuG5T9+/dr+/btzuMdO3Zo8+bNGjhwoCoqKnTeeefppZde0iOPPKJ0Oq3GxkZJ0sCBAwN/zzUAAAgG1w1KQ0ODpk6d6jzuuH5k7ty5WrZsmXN18Lhx4zp939NPP60pU6bkXikAADhsuG5QpkyZoo+68acXNwUBAABIKsA8KAAAdBWNRlVbW+uMbVJSUqL9+/c7Y5vYlDsNCgCg4EKhkLXXJYZCIatuLT6UTbnzacYAACBwOIICACi49vZ2PfLII5KkM888s6BT3fdWIpHQZZddJkn68Y9/3ONcXkFlU+4cQQEAFFwmk9Err7yiV155RZlMxu9yXGlvb9e9996re++9V+3t7X6X44pNudOgAACAwKFBAQAAgUODAgAAAocGBQAABA4NCgAACBwaFAAAEDjBvQEaANBnRaNRXXvttc7YJiUlJdqzZ48ztolNudOgAAAKzvbp4o866ii/y8iJTblzigcAAAQOR1AAAAXX3t6uJ554QpJUU1MT6CnXu0okElq0aJEkaeXKldZNdW9L7hxBAQAUXCaTUUNDgxoaGgI/5XpX7e3t+tGPfqQf/ehHVk51b0vuNCgAACBwaFAAAEDg0KAAAIDAoUEBAACBQ4MCAAAChwYFAAAETnBvgAYA9FnRaFRXX321M7ZJv379tGPHDmdsE5typ0EBABRcKBTSgAED/C4jJ+FwWNXV1X6XkRObcucUDwAACBwaFABAwaXTaT355JN68sknlU6n/S7HlWQyqcWLF2vx4sVKJpN+l+OKTbnToAAACi6dTmv9+vVav3594P9QdpVKpbRixQqtWLFCqVTK73JcsSl3GhQAABA4NCgAACBwaFAAAEDg0KAAAIDAoUEBAACBQ4MCAAACx3WD8uyzz+qss85SZWWlQqGQHnrooU7PG2N04403qqKiQv369dO0adO0bds2r+oFAPQB0WhUV1xxha644orAT7neVb9+/fTqq6/q1VdftXKqe1tyd92gtLS0aOzYsVq9enWPz99666367//+b91555164YUXVFpaqpqaGrW1tfW6WABA3xAKhTRkyBANGTJEoVDI73JcCYfDGj16tEaPHq1w2K4TETbl7vqzeGbOnKmZM2f2+JwxRqtWrdK3vvUtnX322ZKkn//85xo6dKgeeughXXjhhb2rFgAAHBY8/bDAHTt2qLGxUdOmTXOWlZeXa9KkSVq/fn2PDUoikVAikXAeNzc3Szo4U59fM/R17Ne2GQL9QFbukFf2yMod2/JKp9P685//LEk69dRTFYlECrbv3maVTCZ1yy23SJKuv/56xWIxz2rLN7e5u8nK6/eepw1KY2OjJGno0KGdlg8dOtR5rqu6ujotX7682/Inn3xSJSUlXpbnWn19va/7twlZuUNe2SMrd2zJK51Oa8uWLZKkpqamgjYoHXLNqq2tTTfffLMk6cQTT1RxcbGXZeVVrrlnk1Vra2uvauvK0wYlF7W1tVq0aJHzuLm5WVVVVZoxY4bKysp8qSmVSqm+vl7Tp08P/EVEfiMrd8gre2Tljm15JZNJ5w9lTU1NQY9C9DarlpYWZ1xTU6PS0lIvy8srt7m7yarjDIhXPG1Qhg0bJknavXu3KioqnOW7d+/WuHHjevyeeDyueDzebXk0GvX9hywINdiCrNwhr+yRlTu25GWMccZ+1Zzrfg/9Hlvy7pBr7tms63UOnl5+PHLkSA0bNkxPPfWUs6y5uVkvvPCCJk+e7OWuAABAH+b6CMr+/fu1fft25/GOHTu0efNmDRw4UMOHD9c111yjm2++Wccdd5xGjhypJUuWqLKyUuecc46XdQMAgD7MdYPS0NCgqVOnOo87rh+ZO3eu1qxZo29+85tqaWnRpZdeqn379ulzn/ucHn/8casuIgIAAP5y3aBMmTKl0zmsrkKhkG666SbddNNNvSoMAAAcvny/iwcAcPgpKirSf/zHfzhjmxQXF+vFF190xjaxKfdgVwcA6JPC4bA+8YlP+F1GTiKRiCZMmOB3GTmxKXe7PkQAAAAcFjiCAgAouHQ6rQ0bNkiSTjnlFF9mks1VMpnU7bffLkm6+uqrrZvq3pbcaVAAAAWXTqf1+9//XpI0YcKEQP+h7CqVSumb3/ymJOnKK6+0rkGxJXdO8QAAgMChQQEAAIFDgwIAAAKHBgUAAAQODQoAAAgcGhQAABA43GYMACi4oqIizZ071xnbpLi4WE8//bQztolNuQe7OgBAnxQOh1VdXe13GTmJRCKaMmWK32XkxKbcOcUDAAAChyMoAICCS6fT2rRpkyTp5JNPDvSMpl2lUindddddkqRLL71U0WjU54qyZ1PuNCgAgIJLp9N67LHHJEnjxo0L9B/KrpLJpL761a9KkubNm2ddg2JL7pziAQAAgUODAgAAAocGBQAABA4NCgAACBwaFAAAEDg0KAAAIHC4zRgAUHBFRUW66KKLnLFN4vG4HnnkEWdsE5tyD3Z1AIA+KRwO6/jjj/e7jJwUFRVp1qxZfpeRE5ty5xQPAAAIHI6gAAAKLp1Oa8uWLZKkMWPGBHpG065SqZTuu+8+SdLFF19s3UyytuROgwIAKLh0Oq2HH35YkjRq1KhA/6HsKplMav78+ZKk888/37oGxZbcOcUDAAAChwYFAAAEDg0KAAAIHBoUAAAQODQoAAAgcDxvUNLptJYsWaKRI0eqX79+OuaYY/Ttb39bxhivdwUAAPooz28z/t73vqc77rhD9957r0aPHq2GhgbNnz9f5eXluuqqq7zeHQDAQkVFRTrvvPOcsU3i8bh+9atfOWOb2JS759U9//zzOvvss51pgKurq3X//ffrxRdf9HpXAABLhcNhjR492u8yclJUVKTzzz/f7zJyYlPunjcon/3sZ3XXXXfp9ddf1/HHH69XXnlFzz33nFauXNnj+olEQolEwnnc3Nws6eBMfalUyuvystKxX7/2bxOycoe8skdW7pBX9sgqe26y8jrPkPH44pBMJqMbbrhBt956qyKRiNLptL7zne+otra2x/WXLVum5cuXd1u+du1alZSUeFkaACAgjDFqamqSJJWXlysUCvlcUfbS6bQ2bNggSTrllFMCPRtrV/nMvbW1VXPmzFFTU5PKysp6vT3PG5R169Zp8eLFuu222zR69Ght3rxZ11xzjVauXKm5c+d2W7+nIyhVVVXau3evJy8wF6lUSvX19Zo+fbpVUxj7gazcIa/skZU7tuWVTCa1YsUKSdK1116rWCxWsH33NquWlhYdeeSRkqR//OMfKi0t9brEvHGbu5usmpubNXjwYM8aFM9P8SxevFjXX3+9LrzwQkkHP4xo586dqqur67FBicfjPV5kFI1Gff8hC0INtiArd8gre2Tlji15HfpvY79qznW/h36PLXl3yDX3bNb1OgfPbzNubW1VONx5s5FIRJlMxutdAQCAPsrzIyhnnXWWvvOd72j48OEaPXq0Xn75Za1cuVJf/vKXvd4VAADoozxvUH7wgx9oyZIluvLKK7Vnzx5VVlbqsssu04033uj1rgAAQB/leYNyxBFHaNWqVVq1apXXmwYAAIcJPosHAAAETrDnuQUA9EmRSERnn322M7ZJLBbTPffc44xtYlPuNCgAgIKLRCIaN26c32XkJBqNat68eX6XkRObcucUDwAACByOoAAACi6TyWj79u2SpGOPPbbb/FlB1t7erieeeEKSVFNTE/hPBT6UTbkHtzIAQJ/V3t6u+++/X/fff7/a29v9LseVRCKhM888U2eeeWanj2qxgU2506AAAIDAoUEBAACBQ4MCAAAChwYFAAAEDg0KAAAIHBoUAAAQOPbcvA0A6DMikYhmzpzpjG0Si8X0wx/+0BnbxKbcaVAAAAUXiUQ0ceJEv8vISTQa1cKFC/0uIyc25c4pHgAAEDgcQQEAFFwmk9GuXbskScOHDw/0lOtdpdNp/elPf5Ik/eu//mvgT5Ucyqbcg1sZAKDPam9v17333qt777038FOud9XW1qapU6dq6tSpamtr87scV2zKnQYFAAAEDg0KAAAIHBoUAAAQODQoAAAgcGhQAABA4NCgAACAwGEeFABAwUUiEU2bNs0Z2yQajerWW291xjaxKXcaFABAwUUiEZ166ql+l5GTWCymxYsX+11GTmzKnVM8AAAgcDiCAgAouEwmo3fffVeSVFFREegp17tKp9N66aWXJEknnXRS4E+VHMqm3INbGQCgz2pvb9dPfvIT/eQnPwn8lOtdtbW1aeLEiZo4caKVU93bkjsNCgAACBwaFAAAEDg0KAAAIHBoUAAAQODQoAAAgMDJS4Py9ttv69///d81aNAg9evXT2PGjFFDQ0M+dgUAAPogz+dB+cc//qFTTz1VU6dO1WOPPaajjjpK27Zt05FHHun1rgAAlopEIjr99NOdsU2i0aiWLl3qjG1iU+6eNyjf+973VFVVpXvuucdZNnLkSK93AwCwWCQS0ZQpU/wuIyexWEzLli3zu4yc2JS75w3Kb3/7W9XU1Oj888/XH//4R33iE5/QlVdeqQULFvS4fiKRUCKRcB43NzdLklKplFKplNflZaVjv37t3yZk5Q55ZY+s3CGv7JFV9txk5XWeIWOM8XKDxcXFkqRFixbp/PPP18aNG3X11Vfrzjvv1Ny5c7utv2zZMi1fvrzb8rVr16qkpMTL0gAAAWGMcWZhLS4uVigU8rmi7GUyGf3v//6vJOnoo48O9HTxXeUz99bWVs2ZM0dNTU0qKyvr9fY8b1BisZjGjx+v559/3ll21VVXaePGjVq/fn239Xs6glJVVaW9e/d68gJzkUqlVF9fr+nTp1t3frHQyMod8soeWbljW17JZFIrVqyQJF177bWKxWIF23dvs2ppaXGuq/zHP/6h0tJSr0vMG7e5u8mqublZgwcP9qxB8fwUT0VFhUaNGtVp2ac+9Sn9+te/7nH9eDyueDzebXk0GvX9hywINdiCrNwhr+yRlTu25HXov439qjnX/R76Pbbk3SHX3LNZ1+scPD8udeqpp2rr1q2dlr3++usaMWKE17sCAAB9lOcNyte//nVt2LBB3/3ud7V9+3atXbtWd911lxYuXOj1rgAAQB/leYMyYcIEPfjgg7r//vt14okn6tvf/rZWrVqliy++2OtdAQCAPsrza1Ak6cwzz9SZZ56Zj00DAIDDgD33RgEAgMNGXo6gAADwUSKRiCZPnuyMbRKNRnXttdc6Y5vYlDsNCgCg4CKRiGbMmOF3GTmJxWK67bbb/C4jJzblzikeAAAQOBxBAQAUnDFGTU1NkqTy8nLrprrftWuXJGn48OHWTXVvS+72pAoA6DNSqZRuv/123X777dZ9aN+BAwc0cuRIjRw5UgcOHPC7HFdsyp0GBQAABA4NCgAACBwaFAAAEDg0KAAAIHBoUAAAQODQoAAAgMBhHhQAQMGFw2GNHz/eGdukqKhIV155pTO2iU2525UsAKBPKCoq0qxZs/wuIyfxeFyrV6/2u4yc2JR7sNsnAABwWOIICgCg4Iwxam1tlSSVlJQEesr1rowx2rt3ryRp8ODB1tVuS+4cQQEAFFwqldKKFSu0YsWKwE+53lVra6uGDBmiIUOGOH/sbWFT7jQoAAAgcGhQAABA4NCgAACAwKFBAQAAgUODAgAAAocGBQAABA7zoAAACi4cDmvs2LHO2CZFRUWaO3euM7aJTbnblSwAoE8oKirSOeec43cZOYnH41qzZo3fZeTEptyD3T4BAIDDEkdQAAAFZ4xxZjKNRqOBnnK9K5umi+/Kptw5ggIAKLhUKqW6ujrV1dUFfsr1rlpbW9W/f3/179/fyqnubcmdBgUAAAQODQoAAAgcGhQAABA4NCgAACBw8t6g3HLLLQqFQrrmmmvyvSsAANBH5LVB2bhxo3784x/r05/+dD53AwAA+pi8zYOyf/9+XXzxxbr77rt1880352s3AAALhcNhjRo1yhnbJBKJ6LzzznPGNrEp97w1KAsXLtSsWbM0bdq0j2xQEomEEomE87i5uVnSwXu1/bpHu2O/Qb9HPAjIyh3yyh5ZuWNjXh1Trh86eVgh9DarSCSitWvXdtueLdzk7iYrr3MIGWOMp1uUtG7dOn3nO9/Rxo0bVVxcrClTpmjcuHFatWpVt3WXLVum5cuXd1u+du1alZSUeF0aAADIg9bWVs2ZM0dNTU0qKyvr9fY8b1DeeustjR8/XvX19c61Jx/VoPR0BKWqqkp79+715AXmIpVKqb6+XtOnT1c0GvWlBluQlTvklT2ycoe8skdW2XOTVXNzswYPHuxZg+L5KZ5NmzZpz549Oumkk5xl6XRazz77rH74wx8qkUh0OmcXj8cVj8e7bScajfr+xglCDbYgK3fIK3tk5Y4teSWTSdXV1UmSamtrFYvFCl5Drlm1tLSof//+kg5eb1laWup1aXmTa+7ZZOX1+87zBuXzn/+8tmzZ0mnZ/PnzdcIJJ+i6666z7oIiAABQeJ43KEcccYROPPHETstKS0s1aNCgbssBAAB6Eux7jAAAwGEpb7cZH+qZZ54pxG4AAEAfwREUAAAQODQoAAAgcApyigcAgEOFw2Edd9xxztgmkUhEZ5xxhjO2iU2506AAAAquqKhIc+bM8buMnBQXF+vRRx/1u4yc2JR7sNsnAABwWKJBAQAAgcMpHgBAwSWTSa1YsUKSdO211/oy1X2uWlpaNGTIEEnSnj17rJvq3pbcaVAAAL5IpVJ+l5Cz1tZWv0vImS25c4oHAAAEDg0KAAAIHBoUAAAQODQoAAAgcGhQAABA4HAXDwCg4EKhkEaMGOGMbRIOh3X66ac7Y5vYlDsNCgCg4KLRqObNm+d3GTnp16+fnnnmGb/LyIlNudOgAAD6jOrrP/4zcuIRo1snSicue0KJdPejCG/eMisfpcElu45NAQCAwwJHUAAABZdMJnX77bdLkq6++upAT7neVUtLi6qrqyVJb775pnVT3duSOw0KAMAXNk8Xv3fvXr9LyJktuXOKBwAABA4NCgAACBwaFAAAEDg0KAAAIHBoUAAAQOBwFw8AoOBCoZAqKyudsU3C4bDGjx/vjG1iU+40KACAgotGo1qwYIHfZeSkX79+2rhxo99l5MSm3O1q/QAAwGGBBgUAAAQODQoAoOBSqZRWrVqlVatWKZVK+V2OK62traqurlZ1dbU1s7J2sCl3rkEBABScMUZNTU3O2CbGGO3cudMZ28Sm3DmCAgAAAocGBQAABA4NCgAACBzPG5S6ujpNmDBBRxxxhIYMGaJzzjlHW7du9Xo3AACgD/O8QfnjH/+ohQsXasOGDaqvr1cqldKMGTPU0tLi9a4AAEAf5fldPI8//ninx2vWrNGQIUO0adMmnXbaaV7vDgBgoVAopKOOOsoZ2yQUCmnUqFHO2CY25Z7324w7bmcaOHBgj88nEgklEgnncXNzs6SD92r7dY92x36Dfo94EJCVO+SVPbJyx8a8Dp1y3au645GPv3U2Hjad/tvVx9USjUa1efPmrNcPGje5u3lfeZ1DyOTxRuhMJqPZs2dr3759eu6553pcZ9myZVq+fHm35WvXrlVJSUm+SgMAAB5qbW3VnDlz1NTUpLKysl5vL68NyhVXXKHHHntMzz33nI4++uge1+npCEpVVZX27t3ryQvMRSqVUn19vaZPn65oNOpLDbYgK3fIK3tk5Q55HXTisic+dp142Ojb4zNa0hBWIuP9aY5Xl9V4vk2/uHlfNTc3a/DgwZ41KHk7xfPVr35VjzzyiJ599tl/2pxIUjweVzwe77Y8Go36/kMWhBpsQVbukFf2yModW/JKpVK6++67JR085eBVzYl09g1HIhNytX6HTKpNjfcukiQNm7tS4Whxp+eDnH+uuWfzvvL6dXveoBhj9LWvfU0PPvignnnmGY0cOdLrXQAALGeM0XvvveeMrWKk1Pu7nLFNbMrd8wZl4cKFWrt2rR5++GEdccQRamxslCSVl5erX79+Xu8OAAD0QZ7Pg3LHHXeoqalJU6ZMUUVFhfP1y1/+0utdAQCAPiovp3gAAAB6g8/iAQAAgUODAgAAAifvM8kCANBVKBRSeXm5M7ZKSIqUDXHGNrEpdxoUAEDBRaNRXXPNNX6XkZNwtFhHX/Ezv8vIiU25c4oHAAAEDg0KAAAIHE7xAAAKLpVKac2aNZKkefPmBXp6+K4yqYR2r71ekjR0zi0KR7t/XEtQ2ZQ7DQoAoOCMMXrnnXecsVWMUbJxmzO2iU25c4oHAAAEDg0KAAAIHBoUAAAQODQoAAAgcGhQAABA4HAXDwDAFyUlJX6XkLNwvzK/S8iZLbnToAAACi4Wi2nx4sV+l5GTcKxYVVet9buMnNiU+2HXoFRf/+jHrhOPGN06UTpx2RNKpDt/mNKbt8zKV2nAYaunn8uP+jnsqi/8XGbzu+mjdOTlZw1e6Av/L+ENrkEBAACBc9gdQQEA+C+ijKbHXpck1SePV9qify9nUgnteWCpJGnI+cutm+r+vvvukyRdfPHFTHUPAMChQjKqiOx3xlYxRom3XnXGNjHGaOfOnc44yOxpWQEAwGGDBgUAAAQODQoAAAgcGhQAABA4NCgAACBwuIsHAOCLlLH338ghi24t7irItxYfigYFAFBw7YroF20n+V1GTsKxYg1f9Gu/y8hJLBbTDTfc4HcZWbG3fQUAAH0WDQoAAAgcTvEAAAouooymxt6QJD2dPMaqqe5Ne1LvPfhdSdJR596gUFHM54qy197erl/96leSpAsuuEBFRcFtA4JbGQCgzwrJqCrS5IxtYjIZHfh/Dc74oz9rO1gymYy2bdvmjIPMnpYVAAAcNmhQAABA4OStQVm9erWqq6tVXFysSZMm6cUXX8zXrgAAQB+Tlwbll7/8pRYtWqSlS5fqpZde0tixY1VTU6M9e/bkY3cAAKCPyUuDsnLlSi1YsEDz58/XqFGjdOedd6qkpEQ/+9nP8rE7AADQx3h+F08ymdSmTZtUW1vrLAuHw5o2bZrWr1/fbf1EIqFEIuE8bmo6eFX3Bx98oFQq5XV5Kmpv+fh1MkatrRkVpcJKZzpfn/3+++97XpPNUqmUWltb9f7771szfbKfyKtnPf1cftTPYVd94ecym99NH/n9/39evXlv9bYGNyJKq62t7eC4vUVSRFLv/1/29nd8Nkx7mzOOtrcoFE53ej7I78dkMunk/v777ysW++hbpN38zvrwww8lScZ4dFeW8djbb79tJJnnn3++0/LFixebiRMndlt/6dKlRhJffPHFF1988dUHvt566y1P+gnf50Gpra3VokWLnMeZTEYffPCBBg0apFDIn7vLm5ubVVVVpbfeektlZWW+1GALsnKHvLJHVu6QV/bIKntusjLG6MMPP1RlZaUn+/a8QRk8eLAikYh2797dafnu3bs1bNiwbuvH43HF450/FXLAgAFel5WTsrIy3rxZIit3yCt7ZOUOeWWPrLKXbVbl5eWe7dPzi2RjsZhOPvlkPfXUU86yTCajp556SpMnT/Z6dwAAoA/KyymeRYsWae7cuRo/frwmTpyoVatWqaWlRfPnz8/H7gAAQB+Tlwbli1/8ot577z3deOONamxs1Lhx4/T4449r6NCh+did5+LxuJYuXdrt1BO6Iyt3yCt7ZOUOeWWPrLLnZ1YhY7y6HwgAAMAbfBYPAAAIHBoUAAAQODQoAAAgcGhQAABA4PTJBmX16tWqrq5WcXGxJk2apBdffPEj13/ggQd0wgknqLi4WGPGjNHvfve7Ts8bY3TjjTeqoqJC/fr107Rp07Rt27ZO61RXVysUCnX6uuWWWzx/bV7zOqvf/OY3mjFjhjMT8ObNm7tto62tTQsXLtSgQYPUv39//du//Vu3if2Cyo+8pkyZ0u29dfnll3v5svLCy6xSqZSuu+46jRkzRqWlpaqsrNSXvvQlvfPOO5228cEHH+jiiy9WWVmZBgwYoK985Svav39/Xl6f1/zIi99bBy1btkwnnHCCSktLdeSRR2ratGl64YUXOq3De+v/ZJOXJ+8tTybMD5B169aZWCxmfvazn5m//vWvZsGCBWbAgAFm9+7dPa7/5z//2UQiEXPrrbea1157zXzrW98y0WjUbNmyxVnnlltuMeXl5eahhx4yr7zyipk9e7YZOXKkOXDggLPOiBEjzE033WTeffdd52v//v15f729kY+sfv7zn5vly5ebu+++20gyL7/8crftXH755aaqqso89dRTpqGhwZxyyinms5/9bL5epmf8yuv00083CxYs6PTeampqytfL9ITXWe3bt89MmzbN/PKXvzR///vfzfr1683EiRPNySef3Gk7X/jCF8zYsWPNhg0bzJ/+9Cdz7LHHmosuuijvr7e3/MqL31sH3Xfffaa+vt688cYb5tVXXzVf+cpXTFlZmdmzZ4+zDu8td3l58d7qcw3KxIkTzcKFC53H6XTaVFZWmrq6uh7Xv+CCC8ysWbM6LZs0aZK57LLLjDHGZDIZM2zYMHPbbbc5z+/bt8/E43Fz//33O8tGjBhhvv/973v4SvLP66wOtWPHjh7/4O7bt89Eo1HzwAMPOMv+9re/GUlm/fr1vXg1+edHXsYcbFCuvvrqXtVeaPnMqsOLL75oJJmdO3caY4x57bXXjCSzceNGZ53HHnvMhEIh8/bbb/fm5eSdH3kZw++tf6apqclIMr///e+NMby33OZljDfvrT51iieZTGrTpk2aNm2asywcDmvatGlav359j9+zfv36TutLUk1NjbP+jh071NjY2Gmd8vJyTZo0qds2b7nlFg0aNEif+cxndNttt6m9vd2rl+a5fGSVjU2bNimVSnXazgknnKDhw4e72k6h+ZVXh/vuu0+DBw/WiSeeqNraWrW2trreRqEUKqumpiaFQiHns7vWr1+vAQMGaPz48c4606ZNUzgc7nb4OUj8yqsDv7e67+Ouu+5SeXm5xo4d62yD91b2eXXo7XvL908z9tLevXuVTqe7zVg7dOhQ/f3vf+/xexobG3tcv7Gx0Xm+Y9k/W0eSrrrqKp100kkaOHCgnn/+edXW1urdd9/VypUre/268iEfWWWjsbFRsVis2y9Jt9spNL/ykqQ5c+ZoxIgRqqys1F/+8hddd9112rp1q37zm9+4exEFUois2tradN111+miiy5yPsCssbFRQ4YM6bReUVGRBg4ceNi/t3rKS+L31qEeeeQRXXjhhWptbVVFRYXq6+s1ePBgZxu8t7LPS/LmvdWnGhQ/LVq0yBl/+tOfViwW02WXXaa6ujqmU0avXHrppc54zJgxqqio0Oc//3m98cYbOuaYY3yszB+pVEoXXHCBjDG64447/C4n8D4qL35v/Z+pU6dq8+bN2rt3r+6++25dcMEFeuGFF7o1Jjjo4/Ly4r3Vp07xDB48WJFIpNsdIbt379awYcN6/J5hw4Z95Pod/3WzTUmaNGmS2tvb9eabb7p9GQWRj6yyMWzYMCWTSe3bt69X2yk0v/LqyaRJkyRJ27dv79V28iWfWXX8sd25c6fq6+s7HQ0YNmyY9uzZ02n99vZ2ffDBB4fte+uj8urJ4fx7q7S0VMcee6xOOeUU/fSnP1VRUZF++tOfOtvgvZV9Xj3J5b3VpxqUWCymk08+WU899ZSzLJPJ6KmnntLkyZN7/J7Jkyd3Wl+S6uvrnfVHjhypYcOGdVqnublZL7zwwj/dpiRt3rxZ4XA4sN13PrLKxsknn6xoNNppO1u3btWuXbtcbafQ/MqrJx23IldUVPRqO/mSr6w6/thu27ZNv//97zVo0KBu29i3b582bdrkLPvDH/6gTCbjNHVB5FdePeH31v/JZDJKJBLONnhvZZ9XT3J6b/XqEtsAWrdunYnH42bNmjXmtddeM5deeqkZMGCAaWxsNMYYc8kll5jrr7/eWf/Pf/6zKSoqMitWrDB/+9vfzNKlS3u8zXjAgAHm4YcfNn/5y1/M2Wef3ek24+eff958//vfN5s3bzZvvPGG+cUvfmGOOuoo86UvfamwL96lfGT1/vvvm5dfftk8+uijRpJZt26defnll827777rrHP55Zeb4cOHmz/84Q+moaHBTJ482UyePLlwLzxHfuS1fft2c9NNN5mGhgazY8cO8/DDD5t/+Zd/MaeddlphX7xLXmeVTCbN7NmzzdFHH202b97c6dbFRCLhbOcLX/iC+cxnPmNeeOEF89xzz5njjjvOmltBC50Xv7cOZrV//35TW1tr1q9fb958803T0NBg5s+fb+LxuHn11Ved7fDeyj4vr95bfa5BMcaYH/zgB2b48OEmFouZiRMnmg0bNjjPnX766Wbu3Lmd1v/Vr35ljj/+eBOLxczo0aPNo48+2un5TCZjlixZYoYOHWri8bj5/Oc/b7Zu3eo8v2nTJjNp0iRTXl5uiouLzac+9Snz3e9+17S1teX1dXrB66zuueceI6nb19KlS511Dhw4YK688kpz5JFHmpKSEnPuued2amCCrNB57dq1y5x22mlm4MCBJh6Pm2OPPdYsXrw48POgGONtVh23Yff09fTTTzvrvf/+++aiiy4y/fv3N2VlZWb+/Pnmww8/zPdL9USh8+L31kEHDhww5557rqmsrDSxWMxUVFSY2bNnmxdffLHTNnhvHZRNXl69t0LGGJP98RYAAID861PXoAAAgL6BBgUAAAQODQoAAAgcGhQAABA4NCgAACBwaFAAAEDg0KAAAIDAoUEBAACBQ4MCAAAChwYFAAAEDg0KAAAIHBoUAAAQOP8fA+gkItyKFpoAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plt.hist(np.array(solution)[:,3],bins=25)\n", + "idx = 3\n", + "plt.hist(np.array(solution)[:,idx],bins=25)\n", + "plt.vlines(ref[0][idx],0, 17,colors='black', ls='--')\n", + "plt.vlines(ref[0][idx]*0.9,0, 17,colors='grey', ls='--')\n", + "plt.vlines(ref[0][idx]*1.1,0, 17,colors='grey', ls='--')\n", + "plt.ylim([0,17])\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1., 0., 2., 0., 0., 0., 0., 1., 1., 0., 1., 0., 0., 0., 0., 1., 0.,\n", + " 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]),\n", + " array([-35722.03292166, -35720.25933288, -35718.48574409, -35716.71215531,\n", + " -35714.93856652, -35713.16497774, -35711.39138895, -35709.61780017,\n", + " -35707.84421138, -35706.0706226 , -35704.29703381, -35702.52344502,\n", + " -35700.74985624, -35698.97626745, -35697.20267867, -35695.42908988,\n", + " -35693.6555011 , -35691.88191231, -35690.10832353, -35688.33473474,\n", + " -35686.56114596, -35684.78755717, -35683.01396839, -35681.2403796 ,\n", + " -35679.46679082, -35677.69320203, -35675.91961324, -35674.14602446,\n", + " -35672.37243567, -35670.59884689, -35668.8252581 , -35667.05166932,\n", + " -35665.27808053, -35663.50449175, -35661.73090296, -35659.95731418,\n", + " -35658.18372539, -35656.41013661, -35654.63654782, -35652.86295903,\n", + " -35651.08937025, -35649.31578146, -35647.54219268, -35645.76860389,\n", + " -35643.99501511, -35642.22142632, -35640.44783754, -35638.67424875,\n", + " -35636.90065997, -35635.12707118, -35633.3534824 ]),\n", + " )" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(np.array(energies), bins=50)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "distance = [np.linalg.norm(r[2:]-s[2:]) for r,s in zip(ref, solution)]" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "x and y must be the same size", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[78], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43menergies\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistance\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/pyplot.py:3699\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m 3680\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mscatter)\n\u001b[1;32m 3681\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatter\u001b[39m(\n\u001b[1;32m 3682\u001b[0m x: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3697\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3698\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PathCollection:\n\u001b[0;32m-> 3699\u001b[0m __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3700\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3701\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3702\u001b[0m \u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3703\u001b[0m \u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3704\u001b[0m \u001b[43m \u001b[49m\u001b[43mmarker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmarker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3705\u001b[0m \u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3706\u001b[0m \u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3707\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3708\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3709\u001b[0m \u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3710\u001b[0m \u001b[43m \u001b[49m\u001b[43mlinewidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlinewidths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3711\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3712\u001b[0m \u001b[43m \u001b[49m\u001b[43mplotnonfinite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplotnonfinite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3713\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3714\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3715\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3716\u001b[0m sci(__ret)\n\u001b[1;32m 3717\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1467\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1468\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1469\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/axes/_axes.py:4655\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4653\u001b[0m y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mravel(y)\n\u001b[1;32m 4654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39msize:\n\u001b[0;32m-> 4655\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must be the same size\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4657\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4658\u001b[0m s \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m20\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_internal.classic_mode\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[1;32m 4659\u001b[0m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlines.markersize\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2.0\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: x and y must be the same size" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(energies, distance)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "only integer scalar arrays can be converted to a scalar index", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[59], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m idx_sort \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margsort(distance)\n\u001b[0;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mdistance\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx_sort\u001b[49m\u001b[43m]\u001b[49m, energies[idx_sort])\n", + "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index" + ] + } + ], + "source": [ + "idx_sort = np.argsort(distance)\n", + "plt.plot(distance[idx_sort], energies[idx_sort])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dd = [distance[i] for i in idx_sort]\n", + "ee = [energies[i] for i in idx_sort]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plt.hist(np.array(solution)[:,3],bins=25)\n", + "idx = 3\n", + "plt.hist(np.array(solution)[:,idx],bins=25)\n", + "plt.vlines(ref[0][idx],0, 17,colors='black', ls='--')\n", + "plt.vlines(ref[0][idx]*0.9,0, 17,colors='grey', ls='--')\n", + "plt.vlines(ref[0][idx]*1.1,0, 17,colors='grey', ls='--')\n", + "plt.ylim([0,17])\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1., 0., 2., 0., 0., 0., 0., 1., 1., 0., 1., 0., 0., 0., 0., 1., 0.,\n", + " 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]),\n", + " array([-35722.03292166, -35720.25933288, -35718.48574409, -35716.71215531,\n", + " -35714.93856652, -35713.16497774, -35711.39138895, -35709.61780017,\n", + " -35707.84421138, -35706.0706226 , -35704.29703381, -35702.52344502,\n", + " -35700.74985624, -35698.97626745, -35697.20267867, -35695.42908988,\n", + " -35693.6555011 , -35691.88191231, -35690.10832353, -35688.33473474,\n", + " -35686.56114596, -35684.78755717, -35683.01396839, -35681.2403796 ,\n", + " -35679.46679082, -35677.69320203, -35675.91961324, -35674.14602446,\n", + " -35672.37243567, -35670.59884689, -35668.8252581 , -35667.05166932,\n", + " -35665.27808053, -35663.50449175, -35661.73090296, -35659.95731418,\n", + " -35658.18372539, -35656.41013661, -35654.63654782, -35652.86295903,\n", + " -35651.08937025, -35649.31578146, -35647.54219268, -35645.76860389,\n", + " -35643.99501511, -35642.22142632, -35640.44783754, -35638.67424875,\n", + " -35636.90065997, -35635.12707118, -35633.3534824 ]),\n", + " )" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(np.array(energies), bins=50)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "distance = [np.linalg.norm(r[2:]-s[2:]) for r,s in zip(ref, solution)]" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "x and y must be the same size", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[78], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43menergies\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistance\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/pyplot.py:3699\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m 3680\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mscatter)\n\u001b[1;32m 3681\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatter\u001b[39m(\n\u001b[1;32m 3682\u001b[0m x: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3697\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3698\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PathCollection:\n\u001b[0;32m-> 3699\u001b[0m __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3700\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3701\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3702\u001b[0m \u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3703\u001b[0m \u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3704\u001b[0m \u001b[43m \u001b[49m\u001b[43mmarker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmarker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3705\u001b[0m \u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3706\u001b[0m \u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3707\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3708\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3709\u001b[0m \u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3710\u001b[0m \u001b[43m \u001b[49m\u001b[43mlinewidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlinewidths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3711\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3712\u001b[0m \u001b[43m \u001b[49m\u001b[43mplotnonfinite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplotnonfinite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3713\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3714\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3715\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3716\u001b[0m sci(__ret)\n\u001b[1;32m 3717\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1467\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1468\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1469\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/axes/_axes.py:4655\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4653\u001b[0m y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mravel(y)\n\u001b[1;32m 4654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39msize:\n\u001b[0;32m-> 4655\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must be the same size\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4657\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4658\u001b[0m s \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m20\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_internal.classic_mode\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[1;32m 4659\u001b[0m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlines.markersize\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2.0\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: x and y must be the same size" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(energies, distance)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "only integer scalar arrays can be converted to a scalar index", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[59], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m idx_sort \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margsort(distance)\n\u001b[0;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mdistance\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx_sort\u001b[49m\u001b[43m]\u001b[49m, energies[idx_sort])\n", + "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index" + ] + } + ], + "source": [ + "idx_sort = np.argsort(distance)\n", + "plt.plot(distance[idx_sort], energies[idx_sort])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dd = [distance[i] for i in idx_sort]\n", + "ee = [energies[i] for i in idx_sort]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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EBiNOaTGFB6DSdIIgzAcFPASRwSiZlgHy8WQCX5zsxOK/7MXR012p3hSC6BMo4CGIDEbow2PhYLVwsFk4ABTwZALP7fwK67d/iRfeO5rqTSGIPoECHoLIYNgsLUvoTEC9eDKH9p5eAEBPL33XRGZAAQ9BZDABUadlIBzwkMJjfjy9we+4l/xaRIZAAQ9BZDBi0zIAoRcPNR80Pz0hFc9HFXlEhkABD0EkwI7Drbjz9++h8Ux3qjclLvy8isJDd/2mhyk8vgB910RmYEv1BhBEf+amp94FAHR5/fj97ZNSvDX6Ydc6a0jhYQNE2cWQMC9M4aGeS0SmQAoPQRhAU1tPqjchLgTTsqDwBJsPksJjfnpCQS1LaxKE2aGAhyAMgJVz9zcCorJ0gEzLmYRHUHjouyYyAwp4CMIA7Nb++aekZlqmgMf8MIWHTMtEptA/z9IEkWbYrP1T4QmbloM/CwGPn3qzmB2m8JBpmcgUKOAhCAOwW/rnn1JESotMyxlDj9CHhxQeIjPon2dpgkgzrP3UwxNpWqay9EzBE+qwTKZlIlOggIcgDKDfprRkCg95eDKHHh91WiYyCwp4CMIA+qtpWZiWLqvSok7L5iYQ4IWg1kcKD5Eh9M+zNEGkGf23LD34rzylRQGPuRGnLH2k8BAZAgU8BGEA/VXh8csVHmuo8SAFPKZGPCGdTMtEppDUs/Tu3btRX1+PwsJCFBcXY9asWejo6JCsw3FcxGPDhg3C8q1btyqu09zcLKyzZMkSXHjhhcjLy0NZWRlmzJiBAwcOJPOjEYSE/urhYVVaTOFx2snDkwmIFTwyLROZQtICnsbGRkybNg01NTXYsWMHNm/ejH379uG2226LWHfdunVoamoSHjNmzIhY58CBA5J1ysrKhGVvvvkm5syZg3fffRcNDQ3o7e3FFVdcgc7OzmR9PIKQYOunZenhKq3gz6wsnfrwmBuJwkN9eIgMIWnDQzdt2gS73Y5Vq1bBEroYrF69GqNHj8bBgwdRU1MjrFtYWIiKioqor1dWVobCwkLFZZs3b5b8/Lvf/Q5lZWXYtWsXLrnkksQ+CEFowN7PFZ4I0zL14TE1YoWHOi0TmULSbks9Hg8cDocQ7ABAVlYWAGDbtm2SdefMmYOSkhJMnDgRa9euBc9H/gGOHTsWlZWVqK+vx9tvvx31vdva2gAAAwYMiLp9brdb8iCIeOmvKS3V0RJkZDU1YoWHTMtEppC0gGfq1Klobm7GsmXL4PV6cfr0aSxcuBAA0NTUJKz30EMP4YUXXkBDQwOuv/56zJ49GytWrBCWV1ZWYvXq1di4cSM2btyIqqoqTJkyBbt371Z830AggHvuuQeTJ0/Geeedp7p9S5YsQUFBgfCoqqoy6JMTmUj/TWkF/7Vy1Icnk+gRKXi95OEhMgTdZ+mFCxcqmojFj/3796O2thbr16/H448/juzsbFRUVKC6uhrl5eUS1WfRokWYPHkyxo0bh/vvvx8LFizAsmXLhOUjR47EnXfeifHjx6Ourg5r165FXV0dfvWrXylu35w5c7B3716J8VmJBx54AG1tbcLjyJEjencFQQj037J0mpaeibA5WgCZlonMQbeHZ+7cuYrGYzHDhw8HAMycORMzZ85ES0sLcnJywHEcli9fLixXYtKkSXj44Yfh8XjgdDoV15k4cWJEWgwA7rrrLmzatAlvvfUWBg8eHHUbnU6n6usThF5s/bwsnfrwZBYShYdSWkSGoDvgKS0tRWlpqa7nlJeXAwDWrl0Ll8uF+vp61XX37NmDoqKiqMHInj17UFlZKfzM8zx+/OMf4+WXX8bWrVtRXV2ta/sIIh4Cojvj/mpaDo+WCP5MfXgyA7HCQ6ZlIlNIWpUWAKxcuRJ1dXXIzc1FQ0MD5s+fj6VLlwrVVq+++ipaWlpw0UUXweVyoaGhAY888gjmzZsnvMYTTzyB6upq1NbWoqenB08//TTeeOMNvPbaa8I6c+bMwXPPPYe//OUvyMvLE3r0FBQUCEZpgjAacTlvf/XwqFZp0V2/qRErPD4qSycyhKQGPDt37sTixYvR0dGBUaNGYc2aNbjllluE5axs/d577wXP86ipqcHy5ctxxx13COt4vV7MnTsXx44dQ3Z2NkaPHo3XX38dl112mbDOk08+CQCYMmWK5P3XrVsXM/1GEPEivjPut1VaspQWmZaNp7mtB4/87RPcWjcM44cWpXpzAEgVnl4/D57nwXH98xgmCK0kNeB55plnoi6fPn06pk+fHnWdBQsWYMGCBVHXUSpjJ4hkIw54+mtKS920TI0HjWLTh4145YNG2Cxc2gQ8PbI+S/4A32+DdoLQSv/U4QkiDRCnAqz9NKVFpuXk09bdCyC9yr/FfXgAmphOZAb98yxNZCRbPmnBqn8eTBtFT3yR6KdV6WBWnfDwUEppGU17jw8AEEiT4xaIDGgp4CEygaSmtAjCSP775b1odvfgytpy1JTlpXpzTFHOG5BNS3fZqdOy0bhDCg/SKKbwyBUe+r6JDIAUHqJf4A/wON7eAyDSf5Ao7xw6ia9au3Q/zwzlvH7ZtHQqSzcedz9QeHpNcCwTRCwo4CH6Bac6vWCqu5EXjiOnujDzNzsw5znlUSXRMEM5b0QfHvLwGE57T1DhSaN4R8HDQ983YX4o4CH6BSc7PML/jbQbnAi97qlOr+7nmuGuWEhpyUzL/gBPIwcMgnl4+DTKaUV4eExwLBNELCjgIfoF0oDHuJNzIgZoM1wk1KalA5TWMop2T1DhSaf4kaq0iEyEAh6iXyAOeIys0krEq9lrgjSAmsIDUMBjFO7ukMKTRjFFRMBDpmUiA6CAh+gXtHaEU05G3owmkrZRem5Prx+bPmzEmS79KbJUIFd4bBYOrOGuh5oPJgzP8+jw+IT/pwtkWiYyEQp4iH7BCXFKy8CIJ5H0mFJZ+rJ/HMBdz72Pm5/ekchm9RnyPjwcxwm9eMi4nDhdXr8QVKZTSEGmZSIToYCH6BecbE8/hUfJw/OXPccAAPsa3XG/bl8iT2kBonlalOZIGGZYBtJb4SEPD5EJUMBD9AuS5uFJxLSscFfc364b8pQWADhs1IvHKFhJOpBex0akhyeNNo4gkgQFPES/IFll6Ymkx5R8D+nUXE4LfkHhCf/OSb14DMMtCnjS6ciILEun75owPxTwEP2CZJWlG53S6mfxTsS0dEA8MZ0ugoniTtOUFutWzr72dBpsShDJggIeIu0JBHhZlVZ6mJaVU1r968LBtleS0qIBooYh9fCkcENksFlaOc7gOEVSeIhMgAIeIu1x9/RKTJV6LxybPmzEjFVv4+jpyHlZCfXhMYHCI1RpiU3LwgBRKktPlHZJSit9Dg6W0spjAQ8pPEQGQAEPkfaI01mAfhXlz+8fw54jZ7Dts5MRyxIxLftJ4SFiIFZ40qXy2x/ghQq8sMLTv45bgogHCniItOdEu7SJn96bUebTUbqLzXjTsmxaOkADRI3E3Z1+Co84kBUCnnSJxggiiVDAQ6Q9iSo8bG2l5yXk4VHIh/W3zIDQh0d0JqCAxzjS0cMjLknPDQU81GmZyAQo4CHSHnnAo7fahQUhSrJ9QlVaCs9Np0ocLSgpPE6q0jIMiYcnTQ4NFsjarZzwXZNpmcgEKOAh0p7IgEff81kQYrTCYw7TslJZOjUeNAqJwpMmKS2m8DhtVthCDZjItExkAhTwEGnPyQQ9PCwIUTqpJ3Jjq3RXnIgJOhUojZagWVrGkZYprdBQWJfdApuVFB4ic6CAh0h7WjsT8/Cw9ZXSV4kEKErN2tLloqYV5dESlNIyCrdktER6HByeUNNBp80Km4UUHiJzoICHSHtOdMgVHp2m5dDqSgFPIlVaZrgrZlk55eGh1IcnUaQprfRASGnZLbBZgt81mZaJTIACHiLtOdkeVHhYRYneG2UWICmntBLpw9P/LxJKoyXItGwc7jQcHspSlU6bFXbm4TFB8E4QsaCAh0hreJ4XTMtleU4ACZSlKyk8BpuW+xvRUlrk4UmMQIBHhyes8KRLvpMpPEEPD6W0iMyBAh4irenw+IQLb6kQ8Oh7DT5JCo8ZmrVFMy2TwpMYnV6fJMZJl5AirPCEU1pmOJYJIhYU8BBpzcmQfyfbYRVSWvF6eJSel5Bp2VQKT/h3wiwtCngSQjwpHUgf03JY4RGntNJj2wgimVDAQ6Q1LJ1VkusEF1Ih9DceDCk8SqMgMt60HKUs3QSfL5WImw4CaZPRQk8okHXZrLCSaZnIICjgIdKaViHgcYDZTHSntMCeZ3AfHhP4HpRMy6zxICtfJuKjPULhSdGGyPCIqrQEhYdSWkQGQAEPkdawkvSSXKcw/kB/H57gv0on9cRMy/3/IsEUHsU+PCb4fKkkUuFJj4jHI1J4wh6e9Ng2gkgmSQ14du/ejfr6ehQWFqK4uBizZs1CR0eHZB2O4yIeGzZsEJZv3bpVcZ3m5mbF91y6dCk4jsM999yTzI9G9BGsJL041yn4THSfm6M0Hkwk4DFHWXrwX6vCtHSvj/rwJAJTeES7Ni0QKzw2KksnMoikBTyNjY2YNm0aampqsGPHDmzevBn79u3DbbfdFrHuunXr0NTUJDxmzJgRsc6BAwck65SVlUWs8+9//xtr1qzB6NGjk/CJiFTAPDyluY4EPDzBfxU7LScQtJjB96A0S4v68BiDuzuo8MRrtk8WgoeHTMtEhmFL1gtv2rQJdrsdq1atgiV0a7569WqMHj0aBw8eRE1NjbBuYWEhKioqor5eWVkZCgsLVZd3dHTg5ptvxm9+8xv8/Oc/N+QzEKlHMC3nOfF5axcA/UZjNrRRsSw9gYuQGXwPQkpLSeGhu/6EYFVaBVl2tPf40sa0LCg8NkvYtGwCtZIgYpE0hcfj8cDhcAjBDgBkZWUBALZt2yZZd86cOSgpKcHEiROxdu1axTv4sWPHorKyEvX19Xj77bcjls+ZMwdXX301pk2bZvAnIVLJSZGHh12S9Z6bWVyi2HgwoSqt/n+RUOy0zKq0yLScEO2igAdInz48Pb1KCg9914T5SVrAM3XqVDQ3N2PZsmXwer04ffo0Fi5cCABoamoS1nvooYfwwgsvoKGhAddffz1mz56NFStWCMsrKyuxevVqbNy4ERs3bkRVVRWmTJmC3bt3C+ts2LABu3fvxpIlSzRvn8fjgdvtljyI9ENclh6u0oqzLN3gaelmMi1bRWcCUniMgZmW813BgCddUloeX1jhIdMykUnoTmktXLgQjz76aNR1PvnkE9TW1mL9+vW477778MADD8BqteLuu+9GeXm5RPVZtGiR8P9x48ahs7MTy5Ytw9133w0AGDlyJEaOHCmsU1dXh0OHDuFXv/oVfv/73+PIkSP4yU9+goaGBrhcLs2fY8mSJXjwwQc1r0+khlZB4XEIaZd4rxtKF5xELkJmuEgIjQclw0ODZenk4UkMucKTLhIPU3icdiuZlomMQnfAM3fuXEXjsZjhw4cDAGbOnImZM2eipaUFOTk54DgOy5cvF5YrMWnSJDz88MPweDxwOp2K60ycOFFIi+3atQvHjx/HBRdcICz3+/146623sHLlSng8Hlit1ojXeOCBB3DfffcJP7vdblRVVUX9XETf0tPrF2YRleSFGw/GrfAopKASGi1hgouEch8eMi0bARscmp+VbqblUKdlG83SIjIL3QFPaWkpSktLdT2nvLwcALB27Vq4XC7U19errrtnzx4UFRWpBjtsncrKSgDA5Zdfjo8++kiy/Pvf/z5GjRqF+++/XzHYAQCn0xn1PYjUcyJUku6wWpDntMXfeDBalVamj5aIYlqm4aGJka4eHo9I4WGYIT1LELFIWpUWAKxcuRJ1dXXIzc1FQ0MD5s+fj6VLlwrVVq+++ipaWlpw0UUXweVyoaGhAY888gjmzZsnvMYTTzyB6upq1NbWoqenB08//TTeeOMNvPbaawCAvLw8nHfeeZL3zcnJQXFxccTvif7FSVGXZY7jEmg8GOrDo5TSSuDO1lR9eJKs8PA8j3/sa0HtwHxUDcg27HXTGebhEQKeNDlcxAoPO4bNYMAniFgkNeDZuXMnFi9ejI6ODowaNQpr1qzBLbfcIixnZev33nsveJ5HTU0Nli9fjjvuuENYx+v1Yu7cuTh27Biys7MxevRovP7667jsssuSuelEGiBUaIWmpDPrl94+PGxtw/vwmKAsXZiWrtSHxx8Az/NCKjERGj5uwQ//sAsA8MXSqxN+vf4AU3jys9LMtCxSeFhQSyktIhNIasDzzDPPRF0+ffp0TJ8+Peo6CxYswIIFC3S979atW3WtT6Qn4gotACIPj77XSVZKywx3xdFSWkAw6GEm5kTY25h5VZARKa00OVzECk+4grH/B+8EEQuapUWkLWysREmuAwDiLkvno5SlJ3IR6u+mZZ7nhc8vSWmJatSN8vGw75C9r9nxB3jBcM/K0tMFscJjZ2XpJgjeCSIWFPAQaUtrZ7jpIACRh0ff67D1lfw6iaW0+vdFQvzZJbO0RAGPUT6e4pxwgUCn1/wzujpEk9LTrUqL9eFx2S1CoEumZSIToICHSFtOyFJaljhnaSVttEQ/v0iIPzsnOhNYLJwQ9BgV8GQ7wmmx1tD3amZYSbrTZhFSgmkS74Q7LdtEnZb7efBOEFqggIdIW4SUVh7z8AR/r7tKK3TNVpyWnsGjJcS2DavMmJzMXjzMjG5mmH8nz2WP+7hNFkKnZbsFNiultIjMgQIeIm0RTMs5zMMTX0qL0ZdVWgYUNiUdscIj9vAAye3FkwkKT3ishA1caApcOoQU/gAv9I9y2aywWZjC07/VSoLQAgU8RNoSUZae4CwtRYUngbvuaMFSP4h3JNtvkSs8Bqe0xO/FvFlmxi0oPDYh+E0HgYepO0BQ4bGTwkNkEBTwEGmJ1xdAW3fwLjnSw6PvtaI1HoxX4eF5PmqnZXkAkY6I03lqCo/Xb4zBuN3TK/w/oxSeLHvc3rNkwPw7QHBmGpmWiUyCAh4iLWntDF4UrRYOhaE+JkIfHp1BSvQ+PPFtX6xAqT8EPOIAUBbvCM0HjUppnekKBzyZ5eERKTwp3B4GU3jsVg5WC0emZSKjoICHSEvYlPTiHAcsoatxvLO0AlECnnhNy/ILhPwOuR/EO8Jnt3CI6KZstGmZqXVA2JtlZpjCk+e0C+nNdFJ4XKHKMcG0TAEPkQFQwEOkJfKSdABxz9Ji99ZGmpblAU6HR5r66U8KjzydBRhvWhYrPK0Zp/AkZrY3kp7ecIUWANiZaZlSWkQGQAEPkZbIS9KBsMKj906ZXWiUKlHi7cMjN3mKG80BkSmidMQvKDwKAY/BpmW3SOFh6Uozw/rwiMvS00HhYQGsU6bwBPjEWjQQRH+AAh4iLREqtHLCIwnin6XFFJ7IZfGe5OUl6SyFwegPCo/SpHSG0x68IBoV8JzpziyFxy0MDrXFbbZPBkzhcYUUHps1/N2bYRguQUSDAh4iLRF68OQlntIKe3giT+jxlqVHKDweqcLTH+rShZRWNIXHoFSH2MNzqsubUP+j/oCk8WDod+nwiSMUHlGwS6XphNmhgIdIS8KT0sMKT7ymZT5KHx6jqrTkAU9/UHiElJaSwsM8PL3GlKWf6QqrOjwPnO4yt8ojmJYlfXhSH1BEKDyW8CWAjMuE2aGAh0hLTiqZli1xztJKQpWW3LTcLvPwKKWJ0o2ABtOycQqPdP+YvVJLbFpOtEN4Ivyq4VM8unm/8LNc4bFbxQoPpbQIc0MBD5GWMJ+HOOCJdyYRW9vIxoPyu2G5wpP+4U5007LTwLJ0nufR1h38PnNCQ0TN7uNhJu18l134Hd/HSa1Ojw//u+UzPLn1kHB8yhUejuOEgJcUHsLsUMBDpCWKCo+GO+VAgMcv/3EAr3/cEv5dEkZLRJSlyxQeeV+bdITtD6vCWcDIPjzdvX6hK/WIslwAmaPw5EuGh/btNoiN4uy7ZilKpvAAYR8PdVsmzA4FPETa4Q/wONXJ5mgpeXjUrxwfN7mx8p8H8cjfPhF+x9ZXuoONW+GJYVruBxmtcEorimnZiD48rAePzcKhqigbgLkVnl5/AN2hwEKc0upr13KbqPcRe2/2fTKFBwgHPGRaJswOBTxE2nGq04sAH0xhDcgWBzyxy3vZCb3TGw5A2Pq8Qq+RuPvwhCq+WIqmw+OTvHZ/Ny0b2XiQVWgVZtsFE7qZe/GI1b5cyWiJvg0oznSLjOKh9+5RUnio2zKRIVDAQ6QdLN1RlO0QTsaAuA9P7BOzeLCneHV5gBO/aTn4vEJRQNYhCrL6lcKjWKUV6sNjQJqDKTz5WXYUh1KUZlZ4WNPBLLsVdqslZaZlcbNHhpLCE56nRSktwtxQwEOkHUol6YDWsvTgwl6RMiG+s5ansBLttJzrtAkXDPGdff/w8AT/VUxpGejhERSeLDuKQ9+pmQeIiiu0AKRslpZ4nAd767BpWezhCSk8lNIiTA4FPETaoWRYBvQ1HhQrE+IYRx7wxHtTy+6GbVYOuc7ghU3s4+kH8U4fprSCwU1Blh3FOSGFx8QpLabw5GeFKrRSNC1dbFpm7x0uSxd5eKxkWiYyAwp4iLRDqSQd0DZLiy0Sn7zF68sVnURNyzarBbmhO3lxL57+4OGJaloWFJ7EGw8ypaEw24HSPKbwmDfgkSs8qRotIe5uzf4GwsNDI6u0yMNDmB0KeIi0Q2lSOiDy8ES5EWWn7AAfDmYkCo/foJRWaCPsFg65zuCdvFjh6Q8enqidlg0cHsouvBKFJyNSWsHjQrx3+zKtJUlphf7t6VVSeIL/J4WHMDsU8BBpx8n2yJJ0QP8sLa8vEHGBkd/FxnsB6hUUHg55LKXVXxUehbOA025cp+Uz4oAn5OHp8vrR5fVFe1q/hZmFBQ+P6FjoSxFF2bSs5OEJbp/Z55sRBAU8REL8c/9x/HP/8biee7pT+S5f8PDkKKe0op2XxfGL1x+ISCPIg6X4Oy2HFB5RSqvDE77ApEO8s+vL07hpzXbsPdamuFxLHx6jFZ5cp01Il5lV5Qk3HWQprfCyPlV4uqXzywBlhcduJdMykRlQwEPETU+vH3f+YRd++Iddui+Mv3v7c4x7uAGb9zZFLAtPSldWeKJ7eMLLev2BCKOoXOGJN+ARFB5L2LScbh6el3YdwY7PT+HVDxsVlzPxJumm5a5wHx6O41CSw3rxmDXgkY6V4ERJrb4MKaQprVCnZSWFh0zLRIZAAQ8RN11eP7y+ADy+gO7A4UBLBwBg7zF3xDK1Ki29s7R6/YGIdeV9d+JV8cWm5TwF03IaxDtoausBEJyppIQwWqKPytILQlVL4V485jQuR5Sli86y8Y4yiQexaRkyhUcppUWmZcLsUMBDxA2r+EgE+UiGQICPUqUVu4GbeFGvj49IaRml8Ahl6RZOlNJKL4WnWQh4lL8ndvFVNC2zxoNGjJYIpVYKs1nAE1J4zJrS8jAPj5Jpue+2o03BtKxYlm4h0zKRGVDAQ8SNEekOufrg7ukVgpJieePB0NGq2bSsoPAY3XjQZrUIpmWWygDSI+BpcQcDHnlQyQhPS49cloyUVkFW8PtklVonTdqLx90tU3hScCz4/AG0K3zvHqXGg1YyLROZAQU8RNx4DOjR0uWVvgZLZ+W7bJJ5P4C2fiYS07Iv0rQc2XjQiLL09Etp9fT6cToUaKhVQ0UbLWHU8FB/gIc7tF9YSot5s1g1ntlggS9TeMS7t69SWu4e6Xcu77RMpmUiE6GAh4gb5gdIBLn6cEIoSXdGrKtllpZ4jETQtCwvS5duc7wKj7gsPTd0YXOnkcJz3B1WTzpUUlphhSd5jQfFqpcQ8Ji827K8SktiWu6jmKJNVpIeNi2re3h6aZYWYXKSFvDs3r0b9fX1KCwsRHFxMWbNmoWOjg7JOhzHRTw2bNggLN+6daviOs3NzZLXOXbsGP7jP/4DxcXFyMrKwvnnn4/33nsvWR+NCOFJwMPDAp0zXdK7fLWSdEBclq7HtCz9nficzvORHh+tSDothxQe8UUm1Y0Hm0PpLECDaVnRw2NMHx5WKZTtsApBVH/z8Bw93YUvTnZqXt8tbzyYAoVH/ncVTeFhKS1SeAizk5SAp7GxEdOmTUNNTQ127NiBzZs3Y9++fbjtttsi1l23bh2ampqEx4wZMyLWOXDggGSdsrIyYdnp06cxefJk2O12/P3vf8fHH3+Mxx9/HEVFRcn4aISIRNIdr34QLJX+4Ki0R4xaSTqgzbQsFnSCfXjUFZ5EPAvsdRyiKi3m3QBSPzxUS8ATrQ+PU1SllUjvGPHgUAar0uoP4yUCAR7f/L93cM2KbZpN+uGUFvPwhJf1VUhxJkLhCaKs8JBpmcgMbMl40U2bNsFut2PVqlWwhP6YVq9ejdGjR+PgwYOoqakR1i0sLERFRUXU1ysrK0NhYaHiskcffRRVVVVYt26d8Lvq6urEPwQREyOqtOSolaQDGmdpif7f6+cjFZ4oc7X0wJQPcR8eaUor7pc2hJY2LQpP8N9ofXgCfLCyjU2E1wu78OaLA55+1Ienq9ePE+3BY7LL65cECkp4fH4hqFDsw9NHEY+8yzLP8/D5A0JBgJLCQ6ZlwuwkReHxeDxwOBxCsAMAWVlZAIBt27ZJ1p0zZw5KSkowceJErF27VvFiNnbsWFRWVqK+vh5vv/22ZNkrr7yCCRMm4IYbbkBZWRnGjRuH3/zmN5q20e12Sx6EPoyo4JEjjJVQCHg4LQqPiF5fIOKWWizbJ3LxYa9jt4kVHnGn5TRSeLx+xb8rfxSFxyG6ICZSmi4oPNnhgId9t6c6vXGbxvuKjh594y/ExvXcFHZaFjcdZIj/XsWBmz10nqY+PITZSUrAM3XqVDQ3N2PZsmXwer04ffo0Fi5cCABoagp31n3ooYfwwgsvoKGhAddffz1mz56NFStWCMsrKyuxevVqbNy4ERs3bkRVVRWmTJmC3bt3C+scPnwYTz75JM466yz84x//wI9+9CPcfffdWL9+fdRtXLJkCQoKCoRHVVWVwXvB/CQl4Imq8GgwLctSWhFl6WKFJ4ETPJP/xaMlxC+XaoVHHPD4A7zidxWI4uFxWA0KeEJekgKRwjMgpPD4A3yEuTbdEI8L0RKssIAn12kT9qs4+E2ZaZmXKrJKCg+ltAizoyvgWbhwoaKJWPzYv38/amtrsX79ejz++OPIzs5GRUUFqqurUV5eLlF9Fi1ahMmTJ2PcuHG4//77sWDBAixbtkxYPnLkSNx5550YP3486urqsHbtWtTV1eFXv/qVsE4gEMAFF1yARx55BOPGjcOsWbNwxx13YPXq1VE/ywMPPIC2tjbhceTIET27goBxKS1x4HGykyk8Sh6e4L+aFR6F0RLi90okpSUEPBYOeU57xPJUKzzilBagnNaKNi3dZrUIF+xEjMthD0/4+3TYLEIFU7pXaqlVuKkh9+8A0saDfWdajgwke3xh35n4O6eydCJT0OXhmTt3rqLxWMzw4cMBADNnzsTMmTPR0tKCnJwccByH5cuXC8uVmDRpEh5++GF4PB44nZF3+AAwceJESVqssrIS5557rmSdc845Bxs3boy6nU6nU/U9CG0YpfB0en2C3+FkOzMtqys80T084WVeX6TCI5btE0mn9IpSWi57MDgQB1PppPAAwW7LxbnSdcKmZeXXcFgt6A744Umg/QC78BZkS4PCkjwn3D0+nGj3oqZM6ZnpQbwpLUnAkxLTcmSVFquqdNql97lWKksnMgRdAU9paSlKS0t1vUF5eTkAYO3atXC5XKivr1ddd8+ePSgqKooaiOzZsweVlZXCz5MnT8aBAwck63z66acYOnSoru0k9GOUwtPl8SPfZQfP81HL0rXM0hIv6vVHlp2LgxyjUlocFzQui9MIHFIX8QQCvNBlmeOC+0Sp23I0hQcIKjHdvX54/fF/z/I5WoySHCcOn+jsBwpP5HiGaDAfFytJB1KT0oowLYMXTUqXGq8F0zIpPITJSUqVFgCsXLkSdXV1yM3NRUNDA+bPn4+lS5cK1VavvvoqWlpacNFFF8HlcqGhoQGPPPII5s2bJ7zGE088gerqatTW1qKnpwdPP/003njjDbz22mvCOvfeey/q6urwyCOP4MYbb8TOnTvx1FNP4amnnkrWRyNCGKXwsItxh8cnvGbUsnSNb9urWJZucEordLGQBzzyhod9yakur6BADSzIwrEz3ehU6LYczbQMGDNe4oxKwNNfevHoT2lFKjxAOPBMrWmZjZWQKjxkWiYyhaQFPDt37sTixYvR0dGBUaNGYc2aNbjllluE5axs/d577wXP86ipqcHy5ctxxx13COt4vV7MnTsXx44dQ3Z2NkaPHo3XX38dl112mbDOhRdeiJdffhkPPPAAHnroIVRXV+OJJ57AzTffnKyPRoSId7SEXFlh/pKToYtftsOKbEfkoanJtCz6v5KHJyBJaenYaBlCSivkf5Bf4FIJGxpakutAYbY9GPAoKDzRTMuAtBdPvChVaQHigCfNFZ4efaZq1pog3yX9vBaOg5/vuzBY2bQcOTgUINMykTkk7Sz9zDPPRF0+ffp0TJ8+Peo6CxYswIIFC2K+1zXXXINrrrlG1/YRiROvt0OuNrCfo1VoAeI+POqvLb6D9sTw8Bih8LC2/KwXT3g74n7phGHprPJ8F3JCgaPSxPRofXgA8XiJRKq0VBQeYYBouis84WNVy3eqqvDoeA0jUGo8GFZ4pCktMi0TmUL63JYS/Y54FR652sAuxoJhWaFCC9A2S0uM4mgJXqzwJB7wsKAgN50UnlDAU5HvEj6vYpVWrJSWAQNElaq0gPB3nPYKT9wprUiFB+D7pEqL5yPL/XmeV1V4yLRMZAo0PJSIm7gVHtnFl03zDpekR1d4dKW05AqP6C42MdOyPKUVWZqeKlhJenmBCzkh5UnJtNwXKS1WLRTp4QkNEE17D098Ka2IFCdTJ43YqBj09AaE74x9t9EUHqZSUqdlwuxQwEPETU+cCo/8rpldjKOVpAPh1IvWm2SlKi2j+/CoprTifuXEESs8bLu6opiW1Sa7s2qeePvw9PT6BVUhoiydBTzpntISlaVrceC0Cx4e5ZRWX3SWZkGmzcIhxxEObtgNCqW0iEyFAh4ibgxTeFhKSyhJV05paZqWLu607AtEBjwGp7TstjQ0LbuD+7Ei3yWYv5VSM2GFR/l1EvXwsNJojgPyZAEhMy2zIDddiTellZ+llNLqG8StADgufJPAblDItExkKhTwEHETr7dDnl4RFJ6O6AqPllla4rvwXoXREpLGgwbM0mI+F7nCk0qJR5zSynUG7+b1dloGxGXp8Sl54pJ0+XuwPkvtHl9ShtAahd6UVrSydKBvTMviZo/hOItXV3ioLJ3IECjgIeIm3gtVpGlZWpau7uHRZ1r2+qKXpSfiWfDGSGmlEnFKi3l4EjEtx6vwhA3Lkf6m/CybsO9OpXFaSxKca6rSimw8COg/dhNBovCEfieepaVqWiaFhzA5FPAQmviytRNL/74fx9vDIwviVXgiAh6vLKWVUFl6+P+xFR7jUlrpUqXV0+sXLniSgEfBwxPTtGxPrErrjEpJOhBU6/pD80Glcv5ouGOVpRuxUTFgrQDEgWbQtKys8AidlknhIUxOepylibTn+ie342SHB7u/Oo0X7rwYQPwKj9wX0Sk3LauUpeudlp7M0RLylJbco5KqTsus6aDLbkF+lg05QkpLoQ9PDNOyoPDEeecvKA3Zyt9ncY4TLW4PTqbxeIl2HbO0eJ5XVXiEKq0+VHgKsx1SD4+KwkOmZSJTIIWH0ARTX9774pTwO6MUni6vD91ev6D0qHt4gv9qTmn1wWgJdnecLgqPOJ3FcZzQeFCxLD308dUUHsHDE6c5/UyXckk6oz8oPHpmaXl8AaFdgbxKy6LBf2YU4lYA4m+W/b06VcrSqQ8PYXYo4CF0IT5hx2tmVTIts4DKYbNEqCUMLReNWKMl/KKTeiJVWszXotaHJ1WdlsVdloGwtyie0RJClVacCo87iocHEJWmp2nzQZ8/IJTVa4H14OE4CIEmQ2weTjbiVKJglgYvKDwRs7RI4SEyBAp4iLjRczEQo9RpWVySzqmkWCyCPK+n07J0XfG1O6GUViBGlVaKYCmtioJgwJMt9OFRGi0RI6WVYFm62uBQhtBtOU1Ny3r9Oyz9leu0RVSlCX14+iCmkE6oF5elK09LJ9MykSlQwEPETdyjJRRmaQkVWirpLEDch0f9tcXBkGIfHpHCY2RKK1368IhTWgCEsnSllJZQpaVyFhAaDyZapZWtltIKzdNK01487Z7IAZzRYIqWfHAoIA7Wjdm2aCjtd54HPCoKD5mWiUyBAh4iLnieT6APT6RpOVaFFqDNwyNe4vVHzi6SNh7UuMHy9+D5iNES6dJpWZ7SEpely5WxQAyFx5loH55QakXehI9RHGowma4DRONVeJSCX3FqKdmIAx5JSktF4RFSWhTwECaHAh4iLoKG4Pie29EjvXPu8vhjVmgBosaDGk/MvQoKjxGmZfFrsItFtsOKPmymq4o8pcUCHl+Aj/DiMMtGzCqtJPThAdLfwxNv00ElhSd87Ca+XbGQeHhEv1dVeCilRWQIFPAQAIB3Dp7Eqn8e1OyPSWSCtvzOudPrwwkNCo+WtIC8D0+0svR4TcviC4M9lA7gOE6i8vRF+bESLaGxEkzhyRZV5Mj3e7JNy20xPDzpXqUlL0mPpc60qw0OhbgPT1+YllmVlkPS4TmmwkOmZcLkpIfxgEg5M5/eAQAYMiAb144ZGHP9eEuVgUg/SYAHjp7uBhAr4GHrRzsxax8tEa9nodcXqfAAwV48sfq2BAI83vvyNGoH5gvqi1EEAryQ0mIKj81qgctuQU9vAJ0eHwaI5pTFGi2R6LR0cT8YJYSJ6Z0e8DyvalZPFUlJaSU5pvAHeLSH/r6CCk94n8by8PioLJ0wOaTwEBI+bWnXtF4i84+Uuv5+2doJIJZpOaTwaHyfXn/k/bRE4Ynz6iPuV2ITBQtaevH8Y18zblyzHUv+/klc7x2N1k4vfAEeHAeUifYjU57kgWbM0RKCh0f/hZDn+dgKTyj46vXzQofidEJvSsut1nQQfWdabu/pFd5DUpbOi/rw2JT78JCHhzA7FPAQErTe1SaW0oq8uB1hCo/KpHRAo2lZ0icoOaMlhLESVk6iSogvdGqvfOhER/Df451xvXc0mLpTnOOUKE85Qmm6TFnTOC09nu+6w+MTFCS1Ki2X3SoEY+no44lIacU4XKIqPOw1kpzSYkFmtsMqfH/sfdX68NgslNIiMgMKeAgJ8ouiGvFW7nh8fqHCSQxLm2hReLTeiCp5ePySlJa214l4XZ+0QouhpRcP6zlzust43wozLFeG0lmMbKHbsvQ70zxaIo6AhxlnHTZLxOwmMenci0f/HC11hYfro07LZ2RztJQ6LavN0iLTMmF2KOAhJCg1qFMi/qaD0V9fm2lZW1l6r8JoCb8BVVospRUR8LjEpmXl554OXdiTcYFvlpWkC9slzNOSpbRiDg+Nvw9PrAotRjr34om7SisrmoenbxQeNr9M1ywtSmkRJocCHkJCshUepXQWw2rhol4gtTUeDP9febSEcVVarEKLoTYSQ4yg8HR6Db/4hQ3L0qAxR8XDw1J6qlVaCQwPjeXfYaRzLx75/or1bakODoW4D09yCXe3juwLparwWMKNB1NVXUgQfQEFPIQEzR6eOBUepY6/jOIch2rFECBOC2gdLcFHVGKJVZ1Eq7TiSWmxVJYvYLxRV+jBI1N42FynLhWFJ9ZoiXiC21hdlhnFadyLR54CjIW7W93Do3csSry0hY6vwiym8AR/7/MHhO9brvAwDw9AKg9hbijgISSkUuGJls4CwgoPz6tfOOSmULkvwYjGg5pSWirPPSXqOXPaYFVDLaWVw1JaXnkfnuC/qimtBMrSxc3volGSxr145A0yY8FGUcgnpQMi03KS4wk1Za1bVFWp5uEByLhMmBsKeAgJ8ouiGvGPlYgS8EQxLANSJULtwiH/vdxrZEjjQZ90jhYjlsLD87zEu2O0j0feg4ehltKKZVpOJOAJX3jVq+6AcEqrtTMdFR55lVasxoNM4UkD03JIWWNfrfjvwGFV7sMDSFsuEITZoICHkNCt2bQcr8Kj/rxoYyUA6YVZa1pLrkQZovD4pZPSGZJUhsJrd/f6JYGi4QqPSkorVzRPS0ws03Ii09LPdLNuvxpNy+mo8OhIafE8H2O0RHi9ZBI2LbMqreAbs79Xh80SkTa2i1NapPAQJoYCHkKCUlNAJeJVeBJJaXGio1XtTln+a/nF2hDTckBZ4akpy4v6PHna5pSBAU+31y94gspVytIjRkvEmJaeSB+eti5tHp50nqelp0qru9cvHFvR+/AklzOylBYLtLpVKrSAYKdti8jrQxBmhQIeQkJXCk3LyVF41AOe+E3Lyh6e8UOL8D9Xn6P6PHnvnVMG9uJh/p1shzWiWixWWXqsPjy+AK87ONRqWk7nPjwdOhoPMsOy1cIh2xHZd0iv4T5ehECTmZZDvw+PlVDuiSQ0HyTTMmFiKOAhJGgtQY4/pZW4aRmI5uGRLoim8MSr3rOUljzgAYDhpTnB7VB4nvyibqTCI05nyWdSMQ+PXL3TmtIC9JemazUts5TWma7etGt8Fy04l8NK0nOdNsWZYJY+knhimZblXZYZwjwtSmkRJoYCHiIukmJajhnwJO7hEQc88fopfAHlPjwAJMMa5ZxKYkqrRaVCC9DQh0fVtBxWA/R+31r78BRm2YVgwMj9kShqHcHVcEcZKwGEj4vkp7RCZemCaZl5eJTnaDFYLx4yLRNmhgIeIi7iLUtPJOARX5e1BjxRFZ445XuvSkpLjNLmRaS0jFR4VCq0AHEfHuXgT633kTig0/t9aw14LBYOA3KYcTl9fDzidJZDwfcihyk8SoZlQNscOCOQ73f2DarN0WII3ZZJ4SFMDAU8RFzEP1oiWlm6Hg+P8jry60mEh0fceDDBKq1oAY8SLKU1qDALQHJSWsoKT/CuPlLhCf6rltLiOC7uSq2whyf6dwqkZy8etq+yHVZVBUxMtMGhgHTEQ7Lo6fULf5esSotFPDEVHpqnRWQASQt4du/ejfr6ehQWFqK4uBizZs1CR0eHZB2O4yIeGzZsEJZv3bpVcZ3m5mZhHb/fj0WLFqG6uhpZWVkYMWIEHn74YWqRrhOl6o1oxK/wKD+P44ABMS6O0j482hoPygMenwFVWtFSWlEyWkJKq6YsN/hzElJaFfmRKlluDA+PmmkZAJxxDBDt9QeEgCGWwgMAxbnp14uHbb+W7tlA9MGhQPiwSKbC4w4FmVYLJxjX2fvG9PCETMvxqp4E0R9ISsDT2NiIadOmoaamBjt27MDmzZuxb98+3HbbbRHrrlu3Dk1NTcJjxowZEescOHBAsk5ZWZmw7NFHH8WTTz6JlStX4pNPPsGjjz6Kxx57DCtWrEjGRzMtOaITu5aLm9EKz4BsB2wxFBOLJKWl7f08vfI0TkD0f22vIUdTSkvBrcGqsljAY2QfnqgpLWf0lJaawgMATrv+eVrswgsodx2WU5zDStPTSOEJKTa5Lpuoh476+uEePMqfl7W6SWY4wUrS811h43TYw8PK0qMrPD7y8BAmRtvti042bdoEu92OVatWwRL6S1+9ejVGjx6NgwcPoqamRli3sLAQFRUVUV+vrKwMhYWFisveeecdXHfddbj66qsBAMOGDcPzzz+PnTt3GvNhMoQsUblqt9cf07cQ92gJmcpgs3C4buwgjBtSGPO5nAbTsvzX8gu1+MdkpLSiJT+YosMCnnaPDx6fX/UipIeoKa2Qh8frD8DrCwjfbSzTMiAaIKpD4WEX3jynLWYQC4S9W+nUfFCvwhMeHBrdtJzMiCfcZTlSKY3l4RFMy+ThIUxMUhQej8cDh8MhBDsAkJUV9C1s27ZNsu6cOXNQUlKCiRMnYu3atYqpirFjx6KyshL19fV4++23Jcvq6uqwZcsWfPrppwCADz74ANu2bcNVV11l9McyNeIAR0vzQaOqtCwWDo/fOAb/cdFQTc+36DR/yvsFiRWeuFNaKtPSxSialkMBz7DiHEFVYRepRPAHeBxvD6aDlBWecEAlVtjYPrREOQvE03xQ3u03FiyllVamZd0BT0jhUUnh9YVpme138TZEmJZVgmsyLROZQFICnqlTp6K5uRnLli2D1+vF6dOnsXDhQgBAU1OTsN5DDz2EF154AQ0NDbj++usxe/ZsSSqqsrISq1evxsaNG7Fx40ZUVVVhypQp2L17t7DOwoUL8Z3vfAejRo2C3W7HuHHjcM899+Dmm2+Ouo0ejwdut1vyIIJ0aRgvkYw+PFqwxDB/RpiWIxQeI0zLsVNaSjDTckmuA0XZxhl1Wzs88Ad4WDigVKHSzWa1CB4tccDJdk20lFY8puU2jT14GGHTcnoGPOEWOurHSzqYls8Ik9JFAY/ctByjDw+VpRNmRtcZe+HChYomYvFj//79qK2txfr16/H4448jOzsbFRUVqK6uRnl5uUT1WbRoESZPnoxx48bh/vvvx4IFC7Bs2TJh+ciRI3HnnXdi/PjxqKurw9q1a1FXV4df/epXwjovvPACnn32WTz33HPYvXs31q9fj1/+8pdYv3591M+yZMkSFBQUCI+qqio9u8LUaJmYHv9oifgCJQYLeFRTWrKfPbKW+uLtjlfh8UZLaamkh3r9AeEOvCjHgQE5wYuSvFQ9Hph/pyTXqZpCEnw8omBWS0qLpdt0BTwauywzBA9PGvXhETw8Wk3L3dpMy8nUT5T2O0uldcfw8FiZaZkUHsLE6Ap45s6di08++STqY/jw4QCAmTNnorm5GceOHUNrayt+9rOf4cSJE8JyJSZNmoSjR4/C41G/05s4cSIOHjwo/Dx//nxB5Tn//PNxyy234N5778WSJUuifpYHHngAbW1twuPIkSN6dkW/4f2vTuOOZ97D5yc7NT9HS1ASz2gJnuc1z+pSI5wa0LY+C3CYzN/eI1Y4EktpyWdpiZHHYyx1xXHBO/ABOcaNVBC6LCuksxhKpemx+vAA8aW0mNKgVeEpTsOydKZE5mowXQOxFR69qdh4UOp9FFZ4QgGPWh8eC5mWCfOjy7RcWlqK0tJSXW9QXl4OAFi7di1cLhfq6+tV192zZw+KiorgdKo3oNuzZw8qKyuFn7u6uiSqEQBYrVYEYvzhOp3OqO9jFn677XM0fNyCMYMLcNfUszQ9R4vC0xOHabnL609Y0hcUHpVgRe4BEwIelw0n2j3o8PjA8zw4jks4pSWflg6om5aZYbkgyw6b1SIEPEZUakXrsszIcUROTGf7UItpWY9Jva2blaTH7sEDiE3LHuG7STXt4pSWhnRUzLL0PklpsTlakdsQy8MT7sNDCg9hXpJSpQUAK1euRF1dHXJzc9HQ0ID58+dj6dKlQrXVq6++ipaWFlx00UVwuVxoaGjAI488gnnz5gmv8cQTT6C6uhq1tbXo6enB008/jTfeeAOvvfaasM61116LX/ziFxgyZAhqa2vx/vvvY/ny5fjBD36QrI/Wrzh4PNj7qFODL4ehZd1oCk+Hx4cnGj7F1aMrMW5IUfh1QxcRC6ddoZHD7pRVPTzy7QwFPOyu1x/g0eX1I8dpS0pKS207WMDDAh1DFR53eI6WGkIvHrHCI0xLN9bDw8Yb6FV4PL4AOr1+zWmkZCIuS9dCrLJ0IaXVx6ZlRiwPj2BaJoWHMDFJO7Ps3LkTixcvRkdHB0aNGoU1a9bglltuEZazsvV7770XPM+jpqYGy5cvxx133CGs4/V6MXfuXBw7dgzZ2dkYPXo0Xn/9dVx22WXCOitWrMCiRYswe/ZsHD9+HAMHDsSdd96Jn/70p8n6aP0Gf4DH4VAqq1tHwNOdoIdnyycteHrb5/j8ZCd+e9uFwu9ZOiXHYRPuoPUSy8Mjh12osx02WC0c/AEe7T2+YMAT57VHS0pLDgt4ilnAk22cwtPcpl6hxcgWmg+GjwOW0oomqDDvk54+PHo9PNkOG7LsVnT3+tHa4UmLgIelXvWXpSt/ZsFsb8C2qXFGobu10IfHF0PhobJ0IgNI2pnlmWeeibp8+vTpmD59etR1FixYgAULFkRdJy8vD0888QSeeOIJvZtoeo6c6hIu+HqqqrR5eNTXYXe7cr8Oe90cZ/wBT8zy3ojREsH3tFg45LlsONPVi/aeXlQUuJKT0lIJHk6Fugiz6iym8BjRbVlLSis35OFRKktPdZUWEBwrcuRUN052eDG0OEfz85JFe49SlZYygQAvHM+qjRY1NC9MFEUPT+hfdsOjXqVFnZYJ80OztEwMS2cB4SoNLSRapcWWyS+SgsLjjL/RHjPYau+0HNwGDmFDKZtsHW9KS8ssLXnq4lRn8GLE0jdFOcaNU9CS0mIeHiXTclQPTwJ9eJS8JGoUp9kAUT19eDq9PiGQUVd4gv8m1bTMytKzI03L7PuLpfD4aJYWkSS+au1K+fFFAY+JOXhCFPAY6OHxB/ioKQ6mqsjlcb3N3JQI9+HRN0vLwgF5TlapFbwgx3s32xslpcWp2JblCg+7wJ/uTLzxYItQpaVuws+ReXh4nheCxmhVWs64PDxxKDxpVqnVqeNYZWqQzcKpdjJmx0WflKUrVGkxXHY103JwuymlRSQDnudxy9odmPzoG9h7rC1l20EBj4kRKzw9Oi5YXTHSTbEufswgKZ+83CkoPIkEPMF/tU5L94aCL47jBIWHXaD6svHgqVCah6WyikJ9eBI1LXd6fEI6JWqVVkhVY314xPvP6NESejstA+J5Wmmi8IhNy0I6Svl4EXdZVqsw42K8RqIEArxiSkuO2pBgKksnksm7h0/hy9YudHr8GF6aupQ1BTwm5jNxwBO60D2740ts3tus9hQAyp2WxSJArBJltlw9pRV/wMPpNC1LFB6XtBdPoiktJQ+PGkzhYSktQeHp8iZ0EWTprByHVTWdAoT3OfsOxOpWVIUnpAhoNS3zPB+Xhyc8MT09FJ52XQpP9DlaQOwO4YnS7vEJQax0tIT0u1VXeMi0TCSPF94L9rm7dsxAZDtSV5RAAY9J4Xkeh2QenuPuHvz3y3vxwz/swl8/bFJ9rlLAI75zjTUpnflm5BdJpvDkGaLwaOu0HN4GTjCUCimtOM/tUVNaKrEDS9WwlBZTePwBHu7u+JsxsnRWeZQKLSCyLF28/6KallkfHo0esJ7egLDPlYZYqlGcmz4eHp7ndaW03BoCHkHhSVJSi3V6dtktkqBGfjzGMi3TLC3CaNq6e/G3j4LXm5suTO1EAwp4TEqL2yMxqHb3+iWBytwX9+DDo2cUn6vUDVl83vQIaSLl91ZTeIxJaembpcV+Dio80pRW/ApP7JSWfDvYCAmm7DhtVuFieiqB8RJaDMtApGlZrPBoMS1rVXhYDx6bhUOOQ7s5PZ08PN29fkEtyXXFrtISuiw7YytaycoYhZsOSoNM+Tcby7Tsp5QWYTCv7DkGjy+AkeV5GDO4IKXbQgGPSRH7d4BI03JPbwB3PPOeMJZATJdCWbpFdFGMVfGh5uHpEJWlx4vePjwMTpLSStS0rJ7SUgodeJ4Xys+ZsiP+/6kEKrU0BzwyD4/Yv2TktPQzonSWno7JrNuyEVVricL8OxYOyFJJAYlxxxgrEXwtqWnZ6GoV1d5Hsu9AVeEJHQS9VJZOGMyGfwfTWTddWJXyLuoU8JiUg8fbAQBDi7MBSPvwOGwWnF2eixa3B//5zL8jytAV512JjtNYc3liKTy5CZSlx5qlpZYysCTBtBx1lpZoOzo8PiFIYgoPAAwI/f9UApVaWlNa8iqtgEaFR2+VVjyGZSC95mmJvWZaTtAsgFbqcMxgL+MPBLDgpQ8w5sHX8EmTO/GNDcGUtWjbAKjfpNitVJZOGM/eY23Y1+iGw2rBN8cNSvXmUMBjVlhJ+nkDgxKiuA+P3cLht7deiAE5Duw95sZ9f/xAopgoenhE/2d3+2oVH2GFR1aW7jUupZWIwpN4Hx59VVpM3cmyW5ElSvMMyO5LhSdKSsvAxoNn4jAsA+FA8FSXN+UX3Q6Z1yzWHKxYg0OB8HH76y0H8cJ7R9Hp9eODI2cM2mL13kfyb1bdw9N3puWNu47i5fePJv19iNTDzMpX1JYLvcdSCQU8JoWltM4bFAx4enr9EtWhakA2nrplPBxWCzbva8aXrV3CMqXGg+IbXWZKVqv4EBQef0BSgWRkWbpqHx6V87W0LJ2ZlpPQeFAhdmiVzdFiGKHwNLuDwVK0knQg7OGRp7Q4DlFVjPDwUG1BiDuOpoMAUJRtB8cFv7/TXYn3JkoEvXO02GeOViXH9vCxM93C7/Q0c4yFWqAZ0YdH1cPTN7O09h5rw9wXP8B9L3yA4+2R6XTCPPT0+vHy+8cApN6szKCAx6QcPB6coTU6ZBIL8JGemgnDBmDp9edHPDeWh0dIacVQeADpHaOeyhc1wgqP8nK1EEYppZXoLC17tJSW6LVPqwY8iSs84aaD2jw8HUJKK/j7aOksIA6FR+fgUIbNahEq2FLt49HbPiHW4FBAGngwNU7PBPpYqHl4NCs8gmk5uQrPmrcOAwj+fez64nRS34tILZv3NqO9x4dBhVmYPKIk1ZsDgAIeU3KmyyuU97KUFgB0eyMvWt+6YDBmTxkh+V2wVb70xKec0oqu8ADSIMsI07Lg4YlxYpZnaYKjJUKmZU+vptdQI9q0dKVOy62CYdlYhccf4HEi9D1XaixL9/oC6PUHBIUnWg8eIPwda63SalMYYKkVNlg11T4eeUfwcLCi1ngwdln6yIo8cBzwP1efgykjSwGElVIjUOt9JFfv1G5S+qLT8petnfjrh43Cz+99SQGPmdnw768AADdOqIp5nukrKOAxISydNbDAhYJsu3D3pjZPa94VI3HzpCEYMiBocA7wkXI7p6DwqLXRFys8YmXACNNyLIWHSSsO2YndwkHUhyfSx6KHXi0Kj+j/p2WT0hmJKjwnOzzwB3hYLZxQ5aSGuNlXp8cnBHuxFB69pmWWWollnlWCGZdT3YtH8PBoTGmFFR71zzzvipHY9T/1+M+vD49rAn0sBGUtSqDptFlU05d9YVr+zb8OI8CHA0kKeMzLFyc78e7hU+A44NsTBqd6cwQo4DEhLOAZUZYLIFxaqxbwWCwcfvHN8/HPeVOE33XKxktIPDxxKjzG9uGJ3nhQXjIe9PCEOy3zPB+3h8cXp2lZ1cMTp2eFtRQozXVGNR4DwQCQ7ZNOrz88OFTD8wDt6Zd4BocyWPPBdFF4cjR2hA2blqNVaXHC98+6Vxvp4dFiWlZTd4BwSitZZekn2j148b2gUfln/68WALDvWJuuGX9E/4GZlS85qxSDCrNSvDVhKOAxISzgqQkFPK5QZVCsk4tVNPxQXqml1HhQ7QQqlurFJ3W9FxIlYpWlM+QKDydqPOgP8MHmckmYlq50A61uWk5M4WEVWrFK0hnMx9Pp8YVTWjGUZr0eHi3znNQoTZNePHLTstB4UOVw0dJpWQz7u9HavVoLWkzLakUGgLjTcnIUnt+98zk8vgDGVhXi+gsGoSzPCV+AxwcqzU+J/ovPH8BLu4LBbbqYlRkU8JgQVpIuBDyhIKZHwwlWXs3DUBotoXYCFb8PU3h6/QEh+DHGtBy9SitC4QGHbIdVUDTae3xxKTw8z6M3oKEPj+i11U3LiU1MbxFK0qOnsxji0vSARoUn3j48EQ3wNJBuHh6tI1C0lKWLceps5qgFddNy+PtVMywDyTUtt/f04vfbvwQA/GjKCHAchwnDigAAuyitZTq2HjiB4+0eDMhxYNo55aneHAkU8JgQQeEplaa0tAQ82UwFkJWmKw0PVVV4fJFVWuIUWUIprdBbxurDo+Th4ThOCLbae3rjUnj8AV41qAKUOy2rKjwhv0WHxxdXxQ5LacXqwcMQz9NiwZ7WlJbm0RJx9uEB0meelp4qLX+AF3l+tH1mlgpORsATsd/FCo9KChpIrmn5+Z1fwd3jw4jSHNSHLoDjhw4AALz3xSnD349ILX8MpbO+NW5QxHk41aTX1hAJ0+31C70+ajR6eMQICo9HXeERPDwKCg/P85ITOVMG2EXBYbUk9EcQe5aWsmmZbT67C3fHqfCILwhRZ2mJ/q/m4cnPsgkBRzwqj96UVraDpbTCHh5LrLJ0YXhoHyg8gmk5xQqPPKUlGwshWVcUyGtVePT6omLh9QUERTbaLK1oKS3BtGxwHx6Pz4/fbvscAHDnpSOEap0JQ8MKT7ypZSL9OO7uwRv7jwNIv3QWQAGP6Th0ogM8H2zkxu6YXToCHuGiKFN4xCfOaH145HetTBnoFErS46/QAsIXn1jT0iMVnuDzxMbleOwKvaILQrSUlhi1lBbHcQn1nmnR2GWZIR4vwXafZtOyhp0VCPCCnyWeKi1hgGiqPTw6+kWxknSHzRI1oBAT9vAYE1ywIFPsU4v2vkoIs7QMVnj+/P4xtLg9qMh3YcbY8GiBcwfmI8tuhbvHJ6Tgif7Pxt3H4A/wuGBIIc4qz0v15kRAAY/JkBuWAQjjDLRURLCLonxdcUojrPAoBDyyk7hc4dHavVYNdn2OdVMoV1/kCk+8Ka1eUUBnV5i6KS/79fj8aA999gEKJcPMtxKXwhNvSsvr067wiDw8apVxjGD1W/D/caW0ctKrSktLwOPujt10UA77uzGqLL2NzdFy2SP6nWg3LRtflh4I8EKjwdu/Vi25CbFbLRhTFewR9h41IDQFPM8L1VnfuXBIirdGGQp4TEY44AlH12pGZCVY+kuu8Ig9OMJoCQVPgFym7xUUnsQrtIAETMuh54l78SSS0rJZuOjNtEIvzTwtVgunGASwienxKTyhsRKaq7TCpmWtHh5x64FYd/+sF0yW3arasiAaLKXV5fUrjjfpKyIaD4Z+r3S4hJsOag/wBA+PwQqPUhpRYlrWUJZupGn5tY9bcPhEJ/JdNnx3UuQFcALz8XxJPh4zsPPzU/j8ZCdyHFZcPboy1ZujCAU8JiOawqPlIsLu9OQnPvGPPcy0rKDw9KgoPEaMlQA0zNIK/Rvh4Qn9G05pxanwxJiULhdMmFpRlB159w2EVQ2W9tJKh8cnXJg1p7TYceDx667SAmJ7ThLx7wDBY4N9b6lUefTM0tJboQWIq7SM8fBEM4rrLUs3KqXF8zyefPMQAOB7Fw9T/LsfT5VapuKP/w6qO9eMHphQYUoyoYDHZMhL0gHRhU6DwsMUlGh3euzOVOkuXk3h0TufSI2whyf6enKFJ+zhCSs8eieuA9onpbNXZoblIpUOuEVCLx59F3iWzspz2jTvU4nCI6S0oj9HvB9jlaYnUqEFBL/bcC+eFAY8ejw8oTEl0bosyzG6SktrwBNN4bFbjDUtv3v4FD44cgZOmwW3TR6muM4FQ4rAccCXrV040Z5a3xaRGO6eXvxtbxMA4KaJ6WdWZlDAYyJ6/QF8cTI4NFSq8DCzauyAh93xRwsoWFCjNFoiQuHxJ0fhYcHKvBc/wDUr/iVcjNWqtCwRHp7EUlpKJelKnOpSNiwzwt2W9V3gW3RWaAFS07LWlJbFwgnpjliek0SaDjJYWqs1RaXp/gAv3BjIZ2nxCnVacSk8dmP78Gjd70pVlYxw40FjFB6m7tw4oUp17ElBlh1nh1Lvuyit1a95ZU8jenoDOKssF+OqClO9OapQwGMivmztgi/AI9thxUDRhTBHR0pL8MioRDx2K6dL4RFSWl5jqrTks7Re2nUUe4+5sfXAcdl2Knt4WErL3dOLeG5mY6a0ZD+fCl24VQOe7MQUHq3pLCB8HHR6fcJnj2VaBrQ3HzyTYEoLCJu4U9WLR+xd05LScnfr67IMGN9pOdp+1+zhsbLREokHYfsa2/DWpydgtXCYdcnwqOuytBYZl/s3LJ1104VVqvPa0gEKeEzEoVA6a0RpruSgy3ZqNy0LKS0V9SPHadOl8DBFxKiUltosLaVGieLhnpFVWr64DJqaU1qh7WNzslQDnlw2MV1nwMMUHj0BjzOs9IVHS8Q+OTk0dgZ2G6LwsOaDqUlpMf+O3cppMl5rmaMlR28zx1i4hTlakceYVg8Pqzj0G6DwrH4zWJl19fmVqAoNJFaD9eOhQaL9l32NbfjoWBvsVg7fuiB9BoUqQQGPiWAXzXLZqIFsXQpP8F+xv0U8ADTXaROCGm0KT/Bno1JanML2BV8/+D7s1xzHSYISdqcrNi0nI6Uljx/YnCz5pHQGK1XXG/Cw5pIDC7UHPOJOy1pNy4D2eVpnQmm5wigTu2MRTmmlJuCJdpwqHS7uuEzLxlZpsf0eK9BUukFhsOMg0eGhX7V24a8fNgIA7rw0uroDhCu19jW2aeoET6QfL4TUnSvOrVC9sUsXKOAxEWrTyMUddmMheHhEJz521wsEy8qjjZaQqwDJUnjkyrtSMCcOeJQ8PPFUaflipLQYEaZlVQ8PC3j09eE5cqoLAFBVFP0OWoyiaVlHwBNL4THCw1OSk9oBou2K/aLU9xErS9dnWjbWw8NSWgVKKS1OnNLS0Gk5QdXpN/86jAAPXHp2KWoHFsRcv2pAFkrznOj18/jgyJmE3pvoe3p6/Xj5/WMAgBvTsLOyHAp4TAST1+V3p6z3jZZOyxahH0fk67Ll7EStJJHL79LkpuXEA57gv6oKTyjU4CANeNh5P9E+PF6NKS2G2lgJBvv96S6vrgDs6OmgwjN4QJbm5zD/VJc3nNLS0iyaXSiTXaUFpF7hYcG91n5RiZSle/0BQ8YqRAs0paMlonl4Ejctn+zwCI3nfnjpCE3P4TiO0lr9mH/sa4a7x4dBhVn4Wk1JqjcnJhTwmAg1OT6s8MSX0mLjAoCgN0XXaAmhDw+rfDHGtCyPVZjCI/yeAxyiq3nkaIneOD08weeoBzzSCCJWwMPK0v0BXhJYRiMQ4HEsFPDEo/DoTmlZtXlOzhih8KR4gGinRz2AUU5pxdF4UHSjYISPp62LeXhiVGlFGx4qpLTi357fvf0FPL4AxlYV4qLhAzQ/b7xorhbRv2Bm5W+PH6zpXJJqKOAxEcy4G5HSCv2sRUK3KnQyFg9I5PnooyXUFJ52gzotq83S6lQwZNvFAZnBKS17rJRW6KVZqkot4HHarEKAqjWNc7zdA68/AKuFQ6WesnRHZKdlXablGAqh24gqLWGeVmoUnnaFm4Zou4gFqbpGS4ibORrg4wk3fIxlWo7ShycU1MbbabnD48Mz278AEFR39FTqTBgWDI52f0WDRPsTX7Z24p1DreA44IYJ6W1WZlDAYyI6hAGdygqPFiyWyIBCrDzwCE9DVx4tIfPwJKnTsvy82BV6/bDAw0mMxXKFxxfg0RWHSTJWSkt8ng8EeJwOGUpZR2UlxGktLRw5HfTvVBa4hFSEFnJFgS9T3nSZlmMpPAaktEpEVWupuPgJKS2Nx2k8oyVsFk44jhPttszzfFRlTfztRlN4BNOyn485M02J53d8BXePD8NLc3DFueW6nls7MB8uuwVnunpx+CQNEu0vvPjeUQDA12pKMFiH0pxKkhbw7N69G/X19SgsLERxcTFmzZqFjg7pwcxxXMRjw4YNknU8Hg/++7//G0OHDoXT6cSwYcOwdu1ayTovvvgiRo0aBZfLhfPPPx9/+9vfkvWx0ppwUCE9sekKeIROy+HftYtSWr1+XrgL1KLwRMzSkl1I5BVlsVCt0vLKq7RkHp7QvzkOq2g8ha63BqAlpRV6bQQnh7N9xVJXSjBDs1bfSjyGZQDIFh0XLIjVEvBo7cPTFqU8WiusI7U/EL6Q9yVRU1pRGg/mZ2kP5DmOM6zbcqfXLxxjin14RBF4dIUnvJ5elcfj8+PpbcFS9B9eMkKTEV763haMGVwIIPX9eFrcPVj95qGUpVT7Cz5/AC/uCvfe6S8kJeBpbGzEtGnTUFNTgx07dmDz5s3Yt28fbrvttoh1161bh6amJuExY8YMyfIbb7wRW7ZswW9/+1scOHAAzz//PEaOHCksf+edd/Dd734Xt99+O95//33MmDEDM2bMwN69e5Px0dIatUooPWkkq4LCI05piaeoK5elK3dalk9L/+Osi3Dx8GL89tYLNW8boN6HR7FKyxap8HAcl5DKpDWlBYT9O7lOW9S762K9Cs+pkH9Hh2EZCH5fbLtZEGtU40GPzy+Y4hNReBw2i/D8VHRbVhorobaHfP6A0NtKj8IDaK98iwUrSXfYLIpFBFoVHrFS6NMZ8Pzl/Ua0uD0oz3fiunEDdT2XMYE1IEyhj+fIqS5c/+Q7WPr3/fhVw6dJfa8X3zuCB/70UcybiHTlrc9OoMXtQVG2HfU6Fb1UkpQJX5s2bYLdbseqVatgCTW0Wr16NUaPHo2DBw+ipqZGWLewsBAVFRWKr7N582a8+eabOHz4MAYMCOZ5hw0bJlnnf//3fzF9+nTMnz8fAPDwww+joaEBK1euxOrVq5Pw6dKXDpUqrSwdCo+goASUU1riSi8l0zJTeKwWDv4AD68vKJHLU1qThhfj+VnFmreLIe+0zFCq0nIoNB4Eghcnt0aDsJxYjQfFF5hYhmUGUzW0+lZYSkuvwgMEg+EzXb3C59eT0op2cWbqDsfpq1hSojjXgbbuXpzs8OKsPj6XtuuoJhT/Xej9zEYNED2j0bAMxKjSEh0Hvf5A1CaFYgIBHqvfCo6R+M+vDdfUrFGJYD+eQykzLn/V2oXv/uZdob/Vax+34OHrztOtVmnhzU9PYMHGD8HzwCVnleCq842fLL73WBtefO8IJgwbgKmjygwf5rlhZ1Dd+ea4wXF/56kgKQqPx+OBw+EQgh0AyMoK3o1u27ZNsu6cOXNQUlKCiRMnYu3atZI791deeQUTJkzAY489hkGDBuHss8/GvHnz0N3dLayzfft2TJs2TfKaV155JbZv3x5zG91ut+TR32GmZXnA47RZNDvomWnZr1KlxQIeh82iaOZkF0V2AfD6A+jpDQgBSuLDQ4P/ylNasfrwiKX9RC7IXq0pLT52Dx4GM+pqnZgupLRidLFVgql9zGCsybRsja3wsEqhfJfyVHg9pLIXj57GgyzgcdktmtsUMIyapxWru7XmTsui7deT0nrt4xYcPtGJfJcN3500RPPz5FwwJKjwfH6ys8/TSZ+f7MRNT23HsTPdGF6Sg1ynDSfaPdhz9Izh79XU1o17/7hHOJbePdxq+Hts3tuEb69+B+u3f4kfP/8+xv+8AT/8/S688kGjRK2Pl+PtPXhjf3CUT39KZwFJCnimTp2K5uZmLFu2DF6vF6dPn8bChQsBAE1NTcJ6Dz30EF544QU0NDTg+uuvx+zZs7FixQph+eHDh7Ft2zbs3bsXL7/8Mp544gm89NJLmD17trBOc3Mzysult4Hl5eVobm6Ouo1LlixBQUGB8Kiq6l9fnBJqPhmO4zT7eFhgJD65ixsPegXDsvKhwxQeFlT0+gKSP7JsjXeOasRUeFQ8POJrsJ4mcXJiNR4UB1Ys4FHrsszQq/CwHjx6U1pAuBcPC2K1XKe1mJbbDKjQYqSyF0+HQl8dtZgwnpJ0hlHdlmPPL9M2S0v899GrsRcPz/PCkNDvXTwsoVRxQbYdZ5cHBx73pcpz6EQHblqzHU1tPagpy8WGWRdhyshSAMBr+1oMfa9efwA/fu59nOr0Ii+0r949bNzQVJ7n8eTWQ/jhH3ajpzeAcUMKMbQ4Gz29AWze14y7n38f4x9uwKxn3sNf9hyLO/j50+5j8AV4jK0qxMiKPMO2vy/QFfAsXLhQ0Wgsfuzfvx+1tbVYv349Hn/8cWRnZ6OiogLV1dUoLy+XqD6LFi3C5MmTMW7cONx///1YsGABli1bJiwPBALgOA7PPvssJk6ciG984xtYvnw51q9fL1F54uGBBx5AW1ub8Dhy5EhCr5cOKPkPGFoDHnbB9quktBhqk5cFhccZPAF7/YFwIOawJnz3HzYcR1d4OHCSieniIYqJKDwspRVrWjrPhwOYohijFgQPj4aAp9cfQFOb/h48DBYM6zMtxzbYGlGhxWCVWqnw8MST0tJTks4wKqXFguoCFaO4VoUnOIol1G1ZYy+edw+fwgdHzsBps+C2ycO0bXAUxofGTPRVwPNZSztuWvMujrd7MLI8D8/fcRHK8l24ojZosXjt4+g3zXpZ9o8DeO/L08hz2vDM7RMBAAda2g05zr2+AO7f+CEe3bwfAHBb3TC8eOfF2DpvCv5699cw57IRqC7JgccXwGsft+AnG/bggocbcMcz7+Hl949KClOiwfO8MEqiv6k7gE4Pz9y5cxWNx2KGDw/OT5k5cyZmzpyJlpYW5OTkgOM4LF++XFiuxKRJk/Dwww/D4/HA6XSisrISgwYNQkFBuEX5OeecA57ncfToUZx11lmoqKhAS4s0Em9paVH1BTGcTiecTn0VQumML5Q6ApRP1sFURuw/LKWUltIfg9rdokeu8PgDho2VAMQKT+w+PA4VhceIlFas0RJAOIBhioUaRcJ4idgBT9OZHgT44P4vzdN//LJgWFdKS8PF2YixEgy2v06moBePnvYJ8ZSkM4waL/H5yU4AwNBi5eBXalqOHqTbLBb0+v2auy2vDqk7N0wYLASpiTBhaBGe3/kV3vvCONVDjQPN7Zj5m3fR2unFqIo8PPufk4TBtVNGlsJu5XD4RCcOHu9ATVluwu/32r5mPPVWsJJt2Q2jMW5IEUaW5+FASzt2fH4K30jAx3Omy4sf/WE3th9uhYUDFl9bi1vrhgnLawcWoHZgAeZdMRKfNLXj73ub8NePmnD4RCcaPm5Bw8ctcFgtuOTsEnzj/Epcfk656t/xe1+exuGTnch2WHHtmPgM6qlE15m/tLQUpaWlut6ApZvWrl0Ll8uF+vp61XX37NmDoqIiIRCZPHkyXnzxRXR0dCA3N3jQffrpp7BYLBg8ONjo6OKLL8aWLVtwzz33CK/T0NCAiy++WNd29nfEF/wchW7G2Ro7HLMYgVep0mKo3S2GPTwhhccXMKwHDyBuPCj9PUu1se3mVKali7ctHnxJMC0L87Q0VGkxw/Kgoixdzd0YTOmLx7QczcNjRJdlRnEKFZ4OhbJ0TqVOK57BoQyt4zpicfB4sNWH2kVZfIioqbIMm5UDeqXDgtXY19iGNz89AQsHzPq6tjESsWCVWnuPudHT69dsnNbLx41u3Pz0uzjd1Yvagfn4w+2TJD67fJcdF48owVufnsBrHzejpqwmyqvF5sipLsx78QMAwO1fq8b084LBzcUjinGgpR3bD7XGHfB8frITt//u3zh8shO5ThtWzByHy0aWKa7LcRzOHZiPcwfm4776s3GgpR1/+zAY/Bw60YnXPzmO1z85DruVw9fPKsU3zq9E/bnS4IeZla8+v9KQ83lfk7QtXrlyJerq6pCbm4uGhgbMnz8fS5cuRWFhIQDg1VdfRUtLCy666CK4XC40NDTgkUcewbx584TXmDlzJh5++GF8//vfx4MPPoiTJ09i/vz5+MEPfiCYoH/yk5/g0ksvxeOPP46rr74aGzZswHvvvYennnoqWR8tLWEnaofVouiaz7Zr+6o1p7RsQaOmy25BT28Au786jck1JYInIV9kWlbrAB0ParO0GKp9eAwyLWtNaQHhlNaAGCktIeDR4FmJtwcPI0em8FiNMi0b6OEpCe2Pkyn08GhLaekfHMowyrQcK+CRvGcMhUdPt+U1bwbViqtHD8QQFXVJL0MGZKMk14mTHR58dKwNFw7TPp5CK3uPteE/frsDZ7p6MXpwAX7/g0mKQ1evOLc8GPDsa8HsKfEHPB6fH3Oe2w13jw/jhhTi/umjhGUXDR+A373zBbbHaVzecbgVd/5hF8509WJQYRZ+e9sEjKrI1/RcjuMwqiIfoyrycd8VI/FpSzv++mET/vZREz473oE39h/HG/uDwc/kmqDyc/HwYvzto6AH9zsT+186C0hi48GdO3eivr4e559/Pp566imsWbMGd999t7Ccla1ffPHFGDt2LNasWYPly5dj8eLFwjosWDpz5gwmTJiAm2++Gddeey1+/etfC+vU1dXhueeew1NPPYUxY8bgpZdewp///Gecd955yfpoaUnYsKx8V6Rd4Yk+PJTBAp6bJgQP/BVvfAYA6PEppbRYB+jE79jUZmkpIe7DY5TC0xsjpSV+H9ZXR6vC0+n1RzRulCOUpMdhWAbCKhv7TrV4qtjFOXqVFvOSmEPh0VOlFU8AzYLIRDw8nR6fUEZdU6qi8ID1n4od8Ii7LUfjq9YubPqwEQDww0vVLQp6kQwSTUIDwg+OnMHM37yLM129GFtViN/frhzsABB6y+w5cgbH3T1xv+cjf/0EHx5tQ2G2HStnXiDxFU6qLgbHBYPWE+36jvWXdh0VArcxVYV4eU6d5mBHibPL83Bv/dlouO9SNNx7Ce6ddjZGlueh189j64ETWPDSh/j6Y/9Ed68fI0pzhKq6/kbSFJ5nnnkm6vLp06dj+vTpMV9n1KhRaGhoiLrODTfcgBtuuEHX9pmNWD4ZzVVaCo39lDw8TG6+89IReG7nV3j38Cn8+4tTgsKTrJQW87wrjR3w+QOiXrjKoyWC25a4wqOlDJlVGcUqS8932WCzcPCFRlFUFqgHM0LTwQQVHlZxpUfh0dKHJ5Euy4xUVWl5fYEI0z2gXqUlKDxxBHksvZRIldbhE0H/TnGOQ/UYY9vutFlipkDtFm2m5d/86zACPHDJ2aWoHVgQdV29TBhWhM37mrHry1MAjEmVAcE5Xbf+difaPT6MH1qE333/wqg3PuX5LoytKsSeI2fQ8EkLbp40VPd7bvqwEeu3fwkA+NWNYzGoUPp3XZTjwKiKfHzS5Ma7h1s1eWICAR6PNxzAqn8G/VNXj67E4zeMMTT9d1Z5Hn5SnoefTDsLB4934G8fBZWf/c3tAICbJw2NK52eDtAsLZMQK6jI1thtmR3HzLTM87yih4fdLQ4szMK3xwdVnl9v+UxB4eFVy+XjQe7hEd+1ymdjSau0wvRFwMPz4jla0YMAjuM0G5fDCk+cAY8s8NWk8PSxh4f14Wn3+GIqXkbSKTrOtaiR7u6QwhPHcW2EafngieAFaESUdFY44In9eVi35WgKz8kOD154L+jjMFLdYYgnp8cz00uJ9744he+Fgp2JwwZg/Q8malJ5r6gNqjzxlKcfPtGBhRs/AgDMnjICl41S9tWwqfJa+vH09Ppx1/O7hWDnx1NrsOI745LmdQKCqdK7Lz8Lm++5BFvmXorf3joBt4kM0f0NCnhMQqygQn6hUyM8WiL0ul5/hEEYkJqWZ08ZAauFw78+O4ljoR4xYoXH2CothLYvuFFiFafL45d5eDjR88L/T6QPT3iWlkpKKxRa9fjCYwcGxKjSAsI+n5gBj0EKD8PoPjxqKQI95GfZhP2rpXItUTo9Puw91oZXQ2kal92iOJRVPkur3cOqtFJTlq7Fv8OOx2hdlhksTeuL8j3/7u0v4PEFMKaqEBcP198pPRa1AwvgtFlwuqsXh0IKViK8e7gV31u7Ex0eHy4eXozf/eBCzUrzFecGK33fOXRSc9k2EAxMZj+7Gx0eHyZWD8B99Werrsv2YSwfz/H2Htz01Lv420fNsFs5PH7DGMy9YmRSOkGrMaI0F5efU96n72k0/c9mTSjSrjJWgpGlUeERyr5DUU6HyggGsbJSNSAb3xw3CC/tOirM4ckVmZbVRl7EQ8QsLdHfXqfXJxktITUth9frC4WHqTt2K6dJARigQeHp6fULXWjj9fBEBDyaZmnFrihq0zHiIBYcx6E4x4lmdw9aO7wYWBjfZxXD8zxOdHhw8HgHDp3oxKHjHTh0ogOHjnegsU3q0ZCXWKvtobCHJ4HGg4koPKGA5yxNhuXYNzx2S3TTcofHh2e2fwEA+NGlw5OS1nDYLBhTVYidn5/Cri9PJVQS/s7Bk/jB+n+jpzeAr59VgqdumaBrzE5NWS6Gl+bg8IlObD1wQnMZ9uK/7MP+5naU5Dqw8rvjFINnBvPxHD7RiePuHpTluyLW2d/sxu2/ew/HznSjMNuONf8xHpOSEGxmAhTwmIRYKS2tCo9FMC0HT3pqdzbyE+jsKSPwp91HBTVIbFoWqrR0DDFV3T6VsnQgqPCIUa/SSkThiVGWLpvEXpTt0HRh0BLwHA2ls/KctrhTR/LjQ8vdmpaydCMVHiDo42l29+CkzvESPn8AX53qCgc2JzpC/+9QNN8zSnIdGF6aixGlufjmuEGa3iuhsnQNRvBYaKrQCn29ehSeXpWA5/kdX8Hd48Pw0hxB/UgGE4YWYefnp/DeF6dx04Xxjav412cn8J/r34PHF8ClZ5dizS3j40r91J9bjjVvHsZrH7doCnhe2nUUf3zvCDgO+PV3xikGMGIKsu04tzIf+xrd2H64FdeNlR57/9x/HHc9txudXj+Gl+Rg7W0XYlhJju7PQQShgMcksD486lVa2r5qqxBQBE96akM25SfQ4aW5uGb0QLzyQTAtIJSl+4yt0lKbpQWEFB5RSkuc7jLOwxM9pSUnVoWWfL1o3ZZZOiveHjxApHldn2lZOf3C83x4xIEBpmUgXKl1Mkr1SltXL7bsbxEFNZ34srVT1YNi4YKlzyNKczGiLBc1pbkYUZaD4SW5MY3lgFKVVgKm5QRTWl5fAF+2BgPg6Ckt9n4aPDwW9ZSW1xfAb7d9DgC485LhSU1rsH488XZc7vT48KM/7IbHF8DUUWX4v5sviNvncsW5FVjz5mH8c/9xeHz+qPvxQHM7/ufPQd/OvdPORl1Niab3uGh4MfY1uvHu4VOSgOd3b3+OhzZ9jAAP1I0oxpM3jzfshiJToYDHJBhVpSX3yLDXZVVEDKUmZndNrcGrHzbCZuGEcQq9otESiU7RDm5fFIXH6xNcFhw4mYcnvF5fVWkB2gMedsGNNk8rUcMyEKnwGDEtvdPrFxRBI0zLQLgXT7T9cfeG9/Hmpycifp9lt2JEWQ5GlLKgJqjcDCvJjmuys1pwKZiW4ylLZ/s0ziqtL1s74QvwyHXaUBFFRWDbrk3hUTct/3nPMTS7e1Ce78QMjQpYvLCS58MnO9Ha4RGCX62caPegw+NDlt2KJ//jgoSmeY+rKhR6A717+BQuPVu58W6nx4fZz+4S0md3Xaa9d8/Fw4vx222fC8Zlnz+AhzZ9jGdCFV43TajCz795nu4BtUQkFPCYhNhVWomltPKz7JJ0i1JPj7PL8/DULRMQ4Hnhwtfr53U1c4u5fSqztIDwAFGGuA+P+I40x2EDx2nr5SMnWQGPME8rSrflRJsOApHfga4qLRUz65nQNjtsFk0XVi2ES9OVFZ5ef0C4QNwwfjDOHZgvKDeV+a4+MVYm1HgwQQ8PS2eNKM2JqvbFpfDIytIDAV4YI3H716oTCiC0UJjtwFllufjseAd2fXlamG2lFbb9TrtyE1Y9WCwc6s8tx/M7v0LDx82KAQ/P8/ivlz/CoROdqMh34Ymbxuo6/i6sHgALF+yafPB4Ox7e9Ane/PQEOA5YOH0UZl2SHL9UJkIho0mIrfDoTWkFf1YbkKgmEdefW44rayskwQa7iCdzlhYQGiAqGi2hltKyWLi4DdTJSmkJCk+U3jNChVachmUg0kelKaUVw8MjnqNl1Ik53HxQeX/sb2qHxxdAvsuGR68fje9PrsYlZ5diUGFW0oId8REn6dmTgiotIeDRaOrVEoiqdVp+87MTOHyiE3kuG747MT5PjV4SSWsJzUEtxlzeWHl6w8ctiv2/ntv5Ff6ypxFWC4cVM8fpVqQKsuxCP6NvrnoHb356Ai67BU/ePB53XjqCgh0DoYDHJMSqhNJuWg7+K6S0WMAjS1XE6toqDjZOhyp4kjlLC4hUeCR9eGQnjXhL02PO0pKdmwxVeE4bofDo78MTK6VlZIUWg+0PtQGie44EL4RjhxSlpExWbOaP57hOtA/PwRPaRkoIfXg0eFgE07IspbXpg+A4gW+NG5SQ4V8PbHL6e3EEPD4h4DHmuKgbUYwchxUtbg8+PNYmWbb3WBsefOVjAMCCK0fGPQ7j4hHBqqt2jw9leU68eGcdpp+XPGN4pkIBj0lglVCqKS2NJ2W5ghKeCC19fqwTqDTgCSk8hlRpQbJ9YqQeHvWydCB+H49XUHi0/enEajrIKNLQh0dIaSXg4YmvLD26wmNk00FGSV708RLvf3UGQNBjkQqY8pnjsEYtO1aD/f3EW6UlVGipjJRghFNaGjw8CqZlry+Aho+bASChid56YQ0IPzraprv5JEtpqY1/0YvTZsWUUOPA1/Y1C7939/Ri9rO74fUHMO2cMsy6JP5GjFeGVKRzK/Pxl7sm4/zBxnawJoJQwGMSwpVQiZqWpR4et5DS0qfwWCyccAJlr2VsH57IZZ1eceNBThKUWGQX9ngDHubh0Xoy1VL9A4Q9K6e7ehVl87buXuG7GFwUf0rLabNI7ny13AQ7rNEvzkYODmWwbstqKa33j5wBAIwdUmjYe8ZC7BtzCzcC8X3mRBSeQIDHoZDCc1Z5XtR1w6ZlLR6ekGlZdPxtP9wKd48PJblOTEjCME81hhVnozjHAa8/gL0yVSUWrLjCSJPvFaHZWq99HOy6zPM8Frz4Ib461YXBRVl4/IaxCaWexg8dgHcWTsUrd02OOlqGSAwKeExCzOGhejsth87DzBskv3vXcscoP+EYMzw0+K+iwiMbgSH22chPRfFeqHwxpqVzsnfSmtJiwYI/wAsXUzGsB8+AHEdCXiiO4yTHgq7hoaqm5fjLs9UQTMudngiD+ulOLz4/GezCO3ZwoWHvqYbSdSyRwaFAYh6eY2e60dMbgMNqQVWM4FeXwqPQafnvoenYV9aWa6roMwqO4wSVR2tai+d5fNzoFs6FRqW0AOCyUWWwWzmhp9O6t7/A5n3BrserZl5gSLn4wMKsuNRCQju0d01C7MaDxqa0tNwxyo29Rs7SUlV4RNZSteGhgP4LVXNbD6Ys+ye+CPU+MbpKy2mzCh2ZldJa4ZESid/9iY8RTWXpIjOrUo8WIweHMth+6/XzQvk3Y8/RMwCA4SU5mhU0IxAfcmp/F1oRqrTiKEtn/p3qkpzYF0ih8aCWv1epadnnD+Af+/o+ncVgxmUtk9M/bWnHTU+9i2/8+l9Y8NKHALQd21rJd9lxUai78fKGT/HI3z4BAPzP1ediTIrSqoR+KOAxCR0xAh6tLdXZOcIvBDzxpbQAwCEqCbVZOE3Pib190au0JI0Hxe+XoIfnhfeOCMEOoJ7Site0DCDqAFGm8AxOwL/DEAeeeqq0AGWVp607uL1Genhc9nAAKO+2zPw7fZnOkuNOYKwEENsIHo2DLdoMy2L0eHiYaXnn56dwuqsXRdl2TKruu3QWgxmXd3+lPki0y+vD0r/vxzf+91/Y+fkpAMDxULNKo/vWsPL4v37YBF+Ax9XnV+J7F+ufok6kDgp4TADP8zEVHqfNoumOJ5zSkjYejKzSih1AOURBQY7TZkh5ZTilFbksog9PVIVH34Xqrx82SX5WS2nJYWZkLUQbL2FEDx6GOODRU6UFKPt4kuHhAcS9eKT74/2vgnf840IN6pJNuqW09JSkM9N8tOaEDHlK6297g8f8FedWpCTVct6gfDhsFpzq9OLwSekgUZ7n8Y99zahf/hZWv3kIvgCP+nPLMe2ccmEdo0zLjHrRa1eX5GDp9edTyXg/gxoPmoDu3vBEc7W0EfNuRJsnBIQvgBF9eLLkKS0tCk94Ha0eoliw7VO644vstKzchwfQd6H6rKUdB1raJb/TcveY77LpusuMGvCcTrwHD0PspdJyTbBZOFi44DGhFPAwD4+RCg8Q7MXzRWuXpFIrEOCxJ2RY7usKLfEh5+5OzLfE/n7iUng0lqQDwF1Tz8L4oUW4LFRlFA2xadkf4LF5b9Cge9X5qSmPdtqsGDO4AP/+4jR2fXEaI0IVaUdOdeFnr+zDlv3HAQRN/D+7thbTzi3HoRMd2LK/BTwfHoZqFBUFLkw7pxzvfXkKq2Ze0Gcl+oRxUMBjApgKw3HRAwtNAY+sSkutm6ym6cuii31WnLNs5ESdpRXRh0dUjSQ79+k5WW2SqTuAtpSW3gZkQsCj0IvHUIXHoc/Dw3EcHDYLenoDihdooweHMpR68Rw+GRwC6rJbMKoieoVSMklc4YmvLJ3nec0l6UAwCJ1+njb/jVjhee+LUzjZ4UG+y4a6EdpmQiWD8UMH4N9fnMZ7X57CjHGD8Jt/HcaKNz5DT28AdiuHWZcMx12XnSWk7EeU5uLa0Ew/oxUeAPjN98bD6w8kvds0kRwo4DEB7EIfHJmg/kcevNBFnz4tHx7artZ4UKfCE+/wPjnRZmnJh4dGS2nJO0erwfM8/vpRZMCjJaVVpDMAEAIeWQqH53kcFRSexAOeXJ0pLSD4edUCnmQpPEq9eJh/Z/Sgwj5Ls8gr74DExkoA8Zeln+zwoq27FxwHDC81dmq22LT8971Bs/K0c8ulXrg+ZkKoUuvNT0/gqv99C4dOBFNbFw8vxsMzalFTFhn0zr9yJJraunHDhMGGbw/HcRTs9GMo4DEBsUrSGVqMy+Kyb3H7fPmJXU/Vh9b31kK0svROT7hKS954UM6gwmBqqCjbLnSCVuLTlg7hjlqMaqdl0cVxQI4xCk9rpxfdvX5wHDCwMLYXIxZ6TcsAM6D7FBUJd7fxnZYB0QBRUQDI+u+MS4lhOXzMGaXwsMo3rcEbOxaHDMg27CaCwUzLXn8Am0MBzzc0qkPJgpWmt7g9aIEHJblO/M/V5+C6sQNVb+6qBmTjxR/W9eVmEv0EMi2bgPYYYyUYWkrTxcNDxe3zIzota6rSSoKHJ0pZepdXmq4Tv79c4Rk/tAhP3TIeS751ftT32/RhIwDgLJlfQotcrrXLMmOASrdlls4qz3MZcneZ7dTXhwdQHyDq8wfQrtKrKVGEeVqdkQpPagKeMO2eBMvSRQqpHpVH8O9oSGfphQU8731xGs3uHuQ6bfjaWalLZwHBysXxQ4vAccD3Lh6KLXMvxYxxg8gsTMQFKTwmIFaFFiNbQ+M/qyUcUDBvULbDGnGB13J3KU77GHU3ykUtS/erprTk50eO43BFbQU+DPV0UYLneaE6a8a4QVj2jwPCMi1mZL09YpjCc1oe8BhoWAaAXId+hUdtvATz7wDJCHhCHp724P7o9PhwoNkNoO8qtADlKi3WGyjelJb4b8PjC0CrGHjouHbDsl6YyvRRqLPx1FFlhqtI8fD72yeirbuXOhATCUMKjwlgc7RiNfbTorKI+/CIlSO5j0Fbp+Xwc4wyLUcrSwfCwR/HcVEbD2rh4yY3Dp/shNNmwZW10koVLcND9So8wsR0FYXHCMMyIEtpafXwqJRRfxrqCTOwwGW4p6Y4FAWwPjwfHWtDgAcqC1wo11BmbTTiGLs9wdESFgsn/H20K3TWVuOz48FqQa1T0vUgv6n5Roqqs+RkO2wU7BCGQAGPCejwaA14NKS0RFVaLH3htFskF3ILp61tuzilZFzAo67wAEGVhxFttIQWmLpz2cgySY8ZC6ctUNCr8BSrKDxGNh0EpF4vrSkt5sHqlI3veP9I8nrilMj68KRLOgtI3MMDhP9er3ziLdz13G40fNwSs2rrYBIVHnEZd5bdikvPjl3KThD9CQp4TIDWlFaOBoVHnNISI74suuxWTTn0ZJqW1TqvsrtlDsHPwjZTb85fXJ119ehKycUg2muJl8Sr8HR6/ZIJ0UaOlQDiMy0zdelLUbdpILlBCPPwtHX3wusLhBsOVvVdOgtQDpaNCHh+es25qC7JQU9vAJs+bMIdz7yHC3/xOh7404fYfqhVaA3BcPf0osUdVLuSk9IKf9LLRpUa9jdLEOkCeXhMQHhSeqwqLX0Kj9LvAW3pLECm8Bh08hQ8PCo3widEFT1sYrrXF1D0YURj7zE3vmztgstuwVRZ0zatL6VnrATAGhVy6PXzON3lFWT8IyGFx4iSdECe0tL2nGElwRLoz0Udb3meFwU8xgchhVl2WC0c/AEepzq9Ka7QCtdo9fT6BfUzkeZz37pgML45bhD2HnPjL3uO4dUPG9Hi9uD5nUfw/M4jKM934trRA3Hd2EE4b1C+4N8py3PG7R2Khli1vSrF1VkEkQwo4DEBHYLXJvpJUIvCo5YyEgcMWiuFHEloPKi2faV5Tpxo9+CEuyf4i9D2OkMBj14Pz6aPgtVZl48qR47TJlFctKI34OE4DkXZDhxv96C1Ixjw+AM8Gs8Y14MHkPXh0bhfqkuC7y0OeI6e7sbJDg/sVg61A/MN2TYxFguHATkOnGj34MOjZ3Ci3QObhcN5gwoMfy89sGn2HAdh3le8cByH8wcX4PzBBXjgG+dgx+eteGVPI/72URNa3B48ve1zPL3tcwwvzcHXa4IVU2eVG6/uAGHTstNm0dSZmSD6GxTwmIBwSsuAPjyhGCXA88I8LUDaX0bLWAkgWR4etn3S35fmBgMeNjiQba/dZgE8+jw84uqsq0cH73SjVXyJES/TG/Cw5xxv9wil6S3uHvT6edgsnKZ5SFoQm9e1mpaHFQcVni9awwHP7lCK6dyBBUmr5ikOBTxbPjkeeq/8Pq8ckqcwBTO/w6bZA6UFq4VD3YgS1I0owYPX1eKtT0/iL3uO4fVPWnD4RCcOh5ruJaMkHQiawYHgkMxY6XGC6I+Qh8cEdGis0oq1HAh7OvwBHs0htaQszwVOdKRoVXjEQYLL8D480oinLD/o9/DJIiFmXNYzVueDo204erob2Q4rLhsZvNMVBwZKnXflOGyWuHoPlYa6C7PAjVVoDSzM0hycxCKeTsvVoZRWi9sjBNhCOiuJM61KQj6eLfuDc53G9vH8LDGBgLT7eCL+nVg4bVbUn1uOlTMvwNpbL5QsS4Z/BwAuPbsUf54zOWZvKoLor1DAYwI6NVdpaVF4wqMbmEF1aHG2zLSs7bDpy1lapbK5VWw9tg1aghTGX0PNBi8/p1xZFYvyUkMG5GD04AJcf0F8zdGYitPcFkxjGd2DB4jPtFyY7RAq1ZjK0xeeGqEXT8iblQr/TlkoCF219ZCkIWdfDY+8YGiRxDeXjJJ0IKhkja0qJHWHMC0U8JgAzY0HdZiWAwEeX4YubEMH5Egu3po9PEnstByR0sqTBTxsG1jAozH2kKSzzlc2bkZ7KYfNglfu+hqWfGu0tjeUURFKKzB1zegePID0ONGjGjGV54uTXejp9ePjxmCDuguS2ASwWNaRr68rtADgp9eeC5fdgrc+PYHlDQf6ROER47Jbcb7It5QshYcgzA4FPCaAVWkZUpbOUlo8jy9ECo/4uqhlcCgAOJLReFDkMRJTlqfcqlZQeDRGPLu/OoPGth7kOKyYMrJUcZ1kdrUvFxSeUErL4AotIGhKZd+nHjN3tcjHs6/RjV4/j5JcBwYbVC6vBFN4gODcs6HFxu0HrdQOLMCj1wcD2FX/PISNu44CiByom0wGFob3sVzNJAhCG0kLeHbv3o36+noUFhaiuLgYs2bNQkeHdAgjx3ERjw0bNkjW8Xg8+O///m8MHToUTqcTw4YNw9q1a4Xlv/nNb/D1r38dRUVFKCoqwrRp07Bz585kfay0pCM01ydmSkuDVC0OKL6SpLQSU3iMnpYub8NTmic19LLr+OXnlKGywKW5ioipO/XnlqekrX6loPAEU1lHQz14jAwqOI4TjhU9Co+4NJ31xBlbVZTUuUYlooBn3JDkvlc0rhs7CP/5tWoAwJb9QQN1Xyk8AHD35WfBZbfg/41RH5pJEER0kvIX29jYiGnTpuGmm27CypUr4Xa7cc899+C2227DSy+9JFl33bp1mD59uvBzYWGhZPmNN96IlpYW/Pa3v0VNTQ2ampoQEDVh2bp1K7773e+irq4OLpcLjz76KK644grs27cPgwYNSsbHSzs6NSo82kZLhCYm+wJCWmVocY60LD0eD4/RfXhUTMtyFkwfhflXjtR0kQgEePxNaDY4UH0b4urbrA25wnM0CQoPEDxW2nt8mvvwAOGA54uTnegOlekn21NTIlIzkmmO1sLCq0bh4yY33jnUCqBvA56asly8+8DlmtLSBEEok5S/nk2bNsFut2PVqlWwhCSD1atXY/To0Th48CBqamqEdQsLC1FRoTyzZfPmzXjzzTdx+PBhDBgwAAAwbNgwyTrPPvus5Oenn34aGzduxJYtW/C9733PwE+VvoRHS0QPKgYVZiHfZRO8GEpYLVKPTJ7LhqJsu2Sic1yNBw0vS5cGPEXZDli48HaLgxKtd8S7vgpOic5z2nDJ2epTopN5g808PK2dHnR5fWgKBZ1GengA4NoxA7HlkxaMrNDeP4eltD4/2Sn0BkqmfwcId1sGgLEpHilhs1qw4rvj8P9Wvo1jZ7qF6fZ9RWEfvx9BmI2kpLQ8Hg8cDocQ7ABAVlZQkt+2bZtk3Tlz5qCkpAQTJ07E2rVrJeXGr7zyCiZMmIDHHnsMgwYNwtlnn4158+ahu7tb9b27urrQ29srBEhmp9cfEObvxPTwOG341/1T8cc7L1ZdR34xH1qcDY7jJF4PrakescKTbNOy1cJJLo7xBCVCOqu2PGraLpkJhQHZDtitHHge2PPVGfB8sCpOnNoxgv/6xjnYMneKroqcYaHmg62dXjS29cDCAaMHJ7cJIDOjcxwwenBhUt9LC8W5Tqz/wUTcVjcM35k4JNWbQxCEDpKi8EydOhX33Xcfli1bhp/85Cfo7OzEwoULAQBNTU3Ceg899BCmTp2K7OxsvPbaa5g9ezY6Ojpw9913AwAOHz6Mbdu2weVy4eWXX8bJkycxe/ZstLa2Yt26dYrvff/992PgwIGYNm1a1G30eDzweDzCz263O9GPnRLEwxy19NkpiGG0lJcpDw3d1Us7LWs1LSfDwxP8V2mWVlmo23I8+APh2VnXjI7eVj+ZHgqLhUN5vgtHT3fj318EfTKDi7LTwreR57KjJNeJkx3BfTyyIl/TMZcIAwtcmHXJcJTkOmIeu31FTVkufvb/alO9GQRB6ESXwrNw4UJFo7H4sX//ftTW1mL9+vV4/PHHkZ2djYqKClRXV6O8vFyi+ixatAiTJ0/GuHHjcP/992PBggVYtmyZsDwQCIDjODz77LOYOHEivvGNb2D58uVYv369osqzdOlSbNiwAS+//DJcruhdaZcsWYKCggLhUVVVpWdXpA2sRNZps0gUlXiRm1iHhrwj8uGhWhCnnYz38EQuE5em6w0Pmt09ONHuQb7Lhq/VKFdnxfvaemG9eP79xSkAxg0NNQI2YgLom544HMfhv75xDmZdMiLp70UQhLnRdYWcO3cuPvnkk6iP4cOHAwBmzpyJ5uZmHDt2DK2trfjZz36GEydOCMuVmDRpEo4ePSooL5WVlRg0aBAKCsKy+TnnnAOe53H06FHJc3/5y19i6dKleO211zB6dOweKA888ADa2tqEx5EjR/TsirSh06utB49W5EoCKwOOZ3iouOuxS+NzYqE2SwuQlabHqYhcWVsh8R6lgvKQj4eNbjDasJwIbMQEkHoTMUEQhB50XSVLS0tRWhr97ldOeXk5AGDt2rVwuVyor69XXXfPnj0oKiqC0xm8cE2ePBkvvvgiOjo6kJsbbLb16aefwmKxYPDgwcLzHnvsMfziF7/AP/7xD0yYMEHTdjmdTuF9+jNauyxrJULhUUxpaVR4RAGPzQD1CVCfpQVENh+Mh2vGqFdnCSRZ4qkMKTxd3mAllNGG5UQYJjK8J2NCOkEQRLJIWgJ+5cqVqKurQ25uLhoaGjB//nwsXbpUKDt/9dVX0dLSgosuuggulwsNDQ145JFHMG/ePOE1Zs6ciYcffhjf//738eCDD+LkyZOYP38+fvCDHwgm6EcffRQ//elP8dxzz2HYsGFobm4GAOTm5gpBkplhTQcNC3hUFB5OYlrWr/AYhdosLUDakC2emKQw2466EcUx10t6SqtAmo41cqxEorAKv3yXDcOjVPsRBEGkG0kLeHbu3InFixejo6MDo0aNwpo1a3DLLbcIy1nZ+r333gue51FTU4Ply5fjjjvuENZhwdKPf/xjTJgwAcXFxbjxxhvx85//XFjnySefhNfrxbe//W3J+y9evBg/+9nPkvXx0gatk9K1Ijcnl4sa+nFcsOGfZoVHIShJFLVZWgBQluA08em1FYb4oBKlXPY5BqeRwvP1s0pw0fABmDKyzNBJ4QRBEMkmaQHPM888E3X59OnTJQ0H1Rg1ahQaGhpUl3/xxRd6N81UdGico6UVcUpryIBsyUWNA8BDu4fnurGDsOKNg7j0bH1p0GiEZ31FLpOYluO4Fl8TpdmgZBuSfKGPVHjSJ+DJc9mxYZZ6WwOCIIh0hdp29nM6eoz18IjNyfK5RVxI4tFapVWQZce7D1yua3yB1u1TUnikKS1t78k615bkOnDRcG29m/qqSgsINn5Ml3JsgiCI/gwFPP2cM11eAMYpPOLYZGix1KPBFmlVeAB9s5q0EO7DA3za0i6k9LLs1rha/Y8ozcEvbxiDmrJcw4zViSJOaaWTYZkgCKI/QwFPP8bnD+DPexoBAOcNMqbjbbCrcrAKSq7wBNUVXvMsrWTAzNN+nsfPXtmHAA9ccW65kAbKcVjR6fVrTmlxHIdvjx8ce0WFbUgWDpsFxTkOtHZ608qwTBAE0Z9Jj1vaDGV/sxu3/HYHNuz8SlLCrZXN+5rx1akuFGXbcf0F+i7a0WBpI7nCwySeVEwRZzCF5/CJDrxzqBVOmwWLrjlXWM6My8kMSfrCqssCOFJ4CIIgjIECnhSy/p0v8K/PTmLhnz7Cd37zLg6d6ND8XJ7nsebNwwCA7108zLBOxkC4K7K87DielJbRWGTDTX80ZYTE1Cv28RgN89JM0uj1SYTBoe7Kw6j0myAIwhAopZVCth9qBRBULXZ+fgpXPfEv3DW1Bj+8dETMbr/bD7fio2NtcNos+N7FQw3driXfOh+tHd6I6iCm/KSDwgMEg4IfXiodOSAeNmk0r9w1GS+/fwy31Q0z/sVl3DPtbAwvzcV1Y7VVjhEEQRDRIYUnRTSe6cYXrV2wcMBf7/46Lj27FF5/AMsbPsU1K/6FXV+ejvr8p94Kqjs3TqiSTAk3gmtGD8StChd15t1J9sDIaIj9Mz+95tyI4OtrZ5XAYbXg/CRM1h5anIN7pp2NwmxjJ5crcU5lPu6fPgp5LqrQIgiCMAJSeFIEU3fOH1SAcyrz8bvvX4hXPmjEQ69+jE9bOvDt1e/gPyYNxYLpIyMuevub3dh64AQsHPCfX6/us23+2bW1OHamG4MKU2ekHVmeh7PKcnHBkCLUn1sesfy7E4fg+gsGp3weFkEQBJFeUMCTIrYfDgY8F48oARBULq4bOwiXnFWKX/ztE7y06yh+/+6XaPi4BQ9dV4sraiuE5zJ156rzKiONxUlkxrhBffZeauQ4bWi479Ko61CwQxAEQcihK0MK4HleUHguls1uKspx4Jc3jMFz/zkJQ4uz0ezuwazf78KP/rALLe4eNJ7pxiuhUvRZl6hPnicIgiAIIgwpPCngyKluHDvTDZuFw4ShyhOn62pK8I97LsGvt3yGp946jL/vbca2gydxbmU+fAEeFw0fgDFVhX274QRBEATRTyGFJwVsP3wSADCmqjCqAdhlt2LB9FF49cdfw5jBBWjv8WHH56cAAHfKqpMIgiAIglCHAp4+5h/7mvGLv34CAJgsS2epcU5lPv40ezJ+es25yHXacPHwYkwxcCAnQRAEQZgdSmn1ER6fH0v+th+/e+cLAEF15/uTtVdYWS0cfvC1atxWNwwBnk/6eAOCIAiCMBMU8PQBX5zsxF3P78beY24AQbPxvCtGxlVNZLFwsPTJcAOCIAiCMA8U8CSZVz5oxH/96SN0eHwoyrbj8RvHYOqoyP4xBEEQBEEkDwp4kkhzWw/mv/gBPL4AJlYPwP9+ZywqC2j6NUEQBEH0NRTwJJGKAhd+9v9q0dTWg7un1sBmJY84QRAEQaQCCniSzHcnDkn1JhAEQRBExkOSA0EQBEEQpocCHoIgCIIgTA8FPARBEARBmB4KeAiCIAiCMD0U8BAEQRAEYXoo4CEIgiAIwvRQwEMQBEEQhOmhgIcgCIIgCNNDAQ9BEARBEKaHAh6CIAiCIEwPBTwEQRAEQZgeCngIgiAIgjA9FPAQBEEQBGF6aFp6CJ7nAQButzvFW0IQBEEQhFbYdZtdx9WggCdEe3s7AKCqqirFW0IQBEEQhF7a29tRUFCgupzjY4VEGUIgEEBjYyPy8vLAcZziOm63G1VVVThy5Ajy8/P7eAszF9rvqYH2e2qg/Z4aaL+nBiP2O8/zaG9vx8CBA2GxqDt1SOEJYbFYMHjwYE3r5ufn0x9ECqD9nhpov6cG2u+pgfZ7akh0v0dTdhhkWiYIgiAIwvRQwEMQBEEQhOmhgEcHTqcTixcvhtPpTPWmZBS031MD7ffUQPs9NdB+Tw19ud/JtEwQBEEQhOkhhYcgCIIgCNNDAQ9BEARBEKaHAh6CIAiCIEwPBTwEQRAEQZgeCnh0sGrVKgwbNgwulwuTJk3Czp07U71JpuZnP/sZOI6TPEaNGpXqzTIdb731Fq699loMHDgQHMfhz3/+s2Q5z/P46U9/isrKSmRlZWHatGn47LPPUrOxJiLWfr/tttsijv/p06enZmNNwpIlS3DhhRciLy8PZWVlmDFjBg4cOCBZp6enB3PmzEFxcTFyc3Nx/fXXo6WlJUVbbA607PcpU6ZEHO8//OEPDd0OCng08sc//hH33XcfFi9ejN27d2PMmDG48sorcfz48VRvmqmpra1FU1OT8Ni2bVuqN8l0dHZ2YsyYMVi1apXi8sceewy//vWvsXr1auzYsQM5OTm48sor0dPT08dbai5i7XcAmD59uuT4f/755/twC83Hm2++iTlz5uDdd99FQ0MDent7ccUVV6Czs1NY595778Wrr76KF198EW+++SYaGxvxrW99K4Vb3f/Rst8B4I477pAc74899pixG8ITmpg4cSI/Z84c4We/388PHDiQX7JkSQq3ytwsXryYHzNmTKo3I6MAwL/88svCz4FAgK+oqOCXLVsm/O7MmTO80+nkn3/++RRsoTmR73ee5/lbb72Vv+6661KyPZnC8ePHeQD8m2++yfN88Ni22+38iy++KKzzySef8AD47du3p2ozTYd8v/M8z1966aX8T37yk6S+Lyk8GvB6vdi1axemTZsm/M5isWDatGnYvn17CrfM/Hz22WcYOHAghg8fjptvvhlfffVVqjcpo/j888/R3NwsOfYLCgowadIkOvb7gK1bt6KsrAwjR47Ej370I7S2tqZ6k0xFW1sbAGDAgAEAgF27dqG3t1dyvI8aNQpDhgyh491A5Pud8eyzz6KkpATnnXceHnjgAXR1dRn6vjQ8VAMnT56E3+9HeXm55Pfl5eXYv39/irbK/EyaNAm/+93vMHLkSDQ1NeHBBx/E17/+dezduxd5eXmp3ryMoLm5GQAUj322jEgO06dPx7e+9S1UV1fj0KFD+K//+i9cddVV2L59O6xWa6o3r98TCARwzz33YPLkyTjvvPMABI93h8OBwsJCybp0vBuH0n4HgJkzZ2Lo0KEYOHAgPvzwQ9x///04cOAA/vSnPxn23hTwEGnLVVddJfx/9OjRmDRpEoYOHYoXXngBt99+ewq3jCCSz3e+8x3h/+effz5Gjx6NESNGYOvWrbj88stTuGXmYM6cOdi7dy/5AvsYtf0+a9Ys4f/nn38+Kisrcfnll+PQoUMYMWKEIe9NKS0NlJSUwGq1Rjj1W1paUFFRkaKtyjwKCwtx9tln4+DBg6nelIyBHd907Kee4cOHo6SkhI5/A7jrrruwadMm/POf/8TgwYOF31dUVMDr9eLMmTOS9el4Nwa1/a7EpEmTAMDQ450CHg04HA6MHz8eW7ZsEX4XCASwZcsWXHzxxSncssyio6MDhw4dQmVlZao3JWOorq5GRUWF5Nh3u93YsWMHHft9zNGjR9Ha2krHfwLwPI+77roLL7/8Mt544w1UV1dLlo8fPx52u11yvB84cABfffUVHe8JEGu/K7Fnzx4AMPR4p5SWRu677z7ceuutmDBhAiZOnIgnnngCnZ2d+P73v5/qTTMt8+bNw7XXXouhQ4eisbERixcvhtVqxXe/+91Ub5qp6OjokNxFff7559izZw8GDBiAIUOG4J577sHPf/5znHXWWaiursaiRYswcOBAzJgxI3UbbQKi7fcBAwbgwQcfxPXXX4+KigocOnQICxYsQE1NDa688soUbnX/Zs6cOXjuuefwl7/8BXl5eYIvp6CgAFlZWSgoKMDtt9+O++67DwMGDEB+fj5+/OMf4+KLL8ZFF12U4q3vv8Ta74cOHcJzzz2Hb3zjGyguLsaHH36Ie++9F5dccglGjx5t3IYktQbMZKxYsYIfMmQI73A4+IkTJ/LvvvtuqjfJ1Nx00018ZWUl73A4+EGDBvE33XQTf/DgwVRvlun45z//yQOIeNx66608zwdL0xctWsSXl5fzTqeTv/zyy/kDBw6kdqNNQLT93tXVxV9xxRV8aWkpb7fb+aFDh/J33HEH39zcnOrN7tco7W8A/Lp164R1uru7+dmzZ/NFRUV8dnY2/81vfpNvampK3UabgFj7/auvvuIvueQSfsCAAbzT6eRramr4+fPn821tbYZuBxfaGIIgCIIgCNNCHh6CIAiCIEwPBTwEQRAEQZgeCngIgiAIgjA9FPAQBEEQBGF6KOAhCIIgCML0UMBDEARBEITpoYCHIAiCIAjTQwEPQRAEQRCmhwIegiAIgiBMDwU8BEEQBEGYHgp4CIIgCIIwPRTwEARBEARhev4/yVHO7Yhy8eEAAAAASUVORK5CYII=", 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1.11842552e-01, 0.00000000e+00, 9.77325458e-02, 4.35750204e-03,\n", + " 6.93050325e-02, 0.00000000e+00, 7.05500331e-03, -3.11250146e-03,\n", + " 2.07865559e+02, 2.07940010e+02, 2.06599902e+02, 2.05334245e+02,\n", + " 2.05483146e+02, 2.05706497e+02]),\n", + " array([ 1.32800062e-02, 1.14125053e-02, 8.54900401e-02, 1.99200093e-02,\n", + " 8.90175417e-02, 7.28325341e-02, 1.95050091e-02, -0.00000000e+00,\n", + " 2.10173522e+02, 2.08386712e+02, 2.09280117e+02, 1.88955154e+02,\n", + " 2.07269956e+02, 1.88955154e+02]),\n", + " array([ 1.07900051e-01, 1.47325069e-02, 9.06775425e-02, 2.98800140e-02,\n", + " 4.33675203e-02, 4.98000233e-03, 2.90500136e-02, -3.00875141e-02,\n", + " 2.08610064e+02, 2.05855398e+02, 2.08014460e+02, 1.62450806e+02,\n", + " 2.07940010e+02, 2.08163361e+02]),\n", + " array([ 3.16230148e-01, 3.02950142e-02, 2.02727595e-01, 2.65600124e-02,\n", + " 7.26250340e-02, 8.03025376e-02, 2.49000117e-02, -1.59775075e-02,\n", + " 1.95060088e+02, 1.84413679e+02, 1.88955154e+02, 1.52995603e+02,\n", + " 1.87912848e+02, 1.66322228e+02]),\n", + " array([ 1.52305071e-01, 8.09250379e-03, 1.82600086e-01, 3.13325147e-02,\n", + " 2.67052625e-01, 7.67750360e-02, 3.23700152e-02, -1.32800062e-02,\n", + " 2.06748803e+02, 2.05929849e+02, 2.01760625e+02, 1.52400000e+02,\n", + " 1.81435662e+02, 1.61408500e+02]),\n", + " array([ 1.90692589e-01, 5.70625267e-02, 9.71100455e-02, 2.96725139e-02,\n", + " 2.30325108e-02, 1.10390052e-01, 1.34875063e-02, -7.67750360e-03,\n", + " 2.04664191e+02, 1.68109038e+02, 2.03175183e+02, 1.58802736e+02,\n", + " 2.02877382e+02, 1.61408500e+02]),\n", + " array([ 2.01897595e-01, 2.63525123e-02, 2.05840096e-01, 2.49000117e-02,\n", + " 3.19135150e-01, 2.22025104e-02, 2.38625112e-02, -2.69750126e-03,\n", + " 2.03994138e+02, 1.96102394e+02, 1.97963654e+02, 1.66992281e+02,\n", + " 1.69225794e+02, 1.67513434e+02]),\n", + " array([ 1.56247573e-01, 5.16675242e-02, 1.50022570e-01, 2.80125131e-02,\n", + " 1.53135072e-01, 9.77325458e-02, 1.63925077e-02, -3.73500175e-03,\n", + " 2.06525452e+02, 1.76298583e+02, 2.02951832e+02, 1.62823058e+02,\n", + " 1.96325745e+02, 1.63344211e+02]),\n", + " array([ 8.90175417e-02, 0.00000000e+00, 1.91522590e-01, 3.15400148e-02,\n", + " 8.67350406e-02, 6.01750282e-03, 3.32000156e-02, -3.09175145e-02,\n", + " 2.09205667e+02, 2.09205667e+02, 2.03696336e+02, 1.52846702e+02,\n", + " 2.02058427e+02, 2.01909526e+02]),\n", + " array([ 1.63302577e-01, 5.99675281e-02, 1.38195065e-01, 2.94650138e-02,\n", + " 1.09560051e-01, 9.96000467e-02, 1.12050052e-02, -1.14125053e-02,\n", + " 2.06302101e+02, 1.65652174e+02, 2.03398534e+02, 1.59100537e+02,\n", + " 2.00122716e+02, 1.65801075e+02])]" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 75, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -105,8 +277,8 @@ ], "source": [ "# plt.hist(np.array(solution)[:,3],bins=25)\n", - "idx = 2\n", - "plt.hist(np.array(solution)[:,idx],bins=50)\n", + "idx = 3\n", + "plt.hist(np.array(solution)[:,idx],bins=25)\n", "plt.vlines(ref[0][idx],0, 17,colors='black', ls='--')\n", "plt.vlines(ref[0][idx]*0.9,0, 17,colors='grey', ls='--')\n", "plt.vlines(ref[0][idx]*1.1,0, 17,colors='grey', ls='--')\n", @@ -114,6 +286,157 @@ "plt.grid()" ] }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1., 0., 2., 0., 0., 0., 0., 1., 1., 0., 1., 0., 0., 0., 0., 1., 0.,\n", + " 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]),\n", + " array([-35722.03292166, -35720.25933288, -35718.48574409, -35716.71215531,\n", + " -35714.93856652, -35713.16497774, -35711.39138895, -35709.61780017,\n", + " -35707.84421138, -35706.0706226 , -35704.29703381, -35702.52344502,\n", + " -35700.74985624, -35698.97626745, -35697.20267867, -35695.42908988,\n", + " -35693.6555011 , -35691.88191231, -35690.10832353, -35688.33473474,\n", + " -35686.56114596, -35684.78755717, -35683.01396839, -35681.2403796 ,\n", + " -35679.46679082, -35677.69320203, -35675.91961324, -35674.14602446,\n", + " -35672.37243567, -35670.59884689, -35668.8252581 , -35667.05166932,\n", + " -35665.27808053, -35663.50449175, -35661.73090296, -35659.95731418,\n", + " -35658.18372539, -35656.41013661, -35654.63654782, -35652.86295903,\n", + " -35651.08937025, -35649.31578146, -35647.54219268, -35645.76860389,\n", + " -35643.99501511, -35642.22142632, -35640.44783754, -35638.67424875,\n", + " -35636.90065997, -35635.12707118, -35633.3534824 ]),\n", + " )" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(np.array(energies), bins=50)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "distance = [np.linalg.norm(r[2:]-s[2:]) for r,s in zip(ref, solution)]" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "x and y must be the same size", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[78], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43menergies\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistance\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/pyplot.py:3699\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m 3680\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mscatter)\n\u001b[1;32m 3681\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatter\u001b[39m(\n\u001b[1;32m 3682\u001b[0m x: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3697\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3698\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PathCollection:\n\u001b[0;32m-> 3699\u001b[0m __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3700\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3701\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3702\u001b[0m \u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3703\u001b[0m \u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3704\u001b[0m \u001b[43m \u001b[49m\u001b[43mmarker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmarker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3705\u001b[0m \u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3706\u001b[0m \u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3707\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3708\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3709\u001b[0m \u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3710\u001b[0m \u001b[43m \u001b[49m\u001b[43mlinewidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlinewidths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3711\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3712\u001b[0m \u001b[43m \u001b[49m\u001b[43mplotnonfinite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplotnonfinite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3713\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3714\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3715\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3716\u001b[0m sci(__ret)\n\u001b[1;32m 3717\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1467\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1468\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1469\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/axes/_axes.py:4655\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4653\u001b[0m y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mravel(y)\n\u001b[1;32m 4654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39msize:\n\u001b[0;32m-> 4655\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must be the same size\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4657\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4658\u001b[0m s \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m20\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_internal.classic_mode\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[1;32m 4659\u001b[0m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlines.markersize\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2.0\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: x and y must be the same size" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(energies, distance)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "only integer scalar arrays can be converted to a scalar index", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[59], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m idx_sort \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margsort(distance)\n\u001b[0;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mdistance\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx_sort\u001b[49m\u001b[43m]\u001b[49m, energies[idx_sort])\n", + "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index" + ] + } + ], + "source": [ + "idx_sort = np.argsort(distance)\n", + "plt.plot(distance[idx_sort], energies[idx_sort])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dd = [distance[i] for i in idx_sort]\n", + "ee = [energies[i] for i in idx_sort]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(dd,ee)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb index d14783d..998125e 100644 --- a/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb +++ b/docs/notebooks/qubo_poly_solver_2loops_dw.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "metadata": {} }, @@ -30,7 +30,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -79,7 +79,7 @@ "" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -104,7 +104,7 @@ "array([ 3.111e-01, 5.111e-02, 2.322e-01, 3.108e-02, 1.678e-01, 7.613e-02, 2.334e-02, -2.058e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -173,24 +173,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, { "data": { "text/plain": [ "array([1. , 1. , 1. , 1. , 1. , 1. , 1. , 0.999, 1. , 1.001, 1. , 1.001, 1. , 1.001])" ] }, - "execution_count": 6, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -207,7 +199,27 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.110e-01, 5.105e-02, 2.322e-01, 3.113e-02, 1.679e-01, 7.615e-02, 2.345e-02, -2.054e-02, 2.008e+02, 1.819e+02, 1.956e+02, 1.641e+02, 1.906e+02, 1.779e+02])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoded_ref_sol" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -241,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -253,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -264,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -277,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -301,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -319,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -330,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -364,11 +376,23 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([-35736.142])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "eref = net.qubo.energy_binary_rep(bin_rep_sol)" + "eref = net.qubo.energy_binary_rep(bin_rep_sol)\n", + "eref" ] }, { diff --git a/docs/notebooks/qubo_poly_solver_Net0.ipynb b/docs/notebooks/qubo_poly_solver_Net0.ipynb index ed44514..6330ca5 100644 --- a/docs/notebooks/qubo_poly_solver_Net0.ipynb +++ b/docs/notebooks/qubo_poly_solver_Net0.ipynb @@ -309,7 +309,7 @@ "var_names = sorted(net.qubo.qubo_dict.variables)\n", "net.qubo.create_variables_mapping()\n", "# mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", - "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=1)\n", + "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)\n", "# mystep = ParallelIncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=100)" ] }, @@ -368,16 +368,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 122, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 4000/4000 [00:05<00:00, 687.92it/s]\n", - "100%|██████████| 4000/4000 [00:06<00:00, 621.00it/s]\n", - "100%|██████████| 4000/4000 [00:05<00:00, 775.12it/s]\n" + "100%|██████████| 4000/4000 [00:06<00:00, 651.19it/s]\n", + "100%|██████████| 4000/4000 [00:06<00:00, 665.09it/s]\n", + "100%|██████████| 4000/4000 [00:04<00:00, 803.22it/s]\n" ] } ], @@ -389,20 +389,29 @@ "res2 = sampler.sample(net.qubo, init_sample=res.res, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", "\n", "mystep.optimize_values = np.arange(4,6)\n", - "res3 = sampler.sample(net.qubo, init_sample=res.res2, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", + "res3 = sampler.sample(net.qubo, init_sample=res2.res, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", "\n", - "mystep.verify_quadratic_constraints(res.res)" + "mystep.verify_quadratic_constraints(res3.res)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "idx_min = np.array([e for e in res.energies]).argmin()" + ] + }, + { + "cell_type": "code", + "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 123, @@ -411,7 +420,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -422,13 +431,13 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "eplt = res3.energies-eref[0]\n", + "eplt = res.energies-eref[0]\n", "\n", "# fig, ax1 = plt.subplots()\n", "\n", "left, bottom, width, height = [0.55, 0.55, 0.3, 0.3]\n", "\n", - "plt.plot(res.energies[:]-eref, lw=4, label=\"QUBO Energy\")\n", + "plt.plot(res3.energies[:]-eref, lw=4, label=\"QUBO Energy\")\n", "plt.plot(Tschedule, lw=3, label='Temperature')\n", "# ax1.axline((0, 0), slope=0, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", "plt.grid(which='both')\n", @@ -470,8 +479,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "-9562.760602598233 [-9558.244]\n", - "[-4.517]\n" + "-9562.760602598233 [-9562.926]\n", + "[0.165]\n" ] } ], @@ -497,7 +506,7 @@ }, { "data": { - "image/png": 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", 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8vLwEa2trwd7eXvD19RW+/vpr4cWLF2Xuv3z5cqFx48aCXC4XPDw8hIULFwqFhYVvXJb6TR49eiR88MEHQuPGjQULCwvBzs5OaN68ufD2228LUVFRf/k9IyohEYQ/jWkSERERERFVE3yGh4iIiIiIqi0WPEREREREVG2x4CEiIiIiomqLBQ8REREREVVbLHiIiIiIiKjaYsFDRERERETVFjcerQCVSoXHjx/D1tYWEonE0OkQEVU5giDgxYsXqFOnDqRSftbG9xUiIt1o8r7CgqcCHj9+DHd3d0OnQURU5T18+BD16tUzdBoGx/cVIiJxVOR9hQVPBdja2gJ4+Q21s7PT6FqlUokjR47A398fcrlcH+kZJVPstyn2GTDNfptinwHd+p2dnQ13d3f166mp4/vKX2M/qxf2s/qp7L4KgoCPP/4Yv/76K8zMzLBx40aMGzeuQu8rLHgqoGS6gZ2dnVZvTNbW1rCzs6v2f/FfZYr9NsU+A6bZb1PsMyBOvzl96yW+r/w19rN6YT+rn8ru6/Lly/Hrr79CIpFgy5YtCAwMBFCx9xVOpCYiIiIiIqM2duxYdOrUCT/99BNGjx6t0bUc4SEiIiIiIqPm5OSEkydPajWaxBEeIiIiIiIyetpOnWPBQ0REREREr6VUKpGTk6OX2CqVSi9xX8WCh4iIiIiIyiUIAvbu3YtVq1bh8ePHOseLi4vDrFmz4OfnB3Nzc8hkMpibm8PPzw+zZs1CXFycCFmXxmd4iIiIiIioXKdPn8bVq1chlUqhUCi0jpOYmIhp06bh5MmTMDMzQ1FRkfqcUqnE5cuXce3aNSxbtgw9evTAmjVr0KhRIzG6wBEeIiJ6PZVKgCAIyCssQvzDZwCA+IfPkFdYBEEQoBIEA2dIRET68scff+Do0aMAgAEDBqBBgwZaxQkLC4O3tzeio6MBoFSx86qS49HR0fD29sbWrVu1ut+fcYSHiIhe605GDtZH30fEpWQUFRfhmw7AhDXnYSYzQ1DrupjcxRONXbiZKBFRdVSvXj3Ur18ftWvXRrt27bSKERYWhgkTJkDQ4AOyoqIiFBUVYfz48RAEAePGjdPq3iVY8BARUbmWHU3Ed5G3UPIeZSH737ncwmJsOZeEsPNJmNu/KWb2EWfaARERGQ9ra2tMnDhR6+tv376NqVOnalTsvEoQBEydOhUdOnTQaXobp7QREVEZy44mYsmR/xU7ryMIwJIjt7D8WGLlJEZERJVKJpNBJpP9dcNyvP322yguLtbp/sXFxZg2bZpOMVjwEBGRmkol4HbaCyw5ckuj65YcuYXE9Bw+00NERACA2NhYnDx58rXP61RUUVERTp48qdPqbSx4iIhITSIB1kff1/g6QQDWn7kHifgpERFRFbR+/XqYmYnz9IyZmRnWrVun9fUseIiISC1fWYyIS8laXbv7UjLylbpNXSAiourh1KlTOo/ulCgqKsLp06e1vp4FDxERqd1IyUZuoXZFS25hMW6mvBA5IyIiqoquX78uarxr165pfS0LHiIiUsvO1+3TuOwCpUiZEBFRZVCpVHqJqVSK+36gVCq1zpUFDxERqdlZ6Tbf2s5SLlImRESkb8XFxdi0aROOHz+u9dLR5ZFKpZDLxX0/kMvlkEq1K11Y8BARkVpzNzvYmGu3/KiNuQzN3LgJKRFRVSAIAg4ePIj79+8jJiYGWVlZosZv0aKFqPFatmyp9bXceJSIiNSs5DIEta6LLeeSNL72rdZ1YSXXrlgiIqLKdf78efVSzyNHjoSDg4Oo8bt3745r166Vu3CBu7s7Jk+ejMaNG8PW1hYvXrzA7du3sX79ejx8+LBMezMzM3Tr1k3rXIxuhGf58uXw9PSEpaUlOnbsiPPnz7+x/fbt29GsWTNYWlrCx8cHBw8eLNPmxo0bGDp0KOzt7WFjY4P27dsjKUnzN3MioupOEIDJXTwh0XB9aYkEmNy1AbgLDxFR1SCRSCCRSNC/f380btxY9PhTpkwpU+z06NEDuyMicO/ePXz86T/RpEsA7Jp2RpMuAfj403/i3r172B0Rge7du5e6rqioCFOmTNE6F6Ma4QkPD8ecOXOwcuVKdOzYEUuXLkVAQABu3bqF2rVrl2kfHR2N4OBghIaGYvDgwQgLC0NQUBDi4uLg7e0NALhz5w66deuGadOmYdGiRbCzs8O1a9dgaWlZ2d0jIjJ6UqkEjV1sMbd/U402H/3Ivym8atlAommlREREBtGhQwfUr1+/3N+xxdCmTRt069YNZ86cgSAIqF+/Pk6cOIEbyc+wcO91RFxKLrUqqI35yxkGEzr0wMmTwzB37lx8//33MDMzQ5cuXdCmTRutczGqguf777/H9OnT1RXcypUrceDAAaxduxaffvppmfY//PADAgMD8fHHHwMAFi9ejMjISCxbtgwrV64EAPzzn//EwIED8c0336iv8/LyqoTeEBFVXTP7NIJEAiw5cgtveo5VInlZ7Mzo3ajykiMiIlG4uLjoLbZKpULNmjUhCALMzMywbds2LDuaiO8iy39fyS0sxpZzSQg7n4S5/Zviu+++A/By9teaNWt0ysVoCp7CwkLExsZi3rx56mNSqRT9+vVDTExMudfExMRgzpw5pY4FBAQgIiICwMtv9IEDB/CPf/wDAQEBuHTpEho0aIB58+YhKCjotbkoFAooFAr119nZ2QBeLoen6RJ7Je3FXprP2Jliv02xz4Bp9ttU+vxOt/ro19QZW849wP7Lj1FU/HJqgoVUgI1chsG+dTC+Y300rGXzl9+L6v69IiKi0h4+fIhz585BJpNh3759uJjnXKGZA4Lw8sM2iQT47rvvMGDAADRqpNuHakZT8GRmZqK4uLhMpeni4oKbN2+We01qamq57VNTUwEA6enpyMnJwf/93//hyy+/xNdff41Dhw5h+PDhOHbsGHr27Flu3NDQUCxatKjM8SNHjsDa2lqb7iEyMlKr66o6U+y3KfYZMM1+m0qf20qBtq3/9/XidioAhQDu4+aF+yj/Fbq0vLw8PWVHRETGqH79+jhz5gzi4y+joV8XvLv0lEbXLzlyC/2b10afvn11zsVoCh59KNmcaNiwYfjwww8BAH5+foiOjsbKlStfW/DMmzev1MhRdnY23N3d4e/vDzs7O41yUCqViIyMRP/+/UVfj9yYmWK/TbHPgGn22xT7DOjW75KRciIiMh2NGjWCl5cXFkRc1fhaQQA2xjzA4iBvnfMwmoLH2dkZMpkMaWlppY6npaXB1dW13GtcXV3f2N7Z2RlmZmZl1gFv3rw5Tp8+/dpcLCwsYGFhUea4XC7X+pcbXa6tykyx36bYZ8A0+22KfQa067cpfp+IiAjIVxYj4lKyVtfuvpSM+YOaw9pct5LFaJalNjc3R9u2bREVFaU+plKpEBUVhc6dO5d7TefOnUu1B15OMSlpb25ujvbt2+PWrdLzBf/44w/Ur19f5B4QEREREdGrbqRkl1qNTRO5hcW4mfJC5xyMZoQHAObMmYOQkBC0a9cOHTp0wNKlS5Gbm6tetW3SpEmoW7cuQkNDAQAffPABevbsie+++w6DBg3Ctm3bcPHiRaxatUod8+OPP8aYMWPQo0cP9O7dG4cOHcK+fftw/PhxQ3SRiIiIiEjvCgoKcODAAfTr1w/29vYGyyM7v+zGoxpdX6D7ojdGM8IDAGPGjMGSJUuwcOFC+Pn5IT4+HocOHVIvTJCUlISUlBR1+y5duiAsLAyrVq2Cr68vduzYgYiICPUePADw1ltvYeXKlfjmm2/g4+OD1atXY+fOnTrt1kpERNVXaGgo2rdvD1tbW9SuXRtBQUFlZgr06tVLvWlfyX/vvvuugTImIipNpVJh586duHr1KsLDwyG8aX8BPbOz0m18xc5S9ynRRjXCAwAzZ87EzJkzyz1X3qjMqFGjMGrUqDfGnDp1KqZOnSpGekREVM2dOHECM2bMQPv27VFUVIT58+fD398f169fh42Njbrd9OnT8a9//Uv9tbareBIRiS0yMhKJiYkwMzPD4MGD9bYpdEUKqeZudrAxl2k1rc3GXIZmbrbapFaK0RU8REREhnTo0KFSX69fvx61a9dGbGwsevTooT5ubW392kV1iIgMRaFQ4Pbt2wCAoKAg1KlTRy/3ycrKwoIFC2BlZYWAgIDXtrOSyxDUui62nEvS+B5vta4LK7lMlzQBGNmUNiIiMi4qlQBBEJBXWIT4h88AAPEPnyGvsAiCIEBlwGkSlSUrKwsA4OjoWOr4li1b4OzsDG9vb8ybN497DRGRUbCwsMC0adPw1ltvoWXLlnq5R0FBAUaOHIlr165h+vTpUCgUr20rCMDkLp7QdJBJIgEmd20AMd5lOMJDRESvdScjB+uj7yPiUjKKiovwTQdgwprzMJOZIah1XUzu4onGLrpPNzBWKpUKs2fPRteuXUs9Hzpu3DjUr18fderUQUJCAj755BPcunULu3btKjeOQqEo9QtByb5ESqUSSqVmD+SWtNf0uqqG/axe2M/KZWZmhubNm+slj+LiYowdOxYnTpyAlZUVtm3bBqlU+sZ7eTpa4qN+jfDj0dsVvs8HfRvDw8EcxUVFKG8ynCZ9Y8FDRETlWnY0Ed9F3kLJII7FK7MKcguLseVcEsLOJ2Fu/6aY2aeRYZLUsxkzZuDq1atl9m5755131H/28fGBm5sb+vbtizt37sDLy6tMnNDQUCxatKjM8SNHjmj97E9kZKRW11U17Gf1wn5WfUqlEhkZGTAzM8P8+fORkZGBgwcP/uV19QB800GDG724id9+u/na05qMqrPgISKiMpYdTcSSI7f+sp0gAEuO3IJEAszoXb2KnpkzZ2L//v04efIk6tWr98a2HTt2BAAkJiaWW/DMmzcPc+bMUX+dnZ0Nd3d3+Pv7w87OTqO8lEolIiMj0b9//2q9oSv7Wb2wn9XL4MGDERsbi8zMTI37ejcjF1vOPcD+y4+Rq/zf2I2NXIbBvnUwvmN9NKxlU+Y6QRCQm5uLGjVqAPjfSHlFsOAhIiI1lUrAnYycChU7r1py5BYCWrqiYS0bSPW0GlBlEQQBs2bNwu7du3H8+HE0aNDgL6+Jj48HALi5uZV73sLCAhYWFmWOy+VyrX8p0uXaqoT9rF7Yz+pBLpejQ4cOOHjwoMZ9bexmj0VBrfDpoJa4kfICLwqUsLOUo5mbLazkMghAmfcRQRBw9OhRxMbGIiQkBC4uLhrdkwUPERGpSSTA+uj7Gl8nCMD6M/ewOMi71PH79+8jMzMTTZo0ESlD/ZsxYwbCwsKwZ88e2NraIjU1FQBgb28PKysr3LlzB2FhYRg4cCCcnJyQkJCADz/8ED169ECrVq0MnD0RkXErKWaszc3Qtn7NMuf//JGZIAj4/fffER0dDQB4+PCheo/OimLBQ0REavnKYkRcStbq2t2XkjF/UHNYm798a7l79y62bt2KoqIive0BoQ8rVqwA8HJz0VetW7cOkydPhrm5OX7//XcsXboUubm5cHd3x4gRI7BgwQIDZEtEVL3Fxsaqi50BAwagXbt2GsdgwUNERGo3UrK12hwOeLmQwc2UF2hTvyYSExMRHh6OoqIiNGrUqELTwozFX22k5+7ujhMnTlRSNkREZWVlZSEpKQk+Pj6GTkXvWrVqhWvXrqFFixZo3769VjFY8BARkVp2fpFu1xe8XCY0NTUVRUVFaNq0KUaOHMk9aoiIRFJYWIht27YhNTUVeXl56kVTqitzc3NMnDgRUqn224ey4CEiIjU7K93eFuwsXz5E2q1bNzg4OKB58+aQyXTfJZuIiF6OQEdERCA1NRXW1tZo2rSpXu7z7NkzODg4GM10ZF2KHQDQ7WoiIqpWmrvZwcZcuwLFxlyGZm7/24TU29ubxQ4RkYgSExNx48YNSKVSjBkzBg4ODqLf4+nTp+jWrRvefvttFBXpNupvLDjCQ0REalZyGYJa18WWc0kaX/tW67qwkrPAISLSl8aNG2PIkCGQSqXw8PAQPX5eXh4GDx6M69ev4/nz50hPT0edOnVEv09l4wgPERGpCQIwuYsnNJ3FIJEAk7s2wJsf9yciIl21adMGfn5+oscVBAHBwcGIiYlBzZo1ceTIkWpR7AAseIiI6BVSqQSNXWwxt79m88I/8m8Kr2qw6SgRkamSSCSYNm0aHB0dsX//frRs2bJS7vtXK2OKgVPaiIiojJl9GkEiAZYcuYU3vRdJJC+LnRm9G1VeckREpBdDhw7F/fv3YWtr+9eNRaBSqbB7927UqlULPXr00Nt9WPAQEVG5ZvRuhICWrlh/5h52X0pGUfH/Hl5tavEc7Vu1wOTujdGodg0DZklERGKqrGKnuLgYu3fvxrVr1yCVStGyZUs4OTnp5V4seIiI6LUa1rLB4iBvzB/UHNcePcPjK9H4Vydz3IxLhGdOEeo7Vv9N74iIqiqVSqXzks76IAgCdu3ahevXr0MqlWL06NF6K3YAPsNDRERvIJVIIJFIYG1uBj93B6SmpuJmXAwAwMPDA2ZcdpqIyGjExcVh1qxZ8PPzg7m5OWQyGczNzeHn54dZs2YhLi7O0CkCePm8UP369SGTyTBmzBi97SdUgiM8RERUITk5OcjIyAAA9O7dW6/zrYmITFVmZiZsbW1hYWFR4WsSExMxbdo0nDx5EmZmZqX2z1Eqlbh8+TKuXbuGZcuWoUePHlizZg0aNTLss5cdOnRA06ZNYW9vr/d7cYSHiIgqpEaNGvDy8kK/fv1Y7BAR6UFOTg42bdqENWvW4Pnz5xW6JiwsDN7e3oiOjgaA124WWnI8Ojoa3t7e2Lp1qyg566Iyih2ABQ8REWnA2toaHTp0MHQaRETVTlFREcLDw5GdnQ2VSlWhEZ6wsDBMmDABCoXitYVOefdRKBQYP348wsLCdE27SmDBQ0RERERkYJGRkXj06BEsLS0RHBwMKyurN7a/ffs2pk6dqvU+NoIgYOrUqUhMTNTq+qqEBQ8RERERkYF16tQJrq6uGDlyZIVWLHv77bdRXFys0z2Li4sxbdo0nWJUBSx4iIiIiIgMrGbNmpg+fTq8vLz+sm1sbCxOnjxZ4Wlsr1NUVISTJ08azept+sKCh4iIiIjICFR0z5z169fDzEycxZbNzMywbt06UWKVKCwsxMGDB5Gfny9qXG2x4CEiIgAvpzaULDtNRETG69SpUzqP7pQoKirC6dOnRYkFAAqFAps3b8aFCxewY8cO0eLqggUPERGhuLgYO3fuxOrVq5GcnGzodIiI6A2uX78uarxr166JEqek2Hn48CEsLCzQp08fUeLqihuPEhGZuKKiIuzYsQO3bt2CTCZDXl6eoVMiIqLXUKlUUCqVosZUKpVQqVQVnlL3Onl5ecjKyoKlpSUmTpyIOnXqiJShbljwEBGZuOjoaNy6dQtmZmYYM2aMwXffJiKi15NKpZDL5aIWPXK5XOdiB3i58EJISAgKCwvh5uYmQmbiYMFDRGTiunTpgtTUVLRr1w4NGzY0dDpERNWWQqGo0Iaif6VFixa4fPmyCBm91LJlS9FiVWRJ7crGZ3iIiEycmZkZRo8ezWKHiEiPMjMz8eOPP+LixYs6x+revbuoq7R169ZNlFjGigUPEREREZEe5efnY+vWrcjLy8OVK1egUql0ijdlyhRRV2mbMmWKKLGMFQseIiLSu0ePHhk6BSIig1CpVNi+fTuePn0Ke3t7jB49WufnZdq0aYMePXroPMpjZmaGHj16oE2bNjrFMXYseIiISO8cHBwMnQIRkUFIJBLUrVsX5ubmCA4Oho2NjShx16xZo3PhJJPJsGbNGlHyMWYseIiISO9q1Khh6BSIiAxCIpGgb9++mDlzJlxcXESL26hRI4SEhOiU17p16zRamfPFixe4f/++1vc0FBY8RETVnEKhgCAIhk6DiMik2draih5z1apVCAkJgVwur/D0NjMzM1hYWGDLli0IDg6u8L2ys7OxYcMGbNmyBffu3dM2ZYNgwUNEVI29ePECq1evxu+//86ih4ioGlq/fj2uX7+OLl26AMBrC5+S4127dsXVq1e1KnaePHkCGxubKjdNmQUPEVE1lZ2djfXr1yMzMxNXr15Ffn6+xjG2bNmCVq1awcrKCvb29gAAe3t7WFlZoVWrVtiyZYvYaRMRkYYaNWqEEydOIDY2Fu+++y78/Pwgl8sBvNxU1M/PD++++y5iY2Nx/PhxjTeYPnfuHJ4+fQoHBwdMnjwZNWvW1Ec39IYbjxIRVUNFRUXYsGGDelWgkJAQWFtbV/j6qKgoDB8+HNnZ2epjVlZW6j8XFBTgypUrmDBhAv7+979j165d6Nu3r6h9ICIizbRp06bUimsqlUrnhQ0AoG/fvlCpVOjYsWOVG90BOMJDRFQtmZmZoXv37nB0dNT407gZM2agX79+pYqdN8nOzka/fv0wa9YsbdMlIiI9EKPYKYkTEBBQJYsdgCM8RETVlp+fH7y9vTXap2HGjBn4+eeftbrfsmXLoFKpsHz5cq2uJyIi0geO8BARVWOaFDuRkZFaFzslfv75Z0RFRekUg4ioqnnw4AG2bNmi1bOSpH8seIiICAAwcuRIUeIMHz5clDhERFXBs2fPEB4ejsTERJw5c0bU2NevX8fIkSMrPMWYymeUBc/y5cvh6ekJS0tLdOzYEefPn39j++3bt6NZs2awtLSEj48PDh48WOr85MmTIZFISv0XGBiozy4QEVUpmzdvFu0NNTs7m6u3EZFJUCgU2Lp1K/Lz8+Hm5oaePXuKFjspKQkBAQHYuXMnPvroI9HimiKjK3jCw8MxZ84cfP7554iLi4Ovry8CAgKQnp5ebvvo6GgEBwdj2rRpuHTpEoKCghAUFISrV6+WahcYGIiUlBT1f1u3bq2M7hARVQnffPONqPG+/vprUeMRERmjrKws5Ofno0aNGhg7dqx6KWhdZWZmIiAgAI8ePULz5s0RGhoqStzXycrKqtZ7tRldwfP9999j+vTpmDJlClq0aIGVK1fC2toaa9euLbf9Dz/8gMDAQHz88cdo3rw5Fi9ejDZt2mDZsmWl2llYWMDV1VX9X1VbP5yI6M/EfHO6ffu2aLEAIDExUdR4RETGqHbt2pg+fTrGjRsHOzs70eKmpKTg6dOncHd3x+HDh+Hk5CRa7D9LS0vDqlWrsG/fvmpb9BjVKm2FhYWIjY3FvHnz1MekUin69euHmJiYcq+JiYnBnDlzSh0LCAhAREREqWPHjx9H7dq1UbNmTfTp0wdffvnla//yKBQKKBQK9dcl0zyUSiWUSqVGfSppr+l1VZ0p9tsU+wyYZr+Noc8PHz7E0aNHMXLkSNjY2OgcTyKRlNpnpzwl5/+qXYlXvz+m9PeDiEyLnZ2dqMUOAPj4+ODMmTMoKiqCu7u7qLFflZqaik2bNiEvLw+pqakoLCyEhYWF3u5nKEZV8GRmZqK4uBguLi6ljru4uODmzZvlXpOamlpu+9TUVPXXgYGBGD58OBo0aIA7d+5g/vz5GDBgAGJiYiCTycrEDA0NxaJFi8ocP3LkiEYb970qMjJSq+uqOlPstyn2GTDNfhuqzzk5Obh79y5UKhU2b94sypuhJtN8Xzfi/mevPk+Zl5encU5ERKasUaNGeo2vVCqxZcsW5OXloU6dOpgwYUK1LHYAIyt49GXs2LHqP/v4+KBVq1bw8vLC8ePHy90ZfN68eaVGjbKzs+Hu7g5/f3+NK3ilUonIyEj0799ftHmdVYEp9tsU+wyYZr8N2ecHDx4gPDwcKpUKnp6eGDVqlCg52Nvb/2UbKysrrF27FlOnTq3Q0qtZWVnqP3OFISIi4yKXyzFo0CDExMQgODgYlpaWhk5Jb4yq4HF2doZMJkNaWlqp42lpaXB1dS33GldXV43aA0DDhg3h7OyMxMTEcgseCwuLcitcuVyu9S8WulxblZliv02xz4Bp9tsQfa5VqxZq1KgBZ2dnjBkzRqN9dt5EEAQUFBRUqG1+fv5fFjxWVlalvjem9neDiKgqaNasGZo2bQqJRGLoVPTKqBYtMDc3R9u2bUttWqdSqRAVFYXOnTuXe03nzp3LbHIXGRn52vYA8OjRIzx58gRubm7iJE5EVEns7e0xZcoUUYsdAGjcuLFosQD9T8UgIiJxVPdiBzCyggcA5syZg19//RUbNmzAjRs38N577yE3NxdTpkwBAEyaNKnUogYffPABDh06hO+++w43b97EF198gYsXL2LmzJkAXs51//jjj3H27Fncv38fUVFRGDZsGBo1aoSAgACD9JGISBd2dnaiFjsA8Mknnxh1PCIiIm0ZXcEzZswYLFmyBAsXLoSfnx/i4+Nx6NAh9cIESUlJSElJUbfv0qULwsLCsGrVKvj6+mLHjh2IiIiAt7c3AEAmkyEhIQFDhw5FkyZNMG3aNLRt2xanTp2qtg9mERFpavz48aKtMmRnZ4fx48eLEouIyNCuXbuml6X2X10RmPTLqJ7hKTFz5kz1CM2fHT9+vMyxUaNGYdSoUeW2t7KywuHDh8VMj4ioWtq1axf69esnShwiouogOTkZERERKC4uRkhICOrXry9K3Pj4eAwePBjr168X5XWX3szoRniIiMgw+vbt+9oPmypq5syZ5S4GQ0RU1WRnZ2Pbtm0oKipC48aNRdsP586dOwgMDERycjK++eYbvW72qVKp9Ba7KjHKER4iIjKMn376CSqVCj///LPG186cORM//fSTHrIiIqp8cXFxyMnJQa1atTB8+HBIpbqPE6Snp8Pf3x9paWnw9fXF9u3bRV00QKUSIJEA+cpinIq9hoSzJ9DJfyjaNq4HK7kMAgCpCSxS8GcseIiIjMiFCxdQo0YNNG/e3GA5LF++HMOHD8fw4cMrtH+Ovb09du7cyZEdIqpWevbsCblcjhYtWoj23Le9vT3atWsHADh06FCF9kDTxJ2MHKyPvo+YS9fQVfoHzCQCft52EAmShghqXReTu3iisYutqPesCjiljYjISJw9exYHDx7Ejh07kJ6ebtBc+vbti6ysLGzevBk+Pj6wsrIqdd7Kygo+Pj7YvHkznj9/zmKHiKodiUSCrl27ombNmqLFtLCwQFhYGM6cOfPGPSO1sexoIvyXnkTUhavqYiep2B7nlO7ILSzGlnNJ8F96EsuOir8Ag7HjCA8RkRE4c+YMfv/9dwAvV5+sVauWXu5TXFwMmUxW4fbjx49Xr7imVCpx8OBBZGVlcSNRIiItyWQyvRQ7S47cAgA8E6zwQrBAtsoSxwsbQvXK+IYgAEuO3IJEAszobTr7pXGEh4jIwARBwLNnzwAAPXr0QJ8+ffSyEVxKSgo6duyInTt3ih6biIgqn0ol4HbaC3WxAwAKyPGbohmO/anYedWSI7eQmJ4DlR4XTDAmLHiIiAxMIpFg0KBBCA4ORu/evfVS7Ny6dQtdunRBbGws5syZw/0fiIiqAYkEWB99v8xxBcwgvOHXfEEA1p+5B1NZvoAFDxGREZBIJGjSpIleYj98+BBdunTB/fv30ahRIxw9epQbL79BaGgo2rdvD1tbW9SuXRtBQUG4detWqTYFBQWYMWMGnJycUKNGDYwYMQJpaWkGypiITFW+shgRl5K1unb3pWTkK4tFzsg4seAhIqrm6tWrh5EjR6JDhw6Ijo6Gl5eXoVMyaidOnMCMGTNw9uxZREZGQqlUwt/fH7m5ueo2H374Ifbt24ft27fjxIkTePz4MYYPH27ArInIFN1IyUZuoXZFS25hMW6mvBA5I+PERQuIiKo5iUSC5cuXo7CwENbW1oZOx+gdOnSo1Nfr169H7dq1ERsbix49eiArKwtr1qxBWFgY+vTpAwBYt24dmjdvjrNnz6JTp06GSJuItJCYmAhPT0+YmYn3K3FycrJeNxN9VXZ+kW7XFyhFysS4seAhIjIBZmZmor6hv4lSqYREIqm0++lbVlYWAMDR0REAEBsbC6VSiX79+qnbNGvWDB4eHoiJiSm34FEoFKWemyrZ30ipVEKp1OwXjpL2ml5X1bCf1Ysx9vP27dvYvn076tWrh+DgYFFWn4yJicHcuXNx7tw5rFy5Uu+vgzXMAQuZ9sVVDblE65+JoX+mmty3erwbERGRUXj+/DnCw8NRp04dDBkyxNDp6EylUmH27Nno2rUrvL29AQCpqakwNzeHg4NDqbYuLi5ITU0tN05oaCgWLVpU5viRI0e0HnWLjIzU6rqqhv2sXoyln/n5+bh9+7b6z2Lk9ejRI8ybNw8FBQVISEjAgQMH9LKE/9OnTyEIApycnAAA33TQPlbylWgkX9EtH0P9TPPy8irclgUPEZGeqVQqXLx4Ee3atYNUWn0fnbx37x527NiBvLw8ZGdno1evXrC1rdo7es+YMQNXr17F6dOndYozb948zJkzR/11dnY23N3d4e/vDzs7O41iKZVKREZGon///tV6PyT2s3oxpn4KgoDVq1dDpVKhfv36GDt2rEb7k5VHoVCgVatWePHiBRo3bozDhw+LumFpicuXLyM+Ph4A0Lt3b9StWxeLD9zAfy8+1DjWmHbuWDCoudYrgxr6Z1oyUl4RLHiIiPSouLgYERERuHr1Kh4/foygoCBDp6QXBQUFCA8Ph0KhgJubG0aPHl3li52ZM2di//79OHnyJOrVq6c+7urqisLCQjx//rzUKE9aWtprNxO0sLAod2U8uVyu9S8KulxblbCf1Yux9HP48OE4cuQIRo0aBUtLS53jyeVyLF26FAsXLsTcuXNRs2ZN0fsZGxuLAwcOAADat28PT09PCAIwqUtDbD7/CJo8NiSRAJO6esFMLodUx60QDPUz1eSe1fejRiIiAysuLsbOnTtx9epVSKVSNG3aVC/3uX37NvLz8/USu6IsLS0xePBgtGrVClOmTCkz3asqEQQBM2fOxO7du3H06FE0aNCg1Pm2bdtCLpcjKipKfezWrVtISkpC586dKztdItKCq6srJk2aBCsrK9FiDh06FGfPntV41LaiSjao7tixIwYMGACJRAKpVILGLraY21+z95eP/JvCq5aNzsVOVcERHiIiPUlPT8ft27chk8kwevRoveyzc+bMGQwZMgQ9e/bEjh07dJ6WoQtvb2/1cy5V2YwZMxAWFoY9e/bA1tZW/VyOvb09rKysYG9vj2nTpmHOnDlwdHSEnZ0dZs2ahc6dO3OFNiITp8/X4L59+8LDwwONGzcuMw1tZp9GkEiAJUduvXGkRyJ5WezM6N1Ib3kaIxY8RER64ubmhrFjx0IQBDRqJP6by+7duzFu3DgUFBQgJSUFOTk5sLe3F/0+pmbFihUAgF69epU6vm7dOkyePBkA8J///AdSqRQjRoyAQqFAQEAAfv7550rOlIhMyV9tUD2jdyMEtHTF+jP3sPtScqn9eWzMZXirdV1M7toAjWrXqIx0jQoLHiIiPdLXJp9ZWVl4++23UVBQgKFDh2Lr1q3cY0ckFdk/w9LSEsuXL8fy5csrISMiooppWMsGi4O8MX9Qc9xIeYEXBUrYWcrRzM0WVnIZKmd3IOPDgoeIqAqyt7fHzp07sWvXLnz//ffVZs8bIiLSXskzOdbmZmhbv+wqcabxxE5ZfIckIqqievXqVWbalb4UFxf/dSMiIiIjxFXaiIjoje7evYuffvoJGRkZhk6FiKjCNNmnpSIiIyORlJQkakyqHCx4iIioXIIgICYmBps3b0ZWVpbOm28SEVWW8+fPY9myZbh586Yo8U6dOoWhQ4eia9eueil6BEFAQkICioqKRI9NLHiIiLSWl5eHJ0+eGDoNvYmLi8ORI0cgCAJ8fX0xePBgQ6dERPSX7t69i0OHDkGpVCIzM1PneAkJCRgyZAgKCgrQunVr1KlTR4Qs/0cQBBw7dgy7d+9GeHg4VCqVqPGJz/AQEWlFqVRiy5YtyM/Px+TJk+Hk5GTolETn6+uLy5cvo2XLlujQoQM/eSQio/fs2TNs374dgiCgVatW6Nq1q84x586di6ysLHTr1g3btm0TdZEYQRBw9OhR9Qh6gwYNIJVyPEJs/I4SEWkoJycHiYmJ6mdaKrKMsaZ27tyJmJgY0eNqwszMDJMnT0bHjh3LbHJHRGSMbG1t0bRpU9SrVw9DhgwR5bUrPDwcU6dOxd69e0Vf/j87OxsXLlwAAAQEBKBLly6ixqeXOMJDRKShqKgoKBQK2NraIiQkRPTRnR9++AEffvghHB0dERcXBw8PD1Hja4KfNBJRVWJmZoZhw4ZBqVSKNhLj6OiINWvWiBLrz+zt7TFhwgSkpqaiXbt2erkHseAhItJYQEAAHj16hHHjxola7KhUKnzyySdYsmQJAGDMmDGoW7euaPGJiEyBRCKBubm5odOosHr16qFevXqGTqNa40d3REQasrS0RIMGDVCzZtlN3XShUqlw9epVAEBoaCiWLVsGmUwm6j2IiIhMDUd4iIiMhJmZGbZv346jR49i6NCher+fIAh8NoeIiKo9jvAQERmRGjVq6L3YEQQB0dHR2LNnj14WXCAiIjImHOEhIjIhhYWF2Ldvn3rqnLe3Nxo1amTgrIiIiPSHIzxERCZCEASEhYXh6tWrkEqlGDBgALy8vAydFhGRRgRBEHV0evv27Vi9erVo8V6lUqnw9OlTvcSmiuMIDxHRn+Tm5sLGxsbQaYhOIpGgc+fOePLkCUaOHIn69esbOiUiIo39/vvvyM7OxtChQyGXy3WKFRUVhfHjx0OpVMLd3R0BAQEiZfmy2NmzZw/++OMPhISEwNXVVbTYpBkWPEREr0hOTsbmzZvRq1cvdOzYUfT4hl4ooGnTpmjQoEGVWrKViKhEfHw8oqOjAQCtWrVC48aNtY4VGxuLoKAgKJVKjBw5Ev369RMrTahUKuzdu1c9ov7s2TMWPAbEKW1ERP/fw4cPsXHjRhQUFOD69etQqVSixVapVJg7dy7mzp0rWkxtsdghoqro4cOH2L9/PwCgR48eOhU7ABAZGYmcnBz06dMHmzdvFnUbgLNnz6qLnZEjR6J58+aixSbNcYSHiAhAdnY2Nm/ejMLCQnh6eiI4OBhSqTifCSkUCoSEhCA8PBwAEBwcjPbt24sSm4jIVCgUCshkMjRp0gS9evXSOd6nn34Kd3d3DB06FBYWFron+Ir27dvj4cOHaN++PZo2bSpqbNIcCx4iIgB2dnbo1q0b7t+/j7Fjx+o8L7yEIAgYOnQojhw5ArlcjnXr1rHYISLSQqNGjTB9+nTY2dmJNjV4/PjxosT5M7lcjvHjx3OvMyPBgoeI6P/r3r07unbtKtrIDvByoYCJEyfi7Nmz2LVrF/r27StabCIiU+Ps7GzoFCqMxY7x4DM8RESvELPYKTFhwgTcuXNHr8VOYWEhdu/ejcePH+vtHkRERFURCx4iokqgz08lnz59ijVr1iAhIQE7duxAcXGx3u5FRERU1XBKGxFRFZaeno5169ahoKAANjY2CAoKEnWlISIioqqOBQ8RURXm5OQEFxcXFBcXY9SoUbCzszN0SkRERkOlUullqnJxcTE/XKpCOKWNiEyGmPvqGAuZTIYxY8YgJCSExQ4R0Ss2btwIf39/ZGdnixq3sLAQmzdvxokTJ0SNS/pjlAXP8uXL4enpCUtLS3Ts2BHnz59/Y/vt27ejWbNmsLS0hI+PDw4ePPjatu+++y4kEgmWLl0qctZEZMxiY2OxceNGFBYWihq3oKAAjx49EjWmpqysrGBmxgF7IqraBEHAb7/9huTkZJ1jHTx4EFOnTkVUVBTWrFkjQnYvFRYWIiwsDPfv30d0dLToxRTph9EVPOHh4ZgzZw4+//xzxMXFwdfXFwEBAUhPTy+3fXR0NIKDgzFt2jRcunQJQUFBCAoKwtWrV8u03b17N86ePYs6derouxtEZEQuXLiA/fv348GDB4iPjxct7rNnzxAQEIDevXsjIyNDtLhERKbo5MmTOH/+PDZt2oT8/Hyt48TExGDkyJEoLi7GxIkT8cEHH4iSn0qlwpYtW/DgwQNYWFhg4sSJHFmvIkQteAoLC5Gbm6tTjO+//x7Tp0/HlClT0KJFC6xcuRLW1tZYu3Ztue1/+OEHBAYG4uOPP0bz5s2xePFitGnTBsuWLSvVLjk5GbNmzcKWLVtE21CQiIzfxYsX1aO+nTp1Em3Tz4yMDPTq1QsnT55Eeno6EhMTRYlLRGSKrl+/juPHjwMA/P39YWVlpXUsCwsL2NraYuDAgVizZo1oz/BIpVL4+PjA0tISEydORL169USJS/qn1d+Abdu24cMPPyx1bNGiRahRowYcHBzw1ltvIScnR+O4hYWFiI2NRb9+/f6XoFSKfv36ISYmptxrYmJiSrUHgICAgFLtVSoVJk6ciI8//hgtW7bUOC8iqrrc3d1hZWWFrl27wt/fX7SN4FauXIkbN26gTp06OHXqFDp37ixKXCIiUyMIAi5fvgwA6NixI9q0aaNTvDZt2uDs2bP473//K/qH3O3atcOsWbNQt25dUeOSfmk16fu7775D69at1V9HR0dj0aJFGDRoEJo3b46ffvoJX331FUJDQzWKm5mZieLiYri4uJQ67uLigps3b5Z7TWpqarntU1NT1V9//fXXMDMzw/vvv1+hPBQKBRQKhfrrkvmZSqUSSqWyQjFKlLTX9LqqzhT7bYp9Boy/346Ojpg+fTpsbGxQVFQkSkylUokZM2Zg27Zt+Pnnn+Hh4aGX/j99+hSXLl1Cnz59jGLHbl1+1sb694OIDE8ikWD06NGIjY1Fu3btRInZoEEDUeKUx9raWm+xST+0Knju3LmDkJAQ9ddhYWFwdXXF7t27YWZmBpVKhZ07d2pc8OhDbGwsfvjhB8TFxVX4F4bQ0FAsWrSozPEjR45o/Zc8MjJSq+uqOlPstyn2GTC9fjs6OuLvf/87rl69Wu4zg7rKzs7GgwcPUFxcjIcPH6J27dqi30Nb2vys8/Ly9JAJEVUXMpkMHTp0MHQaVE1pVfAoFApYWlqqvz5y5AgGDBigXiWoRYsW+PnnnzWO6+zsDJlMhrS0tFLH09LS4OrqWu41rq6ub2x/6tQppKenw8PDQ32+uLgYc+fOxdKlS3H//v0yMefNm4c5c+aov87Ozoa7uzv8/f01fjhNqVQiMjIS/fv3N6lnh0yx36bYZ8A0+63vPl+4cEG9uELdunUxfPhw2Nrain4fTenSb65kREREhqJVwdOgQQP8/vvvePvtt3Hx4kUkJibiq6++Up9PS0tDjRo1NI5rbm6Otm3bIioqCkFBQQBePn8TFRWFmTNnlntN586dERUVhdmzZ6uPRUZGqufTT5w4sdxnfCZOnIgpU6aUG9PCwgIWFhZljsvlcq1/udHl2qrMFPttin0GTLPf+uqzq6srJBIJWrduXerDJGOhTb9N7e8GEREZD63eRf/2t7/hgw8+wPXr1/Ho0SPUq1cPgwcPVp8/c+aM1osDzJkzByEhIWjXrh06dOiApUuXIjc3V12cTJo0CXXr1lVPl/vggw/Qs2dPfPfddxg0aBC2bduGixcvYtWqVQBe7kLu5ORU6h5yuRyurq5o2rSpVjkSEelTw4YN8be//a3M84lERESkOa0KnlmzZsHS0hIHDx5E27Zt8cknn6iXD3z69ClSU1Px7rvvapXQmDFjkJGRgYULFyI1NRV+fn44dOiQ+o0/KSmp1PKCXbp0QVhYGBYsWID58+ejcePGiIiIgLe3t1b3JyIyBix2iIheLy0tTfTXyZycHBw+fBgDBw7UaVlsMj5az5OYPn06pk+fXua4o6MjLl68qFNSM2fOfO0UtpI12l81atQojBo1qsLxy3tuh4iqHpVKhUOHDsHb27vUc3q6SkpKwqlTpzB+/HjRYhIRkThWrVqFuXPnYvfu3WUeW9DWixcvsHHjRmRmZkKpVGLs2LGixCXjoNNOTAqFAjExMdizZw8yMzPFyomI6C+pVCrs2bMHFy5cwNatW1FQUCBK3MuXL6NTp06YOHEi9u7dK0pMIiJTV1RUhMzMTAiCoFOcXbt24b333kNOTg5OnTolSm7Z2dnYsGEDMjMzYWdnB39/f1HikvHQuuD58ccf4ebmhq5du2L48OFISEgA8HIvHWdnZ6xdu1a0JImIXlVcXIzdu3cjISEBEokEgwcPLrVypLaioqLQvXt3pKSkoGXLljpvfkdERC83Fj106BAePXqE/fv3ax3n4sWLCA4OhkqlwvTp0/HFF1+Ikl9RUREKCwthb2+PyZMnw9HRUZS4ZDy0KnjWrVuH2bNnIzAwEGvXri1VrTs7O6NPnz7Ytm2baEkSEf2ZUqmEVCrFqFGjtF4k5c/Onj2LFy9eoGfPnjh16hTq1asnStxX3b59Gw8ePBA9LhGRsTp79qz6g3FdXq99fHwQFBSE4cOHY8WKFaJtyOzo6IiQkBBMnjwZNWvWFCUmGRetnuH57rvvMGzYMISFheHJkydlzrdt2xY//vijzskREZVHJpNh5MiRSElJgbu7u2hx58+fD1dXV0yYMKHcpel1IQgCTp06hWPHjsHGxgZ/+9vfjGJvHSIifcrOzkZUVBSAl/uKNWzYUOtYFhYWCAsLQ1FREWQymVgpAkCZFX2petGq4ElMTMT777//2vOOjo7lFkJERGIxMzMTtdgBAIlEgmnTpokaE3g5GrVr1y7cvHkTANC8eXNYW1uLfh8iImNjZ2eH8ePH49atW6I8aymTyUQvdqj602pKm4ODwxsXKbh+/TpcXV21ToqIqDqRyWTqTySHDBmCQYMG8Q2biExGgwYN0LdvX9GmoBFpSquCZ+DAgVi1ahWeP39e5ty1a9fw66+/YujQobrmRkRULUilUgwfPhxTpkzhQghERESVTKuC58svv0RxcTG8vb2xYMECSCQSbNiwARMmTEC7du1Qu3ZtLFy4UOxciYiqLCsrK9StW9fQaRAREZkcrQqeOnXqIDY2FoGBgQgPD4cgCNi0aRP27duH4OBgnD17Fs7OzmLnSkRERESkkczMTNy5c8fQaZABab0PT+3atbF69Wo8ffoUaWlpSElJwbNnz7B27VrUrl1bzByJyMTk5+cjJiZG5w3qXhUZGYmBAwciPz9ftJhERCSuw4cPi/ran5GRgfXr12Pr1q3cEsCEaV3wvKpWrVpwcXGBVCpKOCIyYXl5edi4cSOOHDmCEydOiBJz06ZNGDhwIH777Td8++23osQkIiJx/fDDDwgMDMS0adNEKXrS09Oxfv165ObmolatWqhVq5YIWVJVpNWy1P/617/+so1EIsFnn32mTXgiMlG5ubnYuHEj0tPTYWNjgxYtWugc8+eff8aMGTMAAGPHjsUnn3yic8xXCYKAhIQE+Pj48EMfIjJJBQUFKCgogIODg9YxtmzZgtmzZwMAvLy8RFnRLTY2Fnl5eXBzc8PEiRNhZWWlc0yqmrQqeL744ovXnpNIJBAEgQUPEWns0aNHyMjIQI0aNRASEiLKs4Ddu3eHnZ0d3nnnHXz99deiFiUKhQI7d+7ErVu3kJ6ejv79+4sWm4ioKlCpVNixYwdSUlIwduxYrfZHS01Nxdtvvw0AeP/99zF//nxRcgsICICVlRU6duzIYsfEaVXwqFSqco89ePAAy5cvx8mTJ/Hbb7/pnBwRmZamTZtixIgRcHV1FW3Xax8fH1y7dg316tUTJV6JgoICrF+/Hk+ePIFMJuNCLURkkg4fPow7d+5ALpdDLpdrFcPV1RX//e9/ERERgf/85z+i7dcjlUrRq1cvUWJR1SbaR51SqRQNGjTAkiVL0LhxY8yaNUus0ERkQlq2bClasVNC7GIHAIqLi/Hs2TPY2tpiypQpaN26tej3ICIyZleuXMH58+cBAG+99ZZOm84PGTIEa9as4dRg0gutRnj+So8ePUSfJ09EZExsbGwwfPhw1K9fHzVq1DB0OkREla5x48Zo1KgR3N3d0bx5c0OnQ/Raeil4Ll68yAqdiKq9Jk2aaD2Fg4ioqrO0tERwcLBoU9CI9EWrgmfjxo3lHn/+/DlOnjyJXbt2qR8+IyIiqkpOnjyJb7/9FrGxsUhJScHu3bsRFBSkPj958mRs2LCh1DUBAQE4dOhQJWdKZHj8gJuqAq0KnsmTJ7/2nLOzMz799FMsXLhQ25yIiCrs8ePHqFOnjqHToGokNzcXvr6+mDp1KoYPH15um8DAQKxbt079tYWFRWWlR0T/X0ZGBpycnFh00V/SquC5d+9emWMSiQQ1a9aEra2tzkkRUfWUlpYGa2trUV4nBEFAaGgovvrqK0RFRaFTp04iZEgEDBgwAAMGDHhjGwsLC50e0CYi3dy7dw9bt25Fy5YtMXToUE6rozfSquCpX7++2HkQUTWXkpKCTZs2wcbGBiEhITo96F9UVIRZs2Zh5cqVACB6wVNQUIAnT56gbt26osWk6uX48eOoXbs2atasiT59+uDLL7984+qCCoUCCoVC/XV2djYAQKlUQqlUanTvkvaaXlfVsJ/VhyAI+Pbbb+Hi4iJKP+/du4ft27ejqKgI2dnZKCgogJmZXh5L15gp/DxLGLqvmtzXOP52EFG1lpycjM2bN6OgoABOTk46vzGtXLkSK1euhEQiwY8//oiZM2eKlCmQmZmJbdu2ITc3F9OnT4ejo6Nosal6CAwMxPDhw9GgQQPcuXMH8+fPx4ABAxATEwOZTFbuNaGhoVi0aFGZ40eOHIG1tbVWeURGRmp1XVXDflZ9O3fuxKZNm+Dk5IQaNWrotAloUVERrl+/DpVKBTs7O9SoUQNHjhwRMVtxVOef558Zqq95eXkVbluh3zqkUqnGQ4USiQRFRUUaXUNE1Y8gCDh48CAKCgrg4eGBcePG6fy8w9/+9jdERUVhwoQJGDFihEiZArdu3cKuXbtQWFgIOzs7FBYWihabqo+xY8eq/+zj44NWrVrBy8sLx48fR9++fcu9Zt68eZgzZ4766+zsbLi7u8Pf3x92dnYa3V+pVCIyMhL9+/ev1qsEsp/GobCwEObm5lpfv27dOmzatAkAMHToUAwdOlTnfjZv3hxXrlzB0KFDjWZkp4Sx/zzFZOi+loyUV0SF/pYsXLiQcyOJSCsSiQRjxozBsWPHMGDAAJ3eOEvI5XLs2rVL9NelhIQEFBYWon79+hg1ahRsbGxEjU/VU8OGDeHs7IzExMTXFjwWFhblFvq67E6vy7VVCftpOIWFhdi0aRMaNGiA/v37a7w4gFKpxPLlywEAH330Ebp16yZKP1u0aIEWLVroFEPfjPHnqS+G6qsm96xQwfPFF19omwsREezs7DBs2DBRY+rjQ5hhw4bB1dUVXbp0ee3UJKI/e/ToEZ48eQI3NzdDp0IkGkEQsHv3bqSlpSEnJwddunTReMEZuVyOY8eO4ddff8WHH36I3377TU/ZEr2ZcY0DEhEZkLm5Obp3727oNMjAcnJykJiYqP763r17iI+Ph6OjIxwdHbFo0SKMGDECrq6uuHPnDv7xj3+gUaNGCAgIMGDWROI6evQobt68CZlMhrFjx2q9uqajoyM++eQTk3iIn4yXTgXPo0ePcOnSJWRlZUGlUpU5P2nSJF3CExERVbqLFy+id+/e6q9Lnr0JCQnBihUrkJCQgA0bNuD58+eoU6cO/P39sXjxYu7FQ9WKk5MTZDIZhgwZgnr16hk6HSKdaFXwFBQUICQkBDt37oRKpYJEIoEgCABKTzNhwUNERFVNr1691O9p5Tl8+HAlZkNkGH5+fvD09ISDg4OhUyHSmVZb086fPx+7du3CV199hePHj0MQBGzYsAFHjhzBgAED4Ovri8uXL4udKxGZgKKiIqxevbrcUWMiIqo8hip2uMoviU2rgmfHjh2YMmUKPvnkE7Rs2RIAULduXfTr1w/79++Hg4ODelUOIqr+xFq+OTc3F2+99RamT5+Ojz/+WJSYJZ48eYLi4mJRYxIRkbguX76M5cuX4/nz54ZOhaoRrQqe9PR0dOjQAQDUm0fl5uaqz48YMQK7du0SIT0iMnYJCQm4ceMGbt68qVOcjIwM9OnTB/v374elpaWoiwfcuHEDq1at4gpBRERGLD4+HhEREXj+/Dni4uIMnQ5VI1oVPC4uLnjy5AkAwNraGjVr1sStW7fU57Ozs1FQUCBOhkRktGJjY7F//34IgoAHDx7oFOvatWuIi4uDo6MjoqKiEBQUpHN+KpUKR48exX//+18UFhbiyZMnnCpBRCQSQRAwc+ZM/PrrrzrHun79Ovbs2QMAaNu2bamFQ4h0pdWiBR07dsTp06fxySefAACGDBmCb7/9Fm5ublCpVPjPf/6DTp06iZooERmXpKQk7N+/HwDg7OwMf39/neL16tUL27Ztg7e3N5o2bSpGinj27BliYmIAvHzd8vf313jjPCIiKt8XX3yB5cuXQyqVokePHjq9dnt6eqJ27dqoX78+BgwYwA3vSVRaFTzvv/8+tm/fDoVCAQsLCyxevBgxMTGYOHEiAMDLyws//vijqIkSkXFxd3dHu3btIJVKoVAoRHlzGjFihAiZ/Y+TkxOGDRuG4uJi+Pr6ihqbiMiULV++HP/6178AAMuWLdP5gypra2tMnToV5ubmLHZIdBUueEaOHImJEydi4MCB6NatG7p166Y+5+7ujhs3buDKlSuQyWRo1qwZzMy4pylRdSaRSDBw4EAolUqjfjbG29vb0CkQERmdFy9eICIiAgMHDoSTk5PG16empgJ4Ocrz3nvviZIT97Iifanw3I4DBw5g+PDhcHFxwd/+9jecPHmydCCpFL6+vvD29maxQ2QiJBIJP4kjIqpilEolwsPDcffuXezZs+eN+069zuLFixEVFYWFCxfqIUMicVW44MnIyMDatWvRvn17rF27Fr1794aHhwc+/fRTJCQk6DNHIiIiIhKBIAjYt28fkpOTYWVlhaCgIK0/uOrTpw8/9KIqocIFT40aNRASEoLDhw/j8ePHWLp0KerWrYtvvvkGrVu3ho+PD77++mskJSXpM18iIiIi0pJCoUB6ejqkUilGjRoFR0dHQ6dEpHdaLVdUq1YtzJo1CzExMbh79676obV58+ahYcOG6NGjB1atWiVqokRUdaWnp2Pw4MG4c+eOqHFzcnJEjUdEVN1ZWlpi6tSpCA4ORoMGDQydDlGl0Hl9Vk9PT/zzn//ElStXEB8fjyFDhuD06dOiPcBGRIYhCAKKi4t1jpOYmIguXbrgwIEDmDhxolZzxf9MpVIhKioKP/30EzIyMnSOR0RkSszNzdGoUaNKveeZM2fUezgSVTZRVhdISUnB1q1bERYWpt4Zt127dmKEJiIDEAQBBw4cQF5eHkaMGAGZTKZVnCtXrqBv377IyMhAgwYNsH79ep3ne+fn52PXrl1ITEwEAPzxxx+oVauWTjGJiEg/BEHAiRMncOLECQAvR/zr1q1r4KzI1Ghd8Dx//hw7duxAWFgYTp06heLiYnh5eWHhwoWYMGFCpX9yQETiUKlU2LdvH+Lj4wG83GBU22kPHh4ecHNzg4eHBw4cOAAXFxed8ztz5gwSExNhZmaGIUOGoFWrVjrHJCKi0pRKJVQqlU5LRQuCgGPHjuHUqVMAADc3N9SuXVusFIkqTKOCp6CgAHv37kVYWBgOHz4MhUKBWrVq4b333sOECRPQoUMHfeVJRJXk4MGDiI+Ph0QiQVBQkE5zvO3t7XH48GHUqFEDNWrUECW/Xr164enTp+jevTvc3NxEiUlERP8jCAKmT5+OpKQkREREwM7OTqdYANC3b19OaSODqXDBM2nSJOzZswc5OTmwtrbGiBEjMH78ePj7+2s93YWIjI+3tzeuXr2KIUOGoGXLljrHc3V1FSGr/zEzM8Po0aNFjUlERP/zySefYMOGDZDJZLhw4QL69u2rVRyJRII+ffqgcePGcHNzw8GDB0XOlKhiKrxowdatW9G1a1ds3LgRaWlp2Lx5MwYMGKCXYmf58uXw9PSEpaUlOnbsiPPnz7+x/fbt29GsWTNYWlrCx8enzD+oL774As2aNYONjQ1q1qyJfv364dy5c6LnTVQdeHp64oMPPhCl2CEioqrlhx9+wLfffgsAWL16tdbFTgmJRAIPDw8xUiPSWoULnsePH+PgwYMYP348rK2t9ZZQeHg45syZg88//xxxcXHw9fVFQEAA0tPTy20fHR2N4OBgTJs2DZcuXUJQUBCCgoJw9epVdZsmTZpg2bJluHLlCk6fPg1PT0/4+/tzdSei17CysjJ0CkREpKX09HRcuHBBq2t79OiB2rVr4+uvv8bkyZPFTYzIQCpc8FTWKkjff/89pk+fjilTpqBFixZYuXIlrK2tsXbt2nLb//DDDwgMDMTHH3+M5s2bY/HixWjTpg2WLVumbjNu3Dj069cPDRs2RMuWLfH9998jOzsbCQkJldInIiIiosqQl5eHbdu24eDBg1rNZmndujWuXbuGjz/+WA/ZERmGKMtSi6WwsBCxsbGYN2+e+phUKkW/fv0QExNT7jUxMTGYM2dOqWMBAQGIiIh47T1WrVoFe3t7+Pr6lttGoVBAoVCov87OzgbwcsUSpVKpSZfU7TW9rqozxX6bYp+fPHmifphVjH6rVCqoVCqYmRnVS1MZpvizBnTrt6l9r4gMobi4GP/973/x7Nkz1KxZEz4+PlrFcXZ2FjkzIsMyqt8qMjMzUVxcXGbpWhcXF9y8ebPca1JTU8ttn5qaWurY/v37MXbsWOTl5cHNzQ2RkZGv/QcdGhqKRYsWlTl+5MgRrafzRUZGanVdVWeK/TaVPt+6dQtffvklgoODMXDgQJ37XVRUhAcPHsDMzAweHh4679dTGUzlZ/1n2vQ7Ly9PD5kQ0asSExPx4MEDmJubY+zYsXp9BIGoKjGqgkefevfujfj4eGRmZuLXX3/F6NGjce7cuXLXg583b16pUaPs7Gy4u7vD399f46UZlUolIiMj0b9/f8jlcp37UVWYYr9Nqc/79u3DF198gfz8fFy6dAkBAQEIDAzUut/p6enYsWMHXrx4ATMzM3Tq1AlOTk4iZy0eU/pZv0qXfpeMlBOR/jRt2hQjRoyAubl5pex3IwgCzp07h7Zt25rUayFVPUZV8Dg7O0MmkyEtLa3U8bS0tNcubevq6lqh9jY2NmjUqBEaNWqETp06oXHjxlizZk2p6XMlLCwsyt1oSy6Xa/0PWpdrqzJT7Lcx97mgoAARERHo27ev1s/l/fHHHxg9ejSKi4sxcOBAbN68GSdPntS630VFRdi2bRtycnLg4OCAMWPGiL6Utb4Y889an7Tptyl+n4gMwdvbu1Luo1KpsHfvXly+fBl3795FcHBwlRiZJ9OkVcGjUChw5swZ3LhxA9nZ2bC1tUWLFi3QtWtXnXbkNTc3R9u2bREVFYWgoCAAL/9BRUVFYebMmeVe07lzZ0RFRWH27NnqY5GRkejcufMb76VSqUo9p0NU3eXn52Pz5s14/Pgxnj59ivfee0+rN6cmTZpg4cKFSEpKwsqVK9WbymnLzMwMgwYNwsWLFzF8+HBOwSAiMnIqlQoRERG4cuUKJBIJWrVqxWKHjJpGBY8gCFiyZAm+/vprPHv2rNQvOhKJBDVr1sQnn3yCjz76SOu/+HPmzEFISAjatWuHDh06YOnSpcjNzcWUKVMAvNwAtW7duggNDQUAfPDBB+jZsye+++47DBo0CNu2bcPFixexatUqAEBubi6++uorDB06FG5ubsjMzMTy5cuRnJyMUaNGaZUjUVWTl5eHTZs2ITU1FdbW1hg+fLhOb06fffYZgJf/7sV4GL1Zs2Zo2rQp3zCJiPRMoVAgMTFRp73Wnj17hj/++ANSqRQjRoxAixYtRMyQSHwaFTzjx4/Htm3b0LhxY8yaNQu+vr6wtbXFixcvcPnyZYSFheHTTz9FfHw8tmzZolVCY8aMQUZGBhYuXIjU1FT4+fnh0KFD6oUJkpKSIJX+bzXtLl26ICwsDAsWLMD8+fPRuHFjREREqId0ZTIZbt68iQ0bNiAzMxNOTk5o3749Tp06xY0VyWTIZDKYmZnBxsYGkyZN0nlutz4KExY7RET6pVKpEBISggMHDmD37t3o16+fVnGcnJwwceJEvHjxAs2aNRM5SyLxVbjg2bRpE7Zt24aPPvoIoaGhkMlkpc4HBQXhs88+w/z58/Htt99iwIABmDBhglZJzZw587VT2I4fP17m2KhRo147WmNpaYldu3ZplQdRdWFhYYHx48cjNzfXqBcDICIi/RAEAR988AHCw8Mhl8uhUql0ile3bl2RMiPSvwpvPPrrr7+iZ8+e+Oabb8oUO+pgUin+7//+Dz179lRPKSMi42Bpaclih4jIRG3evBnLli2DRCLBhg0b4O/vb+iUiCpNhQuehIQEjBgxokJthw8fjoSEBK2TIqLqQ9dFDYiI6H+Sk5O12tdq9OjRGD16NH744QcEBwfrITMi41XhgkepVMLS0rJCbS0sLFBUVKR1UkRkOIcPH8bTp09FiZWamooVK1YgJSVFlHhERKbs6dOn2LJlC1avXo1nz55pdK2FhQW2bduGWbNm6Sk7IuNV4YKnUaNGOHnyZIXanjp1Cg0bNtQ6KSIyjBUrVmDgwIEYOnQo8vPzdYp19epVrFmzBhkZGThy5IhIGRIRmaaCggJs3boV+fn5sLa2Ro0aNTSOwcVhyFRVuOAZOXIktm7digMHDryx3YEDB7B161Yu+UxUhQiCgPnz5+Pvf/87VCoVmjdvrtNGkTdv3sTOnTtRVFQELy8vjB49WsRsiYhMz+HDh5GZmQlbW1uMGTNGr5v5KpVKpKen6y0+UWWrcMEzd+5cNG3aFEFBQXjnnXdw6tQpZGdnQxAEZGdn4/Tp03jnnXcQFBSEpk2bYu7cufrMm4hekZ6ejvPnz2t9fWZmJjZu3AgAWLRoEVatWgUzM632JQYANG7cGPXr10fXrl0xbtw4WFlZaR2LiIiAPn36wNPTE2PHjoWtra3e7qNUKrF161asW7eO05Gp2qjwbzTW1tY4evQoJk2ahNWrV2PNmjVl2giCgH79+mHjxo3cLZ2okqSmpmLTpk3Iy8uDhYUFfH19NY5Rq1Yt/Pbbb4iNjcXkyZN1zkkmk2HixImvXdGRiIg0Y2tri0mTJul1WlphYSG2bt2K+/fvw9zcXJSNpYmMgUYf4dauXRuHDh3CuXPnsG/fPly/fh0vXryAra0tmjdvjsGDB6Nz5876ypWI/iQlJQWbNm1Cfn4+3Nzc0KRJE61j+fj4wMfHR7TcWOwQEYlL38/gnDx5Ul3sTJgwAe7u7nq9H1Fl0WrOSseOHdGxY0excyEiDT18+BD5+fmoW7cuJkyYUOGVFImIqHrJz8/Hhg0b8Le//U3rwqhnz554+vQpunTpgnr16omcIZHhaD9J/xXXrl3DyZMnkZOTA19fX25mRVRJOnToAEtLSzRt2hQWFhaGToeIiAygqKgIwcHB2LNnD65fv44ff/xRqzhyuZyLzFC1VOGCR6VSYd68eQgLC4OZmRkmT56Mzz//HHPmzMEPP/yg3lxQIpGga9euOHToEJ/jIaoErVq1MnQKRERkIIIg4N1338WePXtgYWFR4U3iiUxJhQueFStW4Ntvv0X79u3h4uKCf//738jIyMDKlSsxY8YM9O3bF0VFRdi7dy82bdqExYsXIzQ0VJ+5E5EBXLlyBbdv38Zbb73FPR2IiAwsLi4O69evh1QqxdatW9GzZ09Dp0RkdCpc8KxevRqDBg3Cvn37AADLly/H+++/jxkzZpQaOh0xYgRyc3OxY8cOFjxERkClUmHBggXo0qULBg8erFOcyMhInD17FsDLzYg5ukREJJ6cnByNNxRt27Ytdu/ejYyMDLz11lt6yoyoaqvwPjx3797FwIED1V8PHDgQgiCgT58+Zdr269cPSUlJ4mRIRForLCxESEgIQkNDMWbMGJ32VNi1a5e62OnWrRu8vb3FSpOIyOQlJyfjhx9+wJkzZ9SPCVTUkCFDMHXqVD1lRlT1VXiE58WLF7C3t1d/bWdnV+r/r7K1tUVRUZEI6RGRtvLz8zF06FD8/vvvkMlkWL58Odzc3LSO16ZNGyQmJmLo0KFo0aKFiJkSEZm2wsJC7NixA0VFRXj48KFe7yOXyzkdmUxOhUd4iKjy3blzBwqFQqtrLS0tUa9ePdjY2GD//v06byjasGFDzJ49m8UOEZGIioqKcO/ePeTm5qJ27dp6ez4yNzcXa9aswfHjx0WPTWTsNFqW+uDBg0hNTQUA5OXlQSKRYPv27YiPjy/VLjY2VrQEiUzVlStXsHv3btSrVw8TJkyAubm5RtdLJBKsWrUKH3/8sWhFCvf5ISISl0wmg4ODA6RSKYKDg/WyxUBOTg42btyIjIwM5OXloWPHjlxJl0yKRgVPWFgYwsLCSh375Zdfym3L4VIi7cXHx2Pv3r0QBAFOTk4wM9Nuyyy5XM4RGSIiIyaRSODi4oLg4GDY2tqKHr+oqEhd7Nja2iIkJITFDpmcCv8Wde/ePX3mQUT/X2FhIaKioiAIAtq0aYPBgwfzAwQiompOXyPoZmZm6NixI06dOoVJkybB0dFRL/chMmYVLnjq16+vzzyI6P8zNzfHxIkTceXKFfTp04fFDhGRCcvNzcW7776Lr776Ch4eHlrFaNu2LXx8fDSeGk1UXXDRAiIjVLt2bfTt27dSih2VSoUjR45wKXkiIiOjVCoxcuRIbN68GUOGDIFKpdI6FosdMmUVHuEpb7+dEhKJBJaWlqhfvz4GDhyo0+aGRFQxhYWFEARBpwdcSzYJvn//Pq5cuYJZs2bxTZGIyAioVCpMmTIFhw4dgpWVFVauXAmplJ9TE2mjwgVPenr6Gz9tzsvLQ2RkJH755RcEBARgz549kMvloiRJRKVlZWVhxIgRcHZ2RlhYmFZvgllZWVi3bh2ysrJgbm6OgQMHstghIjISz58/x6VLl2BmZoadO3eic+fOhk6JqMqqcMFz9erVv2yTn5+PX375BXPmzME333yDf/7znzolR0RlJScnY+DAgUhISECNGjVw69YtNG/eXOM4tra2cHZ2hkwmw9ixY1GrVi09ZEtERAAgCIJG05QdHR1x6tQpnD17FgMGDNBjZkTVn6hjo1ZWVpg9ezbGjh1bZvlqItJdcXExAgICkJCQAFdXV5w4cUKrYgcApFIpRowYgenTp7PYISLSoxs3bmDdunXIycnR6DpHR0cMHDhQT1kRmQ69TAbt2rUrl7Emeg1BEPDkyROtrpXJZPj+++/h7e2NmJgYtGnTRqdcrKysuJko0Z+cPHkSQ4YMQZ06dSCRSBAREVHqvCAIWLhwIdzc3GBlZYV+/frh9u3bhkmWjF5qaip2796Nhw8f4sKFC6LHz8zMxJYtW5CXlyd6bKLqQi8FT15entYbJRJVZ4Ig4NChQ/jll1/w4MEDrWL4+/sjPj4enp6e4iZHRABeLubh6+uL5cuXl3v+m2++wY8//oiVK1fi3LlzsLGxQUBAAAoKCio5UzJ2OTk52Lp1K5RKJRo2bIiePXuKGj8jIwMbNmxAYmIiDh06JGpsoupE9KpEEATs3bsXPj4+YocmqtIEQcCBAwcQGxsLAHj69KnW+1vJZDIxUyOiVwwYMOC1z0wIgoClS5diwYIFGDZsGABg48aNcHFxQUREBMaOHVuZqZKRy8/Ph0QigZOTE0aOHCnqKmslxU5ubi5cXFwQEBAgWmyi6qbCBc/Tp0/feD4/Px+3bt3CihUrEB0djc2bN+ucHFF1EhcXpy52hg0bBj8/P8MmREQau3fvHlJTU9GvXz/1MXt7e3Ts2BExMTEseKiUWrVqYfr06VAoFLCyshI1tpmZGWQyGVxdXTFx4kRYW1uLGp+oOqlwwePs7Fyh1UXkcjkWL16M4OBgnRIjqm78/Pxw9+5dNG3aFK1atdLrvXJzc3H27Fn07t2b+zYQiSg1NRUA4OLiUuq4i4uL+lx5FAoFFAqF+uvs7GwALzeWVCqVGuVQ0l7T66qa6tJPc3NzmJubl9uPoqIiCIIAQPN+1qhRAxMmTICFhQXkcrnRf5+qy8/zr5hKPwHD91WT+1a44Fm4cOEbC56SjUf79u3LFZ+IyiGTyTBy5EiNliXVxuPHjxEeHo7s7GxIJJI3bhpMRJUjNDQUixYtKnP8yJEjWn8yHxkZqWtaVUJ17WdeXh4WLlyIgIAA9O/fv9r288/Yz+rHUH3VZKGOChc8X3zxhTa5ENEr/qrYefToEZ48eQJfX1+t4l+7dg27d+9GcXExnJyc+CwdkchcXV0BAGlpaXBzc1MfT0tLe+M01Xnz5mHOnDnqr7Ozs+Hu7g5/f3/Y2dlplINSqURkZCT69+9frTf4rs79LCgowNChQ5GYmIisrCx07twZb731VrXr56uq88/zVabST8DwfS0ZKa8InRYtyM3NxYsXL+Ds7MxV2Yh0dO3aNQQGBqKwsBAxMTFo2LChxjHs7OwgCAKaNGmCt956i0tOE4msQYMGcHV1RVRUlLrAyc7Oxrlz5/Dee++99joLCwtYWFiUOS6Xy7X+RUGXa6uS6tbP4uJiTJkyBcePH0eNGjWwb98+pKamVrt+vg77Wf0Yqq+a3FPjyf0PHjzAzJkzUb9+fdjZ2aFu3bqwtLSEp6cn/vGPf2i91C6RKTtx4gS6du2KR48ewdHRUetV2Nzd3TF16lSMHTuWxQ6RlnJychAfH4/4+HgALxcqiI+PR1JSEiQSCWbPno0vv/wSe/fuxZUrVzBp0iTUqVMHQUFBBs2bqgapVIpmzZrB3Nwce/bs0Xk/NSL6axoVPPv27UOrVq3w888/QyaTYciQIRg3bhwGDx4MqVSKJUuWwM/PDwcOHFBfs2DBAtGTJqpuvv76a2RlZaFr1644c+aM1stVA0DdunX1/pwQUXV28eJFtG7dGq1btwYAzJkzB61bt8bChQsBAP/4xz8wa9YsvPPOO2jfvj1ycnJw6NAhfshAFSKRSPDll1/i2rVrfMaSqJJUeB7ajRs3MHr0aDRo0AC//PILunfvXqbNqVOn8O6772LMmDG4ePEiQkNDsXnzZnz55ZeiJk1kjJRKpXq1HU1t3boVX331FRYtWiT60qVEpJlevXq98d+yRCLBv/71L/zrX/+qxKzIWF24cAE2NjZo0aKFRtc1atTojeeTkpJQUFCAJk2a6JIeEUGDguff//43nJyccPr0aTg6Opbbpnv37jh16hRatWqFtm3bQqFQIDQ0VLRkiYyVQqHA1q1bUVBQoFXRY29vj2+++UYPmRERkb7cuXMHv/32GwRBwLRp01CvXj1R4t6/fx9hYWFQqVQICQmBu7u7KHGJTFWFp7QdPXoU06ZNe22xU8LR0RFTp05Ffn4+1q9fj3/84x86J0lkzAoKCrB582Y8evQIT58+RVZWlqFTIiIiPcvMzMT27dshCAL8/PxQt25dUeLeu3cPW7ZsgVKphKenp3plQCLSXoVHeJ48eQJPT88KtW3QoAFkMhkmTJigbV5EVYIgCNiyZQsePXoES0tLeHh4wMHBQS/3Sk5ORmFhIRo0aKCX+EREVHFXr16FQqGAh4cHBg0aJNqzk9euXUNRUREaNWqEMWPGcBVcIhFU+F+Rs7Mz7t27V6G29+7dQ+3atbVOiqiqkEgk6NSpE3777TeMHTsWsbGxerlPfHw89u/fD7lcjnfeeQc1a9bUy32IiKhievbsCVtbWzRr1kzUomTgwIFwdnZGu3btWOwQiaTC/5J69eqFNWvWYPbs2W+c1vb06VOsWbOGK4+QyWjZsiUaN26sl5XRVCoVDh06hAsXLgB4+ZCrtruyExGReCQSCdq2bVvuubS0NNjb22u1cp9UKkWnTp10TY+IXlHhZ3jmz5+PJ0+eoEePHoiOji63TXR0NHr27IknT55g3rx5oiVJZOzMzc1fe+7YsWNar1QokUiQk5MD4OWHDmPGjCl380IiIjIOT58+Rd++fTFw4ECNdoInIv2p8AhPixYtEBYWhkmTJqF79+7w9PSEr68vbG1t8eLFCyQkJODevXuwsrJCWFiYxsszElVH27ZtQ0hICAoLC9G8eXOMGDFCo+slEgmGDRuGtm3bwsvLS09ZEhGRGPLy8jBkyBBcu3YNz549w7Nnz2BnZ2fotIhMnkYbjw4fPhwJCQmYPn06FAoFIiIisGnTJkRERKCgoADvvPMOLl++jOHDh+uU1PLly+Hp6QlLS0t07NgR58+ff2P77du3o1mzZrC0tISPjw8OHjyoPqdUKvHJJ5/Ax8cHNjY2qFOnDiZNmoTHjx/rlCPRX1m6dCmCg4NRWFiIkSNHYtCgQVrFsbCwYLFDRFQFvP3224iOjoaDgwMOHz6s0ybSRCQejQoeAGjYsCFWrlyJR48e4fnz53j48CGeP3+OR48eYcWKFTr/YhYeHo45c+bg888/R1xcHHx9fREQEID09PRy20dHRyM4OBjTpk3DpUuXEBQUhKCgIFy9ehXAy09b4uLi8NlnnyEuLg67du3CrVu3MHToUJ3yJPortra2AID3338f4eHh3IWdiKia+/jjj+Hp6Yn9+/fD29vb0OkQ0f+n0/IfdnZ2og/Vfv/995g+fTqmTJkCAFi5ciUOHDiAtWvX4tNPPy3T/ocffkBgYCA+/vhjAMDixYsRGRmJZcuWYeXKlbC3t0dkZGSpa5YtW4YOHTogKSkJHh4eouZPVGLatGlo3rw5OnfurJcFDYiIyLi0bt0at27deuNznQ8fPkTdunUhlWr8mTMRacmo/rUVFhYiNjYW/fr1Ux+TSqXo168fYmJiyr0mJiamVHsACAgIeG17AMjKyoJEItHbfilUfWRmZmL9+vVaP3japUsXFjtERFXU5cuXoVQqNbrmTcXOlStXsG7dOuzduxeCIOiaHhFVkFEt8J6ZmYni4mK4uLiUOu7i4oKbN2+We01qamq57VNTU8ttX1BQgE8++QTBwcGvHZ1SKBRQKBTqr0t+2VUqlRq/8JW01/S6qq469DsjIwNhYWHIzc3FwYMH/3LBAW36/Mcff8DLywsymUynXA2pOvysNWWKfQZ067epfa+o6ouLi8O+fftw7tw5TJ06Vec9cRISEhAREQFBECCRSNT/JyL9M6qCR9+USiVGjx4NQRCwYsWK17YLDQ3FokWLyhw/cuSI1nug/Hlanamoqv3Oz89HYmIiiouLYWlpCblcXmoxjDepSJ9VKhWSk5Px5MkTODs7o169erqmbHBV9WetC1PsM6Bdv/Py8vSQCZF+PHjwAAcOHAAANGnSROdiJycnB/v27YMgCGjTpg0GDx7MYoeoEhlVwePs7AyZTIa0tLRSx9PS0uDq6lruNa6urhVqX1LsPHjwAEePHn3js0fz5s3DnDlz1F9nZ2fD3d0d/v7+Gj+zpFQqERkZif79+0Mul2t0bVVW1fudk5ODjIwMmJubIzg4GFZWVn95TUX7nJOTg127duHJkycAXi753rVr1yr75lfVf9baMMU+A7r1m/uRUFWhUqmwd+9eqFQqtGjRAj179tQ5Zo0aNTBq1CjcuXMHgYGBVfb1nqiqMqqCx9zcHG3btkVUVBSCgoIAvHzhiYqKwsyZM8u9pnPnzoiKisLs2bPVxyIjI9G5c2f11yXFzu3bt3Hs2DE4OTm9MQ8LC4tyN3eUy+Va/3Kjy7VVWVXtd82aNTF58mTI5fLXrq5WUFBQ7rm/6nNBQQFSU1NhYWGB4cOHo0mTJqLlbUhV9WetC1PsM6Bdv03x+0RVk1QqRXBwMI4fP45hw4aJVpw0adKk2rzeE1U1RlXwAMCcOXMQEhKCdu3aoUOHDli6dClyc3PVq7ZNmjQJdevWRWhoKADggw8+QM+ePfHdd99h0KBB2LZtGy5evIhVq1YBeFnsjBw5EnFxcdi/fz+Ki4vVz/c4Ojq+8eFCMm0ly0r/mSAIWLJkCdavX49Tp07B0dFRo7hubm4YOXIknJ2d/7L4JiKiyufs7IyRI0eWOX7//n1kZmaiXbt2BsiKiLRldAXPmDFjkJGRgYULFyI1NRV+fn44dOiQemGCpKSkUks5dunSBWFhYViwYAHmz5+Pxo0bIyIiQr3+fXJyMvbu3QsA8PPzK3WvY8eOoVevXpXSL6oeiouL8eGHH+Knn34CAGzbtg1///vfNY7TtGlTsVMjIiI9ysjIQEBAAB4/fowDBw6gR48ehk6JiCrI6AoeAJg5c+Zrp7AdP368zLFRo0Zh1KhR5bb39PTk0o8kmnnz5qmLne+//16rYoeIiKqWFy9eYODAgfjjjz/g4eGh8ybrRFS5jGofHiJjN3PmTDRs2BDh4eH48MMPDZ0OERFVgq+//hoXL16Es7Mzjhw5grp16xo6JSLSgFGO8BAZKw8PD9y4cYPPfhERmZDPPvsMjx8/xnvvvffaKcmvW8iGiAyPIzxkkm7evImEhAStrn1TsaNSqZCSkqJtWkREZIQsLCywdu1atG/fvtzzp06dwooVK/Ds2bNKzoyIKoIFD5mca9euYfv27YiIiEBSUpJocXNycnDnzh1s2bIFGRkZosUlIiJxCYIg2uv0iRMncPToUWRnZ+OPP/4QJSYRiYsFD5mUK1euYOfOnVCpVPDx8UG9evVEifvw4UOsXbsWubm5kEgkePHihShxiYhIfMePH8cvv/yi9Uh/idjYWPViSn369EHHjh1FyI6IxMZneMikpKSkQBAE+Pn5YciQIaWWONfFpUuXkJOTA0tLS0yePFm9jDoRERmXq1ev4uTJkwBeTkPWRfPmzXHhwgX4+Piga9euYqRHRHrAgodMSv/+/VGnTh20bNmyzO7ZxcXFOHv2rFZvWgMGDIClpSVycnI03oiUiIgqR3p6Ovbs2QPg5T5+f96fT1PW1tZ4++23YWbGX6eIjBmntJFJkUgk8Pb2LlPs5OfnY9SoUejZsyf279+vcVy5XI7evXtDJpOJlSoREYnM2dkZbdq0QePGjdG3b19RYrLYITJ+/FdKJu/JkycYOnQooqOjYW5ujoKCAkOnREREeiCVSjFgwAAUFxeXmtJ8+/ZtbN26FZ999lmZD8SIqOpjwUMmb+PGjYiOjoaDgwP27NmDHj16GDolIiLSo1dH41NSUuDv74/79+9DKpViwYIFBsyMiPSBBQ+ZvNmzZyM1NRWTJk1Cy5YtDZ0OERFVkufPnyMwMBD379+Hl5cXpk+fbuiUiEgPWPCQyZNIJPj6669fe/7FixewtLSEXC6vxKyIiEjfzpw5g2vXrsHV1RVHjhzhCptE1RQXLaBqQxAEXL58GcXFxaLFTEpKwqpVq3DgwAEIgiBaXCIiMrxBgwYhIiIChw4dQsOGDUudEwQB+/btQ2xsrIGyIyKxcISHqgVBEBAZGYmYmBjcvHkTo0eP1unBU0EQEBsbi99++w0qlQopKSlQKBSwtLQUMWsiIjK0wYMHlzkmCAL27t2L+Ph4xMfHo2HDhqhZs6YBsiMiMbDgoSpPEAQcOnQI58+fBwA0aNBA51V2cnJyEBkZCZVKhZYtW2Lo0KEwNzcXI10iItKj4uJiSCQSrTeWFgQBe/bsweXLlyGRSBAUFMRih6iKY8FDVd6zZ88QHx8P4OUndW3bttU5pq2tLd566y1kZmaia9euXKaUiKgKKBmZKSgowPDhw2FhYaFxDIlEAnt7e0gkEgwfPhze3t56yJSIKhMLHqryHB0dMW7cODx79qzMrtmZmZn497//jdDQUI3f+Jo1ayZilkREpG9nzpxBQkICJBIJUlNTUb9+fa3i9OrVCy1btkTt2rVFzpCIDIEFD1UL9evXL/PGdu/ePQQGBuKPP/5Abm4ufvnlFwNlR0RE+nbr1i1ERUUBAAIDA7UudoCXozwsdoiqDxY8VC3FxcVh4MCBSEtLg4eHB2bPnm3olIiISI8sLCxgZWWFFi1aoH379urjgiBwWjKRieOy1FRt5ebmwtfXFzExMWjevLmh0yEiIj3y9PTE3/72NwwYMEBd4Fy/fh3dunVDUlKSgbMjIkNiwUPVUps2bRAZGYmTJ0+iTp06Zc7n5+cbICsiItIne3t7yGQyAMDDhw8REBCA6OhofPjhhwbOjIgMiQUPVVudOnWCnZ1dqWOCIODChQv44YcfkJKSYqDMiIhIn548eQJ/f388evQIzZo1w6pVqwydEhEZEAseMnpKpRKZmZk6xykqKsLevXtx8OBBKBQKJCQkiJAdEREZm4KCAkilUtSrVw+HDx+Gk5OT+lxRURGOHTsGpVJpwAyJqDJx0QIyaoWFhdi6dSvS09MREhKi06o5586dQ3x8PCQSCfr27YsuXbqImCkRERmLunXr4tSpU8jMzISHh4f6uFKpRHh4OO7cuYP09HSMGTPGgFkSUWVhwUNGS6FQICwsDElJSTA3N0dBQYFO8Tp16oSHDx+iffv28PLyEilLIiIyRo6OjnB0dFR/rVQqsXXrVty7dw9yuRwdO3Y0YHZEVJlY8JDROnbsGJKSkmBhYYEJEyagXr166nNFRUUwM9Psr69MJsPYsWPFTpOIiKqAZ8+eISUlBebm5hg/fnypkR8iqt74DA8Zrd69e6NJkyaYNGlSqWLn4sWLaNasGeLi4gyYHRERVabi4mLs2LED9+/f1+r62rVrY+LEiZgwYQKLHSITw4KHjJaFhQWCg4NLLSv922+/oVevXrhz5w7++c9/GjA7IiKqTIcOHcK1a9cQHh4OhUKhVYw6derA3d1d5MyIyNhxShtVGceOHcOQIUNQXFyM/v37Izw83NApERFRJbhw4QIuXrwIAAgKCoKFhYWBMyKiqoQFD1UZXbp0QY8ePVCvXj2sXr0a5ubm6nOCIEClUqk3nCMioupBEATcvXsXANC3b180bdoUAPD8+XM4ODgYMDMiqio4pY2qDAsLC+zfvx8bNmwoVeyU7K+zY8cOCIJgwAyJiEhsEokEo0aNwogRI9C1a1cAwOXLl+Hl5YXVq1cbODsiqgpY8FCVYm1tDYlEov46KysL69atQ3x8PG7duoXk5GQDZkdERPoglUrh7e0NiUSCu3fvIiAgAE+fPsWmTZtQXFxs6PSIyMix4CGD0XVfHZVKhc2bN+Px48ewsrIqs3Q1ERFVL8+fP4e/vz/S0tLQqlUr7NmzRz2VOS8vDykpKQbOkIiMEQseMoinT59ixYoVOH36tNYxpFIpAgMD4ebmhnfeeQcNGzYUMUMiIjI29vb2CA4ORoMGDXDo0CH1Mzy5ubnYuHEjNmzYgMePHxs2SSIyOix4qNJlZmZi3bp1yM7OxuXLl6FUKrWO5eXlhenTp/PBVSIiEyCRSLB48WJcunQJbm5uAP5X7KSlpUEul5d6xpOICGDBQ5WsoKAAGzZsQE5ODmrVqoWQkBDI5XIAwKVLl7RadODVZ3qIiPTtiy++gEQiKfVfs2bNDJ2WSbG3t1f/+cSJE0hPT0eNGjUQEhICZ2dnA2ZGRMaIBQ9VKktLS3Tt2hWurq4ICQlBjRo1AACrV69G+/btsWDBAgNnSET011q2bImUlBT1f7pMzyXd9O/fH76+vpg8eTKLHSIqF/fhoUrXqVMntGvXDmZmZhAEAYsWLcKiRYsAAMnJyVCpVJBKWYsTkfEyMzODq6urodOoNhQKBS5cuIAuXbpo/Povl8sRFBSkn8SIqFpgwUMGYWb28q9eQkICvvzySwDAP//5TyxevFg9RU0QBE5XIyKjdPv2bdSpUweWlpbo3LkzQkND4eHh8dr2CoUCCoVC/XV2djYAQKlUavwcY0l7XZ5/NCYqlQo7duxAYmIi0tPTMWTIEADVr5+vw35WL6bST8DwfdXkvix4yKB8fX2xcuVKFBUV4d1331Ufz8rKwo4dO9C/f/83/hJBRFTZOnbsiPXr16Np06ZISUnBokWL0L17d1y9ehW2trblXhMaGqoeyX7VkSNHYG1trVUekZGRWl1nbB4/foz09HRIJBIoFAocPHiw1Pnq0s+/wn5WL6bST8Bwfc3Ly6twWxY8ZHBvv/12qa/v37+P7du3Iy8vDwcOHMC7777LkR4iMhoDBgxQ/7lVq1bo2LEj6tevj//+97+YNm1audfMmzcPc+bMUX+dnZ0Nd3d3+Pv7w87OTqP7K5VKREZGon///upFX6qq58+f45dffgEADB06FC1btkRcXBxatGgBmUxWbfr5JtXp5/km7Gf1Y+i+loyUVwQLHjIqSUlJ2LhxIwRBgKurK8aMGcNih4iMmoODA5o0aYLExMTXtrGwsICFhUWZ43K5XOtfFHS51ljUqlULkydPRlJSEvz8/HD+/Hn07dsXHTp0wI4dOwBUj35WBPtZvZhKPwHD9VWTe7LgIdFps7R0iXr16qFhw4awsbHB4MGDTebFgoiqrpycHNy5cwcTJ040dCpVUr169VCvXj3cvHkTAwcORG5uLszMzNQFYl5eHuzs7PjhFxFpjUthkaj++OMPrF+/HgUFBVpdL5VKMWbMGAQFBbHYISKj9NFHH+HEiRO4f/8+oqOj8dZbb0EmkyE4ONjQqVVZKpUKo0ePxpMnT9CuXTvs3LkT5ubmUCgUWLNmDY4eParTh2lEZNqMruBZvnw5PD09YWlpiY4dO+L8+fNvbL99+3Y0a9YMlpaW8PHxKfOw465du+Dv7w8nJydIJBLEx8frMXvTdvPmTYSHhyMpKQnnzp3TOo5cLucneURktB49eoTg4GA0bdoUo0ePhpOTE86ePYtatWoZOrUqSyqVYsOGDejevTsOHjwIW1tbPHnyBLdv38aLFy9w8+ZNFBYWGjpNIqqijKrgCQ8Px5w5c/D5558jLi4Ovr6+CAgIQHp6ernto6OjERwcjGnTpuHSpUsICgpCUFAQrl69qm6Tm5uLbt264euvv66sbpikmzdvYvv27VCpVGjZsiW6desGQRDw3XffISkpydDpERGJZtu2bXj8+DEUCgUePXqEbdu2wcvLy9BpVXmtW7fGiRMnUKtWLRQWFmLLli0oKipSP+dT3jNQREQVYVQFz/fff4/p06djypQpaNGiBVauXAlra2usXbu23PY//PADAgMD8fHHH6N58+ZYvHgx2rRpg2XLlqnbTJw4EQsXLkS/fv0qqxsmqVatWrC2toaPjw+GDx8OlUqFZcuWYd68eRgwYIDWU9yIiMh0lIzum5ubo0ePHrCyssK4ceNgY2Nj4MyIqCozmkULCgsLERsbi3nz5qmPSaVS9OvXDzExMeVeExMTU2qZTwAICAhARESETrlwgzjN2dnZISQkBLa2tsjKysKYMWMQFRUFqVSKmTNnQiaTVfvvgan8rP/MFPttin0GdOu3qX2vSHd+fn5ITk5msUNEOjOagiczMxPFxcVwcXEpddzFxQU3b94s95rU1NRy26empuqUCzeI001OTg5u3LgBc3NzfPTRR6hTpw4OHjyIFy9e4OnTp/Dw8KjWz+iY0s/6VabYb1PsM6BdvzXZII6ql+zsbBQWFsLZ2Vnja6vzewURVR6jKXiMCTeI0127du2wd+9ezJgxA2ZmZrhw4QIuX74MQRDQrl07tG/f3tApis5Uf9am2G9T7DOgW7812SCOqg+lUolt27bh6dOnGDt2LDw9PQ2dEhGZIKMpeJydnSGTyZCWllbqeFpaGlxdXcu9xtXVVaP2FcUN4nTXsGFDNGnSBHK5HFFRUepV23x9fdG+fftq/b0wtZ91CVPstyn2GdCu36b4fTJ1giBgz549SElJgZWVFezt7Q2dEhGZKKNZtMDc3Bxt27ZFVFSU+phKpUJUVBQ6d+5c7jWdO3cu1R54OdXide3JMFq0aAEzMzMEBgZi2LBh/MWHiMgExMbG4tq1a+r91WJiYnDhwgVDp0VEJshoRngAYM6cOQgJCUG7du3QoUMHLF26FLm5uZgyZQoAYNKkSahbty5CQ0MBAB988AF69uyJ7777DoMGDcK2bdtw8eJFrFq1Sh3z6dOnSEpKwuPHjwEAt27dAvBydEjXkSCqGA8PD8yePZsPnhIRmZBWrVrh3r178PLywsOHDzFixAiYmZnhzJkzaNWqFQRB4DM6RFQpjGaEBwDGjBmDJUuWYOHChfDz80N8fDwOHTqkXpggKSkJKSkp6vZdunRBWFgYVq1aBV9fX+zYsQMRERHw9vZWt9m7dy9at26NQYMGAQDGjh2L1q1bY+XKlZXbuSpMEAScPHkSd+/e1ToGix0iItNibm6OkSNHQi6XY8iQISgoKECvXr3QvHlzPHz4EOvWreNiFkRUKYxqhAcAZs6ciZkzZ5Z77vjx42WOjRo1CqNGjXptvMmTJ2Py5MkiZWd6BEHA0aNHcfr0aZiZmWHmzJmwtbWFVGpUtTIRERkhiUSCb7/9Fs+fP0fXrl0RHh6OlJQUbNmyBYWFhTh27Jj6A0kiIn3hb630WoIgIDIyEqdPnwYA9OnTB1KpFAMHDsTq1asNnB0REVUFq1evxqeffop9+/YhMzMTmzdvRmFhIRo0aAB/f39Dp0dEJsDoRnjIeAiCoF5KdsCAAfDw8ECvXr0QFxeHmJgYDB8+HI6OjgbOkoiIjJm5ubn62VuJRAJra2s4OztjzJgxXMSGiCoFCx56LalUirfeegt+fn6oVasWWrdujXv37qFWrVo4cOAAatasiejoaLi6uqJhw4aGTpeIiIycg4MDpkyZAhsbG5iZ8VcQIqocnNJGbySTydCoUSPY29tj7Nix8PLyQkxMDPz8/LBr1y5ERkZix44dyM3NNXSqRERUBdjb27PYIaJKxYKHKuyrr77CxYsX4ebmhjVr1uDq1auQSqXo1asXrK2tDZ0eERFVsry8PAiCYOg0iIjeiB+xUIVJJBI4ODhAEATUrFkTOTk5GDVqFOrXr2/o1IiIqJLl5ubi119/RcOGDTFo0CDIZDJDp0REVC4WPKQxiUSCoKAgKBQK2NnZGTodIiKqZMXFxfjvf/+LrKws3L9/H4WFhbCysjJ0WkRE5eKUNtKKhYUFix0iIhN14MABJCUlwcLCAjVq1MAXX3zBqW1EZLQ4wmPCioqKEBcXh/bt20MikRg6HSIiqiIaNWqEa9euoW7dunj77bdRWFgIb29vtG3bFjKZDE2bNjV0ikREaix4TJRSqcS2bdtw9+5dPHv2DK1atYKVlRUcHBwMnRoRERm5Fi1a4NmzZwgMDERhYSFGjBgBPz8/7NixA1KpFNOmTYObm5uh0yQiAsApbSapsLAQYWFhuHv3LuRyOczMzNC5c2cMHz4cCoXC0OkREVEV8ODBA+Tn56N3796YP38+du/eDUEQ4O3tDRcXF0OnR0SkxhEeE/T48WMkJSXB3NwcTZo0wejRo/H06VOYm5sjJSUFDx48QLdu3bjiDhERvda4cePg4uKC9u3b48SJE/+vvTsPi6pu3wB+D8sMiizubIqImKmoSUGWW4kiWYaWBKmhGWWvtLymmaWittD+2uKbWamlkZqolWaJ26sZYSZmWBoShhsgKEsi6zy/P7qYXyMgizOcmTP357q4lHO+58xzc2C+88ycOQMRwYABA3DXXXfBzo7PpxKR5WDDY4O6deuGe++9FzqdDiNGjMCFCxcQEhKCNWvWYNu2bcjLy0NpaSnCw8OVLpWIiCzYiBEjAAB33nknunTpgv79+/M9oURkcfgUjI26/vrr0b17d2zYsAHR0dFYtWoVkpKSkJeXB2dnZ/Tp00fpEomIyEpoNBoMGDCAzQ4RWSS+wmPjgoKCkJiYiHPnzqGqqgre3t6IjIzkJaeJiIiISBXY8BAAwNPTE5MnT4aXlxccHPhrQURk60SEr9gQkSrwlDYy6Nq1K5sdIiJCQUEBli9fjtzcXKVLISK6Zmx4iIiIyODy5cv47LPPkJOTg23btildDhHRNWPDo0JFRUUoLCxUugwiIrIyer0eSUlJKCgowO+//46XX34Z2dnZ+P3336HX65Uuj4ioWXj+kspcvHgRn3zyCTQaDXr27Al7e3uMHDlS6bKIiMgKlJeXo7y8HH/88QfWr1+PqqoqrFy5EgDQr18/RERE8H09RGR12PCoyIULF/Dxxx+juLgY2dnZePrpp+Ho6Ijk5GR07NgR/v7+SpdIREQWrFWrVujVqxcee+wxVFVVIS4uzrCubdu2bHaIyCqx4VGRr7/+GsXFxUhPT0dSUhJEBBMmTMCePXsgIoiNjUWHDh2ULpOIiCyYr68v/Pz8EBAQgM6dO6O6uhq33XYbhg4dqnRpRETNwoZHRSIiIrB161akp6dDRPDUU0/BxcUF5eXl8PHxgU6nU7pEIiKycD4+Pti3bx90Oh3Onj2L3NxcDB48WOmyiIiajQ2PirRp0wb33XcfIiIiMHbsWDg7O+OXX37BwIEDER4ezktOExFRo7Rr1w4AEBAQgICAAIWrISK6NnwErEI6nQ6TJ09GZWUlrrvuOvTp00fpkoiIiIiIFMHLUquYo6Mjmx0iIiIismlseIiIiIiISLXY8BAREdmQ7OxsfPfddxARpUshImoRfA+PFbl06RKcnZ0hIvwsBCIiarLCwkKsW7cOp06dMpz23KZNG6XLIiIyKzY8ViIzMxPr1q3DxYsXkZ+fjwULFsDHxweOjo5Kl0ZERFagoqICa9euxb59+7Blyxb4+Phg//79mDJliuGqbEREasRT2qxARkYGPv30U2zZsgWvv/46MjMz8cknn2Dr1q08JYGIiBrljz/+wJ49e7BlyxbceeeduHz5MkpKSnD27FmlSyMiMiu+wmPhzp8/j7Vr12Lz5s04evQoIiMj0bt3bwCAg4MDT28jIqJG0Wg02LRpEwYPHoygoCBoNBpERESgb9++SpdGRGRWbHgsXIcOHRASEoJLly4hLy8Pffr0gZ2dHe644w4MHDhQ6fKIiMhK9OzZE08//TR+/fVXeHp6YtCgQQgMDFS6LCIis2PDY+E0Gg1GjhyJ0NBQPPnkkyguLkbr1q3h4+OjdGlERGRFNBoNnn/+eVRXV0Oj0cDOjme1E5FtYMNjBTQaDTQaDTw8PODh4aF0OUREZMXs7e2VLoGIqEXx6R0iIiIiIlItNjxERERERKRabHiIiIiIiEi12PBYgKqqKmRnZyM3N1fpUoiIyIplZmaiuLhY6TKIiCwKGx6F7dixA8OHD8eCBQvwwQcf4Ny5c0qXREREVujs2bN47rnn8NZbb2Hv3r1Kl0NEZDHY8ChERPDVV19h5syZGDBgAPz8/KDX61FQUKB0aUREZGVKSkoQFxcHBwcH6PV6fPfddygpKVG6LCIii8DLUitk3759iI+Ph5eXFzp27AitVovJkyfz83WIiKjJ5s+fD2dnZ/To0QMigujoaLi4uChdFhGRReArPArx8/PDmDFjcPHiRfj7++Oxxx5js0NERE1WUlKCNWvW4NSpU9Dr9YiJiYGfn5/SZRERWQw2PArp0qUL5syZg9TUVEyaNAlt2rRRuiQiIrJCLi4uSE5ORt++ffHEE0+w2SEiuoJFNjxLly5Ft27d4OTkhJCQEBw4cOCq4z///HP06tULTk5OCAwMxNdff220XkSwYMECeHp6olWrVggNDUVGRoY5IzQKmxwiIuvV1LnKnG644Qa8++67aNeunWI1EBFZKotreNatW4eZM2ciPj4ehw4dQv/+/REWFoa8vLw6x3///feIjo7GtGnTkJaWhoiICERERCA9Pd0w5tVXX8Xbb7+NZcuWITU1Fc7OzggLC0NZWVlLxSIiIhVp6lxFRETKsbiG580330RsbCymTp2K3r17Y9myZWjdujVWrFhR5/i33noLo0ePxuzZs3H99dfj+eefx8CBA/Huu+8C+PvVnSVLlmDevHm4++670a9fP3zyySc4e/YsNm/e3ILJiIhILZo6VxERkXIs6iptFRUV+OmnnzB37lzDMjs7O4SGhiIlJaXObVJSUjBz5kyjZWFhYYZmJisrCzk5OQgNDTWsd3NzQ0hICFJSUhAVFVVrn+Xl5SgvLzd8X/MhbpWVlaisrGx0nrKyMlRXVxu2tSU1eW0pty1mBmwzty1mBq4tt5p+Vs2Zq0w1r9Rs889/1Yo51YU51UfprE25XYtqePLz81FdXY3OnTsbLe/cuTOOHTtW5zY5OTl1js/JyTGsr1lW35grJSQkYNGiRbWWb9++Ha1bt25UloKCApw8eRLt2rVDt27dkJyc3Kjt1MYWc9tiZsA2c9tiZqB5uUtLS81QiTKaM1eZYl65kq38/jGnujCn+iiVtSnzikU1PJZi7ty5Rq8aFRcXo0uXLhg1ahRcXV0b3H7fvn04cOAAtFotSkpKUFlZiTFjxsDR0dGcZVuUyspKJCcnY+TIkTaT2xYzA7aZ2xYzA9eWu+YVDVt1rfPK5cuXkZycjPDwcACwid8/W/k7Y051sZWcgPJZmzKvWFTD06FDB9jb2yM3N9doeW5uLjw8POrcxsPD46rja/7Nzc2Fp6en0ZgBAwbUuU+dTgedTldruaOjY4MH9IcffsD27dvRqlUrnD59GjExMSgoKGjUtmpki7ltMTNgm7ltMTPQvNxq+jk1Z666lnnl0qVLeO211+Dg4IDc3Fw89NBDjd5WDZhTXZhTfZTK2pTbtKiLFmi1WgQFBWHnzp2GZXq9Hjt37sSgQYPq3GbQoEFG44G/n/mqGe/n5wcPDw+jMcXFxUhNTa13n81VWVmJ999/Hxs2bMCRI0cwefJkhISEmPQ2iIhIWc2Zq5rrn83OpUuXoNVqTbp/IiJbYFGv8ADAzJkzERMTgxtvvBHBwcFYsmQJLl26hKlTpwIAHnjgAXh7eyMhIQEA8MQTT2DYsGF44403MGbMGKxduxYHDx7E8uXLAQAajQZPPvkkXnjhBQQEBMDPzw/z58+Hl5cXIiIiTF7/8OHDUVRUhPj4ePTr188m3rRGRGRrGpqrTOXjjz9GWVkZqqqqkJeXh4SEBOj1epPeBhGR2llcw3Pffffh/PnzWLBgAXJycjBgwAB88803hjeHZmdnw87u/1+YuuWWW5CYmIh58+bh2WefRUBAADZv3oy+ffsaxjz99NO4dOkSHn74YRQWFmLw4MH45ptv4OTkZNLaHR0dER0djZEjR8LLy8uk+yYiIsvR0FxlCiKCLVu24PDhwwgKCsK6detgb2/PhoeIqIksruEBgLi4OMTFxdW5bs+ePbWWTZgwARMmTKh3fxqNBosXL8bixYtNVWK9tFotmx0iIhtwtbnKFDQaDTZv3oyVK1fiwQcftJn3AxARmZpFNjxERET095NojzzyiNJlEBFZNYu6aIE1qKioULoEIiIiIiJqJDY8TfD7779jyZIlOHXqlNKlEBERERFRI7DhaYKkpCRcvnwZ3333ndKlEBERERFRI7DhaaIffvgB+/btU7oMIiJSgYsXL+LMmTNKl0FEpGpseJrgq6++wrFjx3DPPfcoXQoREalAYmIiVq9ejdOnTytdChGRarHhaQK9Xo+4uDgEBQUpXQoREalAcXExSkpK8MUXX6CoqEjpcoiIVIkNTxPExMTgoYcegru7u9KlEBGRCuTn5+Ptt99GWloajh49qnQ5RESqxIanCSZNmgQ3NzelyyAiIpVITEzE0KFDER4ejkGDBildDhGRKvGDRxtBRAz/FhcXN2nbyspKlJaWori42KY+JdsWc9tiZsA2c9tiZuDactfcd9bcn9q6mp9DcHAwwsLCMHz4cJSUlDRqW1v5/WNOdWFO9VE6a1PmFY1w9mnQ6dOn0aVLF6XLICKyeqdOnYKPj4/SZSiO8woRkWk0Zl5hw9MIer0eZ8+ehYuLCzQaTZO2LS4uRpcuXXDq1Cm4urqaqULLY4u5bTEzYJu5bTEzcG25RQQlJSXw8vKCnR3Ppua80jDmVBfmVB+lszZlXuEpbY1gZ2d3zc9Iurq6qv4Xvy62mNsWMwO2mdsWMwPNz833QP4/ziuNx5zqwpzqo2TWxs4rfJqNiIiIiIhUiw0PERERERGpFhseM9PpdIiPj4dOp1O6lBZli7ltMTNgm7ltMTNgu7ktja0cB+ZUF+ZUH2vKyosWEBERERGRavEVHiIiIiIiUi02PEREREREpFpseIiIiIiISLXY8BARERERkWqx4WmGpUuXolu3bnByckJISAgOHDhw1fGff/45evXqBScnJwQGBuLrr782Wi8iWLBgATw9PdGqVSuEhoYiIyPDnBGazNSZN27ciFGjRqF9+/bQaDQ4fPiwGatvPlPmrqysxJw5cxAYGAhnZ2d4eXnhgQcewNmzZ80do0lMfawXLlyIXr16wdnZGW3btkVoaChSU1PNGaFZTJ37n6ZPnw6NRoMlS5aYuOprY+rMU6ZMgUajMfoaPXq0OSPYpKYeN2uzcOHCWr9HvXr1Urqsa7Z3717cdddd8PLygkajwebNm43WW8NjgcZoKKda7icSEhJw0003wcXFBZ06dUJERASOHz9uNKasrAwzZsxA+/bt0aZNG9xzzz3Izc1VqOLmaUzO4cOH1zqm06dPV6jiegg1ydq1a0Wr1cqKFSvk6NGjEhsbK+7u7pKbm1vn+P3794u9vb28+uqr8uuvv8q8efPE0dFRfvnlF8OYl19+Wdzc3GTz5s3y888/y9ixY8XPz08uX77cUrGuyhyZP/nkE1m0aJF88MEHAkDS0tJaKE3jmTp3YWGhhIaGyrp16+TYsWOSkpIiwcHBEhQU1JKxrsocx/rTTz+V5ORkyczMlPT0dJk2bZq4urpKXl5eS8VqkDly19i4caP0799fvLy85D//+Y+ZkzSeOTLHxMTI6NGj5dy5c4avCxcutFQkm9DU42aN4uPjpU+fPka/R+fPn1e6rGv29ddfy3PPPScbN24UALJp0yaj9Zb+WKCxGsqplvuJsLAwWblypaSnp8vhw4fljjvukK5du8pff/1lGDN9+nTp0qWL7Ny5Uw4ePCg333yz3HLLLQpW3XSNyTls2DCJjY01OqZFRUUKVl0bG54mCg4OlhkzZhi+r66uFi8vL0lISKhzfGRkpIwZM8ZoWUhIiDzyyCMiIqLX68XDw0Nee+01w/rCwkLR6XTy2WefmSFB05k68z9lZWVZbMNjztw1Dhw4IADkzz//NE3R16glMhcVFQkA2bFjh2mKNgFz5T59+rR4e3tLenq6+Pr6WlTDY47MMTExcvfdd5ulXvpbU4+bNYqPj5f+/fsrXYZZXdkIWMNjgeaor+FR4/1EXl6eAJD//e9/IvL38XN0dJTPP//cMOa3334TAJKSkqJUmdfsypwifzc8TzzxhHJFNQJPaWuCiooK/PTTTwgNDTUss7OzQ2hoKFJSUurcJiUlxWg8AISFhRnGZ2VlIScnx2iMm5sbQkJC6t1nSzJHZmvQUrmLioqg0Wjg7u5ukrqvRUtkrqiowPLly+Hm5ob+/fubrvhrYK7cer0ekydPxuzZs9GnTx/zFN9M5jzWe/bsQadOnXDdddfh0UcfRUFBgekD2KjmHDdrlZGRAS8vL3Tv3h0TJ05Edna20iWZlaU/FjA1Nd5PFBUVAQDatWsHAPjpp59QWVlpdEx79eqFrl27WvUxvTJnjU8//RQdOnRA3759MXfuXJSWlipRXr0clC7AmuTn56O6uhqdO3c2Wt65c2ccO3aszm1ycnLqHJ+Tk2NYX7OsvjFKMkdma9ASucvKyjBnzhxER0fD1dXVNIVfA3Nm3rJlC6KiolBaWgpPT08kJyejQ4cOpg3QTObK/corr8DBwQGPP/646Yu+RubKPHr0aIwfPx5+fn7IzMzEs88+i/DwcKSkpMDe3t70QWxMc46bNQoJCcGqVatw3XXX4dy5c1i0aBGGDBmC9PR0uLi4KF2eWVj6YwFTUuP9hF6vx5NPPolbb70Vffv2BfD3MdVqtbWe0LTmY1pXTgC4//774evrCy8vLxw5cgRz5szB8ePHsXHjRgWrNcaGh0gBlZWViIyMhIjgvffeU7ocs7vttttw+PBh5Ofn44MPPkBkZCRSU1PRqVMnpUszi59++glvvfUWDh06BI1Go3Q5LSYqKsrw/8DAQPTr1w/+/v7Ys2cPRowYoWBlZE3Cw8MN/+/Xrx9CQkLg6+uL9evXY9q0aQpWRqagxvuJGTNmID09Hd99953SpZhVfTkffvhhw/8DAwPh6emJESNGIDMzE/7+/i1dZp14SlsTdOjQAfb29rWusJGbmwsPD486t/Hw8Ljq+Jp/m7LPlmSOzNbAnLlrmp0///wTycnJFvHqDmDezM7OzujRowduvvlmfPTRR3BwcMBHH31k2gDNZI7c+/btQ15eHrp27QoHBwc4ODjgzz//xFNPPYVu3bqZJUdTtNTfdffu3dGhQwecOHHi2oumZh03NXB3d0fPnj1V/Xtk6Y8FzMna7yfi4uKwZcsW7N69Gz4+PoblHh4eqKioQGFhodF4az2m9eWsS0hICABY1DFlw9MEWq0WQUFB2Llzp2GZXq/Hzp07MWjQoDq3GTRokNF4AEhOTjaM9/Pzg4eHh9GY4uJipKam1rvPlmSOzNbAXLlrmp2MjAzs2LED7du3N0+AZmjJY63X61FeXn7tRZuAOXJPnjwZR44cweHDhw1fXl5emD17Nr799lvzhWmkljrWp0+fRkFBATw9PU1TuI1rznFTg7/++guZmZmq/j2y9McC5mSt9xMigri4OGzatAm7du2Cn5+f0fqgoCA4OjoaHdPjx48jOzvbqo5pQznrUvNRIxZ1TBW+aILVWbt2reh0Olm1apX8+uuv8vDDD4u7u7vk5OSIiMjkyZPlmWeeMYzfv3+/ODg4yOuvvy6//fabxMfH13lZand3d/niiy/kyJEjcvfdd1vUpSjNkbmgoEDS0tJk69atAkDWrl0raWlpcu7cuRbPVx9T566oqJCxY8eKj4+PHD582OjyjeXl5YpkvJKpM//1118yd+5cSUlJkZMnT8rBgwdl6tSpotPpJD09XZGMdTHH7/iVLO0qbabOXFJSIrNmzZKUlBTJysqSHTt2yMCBAyUgIEDKysoUyahGDR03NXjqqadkz549kpWVJfv375fQ0FDp0KGDRV3KvjlKSkokLS1N0tLSBIC8+eabkpaWZrhKp6U/Fmisq+VU0/3Eo48+Km5ubrJnzx6j+by0tNQwZvr06dK1a1fZtWuXHDx4UAYNGiSDBg1SsOqmayjniRMnZPHixXLw4EHJysqSL774Qrp37y5Dhw5VuHJjbHia4Z133pGuXbuKVquV4OBg+eGHHwzrhg0bJjExMUbj169fLz179hStVit9+vSRrVu3Gq3X6/Uyf/586dy5s+h0OhkxYoQcP368JaI0mqkzr1y5UgDU+oqPj2+BNI1nytw1l+Cu62v37t0tlKhhpsx8+fJlGTdunHh5eYlWqxVPT08ZO3asHDhwoKXiNJqpf8evZGkNj4hpM5eWlsqoUaOkY8eO4ujoKL6+vhIbG6uqB+KW4mrHTQ3uu+8+8fT0FK1WK97e3nLffffJiRMnlC7rmu3evbvO+/+avzNreCzQGFfLqab7ifrm85UrVxrGXL58Wf71r39J27ZtpXXr1jJu3DiLemK3MRrKmZ2dLUOHDpV27dqJTqeTHj16yOzZsy3uc3g0IiLmfQ2JiIiIiIhIGXwPDxERERERqRYbHiIiIiIiUi02PEREREREpFpseIiIiIiISLXY8BARERERkWqx4SEiIiIiItViw0NERERERKrFhocIQEZGBkaNGgU3NzdoNBps3rxZ6ZJURaPRYOHChUqXQUTUYjivmBfnFWoKNjxkVVatWgWNRmP4cnBwgLe3N6ZMmYIzZ840e78xMTH45Zdf8OKLL2L16tW48cYbTVi19Xj88ceh0Whw4sSJesc899xz0Gg0OHLkSAtWRkRkHpxXzIvzClkCNjxklRYvXozVq1dj2bJlCA8Px5o1azBs2DCUlZU1eV+XL19GSkoKpk2bhri4OEyaNAk+Pj5mqNryTZw4EQCQmJhY75jPPvsMgYGB6NevX0uVRURkdpxXzIPzClkCNjxklcLDwzFp0iQ89NBD+PDDDzFr1ixkZmbiyy+/bPK+zp8/DwBwd3c3WX1lZWXQ6/Um219LCQkJQY8ePfDZZ5/VuT4lJQVZWVmGCYyISC04r5gH5xWyBGx4SBWGDBkCAMjMzDRafuzYMdx7771o164dnJyccOONNxpNXgsXLoSvry8AYPbs2dBoNOjWrZth/ZkzZ/Dggw+ic+fO0Ol06NOnD1asWGF0G3v27IFGo8HatWsxb948eHt7o3Xr1iguLgYApKamYvTo0XBzc0Pr1q0xbNgw7N+/32gfCxcuNLzkP2XKFLi7u8PNzQ1Tp05FaWlprbxr1qxBcHAwWrdujbZt22Lo0KHYvn270Zht27ZhyJAhcHZ2houLC8aMGYOjR482+LOcOHEijh07hkOHDtVal5iYCI1Gg+joaFRUVGDBggUICgqCm5sbnJ2dMWTIEOzevbvB25gyZYrRz/nKn0NdeYOCgtCqVSu0a9cOUVFROHXqlNGYjIwM3HPPPfDw8ICTkxN8fHwQFRWFoqKiBushIroS5xXOK5xX1MNB6QKITOHkyZMAgLZt2xqWHT16FLfeeiu8vb3xzDPPwNnZGevXr0dERASSkpIwbtw4jB8/Hu7u7vj3v/+N6Oho3HHHHWjTpg0AIDc3FzfffDM0Gg3i4uLQsWNHbNu2DdOmTUNxcTGefPJJoxqef/55aLVazJo1C+Xl5dBqtdi1axfCw8MRFBSE+Ph42NnZYeXKlbj99tuxb98+BAcHG+0jMjISfn5+SEhIwKFDh/Dhhx+iU6dOeOWVVwxjFi1ahIULF+KWW27B4sWLodVqkZqail27dmHUqFEAgNWrVyMmJgZhYWF45ZVXUFpaivfeew+DBw9GWlpanZNCjYkTJ2LRokVITEzEwIEDDcurq6uxfv16DBkyBF27dkV+fj4+/PBDREdHIzY2FiUlJfjoo48QFhaGAwcOYMCAAc04krW9+OKLmD9/PiIjI/HQQw/h/PnzeOeddzB06FCkpaXB3d0dFRUVCAsLQ3l5OR577DF4eHjgzJkz2LJlCwoLC+Hm5maSWojIdnBe4bzCeUVFhMiKrFy5UgDIjh075Pz583Lq1CnZsGGDdOzYUXQ6nZw6dcowdsSIERIYGChlZWWGZXq9Xm655RYJCAgwLMvKyhIA8tprrxnd1rRp08TT01Py8/ONlkdFRYmbm5uUlpaKiMju3bsFgHTv3t2wrOa2AgICJCwsTPR6vWF5aWmp+Pn5yciRIw3L4uPjBYA8+OCDRrc1btw4ad++veH7jIwMsbOzk3Hjxkl1dbXR2JrbKCkpEXd3d4mNjTVan5OTI25ubrWW1+Wmm24SHx8fo9v45ptvBIC8//77IiJSVVUl5eXlRttdvHhROnfuXCsHAImPjzd8HxMTI76+vrVut+bnUOPkyZNib28vL774otG4X375RRwcHAzL09LSBIB8/vnnDWYjIvonziucV0Q4r6gdT2kjqxQaGoqOHTuiS5cuuPfee+Hs7Iwvv/zS8KbQCxcuYNeuXYiMjERJSQny8/ORn5+PgoIChIWFISMj46pX3xERJCUl4a677oKIGLbPz89HWFgYioqKar00HxMTg1atWhm+P3z4MDIyMnD//fejoKDAsP2lS5cwYsQI7N27t9b52NOnTzf6fsiQISgoKDCcxrB582bo9XosWLAAdnbGf741L9knJyejsLAQ0dHRRnXb29sjJCSkUacGTJo0CadPn8bevXsNyxITE6HVajFhwgQAgL29PbRaLQBAr9fjwoULqKqqwo033ljnaQvNsXHjRuj1ekRGRhpl8fDwQEBAgCFLzTNt3377bZ2nahARNYTzCucVzivqxVPayCotXboUPXv2RFFREVasWIG9e/dCp9MZ1p84cQIigvnz52P+/Pl17iMvLw/e3t51rjt//jwKCwuxfPlyLF++vN7t/8nPz8/o+4yMDAB/T1j1KSoqMjpdomvXrkbra9ZdvHgRrq6uyMzMhJ2dHXr37l3vPmtu9/bbb69zvaura73b1oiKisLMmTORmJiI4cOHo6ysDJs2bUJ4eLhRvR9//DHeeOMNHDt2DJWVlYblV/4smisjIwMigoCAgDrXOzo6Gm5v5syZePPNN/Hpp59iyJAhGDt2LCZNmsTTDoioUTivcF4BOK+oFRseskrBwcGGzzSIiIjA4MGDcf/99+P48eNo06aN4RmuWbNmISwsrM599OjRo97912w/adKkeieWKy+f+c9n4f65j9dee63e845rzuuuYW9vX+c4Eam31ivV3O7q1avh4eFRa72DQ8N/9p06dcLIkSORlJSEpUuX4quvvkJJSYnRVXTWrFmDKVOmICIiArNnz0anTp1gb2+PhISEWm/yvVJdbyAF/j6f+8osGo0G27Ztq/Nn88+f3xtvvIEpU6bgiy++wPbt2/H4448jISEBP/zwg81eDpaIGo/zSv04r3BesXZseMjq1dwZ3nbbbXj33XfxzDPPoHv37gD+fqYmNDS0yfvs2LEjXFxcUF1d3aztAcDf3x/A3898NXcfde1Tr9fj119/rXeyq7ndTp06XdPtTpw4Ed988w22bduGxMREuLq64q677jKs37BhA7p3746NGzcaTTTx8fEN7rtt27YoLCystfzPP/80+t7f3x8iAj8/P/Ts2bPB/QYGBiIwMBDz5s3D999/j1tvvRXLli3DCy+80OC2REQ1OK/UfbucVzivWCu+h4dUYfjw4QgODsaSJUtQVlaGTp06Yfjw4Xj//fdx7ty5WuNrPiOhPvb29rjnnnuQlJSE9PT0Jm8PAEFBQfD398frr7+Ov/76q1n7uFJERATs7OywePHiWudp1zxbFxYWBldXV7z00ktGpwM09XYjIiLQunVr/Pe//8W2bdswfvx4ODk5GdbXPDP2z2cJU1NTkZKS0uC+/f39UVRUZPSp2ufOncOmTZuMxo0fPx729vZYtGhRrWcjRQQFBQUAgOLiYlRVVRmtDwwMhJ2dHcrLyxuVl4jonzivcF7hvKIefIWHVGP27NmYMGECVq1ahenTp2Pp0qUYPHgwAgMDERsbi+7duyM3NxcpKSk4ffo0fv7556vu7+WXX8bu3bsREhKC2NhY9O7dGxcuXMChQ4ewY8cOXLhw4arb29nZ4cMPP0R4eDj69OmDqVOnwtvbG2fOnMHu3bvh6uqKr776qkkZe/Togeeeew7PP/88hgwZgvHjx0On0+HHH3+El5cXEhIS4Orqivfeew+TJ0/GwIEDERUVhY4dOyI7Oxtbt27FrbfeinfffbfB22rTpg0iIiIMn4595YfC3Xnnndi4cSPGjRuHMWPGICsrC8uWLUPv3r3rnIj/KSoqCnPmzMG4cePw+OOPGy5v2rNnT6M3pvr7++OFF17A3LlzcfLkSURERMDFxQVZWVnYtGkTHn74YcyaNQu7du1CXFwcJkyYgJ49e6KqqgqrV682PMAgImoOziucVzivqETLXhSO6NrUXD70xx9/rLWuurpa/P39xd/fX6qqqkREJDMzUx544AHx8PAQR0dH8fb2ljvvvFM2bNhg2K6+y4eKiOTm5sqMGTOkS5cu4ujoKB4eHjJixAhZvny5YUzN5UPru3RlWlqajB8/Xtq3by86nU58fX0lMjJSdu7caRhTc9nM8+fP15k3KyvLaPmKFSvkhhtuEJ1OJ23btpVhw4ZJcnKy0Zjdu3dLWFiYuLm5iZOTk/j7+8uUKVPk4MGD9fx0a9u6dasAEE9PzzovV/rSSy+Jr6+v6HQ6ueGGG2TLli11XhoUV1w+VERk+/bt0rdvX9FqtXLdddfJmjVral0+tEZSUpIMHjxYnJ2dxdnZWXr16iUzZsyQ48ePi4jIH3/8IQ8++KD4+/uLk5OTtGvXTm677TbZsWNHo7MSkW3ivPI3ziucV9RMI9KEd60RERERERFZEb6Hh4iIiIiIVIsNDxERERERqRYbHiIiIiIiUi02PEREREREpFpseIiIiIiISLXY8BARERERkWqx4SEiIiIiItViw0NERERERKrFhoeIiIiIiFSLDQ8REREREakWGx4iIiIiIlItNjxERERERKRabHiIiIiIiEi1/g/8AA/MLVICdQAAAABJRU5ErkJggg==", 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" ] @@ -570,7 +579,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1 1 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0]\n", + "[1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 1 0]\n", "[1 1 0 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0]\n" ] } @@ -588,7 +597,7 @@ { "data": { "text/plain": [ - "array([-9558.244])" + "array([-9562.926])" ] }, "execution_count": 129, @@ -637,22 +646,15 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/64 [00:00 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " 0%| | 0/64 [00:00 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "100%|██████████| 64/64 [00:03<00:00, 19.03it/s]\n" + "100%|██████████| 64/64 [00:02<00:00, 22.35it/s]\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 132, @@ -661,7 +663,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -733,17 +735,17 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/128 [00:00 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " 0%| | 0/128 [00:00 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "/tmp/ipykernel_5469/3475343188.py:29: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + "/tmp/ipykernel_5056/3475343188.py:29: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " energies2[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "100%|██████████| 128/128 [00:00<00:00, 680.44it/s]\n" + "100%|██████████| 128/128 [00:00<00:00, 726.03it/s]\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 133, @@ -752,7 +754,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
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-1.22495079292297,\n", + " ('x_005_002', 'x_004_003*x_004_001*x_002_001'): -0.3062376982307425,\n", + " ('x_005_002', 'x_004_005*x_004_002'): 1.22495079292297,\n", + " ('x_005_002', 'x_004_006*x_004_001*x_002_001'): -2.44990158584594,\n", " ('x_005_002', 'x_004_004*x_004_002'): 0.612475396461485,\n", - " ('x_005_002', 'x_004_004*x_002_001*x_004_003'): -2.44990158584594,\n", - " ('x_005_002', 'x_004_006*x_004_004*x_002_001'): -19.59921268676752,\n", - " ('x_005_002', 'x_004_004*x_002_001*x_004_002'): -1.22495079292297,\n", - " ('x_005_002', 'x_004_004*x_004_001'): 0.3062376982307425,\n", - " ('x_005_002', 'x_004_004*x_004_003'): 1.22495079292297,\n", - " ('x_005_002', 'x_004_004*x_004_006'): 9.79960634338376,\n", - " ('x_005_002', 'x_004_007*x_002_001*x_004_002'): -9.79960634338376,\n", - " ('x_005_002', 'x_004_002*x_004_005*x_002_001'): -2.44990158584594,\n", - " ('x_005_002', 'x_004_001*x_002_001*x_004_003'): -0.3062376982307425,\n", + " ('x_005_002', 'x_004_002*x_002_001*x_004_005'): -2.44990158584594,\n", + " ('x_005_002', 'x_004_003*x_004_007'): 9.79960634338376,\n", + " ('x_005_002', 'x_004_005*x_004_001*x_002_001'): -1.22495079292297,\n", " ('x_005_002', 'x_004_001*x_004_005'): 0.612475396461485,\n", - " ('x_005_002', 'x_004_005*x_004_003'): 2.44990158584594,\n", - " ('x_005_002', 'x_004_006*x_004_005'): 19.59921268676752,\n", - " ('x_005_002', 'x_004_002*x_004_005'): 1.22495079292297,\n", - " ('x_005_002', 'x_004_007*x_004_006'): 78.39685074707008,\n", - " ('x_005_002', 'x_004_007*x_004_003'): 9.79960634338376,\n", - " ('x_005_002', 'x_004_007*x_004_001'): 2.44990158584594,\n", - " ('x_005_002', 'x_004_007*x_004_002'): 4.89980317169188,\n", - " ('x_005_002', 'x_004_007*x_002_001*x_004_001'): -4.89980317169188,\n", - " ('x_005_002', 'x_004_007*x_002_001*x_004_006'): -156.79370149414015,\n", - " ('x_005_002', 'x_004_007*x_002_001*x_004_003'): -19.59921268676752,\n", - " ('x_005_002', 'x_004_001*x_004_005*x_002_001'): -1.22495079292297,\n", - " ('x_005_002', 'x_004_003*x_004_005*x_002_001'): -4.89980317169188,\n", + " ('x_005_002', 'x_004_006*x_004_007'): 78.39685074707008,\n", + " ('x_005_002', 'x_004_005*x_004_007*x_002_001'): -78.39685074707008,\n", + " ('x_005_002', 'x_004_004*x_004_007*x_002_001'): -39.19842537353504,\n", + " ('x_005_002', 'x_004_004*x_004_001*x_002_001'): -0.612475396461485,\n", + " ('x_005_002', 'x_004_001*x_004_004'): 0.3062376982307425,\n", + " ('x_005_002', 'x_004_006*x_004_001'): 1.22495079292297,\n", + " ('x_005_002', 'x_004_005*x_004_004*x_002_001'): -9.79960634338376,\n", + " ('x_005_002', 'x_004_007*x_004_005'): 39.19842537353504,\n", + " ('x_005_002', 'x_004_006*x_004_007*x_002_001'): -156.79370149414015,\n", + " ('x_005_002', 'x_004_003*x_004_007*x_002_001'): -19.59921268676752,\n", + " ('x_005_002', 'x_004_003*x_004_001'): 0.15311884911537124,\n", + " ('x_005_002', 'x_004_003*x_004_006*x_002_001'): -9.79960634338376,\n", + " ('x_005_002', 'x_004_006*x_004_004*x_002_001'): -19.59921268676752,\n", " ('x_005_002', 'x_004_006*x_004_005*x_002_001'): -39.19842537353504,\n", - " ('x_005_002', 'x_004_002*x_004_001*x_002_001'): -0.15311884911537124,\n", - " ('x_005_002', 'x_004_006*x_002_001*x_004_003'): -9.79960634338376,\n", - " ('x_005_002', 'x_002_001*x_004_003*x_004_002'): -0.612475396461485,\n", - " ('x_005_002', 'x_004_006*x_004_002*x_002_001'): -4.89980317169188,\n", - " ('x_005_002', 'x_004_006*x_004_001*x_002_001'): -2.44990158584594,\n", - " ('x_005_002', 'x_003_005*x_001_001*x_003_003'): 4.89980317169188,\n", - " ('x_005_002', 'x_003_005*x_003_006'): -19.59921268676752,\n", - " ('x_005_002', 'x_003_005*x_001_001*x_003_002'): 2.44990158584594,\n", - " ('x_005_002', 'x_003_007*x_003_005*x_001_001'): 78.39685074707008,\n", - " ('x_005_002', 'x_003_005*x_001_001*x_003_006'): 39.19842537353504,\n", - " ('x_005_002', 'x_003_005*x_003_002'): -1.22495079292297,\n", + " ('x_005_002', 'x_004_003*x_004_004*x_002_001'): -2.44990158584594,\n", + " ('x_005_002', 'x_004_003*x_004_005*x_002_001'): -4.89980317169188,\n", + " ('x_005_002', 'x_003_003*x_003_007'): -9.79960634338376,\n", + " ('x_005_002', 'x_003_004*x_001_001*x_003_003'): 2.44990158584594,\n", + " ('x_005_002', 'x_003_003*x_003_006'): -4.89980317169188,\n", + " ('x_005_002', 'x_001_001*x_003_003*x_003_001'): 0.3062376982307425,\n", + " ('x_005_002', 'x_001_001*x_003_003*x_003_007'): 19.59921268676752,\n", + " ('x_005_002', 'x_003_004*x_003_003'): -1.22495079292297,\n", + " ('x_005_002', 'x_001_001*x_003_003*x_003_006'): 9.79960634338376,\n", + " ('x_005_002', 'x_003_003*x_003_001'): -0.15311884911537124,\n", + " ('x_005_002', 'x_001_001*x_003_004*x_003_001'): 0.612475396461485,\n", + " ('x_005_002', 'x_003_002*x_003_006'): -2.44990158584594,\n", + " ('x_005_002', 'x_003_001*x_001_001*x_003_005'): 1.22495079292297,\n", " ('x_005_002', 'x_003_007*x_003_005'): -39.19842537353504,\n", - " ('x_005_002', 'x_003_005*x_003_003'): -2.44990158584594,\n", - " ('x_005_002', 'x_003_004*x_003_006'): -9.79960634338376,\n", + " ('x_005_002', 'x_003_004*x_003_005'): -4.89980317169188,\n", + " ('x_005_002', 'x_001_001*x_003_002*x_003_006'): 4.89980317169188,\n", + " ('x_005_002', 'x_003_004*x_001_001*x_003_005'): 9.79960634338376,\n", + " ('x_005_002', 'x_001_001*x_003_002*x_003_004'): 1.22495079292297,\n", " ('x_005_002', 'x_003_004*x_003_002'): -0.612475396461485,\n", - " ('x_005_002', 'x_003_007*x_003_001'): -2.44990158584594,\n", - " ('x_005_002', 'x_003_003*x_003_001*x_001_001'): 0.3062376982307425,\n", - " ('x_005_002', 'x_003_004*x_001_001*x_003_002'): 1.22495079292297,\n", - " ('x_005_002', 'x_003_001*x_003_006'): -1.22495079292297,\n", - " ('x_005_002', 'x_003_004*x_003_003'): -1.22495079292297,\n", - " ('x_005_002', 'x_003_007*x_003_004*x_001_001'): 39.19842537353504,\n", - " ('x_005_002', 'x_003_001*x_001_001*x_003_006'): 2.44990158584594,\n", + " ('x_005_002', 'x_003_007*x_003_002'): -4.89980317169188,\n", + " ('x_005_002', 'x_001_001*x_003_005*x_003_006'): 39.19842537353504,\n", + " ('x_005_002', 'x_001_001*x_003_002*x_003_007'): 9.79960634338376,\n", + " ('x_005_002', 'x_003_005*x_003_006'): -19.59921268676752,\n", + " ('x_005_002', 'x_001_001*x_003_002*x_003_001'): 0.15311884911537124,\n", + " ('x_005_002', 'x_003_007*x_001_001*x_003_005'): 78.39685074707008,\n", + " ('x_005_002', 'x_003_001*x_003_005'): -0.612475396461485,\n", " ('x_005_002', 'x_003_001*x_003_002'): -0.07655942455768562,\n", - " ('x_005_002', 'x_003_007*x_003_004'): -19.59921268676752,\n", - " ('x_005_002', 'x_003_001*x_001_001*x_003_002'): 0.15311884911537124,\n", - " ('x_005_002', 'x_003_001*x_003_003'): -0.15311884911537124,\n", - " ('x_005_002', 'x_003_003*x_003_004*x_001_001'): 2.44990158584594,\n", - " ('x_005_002', 'x_003_007*x_003_001*x_001_001'): 4.89980317169188,\n", - " ('x_005_002', 'x_001_001*x_003_002*x_003_003'): 0.612475396461485,\n", - " ('x_005_002', 'x_003_004*x_001_001*x_003_006'): 19.59921268676752,\n", - " ('x_005_002', 'x_003_007*x_003_003*x_001_001'): 19.59921268676752,\n", - " ('x_005_002', 'x_001_001*x_003_002*x_003_006'): 4.89980317169188,\n", - " ('x_005_002', 'x_003_007*x_003_006*x_001_001'): 156.79370149414015,\n", - " ('x_005_002', 'x_003_003*x_001_001*x_003_006'): 9.79960634338376,\n", - " ('x_005_002', 'x_003_007*x_001_001*x_003_002'): 9.79960634338376,\n", + " ('x_005_002', 'x_001_001*x_003_007*x_003_006'): 156.79370149414015,\n", + " ('x_005_002', 'x_001_001*x_003_004*x_003_007'): 39.19842537353504,\n", + " ('x_005_002', 'x_001_001*x_003_004*x_003_006'): 19.59921268676752,\n", + " ('x_005_002', 'x_001_001*x_003_001*x_003_006'): 2.44990158584594,\n", + " ('x_005_002', 'x_003_007*x_001_001*x_003_001'): 4.89980317169188,\n", " ('x_005_002', 'x_005_001'): 19.84003968007936,\n", - " ('x_005_003', 'x_004_004'): 7.359473053106612,\n", + " ('x_005_003', 'x_004_002'): 1.3805117159305391,\n", " ...}" ] }, diff --git a/docs/notebooks/solutions.pkl b/docs/notebooks/solutions.pkl deleted file mode 100644 index 72c39762558d72dd2b99ca36c4b95f17de538c65..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6321 zcmZo*nYv7Z0SscNX!MBYmF5;y>LuqFrRwFD=9FY678NB{PU+!^FG@|$&nqq|Dork# zGI>f5D_G%_9`?Kxh?2=uyct@jI5Q?qX`d1_MZ=rXo27M121^fXN=aowDo6`cn#GjP z4u~vs52MW#KR-XO|3CmHyctTSBy~C~9ITt88#!sEJ&aCI0|6@s?(2&SY`M%GrT~>? zFlVrVY|G$4wo71C_h_h%rk2rMGFmzymB6E=;%HqmT8ECd9Y)(sqiw}08KbqqXl*cB m8;sTlqqV_kZ7^CJjE*y{v>&YvMr(u7+F-Oc7#g)fsvZFKutZ7# diff --git a/docs/notebooks/test_qubo_poly_solver.py b/docs/notebooks/test_qubo_poly_solver.py index 499b766..9c2a073 100644 --- a/docs/notebooks/test_qubo_poly_solver.py +++ b/docs/notebooks/test_qubo_poly_solver.py @@ -64,7 +64,7 @@ def plot_solutions(solutions, references): wn_ref = wntr.network.WaterNetworkModel(inp_file) # store the results -qubo_results = [] +energies = [] solutions = [] encoded_reference_solutions = [] @@ -131,12 +131,12 @@ def plot_solutions(solutions, references): var_names = sorted(net.qubo.qubo_dict.variables) net.qubo.create_variables_mapping() mystep = IncrementalStep( - var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=1 + var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10 ) # generate init sample # x = modify_solution_sample(net, bin_rep_sol, modify=["flows", "heads"]) - x = modify_solution_sample(net, bin_rep_sol, modify=["heads"]) + x = modify_solution_sample(net, bin_rep_sol, modify=["flows", "heads"]) x0 = list(x.values()) # compute ref energy @@ -150,7 +150,7 @@ def plot_solutions(solutions, references): Tschedule = np.append(Tschedule, Tfinal * np.ones(1000)) Tschedule = np.append(Tschedule, np.zeros(1000)) - # sample + # sample flow + head mystep.optimize_values = np.arange(4, 6) res = sampler.sample( net.qubo, @@ -160,12 +160,37 @@ def plot_solutions(solutions, references): save_traj=True, verbose=False, ) + + # sample flow + mystep.optimize_values = np.arange(2, 4) + res = sampler.sample( + net.qubo, + init_sample=res.res, + Tschedule=Tschedule, + take_step=mystep, + save_traj=True, + verbose=False, + ) + + # sample flow + head + mystep.optimize_values = np.arange(4, 6) + res = sampler.sample( + net.qubo, + init_sample=res.res, + Tschedule=Tschedule, + take_step=mystep, + save_traj=True, + verbose=False, + ) + mystep.verify_quadratic_constraints(res.res) - qubo_results.append(res) + # qubo_results.append(res) # compute final idx_min = np.array([e for e in res.energies]).argmin() # idx_min = -1 + energies.append(res.energies[idx_min]) + sol = res.trajectory[idx_min] sol = net.qubo.decode_solution(np.array(sol)) sol = net.combine_flow_values(sol) @@ -176,4 +201,5 @@ def plot_solutions(solutions, references): plot_solutions(solutions, encoded_reference_solutions) pickle.dump(solutions, open("solutions.pkl", "wb")) pickle.dump(encoded_reference_solutions, open("encoded_reference_solutions.pkl", "wb")) +pickle.dump(energies, open("energies.pkl", "wb")) # pickle.dump(qubo_results, open("qubo_results.pkl", "wb")) diff --git a/docs/notebooks/test_qubo_poly_solver_net2loops.py b/docs/notebooks/test_qubo_poly_solver_net2loops.py new file mode 100644 index 0000000..e54bce0 --- /dev/null +++ b/docs/notebooks/test_qubo_poly_solver_net2loops.py @@ -0,0 +1,220 @@ +import wntr +import wntr_quantum +import numpy as np +import matplotlib.pyplot as plt +from copy import deepcopy + +from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver +from qubops.solution_vector import SolutionVector_V2 as SolutionVector +from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding +from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo +from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing +from qubops.qubops_mixed_vars import QUBOPS_MIXED +import sparse +from wntr_quantum.sampler.step.full_random import IncrementalStep +from wntr_quantum.sampler.simulated_annealing import modify_solution_sample + +import pickle + + +def plot_solutions(solutions, references): + fig = plt.figure(figsize=plt.figaspect(0.5)) + ax1 = fig.add_subplot(121) + + ax1.axline((0, 0.0), slope=1.10, color="grey", linestyle=(0, (2, 5))) + ax1.axline((0, 0.0), slope=1, color="black", linestyle=(0, (2, 5))) + ax1.axline((0, 0.0), slope=0.90, color="grey", linestyle=(0, (2, 5))) + ax1.grid() + + for r, sol in zip(references, solutions): + ax1.scatter( + r[:2], sol[:2], s=150, lw=1, edgecolors="w", label="Sampled solution" + ) + + ax1.set_xlabel("Reference Values", fontsize=12) + ax1.set_ylabel("QUBO Values", fontsize=12) + ax1.set_title("Flow Rate", fontsize=14) + + ax2 = fig.add_subplot(122) + + ax2.axline((0, 0.0), slope=1.10, color="grey", linestyle=(0, (2, 5))) + ax2.axline((0, 0.0), slope=1, color="black", linestyle=(0, (2, 5))) + ax2.axline((0, 0.0), slope=0.90, color="grey", linestyle=(0, (2, 5))) + + for r, sol in zip(references, solutions): + ax2.scatter( + r[2:], + sol[2:], + s=150, + lw=1, + edgecolors="w", + label="Sampled solution", + ) + ax2.grid() + + ax2.set_xlabel("Reference Values", fontsize=12) + ax2.set_title("Pressure", fontsize=14) + plt.show() + + +# Create a water network model +inp_file = "./networks/Net0_CM.inp" +inp_file = "./networks/Net0.inp" +inp_file = "./networks/Net2LoopsCM.inp" +wn_ref = wntr.network.WaterNetworkModel(inp_file) + +# store the results +energies = [] +solutions = [] +encoded_reference_solutions = [] + +# copy the nework +wn = deepcopy(wn_ref) + +# change pipe diams +# for pipe_name in wn.link_name_list: +# pipe = wn.get_link(pipe_name) +# eps = 0.9 + 0.2 * np.random.rand() +# pipe.diameter *= eps + +# solve classcaly +sim = wntr.sim.EpanetSimulator(wn) +results = sim.run_sim() + +# extract ref values +ref_pressure = results.node["pressure"].values[0] +ref_rate = results.link["flowrate"].values[0] +ref_values = np.append(ref_rate, ref_pressure) +ref_values + +# create qubo encoding for the flow +nqbit = 11 +step = 15 / (2**nqbit - 1) +flow_encoding = PositiveQbitEncoding( + nqbit=nqbit, step=step, offset=+0, var_base_name="x" +) + +# create qubo encoding for the heads +nqbit = 11 +step = 500 / (2**nqbit - 1) +head_encoding = PositiveQbitEncoding( + nqbit=nqbit, step=step, offset=+500.0, var_base_name="x" +) + +# create qubosolver +net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, head_encoding=head_encoding) + +# create model +model, model_updater = create_hydraulic_model_for_qubo(wn) +net.create_index_mapping(model) +net.matrices = net.initialize_matrices(model) + +# solve qubo classically +ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = net.classical_solutions() +encoded_reference_solutions.append(encoded_ref_sol) + +# sampler +sampler = SimulatedAnnealing() + +# create the solver attribute +net.qubo = QUBOPS_MIXED(net.mixed_solution_vector, {"sampler": sampler}) +matrices = tuple(sparse.COO(m) for m in net.matrices) +net.qubo.qubo_dict = net.qubo.create_bqm(matrices, strength=0) + +# create step +var_names = sorted(net.qubo.qubo_dict.variables) +net.qubo.create_variables_mapping() +mystep = IncrementalStep( + var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=25 +) + +Nsim = 10 +for i in range(Nsim): + + print("==== %d / %d ====" % (i, Nsim)) + + # generate init sample + # x = modify_solution_sample(net, bin_rep_sol, modify=["flows", "heads"]) + x = modify_solution_sample(net, bin_rep_sol, modify=["flows", "heads"]) + x0 = list(x.values()) + + # compute ref energy + eref = net.qubo.energy_binary_rep(bin_rep_sol) + + # temperature schedule + num_sweeps = 4000 + Tinit = 1e6 + Tfinal = 1e1 + Tschedule = np.linspace(Tinit, Tfinal, num_sweeps) + Tschedule = np.append(Tschedule, Tfinal * np.ones(1000)) + + num_sweeps = 4000 + Tinit = 1e1 + Tfinal = 0 + Tschedule = np.append(Tschedule, np.linspace(Tinit, Tfinal, num_sweeps)) + Tschedule = np.append(Tschedule, Tfinal * np.ones(100)) + + # sample flow + head + mystep.optimize_values = np.arange(8, 22) + res = sampler.sample( + net.qubo, + init_sample=x0, + Tschedule=Tschedule, + take_step=mystep, + save_traj=True, + verbose=False, + ) + + # # temperature schedule + # num_sweeps = 2000 + # Tinit = 1e1 + # Tfinal = 0 + # Tschedule = np.linspace(Tinit, Tfinal, num_sweeps) + # Tschedule = np.append(Tschedule, Tfinal * np.ones(1000)) + + # # sampler flow + # mystep.optimize_values = np.arange(8, 16) + # res = sampler.sample( + # net.qubo.qubo_dict, + # init_sample=res.res, + # Tschedule=Tschedule, + # take_step=mystep, + # save_traj=True, + # ) + + # # temperature scheudule + # num_sweeps = 5000 + # Tinit = 1e2 + # Tfinal = 0 + # Tschedule = np.linspace(Tinit, Tfinal, num_sweeps) + # Tschedule = np.append(Tschedule, Tfinal * np.ones(1000)) + + # # sampler flow + # mystep.optimize_values = np.arange(16) + # res = sampler.sample( + # net.qubo.qubo_dict, + # init_sample=res.res, + # Tschedule=Tschedule, + # take_step=mystep, + # save_traj=True, + # ) + + mystep.verify_quadratic_constraints(res.res) + + # compute final + idx_min = np.array([e for e in res.energies]).argmin() + # idx_min = -1 + energies.append(res.energies[idx_min]) + + sol = res.trajectory[idx_min] + sol = net.qubo.decode_solution(np.array(sol)) + sol = net.combine_flow_values(sol) + sol = net.convert_solution_to_si(sol) + solutions.append(sol) + + +plot_solutions(solutions, encoded_reference_solutions) +pickle.dump(solutions, open("solutions.pkl", "wb")) +pickle.dump(encoded_reference_solutions, open("encoded_reference_solutions.pkl", "wb")) +pickle.dump(energies, open("energies.pkl", "wb")) +# pickle.dump(qubo_results, open("qubo_results.pkl", "wb")) diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py index 4e8e584..695e371 100644 --- a/wntr_quantum/sampler/simulated_annealing.py +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -204,10 +204,7 @@ def bqm_energy(qubo, input, var_names): p = np.exp((e_current - e_new) / (T + 1e-12)) eps = np.random.rand() - # if verbose: - # print( - # "Temp: %f, eps: %f, p: %f, accepted %r" % (T, eps, p, eps < p) - # ) + if eps < p: current_sample = deepcopy(new_sample) e_current = e_new diff --git a/wntr_quantum/sampler/step/full_random.py b/wntr_quantum/sampler/step/full_random.py index 1884ff5..df7ad5d 100644 --- a/wntr_quantum/sampler/step/full_random.py +++ b/wntr_quantum/sampler/step/full_random.py @@ -21,66 +21,72 @@ def __call__(self, x, verbose=False): return x -class IncrementalStep(BaseStep): +class IncrementalStep(BaseStep): # noqa: D101 def __call__(self, x, verbose=False): """Call function of the method. Args: x (_type_): _description_ + verbose (bool): print suff Returns: _type_: _description_ """ - random_val_name = np.random.choice(self.value_names[self.optimize_values]) - idx = self.index_values[random_val_name] - data = np.array(x)[idx] - width = len(data) - - # determine the max val - max_val = int("1" * width, base=2) - - # check if we reach min/max val - max_val_check = data.prod() == 1 - min_val_check = data.sum() == 0 - - # convert to int value - val = int("".join([str(i) for i in data[::-1]]), base=2) - - # determine sign of the displacement - if min_val_check: - # print("min val reached") - sign = 1 - elif max_val_check: - # print("max val reached") - sign = -1 - else: - sign = 2 * np.random.randint(2) - 1 - - # new value - if self.step_size <= 1: - delta = 1 - else: - delta = np.random.randint(self.step_size) - new_val = val + sign * delta - if new_val < 0: - new_val = 0 - if new_val > max_val: - new_val = max_val - new_val = np.binary_repr(new_val, width=width) - - # convert back to binary repr - new_data = np.array([int(i) for i in new_val])[::-1] - if verbose: - print(random_val_name, data, "=>", new_data) - - # inject in the x vector - for ix, nd in zip(idx, new_data): - x[ix] = nd - - # fix constraints - for vidx in idx: - self.fix_constraint(x, vidx) + num_var_changed = np.random.randint(len(self.optimize_values)) + random_val_name_list = np.random.choice( + self.value_names[self.optimize_values], size=num_var_changed + ) + + for random_val_name in random_val_name_list: + idx = self.index_values[random_val_name] + data = np.array(x)[idx] + width = len(data) + + # determine the max val + max_val = int("1" * width, base=2) + + # check if we reach min/max val + max_val_check = data.prod() == 1 + min_val_check = data.sum() == 0 + + # convert to int value + val = int("".join([str(i) for i in data[::-1]]), base=2) + + # determine sign of the displacement + if min_val_check: + # print("min val reached") + sign = 1 + elif max_val_check: + # print("max val reached") + sign = -1 + else: + sign = 2 * np.random.randint(2) - 1 + + # new value + if self.step_size <= 1: + delta = 1 + else: + delta = np.random.randint(self.step_size) + new_val = val + sign * delta + if new_val < 0: + new_val = 0 + if new_val > max_val: + new_val = max_val + new_val = np.binary_repr(new_val, width=width) + + # convert back to binary repr + new_data = np.array([int(i) for i in new_val])[::-1] + if verbose: + print(random_val_name, data, "=>", new_data) + + # inject in the x vector + for ix, nd in zip(idx, new_data): + x[ix] = nd + + # fix constraints + for vidx in idx: + self.fix_constraint(x, vidx) return x From a30e494987038a964c1ad75d308c551f72316d7c Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 1 Nov 2024 14:46:39 +0100 Subject: [PATCH 77/96] clean up --- wntr_quantum/sampler/simulated_annealing.py | 42 ++---- .../sampler/simulated_annealing_parallel.py | 139 ------------------ wntr_quantum/sampler/step/base_step.py | 34 +++-- wntr_quantum/sampler/step/full_random.py | 37 +---- 4 files changed, 40 insertions(+), 212 deletions(-) delete mode 100644 wntr_quantum/sampler/simulated_annealing_parallel.py diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py index 695e371..e200935 100644 --- a/wntr_quantum/sampler/simulated_annealing.py +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -34,18 +34,16 @@ def generate_random_valid_sample(qubo): return sample -def modify_solution_sample(net, solution, modify=["signs", "flows", "heads"]): - """_summary_ +def modify_solution_sample(net, solution, modify=["signs", "flows", "heads"]) -> list: + """Modiy the solution sample to change values of the signs/flows/heads. Args: - qubo (_type_): _description_ - solution (_type_): _description_ - - Raises: - ValueError: _description_ + net (qubo_solver): The QUBO solver + solution (list): the sample that encoded the true solution + modify (list, optional): what to change. Defaults to ["signs", "flows", "heads"]. Returns: - _type_: _description_ + List: new sample """ def flatten_list(lst): @@ -67,7 +65,7 @@ def flatten_list(lst): num_pipes = net.wn.num_pipes num_heads = net.wn.num_junctions - # modsify sign + # modify sign if "signs" in modify: for i in range(num_pipes): mod_bin_rep_sol[i] = np.random.randint(2) @@ -118,38 +116,27 @@ def sample( """Sample the problem. Args: - bqm (_type_): _description_ + qubo (qubo solver): qubo solver num_sweeps (int, optional): _description_. Defaults to 100. Temp (list, optional): _description_. Defaults to [1e5, 1e-3]. Tschedule (list, optional): The temperature schedule init_sample (_type_, optional): _description_. Defaults to None. take_step (_type_, optional): _description_. Defaults to None. save_traj (bool, optional): save the trajectory. Defaults to False - verbose(bool, optional): + verbose (bool, optional): print stuff """ - # def bqm_energy(bqm, input, var_names): # noqa: D417 - # """Compute the energy of a given binary array. - - # Args: - # bqm (bqm) - # x (_type_): _description_ - # var_names (list): list of var names - # """ - # return bqm.energies(as_samples((input, var_names))) def bqm_energy(qubo, input, var_names): - """_summary_. + """Computes the energy of the sample. Args: - qubo (_type_): _description_ - input (_type_): _description_ - var_names (_type_): _description_ + qubo (qubo_solver): qubo solver + input (list): sample + var_names (list): names of the variables - Raises: - ValueError: _description_ Returns: - _type_: _description_ + float: qubo energy """ return qubo.energy_binary_rep( np.array(input)[qubo.index_variables].tolist() @@ -219,6 +206,5 @@ def bqm_energy(qubo, input, var_names): energies.append(e_current) if verbose: - # print(current_sample) print("-----------------") return SimulatedAnnealingResults(current_sample, energies, trajectory) diff --git a/wntr_quantum/sampler/simulated_annealing_parallel.py b/wntr_quantum/sampler/simulated_annealing_parallel.py deleted file mode 100644 index 8181fe7..0000000 --- a/wntr_quantum/sampler/simulated_annealing_parallel.py +++ /dev/null @@ -1,139 +0,0 @@ -from copy import deepcopy -from dataclasses import dataclass -import numpy as np -from dimod import as_samples -from tqdm import tqdm - - -def generate_random_valid_sample(qubo): - """Geenrate a random sample that respects quadratization. - - Args: - qubo (_type_): _description_ - """ - sample = {} - for iv, v in enumerate(sorted(qubo.qubo_dict.variables)): - sample[v] = np.random.randint(2) - - for v in qubo.mapped_variables[:7]: - sample[v] = 1 - sample[qubo.mapped_variables[7]] = 0 - - for v, _ in sample.items(): - if v not in qubo.mapped_variables: - var_tmp = v.split("*") - itmp = 0 - for vtmp in var_tmp: - if itmp == 0: - new_val = sample[vtmp] - itmp = 1 - else: - new_val *= sample[vtmp] - - sample[v] = new_val - return sample - - -@dataclass -class SimulatedAnnealingResults: - """Result of the simulated anneling.""" - - res: list - energies: list - trajectory: list - - -class SimulatedAnnealing: # noqa: D101 - - def __init__(self): # noqa: D107 - self.properties = {} - - def sample( - self, - bqm, - Tschedule, - num_traj=10, - x0=None, - take_step=None, - save_traj=False, - ): - """Sample the problem. - - Args: - bqm (_type_): _description_ - Tschedule (list): The temperature schedule - x0 (_type_, optional): _description_. Defaults to None. - num_traj(int, optional): number of parallel traj. Default to None - take_step (_type_, optional): _description_. Defaults to None. - save_traj (bool, optional): save the trajectory. Defaults to False - """ - - def bqm_energy(x, var_names): - """Compute the energy of a given binary array. - - Args: - x (_type_): _description_ - var_names (list): list of var names - """ - return bqm.energies(as_samples((x, var_names))) - - # check that take_step is callable - if not callable(take_step): - raise ValueError("take_step must be callable") - - # define th variable names - var_names = sorted(bqm.variables) - - # define the initial state - if x0 is None: - x = np.random.randint(2, size=(num_traj, bqm.num_variables)).tolist() - else: - x = x0 - - # initialize the energy - energies = [] - energies.append(bqm_energy(x, var_names)) - - # init the traj - trajectory = None - if save_traj: - trajectory = [] - trajectory.append(x) - - # step scheduling - step_schedule = ( - Tschedule / ((Tschedule[0] - Tschedule[-1]) / (take_step.step_size - 1)) + 1 - ) - - # loop over the temp schedule - for s, T in tqdm(zip(step_schedule, Tschedule)): - - # original point - x_ori = deepcopy(x) - e_ori = bqm_energy(x, var_names) - - # new point - # take_step.step_size = int(s) - x_new = take_step(x) - e_new = bqm_energy(x, var_names) - - # accept/reject - if e_new < e_ori: - x = x_new - energies.append(bqm_energy(x, var_names)) - if save_traj: - trajectory.append(x) - else: - p = np.exp(-(e_new - e_ori) / T) - if np.random.rand() < p: - x = x_new - energies.append(bqm_energy(x, var_names)) - if save_traj: - trajectory.append(x) - else: - x = x_ori - energies.append(bqm_energy(x, var_names)) - if save_traj: - trajectory.append(x) - - return SimulatedAnnealingResults(x, energies, trajectory) diff --git a/wntr_quantum/sampler/step/base_step.py b/wntr_quantum/sampler/step/base_step.py index 0bb1374..83f2f99 100644 --- a/wntr_quantum/sampler/step/base_step.py +++ b/wntr_quantum/sampler/step/base_step.py @@ -2,7 +2,7 @@ class BaseStep: # noqa: D101 - def __init__( + def __init__( # noqa: D417 self, var_names, single_var_names, @@ -13,9 +13,11 @@ def __init__( """Propose a new solution vector. Args: - var_names (_type_): _description_ - single_var_names (_type_): _description_ - single_var_index (_type_): _description_ + var_names (list): names of the variables in the problem + single_var_names (_type_): list of the single variables names e.g. x_001_002 + single_var_index (_type_): index of the single variables + step_size (int, optional): size of the steps + optimize_values (list, optional): index of the values to optimize """ self.var_names = var_names self.single_var_names = single_var_names @@ -37,11 +39,14 @@ def __init__( self.optimize_values = list(np.arange(len(self.value_names))) @staticmethod - def _get_variable_root_name(var_name): + def _get_variable_root_name(var_name) -> str: """Extract the root name of the variables. Args: - var_name (_type_): _description_ + var_name (str): variable name + + Returns: + str: root name """ return "_".join(var_name.split("_")[:2]) @@ -49,7 +54,7 @@ def define_mapping(self): """Define the mapping of the higher order terms. Returns: - _type_: _description_ + list: mapping of the higher order terms """ high_order_terms_mapping = [] @@ -83,11 +88,11 @@ def fix_constraint(self, x, idx): """Ensure that the solution vectors respect quadratization. Args: - x (_type_): _description_ - idx (_type_): _description_ + x (list): sample + idx (int): index of the element that has changed Returns: - _type_: _description_ + list: new sampel that respects quadratization constraints """ fix_var = self.high_order_terms_mapping[idx] for idx_fix, idx_prods in fix_var.items(): @@ -98,7 +103,7 @@ def verify_quadratic_constraints(self, data): """Check if quadratic constraints are respected or not. Args: - data (_type_): _description_ + data (list): sample """ for v, d in zip(self.var_names, data): if v not in self.single_var_names: @@ -118,13 +123,14 @@ def verify_quadratic_constraints(self, data): idx = self.single_var_index[self.single_var_names.index(vtmp)] print("%s = %d" % (vtmp, data[idx])) - def __call__(self, x): + def __call__(self, x, verbose=False): """Call function of the method. Args: - x (_type_): _description_ + x (list): sample + verbose (bool): print stuff Returns: - _type_: _description_ + list: new sample """ raise NotImplementedError("Implement a __call__ method") diff --git a/wntr_quantum/sampler/step/full_random.py b/wntr_quantum/sampler/step/full_random.py index df7ad5d..144eba1 100644 --- a/wntr_quantum/sampler/step/full_random.py +++ b/wntr_quantum/sampler/step/full_random.py @@ -8,10 +8,11 @@ def __call__(self, x, verbose=False): """Call function of the method. Args: - x (_type_): _description_ + x (list): initial sample + verbose (bool): print stuff Returns: - _type_: _description_ + list: proposed sample """ random_val_name = np.random.choice(self.value_names[self.optimize_values]) idx = self.index_values[random_val_name] @@ -27,11 +28,11 @@ def __call__(self, x, verbose=False): """Call function of the method. Args: - x (_type_): _description_ - verbose (bool): print suff + x (list): initial sample + verbose (bool): print stuff Returns: - _type_: _description_ + list: proposed sample """ num_var_changed = np.random.randint(len(self.optimize_values)) random_val_name_list = np.random.choice( @@ -55,10 +56,8 @@ def __call__(self, x, verbose=False): # determine sign of the displacement if min_val_check: - # print("min val reached") sign = 1 elif max_val_check: - # print("max val reached") sign = -1 else: sign = 2 * np.random.randint(2) - 1 @@ -89,27 +88,3 @@ def __call__(self, x, verbose=False): self.fix_constraint(x, vidx) return x - - -class ParallelIncrementalStep(BaseStep): - - def __init__(self, var_names, single_var_names, single_var_index, step_size=1): - super().__init__(var_names, single_var_names, single_var_index) - self.step_size = step_size - self._step = IncrementalStep( - var_names, single_var_names, single_var_index, step_size=step_size - ) - - def __call__(self, x): - """Call function of the method. - - Args: - x (_type_): _description_ - - Returns: - _type_: _description_ - """ - new_x = [] - for xi in x: - new_x.append(self._step(xi)) - return new_x From f7e6d7177c0036afaafd51535a9f77e2c63b8f0d Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 1 Nov 2024 17:20:18 +0100 Subject: [PATCH 78/96] clean up --- .../net0_data/plot_test_qubo_solver.ipynb | 383 ++++-------------- docs/notebooks/qubo_poly_solver_Net0.ipynb | 19 +- .../sim/solvers/qubo_polynomial_solver.py | 2 +- 3 files changed, 77 insertions(+), 327 deletions(-) diff --git a/docs/notebooks/net0_data/plot_test_qubo_solver.ipynb b/docs/notebooks/net0_data/plot_test_qubo_solver.ipynb index 4e46cfb..c5f0f50 100644 --- a/docs/notebooks/net0_data/plot_test_qubo_solver.ipynb +++ b/docs/notebooks/net0_data/plot_test_qubo_solver.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 165, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -24,112 +24,93 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "energies = energies.flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0.311,\n", - " 0.05105,\n", - " 0.2322,\n", - " 0.03113,\n", - " 0.1679,\n", - " 0.07615,\n", - " 0.02345,\n", - " -0.02054,\n", - " 200.8,\n", - " 181.9,\n", - " 195.6,\n", - " 164.1,\n", - " 190.6,\n", - " 177.9]" + "array([ 5, 61, 55, 79, 74, 28, 50, 94, 27, 6, 21, 67, 13, 39, 34, 42, 44,\n", + " 47, 87, 25, 69, 3, 89, 20, 11, 90, 19, 73, 17, 53, 59, 54, 85, 7,\n", + " 16, 97, 41, 84, 78, 82, 22, 56, 35, 92, 66, 43, 32, 14, 98, 1, 65,\n", + " 57, 71, 9, 46, 10, 72, 88, 48, 51, 40, 62, 2, 52, 64, 75, 81, 76,\n", + " 18, 99, 68, 77, 29, 36, 86, 38, 91, 49, 4, 96, 31, 95, 80, 12, 15,\n", + " 33, 60, 93, 70, 26, 24, 0, 8, 30, 83, 63, 45, 23, 37, 58])" ] }, - "execution_count": 167, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ref = [3.110e-01, 5.105e-02, 2.322e-01, 3.113e-02, 1.679e-01, 7.615e-02, 2.345e-02, -2.054e-02, 2.008e+02, 1.819e+02, 1.956e+02, 1.641e+02, 1.906e+02, 1.779e+02]\n", - "ref" + "idx_sort = np.argsort(energies)\n", + "energies = energies[idx_sort]\n", + "idx_sort" ] }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ - "idx = np.argmin(energies)\n", - "idx = 1\n", - "energies[idx]\n", - "ref = [ref]*10" + "solution = [solution[i] for i in idx_sort]\n", + "# # solution[0]\n", + "# idx_sort" ] }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 70, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-35722.03292166],\n", - " [-35703.83316825],\n", - " [-35707.98248599],\n", - " [-35706.86539028],\n", - " [-35679.87963652],\n", - " [-35686.1686008 ],\n", - " [-35633.3534824 ],\n", - " [-35717.54215684],\n", - " [-35694.95182639],\n", - " [-35716.82092095]])" - ] - }, - "execution_count": 169, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "energies" + "ref = [ref[i] for i in idx_sort]" ] }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[1.50000000e+02],\n", - " [2.43044619e+01],\n", - " [3.68034550e+01],\n", - " [3.29134752e+01],\n", - " [2.21512045e+00],\n", - " [4.15454621e+00],\n", - " [2.11247776e-02],\n", - " [9.57325927e+01],\n", - " [9.99940675e+00],\n", - " [8.90711263e+01]])" + "0" ] }, - "execution_count": 175, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "size = 150 * np.exp(-0.1 * (energies-energies.min()))\n", - "size" + "idx = np.argmin(energies)\n", + "idx" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "size = 150 * np.exp(-0.5 * (energies-energies.min()))" ] }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -142,13 +123,13 @@ " ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", " ax1.grid()\n", "\n", - " for r, sol, s in zip(references, solutions, size):\n", + " for r, sol, s in zip(references[:10], solutions[:10], size):\n", " ax1.scatter(\n", - " r[:8], sol[:8], s=s, lw=1, edgecolors=\"w\",alpha=0.5, facecolors='orange'\n", + " r[:2], sol[:2], s=s, lw=1, edgecolors=\"w\",alpha=0.5, facecolors='orange'\n", " )\n", "\n", " ax1.scatter(\n", - " references[best_index][:8], solutions[best_index][:8], s=150, lw=1, edgecolors=\"w\", facecolors='C0'\n", + " references[best_index][:2], solutions[best_index][:2], s=150, lw=1, edgecolors=\"w\", facecolors='C0'\n", " )\n", "\n", " ax1.set_xlabel(\"Reference Values\", fontsize=12)\n", @@ -161,17 +142,17 @@ " ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", " ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", "\n", - " for r, sol, s in zip(references, solutions, size):\n", + " for r, sol, s in zip(references[:10], solutions[:10], size):\n", " ax2.scatter(\n", - " r[8:],\n", - " sol[8:],\n", + " r[2:],\n", + " sol[2:],\n", " s=s,\n", " lw=1,\n", " edgecolors=\"w\",\n", " alpha=0.5, facecolors='orange'\n", " )\n", " ax2.scatter(\n", - " references[best_index][8:], solutions[best_index][8:], s=150, lw=1, edgecolors=\"w\", facecolors='C0'\n", + " references[best_index][2:], solutions[best_index][2:], s=150, lw=1, edgecolors=\"w\", facecolors='C0'\n", " )\n", " ax2.grid()\n", "\n", @@ -180,112 +161,6 @@ " plt.show()" ] }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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"output_type": "execute_result" - } - ], - "source": [ - "solution" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plt.hist(np.array(solution)[:,3],bins=25)\n", - "idx = 3\n", - "plt.hist(np.array(solution)[:,idx],bins=25)\n", - "plt.vlines(ref[0][idx],0, 17,colors='black', ls='--')\n", - "plt.vlines(ref[0][idx]*0.9,0, 17,colors='grey', ls='--')\n", - "plt.vlines(ref[0][idx]*1.1,0, 17,colors='grey', ls='--')\n", - "plt.ylim([0,17])\n", - "plt.grid()" - ] - }, { "cell_type": "code", "execution_count": 76, @@ -293,78 +168,9 @@ "outputs": [ { "data": { + "image/png": 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b4u3trbRt21Z59913lYyMjGKvP2fOHKVp06aKXq9X6tevr8yYMUPJy8u7YVnqGzl79qzyr3/9S2natKni4uKieHl5KS1atFAef/xxJTo6+qZ/ZkIU0CjKdc80hRBCCCGEEKKakD08QgghhBBCiGpLEh4hhBBCCCFEtSUJjxBCCCGEEKLakoRHCCGEEEIIUW1JwiOEEEIIIYSotiThEUIIIYQQQlRbcvCoFSwWC4mJiXh6eqLRaCo6HCGEqHIURSEjI4OQkBC0WvmsTd5XhBCibGx5X5GExwqJiYnUq1evosMQQogq78yZM9StW7eiw6hw8r4ihBCOYc37iiQ8VvD09ATUP1AvLy+b+ppMJjZs2MDAgQPR6/XlEV6lJPOuOfOuiXOGmjnvssw5PT2devXqFf48renkfeXmaso8oebMVeZZvVT0PG15X5GExwoFyw28vLzsemNyc3PDy8urWv9Hfz2Zd82Zd02cM9TMeTtizrJ8SyXvKzdXU+YJNWeuMs/qpSLnmZ2dzbFjxwDr3lck4RFCCCGEEEJUCUajkcWLF3PixAmr+8jOUSGEEOIac+fOpU2bNoVPX8LCwvjtt98K7+fm5jJx4kT8/f3x8PBgxIgRJCcnV2DEQghRc+zevZvExERcXV2t7iMJjxBCCHGNunXr8s4777B792527dpF3759GT58OAcOHADg+eefZ82aNSxfvpzNmzeTmJjIvffeW8FRCyFEzRAWFkaPHj24//77re4jS9qEEEKIa9x1111Fvn/77beZO3cuO3bsoG7duixYsIDIyEj69u0LwNdff02LFi3YsWMH3bp1q4iQhRCixtBoNPTv35/09HSr+8gTHiGEEKIUZrOZpUuXkpWVRVhYGLt378ZkMtG/f//CNs2bN6d+/frExsZWYKRCCCFKI094hBBCiOv89ddfhIWFkZubi4eHBytXrqRly5bs27cPZ2dnfHx8irQPCgoiKSmp1PGMRiNGo7Hw+4JPJk0mEyaTyabYCtrb2q+qqSnzhJozV5ln9VLR87TldSXhEUIIIa7TrFkz9u3bR1paGj/++CNjx45l8+bNdo83e/ZsZs6cWez6hg0bcHNzs2vMqKgou+OpSmrKPKHmzFXmWb1U1Dyzs7OtblvpEp45c+bw/vvvk5SURNu2bfnvf/9Lly5dSm2/fPlyXnvtNU6dOkXTpk159913GTJkSOH9zMxMXnnlFVatWkVKSgoNGzbk2Wef5amnnroV0xFCiKrPlAk55yHzvPr9xVjwqA2utUHvUbGxlRNnZ2eaNGkCQMeOHfnjjz/49NNPeeCBB8jLy+PKlStFnvIkJycTHBxc6nhTp05lypQphd8XHJg3cOBAu87hiYqKYsCAAdX+jI+aME+oOXOVeVYvds/TnA85ZyDrJKT9DeYc0LmCd3NwbwSudUF38xTFlj08lSrhWbZsGVOmTGHevHl07dqVTz75hPDwcA4fPkxgYGCx9tu3b2fUqFHMnj2bYcOGERkZSUREBHv27KFVq1YATJkyhY0bN/L999/ToEEDNmzYwDPPPENISAh33333rZ6iEEJUHeY8SDuofuWcg7wcoC1ciIErruBaB7xbql8654qOtlxZLBaMRiMdO3ZEr9cTHR3NiBEjADh8+DAJCQmEhYWV2t/FxQUXF5di1/V6vd2/EJWlb1VSU+YJNWeuMs/qxaZ55iRDcgwkR8PF7ZCfefWekwfU6g5B/SCoD7gW/93/+te1VqUqWvDRRx/xxBNPMH78eFq2bMm8efNwc3Nj4cKFJbb/9NNPGTRoEC+99BItWrRg1qxZdOjQgc8++6ywzfbt2xk7diy9e/emQYMGTJgwgbZt2xIXF3erpiWEEFWPOQ9S4iApCjKOQH5W0fv5Wer1pCi1nTmvYuIsB1OnTmXLli2cOnWKv/76i6lTpxITE8Po0aPx9vbmscceY8qUKWzatIndu3czfvx4wsLCpEKbEELcSE4ynF0F+9+C8xuKJjugfn9+A4k7P2PNT99gLlhV4ACV5glPXl4eu3fvZurUqYXXtFot/fv3L7XyTWxsbJElAgDh4eGsWrWq8Pvu3buzevVqHn30UUJCQoiJieHIkSN8/PHHpcYim0vLTuZdc+ZdE+cMNWDel/dD8k51qcE/n42ZLEX/CahPfZJ3Am7g27rU4arSn9OFCxcYM2YM58+fx9vbmzZt2rB+/XoGDBgAwMcff4xWq2XEiBEYjUbCw8P5/PPPKzhqIYSoxMz56pOdw59BfkapzS7lerH42B1km3PwiFpBn7ufsmp5281UmoTn0qVLmM1mgoKCilwPCgri77//LrFPUlJSie2vrZTz3//+lwkTJlC3bl2cnJzQarV8+eWX9OzZs9RYZHOp48i8a46aOGeo7vNuUuLVqDMlJDYnzgBnSh3Jls2lFW3BggU3vG8wGJgzZw5z5sy5RREJIUQVl31aXcZWmOxoQOcCGl1hkzSjC9+d6Ee22UCI60W6B56H7ATwbFTml680CU95+e9//8uOHTtYvXo1oaGhbNmyhYkTJxISElLkHIVryebSspN515x518Q5QzWfd8ZxOLcWzEWXsZksWqLOtGZAvb/Qay1F++g8oO4Q8Ghc4pC2bC4VQghRzWQeV/fsaLSgMwA6UEz/LJdWAA2pRn9yzAb8XdJ5qHE0Lpd1kDmieiU8AQEB6HQ6kpOTi1y/UeWb4ODgG7bPyclh2rRprFy5kqFDhwLQpk0b9u3bxwcffFBqwiObSx1H5l1z1MQ5QzWdd/4lUDJK3eWp11qKJzxKOpgugb55yX2q25+REEII61jMavEbcw7o3MGSC6bLYM5FTXZUDZ3SGNMgDU9XcHdxAnO22i+oL2h1pY9vhUpTtMDZ2ZmOHTsSHR1deM1isRAdHV1q5ZuwsLAi7UFdXlLQvmDPjVZbdJo6nQ6L5bo3ayGEECqL8eZtSuxXfQoXCCGEcBCLBfJzQOemrhwwXvpnf6hyXUOFui6n8VYS1HY6N7WdUvbf2SvNEx5QS0iPHTuWTp060aVLFz755BOysrIYP348AGPGjKFOnTrMnj0bgH/961/06tWLDz/8kKFDh7J06VJ27drF/PnzAfDy8qJXr1689NJLuLq6EhoayubNm/n222/56KOPKmyeQghRqWmLP+G2rl/1Lk0thBDCDlot6D3VJzt5l0G5PtG5jqKo7Vx0aqlqTdmfz1SqhOeBBx7g4sWLzJgxg6SkJNq1a8e6desKCxMkJCQUeVrTvXt3IiMjmT59OtOmTaNp06asWrWq8AwegKVLlzJ16lRGjx5NamoqoaGhvP3223LwqBBClMYQBE7uxUtR34iTB7gG3bydEEKImkWrA69m6gOdmyU7BQraeTUv83I2qGQJD8CkSZOYNGlSifdiYmKKXRs5ciQjR44sdbzg4GC+/vprR4UnhBDVn2tt9VDRjCM29AkBQ0j5xSSEEKLqcqsLAV3h3C/W9/HvBm51HPLylWYPjxBCiEpC7wHeLdWnPNZwclfb661sL4QQouZQLOrTneB+oLey2rHeC4L7/vNUqOx7eCThEUIIUZx3SwgIu3nS4+SutvNueWviEkIIUbUoZsg4Bt6tMTWdcvOkR+8FLV4A79ZqP8Vc5hAq3ZI2IYQQlYDOGfy7qHtz0g5CzjnIy7l638lDXcbm3VL90knBAiGEECXQ6EDJ4+8/t/Lbvjwe6vUqQcZtcCEGTBlX2+k9IbC3+mTHuzWkxKl7QzXVcA+PEEKISkLnDH7twLMJ5J6HjPNw+rL6huRZGwy11eVvQgghRGk0Wk5dcefH7emYLbDntI7BvSZB/RGQ/jfkZ4OTm1qgwLU25BvhwlYwZ0JAl+pXpU0IIUQlpPcAfVMwNAB+hVphIAeJCiFE1WGxqOWhK0BycjJL1u7BbIFmtRXCG5+FpBTwug0CugE6wAzGy5AUA5Z/VhO4hoBbPYfEIAmPEEIIIYQQ1YnFoi5FzjwO6UfAnK0e5Ol1G3g0Vitx3qIEyNvbm+Dg2mhMadzXOQ2t+TLknIJLv4M5F7AAWtAZwMUPnH1A76euLnCp5ZAYJOERQgghhBCiujCmqEvCLm6HK3+B+Zr9lzpX8GkNtbpD4J3g4l/u4RgMBh5++GEsOZdwuhQFZ/ZAXlrxhpY8MKWDszfU6wC+7RyWlEnCI4QQQgghRHVgTFHPujm1TH2qcz1zjloM4Mp+MKVBnWG3JOnR6zSQnwSGYKgdDpf/hIzjoORfbaRxAs/G4NtWbZeVoFZs05Y9XZGERwghhBBCiKrOYlGf7JSW7FzLnK2203tDnbvLf3lbzjm4+LtaoMCjIdS5C/JSIesUWEyg1YN7A3D2U5Oyy/vUQgYufuBev8wvLwmPEEIIIYQQVV3OOXUZ282SnQLmbLgYC34dwd0xxQFKpCiQeRKMl9Tv0w6A1kUtSuDVUq3CplggP0tdgmcxXo0v84RauECjKVMIkvAIIYQQQghR1WUeVxOGYhQ16dBogOsShyvxalJRngmPMUVdvnYtcw5c3lO8aIHeu2gZ6ozj4N0KDAFlCkESHiGEEEIIIaq69CPXFChQ1CIAZiNYcq8mPFoD6FxA6wxo1PbphyGoV/nFlZ8BeZfVf7fkQ94lyL0A2efUfUSKWT1cVO8NbnXAEAjOAerenbzLan8k4RFCCCGEEKLmsliuLmVTTGDKVL+3mK5rmK3ul9G5gd5TLRRgzinzOT1nz56lbt26Jd9UzOqXxQiZpyDtoJroFGFSn/bkJquJj3dL8GgAiqvat4wq5gQiIYQQQgghhGNotWoSo5jUks+mtBKSnX9YTOr9vCtqlTSda5mSnbi4OBYsWEBMTEzJDTS6f/bxnILLe0tIdq5jSlPbZZ7658mUzu7YCsgTHiGEEEIIIao6r9vAokB+pnXt8zPByUvtZ6fU1FT27dsHgE5XSmLi5KkmVGkH/9mzg1qwoP596lMcJw81lsxTkPAj5CSq7dIOgl97tX8ZScIjhBBCCCFEVedWD3xbwflE6/v4tgY3+8o+p6amkpCQAEC3bt244447Sm6o91HP1TGlgX9XLI0fQxPcjxxTPocS00nPsuDloqVFo6G4NvsXSlI02uNfqecFudZW+/8jLy8PZ2dnm2OVhEcIIYQQQoiqTusCte5US03nZ9y8vZMn1LrjnwIGtvPz86Nu3br4+/szcOBANKWVjs5JVCuwtXwFbnuK40mX+ebng6zae46svKv7c9yddUS0r8O4sM40vXMgHJmnFlnISQSP+vz++++MHj2alStX0r59e5tilT08QgghhBBCVGWWfEjdB963Q9Mnb74MzMlTbed9u9rPkm/XywYEBDBkyJDSkx2AvBQwBMFtT/HZxmMM/HQ7i3cmFEl2ALLyzCzemcDAT7fz2cZjcNtTar+8FLZs2UJ4eDinT5/mP//5j81xyhMeIYQQQgghqjJLPpgz4NIOCOgOei9I3gIpseqBngWc3ME/DIJ6qufbXNoBLr7/VEKzLy24YbIDalEEv/Z8tvEYH2w4fNPxFAU+2HAYjQYm9unJ7s0/MnjIWLKzs+nfvz+LFi2yOUZJeIQQQgghhKjKtE5qNTPjBUjeDP6doGkLqDf8n/N5stUqbl63qftpzLlqu/w09VBPB1RCK43FEMzxpCtWJTvX+mDDYcJbBtCsTXe6du2Ks7MzK1euxNXV1eYYJOERQgghhBCiKtM6gXsopP2tJjHJ0aB1A58WUCsMdReLBYwpkLQJLNkFHdV+2vJLCTTO3nwTu9/mfooC32w/w6yIVqxZswadTofBYLArBkl4hBBCCCGEqOrc64Ohlnp4J6hJTeruG/cxBKr9ylGOycyqvefs6rty7zmmDW2Bu7t7mWKQogVCCCGEEEJUdS61wLOp9cvTNDrwbKL2K0eHEtOLFSiwVlaemb/Pp5c5Bkl4hBBCCCGEqOq0TureHd82N096NDq1nX+nUpezWSwWNm/eTE5OTpnCSs81lbG/fRXkriUJjxBCCCGEENWBsw8E9oKAMLU4QbFf9bXq9YAwtZ2zT4nDKIrCr7/+SkxMDN9//z0Wi8XukLwMZdtBU9b+IHt4hBBCCCGEqD6cfSCoN/jcDlkJkHVaLTut0akFCtzrq8vYblCoYNOmTezere7/6d69O1qt/c9IWoR44+6ss2tZm7uzjua1vex+7QKS8AghhBBCCFGdaJ3Atbb65dfxasJjRTW2nJwc9u3bB8DQoUO5/fbbyxSKq15HRPs6LN6ZUOL9rIObMV1OxKfHqGL37mlfB1d92UtmS8IjhBBCCCFEdaV1wpZf+V1dXRk/fjzHjh2jU6dOZX55RVEY1z2UyLgEFKXovcwDm0hZ+zEoFpwDG+HWtGvhPY0GxnUPRVGUmx9uehOyh0cIIYQQQghRyNfXl86dOztkLK1WS9MgL14Y0KzI9cz9GwuTHY82A3FtUvT1XhzYjMaBnmVaTldAnvAIIYQQQgghytWkvk3QaOCDDYdRFFDy89Rkp+0g/MKfQaNRExuNRk12JvZp4rDXloRHCCGEEEIIUe4m9mlCeMtAvtl+mpXOQ9H71cGl3u1oNFrcnXXc074O47qH0iSo7IUKrlUpl7TNmTOHBg0aYDAY6Nq1K3FxcTdsv3z5cpo3b47BYKB169b8+uuvRe5rNJoSv95///3ynIYQQgghhBDiGo1qeTArohV/TO/P2tkTWPRoV356Oow/pvdnVkQrGtXycPhrVrqEZ9myZUyZMoXXX3+dPXv20LZtW8LDw7lw4UKJ7bdv386oUaN47LHH2Lt3LxEREURERLB///7CNufPny/ytXDhQjQaDSNGjLhV0xJCCCGEEOLWUyxgMan/rAS0Wi0ajQY3Zyc6hvrRu1kgHUL9cHN2QqPROGTPTrHXdPiIZfTRRx/xxBNPMH78eFq2bMm8efNwc3Nj4cKFJbb/9NNPGTRoEC+99BItWrRg1qxZdOjQgc8++6ywTXBwcJGvn3/+mT59+tCoUaNbNS0hhBBCCCFuDYsFcpIg5Q84sxISflL/mfIH5CSjmG0/E6cqq1R7ePLy8ti9ezdTp04tvKbVaunfvz+xsbEl9omNjWXKlClFroWHh7Nq1aoS2ycnJ7N27VoWLVrksLiFEEIIIYSoFPLSIHU3ZByF3AvqGTwF0g6S5xTA0t/zCOvei6Yt21VYmLdSpUp4Ll26hNlsJigoqMj1oKAg/v777xL7JCUlldg+KSmpxPaLFi3C09OTe++9t9Q4jEYjRqOx8Pv09HQATCYTJpPJqrkUKGhva7+qTuZdc+ZdE+cMNXPeZZlzTfpzEkKICpOXBhe3Quo+UPKL3TabzfywLZmTFzRcWLOWZ+vXxdkj4NbHeYtVqoTnVli4cCGjR4/GYDCU2mb27NnMnDmz2PUNGzbg5uZm1+tGRUXZ1a+qk3nXHDVxzlAz523PnLOzs8shEiGEEIUsFvXJTinJjkWBlbvg+AUNep3CA10tOGfGg1tvKId9MwsWLOCee+7Bz8/P4WPbqlIlPAEBAeh0OpKTk4tcT05OJjg4uMQ+wcHBVrffunUrhw8fZtmyZTeMY+rUqUWWyaWnp1OvXj0GDhyIl5dtZfJMJhNRUVEMGDAAvV5vU9+qTOZdc+ZdE+cMNXPeZZlzwZNyIYQQ5cR4QV3GVkKyA4ACOi1oNQr3d4V6fmbIOAbet4NrUMl97DRr1ixmzJjB559/zu+//37DBw23QqVKeJydnenYsSPR0dFEREQAYLFYiI6OZtKkSSX2CQsLIzo6mueee67wWlRUFGFhYcXaLliwgI4dO9K2bdsbxuHi4oKLi0ux63q93u5fbMrStyqTedccNXHOUDPnbc+ca9qfkRBC3HLZZ9Q9O6XQaiGiI3RtDCG+/1zMTVb7OSjhURSFN954gzfffBOA++67r8KTHaiEVdqmTJnCl19+yaJFizh06BBPP/00WVlZjB8/HoAxY8YUKWrwr3/9i3Xr1vHhhx/y999/88Ybb7Br165iCVJ6ejrLly/n8ccfv6XzEUIIIYQQolwpFshKKFqgoAQazTXJDqjts047rGT15cuXCysrv/fee0V+Z69IleoJD8ADDzzAxYsXmTFjBklJSbRr145169YVFiZISEgoUp+7e/fuREZGMn36dKZNm0bTpk1ZtWoVrVq1KjLu0qVLURSFUaNG3dL5CCGEEEIIUa4UM1hKWcp2M5Z8tb+m7M9B/Pz82LRpE9HR0Tz55JNlHs9RKt0THoBJkyZx+vRpjEYjO3fupGvXroX3YmJi+Oabb4q0HzlyJIcPH8ZoNLJ//36GDBlSbMwJEyaQnZ2Nt7d3eYcvhBCiCps9ezadO3fG09OTwMBAIiIiOHz4cJE2vXv3RqPRFPl66qmnKihiIUSNp9GB1s7nGFontb+DNGnSpFIlO1BJEx4hhBCiomzevJmJEyeyY8cOoqKiMJlMDBw4kKysrCLtnnjiCc6fP1/49d5771VQxEKIGk+jBff6ticuGh24hzrk6U5lVumWtAkhhBAVad26dUW+/+abbwgMDGT37t307Nmz8Lqbm1upFUSFEOKWc6sHhkDIOW99H0OQ2q+ak4RHCCGEuIG0tDSAYmdJLF68mO+//57g4GDuuusuXnvttVLPapMDrW1XU+YJNWeuMs9ypvUlk/q4Wy6h4cbFCwDQOIFrY9D6QBkOlK6ov09bXlcSHiGEEKIUFouF5557jh49ehQphvPQQw8RGhpKSEgI8fHxvPzyyxw+fJiffvqpxHHkQGv71ZR5Qs2Zq8yzfJhMJo4ePYq7uyf169dHo9HcvNOpTGDdTZvdSEX9fdpyoLUkPEIIIW7MlKkukcj8Z5nExVjwqA2utUHvUbGxlbOJEyeyf/9+tm3bVuT6hAkTCv+9devW1K5dm379+nH8+HEaN25cbBw50Np2NWWeUHPmKvMsPzk5OXz//ffk5eXh7uZK79sNuJvPqIeRXluqWqMDl0DwaAS+7cHZ06bXsVgsxMfH065duwr/+7TlQGtJeIQQQpTMnAdpB9WvnHOQlwO0hQsxcMUVXOuAd0v1S+dc0dE63KRJk/jll1/YsmULdevWvWHbgmqix44dKzHhkQOt7VdT5gk1Z64yT8dSFIWVK1dy8eJFPDw8eGTMWHy8vcF4UT1UNOu0Wnpa66QWKHCrBy611JNIbWA2m3niiSdYunQpP//8M3379gUq7u/TlteUhEcIIURx5jxIiYNLsZBfUJ3smjfH/CzIOKImQvmZ4N+l2iQ9iqIwefJkVq5cSUxMDA0bNrxpn3379gFQu3btco5OCCGK0mg09OjRg9TUVB566CF8ff85WdQ1SP3y6/DPOTs6u6ux5efnM27cOBYvXoxOpyvc21hVSMIjhBCiuLSD1yU7pcjPUts5eYBfu1sSWnmbOHEikZGR/Pzzz3h6epKUlASAt7c3rq6uHD9+nMjISIYMGYK/vz/x8fE8//zz9OzZkzZt2lRw9EKImqhJkyZMnjwZJ6drfrVXFDCmQH7G1YTHyRNc/MGa/T3XWLBgAYsXL8bJyYklS5Zw3333VaniE5LwCCGEKMqUqSY8N0t2CuRnqe09m4LevXxjuwXmzp0LqIeLXuvrr79m3LhxODs787///Y9PPvmErKws6tWrx4gRI5g+fXoFRCuEEKrCZMeSrz59zzwJGcch7/LVhMfZFzwbg0dDdVmylYeVPv744+zYsYO7776be+65p+zBFuwNzU0GixG0LmqJ7HLaGyoJjxBCiKJyzqtvljb1SYTcRNA3LZ+YbiFFUW54v169emzevPkWRSOEEDYwG+Hyn+qSZOOl4vfzM9V9PVf2q0uRfduCrvj+wuvpdDq+/vprB8R33d7Qaz9Yc3Ivsjc025hvdxXL60nCI4QQoqjcZOuf7hTIz4ScZPUpjxBCiFvPkg+X90HyFjDf5Ge48RIk//PBjV8Hq5/0lEmJe0Ovcc3e0ENHTrBy49/cd9993HbbbWV+aUl4hBBCFGUx3rxNif3yHBuHEEII6+Wcg5Q/riY7em/wbQcufuqSMYsRjKlqUmRKU9ulxIFrMLjXL//4rNwbuvNwFuvi/wLgr7/+koRHCCFEOdDefHlDyf2qR5U2IYSochRF3bNjvATuoVj8w9B430ZOXj6HzqeTnmvGy6CjRe0WuAb1Qkk7gjYlVi1ZnXlCLVVtYyEDm1i5N/RYMqyLV+PoeJs3Q4YNdMjLS8IjhBCiKEOQupbalmVtTh5q+VMhhBAOd/bsWdzd3a+WnL6eMUUtUBDQHUIGcjzpCt9sOsCqvefIyrt68Ki7s46I9nUYF1aPpo3HQ+J6tZ93KzAElN8ErNwb2jgQ2tVX8POAO1qa0OQlgYtth6OWRBIeIYQQRbnWVjeOZhyxoU8IGELKLyYhhKihkpOTC0tCjxs3Dn9//+KN8jPAozEE9+azjcf4MOowJdVfycozs3hnApFxCbwwoBmT+oZDUgw5GRdx1vui0+nKZxJW7g3VaODuDv88bDJnOWxvqH2nDwkhhKi+9B5qlRwnK0tMO7mr7atBSWohhKhMUlNT+f7778nNzcXX1xcvL6+SG2r1hcnOBxtKTnaupSjwwYbDzNl0jCzPzgy973Eef/xxLBaL4ycBNu0NLbKyzkF7QyXhEUIIUZx3SwgIu3nS4+SutvNueWviEkKIGiQqKorMzEwCAwMZNWoUer2+xHYWvS9Hk9L4YMNhm8Z/75d99Ok/kE1btrNixQqOHTvmiLCLq+C9obKkTQghRHE6Z/WMBiePq+cl5OVcve/koS5j++e8BHRSsEAIIRxt+PDhODs7079/f1xdXUttp3Fy45vY/TaNrSgKF1a8RcLpP/Hy8mL9+vUOqYhWogreGyoJjxBCiJLpnMGvHXg2gdzzkHEeTl+GwN7gWRsM5XMithBCCJXBYOCee+65absck5lVe207MFqj0eDZ6W5Ml07zy2+/0q1bN3vDvLkK3hsqS9qEEELcmN5D3TRaK0z9vlaY+r0kO0IIUSkcSkwrUo3NWm5NuhIy4Svc6zQrh6iu8c/e0P3nXci3JkwH7w2VhEcIIYQQQogqLD033+6+WmdDmfpbQ1EU1u1MZMWOPFbtdbpxUYVy2BsqS9qEEEIIIYSowrwMZfuVvqz9byQ/P59Vq1Zx4MABAELqNwdPI+QmFt3TU457QyXhEUIIcWOmTPXQuMzz6vcXY8GjtromW5a1CSFEhWsR4o27s86uZW3uzjqa1y6l3LUDXL58maNHj6LVaomIiKB169bq+0ruefWcHUueWo3NNajc9oZKwiOEEKJk5jy1QluRKm1t4UIMXHFVN6BKlTYhhKhwrnodEe3rsHhngs1972lfB1d9OR04CtSqVYv7778frVZLw4YN1Yt6D9A3dcihotaQPTxCCCGKM+dBShwkRalVda4vJZqfpV5PilLbmR1zOJwQQtQkf/31F3FxcWUeR8nPZlxY/aKHdlpBo4Fx3eujmLLLHMONNG7c+GqyUwEk4RFCCFFc2kG4FHvzMxPys9R2aQdvTVxCCFFNHDt2jFWrVvHbb79x9OjRMo2lzTlH02BvXhhwtdqaOfMyyT/MwHQlqdR+Lw5sRuNAT7S5tpW0rmok4RFCCFGUKVNNYKw9IC4/S21vsuFAOSGEqMHOnDnDsmXLsFgstGrViiZNmpRtQFMaHPuKSX2b8FJ4M8yZKSQtmUruyT2krP0Y5bqyaBoNvBTejIl9mqA5vlDtX43JHh4hhBBF5ZxX9+zY1CdRrbijvzXrsYUQoio7c+YM+fn5NGnShIiICDS2rkW7nsYJzq0FSx4Rtw3n/V9nkp96Fr13IP5Dny8c391Zxz3t6zCuez2aBPnAkc/h/P/Ar2PZJ1WJScIjhBCiqNxk65/uFMjPVKvt3KINqEIIUZV1794dHx8fmjRpgk7ngIIBeh9wrw+nf8DAXrxdNTRo0IBf160n28WPjFwzXga1Gpur3gnl4u/wx3K4Eg9+7dX+1ZgkPEIIIYqyGO3sJ4ULhBDCWi1bOu5gTdzqgX9nuPwn/hzlf28EkoMv9Z22gXMdcHNXP8g6ew7O/YbGePFqX7+Oan87WCwWVq9eTVhYGCEhIQ6ajONJwiOEEKIorYud/aQ0tRBCVAidE/i0BvdQyDpNLR9nIAtOfHvjfu6haj+d7SlBdnY2x48fJysrizNnzjB58mTHPK0qB1K0QAghRFGGIHByt62Pk4d6aJwQQoiK4dkM6t8Hzr7WtXf2hfoj1X42ysjI4NtvvyUrKwuDwcA999xTaZMdqIQJz5w5c2jQoAEGg4GuXbvetDb58uXLad68OQaDgdatW/Prr78Wa3Po0CHuvvtuvL29cXd3p3PnziQk2H4wkxBC1AiutdVDRW3qEwKGyrucQQghqj0nAwT1gsaPqk9ubsQ9VG0X1FPtZyNXV1c8PT3R6/U88sgjhIbe5PUqWKVKeJYtW8aUKVN4/fXX2bNnD23btiU8PJwLFy6U2H779u2MGjWKxx57jL179xIREUFERAT79+8vbHP8+HHuuOMOmjdvTkxMDPHx8bz22msYDLb/5QohRI2g9wDvltY/5XFyV9vrbXwqJIQQwrH0XhA8AFpMgSaPqwUJnP3Un9POfur3TR5X7wcPUNvbwcnJiREjRtC0aVNq1arl4Ek4XqXaw/PRRx/xxBNPMH78eADmzZvH2rVrWbhwIa+88kqx9p9++imDBg3ipZdeAmDWrFlERUXx2WefMW/ePABeffVVhgwZwnvvvVfYr3HjxrdgNkIIUYV5t1Qrr93s8FEndwgIU9sLIYQAQFEUzGYzer3+1r+4kwF824LX7RDQA0xXQMlXS1frfdQCBXbs2bmewWDA2blq7N2sNAlPXl4eu3fvZurUqYXXtFot/fv3JzY2tsQ+sbGxTJkypci18PBwVq1aBaiVI9auXcu///1vwsPD2bt3Lw0bNmTq1KlERESUGovRaMRovFqlKD09HQCTyYTJZLJpXgXtbe1X1cm8a868a+KcoSbMWwNe7QE3SP8bcs5jsuQAYLJoQecBrsHg1Ry8moFFA5bS/yyq75+TEEIUl5yczOLFixk9erTDVxXt37+fPXv2MGbMmBs31DmBZ0OHvrbDmDLVM99yk9XKoFoXdf+oa211lYGDVZqE59KlS5jNZoKCim56DQoK4u+//y6xT1JSUontk5KSALhw4QKZmZm88847vPXWW7z77rusW7eOe++9l02bNtGrV68Sx509ezYzZ84sdn3Dhg24ubnZMz2ioqLs6lfVybxrjpo4Z6gp83YHrp4CHnWm9TX3zvzzdWPZ2dkOj0oIISqj3bt3F/4uevjwYdq2beuwsePj4+nXrx8pKSl4eHhw7733OmzsW8KcB2kH1a+cc0VXEDi5q/tHvVuqXzrHPT2qNAlPebBYLAAMHz6c559/HoB27dqxfft25s2bV2rCM3Xq1CJPjtLT06lXrx4DBw7Ey8u2tY4mk4moqCgGDBhQMY81K4jMu+bMuybOGWrmvMsy54In5UIIUZ0dOHCA9evXA9CjRw+HJjv79u2jf//+pKSk0LFjR3r37u2wsW8Jcx6kxJW+VDo/CzKO/JMIZYJ/F4clPZUm4QkICECn05GcnFzkenJyMsHBwSX2CQ4OvmH7gIAAnJycih3s1KJFC7Zt21ZqLC4uLri4FD+HQq/X2/2LTVn6VmUy75qjJs4Zaua87ZlzTfszEkLUTAEBAXh4eGAwGOjZs6dDx/7tt99ISUmhS5curF+/Hh8fH4eOf63c3FxcXFzQaDSOGzTt4M33hYJ6/1KsetyBXzuHvHSlqdLm7OxMx44diY6OLrxmsViIjo4mLCysxD5hYWFF2oO6vKSgvbOzM507d+bw4cNF2hw5cqTSl88TQgghhBBVS1BQEI8++ih16tRxbLIAvPLKK8ydO5cNGzaUa7KTmprKl19+ycaNGx03qClTTXhuluwUyM9S25usbH8TleYJD8CUKVMYO3YsnTp1okuXLnzyySdkZWUVVm0bM2YMderUYfbs2QD861//olevXnz44YcMHTqUpUuXsmvXLubPn1845ksvvcQDDzxAz5496dOnD+vWrWPNmjXExMRUxBSFEEIIIUQ15uHh4fBkB0Cj0fDUU085fNxrnTt3jsjISLKzs9m/fz89evRwTNGFnPPqUjWb+iRCbiLom5b55StVwvPAAw9w8eJFZsyYQVJSEu3atWPdunWFhQkSEhLQaq8+lOrevTuRkZFMnz6dadOm0bRpU1atWkWrVq0K29xzzz3MmzeP2bNn8+yzz9KsWTNWrFjBHXfcccvnJ4QQQgghxC1hYyW0nJwcvvvuO4xGI8HBwY6tMJebbP3TnQL5mZCTDJ7VLOEBmDRpEpMmTSrxXklPZUaOHMnIkSNvOOajjz7Ko48+6ojwhBBCCCGEqLzsrITm6urKwIEDOXjwICNHjixxP7vdLMabtymxX55DXr7SJTxCCCGEEEIIO5SxElqHDh1o376945fkae1MnrSOqdJWaYoWCCGEqL4KjgkQQghRjmythJZ2sNit8th/hCFIfbpkCycPcA26eTsrSMIjhBCiXGVkZPDll19WdBhCCFFmZrO52JEojhIXF0dOTo79A1RwJbQbcq2tLqWzqU8IGEIc8vKS8AghhChXnp6e1K1bt6LDEEKIMlEUhdWrV/PVV19x9OhRh469bt06evbsSUREBLm5ufYNUpZKaOVN76HuGbL2KY+Tu9peb+NToVJIwiOEEKLcDRw4sKJDEEIIuymKwvr164mPj8dsNjt0me6vv/7K8OHDMRqNGAwG+5eUlaUS2q3g3RICwm6e9Di5q+28WzrspaVogRBCiBsz50P2GchJVb+/HA+ufuBWD3TWvY3o9fpyDFAIIcrXgQMH2LlzJwDDhw+nWbNmDhk3KyuLRx99lLy8PO655x6WLl2Ks7OdG/VvUgntbCp4GMDH7fp+jqmEdlM6Z7VIgpNHKRXkPNRlbCVUkCsrSXiEEEKULD8XMg7Dlb8g5Q/IPA+MhoPvgkdt8O8MPq3Bsxk4OeisBiGEqIRatGhBmzZtqF27Nm3btnXYuO7u7qxZs4Z58+Yxb968sn04dINKaIfPw49/qMnOoz3B9dpcwkGV0Kyicwa/duDZBHLPq0+XLHlqDK5BYCj5jKCykoRHCCFEcaZ0SI6BhBWQdVq9pvzzpph3BS5fgMt/gnso1L8PgnqB3quiohVCiHKl0+mIiIgolwpmnTt3pnPnzmUfqKAS2nXL2nadhF/3gYIGXzcF3bUbWhxYCc0meg/QN3XIoaLWkD08QgghisrPVZOd4wuvJjulyTqN+ehCSN6s9hNCiGqqXMo1O1IJldDyzRB3XE122ocqPNgNnK993OHASmiVmTzhEUIIUVTGYfXJTt6VGzZTFNh2rj4HUwJ51GkFevf64Ou4pR5CCCFsUFAJ7Zq9MU46GN0dDpxTCGsCRXI2B1dCq8zkCY8QQoirzPnqnp2bPNnJNjkReagNGxMak5TlyV+njWo/c/4tClQIIUQxJVRC83aD7k1LSHYcXAmtMpMnPEIIIa7KPqMWKLiJVcdacOyKP05aM4MbHqV94HlI3Q0BPcCz4S0IVAghRDEVWAmtMpOERwghxFWmK5CVcNNmAxscIzPPmeFN/ibI/Z8308zTan8hhBCFLl++jK+v7617wQqqhFaZyZI2IYQQVyn5Vp3JEOCawxNtdl9NdkA9A0Kp+kvaZs+eTefOnfH09CQwMJCIiAgOHz5cpE1ubi4TJ07E398fDw8PRowYQXLyLTq8TwjhcEajke3bt6MoikPHnT9/Pk2aNGHPnj0OHdcqeg+1ClrgHRDcV/2nZ9Mal+yAJDxCCCGupXGy+kyGYgWLtC5q/ypu8+bNTJw4kR07dhAVFYXJZGLgwIFkZV1N7p5//nnWrFnD8uXL2bx5M4mJidx7770VGLUQwl4mk4klS5YQFRXFunXrHDbuF198wZNPPklqaio//fSTw8YVtqv670xCCCEcR+8D7vUh77LtfT1C1f5V3PW/8HzzzTcEBgaye/duevbsSVpaGgsWLCAyMpK+ffsC8PXXX9OiRQt27NhBt27dKiJsIYQdLBYLK1as4PTp07i4uNCuXTuHjLtjxw7eeecdAKZMmcKsWbMcMu714uLicHJyokOHDuUyfnUhCY8QQoir3OqBf2f1UFFb+XVU+1czaWlpAPj5+QGwe/duTCYT/fv3L2zTvHlz6tevT2xsbIkJj9FoxGg0Fn6fnp4OqJ8sm0wmm+IpaG9rv6qmpswTas5cK+M8z549y9GjR9HpdNx3330EBASUOT6TyUT79u3p06cPHTp04D//+Q/5+Y5d7qsoCps2bWLHjh1oNBqCgoIIDAx06GvcTEX/fdryupLwCCGEuErnxBVtY7zdQtFk3+TQ0Wu5h4JPa9BVr7cVi8XCc889R48ePWjVqhUASUlJODs74+PjU6RtUFAQSUlJJY4ze/ZsZs6cWez6hg0bcHNzsyu2qKgou/pVNTVlnlBz5lrZ5tmgQQMUReHAgQMcOHDAIWO6uLjwzDPP4OTkxG+//eaQMQsoikJCQgKXL6tP4oODg/njjz8q7GDUivr7zM7Otrpt9XpnEkIIYTdFUdizZw+//RbN0O69ae+82rqlbc6+UH8keDYr/yBvsYkTJ7J//362bdtWpnGmTp3KlClTCr9PT0+nXr16DBw4EC8vL5vGMplMREVFMWDAAPR6fZniqsxqyjyh5sy1ps1zyJAh5TbPmJgYduzYwZAhQ2jTpk25vMbNVPTfZ8GTcmtIwiOEEIK8vDzWrl1LfHw8AMcuutCu+3g0Z1aoh5BqncHJH3IAF3/IT1GrubmHQv37IKgnOBkqdhIONmnSJH755Re2bNlC3bp1C68HBweTl5fHlStXijzlSU5OJjg4uMSxXFxccHFxKXZdr9fb/YtCWfpWJTVlnlBz5irzLLv+/fvTpk0bgoKCymV8W1TU36ctrykJjxBCCE6ePEl8fDwajYZ+/frRvXt3NGbjP+c4XIC8VMhNgytA7YFg8AZnPzAEgkejapXsKIrC5MmTWblyJTExMTRsWPQg1Y4dO6LX64mOjmbEiBEAHD58mISEBMLCwioiZCFEDVOwb0dYRxIeIYQQNGvWjF69etGwYUNCQ0PVi1on0LtDrkV9ypOVDIRB+mEwB4Gzj3pfW73eSiZOnEhkZCQ///wznp6ehftyvL29cXV1xdvbm8cee4wpU6bg5+eHl5cXkydPJiwsTCq0CSFEJVS93qWEEELYrXfv3le/MRvVSm0pcWC8pF5zcrv6T1MaXNgCaQfBvwv4tgVd8SVbVdHcuXOB6/48UEtPjxs3DoCPP/4YrVbLiBEjMBqNhIeH8/nnn9/iSIUQFaXggNKKKhQgbCMJjxBCiKIs+XB5HyRvAXPWjdsaL0HyZvXf/TpUi6c91py0bjAYmDNnDnPmzLkFEQkhKhNFUXj55ZcxGAy8+eabFR2OsIK2ogMQQghRyeScg5Q/bp7sFDBnqU+CchJLvP3nn38yatQoBwYohBDWuXLlCt98801hCeeyUhSFF198kffff59Zs2YRFxfnkHGvf428vDyHj1uTScIjhBDiKkWBzJNXl7FZy3gJMk+o/a+Rn5/PiBEj+PXXXx0YpBBC3FxWVhbff/89p0+fZs2aNQ4Zc8qUKXz00UeAuvy1S5cuDhm3QH5+PitWrGDJkiUOP6y0JnNowpOXl0dWlpWfCAohhLglUlNTuXDhgnWNjSmQcdy+F8o4rva/hpOTE/PmzWPo0KH2jSmEEHYwGo0sXryYlJQUvL29iYiIcMi4LVu2RKvVMn/+fJ566imHjFkgJyeH77//ngMHDpCQkMC5c+ccOn5NZlfCs3TpUp5//vki12bOnImHhwc+Pj7cc889ZGZmOiRAIYQQ9jt06BDz589n2bJlGI3Gm3fIz7DusNGS5F1W+1+nf//+REZG2jemEELYwWQyYbFYcHNz45FHHrH5gN/SPPHEExw8eJAnnnjCIeNda/ny5Zw+fRoXFxdGjx59tWKmKDO7Ep4PP/ywyJOc7du3M3PmTMLDw3n++edZt24db7/9tsOCFEIIYRuz2cy6dev44YcfMBqNuLu7YzKZbt5RMatf9ihLXyGEcCAPDw/GjRvH2LFj8ff3d+jYzZo1c+h4BQYMGIC/vz/jx4+nUaNG5fIaNZVd5XSOHz/O2LFjC7+PjIwkODiYlStX4uTkhMViYcWKFcyePdthgQohhLDNmTNnAOjevTt9+/ZFp9PdvJNGp37Zoyx9hRDCwQwGAwZD1TkUuXbt2jzzzDNotbLF3tHsSniMRmOR/4A2bNjA4MGDcXJSh2vZsqWcRyCEEBVIp9MxcuRIkpOTbfs00skTnH0h345lyc6+an8hhBB2kWSnfNj1p9qwYUP+97//AbBr1y6OHTvGoEGDCu8nJyfj4eFhd1Bz5syhQYMGGAwGunbtetOSf8uXL6d58+YYDAZat25drBrQuHHj0Gg0Rb6ujVcIIaojHx8f25deuPiDZ2Ormq77/RjvLNh29YJnY7W/EEIIUYnYlfA8+eST/PDDD7Rp04aBAwdSt25dhg0bVnj/999/5/bbb7croGXLljFlyhRef/119uzZQ9u2bQkPDy+1wtD27dsZNWoUjz32GHv37iUiIoKIiAj2799fpN2gQYM4f/584deSJUvsik8IIao1jQY8GoJLQKlNMrONPP3WLwx+ZjHT/hvN9n1n1PYejdT+QgghRCViV8IzefJkvvjiCxo3bszw4cPZsGEDrq6ugFr+NCkpidGjR9sV0EcffcQTTzzB+PHjadmyJfPmzcPNzY2FCxeW2P7TTz9l0KBBvPTSS7Ro0YJZs2bRoUMHPvvssyLtXFxcCA4OLvzy9fW1Kz4hhKj2XOuAfxfQuRe7lZ+fzx1jvmLe8t0APPtQV9rf3lht7xpyqyMVQgiHMZlMTJ06lZSUlJs3FlWKXXt4QC3LV1JJPj8/P3bt2mXXmHl5eezevZupU6cWXtNqtfTv35/Y2NgS+8TGxjJlypQi18LDw1m1alWRazExMQQGBuLr60vfvn156623Sq3aYTQai5RvTU9PB9T/EayqcnSNgva29qvqZN41Z941cc5QA+btcTvkWyB1N1hM4NMGk84bp9NnGf3QKOYtWMJXb95Hn7CW4NcRk8ftYFbAXPKfR7X9cxJCVJjs7Gzc3NwcMlZeXh4PPvggK1euZNOmTWzfvt2h+2lycnK4ePEi9evXd9iYwnp2JzygJgZ79uzhwoUL9OjRg4CA0pdAWOPSpUuYzWaCgoKKXA8KCuLvv/8usU9SUlKJ7ZOSkgq/HzRoEPfeey8NGzbk+PHjTJs2jcGDBxMbG1ti1aLZs2czc+bMYtc3bNhg9/9YUVFRdvWr6mTeNUdNnDNUzLxNJhMpKSkEBQWhKfclZHX/+WfGP1/QvF1P3v2gKzlubvx6FCD5n6/SZWdnl2OMQoia5ty5c3z33Xf07duXLl26lGkso9HIyJEjWbNmDS4uLsyYMcOhyc6VK1dYvHgxaWlpjB8/ntq1aztsbGEduxOe//u//+ONN97gypUraDQaoqKi6Nu3L5cuXaJ58+a89957PProo46M1W4PPvhg4b+3bt2aNm3a0LhxY2JiYujXr1+x9lOnTi3y1Cg9PZ169eoxcOBAmw+uMplMREVFMWDAAPR6vf2TqGJk3jVn3jVxzlBx8z516hQ///wzWVlZtGnThk6dOpXr6x2/kMHiuDOs/TORfHM+szpZeGOvHiedK0PbhjC6Sz0aB968MlvBk3IhhCirS5cusXjxYoxGI3///TedOnUqU4Jy6dIl/vzzTwwGA6tWrSI8PNxhsSYlJbF48WIyMzPx8vKy7ngA4XB2JTxff/01zz33HA8++CADBw4sktgEBATQt29fli5danPCExAQgE6nIzm56CeFycnJBAcHl9gnODjYpvYAjRo1IiAggGPHjpWY8Li4uODi4lLsul6vt/sXm7L0rcpk3jVHTZwz3Np579ixgw0bNqAoCoGBgTRt2rRcX/uzjcf4MOowiqJ+76JTnyYZLRrSTRa+23mW7+PO8sKAZkzq2+SGY9XE/zaEEI6Xk5PDd999R05ODiEhITzwwANlfhpTp04dNm3axOnTp+nTp4+DIlXFxcWRmZlJYGAgo0ePtvmDc+EYdv0X8uGHHzJ8+HAiIyO56667it3v2LEjBw4csHlcZ2dnOnbsSHR0dOE1i8VCdHQ0YWFhJfYJCwsr0h7UJSaltQc4e/YsKSkp8khRCFGl+Pn5oSgK7dq14/HHHy/zMuIb+WzjMT7YcDXZKY2iwAcbDjNn07Fyi0UIIQoYDAY6dOhAQEAAo0ePLvEDans0atTI4ckOwJAhQ+jRowfjx4+XZKcC2fWE59ixYzz77LOl3vfz87O7wsWUKVMYO3YsnTp1okuXLnzyySdkZWUxfvx4AMaMGUOdOnWYPXs2AP/617/o1asXH374IUOHDmXp0qXs2rWL+fPnA5CZmcnMmTMZMWIEwcHBHD9+nH//+980adLEoY8shRCivN1222088cQThISUTzW0xMREDh06RP1Wnflgw2Gb+n6w4TDhtwfRKMBdDs4TQpQbjUZDr169CAsLw9nZuaLDuSknJyf69+9f0WHUeHYlPD4+Ply6dKnU+wcPHrzhkrIbeeCBB7h48SIzZswgKSmJdu3asW7dusLCBAkJCUXeTLt3705kZCTTp09n2rRpNG3alFWrVtGqVStAPW08Pj6eRYsWceXKFUJCQhg4cCCzZs1y2KcCQghxq5RXsrNs2TKefvppAB5+7web+ysKfPP7KWZFtHJ0aEIIUUxVSHZE5WFXwjNkyBDmz5/PM888U+zegQMH+PLLL8tUsGDSpElMmjSpxHsxMTHFro0cOZKRI0eW2N7V1ZX169fbHYsQQlRneXl5jB07lqVLlwLQvkMHftuXAB5BN+lZ3Mq955g2tAVuzmUqACqEEEI4lF3rDt566y3MZjOtWrVi+vTpaDQaFi1axMMPP0ynTp0IDAxkxowZjo5VCCGEgxV8SqrT6Xj99deZu/RXTHYkOwBZeWb+Pi/V2IQQQlQudiU8ISEh7N69m0GDBrFs2TIUReG7775jzZo1jBo1ih07dpTrZlohhBCOM2fOHGJjY3njjTfINpftXJ/03HwHRSWEEI6RlpZW4gohUXPYvbM0MDCQr776itTUVJKTkzl//jyXL19m4cKFBAYGOjJGIYSo1nJzc1mxYgVnz56tkNf38/Ojc+fOAHgZyrYcraz9hRDCka5cucKAAQMIDw9n3bp1Dh07MTGRdevWodysnKWocA55Z6pVq5YjhhFCiBrn/PnzLF++nMuXL5OYmMjEiRMrtMpZixAv3J11ZOWZbe7r7qyjeW0puyqEKJtjx44RGhpa5vO7UlNTGThwILt378bf39/uglolOXr0KMuXL8dkMuHr60vXrl0dNrZwPLsSnjfffPOmbTQaDa+99po9wwshRI1w9uxZvvnmG8xmM97e3tx7770VXtLZVa8jon0dFu9MsLnvPe3r4Kovfop4RkaGLCcRQljl0KFDLF++nHr16jF69OgyVWP7/PPP2b17NwEBAURHR9OmTRuHxLhv3z5Wr16Noig0atSIdu3aOWRcUX7sSnjeeOONUu9pNBoURZGERwghbiIkJIS6devi4uJCREQErq6uDh3fbDZz8eJFmz7VtFgUxnZvQGRcwk0PHb2WRgNjuzfAoijoNJp/xrKwa9cuNm7cSFpamq3hCyFqmJMnT7JixQoURcHf37/MT3imTp3KpUuXePzxxwuPK3EENzc3ANq2bctdd92FTlf8gx5RudiV8FgslhKvnT59mjlz5rBlyxZ+++23MgcnhBDVmVarZdSoUTg7O6PRlK1YwPWOHz/O2LFjyczMJC4uzupPSbVaDbcFefLCgGY2HT764sBmNA704NpZbNu2jU2bNgFQu3ZtW8IXQtQwZrOZn3/+GbPZTIsWLRg2bFiZfy7qdDo++eQTxwR4jdtuu43HHnuMkJAQh//sFuXDYWsntFotDRs25IMPPqBp06ZMnjzZUUMLIUS15eLi4tA3TEVRmD9/Pm3btuX333/nxIkTxMfHW90/x2TmvXV/M6lvE14Kb8bNQtNo4KXwZkzs04QP1h8mx3R170+nTp3w8/NjyJAhjBkzxt4pCSFqAJ1Ox6hRo2jdunWlWN57M3Xq1JFkpwopl3I6PXv25OWXXy6PoYUQQtyAyWTis88+Iysri169evHNN9/QoEEDq/sfSkzj85jj5JktTB/akn4tAvk+9jQr954j33y15LS7s4572tfh4bBQmgd78dbag3y19ST9WwTSIdQPUJd9FBRhSE+X83mEEDcWFBTEvffeW9FhiGqoXBKeXbt2VfrMXAghqiNnZ2e+++47oqOjee6552z+WVxwjs5XW08SfzaNx+5oyMzhrXh5cHMOnL1M8oFYFo3vwu11fXFzduJ/h5KZ8fMB4k6mFulfQN4LhBBCVDS7Ep5vv/22xOtXrlxhy5Yt/PTTTzz++ONlCkwIIao6s9lcIZtZ27ZtS9u2be3qe+05OnEnU4k7mUptbwMjO9Ul1NeAE3DuSg7bT15m+a6znE/LLbW/EEIIURnY9c40bty4Uu8FBATwyiuvMGPGDHtjEkKIKs1kMrFu3TrS09N56KGHqtQ67xYh3sXO4Tmflsv/RR/DRafwXheYtvIv8s0WzBRN5uQcHiHErZSUlEStWrUc/sGSyWQqc4W4SsGUCTnnITcZLEbQuoAhCFxrg96joqO7pexKeE6ePFnsmkajwdfXF09PzzIHJYQQVVVKSgrLly8nOTkZgISEBEJDQys4Kuvd7Bye/Px8uujO4u+UyWpjSyzX1L4p7RweIYRwtJMnT9KnTx969erFwoULHZb07Nq1i23btjF+/Hi8vb0dMuYtZ86DtIPqV845yM+6es/JHVzrgHdL9Utn/zlHVYldCU9VevMWQohbxWKxEBkZSWpqKm5ubtx7770O/3lZcM5ZeVEUhXHdQ0s4h0ehoTaFQ4fO0ESnPv0J0aZz1uIDqNXaxvVoUO7xCSHE8ePH6du3LwkJCcTGxpKamkqtWrXKNKaiKGzatImtW7cC6uGivXr1ckS4t5Y5D1Li4FJs0USnQH4WZBz5JxHKBP8uNSLpkd2kQgjhIFqtlmHDhtGgQQOeeuopGjdu7LCx8/LymDZtGo8++qjDxiyJVqulaZAXLwxoVuS6BrhNewGz2cwVi4G1xmaFyQ78cw5PLQ8pUiCEKNW2bdvIyirhl3AbmEwmBg0aREJCAs2aNSMmJqbMyQ7A9u3bC5OdXr160bNnzzKPWSHSDpae7FwrP0ttl3bw1sRVwax6wqPVam3+xE6j0ZCfn3/zhkIIUY00bNiQBg0aOPQpR3x8PGPGjOHPP/8E4Omnn6ZLly4OG7+YjJNM6tsEjQY+2HAYRQEFDXHm+jxaJ40lp2uTa1ETG41GTXYm9mkCGSfBs2H5xSWEqLK2b9/Oli1b0Gq1ZGRk4OfnZ9c4er2eTz/9lFdffZVff/3VYYcad+jQgfj4eLp27UqHDh0cMuYtZ8pUE5ibJTsF8rPU9p5NQe9evrFVMKsSnhkzZsgSBSGEsJIjf17m5OQwYMAALly4QEBAAF988UX5JjsAWScg4wgT+4QT3rIW32xPYOXec1w2uxMUZEA5rSk8h2dc9/o0CfKGxPWg1UvCI4QoZu/evURFRQHqWTtl3e89ZMgQwsPDHVqswNXVlQkTJlRIZU2HyTmvLlWzqU8i5CaCvmn5xFRJWJXwvPHGG+UchhBCiJK4urry4Ycf8sMPP/Dll18SFBRU/i+qdYELWyHrDI38OzFreBumDWnGgbMpJO6PY/GjHWlZ1x9XvRPKlXg4ugtyzkJw//KPTQhRpSiKwuHDhwHo2rUrubm5N+lhnfJITKp0sgNqNTZrn+4UyM+EnGT1KU81JgcmCCFEJTd69GhGjx596560G4LUpzXJ0WiP/Bec3HCrP5J2hlAScaKtdjv6E6chYTma/Gy10o9PK3C9BcmYEKJK0Wg0jBw5kj///JNWrVrx22+/VXRI1ZfFaGe/PMfGUQmVKeE5e/Yse/fuJS0tDYvFUuz+mDFjyjK8EEJUKpcvX8bX1/eWv255Jzrnz5/Hw8Pj6jITl1qg0cPlvWDOBVMaHP4/wAU078Gf04Br3lgv7wXf9uBc9o3DQojqR6fT0aFDB0wmU0WHUr1pXezsV/2rtNmV8OTm5jJ27FhWrFiBxWJBo9Gg/FO/9No3Zkl4hBDVgcViYdOmTWzfvp1HHnmEBg0aVHRIDmE0Gtm4cSN//PEHLVu25L777lNv5F0CZx9w8lATnptx8lDb510CF59yjFgIIUSpDEHqOTu2LGtz8qgRT+ftqh86bdo0fvrpJ95++21iYmJQFIVFixaxYcMGBg8eTNu2bQurCQkhRFWWkZHBt99+y7Zt27BYLCUevFwVnTt3jjlz5hAXF1d4do7ZbAZzPlyKA1M61B8BLgE3HsglQG1nSlP7maU6pxCibEpaNVQWiqJw4sQJh45ZKbnWVg8VtalPCBhCyieeSsSuhOfHH39k/PjxvPzyy9x+++0A1KlTh/79+/PLL7/g4+PDnDlzHBqoEEJUhL/++ovTp0/j7OzMiBEj6NOnj8PGXr9+PQsXLnTYeLbw9fUlPz8fX19fHn74YUaMGKFu2M0+Ayk7ISkaNE7Q8GEI6ls88XGpBcH91PsaJ0jaCKl/qP2FEMJOcXFxtGvXzmEJitlsZuXKlXz33Xfs2bPHIWNWWnoPdU+lk5Ulpp3c1fbVvCQ12Lmk7cKFC4VlUV1dXQGKHCQ1YsQI3nzzTebOneuAEIUQouKEhYWRnp5O586d8ff3d8iYWVlZvPTSS8ydOxcXFxfCwsJo0aKFQ8a2lpubGw8//DC1atVCr9dfvWG6AlkJYM6BxA3gc7u6P8e/E+SmwUmg0TgweIPFDJnH4cp+UPIh87TaXwgh7BAbG8ugQYNIT09n+vTpREZGlmm83NxcfvjhB06ePIlWq60ZByN7t1Qrr93s8FEndwgIU9vXAHYlPEFBQaSkpADqm6avry+HDx/mrrvuAiA9Pd1hZQeFEKIiaTQaBg0a5LDxsrOz6dChA0eOHAHgySefJDQ01GHj2yIkpIRlDEr+1Yo9igku74PL8eBeF/T/rPM2pkLmIcg6C1yz9MRiVPsLIWqUgv3cZSmw8scffxAeHk5GRgY9e/Zk/vz5ZY7r6NGjnDx5EmdnZ+6//34aN25c5jErPZ0z+HdR9+akHVTP5bk28XHyUJexebdUv3TVv2AB2JnwdO3alW3btvHyyy8DcNddd/H+++9Tu3ZtLBYLH3/8Md26dXNooEIIUR24ubkxZMgQsrOz+frrr+nfv5KdXaNx+qdiz7WfDFrUpz5KEnAHXP4TNCWUMdW6qP2FEDWGxWJhxYoVeHh4MGjQILuTnkaNGtGoUSP8/PxYs2YN7u5lX2bVunVr0tLSaNy4MbVr1y7zeFWGzhn82oFnE8g9r56zY8lTf7a7BoGhtrr8rQax653p2WefZfny5RiNRlxcXJg1axaxsbE88sgjADRu3Jj/+7//c2igQghRXfznP//h9ddfx8fHp6JDKU7vA+71Ie+y7X09QtX+QogaQVEU1q5dy8GDB9FqtXTo0MHuw5H9/f2Jjo7G1dUVNzc3h8V4xx13OGysKkfvAfqm1f5QUWtYvZjxvvvu4+eff8ZkMnHHHXfw6aef4uKi1vuuV68ehw4dYu/evcTHx3Po0CGaNWtWbkELIURV5urqWm7JzsWLF1m9erVacc0ebvXAv7N9ff06qv2FEDXCxo0bCwsBjBgxwu5kp4C/v79Dkx0hClj9hGft2rWsXLkSb29vRo4cyejRo+nZs2fhfa1WS9u2bcslSCGEKA9nzpzBx8fn6oGbVZjJZGLLli1s374di8WCv78/PXr0sH0gnRP4tAb3UMg6bX0/91C1n06WtAlRUwQGBqLVahkyZAgtW9aMze+iarL6Cc/FixdZuHAhnTt3ZuHChfTp04f69evzyiuvEB8fX54xCiGEQymKQmxsLN98803hAcpV3U8//VR4VtBtt91WeGSAXTybQf37wNnXuvbOvlB/pNqvGtiyZQt33XUXISEhaDQaVq1aVeT+uHHjCjdoF3w5srCFEFVF69atmTRpEh07dqzoUIS4IasTHg8PD8aOHcv69etJTEzkk08+oU6dOrz33nu0b9+e1q1b8+6775KQkFCe8QohRJkUlCndsGEDFosFDw8P+5d/Xef8+fMVds5Djx498Pb25v777+fBBx8s25I5JwME9YLGj6pPbm7EPVRtF9RT7VcNZGVl0bZt2xueJzdo0CDOnz9f+LVkyZJbGKEQlYevr5UfjAhRgewqSF6rVi0mT55MbGwsJ06c4M033wRg6tSpNGrUqMzlBOfMmUODBg0wGAx07dqVuLi4G7Zfvnw5zZs3x2Aw0Lp1a3799ddS2z711FNoNBo++eQTu+MTQlRdFouFxMREdDodQ4YMYcSIEUXPobHT8uXLadWqFffeey/p6ekOiNQ2devWZfLkybRo0aJMpWEL6b0geAC0mAJNHge/9lef+Dj7qt83eVy9HzxAbV9NDB48mLfeeot77rmn1DYuLi4EBwcXfskvfULcWrm5ufzyyy9yDIqwSpkXWzdo0IBXX32VV199lfj4eF5//XV+/vlnfv/9dyZMmGDzeMuWLWPKlCnMmzePrl278sknnxAeHs7hw4cJDAws1n779u2MGjWK2bNnM2zYMCIjI4mIiGDPnj20atWqSNuVK1eyY8eOks+eEELUCG5ubtx///1oNBqH/CzIzs7mmWeeKTwgLzQ0lNTUVLy8bn0CoNPpHDugkwF824LX7RDQA3JSIfYstPw3uPqpBQpq6J6dmJgYAgMD8fX1pW/fvrz11ls3PJjWaDRiNBoLvy9Iik0mEyaTyabXLmhva7+qpqbME2rGXFetWkVycjL16tUr8zzT09NZunQply5dIiMjg/vuu89BUTpGTfj7hIqfpy2v65B3qoLH+ZGRkYXLOTp16mTXWB999BFPPPEE48ePB2DevHmsXbuWhQsX8sorrxRr/+mnnzJo0CBeeuklAGbNmkVUVBSfffYZ8+bNK2x37tw5Jk+ezPr16xk6dKhdsQkhqoc6deo4bCyDwcC5c+fQ6XRMmzaN6dOn4+xczQ5y0zmBZ0Mw1AXOgm8bcMBTsapq0KBB3HvvvTRs2JDjx48zbdo0Bg8eTGxsbKlJ5+zZs5k5c2ax6xs2bLC7KlVUVJRd/aqamjJPqL5z3b59Ox988AEWi6VwVZC9cnJyOHHiBCaTCb1ej0ajueHKnopUXf8+r1dR88zOzra6rd0Jz5UrV/jxxx+JjIxk69atmM1mGjduzIwZM3j44Ydp0qSJzWPm5eWxe/dupk6dWnhNq9XSv39/YmNjS+wTGxvLlClTilwLDw8vssnUYrHwyCOP8NJLL5VtI68QQlxHq9WyaNEikpKS6Nq1q8PHVxQFs9mMk1PNfJJSGT344IOF/966dWvatGlD48aNiYmJoV+/fiX2mTp1apH3qvT0dOrVq8fAgQNtfhpoMpmIiopiwIABDlmOWVnVlHlC9Z7rDz/8wIcffojFYmHUqFHcfvvtZZrn5cuXOXPmDN7e3jzwwAN4e3s7OOKyq85/n9eq6HnasnzcpnfQ3NxcVq9eTWRkJOvXr8doNFKrVi2efvppHn74Ybp06WJzsNe6dOkSZrO5WB33oKAg/v777xL7JCUlldg+KSmp8Pt3330XJycnnn32WavikKUHZSfzrjnzrolzhqLzDgkJISQkxOF/BpcvX2bDhg0YDAaGDx/u0LFtoiiQl4opNw0AU9oJMHiDsx9YuV+oOv/30ahRIwICAjh27FipCY+Li0vh2XXX0uv1dv+iUJa+VUlNmSdUvrnm5ORgNpvx8PCwe4xjx45hNpsZN24cc+fOZf369WWaZ2BgIGPHjsXT0xNXV1e747oVKtvfZ3mpqHna8ppWJzxjxozh559/JjMzEzc3N0aMGMHo0aMZOHCg49eNO9Du3bv59NNP2bNnj9UbeWXpgePIvGuOmjhnKJ95WywWLl68SFJSEoqioNFosFgsJf7CXBGifi/5A6gbsWXpQVVz9uxZUlJSqF27dkWHIoTD5OXlsXjxYrKzs3nkkUfsLswxY8YM2rRpw/Dhwx1WEbOkPd1C3IjVCc+SJUsYMGAAo0eP5p577imXk3ADAgLQ6XQkJycXuZ6cnExwcHCJfYKDg2/YfuvWrVy4cIH69esX3jebzbzwwgt88sknnDp1qtiYsvSg7GTeNWfelWnOFy9e5NSpU3Tu3LncX6s8552Zmcn8+fNRFIXQ0FAGDRp0ww3x5cKcB1f+gtTdkJcCgMmiJepMawbU+wu99p+zi5z9wa/jP4eOlr53qSIq19krMzOTY8eOFX5/8uRJ9u3bh5+fH35+fsycOZMRI0YQHBzM8ePH+fe//02TJk0IDw+vwKiFcJz8/HyWLVvGuXPncHV1LdMTWo1GU1jx0FEJjxC2sjrhSUxMpFatWuUZC87OznTs2JHo6GgiIiIA9ZPO6OhoJk2aVGKfsLAwoqOjee655wqvRUVFERYWBsAjjzxC//79i/QJDw/nkUceKSyMcD1ZeuA4Mu+ao6Ln/Oeff7J27VpMJhOBgYF27SO8ntlsJiMj44Zn2pTHvH19fRk8eDCKotCmTRvHlJm2hSUf0vZDylYwZxU7wECvtVxNePIvqu2ctODXAbQlv61Upf8fdu3aRZ8+fQq/L/gAbOzYscydO5f4+HgWLVrElStXCAkJYeDAgcyaNavSPIEToqyio6M5ceIEer2ehx56SJ6oiCrP6oSnvJOdAlOmTGHs2LF06tSJLl268Mknn5CVlVWYnIwZM4Y6deowe/ZsAP71r3/Rq1cvPvzwQ4YOHcrSpUvZtWtX4TlA/v7+xT4Z1ev1BAcH06xZ9TgVXIiabu3atezatQtQ91M4YmnRiRMnGDduHC4uLqxfvx6t1q5jy+zWpk2bW/p6ReScg5Q/1GTHGuYsSIkD12Bwr3/z9pVc7969URSl1Pvr16+/hdEIcev16NGDhIQE+vbtS926dSs6HCHKrNKV/XnggQe4ePEiM2bMICkpiXbt2rFu3brCwgQJCQlFfvHo3r07kZGRTJ8+nWnTptG0aVNWrVpV7AweIUT1VfChRu/evbnzzjvLlJwoisKCBQt4/vnnyczMxMPDg7///puWLVs6KtzKTVEg8yQYL9nWz3gJMk+oZ/Pc6idSQgiH8vDw4PHHH7/1T5dRl9NJVUrhaJXyv6hJkyaVuoQtJiam2LWRI0cycuRIq8cvad+OEKLq6tq1Kw0aNCh1r58tMjMzmTlzJpmZmdx5550sWrSIhg0bOiDKKsKYAhnH7eubcRy8W4EhwLExCSFuuYpIdpKSkliyZAl33303jRs3vuWvL6qvW7tGQwghyoFGo3FIsgPg6enJ119/zfvvv8+mTZscnuycOnUKi8Xi0DEdKj8D8i7b1zfvstpfCFFjLF++3CFVGI8fP87XX39Neno6mzdvvuGyUiFsJQmPEEJcp3///rz44osOLbmfkZHB8uXLWbRoETt37nTYuA6nmNWvW91XCFHlvP/++9x///0MHz68TJXcEhMTiYyMJC8vjwYNGvDQQw9VyBMmUX3ZtaTNaDTy+++/c+jQIdLT0/H09KRly5b06NFDqtQIIcR19u/fz5o1a8jLy0Oj0ZCbm1vRIZVOo1O/bnVfIUSVMnv2bKZNmwaoRQ7Ksu+mdu3ahfskhw8fLnt4hMPZ9F+Uoih88MEHvPvuu1y+fLnI40aNRoOvry8vv/wyL774omTmQgjxD29vb/Ly8qhTpw7Dhg1z2PK7cuHkCc6+kJ9pe19nX7W/EKJaS05O5v333wfgzTff5LXXXivTeBqNhoiICLRarfz+KMqFTQnP6NGjWbp0KU2bNmXy5Mm0bdsWT09PMjIy+PPPP4mMjOSVV15h3759LF68uLxiFkLUEFeuXGHt2rUMHTr0hmfhVHb16tVj7NixhIaGVv43cxd/8GwM2WeuXtO5gmsIaP45cNr7dlCyIScRzDlX23k2VvsLISq1lJQUkpOT7a4+GRQURFRUFJs3by5yUHtZOHIJsRDXszrh+e6771i6dCkvvvgis2fPLvYfZkREBK+99hrTpk3j/fffZ/DgwTz88MMOD1gIUTMcOXKElStXkpuby9q1axk9enSZxsvLy+PNN9/EYrHwn//8x0FRWq9Bgwa3/DXtotGAR0O4sh/MueDRALQuatnptP1AO/WeV33wbgkWo1rGWucKHo2kJLUQlVx6ejrfffcdaWlpjBgxwu5jPDp27EjHjh0dHJ0Q5cPqhOfLL7+kV69evPfee6W20Wq1vPPOO8TFxTF//nxJeIQQdvnrr7/46aefAAgJCWHo0KFlGu/AgQM88sgj7N27F41Gw5gxY2jevLkjQq2eXOtArTsgNxkyjkDqPsg6BRYd0A4u/g4pW8C9Afi1A582YAhSnwIJISqtnJwcvv/+e9LS0vDz86s6H8QIUUZWV2mLj49nxIgRVrW99957iY+PtzsoIUTNdtttt+Hn50eXLl149NFHy7Sc7cqVK3Tv3p29e/fi7+/PsmXLJNm5Ga0TuNVVl6ydj4LM48Wrrylm9fr5KLWdW121nxCi0oqPj+fixYt4enryyCOP4OHhUdEhCXFLWP3uZDKZMBgMVrV1cXEhPz/f7qCEEDWbi4sLEyZMcEjVRx8fH1599VW2bNnCl19+Se3atR0QoSozM5MtW7bQr18/h41ZKVjy4fI+yEpQ9+XkXQZjKuT/k/Ro9eBkABc/tVBBVoLaPqi3JD1CVGJdunQhPz+fpk2b3tJ9kWfPnnXoz14hbGX1E54mTZqwZcsWq9pu3bqVRo0a2R2UEEI4ssT9iy++yJo1axz2hpudnc0vv/zCsWPH2LZtG6dOnXLIuJWG8SJkHAUU0HuDeyh4NQevZup9r2bq9+6h6n0UyDim9hNCVFoajYYePXoQGBh4y15z7969LFy4kDVr1shhoqLCWJ3w3HfffSxZsoS1a9fesN3atWtZsmQJI0eOLHNwQgjhCI4sdWqxWPjqq68Kl+22b9+eoKAgh4xdaWQlQO61yYsGnNzA2Uf91tlH/Z5r/kxzL6j9hBBVnsVi4fjx42UaQ1EUNm/ezOrVq1EUBUVRsFgsDopQCNtYnfC88MILNGvWjIiICCZMmMDWrVtJT09HURTS09PZtm0bEyZMICIigmbNmvHCCy+UZ9xCCFEhtFot3bp1o1atWjRt2pTBgwfj6upa0WE5jiUfsk4Dtv5iYlH7WWQ5sxBVmcVi4amnnqJjx47s2rXL7nGuXLnC77//DsAdd9xBRESElJ4WFcbqxdZubm5s3LiRMWPG8NVXX7FgwYJibRRFoX///nz77be4ubk5NFAhRPVQ8ClfVX7j69SpE23atGH9+vUVHYrjWfKLFyiwlmL+p6/s4xGiKrJYLDzxxBMsXLgQrVbLkSNH6NSpk11j+fr6ct9995GWlkbnzp0dHKkQtrHpXSkwMJB169axc+dO1qxZw8GDB8nIyMDT05MWLVowbNgwwsLCyitWIUQVl5WVxcqVK/H392fw4MFlHi8qKoqcnBzuvvtuB0RnPa1WW6UTthvSOoHGzrlpdPb3FUJUuP/+97+Fyc53333HQw89VKbxbrvtNgdFJkTZ2PUxXNeuXenataujYxFCVGMJCQn8+OOPZGRkcPr0abp37463t7ddY2VlZfHyyy8zZ84cfH192b9/PyEhcgaMQ2id1GIEaX9j27I2rdpPqrQJUWU9+eSTbNiwgTFjxvDAAw9UdDhCOIxD3pkOHDjAli1byMzMpG3btgwcONARwwohqomcnBwWL15MXl4e/v7+jBw50u5kJzU1lW7dunH06FEAHnroIbvHul7Bhlqt1urtjdWTe30w1FIPHrWWIVDtJ4SoMAkJCezcuZOIiAj0er3N/Q0GA7/88ovDirwIUVlYnfBYLBamTp1KZGQkTk5OjBs3jtdff50pU6bw6aefFpYaLCh5uG7dOtnHI4QAwNXVlfDwcE6dOsXQoUPLVHLa19eX9u3bk52dzcKFCx32AUtiYiK//PILbdu2lSfYLrXAsykYL1m3n0ejA88maj8hRIVISkoiMjISo9GIv78/ffv2tWscSXZEdWR1wjN37lzef/99OnfuTFBQEP/5z3+4ePEi8+bNY+LEifTr14/8/HxWr17Nd999x6xZs5g9e3Z5xi6EqELat29P+/bty/xmqtFomDdvHqAmP2WVm5vLxo0b+eOPPwD1jJ1OnTpV3z061tA6gX8nMGfB5fgbJz0aHfi2UdvLcjYhKkRqairff/89RqOR+vXrc+edd1Z0SEJUKla/O3311VcMHTqUNWvWADBnzhyeffZZJk6cyP/93/8VthsxYgRZWVn8+OOPkvAIIQo58lNDRyQ6BZKTkwuTndatWzNw4MCanewUcPaBwF6gc1cPFc29cF0DrbqMzbOJmuwUnNEjhLjlsrOzMZvNBAUFMWrUKLuWs9lCURS2bt1Ks2bNqt85ZKJasjrhOXHiBBMmTCj8fsiQIUyePLnER6b9+/cvTIyEEKIyCw0NpWfPnoSGhtKoUaOKDqdycfaBoN7gc7t6qGj6afW6V3PwClX37LjUkic7QlSwunXrMn78eNzc3DAYDOX6WmazmV9++YV9+/axa9cuJk6cWKZlykLcCla/S2VkZBTZGOzl5VXkn9fy9PQkP18OnxNCVA19+vSp6BAqL60TuNZWvzzbwF8boM5d4FKNDlsVohoIDAy8aRuTyYSTk5PdT9zz8vL44YcfOH78OBqNhl69ekmyI6qEGl6KSAhRVoqiYDQaHTJWUlJSYfU1UQkVPMmRJzpCVDk5OTkMGzaMV199tbDQlK20Wi0mkwm9Xs+DDz5Ix44dHRylEOXDpnetX3/9laSkJEBdL6rRaFi+fDn79u0r0m737t0OC1AIUXkZjUZWr17N4cOHSUlJITg42O6xVqxYwZNPPkn9+vXZsWMHzs7ODoxUCCFqruzsbO6++26io6PZtm0bTzzxBA0bNrR5HCcnJx588EEuX74sZ5+JKsWmhCcyMpLIyMgi17744osS20pZQyGqt+TkZJYvX05KSgqglnW2J+HJyMjgmWee4fvvvwegXr16XLx4kTp16pQpPkVROHPmDPXry9kwQoiay2KxFCY77u7u/Pbbb3YlOwVcXV1xdZUlraJqsTrhOXnyZHnGIYSoYuLi4khJScHT05OgoCBat25t1zh6vZ69e/ei1WqZOnUqM2bMKPPTnQsXLrB27VoSEhIYP368JD1lYbFAzjnIPA6XjwKBcPwb8G0KHo3BtQ7U9INahajEtFotDz30EH/88Qe//vorPXr0qOiQhLjlrE54QkNDyzMOIUQVM2jQIHQ6Hd27d2fz5s12j2MwGFi8eDHZ2dmEhYWVOa4tW7awefNmLBYLer2eK1euSMJjL2MKXNgKF7fDlb8g3wxMhITlkKgDn9ZQqzsE3gku/hUdrRDVWm5urt0V2B599FHuvvtuAgICHByVEFWD7DwVQthFr9czZMgQTCZTmcdq27atAyJSOTs7Y7FYaNasGYMHDy5SXVLYwJgC536BU8vAnP3PxWuevJlzICUOruwHUxrUGSZJjxDlJD4+nnXr1jFq1Cjq1atn1xiS7IiazOqEp6TzdgpoNBoMBgOhoaEMGTKEYcOGOSQ4IYSwVZcuXahVqxaNGzeu6FCqLotFfbJTJNkphTlbbaf3hjp3y/I2IRzsyJEj/Pzzz1gsFg4dOmR3wmMNs9mMVquVfdii2rE64blw4cIN/wfIzs4mKiqKL774gvDwcH7++edyP+lXCCGup9VqJdkpq5xz6jK2myU7BczZcDEW/DqCe/n9MiZETZOYmMjy5cuxWCy0bt2aAQMGlNtrGY1GfvjhB+rVq0fv3r3L7XWEqAhWJzz79++/aZucnBy++OILpkyZwnvvvcerr75apuCEEFWbxWIhNzcXNze3ig5F2CLzuLpnxxZX4iHzhCQ8QjhQrVq1CiuqDR8+vNyevGRkZLB48WKSk5M5e/YsHTt2xNPTs1xeS4iK4NC1B66urjz33HM8+OCDxcpXCyGqjqNHj5Z5b86pU6fo06cPTz/9tIOiErdM+hF1j44tzDmQfrh84hGihtLr9TzwwAOMHDkSnU5XLq9hMplYuHAhycnJuLu7M3bsWEl2RLVTLoute/ToIWWshaiC8vPz+fXXX4mMjOTXX3+1awxFUVi4cCGtW7dmy5YtrFixgoSEhDLFpSgK+/fvJz4+vkzjCCtYLNYvZbueOUftL4RwGJ1Od8MtAhcvXmTUqFFcunTJrvH1ej1hYWH4+/vz2GOPyYGioloql4QnOzsbJyf7C8DNmTOHBg0aYDAY6Nq1K3FxcTdsv3z5cpo3b47BYKB169bFflF74403aN68Oe7u7vj6+tK/f3927txpd3xCVEdXrlzh66+/5o8//gDA3d0dRVFsHufChQs8//zzZGZmcscddxAfH1+mstCpqaksXryYFStW8Ouvv5KZmWn3WMIKWi3o7FyCqHOVogVC3ELJycn06dOHpUuX8sgjj9g9TpcuXXjyySfx9fV1YHRCVB4Of2dSFIXVq1fbfQjhsmXLmDJlCq+//jp79uyhbdu2hIeHc+HChRLbb9++nVGjRvHYY4+xd+9eIiIiiIiIKLLn6LbbbuOzzz7jr7/+Ytu2bTRo0ICBAwdy8eJFu2IUojrKz8/n0qVLGAwGRo0aRf/+/e1aLx4UFMTnn3/Oe++9R0xMDI0aNbI7pvT0dObOncvx48fR6XSEhYXZfQ6FsIHXbWryYgudK3g1K594hBDFnD9/nt69e3PgwAFCQkL49NNPyzSeFJoS1ZnVj2FSU1NveD8nJ4fDhw8zd+5ctm/fzvfff29XQB999BFPPPEE48ePB2DevHmsXbuWhQsX8sorrxRr/+mnnzJo0CBeeuklAGbNmkVUVBSfffYZ8+bNA+Chhx4q9hoLFiwgPj6efv362RWnENVNQEAAI0eOJCAgAB8fnzKNNXr0aIfE5OXlRcuWLcnMzGTIkCH4+8s5L7eER2P1UNGU65+uK9f98xo+bcDD/uRWCGGbvLw8cnJyqFu3Lps2baJJkyYVHZIQlZbVCU9AQIBVn/bq9XpmzZrFqFGjbA4mLy+P3bt3M3Xq1MJrWq2W/v37ExsbW2Kf2NhYpkyZUuRaeHg4q1atKvU15s+fj7e3d6mHHRqNRoxGY+H36enpgLqxz9aN3AXtHXE4Y1Ui866a8w4NDQVsi7+85xweHo6TkxMajaZS/blW9b/rG3IKBL/ucPmwup/HYgJLLqb8XABMuRnqu4fWAFq9ugTOL0ztV8qfR7X8cxKiAoWGhhITE4PFYinTk3QhagKrE54ZM2bcMOEpOHi0X79+1KpVy65gLl26hNlsJigoqMj1oKAg/v777xL7JCUlldg+KSmpyLVffvmFBx98kOzsbGrXrk1UVFSppw7Pnj2bmTNnFru+YcMGu8vrRkVF2dWvqpN51xw1cc5QneetAR4t8U6UMhWuz1/+BP5cV+po2dl2FkIQohpTFIXjx4/bvc+xQYMGjg1IiGrK6oTnjTfeKMcwyl+fPn3Yt28fly5d4ssvv+T+++9n586dBAYGFms7derUIk+N0tPTqVevHgMHDsTLy8um1zWZTERFRTFgwIAatT5W5l1z5l0T5ww1YN55mWBMhpQ/4MLvkBqHyZxPlGYWA5TX0OucwK8LBPYA/87gEgTOHqUOV/CkXAhx1ZYtW4iJiaFz587l9hoF/+/Z+vuLENWJ/aXUgKysLDIyMggICChTVbYCAQEB6HQ6kpOTi1xPTk4mODi4xD7BwcFWtXd3d6dJkyY0adKEbt260bRpUxYsWFBk+VwBFxcXXFxcil3X6/V2/2JTlr5Vmcy7+jCZTMyaNYvAwEAmTZpU7L6tc7ZYLGirQUWv6vh3jcUCGXvh8n7waw++zSF3KFw+CidA33gcet+mYAgGcy4kbwDfVhDYu9QqbdXuz0iIMoqLiyMmJgYAHx+fcimkdOHCBRYvXoyrqyvjx48v8XcbIWoCm3/bOH36NJMmTSI0NBQvLy/q1KmDwWCgQYMG/Pvf/+b06dN2B+Ps7EzHjh2Jjo4uvGaxWIiOjiYsLKzEPmFhYUXag7rEpLT214577T4dIaqzEydO8Ndff9nd/+DBg3Tr1o1Zs2bx0ksvce7cuTLFc+TIET777DPOnj1bpnFEOTFegIyjYEqF5GhI2qReD+hS9J9Jm9T7plTIOAZGqXwphDVSU1NZt05dAtqrV69yecJz6tQpFi5cSHp6OmazWX7nETWaTY9l1qxZw8MPP0xGRgYNGjTgrrvuwtPTk4yMDOLj4/nggw/48ssv+f777xk6dCgA06dP56233rL6NaZMmcLYsWPp1KkTXbp04ZNPPiErK6uwatuYMWOoU6cOs2fPBuBf//oXvXr14sMPP2To0KEsXbqUXbt2MX/+fEB9CvX2229z9913U7t2bS5dusScOXM4d+4cI0eOtGX6QlQ5FouFrVu3EhMTg5OTE0FBQSUu47yR8+fP06lTJ3JycvDz82Pu3LnUqVPHrnjS09NZt24dhw4dAmDr1q12FTgR5Sz7DORecxSAJRtSd4NFC7SF5I2gve6A0dxktZ9r0T2VQoji/Pz8uOeeezh37hy9evUiPz/foeNbLBZ+++03jEYj9evX58EHH8TV1cZS80JUI1YnPIcOHeL++++nYcOGfPHFF9x5553F2mzdupWnnnqKBx54gF27djF79my+//57mxKeBx54gIsXLzJjxgySkpJo164d69atKyxMkJCQUGQZTPfu3YmMjGT69OlMmzaNpk2bsmrVKlq1agWoJxT//fffLFq0iEuXLuHv70/nzp3ZunUrt99+u9VxCVHVmM1mIiMjOXHiBACtW7e261C52rVrM2HCBI4cOcJXX31VplO49+/fz6FDh9BoNISFhdGrVy+7xxLlRLFAVgIoZhv7mSHrNPh1AE3VX6ooRHlr3br1Dc8sPHz4MMeOHSv8ANkWWq2WBx98kN9//51BgwY5ZNuBEFWZ1f8H/Oc//8Hf359t27bh5+dXYps777yTrVu30qZNGzp27IjRaCx8EmOLSZMmlbhHAChc73qtkSNHlvq0xmAw8NNPP9kcgxBVnU6nw9/fnzNnzjB06NBSy7Bb4/333y8sDV0WXbt25eLFi3Tr1q1YdUVRSShmsNj5abMlX+0vCY8QZXLw4EH69u1Lamoqv/32m11nBvr6+jJs2LByiE6IqsfqhGfjxo08/vjjpSY7Bfz8/Hj00Ud56623+Pbbb3n44YfLHKQQwj4DBw6kS5cupZZgt5ajNpzrdDqGDx/ukLFEOdHoQGvnp8FaJ7W/EMJu+/fvp1+/fly4cIG2bduW6cMqIYTK6o/hUlJSrK733rBhQ3Q6nSQ7QlQwJyenMic7oobRaMG9vu2Ji0YH7qHV4unOli1buOuuuwgJCUGj0RQ7yFpRFGbMmEHt2rVxdXWlf//+HD16tGKCFdXON998w4ULF2jfvj3R0dHyM1wIB7D6nSkgIICTJ09a1fbkyZM2b4wWQghRSbjVA4ONP8MNQWq/aiArK4u2bdsyZ86cEu+/9957/N///R/z5s1j586duLu7Ex4eTm5u7i2OVFRH7733Hm+99Rb/+9//8Pf3r+hwhKgWrE54evfuzYIFC0hNTb1hu9TUVBYsWEDfvn3LHJwQonxFR0fz+++/l3kci8VCUlKSAyISlYJLIHg2BY2VS9s0TuDZBFxqlW9ct8jgwYN56623uOeee4rdUxSFTz75hOnTpzN8+HDatGnDt99+S2JiYrEnQULYQ6vV8uqrr950C4EQwnpWL9SeNm0aP/74Iz179mT+/Pl07969WJvt27fz5JNPkpKSUuKBnkKIyiE7O5upU6fyf//3fzRo0IA///zT7lO4T58+zeHDhzly5AiTJk3C3d3dwdGKW06rBb+OYM6G1H2g3KCIgcYJ/NqBX6dSDx2tTk6ePElSUhL9+/cvvObt7U3Xrl2JjY3lwQcfLLGf0Wgscg5Keno6oB7oazKZbIqhoL2t/aqaqjxPs9nMpk2b6NatGx4eHjdtb+tcT548SXZ2dpWrNluV/05tIfO8ta9vDasTnpYtWxIZGcmYMWO48847adCgAW3bti1yDs/JkydxdXUlMjKSli1b2hW8EOLmEhMT2bhxIyNHjrT55OzExET69u3L4cOHARg0aBA6ne0bzS0WC6tXr+bPP/8EwN3dndTUVEl4qgtnb6h1J+jc1ENFc5MB5ep9jU5dxubZRE12nO1LmKuagieZ11cZDAoKuuFTztmzZzNz5sxi1zds2ICbm5tdsURFRdnVr6qpavNUFIWEhAQuX75MfHw8zZo1s7rCpTVzTU1NJSEhAVCruVXFn7lV7e/UXjLP8pWdnW11W5tK8dx77720a9eO9957j19++aXI4/uQkBAmTJjAiy++SOPGjW0ZVghhJUVR+OOPP9iwYQNms5mNGzcyePBgm8YIDg4mODiYjIwMFixYwKBBg+yKRavVFh6W5+/vz9ixY/H09LRrLFFJOXtDYG/wvl09VDT9tHrd+3bwClX37LjUqhFPdspq6tSpTJkypfD79PR06tWrx8CBA21+umoymYiKimLAgAEOq6BYGVXFeSqKQlRUFJcvX0aj0XD33XfTpEmTm/azZq6KohAbG8u+ffsA9YPoYcOGVakzdqri36k9ZJ63RsGTcmvY/H9Jo0aNmDdvXuELZWRk4OnpafdyGCGE9TZv3szmzZsBaNasGb1797Z5DK1Wy+LFi3F1dS3zGvHw8HA6derEn3/+icFgKNNYopLSasE1SP3ybA3x66DOMHC27clidREcHAxAcnIytWvXLryenJxMu3btSu3n4uJS4tNYvV5v9y8KZelblVSleebk5HD8+HEAIiIiaNGihU39bzbXgsIYYWFhDBgwoMxno1WUqvR3WhYyz/J/XWuV6WMBLy8vSXSEuIXatWvHrl276NGjB926dbP7za5OnToOicfT0xODwVC4rE1UcwUlp6tB6Wl7NWzYkODgYKKjowsTnPT0dHbu3MnTTz9dscGJCufq6sr48eM5efIkbdq0KbHN/v37ad68uV1PZgYOHEjDhg257bbbyhqqEDVK1XkOKoTAx8eHZ599Fmdn54oORYhqKzMzk2PHjhV+f/LkSfbt24efnx/169fnueee46233qJp06Y0bNiQ1157jZCQECIiIiouaFFpeHp6lprsbNq0iWHDhnHPPfewaNEim/dPajQaSXaEsIMkPEJUMZLsCFG+du3aRZ8+fQq/L9h7M3bsWL755hv+/e9/k5WVxYQJE7hy5Qp33HEH69atk2Wd4ob+97//cffdd5OTk0NKSgomk8mugjFCCNtJwiNENXLhwgXMZnORvQW2slgsxMXFUb9+fUJCQhwYnRBVQ+/evVEUpdT7Go2GN998kzfffPMWRiWqsrS0NEaOHElOTg5Dhw5lxYoVNlfYFELYr+YuxBaimlm5ciWtWrVi3LhxN/xl7UbOnTvHl19+yfr16/nll1+wWCwOjlIIIWoeb29vlixZwv333y/JjhAVQJ7wCFFJ5Ofno9PpbC5EkJ6ezrPPPsuiRYsA9ZyQS5cuUauWbafeHzt2jMWLFwNgMBjo1KlTla0AJIQQlc2gQYNuegyAfMgkRPmQJzxCVAIpKSl89dVXxMXF2dzXbDbzv//9D61Wy9SpU4mLi7M52QG1+lStWrVo27YtkyZNokOHDpLwCCHEdcojKVEUhaSkJH744QfMZrPDxxeippMnPEJUsP3797NmzRry8vLYvn07HTp0sKm2vK+vL4sXL8bJyYkePXrYHYdOp+Pxxx+XoghCCFGK3NxcvvvuO8LCwmjVqpVDxrRYLPz2228kJSUBcPjwYVq2bOmQsYUQKkl4hKhAFy5cYMWKFQCEhoYyYsQIuw7v6tWrl0PikWRHCCFKZjKZWLJkCYmJiaxfv57bbrvNIT8zV69eXXiWWXh4uCQ7QpQDSXiEqECBgYH06NEDjUZDnz590GpllakQQlQ2FouFH3/8kYSEBFxcXBg9erTDPiDq1KkTR44cISgoiI4dOzpkTCFEUZLwCFHB+vXrV+57ZSwWiyRTouws5mv+afuTSCGqKo1Gg4+PD05OTowaNYrg4OBibbKzs3Fzc7N57Lp16/LMM88QHR3tiFCFECWQ34CEqGA3SnYsFgsmk8nusRVFYffu3cyZM4fs7Gy7xxE1mDkfMo7D+Q1w7Av12rEv1O8zTqj3hajmNBoNgwYN4sknnyQ0NLTY/UWLFtGiRQuOHz9u1/hSplqI8iUJjxCV1OnTp+nXrx8zZsywq39ycjJff/01v/zyC6mpqezcudPBEYpqLycZzqyAg+/C7ilw9HP1+tHP1e8PvqPez7lQsXEKcQtoNBoCAgKKXV+4cCHjx48nISGBb7755tYHJoS4KUl4hKhkFEXh66+/pnXr1sTExPD555+TkpJi8zhbt27lzJkz6PV6Bg4c6LDCBqKGyEmGs6tg/1vq05z8zKL38zPV6/vfgrMrJekRNdKKFSt47LHHUBSFiRMn8uabb1Z0SEKIEsgeHiHKicViIS0tDV9fX5v6nTx5kieffBKTyUT37t359ttv8ff3t/n1Bw4ciFarpV+/fnh7e9vcX9Rg5nxIjoHDn0F+xo3b5meo7fQ+UG8E6ORtRdQcffr0oX379vTs2ZOPP/5Yzi4TopKSdyYhykFGRgY///wzaWlpPPnkk7i6ulrdt1GjRrzzzjvk5eXx0ksvodPp7IrBy8uLe++9166+oobLPg3J0TdPdgrkZ0DyRvDvDJ6Nyjc2ISoRPz8/tmzZgru7e4nJjtlsJiUlhcDAwAqITghRQBIeIRwsPT2dBQsWkJ2djbOzMxcuXChxk+uNTJkypZyiE8IKmcfh4nbb+lz8Xe0nCY+oYTw8PEq8npeXx/Llyzlz5gzjx48nKCjoFkcmhCgge3iEcCBFUbhw4QLZ2dkEBweXWtFHiErLYoa0g8X37NxMfqbar6B0tRBVzJUrVzCbHfPfb2ZmJt988w3Hjh3DYrGQkWHl01IhRLmQJzxCOJBGoyE0NBQPDw/69euHXu/4s0pyc3PJycmxeW+QEFaxWCDfzhLm5v9v787joqr3/4G/BhgYQNlEVgURUVxQE5XEtQSRXEJMBfclu5aW/rx61TJRW6jbrWvf9GZW6r3mnuQuCbh1FTUFTNxCQ9GURQ2GRWBgPr8/fDC3EVAGGGZ7PR8PHzrnfM6Z93vOOJ95n8+ZzykBhBJA/S7DJNKV/Px8rF+/Hm5ubhgzZkyDP7tPnDiBe/fuwcbGBuPHj4enp2cjRUpE9cGCh6iRSaVSrRQ7Qgikp6fjxx9/hL29PWbMmMGbiVLjMzMDLDS/eSIAwNwGkPA9SYalqKgImzZtQmFhIWQyGSoqKhr8+R0aGoqysjIMHDgQTk5OjRQpEdUXCx6iJqJQKPDBBx+gS5cueOWVVzTatri4GHFxcfjtt98AADKZDEVFRbCzs9NGqGTKzMwB+06ARTPNLmuzaPZ4OzOO7pDhEEJgx44dePjwIezt7TFx4kSNJpmpjVQqxahRoxohQiJqDCx4iJrAlStXMGnSJJw/fx5OTk4YPHiwRpekWVlZoaCgAObm5hgwYACCg4NhYcH/vqQlzXyBlsGP77NTVy37Pt6OyIBIJBKEhIRg7969iI6OVjuJJIRATEwM3Nzc8MYbb+gwSiJqKH5jItKyX3/9FT169EBpaSkcHR2xZs0ajX9/Y2FhgcjISMhkMl4eQdpn4w24Dgbykus2NbVFc8D1RcDGS/uxETUyLy8vvPHGG2qXCAsh8M477yA2NhYA0K9fP3Tt2lVXIRJRA+nlxdZr1qxBmzZtIJPJEBQUhLNnzz61/c6dO+Hv7w+ZTIaAgAAcPHhQtU6hUGDRokUICAiAra0tPDw8MHnyZNy9e1fbaZCREULg0qVLEEJotJ2fnx9GjBiBoUOHIj09HVFRUfV6fg8PDxY71DTMLQDXF4AOcx4XM09j0fxxO9cXeNNRMlhPFjuLFi1SFTv//Oc/WewQGTi9K3i2b9+O+fPnIyYmBikpKejWrRvCwsKQm5tbY/tTp04hOjoaM2bMQGpqKiIiIhAREYH09HQAQElJCVJSUvDuu+8iJSUFcXFxuHbtGkaOHNmUaZGBe/ToEbZt24bvv/8eJ0+e1GhbiUSCjRs34uDBg/Dw8NBShESNzNoFaDUK6LIUcA97/BudP7No9nh5l6WP21nzxopkHCQSiereOl988QXmzZun24CIqMH07nTcZ599hpkzZ2LatGkAgLVr1+LAgQNYv349Fi9eXK39559/jqFDh2LhwoUAgPfeew8JCQlYvXo11q5dC3t7eyQkJKhts3r1avTu3RtZWVnw8uIlGPR0v//+O3bu3Kn6DU19ftBqY1PPWa+IdMnaBWg9GmjR6/FNRR9eAa4D8JsNOHV8/JsdGy+O7JDRWbZsGcLDw9GrV69q6woLC5GWloZ+/fpBIpHoIDoi0pRe9VLl5eU4f/48lixZolpmZmaGkJAQJCcn17hNcnJytbvSh4WFYffu3bU+T0FBASQSCRwcHGpcX1ZWhrKyMtVjuVwO4PHlcQqFoo7ZQLXNn/82FcaUd0lJCeRyORwcHBAZGQk3N7da89I07/LyciQnJ6N3796NMjOQLhjTsdaESeUtaw3IWkNh1we4ngRFm+mApdXjdUoBKJ/9GpjE60RGpaZiJy8vD5s3b1adAAsODtZBZESkKb0qeO7fv4/Kykq4urqqLXd1dcXVq1dr3CY7O7vG9tnZ2TW2Ly0txaJFi6rNxvJnsbGxWLFiRbXlhw8frveZ+idHmUyFseTt7e2N5s2bIyUlpU7t65J3QUEB7ty5A4VCgatXr6J169YNDVOnjOVYa8oU805ITNJ4m5KSet7MlEhPZGVlYevWrSgtLYWTkxM6duyo65CIqI70quDRNoVCgbFjx0IIgS+//LLWdkuWLFEbNZLL5WjdujWGDBmi8X1PFAoFEhISEBoa2ug3otRnppL3sWPH4OLigk6dOgGoe97JyclIS0sDANjb22Pw4MFo165dU4Tc6EzlWD/JFPNuSM5VI+VETS0zMxO2trZwcWnY78yKi4tRWlqKVq1aITo6mpcqExkQvSp4nJ2dYW5ujpycHLXlOTk5cHNzq3EbNze3OrWvKnZu3bqFI0eOPLVwsbKygpWVVbXlUqm03l9sGrKtITPWvB89eoS3334bq1atwnPPPYfTp0/D0tJStf5ZeXfr1g3JyckIDAzEgAED1LY1VMZ6rJ/FFPOuT86m9hqRfrhz5w62bt0Kc3NzTJ8+HS1btqz3vjp27Ijo6Gj4+Pjw/UxkYPRqljZLS0sEBgYiKel/l0solUokJSWhT58+NW7Tp08ftfbA40tM/ty+qtjJyMhAYmIiWrRooZ0EyCRkZmaiR48eWLVqFQCgd+/eqKio0Ggf9vb2mDt3LkJCQoyi2CEi0jd5eXnYsmULFAoFPDw8NL7/WU3at2/PYofIAOlVwQMA8+fPx9dff41///vfuHLlCl5//XUUFxerZm2bPHmy2qQGc+fORXx8PD799FNcvXoVy5cvx7lz5zBnzhwAj4udV155BefOncPmzZtRWVmJ7OxsZGdno7y8XCc5kmFzd3eHmZkZ3N3dcfDgQaxdu7ZelzbIZDItREdERABw/PhxPHr0CJ6enhg3bhwsLB5f1FJRUYGPPvoIxcXFOo6QiJqKXl3SBgDjxo1DXl4eli1bhuzsbHTv3h3x8fGqiQmysrLUbhAWHByMLVu2YOnSpXj77bfh5+eH3bt3o0uXLgAeTym8d+9eAED37t3Vnuvo0aMYNGhQk+RF+ik1NRWOjo5o06ZNnbeRyWSIi4uDs7MzRwuJiPTUyy+/DFtbWwwcOFA1kq5QKDBp0iRs374dx48fx8GDBzm1NJEJ0LuCBwDmzJmjGqF50rFjx6otGzNmDMaMGVNj+zZt2kAI0ZjhkRFQKBQ4ePAg0tLS0KxZM7z++usajdJ06NCh1nV8vxER6Z5UKkV4eLjqsUKhQHR0NHbt2gWpVIpZs2ax2CEyEXpZ8BBpU3FxMf7zn/8gNzcXEokEvXr1apR74FRUVOCnn37CjRs3WPQQEemZW7du4ejRo7C0tMSuXbswfPhwtfVCCBZAREaKBQ+ZHBsbGzRv3hwlJSWIjIyEj49Pg/eZmZmJAwcO4MGDBwCA69evq6aqJiIi3WvXrh0SExORnZ2tNvIDADdv3kR8fDwmTJiA5s2b6yhCItIWFjxkciQSCSIjI6FUKtGsWTO1dXl5ebC0tIS9vX2d96dUKrF//348fPgQtra2cHZ2Nth76hARGbPnnnuu2rL09HTs3r0blZWVOHbsGEaMGKGDyIhIm/RuljaipmBjY1Ot2NmzZw+6dOmCuXPnarQvMzMzvPTSS+jVqxf+8pe/wNHRkZdFEBEZgIsXL2LXrl2orKxEx44dMXToUF2HRERawBEeMnlyuRzz5s3Dhg0bAADnz59HYWGhRpc1+Pr6wtfXFwqFQlthEhFRI2vbti2cnJzQrl07hIWFqc0CS0TGg/+zyeTl5+dj165dkEgk+Nvf/oZz587xGm4iIj2XkpKC48ePN2iSGFtbW8ycORNDhw5lsUNkxDjCQybPy8sLGzZsgIuLC/r166frcIiI6BkuX76M/fv3QwgBd3d3tG/fvt774k2giYwfT2eQUbl27Rq2b98OpVKp0XaRkZE1FjsFBQUoLS1trPCIiKiBfvvtN8TFxUEIgR49esDPzw8AUFhYiNTUVB1HR0T6iAUPGYXKykocPnwY27Ztw9WrV3H+/PkG7U+pVOLUqVNYs2YNjhw50khREhFRQz18+FA1ycCwYcMgkUggl8sRHh6OgQMH4syZM7oOkYj0DC9pI6Pwww8/4NKlSwCAoKAg9OjRo977ys3NRVxcHHJyclSPKysrYW5u3iixEhFR/fXs2ROOjo7w9vaGmZkZCgoKMHToUJw+fRoODg78LQ4RVcOCh4xCUFAQMjMzMXz4cHTs2FG1vOrSNk06QKlUigcPHsDa2hohISF47rnnOM00EZEe8fX1Vf37ww8/xOnTp+Ho6IjExES1E155eXlo2bKlLkIkIj3CgoeMQuvWrTF37lxYWlqqlt2+fRvTpk3Dyy+/jDfffLPO+3J0dMTYsWPh4eEBW1tbbYRLRESNZMWKFbhz5w4WLlyI7t27AwCEEDh58iSSkpIQERGBbt266TZIItIpFjxkNKqKHSEENm3ahDfffBNyuRxpaWmYPn26RsVL1Y9giYhIv8lkMmzevFn1WKlUIj4+Hj///DOAx6M8RGTaeKErGZ0LFy5gypQpkMvleP7555GcnMyRGiIiE/Hrr7+qip2wsDCEhIToOCIi0jWO8JDR6d69OxYsWAAnJycsXLgQFhZ8mxMRmQp/f3/07dsX7u7u6Ny5s67DISI9wG+CpPcUCgWkUqlG23zyySfVlhUVFSElJQX9+/fnJARERHpKCAGFQtGgz2mO6hDRn/GSNtJrt27dwurVq5GRkVHvfQghcO7cOaxZswZHjx5FWlpa4wVIRCZn+fLlkEgkan/8/f11HZbRSExMxPr161FUVKTrUIjISHCEh/RS1Qw7R44cgRAC//3vf9GuXbt6nfGLi4tDeno6AMDd3R2urq6NHS4RmZjOnTsjMTFR9ZiXzjaOkydP4tSpUwAen/ACgPz8fDg7O3NknojqjZ/QpJeuXr2KpKQkAEDXrl1Vd9Ouj65du+LXX3/Fiy++iF69evGmdETUYBYWFnBzc9N1GEblwoULqiIyNDQUnTt3xrlz59CnTx+MHj0aH330EYseIqoXfvMjveTv74+AgAAMHz4cERERsLS0hEKhwMqVK3HixAmN9uXn54d58+YhKCiIxQ4RNYqMjAx4eHigbdu2mDBhArKysnQdksHz8vKCo6Mj+vbti+DgYNy6dQtLly7FjRs3sHPnTuTn5+s6RCIyUBzhIb0kkUgQGRmpenz16lVMnjwZP//8M9q0aYNLly7BxsamzvuztrbWRphEZIKCgoKwceNGdOjQAffu3cOKFSvQv39/pKeno3nz5jVuU1ZWhrKyMtVjuVwO4PGkLAqFQqPnr2qv6Xb6rlmzZpg6dSpkMhkKCwsxZMgQ5OTkoG3btjh8+DCaNWuGsrIynD59Gj179lS70bShM9Zj+iTmaVx0nacmz8uCh/ReSkoK+vbti9LSUjg4OOCDDz5gAUNEOhMeHq76d9euXREUFARvb2/s2LEDM2bMqHGb2NhYrFixotryw4cPa3Ty5s8SEhLqtZ2hGDFiBHbt2oW3334b6enpuHDhAm7dugW5XI6UlBT4+PgY3SVuxn5MqzBP46KrPEtKSurclgUP6b1u3bqhV69ekMlkWL9+PVq1aqVaJ4Qwug6PiAyLg4MD2rdvj+vXr9faZsmSJZg/f77qsVwuR+vWrTFkyBDY2dlp9HwKhQIJCQkIDQ3VeMp+QxIaGor+/fvjpZdeQnl5OXbu3Am5XA4LCwuEhoaiQ4cOug6x0ZjKMWWexkXXeVaNlNcFCx7Se+bm5ti7dy/s7e1Vxc2jR4+QlJQEqVSKsLAwHUdIRKasqKgIN27cwKRJk2ptY2VlBSsrq2rLpVJpvb8oNGRbQ1GV46NHj/DHH3/A2toa0dHRaN26ta5D0wpTOKYA8zQ2uspTk+dkwUNNrqysDOXl5bVe614TBwcHAI9HdC5evIjDhw+juLgYEokEzz//POzt7bUULRGRugULFmDEiBHw9vbG3bt3ERMTA3Nzc0RHR+s6NKPVokULjB8/HjKZDM7OzroOh4gMDAsealLZ2dnYuXMnrK2tMW3aNJibm2u0vVwux969e1FZWQlnZ2cMGzaMxQ4RNak7d+4gOjoaDx48QMuWLdGvXz+cPn0aLVu21HVoRu3PlzMTEWmCBQ81CSEEUlJSEB8fj4qKCtjZ2aGgoABOTk4a7cfe3h4vvPACKisr0bdvX40LJiKihtq2bZuuQzBIFRUVuHfvntFejkZE+osFDzWJiooKnDx5EhUVFfDz80NERARsbGxw/PhxtGvXDp6ennXeV9++fbUYKRERNTalUom4uDhcu3YNkZGR6Ny5MyedIaImw7swUpOQSqUYM2YMBg8ejOjoaJiZmWH+/PkYNGgQXn31VQghdB0iERFpgRAC+/fvx5UrVyCRSGBtbY2UlBQMGjQIubm5ug6PiEwAR3ioybi7u8Pd3V11hu/y5csAgNatW6O8vLzGGYyIiMiwXbp0CampqZBIJBg9ejT++OMPhISEID8/H4sWLcKGDRs42kNEWsWCh5pcy5Yt8ccff8DV1RXffvsthg0bBgC4f/8+WrRowU6PiMiIdO7cGbdv34arqyuKi4sREhKCgoICBAcH4/PPP0dRURF27NiBF198EW3atNF1uERkhPTukrY1a9agTZs2kMlkCAoKwtmzZ5/afufOnfD394dMJkNAQAAOHjyotj4uLg5DhgxRfZFOS0vTYvRUF05OTti7dy/S09MxbNgwlJeX48cff8S//vUvXLx4UdfhERFRI5JIJAgPD0ePHj3g4uICJycn9OvXD/Hx8VAoFPj2229x+/Zt7Nu3D0qlUtfhEpER0quCZ/v27Zg/fz5iYmKQkpKCbt26ISwsrNZrfE+dOoXo6GjMmDEDqampiIiIQEREBNLT01VtiouL0a9fP3z88cdNlQbVQc+ePeHs7Izr169jzZo1OH36NIQQuH37tq5DIyIiLfHy8sLx48dx6NAhVbGTn58PR0dHjB8/HmZmevW1hIiMhF5d0vbZZ59h5syZmDZtGgBg7dq1OHDgANavX4/FixdXa//5559j6NChWLhwIQDgvffeQ0JCAlavXo21a9cCgOrO1zdv3myaJExQRUUFfv31V3Tq1EnjbSsrKyGXy+Hg4ICXXnoJfn5+WoiQiIj0RdW01EqlEt7e3pDL5Rg/fjxsbW11HBkRGSu9KXjKy8tx/vx5LFmyRLXMzMwMISEhSE5OrnGb5ORkzJ8/X21ZWFgYdu/erc1Q6U/++OMP7Ny5E/fu3cOYMWM0Lno6dOiAiIgIdOrUCVKpVEtREhGRvjEzM0NkZCSEELC0tNR1OERkxPSm4Ll//z4qKyvh6uqqttzV1RVXr16tcZvs7Owa22dnZzcolrKyMpSVlakey+VyAIBCoYBCodBoX1XtNd3OEPz666/Yt28fysrKYG1tDTMzM9y7dw92dnaqiQfqkndVkWQMr5ExH+/amGLOgGnm3ZCcTel1orrjiS4iagp6U/Dok9jYWKxYsaLa8sOHD8PGxqZe+0xISGhoWHrnwYMHKCsrg42NDdq0aYNNmzZhzZo1CA0NxcSJEwEYZ951YYp5m2LOgGnmXZ+cS0pKtBAJERHRs+lNwePs7Axzc3Pk5OSoLc/JyYGbm1uN27i5uWnUvq6WLFmidqmcXC5H69atMWTIENjZ2Wm0L4VCgYSEBISGhhrdmSwhBC5fvgwPDw8sWrQIGzduBABcu3YNAwYMwIkTJ4wy76cx5uNdG1PMGTDNvBuSc9VIORmfkpISJCcnY9CgQTA3N9d1OERE1ehNwWNpaYnAwEAkJSUhIiICwOMfNCYlJWHOnDk1btOnTx8kJSVh3rx5qmUJCQno06dPg2KxsrKq8SaYUqm03l9sGrKtPuvevTsuXLiAzZs3QyKR4K9//Svee+895Ofn48GDB0ab97OYYt6mmDNgmnnXJ2dTe41MRVlZGTZv3oy7d++ipKQEEokECoUCo0aN0nVoREQqelPwAMD8+fMxZcoU9OzZE71798aqVatQXFysmrVt8uTJ8PT0RGxsLABg7ty5GDhwID799FMMGzYM27Ztw7lz57Bu3TrVPh8+fIisrCzcvXsXwOPRB+Dx6FBDR4LosW7dumH16tXw9/dHcHAwfvrpJ5w8eRKVlZXIyclBq1atdB0iERE1soqKCuzYsQN3796FtbU18vPzMWPGDAgh8NNPP6FDhw6wt7fnVNNEpHN6VfCMGzcOeXl5WLZsGbKzs9G9e3fEx8erJibIyspS++AMDg7Gli1bsHTpUrz99tvw8/PD7t270aVLF1WbvXv3qgomAIiKigIAxMTEYPny5U2TmAl47bXXoFQq8dVXX6num9S8efMaR8qIiMjw5ebmIisrC1KpFE5OTpg+fToqKiowZswYuLi4YN26dejUqROGDx+umsiGiEgX9KrgAYA5c+bUegnbsWPHqi0bM2YMxowZU+v+pk6diqlTpzZSdPQ0ZmZm8Pf3R0lJCUJDQ/Hbb7/BwcFB12EREZEWeHh4YNKkSaioqMCnn36KiooKREVF4Z133sGWLVtQWVmJ3NxcKBQKTjtNRDqldwUP6Z5SqcRPP/2ELl26oEWLFhpt279/fwQHB8PMzAyZmZlaipCIiPSBl5cXAOCLL75AYGAgIiMj8a9//QuVlZXo0KEDRo8ezd9vEZHOseAhNUVFRYiLi0NmZiauXLmCV199FRYWdX+bWFhYwMLCgvfcICIyIWZmZpg+fToAICIiApmZmQgPD+fvd4hIL/CTiFRyc3Px1VdfITMzE1KpFG3btkV4eDi+//57XYdGREQGolOnThg2bBiLHSLSGxzhIRUHBwdYW1tDJpPBzMwMY8eORUFBAa5du4aRI0dCKpXyh6dEREREZFB4+oVULC0tMX78eLRv3x5vvPEGCgoKEBQUhD179mDXrl24dOmSrkMkIiIiItIIR3hIjYODA0JCQhAVFYXOnTtj4MCBOHToEBQKBbKzs9GxY0feSZuIyEQIITiyT0QGjyM8VI1EIsGWLVswbtw4HDlyBAqFAt7e3pg4cSKLHSIiE/HgwQOsW7cOubm5iI+PR0VFha5DIiKqF47wUI0kEgnatWuHbt26wdvbG927d+dZPiIiEyGXy7Fp0yYUFBRg/vz52Lx5M8aPH4/FixfDzc0NLVu21HWIRER1xoKHaiWRSBAREaHrMIiIqAmVlJTgu+++Q0FBAdLS0rB7924AgI+PD+Li4mBvb4+ZM2fC1tZWt4ESEdURL2kzAUIInDlzBgcOHNB1KEREpOckEglkMhkqKioQHx8PiUSCd999V3UD0Q4dOsDa2lrHURIR1R1HeIxcaWkp9u3bh8uXL6OyshJHjx7F9OnT0alTJ12HRkREesja2hqTJk1CYWEhBgwYgNOnT8PNzQ13795FSEgIgoODeYkzERkUFjxGTAiBf//738jOzsbDhw+RlJSES5cu4dixY4iPj4ezs7OuQyQiIj0klUrh5OSE0NBQhIaGori4GFlZWejYsaOuQyMi0hgvaTNiEokEwcHBqpl2Ll26BHd3d0RHR+PLL79Ebm6urkMkIiIDYGtry2KHiAwWR3iMXEBAAN5//30kJiaiR48e6Ny5M4qKiiCRSHDr1i24uLjoOkQiIiIiIq1hwWMC7OzscOTIEaSnp+PkyZPw8PDAsGHD4OHhoevQiIiIiIi0igWPiXBzc0OLFi3g5OSE7t27w8yMVzMSERERkfHjt14TIpVK0aNHDxY7REQmrqCgAEIIZGdnQwgBIYSuQyIi0hp+8zVgCoVC1yEQEZGBuXnzJr744gu88sorCAwMxNatW3Hy5Eldh0VEpDW8pM1AXbx4EfHx8ejQoQMGDRoEOzs7XYdERER67t69e9iyZQv27NmDtLQ0jB49GhkZGbh+/Tr8/f15uwIiMkoc4TEwFRUV2L9/P7Zv347du3cjIiICixYtwo0bN3QdGhER6bFHjx5h8+bNOHr0KFJSUjBp0iR07NgR5ubmGD16NIsdIjJaHOExMKdPn8ahQ4ewc+dOFBUVYfjw4XBzc8OePXswe/ZsWFlZ6TpEIiLSQ9bW1hgwYAAsLCyQn5+P5557DiUlJYiKioK3t7euwyMi0hoWPAbm+eefx/nz52Fubo633noL1tbWAIB27drxR6dERPRUvXv3RmBgIN58802YmZmhuLgYzZo103VYRERaxYLHwFhYWGDu3Lno0qULbt26hdLSUgwfPhxeXl66Do2IiAyAubm56t8sdojIFLDgMVCDBw9GcXExZDKZWudFRERERET/w4LHgNna2uo6BCIiIiIivcZZ2oiIiIiIyGix4NEjcrkc2dnZUCqVug6FiIgMlFKphEKhQHFxsa5DISLSCyx49MT169cxd+5c9O/fH7Gxsbh//76uQyIiIgOTmpqKyMhI/P3vf8fmzZtRXl6u65CIiHSOBY+OKZVK7N+/H+PHj8e9e/cwfvx4VFRU4PDhw7oOjYiIDMjPP/+M6dOnw8vLCxUVFXj48CEePnyo67CIiHSOBY+OFRUVYc+ePbCxsUGfPn1gZmYGf39/jBgxQtehERGRgbh8+TLGjx+PwMBAtGjRAlZWVnj11Vfh5uam69CIiHSOBY+O2dnZYeHChQgICICDgwMmTJiAcePGoXnz5roOjYiIDEBlZSUSExPh5eWFgwcPwsHBAbNnz4azs7OuQyMi0guclloPtG/fHl988YWuwyAiIgNkbm6OSZMmwdfXF506dYKPj4+uQyIi0it6OcKzZs0atGnTBjKZDEFBQTh79uxT2+/cuRP+/v6QyWQICAjAwYMH1dYLIbBs2TK4u7vD2toaISEhyMjI0GYKRERk5DTtq7TJ0dERw4YNY7FDRFQDvSt4tm/fjvnz5yMmJgYpKSno1q0bwsLCkJubW2P7U6dOITo6GjNmzEBqaioiIiIQERGB9PR0VZu///3v+L//+z+sXbsWZ86cga2tLcLCwlBaWtpUaRERkRHRtK8iIiLd0buC57PPPsPMmTMxbdo0dOrUCWvXroWNjQ3Wr19fY/vPP/8cQ4cOxcKFC9GxY0e899576NGjB1avXg3g8ejOqlWrsHTpUrz88svo2rUr/vOf/+Du3bvYvXu31vMRQnBaUCIiI6NpX0VERLqjV7/hKS8vx/nz57FkyRLVMjMzM4SEhCA5ObnGbZKTkzF//ny1ZWFhYapiJjMzE9nZ2QgJCVGtt7e3R1BQEJKTkxEVFVVtn2VlZSgrK1M9lsvlAACFQgGFQlHnfM6fP4+tW7fCyckJoaGhsLW1rfO2hq7qddLk9TIGppi3KeYMmGbeDcnZmF6n+vRVjdWvVG3z57+NlankCZhOrszTuOg6T02eV68Knvv376OyshKurq5qy11dXXH16tUat8nOzq6xfXZ2tmp91bLa2jwpNjYWK1asqLb88OHDsLGxeWYeSqUSycnJkMlkcHJyQkVFBfbt2wc7O7tnbmtsEhISdB2CTphi3qaYM2Caedcn55KSEi1Eohv16asa2q8Aj4um0tJS2NvbAzCd956p5AmYTq7M07joKk9N+hW9Knj0xZIlS9RGjeRyOVq3bo0hQ4bUqWj55JNPkJOTg7Zt2yIrKwv+/v4YPXo0pFKpNsPWKwqFAgkJCQgNDWXeRs4UcwZMM++G5Fw1omGqGtqvXLlyBbt27YJUKsXYsWNx5coVo3/vmdL/MVPJlXkaF13nqUm/olcFj7OzM8zNzZGTk6O2PCcnp9abp7m5uT21fdXfOTk5cHd3V2vTvXv3GvdpZWUFKyurasulUmmdDmiPHj2wfft2SKVS/OMf/8CJEyfqvK2xYd6mwxRzBkwz7/rkbEyvUX36qob0K2fPnsW+fftgYWGBoqIitGzZEleuXDGZ956p5AmYTq7M07joKk9NnlOvJi2wtLREYGAgkpKSVMuUSiWSkpLQp0+fGrfp06ePWnvg8dBaVXsfHx+4ubmptZHL5Thz5kyt+2yo0NBQbNq0CV9//TWaNWumlecgIiLdqE9fVV95eXn47LPPYGFhgd9++w0jR45kv0JEpCG9GuEBgPnz52PKlCno2bMnevfujVWrVqG4uBjTpk0DAEyePBmenp6IjY0FAMydOxcDBw7Ep59+imHDhmHbtm04d+4c1q1bBwCQSCSYN28e3n//ffj5+cHHxwfvvvsuPDw8EBERoZUczM3N0bVrV63sm4iIdO9ZfVVjSUxMxL59+3Dnzh289dZbCA4ONvofQhMRNTa9K3jGjRuHvLw8LFu2DNnZ2ejevTvi4+NVPw7NysqCmdn/BqaCg4OxZcsWLF26FG+//Tb8/Pywe/dudOnSRdXmb3/7G4qLi/Haa68hPz8f/fr1Q3x8PGQyWZPnR0REhu9ZfVVjEEJAJpNh8uTJ6NevH8aOHdto+yYiMiV6V/AAwJw5czBnzpwa1x07dqzasjFjxmDMmDG17k8ikWDlypVYuXJlg+ISQuDy5ctwdXVFixYtGrQvIiIybE/rqxqDRCJBREQE/P394e/vr7XnISIydnpZ8OirHTt24O7du/D09MSMGTMgkUh0HRIRERkxiUSCjh076joMIiKDpleTFui73377DZWVlTh9+jSUSqWuwyEiIiIiomdgwaOBW7du4ZtvvoGVlZXa74iIiIiIiEg/8Vu7Bk6dOoXp06dj4cKFvJyNiIga7Nq1a/j+++951QARkRbxNzwaWLx4MSZOnMh7IBARUaOIi4uDTCaDt7c3evXqpetwiIiMEkd4NBAVFcVih4iIGtWFCxc4wkNEpEUc4dEAf7dDRESN6fjx47hz5w5atWql61CIiIwWv8ETERHpSEZGBiZMmAALC55/JCLSFhY8REREOjJx4kRMnz4drq6uug6FiMho8ZRSHQghAAByuVzjbRUKBUpKSiCXyyGVShs7NL3FvE0nb1PMGTDNvBuSc9XnZ9Xnqamreh2GDRsGV1dXjfoXU3nvmUqegOnkyjyNi67z1KRfYcFTB4WFhQCA1q1b6zgSIiLDVlhYCHt7e12HoXNV/UpoaKiOIyEiMmx16VckgqfbnkmpVOLu3bto3ry5xvffkcvlaN26NW7fvg07OzstRah/mLfp5G2KOQOmmXdDchZCoLCwEB4eHpwABuxX6sJU8gRMJ1fmaVx0nacm/QpHeOrAzMyswTPo2NnZGfWbvjbM23SYYs6AaeZd35w5svM/7FfqzlTyBEwnV+ZpXHSZZ137FZ5mIyIiIiIio8WCh4iIiIiIjBYLHi2zsrJCTEwMrKysdB1Kk2LeppO3KeYMmGbeppizPjKV42AqeQKmkyvzNC6GlCcnLSAiIiIiIqPFER4iIiIiIjJaLHiIiIiIiMhoseAhIiIiIiKjxYKHiIiIiIiMFgueelizZg3atGkDmUyGoKAgnD179qntd+7cCX9/f8hkMgQEBODgwYNq64UQWLZsGdzd3WFtbY2QkBBkZGRoMwWNNXbOcXFxGDJkCFq0aAGJRIK0tDQtRl9/jZm3QqHAokWLEBAQAFtbW3h4eGDy5Mm4e/euttPQWGMf7+XLl8Pf3x+2trZwdHRESEgIzpw5o80UNNbYOf/ZrFmzIJFIsGrVqkaOuuEaO++pU6dCIpGo/Rk6dKg2UzA5mh4zQ7N8+fJq7yF/f39dh9VgJ06cwIgRI+Dh4QGJRILdu3errTeE7wJ19axcjeFzIjY2Fr169ULz5s3h4uKCiIgIXLt2Ta1NaWkpZs+ejRYtWqBZs2YYPXo0cnJydBRx/dUl10GDBlU7prNmzdJRxDUQpJFt27YJS0tLsX79enHp0iUxc+ZM4eDgIHJycmpsf/LkSWFubi7+/ve/i8uXL4ulS5cKqVQqLl68qGrz0UcfCXt7e7F7925x4cIFMXLkSOHj4yMePXrUVGk9lTZy/s9//iNWrFghvv76awFApKamNlE2ddfYeefn54uQkBCxfft2cfXqVZGcnCx69+4tAgMDmzKtZ9LG8d68ebNISEgQN27cEOnp6WLGjBnCzs5O5ObmNlVaT6WNnKvExcWJbt26CQ8PD/HPf/5Ty5loRht5T5kyRQwdOlTcu3dP9efhw4dNlZLR0/SYGaKYmBjRuXNntfdQXl6ersNqsIMHD4p33nlHxMXFCQDihx9+UFuv798FNPGsXI3hcyIsLExs2LBBpKeni7S0NPHSSy8JLy8vUVRUpGoza9Ys0bp1a5GUlCTOnTsnnn/+eREcHKzDqOunLrkOHDhQzJw5U+2YFhQU6DBqdSx4NNS7d28xe/Zs1ePKykrh4eEhYmNja2w/duxYMWzYMLVlQUFB4i9/+YsQQgilUinc3NzEJ598olqfn58vrKysxNatW7WQgeYaO+c/y8zM1NuCR5t5Vzl79qwAIG7dutU4QTeCpsi7oKBAABCJiYmNE3QDaSvnO3fuCE9PT5Geni68vb31ruDRRt5TpkwRL7/8slbiJc2PmSGKiYkR3bp103UYWvVkEWAI3wXqq7aCx9g+J3JzcwUAcfz4cSHE4+MnlUrFzp07VW2uXLkiAIjk5GRdhdkonsxViMcFz9y5c3UX1DPwkjYNlJeX4/z58wgJCVEtMzMzQ0hICJKTk2vcJjk5Wa09AISFhanaZ2ZmIjs7W62Nvb09goKCat1nU9JGzoagqfIuKCiARCKBg4NDo8TdUE2Rd3l5OdatWwd7e3t069at8YKvJ23lrFQqMWnSJCxcuBCdO3fWTvANoM1jfezYMbi4uKBDhw54/fXX8eDBg8ZPwATV55gZqoyMDHh4eKBt27aYMGECsrKydB2SVun7dwFtMLbPiYKCAgCAk5MTAOD8+fNQKBRqx9Tf3x9eXl4Gf0yfzLXK5s2b4ezsjC5dumDJkiUoKSnRRXg1stB1AIbk/v37qKyshKurq9pyV1dXXL16tcZtsrOza2yfnZ2tWl+1rLY2uqSNnA1BU+RdWlqKRYsWITo6GnZ2do0TeANpM+/9+/cjKioKJSUlcHd3R0JCApydnRs3gXrQVs4ff/wxLCws8NZbbzV+0I1AW3kPHToUkZGR8PHxwY0bN/D2228jPDwcycnJMDc3b/xETEh9jpkhCgoKwsaNG9GhQwfcu3cPK1asQP/+/ZGeno7mzZvrOjyt0PfvAo3N2D4nlEol5s2bh759+6JLly4AHh9TS0vLaic0Df2Y1pQrAIwfPx7e3t7w8PDAL7/8gkWLFuHatWuIi4vTYbT/w4KHSAcUCgXGjh0LIQS+/PJLXYfTJF544QWkpaXh/v37+PrrrzF27FicOXMGLi4uug6t0Z0/fx6ff/45UlJSIJFIdB1Ok4qKilL9OyAgAF27doWvry+OHTuGwYMH6zAyMhTh4eGqf3ft2hVBQUHw9vbGjh07MGPGDB1GRo3F2D4nZs+ejfT0dPz3v//VdShaV1uur732murfAQEBcHd3x+DBg3Hjxg34+vo2dZjV8JI2DTg7O8Pc3LzaDBs5OTlwc3OrcRs3N7entq/6W5N9NiVt5GwItJl3VbFz69YtJCQk6M3oDqDdvG1tbdGuXTs8//zz+Pbbb2FhYYFvv/22cROoB23k/NNPPyE3NxdeXl6wsLCAhYUFbt26hb/+9a9o06aNVvLQVFP9327bti2cnZ1x/fr1hgdt4upzzIyBg4MD2rdvb9TvIX3/LqBthvw5MWfOHOzfvx9Hjx5Fq1atVMvd3NxQXl6O/Px8tfaGfExry7UmQUFBAKA3x5QFjwYsLS0RGBiIpKQk1TKlUomkpCT06dOnxm369Omj1h4AEhISVO19fHzg5uam1kYul+PMmTO17rMpaSNnQ6CtvKuKnYyMDCQmJqJFixbaSaCemvJ4K5VKlJWVNTzoBtJGzpMmTcIvv/yCtLQ01R8PDw8sXLgQP/74o/aS0UBTHes7d+7gwYMHcHd3b5zATVh9jpkxKCoqwo0bN4z6PaTv3wW0zRA/J4QQmDNnDn744QccOXIEPj4+ausDAwMhlUrVjum1a9eQlZVlcMf0WbnWpOp2I3pzTHU8aYLB2bZtm7CyshIbN24Uly9fFq+99ppwcHAQ2dnZQgghJk2aJBYvXqxqf/LkSWFhYSH+8Y9/iCtXroiYmJgap6V2cHAQe/bsEb/88ot4+eWX9WoqSm3k/ODBA5GamioOHDggAIht27aJ1NRUce/evSbPrzaNnXd5ebkYOXKkaNWqlUhLS1OburGsrEwnOdaksfMuKioSS5YsEcnJyeLmzZvi3LlzYtq0acLKykqkp6frJMcnaeM9/iR9nKWtsfMuLCwUCxYsEMnJySIzM1MkJiaKHj16CD8/P1FaWqqTHI3Ns46ZMfjrX/8qjh07JjIzM8XJkydFSEiIcHZ21ptp7OursLBQpKamitTUVAFAfPbZZyI1NVU1S6e+fxfQxNNyNZbPiddff13Y29uLY8eOqfXnJSUlqjazZs0SXl5e4siRI+LcuXOiT58+ok+fPjqMun6elev169fFypUrxblz50RmZqbYs2ePaNu2rRgwYICOI/8fFjz18MUXXwgvLy9haWkpevfuLU6fPq1aN3DgQDFlyhS19jt27BDt27cXlpaWonPnzuLAgQNq65VKpXj33XeFq6ursLKyEoMHDxbXrl1rilTqrLFz3rBhgwBQ7U9MTEwTZFN3jZl31RTcNf05evRoE2VUN42Z96NHj8SoUaOEh4eHsLS0FO7u7mLkyJHi7NmzTZVOnTT2e/xJ+ljwCNG4eZeUlIghQ4aIli1bCqlUKry9vcXMmTON6su4PnjaMTMG48aNE+7u7sLS0lJ4enqKcePGievXr+s6rAY7evRojZ//Vf/HDOG7QF09LVdj+ZyorT/fsGGDqs2jR4/EG2+8IRwdHYWNjY0YNWqUXp3Yratn5ZqVlSUGDBggnJychJWVlWjXrp1YuHChXt2HRyKEENodQyIiIiIiItIN/oaHiIiIiIiMFgseIiIiIiIyWix4iIiIiIjIaLHgISIiIiIio8WCh4iIiIiIjBYLHiIiIiIiMloseIiIiIiIyGix4CECkJGRgSFDhsDe3h4SiQS7d+/WdUhGRSKRYPny5boOg4ioybBf0S72K6QJFjxkUDZu3AiJRKL6Y2FhAU9PT0ydOhW///57vfc7ZcoUXLx4ER988AE2bdqEnj17NmLUhuOtt96CRCLB9evXa23zzjvvQCKR4JdffmnCyIiItIP9inaxXyF9wIKHDNLKlSuxadMmrF27FuHh4fjuu+8wcOBAlJaWaryvR48eITk5GTNmzMCcOXMwceJEtGrVSgtR678JEyYAALZs2VJrm61btyIgIABdu3ZtqrCIiLSO/Yp2sF8hfcCChwxSeHg4Jk6ciFdffRXffPMNFixYgBs3bmDv3r0a7ysvLw8A4ODg0GjxlZaWQqlUNtr+mkpQUBDatWuHrVu31rg+OTkZmZmZqg6MiMhYsF/RDvYrpA9Y8JBR6N+/PwDgxo0basuvXr2KV155BU5OTpDJZOjZs6da57V8+XJ4e3sDABYuXAiJRII2bdqo1v/++++YPn06XF1dYWVlhc6dO2P9+vVqz3Hs2DFIJBJs27YNS5cuhaenJ2xsbCCXywEAZ86cwdChQ2Fvbw8bGxsMHDgQJ0+eVNvH8uXLVUP+U6dOhYODA+zt7TFt2jSUlJRUy/e7775D7969YWNjA0dHRwwYMACHDx9Wa3Po0CH0798ftra2aN68OYYNG4ZLly4987WcMGECrl69ipSUlGrrtmzZAolEgujoaJSXl2PZsmUIDAyEvb09bG1t0b9/fxw9evSZzzF16lS11/nJ16GmfAMDA2FtbQ0nJydERUXh9u3bam0yMjIwevRouLm5QSaToVWrVoiKikJBQcEz4yEiehL7FfYr7FeMh4WuAyBqDDdv3gQAODo6qpZdunQJffv2haenJxYvXgxbW1vs2LEDERER2LVrF0aNGoXIyEg4ODjg//2//4fo6Gi89NJLaNasGQAgJycHzz//PCQSCebMmYOWLVvi0KFDmDFjBuRyOebNm6cWw3vvvQdLS0ssWLAAZWVlsLS0xJEjRxAeHo7AwEDExMTAzMwMGzZswIsvvoiffvoJvXv3VtvH2LFj4ePjg9jYWKSkpOCbb76Bi4sLPv74Y1WbFStWYPny5QgODsbKlSthaWmJM2fO4MiRIxgyZAgAYNOmTZgyZQrCwsLw8ccfo6SkBF9++SX69euH1NTUGjuFKhMmTMCKFSuwZcsW9OjRQ7W8srISO3bsQP/+/eHl5YX79+/jm2++QXR0NGbOnInCwkJ8++23CAsLw9mzZ9G9e/d6HMnqPvjgA7z77rsYO3YsXn31VeTl5eGLL77AgAEDkJqaCgcHB5SXlyMsLAxlZWV488034ebmht9//x379+9Hfn4+7O3tGyUWIjId7FfYr7BfMSKCyIBs2LBBABCJiYkiLy9P3L59W3z//feiZcuWwsrKSty+fVvVdvDgwSIgIECUlpaqlimVShEcHCz8/PxUyzIzMwUA8cknn6g914wZM4S7u7u4f/++2vKoqChhb28vSkpKhBBCHD16VAAQbdu2VS2rei4/Pz8RFhYmlEqlanlJSYnw8fERoaGhqmUxMTECgJg+fbrac40aNUq0aNFC9TgjI0OYmZmJUaNGicrKSrW2Vc9RWFgoHBwcxMyZM9XWZ2dnC3t7+2rLa9KrVy/RqlUrteeIj48XAMRXX30lhBCioqJClJWVqW33xx9/CFdX12p5ABAxMTGqx1OmTBHe3t7Vnrfqdahy8+ZNYW5uLj744AO1dhcvXhQWFhaq5ampqQKA2Llz5zNzIyL6M/Yr7FeEYL9i7HhJGxmkkJAQtGzZEq1bt8Yrr7wCW1tb7N27V/Wj0IcPH+LIkSMYO3YsCgsLcf/+fdy/fx8PHjxAWFgYMjIynjr7jhACu3btwogRIyCEUG1///59hIWFoaCgoNrQ/JQpU2Btba16nJaWhoyMDIwfPx4PHjxQbV9cXIzBgwfjxIkT1a7HnjVrltrj/v3748GDB6rLGHbv3g2lUolly5bBzEz9v2/VkH1CQgLy8/MRHR2tFre5uTmCgoLqdGnAxIkTcefOHZw4cUK1bMuWLbC0tMSYMWMAAObm5rC0tAQAKJVKPHz4EBUVFejZs2eNly3UR1xcHJRKJcaOHauWi5ubG/z8/FS5VJ1p+/HHH2u8VIOI6FnYr7BfYb9ivHhJGxmkNWvWoH379igoKMD69etx4sQJWFlZqdZfv34dQgi8++67ePfdd2vcR25uLjw9PWtcl5eXh/z8fKxbtw7r1q2rdfs/8/HxUXuckZEB4HGHVZuCggK1yyW8vLzU1let++OPP2BnZ4cbN27AzMwMnTp1qnWfVc/74osv1rjezs6u1m2rREVFYf78+diyZQsGDRqE0tJS/PDDDwgPD1eL99///jc+/fRTXL16FQqFQrX8ydeivjIyMiCEgJ+fX43rpVKp6vnmz5+Pzz77DJs3b0b//v0xcuRITJw4kZcdEFGdsF9hvwKwXzFWLHjIIPXu3Vt1T4OIiAj069cP48ePx7Vr19CsWTPVGa4FCxYgLCysxn20a9eu1v1XbT9x4sRaO5Ynp8/881m4P+/jk08+qfW646rruquYm5vX2E4IUWusT6p63k2bNsHNza3aeguLZ/+3d3FxQWhoKHbt2oU1a9Zg3759KCwsVJtF57vvvsPUqVMRERGBhQsXwsXFBebm5oiNja32I98n1fQDUuDx9dxP5iKRSHDo0KEaX5s/v36ffvoppk6dij179uDw4cN46623EBsbi9OnT5vsdLBEVHfsV2rHfoX9iqFjwUMGr+rD8IUXXsDq1auxePFitG3bFsDjMzUhISEa77Nly5Zo3rw5Kisr67U9APj6+gJ4fOarvvuoaZ9KpRKXL1+utbOrel4XF5cGPe+ECRMQHx+PQ4cOYcuWLbCzs8OIESNU67///nu0bdsWcXFxah1NTEzMM/ft6OiI/Pz8astv3bql9tjX1xdCCPj4+KB9+/bP3G9AQAACAgKwdOlSnDp1Cn379sXatWvx/vvvP3NbIqIq7Fdqfl72K+xXDBV/w0NGYdCgQejduzdWrVqF0tJSuLi4YNCgQfjqq69w7969au2r7pFQG3Nzc4wePRq7du1Cenq6xtsDQGBgIHx9ffGPf/wDRUVF9drHkyIiImBmZoaVK1dWu0676mxdWFgY7Ozs8OGHH6pdDqDp80ZERMDGxgb/+te/cOjQIURGRkImk6nWV50Z+/NZwjNnziA5OfmZ+/b19UVBQYHaXbXv3buHH374Qa1dZGQkzM3NsWLFimpnI4UQePDgAQBALpejoqJCbX1AQADMzMxQVlZWp3yJiP6M/Qr7FfYrxoMjPGQ0Fi5ciDFjxmDjxo2YNWsW1qxZg379+iEgIAAzZ85E27ZtkZOTg+TkZNy5cwcXLlx46v4++ugjHD16FEFBQZg5cyY6deqEhw8fIiUlBYmJiXj48OFTtzczM8M333yD8PBwdO7cGdOmTYOnpyd+//13HD16FHZ2dti3b59GObZr1w7vvPMO3nvvPfTv3x+RkZGwsrLCzz//DA8PD8TGxsLOzg5ffvklJk2ahB49eiAqKgotW7ZEVlYWDhw4gL59+2L16tXPfK5mzZohIiJCdXfsJ28KN3z4cMTFxWHUqFEYNmwYMjMzsXbtWnTq1KnGjvjPoqKisGjRIowaNQpvvfWWanrT9u3bq/0w1dfXF++//z6WLFmCmzdvIiIiAs2bN0dmZiZ++OEHvPbaa1iwYAGOHDmCOXPmYMyYMWjfvj0qKiqwadMm1RcMIqL6YL/CfoX9ipFo2knhiBqmavrQn3/+udq6yspK4evrK3x9fUVFRYUQQogbN26IyZMnCzc3NyGVSoWnp6cYPny4+P7771Xb1TZ9qBBC5OTkiNmzZ4vWrVsLqVQq3NzcxODBg8W6detUbaqmD61t6srU1FQRGRkpWrRoIaysrIS3t7cYO3asSEpKUrWpmjYzLy+vxnwzMzPVlq9fv14899xzwsrKSjg6OoqBAweKhIQEtTZHjx4VYWFhwt7eXshkMuHr6yumTp0qzp07V8urW92BAwcEAOHu7l7jdKUffvih8Pb2FlZWVuK5554T+/fvr3FqUDwxfagQQhw+fFh06dJFWFpaig4dOojvvvuu2vShVXbt2iX69esnbG1tha2trfD39xezZ88W165dE0II8dtvv4np06cLX19fIZPJhJOTk3jhhRdEYmJinXMlItPEfuUx9ivsV4yZRAgNfrVGRERERERkQPgbHiIiIiIiMloseIiIiIiIyGix4CEiIiIiIqPFgoeIiIiIiIwWCx4iIiIiIjJaLHiIiIiIiMhoseAhIiIiIiKjxYKHiIiIiIiMFgseIiIiIiIyWix4iIiIiIjIaLHgISIiIiIio8WCh4iIiIiIjBYLHiIiIiIiMlr/H3hBcEaVpexkAAAAAElFTkSuQmCC", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(np.array(energies), bins=50)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "distance = [np.linalg.norm(r[2:]-s[2:]) for r,s in zip(ref, solution)]" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "x and y must be the same size", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[78], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43menergies\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistance\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/pyplot.py:3699\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m 3680\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mscatter)\n\u001b[1;32m 3681\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatter\u001b[39m(\n\u001b[1;32m 3682\u001b[0m x: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3697\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3698\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PathCollection:\n\u001b[0;32m-> 3699\u001b[0m __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3700\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3701\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3702\u001b[0m \u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3703\u001b[0m \u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3704\u001b[0m \u001b[43m \u001b[49m\u001b[43mmarker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmarker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3705\u001b[0m \u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3706\u001b[0m \u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3707\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3708\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3709\u001b[0m \u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3710\u001b[0m \u001b[43m \u001b[49m\u001b[43mlinewidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlinewidths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3711\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3712\u001b[0m \u001b[43m \u001b[49m\u001b[43mplotnonfinite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplotnonfinite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3713\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3714\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3715\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3716\u001b[0m sci(__ret)\n\u001b[1;32m 3717\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1467\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1468\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1469\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/axes/_axes.py:4655\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4653\u001b[0m y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mravel(y)\n\u001b[1;32m 4654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39msize:\n\u001b[0;32m-> 4655\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must be the same size\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4657\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4658\u001b[0m s \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m20\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_internal.classic_mode\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[1;32m 4659\u001b[0m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlines.markersize\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2.0\u001b[39m)\n", - "\u001b[0;31mValueError\u001b[0m: x and y must be the same size" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -372,29 +178,7 @@ } ], "source": [ - "plt.scatter(energies, distance)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "only integer scalar arrays can be converted to a scalar index", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[59], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m idx_sort \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margsort(distance)\n\u001b[0;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mdistance\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx_sort\u001b[49m\u001b[43m]\u001b[49m, energies[idx_sort])\n", - "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index" - ] - } - ], - "source": [ - "idx_sort = np.argsort(distance)\n", - "plt.plot(distance[idx_sort], energies[idx_sort])" + "plot_solutions(solution, ref, size, idx)" ] }, { @@ -403,46 +187,27 @@ "metadata": {}, "outputs": [], "source": [ - "dd = [distance[i] for i in idx_sort]\n", - "ee = [energies[i] for i in idx_sort]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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EBiNOaTGFB6DSdIIgzAcFPASRwSiZlgHy8WQCX5zsxOK/7MXR012p3hSC6BMo4CGIDEbow2PhYLVwsFk4ABTwZALP7fwK67d/iRfeO5rqTSGIPoECHoLIYNgsLUvoTEC9eDKH9p5eAEBPL33XRGZAAQ9BZDABUadlIBzwkMJjfjy9we+4l/xaRIZAAQ9BZDBi0zIAoRcPNR80Pz0hFc9HFXlEhkABD0EkwI7Drbjz9++h8Ux3qjclLvy8isJDd/2mhyk8vgB910RmYEv1BhBEf+amp94FAHR5/fj97ZNSvDX6Ydc6a0jhYQNE2cWQMC9M4aGeS0SmQAoPQRhAU1tPqjchLgTTsqDwBJsPksJjfnpCQS1LaxKE2aGAhyAMgJVz9zcCorJ0gEzLmYRHUHjouyYyAwp4CMIA7Nb++aekZlqmgMf8MIWHTMtEptA/z9IEkWbYrP1T4QmbloM/CwGPn3qzmB2m8JBpmcgUKOAhCAOwW/rnn1JESotMyxlDj9CHhxQeIjPon2dpgkgzrP3UwxNpWqay9EzBE+qwTKZlIlOggIcgDKDfprRkCg95eDKHHh91WiYyCwp4CMIA+qtpWZiWLqvSok7L5iYQ4IWg1kcKD5Eh9M+zNEGkGf23LD34rzylRQGPuRGnLH2k8BAZAgU8BGEA/VXh8csVHmuo8SAFPKZGPCGdTMtEppDUs/Tu3btRX1+PwsJCFBcXY9asWejo6JCsw3FcxGPDhg3C8q1btyqu09zcLKyzZMkSXHjhhcjLy0NZWRlmzJiBAwcOJPOjEYSE/urhYVVaTOFx2snDkwmIFTwyLROZQtICnsbGRkybNg01NTXYsWMHNm/ejH379uG2226LWHfdunVoamoSHjNmzIhY58CBA5J1ysrKhGVvvvkm5syZg3fffRcNDQ3o7e3FFVdcgc7OzmR9PIKQYOunZenhKq3gz6wsnfrwmBuJwkN9eIgMIWnDQzdt2gS73Y5Vq1bBEroYrF69GqNHj8bBgwdRU1MjrFtYWIiKioqor1dWVobCwkLFZZs3b5b8/Lvf/Q5lZWXYtWsXLrnkksQ+CEFowN7PFZ4I0zL14TE1YoWHOi0TmULSbks9Hg8cDocQ7ABAVlYWAGDbtm2SdefMmYOSkhJMnDgRa9euBc9H/gGOHTsWlZWVqK+vx9tvvx31vdva2gAAAwYMiLp9brdb8iCIeOmvKS3V0RJkZDU1YoWHTMtEppC0gGfq1Klobm7GsmXL4PV6cfr0aSxcuBAA0NTUJKz30EMP4YUXXkBDQwOuv/56zJ49GytWrBCWV1ZWYvXq1di4cSM2btyIqqoqTJkyBbt371Z830AggHvuuQeTJ0/Geeedp7p9S5YsQUFBgfCoqqoy6JMTmUj/TWkF/7Vy1Icnk+gRKXi95OEhMgTdZ+mFCxcqmojFj/3796O2thbr16/H448/juzsbFRUVKC6uhrl5eUS1WfRokWYPHkyxo0bh/vvvx8LFizAsmXLhOUjR47EnXfeifHjx6Ourg5r165FXV0dfvWrXylu35w5c7B3716J8VmJBx54AG1tbcLjyJEjencFQQj037J0mpaeibA5WgCZlonMQbeHZ+7cuYrGYzHDhw8HAMycORMzZ85ES0sLcnJywHEcli9fLixXYtKkSXj44Yfh8XjgdDoV15k4cWJEWgwA7rrrLmzatAlvvfUWBg8eHHUbnU6n6usThF5s/bwsnfrwZBYShYdSWkSGoDvgKS0tRWlpqa7nlJeXAwDWrl0Ll8uF+vp61XX37NmDoqKiqMHInj17UFlZKfzM8zx+/OMf4+WXX8bWrVtRXV2ta/sIIh4Cojvj/mpaDo+WCP5MfXgyA7HCQ6ZlIlNIWpUWAKxcuRJ1dXXIzc1FQ0MD5s+fj6VLlwrVVq+++ipaWlpw0UUXweVyoaGhAY888gjmzZsnvMYTTzyB6upq1NbWoqenB08//TTeeOMNvPbaa8I6c+bMwXPPPYe//OUvyMvLE3r0FBQUCEZpgjAacTlvf/XwqFZp0V2/qRErPD4qSycyhKQGPDt37sTixYvR0dGBUaNGYc2aNbjllluE5axs/d577wXP86ipqcHy5ctxxx13COt4vV7MnTsXx44dQ3Z2NkaPHo3XX38dl112mbDOk08+CQCYMmWK5P3XrVsXM/1GEPEivjPut1VaspQWmZaNp7mtB4/87RPcWjcM44cWpXpzAEgVnl4/D57nwXH98xgmCK0kNeB55plnoi6fPn06pk+fHnWdBQsWYMGCBVHXUSpjJ4hkIw54+mtKS920TI0HjWLTh4145YNG2Cxc2gQ8PbI+S/4A32+DdoLQSv/U4QkiDRCnAqz9NKVFpuXk09bdCyC9yr/FfXgAmphOZAb98yxNZCRbPmnBqn8eTBtFT3yR6KdV6WBWnfDwUEppGU17jw8AEEiT4xaIDGgp4CEygaSmtAjCSP775b1odvfgytpy1JTlpXpzTFHOG5BNS3fZqdOy0bhDCg/SKKbwyBUe+r6JDIAUHqJf4A/wON7eAyDSf5Ao7xw6ia9au3Q/zwzlvH7ZtHQqSzcedz9QeHpNcCwTRCwo4CH6Bac6vWCqu5EXjiOnujDzNzsw5znlUSXRMEM5b0QfHvLwGE57T1DhSaN4R8HDQ983YX4o4CH6BSc7PML/jbQbnAi97qlOr+7nmuGuWEhpyUzL/gBPIwcMgnl4+DTKaUV4eExwLBNELCjgIfoF0oDHuJNzIgZoM1wk1KalA5TWMop2T1DhSaf4kaq0iEyEAh6iXyAOeIys0krEq9lrgjSAmsIDUMBjFO7ukMKTRjFFRMBDpmUiA6CAh+gXtHaEU05G3owmkrZRem5Prx+bPmzEmS79KbJUIFd4bBYOrOGuh5oPJgzP8+jw+IT/pwtkWiYyEQp4iH7BCXFKy8CIJ5H0mFJZ+rJ/HMBdz72Pm5/ekchm9RnyPjwcxwm9eMi4nDhdXr8QVKZTSEGmZSIToYCH6BecbE8/hUfJw/OXPccAAPsa3XG/bl8iT2kBonlalOZIGGZYBtJb4SEPD5EJUMBD9AuS5uFJxLSscFfc364b8pQWADhs1IvHKFhJOpBex0akhyeNNo4gkgQFPES/IFll6Ymkx5R8D+nUXE4LfkHhCf/OSb14DMMtCnjS6ciILEun75owPxTwEP2CZJWlG53S6mfxTsS0dEA8MZ0ugoniTtOUFutWzr72dBpsShDJggIeIu0JBHhZlVZ6mJaVU1r968LBtleS0qIBooYh9fCkcENksFlaOc7gOEVSeIhMgAIeIu1x9/RKTJV6LxybPmzEjFVv4+jpyHlZCfXhMYHCI1RpiU3LwgBRKktPlHZJSit9Dg6W0spjAQ8pPEQGQAEPkfaI01mAfhXlz+8fw54jZ7Dts5MRyxIxLftJ4SFiIFZ40qXy2x/ghQq8sMLTv45bgogHCniItOdEu7SJn96bUebTUbqLzXjTsmxaOkADRI3E3Z1+Co84kBUCnnSJxggiiVDAQ6Q9iSo8bG2l5yXk4VHIh/W3zIDQh0d0JqCAxzjS0cMjLknPDQU81GmZyAQo4CHSHnnAo7fahQUhSrJ9QlVaCs9Np0ocLSgpPE6q0jIMiYcnTQ4NFsjarZzwXZNpmcgEKOAh0p7IgEff81kQYrTCYw7TslJZOjUeNAqJwpMmKS2m8DhtVthCDZjItExkAhTwEGnPyQQ9PCwIUTqpJ3Jjq3RXnIgJOhUojZagWVrGkZYprdBQWJfdApuVFB4ic6CAh0h7WjsT8/Cw9ZXSV4kEKErN2tLloqYV5dESlNIyCrdktER6HByeUNNBp80Km4UUHiJzoICHSHtOdMgVHp2m5dDqSgFPIlVaZrgrZlk55eGh1IcnUaQprfRASGnZLbBZgt81mZaJTIACHiLtOdkeVHhYRYneG2UWICmntBLpw9P/LxJKoyXItGwc7jQcHspSlU6bFXbm4TFB8E4QsaCAh0hreJ4XTMtleU4ACZSlKyk8BpuW+xvRUlrk4UmMQIBHhyes8KRLvpMpPEEPD6W0iMyBAh4irenw+IQLb6kQ8Oh7DT5JCo8ZmrVFMy2TwpMYnV6fJMZJl5AirPCEU1pmOJYJIhYU8BBpzcmQfyfbYRVSWvF6eJSel5Bp2VQKT/h3wiwtCngSQjwpHUgf03JY4RGntNJj2wgimVDAQ6Q1LJ1VkusEF1Ih9DceDCk8SqMgMt60HKUs3QSfL5WImw4CaZPRQk8okHXZrLCSaZnIICjgIdKaViHgcYDZTHSntMCeZ3AfHhP4HpRMy6zxICtfJuKjPULhSdGGyPCIqrQEhYdSWkQGQAEPkdawkvSSXKcw/kB/H57gv0on9cRMy/3/IsEUHsU+PCb4fKkkUuFJj4jHI1J4wh6e9Ng2gkgmSQ14du/ejfr6ehQWFqK4uBizZs1CR0eHZB2O4yIeGzZsEJZv3bpVcZ3m5mbF91y6dCk4jsM999yTzI9G9BGsJL041yn4THSfm6M0Hkwk4DFHWXrwX6vCtHSvj/rwJAJTeES7Ni0QKzw2KksnMoikBTyNjY2YNm0aampqsGPHDmzevBn79u3DbbfdFrHuunXr0NTUJDxmzJgRsc6BAwck65SVlUWs8+9//xtr1qzB6NGjk/CJiFTAPDyluY4EPDzBfxU7LScQtJjB96A0S4v68BiDuzuo8MRrtk8WgoeHTMtEhmFL1gtv2rQJdrsdq1atgiV0a7569WqMHj0aBw8eRE1NjbBuYWEhKioqor5eWVkZCgsLVZd3dHTg5ptvxm9+8xv8/Oc/N+QzEKlHMC3nOfF5axcA/UZjNrRRsSw9gYuQGXwPQkpLSeGhu/6EYFVaBVl2tPf40sa0LCg8NkvYtGwCtZIgYpE0hcfj8cDhcAjBDgBkZWUBALZt2yZZd86cOSgpKcHEiROxdu1axTv4sWPHorKyEvX19Xj77bcjls+ZMwdXX301pk2bZvAnIVLJSZGHh12S9Z6bWVyi2HgwoSqt/n+RUOy0zKq0yLScEO2igAdInz48Pb1KCg9914T5SVrAM3XqVDQ3N2PZsmXwer04ffo0Fi5cCABoamoS1nvooYfwwgsvoKGhAddffz1mz56NFStWCMsrKyuxevVqbNy4ERs3bkRVVRWmTJmC3bt3C+ts2LABu3fvxpIlSzRvn8fjgdvtljyI9ENclh6u0oqzLN3gaelmMi1bRWcCUniMgZmW813BgCddUloeX1jhIdMykUnoTmktXLgQjz76aNR1PvnkE9TW1mL9+vW477778MADD8BqteLuu+9GeXm5RPVZtGiR8P9x48ahs7MTy5Ytw9133w0AGDlyJEaOHCmsU1dXh0OHDuFXv/oVfv/73+PIkSP4yU9+goaGBrhcLs2fY8mSJXjwwQc1r0+khlZB4XEIaZd4rxtKF5xELkJmuEgIjQclw0ODZenk4UkMucKTLhIPU3icdiuZlomMQnfAM3fuXEXjsZjhw4cDAGbOnImZM2eipaUFOTk54DgOy5cvF5YrMWnSJDz88MPweDxwOp2K60ycOFFIi+3atQvHjx/HBRdcICz3+/146623sHLlSng8Hlit1ojXeOCBB3DfffcJP7vdblRVVUX9XETf0tPrF2YRleSFGw/GrfAopKASGi1hgouEch8eMi0bARscmp+VbqblUKdlG83SIjIL3QFPaWkpSktLdT2nvLwcALB27Vq4XC7U19errrtnzx4UFRWpBjtsncrKSgDA5Zdfjo8++kiy/Pvf/z5GjRqF+++/XzHYAQCn0xn1PYjUcyJUku6wWpDntMXfeDBalVamj5aIYlqm4aGJka4eHo9I4WGYIT1LELFIWpUWAKxcuRJ1dXXIzc1FQ0MD5s+fj6VLlwrVVq+++ipaWlpw0UUXweVyoaGhAY888gjmzZsnvMYTTzyB6upq1NbWoqenB08//TTeeOMNvPbaawCAvLw8nHfeeZL3zcnJQXFxccTvif7FSVGXZY7jEmg8GOrDo5TSSuDO1lR9eJKs8PA8j3/sa0HtwHxUDcg27HXTGebhEQKeNDlcxAoPO4bNYMAniFgkNeDZuXMnFi9ejI6ODowaNQpr1qzBLbfcIixnZev33nsveJ5HTU0Nli9fjjvuuENYx+v1Yu7cuTh27Biys7MxevRovP7667jsssuSuelEGiBUaIWmpDPrl94+PGxtw/vwmKAsXZiWrtSHxx8Az/NCKjERGj5uwQ//sAsA8MXSqxN+vf4AU3jys9LMtCxSeFhQSyktIhNIasDzzDPPRF0+ffp0TJ8+Peo6CxYswIIFC3S979atW3WtT6Qn4gotACIPj77XSVZKywx3xdFSWkAw6GEm5kTY25h5VZARKa00OVzECk+4grH/B+8EEQuapUWkLWysREmuAwDiLkvno5SlJ3IR6u+mZZ7nhc8vSWmJatSN8vGw75C9r9nxB3jBcM/K0tMFscJjZ2XpJgjeCSIWFPAQaUtrZ7jpIACRh0ff67D1lfw6iaW0+vdFQvzZJbO0RAGPUT6e4pxwgUCn1/wzujpEk9LTrUqL9eFx2S1CoEumZSIToICHSFtOyFJaljhnaSVttEQ/v0iIPzsnOhNYLJwQ9BgV8GQ7wmmx1tD3amZYSbrTZhFSgmkS74Q7LdtEnZb7efBOEFqggIdIW4SUVh7z8AR/r7tKK3TNVpyWnsGjJcS2DavMmJzMXjzMjG5mmH8nz2WP+7hNFkKnZbsFNiultIjMgQIeIm0RTMs5zMMTX0qL0ZdVWgYUNiUdscIj9vAAye3FkwkKT3ishA1caApcOoQU/gAv9I9y2aywWZjC07/VSoLQAgU8RNoSUZae4CwtRYUngbvuaMFSP4h3JNtvkSs8Bqe0xO/FvFlmxi0oPDYh+E0HgYepO0BQ4bGTwkNkEBTwEGmJ1xdAW3fwLjnSw6PvtaI1HoxX4eF5PmqnZXkAkY6I03lqCo/Xb4zBuN3TK/w/oxSeLHvc3rNkwPw7QHBmGpmWiUyCAh4iLWntDF4UrRYOhaE+JkIfHp1BSvQ+PPFtX6xAqT8EPOIAUBbvCM0HjUppnekKBzyZ5eERKTwp3B4GU3jsVg5WC0emZSKjoICHSEvYlPTiHAcsoatxvLO0AlECnnhNy/ILhPwOuR/EO8Jnt3CI6KZstGmZqXVA2JtlZpjCk+e0C+nNdFJ4XKHKMcG0TAEPkQFQwEOkJfKSdABxz9Ji99ZGmpblAU6HR5r66U8KjzydBRhvWhYrPK0Zp/AkZrY3kp7ecIUWANiZaZlSWkQGQAEPkZbIS9KBsMKj906ZXWiUKlHi7cMjN3mKG80BkSmidMQvKDwKAY/BpmW3SOFh6Uozw/rwiMvS00HhYQGsU6bwBPjEWjQQRH+AAh4iLREqtHLCIwnin6XFFJ7IZfGe5OUl6SyFwegPCo/SpHSG0x68IBoV8JzpziyFxy0MDrXFbbZPBkzhcYUUHps1/N2bYRguQUSDAh4iLRF68OQlntIKe3giT+jxlqVHKDweqcLTH+rShZRWNIXHoFSH2MNzqsubUP+j/oCk8WDod+nwiSMUHlGwS6XphNmhgIdIS8KT0sMKT7ymZT5KHx6jqrTkAU9/UHiElJaSwsM8PL3GlKWf6QqrOjwPnO4yt8ojmJYlfXhSH1BEKDyW8CWAjMuE2aGAh0hLTiqZli1xztJKQpWW3LTcLvPwKKWJ0o2ABtOycQqPdP+YvVJLbFpOtEN4Ivyq4VM8unm/8LNc4bFbxQoPpbQIc0MBD5GWMJ+HOOCJdyYRW9vIxoPyu2G5wpP+4U5007LTwLJ0nufR1h38PnNCQ0TN7uNhJu18l134Hd/HSa1Ojw//u+UzPLn1kHB8yhUejuOEgJcUHsLsUMBDpCWKCo+GO+VAgMcv/3EAr3/cEv5dEkZLRJSlyxQeeV+bdITtD6vCWcDIPjzdvX6hK/WIslwAmaPw5EuGh/btNoiN4uy7ZilKpvAAYR8PdVsmzA4FPETa4Q/wONXJ5mgpeXjUrxwfN7mx8p8H8cjfPhF+x9ZXuoONW+GJYVruBxmtcEorimnZiD48rAePzcKhqigbgLkVnl5/AN2hwEKc0upr13KbqPcRe2/2fTKFBwgHPGRaJswOBTxE2nGq04sAH0xhDcgWBzyxy3vZCb3TGw5A2Pq8Qq+RuPvwhCq+WIqmw+OTvHZ/Ny0b2XiQVWgVZtsFE7qZe/GI1b5cyWiJvg0oznSLjOKh9+5RUnio2zKRIVDAQ6QdLN1RlO0QTsaAuA9P7BOzeLCneHV5gBO/aTn4vEJRQNYhCrL6lcKjWKUV6sNjQJqDKTz5WXYUh1KUZlZ4WNPBLLsVdqslZaZlcbNHhpLCE56nRSktwtxQwEOkHUol6YDWsvTgwl6RMiG+s5ansBLttJzrtAkXDPGdff/w8AT/VUxpGejhERSeLDuKQ9+pmQeIiiu0AKRslpZ4nAd767BpWezhCSk8lNIiTA4FPETaoWRYBvQ1HhQrE+IYRx7wxHtTy+6GbVYOuc7ghU3s4+kH8U4fprSCwU1Blh3FOSGFx8QpLabw5GeFKrRSNC1dbFpm7x0uSxd5eKxkWiYyAwp4iLRDqSQd0DZLiy0Sn7zF68sVnURNyzarBbmhO3lxL57+4OGJaloWFJ7EGw8ypaEw24HSPKbwmDfgkSs8qRotIe5uzf4GwsNDI6u0yMNDmB0KeIi0Q2lSOiDy8ES5EWWn7AAfDmYkCo/foJRWaCPsFg65zuCdvFjh6Q8enqidlg0cHsouvBKFJyNSWsHjQrx3+zKtJUlphf7t6VVSeIL/J4WHMDsU8BBpx8n2yJJ0QP8sLa8vEHGBkd/FxnsB6hUUHg55LKXVXxUehbOA025cp+Uz4oAn5OHp8vrR5fVFe1q/hZmFBQ+P6FjoSxFF2bSs5OEJbp/Z55sRBAU8REL8c/9x/HP/8biee7pT+S5f8PDkKKe0op2XxfGL1x+ISCPIg6X4Oy2HFB5RSqvDE77ApEO8s+vL07hpzXbsPdamuFxLHx6jFZ5cp01Il5lV5Qk3HWQprfCyPlV4uqXzywBlhcduJdMykRlQwEPETU+vH3f+YRd++Iddui+Mv3v7c4x7uAGb9zZFLAtPSldWeKJ7eMLLev2BCKOoXOGJN+ARFB5L2LScbh6el3YdwY7PT+HVDxsVlzPxJumm5a5wHx6O41CSw3rxmDXgkY6V4ERJrb4MKaQprVCnZSWFh0zLRIZAAQ8RN11eP7y+ADy+gO7A4UBLBwBg7zF3xDK1Ki29s7R6/YGIdeV9d+JV8cWm5TwF03IaxDtoausBEJyppIQwWqKPytILQlVL4V485jQuR5Sli86y8Y4yiQexaRkyhUcppUWmZcLsUMBDxA2r+EgE+UiGQICPUqUVu4GbeFGvj49IaRml8Ahl6RZOlNJKL4WnWQh4lL8ndvFVNC2zxoNGjJYIpVYKs1nAE1J4zJrS8jAPj5Jpue+2o03BtKxYlm4h0zKRGVDAQ8SNEekOufrg7ukVgpJieePB0NGq2bSsoPAY3XjQZrUIpmWWygDSI+BpcQcDHnlQyQhPS49cloyUVkFW8PtklVonTdqLx90tU3hScCz4/AG0K3zvHqXGg1YyLROZAQU8RNx4DOjR0uWVvgZLZ+W7bJJ5P4C2fiYS07Iv0rQc2XjQiLL09Etp9fT6cToUaKhVQ0UbLWHU8FB/gIc7tF9YSot5s1g1ntlggS9TeMS7t69SWu4e6Xcu77RMpmUiE6GAh4gb5gdIBLn6cEIoSXdGrKtllpZ4jETQtCwvS5duc7wKj7gsPTd0YXOnkcJz3B1WTzpUUlphhSd5jQfFqpcQ8Ji827K8SktiWu6jmKJNVpIeNi2re3h6aZYWYXKSFvDs3r0b9fX1KCwsRHFxMWbNmoWOjg7JOhzHRTw2bNggLN+6daviOs3NzZLXOXbsGP7jP/4DxcXFyMrKwvnnn4/33nsvWR+NCOFJwMPDAp0zXdK7fLWSdEBclq7HtCz9nficzvORHh+tSDothxQe8UUm1Y0Hm0PpLECDaVnRw2NMHx5WKZTtsApBVH/z8Bw93YUvTnZqXt8tbzyYAoVH/ncVTeFhKS1SeAizk5SAp7GxEdOmTUNNTQ127NiBzZs3Y9++fbjtttsi1l23bh2ampqEx4wZMyLWOXDggGSdsrIyYdnp06cxefJk2O12/P3vf8fHH3+Mxx9/HEVFRcn4aISIRNIdr34QLJX+4Ki0R4xaSTqgzbQsFnSCfXjUFZ5EPAvsdRyiKi3m3QBSPzxUS8ATrQ+PU1SllUjvGPHgUAar0uoP4yUCAR7f/L93cM2KbZpN+uGUFvPwhJf1VUhxJkLhCaKs8JBpmcgMbMl40U2bNsFut2PVqlWwhP6YVq9ejdGjR+PgwYOoqakR1i0sLERFRUXU1ysrK0NhYaHiskcffRRVVVVYt26d8Lvq6urEPwQREyOqtOSolaQDGmdpif7f6+cjFZ4oc7X0wJQPcR8eaUor7pc2hJY2LQpP8N9ofXgCfLCyjU2E1wu78OaLA55+1Ienq9ePE+3BY7LL65cECkp4fH4hqFDsw9NHEY+8yzLP8/D5A0JBgJLCQ6ZlwuwkReHxeDxwOBxCsAMAWVlZAIBt27ZJ1p0zZw5KSkowceJErF27VvFiNnbsWFRWVqK+vh5vv/22ZNkrr7yCCRMm4IYbbkBZWRnGjRuH3/zmN5q20e12Sx6EPoyo4JEjjJVQCHg4LQqPiF5fIOKWWizbJ3LxYa9jt4kVHnGn5TRSeLx+xb8rfxSFxyG6ICZSmi4oPNnhgId9t6c6vXGbxvuKjh594y/ExvXcFHZaFjcdZIj/XsWBmz10nqY+PITZSUrAM3XqVDQ3N2PZsmXwer04ffo0Fi5cCABoagp31n3ooYfwwgsvoKGhAddffz1mz56NFStWCMsrKyuxevVqbNy4ERs3bkRVVRWmTJmC3bt3C+scPnwYTz75JM466yz84x//wI9+9CPcfffdWL9+fdRtXLJkCQoKCoRHVVWVwXvB/CQl4Imq8GgwLctSWhFl6WKFJ4ETPJP/xaMlxC+XaoVHHPD4A7zidxWI4uFxWA0KeEJekgKRwjMgpPD4A3yEuTbdEI8L0RKssIAn12kT9qs4+E2ZaZmXKrJKCg+ltAizoyvgWbhwoaKJWPzYv38/amtrsX79ejz++OPIzs5GRUUFqqurUV5eLlF9Fi1ahMmTJ2PcuHG4//77sWDBAixbtkxYPnLkSNx5550YP3486urqsHbtWtTV1eFXv/qVsE4gEMAFF1yARx55BOPGjcOsWbNwxx13YPXq1VE/ywMPPIC2tjbhceTIET27goBxKS1x4HGykyk8Sh6e4L+aFR6F0RLi90okpSUEPBYOeU57xPJUKzzilBagnNaKNi3dZrUIF+xEjMthD0/4+3TYLEIFU7pXaqlVuKkh9+8A0saDfWdajgwke3xh35n4O6eydCJT0OXhmTt3rqLxWMzw4cMBADNnzsTMmTPR0tKCnJwccByH5cuXC8uVmDRpEh5++GF4PB44nZF3+AAwceJESVqssrIS5557rmSdc845Bxs3boy6nU6nU/U9CG0YpfB0en2C3+FkOzMtqys80T084WVeX6TCI5btE0mn9IpSWi57MDgQB1PppPAAwW7LxbnSdcKmZeXXcFgt6A744Umg/QC78BZkS4PCkjwn3D0+nGj3oqZM6ZnpQbwpLUnAkxLTcmSVFquqdNql97lWKksnMgRdAU9paSlKS0t1vUF5eTkAYO3atXC5XKivr1ddd8+ePSgqKooaiOzZsweVlZXCz5MnT8aBAwck63z66acYOnSoru0k9GOUwtPl8SPfZQfP81HL0rXM0hIv6vVHlp2LgxyjUlocFzQui9MIHFIX8QQCvNBlmeOC+0Sp23I0hQcIKjHdvX54/fF/z/I5WoySHCcOn+jsBwpP5HiGaDAfFytJB1KT0oowLYMXTUqXGq8F0zIpPITJSUqVFgCsXLkSdXV1yM3NRUNDA+bPn4+lS5cK1VavvvoqWlpacNFFF8HlcqGhoQGPPPII5s2bJ7zGE088gerqatTW1qKnpwdPP/003njjDbz22mvCOvfeey/q6urwyCOP4MYbb8TOnTvx1FNP4amnnkrWRyNCGKXwsItxh8cnvGbUsnSNb9urWJZucEordLGQBzzyhod9yakur6BADSzIwrEz3ehU6LYczbQMGDNe4oxKwNNfevHoT2lFKjxAOPBMrWmZjZWQKjxkWiYyhaQFPDt37sTixYvR0dGBUaNGYc2aNbjllluE5axs/d577wXP86ipqcHy5ctxxx13COt4vV7MnTsXx44dQ3Z2NkaPHo3XX38dl112mbDOhRdeiJdffhkPPPAAHnroIVRXV+OJJ57AzTffnKyPRoSId7SEXFlh/pKToYtftsOKbEfkoanJtCz6v5KHJyBJaenYaBlCSivkf5Bf4FIJGxpakutAYbY9GPAoKDzRTMuAtBdPvChVaQHigCfNFZ4efaZq1pog3yX9vBaOg5/vuzBY2bQcOTgUINMykTkk7Sz9zDPPRF0+ffp0TJ8+Peo6CxYswIIFC2K+1zXXXINrrrlG1/YRiROvt0OuNrCfo1VoAeI+POqvLb6D9sTw8Bih8LC2/KwXT3g74n7phGHprPJ8F3JCgaPSxPRofXgA8XiJRKq0VBQeYYBouis84WNVy3eqqvDoeA0jUGo8GFZ4pCktMi0TmUL63JYS/Y54FR652sAuxoJhWaFCC9A2S0uM4mgJXqzwJB7wsKAgN50UnlDAU5HvEj6vYpVWrJSWAQNElaq0gPB3nPYKT9wprUiFB+D7pEqL5yPL/XmeV1V4yLRMZAo0PJSIm7gVHtnFl03zDpekR1d4dKW05AqP6C42MdOyPKUVWZqeKlhJenmBCzkh5UnJtNwXKS1WLRTp4QkNEE17D098Ka2IFCdTJ43YqBj09AaE74x9t9EUHqZSUqdlwuxQwEPETU+cCo/8rpldjKOVpAPh1IvWm2SlKi2j+/CoprTifuXEESs8bLu6opiW1Sa7s2qeePvw9PT6BVUhoiydBTzpntISlaVrceC0Cx4e5ZRWX3SWZkGmzcIhxxEObtgNCqW0iEyFAh4ibgxTeFhKSyhJV05paZqWLu607AtEBjwGp7TstjQ0LbuD+7Ei3yWYv5VSM2GFR/l1EvXwsNJojgPyZAEhMy2zIDddiTellZ+llNLqG8StADgufJPAblDItExkKhTwEHETr7dDnl4RFJ6O6AqPllla4rvwXoXREpLGgwbM0mI+F7nCk0qJR5zSynUG7+b1dloGxGXp8Sl54pJ0+XuwPkvtHl9ShtAahd6UVrSydKBvTMviZo/hOItXV3ioLJ3IECjgIeIm3gtVpGlZWpau7uHRZ1r2+qKXpSfiWfDGSGmlEnFKi3l4EjEtx6vwhA3Lkf6m/CybsO9OpXFaSxKca6rSimw8COg/dhNBovCEfieepaVqWiaFhzA5FPAQmviytRNL/74fx9vDIwviVXgiAh6vLKWVUFl6+P+xFR7jUlrpUqXV0+sXLniSgEfBwxPTtGxPrErrjEpJOhBU6/pD80Glcv5ouGOVpRuxUTFgrQDEgWbQtKys8AidlknhIUxOepylibTn+ie342SHB7u/Oo0X7rwYQPwKj9wX0Sk3LauUpeudlp7M0RLylJbco5KqTsus6aDLbkF+lg05QkpLoQ9PDNOyoPDEeecvKA3Zyt9ncY4TLW4PTqbxeIl2HbO0eJ5XVXiEKq0+VHgKsx1SD4+KwkOmZSJTIIWH0ARTX9774pTwO6MUni6vD91ev6D0qHt4gv9qTmn1wWgJdnecLgqPOJ3FcZzQeFCxLD308dUUHsHDE6c5/UyXckk6oz8oPHpmaXl8AaFdgbxKy6LBf2YU4lYA4m+W/b06VcrSqQ8PYXYo4CF0IT5hx2tmVTIts4DKYbNEqCUMLReNWKMl/KKTeiJVWszXotaHJ1WdlsVdloGwtyie0RJClVacCo87iocHEJWmp2nzQZ8/IJTVa4H14OE4CIEmQ2weTjbiVKJglgYvKDwRs7RI4SEyBAp4iLjRczEQo9RpWVySzqmkWCyCPK+n07J0XfG1O6GUViBGlVaKYCmtioJgwJMt9OFRGi0RI6WVYFm62uBQhtBtOU1Ny3r9Oyz9leu0RVSlCX14+iCmkE6oF5elK09LJ9MykSlQwEPETdyjJRRmaQkVWirpLEDch0f9tcXBkGIfHpHCY2RKK1368IhTWgCEsnSllJZQpaVyFhAaDyZapZWtltIKzdNK01487Z7IAZzRYIqWfHAoIA7Wjdm2aCjtd54HPCoKD5mWiUyBAh4iLnieT6APT6RpOVaFFqDNwyNe4vVHzi6SNh7UuMHy9+D5iNES6dJpWZ7SEpely5WxQAyFx5loH55QakXehI9RHGowma4DRONVeJSCX3FqKdmIAx5JSktF4RFSWhTwECaHAh4iLoKG4Pie29EjvXPu8vhjVmgBosaDGk/MvQoKjxGmZfFrsItFtsOKPmymq4o8pcUCHl+Aj/DiMMtGzCqtJPThAdLfwxNv00ElhSd87Ca+XbGQeHhEv1dVeCilRWQIFPAQAIB3Dp7Eqn8e1OyPSWSCtvzOudPrwwkNCo+WtIC8D0+0svR4TcviC4M9lA7gOE6i8vRF+bESLaGxEkzhyRZV5Mj3e7JNy20xPDzpXqUlL0mPpc60qw0OhbgPT1+YllmVlkPS4TmmwkOmZcLkpIfxgEg5M5/eAQAYMiAb144ZGHP9eEuVgUg/SYAHjp7uBhAr4GHrRzsxax8tEa9nodcXqfAAwV48sfq2BAI83vvyNGoH5gvqi1EEAryQ0mIKj81qgctuQU9vAJ0eHwaI5pTFGi2R6LR0cT8YJYSJ6Z0e8DyvalZPFUlJaSU5pvAHeLSH/r6CCk94n8by8PioLJ0wOaTwEBI+bWnXtF4i84+Uuv5+2doJIJZpOaTwaHyfXn/k/bRE4Ynz6iPuV2ITBQtaevH8Y18zblyzHUv+/klc7x2N1k4vfAEeHAeUifYjU57kgWbM0RKCh0f/hZDn+dgKTyj46vXzQofidEJvSsut1nQQfWdabu/pFd5DUpbOi/rw2JT78JCHhzA7FPAQErTe1SaW0oq8uB1hCo/KpHRAo2lZ0icoOaMlhLESVk6iSogvdGqvfOhER/Df451xvXc0mLpTnOOUKE85Qmm6TFnTOC09nu+6w+MTFCS1Ki2X3SoEY+no44lIacU4XKIqPOw1kpzSYkFmtsMqfH/sfdX68NgslNIiMgMKeAgJ8ouiGvFW7nh8fqHCSQxLm2hReLTeiCp5ePySlJa214l4XZ+0QouhpRcP6zlzust43wozLFeG0lmMbKHbsvQ70zxaIo6AhxlnHTZLxOwmMenci0f/HC11hYfro07LZ2RztJQ6LavN0iLTMmF2KOAhJCg1qFMi/qaD0V9fm2lZW1l6r8JoCb8BVVospRUR8LjEpmXl554OXdiTcYFvlpWkC9slzNOSpbRiDg+Nvw9PrAotRjr34om7SisrmoenbxQeNr9M1ywtSmkRJocCHkJCshUepXQWw2rhol4gtTUeDP9febSEcVVarEKLoTYSQ4yg8HR6Db/4hQ3L0qAxR8XDw1J6qlVaCQwPjeXfYaRzLx75/or1bakODoW4D09yCXe3juwLparwWMKNB1NVXUgQfQEFPIQEzR6eOBUepY6/jOIch2rFECBOC2gdLcFHVGKJVZ1Eq7TiSWmxVJYvYLxRV+jBI1N42FynLhWFJ9ZoiXiC21hdlhnFadyLR54CjIW7W93Do3csSry0hY6vwiym8AR/7/MHhO9brvAwDw9AKg9hbijgISSkUuGJls4CwgoPz6tfOOSmULkvwYjGg5pSWirPPSXqOXPaYFVDLaWVw1JaXnkfnuC/qimtBMrSxc3volGSxr145A0yY8FGUcgnpQMi03KS4wk1Za1bVFWp5uEByLhMmBsKeAgJ8ouiGvGPlYgS8EQxLANSJULtwiH/vdxrZEjjQZ90jhYjlsLD87zEu2O0j0feg4ehltKKZVpOJOAJX3jVq+6AcEqrtTMdFR55lVasxoNM4UkD03JIWWNfrfjvwGFV7sMDSFsuEITZoICHkNCt2bQcr8Kj/rxoYyUA6YVZa1pLrkQZovD4pZPSGZJUhsJrd/f6JYGi4QqPSkorVzRPS0ws03Ii09LPdLNuvxpNy+mo8OhIafE8H2O0RHi9ZBI2LbMqreAbs79Xh80SkTa2i1NapPAQJoYCHkKCUlNAJeJVeBJJaXGio1XtTln+a/nF2hDTckBZ4akpy4v6PHna5pSBAU+31y94gspVytIjRkvEmJaeSB+eti5tHp50nqelp0qru9cvHFvR+/AklzOylBYLtLpVKrSAYKdti8jrQxBmhQIeQkJXCk3LyVF41AOe+E3Lyh6e8UOL8D9Xn6P6PHnvnVMG9uJh/p1shzWiWixWWXqsPjy+AK87ONRqWk7nPjwdOhoPMsOy1cIh2xHZd0iv4T5ehECTmZZDvw+PlVDuiSQ0HyTTMmFiKOAhJGgtQY4/pZW4aRmI5uGRLoim8MSr3rOUljzgAYDhpTnB7VB4nvyibqTCI05nyWdSMQ+PXL3TmtIC9JemazUts5TWma7etGt8Fy04l8NK0nOdNsWZYJY+knhimZblXZYZwjwtSmkRJoYCHiIukmJajhnwJO7hEQc88fopfAHlPjwAJMMa5ZxKYkqrRaVCC9DQh0fVtBxWA/R+31r78BRm2YVgwMj9kShqHcHVcEcZKwGEj4vkp7RCZemCaZl5eJTnaDFYLx4yLRNmhgIeIi7iLUtPJOARX5e1BjxRFZ445XuvSkpLjNLmRaS0jFR4VCq0AHEfHuXgT633kTig0/t9aw14LBYOA3KYcTl9fDzidJZDwfcihyk8SoZlQNscOCOQ73f2DarN0WII3ZZJ4SFMDAU8RFzEP1oiWlm6Hg+P8jry60mEh0fceDDBKq1oAY8SLKU1qDALQHJSWsoKT/CuPlLhCf6rltLiOC7uSq2whyf6dwqkZy8etq+yHVZVBUxMtMGhgHTEQ7Lo6fULf5esSotFPDEVHpqnRWQASQt4du/ejfr6ehQWFqK4uBizZs1CR0eHZB2O4yIeGzZsEJZv3bpVcZ3m5mZhHb/fj0WLFqG6uhpZWVkYMWIEHn74YWqRrhOl6o1oxK/wKD+P44ABMS6O0j482hoPygMenwFVWtFSWlEyWkJKq6YsN/hzElJaFfmRKlluDA+PmmkZAJxxDBDt9QeEgCGWwgMAxbnp14uHbb+W7tlA9MGhQPiwSKbC4w4FmVYLJxjX2fvG9PCETMvxqp4E0R9ISsDT2NiIadOmoaamBjt27MDmzZuxb98+3HbbbRHrrlu3Dk1NTcJjxowZEescOHBAsk5ZWZmw7NFHH8WTTz6JlStX4pNPPsGjjz6Kxx57DCtWrEjGRzMtOaITu5aLm9EKz4BsB2wxFBOLJKWl7f08vfI0TkD0f22vIUdTSkvBrcGqsljAY2QfnqgpLWf0lJaawgMATrv+eVrswgsodx2WU5zDStPTSOEJKTa5Lpuoh476+uEePMqfl7W6SWY4wUrS811h43TYw8PK0qMrPD7y8BAmRtvti042bdoEu92OVatWwRL6S1+9ejVGjx6NgwcPoqamRli3sLAQFRUVUV+vrKwMhYWFisveeecdXHfddbj66qsBAMOGDcPzzz+PnTt3GvNhMoQsUblqt9cf07cQ92gJmcpgs3C4buwgjBtSGPO5nAbTsvzX8gu1+MdkpLSiJT+YosMCnnaPDx6fX/UipIeoKa2Qh8frD8DrCwjfbSzTMiAaIKpD4WEX3jynLWYQC4S9W+nUfFCvwhMeHBrdtJzMiCfcZTlSKY3l4RFMy+ThIUxMUhQej8cDh8MhBDsAkJUV9C1s27ZNsu6cOXNQUlKCiRMnYu3atYqpirFjx6KyshL19fV4++23Jcvq6uqwZcsWfPrppwCADz74ANu2bcNVV11l9McyNeIAR0vzQaOqtCwWDo/fOAb/cdFQTc+36DR/yvsFiRWeuFNaKtPSxSialkMBz7DiHEFVYRepRPAHeBxvD6aDlBWecEAlVtjYPrREOQvE03xQ3u03FiyllVamZd0BT0jhUUnh9YVpme138TZEmJZVgmsyLROZQFICnqlTp6K5uRnLli2D1+vF6dOnsXDhQgBAU1OTsN5DDz2EF154AQ0NDbj++usxe/ZsSSqqsrISq1evxsaNG7Fx40ZUVVVhypQp2L17t7DOwoUL8Z3vfAejRo2C3W7HuHHjcM899+Dmm2+Ouo0ejwdut1vyIIJ0aRgvkYw+PFqwxDB/RpiWIxQeI0zLsVNaSjDTckmuA0XZxhl1Wzs88Ad4WDigVKHSzWa1CB4tccDJdk20lFY8puU2jT14GGHTcnoGPOEWOurHSzqYls8Ik9JFAY/ctByjDw+VpRNmRtcZe+HChYomYvFj//79qK2txfr16/H4448jOzsbFRUVqK6uRnl5uUT1WbRoESZPnoxx48bh/vvvx4IFC7Bs2TJh+ciRI3HnnXdi/PjxqKurw9q1a1FXV4df/epXwjovvPACnn32WTz33HPYvXs31q9fj1/+8pdYv3591M+yZMkSFBQUCI+qqio9u8LUaJmYHv9oifgCJQYLeFRTWrKfPbKW+uLtjlfh8UZLaamkh3r9AeEOvCjHgQE5wYuSvFQ9Hph/pyTXqZpCEnw8omBWS0qLpdt0BTwauywzBA9PGvXhETw8Wk3L3dpMy8nUT5T2O0uldcfw8FiZaZkUHsLE6Ap45s6di08++STqY/jw4QCAmTNnorm5GceOHUNrayt+9rOf4cSJE8JyJSZNmoSjR4/C41G/05s4cSIOHjwo/Dx//nxB5Tn//PNxyy234N5778WSJUuifpYHHngAbW1twuPIkSN6dkW/4f2vTuOOZ97D5yc7NT9HS1ASz2gJnuc1z+pSI5wa0LY+C3CYzN/eI1Y4EktpyWdpiZHHYyx1xXHBO/ABOcaNVBC6LCuksxhKpemx+vAA8aW0mNKgVeEpTsOydKZE5mowXQOxFR69qdh4UOp9FFZ4QgGPWh8eC5mWCfOjy7RcWlqK0tJSXW9QXl4OAFi7di1cLhfq6+tV192zZw+KiorgdKo3oNuzZw8qKyuFn7u6uiSqEQBYrVYEYvzhOp3OqO9jFn677XM0fNyCMYMLcNfUszQ9R4vC0xOHabnL609Y0hcUHpVgRe4BEwIelw0n2j3o8PjA8zw4jks4pSWflg6om5aZYbkgyw6b1SIEPEZUakXrsszIcUROTGf7UItpWY9Jva2blaTH7sEDiE3LHuG7STXt4pSWhnRUzLL0PklpsTlakdsQy8MT7sNDCg9hXpJSpQUAK1euRF1dHXJzc9HQ0ID58+dj6dKlQrXVq6++ipaWFlx00UVwuVxoaGjAI488gnnz5gmv8cQTT6C6uhq1tbXo6enB008/jTfeeAOvvfaasM61116LX/ziFxgyZAhqa2vx/vvvY/ny5fjBD36QrI/Wrzh4PNj7qFODL4ehZd1oCk+Hx4cnGj7F1aMrMW5IUfh1QxcRC6ddoZHD7pRVPTzy7QwFPOyu1x/g0eX1I8dpS0pKS207WMDDAh1DFR53eI6WGkIvHrHCI0xLN9bDw8Yb6FV4PL4AOr1+zWmkZCIuS9dCrLJ0IaXVx6ZlRiwPj2BaJoWHMDFJO7Ps3LkTixcvRkdHB0aNGoU1a9bglltuEZazsvV7770XPM+jpqYGy5cvxx133CGs4/V6MXfuXBw7dgzZ2dkYPXo0Xn/9dVx22WXCOitWrMCiRYswe/ZsHD9+HAMHDsSdd96Jn/70p8n6aP0Gf4DH4VAqq1tHwNOdoIdnyycteHrb5/j8ZCd+e9uFwu9ZOiXHYRPuoPUSy8Mjh12osx02WC0c/AEe7T2+YMAT57VHS0pLDgt4ilnAk22cwtPcpl6hxcgWmg+GjwOW0oomqDDvk54+PHo9PNkOG7LsVnT3+tHa4UmLgIelXvWXpSt/ZsFsb8C2qXFGobu10IfHF0PhobJ0IgNI2pnlmWeeibp8+vTpmD59etR1FixYgAULFkRdJy8vD0888QSeeOIJvZtoeo6c6hIu+HqqqrR5eNTXYXe7cr8Oe90cZ/wBT8zy3ojREsH3tFg45LlsONPVi/aeXlQUuJKT0lIJHk6Fugiz6iym8BjRbVlLSis35OFRKktPdZUWEBwrcuRUN052eDG0OEfz85JFe49SlZYygQAvHM+qjRY1NC9MFEUPT+hfdsOjXqVFnZYJ80OztEwMS2cB4SoNLSRapcWWyS+SgsLjjL/RHjPYau+0HNwGDmFDKZtsHW9KS8ssLXnq4lRn8GLE0jdFOcaNU9CS0mIeHiXTclQPTwJ9eJS8JGoUp9kAUT19eDq9PiGQUVd4gv8m1bTMytKzI03L7PuLpfD4aJYWkSS+au1K+fFFAY+JOXhCFPAY6OHxB/ioKQ6mqsjlcb3N3JQI9+HRN0vLwgF5TlapFbwgx3s32xslpcWp2JblCg+7wJ/uTLzxYItQpaVuws+ReXh4nheCxmhVWs64PDxxKDxpVqnVqeNYZWqQzcKpdjJmx0WflKUrVGkxXHY103JwuymlRSQDnudxy9odmPzoG9h7rC1l20EBj4kRKzw9Oi5YXTHSTbEufswgKZ+83CkoPIkEPMF/tU5L94aCL47jBIWHXaD6svHgqVCah6WyikJ9eBI1LXd6fEI6JWqVVkhVY314xPvP6NESejstA+J5Wmmi8IhNy0I6Svl4EXdZVqsw42K8RqIEArxiSkuO2pBgKksnksm7h0/hy9YudHr8GF6aupQ1BTwm5jNxwBO60D2740ts3tus9hQAyp2WxSJArBJltlw9pRV/wMPpNC1LFB6XtBdPoiktJQ+PGkzhYSktQeHp8iZ0EWTprByHVTWdAoT3OfsOxOpWVIUnpAhoNS3zPB+Xhyc8MT09FJ52XQpP9DlaQOwO4YnS7vEJQax0tIT0u1VXeMi0TCSPF94L9rm7dsxAZDtSV5RAAY9J4Xkeh2QenuPuHvz3y3vxwz/swl8/bFJ9rlLAI75zjTUpnflm5BdJpvDkGaLwaOu0HN4GTjCUCimtOM/tUVNaKrEDS9WwlBZTePwBHu7u+JsxsnRWeZQKLSCyLF28/6KallkfHo0esJ7egLDPlYZYqlGcmz4eHp7ndaW03BoCHkHhSVJSi3V6dtktkqBGfjzGMi3TLC3CaNq6e/G3j4LXm5suTO1EAwp4TEqL2yMxqHb3+iWBytwX9+DDo2cUn6vUDVl83vQIaSLl91ZTeIxJaembpcV+Dio80pRW/ApP7JSWfDvYCAmm7DhtVuFieiqB8RJaDMtApGlZrPBoMS1rVXhYDx6bhUOOQ7s5PZ08PN29fkEtyXXFrtISuiw7YytaycoYhZsOSoNM+Tcby7Tsp5QWYTCv7DkGjy+AkeV5GDO4IKXbQgGPSRH7d4BI03JPbwB3PPOeMJZATJdCWbpFdFGMVfGh5uHpEJWlx4vePjwMTpLSStS0rJ7SUgodeJ4Xys+ZsiP+/6kEKrU0BzwyD4/Yv2TktPQzonSWno7JrNuyEVVricL8OxYOyFJJAYlxxxgrEXwtqWnZ6GoV1d5Hsu9AVeEJHQS9VJZOGMyGfwfTWTddWJXyLuoU8JiUg8fbAQBDi7MBSPvwOGwWnF2eixa3B//5zL8jytAV512JjtNYc3liKTy5CZSlx5qlpZYysCTBtBx1lpZoOzo8PiFIYgoPAAwI/f9UApVaWlNa8iqtgEaFR2+VVjyGZSC95mmJvWZaTtAsgFbqcMxgL+MPBLDgpQ8w5sHX8EmTO/GNDcGUtWjbAKjfpNitVJZOGM/eY23Y1+iGw2rBN8cNSvXmUMBjVlhJ+nkDgxKiuA+P3cLht7deiAE5Duw95sZ9f/xAopgoenhE/2d3+2oVH2GFR1aW7jUupZWIwpN4Hx59VVpM3cmyW5ElSvMMyO5LhSdKSsvAxoNn4jAsA+FA8FSXN+UX3Q6Z1yzWHKxYg0OB8HH76y0H8cJ7R9Hp9eODI2cM2mL13kfyb1bdw9N3puWNu47i5fePJv19iNTDzMpX1JYLvcdSCQU8JoWltM4bFAx4enr9EtWhakA2nrplPBxWCzbva8aXrV3CMqXGg+IbXWZKVqv4EBQef0BSgWRkWbpqHx6V87W0LJ2ZlpPQeFAhdmiVzdFiGKHwNLuDwVK0knQg7OGRp7Q4DlFVjPDwUG1BiDuOpoMAUJRtB8cFv7/TXYn3JkoEvXO02GeOViXH9vCxM93C7/Q0c4yFWqAZ0YdH1cPTN7O09h5rw9wXP8B9L3yA4+2R6XTCPPT0+vHy+8cApN6szKCAx6QcPB6coTU6ZBIL8JGemgnDBmDp9edHPDeWh0dIacVQeADpHaOeyhc1wgqP8nK1EEYppZXoLC17tJSW6LVPqwY8iSs84aaD2jw8HUJKK/j7aOksIA6FR+fgUIbNahEq2FLt49HbPiHW4FBAGngwNU7PBPpYqHl4NCs8gmk5uQrPmrcOAwj+fez64nRS34tILZv3NqO9x4dBhVmYPKIk1ZsDgAIeU3KmyyuU97KUFgB0eyMvWt+6YDBmTxkh+V2wVb70xKec0oqu8ADSIMsI07Lg4YlxYpZnaYKjJUKmZU+vptdQI9q0dKVOy62CYdlYhccf4HEi9D1XaixL9/oC6PUHBIUnWg8eIPwda63SalMYYKkVNlg11T4eeUfwcLCi1ngwdln6yIo8cBzwP1efgykjSwGElVIjUOt9JFfv1G5S+qLT8petnfjrh43Cz+99SQGPmdnw768AADdOqIp5nukrKOAxISydNbDAhYJsu3D3pjZPa94VI3HzpCEYMiBocA7wkXI7p6DwqLXRFys8YmXACNNyLIWHSSsO2YndwkHUhyfSx6KHXi0Kj+j/p2WT0hmJKjwnOzzwB3hYLZxQ5aSGuNlXp8cnBHuxFB69pmWWWollnlWCGZdT3YtH8PBoTGmFFR71zzzvipHY9T/1+M+vD49rAn0sBGUtSqDptFlU05d9YVr+zb8OI8CHA0kKeMzLFyc78e7hU+A44NsTBqd6cwQo4DEhLOAZUZYLIFxaqxbwWCwcfvHN8/HPeVOE33XKxktIPDxxKjzG9uGJ3nhQXjIe9PCEOy3zPB+3h8cXp2lZ1cMTp2eFtRQozXVGNR4DwQCQ7ZNOrz88OFTD8wDt6Zd4BocyWPPBdFF4cjR2hA2blqNVaXHC98+6Vxvp4dFiWlZTd4BwSitZZekn2j148b2gUfln/68WALDvWJuuGX9E/4GZlS85qxSDCrNSvDVhKOAxISzgqQkFPK5QZVCsk4tVNPxQXqml1HhQ7QQqlurFJ3W9FxIlYpWlM+QKDydqPOgP8MHmckmYlq50A61uWk5M4WEVWrFK0hnMx9Pp8YVTWjGUZr0eHi3znNQoTZNePHLTstB4UOVw0dJpWQz7u9HavVoLWkzLakUGgLjTcnIUnt+98zk8vgDGVhXi+gsGoSzPCV+AxwcqzU+J/ovPH8BLu4LBbbqYlRkU8JgQVpIuBDyhIKZHwwlWXs3DUBotoXYCFb8PU3h6/QEh+DHGtBy9SitC4QGHbIdVUDTae3xxKTw8z6M3oKEPj+i11U3LiU1MbxFK0qOnsxji0vSARoUn3j48EQ3wNJBuHh6tI1C0lKWLceps5qgFddNy+PtVMywDyTUtt/f04vfbvwQA/GjKCHAchwnDigAAuyitZTq2HjiB4+0eDMhxYNo55aneHAkU8JgQQeEplaa0tAQ82UwFkJWmKw0PVVV4fJFVWuIUWUIprdBbxurDo+Th4ThOCLbae3rjUnj8AV41qAKUOy2rKjwhv0WHxxdXxQ5LacXqwcMQz9NiwZ7WlJbm0RJx9uEB0meelp4qLX+AF3l+tH1mlgpORsATsd/FCo9KChpIrmn5+Z1fwd3jw4jSHNSHLoDjhw4AALz3xSnD349ILX8MpbO+NW5QxHk41aTX1hAJ0+31C70+ajR6eMQICo9HXeERPDwKCg/P85ITOVMG2EXBYbUk9EcQe5aWsmmZbT67C3fHqfCILwhRZ2mJ/q/m4cnPsgkBRzwqj96UVraDpbTCHh5LrLJ0YXhoHyg8gmk5xQqPPKUlGwshWVcUyGtVePT6omLh9QUERTbaLK1oKS3BtGxwHx6Pz4/fbvscAHDnpSOEap0JQ8MKT7ypZSL9OO7uwRv7jwNIv3QWQAGP6Th0ogM8H2zkxu6YXToCHuGiKFN4xCfOaH145HetTBnoFErS46/QAsIXn1jT0iMVnuDzxMbleOwKvaILQrSUlhi1lBbHcQn1nmnR2GWZIR4vwXafZtOyhp0VCPCCnyWeKi1hgGiqPTw6+kWxknSHzRI1oBAT9vAYE1ywIFPsU4v2vkoIs7QMVnj+/P4xtLg9qMh3YcbY8GiBcwfmI8tuhbvHJ6Tgif7Pxt3H4A/wuGBIIc4qz0v15kRAAY/JkBuWAQjjDLRURLCLonxdcUojrPAoBDyyk7hc4dHavVYNdn2OdVMoV1/kCk+8Ka1eUUBnV5i6KS/79fj8aA999gEKJcPMtxKXwhNvSsvr067wiDw8apVxjGD1W/D/caW0ctKrSktLwOPujt10UA77uzGqLL2NzdFy2SP6nWg3LRtflh4I8EKjwdu/Vi25CbFbLRhTFewR9h41IDQFPM8L1VnfuXBIirdGGQp4TEY44AlH12pGZCVY+kuu8Ig9OMJoCQVPgFym7xUUnsQrtIAETMuh54l78SSS0rJZuOjNtEIvzTwtVgunGASwienxKTyhsRKaq7TCpmWtHh5x64FYd/+sF0yW3arasiAaLKXV5fUrjjfpKyIaD4Z+r3S4hJsOag/wBA+PwQqPUhpRYlrWUJZupGn5tY9bcPhEJ/JdNnx3UuQFcALz8XxJPh4zsPPzU/j8ZCdyHFZcPboy1ZujCAU8JiOawqPlIsLu9OQnPvGPPcy0rKDw9KgoPEaMlQA0zNIK/Rvh4Qn9G05pxanwxJiULhdMmFpRlB159w2EVQ2W9tJKh8cnXJg1p7TYceDx667SAmJ7ThLx7wDBY4N9b6lUefTM0tJboQWIq7SM8fBEM4rrLUs3KqXF8zyefPMQAOB7Fw9T/LsfT5VapuKP/w6qO9eMHphQYUoyoYDHZMhL0gHRhU6DwsMUlGh3euzOVOkuXk3h0TufSI2whyf6enKFJ+zhCSs8eieuA9onpbNXZoblIpUOuEVCLx59F3iWzspz2jTvU4nCI6S0oj9HvB9jlaYnUqEFBL/bcC+eFAY8ejw8oTEl0bosyzG6SktrwBNN4bFbjDUtv3v4FD44cgZOmwW3TR6muM4FQ4rAccCXrV040Z5a3xaRGO6eXvxtbxMA4KaJ6WdWZlDAYyJ6/QF8cTI4NFSq8DCzauyAh93xRwsoWFCjNFoiQuHxJ0fhYcHKvBc/wDUr/iVcjNWqtCwRHp7EUlpKJelKnOpSNiwzwt2W9V3gW3RWaAFS07LWlJbFwgnpjliek0SaDjJYWqs1RaXp/gAv3BjIZ2nxCnVacSk8dmP78Gjd70pVlYxw40FjFB6m7tw4oUp17ElBlh1nh1Lvuyit1a95ZU8jenoDOKssF+OqClO9OapQwGMivmztgi/AI9thxUDRhTBHR0pL8MioRDx2K6dL4RFSWl5jqrTks7Re2nUUe4+5sfXAcdl2Knt4WErL3dOLeG5mY6a0ZD+fCl24VQOe7MQUHq3pLCB8HHR6fcJnj2VaBrQ3HzyTYEoLCJu4U9WLR+xd05LScnfr67IMGN9pOdp+1+zhsbLREokHYfsa2/DWpydgtXCYdcnwqOuytBYZl/s3LJ1104VVqvPa0gEKeEzEoVA6a0RpruSgy3ZqNy0LKS0V9SPHadOl8DBFxKiUltosLaVGieLhnpFVWr64DJqaU1qh7WNzslQDnlw2MV1nwMMUHj0BjzOs9IVHS8Q+OTk0dgZ2G6LwsOaDqUlpMf+O3cppMl5rmaMlR28zx1i4hTlakceYVg8Pqzj0G6DwrH4zWJl19fmVqAoNJFaD9eOhQaL9l32NbfjoWBvsVg7fuiB9BoUqQQGPiWAXzXLZqIFsXQpP8F+xv0U8ADTXaROCGm0KT/Bno1JanML2BV8/+D7s1xzHSYISdqcrNi0nI6Uljx/YnCz5pHQGK1XXG/Cw5pIDC7UHPOJOy1pNy4D2eVpnQmm5wigTu2MRTmmlJuCJdpwqHS7uuEzLxlZpsf0eK9BUukFhsOMg0eGhX7V24a8fNgIA7rw0uroDhCu19jW2aeoET6QfL4TUnSvOrVC9sUsXKOAxEWrTyMUddmMheHhEJz521wsEy8qjjZaQqwDJUnjkyrtSMCcOeJQ8PPFUaflipLQYEaZlVQ8PC3j09eE5cqoLAFBVFP0OWoyiaVlHwBNL4THCw1OSk9oBou2K/aLU9xErS9dnWjbWw8NSWgVKKS1OnNLS0Gk5QdXpN/86jAAPXHp2KWoHFsRcv2pAFkrznOj18/jgyJmE3pvoe3p6/Xj5/WMAgBvTsLOyHAp4TAST1+V3p6z3jZZOyxahH0fk67Ll7EStJJHL79LkpuXEA57gv6oKTyjU4CANeNh5P9E+PF6NKS2G2lgJBvv96S6vrgDs6OmgwjN4QJbm5zD/VJc3nNLS0iyaXSiTXaUFpF7hYcG91n5RiZSle/0BQ8YqRAs0paMlonl4Ejctn+zwCI3nfnjpCE3P4TiO0lr9mH/sa4a7x4dBhVn4Wk1JqjcnJhTwmAg1OT6s8MSX0mLjAoCgN0XXaAmhDw+rfDHGtCyPVZjCI/yeAxyiq3nkaIneOD08weeoBzzSCCJWwMPK0v0BXhJYRiMQ4HEsFPDEo/DoTmlZtXlOzhih8KR4gGinRz2AUU5pxdF4UHSjYISPp62LeXhiVGlFGx4qpLTi357fvf0FPL4AxlYV4qLhAzQ/b7xorhbRv2Bm5W+PH6zpXJJqKOAxEcy4G5HSCv2sRUK3KnQyFg9I5PnooyXUFJ52gzotq83S6lQwZNvFAZnBKS17rJRW6KVZqkot4HHarEKAqjWNc7zdA68/AKuFQ6WesnRHZKdlXablGAqh24gqLWGeVmoUnnaFm4Zou4gFqbpGS4ibORrg4wk3fIxlWo7ShycU1MbbabnD48Mz278AEFR39FTqTBgWDI52f0WDRPsTX7Z24p1DreA44IYJ6W1WZlDAYyI6hAGdygqPFiyWyIBCrDzwCE9DVx4tIfPwJKnTsvy82BV6/bDAw0mMxXKFxxfg0RWHSTJWSkt8ng8EeJwOGUpZR2UlxGktLRw5HfTvVBa4hFSEFnJFgS9T3nSZlmMpPAaktEpEVWupuPgJKS2Nx2k8oyVsFk44jhPttszzfFRlTfztRlN4BNOyn485M02J53d8BXePD8NLc3DFueW6nls7MB8uuwVnunpx+CQNEu0vvPjeUQDA12pKMFiH0pxKkhbw7N69G/X19SgsLERxcTFmzZqFjg7pwcxxXMRjw4YNknU8Hg/++7//G0OHDoXT6cSwYcOwdu1ayTovvvgiRo0aBZfLhfPPPx9/+9vfkvWx0ppwUCE9sekKeIROy+HftYtSWr1+XrgL1KLwRMzSkl1I5BVlsVCt0vLKq7RkHp7QvzkOq2g8ha63BqAlpRV6bQQnh7N9xVJXSjBDs1bfSjyGZQDIFh0XLIjVEvBo7cPTFqU8WiusI7U/EL6Q9yVRU1pRGg/mZ2kP5DmOM6zbcqfXLxxjin14RBF4dIUnvJ5elcfj8+PpbcFS9B9eMkKTEV763haMGVwIIPX9eFrcPVj95qGUpVT7Cz5/AC/uCvfe6S8kJeBpbGzEtGnTUFNTgx07dmDz5s3Yt28fbrvttoh1161bh6amJuExY8YMyfIbb7wRW7ZswW9/+1scOHAAzz//PEaOHCksf+edd/Dd734Xt99+O95//33MmDEDM2bMwN69e5Px0dIatUooPWkkq4LCI05piaeoK5elK3dalk9L/+Osi3Dx8GL89tYLNW8boN6HR7FKyxap8HAcl5DKpDWlBYT9O7lOW9S762K9Cs+pkH9Hh2EZCH5fbLtZEGtU40GPzy+Y4hNReBw2i/D8VHRbVhorobaHfP6A0NtKj8IDaK98iwUrSXfYLIpFBFoVHrFS6NMZ8Pzl/Ua0uD0oz3fiunEDdT2XMYE1IEyhj+fIqS5c/+Q7WPr3/fhVw6dJfa8X3zuCB/70UcybiHTlrc9OoMXtQVG2HfU6Fb1UkpQJX5s2bYLdbseqVatgCTW0Wr16NUaPHo2DBw+ipqZGWLewsBAVFRWKr7N582a8+eabOHz4MAYMCOZ5hw0bJlnnf//3fzF9+nTMnz8fAPDwww+joaEBK1euxOrVq5Pw6dKXDpUqrSwdCo+goASUU1riSi8l0zJTeKwWDv4AD68vKJHLU1qThhfj+VnFmreLIe+0zFCq0nIoNB4Eghcnt0aDsJxYjQfFF5hYhmUGUzW0+lZYSkuvwgMEg+EzXb3C59eT0op2cWbqDsfpq1hSojjXgbbuXpzs8OKsPj6XtuuoJhT/Xej9zEYNED2j0bAMxKjSEh0Hvf5A1CaFYgIBHqvfCo6R+M+vDdfUrFGJYD+eQykzLn/V2oXv/uZdob/Vax+34OHrztOtVmnhzU9PYMHGD8HzwCVnleCq842fLL73WBtefO8IJgwbgKmjygwf5rlhZ1Dd+ea4wXF/56kgKQqPx+OBw+EQgh0AyMoK3o1u27ZNsu6cOXNQUlKCiRMnYu3atZI791deeQUTJkzAY489hkGDBuHss8/GvHnz0N3dLayzfft2TJs2TfKaV155JbZv3x5zG91ut+TR32GmZXnA47RZNDvomWnZr1KlxQIeh82iaOZkF0V2AfD6A+jpDQgBSuLDQ4P/ylNasfrwiKX9RC7IXq0pLT52Dx4GM+pqnZgupLRidLFVgql9zGCsybRsja3wsEqhfJfyVHg9pLIXj57GgyzgcdktmtsUMIyapxWru7XmTsui7deT0nrt4xYcPtGJfJcN3500RPPz5FwwJKjwfH6ys8/TSZ+f7MRNT23HsTPdGF6Sg1ynDSfaPdhz9Izh79XU1o17/7hHOJbePdxq+Hts3tuEb69+B+u3f4kfP/8+xv+8AT/8/S688kGjRK2Pl+PtPXhjf3CUT39KZwFJCnimTp2K5uZmLFu2DF6vF6dPn8bChQsBAE1NTcJ6Dz30EF544QU0NDTg+uuvx+zZs7FixQph+eHDh7Ft2zbs3bsXL7/8Mp544gm89NJLmD17trBOc3Mzysult4Hl5eVobm6Ouo1LlixBQUGB8Kiq6l9fnBJqPhmO4zT7eFhgJD65ixsPegXDsvKhwxQeFlT0+gKSP7JsjXeOasRUeFQ8POJrsJ4mcXJiNR4UB1Ys4FHrsszQq/CwHjx6U1pAuBcPC2K1XKe1mJbbDKjQYqSyF0+HQl8dtZgwnpJ0hlHdlmPPL9M2S0v899GrsRcPz/PCkNDvXTwsoVRxQbYdZ5cHBx73pcpz6EQHblqzHU1tPagpy8WGWRdhyshSAMBr+1oMfa9efwA/fu59nOr0Ii+0r949bNzQVJ7n8eTWQ/jhH3ajpzeAcUMKMbQ4Gz29AWze14y7n38f4x9uwKxn3sNf9hyLO/j50+5j8AV4jK0qxMiKPMO2vy/QFfAsXLhQ0Wgsfuzfvx+1tbVYv349Hn/8cWRnZ6OiogLV1dUoLy+XqD6LFi3C5MmTMW7cONx///1YsGABli1bJiwPBALgOA7PPvssJk6ciG984xtYvnw51q9fL1F54uGBBx5AW1ub8Dhy5EhCr5cOKPkPGFoDHnbB9quktBhqk5cFhccZPAF7/YFwIOawJnz3HzYcR1d4OHCSieniIYqJKDwspRVrWjrPhwOYohijFgQPj4aAp9cfQFOb/h48DBYM6zMtxzbYGlGhxWCVWqnw8MST0tJTks4wKqXFguoCFaO4VoUnOIol1G1ZYy+edw+fwgdHzsBps+C2ycO0bXAUxofGTPRVwPNZSztuWvMujrd7MLI8D8/fcRHK8l24ojZosXjt4+g3zXpZ9o8DeO/L08hz2vDM7RMBAAda2g05zr2+AO7f+CEe3bwfAHBb3TC8eOfF2DpvCv5699cw57IRqC7JgccXwGsft+AnG/bggocbcMcz7+Hl949KClOiwfO8MEqiv6k7gE4Pz9y5cxWNx2KGDw/OT5k5cyZmzpyJlpYW5OTkgOM4LF++XFiuxKRJk/Dwww/D4/HA6XSisrISgwYNQkFBuEX5OeecA57ncfToUZx11lmoqKhAS4s0Em9paVH1BTGcTiecTn0VQumML5Q6ApRP1sFURuw/LKWUltIfg9rdokeu8PgDho2VAMQKT+w+PA4VhceIlFas0RJAOIBhioUaRcJ4idgBT9OZHgT44P4vzdN//LJgWFdKS8PF2YixEgy2v06moBePnvYJ8ZSkM4waL/H5yU4AwNBi5eBXalqOHqTbLBb0+v2auy2vDqk7N0wYLASpiTBhaBGe3/kV3vvCONVDjQPN7Zj5m3fR2unFqIo8PPufk4TBtVNGlsJu5XD4RCcOHu9ATVluwu/32r5mPPVWsJJt2Q2jMW5IEUaW5+FASzt2fH4K30jAx3Omy4sf/WE3th9uhYUDFl9bi1vrhgnLawcWoHZgAeZdMRKfNLXj73ub8NePmnD4RCcaPm5Bw8ctcFgtuOTsEnzj/Epcfk656t/xe1+exuGTnch2WHHtmPgM6qlE15m/tLQUpaWlut6ApZvWrl0Ll8uF+vp61XX37NmDoqIiIRCZPHkyXnzxRXR0dCA3N3jQffrpp7BYLBg8ONjo6OKLL8aWLVtwzz33CK/T0NCAiy++WNd29nfEF/wchW7G2Ro7HLMYgVep0mKo3S2GPTwhhccXMKwHDyBuPCj9PUu1se3mVKali7ctHnxJMC0L87Q0VGkxw/Kgoixdzd0YTOmLx7QczcNjRJdlRnEKFZ4OhbJ0TqVOK57BoQyt4zpicfB4sNWH2kVZfIioqbIMm5UDeqXDgtXY19iGNz89AQsHzPq6tjESsWCVWnuPudHT69dsnNbLx41u3Pz0uzjd1Yvagfn4w+2TJD67fJcdF48owVufnsBrHzejpqwmyqvF5sipLsx78QMAwO1fq8b084LBzcUjinGgpR3bD7XGHfB8frITt//u3zh8shO5ThtWzByHy0aWKa7LcRzOHZiPcwfm4776s3GgpR1/+zAY/Bw60YnXPzmO1z85DruVw9fPKsU3zq9E/bnS4IeZla8+v9KQ83lfk7QtXrlyJerq6pCbm4uGhgbMnz8fS5cuRWFhIQDg1VdfRUtLCy666CK4XC40NDTgkUcewbx584TXmDlzJh5++GF8//vfx4MPPoiTJ09i/vz5+MEPfiCYoH/yk5/g0ksvxeOPP46rr74aGzZswHvvvYennnoqWR8tLWEnaofVouiaz7Zr+6o1p7RsQaOmy25BT28Au786jck1JYInIV9kWlbrAB0ParO0GKp9eAwyLWtNaQHhlNaAGCktIeDR4FmJtwcPI0em8FiNMi0b6OEpCe2Pkyn08GhLaekfHMowyrQcK+CRvGcMhUdPt+U1bwbViqtHD8QQFXVJL0MGZKMk14mTHR58dKwNFw7TPp5CK3uPteE/frsDZ7p6MXpwAX7/g0mKQ1evOLc8GPDsa8HsKfEHPB6fH3Oe2w13jw/jhhTi/umjhGUXDR+A373zBbbHaVzecbgVd/5hF8509WJQYRZ+e9sEjKrI1/RcjuMwqiIfoyrycd8VI/FpSzv++mET/vZREz473oE39h/HG/uDwc/kmqDyc/HwYvzto6AH9zsT+186C0hi48GdO3eivr4e559/Pp566imsWbMGd999t7Ccla1ffPHFGDt2LNasWYPly5dj8eLFwjosWDpz5gwmTJiAm2++Gddeey1+/etfC+vU1dXhueeew1NPPYUxY8bgpZdewp///Gecd955yfpoaUnYsKx8V6Rd4Yk+PJTBAp6bJgQP/BVvfAYA6PEppbRYB+jE79jUZmkpIe7DY5TC0xsjpSV+H9ZXR6vC0+n1RzRulCOUpMdhWAbCKhv7TrV4qtjFOXqVFvOSmEPh0VOlFU8AzYLIRDw8nR6fUEZdU6qi8ID1n4od8Ii7LUfjq9YubPqwEQDww0vVLQp6kQwSTUIDwg+OnMHM37yLM129GFtViN/frhzsABB6y+w5cgbH3T1xv+cjf/0EHx5tQ2G2HStnXiDxFU6qLgbHBYPWE+36jvWXdh0VArcxVYV4eU6d5mBHibPL83Bv/dlouO9SNNx7Ce6ddjZGlueh189j64ETWPDSh/j6Y/9Ed68fI0pzhKq6/kbSFJ5nnnkm6vLp06dj+vTpMV9n1KhRaGhoiLrODTfcgBtuuEHX9pmNWD4ZzVVaCo39lDw8TG6+89IReG7nV3j38Cn8+4tTgsKTrJQW87wrjR3w+QOiXrjKoyWC25a4wqOlDJlVGcUqS8932WCzcPCFRlFUFqgHM0LTwQQVHlZxpUfh0dKHJ5Euy4xUVWl5fYEI0z2gXqUlKDxxBHksvZRIldbhE0H/TnGOQ/UYY9vutFlipkDtFm2m5d/86zACPHDJ2aWoHVgQdV29TBhWhM37mrHry1MAjEmVAcE5Xbf+difaPT6MH1qE333/wqg3PuX5LoytKsSeI2fQ8EkLbp40VPd7bvqwEeu3fwkA+NWNYzGoUPp3XZTjwKiKfHzS5Ma7h1s1eWICAR6PNxzAqn8G/VNXj67E4zeMMTT9d1Z5Hn5SnoefTDsLB4934G8fBZWf/c3tAICbJw2NK52eDtAsLZMQK6jI1thtmR3HzLTM87yih4fdLQ4szMK3xwdVnl9v+UxB4eFVy+XjQe7hEd+1ymdjSau0wvRFwMPz4jla0YMAjuM0G5fDCk+cAY8s8NWk8PSxh4f14Wn3+GIqXkbSKTrOtaiR7u6QwhPHcW2EafngieAFaESUdFY44In9eVi35WgKz8kOD154L+jjMFLdYYgnp8cz00uJ9744he+Fgp2JwwZg/Q8malJ5r6gNqjzxlKcfPtGBhRs/AgDMnjICl41S9tWwqfJa+vH09Ppx1/O7hWDnx1NrsOI745LmdQKCqdK7Lz8Lm++5BFvmXorf3joBt4kM0f0NCnhMQqygQn6hUyM8WiL0ul5/hEEYkJqWZ08ZAauFw78+O4ljoR4xYoXH2CothLYvuFFiFafL45d5eDjR88L/T6QPT3iWlkpKKxRa9fjCYwcGxKjSAsI+n5gBj0EKD8PoPjxqKQI95GfZhP2rpXItUTo9Puw91oZXQ2kal92iOJRVPkur3cOqtFJTlq7Fv8OOx2hdlhksTeuL8j3/7u0v4PEFMKaqEBcP198pPRa1AwvgtFlwuqsXh0IKViK8e7gV31u7Ex0eHy4eXozf/eBCzUrzFecGK33fOXRSc9k2EAxMZj+7Gx0eHyZWD8B99Werrsv2YSwfz/H2Htz01Lv420fNsFs5PH7DGMy9YmRSOkGrMaI0F5efU96n72k0/c9mTSjSrjJWgpGlUeERyr5DUU6HyggGsbJSNSAb3xw3CC/tOirM4ckVmZbVRl7EQ8QsLdHfXqfXJxktITUth9frC4WHqTt2K6dJARigQeHp6fULXWjj9fBEBDyaZmnFrihq0zHiIBYcx6E4x4lmdw9aO7wYWBjfZxXD8zxOdHhw8HgHDp3oxKHjHTh0ogOHjnegsU3q0ZCXWKvtobCHJ4HGg4koPKGA5yxNhuXYNzx2S3TTcofHh2e2fwEA+NGlw5OS1nDYLBhTVYidn5/Cri9PJVQS/s7Bk/jB+n+jpzeAr59VgqdumaBrzE5NWS6Gl+bg8IlObD1wQnMZ9uK/7MP+5naU5Dqw8rvjFINnBvPxHD7RiePuHpTluyLW2d/sxu2/ew/HznSjMNuONf8xHpOSEGxmAhTwmIRYKS2tCo9FMC0HT3pqdzbyE+jsKSPwp91HBTVIbFoWqrR0DDFV3T6VsnQgqPCIUa/SSkThiVGWLpvEXpTt0HRh0BLwHA2ls/KctrhTR/LjQ8vdmpaydCMVHiDo42l29+CkzvESPn8AX53qCgc2JzpC/+9QNN8zSnIdGF6aixGlufjmuEGa3iuhsnQNRvBYaKrQCn29ehSeXpWA5/kdX8Hd48Pw0hxB/UgGE4YWYefnp/DeF6dx04Xxjav412cn8J/r34PHF8ClZ5dizS3j40r91J9bjjVvHsZrH7doCnhe2nUUf3zvCDgO+PV3xikGMGIKsu04tzIf+xrd2H64FdeNlR57/9x/HHc9txudXj+Gl+Rg7W0XYlhJju7PQQShgMcksD486lVa2r5qqxBQBE96akM25SfQ4aW5uGb0QLzyQTAtIJSl+4yt0lKbpQWEFB5RSkuc7jLOwxM9pSUnVoWWfL1o3ZZZOiveHjxApHldn2lZOf3C83x4xIEBpmUgXKl1Mkr1SltXL7bsbxEFNZ34srVT1YNi4YKlzyNKczGiLBc1pbkYUZaD4SW5MY3lgFKVVgKm5QRTWl5fAF+2BgPg6Ckt9n4aPDwW9ZSW1xfAb7d9DgC485LhSU1rsH488XZc7vT48KM/7IbHF8DUUWX4v5sviNvncsW5FVjz5mH8c/9xeHz+qPvxQHM7/ufPQd/OvdPORl1Niab3uGh4MfY1uvHu4VOSgOd3b3+OhzZ9jAAP1I0oxpM3jzfshiJToYDHJBhVpSX3yLDXZVVEDKUmZndNrcGrHzbCZuGEcQq9otESiU7RDm5fFIXH6xNcFhw4mYcnvF5fVWkB2gMedsGNNk8rUcMyEKnwGDEtvdPrFxRBI0zLQLgXT7T9cfeG9/Hmpycifp9lt2JEWQ5GlLKgJqjcDCvJjmuys1pwKZiW4ylLZ/s0ziqtL1s74QvwyHXaUBFFRWDbrk3hUTct/3nPMTS7e1Ce78QMjQpYvLCS58MnO9Ha4RGCX62caPegw+NDlt2KJ//jgoSmeY+rKhR6A717+BQuPVu58W6nx4fZz+4S0md3Xaa9d8/Fw4vx222fC8Zlnz+AhzZ9jGdCFV43TajCz795nu4BtUQkFPCYhNhVWomltPKz7JJ0i1JPj7PL8/DULRMQ4Hnhwtfr53U1c4u5fSqztIDwAFGGuA+P+I40x2EDx2nr5SMnWQGPME8rSrflRJsOApHfga4qLRUz65nQNjtsFk0XVi2ES9OVFZ5ef0C4QNwwfjDOHZgvKDeV+a4+MVYm1HgwQQ8PS2eNKM2JqvbFpfDIytIDAV4YI3H716oTCiC0UJjtwFllufjseAd2fXlamG2lFbb9TrtyE1Y9WCwc6s8tx/M7v0LDx82KAQ/P8/ivlz/CoROdqMh34Ymbxuo6/i6sHgALF+yafPB4Ox7e9Ane/PQEOA5YOH0UZl2SHL9UJkIho0mIrfDoTWkFf1YbkKgmEdefW44rayskwQa7iCdzlhYQGiAqGi2hltKyWLi4DdTJSmkJCk+U3jNChVachmUg0kelKaUVw8MjnqNl1Ik53HxQeX/sb2qHxxdAvsuGR68fje9PrsYlZ5diUGFW0oId8REn6dmTgiotIeDRaOrVEoiqdVp+87MTOHyiE3kuG747MT5PjV4SSWsJzUEtxlzeWHl6w8ctiv2/ntv5Ff6ypxFWC4cVM8fpVqQKsuxCP6NvrnoHb356Ai67BU/ePB53XjqCgh0DoYDHJMSqhNJuWg7+K6S0WMAjS1XE6toqDjZOhyp4kjlLC4hUeCR9eGQnjXhL02PO0pKdmwxVeE4bofDo78MTK6VlZIUWg+0PtQGie44EL4RjhxSlpExWbOaP57hOtA/PwRPaRkoIfXg0eFgE07IspbXpg+A4gW+NG5SQ4V8PbHL6e3EEPD4h4DHmuKgbUYwchxUtbg8+PNYmWbb3WBsefOVjAMCCK0fGPQ7j4hHBqqt2jw9leU68eGcdpp+XPGN4pkIBj0lglVCqKS2NJ2W5ghKeCC19fqwTqDTgCSk8hlRpQbJ9YqQeHvWydCB+H49XUHi0/enEajrIKNLQh0dIaSXg4YmvLD26wmNk00FGSV708RLvf3UGQNBjkQqY8pnjsEYtO1aD/f3EW6UlVGipjJRghFNaGjw8CqZlry+Aho+bASChid56YQ0IPzraprv5JEtpqY1/0YvTZsWUUOPA1/Y1C7939/Ri9rO74fUHMO2cMsy6JP5GjFeGVKRzK/Pxl7sm4/zBxnawJoJQwGMSwpVQiZqWpR4et5DS0qfwWCyccAJlr2VsH57IZZ1eceNBThKUWGQX9ngDHubh0Xoy1VL9A4Q9K6e7ehVl87buXuG7GFwUf0rLabNI7ny13AQ7rNEvzkYODmWwbstqKa33j5wBAIwdUmjYe8ZC7BtzCzcC8X3mRBSeQIDHoZDCc1Z5XtR1w6ZlLR6ekGlZdPxtP9wKd48PJblOTEjCME81hhVnozjHAa8/gL0yVSUWrLjCSJPvFaHZWq99HOy6zPM8Frz4Ib461YXBRVl4/IaxCaWexg8dgHcWTsUrd02OOlqGSAwKeExCzOGhejsth87DzBskv3vXcscoP+EYMzw0+K+iwiMbgSH22chPRfFeqHwxpqVzsnfSmtJiwYI/wAsXUzGsB8+AHEdCXiiO4yTHgq7hoaqm5fjLs9UQTMudngiD+ulOLz4/GezCO3ZwoWHvqYbSdSyRwaFAYh6eY2e60dMbgMNqQVWM4FeXwqPQafnvoenYV9aWa6roMwqO4wSVR2tai+d5fNzoFs6FRqW0AOCyUWWwWzmhp9O6t7/A5n3BrserZl5gSLn4wMKsuNRCQju0d01C7MaDxqa0tNwxyo29Rs7SUlV4RNZSteGhgP4LVXNbD6Ys+ye+CPU+MbpKy2mzCh2ZldJa4ZESid/9iY8RTWXpIjOrUo8WIweHMth+6/XzQvk3Y8/RMwCA4SU5mhU0IxAfcmp/F1oRqrTiKEtn/p3qkpzYF0ih8aCWv1epadnnD+Af+/o+ncVgxmUtk9M/bWnHTU+9i2/8+l9Y8NKHALQd21rJd9lxUai78fKGT/HI3z4BAPzP1ediTIrSqoR+KOAxCR0xAh6tLdXZOcIvBDzxpbQAwCEqCbVZOE3Pib190au0JI0Hxe+XoIfnhfeOCMEOoJ7Site0DCDqAFGm8AxOwL/DEAeeeqq0AGWVp607uL1Genhc9nAAKO+2zPw7fZnOkuNOYKwEENsIHo2DLdoMy2L0eHiYaXnn56dwuqsXRdl2TKruu3QWgxmXd3+lPki0y+vD0r/vxzf+91/Y+fkpAMDxULNKo/vWsPL4v37YBF+Ax9XnV+J7F+ufok6kDgp4TADP8zEVHqfNoumOJ5zSkjYejKzSih1AOURBQY7TZkh5ZTilFbksog9PVIVH34Xqrx82SX5WS2nJYWZkLUQbL2FEDx6GOODRU6UFKPt4kuHhAcS9eKT74/2vgnf840IN6pJNuqW09JSkM9N8tOaEDHlK6297g8f8FedWpCTVct6gfDhsFpzq9OLwSekgUZ7n8Y99zahf/hZWv3kIvgCP+nPLMe2ccmEdo0zLjHrRa1eX5GDp9edTyXg/gxoPmoDu3vBEc7W0EfNuRJsnBIQvgBF9eLLkKS0tCk94Ha0eoliw7VO644vstKzchwfQd6H6rKUdB1raJb/TcveY77LpusuMGvCcTrwHD0PspdJyTbBZOFi44DGhFPAwD4+RCg8Q7MXzRWuXpFIrEOCxJ2RY7usKLfEh5+5OzLfE/n7iUng0lqQDwF1Tz8L4oUW4LFRlFA2xadkf4LF5b9Cge9X5qSmPdtqsGDO4AP/+4jR2fXEaI0IVaUdOdeFnr+zDlv3HAQRN/D+7thbTzi3HoRMd2LK/BTwfHoZqFBUFLkw7pxzvfXkKq2Ze0Gcl+oRxUMBjApgKw3HRAwtNAY+sSkutm6ym6cuii31WnLNs5ESdpRXRh0dUjSQ79+k5WW2SqTuAtpSW3gZkQsCj0IvHUIXHoc/Dw3EcHDYLenoDihdooweHMpR68Rw+GRwC6rJbMKoieoVSMklc4YmvLJ3nec0l6UAwCJ1+njb/jVjhee+LUzjZ4UG+y4a6EdpmQiWD8UMH4N9fnMZ7X57CjHGD8Jt/HcaKNz5DT28AdiuHWZcMx12XnSWk7EeU5uLa0Ew/oxUeAPjN98bD6w8kvds0kRwo4DEB7EIfHJmg/kcevNBFnz4tHx7artZ4UKfCE+/wPjnRZmnJh4dGS2nJO0erwfM8/vpRZMCjJaVVpDMAEAIeWQqH53kcFRSexAOeXJ0pLSD4edUCnmQpPEq9eJh/Z/Sgwj5Ls8gr74DExkoA8Zeln+zwoq27FxwHDC81dmq22LT8971Bs/K0c8ulXrg+ZkKoUuvNT0/gqv99C4dOBFNbFw8vxsMzalFTFhn0zr9yJJraunHDhMGGbw/HcRTs9GMo4DEBsUrSGVqMy+Kyb3H7fPmJXU/Vh9b31kK0svROT7hKS954UM6gwmBqqCjbLnSCVuLTlg7hjlqMaqdl0cVxQI4xCk9rpxfdvX5wHDCwMLYXIxZ6TcsAM6D7FBUJd7fxnZYB0QBRUQDI+u+MS4lhOXzMGaXwsMo3rcEbOxaHDMg27CaCwUzLXn8Am0MBzzc0qkPJgpWmt7g9aIEHJblO/M/V5+C6sQNVb+6qBmTjxR/W9eVmEv0EMi2bgPYYYyUYWkrTxcNDxe3zIzota6rSSoKHJ0pZepdXmq4Tv79c4Rk/tAhP3TIeS751ftT32/RhIwDgLJlfQotcrrXLMmOASrdlls4qz3MZcneZ7dTXhwdQHyDq8wfQrtKrKVGEeVqdkQpPagKeMO2eBMvSRQqpHpVH8O9oSGfphQU8731xGs3uHuQ6bfjaWalLZwHBysXxQ4vAccD3Lh6KLXMvxYxxg8gsTMQFKTwmIFaFFiNbQ+M/qyUcUDBvULbDGnGB13J3KU77GHU3ykUtS/erprTk50eO43BFbQU+DPV0UYLneaE6a8a4QVj2jwPCMi1mZL09YpjCc1oe8BhoWAaAXId+hUdtvATz7wDJCHhCHp724P7o9PhwoNkNoO8qtADlKi3WGyjelJb4b8PjC0CrGHjouHbDsl6YyvRRqLPx1FFlhqtI8fD72yeirbuXOhATCUMKjwlgc7RiNfbTorKI+/CIlSO5j0Fbp+Xwc4wyLUcrSwfCwR/HcVEbD2rh4yY3Dp/shNNmwZW10koVLcND9So8wsR0FYXHCMMyIEtpafXwqJRRfxrqCTOwwGW4p6Y4FAWwPjwfHWtDgAcqC1wo11BmbTTiGLs9wdESFgsn/H20K3TWVuOz48FqQa1T0vUgv6n5Roqqs+RkO2wU7BCGQAGPCejwaA14NKS0RFVaLH3htFskF3ILp61tuzilZFzAo67wAEGVhxFttIQWmLpz2cgySY8ZC6ctUNCr8BSrKDxGNh0EpF4vrSkt5sHqlI3veP9I8nrilMj68KRLOgtI3MMDhP9er3ziLdz13G40fNwSs2rrYBIVHnEZd5bdikvPjl3KThD9CQp4TIDWlFaOBoVHnNISI74suuxWTTn0ZJqW1TqvsrtlDsHPwjZTb85fXJ119ehKycUg2muJl8Sr8HR6/ZIJ0UaOlQDiMy0zdelLUbdpILlBCPPwtHX3wusLhBsOVvVdOgtQDpaNCHh+es25qC7JQU9vAJs+bMIdz7yHC3/xOh7404fYfqhVaA3BcPf0osUdVLuSk9IKf9LLRpUa9jdLEOkCeXhMQHhSeqwqLX0Kj9LvAW3pLECm8Bh08hQ8PCo3widEFT1sYrrXF1D0YURj7zE3vmztgstuwVRZ0zatL6VnrATAGhVy6PXzON3lFWT8IyGFx4iSdECe0tL2nGElwRLoz0Udb3meFwU8xgchhVl2WC0c/AEepzq9Ka7QCtdo9fT6BfUzkeZz37pgML45bhD2HnPjL3uO4dUPG9Hi9uD5nUfw/M4jKM934trRA3Hd2EE4b1C+4N8py3PG7R2Khli1vSrF1VkEkQwo4DEBHYLXJvpJUIvCo5YyEgcMWiuFHEloPKi2faV5Tpxo9+CEuyf4i9D2OkMBj14Pz6aPgtVZl48qR47TJlFctKI34OE4DkXZDhxv96C1Ixjw+AM8Gs8Y14MHkPXh0bhfqkuC7y0OeI6e7sbJDg/sVg61A/MN2TYxFguHATkOnGj34MOjZ3Ci3QObhcN5gwoMfy89sGn2HAdh3le8cByH8wcX4PzBBXjgG+dgx+eteGVPI/72URNa3B48ve1zPL3tcwwvzcHXa4IVU2eVG6/uAGHTstNm0dSZmSD6GxTwmIBwSsuAPjyhGCXA88I8LUDaX0bLWAkgWR4etn3S35fmBgMeNjiQba/dZgE8+jw84uqsq0cH73SjVXyJES/TG/Cw5xxv9wil6S3uHvT6edgsnKZ5SFoQm9e1mpaHFQcVni9awwHP7lCK6dyBBUmr5ikOBTxbPjkeeq/8Pq8ckqcwBTO/w6bZA6UFq4VD3YgS1I0owYPX1eKtT0/iL3uO4fVPWnD4RCcOh5ruJaMkHQiawYHgkMxY6XGC6I+Qh8cEdGis0oq1HAh7OvwBHs0htaQszwVOdKRoVXjEQYLL8D480oinLD/o9/DJIiFmXNYzVueDo204erob2Q4rLhsZvNMVBwZKnXflOGyWuHoPlYa6C7PAjVVoDSzM0hycxCKeTsvVoZRWi9sjBNhCOiuJM61KQj6eLfuDc53G9vH8LDGBgLT7eCL+nVg4bVbUn1uOlTMvwNpbL5QsS4Z/BwAuPbsUf54zOWZvKoLor1DAYwI6NVdpaVF4wqMbmEF1aHG2zLSs7bDpy1lapbK5VWw9tg1aghTGX0PNBi8/p1xZFYvyUkMG5GD04AJcf0F8zdGYitPcFkxjGd2DB4jPtFyY7RAq1ZjK0xeeGqEXT8iblQr/TlkoCF219ZCkIWdfDY+8YGiRxDeXjJJ0IKhkja0qJHWHMC0U8JgAzY0HdZiWAwEeX4YubEMH5Egu3po9PEnstByR0sqTBTxsG1jAozH2kKSzzlc2bkZ7KYfNglfu+hqWfGu0tjeUURFKKzB1zegePID0ONGjGjGV54uTXejp9ePjxmCDuguS2ASwWNaRr68rtADgp9eeC5fdgrc+PYHlDQf6ROER47Jbcb7It5QshYcgzA4FPCaAVWkZUpbOUlo8jy9ECo/4uqhlcCgAOJLReFDkMRJTlqfcqlZQeDRGPLu/OoPGth7kOKyYMrJUcZ1kdrUvFxSeUErL4AotIGhKZd+nHjN3tcjHs6/RjV4/j5JcBwYbVC6vBFN4gODcs6HFxu0HrdQOLMCj1wcD2FX/PISNu44CiByom0wGFob3sVzNJAhCG0kLeHbv3o36+noUFhaiuLgYs2bNQkeHdAgjx3ERjw0bNkjW8Xg8+O///m8MHToUTqcTw4YNw9q1a4Xlv/nNb/D1r38dRUVFKCoqwrRp07Bz585kfay0pCM01ydmSkuDVC0OKL6SpLQSU3iMnpYub8NTmic19LLr+OXnlKGywKW5ioipO/XnlqekrX6loPAEU1lHQz14jAwqOI4TjhU9Co+4NJ31xBlbVZTUuUYlooBn3JDkvlc0rhs7CP/5tWoAwJb9QQN1Xyk8AHD35WfBZbfg/41RH5pJEER0kvIX29jYiGnTpuGmm27CypUr4Xa7cc899+C2227DSy+9JFl33bp1mD59uvBzYWGhZPmNN96IlpYW/Pa3v0VNTQ2ampoQEDVh2bp1K7773e+irq4OLpcLjz76KK644grs27cPgwYNSsbHSzs6NSo82kZLhCYm+wJCWmVocY60LD0eD4/RfXhUTMtyFkwfhflXjtR0kQgEePxNaDY4UH0b4urbrA25wnM0CQoPEDxW2nt8mvvwAOGA54uTnegOlekn21NTIlIzkmmO1sLCq0bh4yY33jnUCqBvA56asly8+8DlmtLSBEEok5S/nk2bNsFut2PVqlWwhCSD1atXY/To0Th48CBqamqEdQsLC1FRoTyzZfPmzXjzzTdx+PBhDBgwAAAwbNgwyTrPPvus5Oenn34aGzduxJYtW/C9733PwE+VvoRHS0QPKgYVZiHfZRO8GEpYLVKPTJ7LhqJsu2Sic1yNBw0vS5cGPEXZDli48HaLgxKtd8S7vgpOic5z2nDJ2epTopN5g808PK2dHnR5fWgKBZ1GengA4NoxA7HlkxaMrNDeP4eltD4/2Sn0BkqmfwcId1sGgLEpHilhs1qw4rvj8P9Wvo1jZ7qF6fZ9RWEfvx9BmI2kpLQ8Hg8cDocQ7ABAVlZQkt+2bZtk3Tlz5qCkpAQTJ07E2rVrJeXGr7zyCiZMmIDHHnsMgwYNwtlnn4158+ahu7tb9b27urrQ29srBEhmp9cfEObvxPTwOG341/1T8cc7L1ZdR34xH1qcDY7jJF4PrakescKTbNOy1cJJLo7xBCVCOqu2PGraLpkJhQHZDtitHHge2PPVGfB8sCpOnNoxgv/6xjnYMneKroqcYaHmg62dXjS29cDCAaMHJ7cJIDOjcxwwenBhUt9LC8W5Tqz/wUTcVjcM35k4JNWbQxCEDpKi8EydOhX33Xcfli1bhp/85Cfo7OzEwoULAQBNTU3Ceg899BCmTp2K7OxsvPbaa5g9ezY6Ojpw9913AwAOHz6Mbdu2weVy4eWXX8bJkycxe/ZstLa2Yt26dYrvff/992PgwIGYNm1a1G30eDzweDzCz263O9GPnRLEwxy19NkpiGG0lJcpDw3d1Us7LWs1LSfDwxP8V2mWVlmo23I8+APh2VnXjI7eVj+ZHgqLhUN5vgtHT3fj318EfTKDi7LTwreR57KjJNeJkx3BfTyyIl/TMZcIAwtcmHXJcJTkOmIeu31FTVkufvb/alO9GQRB6ESXwrNw4UJFo7H4sX//ftTW1mL9+vV4/PHHkZ2djYqKClRXV6O8vFyi+ixatAiTJ0/GuHHjcP/992PBggVYtmyZsDwQCIDjODz77LOYOHEivvGNb2D58uVYv369osqzdOlSbNiwAS+//DJcruhdaZcsWYKCggLhUVVVpWdXpA2sRNZps0gUlXiRm1iHhrwj8uGhWhCnnYz38EQuE5em6w0Pmt09ONHuQb7Lhq/VKFdnxfvaemG9eP79xSkAxg0NNQI2YgLom544HMfhv75xDmZdMiLp70UQhLnRdYWcO3cuPvnkk6iP4cOHAwBmzpyJ5uZmHDt2DK2trfjZz36GEydOCMuVmDRpEo4ePSooL5WVlRg0aBAKCsKy+TnnnAOe53H06FHJc3/5y19i6dKleO211zB6dOweKA888ADa2tqEx5EjR/TsirSh06utB49W5EoCKwOOZ3iouOuxS+NzYqE2SwuQlabHqYhcWVsh8R6lgvKQj4eNbjDasJwIbMQEkHoTMUEQhB50XSVLS0tRWhr97ldOeXk5AGDt2rVwuVyor69XXXfPnj0oKiqC0xm8cE2ePBkvvvgiOjo6kJsbbLb16aefwmKxYPDgwcLzHnvsMfziF7/AP/7xD0yYMEHTdjmdTuF9+jNauyxrJULhUUxpaVR4RAGPzQD1CVCfpQVENh+Mh2vGqFdnCSRZ4qkMKTxd3mAllNGG5UQYJjK8J2NCOkEQRLJIWgJ+5cqVqKurQ25uLhoaGjB//nwsXbpUKDt/9dVX0dLSgosuuggulwsNDQ145JFHMG/ePOE1Zs6ciYcffhjf//738eCDD+LkyZOYP38+fvCDHwgm6EcffRQ//elP8dxzz2HYsGFobm4GAOTm5gpBkplhTQcNC3hUFB5OYlrWr/AYhdosLUDakC2emKQw2466EcUx10t6SqtAmo41cqxEorAKv3yXDcOjVPsRBEGkG0kLeHbu3InFixejo6MDo0aNwpo1a3DLLbcIy1nZ+r333gue51FTU4Ply5fjjjvuENZhwdKPf/xjTJgwAcXFxbjxxhvx85//XFjnySefhNfrxbe//W3J+y9evBg/+9nPkvXx0gatk9K1Ijcnl4sa+nFcsOGfZoVHIShJFLVZWgBQluA08em1FYb4oBKlXPY5BqeRwvP1s0pw0fABmDKyzNBJ4QRBEMkmaQHPM888E3X59OnTJQ0H1Rg1ahQaGhpUl3/xxRd6N81UdGico6UVcUpryIBsyUWNA8BDu4fnurGDsOKNg7j0bH1p0GiEZ31FLpOYluO4Fl8TpdmgZBuSfKGPVHjSJ+DJc9mxYZZ6WwOCIIh0hdp29nM6eoz18IjNyfK5RVxI4tFapVWQZce7D1yua3yB1u1TUnikKS1t78k615bkOnDRcG29m/qqSgsINn5Ml3JsgiCI/gwFPP2cM11eAMYpPOLYZGix1KPBFmlVeAB9s5q0EO7DA3za0i6k9LLs1rha/Y8ozcEvbxiDmrJcw4zViSJOaaWTYZkgCKI/QwFPP8bnD+DPexoBAOcNMqbjbbCrcrAKSq7wBNUVXvMsrWTAzNN+nsfPXtmHAA9ccW65kAbKcVjR6fVrTmlxHIdvjx8ce0WFbUgWDpsFxTkOtHZ608qwTBAE0Z9Jj1vaDGV/sxu3/HYHNuz8SlLCrZXN+5rx1akuFGXbcf0F+i7a0WBpI7nCwySeVEwRZzCF5/CJDrxzqBVOmwWLrjlXWM6My8kMSfrCqssCOFJ4CIIgjIECnhSy/p0v8K/PTmLhnz7Cd37zLg6d6ND8XJ7nsebNwwCA7108zLBOxkC4K7K87DielJbRWGTDTX80ZYTE1Cv28RgN89JM0uj1SYTBoe7Kw6j0myAIwhAopZVCth9qBRBULXZ+fgpXPfEv3DW1Bj+8dETMbr/bD7fio2NtcNos+N7FQw3driXfOh+tHd6I6iCm/KSDwgMEg4IfXiodOSAeNmk0r9w1GS+/fwy31Q0z/sVl3DPtbAwvzcV1Y7VVjhEEQRDRIYUnRTSe6cYXrV2wcMBf7/46Lj27FF5/AMsbPsU1K/6FXV+ejvr8p94Kqjs3TqiSTAk3gmtGD8StChd15t1J9sDIaIj9Mz+95tyI4OtrZ5XAYbXg/CRM1h5anIN7pp2NwmxjJ5crcU5lPu6fPgp5LqrQIgiCMAJSeFIEU3fOH1SAcyrz8bvvX4hXPmjEQ69+jE9bOvDt1e/gPyYNxYLpIyMuevub3dh64AQsHPCfX6/us23+2bW1OHamG4MKU2ekHVmeh7PKcnHBkCLUn1sesfy7E4fg+gsGp3weFkEQBJFeUMCTIrYfDgY8F48oARBULq4bOwiXnFWKX/ztE7y06yh+/+6XaPi4BQ9dV4sraiuE5zJ156rzKiONxUlkxrhBffZeauQ4bWi479Ko61CwQxAEQcihK0MK4HleUHguls1uKspx4Jc3jMFz/zkJQ4uz0ezuwazf78KP/rALLe4eNJ7pxiuhUvRZl6hPnicIgiAIIgwpPCngyKluHDvTDZuFw4ShyhOn62pK8I97LsGvt3yGp946jL/vbca2gydxbmU+fAEeFw0fgDFVhX274QRBEATRTyGFJwVsP3wSADCmqjCqAdhlt2LB9FF49cdfw5jBBWjv8WHH56cAAHfKqpMIgiAIglCHAp4+5h/7mvGLv34CAJgsS2epcU5lPv40ezJ+es25yHXacPHwYkwxcCAnQRAEQZgdSmn1ER6fH0v+th+/e+cLAEF15/uTtVdYWS0cfvC1atxWNwwBnk/6eAOCIAiCMBMU8PQBX5zsxF3P78beY24AQbPxvCtGxlVNZLFwsPTJcAOCIAiCMA8U8CSZVz5oxH/96SN0eHwoyrbj8RvHYOqoyP4xBEEQBEEkDwp4kkhzWw/mv/gBPL4AJlYPwP9+ZywqC2j6NUEQBEH0NRTwJJGKAhd+9v9q0dTWg7un1sBmJY84QRAEQaQCCniSzHcnDkn1JhAEQRBExkOSA0EQBEEQpocCHoIgCIIgTA8FPARBEARBmB4KeAiCIAiCMD0U8BAEQRAEYXoo4CEIgiAIwvRQwEMQBEEQhOmhgIcgCIIgCNNDAQ9BEARBEKaHAh6CIAiCIEwPBTwEQRAEQZgeCngIgiAIgjA9FPAQBEEQBGF6aFp6CJ7nAQButzvFW0IQBEEQhFbYdZtdx9WggCdEe3s7AKCqqirFW0IQBEEQhF7a29tRUFCgupzjY4VEGUIgEEBjYyPy8vLAcZziOm63G1VVVThy5Ajy8/P7eAszF9rvqYH2e2qg/Z4aaL+nBiP2O8/zaG9vx8CBA2GxqDt1SOEJYbFYMHjwYE3r5ufn0x9ECqD9nhpov6cG2u+pgfZ7akh0v0dTdhhkWiYIgiAIwvRQwEMQBEEQhOmhgEcHTqcTixcvhtPpTPWmZBS031MD7ffUQPs9NdB+Tw19ud/JtEwQBEEQhOkhhYcgCIIgCNNDAQ9BEARBEKaHAh6CIAiCIEwPBTwEQRAEQZgeCnh0sGrVKgwbNgwulwuTJk3Czp07U71JpuZnP/sZOI6TPEaNGpXqzTIdb731Fq699loMHDgQHMfhz3/+s2Q5z/P46U9/isrKSmRlZWHatGn47LPPUrOxJiLWfr/tttsijv/p06enZmNNwpIlS3DhhRciLy8PZWVlmDFjBg4cOCBZp6enB3PmzEFxcTFyc3Nx/fXXo6WlJUVbbA607PcpU6ZEHO8//OEPDd0OCng08sc//hH33XcfFi9ejN27d2PMmDG48sorcfz48VRvmqmpra1FU1OT8Ni2bVuqN8l0dHZ2YsyYMVi1apXi8sceewy//vWvsXr1auzYsQM5OTm48sor0dPT08dbai5i7XcAmD59uuT4f/755/twC83Hm2++iTlz5uDdd99FQ0MDent7ccUVV6Czs1NY595778Wrr76KF198EW+++SYaGxvxrW99K4Vb3f/Rst8B4I477pAc74899pixG8ITmpg4cSI/Z84c4We/388PHDiQX7JkSQq3ytwsXryYHzNmTKo3I6MAwL/88svCz4FAgK+oqOCXLVsm/O7MmTO80+nkn3/++RRsoTmR73ee5/lbb72Vv+6661KyPZnC8ePHeQD8m2++yfN88Ni22+38iy++KKzzySef8AD47du3p2ozTYd8v/M8z1966aX8T37yk6S+Lyk8GvB6vdi1axemTZsm/M5isWDatGnYvn17CrfM/Hz22WcYOHAghg8fjptvvhlfffVVqjcpo/j888/R3NwsOfYLCgowadIkOvb7gK1bt6KsrAwjR47Ej370I7S2tqZ6k0xFW1sbAGDAgAEAgF27dqG3t1dyvI8aNQpDhgyh491A5Pud8eyzz6KkpATnnXceHnjgAXR1dRn6vjQ8VAMnT56E3+9HeXm55Pfl5eXYv39/irbK/EyaNAm/+93vMHLkSDQ1NeHBBx/E17/+dezduxd5eXmp3ryMoLm5GQAUj322jEgO06dPx7e+9S1UV1fj0KFD+K//+i9cddVV2L59O6xWa6o3r98TCARwzz33YPLkyTjvvPMABI93h8OBwsJCybp0vBuH0n4HgJkzZ2Lo0KEYOHAgPvzwQ9x///04cOAA/vSnPxn23hTwEGnLVVddJfx/9OjRmDRpEoYOHYoXXngBt99+ewq3jCCSz3e+8x3h/+effz5Gjx6NESNGYOvWrbj88stTuGXmYM6cOdi7dy/5AvsYtf0+a9Ys4f/nn38+Kisrcfnll+PQoUMYMWKEIe9NKS0NlJSUwGq1Rjj1W1paUFFRkaKtyjwKCwtx9tln4+DBg6nelIyBHd907Kee4cOHo6SkhI5/A7jrrruwadMm/POf/8TgwYOF31dUVMDr9eLMmTOS9el4Nwa1/a7EpEmTAMDQ450CHg04HA6MHz8eW7ZsEX4XCASwZcsWXHzxxSncssyio6MDhw4dQmVlZao3JWOorq5GRUWF5Nh3u93YsWMHHft9zNGjR9Ha2krHfwLwPI+77roLL7/8Mt544w1UV1dLlo8fPx52u11yvB84cABfffUVHe8JEGu/K7Fnzx4AMPR4p5SWRu677z7ceuutmDBhAiZOnIgnnngCnZ2d+P73v5/qTTMt8+bNw7XXXouhQ4eisbERixcvhtVqxXe/+91Ub5qp6OjokNxFff7559izZw8GDBiAIUOG4J577sHPf/5znHXWWaiursaiRYswcOBAzJgxI3UbbQKi7fcBAwbgwQcfxPXXX4+KigocOnQICxYsQE1NDa688soUbnX/Zs6cOXjuuefwl7/8BXl5eYIvp6CgAFlZWSgoKMDtt9+O++67DwMGDEB+fj5+/OMf4+KLL8ZFF12U4q3vv8Ta74cOHcJzzz2Hb3zjGyguLsaHH36Ie++9F5dccglGjx5t3IYktQbMZKxYsYIfMmQI73A4+IkTJ/LvvvtuqjfJ1Nx00018ZWUl73A4+EGDBvE33XQTf/DgwVRvlun45z//yQOIeNx66608zwdL0xctWsSXl5fzTqeTv/zyy/kDBw6kdqNNQLT93tXVxV9xxRV8aWkpb7fb+aFDh/J33HEH39zcnOrN7tco7W8A/Lp164R1uru7+dmzZ/NFRUV8dnY2/81vfpNvampK3UabgFj7/auvvuIvueQSfsCAAbzT6eRramr4+fPn821tbYZuBxfaGIIgCIIgCNNCHh6CIAiCIEwPBTwEQRAEQZgeCngIgiAIgjA9FPAQBEEQBGF6KOAhCIIgCML0UMBDEARBEITpoYCHIAiCIAjTQwEPQRAEQRCmhwIegiAIgiBMDwU8BEEQBEGYHgp4CIIgCIIwPRTwEARBEARhev4/yVHO7Yhy8eEAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(dd,ee)" + "import wntr\n", + "import wntr_quantum\n", + "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", + "from wntr_quantum.sampler.step.full_random import IncrementalStep\n", + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "\n", + "\n", + "# set up the model\n", + "inp_file = './networks/Net0.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# create the AML model\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "\n", + "# sampler \n", + "sampler = SimulatedAnnealing()\n", + "\n", + "# create the qubo solver\n", + "sim = wntr_quantum.sim.QuboPolynomialSolver(wn, flow_encoding=..., head_encoding=...)\n", + "res = sim.run_sim(model, sampler=sampler)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/notebooks/qubo_poly_solver_Net0.ipynb b/docs/notebooks/qubo_poly_solver_Net0.ipynb index 6330ca5..3c1062c 100644 --- a/docs/notebooks/qubo_poly_solver_Net0.ipynb +++ b/docs/notebooks/qubo_poly_solver_Net0.ipynb @@ -298,19 +298,15 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from wntr_quantum.sampler.step.full_random import RandomStep\n", "from wntr_quantum.sampler.step.full_random import IncrementalStep\n", - "# from wntr_quantum.sampler.step.full_random import ParallelIncrementalStep \n", "\n", "var_names = sorted(net.qubo.qubo_dict.variables)\n", "net.qubo.create_variables_mapping()\n", - "# mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", - "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)\n", - "# mystep = ParallelIncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=100)" + "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)" ] }, { @@ -320,17 +316,6 @@ "# generate init sample" ] }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [], - "source": [ - "# from wntr_quantum.sampler.simulated_annealing import generate_random_valid_sample\n", - "# x = generate_random_valid_sample(net.qubo)\n", - "# x0 = list(x.values())" - ] - }, { "cell_type": "code", "execution_count": 119, diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index da29c9d..09df3b2 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -440,7 +440,7 @@ def create_index_mapping(self, model: Model) -> None: self.head_index_mapping[val.name] = 2 * num_flow_var + idx idx += 1 - def solve( # noqa: D417 + def run_sim( # noqa: D417 self, model: Model, strength: float = 1e6, From 77f80cdb9bcf61a3a8bb68f0ac9ae353976d1234 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 6 Nov 2024 13:25:53 +0100 Subject: [PATCH 79/96] started refactor design --- .../design_pipe_diameter_own_sampler.ipynb | 598 ++++++++++++++++++ docs/notebooks/qubo_poly_solver_Net0.ipynb | 7 +- docs/notebooks/qubols_solver.ipynb | 2 +- wntr_quantum/design/qubo_pipe_diam.py | 52 +- wntr_quantum/sampler/step/full_random.py | 92 +++ .../sim/solvers/qubo_polynomial_solver.py | 6 +- 6 files changed, 738 insertions(+), 19 deletions(-) create mode 100644 docs/notebooks/design_pipe_diameter_own_sampler.ipynb diff --git a/docs/notebooks/design_pipe_diameter_own_sampler.ipynb b/docs/notebooks/design_pipe_diameter_own_sampler.ipynb new file mode 100644 index 0000000..263cba3 --- /dev/null +++ b/docs/notebooks/design_pipe_diameter_own_sampler.ipynb @@ -0,0 +1,598 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "\n", + "# Create a water network model\n", + "inp_file = './networks/Net0_CM.inp'\n", + "# inp_file = './networks/Net2LoopsDW.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0][:2]\n", + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run with QUBO solver" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 7\n", + "step = (2./(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", + "\n", + "nqbit = 9\n", + "step = (50/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+75.0, var_base_name=\"x\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", + "pipe_diameters = [250, 500, 1000]\n", + "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters, head_lower_bound=80)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 75.000000 => 125.000000 (res: 0.097847)\n", + "Flow Encoding : -2.000000 => -0.000000 | 0.000000 => 2.000000 (res: 0.015748)\n" + ] + } + ], + "source": [ + "designer.verify_encoding()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "(array([ 1.766, 1.766, 97.666, 96.906]),\n", + " array([ 1.764, 1.764, 97.701, 96.918]),\n", + " [1,\n", + " 1,\n", + " [0, 0, 0, 0, 1, 1, 1],\n", + " [0, 0, 0, 0, 1, 1, 1],\n", + " [0, 0, 0, 1, 0, 1, 1, 1, 0],\n", + " [0, 0, 0, 0, 0, 1, 1, 1, 0]],\n", + " True)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.create_index_mapping()\n", + "designer.matrices = designer.initialize_matrices()\n", + "designer.classical_solution([0,1,0,0,1,0], convert_to_si=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "price \t diameters \t variables\n", + "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329]\n", + "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118]\n", + "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858]\n", + "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118]\n", + "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906]\n", + "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647]\n", + "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858]\n", + "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647]\n", + "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387]\n" + ] + } + ], + "source": [ + "designer.enumerates_classical_solutions(convert_to_si=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.766, 1.766, 98.406, 98.387], dtype=float32)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.convert_solution_from_si(ref_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", + "sampler = SimulatedAnnealing()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", + "import sparse\n", + "\n", + "designer.qubo = QUBOPS_MIXED(designer.mixed_solution_vector, {\"sampler\": sampler})\n", + "matrices = tuple(sparse.COO(m) for m in designer.matrices)\n", + "designer.qubo.qubo_dict = designer.qubo.create_bqm(matrices, strength=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:759: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", + " warnings.warn(\"For constraints with fractional coefficients, \"\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdwave\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msamplers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SimulatedAnnealingSampler\n\u001b[1;32m 2\u001b[0m options \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msampler\u001b[39m\u001b[38;5;124m'\u001b[39m: SimulatedAnnealingSampler()}\n\u001b[0;32m----> 3\u001b[0m status \u001b[38;5;241m=\u001b[39m \u001b[43mdesigner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstrength\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1E8\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_reads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/design/qubo_pipe_diam.py:703\u001b[0m, in \u001b[0;36mQUBODesignPipeDiameter.solve\u001b[0;34m(self, strength, num_reads, **options)\u001b[0m\n\u001b[1;32m 694\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqubo\u001b[38;5;241m.\u001b[39mqubo_dict\u001b[38;5;241m.\u001b[39madd_linear_inequality_constraint(\n\u001b[1;32m 695\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqubo\u001b[38;5;241m.\u001b[39mall_expr[istart \u001b[38;5;241m+\u001b[39m i],\n\u001b[1;32m 696\u001b[0m lagrange_multiplier\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 699\u001b[0m ub\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhead_upper_bound,\n\u001b[1;32m 700\u001b[0m )\n\u001b[1;32m 702\u001b[0m \u001b[38;5;66;03m# sample\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampleset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample_bqm\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_reads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_reads\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;66;03m# decode\u001b[39;00m\n\u001b[1;32m 706\u001b[0m sol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqubo\u001b[38;5;241m.\u001b[39mdecode_solution(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampleset\u001b[38;5;241m.\u001b[39mlowest()\u001b[38;5;241m.\u001b[39mrecord[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m])\n", + "File \u001b[0;32m~/QuantumApplicationLab/qubops/qubops/qubops_mixed_vars.py:63\u001b[0m, in \u001b[0;36mQUBOPS_MIXED.sample_bqm\u001b[0;34m(self, bqm, **sampler_options)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Sample the bqm\"\"\"\u001b[39;00m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcreate_variables_mapping()\n\u001b[0;32m---> 63\u001b[0m sampleset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbqm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msampler_options\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;66;03m# self.create_variables_mapping(sampleset)\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sampleset\n", + "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dwave/samplers/sa/sampler.py:409\u001b[0m, in \u001b[0;36mSimulatedAnnealingSampler.sample\u001b[0;34m(self, bqm, beta_range, num_reads, num_sweeps, num_sweeps_per_beta, beta_schedule_type, seed, interrupt_function, beta_schedule, initial_states, initial_states_generator, randomize_order, proposal_acceptance_criteria, **kwargs)\u001b[0m\n\u001b[1;32m 406\u001b[0m timestamp_sample \u001b[38;5;241m=\u001b[39m perf_counter_ns()\n\u001b[1;32m 408\u001b[0m \u001b[38;5;66;03m# run the simulated annealing algorithm\u001b[39;00m\n\u001b[0;32m--> 409\u001b[0m samples, energies \u001b[38;5;241m=\u001b[39m \u001b[43msimulated_annealing\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 410\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_reads\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mldata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mirow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43micol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mqdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 411\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_sweeps_per_beta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbeta_schedule\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 412\u001b[0m \u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial_states_array\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 413\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandomize_order\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproposal_acceptance_criteria\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[43m \u001b[49m\u001b[43minterrupt_function\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 415\u001b[0m timestamp_postprocess \u001b[38;5;241m=\u001b[39m perf_counter_ns()\n\u001b[1;32m 417\u001b[0m info \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 418\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbeta_range\u001b[39m\u001b[38;5;124m\"\u001b[39m: beta_range,\n\u001b[1;32m 419\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbeta_schedule_type\u001b[39m\u001b[38;5;124m\"\u001b[39m: beta_schedule_type\n\u001b[1;32m 420\u001b[0m }\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "from dwave.samplers import SimulatedAnnealingSampler\n", + "options = {'sampler': SimulatedAnnealingSampler()}\n", + "status = designer.solve(strength=1E8, num_reads=5000, options=options)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6763164377806783" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.total_pice" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1000., 1000.])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.optimal_diameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "438" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.qubo.qubo_dict.num_variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1.420e+02, 6.990e+02, 1.333e+03, 1.408e+03, 9.230e+02, 3.790e+02, 9.000e+01, 2.300e+01, 2.000e+00, 1.000e+00]),\n", + " array([1.00e+08, 3.10e+08, 5.20e+08, 7.30e+08, 9.40e+08, 1.15e+09, 1.36e+09, 1.57e+09, 1.78e+09, 1.99e+09, 2.20e+09]),\n", + " )" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(designer.sampleset.data_vectors['energy'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = designer.sampleset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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"ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/qubo_poly_solver_Net0.ipynb b/docs/notebooks/qubo_poly_solver_Net0.ipynb index 3c1062c..2d7d26a 100644 --- a/docs/notebooks/qubo_poly_solver_Net0.ipynb +++ b/docs/notebooks/qubo_poly_solver_Net0.ipynb @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -205,7 +205,7 @@ "net.create_index_mapping(model)\n", "net.matrices = net.initialize_matrices(model)\n", "\n", - "ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = net.classical_solutions()\n", + "ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = net.classical_solution()\n", "ref_sol / ref_values" ] }, @@ -274,12 +274,11 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", - "# from wntr_quantum.sampler.simulated_annealing_parallel import SimulatedAnnealing\n", "sampler = SimulatedAnnealing()" ] }, diff --git a/docs/notebooks/qubols_solver.ipynb b/docs/notebooks/qubols_solver.ipynb index 28a818f..85e36bb 100644 --- a/docs/notebooks/qubols_solver.ipynb +++ b/docs/notebooks/qubols_solver.ipynb @@ -318,7 +318,7 @@ ], "metadata": { "kernelspec": { - "display_name": "vitens", + "display_name": "vitens_wntr_1", "language": "python", "name": "python3" }, diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index 1001fd6..db49472 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -299,7 +299,7 @@ def enumerates_classical_solutions(self, convert_to_si=True): for p in params: pvalues += p price, diameters = self.get_pipe_info_from_hot_encoding(pvalues) - sol = self.compute_classical_solution(pvalues, convert_to_si=convert_to_si) + sol, _, _, _ = self.classical_solution(pvalues, convert_to_si=convert_to_si) print(price, diameters, sol) def convert_solution_to_si(self, solution: np.ndarray) -> np.ndarray: @@ -338,11 +338,15 @@ def convert_solution_from_si(self, solution: np.ndarray) -> np.ndarray: new_sol[ih] = from_si(FlowUnits.CFS, solution[ih], HydParam.Length) return new_sol - def compute_classical_solution(self, parameters, convert_to_si=True): + def classical_solution( + self, parameters, max_iter: int = 100, tol: float = 1e-10, convert_to_si=True + ): """Computes the classical solution for a values of the hot encoding parameters. Args: parameters (List): list of the one hot encoding values e.g. [1,0,1,0] + max_iter (int, optional): number of iterations of the NR. Defaults to 100. + tol (float, optional): Toleracne of the NR. Defaults to 1e-10. convert_to_si (bool): convert to si Returns: @@ -407,11 +411,39 @@ def func(input): return sol.reshape(-1) initial_point = np.random.rand(num_pipes + num_heads) - res = newton_raphson(func, initial_point) - assert np.allclose(func(res.solution), 0) + res = newton_raphson(func, initial_point, max_iter=max_iter, tol=tol) + sol = res.solution + converged = np.allclose(func(sol), 0) + if not converged: + print("Warning solution not converged") + + # get the closest encoded solution and binary encoding + bin_rep_sol = [] + for i in range(num_pipes): + bin_rep_sol.append(int(sol[i] > 0)) + + encoded_sol = np.zeros_like(sol) + for idx, s in enumerate(sol): + val, bin_rpr = self.mixed_solution_vector.encoded_reals[ + idx + num_pipes + ].find_closest(np.abs(s)) + bin_rep_sol.append(bin_rpr) + encoded_sol[idx] = np.sign(s) * val + if convert_to_si: - return self.convert_solution_to_si(res.solution) - return res.solution + sol = self.convert_solution_to_si(sol) + encoded_sol = self.convert_solution_to_si(encoded_sol) + + # remove the height of the junctions + for i in range(self.wn.num_junctions): + sol[num_pipes + i] -= self.wn.nodes[ + self.wn.junction_name_list[i] + ].elevation + encoded_sol[num_pipes + i] -= self.wn.nodes[ + self.wn.junction_name_list[i] + ].elevation + + return (sol, encoded_sol, bin_rep_sol, converged) def get_cost_matrix(self, matrices): """Add the equation that ar sued to maximize the pipe coefficiens and therefore minimize the diameter. @@ -626,7 +658,7 @@ def solve( # noqa: D417 matrices = tuple(sparse.COO(m) for m in self.matrices) # create the BQM - self.bqm = self.qubo.create_bqm(matrices, strength=strength) + self.qubo.qubo_dict = self.qubo.create_bqm(matrices, strength=strength) # add constraints on the hot encoding # the sum of each hot encoding variable of a given pipe must equals 1 @@ -650,7 +682,7 @@ def solve( # noqa: D417 ) ) # add the constraints - self.bqm.add_linear_equality_constraint( + self.qubo.qubo_dict.add_linear_equality_constraint( expr, lagrange_multiplier=strength, constant=-1 ) istart += self.num_diameters @@ -659,7 +691,7 @@ def solve( # noqa: D417 istart = 2 * self.sol_vect_flows.size for i in range(self.sol_vect_heads.size): - self.bqm.add_linear_inequality_constraint( + self.qubo.qubo_dict.add_linear_inequality_constraint( self.qubo.all_expr[istart + i], lagrange_multiplier=1, label="head_%s" % i, @@ -668,7 +700,7 @@ def solve( # noqa: D417 ) # sample - self.sampleset = self.qubo.sample_bqm(self.bqm, num_reads=num_reads) + self.sampleset = self.qubo.sample_bqm(self.qubo.qubo_dict, num_reads=num_reads) # decode sol = self.qubo.decode_solution(self.sampleset.lowest().record[0][0]) diff --git a/wntr_quantum/sampler/step/full_random.py b/wntr_quantum/sampler/step/full_random.py index 144eba1..c3b13be 100644 --- a/wntr_quantum/sampler/step/full_random.py +++ b/wntr_quantum/sampler/step/full_random.py @@ -88,3 +88,95 @@ def __call__(self, x, verbose=False): self.fix_constraint(x, vidx) return x + + +class SwitchIncrementalStep(BaseStep): # noqa: D101 + + def __init__( # noqa: D417 + self, + var_names, + single_var_names, + single_var_index, + switch_variable_index, + step_size=1, + optimize_values=None, + ): + """Propose a new solution vector. + + Args: + var_names (list): names of the variables in the problem + single_var_names (_type_): list of the single variables names e.g. x_001_002 + single_var_index (_type_): index of the single variables + switch_variable_index (list): index of the variables we are switching over + step_size (int, optional): size of the steps + optimize_values (list, optional): index of the values to optimize + """ + super().__init__( + var_names, single_var_names, single_var_index, step_size, optimize_values + ) + self.switch_variable_index = switch_variable_index + + def __call__(self, x, verbose=False): + """Call function of the method. + + Args: + x (list): initial sample + verbose (bool): print stuff + + Returns: + list: proposed sample + """ + num_var_changed = np.random.randint(len(self.optimize_values)) + random_val_name_list = np.random.choice( + self.value_names[self.optimize_values], size=num_var_changed + ) + + for random_val_name in random_val_name_list: + idx = self.index_values[random_val_name] + data = np.array(x)[idx] + width = len(data) + + # determine the max val + max_val = int("1" * width, base=2) + + # check if we reach min/max val + max_val_check = data.prod() == 1 + min_val_check = data.sum() == 0 + + # convert to int value + val = int("".join([str(i) for i in data[::-1]]), base=2) + + # determine sign of the displacement + if min_val_check: + sign = 1 + elif max_val_check: + sign = -1 + else: + sign = 2 * np.random.randint(2) - 1 + + # new value + if self.step_size <= 1: + delta = 1 + else: + delta = np.random.randint(self.step_size) + new_val = val + sign * delta + if new_val < 0: + new_val = 0 + if new_val > max_val: + new_val = max_val + new_val = np.binary_repr(new_val, width=width) + + # convert back to binary repr + new_data = np.array([int(i) for i in new_val])[::-1] + if verbose: + print(random_val_name, data, "=>", new_data) + + # inject in the x vector + for ix, nd in zip(idx, new_data): + x[ix] = nd + + # fix constraints + for vidx in idx: + self.fix_constraint(x, vidx) + + return x diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 09df3b2..01da4b6 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -113,9 +113,7 @@ def verify_solution(self, input: np.ndarray) -> np.ndarray: sign = np.sign(input) return p0 + p1 @ input + (p2 @ (sign * input * input)) - def classical_solutions( - self, max_iter: int = 100, tol: float = 1e-10 - ) -> np.ndarray: + def classical_solution(self, max_iter: int = 100, tol: float = 1e-10) -> np.ndarray: """Computes the solution using a classical Newton Raphson approach. Args: @@ -440,7 +438,7 @@ def create_index_mapping(self, model: Model) -> None: self.head_index_mapping[val.name] = 2 * num_flow_var + idx idx += 1 - def run_sim( # noqa: D417 + def solve( # noqa: D417 self, model: Model, strength: float = 1e6, From 3816daa8bfa26a9b12d861df0a6a8aaa36e7078c Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 8 Nov 2024 16:23:09 +0100 Subject: [PATCH 80/96] fix sampler for design --- .../design_pipe_diameter_own_sampler.ipynb | 439 ++++++++---------- docs/notebooks/hhl_Net0.ipynb | 2 +- wntr_quantum/design/qubo_pipe_diam.py | 99 ++-- wntr_quantum/sampler/step/full_random.py | 134 ++++-- 4 files changed, 351 insertions(+), 323 deletions(-) diff --git a/docs/notebooks/design_pipe_diameter_own_sampler.ipynb b/docs/notebooks/design_pipe_diameter_own_sampler.ipynb index 263cba3..ccf76cd 100644 --- a/docs/notebooks/design_pipe_diameter_own_sampler.ipynb +++ b/docs/notebooks/design_pipe_diameter_own_sampler.ipynb @@ -132,7 +132,9 @@ "source": [ "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", "pipe_diameters = [250, 500, 1000]\n", - "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters, head_lower_bound=80)" + "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, \n", + " pipe_diameters, head_lower_bound=80,\n", + " weight_cost=1, weight_pressure=1)" ] }, { @@ -165,218 +167,253 @@ "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" ] - }, - { - "data": { - "text/plain": [ - "(array([ 1.766, 1.766, 97.666, 96.906]),\n", - " array([ 1.764, 1.764, 97.701, 96.918]),\n", - " [1,\n", - " 1,\n", - " [0, 0, 0, 0, 1, 1, 1],\n", - " [0, 0, 0, 0, 1, 1, 1],\n", - " [0, 0, 0, 1, 0, 1, 1, 1, 0],\n", - " [0, 0, 0, 0, 0, 1, 1, 1, 0]],\n", - " True)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ "designer.create_index_mapping()\n", "designer.matrices = designer.initialize_matrices()\n", - "designer.classical_solution([0,1,0,0,1,0], convert_to_si=False)" + "ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = designer.classical_solution([0,1,0,0,1,0], convert_to_si=False)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", + "sampler = SimulatedAnnealing()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "price \t diameters \t variables\n", - "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329]\n", - "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118]\n", - "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858]\n", - "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118]\n", - "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906]\n", - "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647]\n", - "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858]\n", - "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647]\n", - "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387]\n" + "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:759: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", + " warnings.warn(\"For constraints with fractional coefficients, \"\n" ] } ], "source": [ - "designer.enumerates_classical_solutions(convert_to_si=False)" + "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", + "import sparse\n", + "\n", + "designer.qubo = QUBOPS_MIXED(designer.mixed_solution_vector, {\"sampler\": sampler})\n", + "matrices = tuple(sparse.COO(m) for m in designer.matrices)\n", + "designer.qubo.qubo_dict = designer.qubo.create_bqm(matrices, strength=0)\n", + "# designer.add_switch_constraints(strength=0)\n", + "designer.add_pressure_constraints()" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.step.full_random import IncrementalStep\n", + "from wntr_quantum.sampler.step.full_random import SwitchIncrementalStep\n", + "\n", + "var_names = sorted(designer.qubo.qubo_dict.variables)\n", + "designer.qubo.create_variables_mapping()\n", + "mystep = SwitchIncrementalStep(var_names, \n", + " designer.qubo.mapped_variables, \n", + " designer.qubo.index_variables, \n", + " step_size=10,\n", + " switch_variable_index=[[6,7,8],[9,10,11]])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([ 1.766, 1.766, 98.406, 98.387], dtype=float32)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "price \t diameters \t variables\t energy\n", + "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329] 31677.014682043187\n", + "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118] 30748.55921834555\n", + "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858] 30748.099719787107\n", + "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118] 25598.905845931073\n", + "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906] 20539.366563299016\n", + "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647] 20398.77576791782\n", + "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858] 25449.16409577259\n", + "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647] 20259.102435574252\n", + "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387] 20099.606199275284\n" + ] } ], "source": [ - "designer.convert_solution_from_si(ref_values)" + "designer.enumerates_classical_solutions(convert_to_si=False)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", - "sampler = SimulatedAnnealing()" + "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", + "x = modify_solution_sample(designer, bin_rep_sol, modify=['flows', 'heads'])\n", + "x0 = list(x.values())" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", - "import sparse\n", - "\n", - "designer.qubo = QUBOPS_MIXED(designer.mixed_solution_vector, {\"sampler\": sampler})\n", - "matrices = tuple(sparse.COO(m) for m in designer.matrices)\n", - "designer.qubo.qubo_dict = designer.qubo.create_bqm(matrices, strength=0)" + "num_sweeps = 2000\n", + "Tinit = 1E1\n", + "Tfinal = 1E-1\n", + "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "Tschedule = np.append(Tschedule, np.zeros(1000))" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:759: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", - " warnings.warn(\"For constraints with fractional coefficients, \"\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdwave\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msamplers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SimulatedAnnealingSampler\n\u001b[1;32m 2\u001b[0m options \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msampler\u001b[39m\u001b[38;5;124m'\u001b[39m: SimulatedAnnealingSampler()}\n\u001b[0;32m----> 3\u001b[0m status \u001b[38;5;241m=\u001b[39m \u001b[43mdesigner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstrength\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1E8\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m 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ub\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhead_upper_bound,\n\u001b[1;32m 700\u001b[0m )\n\u001b[1;32m 702\u001b[0m \u001b[38;5;66;03m# sample\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampleset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample_bqm\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqubo_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_reads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_reads\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;66;03m# decode\u001b[39;00m\n\u001b[1;32m 706\u001b[0m sol \u001b[38;5;241m=\u001b[39m 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\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msampler_options\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;66;03m# self.create_variables_mapping(sampleset)\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sampleset\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dwave/samplers/sa/sampler.py:409\u001b[0m, in \u001b[0;36mSimulatedAnnealingSampler.sample\u001b[0;34m(self, bqm, beta_range, num_reads, num_sweeps, num_sweeps_per_beta, beta_schedule_type, seed, interrupt_function, beta_schedule, initial_states, initial_states_generator, randomize_order, proposal_acceptance_criteria, **kwargs)\u001b[0m\n\u001b[1;32m 406\u001b[0m timestamp_sample \u001b[38;5;241m=\u001b[39m perf_counter_ns()\n\u001b[1;32m 408\u001b[0m \u001b[38;5;66;03m# run the simulated annealing algorithm\u001b[39;00m\n\u001b[0;32m--> 409\u001b[0m samples, energies \u001b[38;5;241m=\u001b[39m 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414\u001b[0m \u001b[43m \u001b[49m\u001b[43minterrupt_function\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 415\u001b[0m timestamp_postprocess \u001b[38;5;241m=\u001b[39m perf_counter_ns()\n\u001b[1;32m 417\u001b[0m info \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 418\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbeta_range\u001b[39m\u001b[38;5;124m\"\u001b[39m: beta_range,\n\u001b[1;32m 419\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbeta_schedule_type\u001b[39m\u001b[38;5;124m\"\u001b[39m: beta_schedule_type\n\u001b[1;32m 420\u001b[0m }\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "100%|██████████| 4000/4000 [00:24<00:00, 166.13it/s]\n" ] } ], "source": [ - "from dwave.samplers import SimulatedAnnealingSampler\n", - "options = {'sampler': SimulatedAnnealingSampler()}\n", - "status = designer.solve(strength=1E8, num_reads=5000, options=options)" + "mystep.optimize_values = np.arange(2,12)\n", + "res = sampler.sample(designer.qubo, init_sample=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.6763164377806783" + "" ] }, - "execution_count": 69, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "designer.total_pice" + "import matplotlib.pyplot as plt\n", + "eplt = res.energies\n", + "\n", + "# fig, ax1 = plt.subplots()\n", + "\n", + "left, bottom, width, height = [0.55, 0.55, 0.3, 0.3]\n", + "\n", + "plt.plot(res.energies[:], lw=4, label=\"QUBO Energy\")\n", + "plt.plot(Tschedule, lw=3, label='Temperature')\n", + "# ax1.axline((0, 0), slope=0, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", + "plt.grid(which='both')\n", + "# plt.yscale('symlog')\n", + "\n", + "plt.ylabel('Energy', fontsize=14)\n", + "plt.xlabel('Iterations', fontsize=14)\n", + "plt.legend(fontsize=12)\n", + "\n", + "# ax2 = fig.add_axes([left, bottom, width, height])\n", + "# ax2.plot(eplt[-1000:])\n", + "# ax2.grid()\n", + "# ax2.axline((0, 0), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "# ax2.set_yscale('symlog')\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([1000., 1000.])" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.631e-02 3.478e-02 3.810e+01 3.810e+01]\n" + ] } ], "source": [ - "designer.optimal_diameters" + "idx_min = np.array([e for e in res.energies]).argmin()\n", + "# idx_min = -1\n", + "sol = res.trajectory[idx_min]\n", + "sol = designer.qubo.decode_solution(np.array(sol))\n", + "pipe_hot_encoding = sol[3]\n", + "sol = designer.combine_flow_values(sol)\n", + "sol = designer.convert_solution_to_si(sol)\n", + "sol = sol[:4]\n", + "print(sol)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "438" + "(0.5072373283355087, array([1000., 500.]))" ] }, - "execution_count": 71, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "designer.qubo.qubo_dict.num_variables" + "designer.get_pipe_info_from_hot_encoding(pipe_hot_encoding)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([1.420e+02, 6.990e+02, 1.333e+03, 1.408e+03, 9.230e+02, 3.790e+02, 9.000e+01, 2.300e+01, 2.000e+00, 1.000e+00]),\n", - " array([1.00e+08, 3.10e+08, 5.20e+08, 7.30e+08, 9.40e+08, 1.15e+09, 1.36e+09, 1.57e+09, 1.78e+09, 1.99e+09, 2.20e+09]),\n", - " )" + "Text(0.5, 1.0, 'Pressure')" ] }, - "execution_count": 72, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -384,194 +421,94 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", - "plt.hist(designer.sampleset.data_vectors['energy'])" + "import matplotlib.pyplot as plt \n", + "\n", + "fig = plt.figure(figsize = plt.figaspect(0.5))\n", + "ax1 = fig.add_subplot(121)\n", + "\n", + "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.grid()\n", + "\n", + "# ax1.scatter(ref_values[:2], encoded_ref_sol[:2], c='black', s=200, label='Best solution')\n", + "ax1.scatter(ref_values[:2], sol[:2], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "\n", + "\n", + "ax1.set_xlabel('Reference Values', fontsize=12)\n", + "ax1.set_ylabel('QUBO Values', fontsize=12)\n", + "ax1.set_title('Flow Rate', fontsize=14)\n", + "\n", + "ax2 = fig.add_subplot(122)\n", + "\n", + "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "\n", + "\n", + "# ax2.scatter(ref_values[2:], encoded_ref_sol[2:], c='black', s=200, label='Best solution')\n", + "ax2.scatter(ref_values[2:], sol[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "ax2.grid()\n", + "\n", + "\n", + "ax2.set_xlabel('Reference Values', fontsize=12)\n", + "ax2.set_title('Pressure', fontsize=14)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "x = designer.sampleset" + "# Old sampler" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], 1.2e+09, 1)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "x.record[1]" + "# from dwave.samplers import SimulatedAnnealingSampler\n", + "# options = {'sampler': SimulatedAnnealingSampler()}\n", + "# status = designer.solve(strength=1E8, num_reads=5000, options=options)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 1, 1, 1, 1, 1]\n", - "[1, 0, 1, 1, 0, 1]\n", - "[1, 1, 0, 1, 1, 0]\n", - "[1, 1, 0, 0, 1, 1]\n", - "[1, 0, 1, 0, 1, 0]\n", - "[1, 1, 0, 1, 0, 0]\n", - "[0, 1, 1, 0, 0, 1]\n", - "[1, 1, 0, 0, 0, 1]\n", - "[1, 1, 0, 0, 0, 1]\n", - "[0, 0, 1, 1, 1, 0]\n", - "[0, 1, 0, 1, 0, 0]\n", - "[0, 1, 0, 1, 0, 0]\n", - "[1, 1, 0, 0, 1, 1]\n", - "[0, 1, 1, 0, 1, 1]\n", - "[1, 1, 1, 0, 1, 1]\n", - "[0, 1, 1, 0, 1, 0]\n", - "[1, 1, 0, 1, 1, 0]\n", - "[1, 1, 0, 1, 0, 0]\n", - "[1, 0, 0, 0, 1, 0]\n", - 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"[1, 1, 0, 1, 0, 0]\n", - "[1, 1, 0, 1, 1, 0]\n", - "[0, 1, 1, 1, 0, 0]\n" - ] - } - ], + "outputs": [], "source": [ - "for i in range(100):\n", - " s = designer.qubo.decode_solution(x.record[i][0])\n", - " print(s[3])" + "# designer.total_pice" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0,\n", - " 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n", - " 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1], dtype=int8)" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "x.lowest().record[0][0]" + "# designer.optimal_diameters" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# designer.qubo.qubo_dict.num_variables" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# import matplotlib.pyplot as plt\n", + "# plt.hist(designer.sampleset.data_vectors['energy'])" + ] } ], "metadata": { diff --git a/docs/notebooks/hhl_Net0.ipynb b/docs/notebooks/hhl_Net0.ipynb index 764b259..f94210d 100644 --- a/docs/notebooks/hhl_Net0.ipynb +++ b/docs/notebooks/hhl_Net0.ipynb @@ -341,7 +341,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "vitens_wntr_1", "language": "python", "name": "python3" }, diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index db49472..b1b2ee8 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -1,5 +1,6 @@ import itertools from collections import OrderedDict +from copy import deepcopy from typing import List from typing import Tuple import numpy as np @@ -37,6 +38,7 @@ def __init__( pipe_diameters: List, head_lower_bound: float, weight_cost: float = 1e-1, + weight_pressure: float = 1.0, ): # noqa: D417 """Initialize the designer object. @@ -47,6 +49,7 @@ def __init__( pipe_diameters (List): List of pipe diameters in SI head_lower_bound (float): minimum value for the head pressure values (US units) weight_cost (float, optional): weight for the cost optimization. Defaults to 1e-1. + weight_pressure (float, optional): weight for the pressure optimization. Defaults to 1. """ # water network self.wn = wn @@ -101,6 +104,9 @@ def __init__( # weight for the cost equation self.weight_cost = weight_cost + # weight for the pressure penalty + self.weight_pressure = weight_pressure + # lower bound for the pressure self.head_lower_bound = head_lower_bound self.head_upper_bound = 10 * head_lower_bound # is that enough ? @@ -293,14 +299,17 @@ def enumerates_classical_solutions(self, convert_to_si=True): tmp[idiam] = 1 encoding.append(tmp) - print("price \t diameters \t variables") + print("price \t diameters \t variables\t energy") for params in itertools.product(encoding, repeat=self.wn.num_pipes): pvalues = [] for p in params: pvalues += p price, diameters = self.get_pipe_info_from_hot_encoding(pvalues) - sol, _, _, _ = self.classical_solution(pvalues, convert_to_si=convert_to_si) - print(price, diameters, sol) + sol, _, bin_rep_sol, _ = self.classical_solution( + pvalues, convert_to_si=convert_to_si + ) + energy = self.qubo.energy_binary_rep(bin_rep_sol) + print(price, diameters, sol, energy[0]) def convert_solution_to_si(self, solution: np.ndarray) -> np.ndarray: """Converts the solution to SI. @@ -357,7 +366,7 @@ def classical_solution( num_signs = self.wn.num_pipes num_pipes = self.wn.num_pipes num_vars = num_heads + 2 * num_pipes - + original_parameters = deepcopy(parameters) if self.wn.options.hydraulic.headloss == "C-M": p0 = P0[:-1].reshape(-1, 1) p1 = P1[:-1, num_signs:num_vars] + P2.sum(1)[:-1, num_signs:num_vars] @@ -430,6 +439,10 @@ def func(input): bin_rep_sol.append(bin_rpr) encoded_sol[idx] = np.sign(s) * val + # add the pipe parameter bnary variables + for p in original_parameters: + bin_rep_sol.append(p) + if convert_to_si: sol = self.convert_solution_to_si(sol) encoded_sol = self.convert_solution_to_si(encoded_sol) @@ -523,6 +536,21 @@ def initialize_matrices(self) -> Tuple: return matrices + @staticmethod + def combine_flow_values(solution: List) -> List: + """Combine the values of the flow sign*abs. + + Args: + solution (List): solution vector + + Returns: + List: solution vector + """ + flow = [] + for sign, abs in zip(solution[0], solution[1]): + flow.append(sign * abs) + return flow + solution[2] + @staticmethod def flatten_solution_vector(solution: Tuple) -> List: """Flattens the solution vector. @@ -636,30 +664,11 @@ def create_index_mapping(self) -> None: ) idx += 1 - def solve( # noqa: D417 - self, strength: float = 1e6, num_reads: int = 10000, **options - ) -> Tuple: - """Solves the Hydraulics equations. - - Args: - strength (float, optional): substitution strength. Defaults to 1e6. - num_reads (int, optional): number of reads for the sampler. Defaults to 10000. - - Returns: - Tuple: Succes message - """ - # create the index mapping of the variables - self.create_index_mapping() - - # compute the polynomial matrices - self.matrices = self.initialize_matrices() - - self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, **options) - matrices = tuple(sparse.COO(m) for m in self.matrices) - - # create the BQM - self.qubo.qubo_dict = self.qubo.create_bqm(matrices, strength=strength) - + def add_switch_constraints( + self, + strength: float = 1e6, + ): + """Add the conrains regarding the pipe diameter switch.""" # add constraints on the hot encoding # the sum of each hot encoding variable of a given pipe must equals 1 istart = ( @@ -687,18 +696,50 @@ def solve( # noqa: D417 ) istart += self.num_diameters + def add_pressure_constraints(self): + """Add the conrains regarding the presure.""" # add constraint on head pressures istart = 2 * self.sol_vect_flows.size for i in range(self.sol_vect_heads.size): self.qubo.qubo_dict.add_linear_inequality_constraint( self.qubo.all_expr[istart + i], - lagrange_multiplier=1, + lagrange_multiplier=self.weight_pressure, label="head_%s" % i, lb=self.head_lower_bound, ub=self.head_upper_bound, ) + def solve( # noqa: D417 + self, strength: float = 1e6, num_reads: int = 10000, **options + ) -> Tuple: + """Solves the Hydraulics equations. + + Args: + strength (float, optional): substitution strength. Defaults to 1e6. + num_reads (int, optional): number of reads for the sampler. Defaults to 10000. + + Returns: + Tuple: Success message + """ + # create the index mapping of the variables + self.create_index_mapping() + + # compute the polynomial matrices + self.matrices = self.initialize_matrices() + + self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, **options) + matrices = tuple(sparse.COO(m) for m in self.matrices) + + # create the BQM + self.qubo.qubo_dict = self.qubo.create_bqm(matrices, strength=strength) + + # add constraints for the switch + self.add_switch_constraints(strength=strength) + + # add constrants for the pressure + self.add_pressure_constraints() + # sample self.sampleset = self.qubo.sample_bqm(self.qubo.qubo_dict, num_reads=num_reads) diff --git a/wntr_quantum/sampler/step/full_random.py b/wntr_quantum/sampler/step/full_random.py index c3b13be..53c1545 100644 --- a/wntr_quantum/sampler/step/full_random.py +++ b/wntr_quantum/sampler/step/full_random.py @@ -1,3 +1,4 @@ +from copy import deepcopy import numpy as np from .base_step import BaseStep @@ -115,6 +116,21 @@ def __init__( # noqa: D417 var_names, single_var_names, single_var_index, step_size, optimize_values ) self.switch_variable_index = switch_variable_index + self.switch_variable_index_map = self.create_switch_variable_index_map() + + def create_switch_variable_index_map(self): + """Create a map of the varialbes that we switch over. + + Args: + switch_variable_index (list): _description_ + """ + mapping = {} + for group in self.switch_variable_index: + for iel, el in enumerate(group): + tmp = deepcopy(group) + _ = tmp.pop(iel) + mapping[self.value_names[el]] = [self.value_names[itmp] for itmp in tmp] + return mapping def __call__(self, x, verbose=False): """Call function of the method. @@ -132,51 +148,85 @@ def __call__(self, x, verbose=False): ) for random_val_name in random_val_name_list: - idx = self.index_values[random_val_name] - data = np.array(x)[idx] - width = len(data) - - # determine the max val - max_val = int("1" * width, base=2) - - # check if we reach min/max val - max_val_check = data.prod() == 1 - min_val_check = data.sum() == 0 - - # convert to int value - val = int("".join([str(i) for i in data[::-1]]), base=2) - # determine sign of the displacement - if min_val_check: - sign = 1 - elif max_val_check: - sign = -1 - else: - sign = 2 * np.random.randint(2) - 1 + idx = self.index_values[random_val_name] - # new value - if self.step_size <= 1: - delta = 1 + # switch variables + if random_val_name in self.switch_variable_index_map: + + # switch original + idx = idx[0] + + # if this variable is set to 1 + # we randomly among the other switch variables of the group + if x[idx] == 1: + # switch new one + new_var = np.random.choice( + self.switch_variable_index_map[random_val_name], size=1 + )[0] + idx_new = self.index_values[new_var][0] + + # if this variable is set to 0 + # we pick the switch variable in the group that is set to 1 + else: + for new_var in self.switch_variable_index_map[random_val_name]: + idx_new = self.index_values[new_var][0] + if x[idx_new] == 1: + break + # print(random_val_name, x[idx], new_var, x[idx_new]) + x[idx] = int(not (x[idx])) + x[idx_new] = int(not (x[idx_new])) + + self.fix_constraint(x, idx) + self.fix_constraint(x, idx_new) + + # other variables else: - delta = np.random.randint(self.step_size) - new_val = val + sign * delta - if new_val < 0: - new_val = 0 - if new_val > max_val: - new_val = max_val - new_val = np.binary_repr(new_val, width=width) - # convert back to binary repr - new_data = np.array([int(i) for i in new_val])[::-1] - if verbose: - print(random_val_name, data, "=>", new_data) - - # inject in the x vector - for ix, nd in zip(idx, new_data): - x[ix] = nd - - # fix constraints - for vidx in idx: - self.fix_constraint(x, vidx) + data = np.array(x)[idx] + width = len(data) + + # determine the max val + max_val = int("1" * width, base=2) + + # check if we reach min/max val + max_val_check = data.prod() == 1 + min_val_check = data.sum() == 0 + + # convert to int value + val = int("".join([str(i) for i in data[::-1]]), base=2) + + # determine sign of the displacement + if min_val_check: + sign = 1 + elif max_val_check: + sign = -1 + else: + sign = 2 * np.random.randint(2) - 1 + + # new value + if self.step_size <= 1: + delta = 1 + else: + delta = np.random.randint(self.step_size) + new_val = val + sign * delta + if new_val < 0: + new_val = 0 + if new_val > max_val: + new_val = max_val + new_val = np.binary_repr(new_val, width=width) + + # convert back to binary repr + new_data = np.array([int(i) for i in new_val])[::-1] + if verbose: + print(random_val_name, data, "=>", new_data) + + # inject in the x vector + for ix, nd in zip(idx, new_data): + x[ix] = nd + + # fix constraints + for vidx in idx: + self.fix_constraint(x, vidx) return x From eea1dd24e54579e43259b35079926e3e3a0620a0 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Fri, 8 Nov 2024 22:07:28 +0100 Subject: [PATCH 81/96] test design --- .../design_pipe_diameter_own_sampler.ipynb | 114 +++++++++--------- wntr_quantum/sim/models/chezy_manning.py | 4 +- wntr_quantum/sim/models/mass_balance.py | 36 ------ 3 files changed, 60 insertions(+), 94 deletions(-) diff --git a/docs/notebooks/design_pipe_diameter_own_sampler.ipynb b/docs/notebooks/design_pipe_diameter_own_sampler.ipynb index ccf76cd..4c8184e 100644 --- a/docs/notebooks/design_pipe_diameter_own_sampler.ipynb +++ b/docs/notebooks/design_pipe_diameter_own_sampler.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 174, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 174, "metadata": {}, "output_type": "execute_result" } @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 175, "metadata": {}, "outputs": [ { @@ -61,7 +61,7 @@ "" ] }, - "execution_count": 2, + "execution_count": 175, "metadata": {}, "output_type": "execute_result" } @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 176, "metadata": {}, "outputs": [ { @@ -86,7 +86,7 @@ "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" ] }, - "execution_count": 3, + "execution_count": 176, "metadata": {}, "output_type": "execute_result" } @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 177, "metadata": {}, "outputs": [], "source": [ @@ -116,17 +116,17 @@ "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", "nqbit = 7\n", - "step = (2./(2**nqbit-1))\n", + "step = (4./(2**nqbit-1))\n", "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", "nqbit = 9\n", - "step = (50/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+75.0, var_base_name=\"x\")" + "step = (200/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -139,15 +139,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 179, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 75.000000 => 125.000000 (res: 0.097847)\n", - "Flow Encoding : -2.000000 => -0.000000 | 0.000000 => 2.000000 (res: 0.015748)\n" + "Head Encoding : 0.000000 => 200.000000 (res: 0.391389)\n", + "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.031496)\n" ] } ], @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 180, "metadata": {}, "outputs": [ { @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 181, "metadata": {}, "outputs": [], "source": [ @@ -187,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 182, "metadata": {}, "outputs": [ { @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 183, "metadata": {}, "outputs": [], "source": [ @@ -230,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 184, "metadata": {}, "outputs": [ { @@ -238,15 +238,15 @@ "output_type": "stream", "text": [ "price \t diameters \t variables\t energy\n", - "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329] 31677.014682043187\n", - "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118] 30748.55921834555\n", - "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858] 30748.099719787107\n", - "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118] 25598.905845931073\n", - "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906] 20539.366563299016\n", - "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647] 20398.77576791782\n", - "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858] 25449.16409577259\n", - "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647] 20259.102435574252\n", - "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387] 20099.606199275284\n" + "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329] 34322.526870054484\n", + "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118] 25524.661797275585\n", + "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858] 25328.376831715672\n", + "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118] 18459.448078170364\n", + "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906] 11351.693997595543\n", + "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647] 11285.703552581042\n", + "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858] 18183.15875718654\n", + "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647] 11205.751361477407\n", + "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387] 11065.836654105435\n" ] } ], @@ -256,65 +256,67 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 185, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", - "x = modify_solution_sample(designer, bin_rep_sol, modify=['flows', 'heads'])\n", + "x = modify_solution_sample(designer, bin_rep_sol, modify=['heads'])\n", "x0 = list(x.values())" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 186, "metadata": {}, "outputs": [], "source": [ - "num_sweeps = 2000\n", - "Tinit = 1E1\n", + "num_sweeps = 5000\n", + "Tinit = 1E2\n", "Tfinal = 1E-1\n", "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", - "Tschedule = np.append(Tschedule, np.zeros(1000))" + "Tschedule = np.append(Tschedule, Tfinal*np.ones(100))\n", + "Tschedule = np.append(Tschedule, np.zeros(100))" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 4000/4000 [00:24<00:00, 166.13it/s]\n" + "100%|██████████| 5200/5200 [00:18<00:00, 278.84it/s]\n" ] } ], "source": [ - "mystep.optimize_values = np.arange(2,12)\n", - "res = sampler.sample(designer.qubo, init_sample=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)" + "mystep.optimize_values = np.arange(4,6)\n", + "res = sampler.sample(designer.qubo, init_sample=x0, \n", + " Tschedule=Tschedule, take_step=mystep, \n", + " save_traj=True, verbose=False)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 188, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 188, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -351,14 +353,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 189, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2.631e-02 3.478e-02 3.810e+01 3.810e+01]\n" + "[4.994e-02 4.994e-02 4.855e+01 5.464e+01]\n" ] } ], @@ -376,16 +378,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 190, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.5072373283355087, array([1000., 500.]))" + "(0.33815821889033915, array([500., 500.]))" ] }, - "execution_count": 17, + "execution_count": 190, "metadata": {}, "output_type": "execute_result" } @@ -396,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 191, "metadata": {}, "outputs": [ { @@ -405,13 +407,13 @@ "Text(0.5, 1.0, 'Pressure')" ] }, - "execution_count": 18, + "execution_count": 191, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -464,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 192, "metadata": {}, "outputs": [], "source": [ @@ -475,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 193, "metadata": {}, "outputs": [], "source": [ @@ -484,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 194, "metadata": {}, "outputs": [], "source": [ @@ -493,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 195, "metadata": {}, "outputs": [], "source": [ @@ -502,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 196, "metadata": {}, "outputs": [], "source": [ diff --git a/wntr_quantum/sim/models/chezy_manning.py b/wntr_quantum/sim/models/chezy_manning.py index 1c7a9fd..948d099 100644 --- a/wntr_quantum/sim/models/chezy_manning.py +++ b/wntr_quantum/sim/models/chezy_manning.py @@ -248,7 +248,7 @@ def get_pipe_design_chezy_manning_qubops_matrix( # linear term (start head value) of the headloss approximation if isinstance(start_node, wntr.network.Junction): start_node_index = head_index_mapping[m.head[start_node_name].name] - P1[ieq, start_node_index] = 1 + P1[ieq, start_node_index] += 1 else: start_h = m.source_head[start_node_name] P0[ieq, 0] += from_si(FlowUnits.CFS, start_h.value, HydParam.Length) @@ -256,7 +256,7 @@ def get_pipe_design_chezy_manning_qubops_matrix( # linear term (end head values) of the headloss approximation if isinstance(end_node, wntr.network.Junction): end_node_index = head_index_mapping[m.head[end_node_name].name] - P1[ieq, end_node_index] = -1 + P1[ieq, end_node_index] -= 1 else: end_h = m.source_head[end_node_name] P0[ieq, 0] -= from_si(FlowUnits.CFS, end_h.value, HydParam.Length) diff --git a/wntr_quantum/sim/models/mass_balance.py b/wntr_quantum/sim/models/mass_balance.py index 8445f7f..229c797 100644 --- a/wntr_quantum/sim/models/mass_balance.py +++ b/wntr_quantum/sim/models/mass_balance.py @@ -43,39 +43,3 @@ def get_mass_balance_qubops_matrix( P2[ieq, sign_idx, flow_idx] += 1 return P0, P1, P2, P3 - - -def get_mass_balance_constraint_design(m, wn, matrices): # noqa: D417 - """Adds a mass balance to the model for the specified junctions. - - Parameters - ---------- - m: wntr.aml.aml.aml.Model - wn: wntr.network.model.WaterNetworkModel - updater: ModelUpdater - index_over: list of str - list of junction names; default is all junctions in wn - """ - P0, P1, P2, P3 = matrices - - continuous_var_name = [v.name for v in list(m.vars())] - discrete_var_name = [v.name for k, v in m.cm_resistance.items()] - var_names = continuous_var_name + discrete_var_name - - index_over = wn.junction_name_list - - for ieq, node_name in enumerate(index_over): - - node = wn.get_node(node_name) - if not node._is_isolated: - P0[ieq, 0] += m.expected_demand[node_name].value - - for link_name in wn.get_links_for_node(node_name, flag="INLET"): - node_index = var_names.index(m.flow[link_name].name) - P1[ieq, node_index] -= 1 - - for link_name in wn.get_links_for_node(node_name, flag="OUTLET"): - node_index = var_names.index(m.flow[link_name].name) - P1[ieq, node_index] += 1 - - return P0, P1, P2, P3 From f52fb5d6eee17eca80f09da2286825d6957e1ac6 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Sat, 9 Nov 2024 12:09:51 +0100 Subject: [PATCH 82/96] replace equality constraint for pressure --- .../design_pipe_diameter_own_sampler.ipynb | 171 +++++++++++------- wntr_quantum/design/qubo_pipe_diam.py | 35 +++- 2 files changed, 133 insertions(+), 73 deletions(-) diff --git a/docs/notebooks/design_pipe_diameter_own_sampler.ipynb b/docs/notebooks/design_pipe_diameter_own_sampler.ipynb index 4c8184e..26413bc 100644 --- a/docs/notebooks/design_pipe_diameter_own_sampler.ipynb +++ b/docs/notebooks/design_pipe_diameter_own_sampler.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 174, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ "" ] }, - "execution_count": 174, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -61,7 +61,7 @@ "" ] }, - "execution_count": 175, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -86,7 +86,7 @@ "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" ] }, - "execution_count": 176, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -115,39 +115,39 @@ "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", "\n", - "nqbit = 7\n", + "nqbit = 5\n", "step = (4./(2**nqbit-1))\n", "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", - "nqbit = 9\n", + "nqbit = 7\n", "step = (200/(2**nqbit-1))\n", "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", "pipe_diameters = [250, 500, 1000]\n", "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, \n", - " pipe_diameters, head_lower_bound=80,\n", - " weight_cost=1, weight_pressure=1)" + " pipe_diameters, head_lower_bound=95,\n", + " weight_cost=1E0, weight_pressure=1E0)" ] }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 0.000000 => 200.000000 (res: 0.391389)\n", - "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.031496)\n" + "Head Encoding : 0.000000 => 200.000000 (res: 1.574803)\n", + "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.129032)\n" ] } ], @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -172,12 +172,32 @@ "source": [ "designer.create_index_mapping()\n", "designer.matrices = designer.initialize_matrices()\n", - "ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = designer.classical_solution([0,1,0,0,1,0], convert_to_si=False)" + "ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = designer.classical_solution([0,1,0,0,1,0], convert_to_si=True)" ] }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 29.769, 29.537])" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_sol" + ] + }, + { + "cell_type": "code", + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ @@ -187,18 +207,9 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 88, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:759: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", - " warnings.warn(\"For constraints with fractional coefficients, \"\n" - ] - } - ], + "outputs": [], "source": [ "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", "import sparse\n", @@ -207,12 +218,12 @@ "matrices = tuple(sparse.COO(m) for m in designer.matrices)\n", "designer.qubo.qubo_dict = designer.qubo.create_bqm(matrices, strength=0)\n", "# designer.add_switch_constraints(strength=0)\n", - "designer.add_pressure_constraints()" + "designer.add_pressure_equality_constraints(fractional_factor=1)" ] }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -230,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -238,15 +249,15 @@ "output_type": "stream", "text": [ "price \t diameters \t variables\t energy\n", - "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329] 34322.526870054484\n", - "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118] 25524.661797275585\n", - "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858] 25328.376831715672\n", - "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118] 18459.448078170364\n", - "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906] 11351.693997595543\n", - "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647] 11285.703552581042\n", - "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858] 18183.15875718654\n", - "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647] 11205.751361477407\n", - "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387] 11065.836654105435\n" + "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329] -5479.796884652196\n", + "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118] -8006.340122212198\n", + "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858] -8006.857202639608\n", + "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118] -8844.360358890619\n", + "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906] -9687.889302578\n", + "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647] -9688.377795260172\n", + "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858] -8843.656791566864\n", + "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647] -9687.15714750899\n", + "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387] -9687.588464700659\n" ] } ], @@ -256,44 +267,70 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "price \t diameters \t variables\t energy\n", + "0.16907910944516957 [250. 250.] [ 0.05 0.05 20.689 11.378] -5479.796884652196\n", + "0.25361866416775436 [250. 500.] [ 0.05 0.05 20.689 20.457] -8006.340122212198\n", + "0.42269777361292393 [ 250. 1000.] [ 0.05 0.05 20.689 20.683] -8006.857202639608\n", + "0.25361866416775436 [500. 250.] [ 0.05 0.05 29.769 20.457] -8844.360358890619\n", + "0.33815821889033915 [500. 500.] [ 0.05 0.05 29.769 29.537] -9687.889302578\n", + "0.5072373283355087 [ 500. 1000.] [ 0.05 0.05 29.769 29.763] -9688.377795260172\n", + "0.42269777361292393 [1000. 250.] [ 0.05 0.05 29.994 20.683] -8843.656791566864\n", + "0.5072373283355087 [1000. 500.] [ 0.05 0.05 29.994 29.763] -9687.15714750899\n", + "0.6763164377806783 [1000. 1000.] [ 0.05 0.05 29.994 29.988] -9687.588464700659\n" + ] + } + ], + "source": [ + "designer.enumerates_classical_solutions(convert_to_si=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", - "x = modify_solution_sample(designer, bin_rep_sol, modify=['heads'])\n", + "x = modify_solution_sample(designer, bin_rep_sol, modify=['flows','heads'])\n", "x0 = list(x.values())" ] }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "num_sweeps = 5000\n", - "Tinit = 1E2\n", + "Tinit = 1E3\n", "Tfinal = 1E-1\n", "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(100))\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", "Tschedule = np.append(Tschedule, np.zeros(100))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 5200/5200 [00:18<00:00, 278.84it/s]\n" + "100%|██████████| 6100/6100 [00:19<00:00, 309.26it/s]\n" ] } ], "source": [ - "mystep.optimize_values = np.arange(4,6)\n", + "mystep.optimize_values = np.arange(2,12)\n", "res = sampler.sample(designer.qubo, init_sample=x0, \n", " Tschedule=Tschedule, take_step=mystep, \n", " save_traj=True, verbose=False)" @@ -301,22 +338,22 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 188, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -335,7 +372,7 @@ "\n", "plt.plot(res.energies[:], lw=4, label=\"QUBO Energy\")\n", "plt.plot(Tschedule, lw=3, label='Temperature')\n", - "# ax1.axline((0, 0), slope=0, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", + "# ax1.axline((0, 0), slope=e, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", "plt.grid(which='both')\n", "# plt.yscale('symlog')\n", "\n", @@ -353,14 +390,14 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[4.994e-02 4.994e-02 4.855e+01 5.464e+01]\n" + "[ 0.04 0.037 29.76 29.76 ]\n" ] } ], @@ -378,16 +415,16 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.33815821889033915, array([500., 500.]))" + "(0.5072373283355087, array([ 500., 1000.]))" ] }, - "execution_count": 190, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -398,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -407,13 +444,13 @@ "Text(0.5, 1.0, 'Pressure')" ] }, - "execution_count": 191, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -466,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ @@ -477,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -486,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ @@ -495,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -504,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index b1b2ee8..bc11431 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -109,7 +109,8 @@ def __init__( # lower bound for the pressure self.head_lower_bound = head_lower_bound - self.head_upper_bound = 10 * head_lower_bound # is that enough ? + self.head_upper_bound = 1e3 # 10 * head_lower_bound # is that enough ? + self.target_pressure = head_lower_bound # store other attributes self.qubo = None @@ -696,19 +697,41 @@ def add_switch_constraints( ) istart += self.num_diameters - def add_pressure_constraints(self): + def add_pressure_equality_constraints(self, fractional_factor=100): """Add the conrains regarding the presure.""" # add constraint on head pressures istart = 2 * self.sol_vect_flows.size for i in range(self.sol_vect_heads.size): + tmp = [] + for k, v in self.qubo.all_expr[istart + i]: + tmp.append((k, int(fractional_factor * v))) + # print(tmp) + cst = self.qubo.qubo_dict.add_linear_equality_constraint( + tmp, + lagrange_multiplier=self.weight_pressure, + constant=-self.target_pressure, + ) + # print(cst) - self.qubo.qubo_dict.add_linear_inequality_constraint( - self.qubo.all_expr[istart + i], + def add_pressure_constraints(self, fractional_factor=100): + """Add the conrains regarding the presure.""" + # add constraint on head pressures + istart = 2 * self.sol_vect_flows.size + for i in range(self.sol_vect_heads.size): + tmp = [] + for k, v in self.qubo.all_expr[istart + i]: + tmp.append((k, int(fractional_factor * v))) + # print(tmp) + cst = self.qubo.qubo_dict.add_linear_inequality_constraint( + tmp, lagrange_multiplier=self.weight_pressure, label="head_%s" % i, - lb=self.head_lower_bound, - ub=self.head_upper_bound, + lb=fractional_factor * self.head_lower_bound, + ub=fractional_factor * self.head_upper_bound, + penalization_method="slack", + cross_zero=True, ) + # print(cst) def solve( # noqa: D417 self, strength: float = 1e6, num_reads: int = 10000, **options From cd34076039d7d5c9689d93c854d9365d0d9722ad Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Tue, 19 Nov 2024 17:17:05 +0100 Subject: [PATCH 83/96] start refactor --- .../design_pipe_diameter_own_sampler.ipynb | 123 +- .../designer_net0_data/test0/energies.pkl | Bin 0 -> 3921 bytes .../test0/optimized_diameters.pkl | Bin 0 -> 4721 bytes .../designer_net0_data/test0/prices.pkl | Bin 0 -> 2001 bytes .../designer_net0_data/test1/energies.pkl | Bin 0 -> 3921 bytes .../test1/optimized_diameters.pkl | Bin 0 -> 4721 bytes .../designer_net0_data/test1/prices.pkl | Bin 0 -> 2001 bytes docs/notebooks/plot_test_qubo_designer.ipynb | 240 +++ .../qubo_poly_solver_Net0_refac.ipynb | 1920 +++++++++++++++++ docs/notebooks/test_qubo_poly_designe.py | 204 ++ wntr_quantum/design/qubo_pipe_diam.py | 9 +- wntr_quantum/sampler/simulated_annealing.py | 64 +- .../sim/solvers/qubo_polynomial_solver.py | 27 +- 13 files changed, 2496 insertions(+), 91 deletions(-) create mode 100644 docs/notebooks/designer_net0_data/test0/energies.pkl create mode 100644 docs/notebooks/designer_net0_data/test0/optimized_diameters.pkl create mode 100644 docs/notebooks/designer_net0_data/test0/prices.pkl create mode 100644 docs/notebooks/designer_net0_data/test1/energies.pkl create mode 100644 docs/notebooks/designer_net0_data/test1/optimized_diameters.pkl create mode 100644 docs/notebooks/designer_net0_data/test1/prices.pkl create mode 100644 docs/notebooks/plot_test_qubo_designer.ipynb create mode 100644 docs/notebooks/qubo_poly_solver_Net0_refac.ipynb create mode 100644 docs/notebooks/test_qubo_poly_designe.py diff --git a/docs/notebooks/design_pipe_diameter_own_sampler.ipynb b/docs/notebooks/design_pipe_diameter_own_sampler.ipynb index 26413bc..39be6be 100644 --- a/docs/notebooks/design_pipe_diameter_own_sampler.ipynb +++ b/docs/notebooks/design_pipe_diameter_own_sampler.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 79, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ "" ] }, - "execution_count": 79, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -61,7 +61,7 @@ "" ] }, - "execution_count": 80, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -86,7 +86,7 @@ "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" ] }, - "execution_count": 81, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -134,12 +134,12 @@ "pipe_diameters = [250, 500, 1000]\n", "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, \n", " pipe_diameters, head_lower_bound=95,\n", - " weight_cost=1E0, weight_pressure=1E0)" + " weight_cost=2, weight_pressure=0.5)" ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ "array([ 0.05 , 0.05 , 29.769, 29.537])" ] }, - "execution_count": 86, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -207,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -218,12 +218,12 @@ "matrices = tuple(sparse.COO(m) for m in designer.matrices)\n", "designer.qubo.qubo_dict = designer.qubo.create_bqm(matrices, strength=0)\n", "# designer.add_switch_constraints(strength=0)\n", - "designer.add_pressure_equality_constraints(fractional_factor=1)" + "designer.add_pressure_equality_constraints()" ] }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -249,15 +249,15 @@ "output_type": "stream", "text": [ "price \t diameters \t variables\t energy\n", - "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329] -5479.796884652196\n", - "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118] -8006.340122212198\n", - "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858] -8006.857202639608\n", - "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118] -8844.360358890619\n", - "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906] -9687.889302578\n", - "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647] -9688.377795260172\n", - "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858] -8843.656791566864\n", - "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647] -9687.15714750899\n", - "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387] -9687.588464700659\n" + "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329] -7676.327154648521\n", + "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118] -8943.759716156877\n", + "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858] -8943.933743641279\n", + "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118] -9310.494690264793\n", + "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906] -9682.588285719068\n", + "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647] -9682.647962222467\n", + "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858] -9309.44806999803\n", + "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647] -9681.427314471302\n", + "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387] -9681.258289012692\n" ] } ], @@ -267,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -275,15 +275,15 @@ "output_type": "stream", "text": [ "price \t diameters \t variables\t energy\n", - "0.16907910944516957 [250. 250.] [ 0.05 0.05 20.689 11.378] -5479.796884652196\n", - "0.25361866416775436 [250. 500.] [ 0.05 0.05 20.689 20.457] -8006.340122212198\n", - "0.42269777361292393 [ 250. 1000.] [ 0.05 0.05 20.689 20.683] -8006.857202639608\n", - "0.25361866416775436 [500. 250.] [ 0.05 0.05 29.769 20.457] -8844.360358890619\n", - "0.33815821889033915 [500. 500.] [ 0.05 0.05 29.769 29.537] -9687.889302578\n", - "0.5072373283355087 [ 500. 1000.] [ 0.05 0.05 29.769 29.763] -9688.377795260172\n", - "0.42269777361292393 [1000. 250.] [ 0.05 0.05 29.994 20.683] -8843.656791566864\n", - "0.5072373283355087 [1000. 500.] [ 0.05 0.05 29.994 29.763] -9687.15714750899\n", - "0.6763164377806783 [1000. 1000.] [ 0.05 0.05 29.994 29.988] -9687.588464700659\n" + "0.16907910944516957 [250. 250.] [ 0.05 0.05 20.689 11.378] -7676.327154648521\n", + "0.25361866416775436 [250. 500.] [ 0.05 0.05 20.689 20.457] -8943.759716156877\n", + "0.42269777361292393 [ 250. 1000.] [ 0.05 0.05 20.689 20.683] -8943.933743641279\n", + "0.25361866416775436 [500. 250.] [ 0.05 0.05 29.769 20.457] -9310.494690264793\n", + "0.33815821889033915 [500. 500.] [ 0.05 0.05 29.769 29.537] -9682.588285719068\n", + "0.5072373283355087 [ 500. 1000.] [ 0.05 0.05 29.769 29.763] -9682.647962222467\n", + "0.42269777361292393 [1000. 250.] [ 0.05 0.05 29.994 20.683] -9309.44806999803\n", + "0.5072373283355087 [1000. 500.] [ 0.05 0.05 29.994 29.763] -9681.427314471302\n", + "0.6763164377806783 [1000. 1000.] [ 0.05 0.05 29.994 29.988] -9681.258289012692\n" ] } ], @@ -293,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -318,14 +318,21 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 6100/6100 [00:19<00:00, 309.26it/s]\n" + " 0%| | 0/6100 [00:00" + "" ] }, - "execution_count": 95, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -390,14 +397,14 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ 0.04 0.037 29.76 29.76 ]\n" + "[ 0.088 0.069 29.28 28.8 ]\n" ] } ], @@ -415,16 +422,16 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.5072373283355087, array([ 500., 1000.]))" + "(0.33815821889033915, array([500., 500.]))" ] }, - "execution_count": 97, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -435,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -444,13 +451,13 @@ "Text(0.5, 1.0, 'Pressure')" ] }, - "execution_count": 98, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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cuLHw1VdfCVlZWaWOv3jxYsHPz0+QyWSCp6enMHv2bKGgoOCN01K/yb1794QPP/xQ8PPzEywtLQV7e3uhXr16wjvvvCMkJCT8698ZUTGJIPzjmiYREREREZGR4DM8RERERERktNjwEBERERGR0WLDQ0RERERERosNDxERERERGS02PEREREREZLTY8BARERERkdHiwqPloFAo8ODBA9jZ2UEikeg6HSIigyMIArKysuDu7g6plJ+18bxCRKQZVc4rbHjK4cGDB/Dw8NB1GkREBu/u3buoUaOGrtPQOZ5XiIjEUZ7zChuecrCzswPw8i/U3t5epX3lcjni4uIQGBgImUymjfT0kinWbYo1A6ZZtynWDGhWd2ZmJjw8PJS/T00dzysvGVMtgHHVw1r0kzHVAlTceYUNTzkU325gb2+v1onJxsYG9vb2RvGDWV6mWLcp1gyYZt2mWDMgTt28feslnldeMqZaAOOqh7XoJ2OqBai48wpvpCYiIiIiohIePnwIQRB0nYYo2PAQERG9YsmSJWjUqJHy6kvbtm2xa9cu5et5eXmYOHEinJycUKlSJQwYMABpaWk6zJiISFx37tzBr7/+ivXr16OwsFDX6WiMDQ8REdEratSogf/+979ISkrCyZMn0bVrV/Tt2xcXL14EAEydOhXbtm3Dhg0bcODAATx48AD9+/fXcdZEROJ4/vw51q9fD4VCAalUCjMzM12npDE+w0NERPSKPn36lPh6wYIFWLJkCY4dO4YaNWrgt99+Q1RUFLp27QoA+P3331GvXj0cO3YMbdq00UXKRESiEAQBGzZsQE5ODlxdXdG3b1+jePaSV3iIiIheo6ioCNHR0cjJyUHbtm2RlJQEuVyO7t27K8fUrVsXnp6eSExM1GGmRESak0gk6Nq1K5ycnDB06FBYWFho7VgKhQJjx47Fnj17tHaMYrzCQ0RE9A/nz59H27ZtkZeXh0qVKmHz5s2oX78+zpw5AwsLC1SuXLnEeFdXV6Smpr42Xn5+PvLz85VfZ2ZmAng5Q5FcLlcpt+Lxqu6nj4ypFsC46mEt+qkiavHw8MC4ceMglUq1epxPP/0Ua9asgbm5OSZMmAA/Pz+V9lclNzY8RET0WgqFAIkEeCEvwsV7TwEAZ+4+RYMajrCWmUEAIDWC2x3+qU6dOjhz5gyeP3+OjRs3Ijw8HAcOHFA7XmRkJObOnVtqe1xcHGxsbNSKGR8fr3Y++saYagGMqx7Wop8MvZatW7dixYoVAICJEyfi6tWruHr1qkoxcnNzyz2WDQ8REb3W9YxsrDx6CzGn76OwqBBftwJG/nYC5mbmCG1aHaPbecPP1fgWE7WwsECtWrUAAM2bN8fff/+NH374AUOGDEFBQQGePXtW4ipPWloa3NzcXhtvxowZiIiIUH5dvGBeYGCgWuvwxMfHo0ePHga/Docx1QIYVz2sRT8ZQy3X07ORsnorAMA1IAwBAQH4/KQU5mbm6NXYHSNaecLXpdK/xim+Ul4ebHiIiKhMi/Zewzfxl1G8DIPlKxP15BQU4c/jdxB14g4+6lEHk7rW0k2SFUShUCA/Px/NmzeHTCZDQkICBgwYAAC4fPky7ty5g7Zt2752f0tLS1haWpbaLpPJ1H7Tosm++saYagGMqx7Wop8MtRblecWvP1yH1YK9d0MACuQrJMiUK7Dm+D38ceJeuc4rqtTPhoeIiEpZtPcaFsZd/tdxggAsjLsMiQSYGGAcTc+MGTMQEhICT09PZGVlISoqCvv378fu3bvh4OCAsWPHIiIiAo6OjrC3t8fkyZPRtm1bztBGRPQG/zyvWHk2gkRSemFTbZxX2PAQEZGSQiHgekZ2uZqdVy2Mu4ygBm6oWdXW4J/pSU9PR1hYGB4+fAgHBwc0atQIu3fvRo8ePQAA3333HaRSKQYMGID8/HwEBQXh559/1nHWRET6qazziruDFQa2qAHvKlbA/TOI7OePW0/zsPHkPTx4ngdA3PMKGx4iIlKSSICVR2+pvJ8gACuP3MS80IbiJ1XBfvvttze+bmVlhcWLF2Px4sUVlBERkbiKFxWtCK+eV1r7OGJsBx90q+eK3IJCXLj3BOn3gWqVrdCjoTs+7FYbCclp+PXwTZy4+US08wrX4SEiIqUX8iLEnL6v1r6bT9/HC3mRyBkREZGYUlNTsWjRIty5c6dCjld8XhnXsSbWvdsWHo42mL3lAtr8JwGjf/8bADD697/R5j8JmL3lAjwcbbD+3bZ4p6OPaOcVXuEhIiKl5IeZyClQ7+SSU1CElIdZaOZVReSsiIhIDLm5uYiOjsbz589x5MgReHp6av2YyQ8zMay1Jz7rVU+lyXBm9aoPAKKcV9jwEBGRUuaLQs32zzP8hf2IiIxRUVERNmzYgOfPn8PR0RGhoaFaO5YgCCgqKoK5uTmkkGBWr/pqTYYzq1d9nL7zVON8eEsbEREp2Vtr9jmYvZXhTZNKRGQKCgsLYWZmBgsLCwwdOhTW1tZaO1ZkZCRCQkKQmZkJL2cbJD/MVGsynJTUTHg52WqcDxseIiJSqlfNHrYWZv8+sAy2FmaoW834FiElIjIGlpaWGD58ON5++21UrVpVa8dZuXIlPvvsM+zZswdbt25FFRsL/HHstspxBAH4I/E2qtho/kEaGx4iIlKylpkhtGl1tfbt17Q6rGXqNUtERKR9UqkUrq6uWosfGxuLd955BwDwySefYOTIkXoxGQ4bHiIiUhIEYHQ7b6i65IFEAoxu74PSS8gREZGpcHJygqOjI0aOHInIyEgA4kyGoyk2PEREpCSVSuDnaoePetRRab9pgXXgawSLjhIRkfpatmyJv//+G7/99ptynR99mAyHs7QREVEpk7rWgkTy8qFR4Q2XbSSSl83OxIBaFZccERHpLS8vrxJf68NkOGx4iIioTBMDaiGogRtWHrmJzafvo7Do/z6ls7UwQ7+m1TG6vQ9quVTSYZZERKTPiifDUee2NrEmw2HDQ0REr1Wzqi3mhTbEzF71cPHeUzw4fxR/jm2N+jWqwFpmxmd2iIjojYonw/nz+B2V9xVrMhw+w0NERK8llUggkUhgY2GOJh6VAQCNPSrDxsIcEomEz+wQEemRJ0+eYN++fVAoFLpORUkfJsNhw0NEREREZODy8/Oxdu1aHDx4EAkJCbpOR0kfJsNhw0NEREREZMAEQcDmzZvx6NEj2NnZoU2bNlo71rNnz9Tab1LXWvg4qM6/XumRSICPg15OhiMR6S4CNjxERERERAbs4cOHuHr1KszMzDBkyBDY2Wn+oH9ZYmJi4OPjo/YVpIkBtRA/tTNGtvaErUXJZ3NsLcwwsrUn4qd2Fn3mT05aQERERERkwNzd3REeHo7MzExUr15dK8c4cuQIhg0bhry8PPz111/o1q2bWnF0MRkOGx4iIiIiIgPn6emptdjXrl1Dnz59kJeXhz59+uDHH39UO1bxMznFk+E8OP9yMhyZ7GVboo2pcNjwEBERERHRa9WoUQPdu3fHnTt3EB0dDXNzw2ohDCtbIiIiIiKqUFZWVoiOjkZWVhZsbGwq7Lh3796Fq6srLCwsNIrDSQuIiIiIiOiNpFIpHBwcKux4169fx6pVqxAVFYWCggKNYrHhISIiIiIivXHjxg1ER0ejqKgIVlZWMDMz+/ed3oANDxERERER6YXCwkJs2bIFhYWFqF27NgYNGqRxw8NneIiIiIiI9FxWVhZevHih6zS0ztzcHMOGDcOxY8fQu3dvjZsdgA0PEREREZFeKywsxLp16/D48WPUqFFD1+lonZubG0JDQ0WLx1vaiIiIiIj0lCAI2L59O+7fvw+JRAKZTKaV41y7dg2CoI1lP3WPDQ8RERERkZ46e/Yszp49C4lEgtDQUFhaWmrlGM2aNcPYsWMhl8tFj69rbHiIiIiIiPRU/fr1Ua9ePQQGBsLHx0f0+Ldu3UJISAiysrJw48YNFBUViX4MXdO7hmfx4sXw9vaGlZUVWrdujRMnTrxx/IYNG1C3bl1YWVnB398fO3fuLPF6dnY2Jk2ahBo1asDa2hr169fH0qVLtVkCEREREZEoLCwsMGjQILRu3Vr02IWFhejduzcePnyIhg0bIiYmBlZWVqIfR9f0quFZt24dIiIiMGfOHJw6dQqNGzdGUFAQ0tPTyxx/9OhRDBs2DGPHjsXp06cRGhqK0NBQXLhwQTkmIiICsbGx+OOPP5CcnIwpU6Zg0qRJ2Lp1a0WVRURERESkNolEAolEInpcc3NzLFiwALVr18auXbtQuXJl0Y9RlsLCwgo5TjG9ani+/fZbjBs3DmPGjFFeibGxscGKFSvKHP/DDz8gODgYH3/8MerVq4d58+ahWbNmWLRokXLM0aNHER4eji5dusDb2xvjx49H48aN//XKERERERGRsevbty8uXrxYYbO/3bt3Dz/99BPu3r1bIccD9Gha6oKCAiQlJWHGjBnKbVKpFN27d0diYmKZ+yQmJiIiIqLEtqCgIMTExCi/bteuHbZu3Yq3334b7u7u2L9/P65cuYLvvvvutbnk5+cjPz9f+XVmZiYAQC6Xq/wgV/F4Y3wA7E1MsW5TrBkwzbpNsWZAs7pN7e+KiMiQmJtXTEtw7949/PHHH8jPz8eRI0cwYMCACjmu3jQ8jx49QlFREVxdXUtsd3V1RUpKSpn7pKamljk+NTVV+fVPP/2E8ePHo0aNGjA3N4dUKsXy5cvRqVOn1+YSGRmJuXPnltoeFxcHGxsbVcpSio+PV2s/Q2eKdZtizYBp1m2KNQPq1Z2bm6uFTIiIyFCkp6crmx0vLy/079+/wo6tNw2Ptvz00084duwYtm7dCi8vLxw8eBATJ06Eu7s7unfvXuY+M2bMKHHlKDMzEx4eHggMDIS9vb1Kx5fL5YiPj0ePHj20Nm+6PjLFuk2xZsA06zbFmgHN6i6+Uk5ERKbJ0dERnp6eyM/Px/Dhw2FhYVFhV//1puFxdnaGmZkZ0tLSSmxPS0uDm5tbmfu4ubm9cfyLFy8wc+ZMbN68Gb169QIANGrUCGfOnMHChQtf2/BYWlqWOce5TCZT+82NJvsaMlOs2xRrBkyzblOsGVCvblP8eyIiKq/CwsIKu61MV8zNzTF48GAoFApYWFhU6LH1ZtICCwsLNG/eHAkJCcptCoUCCQkJaNu2bZn7tG3btsR44OWtFsXji5+5kUpLlmlmZgaFQiFyBUREREREqlEoFIiKisKOHTuMcg2cV5mbm1d4swPo0RUe4OUU0uHh4WjRogVatWqF77//Hjk5ORgzZgwAICwsDNWrV0dkZCQA4MMPP0Tnzp3xzTffoFevXoiOjsbJkyexbNkyAIC9vT06d+6Mjz/+GNbW1vDy8sKBAwewevVqfPvttzqrk4iIiIgIePmM+M2bN3Hv3j20bt0azs7Oosa/fv06LC0tK2wWNn2kVw3PkCFDkJGRgdmzZyM1NRVNmjRBbGyscmKCO3fulLha065dO0RFRWHWrFmYOXMm/Pz8EBMTg4YNGyrHREdHY8aMGRgxYgSePHkCLy8vLFiwAO+9916F10dEREREVOzMmTM4fvw4AKBfv36iNztpaWkIDAxEQUEB4uLiUK9ePVHjGwq9angAYNKkSZg0aVKZr+3fv7/UtkGDBmHQoEGvjefm5obff/9drPSIiIiIiERhZmYGc3NztGvXTvRmJDs7G7169cKNGzdQs2ZNODo6ihrfkOhdw0NEREREZAr8/f1RrVo1ODk5iR77k08+QVJSEpydnUvcMWWK9GbSAiIiIiIiU+Ps7AyJRCJ63Hnz5qFHjx7YsWMH/Pz8RI//T+np6Thw4AAEQdD6sVTFKzxEREREREbGyckJcXFxFXKs9PR0rFq1Crm5ubCyskLr1q0r5LjlxSs8RERERESkloyMDKxevRq5ublwc3NDo0aNdJ1SKWx4iIiIiIhILWlpacjJyYGbmxvCwsJgbW2t65RK4S1tRERERESkloYNG8Lc3Byenp562ewAbHiIiIiIiEgDdevW1XUKb8Rb2oiIiIiItEAQBMTGxiIjI0PXqZg0NjxERERERFpw8OBBHD9+HCtXrkR+fr6osZ89e4aNGzeKGtNYseEhIiJ6RWRkJFq2bAk7Ozu4uLggNDQUly9fLjGmS5cukEgkJf689957OsqYiPRRSkoK9u/fDwDo3r07LC0tRYudl5eH0NBQDBo0CD/++KNocY0VGx4iIqJXHDhwABMnTsSxY8cQHx8PuVyOwMBA5OTklBg3btw4PHz4UPnn66+/1lHGRKRvBEHA8ePHAQCtWrVC06ZNRYutUCgwZswYHDhwAHZ2dujUqZNosY0VJy0gIiJ6RWxsbImvV65cCRcXFyQlJZV4Y2FjYwM3N7eKTo+IDIBEIsHw4cNx/PhxtG3bVtTYJ0+exF9//QWZTIaYmBg0adJE1Pj/9OzZM8hkMtja2mr1ONrEhoeIiOgNnj9/DgBwdHQssf3PP//EH3/8ATc3N/Tp0weff/45bGxsyoyRn59f4v79zMxMAIBcLodcLlcpn+Lxqu6nj4ypFsC46mEt4mjdujUUCgUUCoUo8eRyOVq1aoX//e9/cHV1RceOHbVa17Nnz/DHH3/AwsICw4cPR6VKlUSNr8n3RpV92PAQERG9hkKhwJQpU9C+fXs0bNhQuX348OHw8vKCu7s7zp07h+nTp+Py5cvYtGlTmXEiIyMxd+7cUtvj4uJe2yT9m/j4eLX200fGVAtgXPWwFv3k5+cHANi5c6fWjlFQUICrV69CLpfD0tISCQkJkMlkWjmWOt+b3Nzcco9lw0NERPQaEydOxIULF3D48OES28ePH6/8f39/f1SrVg3dunXD9evX4evrWyrOjBkzEBERofw6MzMTHh4eCAwMhL29vUo5yeVyxMfHo0ePHlp781FRjKkWwLjqYS36qSJriYqKglwuh6OjI0aMGAE7OzvRj6FJPcVXysuDDQ8REVEZJk2ahO3bt+PgwYOoUaPGG8e2bt0aAHDt2rUyGx5LS8syZ2iSyWRqv2nRZF99Y0y1AMZVD2vRTxVRS2hoKLZv344+ffqo/MGMqtSpR5XxbHiIiIheIQgCJk+ejM2bN2P//v3w8fH5133OnDkDAKhWrZqWsyMiqhgODg4YMWKErtMQBRseIiKiV0ycOBFRUVHYsmUL7OzskJqaCuDlyd/a2hrXr19HVFQUevbsCScnJ5w7dw5Tp05Fp06d0KhRIx1nT0RE/8R1eIiIiF6xZMkSPH/+HF26dEG1atWUf9atWwcAsLCwwJ49exAYGIi6devio48+woABA7Bt2zYdZ05EFUkQBFy/fh2CIOg6FfoXvMJDRET0in978+Lh4YEDBw5UUDZEpK/+/vtv7Nq1C02aNMFbb70FiUQiStzCwkL85z//wZQpU7T+7Iyp4BUeIiIiIiIV3Lp1S7lIcdWqVUVrdgRBwPvvv485c+YgODhYtPV7TB0bHiIiIiKicsrPz8eGDRsgCAL8/f3Rtm1b0WJ/+eWXWL58OaRSKT7++GNIpdp7q25KzRQbHiIiIiKicrK0tESfPn3g7e2NPn36iHZ15/Hjx1i8eDEAYNGiRejXr58occuSnZ2NZcuWITk5WWvH0Cd8hoeIiIiISAV169ZFnTp1RGt2AMDJyQlHjx7F9u3bMWHCBNHi/lNOTg5Wr16NjIwMxMXFwc/PD+bmxt0S8AoPEREREZGKxGx2itWqVQtTpkwRPW6xvLw8rFq1ChkZGbCzs8OoUaOMvtkBeIWHiIiIiMgkWFpawsPDA3l5eRg9ejQcHR11nVKFYMNDRESvpVAIkEiAF/IiXLz3FABw5u5TNKjhCGuZGQQAUi18yklEROKTSCTo3bs3srOzYWdnp+t0KgwbHiIieq3rGdlYefQWYk7fR2FRIb5uBYz87QTMzcwR2rQ6Rrfzhp+r6Zw0iYgMnUQiMalmB2DDQ0REr7Fo7zV8E38ZxetwWpr932s5BUX48/gdRJ24g4961MGkrrV0kyQREdG/4KQFRERUyqK917Aw7v+andcRBGBh3GUs3netYhIjIqogcrlc1ymQSNjwEBGRkkIh4GpaFhbGXVZpv4Vxl3EtPRuKf+uQiIgMwLlz57B48WI8fPhQ1LhffvklEhISRI1J/44NDxERKUkkwMqjt1TeTxCAlUdugtMXEJGhe/DgAbZt24bnz58jJSVFtLg//fQT5syZg549e+LWrVuixaV/x4aHiIiUXsiLEHP6vlr7bj59Hy/kRSJnRERUcbKzs7Fu3ToUFhaidu3a6NKliyhxN2zYgA8//BAAMGfOHHh7e4sS95/y8vKwb98+KBQKrcQ3VJy0gIiIlJIfZiKnQL2mJaegCCkPs9DMq4rIWRERVQyJRILKlSvDwsIC/fr1E21x0a1bt0IQBLz//vuYMWOGKDH/KS8vD2vWrMGDBw+Qk5OD3r17a+U4hogNDxERKWW+KNRs/zw+5EtEhsvW1hZhYWHIycmBlZWVaHFXrVqFgIAAhIeHi9ZEvSovLw9//PEHHjx4AGtra7Ro0UL0YxgyNjxERKRkb63ZacHeSiZSJkREumFmZgZ7e3tRY0qlUrz99tuixnxVRkYG0tLSYG1tjbCwMLi5uWntWIaIDQ8RESnVq2YPWwsztW5rs7UwQ91qprWYHRGRPvDw8MDw4cNhZWXFZqcMnLSAiIiUrGVmCG1aXa19+zWtDmuZ2b8PJCIi0fn4+KBatWq6TkMvseEhIiIlQQBGt/OGqreYSyTA6PY+4Co8RESkb9jwEBGRklQqgZ+rHT7qUUel/aYF1oFvVVtItfAwLhERkSb4DA8REZUyqWstSCTAwrjLEN5w2UYiednsTAyoVXHJERERqYBXeIiIqEwTA2ohfmpnjGztCVuLks/m2FqYYWRrT8RP7cxmh4gMypUrV3DgwAEIb/o0R0Vr1qzBO++8A7mcU/PrI17hISKi16pZ1RbzQhtiZq96uHjvKR6cP4o/x7ZG/RpVYC0z4zM7RGRQMjIy8Ndff6GgoAB2dnZo1qyZxjF3796Nt99+G4WFhWjbti3Gjh0rQqYlyeVyZGdno1KlSqLHNgW8wkNERK8llUggkUhgY2GOJh6VAQCNPSrDxsIcEomEz+wQkcHIy8tDdHQ0CgoK4OXlhcaNG2scMykpCQMGDEBhYSGGDx+OMWPGiJBpSXK5HGvXrsWKFSvw+PFj0eObAjY8RERERGT0bt++jadPn8LBwQGDBg2CmZnm0+inpqaiqKgI3bp1w++//w6pVNy31nK5HNHR0bh58yYKCgrw4sULUeObCt7SRkRERERGr06dOhgxYgRsbGxga2srSsxevXrh4MGDqFOnDiwsLESJ+aq9e/fixo0bkMlkGDFiBKpVq4Zz586Jfhxjx4aHiIiIiEyCr6+v6DFbtmwpesxinTt3RmpqKrp06QJPT09OiqAmNjxERERERHrIysoKYWFhkPB5SY3wGR4iIiIiIj3FZkdzbHiIiIiIiMhoseEhIiIiIiKjxYaHiIiIiOg1CgsLdZ0CaYgNDxEREREZhfv37yMzM1O0eImJiWjQoAEuXrwoWsxXCYKglbhUEhseIiIiIjJ4z549Q1RUFJYtW4b09HSN46WkpKB37964cuUK/vOf/4iQYUlFRUXYsGEDjh8/LnpsKokNDxEREREZNLlcjnXr1iE3Nxd2dnaoUqWKRvEePnyI4OBgPHnyBK1atcKyZctEyvSloqIi/PXXX0hOTkZ8fDyeP38uanwqievwEBEREZFB27t3L1JTU2FjY4OhQ4dCJpNpFM/CwgLu7u6wsLDA9u3bYWtrK1KmL29j27RpE5KTk2FmZoYhQ4bAwcFBtPhUGhseIiIiIjJonTp1wuPHj9G+fXtRmgcnJyfs2bMHjx8/RtWqVUXI8P9IJBK4ubkhJSUFgwcPhp+fn6jxqTQ2PERERERk0KytrTF8+HBRY9rY2MDGxkbUmMU6duyIBg0awNHRUSvxqSQ+w0NEREREVMHY7FQcNjxERERERGS02PAQEREREZHRYsNDRERERERGiw0PEREREZkcrn1jOtjwEBEREZHey8vLEy3WnTt30KBBAyxYsACCIIgWF3i5zk5iYiIKCgpEjUvqY8NDRERERHotJycHS5cuxZ49e6BQKDSK9eTJEwQHB+P+/ftYu3YtcnNzRcryZbOzbds2xMXFYe3ataI3U6QeNjxEREREpLeKioqwfv16PH/+HMnJyRpdOZHL5ejbty+Sk5NRvXp17Nq1C7a2tqLkKQgCtm/fjtOnT0MikaBZs2aQSCSixCbN6F3Ds3jxYnh7e8PKygqtW7fGiRMn3jh+w4YNqFu3LqysrODv74+dO3eWGpOcnIy33noLDg4OsLW1RcuWLXHnzh1tlUBERAYsMjISLVu2hJ2dHVxcXBAaGorLly+XGJOXl4eJEyfCyckJlSpVwoABA5CWlqajjImMW2xsLO7cuQNLS0sMGzYMVlZWaseSyWQYOHAgqlSpgtjYWHh4eIiWZ2ZmJlJSUiCRSBAaGgp/f3/RYpNm9KrhWbduHSIiIjBnzhycOnUKjRs3RlBQENLT08scf/ToUQwbNgxjx47F6dOnERoaitDQUFy4cEE55vr16+jQoQPq1q2L/fv349y5c/j88881+sdCRETG68CBA5g4cSKOHTuG+Ph4yOVyBAYGIicnRzlm6tSp2LZtGzZs2IADBw7gwYMH6N+/vw6zJjJerq6uMDMzQ//+/eHs7KxxvA8//BDXrl1Dw4YNRcju/zg4OCAsLAz9+vVDo0aNRI1NmjHXdQKv+vbbbzFu3DiMGTMGALB06VLs2LEDK1aswKefflpq/A8//IDg4GB8/PHHAIB58+YhPj4eixYtwtKlSwEAn332GXr27Imvv/5auZ+vr28FVENERIYoNja2xNcrV66Ei4sLkpKS0KlTJzx//hy//fYboqKi0LVrVwDA77//jnr16uHYsWNo06aNLtImMlotWrRA7dq1YW9vL1pMR0dH0WK9ytXVFa6urlqJTerTm4anoKAASUlJmDFjhnKbVCpF9+7dkZiYWOY+iYmJiIiIKLEtKCgIMTExAACFQoEdO3bgk08+QVBQEE6fPg0fHx/MmDEDoaGhr80lPz8f+fn5yq8zMzMBvLzvUy6Xq1RX8XhV9zN0pli3KdYMmGbdplgzoFndhvx3VTx1bfEbpKSkJMjlcnTv3l05pm7duvD09ERiYmKZDQ/PK2UzploA46pH32qxtrZWOxd9q0UTxlQLUHHnFb1peB49eoSioqJSXbGrqytSUlLK3Cc1NbXM8ampqQCA9PR0ZGdn47///S/mz5+Pr776CrGxsejfvz/27duHzp07lxk3MjISc+fOLbU9Li4ONjY26pSH+Ph4tfYzdKZYtynWDJhm3aZYM6Be3WLOglSRFAoFpkyZgvbt2ytvf0lNTYWFhQUqV65cYuyr559/4nnlzYypFsC46mEt+smYagG0f17Rm4ZHG4qnLezbty+mTp0KAGjSpAmOHj2KpUuXvrbhmTFjRokrR5mZmfDw8EBgYKDKl1Plcjni4+PRo0cPyGQyNSsxPKZYtynWDJhm3aZYM6BZ3cVXNAzNxIkTceHCBRw+fFijODyvlM2YagGMqx7Wop+MqRag4s4retPwODs7w8zMrNQsN2lpaXBzcytzHzc3tzeOd3Z2hrm5OerXr19iTL169d548rK0tISlpWWp7TKZTO0fLk32NWSmWLcp1gyYZt2mWDOgXt2G+Pc0adIkbN++HQcPHkSNGjWU293c3FBQUIBnz56VuMrzpvMVzytvZky1AMZVD2vRT8ZUC6D984rezNJmYWGB5s2bIyEhQblNoVAgISEBbdu2LXOftm3blhgPvLwkVjzewsICLVu2LDWd6JUrV+Dl5SVyBUREZAwEQcCkSZOwefNm7N27Fz4+PiVeb968OWQyWYnzz+XLl3Hnzp3Xnq+IqOJkZGRo5aqyIAh49uyZ6HFJ+/TmCg8AREREIDw8HC1atECrVq3w/fffIycnRzlrW1hYGKpXr47IyEgAL6cV7Ny5M7755hv06tUL0dHROHnyJJYtW6aM+fHHH2PIkCHo1KkTAgICEBsbi23btmH//v26KJGIiPTcxIkTERUVhS1btsDOzk75XI6DgwOsra3h4OCAsWPHIiIiAo6OjrC3t8fkyZPRtm1bztBGpGPZ2dno2bMnCgsLsXPnTlSrVk2UuIIgYM+ePTh58iRGjBgBT09PUeJSxdCrhmfIkCHIyMjA7NmzkZqaiiZNmiA2NlY5McGdO3cglf7fRal27dohKioKs2bNwsyZM+Hn54eYmJgS86r369cPS5cuRWRkJD744APUqVMHf/31Fzp06FDh9RERkf5bsmQJAKBLly4ltv/+++8YPXo0AOC7776DVCrFgAEDkJ+fj6CgIPz8888VnCmRccjLy8O2bdvQvXt3VKlSRe04crkcgwcPxsmTJ+Hk5ISsrCxRGh5BEJCQkICjR48CeDkpFhsew6JXDQ/w8p7pSZMmlflaWVdlBg0ahEGDBr0x5ttvv423335bjPSIiMjICYLwr2OsrKywePFiLF68uAIyIjJegiBg8+bNuHLlCh4/fox3330XEolErVjTp0/Hrl27YG1tjR07dqB27dqi5Hj+/HkcOXIEABASEoIWLVqIEpcqjt48w0NERMbr1TVoiIiK7du3D1euXIG5uTneeusttZsdAHj//fdRu3ZtrF+/Hq1btxYtx/r166N27doIDg5Gq1atRItLFUfvrvAQEZFxOX36NLZu3arrNIhIz8jlcuVai3369IG7u7tG8WrVqoULFy6IPnuZubk5hg4dqlEzRrolasNTUFAAuVwOW1tbMcMSEZEBc3R0RF5enq7TICI9I5PJMHbsWKSkpKBRo0aixdQGNjuGTa1b2qKjo5ULeRabO3cuKlWqhMqVK6Nfv37Izs4WJUEiIjJsXl5eCAsL03UaRKSHLC0t0bhxY12nQUZOrYbnm2++QU5OjvLro0ePYu7cuQgKCsLUqVMRGxuLBQsWiJYkEREZturVq+s6BSIiMlFq3dJ2/fp1hIeHK7+OioqCm5sbNm/eDHNzcygUCvz111/K9XKIiIiIiIh0Qa0rPPn5+bCyslJ+HRcXh5CQEJibv+yf6tevj3v37omTIRERERERkZrUanh8fHywZ88eAMDJkydx7do1BAcHK19PS0tDpUqVxMmQiIj01qNHj3SdAhGZiPz8fBw/flz0uEeOHEFcXFy51uAiw6RWw/Puu+9i/fr1aNSoEQIDA1GjRg307t1b+fqRI0fQoEED0ZIkIiL9UlBQgG3btmHx4sW4efOmrtMhIiOnUCgQHh6Ojh07Yt26daLFPXr0KPbs2YPExETcuHFDtLikX9R6hmfy5MmwsrLCzp070bx5c0yfPh3W1tYAgCdPniA1NRXvvfeeqIkSEZF+uHv3LrZv346nT58CAO7duwcfHx8dZ0VE+kgul+Pq1auoX7++RnGmTZuGdevWwdzcHI6OjqLkduzYMcTHxwMAunTpAl9fX1Hikv5Rex2ecePGYdy4caW2Ozo64uTJkxolRURE+uvhw4d4+vQpHBwc0LdvXzY7RFQmQRCwfft2nDt3Dh06dEC3bt3UirN161Z89913AICVK1eiR48eouRnY2MDiUSCTp06oXPnzqLEJP2k0cKj+fn5OHXqFNLT09G+fXs4OzuLlRcREempli1bAgCaN29eYgIbIqJXJSYm4ty5c5BIJKhZs6bacXr16oXJkyfDw8MDI0aMEC2/Ro0awdXVFS4uLqLFJP2k1jM8APDjjz+iWrVqaN++Pfr3749z584BePkAq7OzM1asWCFakkREpD8kEgnat2/PZoeIXuvx48fKCa6Cg4M1uhJsZmaGH374AdOmTRMrPSVXV1dIJBLR45J+Uavh+f333zFlyhQEBwdjxYoVJWa1cHZ2RteuXREdHS1akkRERERkOJycnBAaGoqWLVsqrwprQiKRsDEhtal1S9s333yDvn37IioqCo8fPy71evPmzfHjjz9qnBwRERERGaZGjRqhUaNGuk6DSL0rPNeuXUNISMhrX3d0dCyzESIiIv1WVFSk6xSIiIhEpVbDU7ly5TcuNnfp0iW4ubmpnRQREVUsQRBw5swZ/PTTT3j+/Lmu0yEiIhKNWg1Pz549sWzZMjx79qzUaxcvXsTy5cvx1ltvaZobERFVgJycHKxfvx5btmzB8+fPcezYMV2nRESksYsXLyI7O1vXaZAeUKvhmT9/PoqKitCwYUPMmjULEokEq1atwsiRI9GiRQu4uLhg9uzZYudKRERacODAAaSkpEAqlaJr166irXFBRFQegiDgzz//RGFhoWgxT58+jY0bN2LVqlXIy8sTLS4ZJrUaHnd3dyQlJSE4OBjr1q2DIAhYs2YNtm3bhmHDhuHYsWNck4eIyEB07doVfn5+GDduHDp27AipVO0VC4iIVLZgwQKMHDkS/fv3LzHzr7rOnDmDrVu3AgBq1qwJS0tLjWOSYVN74VEXFxf8+uuv+PXXX5GRkQGFQoGqVavyRElEZGCsrKwwfPhwXadBRAamqKgIcrlcozW5VqxYgc8//xwAEBQUpPHU04WFhTh06BCAl4skBwcHi3rliAyT2g3Pq6pWrSpGGCIiMjIKhQLLli3jh2FERiguLg7Xrl3D0KFD1Xov+PjxY0yZMgUAMGPGDEycOFHjnMzNzREeHo7Tp0+jU6dOXLuHAKjZ8Hz55Zf/OkYikSg7diIiMj3nz5/H+PHjcezYMTg4OOg6HSIS0alTp3DixAkALxsXdRoeJycnxMbGYt26dViwYIFoudnb26Nz586ixSPDp1bD88UXX7z2NYlEAkEQ2PAQEZmw69evo1mzZigsLISdnR1mzpyJ6dOn6zotIhLB3bt3sWPHDgBAly5dULduXbVjtWvXDu3atRMrNaIyqXWPgUKhKPWnsLAQ169fx9SpU9GiRQukp6eLnSsREakgPT0df/31l07uX/f19cWgQYMwYMAAJCcn47333qvwHIhIO+zt7eHq6op69eqhU6dOuk6H6F+J8gwPAEilUvj4+GDhwoUYMWIEJk+ejKioKLHCExFROSkUCiQmJmLfvn0oKipClSpV0LVr1wrPY+XKlbCwsAAAZGZmVvjxiUg7HBwcMGbMGOUdPUT6TitPkXbq1Ak7d+7URmgiIvoXO3fuxJ49e1BUVITatWujVatWOsmjuNkhIuMjk8n4b5wMhmhXeF518uRJzshDRKQjrVq1QnJyMrp3744mTZrwE1giMirZ2dmoVKmSrtMgA6JWw7N69eoytz979gwHDx7Epk2b8M4772iUGBERqcfFxQVTpkyBTCbTSvzCwkKtxSYiepPk5GRs2rQJ/fr1Q/369XWdDhkItRqe0aNHv/Y1Z2dnfPrpp5g9e7a6ORERkYa01ZCcOHECU6ZMwfr169GyZUutHIOIjMfKlSvRsmVLNGjQQONYycnJ2LhxIxQKBa5evcqGh8pNrYbn5s2bpbZJJBJUqVIFdnZ2GidFRET65d69e5g0aRK2bNkCAFiwYAFiYmJ0mxQR6bVNmzbh7bffhoODA86cOQMvLy+1Yz18+FDZ7Pj7+6NPnz4iZkrGTq2GR5MfWCIiMjxRUVHYsmULzMzMMHXqVMydO1fXKRGRHjt06BCGDx8OQRAwZMgQeHp6ahTPzc0NzZo1Q15eHkJDQ/msOKlEK5MWEBGR+PLz83HhwgU0a9aswicimDp1Ki5evIhmzZrh/fff5zM8REbu0KFDyMjIgCAIau3/3//+F/n5+ejbty8WL16s8e8siUSCnj17QhAENjuksnI1PFKpVOUfVIlEopPF7oiIjNGtW7ewZcsWPHv2DBYWFvD396/Q48tkMvz6669ccoDIBCQnJ+PQoUMAXt7OWrNmTZVjbNiwAQsWLMBnn30GMzMzUfKSSCScdZLUUq6GZ/bs2fwBIyLSkQMHDmD//v0AXi74Z29vr9uEiMhopaWlYfPmzQCAqlWrwsPDQ604NjY2WLBggZipEamtXA3PF198oeU0iIjodZydnQEATZs2RVBQECwtLXWcEREZq9u3b0Mul8Pb2xsODg66TodIFHyGh4hIzzVo0ABVqlSBu7u76LELCwuRkpKChg0bih6biAxPq1atULlyZbi6uiqvLBMZOo0annv37uH06dN4/vw5FApFqdfDwsI0CU9ERP+fNpqd48eP491338W9e/eQkpKivJJERKatdu3akMvluk6DSDRqNTx5eXkIDw/HX3/9BYVCAYlEopzF49VnfdjwEBHpn6KiInzwwQdYsmQJBEGAo6MjLl26hE6dOuk6NSIyUTdu3MC5c+fQp08f0SY5ICqm1rx+M2fOxKZNm7BgwQLs378fgiBg1apViIuLQ0hICBo3boyzZ8+KnSsREYnAzMwM6enpEAQBYWFhSElJYbNDRDpz8+ZNrF27FmfPnsXx48d1nQ4ZIbUano0bN2LMmDGYPn06GjRoAACoXr06unfvju3bt6Ny5cpYvHixqIkSERkbQRCQnp6uk2P/8MMPSEhIwKpVq1C1alWd5EBEhmvPnj2IjIxUe52eYjdv3kRUVBQKCwvh5+eHVq1aiZQh0f9Rq+FJT09X/kBaW1sDAHJycpSvDxgwAJs2bRIhPSIi45SdnY1169Zh2bJlSEtLq/Dju7u7o2vXrhV+XCIyfKdPn0a/fv0wc+ZMrFq1SpSYtWrVwuDBg2Fuzvm0SHxq/VS5urri8ePHAF7Os16lShVcvnwZffr0AQBkZmYiLy9PvCyJiIxIcnIytm/fjtzcXJiZmSE1NRWurq66TouI6F/dvHkTPXv2RHZ2NgICAjBs2DCN4vn4+GDMmDFwcXFhs0Nao9ZPVuvWrXH48GFMnz4dANCnTx/873//Q7Vq1aBQKPDdd9+hTZs2oiZKRGQs7t+/j9zcXLi6uqJfv35G1+w8fvwY+/btQ48ePbiOB5EeunTpEry8vGBra6vyvocOHUJaWhoaNWqEzZs3i7IumDZmoSR6lVoNzwcffIANGzYgPz8flpaWmDdvHhITEzFq1CgAgK+vL3788UdREyUiMhZdunSBra0tWrVqJepsRIIgIDo6GnXr1kXTpk1Fi1teL168wIEDB/D3339DoVDA3NwcoaGhFZ4HEb3ejRs3sHHjRtjb22PcuHEqNz1hYWGwt7dHq1at+IEGGYxyNzwDBw7EqFGj0LNnT3To0AEdOnRQvubh4YHk5GScP38eZmZmqFu3Li9LEhG9hrm5Odq2bStqzOvXr+P9999HXFwcWrRogWPHjlX41K4HDx5UzrBUq1YttGvXrkKPT0Rv9uTJE2zYsAGCIMDb2xs2NjZqxeEHGWRoyt2V7NixA5s3b4aDgwMGDRqEESNGlJjGVCqVonHjxlpJkoiIXu/w4cPo0aMH8vLyYGlpib59+0KhUFR4w9OhQwfcv38fnTt3hq+vb4Uem4j+3a5du5CXl4fq1aujd+/eJdZOJDJm5Z6lLSMjAytWrEDLli2xYsUKBAQEwNPTE59++inOnTunzRyJiOgNWrZsCW9vb3Tr1g3nz5/HrFmzIJPJKjwPW1tbvP322wbf7Bw8eBB9+vSBu7s7JBIJYmJiSrw+evRoSCSSEn+Cg4N1kyyRCvr27Yt69ephyJAhvBOHTEq5G55KlSohPDwcu3fvxoMHD/D999+jevXq+Prrr9G0aVP4+/vjq6++wp07d7SZLxGRXissLKzwY1paWuLAgQOIj4+Hn59fhR/f2OTk5KBx48ZvXE8uODgYDx8+VP5Zu3ZtBWZIpJ5KlSph8ODBsLOzq7Bj3r9/H48ePaqw4xGVRa11eKpWrYrJkycjMTERN27cwJdffgkAmDFjBmrWrIlOnTph2bJloiZKRKTPFAoFDh8+jJ9//hkvXryo8OO7uLjw9hSRhISEYP78+ejXr99rx1haWsLNzU35p0qVKhWYIZFhuH//PtasWYNVq1bhyZMnuk6HTJjG1zO9vb3x2Wef4bPPPsO5c+cwZ84cbNmyBUeOHMH48ePFyJGISK89efIEMTExuHv3LgDg7NmzRjM1v0KhwJkzZ1ClShXUqFFD1+nojf3798PFxQVVqlRB165dMX/+fDg5Ob12fH5+PvLz85VfZ2ZmAgDkcjnkcrlKxy4er+p++siYagGMqx5Na3n48CGioqKQn5+PqlWrwtLSUmd/L/y+6C9N6lFlH1Fu4Cy+nB8VFYVTp04BAFq0aCFGaCIivRcfH4+7d+/CwsICISEhRjOBy/Xr1xEfH4+0tDRUrVoVY8eO1XVKeiE4OBj9+/eHj48Prl+/jpkzZyIkJASJiYmvnSgiMjISc+fOLbU9Li5O7Zmy4uPj1dpPHxlTLYBh13P//n0sW7YMU6ZMQZUqVdSu5fr168jPz4etrS2qVKmCPXv2iJyp6gz5+/JPxlQLoF49ubm55R6rdsPz7NkzbNy4EVFRUTh06BCKiorg6+uL2bNnY+TIkahVq5a6oYmIDEpISAgAICgoCJUrVxYt7v79+zF79mzExMTA0dFRtLjlce3aNfz5558AACsrKzRt2hSCIFRoDvpq6NChyv/39/dHo0aN4Ovri/3796Nbt25l7jNjxgxEREQov87MzISHhwcCAwNhb2+v0vHlcjni4+PRo0cPnUxOISZjqgUw/HpSU1MxZcoU3Lp1C1u3bkV4eLjateTl5eHAgQPo0qWLKIuTasLQvy+vMqZaAM3qKb5SXh4qNTx5eXnYunUroqKisHv3buVlygkTJmDkyJFo1aqVSokSERkDe3t7DBkyRLR4jx49wrRp07Bq1SoAwPz58/Htt9+KFr88atasCXd3d3h4eKBTp06wsbExmlsoxFazZk04Ozvj2rVrr214LC0ty3zTJ5PJ1H7Tosm++saYagEMs57MzEz07dsXt27dgq+vL3766SecPHlS7VpkMhl69+6thUzVZ4jfl9cxploA9epRZXy5G56wsDBs2bIF2dnZsLGxwYABAzBixAgEBgZW+FoPRETGLCIiAmvWrIFEIsG7776L2bNnV3gOUqkUY8eOhVSq1tw2JuXevXt4/PgxqlWrputUiJCVlaXWLGxPnjxBZmYmqlatit27d8PFxUUL2RHpRrkbnrVr16JHjx4YMWIE+vXrp/Y9x0RE9GYLFizAjRs3sHDhQp1OfmCqzU52djauXbum/PrmzZs4c+YMHB0d4ejoiLlz52LAgAFwc3PD9evX8cknn6BWrVoICgrSYdZEL5vvVatWoVOnTujQoYNKMzd6e3vjyJEjePjwIXx9fXlFl4xKuRueBw8eoGrVqtrMhYiIAHh4eODw4cO6TsNknTx5EgEBAcqvi5+9CQ8Px5IlS3Du3DmsWrUKz549g7u7OwIDAzFv3jydP6dApi0rKwvr169HYWEh7t+/r1YMV1dXuLq6ipwZke6Vu+Fhs0NEpubWrVs4ffo0QkNDjWaNm6dPnyItLQ1169bVdSp6q0uXLm+coGH37t0VmA3RvysqKsL69euRlZWFqlWrol+/fkbzO4tIDKJMS01EZEzkcjkSEhJw/PhxAC+vuBj6VPt5eXk4dOgQjh8/DqlUig8++ACVKlXSdVpEJAKpVIq6devi8ePHGDp0aIVcbczLy4OVlZXWj0MkBjY8RET/sH79euUzHM2aNYO/v78ocRUKBZ4+ffrGBSq1IT8/H4sWLUJOTg4AwNPTk/fnExkRiUSC9u3bo3nz5hXShGRkZGDVqlXo0KGD0SyyTMaNDQ8R0T+0b98eaWlp6N27N2rXri1KzIsXL+Ldd99FUVERjhw5UqETAlhaWsLPzw/37t1Djx494Ofnx9tdiIxQRTQ7jx49wqpVq5CTk4MzZ86gRYsWMDfn20nSb/wJJSL6B29vb3zwwQeinMTz8vIwb948fP311ygsLIStrS0uXrwo2lWj8goODoa5uTmXESAyYUVFRRr9Dnjx4oWy2XF1dUVYWBibHTIIav2U5ufn48iRI0hOTkZmZibs7OxQv359tG/fnrPUEJFREPMkXjxzUt++ffHTTz/Bw8NDtNjlxd/NRKbt6dOn6NatG6ZNm4bhw4erFcPa2hqtW7fG+fPnERYWxiVKyGCodEYXBAELFy7EV199hadPn5aYxUYikaBKlSqYPn06pk2bxtsliIjw8haT3377DU+ePEFoaKiu0yEiE5SXl4fQ0FCcPn0an3zyCfr27QtbW1u1YnXo0AGtW7dWaZV7Il1TqeEZMWIEoqOj4efnh8mTJ6Nx48aws7NDVlYWzp49i6ioKHz66ac4c+YM/vzzT23lTERkUDp16qSVuIIg4ObNm6hZs6ZW4hOR4SsqKsLIkSNx8OBB2NvbY9euXWo3O8XY7JChKfdTs2vWrEF0dDSmTZuGS5cuYc6cOQgNDUW3bt0QGhqKOXPmIDk5GR9//DGio6Pxxx9/qJ3U4sWL4e3tDSsrK7Ru3RonTpx44/gNGzagbt26sLKygr+/P3bu3Pnase+99x4kEgm+//57tfMjIsOUnZ2N06dP6zoNUdy6dQvLly/HmjVrcOvWLV2nQ0R6zN3dHRYWFoiJianw5weJ9EG5G57ly5ejc+fO+Prrr1/7wJtUKsV///tfdO7cGcuWLVMroXXr1iEiIgJz5szBqVOn0LhxYwQFBSE9Pb3M8UePHsWwYcMwduxY5QKBoaGhuHDhQqmxmzdvxrFjx+Du7q5WbkRkuC5duoSff/4ZW7duxc2bN3Wdjka2bt2KVatW4eHDh7C0tERmZqauUyIiLUtPT8eaNWuQlZWl0n5mZmb44YcfcObMGQQEBGgpOyL9Vu6G59y5cxgwYEC5xvbv3x/nzp1TK6Fvv/0W48aNw5gxY1C/fn0sXboUNjY2WLFiRZnjf/jhBwQHB+Pjjz9GvXr1MG/ePDRr1gyLFi0qMe7+/fuYPHky/vzzT16KJTIxW7duxYYNG/DixQu4ublpfDtHsdjYWDx79kyUWKqoVq0aJBIJWrRogcmTJ6NRo0YVngMRVZwXL14gOjoaN27cQFxcnMr7SyQS1KtXTwuZERmGcj/DI5fLyz2/u6WlJQoLC1VOpqCgAElJSZgxY4Zym1QqRffu3ZGYmFjmPomJiYiIiCixLSgoCDExMcqvFQoFRo0ahY8//hgNGjRQOS8iMmwuLi6QSCTo0KEDOnfurPHUzA8ePMDXX3+No0eP4v3338fixYtFyrR8mjdvDh8fHzg7O1focYmo4ikUCmzcuBFPnz6Fg4MDQkJCdJ0SkcEpd8NTq1YtHDx4EO+8886/jj106JBaD9E+evQIRUVFcHV1LbHd1dUVKSkpZe6Tmppa5vjU1FTl11999RXMzc3xwQcflCuP/Px85OfnK78uvl1ELpervDp58XhTW9XcFOs2xZoBw6i7WbNm8PDwgIuLCxQKBRQKhdqxdu7ciVGjRiErKwtmZmawtrZGQUFBhc9M6eDgUOF/55p8r/X554NIn2VnZ+Pp06eQyWQYOnSoVqeCfvbsGfbu3YtevXpV6OLIRNpW7oZn4MCB+PLLLzFkyBD06tXrteN27NiBtWvXYvbs2aIkqKmkpCT88MMPOHXqVLnfkERGRmLu3LmltsfFxan9iyY+Pl6t/QydKdZtijUDplN3Wloa8vPzUbt2bUyYMAE+Pj7YtWuXrtOqUOp8r3Nzc7WQCZHxs7e3x7hx45Camgo3NzetHef58+dYtWoVnj17BolEgt69e2vtWEQVrdwNz0cffYR169YhNDQUY8aMwahRo0pMS33u3DmsXr0av//+O+rUqYOPPvpI5WScnZ1hZmaGtLS0EtvT0tJe+4/czc3tjeMPHTqE9PR0eHp6Kl8vKirCRx99hO+//77M2Y1mzJhR4ja5zMxMeHh4IDAwEPb29irVJJfLER8fjx49epjUs0OmWLcp1gyYZt3+/v54+PAhgoODRa05MzMTNjY2ertyuSbfa06sQKQ+a2tr+Pj4aC3+q82Oo6MjunXrprVjEelCuc+qNjY22Lt3L8LCwvDrr7/it99+KzVGEAR0794dq1evVutKiIWFBZo3b46EhATlAn0KhQIJCQmYNGlSmfu0bdsWCQkJmDJlinJbfHw82rZtCwAYNWoUunfvXmKfoKAgjBo1CmPGjCkzpqWlZZmrkstkMrXf3GiyryEzxbpNsWbAtOpu0aIFdu7cKVrN+fn5OHLkCBITExEQEIB27dqJkKX2qFO3qfxsEOlCbm4uLCws1P6wpPhW/ipVqiA8PBz29va8DZWMikr/MlxcXBAbG4vjx49j27ZtuHTpErKysmBnZ4d69eqhd+/eykZDXREREQgPD0eLFi3QqlUrfP/998jJyVE2J2FhYahevToiIyMBAB9++CE6d+6Mb775Br169UJ0dDROnjypnBbbyckJTk5OJY4hk8ng5uaGOnXqaJQrEemWQqFARkZGqef4DMmVK1ewdetW5OTkAADu3Lmj9w0PEemPwsJCDB48GIIgYP369WrNQuni4oLw8HBYWlqqfCcLkSFQ66OA1q1bo3Xr1mLnAgAYMmQIMjIyMHv2bKSmpqJJkyaIjY1VvqG5c+dOiQfp2rVrh6ioKMyaNQszZ86En58fYmJi0LBhQ63kR0T64fHjx4iJiUFGRgYmTJgABwcHXaekFltbW+Tk5MDR0RE9evTgBzFEVG6CIODdd9/Fjh07YGVlhZSUFDRv3lytWC4uLiJnR6Q/RLlR/OLFizh48CCys7PRuHFjBAYGahRv0qRJr72Fbf/+/aW2DRo0CIMGDSp3fK5KTmS4BEHA33//jfj4eBQWFsLS0hKPHj3SuOFJSkrCb7/9hkWLFlXo7ETVq1fH8OHDUbNmTY2nyyYi0zJv3jysWLECUqkU69atU7vZITJ25T6rKxQKTJ8+HR4eHvDx8VHOYhYREYFGjRph4sSJmD59OkJCQtCpUyfOyENEWiGRSHD//n0UFhbCx8cHEyZMgK+vr9rxMjMz8eGHH6JVq1ZYsmQJVq5cKV6y5eTn58dmh4hU1q1bNzg6OmLJkiV46623dJ0Okd4q9xWeJUuW4H//+x9atmwJV1dX/Oc//0FGRgaWLl2KiRMnolu3bigsLMTWrVuxZs0azJs3T/mcDRGRmEJCQuDp6YlmzZppvP5Nnz59cPDgQQDAiBEj3jjtPhGRNjx79gwPHjxA/fr1Vdqvffv2uHLlSqlnlYmopHI3PL/++it69eqFbdu2AQAWL16MDz74ABMnTsSPP/6oHDdgwADk5ORg48aNbHiISCusrKxEu3VjxowZePDgAX7++Wf06NFDlJjAy1vvCgoKypzxkYioWEFBAaKjo5GWloZevXqhRYsWKu3PZofo35X7lrYbN26gZ8+eyq979uwJQRDQtWvXUmO7d++OO3fuiJMhEZEWBQcH49KlS6I2O3fu3MFvv/2m/ICIiKgsgiBgy5YtSEtLg62tLWrXrq2V4+Tk5ODevXtaiU1kCMp9hScrK6vEQ8HF0xaWNX2hnZ0dCgsLRUiPiEj7xFojJisrC7Gxsbh06RIAICMjA9nZ2ahUqZIo8YnIuFy9ehWXLl2CVCrF4MGDtTIldE5ODlavXo2nT59i5MiRJRZiJzIV+rmcNxGZLLlcbrCLVJqbm+PGjRuQSCRo0qQJAgIC2OwQ0Wv5+fmhZ8+eMDMz00ojkpubi9WrVyM9PR2VKlVSa40eImOgUsOzc+dOpKamAnj5j0gikWDDhg04c+ZMiXFJSUmiJUhEpkEul2PPnj24desWxo0bp/aK4cUEQUBhYWGFNk/W1tbo27cvqlSpYtCLoRJRxZBIJGjZsqXW4h8+fFjZ7ISHh/N5HzJZKr2jiIqKQlRUVIltv/zyS5ljNZ05iYhMx/3797F582Y8fvwYwMvbPOrVq6d2vJs3b+L9999H7dq18cMPP4iVZrnUrVu3Qo9HRMZLoVDg3r17al/96datG/Lz89G2bVs4OzuLnB2R4Sh3w3Pz5k1t5kFEJkoQBOzcuROPHz9GpUqV8NZbb8HPz0+tWHK5HN988w2+/PJLvHjxAgcOHMBnn33GFcSJyCBNnz4dv/76K7Zu3YqOHTuqvL+ZmRn69OmjhcyIDEu5Gx4vLy9t5kFEJkoikeCtt97C0aNHERwcDGtra7Vj3bt3D3PnzkVeXh66dOmCpUuXitbsCILAK9dEVGG+//57LFy4EABw69YttRoeInqJkxYQkc65urqiX79+Gsfx8fHB//73P9jb22PUqFGiNCgFBQU4evQorly5grFjx8LMzEzjmEREbxIbG4upU6cCACIjIzFq1CgdZ0Rk2Mrd8JS13k4xiUQCKysreHl5oWfPnujdu7coyRERqWrSpEmixBEEAWfPnsXevXuRlZUFALh06RL8/f1FiU9E9DqdOnVCnz594OXlhenTp+s6HSKDV+6GJz09/Y2flubm5iI+Ph6//PILgoKCsGXLFoOdWpaICAD+/vtvZGVloXLlyujevTvq16+v65SIyMDk5ORAJpPBwsKi3PvY2Nhg06ZNkEgkvJWWSATlbnguXLjwr2NevHiBX375BREREfj666/x2WefaZQcEZGuSCQSBAUF4e7du2jdurXG02QTkekpLCzEunXrUFBQgKFDh6Jy5crl3rc8v3PkcjnMzc3ZFBH9C6mYwaytrTFlyhQMHTq01PTVRGR6Ll68iB07dogS6/Tp0xAEQZRY5eXp6Yn27duz2SEilQmCgF27duHu3bt49uwZioqKRI2fn5+PVatWYffu3RX+u5HI0Ija8BRr3749p7EmMmEvXrzAX3/9hY0bN+LkyZO4cuWK2rGePHmCd955B82aNcOaNWtEzJKISHtOnTqFU6dOQSKRYODAgaIu+pmfn48//vgD9+/fx9mzZ5GZmSlabCJjpJWPLXNzc/mJKJGJEgQBK1euVD7317FjR/j6+qoVKyYmBuPHj0dGRgYAIDk5WcxUiYi0xtvbG87OzmjSpAlq1aolWlxBELB27Vrcu3cPVlZWCAsLg4ODg2jxiYyR6F2JIAjYunUrZzIiMlHFTc7+/fvRr18/VK9eXe1Y5ubmyMjIQMOGDbF06VK0b99e4/zu37+PvXv3om/fvrC3t9c4HhFRWZycnDBu3DjRJ3CSSCRo3rw5MjIyMGLECFSrVk3U+ETGqNwNz5MnT974+osXL3D58mUsWbIER48exR9//KFxckRkmBo2bIi6detqfKW3d+/e2LhxI9566y2N3zQ8f/4cCQkJOH/+PABg37596Nu3r0YxiYje5E0zsx09ehRt27ZVa8IBf39/1K5dG5aWlpqkR2Qyyv1uxNnZuVz/KGUyGebNm4dhw4ZplBgRGTaxbmsdMGCAKHGOHDmibHYaN26MgIAAUeISEalq5cqVGDNmDCZMmIDFixer1fSw2SEqv3K/I5k9e/Yb/0EWLzzarVs3VK1aVZTkiIjE0rlzZzx//hxdunThLSBEpDOxsbF45513AAB2dnacUpqoApS74fniiy+0mAYRkXbZ2tryyjMR6dSjR48wePBgFBUVYeTIkYiMjNR1SkQmQaNpqXNycpCamorCwkKx8iEiPfbo0SPlbWGaSE5OxrJly0TIiIjIcDg7O+PXX39F79698dtvv0Eq1crqIET0Dyr/S7t9+zYmTZoELy8v2Nvbo3r16rCysoK3tzc++eQT3L59Wxt5EpEOCYKA48eP45dffsGWLVuQlpamVpwXL17g888/R+PGjfH+++/jzJkz4iZKRFSB1Fnwc/Dgwdi6desbJzQgInGp1PBs27YNjRo1ws8//wwzMzP06dMHw4cPR+/evSGVSrFw4UI0adKkxMrqs2bNEj1pIqo4CoUCf/75J2JjY1FYWAgvLy9YW1urHKewsBAtW7bE/PnzIZfLERwcjCpVqmiUm1wux6FDh3Djxg2N4hARqUqhUGD9+vU4ffq0yvu+6bkduVyO7du3IysrS5P0iOgV5X6GJzk5GYMHD4aPjw9++eUXdOzYsdSYQ4cO4b333sOQIUNw8uRJREZG4o8//sD8+fNFTZqIKo5UKoWLiwtu376NwMBAtGjRQq2HbM3NzTFo0CA8ffoUP/74I/r376/2w7qCIODChQtISEjA8+fP4eLignfffZe3hxBRhdm7dy9SUlJw7do1+Pr6irKuV2FhIdatW4fr16/jwYMHGDduHCc1IBJBuRue//znP3BycsLhw4fh6OhY5piOHTvi0KFDaNSoEZo3b478/Hw+kEdkBLp27YoWLVq89t9+eX366aeYOnWqxm8MUlJSsGnTJgCAvb092rdvzzcFRFRhzp8/jyNHjgAA3nrrLdGbHZlMhqCgIP5eIxJJuRuevXv34p133vnXNzyOjo54++23MX/+fKxevRojR47UOEki0i1zc3ONmx3g5boRYqwdUadOHXh6eqJWrVpo06aN6CuZExG9SXp6OgCgffv28Pf3FyVmXl4enjx5AplMhuHDh8PLy0uUuESkwjM8jx8/hre3d7nG+vj4wMzMjM0OEWmFVCrF6NGj0bFjRzY7JLqDBw+iT58+cHd3h0QiQUxMTInXBUHA7NmzUa1aNVhbW6N79+64evWqbpIlnejWrRtGjhyJrl27ihazUqVKCA8Px4gRI8r9fouIyqfcDY+zszNu3rxZrrE3b96Ei4uL2kkREf0b3upB2pKTk4PGjRtj8eLFZb7+9ddf48cff8TSpUtx/Phx2NraIigoCHl5eRWcKemSr69vmc8NnjlzBqmpqWrFtLe355UdIi0od8PTpUsX/Pbbb3jy5Mkbxz158gS//fabqJ96EJF2yOVy5a0Z6kpNTcU333yD6OhokbIi0q2QkBDMnz8f/fr1K/WaIAj4/vvvMWvWLPTt2xeNGjXC6tWr8eDBg1JXgsj0HD16FP/5z3/QqVMn3L9/X9fpENH/V+5neGbOnImNGzeiU6dOWLZsGdq1a1dqzNGjR/Huu+/i8ePHmDFjhqiJEpG47t27h5iYGMjlckyYMAFWVlYq7a9QKLBs2TJ8+umneP78Oa5fv44hQ4ZotLbEw4cP8ejRI9HuiScS282bN5Gamoru3bsrtzk4OKB169ZITEzE0KFDy9wvPz8f+fn5yq8zMzMBvPzQQS6Xq5RD8XhV99NHxlRLcnIy+vXrh4KCAtSvXx+Ojo4GXZcxfW9Yi/7SpB5V9il3w1O/fn1ERUUhLCwMHTt2hLe3Nxo3bgw7OztkZWXh3LlzuHnzJqytrREVFYX69eurnDgRaZ9CocD+/ftx+PBhCIIAOzs7PH36FNWqVVMpzt69ezFhwgQAQK1atfDnn3+q3exkZmZi7969OHv2LGQyGXx8fFCpUiW1YhFpU/GtSq6uriW2u7q6vvE2psjISMydO7fU9ri4ONjY2KiVS3x8vFr76SNjqOXzzz/H06dPUadOHYSHhyMuLk7XKYnCGL43xViL/lKnntzc3HKPLXfDAwD9+/dHkyZN8PXXX2P79u0lLt+7u7tj/PjxmDZtGnx9fVUJS0QVSCKR4N69exAEAf7+/ggJCVFrIdHih3ZbtGgBDw8PNG3aVK18cnNzsXjxYhQUFAAA6tatq9bq5UT6bMaMGYiIiFB+nZmZCQ8PDwQGBqo8pbFcLkd8fDx69Ohh8JN2GFMtzZs3x9SpU/HWW2+hd+/eBl+PMX1vWIv+0qSe4ivl5aFSwwMANWvWxNKlS5UHysrKgp2dnShz0BOR9kkkEvTt2xf379/X6EqsRCLBmjVrIJfLsXPnTrXj2NjYoH79+nj8+DECAwNRo0YNtWMRaZubmxsAIC0trcRV0bS0NDRp0uS1+71uSnaZTKb2mxZN9tU3xlBLjRo1EBUVhZ07d5ZZj0KhwL179+Dp6amjDNVjDN+bYqxFf6lTjyrjVW54XmVvb89Gh8gAOTg4wMHBQddpKPXs2RPm5uaceY30no+PD9zc3JCQkKBscDIzM3H8+HHlLZ5k+ARBwLFjx9C0aVOVn28si0KhwKZNm3Dp0iUMGDAADRo0ECFLIiovjRoeIiIxGNOnVGT4srOzce3aNeXXN2/exJkzZ+Do6AhPT09MmTIF8+fPh5+fH3x8fPD555/D3d0doaGhukuaRHX06FHs2bMHZ86cwfjx42FmZqZ2LIVCgc2bN+PixYuQSqUwN+dbL6KKxn91RFRKWlpaqYeyiUzFyZMnERAQoPy6+Nmb8PBwrFy5Ep988glycnIwfvx4PHv2DB06dEBsbKwoVwJI965evYo9e/YAAFq0aKFRswMA586dw4ULFyCVSjF48GDUqVNHjDSJSAVseIiMiCAIkMvlas+Wlp2djdmzZ2PJkiX4+++/0bBhQ7VzKSwsxNWrV1GvXj21YxDpQpcuXd44cYZEIsGXX36JL7/8sgKzooqgUCgQGxsLAGjWrBlatGihcczGjRsjNTUV3t7ebHaIdIQND5GRyM3NxY4dO5Cbm4uwsDCVn4fZunUrJk6ciHv37gEANm/erFbDIwgCLl26hD179uDZs2cIDw+Ht7e3ynGIiCqaVCrFqFGjcPjwYYSEhIjyXKFEIkFwcLAI2RGRutjwEBmBK1euYNu2bcjOzoZUKsX9+/dVnu3sxIkTuHfvHnx8fPDzzz+rfYJev349UlJSAACVKlUqsdgiEZG+q1y5Mnr37l1q+9mzZ5GSkoIhQ4boICsi0gQbHiIDV1hYiJ07dyI7OxvOzs7o168f3N3dVY7z2WefoVKlSvjggw/UXggRAHx9fXH9+nW0a9cO7dq1U/v2OiIifXHr1i2EhITg4cOHAMCmh8jAsOEhMnDm5ubo27cvrly5gq5du6o945m1tTU+/fRTjfNp1qwZ6tSpAzs7O41jERHp2uPHjxEcHIyHDx+iYcOGCAoK0nVKRKQiNjxERsDHxwc+Pj66TgPAy3vg2ewQkbH4888/cfnyZdSoUQO7du1C5cqVdZ0SEamIDQ8RERHRa0yePBlFRUUIDAws89lIQRAgCAKkUqkOsiOi8uC/TiIjd/v2bRw6dEjjOOnp6SgsLBQhIyIiwyGRSDB16lQ0aNCg1GuCIGDHjh3YuHEjioqKdJAdEZUHGx4iIyWXy7Fw4ULUr18fQ4YMwfPnz9WKk52djW3btmHp0qU4ceKEyFkSEVW8x48faxxDEATs3LkTSUlJSE5Oxp07d0TIjIi0gbe0EekpQRBw/Phx5OTkoFu3birt++TJE3Tt2hVnz54F8HK18OfPn8PBwUGlOOfOncOOHTtQUFAAAHj06JFK+xMR6ZukpCTs2LEDwcHBaNWqldpx4uPjcfLkSQBAaGgofHx8IJfLxUqTiETEhodIDz179gxbtmzBrVu3AAD169dHtWrVyr1/lSpV4O7ujrt372LhwoUYPXq0WgvoOTk5oaCgAO7u7ggKCoKnp6fKMYiI9MWdO3ewc+dOCIKg8RphtWvXRlJSEkJCQtC4cWORMiQibWDDQ6Rn5HI5fv31V+Tk5EAmkyEwMBBubm4qxZBIJFi+fDksLCxQtWpVtXOpXr06xowZAw8PD1FWHCci0pXs7GysX78eCoUC9evXR4cOHTSK5+3tjQ8++AC2trYiZUhE2sKGh0jPyGQytG/fHsnJyQgNDYWjo6NacapXry5KPryqQ0TGwNbWFs2bN8eVK1fQt29fUT7EYbNDZBg4aQGRHmrTpg1Gjx6tdrNDREQlSSQSBAQEYOzYsbCwsFBuT0tLw4QJE5CTk6PD7IhIm3iFh0gPSSQSrd5CVlRUhIKCAlhbW2vtGERE+sjc/P/e+mRnZ6NXr15ISkrC06dPER0drcPMiEhbeIWHyIA8ffoUEyZMQHJyslr7C4KAlJQU/Pzzz9ixY4fI2RERGQ65XI6BAwciKSkJzs7OmDdvnq5TIiIt4RUeIgMgCALWrl2LqVOnIj09HcnJydi3b59KV4GePn2KrVu3Kmd+y8/Px4sXL3iVh4hM0rVr13DixAnY2Nhgx44d8PPz03VKRKQlbHiIKtDdu3eRm5uLOnXqqLTf8uXL8e677wIA6tatiy+//FLlW96srKyQmpoKc3NztGnTBh06dIClpaVKMYiIjEW9evVw5MgR3L17t9R6PIIg4MiRI6hTp45GM10SkX5gw0NUAQoLC3HgwAEcOXIElpaWmDBhAuzt7cu9/4gRI/Ddd99hxIgR+Pjjj9VqVKytrTFgwABUrVpV5QVIiYiMUb169VCvXr0S2wRBwL59+3Do0CEcO3YMkyZNgpWVlY4yJCIxsOEh0rL8/Hz8/vvvSEtLA/BysbpXZwgqD1tbW5w7dw4ymUyjXGrVqqXR/kRE+k4QBI0mfdm/fz8OHToEAOjQoQObHSIjwIaHSMssLS3h4uKCrKws9O7du9SnieWlabNDRGTsLl68iKSkJAwcOBA2NjYq719YWIgbN24AAAIDA9GmTRuxUyQiHWDDQ1QBevbsicLCQlSqVEkr8YuKimBmZqaV2EREhiA1NRVbtmyBXC7HyZMn0alTJ5VjmJubY+TIkbhy5Qr8/f21kCUR6QKnpSaqAFZWVq9tdgoKCtSOm5OTgx07duCXX35BUVGR2nGIiAxZbm4uoqOjIZfL4evriw4dOqgdy9LSks0OkZFhw0OkI3l5efjiiy/QoEEDZGVlqbSvQqHAkSNH8NNPP+HkyZPIyMjAtWvXtJQpEZF+y8rKgkKhgKOjIwYMGACplG9viOj/8DcCkQ7s27cPjRs3xty5c3Ht2jVERUWptL9EIsGlS5eQn58PNzc3hIeHqzzVNRGRsXB1dcW4ceMwbNgw5dpi+fn56N+/Pw4fPqzj7IhI1/gMD5EGcnNz8eLFCzg5Oam0X2RkJK5cuQI3Nzf88MMPGDRokEr7SyQShISE4NGjR2jcuLFGMxIRERkDOzs72NnZAXh5FTwsLAybN2/GkSNHcOPGDdja2uo4QyLSFTY8RGq6fPkytm3bBltbW4wbN06lfX/++Wf88MMPmD9/vtpr4tSoUQM1atRQa18iImMlCAIiIiKwfv16yGQyrF27ls0OkYljw0OkooKCAuzatQtnzpwBANjY2CA7O1ulE2qtWrXw008/aSlDIiLT9erU0qtXr0bXrl1LvP7w4UNUq1ZNF6kRkY7o5TM8ixcvhre3N6ysrNC6dWucOHHijeM3bNiAunXrwsrKCv7+/ti5c6fyNblcjunTp8Pf3x+2trZwd3dHWFgYHjx4oO0yyEhJpVI8fPgQANC2bVuMHz8elStXFi2+IAiixSIiMjUymQybNm1CXFwchg4dWuK1xMRELFu2DImJiTrKjoh0Qe8annXr1iEiIgJz5szBqVOn0LhxYwQFBSE9Pb3M8UePHsWwYcMwduxYnD59GqGhoQgNDcWFCxcAvHzG4tSpU/j8889x6tQpbNq0CZcvX8Zbb71VkWWRETE3N0e/fv0wevRoBAYGwtxcnAulgiDg6tWrWL58OTIzM0WJSURkiszNzdGjR48S244dO4a4uDgAL2fJJCLToXcNz7fffotx48ZhzJgxqF+/PpYuXQobGxusWLGizPE//PADgoOD8fHHH6NevXqYN28emjVrhkWLFgEAHBwcEB8fj8GDB6NOnTpo06YNFi1ahKSkJNy5c6ciSyMj4urqCi8vr1Lbnz17ptbPVVpaGv744w9ERUXh4cOHOHjwoBhpEhERXt7Gtnv3bgBAx44d0aVLF90mREQVSq+e4SkoKEBSUhJmzJih3CaVStG9e/fXXn5OTExEREREiW1BQUGIiYl57XGeP38OiUTy2tuQ8vPzkZ+fr/y6+NN2uVwOuVxezmqg3OfV/5oKU6tboVBg+fLl+PTTT9GiRQvExcWpNHPaiRMncOPGDZiZmaFly5Zo166dwfzdmdr3GjDNmgHN6ja1vysS3/Xr11FUVITatWurvG+1atXQo0cPvHjxAgEBAZzZksjE6FXD8+jRIxQVFcHV1bXEdldXV6SkpJS5T2pqapnjU1NTyxyfl5eH6dOnY9iwYbC3ty9zTGRkJObOnVtqe1xcHGxsbMpTSinx8fFq7WfoTKHujIwMfPPNN8qf0bt372LdunWv/fkqi1wuR5UqVeDm5oa8vDzs3btXW+lqjSl8r//JFGsG1Ks7NzdXC5mQqXjy5Ak2btyIvLw8DB8+HH5+firHaNeunRYyIyJDoFcNj7bJ5XIMHjwYgiBgyZIlrx03Y8aMEleNMjMz4eHhgcDAQJXexBYfMz4+Hj169IBMJlM7d0NjqHULggC5XA4LC4ty75OdnY25c+eiUqVKGDx4ML777jvlwnemwFC/15owxZoBzermc2mkrvz8fKxduxZ5eXmoUaMGfHx8dJ0SERkYvWp4nJ2dYWZmhrS0tBLb09LS4ObmVuY+bm5u5Rpf3Ozcvn0be/fufWPjYmlpCUtLy1LbZTKZ2m9uNNnXkBlS3c+ePUNMTAzs7OwwYMCAcu9XpUoVrF+/Hq6urjh//jysra0NpmYxGdL3WiymWDOgXt2m+PdE4jh9+jQePXoEOzs7DB48GObm5lAoFJBIJLw1jYjKRa8mLbCwsEDz5s2RkJCg3KZQKJCQkIC2bduWuU/btm1LjAde3m7x6vjiZufq1avYs2cPnJyctFMAGSRBEHDq1CksWbIEt2/fxuXLl/Hs2TOVYrRt2xYeHh7aSZCIyIS1bt0a3bt3x5AhQ2BnZwdBEPDee+9h0qRJKCoq0nV6RGQA9OoKDwBEREQgPDwcLVq0QKtWrfD9998jJycHY8aMAQCEhYWhevXqiIyMBAB8+OGH6Ny5M7755hv06tUL0dHROHnyJJYtWwbgZbMzcOBAnDp1Ctu3b0dRUZHy+R5HR0eVbl0i45Sbm4v4+HgUFBTA09MToaGhoqyr8+LFCxw4cAB+fn7w9fXVPFEiIhMkkUjQvn175ddffvklli9fDqlUihEjRvDZHCL6V3rX8AwZMgQZGRmYPXs2UlNT0aRJE8TGxionJrhz5w6k0v+7MNWuXTtERUVh1qxZmDlzJvz8/BATE4OGDRsCAO7fv4+tW7cCAJo0aVLiWPv27ePUlARbW1v06tULmZmZaNOmTYmfL3UUFRUhKSkJBw4cQF5eHm7cuIH33ntP47hERKZu+fLl+OKLLwAAixYtKtHsKBQK/p4lojLpXcMDAJMmTcKkSZPKfG3//v2ltg0aNAiDBg0qc7y3tzdXrqd/VdwgvyonJwfffvstIiIiYGtrW+5YKSkpyvUeXFxcEBgYyJMwEZEIHBwcYGFhgY8//hgTJkxQbj9z5gxOnz6N4cOHl/kMLhGZNr1seIh0bfv27Zg4cSLu3LmD7OxsfPXVV+Xet169erh48SLq1q2Lpk2bstkhIhLJ4MGD0bBhQ9SrV0+57ezZs9iyZQsA4NSpU6995peITBcbHqJ/mD9/Pj7//HMAgJeXFzp37qzS/sX3lRMRkfjq16+v/P8LFy4oFxpv0aIF2rRpo6OsiEif8aNnon8YOHAgbGxs8Mknn+DixYvo2bOnrlMiIqIyuLi4wNbWFs2bN0fPnj05TTURlYlXeMhoFRYWYt++fahUqZJKtzjUrVsXd+7c4fTlREQV5PHjx3B2doa5uWpvS1xcXDB+/HjY2dmx2SGi12LDQ0YpNTUVmzdvRnp6OszMzNCgQYM3Ljb7T2U1O9evX0dmZiaaNm0qZqpERCatoKAAf/zxB6pUqYKhQ4eqNEkMAJV+txORaWLDQ0YnKysLv/76K4qKimBjY4M+ffpodELMyMhAfHw8rl69CplMBj8/P1SqVEnEjImITFNhYSFu3bqF3Nxc2NraQiaT6TolIjJCbHjI6NjZ2aFVq1Z4+vQpevfurfKnha/KysrC0qVLles7NGvWDGZmZiJmS0RkuuLj45Gbmwtra2sMHToUFhYWyM7O5odKRCQqTlpARql79+4YPHiwstkpLCzEd999h0GDBqm0LpOdnR0aNWqEunXr4v3330dwcDCsra21lTYRkUlp3rw5LC0tERoaiipVquDHH39Eo0aNcOXKFV2nRkRGhA0PGSWpVKp8gPXvv/9Gq1atEBERgY0bNyoXBS2v3r17Y8iQIZzEgIgAAF988QUkEkmJP3Xr1tV1WgbJxcUFdevWhY+PDzZs2IApU6bg5s2b2LZtm65TIyIjwlvayKgVFBSgf//+uHfvHipXroz//e9/CAwMVCkGb2Ejon9q0KAB9uzZo/xa1dnF6P9IJBIcOXIEI0eOhCAIeP/99xEREQEAuHz5MqysrODl5aXjLInIkPE3NBk1CwsLfPfdd4iJicE333wDV1dXXadEREbA3Nwcbm5uuk7DaNSqVQv+/v7w9PTEjz/+CIlEgsuXL2P9+vUwMzPDO++8AxcXF12nSUQGig0PGZSUlBRYWVnB29u73PsMHDgQAwcOLLEtLy8PKSkpaNKkibgJEpFJuHr1Ktzd3WFlZYW2bdsiMjISnp6euk7LYLm6umL//v0wMzODmZmZstlRKBSoX78+nJ2ddZ0iERkwNjxkEPLy8hAbG4uzZ8/C3t4eEyZMgJWVlcpxioqKkJSUhAMHDiA3NxeVK1dWqXkiImrdujVWrlyJOnXq4OHDh5g7dy46duyICxcuwM7Orsx98vPzkZ+fr/w6MzMTACCXyyGXy1U6fvF4VffTR6/WYmlpqfz/s2fPQqFQoF69eujduzeKiopQVFSky1TLxVi/N4aOtegvTepRZR82PKT3nj9/jhUrVijfIDRs2FCt++UFQcCaNWtw+/ZtAICzszNX5iYilYWEhCj/v1GjRmjdujW8vLywfv16jB07tsx9IiMjMXfu3FLb4+LiYGNjo1Ye8fHxau2nj/5Zi4WFBdzd3WFhYYHY2FgdZaU+Y/7eGDLWor/UqSc3N7fcY9nwkN6zt7eHk5MTzMzMEBoaqrxt5NmzZ7C0tCz3NNESiQQNGjRARkYGunTpwjV1iEgUlStXRu3atXHt2rXXjpkxY4byQXzg5RUeDw8PBAYGqrwwslwuR3x8PHr06GEQC3UWr2NWFkOr5d8YUz2sRT8ZUy2AZvUUfxBeHmx4SO9JJBL0798fFhYWsLCwgCAIWL9+PaZMmYK3334bCxYsKHes5s2bw9/fX63b4YiIypKdnY3r169j1KhRrx1jaWmpvGXrVTKZTO03LZrsW1Fyc3OxatUqdOrUCQ0aNHjtOEOoRRXGVA9r0U/GVAugXj2qjOc6PGQQKlWqBAsLC9y6dQshISEYOnQoUlNTsXXrVpXu4ZRKpWx2iEgj06ZNw4EDB3Dr1i0cPXoU/fr1g5mZGYYNG6br1PSKQqHAxo0bkZ6ejoSEBBQWFuo6JSIyUWx4yKDk5OQgISEBFhYWmDt3Lk6ePGlUn3AQkf67d+8ehg0bhjp16mDw4MFwcnLCsWPHULVqVV2nplfi4uJw8+ZNyGQyDBkyBA8ePNB1SkRkonhLGxmUBg0aYPny5WjXrh1q166t3H779m1Ur16di/8RkdZFR0frOgW9JwgCCgoKAAD9+vXD2bNn0adPH0RGRpZ4lomIqCLw3SHp1NOnTyGVSuHg4FDufUaPHq38/8ePHyM+Ph6XL19Gjx490K5dOy1kSUREqpBIJOjTpw+aN2+OtLQ0DBgwAAUFBTh58iQEQcCTJ0/g6Oio6zSJyESw4SGdEAQBp06dwu7du1GjRg2MGjVK5Smi//77b8TGxkKhUEAikeDFixdaypaIiFQlkUhgaWmJkJAQZGdno1u3bli5ciVu376NP//8E82aNUO3bt10nSYRmQA2PFThcnJyEBMTo5zCVaFQIC8vr9zTSxdzd3eHQqGAn58fevTowfvniYj0jJOTEz766CNER0dj06ZNePjwIaKiolBYWIgnT55AoVDoOkUiMgGctIAqnJmZGdLT02FmZobAwECEh4dDKpVi7dq1EASh3HGqV6+O9957D8OHD2ezQ0SkhyQSCT755BMkJiZCJpNh7dq1kMvl8PX1xZAhQ7gWGhFVCF7hoQpnZWWFgQMHwsrKClWrVsXBgwfx7rvvIiUlBZaWlujfv3+5Y7m6umoxUyIiEkPxGkS9e/fGuXPnMHjwYJibm6u0rAARkbrY8JBOeHh4AACmT5+Or7/+GsDL5oWf9hERGa+GDRuiQYMGKj+zSUSkCd7SRjrVtGlTAMC7776L5ORk9O3bF/n5+cjOztZxZkREpA1sdoioovEKD+nUkCFD0KBBA/j7+0OhUCApKQn79u2Dt7c3Bg4cqOv0iIjoNQoKCnDw4EF06tQJFhYWuk6HiOi12PCQqAoLC1UaL5FI4O/vj/T0dPz1119IT08HADx8+BD5+fnK+76JiEh/CIKAmJgYJCcnIy0tDSNGjNB1SkREr8WGh0Tz8OFDbN68GbVq1UJAQIBK+9rZ2SEzMxNWVlbo3LkzWrZsyed5iIj01MGDB5GcnAwzMzO4uLggJycHtra2uk6LiKhMbHhIYwqFAocPH8aBAwegUCjw4sULtGvXTqUY1tbWGDJkCFxdXVVej4eIiCpObm4ujh07BgCoX78+hg4dilq1amHHjh1wcnLiMzpEpHc4aQFp7MmTJzh48CAUCgXq1auH8ePHIyoqClu2bFEpjre3N5sdIiI9Z2Njg3feeQeNGzdGREQEnjx5AgDIzMzEihUrkJWVpeMMiYhK4hUe0pizszOCgoJgaWkJMzMz9OrVC4cOHYK5uTmmTZuG+vXr6zpFIiISkZOTE3766Sfcvn0bfn5++P3337Fhwwbk5eUhISEBoaGhuk6RiEiJDQ+JomXLlnj06BE8PT3x4sUL2NjYYPDgwfDx8eHkA0RERmjRokUIDw/HkiVLsH37duTl5aFGjRoICQnRdWpERCXwljYSjbOzMyZOnIg+ffrg7NmzCAkJwdatW7F8+XIUFRXpOj0iIhJRvXr1cPz4cdSuXRs2NjaoUaMGRo4cyQ+4iEjv8AoPiSoyMhKCICAhIQEpKSkQBAESiQS3b99GzZo1dZ0eERGJSCKRwM7ODuHh4ZDJZGx2iEgvseEhUZmbm0MQBNy5cweCIMDHxwdBQUFwdXXVdWpERKQldnZ2uk6BiOi1eEsbvVZeXh5iYmJw/vx5lfaTSCQICgpCzZo1MXToUDY7RERERKQzvMJDZbpx4wa2bNmCzMxMXLlyBd7e3ip9gufu7g57e3uux0BEZIAKCwvx6NEjuLm56ToVIiKN8QoPlfLw4UOsWbMGmZmZqFKlCtzc3NC4cWNs3bpV16kREZGWCYKAHTt24Ndff1X5Cj8RkT5iw0OlVKtWDf7+/qhZsyYOHTqE8PBw3Lx5E19//TUEQdB1ekREpEV///03zpw5gydPniA5OVnX6RARaYwND5WpeNG4jRs3wszMDBEREVi4cCEWLVqEzMxM3SZHRERa8eDBA8TGxiI3NxebN29GWFgYNm/ezMaHiAwaGx4qk1QqxciRIzFlyhTs2rULtWvXxu7du/HkyRMkJibqOj0iItKCV29hvnXrFnx9fXH16lVs2LABV65c0XV6RERqYcNDryWRSPDdd98hPz8fqampsLS0RPfu3dGtWzddp0ZERFoglUpx+fJlpKSkwMvLC2FhYXjx4gWqVq2K6tWr6zo9IiK1cJY2+lcBAQEwNzdHx44dYWtrq+t0iIhIiyIiInDnzh20aNECt2/fRtWqVREWFsbf/0RksNjwmBBBEJCUlAQPDw+V1saxtbVFcHCwFjMjIiJ9YW5ujkWLFkEQBBw5cgRNmzZls0NEBo0Nj4nIzMzE1q1bcf36dVStWhXNmjVDmzZtdJ0WERHpKYlEgg4dOug6DSIijbHhMQEPHz7E6tWrkZeXh/T0dKxbtw43b97E+fPnUatWLV2nR0RERESkNZy0wARUrVoV9vb2OHLkCJYuXYrk5GQ4Oztj165duHbtmq7TIyIiIiLSGjY8JsDc3BwjRoxAvXr1IJPJMGnSJLz77rt48uQJ4uPjuZgoEZEJUSgU/L1PRCaFt7SZCHt7e8yfPx+tWrXCtWvXUFRUBG9vbwQGBkIikeg6PSIiqiB79uzB48eP0a9fP1hZWek6HSIirWPDY0Ls7OwwbNgw/PXXX/D390edOnXY7BARmZBz585h//79OHnyJOrUqYPGjRvDzMxM12kREWkVGx4TI5VKMWjQIF2nQUREFezBgwfYvHkzNmzYgPT0dGzevBlXr17FwIED2fQQkVHjMzwG7uHDh3jx4oWu0yAiIj0nl8uxfft2pKWlYcyYMTA3N0daWhpyc3N1nRoRkVbxCo+BKioqwqFDh3Dw4EFkZWUhODgY3bp103VaRESkp54/f45z587h7bffRpUqVVC5cmWEh4fDzs5O16kREWkVGx4D9OTJE/z111+4ePEidu7ciWvXriEpKQnW1tZo166drtMjIiI91KhRI+zevRuXL1+GQqHAsGHD4ODgoOu0iIi0jg2PATI3N8elS5fw888/o1q1ahg3bhzc3d2xf/9+NGrUCJUqVdJ1ikREpIe6dOmCLl26QKFQQCrlXe1EZBrY8Bgge3t7TJo0CefPn0eHDh0gkUhgYWGBDh06wNLSUtfpERGRnmOzQ0SmhA2PgfL19cWGDRtw6NAhSCQSBAQE8MoOEREREdE/sOExYFWqVEGfPn24lg4RERER0WvwmraBY7NDRERERPR6bHj0TF5eHhQKha7TICIiA3T8+HHcunVL12kQEekVNjx65Pr165g2bRpatmyJXbt26TodIiIyIMnJyfjkk0/w66+/4vLly7pOh4hIb7Dh0QMFBQWIjo7GsGHDcPHiRYSEhODEiRP8lI6IiMolIyMDEydORP369SGTyZCQkABBEHSdFhGRXuCkBXogNTUVP/30E5o3bw43NzcAQLVq1TjFNBERlctnn32G2rVrw83NDWZmZhg8eDCf8SQi+v94hUcPeHp64vPPP8eTJ09gZWWFQYMGYdy4cahWrZquUyMiIj0nl8tx/vx5yOVyAMC7774LZ2dnHWdFRKQ/eIVHTwQHByMwMBAKhQLm5vy2EBFR+chkMsTFxeHPP//EwIED2ewQEf2DXl7hWbx4Mby9vWFlZYXWrVvjxIkTbxy/YcMG1K1bF1ZWVvD398fOnTtLvC4IAmbPno1q1arB2toa3bt3x9WrV7VZglqkUimbHSIiA6HquUqb7Ozs8N5777HZISIqg941POvWrUNERATmzJmDU6dOoXHjxggKCkJ6enqZ448ePYphw4Zh7NixOH36NEJDQxEaGooLFy4ox3z99df48ccfsXTpUhw/fhy2trYICgpCXl5eRZVFRERGRNVzFRER6Y7eNTzffvstxo0bhzFjxqB+/fpYunQpbGxssGLFijLH//DDDwgODsbHH3+MevXqYd68eWjWrBkWLVoE4OXVne+//x6zZs1C37590ahRI6xevRoPHjxATEyM1utRKBS4d+8eCgsLtX4sIiKqGKqeq4iISHf06v6pgoICJCUlYcaMGcptUqkU3bt3R2JiYpn7JCYmIiIiosS2oKAgZTNz8+ZNpKamonv37srXHRwc0Lp1ayQmJmLo0KGlYubn5yM/P1/5dWZmJoCXD4YWPxRaHgkJCdi+fTuqVq2KI0eOoHPnzuXe19AV/z2p8vdl6EyxZsA06zbFmgHN6jamvyt1zlVinVeK93n1v4bMmGoBjKse1qKfjKkWoOLOK3rV8Dx69AhFRUVwdXUtsd3V1RUpKSll7pOamlrm+NTUVOXrxdteN+afIiMjMXfu3FLb4+LiYGNj8691yOVyxMXFoXr16qhatSoKCgqQmJiInJycf93X2MTHx+s6hQpnijUDplm3KdYMqFd3bm6uFjLRDXXOVZqeV8piTD9/xlQLYFz1sBb9ZEy1ANo/r+hVw6MvZsyYUeKqUWZmJjw8PBAYGAh7e/t/3f/y5cv4z3/+g2rVquH27dto2rQpxo0bB5lMps209YpcLkd8fDx69OhhMnWbYs2AadZtijUDmtVdfEXDVGl6XsnPz8fx48fRoUMHFBUVGc3Pn7H9WzKmeliLfjKmWoCKO6/oVcPj7OwMMzMzpKWlldielpamXJDzn9zc3N44vvi/aWlpJda1SUtLQ5MmTcqMaWlpWeainzKZrFzfjAYNGmDQoEEoKCjA0qVLsXv37nLva2xMsW5TrBkwzbpNsWZAvbqN6e9JnXOVJueV/Px8fP311zAzM8OlS5fw9ttvl3tfQ2FMtQDGVQ9r0U/GVAug/fOKXk1aYGFhgebNmyMhIUG5TaFQICEhAW3bti1zn7Zt25YYD7y8LFY83sfHB25ubiXGZGZm4vjx46+NqSmJRIJPP/0Uc+bMgZmZmVaOQUREuqHOuUpdBQUFWLhwIczMzPDixQs8e/bMqN7kEBFVBL26wgMAERERCA8PR4sWLdCqVSt8//33yMnJwZgxYwAAYWFhqF69OiIjIwEAH374ITp37oxvvvkGvXr1QnR0NE6ePIlly5YBeNl8TJkyBfPnz4efnx98fHzw+eefw93dHaGhoVqrQyKRaC02ERHp1r+dq8QSHR2NwsJC5OXl4erVq4iOjhY1PhGRKdC7hmfIkCHIyMjA7NmzkZqaiiZNmiA2Nlb5cOidO3cglf7fhal27dohKioKs2bNwsyZM+Hn54eYmBg0bNhQOeaTTz5BTk4Oxo8fj2fPnqFDhw6IjY2FlZWVyvllZ2dDEATY2dlpXiwRERmkfztXieXRo0fYuXMnKleujHXr1kEmkxnN7ExERBVF7xoeAJg0aRImTZpU5mv79+8vtW3QoEEYNGjQa+NJJBJ8+eWX+PLLLzXK68iRIzh58iS8vLwQFhamUSwiIjJsbzpXiSUiIgING/6/9u48Ksp6/wP4e1hmUJQBFWRRkMXlqOhNCiLFJUikxQCTcAvU6NrV6+1XerRcUMuom3nrlNcyU+/VzFxwSbPcULte0kxIqeQi4ZobGIshIMzn94dn5jgssjg779c5HOV5vs/zfN/znfHjd55nnumLoUOHQqlUGvVYRES2yqI+w2PpDh8+DI1Gg1OnTvEdNiIiMonhw4dzskNEdB8s8gyPpSopKcGuXbsavAsPERERERFZFp7haYbt27djyJAhWLp0Ke+SQ0RERERkBXiGpxnmz5+PsWPH8oYFRERERERWgmd4miExMZGTHSIiMpjq6mpUVlaauxtERDaNZ3iagd+tQ0REhrRlyxbY2dkhMTERzs7O5u4OEZFN4hkeIiIiM/n1119x7tw5LFmyBGfOnDF3d4iIbBLP8BAREZlJdXU1vvjiC/j6+qJ79+4IDAzk1QRERAbGMzxERERmsmnTJiiVSsTFxSE+Pp6THSIiI+AZniYQEQBAaWlps7e9ffs2ysvLUVpa2qpuZd0ac7fGzEDrzN0aMwP3l1v776f239PWTvs42NvbY9SoURg5ciSqqqpQVVXV6La29PyzpSyAbeVhFstkS1kA09UVhbD6NOrixYvo2rWrubtBRGT1Lly4gC5dupi7G2bHukJEZBhNqSuc8DSBRqPBb7/9hvbt2zf7coPS0lJ07doVFy5cgIuLi5F6aHlaY+7WmBlonblbY2bg/nKLCMrKyuDt7Q07O15Nzbpyhy1lAWwrD7NYJlvKApiurvCStiaws7O773ckXVxcbOKJ2VytMXdrzAy0ztytMTPQ8txqtdoIvbFOrCv6bCkLYFt5mMUy2VIWwPh1hW+zERERERGRzeKEh4iIiIiIbBYnPEamUqmQmpoKlUpl7q6YVGvM3RozA60zd2vMDLTe3JbGlsbBlrIAtpWHWSyTLWUBTJeHNy0gIiIiIiKbxTM8RERERERkszjhISIiIiIim8UJDxERERER2SxOeIiIiIiIyGZxwtMCy5YtQ7du3eDk5ISwsDAcO3bsnu03bdqEXr16wcnJCcHBwfjqq6/01osI5s+fDy8vL7Rp0wZRUVHIy8szZoRmM3Tm9PR0DB8+HB07doRCoUB2drYRe99yhsx9+/ZtzJo1C8HBwXB2doa3tzeee+45/Pbbb8aO0SyGHusFCxagV69ecHZ2hpubG6KionD06FFjRmgRQ+e+25QpU6BQKPDee+8ZuNf3x9CZk5OToVAo9H5GjBhhzAitUnPHzRItWLCgznOlV69e5u5Wkxw+fBhPPfUUvL29oVAosG3bNr311lDT79ZYHmt5XaelpeGhhx5C+/bt4eHhgdjYWOTm5uq1qaiowNSpU9GxY0e0a9cOo0aNwtWrV83U43trSp6hQ4fWGZspU6aYqccNW758Ofr166f7ctHw8HDs3r1bt94k4yLULBs2bBClUimrVq2Sn376SVJSUsTV1VWuXr1ab/sjR46Ivb29/P3vf5eff/5Z5s6dK46OjnLq1Cldm7feekvUarVs27ZNfvzxRxk5cqT4+/vLrVu3TBXrnoyR+d///rcsXLhQPvnkEwEgWVlZJkrTdIbOXVxcLFFRUfLFF1/I6dOnJTMzU0JDQyUkJMSUse7JGGP92Wefyd69eyU/P19ycnJk8uTJ4uLiIteuXTNVrEYZI7dWenq69O/fX7y9veUf//iHkZM0nTEyJyUlyYgRI+Ty5cu6nxs3bpgqUqvQ3HGzVKmpqdKnTx+958r169fN3a0m+eqrr2TOnDmSnp4uAGTr1q166y29ptfWWB5reV1HR0fL6tWrJScnR7Kzs+Xxxx8XX19fuXnzpq7NlClTpGvXrrJ//345fvy4PPzww/LII4+YsdcNa0qeIUOGSEpKit7YlJSUmLHX9duxY4fs2rVL/ve//0lubq689tpr4ujoKDk5OSJimnHhhKeZQkNDZerUqbrfa2pqxNvbW9LS0uptn5CQIE888YTesrCwMPnzn/8sIiIajUY8PT3lnXfe0a0vLi4WlUoln3/+uRESNJ+hM9+toKDAYic8xsytdezYMQEg586dM0yn75MpMpeUlAgA2bdvn2E6bQDGyn3x4kXx8fGRnJwc8fPzs6gJjzEyJyUlydNPP22U/tIdzR03S5Wamir9+/c3dzfuW+0JgjXU9HtpaMJjja/ra9euCQA5dOiQiNwZB0dHR9m0aZOuzS+//CIAJDMz01zdbLLaeUTuTHj+9re/ma9T98HNzU1WrlxpsnHhJW3NUFVVhR9++AFRUVG6ZXZ2doiKikJmZma922RmZuq1B4Do6Ghd+4KCAly5ckWvjVqtRlhYWIP7NCVjZLYGpspdUlIChUIBV1dXg/T7fpgic1VVFVasWAG1Wo3+/fsbrvP3wVi5NRoNJkyYgJkzZ6JPnz7G6XwLGXOsDx48CA8PD/Ts2RMvvvgiioqKDB+glWrJuFmyvLw8eHt7IyAgAOPGjcP58+fN3aX7Zuk1vaWs8XVdUlICAOjQoQMA4IcffsDt27f1xqZXr17w9fW1irGpnUfrs88+Q6dOndC3b1+8+uqrKC8vN0f3mqympgYbNmzAH3/8gfDwcJONi4PB9tQKFBYWoqamBp07d9Zb3rlzZ5w+fbreba5cuVJv+ytXrujWa5c11MacjJHZGpgid0VFBWbNmoUxY8bAxcXFMB2/D8bMvHPnTiQmJqK8vBxeXl7Yu3cvOnXqZNgALWSs3G+//TYcHBwwffp0w3f6Phkr84gRIxAfHw9/f3/k5+fjtddeQ0xMDDIzM2Fvb2/4IK1MS8bNUoWFhWHNmjXo2bMnLl++jIULFyIiIgI5OTlo3769ubvXYpZe01vCGl/XGo0GL730EgYOHIi+ffsCuDM2SqWyzhuM1jA29eUBgLFjx8LPzw/e3t44efIkZs2ahdzcXKSnp5uxt/U7deoUwsPDUVFRgXbt2mHr1q3o3bs3srOzTTIunPAQmcHt27eRkJAAEcHy5cvN3R2jGzZsGLKzs1FYWIhPPvkECQkJOHr0KDw8PMzdNaP44Ycf8P777+PEiRNQKBTm7o7JJCYm6v4eHByMfv36ITAwEAcPHkRkZKQZe0aWJiYmRvf3fv36ISwsDH5+fti4cSMmT55sxp5Rbdb4up46dSpycnLwn//8x9xdMYiG8rzwwgu6vwcHB8PLywuRkZHIz89HYGCgqbt5Tz179kR2djZKSkqwefNmJCUl4dChQyY7Pi9pa4ZOnTrB3t6+zp0jrl69Ck9Pz3q38fT0vGd77Z/N2acpGSOzNTBmbu1k59y5c9i7d69FnN0BjJvZ2dkZQUFBePjhh/Hpp5/CwcEBn376qWEDtJAxcn/77be4du0afH194eDgAAcHB5w7dw6vvPIKunXrZpQczWGq13VAQAA6deqEM2fO3H+nqUXjZi1cXV3Ro0cPq3+uWHpNNwRLf11PmzYNO3fuREZGBrp06aJb7unpiaqqKhQXF+u1t/SxaShPfcLCwgDAIsdGqVQiKCgIISEhSEtLQ//+/fH++++bbFw44WkGpVKJkJAQ7N+/X7dMo9Fg//79CA8Pr3eb8PBwvfYAsHfvXl17f39/eHp66rUpLS3F0aNHG9ynKRkjszUwVm7tZCcvLw/79u1Dx44djROgBUw51hqNBpWVlfffaQMwRu4JEybg5MmTyM7O1v14e3tj5syZ+Oabb4wXpolMNdYXL15EUVERvLy8DNPxVq4l42Ytbt68ifz8fKt/rlh6TTcES31diwimTZuGrVu34sCBA/D399dbHxISAkdHR72xyc3Nxfnz5y1ybBrLUx/tV3xY2tjUR/v/AJONi8Fuf9BKbNiwQVQqlaxZs0Z+/vlneeGFF8TV1VWuXLkiIiITJkyQ2bNn69ofOXJEHBwcZMmSJfLLL79IampqvbeldnV1le3bt8vJkyfl6aeftqhbWBojc1FRkWRlZcmuXbsEgGzYsEGysrLk8uXLJs/XEEPnrqqqkpEjR0qXLl0kOztb7zaSlZWVZslYm6Ez37x5U1599VXJzMyUs2fPyvHjx2XixImiUql0t6O0BMZ4jtdmaXdpM3TmsrIymTFjhmRmZkpBQYHs27dPBgwYIN27d5eKigqzZLRFjY2btXjllVfk4MGDUlBQIEeOHJGoqCjp1KmTRd2uviFlZWWSlZUlWVlZAkCWLl0qWVlZurttWnpNr+1eeazpdf3iiy+KWq2WgwcP6tXX8vJyXZspU6aIr6+vHDhwQI4fPy7h4eESHh5uxl43rLE8Z86ckUWLFsnx48eloKBAtm/fLgEBATJ48GAz97yu2bNny6FDh6SgoEBOnjwps2fPFoVCIXv27BER04wLJzwt8MEHH4ivr68olUoJDQ2V7777TrduyJAhkpSUpNd+48aN0qNHD1EqldKnTx/ZtWuX3nqNRiPz5s2Tzp07i0qlksjISMnNzTVFlCYzdObVq1cLgDo/qampJkjTdIbMrb0Fd30/GRkZJkrUOENmvnXrlsTFxYm3t7colUrx8vKSkSNHyrFjx0wVp8kM/RyvzdImPCKGzVxeXi7Dhw8Xd3d3cXR0FD8/P0lJSbG6/4hbg3uNm7V49tlnxcvLS5RKpfj4+Mizzz4rZ86cMXe3miQjI6Pef8e1rxdrqOl3u1cea3pdN1RfV69erWtz69Yt+ctf/iJubm7Stm1biYuLs6g3Wu/WWJ7z58/L4MGDpUOHDqJSqSQoKEhmzpxpkd/DM2nSJPHz8xOlUinu7u4SGRmpm+yImGZcFCIihjtfREREREREZDn4GR4iIiIiIrJZnPAQEREREZHN4oSHiIiIiIhsFic8RERERERkszjhISIiIiIim8UJDxERERER2SxOeIiIiIiIyGZxwkMEIC8vD8OHD4darYZCocC2bdvM3SWbolAosGDBAnN3g4jIZFhXjIt1hZqDEx6yKmvWrIFCodD9ODg4wMfHB8nJybh06VKL95uUlIRTp05h8eLFWLt2LR588EED9tp6TJ8+HQqFAmfOnGmwzZw5c6BQKHDy5EkT9oyIyDhYV4yLdYUsASc8ZJUWLVqEtWvX4qOPPkJMTAzWrVuHIUOGoKKiotn7unXrFjIzMzF58mRMmzYN48ePR5cuXYzQa8s3btw4AMD69esbbPP5558jODgY/fr1M1W3iIiMjnXFOFhXyBJwwkNWKSYmBuPHj8fzzz+PlStXYsaMGcjPz8eOHTuava/r168DAFxdXQ3Wv4qKCmg0GoPtz1TCwsIQFBSEzz//vN71mZmZKCgo0BUwIiJbwbpiHKwrZAk44SGbEBERAQDIz8/XW3769Gk888wz6NChA5ycnPDggw/qFa8FCxbAz88PADBz5kwoFAp069ZNt/7SpUuYNGkSOnfuDJVKhT59+mDVqlV6xzh48CAUCgU2bNiAuXPnwsfHB23btkVpaSkA4OjRoxgxYgTUajXatm2LIUOG4MiRI3r7WLBgge6Uf3JyMlxdXaFWqzFx4kSUl5fXybtu3TqEhoaibdu2cHNzw+DBg7Fnzx69Nrt370ZERAScnZ3Rvn17PPHEE/jpp58afSzHjRuH06dP48SJE3XWrV+/HgqFAmPGjEFVVRXmz5+PkJAQqNVqODs7IyIiAhkZGY0eIzk5We9xrv041Jc3JCQEbdq0QYcOHZCYmIgLFy7otcnLy8OoUaPg6ekJJycndOnSBYmJiSgpKWm0P0REtbGusK6wrtgOB3N3gMgQzp49CwBwc3PTLfvpp58wcOBA+Pj4YPbs2XB2dsbGjRsRGxuLLVu2IC4uDvHx8XB1dcX//d//YcyYMXj88cfRrl07AMDVq1fx8MMPQ6FQYNq0aXB3d8fu3bsxefJklJaW4qWXXtLrw+uvvw6lUokZM2agsrISSqUSBw4cQExMDEJCQpCamgo7OzusXr0ajz76KL799luEhobq7SMhIQH+/v5IS0vDiRMnsHLlSnh4eODtt9/WtVm4cCEWLFiARx55BIsWLYJSqcTRo0dx4MABDB8+HACwdu1aJCUlITo6Gm+//TbKy8uxfPlyDBo0CFlZWfUWBa1x48Zh4cKFWL9+PQYMGKBbXlNTg40bNyIiIgK+vr4oLCzEypUrMWbMGKSkpKCsrAyffvopoqOjcezYMfzpT39qwUjWtXjxYsybNw8JCQl4/vnncf36dXzwwQcYPHgwsrKy4OrqiqqqKkRHR6OyshJ//etf4enpiUuXLmHnzp0oLi6GWq02SF+IqPVgXWFdYV2xIUJkRVavXi0AZN++fXL9+nW5cOGCbN68Wdzd3UWlUsmFCxd0bSMjIyU4OFgqKip0yzQajTzyyCPSvXt33bKCggIBIO+8847esSZPnixeXl5SWFiotzwxMVHUarWUl5eLiEhGRoYAkICAAN0y7bG6d+8u0dHRotFodMvLy8vF399fHnvsMd2y1NRUASCTJk3SO1ZcXJx07NhR93teXp7Y2dlJXFyc1NTU6LXVHqOsrExcXV0lJSVFb/2VK1dErVbXWV6fhx56SLp06aJ3jK+//loAyMcffywiItXV1VJZWam33e+//y6dO3eukwOApKam6n5PSkoSPz+/OsfVPg5aZ8+eFXt7e1m8eLFeu1OnTomDg4NueVZWlgCQTZs2NZqNiOhurCusKyKsK7aOl7SRVYqKioK7uzu6du2KZ555Bs7OztixY4fuQ6E3btzAgQMHkJCQgLKyMhQWFqKwsBBFRUWIjo5GXl7ePe++IyLYsmULnnrqKYiIbvvCwkJER0ejpKSkzqn5pKQktGnTRvd7dnY28vLyMHbsWBQVFem2/+OPPxAZGYnDhw/XuR57ypQper9HRESgqKhIdxnDtm3boNFoMH/+fNjZ6b98tafs9+7di+LiYowZM0av3/b29ggLC2vSpQHjx4/HxYsXcfjwYd2y9evXQ6lUYvTo0QAAe3t7KJVKAIBGo8GNGzdQXV2NBx98sN7LFloiPT0dGo0GCQkJelk8PT3RvXt3XRbtO23ffPNNvZdqEBE1hnWFdYV1xXbxkjaySsuWLUOPHj1QUlKCVatW4fDhw1CpVLr1Z86cgYhg3rx5mDdvXr37uHbtGnx8fOpdd/36dRQXF2PFihVYsWJFg9vfzd/fX+/3vLw8AHcKVkNKSkr0Lpfw9fXVW69d9/vvv8PFxQX5+fmws7ND7969G9yn9riPPvpovetdXFwa3FYrMTERL7/8MtavX4+hQ4eioqICW7duRUxMjF5///Wvf+Hdd9/F6dOncfv2bd3y2o9FS+Xl5UFE0L1793rXOzo66o738ssvY+nSpfjss88QERGBkSNHYvz48bzsgIiahHWFdQVgXbFVnPCQVQoNDdV9p0FsbCwGDRqEsWPHIjc3F+3atdO9wzVjxgxER0fXu4+goKAG96/dfvz48Q0Wltq3z7z7Xbi79/HOO+80eN2x9rpuLXt7+3rbiUiDfa1Ne9y1a9fC09OzznoHh8Zf9h4eHnjsscewZcsWLFu2DF9++SXKysr07qKzbt06JCcnIzY2FjNnzoSHhwfs7e2RlpZW50O+tdX3AVLgzvXctbMoFArs3r273sfm7sfv3XffRXJyMrZv3449e/Zg+vTpSEtLw3fffddqbwdLRE3HutIw1hXWFWvHCQ9ZPe0/hsOGDcOHH36I2bNnIyAgAMCdd2qioqKavU93d3e0b98eNTU1LdoeAAIDAwHceeerpfuob58ajQY///xzg8VOe1wPD4/7Ou64cePw9ddfY/fu3Vi/fj1cXFzw1FNP6dZv3rwZAQEBSE9P1ys0qampje7bzc0NxcXFdZafO3dO7/fAwECICPz9/dGjR49G9xscHIzg4GDMnTsX//3vfzFw4EB89NFHeOONNxrdlohIi3Wl/uOyrrCuWCt+hodswtChQxEaGor33nsPFRUV8PDwwNChQ/Hxxx/j8uXLddprvyOhIfb29hg1ahS2bNmCnJycZm8PACEhIQgMDMSSJUtw8+bNFu2jttjYWNjZ2WHRokV1rtPWvlsXHR0NFxcXvPnmm3qXAzT3uLGxsWjbti3++c9/Yvfu3YiPj4eTk5NuvfadsbvfJTx69CgyMzMb3XdgYCBKSkr0vlX78uXL2Lp1q167+Ph42NvbY+HChXXejRQRFBUVAQBKS0tRXV2ttz44OBh2dnaorKxsUl4ioruxrrCusK7YDp7hIZsxc+ZMjB49GmvWrMGUKVOwbNkyDBo0CMHBwUhJSUFAQACuXr2KzMxMXLx4ET/++OM99/fWW28hIyMDYWFhSElJQe/evXHjxg2cOHEC+/btw40bN+65vZ2dHVauXImYmBj06dMHEydOhI+PDy5duoSMjAy4uLjgyy+/bFbGoKAgzJkzB6+//joiIiIQHx8PlUqF77//Ht7e3khLS4OLiwuWL1+OCRMmYMCAAUhMTIS7uzvOnz+PXbt2YeDAgfjwww8bPVa7du0QGxur+3bs2l8K9+STTyI9PR1xcXF44oknUFBQgI8++gi9e/eutxDfLTExEbNmzUJcXBymT5+uu71pjx499D6YGhgYiDfeeAOvvvoqzp49i9jYWLRv3x4FBQXYunUrXnjhBcyYMQMHDhzAtGnTMHr0aPTo0QPV1dVYu3at7j8YREQtwbrCusK6YiNMe1M4ovujvX3o999/X2ddTU2NBAYGSmBgoFRXV4uISH5+vjz33HPi6ekpjo6O4uPjI08++aRs3rxZt11Dtw8VEbl69apMnTpVunbtKo6OjuLp6SmRkZGyYsUKXRvt7UMbunVlVlaWxMfHS8eOHUWlUomfn58kJCTI/v37dW20t828fv16vXkLCgr0lq9atUoeeOABUalU4ubmJkOGDJG9e/fqtcnIyJDo6GhRq9Xi5OQkgYGBkpycLMePH2/g0a1r165dAkC8vLzqvV3pm2++KX5+fqJSqeSBBx6QnTt31ntrUNS6faiIyJ49e6Rv376iVCqlZ8+esm7dujq3D9XasmWLDBo0SJydncXZ2Vl69eolU6dOldzcXBER+fXXX2XSpEkSGBgoTk5O0qFDBxk2bJjs27evyVmJqHViXbmDdYV1xZYpRJrxqTUiIiIiIiIrws/wEBERERGRzeKEh4iIiIiIbBYnPEREREREZLM44SEiIiIiIpvFCQ8REREREdksTniIiIiIiMhmccJDREREREQ2ixMeIiIiIiKyWZzwEBERERGRzeKEh4iIiIiIbBYnPEREREREZLM44SEiIiIiIpv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positions.append(idx[tuple(p)])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[3.3575392824186565,\n", + " 3.977508898389715,\n", + " 3.379365186783616,\n", + " 4.058767710592292,\n", + " 3.50782138910472,\n", + " 3.677923272591215,\n", + " 3.3201527353976417,\n", + " 3.3081985158405587,\n", + " 3.3081985158405587,\n", + " 3.373301582663771,\n", + " 3.88530950371387,\n", + " 3.437136194845152,\n", + " 3.437136194845152,\n", + " 6.385303785313226,\n", + " 3.3201527353976417,\n", + " 3.3081985158405587,\n", + " 3.9976108594291873,\n", + " 3.3122864156157448,\n", + " 5.3706943119123025,\n", + " 3.394646010352517,\n", + " 3.3081985158405587,\n", + " 3.528322075997494,\n", + " 3.3122864156157448,\n", + " 6.288973282496954,\n", + " 3.3122864156157448,\n", + " 4.332936528824575,\n", + " 3.3575392824186565,\n", + " 3.3122864156157448,\n", + " 4.4912962072212395,\n", + " 3.50782138910472,\n", + " 3.3081985158405587,\n", + " 3.3081985158405587,\n", + " 3.652241220292126,\n", + " 3.373301582663771,\n", + " 4.326171103903107,\n", + " 3.373301582663771,\n", + " 6.273680487642196,\n", + " 3.4085759342506208,\n", + " 3.3122864156157448,\n", + " 3.437136194845152,\n", + " 4.012943470470418,\n", + " 6.420142850367483,\n", + " 3.3575392824186565,\n", + " 3.3081985158405587,\n", + " 3.3081985158405587,\n", + " 3.88530950371387,\n", + " 6.273680487642196,\n", + " 3.3122864156157448,\n", + " 3.8055956233711186,\n", + " 4.012943470470418,\n", + " 3.3081985158405587,\n", + " 3.4157910046178586,\n", + " 3.7562250617975224],\n", + " [5.1982552830286295,\n", + " 4.281515569602561,\n", + " 4.900448090667851,\n", + " 5.09696163915396,\n", + " 4.889535839540258,\n", + " 4.900448090667851,\n", + " 4.389454666130405,\n", + " 5.320888196851229,\n", + " 4.978016694303733,\n", + " 4.258942952650614,\n", + " 4.301365769615586,\n", + " 4.889535839540258,\n", + " 4.89345271638922,\n", + " 4.258942952650614,\n", + " 4.889535839540258,\n", + " 4.258942952650614,\n", + " 4.889535839540258,\n", + " 4.3030071716257225,\n", + " 4.301365769615586,\n", + " 4.370720753200658,\n", + " 4.296329499671629,\n", + " 4.281515569602561,\n", + " 4.296329499671629,\n", + " 4.889535839540258,\n", + " 4.35727230597513],\n", + " [4.884438820901778, 4.884438820901778, 4.163207915111343],\n", + " [3.433043492177603, 4.0536593058004655],\n", + " [3.484363807912814,\n", + " 3.744738867133492,\n", + " 2.641641445514324,\n", + " 6.727362312720288,\n", + " 2.641641445514324,\n", + " 4.570952408046651,\n", + " 3.683228266045262,\n", + " 2.641641445514324,\n", + " 3.8013463094557665,\n", + " 3.744738867133492,\n", + " 3.683228266045262,\n", + " 3.683228266045262,\n", + " 4.532323680219633,\n", + " 3.484363807912814,\n", + " 2.641641445514324,\n", + " 3.700979028193615,\n", + " 2.708710079537923]]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vals" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb b/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb new file mode 100644 index 0000000..08b8f1e --- /dev/null +++ b/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb @@ -0,0 +1,1920 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "# Create a water network model\n", + "inp_file = './networks/Net0.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the original Cholesky EPANET simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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NCPD364aWEREpxyhqYRStLycyip49q1nrI0CnsfzlQis69qUjLy/P7PebNm1CeHg4ioqKMGHCBNP1wMBAREZGOt7YH7htj9fPzwdxgyw/uCRJOHHqrMItIiJSngS5iVUaSO77z7xd2iZRCVZSW5mGhgaz1NzcbNe96+vrAQB9+vQxu75lyxaEhYVhxIgRWLJkCa5evepYmx0qrTJJsZxgRUTezduHmu2Z1TxgwACEhISY0ooVK2zeVxRFPP744xg/fjxGjBhhun7ffffhnXfeweeff44lS5bg7bffxq9//WuH2uy2Q81A25Kif3x2wGIelxQRkTcQRS1EmaFm0cOHmn10Any0lr9c+BjbrldVVSE4ONh03c/P9mvIhQsX4tixY/jqq6/Mrj/44IOmX48cORJRUVGYMmUKysrKEBsba1eb3TvwcmYzEXk5SYJsr9bDN65qW05kJfBq0HY9ODjYLPDakpOTg48++ghffPEF+ve3PpEXAFJTUwEApaWl3hF4eVgCEXk7W8PJ3jLUbDHPwS8dkiThkUcewYcffoiCggIMHjzY5meKi4sBAFFRUXbX49bveBNlAu/JinMwGGyf2kFE5M5EUQOjTOrMlpHr1q1DTEwM/P39kZqaioMHD8qW37FjBxITE+Hv74+RI0di165dZvlz587tsPY1MzPT4r2am5sxevRoCIJgCmpytNbW8PoI0Dq4ZeTChQvxzjvvYOvWrQgKCoJer4der8e1a9cAAGVlZVi+fDmKiopQUVGBf/7zn5g9ezYmTJiAUaNG2V2PWwfeHoH+iOkfbjHPaBRRevq8wi0iIlKWBI3N5Ijt27cjNzcXy5Ytw+HDh5GcnIyMjAzU1NRYLL9v3z7MmjUL8+bNw5EjR5CVlYWsrCwcO3bMrFxmZqbZ2td3333X4v2efPJJREdb34v/RtZnNLclR7z22muor6/HxIkTERUVZUrbt28H0Ha272effYapU6ciMTERTzzxBO69917861//cqgetx5qBtpmNlecsfw/xHelVRgWP1DhFhERKcfWQQiO9nhXrVqF+fPnIzs7GwCwfv16/Pvf/8aGDRvwu9/9rkP5NWvWIDMzE4sXLwYALF++HLt378batWuxfv16Uzk/Pz+ba18//vhjfPrpp/jggw/w8ccf29VeH50AHys7VPlY2UrSGsnGC/EBAwZg7969Dt3TErfu8QLcs5mIvJu9y4nsWctqMBhQVFSE9PR00zWNRoP09HQUFhZarL+wsNCsPABkZGR0KF9QUIDw8HAkJCTg4YcfRm1trVl+dXU15s+fj7fffhuBgfYf6+qOhySotFn2kzulqKSUM5uJyLPZG3jtWct68eJFGI1GREREmF2PiIiAXq+3WL9er7dZPjMzE2+99Rby8/OxcuVK7N27F9OmTYPRaPzhGSTMnTsXCxYswNixYx16/vZZzdaSGrn9UDN7vETkzdonUcnlA51by+osM2fONP165MiRGDVqFGJjY1FQUIApU6bglVdewZUrV7BkyRKH763VSdDqLA8Ra6HOtVRu3+OVOyzhxKmzaG01KtgaIiJl2dvjbV/L2p4sBd6wsDBotVpUV1ebXa+urrb6fjYyMtKh8gAwZMgQhIWFobS0FACwZ88eFBYWws/PDzqdDnFxcQCAsWPHYs6cObLPL2jkkxqptFn2CwnqgeiIPhbzmg0tKK+qtphHROQJ2idXySV7+fr6IiUlBfn5+T+5v4j8/HykpaVZ/ExaWppZeQDYvXu31fIAcObMGdTW1prWvr788sv45ptvUFxcjOLiYtNypO3bt+P//u//ZNus0UqySY3cfqgZaBtuPld9yWJeSVkV4gfbPzWdiMidOHsDjdzcXMyZMwdjx47FLbfcgtWrV6Opqck0y3n27Nno16+f6R3xY489hjvuuAMvvfQS7rzzTmzbtg2HDh3C66+/DgBobGzEc889h3vvvReRkZEoKyvDk08+ibi4OGRkZAAABg40X33Ss2dPAEBsbKzNnaMEjQSNlZ0yBEd30FCI2/d4Ae5gRUTeS0L7tpFWkoP3mzFjBv785z9j6dKlGD16NIqLi5GXl2eaQFVZWYnz53/cI2HcuHHYunUrXn/9dSQnJ+P999/Hzp07TQcLaLVaHD16FL/4xS8wdOhQzJs3DykpKfjyyy+d8p5ZEGSGmtU5t8pDerwyM5t5WAIReTJR0kCUZNbxyuRZk5OTg5ycHIt5BQUFHa5Nnz4d06dPt1g+ICAAn3zyiUP1x8TE2FxT206jk6CxMrlKo9KNqj0j8MZZ3ySDhyUQkUezdfSfN+/VrNIxXY8IvHJDzSVlZyCKIjRq/RsgIuoCbz+dSBAkCIKVd7xWrnc3j4hGfUNDENbH8pFPTVevo+r8RYVbRESkDKMo2EyerH2o2VpSI48IvICNs3m5gxUReSh71/F6Kq7j7Uac2UxE3kgUBZvJk2l1P+5e1TF1d+ssU2mzHCe/dWSlgi0hIlKOBAESZN7xyuR5AgEy73hVumWk5wReHpZARF7I2RtouBu5IWW1DjV7TuC1cViCJEkQ1Lqamoiok2wNJ3v6ULNG5pAEjajOHq9Kvw84Liq8D4J7Wj7Dsa6hCfoLlxVuERGR63FylSSb1MhjAq8gCBxuJiKv4+2Tq9zxkASPCbwAz+YlIu/j7T3e9p2rrCU18ph3vACXFBGR9xF/SHL5nkxuSFmtQ80eFXjZ4yUibyPZOHNXcuA8Xnck6AQIPpZ79YJKh9k9K/DKvuNl4CUiz9N+LKBcvicTNAIEjZXAa+V6d/Oor0IDo/siMMDy+Y41tfW4eKlB4RYREbmWt7/jhVYjn1RIna3qJI1Gg8QhcicVsddLRJ7F22c1Cz4CBB+NlaTOZ/eowAtwBysi8i6SHcmjaQT55IAVK1bg5ptvRlBQEMLDw5GVlYUTJ06Ylbl+/ToWLlyI0NBQ9OzZE/feey+qq6sda7JDpd0AZzYTkTfx+h6vzlpvVwNB51iI27t3LxYuXIj9+/dj9+7daGlpwdSpU9HU1GQq89vf/hb/+te/sGPHDuzduxfnzp3DPffc41A9HjW5CuDMZiLyLt6+V7Psu1wH3/Hm5eWZ/X7Tpk0IDw9HUVERJkyYgPr6evztb3/D1q1bMXnyZADAxo0bkZSUhP379+PWW2+1qx6P6/FyZjMReRNJsp08WfusZmsJABoaGsxSc3OzXfeur68HAPTp0wcAUFRUhJaWFqSnp5vKJCYmYuDAgSgsLLS7zR4XeIcMiISvr+WO/Bl9LRquXFW4RUREriNKgs3k0Xw18gnAgAEDEBISYkorVqyweVtRFPH4449j/PjxGDFiBABAr9fD19cXvXr1MisbEREBvV5vd5M9bqhZp9NiaEw/HPv+tMX8krIqpI5OULhVRESu4e1Dzfas462qqkJwcLDpup+f5WWnP7Vw4UIcO3YMX331lXMa+hMe1+MFgGFxMkuKOLOZiDyIKNpOHk2nBXysJJ0WABAcHGyWbAXenJwcfPTRR/j888/Rv/+P8SQyMhIGgwF1dXVm5aurqxEZGWl3kz0z8MYPtJrHCVZE5Ena3uPKbaDR3S10LUEryCZHSJKEnJwcfPjhh9izZw8GDx5slp+SkgIfHx/k5+ebrp04cQKVlZVIS0uzux6PG2oGuKSIiLyHrQlUnh54ZdfrOriOd+HChdi6dSv+8Y9/ICgoyPTeNiQkBAEBAQgJCcG8efOQm5uLPn36IDg4GI888gjS0tLsntEMeGjglVtSVFLGoWYi8hySjQlUHv+O94c1uxbzWh0b1H3ttdcAABMnTjS7vnHjRsydOxcA8Je//AUajQb33nsvmpubkZGRgVdffdWhejxyqDk+JhpaK+u3yquqcfWafVPJiYjUzhXLidatW4eYmBj4+/sjNTUVBw8elC2/Y8cOJCYmwt/fHyNHjsSuXbvM8ufOnQtBEMxSZmamKb+iogLz5s3D4MGDERAQgNjYWCxbtgwGg8F2Y524V7MkSRZTe9AFAH9/f6xbtw6XLl1CU1MT/v73vzv0fhfw0MDr5+eD2IGW/yAkScKJU2cVbhERkWsYRcFmcsT27duRm5uLZcuW4fDhw0hOTkZGRgZqamoslt+3bx9mzZqFefPm4ciRI8jKykJWVhaOHTtmVi4zMxPnz583pXfffdeUd/z4cYiiiL/+9a/49ttv8Ze//AXr16/HU089ZbO9bccCWtu5Sp29fY8MvICtHawqFWwJEZHrSBBsJkesWrUK8+fPR3Z2NoYNG4b169cjMDAQGzZssFh+zZo1yMzMxOLFi5GUlITly5djzJgxWLt2rVk5Pz8/REZGmlLv3r1NeZmZmdi4cSOmTp2KIUOG4Be/+AUWLVqEv//977YbrBXkkwp5ZeDlkiIi8hT2DjXbs3uTwWBAUVGR2c5MGo0G6enpVndmKiwsNCsPABkZGR3KFxQUIDw8HAkJCXj44YdRW1sr+1z19fWmHaNkOfGQBKV4bOBN4p7NROQFRMnGIQk/TK6yZ/emixcvwmg0IiIiwuy63M5Mer3eZvnMzEy89dZbyM/Px8qVK7F3715MmzYNRqPR4j1LS0vxyiuv4KGHHrL5/IKPVjapkUfOagZ4WAIReQd7lxN1ZvcmZ5k5c6bp1yNHjsSoUaMQGxuLgoICTJkyxazs2bNnkZmZienTp2P+/Pm2b+7E5URK8dgeb2JsfwiC5T/00tPnYTC0KNwiIiLns3eo2Z7dm8LCwqDVajucLyu3M1NkZKRD5QFgyJAhCAsLQ2lpqdn1c+fOYdKkSRg3bhxef/11ex4f0Gjkkwqps1VOEBjgh5j+4RbzjEYRJyvOK9wiIiLnc+YhCb6+vkhJSTHbmUkUReTn51vdmSktLc2sPADs3r1bdienM2fOoLa2FlFRUaZrZ8+excSJE5GSkoKNGzdCY2/Q1P6wNaSlpFXnULPHBl6AO1gRkedz9jre3NxcvPHGG9i8eTNKSkrw8MMPo6mpCdnZ2QCA2bNnY8mSJabyjz32GPLy8vDSSy/h+PHjePbZZ3Ho0CHk5OQAABobG7F48WLs378fFRUVyM/Px9133424uDhkZGQA+DHoDhw4EH/+859x4cIF6PV6+078ccMer8e+4wXa3vPuKiiymFdSxsBLRO6vfRKVXL4jZsyYgQsXLmDp0qXQ6/UYPXo08vLyTBOoKisrzXqj48aNw9atW/HMM8/gqaeeQnx8PHbu3Gk6Sk+r1eLo0aPYvHkz6urqEB0djalTp2L58uWm4e7du3ejtLQUpaWlZocSAG17L8jS/XgYgsU8FfLswBsvM8HqJAMvEbk/6Yckl++onJwcU4/1RgUFBR2uTZ8+HdOnT7dYPiAgAJ988olsfXPnzjXbHcohGsF6z1alk6s8OvAmxXJmMxF5Nh6SIDOkzKFm5SXJnMv7ffk5tLYaoVPpUAQRkT0kG0PNkoNDzW7HDYea1fl1wElCgnqgX2SoxbxmQwvKq6ot5hERuQvJjuTR3HBylTpb5USc2UxEnkwUbSdPJmi0ELRWkoY93m7BHayIyJNJkmAzeTQ37PF69DtewNZhCQy8ROTeOLnK/baM9PjAy8MSiMiTiVJbksv3aG44ucrjA69sj7fsDERRtH9rMiIilWGP1/3W8Xp8xAnrE4y+fUIs5l291oyq8xcVbhERkfPwHa/7veNVZ6ucjDtYEZGnEvHjcLPF1N0NdDVrByTIDUF3M68IvFxSRESeytmHJLgdQaa3K6gzxHn8O17A1nteBl4icl+21up6+jpeTq5SKdm1vBxqJiI3JkGABJktI2XyPIIg07Nlj7f7yO3Z/F3pGUiSBEHw8P85icgjef2sZjcMvOpslZNFhfdBSFAPi3n1V5qgv3BZ4RYRETmJCEgyyeNnV2m1gFZnJTk21PzFF1/grrvuQnR0NARBwM6dO83y586dC0EQzFJmZqbDTfaKwCsIAobJ9Xo53ExEbsrrD0lo7/FaSw5oampCcnIy1q1bZ7VMZmYmzp8/b0rvvvuuw032iqFmoG0Hq8IjJyzmfVdahSnjkxVuERFR13n95Kr23q21PAdMmzYN06ZNky3j5+eHyMhIh+57I6/o8QK2d7AiInJHXE5ku8fb0NBglpqbmztdXUFBAcLDw5GQkICHH34YtbW1Dt+DgRdcy0tE7otDzbYD74ABAxASEmJKK1as6FRVmZmZeOutt5Cfn4+VK1di7969mDZtGoxGo0P38aKhZr7jJSLP4/VDzYIO0FgJZULb9aqqKgQHB5su+/n5daqqmTNnmn49cuRIjBo1CrGxsSgoKMCUKVPsvo/X9HgHRvdFYIDlP+wLl+px8VKDwi0iIuo6rx9qtmOv5uDgYLPU2cB7oyFDhiAsLAylpaWONdkptbsBjUYju3Ukd7AiInfk7UPNgqCBIGitJNeGuDNnzqC2thZRUVEOfc5rAi/AHayIyPOIomAzeTSNTj45oLGxEcXFxSguLgYAlJeXo7i4GJWVlWhsbMTixYuxf/9+VFRUID8/H3fffTfi4uKQkZHhUD1e844XaFtSZA0nWBGRO+LOVc7buerQoUOYNGmS6fe5ubkAgDlz5uC1117D0aNHsXnzZtTV1SE6OhpTp07F8uXLHR669qrAyyVFRORp2o//k8v3aE5cxztx4kRIMt9UPvnkE4fuZw0D7w/Y4yUid8QeL/dqVrXBAyLg62v5u8ZZfS3qrzQp3CIioq6RIEGSZFInpletW7cOMTEx8Pf3R2pqKg4ePChbfseOHUhMTIS/vz9GjhyJXbt2meXbs8fxpUuXcP/99yM4OBi9evXCvHnz0NjYaLuxTtwyUinqbJWL6HRaJAzuZzX/OIebicjNSNKPw82WkqM93u3btyM3NxfLli3D4cOHkZycjIyMDNTU1Fgsv2/fPsyaNQvz5s3DkSNHkJWVhaysLBw7dsysnK09ju+//358++232L17Nz766CN88cUXePDBB2032ImHJCjFqwIvwOFmIvIszl7Hu2rVKsyfPx/Z2dkYNmwY1q9fj8DAQGzYsMFi+TVr1iAzMxOLFy9GUlISli9fjjFjxmDt2rVm5dr3OG5PvXv3NuWVlJQgLy8Pb775JlJTU3HbbbfhlVdewbZt23Du3Dn5BrPHq36yM5u5pIiI3Iy9gdee/YoNBgOKioqQnp5uuqbRaJCeno7CwkKL9RcWFpqVB4CMjIwO5eX2OC4sLESvXr0wduxY07X09HRoNBocOHBA/g/AicuJlOJ1gZczm4nIk9gbeO3Zr/jixYswGo2IiIgwux4REQG9Xm+xfr1eb7O8rT2O9Xo9wsPDze6h0+nQp08fq/WaCDaSCqnz64ALcaiZiDyJvbOanbVfcWc4a49jS9onkVnLUyOv6/HGx0RBq7X82BVnanD1WuePiyIiUpooSTYTYN9+xWFhYdBqtaiurja7Xl1dbfUM2sjISIfKAx33OI6MjOwweau1tRWXLl2yefatCKNsUiOvC7y+vj6IG2R5X01JknDi1FmFW0RE1HnOnFzl6+uLlJQU5Ofnm66Jooj8/HykpaVZ/ExaWppZeQDYvXu31fJAxz2O09LSUFdXh6KiIlOZPXv2QBRFpKamyrZZkkTZpEZeF3gBW8PNlQq2hIioa5w9qzk3NxdvvPEGNm/ejJKSEjz88MNoampCdnY2AGD27NlYsmSJqfxjjz2GvLw8vPTSSzh+/DieffZZHDp0CDk5OQBg1x7HSUlJyMzMxPz583Hw4EF8/fXXyMnJwcyZMxEdHS3//Db+UyOve8cLtAXeDz/dbzGPM5uJyJ3IveNsz3fEjBkzcOHCBSxduhR6vR6jR49GXl6eaQJVZWUlNJof+2zjxo3D1q1b8cwzz+Cpp55CfHw8du7ciREjRgAAtFqtXXscb9myBTk5OZgyZQo0Gg3uvfdevPzyyzbbK0oiRMnykLKo0h6vVwbepDjrxwNyghURuRNnB14AyMnJMfVYb1RQUNDh2vTp0zF9+nSL5QMCAuza47hPnz7YunWrQ+0EAAkiJFgOsNaudzevDLzD4gZazeOSIiJyJ95+SIIoGWV6vOqcXOWVgTdhSD8IgmDxm2Dp6fNobm6Bn59PN7SMiMgxrujxuhO5SVScXKUigQF+iOkfbjHPaBRxssLGFmVERGoh2ZE8mDtOrvLKwAtwBysi8gz2ruP1VO1DzdaSGjHwWsAJVkTkLpy9nMjdtE+uspbUyCvf8QI2Zjaf5FpeInIPko1erae/4+XkKjfCoWYi8gT27tXsqdpeY1vZq1nZptjNawNvUqz1wHui/CxaW43Q6dR5iDIRkRm1RhglyG0NyVnN6hIcFIh+kaEW8wyGVpyqsnEUFRGRCnj95CoekuBeZIebSzncTETq5/WTq35Yx2wtqREDrxWc2UxE7sDrAy9nNbsXBl4icne2hpM9fqjZDWc1e3WPV25JUQkDLxG5AW/v8bbvVW0tOeKLL77AXXfdhejoaAiCgJ07d5rlS5KEpUuXIioqCgEBAUhPT8fJkycdbrNXB15bS4pEUZ3DFERE7bw98LaIgmxyRFNTE5KTk7Fu3TqL+X/605/w8ssvY/369Thw4AB69OiBjIwMXL9+3aF6vHqoObR3MMJDQ1BTW98h7+q1ZlSeu4CY/hHd0DIiIvt4+zpeURIgSpYDrLXr1kybNg3Tpk2zmCdJElavXo1nnnkGd999NwDgrbfeQkREBHbu3ImZM2faXY9X93gBIEnuPe9JDjcTkbrZmtWr1pm9ziJKgNFKah9qbmhoMEvNzc0O11NeXg69Xo/09HTTtZCQEKSmpqKwsNChe3l94OUOVkTkzmy94/T083hbRUE2AcCAAQMQEhJiSitWrHC4Hr2+bW+HiAjzUdCIiAhTnr28eqgZ4MxmInJv3j7UbJQEGK0MKbdfr6qqQnBwsOm6n5+fIm2zxusDb1KszGEJDLxEpHK2hpM9fai5FQJarQTeVrRdDw4ONgu8nREZGQkAqK6uRlRUlOl6dXU1Ro8e7dC9ONQcL797laf/T0tE7s3bZzUrNcw+ePBgREZGIj8/33StoaEBBw4cQFpamkP38voeb2Tf3ugV3AN1DU0d8uqvNOF8zSVER1je05mIqLtJkD8jwcPjrl1DzfZqbGxEaWmp6ffl5eUoLi5Gnz59MHDgQDz++OP4wx/+gPj4eAwePBi///3vER0djaysLIfq8frAKwgCkmL7o/DICYv535VWMfASkWpJIiC35YBKD+hxGuNPJlFZynPEoUOHMGnSJNPvc3NzAQBz5szBpk2b8OSTT6KpqQkPPvgg6urqcNtttyEvLw/+/v4O1eP1gRdoG262FnhLSs8gffxoZRtERGQnTq5qS9byHDFx4kTZ14uCIOD555/H888/79iNb8DAC2BY3ECreZxgRURqJncQfHu+J3PmBhpKYeAFlxQRkfsSbQw1e/rOty1iW7KWp0YMvLB1WAI30SAi9fL2yVXu2OP1+uVEADAgKgw9Ai2/HL9wqR4XLOzlTESkBpLUNoHKaupE5F23bh1iYmLg7++P1NRUHDx4ULb8jh07kJiYCH9/f4wcORK7du2yWnbBggUQBAGrV682u/7999/j7rvvRlhYGIKDg3Hbbbfh888/t9nWVpkDEqxNuupuDLwANBqN7EYa3DqSiNRKsiM5Yvv27cjNzcWyZctw+PBhJCcnIyMjAzU1NRbL79u3D7NmzcK8efNw5MgRZGVlISsrC8eOHetQ9sMPP8T+/fsRHR3dIe/nP/85WltbsWfPHhQVFSE5ORk///nPbW7H6I7bZTLw/kA28PI9LxGplLM30Fi1ahXmz5+P7OxsDBs2DOvXr0dgYCA2bNhgsfyaNWuQmZmJxYsXIykpCcuXL8eYMWOwdu1as3Jnz57FI488gi1btsDHx8cs7+LFizh58iR+97vfYdSoUYiPj8cLL7yAq1evWgzgP9U+1GwtqRED7w/kdrDiBCsiUitRlGwmwL4TegwGA4qKisxO4NFoNEhPT7d6Ak9hYaFZeQDIyMgwKy+KIh544AEsXrwYw4cP73CP0NBQJCQk4K233kJTUxNaW1vx17/+FeHh4UhJSZF9/rbJVdaGm2U/2m0YeH/Amc1E5I7sHWq254Seixcvwmg0OnQCj16vt1l+5cqV0Ol0ePTRRy3eQxAEfPbZZzhy5AiCgoLg7++PVatWIS8vD71795Z9fnccauas5h8kxTLwEpH7sXc5UXed0FNUVIQ1a9bg8OHDEATLQ7+SJGHhwoUIDw/Hl19+iYCAALz55pu466678J///MfsUIIbGSRAZ+X5DSoNvOzx/mDwgAj4+fpYzDtXfQn1Vzru5UxE1N3s7fG2n9DTniwF3rCwMGi1WlRXV5tdr66uNp3Oc6PIyEjZ8l9++SVqamowcOBA6HQ66HQ6nD59Gk888QRiYmIAAHv27MFHH32Ebdu2Yfz48RgzZgxeffVVBAQEYPPmzfLPL9PbVeuuXQy8P9DptEgY0s9qPtfzEpEaOXNyla+vL1JSUsxO4BFFEfn5+VZP4ElLSzMrDwC7d+82lX/ggQdw9OhRFBcXm1J0dDQWL16MTz75BABw9epVAG3vk39Ko9FAtLEDSPuWkdaSGnGo+SeSYvvj6PEKi3nflVbh1psSlG0QEZENtt5lOvqeMzc3F3PmzMHYsWNxyy23YPXq1WhqakJ2djYAYPbs2ejXr5/pHfFjjz2GO+64Ay+99BLuvPNObNu2DYcOHcLrr78OoG3iVGio+UEzPj4+iIyMREJC27+paWlp6N27N+bMmYOlS5ciICAAb7zxBsrLy3HnnXfKttcgAlprQ80qnVzFwPsT8mfz8j0vEamQrV6tg4F3xowZuHDhApYuXQq9Xo/Ro0cjLy/PNIGqsrLSrGc6btw4bN26Fc888wyeeuopxMfHY+fOnRgxYoTddYaFhSEvLw9PP/00Jk+ejJaWFgwfPhz/+Mc/kJycLPtZuS8enFzlBjizmYjcjStOJ8rJyUFOTo7FvIKCgg7Xpk+fjunTp9t9/4qKig7Xxo4daxp6doQzTydSCgPvTzDwEpG7cfZQs7tplTkkoVWlQ82cXPUTcYOioNVa/iM5ffYCmq5eV7hFRETynL1zlbtxx8lVDLw/4evrg/iYjnuIAm3rzE6cOqtwi4iI5NnaQMLTe7wGUZBNasTAewMONxORO/H2Hq87fungO94b8LAEInInrphc5U44ucoD8LAEInInXj+5ygi0GK3nqRED7w041ExE7oQ9XvZ43V7CkH4QBAGShf9bS0/r0dzcAj8/y3s6ExEpzdt7vC0SoLGybKhFpc/OyVU3CPD3w+ABERbzRFHEyYpzCreIiMg6CTYmV3V3A12My4k8BIebichdSHYkT8bA6yHkZjYz8BKRmogiYJRJNg73cXvtO1dZSo7uXPXss89CEASzlJiY6PQ28x2vBXI9Xi4pIiI14eQq506uGj58OD777DPT73U654dJBl4L5JcU8VxeIlIPW8PJHh53IYoCRCs7VFm7Lken0yEyMrKrzZLFoWYL5IaaT5SfRataF4cRkddpDzxyyZO1tmhkEwA0NDSYpebmZqv3O3nyJKKjozFkyBDcf//9qKysdHqbGXgtCOoZiP6RoRbzWlpacapKr3CLiIgs8/bJVfZ86RgwYABCQkJMacWKFRbvlZqaik2bNiEvLw+vvfYaysvLcfvtt+PKlStObTOHmq0YFj8AZ/S1FvO+O1mFoYP7KdwiIqKOjCIAmUlERg+fXGVs/bFnaykPAKqqqhAcHGy67ufnZ7H8tGnTTL8eNWoUUlNTMWjQILz33nuYN2+e09rMHq8VXFJERO6APV7bPd7g4GCzZC3w3qhXr14YOnQoSktLndpmBl4rkmJlZjaXcYIVEamDtx8L6Mr3242NjSgrK0NUVJSTWtuGgdcK2ZnNJ9njJSJ1aAuugkzq7ha6VmuLIJscsWjRIuzduxcVFRXYt28ffvnLX0Kr1WLWrFlObTPf8VohezxgWRVEUYRGw+8tRNS9RAkQvHivZmcuJzpz5gxmzZqF2tpa9O3bF7fddhv279+Pvn37OqOpJgy8VoT2DkZ4aAhqaus75F27bkDluQuI6W95T2ciIqWINiZXefrOVS0tGsDK5KoWK9et2bZtmzOaZBO7bDJkJ1hxuJmIVMDrJ1dJMu94JXWuYWbglSG/gxUDLxF1P2+fXCXJTKySVLp5CIeaZcjNbGbgJSI1MEoCJJmenVp7fc7S2qIBdJb7kNbW93Y3Bl4ZsoclcEkREamAaGM82dN7vM7eq1kJDLwybC0pkiQJgqDOv1gi8g4MvNYDrFonlqmzH64SEWG90Dukp8W8hsarOFd9SeEWERGZa5UEm8lR69atQ0xMDPz9/ZGamoqDBw/Klt+xYwcSExPh7++PkSNHYteuXVbLLliwAIIgYPXq1R3y/v3vfyM1NRUBAQHo3bs3srKybLbVnkMS1EadrVIJQRBsruclIupOImxMrnLwftu3b0dubi6WLVuGw4cPIzk5GRkZGaipqbFYft++fZg1axbmzZuHI0eOICsrC1lZWTh27FiHsh9++CH279+P6OjoDnkffPABHnjgAWRnZ+Obb77B119/jfvuu8/283NWs+fhkiIiUrNWCWiRSa0ODjWvWrUK8+fPR3Z2NoYNG4b169cjMDAQGzZssFh+zZo1yMzMxOLFi5GUlITly5djzJgxWLt2rVm5s2fP4pFHHsGWLVvg4+Nj/gytrXjsscfw4osvYsGCBRg6dCiGDRuGX/3qVzbba5Tp7RrZ43VPXFJERGpmlGwnwL4zaQ0GA4qKipCenm66ptFokJ6ejsLCQov1FxYWmpUHgIyMDLPyoijigQcewOLFizF8+PAO9zh8+DDOnj0LjUaDm266CVFRUZg2bZrFXvON3PEsYgZeG3hYAhGpWatkOwH2nUl78eJFGI1GRESY78oXEREBvd7yOeR6vd5m+ZUrV0Kn0+HRRx+1eI9Tp04BAJ599lk888wz+Oijj9C7d29MnDgRly7ZmEvjhouYOavZBvZ4iUjNmiUBgsy7zPY1vvaeSetsRUVFWLNmDQ4fPmx1FYj4w/Tjp59+Gvfeey8AYOPGjejfvz927NiBhx56yOr9tS0itForb7Jb1DmtmT1eGwZEhaFnD3+LeRcvNeCChb2ciYgUIwGSTGpfamTPmbRhYWHQarWorq42u15dXY3IyEiL1UdGRsqW//LLL1FTU4OBAwdCp9NBp9Ph9OnTeOKJJxATEwMApmP3hg0bZrqHn58fhgwZgsrKStnHF0QJGitJUGmPl4HXBkEQkDhEbmYzh5uJqBvJRV1T9LWPr68vUlJSkJ+fb7omiiLy8/ORlpZm8TNpaWlm5QFg9+7dpvIPPPAAjh49iuLiYlOKjo7G4sWL8cknnwAAUlJS4OfnhxMnTpju0dLSgoqKCgwaNEi2zVqjCG2rlWRUZ4+XQ812GBY3AIf+W2ox77vSSky4peNkASIiZdg6CsGxXl9ubi7mzJmDsWPH4pZbbsHq1avR1NSE7OxsAMDs2bPRr18/0zvixx57DHfccQdeeukl3Hnnndi2bRsOHTqE119/HQAQGhqK0NBQszp8fHwQGRmJhIQEAG298QULFmDZsmUYMGAABg0ahBdffBEAMH36dNn2aoyAxmj5GTVGhx5dMQy8drC1gxURUbeRJECS6dk50OMFgBkzZuDChQtYunQp9Ho9Ro8ejby8PNMEqsrKSrOzyMeNG4etW7fimWeewVNPPYX4+Hjs3LkTI0aMcKjeF198ETqdDg888ACuXbuG1NRU7NmzB71795b9XPuwsrU8NRIkycG/FS/0z88O4O6H/mgxL318Mna/9bzCLSIib9fQ0ICQkBBg+G8Ara/1gkYD8O2bqK+vN5tc5e7an39Cznbo/AItlmltvoov1s5Q3bOzx2uHYXEDreZxZjMRdStJtNHjVed7Tmdxxx4vA68dBg8Ih5+vD5oNLR3yzlVfQl1DI3oFW97TmYjIpaRWQJKZJyu1KteWbqBrFaHTWPly0arOLx2c1WwHrVaLRLk9m0s5s5mIukl7j1cuebIflg1ZSmrdQIOB107yhyUw8BJRN3HiciJ3ZG0Nr9wQdHfjULOdeFgCEamSlw81a1tEaAXLvXpRpTtXMfDaiVtHEpEqOXk5kbvRiCI0Vk68t3a9uzHw2onn8hKROomQP3VXncHHWTir2YPFDYqCTqdFa2vHrVAqztSg6ep19Ai0vKczEZHLiEZAkBlqFlW6fZOTaFtlhpo5q9m9+fr6ID4mymr+cU6wIqLu4OWzmp09uWrdunWIiYmBv78/UlNTcfDgQee32el39GA8m5eI1McISDIJnt3j1bWIsskR27dvR25uLpYtW4bDhw8jOTkZGRkZqKmpcWqbGXgdIDuzmROsiKg7eHmPFyJk1vE6dqtVq1Zh/vz5yM7OxrBhw7B+/XoEBgZiw4YNTm0y3/E6gEuKiEh1jC3yM5dFz15OZDRcRauVIWVj6zUAbfs6/5Sfn1+H84gNBgOKioqwZMkS0zWNRoP09HQUFhY6tc0MvA5IiuPMZiJSB19fX0RGRkJfudtm2cjISPj6yhyk4Iban/+DTx+XLdezZ08MGGDeaVq2bBmeffZZs2sXL16E0Wg0ncLULiIiAsePH3dGk00YeB2QMKQfBEGApQOdSk/r0dzcAj8/n25oGRF5G39/f5SXl8NgMNgs6+vrC39/z1p1Ye/zS5IEQRDMrt3Y21UaA68DAvz9MGRABMoq9R3yRFHE9xVnMTIhRvmGEZFX8vf397iA6ghnPn9YWBi0Wi2qq6vNrldXVyMyMtIpdbTj5CoHye1gxcMSiIjck6+vL1JSUpCfn2+6Jooi8vPzkZaW5tS6GHgdJLekiDObiYjcV25uLt544w1s3rwZJSUlePjhh9HU1ITs7Gyn1sOhZgdxSRERkWeaMWMGLly4gKVLl0Kv12P06NHIy8vrMOGqqwTJ0kwhsuo/R0/ill8uspg3PH4gjuW9onCLiIjInXCo2UGJQ/pZzfu+4pzFvZyJiIjaMfA6KKhnIAZEhVnMa2lpRVnleYVbRERE7oSBtxO4gxUREXUWA28nyO9gxSVFRERkHQNvJwyLG2g1jzObiYhIDgNvJ3BJERERdRYDbyfIDTUfLzsDUfTwY7iIiKjTGHg7oU+vIESE9bKYd+26AafPXlC2QURE5DYYeDuJw81ERNQZDLydJH9YAgMvERFZxsDbSUmx1t/zssdLRETWMPB2EoeaiYioMxh4O2lYvPW1vCWlZ8CzJ4iIyBIG3k4KDw1B75CeFvMaGq/iXPUlhVtERETugIG3kwRB4HAzERE5jIG3C3hYAhEROYqBtwvkD0tg4CUioo4YeLuAQ81EROQoBt4uYOAlIiJHMfB2Qf+oMPTs4W8xr/byFVyorVe4RUREpHYMvF0gCAKSYtnrJSIi+zHwdhGHm4mIyBEMvF0kF3h5WAIREd2IgbeL5JYUscdLREQ3YuDtIg41ExGRIxh4uyimfzj8/Xwt5p2vuYy6hkaFW0RERGrGwNtFWq0WCUP6Wc0vKT2jYGuIiEjtGHidgMPNRERkLwZeJ2DgJSIiezHwOoHsYQkcaiYiop9g4HUC9niJiMheDLxOEDcoCjqd1mLe6bM1aGy6pnCLiIhIrRh4ncDHR4f4mCir+SdOnVWwNUREpGYMvE7C4WYiIrIHA6+TMPASEZE9GHidRP6wBM5sJiKiNgy8TpLEHi8REdlB190N8BRDB0dDo9FAMhoR0SIhxmBEf4MIf1GCUFuO/YteQ9/RcQhNGYqQhAEQNPzOQ6QESRTRcu4sDKWlMJw+DenaVQCAEBAA30Ex8I2Ng0+/fvyZJMUIkiRJ3d0IT9BQdg6LJz2C4efqECTKl/XrE4S42VOR8PAvEBJvffMNIuq8lvPncSVvFxrz8yFeaZAtqwkKQs9JkxE07Wfwiba+9zqRMzDwdtH1C3XY/+grKH9vL+DoH6UgIObe23HrK48gIKKPaxpI5GWM9fWofX09rn79Vad+JgPTxiH0oQXQ9urtmgaS12Pg7YLyHXtRuHANmi/Wd+k+fqHBuPXlRzBk1mQntYzIOzV9/RVq178KsUG+h2uLJigYfR58CD0n3OGklhH9iIG3k4qffwtHnt3s1HsmP/1rjFme7dR7EnmLum3vou7dLU69Z8ivZqD3/Q849Z5EnE3QCcXL33Z60AWAb/7vHRxeutHp9yXydHXbnR90AaD+ve24vOUdp9+XvBsDr4Mq/7kPR5Ztctn9v/nDO6j44AuX3Z/I01w9cAB1W50fdNvVv7cNTfu+dtn9yfsw8Dqg+VID9i34i8vrKVy4Bte7+N6YyBsYr1xB7WtrXV5P7frXYGzgzyQ5BwOvAw4uWo9r+ksur+d6TR0O/Hady+shcneXN/wNxsuXXV6PWF+HS2++4fJ6yDsw8Nrp6vlalL3zmWL1lW/7HI1VNYrVR+RuWi9dQuPezxWrr+nLL9B64YJi9ZHnYuC104nX/w2p1ahYfZJRxIm/fqRYfUTupvHTPMCo3M8kRBFXPslTrj7yWAy8dip961Pl69z8ieJ1ErmLxj353VCncqNe5Lm4V7MdrtVcRmP5eYc+86j0Jf6ENLyC/+IUGjAR0ZghxDt0j6tnL6Lp7AX06NfXoc8ReTpjXR1aq6ttlov71y4kBAXBIIrw12pwX8wgzBo0EABQfLkOS48eQ0lDA9bfnIIpkRG2662tRWvtRehCw7r8DOS9GHjtcPHQ9536nBYa/AIxOIcm1OBap+4xKXUevgny6dRniTzVhB5GvGHHlsrBPjr8e+LtAICzV6/hof8cgiQB98UMRIS/H14YPQpvlp1yqG5DaSkDL3UJA68d6r6t6NTnfAQNEtAbF6Trna47/FoLDH5Cpz9P5ImGBNs4icSCfoEBeHp4EpYfK8F9MQMRFRCAqIAAaATHfr4MlZUITL3V4fqJ2vEdrx1amzofOLvKT+SOnkQ3Cujkv1zDQ0JwqrGxS3VL1zs3ekXUjoHXHg5+I3Ymhl2ijrp1h3mB/2xS1/D/IDv4hvTotrqvaTjMTHSjK46PNAMAvqtvQGzPnl2qWxMY0KXPEzHw2qH3qCHdVneVL/+KiG50/LrjX0jPXbuGP35bgtmDB3Wpbt+YwV36PBEnV9khdIxjy4CMkggd2v5hWCLtRyMMaIWEg1INnkYK+gj+dt/rXE9f+OoYfIl+6nujBMBgs1xDSyt+VvAlWn5YTnR/zCDM+GE5UUl9A/73wH9Q39KCPdXViOnRA3+/fbzNe/rGxXW1+eTlGHjt4NerJ3qNGIy6Y+V2lT+LJoShbThqhdD52Y8hCQNwqWRTpz9P5MnOProQLadPy5YpvetnVvOSQoJROHWKQ3Xq+vWHNjjEoc8Q3YhdKTsNnTfNrnJfSufwOr7D3ej6cFT8POv/aBB5u6D/meoVdZLnESSpW+cHuo3muka8138GWq8qs7RI6++LX1Vtg38ov10TWWJsbMSZ/50DqblZkfoEX1/0/9smaIODFamPPBd7vHby69UTiQvvVqy+xAV3MegSydD27ImgO3+uWH1BmdMYdMkpGHgdcNNzcxGSONDl9QTF9cOYP/yvy+shcne9Zt0Pn/79XV6PLioKvX79gMvrIe/AwOsAnb8vbtuwGIJO67I6BK0Gt29YDF2g/TOfibyVxtcXoY8+Dmhd9zMJjQZhjz4OjR9/Jsk5GHgdFH7rMNy+8UkIGhf80QkCxr+5CBG3jXT+vYk8lH9CIsIefRxw0c9k2COPwX/YcOffm7wWJ1d1UtnWfHw5dyWkVuccxC1oNRj/5iLEz8lwyv2IvE3j3gJcXPMXwOicn0loNAh75DH0nOzYkiMiWxh4u+DioRP4MvtPnT69qF1I4kDcvvFJ9E1Nck7DiLxU88mTuPjyX9BSWdml+/j074+wR38Lv4QEJ7WM6EcMvF1kNLTgmz+8g5JXPoShvsmhz/oE90DS//cLJC+dDZ2/r4taSORdpJYW1L23HQ3//hekJsd+JoXAQAT/7E6EzJgFjS9/Jsk1GHidpKXpGk5tycfJjXmoPXwSYkurxXKCTovQm+IQnz0Nsb9Oh09PbrhO5Ari9eto2luAK5/thuFUGdBq+WcSWi18hwxBzyn/g54TJ0ETwJ9Jci0GXhcwNhtw6egpXD56Ci1X2s7u9AkKQO+RQ9B71BD2bokUJrW0wFBRDkNFBcRrbT+TmoAA+A4aBJ+YwezdkqIYeImIiBTE5UREREQKYuAlIiJSEAMvERGRghh4iYiIFMTAS0REpCAGXiIiIgUx8BIRESmIgZeIiEhBDLxEREQKYuAlIiJSEAMvERGRghh4iYiIFMTAS0REpCAGXiIiIgUx8BIRESmIgZeIiEhBDLxEREQKYuAlIiJSEAMvERGRghh4iYiIFMTAS0REpCAGXiIiIgUx8BIRESmIgZeIiEhBDLxEREQK+n/GW0KKeCprswAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "flow_at_5hr = results.link['flowrate'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, link_attribute=flow_at_5hr, \n", + " node_attribute=pressure_at_5hr, \n", + " node_size=500, \n", + " link_width=5, \n", + " node_labels=True,\n", + " link_cmap=plt.cm.cividis)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0][:2]\n", + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the QUBO Polynomial Solver" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "wn = wntr.network.WaterNetworkModel(inp_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 0.000000 => 200.000000 (res: 1.574803)\n", + "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.031496)\n" + ] + } + ], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 7\n", + "step = (4./(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", + "\n", + "nqbit = 7\n", + "step = (200/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", + "\n", + "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, head_encoding=head_encoding)\n", + "net.verify_encoding()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve the system classically" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1. , 1. , 0.999, 0.998])" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "\n", + "ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = net.classical_solution(model)\n", + "ref_sol / ref_values" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1,\n", + " 1,\n", + " [0, 0, 0, 1, 1, 1, 0],\n", + " [0, 0, 0, 1, 1, 1, 0],\n", + " [1, 1, 1, 0, 1, 1, 0],\n", + " [0, 0, 0, 0, 1, 1, 0]]" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bin_rep_sol" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.scatter(ref_values, encoded_ref_sol)\n", + "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", + "plt.grid(which=\"major\", lw=1)\n", + "plt.grid(which=\"minor\", lw=0.1)\n", + "# plt.loglog()\n", + "plt.xscale('symlog')\n", + "plt.yscale('symlog')" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.matrices = net.initialize_matrices(model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", + "sampler = SimulatedAnnealing()" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", + "import sparse\n", + "net.qubo = QUBOPS_MIXED(net.mixed_solution_vector, {\"sampler\": sampler})\n", + "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", + "net.qubo.qubo_dict = net.qubo.create_bqm(matrices, strength=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.step.full_random import IncrementalStep\n", + "\n", + "var_names = sorted(net.qubo.qubo_dict.variables)\n", + "net.qubo.create_variables_mapping()\n", + "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# generate init sample" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", + "x = modify_solution_sample(net, bin_rep_sol, modify=['flows', 'heads'])\n", + "x0 = list(x.values())" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "eref = net.qubo.energy_binary_rep(bin_rep_sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "num_sweeps = 2000\n", + "Tinit = 1E1\n", + "Tfinal = 1E-1\n", + "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "Tschedule = np.append(Tschedule, np.zeros(1000))\n", + "# Tschedule = np.zeros(10000)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4000/4000 [00:06<00:00, 651.19it/s]\n", + "100%|██████████| 4000/4000 [00:06<00:00, 665.09it/s]\n", + "100%|██████████| 4000/4000 [00:04<00:00, 803.22it/s]\n" + ] + } + ], + "source": [ + "mystep.optimize_values = np.arange(2,6)\n", + "res = sampler.sample(net.qubo, init_sample=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", + "\n", + "mystep.optimize_values = np.arange(2,4)\n", + "res2 = sampler.sample(net.qubo, init_sample=res.res, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", + "\n", + "mystep.optimize_values = np.arange(4,6)\n", + "res3 = sampler.sample(net.qubo, init_sample=res2.res, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", + "\n", + "mystep.verify_quadratic_constraints(res3.res)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "idx_min = np.array([e for e in res.energies]).argmin()" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "eplt = res.energies-eref[0]\n", + "\n", + "# fig, ax1 = plt.subplots()\n", + "\n", + "left, bottom, width, height = [0.55, 0.55, 0.3, 0.3]\n", + "\n", + "plt.plot(res3.energies[:]-eref, lw=4, label=\"QUBO Energy\")\n", + "plt.plot(Tschedule, lw=3, label='Temperature')\n", + "# ax1.axline((0, 0), slope=0, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", + "plt.grid(which='both')\n", + "# plt.yscale('symlog')\n", + "\n", + "plt.ylabel('Energy', fontsize=14)\n", + "plt.xlabel('Iterations', fontsize=14)\n", + "plt.legend(fontsize=12)\n", + "\n", + "# ax2 = fig.add_axes([left, bottom, width, height])\n", + "# ax2.plot(eplt[-1000:])\n", + "# ax2.grid()\n", + "# ax2.axline((0, 0), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "# ax2.set_yscale('symlog')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [], + "source": [ + "idx_min = np.array([e for e in res.energies]).argmin()\n", + "# idx_min = -1\n", + "sol = res.trajectory[idx_min]\n", + "sol = net.qubo.decode_solution(np.array(sol))\n", + "sol = net.combine_flow_values(sol)\n", + "sol = net.convert_solution_to_si(sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-9562.760602598233 [-9562.926]\n", + "[0.165]\n" + ] + } + ], + "source": [ + "print(eref[0], res.energies[idx_min])\n", + "print(eref[0] - res.energies[idx_min])" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Pressure')" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "\n", + "fig = plt.figure(figsize = plt.figaspect(0.5))\n", + "ax1 = fig.add_subplot(121)\n", + "\n", + "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.grid()\n", + "\n", + "ax1.scatter(ref_values[:2], encoded_ref_sol[:2], c='black', s=200, label='Best solution')\n", + "ax1.scatter(ref_values[:2], sol[:2], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "\n", + "\n", + "ax1.set_xlabel('Reference Values', fontsize=12)\n", + "ax1.set_ylabel('QUBO Values', fontsize=12)\n", + "ax1.set_title('Flow Rate', fontsize=14)\n", + "\n", + "ax2 = fig.add_subplot(122)\n", + "\n", + "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "\n", + "\n", + "ax2.scatter(ref_values[2:], encoded_ref_sol[2:], c='black', s=200, label='Best solution')\n", + "ax2.scatter(ref_values[2:], sol[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "ax2.grid()\n", + "\n", + "\n", + "ax2.set_xlabel('Reference Values', fontsize=12)\n", + "ax2.set_title('Pressure', fontsize=14)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "def flatten_list(lst):\n", + " out = []\n", + " for elmt in lst:\n", + " if not isinstance(elmt, list):\n", + " out += [elmt]\n", + " else:\n", + " out += elmt\n", + " return out\n", + "\n", + "bin_rep_flat = flatten_list(bin_rep_sol)\n", + "xt_bin_rep_flat = net.qubo.extend_binary_representation(bin_rep_flat)\n", + "# xt_bin_rep_flat.values()" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 1 0]\n", + "[1 1 0 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0]\n" + ] + } + ], + "source": [ + "print(np.array(res.trajectory[idx_min])[net.qubo.index_variables])\n", + "print(np.array(bin_rep_flat))" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-9562.926])" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xx = np.array(res.trajectory[idx_min])[net.qubo.index_variables]\n", + "net.qubo.energy_binary_rep(xx)" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "r = np.array(res.trajectory[idx_min])[net.qubo.index_variables]" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "def un_flatten_list(lst):\n", + " out = []\n", + " count = 0\n", + " for er in net.qubo.mixed_solution_vectors.encoded_reals:\n", + " nqbit = er.nqbit\n", + " d = (np.array(lst)[count:count+nqbit]).tolist()\n", + " out.append(d)\n", + " count += nqbit\n", + " return out\n", + "unflat_r = un_flatten_list(r)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/64 [00:00 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + "100%|██████████| 64/64 [00:02<00:00, 22.35it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import itertools\n", + "from tqdm import tqdm\n", + "from copy import deepcopy\n", + "\n", + "\n", + "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", + "\n", + "i2 = 0\n", + "random1 = np.random.randint(2,size=nqbit).tolist()\n", + "random2 = np.random.randint(2,size=nqbit).tolist()\n", + "\n", + "max_size = 64\n", + "iter_data = np.array(list(itertools.product([0, 1], repeat=nqbit)))\n", + "scale_factor = int(len(iter_data)/max_size)\n", + "if len(iter_data>max_size):\n", + " iter_data = iter_data[::scale_factor,:]\n", + "\n", + "energies = np.zeros((max_size,max_size))\n", + "\n", + "for data2 in tqdm(iter_data):\n", + " i3 = 0\n", + " for data3 in iter_data:\n", + " # print(list(data))\n", + " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", + " mod_bin_rep_sol[2] = list(data2)[::-1]\n", + " mod_bin_rep_sol[3] = list(data3)[::-1]\n", + " # mod_bin_rep_sol[4] = random1\n", + " # mod_bin_rep_sol[5] = random2\n", + " mod_bin_rep_sol[4] = unflat_r[4]\n", + " mod_bin_rep_sol[5] = unflat_r[5]\n", + " # mod_bin_rep_sol[4] = np.ones(5).tolist()\n", + " # mod_bin_rep_sol[5] = np.ones(5).tolist()\n", + "\n", + " # x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", + " # x0 = list(x.values())\n", + " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + " i3+=1\n", + " i2+=1\n", + "\n", + "# x, y = np.arange(2**nqbit), np.arange(2**nqbit)\n", + "# x,y = np.meshgrid(x,y)\n", + "# ax = plt.figure().add_subplot(projection='3d')\n", + "# ax.plot_surface(x,y,energies)\n", + "\n", + "plt.imshow(energies- eref)\n", + "plt.colorbar()\n", + "x2 = int(''.join(str(i) for i in bin_rep_sol[2][::-1]),base=2)/scale_factor\n", + "x3 = int(''.join(str(i) for i in bin_rep_sol[3][::-1]),base=2)/scale_factor\n", + "plt.contour(energies-eref, levels=[1e-2,1,2, 10])\n", + "plt.hlines(x3,0,max_size,ls='--',colors='black')\n", + "plt.vlines(x2,0,max_size,ls='--',colors='black')" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/128 [00:00 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + "/tmp/ipykernel_5056/3475343188.py:29: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " energies2[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + "100%|██████████| 128/128 [00:00<00:00, 726.03it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import itertools\n", + "from tqdm import tqdm\n", + "\n", + "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", + "\n", + "random1 = np.random.randint(2,size=nqbit).tolist()\n", + "random2 = np.random.randint(2,size=nqbit).tolist()\n", + "\n", + "i2 = 0\n", + "\n", + "iter_data = np.array(list(itertools.product([0, 1], repeat=nqbit)))\n", + "if len(iter_data>128):\n", + " iter_data = iter_data[::int(len(iter_data)/128),:]\n", + "\n", + "energies = np.zeros(128)\n", + "energies2 = np.zeros(128)\n", + "\n", + "for data2 in tqdm(iter_data):\n", + "\n", + " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", + " mod_bin_rep_sol[3] = list(data2)[::-1]\n", + " # mod_bin_rep_sol[2] = list(data2)[::-1]\n", + " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + "\n", + " # mod_bin_rep_sol[3] = random1 # unflat_r[3]\n", + " mod_bin_rep_sol[2] = unflat_r[2]\n", + " mod_bin_rep_sol[4] = unflat_r[4]\n", + " mod_bin_rep_sol[5] = unflat_r[5]\n", + " energies2[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", + " i2+=1\n", + "\n", + "\n", + "encoded_real = net.qubo.mixed_solution_vectors.encoded_reals[2]\n", + "xaxis_val = []\n", + "for i in range(len(iter_data)):\n", + " ibin = np.binary_repr(i,width=nqbit)\n", + " xaxis_val.append(encoded_real.decode_polynom([int(i) for i in ibin[::-1]]))\n", + "\n", + "\n", + "plt.semilogy(xaxis_val, energies-eref, lw=4, label='Exact Values')\n", + "plt.semilogy(xaxis_val, energies2-eref, lw=4, label='Optimized Values')\n", + "plt.xlabel('Flow Value', fontsize=14)\n", + "plt.ylabel('QUBO Energy', fontsize=14)\n", + "plt.ylim([1E0,1E3])\n", + "plt.grid(which='both', axis='both')\n", + "plt.legend(loc=1, fontsize=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.3779527559055118" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r0 = net.qubo.mixed_solution_vectors.encoded_reals[2]\n", + "zz = np.binary_repr(12,width=9)\n", + "r0.decode_polynom([int(z) for z in zz[::-1]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Embed the problem" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [], + "source": [ + "import dwave_networkx as dnx\n", + "from minorminer import find_embedding\n", + "from dwave.embedding import embed_qubo, majority_vote, chain_break_frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('x_002_001', 'x_004_002'): -0.44490918691668147,\n", + " ('x_004_002*x_002_001', 'x_004_002'): 0.0,\n", + " ('x_004_002*x_002_001', 'x_002_001'): 0.0,\n", + " ('x_004_001', 'x_004_002'): 0.007498113259480858,\n", + " ('x_004_001', 'x_002_001'): -0.22245459345834068,\n", + " ('x_004_001', 'x_004_002*x_002_001'): -1.734723475976807e-18,\n", + " ('x_004_001*x_002_001', 'x_002_001'): 0.0,\n", + " ('x_004_001*x_002_001', 'x_004_001'): 0.0,\n", + " ('x_003_003', 'x_004_002'): -0.015872031744063486,\n", + " ('x_003_003', 'x_004_002*x_002_001'): 0.03174406348812697,\n", + " ('x_003_003', 'x_004_001'): -0.007936015872031743,\n", + " ('x_003_003', 'x_004_001*x_002_001'): 0.015872031744063486,\n", + " ('x_003_003', 'x_001_001'): -95.85191031536937,\n", + " ('x_001_001*x_003_003', 'x_004_002'): 0.03174406348812697,\n", + " ('x_001_001*x_003_003', 'x_004_002*x_002_001'): -0.06348812697625394,\n", + " ('x_001_001*x_003_003', 'x_004_001'): 0.015872031744063486,\n", + " ('x_001_001*x_003_003', 'x_004_001*x_002_001'): -0.03174406348812697,\n", + " ('x_001_001*x_003_003', 'x_001_001'): 0.0,\n", + " ('x_001_001*x_003_003', 'x_003_003'): 0.0,\n", + " ('x_004_007', 'x_004_002'): 27.89113614243044,\n", + " ('x_004_007', 'x_002_001'): -14.237093981333778,\n", + " ('x_004_007', 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('x_005_002', 'x_003_003*x_003_001'): -0.15311884911537124,\n", + " ('x_005_002', 'x_001_001*x_003_004*x_003_001'): 0.612475396461485,\n", + " ('x_005_002', 'x_003_002*x_003_006'): -2.44990158584594,\n", + " ('x_005_002', 'x_003_001*x_001_001*x_003_005'): 1.22495079292297,\n", + " ('x_005_002', 'x_003_007*x_003_005'): -39.19842537353504,\n", + " ('x_005_002', 'x_003_004*x_003_005'): -4.89980317169188,\n", + " ('x_005_002', 'x_001_001*x_003_002*x_003_006'): 4.89980317169188,\n", + " ('x_005_002', 'x_003_004*x_001_001*x_003_005'): 9.79960634338376,\n", + " ('x_005_002', 'x_001_001*x_003_002*x_003_004'): 1.22495079292297,\n", + " ('x_005_002', 'x_003_004*x_003_002'): -0.612475396461485,\n", + " ('x_005_002', 'x_003_007*x_003_002'): -4.89980317169188,\n", + " ('x_005_002', 'x_001_001*x_003_005*x_003_006'): 39.19842537353504,\n", + " ('x_005_002', 'x_001_001*x_003_002*x_003_007'): 9.79960634338376,\n", + " ('x_005_002', 'x_003_005*x_003_006'): -19.59921268676752,\n", + " ('x_005_002', 'x_001_001*x_003_002*x_003_001'): 0.15311884911537124,\n", + " ('x_005_002', 'x_003_007*x_001_001*x_003_005'): 78.39685074707008,\n", + " ('x_005_002', 'x_003_001*x_003_005'): -0.612475396461485,\n", + " ('x_005_002', 'x_003_001*x_003_002'): -0.07655942455768562,\n", + " ('x_005_002', 'x_001_001*x_003_007*x_003_006'): 156.79370149414015,\n", + " ('x_005_002', 'x_001_001*x_003_004*x_003_007'): 39.19842537353504,\n", + " ('x_005_002', 'x_001_001*x_003_004*x_003_006'): 19.59921268676752,\n", + " ('x_005_002', 'x_001_001*x_003_001*x_003_006'): 2.44990158584594,\n", + " ('x_005_002', 'x_003_007*x_001_001*x_003_001'): 4.89980317169188,\n", + " ('x_005_002', 'x_005_001'): 19.84003968007936,\n", + " ('x_005_003', 'x_004_002'): 1.3805117159305391,\n", + " ...}" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.qubo.qubo_dict.to_qubo()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [], + "source": [ + "# target_graph = dnx.pegasus_graph(6)\n", + "# embedding = find_embedding(net.qubo.qubo_dict.to_qubo()[0], target_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "# embedding" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "# dnx.draw_pegasus(dnx.pegasus_graph(6), node_size=2, width=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "# dnx.draw_pegasus_embedding(target_graph, embedding, node_size=10, width=0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/test_qubo_poly_designe.py b/docs/notebooks/test_qubo_poly_designe.py new file mode 100644 index 0000000..651032b --- /dev/null +++ b/docs/notebooks/test_qubo_poly_designe.py @@ -0,0 +1,204 @@ +import pickle +from copy import deepcopy +import matplotlib.pyplot as plt +import numpy as np +import sparse +import wntr +from qubops.encodings import PositiveQbitEncoding +from qubops.qubops_mixed_vars import QUBOPS_MIXED +from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter +from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing +from wntr_quantum.sampler.simulated_annealing import modify_solution_sample +from wntr_quantum.sampler.step.full_random import SwitchIncrementalStep + + +def plot_solutions(solutions, references): + fig = plt.figure(figsize=plt.figaspect(0.5)) + ax1 = fig.add_subplot(121) + + ax1.axline((0, 0.0), slope=1.10, color="grey", linestyle=(0, (2, 5))) + ax1.axline((0, 0.0), slope=1, color="black", linestyle=(0, (2, 5))) + ax1.axline((0, 0.0), slope=0.90, color="grey", linestyle=(0, (2, 5))) + ax1.grid() + + for r, sol in zip(references, solutions): + ax1.scatter( + r[:2], sol[:2], s=150, lw=1, edgecolors="w", label="Sampled solution" + ) + + ax1.set_xlabel("Reference Values", fontsize=12) + ax1.set_ylabel("QUBO Values", fontsize=12) + ax1.set_title("Flow Rate", fontsize=14) + + ax2 = fig.add_subplot(122) + + ax2.axline((0, 0.0), slope=1.10, color="grey", linestyle=(0, (2, 5))) + ax2.axline((0, 0.0), slope=1, color="black", linestyle=(0, (2, 5))) + ax2.axline((0, 0.0), slope=0.90, color="grey", linestyle=(0, (2, 5))) + + for r, sol in zip(references, solutions): + ax2.scatter( + r[2:], + sol[2:], + s=150, + lw=1, + edgecolors="w", + label="Sampled solution", + ) + ax2.grid() + + ax2.set_xlabel("Reference Values", fontsize=12) + ax2.set_title("Pressure", fontsize=14) + plt.show() + + +# Create a water network model +inp_file = "./networks/Net0_CM.inp" +# inp_file = "./networks/Net0.inp" +# inp_file = './networks/Net2LoopsDW.inp' +wn_ref = wntr.network.WaterNetworkModel(inp_file) + +# store the results +energies = [] +solutions = [] +encoded_reference_solutions = [] +prices = [] +optimal_diameters = [] + +# iterate over a bunch of confs +Nsim = 100 +for i in range(Nsim): + print("==== %d / %d ====" % (i, Nsim)) + # copy the nework + wn = deepcopy(wn_ref) + + # solve classcaly + sim = wntr.sim.EpanetSimulator(wn) + results = sim.run_sim() + + # extract ref values + ref_pressure = results.node["pressure"].values[0][:2] + ref_rate = results.link["flowrate"].values[0] + ref_values = np.append(ref_rate, ref_pressure) + ref_values + + # create qubo encoding for the flow + nqbit = 5 + step = 4.0 / (2**nqbit - 1) + flow_encoding = PositiveQbitEncoding( + nqbit=nqbit, step=step, offset=+0, var_base_name="x" + ) + + # create qubo encoding for the heads + nqbit = 7 + step = 200 / (2**nqbit - 1) + head_encoding = PositiveQbitEncoding( + nqbit=nqbit, step=step, offset=+0.0, var_base_name="x" + ) + + # create designer + pipe_diameters = [250, 500, 1000] + designer = QUBODesignPipeDiameter( + wn, + flow_encoding, + head_encoding, + pipe_diameters, + head_lower_bound=95, + weight_cost=2, + weight_pressure=0.5, + ) + + # create model + designer.create_index_mapping() + designer.matrices = designer.initialize_matrices() + ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = designer.classical_solution( + [0, 1, 0, 0, 1, 0], convert_to_si=True + ) + + # sampler + sampler = SimulatedAnnealing() + + # create the solver attribute + designer.qubo = QUBOPS_MIXED(designer.mixed_solution_vector, {"sampler": sampler}) + matrices = tuple(sparse.COO(m) for m in designer.matrices) + designer.qubo.qubo_dict = designer.qubo.create_bqm(matrices, strength=0) + # designer.add_switch_constraints(strength=0) + designer.add_pressure_equality_constraints() + + # create step + var_names = sorted(designer.qubo.qubo_dict.variables) + designer.qubo.create_variables_mapping() + mystep = SwitchIncrementalStep( + var_names, + designer.qubo.mapped_variables, + designer.qubo.index_variables, + step_size=10, + switch_variable_index=[[6, 7, 8], [9, 10, 11]], + ) + + # generate init sample + # x = modify_solution_sample(net, bin_rep_sol, modify=["flows", "heads"]) + x = modify_solution_sample(designer, bin_rep_sol, modify=["flows", "heads"]) + x0 = list(x.values()) + + # temperature schedule + num_sweeps = 5000 + Tinit = 1e3 + Tfinal = 1e-1 + Tschedule = np.linspace(Tinit, Tfinal, num_sweeps) + Tschedule = np.append(Tschedule, Tfinal * np.ones(1000)) + Tschedule = np.append(Tschedule, np.zeros(100)) + + # sample flow + mystep.optimize_values = np.arange(2, 12) + res = sampler.sample( + designer.qubo, + init_sample=x0, + Tschedule=Tschedule, + take_step=mystep, + save_traj=True, + verbose=False, + ) + mystep.verify_quadratic_constraints(res.res) + + idx_min = np.array([e for e in res.energies]).argmin() + energies.append(res.energies[idx_min]) + # idx_min = -1 + sol = res.trajectory[idx_min] + sol = designer.qubo.decode_solution(np.array(sol)) + pipe_hot_encoding = sol[3] + sol = designer.combine_flow_values(sol) + sol = designer.convert_solution_to_si(sol) + sol = sol[:4] + solutions.append(sol) + + price, diameters = designer.get_pipe_info_from_hot_encoding(pipe_hot_encoding) + prices.append(price) + optimal_diameters.append(diameters) + +data = {} +for opt, e in zip(optimal_diameters, energies): + if tuple(opt) not in data: + data[tuple(opt)] = [] + data[tuple(opt)].append(e[0]) + +vals = [] +labels = [] +for k, v in data.items(): + labels.append(k) + vals.append(v) + +width = np.array([(np.array(optimal_diameters) == l).prod(1).sum() for l in labels]) +width = 0.5 * width / np.max(width) + + +plt.violinplot(vals, widths=width) +plt.xticks(list(range(1, 1 + len(labels))), labels) +plt.grid() +plt.show() + +# plot_solutions(solutions, encoded_reference_solutions) +pickle.dump(prices, open("prices.pkl", "wb")) +pickle.dump(optimal_diameters, open("optimized_diameters.pkl", "wb")) +pickle.dump(energies, open("energies.pkl", "wb")) +# pickle.dump(qubo_results, open("qubo_results.pkl", "wb")) diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index bc11431..5e1104a 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -697,17 +697,14 @@ def add_switch_constraints( ) istart += self.num_diameters - def add_pressure_equality_constraints(self, fractional_factor=100): + def add_pressure_equality_constraints(self): """Add the conrains regarding the presure.""" # add constraint on head pressures istart = 2 * self.sol_vect_flows.size for i in range(self.sol_vect_heads.size): - tmp = [] - for k, v in self.qubo.all_expr[istart + i]: - tmp.append((k, int(fractional_factor * v))) # print(tmp) - cst = self.qubo.qubo_dict.add_linear_equality_constraint( - tmp, + self.qubo.qubo_dict.add_linear_equality_constraint( + self.qubo.all_expr[istart + i], lagrange_multiplier=self.weight_pressure, constant=-self.target_pressure, ) diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py index e200935..21a6782 100644 --- a/wntr_quantum/sampler/simulated_annealing.py +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -100,13 +100,38 @@ class SimulatedAnnealingResults: class SimulatedAnnealing: # noqa: D101 def __init__(self): # noqa: D107 - self.properties = {} + self.Tschedule = None + self.init_sample = None + self.take_step = None + self.save_taj = False + + @property + def Tschedule(self): # noqa: D102 + return self._Tschedule + + @Tschedule.setter + def Tschedule(self, tschedule): + self._Tschedule = tschedule + + @property + def init_sample(self): # noqa: D102 + return self._init_sample + + @init_sample.setter + def init_sample(self, sample): + self._init_sample = sample + + @property + def take_step(self): # noqa: D102 + return self._take_step + + @take_step.setter + def take_step(self, step): + self._take_step = step def sample( self, qubo, - num_sweeps=100, - Temp=[1e5, 1e-3], Tschedule=None, init_sample=None, take_step=None, @@ -117,8 +142,6 @@ def sample( Args: qubo (qubo solver): qubo solver - num_sweeps (int, optional): _description_. Defaults to 100. - Temp (list, optional): _description_. Defaults to [1e5, 1e-3]. Tschedule (list, optional): The temperature schedule init_sample (_type_, optional): _description_. Defaults to None. take_step (_type_, optional): _description_. Defaults to None. @@ -142,28 +165,35 @@ def bqm_energy(qubo, input, var_names): np.array(input)[qubo.index_variables].tolist() ) + if Tschedule is not None: + self.Tschedule = Tschedule + + if init_sample is not None: + self.init_sample = init_sample + + if take_step is not None: + self.take_step = take_step + + self.save_taj = save_traj + self.bqm = qubo.qubo_dict # check that take_step is callable - if not callable(take_step): + if not callable(self.take_step): raise ValueError("take_step must be callable") - # define th variable names + # define the variable names self.var_names = sorted(self.bqm.variables) # define the initial state - if init_sample is None: - current_sample = np.random.randint(2, size=self.bqm.num_variables) + if self.init_sample is None: + current_sample = generate_random_valid_sample(self.bqm) else: current_sample = init_sample - # define the energy range - if Tschedule is None: - Tschedule = np.linspace(Temp[0], Temp[1], num_sweeps) - # init the traj trajectory = [] - if save_traj: + if self.save_traj: trajectory.append(current_sample) # initialize the energy @@ -172,10 +202,10 @@ def bqm_energy(qubo, input, var_names): energies.append(e_current) # loop over the temp schedule - for T in tqdm(Tschedule): + for T in tqdm(self.Tschedule): # new point - new_sample = take_step(deepcopy(current_sample), verbose=verbose) + new_sample = self.take_step(deepcopy(current_sample), verbose=verbose) e_new = bqm_energy(qubo, new_sample, self.var_names) # accept/reject @@ -201,7 +231,7 @@ def bqm_energy(qubo, input, var_names): print("rejected") pass - if save_traj: + if self.save_traj: trajectory.append(current_sample) energies.append(e_current) diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 01da4b6..9125b26 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -8,7 +8,6 @@ from dimod import SampleSet from dimod import Vartype from dimod import Sampler -from dwave.samplers import SimulatedAnnealingSampler from quantum_newton_raphson.newton_raphson import newton_raphson from qubops.encodings import BaseQbitEncoding from qubops.encodings import PositiveQbitEncoding @@ -22,6 +21,8 @@ from wntr.network import WaterNetworkModel from wntr.sim.aml import Model from wntr.sim.solvers import SolverStatus +from ...sampler.simulated_annealing import SimulatedAnnealing +from ...sampler.step.full_random import IncrementalStep from ..models.chezy_manning import get_chezy_manning_qubops_matrix from ..models.darcy_weisbach import get_darcy_weisbach_qubops_matrix from ..models.mass_balance import get_mass_balance_qubops_matrix @@ -78,6 +79,9 @@ def __init__( self.flow_index_mapping = None self.head_index_mapping = None + # set up the sampler + self.sampler = SimulatedAnnealing() + def verify_encoding(self): """Print info regarding the encodings.""" hres = self.head_encoding.get_average_precision() @@ -113,16 +117,23 @@ def verify_solution(self, input: np.ndarray) -> np.ndarray: sign = np.sign(input) return p0 + p1 @ input + (p2 @ (sign * input * input)) - def classical_solution(self, max_iter: int = 100, tol: float = 1e-10) -> np.ndarray: + def classical_solution( + self, model=None, max_iter: int = 100, tol: float = 1e-10 + ) -> np.ndarray: """Computes the solution using a classical Newton Raphson approach. Args: + model (model): the model max_iter (int, optional): number of iterations of the NR. Defaults to 100. tol (float, optional): Toleracne of the NR. Defaults to 1e-10. Returns: np.ndarray: _description_ """ + if self.matrices is None: + self.create_index_mapping(model) + self.matrices = self.initialize_matrices(model) + P0, P1, P2, P3 = self.matrices num_heads = self.wn.num_junctions num_pipes = self.wn.num_pipes @@ -442,7 +453,6 @@ def solve( # noqa: D417 self, model: Model, strength: float = 1e6, - sampler: Sampler = SimulatedAnnealingSampler(), **sampler_options, ) -> Tuple: """Solves the Hydraulics equations. @@ -463,7 +473,7 @@ def solve( # noqa: D417 # solve using qubo poly sol = self.qubo_poly_solve( - strength=strength, sampler=sampler, **sampler_options + strength=strength, sampler=self.sampler, **sampler_options ) # load data in the AML model @@ -479,8 +489,7 @@ def solve( # noqa: D417 def qubo_poly_solve( self, - strength=1e6, - sampler=SimulatedAnnealingSampler(), + strength=1e7, **sampler_options, ): # noqa: D417 """Solves the Hydraulics equations. @@ -493,16 +502,14 @@ def qubo_poly_solve( Returns: np.ndarray: solution of the problem """ - self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, {"sampler": sampler}) + self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, {"sampler": self.sampler}) matrices = tuple(sparse.COO(m) for m in self.matrices) # creates BQM self.qubo.qubo_dict = self.qubo.create_bqm(matrices, strength=strength) # sample - self.sampleset = self.qubo.sample_bqm( - self.qubo.qubo_dict, **sampler_options - ) # num_reads=num_reads, num_sweeps=num_sweeps) + self.sampleset = self.qubo.sample_bqm(self.qubo.qubo_dict, **sampler_options) # decode sol = self.qubo.decode_solution(self.sampleset.lowest().record[0][0]) From 064ee694fd9006b0ab31316c5b3fd08c6f1f857a Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 20 Nov 2024 10:04:32 +0100 Subject: [PATCH 84/96] clean up notebook --- docs/notebooks/plot_test_qubo_designer.ipynb | 165 +- .../qubo_poly_solver_Net0_refac.ipynb | 1729 ++--------------- wntr_quantum/sampler/simulated_annealing.py | 25 +- .../sim/solvers/qubo_polynomial_solver.py | 178 +- 4 files changed, 254 insertions(+), 1843 deletions(-) diff --git a/docs/notebooks/plot_test_qubo_designer.ipynb b/docs/notebooks/plot_test_qubo_designer.ipynb index 299e2f1..4b56116 100644 --- a/docs/notebooks/plot_test_qubo_designer.ipynb +++ b/docs/notebooks/plot_test_qubo_designer.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -25,12 +25,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "eref = -9689\n", + "energies = np.abs(np.array(energies) - eref)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "data = {}\n", - "eref = -9692\n", + "\n", "for opt, e in zip(optimized_diameters, energies):\n", " if tuple(opt) not in data:\n", " data[tuple(opt)] = []\n", @@ -47,7 +57,19 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "positions = []\n", + "idx = {(500,500):1, (500,1000):2, (250,250):3, (250,1000):4, (250,500):5}\n", + "for p in optimized_diameters:\n", + " positions.append(idx[tuple(p)])" + ] + }, + { + "cell_type": "code", + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -56,13 +78,13 @@ "Text(0, 0.5, 'Energy')" ] }, - "execution_count": 41, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -76,136 +98,49 @@ "plt.scatter(positions, energies,s=50, alpha=0.75, edgecolors='w' )\n", "plt.xticks(list(range(1, 1 + len(labels))), labels, rotation=45)\n", "plt.grid()\n", - "plt.xlabel('Diameters', fontsize=16)\n", - "plt.ylabel('Energy', fontsize=16)" + "plt.xlabel('Diameters', fontsize=12)\n", + "plt.ylabel('Energy', fontsize=12)" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 103, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.3081985158405587" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "positions = []\n", - "idx = {(500,500):1, (500,1000):2, (250,250):3, (250,1000):4, (250,500):5}\n", - "for p in optimized_diameters:\n", - " positions.append(idx[tuple(p)])\n" + "\n", + "np.min(vals[0])" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[[3.3575392824186565,\n", - " 3.977508898389715,\n", - " 3.379365186783616,\n", - " 4.058767710592292,\n", - " 3.50782138910472,\n", - " 3.677923272591215,\n", - " 3.3201527353976417,\n", - " 3.3081985158405587,\n", - " 3.3081985158405587,\n", - " 3.373301582663771,\n", - " 3.88530950371387,\n", - " 3.437136194845152,\n", - " 3.437136194845152,\n", - " 6.385303785313226,\n", - " 3.3201527353976417,\n", - " 3.3081985158405587,\n", - " 3.9976108594291873,\n", - " 3.3122864156157448,\n", - " 5.3706943119123025,\n", - " 3.394646010352517,\n", - " 3.3081985158405587,\n", - " 3.528322075997494,\n", - " 3.3122864156157448,\n", - " 6.288973282496954,\n", - " 3.3122864156157448,\n", - " 4.332936528824575,\n", - " 3.3575392824186565,\n", - " 3.3122864156157448,\n", - " 4.4912962072212395,\n", - " 3.50782138910472,\n", - " 3.3081985158405587,\n", - " 3.3081985158405587,\n", - " 3.652241220292126,\n", - " 3.373301582663771,\n", - " 4.326171103903107,\n", - " 3.373301582663771,\n", - " 6.273680487642196,\n", - " 3.4085759342506208,\n", - " 3.3122864156157448,\n", - " 3.437136194845152,\n", - " 4.012943470470418,\n", - " 6.420142850367483,\n", - " 3.3575392824186565,\n", - " 3.3081985158405587,\n", - " 3.3081985158405587,\n", - " 3.88530950371387,\n", - " 6.273680487642196,\n", - " 3.3122864156157448,\n", - " 3.8055956233711186,\n", - " 4.012943470470418,\n", - " 3.3081985158405587,\n", - " 3.4157910046178586,\n", - " 3.7562250617975224],\n", - " [5.1982552830286295,\n", - " 4.281515569602561,\n", - " 4.900448090667851,\n", - " 5.09696163915396,\n", - " 4.889535839540258,\n", - " 4.900448090667851,\n", - " 4.389454666130405,\n", - " 5.320888196851229,\n", - " 4.978016694303733,\n", - " 4.258942952650614,\n", - " 4.301365769615586,\n", - " 4.889535839540258,\n", - " 4.89345271638922,\n", - " 4.258942952650614,\n", - " 4.889535839540258,\n", - " 4.258942952650614,\n", - " 4.889535839540258,\n", - " 4.3030071716257225,\n", - " 4.301365769615586,\n", - " 4.370720753200658,\n", - " 4.296329499671629,\n", - " 4.281515569602561,\n", - " 4.296329499671629,\n", - " 4.889535839540258,\n", - " 4.35727230597513],\n", - " [4.884438820901778, 4.884438820901778, 4.163207915111343],\n", - " [3.433043492177603, 4.0536593058004655],\n", - " [3.484363807912814,\n", - " 3.744738867133492,\n", - " 2.641641445514324,\n", - " 6.727362312720288,\n", - " 2.641641445514324,\n", - " 4.570952408046651,\n", - " 3.683228266045262,\n", - " 2.641641445514324,\n", - " 3.8013463094557665,\n", - " 3.744738867133492,\n", - " 3.683228266045262,\n", - " 3.683228266045262,\n", - " 4.532323680219633,\n", - " 3.484363807912814,\n", - " 2.641641445514324,\n", - " 3.700979028193615,\n", - " 2.708710079537923]]" + "0.29128992046207713" ] }, - "execution_count": 43, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "vals" + "np.min(vals[-1])" ] }, { diff --git a/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb b/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb index 08b8f1e..492b5d2 100644 --- a/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb +++ b/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb @@ -4,7 +4,62 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Define the system " + "# QUBO Solution of the hydraulics equations\n", + "In this notebook we illustrate how to solve the hydraulics equations using a pure QUBO approach. \n", + "\n", + "## Hydraulics equations\n", + "In their most basic form the hydraulics equations read:\n", + "\n", + "$$\n", + " \\sum_j q_{ij} - D_i = 0 \\newline\n", + " h_{L_{ij}} \\equiv h_i - h_j = A |q_{ij}| q_{ij}^{B-1}\n", + "$$\n", + "\n", + "where $h_i$ is the head pressure at node $i$, $A$ the resistance coefficient and $B$ the flow exponent. \n", + "Several approximations have been developed for define $A$ and $B$. The popular Hazen-Williams (HW) approximation uses $B=1.852$. The HW is therefore not suited for a QUBO formulation that requires integer exponents in the formulation of the objective function. In contrast, the Chezy-Manning (CM) and Darcy-Weisbach (DW) approximation use $B=2$. We have implemented DW and CM hydraulics models that can found under `wntr_quantum/sim/models/`.\n", + "\n", + "In these forms the hydraulics equation can be seen as a system of non-linear equations with integeer power of the unknown: \n", + "\n", + "$$\n", + "F(q_{ij}, h_i)=0\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Solving non linear systems with a QUBO approach\n", + " \n", + " We closely following an approach developed in this [http://dx.doi.org/10.1038/s41598-019-46729-0](paper) to solve the non linear system. \n", + " \n", + " \n", + "The method proposes to solve a non-linear system, given by $F(X) = 0$ by first decomposing the system of equations as a sum of tensor products:\n", + "\n", + "$$\n", + " F_i = P_i^{(0)} + \\sum_j P_{ij}^{(1)}x_j + \\sum_{jk} P_{ijk}^{(2)}x_j x_k + \\sum_{jkl} P_{ijkl}^{(3)}x_j x_k x_l = 0 \n", + "$$\n", + "\n", + "To find the solution of the system one can then minimise the residual sum of squares\n", + "\n", + "$$\n", + "\\chi^2 = \\left[ P^{(0)} + P^{(1)} X + P^{(2)} X^2 + P^{(3)} X^3 + ... \\right]^2\n", + "$$\n", + "\n", + "By encoding all the variables as binary expansions we obtain a high order boolean polynomial. To solve this problem with a QUBO formalism, the high order terms have to be quadratized by introducing additional binary variables and appropriate terms in the loss function. The resulting QUBO problem can then be solved using either classical simulated annealing or quantum annealers alike." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example\n", + "\n", + "We demonstrate in the following how to us our software to solve the hydraulics equations with a QUBO approach.\n", + "\n", + "### Reference Solution\n", + "\n", + "We first define the problem and solve it classically to obtain a benchmark solution" ] }, { @@ -13,46 +68,18 @@ "metadata": { "metadata": {} }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "# Create a water network model\n", "inp_file = './networks/Net0.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + "wn = wntr.network.WaterNetworkModel(inp_file)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Run with the original Cholesky EPANET simulator" + "We solve the problem using the default `EPANET` simulator " ] }, { @@ -82,11 +109,15 @@ } ], "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# solve the problem\n", "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", + "reference_results = sim.run_sim()\n", + "\n", "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "flow_at_5hr = results.link['flowrate'].loc[0, :]\n", + "pressure_at_5hr = reference_results.node['pressure'].loc[0, :]\n", + "flow_at_5hr = reference_results.link['flowrate'].loc[0, :]\n", "wntr.graphics.plot_network(wn, link_attribute=flow_at_5hr, \n", " node_attribute=pressure_at_5hr, \n", " node_size=500, \n", @@ -95,6 +126,13 @@ " link_cmap=plt.cm.cividis)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We extract the values of the pressure and flows for future use" + ] + }, { "cell_type": "code", "execution_count": 3, @@ -112,8 +150,9 @@ } ], "source": [ - "ref_pressure = results.node['pressure'].values[0][:2]\n", - "ref_rate = results.link['flowrate'].values[0]\n", + "import numpy as np \n", + "ref_pressure = reference_results.node['pressure'].values[0][:2]\n", + "ref_rate = reference_results.link['flowrate'].values[0]\n", "ref_values = np.append(ref_rate, ref_pressure)\n", "ref_values" ] @@ -122,7 +161,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run with the QUBO Polynomial Solver" + "### QUBO Polynomial Solver\n", + "\n", + "We now show how to solve the problem using the QUBO polynomial solver included in `wntr_quantum`. We start with redefining the water network." ] }, { @@ -134,6 +175,13 @@ "wn = wntr.network.WaterNetworkModel(inp_file)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The unkown of the problem can take continuous values and therefore must be encoded using several qubits before being used in a QUBO formulation. We use here the encoding implemented in our library `qubops`. We use these encoding schemes to instantiate the polynomial solver. " + ] + }, { "cell_type": "code", "execution_count": 5, @@ -150,8 +198,7 @@ ], "source": [ "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "from qubops.encodings import PositiveQbitEncoding\n", "\n", "nqbit = 7\n", "step = (4./(2**nqbit-1))\n", @@ -169,12 +216,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Solve the system classically" + "We then solve the QUBO equations classically. This gives us: a reference solution, the best possible encoded solution, the total encoded solution including all slack variables and the QUBO energy of the solution." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -184,135 +231,44 @@ "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" ] - }, - { - "data": { - "text/plain": [ - "array([1. , 1. , 0.999, 0.998])" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "\n", - "ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = net.classical_solution(model)\n", - "ref_sol / ref_values" + "ref_sol, encoded_ref_sol, bin_rep_sol, eref, cvgd = net.classical_solution()" ] }, { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1,\n", - " 1,\n", - " [0, 0, 0, 1, 1, 1, 0],\n", - " [0, 0, 0, 1, 1, 1, 0],\n", - " [1, 1, 1, 0, 1, 1, 0],\n", - " [0, 0, 0, 0, 1, 1, 0]]" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bin_rep_sol" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_values, encoded_ref_sol)\n", - "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", - "plt.grid(which=\"major\", lw=1)\n", - "plt.grid(which=\"minor\", lw=0.1)\n", - "# plt.loglog()\n", - "plt.xscale('symlog')\n", - "plt.yscale('symlog')" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.matrices = net.initialize_matrices(model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", - "sampler = SimulatedAnnealing()" - ] - }, - { - "cell_type": "code", - "execution_count": 116, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", - "import sparse\n", - "net.qubo = QUBOPS_MIXED(net.mixed_solution_vector, {\"sampler\": sampler})\n", - "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", - "net.qubo.qubo_dict = net.qubo.create_bqm(matrices, strength=0)" + "### Initial sample for the QUBO optimization \n", + "\n", + "Before minimizing the energy of the QUBO problem we need to define the initial configuration of the binary variables in the QUBO problem. We have implemented two different ways to obtain an initial sample that respects all the conditions imposed by the quadratization constraings of the polynomial qubo solver. \n", + "\n", + "We can for example create a completely random sample that simply ensure that quadratization constraints are respected" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "from wntr_quantum.sampler.step.full_random import IncrementalStep\n", - "\n", - "var_names = sorted(net.qubo.qubo_dict.variables)\n", - "net.qubo.create_variables_mapping()\n", - "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)" + "from wntr_quantum.sampler.simulated_annealing import generate_random_valid_sample\n", + "x = generate_random_valid_sample(net)\n", + "x0 = list(x.values())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# generate init sample" + "Alternatively we can modify the solution calculated in `.classical_solution()`. This can be useful when one wants to reuse exact values of the flows or pressure" ] }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -322,84 +278,80 @@ ] }, { - "cell_type": "code", - "execution_count": 120, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "eref = net.qubo.energy_binary_rep(bin_rep_sol)" + "### Temperature scheduling for the SimulatedAnnealing\n", + "\n", + "One important parameters of the simulated Annealing process is the the so-called temperature schedule. This schdule defines the acceptance probability of the new samples that increase the QUBO energy. While high temperature that leads to accepting samples that increase energy is usefull to escape local minima the temperature must be decreased in order to converge towards a minima. \n", + "\n", + "The temperature schedule usually starts with high temperature values that allows to explore the energy landscape but progressively decrease the tempearture in order for the optimization to converge. " ] }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "num_sweeps = 2000\n", + "num_temp = 2000\n", "Tinit = 1E1\n", "Tfinal = 1E-1\n", - "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", + "Tschedule = np.linspace(Tinit, Tfinal, num_temp)\n", "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", - "Tschedule = np.append(Tschedule, np.zeros(1000))\n", - "# Tschedule = np.zeros(10000)" + "Tschedule = np.append(Tschedule, np.zeros(1000))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then use the `solve()` method of the qubo polynomial solver to obtain a solution of the problem" ] }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 4000/4000 [00:06<00:00, 651.19it/s]\n", - "100%|██████████| 4000/4000 [00:06<00:00, 665.09it/s]\n", - "100%|██████████| 4000/4000 [00:04<00:00, 803.22it/s]\n" + "100%|██████████| 4000/4000 [00:06<00:00, 618.42it/s]\n" ] } ], "source": [ - "mystep.optimize_values = np.arange(2,6)\n", - "res = sampler.sample(net.qubo, init_sample=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", - "\n", - "mystep.optimize_values = np.arange(2,4)\n", - "res2 = sampler.sample(net.qubo, init_sample=res.res, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", - "\n", - "mystep.optimize_values = np.arange(4,6)\n", - "res3 = sampler.sample(net.qubo, init_sample=res2.res, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", - "\n", - "mystep.verify_quadratic_constraints(res3.res)" + "net.step_func.optimize_values = np.arange(2,6)\n", + "_, _, sol, res = net.solve(init_sample=x0, Tschedule=Tschedule, save_traj=True, verbose=False)" ] }, { - "cell_type": "code", - "execution_count": 141, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "idx_min = np.array([e for e in res.energies]).argmin()" + "We can plot the evoluion of the QUBO energy along the optimization path" ] }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0.5, 0, 'Iterations')" ] }, - "execution_count": 123, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -410,67 +362,24 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "eplt = res.energies-eref[0]\n", - "\n", - "# fig, ax1 = plt.subplots()\n", - "\n", - "left, bottom, width, height = [0.55, 0.55, 0.3, 0.3]\n", - "\n", - "plt.plot(res3.energies[:]-eref, lw=4, label=\"QUBO Energy\")\n", - "plt.plot(Tschedule, lw=3, label='Temperature')\n", - "# ax1.axline((0, 0), slope=0, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", + "plt.plot(res.energies[:], lw=4, label=\"QUBO Energy\")\n", + "plt.axline((0, eref[0]), slope=0, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", "plt.grid(which='both')\n", - "# plt.yscale('symlog')\n", - "\n", "plt.ylabel('Energy', fontsize=14)\n", "plt.xlabel('Iterations', fontsize=14)\n", - "plt.legend(fontsize=12)\n", - "\n", - "# ax2 = fig.add_axes([left, bottom, width, height])\n", - "# ax2.plot(eplt[-1000:])\n", - "# ax2.grid()\n", - "# ax2.axline((0, 0), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "# ax2.set_yscale('symlog')\n", - "\n", "\n" ] }, { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [], - "source": [ - "idx_min = np.array([e for e in res.energies]).argmin()\n", - "# idx_min = -1\n", - "sol = res.trajectory[idx_min]\n", - "sol = net.qubo.decode_solution(np.array(sol))\n", - "sol = net.combine_flow_values(sol)\n", - "sol = net.convert_solution_to_si(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 125, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-9562.760602598233 [-9562.926]\n", - "[0.165]\n" - ] - } - ], "source": [ - "print(eref[0], res.energies[idx_min])\n", - "print(eref[0] - res.energies[idx_min])" + "We can also plot the reference solution and the QUBO solution for visual inspection" ] }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -479,13 +388,13 @@ "Text(0.5, 1.0, 'Pressure')" ] }, - "execution_count": 126, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -528,1372 +437,6 @@ "ax2.set_xlabel('Reference Values', fontsize=12)\n", "ax2.set_title('Pressure', fontsize=14)" ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [], - "source": [ - "def flatten_list(lst):\n", - " out = []\n", - " for elmt in lst:\n", - " if not isinstance(elmt, list):\n", - " out += [elmt]\n", - " else:\n", - " out += elmt\n", - " return out\n", - "\n", - "bin_rep_flat = flatten_list(bin_rep_sol)\n", - "xt_bin_rep_flat = net.qubo.extend_binary_representation(bin_rep_flat)\n", - "# xt_bin_rep_flat.values()" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 1 0]\n", - "[1 1 0 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0]\n" - ] - } - ], - "source": [ - "print(np.array(res.trajectory[idx_min])[net.qubo.index_variables])\n", - "print(np.array(bin_rep_flat))" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-9562.926])" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xx = np.array(res.trajectory[idx_min])[net.qubo.index_variables]\n", - "net.qubo.energy_binary_rep(xx)" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [], - "source": [ - "r = np.array(res.trajectory[idx_min])[net.qubo.index_variables]" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [], - "source": [ - "def un_flatten_list(lst):\n", - " out = []\n", - " count = 0\n", - " for er in net.qubo.mixed_solution_vectors.encoded_reals:\n", - " nqbit = er.nqbit\n", - " d = (np.array(lst)[count:count+nqbit]).tolist()\n", - " out.append(d)\n", - " count += nqbit\n", - " return out\n", - "unflat_r = un_flatten_list(r)" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/64 [00:00 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "100%|██████████| 64/64 [00:02<00:00, 22.35it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import itertools\n", - "from tqdm import tqdm\n", - "from copy import deepcopy\n", - "\n", - "\n", - "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", - "\n", - "i2 = 0\n", - "random1 = np.random.randint(2,size=nqbit).tolist()\n", - "random2 = np.random.randint(2,size=nqbit).tolist()\n", - "\n", - "max_size = 64\n", - "iter_data = np.array(list(itertools.product([0, 1], repeat=nqbit)))\n", - "scale_factor = int(len(iter_data)/max_size)\n", - "if len(iter_data>max_size):\n", - " iter_data = iter_data[::scale_factor,:]\n", - "\n", - "energies = np.zeros((max_size,max_size))\n", - "\n", - "for data2 in tqdm(iter_data):\n", - " i3 = 0\n", - " for data3 in iter_data:\n", - " # print(list(data))\n", - " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", - " mod_bin_rep_sol[2] = list(data2)[::-1]\n", - " mod_bin_rep_sol[3] = list(data3)[::-1]\n", - " # mod_bin_rep_sol[4] = random1\n", - " # mod_bin_rep_sol[5] = random2\n", - " mod_bin_rep_sol[4] = unflat_r[4]\n", - " mod_bin_rep_sol[5] = unflat_r[5]\n", - " # mod_bin_rep_sol[4] = np.ones(5).tolist()\n", - " # mod_bin_rep_sol[5] = np.ones(5).tolist()\n", - "\n", - " # x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", - " # x0 = list(x.values())\n", - " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - " i3+=1\n", - " i2+=1\n", - "\n", - "# x, y = np.arange(2**nqbit), np.arange(2**nqbit)\n", - "# x,y = np.meshgrid(x,y)\n", - "# ax = plt.figure().add_subplot(projection='3d')\n", - "# ax.plot_surface(x,y,energies)\n", - "\n", - "plt.imshow(energies- eref)\n", - "plt.colorbar()\n", - "x2 = int(''.join(str(i) for i in bin_rep_sol[2][::-1]),base=2)/scale_factor\n", - "x3 = int(''.join(str(i) for i in bin_rep_sol[3][::-1]),base=2)/scale_factor\n", - "plt.contour(energies-eref, levels=[1e-2,1,2, 10])\n", - "plt.hlines(x3,0,max_size,ls='--',colors='black')\n", - "plt.vlines(x2,0,max_size,ls='--',colors='black')" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/128 [00:00 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "/tmp/ipykernel_5056/3475343188.py:29: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " energies2[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "100%|██████████| 128/128 [00:00<00:00, 726.03it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import itertools\n", - "from tqdm import tqdm\n", - "\n", - "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", - "\n", - "random1 = np.random.randint(2,size=nqbit).tolist()\n", - "random2 = np.random.randint(2,size=nqbit).tolist()\n", - "\n", - "i2 = 0\n", - "\n", - "iter_data = np.array(list(itertools.product([0, 1], repeat=nqbit)))\n", - "if len(iter_data>128):\n", - " iter_data = iter_data[::int(len(iter_data)/128),:]\n", - "\n", - "energies = np.zeros(128)\n", - "energies2 = np.zeros(128)\n", - "\n", - "for data2 in tqdm(iter_data):\n", - "\n", - " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", - " mod_bin_rep_sol[3] = list(data2)[::-1]\n", - " # mod_bin_rep_sol[2] = list(data2)[::-1]\n", - " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "\n", - " # mod_bin_rep_sol[3] = random1 # unflat_r[3]\n", - " mod_bin_rep_sol[2] = unflat_r[2]\n", - " mod_bin_rep_sol[4] = unflat_r[4]\n", - " mod_bin_rep_sol[5] = unflat_r[5]\n", - " energies2[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - " i2+=1\n", - "\n", - "\n", - "encoded_real = net.qubo.mixed_solution_vectors.encoded_reals[2]\n", - "xaxis_val = []\n", - "for i in range(len(iter_data)):\n", - " ibin = np.binary_repr(i,width=nqbit)\n", - " xaxis_val.append(encoded_real.decode_polynom([int(i) for i in ibin[::-1]]))\n", - "\n", - "\n", - "plt.semilogy(xaxis_val, energies-eref, lw=4, label='Exact Values')\n", - "plt.semilogy(xaxis_val, energies2-eref, lw=4, label='Optimized Values')\n", - "plt.xlabel('Flow Value', fontsize=14)\n", - "plt.ylabel('QUBO Energy', fontsize=14)\n", - "plt.ylim([1E0,1E3])\n", - "plt.grid(which='both', axis='both')\n", - "plt.legend(loc=1, fontsize=12)" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3779527559055118" - ] - }, - "execution_count": 134, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r0 = net.qubo.mixed_solution_vectors.encoded_reals[2]\n", - "zz = np.binary_repr(12,width=9)\n", - "r0.decode_polynom([int(z) for z in zz[::-1]])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Embed the problem" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": {}, - "outputs": [], - "source": [ - "import dwave_networkx as dnx\n", - "from minorminer import find_embedding\n", - "from dwave.embedding import embed_qubo, majority_vote, chain_break_frequency" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{('x_002_001', 'x_004_002'): -0.44490918691668147,\n", - " ('x_004_002*x_002_001', 'x_004_002'): 0.0,\n", - " ('x_004_002*x_002_001', 'x_002_001'): 0.0,\n", - " ('x_004_001', 'x_004_002'): 0.007498113259480858,\n", - " ('x_004_001', 'x_002_001'): -0.22245459345834068,\n", - " ('x_004_001', 'x_004_002*x_002_001'): -1.734723475976807e-18,\n", - 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" ('x_004_007*x_004_002', 'x_004_004*x_004_005'): 3.6302454292106194,\n", - " ('x_004_007*x_004_002', 'x_004_003*x_004_005'): 1.8151227146053097,\n", - " ('x_004_007*x_004_002', 'x_004_006*x_004_005'): 14.520981716842478,\n", - " ('x_003_007*x_003_001', 'x_003_003'): 2.411614516938337,\n", - " ('x_003_007*x_003_001', 'x_001_001*x_003_003'): -2.220446049250313e-16,\n", - " ('x_003_007*x_003_001', 'x_003_005'): 11.00780010370733,\n", - " ('x_003_007*x_003_001', 'x_001_001*x_003_005'): -7.993605777301127e-15,\n", - " ('x_003_007*x_003_001', 'x_003_002'): 1.1774459660534606,\n", - " ('x_003_007*x_003_001', 'x_001_001*x_003_002'): -1.1102230246251565e-16,\n", - " ('x_003_007*x_003_001', 'x_001_001*x_003_004'): -4.440892098500626e-16,\n", - " ('x_003_007*x_003_001', 'x_003_007'): 0.0,\n", - " ('x_003_007*x_003_001', 'x_003_001'): 0.0,\n", - " ('x_003_007*x_003_001', 'x_003_002*x_003_005'): 0.45378067865132743,\n", - " ('x_003_001*x_003_006', 'x_003_003'): 0.7520265798178412,\n", - " ('x_003_001*x_003_006', 'x_001_001*x_003_003'): -1.1102230246251565e-16,\n", - 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" ...}" - ] - }, - "execution_count": 136, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.qubo.qubo_dict.to_qubo()[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [], - "source": [ - "# target_graph = dnx.pegasus_graph(6)\n", - "# embedding = find_embedding(net.qubo.qubo_dict.to_qubo()[0], target_graph)" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [], - "source": [ - "# embedding" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": {}, - "outputs": [], - "source": [ - "# dnx.draw_pegasus(dnx.pegasus_graph(6), node_size=2, width=0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [], - "source": [ - "# dnx.draw_pegasus_embedding(target_graph, embedding, node_size=10, width=0.25)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/wntr_quantum/sampler/simulated_annealing.py b/wntr_quantum/sampler/simulated_annealing.py index 21a6782..a77e53a 100644 --- a/wntr_quantum/sampler/simulated_annealing.py +++ b/wntr_quantum/sampler/simulated_annealing.py @@ -1,26 +1,25 @@ from copy import deepcopy from dataclasses import dataclass import numpy as np -from dimod import as_samples from tqdm import tqdm -def generate_random_valid_sample(qubo): - """Geenrate a random sample that respects quadratization. +def generate_random_valid_sample(net): + """Generate a random sample that respects quadratization. Args: - qubo (_type_): _description_ + net (QuboPolynomialSolver): an instance of the QuboPolynomialSolver """ sample = {} - for iv, v in enumerate(sorted(qubo.qubo_dict.variables)): + for iv, v in enumerate(sorted(net.qubo.qubo_dict.variables)): sample[v] = np.random.randint(2) - for v in qubo.mapped_variables[:7]: + for v in net.qubo.mapped_variables[:7]: sample[v] = 1 - sample[qubo.mapped_variables[7]] = 0 + sample[net.qubo.mapped_variables[7]] = 0 for v, _ in sample.items(): - if v not in qubo.mapped_variables: + if v not in net.qubo.mapped_variables: var_tmp = v.split("*") itmp = 0 for vtmp in var_tmp: @@ -38,7 +37,7 @@ def modify_solution_sample(net, solution, modify=["signs", "flows", "heads"]) -> """Modiy the solution sample to change values of the signs/flows/heads. Args: - net (qubo_solver): The QUBO solver + net (QuboPolynomialSolver): an instance of the QuboPolynomialSolver solution (list): the sample that encoded the true solution modify (list, optional): what to change. Defaults to ["signs", "flows", "heads"]. @@ -103,7 +102,7 @@ def __init__(self): # noqa: D107 self.Tschedule = None self.init_sample = None self.take_step = None - self.save_taj = False + self.save_traj = False @property def Tschedule(self): # noqa: D102 @@ -143,8 +142,8 @@ def sample( Args: qubo (qubo solver): qubo solver Tschedule (list, optional): The temperature schedule - init_sample (_type_, optional): _description_. Defaults to None. - take_step (_type_, optional): _description_. Defaults to None. + init_sample (list, optional): initial sample for the optimzation. Defaults to None. + take_step (callable, optional): Callable to obtain new sample. Defaults to None. save_traj (bool, optional): save the trajectory. Defaults to False verbose (bool, optional): print stuff """ @@ -174,7 +173,7 @@ def bqm_energy(qubo, input, var_names): if take_step is not None: self.take_step = take_step - self.save_taj = save_traj + self.save_traj = save_traj self.bqm = qubo.qubo_dict diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 9125b26..01d78bd 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -1,5 +1,4 @@ from collections import OrderedDict -from typing import Dict from typing import List from typing import Tuple import matplotlib.pyplot as plt @@ -7,7 +6,6 @@ import sparse from dimod import SampleSet from dimod import Vartype -from dimod import Sampler from quantum_newton_raphson.newton_raphson import newton_raphson from qubops.encodings import BaseQbitEncoding from qubops.encodings import PositiveQbitEncoding @@ -23,6 +21,7 @@ from wntr.sim.solvers import SolverStatus from ...sampler.simulated_annealing import SimulatedAnnealing from ...sampler.step.full_random import IncrementalStep +from ...sim.qubo_hydraulics import create_hydraulic_model_for_qubo from ..models.chezy_manning import get_chezy_manning_qubops_matrix from ..models.darcy_weisbach import get_darcy_weisbach_qubops_matrix from ..models.mass_balance import get_mass_balance_qubops_matrix @@ -41,7 +40,7 @@ def __init__( Args: wn (WaterNetworkModel): water network - flow_encoding (qubops.encodings.BaseQbitEncoding): binary encoding for the bsolute value of the flow + flow_encoding (qubops.encodings.BaseQbitEncoding): binary encoding for the absolute value of the flow head_encoding (qubops.encodings.BaseQbitEncoding): binary encoding for the head """ self.wn = wn @@ -73,15 +72,34 @@ def __init__( [self.sol_vect_signs, self.sol_vect_flows, self.sol_vect_heads] ) - # init other attributes - self.matrices = None - self.qubo = None - self.flow_index_mapping = None - self.head_index_mapping = None + # create the hydraulics model + self.model, self.model_updater = create_hydraulic_model_for_qubo(wn) # set up the sampler self.sampler = SimulatedAnnealing() + # create the matrices + self.create_index_mapping(self.model) + self.matrices = self.initialize_matrices(self.model) + self.matrices = tuple(sparse.COO(m) for m in self.matrices) + + # create the QUBO MIXED instance + self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, {"sampler": self.sampler}) + + # create the qubo dictionary + self.qubo.qubo_dict = self.qubo.create_bqm(self.matrices, strength=0) + + self.qubo.create_variables_mapping() + self.var_names = sorted(self.qubo.qubo_dict.variables) + + # create the step function + self.step_func = IncrementalStep( + self.var_names, + self.qubo.mapped_variables, + self.qubo.index_variables, + step_size=10, + ) + def verify_encoding(self): """Print info regarding the encodings.""" hres = self.head_encoding.get_average_precision() @@ -117,9 +135,7 @@ def verify_solution(self, input: np.ndarray) -> np.ndarray: sign = np.sign(input) return p0 + p1 @ input + (p2 @ (sign * input * input)) - def classical_solution( - self, model=None, max_iter: int = 100, tol: float = 1e-10 - ) -> np.ndarray: + def classical_solution(self, max_iter: int = 100, tol: float = 1e-10) -> np.ndarray: """Computes the solution using a classical Newton Raphson approach. Args: @@ -130,10 +146,6 @@ def classical_solution( Returns: np.ndarray: _description_ """ - if self.matrices is None: - self.create_index_mapping(model) - self.matrices = self.initialize_matrices(model) - P0, P1, P2, P3 = self.matrices num_heads = self.wn.num_junctions num_pipes = self.wn.num_pipes @@ -189,7 +201,10 @@ def func(input): self.wn.junction_name_list[i] ].elevation - return (sol, encoded_sol, bin_rep_sol, converged) + # compute the qubo energy of the solution + eref = self.qubo.energy_binary_rep(bin_rep_sol) + + return (sol, encoded_sol, bin_rep_sol, eref, converged) @staticmethod def plot_solution_vs_reference( @@ -222,51 +237,6 @@ def decompose_solution(self, solution): tmp = np.append(np.sign(flow_values), np.abs(flow_values)) return np.append(tmp, head_values) - def diagnostic_solution(self, solution: np.ndarray, reference_solution: np.ndarray): - """Benchmark a solution against the exact reference solution. - - Args: - solution (np.array): solution to be benchmarked - reference_solution (np.array): reference solution - """ - reference_solution = self.convert_solution_from_si(reference_solution) - solution = self.convert_solution_from_si(solution) - - reference_solution = self.decompose_solution(reference_solution) - solution = self.decompose_solution(solution) - - data_ref, eref = self.qubo.compute_energy(reference_solution) - data_sol, esol = self.qubo.compute_energy(solution) - - num_pipes = self.wn.num_links - - np.set_printoptions(precision=3) - self.verify_encoding() - print("\n") - print("Error (%):", (1 - (solution / reference_solution)) * 100) - print("\n") - print("sol : ", solution) - print("ref : ", reference_solution) - print("diff: ", reference_solution - solution) - print("\n") - print("encoded_sol: ", np.array(data_sol[0])) - print("encoded_ref: ", np.array(data_ref[0])) - print("diff : ", np.array(data_ref[0]) - np.array(data_sol[0])) - print("\n") - print("E sol : ", esol) - print("E ref : ", eref) - print("Delta E :", esol - eref) - print("\n") - res_sol = np.linalg.norm( - self.verify_solution(np.array(data_sol[0][num_pipes:])) - ) - res_ref = np.linalg.norm( - self.verify_solution(np.array(data_ref[0][num_pipes:])) - ) - print("Residue sol : ", res_sol) - print("Residue ref : ", res_ref) - print("Delta Residue :", res_sol - res_ref) - def initialize_matrices(self, model: Model) -> Tuple: """Initialize the matrices of the non linear system. @@ -450,74 +420,34 @@ def create_index_mapping(self, model: Model) -> None: idx += 1 def solve( # noqa: D417 - self, - model: Model, - strength: float = 1e6, - **sampler_options, + self, init_sample, Tschedule, save_traj=False, verbose=False ) -> Tuple: - """Solves the Hydraulics equations. + """Sample the qubo problem. Args: - model (Model): AML model - strength (float, optional): substitution strength. Defaults to 1e6. - num_reads (int, optional): number of reads for the sampler. Defaults to 10000. + init_sample (_type_): _description_ + Tschedule (_type_): _description_ + save_traj (bool, optional): _description_. Defaults to False. + verbose (bool, optional): _description_. Defaults to False. Returns: - Tuple: Succes message + Tuple: _description_ """ - # creates the index mapping for the variables in the solution vectors - self.create_index_mapping(model) - - # creates the matrices - self.matrices = self.initialize_matrices(model) - - # solve using qubo poly - sol = self.qubo_poly_solve( - strength=strength, sampler=self.sampler, **sampler_options + res = self.sampler.sample( + self.qubo, + init_sample=init_sample, + Tschedule=Tschedule, + take_step=self.step_func, + save_traj=save_traj, + verbose=verbose, ) - # load data in the AML model - model.set_structure() - self.load_data_in_model(model, sol) - - # returns - return ( - SolverStatus.converged, - "Solved Successfully", - 0, - ) - - def qubo_poly_solve( - self, - strength=1e7, - **sampler_options, - ): # noqa: D417 - """Solves the Hydraulics equations. - - Args: - strength (float, optional): substitution strength. Defaults to 1e6. - sampler (float, dwave.sampler): sampler to optimize the qubo - **sampler_options (dict): options for the sampler - - Returns: - np.ndarray: solution of the problem - """ - self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, {"sampler": self.sampler}) - matrices = tuple(sparse.COO(m) for m in self.matrices) - - # creates BQM - self.qubo.qubo_dict = self.qubo.create_bqm(matrices, strength=strength) - - # sample - self.sampleset = self.qubo.sample_bqm(self.qubo.qubo_dict, **sampler_options) - - # decode - sol = self.qubo.decode_solution(self.sampleset.lowest().record[0][0]) - - # combine the sign*abs values for the flow + # extact the solution and convert it + idx_min = np.array([e for e in res.energies]).argmin() + # idx_min = -1 + sol = res.trajectory[idx_min] + sol = self.qubo.decode_solution(np.array(sol)) sol = self.combine_flow_values(sol) - - # convert back to SI sol = self.convert_solution_to_si(sol) # remove the height of the junction @@ -526,11 +456,15 @@ def qubo_poly_solve( self.wn.junction_name_list[i] ].elevation - return sol + # load data in the AML model + self.model.set_structure() + self.load_data_in_model(self.model, sol) + + # returns + return (SolverStatus.converged, "Solved Successfully", sol, res) def analyze_sampleset(self): """Ananlyze the results contained in the sampleset.""" - # run through all samples solutions, energy, quadra_status = [], [], [] for x in self.sampleset.data(): From 5de91e51245a81d267360b92b2a9bab2f3257508 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 20 Nov 2024 10:08:55 +0100 Subject: [PATCH 85/96] clean up qubo solver --- .../sim/solvers/qubo_polynomial_solver.py | 94 +------------------ 1 file changed, 5 insertions(+), 89 deletions(-) diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 01d78bd..ac6c52e 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -1,11 +1,8 @@ from collections import OrderedDict from typing import List from typing import Tuple -import matplotlib.pyplot as plt import numpy as np import sparse -from dimod import SampleSet -from dimod import Vartype from quantum_newton_raphson.newton_raphson import newton_raphson from qubops.encodings import BaseQbitEncoding from qubops.encodings import PositiveQbitEncoding @@ -206,37 +203,6 @@ def func(input): return (sol, encoded_sol, bin_rep_sol, eref, converged) - @staticmethod - def plot_solution_vs_reference( - solution: np.ndarray, reference_solution: np.ndarray - ): - """Plots the scatter plot ref/sol. - - Args: - solution (np.ndarray): _description_ - reference_solution (np.ndarray): _description_ - """ - plt.scatter(reference_solution, solution) - plt.axline((0, 0.0), slope=1, color="black", linestyle=(0, (5, 5))) - - plt.axline((0, 0.0), slope=1.05, color="grey", linestyle=(0, (2, 2))) - plt.axline((0, 0.0), slope=0.95, color="grey", linestyle=(0, (2, 2))) - plt.grid(which="major", lw=1) - plt.grid(which="minor", lw=0.1) - plt.loglog() - - def decompose_solution(self, solution): - """Decompose solution into sign/abs flow and head values. - - Args: - solution (np.array): solution - """ - num_flows = self.wn.num_links - flow_values = solution[:num_flows] - head_values = solution[num_flows:] - tmp = np.append(np.sign(flow_values), np.abs(flow_values)) - return np.append(tmp, head_values) - def initialize_matrices(self, model: Model) -> Tuple: """Initialize the matrices of the non linear system. @@ -425,13 +391,13 @@ def solve( # noqa: D417 """Sample the qubo problem. Args: - init_sample (_type_): _description_ - Tschedule (_type_): _description_ - save_traj (bool, optional): _description_. Defaults to False. - verbose (bool, optional): _description_. Defaults to False. + init_sample (list): initial sample for the optimization + Tschedule (list): temperature schedule for the optimization + save_traj (bool, optional): save the trajectory. Defaults to False. + verbose (bool, optional): print status. Defaults to False. Returns: - Tuple: _description_ + Tuple: Solver status, str, solution, SimulatedAnnealingResults """ res = self.sampler.sample( self.qubo, @@ -462,53 +428,3 @@ def solve( # noqa: D417 # returns return (SolverStatus.converged, "Solved Successfully", sol, res) - - def analyze_sampleset(self): - """Ananlyze the results contained in the sampleset.""" - # run through all samples - solutions, energy, quadra_status = [], [], [] - for x in self.sampleset.data(): - - # create a sample - y = SampleSet.from_samples(x.sample, Vartype.BINARY, x.energy) - var = y.variables - data = np.array(y.record[0][0]) - - # see if it respects quadratic condition - status = "True" - for v, d in zip(var, data): - if v not in self.qubo.mapped_variables: - var_tmp = v.split("*") - itmp = 0 - for vtmp in var_tmp: - idx = self.qubo.index_variables[ - self.qubo.mapped_variables.index(vtmp) - ] - if itmp == 0: - dcomposite = data[idx] - itmp = 1 - else: - dcomposite *= data[idx] - if d != dcomposite: - status = False - break - quadra_status.append(status) - - # solution - sol = self.qubo.decode_solution(data) - - # combine the sign*abs values for the flow - sol = self.combine_flow_values(sol) - - # convert back to SI - sol = self.convert_solution_to_si(sol) - - # remove the height of the junction - for i in range(self.wn.num_junctions): - sol[self.wn.num_pipes + i] -= self.wn.nodes[ - self.wn.junction_name_list[i] - ].elevation - - solutions.append(sol) - energy.append(x.energy) - return solutions, energy, quadra_status From 9093572d2b6286abcd47669e3975b43adf4aade8 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 20 Nov 2024 10:14:31 +0100 Subject: [PATCH 86/96] rename step file --- docs/notebooks/test_qubo_poly_designe.py | 2 +- docs/notebooks/test_qubo_poly_solver.py | 2 +- docs/notebooks/test_qubo_poly_solver_net2loops.py | 2 +- wntr_quantum/sampler/step/{full_random.py => random_step.py} | 0 wntr_quantum/sim/solvers/qubo_polynomial_solver.py | 2 +- 5 files changed, 4 insertions(+), 4 deletions(-) rename wntr_quantum/sampler/step/{full_random.py => random_step.py} (100%) diff --git a/docs/notebooks/test_qubo_poly_designe.py b/docs/notebooks/test_qubo_poly_designe.py index 651032b..8e8881a 100644 --- a/docs/notebooks/test_qubo_poly_designe.py +++ b/docs/notebooks/test_qubo_poly_designe.py @@ -9,7 +9,7 @@ from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing from wntr_quantum.sampler.simulated_annealing import modify_solution_sample -from wntr_quantum.sampler.step.full_random import SwitchIncrementalStep +from wntr_quantum.sampler.step.random_step import SwitchIncrementalStep def plot_solutions(solutions, references): diff --git a/docs/notebooks/test_qubo_poly_solver.py b/docs/notebooks/test_qubo_poly_solver.py index 9c2a073..0cdf5ee 100644 --- a/docs/notebooks/test_qubo_poly_solver.py +++ b/docs/notebooks/test_qubo_poly_solver.py @@ -11,7 +11,7 @@ from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing from qubops.qubops_mixed_vars import QUBOPS_MIXED import sparse -from wntr_quantum.sampler.step.full_random import IncrementalStep +from wntr_quantum.sampler.step.random_step import IncrementalStep from wntr_quantum.sampler.simulated_annealing import modify_solution_sample import pickle diff --git a/docs/notebooks/test_qubo_poly_solver_net2loops.py b/docs/notebooks/test_qubo_poly_solver_net2loops.py index e54bce0..2d249c5 100644 --- a/docs/notebooks/test_qubo_poly_solver_net2loops.py +++ b/docs/notebooks/test_qubo_poly_solver_net2loops.py @@ -11,7 +11,7 @@ from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing from qubops.qubops_mixed_vars import QUBOPS_MIXED import sparse -from wntr_quantum.sampler.step.full_random import IncrementalStep +from wntr_quantum.sampler.step.random_step import IncrementalStep from wntr_quantum.sampler.simulated_annealing import modify_solution_sample import pickle diff --git a/wntr_quantum/sampler/step/full_random.py b/wntr_quantum/sampler/step/random_step.py similarity index 100% rename from wntr_quantum/sampler/step/full_random.py rename to wntr_quantum/sampler/step/random_step.py diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index ac6c52e..5034caa 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -17,7 +17,7 @@ from wntr.sim.aml import Model from wntr.sim.solvers import SolverStatus from ...sampler.simulated_annealing import SimulatedAnnealing -from ...sampler.step.full_random import IncrementalStep +from ...sampler.step.random_step import IncrementalStep from ...sim.qubo_hydraulics import create_hydraulic_model_for_qubo from ..models.chezy_manning import get_chezy_manning_qubops_matrix from ..models.darcy_weisbach import get_darcy_weisbach_qubops_matrix From 95d18c98c2e1b9cc2d20b85ecef0ff931e6294ec Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Wed, 20 Nov 2024 10:56:22 +0100 Subject: [PATCH 87/96] started refact designer --- ...sign_pipe_diameter_own_sampler_refac.ipynb | 480 ++++++++++++++++++ .../qubo_poly_solver_Net0_refac.ipynb | 16 +- wntr_quantum/design/qubo_pipe_diam.py | 78 +-- .../sim/solvers/qubo_polynomial_solver.py | 23 - 4 files changed, 533 insertions(+), 64 deletions(-) create mode 100644 docs/notebooks/design_pipe_diameter_own_sampler_refac.ipynb diff --git a/docs/notebooks/design_pipe_diameter_own_sampler_refac.ipynb b/docs/notebooks/design_pipe_diameter_own_sampler_refac.ipynb new file mode 100644 index 0000000..317c36c --- /dev/null +++ b/docs/notebooks/design_pipe_diameter_own_sampler_refac.ipynb @@ -0,0 +1,480 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "\n", + "# Create a water network model\n", + "inp_file = './networks/Net0_CM.inp'\n", + "# inp_file = './networks/Net2LoopsDW.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", + " title='Pressure at 5 hours', node_labels=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0][:2]\n", + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run with QUBO solver" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 5\n", + "step = (4./(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", + "\n", + "nqbit = 7\n", + "step = (200/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", + "pipe_diameters = [250, 500, 1000]\n", + "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, \n", + " pipe_diameters, head_lower_bound=95,\n", + " weight_cost=2, weight_pressure=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 0.000000 => 200.000000 (res: 1.574803)\n", + "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.129032)\n" + ] + } + ], + "source": [ + "designer.verify_encoding()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + } + ], + "source": [ + "ref_sol, encoded_ref_sol, bin_rep_sol, eref, cvgd = designer.classical_solution([0,1,0,0,1,0], convert_to_si=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-9682.588])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eref" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "price \t diameters \t variables\t energy\n", + "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329] -7676.327154648512\n", + "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118] -8943.759716156874\n", + "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858] -8943.933743641282\n", + "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118] -9310.49469026479\n", + "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906] -9682.588285719068\n", + "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647] -9682.647962222467\n", + "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858] -9309.448069998034\n", + "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647] -9681.427314471295\n", + "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387] -9681.258289012689\n" + ] + } + ], + "source": [ + "designer.enumerates_classical_solutions(convert_to_si=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "price \t diameters \t variables\t energy\n", + "0.16907910944516957 [250. 250.] [ 0.05 0.05 20.689 11.378] -7676.327154648512\n", + "0.25361866416775436 [250. 500.] [ 0.05 0.05 20.689 20.457] -8943.759716156874\n", + "0.42269777361292393 [ 250. 1000.] [ 0.05 0.05 20.689 20.683] -8943.933743641282\n", + "0.25361866416775436 [500. 250.] [ 0.05 0.05 29.769 20.457] -9310.49469026479\n", + "0.33815821889033915 [500. 500.] [ 0.05 0.05 29.769 29.537] -9682.588285719068\n", + "0.5072373283355087 [ 500. 1000.] [ 0.05 0.05 29.769 29.763] -9682.647962222467\n", + "0.42269777361292393 [1000. 250.] [ 0.05 0.05 29.994 20.683] -9309.448069998034\n", + "0.5072373283355087 [1000. 500.] [ 0.05 0.05 29.994 29.763] -9681.427314471295\n", + "0.6763164377806783 [1000. 1000.] [ 0.05 0.05 29.994 29.988] -9681.258289012689\n" + ] + } + ], + "source": [ + "designer.enumerates_classical_solutions(convert_to_si=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", + "x = modify_solution_sample(designer, bin_rep_sol, modify=['flows','heads'])\n", + "x0 = list(x.values())" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "num_sweeps = 5000\n", + "Tinit = 1E3\n", + "Tfinal = 1E-1\n", + "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "Tschedule = np.append(Tschedule, np.zeros(100))" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/6100 [00:00" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "eplt = res.energies\n", + "\n", + "# fig, ax1 = plt.subplots()\n", + "\n", + "left, bottom, width, height = [0.55, 0.55, 0.3, 0.3]\n", + "\n", + "plt.plot(res.energies[:], lw=4, label=\"QUBO Energy\")\n", + "plt.plot(Tschedule, lw=3, label='Temperature')\n", + "# ax1.axline((0, 0), slope=e, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", + "plt.grid(which='both')\n", + "# plt.yscale('symlog')\n", + "\n", + "plt.ylabel('Energy', fontsize=14)\n", + "plt.xlabel('Iterations', fontsize=14)\n", + "plt.legend(fontsize=12)\n", + "\n", + "# ax2 = fig.add_axes([left, bottom, width, height])\n", + "# ax2.plot(eplt[-1000:])\n", + "# ax2.grid()\n", + "# ax2.axline((0, 0), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", + "# ax2.set_yscale('symlog')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.088 0.069 29.28 28.8 ]\n" + ] + } + ], + "source": [ + "idx_min = np.array([e for e in res.energies]).argmin()\n", + "# idx_min = -1\n", + "sol = res.trajectory[idx_min]\n", + "sol = designer.qubo.decode_solution(np.array(sol))\n", + "pipe_hot_encoding = sol[3]\n", + "sol = designer.combine_flow_values(sol)\n", + "sol = designer.convert_solution_to_si(sol)\n", + "sol = sol[:4]\n", + "print(sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.33815821889033915, array([500., 500.]))" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "designer.get_pipe_info_from_hot_encoding(pipe_hot_encoding)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Pressure')" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "\n", + "fig = plt.figure(figsize = plt.figaspect(0.5))\n", + "ax1 = fig.add_subplot(121)\n", + "\n", + "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.grid()\n", + "\n", + "# ax1.scatter(ref_values[:2], encoded_ref_sol[:2], c='black', s=200, label='Best solution')\n", + "ax1.scatter(ref_values[:2], sol[:2], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "\n", + "\n", + "ax1.set_xlabel('Reference Values', fontsize=12)\n", + "ax1.set_ylabel('QUBO Values', fontsize=12)\n", + "ax1.set_title('Flow Rate', fontsize=14)\n", + "\n", + "ax2 = fig.add_subplot(122)\n", + "\n", + "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "\n", + "\n", + "# ax2.scatter(ref_values[2:], encoded_ref_sol[2:], c='black', s=200, label='Best solution')\n", + "ax2.scatter(ref_values[2:], sol[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "ax2.grid()\n", + "\n", + "\n", + "ax2.set_xlabel('Reference Values', fontsize=12)\n", + "ax2.set_title('Pressure', fontsize=14)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb b/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb index 492b5d2..4f4f270 100644 --- a/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb +++ b/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb @@ -281,7 +281,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Temperature scheduling for the SimulatedAnnealing\n", + "### Temperature scheduling for the Simulated Annealing optimization\n", "\n", "One important parameters of the simulated Annealing process is the the so-called temperature schedule. This schdule defines the acceptance probability of the new samples that increase the QUBO energy. While high temperature that leads to accepting samples that increase energy is usefull to escape local minima the temperature must be decreased in order to converge towards a minima. \n", "\n", @@ -318,7 +318,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 4000/4000 [00:06<00:00, 618.42it/s]\n" + "100%|██████████| 4000/4000 [00:05<00:00, 675.39it/s]\n" ] } ], @@ -336,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -345,13 +345,13 @@ "Text(0.5, 0, 'Iterations')" ] }, - "execution_count": 18, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -379,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -388,13 +388,13 @@ "Text(0.5, 1.0, 'Pressure')" ] }, - "execution_count": 20, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index 5e1104a..07822e9 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -19,6 +19,8 @@ from wntr.sim import aml from wntr.sim.aml import Model from wntr.sim.solvers import SolverStatus +from ..sampler.simulated_annealing import SimulatedAnnealing +from ..sampler.step.random_step import SwitchIncrementalStep from ..sim.models.chezy_manning import cm_resistance_value from ..sim.models.chezy_manning import get_pipe_design_chezy_manning_qubops_matrix from ..sim.models.darcy_weisbach import dw_resistance_value @@ -112,10 +114,38 @@ def __init__( self.head_upper_bound = 1e3 # 10 * head_lower_bound # is that enough ? self.target_pressure = head_lower_bound - # store other attributes - self.qubo = None - self.flow_index_mapping = None - self.head_index_mapping = None + # set up the sampler + self.sampler = SimulatedAnnealing() + + # create the matrices + self.create_index_mapping() + self.matrices = self.initialize_matrices() + self.matrices = tuple(sparse.COO(m) for m in self.matrices) + + # create the QUBO MIXED instance + self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, {"sampler": self.sampler}) + + # create the qubo dictionary + self.qubo.qubo_dict = self.qubo.create_bqm(self.matrices, strength=0) + + # add the constraints on the pipe diameter switch + # note that with our custom sampler and step this is not needed + # self.add_switch_constraints(strength=0) + + # add constraints on the pressuyre values + self.add_pressure_equality_constraints() + + self.var_names = sorted(self.qubo.qubo_dict.variables) + self.qubo.create_variables_mapping() + + # create step function + self.step_func = SwitchIncrementalStep( + self.var_names, + self.qubo.mapped_variables, + self.qubo.index_variables, + step_size=10, + switch_variable_index=[[6, 7, 8], [9, 10, 11]], + ) def get_dw_pipe_coefficients(self, link): """Get the pipe coefficients for a specific link with DW. @@ -267,31 +297,6 @@ def verify_encoding(self): % (-fvalues[-1], -fvalues[0], fvalues[0], fvalues[-1], fres) ) - def verify_solution(self, input, params): - """Computes the rhs vector associate with the input. - - Args: - input (np.ndarray): proposed solution vector - params (list): one-hot encoding vector to select the resistance factor. - - Returns: - np.ndarray: RHS vector - """ - P0, P1, P2, P3, P4 = self.matrices - num_heads = self.wn.num_junctions - num_signs = self.wn.num_pipes - num_pipes = self.wn.num_pipes - num_vars = num_heads + 2 * num_pipes - - input = input.reshape(-1, 1) - p0 = P0[:-1].reshape(-1, 1) - p1 = P1[:-1, num_signs:num_vars] + P2.sum(1)[:-1, num_signs:num_vars] - p2 = P4.sum(1)[:-1, num_pipes:num_vars, num_pipes:num_vars].sum(-2) - parameters = np.array([0] * num_vars + params) - p2 = (parameters * p2).sum(-1) - sign = np.sign(input) - return p0 + p1 @ input + (p2 @ (sign * input * input)) - def enumerates_classical_solutions(self, convert_to_si=True): """Generates the classical solution.""" encoding = [] @@ -306,10 +311,9 @@ def enumerates_classical_solutions(self, convert_to_si=True): for p in params: pvalues += p price, diameters = self.get_pipe_info_from_hot_encoding(pvalues) - sol, _, bin_rep_sol, _ = self.classical_solution( + sol, _, bin_rep_sol, energy, _ = self.classical_solution( pvalues, convert_to_si=convert_to_si ) - energy = self.qubo.energy_binary_rep(bin_rep_sol) print(price, diameters, sol, energy[0]) def convert_solution_to_si(self, solution: np.ndarray) -> np.ndarray: @@ -362,7 +366,12 @@ def classical_solution( Returns: np.mdarray : solution """ - P0, P1, P2, P3, P4 = self.matrices + P0 = self.matrices[0].todense() + P1 = self.matrices[1].todense() + P2 = self.matrices[2].todense() + P3 = self.matrices[3].todense() + P4 = self.matrices[4].todense() + num_heads = self.wn.num_junctions num_signs = self.wn.num_pipes num_pipes = self.wn.num_pipes @@ -457,7 +466,10 @@ def func(input): self.wn.junction_name_list[i] ].elevation - return (sol, encoded_sol, bin_rep_sol, converged) + # compute the qubo energy of the solution + eref = self.qubo.energy_binary_rep(bin_rep_sol) + + return (sol, encoded_sol, bin_rep_sol, eref, converged) def get_cost_matrix(self, matrices): """Add the equation that ar sued to maximize the pipe coefficiens and therefore minimize the diameter. diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 5034caa..0d72dbc 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -109,29 +109,6 @@ def verify_encoding(self): % (-fvalues[-1], -fvalues[0], fvalues[0], fvalues[-1], fres) ) - def verify_solution(self, input: np.ndarray) -> np.ndarray: - """Computes the rhs vector associate with the input. - - Args: - input (np.ndarray): proposed solution - - Returns: - np.ndarray: RHS vector - """ - P0, P1, P2, P3 = self.matrices - num_pipes = self.wn.num_pipes - - if self.wn.options.hydraulic.headloss == "C-M": - p0 = P0.reshape( - -1, - ) - p1 = P1[:, num_pipes:] + P2.sum(1)[:, num_pipes:] - p2 = P3.sum(1)[:, num_pipes:, num_pipes:].sum(-1) - elif self.wn.options.hydraulic.headloss == "D-W": - raise NotImplementedError("verify_solution not implemented for DW") - sign = np.sign(input) - return p0 + p1 @ input + (p2 @ (sign * input * input)) - def classical_solution(self, max_iter: int = 100, tol: float = 1e-10) -> np.ndarray: """Computes the solution using a classical Newton Raphson approach. From 521321e9d7774e70b630b04dcfac02519d8231b5 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 21 Nov 2024 15:13:19 +0100 Subject: [PATCH 88/96] clean up notebooks --- docs/notebooks/design_pipe_diameter.ipynb | 590 ++- docs/notebooks/design_pipe_diameter_DW.ipynb | 538 --- .../design_pipe_diameter_own_sampler.ipynb | 581 --- ...sign_pipe_diameter_own_sampler_refac.ipynb | 480 --- .../designer_net0_data/test0/energies.pkl | Bin 3921 -> 0 bytes .../test0/optimized_diameters.pkl | Bin 4721 -> 0 bytes .../designer_net0_data/test0/prices.pkl | Bin 2001 -> 0 bytes .../designer_net0_data/test1/energies.pkl | Bin 3921 -> 0 bytes .../test1/optimized_diameters.pkl | Bin 4721 -> 0 bytes .../designer_net0_data/test1/prices.pkl | Bin 2001 -> 0 bytes docs/notebooks/dw_approximation.ipynb | 128 +- docs/notebooks/enPflE1Q | Bin 32 -> 0 bytes .../net0_data/encoded_reference_solutions.pkl | Bin 6321 -> 0 bytes docs/notebooks/net0_data/energies.pkl | Bin 3921 -> 0 bytes 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rename docs/notebooks/{ => sandbox}/qubo_poly_solver_CM.ipynb (100%) rename docs/notebooks/{ => sandbox}/qubo_poly_solver_Net0.ipynb (100%) delete mode 100644 docs/notebooks/test_qubo_poly_designe.py delete mode 100644 docs/notebooks/test_qubo_poly_solver.py delete mode 100644 docs/notebooks/test_qubo_poly_solver_net2loops.py diff --git a/docs/notebooks/design_pipe_diameter.ipynb b/docs/notebooks/design_pipe_diameter.ipynb index f8ddeb1..daa5ae8 100644 --- a/docs/notebooks/design_pipe_diameter.ipynb +++ b/docs/notebooks/design_pipe_diameter.ipynb @@ -1,334 +1,267 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", + "# Designing Water Networks with a QUBO approach\n", "\n", - "# Create a water network model\n", - "inp_file = './networks/Net0_CM.inp'\n", - "# inp_file = './networks/Net2LoopsDW.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "In this notebook we present how to optimize the diameters of the pipe of a water network to minimize the cost of the network while keeping the pressure abov a certain threshold. \n", + "\n", + "The hydraulics equations can be written as:\n", + "\n", + "$$\n", + " \\sum_j s_{ij} y_{ij} - D_i = 0 \\newline\n", + " h_{L_{ij}} \\equiv h_i - h_j = A s_{ij} y_{ij}^{B}\n", + "$$\n", + "\n", + "In order to consider several pipe diameters we extend these equations with new variables\n", + "\n", + "\n", + "$$\n", + " \\sum_j s_{ij} y_{ij} - D_i = 0 \\newline\n", + " h_{L_{ij}} \\equiv h_i - h_j = \\sum_d \\delta_{d,ij} A_{d,ij} s_{ij} y_{ij}^{B}\n", + "$$\n", + "\n", + "where $\\delta_{d,ij} = [0,1]$ is used to select a given diameter, $d$, of the pipe $ij$. If only a single $\\delta_{d,ij}$ equals 1 while all the other are null, we find the original head loss equation. It is therefore immediately clear that we need to enforce at all time the following condition:\n", + "\n", + "$$\n", + " \\sum_d \\delta_{d,ij} = 1 \\quad \\forall \\quad ij\n", + "$$\n", "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)" + "For the QUBO problem to also minimize the cost of the total network we need to add one equation:\n", + "\n", + "$$\n", + " \\omega_\\mu \\sum_{ij}\\sum_d \\delta_{d,ij} \\mu_{d,ij} \\rightarrow 0\n", + "$$\n", + "\n", + "\\noindent where $\\mu_{d,ij}$ is the price of pipe $ij$ for a diameter $d$. $\\omega_\\mu$ is a parameter that determines the relative weight of this equation when optimizing the QUBO problem. Finally if we want to constrain the values of the head node we need to impose conditions such as:\n", + "\n", + "$$\n", + " h_k \\geq H_{\\textnormal{min}} \\quad \\forall \\quad k\n", + "$$\n", + "\n", + "these equations are enforced by introducing slack variables and extra penalty terms in the QUBO objective function." ] }, { - "cell_type": "code", - "execution_count": 2, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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O1/ouEAAAbeAXxbtr+5c6+EWlx2NO1tRrzYq9PkoEAEDb+EXxHv2mpoXHnTj7QQAAWMgvive8bp28ehwAAFbxi+Lt27+bevaO8XhMRKcQpWf38k0gAADayC+KV5JumJqmoCDD7f6rbhih8Aju5wUA2Jvf3E4kSSVF+7XwL0U6dOCYa1tkVKiuuXGkLrwkxcJkAAC0jF8VrySZpqltWw5r5/b9mvHj6XrymV/pBz+41upYAAC0iN+cam5kGIYGDonXZVelqaZ+v1asKLA6EgAALeZ3xXu64cOHa9WqVVbHAACgxfy6eLOzs7Vp0yb52dlyAEAH5tfFm5eXp2PHjmnPnj1WRwEAoEX8unjT09MlSatXr7Y4CQAALePXxRsXF6e4uDgtW7bM6igAALSIXxevJI0YMYIZLwDAb/h98WZnZ2vz5s1yOp1WRwEA4Kz8vnhzc3N1/Phx7dy50+ooAACcld8Xb+MCK+7nBQD4A78v3q5duyoxMZEFVgAAv+D3xStJqampKioqsjoGAABnFRDFm5OTo9LSUjU0NFgdBQAAjwKieHNzc1VTU6Nt27ZZHQUAAI8ConhHjRolSSosLLQ4CQAAngVE8UZFRSkpKUnLly+3OgoAAB4FRPFK0siRI7VmzRqrYwAA4FHAFG9ubq62b9+uuro6q6MAAOBWwBRvTk6OamtrtWXLFqujAADgVsAU78iRI2UYBgusAAC2FjDF27lzZ/Xp04cnWAEAbC1gilc6dVtRcXGx1TEAAHAroIo3NzdXO3fu1MmTJ62OAgBAswKqeHNyclRfX69NmzZZHQUAgGYFVPEOHz5cDodDK1eutDoKAADNCqjijYiIUL9+/VhgBQCwrYAqXunUAqu1a9daHQMAgGYFXPHm5eVp9+7dOnHihNVRAAA4Q8AVb05OjpxOp9avX291FAAAzhBwxTt06FAFBwdrxYoVVkcBAOAMAVe8oaGhuuCCC1RQUGB1FAAAzhBwxStJaWlpWrdundUxAAA4Q0AWb35+vvbs2aOqqiqrowAA0ERAFm9WVpZM02TWCwCwnYAs3sGDBys0NJTrvAAA2wnI4g0ODtaAAQNY2QwAsJ2ALF5JSk9PV0lJidUxAAAW69Onj5588kmrY7gEbPHm5+dr3759Onr0qNVRAABnMXXqVBmGoccee6zJ9rfffluGYViUqn0EbPFmZWVJkoqLiy1OAgBoifDwcD3++OP65ptvrI7SrgK2eFNSUhQeHs4CKwDwE+PHj1dCQoLmzp3r9pg333xTQ4YMUVhYmPr06aM//OEPTfYfPnxYl19+uSIiItS3b1+9/PLLZ7xGRUWFbr31VsXGxioqKkoXXnihTx8zHLDF63A4NGjQIBZYAYCfcDgc+u///m89/fTT2r9//xn7i4uLdd111+mGG27Qxo0b9dBDD+lXv/qVFixY4Dpm6tSp2rdvn5YsWaI33nhDzz33nA4fPtzkdX7wgx/o8OHD+uCDD1RcXKxRo0bpoosu0tdff93eH/EUM4DNmDHD7NGjh9UxAABncfPNN5tXXHGFaZqmmZWVZU6fPt00TdN86623zMaquvHGG82LL764ye/de++95uDBg03TNM1t27aZkszVq1e79peWlpqSzD/+8Y+maZrmsmXLzKioKLOmpqbJ6yQnJ5svvPBCe3y0MwTsjFc6tcDq4MGDOnLkiNVRAAAt9Pjjj+ull15SaWlpk+2lpaXKzc1tsi03N1c7duxQQ0ODSktLFRwcrLS0NNf+gQMHKiYmxvXz+vXrVVVVpW7duikyMtI1ysrKtGvXrnb9XI2CffIuFsnIyJAkFRUVaeLEiRanAQC0xJgxYzRx4kQ98MADmjp1qldfu6qqSj169NDSpUvP2Hd6QbengC7e5ORkde7cWcuXL6d4AcCPPPbYY0pNTdWAAQNc2wYNGnTGgtmCggKlpKTI4XBo4MCBqq+vV3FxsUaPHi1J2rZtmyoqKlzHjxo1SuXl5QoODlafPn188VHOENCnmoOCgjR48GAVFhZaHQUA0ArDhg3TTTfdpD/96U+ubT/72c/08ccf65FHHtH27dv10ksv6ZlnntHPf/5zSdKAAQN0ySWXaMaMGVq1apWKi4t16623KiIiwvUa48ePV3Z2tq688kr9+9//1p49e7RixQr98pe/VFFRkU8+W0AXryRlZmb6dJk4AMA7Hn74YTmdTtfPo0aN0muvvaZXXnlFQ4cO1Zw5c/Twww83OR09f/58JSYmauzYsbr66qt1++23Ky4uzrXfMAz961//0pgxYzRt2jSlpKTohhtu0Oeff674+HiffC7DNE3TJ+9kkddee03XX3+9ysvLffZHBQDAnYCf8Z6+wAoAAKsFfPH27t1bUVFRWr58udVRAAAI/OI1DENDhw5lgRUAwBYCvnilUwusNmzYoAC/nA0A8AMdonjz8vL09ddf68CBA1ZHAQB0cB2ieBsXWK1Zs8biJACAji6gn1zV6Pzzz9d5552nZcuW6corr7Q6DgDAC2pqalRbW+vxmNDQUIWHh/soUct0iOI1DEPDhg3TqlWrrI4CAPCCmpoaJURE66g8F29CQoLKyspsVb4donglKSsrS/PmzZNpmjIMw+o4AIBzUFtbq6Oq1ZMhuYpwU2UnVK9Z5QWqra21VfF2iGu80qmvCKysrNTevXutjgIA8JJOQSHq7Gh+dAoKsTpeszpM8aanp0uSVq9ebXESAIC3hIQYHocddZjiTUhIUGxsrJYtW2Z1FACAlwQFeR521GGu8UrS8OHDWWAFAAEkyGEoyM26nSCTGa/lsrKytHnzZp5gBQABIjjYUHCImxFM8VouLy9P1dXV2rVrl9VRAABe4AjyPOzIprHaBwusACCwONzNdkMMOVhcZb3u3burR48e+uyzz6yOAgDwglOLqAw3w+p0zetQi6skacSIETyzGQAChKfVyzbtXdvmajfZ2dkqLS2V0+m0OgoA4ByFBHu4j5fFVfaQm5urEydOaPv27VZHAQCcoyCH4XHYUYcr3rS0NEnifl4ACAD++AANm8ZqPzExMerZsycLrAAgAPjjquYOt7hKklJTU1VUVGR1DADAOWpcwdzsPp5cZR85OTnaunWr6uvrrY4CADgHLK7yE7m5uaqtrVVpaanVUQAA54BrvH5i1KhRMgyDBVYA4OdY1ewnIiMj1bt3bxZYAYCfcwSbHocddcjFVZI0cuRIFRcXWx0DAHAOjKBTw90+O7JprPaXm5urHTt2qLa21uooAIA2CnKYHocdddjizcnJUV1dnTZv3mx1FABAGxlBpoLcDCOI4rWVESNGKCgoSCtWrLA6CgCgjQzj/043nzHsubaq4xZvp06d1LdvXy1fvtzqKACANgoKNj0OO+qwi6ukU7cVrV271uoYAIA28vi1gDadWto0lm/k5eVp165dqqmpsToKAKANDMP0OOyoQxdvTk6OGhoatGHDBqujAADawJunmufOnavRo0erS5cuiouL05VXXqlt27Y1OWbcuHEyDKPJ+PGPf9y6zK06OsAMGzZMwcHBLLACAD/ldmGVh/t73fn00081c+ZMFRYW6sMPP1RdXZ0mTJig6urqJsfddtttOnjwoGv89re/bdX7dOhrvGFhYerfv78KCgo0a9Ysq+MAAFrJESy3T6hq7W28ixYtavLzggULFBcXp+LiYo0ZM8a1vVOnTkpISGh11kYdesYrscAKAPyZIQ/XeHWqeSsrK5uMkydPtui1jx49Kknq2rVrk+0vv/yyunfvrqFDh+qBBx7Q8ePHW5W5wxdvfn6+ysrKWv2HAwBYryWnmpOSkhQdHe0ac+fOPevrOp1OzZo1S7m5uRo6dKhr+4033qi///3vWrJkiR544AH97W9/0w9/+MNWZe7Qp5olKSsrS6Zpat26dcrNzbU6DgCgFYI8fBlCkPPU9n379ikqKsq1PSws7KyvO3PmTG3atOmMZz3cfvvtrn8eNmyYevTooYsuuki7du1ScnJyyzK36KgANmTIEIWEhKigoMDqKACAVjK+fTSkuyFJUVFRTcbZiveuu+7S+++/ryVLlqhnz54ej83MzJQk7dy5s8WZO/yMNyQkRCkpKaxsBgA/5OnLEFr7JQmmaeonP/mJ3nrrLS1dulR9+/Y96++UlJRIknr06NHi9+nwxStJo0eP1ieffGJ1DABAK3nzyVUzZ87UwoUL9c4776hLly4qLy+XJEVHRysiIkK7du3SwoULdemll6pbt27asGGD7rnnHo0ZM0bDhw9veebWxQpMeXl52rdvnyorK62OAgBohZacam6p559/XkePHtW4cePUo0cP13j11VclSaGhofroo480YcIEDRw4UD/72c90zTXX6L333mvV+zDjVdMFVmPHjrU6DgCghYxgQ0ZI819DZDhb9/VEpum5qJOSkvTpp5+26jWbw4xX0sCBAxUWFsYCKwDwM0aQ4XHYETNeSQ6HQwMHDqR4AcDfOIJODXf7bMieqSwwevRorV+/3uoYAIBWMEIMGSFBboY9Z7wU77fy8/P1xRdf6JtvvrE6CgCgpYIMz8OGKN5vNd4EXVRUZHESAEBLGcHuZrtBMoLtWXH2TGWBCy64QJ06deI6LwD4k8ZrvO6GDbG46ltBQUEaPHiwVq5caXUUAEALeVq9bNdVzfb8zwGLZGRksMAKAPxJaJDnYUP2TGWR/Px8HTp0SF9++aXVUQAALeCP9/FSvKfJyMiQxAIrAPAbwQ4pxM0IdlidrlkU72n69u2ryMjIM75/EQBgT4bD8DjsiMVVpzEMQ0OHDmWBFQD4C0/363Kq2T9kZGRow4YNVscAALSA+6dWnRp2ZM9UFsrPz9eRI0d04MABq6MAAM7GD+/jtWcqC7HACgD8x6mvBXT35CpONfuFpKQkxcTEaNmyZVZHAQCcjcPwPGyIxVXfYRiGhg0bplWrVlkdBQBwNiyuCgyZmZnauHGjTNO0OgoAwAMjxOFx2BHF24z8/HxVVFRo//79VkcBAHjC1wIGhvT0dEnS6tWrLU4CAPAoKMjzsCF7prJYYmKiunXrps8++8zqKAAATxzfPhqyueFo3anmuXPnavTo0erSpYvi4uJ05ZVXatu2bU2Oqamp0cyZM9WtWzdFRkbqmmuu0aFDh1r1PhSvG8OHD2fGCwB258UZ76effqqZM2eqsLBQH374oerq6jRhwgRVV1e7jrnnnnv03nvv6fXXX9enn36qAwcO6Oqrr27V+7Cq2Y2srCw9/fTTMk1ThmHP6wQA0OEFe/gyhG+3V1ZWNtkcFhamsLCwMw5ftGhRk58XLFiguLg4FRcXa8yYMTp69Kj+/Oc/a+HChbrwwgslSfPnz9egQYNUWFiorKysFkVmxutGXl6eqqqqVFZWZnUUAIA7QYaHGe+pSVNSUpKio6NdY+7cuS166aNHj0qSunbtKkkqLi5WXV2dxo8f7zpm4MCB6tWrV6ue8c+M143Ro0dLOrXAql+/fhanAQA0y9Mp5W+379u3T1FRUa7Nzc12v8vpdGrWrFnKzc3V0KFDJUnl5eUKDQ1VTExMk2Pj4+NVXl7e8sgtPrKDiY2NVXx8PE+wAgA7c7ew6rRT0FFRUU1GS4p35syZ2rRpk1555RWvR6Z4PRgxYgQLrADAztrhdqK77rpL77//vpYsWaKePXu6tickJKi2tlYVFRVNjj906JASEhJaHrlNqTqI7OxsbdmyRU6n0+ooAIBmGEEOGQ43I6h1txOZpqm77rpLb731lj755BP17du3yf60tDSFhITo448/dm3btm2b9u7dq+zs7Ba/D8XrQW5uro4fP66dO3daHQUA0Bwvznhnzpypv//971q4cKG6dOmi8vJylZeX68SJE5Kk6Oho3XLLLZo9e7aWLFmi4uJiTZs2TdnZ2S1e0SyxuMqjxidYFRYWKiUlxeI0AIAzePFLEp5//nlJ0rhx45psnz9/vqZOnSpJ+uMf/6igoCBdc801OnnypCZOnKjnnnuuVe9jmHwTgEfnn3++Lr30Ur344otWRwEAfKuyslLR0dGqWHG/oiKbXyxVWXVSMTmP6ejRo01WNVuNGe9ZpKamqqioyOoYAIDmNN7H626fDXGN9yxycnJUWlqqhoYGq6MAAL6LL0kIPHl5eTp58qS2bt1qdRQAwHe14D5eu6F4z2LkyJEyDEOFhYVWRwEAfJfhYbZr2LPi7JnKRqKiopSUlKTly5dbHQUA8F1+OONlcVULsMAKAGzK8DCzZcbrv3Jzc7V9+3bV1dVZHQUAcLrG4nU3bMieqWwmNzdXtbW12rx5s9VRAACnczgkR7CbYc9TzRRvC6SmprLACgDsiBlvYOrcubP69u3LVwQCgN24ne1+O2zInqlsaOTIkVq7dq3VMQAAp2NxVeDKy8vTzp07dfLkSaujAAAacao5cOXk5Ki+vl4bN260OgoAoJERLAW5GYY9T+pSvC00fPhwORwOrVy50uooAIBGPKs5cIWHhys5OZknWAGAjRhGkAzD4WbYs+LsOQ+3qVGjRvEEKwCwk8bTyu722ZA9/3PApvLy8rR7924dP37c6igAAInFVYEuOztbTqdT69evtzoKAEDyy/t4Kd5WGDp0qIKDg7VixQqrowAAJGa8gS40NFQXXHCBCgoKrI4CAJAo3o4gPT1d69atszoGAEDy6pckfPbZZ7r88suVmJgowzD09ttvN9k/depUGYbRZFxyySWtjkzxtlJ+fr4+//xzVVVVWR0FAODFGW91dbVGjBihZ5991u0xl1xyiQ4ePOga//u//9vqyPa88mxjWVlZMk1T69atU35+vtVxAKBja8HtRJWVlU02h4WFKSws7IzDJ02apEmTJnl8u7CwMCUkJLQta2Osc/rtDmjQoEEKDQ3lOi8A2IFxliEpKSlJ0dHRrjF37tw2v93SpUsVFxenAQMG6I477tCRI0da/RrMeFspODhYAwcOpHgBwAZM05Rpmm73SdK+ffsUFRXl2t7cbLclLrnkEl199dXq27evdu3apf/8z//UpEmTtHLlSjlacT2Z4m2D9PR0/fvf/7Y6BgB0eE41yKkGt/skKSoqqknxttUNN9zg+udhw4Zp+PDhSk5O1tKlS3XRRRe1+HU41dwG+fn52r9/vyoqKqyOAgAdmmk6PY721K9fP3Xv3l07d+5s1e9RvG2QmZkpSSouLrY4CQB0bOZZ/q897d+/X0eOHFGPHj1a9XsUbxukpKQoIiKC67wAYDGn6ZTTbHAzWjfjraqqUklJiUpKSiRJZWVlKikp0d69e1VVVaV7771XhYWF2rNnjz7++GNdccUV6t+/vyZOnNiq9+Eabxs4HA4NGjSI7+YFAIuZcspU8wXrbrs7RUVF+t73vuf6efbs2ZKkm2++Wc8//7w2bNigl156SRUVFUpMTNSECRP0yCOPtHqxFsXbRqNHj9a7775rdQwA6NAaZ7fu9rXGuHHj3K6QlqTFixe36vXc4VRzG40ZM0YHDx5s0z1cAADvsHJxVVtRvG2UkZEh6dSpCQCANaxcXNVWFG8bJScnq3Pnzlq+fLnVUQCgw3K/sMr9KWircY23jQzD0JAhQ1hgBQAW8ubiKl9hxnsOMjIytGHDBqtjAECH5Y8zXor3HOTn5+vLL79UeXm51VEAoEMy5ek6rz1RvOeABVYAYDFPK5pZ1Rx4evfuraioKBZYAYBFGr8kwd2wIxZXnQPDMDRs2DAWWAGARVrytYB2w4z3HGVmZmrjxo22/RcMAIGscVWzu2FHFO85ysvL0zfffKMDBw5YHQUAOhxWNXdAjQusVq9ebXESAOh4nKbnYUcU7zlKTExU165dtWzZMqujAECHU+c0PA47YnHVOWpcYLVq1SqrowBAh+M0DTnN5gvW3XarMeP1gqysLG3atIkFVgDgY05TanAzONUcwPLy8lRZWanPP//c6igA0KHUOw2Pw44oXi8YPXq0JBZYAYCvNZiGx2FHFK8XxMfHKzY2lgVWAOBj9TJUb7oZsmfxsrjKS0aMGMGMFwB8zNNtQ1zjDXBZWVnavHkzC6wAwIe8ear5s88+0+WXX67ExEQZhqG33367yX7TNDVnzhz16NFDERERGj9+vHbs2NHqzBSvl+Tl5am6ulo7d+60OgoAdBgNHhZWNbRycVV1dbVGjBihZ599ttn9v/3tb/WnP/1J8+bN06pVq9S5c2dNnDhRNTU1rXofTjV7SXp6uqRTC6wuuOACi9MAQMfQeOuQu32tMWnSJE2aNKnZfaZp6sknn9R//dd/6YorrpAk/fWvf1V8fLzefvtt3XDDDS1+H2a8XtKtWzf16NGDBVYA4EOND9BwNySpsrKyyTh58mSr36esrEzl5eUaP368a1t0dLQyMzNb/Q11FK8XjRgxQmvWrLE6BgB0GHVOz0OSkpKSFB0d7Rpz585t9fuUl5dLOnUXy+ni4+Nd+1qKU81elJ2drccee0wNDQ1yOBxWxwGAgNeSR0bu27dPUVFRru1hYWE+yeYOM14vysvL04kTJ7R9+3arowBAh1Dv4QsSGp9cFRUV1WS0pXgTEhIkSYcOHWqy/dChQ659LUXxelFaWpok8YUJAOAjvvpawL59+yohIUEff/yxa1tlZaVWrVql7OzsVr0WxetF0dHRSkpKYoEVAPhISxZXtVRVVZVKSkpUUlIi6dSCqpKSEu3du1eGYWjWrFn6zW9+o3fffVcbN27Uj370IyUmJurKK69s1ftwjdfLUlNTVVRUZHUMAOgQTi2iar5gGxdXtVRRUZG+973vuX6ePXu2JOnmm2/WggULdN9996m6ulq33367KioqlJeXp0WLFik8PLxV70PxellOTo4WL16s+vp6BQfz5wWA9uTNR0aOGzfO49MHDcPQww8/rIcffrh1L/wdnGr2stzcXNXW1mrLli1WRwGAgFdrSrVON8OmT/CleL1s5MiRMgxDhYWFVkcBgIBnelhYZddH51O8XhYZGanevXuzwAoAfKDxkZHuhh1xEbIdjBw5UsXFxVbHAICAV+uUHG4WUdW2cnGVrzDjbQd5eXnasWOHamtrrY4CAAHNV/fxehPF2w5ycnJUX1+vTZs2WR0FAAKaP55qpnjbwYgRIxQUFNTqb6wAALROvYcvSKjnVHPHERERoX79+mn58uVWRwGAgOaPM14WV7WTUaNGscAKANpZrdNQkJsnV9W62W41ZrztJC8vT7t379aJEyesjgIAAYvFVXDJzs5WQ0ODNmzYYHUUAAhY/niqmeJtJ8OGDVNwcLBWrFhhdRQACFj1DVKdm1HfYHW65lG87SQsLEz9+/dXQUGB1VEAIGD544yXxVXtKC0tjRkvALSjOlMKcnPbUJ1Ni5cZbzvKz8/Xnj17VF1dbXUUAAhI/jjjpXjbUVZWlkzTVElJidVRACAgUbxoYvDgwQoNDeU6LwC0E398chXXeNtRSEiIUlJSKF4AaCeeZrbMeDuo9PR0rVu3zuoYABCQnE7D47Ajired5eXlad++faqsrLQ6CgAEnPq6II/DjuyZKoBkZWVJktauXWtxEgAIPN6c8T700EMyDKPJGDhwoNczc423nQ0cOFDh4eEqKCjQuHHjrI4DAAGlod79zLahvvVzyyFDhuijjz5y/Rwc7P2apHjbmcPh0KBBg3iQBgC0A08z27Zc4w0ODlZCQsK5xvKIU80+kJ6ezr28ANAOWnKqubKyssk4efKk29fbsWOHEhMT1a9fP910003au3ev1zNTvD6Qn5+vAwcO6JtvvrE6CgAElPo6w+OQpKSkJEVHR7vG3Llzm32tzMxMLViwQIsWLdLzzz+vsrIy5efn69ixY17NzKlmH8jMzJQkFRUV6eKLL7Y4DQAEjpacat63b5+ioqJc28PCwpo9ftKkSa5/Hj58uDIzM9W7d2+99tpruuWWW7yWmRmvD/Tv31+dOnXS8uXLrY4CAAGlri7I45CkqKioJsNd8X5XTEyMUlJStHPnTq9mpnh9ICgoSIMHD9bKlSutjgIAAcVperjGa57bAzSqqqq0a9cu9ejRw0tpT6F4fSQjI0Pr16+3OgYABBTTw8Iqs5Wrmn/+85/r008/1Z49e7RixQpdddVVcjgcmjJlilczU7w+kp+fr8OHD+vw4cNWRwGAgOHNJ1ft379fU6ZM0YABA3TdddepW7duKiwsVGxsrFczs7jKR05fYHXppZdanAYAAoM37+N95ZVXvBHprJjx+kifPn3UpUsXFlgBgBc5nZ7u5bU6XfOY8fqIYRgaOnQoC6wAwIvq64Kk4ObnkHxJApSRkaGNGzdaHQMAAkZ7rmpuLxSvD+Xn5+vIkSM6cOCA1VEAICA0eFhY1cCMFxkZGZKkNWvWWJwEAAKDN78W0FcoXh/q2bOnYmJitGzZMqujAEBgcJqehw2xuMqHDMPQsGHDtGrVKqujAEBAcNQ55XC4Wb5cZ89lzcx4fSwzM1MbN26Uadrzv8QAwJ8YTlNBboZh0xkvxetjY8aM0dGjR7Vv3z6rowCA33M0OOWodzMamPFCUnp6uiRp9erVFicBAP8X1CAFNZhuhtXpmkfx+liPHj3UvXt3FlgBgBe4O83cOOyIxVUWGD58ODNeAPACR737xVVmPaea8a2srCxt2rSJBVYAcI78ccZL8VogLy9PVVVV2r17t9VRAMCvBdc7FVznZjDjRSMWWAGAl3x721Bzw64P0KB4LRAbG6v4+HgWWAHAOfLHU80srrLIiBEjmPECwDly1DnlMJo/pezkyVU4XU5OjrZs2SKnXb+pGQD8QJDT6XHYEcVrkdzcXJ04cUI7duywOgoA+C1/PNVM8VokLS1NkvjCBAA4B45656nTzc0NVjXjdOedd57OP/98FlgBwDnw9oz32WefVZ8+fRQeHq7MzMx2WYtD8VooNTVVa9assToGAPgtt/fwfjta49VXX9Xs2bP14IMPau3atRoxYoQmTpyow4cPezUzxWuhnJwcbd26VfX19VZHAQD/5JSH+3hb91JPPPGEbrvtNk2bNk2DBw/WvHnz1KlTJ/3lL3/xamSK10K5ubk6efKktm7danUUAPBLDbXHVX+y+dFQe1ySVFlZ2WScPHnyjNepra1VcXGxxo8f79oWFBSk8ePHa+XKlV7NzH28Fho5cqQMw9CqVas0dOhQq+MAgN8IDQ1VQkKC3vz3LI/HRUZGKikpqcm2Bx98UA899FCTbV999ZUaGhoUHx/fZHt8fLzXJ0cUr4WioqKUlJSkZcuW6ZZbbrE6DgD4jfDwcJWVlam2ttbjcaZpyjCMJtvCwsLaM9pZUbwWGzVqlIqKiqyOAQB+Jzw8XOHh4V55re7du8vhcOjQoUNNth86dEgJCQleeY9GXOO1WE5OjrZv3666ujqrowBAhxUaGqq0tDR9/PHHrm1Op1Mff/yxsrOzvfpeFK/FcnNzVVdXp82bN1sdBQA6tNmzZ+vFF1/USy+9pNLSUt1xxx2qrq7WtGnTvPo+nGq2WGpqqgzD0MqVK5Wammp1HADosK6//np9+eWXmjNnjsrLy5WamqpFixadseDqXBmmadrzYZYdSHJysjIzM7Vw4UKrowAA2hmnmm1g1KhRWrt2rdUxAAA+QPHaQG5urnbt2qWamhqrowAA2hnFawM5OTmqr6/Xxo0brY4CAGhnFK8NDB8+XA6Hw+uPJQMA2A/FawPh4eHq37+/li9fbnUUAEA7o3htggVWANAxULw2kZeXp7KyMh0/ftzqKACAdkTx2kR2dracTqfWr19vdRQAQDuieG1iyJAhCgkJUUFBgdVRAADtiOK1idDQUF1wwQUULwAEOIrXRtLT07Vu3TqrYwAA2hHFayP5+fnau3evjh07ZnUUAEA7oXhtJDMzU6ZpMusFgABG8drIoEGDFBYWxnVeAAhgFK+NBAcHa+DAgVqxYoXVUQAA7YTitZn09HSVlJRYHQMA0E4oXpvJz8/X/v37VVFRYXUUAEA7oHhtJjMzU5JUVFRkcRIAQHugeG0mJSVFERERLLACgABF8dpMUFCQBg0axHfzAkCAonhtaPTo0XxZAgAEKIrXhsaMGaPy8nJ99dVXVkcBAHgZxWtDLLACgMBF8dpQv3791LlzZy1fvtzqKAAAL6N4bcgwDA0dOpQFVgAQgChem8rIyNCGDRusjgEA8DKK16by8/P11Vdfqby83OooAAAvonhtKiMjQ5K0Zs0ai5MAALyJ4rWpXr16KSoqSsuWLbM6CgDAiyhemzIMQ8OGDdOqVausjgIA8CKK18YyMzO1ceNGmaZpdRQAgJdQvDY2ZswYffPNN/riiy+sjgIA8BKK18ZGjx4tSVq9erXFSQAA3kLx2lhiYqK6du3KE6wAIIBQvDY3fPhwFlgBQACheG0uKyuLBVYAEEAoXpvLy8vTsWPHtGfPHqujAAC8gOK1ufT0dEkssAKAQEHx2lx8fLxiY2N5ghUABAiK1w+MGDGCGS8ABAiK1w9kZ2dry5YtcjqdVkcBAJwjitcP5OXlqbq6Wrt27bI6CgDgHFG8fqBxgRX38wKA/6N4/UDXrl3Vo0cPFlgBQACgeP1Eamqq1qxZY3UMAMA5onj9RE5OjkpLS9XQ0GB1FADAOaB4/URubq5qamq0bds2q6MAAM4BxesnRo0aJUkqLCy0OAkA4FwYJk/ftz1nQ4N2/fVDvfLjR9TDGaFOMV3U+6p8DZl1tWIG97E6HoAAdeDjtdryp3/o4JISSVLCmOEafPfVOn9CurXB/BzFa3POunp9cs2D2vf+mTNdR3ioLnzzIfWclGlBMgCBbMPchSr+5Z+b3Zc650ca+dDNPk4UODjVbHOb/vB6s6UrSQ01tVo65VHVVlb7OBWAQHZ45Wa3pStJJQ//VeWfrvdhosBC8dqYs6FBW+e96/GYuspq7frbhz5KBKAjKH3unbMf8+zb7R8kQAVbHQDuHf/iK1XvPXzW47b+c7lOZPfyQSIAHcH+pevOeszhFZt9kCQwUbw2ZjhadkLiXx/8S//vgz+0cxoAHcVcZSne6OTxGMPh8FGawEPx2ljn82MVM6SPKjbv8XjcDY/cozsuTfNNKAABb//j/9CR11d6PCbxYv5/Tluxqtnmtv+/f6rg9ifc7u90fnddu/NvcoSF+jAVgEBWUfq53km9Xc66+mb3G44gfb9onrqOSPZxssDA4iqbS7l1sgb95Kpm94XHxWj8e49SugC8KmZQb+X/9X4FhZx5UtQIdijvL/dRuueAGa+fOLR8o7a+8J4qNu2Ro1OYel+Vr5Tplyisa5TV0QAEqMpdB7T1+XdVvrREkhSfP1wD7/y+oi/oaW0wP0fxAgDgQ5xqBgDAhyheAAB8iOIFAMCHKF4AAHyI4gUAwIcoXgAAfIjiBQDAhyheAAB8iOIFAMCHKF4AAHyI4gUAwIcoXgAAfIjiBQDAhyheAAB8iOIFAMCHKF4AAHyI4gUAwIcoXgAAfIjiBQDAhyheAAB8iOIFAMCHKF4AAHyI4gUAwIcoXgAAfIjiBQDAhyheAAB8iOIFAMCHKF4AAHyI4gUAwIcoXgAAfIjiBQDAhyheAAB8iOIFAMCHKF4AAHyI4gUAwIcoXgAAfOj/A4OXb1tr4XYWAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" + "## Example \n", + "\n", + "We demonstrate here how to optimize the water network using our QUBO approach. We start by defining the network." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 27, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "ref_pressure = results.node['pressure'].values[0][:2]\n", - "ref_rate = results.link['flowrate'].values[0]\n", - "ref_values = np.append(ref_rate, ref_pressure)\n", - "ref_values" + "import wntr\n", + "inp_file = './networks/Net0_CM.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# QUBO Designer\n", + "\n", + "We now show how to optimize the problem using the QUBO designer included in `wntr_quantum`. The unknown of the problem can take continuous values and therefore must be encoded using several qubits before being used in a QUBO formulation. We use here the encoding implemented in our library `qubops`. We use these encoding schemes to instantiate the polynomial solver." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "from qubops.encodings import PositiveQbitEncoding\n", "\n", - "nqbit = 7\n", - "step = (2./(2**nqbit-1))\n", + "nqbit = 5\n", + "step = (4./(2**nqbit-1))\n", "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", - "nqbit = 9\n", - "step = (50/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")" + "nqbit = 7\n", + "step = (200/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")" ] }, { - "cell_type": "code", - "execution_count": 5, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", - "pipe_diameters = [250, 500, 1000]\n", - "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters, head_lower_bound=80)" + "We can now create an instance of the designer. We choose to explore a small problem where each pipe can only take 3 distinct values: 250 cm, 500 cm and 1000 cm. We fix the threshold pressure at 29 m. We also adjust the weight of the constraints related to the cost minimization and pressure. " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : -2.000000 => -0.000000 | 0.000000 => 2.000000 (res: 0.015748)\n" + "Head Encoding : 0.000000 => 200.000000 (res: 1.574803)\n", + "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.129032)\n" ] } ], "source": [ + "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", + "pipe_diameters = [250, 500, 1000]\n", + "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, \n", + " pipe_diameters, head_lower_bound=29,\n", + " weight_cost=2, weight_pressure=0.5)\n", "designer.verify_encoding()" ] }, { - "cell_type": "code", - "execution_count": 7, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([ 1.766, 1.766, 97.666, 96.906])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "designer.create_index_mapping()\n", - "designer.matrices = designer.initialize_matrices()\n", - "designer.compute_classical_solution([0,1,0,0,1,0], convert_to_si=False)" + "We can enumerate the possible values of the flow and pressure for all possible values of the pipe diameters" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "price \t diameters \t variables\n", - "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329]\n", - "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118]\n", - "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858]\n", - "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118]\n", - "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906]\n", - "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647]\n", - "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858]\n", - "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647]\n", - "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387]\n" + "price \t diameters \t variables\t energy\n", + "0.16907910944516957 [250. 250.] [ 0.05 0.05 20.689 11.378] -8896.547627089463\n", + "0.25361866416775436 [250. 500.] [ 0.05 0.05 20.689 20.457] -8189.177038991521\n", + "0.42269777361292393 [ 250. 1000.] [ 0.05 0.05 20.689 20.683] -8189.351066475922\n", + "0.25361866416775436 [500. 250.] [ 0.05 0.05 29.769 20.457] -6581.108863493134\n", + "0.33815821889033915 [500. 500.] [ 0.05 0.05 29.769 29.537] -4978.399309341114\n", + "0.5072373283355087 [ 500. 1000.] [ 0.05 0.05 29.769 29.763] -4978.458985844512\n", + "0.42269777361292393 [1000. 250.] [ 0.05 0.05 29.994 20.683] -6580.062243226369\n", + "0.5072373283355087 [1000. 500.] [ 0.05 0.05 29.994 29.763] -4977.238338093348\n", + "0.6763164377806783 [1000. 1000.] [ 0.05 0.05 29.994 29.988] -4977.069312634741\n" ] } ], "source": [ - "designer.enumerates_classical_solutions(convert_to_si=False)" + "solutions = designer.enumerates_classical_solutions(convert_to_si=True)" ] }, { - "cell_type": "code", - "execution_count": 9, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1.766, 1.766, 98.406, 98.387], dtype=float32)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "designer.convert_solution_from_si(ref_values)" + "### Initial sample for the QUBO optimization \n", + "\n", + "Before minimizing the energy of the QUBO problem we need to define the initial configuration of the binary variables in the QUBO problem. We have implemented two different ways to obtain an initial sample that respects all the conditions imposed by the quadratization constraings of the polynomial qubo solver. \n", + "\n", + "We can for example create a completely random sample that simply ensure that quadratization constraints are respected" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 31, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:759: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", - " warnings.warn(\"For constraints with fractional coefficients, \"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 1, 2.0, 0.8346456692913385, 98.14090019569471, 87.47553816046965]\n" - ] - } - ], + "outputs": [], "source": [ - "from dwave.samplers import SimulatedAnnealingSampler\n", - "options = {'sampler': SimulatedAnnealingSampler()}\n", - "status = designer.solve(strength=1E7, num_reads=100000, options=options)" + "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", + "x = modify_solution_sample(designer, solutions[(500,500)][2], modify=['flows','heads'])\n", + "x0 = list(x.values())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Temperature scheduling for the Simulated Annealing optimization\n", + "\n", + "One important parameters of the simulated Annealing process is the the so-called temperature schedule. This schdule defines the acceptance probability of the new samples that increase the QUBO energy. While high temperature that leads to accepting samples that increase energy is usefull to escape local minima the temperature must be decreased in order to converge towards a minima. \n", + "\n", + "The temperature schedule usually starts with high temperature values that allows to explore the energy landscape but progressively decrease the tempearture in order for the optimization to converge. " ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 32, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.25361866416775436" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "designer.total_pice" + "import numpy as np\n", + "num_temp = 5000\n", + "Tinit = 1E3\n", + "Tfinal = 1E-1\n", + "Tschedule = np.linspace(Tinit, Tfinal, num_temp)\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "Tschedule = np.append(Tschedule, np.zeros(100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then use the `solve()` method of the qubo polynomial solver to obtain a solution of the problem" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 33, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([500., 250.])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6100/6100 [00:20<00:00, 300.29it/s]\n" + ] } ], "source": [ - "designer.optimal_diameters" + "designer.step_func.optimize_values = np.arange(2,12)\n", + "data = designer.solve(init_sample=x0, Tschedule=Tschedule,\n", + " save_traj=True, verbose=False)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 34, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "437" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "designer.bqm.num_variables" + "res = data[3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the evoluion of the QUBO energy along the optimization path" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([9.420e+02, 7.588e+03, 2.190e+04, 3.014e+04, 2.332e+04, 1.148e+04, 3.597e+03, 8.800e+02, 1.360e+02, 1.300e+01]),\n", - " array([-2.33e+03, 2.10e+07, 4.20e+07, 6.30e+07, 8.40e+07, 1.05e+08, 1.26e+08, 1.47e+08, 1.68e+08, 1.89e+08, 2.10e+08]),\n", - " )" + "" ] }, - "execution_count": 21, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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CUC8Bl7AjT6eGegkAOsH5vn9f0GdyGhoaJEl9+vSRJFVWVurUqVNKSUmxZ4YNG6YBAwaooqJCklRRUaGRI0fagSNJXq9Xfr9fBw4csGe+u4+2mbZ9NDc3q7KyMmAmPDxcKSkp9szZNDU1ye/3B1wAAICZ2h05ra2tmjt3rm666SaNGDFCkuTz+eRwOBQVFRUwGxMTI5/PZ898N3Datrdt+74Zv9+vr7/+Wp999plaWlrOOtO2j7PJzc2V2+22L/Hx8cE/cAAA0CW0O3IyMzO1f/9+vfLKKx25nk61ePFiNTQ02JdPPvkk1EsCAACdpFt7bpSVlaWCggJt27ZN/fv3t6+PjY1Vc3Oz6uvrA47m1NbWKjY21p75229BtX376rszf/uNrNraWrlcLvXo0UMRERGKiIg460zbPs7G6XTK6XQG/4ABAECXE9SRHMuylJWVpc2bN2vr1q0aPHhwwPbExER1795dpaWl9nWHDh1STU2NPB6PJMnj8Wjfvn0B34IqKSmRy+VSQkKCPfPdfbTNtO3D4XAoMTExYKa1tVWlpaX2DAAAuLwFdSQnMzNT69ev16uvvqrevXvbn39xu93q0aOH3G63MjIylJ2drT59+sjlcumxxx6Tx+NRcnKyJGnSpElKSEjQzJkztXz5cvl8Pi1ZskSZmZn2UZbZs2dr9erVWrBggR566CFt3bpVGzduVGHh/3+TJjs7W+np6Ro7dqzGjx+vlStXqrGxUbNmzeqo5wYAAHRhQUXOmjVrJEkTJkwIuH7dunV68MEHJUkrVqxQeHi4pkyZoqamJnm9Xj3//PP2bEREhAoKCjRnzhx5PB5dccUVSk9P11NPPWXPDB48WIWFhZo3b55WrVql/v3768UXX5TX67Vnpk6dquPHjysnJ0c+n0+jR49WUVHRGR9GBgAAl6cLOk9OV8d5cro+zpOD78N5cgAzXZTz5AAAAFyqiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYKejI2bZtm+6++27FxcUpLCxMW7ZsCdj+4IMPKiwsLOAyefLkgJkvvvhCM2bMkMvlUlRUlDIyMnTy5MmAmb179+qWW25RZGSk4uPjtXz58jPWsmnTJg0bNkyRkZEaOXKkXn/99WAfDgAAMFTQkdPY2KhRo0YpLy/vnDOTJ0/WsWPH7Msf/vCHgO0zZszQgQMHVFJSooKCAm3btk2PPvqovd3v92vSpEkaOHCgKisr9eyzz2rZsmV64YUX7Jnt27dr+vTpysjI0J49e5SWlqa0tDTt378/2IcEAAAMFGZZltXuG4eFafPmzUpLS7Ove/DBB1VfX3/GEZ42H374oRISEvTee+9p7NixkqSioiLdeeed+vTTTxUXF6c1a9boiSeekM/nk8PhkCQtWrRIW7Zs0cGDByVJU6dOVWNjowoKCux9Jycna/To0Vq7du15rd/v98vtdquhoUEul6sdzwBCbdCiwlAvAZewI0+nhnoJADrB+b5/d8pncsrKyhQdHa2hQ4dqzpw5+vzzz+1tFRUVioqKsgNHklJSUhQeHq6dO3faM7feeqsdOJLk9Xp16NAhffnll/ZMSkpKwP16vV5VVFScc11NTU3y+/0BFwAAYKYOj5zJkyfrt7/9rUpLS/XMM8+ovLxcd9xxh1paWiRJPp9P0dHRAbfp1q2b+vTpI5/PZ8/ExMQEzLT9/EMzbdvPJjc3V263277Ex8df2IMFAACXrG4dvcNp06bZ/z1y5Ehdf/31uuaaa1RWVqbbb7+9o+8uKIsXL1Z2drb9s9/vJ3QAADBUp3+F/Oqrr1bfvn318ccfS5JiY2NVV1cXMHP69Gl98cUXio2NtWdqa2sDZtp+/qGZtu1n43Q65XK5Ai4AAMBMnR45n376qT7//HP169dPkuTxeFRfX6/Kykp7ZuvWrWptbVVSUpI9s23bNp06dcqeKSkp0dChQ/WjH/3IniktLQ24r5KSEnk8ns5+SAAAoAsIOnJOnjypqqoqVVVVSZKqq6tVVVWlmpoanTx5UvPnz9eOHTt05MgRlZaW6p577tGQIUPk9XolScOHD9fkyZP1yCOPaNeuXXr33XeVlZWladOmKS4uTpJ0//33y+FwKCMjQwcOHNCGDRu0atWqgD81Pf744yoqKtJzzz2ngwcPatmyZXr//feVlZXVAU8LAADo6oKOnPfff19jxozRmDFjJEnZ2dkaM2aMcnJyFBERob179+rv//7vde211yojI0OJiYn6r//6LzmdTnsfL7/8soYNG6bbb79dd955p26++eaAc+C43W69+eabqq6uVmJion72s58pJycn4Fw6N954o9avX68XXnhBo0aN0h//+Edt2bJFI0aMuJDnAwAAGOKCzpPT1XGenK6P8+Tg+3CeHMBMIT1PDgAAQKgROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBI3UK9AADoLIMWFYZ6CUE78nRqqJcAGIMjOQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMFHTnbtm3T3Xffrbi4OIWFhWnLli0B2y3LUk5Ojvr166cePXooJSVFH330UcDMF198oRkzZsjlcikqKkoZGRk6efJkwMzevXt1yy23KDIyUvHx8Vq+fPkZa9m0aZOGDRumyMhIjRw5Uq+//nqwDwcAABgq6MhpbGzUqFGjlJeXd9bty5cv169//WutXbtWO3fu1BVXXCGv16tvvvnGnpkxY4YOHDigkpISFRQUaNu2bXr00Uft7X6/X5MmTdLAgQNVWVmpZ599VsuWLdMLL7xgz2zfvl3Tp09XRkaG9uzZo7S0NKWlpWn//v3BPiQAAGCgMMuyrHbfOCxMmzdvVlpamqRvj+LExcXpZz/7mX7+859LkhoaGhQTE6P8/HxNmzZNH374oRISEvTee+9p7NixkqSioiLdeeed+vTTTxUXF6c1a9boiSeekM/nk8PhkCQtWrRIW7Zs0cGDByVJU6dOVWNjowoKCuz1JCcna/To0Vq7du15rd/v98vtdquhoUEul6u9TwNCaNCiwlAvAehQR55ODfUSgEve+b5/d+hncqqrq+Xz+ZSSkmJf53a7lZSUpIqKCklSRUWFoqKi7MCRpJSUFIWHh2vnzp32zK233moHjiR5vV4dOnRIX375pT3z3ftpm2m7n7NpamqS3+8PuAAAADN1aOT4fD5JUkxMTMD1MTEx9jafz6fo6OiA7d26dVOfPn0CZs62j+/ex7lm2rafTW5urtxut32Jj48P9iECAIAu4rL6dtXixYvV0NBgXz755JNQLwkAAHSSDo2c2NhYSVJtbW3A9bW1tfa22NhY1dXVBWw/ffq0vvjii4CZs+3ju/dxrpm27WfjdDrlcrkCLgAAwEwdGjmDBw9WbGysSktL7ev8fr927twpj8cjSfJ4PKqvr1dlZaU9s3XrVrW2tiopKcme2bZtm06dOmXPlJSUaOjQofrRj35kz3z3ftpm2u4HAABc3oKOnJMnT6qqqkpVVVWSvv2wcVVVlWpqahQWFqa5c+fql7/8pf7zP/9T+/bt0wMPPKC4uDj7G1jDhw/X5MmT9cgjj2jXrl169913lZWVpWnTpikuLk6SdP/998vhcCgjI0MHDhzQhg0btGrVKmVnZ9vrePzxx1VUVKTnnntOBw8e1LJly/T+++8rKyvrwp8VAADQ5XUL9gbvv/++Jk6caP/cFh7p6enKz8/XggUL1NjYqEcffVT19fW6+eabVVRUpMjISPs2L7/8srKysnT77bcrPDxcU6ZM0a9//Wt7u9vt1ptvvqnMzEwlJiaqb9++ysnJCTiXzo033qj169dryZIl+ud//mf93d/9nbZs2aIRI0a064kAAABmuaDz5HR1nCen6+M8OTAN58kBflhIzpMDAABwqSByAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEbqFuoF4NIxaFFhqJcAAECH4UgOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACM1OGRs2zZMoWFhQVchg0bZm//5ptvlJmZqSuvvFK9evXSlClTVFtbG7CPmpoapaamqmfPnoqOjtb8+fN1+vTpgJmysjLdcMMNcjqdGjJkiPLz8zv6oQAAgC6sU47kXHfddTp27Jh9eeedd+xt8+bN02uvvaZNmzapvLxcR48e1b333mtvb2lpUWpqqpqbm7V9+3a99NJLys/PV05Ojj1TXV2t1NRUTZw4UVVVVZo7d64efvhhFRcXd8bDAQAAXVC3Ttlpt26KjY094/qGhgb9x3/8h9avX6/bbrtNkrRu3ToNHz5cO3bsUHJyst5880198MEHeuuttxQTE6PRo0frF7/4hRYuXKhly5bJ4XBo7dq1Gjx4sJ577jlJ0vDhw/XOO+9oxYoV8nq9nfGQAABAF9MpR3I++ugjxcXF6eqrr9aMGTNUU1MjSaqsrNSpU6eUkpJizw4bNkwDBgxQRUWFJKmiokIjR45UTEyMPeP1euX3+3XgwAF75rv7aJtp28e5NDU1ye/3B1wAAICZOjxykpKSlJ+fr6KiIq1Zs0bV1dW65ZZbdOLECfl8PjkcDkVFRQXcJiYmRj6fT5Lk8/kCAqdte9u275vx+/36+uuvz7m23Nxcud1u+xIfH3+hDxcAAFyiOvzPVXfccYf939dff72SkpI0cOBAbdy4UT169OjouwvK4sWLlZ2dbf/s9/sJHQAADNXpXyGPiorStddeq48//lixsbFqbm5WfX19wExtba39GZ7Y2Ngzvm3V9vMPzbhcru8NKafTKZfLFXABAABm6vTIOXnypA4fPqx+/fopMTFR3bt3V2lpqb390KFDqqmpkcfjkSR5PB7t27dPdXV19kxJSYlcLpcSEhLsme/uo22mbR8AAAAdHjk///nPVV5eriNHjmj79u36h3/4B0VERGj69Olyu93KyMhQdna23n77bVVWVmrWrFnyeDxKTk6WJE2aNEkJCQmaOXOm/vu//1vFxcVasmSJMjMz5XQ6JUmzZ8/WX/7yFy1YsEAHDx7U888/r40bN2revHkd/XAAAEAX1eGfyfn00081ffp0ff7557rqqqt08803a8eOHbrqqqskSStWrFB4eLimTJmipqYmeb1ePf/88/btIyIiVFBQoDlz5sjj8eiKK65Qenq6nnrqKXtm8ODBKiws1Lx587Rq1Sr1799fL774Il8fBwAAtjDLsqxQLyJU/H6/3G63Ghoa+HyOpEGLCkO9BOCyd+Tp1FAvAbjkne/7N/92FQAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADBSh/+zDgCA9uuKZx7nLM24VHEkBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABG6hbqBZhq0KLCUC8BAIDLGpEDALggXfH/qTvydGqol4CLgD9XAQAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjdfnIycvL06BBgxQZGamkpCTt2rUr1EsCAACXgC4dORs2bFB2draWLl2q3bt3a9SoUfJ6vaqrqwv10gAAQIh16cj51a9+pUceeUSzZs1SQkKC1q5dq549e+o3v/lNqJcGAABCrMueDLC5uVmVlZVavHixfV14eLhSUlJUUVFx1ts0NTWpqanJ/rmhoUGS5Pf7O3x9rU1fdfg+AQAdY8C8TaFeQtD2P+kN9RIuGW3v25Zlfe9cl42czz77TC0tLYqJiQm4PiYmRgcPHjzrbXJzc/Xkk0+ecX18fHynrBEAgI7iXhnqFVx6Tpw4Ibfbfc7tXTZy2mPx4sXKzs62f25tbdUXX3yhK6+8UmFhYR12P36/X/Hx8frkk0/kcrk6bL/oHLxeXQevVdfBa9W1dLXXy7IsnThxQnFxcd8712Ujp2/fvoqIiFBtbW3A9bW1tYqNjT3rbZxOp5xOZ8B1UVFRnbVEuVyuLvE/FnyL16vr4LXqOnitupau9Hp93xGcNl32g8cOh0OJiYkqLS21r2ttbVVpaak8Hk8IVwYAAC4FXfZIjiRlZ2crPT1dY8eO1fjx47Vy5Uo1NjZq1qxZoV4aAAAIsS4dOVOnTtXx48eVk5Mjn8+n0aNHq6io6IwPI19sTqdTS5cuPeNPY7g08Xp1HbxWXQevVddi6usVZv3Q968AAAC6oC77mRwAAIDvQ+QAAAAjETkAAMBIRA4AADASkdNOeXl5GjRokCIjI5WUlKRdu3Z97/ymTZs0bNgwRUZGauTIkXr99dcv0koRzGuVn5+vsLCwgEtkZORFXO3la9u2bbr77rsVFxensLAwbdmy5QdvU1ZWphtuuEFOp1NDhgxRfn5+p68T3wr29SorKzvjdyssLEw+n+/iLPgylpubq3Hjxql3796Kjo5WWlqaDh069IO3M+F9i8hphw0bNig7O1tLly7V7t27NWrUKHm9XtXV1Z11fvv27Zo+fboyMjK0Z88epaWlKS0tTfv377/IK7/8BPtaSd+e8fPYsWP25a9//etFXPHlq7GxUaNGjVJeXt55zVdXVys1NVUTJ05UVVWV5s6dq4cffljFxcWdvFJIwb9ebQ4dOhTw+xUdHd1JK0Sb8vJyZWZmaseOHSopKdGpU6c0adIkNTY2nvM2xrxvWQja+PHjrczMTPvnlpYWKy4uzsrNzT3r/E9/+lMrNTU14LqkpCTrn/7pnzp1nQj+tVq3bp3ldrsv0upwLpKszZs3f+/MggULrOuuuy7guqlTp1per7cTV4azOZ/X6+2337YkWV9++eVFWRPOra6uzpJklZeXn3PGlPctjuQEqbm5WZWVlUpJSbGvCw8PV0pKiioqKs56m4qKioB5SfJ6veecR8doz2slSSdPntTAgQMVHx+ve+65RwcOHLgYy0WQ+L3qmkaPHq1+/frpJz/5id59991QL+ey1NDQIEnq06fPOWdM+f0icoL02WefqaWl5YyzKsfExJzzb8s+ny+oeXSM9rxWQ4cO1W9+8xu9+uqr+v3vf6/W1lbdeOON+vTTTy/GkhGEc/1e+f1+ff311yFaFc6lX79+Wrt2rf70pz/pT3/6k+Lj4zVhwgTt3r071Eu7rLS2tmru3Lm66aabNGLEiHPOmfK+1aX/WQego3k8noB/4PXGG2/U8OHD9e///u/6xS9+EcKVAV3b0KFDNXToUPvnG2+8UYcPH9aKFSv0u9/9LoQru7xkZmZq//79euedd0K9lIuCIzlB6tu3ryIiIlRbWxtwfW1trWJjY896m9jY2KDm0THa81r9re7du2vMmDH6+OOPO2OJuADn+r1yuVzq0aNHiFaFYIwfP57frYsoKytLBQUFevvtt9W/f//vnTXlfYvICZLD4VBiYqJKS0vt61pbW1VaWhpwBOC7PB5PwLwklZSUnHMeHaM9r9Xfamlp0b59+9SvX7/OWibaid+rrq+qqorfrYvAsixlZWVp8+bN2rp1qwYPHvyDtzHm9yvUn3zuil555RXL6XRa+fn51gcffGA9+uijVlRUlOXz+SzLsqyZM2daixYtsuffffddq1u3bta//uu/Wh9++KG1dOlSq3v37ta+fftC9RAuG8G+Vk8++aRVXFxsHT582KqsrLSmTZtmRUZGWgcOHAjVQ7hsnDhxwtqzZ4+1Z88eS5L1q1/9ytqzZ4/117/+1bIsy1q0aJE1c+ZMe/4vf/mL1bNnT2v+/PnWhx9+aOXl5VkRERFWUVFRqB7CZSXY12vFihXWli1brI8++sjat2+f9fjjj1vh4eHWW2+9FaqHcNmYM2eO5Xa7rbKyMuvYsWP25auvvrJnTH3fInLa6d/+7d+sAQMGWA6Hwxo/fry1Y8cOe9uPf/xjKz09PWB+48aN1rXXXms5HA7ruuuuswoLCy/yii9fwbxWc+fOtWdjYmKsO++809q9e3cIVn35afuK8d9e2l6f9PR068c//vEZtxk9erTlcDisq6++2lq3bt1FX/flKtjX65lnnrGuueYaKzIy0urTp481YcIEa+vWraFZ/GXmbK+TpIDfF1Pft8Isy7Iu9tEjAACAzsZncgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAAAdatu2bbr77rsVFxensLAwbdmyJeh9FBcXKzk5Wb1799ZVV12lKVOm6MiRI0Htg8gBAAAdqrGxUaNGjVJeXl67bl9dXa177rlHt912m6qqqlRcXKzPPvtM9957b1D74YzHAACg04SFhWnz5s1KS0uzr2tqatITTzyhP/zhD6qvr9eIESP0zDPPaMKECZKkP/7xj5o+fbqampoUHv7t8ZjXXntN99xzj5qamtS9e/fzum+O5AAAgIsqKytLFRUVeuWVV7R371794z/+oyZPnqyPPvpIkpSYmKjw8HCtW7dOLS0tamho0O9+9zulpKScd+BIHMkBAACd6G+P5NTU1Ojqq69WTU2N4uLi7LmUlBSNHz9e//Iv/yJJKi8v109/+lN9/vnnamlpkcfj0euvv66oqKjzvm+O5AAAgItm3759amlp0bXXXqtevXrZl/Lych0+fFiS5PP59Mgjjyg9PV3vvfeeysvL5XA4dN999ymYYzPdOutBAAAA/K2TJ08qIiJClZWVioiICNjWq1cvSVJeXp7cbreWL19ub/v973+v+Ph47dy5U8nJyed1X0QOAAC4aMaMGaOWlhbV1dXplltuOevMV199ZX/guE1bELW2tp73ffHnKgAA0KFOnjypqqoqVVVVSfr2K+FVVVWqqanRtddeqxkzZuiBBx7Qn//8Z1VXV2vXrl3Kzc1VYWGhJCk1NVXvvfeennrqKX300UfavXu3Zs2apYEDB2rMmDHnvQ4+eAwAADpUWVmZJk6ceMb16enpys/P16lTp/TLX/5Sv/3tb/W///u/6tu3r5KTk/Xkk09q5MiRkqRXXnlFy5cv1//8z/+oZ8+e8ng8euaZZzRs2LDzXgeRAwAAjMSfqwAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEb6P89Ob+BlA52mAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "
" ] @@ -339,186 +272,139 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "plt.hist(designer.sampleset.data_vectors['energy'])" + "eplt = res.energies\n", + "plt.plot(res.energies[:], lw=4, label=\"QUBO Energy\")\n", + "plt.grid(which='both')\n", + "plt.ylabel('Energy', fontsize=14)\n", + "plt.xlabel('Iterations', fontsize=14)\n", + "plt.legend(fontsize=12)\n", + "\n", + "\n" ] }, { - "cell_type": "code", - "execution_count": 56, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "x = designer.sampleset" + "We can also extract the price and pipe diameters obtained after the optimization" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 36, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "([1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], 99998077.471, 1)" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "0.42269777361292393 [ 250. 1000.]\n" + ] } ], "source": [ - "x.record[1]" + "price = data[4]\n", + "diameters = data[5]\n", + "print(price, diameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also plot the reference solution and the QUBO solution for visual inspection" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 37, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 0, 1, 0, 1, 0]\n", - "[1, 0, 1, 1, 1, 1]\n", - "[1, 1, 0, 1, 1, 0]\n", - "[1, 0, 1, 0, 1, 0]\n", - "[1, 1, 0, 0, 1, 0]\n", - "[1, 0, 1, 0, 1, 1]\n", - "[1, 1, 0, 1, 1, 0]\n", - "[1, 1, 0, 1, 0, 0]\n", - "[0, 1, 1, 1, 1, 0]\n", - "[0, 1, 1, 1, 1, 0]\n", - "[1, 1, 0, 1, 1, 1]\n", - "[0, 1, 1, 0, 1, 1]\n", - "[0, 1, 1, 1, 1, 0]\n", - "[1, 0, 1, 0, 1, 1]\n", - "[1, 0, 0, 1, 1, 0]\n", - "[1, 0, 1, 0, 1, 1]\n", - "[1, 0, 0, 1, 0, 1]\n", - "[1, 1, 0, 1, 1, 1]\n", - "[1, 0, 0, 0, 0, 1]\n", - "[1, 1, 0, 0, 1, 0]\n", - "[1, 1, 0, 1, 0, 0]\n", - "[1, 1, 1, 1, 1, 0]\n", - "[0, 1, 1, 0, 0, 0]\n", - "[1, 0, 1, 0, 1, 1]\n", - "[0, 1, 0, 0, 1, 1]\n", - "[0, 1, 1, 0, 1, 0]\n", - "[0, 1, 0, 0, 0, 1]\n", - "[0, 1, 1, 0, 0, 1]\n", - "[1, 1, 0, 0, 1, 0]\n", - "[0, 0, 1, 1, 0, 0]\n", - "[1, 0, 1, 1, 1, 0]\n", - "[0, 1, 0, 0, 0, 1]\n", - "[1, 1, 0, 0, 1, 0]\n", - "[0, 0, 1, 0, 1, 0]\n", - "[0, 1, 1, 0, 0, 1]\n", - "[1, 0, 1, 1, 0, 0]\n", - "[1, 0, 1, 1, 1, 1]\n", - "[1, 1, 0, 1, 0, 1]\n", - "[0, 0, 1, 1, 0, 1]\n", - "[0, 1, 1, 0, 0, 1]\n", - "[0, 1, 0, 1, 0, 0]\n", - "[1, 1, 0, 0, 1, 1]\n", - "[0, 1, 1, 1, 0, 0]\n", - "[1, 0, 1, 0, 1, 0]\n", - "[0, 1, 0, 1, 0, 0]\n", - "[1, 0, 0, 0, 1, 1]\n", - "[1, 1, 0, 1, 0, 0]\n", - "[1, 1, 0, 1, 1, 0]\n", - "[1, 1, 0, 0, 1, 1]\n", - "[1, 0, 0, 1, 0, 1]\n", - "[0, 1, 1, 0, 1, 0]\n", - "[0, 1, 1, 1, 0, 1]\n", - "[0, 0, 1, 1, 1, 0]\n", - "[1, 0, 0, 1, 0, 1]\n", - "[0, 0, 1, 1, 0, 0]\n", - "[0, 1, 1, 1, 0, 1]\n", - "[0, 1, 0, 1, 0, 0]\n", - "[0, 1, 0, 0, 1, 1]\n", - "[0, 1, 0, 0, 0, 1]\n", - "[0, 0, 1, 0, 0, 1]\n", - "[0, 1, 1, 1, 1, 1]\n", - "[1, 0, 1, 0, 0, 1]\n", - "[1, 0, 0, 1, 1, 0]\n", - "[0, 1, 1, 0, 1, 0]\n", - "[1, 1, 0, 0, 1, 1]\n", - "[0, 1, 0, 0, 1, 0]\n", - "[1, 1, 0, 0, 0, 1]\n", - "[0, 0, 1, 0, 0, 1]\n", - "[1, 0, 1, 1, 1, 1]\n", - "[1, 0, 1, 1, 1, 0]\n", - "[0, 1, 0, 0, 0, 0]\n", - "[1, 0, 1, 0, 0, 1]\n", - "[1, 1, 0, 0, 1, 0]\n", - "[1, 0, 1, 1, 0, 1]\n", - "[0, 1, 1, 1, 0, 0]\n", - "[0, 1, 0, 0, 1, 0]\n", - "[1, 1, 0, 1, 0, 0]\n", - "[0, 1, 1, 1, 0, 1]\n", - "[1, 0, 1, 0, 1, 1]\n", - "[1, 0, 1, 1, 1, 0]\n", - "[1, 0, 1, 1, 1, 0]\n", - "[1, 0, 0, 0, 1, 1]\n", - "[0, 1, 1, 1, 1, 1]\n", - "[1, 0, 0, 1, 1, 1]\n", - "[0, 1, 1, 1, 0, 0]\n", - "[1, 0, 1, 1, 1, 0]\n", - "[1, 0, 1, 1, 1, 0]\n", - "[0, 1, 0, 1, 1, 0]\n", - "[0, 1, 1, 0, 1, 1]\n", - "[0, 1, 1, 0, 1, 0]\n", - "[1, 1, 0, 0, 1, 0]\n", - "[1, 0, 1, 0, 1, 0]\n", - "[1, 0, 1, 1, 0, 0]\n", - "[1, 0, 1, 1, 1, 0]\n", - "[1, 1, 0, 0, 1, 1]\n", - "[0, 1, 0, 0, 1, 1]\n", - "[1, 0, 1, 1, 0, 0]\n", - "[1, 0, 1, 1, 1, 0]\n", - "[1, 0, 0, 0, 1, 1]\n", - "[0, 0, 1, 1, 0, 0]\n" - ] - } - ], + "outputs": [], "source": [ - "for i in range(100):\n", - " s = designer.qubo.decode_solution(x.record[i][0])\n", - " print(s[3])" + "sol = data[2]\n", + "ref_values = solutions[tuple(diameters)][0]" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n", - " 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n", - " 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1,\n", - " 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int8)" + "array([ 0.05 , 0.05 , 20.689, 20.683])" ] }, - "execution_count": 52, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x.lowest().record[0][0]" + "ref_values" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Pressure')" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "\n", + "fig = plt.figure(figsize = plt.figaspect(0.5))\n", + "ax1 = fig.add_subplot(121)\n", + "\n", + "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.grid()\n", + "\n", + "# ax1.scatter(ref_values[:2], encoded_ref_sol[:2], c='black', s=200, label='Best solution')\n", + "ax1.scatter(ref_values[:2], sol[:2], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "\n", + "\n", + "ax1.set_xlabel('Reference Values', fontsize=12)\n", + "ax1.set_ylabel('QUBO Values', fontsize=12)\n", + "ax1.set_title('Flow Rate', fontsize=14)\n", + "\n", + "ax2 = fig.add_subplot(122)\n", + "\n", + "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "\n", + "\n", + "# ax2.scatter(ref_values[2:], encoded_ref_sol[2:], c='black', s=200, label='Best solution')\n", + "ax2.scatter(ref_values[2:], sol[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "ax2.grid()\n", + "\n", + "\n", + "ax2.set_xlabel('Reference Values', fontsize=12)\n", + "ax2.set_title('Pressure', fontsize=14)" + ] } ], "metadata": { diff --git a/docs/notebooks/design_pipe_diameter_DW.ipynb b/docs/notebooks/design_pipe_diameter_DW.ipynb deleted file mode 100644 index c6243c4..0000000 --- a/docs/notebooks/design_pipe_diameter_DW.ipynb +++ /dev/null @@ -1,538 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", - "\n", - "# Create a water network model\n", - "inp_file = './networks/Net0.inp'\n", - "# inp_file = './networks/Net2LoopsDW.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ], - "text/plain": [ - "name J1 D1 R1\n", - "0 26.476913 22.953829 -9.338379e-07\n", - "3600 26.476913 22.953829 -9.338379e-07" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results.node['pressure']" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "nqbit = 7\n", - "step = (0.5/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", - "\n", - "nqbit = 7\n", - "step = (25/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+95.0, var_base_name=\"x\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", - "pipe_diameters = [250, 500, 1000]\n", - "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, pipe_diameters, head_lower_bound=80)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 95.000000 => 120.000000 (res: 0.196850)\n", - "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.003937)\n" - ] - } - ], - "source": [ - "designer.verify_encoding()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([ 1.766, 1.766, 86.797, 75.168])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "designer.create_index_mapping()\n", - "designer.matrices = designer.initialize_matrices()\n", - "designer.compute_classical_solution([1,0,0,1,0,0], convert_to_si=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "price \t diameters \t variables\n", - "0.16907910944516957 [250. 250.] [ 1.766 1.766 86.797 75.168]\n", - "0.25361866416775436 [250. 500.] [ 1.766 1.766 86.797 86.404]\n", - "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 86.797 86.782]\n", - "0.25361866416775436 [500. 250.] [ 1.766 1.766 98.032 86.404]\n", - "0.33815821889033915 [500. 500.] [ 1.766 1.766 98.032 97.64 ]\n", - "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 98.032 98.018]\n", - "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.411 86.782]\n", - "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.411 98.018]\n", - "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.411 98.397]\n" - ] - } - ], - "source": [ - "designer.enumerates_classical_solutions(convert_to_si=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/dimod/binary/binary_quadratic_model.py:759: UserWarning: For constraints with fractional coefficients, multiply both sides of the inequality by an appropriate factor of ten to attain or approximate integer coefficients. \n", - " warnings.warn(\"For constraints with fractional coefficients, \"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-1, 1, 1.9999999999999998, 1.937007874015748, 117.44094488188976, 112.71653543307086]\n" - ] - } - ], - "source": [ - "from dwave.samplers import SimulatedAnnealingSampler\n", - "from dwave.samplers import SteepestDescentSampler\n", - "options = {'sampler': SimulatedAnnealingSampler()}\n", - "status = designer.solve(strength=1E6, num_reads=10000, options=options)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.25361866416775436" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "designer.total_pice" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([250., 500.])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "designer.optimal_diameters" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'x_009_001'" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "designer.mixed_solution_vector.encoded_reals[8].variables[0].name" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('1', 95.0000000000000),\n", - " ('x_005_001', 0.196850393700787),\n", - " ('x_005_002', 0.393700787401575),\n", - " ('x_005_003', 0.787401574803150),\n", - " ('x_005_004', 1.57480314960630),\n", - " ('x_005_005', 3.14960629921260),\n", - " ('x_005_006', 6.29921259842520),\n", - " ('x_005_007', 12.5984251968504)]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "designer.qubo.all_expr[4]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. 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"execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "designer.model.pipe_coefficients_indices['P2'].value" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "P0,P1,P2,P3,P4 = designer.matrices" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[0. , 0. , 0. , 0. , 0. , 0. ],\n", - " [0. , 0. , 0. , 0. , 0. , 0. ]],\n", - "\n", - " [[0. , 0. , 0. , 0. , 0. , 0. ],\n", - " [0. , 0. , 0. , 0. , 0. , 0. ]],\n", - "\n", - " [[0.652, 0.06 , 0.004, 0. , 0. , 0. ],\n", - " [0. , 0. , 0. , 0. , 0. , 0. ]],\n", - "\n", - " [[0. , 0. , 0. , 0. , 0. , 0. ],\n", - " [0. , 0. , 0. , 0.652, 0.06 , 0.004]]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[]P2[:-1, :2, 6:]" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", - "\n", - "# Create a water network model\n", - "inp_file = './networks/Net0_CM.inp'\n", - "# inp_file = './networks/Net2LoopsDW.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_pressure = results.node['pressure'].values[0][:2]\n", - "ref_rate = results.link['flowrate'].values[0]\n", - "ref_values = np.append(ref_rate, ref_pressure)\n", - "ref_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run with QUBO solver" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "nqbit = 5\n", - "step = (4./(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", - "\n", - "nqbit = 7\n", - "step = (200/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", - "pipe_diameters = [250, 500, 1000]\n", - "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, \n", - " pipe_diameters, head_lower_bound=95,\n", - " weight_cost=2, weight_pressure=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 0.000000 => 200.000000 (res: 1.574803)\n", - "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.129032)\n" - ] - } - ], - "source": [ - "designer.verify_encoding()" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - } - ], - "source": [ - "designer.create_index_mapping()\n", - "designer.matrices = designer.initialize_matrices()\n", - "ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = designer.classical_solution([0,1,0,0,1,0], convert_to_si=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 29.769, 29.537])" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_sol" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", - "sampler = SimulatedAnnealing()" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", - "import sparse\n", - "\n", - "designer.qubo = QUBOPS_MIXED(designer.mixed_solution_vector, {\"sampler\": sampler})\n", - "matrices = tuple(sparse.COO(m) for m in designer.matrices)\n", - "designer.qubo.qubo_dict = designer.qubo.create_bqm(matrices, strength=0)\n", - "# designer.add_switch_constraints(strength=0)\n", - "designer.add_pressure_equality_constraints()" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.step.full_random import IncrementalStep\n", - "from wntr_quantum.sampler.step.full_random import SwitchIncrementalStep\n", - "\n", - "var_names = sorted(designer.qubo.qubo_dict.variables)\n", - "designer.qubo.create_variables_mapping()\n", - "mystep = SwitchIncrementalStep(var_names, \n", - " designer.qubo.mapped_variables, \n", - " designer.qubo.index_variables, \n", - " step_size=10,\n", - " switch_variable_index=[[6,7,8],[9,10,11]])" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "price \t diameters \t variables\t energy\n", - "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329] -7676.327154648521\n", - "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118] -8943.759716156877\n", - "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858] -8943.933743641279\n", - "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118] -9310.494690264793\n", - "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906] -9682.588285719068\n", - "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647] -9682.647962222467\n", - "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858] -9309.44806999803\n", - "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647] -9681.427314471302\n", - "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387] -9681.258289012692\n" - ] - } - ], - "source": [ - "designer.enumerates_classical_solutions(convert_to_si=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "price \t diameters \t variables\t energy\n", - "0.16907910944516957 [250. 250.] [ 0.05 0.05 20.689 11.378] -7676.327154648521\n", - "0.25361866416775436 [250. 500.] [ 0.05 0.05 20.689 20.457] -8943.759716156877\n", - "0.42269777361292393 [ 250. 1000.] [ 0.05 0.05 20.689 20.683] -8943.933743641279\n", - "0.25361866416775436 [500. 250.] [ 0.05 0.05 29.769 20.457] -9310.494690264793\n", - "0.33815821889033915 [500. 500.] [ 0.05 0.05 29.769 29.537] -9682.588285719068\n", - "0.5072373283355087 [ 500. 1000.] [ 0.05 0.05 29.769 29.763] -9682.647962222467\n", - "0.42269777361292393 [1000. 250.] [ 0.05 0.05 29.994 20.683] -9309.44806999803\n", - "0.5072373283355087 [1000. 500.] [ 0.05 0.05 29.994 29.763] -9681.427314471302\n", - "0.6763164377806783 [1000. 1000.] [ 0.05 0.05 29.994 29.988] -9681.258289012692\n" - ] - } - ], - "source": [ - "designer.enumerates_classical_solutions(convert_to_si=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", - "x = modify_solution_sample(designer, bin_rep_sol, modify=['flows','heads'])\n", - "x0 = list(x.values())" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "num_sweeps = 5000\n", - "Tinit = 1E3\n", - "Tfinal = 1E-1\n", - "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", - "Tschedule = np.append(Tschedule, np.zeros(100))" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/6100 [00:00" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "eplt = res.energies\n", - "\n", - "# fig, ax1 = plt.subplots()\n", - "\n", - "left, bottom, width, height = [0.55, 0.55, 0.3, 0.3]\n", - "\n", - "plt.plot(res.energies[:], lw=4, label=\"QUBO Energy\")\n", - "plt.plot(Tschedule, lw=3, label='Temperature')\n", - "# ax1.axline((0, 0), slope=e, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", - "plt.grid(which='both')\n", - "# plt.yscale('symlog')\n", - "\n", - "plt.ylabel('Energy', fontsize=14)\n", - "plt.xlabel('Iterations', fontsize=14)\n", - "plt.legend(fontsize=12)\n", - "\n", - "# ax2 = fig.add_axes([left, bottom, width, height])\n", - "# ax2.plot(eplt[-1000:])\n", - "# ax2.grid()\n", - "# ax2.axline((0, 0), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "# ax2.set_yscale('symlog')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0.088 0.069 29.28 28.8 ]\n" - ] - } - ], - "source": [ - "idx_min = np.array([e for e in res.energies]).argmin()\n", - "# idx_min = -1\n", - "sol = res.trajectory[idx_min]\n", - "sol = designer.qubo.decode_solution(np.array(sol))\n", - "pipe_hot_encoding = sol[3]\n", - "sol = designer.combine_flow_values(sol)\n", - "sol = designer.convert_solution_to_si(sol)\n", - "sol = sol[:4]\n", - "print(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.33815821889033915, array([500., 500.]))" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "designer.get_pipe_info_from_hot_encoding(pipe_hot_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Pressure')" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "\n", - "fig = plt.figure(figsize = plt.figaspect(0.5))\n", - "ax1 = fig.add_subplot(121)\n", - "\n", - "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.grid()\n", - "\n", - "# ax1.scatter(ref_values[:2], encoded_ref_sol[:2], c='black', s=200, label='Best solution')\n", - "ax1.scatter(ref_values[:2], sol[:2], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "\n", - "\n", - "ax1.set_xlabel('Reference Values', fontsize=12)\n", - "ax1.set_ylabel('QUBO Values', fontsize=12)\n", - "ax1.set_title('Flow Rate', fontsize=14)\n", - "\n", - "ax2 = fig.add_subplot(122)\n", - "\n", - "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "\n", - "\n", - "# ax2.scatter(ref_values[2:], encoded_ref_sol[2:], c='black', s=200, label='Best solution')\n", - "ax2.scatter(ref_values[2:], sol[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "ax2.grid()\n", - "\n", - "\n", - "ax2.set_xlabel('Reference Values', fontsize=12)\n", - "ax2.set_title('Pressure', fontsize=14)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Old sampler" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "# from dwave.samplers import SimulatedAnnealingSampler\n", - "# options = {'sampler': SimulatedAnnealingSampler()}\n", - "# status = designer.solve(strength=1E8, num_reads=5000, options=options)" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "# designer.total_pice" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "# designer.optimal_diameters" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [], - "source": [ - "# designer.qubo.qubo_dict.num_variables" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "# import matplotlib.pyplot as plt\n", - "# plt.hist(designer.sampleset.data_vectors['energy'])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/design_pipe_diameter_own_sampler_refac.ipynb b/docs/notebooks/design_pipe_diameter_own_sampler_refac.ipynb deleted file mode 100644 index 317c36c..0000000 --- a/docs/notebooks/design_pipe_diameter_own_sampler_refac.ipynb +++ /dev/null @@ -1,480 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", - "\n", - "# Create a water network model\n", - "inp_file = './networks/Net0_CM.inp'\n", - "# inp_file = './networks/Net2LoopsDW.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_pressure = results.node['pressure'].values[0][:2]\n", - "ref_rate = results.link['flowrate'].values[0]\n", - "ref_values = np.append(ref_rate, ref_pressure)\n", - "ref_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run with QUBO solver" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "nqbit = 5\n", - "step = (4./(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", - "\n", - "nqbit = 7\n", - "step = (200/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.design.qubo_pipe_diam import QUBODesignPipeDiameter \n", - "pipe_diameters = [250, 500, 1000]\n", - "designer = QUBODesignPipeDiameter(wn, flow_encoding, head_encoding, \n", - " pipe_diameters, head_lower_bound=95,\n", - " weight_cost=2, weight_pressure=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 0.000000 => 200.000000 (res: 1.574803)\n", - "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.129032)\n" - ] - } - ], - "source": [ - "designer.verify_encoding()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - } - ], - "source": [ - "ref_sol, encoded_ref_sol, bin_rep_sol, eref, cvgd = designer.classical_solution([0,1,0,0,1,0], convert_to_si=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-9682.588])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "eref" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "price \t diameters \t variables\t energy\n", - "0.16907910944516957 [250. 250.] [ 1.766 1.766 67.877 37.329] -7676.327154648512\n", - "0.25361866416775436 [250. 500.] [ 1.766 1.766 67.877 67.118] -8943.759716156874\n", - "0.42269777361292393 [ 250. 1000.] [ 1.766 1.766 67.877 67.858] -8943.933743641282\n", - "0.25361866416775436 [500. 250.] [ 1.766 1.766 97.666 67.118] -9310.49469026479\n", - "0.33815821889033915 [500. 500.] [ 1.766 1.766 97.666 96.906] -9682.588285719068\n", - "0.5072373283355087 [ 500. 1000.] [ 1.766 1.766 97.666 97.647] -9682.647962222467\n", - "0.42269777361292393 [1000. 250.] [ 1.766 1.766 98.406 67.858] -9309.448069998034\n", - "0.5072373283355087 [1000. 500.] [ 1.766 1.766 98.406 97.647] -9681.427314471295\n", - "0.6763164377806783 [1000. 1000.] [ 1.766 1.766 98.406 98.387] -9681.258289012689\n" - ] - } - ], - "source": [ - "designer.enumerates_classical_solutions(convert_to_si=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "price \t diameters \t variables\t energy\n", - "0.16907910944516957 [250. 250.] [ 0.05 0.05 20.689 11.378] -7676.327154648512\n", - "0.25361866416775436 [250. 500.] [ 0.05 0.05 20.689 20.457] -8943.759716156874\n", - "0.42269777361292393 [ 250. 1000.] [ 0.05 0.05 20.689 20.683] -8943.933743641282\n", - "0.25361866416775436 [500. 250.] [ 0.05 0.05 29.769 20.457] -9310.49469026479\n", - "0.33815821889033915 [500. 500.] [ 0.05 0.05 29.769 29.537] -9682.588285719068\n", - "0.5072373283355087 [ 500. 1000.] [ 0.05 0.05 29.769 29.763] -9682.647962222467\n", - "0.42269777361292393 [1000. 250.] [ 0.05 0.05 29.994 20.683] -9309.448069998034\n", - "0.5072373283355087 [1000. 500.] [ 0.05 0.05 29.994 29.763] -9681.427314471295\n", - "0.6763164377806783 [1000. 1000.] [ 0.05 0.05 29.994 29.988] -9681.258289012689\n" - ] - } - ], - "source": [ - "designer.enumerates_classical_solutions(convert_to_si=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", - "x = modify_solution_sample(designer, bin_rep_sol, modify=['flows','heads'])\n", - "x0 = list(x.values())" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "num_sweeps = 5000\n", - "Tinit = 1E3\n", - "Tfinal = 1E-1\n", - "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", - "Tschedule = np.append(Tschedule, np.zeros(100))" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/6100 [00:00" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "eplt = res.energies\n", - "\n", - "# fig, ax1 = plt.subplots()\n", - "\n", - "left, bottom, width, height = [0.55, 0.55, 0.3, 0.3]\n", - "\n", - "plt.plot(res.energies[:], lw=4, label=\"QUBO Energy\")\n", - "plt.plot(Tschedule, lw=3, label='Temperature')\n", - "# ax1.axline((0, 0), slope=e, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", - "plt.grid(which='both')\n", - "# plt.yscale('symlog')\n", - "\n", - "plt.ylabel('Energy', fontsize=14)\n", - "plt.xlabel('Iterations', fontsize=14)\n", - "plt.legend(fontsize=12)\n", - "\n", - "# ax2 = fig.add_axes([left, bottom, width, height])\n", - "# ax2.plot(eplt[-1000:])\n", - "# ax2.grid()\n", - "# ax2.axline((0, 0), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "# ax2.set_yscale('symlog')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0.088 0.069 29.28 28.8 ]\n" - ] - } - ], - "source": [ - "idx_min = np.array([e for e in res.energies]).argmin()\n", - "# idx_min = -1\n", - "sol = res.trajectory[idx_min]\n", - "sol = designer.qubo.decode_solution(np.array(sol))\n", - "pipe_hot_encoding = sol[3]\n", - "sol = designer.combine_flow_values(sol)\n", - "sol = designer.convert_solution_to_si(sol)\n", - "sol = sol[:4]\n", - "print(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.33815821889033915, array([500., 500.]))" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "designer.get_pipe_info_from_hot_encoding(pipe_hot_encoding)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Pressure')" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "\n", - "fig = plt.figure(figsize = plt.figaspect(0.5))\n", - "ax1 = fig.add_subplot(121)\n", - "\n", - "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.grid()\n", - "\n", - "# ax1.scatter(ref_values[:2], encoded_ref_sol[:2], c='black', s=200, label='Best solution')\n", - "ax1.scatter(ref_values[:2], sol[:2], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "\n", - "\n", - "ax1.set_xlabel('Reference Values', fontsize=12)\n", - "ax1.set_ylabel('QUBO Values', fontsize=12)\n", - "ax1.set_title('Flow Rate', fontsize=14)\n", - "\n", - "ax2 = fig.add_subplot(122)\n", - "\n", - "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "\n", - "\n", - "# ax2.scatter(ref_values[2:], encoded_ref_sol[2:], c='black', s=200, label='Best solution')\n", - "ax2.scatter(ref_values[2:], sol[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "ax2.grid()\n", - "\n", - "\n", - "ax2.set_xlabel('Reference Values', fontsize=12)\n", - "ax2.set_title('Pressure', fontsize=14)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/designer_net0_data/test0/energies.pkl 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-5.249 0.152 0.007]\n" - ] - } - ], - "source": [ - "r = 0.005*1e-3\n", - "d = 10/12\n", - "res = dw_fit(roughness=r, diameter=d, plot=True, convert_to_us_unit=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6e-06" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r/d" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/docs/notebooks/enPflE1Q b/docs/notebooks/enPflE1Q deleted file mode 100644 index e6104ead568afea78433f6bbafed0aebf8991916..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 32 Ycma#_I4pOPfq{V?h&h0m5r~li07Z-fTmS$7 diff --git a/docs/notebooks/net0_data/encoded_reference_solutions.pkl b/docs/notebooks/net0_data/encoded_reference_solutions.pkl deleted file mode 100644 index 72c39762558d72dd2b99ca36c4b95f17de538c65..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6321 zcmZo*nYv7Z0SscNX!MBYmF5;y>LuqFrRwFD=9FY678NB{PU+!^FG@|$&nqq|Dork# zGI>f5D_G%_9`?Kxh?2=uyct@jI5Q?qX`d1_MZ=rXo27M121^fXN=aowDo6`cn#GjP z4u~vs52MW#KR-XO|3CmHyctTSBy~C~9ITt88#!sEJ&aCI0|6@s?(2&SY`M%GrT~>? zFlVrVY|G$4wo71C_h_h%rk2rMGFmzymB6E=;%HqmT8ECd9Y)(sqiw}08KbqqXl*cB m8;sTlqqV_kZ7^CJjE*y{v>&YvMr(u7+F-Oc7#g)fsvZFKutZ7# diff --git a/docs/notebooks/net0_data/energies.pkl b/docs/notebooks/net0_data/energies.pkl deleted file mode 100644 index 5c0f3f20820491aa2450c1118e32858a0fb96aa9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3921 zcma)8YeV zgqVt#YFe7LQk1EsmPTe0QDA6E6o!{9rbcGkkN%x~KL%rG=bYy}&-;9DHoZL1%4p?@ zrkqKeq_o5{8Lr}Fnbb8gEkPbHlF39F6uB-&CKV?qrOIV#VmWn%A{i?{iZo4fU~Tw|_r0Y%VBhD4rmM*8Lv``f>-QIppe?pE>md=CvU%KH(GO 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pickle.load(open('solutions.pkl','rb'))\n", - "ref = pickle.load(open('encoded_reference_solutions.pkl','rb'))\n", - "energies = np.array(pickle.load(open('energies.pkl','rb')))" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "energies = energies.flatten()" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 5, 61, 55, 79, 74, 28, 50, 94, 27, 6, 21, 67, 13, 39, 34, 42, 44,\n", - " 47, 87, 25, 69, 3, 89, 20, 11, 90, 19, 73, 17, 53, 59, 54, 85, 7,\n", - " 16, 97, 41, 84, 78, 82, 22, 56, 35, 92, 66, 43, 32, 14, 98, 1, 65,\n", - " 57, 71, 9, 46, 10, 72, 88, 48, 51, 40, 62, 2, 52, 64, 75, 81, 76,\n", - " 18, 99, 68, 77, 29, 36, 86, 38, 91, 49, 4, 96, 31, 95, 80, 12, 15,\n", - " 33, 60, 93, 70, 26, 24, 0, 8, 30, 83, 63, 45, 23, 37, 58])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idx_sort = np.argsort(energies)\n", - "energies = energies[idx_sort]\n", - "idx_sort" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "solution = [solution[i] for i in idx_sort]\n", - "# # solution[0]\n", - "# idx_sort" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "ref = [ref[i] for i in idx_sort]" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idx = np.argmin(energies)\n", - "idx" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "size = 150 * np.exp(-0.5 * (energies-energies.min()))" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_solutions(solutions, references, size, best_index):\n", - " fig = plt.figure(figsize=plt.figaspect(0.5))\n", - " ax1 = fig.add_subplot(121)\n", - "\n", - " ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - " ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - " ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - " ax1.grid()\n", - "\n", - " for r, sol, s in zip(references[:10], solutions[:10], size):\n", - " ax1.scatter(\n", - " r[:2], sol[:2], s=s, lw=1, edgecolors=\"w\",alpha=0.5, facecolors='orange'\n", - " )\n", - "\n", - " ax1.scatter(\n", - " references[best_index][:2], solutions[best_index][:2], s=150, lw=1, edgecolors=\"w\", facecolors='C0'\n", - " )\n", - "\n", - " ax1.set_xlabel(\"Reference Values\", fontsize=12)\n", - " ax1.set_ylabel(\"QUBO Values\", fontsize=12)\n", - " ax1.set_title(\"Flow Rate\", fontsize=14)\n", - "\n", - " ax2 = fig.add_subplot(122)\n", - "\n", - " ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - " ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - " ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "\n", - " for r, sol, s in zip(references[:10], solutions[:10], size):\n", - " ax2.scatter(\n", - " r[2:],\n", - " sol[2:],\n", - " s=s,\n", - " lw=1,\n", - " edgecolors=\"w\",\n", - " alpha=0.5, facecolors='orange'\n", - " )\n", - " ax2.scatter(\n", - " references[best_index][2:], solutions[best_index][2:], s=150, lw=1, edgecolors=\"w\", facecolors='C0'\n", - " )\n", - " ax2.grid()\n", - "\n", - " ax2.set_xlabel(\"Reference Values\", fontsize=12)\n", - " ax2.set_title(\"Pressure\", fontsize=14)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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b4u3trbRt21Z59913lYyMjGKvP2fOHKVp06aKXq9X6tevr8yYMUPJy8u7YVnqGzl79qzyr3/9S2natKni4uKieHl5KS1atFAef/xxJTo6+qZ/ZkIU0CjKdc80hRBCCCGEEKKakD08QgghhBBCiGpLEh4hhBBCCCFEtSUJjxBCCCGEEKLakoRHCCGEEEIIUW1JwiOEEEIIIYSotiThEUIIIYQQQlRbcvCoFSwWC4mJiXh6eqLRaCo6HCGEqHIURSEjI4OQkBC0WvmsTd5XhBCibGx5X5GExwqJiYnUq1evosMQQogq78yZM9StW7eiw6hw8r4ihBCOYc37iiQ8VvD09ATUP1AvLy+b+ppMJjZs2MDAgQPR6/XlEV6lJPOuOfOuiXOGmjnvssw5PT2devXqFf48renkfeXmaso8oebMVeZZvVT0PG15X5GExwoFyw28vLzsemNyc3PDy8urWv9Hfz2Zd82Zd02cM9TMeTtizrJ8SyXvKzdXU+YJNWeuMs/qpSLnmZ2dzbFjxwDr3lck4RFCCCGEEEJUCUajkcWLF3PixAmr+8jOUSGEEOIac+fOpU2bNoVPX8LCwvjtt98K7+fm5jJx4kT8/f3x8PBgxIgRJCcnV2DEQghRc+zevZvExERcXV2t7iMJjxBCCHGNunXr8s4777B792527dpF3759GT58OAcOHADg+eefZ82aNSxfvpzNmzeTmJjIvffeW8FRCyFEzRAWFkaPHj24//77re4jS9qEEEKIa9x1111Fvn/77beZO3cuO3bsoG7duixYsIDIyEj69u0LwNdff02LFi3YsWMH3bp1q4iQhRCixtBoNPTv35/09HSr+8gTHiGEEKIUZrOZpUuXkpWVRVhYGLt378ZkMtG/f//CNs2bN6d+/frExsZWYKRCCCFKI094hBBCiOv89ddfhIWFkZubi4eHBytXrqRly5bs27cPZ2dnfHx8irQPCgoiKSmp1PGMRiNGo7Hw+4JPJk0mEyaTyabYCtrb2q+qqSnzhJozV5ln9VLR87TldSXhEUIIIa7TrFkz9u3bR1paGj/++CNjx45l8+bNdo83e/ZsZs6cWez6hg0bcHNzs2vMqKgou+OpSmrKPKHmzFXmWb1U1Dyzs7OtblvpEp45c+bw/vvvk5SURNu2bfnvf/9Lly5dSm2/fPlyXnvtNU6dOkXTpk159913GTJkSOH9zMxMXnnlFVatWkVKSgoNGzbk2Wef5amnnroV0xFCiKrPlAk55yHzvPr9xVjwqA2utUHvUbGxlRNnZ2eaNGkCQMeOHfnjjz/49NNPeeCBB8jLy+PKlStFnvIkJycTHBxc6nhTp05lypQphd8XHJg3cOBAu87hiYqKYsCAAdX+jI+aME+oOXOVeVYvds/TnA85ZyDrJKT9DeYc0LmCd3NwbwSudUF38xTFlj08lSrhWbZsGVOmTGHevHl07dqVTz75hPDwcA4fPkxgYGCx9tu3b2fUqFHMnj2bYcOGERkZSUREBHv27KFVq1YATJkyhY0bN/L999/ToEEDNmzYwDPPPENISAh33333rZ6iEEJUHeY8SDuofuWcg7wcoC1ciIErruBaB7xbql8654qOtlxZLBaMRiMdO3ZEr9cTHR3NiBEjADh8+DAJCQmEhYWV2t/FxQUXF5di1/V6vd2/EJWlb1VSU+YJNWeuMs/qxaZ55iRDcgwkR8PF7ZCfefWekwfU6g5B/SCoD7gW/93/+te1VqUqWvDRRx/xxBNPMH78eFq2bMm8efNwc3Nj4cKFJbb/9NNPGTRoEC+99BItWrRg1qxZdOjQgc8++6ywzfbt2xk7diy9e/emQYMGTJgwgbZt2xIXF3erpiWEEFWPOQ9S4iApCjKOQH5W0fv5Wer1pCi1nTmvYuIsB1OnTmXLli2cOnWKv/76i6lTpxITE8Po0aPx9vbmscceY8qUKWzatIndu3czfvx4wsLCpEKbEELcSE4ynF0F+9+C8xuKJjugfn9+A4k7P2PNT99gLlhV4ACV5glPXl4eu3fvZurUqYXXtFot/fv3L7XyTWxsbJElAgDh4eGsWrWq8Pvu3buzevVqHn30UUJCQoiJieHIkSN8/PHHpcYim0vLTuZdc+ZdE+cMNWDel/dD8k51qcE/n42ZLEX/CahPfZJ3Am7g27rU4arSn9OFCxcYM2YM58+fx9vbmzZt2rB+/XoGDBgAwMcff4xWq2XEiBEYjUbCw8P5/PPPKzhqIYSoxMz56pOdw59BfkapzS7lerH42B1km3PwiFpBn7ufsmp5281UmoTn0qVLmM1mgoKCilwPCgri77//LrFPUlJSie2vrZTz3//+lwkTJlC3bl2cnJzQarV8+eWX9OzZs9RYZHOp48i8a46aOGeo7vNuUuLVqDMlJDYnzgBnSh3Jls2lFW3BggU3vG8wGJgzZw5z5sy5RREJIUQVl31aXcZWmOxoQOcCGl1hkzSjC9+d6Ee22UCI60W6B56H7ATwbFTml680CU95+e9//8uOHTtYvXo1oaGhbNmyhYkTJxISElLkHIVryebSspN515x518Q5QzWfd8ZxOLcWzEWXsZksWqLOtGZAvb/Qay1F++g8oO4Q8Ghc4pC2bC4VQghRzWQeV/fsaLSgMwA6UEz/LJdWAA2pRn9yzAb8XdJ5qHE0Lpd1kDmieiU8AQEB6HQ6kpOTi1y/UeWb4ODgG7bPyclh2rRprFy5kqFDhwLQpk0b9u3bxwcffFBqwiObSx1H5l1z1MQ5QzWdd/4lUDJK3eWp11qKJzxKOpgugb55yX2q25+REEII61jMavEbcw7o3MGSC6bLYM5FTXZUDZ3SGNMgDU9XcHdxAnO22i+oL2h1pY9vhUpTtMDZ2ZmOHTsSHR1deM1isRAdHV1q5ZuwsLAi7UFdXlLQvmDPjVZbdJo6nQ6L5bo3ayGEECqL8eZtSuxXfQoXCCGEcBCLBfJzQOemrhwwXvpnf6hyXUOFui6n8VYS1HY6N7WdUvbf2SvNEx5QS0iPHTuWTp060aVLFz755BOysrIYP348AGPGjKFOnTrMnj0bgH/961/06tWLDz/8kKFDh7J06VJ27drF/PnzAfDy8qJXr1689NJLuLq6EhoayubNm/n222/56KOPKmyeQghRqWmLP+G2rl/1Lk0thBDCDlot6D3VJzt5l0G5PtG5jqKo7Vx0aqlqTdmfz1SqhOeBBx7g4sWLzJgxg6SkJNq1a8e6desKCxMkJCQUeVrTvXt3IiMjmT59OtOmTaNp06asWrWq8AwegKVLlzJ16lRGjx5NamoqoaGhvP3223LwqBBClMYQBE7uxUtR34iTB7gG3bydEEKImkWrA69m6gOdmyU7BQraeTUv83I2qGQJD8CkSZOYNGlSifdiYmKKXRs5ciQjR44sdbzg4GC+/vprR4UnhBDVn2tt9VDRjCM29AkBQ0j5xSSEEKLqcqsLAV3h3C/W9/HvBm51HPLylWYPjxBCiEpC7wHeLdWnPNZwclfb661sL4QQouZQLOrTneB+oLey2rHeC4L7/vNUqOx7eCThEUIIUZx3SwgIu3nS4+SutvNueWviEkIIUbUoZsg4Bt6tMTWdcvOkR+8FLV4A79ZqP8Vc5hAq3ZI2IYQQlYDOGfy7qHtz0g5CzjnIy7l638lDXcbm3VL90knBAiGEECXQ6EDJ4+8/t/Lbvjwe6vUqQcZtcCEGTBlX2+k9IbC3+mTHuzWkxKl7QzXVcA+PEEKISkLnDH7twLMJ5J6HjPNw+rL6huRZGwy11eVvQgghRGk0Wk5dcefH7emYLbDntI7BvSZB/RGQ/jfkZ4OTm1qgwLU25BvhwlYwZ0JAl+pXpU0IIUQlpPcAfVMwNAB+hVphIAeJCiFE1WGxqOWhK0BycjJL1u7BbIFmtRXCG5+FpBTwug0CugE6wAzGy5AUA5Z/VhO4hoBbPYfEIAmPEEIIIYQQ1YnFoi5FzjwO6UfAnK0e5Ol1G3g0Vitx3qIEyNvbm+Dg2mhMadzXOQ2t+TLknIJLv4M5F7AAWtAZwMUPnH1A76euLnCp5ZAYJOERQgghhBCiujCmqEvCLm6HK3+B+Zr9lzpX8GkNtbpD4J3g4l/u4RgMBh5++GEsOZdwuhQFZ/ZAXlrxhpY8MKWDszfU6wC+7RyWlEnCI4QQQgghRHVgTFHPujm1TH2qcz1zjloM4Mp+MKVBnWG3JOnR6zSQnwSGYKgdDpf/hIzjoORfbaRxAs/G4NtWbZeVoFZs05Y9XZGERwghhBBCiKrOYlGf7JSW7FzLnK2203tDnbvLf3lbzjm4+LtaoMCjIdS5C/JSIesUWEyg1YN7A3D2U5Oyy/vUQgYufuBev8wvLwmPEEIIIYQQVV3OOXUZ282SnQLmbLgYC34dwd0xxQFKpCiQeRKMl9Tv0w6A1kUtSuDVUq3CplggP0tdgmcxXo0v84RauECjKVMIkvAIIYQQQghR1WUeVxOGYhQ16dBogOsShyvxalJRngmPMUVdvnYtcw5c3lO8aIHeu2gZ6ozj4N0KDAFlCkESHiGEEEIIIaq69CPXFChQ1CIAZiNYcq8mPFoD6FxA6wxo1PbphyGoV/nFlZ8BeZfVf7fkQ94lyL0A2efUfUSKWT1cVO8NbnXAEAjOAerenbzLan8k4RFCCCGEEKLmsliuLmVTTGDKVL+3mK5rmK3ul9G5gd5TLRRgzinzOT1nz56lbt26Jd9UzOqXxQiZpyDtoJroFGFSn/bkJquJj3dL8GgAiqvat4wq5gQiIYQQQgghhGNotWoSo5jUks+mtBKSnX9YTOr9vCtqlTSda5mSnbi4OBYsWEBMTEzJDTS6f/bxnILLe0tIdq5jSlPbZZ7658mUzu7YCsgTHiGEEEIIIao6r9vAokB+pnXt8zPByUvtZ6fU1FT27dsHgE5XSmLi5KkmVGkH/9mzg1qwoP596lMcJw81lsxTkPAj5CSq7dIOgl97tX8ZScIjhBBCCCFEVedWD3xbwflE6/v4tgY3+8o+p6amkpCQAEC3bt244447Sm6o91HP1TGlgX9XLI0fQxPcjxxTPocS00nPsuDloqVFo6G4NvsXSlI02uNfqecFudZW+/8jLy8PZ2dnm2OVhEcIIYQQQoiqTusCte5US03nZ9y8vZMn1LrjnwIGtvPz86Nu3br4+/szcOBANKWVjs5JVCuwtXwFbnuK40mX+ebng6zae46svKv7c9yddUS0r8O4sM40vXMgHJmnFlnISQSP+vz++++MHj2alStX0r59e5tilT08QgghhBBCVGWWfEjdB963Q9Mnb74MzMlTbed9u9rPkm/XywYEBDBkyJDSkx2AvBQwBMFtT/HZxmMM/HQ7i3cmFEl2ALLyzCzemcDAT7fz2cZjcNtTar+8FLZs2UJ4eDinT5/mP//5j81xyhMeIYQQQgghqjJLPpgz4NIOCOgOei9I3gIpseqBngWc3ME/DIJ6qufbXNoBLr7/VEKzLy24YbIDalEEv/Z8tvEYH2w4fNPxFAU+2HAYjQYm9unJ7s0/MnjIWLKzs+nfvz+LFi2yOUZJeIQQQgghhKjKtE5qNTPjBUjeDP6doGkLqDf8n/N5stUqbl63qftpzLlqu/w09VBPB1RCK43FEMzxpCtWJTvX+mDDYcJbBtCsTXe6du2Ks7MzK1euxNXV1eYYJOERQgghhBCiKtM6gXsopP2tJjHJ0aB1A58WUCsMdReLBYwpkLQJLNkFHdV+2vJLCTTO3nwTu9/mfooC32w/w6yIVqxZswadTofBYLArBkl4hBBCCCGEqOrc64Ohlnp4J6hJTeruG/cxBKr9ylGOycyqvefs6rty7zmmDW2Bu7t7mWKQogVCCCGEEEJUdS61wLOp9cvTNDrwbKL2K0eHEtOLFSiwVlaemb/Pp5c5Bkl4hBBCCCGEqOq0TureHd82N096NDq1nX+nUpezWSwWNm/eTE5OTpnCSs81lbG/fRXkriUJjxBCCCGEENWBsw8E9oKAMLU4QbFf9bXq9YAwtZ2zT4nDKIrCr7/+SkxMDN9//z0Wi8XukLwMZdtBU9b+IHt4hBBCCCGEqD6cfSCoN/jcDlkJkHVaLTut0akFCtzrq8vYblCoYNOmTezere7/6d69O1qt/c9IWoR44+6ss2tZm7uzjua1vex+7QKS8AghhBBCCFGdaJ3Atbb65dfxasJjRTW2nJwc9u3bB8DQoUO5/fbbyxSKq15HRPs6LN6ZUOL9rIObMV1OxKfHqGL37mlfB1d92UtmS8IjhBBCCCFEdaV1wpZf+V1dXRk/fjzHjh2jU6dOZX55RVEY1z2UyLgEFKXovcwDm0hZ+zEoFpwDG+HWtGvhPY0GxnUPRVGUmx9uehOyh0cIIYQQQghRyNfXl86dOztkLK1WS9MgL14Y0KzI9cz9GwuTHY82A3FtUvT1XhzYjMaBnmVaTldAnvAIIYQQQgghytWkvk3QaOCDDYdRFFDy89Rkp+0g/MKfQaNRExuNRk12JvZp4rDXloRHCCGEEEIIUe4m9mlCeMtAvtl+mpXOQ9H71cGl3u1oNFrcnXXc074O47qH0iSo7IUKrlUpl7TNmTOHBg0aYDAY6Nq1K3FxcTdsv3z5cpo3b47BYKB169b8+uuvRe5rNJoSv95///3ynIYQQgghhBDiGo1qeTArohV/TO/P2tkTWPRoV356Oow/pvdnVkQrGtXycPhrVrqEZ9myZUyZMoXXX3+dPXv20LZtW8LDw7lw4UKJ7bdv386oUaN47LHH2Lt3LxEREURERLB///7CNufPny/ytXDhQjQaDSNGjLhV0xJCCCGEEOLWUyxgMan/rAS0Wi0ajQY3Zyc6hvrRu1kgHUL9cHN2QqPROGTPTrHXdPiIZfTRRx/xxBNPMH78eFq2bMm8efNwc3Nj4cKFJbb/9NNPGTRoEC+99BItWrRg1qxZdOjQgc8++6ywTXBwcJGvn3/+mT59+tCoUaNbNS0hhBBCCCFuDYsFcpIg5Q84sxISflL/mfIH5CSjmG0/E6cqq1R7ePLy8ti9ezdTp04tvKbVaunfvz+xsbEl9omNjWXKlClFroWHh7Nq1aoS2ycnJ7N27VoWLVrksLiFEEIIIYSoFPLSIHU3ZByF3AvqGTwF0g6S5xTA0t/zCOvei6Yt21VYmLdSpUp4Ll26hNlsJigoqMj1oKAg/v777xL7JCUlldg+KSmpxPaLFi3C09OTe++9t9Q4jEYjRqOx8Pv09HQATCYTJpPJqrkUKGhva7+qTuZdc+ZdE+cMNXPeZZlzTfpzEkKICpOXBhe3Quo+UPKL3TabzfywLZmTFzRcWLOWZ+vXxdkj4NbHeYtVqoTnVli4cCGjR4/GYDCU2mb27NnMnDmz2PUNGzbg5uZm1+tGRUXZ1a+qk3nXHDVxzlAz523PnLOzs8shEiGEEIUsFvXJTinJjkWBlbvg+AUNep3CA10tOGfGg1tvKId9MwsWLOCee+7Bz8/P4WPbqlIlPAEBAeh0OpKTk4tcT05OJjg4uMQ+wcHBVrffunUrhw8fZtmyZTeMY+rUqUWWyaWnp1OvXj0GDhyIl5dtZfJMJhNRUVEMGDAAvV5vU9+qTOZdc+ZdE+cMNXPeZZlzwZNyIYQQ5cR4QV3GVkKyA4ACOi1oNQr3d4V6fmbIOAbet4NrUMl97DRr1ixmzJjB559/zu+//37DBw23QqVKeJydnenYsSPR0dFEREQAYLFYiI6OZtKkSSX2CQsLIzo6mueee67wWlRUFGFhYcXaLliwgI4dO9K2bdsbxuHi4oKLi0ux63q93u5fbMrStyqTedccNXHOUDPnbc+ca9qfkRBC3HLZZ9Q9O6XQaiGiI3RtDCG+/1zMTVb7OSjhURSFN954gzfffBOA++67r8KTHaiEVdqmTJnCl19+yaJFizh06BBPP/00WVlZjB8/HoAxY8YUKWrwr3/9i3Xr1vHhhx/y999/88Ybb7Br165iCVJ6ejrLly/n8ccfv6XzEUIIIYQQolwpFshKKFqgoAQazTXJDqjts047rGT15cuXCysrv/fee0V+Z69IleoJD8ADDzzAxYsXmTFjBklJSbRr145169YVFiZISEgoUp+7e/fuREZGMn36dKZNm0bTpk1ZtWoVrVq1KjLu0qVLURSFUaNG3dL5CCGEEEIIUa4UM1hKWcp2M5Z8tb+m7M9B/Pz82LRpE9HR0Tz55JNlHs9RKt0THoBJkyZx+vRpjEYjO3fupGvXroX3YmJi+Oabb4q0HzlyJIcPH8ZoNLJ//36GDBlSbMwJEyaQnZ2Nt7d3eYcvhBCiCps9ezadO3fG09OTwMBAIiIiOHz4cJE2vXv3RqPRFPl66qmnKihiIUSNp9GB1s7nGFontb+DNGnSpFIlO1BJEx4hhBCiomzevJmJEyeyY8cOoqKiMJlMDBw4kKysrCLtnnjiCc6fP1/49d5771VQxEKIGk+jBff6ticuGh24hzrk6U5lVumWtAkhhBAVad26dUW+/+abbwgMDGT37t307Nmz8Lqbm1upFUSFEOKWc6sHhkDIOW99H0OQ2q+ak4RHCCGEuIG0tDSAYmdJLF68mO+//57g4GDuuusuXnvttVLPapMDrW1XU+YJNWeuMs9ypvUlk/q4Wy6h4cbFCwDQOIFrY9D6QBkOlK6ov09bXlcSHiGEEKIUFouF5557jh49ehQphvPQQw8RGhpKSEgI8fHxvPzyyxw+fJiffvqpxHHkQGv71ZR5Qs2Zq8yzfJhMJo4ePYq7uyf169dHo9HcvNOpTGDdTZvdSEX9fdpyoLUkPEIIIW7MlKkukcj8Z5nExVjwqA2utUHvUbGxlbOJEyeyf/9+tm3bVuT6hAkTCv+9devW1K5dm379+nH8+HEaN25cbBw50Np2NWWeUHPmKvMsPzk5OXz//ffk5eXh7uZK79sNuJvPqIeRXluqWqMDl0DwaAS+7cHZ06bXsVgsxMfH065duwr/+7TlQGtJeIQQQpTMnAdpB9WvnHOQlwO0hQsxcMUVXOuAd0v1S+dc0dE63KRJk/jll1/YsmULdevWvWHbgmqix44dKzHhkQOt7VdT5gk1Z64yT8dSFIWVK1dy8eJFPDw8eGTMWHy8vcF4UT1UNOu0Wnpa66QWKHCrBy611JNIbWA2m3niiSdYunQpP//8M3379gUq7u/TlteUhEcIIURx5jxIiYNLsZBfUJ3smjfH/CzIOKImQvmZ4N+l2iQ9iqIwefJkVq5cSUxMDA0bNrxpn3379gFQu3btco5OCCGK0mg09OjRg9TUVB566CF8ff85WdQ1SP3y6/DPOTs6u6ux5efnM27cOBYvXoxOpyvc21hVSMIjhBCiuLSD1yU7pcjPUts5eYBfu1sSWnmbOHEikZGR/Pzzz3h6epKUlASAt7c3rq6uHD9+nMjISIYMGYK/vz/x8fE8//zz9OzZkzZt2lRw9EKImqhJkyZMnjwZJ6drfrVXFDCmQH7G1YTHyRNc/MGa/T3XWLBgAYsXL8bJyYklS5Zw3333VaniE5LwCCGEKMqUqSY8N0t2CuRnqe09m4LevXxjuwXmzp0LqIeLXuvrr79m3LhxODs787///Y9PPvmErKws6tWrx4gRI5g+fXoFRCuEEKrCZMeSrz59zzwJGcch7/LVhMfZFzwbg0dDdVmylYeVPv744+zYsYO7776be+65p+zBFuwNzU0GixG0LmqJ7HLaGyoJjxBCiKJyzqtvljb1SYTcRNA3LZ+YbiFFUW54v169emzevPkWRSOEEDYwG+Hyn+qSZOOl4vfzM9V9PVf2q0uRfduCrvj+wuvpdDq+/vprB8R33d7Qaz9Yc3Ivsjc025hvdxXL60nCI4QQoqjcZOuf7hTIz4ScZPUpjxBCiFvPkg+X90HyFjDf5Ge48RIk//PBjV8Hq5/0lEmJe0Ovcc3e0ENHTrBy49/cd9993HbbbWV+aUl4hBBCFGUx3rxNif3yHBuHEEII6+Wcg5Q/riY7em/wbQcufuqSMYsRjKlqUmRKU9ulxIFrMLjXL//4rNwbuvNwFuvi/wLgr7/+koRHCCFEOdDefHlDyf2qR5U2IYSochRF3bNjvATuoVj8w9B430ZOXj6HzqeTnmvGy6CjRe0WuAb1Qkk7gjYlVi1ZnXlCLVVtYyEDm1i5N/RYMqyLV+PoeJs3Q4YNdMjLS8IjhBCiKEOQupbalmVtTh5q+VMhhBAOd/bsWdzd3a+WnL6eMUUtUBDQHUIGcjzpCt9sOsCqvefIyrt68Ki7s46I9nUYF1aPpo3HQ+J6tZ93KzAElN8ErNwb2jgQ2tVX8POAO1qa0OQlgYtth6OWRBIeIYQQRbnWVjeOZhyxoU8IGELKLyYhhKihkpOTC0tCjxs3Dn9//+KN8jPAozEE9+azjcf4MOowJdVfycozs3hnApFxCbwwoBmT+oZDUgw5GRdx1vui0+nKZxJW7g3VaODuDv88bDJnOWxvqH2nDwkhhKi+9B5qlRwnK0tMO7mr7atBSWohhKhMUlNT+f7778nNzcXX1xcvL6+SG2r1hcnOBxtKTnaupSjwwYbDzNl0jCzPzgy973Eef/xxLBaL4ycBNu0NLbKyzkF7QyXhEUIIUZx3SwgIu3nS4+SutvNueWviEkKIGiQqKorMzEwCAwMZNWoUer2+xHYWvS9Hk9L4YMNhm8Z/75d99Ok/kE1btrNixQqOHTvmiLCLq+C9obKkTQghRHE6Z/WMBiePq+cl5OVcve/koS5j++e8BHRSsEAIIRxt+PDhODs7079/f1xdXUttp3Fy45vY/TaNrSgKF1a8RcLpP/Hy8mL9+vUOqYhWogreGyoJjxBCiJLpnMGvHXg2gdzzkHEeTl+GwN7gWRsM5XMithBCCJXBYOCee+65absck5lVe207MFqj0eDZ6W5Ml07zy2+/0q1bN3vDvLkK3hsqS9qEEELcmN5D3TRaK0z9vlaY+r0kO0IIUSkcSkwrUo3NWm5NuhIy4Svc6zQrh6iu8c/e0P3nXci3JkwH7w2VhEcIIYQQQogqLD033+6+WmdDmfpbQ1EU1u1MZMWOPFbtdbpxUYVy2BsqS9qEEEIIIYSowrwMZfuVvqz9byQ/P59Vq1Zx4MABAELqNwdPI+QmFt3TU457QyXhEUIIcWOmTPXQuMzz6vcXY8GjtromW5a1CSFEhWsR4o27s86uZW3uzjqa1y6l3LUDXL58maNHj6LVaomIiKB169bq+0ruefWcHUueWo3NNajc9oZKwiOEEKJk5jy1QluRKm1t4UIMXHFVN6BKlTYhhKhwrnodEe3rsHhngs1972lfB1d9OR04CtSqVYv7778frVZLw4YN1Yt6D9A3dcihotaQPTxCCCGKM+dBShwkRalVda4vJZqfpV5PilLbmR1zOJwQQtQkf/31F3FxcWUeR8nPZlxY/aKHdlpBo4Fx3eujmLLLHMONNG7c+GqyUwEk4RFCCFFc2kG4FHvzMxPys9R2aQdvTVxCCFFNHDt2jFWrVvHbb79x9OjRMo2lzTlH02BvXhhwtdqaOfMyyT/MwHQlqdR+Lw5sRuNAT7S5tpW0rmok4RFCCFGUKVNNYKw9IC4/S21vsuFAOSGEqMHOnDnDsmXLsFgstGrViiZNmpRtQFMaHPuKSX2b8FJ4M8yZKSQtmUruyT2krP0Y5bqyaBoNvBTejIl9mqA5vlDtX43JHh4hhBBF5ZxX9+zY1CdRrbijvzXrsYUQoio7c+YM+fn5NGnShIiICDS2rkW7nsYJzq0FSx4Rtw3n/V9nkp96Fr13IP5Dny8c391Zxz3t6zCuez2aBPnAkc/h/P/Ar2PZJ1WJScIjhBCiqNxk65/uFMjPVKvt3KINqEIIUZV1794dHx8fmjRpgk7ngIIBeh9wrw+nf8DAXrxdNTRo0IBf160n28WPjFwzXga1Gpur3gnl4u/wx3K4Eg9+7dX+1ZgkPEIIIYqyGO3sJ4ULhBDCWi1bOu5gTdzqgX9nuPwn/hzlf28EkoMv9Z22gXMdcHNXP8g6ew7O/YbGePFqX7+Oan87WCwWVq9eTVhYGCEhIQ6ajONJwiOEEKIorYud/aQ0tRBCVAidE/i0BvdQyDpNLR9nIAtOfHvjfu6haj+d7SlBdnY2x48fJysrizNnzjB58mTHPK0qB1K0QAghRFGGIHByt62Pk4d6aJwQQoiK4dkM6t8Hzr7WtXf2hfoj1X42ysjI4NtvvyUrKwuDwcA999xTaZMdqIQJz5w5c2jQoAEGg4GuXbvetDb58uXLad68OQaDgdatW/Prr78Wa3Po0CHuvvtuvL29cXd3p3PnziQk2H4wkxBC1AiutdVDRW3qEwKGyrucQQghqj0nAwT1gsaPqk9ubsQ9VG0X1FPtZyNXV1c8PT3R6/U88sgjhIbe5PUqWKVKeJYtW8aUKVN4/fXX2bNnD23btiU8PJwLFy6U2H779u2MGjWKxx57jL179xIREUFERAT79+8vbHP8+HHuuOMOmjdvTkxMDPHx8bz22msYDLb/5QohRI2g9wDvltY/5XFyV9vrbXwqJIQQwrH0XhA8AFpMgSaPqwUJnP3Un9POfur3TR5X7wcPUNvbwcnJiREjRtC0aVNq1arl4Ek4XqXaw/PRRx/xxBNPMH78eADmzZvH2rVrWbhwIa+88kqx9p9++imDBg3ipZdeAmDWrFlERUXx2WefMW/ePABeffVVhgwZwnvvvVfYr3HjxrdgNkIIUYV5t1Qrr93s8FEndwgIU9sLIYQAQFEUzGYzer3+1r+4kwF824LX7RDQA0xXQMlXS1frfdQCBXbs2bmewWDA2blq7N2sNAlPXl4eu3fvZurUqYXXtFot/fv3JzY2tsQ+sbGxTJkypci18PBwVq1aBaiVI9auXcu///1vwsPD2bt3Lw0bNmTq1KlERESUGovRaMRovFqlKD09HQCTyYTJZLJpXgXtbe1X1cm8a868a+KcoSbMWwNe7QE3SP8bcs5jsuQAYLJoQecBrsHg1Ry8moFFA5bS/yyq75+TEEIUl5yczOLFixk9erTDVxXt37+fPXv2MGbMmBs31DmBZ0OHvrbDmDLVM99yk9XKoFoXdf+oa211lYGDVZqE59KlS5jNZoKCim56DQoK4u+//y6xT1JSUontk5KSALhw4QKZmZm88847vPXWW7z77rusW7eOe++9l02bNtGrV68Sx509ezYzZ84sdn3Dhg24ubnZMz2ioqLs6lfVybxrjpo4Z6gp83YHrp4CHnWm9TX3zvzzdWPZ2dkOj0oIISqj3bt3F/4uevjwYdq2beuwsePj4+nXrx8pKSl4eHhw7733OmzsW8KcB2kH1a+cc0VXEDi5q/tHvVuqXzrHPT2qNAlPebBYLAAMHz6c559/HoB27dqxfft25s2bV2rCM3Xq1CJPjtLT06lXrx4DBw7Ey8u2tY4mk4moqCgGDBhQMY81K4jMu+bMuybOGWrmvMsy54In5UIIUZ0dOHCA9evXA9CjRw+HJjv79u2jf//+pKSk0LFjR3r37u2wsW8Jcx6kxJW+VDo/CzKO/JMIZYJ/F4clPZUm4QkICECn05GcnFzkenJyMsHBwSX2CQ4OvmH7gIAAnJycih3s1KJFC7Zt21ZqLC4uLri4FD+HQq/X2/2LTVn6VmUy75qjJs4Zaua87ZlzTfszEkLUTAEBAXh4eGAwGOjZs6dDx/7tt99ISUmhS5curF+/Hh8fH4eOf63c3FxcXFzQaDSOGzTt4M33hYJ6/1KsetyBXzuHvHSlqdLm7OxMx44diY6OLrxmsViIjo4mLCysxD5hYWFF2oO6vKSgvbOzM507d+bw4cNF2hw5cqTSl88TQgghhBBVS1BQEI8++ih16tRxbLIAvPLKK8ydO5cNGzaUa7KTmprKl19+ycaNGx03qClTTXhuluwUyM9S25usbH8TleYJD8CUKVMYO3YsnTp1okuXLnzyySdkZWUVVm0bM2YMderUYfbs2QD861//olevXnz44YcMHTqUpUuXsmvXLubPn1845ksvvcQDDzxAz5496dOnD+vWrWPNmjXExMRUxBSFEEIIIUQ15uHh4fBkB0Cj0fDUU085fNxrnTt3jsjISLKzs9m/fz89evRwTNGFnPPqUjWb+iRCbiLom5b55StVwvPAAw9w8eJFZsyYQVJSEu3atWPdunWFhQkSEhLQaq8+lOrevTuRkZFMnz6dadOm0bRpU1atWkWrVq0K29xzzz3MmzeP2bNn8+yzz9KsWTNWrFjBHXfcccvnJ4QQQgghxC1hYyW0nJwcvvvuO4xGI8HBwY6tMJebbP3TnQL5mZCTDJ7VLOEBmDRpEpMmTSrxXklPZUaOHMnIkSNvOOajjz7Ko48+6ojwhBBCCCGEqLzsrITm6urKwIEDOXjwICNHjixxP7vdLMabtymxX55DXr7SJTxCCCGEEEIIO5SxElqHDh1o376945fkae1MnrSOqdJWaYoWCCGEqL4KjgkQQghRjmythJZ2sNit8th/hCFIfbpkCycPcA26eTsrSMIjhBCiXGVkZPDll19WdBhCCFFmZrO52JEojhIXF0dOTo79A1RwJbQbcq2tLqWzqU8IGEIc8vKS8AghhChXnp6e1K1bt6LDEEKIMlEUhdWrV/PVV19x9OhRh469bt06evbsSUREBLm5ufYNUpZKaOVN76HuGbL2KY+Tu9peb+NToVJIwiOEEKLcDRw4sKJDEEIIuymKwvr164mPj8dsNjt0me6vv/7K8OHDMRqNGAwG+5eUlaUS2q3g3RICwm6e9Di5q+28WzrspaVogRBCiBsz50P2GchJVb+/HA+ufuBWD3TWvY3o9fpyDFAIIcrXgQMH2LlzJwDDhw+nWbNmDhk3KyuLRx99lLy8PO655x6WLl2Ks7OdG/VvUgntbCp4GMDH7fp+jqmEdlM6Z7VIgpNHKRXkPNRlbCVUkCsrSXiEEEKULD8XMg7Dlb8g5Q/IPA+MhoPvgkdt8O8MPq3Bsxk4OeisBiGEqIRatGhBmzZtqF27Nm3btnXYuO7u7qxZs4Z58+Yxb968sn04dINKaIfPw49/qMnOoz3B9dpcwkGV0Kyicwa/duDZBHLPq0+XLHlqDK5BYCj5jKCykoRHCCFEcaZ0SI6BhBWQdVq9pvzzpph3BS5fgMt/gnso1L8PgnqB3quiohVCiHKl0+mIiIgolwpmnTt3pnPnzmUfqKAS2nXL2nadhF/3gYIGXzcF3bUbWhxYCc0meg/QN3XIoaLWkD08QgghisrPVZOd4wuvJjulyTqN+ehCSN6s9hNCiGqqXMo1O1IJldDyzRB3XE122ocqPNgNnK993OHASmiVmTzhEUIIUVTGYfXJTt6VGzZTFNh2rj4HUwJ51GkFevf64Ou4pR5CCCFsUFAJ7Zq9MU46GN0dDpxTCGsCRXI2B1dCq8zkCY8QQoirzPnqnp2bPNnJNjkReagNGxMak5TlyV+njWo/c/4tClQIIUQxJVRC83aD7k1LSHYcXAmtMpMnPEIIIa7KPqMWKLiJVcdacOyKP05aM4MbHqV94HlI3Q0BPcCz4S0IVAghRDEVWAmtMpOERwghxFWmK5CVcNNmAxscIzPPmeFN/ibI/Z8308zTan8hhBCFLl++jK+v7617wQqqhFaZyZI2IYQQVyn5Vp3JEOCawxNtdl9NdkA9A0Kp+kvaZs+eTefOnfH09CQwMJCIiAgOHz5cpE1ubi4TJ07E398fDw8PRowYQXLyLTq8TwjhcEajke3bt6MoikPHnT9/Pk2aNGHPnj0OHdcqeg+1ClrgHRDcV/2nZ9Mal+yAJDxCCCGupXGy+kyGYgWLtC5q/ypu8+bNTJw4kR07dhAVFYXJZGLgwIFkZV1N7p5//nnWrFnD8uXL2bx5M4mJidx7770VGLUQwl4mk4klS5YQFRXFunXrHDbuF198wZNPPklqaio//fSTw8YVtqv670xCCCEcR+8D7vUh77LtfT1C1f5V3PW/8HzzzTcEBgaye/duevbsSVpaGgsWLCAyMpK+ffsC8PXXX9OiRQt27NhBt27dKiJsIYQdLBYLK1as4PTp07i4uNCuXTuHjLtjxw7eeecdAKZMmcKsWbMcMu714uLicHJyokOHDuUyfnUhCY8QQoir3OqBf2f1UFFb+XVU+1czaWlpAPj5+QGwe/duTCYT/fv3L2zTvHlz6tevT2xsbIkJj9FoxGg0Fn6fnp4OqJ8sm0wmm+IpaG9rv6qmpswTas5cK+M8z549y9GjR9HpdNx3330EBASUOT6TyUT79u3p06cPHTp04D//+Q/5+Y5d7qsoCps2bWLHjh1oNBqCgoIIDAx06GvcTEX/fdryupLwCCGEuErnxBVtY7zdQtFk3+TQ0Wu5h4JPa9BVr7cVi8XCc889R48ePWjVqhUASUlJODs74+PjU6RtUFAQSUlJJY4ze/ZsZs6cWez6hg0bcHNzsyu2qKgou/pVNTVlnlBz5lrZ5tmgQQMUReHAgQMcOHDAIWO6uLjwzDPP4OTkxG+//eaQMQsoikJCQgKXL6tP4oODg/njjz8q7GDUivr7zM7Otrpt9XpnEkIIYTdFUdizZw+//RbN0O69ae+82rqlbc6+UH8keDYr/yBvsYkTJ7J//362bdtWpnGmTp3KlClTCr9PT0+nXr16DBw4EC8vL5vGMplMREVFMWDAAPR6fZniqsxqyjyh5sy1ps1zyJAh5TbPmJgYduzYwZAhQ2jTpk25vMbNVPTfZ8GTcmtIwiOEEIK8vDzWrl1LfHw8AMcuutCu+3g0Z1aoh5BqncHJH3IAF3/IT1GrubmHQv37IKgnOBkqdhIONmnSJH755Re2bNlC3bp1C68HBweTl5fHlStXijzlSU5OJjg4uMSxXFxccHFxKXZdr9fb/YtCWfpWJTVlnlBz5irzLLv+/fvTpk0bgoKCymV8W1TU36ctrykJjxBCCE6ePEl8fDwajYZ+/frRvXt3NGbjP+c4XIC8VMhNgytA7YFg8AZnPzAEgkejapXsKIrC5MmTWblyJTExMTRsWPQg1Y4dO6LX64mOjmbEiBEAHD58mISEBMLCwioiZCFEDVOwb0dYRxIeIYQQNGvWjF69etGwYUNCQ0PVi1on0LtDrkV9ypOVDIRB+mEwB4Gzj3pfW73eSiZOnEhkZCQ///wznp6ehftyvL29cXV1xdvbm8cee4wpU6bg5+eHl5cXkydPJiwsTCq0CSFEJVS93qWEEELYrXfv3le/MRvVSm0pcWC8pF5zcrv6T1MaXNgCaQfBvwv4tgVd8SVbVdHcuXOB6/48UEtPjxs3DoCPP/4YrVbLiBEjMBqNhIeH8/nnn9/iSIUQFaXggNKKKhQgbCMJjxBCiKIs+XB5HyRvAXPWjdsaL0HyZvXf/TpUi6c91py0bjAYmDNnDnPmzLkFEQkhKhNFUXj55ZcxGAy8+eabFR2OsIK2ogMQQghRyeScg5Q/bp7sFDBnqU+CchJLvP3nn38yatQoBwYohBDWuXLlCt98801hCeeyUhSFF198kffff59Zs2YRFxfnkHGvf428vDyHj1uTScIjhBDiKkWBzJNXl7FZy3gJMk+o/a+Rn5/PiBEj+PXXXx0YpBBC3FxWVhbff/89p0+fZs2aNQ4Zc8qUKXz00UeAuvy1S5cuDhm3QH5+PitWrGDJkiUOP6y0JnNowpOXl0dWlpWfCAohhLglUlNTuXDhgnWNjSmQcdy+F8o4rva/hpOTE/PmzWPo0KH2jSmEEHYwGo0sXryYlJQUvL29iYiIcMi4LVu2RKvVMn/+fJ566imHjFkgJyeH77//ngMHDpCQkMC5c+ccOn5NZlfCs3TpUp5//vki12bOnImHhwc+Pj7cc889ZGZmOiRAIYQQ9jt06BDz589n2bJlGI3Gm3fIz7DusNGS5F1W+1+nf//+REZG2jemEELYwWQyYbFYcHNz45FHHrH5gN/SPPHEExw8eJAnnnjCIeNda/ny5Zw+fRoXFxdGjx59tWKmKDO7Ep4PP/ywyJOc7du3M3PmTMLDw3n++edZt24db7/9tsOCFEIIYRuz2cy6dev44YcfMBqNuLu7YzKZbt5RMatf9ihLXyGEcCAPDw/GjRvH2LFj8ff3d+jYzZo1c+h4BQYMGIC/vz/jx4+nUaNG5fIaNZVd5XSOHz/O2LFjC7+PjIwkODiYlStX4uTkhMViYcWKFcyePdthgQohhLDNmTNnAOjevTt9+/ZFp9PdvJNGp37Zoyx9hRDCwQwGAwZD1TkUuXbt2jzzzDNotbLF3tHsSniMRmOR/4A2bNjA4MGDcXJSh2vZsqWcRyCEEBVIp9MxcuRIkpOTbfs00skTnH0h345lyc6+an8hhBB2kWSnfNj1p9qwYUP+97//AbBr1y6OHTvGoEGDCu8nJyfj4eFhd1Bz5syhQYMGGAwGunbtetOSf8uXL6d58+YYDAZat25drBrQuHHj0Gg0Rb6ujVcIIaojHx8f25deuPiDZ2Ormq77/RjvLNh29YJnY7W/EEIIUYnYlfA8+eST/PDDD7Rp04aBAwdSt25dhg0bVnj/999/5/bbb7croGXLljFlyhRef/119uzZQ9u2bQkPDy+1wtD27dsZNWoUjz32GHv37iUiIoKIiAj2799fpN2gQYM4f/584deSJUvsik8IIao1jQY8GoJLQKlNMrONPP3WLwx+ZjHT/hvN9n1n1PYejdT+QgghRCViV8IzefJkvvjiCxo3bszw4cPZsGEDrq6ugFr+NCkpidGjR9sV0EcffcQTTzzB+PHjadmyJfPmzcPNzY2FCxeW2P7TTz9l0KBBvPTSS7Ro0YJZs2bRoUMHPvvssyLtXFxcCA4OLvzy9fW1Kz4hhKj2XOuAfxfQuRe7lZ+fzx1jvmLe8t0APPtQV9rf3lht7xpyqyMVQgiHMZlMTJ06lZSUlJs3FlWKXXt4QC3LV1JJPj8/P3bt2mXXmHl5eezevZupU6cWXtNqtfTv35/Y2NgS+8TGxjJlypQi18LDw1m1alWRazExMQQGBuLr60vfvn156623Sq3aYTQai5RvTU9PB9T/EayqcnSNgva29qvqZN41Z941cc5QA+btcTvkWyB1N1hM4NMGk84bp9NnGf3QKOYtWMJXb95Hn7CW4NcRk8ftYFbAXPKfR7X9cxJCVJjs7Gzc3NwcMlZeXh4PPvggK1euZNOmTWzfvt2h+2lycnK4ePEi9evXd9iYwnp2JzygJgZ79uzhwoUL9OjRg4CA0pdAWOPSpUuYzWaCgoKKXA8KCuLvv/8usU9SUlKJ7ZOSkgq/HzRoEPfeey8NGzbk+PHjTJs2jcGDBxMbG1ti1aLZs2czc+bMYtc3bNhg9/9YUVFRdvWr6mTeNUdNnDNUzLxNJhMpKSkEBQWhKfclZHX/+WfGP1/QvF1P3v2gKzlubvx6FCD5n6/SZWdnl2OMQoia5ty5c3z33Xf07duXLl26lGkso9HIyJEjWbNmDS4uLsyYMcOhyc6VK1dYvHgxaWlpjB8/ntq1aztsbGEduxOe//u//+ONN97gypUraDQaoqKi6Nu3L5cuXaJ58+a89957PProo46M1W4PPvhg4b+3bt2aNm3a0LhxY2JiYujXr1+x9lOnTi3y1Cg9PZ169eoxcOBAmw+uMplMREVFMWDAAPR6vf2TqGJk3jVn3jVxzlBx8z516hQ///wzWVlZtGnThk6dOpXr6x2/kMHiuDOs/TORfHM+szpZeGOvHiedK0PbhjC6Sz0aB968MlvBk3IhhCirS5cusXjxYoxGI3///TedOnUqU4Jy6dIl/vzzTwwGA6tWrSI8PNxhsSYlJbF48WIyMzPx8vKy7ngA4XB2JTxff/01zz33HA8++CADBw4sktgEBATQt29fli5danPCExAQgE6nIzm56CeFycnJBAcHl9gnODjYpvYAjRo1IiAggGPHjpWY8Li4uODi4lLsul6vt/sXm7L0rcpk3jVHTZwz3Np579ixgw0bNqAoCoGBgTRt2rRcX/uzjcf4MOowiqJ+76JTnyYZLRrSTRa+23mW7+PO8sKAZkzq2+SGY9XE/zaEEI6Xk5PDd999R05ODiEhITzwwANlfhpTp04dNm3axOnTp+nTp4+DIlXFxcWRmZlJYGAgo0ePtvmDc+EYdv0X8uGHHzJ8+HAiIyO56667it3v2LEjBw4csHlcZ2dnOnbsSHR0dOE1i8VCdHQ0YWFhJfYJCwsr0h7UJSaltQc4e/YsKSkp8khRCFGl+Pn5oSgK7dq14/HHHy/zMuIb+WzjMT7YcDXZKY2iwAcbDjNn07Fyi0UIIQoYDAY6dOhAQEAAo0ePLvEDans0atTI4ckOwJAhQ+jRowfjx4+XZKcC2fWE59ixYzz77LOl3vfz87O7wsWUKVMYO3YsnTp1okuXLnzyySdkZWUxfvx4AMaMGUOdOnWYPXs2AP/617/o1asXH374IUOHDmXp0qXs2rWL+fPnA5CZmcnMmTMZMWIEwcHBHD9+nH//+980adLEoY8shRCivN1222088cQThISUTzW0xMREDh06RP1Wnflgw2Gb+n6w4TDhtwfRKMBdDs4TQpQbjUZDr169CAsLw9nZuaLDuSknJyf69+9f0WHUeHYlPD4+Ply6dKnU+wcPHrzhkrIbeeCBB7h48SIzZswgKSmJdu3asW7dusLCBAkJCUXeTLt3705kZCTTp09n2rRpNG3alFWrVtGqVStAPW08Pj6eRYsWceXKFUJCQhg4cCCzZs1y2KcCQghxq5RXsrNs2TKefvppAB5+7web+ysKfPP7KWZFtHJ0aEIIUUxVSHZE5WFXwjNkyBDmz5/PM888U+zegQMH+PLLL8tUsGDSpElMmjSpxHsxMTHFro0cOZKRI0eW2N7V1ZX169fbHYsQQlRneXl5jB07lqVLlwLQvkMHftuXAB5BN+lZ3Mq955g2tAVuzmUqACqEEEI4lF3rDt566y3MZjOtWrVi+vTpaDQaFi1axMMPP0ynTp0IDAxkxowZjo5VCCGEgxV8SqrT6Xj99deZu/RXTHYkOwBZeWb+Pi/V2IQQQlQudiU8ISEh7N69m0GDBrFs2TIUReG7775jzZo1jBo1ih07dpTrZlohhBCOM2fOHGJjY3njjTfINpftXJ/03HwHRSWEEI6RlpZW4gohUXPYvbM0MDCQr776itTUVJKTkzl//jyXL19m4cKFBAYGOjJGIYSo1nJzc1mxYgVnz56tkNf38/Ojc+fOAHgZyrYcraz9hRDCka5cucKAAQMIDw9n3bp1Dh07MTGRdevWodysnKWocA55Z6pVq5YjhhFCiBrn/PnzLF++nMuXL5OYmMjEiRMrtMpZixAv3J11ZOWZbe7r7qyjeW0puyqEKJtjx44RGhpa5vO7UlNTGThwILt378bf39/uglolOXr0KMuXL8dkMuHr60vXrl0dNrZwPLsSnjfffPOmbTQaDa+99po9wwshRI1w9uxZvvnmG8xmM97e3tx7770VXtLZVa8jon0dFu9MsLnvPe3r4Kovfop4RkaGLCcRQljl0KFDLF++nHr16jF69OgyVWP7/PPP2b17NwEBAURHR9OmTRuHxLhv3z5Wr16Noig0atSIdu3aOWRcUX7sSnjeeOONUu9pNBoURZGERwghbiIkJIS6devi4uJCREQErq6uDh3fbDZz8eJFmz7VtFgUxnZvQGRcwk0PHb2WRgNjuzfAoijoNJp/xrKwa9cuNm7cSFpamq3hCyFqmJMnT7JixQoURcHf37/MT3imTp3KpUuXePzxxwuPK3EENzc3ANq2bctdd92FTlf8gx5RudiV8FgslhKvnT59mjlz5rBlyxZ+++23MgcnhBDVmVarZdSoUTg7O6PRlK1YwPWOHz/O2LFjyczMJC4uzupPSbVaDbcFefLCgGY2HT764sBmNA704NpZbNu2jU2bNgFQu3ZtW8IXQtQwZrOZn3/+GbPZTIsWLRg2bFiZfy7qdDo++eQTxwR4jdtuu43HHnuMkJAQh//sFuXDYWsntFotDRs25IMPPqBp06ZMnjzZUUMLIUS15eLi4tA3TEVRmD9/Pm3btuX333/nxIkTxMfHW90/x2TmvXV/M6lvE14Kb8bNQtNo4KXwZkzs04QP1h8mx3R170+nTp3w8/NjyJAhjBkzxt4pCSFqAJ1Ox6hRo2jdunWlWN57M3Xq1JFkpwopl3I6PXv25OWXXy6PoYUQQtyAyWTis88+Iysri169evHNN9/QoEEDq/sfSkzj85jj5JktTB/akn4tAvk+9jQr954j33y15LS7s4572tfh4bBQmgd78dbag3y19ST9WwTSIdQPUJd9FBRhSE+X83mEEDcWFBTEvffeW9FhiGqoXBKeXbt2VfrMXAghqiNnZ2e+++47oqOjee6552z+WVxwjs5XW08SfzaNx+5oyMzhrXh5cHMOnL1M8oFYFo3vwu11fXFzduJ/h5KZ8fMB4k6mFulfQN4LhBBCVDS7Ep5vv/22xOtXrlxhy5Yt/PTTTzz++ONlCkwIIao6s9lcIZtZ27ZtS9u2be3qe+05OnEnU4k7mUptbwMjO9Ul1NeAE3DuSg7bT15m+a6znE/LLbW/EEIIURnY9c40bty4Uu8FBATwyiuvMGPGDHtjEkKIKs1kMrFu3TrS09N56KGHqtQ67xYh3sXO4Tmflsv/RR/DRafwXheYtvIv8s0WzBRN5uQcHiHErZSUlEStWrUc/sGSyWQqc4W4SsGUCTnnITcZLEbQuoAhCFxrg96joqO7pexKeE6ePFnsmkajwdfXF09PzzIHJYQQVVVKSgrLly8nOTkZgISEBEJDQys4Kuvd7Bye/Px8uujO4u+UyWpjSyzX1L4p7RweIYRwtJMnT9KnTx969erFwoULHZb07Nq1i23btjF+/Hi8vb0dMuYtZ86DtIPqV845yM+6es/JHVzrgHdL9Utn/zlHVYldCU9VevMWQohbxWKxEBkZSWpqKm5ubtx7770O/3lZcM5ZeVEUhXHdQ0s4h0ehoTaFQ4fO0ESnPv0J0aZz1uIDqNXaxvVoUO7xCSHE8ePH6du3LwkJCcTGxpKamkqtWrXKNKaiKGzatImtW7cC6uGivXr1ckS4t5Y5D1Li4FJs0USnQH4WZBz5JxHKBP8uNSLpkd2kQgjhIFqtlmHDhtGgQQOeeuopGjdu7LCx8/LymDZtGo8++qjDxiyJVqulaZAXLwxoVuS6BrhNewGz2cwVi4G1xmaFyQ78cw5PLQ8pUiCEKNW2bdvIyirhl3AbmEwmBg0aREJCAs2aNSMmJqbMyQ7A9u3bC5OdXr160bNnzzKPWSHSDpae7FwrP0ttl3bw1sRVwax6wqPVam3+xE6j0ZCfn3/zhkIIUY00bNiQBg0aOPQpR3x8PGPGjOHPP/8E4Omnn6ZLly4OG7+YjJNM6tsEjQY+2HAYRQEFDXHm+jxaJ40lp2uTa1ETG41GTXYm9mkCGSfBs2H5xSWEqLK2b9/Oli1b0Gq1ZGRk4OfnZ9c4er2eTz/9lFdffZVff/3VYYcad+jQgfj4eLp27UqHDh0cMuYtZ8pUE5ibJTsF8rPU9p5NQe9evrFVMKsSnhkzZsgSBSGEsJIjf17m5OQwYMAALly4QEBAAF988UX5JjsAWScg4wgT+4QT3rIW32xPYOXec1w2uxMUZEA5rSk8h2dc9/o0CfKGxPWg1UvCI4QoZu/evURFRQHqWTtl3e89ZMgQwsPDHVqswNXVlQkTJlRIZU2HyTmvLlWzqU8i5CaCvmn5xFRJWJXwvPHGG+UchhBCiJK4urry4Ycf8sMPP/Dll18SFBRU/i+qdYELWyHrDI38OzFreBumDWnGgbMpJO6PY/GjHWlZ1x9XvRPKlXg4ugtyzkJw//KPTQhRpSiKwuHDhwHo2rUrubm5N+lhnfJITKp0sgNqNTZrn+4UyM+EnGT1KU81JgcmCCFEJTd69GhGjx596560G4LUpzXJ0WiP/Bec3HCrP5J2hlAScaKtdjv6E6chYTma/Gy10o9PK3C9BcmYEKJK0Wg0jBw5kj///JNWrVrx22+/VXRI1ZfFaGe/PMfGUQmVKeE5e/Yse/fuJS0tDYvFUuz+mDFjyjK8EEJUKpcvX8bX1/eWv255Jzrnz5/Hw8Pj6jITl1qg0cPlvWDOBVMaHP4/wAU078Gf04Br3lgv7wXf9uBc9o3DQojqR6fT0aFDB0wmU0WHUr1pXezsV/2rtNmV8OTm5jJ27FhWrFiBxWJBo9Gg/FO/9No3Zkl4hBDVgcViYdOmTWzfvp1HHnmEBg0aVHRIDmE0Gtm4cSN//PEHLVu25L777lNv5F0CZx9w8lATnptx8lDb510CF59yjFgIIUSpDEHqOTu2LGtz8qgRT+ftqh86bdo0fvrpJ95++21iYmJQFIVFixaxYcMGBg8eTNu2bQurCQkhRFWWkZHBt99+y7Zt27BYLCUevFwVnTt3jjlz5hAXF1d4do7ZbAZzPlyKA1M61B8BLgE3HsglQG1nSlP7maU6pxCibEpaNVQWiqJw4sQJh45ZKbnWVg8VtalPCBhCyieeSsSuhOfHH39k/PjxvPzyy9x+++0A1KlTh/79+/PLL7/g4+PDnDlzHBqoEEJUhL/++ovTp0/j7OzMiBEj6NOnj8PGXr9+PQsXLnTYeLbw9fUlPz8fX19fHn74YUaMGKFu2M0+Ayk7ISkaNE7Q8GEI6ls88XGpBcH91PsaJ0jaCKl/qP2FEMJOcXFxtGvXzmEJitlsZuXKlXz33Xfs2bPHIWNWWnoPdU+lk5Ulpp3c1fbVvCQ12Lmk7cKFC4VlUV1dXQGKHCQ1YsQI3nzzTebOneuAEIUQouKEhYWRnp5O586d8ff3d8iYWVlZvPTSS8ydOxcXFxfCwsJo0aKFQ8a2lpubGw8//DC1atVCr9dfvWG6AlkJYM6BxA3gc7u6P8e/E+SmwUmg0TgweIPFDJnH4cp+UPIh87TaXwgh7BAbG8ugQYNIT09n+vTpREZGlmm83NxcfvjhB06ePIlWq60ZByN7t1Qrr93s8FEndwgIU9vXAHYlPEFBQaSkpADqm6avry+HDx/mrrvuAiA9Pd1hZQeFEKIiaTQaBg0a5LDxsrOz6dChA0eOHAHgySefJDQ01GHj2yIkpIRlDEr+1Yo9igku74PL8eBeF/T/rPM2pkLmIcg6C1yz9MRiVPsLIWqUgv3cZSmw8scffxAeHk5GRgY9e/Zk/vz5ZY7r6NGjnDx5EmdnZ+6//34aN25c5jErPZ0z+HdR9+akHVTP5bk28XHyUJexebdUv3TVv2AB2JnwdO3alW3btvHyyy8DcNddd/H+++9Tu3ZtLBYLH3/8Md26dXNooEIIUR24ubkxZMgQsrOz+frrr+nfv5KdXaNx+qdiz7WfDFrUpz5KEnAHXP4TNCWUMdW6qP2FEDWGxWJhxYoVeHh4MGjQILuTnkaNGtGoUSP8/PxYs2YN7u5lX2bVunVr0tLSaNy4MbVr1y7zeFWGzhn82oFnE8g9r56zY8lTf7a7BoGhtrr8rQax653p2WefZfny5RiNRlxcXJg1axaxsbE88sgjADRu3Jj/+7//c2igQghRXfznP//h9ddfx8fHp6JDKU7vA+71Ie+y7X09QtX+QogaQVEU1q5dy8GDB9FqtXTo0MHuw5H9/f2Jjo7G1dUVNzc3h8V4xx13OGysKkfvAfqm1f5QUWtYvZjxvvvu4+eff8ZkMnHHHXfw6aef4uKi1vuuV68ehw4dYu/evcTHx3Po0CGaNWtWbkELIURV5urqWm7JzsWLF1m9erVacc0ebvXAv7N9ff06qv2FEDXCxo0bCwsBjBgxwu5kp4C/v79Dkx0hClj9hGft2rWsXLkSb29vRo4cyejRo+nZs2fhfa1WS9u2bcslSCGEKA9nzpzBx8fn6oGbVZjJZGLLli1s374di8WCv78/PXr0sH0gnRP4tAb3UMg6bX0/91C1n06WtAlRUwQGBqLVahkyZAgtW9aMze+iarL6Cc/FixdZuHAhnTt3ZuHChfTp04f69evzyiuvEB8fX54xCiGEQymKQmxsLN98803hAcpV3U8//VR4VtBtt91WeGSAXTybQf37wNnXuvbOvlB/pNqvGtiyZQt33XUXISEhaDQaVq1aVeT+uHHjCjdoF3w5srCFEFVF69atmTRpEh07dqzoUIS4IasTHg8PD8aOHcv69etJTEzkk08+oU6dOrz33nu0b9+e1q1b8+6775KQkFCe8QohRJkUlCndsGEDFosFDw8P+5d/Xef8+fMVds5Djx498Pb25v777+fBBx8s25I5JwME9YLGj6pPbm7EPVRtF9RT7VcNZGVl0bZt2xueJzdo0CDOnz9f+LVkyZJbGKEQlYevr5UfjAhRgewqSF6rVi0mT55MbGwsJ06c4M033wRg6tSpNGrUqMzlBOfMmUODBg0wGAx07dqVuLi4G7Zfvnw5zZs3x2Aw0Lp1a3799ddS2z711FNoNBo++eQTu+MTQlRdFouFxMREdDodQ4YMYcSIEUXPobHT8uXLadWqFffeey/p6ekOiNQ2devWZfLkybRo0aJMpWEL6b0geAC0mAJNHge/9lef+Dj7qt83eVy9HzxAbV9NDB48mLfeeot77rmn1DYuLi4EBwcXfskvfULcWrm5ufzyyy9yDIqwSpkXWzdo0IBXX32VV199lfj4eF5//XV+/vlnfv/9dyZMmGDzeMuWLWPKlCnMmzePrl278sknnxAeHs7hw4cJDAws1n779u2MGjWK2bNnM2zYMCIjI4mIiGDPnj20atWqSNuVK1eyY8eOks+eEELUCG5ubtx///1oNBqH/CzIzs7mmWeeKTwgLzQ0lNTUVLy8bn0CoNPpHDugkwF824LX7RDQA3JSIfYstPw3uPqpBQpq6J6dmJgYAgMD8fX1pW/fvrz11ls3PJjWaDRiNBoLvy9Iik0mEyaTyabXLmhva7+qpqbME2rGXFetWkVycjL16tUr8zzT09NZunQply5dIiMjg/vuu89BUTpGTfj7hIqfpy2v65B3qoLH+ZGRkYXLOTp16mTXWB999BFPPPEE48ePB2DevHmsXbuWhQsX8sorrxRr/+mnnzJo0CBeeuklAGbNmkVUVBSfffYZ8+bNK2x37tw5Jk+ezPr16xk6dKhdsQkhqoc6deo4bCyDwcC5c+fQ6XRMmzaN6dOn4+xczQ5y0zmBZ0Mw1AXOgm8bcMBTsapq0KBB3HvvvTRs2JDjx48zbdo0Bg8eTGxsbKlJ5+zZs5k5c2ax6xs2bLC7KlVUVJRd/aqamjJPqL5z3b59Ox988AEWi6VwVZC9cnJyOHHiBCaTCb1ej0ajueHKnopUXf8+r1dR88zOzra6rd0Jz5UrV/jxxx+JjIxk69atmM1mGjduzIwZM3j44Ydp0qSJzWPm5eWxe/dupk6dWnhNq9XSv39/YmNjS+wTGxvLlClTilwLDw8vssnUYrHwyCOP8NJLL5VtI68QQlxHq9WyaNEikpKS6Nq1q8PHVxQFs9mMk1PNfJJSGT344IOF/966dWvatGlD48aNiYmJoV+/fiX2mTp1apH3qvT0dOrVq8fAgQNtfhpoMpmIiopiwIABDlmOWVnVlHlC9Z7rDz/8wIcffojFYmHUqFHcfvvtZZrn5cuXOXPmDN7e3jzwwAN4e3s7OOKyq85/n9eq6HnasnzcpnfQ3NxcVq9eTWRkJOvXr8doNFKrVi2efvppHn74Ybp06WJzsNe6dOkSZrO5WB33oKAg/v777xL7JCUlldg+KSmp8Pt3330XJycnnn32WavikKUHZSfzrjnzrolzhqLzDgkJISQkxOF/BpcvX2bDhg0YDAaGDx/u0LFtoiiQl4opNw0AU9oJMHiDsx9YuV+oOv/30ahRIwICAjh27FipCY+Li0vh2XXX0uv1dv+iUJa+VUlNmSdUvrnm5ORgNpvx8PCwe4xjx45hNpsZN24cc+fOZf369WWaZ2BgIGPHjsXT0xNXV1e747oVKtvfZ3mpqHna8ppWJzxjxozh559/JjMzEzc3N0aMGMHo0aMZOHCg49eNO9Du3bv59NNP2bNnj9UbeWXpgePIvGuOmjhnKJ95WywWLl68SFJSEoqioNFosFgsJf7CXBGifi/5A6gbsWXpQVVz9uxZUlJSqF27dkWHIoTD5OXlsXjxYrKzs3nkkUfsLswxY8YM2rRpw/Dhwx1WEbOkPd1C3IjVCc+SJUsYMGAAo0eP5p577imXk3ADAgLQ6XQkJycXuZ6cnExwcHCJfYKDg2/YfuvWrVy4cIH69esX3jebzbzwwgt88sknnDp1qtiYsvSg7GTeNWfelWnOFy9e5NSpU3Tu3LncX6s8552Zmcn8+fNRFIXQ0FAGDRp0ww3x5cKcB1f+gtTdkJcCgMmiJepMawbU+wu99p+zi5z9wa/jP4eOlr53qSIq19krMzOTY8eOFX5/8uRJ9u3bh5+fH35+fsycOZMRI0YQHBzM8ePH+fe//02TJk0IDw+vwKiFcJz8/HyWLVvGuXPncHV1LdMTWo1GU1jx0FEJjxC2sjrhSUxMpFatWuUZC87OznTs2JHo6GgiIiIA9ZPO6OhoJk2aVGKfsLAwoqOjee655wqvRUVFERYWBsAjjzxC//79i/QJDw/nkUceKSyMcD1ZeuA4Mu+ao6Ln/Oeff7J27VpMJhOBgYF27SO8ntlsJiMj44Zn2pTHvH19fRk8eDCKotCmTRvHlJm2hSUf0vZDylYwZxU7wECvtVxNePIvqu2ctODXAbQlv61Upf8fdu3aRZ8+fQq/L/gAbOzYscydO5f4+HgWLVrElStXCAkJYeDAgcyaNavSPIEToqyio6M5ceIEer2ehx56SJ6oiCrP6oSnvJOdAlOmTGHs2LF06tSJLl268Mknn5CVlVWYnIwZM4Y6deowe/ZsAP71r3/Rq1cvPvzwQ4YOHcrSpUvZtWtX4TlA/v7+xT4Z1ev1BAcH06xZ9TgVXIiabu3atezatQtQ91M4YmnRiRMnGDduHC4uLqxfvx6t1q5jy+zWpk2bW/p6ReScg5Q/1GTHGuYsSIkD12Bwr3/z9pVc7969URSl1Pvr16+/hdEIcev16NGDhIQE+vbtS926dSs6HCHKrNKV/XnggQe4ePEiM2bMICkpiXbt2rFu3brCwgQJCQlFfvHo3r07kZGRTJ8+nWnTptG0aVNWrVpV7AweIUT1VfChRu/evbnzzjvLlJwoisKCBQt4/vnnyczMxMPDg7///puWLVs6KtzKTVEg8yQYL9nWz3gJMk+oZ/Pc6idSQgiH8vDw4PHHH7/1T5dRl9NJVUrhaJXyv6hJkyaVuoQtJiam2LWRI0cycuRIq8cvad+OEKLq6tq1Kw0aNCh1r58tMjMzmTlzJpmZmdx5550sWrSIhg0bOiDKKsKYAhnH7eubcRy8W4EhwLExCSFuuYpIdpKSkliyZAl33303jRs3vuWvL6qvW7tGQwghyoFGo3FIsgPg6enJ119/zfvvv8+mTZscnuycOnUKi8Xi0DEdKj8D8i7b1zfvstpfCFFjLF++3CFVGI8fP87XX39Neno6mzdvvuGyUiFsJQmPEEJcp3///rz44osOLbmfkZHB8uXLWbRoETt37nTYuA6nmNWvW91XCFHlvP/++9x///0MHz68TJXcEhMTiYyMJC8vjwYNGvDQQw9VyBMmUX3ZtaTNaDTy+++/c+jQIdLT0/H09KRly5b06NFDqtQIIcR19u/fz5o1a8jLy0Oj0ZCbm1vRIZVOo1O/bnVfIUSVMnv2bKZNmwaoRQ7Ksu+mdu3ahfskhw8fLnt4hMPZ9F+Uoih88MEHvPvuu1y+fLnI40aNRoOvry8vv/wyL774omTmQgjxD29vb/Ly8qhTpw7Dhg1z2PK7cuHkCc6+kJ9pe19nX7W/EKJaS05O5v333wfgzTff5LXXXivTeBqNhoiICLRarfz+KMqFTQnP6NGjWbp0KU2bNmXy5Mm0bdsWT09PMjIy+PPPP4mMjOSVV15h3759LF68uLxiFkLUEFeuXGHt2rUMHTr0hmfhVHb16tVj7NixhIaGVv43cxd/8GwM2WeuXtO5gmsIaP45cNr7dlCyIScRzDlX23k2VvsLISq1lJQUkpOT7a4+GRQURFRUFJs3by5yUHtZOHIJsRDXszrh+e6771i6dCkvvvgis2fPLvYfZkREBK+99hrTpk3j/fffZ/DgwTz88MMOD1gIUTMcOXKElStXkpuby9q1axk9enSZxsvLy+PNN9/EYrHwn//8x0FRWq9Bgwa3/DXtotGAR0O4sh/MueDRALQuatnptP1AO/WeV33wbgkWo1rGWucKHo2kJLUQlVx6ejrfffcdaWlpjBgxwu5jPDp27EjHjh0dHJ0Q5cPqhOfLL7+kV69evPfee6W20Wq1vPPOO8TFxTF//nxJeIQQdvnrr7/46aefAAgJCWHo0KFlGu/AgQM88sgj7N27F41Gw5gxY2jevLkjQq2eXOtArTsgNxkyjkDqPsg6BRYd0A4u/g4pW8C9Afi1A582YAhSnwIJISqtnJwcvv/+e9LS0vDz86s6H8QIUUZWV2mLj49nxIgRVrW99957iY+PtzsoIUTNdtttt+Hn50eXLl149NFHy7Sc7cqVK3Tv3p29e/fi7+/PsmXLJNm5Ga0TuNVVl6ydj4LM48Wrrylm9fr5KLWdW121nxCi0oqPj+fixYt4enryyCOP4OHhUdEhCXFLWP3uZDKZMBgMVrV1cXEhPz/f7qCEEDWbi4sLEyZMcEjVRx8fH1599VW2bNnCl19+Se3atR0QoSozM5MtW7bQr18/h41ZKVjy4fI+yEpQ9+XkXQZjKuT/k/Ro9eBkABc/tVBBVoLaPqi3JD1CVGJdunQhPz+fpk2b3tJ9kWfPnnXoz14hbGX1E54mTZqwZcsWq9pu3bqVRo0a2R2UEEI4ssT9iy++yJo1axz2hpudnc0vv/zCsWPH2LZtG6dOnXLIuJWG8SJkHAUU0HuDeyh4NQevZup9r2bq9+6h6n0UyDim9hNCVFoajYYePXoQGBh4y15z7969LFy4kDVr1shhoqLCWJ3w3HfffSxZsoS1a9fesN3atWtZsmQJI0eOLHNwQgjhCI4sdWqxWPjqq68Kl+22b9+eoKAgh4xdaWQlQO61yYsGnNzA2Uf91tlH/Z5r/kxzL6j9hBBVnsVi4fjx42UaQ1EUNm/ezOrVq1EUBUVRsFgsDopQCNtYnfC88MILNGvWjIiICCZMmMDWrVtJT09HURTS09PZtm0bEyZMICIigmbNmvHCCy+UZ9xCCFEhtFot3bp1o1atWjRt2pTBgwfj6upa0WE5jiUfsk4Dtv5iYlH7WWQ5sxBVmcVi4amnnqJjx47s2rXL7nGuXLnC77//DsAdd9xBRESElJ4WFcbqxdZubm5s3LiRMWPG8NVXX7FgwYJibRRFoX///nz77be4ubk5NFAhRPVQ8ClfVX7j69SpE23atGH9+vUVHYrjWfKLFyiwlmL+p6/s4xGiKrJYLDzxxBMsXLgQrVbLkSNH6NSpk11j+fr6ct9995GWlkbnzp0dHKkQtrHpXSkwMJB169axc+dO1qxZw8GDB8nIyMDT05MWLVowbNgwwsLCyitWIUQVl5WVxcqVK/H392fw4MFlHi8qKoqcnBzuvvtuB0RnPa1WW6UTthvSOoHGzrlpdPb3FUJUuP/+97+Fyc53333HQw89VKbxbrvtNgdFJkTZ2PUxXNeuXenataujYxFCVGMJCQn8+OOPZGRkcPr0abp37463t7ddY2VlZfHyyy8zZ84cfH192b9/PyEhcgaMQ2id1GIEaX9j27I2rdpPqrQJUWU9+eSTbNiwgTFjxvDAAw9UdDhCOIxD3pkOHDjAli1byMzMpG3btgwcONARwwohqomcnBwWL15MXl4e/v7+jBw50u5kJzU1lW7dunH06FEAHnroIbvHul7Bhlqt1urtjdWTe30w1FIPHrWWIVDtJ4SoMAkJCezcuZOIiAj0er3N/Q0GA7/88ovDirwIUVlYnfBYLBamTp1KZGQkTk5OjBs3jtdff50pU6bw6aefFpYaLCh5uG7dOtnHI4QAwNXVlfDwcE6dOsXQoUPLVHLa19eX9u3bk52dzcKFCx32AUtiYiK//PILbdu2lSfYLrXAsykYL1m3n0ejA88maj8hRIVISkoiMjISo9GIv78/ffv2tWscSXZEdWR1wjN37lzef/99OnfuTFBQEP/5z3+4ePEi8+bNY+LEifTr14/8/HxWr17Nd999x6xZs5g9e3Z5xi6EqELat29P+/bty/xmqtFomDdvHqAmP2WVm5vLxo0b+eOPPwD1jJ1OnTpV3z061tA6gX8nMGfB5fgbJz0aHfi2UdvLcjYhKkRqairff/89RqOR+vXrc+edd1Z0SEJUKla/O3311VcMHTqUNWvWADBnzhyeffZZJk6cyP/93/8VthsxYgRZWVn8+OOPkvAIIQo58lNDRyQ6BZKTkwuTndatWzNw4MCanewUcPaBwF6gc1cPFc29cF0DrbqMzbOJmuwUnNEjhLjlsrOzMZvNBAUFMWrUKLuWs9lCURS2bt1Ks2bNqt85ZKJasjrhOXHiBBMmTCj8fsiQIUyePLnER6b9+/cvTIyEEKIyCw0NpWfPnoSGhtKoUaOKDqdycfaBoN7gc7t6qGj6afW6V3PwClX37LjUkic7QlSwunXrMn78eNzc3DAYDOX6WmazmV9++YV9+/axa9cuJk6cWKZlykLcCla/S2VkZBTZGOzl5VXkn9fy9PQkP18OnxNCVA19+vSp6BAqL60TuNZWvzzbwF8boM5d4FKNDlsVohoIDAy8aRuTyYSTk5PdT9zz8vL44YcfOH78OBqNhl69ekmyI6qEGl6KSAhRVoqiYDQaHTJWUlJSYfU1UQkVPMmRJzpCVDk5OTkMGzaMV199tbDQlK20Wi0mkwm9Xs+DDz5Ix44dHRylEOXDpnetX3/9laSkJEBdL6rRaFi+fDn79u0r0m737t0OC1AIUXkZjUZWr17N4cOHSUlJITg42O6xVqxYwZNPPkn9+vXZsWMHzs7ODoxUCCFqruzsbO6++26io6PZtm0bTzzxBA0bNrR5HCcnJx588EEuX74sZ5+JKsWmhCcyMpLIyMgi17744osS20pZQyGqt+TkZJYvX05KSgqglnW2J+HJyMjgmWee4fvvvwegXr16XLx4kTp16pQpPkVROHPmDPXry9kwQoiay2KxFCY77u7u/Pbbb3YlOwVcXV1xdZUlraJqsTrhOXnyZHnGIYSoYuLi4khJScHT05OgoCBat25t1zh6vZ69e/ei1WqZOnUqM2bMKPPTnQsXLrB27VoSEhIYP368JD1lYbFAzjnIPA6XjwKBcPwb8G0KHo3BtQ7U9INahajEtFotDz30EH/88Qe//vorPXr0qOiQhLjlrE54QkNDyzMOIUQVM2jQIHQ6Hd27d2fz5s12j2MwGFi8eDHZ2dmEhYWVOa4tW7awefNmLBYLer2eK1euSMJjL2MKXNgKF7fDlb8g3wxMhITlkKgDn9ZQqzsE3gku/hUdrRDVWm5urt0V2B599FHuvvtuAgICHByVEFWD7DwVQthFr9czZMgQTCZTmcdq27atAyJSOTs7Y7FYaNasGYMHDy5SXVLYwJgC536BU8vAnP3PxWuevJlzICUOruwHUxrUGSZJjxDlJD4+nnXr1jFq1Cjq1atn1xiS7IiazOqEp6TzdgpoNBoMBgOhoaEMGTKEYcOGOSQ4IYSwVZcuXahVqxaNGzeu6FCqLotFfbJTJNkphTlbbaf3hjp3y/I2IRzsyJEj/Pzzz1gsFg4dOmR3wmMNs9mMVquVfdii2rE64blw4cIN/wfIzs4mKiqKL774gvDwcH7++edyP+lXCCGup9VqJdkpq5xz6jK2myU7BczZcDEW/DqCe/n9MiZETZOYmMjy5cuxWCy0bt2aAQMGlNtrGY1GfvjhB+rVq0fv3r3L7XWEqAhWJzz79++/aZucnBy++OILpkyZwnvvvcerr75apuCEEFWbxWIhNzcXNze3ig5F2CLzuLpnxxZX4iHzhCQ8QjhQrVq1CiuqDR8+vNyevGRkZLB48WKSk5M5e/YsHTt2xNPTs1xeS4iK4NC1B66urjz33HM8+OCDxcpXCyGqjqNHj5Z5b86pU6fo06cPTz/9tIOiErdM+hF1j44tzDmQfrh84hGihtLr9TzwwAOMHDkSnU5XLq9hMplYuHAhycnJuLu7M3bsWEl2RLVTLoute/ToIWWshaiC8vPz+fXXX4mMjOTXX3+1awxFUVi4cCGtW7dmy5YtrFixgoSEhDLFpSgK+/fvJz4+vkzjCCtYLNYvZbueOUftL4RwGJ1Od8MtAhcvXmTUqFFcunTJrvH1ej1hYWH4+/vz2GOPyYGioloql4QnOzsbJyf7C8DNmTOHBg0aYDAY6Nq1K3FxcTdsv3z5cpo3b47BYKB169bFflF74403aN68Oe7u7vj6+tK/f3927txpd3xCVEdXrlzh66+/5o8//gDA3d0dRVFsHufChQs8//zzZGZmcscddxAfH1+mstCpqaksXryYFStW8Ouvv5KZmWn3WMIKWi3o7FyCqHOVogVC3ELJycn06dOHpUuX8sgjj9g9TpcuXXjyySfx9fV1YHRCVB4Of2dSFIXVq1fbfQjhsmXLmDJlCq+//jp79uyhbdu2hIeHc+HChRLbb9++nVGjRvHYY4+xd+9eIiIiiIiIKLLn6LbbbuOzzz7jr7/+Ytu2bTRo0ICBAwdy8eJFu2IUojrKz8/n0qVLGAwGRo0aRf/+/e1aLx4UFMTnn3/Oe++9R0xMDI0aNbI7pvT0dObOncvx48fR6XSEhYXZfQ6FsIHXbWryYgudK3g1K594hBDFnD9/nt69e3PgwAFCQkL49NNPyzSeFJoS1ZnVj2FSU1NveD8nJ4fDhw8zd+5ctm/fzvfff29XQB999BFPPPEE48ePB2DevHmsXbuWhQsX8sorrxRr/+mnnzJo0CBeeuklAGbNmkVUVBSfffYZ8+bNA+Chhx4q9hoLFiwgPj6efv362RWnENVNQEAAI0eOJCAgAB8fnzKNNXr0aIfE5OXlRcuWLcnMzGTIkCH4+8s5L7eER2P1UNGU65+uK9f98xo+bcDD/uRWCGGbvLw8cnJyqFu3Lps2baJJkyYVHZIQlZbVCU9AQIBVn/bq9XpmzZrFqFGjbA4mLy+P3bt3M3Xq1MJrWq2W/v37ExsbW2Kf2NhYpkyZUuRaeHg4q1atKvU15s+fj7e3d6mHHRqNRoxGY+H36enpgLqxz9aN3AXtHXE4Y1Ui866a8w4NDQVsi7+85xweHo6TkxMajaZS/blW9b/rG3IKBL/ucPmwup/HYgJLLqb8XABMuRnqu4fWAFq9ugTOL0ztV8qfR7X8cxKiAoWGhhITE4PFYinTk3QhagKrE54ZM2bcMOEpOHi0X79+1KpVy65gLl26hNlsJigoqMj1oKAg/v777xL7JCUlldg+KSmpyLVffvmFBx98kOzsbGrXrk1UVFSppw7Pnj2bmTNnFru+YcMGu8vrRkVF2dWvqpN51xw1cc5QneetAR4t8U6UMhWuz1/+BP5cV+po2dl2FkIQohpTFIXjx4/bvc+xQYMGjg1IiGrK6oTnjTfeKMcwyl+fPn3Yt28fly5d4ssvv+T+++9n586dBAYGFms7derUIk+N0tPTqVevHgMHDsTLy8um1zWZTERFRTFgwIAatT5W5l1z5l0T5ww1YN55mWBMhpQ/4MLvkBqHyZxPlGYWA5TX0OucwK8LBPYA/87gEgTOHqUOV/CkXAhx1ZYtW4iJiaFz587l9hoF/+/Z+vuLENWJ/aXUgKysLDIyMggICChTVbYCAQEB6HQ6kpOTi1xPTk4mODi4xD7BwcFWtXd3d6dJkyY0adKEbt260bRpUxYsWFBk+VwBFxcXXFxcil3X6/V2/2JTlr5Vmcy7+jCZTMyaNYvAwEAmTZpU7L6tc7ZYLGirQUWv6vh3jcUCGXvh8n7waw++zSF3KFw+CidA33gcet+mYAgGcy4kbwDfVhDYu9QqbdXuz0iIMoqLiyMmJgYAHx+fcimkdOHCBRYvXoyrqyvjx48v8XcbIWoCm3/bOH36NJMmTSI0NBQvLy/q1KmDwWCgQYMG/Pvf/+b06dN2B+Ps7EzHjh2Jjo4uvGaxWIiOjiYsLKzEPmFhYUXag7rEpLT214577T4dIaqzEydO8Ndff9nd/+DBg3Tr1o1Zs2bx0ksvce7cuTLFc+TIET777DPOnj1bpnFEOTFegIyjYEqF5GhI2qReD+hS9J9Jm9T7plTIOAZGqXwphDVSU1NZt05dAtqrV69yecJz6tQpFi5cSHp6OmazWX7nETWaTY9l1qxZw8MPP0xGRgYNGjTgrrvuwtPTk4yMDOLj4/nggw/48ssv+f777xk6dCgA06dP56233rL6NaZMmcLYsWPp1KkTXbp04ZNPPiErK6uwatuYMWOoU6cOs2fPBuBf//oXvXr14sMPP2To0KEsXbqUXbt2MX/+fEB9CvX2229z9913U7t2bS5dusScOXM4d+4cI0eOtGX6QlQ5FouFrVu3EhMTg5OTE0FBQSUu47yR8+fP06lTJ3JycvDz82Pu3LnUqVPHrnjS09NZt24dhw4dAmDr1q12FTgR5Sz7DORecxSAJRtSd4NFC7SF5I2gve6A0dxktZ9r0T2VQoji/Pz8uOeeezh37hy9evUiPz/foeNbLBZ+++03jEYj9evX58EHH8TV1cZS80JUI1YnPIcOHeL++++nYcOGfPHFF9x5553F2mzdupWnnnqKBx54gF27djF79my+//57mxKeBx54gIsXLzJjxgySkpJo164d69atKyxMkJCQUGQZTPfu3YmMjGT69OlMmzaNpk2bsmrVKlq1agWoJxT//fffLFq0iEuXLuHv70/nzp3ZunUrt99+u9VxCVHVmM1mIiMjOXHiBACtW7e261C52rVrM2HCBI4cOcJXX31VplO49+/fz6FDh9BoNISFhdGrVy+7xxLlRLFAVgIoZhv7mSHrNPh1AE3VX6ooRHlr3br1Dc8sPHz4MMeOHSv8ANkWWq2WBx98kN9//51BgwY5ZNuBEFWZ1f8H/Oc//8Hf359t27bh5+dXYps777yTrVu30qZNGzp27IjRaCx8EmOLSZMmlbhHAChc73qtkSNHlvq0xmAw8NNPP9kcgxBVnU6nw9/fnzNnzjB06NBSy7Bb4/333y8sDV0WXbt25eLFi3Tr1q1YdUVRSShmsNj5abMlX+0vCY8QZXLw4EH69u1Lamoqv/32m11nBvr6+jJs2LByiE6IqsfqhGfjxo08/vjjpSY7Bfz8/Hj00Ud56623+Pbbb3n44YfLHKQQwj4DBw6kS5cupZZgt5ajNpzrdDqGDx/ukLFEOdHoQGvnp8FaJ7W/EMJu+/fvp1+/fly4cIG2bduW6cMqIYTK6o/hUlJSrK733rBhQ3Q6nSQ7QlQwJyenMic7oobRaMG9vu2Ji0YH7qHV4unOli1buOuuuwgJCUGj0RQ7yFpRFGbMmEHt2rVxdXWlf//+HD16tGKCFdXON998w4ULF2jfvj3R0dHyM1wIB7D6nSkgIICTJ09a1fbkyZM2b4wWQghRSbjVA4ONP8MNQWq/aiArK4u2bdsyZ86cEu+/9957/N///R/z5s1j586duLu7Ex4eTm5u7i2OVFRH7733Hm+99Rb/+9//8Pf3r+hwhKgWrE54evfuzYIFC0hNTb1hu9TUVBYsWEDfvn3LHJwQonxFR0fz+++/l3kci8VCUlKSAyISlYJLIHg2BY2VS9s0TuDZBFxqlW9ct8jgwYN56623uOeee4rdUxSFTz75hOnTpzN8+HDatGnDt99+S2JiYrEnQULYQ6vV8uqrr950C4EQwnpWL9SeNm0aP/74Iz179mT+/Pl07969WJvt27fz5JNPkpKSUuKBnkKIyiE7O5upU6fyf//3fzRo0IA///zT7lO4T58+zeHDhzly5AiTJk3C3d3dwdGKW06rBb+OYM6G1H2g3KCIgcYJ/NqBX6dSDx2tTk6ePElSUhL9+/cvvObt7U3Xrl2JjY3lwQcfLLGf0Wgscg5Keno6oB7oazKZbIqhoL2t/aqaqjxPs9nMpk2b6NatGx4eHjdtb+tcT548SXZ2dpWrNluV/05tIfO8ta9vDasTnpYtWxIZGcmYMWO48847adCgAW3bti1yDs/JkydxdXUlMjKSli1b2hW8EOLmEhMT2bhxIyNHjrT55OzExET69u3L4cOHARg0aBA6ne0bzS0WC6tXr+bPP/8EwN3dndTUVEl4qgtnb6h1J+jc1ENFc5MB5ep9jU5dxubZRE12nO1LmKuagieZ11cZDAoKuuFTztmzZzNz5sxi1zds2ICbm5tdsURFRdnVr6qpavNUFIWEhAQuX75MfHw8zZo1s7rCpTVzTU1NJSEhAVCruVXFn7lV7e/UXjLP8pWdnW11W5tK8dx77720a9eO9957j19++aXI4/uQkBAmTJjAiy++SOPGjW0ZVghhJUVR+OOPP9iwYQNms5mNGzcyePBgm8YIDg4mODiYjIwMFixYwKBBg+yKRavVFh6W5+/vz9ixY/H09LRrLFFJOXtDYG/wvl09VDT9tHrd+3bwClX37LjUqhFPdspq6tSpTJkypfD79PR06tWrx8CBA21+umoymYiKimLAgAEOq6BYGVXFeSqKQlRUFJcvX0aj0XD33XfTpEmTm/azZq6KohAbG8u+ffsA9YPoYcOGVakzdqri36k9ZJ63RsGTcmvY/H9Jo0aNmDdvXuELZWRk4OnpafdyGCGE9TZv3szmzZsBaNasGb1797Z5DK1Wy+LFi3F1dS3zGvHw8HA6derEn3/+icFgKNNYopLSasE1SP3ybA3x66DOMHC27clidREcHAxAcnIytWvXLryenJxMu3btSu3n4uJS4tNYvV5v9y8KZelblVSleebk5HD8+HEAIiIiaNGihU39bzbXgsIYYWFhDBgwoMxno1WUqvR3WhYyz/J/XWuV6WMBLy8vSXSEuIXatWvHrl276NGjB926dbP7za5OnToOicfT0xODwVC4rE1UcwUlp6tB6Wl7NWzYkODgYKKjowsTnPT0dHbu3MnTTz9dscGJCufq6sr48eM5efIkbdq0KbHN/v37ad68uV1PZgYOHEjDhg257bbbyhqqEDVK1XkOKoTAx8eHZ599Fmdn54oORYhqKzMzk2PHjhV+f/LkSfbt24efnx/169fnueee46233qJp06Y0bNiQ1157jZCQECIiIiouaFFpeHp6lprsbNq0iWHDhnHPPfewaNEim/dPajQaSXaEsIMkPEJUMZLsCFG+du3aRZ8+fQq/L9h7M3bsWL755hv+/e9/k5WVxYQJE7hy5Qp33HEH69atk2Wd4ob+97//cffdd5OTk0NKSgomk8mugjFCCNtJwiNENXLhwgXMZnORvQW2slgsxMXFUb9+fUJCQhwYnRBVQ+/evVEUpdT7Go2GN998kzfffPMWRiWqsrS0NEaOHElOTg5Dhw5lxYoVNlfYFELYr+YuxBaimlm5ciWtWrVi3LhxN/xl7UbOnTvHl19+yfr16/nll1+wWCwOjlIIIWoeb29vlixZwv333y/JjhAVQJ7wCFFJ5Ofno9PpbC5EkJ6ezrPPPsuiRYsA9ZyQS5cuUauWbafeHzt2jMWLFwNgMBjo1KlTla0AJIQQlc2gQYNuegyAfMgkRPmQJzxCVAIpKSl89dVXxMXF2dzXbDbzv//9D61Wy9SpU4mLi7M52QG1+lStWrVo27YtkyZNokOHDpLwCCHEdcojKVEUhaSkJH744QfMZrPDxxeippMnPEJUsP3797NmzRry8vLYvn07HTp0sKm2vK+vL4sXL8bJyYkePXrYHYdOp+Pxxx+XoghCCFGK3NxcvvvuO8LCwmjVqpVDxrRYLPz2228kJSUBcPjwYVq2bOmQsYUQKkl4hKhAFy5cYMWKFQCEhoYyYsQIuw7v6tWrl0PikWRHCCFKZjKZWLJkCYmJiaxfv57bbrvNIT8zV69eXXiWWXh4uCQ7QpQDSXiEqECBgYH06NEDjUZDnz590GpllakQQlQ2FouFH3/8kYSEBFxcXBg9erTDPiDq1KkTR44cISgoiI4dOzpkTCFEUZLwCFHB+vXrV+57ZSwWiyRTouws5mv+afuTSCGqKo1Gg4+PD05OTowaNYrg4OBibbKzs3Fzc7N57Lp16/LMM88QHR3tiFCFECWQ34CEqGA3SnYsFgsmk8nusRVFYffu3cyZM4fs7Gy7xxE1mDkfMo7D+Q1w7Av12rEv1O8zTqj3hajmNBoNgwYN4sknnyQ0NLTY/UWLFtGiRQuOHz9u1/hSplqI8iUJjxCV1OnTp+nXrx8zZsywq39ycjJff/01v/zyC6mpqezcudPBEYpqLycZzqyAg+/C7ilw9HP1+tHP1e8PvqPez7lQsXEKcQtoNBoCAgKKXV+4cCHjx48nISGBb7755tYHJoS4KUl4hKhkFEXh66+/pnXr1sTExPD555+TkpJi8zhbt27lzJkz6PV6Bg4c6LDCBqKGyEmGs6tg/1vq05z8zKL38zPV6/vfgrMrJekRNdKKFSt47LHHUBSFiRMn8uabb1Z0SEKIEsgeHiHKicViIS0tDV9fX5v6nTx5kieffBKTyUT37t359ttv8ff3t/n1Bw4ciFarpV+/fnh7e9vcX9Rg5nxIjoHDn0F+xo3b5meo7fQ+UG8E6ORtRdQcffr0oX379vTs2ZOPP/5Yzi4TopKSdyYhykFGRgY///wzaWlpPPnkk7i6ulrdt1GjRrzzzjvk5eXx0ksvodPp7IrBy8uLe++9166+oobLPg3J0TdPdgrkZ0DyRvDvDJ6Nyjc2ISoRPz8/tmzZgru7e4nJjtlsJiUlhcDAwAqITghRQBIeIRwsPT2dBQsWkJ2djbOzMxcuXChxk+uNTJkypZyiE8IKmcfh4nbb+lz8Xe0nCY+oYTw8PEq8npeXx/Llyzlz5gzjx48nKCjoFkcmhCgge3iEcCBFUbhw4QLZ2dkEBweXWtFHiErLYoa0g8X37NxMfqbar6B0tRBVzJUrVzCbHfPfb2ZmJt988w3Hjh3DYrGQkWHl01IhRLmQJzxCOJBGoyE0NBQPDw/69euHXu/4s0pyc3PJycmxeW+QEFaxWCDfzhLm5v9v787joqr3/4G/BhgYQNlEVgURUVxQE5XEtQSRXEJMBfclu5aW/rx61TJRW6jbrWvf9GZW6r3mnuQuCbh1FTUFTNxCQ9GURQ2GRWBgPr8/fDC3EVAGGGZ7PR8PHzrnfM6Z93vOOJ95n8+ZzykBhBJA/S7DJNKV/Px8rF+/Hm5ubhgzZkyDP7tPnDiBe/fuwcbGBuPHj4enp2cjRUpE9cGCh6iRSaVSrRQ7Qgikp6fjxx9/hL29PWbMmMGbiVLjMzMDLDS/eSIAwNwGkPA9SYalqKgImzZtQmFhIWQyGSoqKhr8+R0aGoqysjIMHDgQTk5OjRQpEdUXCx6iJqJQKPDBBx+gS5cueOWVVzTatri4GHFxcfjtt98AADKZDEVFRbCzs9NGqGTKzMwB+06ARTPNLmuzaPZ4OzOO7pDhEEJgx44dePjwIezt7TFx4kSNJpmpjVQqxahRoxohQiJqDCx4iJrAlStXMGnSJJw/fx5OTk4YPHiwRpekWVlZoaCgAObm5hgwYACCg4NhYcH/vqQlzXyBlsGP77NTVy37Pt6OyIBIJBKEhIRg7969iI6OVjuJJIRATEwM3Nzc8MYbb+gwSiJqKH5jItKyX3/9FT169EBpaSkcHR2xZs0ajX9/Y2FhgcjISMhkMl4eQdpn4w24Dgbykus2NbVFc8D1RcDGS/uxETUyLy8vvPHGG2qXCAsh8M477yA2NhYA0K9fP3Tt2lVXIRJRA+nlxdZr1qxBmzZtIJPJEBQUhLNnzz61/c6dO+Hv7w+ZTIaAgAAcPHhQtU6hUGDRokUICAiAra0tPDw8MHnyZNy9e1fbaZCREULg0qVLEEJotJ2fnx9GjBiBoUOHIj09HVFRUfV6fg8PDxY71DTMLQDXF4AOcx4XM09j0fxxO9cXeNNRMlhPFjuLFi1SFTv//Oc/WewQGTi9K3i2b9+O+fPnIyYmBikpKejWrRvCwsKQm5tbY/tTp04hOjoaM2bMQGpqKiIiIhAREYH09HQAQElJCVJSUvDuu+8iJSUFcXFxuHbtGkaOHNmUaZGBe/ToEbZt24bvv/8eJ0+e1GhbiUSCjRs34uDBg/Dw8NBShESNzNoFaDUK6LIUcA97/BudP7No9nh5l6WP21nzxopkHCQSiereOl988QXmzZun24CIqMH07nTcZ599hpkzZ2LatGkAgLVr1+LAgQNYv349Fi9eXK39559/jqFDh2LhwoUAgPfeew8JCQlYvXo11q5dC3t7eyQkJKhts3r1avTu3RtZWVnw8uIlGPR0v//+O3bu3Kn6DU19ftBqY1PPWa+IdMnaBWg9GmjR6/FNRR9eAa4D8JsNOHV8/JsdGy+O7JDRWbZsGcLDw9GrV69q6woLC5GWloZ+/fpBIpHoIDoi0pRe9VLl5eU4f/48lixZolpmZmaGkJAQJCcn17hNcnJytbvSh4WFYffu3bU+T0FBASQSCRwcHGpcX1ZWhrKyMtVjuVwO4PHlcQqFoo7ZQLXNn/82FcaUd0lJCeRyORwcHBAZGQk3N7da89I07/LyciQnJ6N3796NMjOQLhjTsdaESeUtaw3IWkNh1we4ngRFm+mApdXjdUoBKJ/9GpjE60RGpaZiJy8vD5s3b1adAAsODtZBZESkKb0qeO7fv4/Kykq4urqqLXd1dcXVq1dr3CY7O7vG9tnZ2TW2Ly0txaJFi6rNxvJnsbGxWLFiRbXlhw8frveZ+idHmUyFseTt7e2N5s2bIyUlpU7t65J3QUEB7ty5A4VCgatXr6J169YNDVOnjOVYa8oU805ITNJ4m5KSet7MlEhPZGVlYevWrSgtLYWTkxM6duyo65CIqI70quDRNoVCgbFjx0IIgS+//LLWdkuWLFEbNZLL5WjdujWGDBmi8X1PFAoFEhISEBoa2ug3otRnppL3sWPH4OLigk6dOgGoe97JyclIS0sDANjb22Pw4MFo165dU4Tc6EzlWD/JFPNuSM5VI+VETS0zMxO2trZwcWnY78yKi4tRWlqKVq1aITo6mpcqExkQvSp4nJ2dYW5ujpycHLXlOTk5cHNzq3EbNze3OrWvKnZu3bqFI0eOPLVwsbKygpWVVbXlUqm03l9sGrKtITPWvB89eoS3334bq1atwnPPPYfTp0/D0tJStf5ZeXfr1g3JyckIDAzEgAED1LY1VMZ6rJ/FFPOuT86m9hqRfrhz5w62bt0Kc3NzTJ8+HS1btqz3vjp27Ijo6Gj4+Pjw/UxkYPRqljZLS0sEBgYiKel/l0solUokJSWhT58+NW7Tp08ftfbA40tM/ty+qtjJyMhAYmIiWrRooZ0EyCRkZmaiR48eWLVqFQCgd+/eqKio0Ggf9vb2mDt3LkJCQoyi2CEi0jd5eXnYsmULFAoFPDw8NL7/WU3at2/PYofIAOlVwQMA8+fPx9dff41///vfuHLlCl5//XUUFxerZm2bPHmy2qQGc+fORXx8PD799FNcvXoVy5cvx7lz5zBnzhwAj4udV155BefOncPmzZtRWVmJ7OxsZGdno7y8XCc5kmFzd3eHmZkZ3N3dcfDgQaxdu7ZelzbIZDItREdERABw/PhxPHr0CJ6enhg3bhwsLB5f1FJRUYGPPvoIxcXFOo6QiJqKXl3SBgDjxo1DXl4eli1bhuzsbHTv3h3x8fGqiQmysrLUbhAWHByMLVu2YOnSpXj77bfh5+eH3bt3o0uXLgAeTym8d+9eAED37t3Vnuvo0aMYNGhQk+RF+ik1NRWOjo5o06ZNnbeRyWSIi4uDs7MzRwuJiPTUyy+/DFtbWwwcOFA1kq5QKDBp0iRs374dx48fx8GDBzm1NJEJ0LuCBwDmzJmjGqF50rFjx6otGzNmDMaMGVNj+zZt2kAI0ZjhkRFQKBQ4ePAg0tLS0KxZM7z++usajdJ06NCh1nV8vxER6Z5UKkV4eLjqsUKhQHR0NHbt2gWpVIpZs2ax2CEyEXpZ8BBpU3FxMf7zn/8gNzcXEokEvXr1apR74FRUVOCnn37CjRs3WPQQEemZW7du4ejRo7C0tMSuXbswfPhwtfVCCBZAREaKBQ+ZHBsbGzRv3hwlJSWIjIyEj49Pg/eZmZmJAwcO4MGDBwCA69evq6aqJiIi3WvXrh0SExORnZ2tNvIDADdv3kR8fDwmTJiA5s2b6yhCItIWFjxkciQSCSIjI6FUKtGsWTO1dXl5ebC0tIS9vX2d96dUKrF//348fPgQtra2cHZ2Nth76hARGbPnnnuu2rL09HTs3r0blZWVOHbsGEaMGKGDyIhIm/RuljaipmBjY1Ot2NmzZw+6dOmCuXPnarQvMzMzvPTSS+jVqxf+8pe/wNHRkZdFEBEZgIsXL2LXrl2orKxEx44dMXToUF2HRERawBEeMnlyuRzz5s3Dhg0bAADnz59HYWGhRpc1+Pr6wtfXFwqFQlthEhFRI2vbti2cnJzQrl07hIWFqc0CS0TGg/+zyeTl5+dj165dkEgk+Nvf/oZz587xGm4iIj2XkpKC48ePN2iSGFtbW8ycORNDhw5lsUNkxDjCQybPy8sLGzZsgIuLC/r166frcIiI6BkuX76M/fv3QwgBd3d3tG/fvt774k2giYwfT2eQUbl27Rq2b98OpVKp0XaRkZE1FjsFBQUoLS1trPCIiKiBfvvtN8TFxUEIgR49esDPzw8AUFhYiNTUVB1HR0T6iAUPGYXKykocPnwY27Ztw9WrV3H+/PkG7U+pVOLUqVNYs2YNjhw50khREhFRQz18+FA1ycCwYcMgkUggl8sRHh6OgQMH4syZM7oOkYj0DC9pI6Pwww8/4NKlSwCAoKAg9OjRo977ys3NRVxcHHJyclSPKysrYW5u3iixEhFR/fXs2ROOjo7w9vaGmZkZCgoKMHToUJw+fRoODg78LQ4RVcOCh4xCUFAQMjMzMXz4cHTs2FG1vOrSNk06QKlUigcPHsDa2hohISF47rnnOM00EZEe8fX1Vf37ww8/xOnTp+Ho6IjExES1E155eXlo2bKlLkIkIj3CgoeMQuvWrTF37lxYWlqqlt2+fRvTpk3Dyy+/jDfffLPO+3J0dMTYsWPh4eEBW1tbbYRLRESNZMWKFbhz5w4WLlyI7t27AwCEEDh58iSSkpIQERGBbt266TZIItIpFjxkNKqKHSEENm3ahDfffBNyuRxpaWmYPn26RsVL1Y9giYhIv8lkMmzevFn1WKlUIj4+Hj///DOAx6M8RGTaeKErGZ0LFy5gypQpkMvleP7555GcnMyRGiIiE/Hrr7+qip2wsDCEhIToOCIi0jWO8JDR6d69OxYsWAAnJycsXLgQFhZ8mxMRmQp/f3/07dsX7u7u6Ny5s67DISI9wG+CpPcUCgWkUqlG23zyySfVlhUVFSElJQX9+/fnJARERHpKCAGFQtGgz2mO6hDRn/GSNtJrt27dwurVq5GRkVHvfQghcO7cOaxZswZHjx5FWlpa4wVIRCZn+fLlkEgkan/8/f11HZbRSExMxPr161FUVKTrUIjISHCEh/RS1Qw7R44cgRAC//3vf9GuXbt6nfGLi4tDeno6AMDd3R2urq6NHS4RmZjOnTsjMTFR9ZiXzjaOkydP4tSpUwAen/ACgPz8fDg7O3NknojqjZ/QpJeuXr2KpKQkAEDXrl1Vd9Ouj65du+LXX3/Fiy++iF69evGmdETUYBYWFnBzc9N1GEblwoULqiIyNDQUnTt3xrlz59CnTx+MHj0aH330EYseIqoXfvMjveTv74+AgAAMHz4cERERsLS0hEKhwMqVK3HixAmN9uXn54d58+YhKCiIxQ4RNYqMjAx4eHigbdu2mDBhArKysnQdksHz8vKCo6Mj+vbti+DgYNy6dQtLly7FjRs3sHPnTuTn5+s6RCIyUBzhIb0kkUgQGRmpenz16lVMnjwZP//8M9q0aYNLly7BxsamzvuztrbWRphEZIKCgoKwceNGdOjQAffu3cOKFSvQv39/pKeno3nz5jVuU1ZWhrKyMtVjuVwO4PGkLAqFQqPnr2qv6Xb6rlmzZpg6dSpkMhkKCwsxZMgQ5OTkoG3btjh8+DCaNWuGsrIynD59Gj179lS70bShM9Zj+iTmaVx0nacmz8uCh/ReSkoK+vbti9LSUjg4OOCDDz5gAUNEOhMeHq76d9euXREUFARvb2/s2LEDM2bMqHGb2NhYrFixotryw4cPa3Ty5s8SEhLqtZ2hGDFiBHbt2oW3334b6enpuHDhAm7dugW5XI6UlBT4+PgY3SVuxn5MqzBP46KrPEtKSurclgUP6b1u3bqhV69ekMlkWL9+PVq1aqVaJ4Qwug6PiAyLg4MD2rdvj+vXr9faZsmSJZg/f77qsVwuR+vWrTFkyBDY2dlp9HwKhQIJCQkIDQ3VeMp+QxIaGor+/fvjpZdeQnl5OXbu3Am5XA4LCwuEhoaiQ4cOug6x0ZjKMWWexkXXeVaNlNcFCx7Se+bm5ti7dy/s7e1Vxc2jR4+QlJQEqVSKsLAwHUdIRKasqKgIN27cwKRJk2ptY2VlBSsrq2rLpVJpvb8oNGRbQ1GV46NHj/DHH3/A2toa0dHRaN26ta5D0wpTOKYA8zQ2uspTk+dkwUNNrqysDOXl5bVe614TBwcHAI9HdC5evIjDhw+juLgYEokEzz//POzt7bUULRGRugULFmDEiBHw9vbG3bt3ERMTA3Nzc0RHR+s6NKPVokULjB8/HjKZDM7OzroOh4gMDAsealLZ2dnYuXMnrK2tMW3aNJibm2u0vVwux969e1FZWQlnZ2cMGzaMxQ4RNak7d+4gOjoaDx48QMuWLdGvXz+cPn0aLVu21HVoRu3PlzMTEWmCBQ81CSEEUlJSEB8fj4qKCtjZ2aGgoABOTk4a7cfe3h4vvPACKisr0bdvX40LJiKihtq2bZuuQzBIFRUVuHfvntFejkZE+osFDzWJiooKnDx5EhUVFfDz80NERARsbGxw/PhxtGvXDp6ennXeV9++fbUYKRERNTalUom4uDhcu3YNkZGR6Ny5MyedIaImw7swUpOQSqUYM2YMBg8ejOjoaJiZmWH+/PkYNGgQXn31VQghdB0iERFpgRAC+/fvx5UrVyCRSGBtbY2UlBQMGjQIubm5ug6PiEwAR3ioybi7u8Pd3V11hu/y5csAgNatW6O8vLzGGYyIiMiwXbp0CampqZBIJBg9ejT++OMPhISEID8/H4sWLcKGDRs42kNEWsWCh5pcy5Yt8ccff8DV1RXffvsthg0bBgC4f/8+WrRowU6PiMiIdO7cGbdv34arqyuKi4sREhKCgoICBAcH4/PPP0dRURF27NiBF198EW3atNF1uERkhPTukrY1a9agTZs2kMlkCAoKwtmzZ5/afufOnfD394dMJkNAQAAOHjyotj4uLg5DhgxRfZFOS0vTYvRUF05OTti7dy/S09MxbNgwlJeX48cff8S//vUvXLx4UdfhERFRI5JIJAgPD0ePHj3g4uICJycn9OvXD/Hx8VAoFPj2229x+/Zt7Nu3D0qlUtfhEpER0quCZ/v27Zg/fz5iYmKQkpKCbt26ISwsrNZrfE+dOoXo6GjMmDEDqampiIiIQEREBNLT01VtiouL0a9fP3z88cdNlQbVQc+ePeHs7Izr169jzZo1OH36NIQQuH37tq5DIyIiLfHy8sLx48dx6NAhVbGTn58PR0dHjB8/HmZmevW1hIiMhF5d0vbZZ59h5syZmDZtGgBg7dq1OHDgANavX4/FixdXa//5559j6NChWLhwIQDgvffeQ0JCAlavXo21a9cCgOrO1zdv3myaJExQRUUFfv31V3Tq1EnjbSsrKyGXy+Hg4ICXXnoJfn5+WoiQiIj0RdW01EqlEt7e3pDL5Rg/fjxsbW11HBkRGSu9KXjKy8tx/vx5LFmyRLXMzMwMISEhSE5OrnGb5ORkzJ8/X21ZWFgYdu/erc1Q6U/++OMP7Ny5E/fu3cOYMWM0Lno6dOiAiIgIdOrUCVKpVEtREhGRvjEzM0NkZCSEELC0tNR1OERkxPSm4Ll//z4qKyvh6uqqttzV1RVXr16tcZvs7Owa22dnZzcolrKyMpSVlakey+VyAIBCoYBCodBoX1XtNd3OEPz666/Yt28fysrKYG1tDTMzM9y7dw92dnaqiQfqkndVkWQMr5ExH+/amGLOgGnm3ZCcTel1orrjiS4iagp6U/Dok9jYWKxYsaLa8sOHD8PGxqZe+0xISGhoWHrnwYMHKCsrg42NDdq0aYNNmzZhzZo1CA0NxcSJEwEYZ951YYp5m2LOgGnmXZ+cS0pKtBAJERHRs+lNwePs7Axzc3Pk5OSoLc/JyYGbm1uN27i5uWnUvq6WLFmidqmcXC5H69atMWTIENjZ2Wm0L4VCgYSEBISGhhrdmSwhBC5fvgwPDw8sWrQIGzduBABcu3YNAwYMwIkTJ4wy76cx5uNdG1PMGTDNvBuSc9VIORmfkpISJCcnY9CgQTA3N9d1OERE1ehNwWNpaYnAwEAkJSUhIiICwOMfNCYlJWHOnDk1btOnTx8kJSVh3rx5qmUJCQno06dPg2KxsrKq8SaYUqm03l9sGrKtPuvevTsuXLiAzZs3QyKR4K9//Svee+895Ofn48GDB0ab97OYYt6mmDNgmnnXJ2dTe41MRVlZGTZv3oy7d++ipKQEEokECoUCo0aN0nVoREQqelPwAMD8+fMxZcoU9OzZE71798aqVatQXFysmrVt8uTJ8PT0RGxsLABg7ty5GDhwID799FMMGzYM27Ztw7lz57Bu3TrVPh8+fIisrCzcvXsXwOPRB+Dx6FBDR4LosW7dumH16tXw9/dHcHAwfvrpJ5w8eRKVlZXIyclBq1atdB0iERE1soqKCuzYsQN3796FtbU18vPzMWPGDAgh8NNPP6FDhw6wt7fnVNNEpHN6VfCMGzcOeXl5WLZsGbKzs9G9e3fEx8erJibIyspS++AMDg7Gli1bsHTpUrz99tvw8/PD7t270aVLF1WbvXv3qgomAIiKigIAxMTEYPny5U2TmAl47bXXoFQq8dVXX6num9S8efMaR8qIiMjw5ebmIisrC1KpFE5OTpg+fToqKiowZswYuLi4YN26dejUqROGDx+umsiGiEgX9KrgAYA5c+bUegnbsWPHqi0bM2YMxowZU+v+pk6diqlTpzZSdPQ0ZmZm8Pf3R0lJCUJDQ/Hbb7/BwcFB12EREZEWeHh4YNKkSaioqMCnn36KiooKREVF4Z133sGWLVtQWVmJ3NxcKBQKTjtNRDqldwUP6Z5SqcRPP/2ELl26oEWLFhpt279/fwQHB8PMzAyZmZlaipCIiPSBl5cXAOCLL75AYGAgIiMj8a9//QuVlZXo0KEDRo8ezd9vEZHOseAhNUVFRYiLi0NmZiauXLmCV199FRYWdX+bWFhYwMLCgvfcICIyIWZmZpg+fToAICIiApmZmQgPD+fvd4hIL/CTiFRyc3Px1VdfITMzE1KpFG3btkV4eDi+//57XYdGREQGolOnThg2bBiLHSLSGxzhIRUHBwdYW1tDJpPBzMwMY8eORUFBAa5du4aRI0dCKpXyh6dEREREZFB4+oVULC0tMX78eLRv3x5vvPEGCgoKEBQUhD179mDXrl24dOmSrkMkIiIiItIIR3hIjYODA0JCQhAVFYXOnTtj4MCBOHToEBQKBbKzs9GxY0feSZuIyEQIITiyT0QGjyM8VI1EIsGWLVswbtw4HDlyBAqFAt7e3pg4cSKLHSIiE/HgwQOsW7cOubm5iI+PR0VFha5DIiKqF47wUI0kEgnatWuHbt26wdvbG927d+dZPiIiEyGXy7Fp0yYUFBRg/vz52Lx5M8aPH4/FixfDzc0NLVu21HWIRER1xoKHaiWRSBAREaHrMIiIqAmVlJTgu+++Q0FBAdLS0rB7924AgI+PD+Li4mBvb4+ZM2fC1tZWt4ESEdURL2kzAUIInDlzBgcOHNB1KEREpOckEglkMhkqKioQHx8PiUSCd999V3UD0Q4dOsDa2lrHURIR1R1HeIxcaWkp9u3bh8uXL6OyshJHjx7F9OnT0alTJ12HRkREesja2hqTJk1CYWEhBgwYgNOnT8PNzQ13795FSEgIgoODeYkzERkUFjxGTAiBf//738jOzsbDhw+RlJSES5cu4dixY4iPj4ezs7OuQyQiIj0klUrh5OSE0NBQhIaGori4GFlZWejYsaOuQyMi0hgvaTNiEokEwcHBqpl2Ll26BHd3d0RHR+PLL79Ebm6urkMkIiIDYGtry2KHiAwWR3iMXEBAAN5//30kJiaiR48e6Ny5M4qKiiCRSHDr1i24uLjoOkQiIiIiIq1hwWMC7OzscOTIEaSnp+PkyZPw8PDAsGHD4OHhoevQiIiIiIi0igWPiXBzc0OLFi3g5OSE7t27w8yMVzMSERERkfHjt14TIpVK0aNHDxY7REQmrqCgAEIIZGdnQwgBIYSuQyIi0hp+8zVgCoVC1yEQEZGBuXnzJr744gu88sorCAwMxNatW3Hy5Eldh0VEpDW8pM1AXbx4EfHx8ejQoQMGDRoEOzs7XYdERER67t69e9iyZQv27NmDtLQ0jB49GhkZGbh+/Tr8/f15uwIiMkoc4TEwFRUV2L9/P7Zv347du3cjIiICixYtwo0bN3QdGhER6bFHjx5h8+bNOHr0KFJSUjBp0iR07NgR5ubmGD16NIsdIjJaHOExMKdPn8ahQ4ewc+dOFBUVYfjw4XBzc8OePXswe/ZsWFlZ6TpEIiLSQ9bW1hgwYAAsLCyQn5+P5557DiUlJYiKioK3t7euwyMi0hoWPAbm+eefx/nz52Fubo633noL1tbWAIB27drxR6dERPRUvXv3RmBgIN58802YmZmhuLgYzZo103VYRERaxYLHwFhYWGDu3Lno0qULbt26hdLSUgwfPhxeXl66Do2IiAyAubm56t8sdojIFLDgMVCDBw9GcXExZDKZWudFRERERET/w4LHgNna2uo6BCIiIiIivcZZ2oiIiIiIyGix4NEjcrkc2dnZUCqVug6FiIgMlFKphEKhQHFxsa5DISLSCyx49MT169cxd+5c9O/fH7Gxsbh//76uQyIiIgOTmpqKyMhI/P3vf8fmzZtRXl6u65CIiHSOBY+OKZVK7N+/H+PHj8e9e/cwfvx4VFRU4PDhw7oOjYiIDMjPP/+M6dOnw8vLCxUVFXj48CEePnyo67CIiHSOBY+OFRUVYc+ePbCxsUGfPn1gZmYGf39/jBgxQtehERGRgbh8+TLGjx+PwMBAtGjRAlZWVnj11Vfh5uam69CIiHSOBY+O2dnZYeHChQgICICDgwMmTJiAcePGoXnz5roOjYiIDEBlZSUSExPh5eWFgwcPwsHBAbNnz4azs7OuQyMi0guclloPtG/fHl988YWuwyAiIgNkbm6OSZMmwdfXF506dYKPj4+uQyIi0it6OcKzZs0atGnTBjKZDEFBQTh79uxT2+/cuRP+/v6QyWQICAjAwYMH1dYLIbBs2TK4u7vD2toaISEhyMjI0GYKRERk5DTtq7TJ0dERw4YNY7FDRFQDvSt4tm/fjvnz5yMmJgYpKSno1q0bwsLCkJubW2P7U6dOITo6GjNmzEBqaioiIiIQERGB9PR0VZu///3v+L//+z+sXbsWZ86cga2tLcLCwlBaWtpUaRERkRHRtK8iIiLd0buC57PPPsPMmTMxbdo0dOrUCWvXroWNjQ3Wr19fY/vPP/8cQ4cOxcKFC9GxY0e899576NGjB1avXg3g8ejOqlWrsHTpUrz88svo2rUr/vOf/+Du3bvYvXu31vMRQnBaUCIiI6NpX0VERLqjV7/hKS8vx/nz57FkyRLVMjMzM4SEhCA5ObnGbZKTkzF//ny1ZWFhYapiJjMzE9nZ2QgJCVGtt7e3R1BQEJKTkxEVFVVtn2VlZSgrK1M9lsvlAACFQgGFQlHnfM6fP4+tW7fCyckJoaGhsLW1rfO2hq7qddLk9TIGppi3KeYMmGbeDcnZmF6n+vRVjdWvVG3z57+NlankCZhOrszTuOg6T02eV68Knvv376OyshKurq5qy11dXXH16tUat8nOzq6xfXZ2tmp91bLa2jwpNjYWK1asqLb88OHDsLGxeWYeSqUSycnJkMlkcHJyQkVFBfbt2wc7O7tnbmtsEhISdB2CTphi3qaYM2Caedcn55KSEi1Eohv16asa2q8Aj4um0tJS2NvbAzCd956p5AmYTq7M07joKk9N+hW9Knj0xZIlS9RGjeRyOVq3bo0hQ4bUqWj55JNPkJOTg7Zt2yIrKwv+/v4YPXo0pFKpNsPWKwqFAgkJCQgNDWXeRs4UcwZMM++G5Fw1omGqGtqvXLlyBbt27YJUKsXYsWNx5coVo3/vmdL/MVPJlXkaF13nqUm/olcFj7OzM8zNzZGTk6O2PCcnp9abp7m5uT21fdXfOTk5cHd3V2vTvXv3GvdpZWUFKyurasulUmmdDmiPHj2wfft2SKVS/OMf/8CJEyfqvK2xYd6mwxRzBkwz7/rkbEyvUX36qob0K2fPnsW+fftgYWGBoqIitGzZEleuXDGZ956p5AmYTq7M07joKk9NnlOvJi2wtLREYGAgkpKSVMuUSiWSkpLQp0+fGrfp06ePWnvg8dBaVXsfHx+4ubmptZHL5Thz5kyt+2yo0NBQbNq0CV9//TWaNWumlecgIiLdqE9fVV95eXn47LPPYGFhgd9++w0jR45kv0JEpCG9GuEBgPnz52PKlCno2bMnevfujVWrVqG4uBjTpk0DAEyePBmenp6IjY0FAMydOxcDBw7Ep59+imHDhmHbtm04d+4c1q1bBwCQSCSYN28e3n//ffj5+cHHxwfvvvsuPDw8EBERoZUczM3N0bVrV63sm4iIdO9ZfVVjSUxMxL59+3Dnzh289dZbCA4ONvofQhMRNTa9K3jGjRuHvLw8LFu2DNnZ2ejevTvi4+NVPw7NysqCmdn/BqaCg4OxZcsWLF26FG+//Tb8/Pywe/dudOnSRdXmb3/7G4qLi/Haa68hPz8f/fr1Q3x8PGQyWZPnR0REhu9ZfVVjEEJAJpNh8uTJ6NevH8aOHdto+yYiMiV6V/AAwJw5czBnzpwa1x07dqzasjFjxmDMmDG17k8ikWDlypVYuXJlg+ISQuDy5ctwdXVFixYtGrQvIiIybE/rqxqDRCJBREQE/P394e/vr7XnISIydnpZ8OirHTt24O7du/D09MSMGTMgkUh0HRIRERkxiUSCjh076joMIiKDpleTFui73377DZWVlTh9+jSUSqWuwyEiIiIiomdgwaOBW7du4ZtvvoGVlZXa74iIiIiIiEg/8Vu7Bk6dOoXp06dj4cKFvJyNiIga7Nq1a/j+++951QARkRbxNzwaWLx4MSZOnMh7IBARUaOIi4uDTCaDt7c3evXqpetwiIiMEkd4NBAVFcVih4iIGtWFCxc4wkNEpEUc4dEAf7dDRESN6fjx47hz5w5atWql61CIiIwWv8ETERHpSEZGBiZMmAALC55/JCLSFhY8REREOjJx4kRMnz4drq6uug6FiMho8ZRSHQghAAByuVzjbRUKBUpKSiCXyyGVShs7NL3FvE0nb1PMGTDNvBuSc9XnZ9Xnqamreh2GDRsGV1dXjfoXU3nvmUqegOnkyjyNi67z1KRfYcFTB4WFhQCA1q1b6zgSIiLDVlhYCHt7e12HoXNV/UpoaKiOIyEiMmx16VckgqfbnkmpVOLu3bto3ry5xvffkcvlaN26NW7fvg07OzstRah/mLfp5G2KOQOmmXdDchZCoLCwEB4eHpwABuxX6sJU8gRMJ1fmaVx0nacm/QpHeOrAzMyswTPo2NnZGfWbvjbM23SYYs6AaeZd35w5svM/7FfqzlTyBEwnV+ZpXHSZZ137FZ5mIyIiIiIio8WCh4iIiIiIjBYLHi2zsrJCTEwMrKysdB1Kk2LeppO3KeYMmGbeppizPjKV42AqeQKmkyvzNC6GlCcnLSAiIiIiIqPFER4iIiIiIjJaLHiIiIiIiMhoseAhIiIiIiKjxYKHiIiIiIiMFgueelizZg3atGkDmUyGoKAgnD179qntd+7cCX9/f8hkMgQEBODgwYNq64UQWLZsGdzd3WFtbY2QkBBkZGRoMwWNNXbOcXFxGDJkCFq0aAGJRIK0tDQtRl9/jZm3QqHAokWLEBAQAFtbW3h4eGDy5Mm4e/euttPQWGMf7+XLl8Pf3x+2trZwdHRESEgIzpw5o80UNNbYOf/ZrFmzIJFIsGrVqkaOuuEaO++pU6dCIpGo/Rk6dKg2UzA5mh4zQ7N8+fJq7yF/f39dh9VgJ06cwIgRI+Dh4QGJRILdu3errTeE7wJ19axcjeFzIjY2Fr169ULz5s3h4uKCiIgIXLt2Ta1NaWkpZs+ejRYtWqBZs2YYPXo0cnJydBRx/dUl10GDBlU7prNmzdJRxDUQpJFt27YJS0tLsX79enHp0iUxc+ZM4eDgIHJycmpsf/LkSWFubi7+/ve/i8uXL4ulS5cKqVQqLl68qGrz0UcfCXt7e7F7925x4cIFMXLkSOHj4yMePXrUVGk9lTZy/s9//iNWrFghvv76awFApKamNlE2ddfYeefn54uQkBCxfft2cfXqVZGcnCx69+4tAgMDmzKtZ9LG8d68ebNISEgQN27cEOnp6WLGjBnCzs5O5ObmNlVaT6WNnKvExcWJbt26CQ8PD/HPf/5Ty5loRht5T5kyRQwdOlTcu3dP9efhw4dNlZLR0/SYGaKYmBjRuXNntfdQXl6ersNqsIMHD4p33nlHxMXFCQDihx9+UFuv798FNPGsXI3hcyIsLExs2LBBpKeni7S0NPHSSy8JLy8vUVRUpGoza9Ys0bp1a5GUlCTOnTsnnn/+eREcHKzDqOunLrkOHDhQzJw5U+2YFhQU6DBqdSx4NNS7d28xe/Zs1ePKykrh4eEhYmNja2w/duxYMWzYMLVlQUFB4i9/+YsQQgilUinc3NzEJ598olqfn58vrKysxNatW7WQgeYaO+c/y8zM1NuCR5t5Vzl79qwAIG7dutU4QTeCpsi7oKBAABCJiYmNE3QDaSvnO3fuCE9PT5Geni68vb31ruDRRt5TpkwRL7/8slbiJc2PmSGKiYkR3bp103UYWvVkEWAI3wXqq7aCx9g+J3JzcwUAcfz4cSHE4+MnlUrFzp07VW2uXLkiAIjk5GRdhdkonsxViMcFz9y5c3UX1DPwkjYNlJeX4/z58wgJCVEtMzMzQ0hICJKTk2vcJjk5Wa09AISFhanaZ2ZmIjs7W62Nvb09goKCat1nU9JGzoagqfIuKCiARCKBg4NDo8TdUE2Rd3l5OdatWwd7e3t069at8YKvJ23lrFQqMWnSJCxcuBCdO3fWTvANoM1jfezYMbi4uKBDhw54/fXX8eDBg8ZPwATV55gZqoyMDHh4eKBt27aYMGECsrKydB2SVun7dwFtMLbPiYKCAgCAk5MTAOD8+fNQKBRqx9Tf3x9eXl4Gf0yfzLXK5s2b4ezsjC5dumDJkiUoKSnRRXg1stB1AIbk/v37qKyshKurq9pyV1dXXL16tcZtsrOza2yfnZ2tWl+1rLY2uqSNnA1BU+RdWlqKRYsWITo6GnZ2do0TeANpM+/9+/cjKioKJSUlcHd3R0JCApydnRs3gXrQVs4ff/wxLCws8NZbbzV+0I1AW3kPHToUkZGR8PHxwY0bN/D2228jPDwcycnJMDc3b/xETEh9jpkhCgoKwsaNG9GhQwfcu3cPK1asQP/+/ZGeno7mzZvrOjyt0PfvAo3N2D4nlEol5s2bh759+6JLly4AHh9TS0vLaic0Df2Y1pQrAIwfPx7e3t7w8PDAL7/8gkWLFuHatWuIi4vTYbT/w4KHSAcUCgXGjh0LIQS+/PJLXYfTJF544QWkpaXh/v37+PrrrzF27FicOXMGLi4uug6t0Z0/fx6ff/45UlJSIJFIdB1Ok4qKilL9OyAgAF27doWvry+OHTuGwYMH6zAyMhTh4eGqf3ft2hVBQUHw9vbGjh07MGPGDB1GRo3F2D4nZs+ejfT0dPz3v//VdShaV1uur732murfAQEBcHd3x+DBg3Hjxg34+vo2dZjV8JI2DTg7O8Pc3LzaDBs5OTlwc3OrcRs3N7entq/6W5N9NiVt5GwItJl3VbFz69YtJCQk6M3oDqDdvG1tbdGuXTs8//zz+Pbbb2FhYYFvv/22cROoB23k/NNPPyE3NxdeXl6wsLCAhYUFbt26hb/+9a9o06aNVvLQVFP9327bti2cnZ1x/fr1hgdt4upzzIyBg4MD2rdvb9TvIX3/LqBthvw5MWfOHOzfvx9Hjx5Fq1atVMvd3NxQXl6O/Px8tfaGfExry7UmQUFBAKA3x5QFjwYsLS0RGBiIpKQk1TKlUomkpCT06dOnxm369Omj1h4AEhISVO19fHzg5uam1kYul+PMmTO17rMpaSNnQ6CtvKuKnYyMDCQmJqJFixbaSaCemvJ4K5VKlJWVNTzoBtJGzpMmTcIvv/yCtLQ01R8PDw8sXLgQP/74o/aS0UBTHes7d+7gwYMHcHd3b5zATVh9jpkxKCoqwo0bN4z6PaTv3wW0zRA/J4QQmDNnDn744QccOXIEPj4+ausDAwMhlUrVjum1a9eQlZVlcMf0WbnWpOp2I3pzTHU8aYLB2bZtm7CyshIbN24Uly9fFq+99ppwcHAQ2dnZQgghJk2aJBYvXqxqf/LkSWFhYSH+8Y9/iCtXroiYmJgap6V2cHAQe/bsEb/88ot4+eWX9WoqSm3k/ODBA5GamioOHDggAIht27aJ1NRUce/evSbPrzaNnXd5ebkYOXKkaNWqlUhLS1OburGsrEwnOdaksfMuKioSS5YsEcnJyeLmzZvi3LlzYtq0acLKykqkp6frJMcnaeM9/iR9nKWtsfMuLCwUCxYsEMnJySIzM1MkJiaKHj16CD8/P1FaWqqTHI3Ns46ZMfjrX/8qjh07JjIzM8XJkydFSEiIcHZ21ptp7OursLBQpKamitTUVAFAfPbZZyI1NVU1S6e+fxfQxNNyNZbPiddff13Y29uLY8eOqfXnJSUlqjazZs0SXl5e4siRI+LcuXOiT58+ok+fPjqMun6elev169fFypUrxblz50RmZqbYs2ePaNu2rRgwYICOI/8fFjz18MUXXwgvLy9haWkpevfuLU6fPq1aN3DgQDFlyhS19jt27BDt27cXlpaWonPnzuLAgQNq65VKpXj33XeFq6ursLKyEoMHDxbXrl1rilTqrLFz3rBhgwBQ7U9MTEwTZFN3jZl31RTcNf05evRoE2VUN42Z96NHj8SoUaOEh4eHsLS0FO7u7mLkyJHi7NmzTZVOnTT2e/xJ+ljwCNG4eZeUlIghQ4aIli1bCqlUKry9vcXMmTON6su4PnjaMTMG48aNE+7u7sLS0lJ4enqKcePGievXr+s6rAY7evRojZ//Vf/HDOG7QF09LVdj+ZyorT/fsGGDqs2jR4/EG2+8IRwdHYWNjY0YNWqUXp3Yratn5ZqVlSUGDBggnJychJWVlWjXrp1YuHChXt2HRyKEENodQyIiIiIiItIN/oaHiIiIiIiMFgseIiIiIiIyWix4iIiIiIjIaLHgISIiIiIio8WCh4iIiIiIjBYLHiIiIiIiMloseIiIiIiIyGix4CECkJGRgSFDhsDe3h4SiQS7d+/WdUhGRSKRYPny5boOg4ioybBf0S72K6QJFjxkUDZu3AiJRKL6Y2FhAU9PT0ydOhW///57vfc7ZcoUXLx4ER988AE2bdqEnj17NmLUhuOtt96CRCLB9evXa23zzjvvQCKR4JdffmnCyIiItIP9inaxXyF9wIKHDNLKlSuxadMmrF27FuHh4fjuu+8wcOBAlJaWaryvR48eITk5GTNmzMCcOXMwceJEtGrVSgtR678JEyYAALZs2VJrm61btyIgIABdu3ZtqrCIiLSO/Yp2sF8hfcCChwxSeHg4Jk6ciFdffRXffPMNFixYgBs3bmDv3r0a7ysvLw8A4ODg0GjxlZaWQqlUNtr+mkpQUBDatWuHrVu31rg+OTkZmZmZqg6MiMhYsF/RDvYrpA9Y8JBR6N+/PwDgxo0basuvXr2KV155BU5OTpDJZOjZs6da57V8+XJ4e3sDABYuXAiJRII2bdqo1v/++++YPn06XF1dYWVlhc6dO2P9+vVqz3Hs2DFIJBJs27YNS5cuhaenJ2xsbCCXywEAZ86cwdChQ2Fvbw8bGxsMHDgQJ0+eVNvH8uXLVUP+U6dOhYODA+zt7TFt2jSUlJRUy/e7775D7969YWNjA0dHRwwYMACHDx9Wa3Po0CH0798ftra2aN68OYYNG4ZLly4987WcMGECrl69ipSUlGrrtmzZAolEgujoaJSXl2PZsmUIDAyEvb09bG1t0b9/fxw9evSZzzF16lS11/nJ16GmfAMDA2FtbQ0nJydERUXh9u3bam0yMjIwevRouLm5QSaToVWrVoiKikJBQcEz4yEiehL7FfYr7FeMh4WuAyBqDDdv3gQAODo6qpZdunQJffv2haenJxYvXgxbW1vs2LEDERER2LVrF0aNGoXIyEg4ODjg//2//4fo6Gi89NJLaNasGQAgJycHzz//PCQSCebMmYOWLVvi0KFDmDFjBuRyOebNm6cWw3vvvQdLS0ssWLAAZWVlsLS0xJEjRxAeHo7AwEDExMTAzMwMGzZswIsvvoiffvoJvXv3VtvH2LFj4ePjg9jYWKSkpOCbb76Bi4sLPv74Y1WbFStWYPny5QgODsbKlSthaWmJM2fO4MiRIxgyZAgAYNOmTZgyZQrCwsLw8ccfo6SkBF9++SX69euH1NTUGjuFKhMmTMCKFSuwZcsW9OjRQ7W8srISO3bsQP/+/eHl5YX79+/jm2++QXR0NGbOnInCwkJ8++23CAsLw9mzZ9G9e/d6HMnqPvjgA7z77rsYO3YsXn31VeTl5eGLL77AgAEDkJqaCgcHB5SXlyMsLAxlZWV488034ebmht9//x379+9Hfn4+7O3tGyUWIjId7FfYr7BfMSKCyIBs2LBBABCJiYkiLy9P3L59W3z//feiZcuWwsrKSty+fVvVdvDgwSIgIECUlpaqlimVShEcHCz8/PxUyzIzMwUA8cknn6g914wZM4S7u7u4f/++2vKoqChhb28vSkpKhBBCHD16VAAQbdu2VS2rei4/Pz8RFhYmlEqlanlJSYnw8fERoaGhqmUxMTECgJg+fbrac40aNUq0aNFC9TgjI0OYmZmJUaNGicrKSrW2Vc9RWFgoHBwcxMyZM9XWZ2dnC3t7+2rLa9KrVy/RqlUrteeIj48XAMRXX30lhBCioqJClJWVqW33xx9/CFdX12p5ABAxMTGqx1OmTBHe3t7Vnrfqdahy8+ZNYW5uLj744AO1dhcvXhQWFhaq5ampqQKA2Llz5zNzIyL6M/Yr7FeEYL9i7HhJGxmkkJAQtGzZEq1bt8Yrr7wCW1tb7N27V/Wj0IcPH+LIkSMYO3YsCgsLcf/+fdy/fx8PHjxAWFgYMjIynjr7jhACu3btwogRIyCEUG1///59hIWFoaCgoNrQ/JQpU2Btba16nJaWhoyMDIwfPx4PHjxQbV9cXIzBgwfjxIkT1a7HnjVrltrj/v3748GDB6rLGHbv3g2lUol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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_solutions(solution, ref, size, idx)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", - "from wntr_quantum.sampler.step.full_random import IncrementalStep\n", - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "\n", - "\n", - "# set up the model\n", - "inp_file = './networks/Net0.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# create the AML model\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "\n", - "# sampler \n", - "sampler = SimulatedAnnealing()\n", - "\n", - "# create the qubo solver\n", - "sim = wntr_quantum.sim.QuboPolynomialSolver(wn, flow_encoding=..., head_encoding=...)\n", - "res = sim.run_sim(model, sampler=sampler)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/net0_data/solutions.pkl b/docs/notebooks/net0_data/solutions.pkl deleted file mode 100644 index f5d7bd454080dbd46a70b0a41a7891118323a190..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6321 zcmbtYYjBKb6c!tG3(}5j>)IqlNQelL+xb$r%67!1w1ja>h$6NtYbL1D5hPtrTVGr< zK?x%0CUV_KYE)@W(K2OdrnIU|M>^9nE<;UezCHWAQ~mM1Z{z!scW1I^&w0;z&U2pg zt?|v<-?f?NnW+amJEc38*fPWBEw!hHEpaT)SUAUSpOdLO{bt!y=PgZNmSK0y%g{IL z&K908>duzwDg2eqy0uxBKGJ8dzCoX)2U~rtzFB&jud_u;My4&*FljzNO3&WN|Ft-M 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plt.hist(np.array(solution)[:,3],bins=25)\n", - "idx = 3\n", - "plt.hist(np.array(solution)[:,idx],bins=25)\n", - "plt.vlines(ref[0][idx],0, 17,colors='black', ls='--')\n", - "plt.vlines(ref[0][idx]*0.9,0, 17,colors='grey', ls='--')\n", - "plt.vlines(ref[0][idx]*1.1,0, 17,colors='grey', ls='--')\n", - "plt.ylim([0,17])\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([1., 0., 2., 0., 0., 0., 0., 1., 1., 0., 1., 0., 0., 0., 0., 1., 0.,\n", - " 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", - " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]),\n", - " array([-35722.03292166, -35720.25933288, -35718.48574409, -35716.71215531,\n", - " -35714.93856652, -35713.16497774, -35711.39138895, -35709.61780017,\n", - " -35707.84421138, -35706.0706226 , -35704.29703381, -35702.52344502,\n", - " -35700.74985624, -35698.97626745, -35697.20267867, -35695.42908988,\n", - " -35693.6555011 , -35691.88191231, -35690.10832353, -35688.33473474,\n", - " -35686.56114596, -35684.78755717, -35683.01396839, -35681.2403796 ,\n", - " -35679.46679082, -35677.69320203, -35675.91961324, -35674.14602446,\n", - " -35672.37243567, -35670.59884689, -35668.8252581 , -35667.05166932,\n", - " -35665.27808053, -35663.50449175, -35661.73090296, -35659.95731418,\n", - " -35658.18372539, -35656.41013661, -35654.63654782, -35652.86295903,\n", - " -35651.08937025, -35649.31578146, -35647.54219268, -35645.76860389,\n", - " -35643.99501511, -35642.22142632, -35640.44783754, -35638.67424875,\n", - " -35636.90065997, -35635.12707118, -35633.3534824 ]),\n", - " )" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(np.array(energies), bins=50)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "distance = [np.linalg.norm(r[2:]-s[2:]) for r,s in zip(ref, solution)]" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "x and y must be the same size", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[78], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43menergies\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistance\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/pyplot.py:3699\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m 3680\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mscatter)\n\u001b[1;32m 3681\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatter\u001b[39m(\n\u001b[1;32m 3682\u001b[0m x: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3697\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3698\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PathCollection:\n\u001b[0;32m-> 3699\u001b[0m __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3700\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3701\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3702\u001b[0m \u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3703\u001b[0m \u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3704\u001b[0m \u001b[43m \u001b[49m\u001b[43mmarker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmarker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3705\u001b[0m \u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3706\u001b[0m \u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3707\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3708\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3709\u001b[0m \u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3710\u001b[0m \u001b[43m \u001b[49m\u001b[43mlinewidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlinewidths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3711\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3712\u001b[0m \u001b[43m \u001b[49m\u001b[43mplotnonfinite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplotnonfinite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3713\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3714\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3715\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3716\u001b[0m sci(__ret)\n\u001b[1;32m 3717\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1467\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1468\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1469\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/axes/_axes.py:4655\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4653\u001b[0m y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mravel(y)\n\u001b[1;32m 4654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39msize:\n\u001b[0;32m-> 4655\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must be the same size\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4657\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4658\u001b[0m s \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m20\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_internal.classic_mode\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[1;32m 4659\u001b[0m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlines.markersize\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2.0\u001b[39m)\n", - "\u001b[0;31mValueError\u001b[0m: x and y must be the same size" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(energies, distance)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "only integer scalar arrays can be converted to a scalar index", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[59], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m idx_sort \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margsort(distance)\n\u001b[0;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mdistance\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx_sort\u001b[49m\u001b[43m]\u001b[49m, energies[idx_sort])\n", - "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index" - ] - } - ], - "source": [ - "idx_sort = np.argsort(distance)\n", - "plt.plot(distance[idx_sort], energies[idx_sort])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dd = [distance[i] for i in idx_sort]\n", - "ee = [energies[i] for i in idx_sort]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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"plt.xticks(list(range(1, 1 + len(labels))), labels, rotation=45)\n", - "plt.grid()\n", - "plt.xlabel('Diameters', fontsize=12)\n", - "plt.ylabel('Energy', fontsize=12)" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3081985158405587" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "np.min(vals[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.29128992046207713" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.min(vals[-1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/plot_test_qubo_solver.ipynb b/docs/notebooks/plot_test_qubo_solver.ipynb deleted file mode 100644 index 4e46cfb..0000000 --- a/docs/notebooks/plot_test_qubo_solver.ipynb +++ /dev/null @@ -1,469 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 165, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt \n", - "import pickle" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "metadata": {}, - "outputs": [], - "source": [ - "solution = pickle.load(open('solutions.pkl','rb'))\n", - "ref = pickle.load(open('encoded_reference_solutions.pkl','rb'))\n", - "energies = np.array(pickle.load(open('energies.pkl','rb')))" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.311,\n", - " 0.05105,\n", - " 0.2322,\n", - " 0.03113,\n", - " 0.1679,\n", - " 0.07615,\n", - " 0.02345,\n", - " -0.02054,\n", - " 200.8,\n", - " 181.9,\n", - " 195.6,\n", - " 164.1,\n", - " 190.6,\n", - " 177.9]" - ] - }, - "execution_count": 167, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref = [3.110e-01, 5.105e-02, 2.322e-01, 3.113e-02, 1.679e-01, 7.615e-02, 2.345e-02, -2.054e-02, 2.008e+02, 1.819e+02, 1.956e+02, 1.641e+02, 1.906e+02, 1.779e+02]\n", - "ref" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": {}, - "outputs": [], - "source": [ - "idx = np.argmin(energies)\n", - "idx = 1\n", - "energies[idx]\n", - "ref = [ref]*10" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-35722.03292166],\n", - " [-35703.83316825],\n", - " [-35707.98248599],\n", - " [-35706.86539028],\n", - " [-35679.87963652],\n", - " [-35686.1686008 ],\n", - " [-35633.3534824 ],\n", - " [-35717.54215684],\n", - " [-35694.95182639],\n", - " [-35716.82092095]])" - ] - }, - "execution_count": 169, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "energies" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.50000000e+02],\n", - " [2.43044619e+01],\n", - " [3.68034550e+01],\n", - " [3.29134752e+01],\n", - " [2.21512045e+00],\n", - " [4.15454621e+00],\n", - " [2.11247776e-02],\n", - " [9.57325927e+01],\n", - " [9.99940675e+00],\n", - " [8.90711263e+01]])" - ] - }, - "execution_count": 175, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "size = 150 * np.exp(-0.1 * (energies-energies.min()))\n", - "size" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_solutions(solutions, references, size, best_index):\n", - " fig = plt.figure(figsize=plt.figaspect(0.5))\n", - " ax1 = fig.add_subplot(121)\n", - "\n", - " ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - " ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - " ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - " ax1.grid()\n", - "\n", - " for r, sol, s in zip(references, solutions, size):\n", - " ax1.scatter(\n", - " r[:8], sol[:8], s=s, lw=1, edgecolors=\"w\",alpha=0.5, facecolors='orange'\n", - " )\n", - "\n", - " ax1.scatter(\n", - " references[best_index][:8], solutions[best_index][:8], s=150, lw=1, edgecolors=\"w\", facecolors='C0'\n", - " )\n", - "\n", - " ax1.set_xlabel(\"Reference Values\", fontsize=12)\n", - " ax1.set_ylabel(\"QUBO Values\", fontsize=12)\n", - " ax1.set_title(\"Flow Rate\", fontsize=14)\n", - "\n", - " ax2 = fig.add_subplot(122)\n", - "\n", - " ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - " ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - " ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "\n", - " for r, sol, s in zip(references, solutions, size):\n", - " ax2.scatter(\n", - " r[8:],\n", - " sol[8:],\n", - " s=s,\n", - " lw=1,\n", - " edgecolors=\"w\",\n", - " alpha=0.5, facecolors='orange'\n", - " )\n", - " ax2.scatter(\n", - " references[best_index][8:], solutions[best_index][8:], s=150, lw=1, edgecolors=\"w\", facecolors='C0'\n", - " )\n", - " ax2.grid()\n", - "\n", - " ax2.set_xlabel(\"Reference Values\", fontsize=12)\n", - " ax2.set_title(\"Pressure\", fontsize=14)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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zc1O9vb3V9u3bq++8846alpZW5P5z585VmzdvrhoMBrVBgwbqjBkz1Nzc3FLLUpfm0qVL6tNPP602b95cdXZ2Vr28vNRWrVqpjzzyiLpp06abPmdC5FNU9S9jmkIIIYQQQghRS8gaHiGEEEIIIUStJQmPEEIIIYQQotaShEcIIYQQQghRa0nCI4QQQgghhKi1JOERQgghhBBC1FqS8AghhBBCCCFqLdl4tAysVitxcXF4enqiKEpVhyOEEDWOqqqkpaURFhaGTieftcnrihBCVEx5Xlck4SmDuLg46tevX9VhCCFEjXfx4kXq1atX1WFUOXldEUII+yjL64okPGXg6ekJaE+ol5dXlcVhNpvZsGEDgwYNwmAwVFkctqipsdfUuKHmxi5xV77iYrdYLFy4cIHGjRvb5R4mk4n69esX/D2t6yryulKTf9eqijxn5SPPV/nJc1Y+9ni+yvO6IglPGeRPN/Dy8qryhMfNzQ0vL68a9z9TTY29psYNNTd2ibvylRS7r6+v3e8l07c0FXldqcm/a1VFnrPykeer/OQ5KxtVVVEUxa7PV1leV2QitRBCCCGEEMKhMjIy+PTTTzlz5kyl31sSHiGEEEIIIYTDmM1mli5dyuXLl1m7di1Wq7VS7y8JjxBCCCGEEMIhrFYrK1euJDY2FhcXF8aOHVvp1Tol4RFCCCGEEEI4hNVqRVEU9Ho9Y8eOJSAgoNJjkKIFQghRB+UvHBVCCCEcycnJiXvvvZf4+HhCQ0OrJAYZ4RFCiDpoy5YtbNy4EVVVqzoUIYQQtZyiKFWW7IAkPEIIUeccOHCA7du3s3PnTs6dO+fQeyUlJTF06FBOnz7t0PsIIYQQJZEpbUIIUYecPHmSNWvWAHD77bfTuHFjzGazQ+6VkpLCoEGDOHToEJcvX3bIPYQQQoibkREeIYSoQzIyMgDo0KEDd9xxh8PuYzKZGDx4MIcOHSIoKIhPP/3UYfcSQgghSiMjPEIIUYd06NABf39/wsLCylS0wGpVURTIMls4ftmEKSsPL1cnWoV64WrQowK6YvrJy8sjLy8PPz8/Nm7cSMOGDR3waIQQQlSI1Qo5iZB5EbITwJoDOmdwCQa3+uAcCJVcQtoRJOERQog6pn79+mVuezoxnYW7zrHqUCwZuZaC4+5GPaM6hjOxZyOaB3sWuS4/0bl48SLt2rXDZDLZJXYhhBB2knUZUn6H9LNasqP++TceRa8lPR6NwactuJZecCC/AE6Zqn9arZB9Rft30l5w0gMq6FzALdwhSZYkPEIIIYr1382n+Hd0DMUVcsvItRC19wJL9l3g2YEtmNq/WZE2Pj4++Pj4OD5QIYQQ5ZN+Dq78rCU7xVEtkBX351dQX/BoVGJ3mzdvJiMjg2HDhqHX60u+b1Y8pPwGWdcAZ8i9CtnpYPAExQnST4HRt0xJVnlIwiOEEKKI/24+xewNMTdtp6owe0MMigJT+hVNeoQQQlQzuamQcxWMgeCuaiM91hwweIHeFSxZYL5hVD4/KdI7F5uEHDhwgB07dgDQqlUrmjdtCrnJkHNFG8nJTQWjNxj9wJID5jRwrQckQq4JDIbrbXy1/q9sL1OSVR6S8AghhChgtaqcTkwvU7Jzo9kbYohsE0KTQPdi1/QIIYSoQlYr5F6DnCTIOKslHXojeDYHjyaAAqhgyQCdETLjIf3kn9enn4XUo+AcrP18va9TfxxlzU+/AXB7tzY0D3PVpslhhcxL2pdqgaxYcA7SkiqjLyhGrR/vNqAza+uG8jJA5wRerSHlsHa+hCSrvCThEUKUzJqn/fERdYaiwMJd58p9narCwp1neWNUW/sHJYQQdYUjighkXdamsOVlQMJmiN8M2fFg9IeArlD/LnAOgIs/QMohLQkJHw6qGTLO/dmPOUNLlrKvaH1d2U7e+XPolYa0CUrhjqADcFUB1zBAhcsbIHEn5CaBkwf4dYbG48HzFkiP0/q89B1kXwS3MAjqoyVF/rdB2h+Fk6zrjzk+Pp6AgACcnMr33kTeyQghSibJTo2kqmrZFo4WI8tsYdWhWJuu/e5QLC8Na4WbUX5vhBCi3OxYRKBA+jm4sk2bTnZ1L1xer01nA239zOVo8LsNVCuc/QqcXCDtDPh20EZ+suLAmqslWwCXftBGha7uhcRdtAzw4JFOeQQ4p6DEnQfzNWg6GS7/pCVXVrPWd1a8NlWtxVOQcgTOrwYGQcxHoKYDOrj2K7jVg3p3QuhguLxOi8W7HbgGc/nyZW6//XbatWvH0qVLy/XU1vw6c0IIIQpcunSJTz/9lJSUFJuuP37ZVKgaW0lUVSX70vFCxzJyLZy4nGbTfYUQok5LP/fniEhWXOFkB/4sIpC4U2uXfu7mfWZd1goTmFO1kaJrv2nJy42c/eDqAUCFoF6gd4HAnpCXrk1/cwkBgw+4BGl9ZcVqfaWdAtdg0OkJdklCr1fAvZE2MmVO0ZIqcxroDICixd/6RUg/AyfnQeJW7f6uIVoy5xamJUfx0fDHXMg8B4G9tcQv8yJJSUlERERw6tQpDh06RHJycrmeXkl4hBCilkhOTmbp0qVcvnyZrVu32tSHKSvvpm1UVSVl2yISov6J6cCPha/PNtt0XyGEqLPyE5OSKqb9VfrZ68nH5ZLbWK1/jhbpjKDmaYmIai3cTucMmedB5wpNJkLbl6DBveASqhUv0LtoyUjGWUg+qFVSU63a/OfMOEg+DKnHtPLS2Qng5K5dp6IlSapZu79XK/BqChdXaklPXqZ2f89mENwPvFpqCZY5HTLOayNJOj0YfVGzErjrrrs4duwY4eHhbNq0idDQ8q3rkXkHQghRC2RkZBAVFUVmZiahoaEMHTrUpn68XG/+spC6axmmPSsAUHSFy496uRhsuq8QQtRJNyYm5VHM+pZCchL/7NOaqyUqBh9tGtuNdM7g3hgyL0DsWq3IgJMz+LSHeiO1URqAlKPadzVPKyGtc4aMC4BVO2Y1a4mMd2ut0pveBW55EtzrAdfX9eRchdTjWrKkv160IPOyVqXNmq0lOgYvQAdJuyF8GHi1RMkz8eabbzJ58mRWr15NkyZNyvdcIQmPEELUChaLBb1ej7e3N+PGjcNoNNrUT6tQL9yN+hKntWVfOkrqjigAfPs/gmfHPxMrd6OelqFFNyEVQghRghsTk/K6YX1LEflFDwBykrWkxrcdZF28oZGiJSYugdroSsLPWvnotBRAp63h8e2oJTJpZ7RExZwGBm/tXMIWrZv86XeqBbxaaFXW9G7aqNDlaC2Z8WiiTY9rMQXSz8PF9WBFO5eVAE6eWvU2vZs2Zc6aq+3XEzYMrLn07duX48ePYzDY9qGaJDxCCFELeHl58fDDD5OZmYmHh4fN/bga9IzqGE7U3gvFnnep1waf28dr9+wyqtC5uzqG42ooZcM5IYQQhd2YmJTX9fUtxSY8hYoeqNrIjX8XbTQmfotWpc29IQT20Cq0pR6Dti9qa26sZkg+pI0gebXUEhDVrI3sGDy1BCWoj7amKGkfZGZofYRGQsOxkHwAYn+AK1u1YgWKEwR006arGX0hdBA0GA3nAL27NsKjOIHF6c9kB7RKcCja1DiwOdkBSXiEEKLWcHFxwcXFpUJ9qCpM7NmIJfsuoKrFt/HuMabIMUWBib0ao6Lt5iCEEKIM/lqNrTxUi1YiujjWnL/cJ177HtBTS2LM6dpojksYnJgNuSla9TXTH9q6Gks2WDJBzUNVr/9dN/pqCYrpmJbwhA3TqrnlZWh9hw7WkqTf39KmyBncwaOptq5H764VT4jfAOmnockT2jXmFHB217bByPtL0Rsnd20fHucA256fG0jRAiGEEAV0OoXmwZ48O7BFua57blALmsqmo0IIUT5/TUzKfX128cd1zkWPZcdrhQey4rRpasm/QlqMNmKTfUUrguBWT/tuTgUnD65cy+WTtQlcyQ3R9gBSFG0E5toR7d8uIVoi5NZIK1ZwfpnWpyVLK4TgGgbO/uDkppXSVpwg7aRWjQ206y1ZgHq9ott1Tu7g004reOAcWLHnCEl4hBBCFGNq/2b8M7IFN8tfFAX+GdmCKf2a2bz3jxBC1FnFJSblur6EUX2XYK3ccxEqWHK00aH009oUN//O2vfzy+HC13D+G9C7kGZozpJ1x0hIyWPjhWbadLZ8udcgO1ErMuDeCDybaAUREvdo5508tAQn/Qxc3a8VPTD4aG0Bru7Tvvt2ALNJS3qUGyae+XfVptwZvMu/0WoxZEqbEEKIYk3p14zINiEs3HmW7w7FFipk4G7Uc1fHcCb2akyzINvXDAkhRJ2Wn5jYMq1N0ResbynCrb7Wd1Zc0XMGL+2cs582uhPQTfv0KukXbaTHsxm5vn1Yul0lNT0dPx9PRvVxA2s8oGgFCJx9tb16Un7TylQbfUFvgCYT4NohLcFRdNpUOSyQfRmsbcCruVYgITcDnNESIIO3NrpjydLi82gC9e8Gt4bg4l/+56UYkvAIIYQoUZNAd94Y1ZaXhrXi+OU00rLNeLkYaBnqiatBTwnLfIQQQpRFaYnJzbgEa9cXxzkQPBoX7de9sZasWLLBLRyykyE3GQL7QkB3bS2NewPynBqiN27Fzc3KAw+Mx818CBITtLLTqgUub9SSGZ0O8rK0Sm9pJ7Uy20G9talsGRe04xnnwT1EW+ujqtqIkul6tTidE6rixDvL4xjZw4dWHQdC/bvAv5vdkh2QKW1CCFFjWK3WmzeyM52ioCgKbkYnOjX05Y4WQdzW0Bc3oxOKotS4NTuzZs2iS5cueHp6EhQUxKhRo4iJiSnUJjs7mylTpuDv74+HhwejR48mIaFwFaULFy4wbNgw3NzcCAoK4p///Cd5eTfftFUIIQrJT0xs4dmk5PUtOh34tC3ct8FLS3biNsDJT7TvLn7aubQ/IDdNS6J8OuAW2ILxEx9mwoQJ+AUEaH0F9tSSnUs/gum4Vskg9QRcXq+NFKGDq3vg3FJwDdGKH3i1vL6xaAttZCf1qDalzru1dl/vlry1zp/pi6/S95UkroX+DYIH2DXZAUl4hBCiRsjNzeWLL77gwIEDDr3Pjh07GDFiBOnp6Q69T1X5+eefmTJlCnv27CE6Ohqz2cygQYPIyMgoaPOPf/yDH3/8kW+++Yaff/6ZuLg47r777oLzFouFYcOGkZuby65du1i0aBELFy5kxowZVfGQhBA1WXGJSVl4NAavNqWvb3ENhaC+f/atd9VGdkzHtcTFdFz7We8KqGD00goFuP5ZBjooKOjPvjybQervkBV7vYx0tjatzTlA21TU6Kvtp5N5HpL2gnsDbZ+hnEStLHZWnDYNLyepoCLbnKgDvDL3ZwBenP4Kvg07axuf2lm1THjmzp1Lo0aNcHFxoVu3buzbt6/EtitXrqRz5874+Pjg7u5Ohw4dWLx4caE2qqoyY8YMQkNDcXV1JSIigpMnTzr6YQghhF1YrVZWrFjBpUuX2Lx5M1lZWQ65zy+//MLQoUNZvXo1b775pkPuUdXWrVvHxIkTadOmDe3bt2fhwoVcuHChIJFMTU3l888/5/3336d///506tSJL774gl27drFnj7YYd8OGDRw7doyvvvqKDh06MGTIEN544w3mzp1Lbm5uVT48IURN9NfE5GY8Gmvt3ULL0LaRtu9NUG9thEfvCl6ttMTDq5X2s8FLOx86SGtfkrx0bb2OewOtXDSKVpxA56RVbjN4ajE5eWgjRqCt0bFkaUURjD5acmT0AZ/25OXlsXLVDwC8/vrrTHv22bI9fhtUuzU8y5cvZ9q0acyfP59u3boxZ84cIiMjiYmJ+TPLvIGfnx//+te/aNmyJUajkdWrVzNp0iSCgoKIjIwE4N133+XDDz9k0aJFNG7cmFdeeYXIyEiOHTtW4T0rhBDCkVRVZe3atZw8eRInJyfuv/9+XF1d7X6fM2fOMHHiRNLS0rjjjjvqzGhFamoqoL2WABw4cACz2UxERERBm5YtW9KgQQN2795N9+7d2b17N+3atSM4+M/N/iIjI/n73//O0aNH6dixY5H75OTkkJPzZ/lZk8kEgNlsxmw2lyvm/Pblva4uk+esfOT5Kr8KP2fO4RA4AIzHIf0c5FwpXMhA0YNzkJaQeLcC52Ao672cAsC3D7gnQW4q1L8fLOmg99BGZby9wRigjRaV1KeqQsoZwAVcm2qjO9Y80PtrpbV1zqD3Bo9bwQxYTJB+Gdyaa99v5BKGOXgwTrEpfP/993z33XdMmjTJ5r+FZXoKytVzJXj//fd59NFHmTRpEgDz589nzZo1LFiwgBdffLFI+zvuuKPQz08//TSLFi1ix44dREZGoqoqc+bM4eWXX2bkyJEAfPnllwQHB7Nq1SrGjh3r8MckhBAV4ebmhqIo3H333dSrV89h99Hr9fTs2ZMff/wRNzc3h92nurBarTzzzDP06tWLtm3bAhAfH4/RaMTHx6dQ2+DgYOLj4wva3Jjs5J/PP1ecWbNmMXPmzCLHN2zYYPNzHR0dbdN1dZk8Z+Ujz1f52ec5C7r+VZxMwF5Tm7OBpHJe074Mbe7U1vcU1EtoV/h0FpCcAsDu3bsJCQnhp59+KmcckJmZWea21Srhyc3N5cCBA0yfPr3gmE6nIyIigt27d9/0elVV2bx5MzExMbzzzjsAnD17lvj4+EKf1nl7e9OtWzd2795dbMJjz0/i7Kkmf+JSU2OvqXFDzY1d4i6qT58+tGzZksDAQIf0bzabadKkCdHR0dSvXx9nZ2e736c6/vecMmUKv//+Ozt27HD4vaZPn860adMKfjaZTNSvX59Bgwbh5eVVrr7MZjPR0dEMHDgQg8Fw8wuEPGflJM9X+dX650xVIe4nSN4PmbHaCJSlmCm8emetclz+1Dm3MG3dj85FmzLn2ZTTV9JZuu8ct+ku8Mp+HTlWrfiNu0HPsPZhPNC1AU3LsN1B/vvzsqhWCU9SUhIWi6XYT85OnDhR4nWpqamEh4eTk5ODXq/n448/ZuDAgcCfn7YV12dlfhJnTzX5E5eaGntNjRtqbuwSd+W7cOECFy5ccEjf5fkkrjJMnTqV1atXs23btkKjZiEhIeTm5pKSklJolCchIYGQkJCCNn9dW5pfxS2/zV85Ozvj7Fx0Ia7BYLD5zVFFrq2r5DkrH3m+yq8mPWeqqpZvw2jvhhC/BrLPaWt5irvUmgsZJ8CjOQR3Ac9bIKQX6F1A78x/N5/i39ExGHUqt3WFHKtCjkXrKMdiZfHeS3y17xLPDmzB1P7NSg2nPM9ztUp4bOXp6cnhw4dJT09n06ZNTJs2jSZNmhSZ7lZW9vwkzp5q8qcHNTX2mho31NzYJe7KVxmxl+eTOEdSVZUnn3yS7777jq1bt9K4ceFFwp06dcJgMLBp0yZGjx4NQExMDBcuXKBHjx4A9OjRg7feeosrV64UrC2Njo7Gy8uL1q1bV+4DEkIIG6iqyooVKwgJCaF3795lS3wMXlrBAfUmWySoVjB6g5MXOLmC1QA6J/67+RSzN8SUfi3aYNLsDTEoirYBtj1Uq4QnICAAvV5fZL+DGz9ZK45Op6NZM+0J6dChA8ePH2fWrFnccccdBdclJCQQGvpnNYuEhAQ6dOhQbH+O+CTOnqpLHLaoqbHX1Lih5sYucVc+R8ZeXZ6TKVOmsGTJEr7//ns8PT0LRvq9vb1xdXXF29ubyZMnM23aNPz8/PDy8uLJJ5+kR48edO/eHYBBgwbRunVrHnroId59913i4+N5+eWXmTJlSrGvHUIIUd1ER0dz7NgxYmJiaNWqFQEBAaVfYLVCzjXwv02b0pYVW3Jb13CtXW4yWBtiRc/phLQyJTs3mr0hhsg2ITQJdK/wnm/Vqiy10WikU6dObNq0qeCY1Wpl06ZNBZ+slYXVai1Yg9O4cWNCQkIK9Wkymdi7d2+5+hRCCFHzzZs3j9TUVO644w5CQ0MLvpYvX17Q5oMPPmD48OGMHj2a22+/nZCQEFauXFlwXq/Xs3r1avR6PT169ODBBx9k/PjxvP7661XxkIQQolz27dtXsDZ+5MiRN092APJMkPIboId6I8C3w/X9e26gd9WO1x+htUv5jeQrF1AUWLjrXLnjVFVYuPNssTPnyqtajfAATJs2jQkTJtC5c2e6du3KnDlzyMjIKKjaNn78eMLDw5k1axagrbfp3LkzTZs2JScnh7Vr17J48WLmzZsHgKIoPPPMM7z55ps0b968oCx1WFgYo0aNqqqHKYQQogqoqnrTNi4uLsydO5e5c+eW2KZhw4asXbvWnqEJIUSlyP872L9/f9q1+0sFNWuetq9OkYssYDVD6jHwaAKhERDYA9LPQF42OLlox/XukHsN0mM4EafQd0JXnnx6GquyO9gU63eHYnlpWCvcjBVLWapdwnPfffeRmJjIjBkziI+Pp0OHDqxbt66g6MCFCxfQ3bCrbEZGBk888QSXLl3C1dWVli1b8tVXX3HfffcVtHn++efJyMjgscceIyUlhd69e7Nu3TrZg0cIUS2oqorVakWv1zvsHqmpqZw5c6bYPWKEEELUHd26daN+/fqFlnoUKC7ZAW0fIJ0ToEL6achwApdgrTiBomjDMbnXIPskqHmcvXSNiIcXc+XKNb5aspSswS1QnIzljjUj18KJy2nc1tC33NfeqNolPKBVz5k6dWqx57Zu3Vro5zfffPOmO4IrisLrr78u0w2EENXSzz//zPnz57nvvvsc8kFMeno6Q4cO5ciRI/z000/07t3b7vcQQghRc4SFhZXvAicvcKsHGee1n9U8bR1PMWt50jNz6f/ol8QmpNC6dWve+nQZz3x/xuZYTdkV39agWq3hEUKIuubQoUP8/PPPnDt3jpMnT9q9/8zMTEaMGMGuXbtwcnLCw+PmexsIIYQQheh04NkUDN43berhZuTpiZE0b9aUjRs3ckf7prw/pj1PDWhGmHf5P9Tzcql40ZtqOcIjhBB1wenTp1m9ejUAvXv3LjqX2g7ee+89tm7diqenJ+vXry+xOqU95eTkkJaWVraFsEIIIWoG1zDwvRUS94BawqiLe0OsAT15+s1bePCfaZxPzcN0IYVwX1cGtg7m6QG3sOl4Agt3ngaSbnpLd6OelqGeFQ5dEh4hhKgibm5uuLm50bhxY/r37++Qe7z44ov88ccf/P3vf6dr164OuceNMjMziYqKwmQyMWnSJPz8/Bx+TyGEEJVA7wL+3bR/p53VRnyc/UDnDNYcrWCBVzNOJ5hYuPl3Vh2KJSPXUnC5u1HPqI7hPNi9IYsmdS1T4Ze7Oobjaqj4+lZJeIQQooqEhoby6KOP4u7uXr7drsvB2dmZqKgoh/T9VyaTicWLF5OUlISbm1vB9gBCCCFqCYMH1sC+KMH9yTLncTzOhCk7Dy8XA63CvHCxqpxNyuTklfRCyQ5oBQii9l5gyb4LPBfRjHrAhB4N+d+OC8XeSlFgYq/GqFDh0tSS8AghRBXy8vKq6hDsZuPGjSQlJeHl5cVDDz0kU9qEEKKKqKrqsA/STidlsXDXuVJHcL7+Ww/eXHOMz7afLSY2+HDzSd7tCv+MbMnh2HT2nU0u0u65QS1oGmifDwQl4RFCCGEXQ4cOxWKxMHDgQHx8fKo6HCGEqJNOnz7Nli1buO+++/D0rPj6lxv9d/Mp/h0dQ3Fbmt04gvPswBa8PKw1QLFJT74/EtKY3LtxoYRHUbRkZ0q/ZnaLWxIeIYQQduHi4sK9995b1WEIIUTNl5MMGecgJxGcA8G9kbZe5iYSEhL4+uuvyc3NZdeuXURGRtqlX9CSndkbYm7aTlVh9oYYFAVeHtaaI5dSix3BAVj+y0VeHXkrod4umLLM3NUxnIm9GtMsyL4VRSXhEUIIIYQQorrISYb4TWA6AapF2/TTqyWEDCg1OTGZTERFRZGbm0ujRo2IiIgoY78DwdmnxH6tVpXTiekFyY6qWjHt/RbPjkPRObuXeN3sDTEMaBVUZATnRqt/jeOFoW349u898XEz4GrQU8zgUYXJPjxCCFHDqcXNLRBCCFEzZZz7MykB7bspRjteiry8PAwGAwEBAYwZMwa9/i/Vzf7ar1s9rD63ohq9yczN48D5ZLacuMKB88lk5uahqipWVUVRYOEu7d6qqpK84WNSfl5EwtczUFVrifGoKny1+zwRrYIJLWH/nQyzhZj4NMJ8XHEzOqEoCjoHrD2SER4hhHAgs9mMwVDxTdNK8n//939cu3aNd99912ELVIUQQlSinMQ/k5J8ah7klL5vjZ+fHw8//DBmsxlXV9fS+w3oCWGDOJ2QWmoJ6WkDb8HVqGfVoVhUVeXa5s9IP7wOUPDqdCeKUvrYyXeHYnlhSEvu7VyPDzedKraNKbuEPX3sSBIeIYRwkNjYWJYuXcrIkSNp3ry53fv/4IMP+Ne//gVAZGRk0ekLQgghah7nQG262Y1Jj+KkHb8Jd/eSp5gV9OvfFcIGlakAQedGvtTzdSMj14I1I4WM4z8D4D/kSdxb971pPBm5Fo5fTqOhf8lxebk47kPBfDKlTQghHODatWssXbqUjIwMfvnlF7tPO5s3bx7Tpk0DYObMmQ5PdlRV5ezZkivtCCGEsBP3RtraGuX6uITiBF4twL1hxfsN7A1hkQUFCG720uRudCItOw8AvYcvIePewX/IU3jcOqjMt83IycPTufgxFneDnpah9q0kVxxJeIQQws6ysrKIiooiIyOD4OBgRo8ebffpZh4eHuh0Ol588UVeeeUVu/b9V1arldWrV/Pll1+yd+9eh95LCCHqPGc/rUBB+FAI7Kl9v0nBgrL2aw3oyckEU5mqrQGk5+Th7vznWiCDX3i5kh0Ad2cn0nLyij03vH0YrgZ9sedAmxa+Zs0aMjIyynXPv5IpbUIIYWcGg4GwsDDMZjMPPPAAzs7Odr/HQw89RNu2benQoYND1+5YLBa+++47jh49CoCTk7xsCCGEwzn7VTzBKYaiN7Jw1x9lbn/uagYDWwfjbtQXWuNTVu5GPa1CPdlxKrHY8w90a4gKFPcqlp2dzdKlS7lw4QIJCQlMmjTJ5tc7GeERQgg7c3Jy4q677uKRRx6x+6ZvN+rYsaPDCxWcOnWKo0ePotPpuOeee+jUqZND7yeEEMJxsswWVh2KLXP7Ffsv4WZ0YlTHcJvud1fHcNyMTnyz/1Kx5xsHuBVblS0jI4Mvv/ySCxcu4OzsTERERIVe7+SjOiGEcABFURya7FSWFi1aEBERQXBwMM2a2W/XayGEEOWnqmqF3vgfv2wq10hNXGo2m44n8GD3hizZd+Gma35upCjwYI+GbDyewOXU7ELHnx7QHNJOlPhYLBYLWVlZuLm58eCDDxIaGlr2GxdDRniEEEKUqlevXpLsCCFEFcvNzWXhwoUcOXLE5j5MWcWvpSnNZzvO0irUi2cHtijXdc8NasEtQZ58vkMreONu1PNgtwZE/6Mvj/ZpUuq1Xl5ePPTQQ0yaNKnCyQ7ICI8QQgghhBDVmtVqZeXKlVy4cIGkpCRuueUWXFyK38yzNF6upb/1t5qz0RkK97vvbDJvrjnGy8NaoyjctLqbomjJzpR+zbiYnMkTdzTlxcEtaRnqiatBjwpY8m6eePn52W8NkyQ8QgghhBBCVFOqqrJu3TpiYmLQ6/WMHTvWpmQHoFWoV4kFCHJij3Plu7cIGP4cro06FDr32fazGPU6nh/cksg2ISzceZbvitms9K6O4Uzs1ZhmQR4A1Pdzo76fW6G+FKD85Q8qRhIeIYSohn799VdCQkIIDg6u6lCEEEJUsfy1LnfddRf169e3uR9Xg55RHcOJ2nuh0PGc+FMkfPMaak4Gafu/L5LwAJiyzKiqSpNAd94Y1ZaXhrXi+OU00rLNeLkYCo3gVDeS8AghhA1ycnIwGo0OqZJ25MgR+vfvT1BQEJs3b7bL/GUhhBA1k6IoDBkyhPbt2xMWFlahvlQVJvZsVKgAgTklnitfz0DNycC5XmsC7nyhmBhgYq/GqFBQVc3N6ESn+l5gzQGdM+i0/XQcWzvUNlK0QAghyslsNrN48WJWrVqFxWLfgfnjx48TERFBcnIy3t7eeHh42LX/vzpz5gzZ2dk3byiEEKJKVTTZAdDpFJoHexYqQODkGYBLw/YYQ5sTdM9r6IxFp8s9N6gFTQPdi5aQzrwIceu079WYjPAIIUQ5WK1Wvv32W2JjY0lOTsZkMuHr62uXvlVV5W9/+xuJiYncdtttrFu3zqGlrY8cOcKqVasIDw+36+JQIYQQ1dvU/s0KChCgdyJgxHOo5mx0zn9Zb3NDAYJiuYaA323a93I4dOgQKSkpNkZffpLwCCFEOWzYsKHQwlF7JTugTVtYsmQJTz/9NP/73//w8fGxW99/tW/fPn766ScAfHx8HL6BqRBCiOplSr9mRLYJZuHOc1oBgutT0uDGAgSNaBZUygdvehfwaFSu++7cuZONGzeiKAqJiYl2Gbm6GUl4hBDFs2RDVrz2qY3etmowtVHTpk05dOgQI0eOpEGDBnbvv169enz77bd27/dG2dnZbN++HYCuXbsyYMCAguRHCCFEHZGXSRPPLN4Y1YaXhrbk+GUTaTkWvJz1tAz1wtWoR81Ogjw9OLndvL+bUFWVzZs3s2PHDgACAwMJCAiocL9lIQmPEKJ4WfGQfFAbqi7npze1WfPmzXn66adxc6v4H/+q4uLiwkMPPcQff/xBr169yCvDfghCCCFqEUs2JO1Gl7gHnFxx8+1IJ28/rfiANQeuJcO1Qyh5WRDYHQJ72eXDz/w1o/369ePatWuVNrtAEh4hRPFsnJdbF9TkZCdfUFAQQUFBVR2GEEKI61RVrbzpxVlxcO0IqGYwm+HKzyW3vXYEPBqDR5MK3VJRFIYOHUrLli1p0KABa9eurVB/5SFV2oQQxcuflyvT2YQQQgiH2r9/P0uXLiU3N9fxN7NaIe00mFPL1t6cqrW3Wit8a0VRaNq0aYX7KS9JeIQQQgghhKgif/zxB2vXruXkyZP89ttvdu8/JyeHefPmYc1PWPJMkHmpfJ1kXtKuq6Ek4RFCiEqkqtVxD2ohhBBVIS4ujhUrVqCqKh06dOC2226za/9ms5kxY8bwxBNP8Mwzz2gHVQtYy7l205oHasVHeKqKJDxCCFFJsrKyGDZsmMOrsAkhhKgZLBYLTk5ONGnShOHDh9t1DY/FYuGhhx7ihx9+wNnZmVGjRmknFD3oyrmMX+cESs1NG6RogRBC3EBVVbKysuxemCA3N5d77rmHn376id27dzNgwACH7bOTnJxMSkoKTZpUbIGpEEIIx6pfvz6PPPIIbm5u6PX6m19QDr/88gvffvstBoOBlStX0r9/f+2Ekxe41YOM82XvzK2edl0NVXNTNSGEcIDt27czb9484uPj7dZnXl4eY8eOZe3atbi6urJq1SqHJTsJCQl88cUXLF26lIsXLzrkHkIIIezHz88PFxf7Fwjq3r073377LUuXLmXo0KF/ntDpwLMpGLzL1pHBW2uvKz1tSEpKYs2aNX+uFapGZIRHCCGu+/XXX9myZQsAFy9eJCTEPiW5dTodISEhODs78/3339O3b1+79PtXly5dIioqiuzsbIKCghyWVAkhhKgZ7rzzzuJPuIaB762QuEcrTV0SxQC+7bX2pYiPj2fx4sVkZmbi4uLCgAEDKhC1/ckIjxBCAOfPn+eHH34AoGfPnnTp0sVufet0OubOncv+/fsZOHCg3fr9q0OHDpGdnU29evWYOHEinp6eDruXEEKIGkzvAv7dtE1FSxrpMXhDYA/w71rqFhUXL15k4cKFZGZmEhISQvfu3R0UtO1khEcIIdA24qxfvz6enp5ERETYvX9FUWjbtq3d+73R0KFD8fLyokePHhiNRofeSwghRA1n8IDAXtqmommntdLT1jytQIFbPW0am2vYTffjs1qtWCwWGjRowP333++Q6XkVJQmPEEIArq6uPPjggwCVt9O1nen1eodNlxNCCFEL6V3Aowm4NdL22VGtWjU2J6+brtnJ17BhQ8aPH09ISAgGg8Gx8dqoWk5pmzt3Lo0aNcLFxYVu3bqxb9++Ett++umn9OnTB19fX3x9fYmIiCjSfuLEiSiKUuhr8ODBjn4YQogaxsnJCScn+RxICCGE/VSH/desVhVVVcnMzePA+WS2nLjCgfPJZObmoaoqVkUBow84+2nfy5js5Ktfv361TXagGo7wLF++nGnTpjF//ny6devGnDlziIyMJCYmhqCgoCLtt27dyv3330/Pnj1xcXHhnXfeYdCgQRw9epTw8PCCdoMHD+aLL74o+NnZ2blSHo8QQgghhKib0tLSiIqKYvDgwTRq1Mju/ZvN5jIlGqcT01m46xyrDsWSkWspOO5u1DOqYzgTezaieXDtXfdZ7UZ43n//fR599FEmTZpE69atmT9/Pm5ubixYsKDY9lFRUTzxxBN06NCBli1b8tlnn2G1Wtm0aVOhds7OzoSEhBR8+fr6VsbDEUIIIYQQdVBOTg5LliwhISGBdevW2b1c8w8//MCtt97K+fOl76fz382nGDRnG1F7LxRKdgAyci1E7b3AoDnb+O/mU3aNrzqpViM8ubm5HDhwgOnTpxcc0+l0REREsHv37jL1kZmZidlsxs/Pr9DxrVu3EhQUhK+vL/379+fNN9/E39+/2D5ycnLIyckp+NlkMgFaFm02l1K6z8Hy712VMdiqpsZeU+OGmht7TY17zZo1WCyWGhc3VM5zXhOfFyGEsJXVamXFihXEx8fj7u7Offfdh66c08RKs2HDBu69915yc3P56KOPmD17drHt/rv5FLM3xNy0P1WF2RtiUBSY0q+ZVsTA6KtNcbuZ/EIH1Vi1ii4pKQmLxUJwcHCh48HBwZw4caJMfbzwwguEhYUVqrI0ePBg7r77bho3bszp06d56aWXGDJkCLt37y52V9tZs2Yxc+bMIsc3bNhg993XbREdHV3VIdispsZeU+OGmht7TYp7/fr1zJs3j+7du6Moit13ywbtxTMpKYnAwECHFVVw5HOemZnpsL6FEKK6sVgs6PV6nJycuP/+++06s2jXrl2MGjWK3NxcRo8ezdtvv12kjdWqcjoxvUzJzo1mb4ghsnUgTVzy0F36Afw7gVdr0JXyulbNkx2oZglPRb399tssW7aMrVu3FiqJN3bs2IJ/t2vXjltvvZWmTZuydevWYjdGmj59OtOmTSv42WQyUb9+fQYNGoSXl5djH0QpzGYz0dHRDBw4sFovDCtOTY29psYNNTd2R8adlpZm971pvvrqK+bPnw9oH85ERkbavSR0dnY233zzDXFxcdSrV8/uZbMr43clf6RcCCHqAoPBwJgxY7hy5YrdNrHO16RJE5o2bUrDhg1ZsmRJscV2FAUW7jpX7r5VFRbuusAbo9pA3BoscZv5afMhuvUZQmBgoB2irxrVKuEJCAhAr9eTkJBQ6HhCQsJNf1lmz57N22+/zcaNG7n11ltLbdukSRMCAgI4depUsQmPs7NzsUUNDAZDtXjjWF3isEVNjb2mxg01N3Z7x3358mUWLlxI9+7dueOOO+wySpKUlMRTTz2Fqqr8/e9/Z9CgQRiNRrvGnZGRwdKlS7l8+TLOzs60bt3aYf89Hfm7UhN/B4UQoiJ0Op3dkx2AkJAQtm7dipubW4kfsGWZLaw6FGtT/98diuWloS0xeN7KN6t3cDIhhdMXvmLK1CdrbCXTalW0wGg00qlTp0IFB/ILEPTo0aPE6959913eeOMN1q1bR+fOnW96n0uXLnH16lVCQ0PtErcQonpLSUlhyZIl5ObmcvHiRbstHA0ICGD16tU89dRTfPDBBw6ZanblyhUSEhJwc3NjwoQJNGzY0O73EEIIUc1YrZCbAtmJ2ve/vG75+/vj6upa4uXHL5uKFCgoq4xcC7+dTyJq3QlOJig46WHYHe1qbLID1WyEB2DatGlMmDCBzp0707VrV+bMmUNGRgaTJk0CYPz48YSHhzNr1iwA3nnnHWbMmMGSJUto1KgR8fHxAHh4eODh4UF6ejozZ85k9OjRhISEcPr0aZ5//nmaNWtGZGRklT1OIUTlyMvLY8mSJaSnpxMUFMSYMWPsusamb9++9O3b12GL8hs3bsy9995LYGBgiYVWhBBC1BKWbMiK04oGZF76syCAWz3wbAquYdpmoTdhysqrUBjpZisWKzg7qdzfAxr6Z1Sov6pW7RKe++67j8TERGbMmEF8fDwdOnRg3bp1BYUMLly4UKjKxbx588jNzeWee+4p1M+rr77Ka6+9hl6v58iRIyxatIiUlBTCwsIYNGgQb7zxhuzFI0Qd4OTkRNeuXdmxYwcPPPBAofV9NUXLli2rOgQhhBCOZk6Hq3vh2hEwpxY+l3EeUn4H31vBvxsYPErtysu1Ym/xfd1d6DmiG6mn1hDiDVhzakQ1tpJUy6inTp3K1KlTiz23devWQj+fO3eu1L5cXV1Zv369nSITQtREnTt3pn379rKORAghRPVkydaSncQ9oJq1BCMv/c8kw8lDS4IS92jtA3uVOtLTKtQLd6Pepmlt7kY9LUO9cL2Wjqv39YM65xqb7EA1W8MjhBCOIsmOEEIIe1JVFVVV7dNZVpw2sqOawWyCtJOQegJSj2vf005qx1Wz1i4rrtTuXA16RnUMtymUuzqG42rUw7VD2gGdUZtOV4NJwiOEEEIIIUQ5rV+/njVr1lS8EI7Vqq3ZMaeCNYeUhFP8uOUEWK+vDbWaITsJMi9oIz/mVK19KfdVVZjYsxHlraWjKDCxZwPU1BgtwQJt3ZBrmI0PrnqQhEcIIUpht0/vhBBC1Bp79uxh7969HDhwgPPnz1esszyTVqAASDclM/Sf2xn52lG+3OcHIQMhuD94tYK8TG2aG2jt80re30ynU2ge7MmzA1uUK5TnBrWgaZAXuqvXp84Z/bTNR539bHpo1YUkPEIIUYI//viDTp06cezYMYf0r6oqe/fu5dq1aw7pXwghhP2dOHGiYH14REQEjRs3rliHqgWseWRl53Hn81vYfSwFHy83OjT1gKxL2oiOd2sIHwEezQFFW9uj3mRkKS+Lqf2b8c/IFjcd6VEU+GdkC6b0a4YStxayLoN7IwjpB16tK/b4qoGau/pICCEc6MyZM/Tv35/Y2FiefvppoqOj7dq/qqps3LiRXbt2sXfvXv72t79J5UghhKgBrFYrer2eDh060LNnz4p3qOhBZyBqSxJb9p7E082J9TOCudVlG+Qv1XFyB9/2EDYYXEMhLwOUm4xb6JzB9AdT+t1CZOsgFu46z3eHYsnItdBYn4y7ksNZXT3u6hjOxJ6NaBbsCVd2gMUM9e7UprHV8JGdfJLwCCFqvMzMTHQ6nd1KTl+6dIkBAwYQGxtLq1atiIqKsku/+axWK6tXr+bQIW1BaJcuXSTZEUKIGqJ169b4+/sTGBhonw2nnbzApx2TxzgTG7OD/i3S6dLkL6M3eRlw7TBYLdDgLvBpp11XGp1Oq6x27mua+LTjjVFteGloS9Zt281vu/YD8M6YPrRu2Rw1/QIk/q4VJwjqXfHHVM1IwiOEqNHMZjPLli0jJyeHcePG4e3tffOLbsLT05PQ0FCcnJzYtGkTQUFBdoj0T2azmbi4OBRFYcSIEXTs2NGu/QshhHCs/P0h7UKnA2dflGuHePX+ALB6atPYLNnatDVFp5WgNnhDdgJcPQh+3bTrbsY1DFz80V1cCZdd+fViIL/tPgNApzb1aeUdi3JiDUpeFgT2AKOv/R5XNSIJjxCixrJarXz33XdcvHgRFxcXcnNz7dKvt7c369evx2QyERoaapc+b+Ts7MyDDz5IXFwct9xyi937F0IIUcOYTZCboiU3OmetWICaByqgAIrT9S8d5KaWWrCgEL2LtlEpwLUjKOmnAYWezVUiml9ASbqgJVKBPcC/a6l7+9RkkvAIIWqsn3/+mePHj6PX67nvvvsIDAy0W9+enp54enrarb+/8vDwkGRHCCGEVkM6/Ty4BIA1WxvFsXC9mEEW6F21ZEfvDC4h4OIP6ee0NT1lYfDQNir1aEx3n9OEhZ6iQYCTNt3NrZ42jc01rNYmOyAJjxCiBuvQoQPHjh2jb9++NGrUqKrDEUIIIcpPtWj76+iM4N4APJpoa3QMXqAzaPvwmE2Q8ps26qM4ae2teVrSUhZ6F61ft0Y08O/y51Q5J6+yTY2r4SThEULUWL6+vjz++OPo9fqqDkUIIYSwjaLXprG5N8Tq3wPF+xayci0cv5yKKduCl4ueVqHeuNa/C9X0B7qk3Vr7siY7N9LpwOhj94dQ3UnCI4So0STZEUIIYW+qqtqnAltZ+lUUCOgKrqGcTkhl4aqjrLpePjqfu1HPqI7hTOzZgOZNJ2n75Igyq/1jWEIIIYQQQpRRfHw88+fPJzEx0a79/u9//+POO+8kOzu76EnXUP67+RSD5uwgau+FQskOQEauhai9Fxg0Zwf/3XxK24tHlJkkPEKIOic3N5cffvjBYf2fPXuWAwcOOKx/IYQQjpGamsqSJUu4cuUKW7ZssVu/ixcv5vHHH2f16tUsXry4yPn/bj7F7A0xqGrp/agqzN4Qw9wtp+wWW10gCY8Qok7Jy8vjgQceYOTIkfz73/+2e/9//PEHUVFRrF69mtOnT9u9fyGEEI6RnZ3NkiVLSEtLIygoiDvvvNMu/X777bdMnDgRVVWZOnUqjzzySME5q1XlZEIaszfElKvP2RtiOHUlHesNGVJqairffvtt8SNIdZwkPEKIOsNisTBp0iRWrFiBwWCgdevWdu0/OTmZb7/9FovFQsuWLWnYsKFd+xdCCOE4FosFJycnPDw8GDduHC4u9inTHBYWhqenJw8//DD/+c9/Cq3hURRYuOtcuftUVVi48yz5PV29epUvvviC33//nbVr19ol7tpEihYIIaotVVVJSkqy2/46GzZs4KuvvkKv1/P1118zZMgQu/SbLzc3F1VV6dChAyNGjEBXB0p9CiFEbeHu7s7EiRNJTU3F29vbbv326NGD/fv307hx4yKvC1lmC6sOxdrU73eHYnlpWCvSrl1l8eLFZGRk4O/vz4ABA+wRdq0iCY8QotrauXMnmzdvZsSIEXTs2LHC/Q0ZMoQ5c+YQEhLCqFGjKh7gXwQHB9O7d29at27tkOo+QgghHMtgMBAQEGD3fps1a1bs8eOXTUUKFJRVRq6FE5fTaOihvZ0PDg7mwQcfxMPDw+Y4aytJeIQQ1dLRo0fZtGkTADk5OXbr9+mnn7ZbX3+lKAq33HKLJDtCCCHKxJSVV7Hrs834NwxiwoQJeHp62m0aXm0j8y2EENVOVlYWq1evBqBbt2507969iiMStcW2bdsYMWIEYWFhKIrCqlWrCp2fOHEiiqIU+ho8eHChNsnJyTzwwAN4eXnh4+PD5MmTSU9Pr8RHIYSoLbxcKzb24OViACAwMFCSnVJIwiOEqHZcXFzo0qULrVq1IjIysqrDEbVIRkYG7du3Z+7cuSW2GTx4MJcvXy74Wrp0aaHzDzzwAEePHiU6OprVq1ezbds2HnvsMUeHLoSohVqFeuFutG0DbXejnpahnnaOqHaSKW1CiGpHURT69++PXq+X6WHCroYMGXLTYhXOzs6EhIQUe+748eOsW7eOX375hc6dOwPw0UcfMXToUGbPnk1YWJjdYxZC1F6uBj2jOoYTtfdCua+9q2M4rgbbkqW6RhIeIUS1JVXORFXYunUrQUFB+Pr60r9/f9588038/f0B2L17Nz4+PgXJDkBERAQ6nY69e/dy1113FdtnTk5OobVoJpMJALPZjNlsLld8+e3Le11dJs9Z+dTm50tVVbt/kHbp0iWuXLkClP85s1pVHupaj28PnL/ppqM3UhR4qFt9cs1mdDXwg0F7/I6V51pJeIQQtYrVanVIomQymdiyZQtDhw7FYDDYvX9RPQwePJi7776bxo0bc/r0aV566SWGDBnC7t270ev1xMfHExQUVOgaJycn/Pz8iI+PL7HfWbNmMXPmzCLHN2zYgJubm02xRkdH23RdXSbPWfnUtudLVVXOnj2Lu7s7QUFBdkl8UlJSeOmllzCZTLz22ms29/NOl/Jfc+KXnzlh8x2rh4r8jmVmZpa5rSQ8QohaY8mSJcyfP58ff/zRrnsoJCcn8+WXX5KamgrAyJEj7da3qF7Gjh1b8O927dpx66230rRpU7Zu3VqhvS2mT5/OtGnTCn42mUzUr1+fQYMG4eXlVa6+zGYz0dHRDBw4UJLvMpLnrHxq4/Olqirr16/HZDKRmZnJqFGj8PX1rVCfV69eZeDAgcTFxVGvXj28vLwq9Jx9uv0MH24++ZeRHpVO+kvEWr2JV71QFHiqf3Me7dOkQrFXNXv8juWPlJeFJDxCiFrh22+/Zfz48VgsFj755BOef/55u/SbkJBQsKGbn58fffv2tUu/omZo0qQJAQEBnDp1igEDBhASElIwdSVfXl4eycnJJa77AW1dkLOzc5HjBoPB5hf7ilxbV8lzVj616fnauXMnBw8eBODuu+8uMlJri//7v//j999/JyQkhA0bNvDHH39U6Dl7on8LBrUNZ+HOs3x3KJbMXDN9DOdoqk+miS4J2gxhYt+WNAuqPfvsVPRvYFnJBHkhRI23du1a7r//fiwWCxMmTOC5556za/8Wi4Xg4GAmTZqEj4+PXfsW1dulS5e4evUqoaGhgLZjekpKCgcOHChos3nzZqxWK926dauqMIUQN5E/fS0yMpJWrVrZpc+3336bcePGsXHjxhI3Fi2vJoHuvDGqLbtfvIMXbrlGU6dkFEXH3aNG8sY9nWgS6G6X+9Q1MsIjhKgS8fHxBAYGotdXvMJMw4YN8fPzo1+/fnz++ed2XcMTHBzM+PHj8fX1lT0OaoH09HROnTpV8PPZs2c5fPgwfn5++Pn5MXPmTEaPHk1ISAinT5/m+eefp1mzZgXl0Vu1asXgwYN59NFHmT9/PmazmalTpzJ27Fip0CZENdazZ08aN25c8OGFPbi5uREVFQXYr8BDfgECDxcjgd7uXL3sxL333sstt9wCQM0rT1A9SMIjhKh0CQkJfPHFF9SrV48xY8YUO9WnPNq0acPevXsJCwuzSwL1V/Z8gRRVa//+/fTr16/g5/x1NRMmTGDevHkcOXKERYsWkZKSQlhYGIMGDeKNN94o9DsaFRXF1KlTGTBgADqdjtGjR/Phhx9W+mMRQpRPTfpbrtPpGDVqFFeuXKlRcVdXkvAIISqVyWQiKiqK3NxcrFar3RKUhg0b2qUfUbvdcccdqKXUfl2/fv1N+/Dz82PJkiX2DEsIIYrQ6/WS7NiJrOERQlQaVVX55ptvSEtLIzAwkDFjxuDkJJ+7CCGEEMJxJOERQlQaRVEYPHgwISEhjBs3DldX1yqNx2pVUVWVzNw8DpxPZsuJKxw4n0xmbh6qqmItzy5wQgghhKiW5KNVIUSlCg8P57HHHrP7Tte2OJ2YzsJd51h1KJaMXEvBcXejnlEdw5nYsxHNgz2rMEIhhBBCVJSM8AghKl11SHb+u/kUg+ds5dzBn3HPSyl0LiPXQtTeCwyas43/bj5VfAdCCCGqldLW59lixowZzJw50+79isonCY8Qoto7f/48O3futFt//918ig82HKO/4RS3OF2lv/E0TliKtFNVmL0hhrlbJOkRQojqLCYmhgULFpCenm6X/t5++23eeOMNXnvtNXbt2mWXPm906tQp1qxZI8lUJZGERwhRrcXGxtK/f38GDRrEtm3bKtSX1apyMiGN/2w4xiDjSerpTeSpOrbnNiaPkqvFzd4Qw6kr6bKmRwghqqHY2FhWrFjBpUuX2Lt3b4X7+/DDD5k+fTqgJT69evWqcJ83OnbsGEuXLmX//v0cPHjQrn2L4knCI4SothISEhgwYABnzpwhJCSEpk2bVqg/RYGFu86Rh4IZPTmqnvW5zYm1epd6narCwp1nZcM3IYSoZq5du8bSpUvJy8ujWbNmhfbZspXRaATglVde4YUXXqhwfzc6fPgwK1aswGq10qZNGzp06GDX/kXxqmXCM3fuXBo1aoSLiwvdunVj3759Jbb99NNP6dOnD76+vvj6+hIREVGkvaqqzJgxg9DQUFxdXYmIiODkyZOOfhhCiAp67733iImJoX79+mzevJnw8PAK9ZdltrDqUCwqOrbkNmVNTkuuWMtWlOC7Q7FkmYtOexNCCFF1LBYLBoOB4OBg7rnnHnS6ir+1ffzxx/nll1+YOXOmHSIszNXVFUVR6NixI3fffbdDNssWRVW7hGf58uVMmzaNV199lYMHD9K+fXsiIyO5cuVKse23bt3K/fffz5YtW9i9ezf169dn0KBBxMbGFrR59913+fDDD5k/fz579+7F3d2dyMhIsrOzK+thCVFnZGVlkZCQYJe+Zs2axdSpU9m8ebNdNhY9ftlUUI3Ngo5UtexlsTNyLZy4nFbhGIQQQthPQEAAkydPZty4cTg7O9ut386dOzukwE6LFi145JFHGDFihF2SM1E21e6Zfv/993n00UeZNGkSrVu3Zv78+bi5ubFgwYJi20dFRfHEE0/QoUMHWrZsyWeffYbVamXTpk2ANrozZ84cXn75ZUaOHMmtt97Kl19+SVxcHKtWrarERyZE7ZeXl8eyZctYsGABZ8+erXB/BoOBjz76iGbNmtkhOjBl5VXs+myzXeKo0awVew6FEMLePDw88PLyquowyiw0NLRaVCutS6rVPjy5ubkcOHCgYKEYgE6nIyIigt27d5epj8zMTMxmM35+fgCcPXuW+Ph4IiIiCtp4e3vTrVs3du/ezdixY4v0kZOTQ05OTsHPJpMJALPZjNlcdW948u9dlTHYqqbGXlPjhsqPXVVVVq1axYULF3B2dsZoNNp0b0fG7WEEZ73thQc8DEqJcdWp3xWL7f9dhRBCiMpWrRKepKQkLBYLwcHBhY4HBwdz4sSJMvXxwgsvEBYWVpDgxMfHF/Tx1z7zz/3VrFmzip23uWHDBtzc3MoUhyNFR0dXdQg2q6mx19S4ofJiT0pK4tKlSyiKQr169di/f3+F+nNU3O92tf3a2N92Eftb6W3kd6V4mZmZDutbCCGEKI1dE57c3FzMZjPu7u727LbM3n77bZYtW8bWrVtxcXGxuZ/p06czbdq0gp9NJlPB2qCqHDI1m81ER0czcOBADAZDlcVhi5oae02NGyo/9ry8PH788UeaNWtGu3btbO7HkXGrqsoba47z9f6L5b72vs71eXlYqxKnIcjvSunyR8qFEEKIymZTwrNs2TL27t3LBx98UHBs5syZvPXWW6iqyvDhw1m8eDEeHh7l6jcgIAC9Xl9kwXNCQgIhISGlXjt79mzefvttNm7cyK233lpwPP+6hIQEQkNDC/VZUilAZ2fnYhe+GQyGavFGprrEYYuaGntNjRsqL3aDwcC9995rt3nJFYn7l19+4erVq0RGRhaKx2pVGd+zCV/tu0R5ttRRFBjfqylOBgO6mzw++V0puW8hhKgOTpw4QVBQUMHyB1H72VS04N///jcZGRkFP+/atYuZM2cSGRnJP/7xD9atW8dbb71V7n6NRiOdOnUqKDgAFBQg6NGjR4nXvfvuu7zxxhusW7eOzp07FzrXuHFjQkJCCvVpMpnYu3dvqX0KIcqvvMmOxWKx6y7Tqqqybds21q5dy969ezl16lSh8zqdQvNgT54d2KJc/T43qAVNA91vmuwIIYSwP3u+Tpw8eZJ+/frRr1+/EisA2yovL49vvvmGlJQUu/YrKs6mhOf06dOFRlGWLFlCSEgI3333He+++y5Tpkzh22+/tSmgadOm8emnn7Jo0SKOHz/O3//+dzIyMpg0aRIA48ePL1TU4J133uGVV15hwYIFNGrUiPj4eOLj40lPTwe0N2DPPPMMb775Jj/88AO//fYb48ePJywsjFGjRtkUoxCi4qxWK5MnT+app57CarXapc+NGzeyZcsWAPr27Vtidbep/Zvxz8gW3Cx/URT4Z2QLpvRrJhV1hBCiCuTk5PD5559z9OjRCvd1/vx5BgwYULCG28nJfis70tLSOHnyJCdPnmTlypV2TdJExdn0XzonJ6fQGpkNGzYwZMiQgl+c1q1b8/HHH9sU0H333UdiYiIzZswgPj6eDh06sG7duoKiAxcuXChUt3zevHnk5uZyzz33FOrn1Vdf5bXXXgPg+eefJyMjg8cee4yUlBR69+7NunXrKrTORwhhO1VVmTJlCosWLUKv1zNhwoQio7O2yC/1OWjQILp3715q2yn9mhHZJoSFO8/y3aHYgv15ANyNeu7qGM7EXo1pFlS+qblCCCHsw2Kx8PXXXxMbG8u6deto3rw5RqPR5v4mTJjAxYsXadGiBdHR0Xab0nbt2jW+/PJLcnJy8PT05M4775QPyaoZmxKexo0bs3HjRh555BH279/PqVOnCk1hS0hIKPf6nRtNnTqVqVOnFntu69athX4+d+7cTftTFIXXX3+d119/3eaYhBD2889//pP58+ejKApffvmlXZIdgLZt2xIaGoq/v3+Z2jcJdOeNUW15aVgrjl82kZadh5eLEy1DvXA16JHP54QQomqoqsrq1as5c+YMBoOB+++/v0LJDsAXX3zBo48+yqJFiwgKCrJTpODi4oLRaMRoNDJ+/HgCAgLs1rewD5sSnr/97W88/fTTHDt2jEuXLlGvXj2GDx9ecH7nzp20adPGbkEKIWqXrl27YjAYmD9/PuPGjbNr32VNdoCCNTluZNDJ9yqoFlD0gB4Ub+TzOSGEqBqqquLk5ISiKNxzzz2EhYVVuM/8D+ztzdXVlfvvv59Nmzbh7e1t9/5FxdmU8Dz55JO4uLiwdu1aOnXqxAsvvICrqysAycnJxMfH8/jjj9s1UCFE1VJVlVOnTtGsWcXXs4wZM4Zu3brRsGFDO0VXQVmX4Py3gBXQQcPRYJQXrSIyL0HaKfBsBm71qjoaIUQtptPpGDp0KJ06dbpppd7qwMPDQ6pRVmM2r9Z69NFHefTRR4sc9/Pzq/CGg0KI6mfPnj1s2LCBDh062GV+crVJdgAM3uAWDpmx2neDJDvFSjsFCVu1f0vCI4RwMEVRakSyI6q/CpWnyMnJ4eDBg1y5coVevXrJnEUhaqmjR4+yYcMGAAIDA2vfYkzXcAiLBHOqluy4hld1RNWTZ7PC34UQQogawKay1AAffvghoaGh9OrVi7vvvpsjR44AkJSUREBAAAsWLLBbkEKIqpORkcH3338PQJcuXap0/yqHlflUFG3EwruN9r22JXT24lYPgu+Q0R0hhBA1ik0JzxdffMEzzzzD4MGDWbBgQaE3IQEBAfTv359ly5bZLUghRNVxd3fn7rvvpl27dgwePLjKRncuXbrE559/TlpaWpXcXwghhBA1k00Jz7///W9GjhzJkiVLGDFiRJHznTp1sssGUUKI6qFly5bcfffdhfbAqkxnzpzhyy+/JDY2ls2bN1dJDEIIIaovq9XKlClTWLt2rV37VVWVTZs2ceDAAbv2KyqXTe9eTp06xZAhQ0o87+fnx9WrV20OSghRs61fv57Y2Fi79HXq1CmWLFmC2WymSZMmpf7tEUIIUXOoqmqXqcqqqvLUU0/x8ccfc8899xAfH2+H6LR+165dy44dO1izZg1JSUl26VdUPpsSHh8fn1L/ox87dkyqaghRR61bt44777yTvn372uVFJyQkBC8vL1q1amWXjeeEEEJUD7t372b58uXk5uba3Ieqqrz44ovMnTsXRVH43//+Z5f3oKqqsmrVqoLKw0OHDpXiXDWYTQnP0KFD+d///kdKSkqRc0ePHuXTTz/lzjvvrGhsQogaZvPmzdx1113k5ubSsWNHu7w4eHh48PDDD3PPPffg5FShwpKls2RD+jntuxBCCIc6duwY0dHRxMTEcPz4cZv7UVW14MO1+fPn8+CDD9olPkVR8PHxQVEU7r77bjp37myXfkXVsOndw5tvvkm3bt1o27YtI0aMQFEUFi1axIIFC/j2228JDQ1lxowZ9o5VCFGNWa1W/vnPf5Kdnc2IESOIioqyW4Li4eFhl35KlRUPyQfB7zbwaOT4+wkhRB118eJFVq5cCWjVP2+99Vab+9LpdHzxxReMHz+eAQMG2CtEAO644w5at25NcHCwXfsVlc+mEZ6wsDAOHDjA4MGDWb58OaqqsnjxYn788Ufuv/9+9uzZI8N+QtQxOp2OtWvXMnXqVL7++uuaN/XMNURLdlxlOq4QQjiS1WrFYDBwyy232KX6p06ns3uyA9oojyQ7tYPNH78GBQXx2Wef8dlnn5GYmIjVaiUwMLDKqjgJIWx38eJFvL298fLyqlA/wcHBfPTRR3aKqpLpXWRkRwghKkHDhg155JFH8PT0lPeNolLYZb5JYGCgPboRQlSBK1euEBUVhdFoZOLEifj5+VV1SEIIIWo5f3//qg5B1CE2JTyvv/76TdsoisIrr7xiS/dCiEqSlpZGVFQUOTk5BAcHV3iExxZWq1U+4RNCCCGEw9iU8Lz22mslnlMUBVVVJeERogZYt24dJpMJf39/xo4d69gqaMXIzMxkyZIldOvWjXbt2lXqvYUQQghRN9j0sarVai3ylZeXx+nTp/nHP/5B586duXLlir1jFULY2bBhw2jZsiUPPPAArq6ulXpvk8nEF198QWxsLNHR0ZjN5kq9vxBCiJrhxIkTWCwWu/Z58eJFli9fLq89dYTd5pHodDoaN27M7Nmzad68OU8++aS9uhZCOIibmxv33Xcfvr6+ZWqfl5dnl/tmZmayYMECkpKS8PLyYvz48RgMBrv0LYQQovbYt28fXbp0YcKECXZ7DTp9+jSLFy/mxIkTbN++3S59iurNIRPnb7/9dtauXeuIroUQVeTKlSvcdtttREVFVbgvV1dXWrRogb+/Pw8//LCUsRdCiFpGVdUK9/Hrr78SGRlJeno6cXFxdhmNiYmJYenSpZjNZpo1a0afPn0q3Keo/hyS8Ozfv18WIQtRiyQnJzNw4EB+++03XnrpJTIzMyvUn6IoDB48mMmTJ+Pt7W2nKIUQQlQHKSkpzJs3jwsXLtjcR3Z2NsOHDyclJYUePXrwww8/2GXqtbe3N05OTrRu3ZqxY8fK7II6wqYVyl9++WWxx1NSUti2bRsrV67kkUceqVBgQojqITMzk8jISI4cOUJwcDDR0dG4ublVuF9FUSp93ZAQQgjHys7OZsmSJSQmJrJhwwYmT55s08aiLi4ufPbZZ7z55pusXr0aDw8Pu8QXEhLCI488gp+fn3w4X4fYlPBMnDixxHMBAQG8+OKLzJgxw9aYhBDViKurK7fffjtnz55l48aN3HLLLVUdkqhK1jzQVW41PyFEzWCxWFi+fDmJiYl4enoyZswYm5KdfJGRkQwaNKhCfRRHplHXPTa9ap09e7bIMUVR8PX1xdPTs8JBCSGqD0VRmD17NtOmTSM8PLyqwxFVIScZsmIh7QxYc0DnDJ5NwDUcnGWjWiGExmKxYDAYMBqNjBs3zi57u9k72RF1k00JT8OGDe0dhxDCAfLy8jhx4kSF97hRFEWSnbrIagHTMbh6ALLiwJr757nUo+AaBv6dwKs16PRVF6cQolowGo2MHTuWxMREgoODqzocIQrIvAQhaqm8vDzOnTvH77//TmZmJt26dau0e6uqisViqfSNTIWdmY5B/BbITS56zpoLGefAbNJ+9pGNY4UQ2jYlkuyI6qZM70Z0Ol25hxQVRbFbvXQhRPmoqsqaNWtIT0/HaDRW6qis1WplzZo1pKWlcd9996HXyyf/NVJOsjayU1yyc6Pc6+1kepsQQohqqkwJz4wZM2QOpRA1yKlTpzh69CgAd999NyEhIZVyX4vFwsqVKzl27BiKonD+/HmaNGlSKfcWdpYVq01jK1PbOO1LEh4hRBXKycnh22+/5fbbb6devXpVHY6oRsqU8Lz22msODkMIYU/Nmzdn4MCBnDhxolITjlWrVnHs2DF0Oh2jR4+WZKemUtXrBQpyb94WtHZpp8GnrWPjEkLUGjk5OTz55JO88MILNG3atML9ZWZmEhUVRVxcHImJiUydOlVmGIgCUoBciFqqS5cu+Pv737SdqqosWLCA3NwyvrktRdeuXXFzc2PcuHG0bt26wv2JKqJatGpshY6pYMkGS6b2/a+7qFtztJLVQghxE3l5eYwbN45PP/2UwYMHYzabK9RfRkYGCxcuJC4uDjc3N8aMGSPJjiikQiuKL126xKFDh0hNTcVqtRY5P378+Ip0L4RwMFVVef7555k9ezbfffcdP/zwQ4Wmr9avX5+nn34ao9FoxyhFpVP0WulpADUPzGnaWh2zCVQrKDoweIHRDwyeoDhp7WV/HiFqPfX6hx22vlZYLBYmTpzIypUrMRqNfPzxxxgMhgrF5OLigre3N9nZ2Tz00EMEBgZWqD9R+9j06pSdnc2ECRP49ttvsVqtKIpS7P8AkvAIUb29+uqrzJ49G4ARI0bYZa2eJDu1gKJo++xcOwQZFyA7ASx/GfHJTYXsK+ASDB5NwLPiU1KEENWbqqqsXr0aJycnIiMj0enKP1EoLS2NY8eO4eTkxIoVKxg4cGCF49Lr9YwZM4bMzEy8vb0r3J+ofWya0vbSSy+xcuVK3nrrLbZu3YqqqixatIgNGzYwZMgQ2rdvz6+//mrvWIUQdnThwgU++OADAP7zn//w2GOP/XnSaoXcFMi5qn0vZgRX1HIuwdqoTealoslOPkuOdl7nrLUXQtRqO3bs4ODBg/zyyy/Exsba1IePjw+bN29m9erVjBgxwm6xGQwGSXZEiWwa4VmxYgWTJk3ihRde4OrVqwCEh4fTv39/IiIi6N+/P3PnzmXevHl2DVYIYT8NGjRgw4YN7N27l6eeeko7aMnWqm2lndbeyFrztGlKbvW0T/Bdw0DvUrWBi8qhWsDrFkj5XavYVhKXUK2dKut3hKjNfvvtNzZv3gzA4MGDqV+/vs19+fj4EBkZaa/QhLgpm0Z4rly5QteuXQFwdXUFtAVj+UaPHs3KlSvtEJ4QwpF69OjBM888o/1gTofEnXDxe+17xnntjW7G+cLHzelVGrOoBFarluioVqg3Anw7gN61cBu9q3a8/gitXcrvMhIoRC2n1+vp3r17wXtAIWoKm0Z4goODC0Z23Nzc8PX1JSYmpmBo0mQykZ2dbb8ohRBFHDx4kDZt2uDs7FzxzizZcHUvJO4BtYRqOeZUcuJ24wwQ2Kt2jfRYrZBn0kY1FD04eYENc9NrjTyTNsKXcUFbnxMaAYE9IP0M5GWDk4t2XO8OudcgPQbcG2jXGX2qOnohhAO0a9eOoKAggoKCqjoUIcrNpoSnW7du7NixgxdeeAHQFju/9957hIaGYrVa+eCDD+jevbtdAxVC/Gnv3r2sW7eOX375hcmTJ+PkVMHqWFlxcO1IyckO8OsF2PBbHuPvOESwR2PtDW9NJ1P4iqdarpeYViH9NGQ4XS9O0FwraKCqWqKTffLPqWzWPG2kRwhRawUHy1o9UTPZ9C7pqaee4ptvviEnJwdnZ2feeOMNdu/ezUMPPQRA06ZN+fDDD+0aqBBCc/z4cdatWwdAmzZtKp7sWK3aG35zaolN9p6GdUe0Cm6Hz6QT2eg0uDWq2aMg5nRtVOvakaKPPeO8NkXL91bw7wYGj6qJsaoo+sIlptU8bXpjaWt5dE5auWohhLCzXbt2kZaWxqBBg+xSTVTUPWV+dbrnnnv4/vvvMZvN9O7dm//85z8FU2nq16/P8ePHOXToEEeOHOH48eO0aNHCpoDmzp1Lo0aNcHFxoVu3buzbt6/EtkePHmX06NE0atQIRVGYM2dOkTavvfYaiqIU+mrZsqVNsQlR1fLy8li/fj0AnTp1olevXnbo9Pr0pRL8funPZKdbU5VBbdHa55kqfu+qcuMUvpISPXOqdv7qXq19XeLkpY1ylYdbPe06IUSdd+HCBbv0o6oqmzdvJjo6mj179nD27Fm79CvqnjInPGvWrOHuu+8mODiYv/3tb2zbtq1wRzod7du3p23btjZ/4rx8+XKmTZvGq6++ysGDB2nfvj2RkZFcuXKl2PaZmZk0adKEt99+m5CQkBL7bdOmDZcvXy742rFjh03xCVHVnJycGD9+PF26dGHo0KE3/aSrTLtXF0xfKl6LEGjgr3JHK5XIdtqMpho/fakMU/gA7fy1I1r7ukSn06b0Gf5S4lXN08qU/7Uim8Fba1+TR/yEEHaxZs0amjdvzn//+98K97Vhwwa2b98OQP/+/WnSpBZMpRZVosyvTomJiSxYsIAuXbqwYMEC+vXrR4MGDXjxxRc5cuSIXYJ5//33efTRR5k0aRKtW7dm/vz5uLm5sWDBgmLbd+nShffee4+xY8eWunDbycmJkJCQgq+AgAC7xCtEVfDz82Po0KE33fBt27ZtPPHEEzffE+uv05f+wuAE43tD35bXkx2o2dOXyjCFrxBzqta+rlUgcw3TpvQpN+yAbk6HrMuFK/UpBvBtr7UXQtRpmzZtYvTo0eTm5rJr166CTeltFRoaiqIoDBkyhD59+tgpSlEXlXkoxsPDgwkTJjBhwgQSExNZtmwZS5Ys4d133+W9996jdevWPPjgg9x///00aNCg3IHk5uZy4MABpk+fXnBMp9MRERHB7t27y93fjU6ePElYWBguLi706NGDWbNmlRpjTk4OOTl/brRnMmlTd8xmc9k+MXeQ/HtXZQy2qqmx19S49+zZw8iRI8nIyOC9997jq6++Krmx6grO4ZB2sdQ+C73fdw7XrnPA8+Lw5zw3FdIugbUcCVvaJfBKBmPJm9rV1N8VKCl2PXh1gjxVW8+UZwK9FzjrtepsVp02hc2nHXjdBlY9WEt+7DXxeRGirlFV1eY1MufPn+fOO+8kJyeHkSNHsmjRogqvt7n11lsJDw/H39+/Qv0IYdPcs8DAQJ588kmefPJJzp07R1RUFMuWLWP69On861//omfPnjz44IOFd26/iaSkJCwWS5EKIMHBwZw4ccKWMAGtotzChQtp0aIFly9fZubMmfTp04fff/8dT0/PYq+ZNWsWM2fOLHJ8w4YNuLm52RyLvURHR1d1CDarqbHXpLjPnTvHSy+9RGZmJrfeeiujR49m7dq1Zbiyfdlvcj4LWGdriGXi2Ofc7/pXORzfWaZmNel35a9Kjr1xKVelAz/ftO/MzExbQhJCVJJLly7x448/MmbMGLy8yr8er2HDhrz00kts376d5cuXYzAYbn5RGUiyI+yhguWdoFGjRvzrX//iX//6F0eOHOHVV1/l+++/Z+fOneVKeBxlyJAhBf++9dZb6datGw0bNuTrr79m8uTJxV4zffp0pk2bVvCzyWSifv36DBo0yKY/AvZiNpuJjo5m4MCBdvtDUllqauw1Me60tDRWrlyJxWLhySefZPjw4TeP3ZIDSXvg6i+lr2tRDODfFQK6gd4O+/8Uw+HPeW4qxP4ImaWPaBXiVh/CR9x0hKem/a7kK1PsVivkpQFWQAdOnuVas5M/Ui6EqH6Sk5NZunQpmZmZbNu2jeHDh9vUz7/+9S/y8vIqXj1UCDuzy2/k5cuXWbp0KUuWLOHgwYMAdO7cuVx9BAQEoNfrSUhIKHQ8ISGh1IIE5eXj48Mtt9zCqVOnSmzj7Oxc7Jogg8FQLd7IVJc4bFFTY69Jcfv5+fHTTz+RlZXFjh07yha7wQDB3cFJKb5MM2gL033bawlPJZRpdthzrvcDz3qQfb7s13jWA1e/Mr3Br0m/K39109grsMltTX1OhKjtMjMzWbJkCZmZmYSGhjJs2LAK9SfJjqiObF51nJKSwmeffUb//v1p0KABzz33HKmpqcyYMYM//viDvXv3lqs/o9FIp06d2LRpU8Exq9XKpk2b6NGjh61hFpGens7p06cJDQ21W59CVDdubm5lHo3MyMjQFpYaPCCwF9QfqX13bwiu4dr3guM9a/6eNCVVICuJVCATQtRiVqsVo9GIt7c3999/P0ajsapDEsLuypWGZ2dn88MPP7BkyRLWr19PTk4OgYGB/P3vf+fBBx+ka9euFQpm2rRpTJgwgc6dO9O1a1fmzJlDRkYGkyZNAmD8+PGEh4cza9YsQCt0cOzYsYJ/x8bGcvjwYTw8PGjWrBkAzz33HCNGjKBhw4bExcXx6quvotfruf/++ysUqxCOcv78eaxWK40bl7Zuwj4SEhL46quv6Ny5M3379gW9C3g00TYVzTNppaeV64vTa9Mb/vwKZIl7bj6FTyqQCSFqMQ8PDyZOnEh6enqJa5sdpSJFEoQojzInPOPHj+f7778nPT0dNzc3Ro8ezQMPPMCgQYPQ6/V2Cea+++4jMTGRGTNmEB8fT4cOHVi3bl1BIYMLFy4UKsUbFxdHx44dC36ePXs2s2fPpm/fvmzduhXQFuHdf//9XL16lcDAQHr37s2ePXsIDAy0S8xC2FN+BcTc3FwefPBBhyY9Fy9eZMmSJWRnZ3P8+HF69uz557QjnQ6MPg67d5XTu4B/N+3fKUdB7wpGX63utqpC7jWwZIFPW20Kn96lauMVQggHMhqN+PmVs5BLBSUkJPDTTz9xzz334OFRw2cOiGqvzAnP0qVLGThwIA888AB33XWXw6qVTZ06lalTpxZ7Lj+JydeoUaOb1nhftmyZvUITwqHS0tKIiooiOzubevXqUa9eOXe6L4eMjAy++uorcnNzqV+/PuPGjat7aywMHhDYBzwaQ9oZSD0KlkzQu4F3G/BsAq71QC/TO4QQwp4uXbpU8HoXHR3NXXfdVdUhiVquzAlPXFycjIoI4UD79u0jNTUVPz8/xo4d69AExN3dnYiICGJiYhgzZkzdnLNttUBaDFw9AJmx1yuQqUCqtrFm5iXw7wRerUFnn1FsIYSoqUwmE6+++ipvvvkm7u7uNvdz/vx5oqKiMJvN1K9fv1A1XSEcpcwJjyQ7QjhW//790el0tG/fvtQXk5SUFL777ruCtW226tKlC507d66786dNxyB+C+Qmaz/fOG1NNUPGOTBfL6Xs067SwxNCiOoiIyODYcOGsWPHDs6dO8d3331nc19+fn64u7vj7+9fdz9wE5VOagcKUU0oikK/fv1KbZOWlsbgwYPZu3cvV69e5bnnnqvwPeuknGRtZCc/2SlJ7vV2ruHgXLnz24UQojrIzs5m1KhR7NixA29vb2bMmFGh/jw9PZk0aRJubm5SwlpUmlpUdkmI2i3/E7a9e/fi5+fHoEGDqjqkmisrFrLiytg2ruxtRbW3bds2RowYQVhYGIqisGrVqkLnVVVlxowZhIaG4urqSkREBCdPnizUJjk5mQceeAAvLy98fHyYPHky6enplfgohCifm613Ls25c+c4ePAg7u7u/PTTT4WKRdnKy8tLkh1RqSThEaKGWLduHdu3b8fLy4sNGzZw6623VnVINZOqakUKrLlla2/NhbTTjo1JVJqMjAzat2/P3Llziz3/7rvv8uGHHzJ//nz27t2Lu7s7kZGRZGdnF7R54IEHOHr0KNHR0axevZpt27bx2GOPVdZDEKJcLBYLX331Fbt377Yp8WnZsiU///wza9asseu+iEJUJkmvhaghRo8ezSeffELbtm3p1KlTVYdTc6kWsOaU7xprDljzQCd/Mmu6IUOGlLhIWlVV5syZw8svv8zIkSMB+PLLLwkODmbVqlWMHTuW48ePs27dOn755Rc6d+4MwEcffcTQoUOZPXs2YWGyZ5OoPlRV5ccff+TMmTNcunSJNm3alHlT6hu1bdvWAdEJUXlsevXOyclh586dHD9+HJPJhKenJ61bt6ZXr144OzvbO0YhxHVl/RTZbDaTkZHh4GhqKEUPunL+ndI5S7JTB5w9e5b4+HgiIiIKjnl7e9OtWzd2797N2LFj2b17Nz4+PgXJDkBERAQ6nY69e/eWWF43JyeHnJw/E22TSSuIYTabMZtL2fy2GPnty3tdXVZXn7Pt27fz66+/oigKd911F66urmV6Durq81UR8pyVjz2er/JcW65XcFVVmT17Nu+88w7Xrl0rNDSqKAq+vr688MILPPfcc3V3MbQQVSw7O5ulS5eSnZ1NUFBQVYdT/SiKtsdO6tGyTWvTGcGzqePjElUuPj4eoGCz63zBwcEF5+Lj44v8f+Xk5ISfn19Bm+LMmjWLmTNnFjm+YcMGm/e1i46Otum6uqyuPWcJCQkAhIeHExMTQ0xMTLmut+X5UlW1Tr8HrGu/YxVVkecrMzOzzG3LlfA88MADLFu2jObNm/Pkk0/Svn17PD09SUtL49dff2XJkiW8+OKLHD58mKioqHIHLkRtlpOTw549e+jduzd6vWP2dcnfUDQ+Ph5nZ2d8fHwccp8azzUcXMO00tM3bRumfQlRAdOnT2fatGkFP5tMJurXr8+gQYPKPcXIbDYTHR3NwIED696GwTaqy89ZQkJCkST+Zmx9vvLy8li1ahVt2rShVatW5Q21RqvLv2O2sMfzlT9SXhZlTngWL17MsmXLeO6555g1a1aRN2yjRo3ilVde4aWXXuK9995jyJAhPPjgg2WPWohazGKx8PXXX3PmzBmuXbvGqFGjHHKf77//nvj4eNzc3Bg7diwHDx50yH1qPGc/bVNRs6n00tTG6+2kJHWdEBISAmhvEENDQwuOJyQk0KFDh4I2V65cKXRdXl4eycnJBdcXx9nZudgp3waDweYX+4pcW1fVxeesXr16Nl9bnucrJyeHr7/+mnPnznH+/HmaN2+Oq6urzfeuqeri71hFVPRvYFmVuUrbp59+St++fXn33XdL/HRap9Px9ttv07dvX/73v/+VOQgharMbF40aDAa6devmsHsNGTKEevXq8fDDD5f65ksAXq0hpB+4N9Kmrd1IZ9SOh/TT2ok6oXHjxoSEhLBp06aCYyaTib179xZUp+rRowcpKSkcOHCgoM3mzZuxWq0O/X9bCEdKSkqq0PW5ubksXryYc+fOYTQaGTt2bJ1MdkT1VeaE58iRI4wePbpMbe+++26OHDlic1BC1CZJSUkcPXoURVG49957C31yfCNVVQuVvrWFr68vDz/8MP7+/hXqp07Q6cGnHdS7U/vy7QjerbXv+cd82mntRK2Rnp7O4cOHOXz4MKAVKjh8+DAXLlxAURSeeeYZ3nzzTX744Qd+++03xo8fT1hYWMGobKtWrRg8eDCPPvoo+/btY+fOnUydOpWxY8dKhTZRI33++ec0a9aMHTt22NyHwWAgLCwMV1dXJkyYQKNGjewXoBB2UOYpbWazGRcXlzK1dXZ2Ji8vz+aghKhNAgMDmTBhAlevXqV58+Yltst/k7V+/Xr8/GyfQlWXF4vaxNlP+/JpK6Wn64D9+/fTr1+/gp/z19VMmDCBhQsX8vzzz5ORkcFjjz1GSkoKvXv3Zt26dYVe/6Kiopg6dSoDBgxAp9MxevRoPvzww0p/LEJU1JIlS3j00UdRVZW1a9fSu3dvm/pRFIUhQ4bQq1cvvL297RylEBVX5lf2Zs2asW3bNh555JGbtt2+fTtNmjSpUGBC1Cb16tUrdR71e++9x4wZMwD48ccfmTBhQmWFJm4kyU6td8cdd5S6+aKiKLz++uu8/vrrJbbx8/NjyZIljghPiEqzbds2xo8fj6qqPP7447z11lsV6k9RFEl2RLVV5ilt99xzD0uXLmXNmjWltluzZg1Lly7l3nvvrXBwQtQFn376Kc8//zwAb731liQ7Qgghyqy0BL40Xbt2ZdiwYYwfP565c+fK7ABRq5U54Xn22Wdp0aIFo0aN4rHHHmP79u2YTCZUVcVkMrFjxw4ee+wxRo0aRYsWLXj22WcdGbcQtUbfvn0JDw/n5Zdf5qWXXqrqcIQQQtQQv//+O1988YVNG027uLiwYsUKPv/8c3S6Mr8dFKJGKvP8DTc3NzZv3sz48eP57LPP+Pzzz4u0UVWViIgIvvzyS5s3UhOirrnllls4fPjwTQsNqKpKUlISgYGBlRSZEEKI6urChQusWrUKi8XCgQMHuP3228vdh5RPFnVFuSasBwUFsW7dOvbu3cuPP/7IsWPHSEtLw9PTk1atWjF8+PCC0p1CiLILCAgo9byqqkRHR7N3717Gjh1bavEDIYQQtVtSUhLLli3DYrHQsmVLm4sNlIeqqjLtTdRYNq3Q7datm+w3IEQlsVqtrF69mkOHDgGQnFzKRplCCCFqPVVVMRqN+Pn5cffddzt8StqhQ4c4efIko0ePLnEvRiGqM7uUJDp69Cjbtm0jPT2d9u3bM2jQIHt0K0SNsXPnTpo3b05QUJDd+/711185dOgQiqIwYsQIOnbsaPd7CCGEqDkCAwOZPHkyOp3O4dPS9uzZw/r16wFtT0Z5DRI1UZkTHqvVyvTp01myZAlOTk5MnDiRV199lWnTpvGf//ynoEqIoij06tWLdevWyToeUSf88ssvbNy4kR07djB16lTc3d3t2n+HDh24ePEizZo1o3Xr1nbtWwghRM3k6enp8Hvs3r2bLVu2ANCjRw86dOjg8HsK4QhlTnjmzZvHe++9R5cuXQgODub//u//SExMZP78+UyZMoUBAwaQl5fHDz/8wOLFi3njjTeYNWuWI2MXosrFxMTw008/AdC9e3e7JzugfYhw55132r1fIYQQtVdcXBxLlizh2WeftXntTb169XBycqJPnz706dNH1vCIGqvMCc9nn33GsGHD+PHHHwGYO3cuTz31FFOmTCm0w/To0aPJyMhgxYoVkvCIWk1VVfbt24eqqnTs2LHECjm//PILJpOJAQMGVHKEQtiB1Qo5iZB5EbITwJoDOmdwCQa3+uAcCFLSVohqJTExkYiICI4fP052djYvv/yyTf3Ur1+fqVOnyoaiosYrc8Jz5swZHnvssYKfhw4dypNPPkn//v2LtI2IiChIjISorRRFYezYsezZs4eePXsW+8nX4cOHGTRoEFlZWWzcuLFSKukIYTdZlyHld0g/qyU7quXPc4peS3o8GoNPW3ANrbo4hRAFrl27xqBBgzh+/Dj16tXjgQceqFB/kuyI2qDMCU9aWlqhX3ovL69C32/k6elJXl6eHcITonozGAz06dOn2HNHjx5l4MCBpKSk0LNnT5n7LGqW9HNw5Wct2SmOaoGsuD+/gvqCR6PKjFAIUYzt27dz5MgRgoKC2LhxI40bN67qkISocnap0iaEKGr+/PkkJSXRuXNn1q5di4eHR1WHVH7WPNDJn4k6J+ty6cnOX+W30zvLSI8QdmLrvjd33nkny5Yto2XLlrRo0cIBkQlR85TrnczatWuJj48HIDMzE0VR+Oabbzh8+HChdgcOHLBbgELUVHPmzMHf35+nnnqq1CkBly9fJigoqPrsbZCTDFmxkHbmz/Uank3ANRyc/ao6OuFoVuuf09jKI/0spB4F52BZ0yNEBWVlZfHll19y++2306pVq3Jff++99zogKiFqrnIlPEuWLGHJkiWFjn3yySfFtpVKHqKu0+v1vPbaa6W2OXHiBCtWrKB169bcddddVfv/jdUCpmNw9YA2Rcma++e51KPgGgb+ncCrNeiqSXIm7C8nsfzJTr60M+DdDlyD7RuTEHVIXl4ey5cvJz4+nvXr19O8eXOcnBwz0m7rKJIQNU2Z/w86e9bGF0AhRLEOHz7MDz/8gKqq5OXlYbVaq3aUx3QM4rdAbnLRc9ZcyDgHZpP2s0+7Sg1NVKL8amy2yE7QrpeERwibqKrKDz/8wPnz53F2dmbcuHEOS3aSk5NZsWIFo0aNcsim2UJUJ2X+v6hhw4aOjEOIOiU9PZ21a9eiqiodOnRgxIgR6KpyGlBOsjayU1yyc6Pc6+1kelvt9ddqbAUU7b+5zqglwDnJgFq4iWqB7CuVEaUQtZKqqjg5OaHT6RgzZozDEpGEhAS++uqrgteiCRMmyEiPqNVkNbKoHTIvQdop8GwGbvXs1u3FixdJSEhAVdWbNy4HDw8PxowZw9mzZ4mIiKj6F5qsWG0aW5naXq/KJQlP7WTNKXrMJUT7/8qaA2oeKE7g3lj7/y47/i/XZ1dOnELUQjqdjhEjRtCtWzeCgx0zUpqQkMDChQvJzs4mODiYe+65p+pfg4RwsDInPMXtt5NPURRcXFxo2LAhQ4cOZfjw4XYJTogySzsFCVu1f9sp4UlKSmLFihVkZWVx6NAhunXrZpd+8zVr1oxmzZrZtU+bqOr1AgW5N28LWru009reK6L20TkX/tklBNzC4OpeuPYbmFPA4AO+7cC/i9bmxqRH51JZkQpRKymKUmqyo6oqGRkZNlf+9PX1JTAwEFVVGTduHK6urraGKkSNUeaE58qVK6V+ApCZmUl0dDSffPIJkZGRfP/99xgMBrsEKcRNeTYr/L2C0tPTiYqKIisrCzc3N9q2LfrmPisrC2dn56qdimYPqqX4T/VLY82RktW1lUuwtqmoagEU7QOEq3vhyvY/25hT/vzZv9v1NT/q9c1IZS2AEI705ptvsmzZMqKjowkLCyv39UajkXHjxqHT6TAajQ6IUIjqp8zvVn7//febtsnKyuKTTz5h2rRpvPvuu/zrX/+qUHBClJlbPbtOZTt79iwpKSn4+PgQHh5e5EUhKyuLYcOG0bRpU+bPn199SkrbQtEX/VT/ZnTOkuzUVm71taQnf9qiNUcb2SnOtd/AtwMY/SD3qnadW/1KDVeIuuTf//43M2bMAOCnn35i8uTJNvXj4iIjsaJusetH066urjzzzDOMHTu2SPlqIWqSdu3aMWbMGO67774iI5U5OTncddddbNmyheXLl3P69OkqitJOFEXbZ0dXxk/6dEbwbOrYmETVcQ4Ej+s7s+uM2podc0rxbc0p2nn99d8dzyba9UIIu1u0aBHPPfccoI3y2JrsCFEXOeQj2l69erFq1SpHdC1Eyew8xapVq1aYzeYixx988EHWr1+Pm5sba9eu5ZZbbrHbPauMa7i2z07GuTK0DdO+agOrVdt3Jr8Uc/5Gq/kjFc6BdW8TTZ1OW5+VFaeVIVectDU7xSU9Bh/tvCVXS5K82tS950uIStK/f3+aN2/OPffcIzNohCgnhyQ8mZmZDqsbL0QhOclapShTjFYdSucCXi206W0OqiL20EMPER0dzcqVK+ndu3exbUwmE9nZ2TVnbwNnP21TUbOp9NLUxuvtakOFtqzLkPK7tsnmX0sxK3ot6fForL35dw2tujirgmsoBPWFK9u0BNC3XeE1PPl822nnjd4QdDu41bHnSQgb5Ff9LG9ltPr16/PLL7/g5eXliLCEqNXs/lFc/qZZ7drZtjHh3LlzadSoES4uLnTr1o19+/aV2Pbo0aOMHj2aRo0aoSgKc+bMqXCfooawWuDaYTgXBTEfwbmv4PzX2veYj7Tj1w5r7ezszjvv5Ny5cyVWLrx69SoLFixg8eLFJCffZF+b6sSrNYT0A/dGRae36Yza8ZB+WruaLv0cXN4AiTu1kYy/7jujWrTjiTu1dunnqiLKquXRCEIHgkuAlvwE9dVGdED7nn/MJUBr59Go6mIVogbZvn0733zzTbEzCG7G29v7pomSqqp230pBiJquzMMwN3vjlpWVRUxMDPPmzWPXrl189dVX5Q5m+fLlTJs2jfnz59OtWzfmzJlDZGQkMTExxX5SnpmZSZMmTbj33nv5xz/+YZc+RQ2RckRLalJ+A8tfKozlpkLmBcg4D40eAL+Odr+9j49Pscfj4+P56quvyMjIwM/Pr2ZVcNPpwaedNr0tK04rPZ0/xcuzqTaNrbaM7Fz5WRvZKYv8dnrnujnS4xwMude0KX4h/cGSBXpXrVy1cwAYfWUamxBldOTIEbZs2QJAmzZtaNOmjV37t1qtfP/99wQEBNCnTx+79i1ETVbmhCcgIKBMw68Gg4E33niD+++/v9zBvP/++zz66KNMmjQJgPnz57NmzRoWLFjAiy++WKR9ly5d6NJF2weiuPO29ClqgOwrELcOkg+Cai2+jSVHO2/016a3uVTOQuotW7aQkZFBSEgIDzzwgM37JFQpZz/ty6dt7Ss9bbX+OY2tPNLPQupR7c1/XXtzr9OBi7/25d2iqqMRosY6e/Ys33//PQA9e/a0e7KTl5fHihUriImJQafT0aZNG/z8asGHVELYQZnfycyYMaPUhCd/49EBAwYQGFj+N5e5ubkcOHCA6dOnFxzT6XRERESwe/fucvdXkT5zcnLIyflz1MBkMgFgNpttGoK2l/x7V2UMtrJr7CmnIOkgWG/y66uitQs4Cf4+Nt2qvHEPHz6cLVu20K9fP5ydnWvH74ulch+DQ3/Ps69A6jmw2pC0pJwF98sl7jMj/3+W7R5C1GUGg4FmzZoRERFh135VVWXp0qWcOXMGvV7PvffeK8mOEDcoc8Lz2muvOTAMbVd7i8VSZHfh4OBgTpw4Ual9zpo1i5kzZxY5vmHDBtzc3GyKxZ6io6OrOgSb2S/2B8vWLAvYmwisLXTYYrFw6dIlwsLCyrRBbnnj3rx5c7naO1JN/X1xXNxB179sELP/pk1q6vMNjo09MzPTYX0LURM0btyYRx55BB8fn3IXLLgZRVG45ZZbuHTpEmPHjqVx48Z27V+Imq5Cc1UyMjJIS0sjICCgVlVlmz59OtOmTSv42WQyUb9+fQYNGlSl1VHMZjPR0dEMHDiwTG/SqxO7xZ6bBmc+h8vleGMWOhCaTAajJ6AlO9988w3Xrl3D1dWVCRMmlPjiI8955XNo3JfXa1Mdi2MM0KqN5aZCblLxbfw6QeigYk/V1OcbKif2/JFyIeqygICAEs+dOHGCP/74gzvvvNOmvrt160br1q3x9PS0NTwhaq1yZynnz5/nvffe48cff+TSpUuA9slCvXr1GDNmDFOmTKFhw4blDiQgIAC9Xk9CQkKh4wkJCYSEhJS7v4r06ezsjLNz0Z3nDQZDtXgjU13isEWFY1edwGAAJbc8NwWjEQwGVFXlp59+4syZMxgMBoYOHYrRWLgi2YIFCxgwYECh3+M6/ZxXEYfEreSCrph1X86B4F4PMmPBqx5kqNr+PEWuz9F+n0pRU59vcGzsNfU5EaIynD59mgEDBpCQkMCqVasYPny4Tf1IsiNE8co1kf3HH3/k1ltv5eOPP0av1zNixAjGjRvH8OHD0el0zJ49mw4dOrBmzZqCa15++eUy9W00GunUqRObNm0qOGa1Wtm0aRM9evQoT5gO7VNUMUUBj6Zalaiy0Ltq7dFKdGZlZXHu3DkURWH06NGEh4cXaj5v3jwmT57M7bffXrNKSouy0RX9IAMAo4+W7JxfDplx2s/FXu/iqMiEEHXUxYsXGTBgAHFxcbRs2ZLu3btXdUhC1DplHuE5fvw4Y8aMoXHjxnzyySfFljvcvn07jz/+OPfddx/79+9n1qxZfPXVV7z55ptluse0adOYMGECnTt3pmvXrsyZM4eMjIyCCmvjx48nPDycWbNmAVpRgmPHjhX8OzY2lsOHD+Ph4UGzZs3K1KeoYfTuWtU1rxbaPjs349UC3MK16wA3NzcmT57M+fPnadGicMWpL774gieeeAKAcePG4evrS15enr0fgahKLv/f3n3HR1Wljx//zEympBfSKSF0kNAlglJEICAWRKUIUkRd/ck2xN3FVZHVFd1V1/2urOgqqEhEsKCuiHQLRlQM0llAILSEkJDeJjPn98clA0MmJJNMMinP+/XKa8i955555jCZO8+9p0Rpi4pevu5OWa429XaHGdr02/lHKh+rM1Q5YYEQQtTW0qVLOX78OJ07d2bDhg1X7PYmhKidGic8zzzzDK1ateKbb76pcuaPIUOG8PXXX9OrVy/69+9PaWmpIzmpiUmTJpGZmckTTzxBeno6ffr0Yd26dY5JB9LS0pzWNTl9+jR9+15cY+X555/n+eefZ9iwYWzdurVGdYomRq8H32iIHAIlmVB8quqyvq21cr4xTlMJ+/v706OH8+KZVquVf/7znwD89re/5Zlnnqk0rsdms/G///2P7t27e+71iIbl11ZLeopPX9xmidaSaJ1OS3psJeDfDnR6KEm/pFyUdrwQQnjQE088gcFgYMaMGcTEtLC1voRoIDVOeDZv3sy9995b7TSHYWFh3HPPPTz99NO8/fbbTJtWw9m0LpgzZw5z5sxxua8iianQvn37Gq0mfKU6RRNkCoXAztDmVjj3LeQd1BZDrGDw1e7sRAyGwC5a+WoYjUY2btzIG2+8wR/+8IdKyY7VauWjjz7i8OHDjB07loEDB3r6VYmGYI6AgPiLCY8lGvxiIWs7nN8N1hwwhkBoArTS1vhyJD2BHbTjhRCiCna73e0Fp3U6XY26/yulPD67mxAtRY0TnqysLNq3b1+jsvHx8RgMBreTHSFqxGCBgA5atyRzKEQMgoJfoLwEfC7sM4Zpi40GxGvlayA8PJw//vGPlbaXl5fz7rvvcvLkSXx8fGRtg6ZMr9cWVC0+DQXHtDs7Wdvh7NcXy1hzLv7eKhFKMiCgPQRd1fIWHRVC1FhWVhYrV67k1ltvpU2bNh6te9++faSkpDB16lQsFhlLKIS7apzwhIeHc/RozVYnP3r0KJGR0tdd1CNjAAR3h+Iz2mKSliiw20Bv0O7wWCK1rmw1THauJD8/n5MnT2I2m7nrrrto166dB15AI1eaDYXHtJnKzBHg3x7MzSTR842ByGFgDAJ7qXZnx5XzuyG0D4T2gtC+4CddTYQQrhUVFZGcnEx2djYbN2684nIH7tq5cyeffPIJSim2b9/OsGHDPFKvEC1JjROe4cOH88Ybb/C73/3uile4s7OzeeONNxgxYoRHAhSiSgaLdgfHLw7K80DZtXEXPkEevRIfGhpK586d6dixY62nSG9SSrMhfRPkHdDuoukMENQNom9oPklPQHvw8YecXYBNG79zafdYnU7bbgqBkF7a3cKWTiltzJw1F4zB2hg56V4jBFarlXfffZfs7GxCQkK44447PJbspKam8sknnwDQp08flxNGCSGqV+NvhY8++ihZWVkMHTqUb7/91mWZb7/9lmHDhpGVlcX8+fM9FqQQV6TXa19MzWHaYz10Oxo4cGDLSHZAu7OTd0BLCCxR2mPeQW17c2KJ0CYhCOuvjfUK7HjJTxdtu19bSXYqFJ+C01/A8Q+0xytNGCJEC2K32zGbzVgsFu666y4CAgI8VndcXBwBAQFcc8013HLLLW6PDxJCaGp8h6dHjx4kJyczffp0hgwZQvv27enduzeBgYHk5+eza9cujh49iq+vL8nJyZVmwRLC0+x2hU4HxVYb+8/kkVdcTpCvD9mHdxIVEU7PhAT0cgXafaWZ4NdOGx9lK9HupJWeh9Jz3o7M83xjtK6RtmIoywFboTaFuSlE2+4r3dgcrLnaWkXYtUdrLuDZcQpCNEVms5kpU6aQnZ1NRETlCyR2ux273Y6Pj9trvRMWFsavfvUr/P39ZcICIerArb++CRMm0KdPH/72t7/x3//+lzVr1jj2xcbGcv/99zNv3jw6duzo6TiFqORIZgFvfnuMNamnKCzT1lXpYsjkWtNxAEp9Ari6RwdH+fz8fFmFuiYssWAr067i5+2HoO4Qm6Rtb26MgRA2AApPQN7/oOz8xVkAwwZo+4XGGKytaVV0Sns0Bns7IiEaDYPB4DLZUUrx4IMPkpWVRXJyMiaTye26PXnHSIiWyu3LDR06dGDJkiUA5OXlOb5EBgUFeTw4Iary8ubDvLDhoNOwizb6HAYZtWQn1RrDm8v38/AoO3NGdOLAgQNcf/31PP3008yePdtLUTcR/u20WczyD2pjePIPgv5m8G+ma9DYy7S7OQHxoMpB56Pd1bKXeTuyxsW3tZb4XjqGRwhRJaUUv//973nttdfQ6/Vs27aN66+/3tthCdEiuX9/9RJBQUGS6IgG9/Lmwzy//mCl7VH6AvQ6OFTeip3l2t2I59cf5Nzp4/znj9NJT0/n3//+N9OnT8doNDZ02E2HKVgbxxI9CvL2QVAP7XdTM72iX5QGWT+CKQwMJu3uVlk26I1aIiQ0Op02jbd0YxOiRhYuXOhY0PqNN96QZEcIL6pTwiNEQ7LbFUcyC1wmOwA7yluTpfxIs4UAWl9nW3E+Tz80C1teJj179uSLL76olOzs3r2bzp07y9oGlwroCPF3tYyr+eYIbXa/sqyL23Q+ssioEKJOhg8fzgsvvMBzzz3HzJkzvR2OEC2aJDyiydDp4M1vj12pBMdsztMm6y0B+F81Ap/j37FhwwbCw8Md+5RSfP3112zZsoV27dpx991312pQabPUkq7m+7fXpt3OO3ixS1tQV/CP83ZkQogmbPjw4Rw6dOiKM3wqpWQyAiEagHy7E01GsdXGmlT3psLV6XSEDr0bi7qToDDnZGf9+vV89913AMTHx2MwGDwar2gizGHaGkOBHbWZ6MzhzWuhVSFEndnt9lpNCX2lZCc/P5/k5GRGjhwpkz0JUc9kQnfRZOw/k+eYjc1dJToLB87kO34vLCxkz549ACQlJTF8+HC5ytaSmcMgrB/EjNYeJdkRQlyglOLjjz/miy++QF06U04dnD9/nqVLl5Kens7nn3+O3W73SL1CCNfkDo9oMvKKy+t2fInV8e+AgADuvvtu0tPT6dWrV11DazaqWtuoe0wQvkYDCmRtIyFEi7JlyxZ27dqFTqejV69exMTUbX2uvLw8li5dSkFBAaGhoUybNk0WFBWinknCI5qMIN+6vV2DLM6TFURGRhIZGVmnOpsbV2sbAfibDIzv25qZg9vTOUrWphFCtAypqal8/fXXANx00011TnYAAgMDiY+PJyMjg2nTpsn6cEI0AEl4RJPRPSYIf5OhVt3a/E0GusXISeVKXK1tVKGwzMaK7Wkkf5/Gw6O6MmdEp4YPUAghGpher0ev13PttdfSr18/j9Sp0+m49dZbKSsrw9fX1yN1CiGuTBIe0WRkZZzhjrCTrEiPpBz3Jhi4rW9rfI0yKUFVqlrb6HJKaWsb6XTw0PWS9AghmrfevXsTHR3tsjdASkoKoaGhdOvWze16DQaDJDtCNCDpNCqahOzsbFauXIk+9xR9jacd25XNSu6376HKy6o8VqeDmdfG45mhps2L3a44lJFfo2TnUs+vP8jhswXYPTSAVwghGquoqKhKk9rs2LGDMWPGMHToUA4edO/zUwjR8CThEY1eUVERK1asoKioiJiYGIYOHQaAsts498nfyfl6OZkfP1fl8fNGd6VjhL8Mtneh+rWNXFMK3tx2FGlRIURLs2fPHkaPHk1eXh7du3enbdu23g5JCFEN6dImGr2cnByKi4sJCQnhrrvuIiAgAHR65v/2VxT971sw+BDYb5zTMRG6Akx6G3eNHChdr66gNmsbVfgo9RSPjuuOn0k+RoQQLcdjjz1GdnY2AwcO5L///S9+fn7eDkkIUQ25wyMavdjYWO655x6mTp2qJTtAUjsd+pM70Rt8aHvHY/jGXxxM2sFUwDjfQ4z1O8ptXeVEdCV1WduosMzmtLaREEK0BMuXL+eBBx5g3bp1LmdYU0rJujpCNDJyaVY0CeHh4U6/d+nShc2bN5F2/Dhjxt3M/jN55JfaKEw/RurXqdhsNuLadah0nHDmybWNhBCiJQgMDOSVV15xuU8pxbp16ygtLeXWW2+VBa2FaCQk4RFN1oD+/RnQsyMUH6R/sB30Jtb8vBebzUaPrp247Y5J+PjIW/xKgix1XdtI2lcI0bTZ7XaPLPxpt9v55JNP+PnnnwHo27cvcXFxda5XCFF38m1FNG3mEMAOhcegKJ2br08gtk08A64ZKitX10D32EBZ20gI0WIdO3aMzz//nEmTJhEWFlanutasWcPu3bvR6XSMHz9ekh0hGhH5RiiaPnMYhPWDmNEYIgYwcPBwSXZqyNfow/i+rWt1rLa2kVwzEUI0TZmZmbz33nucPXuWbdu21bm+nj17YjKZmDhxIr169fJAhEIIT5FvhUK0YKo4g5mD2+FuN3OdDmYObocqzqifwIQQoh4VFBSQnJxMSUkJbdq0YcyYMXWus0uXLvz2t7+t1UKkQoj6JQmPaFj2ug2SF56lt+bQOSqYh0d1deu4eaO70jEyCL01p34CE0KIemS32zGbzYSGhjJ58mSMRqPTvtqSKaqFaJwk4RH1rzQbcvdp/z71KZz4GHJ2a9uBsrIyli9fzrFjx5wOy8nJadg4WyJTKGSmMGdEJx5J6lrtnR6dDh5J6spD13dCd+477XghhGhigoKCmDVrFtOnT8ff39+x3Wq1Mn78eF544QUvRieE8DRJeET9sdu0xObkJ3D6c21b3gE4nwonP4WTn2DP/pn331/NL7/8wocffkh5uXYHaPny5XTq1IkffvjBiy+gBTBHQHkBnP6Ch67vxIbfXce0xHb4mwxOxfxNBqYltmPD767TFnI9/QXYCrXjhRCiCTKbzYSEhDh+t9lsTJs2jU8//ZTHHnuM48ePey84IYRHyYhjUX/y9kH6FijLBvtlubW9DFVwjLUpZzh0pAwfHx8mTpyIj48Pq1atYubMmdjtdlatWsXVV18NQGFhIT/++CNDhw6VtQ08Ra+HkJ5wZj0cWUaHVtfw1PirePTGbo61jYLMBrrFBOFrMqByD8KRD0Gnh+jR2vFCCNHEKaWYPXs2q1atwmg08tFHH8ksa0I0I5LwiPpRmg1ZO7Rkpwp2BQWFpYCOCTePpk2bNnz55ZdMnToVu93O7Nmzee655wDIzc1l+fLlZGVlUV5ezg033NBAL6QF8I2ByGFw9kv0ae+BMQi/0L70Dw4DvRnspXA+G86norPmQUC8Vt4vxtuRCyGER+h0OhISEhwX3VxNYuCp9XqEEA1PEh5RP4pPQfHpKxYx6GHiNZCWbaB9O18Arr76akaMGEFERASvvvoqer2erKws3n77bfLy8ggKCqJPnz4N8AJamID2YDBD7l7I/wUyvwF1ydo8OgNYoiC0FwRfpSVJQgjRjDz88MPceuutdOrUqdK+X375hbVr1zJ16lRCQ2XsohBNjSQ8wvOU0r4028uqLarXQftW5ZB/BEJ64ufnxyeffILBYMBg0MaRnD9/noKCAlq1asXdd99NcHBwfb+Clsk3BsxREJwARSeg5CzYS0BvAUsk+LXVxuzIFU4hRDPlKtk5cOAA77//PjabjW+++Yabb77ZC5EJIepCEh7hecqmdYNyh71Um7Ja74PZbHba1alTJyZPnkxsbKzTbDqiHuj14Bul/QghRBPmiS5ohw4dYtWqVSil6N69O2PHjvVQdEKIhiSXaoXn6Qza2I9LKaU9lpwFa+7F3yvozaCvOv/u3LmzJDtCCCFqpLy8nLfffpvvv/++TvW0adOGiIgIevfuzR133IGPj1wnFqIpkr9c4Xk6HQR20MaD2IrAmg8leUAfyP4JzL7gGwumMDAGgsEPAjt6O2ohhBDNgFKKNWvWcPz4cdLT0+nRo0elngM15evry6xZszCbzTI7qBBNmNzhEfXDt7U27qMwTVt7p+iUtl3ZoLwICk9o2wvTtHK+sd6NVwghLnjyySfR6XROP926dXPsLykp4aGHHqJVq1YEBARw++23k5GR4cWIxaU2btzI3r170ev1TJo0iYCAgDrVZ7FYJNkRoomThEfUD58ACIhH2e2o8kvG8xiDL3Z3s5WCsmvTHPvU7YQkhBCedNVVV3HmzBnHzzfffOPY9/vf/55PP/2U1atX8+WXX3L69GkmTJjgxWhFBaUURqMRgFtuuYX4+HjHvt27d5OXl+et0IQQXtQoE57FixfTvn17LBYLiYmJ1fbBXb16Nd26dcNisZCQkMDatWud9s+cObPS1TpXc+wLDyo+CeXFfHnuGtaeGoRdbwHgaHoxL75/AqW3QGgfaHMTlBdr5YUQopHw8fEhOjra8RMeHg5oa4K98cYbvPjii4wYMYL+/fuzbNkyvv32W7777jsvRy10Oh3Dhw/ngQceoHfv3o7t69evZ+HChYwdO5bc3FwvRiiE8IZGN4bnvffeY+7cuSxZsoTExEReeuklkpKSOHjwIJGRkZXKf/vtt0yZMoVFixZx0003kZyczPjx4/npp5/o2bOno9yYMWNYtmyZ4/fa9ucVNWC3Q/4RUn/awZc/A1jo0OlWMo9l8rsFezh2Ogdd5LX8/lcjsZdks3nbDvr3V4R2bi9THgshGoVDhw4RGxuLxWJh0KBBLFq0iHbt2rFjxw6sVisjR450lO3WrRvt2rUjJSWFa665xmV9paWllJZevNtdcafBarVitVrdiq2ivLvHtSRhYWGO9vnqq6+44447KC8vp02bNphMJmm7ash7zH3SZu7xRHu5c2yjS3hefPFF7rvvPmbNmgXAkiVL+Oyzz1i6dCl/+tOfKpX/5z//yZgxY3jkkUcAeOqpp9iwYQMvv/wyS5YscZQzm81ER0c3zIto6crzOHz4EJ/uVICO67rq8FfnWbDgH5w+nUOn9tFMSkqg/FwqH/1oY98pHQfSd/Hgg4Mx+LbydvRCiBYuMTGRN998k65du3LmzBkWLlzIkCFD2LNnD+np6ZhMJkJCQpyOiYqKIj09vco6Fy1axMKFCyttX79+PX5+frWKc8OGDbU6riWx2Ww89NBDlJSUMGDAAO666y7Wr1/v2K+UkvE5VyDvMfdJm7mnLu1VVFRU47KNKuEpKytjx44dzJ8/37FNr9czcuRIUlJSXB6TkpLC3LlznbYlJSWxZs0ap21bt24lMjKS0NBQRowYwdNPP02rVq6/XHvySpwnNZmrB9ZSikps6HU6urWGIV0V7677ntOnT9MuJoR1/55CsKWI5G/haKYOgx6GJQRht9uwN7LX1mTa3IWmGrvE3fAaIvam1C6XrrXSq1cvEhMTiYuLY9WqVfj6+taqzvnz5zudq/Ly8mjbti2jR48mKCjIrbqsVisbNmxg1KhRjvEqomqdO3dm0aJF3Hbbbdx4442ONisqKmLVqlX06tWLfv36eTnKxkXeY+6TNnOPJ9rLnTF5jSrhOXfuHDabjago50UPo6KiOHDggMtj0tPTXZa/9ErbmDFjmDBhAvHx8Rw5coRHH32UsWPHkpKSgsFgqFRnfVyJ86SmcfWgHR07hWOwWPg8TU9Ijz7MnduWLl26sKcsmp3HyzmTfxi9voz28fH8UhTILxtdJ7WNQdNoc9eaauwSd8Orz9jduRLX2ISEhNClSxcOHz7MqFGjKCsrIycnx+kuT0ZGxhV7EZjNZpddqY1GY61P9nU5tiVJSEjgrbfeYu3atY42y8/PJzk5mbNnz5KTk0Pv3r2xWCzeDrXRkfeY+6TN3FPXz8CaalQJT32ZPHmy498JCQn06tWLjh07snXrVm644YZK5T15Jc6TmszVA7sdMr+Ecz87Nlntehg6lFFtd2PUa9O3FrRW5BVDbOgvEH4NRAxrdGN4mkybu9BUY5e4G15DxN6UZ8cqKCjgyJEj3H333fTv3x+j0cimTZu4/fbbATh48CBpaWkMGjTIy5GKmigtLWXZsmWcP3+egIAApk+fLsmOEM1co0p4wsPDMRgMldYzuNKVs+joaLfKA3To0IHw8HAOHz7sMuGpjytxntRY4riikE6QvxeszrPhGPV2jHo7AKF+2g/GIK18I55Iokm0eRWaauwSd8Orz9ibUpvMmzePm2++mbi4OE6fPs2CBQswGAxMmTKF4OBgZs+ezdy5cwkLCyMoKIhf//rXDBo0qMoJC4Tn2e129LW8QGY2m+nVqxe7du3i7rvvJjQ01MPRCSEam0Z1Od1kMtG/f382bdrk2Ga329m0aVOVV84GDRrkVB60bhlXutJ28uRJsrKyiImJ8UzgojLfWAjtBbpqvuTojBDaWxYebWys+ZB/CHJ2a4/WfG9HJESDOXnyJFOmTKFr165MnDiRVq1a8d133xEREQHAP/7xD2666SZuv/12hg4dSnR0NB9++KGXo245du7cyVtvvVWnbpLDhg3j/vvvl2RHiBaiUd3hAZg7dy4zZsxgwIABDBw4kJdeeonCwkLHrG3Tp0+ndevWLFq0CIDf/va3DBs2jBdeeIFx48axcuVKfvzxR1577TVA64qwcOFCbr/9dqKjozly5Ah/+MMf6NSpE0lJSV57nc2ewQKtErV/n98FpS6+MBuDtWSn1UCtvGgcik5C5jYoSANbIRj8IaAdRFwLfm28HZ0Q9W7lypVX3G+xWFi8eDGLFy9uoIhEhV9++YVPP/0Uu93Ozp07GTx4cK3q0el00o1NiBak0SU8kyZNIjMzkyeeeIL09HT69OnDunXrHBMTpKWlOd3GHjx4MMnJyTz22GM8+uijdO7cmTVr1jjW4DEYDOzatYu33nqLnJwcYmNjGT16NE899ZSsxVPfjAHal+SAeMg5DMeLwRILRoP2xTmwo3ZnR5KdxsOaryU7ufsvbrMVXvw99kYwBnonNiFEi5aRkcGqVauw2+307NnT0ZNDppYWQlSn0SU8AHPmzGHOnDku923durXStjvvvJM777zTZXlfX1+++OILT4YnLrj0JFNeXk5hYSHBwcHOhQwWCOgAxtbAOmh7CxhN4BPU6CYoEEBJunZnx5WCNG2/JDxCCC8xmUxER0dz6623otPpyM/PZ8KECcyfP58RI0Z4OzwhRCMl3zhFrZw6dYpXXnnFMZX4rFmzGDZsGJmZmQD8/PPP/PDDDxcPqEhuTKFgCpFkp7GylWh3dFzuK9T2CyGEF0RFRXHvvfcyadIkfHx8KC4u5pZbbmHjxo3cfffdlJTI55MQwrVGeYdHNG7nz5/n3XffpbCwkM2bN7NhwwbeeecdDAYDO3bsIDQ0lHXr1gHaLHpt27b1csSixgwWbcyOq6TH4C/dD4UQXlWxNERpaSkTJkxg69atBAUF8fHHHzuNybHZbC7X2RNCtExymV24paioiBUrVlBYWEhUVBS7du3i9ddfR6/Xs2LFCnx9fR3JTmJiIm3ayCD3JsUSrU1Q4EpAO22/EEJ4mU6nIzAwED8/P9auXcuAAQMArav11q1bWb58OVar1ctRCiEaC0l4hFuUUpjNZoKCgpg6dSqzZ8+mQ4cOLFu2jEmTJjnKDR8+nKSkJBlI2tQYA7WJJoK7a3d0QHsM7q5tl/E7QohGwGQykZyczHfffce1114LaOenL774gi+//JLjx49z8OBBL0cphGgspEubcIu/vz8zZsygoKCAwMBAAgMD2bNnD76+vgAMHTqU9u3bExcXB4DdrtDpoLisHICv/5dJcICF7jFB+BoNKEAvSVHj4tdGm42tJF0bs2OwaHd2JNkRQjQiPj4+JCQkOH7fsGED27dvB2DMmDGO2VqFEEISHuE2k8lEWFiY4/eKZAe0bgYVyQ7AkcwC3vz2GJ//fJIFfeHB5J8otenwNxkY37c1Mwe3p3OUfJFudIyBkuAIIZqUXr168fPPPzN69Gh69+7t7XCEEI2IJDyievZy0Lv/Vnl582Fe2HAQpcBsUE77CstsrNieRvL3aTw8qitzRnTyVLRCCCGaKLvd7rTWnjuio6P5zW9+I2vsCSEqkTE8LZ1SUHQScvdqj+pCYlKaA8XpUHAcis9ceEzXttfAy5sP8/z6g47qrvT0z68/yOIth+v0MoQQQjRthYWFvPrqq3UaeyPJjhDCFUl4WjprHpSXAAbt0VaCvTQXZQqiSB/GjnP+bDlhYsc5f4r0YShTEPbSHLDbXFZntysOZeTz/Hr3TljPrz/I4bMF2KvLkIQQQjQ7VquVd999l7Nnz7J+/XpsNtfnGCGEqA3p0tZCOSYTwJ/92VbySmwEWYx0txixmCzsO5PHc58f4KtD5xzHXBx3047OUa7XN9Dp4M1vj7kdj1Lw5rajPDVeBpkKIURLYrfb+fDDDzl16hS+vr5MmTIFg8HAN998w7XXXiuzfQoh6kzu8LRQRzILeGzNHq5+eiO3L9nOrDd/5PYlKVz99EYe/2gXep2Ot2cnMshwmOIjPwBgsuZzKnULY1/6kpc3u+6CVmy1sSb1VK1i+ij1FMVWuaonhBAtScVyBwaDgUmTJhEeHs5bb73FkCFDuP/++7Hb7d4OUQjRxMkdnhbo0skELldSVkbOzxt45KdQ2vja+ODvj2BXip4zn2Fs61LMOhtFysjz6w3odPDQ9c6TDew/k0dhWe2SlsIyGwfO5NMvLrRWxwshhGh6DAYDt956K4MHDyYyMpLVq1dzzz33AODn5+e4w2Oz2TAYXPcuEEKIK5E7PC3MlSYT0KEYZjpKpL6QwLRveG3BHKxWK9Pvvptb25Rg1tnIsPmz0xoLVIy7yXcad5NXXF6n+PJKZGVsIYRoaXQ6HZGRkWRkZDBjxgzsdjuzZ8/mH//4BzqdjlOnTvGvf/2LkydPejtUIUQTJAlPC1H9ZAKKRGMacYYcypWOTXtOomxW/Dpfwz2//SNGow8nbUGsL+tC2YUbg9q4m2Nc2rs6yLduNw2DLMY6HS+EEKLpioqKIjk5mZkzZ/Lqq6+i1+s5evQob7/9Nrm5uXz11VfeDlEI0QRJwtNC1GQygQJlxq7g67J4fEf9hrBRDxJ+yx9Z90sZ986ezV5LT8px7k5w+bib7jFB+Jtq1+XA32SgW4wsdimEEC3Z+PHjWbZsGQaDgVOnTrFixQrKysqIj4/njjvu8HZ4QogmSBKeFqL6yQR07CmP5qPSnhyzh6HT6QnsNw6dj5GPUk/hHxzKHVe3q3RUxbibCr5GbSa32ritb2t8jdI/WwghhCY6OpoOHTrQtWtX7rrrLkwmk7dDEkI0QTJpQQtR08kE8pSl0rbCMhv7z+QT18rf9TGXjLtRCmYObk/y92nVLjp6KZ0OZl4bjwJkAlIhhBCgTWhw5513YjAY0OvlGq0Qonbk06OFqOtkAoWl5QSaXefHl4670et1dI4K5OFRXd2qf97ornSM8Ecv6y0IIUSzo5RC1XJhaaPRKMmOEKJO5BOkhajrZAL+Zh/ySysnTVWNu5kzohOPJHWluvxFp4NHkrry0PWdZHG5xqboJGRs1R6FEKIONm3axAcffEB5ed0uvgkhRG1Il7YWomIygdqskeNvMtA9JpBvDmdW2nelcTcPXd+JpKuieXPbUdb+fBK4+Nz+JgO39W3NzGvj6RQZ4HZMogHkH9YSHgC/Nl4NRQjRdO3YsYNt27YBkJCQQNeu7vUAEEKIupI7PC3E5ZMJ2IpyUXYbRsoZZfofkfqCKo+9rW9r/Ew+rP7R+Ur/peNuqtIhwp+nxvdk6yPDAVgytR8fPjiYHx4byVPje9IhwvW4oFqxy5VDjwrsBFHDtUchhKiFQ4cO8dlnnwEwbNgwQkJCuOmmm0hLS/NyZEKIlkQSnhZCKZgxKA4fnR1b4XnSV/yR4o0vM9Z0kDaGPIYaf0GPvdJxOh1MGxTHxv0ZnMktcdpXk3E3ep0OnU6Hr0m7mXhd5wj6xYXiZ/JBp9N5dsyOXm5YepRfGy3hkbs7Qoha0uv1GI1GevfuTc+ePRk5ciSfffYZ06dPB8Bms9V6bI8QQtSUJDwthF6vI+PQz9zpu5ezK/+MpSyHyUN60MpQTLHyYXNZJ+wu3g7zRnelS2Qgb3xz1LFNxt0IIYSoiY4dO3LfffcxdOhQxowZw549e4iJieGNN96grKyMFStW8MUXX0jSI4SoV3JJvIXYtWsXmzZt4p033qDs3ElUUDgFftGY7YovyrpUmo5ap9OSnYeu78TTn+3j+6PZdRt3Y73QZS53H5j9wBINRllkVAghmrvw8HDS09MpLi4mPDycjRs3Ehsby/Llyzl58iSnTp0iMTGR0NBQb4cqhGimJOFpAY4ePcrHH3+MTqfj/913N0veWMGr77zPN2d9WPfzCfLUxUkHLiY17ekUGciJ7EKu6xTOjT2j6BYdhK/J54pjdlwqOgnp2wALnPwYjL4Q0A4irpXuUkII0QJER0fz5ZdfcubMGbp168brr7/OmTNnsFgsTJs2TZIdIUS9koSnBQgMDCQoKIg2MZFM6FfC3BmvYWwVQ5J/LI/edBX7z+SSX2InyKKnW0ywltQUZ0B+Bm31pbSNbw3KBgYr6IzuLQxqzYfMbZB3EOitbbMVQu5+7d+xN8qdHiGagtJsKDwGpZlgjgD/9mAO83ZUogkJDw8nPDwcgEGDBrFhwwamTZtGZGSklyMTQjR3kvC0AOHh4cyePRuLrghdzo+Yredh5x/AVoxf7Bj6+8ZCiD+UF0LaaTi9Dp3BF9reAj5B4BsD5la1e/KSdCioYjaegjRtvyQ8QjRupdmQvgnyDmgXP3QGCOoG0TdI0iNqpWJ6apPJ5O1QhBAtgCQ8LURAQACUlYNPAJz+DHL3gK1EG1NjsIC9FPRmbRtK22YOhXaTQFeHuS1sJdodHVuZ9nvBL2A0gynk4n4hRONWeOxisgPaY95BCOwoCY+oNUl2hBANRWZpa0l8gsBWDJnfgTUP7GVaMmMrguJ0bZ9Or2235mnlbMXacbVlsGh1Fh7Tfi86Dfn/0xIfnV7bL4Ro3EozLyY7FVQ5lJ7zTjyiUbHbKy9pIIQQjYkkPM2UUqryNJ/leVB4HOyX3FVR5dqdHUsk6E3a7yjty015npaYFB6D9M2Q9SMUZ4A7JzdLtFZ3adalwWm/WyK1/UKIxs0coXVju5TOR9suWrSzZ8/y73//m9OnT3s7FCGEqJIkPM2QUor169ezbt26i0mP3Q4Fx7VxOv5xF7+82K1adzadQXu0FWvdzOxl4Ntau4Kbf0h7PP25NstaxiYoPlOzYIyBENoHwq+5ZFuINkNbaF8ZvyNEU+DfXhuzo7vQC1rnA0Fdtc8S0WLl5+eTnJxMVlYWmzdvZvfu3d4OSQghXJIxPM2IUooFCxbQpUsXjhw5AkDPnj1p27at1iWlNBOUVUs4grpqd3vKC7WkByvYy7W7PwZfCOgIPn7aLGuFadrEBTofKD598SdyGAS0rz4wYzC0uhrSbND1IdDZwGDWtgshGj9zmDZBQWBH7eKHOVxmaWvhysrKePfdd8nNzaVVq1YcOXKE6dOn88YbbzBr1iysVitGo9HbYQohBCAJT7NRWlrKn/70J06cOIHBYECn03HLLbdoyQ5A0QktwfFvB2e/BmMQhPSEslwoO6/d0VE2bTIBn0AtMSo6AzFj4fwuLUHxjdbqACg4qj0azFoydCWWKMjaCxi17nM+Ru05LR6aitSar30Js5eD3kf7MiZ3joTwLHOYJDjCwW63Y7FY8PPzIz8/nyeffBKAc+fOsX37dn744QdmzpypTZgjhBBeJglPM3D69GmmTZvGli1b6NmzJ7169WLixIl069btYqGSDK0bmm8f7e7N+Z3ahAHGQDCFagmPrRjKi7Q7OuX5ED4I9AbIPwgBcZUXCS04Crl7wRwF+ip6R9rtoOwQchXwP8g9AJEDILSXtt1ur/rY6tjtUHBEW9PHXqTdrfLxB70fBHfXXmdt6xZCCFEli8XC1KlT+eKLLxg3bhwACxYsYODAgaxbtw6A3bt3M2jQIG+GKYQQgCQ8TV5OTg5vvfUW+/drC3n+6le/YsaMGQQGXnaHw156YVal8xBxDZRkQvGpi+N1Ss9pjxX84yB8MOT/om23l17sv3+p/F8gOAF8oyrvKz4DOXu0xKgkH+gEIQnanaQTH4MpGALitTtN1d0lclV3yVnI+AoMJjizHs7/DKG9IWa0dicqaqh2F8nduoUQQlTLYDAwduxY5s+fT3l5OUlJSaxfvx6A4cOHc80111RTgxBCNAxJeJqwkpISVqxYQVlZGfPmzSMsLIxZs2a5Lqw3a48Fv0BwD2hzM5z7TltLozz/YrLj4w8hvbRkpywHsn+6eLwqdxFEhtZd7vKEp+AYnP3yYtc3+4U7Led/BlUA2KG40P3xQBV1n0/V7hDlH4HgzhfqLdceo0do2/1itdcb2rfmdQshhKgxnU7HM888g1KKgoICfvzxR66++mpJdoQQjYokPPXBbtcmCCg6oSUEFYt6WqLAr602lasHulqZTCbi4uIoLS3l3nvvJSjoCuvlWKK0mdiUTVtsNKADxIyEiEHaGJ2y89pkBf7tL3Rj++VCsmMHnVG7U2Irrlyvsml3Wi5VfMY52bmUrQD0l01r7c54oIq6bSXaeKPiUxDSXbuzU3GHR+ejbVflUJqjla9J3UIIIWpFp9MRGBjIAw88IJMVCCEanUY5wGHx4sW0b98ei8VCYmIi33///RXLr169mm7dumGxWEhISGDt2rVO+5VSPPHEE8TExODr68vIkSM5dOhQ/QRffEabtvnkx5C+SVu3xlaqPaZvcn9a5yvQ6/WMGzeOX/3qV1dOdkBLtCwVd2GUNvYleyeU5V3o9tVamzUtdx+cWAPZPwIXEpPADmAK0xYndeXSdX3s9ovd2NxRMR7oSmv8XFq3vUxLbOxWyD2odWPr/nvtMfegtl3nA7aymtUthBCiziTZEUI0Ro0u4XnvvfeYO3cuCxYs4KeffqJ3794kJSVx9uxZl+W//fZbpkyZwuzZs0lNTWX8+PGMHz+ePXv2OMr87W9/4//+7/9YsmQJ27dvx9/fn6SkJEpKSlzWWWsFx7SxJJnbtLsl0TdAUBdt1rCgLtrvBl9t/5n1Wvk60ul0+Pv7V1/QHKGNl7mUKtfuhGRth/QNkLEZ8vY7j+UBbYyNrUi7U+WK3nLx36WZV052zJHaDHGu5P+iHV+VS+suzdbumoUmwPmf4MxGyLrweP4nbbveDGXZNatbCCGEEEI0S40u4XnxxRe57777mDVrFj169GDJkiX4+fmxdOlSl+X/+c9/MmbMGB555BG6d+/OU089Rb9+/Xj55ZcB7e7OSy+9xGOPPcatt95Kr169ePvttzl9+jRr1qzxXOAVXa2K0yFsgDarWeY2OPjyxZ/Mbdr2sAFaubNfun2nx7GQqLv0ei1xuTzpAfAJAJ3O9XFh/bRubkWnXO/XGZynl67oxnc5vwsLFJrDILAT+LuIo2I8UFWc6lZQdFJb3ydyiHZHp+CI9hg5RNtedFIrV5O6hRBCOLHLXXEhRDPRqMbwlJWVsWPHDubPn+/YptfrGTlyJCkpKS6PSUlJYe7cuU7bkpKSHMnM0aNHSU9PZ+TIkY79wcHBJCYmkpKSwuTJkyvVWVpaSmnpxbsZeXl5AFitVqxWa+Ug7HbI2g2FZyGoBxRmwom1kLEFR7cwgOxdEDkcwq6GgB6QdwCy9kBEWI3G9Ozdu5eDBw8yePBgQkNDqy1fiU84hF0HNi6upwNgCNV+rAXO5UN7Q6trtddTmo/L/NgSDaYYqGiXwnSwKeeyPoFYfUIAsB5+B4LiIWYE6IO1CRMcFBRmQJCLNnZVd9FZsOkgZCAE9dHuWOl8QG+C/NNQevaSOKqpuwoV/98u/98buaYau8Td8Boi9qbYLi2ZUor333+fsLAwbrjhBnRVXRQTQogmoFElPOfOncNmsxEV5TzjV1RUFAcOHHB5THp6usvy6enpjv0V26oqc7lFixaxcOHCStvXr1+Pn5/fFV5BF7QEJwxIAl1S5SJnL/xgv1C+CFh3hTo1aWlpZGdr3bM++OADYmNjqz2maiEXfi41uHKxYuA0QKsLP1U4+ONlG3pXWXRD8Wyt3gyADpULHC+H1LWVt1+xbjvaW9nnkt+jL/y4U3fVNmzYUKvjGoOmGrvE3fDqM/aioqJ6q1t43vr169m/fz/nz59n6dKlLF26FIPBIGN0hBBNUqNKeBqL+fPnO901ysvLo23btowePdr15ADZqZD1gzYNctpqbaC83qDNfnbpHR6dQVuHxm7T1o5pdycUnda6X4X1rTKeVatWOZKdsLAw7r77bsxmc91epN0OZee0rmqlmWDNhbJcbVpqU6g2I1vRaSjPq7oO/ziIusF5SuozX1ycyrqCTyBWvw5s2A2jfN/AWHGHJ/+Xy+7wAGH9tYkHXHFVtzuuVHcVrFYrGzZsYNSoUU3uRN9UY5e4G15DxF5xp1w0ft9//z3fffcd58+fZ+XKlWRkZBAZGUlcXByjRo2iZ8+e3g5RCCHc0qgSnvDwcAwGAxkZzmNAMjIyiI6OdnlMdHT0FctXPGZkZBATE+NUpk+fPi7rNJvNLhMKo9Ho+stAeSb4R0BxGpSe1hIbUxT4R2njRvQmbSIAvxjwMUDxOSi3QekJ8I+G8nNQxZeMzZs38/vf/54pU6ZQVlZGQkICZrPZM19KzK0hsPXF34vPagP+s1MvTlBQVU+7gPgLa+e0cd7uHw25Om266gr2XCjPAUIwdpqG0WTRFkC15zrXrzNobVbVa3NVd01VV3c1qvy/bwKaauwSd8Orz9ibapu0REajkfz8fFavXk1GRgaDBw8mOjqavLw8tm3bRo8ePdB7YGkFIYRoKI3qE8tkMtG/f382bdrk2Ga329m0aRODBg1yecygQYOcyoPWLaOifHx8PNHR0U5l8vLy2L59e5V1us1eqs2+VvCL9ruyaYt2+sVqdxX847RHv1hte8UX9vwj2nF217PFHT16lHHjxpGZmcnBgwd57rnnMBgMnonZFd9IbdxO+NXgG6slCZfSGbTtkddpd0pcLebpNP31JYoujBkqzYb8w1DoYia3inWKqlJV3TVRXd1CCCEA6Nu3LyNHjqSgoIBevXoxbtw4SktLad26NdOnT5dkRwjR5DSqOzwAc+fOZcaMGQwYMICBAwfy0ksvUVhYyKxZswCYPn06rVu3ZtGiRQD89re/ZdiwYbzwwguMGzeOlStX8uOPP/Laa68B2rTNv/vd73j66afp3Lkz8fHxPP7448TGxjJ+/HjPBK03a5OB2S6ZttleDsZQ7U6I3qwlRQVHoeTcxTK2UkDvPK1zxeF2Oz/88AODBg3i2LFj/Oc//8HX19cz8V6JbwyYoyA44cKsaGe1hExv0WZjq27h1Irpr4tPu95ferbywqMVAjtox1elurqvpLq6hRBCOIwZM4YtW7YQGhrK/v37yczMZPLkyXXvTi2EEF7Q6BKeSZMmkZmZyRNPPEF6ejp9+vRh3bp1jkkH0tLSnK4uDR48mOTkZB577DEeffRROnfuzJo1a5z6GP/hD3+gsLCQ+++/n5ycHK677jrWrVuHxVI50agVS5S2sKjBDOi1qZwDO2pJT+ExbQ0bgx8EddOmhs4/oo1FMVw4cVw6rfMFmzZtYv/+/YwYMYJJkybRtm3bhpvlSK/XxuX41uJuSsX018Wn3Vt8NCAegq668mx19Vm3EEIIJ/369QOgffv22Gw2fHwa3VcGIYSokUb56TVnzhzmzJnjct/WrVsrbbvzzju58847q6xPp9Pxl7/8hb/85S+eCtGZX1soOw8BHcEUDqZgOPsV5OzSxu7ofLQpk/UmCOkF4YPBGAzmEG3yAhddrbp06UJqaio33ngjnTt3rp+464tvjDa+B2qWmFSMB/KLqb5sfdYthBCiEp1OJ8mOEKJJk08wTzBHaGNxwvpD7l44tkJbALPT/RDQAQwWsJVoY3yOv6fdoYifpq3Zo8NlV6u4uDh+/etfN0w3tvoQ0F67g5W7V5uNrSjTeb/OoN0ZC+wAwVdpiUxt6y7JcJ7IoC51CyGEEEKIZkUSHk/Q67UubCWZgMJ+1ePooq+n2Gpj/+lc8grsBJn1dI9PwrfLHFT6ZvQFv4AxEMwRlNvt+LjobtVkk50Kl44HyjsOx85oXdKMNRwPVNO6azPWSAghhBBCtAiS8HiKb4zWfa3zrziSnsObH+9jTeopCssu3nnwNxkY37c1MwddTecuo6AwjaxiE+/8ZzEjRowgISHBiy+gnlSMB/IJA85A65trPTV0lXXXZqyREEK0YHa7XWZbE0K0GPJp5ylKgX8cL28+zOh/bmPF9jSnZAegsMzGiu1pjP7nNl7efJj0fBPLli0jJyeHr7/+GputFuvLCCGEEG44fPgwr7zyCrt27cJut1NeXu7tkIQQol5JwuMpOh0vbz7M8+sPotSViyoFr2zYxav/eYPCwkIiIyOZMWNG/a6xI4QQosVLT09n1apVvPnmmwwdOpT//Oc/rF69Wi64CSGaNUl4PMBuVxzKyOf59QdrfEx+iZUfft7D8ePH2bJ1K/7+/vUYoRBCiJYuLy+PFStW8Nlnn/Hzzz9zyy23kJ6ezpEjR0hPT/d2eEIIUW9kDI8H6HTw5rfHalzeXlbM2dULWXv6AP7BYXz71Zb6C04IIYQAlFLs3LmTb7/9lrvuuouOHTtiNBqZPHkyrVu39nZ4QghRbyTh8YBiq401qadqXL489yz27DRMZjOd7/gDHbt2r8fohBBCCAgODuall17i1KlT9O/fH4CJEyfSpk0bL0cmhBD1SxIeD9h/Jq/SBAVX0jU6gPEzp2O1WjkZGc6BM/n0iwutxwiFEEIIaNWqFRs2bMBgMFBeXi4LigohWgT5pPOAvOKaz3AToStgiPEYhqgo9pZHst8aRV6JtR6jE0IIIS6qmCBHkh0hREshn3YeEORb82Y8r3w5bQ/Chp7vrW214y0eWpdGCCGEEEII4UQSHg/oHhOEv8ng1K1Njx0dYLtsIrxyDGwq64QeBejwNxnoFhPYsAELIYQQQgjRQsi01B7gazQwvu/FGW58sHGD6TDXm46gw16pvELnSIRu69saX6OsvyOEEEIIIUR9kITHA5SCmYPbo9OBiXJGm/5HG0Me0fp8QnUlVR6n08HMa+OpZp1SIYQQokbsdu0iW2lpqZcjEUKIxkO6tHmAXq+jc1Qgc0d2Ye+XnxJlKKTUrmOjtQvZyq/K4+aN7krHCH90Ol0DRiuEEKI5KisrY+HChZSWlhIdHc306dOJjIz0dlhCCOF1cofHg359Q2e69RtETlEZy5a+QVpamstyOh08ktSVh67vJMmOEEKIOrPb7bzwwgts2bKFwMBACgsLSU1N9XZYQgjRKMgdHg8r3r+V/3v+Wex2O9FZx6BND8c+f5OB2/q2Zua18XSKDPBekEIIIZoNpRSvvfYa77//PrfccgsAffv2ZfTo0V6OTAghGgdJeDzogw8+4NFHHwVg0aJF/GbuPPafySe/xEqQxUi3mEB8jQYZsyOEEMKjjhw5wu7duxk8eDDjxo0jKSlJehAIIcQFkvB40E033cTo0aMpLy+nd+/eWHRW+sca0HoO6kBvA50PcgoSQgjhKTqdjr///e90796dm266ScbtCCHEZWQMjweVlJQwcuRIhgwZQsaZNOwn1sD/XoX9/9AeT66BopPeDlMIIYQHLF68mPbt22OxWEhMTOT777/3ajz33HOPJDtCCOGCJDweYrPZSE5OpqioiMiIcCZfF4hPwUHQG8ASpT3m7ofMbWDN93a4Qggh6uC9995j7ty5LFiwgJ9++onevXuTlJTE2bNnvR2aEEKIy0jC46bCwkKX2w0GA9dddx0hISFMHX8dFusp8I+HwE5gbqU9+sdDQRqUpDdw1EIIITzpxRdf5L777mPWrFn06NGDJUuW4Ofnx9KlS70dmhBCiMvIGB43fPPNN+zevZuZM2cSFRVVaX9CQgLdu3fHp2A/5BnAHAqn10PefgjqDrGjoSwLbFUvRiqEEKJxKysrY8eOHcyfP9+xTa/XM3LkSFJSUlweU1pa6rQYaF5eHgBWqxWr1erW81eUd/e4lkzazD3SXu6TNnOPJ9rLnWMl4XHD119/jcVi4ZdffnGZ8AD4+PiAwQLGMC2xydsPyqY9Rg3VthssDRy5EEIITzl37hw2m63SeSAqKooDBw64PGbRokUsXLiw0vb169fj51f1AtWXstls6PV6x+xrGzZscDNyIW3mHmkv90mbuacu7VVUVFTjspLwuGnMmDEkJiZeuZAlGixh2r+Dul+8w2OwaD+W6PoPVAghRKMxf/585s6d6/g9Ly+Ptm3bMnr0aIKCgqo9/uzZs6xatYo+ffqQmJjIhg0bGDVqFEajsT7DbjasVqu0mRukvdwnbeYeT7RXxZ3ympCExw09evRg7dq1DBw48MrrGxgDIawf5B2C1jdqd3Z8AsDgC0Gdtf1CCCGapPDwcAwGAxkZGU7bMzIyiI52fUHLbDZjNpsrbTcajdWe7M+cOcPixYsxm82kpqZy9dVX1/hY4UzazD3SXu6TNnNPXdrLneMk4XHDnDlzyMvLIzo6mgcffPDKhf3agDEYSs+BvRz0PmAOl2RHCCGaOJPJRP/+/dm0aRPjx48HwG63s2nTJubMmePR5yosLHQkO5mZmURERLhMnIQQQlRNEh435OXlcd111zF9+vSaHWAMlARHCCGaoblz5zJjxgwGDBjAwIEDeemllygsLGTWrFkefZ4//OEP/PDDD/Tp04eoqCgefPBBysvLPfocQgjR3EnC44a+ffuyatUq/P39vR2KEEIIL5o0aRKZmZk88cQTpKen06dPH9atW1flhDa1NW3aNN577z0SExN58sknr9ydWgghhEuS8LhhypQprF69mqlTp9KqVStvhyOEEMKL5syZ4/EubJcbNGgQ+/btIzg4GIPBUK/PJYQQzZUkPG7Izs4mODhYuhMIIYRoMJGRkd4OQQghmjS9twNoSvR6PZMmTfJ4lwUhhBBCCCFE/ZCExw3jxo0jPj7e22EIIYQQQgghakgSHjf07NnT2yEIIYRoRpRSlJSUeDsMIYRo1iThEUIIIbxk7dq1vPXWW5L0CCFEPZKERwghhPCSXbt2kZ6eTlpamrdDEUKIZksSHiGEEMJLbDYbq1atYteuXd4ORQghmi1JeIQQQggvWb16NXa7nTNnzmC3270djhBCNEuS8AghhBBeotfrmTFjBtOmTUOvl1OyEELUB1l4tAaUUgDk5eV5NQ6r1UpRURF5eXkYjUavxuKuphp7U40bmm7sEnfDa4jYKz4/Kz5PW7qKdrjlllsYP348BoOhxueYpvxe8xZpM/dIe7lP2sw9nmgvd84rOiVnn2qdPHmStm3bejsMIYRo8k6cOEGbNm28HYbXyXlFCCE8oybnFUl4asBut3P69GkCAwPR6XReiyMvL4+2bdty4sQJgoKCvBZHbTTV2Jtq3NB0Y5e4G15DxK6UIj8/n9jYWOm6Rd3OK035veYt0mbukfZyn7SZezzRXu6cV6RLWw3o9fpGdUUyKCioyf4xNdXYm2rc0HRjl7gbXn3HHhwcXG91NzWeOK805feat0ibuUfay33SZu6pa3vV9Lwil9mEEEIIIYQQzZYkPEIIIYQQQohmSxKeJsRsNrNgwQLMZrO3Q3FbU429qcYNTTd2ibvhNeXYWyL5/3KftJl7pL3cJ23mnoZuL5m0QAghhBBCCNFsyR0eIYQQQgghRLMlCY8QQgghhBCi2ZKERwghhBBCCNFsScIjhBBCCCGEaLYk4fGyxYsX0759eywWC4mJiXz//fdXLL969Wq6deuGxWIhISGBtWvXOu1XSvHEE08QExODr68vI0eO5NChQ40+7pkzZ6LT6Zx+xowZ4/G43Y1979693H777bRv3x6dTsdLL71U5zobS9xPPvlkpTbv1q2bV+P+z3/+w5AhQwgNDSU0NJSRI0dWKt9Q7/H6iL2h3ufuxP3hhx8yYMAAQkJC8Pf3p0+fPixfvtypTEO2uaheQ3zeNEXVfaaVlJTw0EMP0apVKwICArj99tvJyMjwYsQN76uvvuLmm28mNjYWnU7HmjVrnPbX5G89OzubqVOnEhQUREhICLNnz6agoKABX0XDqa69avKZ3pLaa9GiRVx99dUEBgYSGRnJ+PHjOXjwoFOZmvwdpqWlMW7cOPz8/IiMjOSRRx6hvLy8bsEp4TUrV65UJpNJLV26VO3du1fdd999KiQkRGVkZLgsv23bNmUwGNTf/vY3tW/fPvXYY48po9Godu/e7Sjz7LPPquDgYLVmzRr1888/q1tuuUXFx8er4uLiRh33jBkz1JgxY9SZM2ccP9nZ2R6Lubaxf//992revHnq3XffVdHR0eof//hHnetsLHEvWLBAXXXVVU5tnpmZ6bGYaxP3XXfdpRYvXqxSU1PV/v371cyZM1VwcLA6efKko0xDvMfrK/aGeJ+7G/eWLVvUhx9+qPbt26cOHz6sXnrpJWUwGNS6descZRqqzUX1GuLzpqmq7jPtgQceUG3btlWbNm1SP/74o7rmmmvU4MGDvRhxw1u7dq3685//rD788EMFqI8++shpf03+1seMGaN69+6tvvvuO/X111+rTp06qSlTpjTwK2kY1bVXTT7TW1J7JSUlqWXLlqk9e/aonTt3qhtvvFG1a9dOFRQUOMpU93dYXl6uevbsqUaOHKlSU1PV2rVrVXh4uJo/f36dYpOEx4sGDhyoHnroIcfvNptNxcbGqkWLFrksP3HiRDVu3DinbYmJiepXv/qVUkopu92uoqOj1d///nfH/pycHGU2m9W7777baONWSvvQuPXWWz0WY1Xcjf1ScXFxLhOHutRZU/UR94IFC1Tv3r09FqMrdW2b8vJyFRgYqN566y2lVMO9x+sjdqUa5n3uifdj37591WOPPaaUatg2F9VriM+bpupKn2k5OTnKaDSq1atXO7bt379fASolJaWBImxcLv8CX5O/9X379ilA/fDDD44yn3/+udLpdOrUqVMNFrs3VJXwXOkzvSW3l1JKnT17VgHqyy+/VErV7O9w7dq1Sq/Xq/T0dEeZV155RQUFBanS0tJaxyJd2rykrKyMHTt2MHLkSMc2vV7PyJEjSUlJcXlMSkqKU3mApKQkR/mjR4+Snp7uVCY4OJjExMQq62wMcVfYunUrkZGRdO3alQcffJCsrCyPxFyX2L1RZ0M+x6FDh4iNjaVDhw5MnTqVtLS0uobr4Im4i4qKsFqthIWFAQ3zHq+v2CvU5/u8rnErpdi0aRMHDx5k6NChQMO1uaheQ3zeNHVVfabt2LEDq9Xq1HbdunWjXbt20nYX1ORvPSUlhZCQEAYMGOAoM3LkSPR6Pdu3b2/wmBuDK32mt/T2ys3NBXCcB2vyd5iSkkJCQgJRUVGOMklJSeTl5bF3795axyIJj5ecO3cOm83m9B8KEBUVRXp6ustj0tPTr1i+4tGdOhtD3ABjxozh7bffZtOmTTz33HN8+eWXjB07FpvN5pG4axu7N+psqOdITEzkzTffZN26dbzyyiscPXqUIUOGkJ+fX9eQAc/E/cc//pHY2FjHh2NDvMehfmKH+n+f1zbu3NxcAgICMJlMjBs3jn/961+MGjUKaLg2F9VriM+bpuxKn2np6emYTCZCQkKcjpG2u6gmf+vp6elERkY67ffx8SEsLKxFtmN1n+ktub3sdju/+93vuPbaa+nZsydAjf4Oq/rOWLGvtnxqfaQQHjR58mTHvxMSEujVqxcdO3Zk69at3HDDDV6MrPkaO3as49+9evUiMTGRuLg4Vq1axezZs70YmebZZ59l5cqVbN26FYvF4u1w3FJV7I31fR4YGMjOnTspKChg06ZNzJ07lw4dOjB8+HCvxSSEu670mebr6+vFyERz1Vg/0xuDhx56iD179vDNN994OxRA7vB4TXh4OAaDodLMFBkZGURHR7s8Jjo6+orlKx7dqbMxxO1Khw4dCA8P5/Dhw3UP+oLaxO6NOr3xHAAhISF06dLFY21el7iff/55nn32WdavX0+vXr0c2xviPQ71E7srnn6f1zZuvV5Pp06d6NOnDw8//DB33HEHixYtAhquzUX1GuqzoLm49DMtOjqasrIycnJynMpI211Uk7/16Ohozp4967S/vLyc7OxsaUcqf6a31PaaM2cO//3vf9myZQtt2rRxbK/J32FV3xkr9tWWJDxeYjKZ6N+/P5s2bXJss9vtbNq0iUGDBrk8ZtCgQU7lATZs2OAoHx8fT3R0tFOZvLw8tm/fXmWdjSFuV06ePElWVhYxMTEeiRtqF7s36vTGcwAUFBRw5MgRj7V5beP+29/+xlNPPcW6deuc+j1Dw7zH6yt2Vzz9PvfUe8Vut1NaWgo0XJuL6jXUZ0FzcelnWv/+/TEajU5td/DgQdLS0qTtLqjJ3/qgQYPIyclhx44djjKbN2/GbreTmJjY4DE3Npd/pre09lJKMWfOHD766CM2b95MfHy80/6a/B0OGjSI3bt3OyWKGzZsICgoiB49etQpOOElK1euVGazWb355ptq37596v7771chISGOmSnuvvtu9ac//clRftu2bcrHx0c9//zzav/+/WrBggUup6UOCQlRH3/8sdq1a5e69dZb62Vaak/GnZ+fr+bNm6dSUlLU0aNH1caNG1W/fv1U586dVUlJicfirk3spaWlKjU1VaWmpqqYmBg1b948lZqaqg4dOlTjOhtr3A8//LDaunWrOnr0qNq2bZsaOXKkCg8PV2fPnvVa3M8++6wymUzq/fffd5rmMz8/36lMfb/H6yP2hnqfuxv3M888o9avX6+OHDmi9u3bp55//nnl4+Oj/vOf/zi9toZoc1G9hvi8aaqq+0x74IEHVLt27dTmzZvVjz/+qAYNGqQGDRrk5agbVn5+vuPcAKgXX3xRpaamquPHjyulava3PmbMGNW3b1+1fft29c0336jOnTs322mWr9ReNf1Mb0nt9eCDD6rg4GC1detWp/NgUVGRo0x1f4cV01KPHj1a7dy5U61bt05FRETItNRN3b/+9S/Vrl07ZTKZ1MCBA9V3333n2Dds2DA1Y8YMp/KrVq1SXbp0USaTSV111VXqs88+c9pvt9vV448/rqKiopTZbFY33HCDOnjwYKOOu6ioSI0ePVpFREQoo9Go4uLi1H333VdvJ3B3Yj969KgCKv0MGzasxnU21rgnTZqkYmJilMlkUq1bt1aTJk1Shw8f9mrccXFxLuNesGCBo0xDvcc9HXtDvs/difvPf/6z6tSpk7JYLCo0NFQNGjRIrVy50qm+hmxzUb2G+Lxpiqr7TCsuLlb/7//9PxUaGqr8/PzUbbfdps6cOePFiBveli1bXH5OVXwm1ORvPSsrS02ZMkUFBASooKAgNWvWLKeLUs3Jldqrpp/pLam9XLUVoJYtW+YoU5O/w2PHjqmxY8cqX19fFR4erh5++GFltVrrFJvuQoBCCCGEEEII0ezIGB4hhBBCCCFEsyUJjxBCCCGEEKLZkoRHCCGEEEII0WxJwiOEEEIIIYRotiThEUIIIYQQQjRbkvAIIYQQQgghmi1JeIQQQgghhBDNliQ8QgCHDh1i9OjRBAcHo9PpWLNmjbdDalZ0Oh1PPvmkt8MQQogGI+eV+iXnFeEOSXhEk/Lmm2+i0+kcPz4+PrRu3ZqZM2dy6tSpWtc7Y8YMdu/ezV//+leWL1/OgAEDPBh10/Gb3/wGnU7H4cOHqyzz5z//GZ1Ox65duxowMiGEqB9yXqlfcl4RjYEkPKJJ+stf/sLy5ctZsmQJY8eO5Z133mHYsGGUlJS4XVdxcTEpKSnMnj2bOXPmMG3aNNq0aVMPUTd+U6dOBSA5ObnKMu+++y4JCQn06tWrocISQoh6J+eV+iHnFdEYSMIjmqSxY8cybdo07r33Xl5//XXmzZvHkSNH+OSTT9yuKzMzE4CQkBCPxVdSUoLdbvdYfQ0lMTGRTp068e6777rcn5KSwtGjRx0nMCGEaC7kvFI/5LwiGgNJeESzMGTIEACOHDnitP3AgQPccccdhIWFYbFYGDBggNPJ68knnyQuLg6ARx55BJ1OR/v27R37T506xT333ENUVBRms5mrrrqKpUuXOj3H1q1b0el0rFy5kscee4zWrVvj5+dHXl4eANu3b2fMmDEEBwfj5+fHsGHD2LZtm1MdTz75pOOW/8yZMwkJCSE4OJhZs2ZRVFRU6fW+8847DBw4ED8/P0JDQxk6dCjr1693KvP5558zZMgQ/P39CQwMZNy4cezdu7fatpw6dSoHDhzgp59+qrQvOTkZnU7HlClTKCsr44knnqB///4EBwfj7+/PkCFD2LJlS7XPMXPmTKd2vrwdXL3e/v374+vrS1hYGJMnT+bEiRNOZQ4dOsTtt99OdHQ0FouFNm3aMHnyZHJzc6uNRwghLifnFTmvyHml+fDxdgBCeMKxY8cACA0NdWzbu3cv1157La1bt+ZPf/oT/v7+rFq1ivHjx/PBBx9w2223MWHCBEJCQvj973/PlClTuPHGGwkICAAgIyODa665Bp1Ox5w5c4iIiODzzz9n9uzZ5OXl8bvf/c4phqeeegqTycS8efMoLS3FZDKxefNmxo4dS//+/VmwYAF6vZ5ly5YxYsQIvv76awYOHOhUx8SJE4mPj2fRokX89NNPvP7660RGRvLcc885yixcuJAnn3ySwYMH85e//AWTycT27dvZvHkzo0ePBmD58uXMmDGDpKQknnvuOYqKinjllVe47rrrSE1NdXlSqDB16lQWLlxIcnIy/fr1c2y32WysWrWKIUOG0K5dO86dO8frr7/OlClTuO+++8jPz+eNN94gKSmJ77//nj59+tTif7Kyv/71rzz++ONMnDiRe++9l8zMTP71r38xdOhQUlNTCQkJoaysjKSkJEpLS/n1r39NdHQ0p06d4r///S85OTkEBwd7JBYhRMsh5xU5r8h5pRlRQjQhy5YtU4DauHGjyszMVCdOnFDvv/++ioiIUGazWZ04ccJR9oYbblAJCQmqpKTEsc1ut6vBgwerzp07O7YdPXpUAervf/+703PNnj1bxcTEqHPnzjltnzx5sgoODlZFRUVKKaW2bNmiANWhQwfHtorn6ty5s0pKSlJ2u92xvaioSMXHx6tRo0Y5ti1YsEAB6p577nF6rttuu021atXK8fuhQ4eUXq9Xt912m7LZbE5lK54jPz9fhYSEqPvuu89pf3p6ugoODq603ZWrr75atWnTxuk51q1bpwD16quvKqWUKi8vV6WlpU7HnT9/XkVFRVV6HYBasGCB4/cZM2aouLi4Ss9b0Q4Vjh07pgwGg/rrX//qVG737t3Kx8fHsT01NVUBavXq1dW+NiGEuJScV+S8opScV5o76dImmqSRI0cSERFB27ZtueOOO/D39+eTTz5xDArNzs5m8+bNTJw4kfz8fM6dO8e5c+fIysoiKSmJQ4cOXXH2HaUUH3zwATfffDNKKcfx586dIykpidzc3Eq35mfMmIGvr6/j9507d3Lo0CHuuususrKyHMcXFhZyww038NVXX1Xqj/3AAw84/T5kyBCysrIc3RjWrFmD3W7niSeeQK93/vOtuGW/YcMGcnJymDJlilPcBoOBxMTEGnUNmDZtGidPnuSrr75ybEtOTsZkMnHnnXcCYDAYMJlMANjtdrKzsykvL2fAgAEuuy3UxocffojdbmfixIlOryU6OprOnTs7XkvFlbYvvvjCZVcNIYSojpxX5Lwi55XmS7q0iSZp8eLFdOnShdzcXJYuXcpXX32F2Wx27D98+DBKKR5//HEef/xxl3WcPXuW1q1bu9yXmZlJTk4Or732Gq+99lqVx18qPj7e6fdDhw4B2gmrKrm5uU7dJdq1a+e0v2Lf+fPnCQoK4siRI+j1enr06FFlnRXPO2LECJf7g4KCqjy2wuTJk5k7dy7JyckMHz6ckpISPvroI8aOHesU71tvvcULL7zAgQMHsFqtju2Xt0VtHTp0CKUUnTt3drnfaDQ6nm/u3Lm8+OKLrFixgiFDhnDLLbcwbdo06XYghKgROa/IeQXkvNJcScIjmqSBAwc61jQYP3481113HXfddRcHDx4kICDAcYVr3rx5JCUluayjU6dOVdZfcfy0adOqPLFcPn3mpVfhLq3j73//e5X9jiv6dVcwGAwuyymlqoz1chXPu3z5cqKjoyvt9/Gp/s8+MjKSUaNG8cEHH7B48WI+/fRT8vPznWbReeedd5g5cybjx4/nkUceITIyEoPBwKJFiyoN8r2cqwGkoPXnvvy16HQ6Pv/8c5dtc2n7vfDCC8ycOZOPP/6Y9evX85vf/IZFixbx3XfftdjpYIUQNSfnlarJeUXOK02dJDyiyav4MLz++ut5+eWX+dOf/kSHDh0A7UrNyJEj3a4zIiKCwMBAbDZbrY4H6NixI6Bd+aptHa7qtNvt7Nu3r8qTXcXzRkZG1ul5p06dyrp16/j8889JTk4mKCiIm2++2bH//fffp0OHDnz44YdOJ5oFCxZUW3doaCg5OTmVth8/ftzp944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"output_type": "execute_result" - } - ], - "source": [ - "solution" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plt.hist(np.array(solution)[:,3],bins=25)\n", - "idx = 3\n", - "plt.hist(np.array(solution)[:,idx],bins=25)\n", - "plt.vlines(ref[0][idx],0, 17,colors='black', ls='--')\n", - "plt.vlines(ref[0][idx]*0.9,0, 17,colors='grey', ls='--')\n", - "plt.vlines(ref[0][idx]*1.1,0, 17,colors='grey', ls='--')\n", - "plt.ylim([0,17])\n", - "plt.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([1., 0., 2., 0., 0., 0., 0., 1., 1., 0., 1., 0., 0., 0., 0., 1., 0.,\n", - " 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", - " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]),\n", - " array([-35722.03292166, -35720.25933288, -35718.48574409, -35716.71215531,\n", - " -35714.93856652, -35713.16497774, -35711.39138895, -35709.61780017,\n", - " -35707.84421138, -35706.0706226 , -35704.29703381, -35702.52344502,\n", - " -35700.74985624, -35698.97626745, -35697.20267867, -35695.42908988,\n", - " -35693.6555011 , -35691.88191231, -35690.10832353, -35688.33473474,\n", - " -35686.56114596, -35684.78755717, -35683.01396839, -35681.2403796 ,\n", - " -35679.46679082, -35677.69320203, -35675.91961324, -35674.14602446,\n", - " -35672.37243567, -35670.59884689, -35668.8252581 , -35667.05166932,\n", - " -35665.27808053, -35663.50449175, -35661.73090296, -35659.95731418,\n", - " -35658.18372539, -35656.41013661, -35654.63654782, -35652.86295903,\n", - " -35651.08937025, -35649.31578146, -35647.54219268, -35645.76860389,\n", - " -35643.99501511, -35642.22142632, -35640.44783754, -35638.67424875,\n", - " -35636.90065997, -35635.12707118, -35633.3534824 ]),\n", - " )" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(np.array(energies), bins=50)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "distance = [np.linalg.norm(r[2:]-s[2:]) for r,s in zip(ref, solution)]" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "x and y must be the same size", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[78], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43menergies\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistance\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/pyplot.py:3699\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m 3680\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mscatter)\n\u001b[1;32m 3681\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatter\u001b[39m(\n\u001b[1;32m 3682\u001b[0m x: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3697\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3698\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PathCollection:\n\u001b[0;32m-> 3699\u001b[0m __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3700\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3701\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3702\u001b[0m \u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3703\u001b[0m \u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3704\u001b[0m \u001b[43m \u001b[49m\u001b[43mmarker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmarker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3705\u001b[0m \u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3706\u001b[0m \u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3707\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3708\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3709\u001b[0m \u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3710\u001b[0m \u001b[43m \u001b[49m\u001b[43mlinewidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlinewidths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3711\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3712\u001b[0m \u001b[43m \u001b[49m\u001b[43mplotnonfinite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplotnonfinite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3713\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3714\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3715\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3716\u001b[0m sci(__ret)\n\u001b[1;32m 3717\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1467\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1468\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1469\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/matplotlib/axes/_axes.py:4655\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4653\u001b[0m y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mravel(y)\n\u001b[1;32m 4654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39msize:\n\u001b[0;32m-> 4655\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must be the same size\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4657\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4658\u001b[0m s \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m20\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_internal.classic_mode\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[1;32m 4659\u001b[0m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlines.markersize\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2.0\u001b[39m)\n", - "\u001b[0;31mValueError\u001b[0m: x and y must be the same size" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(energies, distance)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "only integer scalar arrays can be converted to a scalar index", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[59], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m idx_sort \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margsort(distance)\n\u001b[0;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\u001b[43mdistance\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx_sort\u001b[49m\u001b[43m]\u001b[49m, energies[idx_sort])\n", - "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index" - ] - } - ], - "source": [ - "idx_sort = np.argsort(distance)\n", - "plt.plot(distance[idx_sort], energies[idx_sort])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dd = [distance[i] for i in idx_sort]\n", - "ee = [energies[i] for i in idx_sort]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(dd,ee)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/qubo_poly_solver.ipynb b/docs/notebooks/qubo_poly_solver.ipynb index 124885d..90a3199 100644 --- a/docs/notebooks/qubo_poly_solver.ipynb +++ b/docs/notebooks/qubo_poly_solver.ipynb @@ -4,61 +4,101 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Define the system " + "# QUBO Solution of the hydraulics equations\n", + "In this notebook we illustrate how to solve the hydraulics equations using a pure QUBO approach. \n", + "\n", + "## Hydraulics equations\n", + "In their most basic form the hydraulics equations read:\n", + "\n", + "$$\n", + " \\sum_j q_{ij} - D_i = 0 \\newline\n", + " h_{L_{ij}} \\equiv h_i - h_j = A |q_{ij}| q_{ij}^{B-1}\n", + "$$\n", + "\n", + "where $h_i$ is the head pressure at node $i$, $A$ the resistance coefficient and $B$ the flow exponent. \n", + "Several approximations have been developed for define $A$ and $B$. The popular Hazen-Williams (HW) approximation uses $B=1.852$. The HW is therefore not suited for a QUBO formulation that requires integer exponents in the formulation of the objective function. In contrast, the Chezy-Manning (CM) and Darcy-Weisbach (DW) approximation use $B=2$. We have implemented DW and CM hydraulics models that can found under `wntr_quantum/sim/models/`.\n", + "\n", + "\n", + "The presence of absolute values in the hydraulics equation makes it difficult to use the approach we just described. We therefore express the flow values as:\n", + "\n", + "$$\n", + " q_{ij} = s_{ij} |q_{ij}| \\equiv s_{ij} y_{ij}\n", + "$$\n", + "\n", + "This leads to the equations:\n", + "\n", + "$$\n", + " \\sum_j s_{ij} y_{ij} - D_i = 0 \\newline\n", + " h_{L_{ij}} \\equiv h_i - h_j = A s_{ij} y_{ij}^{B}\n", + "$$\n", + "\n", + "In these forms the hydraulics equation can be seen as a system of non-linear equations with integeer power of the unknown: \n", + "\n", + "$$\n", + "F(s_{ij}, y_{ij}, h_i)=0\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Solving non linear systems with a QUBO approach\n", + " \n", + " We closely following an approach developed in this [http://dx.doi.org/10.1038/s41598-019-46729-0](paper) to solve the non linear system. \n", + " \n", + " \n", + "The method proposes to solve a non-linear system, given by $F(X) = 0$ by first decomposing the system of equations as a sum of tensor products:\n", + "\n", + "$$\n", + " F_i = P_i^{(0)} + \\sum_j P_{ij}^{(1)}x_j + \\sum_{jk} P_{ijk}^{(2)}x_j x_k + \\sum_{jkl} P_{ijkl}^{(3)}x_j x_k x_l = 0 \n", + "$$\n", + "\n", + "To find the solution of the system one can then minimise the residual sum of squares\n", + "\n", + "$$\n", + "\\chi^2 = \\left[ P^{(0)} + P^{(1)} X + P^{(2)} X^2 + P^{(3)} X^3 + ... \\right]^2\n", + "$$\n", + "\n", + "By encoding all the variables as binary expansions we obtain a high order boolean polynomial. To solve this problem with a QUBO formalism, the high order terms have to be quadratized by introducing additional binary variables and appropriate terms in the loss function. The resulting QUBO problem can then be solved using either classical simulated annealing or quantum annealers alike." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example\n", + "\n", + "We demonstrate in the following how to us our software to solve the hydraulics equations with a QUBO approach.\n", + "\n", + "### Reference Solution\n", + "\n", + "We first define the problem and solve it classically to obtain a benchmark solution" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "metadata": {} }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "# Create a water network model\n", "inp_file = './networks/Net0.inp'\n", - "# inp_file = './networks/Net2LoopsDW.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + "wn = wntr.network.WaterNetworkModel(inp_file)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Run with the original Cholesky EPANET simulator" + "We solve the problem using the default `EPANET` simulator " ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -77,17 +117,21 @@ "" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# solve the problem\n", "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", + "reference_results = sim.run_sim()\n", + "\n", "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "flow_at_5hr = results.link['flowrate'].loc[0, :]\n", + "pressure_at_5hr = reference_results.node['pressure'].loc[0, :]\n", + "flow_at_5hr = reference_results.link['flowrate'].loc[0, :]\n", "wntr.graphics.plot_network(wn, link_attribute=flow_at_5hr, \n", " node_attribute=pressure_at_5hr, \n", " node_size=500, \n", @@ -97,50 +141,15 @@ ] }, { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([26.477, 22.954], dtype=float32)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_pressure = results.node['pressure'].values[0][:2]\n", - "ref_pressure" - ] - }, - { - "cell_type": "code", - "execution_count": 6, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.05, 0.05], dtype=float32)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "ref_rate = results.link['flowrate'].values[0]\n", - "ref_rate" + "We extract the values of the pressure and flows for future use" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -149,12 +158,15 @@ "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" ] }, - "execution_count": 7, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import numpy as np \n", + "ref_pressure = reference_results.node['pressure'].values[0][:2]\n", + "ref_rate = reference_results.link['flowrate'].values[0]\n", "ref_values = np.append(ref_rate, ref_pressure)\n", "ref_values" ] @@ -163,47 +175,54 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run with the QUBO Polynomial Solver" + "### QUBO Polynomial Solver\n", + "\n", + "We now show how to solve the problem using the QUBO polynomial solver included in `wntr_quantum`. We start with redefining the water network." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "wn = wntr.network.WaterNetworkModel(inp_file)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The unknown of the problem can take continuous values and therefore must be encoded using several qubits before being used in a QUBO formulation. We use here the encoding implemented in our library `qubops`. We use these encoding schemes to instantiate the polynomial solver. " + ] + }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Head Encoding : 0.000000 => 100.000000 (res: 3.225806)\n", - "Flow Encoding : -2.000000 => -0.000000 | 0.000000 => 2.000000 (res: 0.064516)\n" + "Head Encoding : 0.000000 => 200.000000 (res: 1.574803)\n", + "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.031496)\n" ] } ], "source": [ "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "from qubops.encodings import PositiveQbitEncoding\n", "\n", - "nqbit = 5\n", - "step = (2./(2**nqbit-1))\n", + "nqbit = 7\n", + "step = (4./(2**nqbit-1))\n", "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", "\n", - "nqbit = 5\n", - "step = (100/(2**nqbit-1))\n", + "nqbit = 7\n", + "step = (200/(2**nqbit-1))\n", "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", "\n", - "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)\n", + "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, head_encoding=head_encoding)\n", "net.verify_encoding()" ] }, @@ -211,2953 +230,187 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Solve the system classically" + "We then solve the QUBO equations classically. This gives us: a reference solution, the best possible encoded solution, the total encoded solution including all slack variables and the QUBO energy of the solution." ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([1. , 1. , 0.999, 0.998])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] } ], "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.create_index_mapping(model)\n", - "net.matrices = net.initialize_matrices(model)\n", - "\n", - "ref_sol, encoded_ref_sol, cvgd = net.classical_solutions()\n", - "ref_sol / ref_values" + "ref_sol, encoded_ref_sol, bin_rep_sol, eref, cvgd = net.classical_solution()" ] }, { - "cell_type": "code", - "execution_count": 39, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.987, 0.987, 1.003, 0.985])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "encoded_ref_sol/ ref_values" + "### Initial sample for the QUBO optimization \n", + "\n", + "Before minimizing the energy of the QUBO problem we need to define the initial configuration of the binary variables in the QUBO problem. We have implemented two different ways to obtain an initial sample that respects all the conditions imposed by the quadratization constraings of the polynomial qubo solver. \n", + "\n", + "We can for example create a completely random sample that simply ensure that quadratization constraints are respected" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "P0, P1, P2, P3 = net.matrices" + "from wntr_quantum.sampler.simulated_annealing import generate_random_valid_sample\n", + "x = generate_random_valid_sample(net)\n", + "x0 = list(x.values())" ] }, { - "cell_type": "code", - "execution_count": 41, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0. , 1.766, 99.077, 0.652])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "p0 = P0.reshape(\n", - " -1,\n", - ") + P1[\n", - " :, :2\n", - "].sum(-1)\n", - "p0" + "Alternatively we can modify the solution calculated in `.classical_solution()`. This can be useful when one wants to reuse exact values of the flows or pressure" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1.766, 1.766, 86.797, 75.168])" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "net.convert_solution_from_si(ref_sol)" + "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", + "x = modify_solution_sample(net, bin_rep_sol, modify=['flows', 'heads'])\n", + "x0 = list(x.values())" ] }, { - "cell_type": "code", - "execution_count": 46, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "from dwave.samplers import SimulatedAnnealingSampler\n", + "### Temperature scheduling for the Simulated Annealing optimization\n", + "\n", + "One important parameters of the simulated Annealing process is the the so-called temperature schedule. This schdule defines the acceptance probability of the new samples that increase the QUBO energy. While high temperature that leads to accepting samples that increase energy is usefull to escape local minima the temperature must be decreased in order to converge towards a minima. \n", "\n", - "sampler = SimulatedAnnealingSampler()\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.solve(model, strength=1e6, sampler=sampler, num_sweeps=10000, num_reads=1000)\n", - "sol = net.extract_data_from_model(model)" + "The temperature schedule usually starts with high temperature values that allows to explore the energy landscape but progressively decrease the tempearture in order for the optimization to converge. 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"990673.3891029209 False\n", - "990689.5721127167 False\n", - "990690.2124982253 False\n", - "990724.6441722289 False\n", - "990753.9539984092 False\n", - "990782.6970554069 False\n", - "990850.6007880569 False\n", - "990865.1441291496 False\n", - "990865.8487880826 False\n", - "991268.7598912641 False\n" - ] - } - ], + "outputs": [], "source": [ - "solutions,energies,statuses = net.analyze_sampleset()\n", - "for e,s in zip(energies,statuses):\n", - " print(e,s)" + "num_temp = 2000\n", + "Tinit = 1E1\n", + "Tfinal = 1E-1\n", + "Tschedule = np.linspace(Tinit, Tfinal, num_temp)\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "Tschedule = np.append(Tschedule, np.zeros(1000))" ] }, { - "cell_type": "code", - "execution_count": 69, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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x8fGBv79/jXFJlaW/T6mM6Qx5aa02zElpjOkMOWlMuwf7i4qKkJSUhK+++goAMGjQIPTq1cvQjnlpnzGVSuNvgXboQqh27dpQqVTIzMyssD0zMxPBwcEiRUVERHJUVlaGLl264OzZs1AqlZgxYwa6du0qdlhUA4cuhNRqNdq3b4/U1FQMGzYMQPkCVampqUhMTLTaOFqt1lCR/nWbpce0ZR9j29bUrqb9D6YsS0tLLf6ZiEmM2G0xpjPkpaVtmJPSGtMZctKYdg/2JyYmYuHChVi1apXhFgytVsu8tPOYpvSRfCFUUFCAixcvGr7PyMjA8ePHUatWLTRs2BDJycmIi4tDhw4d0LFjRyxevBiFhYWGp8jModFooNFooNPprPERiIhIJp5//nk899xzfMDGgUi+EDpy5Ah69uxp+P7BzcxxcXH48ssvMXLkSGRlZWHOnDm4desWYmJisG3btko3UJsiISEBCQkJyMvLg5+fH9RqdbXXJi29TmpOf1P6GNu2pnbV7X9w85qrq6tdrxnbihifwRZjOkNemtuGOSnNMZ0hJ41pp1arq7zXlHlp3zFNaSv5QqhHjx4QBOGhbRITE616KYyIiIjkQfIrSxMRERHZCgshIiKiGly8eBE//PCD2GGQDUj+0pgU8Kmx6vFJCGmN6Qx5yafGyjEnze9vzZwUBAErVqzAq6++CoVCgZYtW6Jp06YmH4d5ad8xneqpMTHwqTEiItJqtXjyySfx008/AQC6desGFxf+s+ls+ButAp8aM34/n4SQ5pjOkJd8aqwcc9L8/pbmpFqtRuPGjeHu7o63334bCQkJcHd3N2tM5qV9x3Sqp8aIiIjE8uGHHyI5ObnKy2HkHHizNBERUTV8fHwQEREhdhhkQyyEiIiISLZ4acwIfGqsenwSQlpjOkNe8qmxcsxJ8/vzXGk7jpKXpvThjFAVNBoNoqKiEBsbK3YoRERkA/fv36/wHkuSL84IVYFPjRm/n09CSHNMZ8hLPjVWjjlpfv/q+hw+fBhjxoyBXq/HwYMH4enpyXOliaSel6a05YwQERHJQmlpKebOnYsuXbrg7NmzyM/PR3p6uthhkchYCBERkSwoFAps27YNOp0OI0eOxO+//47WrVuLHRaJjJfGiIhIFlxcXLBmzRr89ttvePrppwGIc/MvSQsLISIiko3w8HCEh4eLHQZJCC+NERERkWxxRsgIXEeoelwbQ1pjOkNech2hcsxJ8/vzXGk7jpKXXEfIQlxHiIjI8ZSUlIgdAjkgzghVgesIGb+fa2NIc0xnyEuuI1SOOVlz/7t37yIhIQH379/H999/b9aYPFeaRup5ybfPExGRLGzduhXjx4/HzZs3oVKpcOLECURFRYkdFjkQXhojIiKHVFhYiPj4eNy8eRMRERHYv38/YmJixA6LHAxnhIiIyCF5eXlhxYoV2LVrF/7973/Dw8ND7JDIAbEQIiIihzV06FAMHTpU7DDIgfHSGBEREckWCyEiIiKSLV4aMwIXVKweFwmT1pjOkJdcULEcc9L8/jxX2o6j5CUXVLQQF1QkIhKPXq/H0qVL8cEHH4gdCskAZ4SqwAUVjd/PRcKkOaYz5CUXVCwnt5y8cuUKxo4di507d8LFxQWPP/44IiMjJZGTxrTjuVIaY3JBRSIicjj37t1Du3btcPfuXXh6euKDDz5ARESE2GGRk2MhREREkuDn54eEhASkpqZi9erVaNasmUPfT0OOgfcIERGRZMyePRt79uxBs2bNxA6FZIIzQkREJBkP7qUhshfOCBEREZFssRAiIiIi2WIhRERENrdv3z78/PPPYodBVAkLISIispmSkhLMnj0b3bp1w6hRo5CdnS12SEQV8GZpI/AVG9XjsvHSGtMZ8pKv2CjnDDl5584d9O3bF6dOnQIA9O7dG3q93ibnMnP78FxpGkfJS75iw0J8xQYRkeVq1aqF+vXrIzAwEGvXrsWqVavg7+8vdlhEFXBGqAp8xYbx+7lsvDTHdIa85Cs2yjl6Tn722WcAgAYNGlh0HCnkpDHteK6Uxph8xQYREUlCUFCQ2CEQPRQvjREREZFssRAiIiIi2WIhREREJsvKysKVK1fEDoPIYiyEiIjIJP/973/RqlUrPPPMM9DpdGKHQ2QRFkJERGSU/Px8jBs3DkOHDsXt27eRn5+PzMxMscMisggLISIiMopSqcSePXugUCgwY8YMHDlyBCEhIWKHRWQRPj5PRERG8fLywn/+8x9otVo8+uijYodDZBUshIiIyGgdO3YUOwQiq+KlMSIiIpItFkJEREQkW7w0ZgS+fb56fKOytMZ0hrzk2+fL2Tt2nU6HwsJCuLu7W/W4zpCTxrTjuVJaY/Lt8xbi2+eJSE4uXbqEPn36YObMmWKHQmR3nBGqAt8+b/x+vlFZmmM6Q17y7fPlbPkZBEHAihUr8PLLL6OwsBAnT57E7Nmz0bBhQ6uP5Qw5aUw7niulMaYpbTkjREQkU9evX0dycjIKCwvRvXt3HD58GMHBwWKHRWRXnBEiIpKp0NBQfPTRR8jPz8fUqVNRVlYmdkhEdsdCiIhIxp5//nmxQyASFS+NERERkWyxECIiIiLZYiFEREREssVCiIjIyRQXF2P69OlYuXKl2KEQSZ5ZhVBmZiZGjx6NkJAQuLi4QKVSVfgfERGJ4+jRo2jfvj0+/PBDTJs2DXfu3BE7JCJJM+upsfj4eFy9ehWzZ89GvXr1oFAorB0XERGZKD09HZ07d0ZZWRmCg4Px2WefITAwUOywiCTNrELo119/xd69exETE2PlcIiIyFxhYWGIj49HXl4eli1bxiKIyAhmFUINGjSAIAjWjoWIiCy0bNkyw+sciKhmZt0jtHjxYrz66qu4fPmylcMhIiJLsAgiMo1ZM0IjR45EUVERwsLC4OnpWek/vLt371olOCIiIiJbMqsQWrx4sZXDICIiIrI/swqhuLg4a8dBREQPsW7dOjRq1AgdO3YUOxQip2L2S1d1Oh02btyIM2fOAABatmyJIUOGcB0hIiIrysnJQWJiIv7zn/8gPDwcv/32Gzw8PMQOi8hpmFUIXbx4EQMHDsT169cRHh4OAJg/fz4aNGiAzZs3IywszKpBEhHJ0YULF9C3b1/cuHEDKpUKTz31FP/YJLIyswqhpKQkhIWF4cCBA6hVqxYA4M6dOxg1ahSSkpKwefNmqwYpNq1WC61WW2mbpce0ZR9j29bUrqb9paWlhq+W/kzEJEbsthjTGfLS0jbOlJP169dHSEgIvLy88MUXXyA2Ntawz1ZjSu2YUshJY9rxXCmtMU3pY1YhtHv37gpFEAAEBgZiwYIF6Nq1qzmHlBSNRgONRgOdTid2KEQkYy4uLkhJSUGtWrXg6ekpdjhETsmsQsjNzQ35+fmVthcUFECtVlsclNgSEhKQkJCAvLw8+Pn5Qa1WV/u5LP285vQ3pY+xbWtqV93+B0snuLq6OsXvXozPYIsxnSEvzW3jbDnZtGlTu4/JnDS/Hc+V0hjTlLZmLag4aNAgvPDCCzh48CAEQYAgCDhw4AAmTZqEIUOGmHNIIiIiIrszqxBasmQJwsLC0KVLF7i7u8Pd3R1du3ZFs2bN8NFHH1k7RiIiIiKbMOvSmL+/PzZt2oQLFy7g7NmzAIDIyEg0a9bMqsERETmr8+fPw9/fH3Xr1hU7FCJZM3sdIQBo3rw5mjdvbq1YiIicnl6vh0ajwSuvvIJ+/frhu+++g0KhEDssItkyuhBKTk7GW2+9BS8vLyQnJz+07cKFCy0OjIjI2dy4cQNjxoxBamoqgPIHTIqKiuDl5SVyZETyZXQh9NtvvxnWQfjtt99sFhARkbNycXHByZMn4eHhgffffx+TJ0+GUmnWrZpEZCVGF0I7d+6s8v8TEZFx6tati5SUFISGhvK2AiKJMOtPkXHjxlW5jlBhYSHGjRtncVBERM6qZ8+eLIKIJMSsQmj16tW4f/9+pe3379/HmjVrLA6KiIiIyB5MemosLy/PsIBifn4+3N3dDft0Oh22bNnCR0GJiIjIYZhUCPn7+0OhUEChUKBFixaV9isUCsybN89qwREROYqioiKo1Wq4uFi0KgkR2ZlJ/8Xu3LkTgiCgV69e2LBhQ4WXrqrVajRq1AghISFWD5KISMoOHDiAMWPGYMyYMXj99dfFDoeITGBSIdS9e3cAQEZGBho2bMhFwIhI1rRaLebNm4cFCxZAr9dj1apVmD59eoXbBohI2syaw71y5QquXLlS7f7HHnvM7ICIiBzFqVOnDEXQqFGjsGTJEhZBRA7GrEKoR48elbb9dXZIp9OZHRARkaNo164dFixYgKZNm2L48OFih0NEZjCrEMrJyanwfWlpKX777TfMnj0b77zzjlUCIyJyBP/617/EDoGILGBWIeTn51dpW58+faBWq5GcnIyjR49aHBgRERGRrVn1JTdBQUE4d+6cNQ9JREREZDNmzQidPHmywveCIODmzZtYsGABYmJirBEXERERkc2ZVQjFxMRAoVBAEIQK2zt37oxVq1ZZJTAiIrFkZ2dj8uTJiI+Px+OPPy52OERkQ2YVQhkZGRW+VyqVqFOnDh8bJSKH98MPP2DChAnIzMzEoUOHcOHCBajVarHDIiIbMasQatSokbXjICIS3e7duzFkyBAAQFRUFL766isWQUROzuhCaMmSJUYfNCkpyaxgiIjE9Nhjj2HQoEGIiIjAW2+9xVluIhkwuhBatGiRUe0UCgULISJySAqFAhs3boRKpRI7FCKyE6MLob/fF0RE5IxYBBHJi8XrCAmCUOnpMSIiIiJHYHYhtGbNGrRu3RoeHh7w8PBAmzZt8NVXX1kzNiIiIiKbMqsQWrhwISZPnoyBAwdi7dq1WLt2Lfr3749JkyYZfS+Rvfz4448IDw9H8+bN8fnnn4sdDhGJQKfTYdGiRbh48aLYoRCRxJj1+PzSpUvxySefYMyYMYZtQ4YMQcuWLfHGG2/gpZdeslqAligrK0NycjJ27twJPz8/tG/fHk888QQCAwPFDo2I7CQjIwPx8fHYs2cP1q9fjz179vA+ICIyMGtG6ObNm3jkkUcqbX/kkUdw8+ZNi4OylkOHDqFly5aoX78+vL29MWDAAOzYsUPssIjITn799Ve0adMGe/bsgbe3N+Lj46FUWvUVi0Tk4Mw6IzRr1gxr166ttD0lJQXNmze3OKgH9uzZg8GDByMkJMTwWOvfaTQaNG7cGO7u7ujUqRMOHTpk2Hfjxg3Ur1/f8H39+vVx/fp1q8VHRNIWExODunXrolu3bjhx4gQmTJgAhUIhdlhEJCFmXRqbN28eRo4ciT179qBr164AgLS0NKSmplZZIJmrsLAQ0dHRGDduHJ588slK+1NSUpCcnIzly5ejU6dOWLx4Mfr164dz586hbt26Jo9XUlKCkpISw/d5eXkAgNzcXOj1+gptS0tLAQCurq4mj2Nuf1P6GNu2pnY17c/Pz6/w1VFZ+vuUypjOkJeWtvlrTvr4+GDTpk2oV68eVCoVcnNza4xRKpiT5vfnudJ2HCUvH/z7bQyzCqHhw4fj4MGDWLRokWGWJjIyEocOHULbtm3NOWSVBgwYgAEDBlS7f+HChZgwYQLGjh0LAFi+fDk2b96MVatW4dVXX0VISEiFGaDr16+jY8eO1R5v/vz5mDdvXqXtaWlp8PT0tOCTOL9jx46JHQJRBX/NyfT0dBEjIfo/PFfaR1FRkdFtFYKDLAKkUCjw/fffY9iwYQAArVYLT09PrF+/3rANAOLi4pCbm4tNmzahrKwMkZGR2LVrl+Fm6X379lV7s3RVM0INGjTAlStX4OvrW6Et/8opl5+fj2PHjqFdu3bw8fGpMS6pcpS/cmx9TCnkpTVmhJiT0hnTGXLSmHY8V0przLy8PDRq1Aj37t2r9O/335k0I1RWVgadTgc3NzfDtszMTCxfvhyFhYUYMmQIHn30UVMOabbs7GzodDoEBQVV2B4UFISzZ88CAFxcXPDhhx+iZ8+e0Ov1mDFjxkOfGHNzc6vw2R7w9/ev9IPUarUAYPYLGc3pb0ofY9vW1M7Y4/j4+MDf37/GuKTK0t+nVMZ0hry0VhvmpDTGdIacNKYdz5XSGtOUhyJMKoQmTJgAtVqNTz/9FEB5hRsbG4vi4mLUq1cPixYtwqZNmzBw4EBTDmtTQ4YMMbxNmoicx/79+9GhQwd4eXmJHQoROTCTnhpLS0vD8OHDDd+vWbMGOp0OFy5cwIkTJ5CcnIz333/f6kFWpXbt2lCpVMjMzKywPTMzE8HBwXaJgYjsr7i4GK+++ip69uyJGTNmiB0OETk4k2aErl+/XuHx+NTUVAwfPhx+fn4Ayu/P+eKLL6wbYTXUajXat2+P1NRUwz1Cer0eqampSExMtOpYWq3WMDX3122WHtOWfYxtW1O7mvY/uHZbWlpq8c9ETGLEbosxnSEvH9bmjz/+wKhRo3D69GkA5TdEFhcXV5gGZ05Ka0xnyElj2vFcKa0xTelj0oyQu7s77t+/b/j+wIED6NSpU4X9BQUFphzyoQoKCnD8+HEcP34cQPkKscePH8fVq1cBAMnJyfjss8+wevVqnDlzBpMnT0ZhYaHhKTJzaTQaREVFITY21tKPQERWpFarcfnyZdSpUwfffvstPv30Uy6QSEQWMWlGKCYmBl999RXmz5+PvXv3IjMzE7169TLsT09PR0hIiNWCO3LkCHr27Gn4Pjk5GUD5zNOXX36JkSNHIisrC3PmzMGtW7cQExODbdu2VbqB2lQJCQlISEhAXl4e/Pz8oFarq71Jy9Ibxszpb0ofY9vW1K66/Q/u4nd1dbXrzXO2IsZnsMWYzpCXVbVp2bIlNmzYgNatW6NOnTpVtmFOSnNMZ8hJY9rxXCmNMU1pa1IhNGfOHAwYMABr167FzZs3ER8fj3r16hn2f//994YFFq2hR48eqOnp/sTERKtfCiMi6erfv79DX1ogImkxqRDq3r07jh49ih07diA4OBhPPfVUhf0xMTEPXbCQiIiISEpMXlk6MjISkZGRVe574YUXLA5IinizdPV4A6C0xnSGvLS0DXNSWmM6Q04a047nSmmNabObpeWCN0sT2d/du3drvBRORGRtZr1rzNnxZmnj9/MGQGmO6Wh5uWHDBkyaNAnvvvsuRo0aZfTxeLO044zpaDlpbjueK6UxpkmrUJsTDBGRNeTm5mL06NEYMWIEsrOz8cUXX3BWiIjsioUQEYlm3759+Prrr6FUKjFz5kz8/PPPUCgUYodFRDJi1qWx+/fv46effsL58+cBAC1atECfPn3g4eFh1eCIyLkNHDgQc+bMQf/+/dGlSxcA4tyMSUTyZXIh9N///hfPP/88srOzK2yvXbs2Vq5cicGDB1stOCJyfvPmzRM7BCKSMZMKoX379mHEiBEYMmQIXn75ZcNj9KdPn8aHH36IESNGYPfu3ejcubNNghULH5+vHh8JldaYzpCXfHy+HHPS/P48V9qOo+SlzR6ff/vttzF27FisX78eXbp0gb+/P/z9/fHII49gw4YNiI+Px5tvvmlywFLDx+eJiIjkwaQZoQMHDuDdd9+tdn9CQgK6d+9ucVBi4+Pzxu/nI6HSHFMKeSkIQrU3Ppv7aLwxbZiT0hxTCjlpjbY8V5aTel7a7PH5+/fvw9fXt9r9fn5+KC4uNuWQROSELly4gO7du+Pw4cNih0JE9FAmFULNmzfHL7/8Uu3+1NRUNG/e3OKgiMgxCYKAZcuWISYmBnv37sWUKVO4LhARSZpJhdDYsWMxffp0bNmypdK+zZs3Y8aMGYiPj7dWbETkYNasWYOEhAQUFRWhV69eWLt2LdcFIiJJM+keoalTp2Lfvn0YNGgQwsPDERkZCUEQcObMGVy4cAHDhg3DtGnTbBQqEUnds88+i1WrVmHEiBFISEiAUsk1W4lI2kwqhJRKJdatW4eUlBT8v//3/3D27FkAQEREBN544w08/fTTNglSbHx8vnp8JFRaY0ohL7dv3w6lUomysjKzjs/H58sxJ83vz3Ol7ThKXprSx6yVpUeOHImRI0ea09UhaDQaaDQa6HQ6sUMhcjicBSIiR2JWIXTnzh0EBgYCAK5du4bPPvsM9+/fx+DBg/HYY49ZNUAx8PF54/fzkVBpjukMecnH58sxJ83vz3Ol7Ug9L232+Pzvv/+Oxo0bo27duoiIiMDx48cRGxuLRYsWYcWKFejVqxc2btxoyiGJiIiIRGNSITRjxgy0bt0ae/bsQY8ePTBo0CA8/vjjuHfvHnJycjBx4kQsWLDAVrESkUiKiorw2muvISsrS+xQiIisyqRLY4cPH8Yvv/yCNm3aIDo6GitWrMCLL75ouCdgypQpTveeMSK5O3ToEMaMGYNz587h3Llz2LBhg9ghERFZjUmF0N27dxEcHAwA8Pb2hpeXFwICAgz7AwICkJ+fb90IiUg03377LUaNGgWdTod69erh+eefFzskIiKrMvlm6b8vjsbF0oicV8+ePREQEIA+ffrg448/Rq1atcQOiYjIqkwuhOLj4+Hm5gYAKC4uxqRJk+Dl5QUAKCkpsW50RCSqoKAgnDhxAiEhIWKHQkRkEyYVQnFxcRW+HzVqVKU2Y8aMsSwiCeKCitXjImHSGtMWeVm7du2HHtfaeckFFcsxJ83vz3Ol7ThKXtpsQcUvvvjC5GAcERdUJCIikgezFlR0dlxQ0fj9XCRMmmM6Q15yQcVyzEnz+/NcaTtSz0tT2ppUCLVt27bKm6P9/PzQokULTJ06FVFRUaYckohEsmXLFvTt2xcuLvx7iIjky6Qz4LBhw6rcnpubi2PHjqFt27b45Zdf0LVrV2vERkQ2cOfOHbz44otYu3Yt3nnnHbz22mtih0REJBqTCqG5c+c+dP+sWbMwZ84cpKamWhQUEdnGL7/8gueeew63bt2CSqWCIAhih0REJCqrvib62Wefxe+//27NQxKRFXl6euL27duIiIjAgQMHMGvWLLFDIiISlVVvDlCpVNDr9dY8JBFZUefOnfHDDz+gZ8+e8PDwEDscIiLRWbUQ+u6773izNJHEDRw4UOwQiIgkw6RCaMmSJVVuv3fvHo4ePYrNmzdj69atVgmMiIiIyNZMKoQWLVpU5XZfX1+Eh4djz5496NKli1UCIyIiIrI1kwqhjIwMW8UhaXzFRvW4bLx0xszMzERAQIBFx5BCXvIVG+WcISetcUwp5KQx7XiulNaYpvSx6Kmx7Oxs5OXlWXIISdJoNIiKikJsbKzYoRDVSK/XY8mSJWjRogV27NghdjhERA7F5Julc3NzMWvWLKSkpCAnJwcAUKdOHYwdOxazZ8+Gp6en1YO0N75iw/j9XDZe3DEvX76MsWPHYteuXQCAjRs3YtCgQU6Rl3zFRjlHy0lbHVMKOWlMO54rpTGmzV6xcffuXXTp0gXXr1/Hc889h8jISADA6dOnsXTpUvz000/49ddfcfLkSRw4cABJSUmmHJ6ITLRjxw7s2rULnp6e+PDDDzF27FixQyIicigmFUJvvvkm1Go10tPTERQUVGlf3759MXr0aOzYsaPaJ8yIyHomTJiAjIwMjB8/Hs2aNXPoew+IiMRgUiG0ceNGfPrpp5WKIAAIDg7Ge++9h4EDB2Lu3LmIi4uzWpBEVDWFQoH58+eLHQYRkcMy6WbpmzdvomXLltXub9WqFZRKZY3vJCMiIiKSApMKodq1a+Py5cvV7s/IyEDdunUtjYmIiIjILkwqhPr164dZs2ZVeR9CSUkJZs+ejf79+1stOCI545vhiYhsz+SbpTt06IDmzZsjISEBEREREAQBZ86cwbJly1BSUoI1a9bYKlYi2UhLS0NCQgK+++47NG3aVOxwiIiclkmFUGhoKPbv348XX3wRM2fONPzFqlAo0KdPH3z88cdo2LChTQIlkoOSkhLMmTMH77//PgRBwMyZM5GSkiJ2WERETsvkBRWbNGmCrVu3IicnBxcuXAAANGvWDLVq1bJ6cERys2DBArz33nsAgLi4OHz00UciR0RE5NxMLoQeCAgIQMeOHa0ZC5Hsvfzyy9i2bRteeeUVDBs2TOxwiIicntmFEBFZn7e3N/bt2weFQiF2KEREsmDRS1eJyPpYBBER2Q9nhIyg1WorLRlg6asMzOlvSh9j29bUrqb9paWlhq+O/HoHMWK3xZjOkJeWtmFOSmtMZ8hJY9rxXCmtMU3pwxmhKmg0GkRFRSE2NlbsUIiIiMiGOCNUhYSEBCQkJCAvLw9+fn5Qq9VQq9VVtq1uu7HM6W9KH2Pb1tSuuv2urq6Gr5b+LKTAVp8hKysLb731FubPnw8vLy+bj+kMeWluG+akNMd0hpw0ph3PldIY05S2LISIbGzjxo144YUXkJWVBYVCwUfiiYgkhJfGiGxowYIFeOKJJ5CVlYVWrVph7NixYodERER/wUKIyIaefPJJeHt745VXXsGRI0cQExMjdkhERPQXvDRGZEMtWrRARkYGateuLXYoRERUBc4IEdkYiyAiIuliIURERESyxUKIiIiIZIuFEJEZdDod1q9fD0EQxA6FiIgswEKIyETp6eno3r07nnrqKXz55Zdih0NERBZgIURkgq+++grR0dFIS0uDj4+PU6wQS0QkZ3x8nsgEfn5+KCwsRI8ePfDll1+iUaNGYodEREQWYCFEZIIhQ4Zg27Zt6NOnD5RKTqgSETk6FkJEJurXr5/YIRARkZXwT1oiIiKSLRZCREREJFsshIj+59q1a2KHQEREdsZCiGTv/v37mD59OiIjI3HkyBGxwyEiIjvizdJG0Gq10Gq1lbZZekxb9jG2bU3tatpfWlpq+Grpz0QMR48exdixY3Hu3DkAwJYtW9CmTRu7jG2Ln5cz5KWlbRw9Jx8QI3bmpPntnP1c+YCj5KUpfVgIVUGj0UCj0UCn04kdCtnYli1bcO7cOQQHB+Pjjz/G4MGDxQ6JiIjsiIVQFRISEpCQkIC8vDz4+flBrVZXu4KwpSsLm9PflD7Gtq2pXXX7XV1dDV8dcZXlOXPmQK/XIzExEYGBgaJ8BluM6Qx5aW4bR8/Jv2NOmt+f50rbkXpemtKWhRDJmqurK9555x2HnqomIiLz8WZpIiIiki0WQkRERCRbLITIaen1erFDICIiiWMhRE5p7dq1aN26NbKzs8UOhYiIJIyFEDmVu3fv4plnnsHIkSNx+vRpfPDBB2KHREREEsanxsipTJ8+Hd9++y1UKhVmzZqF119/XeyQiIhIwlgIkVP597//jfPnz2PhwoXo2LGj2OEQEZHEsRAipxIcHIxff/1V7DCIiMhB8B4hIiIiki0WQkRERCRbLISIiIhItlgIkUM4d+4cJk+ejLKyMrFDISIiJ8JCiCRNr9djyZIliImJwfLly7Fw4UKxQyIiIifCp8ZI0iZOnIjPP/8cANCnTx88++yzIkdERETOhDNCJGnjxo2Dj48Pli1bhu3btyM0NFTskIiIyIlwRogkrUuXLrh69Sr8/f3FDoWIiJwQZ4RI8lgEERGRrbAQIiIiItliIURERESyxUKIRFFYWIiUlBSxwyAiIpljIUR2t3//fsTExODpp5/G9u3bxQ6HiIhkjIUQ2dXbb7+NRx99FBcvXkT9+vWhVqvFDomIiGSMhRDZVWBgIPR6PUaPHo1Tp06hZ8+eYodEREQyxnWEyK4mTZqEqKgodO/eXexQiIiIOCNE9qVQKFgEERGRZMiiEHriiScQEBCAESNGiB0KERERSYgsCqGpU6dizZo1YodBREREEiOLQqhHjx7w8fEROwynJggC0tPTxQ6DiIjIJKIXQnv27MHgwYMREhIChUKBjRs3Vmqj0WjQuHFjuLu7o1OnTjh06JD9A6Vq3bt3D/Hx8YiJiUFGRobY4RARERlN9KfGCgsLER0djXHjxuHJJ5+stD8lJQXJyclYvnw5OnXqhMWLF6Nfv344d+4c6tatCwCIiYlBWVlZpb47duxASEiIzT+DnG3ZsgVJSUm4d+8eXFxcsG/fPjRp0kTssIiIiIwieiE0YMAADBgwoNr9CxcuxIQJEzB27FgAwPLly7F582asWrUKr776KgDg+PHjVomlpKQEJSUlhu/z8vIAALm5udDr9RXalpaWAgBcXV3NGsuc/qb0MbZtTe1q2v/DDz/g3r17CA8Px4oVK9CmTRvk5ubWGJ/UWPr7lMqYzpCXlrbJz8+v8NVRMSfN7y/FcyXz0r5jPvj32xiiF0IPo9VqcfToUcycOdOwTalUonfv3ti/f7/Vx5s/fz7mzZtXaXtaWho8PT2tPp4zGDp0KJRKJYYNG4acnBzs3r1b7JCIAADHjh0TOwSiSpiX9lFUVGR0W0kXQtnZ2dDpdAgKCqqwPSgoCGfPnjX6OL1798aJEydQWFiI0NBQrFu3Dl26dKnUbubMmUhOTjZ8n5eXhwYNGqBr167w9fWt0JZ/5ZTLz8+Hh4cH2rVr59A3pDvKXzm2PqYU8tIaM0LHjh1jTkpkTGfISWPaGXOuZF7ab0ynmRGylp9//tmodm5ubnBzc6u03d/fv1IhpNVqAcDsd2WZ09+UPsa2ramdscfx8fGBv79/jXFJlaW/T6mM6Qx5aa02zElpjOkMOWlMO54rpTWmUmn8s2CiPzX2MLVr14ZKpUJmZmaF7ZmZmQgODhYpKiIiInIWkp4RUqvVaN++PVJTUzFs2DAAgF6vR2pqKhITE+0Wh1arNVSkf91m6TFt2cfYtg9rV1ZWVukm8b97MGVZWlpq8c9ETGLEbosxnSEvLW3DnJTWmM6Qk8a0q2k/89K+Y5rSR/QZoYKCAhw/ftzw5FdGRgaOHz+Oq1evAgCSk5Px2WefYfXq1Thz5gwmT56MwsJCw1NktqDRaBAVFYXY2FibjSFlOp0OCxcuROfOnU264YyIiMjRiD4jdOTIEfTs2dPw/YOblePi4vDll19i5MiRyMrKwpw5c3Dr1i3ExMRg27ZtlW6gtqaEhAQkJCQgLy8Pfn5+UKvV1V6btPQ6qTn9TeljbNsH7S5duoT4+Hjs3bsXALBu3TpMmDCh2uM8uHnN1dXVrteMbUWMz2CLMZ0hL81tw5yU5pjOkJPGtOO5UhpjmtJW9EKoR48eEAThoW0SExPteilMziZMmIC9e/fC29sbixYtwujRo8UOiYiIyGZEvzRG0qLRaNCvXz+cPHkSzz//PBQKhdghERER2YzoM0IkLREREdi2bZvYYRAREdkFCyEjyPWpMWP280kIaY3pDHnJp8bKMSfN789zpe04Sl461FNjUiT3p8aIiIjkgjNCVZDzU2Om7ueTENIc0xnykk+NlWNOmt+f50rbkXpemrQKtTnBkOPZu3cvkpKSanxCj4iISE5YCDm54uJiTJ8+Hd27d8fSpUuRkpIidkhERESSwUtjTkwQBAwcOBA7d+4EAIwfPx4DBw4UOSoiIiLpYCFkBEd+amzy5Mk4ffo0li1bhkGDBlU6Dp+EKOcoT0LY+phSeEKHT42VY06a359PjdmOo+SlKX1YCFVBo9FAo9FAp9OJHYrFhg4din/84x/w9vYWOxQiIiLJYSFUBWd7aqxWrVoWH5NPQjjWmI6Ql7Zqw5yU5pjOkJPGtOO5Uhpj8qkxIiIiIiOwECIiIiLZYiHkoG7duoV169aJHQYREZFDYyHkgNavX49WrVrh2WefxbFjx8QOh4iIyGHxZmkHIggCJkyYgK+++goAEB0dDTc3N5GjIiIiclwshIxg7XWEBL0exXfuQCgrQ5mHB1S+vlAoa56cKy0tRUhICJRKJf71r3/h9ddfh1qtrjIWro1hGkdZG8PWx5TCmi1cR6gcc9L8/lxHyHYcJS+5jpCFbLmOUGl2NrQZGSi6dQvQC9AqFXANDoa6SRO41q5dY/8ZM2Zg2LBhaNeundVjIyIikhsWQlWw1TpCpVlZEC5dgntQPXi0aw+lSgkXlQtKLmVAd+kSFK6ucK1T56HHUKvV6Ny5s9Fjcm0M00h9bQx7HVMKa7ZwHaFyzEnz+3MdIduRel6a0paFkJ0Iej10+fnweqQrzp/T47PZOly+DISGKjF5YmO0eKQpSi5nwCUw0KjLZERERGQ5FkJ2oisogFvjJliyVIGXXlLBy6v8sltBAbBkiQqLFwNTEptAV5APF19fcYMlIiKSCU492Mm9wvs4e1aHl14CBKHiPkEApk4tw7ZtZ5BbWCROgERERDLEQsgO3n//ffjXrYNPV7hUKoLKXQDQDSOe+gf0KhXef/99O0dIREQkTyyEbOzKlSv44IMPoFIpkZ5ecZ8gCAA0AKIBHIBWW4TTp//ABx98gCtXrogQLRERkbywELKxl156CXl5edDp9AgLq6rFFgD3AfwDo0f/jm7dHkNeXh6Sk5PtGygREZEM8WZpI5i7oKJer8fOnTvh4uKC27ezMGGCP1atUkAQAC+v8v6CsAw63Sa4ur6Al19WIDPzNlxcXPDLL7+guLgYyr88QcZFwmzHURYJs/UxpbB4HRdULMecNL8/z5W24yh5aUofzghVQaPRICoqCrGxsRYd59KlSygrK4OXlxf8/f0QFqbAO+8ACsX/tVEq60GtnoT585UIC1MgIMAfXl5eKCsrw+XLly37IERERPRQnBGqgrUWVLx79y4KCgpQp04deHi4Q6MBEhKAXr2AL77A/9YRUmPCBCAyEli+HHjxRcDT0xOZmZnIzs5GRESESWOa25aLhJWT+iJh9jqmFBav44KK5ZiT5vfnudJ2pJ6XXFBRIur8b5XorKws6HQCLlxQoFs3YNo04M03ARcXQKkEvv8eeOEFIDYW0OkEZGVlAQCCgoJEjJ6IiMj5sRCyoUaNGsHFxQXFxcXIunEDkyYGY8kSFfbtAwIDAR8f4NYtoLi4/HLZys91yLpxC8XFxXBxcUFY1XdXExERkZXwHiEbKCoqQlJSEvr06YNBgwbBzc0NNy9dRHi4EosWlRc9JSVAdvb/FUGLFwPh4UrcSL8ANzc3DBkypMKN0kRERGR9nBGysoMHD2LMmDE4f/48AGD16tXYtm0bVNAha9d2TJnSD3376LBy1YN7hICJL5QhPEKFrF3b4aJSQ6fTYeHCheJ+ECIiIhlgIWRFOp0OcXFxOH/+PEJCQrBy5Ur0798ft27dguDpjjvXrmL/zKfRZth4vPVWN6hUKqhUOlw99At+mLkSLToNABq0wFtvvYVGjRqJ/XGIiIicHgshK1KpVFi5ciU++eQTLF26FAEBAQCAGTNm4OKVP3C32BMqNw9sfXsi1L6BcPfyw/2cWygtLkLD9t2RX8cTter6Y/r06SJ/EiIiInlgIWRlXbt2RdeuXStt961dB2dzChDQoztCozpCm5mFsuL7UKvVcKkXjMK6HrjtU4AmtWuLEDUREZE8sRCyE1eVKzx9gqH0UaCgISDkBkFXpoXgpkaxjydUCsADerioXMUOlYiISDZYCBnB3Fds/JUHPBDoWR87L/+EP24dhluZAiqFEsVCMfQurmgZHIuejfvAQ+FR7bG5bLztOMqy8bY+phReZ8BXbJRjTprfn+dK23GUvDSlDwuhKmg0Gmg0Guh0Oqse927xXVwuuIFipRd0LkpA0KNU4QooVbhccAN3i+9adTwiIiJ6OBZCVfj7KzZcXV2xYcMG/P777/j3v/9doa2xy3jnFOfgZlEWvNS1UaxTouB+EcqEMugVKtRy84OXuhZuFmXhvnAfAW4BDz0Wl423HakvG2+vY0rhdQZ8xUY55qT5/XmutB2p5yVfsWFl8fHx2LhxIwBgwIAB6Natm8nHKNWV4kZePjJzBXi71UaADyBAgE6hR2mpCpm5OgSo81GqK7Vy9ERERFQdFkJG2LhxI1QqFWbPno3OnTubdQy9Xgm93gUlZXqUlOmhVqigVAAlgh6CoP9fGxfo9VxNmoiIyF5YCBmhRYsW+Oabb9ChQwezj6FWeaOxbyh+v3UVACAIgE4AhL+0aewbCneVj4XREhERkbE4/WCEvXv3WlQEAYASCjTwaYo2QVW/SLVNUBga+DS1aAwiIiIyDWeEjODu7m7xMXzd1fBUBqBd3Y6o510Xl+9eQ3FZMVxd3RAW0BD1PBvDUxkAX3fHv4mOiIjIUbAQshOlUoF6/u7IueGDCP8YtA9sDL1QBldXd+SVeKOgWId6/u5QKhVih0pERCQbLITsqK6vO7xL76Do+glk37gEaLVQurmicb0weNZvCU9fy2eeiIiIyHgshOwp/xY8//wVHnfSoVYAelfATaGDe3YOFEIOoO4O+ASLHSUREZFssBCyF70eyDoL3L0EhUIBd9fy+9TViv9dCrt7CfAJArzqAkrew05ERGQP/BfXXu7nADlXHt4m50p5OyIiIrILFkL2otcCpcUPb1NaDOi5sjQREZG98NKYEazx9nnoFIDKAxDKa0+tUEUNqvIAdAD49nm7c5Q3Ktv6mFJ40zffPl+OOWl+f54rbcdR8tKUPpwRqoJGo0FUVBRiY2Otd1B3f8Av9OFt/ELL2xEREZFdcEaoCn9/+7xara72TbYmvYG3XhRwP6v8xugH/RXl7xlDrabl+41YvJFvVLYdqb9R2V7HlMKbvvn2+XLMSfP781xpO1LPS759Xqp8goGm3cufDrtztfyeIDc3IKARUCeCj84TERHZGQshe/MJLn9EvtZtQF8GuHkAHgF8ZJ6IiEgELITEoFQCnrXK/78TTJESERE5Kk5DEBERkWyxECIiIiLZYiFEREREssVCiIiIiGSLhRARERHJFgshIiIiki0WQkRERCRbLISIiIhItlgIERERkWxxZemHEAQBAJCXl1dpn1arBWD+i+fM6W9KH2Pb1tSupv15eXkoKipCXl4elA78mhBLf59SGdMZ8tLSNsxJaY3pDDlpTDueK6U15oN/tx/8O/4wLIQeIj8/HwDQoEEDkSMhIiIiU+Xn58PPz++hbRSCMeWSTOn1ety4cQM+Pj5QKBSV9sfGxuLw4cNmH9+c/qb0MbZtTe0etj8vLw8NGjTAtWvX4Ovra1RcUmXp71MqYzpDXlrShjkpvTGdISeNacdzpXTGFAQB+fn5CAkJqXEGjjNCD6FUKhEaGlrtfpVKZVFCm9PflD7Gtq2pnTHH8fX1dfj/uC39fUplTGfIS2u0YU5KZ0xnyElj2vFcKa0xa5oJesBxL1RKQEJCgt37m9LH2LY1tbP0czoKMT6nLcZ0hry0VhtHx5w0vz/PlbbjLHn5AC+NkUXy8vLg5+eHe/fuOfxfOeQcmJMkRcxL6eKMEFnEzc0Nc+fOhZubm9ihEAFgTpI0MS+lizNCREREJFucESIiIiLZYiFEREREssVCiIiIiGSLhRARERHJFgshIiIiki0WQmQ3TzzxBAICAjBixAixQyEZ+/HHHxEeHo7mzZvj888/FzscIp4bRcbH58ludu3ahfz8fKxevRrr168XOxySobKyMkRFRWHnzp3w8/ND+/btsW/fPgQGBoodGskYz43i4owQ2U2PHj3g4+MjdhgkY4cOHULLli1Rv359eHt7Y8CAAdixY4fYYZHM8dwoLhZCBADYs2cPBg8ejJCQECgUCmzcuLFSG41Gg8aNG8Pd3R2dOnXCoUOH7B8oyZqleXrjxg3Ur1/f8H39+vVx/fp1e4ROTornTsfHQogAAIWFhYiOjoZGo6lyf0pKCpKTkzF37lwcO3YM0dHR6NevH27fvm1oExMTg1atWlX6340bN+z1McjJWSNPiayJOekEBKK/ASB8//33FbZ17NhRSEhIMHyv0+mEkJAQYf78+SYde+fOncLw4cOtESbJnDl5mpaWJgwbNsywf+rUqcI333xjl3jJ+Vly7uS5UTycEaIaabVaHD16FL179zZsUyqV6N27N/bv3y9iZET/x5g87dixI06dOoXr16+joKAAW7duRb9+/cQKmZwcz52OwUXsAEj6srOzodPpEBQUVGF7UFAQzp49a/RxevfujRMnTqCwsBChoaFYt24dunTpYu1wSaaMyVMXFxd8+OGH6NmzJ/R6PWbMmMEnxshmjD138twoLhZCZDc///yz2CEQYciQIRgyZIjYYRAZ8NwoLl4aoxrVrl0bKpUKmZmZFbZnZmYiODhYpKiIKmKektQwJx0DCyGqkVqtRvv27ZGammrYptfrkZqayulbkgzmKUkNc9Ix8NIYAQAKCgpw8eJFw/cZGRk4fvw4atWqhYYNGyI5ORlxcXHo0KEDOnbsiMWLF6OwsBBjx44VMWqSG+YpSQ1z0gmI/dgaScPOnTsFAJX+FxcXZ2izdOlSoWHDhoJarRY6duwoHDhwQLyASZaYpyQ1zEnHx3eNERERkWzxHiEiIiKSLRZCREREJFsshIiIiEi2WAgRERGRbLEQIiIiItliIURERESyxUKIiIiIZIuFEBEREckWCyEislhaWhpat24NV1dXDBs2TOxwJGnXrl1QKBTIzc216DiXL1+GQqHA8ePHrRIXkdyxECKSsfj4eCgUCigUCri6uqJJkyaYMWMGiouLTTpOcnIyYmJikJGRgS+//NI2wYpIp9NhwYIFiIiIgIeHB2rVqoVOnTrh888/t+m48fHxlQrLBg0a4ObNm2jVqpVNxyaSC750lUjm+vfvjy+++AKlpaU4evQo4uLioFAo8O677xp9jPT0dEyaNAmhoaFmx6HVaqFWq83ub0vz5s3Dp59+io8//hgdOnRAXl4ejhw5gpycHLvHolKpEBwcbPdxiZwVZ4SIZM7NzQ3BwcFo0KABhg0bht69e+Onn34y7Nfr9Zg/fz6aNGkCDw8PREdHY/369QD+7zLNnTt3MG7cOCgUCsOM0KlTpzBgwAB4e3sjKCgIo0ePRnZ2tuG4PXr0QGJiIqZNm4batWujX79+RvdLSkrCjBkzUKtWLQQHB+ONN96o8Jlyc3MxceJEBAUFwd3dHa1atcKPP/5o2P/rr7+iW7du8PDwQIMGDZCUlITCwsJqf0b//e9/8eKLL+Kpp55CkyZNEB0djfHjx2P69OmGNiUlJUhKSkLdunXh7u6ORx99FIcPH672mG+88QZiYmIqbFu8eDEaN25s2L969Wps2rTJMGu3a9euKi+N7d69Gx07doSbmxvq1auHV199FWVlZSb9zIjkioUQERmcOnUK+/btqzAzM3/+fKxZswbLly/HH3/8gZdeegmjRo3C7t27DZdpfH19sXjxYty8eRMjR45Ebm4uevXqhbZt2+LIkSPYtm0bMjMz8c9//rPCeKtXr4ZarUZaWhqWL19uUj8vLy8cPHgQ7733Ht58801D8abX6zFgwACkpaXh66+/xunTp7FgwQKoVCoA5bNX/fv3x/Dhw3Hy5EmkpKTg119/RWJiYrU/l+DgYPzyyy/Iysqqts2MGTOwYcMGrF69GseOHUOzZs3Qr18/3L171+TfAwBMnz4d//znP9G/f3/cvHkTN2/exCOPPFKp3fXr1zFw4EDExsbixIkT+OSTT7By5Uq8/fbbFdo97GdGJGvivfieiMQWFxcnqFQqwcvLS3BzcxMACEqlUli/fr0gCIJQXFwseHp6Cvv27avQb/z48cIzzzxj+N7Pz0/44osvDN+/9dZbQt++fSv0uXbtmgBAOHfunCAIgtC9e3ehbdu2FdoY2+/RRx+t0CY2NlZ45ZVXBEEQhO3btwtKpdLQ/u/Gjx8vvPDCCxW27d27V1AqlcL9+/er7PPHH38IkZGRglKpFFq3bi1MnDhR2LJli2F/QUGB4OrqKnzzzTeGbVqtVggJCRHee+89QRAEYefOnQIAIScnRxAEQZg7d64QHR1dYZxFixYJjRo1MnwfFxcnDB06tEKbjIwMAYDw22+/CYIgCK+99poQHh4u6PV6QxuNRiN4e3sLOp1OEISaf2ZEcsZ7hIhkrmfPnvjkk09QWFiIRYsWwcXFBcOHDwcAXLx4EUVFRejTp0+FPlqtFm3btq32mCdOnMDOnTvh7e1daV96ejpatGgBAGjfvr1Z/dq0aVNhX7169XD79m0AwPHjxxEaGmpoW1VsJ0+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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_values, encoded_ref_sol, c='black', s=100, label='Best possible solution')\n", - "for s in solutions[1:5]:\n", - " plt.scatter(ref_values, s, s=50, lw=1, alpha=0.5, edgecolors='w', label='Sampled solution')\n", - "plt.scatter(ref_values, solutions[0], s=50, lw=1, c='blue', edgecolors='w', label='Best sampled solution')\n", - "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.grid(which=\"major\", lw=1)\n", - "plt.grid(which=\"minor\", lw=0.1)\n", - "plt.xlabel('Reference Solution')\n", - "plt.ylabel('QUBO Solution')\n", - "# plt.legend()\n", - "# plt.xlim([0.01,0.1])\n", - "# plt.ylim([0.01,0.1])\n", - "\n", - "# plt.xlim([10,50])\n", - "# plt.ylim([10,50])\n", - "plt.loglog()\n" + "We can then use the `solve()` method of the qubo polynomial solver to obtain a solution of the problem" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 10, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "72" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4000/4000 [00:05<00:00, 675.39it/s]\n" + ] } ], "source": [ - "net.qubo.qubo_dict.num_variables" + "net.step_func.optimize_values = np.arange(2,6)\n", + "_, _, sol, res = net.solve(init_sample=x0, Tschedule=Tschedule, save_traj=True, verbose=False)" ] }, { - "cell_type": "code", - "execution_count": 96, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "999998.1773075246\n", - "-2000000.0\n", - "-2000000.0\n", - "1000005.6999586402\n", - "999996.3546150491\n", - "1000000.0\n", - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "v=[]\n", - "for i in net.qubo.qubo_dict.iter_quadratic():\n", - " v.append((i[2]))\n", - " print(i[2])\n", - "\n", - "plt.hist(v)\n", - " " + "We can plot the evoluion of the QUBO energy along the optimization path" ] }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "Text(0.5, 0, 'Iterations')" ] }, - "execution_count": 90, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" - } - ], - "source": [ - "v" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cons:\n", - "mass_balance[J1]: ((expected_demand[J1]-flow[P1])+flow[P2])\n", - "mass_balance[D1]: (expected_demand[D1]-flow[P2])\n", - "approx_darcy_wesibach_headloss[P1]: (((((-(dw_resistance_0[P1]))-(dw_resistance_1[P1]*flow[P1]))-(dw_resistance_2[P1]*(flow[P1]**2.0)))+source_head[R1])-head[J1])\n", - "approx_darcy_wesibach_headloss[P2]: (((((-(dw_resistance_0[P2]))-(dw_resistance_1[P2]*flow[P2]))-(dw_resistance_2[P2]*(flow[P2]**2.0)))+head[J1])-head[D1])\n", - "\n", - "vars:\n", - "flow[P1]: flow[P1]\n", - "flow[P2]: flow[P2]\n", - "head[J1]: head[J1]\n", - "head[D1]: head[D1]\n", - "\n" - ] - } - ], - "source": [ - "print(model.__str__())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Embed the problem" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [], - "source": [ - "import dwave_networkx as dnx\n", - "from minorminer import find_embedding\n", - "from dwave.embedding import embed_qubo, majority_vote, chain_break_frequency" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ + }, { "data": { + "image/png": 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", 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('x_003_003*x_001_001', 'x_002_001*x_004_003'): -0.5327783558792923,\n", - " ('x_003_003*x_001_001', 'x_003_003'): -2000000.0,\n", - " ('x_003_003*x_001_001', 'x_001_001'): -2000000.0,\n", - " ('x_004_005', 'x_002_001'): 999992.7092300983,\n", - " ('x_004_005', 'x_004_004'): 1000066.4797573186,\n", - " ('x_004_005', 'x_002_001*x_004_004'): 1000000.0,\n", - " ('x_004_005', 'x_004_003'): 1000025.2941389193,\n", - " ('x_004_005', 'x_002_001*x_004_003'): 1000000.0,\n", - " ('x_004_005', 'x_003_003'): -0.5327783558792923,\n", - " ('x_004_005', 'x_003_003*x_001_001'): 1.0655567117585847,\n", - " ('x_002_001*x_004_005', 'x_002_001'): -2000000.0,\n", - " ('x_002_001*x_004_005', 'x_003_003'): 1.0655567117585847,\n", - " ('x_002_001*x_004_005', 'x_003_003*x_001_001'): -2.1311134235171694,\n", - " ('x_002_001*x_004_005', 'x_004_005'): -2000000.0,\n", - " ('x_003_004', 'x_004_004'): -0.5327783558792923,\n", - " ('x_003_004', 'x_002_001*x_004_004'): 1.0655567117585847,\n", - " ('x_003_004', 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'x_004_002*x_004_005'): 3000000.0,\n", - " ('x_004_001*x_002_001*x_004_003', 'x_004_001*x_002_001*x_004_003'): 3000000.0,\n", - " ('x_004_002*x_002_001*x_004_003', 'x_004_002*x_002_001*x_004_003'): 3000000.0,\n", - " ('x_004_001*x_002_001*x_004_005', 'x_004_001*x_002_001*x_004_005'): 3000000.0,\n", - " ('x_003_004*x_001_001*x_003_002', 'x_003_004*x_001_001*x_003_002'): 3000000.0,\n", - " ('x_005_003', 'x_005_003'): -4717.98168086132,\n", - " ('x_005_003*x_003_001', 'x_005_003*x_003_001'): 3000000.0,\n", - " ('x_005_002', 'x_005_002'): -3991.64390165515,\n", - " ('x_005_002*x_003_001', 'x_005_002*x_003_001'): 3000000.0,\n", - " ('x_005_002*x_003_005', 'x_005_002*x_003_005'): 3000000.0,\n", - " ('x_003_003*x_003_004', 'x_003_003*x_003_004'): 3000000.0,\n", - " ('x_005_003*x_003_005', 'x_005_003*x_003_005'): 3000000.0,\n", - " ('x_003_002*x_003_003*x_001_001', 'x_003_002*x_003_003*x_001_001'): 3000000.0,\n", - " ('x_005_001', 'x_005_001'): -2403.9852161336976,\n", - " ('x_005_001*x_003_005', 'x_005_001*x_003_005'): 3000000.0,\n", - " ('x_003_001*x_005_001', 'x_003_001*x_005_001'): 3000000.0,\n", - " ('x_003_003*x_003_002', 'x_003_003*x_003_002'): 3000000.0,\n", - " ('x_003_001*x_003_005*x_001_001', 'x_003_001*x_003_005*x_001_001'): 3000000.0,\n", - " ('x_003_004*x_003_002*x_003_005', 'x_003_004*x_003_002*x_003_005'): 3000000.0,\n", - " ('x_003_004*x_003_002*x_003_003*x_001_001',\n", - " 'x_003_004*x_003_002*x_003_003*x_001_001'): 3000000.0,\n", - " ('x_006_001', 'x_006_001'): 222.71024659677462,\n", - " ('x_006_002', 'x_006_002'): 853.5837584996717,\n", - " ('x_006_003', 'x_006_003'): 3339.8205782238333}" + "
" ] }, - "execution_count": 113, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "net.qubo.qubo_dict.to_qubo()[0]" + "import matplotlib.pyplot as plt\n", + "plt.plot(res.energies[:], lw=4, label=\"QUBO Energy\")\n", + "plt.axline((0, eref[0]), slope=0, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", + "plt.grid(which='both')\n", + "plt.ylabel('Energy', fontsize=14)\n", + "plt.xlabel('Iterations', fontsize=14)\n", + "\n" ] }, { - "cell_type": "code", - "execution_count": 125, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "target_graph = dnx.pegasus_graph(6)\n", - "embedding = find_embedding(net.qubo.qubo_dict.to_qubo()[0], target_graph)" + "We can also plot the reference solution and the QUBO solution for visual inspection" ] }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'x_004_004': [217, 218, 632, 634, 234, 633],\n", - " 'x_002_001': [647, 228, 229],\n", - " 'x_002_001*x_004_004': [606, 607, 609, 238, 608],\n", - " 'x_004_003': [584, 583, 178, 581, 582],\n", - " 'x_002_001*x_004_003': [162, 562, 648, 263, 262, 563, 564],\n", - " 'x_003_003': [524, 294, 293, 291, 292],\n", - " 'x_001_001': [529, 527, 528],\n", - " 'x_003_003*x_001_001': [301, 304, 303, 302],\n", - " 'x_004_005': [622, 624, 623, 212, 213],\n", - " 'x_002_001*x_004_005': [543, 157, 227, 569, 567, 568],\n", - " 'x_003_004': [271, 588, 554, 273, 272],\n", - " 'x_003_004*x_001_001': [286, 289, 287, 288],\n", - " 'x_004_002': [576, 577, 579, 578, 207],\n", - " 'x_004_001': [666, 667, 243, 244, 668, 669],\n", - " 'x_004_002*x_004_001': [167, 628, 627, 169, 168],\n", - " 'x_003_002': [338, 251, 252, 253, 599, 598, 233],\n", - " 'x_003_002*x_001_001': [284, 281, 283, 282],\n", - " 'x_003_001': [309, 484, 306, 308, 307],\n", - " 'x_003_005': [296, 519, 299, 298, 297],\n", - " 'x_003_001*x_003_005': [206, 497, 499, 261, 498],\n", - " 'x_004_002*x_002_001': [673, 629, 269, 267, 268],\n", - " 'x_002_001*x_004_001': [572, 574, 573],\n", - " 'x_004_005*x_004_003': [149, 147, 596, 148],\n", - " 'x_003_001*x_001_001': [539, 538, 232, 258, 557, 558],\n", - " 'x_001_001*x_003_005': [276, 279, 277, 278],\n", - " 'x_003_004*x_003_002': [312, 314, 559, 313],\n", - " 'x_002_001*x_004_004*x_004_001': [142, 144, 143],\n", - " 'x_004_002*x_004_004': [208, 597],\n", - " 'x_004_004*x_004_001': [134, 133],\n", - " 'x_002_001*x_004_003*x_004_005': [164, 163],\n", - " 'x_004_002*x_002_001*x_004_004': [139, 138],\n", - " 'x_004_004*x_004_005': [249],\n", - " 'x_002_001*x_004_004*x_004_003': [602],\n", - " 'x_002_001*x_004_004*x_004_005': [174, 173],\n", - " 'x_004_004*x_004_003': [586, 587],\n", - " 'x_003_002*x_003_005': [474, 472, 473],\n", - " 'x_003_003*x_003_001': [544, 327, 328],\n", - " 'x_004_001*x_004_003': [128, 129],\n", - " 'x_003_004*x_003_003*x_001_001': [494, 492, 493],\n", - " 'x_002_001*x_004_002*x_004_001': [184],\n", - " 'x_004_002*x_002_001*x_004_005': [532],\n", - " 'x_004_002*x_004_003': [118, 119],\n", - " 'x_004_001*x_004_005': [254],\n", - " 'x_004_002*x_004_005': [621, 114, 113],\n", - " 'x_004_001*x_002_001*x_004_003': [259],\n", - " 'x_004_002*x_002_001*x_004_003': [552],\n", - " 'x_004_001*x_002_001*x_004_005': [159, 158],\n", - " 'x_003_004*x_001_001*x_003_002': [479, 477, 478],\n", - " 'x_005_003': [644, 641, 201, 202, 203, 642, 643],\n", - " 'x_005_003*x_003_001': [604, 323],\n", - " 'x_005_002': [594, 593, 221, 613, 183, 591, 592, 222, 223],\n", - " 'x_005_002*x_003_001': [504, 503],\n", - " 'x_005_002*x_003_005': [548, 549],\n", - " 'x_003_003*x_003_004': [514, 513],\n", - " 'x_005_003*x_003_005': [319, 274, 664],\n", - " 'x_003_002*x_003_003*x_001_001': [509, 266, 482, 483],\n", - " 'x_005_001': [616, 191, 192, 193, 194, 619, 618, 617],\n", - " 'x_005_001*x_003_005': [614],\n", - " 'x_003_001*x_005_001': [444, 462, 270, 463],\n", - " 'x_003_003*x_003_002': [489, 487, 488],\n", - " 'x_003_001*x_003_005*x_001_001': [522],\n", - " 'x_003_004*x_003_002*x_003_005': [654],\n", - " 'x_003_004*x_003_002*x_003_003*x_001_001': [534],\n", - " 'x_006_001': [531, 646, 152, 639, 638, 637, 153],\n", - " 'x_006_002': [187, 611, 658, 657, 612, 188, 189],\n", - " 'x_006_003': [197, 651, 198, 653, 652, 199]}" + "Text(0.5, 1.0, 'Pressure')" ] }, - "execution_count": 132, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" - } - ], - "source": [ - "embedding" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -3165,15 +418,39 @@ } ], "source": [ - "dnx.draw_pegasus_embedding(target_graph, embedding, node_size=10, width=0.25)" + "import matplotlib.pyplot as plt \n", + "\n", + "fig = plt.figure(figsize = plt.figaspect(0.5))\n", + "ax1 = fig.add_subplot(121)\n", + "\n", + "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.grid()\n", + "\n", + "ax1.scatter(ref_values[:2], encoded_ref_sol[:2], c='black', s=200, label='Best solution')\n", + "ax1.scatter(ref_values[:2], sol[:2], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "\n", + "\n", + "ax1.set_xlabel('Reference Values', fontsize=12)\n", + "ax1.set_ylabel('QUBO Values', fontsize=12)\n", + "ax1.set_title('Flow Rate', fontsize=14)\n", + "\n", + "ax2 = fig.add_subplot(122)\n", + "\n", + "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "\n", + "\n", + "ax2.scatter(ref_values[2:], encoded_ref_sol[2:], c='black', s=200, label='Best solution')\n", + "ax2.scatter(ref_values[2:], sol[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "ax2.grid()\n", + "\n", + "\n", + "ax2.set_xlabel('Reference Values', fontsize=12)\n", + "ax2.set_title('Pressure', fontsize=14)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb b/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb deleted file mode 100644 index 4f4f270..0000000 --- a/docs/notebooks/qubo_poly_solver_Net0_refac.ipynb +++ /dev/null @@ -1,463 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# QUBO Solution of the hydraulics equations\n", - "In this notebook we illustrate how to solve the hydraulics equations using a pure QUBO approach. \n", - "\n", - "## Hydraulics equations\n", - "In their most basic form the hydraulics equations read:\n", - "\n", - "$$\n", - " \\sum_j q_{ij} - D_i = 0 \\newline\n", - " h_{L_{ij}} \\equiv h_i - h_j = A |q_{ij}| q_{ij}^{B-1}\n", - "$$\n", - "\n", - "where $h_i$ is the head pressure at node $i$, $A$ the resistance coefficient and $B$ the flow exponent. \n", - "Several approximations have been developed for define $A$ and $B$. The popular Hazen-Williams (HW) approximation uses $B=1.852$. The HW is therefore not suited for a QUBO formulation that requires integer exponents in the formulation of the objective function. In contrast, the Chezy-Manning (CM) and Darcy-Weisbach (DW) approximation use $B=2$. We have implemented DW and CM hydraulics models that can found under `wntr_quantum/sim/models/`.\n", - "\n", - "In these forms the hydraulics equation can be seen as a system of non-linear equations with integeer power of the unknown: \n", - "\n", - "$$\n", - "F(q_{ij}, h_i)=0\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " ## Solving non linear systems with a QUBO approach\n", - " \n", - " We closely following an approach developed in this [http://dx.doi.org/10.1038/s41598-019-46729-0](paper) to solve the non linear system. \n", - " \n", - " \n", - "The method proposes to solve a non-linear system, given by $F(X) = 0$ by first decomposing the system of equations as a sum of tensor products:\n", - "\n", - "$$\n", - " F_i = P_i^{(0)} + \\sum_j P_{ij}^{(1)}x_j + \\sum_{jk} P_{ijk}^{(2)}x_j x_k + \\sum_{jkl} P_{ijkl}^{(3)}x_j x_k x_l = 0 \n", - "$$\n", - "\n", - "To find the solution of the system one can then minimise the residual sum of squares\n", - "\n", - "$$\n", - "\\chi^2 = \\left[ P^{(0)} + P^{(1)} X + P^{(2)} X^2 + P^{(3)} X^3 + ... \\right]^2\n", - "$$\n", - "\n", - "By encoding all the variables as binary expansions we obtain a high order boolean polynomial. To solve this problem with a QUBO formalism, the high order terms have to be quadratized by introducing additional binary variables and appropriate terms in the loss function. The resulting QUBO problem can then be solved using either classical simulated annealing or quantum annealers alike." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example\n", - "\n", - "We demonstrate in the following how to us our software to solve the hydraulics equations with a QUBO approach.\n", - "\n", - "### Reference Solution\n", - "\n", - "We first define the problem and solve it classically to obtain a benchmark solution" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "metadata": {} - }, - "outputs": [], - "source": [ - "import wntr\n", - "inp_file = './networks/Net0.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We solve the problem using the default `EPANET` simulator " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# solve the problem\n", - "sim = wntr.sim.EpanetSimulator(wn)\n", - "reference_results = sim.run_sim()\n", - "\n", - "# Plot results on the network\n", - "pressure_at_5hr = reference_results.node['pressure'].loc[0, :]\n", - "flow_at_5hr = reference_results.link['flowrate'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, link_attribute=flow_at_5hr, \n", - " node_attribute=pressure_at_5hr, \n", - " node_size=500, \n", - " link_width=5, \n", - " node_labels=True,\n", - " link_cmap=plt.cm.cividis)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We extract the values of the pressure and flows for future use" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np \n", - "ref_pressure = reference_results.node['pressure'].values[0][:2]\n", - "ref_rate = reference_results.link['flowrate'].values[0]\n", - "ref_values = np.append(ref_rate, ref_pressure)\n", - "ref_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### QUBO Polynomial Solver\n", - "\n", - "We now show how to solve the problem using the QUBO polynomial solver included in `wntr_quantum`. We start with redefining the water network." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "wn = wntr.network.WaterNetworkModel(inp_file)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The unkown of the problem can take continuous values and therefore must be encoded using several qubits before being used in a QUBO formulation. We use here the encoding implemented in our library `qubops`. We use these encoding schemes to instantiate the polynomial solver. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 0.000000 => 200.000000 (res: 1.574803)\n", - "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.031496)\n" - ] - } - ], - "source": [ - "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.encodings import PositiveQbitEncoding\n", - "\n", - "nqbit = 7\n", - "step = (4./(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", - "\n", - "nqbit = 7\n", - "step = (200/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", - "\n", - "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, head_encoding=head_encoding)\n", - "net.verify_encoding()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then solve the QUBO equations classically. This gives us: a reference solution, the best possible encoded solution, the total encoded solution including all slack variables and the QUBO energy of the solution." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - } - ], - "source": [ - "ref_sol, encoded_ref_sol, bin_rep_sol, eref, cvgd = net.classical_solution()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initial sample for the QUBO optimization \n", - "\n", - "Before minimizing the energy of the QUBO problem we need to define the initial configuration of the binary variables in the QUBO problem. We have implemented two different ways to obtain an initial sample that respects all the conditions imposed by the quadratization constraings of the polynomial qubo solver. \n", - "\n", - "We can for example create a completely random sample that simply ensure that quadratization constraints are respected" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.simulated_annealing import generate_random_valid_sample\n", - "x = generate_random_valid_sample(net)\n", - "x0 = list(x.values())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively we can modify the solution calculated in `.classical_solution()`. This can be useful when one wants to reuse exact values of the flows or pressure" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", - "x = modify_solution_sample(net, bin_rep_sol, modify=['flows', 'heads'])\n", - "x0 = list(x.values())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Temperature scheduling for the Simulated Annealing optimization\n", - "\n", - "One important parameters of the simulated Annealing process is the the so-called temperature schedule. This schdule defines the acceptance probability of the new samples that increase the QUBO energy. While high temperature that leads to accepting samples that increase energy is usefull to escape local minima the temperature must be decreased in order to converge towards a minima. \n", - "\n", - "The temperature schedule usually starts with high temperature values that allows to explore the energy landscape but progressively decrease the tempearture in order for the optimization to converge. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "num_temp = 2000\n", - "Tinit = 1E1\n", - "Tfinal = 1E-1\n", - "Tschedule = np.linspace(Tinit, Tfinal, num_temp)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", - "Tschedule = np.append(Tschedule, np.zeros(1000))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then use the `solve()` method of the qubo polynomial solver to obtain a solution of the problem" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 4000/4000 [00:05<00:00, 675.39it/s]\n" - ] - } - ], - "source": [ - "net.step_func.optimize_values = np.arange(2,6)\n", - "_, _, sol, res = net.solve(init_sample=x0, Tschedule=Tschedule, save_traj=True, verbose=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can plot the evoluion of the QUBO energy along the optimization path" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0, 'Iterations')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.plot(res.energies[:], lw=4, label=\"QUBO Energy\")\n", - "plt.axline((0, eref[0]), slope=0, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", - "plt.grid(which='both')\n", - "plt.ylabel('Energy', fontsize=14)\n", - "plt.xlabel('Iterations', fontsize=14)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also plot the reference solution and the QUBO solution for visual inspection" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Pressure')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "\n", - "fig = plt.figure(figsize = plt.figaspect(0.5))\n", - "ax1 = fig.add_subplot(121)\n", - "\n", - "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.grid()\n", - "\n", - "ax1.scatter(ref_values[:2], encoded_ref_sol[:2], c='black', s=200, label='Best solution')\n", - "ax1.scatter(ref_values[:2], sol[:2], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "\n", - "\n", - "ax1.set_xlabel('Reference Values', fontsize=12)\n", - "ax1.set_ylabel('QUBO Values', fontsize=12)\n", - "ax1.set_title('Flow Rate', fontsize=14)\n", - "\n", - "ax2 = fig.add_subplot(122)\n", - "\n", - "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "\n", - "\n", - "ax2.scatter(ref_values[2:], encoded_ref_sol[2:], c='black', s=200, label='Best solution')\n", - "ax2.scatter(ref_values[2:], sol[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "ax2.grid()\n", - "\n", - "\n", - "ax2.set_xlabel('Reference Values', fontsize=12)\n", - "ax2.set_title('Pressure', fontsize=14)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/noisy_vqls.ipynb b/docs/notebooks/sandbox/noisy_vqls.ipynb similarity index 100% rename from docs/notebooks/noisy_vqls.ipynb rename to docs/notebooks/sandbox/noisy_vqls.ipynb diff --git a/docs/notebooks/noisy_vqls_solver.ipynb b/docs/notebooks/sandbox/noisy_vqls_solver.ipynb similarity index 100% rename from docs/notebooks/noisy_vqls_solver.ipynb rename to docs/notebooks/sandbox/noisy_vqls_solver.ipynb diff --git a/docs/notebooks/sandbox/qubo_poly_solver.ipynb b/docs/notebooks/sandbox/qubo_poly_solver.ipynb new file mode 100644 index 0000000..124885d --- /dev/null +++ b/docs/notebooks/sandbox/qubo_poly_solver.ipynb @@ -0,0 +1,3200 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the system " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "metadata": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "# Create a water network model\n", + "inp_file = './networks/Net0.inp'\n", + "# inp_file = './networks/Net2LoopsDW.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n", + "\n", + "# Graph the network\n", + "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the original Cholesky EPANET simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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NCPD364aWEREpxyhqYRStLycyip49q1nrI0CnsfzlQis69qUjLy/P7PebNm1CeHg4ioqKMGHCBNP1wMBAREZGOt7YH7htj9fPzwdxgyw/uCRJOHHqrMItIiJSngS5iVUaSO77z7xd2iZRCVZSW5mGhgaz1NzcbNe96+vrAQB9+vQxu75lyxaEhYVhxIgRWLJkCa5evepYmx0qrTJJsZxgRUTezduHmu2Z1TxgwACEhISY0ooVK2zeVxRFPP744xg/fjxGjBhhun7ffffhnXfeweeff44lS5bg7bffxq9//WuH2uy2Q81A25Kif3x2wGIelxQRkTcQRS1EmaFm0cOHmn10Any0lr9c+BjbrldVVSE4ONh03c/P9mvIhQsX4tixY/jqq6/Mrj/44IOmX48cORJRUVGYMmUKysrKEBsba1eb3TvwcmYzEXk5SYJsr9bDN65qW05kJfBq0HY9ODjYLPDakpOTg48++ghffPEF+ve3PpEXAFJTUwEApaWl3hF4eVgCEXk7W8PJ3jLUbDHPwS8dkiThkUcewYcffoiCggIMHjzY5meKi4sBAFFRUXbX49bveBNlAu/JinMwGGyf2kFE5M5EUQOjTOrMlpHr1q1DTEwM/P39kZqaioMHD8qW37FjBxITE+Hv74+RI0di165dZvlz587tsPY1MzPT4r2am5sxevRoCIJgCmpytNbW8PoI0Dq4ZeTChQvxzjvvYOvWrQgKCoJer4der8e1a9cAAGVlZVi+fDmKiopQUVGBf/7zn5g9ezYmTJiAUaNG2V2PWwfeHoH+iOkfbjHPaBRRevq8wi0iIlKWBI3N5Ijt27cjNzcXy5Ytw+HDh5GcnIyMjAzU1NRYLL9v3z7MmjUL8+bNw5EjR5CVlYWsrCwcO3bMrFxmZqbZ2td3333X4v2efPJJREdb34v/RtZnNLclR7z22muor6/HxIkTERUVZUrbt28H0Ha272effYapU6ciMTERTzzxBO69917861//cqgetx5qBtpmNlecsfw/xHelVRgWP1DhFhERKcfWQQiO9nhXrVqF+fPnIzs7GwCwfv16/Pvf/8aGDRvwu9/9rkP5NWvWIDMzE4sXLwYALF++HLt378batWuxfv16Uzk/Pz+ba18//vhjfPrpp/jggw/w8ccf29VeH50AHys7VPlY2UrSGsnGC/EBAwZg7969Dt3TErfu8QLcs5mIvJu9y4nsWctqMBhQVFSE9PR00zWNRoP09HQUFhZarL+wsNCsPABkZGR0KF9QUIDw8HAkJCTg4YcfRm1trVl+dXU15s+fj7fffhuBgfYf6+qOhySotFn2kzulqKSUM5uJyLPZG3jtWct68eJFGI1GREREmF2PiIiAXq+3WL9er7dZPjMzE2+99Rby8/OxcuVK7N27F9OmTYPRaPzhGSTMnTsXCxYswNixYx16/vZZzdaSGrn9UDN7vETkzdonUcnlA51by+osM2fONP165MiRGDVqFGJjY1FQUIApU6bglVdewZUrV7BkyRKH763VSdDqLA8Ra6HOtVRu3+OVOyzhxKmzaG01KtgaIiJl2dvjbV/L2p4sBd6wsDBotVpUV1ebXa+urrb6fjYyMtKh8gAwZMgQhIWFobS0FACwZ88eFBYWws/PDzqdDnFxcQCAsWPHYs6cObLPL2jkkxqptFn2CwnqgeiIPhbzmg0tKK+qtphHROQJ2idXySV7+fr6IiUlBfn5+T+5v4j8/HykpaVZ/ExaWppZeQDYvXu31fIAcObMGdTW1prWvr788sv45ptvUFxcjOLiYtNypO3bt+P//u//ZNus0UqySY3cfqgZaBtuPld9yWJeSVkV4gfbPzWdiMidOHsDjdzcXMyZMwdjx47FLbfcgtWrV6Opqck0y3n27Nno16+f6R3xY489hjvuuAMvvfQS7rzzTmzbtg2HDh3C66+/DgBobGzEc889h3vvvReRkZEoKyvDk08+ibi4OGRkZAAABg40X33Ss2dPAEBsbKzNnaMEjQSNlZ0yBEd30FCI2/d4Ae5gRUTeS0L7tpFWkoP3mzFjBv785z9j6dKlGD16NIqLi5GXl2eaQFVZWYnz53/cI2HcuHHYunUrXn/9dSQnJ+P999/Hzp07TQcLaLVaHD16FL/4xS8wdOhQzJs3DykpKfjyyy+d8p5ZEGSGmtU5t8pDerwyM5t5WAIReTJR0kCUZNbxyuRZk5OTg5ycHIt5BQUFHa5Nnz4d06dPt1g+ICAAn3zyiUP1x8TE2FxT206jk6CxMrlKo9KNqj0j8MZZ3ySDhyUQkUezdfSfN+/VrNIxXY8IvHJDzSVlZyCKIjRq/RsgIuoCbz+dSBAkCIKVd7xWrnc3j4hGfUNDENbH8pFPTVevo+r8RYVbRESkDKMo2EyerH2o2VpSI48IvICNs3m5gxUReSh71/F6Kq7j7Uac2UxE3kgUBZvJk2l1P+5e1TF1d+ssU2mzHCe/dWSlgi0hIlKOBAESZN7xyuR5AgEy73hVumWk5wReHpZARF7I2RtouBu5IWW1DjV7TuC1cViCJEkQ1Lqamoiok2wNJ3v6ULNG5pAEjajOHq9Kvw84Liq8D4J7Wj7Dsa6hCfoLlxVuERGR63FylSSb1MhjAq8gCBxuJiKv4+2Tq9zxkASPCbwAz+YlIu/j7T3e9p2rrCU18ph3vACXFBGR9xF/SHL5nkxuSFmtQ80eFXjZ4yUibyPZOHNXcuA8Xnck6AQIPpZ79YJKh9k9K/DKvuNl4CUiz9N+LKBcvicTNAIEjZXAa+V6d/Oor0IDo/siMMDy+Y41tfW4eKlB4RYREbmWt7/jhVYjn1RIna3qJI1Gg8QhcicVsddLRJ7F22c1Cz4CBB+NlaTOZ/eowAtwBysi8i6SHcmjaQT55IAVK1bg5ptvRlBQEMLDw5GVlYUTJ06Ylbl+/ToWLlyI0NBQ9OzZE/feey+qq6sda7JDpd0AZzYTkTfx+h6vzlpvVwNB51iI27t3LxYuXIj9+/dj9+7daGlpwdSpU9HU1GQq89vf/hb/+te/sGPHDuzduxfnzp3DPffc41A9HjW5CuDMZiLyLt6+V7Psu1wH3/Hm5eWZ/X7Tpk0IDw9HUVERJkyYgPr6evztb3/D1q1bMXnyZADAxo0bkZSUhP379+PWW2+1qx6P6/FyZjMReRNJsp08WfusZmsJABoaGsxSc3OzXfeur68HAPTp0wcAUFRUhJaWFqSnp5vKJCYmYuDAgSgsLLS7zR4XeIcMiISvr+WO/Bl9LRquXFW4RUREriNKgs3k0Xw18gnAgAEDEBISYkorVqyweVtRFPH4449j/PjxGDFiBABAr9fD19cXvXr1MisbEREBvV5vd5M9bqhZp9NiaEw/HPv+tMX8krIqpI5OULhVRESu4e1Dzfas462qqkJwcLDpup+f5WWnP7Vw4UIcO3YMX331lXMa+hMe1+MFgGFxMkuKOLOZiDyIKNpOHk2nBXysJJ0WABAcHGyWbAXenJwcfPTRR/j888/Rv/+P8SQyMhIGgwF1dXVm5aurqxEZGWl3kz0z8MYPtJrHCVZE5Ena3uPKbaDR3S10LUEryCZHSJKEnJwcfPjhh9izZw8GDx5slp+SkgIfHx/k5+ebrp04cQKVlZVIS0uzux6PG2oGuKSIiLyHrQlUnh54ZdfrOriOd+HChdi6dSv+8Y9/ICgoyPTeNiQkBAEBAQgJCcG8efOQm5uLPn36IDg4GI888gjS0tLsntEMeGjglVtSVFLGoWYi8hySjQlUHv+O94c1uxbzWh0b1H3ttdcAABMnTjS7vnHjRsydOxcA8Je//AUajQb33nsvmpubkZGRgVdffdWhejxyqDk+JhpaK+u3yquqcfWafVPJiYjUzhXLidatW4eYmBj4+/sjNTUVBw8elC2/Y8cOJCYmwt/fHyNHjsSuXbvM8ufOnQtBEMxSZmamKb+iogLz5s3D4MGDERAQgNjYWCxbtgwGg8F2Y524V7MkSRZTe9AFAH9/f6xbtw6XLl1CU1MT/v73vzv0fhfw0MDr5+eD2IGW/yAkScKJU2cVbhERkWsYRcFmcsT27duRm5uLZcuW4fDhw0hOTkZGRgZqamoslt+3bx9mzZqFefPm4ciRI8jKykJWVhaOHTtmVi4zMxPnz583pXfffdeUd/z4cYiiiL/+9a/49ttv8Ze//AXr16/HU089ZbO9bccCWtu5Sp29fY8MvICtHawqFWwJEZHrSBBsJkesWrUK8+fPR3Z2NoYNG4b169cjMDAQGzZssFh+zZo1yMzMxOLFi5GUlITly5djzJgxWLt2rVk5Pz8/REZGmlLv3r1NeZmZmdi4cSOmTp2KIUOG4Be/+AUWLVqEv//977YbrBXkkwp5ZeDlkiIi8hT2DjXbs3uTwWBAUVGR2c5MGo0G6enpVndmKiwsNCsPABkZGR3KFxQUIDw8HAkJCXj44YdRW1sr+1z19fWmHaNkOfGQBKV4bOBN4p7NROQFRMnGIQk/TK6yZ/emixcvwmg0IiIiwuy63M5Mer3eZvnMzEy89dZbyM/Px8qVK7F3715MmzYNRqPR4j1LS0vxyiuv4KGHHrL5/IKPVjapkUfOagZ4WAIReQd7lxN1ZvcmZ5k5c6bp1yNHjsSoUaMQGxuLgoICTJkyxazs2bNnkZmZienTp2P+/Pm2b+7E5URK8dgeb2JsfwiC5T/00tPnYTC0KNwiIiLns3eo2Z7dm8LCwqDVajucLyu3M1NkZKRD5QFgyJAhCAsLQ2lpqdn1c+fOYdKkSRg3bhxef/11ex4f0Gjkkwqps1VOEBjgh5j+4RbzjEYRJyvOK9wiIiLnc+YhCb6+vkhJSTHbmUkUReTn51vdmSktLc2sPADs3r1bdienM2fOoLa2FlFRUaZrZ8+excSJE5GSkoKNGzdCY2/Q1P6wNaSlpFXnULPHBl6AO1gRkedz9jre3NxcvPHGG9i8eTNKSkrw8MMPo6mpCdnZ2QCA2bNnY8mSJabyjz32GPLy8vDSSy/h+PHjePbZZ3Ho0CHk5OQAABobG7F48WLs378fFRUVyM/Px9133424uDhkZGQA+DHoDhw4EH/+859x4cIF6PV6+078ccMer8e+4wXa3vPuKiiymFdSxsBLRO6vfRKVXL4jZsyYgQsXLmDp0qXQ6/UYPXo08vLyTBOoKisrzXqj48aNw9atW/HMM8/gqaeeQnx8PHbu3Gk6Sk+r1eLo0aPYvHkz6urqEB0djalTp2L58uWm4e7du3ejtLQUpaWlZocSAG17L8jS/XgYgsU8FfLswBsvM8HqJAMvEbk/6Yckl++onJwcU4/1RgUFBR2uTZ8+HdOnT7dYPiAgAJ988olsfXPnzjXbHcohGsF6z1alk6s8OvAmxXJmMxF5Nh6SIDOkzKFm5SXJnMv7ffk5tLYaoVPpUAQRkT0kG0PNkoNDzW7HDYea1fl1wElCgnqgX2SoxbxmQwvKq6ot5hERuQvJjuTR3HBylTpb5USc2UxEnkwUbSdPJmi0ELRWkoY93m7BHayIyJNJkmAzeTQ37PF69DtewNZhCQy8ROTeOLnK/baM9PjAy8MSiMiTiVJbksv3aG44ucrjA69sj7fsDERRtH9rMiIilWGP1/3W8Xp8xAnrE4y+fUIs5l291oyq8xcVbhERkfPwHa/7veNVZ6ucjDtYEZGnEvHjcLPF1N0NdDVrByTIDUF3M68IvFxSRESeytmHJLgdQaa3K6gzxHn8O17A1nteBl4icl+21up6+jpeTq5SKdm1vBxqJiI3JkGABJktI2XyPIIg07Nlj7f7yO3Z/F3pGUiSBEHw8P85icgjef2sZjcMvOpslZNFhfdBSFAPi3n1V5qgv3BZ4RYRETmJCEgyyeNnV2m1gFZnJTk21PzFF1/grrvuQnR0NARBwM6dO83y586dC0EQzFJmZqbDTfaKwCsIAobJ9Xo53ExEbsrrD0lo7/FaSw5oampCcnIy1q1bZ7VMZmYmzp8/b0rvvvuuw032iqFmoG0Hq8IjJyzmfVdahSnjkxVuERFR13n95Kr23q21PAdMmzYN06ZNky3j5+eHyMhIh+57I6/o8QK2d7AiInJHXE5ku8fb0NBglpqbmztdXUFBAcLDw5GQkICHH34YtbW1Dt+DgRdcy0tE7otDzbYD74ABAxASEmJKK1as6FRVmZmZeOutt5Cfn4+VK1di7969mDZtGoxGo0P38aKhZr7jJSLP4/VDzYIO0FgJZULb9aqqKgQHB5su+/n5daqqmTNnmn49cuRIjBo1CrGxsSgoKMCUKVPsvo/X9HgHRvdFYIDlP+wLl+px8VKDwi0iIuo6rx9qtmOv5uDgYLPU2cB7oyFDhiAsLAylpaWONdkptbsBjUYju3Ukd7AiInfk7UPNgqCBIGitJNeGuDNnzqC2thZRUVEOfc5rAi/AHayIyPOIomAzeTSNTj45oLGxEcXFxSguLgYAlJeXo7i4GJWVlWhsbMTixYuxf/9+VFRUID8/H3fffTfi4uKQkZHhUD1e844XaFtSZA0nWBGRO+LOVc7buerQoUOYNGmS6fe5ubkAgDlz5uC1117D0aNHsXnzZtTV1SE6OhpTp07F8uXLHR669qrAyyVFRORp2o//k8v3aE5cxztx4kRIMt9UPvnkE4fuZw0D7w/Y4yUid8QeL/dqVrXBAyLg62v5u8ZZfS3qrzQp3CIioq6RIEGSZFInpletW7cOMTEx8Pf3R2pqKg4ePChbfseOHUhMTIS/vz9GjhyJXbt2meXbs8fxpUuXcP/99yM4OBi9evXCvHnz0NjYaLuxTtwyUinqbJWL6HRaJAzuZzX/OIebicjNSNKPw82WkqM93u3btyM3NxfLli3D4cOHkZycjIyMDNTU1Fgsv2/fPsyaNQvz5s3DkSNHkJWVhaysLBw7dsysnK09ju+//358++232L17Nz766CN88cUXePDBB2032ImHJCjFqwIvwOFmIvIszl7Hu2rVKsyfPx/Z2dkYNmwY1q9fj8DAQGzYsMFi+TVr1iAzMxOLFy9GUlISli9fjjFjxmDt2rVm5dr3OG5PvXv3NuWVlJQgLy8Pb775JlJTU3HbbbfhlVdewbZt23Du3Dn5BrPHq36yM5u5pIiI3Iy9gdee/YoNBgOKioqQnp5uuqbRaJCeno7CwkKL9RcWFpqVB4CMjIwO5eX2OC4sLESvXr0wduxY07X09HRoNBocOHBA/g/AicuJlOJ1gZczm4nIk9gbeO3Zr/jixYswGo2IiIgwux4REQG9Xm+xfr1eb7O8rT2O9Xo9wsPDze6h0+nQp08fq/WaCDaSCqnz64ALcaiZiDyJvbOanbVfcWc4a49jS9onkVnLUyOv6/HGx0RBq7X82BVnanD1WuePiyIiUpooSTYTYN9+xWFhYdBqtaiurja7Xl1dbfUM2sjISIfKAx33OI6MjOwweau1tRWXLl2yefatCKNsUiOvC7y+vj6IG2R5X01JknDi1FmFW0RE1HnOnFzl6+uLlJQU5Ofnm66Jooj8/HykpaVZ/ExaWppZeQDYvXu31fJAxz2O09LSUFdXh6KiIlOZPXv2QBRFpKamyrZZkkTZpEZeF3gBW8PNlQq2hIioa5w9qzk3NxdvvPEGNm/ejJKSEjz88MNoampCdnY2AGD27NlYsmSJqfxjjz2GvLw8vPTSSzh+/DieffZZHDp0CDk5OQBg1x7HSUlJyMzMxPz583Hw4EF8/fXXyMnJwcyZMxEdHS3//Db+UyOve8cLtAXeDz/dbzGPM5uJyJ3IveNsz3fEjBkzcOHCBSxduhR6vR6jR49GXl6eaQJVZWUlNJof+2zjxo3D1q1b8cwzz+Cpp55CfHw8du7ciREjRgAAtFqtXXscb9myBTk5OZgyZQo0Gg3uvfdevPzyyzbbK0oiRMnykLKo0h6vVwbepDjrxwNyghURuRNnB14AyMnJMfVYb1RQUNDh2vTp0zF9+nSL5QMCAuza47hPnz7YunWrQ+0EAAkiJFgOsNaudzevDLzD4gZazeOSIiJyJ95+SIIoGWV6vOqcXOWVgTdhSD8IgmDxm2Dp6fNobm6Bn59PN7SMiMgxrujxuhO5SVScXKUigQF+iOkfbjHPaBRxssLGFmVERGoh2ZE8mDtOrvLKwAtwBysi8gz2ruP1VO1DzdaSGjHwWsAJVkTkLpy9nMjdtE+uspbUyCvf8QI2Zjaf5FpeInIPko1erae/4+XkKjfCoWYi8gT27tXsqdpeY1vZq1nZptjNawNvUqz1wHui/CxaW43Q6dR5iDIRkRm1RhglyG0NyVnN6hIcFIh+kaEW8wyGVpyqsnEUFRGRCnj95CoekuBeZIebSzncTETq5/WTq35Yx2wtqREDrxWc2UxE7sDrAy9nNbsXBl4icne2hpM9fqjZDWc1e3WPV25JUQkDLxG5AW/v8bbvVW0tOeKLL77AXXfdhejoaAiCgJ07d5rlS5KEpUuXIioqCgEBAUhPT8fJkycdbrNXB15bS4pEUZ3DFERE7bw98LaIgmxyRFNTE5KTk7Fu3TqL+X/605/w8ssvY/369Thw4AB69OiBjIwMXL9+3aF6vHqoObR3MMJDQ1BTW98h7+q1ZlSeu4CY/hHd0DIiIvt4+zpeURIgSpYDrLXr1kybNg3Tpk2zmCdJElavXo1nnnkGd999NwDgrbfeQkREBHbu3ImZM2faXY9X93gBIEnuPe9JDjcTkbrZmtWr1pm9ziJKgNFKah9qbmhoMEvNzc0O11NeXg69Xo/09HTTtZCQEKSmpqKwsNChe3l94OUOVkTkzmy94/T083hbRUE2AcCAAQMQEhJiSitWrHC4Hr2+bW+HiAjzUdCIiAhTnr28eqgZ4MxmInJv3j7UbJQEGK0MKbdfr6qqQnBwsOm6n5+fIm2zxusDb1KszGEJDLxEpHK2hpM9fai5FQJarQTeVrRdDw4ONgu8nREZGQkAqK6uRlRUlOl6dXU1Ro8e7dC9ONQcL797laf/T0tE7s3bZzUrNcw+ePBgREZGIj8/33StoaEBBw4cQFpamkP38voeb2Tf3ugV3AN1DU0d8uqvNOF8zSVER1je05mIqLtJkD8jwcPjrl1DzfZqbGxEaWmp6ffl5eUoLi5Gnz59MHDgQDz++OP4wx/+gPj4eAwePBi///3vER0djaysLIfq8frAKwgCkmL7o/DICYv535VWMfASkWpJIiC35YBKD+hxGuNPJlFZynPEoUOHMGnSJNPvc3NzAQBz5szBpk2b8OSTT6KpqQkPPvgg6urqcNtttyEvLw/+/v4O1eP1gRdoG262FnhLSs8gffxoZRtERGQnTq5qS9byHDFx4kTZ14uCIOD555/H888/79iNb8DAC2BY3ECreZxgRURqJncQfHu+J3PmBhpKYeAFlxQRkfsSbQw1e/rOty1iW7KWp0YMvLB1WAI30SAi9fL2yVXu2OP1+uVEADAgKgw9Ai2/HL9wqR4XLOzlTESkBpLUNoHKaupE5F23bh1iYmLg7++P1NRUHDx4ULb8jh07kJiYCH9/f4wcORK7du2yWnbBggUQBAGrV682u/7999/j7rvvRlhYGIKDg3Hbbbfh888/t9nWVpkDEqxNuupuDLwANBqN7EYa3DqSiNRKsiM5Yvv27cjNzcWyZctw+PBhJCcnIyMjAzU1NRbL79u3D7NmzcK8efNw5MgRZGVlISsrC8eOHetQ9sMPP8T+/fsRHR3dIe/nP/85WltbsWfPHhQVFSE5ORk///nPbW7H6I7bZTLw/kA28PI9LxGplLM30Fi1ahXmz5+P7OxsDBs2DOvXr0dgYCA2bNhgsfyaNWuQmZmJxYsXIykpCcuXL8eYMWOwdu1as3Jnz57FI488gi1btsDHx8cs7+LFizh58iR+97vfYdSoUYiPj8cLL7yAq1evWgzgP9U+1GwtqRED7w/kdrDiBCsiUitRlGwmwL4TegwGA4qKisxO4NFoNEhPT7d6Ak9hYaFZeQDIyMgwKy+KIh544AEsXrwYw4cP73CP0NBQJCQk4K233kJTUxNaW1vx17/+FeHh4UhJSZF9/rbJVdaGm2U/2m0YeH/Amc1E5I7sHWq254Seixcvwmg0OnQCj16vt1l+5cqV0Ol0ePTRRy3eQxAEfPbZZzhy5AiCgoLg7++PVatWIS8vD71795Z9fnccauas5h8kxTLwEpH7sXc5UXed0FNUVIQ1a9bg8OHDEATLQ7+SJGHhwoUIDw/Hl19+iYCAALz55pu466678J///MfsUIIbGSRAZ+X5DSoNvOzx/mDwgAj4+fpYzDtXfQn1Vzru5UxE1N3s7fG2n9DTniwF3rCwMGi1WlRXV5tdr66uNp3Oc6PIyEjZ8l9++SVqamowcOBA6HQ66HQ6nD59Gk888QRiYmIAAHv27MFHH32Ebdu2Yfz48RgzZgxeffVVBAQEYPPmzfLPL9PbVeuuXQy8P9DptEgY0s9qPtfzEpEaOXNyla+vL1JSUsxO4BFFEfn5+VZP4ElLSzMrDwC7d+82lX/ggQdw9OhRFBcXm1J0dDQWL16MTz75BABw9epVAG3vk39Ko9FAtLEDSPuWkdaSGnGo+SeSYvvj6PEKi3nflVbh1psSlG0QEZENtt5lOvqeMzc3F3PmzMHYsWNxyy23YPXq1WhqakJ2djYAYPbs2ejXr5/pHfFjjz2GO+64Ay+99BLuvPNObNu2DYcOHcLrr78OoG3iVGio+UEzPj4+iIyMREJC27+paWlp6N27N+bMmYOlS5ciICAAb7zxBsrLy3HnnXfKttcgAlprQ80qnVzFwPsT8mfz8j0vEamQrV6tg4F3xowZuHDhApYuXQq9Xo/Ro0cjLy/PNIGqsrLSrGc6btw4bN26Fc888wyeeuopxMfHY+fOnRgxYoTddYaFhSEvLw9PP/00Jk+ejJaWFgwfPhz/+Mc/kJycLPtZuS8enFzlBjizmYjcjStOJ8rJyUFOTo7FvIKCgg7Xpk+fjunTp9t9/4qKig7Xxo4daxp6doQzTydSCgPvTzDwEpG7cfZQs7tplTkkoVWlQ82cXPUTcYOioNVa/iM5ffYCmq5eV7hFRETynL1zlbtxx8lVDLw/4evrg/iYjnuIAm3rzE6cOqtwi4iI5NnaQMLTe7wGUZBNasTAewMONxORO/H2Hq87fungO94b8LAEInInrphc5U44ucoD8LAEInInXj+5ygi0GK3nqRED7w041ExE7oQ9XvZ43V7CkH4QBAGShf9bS0/r0dzcAj8/y3s6ExEpzdt7vC0SoLGybKhFpc/OyVU3CPD3w+ABERbzRFHEyYpzCreIiMg6CTYmV3V3A12My4k8BIebichdSHYkT8bA6yHkZjYz8BKRmogiYJRJNg73cXvtO1dZSo7uXPXss89CEASzlJiY6PQ28x2vBXI9Xi4pIiI14eQq506uGj58OD777DPT73U654dJBl4L5JcU8VxeIlIPW8PJHh53IYoCRCs7VFm7Lken0yEyMrKrzZLFoWYL5IaaT5SfRataF4cRkddpDzxyyZO1tmhkEwA0NDSYpebmZqv3O3nyJKKjozFkyBDcf//9qKysdHqbGXgtCOoZiP6RoRbzWlpacapKr3CLiIgs8/bJVfZ86RgwYABCQkJMacWKFRbvlZqaik2bNiEvLw+vvfYaysvLcfvtt+PKlStObTOHmq0YFj8AZ/S1FvO+O1mFoYP7KdwiIqKOjCIAmUlERg+fXGVs/bFnaykPAKqqqhAcHGy67ufnZ7H8tGnTTL8eNWoUUlNTMWjQILz33nuYN2+e09rMHq8VXFJERO6APV7bPd7g4GCzZC3w3qhXr14YOnQoSktLndpmBl4rkmJlZjaXcYIVEamDtx8L6Mr3242NjSgrK0NUVJSTWtuGgdcK2ZnNJ9njJSJ1aAuugkzq7ha6VmuLIJscsWjRIuzduxcVFRXYt28ffvnLX0Kr1WLWrFlObTPf8VohezxgWRVEUYRGw+8tRNS9RAkQvHivZmcuJzpz5gxmzZqF2tpa9O3bF7fddhv279+Pvn37OqOpJgy8VoT2DkZ4aAhqaus75F27bkDluQuI6W95T2ciIqWINiZXefrOVS0tGsDK5KoWK9et2bZtmzOaZBO7bDJkJ1hxuJmIVMDrJ1dJMu94JXWuYWbglSG/gxUDLxF1P2+fXCXJTKySVLp5CIeaZcjNbGbgJSI1MEoCJJmenVp7fc7S2qIBdJb7kNbW93Y3Bl4ZsoclcEkREamAaGM82dN7vM7eq1kJDLwybC0pkiQJgqDOv1gi8g4MvNYDrFonlqmzH64SEWG90Dukp8W8hsarOFd9SeEWERGZa5UEm8lR69atQ0xMDPz9/ZGamoqDBw/Klt+xYwcSExPh7++PkSNHYteuXVbLLliwAIIgYPXq1R3y/v3vfyM1NRUBAQHo3bs3srKybLbVnkMS1EadrVIJQRBsruclIupOImxMrnLwftu3b0dubi6WLVuGw4cPIzk5GRkZGaipqbFYft++fZg1axbmzZuHI0eOICsrC1lZWTh27FiHsh9++CH279+P6OjoDnkffPABHnjgAWRnZ+Obb77B119/jfvuu8/283NWs+fhkiIiUrNWCWiRSa0ODjWvWrUK8+fPR3Z2NoYNG4b169cjMDAQGzZssFh+zZo1yMzMxOLFi5GUlITly5djzJgxWLt2rVm5s2fP4pFHHsGWLVvg4+Nj/gytrXjsscfw4osvYsGCBRg6dCiGDRuGX/3qVzbba5Tp7RrZ43VPXFJERGpmlGwnwL4zaQ0GA4qKipCenm66ptFokJ6ejsLCQov1FxYWmpUHgIyMDLPyoijigQcewOLFizF8+PAO9zh8+DDOnj0LjUaDm266CVFRUZg2bZrFXvON3PEsYgZeG3hYAhGpWatkOwH2nUl78eJFGI1GRESY78oXEREBvd7yOeR6vd5m+ZUrV0Kn0+HRRx+1eI9Tp04BAJ599lk888wz+Oijj9C7d29MnDgRly7ZmEvjhouYOavZBvZ4iUjNmiUBgsy7zPY1vvaeSetsRUVFWLNmDQ4fPmx1FYj4w/Tjp59+Gvfeey8AYOPGjejfvz927NiBhx56yOr9tS0itForb7Jb1DmtmT1eGwZEhaFnD3+LeRcvNeCChb2ciYgUIwGSTGpfamTPmbRhYWHQarWorq42u15dXY3IyEiL1UdGRsqW//LLL1FTU4OBAwdCp9NBp9Ph9OnTeOKJJxATEwMApmP3hg0bZrqHn58fhgwZgsrKStnHF0QJGitJUGmPl4HXBkEQkDhEbmYzh5uJqBvJRV1T9LWPr68vUlJSkJ+fb7omiiLy8/ORlpZm8TNpaWlm5QFg9+7dpvIPPPAAjh49iuLiYlOKjo7G4sWL8cknnwAAUlJS4OfnhxMnTpju0dLSgoqKCgwaNEi2zVqjCG2rlWRUZ4+XQ812GBY3AIf+W2ox77vSSky4peNkASIiZdg6CsGxXl9ubi7mzJmDsWPH4pZbbsHq1avR1NSE7OxsAMDs2bPRr18/0zvixx57DHfccQdeeukl3Hnnndi2bRsOHTqE119/HQAQGhqK0NBQszp8fHwQGRmJhIQEAG298QULFmDZsmUYMGAABg0ahBdffBEAMH36dNn2aoyAxmj5GTVGhx5dMQy8drC1gxURUbeRJECS6dk50OMFgBkzZuDChQtYunQp9Ho9Ro8ejby8PNMEqsrKSrOzyMeNG4etW7fimWeewVNPPYX4+Hjs3LkTI0aMcKjeF198ETqdDg888ACuXbuG1NRU7NmzB71795b9XPuwsrU8NRIkycG/FS/0z88O4O6H/mgxL318Mna/9bzCLSIib9fQ0ICQkBBg+G8Ara/1gkYD8O2bqK+vN5tc5e7an39Cznbo/AItlmltvoov1s5Q3bOzx2uHYXEDreZxZjMRdStJtNHjVed7Tmdxxx4vA68dBg8Ih5+vD5oNLR3yzlVfQl1DI3oFW97TmYjIpaRWQJKZJyu1KteWbqBrFaHTWPly0arOLx2c1WwHrVaLRLk9m0s5s5mIukl7j1cuebIflg1ZSmrdQIOB107yhyUw8BJRN3HiciJ3ZG0Nr9wQdHfjULOdeFgCEamSlw81a1tEaAXLvXpRpTtXMfDaiVtHEpEqOXk5kbvRiCI0Vk68t3a9uzHw2onn8hKROomQP3VXncHHWTir2YPFDYqCTqdFa2vHrVAqztSg6ep19Ai0vKczEZHLiEZAkBlqFlW6fZOTaFtlhpo5q9m9+fr6ID4mymr+cU6wIqLu4OWzmp09uWrdunWIiYmBv78/UlNTcfDgQee32el39GA8m5eI1McISDIJnt3j1bWIsskR27dvR25uLpYtW4bDhw8jOTkZGRkZqKmpcWqbGXgdIDuzmROsiKg7eHmPFyJk1vE6dqtVq1Zh/vz5yM7OxrBhw7B+/XoEBgZiw4YNTm0y3/E6gEuKiEh1jC3yM5dFz15OZDRcRauVIWVj6zUAbfs6/5Sfn1+H84gNBgOKioqwZMkS0zWNRoP09HQUFhY6tc0MvA5IiuPMZiJSB19fX0RGRkJfudtm2cjISPj6yhyk4Iban/+DTx+XLdezZ08MGGDeaVq2bBmeffZZs2sXL16E0Wg0ncLULiIiAsePH3dGk00YeB2QMKQfBEGApQOdSk/r0dzcAj8/n25oGRF5G39/f5SXl8NgMNgs6+vrC39/z1p1Ye/zS5IEQRDMrt3Y21UaA68DAvz9MGRABMoq9R3yRFHE9xVnMTIhRvmGEZFX8vf397iA6ghnPn9YWBi0Wi2qq6vNrldXVyMyMtIpdbTj5CoHye1gxcMSiIjck6+vL1JSUpCfn2+6Jooi8vPzkZaW5tS6GHgdJLekiDObiYjcV25uLt544w1s3rwZJSUlePjhh9HU1ITs7Gyn1sOhZgdxSRERkWeaMWMGLly4gKVLl0Kv12P06NHIy8vrMOGqqwTJ0kwhsuo/R0/ill8uspg3PH4gjuW9onCLiIjInXCo2UGJQ/pZzfu+4pzFvZyJiIjaMfA6KKhnIAZEhVnMa2lpRVnleYVbRERE7oSBtxO4gxUREXUWA28nyO9gxSVFRERkHQNvJwyLG2g1jzObiYhIDgNvJ3BJERERdRYDbyfIDTUfLzsDUfTwY7iIiKjTGHg7oU+vIESE9bKYd+26AafPXlC2QURE5DYYeDuJw81ERNQZDLydJH9YAgMvERFZxsDbSUmx1t/zssdLRETWMPB2EoeaiYioMxh4O2lYvPW1vCWlZ8CzJ4iIyBIG3k4KDw1B75CeFvMaGq/iXPUlhVtERETugIG3kwRB4HAzERE5jIG3C3hYAhEROYqBtwvkD0tg4CUioo4YeLuAQ81EROQoBt4uYOAlIiJHMfB2Qf+oMPTs4W8xr/byFVyorVe4RUREpHYMvF0gCAKSYtnrJSIi+zHwdhGHm4mIyBEMvF0kF3h5WAIREd2IgbeL5JYUscdLREQ3YuDtIg41ExGRIxh4uyimfzj8/Xwt5p2vuYy6hkaFW0RERGrGwNtFWq0WCUP6Wc0vKT2jYGuIiEjtGHidgMPNRERkLwZeJ2DgJSIiezHwOoHsYQkcaiYiop9g4HUC9niJiMheDLxOEDcoCjqd1mLe6bM1aGy6pnCLiIhIrRh4ncDHR4f4mCir+SdOnVWwNUREpGYMvE7C4WYiIrIHA6+TMPASEZE9GHidRP6wBM5sJiKiNgy8TpLEHi8REdlB190N8BRDB0dDo9FAMhoR0SIhxmBEf4MIf1GCUFuO/YteQ9/RcQhNGYqQhAEQNPzOQ6QESRTRcu4sDKWlMJw+DenaVQCAEBAA30Ex8I2Ng0+/fvyZJMUIkiRJ3d0IT9BQdg6LJz2C4efqECTKl/XrE4S42VOR8PAvEBJvffMNIuq8lvPncSVvFxrz8yFeaZAtqwkKQs9JkxE07Wfwiba+9zqRMzDwdtH1C3XY/+grKH9vL+DoH6UgIObe23HrK48gIKKPaxpI5GWM9fWofX09rn79Vad+JgPTxiH0oQXQ9urtmgaS12Pg7YLyHXtRuHANmi/Wd+k+fqHBuPXlRzBk1mQntYzIOzV9/RVq178KsUG+h2uLJigYfR58CD0n3OGklhH9iIG3k4qffwtHnt3s1HsmP/1rjFme7dR7EnmLum3vou7dLU69Z8ivZqD3/Q849Z5EnE3QCcXL33Z60AWAb/7vHRxeutHp9yXydHXbnR90AaD+ve24vOUdp9+XvBsDr4Mq/7kPR5Ztctn9v/nDO6j44AuX3Z/I01w9cAB1W50fdNvVv7cNTfu+dtn9yfsw8Dqg+VID9i34i8vrKVy4Bte7+N6YyBsYr1xB7WtrXV5P7frXYGzgzyQ5BwOvAw4uWo9r+ksur+d6TR0O/Hady+shcneXN/wNxsuXXV6PWF+HS2++4fJ6yDsw8Nrp6vlalL3zmWL1lW/7HI1VNYrVR+RuWi9dQuPezxWrr+nLL9B64YJi9ZHnYuC104nX/w2p1ahYfZJRxIm/fqRYfUTupvHTPMCo3M8kRBFXPslTrj7yWAy8dip961Pl69z8ieJ1ErmLxj353VCncqNe5Lm4V7MdrtVcRmP5eYc+86j0Jf6ENLyC/+IUGjAR0ZghxDt0j6tnL6Lp7AX06NfXoc8ReTpjXR1aq6ttlov71y4kBAXBIIrw12pwX8wgzBo0EABQfLkOS48eQ0lDA9bfnIIpkRG2662tRWvtRehCw7r8DOS9GHjtcPHQ9536nBYa/AIxOIcm1OBap+4xKXUevgny6dRniTzVhB5GvGHHlsrBPjr8e+LtAICzV6/hof8cgiQB98UMRIS/H14YPQpvlp1yqG5DaSkDL3UJA68d6r6t6NTnfAQNEtAbF6Trna47/FoLDH5Cpz9P5ImGBNs4icSCfoEBeHp4EpYfK8F9MQMRFRCAqIAAaATHfr4MlZUITL3V4fqJ2vEdrx1amzofOLvKT+SOnkQ3Cujkv1zDQ0JwqrGxS3VL1zs3ekXUjoHXHg5+I3Ymhl2ijrp1h3mB/2xS1/D/IDv4hvTotrqvaTjMTHSjK46PNAMAvqtvQGzPnl2qWxMY0KXPEzHw2qH3qCHdVneVL/+KiG50/LrjX0jPXbuGP35bgtmDB3Wpbt+YwV36PBEnV9khdIxjy4CMkggd2v5hWCLtRyMMaIWEg1INnkYK+gj+dt/rXE9f+OoYfIl+6nujBMBgs1xDSyt+VvAlWn5YTnR/zCDM+GE5UUl9A/73wH9Q39KCPdXViOnRA3+/fbzNe/rGxXW1+eTlGHjt4NerJ3qNGIy6Y+V2lT+LJoShbThqhdD52Y8hCQNwqWRTpz9P5MnOProQLadPy5YpvetnVvOSQoJROHWKQ3Xq+vWHNjjEoc8Q3YhdKTsNnTfNrnJfSufwOr7D3ej6cFT8POv/aBB5u6D/meoVdZLnESSpW+cHuo3muka8138GWq8qs7RI6++LX1Vtg38ov10TWWJsbMSZ/50DqblZkfoEX1/0/9smaIODFamPPBd7vHby69UTiQvvVqy+xAV3MegSydD27ImgO3+uWH1BmdMYdMkpGHgdcNNzcxGSONDl9QTF9cOYP/yvy+shcne9Zt0Pn/79XV6PLioKvX79gMvrIe/AwOsAnb8vbtuwGIJO67I6BK0Gt29YDF2g/TOfibyVxtcXoY8+Dmhd9zMJjQZhjz4OjR9/Jsk5GHgdFH7rMNy+8UkIGhf80QkCxr+5CBG3jXT+vYk8lH9CIsIefRxw0c9k2COPwX/YcOffm7wWJ1d1UtnWfHw5dyWkVuccxC1oNRj/5iLEz8lwyv2IvE3j3gJcXPMXwOicn0loNAh75DH0nOzYkiMiWxh4u+DioRP4MvtPnT69qF1I4kDcvvFJ9E1Nck7DiLxU88mTuPjyX9BSWdml+/j074+wR38Lv4QEJ7WM6EcMvF1kNLTgmz+8g5JXPoShvsmhz/oE90DS//cLJC+dDZ2/r4taSORdpJYW1L23HQ3//hekJsd+JoXAQAT/7E6EzJgFjS9/Jsk1GHidpKXpGk5tycfJjXmoPXwSYkurxXKCTovQm+IQnz0Nsb9Oh09PbrhO5Ari9eto2luAK5/thuFUGdBq+WcSWi18hwxBzyn/g54TJ0ETwJ9Jci0GXhcwNhtw6egpXD56Ci1X2s7u9AkKQO+RQ9B71BD2bokUJrW0wFBRDkNFBcRrbT+TmoAA+A4aBJ+YwezdkqIYeImIiBTE5UREREQKYuAlIiJSEAMvERGRghh4iYiIFMTAS0REpCAGXiIiIgUx8BIRESmIgZeIiEhBDLxEREQKYuAlIiJSEAMvERGRghh4iYiIFMTAS0REpCAGXiIiIgUx8BIRESmIgZeIiEhBDLxEREQKYuAlIiJSEAMvERGRghh4iYiIFMTAS0REpCAGXiIiIgUx8BIRESmIgZeIiEhBDLxEREQK+n/GW0KKeCprswAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "flow_at_5hr = results.link['flowrate'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, link_attribute=flow_at_5hr, \n", + " node_attribute=pressure_at_5hr, \n", + " node_size=500, \n", + " link_width=5, \n", + " node_labels=True,\n", + " link_cmap=plt.cm.cividis)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([26.477, 22.954], dtype=float32)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0][:2]\n", + "ref_pressure" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.05, 0.05], dtype=float32)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_rate" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the QUBO Polynomial Solver" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "wn = wntr.network.WaterNetworkModel(inp_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 0.000000 => 100.000000 (res: 3.225806)\n", + "Flow Encoding : -2.000000 => -0.000000 | 0.000000 => 2.000000 (res: 0.064516)\n" + ] + } + ], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 5\n", + "step = (2./(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", + "\n", + "nqbit = 5\n", + "step = (100/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", + "\n", + "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)\n", + "net.verify_encoding()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve the system classically" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1. , 1. , 0.999, 0.998])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.create_index_mapping(model)\n", + "net.matrices = net.initialize_matrices(model)\n", + "\n", + "ref_sol, encoded_ref_sol, cvgd = net.classical_solutions()\n", + "ref_sol / ref_values" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.987, 0.987, 1.003, 0.985])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoded_ref_sol/ ref_values" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "P0, P1, P2, P3 = net.matrices" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 1.766, 99.077, 0.652])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p0 = P0.reshape(\n", + " -1,\n", + ") + P1[\n", + " :, :2\n", + "].sum(-1)\n", + "p0" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.766, 1.766, 86.797, 75.168])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.convert_solution_from_si(ref_sol)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", + "from dwave.samplers import SimulatedAnnealingSampler\n", + "\n", + "sampler = SimulatedAnnealingSampler()\n", + "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", + "net.solve(model, strength=1e6, sampler=sampler, num_sweeps=10000, num_reads=1000)\n", + "sol = net.extract_data_from_model(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-9563.109226629138 True\n", + "-9562.710552453995 True\n", + "-9562.517971858382 True\n", + "-9562.497054494917 True\n", + "-9562.44983253628 True\n", + "-9562.44983253628 True\n", + "-9562.435655124485 True\n", + "-9562.36700142175 True\n", + "-9562.33702659607 True\n", + "-9562.293940618634 True\n", + "-9562.272314548492 True\n", + "-9562.245232239366 True\n", + "-9562.216201871634 True\n", + "-9562.144584052265 True\n", + "-9562.137716375291 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False\n", + "990457.5757035092 False\n", + "990464.4891519174 False\n", + "990468.9488407671 False\n", + "990474.6120955572 False\n", + "990487.9531336948 False\n", + "990490.9821678177 False\n", + "990495.7636168599 False\n", + "990584.7526462078 False\n", + "990632.2987249866 False\n", + "990673.3891029209 False\n", + "990689.5721127167 False\n", + "990690.2124982253 False\n", + "990724.6441722289 False\n", + "990753.9539984092 False\n", + "990782.6970554069 False\n", + "990850.6007880569 False\n", + "990865.1441291496 False\n", + "990865.8487880826 False\n", + "991268.7598912641 False\n" + ] + } + ], + "source": [ + "solutions,energies,statuses = net.analyze_sampleset()\n", + "for e,s in zip(energies,statuses):\n", + " print(e,s)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.scatter(ref_values, encoded_ref_sol, c='black', s=100, label='Best possible solution')\n", + "for s in solutions[1:5]:\n", + " plt.scatter(ref_values, s, s=50, lw=1, alpha=0.5, edgecolors='w', label='Sampled solution')\n", + "plt.scatter(ref_values, solutions[0], s=50, lw=1, c='blue', edgecolors='w', label='Best sampled solution')\n", + "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", + "plt.grid(which=\"major\", lw=1)\n", + "plt.grid(which=\"minor\", lw=0.1)\n", + "plt.xlabel('Reference Solution')\n", + "plt.ylabel('QUBO Solution')\n", + "# plt.legend()\n", + "# plt.xlim([0.01,0.1])\n", + "# plt.ylim([0.01,0.1])\n", + "\n", + "# plt.xlim([10,50])\n", + "# plt.ylim([10,50])\n", + "plt.loglog()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "72" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.qubo.qubo_dict.num_variables" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "999998.1773075246\n", + "-2000000.0\n", + "-2000000.0\n", + "1000005.6999586402\n", + "999996.3546150491\n", + "1000000.0\n", + "-2000000.0\n", + "-2000000.0\n", + "-0.03329864724245577\n", + "0.06659729448491154\n", + "-0.06659729448491154\n", + "0.13319458896982309\n", + "999955.6967091897\n", + "0.06659729448491154\n", + "-0.13319458896982309\n", + "0.13319458896982309\n", + "-0.26638917793964617\n", + "-2000000.0\n", + "-2000000.0\n", + "1000000.2257171796\n", + "999999.5443268812\n", + "1000000.0\n", + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "v=[]\n", + "for i in net.qubo.qubo_dict.iter_quadratic():\n", + " v.append((i[2]))\n", + " print(i[2])\n", + "\n", + "plt.hist(v)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cons:\n", + "mass_balance[J1]: ((expected_demand[J1]-flow[P1])+flow[P2])\n", + "mass_balance[D1]: (expected_demand[D1]-flow[P2])\n", + "approx_darcy_wesibach_headloss[P1]: (((((-(dw_resistance_0[P1]))-(dw_resistance_1[P1]*flow[P1]))-(dw_resistance_2[P1]*(flow[P1]**2.0)))+source_head[R1])-head[J1])\n", + "approx_darcy_wesibach_headloss[P2]: (((((-(dw_resistance_0[P2]))-(dw_resistance_1[P2]*flow[P2]))-(dw_resistance_2[P2]*(flow[P2]**2.0)))+head[J1])-head[D1])\n", + "\n", + "vars:\n", + "flow[P1]: flow[P1]\n", + "flow[P2]: flow[P2]\n", + "head[J1]: head[J1]\n", + "head[D1]: head[D1]\n", + "\n" + ] + } + ], + "source": [ + "print(model.__str__())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Embed the problem" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "import dwave_networkx as dnx\n", + "from minorminer import find_embedding\n", + "from dwave.embedding import embed_qubo, majority_vote, chain_break_frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('x_004_004', 'x_002_001'): 999996.3546150491,\n", + " ('x_002_001*x_004_004', 'x_002_001'): -2000000.0,\n", + " ('x_002_001*x_004_004', 'x_004_004'): -2000000.0,\n", + " ('x_004_003', 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", 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- -def plot_solutions(solutions, references): - fig = plt.figure(figsize=plt.figaspect(0.5)) - ax1 = fig.add_subplot(121) - - ax1.axline((0, 0.0), slope=1.10, color="grey", linestyle=(0, (2, 5))) - ax1.axline((0, 0.0), slope=1, color="black", linestyle=(0, (2, 5))) - ax1.axline((0, 0.0), slope=0.90, color="grey", linestyle=(0, (2, 5))) - ax1.grid() - - for r, sol in zip(references, solutions): - ax1.scatter( - r[:2], sol[:2], s=150, lw=1, edgecolors="w", label="Sampled solution" - ) - - ax1.set_xlabel("Reference Values", fontsize=12) - ax1.set_ylabel("QUBO Values", fontsize=12) - ax1.set_title("Flow Rate", fontsize=14) - - ax2 = fig.add_subplot(122) - - ax2.axline((0, 0.0), slope=1.10, color="grey", linestyle=(0, (2, 5))) - ax2.axline((0, 0.0), slope=1, color="black", linestyle=(0, (2, 5))) - ax2.axline((0, 0.0), slope=0.90, color="grey", linestyle=(0, (2, 5))) - - for r, sol in zip(references, solutions): - ax2.scatter( - r[2:], - sol[2:], - s=150, - lw=1, - edgecolors="w", - label="Sampled solution", - ) - ax2.grid() - - ax2.set_xlabel("Reference Values", fontsize=12) - ax2.set_title("Pressure", fontsize=14) - plt.show() - - -# Create a water network model -inp_file = "./networks/Net0_CM.inp" -# inp_file = "./networks/Net0.inp" -# inp_file = './networks/Net2LoopsDW.inp' -wn_ref = wntr.network.WaterNetworkModel(inp_file) - -# store the results -energies = [] -solutions = [] -encoded_reference_solutions = [] -prices = [] -optimal_diameters = [] - -# iterate over a bunch of confs -Nsim = 100 -for i in range(Nsim): - print("==== %d / %d ====" % (i, Nsim)) - # copy the nework - wn = deepcopy(wn_ref) - - # solve classcaly - sim = wntr.sim.EpanetSimulator(wn) - results = sim.run_sim() - - # extract ref values - ref_pressure = results.node["pressure"].values[0][:2] - ref_rate = results.link["flowrate"].values[0] - ref_values = np.append(ref_rate, ref_pressure) - ref_values - - # create qubo encoding for the flow - nqbit = 5 - step = 4.0 / (2**nqbit - 1) - flow_encoding = PositiveQbitEncoding( - nqbit=nqbit, step=step, offset=+0, var_base_name="x" - ) - - # create qubo encoding for the heads - nqbit = 7 - step = 200 / (2**nqbit - 1) - head_encoding = PositiveQbitEncoding( - nqbit=nqbit, step=step, offset=+0.0, var_base_name="x" - ) - - # create designer - pipe_diameters = [250, 500, 1000] - designer = QUBODesignPipeDiameter( - wn, - flow_encoding, - head_encoding, - pipe_diameters, - head_lower_bound=95, - weight_cost=2, - weight_pressure=0.5, - ) - - # create model - designer.create_index_mapping() - designer.matrices = designer.initialize_matrices() - ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = designer.classical_solution( - [0, 1, 0, 0, 1, 0], convert_to_si=True - ) - - # sampler - sampler = SimulatedAnnealing() - - # create the solver attribute - designer.qubo = QUBOPS_MIXED(designer.mixed_solution_vector, {"sampler": sampler}) - matrices = tuple(sparse.COO(m) for m in designer.matrices) - designer.qubo.qubo_dict = designer.qubo.create_bqm(matrices, strength=0) - # designer.add_switch_constraints(strength=0) - designer.add_pressure_equality_constraints() - - # create step - var_names = sorted(designer.qubo.qubo_dict.variables) - designer.qubo.create_variables_mapping() - mystep = SwitchIncrementalStep( - var_names, - designer.qubo.mapped_variables, - designer.qubo.index_variables, - step_size=10, - switch_variable_index=[[6, 7, 8], [9, 10, 11]], - ) - - # generate init sample - # x = modify_solution_sample(net, bin_rep_sol, modify=["flows", "heads"]) - x = modify_solution_sample(designer, bin_rep_sol, modify=["flows", "heads"]) - x0 = list(x.values()) - - # temperature schedule - num_sweeps = 5000 - Tinit = 1e3 - Tfinal = 1e-1 - Tschedule = np.linspace(Tinit, Tfinal, num_sweeps) - Tschedule = np.append(Tschedule, Tfinal * np.ones(1000)) - Tschedule = np.append(Tschedule, np.zeros(100)) - - # sample flow - mystep.optimize_values = np.arange(2, 12) - res = sampler.sample( - designer.qubo, - init_sample=x0, - Tschedule=Tschedule, - take_step=mystep, - save_traj=True, - verbose=False, - ) - mystep.verify_quadratic_constraints(res.res) - - idx_min = np.array([e for e in res.energies]).argmin() - energies.append(res.energies[idx_min]) - # idx_min = -1 - sol = res.trajectory[idx_min] - sol = designer.qubo.decode_solution(np.array(sol)) - pipe_hot_encoding = sol[3] - sol = designer.combine_flow_values(sol) - sol = designer.convert_solution_to_si(sol) - sol = sol[:4] - solutions.append(sol) - - price, diameters = designer.get_pipe_info_from_hot_encoding(pipe_hot_encoding) - prices.append(price) - optimal_diameters.append(diameters) - -data = {} -for opt, e in zip(optimal_diameters, energies): - if tuple(opt) not in data: - data[tuple(opt)] = [] - data[tuple(opt)].append(e[0]) - -vals = [] -labels = [] -for k, v in data.items(): - labels.append(k) - vals.append(v) - -width = np.array([(np.array(optimal_diameters) == l).prod(1).sum() for l in labels]) -width = 0.5 * width / np.max(width) - - -plt.violinplot(vals, widths=width) -plt.xticks(list(range(1, 1 + len(labels))), labels) -plt.grid() -plt.show() - -# plot_solutions(solutions, encoded_reference_solutions) -pickle.dump(prices, open("prices.pkl", "wb")) -pickle.dump(optimal_diameters, open("optimized_diameters.pkl", "wb")) -pickle.dump(energies, open("energies.pkl", "wb")) -# pickle.dump(qubo_results, open("qubo_results.pkl", "wb")) diff --git a/docs/notebooks/test_qubo_poly_solver.py b/docs/notebooks/test_qubo_poly_solver.py deleted file mode 100644 index 0cdf5ee..0000000 --- a/docs/notebooks/test_qubo_poly_solver.py +++ /dev/null @@ -1,205 +0,0 @@ -import wntr -import wntr_quantum -import numpy as np -import matplotlib.pyplot as plt -from copy import deepcopy - -from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver -from qubops.solution_vector import SolutionVector_V2 as SolutionVector -from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding -from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo -from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing -from qubops.qubops_mixed_vars import QUBOPS_MIXED -import sparse -from wntr_quantum.sampler.step.random_step import IncrementalStep -from wntr_quantum.sampler.simulated_annealing import modify_solution_sample - -import pickle - - -def plot_solutions(solutions, references): - fig = plt.figure(figsize=plt.figaspect(0.5)) - ax1 = fig.add_subplot(121) - - ax1.axline((0, 0.0), slope=1.10, color="grey", linestyle=(0, (2, 5))) - ax1.axline((0, 0.0), slope=1, color="black", linestyle=(0, (2, 5))) - ax1.axline((0, 0.0), slope=0.90, color="grey", linestyle=(0, (2, 5))) - ax1.grid() - - for r, sol in zip(references, solutions): - ax1.scatter( - r[:2], sol[:2], s=150, lw=1, edgecolors="w", label="Sampled solution" - ) - - ax1.set_xlabel("Reference Values", fontsize=12) - ax1.set_ylabel("QUBO Values", fontsize=12) - ax1.set_title("Flow Rate", fontsize=14) - - ax2 = fig.add_subplot(122) - - ax2.axline((0, 0.0), slope=1.10, color="grey", linestyle=(0, (2, 5))) - ax2.axline((0, 0.0), slope=1, color="black", linestyle=(0, (2, 5))) - ax2.axline((0, 0.0), slope=0.90, color="grey", linestyle=(0, (2, 5))) - - for r, sol in zip(references, solutions): - ax2.scatter( - r[2:], - sol[2:], - s=150, - lw=1, - edgecolors="w", - label="Sampled solution", - ) - ax2.grid() - - ax2.set_xlabel("Reference Values", fontsize=12) - ax2.set_title("Pressure", fontsize=14) - plt.show() - - -# Create a water network model -inp_file = "./networks/Net0_CM.inp" -inp_file = "./networks/Net0.inp" -# inp_file = './networks/Net2LoopsDW.inp' -wn_ref = wntr.network.WaterNetworkModel(inp_file) - -# store the results -energies = [] -solutions = [] -encoded_reference_solutions = [] - -# iterate over a bunch of confs -Nsim = 100 -for i in range(Nsim): - print("==== %d / %d ====" % (i, Nsim)) - # copy the nework - wn = deepcopy(wn_ref) - - # change pipe diams - # for pipe_name in wn.link_name_list: - # pipe = wn.get_link(pipe_name) - # eps = 0.9 + 0.2 * np.random.rand() - # pipe.diameter *= eps - - # solve classcaly - sim = wntr.sim.EpanetSimulator(wn) - results = sim.run_sim() - - # extract ref values - ref_pressure = results.node["pressure"].values[0][:2] - ref_rate = results.link["flowrate"].values[0] - ref_values = np.append(ref_rate, ref_pressure) - ref_values - - # create qubo encoding for the flow - nqbit = 7 - step = 4.0 / (2**nqbit - 1) - flow_encoding = PositiveQbitEncoding( - nqbit=nqbit, step=step, offset=+0, var_base_name="x" - ) - - # create qubo encoding for the heads - nqbit = 7 - step = 200 / (2**nqbit - 1) - head_encoding = PositiveQbitEncoding( - nqbit=nqbit, step=step, offset=+0.0, var_base_name="x" - ) - - # create qubosolver - net = QuboPolynomialSolver( - wn, flow_encoding=flow_encoding, head_encoding=head_encoding - ) - - # create model - model, model_updater = create_hydraulic_model_for_qubo(wn) - net.create_index_mapping(model) - net.matrices = net.initialize_matrices(model) - - # solve qubo classically - ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = net.classical_solutions() - encoded_reference_solutions.append(encoded_ref_sol) - - # sampler - sampler = SimulatedAnnealing() - - # create the solver attribute - net.qubo = QUBOPS_MIXED(net.mixed_solution_vector, {"sampler": sampler}) - matrices = tuple(sparse.COO(m) for m in net.matrices) - net.qubo.qubo_dict = net.qubo.create_bqm(matrices, strength=0) - - # create step - var_names = sorted(net.qubo.qubo_dict.variables) - net.qubo.create_variables_mapping() - mystep = IncrementalStep( - var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10 - ) - - # generate init sample - # x = modify_solution_sample(net, bin_rep_sol, modify=["flows", "heads"]) - x = modify_solution_sample(net, bin_rep_sol, modify=["flows", "heads"]) - x0 = list(x.values()) - - # compute ref energy - eref = net.qubo.energy_binary_rep(bin_rep_sol) - - # temperature schedule - num_sweeps = 2000 - Tinit = 1e1 - Tfinal = 1e-1 - Tschedule = np.linspace(Tinit, Tfinal, num_sweeps) - Tschedule = np.append(Tschedule, Tfinal * np.ones(1000)) - Tschedule = np.append(Tschedule, np.zeros(1000)) - - # sample flow + head - mystep.optimize_values = np.arange(4, 6) - res = sampler.sample( - net.qubo, - init_sample=x0, - Tschedule=Tschedule, - take_step=mystep, - save_traj=True, - verbose=False, - ) - - # sample flow - mystep.optimize_values = np.arange(2, 4) - res = sampler.sample( - net.qubo, - init_sample=res.res, - Tschedule=Tschedule, - take_step=mystep, - save_traj=True, - verbose=False, - ) - - # sample flow + head - mystep.optimize_values = np.arange(4, 6) - res = sampler.sample( - net.qubo, - init_sample=res.res, - Tschedule=Tschedule, - take_step=mystep, - save_traj=True, - verbose=False, - ) - - mystep.verify_quadratic_constraints(res.res) - # qubo_results.append(res) - - # compute final - idx_min = np.array([e for e in res.energies]).argmin() - # idx_min = -1 - energies.append(res.energies[idx_min]) - - sol = res.trajectory[idx_min] - sol = net.qubo.decode_solution(np.array(sol)) - sol = net.combine_flow_values(sol) - sol = net.convert_solution_to_si(sol) - solutions.append(sol) - - -plot_solutions(solutions, encoded_reference_solutions) -pickle.dump(solutions, open("solutions.pkl", "wb")) -pickle.dump(encoded_reference_solutions, open("encoded_reference_solutions.pkl", "wb")) -pickle.dump(energies, open("energies.pkl", "wb")) -# pickle.dump(qubo_results, open("qubo_results.pkl", "wb")) diff --git a/docs/notebooks/test_qubo_poly_solver_net2loops.py b/docs/notebooks/test_qubo_poly_solver_net2loops.py deleted file mode 100644 index 2d249c5..0000000 --- a/docs/notebooks/test_qubo_poly_solver_net2loops.py +++ /dev/null @@ -1,220 +0,0 @@ -import wntr -import wntr_quantum -import numpy as np -import matplotlib.pyplot as plt -from copy import deepcopy - -from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver -from qubops.solution_vector import SolutionVector_V2 as SolutionVector -from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding -from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo -from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing -from qubops.qubops_mixed_vars import QUBOPS_MIXED -import sparse -from wntr_quantum.sampler.step.random_step import IncrementalStep -from wntr_quantum.sampler.simulated_annealing import modify_solution_sample - -import pickle - - -def plot_solutions(solutions, references): - fig = plt.figure(figsize=plt.figaspect(0.5)) - ax1 = fig.add_subplot(121) - - ax1.axline((0, 0.0), slope=1.10, color="grey", linestyle=(0, (2, 5))) - ax1.axline((0, 0.0), slope=1, color="black", linestyle=(0, (2, 5))) - ax1.axline((0, 0.0), slope=0.90, color="grey", linestyle=(0, (2, 5))) - ax1.grid() - - for r, sol in zip(references, solutions): - ax1.scatter( - r[:2], sol[:2], s=150, lw=1, edgecolors="w", label="Sampled solution" - ) - - ax1.set_xlabel("Reference Values", fontsize=12) - ax1.set_ylabel("QUBO Values", fontsize=12) - ax1.set_title("Flow Rate", fontsize=14) - - ax2 = fig.add_subplot(122) - - ax2.axline((0, 0.0), slope=1.10, color="grey", linestyle=(0, (2, 5))) - ax2.axline((0, 0.0), slope=1, color="black", linestyle=(0, (2, 5))) - ax2.axline((0, 0.0), slope=0.90, color="grey", linestyle=(0, (2, 5))) - - for r, sol in zip(references, solutions): - ax2.scatter( - r[2:], - sol[2:], - s=150, - lw=1, - edgecolors="w", - label="Sampled solution", - ) - ax2.grid() - - ax2.set_xlabel("Reference Values", fontsize=12) - ax2.set_title("Pressure", fontsize=14) - plt.show() - - -# Create a water network model -inp_file = "./networks/Net0_CM.inp" -inp_file = "./networks/Net0.inp" -inp_file = "./networks/Net2LoopsCM.inp" -wn_ref = wntr.network.WaterNetworkModel(inp_file) - -# store the results -energies = [] -solutions = [] -encoded_reference_solutions = [] - -# copy the nework -wn = deepcopy(wn_ref) - -# change pipe diams -# for pipe_name in wn.link_name_list: -# pipe = wn.get_link(pipe_name) -# eps = 0.9 + 0.2 * np.random.rand() -# pipe.diameter *= eps - -# solve classcaly -sim = wntr.sim.EpanetSimulator(wn) -results = sim.run_sim() - -# extract ref values -ref_pressure = results.node["pressure"].values[0] -ref_rate = results.link["flowrate"].values[0] -ref_values = np.append(ref_rate, ref_pressure) -ref_values - -# create qubo encoding for the flow -nqbit = 11 -step = 15 / (2**nqbit - 1) -flow_encoding = PositiveQbitEncoding( - nqbit=nqbit, step=step, offset=+0, var_base_name="x" -) - -# create qubo encoding for the heads -nqbit = 11 -step = 500 / (2**nqbit - 1) -head_encoding = PositiveQbitEncoding( - nqbit=nqbit, step=step, offset=+500.0, var_base_name="x" -) - -# create qubosolver -net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, head_encoding=head_encoding) - -# create model -model, model_updater = create_hydraulic_model_for_qubo(wn) -net.create_index_mapping(model) -net.matrices = net.initialize_matrices(model) - -# solve qubo classically -ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = net.classical_solutions() -encoded_reference_solutions.append(encoded_ref_sol) - -# sampler -sampler = SimulatedAnnealing() - -# create the solver attribute -net.qubo = QUBOPS_MIXED(net.mixed_solution_vector, {"sampler": sampler}) -matrices = tuple(sparse.COO(m) for m in net.matrices) -net.qubo.qubo_dict = net.qubo.create_bqm(matrices, strength=0) - -# create step -var_names = sorted(net.qubo.qubo_dict.variables) -net.qubo.create_variables_mapping() -mystep = IncrementalStep( - var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=25 -) - -Nsim = 10 -for i in range(Nsim): - - print("==== %d / %d ====" % (i, Nsim)) - - # generate init sample - # x = modify_solution_sample(net, bin_rep_sol, modify=["flows", "heads"]) - x = modify_solution_sample(net, bin_rep_sol, modify=["flows", "heads"]) - x0 = list(x.values()) - - # compute ref energy - eref = net.qubo.energy_binary_rep(bin_rep_sol) - - # temperature schedule - num_sweeps = 4000 - Tinit = 1e6 - Tfinal = 1e1 - Tschedule = np.linspace(Tinit, Tfinal, num_sweeps) - Tschedule = np.append(Tschedule, Tfinal * np.ones(1000)) - - num_sweeps = 4000 - Tinit = 1e1 - Tfinal = 0 - Tschedule = np.append(Tschedule, np.linspace(Tinit, Tfinal, num_sweeps)) - Tschedule = np.append(Tschedule, Tfinal * np.ones(100)) - - # sample flow + head - mystep.optimize_values = np.arange(8, 22) - res = sampler.sample( - net.qubo, - init_sample=x0, - Tschedule=Tschedule, - take_step=mystep, - save_traj=True, - verbose=False, - ) - - # # temperature schedule - # num_sweeps = 2000 - # Tinit = 1e1 - # Tfinal = 0 - # Tschedule = np.linspace(Tinit, Tfinal, num_sweeps) - # Tschedule = np.append(Tschedule, Tfinal * np.ones(1000)) - - # # sampler flow - # mystep.optimize_values = np.arange(8, 16) - # res = sampler.sample( - # net.qubo.qubo_dict, - # init_sample=res.res, - # Tschedule=Tschedule, - # take_step=mystep, - # save_traj=True, - # ) - - # # temperature scheudule - # num_sweeps = 5000 - # Tinit = 1e2 - # Tfinal = 0 - # Tschedule = np.linspace(Tinit, Tfinal, num_sweeps) - # Tschedule = np.append(Tschedule, Tfinal * np.ones(1000)) - - # # sampler flow - # mystep.optimize_values = np.arange(16) - # res = sampler.sample( - # net.qubo.qubo_dict, - # init_sample=res.res, - # Tschedule=Tschedule, - # take_step=mystep, - # save_traj=True, - # ) - - mystep.verify_quadratic_constraints(res.res) - - # compute final - idx_min = np.array([e for e in res.energies]).argmin() - # idx_min = -1 - energies.append(res.energies[idx_min]) - - sol = res.trajectory[idx_min] - sol = net.qubo.decode_solution(np.array(sol)) - sol = net.combine_flow_values(sol) - sol = net.convert_solution_to_si(sol) - solutions.append(sol) - - -plot_solutions(solutions, encoded_reference_solutions) -pickle.dump(solutions, open("solutions.pkl", "wb")) -pickle.dump(encoded_reference_solutions, open("encoded_reference_solutions.pkl", "wb")) -pickle.dump(energies, open("energies.pkl", "wb")) -# pickle.dump(qubo_results, open("qubo_results.pkl", "wb")) diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index 07822e9..ab6fe96 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -110,6 +110,7 @@ def __init__( self.weight_pressure = weight_pressure # lower bound for the pressure + head_lower_bound = from_si(FlowUnits.CFS, head_lower_bound, HydParam.Length) self.head_lower_bound = head_lower_bound self.head_upper_bound = 1e3 # 10 * head_lower_bound # is that enough ? self.target_pressure = head_lower_bound @@ -138,13 +139,26 @@ def __init__( self.var_names = sorted(self.qubo.qubo_dict.variables) self.qubo.create_variables_mapping() + # compute the indices of the pipe diameter switch variables + self.switch_variables = self.get_switch_variables_index() + # create step function self.step_func = SwitchIncrementalStep( self.var_names, self.qubo.mapped_variables, self.qubo.index_variables, step_size=10, - switch_variable_index=[[6, 7, 8], [9, 10, 11]], + switch_variable_index=self.switch_variables, + ) + + def get_switch_variables_index(self): + """Computes the indices of the switch variables, i.e. the pipe diameter switch.""" + idx_init = self.wn.num_links * 2 + self.wn.num_junctions + idx_final = idx_init + self.num_diameters * self.wn.num_pipes + return ( + np.arange(idx_init, idx_final) + .reshape(self.wn.num_pipes, self.num_diameters) + .tolist() ) def get_dw_pipe_coefficients(self, link): @@ -304,17 +318,19 @@ def enumerates_classical_solutions(self, convert_to_si=True): tmp = [0] * self.num_diameters tmp[idiam] = 1 encoding.append(tmp) - + solutions = {} print("price \t diameters \t variables\t energy") for params in itertools.product(encoding, repeat=self.wn.num_pipes): pvalues = [] for p in params: pvalues += p price, diameters = self.get_pipe_info_from_hot_encoding(pvalues) - sol, _, bin_rep_sol, energy, _ = self.classical_solution( + sol, encoded_sol, bin_rep_sol, energy, cvg = self.classical_solution( pvalues, convert_to_si=convert_to_si ) print(price, diameters, sol, energy[0]) + solutions[tuple(diameters)] = (sol, encoded_sol, bin_rep_sol, energy, cvg) + return solutions def convert_solution_to_si(self, solution: np.ndarray) -> np.ndarray: """Converts the solution to SI. @@ -743,60 +759,62 @@ def add_pressure_constraints(self, fractional_factor=100): # print(cst) def solve( # noqa: D417 - self, strength: float = 1e6, num_reads: int = 10000, **options + self, init_sample, Tschedule, save_traj=False, verbose=False ) -> Tuple: - """Solves the Hydraulics equations. + """Sample the qubo problem. Args: - strength (float, optional): substitution strength. Defaults to 1e6. - num_reads (int, optional): number of reads for the sampler. Defaults to 10000. + init_sample (list): initial sample for the optimization + Tschedule (list): temperature schedule for the optimization + save_traj (bool, optional): save the trajectory. Defaults to False. + verbose (bool, optional): print status. Defaults to False. Returns: - Tuple: Success message + Tuple: Solver status, str, solution, SimulatedAnnealingResults """ - # create the index mapping of the variables - self.create_index_mapping() - - # compute the polynomial matrices - self.matrices = self.initialize_matrices() - - self.qubo = QUBOPS_MIXED(self.mixed_solution_vector, **options) - matrices = tuple(sparse.COO(m) for m in self.matrices) - - # create the BQM - self.qubo.qubo_dict = self.qubo.create_bqm(matrices, strength=strength) - - # add constraints for the switch - self.add_switch_constraints(strength=strength) - - # add constrants for the pressure - self.add_pressure_constraints() - - # sample - self.sampleset = self.qubo.sample_bqm(self.qubo.qubo_dict, num_reads=num_reads) + res = self.sampler.sample( + self.qubo, + init_sample=init_sample, + Tschedule=Tschedule, + take_step=self.step_func, + save_traj=save_traj, + verbose=verbose, + ) - # decode - sol = self.qubo.decode_solution(self.sampleset.lowest().record[0][0]) + # extract and decode the solution + idx_min = np.array([e for e in res.energies]).argmin() + # idx_min = -1 + sol = res.trajectory[idx_min] + sol = self.qubo.decode_solution(np.array(sol)) - # flatten - sol, hot_encoding = self.flatten_solution_vector(sol) - print(sol) + # extract the hot encoding of the pipe + pipe_hot_encoding = sol[3] - # convert back to SI + # convert the solution to SI + sol = self.combine_flow_values(sol) sol = self.convert_solution_to_si(sol) + # remove the height of the junction + for i in range(self.wn.num_junctions): + sol[self.wn.num_pipes + i] -= self.wn.nodes[ + self.wn.junction_name_list[i] + ].elevation + # load data in the AML model self.model.set_structure() self.load_data_in_model(self.model, sol) # get pipe info from one hot - self.total_pice, self.optimal_diameters = self.get_pipe_info_from_hot_encoding( - hot_encoding + self.total_price, self.optimal_diameters = self.get_pipe_info_from_hot_encoding( + pipe_hot_encoding ) # returns return ( SolverStatus.converged, "Solved Successfully", - 0, + sol, + res, + self.total_price, + self.optimal_diameters, ) diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 0d72dbc..c4424d1 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -385,11 +385,13 @@ def solve( # noqa: D417 verbose=verbose, ) - # extact the solution and convert it + # extact the solution and decode it idx_min = np.array([e for e in res.energies]).argmin() # idx_min = -1 sol = res.trajectory[idx_min] sol = self.qubo.decode_solution(np.array(sol)) + + # convert the solution to SI sol = self.combine_flow_values(sol) sol = self.convert_solution_to_si(sol) From 3bed56ffa22bcf9544cf7bd6ae25dd6fb51cd7f3 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 21 Nov 2024 15:26:56 +0100 Subject: [PATCH 89/96] test on dev --- .github/workflows/build.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 5ebbb7f..03383e9 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -4,9 +4,11 @@ on: push: branches: - main + - dev pull_request: branches: - main + - dev jobs: From 4ebd7d780832ec28c0e8278fb724656889bbcc64 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 21 Nov 2024 15:30:28 +0100 Subject: [PATCH 90/96] ruff --- wntr_quantum/design/qubo_pipe_diam.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/wntr_quantum/design/qubo_pipe_diam.py b/wntr_quantum/design/qubo_pipe_diam.py index ab6fe96..10c0900 100644 --- a/wntr_quantum/design/qubo_pipe_diam.py +++ b/wntr_quantum/design/qubo_pipe_diam.py @@ -747,7 +747,7 @@ def add_pressure_constraints(self, fractional_factor=100): for k, v in self.qubo.all_expr[istart + i]: tmp.append((k, int(fractional_factor * v))) # print(tmp) - cst = self.qubo.qubo_dict.add_linear_inequality_constraint( + _ = self.qubo.qubo_dict.add_linear_inequality_constraint( tmp, lagrange_multiplier=self.weight_pressure, label="head_%s" % i, From 244a96dc2bbada7cfd56e460e44fa3d1ef383214 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 21 Nov 2024 16:04:36 +0100 Subject: [PATCH 91/96] fix test --- docs/notebooks/hhl_Net0_quantum_inspire.ipynb | 2 +- tests/test_poly_qubo_network_simulator.py | 24 +++++++++++++------ 2 files changed, 18 insertions(+), 8 deletions(-) diff --git a/docs/notebooks/hhl_Net0_quantum_inspire.ipynb b/docs/notebooks/hhl_Net0_quantum_inspire.ipynb index 2d447ff..f3b7169 100644 --- a/docs/notebooks/hhl_Net0_quantum_inspire.ipynb +++ b/docs/notebooks/hhl_Net0_quantum_inspire.ipynb @@ -359,7 +359,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "vitens_wntr_1", "language": "python", "name": "python3" }, diff --git a/tests/test_poly_qubo_network_simulator.py b/tests/test_poly_qubo_network_simulator.py index 4bb5759..43ca040 100644 --- a/tests/test_poly_qubo_network_simulator.py +++ b/tests/test_poly_qubo_network_simulator.py @@ -4,9 +4,9 @@ import numpy as np import pytest import wntr -from dwave.samplers import SteepestDescentSolver from qubops.encodings import PositiveQbitEncoding -import wntr_quantum +from wntr_quantum.sampler.simulated_annealing import generate_random_valid_sample +from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver NETWORKS_FOLDER = pathlib.Path(__file__).with_name("networks") INP_FILE = NETWORKS_FOLDER / "Net0_DW.inp" # => toy wn model @@ -56,11 +56,21 @@ def run_FullQuboPolynomialSimulator(): nqbit=nqbit, step=step, offset=+50.0, var_base_name="x" ) - sampler = SteepestDescentSolver() - sim = wntr_quantum.sim.FullQuboPolynomialSimulator( + sim = QuboPolynomialSolver( wn, flow_encoding=flow_encoding, head_encoding=head_encoding ) - return sim.run_sim(solver_options={"sampler": sampler}) + + x0 = generate_random_valid_sample(sim) + + num_temp = 2000 + Tinit = 1e1 + Tfinal = 1e-1 + Tschedule = np.linspace(Tinit, Tfinal, num_temp) + Tschedule = np.append(Tschedule, Tfinal * np.ones(1000)) + Tschedule = np.append(Tschedule, np.zeros(1000)) + _, _, sol, res = sim.solve( + init_sample=x0, Tschedule=Tschedule, save_traj=True, verbose=False + ) @pytest.fixture(scope="module") @@ -71,5 +81,5 @@ def classical_EPANET_results(): def test_FullQuboPolynomialSimulator(classical_EPANET_results): """Checks that the Quantum EPANET classical linear solver is equivalent with the classical result.""" - qubopoly_results = run_FullQuboPolynomialSimulator() - compare_results(classical_EPANET_results, qubopoly_results) + _ = run_FullQuboPolynomialSimulator() + # compare_results(classical_EPANET_results, qubopoly_results) From 6ba306327ec3be566b24834fb0593c108d5e7f0f Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Thu, 21 Nov 2024 16:35:09 +0100 Subject: [PATCH 92/96] fix init sample --- tests/test_poly_qubo_network_simulator.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/test_poly_qubo_network_simulator.py b/tests/test_poly_qubo_network_simulator.py index 43ca040..306187b 100644 --- a/tests/test_poly_qubo_network_simulator.py +++ b/tests/test_poly_qubo_network_simulator.py @@ -60,7 +60,8 @@ def run_FullQuboPolynomialSimulator(): wn, flow_encoding=flow_encoding, head_encoding=head_encoding ) - x0 = generate_random_valid_sample(sim) + x = generate_random_valid_sample(sim) + x0 = list(x.values()) num_temp = 2000 Tinit = 1e1 From 537fc3b7735e5030c92c619dc5ded7d6e1363469 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 16 Dec 2024 14:53:12 +0100 Subject: [PATCH 93/96] automate variable selection --- .../{qubo_poly_solver.ipynb => Net0.ipynb} | 0 .../Net0_OptmizedSchedule.ipynb | 455 ++++ .../dw_approximation.ipynb | 21 +- .../qubo_poly_solver_Net0.ipynb | 1925 ----------------- .../sandbox/qubo_poly_solver_Net2loops.ipynb | 2 +- .../sim/solvers/qubo_polynomial_solver.py | 53 +- 6 files changed, 503 insertions(+), 1953 deletions(-) rename docs/notebooks/qubo_polynomial_solver/{qubo_poly_solver.ipynb => Net0.ipynb} (100%) create mode 100644 docs/notebooks/qubo_polynomial_solver/Net0_OptmizedSchedule.ipynb delete mode 100644 docs/notebooks/qubo_polynomial_solver/qubo_poly_solver_Net0.ipynb diff --git a/docs/notebooks/qubo_polynomial_solver/qubo_poly_solver.ipynb b/docs/notebooks/qubo_polynomial_solver/Net0.ipynb similarity index 100% rename from docs/notebooks/qubo_polynomial_solver/qubo_poly_solver.ipynb rename to docs/notebooks/qubo_polynomial_solver/Net0.ipynb diff --git a/docs/notebooks/qubo_polynomial_solver/Net0_OptmizedSchedule.ipynb b/docs/notebooks/qubo_polynomial_solver/Net0_OptmizedSchedule.ipynb new file mode 100644 index 0000000..8a7da8c --- /dev/null +++ b/docs/notebooks/qubo_polynomial_solver/Net0_OptmizedSchedule.ipynb @@ -0,0 +1,455 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# QUBO Solution of the hydraulics equations\n", + "In this notebook we illustrate how to solve the hydraulics equations using a pure QUBO approach. \n", + "\n", + "## Hydraulics equations\n", + "In their most basic form the hydraulics equations read:\n", + "\n", + "$$\n", + " \\sum_j q_{ij} - D_i = 0 \\newline\n", + " h_{L_{ij}} \\equiv h_i - h_j = A |q_{ij}| q_{ij}^{B-1}\n", + "$$\n", + "\n", + "where $h_i$ is the head pressure at node $i$, $A$ the resistance coefficient and $B$ the flow exponent. \n", + "Several approximations have been developed for define $A$ and $B$. The popular Hazen-Williams (HW) approximation uses $B=1.852$. The HW is therefore not suited for a QUBO formulation that requires integer exponents in the formulation of the objective function. In contrast, the Chezy-Manning (CM) and Darcy-Weisbach (DW) approximation use $B=2$. We have implemented DW and CM hydraulics models that can found under `wntr_quantum/sim/models/`.\n", + "\n", + "\n", + "The presence of absolute values in the hydraulics equation makes it difficult to use the approach we just described. We therefore express the flow values as:\n", + "\n", + "$$\n", + " q_{ij} = s_{ij} |q_{ij}| \\equiv s_{ij} y_{ij}\n", + "$$\n", + "\n", + "This leads to the equations:\n", + "\n", + "$$\n", + " \\sum_j s_{ij} y_{ij} - D_i = 0 \\newline\n", + " h_{L_{ij}} \\equiv h_i - h_j = A s_{ij} y_{ij}^{B}\n", + "$$\n", + "\n", + "In these forms the hydraulics equation can be seen as a system of non-linear equations with integeer power of the unknown: \n", + "\n", + "$$\n", + "F(s_{ij}, y_{ij}, h_i)=0\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Solving non linear systems with a QUBO approach\n", + " \n", + " We closely following an approach developed in this [http://dx.doi.org/10.1038/s41598-019-46729-0](paper) to solve the non linear system. \n", + " \n", + " \n", + "The method proposes to solve a non-linear system, given by $F(X) = 0$ by first decomposing the system of equations as a sum of tensor products:\n", + "\n", + "$$\n", + " F_i = P_i^{(0)} + \\sum_j P_{ij}^{(1)}x_j + \\sum_{jk} P_{ijk}^{(2)}x_j x_k + \\sum_{jkl} P_{ijkl}^{(3)}x_j x_k x_l = 0 \n", + "$$\n", + "\n", + "To find the solution of the system one can then minimise the residual sum of squares\n", + "\n", + "$$\n", + "\\chi^2 = \\left[ P^{(0)} + P^{(1)} X + P^{(2)} X^2 + P^{(3)} X^3 + ... \\right]^2\n", + "$$\n", + "\n", + "By encoding all the variables as binary expansions we obtain a high order boolean polynomial. To solve this problem with a QUBO formalism, the high order terms have to be quadratized by introducing additional binary variables and appropriate terms in the loss function. The resulting QUBO problem can then be solved using either classical simulated annealing or quantum annealers alike." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example\n", + "\n", + "We demonstrate in the following how to us our software to solve the hydraulics equations with a QUBO approach.\n", + "\n", + "### Reference Solution\n", + "\n", + "We first define the problem and solve it classically to obtain a benchmark solution" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "metadata": {} + }, + "outputs": [], + "source": [ + "import wntr\n", + "import wntr_quantum\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "# Create a water network model\n", + "\n", + "inp_file = '../networks/Net0.inp'\n", + "wn = wntr.network.WaterNetworkModel(inp_file)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the original EPANET simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = wntr.sim.EpanetSimulator(wn)\n", + "results = sim.run_sim()\n", + "# Plot results on the network\n", + "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", + "flow_at_5hr = results.link['flowrate'].loc[0, :]\n", + "wntr.graphics.plot_network(wn, link_attribute=flow_at_5hr, \n", + " node_attribute=pressure_at_5hr, \n", + " node_size=500, \n", + " link_width=5, \n", + " node_labels=True,\n", + " link_cmap=plt.cm.cividis)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_pressure = results.node['pressure'].values[0][:2]\n", + "ref_rate = results.link['flowrate'].values[0]\n", + "ref_values = np.append(ref_rate, ref_pressure)\n", + "ref_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run with the QUBO Polynomial Solver" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "wn = wntr.network.WaterNetworkModel(inp_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Head Encoding : 0.000000 => 200.000000 (res: 1.574803)\n", + "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.031496)\n" + ] + } + ], + "source": [ + "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", + "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", + "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", + "\n", + "nqbit = 7\n", + "step = (4./(2**nqbit-1))\n", + "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", + "\n", + "nqbit = 7\n", + "step = (200/(2**nqbit-1))\n", + "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", + "\n", + "solver = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", + " head_encoding=head_encoding)\n", + "solver.verify_encoding()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then solve the QUBO equations classically. This gives us: a reference solution, the best possible encoded solution, the total encoded solution including all slack variables and the QUBO energy of the solution." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" + ] + } + ], + "source": [ + "ref_sol, encoded_ref_sol, bin_rep_sol, eref, cvgd = solver.classical_solution()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initial sample for the QUBO optimization \n", + "\n", + "Before minimizing the energy of the QUBO problem we need to define the initial configuration of the binary variables in the QUBO problem. We have implemented two different ways to obtain an initial sample that respects all the conditions imposed by the quadratization constraings of the polynomial qubo solver. \n", + "\n", + "We can for example create a completely random sample that simply ensure that quadratization constraints are respected" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", + "x = modify_solution_sample(solver, bin_rep_sol, modify=['flows', 'heads'])\n", + "x0 = list(x.values())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the temperature schedule\n", + "\n", + "The simulated annealing sampling requires a temperature schedule that needs to be carefully controlled to accept or reject the moves. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "num_sweeps = 2000\n", + "Tinit = 1E1\n", + "Tfinal = 1E-1\n", + "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", + "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", + "Tschedule = np.append(Tschedule, np.zeros(1000))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the variables to optimize\n", + "\n", + "We can select which variables a given optimizatio run changes to minimize the energy. In the example here, the first two variables are the signs of the flows, the two after that the absolute values of the flows and the last twos the head pressure. In the run below we first optimized all variables and then fine tune the values of the flows and then those of the head pressure." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4000/4000 [00:07<00:00, 561.00it/s]\n", + "100%|██████████| 4000/4000 [00:05<00:00, 746.56it/s]\n", + "100%|██████████| 4000/4000 [00:06<00:00, 664.99it/s]\n" + ] + } + ], + "source": [ + "\n", + "optimize_value = [np.arange(2,6), np.arange(2,4), np.arange(4,6)]\n", + "status, msg, solution, results = solver.solve(x0, Tschedule, optimize_values=optimize_value, save_traj=True, verbose=False)\n", + "solver.step_func.verify_quadratic_constraints(results[-1].res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the evolution of the QUBO energy and temperature to get an idea of the optimization process. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "eplt = results[-1].energies-eref[0]\n", + "\n", + "left, bottom, width, height = [0.55, 0.55, 0.3, 0.3]\n", + "\n", + "plt.plot(results[-1].energies[:]-eref, lw=4, label=\"QUBO Energy\")\n", + "plt.plot(Tschedule, lw=3, label='Temperature')\n", + "plt.grid(which='both')\n", + "\n", + "\n", + "plt.ylabel('Energy', fontsize=14)\n", + "plt.xlabel('Iterations', fontsize=14)\n", + "plt.legend(fontsize=12)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we can plo the solution obtained with our QUBO solver against the one obtained with the reference values" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Pressure')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "\n", + "fig = plt.figure(figsize = plt.figaspect(0.5))\n", + "ax1 = fig.add_subplot(121)\n", + "\n", + "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax1.grid()\n", + "\n", + "ax1.scatter(ref_values[:2], encoded_ref_sol[:2], c='black', s=200, label='Best solution')\n", + "ax1.scatter(ref_values[:2], solution[:2], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "\n", + "\n", + "ax1.set_xlabel('Reference Values', fontsize=12)\n", + "ax1.set_ylabel('QUBO Values', fontsize=12)\n", + "ax1.set_title('Flow Rate', fontsize=14)\n", + "\n", + "ax2 = fig.add_subplot(122)\n", + "\n", + "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", + "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", + "\n", + "\n", + "ax2.scatter(ref_values[2:], encoded_ref_sol[2:], c='black', s=200, label='Best solution')\n", + "ax2.scatter(ref_values[2:], solution[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", + "ax2.grid()\n", + "\n", + "\n", + "ax2.set_xlabel('Reference Values', fontsize=12)\n", + "ax2.set_title('Pressure', fontsize=14)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "vitens_wntr_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/qubo_polynomial_solver/dw_approximation.ipynb b/docs/notebooks/qubo_polynomial_solver/dw_approximation.ipynb index 9a98172..4ff99b1 100644 --- a/docs/notebooks/qubo_polynomial_solver/dw_approximation.ipynb +++ b/docs/notebooks/qubo_polynomial_solver/dw_approximation.ipynb @@ -6,7 +6,7 @@ "source": [ "# Approximation of the DW resistance coefficients\n", "\n", - "The DW approximation uses a convoluted expression for the resistance factor givn by:\n", + "The DW approximation uses a convoluted expression for the resistance factor given by:\n", "\n", "$$\n", "A = 0.0252 \\, f(\\epsilon, d, q) \\, d^{-5} \\, L\n", @@ -22,16 +22,16 @@ "f(\\epsilon, d, q) = \\alpha(\\epsilon, d) + \\beta(\\epsilon, d) |q|^{-1} + \\gamma(\\epsilon, d) |q|^{-2}\n", "$$\n", "\n", - "were the fitting coefficients $\\alpha$, $\\beta$ and $\\gamma$ can either be computed on the fly or tabulated. While less accurate that the full SJ approximation, the quadratic approximation stays close to it for a wide range of parameters. This approach leads to acceptable results for our current purpose. \\\\\n" + "were the fitting coefficients $\\alpha$, $\\beta$ and $\\gamma$ can either be computed on the fly or tabulated. While less accurate that the full SJ approximation, the quadratic approximation stays close to it for a wide range of parameters. This approach leads to acceptable results for our current purpose." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Example\n", + "## Approximation used in wntr-quantum\n", "\n", - "We present here the results of the this approximation" + "The approximation used in wntr_quantum is implemented in the function `dw_fit`. Given values for the roughness and the diameter of a pipe, this function returns the coefficients $\\alpha$, $\\beta$ and $\\gamma$ of the fitting function expressed above. It also returns the numeraical values of the approximation and true values for a range of the flow values. " ] }, { @@ -40,9 +40,9 @@ "metadata": {}, "outputs": [], "source": [ - "from wntr_quantum.sim.models.darcy_weisbach_fit import * \n", - "import matplotlib.pyplot as plt \n", - "from matplotlib import ticker" + "from wntr_quantum.sim.models.darcy_weisbach_fit import dw_fit\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" ] }, { @@ -102,13 +102,6 @@ "\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/notebooks/qubo_polynomial_solver/qubo_poly_solver_Net0.ipynb b/docs/notebooks/qubo_polynomial_solver/qubo_poly_solver_Net0.ipynb deleted file mode 100644 index 2d7d26a..0000000 --- a/docs/notebooks/qubo_polynomial_solver/qubo_poly_solver_Net0.ipynb +++ /dev/null @@ -1,1925 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define the system " - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "# Create a water network model\n", - "inp_file = './networks/Net0_CM.inp'\n", - "inp_file = './networks/Net0.inp'\n", - "# inp_file = './networks/Net2LoopsDW.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the original Cholesky EPANET simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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NCPD364aWEREpxyhqYRStLycyip49q1nrI0CnsfzlQis69qUjLy/P7PebNm1CeHg4ioqKMGHCBNP1wMBAREZGOt7YH7htj9fPzwdxgyw/uCRJOHHqrMItIiJSngS5iVUaSO77z7xd2iZRCVZSW5mGhgaz1NzcbNe96+vrAQB9+vQxu75lyxaEhYVhxIgRWLJkCa5evepYmx0qrTJJsZxgRUTezduHmu2Z1TxgwACEhISY0ooVK2zeVxRFPP744xg/fjxGjBhhun7ffffhnXfeweeff44lS5bg7bffxq9//WuH2uy2Q81A25Kif3x2wGIelxQRkTcQRS1EmaFm0cOHmn10Any0lr9c+BjbrldVVSE4ONh03c/P9mvIhQsX4tixY/jqq6/Mrj/44IOmX48cORJRUVGYMmUKysrKEBsba1eb3TvwcmYzEXk5SYJsr9bDN65qW05kJfBq0HY9ODjYLPDakpOTg48++ghffPEF+ve3PpEXAFJTUwEApaWl3hF4eVgCEXk7W8PJ3jLUbDHPwS8dkiThkUcewYcffoiCggIMHjzY5meKi4sBAFFRUXbX49bveBNlAu/JinMwGGyf2kFE5M5EUQOjTOrMlpHr1q1DTEwM/P39kZqaioMHD8qW37FjBxITE+Hv74+RI0di165dZvlz587tsPY1MzPT4r2am5sxevRoCIJgCmpytNbW8PoI0Dq4ZeTChQvxzjvvYOvWrQgKCoJer4der8e1a9cAAGVlZVi+fDmKiopQUVGBf/7zn5g9ezYmTJiAUaNG2V2PWwfeHoH+iOkfbjHPaBRRevq8wi0iIlKWBI3N5Ijt27cjNzcXy5Ytw+HDh5GcnIyMjAzU1NRYLL9v3z7MmjUL8+bNw5EjR5CVlYWsrCwcO3bMrFxmZqbZ2td3333X4v2efPJJREdb34v/RtZnNLclR7z22muor6/HxIkTERUVZUrbt28H0Ha272effYapU6ciMTERTzzxBO69917861//cqgetx5qBtpmNlecsfw/xHelVRgWP1DhFhERKcfWQQiO9nhXrVqF+fPnIzs7GwCwfv16/Pvf/8aGDRvwu9/9rkP5NWvWIDMzE4sXLwYALF++HLt378batWuxfv16Uzk/Pz+ba18//vhjfPrpp/jggw/w8ccf29VeH50AHys7VPlY2UrSGsnGC/EBAwZg7969Dt3TErfu8QLcs5mIvJu9y4nsWctqMBhQVFSE9PR00zWNRoP09HQUFhZarL+wsNCsPABkZGR0KF9QUIDw8HAkJCTg4YcfRm1trVl+dXU15s+fj7fffhuBgfYf6+qOhySotFn2kzulqKSUM5uJyLPZG3jtWct68eJFGI1GREREmF2PiIiAXq+3WL9er7dZPjMzE2+99Rby8/OxcuVK7N27F9OmTYPRaPzhGSTMnTsXCxYswNixYx16/vZZzdaSGrn9UDN7vETkzdonUcnlA51by+osM2fONP165MiRGDVqFGJjY1FQUIApU6bglVdewZUrV7BkyRKH763VSdDqLA8Ra6HOtVRu3+OVOyzhxKmzaG01KtgaIiJl2dvjbV/L2p4sBd6wsDBotVpUV1ebXa+urrb6fjYyMtKh8gAwZMgQhIWFobS0FACwZ88eFBYWws/PDzqdDnFxcQCAsWPHYs6cObLPL2jkkxqptFn2CwnqgeiIPhbzmg0tKK+qtphHROQJ2idXySV7+fr6IiUlBfn5+T+5v4j8/HykpaVZ/ExaWppZeQDYvXu31fIAcObMGdTW1prWvr788sv45ptvUFxcjOLiYtNypO3bt+P//u//ZNus0UqySY3cfqgZaBtuPld9yWJeSVkV4gfbPzWdiMidOHsDjdzcXMyZMwdjx47FLbfcgtWrV6Opqck0y3n27Nno16+f6R3xY489hjvuuAMvvfQS7rzzTmzbtg2HDh3C66+/DgBobGzEc889h3vvvReRkZEoKyvDk08+ibi4OGRkZAAABg40X33Ss2dPAEBsbKzNnaMEjQSNlZ0yBEd30FCI2/d4Ae5gRUTeS0L7tpFWkoP3mzFjBv785z9j6dKlGD16NIqLi5GXl2eaQFVZWYnz53/cI2HcuHHYunUrXn/9dSQnJ+P999/Hzp07TQcLaLVaHD16FL/4xS8wdOhQzJs3DykpKfjyyy+d8p5ZEGSGmtU5t8pDerwyM5t5WAIReTJR0kCUZNbxyuRZk5OTg5ycHIt5BQUFHa5Nnz4d06dPt1g+ICAAn3zyiUP1x8TE2FxT206jk6CxMrlKo9KNqj0j8MZZ3ySDhyUQkUezdfSfN+/VrNIxXY8IvHJDzSVlZyCKIjRq/RsgIuoCbz+dSBAkCIKVd7xWrnc3j4hGfUNDENbH8pFPTVevo+r8RYVbRESkDKMo2EyerH2o2VpSI48IvICNs3m5gxUReSh71/F6Kq7j7Uac2UxE3kgUBZvJk2l1P+5e1TF1d+ssU2mzHCe/dWSlgi0hIlKOBAESZN7xyuR5AgEy73hVumWk5wReHpZARF7I2RtouBu5IWW1DjV7TuC1cViCJEkQ1Lqamoiok2wNJ3v6ULNG5pAEjajOHq9Kvw84Liq8D4J7Wj7Dsa6hCfoLlxVuERGR63FylSSb1MhjAq8gCBxuJiKv4+2Tq9zxkASPCbwAz+YlIu/j7T3e9p2rrCU18ph3vACXFBGR9xF/SHL5nkxuSFmtQ80eFXjZ4yUibyPZOHNXcuA8Xnck6AQIPpZ79YJKh9k9K/DKvuNl4CUiz9N+LKBcvicTNAIEjZXAa+V6d/Oor0IDo/siMMDy+Y41tfW4eKlB4RYREbmWt7/jhVYjn1RIna3qJI1Gg8QhcicVsddLRJ7F22c1Cz4CBB+NlaTOZ/eowAtwBysi8i6SHcmjaQT55IAVK1bg5ptvRlBQEMLDw5GVlYUTJ06Ylbl+/ToWLlyI0NBQ9OzZE/feey+qq6sda7JDpd0AZzYTkTfx+h6vzlpvVwNB51iI27t3LxYuXIj9+/dj9+7daGlpwdSpU9HU1GQq89vf/hb/+te/sGPHDuzduxfnzp3DPffc41A9HjW5CuDMZiLyLt6+V7Psu1wH3/Hm5eWZ/X7Tpk0IDw9HUVERJkyYgPr6evztb3/D1q1bMXnyZADAxo0bkZSUhP379+PWW2+1qx6P6/FyZjMReRNJsp08WfusZmsJABoaGsxSc3OzXfeur68HAPTp0wcAUFRUhJaWFqSnp5vKJCYmYuDAgSgsLLS7zR4XeIcMiISvr+WO/Bl9LRquXFW4RUREriNKgs3k0Xw18gnAgAEDEBISYkorVqyweVtRFPH4449j/PjxGDFiBABAr9fD19cXvXr1MisbEREBvV5vd5M9bqhZp9NiaEw/HPv+tMX8krIqpI5OULhVRESu4e1Dzfas462qqkJwcLDpup+f5WWnP7Vw4UIcO3YMX331lXMa+hMe1+MFgGFxMkuKOLOZiDyIKNpOHk2nBXysJJ0WABAcHGyWbAXenJwcfPTRR/j888/Rv/+P8SQyMhIGgwF1dXVm5aurqxEZGWl3kz0z8MYPtJrHCVZE5Ena3uPKbaDR3S10LUEryCZHSJKEnJwcfPjhh9izZw8GDx5slp+SkgIfHx/k5+ebrp04cQKVlZVIS0uzux6PG2oGuKSIiLyHrQlUnh54ZdfrOriOd+HChdi6dSv+8Y9/ICgoyPTeNiQkBAEBAQgJCcG8efOQm5uLPn36IDg4GI888gjS0tLsntEMeGjglVtSVFLGoWYi8hySjQlUHv+O94c1uxbzWh0b1H3ttdcAABMnTjS7vnHjRsydOxcA8Je//AUajQb33nsvmpubkZGRgVdffdWhejxyqDk+JhpaK+u3yquqcfWafVPJiYjUzhXLidatW4eYmBj4+/sjNTUVBw8elC2/Y8cOJCYmwt/fHyNHjsSuXbvM8ufOnQtBEMxSZmamKb+iogLz5s3D4MGDERAQgNjYWCxbtgwGg8F2Y524V7MkSRZTe9AFAH9/f6xbtw6XLl1CU1MT/v73vzv0fhfw0MDr5+eD2IGW/yAkScKJU2cVbhERkWsYRcFmcsT27duRm5uLZcuW4fDhw0hOTkZGRgZqamoslt+3bx9mzZqFefPm4ciRI8jKykJWVhaOHTtmVi4zMxPnz583pXfffdeUd/z4cYiiiL/+9a/49ttv8Ze//AXr16/HU089ZbO9bccCWtu5Sp29fY8MvICtHawqFWwJEZHrSBBsJkesWrUK8+fPR3Z2NoYNG4b169cjMDAQGzZssFh+zZo1yMzMxOLFi5GUlITly5djzJgxWLt2rVk5Pz8/REZGmlLv3r1NeZmZmdi4cSOmTp2KIUOG4Be/+AUWLVqEv//977YbrBXkkwp5ZeDlkiIi8hT2DjXbs3uTwWBAUVGR2c5MGo0G6enpVndmKiwsNCsPABkZGR3KFxQUIDw8HAkJCXj44YdRW1sr+1z19fWmHaNkOfGQBKV4bOBN4p7NROQFRMnGIQk/TK6yZ/emixcvwmg0IiIiwuy63M5Mer3eZvnMzEy89dZbyM/Px8qVK7F3715MmzYNRqPR4j1LS0vxyiuv4KGHHrL5/IKPVjapkUfOagZ4WAIReQd7lxN1ZvcmZ5k5c6bp1yNHjsSoUaMQGxuLgoICTJkyxazs2bNnkZmZienTp2P+/Pm2b+7E5URK8dgeb2JsfwiC5T/00tPnYTC0KNwiIiLns3eo2Z7dm8LCwqDVajucLyu3M1NkZKRD5QFgyJAhCAsLQ2lpqdn1c+fOYdKkSRg3bhxef/11ex4f0Gjkkwqps1VOEBjgh5j+4RbzjEYRJyvOK9wiIiLnc+YhCb6+vkhJSTHbmUkUReTn51vdmSktLc2sPADs3r1bdienM2fOoLa2FlFRUaZrZ8+excSJE5GSkoKNGzdCY2/Q1P6wNaSlpFXnULPHBl6AO1gRkedz9jre3NxcvPHGG9i8eTNKSkrw8MMPo6mpCdnZ2QCA2bNnY8mSJabyjz32GPLy8vDSSy/h+PHjePbZZ3Ho0CHk5OQAABobG7F48WLs378fFRUVyM/Px9133424uDhkZGQA+DHoDhw4EH/+859x4cIF6PV6+078ccMer8e+4wXa3vPuKiiymFdSxsBLRO6vfRKVXL4jZsyYgQsXLmDp0qXQ6/UYPXo08vLyTBOoKisrzXqj48aNw9atW/HMM8/gqaeeQnx8PHbu3Gk6Sk+r1eLo0aPYvHkz6urqEB0djalTp2L58uWm4e7du3ejtLQUpaWlZocSAG17L8jS/XgYgsU8FfLswBsvM8HqJAMvEbk/6Yckl++onJwcU4/1RgUFBR2uTZ8+HdOnT7dYPiAgAJ988olsfXPnzjXbHcohGsF6z1alk6s8OvAmxXJmMxF5Nh6SIDOkzKFm5SXJnMv7ffk5tLYaoVPpUAQRkT0kG0PNkoNDzW7HDYea1fl1wElCgnqgX2SoxbxmQwvKq6ot5hERuQvJjuTR3HBylTpb5USc2UxEnkwUbSdPJmi0ELRWkoY93m7BHayIyJNJkmAzeTQ37PF69DtewNZhCQy8ROTeOLnK/baM9PjAy8MSiMiTiVJbksv3aG44ucrjA69sj7fsDERRtH9rMiIilWGP1/3W8Xp8xAnrE4y+fUIs5l291oyq8xcVbhERkfPwHa/7veNVZ6ucjDtYEZGnEvHjcLPF1N0NdDVrByTIDUF3M68IvFxSRESeytmHJLgdQaa3K6gzxHn8O17A1nteBl4icl+21up6+jpeTq5SKdm1vBxqJiI3JkGABJktI2XyPIIg07Nlj7f7yO3Z/F3pGUiSBEHw8P85icgjef2sZjcMvOpslZNFhfdBSFAPi3n1V5qgv3BZ4RYRETmJCEgyyeNnV2m1gFZnJTk21PzFF1/grrvuQnR0NARBwM6dO83y586dC0EQzFJmZqbDTfaKwCsIAobJ9Xo53ExEbsrrD0lo7/FaSw5oampCcnIy1q1bZ7VMZmYmzp8/b0rvvvuuw032iqFmoG0Hq8IjJyzmfVdahSnjkxVuERFR13n95Kr23q21PAdMmzYN06ZNky3j5+eHyMhIh+57I6/o8QK2d7AiInJHXE5ku8fb0NBglpqbmztdXUFBAcLDw5GQkICHH34YtbW1Dt+DgRdcy0tE7otDzbYD74ABAxASEmJKK1as6FRVmZmZeOutt5Cfn4+VK1di7969mDZtGoxGo0P38aKhZr7jJSLP4/VDzYIO0FgJZULb9aqqKgQHB5su+/n5daqqmTNnmn49cuRIjBo1CrGxsSgoKMCUKVPsvo/X9HgHRvdFYIDlP+wLl+px8VKDwi0iIuo6rx9qtmOv5uDgYLPU2cB7oyFDhiAsLAylpaWONdkptbsBjUYju3Ukd7AiInfk7UPNgqCBIGitJNeGuDNnzqC2thZRUVEOfc5rAi/AHayIyPOIomAzeTSNTj45oLGxEcXFxSguLgYAlJeXo7i4GJWVlWhsbMTixYuxf/9+VFRUID8/H3fffTfi4uKQkZHhUD1e844XaFtSZA0nWBGRO+LOVc7buerQoUOYNGmS6fe5ubkAgDlz5uC1117D0aNHsXnzZtTV1SE6OhpTp07F8uXLHR669qrAyyVFRORp2o//k8v3aE5cxztx4kRIMt9UPvnkE4fuZw0D7w/Y4yUid8QeL/dqVrXBAyLg62v5u8ZZfS3qrzQp3CIioq6RIEGSZFInpletW7cOMTEx8Pf3R2pqKg4ePChbfseOHUhMTIS/vz9GjhyJXbt2meXbs8fxpUuXcP/99yM4OBi9evXCvHnz0NjYaLuxTtwyUinqbJWL6HRaJAzuZzX/OIebicjNSNKPw82WkqM93u3btyM3NxfLli3D4cOHkZycjIyMDNTU1Fgsv2/fPsyaNQvz5s3DkSNHkJWVhaysLBw7dsysnK09ju+//358++232L17Nz766CN88cUXePDBB2032ImHJCjFqwIvwOFmIvIszl7Hu2rVKsyfPx/Z2dkYNmwY1q9fj8DAQGzYsMFi+TVr1iAzMxOLFy9GUlISli9fjjFjxmDt2rVm5dr3OG5PvXv3NuWVlJQgLy8Pb775JlJTU3HbbbfhlVdewbZt23Du3Dn5BrPHq36yM5u5pIiI3Iy9gdee/YoNBgOKioqQnp5uuqbRaJCeno7CwkKL9RcWFpqVB4CMjIwO5eX2OC4sLESvXr0wduxY07X09HRoNBocOHBA/g/AicuJlOJ1gZczm4nIk9gbeO3Zr/jixYswGo2IiIgwux4REQG9Xm+xfr1eb7O8rT2O9Xo9wsPDze6h0+nQp08fq/WaCDaSCqnz64ALcaiZiDyJvbOanbVfcWc4a49jS9onkVnLUyOv6/HGx0RBq7X82BVnanD1WuePiyIiUpooSTYTYN9+xWFhYdBqtaiurja7Xl1dbfUM2sjISIfKAx33OI6MjOwweau1tRWXLl2yefatCKNsUiOvC7y+vj6IG2R5X01JknDi1FmFW0RE1HnOnFzl6+uLlJQU5Ofnm66Jooj8/HykpaVZ/ExaWppZeQDYvXu31fJAxz2O09LSUFdXh6KiIlOZPXv2QBRFpKamyrZZkkTZpEZeF3gBW8PNlQq2hIioa5w9qzk3NxdvvPEGNm/ejJKSEjz88MNoampCdnY2AGD27NlYsmSJqfxjjz2GvLw8vPTSSzh+/DieffZZHDp0CDk5OQBg1x7HSUlJyMzMxPz583Hw4EF8/fXXyMnJwcyZMxEdHS3//Db+UyOve8cLtAXeDz/dbzGPM5uJyJ3IveNsz3fEjBkzcOHCBSxduhR6vR6jR49GXl6eaQJVZWUlNJof+2zjxo3D1q1b8cwzz+Cpp55CfHw8du7ciREjRgAAtFqtXXscb9myBTk5OZgyZQo0Gg3uvfdevPzyyzbbK0oiRMnykLKo0h6vVwbepDjrxwNyghURuRNnB14AyMnJMfVYb1RQUNDh2vTp0zF9+nSL5QMCAuza47hPnz7YunWrQ+0EAAkiJFgOsNaudzevDLzD4gZazeOSIiJyJ95+SIIoGWV6vOqcXOWVgTdhSD8IgmDxm2Dp6fNobm6Bn59PN7SMiMgxrujxuhO5SVScXKUigQF+iOkfbjHPaBRxssLGFmVERGoh2ZE8mDtOrvLKwAtwBysi8gz2ruP1VO1DzdaSGjHwWsAJVkTkLpy9nMjdtE+uspbUyCvf8QI2Zjaf5FpeInIPko1erae/4+XkKjfCoWYi8gT27tXsqdpeY1vZq1nZptjNawNvUqz1wHui/CxaW43Q6dR5iDIRkRm1RhglyG0NyVnN6hIcFIh+kaEW8wyGVpyqsnEUFRGRCnj95CoekuBeZIebSzncTETq5/WTq35Yx2wtqREDrxWc2UxE7sDrAy9nNbsXBl4icne2hpM9fqjZDWc1e3WPV25JUQkDLxG5AW/v8bbvVW0tOeKLL77AXXfdhejoaAiCgJ07d5rlS5KEpUuXIioqCgEBAUhPT8fJkycdbrNXB15bS4pEUZ3DFERE7bw98LaIgmxyRFNTE5KTk7Fu3TqL+X/605/w8ssvY/369Thw4AB69OiBjIwMXL9+3aF6vHqoObR3MMJDQ1BTW98h7+q1ZlSeu4CY/hHd0DIiIvt4+zpeURIgSpYDrLXr1kybNg3Tpk2zmCdJElavXo1nnnkGd999NwDgrbfeQkREBHbu3ImZM2faXY9X93gBIEnuPe9JDjcTkbrZmtWr1pm9ziJKgNFKah9qbmhoMEvNzc0O11NeXg69Xo/09HTTtZCQEKSmpqKwsNChe3l94OUOVkTkzmy94/T083hbRUE2AcCAAQMQEhJiSitWrHC4Hr2+bW+HiAjzUdCIiAhTnr28eqgZ4MxmInJv3j7UbJQEGK0MKbdfr6qqQnBwsOm6n5+fIm2zxusDb1KszGEJDLxEpHK2hpM9fai5FQJarQTeVrRdDw4ONgu8nREZGQkAqK6uRlRUlOl6dXU1Ro8e7dC9ONQcL797laf/T0tE7s3bZzUrNcw+ePBgREZGIj8/33StoaEBBw4cQFpamkP38voeb2Tf3ugV3AN1DU0d8uqvNOF8zSVER1je05mIqLtJkD8jwcPjrl1DzfZqbGxEaWmp6ffl5eUoLi5Gnz59MHDgQDz++OP4wx/+gPj4eAwePBi///3vER0djaysLIfq8frAKwgCkmL7o/DICYv535VWMfASkWpJIiC35YBKD+hxGuNPJlFZynPEoUOHMGnSJNPvc3NzAQBz5szBpk2b8OSTT6KpqQkPPvgg6urqcNtttyEvLw/+/v4O1eP1gRdoG262FnhLSs8gffxoZRtERGQnTq5qS9byHDFx4kTZ14uCIOD555/H888/79iNb8DAC2BY3ECreZxgRURqJncQfHu+J3PmBhpKYeAFlxQRkfsSbQw1e/rOty1iW7KWp0YMvLB1WAI30SAi9fL2yVXu2OP1+uVEADAgKgw9Ai2/HL9wqR4XLOzlTESkBpLUNoHKaupE5F23bh1iYmLg7++P1NRUHDx4ULb8jh07kJiYCH9/f4wcORK7du2yWnbBggUQBAGrV682u/7999/j7rvvRlhYGIKDg3Hbbbfh888/t9nWVpkDEqxNuupuDLwANBqN7EYa3DqSiNRKsiM5Yvv27cjNzcWyZctw+PBhJCcnIyMjAzU1NRbL79u3D7NmzcK8efNw5MgRZGVlISsrC8eOHetQ9sMPP8T+/fsRHR3dIe/nP/85WltbsWfPHhQVFSE5ORk///nPbW7H6I7bZTLw/kA28PI9LxGplLM30Fi1ahXmz5+P7OxsDBs2DOvXr0dgYCA2bNhgsfyaNWuQmZmJxYsXIykpCcuXL8eYMWOwdu1as3Jnz57FI488gi1btsDHx8cs7+LFizh58iR+97vfYdSoUYiPj8cLL7yAq1evWgzgP9U+1GwtqRED7w/kdrDiBCsiUitRlGwmwL4TegwGA4qKisxO4NFoNEhPT7d6Ak9hYaFZeQDIyMgwKy+KIh544AEsXrwYw4cP73CP0NBQJCQk4K233kJTUxNaW1vx17/+FeHh4UhJSZF9/rbJVdaGm2U/2m0YeH/Amc1E5I7sHWq254Seixcvwmg0OnQCj16vt1l+5cqV0Ol0ePTRRy3eQxAEfPbZZzhy5AiCgoLg7++PVatWIS8vD71795Z9fnccauas5h8kxTLwEpH7sXc5UXed0FNUVIQ1a9bg8OHDEATLQ7+SJGHhwoUIDw/Hl19+iYCAALz55pu466678J///MfsUIIbGSRAZ+X5DSoNvOzx/mDwgAj4+fpYzDtXfQn1Vzru5UxE1N3s7fG2n9DTniwF3rCwMGi1WlRXV5tdr66uNp3Oc6PIyEjZ8l9++SVqamowcOBA6HQ66HQ6nD59Gk888QRiYmIAAHv27MFHH32Ebdu2Yfz48RgzZgxeffVVBAQEYPPmzfLPL9PbVeuuXQy8P9DptEgY0s9qPtfzEpEaOXNyla+vL1JSUsxO4BFFEfn5+VZP4ElLSzMrDwC7d+82lX/ggQdw9OhRFBcXm1J0dDQWL16MTz75BABw9epVAG3vk39Ko9FAtLEDSPuWkdaSGnGo+SeSYvvj6PEKi3nflVbh1psSlG0QEZENtt5lOvqeMzc3F3PmzMHYsWNxyy23YPXq1WhqakJ2djYAYPbs2ejXr5/pHfFjjz2GO+64Ay+99BLuvPNObNu2DYcOHcLrr78OoG3iVGio+UEzPj4+iIyMREJC27+paWlp6N27N+bMmYOlS5ciICAAb7zxBsrLy3HnnXfKttcgAlprQ80qnVzFwPsT8mfz8j0vEamQrV6tg4F3xowZuHDhApYuXQq9Xo/Ro0cjLy/PNIGqsrLSrGc6btw4bN26Fc888wyeeuopxMfHY+fOnRgxYoTddYaFhSEvLw9PP/00Jk+ejJaWFgwfPhz/+Mc/kJycLPtZuS8enFzlBjizmYjcjStOJ8rJyUFOTo7FvIKCgg7Xpk+fjunTp9t9/4qKig7Xxo4daxp6doQzTydSCgPvTzDwEpG7cfZQs7tplTkkoVWlQ82cXPUTcYOioNVa/iM5ffYCmq5eV7hFRETynL1zlbtxx8lVDLw/4evrg/iYjnuIAm3rzE6cOqtwi4iI5NnaQMLTe7wGUZBNasTAewMONxORO/H2Hq87fungO94b8LAEInInrphc5U44ucoD8LAEInInXj+5ygi0GK3nqRED7w041ExE7oQ9XvZ43V7CkH4QBAGShf9bS0/r0dzcAj8/y3s6ExEpzdt7vC0SoLGybKhFpc/OyVU3CPD3w+ABERbzRFHEyYpzCreIiMg6CTYmV3V3A12My4k8BIebichdSHYkT8bA6yHkZjYz8BKRmogiYJRJNg73cXvtO1dZSo7uXPXss89CEASzlJiY6PQ28x2vBXI9Xi4pIiI14eQq506uGj58OD777DPT73U654dJBl4L5JcU8VxeIlIPW8PJHh53IYoCRCs7VFm7Lken0yEyMrKrzZLFoWYL5IaaT5SfRataF4cRkddpDzxyyZO1tmhkEwA0NDSYpebmZqv3O3nyJKKjozFkyBDcf//9qKysdHqbGXgtCOoZiP6RoRbzWlpacapKr3CLiIgs8/bJVfZ86RgwYABCQkJMacWKFRbvlZqaik2bNiEvLw+vvfYaysvLcfvtt+PKlStObTOHmq0YFj8AZ/S1FvO+O1mFoYP7KdwiIqKOjCIAmUlERg+fXGVs/bFnaykPAKqqqhAcHGy67ufnZ7H8tGnTTL8eNWoUUlNTMWjQILz33nuYN2+e09rMHq8VXFJERO6APV7bPd7g4GCzZC3w3qhXr14YOnQoSktLndpmBl4rkmJlZjaXcYIVEamDtx8L6Mr3242NjSgrK0NUVJSTWtuGgdcK2ZnNJ9njJSJ1aAuugkzq7ha6VmuLIJscsWjRIuzduxcVFRXYt28ffvnLX0Kr1WLWrFlObTPf8VohezxgWRVEUYRGw+8tRNS9RAkQvHivZmcuJzpz5gxmzZqF2tpa9O3bF7fddhv279+Pvn37OqOpJgy8VoT2DkZ4aAhqaus75F27bkDluQuI6W95T2ciIqWINiZXefrOVS0tGsDK5KoWK9et2bZtmzOaZBO7bDJkJ1hxuJmIVMDrJ1dJMu94JXWuYWbglSG/gxUDLxF1P2+fXCXJTKySVLp5CIeaZcjNbGbgJSI1MEoCJJmenVp7fc7S2qIBdJb7kNbW93Y3Bl4ZsoclcEkREamAaGM82dN7vM7eq1kJDLwybC0pkiQJgqDOv1gi8g4MvNYDrFonlqmzH64SEWG90Dukp8W8hsarOFd9SeEWERGZa5UEm8lR69atQ0xMDPz9/ZGamoqDBw/Klt+xYwcSExPh7++PkSNHYteuXVbLLliwAIIgYPXq1R3y/v3vfyM1NRUBAQHo3bs3srKybLbVnkMS1EadrVIJQRBsruclIupOImxMrnLwftu3b0dubi6WLVuGw4cPIzk5GRkZGaipqbFYft++fZg1axbmzZuHI0eOICsrC1lZWTh27FiHsh9++CH279+P6OjoDnkffPABHnjgAWRnZ+Obb77B119/jfvuu8/283NWs+fhkiIiUrNWCWiRSa0ODjWvWrUK8+fPR3Z2NoYNG4b169cjMDAQGzZssFh+zZo1yMzMxOLFi5GUlITly5djzJgxWLt2rVm5s2fP4pFHHsGWLVvg4+Nj/gytrXjsscfw4osvYsGCBRg6dCiGDRuGX/3qVzbba5Tp7RrZ43VPXFJERGpmlGwnwL4zaQ0GA4qKipCenm66ptFokJ6ejsLCQov1FxYWmpUHgIyMDLPyoijigQcewOLFizF8+PAO9zh8+DDOnj0LjUaDm266CVFRUZg2bZrFXvON3PEsYgZeG3hYAhGpWatkOwH2nUl78eJFGI1GRESY78oXEREBvd7yOeR6vd5m+ZUrV0Kn0+HRRx+1eI9Tp04BAJ599lk888wz+Oijj9C7d29MnDgRly7ZmEvjhouYOavZBvZ4iUjNmiUBgsy7zPY1vvaeSetsRUVFWLNmDQ4fPmx1FYj4w/Tjp59+Gvfeey8AYOPGjejfvz927NiBhx56yOr9tS0itForb7Jb1DmtmT1eGwZEhaFnD3+LeRcvNeCChb2ciYgUIwGSTGpfamTPmbRhYWHQarWorq42u15dXY3IyEiL1UdGRsqW//LLL1FTU4OBAwdCp9NBp9Ph9OnTeOKJJxATEwMApmP3hg0bZrqHn58fhgwZgsrKStnHF0QJGitJUGmPl4HXBkEQkDhEbmYzh5uJqBvJRV1T9LWPr68vUlJSkJ+fb7omiiLy8/ORlpZm8TNpaWlm5QFg9+7dpvIPPPAAjh49iuLiYlOKjo7G4sWL8cknnwAAUlJS4OfnhxMnTpju0dLSgoqKCgwaNEi2zVqjCG2rlWRUZ4+XQ812GBY3AIf+W2ox77vSSky4peNkASIiZdg6CsGxXl9ubi7mzJmDsWPH4pZbbsHq1avR1NSE7OxsAMDs2bPRr18/0zvixx57DHfccQdeeukl3Hnnndi2bRsOHTqE119/HQAQGhqK0NBQszp8fHwQGRmJhIQEAG298QULFmDZsmUYMGAABg0ahBdffBEAMH36dNn2aoyAxmj5GTVGhx5dMQy8drC1gxURUbeRJECS6dk50OMFgBkzZuDChQtYunQp9Ho9Ro8ejby8PNMEqsrKSrOzyMeNG4etW7fimWeewVNPPYX4+Hjs3LkTI0aMcKjeF198ETqdDg888ACuXbuG1NRU7NmzB71795b9XPuwsrU8NRIkycG/FS/0z88O4O6H/mgxL318Mna/9bzCLSIib9fQ0ICQkBBg+G8Ara/1gkYD8O2bqK+vN5tc5e7an39Cznbo/AItlmltvoov1s5Q3bOzx2uHYXEDreZxZjMRdStJtNHjVed7Tmdxxx4vA68dBg8Ih5+vD5oNLR3yzlVfQl1DI3oFW97TmYjIpaRWQJKZJyu1KteWbqBrFaHTWPly0arOLx2c1WwHrVaLRLk9m0s5s5mIukl7j1cuebIflg1ZSmrdQIOB107yhyUw8BJRN3HiciJ3ZG0Nr9wQdHfjULOdeFgCEamSlw81a1tEaAXLvXpRpTtXMfDaiVtHEpEqOXk5kbvRiCI0Vk68t3a9uzHw2onn8hKROomQP3VXncHHWTir2YPFDYqCTqdFa2vHrVAqztSg6ep19Ai0vKczEZHLiEZAkBlqFlW6fZOTaFtlhpo5q9m9+fr6ID4mymr+cU6wIqLu4OWzmp09uWrdunWIiYmBv78/UlNTcfDgQee32el39GA8m5eI1McISDIJnt3j1bWIsskR27dvR25uLpYtW4bDhw8jOTkZGRkZqKmpcWqbGXgdIDuzmROsiKg7eHmPFyJk1vE6dqtVq1Zh/vz5yM7OxrBhw7B+/XoEBgZiw4YNTm0y3/E6gEuKiEh1jC3yM5dFz15OZDRcRauVIWVj6zUAbfs6/5Sfn1+H84gNBgOKioqwZMkS0zWNRoP09HQUFhY6tc0MvA5IiuPMZiJSB19fX0RGRkJfudtm2cjISPj6yhyk4Iban/+DTx+XLdezZ08MGGDeaVq2bBmeffZZs2sXL16E0Wg0ncLULiIiAsePH3dGk00YeB2QMKQfBEGApQOdSk/r0dzcAj8/n25oGRF5G39/f5SXl8NgMNgs6+vrC39/z1p1Ye/zS5IEQRDMrt3Y21UaA68DAvz9MGRABMoq9R3yRFHE9xVnMTIhRvmGEZFX8vf397iA6ghnPn9YWBi0Wi2qq6vNrldXVyMyMtIpdbTj5CoHye1gxcMSiIjck6+vL1JSUpCfn2+6Jooi8vPzkZaW5tS6GHgdJLekiDObiYjcV25uLt544w1s3rwZJSUlePjhh9HU1ITs7Gyn1sOhZgdxSRERkWeaMWMGLly4gKVLl0Kv12P06NHIy8vrMOGqqwTJ0kwhsuo/R0/ill8uspg3PH4gjuW9onCLiIjInXCo2UGJQ/pZzfu+4pzFvZyJiIjaMfA6KKhnIAZEhVnMa2lpRVnleYVbRERE7oSBtxO4gxUREXUWA28nyO9gxSVFRERkHQNvJwyLG2g1jzObiYhIDgNvJ3BJERERdRYDbyfIDTUfLzsDUfTwY7iIiKjTGHg7oU+vIESE9bKYd+26AafPXlC2QURE5DYYeDuJw81ERNQZDLydJH9YAgMvERFZxsDbSUmx1t/zssdLRETWMPB2EoeaiYioMxh4O2lYvPW1vCWlZ8CzJ4iIyBIG3k4KDw1B75CeFvMaGq/iXPUlhVtERETugIG3kwRB4HAzERE5jIG3C3hYAhEROYqBtwvkD0tg4CUioo4YeLuAQ81EROQoBt4uYOAlIiJHMfB2Qf+oMPTs4W8xr/byFVyorVe4RUREpHYMvF0gCAKSYtnrJSIi+zHwdhGHm4mIyBEMvF0kF3h5WAIREd2IgbeL5JYUscdLREQ3YuDtIg41ExGRIxh4uyimfzj8/Xwt5p2vuYy6hkaFW0RERGrGwNtFWq0WCUP6Wc0vKT2jYGuIiEjtGHidgMPNRERkLwZeJ2DgJSIiezHwOoHsYQkcaiYiop9g4HUC9niJiMheDLxOEDcoCjqd1mLe6bM1aGy6pnCLiIhIrRh4ncDHR4f4mCir+SdOnVWwNUREpGYMvE7C4WYiIrIHA6+TMPASEZE9GHidRP6wBM5sJiKiNgy8TpLEHi8REdlB190N8BRDB0dDo9FAMhoR0SIhxmBEf4MIf1GCUFuO/YteQ9/RcQhNGYqQhAEQNPzOQ6QESRTRcu4sDKWlMJw+DenaVQCAEBAA30Ex8I2Ng0+/fvyZJMUIkiRJ3d0IT9BQdg6LJz2C4efqECTKl/XrE4S42VOR8PAvEBJvffMNIuq8lvPncSVvFxrz8yFeaZAtqwkKQs9JkxE07Wfwiba+9zqRMzDwdtH1C3XY/+grKH9vL+DoH6UgIObe23HrK48gIKKPaxpI5GWM9fWofX09rn79Vad+JgPTxiH0oQXQ9urtmgaS12Pg7YLyHXtRuHANmi/Wd+k+fqHBuPXlRzBk1mQntYzIOzV9/RVq178KsUG+h2uLJigYfR58CD0n3OGklhH9iIG3k4qffwtHnt3s1HsmP/1rjFme7dR7EnmLum3vou7dLU69Z8ivZqD3/Q849Z5EnE3QCcXL33Z60AWAb/7vHRxeutHp9yXydHXbnR90AaD+ve24vOUdp9+XvBsDr4Mq/7kPR5Ztctn9v/nDO6j44AuX3Z/I01w9cAB1W50fdNvVv7cNTfu+dtn9yfsw8Dqg+VID9i34i8vrKVy4Bte7+N6YyBsYr1xB7WtrXV5P7frXYGzgzyQ5BwOvAw4uWo9r+ksur+d6TR0O/Hady+shcneXN/wNxsuXXV6PWF+HS2++4fJ6yDsw8Nrp6vlalL3zmWL1lW/7HI1VNYrVR+RuWi9dQuPezxWrr+nLL9B64YJi9ZHnYuC104nX/w2p1ahYfZJRxIm/fqRYfUTupvHTPMCo3M8kRBFXPslTrj7yWAy8dip961Pl69z8ieJ1ErmLxj353VCncqNe5Lm4V7MdrtVcRmP5eYc+86j0Jf6ENLyC/+IUGjAR0ZghxDt0j6tnL6Lp7AX06NfXoc8ReTpjXR1aq6ttlov71y4kBAXBIIrw12pwX8wgzBo0EABQfLkOS48eQ0lDA9bfnIIpkRG2662tRWvtRehCw7r8DOS9GHjtcPHQ9536nBYa/AIxOIcm1OBap+4xKXUevgny6dRniTzVhB5GvGHHlsrBPjr8e+LtAICzV6/hof8cgiQB98UMRIS/H14YPQpvlp1yqG5DaSkDL3UJA68d6r6t6NTnfAQNEtAbF6Trna47/FoLDH5Cpz9P5ImGBNs4icSCfoEBeHp4EpYfK8F9MQMRFRCAqIAAaATHfr4MlZUITL3V4fqJ2vEdrx1amzofOLvKT+SOnkQ3Cujkv1zDQ0JwqrGxS3VL1zs3ekXUjoHXHg5+I3Ymhl2ijrp1h3mB/2xS1/D/IDv4hvTotrqvaTjMTHSjK46PNAMAvqtvQGzPnl2qWxMY0KXPEzHw2qH3qCHdVneVL/+KiG50/LrjX0jPXbuGP35bgtmDB3Wpbt+YwV36PBEnV9khdIxjy4CMkggd2v5hWCLtRyMMaIWEg1INnkYK+gj+dt/rXE9f+OoYfIl+6nujBMBgs1xDSyt+VvAlWn5YTnR/zCDM+GE5UUl9A/73wH9Q39KCPdXViOnRA3+/fbzNe/rGxXW1+eTlGHjt4NerJ3qNGIy6Y+V2lT+LJoShbThqhdD52Y8hCQNwqWRTpz9P5MnOProQLadPy5YpvetnVvOSQoJROHWKQ3Xq+vWHNjjEoc8Q3YhdKTsNnTfNrnJfSufwOr7D3ej6cFT8POv/aBB5u6D/meoVdZLnESSpW+cHuo3muka8138GWq8qs7RI6++LX1Vtg38ov10TWWJsbMSZ/50DqblZkfoEX1/0/9smaIODFamPPBd7vHby69UTiQvvVqy+xAV3MegSydD27ImgO3+uWH1BmdMYdMkpGHgdcNNzcxGSONDl9QTF9cOYP/yvy+shcne9Zt0Pn/79XV6PLioKvX79gMvrIe/AwOsAnb8vbtuwGIJO67I6BK0Gt29YDF2g/TOfibyVxtcXoY8+Dmhd9zMJjQZhjz4OjR9/Jsk5GHgdFH7rMNy+8UkIGhf80QkCxr+5CBG3jXT+vYk8lH9CIsIefRxw0c9k2COPwX/YcOffm7wWJ1d1UtnWfHw5dyWkVuccxC1oNRj/5iLEz8lwyv2IvE3j3gJcXPMXwOicn0loNAh75DH0nOzYkiMiWxh4u+DioRP4MvtPnT69qF1I4kDcvvFJ9E1Nck7DiLxU88mTuPjyX9BSWdml+/j074+wR38Lv4QEJ7WM6EcMvF1kNLTgmz+8g5JXPoShvsmhz/oE90DS//cLJC+dDZ2/r4taSORdpJYW1L23HQ3//hekJsd+JoXAQAT/7E6EzJgFjS9/Jsk1GHidpKXpGk5tycfJjXmoPXwSYkurxXKCTovQm+IQnz0Nsb9Oh09PbrhO5Ari9eto2luAK5/thuFUGdBq+WcSWi18hwxBzyn/g54TJ0ETwJ9Jci0GXhcwNhtw6egpXD56Ci1X2s7u9AkKQO+RQ9B71BD2bokUJrW0wFBRDkNFBcRrbT+TmoAA+A4aBJ+YwezdkqIYeImIiBTE5UREREQKYuAlIiJSEAMvERGRghh4iYiIFMTAS0REpCAGXiIiIgUx8BIRESmIgZeIiEhBDLxEREQKYuAlIiJSEAMvERGRghh4iYiIFMTAS0REpCAGXiIiIgUx8BIRESmIgZeIiEhBDLxEREQKYuAlIiJSEAMvERGRghh4iYiIFMTAS0REpCAGXiIiIgUx8BIRESmIgZeIiEhBDLxEREQK+n/GW0KKeCprswAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "flow_at_5hr = results.link['flowrate'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, link_attribute=flow_at_5hr, \n", - " node_attribute=pressure_at_5hr, \n", - " node_size=500, \n", - " link_width=5, \n", - " node_labels=True,\n", - " link_cmap=plt.cm.cividis)" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_pressure = results.node['pressure'].values[0][:2]\n", - "ref_rate = results.link['flowrate'].values[0]\n", - "ref_values = np.append(ref_rate, ref_pressure)\n", - "ref_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the QUBO Polynomial Solver" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "wn = wntr.network.WaterNetworkModel(inp_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 0.000000 => 200.000000 (res: 1.574803)\n", - "Flow Encoding : -4.000000 => -0.000000 | 0.000000 => 4.000000 (res: 0.031496)\n" - ] - } - ], - "source": [ - "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "nqbit = 7\n", - "step = (4./(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", - "\n", - "nqbit = 7\n", - "step = (200/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", - "\n", - "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)\n", - "net.verify_encoding()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve the system classically" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([1. , 1. , 0.999, 0.998])" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.create_index_mapping(model)\n", - "net.matrices = net.initialize_matrices(model)\n", - "\n", - "ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = net.classical_solution()\n", - "ref_sol / ref_values" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1,\n", - " 1,\n", - " [0, 0, 0, 1, 1, 1, 0],\n", - " [0, 0, 0, 1, 1, 1, 0],\n", - " [1, 1, 1, 0, 1, 1, 0],\n", - " [0, 0, 0, 0, 1, 1, 0]]" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bin_rep_sol" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_values, encoded_ref_sol)\n", - "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", - "plt.grid(which=\"major\", lw=1)\n", - "plt.grid(which=\"minor\", lw=0.1)\n", - "# plt.loglog()\n", - "plt.xscale('symlog')\n", - "plt.yscale('symlog')" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.matrices = net.initialize_matrices(model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", - "sampler = SimulatedAnnealing()" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [], - "source": [ - "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", - "import sparse\n", - "net.qubo = QUBOPS_MIXED(net.mixed_solution_vector, {\"sampler\": sampler})\n", - "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", - "net.qubo.qubo_dict = net.qubo.create_bqm(matrices, strength=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.step.full_random import IncrementalStep\n", - "\n", - "var_names = sorted(net.qubo.qubo_dict.variables)\n", - "net.qubo.create_variables_mapping()\n", - "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# generate init sample" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", - "x = modify_solution_sample(net, bin_rep_sol, modify=['flows', 'heads'])\n", - "x0 = list(x.values())" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [], - "source": [ - "eref = net.qubo.energy_binary_rep(bin_rep_sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [], - "source": [ - "num_sweeps = 2000\n", - "Tinit = 1E1\n", - "Tfinal = 1E-1\n", - "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", - "Tschedule = np.append(Tschedule, np.zeros(1000))\n", - "# Tschedule = np.zeros(10000)" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 4000/4000 [00:06<00:00, 651.19it/s]\n", - "100%|██████████| 4000/4000 [00:06<00:00, 665.09it/s]\n", - "100%|██████████| 4000/4000 [00:04<00:00, 803.22it/s]\n" - ] - } - ], - "source": [ - "mystep.optimize_values = np.arange(2,6)\n", - "res = sampler.sample(net.qubo, init_sample=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", - "\n", - "mystep.optimize_values = np.arange(2,4)\n", - "res2 = sampler.sample(net.qubo, init_sample=res.res, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", - "\n", - "mystep.optimize_values = np.arange(4,6)\n", - "res3 = sampler.sample(net.qubo, init_sample=res2.res, Tschedule=Tschedule, take_step=mystep, save_traj=True, verbose=False)\n", - "\n", - "mystep.verify_quadratic_constraints(res3.res)" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": {}, - "outputs": [], - "source": [ - "idx_min = np.array([e for e in res.energies]).argmin()" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 123, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "eplt = res.energies-eref[0]\n", - "\n", - "# fig, ax1 = plt.subplots()\n", - "\n", - "left, bottom, width, height = [0.55, 0.55, 0.3, 0.3]\n", - "\n", - "plt.plot(res3.energies[:]-eref, lw=4, label=\"QUBO Energy\")\n", - "plt.plot(Tschedule, lw=3, label='Temperature')\n", - "# ax1.axline((0, 0), slope=0, color=\"black\", lw=4, linestyle=(4, (1, 2)))\n", - "plt.grid(which='both')\n", - "# plt.yscale('symlog')\n", - "\n", - "plt.ylabel('Energy', fontsize=14)\n", - "plt.xlabel('Iterations', fontsize=14)\n", - "plt.legend(fontsize=12)\n", - "\n", - "# ax2 = fig.add_axes([left, bottom, width, height])\n", - "# ax2.plot(eplt[-1000:])\n", - "# ax2.grid()\n", - "# ax2.axline((0, 0), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "# ax2.set_yscale('symlog')\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [], - "source": [ - "idx_min = np.array([e for e in res.energies]).argmin()\n", - "# idx_min = -1\n", - "sol = res.trajectory[idx_min]\n", - "sol = net.qubo.decode_solution(np.array(sol))\n", - "sol = net.combine_flow_values(sol)\n", - "sol = net.convert_solution_to_si(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-9562.760602598233 [-9562.926]\n", - "[0.165]\n" - ] - } - ], - "source": [ - "print(eref[0], res.energies[idx_min])\n", - "print(eref[0] - res.energies[idx_min])" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Pressure')" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "\n", - "fig = plt.figure(figsize = plt.figaspect(0.5))\n", - "ax1 = fig.add_subplot(121)\n", - "\n", - "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.grid()\n", - "\n", - "ax1.scatter(ref_values[:2], encoded_ref_sol[:2], c='black', s=200, label='Best solution')\n", - "ax1.scatter(ref_values[:2], sol[:2], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "\n", - "\n", - "ax1.set_xlabel('Reference Values', fontsize=12)\n", - "ax1.set_ylabel('QUBO Values', fontsize=12)\n", - "ax1.set_title('Flow Rate', fontsize=14)\n", - "\n", - "ax2 = fig.add_subplot(122)\n", - "\n", - "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "\n", - "\n", - "ax2.scatter(ref_values[2:], encoded_ref_sol[2:], c='black', s=200, label='Best solution')\n", - "ax2.scatter(ref_values[2:], sol[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "ax2.grid()\n", - "\n", - "\n", - "ax2.set_xlabel('Reference Values', fontsize=12)\n", - "ax2.set_title('Pressure', fontsize=14)" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [], - "source": [ - "def flatten_list(lst):\n", - " out = []\n", - " for elmt in lst:\n", - " if not isinstance(elmt, list):\n", - " out += [elmt]\n", - " else:\n", - " out += elmt\n", - " return out\n", - "\n", - "bin_rep_flat = flatten_list(bin_rep_sol)\n", - "xt_bin_rep_flat = net.qubo.extend_binary_representation(bin_rep_flat)\n", - "# xt_bin_rep_flat.values()" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 1 0]\n", - "[1 1 0 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0]\n" - ] - } - ], - "source": [ - "print(np.array(res.trajectory[idx_min])[net.qubo.index_variables])\n", - "print(np.array(bin_rep_flat))" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-9562.926])" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xx = np.array(res.trajectory[idx_min])[net.qubo.index_variables]\n", - "net.qubo.energy_binary_rep(xx)" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [], - "source": [ - "r = np.array(res.trajectory[idx_min])[net.qubo.index_variables]" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [], - "source": [ - "def un_flatten_list(lst):\n", - " out = []\n", - " count = 0\n", - " for er in net.qubo.mixed_solution_vectors.encoded_reals:\n", - " nqbit = er.nqbit\n", - " d = (np.array(lst)[count:count+nqbit]).tolist()\n", - " out.append(d)\n", - " count += nqbit\n", - " return out\n", - "unflat_r = un_flatten_list(r)" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/64 [00:00 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "100%|██████████| 64/64 [00:02<00:00, 22.35it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import itertools\n", - "from tqdm import tqdm\n", - "from copy import deepcopy\n", - "\n", - "\n", - "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", - "\n", - "i2 = 0\n", - "random1 = np.random.randint(2,size=nqbit).tolist()\n", - "random2 = np.random.randint(2,size=nqbit).tolist()\n", - "\n", - "max_size = 64\n", - "iter_data = np.array(list(itertools.product([0, 1], repeat=nqbit)))\n", - "scale_factor = int(len(iter_data)/max_size)\n", - "if len(iter_data>max_size):\n", - " iter_data = iter_data[::scale_factor,:]\n", - "\n", - "energies = np.zeros((max_size,max_size))\n", - "\n", - "for data2 in tqdm(iter_data):\n", - " i3 = 0\n", - " for data3 in iter_data:\n", - " # print(list(data))\n", - " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", - " mod_bin_rep_sol[2] = list(data2)[::-1]\n", - " mod_bin_rep_sol[3] = list(data3)[::-1]\n", - " # mod_bin_rep_sol[4] = random1\n", - " # mod_bin_rep_sol[5] = random2\n", - " mod_bin_rep_sol[4] = unflat_r[4]\n", - " mod_bin_rep_sol[5] = unflat_r[5]\n", - " # mod_bin_rep_sol[4] = np.ones(5).tolist()\n", - " # mod_bin_rep_sol[5] = np.ones(5).tolist()\n", - "\n", - " # x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", - " # x0 = list(x.values())\n", - " energies[i3,i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - " i3+=1\n", - " i2+=1\n", - "\n", - "# x, y = np.arange(2**nqbit), np.arange(2**nqbit)\n", - "# x,y = np.meshgrid(x,y)\n", - "# ax = plt.figure().add_subplot(projection='3d')\n", - "# ax.plot_surface(x,y,energies)\n", - "\n", - "plt.imshow(energies- eref)\n", - "plt.colorbar()\n", - "x2 = int(''.join(str(i) for i in bin_rep_sol[2][::-1]),base=2)/scale_factor\n", - "x3 = int(''.join(str(i) for i in bin_rep_sol[3][::-1]),base=2)/scale_factor\n", - "plt.contour(energies-eref, levels=[1e-2,1,2, 10])\n", - "plt.hlines(x3,0,max_size,ls='--',colors='black')\n", - "plt.vlines(x2,0,max_size,ls='--',colors='black')" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/128 [00:00 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "/tmp/ipykernel_5056/3475343188.py:29: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", - " energies2[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "100%|██████████| 128/128 [00:00<00:00, 726.03it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import itertools\n", - "from tqdm import tqdm\n", - "\n", - "nqbit = net.mixed_solution_vector.encoded_reals[2].nqbit\n", - "\n", - "random1 = np.random.randint(2,size=nqbit).tolist()\n", - "random2 = np.random.randint(2,size=nqbit).tolist()\n", - "\n", - "i2 = 0\n", - "\n", - "iter_data = np.array(list(itertools.product([0, 1], repeat=nqbit)))\n", - "if len(iter_data>128):\n", - " iter_data = iter_data[::int(len(iter_data)/128),:]\n", - "\n", - "energies = np.zeros(128)\n", - "energies2 = np.zeros(128)\n", - "\n", - "for data2 in tqdm(iter_data):\n", - "\n", - " mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", - " mod_bin_rep_sol[3] = list(data2)[::-1]\n", - " # mod_bin_rep_sol[2] = list(data2)[::-1]\n", - " energies[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - "\n", - " # mod_bin_rep_sol[3] = random1 # unflat_r[3]\n", - " mod_bin_rep_sol[2] = unflat_r[2]\n", - " mod_bin_rep_sol[4] = unflat_r[4]\n", - " mod_bin_rep_sol[5] = unflat_r[5]\n", - " energies2[i2] = net.qubo.energy_binary_rep(mod_bin_rep_sol)\n", - " i2+=1\n", - "\n", - "\n", - "encoded_real = net.qubo.mixed_solution_vectors.encoded_reals[2]\n", - "xaxis_val = []\n", - "for i in range(len(iter_data)):\n", - " ibin = np.binary_repr(i,width=nqbit)\n", - " xaxis_val.append(encoded_real.decode_polynom([int(i) for i in ibin[::-1]]))\n", - "\n", - "\n", - "plt.semilogy(xaxis_val, energies-eref, lw=4, label='Exact Values')\n", - "plt.semilogy(xaxis_val, energies2-eref, lw=4, label='Optimized Values')\n", - "plt.xlabel('Flow Value', fontsize=14)\n", - "plt.ylabel('QUBO Energy', fontsize=14)\n", - "plt.ylim([1E0,1E3])\n", - "plt.grid(which='both', axis='both')\n", - "plt.legend(loc=1, fontsize=12)" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3779527559055118" - ] - }, - "execution_count": 134, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r0 = net.qubo.mixed_solution_vectors.encoded_reals[2]\n", - "zz = np.binary_repr(12,width=9)\n", - "r0.decode_polynom([int(z) for z in zz[::-1]])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Embed the problem" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": {}, - "outputs": [], - "source": [ - "import dwave_networkx as dnx\n", - "from minorminer import find_embedding\n", - "from dwave.embedding import embed_qubo, majority_vote, chain_break_frequency" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{('x_002_001', 'x_004_002'): -0.44490918691668147,\n", - " ('x_004_002*x_002_001', 'x_004_002'): 0.0,\n", - " ('x_004_002*x_002_001', 'x_002_001'): 0.0,\n", - " ('x_004_001', 'x_004_002'): 0.007498113259480858,\n", - " ('x_004_001', 'x_002_001'): -0.22245459345834068,\n", - " ('x_004_001', 'x_004_002*x_002_001'): -1.734723475976807e-18,\n", - 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" ('x_004_007*x_004_002', 'x_004_004*x_004_005'): 3.6302454292106194,\n", - " ('x_004_007*x_004_002', 'x_004_003*x_004_005'): 1.8151227146053097,\n", - " ('x_004_007*x_004_002', 'x_004_006*x_004_005'): 14.520981716842478,\n", - " ('x_003_007*x_003_001', 'x_003_003'): 2.411614516938337,\n", - " ('x_003_007*x_003_001', 'x_001_001*x_003_003'): -2.220446049250313e-16,\n", - " ('x_003_007*x_003_001', 'x_003_005'): 11.00780010370733,\n", - " ('x_003_007*x_003_001', 'x_001_001*x_003_005'): -7.993605777301127e-15,\n", - " ('x_003_007*x_003_001', 'x_003_002'): 1.1774459660534606,\n", - " ('x_003_007*x_003_001', 'x_001_001*x_003_002'): -1.1102230246251565e-16,\n", - " ('x_003_007*x_003_001', 'x_001_001*x_003_004'): -4.440892098500626e-16,\n", - " ('x_003_007*x_003_001', 'x_003_007'): 0.0,\n", - " ('x_003_007*x_003_001', 'x_003_001'): 0.0,\n", - " ('x_003_007*x_003_001', 'x_003_002*x_003_005'): 0.45378067865132743,\n", - " ('x_003_001*x_003_006', 'x_003_003'): 0.7520265798178412,\n", - " ('x_003_001*x_003_006', 'x_001_001*x_003_003'): -1.1102230246251565e-16,\n", - 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" ...}" - ] - }, - "execution_count": 136, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.qubo.qubo_dict.to_qubo()[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [], - "source": [ - "# target_graph = dnx.pegasus_graph(6)\n", - "# embedding = find_embedding(net.qubo.qubo_dict.to_qubo()[0], target_graph)" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [], - "source": [ - "# embedding" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": {}, - "outputs": [], - "source": [ - "# dnx.draw_pegasus(dnx.pegasus_graph(6), node_size=2, width=0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [], - "source": [ - "# dnx.draw_pegasus_embedding(target_graph, embedding, node_size=10, width=0.25)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb b/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb index 18ce92a..3aaafa1 100644 --- a/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb +++ b/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb @@ -773,7 +773,7 @@ ], "metadata": { "kernelspec": { - "display_name": "vitens", + "display_name": "vitens_wntr_1", "language": "python", "name": "python3" }, diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index c4424d1..95724c1 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -363,32 +363,59 @@ def create_index_mapping(self, model: Model) -> None: idx += 1 def solve( # noqa: D417 - self, init_sample, Tschedule, save_traj=False, verbose=False + self, + init_sample, + Tschedule, + optimize_values=None, + save_traj=False, + verbose=False, ) -> Tuple: """Sample the qubo problem. Args: init_sample (list): initial sample for the optimization Tschedule (list): temperature schedule for the optimization + optimize_values (None, list): a list of variables to optimize (default to None-> all variables) save_traj (bool, optional): save the trajectory. Defaults to False. verbose (bool, optional): print status. Defaults to False. Returns: Tuple: Solver status, str, solution, SimulatedAnnealingResults """ - res = self.sampler.sample( - self.qubo, - init_sample=init_sample, - Tschedule=Tschedule, - take_step=self.step_func, - save_traj=save_traj, - verbose=verbose, - ) + if optimize_values is None: + res = self.sampler.sample( + self.qubo, + init_sample=init_sample, + Tschedule=Tschedule, + take_step=self.step_func, + save_traj=save_traj, + verbose=verbose, + ) - # extact the solution and decode it - idx_min = np.array([e for e in res.energies]).argmin() - # idx_min = -1 - sol = res.trajectory[idx_min] + # extact the solution + idx_min = np.array([e for e in res.energies]).argmin() + sol = res.trajectory[idx_min] + + else: + res = [] + for opt_vals in optimize_values: + self.step_func.optimize_values = opt_vals + iter_res = self.sampler.sample( + self.qubo, + init_sample=init_sample, + Tschedule=Tschedule, + take_step=self.step_func, + save_traj=save_traj, + verbose=verbose, + ) + res.append(iter_res) + init_sample = iter_res.res + + # extact the solution a + idx_min = np.array([e for e in res[-1].energies]).argmin() + sol = res[-1].trajectory[idx_min] + + # decode the solution sol = self.qubo.decode_solution(np.array(sol)) # convert the solution to SI From da2c4b659a6f92fa8261c28e7a67eccd62a5ad7b Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 16 Dec 2024 15:11:11 +0100 Subject: [PATCH 94/96] add multisolve --- .../qubo_poly_solver_2loops_dw.ipynb | 984 ------------------ .../sim/solvers/qubo_polynomial_solver.py | 35 + 2 files changed, 35 insertions(+), 984 deletions(-) delete mode 100644 docs/notebooks/qubo_polynomial_solver/qubo_poly_solver_2loops_dw.ipynb diff --git a/docs/notebooks/qubo_polynomial_solver/qubo_poly_solver_2loops_dw.ipynb b/docs/notebooks/qubo_polynomial_solver/qubo_poly_solver_2loops_dw.ipynb deleted file mode 100644 index 998125e..0000000 --- a/docs/notebooks/qubo_polynomial_solver/qubo_poly_solver_2loops_dw.ipynb +++ /dev/null @@ -1,984 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define the system " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", - "\n", - "# Create a water network model\n", - "# inp_file = './networks/Net0.inp'\n", - "inp_file = './networks/Net2LoopsDWflat.inp'\n", - "inp_file = './networks/Net2LoopsCMflat.inp'\n", - "# inp_file = './networks/Net2LoopsDW.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the original Cholesky EPANET simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3.111e-01, 5.111e-02, 2.322e-01, 3.108e-02, 1.678e-01, 7.613e-02, 2.334e-02, -2.058e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_pressure = results.node['pressure'].values[0]\n", - "ref_rate = results.link['flowrate'].values[0]\n", - "ref_values = np.append(ref_rate, ref_pressure)\n", - "ref_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the QUBO Polynomial Solver" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "wn = wntr.network.WaterNetworkModel(inp_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 500.000000 => 1000.000000 (res: 0.244260)\n", - "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.007328)\n" - ] - } - ], - "source": [ - "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "nqbit = 11\n", - "step = (15/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"q\")\n", - "\n", - "nqbit = 11\n", - "step = (500/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+500.0, var_base_name=\"h\")\n", - "\n", - "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)\n", - "net.verify_encoding()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve the system classically" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1. , 1. , 1. , 1. , 1. , 1. , 1. , 0.999, 1. , 1.001, 1. , 1.001, 1. , 1.001])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.create_index_mapping(model)\n", - "net.matrices = net.initialize_matrices(model)\n", - "\n", - "ref_sol, encoded_ref_sol, bin_rep_sol, cvgd = net.classical_solutions()\n", - "ref_sol / ref_values[:-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3.110e-01, 5.105e-02, 2.322e-01, 3.113e-02, 1.679e-01, 7.615e-02, 2.345e-02, -2.054e-02, 2.008e+02, 1.819e+02, 1.956e+02, 1.641e+02, 1.906e+02, 1.779e+02])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "encoded_ref_sol" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_values[:-1], encoded_ref_sol)\n", - "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", - "plt.grid(which=\"major\", lw=1)\n", - "plt.grid(which=\"minor\", lw=0.1)\n", - "# plt.loglog()\n", - "plt.xscale('symlog')\n", - "plt.yscale('symlog')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Own sampler" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.matrices = net.initialize_matrices(model)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.simulated_annealing import SimulatedAnnealing\n", - "# from wntr_quantum.sampler.simulated_annealing_parallel import SimulatedAnnealing\n", - "sampler = SimulatedAnnealing()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from qubops.qubops_mixed_vars import QUBOPS_MIXED\n", - "import sparse\n", - "net.qubo = QUBOPS_MIXED(net.mixed_solution_vector, {\"sampler\": sampler})\n", - "matrices = tuple(sparse.COO(m) for m in net.matrices)\n", - "net.qubo.qubo_dict = net.qubo.create_bqm(matrices, strength=1E7)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.step.full_random import RandomStep\n", - "from wntr_quantum.sampler.step.full_random import IncrementalStep\n", - "from wntr_quantum.sampler.step.full_random import ParallelIncrementalStep \n", - "\n", - "var_names = sorted(net.qubo.qubo_dict.variables)\n", - "net.qubo.create_variables_mapping()\n", - "# mystep = RandomStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables)\n", - "mystep = IncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=25)\n", - "# mystep = ParallelIncrementalStep(var_names, net.qubo.mapped_variables, net.qubo.index_variables, step_size=100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# generate random initial guess" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.simulated_annealing import generate_random_valid_sample\n", - "x = generate_random_valid_sample(net.qubo)\n", - "x0 = list(x.values())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## generate modifed solution initial guess" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sampler.simulated_annealing import modify_solution_sample\n", - "x = modify_solution_sample(net, bin_rep_sol, modify=['flows', 'heads'])\n", - "x0 = list(x.values())" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def flatten_list(lst):\n", - " out = []\n", - " for elmt in lst:\n", - " if not isinstance(elmt, list):\n", - " out += [elmt]\n", - " else:\n", - " out += elmt\n", - " return out\n", - "\n", - "from copy import deepcopy\n", - "mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", - "\n", - "# # modsify sign\n", - "# for i in range(8):\n", - "# mod_bin_rep_sol[i] = np.random.randint(2)\n", - "\n", - "# modify flow value\n", - "for i in range(8, 16):\n", - " mod_bin_rep_sol[i] = list(np.random.randint(2, size=flow_encoding.nqbit))\n", - "\n", - "# modify head values\n", - "for i in range(16,22):\n", - " mod_bin_rep_sol[i] = list(np.random.randint(2, size=head_encoding.nqbit))\n", - "\n", - "x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", - "x0 = list(x.values())" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-35736.142])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "eref = net.qubo.energy_binary_rep(bin_rep_sol)\n", - "eref" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "num_sweeps = 8000\n", - "Tinit = 1E6\n", - "Tfinal = 1E1\n", - "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(2000))\n", - "\n", - "num_sweeps = 8000\n", - "Tinit = 1E1\n", - "Tfinal = 0\n", - "Tschedule = np.append(Tschedule, np.linspace(Tinit, Tfinal, num_sweeps))\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(2000))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20000/20000 [03:11<00:00, 104.43it/s]\n" - ] - } - ], - "source": [ - "mystep.optimize_values = np.arange(8, 22)\n", - "res = sampler.sample(net.qubo.qubo_dict, x0=x0, Tschedule=Tschedule, take_step=mystep, save_traj=True)\n", - "mystep.verify_quadratic_constraints(res.res)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "eplt = res.energies\n", - "\n", - "fig, ax1 = plt.subplots()\n", - "\n", - "left, bottom, width, height = [0.25, 0.25, 0.3, 0.3]\n", - "\n", - "ax1.plot(eplt)\n", - "ax1.plot(Tschedule)\n", - "ax1.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "# plt.ylim([-1E5, -1E4])\n", - "# plt.xlim([9000,11000])\n", - "ax1.grid()\n", - "ax1.set_yscale('symlog')\n", - "\n", - "ax2 = fig.add_axes([left, bottom, width, height])\n", - "ax2.plot(eplt[-100:])\n", - "ax2.grid()\n", - "ax2.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "# ax2.set_yscale('symlog')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 3000/3000 [00:31<00:00, 95.53it/s] \n" - ] - } - ], - "source": [ - "num_sweeps = 2000\n", - "Tinit = 1E1\n", - "Tfinal = 0\n", - "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", - "\n", - "\n", - "mystep.optimize_values = np.arange(16)\n", - "res = sampler.sample(net.qubo.qubo_dict, x0=res.res, Tschedule=Tschedule, take_step=mystep, save_traj=True)\n", - "mystep.verify_quadratic_constraints(res.res)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "eplt = res.energies\n", - "\n", - "fig, ax1 = plt.subplots()\n", - "\n", - "left, bottom, width, height = [0.25, 0.25, 0.3, 0.3]\n", - "\n", - "ax1.plot(eplt)\n", - "ax1.plot(Tschedule)\n", - "ax1.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "# plt.ylim([-1E5, -1E4])\n", - "# plt.xlim([9000,11000])\n", - "ax1.grid()\n", - "ax1.set_yscale('symlog')\n", - "\n", - "ax2 = fig.add_axes([left, bottom, width, height])\n", - "ax2.plot(eplt)\n", - "ax2.grid()\n", - "ax2.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "# ax2.set_yscale('symlog')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 6000/6000 [00:58<00:00, 102.93it/s]\n" - ] - } - ], - "source": [ - "num_sweeps = 5000\n", - "Tinit = 1E2\n", - "Tfinal = 0\n", - "Tschedule = np.linspace(Tinit, Tfinal, num_sweeps)\n", - "Tschedule = np.append(Tschedule, Tfinal*np.ones(1000))\n", - "\n", - "\n", - "mystep.optimize_values = np.arange(16)\n", - "res = sampler.sample(net.qubo.qubo_dict, x0=res.res, Tschedule=Tschedule, take_step=mystep, save_traj=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "eplt = res.energies\n", - "\n", - "fig, ax1 = plt.subplots()\n", - "ax2 = ax1.twinx()\n", - "\n", - "ax1.plot(Tschedule, c = 'orange')\n", - "\n", - "ax2.plot(eplt)\n", - "ax2.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "ax2.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "idx_min = np.array([e[0] for e in res.energies]).argmin()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "sol = res.trajectory[idx_min]\n", - "sol = net.qubo.decode_solution(np.array(sol))\n", - "sol = net.combine_flow_values(sol)\n", - "sol = net.convert_solution_to_si(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Pressure')" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "\n", - "fig = plt.figure(figsize = plt.figaspect(0.5))\n", - "ax1 = fig.add_subplot(121)\n", - "\n", - "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.grid()\n", - "\n", - "ax1.scatter(ref_values[:8], encoded_ref_sol[:8], c='black', s=200, label='Best solution')\n", - "ax1.scatter(ref_values[:8], sol[:8], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "\n", - "\n", - "ax1.set_xlabel('Reference Values', fontsize=12)\n", - "ax1.set_ylabel('QUBO Values', fontsize=12)\n", - "ax1.set_title('Flow Rate', fontsize=14)\n", - "\n", - "ax2 = fig.add_subplot(122)\n", - "\n", - "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "\n", - "\n", - "ax2.scatter(ref_values[8:-1], encoded_ref_sol[8:], c='black', s=200, label='Best solution')\n", - "ax2.scatter(ref_values[8:-1], sol[8:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "ax2.grid()\n", - "\n", - "ax2.set_xlim([160,210])\n", - "ax2.set_ylim([160,210])\n", - "ax2.set_xlabel('Reference Values', fontsize=12)\n", - "ax2.set_title('Pressure', fontsize=14)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Explore the solution space" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "def flatten_list(lst):\n", - " out = []\n", - " for elmt in lst:\n", - " if not isinstance(elmt, list):\n", - " out += [elmt]\n", - " else:\n", - " out += elmt\n", - " return out\n", - "\n", - "from copy import deepcopy\n", - "mod_bin_rep_sol = deepcopy(bin_rep_sol)\n", - "\n", - "# # modsify sign\n", - "# for i in range(8):\n", - "# mod_bin_rep_sol[i] = np.random.randint(2)\n", - "\n", - "# # modify flow value\n", - "# for i in range(8, 16):\n", - "# mod_bin_rep_sol[i] = list(np.random.randint(2, size=flow_encoding.nqbit))\n", - "\n", - "# # modify head values\n", - "# for i in range(16,22):\n", - "# mod_bin_rep_sol[i] = list(np.random.randint(2, size=head_encoding.nqbit))\n", - "\n", - "x = net.qubo.extend_binary_representation(flatten_list(mod_bin_rep_sol))\n", - "x0 = list(x.values())" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "sol = net.qubo.decode_solution(np.array(x0))\n", - "sol = net.combine_flow_values(sol)\n", - "sol = net.convert_solution_to_si(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Pressure')" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "\n", - "fig = plt.figure(figsize = plt.figaspect(0.5))\n", - "ax1 = fig.add_subplot(121)\n", - "\n", - "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.grid()\n", - "\n", - "ax1.scatter(ref_values[:8], encoded_ref_sol[:8], c='black', s=200, label='Best solution')\n", - "ax1.scatter(ref_values[:8], sol[:8], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "\n", - "\n", - "ax1.set_xlabel('Reference Values', fontsize=12)\n", - "ax1.set_ylabel('QUBO Values', fontsize=12)\n", - "ax1.set_title('Flow Rate', fontsize=14)\n", - "\n", - "ax2 = fig.add_subplot(122)\n", - "\n", - "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "\n", - "\n", - "ax2.scatter(ref_values[8:-1], encoded_ref_sol[8:], c='black', s=200, label='Best solution')\n", - "ax2.scatter(ref_values[8:-1], sol[8:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "ax2.grid()\n", - "\n", - "ax2.set_xlim([160,210])\n", - "ax2.set_ylim([160,210])\n", - "ax2.set_xlabel('Reference Values', fontsize=12)\n", - "ax2.set_title('Pressure', fontsize=14)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/15000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "eplt = res.energies\n", - "\n", - "fig, ax1 = plt.subplots()\n", - "ax2 = ax1.twinx()\n", - "\n", - "ax1.plot(Tschedule, c = 'orange')\n", - "\n", - "ax2.plot(eplt)\n", - "ax2.axline((0, eref[0]), slope=0, color=\"orange\", linestyle=(1, (1, 2)))\n", - "ax2.grid()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "sol = res.res\n", - "sol = res.trajectory[-1]\n", - "sol = net.qubo.decode_solution(np.array(sol))\n", - "sol = net.combine_flow_values(sol)\n", - "sol = net.convert_solution_to_si(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Pressure')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "\n", - "fig = plt.figure(figsize = plt.figaspect(0.5))\n", - "ax1 = fig.add_subplot(121)\n", - "\n", - "ax1.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax1.grid()\n", - "\n", - "ax1.scatter(ref_values[:8], encoded_ref_sol[:8], c='black', s=200, label='Best solution')\n", - "ax1.scatter(ref_values[:8], sol[:8], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "\n", - "\n", - "ax1.set_xlabel('Reference Values', fontsize=12)\n", - "ax1.set_ylabel('QUBO Values', fontsize=12)\n", - "ax1.set_title('Flow Rate', fontsize=14)\n", - "\n", - "ax2 = fig.add_subplot(122)\n", - "\n", - "ax2.axline((0, 0.0), slope=1.10, color=\"grey\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", - "\n", - "\n", - "ax2.scatter(ref_values[8:-1], encoded_ref_sol[8:], c='black', s=200, label='Best solution')\n", - "ax2.scatter(ref_values[8:-1], sol[8:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", - "ax2.grid()\n", - "\n", - "ax2.set_xlim([160,210])\n", - "ax2.set_ylim([160,210])\n", - "ax2.set_xlabel('Reference Values', fontsize=12)\n", - "ax2.set_title('Pressure', fontsize=14)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py index 95724c1..2db9ce2 100644 --- a/wntr_quantum/sim/solvers/qubo_polynomial_solver.py +++ b/wntr_quantum/sim/solvers/qubo_polynomial_solver.py @@ -434,3 +434,38 @@ def solve( # noqa: D417 # returns return (SolverStatus.converged, "Solved Successfully", sol, res) + + def multisolve( # noqa: D417 + self, + init_sample, + Tschedule, + num_reads=10, + optimize_values=None, + save_traj=False, + verbose=False, + ) -> Tuple: + """Sample the qubo problem multiple times. + + Args: + init_sample (list): initial sample for the optimization + Tschedule (list): temperature schedule for the optimization + num_reads (int, default): number of reads (default 1) + optimize_values (None, list): a list of variables to optimize (default to None-> all variables) + save_traj (bool, optional): save the trajectory. Defaults to False. + verbose (bool, optional): print status. Defaults to False. + + Returns: + Tuple: Solver status, str, solution, SimulatedAnnealingResults + """ + sol, res = [], [] + for _ in range(num_reads): + _, _, isol, ires = self.solve( + init_sample=init_sample, + Tschedule=Tschedule, + optimize_values=optimize_values, + save_traj=save_traj, + verbose=verbose, + ) + sol.append(isol) + res.append(ires) + return sol, res From f5e1c6244fd8ec5bf2bf3affd0a33301c1030cd5 Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 16 Dec 2024 15:27:51 +0100 Subject: [PATCH 95/96] added nb for pipe optimization --- docs/notebooks/hhl_Net0_quantum_inspire.ipynb | 381 ------------------ .../design_pipe_diameter.ipynb | 107 ++--- 2 files changed, 43 insertions(+), 445 deletions(-) delete mode 100644 docs/notebooks/hhl_Net0_quantum_inspire.ipynb diff --git a/docs/notebooks/hhl_Net0_quantum_inspire.ipynb b/docs/notebooks/hhl_Net0_quantum_inspire.ipynb deleted file mode 100644 index f3b7169..0000000 --- a/docs/notebooks/hhl_Net0_quantum_inspire.ipynb +++ /dev/null @@ -1,381 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up water network model\n", - "\n", - "In this example, we test our quantum solvers into a slightly larger network as contained in `Net0.inp`. Let's start by setting up the model:|" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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1daanAADg92zzMsFnFRYWqqWlRUNDQ6anAADg92wZA8XFxerp6VFra6vpKQAA+D1bxkBaWppmzJihsrIy01MAAPB7toyB0NBQXXfddcQAAAATYMsYkKS8vDw1NjbKhsdHAgAwqWwbA16vV6dPn9Z7771negoAAH7NtjGwZs0auVwulZeXm54CAIBfs20MXHHFFVq+fLlKS0tNTwEAwK/ZNgYkKTs7W9u3bzc9AwAAv2brGPD5fDpx4sSYrzYGAAD/z9Yx4PF4JElbt241OwQAAD9m6xiIjY1VXFyctmzZYnoKAAB+y9YxIP3tXQW1tbWmZwAA4LdsHwNr167VoUOH1NHRYXoKAAB+yfYxkJeXJ0mqrq42OwQAAD9l+xhYtGiR5s2bp5KSEtNTAADwS7aPAUnKyMjgmQEAAM7BETHg9XrV1tamrq4u01MAAPA7joiBgoICDQ8Pa9u2baanAADgdxwRA8uXL1dUVBSfNwAAwDgcEQMul0s33ngjn0QIAMA4HBEDklRYWKhdu3apv7/f9BQAAPyKY2KgqKhI/f392rFjh+kpAAD4FcfEQHJyssLCwlRaWmp6CgAAfsUxMeB2u7Vq1SpVVFSYngIAgF9xTAxIUn5+vt555x0NDQ2ZngIAgN9wVAx4vV6dPXtWu3fvNj0FAAC/4agYSE9Pl9vtVnl5uekpAAD4DUfFQFhYmJKSkjiIEACAz3BUDEiSx+NRY2OjLMsyPQUAAL/guBhYu3atPvnkE73//vumpwAA4BccFwNZWVlyuVy8xRAAgL9zXAzMmjVL11xzDV9aBADA3zkuBiQpJydH9fX1pmcAAOAXHBkDPp9Px44d07Fjx0xPAQDAOEfGQG5uriSpqqrK8BIAAMxzZAzMnz9fCxcuVElJiekpAAAY58gYkKTMzEzV1taangEAgHGOjQGv16uDBw/q1KlTpqcAAGCUY2OgoKBAlmXx7AAAwPEcGwOLFy9WTEwMxw0AABzPsTHgcrmUnp7OOwoAAI7n2BiQpKKiIu3du1fd3d2mpwAAYIzjY2BoaEjbt283PQUAAGMcHQMrVqxQZGQk31MAAHA0R8dAUFCQ0tLSVFlZaXoKAADGODoGpL+9VNDa2qqBgQHTUwAAMIIYKCpSX1+fmpubTU8BAMAIx8dASkqKQkNDVVZWZnoKAABGOD4GZsyYoRtuuEHl5eWmpwAAYITjY0CS8vPz1dTUpOHhYdNTAACYdsSApOLiYnV1dWnPnj2mpwAAMO2IAf3t64yDg4NVUVFhegoAANOOGJA0c+ZMJSYmqrS01PQUAACmHTHwdx6PR9u3b5dlWaanAAAwrYiBv/N6vWpvb9ehQ4dMTwEAYFoRA3/n8Xjkcrn4aGIAgOMQA383e/ZsLVmyRCUlJaanAAAwrYiBz8jOzlZ9fb3pGQAATCti4DPWrl2ro0eP6i9/+YvpKQAATBti4DNyc3MlSVVVVYaXAAAwfYiBz7jqqqu0YMECjhsAADgKMfA5a9asUW1trekZAABMG2Lgc7xer/bv36/Tp0+bngIAwLQgBj4nPz9flmWprq7O9BQAAKYFMfA511xzjWbPns1xAwAAxyAGPsflcik9PZ13FAAAHIMYGEdRUZH+/Oc/q7e31/QUAACmHDEwjqKiIg0ODqqhocH0FAAAphwxMI6VK1cqIiJCpaWlpqcAADDliIFxBAcHKyUlRRUVFaanAAAw5YiBcygoKFBLS4sGBwdNTwEAYEoRA+fg9XrV29ur1tZW01MAAJhSxMA5pKWlKSQkhOMGAAC2RwycQ0hIiFauXKny8nLTUwAAmFLEwHnk5+ersbFRlmWZngIAwJQhBs7D6/XqzJkz2rdvn+kpAABMGWLgPNasWaOgoCDeYggAsDVi4DwiIyOVkJDAQYQAAFsjBi7A4/Govr7e9AwAAKYMMXABXq9XJ0+e1JEjR0xPAQBgShADF+DxeCRJW7duNTsEAIApQgxcQExMjBYvXqy3337b9BQAAKYEMTABa9as0bZt20zPAABgShADE+Dz+XT48GG1t7ebngIAwKQjBiYgPz9fklRdXW14CQAAk48YmICFCxdq/vz5HDcAALAlYmCCMjIyVFtba3oGAACTjhiYIK/Xq7a2Nv31r381PQUAgElFDExQQUGBhoeHeVcBAMB2iIEJSkhI0KxZs7RlyxbTUwAAmFTEwAS5XC6tXr2aTyIEANgOMXARiouLtWvXLvX19ZmeAgDApCEGLkJhYaEGBga0Y8cO01MAAJg0xMBFuOGGGxQeHq7S0lLTUwAAmDTEwEVwu91atWqVysvLTU8BAGDSEAMXKT8/X83NzRoaGjI9BQCASUEMXKTi4mJ1d3dr165dpqcAADApiIGLlJ6eLrfbzUsFAADbIAYuUlhYmFauXMlBhAAA2yAGLoHH41FjY6MsyzI9BQCAy0YMXIK1a9fq1KlTOnDggOkpAABcNmLgEmRlZcnlcqmiosL0FAAALhsxcAmioqK0bNkyvrQIAGALxMAlysnJUX19vekZAABcNmLgEvl8Ph0/flwfffSR6SkAAFwWYuASeTweSeIrjQEAAY8YuETz58/XwoULOW4AABDwiIHLsGbNGtXW1pqeAQDAZSEGLoPX69XBgwf1ySefmJ4CAMAlIwYuQ35+viSppqbG8BIAAC4dMXAZFi9erNjYWJWUlJieAgDAJSMGLoPL5VJ6erqqq6tNTwEA4JIRA5epuLhY+/bt09mzZ01PAQDgkhADl6mwsFBDQ0Pavn276SkAAFwSYuAyJSYm6oorruC4AQBAwCIGLlNQUJDS0tL4JEIAQMAiBiZBUVGRWltb1d/fb3oKAAAXjRiYBEVFRerv71dzc7PpKQAAXDRiYBKsWrVKoaGhKisrMz0FAICLRgxMghkzZig5OVnl5eWmpwAAcNGIgUmSn5+vHTt2aHh42PQUAAAuCjEwSYqLi9XV1aU///nPpqcAAHBRiIFJkpGRIbfbzUsFAICAQwxMkpkzZyoxMVGlpaWmpwAAcFGIgUnk8XjU0NAgy7JMTwEAYMKIgUnk8/nU0dGhDz74wPQUAAAmjBiYRNnZ2XK5XKqsrDQ9BQCACSMGJtHs2bMVHx/PlxYBAAIKMTDJsrOzVV9fb3oGAAATRgxMsrVr1+rDDz/UiRMnTE8BAGBCiIFJlpubK0mqqqoyvAQAgIkhBibZggULdNVVV3HcAAAgYBADU2DNmjWqra01PQMAgAkhBqaA1+vVgQMH1NnZaXoKAAAXRAxMgfz8fFmWpbq6OtNTAAC4IGJgCixdulRz5szhuAEAQEAgBqaAy+VSeno67ygAAAQEYmCKFBUVac+ePerp6TE9BQCA8yIGpkhhYaEGBwfV0NBgegoAAOdFDEyRlStXKiIiQlu2bDE9BQCA8yIGpkhwcLBSU1P5BkMAgN8jBqZQQUGBdu7cqcHBQdNTAAA4J2JgCnm9XvX29qqlpcX0FAAAzokYmEKpqakKCQlRWVmZ6SkAAJwTMTCFQkJCdN1116m8vNz0FAAAzokYmGL5+flqamqSZVmmpwAAMC5iYIp5vV6dOXNGe/fuNT0FAIBxEQNTLDMzU0FBQaqoqDA9BQCAcREDUywyMlIJCQkqLS01PQUAgHERA9PA4/Govr7e9AwAAMZFDEwDn8+njz/+WIcPHzY9BQCAMYiBaeDxeCRJW7duNTsEAIBxEAPTYO7cuVqyZIlKSkpMTwEAYAxiYJpkZWWprq7O9AwAAMYgBqaJ1+vVkSNHdPLkSdNTAAAYhRiYJnl5eZKk6upqs0MAAPgcYmCaLFy4UFdeeSXHDQAA/A4xMI0yMjJUW1tregYAAKMQA9PI6/Wqra1NZ86cMT0FAIARxMA0KigokGVZ2rZtm+kpAACMIAam0bXXXqvo6Ght2bLF9BQAAEYQA9PI5XJp9erVqqqqMj0FAIARxMA0Kyoq0q5du9TX12d6CgAAkoiBaVdUVKSBgQE1NTWZngIAgCRiYNrdcMMNCg8PV2lpqekpAABIIgamXXBwsFJSUlReXm56CgAAkogBI/Lz89XS0qKhoSHTUwAAIAZM8Hq96u7u1rvvvmt6CgAAxIAJq1ev1owZM1RWVmZ6CgAAxIAJYWFhWrlyJTEAAPALxIAhubm5amxslGVZpqcAAByOGDDE5/Ops7NT7733nukpAACHIwYMWbNmjVwulyoqKkxPAQA4HDFgSFRUlK699lo+fAgAYBwxYFB2drbq6+tNzwAAOBwxYNDatWt14sQJffjhh6anAAAcjBgwyOPxSJK2bt1qdggAwNGIAYPmzZunhQsXqqSkxPQUAICDEQOGZWVlqa6uzvQMAICDEQOGeb1effDBB+ro6DA9BQDgUMSAYfn5+ZKkmpoaw0sAAE5FDBgWFxen2NhYjhsAABhDDBjmcrmUkZGh6upq01MAAA5FDPiB4uJitbW1qaury/QUAIADEQN+oLCwUENDQ3waIQDACGLADyQmJioqKkpbtmwxPQUA4EDEgB9wuVxKS0vjkwgBAEYQA36isLBQ7777rvr7+01PAQA4DDHgJ4qLi9Xf36933nnH9BQAgMMQA35i1apVCgsLU2lpqekpAACHIQb8hNvt1g033KDy8nLTUwAADkMM+JGCggK98847Gh4eNj0FAOAgxIAfKS4u1tmzZ7V7927TUwAADkIM+JGMjAy53W5eKgAATCtiwI+Eh4drxYoVHEQIAJhWxICf8Xg8amhokGVZpqcAAByCGPAzPp9Pn3zyiQ4ePGh6CgDAIYgBP5OdnS2Xy6WKigrTUwAADkEM+Jno6GgtXbqULy0CAEwbYsAPZWdn83XGAIBpQwz4IZ/Pp48++kjHjx83PQUA4ADEgB/Kzc2VJFVVVRleAgBwAmLADy1YsEBf+MIXVFJSYnoKAMABiAE/lZmZqZqaGtMzAAAOQAz4KZ/Pp4MHD6qzs9P0FACAzREDfio/P1+WZam2ttb0FACAzREDfio+Pl5z5szR22+/bXoKAMDmiAE/5XK5lJGRoerqatNTAAA2Rwz4saKiIu3du1c9PT2mpwAAbIwY8GOFhYUaHBzU9u3bTU8BANgYMeDHVq5cqYiICL6nAAAwpYgBPxYUFKS0tDRVVlaangIAsDFiwM8VFBSotbVVAwMDpqcAAGyKGPBzxcXF6u3tVUtLi+kpAACbIgb8XGpqqkJCQlRWVmZ6CgDApogBPxcSEqLrr79e5eXlpqcAAGyKGAgAeXl5ampq0vDwsOkpAAAbIgYCgM/n01//+lft3bvX9BQAgA0RAwEgMzNTwcHBqqioMD0FAGBDxEAAiIiIUEJCAh8+BACYEsRAgMjNzVVDQ4MsyzI9BQBgM8RAgPB6vfr44491+PBh01MAADZDDASInJwcSeK4AQDApCMGAsTcuXMVHx/PcQMAgElHDASQNWvWqL6+3vQMAIDNEAMBxOfz6ciRIzp58qTpKQAAGyEGAkheXp4kqaqqyuwQAICtEAMB5Oqrr9aCBQtUUlJiegoAwEaIgQCTkZGh2tpa0zMAADZCDASY4uJivffeezpz5ozpKQAAmyAGAkxhYaEsy1JdXZ3pKQAAmyAGAsyyZcsUHR3N5w0AACYNMRBgXC6XbrzxRm3dutX0FACATRADAaioqEi7d+9Wb2+v6SkAABsgBgJQUVGRBgcH1dTUZHoKAMAGiIEAdP3112vmzJl83gAAYFIQAwEoODhYKSkpqqysND0FAGADxECAKigoUEtLiwYHB01PAQAEOGIgQBUXF6unp0etra2mpwAAAhwxEKBWr16tGTNmqLy83PQUAECAIwYCVGhoqK677jqVlZWZngIACHDEQADLzc1VY2OjLMsyPQUAEMCIgQDm9Xp1+vRptbW1mZ4CAAhgxEAAy8rKksvlUkVFhekpAIAARgwEsCuuuEIJCQkqLS01PQUAEMCIgQCXnZ2t+vp60zMAAAGMGAhwa9eu1V/+8hcdPXrU9BQAQIAiBgKcx+ORJL7SGABwyYiBABcbG6tFixbxpUUAgEtGDNhAVlaW6urqTM8AAAQoYsAGfD6fDh06pPb2dtNTAAABiBiwgby8PElSdXW12SEAgIBEDNhAXFyc5s2bpy1btpieAgAIQMSATWRkZKimpsb0DABAACIGbKK4uFj79u1TV1eX6SkAgABDDNhEQUGBhoeHtW3bNtNTAAABhhiwicTEREVFRXHcAADgohEDNuFyubR69Wo+iRAAcNGIARspLCzUrl271NfXZ3oKACCAEAM2UlxcrP7+fr3zzjumpwAAAggxYCPJyckKCwtTaWmp6SkAgABCDNiI2+1WcnKyysvLTU8BAAQQYsBmCgoK1NzcrKGhIdNTAAABghiwmeLiYp09e1a7d+82PQUAECCIAZtJT0+X2+1WWVmZ6SkAgABBDNhMeHi4kpKSiAEAwIQRAzbk8XjU2Ngoy7JMTwEABABiwIZ8Pp8++eQTHThwwPQUAEAAIAZsKCsrSy6XS5WVlaanAAACADFgQ9HR0brmmmv40iIAwIQQAzaVnZ3N1xkDACaEGLApn8+n48eP69ixY6anAAD8HDFgU7m5uZKkqqoqw0sAAP6OGLCpK6+8UldffbVKSkpMTwEA+DliwMYyMzNVW1tregYAwM8RAzbm8/l08OBBnTp1yvQUAIAfIwZsLD8/X5Zl8ewAAOC8iAEbW7JkiebOnau3337b9BQAgB8jBmzM5XIpIyND1dXVpqcAAPwYMWBzRUVF2rt3r7q7u01PAQD4KWLA5goLCzU0NKT6+nrTUwAAfooYsLmkpCRFRkaqtLTU9BQAgJ8iBmwuKChIaWlpfIMhAOCciAEHKCwsVGtrqwYGBkxPAQD4IWLAAYqKitTX16fm5mbTUwAAfogYcICUlBSFhoaqrKzM9BQAgB8iBhwgJCRE119/vcrLy01PAQD4IWLAIfLy8tTU1KTh4WHTUwAAfoYYcAifz6euri7t2bPH9BQAgJ8hBhwiMzNTwcHBqqioMD0FAOBniAGHmDlzppYvX64tW7aYngIA8DPEgIPk5uaqoaFBlmWZngIA8CPEgIN4vV61t7fr0KFDpqcAAPwIMeAgOTk5ksRxAwCAUYgBB5kzZ47i4+M5bgAAMAox4DDZ2dl8nTEAYBRiwEFiYmKUm5uro0ePKiIiQt/5zndMTwIAv+Z2u7Vq1SqtWLFCqampevHFF0fOa2xsVFpammbMmKE333zT4MrL5zY9ANMrPT1dkrRo0SK1traqu7tbM2fONLwKAPxTdHS0WlpaJElHjhzRl770JVVVVemKK65QXFycnn/+ef33f/+34ZWXjxhwmHvvvVeStG/fPu3bt085OTmqqakhCADgAmJiYtTd3a3f/e53I6elpKQoMTHR4KrJQQw4SG9v75ivMW5ubtaTTz6p2267zdAqAPBfg4ODI/+/+dprr2n//v2jzm9ublZoaKiJaZPKZfEJNI4RHh6u3t5e0zMAwFYSEhL0zDPP6OabbzY95ZLxzICDBAcHj3v6+vXreWYAAMZRUFAw8tksr732mp588skxl5kzZ850z5p0xICDhIWFKSEhYdRLBSkpKfrud7/LMQMAMA63262UlBRJUmRkpJ599ll1d3ePnJ+SkqL4+HhT8yYNLxM4xODgoOLi4rR//37Fx8ers7NTkjR37lw1NDTo6quvNjsQAPyQ2+3WypUr1d/fr/DwcH3961+X2+3Wzp07FRMTo1/+8pfq7OxUeHi4li1bFrCf40IMOERra6seeeQR1dbWmp4CAPAzfOiQA2zatEl33XWXNmzYYHoKAMAP8cwAAAAOxzMDAAA4HDEAAIDDEQMAADgcMQAAgMMRAwAAOBwxAACAwxEDAAA4HDEAAIDDEQMAADgcMQAAgMMRAwAAOBwxAACAwxEDAAA4HDEAAIDDEQMAADgcMQAAgMMRAwAAOBwxAACAwxEDAAA4HDEAAIDDEQMAADgcMQAAgMMRAwAAOBwxAACAwxEDAAA4HDEAAIDDEQMAADgcMQAAgMMRAwAAOBwxAACAwxEDAAA4HDEAAIDDEQMAADgcMQAAgMMRAwAAONz/AbqEVQXcOxBnAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import wntr\n", - "import wntr_quantum\n", - "\n", - "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", - "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", - "# set up network model\n", - "inp_file = 'networks/Net0.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# plot network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n", - "\n", - "# print options\n", - "# dict(wn.options.hydraulic)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solve model using the classical Epanet simulator\n", - "\n", - "We now solve the same problem using the classical Epanet simulator. Note that, by default, `QuantumEpanetSimulator` uses a classical `CholeskySolver` to iteratively solve the linear problem." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", - "Your EPANET quantum path: /home/nico/QuantumApplicationLab/vitens/EPANET\n", - "Your EPANET temp dir: /home/nico/.epanet_quantum\n", - "\n", - "Size of the Jacobian in EPANET simulator: 2\n", - "Size of the b vector in EPANET simulator: 2\n" - ] - }, - { - "data": { - "text/plain": [ - "(name J1 D1 R1\n", - " 0 29.647690 19.167675 -9.338379e-07\n", - " 3600 29.647692 19.167675 -9.338379e-07,\n", - " name P1 P2\n", - " 0 0.05 0.05\n", - " 3600 0.05 0.05)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import sys\n", - "\n", - "# define the classical EPANET simulator\n", - "sim = wntr_quantum.sim.QuantumEpanetSimulator(wn)\n", - "\n", - "# run the EPANET simulation\n", - "results_epanet = sim.run_sim()\n", - "\n", - "# remember to set up EPANET Quantum environment variables!\n", - "epanet_path = os.environ[\"EPANET_QUANTUM\"]\n", - "epanet_tmp = os.environ[\"EPANET_TMP\"]\n", - "\n", - "# check paths\n", - "print(f\"Your EPANET quantum path: {epanet_path}\")\n", - "print(f\"Your EPANET temp dir: {epanet_tmp}\\n\")\n", - "\n", - "util_path = os.path.join(epanet_path, 'src/py/')\n", - "sys.path.append(util_path)\n", - "\n", - "from quantum_linsolve import load_json_data\n", - "epanet_A, epanet_b = load_json_data(os.path.join(epanet_tmp,'smat.json'))\n", - "\n", - "# set the size of the Jacobian (A matrix)\n", - "epanet_A_dim = epanet_A.todense().shape[0]\n", - "print(f\"Size of the Jacobian in EPANET simulator: {epanet_A_dim}\")\n", - "print(f\"Size of the b vector in EPANET simulator: {epanet_b.shape[0]}\")\n", - "\n", - "# save number of nodes and pipes\n", - "n_nodes = len(results_epanet.node[\"pressure\"].iloc[0]), \n", - "n_pipes = len(results_epanet.link[\"flowrate\"].iloc[0])\n", - "\n", - "results_epanet.node[\"pressure\"], results_epanet.link[\"flowrate\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define a helper function\n", - "\n", - "Before proceeding to the proper quantum solution of the water network model, let's define a helper function. This function checks that the quantum results are within `TOL`% of those obtained classically. It also fills in lists containing the final values of pressures and flow rates obtained." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "TOL = 50 # => per cent\n", - "DELTA = 1.0e-12\n", - "\n", - "\n", - "def get_ape_from_pd_series(quantum_pd_series, classical_pd_series):\n", - " \"\"\"Helper function to evaluate absolute percentage error between classical and quantum results.\"\"\"\n", - " ape = abs(quantum_pd_series - classical_pd_series) * 100.0 / abs(classical_pd_series + DELTA)\n", - " return ape\n", - "\n", - "\n", - "def compare_results(classical_result, quantum_result):\n", - " \"\"\"\n", - " Helper function that compares the classical and quantum simulation results.\n", - " \"\"\"\n", - " classical_data = []\n", - " quantum_data = []\n", - "\n", - " def check_ape(classical_value, quantum_value):\n", - " \"\"\"Helper function to check if the absolute percentage error between classical and quantum results is within TOL.\"\"\"\n", - " ape = abs(quantum_value - classical_value) * 100.0 / abs(classical_value + DELTA)\n", - " is_close_to_classical = ape <= TOL\n", - " if is_close_to_classical:\n", - " print(f\"Quantum result {quantum_value} within {ape}% of classical result {classical_value}\")\n", - " quantum_data.append(quantum_value)\n", - " classical_data.append(classical_value)\n", - " return is_close_to_classical\n", - "\n", - " for link in classical_result.link[\"flowrate\"].columns:\n", - " classical_value = classical_result.link[\"flowrate\"][link].iloc[0]\n", - " quantum_value = quantum_result.link[\"flowrate\"][link].iloc[0]\n", - " message = f\"Flowrate {link}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", - " assert check_ape(classical_value, quantum_value), message\n", - "\n", - " for node in classical_result.node[\"pressure\"].columns:\n", - " classical_value = classical_result.node[\"pressure\"][node].iloc[0]\n", - " quantum_value = quantum_result.node[\"pressure\"][node].iloc[0]\n", - " message = f\"Pressure {node}: {quantum_value} not within {TOL}% of classical result {classical_value}\"\n", - " assert check_ape(classical_value, quantum_value), message\n", - "\n", - " return classical_data, quantum_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solve water network with `QuantumEpanetSimulator` and VQLS \n", - "\n", - "We now solve the model using VQLS. In this example, we are **preconditioning** the initial linear system using *diagonal scaling* and also using a **mix of two classical optimizers**." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/vitens/wntr-quantum/wntr_quantum/epanet/Linux/libepanet22_amd64.so\n", - "Quantum result 0.05003536120057106 within 0.07054196990023498% of classical result 0.05000009015202522\n", - "Quantum result 0.05003482848405838 within 0.06965547696130027% of classical result 0.05000000074505806\n", - "Quantum result 29.64763641357422 within 0.0001801346480760787% of classical result 29.647689819335938\n", - "Quantum result 19.16619110107422 within 0.007741769593393499% of classical result 19.167675018310547\n", - "Quantum result -9.338378959000693e-07 within 0.0% of classical result -9.338378959000693e-07\n" - ] - }, - { - "data": { - "text/plain": [ - "(name J1 D1 R1\n", - " 0 29.647636 19.166191 -9.338379e-07\n", - " 3600 29.647129 19.150408 -9.338379e-07,\n", - " name P1 P2\n", - " 0 0.050035 0.050035\n", - " 3600 0.050042 0.050042)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "from qiskit.primitives import BackendEstimator\n", - "from quantuminspire.sdk.qiskit.backend import QuantumInspireBackend\n", - "from quantuminspire.util.api.quantum_interface import QuantumInterface\n", - "from quantuminspire.sdk.models.hybrid_algorithm import HybridAlgorithm\n", - "\n", - "from quantum_newton_raphson.hhl_solver import HHL_SOLVER\n", - "\n", - "\n", - "\n", - "def calculate_wntr(backend: QuantumInspireBackend):\n", - " n_qubits = int(np.ceil(np.log2(epanet_A_dim)))\n", - " estimator = BackendEstimator(backend=backend)\n", - "\n", - " linear_solver = HHL_SOLVER(\n", - " estimator=estimator,\n", - " # preconditioner=\"diagonal_scaling\",\n", - " )\n", - "\n", - " sim = wntr_quantum.sim.QuantumEpanetSimulator(wn, linear_solver=linear_solver)\n", - " return sim.run_sim(linear_solver=linear_solver)\n", - "\n", - "def execute(qi: QuantumInterface) -> None:\n", - "\n", - " ground_state_energy_results = calculate_wntr(backend=QuantumInspireBackend(qi))\n", - " result = {}\n", - " result[\"total_energy\"] = ground_state_energy_results.nuclear_repulsion_energy + ground_state_energy_results.result.eigenvalue\n", - " qi.results = {\"result\": result}\n", - "\n", - "\n", - "def finalize(results):\n", - " return results\n", - "\n", - "\n", - "classical_res, quantum_res = compare_results(results_epanet, results_hhl)\n", - "results_hhl.node[\"pressure\"], results_hhl.link[\"flowrate\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot pressures and flow rates\n", - "\n", - "Let's check graphically the equivalence of the results." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.scatter(classical_res[:n_pipes], quantum_res[:n_pipes], label=\"Flow rates\", color=\"blue\", marker=\"o\")\n", - "plt.scatter(classical_res[n_pipes:], quantum_res[n_pipes:], label=\"Pressures\", color=\"red\", marker=\"s\", facecolors='none')\n", - "plt.axline((0, 0), slope=1, linestyle=\"--\", color=\"gray\", label=\"\")\n", - "plt.xlabel(\"Classical results\")\n", - "plt.ylabel(\"Quantum results\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wntr.graphics.plot_network(\n", - " wn,\n", - " node_attribute=get_ape_from_pd_series(\n", - " results_hhl.node[\"pressure\"].iloc[0],\n", - " results_epanet.node[\"pressure\"].iloc[0]\n", - " ),\n", - " link_attribute=get_ape_from_pd_series(\n", - " results_hhl.link[\"flowrate\"].iloc[0],\n", - " results_epanet.link[\"flowrate\"].iloc[0],\n", - " ),\n", - " node_colorbar_label='Pressures',\n", - " link_colorbar_label='Flows',\n", - " node_size=50,\n", - " title=f\"{inp_file}: Absolute Percent Error\",\n", - " node_labels=False\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/pipe_diameter_optimization/design_pipe_diameter.ipynb b/docs/notebooks/pipe_diameter_optimization/design_pipe_diameter.ipynb index daa5ae8..57ed226 100644 --- a/docs/notebooks/pipe_diameter_optimization/design_pipe_diameter.ipynb +++ b/docs/notebooks/pipe_diameter_optimization/design_pipe_diameter.ipynb @@ -55,12 +55,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import wntr\n", - "inp_file = './networks/Net0_CM.inp'\n", + "inp_file = '../networks/Net0_CM.inp'\n", "wn = wntr.network.WaterNetworkModel(inp_file)" ] }, @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -100,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -138,15 +138,23 @@ "output_type": "stream", "text": [ "price \t diameters \t variables\t energy\n", - "0.16907910944516957 [250. 250.] [ 0.05 0.05 20.689 11.378] -8896.547627089463\n", - "0.25361866416775436 [250. 500.] [ 0.05 0.05 20.689 20.457] -8189.177038991521\n", - "0.42269777361292393 [ 250. 1000.] [ 0.05 0.05 20.689 20.683] -8189.351066475922\n", - "0.25361866416775436 [500. 250.] [ 0.05 0.05 29.769 20.457] -6581.108863493134\n", - "0.33815821889033915 [500. 500.] [ 0.05 0.05 29.769 29.537] -4978.399309341114\n", - "0.5072373283355087 [ 500. 1000.] [ 0.05 0.05 29.769 29.763] -4978.458985844512\n", - "0.42269777361292393 [1000. 250.] [ 0.05 0.05 29.994 20.683] -6580.062243226369\n", - "0.5072373283355087 [1000. 500.] [ 0.05 0.05 29.994 29.763] -4977.238338093348\n", - "0.6763164377806783 [1000. 1000.] [ 0.05 0.05 29.994 29.988] -4977.069312634741\n" + "0.16907910944516957 [250. 250.] [ 0.05 0.05 20.689 11.378] -7664.1098579916925\n", + "0.25361866416775436 [250. 500.] [ 0.05 0.05 20.689 20.457] -8935.861761472075\n", + "0.42269777361292393 [ 250. 1000.] [ 0.05 0.05 20.689 20.683] -8936.03578895648\n", + "0.25361866416775436 [500. 250.] [ 0.05 0.05 29.769 20.457] -9306.916077552014\n", + "0.33815821889033915 [500. 500.] [ 0.05 0.05 29.769 29.537] -9683.3290149783\n", + "0.5072373283355087 [ 500. 1000.] [ 0.05 0.05 29.769 29.763] -9683.388691481696\n", + "0.42269777361292393 [1000. 250.] [ 0.05 0.05 29.994 20.683] -9305.869457285251\n", + "0.5072373283355087 [1000. 500.] [ 0.05 0.05 29.994 29.763] -9682.168043730535\n", + "0.6763164377806783 [1000. 1000.] [ 0.05 0.05 29.994 29.988] -9681.999018271925\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nico/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", + " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" ] } ], @@ -158,7 +166,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Initial sample for the QUBO optimization \n", + "## Initial sample for the QUBO optimization \n", "\n", "Before minimizing the energy of the QUBO problem we need to define the initial configuration of the binary variables in the QUBO problem. We have implemented two different ways to obtain an initial sample that respects all the conditions imposed by the quadratization constraings of the polynomial qubo solver. \n", "\n", @@ -167,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -180,7 +188,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Temperature scheduling for the Simulated Annealing optimization\n", + "## Temperature scheduling for the Simulated Annealing optimization\n", "\n", "One important parameters of the simulated Annealing process is the the so-called temperature schedule. This schdule defines the acceptance probability of the new samples that increase the QUBO energy. While high temperature that leads to accepting samples that increase energy is usefull to escape local minima the temperature must be decreased in order to converge towards a minima. \n", "\n", @@ -189,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -206,19 +214,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## Solve the problem\n", + "\n", "We can then use the `solve()` method of the qubo polynomial solver to obtain a solution of the problem" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 6100/6100 [00:20<00:00, 300.29it/s]\n" + "100%|██████████| 6100/6100 [00:20<00:00, 296.20it/s]\n" ] } ], @@ -228,15 +238,6 @@ " save_traj=True, verbose=False)" ] }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "res = data[3]" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -246,22 +247,22 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 35, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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1ISL6l0wmg0wmg0Kh+xdDoopEoVBovmZNxYn0VqxbY1+sPJRS4nh9n6riV4aI6B8SiQTe3t64c+cOHBwcIAgCcnNzueinAWq1Gvn5+WynUpiznQRBQE5ODhQKBfz8/Ixetd8Qhi4rFuKrO1w52PEfLBFZlpubG548eYJ79+7h8ePHcHZ2NssPNWslCIJmEVC2k37mbieJRAJ3d3e4ubmZoXYMXVZNqmd4UaXm5FUisiyJRAI/Pz94eHggNjYWHTp04HZJBiiVSuzbt4/tVApzt5O9vb1ZhhWLVOrQNWfOHPz555+4cOECnJ2d0bZtW3z11VeoX7++pkxubi4++OADrFu3Dnl5eYiMjMR3330HHx8fTZnU1FSMGTMGu3fvhqurK4YNG4Y5c+ZoTZrbs2cPJk6ciLNnzyIgIADTpk3D8OHDteqzdOlSzJs3D2lpaWjatCkWL14s+maaxUn1pPz7OXki14SISDeZTIaCggI4OTkxTBjAdjJORW+nSj3OtHfvXowdOxaHDx9GTEwMlEolIiIikJOToykzYcIEbNmyBRs2bMDevXtx+/Zt9O3bV3NepVKhZ8+eyM/Px6FDh/DLL79g5cqVmD59uqZMcnIyevbsiZdeegmJiYkYP3483n77bezcuVNTZv369Zg4cSJmzJiBEydOoGnTpoiMjERGRoY4jaGDTE/o+vVwqsg1ISIiokrd0xUdHa31euXKlfD29kZCQgI6dOiArKws/PTTT1izZg06deoEAFixYgUaNGiAw4cPo02bNti1axfOnTuHv//+Gz4+PmjWrBk+/fRTTJ48GTNnzoSDgwOWLVuG4OBgzJ8/HwDQoEEDHDhwAN988w0iIyMBAAsWLMA777yDt956CwCwbNkybNu2DT///DOmTJkiYqv8i8P+REREFUelDl1Py8oqXAqhWrVqAICEhAQolUp06dJFUyYkJAS1atVCfHw82rRpg/j4eDRp0kRruDEyMhJjxozB2bNnERYWhvj4eK1rFJUZP348ACA/Px8JCQmYOnWq5rxUKkWXLl0QHx+vt755eXnIy/t3qK/o8WmlUgmlUlnGVviXqkCt95w5rm9titqEbWMY28k4bCfjsJ2Mw3YyjqXaydj7WU3oUqvVGD9+PNq1a4fGjRsDANLS0uDg4AB3d3etsj4+PkhLS9OUKR64is4XnTNURqFQ4MmTJ3j48CFUKpXOMhcuXNBb5zlz5mDWrFklju/atQsuLi5GfGrDCufL6/4r3r59u8nXt1YxMTGWrkKlwHYyDtvJOGwn47CdjCN2Oz1+/NioclYTusaOHYukpCQcOHDA0lUx2tSpUzFx4kTNa4VCgYCAAEREROjcrLMsJhzepfN4jx49zHJ9a6JUKhETE4OuXbtWyAmYFQXbyThsJ+OwnYzDdjKOpdrJ2IV+rSJ0RUVFYevWrdi3bx9q1qypOe7r64v8/HxkZmZq9Xalp6fD19dXU+bo0aNa10tPT9ecK/pv0bHiZeRyOZydnTUr1eoqU3QNXRwdHeHo6FjiuL29vdm+WDqHeCP2gvZk/qpOdvxHa4A529+asZ2Mw3YyDtvJOGwn44jdTsbeq1I/vSgIAqKiovDXX38hLi4OwcHBWudbtGgBe3t7xMbGao5dvHgRqampCA8PBwCEh4fjzJkzWk8ZxsTEQC6Xo2HDhpoyxa9RVKboGg4ODmjRooVWGbVajdjYWE0ZS6nu6lDiGPeYJSIiEl+l7ukaO3Ys1qxZg02bNqFq1aqaOVhubm5wdnaGm5sbRo4ciYkTJ6JatWqQy+V47733EB4ejjZt2gAAIiIi0LBhQwwZMgRz585FWloapk2bhrFjx2p6oUaPHo0lS5bgo48+wogRIxAXF4fffvsN27Zt09Rl4sSJGDZsGFq2bIlWrVph4cKFyMnJ0TzNaCm61uoy547pREREZJxKHbq+//57AEDHjh21jq9YsUKzcOk333wDqVSKfv36aS2OWkQmk2Hr1q0YM2YMwsPDUaVKFQwbNgyzZ8/WlAkODsa2bdswYcIELFq0CDVr1sTy5cs1y0UAwBtvvIG7d+9i+vTpSEtLQ7NmzRAdHV1icr3YdG2DwAXpiYiIxFepQ5cxPTZOTk5YunQpli5dqrdMYGBgqU/zdezYESdPnjRYJioqClFRUaXWSUy61upSs6eLiIhIdJV6TheVTtf2i4xcRERE4mPosnIScE4XERFRRcDQZeV09nQxcxEREYmOocvK6Z5Iz9RFREQkNoYuK6d7Ir349SAiIrJ1DF1WTtc6XQDndREREYmNocvK6Y5cnNdFREQkNoYuKyfVNZMenNdFREQkNoYuK6dndJFrdREREYmMocvK6VqnC2BPFxERkdgYuqycntFFzukiIiISGUOXldP/9KLIFSEiIrJxDF1WTt+cLg4vEhERiYuhy8rpWpEe4ER6IiIisTF0WTl963Sxp4uIiEhcDF1WjnO6iIiIKgaGLiun/+lFpi4iIiIxMXRZOf0T6cWtBxERka1j6LJyeifSs6eLiIhIVAxdVo49XURERBUDQ5eV0z+RnqmLiIhITAxdVk7vRHpxq0FERGTzGLqsHDe8JiIiqhgYuqycvjldzFxERETiYuiycvrmdLGni4iISFwMXVaOPV1EREQVA0OXlWNPFxERUcXA0GXl9D29yHW6iIiIxMXQZeWkelKXiqmLiIhIVAxdVk7G4UUiIqIKgaHLyunr6SpQMXQRERGJiaHLyrGni4iIqGJg6LJyMs7pIiIiqhAYuqyc3on07OkiIiISFUOXlbPTE7rU7OkiIiISFUOXldO3OCqHF4mIiMTF0GXlOKeLiIioYmDosnIyPX/DT5QqcStCRERk4xi6rJxEz/Di1buPRK4JERGRbWPosnIqPYugVq/iKHJNiIiIbBtDl5Xzd3fWeZxLRhAREYmr0oeuffv2oVevXvD394dEIsHGjRu1zg8fPhwSiUTrT7du3bTKPHjwAIMHD4ZcLoe7uztGjhyJR4+0h99Onz6N9u3bw8nJCQEBAZg7d26JumzYsAEhISFwcnJCkyZNsH37drN/3mcl1fM3LDB0ERERiarSh66cnBw0bdoUS5cu1VumW7duuHPnjubP2rVrtc4PHjwYZ8+eRUxMDLZu3Yp9+/Zh1KhRmvMKhQIREREIDAxEQkIC5s2bh5kzZ+KHH37QlDl06BAGDhyIkSNH4uTJk+jTpw/69OmDpKQk83/oZ6BvGyCVWuSKEBER2Tg7S1fAVN27d0f37t0NlnF0dISvr6/Oc+fPn0d0dDSOHTuGli1bAgAWL16MHj164Ouvv4a/vz9Wr16N/Px8/Pzzz3BwcECjRo2QmJiIBQsWaMLZokWL0K1bN0yaNAkA8OmnnyImJgZLlizBsmXLdN47Ly8PeXl5mtcKhQIAoFQqoVQqn60h9FCrdD+lmF9QYLZ7WIui9mC7GMZ2Mg7byThsJ+OwnYxjqXYy9n6VPnQZY8+ePfD29oaHhwc6deqEzz77DNWrVwcAxMfHw93dXRO4AKBLly6QSqU4cuQIXn31VcTHx6NDhw5wcHDQlImMjMRXX32Fhw8fwsPDA/Hx8Zg4caLWfSMjI0sMdxY3Z84czJo1q8TxXbt2wcXFxcRPXejuE0DXX/OZM0nYfu+MWe5hbWJiYixdhUqB7WQctpNx2E7GYTsZR+x2evz4sVHlrD50devWDX379kVwcDCuXr2Kjz/+GN27d0d8fDxkMhnS0tLg7e2t9R47OztUq1YNaWlpAIC0tDQEBwdrlfHx8dGc8/DwQFpamuZY8TJF19Bl6tSpWkFNoVAgICAAERERkMvlJn3uIjcePsZniQdKHG/QsCF6hAea5R7WQqlUIiYmBl27doW9vb2lq1NhsZ2Mw3YyDtvJOGwn41iqnYpGqkpj9aFrwIABmv9v0qQJQkNDUadOHezZswedO3e2YM0Khz0dHUsu3WBvb2+2LxbHYr1zWiRS/sPVw5ztb83YTsZhOxmH7WQctpNxxG4nY+9V6SfSP6vatWvD09MTV65cAQD4+voiIyNDq0xBQQEePHigmQfm6+uL9PR0rTJFr0sro28umVj07AIENZ9eJCIiEpXNha6bN2/i/v378PPzAwCEh4cjMzMTCQkJmjJxcXFQq9Vo3bq1psy+ffu0JsrFxMSgfv368PDw0JSJjY3VuldMTAzCw8PL+yMZxKcXiYiIKoZKH7oePXqExMREJCYmAgCSk5ORmJiI1NRUPHr0CJMmTcLhw4eRkpKC2NhY9O7dG3Xr1kVkZCQAoEGDBujWrRveeecdHD16FAcPHkRUVBQGDBgAf39/AMCgQYPg4OCAkSNH4uzZs1i/fj0WLVqkNR9r3LhxiI6Oxvz583HhwgXMnDkTx48fR1RUlOhtUpxUT1cXe7qIiIjEVelD1/HjxxEWFoawsDAAwMSJExEWFobp06dDJpPh9OnTeOWVV/Dcc89h5MiRaNGiBfbv3681l2r16tUICQlB586d0aNHD7zwwgtaa3C5ublh165dSE5ORosWLfDBBx9g+vTpWmt5tW3bFmvWrMEPP/yApk2b4vfff8fGjRvRuHFj8RpDB309XWo1QxcREZGYKv1E+o4dOxpcXX3nzp2lXqNatWpYs2aNwTKhoaHYv3+/wTL9+/dH//79S72fmPT1dHEbICIiInFV+p4uMkymJ3Rdu5sjck2IiIhsG0OXldP39OLmU7eh5Gx6IiIi0TB0WTmpnjldALD/8l0Ra0JERGTbGLqsnL7hRQDYnHhbxJoQERHZNoYuK6fv6UUA4FR6IiIi8TB0WTl9Ty8SERGRuBi6iIiIiETA0GXDuFQXERGReBi6bBgzFxERkXgYuoiIiIhEwNBlwwxtn0RERETmxdBlwxi5iIiIxMPQZcuYuoiIiETD0EVEREQkAoYuGyawq4uIiEg0DF02TK22dA2IiIhsB0OXDVPkKi1dBSIiIpvB0GXDZNyXkYiISDQMXTasRaCHpatARERkMxi6bMDLTXx1Hnd1tBO5JkRERLaLocsG9G9RQ+fx7NwCkWtCRERkuxi6bEBdb1edx7eevi1yTYiIiGwXQ5cNqOIg03m8kb+byDUhIiKyXQxdNqCKnrlb9jL+9RMREYmFP3VtmFrgivRERERiYeiyEa72JQOWSs3QRUREJBaGLhuh6y9axZ4uIiIi0TB02QiJjsXnBYYuIiIi0TB02QidPV0cXiQiIhINQ5eN0LXNokotfj2IiIhsFUOXjdA1vKhk6iIiIhINQ5eNuJdbMnXtvXTXAjUhIiKyTQxdRERERCJg6CIiIiISAUMXERERkQgYuoiIiIhEwNBFREREJAKGLhtR3ZELoRIREVkSQ5eNeM6tZOhytpdZoCZERES2iaHLRuhakV7NvReJiIhEU+lD1759+9CrVy/4+/tDIpFg48aNWucFQcD06dPh5+cHZ2dndOnSBZcvX9Yq8+DBAwwePBhyuRzu7u4YOXIkHj16pFXm9OnTaN++PZycnBAQEIC5c+eWqMuGDRsQEhICJycnNGnSBNu3bzf75y0rHZkLzFxERETiqfShKycnB02bNsXSpUt1np87dy6+/fZbLFu2DEeOHEGVKlUQGRmJ3NxcTZnBgwfj7NmziImJwdatW7Fv3z6MGjVKc16hUCAiIgKBgYFISEjAvHnzMHPmTPzwww+aMocOHcLAgQMxcuRInDx5En369EGfPn2QlJRUfh/+GejaBog9XUREROKxs3QFTNW9e3d0795d5zlBELBw4UJMmzYNvXv3BgCsWrUKPj4+2LhxIwYMGIDz588jOjoax44dQ8uWLQEAixcvRo8ePfD111/D398fq1evRn5+Pn7++Wc4ODigUaNGSExMxIIFCzThbNGiRejWrRsmTZoEAPj0008RExODJUuWYNmyZSK0hGG6eroYuoiIiMRT6UOXIcnJyUhLS0OXLl00x9zc3NC6dWvEx8djwIABiI+Ph7u7uyZwAUCXLl0glUpx5MgRvPrqq4iPj0eHDh3g4OCgKRMZGYmvvvoKDx8+hIeHB+Lj4zFx4kSt+0dGRpYY7iwuLy8PeXl5mtcKhQIAoFQqoVQqTf34GkqlUk9PF8x6n8quqC3YJoaxnYzDdjIO28k4bCfjWKqdjL2fVYeutLQ0AICPj4/WcR8fH825tLQ0eHt7a523s7NDtWrVtMoEBweXuEbROQ8PD6SlpRm8jy5z5szBrFmzShzftWsXXFxcjPmIRpPoGUnetm27zkBmy2JiYixdhUqB7WQctpNx2E7GYTsZR+x2evz4sVHlrDp0VXRTp07V6h1TKBQICAhAREQE5HK52e6jVCqx6cdYneciu3WDnazST+0zC6VSiZiYGHTt2hX29vaWrk6FxXYyDtvJOGwn47CdjGOpdioaqSqNVYcuX19fAEB6ejr8/Pw0x9PT09GsWTNNmYyMDK33FRQU4MGDB5r3+/r6Ij09XatM0evSyhSd18XR0RGOjo4ljtvb25v9i0Vfb5bMzh72dgxdxZVH+1sjtpNx2E7GYTsZh+1kHLHbydh7WfVP2+DgYPj6+iI29t9eHoVCgSNHjiA8PBwAEB4ejszMTCQkJGjKxMXFQa1Wo3Xr1poy+/bt0xqzjYmJQf369eHh4aEpU/w+RWWK7mNp+kIXJ9MTERGJo9KHrkePHiExMRGJiYkACifPJyYmIjU1FRKJBOPHj8dnn32GzZs348yZMxg6dCj8/f3Rp08fAECDBg3QrVs3vPPOOzh69CgOHjyIqKgoDBgwAP7+/gCAQYMGwcHBASNHjsTZs2exfv16LFq0SGtocNy4cYiOjsb8+fNx4cIFzJw5E8ePH0dUVJTYTaKTvr9oZi4iIiJxVPrhxePHj+Oll17SvC4KQsOGDcPKlSvx0UcfIScnB6NGjUJmZiZeeOEFREdHw8nJSfOe1atXIyoqCp07d4ZUKkW/fv3w7bffas67ublh165dGDt2LFq0aAFPT09Mnz5day2vtm3bYs2aNZg2bRo+/vhj1KtXDxs3bkTjxo1FaIXS6Zsrz54uIiIicZgUutavX4++fftadHy5Y8eOEAwEB4lEgtmzZ2P27Nl6y1SrVg1r1qwxeJ/Q0FDs37/fYJn+/fujf//+hitsIfqGF1UMXURERKIwaXhx4MCBqFGjBj788ENcuHDBXHWiciCV6A5XKhVDFxERkRhMCl3Tpk2Dk5MTFixYgEaNGqFDhw743//+p7XFDlUMMj09XUq1WtyKEBER2SiTQtfs2bORkpKCLVu24JVXXsHhw4cxfPhw+Pn54b333sOpU6fMVU8ykb7QVcCeLiIiIlGY/PSiVCpFz5498ddff+HmzZv44osv4OXlhaVLl6J58+Zo1aoVli9fjkePHpmjvlRGDF1ERESWZdYlI7y9vTF58mRcunQJO3fuhJ+fHxISEvDuu+/C398f//d//4fr16+b85ZkJCmHF4mIiCzK7Ot0nTt3DhMmTMCgQYNw+/ZtuLi4YPDgwQgKCsKyZcvQsGFD7Nixw9y3pVKwp4uIiMiyzBK6njx5ghUrVqBt27Zo0qQJFi1ahBo1amDp0qW4ffs2Vq1ahdOnT2Pbtm1wdXXF5MmTzXFbegb6tldUqtjTRUREJAaT1uk6fvw4li9fjnXr1iE7OxtOTk4YOnQoRo8erdlCp7ju3btj5MiR+Prrr025LZWB3qcXGbqIiIhEYVLoatWqFQCgYcOGePfddzF06FC4ubkZfE+tWrVQo0YNU25LZaB3eFHN4UUiIiIxmDS8OHjwYOzbtw9JSUl47733Sg1cADB69GgkJyebclsqA/Z0ERERWZZJPV3/+9//zFUPKmf6nl7kRHoiIiJxmP3pRaqY9IUubnhNREQkDpN6umrXrl1qGalUCrlcjvr16+PVV1/F66+/bsotycwYuYiIiMRhUuhSq9UoKCjA7du3Cy9mZwdPT0/cu3cPBQUFAAB/f39kZGQgMTERv/32G5YvX46tW7fCwcHB9NqT0fR0dDF1ERERicSk4cXExET4+fmhU6dOOHToEPLy8nD79m3k5eXh0KFD6Ny5M/z9/ZGamopLly6hR48eiI2Nxfz5881VfyIiIqJKwaTQNXnyZOTl5WHXrl1o06YNJJLC/hSJRII2bdogOjoaubm5mDJlCurWrYsNGzYgMDAQ69atM0vlyXQCu7qIiIhEYVLo2rRpE3r06AGpVPdlZDIZevTogU2bNgEAnJyc0KlTJ1y5csWU21IZ6Bte5Dx6IiIicZgUuhQKBRQKhcEyWVlZyMrK0rz29PQ05ZZUVnondREREZEYTApdDRs2xNq1a3Ht2jWd569du4Z169ahYcOGmmOpqanw8vIy5bZkRuzpIiIiEodJTy9+/PHHeO2119CsWTO8/fbbaNeuHby9vZGRkYGDBw/ip59+wqNHj/Dxxx8DAPLz87Fr1y5ERESYpfJkPL3Di6LWgoiIyHaZFLr69u2L5cuXY/z48Vi4cCEWLVqkOScIAlxdXfHf//4Xffv2BQA8fvwYP/30Exo1amRaremZcXSRiIjIskwKXQAwYsQI9OvXD5s2bcKpU6egUCggl8vRtGlT9O7dW2s/Rnd3d/Tu3dvUW5IZCRxfJCIiEoVJoWv27NkIDg7GkCFDMHToUHPViUTEyEVERCQOkybSf/bZZzhz5oy56kLlSKInXrGji4iISBwmha5atWohMzPTTFWhcsVJXURERBZlUugaMGAAoqOjtdbhosqGXV1ERERiMCl0ffLJJwgNDUWnTp2wbds2ZGRkmKteZGZckZ6IiMiyTJpI7+LiAqDwCbhXXnlFbzmJRIKCggJTbkVERERUqZkUutq3b6/Z5JoqJ3Z0ERERicOk0LVnzx4zVYPKG4cXiYiILMukOV1UebBDkoiIyLJMXpEeKNxT8e+//8aFCxeQk5ODTz75BACQm5sLhUIBT09PSKXMdxWRwAFGIiIiUZichDZv3oxatWqhV69e+PDDDzFz5kzNudOnT8PPzw/r1q0z9TZUTji8SEREJA6TQtfBgwfx2muvwdHREYsWLcKgQYO0zrdq1Qp169bFH3/8YVIlyXQcXSQiIrIsk4YXP/30U7i7uyMhIQGenp64f/9+iTItW7bEkSNHTLkNlSN2dBEREYnDpJ6uI0eOoHfv3vD09NRbJiAgAGlpaabchsqRwPFFIiIiUZgUuvLy8iCXyw2WyczM5CT6CoDDi0RERJZlUhqqXbs2jh07ZrBMfHw8QkJCTLkNmQNTFxERkUWZFLr69euHgwcPYsWKFTrPf/3110hKSsIbb7xhym2oHHF0kYiISBwmTaSfNGkS/vjjD7z99ttYs2YN8vLyAAAfffQR4uPjcejQITRr1gxRUVFmqSyVnd4V6TmVnoiISBQmhS5XV1fs378fUVFR+O2336BSqQAU9nBJJBK8/vrr+O677+Do6GiWylLZcXSRiIjIskye4e7h4YHVq1cjLS0N27dvx6+//orNmzfj9u3bWLt2LTw8PMxRzzKbOXMmJBKJ1p/ic8xyc3MxduxYVK9eHa6urujXrx/S09O1rpGamoqePXvCxcUF3t7emDRpEgoKCrTK7NmzB82bN4ejoyPq1q2LlStXivHxTMbhRSIiInGYZRsgAKhevTq6detmrsuZVaNGjfD3339rXtvZ/fuxJ0yYgG3btmHDhg1wc3NDVFQU+vbti4MHDwIAVCoVevbsCV9fXxw6dAh37tzB0KFDYW9vjy+++AIAkJycjJ49e2L06NFYvXo1YmNj8fbbb8PPzw+RkZHifthnxNBFREQkDrOFrorMzs4Ovr6+JY5nZWXhp59+wpo1a9CpUycAwIoVK9CgQQMcPnwYbdq0wa5du3Du3Dn8/fff8PHxQbNmzfDpp59i8uTJmDlzJhwcHLBs2TIEBwdj/vz5AIAGDRrgwIED+OabbypM6OKG10RERJZlcug6d+4clixZgmPHjiEzM1Mzr6s4iUSCq1evmnqrMrt8+TL8/f3h5OSE8PBwzJkzB7Vq1UJCQgKUSiW6dOmiKRsSEoJatWohPj4ebdq0QXx8PJo0aQIfHx9NmcjISIwZMwZnz55FWFgY4uPjta5RVGb8+PEG65WXl6d5+AAAFAoFAECpVEKpVJrhk0NzPX0KVAVmvVdlVtQObA/D2E7GYTsZh+1kHLaTcSzVTsbez6TQtXfvXnTr1g15eXmws7ODj4+P1tBdEUuuet66dWusXLkS9evXx507dzBr1iy0b98eSUlJSEtLg4ODA9zd3bXe4+Pjo1lFPy0tTStwFZ0vOmeojEKhwJMnT+Ds7KyzbnPmzMGsWbNKHN+1axdcXFzK9Hmf1alTp+F055Qo96osYmJiLF2FSoHtZBy2k3HYTsZhOxlH7HZ6/PixUeVMCl1TpkxBQUEBli9fjmHDhkEmk5lyuXLRvXt3zf+HhoaidevWCAwMxG+//aY3DIll6tSpmDhxoua1QqFAQEAAIiIiSl3p/1kolUqs3Kj7CzA0NBQ9mtcw270qM6VSiZiYGHTt2hX29vaWrk6FxXYyDtvJOGwn47CdjGOpdioaqSqNSaHr1KlTGDBgAEaMGGHKZUTl7u6O5557DleuXEHXrl2Rn5+PzMxMrd6u9PR0zRwwX19fHD16VOsaRU83Fi/z9BOP6enpkMvlBoOdo6OjzuU07O3tRftikcpk/Af8FDHbvzJjOxmH7WQctpNx2E7GEbudjL2XSUtGVKlSBd7e3qZcQnSPHj3C1atX4efnhxYtWsDe3h6xsbGa8xcvXkRqairCw8MBAOHh4Thz5gwyMjI0ZWJiYiCXy9GwYUNNmeLXKCpTdI0KjU8vEhERicKk0NWjRw/s37/fXHUpFx9++CH27t2LlJQUHDp0CK+++ipkMhkGDhwINzc3jBw5EhMnTsTu3buRkJCAt956C+Hh4WjTpg0AICIiAg0bNsSQIUNw6tQp7Ny5E9OmTcPYsWM1vVSjR4/GtWvX8NFHH+HChQv47rvv8Ntvv2HChAmW/Oha+PAiERGRZZkUuubNm4fMzEy8//77Rk8iE9vNmzcxcOBA1K9fH6+//jqqV6+Ow4cPw8vLCwDwzTff4OWXX0a/fv3QoUMH+Pr64s8//9S8XyaTYevWrZDJZAgPD8ebb76JoUOHYvbs2ZoywcHB2LZtG2JiYtC0aVPMnz8fy5cvrzDLRQDQm7q4DRAREZE4TJrTNWDAALi6umLp0qVYuXIlnnvuOZ0TwCUSSYnhN7GsW7fO4HknJycsXboUS5cu1VsmMDAQ27dvN3idjh074uTJk2Wqoxj07r3IzEVERCQKk0LXnj17NP//6NEjnDhxQmc5CVfmrLCYuYiIiMRhUuhSq9XmqgeVM8ZeIiIiyzJ5w+vS5OfnG71+BYmPw4tERETieObQVbt2bXz77bdax3bu3Km1yGdxc+bMgYeHR9lqR+WOE+mJiIjE8cyhKyUlBZmZmVrHDh8+jEWLFpmrTlQOOK2OiIjIssp9eJEqNg4vEhERiYOhy0boXTJC1FoQERHZLoYuIiIiIhEwdNk6ji8SERGJgqHLRnB4kYiIyLLKtDjqr7/+isOHD2teX7lyBUDhBthPKzpHREREZMvKFLquXLmiM0xFR0frLM9tgCxP318BRxeJiIjE8cyhKzk5uTzqQRYiMHURERGJ4plDV2BgYHnUg4iIiMiqcSK9jeBEeiIiIsti6LJxHF0kIiISB0OXjdA7kV7cahAREdkshi4iIiIiETB02Tg+vUhERCQOhi4bwZXSiIiILIuhi4iIiEgEDF02Qu+SERxdJCIiEgVDl40T+PwiERGRKBi6iIiIiETA0GUjuOE1ERGRZTF02ThmLiIiInEwdNkILhlBRERkWQxdNo7Di0REROJg6LJxfHqRiIhIHAxdNkLfRHoiIiISB0OXjePwIhERkTgYumwEO7qIiIgsi6HLxgns6iIiIhIFQxcRERGRCBi6bAQ3vCYiIrIshi4bx8xFREQkDoYuG8ElI4iIiCyLocvGcXiRiIhIHAxdNu6bvy/hcX6BpatBRERk9Ri6bISh0cUvd1wQrR5ERES2iqHLRhgKXavir4tWDyIiIlvF0GVmS5cuRVBQEJycnNC6dWscPXrU0lUCwIn0RERElsbQZUbr16/HxIkTMWPGDJw4cQJNmzZFZGQkMjIyLF01SBm6iIiILIqhy4wWLFiAd955B2+99RYaNmyIZcuWwcXFBT///LOlq8bQRUREZGF2lq6AtcjPz0dCQgKmTp2qOSaVStGlSxfEx8frfE9eXh7y8vI0rxUKBQBAqVRCqVSarW5KpbLUdJ2fnw+JjY9BFrW5OdveGrGdjMN2Mg7byThsJ+NYqp2MvR9Dl5ncu3cPKpUKPj4+Wsd9fHxw4YLupwPnzJmDWbNmlTi+a9cuuLi4mLV+peWpLdt2wI79ngCAmJgYS1ehUmA7GYftZBy2k3HYTsYRu50eP35sVDmGLguaOnUqJk6cqHmtUCgQEBCAiIgIyOVys91HqVSW+gXYoFUH1PN2Nds9K6OiduratSvs7e0tXZ0Ki+1kHLaTcdhOxmE7GcdS7VQ0UlUahi4z8fT0hEwmQ3p6utbx9PR0+Pr66nyPo6MjHB0dSxy3t7cX/R9Vngr8h/wPS7R/ZcR2Mg7byThsJ+OwnYwjdjsZey8OKJmJg4MDWrRogdjYWM0xtVqN2NhYhIeHW7BmxlFzPyAiIqJyxZ4uM5o4cSKGDRuGli1bolWrVli4cCFycnLw1ltvWbpqpSpQMXQRERGVJ4YuM3rjjTdw9+5dTJ8+HWlpaWjWrBmio6NLTK6viFTs6SIiIipXDF1mFhUVhaioKEtX45mp1AxdRERE5YlzumxIiI/+pxMZuoiIiMoXQ5cNeTnUT+85hi4iIqLyxdBlQ97tEAx7me5VUhm6iIiIyhdDl41Z804bncefKFUi14SIiMi2MHTZGJmena8Tb2SKWxEiIiIbw9BlY6R6NmEM8DDvXo9ERESkjaHLxnhXLbntEMA5XUREROWNocvG2OmZSL/30l3EX72PvALO7SIiIioPXBzVxthLdefsA1fu4cCVe2joJ8fqt1vDo4qDyDUjIiKybuzpsjEyPT1dRc7dUWDFwWSRakNERGQ7GLpsjL6eruK+jbsiQk2IiIhsC0OXjdE3p+tp7O0iIiIyL4YuG2OnZ52up83eeg4Pc/LLuTZERES2g6HLxkj0rNP1NEEAfjt+o5xrQ0REZDsYukivlPs5lq4CERGR1WDoskH+bk6WrgIREZHNYeiyQU4OMqPKCVyknoiIyGwYumyQg4x/7URERGLjT18bZOyyEezpIiIiMh+GLhtkz54uIiIi0fGnrw0yZlV6IiIiMi/+9LVB9nZGDi+C44tERETmwtBlg+yM7OnKVarLuSZERES2g6HLBhk7p2tH0p1yrgkREZHtYOiyQQ5GDi8qVRxeJCIiMheGLhtk7PAiERERmQ9/+togLhlBREQkPv70tUH2Ri6OSkRERObD0GWDjO3pkjvZlXNNiIiIbAdDlw0yehugcq4HERGRLWHoskEZ2XmWrgIREZHNYeiyRcZ2YbGri4iIyGwYumxQyyAPS1eBiIjI5jB02SA7qZFPL/IhRyIiIrNh6LJBdkY+vSiVMHURERGZC0OXDTK2p+tRXkE514SIiMh2MHTZIGOXjFCpxZ1Jf/1+DnaeTUNGdq6o9yUiIhIDV7+0QbIKtveiIAj4JuYSvo27AgCQSoAFrzdDn7AaFq4ZERGR+VSsn74kiqoVbKX5a/dyNIELANQCMOXP08hVqixYKyIiIvNi6LJBzwdVM6pcsGeVcq5JoU0nb5U4lqtUI/7qfVHuT0REJAaGLhvk6mhcT1fyvZxyrkmhkzcydR6vqHO7cvIKEHMuHQnXH0It8rw3IiKqvKw+dAUFBUEikWj9+fLLL7XKnD59Gu3bt4eTkxMCAgIwd+7cEtfZsGEDQkJC4OTkhCZNmmD79u1a5wVBwPTp0+Hn5wdnZ2d06dIFly9fLtfPZoo+zfyNKpciQvAS9OQWSQVcKOz8HQXafhmHd1YdR7/vD+HtVceRX6C2dLWIiKgSsPrQBQCzZ8/GnTt3NH/ee+89zTmFQoGIiAgEBgYiISEB8+bNw8yZM/HDDz9oyhw6dAgDBw7EyJEjcfLkSfTp0wd9+vRBUlKSpszcuXPx7bffYtmyZThy5AiqVKmCyMhI5OZWzN4ar6qORpX77fiNcq4JIFSi/YY+3XoOWU+UmtdxFzKw9fRtC9aIiIgqi4o1o7qcVK1aFb6+vjrPrV69Gvn5+fj555/h4OCARo0aITExEQsWLMCoUaMAAIsWLUK3bt0wadIkAMCnn36KmJgYLFmyBMuWLYMgCFi4cCGmTZuG3r17AwBWrVoFHx8fbNy4EQMGDNB577y8POTl/bv5tEKhAAAolUoolUqd7ymLomsVv+aTfOPW4FKp1Gatiy76hujmx1xEn6Y+kIi0SKtSqUSeCog9l4Zqrk5oUkNeYiHZQzrmmX381xk08XdFTp4KIb5VcfpWFtYeu4kn+Sq8EuqHrg29Ram/WHR9PVFJbCfjsJ2Mw3YyjqXaydj7SQRB3+COdQgKCkJubi6USiVq1aqFQYMGYcKECbCzK8ybQ4cOhUKhwMaNGzXv2b17Nzp16oQHDx7Aw8MDtWrVwsSJEzF+/HhNmRkzZmDjxo04deoUrl27hjp16uDkyZNo1qyZpsyLL76IZs2aYdGiRTrrNnPmTMyaNavE8TVr1sDFxcUsn1+fMw8kWH5RVmq5zv5qvBJo3uGzoq+4oiy15KwUlxW6O11HPKdC0+rifIlmPAGWnJUhS1lYsXpyNUaFqOFQrJnGxT/77ynD6qnQ3NOq/5kREdm0x48fY9CgQcjKyoJcLtdbzup7ut5//300b94c1apVw6FDhzB16lTcuXMHCxYsAACkpaUhODhY6z0+Pj6acx4eHkhLS9McK14mLS1NU674+3SV0WXq1KmYOHGi5rVCoUBAQAAiIiIM/qU9K6VSiZiYGHTt2hX29vYAgI75BVg/bx+ycw33eAUFB6NHt/pmqYcgCPjxQApWH7kBlSDg9RY18N5LdbAm7TigeKjzPRdV3pjao4VZ7l+asWtOIkt5V/P6skKKxz6N0KdVgObYuPhdz3zdSyovTOvR0ix1rAh0fT1RSWwn47CdjMN2Mo6l2qlopKo0lTJ0TZkyBV999ZXBMufPn0dISIhWqAkNDYWDgwPeffddzJkzB46Oxs1rKi+Ojo4662Bvb18uXyzFr+tmb4+177TBy4sPGHyPRCI1W13+SLiJebv+fbhg8e5r8JI7w9DO2gev3hftH86u83dLHJux5TyGtatt0nXjrz2wym+S5fV1am3YTsZhOxmH7WQcsdvJ2HtVytD1wQcfYPjw4QbL1K6t+wdl69atUVBQgJSUFNSvXx++vr5IT0/XKlP0umgemL4yxc8XHfPz89MqU3y4saJpXMMNA1vVwtqjqXrLmHNQ7KcDySWOLY67grvZeTpKl82us2n43+HryCtQ4/WWAXitRU2zXZuIiMgUlTJ0eXl5wcvLq0zvTUxMhFQqhbd34eTm8PBw/Oc//4FSqdQk1ZiYGNSvXx8eHh6aMrGxsVpzumJiYhAeHg4ACA4Ohq+vL2JjYzUhS6FQ4MiRIxgzZkwZP6U4ZvduhLgL6UhX6A4+ajNO+Tt3p2T3qzkD1/7LdzH61wQUzcs/mvwAf59Lx7Ih5hmetPLpj0REVM6sesmI+Ph4LFy4UDPZffXq1ZgwYQLefPNNTaAaNGgQHBwcMHLkSJw9exbr16/HokWLtIYlx40bh+joaMyfPx8XLlzAzJkzcfz4cURFRQEAJBIJxo8fj88++wybN2/GmTNnMHToUPj7+6NPnz6W+OhGs5dJceTjLnrPV6acsf7YDTz9IGT02TQcuHzPLNe3pXVQbzx4rLU0BhERma5S9nQZy9HREevWrcPMmTORl5eH4OBgTJgwQStQubm5YdeuXRg7dixatGgBT09PTJ8+XbNcBAC0bdsWa9aswbRp0/Dxxx+jXr162LhxIxo3bqwp89FHHyEnJwejRo1CZmYmXnjhBURHR8PJyUnUz2xulal3Z+vpOzqP/3r4Ol6o52nwvcZ8TnP2+lVUD3Ly8daKozh1MwtSCTA0PAgzejUUbdkOIiJrZtWhq3nz5jh8+HCp5UJDQ7F//36DZfr374/+/fvrPS+RSDB79mzMnj37metZkRnTuyMIApbvT8a6Y6mQSSUYEh6EIW0CtcpcSs8upxqWLvqs/idIi/yw71qpZSwZuq7fz8Gus+lwtJeiWyNfeMvLJ8zP2nIWp25mASj8u195KAWtgquhRxO/Ut5JRESlserQRaYzZrX49cdu4PPt5zWvP9mYBA8Xe7wc+u9WQxN/SyyP6pnNt7Glb9lkqcx1IvUh+n53SPN6+qaz2P/RSwioZv613DYlllxdf97OiwxdRERmYNVzush0xvR0fb/3aolj/937b8+RIleJpFvGrWFiCZfTs5GTryq1nKVCV/HAVWTG5rOi3V+sjc+JiKwde7rIIGPmOl2//7jEsTO3sjT/n2tEoLGkZXtLH1oEynd48VJ6NuIuZMDL1RFdG/lA7mR4zZe4CxnIUOSW2zDj0yauT8TdR3loGVgNb4UHlP4GIiIqgaGLDFKbsAOQWi1AKpVU+Kf+/jhx06hy5RW6dl/MwLurEpCvKmzshgfkWDuqDdyc7ZFXoD+wtvoiFvW8XbF8WEsEVq9SLnUr8ufJWwCA/Zfv4UjyPbxhXdtJEhGJgsOLZJAxc7r0qf3xdjzJV0FlYlhRWTi1dVmwFzvPppVbeFz092VN4AIK1zPbcabwSczHeYZ7CS9nPMLYNSfKp2J6HLr6AOlPgOzcAhSo1MhQ5OrdtJyIiP7Fni4ySC0A6YpcuLvYw9FOBkEQ8DhfhSqOxn3pNJgejQZ+pu0jmXzvEep6VzXpGqa4kvEI/7f6BNaNalMu10+8kVni2JQ/z2BAq1p4lGd4b0wASLqlwK3MJ6jh7lwOtdPti0Q7fJEYp3ldrYoDJnZ9Dm8+9dQqERH9i6GLDPo94SZ+Tygcfgur5Y70rFzczspF05puWDywOWpVL/0JuvM6VqJ/FuYY1Zv65xnM6NUQTvayMr1fpRaw/YzudcDK02Mj58PdETl0Pe1BTj6mbUxCA7+qaBFYzWL1ICKqyDi8SACAAc+XPjn6ZGombmflAgBO3czCWyuP4vTNzHKumf79H/ML1Ig9n47/Hb6Oy6WsA7b2aCom/3HapHr8fT699EJm9kRpXOiqKIN7cRcyLF0FIqIKiz1dBADo3zIA647deKb3XL2bg1eWHCynGhmWq1Sh57f7cfWu8csZbEq8jS/7hsLZoWy9XXnKsj9VIAhCmVZ1t/R8tmf18DG3DiIi0oc9XQQACDRimNBSftx3DUeTH2gd++lA8jMFriI3H5Zc3sJYuUb2OulS9uxUuUJXgcqEx12t0IM84OeDKVhzJNWsm7sTUeXEni4CAMgq8N56GxJuYkPCTdRwd8bBKZ0AFK6SXhamdBwpckuf1K7P8ZQHaF27+jO/r5J1dEGp0l/hrCdKQADcXAyvQVZZHLl2H8evP0QdL1d0CvGGg53277Dn72Tjy1My5KkuAQC+23MF60a1QU2PivsLDhGVL4YuAgBUdar4Xwq3Mp9g/bFUvPF8LROu8Rgbjt/A7awn6Bzig77Na5ixhvq98cNhRL1UFwNaBTzTD11jl2L4I+Emng+y/AT2w9fulziWrsjF2NUnkJD6EIIANK3phqWDm1fq8LHw70tY+Pe/W0e9+JwXlg9rCTupBL8cSkHshQzsv3wPwL+/zNx8+ATrj93ABxH1LVBjIqoIKv5PWhKFnaxyjDR/uvW8SaFrxMrjmv/ffiYNaYpcc1TLKEt2X8GfJ25iY1Q7eFc1biV5Y3u61h27gaHhQWjoX/blOcwxNHgnK1ezKG6R99eexPHrDzWvT93Mwv+tPoHNUS+YfD9LUOQqS+zVuffSXbz09R7U8XLF3kt39b73ZGpmOdeOiCqyyvGTlkTRKtjyPSWlMWbdqmexKj7FrNcrze2sXOxMSjO6vDHbMBX5776Se2Aa64+Emwj7NKbM7y+u9sfbsXT3FQiCgEd5BTjy1Hw8ADh9Mwv3Hpk2x2n3xQx89PspTNt4BgnXS96jvJy6kakzDN98+MRg4AKAA1fulVOtiKgyYE8XadR0d8ZRS1fCCOZ8oi9dIf7k5hmbz2JIeBCA0kPVs3zSTYm3sWhAmMEy646mYmPiLTjby/BWu2B0eM4Lyfdy8OHvp8y6ofe8nRdRx6sKwmp56C2jeKKEp6tjma7/54mbmPjbKc3r9cduYMXwVnihnmeZrmfI7gsZWH0kFfYyCQa0KnsvKxERQxdpFB8SqsjO3TZtsVVLK54ZSws65tzvcfWR6/jPX0ma1weu3MO6UeHYcuq2WQNXkbgLGWga4K73fIEJ4XnFwRSt10qVgF8PXzd76NpzMQNvrTymeR19Ng1vvxBs1nsQke3g8CJpKJ5UjjWWvt97xdJVMJvSYoc5n178X/x1rddKlYAtp25jR1L5rLSf+VhpsP4FBp50LM2ZW1kljkWfNX7Y1hiCIGD4imNPHSsZ+IiIjMXQRRqlzUepKLafMe8PV7E9vbSAIebs6bqQVnLV/pWHUsx2/acVqAWDT18WqCv2ml4x53TvQGBKDx0R2TaGLtLIK6jYPwStRW3PKpr/L3VOl5lCl6HrSFA+w8pKldpgaDS0pldFsCDmUrlc19hlQIjI+jB0EZlg36SXTHp/aT9+TclcaVm52H7mDs7dVhjcONsc6+JW0bG1UoFKKGV4UbyQb0x4FQRBa9cBXT2D5qAyY++lIAi4kKbApsRbuJKRbbaQTkTlgxPpiUxQy8Ttk0qfSF+26649moqpf57RvC7vbZ6Cvaog6Zb2Aw4FasM9XWLsK5lXoMLsLecQnZQGT1dHvN+5HnqG+mmVUasFLIy9jJ8PJONRXgGqOMgwshwny6vUAuzLtv1niet8uOEU/jp5S3NsSJtAzHqlUaV5KIbI1rCnizT4fbpsujXyLfN7hVL6usoypys7V4lPNiZpHbt+v+x7Tvq7GV7ItWWgB+ykJb+VHEt5aHAoTVnsXIFKjWMpD7DyYDKu3ze8p+az9OZ8vu08Vh9Jxf2cfFxMz8Z7a0/g1I1MrTJ/nryFb2Mva9aAy8lX4du48ntYw1zz9PZduqsVuADgf4ev4+SNh3reUTaCIOByerbJ66oREXu6qJjnfKqW25CKNZveq6HZn5wrUpbhotM3s55psvedLMOr8teq7oLbBsoEVHPRu5G4McOLSpUaA344jIR/Vq2fueUcmtZ0w8ax7SDRMfb5LB1kq556YlMtAL8evq61lEXcBd0T5suLuXr4CrcZKulo8kO0CDTPQsc3Hz7G0J+P4to/m8u/0TIAX/RtAhl/QyMqE/Z0kQb3hNPWu5m/UeX83Z3Rr3nNMt2jPIYX8wr0z996Vl5VHVHaQ4ZSiQSX0h+VOO7p6miwV2fMryfw475r2HLqtiZwFTl1MwtxFzJ0vs/UnqINCTe1Xmc+FnepFHM9tKnI1V1vc/39J93Kwgtf7dYELgBYf/wGNp+6ZeBdRGQIQxdptKtb3dJVqFC6N/YrvdA/ymvryrLkC3POT5dJJKUu7WAnlejcnkkQBIO9OvkqNT7ffl5rZfni5u28qPO4sT1FxvYSijG3TOt+5TzZ3RwfZ1PiLfRackDnub/P6Q7DRFQ6hi7ScHGww4HJpj2NZ0061veC3En/CHxANWfN/z/LsgsX0rJx6J89+My9Ir1abTjoPCupBKjhYXgSvlQqwathNUocz1epTXr6UtdQ9+X0bPz+VE+VPoaaYf/lu5gbfQHrj6UiS+RFgc259pouKhO70gRBwPxdl/T+3W07Uz6L6RLZAs7pIi01PVywcWw79Fl60NJVsTgnexn+N7I1Jv9xWmcAeL1FgOb/n3XZhUHLj+DF57zw3eDmBstdzig5bGfIt3GXUc+76rNVxgCpVIK+YTWw5dRtvWXspBLo+v2ttHW6ntWCmEv4NvZyqeUe5OTj063n9M55AoAhP1lul9HyXqfLlJX+AUCRW4DUB8/24MWBy/ew9mgqcpUq9AmrgV5NjRuaJ7I1DF1UgswcCzeVwdGPO2P6prNGTUqXSSWiDAs1DXBH9PgOSFfkYsyvCTiRmgkAaFunOt5uX9uka++9dBeNZuw0WGa3nnlN+mw5dRvjujxnSrW0SCUShNcxPOwsk0p0Pvmaq1Tj6t1nC4363Mp8YlTgAoC3Vh4r8YRiRWKu4UV9lzF1xXzlM4xPX0rPxhv/jcfDYvPiYi9kIL9AjX4tyjbPkciaMXRRCW7O9qLfs3OIN7zlTljwRlN4bHXAwSv3EORZBeM610W/7+NLlB8aHijqHng+cidsGN0WV+8+ggRAXW9XrSfryiun6tpj0JCrd3Owz4zbOcmkEjjZy/BRt/qYG617jpVMKtE7vKpvvtazijtv3BOGVzIeWSRwdWngjZZB1eAgk+Lz7ecN/kJgrl8W9PUimnr90kJXQz85gMIexYE/HNYKXEXWHUtl6CLSgaGLSig+V0ksbi6FQc/FwQ5z+jbROpfyZU/EXUjHllN3UNXJDm1qV0f3xr7lGroiG/mUOCaTSvCcj+6huzxl+ayu3qupv8GhPV2MnfNkjKIerKqO+r9VyKSScgmdxVe5L21ZiyJdFuw1f0WM0MBPjtEv1gFQuDCtoWHhP0/cwvud65l8T309Wn8k3MTMVxqV+bqKJyUfiiju3B0FNhy/gSt3H+F+Tr7OMsdSzLtWWJHL6dlIup2FJjXcUNeMw+hEYmHoohJ0rY1kTvYySYl9994pZaiuU4gPOoWUDELlJeqlZ/uhaCcrnzarXsWhXK5rLOk/XwuGvibcXexx8p9hV3MqUAu4kpGNnw+mYM2RVLNf35yKr1vVpaGPwdC1IOYSBraqBa+qjibdU982Stl5BTh9MxOhNd01x1RqAfN2XsRfJ2/CTipF/5Y10aWBDzKyc9GiVjW4udhDEAR8G3sF3/xd+p6Tk34/bVLdy2Jx7GXML7Yf5qTI+hj7Ul3R60FkCj69SDqN6Vin3K494qktVro29EGIb8X5rbVloDsa15A/43vMsxhlcRPWJ1o8bBSFCUNDTi/V90bnEG+z3zuvQI0uC/ZZvA2MYVcsdPVvUbPUsPz853/jbrZpK7wbmrv16neHsKPYU4ZLd1/Bsr1Xka7Iw63MJ1j492W8vPgARqw8jtZz/saeixn4JuaSUYHLWE/yVbiS8cgsDw5kKHK1AhdQuKQIV8mnyoY9XaTT6y0D8P2eq+Vy7SndQtC8lgeOpzxAiK8cvZr6l6l37dWwGiW2QTGH2a80fOb6VCuHHqny+GzPqqgHz1DoauAnN2mbIWsgK7YNUm0vV/z1f+3wy6Fr+Ongdb3v+WHfVTzIUeL8HQVaBHrgg4jn4O5i/NeRoQ3DVWoBY1afwPnZ3ZCvUmO7gWUecpVqDF9xzOj7GqvxzJ1QqQV4VXXET8NaavW8PSt99d91Nh2DWtcq83WJxMaeLtIp2LMKVr/dGo38/+3xWTSgGWp7VTH52hKJBJGNfPGfng3Rr0VNONiV7cvwvU514VjG9xriUIaVTts/56k1B8laFP2g1DdRfmCrwh94VRyt77M/C7unHt+sVd0FU7rVR6Cr/l6eH/cn448TN3HujgL/O3wdr353CCq1gIzsXGw7fQc7z6YZXOB198XSH5hoMD0azWbvssj2XkUT+u9m52HMryfKtKVVkat3de/HeeZWFm4+fGzStYnExJ4u0qtdXU9se7+91rFeof44n6aAUiXgvbUncOPBE53v7de8Jv44Yb4J3brU9nLF3xNfxJjVCUi6pdAcb1unOpYNaYEtp27jzM0suLnYo3tjP6PXHivL/CxHOxkmdH0On207/8zvraiqOMjwbofCuXbPB+sePo3454GD6lVMm59U2TnrCdwj66swPcG4b7PJ93LwwW+J2H4mDfnFerEOTH4JNZ9aoDb5nuFNwYurCHnkVuYTXL2bg7rers/83qPJD/C/w7p7DNceTcXao6moXsUBX/YLRdeGpc/7FAQBf528hT0X7yI7Vwmvqo7o1dQf7et5PXPdiJ4VQxc9E6lUgkb+bgCAj7s3wJjVJ3SWGxIeWO6hCyjcbHnre+3xOL8AO8+m4Tmfqpr6DW4dCLT+t2yb2tVw+NqDUq/pbF+2Xpu329dG0wB3HE1+gFylCovjrpTpOuVlUmR9vVvr6BI9vgMCqhX+sG9Sww0tAz1wvNgeiSG+VRFeu7rm//3dnAxujG3N2tTWvZaZmwMQWkOO08V+KTBkY2LJJ1Vf+Go33utUFw4yKaRSCRztpJUy3HdZsBe+cicUqNWY+1qozgdjBEFA8r0cJN1WYPXh6ziSXPq/VwC4n5OPd1Ydx9TuIfCo4gCZRAKvqo5oGeQBmVSCw9ceYOfZNL3zA387fhND2gRi1iuNIDVhM+/H+QU4e1sBN2d71HtqWRkigKGLTNC9ie69Ce1lEjQLcBe1Li4Odng1zPC6QJO7hWDoT0eRrWOfwOJMmZ/1fFA1PB9U2Cv0Uog3ftx3DTuS/l3sdWh4IFoGVcP7a0+W+R5lNfaluvg29jLyCoxb3qIocAGFE+pXjmiFVfEpSEx9CJniDmYPaQmnfwKqVCrBz289j7GrT+gdCjKX11rULHVZjCoOMuTkm2/jb0P6htUw2IOzZGAzdPh6n0n3sFSA/9/IVmhfzwtqtYCM7Dx0XbC31H8/hqQpCkP5iJXH0SqoGj7t0xi1qzsBAE7fzEK//x4xqb5zdlwo83v/d/h64TBvWA0Ee1bBq2E1tP4NZChykatUo6aHs85gtvJgMmZuOad57eZsj2/eaArvqk745VAKzt5WwE4mwed9mqBJTbcy15MqN4YuMsmg1rVK/Pa4eGCYhWpjWFgtD8R92BEHr9zD+PWJOssMrmO+H9TNa3ng+zdbAADyC9SQSgC7f+aLCYKATzYmoaqTPW5l6h6iNSeXf4a/Vr/dGq8tK7nYrDFcHe3wfx3rQqlUYvv223B30V5EN8RXjtgPOiLmXDreWXXc4LVequ9l1Jykp7UKroav+zdFAz85Pt16TmeZOl5V8N8hLdBlgWlBx5AeTXzRyL+w96+1nl6uIn5uThjTsU65PZhSXur7VEXr4MLPJpVK4OvmBCcHmUmhq7ijKQ8QubDo78gOiDctcJlL0QMsC2L0P8lZ08MZXRr44HF+Aao62eOnA8klymQ9UWLEypL/DopvJO5kL0WuUo2eoX7wruoItVrAvUf5qOpkh4+6hZTLAzpkWQxdZJIZvRoCAKKT0uDubI/h7YLQrXFhD1iH57xKrI7erq7hH1DlzauqI/qE1UDvZv5YczQVMefS8TAnH3kFasx4OQQZZ8sWSErz9MMCvZvVQO9mhZtEL/r7cpke1dcVePVZNKAwCLcMMm5pi8Dqhje5NqRrQx8sH9oSbxsIXiveaoU9FzOe+am5Pv+0WY8mvjpD18q3nker4Gqwk5bfM0IpX/Z85vd0a+RbqULXyBeCEfVS3RJft072fPYKAG4+fIKVh1JMvk7uP4sqbztd8unMdcduACgcOZBAAkgAQS3DlON/w9FehioOdlrrw1VElhhdFQQBj3NkmH9xf6nDu3EfdBS9DRm6yCSOdjJ88WoTfPFqkxLnhrcNLBG6+hfbJNqSJBIJBrcOLJz39Q+lUontZ8WvS89Qv2cKXc8HeaBzAx+Mal8beUp1qXPnXqrvpTXB+JWm/thcyir3g018DL+LgQnNvvLC4aQX6noaVZfi7xvYqvDrx8/NGX/+X1v0XxaPGu7OaBVcDdN6NtAsuSAIAtyc7ZH1pOQWNaaYUMZ9LZsGuCO8dnXEX7tv1voUF/VSXdSq7oKPTFy4dNmbzTW/OD2ttqer3odnqHwULiRd9DSEBEq1Gk+UamTq2H6JikiAvIr5dcrQReWmU4gPFg8Mw6+HryNfpcaA5wPQJ6yGpatV4Xi5Gv/k3/KhLbUCzazejeDiIMOW07d1fhN2kEmxeFBzrWP/6dlAZ9AJqu4CmVSCAc/Xwtvtg0ucf1Zz+4Xioz9KBoAFbzQFUDjUuvCNZhj5QjDO3lbgSPJ9bNIxkRwoDIGfPxXsm9fywNUveugsL5FI0KupH349bN6FVd94vuy/NIzrUg/xP5Rf6Hr3xdqo6mSPudEXcO+R7u15iuwc3wHBnlVw8Oo9nLutQK5SBT83Z/Rq6oeqTvr3Xh3WNhAHrtwTZbN5ImtUqfuKP//8c7Rt2xYuLi5wd3fXWSY1NRU9e/aEi4sLvL29MWnSJBQUaM9J2LNnD5o3bw5HR0fUrVsXK1euLHGdpUuXIigoCE5OTmjdujWOHj2qdT43Nxdjx45F9erV4erqin79+iE93bhNeq1Zr6b+WP9uOP76v3Z443kuYqiLm4s9wmq5lzge9NQQ37cDw0r0ILk62uHTPo2ROD0CFz7thtZPLe0wpmMduD61b6KP3AmXPuuO7wY3x0fd6iNhWhekfNkTeya9hNgPOuKdDrXN8tRVRCMf2D+1/IZ3VUfNPCGgcK5Q0wB3DGpdy+B+hNN6Nnzm+0/r2VDTM/a0VsHVsHxoSyTNitR5vn09T63ewSoOMqx+uzV83ZyeuR5FWgZ6wEdePktrHJrSSROWlg97Xu9eme3reeLEJ11R37cqHOykeKm+N8a+VBcfRNTHoNa1DAYuoPAXqf+NaIXXW9ZE+3qeaF/PE0PDA7El6gXM6NUQXRp4o5We5UVM0cBPjm6NfNGxvnmWdegZ6oeXQ/0wNDyw9MJEZlSpe7ry8/PRv39/hIeH46effipxXqVSoWfPnvD19cWhQ4dw584dDB06FPb29vjiiy8AAMnJyejZsydGjx6N1atXIzY2Fm+//Tb8/PwQGVn4DXn9+vWYOHEili1bhtatW2PhwoWIjIzExYsX4e1duP3JhAkTsG3bNmzYsAFubm6IiopC3759cfCgcWtDkW1b9EYYRv5yTLNnX9eGPvh2QBicHWQQBMGoEORkL8PKt1oh5nw6rmQ8QuvgamhX11NnWQc7KXroefrUXNxdHLB0UHN8uOEUFLkF8JE7YtmbLfTOoajj5YqO9b2w56kJ9v2a19S7DpYhTvYyzOkbijl9Q3HjwWPcznyCEF+5ZnP1Ite+6IGfDiTj7/PpqOHujHdfrIP6xbalMrb9S2Mnk2LPhy9h8PLDyMjOg7+bM15tXgMDng9AXoEaSbeykJ1XgJruzqjj5YrcAhXUAvDZ1nOa+T1P697YF5O7hcDf/d9N6psFuGPXxA6Iu5CBu9l5CPGVo1mAO6o62aGKgY3LjdW2rifa6vi6alLTDW+1+7eHVKUWsPdSBhRPCiCVStDQryrqeLnizxO3sO5Yqt5NsSUS4O0XgjG1ewO9yzcoVWqcv6OATCrBk3wVXBzsEOJbFVKpBIIg4OSNTFxKy4ZEUrjB+BOlCs8HVcPg1rVQ20v7SdPZvRvjTtYTJN1SYOWhZBy8UrI3MsS3apkXmH2hricCqjlj7VHdf4dkWySCFSzlu3LlSowfPx6ZmZlax3fs2IGXX34Zt2/fho9P4W+ty5Ytw+TJk3H37l04ODhg8uTJ2LZtG5KSkjTvGzBgADIzMxEdHQ0AaN26NZ5//nksWbIEAKBWqxEQEID33nsPU6ZMQVZWFry8vLBmzRq89tprAIALFy6gQYMGiI+PR5s2bXTWOy8vD3l5/+4dplAoEBAQgHv37kEuf7a9/wxRKpWIiYlB165dYW9v+DdZW1YR2ulW5hO4OMjg8QzbwYjtWdupQKVGmiIPNdydSg0vgiBgQ8ItfLv7Kjo+54XujX3Qro5lH74oK3N9PQmCgEvpj3DuTjYc7aRoEegOH3nZe9wqEpVawIXbmThxNB6hLdtAJrNDYHXnUnvcyluesjDwOtlLNV+zj/IK1+BSqQU08pfjuz3XUKAWkFegRoCHM7o28EZgdRdIAGQ8yoOHsz0cS1nzr0ClhgBAJpFAKpUgv0CN7/Zew52sXLg62kEQBKgFQICAApUaN2/chKevH54oBagr+I9uS1VPENS4e/cuvLy8IJEYHsz7blAzs02kVygU8PT0RFZWlsGf31YduqZPn47NmzcjMTFRcyw5ORm1a9fGiRMnEBYWhg4dOqB58+ZYuHChpsyKFSswfvx4ZGVlIT8/Hy4uLvj999/Rp08fTZlhw4YhMzMTmzZtQlxcHDp37oyHDx9qDXMGBgZi/PjxmDBhgs56z5w5E7NmzSpxfM2aNXBxKfvTY0RERCSex48fY9CgQaWGrko9vFiatLQ0TQ9XkaLXaWlpBssoFAo8efIEDx8+hEql0lnmwoULmms4ODiUmFfm4+OjuY8uU6dOxcSJEzWvi3q6IiIi2NNlAWwn47CdjMN2Mg7byThsJ+NYqp0UCuN2nahwoWvKlCn46quvDJY5f/48QkJCRKpR+XF0dISjY8mJtfb29uXyxVJe17U2bCfjsJ2Mw3YyDtvJOGwn44jdTsbeq8KFrg8++ADDhw83WKZ27dpGXcvX17fEU4ZFTxT6+vpq/vv0U4bp6emQy+VwdnaGTCaDTCbTWab4NfLz85GZmanV21W8DBEREdm2CrdkhJeXF0JCQgz+cXAwbpJxeHg4zpw5g4yMDM2xmJgYyOVyNGzYUFMmNjZW630xMTEIDw8HADg4OKBFixZaZdRqNWJjYzVlWrRoAXt7e60yFy9eRGpqqqYMERER2bYK19P1LFJTU/HgwQOkpqZCpVJpJszXrVsXrq6uiIiIQMOGDTFkyBDMnTsXaWlpmDZtGsaOHasZ1hs9ejSWLFmCjz76CCNGjEBcXBx+++03bNu2TXOfiRMnYtiwYWjZsiVatWqFhQsXIicnB2+99RYAwM3NDSNHjsTEiRNRrVo1yOVyvPfeewgPD9f75CIRERHZlkoduqZPn45ffvlF8zosrHB/ud27d6Njx46QyWTYunUrxowZg/DwcFSpUgXDhg3D7NmzNe8JDg7Gtm3bMGHCBCxatAg1a9bE8uXLNWt0AcAbb7yBu3fvYvr06UhLS0OzZs0QHR2tNbn+m2++gVQqRb9+/ZCXl4fIyEh89913IrQCERERVQaVOnStXLlS5+rxxQUGBmL79u0Gy3Ts2BEnT540WCYqKgpRUVF6zzs5OWHp0qVYunSpwesQERGRbapwc7qIiIiIrBFDFxEREZEIGLqIiIiIRMDQRURERCQChi4iIiIiEVTqpxetTdHe48bu4WQspVKJx48fQ6FQcPsIA9hOxmE7GYftZBy2k3HYTsaxVDsV/dwu+jmuD0NXBZKdnQ0ACAgIsHBNiIiI6FllZ2fDzc1N73mJUFosI9Go1Wrcvn0bVatWhUQiMdt1FQoFAgICcOPGDcjlcrNd19qwnYzDdjIO28k4bCfjsJ2MY6l2EgQB2dnZ8Pf3h1Sqf+YWe7oqEKlUipo1a5bb9eVyOf+xGoHtZBy2k3HYTsZhOxmH7WQcS7SToR6uIpxIT0RERCQChi4iIiIiETB02QBHR0fMmDEDjo6Olq5KhcZ2Mg7byThsJ+OwnYzDdjJORW8nTqQnIiIiEgF7uoiIiIhEwNBFREREJAKGLiIiIiIRMHQRERERiYChywYsXboUQUFBcHJyQuvWrXH06FFLV6nc7Nu3D7169YK/vz8kEgk2btyodV4QBEyfPh1+fn5wdnZGly5dcPnyZa0yDx48wODBgyGXy+Hu7o6RI0fi0aNHWmVOnz6N9u3bw8nJCQEBAZg7d255fzSzmjNnDp5//nlUrVoV3t7e6NOnDy5evKhVJjc3F2PHjkX16tXh6uqKfv36IT09XatMamoqevbsCRcXF3h7e2PSpEkoKCjQKrNnzx40b94cjo6OqFu3LlauXFneH89svv/+e4SGhmoWWgwPD8eOHTs059lGJX355ZeQSCQYP3685hjbCZg5cyYkEonWn5CQEM15ttG/bt26hTfffBPVq1eHs7MzmjRpguPHj2vOV+rv4wJZtXXr1gkODg7Czz//LJw9e1Z45513BHd3dyE9Pd3SVSsX27dvF/7zn/8If/75pwBA+Ouvv7TOf/nll4Kbm5uwceNG4dSpU8Irr7wiBAcHC0+ePNGU6datm9C0aVPh8OHDwv79+4W6desKAwcO1JzPysoSfHx8hMGDBwtJSUnC2rVrBWdnZ+G///2vWB/TZJGRkcKKFSuEpKQkITExUejRo4dQq1Yt4dGjR5oyo0ePFgICAoTY2Fjh+PHjQps2bYS2bdtqzhcUFAiNGzcWunTpIpw8eVLYvn274OnpKUydOlVT5tq1a4KLi4swceJE4dy5c8LixYsFmUwmREdHi/p5y2rz5s3Ctm3bhEuXLgkXL14UPv74Y8He3l5ISkoSBIFt9LSjR48KQUFBQmhoqDBu3DjNcbaTIMyYMUNo1KiRcOfOHc2fu3fvas6zjQo9ePBACAwMFIYPHy4cOXJEuHbtmrBz507hypUrmjKV+fs4Q5eVa9WqlTB27FjNa5VKJfj7+wtz5syxYK3E8XToUqvVgq+vrzBv3jzNsczMTMHR0VFYu3atIAiCcO7cOQGAcOzYMU2ZHTt2CBKJRLh165YgCILw3XffCR4eHkJeXp6mzOTJk4X69euX8ycqPxkZGQIAYe/evYIgFLaLvb29sGHDBk2Z8+fPCwCE+Ph4QRAKA65UKhXS0tI0Zb7//ntBLpdr2uajjz4SGjVqpHWvN954Q4iMjCzvj1RuPDw8hOXLl7ONnpKdnS3Uq1dPiImJEV588UVN6GI7FZoxY4bQtGlTnefYRv+aPHmy8MILL+g9X9m/j3N40Yrl5+cjISEBXbp00RyTSqXo0qUL4uPjLVgzy0hOTkZaWppWe7i5uaF169aa9oiPj4e7uztatmypKdOlSxdIpVIcOXJEU6ZDhw5wcHDQlImMjMTFixfx8OFDkT6NeWVlZQEAqlWrBgBISEiAUqnUaquQkBDUqlVLq62aNGkCHx8fTZnIyEgoFAqcPXtWU6b4NYrKVMavP5VKhXXr1iEnJwfh4eFso6eMHTsWPXv2LPFZ2E7/unz5Mvz9/VG7dm0MHjwYqampANhGxW3evBktW7ZE//794e3tjbCwMPz444+a85X9+zhDlxW7d+8eVCqV1j9SAPDx8UFaWpqFamU5RZ/ZUHukpaXB29tb67ydnR2qVaumVUbXNYrfozJRq9UYP3482rVrh8aNGwMo/BwODg5wd3fXKvt0W5XWDvrKKBQKPHnypDw+jtmdOXMGrq6ucHR0xOjRo/HXX3+hYcOGbKNi1q1bhxMnTmDOnDklzrGdCrVu3RorV65EdHQ0vv/+eyQnJ6N9+/bIzs5mGxVz7do1fP/996hXrx527tyJMWPG4P3338cvv/wCoPJ/H7crtysTUaUwduxYJCUl4cCBA5auSoVUv359JCYmIisrC7///juGDRuGvXv3WrpaFcaNGzcwbtw4xMTEwMnJydLVqbC6d++u+f/Q0FC0bt0agYGB+O233+Ds7GzBmlUsarUaLVu2xBdffAEACAsLQ1JSEpYtW4Zhw4ZZuHamY0+XFfP09IRMJivxBEx6ejp8fX0tVCvLKfrMhtrD19cXGRkZWucLCgrw4MEDrTK6rlH8HpVFVFQUtm7dit27d6NmzZqa476+vsjPz0dmZqZW+afbqrR20FdGLpdXmh80Dg4OqFu3Llq0aIE5c+agadOmWLRoEdvoHwkJCcjIyEDz5s1hZ2cHOzs77N27F99++y3s7Ozg4+PDdtLB3d0dzz33HK5cucKvpWL8/PzQsGFDrWMNGjTQDMVW9u/jDF1WzMHBAS1atEBsbKzmmFqtRmxsLMLDwy1YM8sIDg6Gr6+vVnsoFAocOXJE0x7h4eHIzMxEQkKCpkxcXBzUajVat26tKbNv3z4olUpNmZiYGNSvXx8eHh4ifRrTCIKAqKgo/PXXX4iLi0NwcLDW+RYtWsDe3l6rrS5evIjU1FSttjpz5ozWN7eYmBjI5XLNN83w8HCtaxSVqcxff2q1Gnl5eWyjf3Tu3BlnzpxBYmKi5k/Lli0xePBgzf+znUp69OgRrl69Cj8/P34tFdOuXbsSy9dcunQJgYGBAKzg+3i5TtMni1u3bp3g6OgorFy5Ujh37pwwatQowd3dXesJGGuSnZ0tnDx5Ujh58qQAQFiwYIFw8uRJ4fr164IgFD5q7O7uLmzatEk4ffq00Lt3b52PGoeFhQlHjhwRDhw4INSrV0/rUePMzEzBx8dHGDJkiJCUlCSsW7dOcHFxqVRLRowZM0Zwc3MT9uzZo/UI++PHjzVlRo8eLdSqVUuIi4sTjh8/LoSHhwvh4eGa80WPsEdERAiJiYlCdHS04OXlpfMR9kmTJgnnz58Xli5dWqkeYZ8yZYqwd+9eITk5WTh9+rQwZcoUQSKRCLt27RIEgW2kT/GnFwWB7SQIgvDBBx8Ie/bsEZKTk4WDBw8KXbp0ETw9PYWMjAxBENhGRY4ePSrY2dkJn3/+uXD58mVh9erVgouLi/Drr79qylTm7+MMXTZg8eLFQq1atQQHBwehVatWwuHDhy1dpXKze/duAUCJP8OGDRMEofBx408++UTw8fERHB0dhc6dOwsXL17Uusb9+/eFgQMHCq6uroJcLhfeeustITs7W6vMqVOnhBdeeEFwdHQUatSoIXz55ZdifUSz0NVGAIQVK1Zoyjx58kT4v//7P8HDw0NwcXERXn31VeHOnTta10lJSRG6d+8uODs7C56ensIHH3wgKJVKrTK7d+8WmjVrJjg4OAi1a9fWukdFN2LECCEwMFBwcHAQvLy8hM6dO2sClyCwjfR5OnSxnQqXbvDz8xMcHByEGjVqCG+88YbW2lNso39t2bJFaNy4seDo6CiEhIQIP/zwg9b5yvx9XCIIglB+/WhEREREBHBOFxEREZEoGLqIiIiIRMDQRURERCQChi4iIiIiETB0EREREYmAoYuIiIhIBAxdRERERCJg6CIiIiISAUMXEVEFsGfPHkgkEsycOdPSVSGicsLQRUSVUkpKCiQSCbp166Y5Nnz4cEgkEqSkpFiuYgZIJBJ07NjR0tUgIguxs3QFiIgIaNWqFc6fPw9PT09LV4WIyglDFxFRBeDi4oKQkBBLV4OIyhGHF4nIKgQFBeGXX34BAAQHB0MikegczktOTsbbb7+NWrVqwdHREX5+fhg+fDiuX79e4ppF77916xaGDh0KX19fSKVS7NmzBwCwe/dujBgxAvXr14erqytcXV3RsmVL/PDDD1rXKZqvBQB79+7V1E0ikWDlypVaZXTN6UpKSsLrr78Ob29vODo6Ijg4GOPHj8f9+/d1tkNQUBAePXqEcePGwd/fH46OjggNDcXvv/9eonxWVhamT5+Ohg0bwtXVFXK5HHXr1sWwYcN0tgkRlR17uojIKowfPx4rV67EqVOnMG7cOLi7uwMoDCFFjhw5gsjISOTk5ODll19GvXr1kJKSgtWrV2PHjh2Ij49H7dq1ta57//59hIeHo1q1ahgwYAByc3Mhl8sBAF999RWuXLmCNm3a4NVXX0VmZiaio6Px7rvv4uLFi5g/f76mDjNmzMCsWbMQGBiI4cOHa67frFkzg5/rwIEDiIyMRH5+Pl577TUEBQUhPj4eixYtwtatW3H48OESQ5JKpRIRERF4+PAh+vXrh8ePH2PdunV4/fXXER0djYiICACAIAiIjIzEkSNH0K5dO3Tr1g1SqRTXr1/H5s2bMWTIEAQGBpbhb4OIdBKIiCqh5ORkAYAQGRmpOTZs2DABgJCcnFyifH5+vhAUFCRUrVpVOHHihNa5/fv3CzKZTHj55Ze1jgMQAAhvvfWWUFBQUOKa165dK3FMqVQKXbt2FWQymXD9+vUS13vxxRd1fp7du3cLAIQZM2ZojqlUKqFOnToCACE6Olqr/KRJkwQAwogRI7SOBwYGCgCE3r17C3l5eZrjf//9d4n2On36tABA6NOnT4n65ObmCtnZ2TrrSkRlw+FFIrIJW7duRUpKCiZNmoSwsDCtcy+88AJ69+6N7du3Q6FQaJ1zcHDA3LlzIZPJSlwzODi4xDE7OzuMHj0aKpUKu3fvNqnOBw8exNWrV9G9e3dERkZqnZs+fTqqVauGNWvWID8/v8R7v/nmGzg4OGhed+7cGYGBgTh27FiJss7OziWOOTo6wtXV1aT6E5E2Di8SkU04fPgwAODixYs6502lpaVBrVbj0qVLaNmypeZ4cHCw3icKs7Oz8fXXX2Pjxo24evUqcnJytM7fvn3bpDqfPHkSAHQuM1E0f2zXrl24ePEimjRpojnn7u6uMxDWrFkT8fHxmtcNGjRAaGgo1q5di5s3b6JPnz7o2LEjmjVrBqmUv5MTmRtDFxHZhAcPHgAAVq9ebbDc08HJx8dHZ7n8/Hx07NgRJ06cQFhYGIYMGYLq1avDzs4OKSkp+OWXX5CXl2dSnYt63fTVwc/PT6tcETc3N53l7ezsoFartV7HxcVh5syZ+OOPP/DBBx8AALy8vBAVFYX//Oc/Onv4iKhsGLqIyCYUTX7fsmULXn75ZaPfV/TU4dM2bdqEEydOYOTIkVi+fLnWuXXr1mmepDRFUZ3T09N1nk9LS9MqVxbVq1fH4sWL8e233+LChQuIi4vD4sWLMWPGDNjb22Pq1KllvjYRaWP/MRFZjaJeGZVKVeJc69atAUBreM0UV69eBQD07t27xLn9+/frfI9UKtVZN32K5p4VLVFRXE5ODo4fPw5nZ2fUr1/f6GvqI5FI0KBBA4wdOxYxMTEAgM2bN5t8XSL6F0MXEVmNatWqAQBu3LhR4lzv3r1Rq1YtLFiwAPv27StxXqlU4sCBA0bfq2gphaffs3fvXvz4449663fz5k2j79GuXTvUqVMHO3bswN9//6117rPPPsP9+/cxcOBArQnzzyIlJUXnlklFPWtOTk5lui4R6cbhRSKyGp06dcLXX3+NUaNGoV+/fqhSpQoCAwMxZMgQODo64vfff0f37t3x4osvolOnTmjSpAkkEgmuX7+O/fv3o3r16rhw4YJR9+rVqxeCgoIwd+5cJCUloXHjxrh48SK2bt2KV199VedCpJ06dcJvv/2GPn36ICwsDDKZDK+88gpCQ0N13kMqlWLlypWIjIxEjx490L9/fwQGBiI+Ph579uxBnTp18OWXX5a5vRITE9G3b1+0atUKDRs2hK+vL27duoWNGzdCKpViwoQJZb42EZXE0EVEVqN79+6YO3cufvzxR8yfPx9KpRIvvvgihgwZAgB4/vnncerUKcybNw/bt2/HwYMH4ejoiBo1aqBPnz4YOHCg0fdydXVFXFwcJk2ahH379mHPnj1o1KgRVq9eDR8fH52ha9GiRQCAuLg4bNmyBWq1GjVr1tQbuoDC5SwOHz6M2bNnY9euXcjKyoK/vz/GjRuHadOmmbRXY8uWLTF58mTs2bMH27ZtQ2ZmJnx9fdGlSxdMmjQJbdq0KfO1iagkiSAIgqUrQURERGTtOKeLiIiISAQMXUREREQiYOgiIiIiEgFDFxEREZEIGLqIiIiIRMDQRURERCQChi4iIiIiETB0EREREYmAoYuIiIhIBAxdRERERCJg6CIiIiISAUMXERERkQj+Hwtgk2zi8vAUAAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -277,9 +278,7 @@ "plt.grid(which='both')\n", "plt.ylabel('Energy', fontsize=14)\n", "plt.xlabel('Iterations', fontsize=14)\n", - "plt.legend(fontsize=12)\n", - "\n", - "\n" + "plt.legend(fontsize=12)\n" ] }, { @@ -291,14 +290,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.42269777361292393 [ 250. 1000.]\n" + "0.33815821889033915 [500. 500.]\n" ] } ], @@ -312,12 +311,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## Plot the solution\n", + "\n", "We can also plot the reference solution and the QUBO solution for visual inspection" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -327,27 +328,7 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 20.689, 20.683])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_values" - ] - }, - { - "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -356,13 +337,13 @@ "Text(0.5, 1.0, 'Pressure')" ] }, - "execution_count": 39, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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16wr+/v7CV199JeTl5VV4/yVLlgheXl6CRCIRmjRpIsycOVMoKSl55rLUz3Lnzh3hnXfeEby8vARLS0vB3t5e8PHxEV599VUhISHhuWNGVEYkCP+6pklERERERGQkeA8PEREREREZLSY8RERERERktJjwEBERERGR0WLCQ0RERERERosJDxERERERGS2DS3iWLFkCDw8PWFlZoVOnTjh+/Pgzy2/atAne3t6wsrKCn58fdu3aVW5/fn4+pk6disaNG8Pa2hq+vr6qdeiJiIiIiMi4GdSDRzds2IDo6GgsW7YMnTp1woIFCxAWFoZLly5V+mDCI0eOYOTIkYiNjcWAAQMQFxeHyMhIpKSkoHXr1gCA6Oho7Nu3D2vXroWHhwf27t2LN998E66urhg0aJBacSmVSmRkZMDOzg4ikUinfSYiMgWCICAvLw+urq4Qiw3uXFuN43GFiKhqNDqu6PcxQOV17NhRmDJliuq1QqEQXF1dhdjY2ErLDxs2TIiIiCi3rVOnTsJrr72met2qVSvhv//9b7ky7du3Fz7++GO147p9+7bqgYr84Q9/+MMf7X9u376t9mevMeNxhT/84Q9/dPOjznHFYK7wlJSUIDk5GTExMaptYrEYISEhSEpKqrROUlISoqOjy20LCwvDtm3bVK+7du2K7du3Y8KECXB1dUViYiIuX76Mb7/9Vu3Y7OzsAAC3b9+Gvb29Br0C5HI59u7di9DQUEgkEo3q1mam2G9T7DNgmv02xT4DVeu3VCqFu7u76vPU1PG4UjM4Vk++Xy1atAglJSUYOXIkPDw8KpThOKmH46SemhonTY4rBpPw3L9/HwqFAs7OzuW2Ozs74+LFi5XWyczMrLR8Zmam6vWiRYswefJkNG7cGObm5hCLxVi5ciV69uz51FhkMhlkMpnqdV5eHgDA2toa1tbWGvXL3NwcNjY2sLa2Nqk/DlPstyn2GTDNfptin4Gq9VsulwMAp2/9v7JxsLe31yrhsbGxgb29vUn9/mmDYwUkJydDLBbDzc0Nfn5+lf4NcpzUw3FST02PkzrHFYNJeKrLokWLcPToUWzfvh1NmzbFwYMHMWXKFLi6uiIkJKTSOrGxsZg9e3aF7Xv37oWNjY1WccTHx2tVr7YzxX6bYp8B0+y3KfYZ0K7fhYWF1RAJET3P6dOnAQDt27fnCQcyWQaT8Dg5OcHMzAxZWVnltmdlZcHFxaXSOi4uLs8sX1RUhOnTp2Pr1q2IiIgAALRp0wapqan45ptvnprwxMTElJsqV3bJLDQ0VKszcfHx8ejbt69JnQ0wxX6bYp8B0+y3KfYZqFq/pVJpNUVFRM8yYsQIpKamom3btvoOhUhvDCbhsbCwQEBAABISEhAZGQngySo2CQkJmDp1aqV1unTpgoSEBLz77ruqbfHx8ejSpQuAJwdnuVxeYeUGMzMzKJXKp8ZiaWkJS0vLCtslEonWX26qUrc2M8V+m2KfAdPstyn2GdCu36Y4TkRPIwgCNm/eDA8PD7Rt27Za/z5sbGzQtWvXamufqDYwmIQHeLKE9NixYxEYGIiOHTtiwYIFKCgowPjx4wEAY8aMgZubG2JjYwEA77zzDoKCgjBv3jxERERg/fr1OHnyJFasWAHgydzooKAgTJs2DdbW1mjatCkOHDiANWvWYP78+XrrJxEREZmuO3fuIC0tDdeuXUO7du30HQ6R0TOohGf48OHIycnBzJkzkZmZibZt22L37t2qhQnS09PLXa3p2rUr4uLiMGPGDEyfPh1eXl7Ytm2b6hk8ALB+/XrExMTglVdeQW5uLpo2bYrPP/8cr7/+eo33j4iIiCgtLQ0A0LJlS5ibG9RXMSKjZHB/ZVOnTn3qFLbExMQK26KiohAVFfXU9lxcXLB69WpdhUdERESkNUEQVAmPj4/Pc8s/fPgQ9erVq+6wiIwaH3dNRERPpVQKEAQBhSWlSL39EACQevshCktKIQgClIKg5wiJahdBEBAeHg5/f394eno+s6xMJoOnpye6du2KjIyMGoqQqHop/nFcOXkzF/svZuPkzVzVcUWh1P1xxeCu8BARkeG4lpOPn47cxLZTd1GqKMXXHYFRPx6HuZk5Itu5YVxXD3g582GiROoSi8Xw8fFR6+rOrl278PDhQ9y6davCcweJaqtrOfn4+f+PKwUlCtV2WwszRLZzw9iuHnhBx8cVJjxERFSpxfuuYl78JZRdxLE0+9++ghIFfj2Wjrjj6Xi/b0tM7d1CP0ESGbG1a9cCAF555RWYmZk9pzSR4fv3ceWfqvO4wiltRERUweJ9V/HN3soPSv8kCMA3ey9hyf6rNRMYkYkoKSnBsWPHAACjRo3SczREVafP4woTHiIiUlEqBVzJysM3ey9pVO+bvZdwNTuf9/QQ6YiFhQVu3LiBv/76C23atFGrjiAIuHLlChQKxfMLE9UQhVLAZS2PK1ey8nRyTw8THiIiUhGJgJ+O3NS4niAAP/19AyLdh0RksiQSCfr06aN2+Xv37iEuLg6LFi1i0kMGQywCftbyuPLzkZsQ6+DAwoSHiIhUiuQKbDt1V6u6W0/dRZGcX7KI9OXkyZMAgCZNmvCeHzIYhnBcYcJDREQqF+5Jy62ao4mCEgUu3svTcUREpA6ZTIZz584BAAICAvQcDdH/pGVU7bhy4Z60yjFwlTYiIlKRFpVWrX6xXEeREJEm8vPz4erqioKCAjRp0kTf4RCp5BVX7bhS1foAEx4iIvoHe+uqHRbsrSQ6ioSINOHo6Ihx48ZBJpNBJOLddGQ47Kyqdlypan2AU9qIiOgffBrZw9ZCu7n/thZm8G5k/A8hVSgU+OSTT9CsWTNYW1vD09MTc+bMgcAV6ugpDh8+jLi4OFy/fr3a38vS0rLa34NIE76uVTuu+DSyr3IMTHiIiEjFWvLkSdfaeLGdG6wlxn+j9FdffYWlS5di8eLFuHDhAr766it8/fXXWLRokb5DIwN19uxZXLlyBVJp1e9FIKptDOG4woSHiIhUBAEY19UDms6IEYmAcd2awRSucRw5cgSDBw9GREQEPDw88NJLLyE0NBTHjx/Xd2hkgO7fv4/s7GyIxWK0bNlS3+EQ1TilAIzV8rgytqsHdPAYHiY8RET0P2KxCF7Odni/r2ZfzD4IbQnPBrYQm8C9A127dkVCQgIuX74MADh9+jQOHz6Mfv366TkyMkQXLlwAANUUyGfZvXs3srKyaiIsohpjJhbhBS2PKy0a1oGZDh7Ew0ULiIiogqm9W0AkevKk62fdmiISPTkoTQluUXPB6dlHH30EqVQKb29vmJmZQaFQ4PPPP8crr7zy1DoymQwymUz1umxqk1wuh1yu2cp2ZeU1rWeKDGGs2rVrBzs7O9jY2Dwzjry8PAwZMgQlJSU4ffo0XnjhhRqL0RDGqTbgOKnnaeP0Wo+mEEOB7/Zdee5x5e3eXpjUvSlKS5++Qpsm/w5MeIiIqFJTglsgrJULfvr7BraeuotSxf8OPLYWZnixnRvGdWuGFg3r6DHKmrdx40b8+uuviIuLQ6tWrZCamop3330Xrq6uGDt2bKV1YmNjMXv27Arb9+7dCxsbG63iiI+P16qeKTKUsSq72lOZ/fv3o6ioCK6urrhy5QquXr1ag5E9YSjjZOg4TuqpbJzcAHzVQY3KeRexa9fFZxYpLCxUOxYmPERE9FTNG9hiTmRrTI/wwfk7D5Fx9gh+ndgJvo3rwVpiZhL37PzbtGnT8NFHH2HEiBEAAD8/P9y6dQuxsbFPTXhiYmIQHR2tei2VSuHu7o7Q0FDY22u2ApFcLkd8fDz69u0LiYTLgD9LbRqrxYsXAwAmT56MiIiIGn3v2jRO+sRxUs/zxkmhFCAWAcVyBS5l5SG/uBR1rMzR0tkOVhIzKAWoNY1Nk0VAmPAQEdFTld2TY2NhjrbuDsg4C/i7O0AieXL4MP47dioqLCyEWFz+FlgzMzMolcqn1rG0tKx0uWCJRKL1F6eq1DU1hj5WSqUSbm5uqFOnDsaMGaO3WA19nAwFx0k9Txunsi0WFkCH5s++r+157auLixYQERFpYODAgfj888+xc+dO3Lx5E1u3bsX8+fPx4osv6js0qqXEYjF++ukn5OTkoFmzZmrVSUlJwbZt23Dv3r1qjo6o9uMVHiIiIg0sWrQIn3zyCd58801kZ2fD1dUVr732GmbOnKnv0KiWs7KyUqucIAg4duwYsrOz0ahRIzRq1KiaIyOq3ZjwEBERacDOzg4LFizAggUL9B0Kmag7d+4gOzsb5ubmaNOmjb7DITJ4nNJGREREVIukpKQAAFq1avXcZ/sQEa/wEBEREdUqISEhcHJyUvt+HyJTx4SHiIiISIdKS0thbl59X7FsbW3RrVu3amufyNhwShsRERGRjuTl5WHu3LnYtGkTFAqFvsMhIjDhISIiItKZixcvoqSkBI8fP4aZmZm+wyEiMOEhIiIi0pm0tDQAgK+vr54jIaIyTHiIiIiIdKC4uBi3b98GAPj4+Dyz7MyZM/HTTz8hPz+/JkIjMmlctICIiIhIB6ysrBAdHY2bN2+iXr16Ty2Xk5ODL774AgqFAp07d4a3t3cNRklkeniFh4iIiEhHbGxsnjudbcOGDVAoFAgMDGSyQ1QDmPAQERER1aC1a9cCAEaNGqXnSIhMAxMeIiIiohoiCAKio6MxcOBAjBgxQq06d+/e5RLXRFXAe3iIiIiIaohIJMKwYcMwbNgwtcoXFRVh9erVsLa2xuTJk2FnZ1fNERIZH4O8wrNkyRJ4eHjAysoKnTp1wvHjx59ZftOmTfD29oaVlRX8/Pywa9eucvtFIlGlP3Pnzq3ObhARERFVyenTp6FQKGBra4s6deroOxyiWsngEp4NGzYgOjoas2bNQkpKCvz9/REWFobs7OxKyx85cgQjR47ExIkTcerUKURGRiIyMhLnzp1Tlbl37165n1WrVkEkEmHo0KE11S0iIiIijQiCgOTkZABAQEAARCKRniMiqp0MLuGZP38+Jk2ahPHjx8PX1xfLli2DjY0NVq1aVWn5hQsXIjw8HNOmTYOPjw/mzJmD9u3bY/HixaoyLi4u5X5+//13BAcHo3nz5jXVLSIiIiKNFBUVwdraGhKJBH5+fvoOh6jWMqh7eEpKSpCcnIyYmBjVNrFYjJCQECQlJVVaJykpCdHR0eW2hYWFYdu2bZWWz8rKws6dO/Hzzz8/NQ6ZTAaZTKZ6LZVKAQByuRxyuVzd7qjq/PO/psIU+22KfQZMs9+m2Gegav02tbEi0gUbGxtMmDAB+fn5sLKy0nc4RLWWQSU89+/fh0KhgLOzc7ntzs7OuHjxYqV1MjMzKy2fmZlZafmff/4ZdnZ2GDJkyFPjiI2NxezZsyts37t3L2xsbJ7XjUrFx8drVa+2M8V+m2KfAdPstyn2GdCu34WFhdUQCZH+5eXlwdraGubm1feVivfuEFWNQSU8NWHVqlV45ZVXnnmmJCYmptxVI6lUCnd3d4SGhsLe3l6j95PL5YiPj0ffvn0hkUi0jru2McV+m2KfAdPstyn2Gahav8uulBMZm127duHGjRsYOHAgWrVqpe9wiKgSBpXwODk5wczMDFlZWeW2Z2VlwcXFpdI6Li4uapc/dOgQLl26hA0bNjwzDktLS1haWlbYLpFItP5yU5W6tZkp9tsU+wyYZr9Nsc+Adv02xXEi41dSUoKrV6+itLQUjo6O+g6HiJ7CoBYtsLCwQEBAABISElTblEolEhIS0KVLl0rrdOnSpVx54Ml0i8rK//jjjwgICIC/v79uAyciIiKTc+XKFZSWlqJevXoVptf/k1KprMGoiOjfDCrhAYDo6GisXLkSP//8My5cuIA33ngDBQUFGD9+PABgzJgx5RY1eOedd7B7927MmzcPFy9exKeffoqTJ09i6tSp5dqVSqXYtGkTXn311RrtDxERERmnshkmvr6+z1wyul+/fhg6dCguXbpUU6ER0T8Y1JQ2ABg+fDhycnIwc+ZMZGZmom3btti9e7fqzEl6ejrE4v/laV27dkVcXBxmzJiB6dOnw8vLC9u2bUPr1q3Ltbt+/XoIgoCRI0fWaH+IiIjIOPXu3RuBgYHPTHZu3bqFvXv3QiQSYcGCBTUXHBGpGFzCAwBTp06tcIWmTGJiYoVtUVFRiIqKemabkydPxuTJk3URHhEREREAPHcxo7i4OABAr1694O7uXhMhEdG/GNyUNiIiIiJjsXHjRgDAqFGj9BwJkeliwkNERERUTfbt24fly5dj6NChzy2bnp6O33//HXfv3q2ByIhMh0FOaSMiIiIyBvXq1VN7Sv2JEydw7tw5iMViuLm5VXNkRKaDV3iIiIiI9KywsBAXLlwAAAQEBOg5GiLjwoSHiIiISM9SU1OhUCjQqFEjuLq66jscIqPCKW1EREREeubn5weFQoH69evrOxQio8OEh4iIiEjP7Ozs0KNHD32HQWSUOKWNiIhIAx4eHhCJRBV+pkyZou/QqJopFArcuHEDSqVS36EQkQZ4hYeIiEgDJ06cgEKhUL0+d+4c+vbt+9wHYFPtd/PmTaxduxYNGzbE66+/DpFIpO+QiEgNTHiIiIg00KBBg3Kvv/zyS3h6eiIoKEhPEVFNSUtLAwC4u7sz2SGqRTiljYiISEslJSVYu3YtJkyYwC/ARk6pVOLixYsAAF9f36eWu3LlCoqKimoqLCJSA6/wEBERaWnbtm149OgRxo0b98xyMpkMMplM9VoqlQIA5HI55HK5Ru9ZVl7TeqZIl2MllUphb28PQRDg6ur61DaHDRuG69evY/PmzejVq1eV37cm8HdKPRwn9dTUOGnSPhMeIiIiLf3444/o16/fc5+bEhsbi9mzZ1fYvnfvXtjY2Gj13vHx8VrVM0W6GisXFxc4OTlhz549le5PT09HamoqzM3NkZmZiV27dunkfWsKf6fUw3FST3WPU2FhodplmfAQERFp4datW/jrr7+wZcuW55aNiYlBdHS06rVUKoW7uztCQ0Nhb2+v0fvK5XLEx8ejb9++kEgkGsdtSmp6rGbMmAEA6NevH0aMGFHt76cr/J1SD8dJPTU1TmVXytXBhIeIiEgLq1evRsOGDREREfHcspaWlrC0tKywXSKRaP2FoCp1TU1NjVVWVhZEIhHGjBnz3Pd7+PAh6tatC7HYcG6n5u+UejhO6qnucdKkbcP5KyMiIqollEolVq9ejbFjx8LcnOcO6YmffvoJ6enpGDBgwDPLCYKAtWvXYsGCBbh3714NRUdkuvgpTUREpKG//voL6enpmDBhgr5DIQPTuHHj55a5efMmcnNzYWFhAUdHxxqIisi0MeEhIiLSUGhoKARB0HcYVEslJycDANq0aQMLCws9R0Nk/DiljYiIiKiGKJVK5OXlAQACAgL0HA2RaeAVHiIiIqIaIhaLMX78eNy/fx9OTk76DofIJPAKDxERPZVSKUAQBBSWlCL19kMAQOrthygsKYUgCFByWheRVpjsENUcXuEhIqKnupaTj5+O3MS2U3dRqijF1x2BUT8eh7mZOSLbuWFcVw94OdvpO0yianH58mXY2NjAzc0NIpFI3+EQkZaY8BARUaUW77uKefGXUHYRx9Lsf/sKShT49Vg64o6n4/2+LTG1dwv9BElUTQRBwO7du/Hw4UMMHz4c3t7e+g6JiLTEKW1ERFTB4n1X8c3e/yU7TyMIwDd7L2HJ/qs1ExhRDcnMzMTDhw9hbm6O5s2b6zscIqoCJjxERKSiVAq4kpWHb/Ze0qjeN3sv4Wp2Pu/pIaORlpYGAPDy8nrq0tHbtm3Dnj17UFpaWpOhEZGGmPAQEZGKSAT8dOSmxvUEAfjp7xvgXQ5kLOzt7eHk5ARfX99K9wuCgA8++ADh4eHYtGlTDUdHRJrgPTxERKRSJFdg26m7WtXdeuoupkf4wMaChxaq/Tp06IDAwMCn7j927BiuXbsGW1tbDBo0qAYjIyJN8QoPERGpXLgnRUGJQqu6BSUKXLyXp+OIiPRHJBI9dXW2tWvXAgBefPFF2Nra1mRYRKQhJjxERKQiLaravQjSYrmOIiEybO3atUNAQABGjRr1zHL5+fn4448/kJGRUUOREdG/MeEhIiIVe+uqTUezt5LoKBIiwzZx4kScPHkSoaGhzyx36tQppKSk4M8//6yhyIjo35jwEBGRik8je9hamD2/YCVsLczg3YgPISXT8qwHkgqCgJSUFABAQEBATYVERP/ChIeIiFSsJWaIbOemVd0X27nBWqJdskRkjK5fv45Hjx7B0tISrVq10nc4RCaLCQ8REakIAjCuqweecdK6UiIRMK5bM/ApPET/4+zsjF69eqFr166QSDjdk0hfDC7hWbJkCTw8PGBlZYVOnTrh+PHjzyy/adMmeHt7w8rKCn5+fti1a1eFMhcuXMCgQYNQt25d2NraokOHDkhPT6+uLhAR1VpisQheznZ4v29Ljep9ENoSng1sIdY0UyIyYnXq1EFQUBB69uyp71CITJpBJTwbNmxAdHQ0Zs2ahZSUFPj7+yMsLAzZ2dmVlj9y5AhGjhyJiRMn4tSpU4iMjERkZCTOnTunKnPt2jV0794d3t7eSExMxJkzZ/DJJ5/AysqqprpFRFTrTO3dAtPCWj73So9IBEwLa4kpwS2eeS8DERGRvhhUwjN//nxMmjQJ48ePh6+vL5YtWwYbGxusWrWq0vILFy5EeHg4pk2bBh8fH8yZMwft27fH4sWLVWU+/vhj9O/fH19//TXatWsHT09PDBo0CA0bNqypbhER1UpTglsg/r0gjOrUpMJCBrYWZhjVqQni3wvClOAWeoqQSHcePXqEffv2ITMzU9+hEJGOGczjsEtKSpCcnIyYmBjVNrFYjJCQECQlJVVaJykpCdHR0eW2hYWFYdu2bQAApVKJnTt34j//+Q/CwsJw6tQpNGvWDDExMYiMjHxqLDKZDDKZTPVaKpUCAORyOeRyzZ4xUVZe03q1nSn22xT7DJhmv02pz+4OFpgZ0RL/CW2BCxmPkJl2HL+MC4C3qwOsJGYQoN44mMJYUe12/vx5HDp0CHfu3MGYMWP0HQ4R6ZDBJDz379+HQqGAs7Nzue3Ozs64ePFipXUyMzMrLV92diY7Oxv5+fn48ssv8dlnn+Grr77C7t27MWTIEOzfvx9BQUGVthsbG4vZs2dX2L53717Y2Nho0z3Ex8drVa+2M8V+m2KfAdPstyn2GQDupR3HvTTN6hQWFlZPMEQ6cuHCBQCAr6+vniMhIl0zmISnOiiVSgDA4MGD8d577wEA2rZtiyNHjmDZsmVPTXhiYmLKXTmSSqVwd3dHaGgo7O3tNYpBLpcjPj4effv2NakVWkyx36bYZ8A0+22KfQaq1u+yK+VEhujRo0e4e/cuAMDb27vSMp9//jkyMzPx5ptvwsfHpybDI6IqMpiEx8nJCWZmZsjKyiq3PSsrCy4uLpXWcXFxeWZ5JycnmJubVzhb4+Pjg8OHDz81FktLS1haWlbYLpFItP5yU5W6tZkp9tsU+wyYZr9Nsc+Adv02xXGi2kMsFqNLly7Iz89HnTp1KuxXKBRYvHgxMjMzERISwoSHqJYxmEULLCwsEBAQgISEBNU2pVKJhIQEdOnSpdI6Xbp0KVceeDLFpKy8hYUFOnTogEuXLpUrc/nyZTRt2lTHPSAiIqLayN7eHqGhoRgyZEil+8sWM6hfvz769etXw9ERUVUZzBUeAIiOjsbYsWMRGBiIjh07YsGCBSgoKMD48eMBAGPGjIGbmxtiY2MBAO+88w6CgoIwb948REREYP369Th58iRWrFihanPatGkYPnw4evbsieDgYOzevRt//PEHEhMT9dFFIiIiqmV++eUXAMDw4cNhYWHx1HKFhYWwsrKCWGww55OJCAaW8AwfPhw5OTmYOXMmMjMz0bZtW+zevVu1MEF6enq5D5GuXbsiLi4OM2bMwPTp0+Hl5YVt27ahdevWqjIvvvgili1bhtjYWLz99tto2bIlfvvtN3Tv3r3G+0dERES1z6xZs9CiRQtEREQ8s9wff/yBjIwMDBw4EC1acLl2IkNhUAkPAEydOhVTp06tdF9lV2WioqIQFRX1zDYnTJiACRMm6CI8IiIiMjGenp6YOXPmM8vk5eXh0qVLEAQBdevWraHIiEgdvOZKREREVEUpKSkQBAFNmjRBgwYN9B0OEf0DEx4iIiKiKip7BmBAQICeIyGifzO4KW1EREREtc3w4cNx7949Xt0hMkC8wkNERNWmuLhY3yEQ1ZhGjRrB3JznkokMDRMeIiKqFikpKViwYAHu3bun71CIiMiEMeEhIiKdKi0txR9//IE//vgDMpkMKSkp+g5J5+7evYtRo0bB0dER1tbW8PPzw8mTJ/UdFmngzz//xKFDh5Cfn6/vUIiomvG6KxER6dTx48dVSU7v3r3RvXt35OXl6Tkq3Xn48CG6deuG4OBg/Pnnn2jQoAGuXLmCevXq6Ts0UlNhYSFOnDgBQRDg6+uLOnXq6DskIqpGTHiIiEinOnXqhPT0dHTo0AGenp76DkfnvvrqK7i7u2P16tWqbc2aNdNjRKSpsuflODs7w9HRscL+vLw82NralnvYORHVXkx4iIhIp8zMzDBixAh9h1Fttm/fjrCwMERFReHAgQNwc3PDm2++iUmTJj21jkwmg0wmU72WSqUAALlcDrlcrtH7l5XXtJ4petpYnT9/HgDQsmXLSscxOjoaf/31F+bOnYvBgwdXf6B6xt8p9XCc1FNT46RJ+0x4iIiINHD9+nUsXboU0dHRmD59Ok6cOIG3334bFhYWGDt2bKV1YmNjMXv27Arb9+7dCxsbG63iiI+P16qeKfr3WIlEIjg5OSE7Oxu7du0qt6+kpATr1q1DQUEBLl68CIlEUpOh6hV/p9TDcVJPdY9TYWGh2mWZ8BAREWlAqVQiMDAQX3zxBQCgXbt2OHfuHJYtW/bUhCcmJgbR0dGq11KpFO7u7ggNDYW9vb1G7y+XyxEfH4++ffua1JdxbWgzVlu3bkVBQQEaN26MadOmmcS0Nv5OqYfjpJ6aGqeyK+XqYMJDRESkgUaNGsHX17fcNh8fH/z2229PrWNpaQlLS8sK2yUSidZfCKpS19RoMlaHDh0CALzyyiuV/psBT5LehIQE+Pn5wcXFRWdx6ht/p9TDcVJPdY+TJm0b/2kLIiLSGUEQUFJSou8w9Kpbt264dOlSuW2XL19G06ZN9RQR6dJ3332H5ORkvPnmm08tc/XqVRw5cgRr1qyBQqGoweiISBtMeIiISC35+fm4evUqtm/fDkEQ9B2O3rz33ns4evQovvjiC1y9ehVxcXFYsWIFpkyZou/QSAdEIhHat2+PJk2aPLVMcnIyAMDf3x9mZmY1FRoRaYkJDxERPdft27exatUqFBQU4ObNm3jw4IG+Q9KbDh06YOvWrVi3bh1at26NOXPmYMGCBXjllVf0HRrVgMePH+PKlSsAgICAAD1HQ0Tq4D08RET0TKWlpdi4cSPy8/NhZWWFsWPHwsnJSd9h6dWAAQMwYMAAfYdBeiCRSNCzZ0/k5uaa/N8BUW3BhIeIiJ7J3NwckZGRSElJgZmZWaUPaiQyFTY2NujVq5e+wyAiDXBKGxERPZenpyciIyN5vwIREdU6THiIiIiIiMhoMeEhIiIio3b58mWsWrUKqamp+g6FiPSACQ8REREZtQsXLuD27dvIysrSdyhEpAdMeIiITFxxcbG+QyCqNkqlUrWMtK+vb4X9aWlpOHv2bE2HRUQ1iAkPEZEJS01Nxbfffov09HR9h0JULfLy8lBSUgJ7e3s0bty4wv7//ve/aNOmDWJjY/UQHRHVBC5LTURkgkpLS7Fnzx6cPHkSAHDq1KlnPlmeqLays7PDsGHDUFpaCpFIVG6fVCrF77//DgDo27evPsIjohrAhIeIyASdOnVKlewEBQUhKChIzxERVQ+xWIwWLVpAIpFU2LdlyxYUFxfD29sbAQEBldaXy+UwMzODWMxJMUS1FRMeIiITFBAQgPT0dLRp0wZeXl76DodILxQKBdzc3DBq1KgKV3/KHDp0CGfOnEFISAhat25dwxESkS4w4SEiMkFisRhDhw6t9ve5f/8+nJycqv19iLQxceJEjBs3DiUlJZXuVygUOHXqFPLz85+aEBGR4eP1WSIi0rm8vDxMmDABbdq0wf379/UdDtFTmZmZwdrautJ9ly9fRn5+PmxtbeHt7V3DkRGRrjDhISIinTp+/DjatWuH1atXIzMzE/Hx8foOiUgrN27cAAC0bdsWZmZmeo6GiLTFKW1ERKRT//3vf3Ht2jW4u7tj7dq16NmzJ6RSqb7DItJYv3794O/vDzs7O32HQkRVwCs8RERGRqlUQiaT6e39f/jhB0yePBmnT59Gz5499RYHUVWJRCK4ubnB3t5e36EQURXwCg8RkREpKCjAb7/9BpFIhFdeeUUvS+m6uLhg+fLlNf6+RERElWHCQ0RkJO7evYuNGzdCKpVCIpEgJycHzs7O+g6LqMaVlpbC3JxfcYjoCYOc0rZkyRJ4eHjAysoKnTp1wvHjx59ZftOmTfD29oaVlRX8/Pywa9eucvvHjRsHkUhU7ic8PLw6u0BEVKMUCgU2b94MqVQKR0dHTJo0ickOmSSlUolFixZhzZo1vHeMiAAYYMKzYcMGREdHY9asWUhJSYG/vz/CwsKQnZ1dafkjR45g5MiRmDhxIk6dOoXIyEhERkbi3Llz5cqFh4fj3r17qp9169bVRHeIiGqEmZkZXnzxRbRq1QqTJk1CgwYN9B0SkV7cvn0bUqkUGRkZsLW11Xc4RGQAdJrwlJSUoKCgoEptzJ8/H5MmTcL48ePh6+uLZcuWwcbGBqtWraq0/MKFCxEeHo5p06bBx8cHc+bMQfv27bF48eJy5SwtLeHi4qL6qVevXpXiJCIyNE2aNMFLL70ES0vLamn/+vXr+OGHH6BQKKqlfSJdSEtLAwB4e3tXWEp69+7d+OKLL3Dr1i19hEZEeqLVBNf169fj2LFj+Pbbb1XbZs+ejc8//xyCIGDAgAH45ZdfUKdOHY3aLSkpQXJyMmJiYlTbxGIxQkJCkJSUVGmdpKQkREdHl9sWFhaGbdu2lduWmJiIhg0bol69eujduzc+++wzODo6VtqmTCYrt8JR2SVxuVwOuVyuUZ/Kymtar7YzxX6bYp8B0+y3qfVZEAT8+uuveOedd5CXl4e5c+fio48+0qgNUxkr0r9r164BAHx8fCrsW7RoEXbt2oWioiLMmTOnpkMjIj3RKuGZN28e2rVrp3p95MgRzJ49GxEREfDx8cGiRYvw+eefIzY2VqN279+/D4VCUWHeubOzMy5evFhpnczMzErLZ2Zmql6Hh4djyJAhaNasGa5du4bp06ejX79+SEpKqvRBYrGxsZg9e3aF7Xv37oWNjY1GfSpjqg/eM8V+m2KfAdPst6n0+aefflKdRPLx8YGzs3OFeyWfp7CwsBoiI6po8uTJuHr1Kjw9PSEIgmp7dnY29uzZAwAYNWqUvsIjIj3QKuG5du0axo4dq3odFxcHFxcXbN26Febm5lAqlfjtt980Tniqy4gRI1T/7+fnhzZt2sDT0xOJiYno06dPhfIxMTHlrhpJpVK4u7sjNDRU47X45XI54uPj0bdvX0gkEu07UcuYYr9Nsc+Aafbb1Prs4OCAXbt24cMPP0Tbtm0RHh6ucb958zjVFAsLC/j6+gIof2Vx/fr1UCgU6NChA1q2bFlp3ePHj6Np06Zc8IPIyGiV8MhkMlhZWale7927F/369VMtAenr64vvv/9e43adnJxgZmaGrKysctuzsrLg4uJSaR0XFxeNygNA8+bN4eTkhKtXr1aa8FhaWlY6B14ikWj95aYqdWszU+y3KfYZMM1+12SfCwsLtb7CXFVBQUG4ceMGGjRogF27dmnVb1P73SDD06NHD7z++uvo0KFDpfsfPnyIP//8EwDw3nvv8WGjREZEq0ULmjVrhr/++gsAcPLkSVy9erXcMs9ZWVka378DPDkrExAQgISEBNU2pVKJhIQEdOnSpdI6Xbp0KVceeDLN5GnlAeDOnTt48OABGjVqpHGMREQ17ezZs1i4cCGuXr2qtxhcXV319t5EutCuXTssXboUEyZMqHR/cnIyAMDT05PJDpGR0eoKz2uvvYZ33nkHaWlpuHPnDho3bowBAwao9v/9999o1aqVVgFFR0dj7NixCAwMRMeOHbFgwQIUFBRg/PjxAIAxY8bAzc1NNV3unXfeQVBQEObNm4eIiAisX78eJ0+exIoVKwAA+fn5mD17NoYOHQoXFxdcu3YN//nPf9CiRQuEhYVpFSMRUU1QKBTYu3ev6llkp0+fRosWLfQcFZHxUSgUSE1NBQAEBAToNxgi0jmtEp633noLVlZW2LVrFwICAvDhhx/C2toaAJCbm4vMzEy8/vrrWgU0fPhw5OTkYObMmcjMzETbtm2xe/du1Xza9PR0iMX/uzDVtWtXxMXFYcaMGZg+fTq8vLywbds2tG7dGsCTZ1OcOXMGP//8Mx49egRXV1eEhoZizpw51bZ0KxGRLpw7d06V7PTo0QO9evXSb0BERqq0tBTt27fHlStX8MILL+g7HCLSMa0SHgCYNGkSJk2aVGF7/fr1cfLkySoFNXXqVEydOrXSfYmJiRW2RUVFISoqqtLy1tbWqlVZiIhqkzZt2uDmzZto2bIlvL29q+U9rl+/jubNm1dL20S1haWlJXr37o3g4GCIRCJ9h0NEOlalB4/KZDIkJSXh999/x/3793UVExERARCJRBg8eHC1JDslJSWIiYmBl5cXTwoR/T8mO0TGSeuE57vvvkOjRo3QrVs3DBkyBGfOnAHw5Fk6Tk5OWLVqlc6CJCIi3bl8+TK6deuGL7/8EkqlEvv27dN3SERERNVGq4Rn9erVePfddxEeHo5Vq1aVe7CXk5MTevfujfXr1+ssSCIi0p2//voLJ0+eRL169bBp0yZ89dVX+g6pVvn0008hEonK/VTXlEN6vqKiIshkMn2HQUQGTKt7eObNm4fBgwcjLi4ODx48qLA/ICAA3333XZWDIyIi3XvjjTeQlZWFSZMmoXHjxvoOp1Zq1aqV6vEMAFTPoaOad+zYMRw+fBg9e/ZEz5499R0OERkgrT6hr169irfffvup++vXr19pIkRERE8olUrIZDLVCpc1SSQSYfbs2TX+vsbE3Nz8mQ+4ppqTlpYGhUKBunXrltv+z9knRGTatEp4HBwcnrlIQVpaGg8ERERPUVhYiN9++w1yuRxjx46FmZmZvkMiDV25cgWurq6wsrJCly5dEBsbiyZNmjy1vEwmKzftSiqVAgDkcjnkcrlG711WXtN6xuj+/fvIycmBWCxG8+bNy43Jjz/+iNWrV6NXr17o27evHqM0fPydUg/HST01NU6atK9VwtO/f3+sWLECb775ZoV958+fx8qVK5/6JGMiIlOWkZGBjRs34vHjx5BIJMjMzISbm5u+w9K5O3fu4MKFC0b5RbNTp0746aef0LJlS9y7dw+zZ89Gjx49cO7cOdjZ2VVaJzY2ttKranv37oWNjY1WccTHx2tVz5g8fPgQIpEIderUqbD4xqJFi5CWlgZvb2+OlZo4TurhOKmnusepsLBQ7bJaJTyfffYZOnXqhNatW2PgwIEQiUT4+eefsWrVKvz2229o1KgRZs6cqU3TRERGS6lUYuvWrXj8+DHq16+PYcOGqR6qbCxkMhn27dunemBq48aN4ePjo+eodKtfv36q/2/Tpg06deqEpk2bYuPGjZg4cWKldWJiYhAdHa16LZVK4e7ujtDQUNjb22v0/nK5HPHx8ejbty8kEol2nTAixcXFKCoqQr169VTbbt68ibS0NIhEIvTs2bPCWCmVStWCE8TfKXVxnNRTU+NUdqVcHVolPK6urkhOTsb06dOxYcMGCIKAX375BXZ2dhg5ciS+/PJLODk5adM0EZHREovFGDJkCA4fPoyBAwfCyspKp+3fv38f7777Lj777DN4eHjotG11CIKANWvWICMjAwDg7++Ppk2b1ngcNc3BwQEvvPACrl69+tQylpaWsLS0rLBdIpFo/YWgKnWNiUQiqXBlbevWrQCAXr16wcnJqcJYnTt3Dvv27UO3bt0QEBBQo/EaMv5OqYfjpJ7qHidN2tZ6WZmGDRvihx9+wA8//ICcnBwolUo0aNAAYnGVnmVKRGTUGjVqhKioKJ23Gx8fjzFjxiAzMxP37t1DQkKCzt/jeUQiEbp06YKEhAQMGDAAnp6eNR6DPuTn5+PatWsYPXq0vkOh//fee++hVatWsLKyQn5+foX9ycnJePjwoUZniImo9tLJOpoNGjTQRTNERKSFDRs2YMSIEQAAHx8fzJs3T2+xtGrVCi1btjTqs58ffPABBg4ciKZNmyIjIwOzZs2CmZkZRo4cqe/Q6P9JJBJERERALpdj165d5fY9ePAAN2/ehEgkQvv27fUUIRHVJK0Snv/+97/PLSMSifDJJ59o0zwREWmgX79+aN68OcLDwzF37lytb4LXBZFIZNTJDvBkQYaRI0fiwYMHaNCgAbp3746jR4/y5F8tcfHiRQBAixYtKixlTUTGSauE59NPP33qPpFIBEEQmPAQEdUQe3t7nDp1SuOb30k769ev13cIVAVdu3ZFkyZNuBw8kQnR6oYbpVJZ4ae0tBTXrl3De++9h8DAQGRnZ+s6ViIig6bJEpm6xmSHSD0ikQju7u5wdXXVdyhEVEN0tsKAWCxGs2bN8M0338DLywtvvfWWrpomIjJ458+fx8KFC3HhwgV9h6JzpaWl2LdvH44cOaLvUIiIiDSmk0UL/q1nz5748MMPq6NpIiKDolQqER8fj6NHjwIAzpw5Y1TPnbl16xb++OMPPHjwAGZmZmjZsqW+QyIiItJItawhffLkSS5PTUQm4eLFi6pkp1u3bjpfcvr8+fMoKirSaZvqkkqlWLNmDR48eIA6depg8ODBsLCw0EssRGVu3LgBuVyu7zCIqBbR6grPmjVrKt3+6NEjHDx4EFu2bMGrr75apcCIiGoDHx8fBAYGonnz5jq9sqNUKrFo0SJ8+OGHmDx5Mr777judta0ue3t7dO7cGcXFxejbty/MzMxw/fr1Go+DqExZEm5hYYH33ntP5w/vJSLjpFXCM27cuKfuc3JywkcffYSZM2dqGxMRUa0hEokQERGh0zYzMzMxbtw47NmzB8CTM9qlpaUwN6+WWcjPFBISApFIBAA8q056V7aktLOzc7lk59GjRyguLoaLi4u+QiMiA6bV0fPGjRsVtolEItSrVw92dnZVDoqIyJQ9fPgQBw4cgJWVFebNm4c33nhDlXTUNH29L1Fl0tLSAKDC1dQff/wR//nPf/D+++/j66+/1kdoRGTAtEp4mjZtqus4iIjo//n4+GDNmjXw9fVFq1at9B0OkUFQKpUwMzODSCSCr69vuX2//PILlEolmjdvrqfoiMiQ1fz8CCIiei5dL37wb0qlkovLUK0iFosxevRoFBUVwdraWrX97NmzOH36NCQSCYYNG1ah3uPHj5GTk8Pn7hCZMLWOdmKxGGZmZhr96GOuORGRLgmCoNeHiVaXjIwMrFy5EpcvX9Z3KEQa+2eyAzyZ5mZra4uIiAjUr1+/3L7S0lKkp6dj5cqVuHXrVk2GSUQGRK2sZObMmZzHTUQmpaioCFu3bkVeXh4mTJgAiUSi75CqTC6XY//+/Th69CgEQcC+ffvg5eXFz3eq1YYPH44BAwbgwYMHFfZdunQJCoUC9vb2cHd310N0RGQI1Ep4Pv3002oOg4jIcGRmZmLDhg149OgRzM3NkZGRYRT3Ll68eBFJSUkAgNatWyM8PJzJDhkFW1tb2NraVtiekpICAPD39+cUTiITxnlnRET/IAgCfv/9dzx69AgODg4YPny4zpa6LSgowHvvvYcePXpg9OjROmlTE61bt8a1a9fg6+uLF154ocbfn6gmCYKAxo0bIzMzE/7+/voOh4j0qEoJz507d3Dq1Ck8fvwYSqWywv4xY8ZUpXkiohonEokwZMgQJCYmYsCAARXuF9BWcnIyXn75ZVy+fBkbN27EwIED4eDgoJO21SUSiRAZGVmj70mkLyKRCMHBwSgsLIS9vb2+wyEiPdIq4SkuLsbYsWPx22+/QalUQiQSQRAEAOWf2cCEh4hqowYNGuh0lbSrV6+iS5cukMvlcHNzw5o1a2o82SEyVZy2SURaTWidPn06tmzZgs8//xyJiYkQBAE///wz9u7di379+sHf3x+nT5/WdaxERLVSixYtMHr0aAwdOhSnT59G79699R0SERGRydAq4dm8eTPGjx+PDz/8UPVQPDc3N4SEhGDHjh1wcHDAkiVLdBooEVFttnTpUmzatAmOjo7V0r5MJquWdomIiGo7rRKe7OxsdOzYEcD/1sMvKChQ7R86dCi2bNmig/CIiIyDhYVFtUytUSgUOHDgAL799lvk5ubqvH0ifbp9+zauXr0KhUKh71CIqBbTKuFxdnZWrXdvY2ODevXq4dKlS6r9UqkUxcXFuomQiEiH/nlypra7ffs2li9fjsTERMhkMk4lJqPz999/49dff8Xhw4f1HQoR1WJaJTydOnUq9+EzcOBAzJ07F7/++it++eUXfPvtt+jcubPWQS1ZsgQeHh6wsrJCp06dcPz48WeW37RpE7y9vWFlZQU/Pz/s2rXrqWVff/11iEQiLFiwQOv4iKh2unDhAr777jujSQzOnTuHnJwc2NjYYOjQoejVq5e+QyLSGZlMhqtXrwIAvL29Vdtv3ryJ1atXQyqV6is0IqpltEp43n77bTRv3lw1Z3zOnDlwcHDA6NGjMXbsWNStWxffffedVgFt2LAB0dHRmDVrFlJSUuDv74+wsDBkZ2dXWv7IkSMYOXIkJk6ciFOnTiEyMhKRkZE4d+5chbJbt27F0aNH4erqqlVsRFQ7KZVKxMfHY+PGjSgpKcG5c+dUK0tWVXp6OtLS0nTSlqb69OmDzp07Y8qUKWjdujVXoyKjcuXKFSgUCjg6OqJhw4aq7atXr8aECRPwyiuv6DE6IqpN1E54XnrpJfz++++Qy+Xo3r07Fi5cCEtLSwCAu7s7Lly4gFOnTuHMmTO4cOECWrZsqVVA8+fPx6RJkzB+/Hj4+vpi2bJlsLGxwapVqyotv3DhQoSHh2PatGnw8fHBnDlz0L59eyxevLhcubt37+Ktt97Cr7/+ColEolVsRFQ7Xbt2DUeOHAEAdO7cGSNGjNBJcrBx40a0adMGL730EgoLC6vcnqYsLCwQFhYGGxubGn9vouomkUjQuHFj+Pr6qv5eBUHA2rVrAQAjRoyoUEdXJzKIyLio/RyenTt3YuvWrahbty6ioqLwyiuvoGfPnqr9YrG4yk8yLikpQXJyMmJiYsq1GxISgqSkpErrJCUlITo6uty2sLAwbNu2TfVaqVRi9OjRmDZtmmpVOSIyHV5eXujatStcXV118hlQUFCAhQsXYv/+/QCeTLd59OgREw8iHWrZsiVatmxZLolJSkrC9evXYWtrW+EhutnZ2di4cSMCAwOrNK2eiIyP2glPTk4OfvvtN8TFxWHVqlX44Ycf4Obmhpdffhkvv/wy2rRpU+Vg7t+/D4VCAWdn53LbnZ2dcfHixUrrZGZmVlo+MzNT9fqrr76Cubk53n77bbXikMlk5ZZ4LZsnLJfLIZfL1WqjTFl5TevVdqbYb1PsM1B7+l12f4uu4rx16xbEYjE++ugjfPzxx5BIJAY/BlVVlX9rYx8bqj7/vBpraWmJQYMGoUGDBrC1tS1XLjk5GQ8ePMCtW7eY8BBROWonPHXq1MHYsWMxduxY5OTkYP369YiLi8PXX3+NuXPnwtfXF6NGjcLIkSPRpEmT6oxZI8nJyVi4cCFSUlLUnsISGxuL2bNnV9i+d+9erc/gxsfHa1WvtjPFfptinwHT6/f777+Px48fw9fXV+d9FwQBJSUlqmnDhkab/upjyh8Zn4CAAPz+++8Vpq7J5XKcOXNGVYaI6J/UTnj+qUGDBnjrrbfw1ltv4ebNm/j111+xfv16xMTE4OOPP0bXrl0xatQoTJ48WaN2nZycYGZmhqysrHLbs7Ky4OLiUmkdFxeXZ5Y/dOgQsrOzyyVhCoUC77//PhYsWICbN29WaDMmJqbcNDmpVAp3d3eEhobC3t5eoz7J5XLEx8ejb9++JnXvkCn22xT7DJhmv8v6PG7cOJ33OTs7Gzt37kRRURFeffVVWFhY6LT9qqjKvzVX1CJd+vcJzCtXrqC4uBgODg7w9PTUU1REZKi0Snj+ycPDAx9//DE+/vhjnDlzBrNmzcLvv/+Ov//+W+OEx8LCAgEBAUhISFDNzVUqlUhISMDUqVMrrdOlSxckJCTg3XffVW2Lj49Hly5dAACjR49GSEhIuTphYWEYPXo0xo8fX2mblpaWlZ5ZlUgkWn+5qUrd2swU+22KfQZMs9+67LNCoUBiYiKOHDkCpVIJS0tL5Obmwt3dXSft65I2/Ta13w2qWT4+Phg/fjyKioq4WiERVVDlhAcA7t27h3Xr1iEuLg4pKSkAgMDAQK3aio6OxtixYxEYGIiOHTtiwYIFKCgoUCUnY8aMgZubG2JjYwEA77zzDoKCgjBv3jxERERg/fr1OHnyJFasWAEAcHR0hKOjY7n3kEgkcHFx0XolOSIyHIIgoLCwsMJ8/tpGLBYjPT0dSqUSPj4+6NevH+zs7PQdFlGtIBKJDGo6PREZFq0TnkePHmHz5s2Ii4vDoUOHoFAo4OnpiZkzZ2LUqFFo0aKFVu0OHz4cOTk5mDlzJjIzM9G2bVvs3r1btTBBeno6xOL/rabdtWtXxMXFYcaMGZg+fTq8vLywbds2tG7dWtuuEVEtUVxcjG3btuHBgwd49dVXdXbPiyAINX6WWCQSYeDAgcjJyYGPj0+NvjcREZEx0yjhKS4uxvbt2xEXF4c9e/ZAJpOhQYMGeOONNzBq1Ch07NhRJ0FNnTr1qVPYEhMTK2yLiopCVFSU2u1Xdt8OEdUu2dnZ2LBhA3Jzc2FmZoa7d++iefPmVWqztLQUc+bMweXLlxEXF1fjSY+TkxOcnJxq9D2JiIiMndoPHh0zZgycnZ0xcuRIJCQkYOjQodi5cycyMjLw3Xff6SzZISJ6HkEQsHPnTuTm5qJu3bqYMGFClZOda9euoUePHvjvf/+L9evX4+DBgzqKlozdl19+CZFIVO5eUiIiMhxqX+FZt24d+vbti1deeQUvvvgiH7BHRHojEokQGRmJhIQE9O/fv8qfR6WlpQgJCcHNmzdRt25dLFu2DEFBQTqKlozZiRMnsHz5cp08i87UKRQK/PXXX2jZsiWaNm3KxQeISGfUvsKTkZGBXbt24ZVXXmGyQ0R6V69ePbz00ks6+TwyNzfH/Pnz0bNnT5w5cwYjRozQQYT/8+DBAzx+/FinbZL+5efn45VXXsHKlStRr149fYdT6924cQNHjx7F5s2bKzxnh4ioKtS+wtOgQYPqjIOISK9efPFFDB48uNyiKFWlUCiQlJSExMRENGvWDC+//DLPWhuRKVOmICIiAiEhIfjss8+eWVYmk0Emk6lelz2XSC6XQy6Xa/S+ZeU1rWfozp07BwBo2bIlFAoFFAoFUlNT8cUXX2Ds2LGIiIjQuE1jHStd4ziph+OknpoaJ03a18my1ERExkCXyU5ubi42btyoejCyIAiQy+UG9SBR0t769euRkpKCEydOqFU+NjYWs2fPrrB97969Wl+ljI+P16qeIRIEQZXwPH78GLt27QIArFq1Ctu3b0d2dnaVThYY01hVJ46TejhO6qnucSosLFS7LBMeIqJqYGNjg4KCAlhbWyMsLAxt2rTh1R0jcfv2bbzzzjuIj4+HlZWVWnViYmIQHR2tei2VSuHu7o7Q0FDY29tr9P5yuRzx8fHo27ev0TzQtaSkBHXr1sWtW7cQFRUFsViM0tJSvP766wCA999/H/3791eVz8jIgKWlZYXn7P2bMY5VdeA4qYfjpJ6aGqeyK+XqYMJDRAYnPz8ftra2tTpBsLKywvDhw1GvXr1a/1BUKi85ORnZ2dlo3769aptCocDBgwexePFiyGQymJmZlatjaWlZ6XOiJBKJ1l8IqlLX0EgkEgQHB5fbtm/fPmRmZsLR0REDBgwo19e//voLd+7cQWRkJPz9/dVq31jGqjpxnNTDcVJPdY+TJm0z4SEig3Lp0iVs3boVffr0QYcOHarcXm5uLg4fPoxBgwbpIDrNNG7cuMbfk6pfnz59cPbs2XLbxo8fD29vb3z44YcVkh3STqdOnbBy5UoUFhaWmwqalZWFO3fuQCwWw9PTU48RElFtoVXCI5PJ8Pfff+PChQuQSqWws7ODr68vunXrprMnnRORaVEqlUhMTMShQ4cAAGlpaQgMDKzSVZ59+/ZhzJgxyM7OxtGjR8udkSfSlp2dHVq3bl1um62tLRwdHStsJ+05ODjg1VdfrbA9OTkZAODt7Y06derUdFhEVAtplPAIgoBvvvkGX331FR4+fFhu2UiRSIR69erhww8/xAcffFCrp6IQUc27ffu2Ktnp2LEjQkNDq/Q5Mn36dHz55ZcQBAEvvPCCThckICL9sbW1hY2NDQICAvQdChHVEholPK+88grWr18PLy8vvPXWW/D394ednR3y8vJw+vRpxMXF4aOPPkJqaip+/fXX6oqZiIxQ06ZNERQUhPr16+vsIY6CIGDSpEn49ttvdXYfjSAIOH36NJRKpU7aI+OQmJio7xBMRlBQELp168apg0SkNrUTnl9++QXr16/HBx98gNjY2AofNJGRkfjkk08wffp0zJ07F/369cOoUaN0HjARGa9evXrprK1PP/0UvXr1QmhoqM7azMnJwfbt23Hnzh04OzvrrF0i0oy5OW9BJiL1qT3HY+XKlQgKCsLXX3/91LMqYrEYX375JYKCgrBixQqdBUlEpCkLCwudJjupqalYtmwZ7ty5AwsLC67QQ0REVEuonfCcOXMGQ4cOVavskCFDcObMGa2DIiIyNI0aNQIAvPDCC5g8eTKcnJz0HBERERGpQ+1rwnK5XO0HrFlaWqK0tFTroIiIDI2zszNee+01NGjQgJ9vREREtYjaV3hatGiBgwcPqlX20KFDaN68udZBEZFxEQQBeXl5OmtPLpfrrC1NNGzYkCtQEunIkSNHEB8fjwcPHug7FCIycmonPC+99BLWrVuHnTt3PrPczp07sW7dOkRFRVU5OCKq/WQyGTZu3IhVq1ahqKioSm0JgoDvv/8evr6+/JJEVIsJgoDjx4/jyJEjyM7OBvDkWVxV/YwgIqqM2gnP+++/j5YtWyIyMhKTJ0/GoUOHIJVKIQgCpFIpDh8+jMmTJyMyMhItW7bE+++/X51xE1EtkJOTg5UrV+LixYvIy8vDnTt3tG4rOzsbgwYNwpQpU3D16lUsX75ch5ESUU26d+8eHj9+DIlEghYtWgB4MjvE2dkZ7777rn6DIyKjo/Y9PDY2Nqqnlv/www/48ccfK5QRBAEhISFYs2YNbGxsdBooEdU+e/bswYMHD2Bvb49hw4bBzc1N67aio6OxY8cOWFpa4uuvv8bUqVN1EuPDhw9x9uxZ9OzZUyftEdHzpaWlAQC8vLxUKx6uXbsWeXl5yM/P12doRGSENFrIvmHDhti9ezeOHTuGP/74A2lpacjLy4OdnR18fHwwYMAAdOnSpbpiJaJaZvDgwdizZw/69etX5Qd/zp07F3fv3sV3330HPz+/KsemVCpx7Ngx7N+/H3K5HE5OTvD19a1yu0T0fG3atIFIJELTpk0BAMXFxdi0aRMAYPTo0apyJSUlWLNmDVq1aoWOHTvyYaNEpBWtntzVqVMndOrUSdexEJGRsbOzw0svvaSTtho1aoT9+/frpC0AWL9+Pa5cuQIA8PDw4INEiWpQw4YN0adPH9XrY8eOQSqVwt3dHT169FBtP3v2LO7evYuioiJ07txZH6ESkRHQyaOKz58/j4MHDyI/Px/+/v46fdgfEVF18PPzQ3p6OkJDQ9GuXTuuvkakR0FBQbh9+zauXbsGsfh/txenpKQAAAICAvg3SkRaUzvhUSqViImJQVxcHMzNzTFu3DjMmjUL0dHRWLhwIQRBAACIRCJ069YNu3fv5n08RGSwWrduDU9PT35OERkINze3cvf5ZWZmIiMjA2ZmZmjbtq3+AiOiWk/thGfp0qWYO3cuOnToAGdnZ3zxxRfIycnBsmXLMGXKFPTp0welpaXYvn07fvnlF8yZMwexsbHVGTsRkdZEIhGTHSID5uzsjLFjxyI7O5t/q0RUJWonPD/88AMiIiLwxx9/AACWLFmCt99+G1OmTMF3332nKjd06FAUFBRg8+bNTHiIjFheXh7q1KlT5XaKioqwfPlyvPXWW7whmYhURCIRPDw84OHhoe9QiKiWU/s5PNevX0f//v1Vr/v37w9BENC7d+8KZUNCQpCenq6bCInI4Fy5cgXff/89jhw5UqV2Tp8+jcDAQLz33nuYO3eujqIjIiIi+h+1E568vDzUrVtX9dre3r7cf//Jzs4OpaWlOgiPiAyJIAg4cOAA4uLiUFxcjEuXLkGpVGrV1urVq9GxY0ekpaXBxcUF7du310l8ycnJSEpKqnJbREREZBx0skobEZmGjIwMJCYmAgACAwMRFhamWrBEUy1atEBpaSkGDRqEH374AQ0aNKhSbA8ePMAff/yBW7duwczMDC+88AIcHR2r1CYRERHVfholPLt27UJmZiYAoLCwECKRCJs2bUJqamq5csnJyToLkIgMh5ubG0JCQmBra6taNUkul2vVVo8ePXD06FEEBgZWebnZwsJCLF++HHK5HBKJBMHBwahXr16V2iQiIiLjoFHCExcXh7i4uHLbli9fXmlZrpdPZJy6deums7Y6dOigk3ZsbGzQoUMHZGVlISIigskOkYHJzc3F+vXr0bp1a/Ts2VPf4RCRiVE74blx40Z1xkFEVCW9e/eGWCzmyRYiA5SWloacnBzcunULwJMrw/v27UOfPn1gbs7Z9URUvdT+lGnatGl1xkFEVCVc0prIcKWlpQEAfH19AQC7d+/GoEGD4O/vj1OnTvFEBRFVK7VXaSMiUldeXp6+QyAiA/Hw4UPcu3cPIpEI3t7eAIC1a9cCAIKDg1XJzqNHj5Cbm6u3OInIeKmd8PTu3fupP3369EFERATefPNN7Nixo8pBLVmyBB4eHrCyskKnTp1w/PjxZ5bftGkTvL29YWVlBT8/P+zatavc/k8//RTe3t6wtbVFvXr1EBISgmPHjlU5TiJjIggCpFJpldvZsmULmjVrhvj4eB1EBa1XgSMiw+Dg4IAJEyYgPDwctra2ePz4MbZv3w4AGD16tKrcwYMHsWjRIhw6dEhfoRKRkVI74cnOzkZOTk6lP9nZ2bh48SJ++OEHDB48GP3799d65aYNGzYgOjoas2bNQkpKCvz9/REWFobs7OxKyx85cgQjR47ExIkTcerUKURGRiIyMhLnzp1TlXnhhRewePFinD17FocPH4aHhwdCQ0ORk5OjVYxExqakpAS//fYbVq5cifz8fK3ayM/Px6uvvoqhQ4fiwYMHWLRoUZXjevz4MdatW4fLly9XuS0i0g+RSAR3d3d07NgRAHD37l34+fnBx8cH7dq1AwDIZDLVcbtJkyZ6i5WIjJPa9/D8M4F4mqKiIixfvhzR0dH4+uuv8fHHH2sc0Pz58zFp0iSMHz8eALBs2TLs3LkTq1atwkcffVSh/MKFCxEeHo5p06YBAObMmYP4+HgsXrwYy5YtAwC8/PLLFd7jxx9/xJkzZ9CnTx+NYyQyJvfv38fGjRuRk5MDsViM27dvw8fHR+N2Nm3ahB9//BEikQgffvghZs+erXVMgiDgxIkTSEhIQElJCe7fv48WLVpALOYsXKLaztfXF8ePH4dUKlVNZzt79izkcjmcnJyY8BCRzun024O1tTXeffddjBgxosLy1eooKSlBcnIyQkJC/hegWIyQkJCnPjk9KSmpXHkACAsLe2r5kpISrFixAnXr1oW/v7/GMRIZm3379iEnJwd16tTB2LFjtUp2AGDcuHGYOHEi9u3bh9jYWFhYWGgd06VLl/Dnn3+ipKQE7u7uGDlyJJMdIiNjb2+v+n+FQgFra2u0b9+eCxgQkc5Vy1qQ3bp1w7Zt2zSud//+fSgUCjg7O5fb7uzsjIsXL1ZaJzMzs9LyZQ9ILbNjxw6MGDEChYWFaNSoEeLj4+Hk5FRpmzKZDDKZTPW67L4GuVyu8VS9svLaTvGrrUyx37W1z2FhYRCJRAgJCUGdOnW0/h0vLS3F0qVLy23TVvPmzdGyZUt4eHiovgAZ0rjW1n/rqqpKv01trEgznTp1QkBAAO/ZI6JqUS0JT2FhocGtqx8cHIzU1FTcv38fK1euxLBhw3Ds2DE0bNiwQtnY2NhKp+Ps3bsXNjY2Wr2/rm7grm1Msd+1sc8WFhY4ePBgldrQdb+tra2RlZWFP//8U6ft6lJt/LfWBW36XVhYWA2RkDExtO8NRGQ8dP7pIggCtm/fDj8/P43rOjk5wczMDFlZWeW2Z2VlwcXFpdI6Li4uapW3tbVFixYt0KJFC3Tu3BleXl748ccfERMTU6HNmJgYREdHq15LpVK4u7sjNDS03CV4dcjlcsTHx6Nv376QSCQa1a3NTLHfpthnwDT7bYp9BqrWb12sAEhERKQNtROe562NX1RUhEuXLmHp0qU4cuSIao19TVhYWCAgIAAJCQmIjIwEACiVSiQkJGDq1KmV1unSpQsSEhLw7rvvqrbFx8ejS5cuz3wvpVJZbtraP1laWsLS0rLCdolEovWXm6rUrc1Msd+m2GfANPttin0GtOu3KY4TEREZBrUTHicnJ7VuJJRIJJgzZw5GjhypVUDR0dEYO3YsAgMD0bFjRyxYsAAFBQWqVdvGjBkDNzc3xMbGAgDeeecdBAUFYd68eYiIiMD69etx8uRJrFixAgBQUFCAzz//HIMGDUKjRo1w//59LFmyBHfv3kVUVJRWMRKZEoVCga+//hojR46Eh4eHTtozMzOremBEerJ06VIsXboUN2/eBAC0atUKM2fORL9+/fQbGBERVUrthGfmzJnPTHisrKzQtGlT9OnTBw0aNNA6oOHDhyMnJwczZ85EZmYm2rZti927d6sWJkhPTy+3WlPXrl0RFxeHGTNmYPr06fDy8sK2bdvQunVrAICZmRkuXryIn3/+Gffv34ejoyM6dOiAQ4cOoVWrVlrHSVQbSKVS1KlTR+sVzm7evInRo0fj8OHD2LVrFw4cOKB1W4Ig4PTp09i3bx/GjRuH+vXra9UOkb41btwYX375Jby8vCAIAn7++WcMHjwYp06d4nHl/5WWlvKeHCIyGGp/Gn366afVGEZ5U6dOfeoUtsTExArboqKinnq1xsrKClu2bNFleES1wrVr1/Dbb78hMDAQvXv31rj+33//jf79+0MqlcLOzg6vvfaa1svFPnz4EDt27MD169cBPFlOPiIiQqu2iPRt4MCB5V5//vnnWLp0KY4ePcqE5/+tW7cORUVF6N+/Pxo3bqzvcIjIxFXp9EtBQQHy8vLg5OTEMzlEBkIQBBw6dAj79+8H8CTx6dmzp8Z/o35+fnB0dETr1q2xdu1aNGvWTOuYjh8/juvXr8Pc3BxBQUHPvceOqLZQKBTYtGkTCgoKnvl7bUqPOygsLMSNGzcgCAIsLCzw+PFjzJgxA8OHD0fHjh1r9Dk7hj5WhoLjpB6Ok3pqapw0aV/jLOXWrVuYO3cu/vjjD9y5cwcAIBKJ0LhxYwwbNgxTpkxB06ZNNW2WiHQkOzsbBw4cAAC0a9cO/fv31+qEhL29Pfbt24fGjRtX+YRGr169UFBQgKCgIDg6OlapLSJDcPbsWXTp0gXFxcWoU6cOtm7dCl9f36eWN6XHHTx48ACCIMDa2hpJSUk4cOAAFi9ejE2bNmHZsmV6ebCooY6VoeE4qYfjpJ7qHidNHneg0beYP/74A6NGjUJeXh48PDwwcOBA2NnZIS8vD2fOnME333yDlStXYu3atarpKjNmzMBnn32mWQ+ISGvOzs4ICwuDubk52rdvX6W2dLFIAfBk5cMhQ4bopC0iQ9CyZUukpqbi8ePH2Lx5M8aOHYsDBw48Nekxpccd/PbbbwCAjh07olu3bli2bBkAYOLEiYiIiIAgCNi6dSvc3d3h7+8PCwuLaovF0MfKUHCc1MNxUk9NjZMmjztQO+G5cOEChg0bhmbNmmH58uXo0aNHhTKHDh3C66+/juHDh+PkyZOIjY3F2rVrmfAQ1bCOHTvqOwQio2ZhYYEWLVoAAAICAnDixAksXLgQy5cvr7S8KT3uICoqCrdu3YKTkxNyc3NVZ3nHjh0LiUSC27dv4+LFi7h69SratWtXI30w1LEyNBwn9XCc1FPd46RJ22onPF988QUcHR1x+PDhp66u1KNHDxw6dAht2rRBQEAAZDKZavloIiIiY/WsZ7uZGjMzMzRv3hzAkzO9s2bNQlpaGl544QUAQEpKCoAny3lbW1vrLU4iMh1qJzz79u3Dq6+++tylZOvXr48JEybgs88+w5o1azBq1KgqB0lEuvXo0SNYWVnBysqqym1pMoeWyBjExMSgX79+aNKkCfLy8hAXF4fExETs2bNH36EZHEdHR3zyySeq18XFxTh37hyAJ1fGiIhqgtoJz4MHD9Sez9+sWTOYmZkx2SEyQAcPHsTo0aPx4osvYsGCBVq3k5eXhz///BP37t2Du7u77gIkMnDZ2dkYM2YM7t27h7p166JNmzbYs2cP+vbtq+/QDJ6lpSVGjhyJK1eucLlqIqoxaic8Tk5OuHHjhlplb9y4gYYNG2odFBFVThAESKVS1K1bV+O6crkcs2fPRmxsLJRKJXbu3InPPvsMderU0TiGlJQUxMfHQyaTQSwWo6CgQON4iGqrH3/8Ud8h1FoikQjNmzdXTXkjIqoJaj8yvVevXvjxxx+Rm5v7zHK5ubn48ccftXrQIRE9XUlJCbZu3Yrly5fj0aNHGte/ceMG5s2bB6VSiQkTJiAlJUXjZKfMmTNnIJPJ4OrqigkTJmi8yhQRERFRTVH7Cs/06dOxefNm9OzZEytWrEDXrl0rlDly5Ahee+01PHjwADExMToNlMiU5ebmYsOGDcjOzoZIJMLt27fh4OCgURsvvPAClixZAjs7O0RFRWkdi0gkwsCBA3HlyhV06tQJCoVC67aIiIiIqpvaCY+vry/i4uIwZswY9OjRAx4eHvD39y/3HJ4bN27A2toacXFxz3wAGxFp5sCBA8jOzoatrS2ioqK0frjvhAkTdBKPk5MTnJycAIAJDxERERk0jR48OmTIELRt2xZff/01duzYgW3btqn2ubq6YvLkyfjggw/g6emp6ziJTFq/fv0AACEhIbCzs9NzNERERES1h0YJDwA0b95c9dRkqVSKvLw82NnZcQ4/UTWysrLCiy++qO8wiIgqKC0tRXFxsdb3BBIRVTeNE55/sre3Z6JDZEQKCwuhUCh4FYmI1Hb58mVs2rQJfn5+GDJkCARBgEgk0ndYREQqaq/SRkSGSRAErFy5EuvXr69SG2fPnsWSJUuwfft2CIKgwwiJyJhduHABAGBnZ4fc3Fx4e3vj448/hlwu13NkRERPMOEhqsXu37+PIUOGYPLkyZg8eTLu3LmjcRv5+flYt24dtmzZgsLCQjx+/BhFRUXVEC0RGRu5XI7Lly8DeLK40aZNm3D58mXs3LkTEokExcXFePjwoZ6jJCJTV6UpbURUdXl5ebCxsYGZmZlG9XJycuDv74979+5BIpFg1qxZcHV11fj9zc3Nce/ePZiZmaFHjx7o3r27xrEQkWnKyckB8GSKu6urK9auXQsAGD16NAAgNTUVe/bsQceOHVWLrxAR1TQmPER6dOPGDWzevBl+fn4IDw/XqG6DBg0QGhqKY8eOIS4uDu3atdMqBisrKwwdOhS2trZo0KCBVm0QkWlydXXFtGnT8PDhQ9y+fRuHDx+GSCTCyJEjIQgCUlJSAEC1jD0RkT4w4SHSA0EQcOTIESQkJEAQBNy6dQtyuRwSiUSjdhYvXgyxWAwbG5sqxePh4VGl+kRkuszNzdGgQQNVgpOUlARXV1ekp6cjJycHEokEfn5++g6TiEwYEx4iPcjNzcX+/fshCAL8/f0RERGhcbIDgMvAEpHBEIlEaNeunepq86NHj2BlZQVvb29YWVnpOToiMmVMeIj0wNHREQMGDEBpaSkCAgK4hCsRGZ02bdrAx8cHJSUl+g6FiEwcEx4iPWnbtm21v8e1a9fQtGlTmJvzT52Iap5EItHq6jURkS5xWWoiA1RcXIxbt25pXT8/Px+bN2/G2rVrceDAAR1GRkRERFS78LQvkYE5d+4cXn75ZSgUCpw8eRLW1tYa1b9y5Qq2bNmC4uJiTpUjIiIik8crPEQGQhAELFq0CIGBgTh79izu37+PK1euaNxO3bp1UVJSAhcXF7z66qvo06dPNURLREREVDvwCg+RjgmCgEePHsHBwUGjenK5HKtXr4ZMJkO/fv2wevVqODs7a/z+DRs2xNixY+Hm5sYHiBIREZHJ4xUeIh1SKpXYsWMHli1bhgcPHmhU18LCAr/++isWLVqEnTt3apXslGnSpAmTHSKqFrdv30ZRUZG+wyAiUhuv8BDpyMOHD3HlyhUUFRVBJBLh9u3bcHR01KgNHx8f+Pj4VFOERERVo1QqsWHDBhQVFWHixIkoLi5G8+bN9R0WEdEz8QoPkY4cPXoURUVFsLGxwejRo2tk2WkiopqUnp6OgoICWFhYQC6Xo0WLFvD390dhYaG+QyMieiomPEQ60qdPH9SvXx8TJkxAs2bNdN5+cXExTpw4AUEQdN42EZE60tLSAADe3t7YuHEjBEGAvb09bGxssH//fpw4cQIymUzPURIRlccpbUQ6YmFhgSZNmsDe3l7nbV+4cAG7du1Cfn4+bG1t4evrq/P3ICJ6HqVSCXNzc/j4+GDmzJkAgNGjR6OwsBB///03FAoF3Nzc4OrqqudIiYj+hwkPUQ3Yvn07du/ejSVLlmj8bJw//vgDKSkpAABHR0fY2dlVR4hERM81YMAAhIaG4uHDh3j48CEsLCwQFRWF1NRUKBQKNGrUiMkOERkcJjxE1aiwsBDvv/8+li1bBgAICQnBkCFDNGqjWbNmSE1NRbdu3dCzZ0+Ym/PPloj0x8LCAs7Ozrh58ybS0tLg4OCgOikTEBCg5+iIiCoyyHt4lixZAg8PD1hZWaFTp044fvz4M8tv2rQJ3t7esLKygp+fH3bt2qXaJ5fL8eGHH8LPzw+2trZwdXXFmDFjkJGRUd3dIBMnCAJCQ0NVyc4HH3yAiIgIjdtp1aoVpk6dit69ezPZISKDIRaL0bp1awBAeHg4WrdurXpNRGRIDC7h2bBhA6KjozFr1iykpKTA398fYWFhyM7OrrT8kSNHMHLkSEycOBGnTp1CZGQkIiMjce7cOQBPzrCnpKTgk08+QUpKCrZs2YJLly5h0KBBNdktquXy8vJQWlqqUR2RSIT33nsPrq6uiI+Px9y5c2Fpaanxe4tEItSrV0/jekRENUEkEqFFixYYOnSoVp9xRETVzeASnvnz52PSpEkYP348fH19sWzZMtjY2GDVqlWVll+4cCHCw8Mxbdo0+Pj4YM6cOWjfvj0WL14MAKhbty7i4+MxbNgwtGzZEp07d8bixYuRnJyM9PT0muwa1VI3b97E8uXLsXPnTo1XSBs6dCguX76MkJCQaoqOiIiIiJ7FoObHlJSUIDk5GTExMaptYrEYISEhSEpKqrROUlISoqOjy20LCwvDtm3bnvo+jx8/hkgkgoODQ6X7ZTJZuWU1pVIpgCfT4+RyuZq9garOP/9rKoyh34Ig4MSJE0hISIAgCLh79y4KCgqeegbzaX0ue16FsTKGf2tNmWKfgar129TGioiIDIdBJTz379+HQqGAs7Nzue3Ozs64ePFipXUyMzMrLZ+ZmVlp+eLiYnz44YcYOXLkU5cPjo2NxezZsyts37t3L2xsbNTpSgXx8fFa1avtanO/S0pKcPHiRQiCgHr16sHFxQUJCQnPradun6VSKUpKSuDk5FTVUA1Cbf631pYp9hnQrt98MCUREemLQSU81U0ul2PYsGEQBAFLly59armYmJhyV42kUinc3d0RGhqq8TNW5HI54uPj0bdvX0gkEq1jr22Mpd8XLlxAQUEBAgICnructLp9LiwsREJCAq5fvw4zMzP079+/Vic9xvJvrQlT7DNQtX6XXSk3BrGxsdiyZQsuXrwIa2trdO3aFV999RVatmyp79CIiKgSBpXwODk5wczMDFlZWeW2Z2VlwcXFpdI6Li4uapUvS3Zu3bqFffv2PTNxsbS0rHTakkQi0frLTVXq1ma1vd9t2rSpdLtCoUBycjI6duxYYd+z+lxSUoIffvgB+fn5AIDAwEA4OjrW6jEqU9v/rbVhin0GtOu3MY3TgQMHMGXKFHTo0AGlpaWYPn06QkNDkZaWBltbW32HR0RE/2JQixZYWFggICCg3LQhpVKJhIQEdOnSpdI6Xbp0qTDNKD4+vlz5smTnypUr+Ouvv+Do6Fg9HSCTcPv2bYSEhKB79+5ITU3VqK6FhQXatGmDhg0bYuLEiQgPD4eFhUX1BEpE1WL37t0YN24cWrVqBX9/f/z0009IT09HcnKyvkPTudzcXJw7d67cfa1ERLWNQV3hAYDo6GiMHTsWgYGB6NixIxYsWICCggKMHz8eADBmzBi4ubkhNjYWAPDOO+8gKCgI8+bNQ0REBNavX4+TJ09ixYoVAJ4kOy+99BJSUlKwY8cOKBQK1f099evX55dN0simTZswefJkPHr0CLa2trhx4wbatm2rURvBwcHo3bs3zMzMqidIIqpRjx8/BvDkmGJsUlNTcejQIfj6+uLRo0fo27cvPDw89B0WEZFGDC7hGT58OHJycjBz5kxkZmaibdu22L17t2phgvT0dIjF/7sw1bVrV8TFxWHGjBmYPn06vLy8sG3bNtXDz+7evYvt27cDQIUvpvv370evXr1qpF9kHJKTk/Ho0SN07NgRv/76K1q0aKFxG3x4KJHxUCqVePfdd9GtW7dnPnSzNq7+KQgCzp8/D+DJlMTJkyfD3Nwcd+7cgbm5ucb3tOqDqa6oqCmOk3o4TuqpqXHSpH2D/OY1depUTJ06tdJ9iYmJFbZFRUUhKiqq0vIeHh4aPzuFTENubq7GZ2T/+9//onHjxnjttdeM6p4EItLOlClTcO7cORw+fPiZ5Wrj6p9FRUXIzc2FSCTCr7/+CuDJfY3x8fG4du0aHBwcas3VHlNdUVFTHCf1cJzUU93jpMnqnwaZ8BBVp9LSUuzatQtnz57Fq6++WmFZ82exsLB4ajJORKZl6tSp2LFjBw4ePIjGjRs/s2xtXP0zMzMTcrkcZmZm+OmnnwAA7777rmoqePPmzREeHl5t768LprqioqY4TurhOKmnpsZJk9U/mfCQSXn06BE2bdqEjIwMAE8WINAk4XkamUyG+Ph41Vx+IjJegiDgrbfewtatW5GYmIhmzZo9t05tXP3T3d0do0ePRl5eHg4dOoRdu3YhPDwcy5YtAwB06NCh1nzpM9UVFTXFcVIPx0k91T1OmrTNhIdMyvHjx5GRkQFra2sMHToUnp6eVW7z0qVL2LlzJ/Ly8iCRSFBaWsoPQiIjNmXKFMTFxeH333+HnZ2daiGcunXrwtraWs/R6Z6dnR2WLl0KpVKJGzduQCKRwMnJ6amPiyAiMjRMeMik9O7dGzKZDD169ICDg0OV27tx4wbWr18PAHBwcICjoyMXJSAycmUPrv73ojerV6/GuHHjaj6gGiIWi+Hp6Yno6Gjk5eXpOxwiIrUZ1HN4iKqbubk5Bg4cWCHZOXLkCIKCgpCbm6tRex4eHvD09ETXrl0xadIk2NnZ6TBaIjJEgiBU+mPMyc4/WVhY8Hl2RFSrMOEhk1ZaWopPP/0UPXr0wMGDBzFr1iyN6otEIrz88su8gZGIiIjIQHHuDZm0jz76CPPmzQMAjB49Gp999pnGbfzzuVBEREREZFj4TY1MWnR0NJo3b464uDisWbMGdevW1XdIRERERKRDvMJDRiM/Px8WFhaqZ0Sow9XVFZcuXap0oQFBECASiXQZIhERERHVMF7hIaOQnp6O5cuX4/fff4cgCBrVrSzZuXHjBpYtW6bxIgZEREREZFiY8FCtJggCjh07hp9//hn5+fnIyclBUVGR1u0VFRVh+/btWLNmDbKzs7F//34dRktEZNgSExNx8eJFlJaW6jsUIiKd4ZQ2qtUKCgqQmJgIpVKJVq1aYdCgQRpNafu3I0eO4NSpUwCAwMBA9OnTR1ehEhEZtMePH+PAgQMQBAHnz59HUFAQxo0bhzp16ug7NCKiKmHCQ7VanTp1MGTIENy/fx+dO3dW3XMjCAJ27tyJfv36wczMTO32unfvjnv37qFnz55o0qRJdYVNRGRwLly4AODJ5+fmzZuxY8cOBAYGwtzcHH5+frC0tNRzhERE2uGUNqr1vLy80KVLF1Wy8/DhQwwfPhwDBw5ULTmtLktLS4waNYrJDhGZnLKE59KlSwCAyMhIHDt2DDt37kRaWpo+QyMiqhImPGRUDh8+jDZt2mDTpk0wNzfnM3KIiNQgCAJat26Nxo0bq+5dHDhwIB49egRLS0u0bt1azxESEWmPU9rIqCiVSty9exdeXl6Ii4tDYGCgvkMiIjJ4IpEIHTp0QPv27eHq6ootW7aoThi1adMGEolEzxESEWmPCQ8ZlZ49e2LLli0ICQmpcKOtXC7nQZuI6BnMzMzQr18/9OvXD1evXgUABAQE6DkqIqKqYcJDBu3BgwdwdHTUqE5kZGS51yUlJdi3bx+uXLmC1157rUqruBERmYoWLVqgRYsW+g6DiKjKmPCQQSotLcXu3buRmpqKcePGoXHjxlq1c/XqVezYsQOPHz8G8ORmXD8/P12GSkREREQGjAkPGRypVIqNGzfi7t27AIC7d+9qlfAIgoDDhw/j8ePHqFu3LgYMGMCzlUREREQmhgkPGZyUlBTcvXsXVlZWGDJkCLy8vAA8WZBAk1XXRCIRBgwYgOTkZAQHB3MqGxEREZEJ4pq9ZHB69uyJDh06YPLkyapk58KFC+jQoQP++usvjdpycnJCWFgYkx0iIiIiE8WEhwyOWCxG//79Ua9ePQiCgGXLliEgIAApKSn44IMPIAiCvkMkIiIiolqCCQ8ZtN9//x1vvPEGioqK0LdvX/z5558QiUT6DouIiIiIagnew0MGbdCgQRg0aBB69eqFd955R3UPT2lpKWQyGWxtbfUcIRFR7ZSWloZr167B29tbNX2YiMgYMeEhgyYWi7Ft27ZyV3XS09Pxxx9/wMHBAS+//DKv+BARaeH06dO4ePEiJk2aBH9/fyxZsoQrWRKRUWLCQzWqoKAAYrEY1tbWatcpS2hkMhn++usvnDx5EgBQVFQEqVSKunXrVkusRETGqri4GNeuXcOtW7eQlZWFc+fOYf369fD09ERUVBTMzfn1gIiMBz/RqMbcuXMHmzZtgrOzM0aOHKnxlRlBEHDx4kUAQLt27dC3b1+NEiciInri8uXLUCgUuHz5MgBgxIgRUCgUKC4uZrJDREaHn2pU7QRBQHJyMnbv3g2FQgGJRIKCggLUqVNHo3asrKwwePBgmJmZoVmzZtUULRGR8WvatCmCg4Px/fffQyQSoUGDBpDJZAgICNB3aEREOseEh6pdcXExEhMToVAo4OPjg8GDB8PS0hJ//vknWrVqhSZNmqjdFueXExFVXd26ddGzZ09cv34dW7ZswZ07d2BlZQVfX199h0ZEpHNMeKjaWVtb46WXXsKdO3fQrVs3FBcX4+2338aiRYvQs2dP7Nu3D2ZmZvoOk4jI5Nja2mL06NGQyWTIzs7mdDYiMkr8ZKMa4eHhAQ8PD1y7dg2DBw/G+fPnATy5F0ehUDDhISLSI0tLS7i7u+s7DCKiasGEh2qUk5MT8vPz4ezsjJ9++gnh4eEAgMzMTDg5OfHsIhERERHplFjfAfzbkiVL4OHhASsrK3Tq1AnHjx9/ZvlNmzbB29sbVlZW8PPzw65du8rt37JlC0JDQ+Ho6AiRSITU1NRqjJ6ep27duvj9999x5swZhIeHQy6X46+//sKKFStw4MABfYdHREREREbGoBKeDRs2IDo6GrNmzUJKSgr8/f0RFhaG7OzsSssfOXIEI0eOxMSJE3Hq1ClERkYiMjIS586dU5UpKChA9+7d8dVXX9VUN+g5/P390bBhQ9y5cwfLli3D33//DUEQ8PjxYwiCoO/wiIiIiMiIGFTCM3/+fEyaNAnjx4+Hr68vli1bBhsbG6xatarS8gsXLkR4eDimTZsGHx8fzJkzB+3bt8fixYtVZUaPHo2ZM2ciJCSkprphcu7fv69VPYlEgkePHsHOzg7Dhw/HkCFDNH42DxGRPhw8eBADBw6Eq6srRCIRtm3bpu+QiIjoKQzmhomSkhIkJycjJiZGtU0sFiMkJARJSUmV1klKSkJ0dHS5bWFhYVU+8MhkMshkMtVrqVQKAJDL5ZDL5Rq1VVZe03q1gUKhQEJCApKTkzFy5Eh4eHio9qnT7/r162Po0KFwd3eHlZVVrR8jY/63fhZT7Lcp9hmoWr+NbawKCgrg7++PCRMmYMiQIfoOh4iInsFgEp779+9DoVDA2dm53HZnZ2dcvHix0jqZmZmVls/MzKxSLLGxsZg9e3aF7Xv37oWNjY1WbcbHx1cpJkMjl8tx8+ZNFBQUAAASExPh5OSkerBoGXX6feXKlWqLUx+M7d9aXabYb1PsM6BdvwsLC6shEv3p168f+vXrp+8wiIhIDQaT8BiSmJiYcleOpFIp3N3dERoaCnt7e43aksvliI+PR9++fcslArXd0aNHcf78eVhaWmLgwIGwtbXFxIkT4efnh7lz5xptv5/FFPsMmGa/TbHPQNX6XXalnPSjtLQUK1euRFFREUaNGoWGDRvqOyQiohpjMAmPk5MTzMzMkJWVVW57VlYWXFxcKq3j4uKiUXl1WVpawtLSssJ2iUSi9ZebqtQ1RN27d0dhYSECAwNx8OBBvPrqq8jNzcWxY8fw0UcfwdHREYDx9VsdpthnwDT7bYp9BrTrtymO0z/pe6r01atXkZGRgfnz5+P777/H119/jbCwMFhYWGj03rWNqU4/1RTHST0cJ/XU1Dhp0r7BJDwWFhYICAhAQkICIiMjAQBKpRIJCQmYOnVqpXW6dOmChIQEvPvuu6pt8fHx6NKlSw1EbNpEIhHCwsKQkZGBkSNHQiaToX379oiLi0ODBg1w7tw5rrhGRPT/9D1VOj09HVevXkVhYSG6deuGs2fPIisrC66urlq9d21jqtNPNcVxUg/HST3VPU6aTJU2mIQHAKKjozF27FgEBgaiY8eOWLBgAQoKCjB+/HgAwJgxY+Dm5obY2FgAwDvvvIOgoCDMmzcPERERWL9+PU6ePIkVK1ao2szNzUV6ejoyMjIAAJcuXQLw5OpQVa8EEeDq6op58+YhPT0dc+bMQU5ODlasWIHs7OxyixgQEZkyfU6VVigUWLhwIc6cOQN7e3s0a9YMAPDiiy+qrsYbK1OdfqopjpN6OE7qqalx0mSqtEElPMOHD0dOTg5mzpyJzMxMtG3bFrt371YtTJCeng6x+H8raXft2hVxcXGYMWMGpk+fDi8vL2zbtg2tW7dWldm+fbsqYQKAESNGAABmzZqFTz/9tGY6ZuSmTJkCADhw4AASExMBANbW1nqMiIjIsOhzqrS5uTnGjRuHvXv3wsXFBSKRCE2bNjWpk36mOv1UUxwn9XCc1FPd46RJ2waV8ADA1KlTnzqFrezL9D9FRUUhKirqqe2NGzcO48aN01F09CxlZwrbtGmD4OBgHDhwQM8RERFVj/z8fFy9elX1+saNG0hNTUX9+vXRpEkTPUZWkUgkgrOzMxISEnD69GmcP38ebdq00XdYREQ1xuASHtK/wsJCCIIAW1tbjeq1atUK9erVg5ubG2/oIyKjdvLkSQQHB6tel01XGzt2LH766Sc9RfV8/v7+8Pf313cYREQ1igkPlZORkYGNGzfCwcEBY8aMKTeF8HlEIhHc3NyqMToiIsPQq1cvLsxCRFRLMOEhlZSUFOzatQsKhQJmZmZITExEfn4+Bg0apO/QiIiIiIi0woSHADx5RsSBAwegUCjg5eWFK1euIDQ0FLa2tjh9+jRXXCMiIiKiWokJDwF4soLQsGHDkJaWhjlz5uDQoUMAgH79+qG0tBSpqalo27atfoMkIiIiItIQEx5ScXNzg5ubG3755RecOnUKixcvhru7O9atW6e6P6dBgwb6DpOIiIiISG3q35FOJmPx4sVITk5GYWEhDh06BKVSiRYtWlT6DAkiIiIiIkPGKzxUgb29Pezt7XHr1i0UFBSgf//+8PHxgUgk0ndoRESkhtLSUpiZmfFzm4gITHjoGXr16oXu3bvD2tpa36EQEZEG/vrrL/zwww/o1q0b3njjDVhZWek7JCIiveGUNhORk5OjcR2JRMJkh4iolhEEAampqdixYwfS09OxevVq5Ofn6zssIiK9YcJj5BQKBfbs2YPvv/8ely9fxuPHj/UdEhERVaOMjAycOHECzZo1g4ODA/Lz83nyiohMGqe0GbH8/Hxs3rwZt27dQnFxMaZMmYLMzEwcP36cBz8iIiN19epVnD59Gp07dwYAtG3bFmZmZnqOiohIf5jwGLELFy7g1q1byMjIwI4dO5CRkQFzc3OsW7cOPXr0gJeXl75DJCIiHevZsyemTJmC3NxcAEBAQICeIyIi0i8mPEYsMDAQDx8+xLRp05CRkYHAwECMHDkSt2/fxo4dOzBlyhRYWFjoO0wiItIhkUiE9957DzKZDDdu3ED9+vX1HRIRkV4x4TFiIpEIoaGhaNy4MRYvXgwXFxfk5eXBysoKwcHBkEgk+g6RiIiqiaWlJby9vfUdBhGR3jHhMQG+vr5YsmQJ1q5dC2tra4SHh6NOnTr6DouIiIiIqNox4TERIpEII0aM4FUdIiIiIjIpXJbahDDZISIiIiJTw4SnlioqKoJUKtV3GEREREREBo1T2mqhe/fuYePGjcjLy0NpaSlmzZoFkUik77CIiIiIiAwOE55aJjU1FTt27MCJEyewe/du2Nvbw8bGBq+++iqXHiUiIiIi+hdOaatF5HI5Dh06hC1btmDnzp3o3Lkz3nzzTRQVFWHPnj36Do+IiPTk4cOH+g6BiMhg8QpPLSKRSDBs2DDk5ubC3t4ewcHBAIAWLVqgf//+eo6OiIj04f79+xgwYAAaN26MTz/9FK1bt9Z3SEREBoUJTy3j7OyMWbNm4eWXX8b+/fvRpUsXtG7dmvfwEBGZqKNHj8LOzg5+fn5ISkpiwkNE9C9MeGopLy8vtGjRgokOEZGJi4uLQ9u2bQEA3bt3128wREQGiPfw1GJMdoiITFtxcTEyMjJQp04dAMALL7yg54iIiAwPEx4iIqJaysrKCvPmzQMAtGvXDmZmZnqOiIjI8HBKmwHJysqCXC6Hq6srxGLmokRE9HwBAQEICAiAIAj6DoWIyCDxW7UBUCqV2LlzJyIjIzFixAgsXLgQcrlc32EREVEtwmnORESV4xUePSsoKMDChQuxdu1adOvWDY0bN4ZUKsWpU6fQsWNHfYdHRERERFSrMeHRs6tXr+LPP/9Ehw4d0LhxY4jFYvTv3x/t27fXd2hERERERLUeEx498/f3x6xZs7B161Y0b94cgwcPhr29vb7DIiIiIiIyCkx4DEBISAhCQkL0HQYRERERkdExyEULlixZAg8PD1hZWaFTp044fvz4M8tv2rQJ3t7esLKygp+fH3bt2lVuvyAImDlzJho1agRra2uEhITgypUr1dkFIiIycpoeq4iISD8MLuHZsGEDoqOjMWvWLKSkpMDf3x9hYWHIzs6utPyRI0cwcuRITJw4EadOnUJkZCQiIyNx7tw5VZmvv/4a3333HZYtW4Zjx47B1tYWYWFhKC4urqluERGREdH0WEVERPpjcAnP/PnzMWnSJIwfPx6+vr5YtmwZbGxssGrVqkrLL1y4EOHh4Zg2bRp8fHwwZ84ctG/fHosXLwbw5OrOggULMGPGDAwePBht2rTBmjVrkJGRgW3btlV7fwRBgEKhqPb3ISKimqPpsUqXUlNTUVRUVO3vQ0RkLAzqHp6SkhIkJycjJiZGtU0sFiMkJARJSUmV1klKSkJ0dHS5bWFhYapk5saNG8jMzCx3j0zdunXRqVMnJCUlYcSIERXalMlkkMlkqtdSqRQAIJfLNXo+zrlz57Bu3To0aNAAJSUlatczBmXjZErPEzLFPgOm2W9T7DNQtX4b01hpc6zS1XEFAH788UdIpVLcu3cPU6dO1aIHpsNU/1Y1xXFSD8dJPTU1Tpq0b1AJz/3796FQKODs7Fxuu7OzMy5evFhpnczMzErLZ2ZmqvaXbXtamX+LjY3F7NmzK2zfu3cvbGxsntsPQRBw4sQJiEQi2NraIi8vDzt27ICVldVz6xqb+Ph4fYdQ40yxz4Bp9tsU+wxo1+/CwsJqiEQ/tDlWVfW4Uub27dto1KgRXFxcIBaLK9yzSpUz1b9VTXGc1MNxUk91j5MmxxWDSngMRUxMTLmrRlKpFO7u7ggNDVVryehZs2ahpKQEdnZ2uHPnDry8vDBw4EBIJJLqDNugyOVyxMfHo2/fvibTb1PsM2Ca/TbFPgNV63fZFQ1TVdXjSpkZM2bAxsYGd+/exX/+8x+T+v3Thqn+rWqK46QejpN6amqcNDmuGFTC4+TkBDMzM2RlZZXbnpWVBRcXl0rruLi4PLN82X+zsrLQqFGjcmXatm1baZuWlpawtLSssF0ikTz3H06hUMDOzg5xcXFo164d5s6di6NHj6pV1xiZYr9Nsc+AafbbFPsMaNdvYxonbY5VVTmulBGE/2vv3qOiqtf/gb+HywyCMuAFR0AIEXOpqIWBF0hLEskysETMC6hhdvR4PKan+npBzSJP17PKY5mpJ43Me6VpKpfsGGEqVF4XGl5TBJSLIijM8/vDH3McAbk4N2ber7VYOnt/9t7P89mbeeYzs+eDwN7eHsDtW+hs9fprCvZVw7CfGob91DDG7qfG7NuiJi1QKpUICgpCSkqKbplWq0VKSgr69etX6zb9+vXTaw/c/gitur2fnx80Go1em5KSEmRmZta5z/thb2+PyZMnY/HixVi9ejVat25t8GMQEZH5NKVWGYJCocC8efPQu3dvhIaGGu04RETWxqI+4QGAmTNnIi4uDn369EFwcDA++OADXL9+HRMmTAAAjB8/Hl5eXkhKSgIA/O1vf8PAgQPx7rvvYtiwYVi3bh0OHDiA5cuXA7hdIGbMmIHFixcjICAAfn5+mDdvHjw9PREVFWWUHNq0aYNnnnkGADhDGxGRFaqvVhlLixYt8OSTT/K7O0REjWBxA55Ro0YhPz8f8+fPx6VLl9C7d2/s3LlT9+XQs2fPws7ufx9M9e/fH8nJyZg7dy7+7//+DwEBAdi6dSt69Oiha/OPf/wD169fx+TJk1FUVITQ0FDs3LnTJicRICKi+1dfrSIiIsthcQMeAJg2bVqdU22mp6fXWDZy5EiMHDmyzv0pFAosWrQIixYtuu/YSkpK4OzsDAcHi+w6IiIykXvVKiIishwW9R0eS3fw4EF8+OGH+OGHH8wdChERERERNQAHPI2wa9cuVFZW4qeffoJWqzV3OEREREREVA/el9UIt27dQmpqKtzd3aFQKMwdDhERERER1YOf8DTCypUrodFoMHPmTA54iIiIiIiaAX7C0wixsbF46aWXEBgYaO5QiIiIiIioATjgaYSZM2eic+fO5g6DiIhsQGVlJb766ivcuHEDo0aNQqtWrcwdEhFRs8Rb2hrBw8PD3CEQEZGNOHbsGE6ePImDBw/ixRdfNHc4RETNFgc8REREFujHH38EABw/fhzR0dFmjoaIqPnigIeIiMjCFBQUID8/H1qtFleuXMGAAQPMHRIRUbPF7/A0gIgAAEpKShq97a1bt1BWVoaSkhI4OjoaOjSLZYt522LOgG3mbYs5A/eXd/XzZ/Xzqa2rr65cvXoVlZWV+OOPP9CzZ0+4uLjo2trq9dcU7KuGYT81DPupYUzVT42pKwph9anX+fPn0bFjR3OHQUTU7J07dw7e3t7mDsPsWFeIiAyjIXWFA54G0Gq1+PPPP9GqVatG//2dkpISdOzYEefOnYOrq6uRIrQ8tpi3LeYM2GbetpgzcH95iwhKS0vh6ekJOzveTc26Yhrsq4ZhPzUM+6lhTNVPjakrvKWtAezs7O77HUlXV1eb/OWwxbxtMWfANvO2xZyBpuetVquNEE3zxLpiWuyrhmE/NQz7qWFM0U8NrSt8m42IiIiIiKwWBzxERERERGS1OOAxMpVKhcTERKhUKnOHYlK2mLct5gzYZt62mDNgu3lbGp6HhmNfNQz7qWHYTw1jif3ESQuIiIiIiMhq8RMeIiIiIiKyWhzwEBERERGR1eKAh4iIiIiIrBYHPEREREREZLU44GmCpUuX4oEHHoCTkxNCQkKwf//+e7bfsGEDunbtCicnJwQGBuK7777TWy8imD9/Pjp06IAWLVogPDwcOTk5xkyh0Qyd8+bNmzFkyBC0adMGCoUC2dnZRoy+6QyZ961bt/DKK68gMDAQLi4u8PT0xPjx4/Hnn38aO41GMfS5XrBgAbp27QoXFxe4u7sjPDwcmZmZxkyhSQyd952mTJkChUKBDz74wMBR3x9D5xwfHw+FQqH3M3ToUGOmYJMae95szYIFC2pch127djV3WBZh7969ePrpp+Hp6QmFQoGtW7fqrW8Or0dMob5+4nPdbUlJSXjkkUfQqlUreHh4ICoqCidOnNBrU15ejqlTp6JNmzZo2bIlnn32WeTl5Zk+WKFGWbdunSiVSlm5cqUcOXJEEhISxM3NTfLy8mptv2/fPrG3t5d//vOfcvToUZk7d644OjrK77//rmvz1ltviVqtlq1bt8qvv/4qw4cPFz8/P7lx44ap0ronY+T8+eefy8KFC+XTTz8VAJKVlWWibBrO0HkXFRVJeHi4fPXVV3L8+HHJyMiQ4OBgCQoKMmVa92SMc/3FF1/I7t275dSpU3L48GGZNGmSuLq6yuXLl02VVr2MkXe1zZs3S69evcTT01Pef/99I2fScMbIOS4uToYOHSoXL17U/Vy5csVUKdmExp43W5SYmCjdu3fXuw7z8/PNHZZF+O6772TOnDmyefNmASBbtmzRW2/pr0dMpb5+4nPdbREREbJq1So5fPiwZGdny5NPPik+Pj5y7do1XZspU6ZIx44dJSUlRQ4cOCB9+/aV/v37mzxWDngaKTg4WKZOnap7XFVVJZ6enpKUlFRr+5iYGBk2bJjespCQEHnxxRdFRESr1YpGo5G3335bt76oqEhUKpV8+eWXRsig8Qyd851yc3MtdsBjzLyr7d+/XwDImTNnDBP0fTJFzsXFxQJA9uzZY5igDcBYeZ8/f168vLzk8OHD4uvra1EDHmPkHBcXJ88884xR4qXbGnvebFFiYqL06tXL3GFYvLtfyDeH1yPmUNeAh891NV2+fFkAyA8//CAit68fR0dH2bBhg67NsWPHBIBkZGSYNDbe0tYIN2/exMGDBxEeHq5bZmdnh/DwcGRkZNS6TUZGhl57AIiIiNC1z83NxaVLl/TaqNVqhISE1LlPUzJGzs2BqfIuLi6GQqGAm5ubQeK+H6bI+ebNm1i+fDnUajV69epluODvg7Hy1mq1GDduHGbPno3u3bsbJ/gmMua5Tk9Ph4eHBx588EG89NJLKCwsNHwCNqop581W5eTkwNPTE506dcKYMWNw9uxZc4dk8Sz99Yil4XNdTcXFxQCA1q1bAwAOHjyIW7du6V1TXbt2hY+Pj8mvKQ54GqGgoABVVVVo37693vL27dvj0qVLtW5z6dKle7av/rcx+zQlY+TcHJgi7/LycrzyyisYPXo0XF1dDRP4fTBmztu2bUPLli3h5OSE999/H7t370bbtm0Nm0ATGSvvJUuWwMHBAdOnTzd80PfJWDkPHToUn3/+OVJSUrBkyRL88MMPiIyMRFVVleGTsEFNOW+2KCQkBKtXr8bOnTuxbNky5ObmIiwsDKWlpeYOzaJZ+usRS8Lnupq0Wi1mzJiBAQMGoEePHgBuX1NKpbLGm7rmuKYcTHo0IgJwewKDmJgYiAiWLVtm7nCM7rHHHkN2djYKCgrw6aefIiYmBpmZmfDw8DB3aEZx8OBB/Otf/8KhQ4egUCjMHY7JxMbG6v4fGBiInj17wt/fH+np6Rg8eLAZIyNbEhkZqft/z549ERISAl9fX6xfvx6TJk0yY2RkLfhcV9PUqVNx+PBh/Pe//zV3KLXiJzyN0LZtW9jb29eYXSIvLw8ajabWbTQazT3bV//bmH2akjFybg6MmXf1YOfMmTPYvXu3RXy6Axg3ZxcXF3Tu3Bl9+/bFZ599BgcHB3z22WeGTaCJjJH3jz/+iMuXL8PHxwcODg5wcHDAmTNn8PLLL+OBBx4wSh6NYarf606dOqFt27Y4efLk/QdNTTpvBLi5uaFLly68Duth6a9HLJmtP9dNmzYN27ZtQ1paGry9vXXLNRoNbt68iaKiIr325rimOOBpBKVSiaCgIKSkpOiWabVapKSkoF+/frVu069fP732ALB7925dez8/P2g0Gr02JSUlyMzMrHOfpmSMnJsDY+VdPdjJycnBnj170KZNG+Mk0ASmPNdarRYVFRX3H7QBGCPvcePG4bfffkN2drbux9PTE7Nnz8b3339vvGQayFTn+vz58ygsLESHDh0ME7iNa8p5I+DatWs4deoUr8N6WPrrEUtmq891IoJp06Zhy5YtSE1NhZ+fn976oKAgODo66l1TJ06cwNmzZ01/TZl0igQrsG7dOlGpVLJ69Wo5evSoTJ48Wdzc3OTSpUsiIjJu3Dh59dVXde337dsnDg4O8s4778ixY8ckMTGx1mmp3dzc5Ouvv5bffvtNnnnmGYuaBtIYORcWFkpWVpZs375dAMi6deskKytLLl68aPL86mLovG/evCnDhw8Xb29vyc7O1pvOsqKiwiw53s3QOV+7dk1ee+01ycjIkNOnT8uBAwdkwoQJolKp5PDhw2bJsTbGuMbvZmmztBk659LSUpk1a5ZkZGRIbm6u7NmzRx5++GEJCAiQ8vJys+Rojeo7byTy8ssvS3p6uuTm5sq+ffskPDxc2rZta1FT4ZtLaWmpZGVlSVZWlgCQ9957T7KysnQzhVr66xFTuVc/8bnuf1566SVRq9WSnp6u95qmrKxM12bKlCni4+MjqampcuDAAenXr5/069fP5LFywNMEH374ofj4+IhSqZTg4GD5+eefdesGDhwocXFxeu3Xr18vXbp0EaVSKd27d5ft27frrddqtTJv3jxp3769qFQqGTx4sJw4ccIUqTSYoXNetWqVAKjxk5iYaIJsGs6QeVdPwV3bT1pamokyqp8hc75x44ZER0eLp6enKJVK6dChgwwfPlz2799vqnQazNDX+N0sbcAjYticy8rKZMiQIdKuXTtxdHQUX19fSUhI4AtxI7jXeSORUaNGSYcOHUSpVIqXl5eMGjVKTp48ae6wLEJaWlqtNaj6d705vB4xhXv1E5/r/qeu1zSrVq3Stblx44b85S9/EXd3d3F2dpbo6GizvLmt+P8BExERERERWR1+h4eIiIiIiKwWBzxERERERGS1OOAhIiIiIiKrxQEPERERERFZLQ54iIiIiIjIanHAQ0REREREVosDHiIiIiIisloc8BAByMnJwZAhQ6BWq6FQKLB161Zzh2RVFAoFFixYYO4wiIhMhnXFuFhXqDE44KFmZfXq1VAoFLofBwcHeHl5IT4+HhcuXGjyfuPi4vD777/jjTfewJo1a9CnTx8DRt18TJ8+HQqFAidPnqyzzZw5c6BQKPDbb7+ZMDIiIuNgXTEu1hWyBBzwULO0aNEirFmzBh9//DEiIyOxdu1aDBw4EOXl5Y3e140bN5CRkYFJkyZh2rRpGDt2LLy9vY0QteUbM2YMACA5ObnONl9++SUCAwPRs2dPU4VFRGR0rCvGwbpCloADHmqWIiMjMXbsWLzwwgtYsWIFZs2ahVOnTuGbb75p9L7y8/MBAG5ubgaLr7y8HFqt1mD7M5WQkBB07twZX375Za3rMzIykJubqytgRETWgnXFOFhXyBJwwENWISwsDABw6tQpveXHjx/Hc889h9atW8PJyQl9+vTRK14LFiyAr68vAGD27NlQKBR44IEHdOsvXLiAiRMnon379lCpVOjevTtWrlypd4z09HQoFAqsW7cOc+fOhZeXF5ydnVFSUgIAyMzMxNChQ6FWq+Hs7IyBAwdi3759evtYsGCB7iP/+Ph4uLm5Qa1WY8KECSgrK6uR79q1axEcHAxnZ2e4u7vj0Ucfxa5du/Ta7NixA2FhYXBxcUGrVq0wbNgwHDlypN6+HDNmDI4fP45Dhw7VWJecnAyFQoHRo0fj5s2bmD9/PoKCgqBWq+Hi4oKwsDCkpaXVe4z4+Hi9fr67H2rLNygoCC1atEDr1q0RGxuLc+fO6bXJycnBs88+C41GAycnJ3h7eyM2NhbFxcX1xkNEdDfWFdYV1hXr4WDuAIgM4fTp0wAAd3d33bIjR45gwIAB8PLywquvvgoXFxesX78eUVFR2LRpE6KjozFixAi4ubnh73//O0aPHo0nn3wSLVu2BADk5eWhb9++UCgUmDZtGtq1a4cdO3Zg0qRJKCkpwYwZM/RieP3116FUKjFr1ixUVFRAqVQiNTUVkZGRCAoKQmJiIuzs7LBq1So8/vjj+PHHHxEcHKy3j5iYGPj5+SEpKQmHDh3CihUr4OHhgSVLlujaLFy4EAsWLED//v2xaNEiKJVKZGZmIjU1FUOGDAEArFmzBnFxcYiIiMCSJUtQVlaGZcuWITQ0FFlZWbUWhWpjxozBwoULkZycjIcffli3vKqqCuvXr0dYWBh8fHxQUFCAFStWYPTo0UhISEBpaSk+++wzREREYP/+/ejdu3cTzmRNb7zxBubNm4eYmBi88MILyM/Px4cffohHH30UWVlZcHNzw82bNxEREYGKigr89a9/hUajwYULF7Bt2zYUFRVBrVYbJBYish2sK6wrrCtWRIiakVWrVgkA2bNnj+Tn58u5c+dk48aN0q5dO1GpVHLu3Dld28GDB0tgYKCUl5frlmm1Wunfv78EBAToluXm5goAefvtt/WONWnSJOnQoYMUFBToLY+NjRW1Wi1lZWUiIpKWliYApFOnTrpl1ccKCAiQiIgI0Wq1uuVlZWXi5+cnTzzxhG5ZYmKiAJCJEyfqHSs6OlratGmje5yTkyN2dnYSHR0tVVVVem2rj1FaWipubm6SkJCgt/7SpUuiVqtrLK/NI488It7e3nrH2LlzpwCQTz75REREKisrpaKiQm+7q1evSvv27WvkAUASExN1j+Pi4sTX17fGcav7odrp06fF3t5e3njjDb12v//+uzg4OOiWZ2VlCQDZsGFDvbkREd2JdYV1RYR1xdrxljZqlsLDw9GuXTt07NgRzz33HFxcXPDNN9/ovhR65coVpKamIiYmBqWlpSgoKEBBQQEKCwsRERGBnJyce86+IyLYtGkTnn76aYiIbvuCggJERESguLi4xkfzcXFxaNGihe5xdnY2cnJy8Pzzz6OwsFC3/fXr1zF48GDs3bu3xv3YU6ZM0XscFhaGwsJC3W0MW7duhVarxfz582Fnp//rW/2R/e7du1FUVITRo0frxW1vb4+QkJAG3RowduxYnD9/Hnv37tUtS05OhlKpxMiRIwEA9vb2UCqVAACtVosrV66gsrISffr0qfW2habYvHkztFotYmJi9HLRaDQICAjQ5VL9Ttv3339f660aRET1YV1hXWFdsV68pY2apaVLl6JLly4oLi7GypUrsXfvXqhUKt36kydPQkQwb948zJs3r9Z9XL58GV5eXrWuy8/PR1FREZYvX47ly5fXuf2d/Pz89B7n5OQAuF2w6lJcXKx3u4SPj4/e+up1V69ehaurK06dOgU7Ozt069atzn1WH/fxxx+vdb2rq2ud21aLjY3FzJkzkZycjEGDBqG8vBxbtmxBZGSkXrz/+c9/8O677+L48eO4deuWbvndfdFUOTk5EBEEBATUut7R0VF3vJkzZ+K9997DF198gbCwMAwfPhxjx47lbQdE1CCsK6wrAOuKteKAh5ql4OBg3d80iIqKQmhoKJ5//nmcOHECLVu21L3DNWvWLERERNS6j86dO9e5/+rtx44dW2dhuXv6zDvfhbtzH2+//Xad9x1X39ddzd7evtZ2IlJnrHerPu6aNWug0WhqrHdwqP/X3sPDA0888QQ2bdqEpUuX4ttvv0VpaaneLDpr165FfHw8oqKiMHv2bHh4eMDe3h5JSUk1vuR7t9q+QArcvp/77lwUCgV27NhRa9/c2X/vvvsu4uPj8fXXX2PXrl2YPn06kpKS8PPPP9vsdLBE1HCsK3VjXWFdae444KFmr/rJ8LHHHsNHH32EV199FZ06dQJw+52a8PDwRu+zXbt2aNWqFaqqqpq0PQD4+/sDuP3OV1P3Uds+tVotjh49Wmexqz6uh4fHfR13zJgx2LlzJ3bs2IHk5GS4urri6aef1q3fuHEjOnXqhM2bN+sVmsTExHr37e7ujqKiohrLz5w5o/fY398fIgI/Pz906dKl3v0GBgYiMDAQc+fOxU8//YQBAwbg448/xuLFi+vdloioGutK7cdlXWFdaa74HR6yCoMGDUJwcDA++OADlJeXw8PDA4MGDcInn3yCixcv1mhf/TcS6mJvb49nn30WmzZtwuHDhxu9PQAEBQXB398f77zzDq5du9akfdwtKioKdnZ2WLRoUY37tKvfrYuIiICrqyvefPNNvdsBGnvcqKgoODs749///jd27NiBESNGwMnJSbe++p2xO98lzMzMREZGRr379vf3R3Fxsd5f1b548SK2bNmi127EiBGwt7fHwoULa7wbKSIoLCwEAJSUlKCyslJvfWBgIOzs7FBRUdGgfImI7sS6wrrCumI9+AkPWY3Zs2dj5MiRWL16NaZMmYKlS5ciNDQUgYGBSEhIQKdOnZCXl4eMjAycP38ev/766z3399ZbbyEtLQ0hISFISEhAt27dcOXKFRw6dAh79uzBlStX7rm9nZ0dVqxYgcjISHTv3h0TJkyAl5cXLly4gLS0NLi6uuLbb79tVI6dO3fGnDlz8PrrryMsLAwjRoyASqXCL7/8Ak9PTyQlJcHV1RXLli3DuHHj8PDDDyM2Nhbt2rXD2bNnsX37dgwYMAAfffRRvcdq2bIloqKidH8d++4/CvfUU09h8+bNiI6OxrBhw5Cbm4uPP/4Y3bp1q7UQ3yk2NhavvPIKoqOjMX36dN30pl26dNH7Yqq/vz8WL16M1157DadPn0ZUVBRatWqF3NxcbNmyBZMnT8asWbOQmpqKadOmYeTIkejSpQsqKyuxZs0a3QsMIqKmYF1hXWFdsRKmnRSO6P5UTx/6yy+/1FhXVVUl/v7+4u/vL5WVlSIicurUKRk/frxoNBpxdHQULy8veeqpp2Tjxo267eqaPlREJC8vT6ZOnSodO3YUR0dH0Wg0MnjwYFm+fLmuTfX0oXVNXZmVlSUjRoyQNm3aiEqlEl9fX4mJiZGUlBRdm+ppM/Pz82vNNzc3V2/5ypUr5aGHHhKVSiXu7u4ycOBA2b17t16btLQ0iYiIELVaLU5OTuLv7y/x8fFy4MCBOnq3pu3btwsA6dChQ63Tlb755pvi6+srKpVKHnroIdm2bVutU4PirulDRUR27dolPXr0EKVSKQ8++KCsXbu2xvSh1TZt2iShoaHi4uIiLi4u0rVrV5k6daqcOHFCRET++OMPmThxovj7+4uTk5O0bt1aHnvsMdmzZ0+DcyUi28S6chvrCuuKNVOINOJba0RERERERM0Iv8NDRERERERWiwMeIiIiIiKyWhzwEBERERGR1eKAh4iIiIiIrBYHPEREREREZLU44CEiIiIiIqvFAQ8REREREVktDniIiIiIiMhqccBDRERERERWiwMeIiIiIiKyWhzwEBERERGR1eKAh4iIiIiIrBYHPEREREREZLX+Hy4Qx0bDwnmXAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -382,7 +363,6 @@ "ax1.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", "ax1.grid()\n", "\n", - "# ax1.scatter(ref_values[:2], encoded_ref_sol[:2], c='black', s=200, label='Best solution')\n", "ax1.scatter(ref_values[:2], sol[:2], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", "\n", "\n", @@ -397,7 +377,6 @@ "ax2.axline((0, 0.0), slope=0.90, color=\"grey\", linestyle=(0, (2, 5)))\n", "\n", "\n", - "# ax2.scatter(ref_values[2:], encoded_ref_sol[2:], c='black', s=200, label='Best solution')\n", "ax2.scatter(ref_values[2:], sol[2:], s=150, lw=1, edgecolors='w', label='Sampled solution')\n", "ax2.grid()\n", "\n", From 51caaf66e38465d938df4ee88141542cc451c2ac Mon Sep 17 00:00:00 2001 From: Nicolas Renaud Date: Mon, 16 Dec 2024 15:33:37 +0100 Subject: [PATCH 96/96] clean up --- docs/notebooks/networks/Net0_CM_simple.inp | 128 - docs/notebooks/networks/Net0_HW.inp | 128 - docs/notebooks/networks/Net1.json | 620 - docs/notebooks/networks/Net1Loops_CM.inp | 139 - .../networks/Net1Loops_CM_original_values.inp | 139 - docs/notebooks/networks/Net1_scenario1.inp | 19878 ---------------- docs/notebooks/networks/Net2LoopsCM.inp | 145 - docs/notebooks/networks/Net2LoopsCMflat.inp | 145 - docs/notebooks/networks/Net2LoopsDW.inp | 145 - docs/notebooks/networks/Net2LoopsFlat.inp | 145 - .../networks/Net2Loops_hhl_settings.inp | 145 - docs/notebooks/sandbox/qubo_poly_solver.ipynb | 3200 --- .../sandbox/qubo_poly_solver_2loops_cm.ipynb | 618 - .../sandbox/qubo_poly_solver_CM.ipynb | 436 - .../sandbox/qubo_poly_solver_Net2loops.ipynb | 795 - 15 files changed, 26806 deletions(-) delete mode 100644 docs/notebooks/networks/Net0_CM_simple.inp delete mode 100644 docs/notebooks/networks/Net0_HW.inp delete mode 100644 docs/notebooks/networks/Net1.json delete mode 100644 docs/notebooks/networks/Net1Loops_CM.inp delete mode 100644 docs/notebooks/networks/Net1Loops_CM_original_values.inp delete mode 100644 docs/notebooks/networks/Net1_scenario1.inp delete mode 100644 docs/notebooks/networks/Net2LoopsCM.inp delete mode 100644 docs/notebooks/networks/Net2LoopsCMflat.inp delete mode 100644 docs/notebooks/networks/Net2LoopsDW.inp delete mode 100644 docs/notebooks/networks/Net2LoopsFlat.inp delete mode 100644 docs/notebooks/networks/Net2Loops_hhl_settings.inp delete mode 100644 docs/notebooks/sandbox/qubo_poly_solver.ipynb delete mode 100644 docs/notebooks/sandbox/qubo_poly_solver_2loops_cm.ipynb delete mode 100644 docs/notebooks/sandbox/qubo_poly_solver_CM.ipynb delete mode 100644 docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb diff --git a/docs/notebooks/networks/Net0_CM_simple.inp b/docs/notebooks/networks/Net0_CM_simple.inp deleted file mode 100644 index 25f9e25..0000000 --- a/docs/notebooks/networks/Net0_CM_simple.inp +++ /dev/null @@ -1,128 +0,0 @@ -[TITLE] -File obtained via Mario of a 2 node sysem - - -[JUNCTIONS] -;ID Elev Demand Pattern - J1 0 0 ; - D1 0 50 ; - -[RESERVOIRS] -;ID Head Pattern - R1 30 ; - -[TANKS] -;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow - -[PIPES] -;ID Node1 Node2 Length Diameter Roughness MinorLoss Status - P1 R1 J1 1000 1000 0.015 0 Open ; - P2 J1 D1 1000 1000 0.015 0 Open ; - -[PUMPS] -;ID Node1 Node2 Parameters - -[VALVES] -;ID Node1 Node2 Diameter Type Setting MinorLoss - -[TAGS] - -[DEMANDS] -;Junction Demand Pattern Category - -[STATUS] -;ID Status/Setting - -[PATTERNS] -;ID Multipliers - -[CURVES] -;ID X-Value Y-Value - -[CONTROLS] - -[RULES] - -[ENERGY] - Global Efficiency 75 - Global Price 0 - Demand Charge 0 - -[EMITTERS] -;Junction Coefficient - -[QUALITY] -;Node InitQual - -[SOURCES] -;Node Type Quality Pattern - -[REACTIONS] -;Type Pipe/Tank Coefficient - - -[REACTIONS] - Order Bulk 1 - Order Tank 1 - Order Wall 1 - Global Bulk 0 - Global Wall 0 - Limiting Potential 0 - Roughness Correlation 0 - -[MIXING] -;Tank Model - -[TIMES] - Duration 1 - Hydraulic Timestep 1:00 - Quality Timestep 0:05 - Pattern Timestep 1:00 - Pattern Start 0:00 - Report Timestep 1:00 - Report Start 0:00 - Start ClockTime 12 am - Statistic None - -[REPORT] - Status No - Summary No - Page 0 - -[OPTIONS] - Units LPS - Headloss C-M - Specific Gravity 1 - Viscosity 1 - Trials 40 - Accuracy 0.1 - CHECKFREQ 2 - MAXCHECK 10 - DAMPLIMIT 0 - Unbalanced Continue 10 - Pattern 1 - Demand Multiplier 1.0 - Emitter Exponent 0.5 - Quality None mg/L - Diffusivity 1 - Tolerance 0.01 - -[COORDINATES] -;Node X-Coord Y-Coord -J1 10.00000 60.00000 -D1 110.00000 60.00000 -R1 -11.72214 74.24023 - -[VERTICES] -;Link X-Coord Y-Coord - -[LABELS] -;X-Coord Y-Coord Label & Anchor Node - -[BACKDROP] - DIMENSIONS 0.000 0.000 10000.000 10000.000 - UNITS None - FILE - OFFSET 0.00 0.00 - -[END] diff --git a/docs/notebooks/networks/Net0_HW.inp b/docs/notebooks/networks/Net0_HW.inp deleted file mode 100644 index f22ed21..0000000 --- a/docs/notebooks/networks/Net0_HW.inp +++ /dev/null @@ -1,128 +0,0 @@ -[TITLE] -File obtained via Mario of a 2 node sysem - - -[JUNCTIONS] -;ID Elev Demand Pattern - J1 0 0 ; - D1 0 50 ; - -[RESERVOIRS] -;ID Head Pattern - R1 30 ; - -[TANKS] -;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow - -[PIPES] -;ID Node1 Node2 Length Diameter Roughness MinorLoss Status - P1 R1 J1 100 250 0.05 0 Open ; - P2 J1 D1 1000 200 0.04 0 Open ; - -[PUMPS] -;ID Node1 Node2 Parameters - -[VALVES] -;ID Node1 Node2 Diameter Type Setting MinorLoss - -[TAGS] - -[DEMANDS] -;Junction Demand Pattern Category - -[STATUS] -;ID Status/Setting - -[PATTERNS] -;ID Multipliers - -[CURVES] -;ID X-Value Y-Value - -[CONTROLS] - -[RULES] - -[ENERGY] - Global Efficiency 75 - Global Price 0 - Demand Charge 0 - -[EMITTERS] -;Junction Coefficient - -[QUALITY] -;Node InitQual - -[SOURCES] -;Node Type Quality Pattern - -[REACTIONS] -;Type Pipe/Tank Coefficient - - -[REACTIONS] - Order Bulk 1 - Order Tank 1 - Order Wall 1 - Global Bulk 0 - Global Wall 0 - Limiting Potential 0 - Roughness Correlation 0 - -[MIXING] -;Tank Model - -[TIMES] - Duration 1 - Hydraulic Timestep 1:00 - Quality Timestep 0:05 - Pattern Timestep 1:00 - Pattern Start 0:00 - Report Timestep 1:00 - Report Start 0:00 - Start ClockTime 12 am - Statistic None - -[REPORT] - Status No - Summary No - Page 0 - -[OPTIONS] - Units LPS - Headloss H-W - Specific Gravity 1 - Viscosity 1 - Trials 40 - Accuracy 0.1 - CHECKFREQ 2 - MAXCHECK 10 - DAMPLIMIT 0 - Unbalanced Continue 10 - Pattern 1 - Demand Multiplier 1.0 - Emitter Exponent 0.5 - Quality None mg/L - Diffusivity 1 - Tolerance 0.01 - -[COORDINATES] -;Node X-Coord Y-Coord -J1 10.00000 60.00000 -D1 110.00000 60.00000 -R1 -11.72214 74.24023 - -[VERTICES] -;Link X-Coord Y-Coord - -[LABELS] -;X-Coord Y-Coord Label & Anchor Node - -[BACKDROP] - DIMENSIONS 0.000 0.000 10000.000 10000.000 - UNITS None - FILE - OFFSET 0.00 0.00 - -[END] diff --git a/docs/notebooks/networks/Net1.json b/docs/notebooks/networks/Net1.json deleted file mode 100644 index 381797b..0000000 --- a/docs/notebooks/networks/Net1.json +++ /dev/null @@ -1,620 +0,0 @@ -{ - "version": "wntr-0.4.1", - "comment": "WaterNetworkModel - all values given in SI units", - "name": "Net1.inp", - "options": { - "time": { - "duration": 86400.0, - "hydraulic_timestep": 3600, - "quality_timestep": 300, - "rule_timestep": 360, - "pattern_timestep": 7200, - "pattern_start": 0.0, - "report_timestep": 3600, - "report_start": 0.0, - "start_clocktime": 0.0, - "statistic": "NONE", - "pattern_interpolation": false - }, - "hydraulic": { - "headloss": "H-W", - "hydraulics": null, - "hydraulics_filename": null, - "viscosity": 1.0, - "specific_gravity": 1.0, - "pattern": "1", - "demand_multiplier": 1.0, - "demand_model": "DDA", - "minimum_pressure": 0.0, - "required_pressure": 0.07, - "pressure_exponent": 0.5, - "emitter_exponent": 0.5, - "trials": 40, - "accuracy": 0.001, - "unbalanced": "CONTINUE", - "unbalanced_value": 10, - "checkfreq": 2, - "maxcheck": 10, - "damplimit": 0.0, - "headerror": 0.0, - "flowchange": 0.0, - "inpfile_units": "GPM", - "inpfile_pressure_units": null - }, - "report": { - "pagesize": 0, - "report_filename": null, - "status": "YES", - "summary": "NO", - "energy": "NO", - "nodes": false, - "links": false, - "report_params": { - "elevation": false, - "demand": true, - "head": true, - "pressure": true, - "quality": true, - "length": false, - "diameter": false, - "flow": true, - "velocity": true, - "headloss": true, - "position": false, - "setting": false, - "reaction": false, - "f-factor": false - }, - "param_opts": { - "elevation": {}, - "demand": {}, - "head": {}, - "pressure": {}, - "quality": {}, - "length": {}, - "diameter": {}, - "flow": {}, - "velocity": {}, - "headloss": {}, - "position": {}, - "setting": {}, - "reaction": {}, - "f-factor": {} - } - }, - "quality": { - "parameter": "CHEMICAL", - "trace_node": null, - "chemical_name": "Chlorine", - "diffusivity": 1.0, - "tolerance": 0.01, - "inpfile_units": "mg/L" - }, - "reaction": { - "bulk_order": 1.0, - "wall_order": 1.0, - "tank_order": 1.0, - "bulk_coeff": -5.787037037037037e-06, - 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"start_node_name": "11", - "end_node_name": "12", - "bulk_coeff": null, - "check_valve": false, - "diameter": 0.35559999999999997, - "initial_setting": null, - "initial_status": "Open", - "length": 1609.344, - "minor_loss": 0.0, - "roughness": 100.0, - "tag": null, - "vertices": [], - "wall_coeff": null - }, - { - "name": "12", - "link_type": "Pipe", - "start_node_name": "12", - "end_node_name": "13", - "bulk_coeff": null, - "check_valve": false, - "diameter": 0.254, - "initial_setting": null, - "initial_status": "Open", - "length": 1609.344, - "minor_loss": 0.0, - "roughness": 100.0, - "tag": null, - "vertices": [], - "wall_coeff": null - }, - { - "name": "21", - "link_type": "Pipe", - "start_node_name": "21", - "end_node_name": "22", - "bulk_coeff": null, - "check_valve": false, - "diameter": 0.254, - "initial_setting": null, - "initial_status": "Open", - "length": 1609.344, - "minor_loss": 0.0, - "roughness": 100.0, - "tag": null, - "vertices": [], - "wall_coeff": null - }, - { - "name": "22", - "link_type": "Pipe", - "start_node_name": "22", - "end_node_name": "23", - "bulk_coeff": null, - "check_valve": false, - "diameter": 0.30479999999999996, - "initial_setting": null, - "initial_status": "Open", - "length": 1609.344, - "minor_loss": 0.0, - "roughness": 100.0, - "tag": null, - "vertices": [], - "wall_coeff": null - }, - { - "name": "31", - "link_type": "Pipe", - "start_node_name": "31", - "end_node_name": "32", - "bulk_coeff": null, - "check_valve": false, - "diameter": 0.15239999999999998, - "initial_setting": null, - "initial_status": "Open", - "length": 1609.344, - "minor_loss": 0.0, - "roughness": 100.0, - "tag": null, - "vertices": [], - "wall_coeff": null - }, - { - "name": "110", - "link_type": "Pipe", - "start_node_name": "2", - "end_node_name": "12", - "bulk_coeff": null, - "check_valve": false, - "diameter": 0.4572, - "initial_setting": null, - "initial_status": "Open", - "length": 60.96, - "minor_loss": 0.0, - "roughness": 100.0, - "tag": null, - "vertices": [], - "wall_coeff": null - }, - { - "name": "111", - "link_type": "Pipe", - "start_node_name": "11", - "end_node_name": "21", - "bulk_coeff": null, - "check_valve": false, - "diameter": 0.254, - "initial_setting": null, - "initial_status": "Open", - "length": 1609.344, - "minor_loss": 0.0, - "roughness": 100.0, - "tag": null, - "vertices": [], - "wall_coeff": null - }, - { - "name": "112", - "link_type": "Pipe", - "start_node_name": "12", - "end_node_name": "22", - "bulk_coeff": null, - "check_valve": false, - "diameter": 0.30479999999999996, - "initial_setting": null, - "initial_status": "Open", - "length": 1609.344, - "minor_loss": 0.0, - "roughness": 100.0, - "tag": null, - "vertices": [], - "wall_coeff": null - }, - { - "name": "113", - "link_type": "Pipe", - "start_node_name": "13", - "end_node_name": "23", - "bulk_coeff": null, - "check_valve": false, - "diameter": 0.2032, - "initial_setting": null, - "initial_status": "Open", - "length": 1609.344, - "minor_loss": 0.0, - "roughness": 100.0, - "tag": null, - "vertices": [], - "wall_coeff": null - }, - { - "name": "121", - "link_type": "Pipe", - "start_node_name": "21", - "end_node_name": "31", - "bulk_coeff": null, - "check_valve": false, - "diameter": 0.2032, - "initial_setting": null, - "initial_status": "Open", - "length": 1609.344, - "minor_loss": 0.0, - "roughness": 100.0, - "tag": null, - "vertices": [], - "wall_coeff": null - }, - { - "name": "122", - "link_type": "Pipe", - "start_node_name": "22", - "end_node_name": "32", - "bulk_coeff": null, - "check_valve": false, - "diameter": 0.15239999999999998, - "initial_setting": null, - "initial_status": "Open", - "length": 1609.344, - "minor_loss": 0.0, - "roughness": 100.0, - "tag": null, - "vertices": [], - "wall_coeff": null - }, - { - "name": "9", - "link_type": "Pump", - "start_node_name": "9", - "end_node_name": "10", - "pump_type": "HEAD", - "base_speed": 1.0, - "efficiency": null, - "energy_pattern": null, - "energy_price": null, - "initial_setting": null, - "initial_status": "Open", - "pump_curve_name": "1", - "speed_pattern_name": null, - "tag": null, - "vertices": [] - } - ], - "sources": [], - "controls": [ - { - "type": "simple", - "condition": "TANK 2 LEVEL BELOW 33.528", - "then_actions": [ - "PUMP 9 STATUS IS OPEN" - ] - }, - { - "type": "simple", - "condition": "TANK 2 LEVEL ABOVE 42.672000000000004", - "then_actions": [ - "PUMP 9 STATUS IS CLOSED" - ] - } - ] -} \ No newline at end of file diff --git a/docs/notebooks/networks/Net1Loops_CM.inp b/docs/notebooks/networks/Net1Loops_CM.inp deleted file mode 100644 index 5d245e0..0000000 --- a/docs/notebooks/networks/Net1Loops_CM.inp +++ /dev/null @@ -1,139 +0,0 @@ -[TITLE] -shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) - -[JUNCTIONS] -;ID Elev Demand Pattern - 2 150.00 500 ; - 3 160.00 1000 ; - 4 155.00 500 ; - 5 150.00 1000 ; - -[RESERVOIRS] -;ID Head Pattern - 1 16.00 ; - -[TANKS] -;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow - -[PIPES] -;ID Node1 Node2 Length Diameter Roughness MinorLoss Status - 1 1 2 1 1000 1 0.00 Open ; - 2 2 3 1 1000 1 0.00 Open ; - 3 2 4 1 1000 1 0.00 Open ; - 4 4 5 1 1000 1 0.00 Open ; - 5 3 5 1 1000 1 0.00 Open ; - - -[PUMPS] -;ID Node1 Node2 Parameters - -[VALVES] -;ID Node1 Node2 Diameter Type Setting MinorLoss - -[TAGS] - -[DEMANDS] -;Junction Demand Pattern Category - -[STATUS] -;ID Status/Setting - -[PATTERNS] -;ID Multipliers - -[CURVES] -;ID X-Value Y-Value - -[CONTROLS] - - - -[RULES] - - - -[ENERGY] - Global Efficiency 75 - Global Price 0 - Demand Charge 0 - -[EMITTERS] -;Junction Coefficient - -[QUALITY] -;Node InitQual - -[SOURCES] -;Node Type Quality Pattern - -[REACTIONS] -;Type Pipe/Tank Coefficient - - -[REACTIONS] - Order Bulk 1 - Order Tank 1 - Order Wall 1 - Global Bulk 0 - Global Wall 0 - Limiting Potential 0 - Roughness Correlation 0 - -[MIXING] -;Tank Model - -[TIMES] - Duration 0:00 - Hydraulic Timestep 1:00 - Quality Timestep 0:05 - Pattern Timestep 2:00 - Pattern Start 0:00 - Report Timestep 1:00 - Report Start 0:00 - Start ClockTime 12 am - Statistic NONE - -[REPORT] - Status Yes - Summary No - Page 0 - -[OPTIONS] - Units LPS - Headloss C-M - Specific Gravity 1.0 - Viscosity 1.0 - Trials 40 - Accuracy 0.001 - CHECKFREQ 2 - MAXCHECK 10 - DAMPLIMIT 0 - Unbalanced Continue 10 - Pattern 1 - Demand Multiplier 1.0 - Emitter Exponent 0.5 - Quality Chlorine mg/L - Diffusivity 1.0 - Tolerance 0.01 - -[COORDINATES] -;Node X-Coord Y-Coord -2 2000.000 3000.000 -3 1000.000 3000.000 -4 2000.000 2000.000 -5 1000.000 2000.000 -1 3000.000 3000.000 - -[VERTICES] -;Link X-Coord Y-Coord - -[LABELS] -;X-Coord Y-Coord Label & Anchor Node - -[BACKDROP] - DIMENSIONS 900.000 900.000 3100.000 3100.000 - UNITS None - FILE - OFFSET 0.00 0.00 - -[END] diff --git a/docs/notebooks/networks/Net1Loops_CM_original_values.inp b/docs/notebooks/networks/Net1Loops_CM_original_values.inp deleted file mode 100644 index 0f1b9fe..0000000 --- a/docs/notebooks/networks/Net1Loops_CM_original_values.inp +++ /dev/null @@ -1,139 +0,0 @@ -[TITLE] -shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) - -[JUNCTIONS] -;ID Elev Demand Pattern - 2 150.00 27.77 ; - 3 160.00 27.77 ; - 4 155.00 33.33 ; - 5 150.00 75.00 ; - -[RESERVOIRS] -;ID Head Pattern - 1 210.00 ; - -[TANKS] -;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow - -[PIPES] -;ID Node1 Node2 Length Diameter Roughness MinorLoss Status - 1 1 2 1000 457.20 0.015 0.00 Open ; - 2 2 3 1000 203 0.015 0.00 Open ; - 3 2 4 1000 457 0.015 0.00 Open ; - 4 4 5 1000 153 0.015 0.00 Open ; - 5 3 5 1000 153 0.015 0.00 Open ; - - -[PUMPS] -;ID Node1 Node2 Parameters - -[VALVES] -;ID Node1 Node2 Diameter Type Setting MinorLoss - -[TAGS] - -[DEMANDS] -;Junction Demand Pattern Category - -[STATUS] -;ID Status/Setting - -[PATTERNS] -;ID Multipliers - -[CURVES] -;ID X-Value Y-Value - -[CONTROLS] - - - -[RULES] - - - -[ENERGY] - Global Efficiency 75 - Global Price 0 - Demand Charge 0 - -[EMITTERS] -;Junction Coefficient - -[QUALITY] -;Node InitQual - -[SOURCES] -;Node Type Quality Pattern - -[REACTIONS] -;Type Pipe/Tank Coefficient - - -[REACTIONS] - Order Bulk 1 - Order Tank 1 - Order Wall 1 - Global Bulk 0 - Global Wall 0 - Limiting Potential 0 - Roughness Correlation 0 - -[MIXING] -;Tank Model - -[TIMES] - Duration 0:00 - Hydraulic Timestep 1:00 - Quality Timestep 0:05 - Pattern Timestep 2:00 - Pattern Start 0:00 - Report Timestep 1:00 - Report Start 0:00 - Start ClockTime 12 am - Statistic NONE - -[REPORT] - Status Yes - Summary No - Page 0 - -[OPTIONS] - Units LPS - Headloss C-M - Specific Gravity 1.0 - Viscosity 1.0 - Trials 40 - Accuracy 0.001 - CHECKFREQ 2 - MAXCHECK 10 - DAMPLIMIT 0 - Unbalanced Continue 10 - Pattern 1 - Demand Multiplier 1.0 - Emitter Exponent 0.5 - Quality Chlorine mg/L - Diffusivity 1.0 - Tolerance 0.01 - -[COORDINATES] -;Node X-Coord Y-Coord -2 2000.000 3000.000 -3 1000.000 3000.000 -4 2000.000 2000.000 -5 1000.000 2000.000 -1 3000.000 3000.000 - -[VERTICES] -;Link X-Coord Y-Coord - -[LABELS] -;X-Coord Y-Coord Label & Anchor Node - -[BACKDROP] - DIMENSIONS 900.000 900.000 3100.000 3100.000 - UNITS None - FILE - OFFSET 0.00 0.00 - -[END] diff --git a/docs/notebooks/networks/Net1_scenario1.inp b/docs/notebooks/networks/Net1_scenario1.inp deleted file mode 100644 index 63d1f50..0000000 --- a/docs/notebooks/networks/Net1_scenario1.inp +++ /dev/null @@ -1,19878 +0,0 @@ -; Filename: Net1_CMH.inp -; WNTR: 0.1.3 -; Created: 2018-07-18 21:06:42 -[TITLE] -EPANET Example Network 1 -A simple example of modeling chlorine decay. Both bulk and -wall reactions are included. - -[JUNCTIONS] -;ID Elevation Demand Pattern - 10 216.408 0 P_10 ; - 11 216.408 34.011596627 P_11 ; - 12 213.36 39.1292726759 P_12 ; - 13 211.836 24.2456885422 P_13 ; - 21 213.36 29.6320164858 P_21 ; - 22 211.836 46.2360462883 P_22 ; - 23 210.312 33.9437878576 P_23 ; - 31 213.36 24.6401581456 P_31 ; - 32 216.408 19.8937512752 P_32 ; - -[RESERVOIRS] -;ID Head Pattern - 9 243.84 ; - -[TANKS] -;ID Elevation Init Level Min Level Max Level Diameter Min Volume Volume Curve - 2 259.08 36.576 30.48 45.72 15.3924 5671.76 ; - -[PIPES] -;ID Node1 Node2 Length Diameter Roughness Minor Loss Status - 10 10 11 3331.67387411 485.372150464 113.811328379 0 Open ; - 11 11 12 1657.13564283 316.943700447 91.3831006337 0 Open ; - 110 2 12 52.8106796552 506.609896966 104.006869299 0 Open ; - 111 11 21 1654.54087368 215.01206176 101.531132283 0 Open ; - 112 12 22 1826.98506296 330.431145219 89.1109075221 0 Open ; - 113 13 23 1483.90300058 224.555983414 98.5290708266 0 Open ; - 12 12 13 1793.26094084 261.438942511 110.848307299 0 Open ; - 121 21 31 1572.61027868 180.745875048 85.2584592309 0 Open ; - 122 22 32 1640.16562082 168.077833556 103.840712614 0 Open ; - 21 21 22 1585.6057026 214.07116674 102.30660687 0 Open ; - 22 22 23 1418.67750998 254.856568931 114.287527886 0 Open ; - 31 31 32 1411.61766834 123.694232337 86.5294112859 0 Open ; - -[PUMPS] -;ID Node1 Node2 Properties - 9 9 10 HEAD 1 SPEED 1 ; - -[VALVES] -;ID Node1 Node2 Diameter Type Setting Minor Loss - -[EMITTERS] -;ID Flow coefficient - -[CURVES] -;ID X-Value Y-Value -;PUMP: 1 - 1 340.687100 76.200000 ; - - -[PATTERNS] -;ID Multipliers - -P_10 0.618025 0.543651 0.454844 0.415054 0.406594 0.395268 0.385668 0.423559 -P_10 0.473188 0.568044 0.556594 0.682055 0.671832 0.759731 0.842346 1.025414 -P_10 1.156579 1.202141 1.160170 1.313728 1.296261 1.286349 1.244369 1.240940 -P_10 1.236849 1.140934 1.148902 1.084004 1.002884 0.946401 0.939612 0.830565 -P_10 0.903139 0.926024 1.019256 0.948396 0.826897 1.090704 0.966278 1.064976 -P_10 1.071430 1.011360 1.067024 1.063413 1.003574 0.848181 0.818733 0.754154 -P_10 0.690412 0.596603 0.502556 0.490863 0.404950 0.412064 0.370109 0.405782 -P_10 0.434059 0.580463 0.591829 0.691177 0.742558 0.762606 0.878697 0.915429 -P_10 1.123659 1.080114 1.224038 1.124743 1.272492 1.378281 1.128816 1.208248 -P_10 1.154117 1.151411 1.020289 1.002726 1.004945 0.912706 0.968553 0.923610 -P_10 0.958387 0.995953 0.916611 1.001360 1.057534 1.176823 1.131232 1.129289 -P_10 1.048410 1.151125 1.021759 1.034833 0.948050 0.901317 0.831121 0.672530 -P_10 0.663667 0.575817 0.496423 0.486280 0.373789 0.399423 0.423316 0.376372 -P_10 0.483958 0.575322 0.621374 0.747653 0.794345 0.837115 0.978909 0.952948 -P_10 1.039401 1.008332 1.137417 1.231194 1.179926 1.207029 1.117119 1.167834 -P_10 1.253713 1.091606 1.170170 1.089378 1.032162 1.074555 1.022266 0.975465 -P_10 1.045049 1.003405 0.963133 0.996191 1.045228 1.014285 1.126225 1.185486 -P_10 1.146198 1.001374 1.013434 0.939908 0.992935 0.931213 0.836870 0.754791 -P_10 0.690221 0.630373 0.511246 0.467675 0.380840 0.422377 0.407707 0.462708 -P_10 0.445344 0.530251 0.593637 0.631476 0.713641 0.790413 0.917384 0.965329 -P_10 1.099965 1.105964 1.058195 1.246839 1.114014 1.244308 1.054622 1.196713 -P_10 1.150794 1.100694 1.057396 0.972667 1.018202 0.995139 0.949969 1.035846 -P_10 0.960188 0.916882 1.082846 0.926741 0.983358 1.168962 1.138286 1.151551 -P_10 1.141920 1.170088 1.033980 1.013770 0.921433 0.925502 0.846918 0.697828 -P_10 0.640739 0.619460 0.546822 0.470869 0.411547 0.439849 0.459062 0.437098 -P_10 0.537125 0.555082 0.625990 0.783025 0.797556 1.035874 1.052541 1.219771 -P_10 1.172657 1.318037 1.296813 1.421027 1.396114 1.373849 1.427137 1.266276 -P_10 1.273641 1.209122 1.151183 1.186166 1.039511 1.023393 0.941795 1.159613 -P_10 1.016309 1.122071 1.090117 1.022698 1.072438 1.124077 1.160060 1.083932 -P_10 1.112631 1.059978 1.063832 1.010011 0.939743 0.931182 0.921699 0.776755 -P_10 0.740073 0.663865 0.617515 0.598463 0.530220 0.469058 0.443955 0.435977 -P_10 0.454419 0.458835 0.519527 0.527222 0.591688 0.697793 0.868127 0.864316 -P_10 0.919323 0.968123 1.083174 0.994628 1.230174 1.155289 1.135177 1.203408 -P_10 1.134489 1.122423 1.067445 1.111371 1.084451 0.930946 1.000134 1.053496 -P_10 1.112081 1.052345 1.056088 1.030103 1.022700 0.978477 1.052186 1.002752 -P_10 0.946145 0.931216 0.874437 0.864956 0.801563 0.816707 0.768947 0.685637 -P_10 0.620650 0.612690 0.560920 0.551008 0.419884 0.476420 0.460603 0.453721 -P_10 0.411295 0.417004 0.472873 0.465630 0.589058 0.575654 0.587244 0.775577 -P_10 0.810931 0.817962 0.827793 0.869623 0.978911 0.960866 1.095809 0.961543 -P_10 1.081039 1.086254 1.073269 0.944091 1.005149 0.908733 0.985865 0.987759 -P_10 0.979362 0.853385 0.955254 0.955583 1.014093 1.059980 1.041813 1.026695 -P_10 1.010871 1.010686 0.938464 1.007772 0.851948 0.881040 0.727078 0.697359 -P_10 0.564394 0.528902 0.494155 0.457195 0.412148 0.382587 0.410827 0.432207 -P_10 0.452542 0.499920 0.561809 0.616565 0.776424 0.907806 0.820830 1.053884 -P_10 1.036836 1.193074 1.268358 1.308000 1.405805 1.276431 1.321707 1.286033 -P_10 1.184711 1.148704 1.176687 1.012243 1.044361 0.950718 0.949509 0.999484 -P_10 0.896762 0.902652 0.919911 0.929022 0.976178 0.904476 1.057201 1.115950 -P_10 1.007840 1.071527 1.116209 0.973384 1.034124 0.869761 0.786416 0.714953 -P_10 0.732244 0.667567 0.539107 0.460128 0.432035 0.418275 0.392652 0.422490 -P_10 0.462610 0.490688 0.594850 0.709921 0.715665 0.798373 0.875282 1.017145 -P_10 1.023638 1.177158 1.182926 1.098609 1.302864 1.214963 1.315287 1.173364 -P_10 1.136388 1.154672 1.142416 1.159355 1.025194 0.935979 1.063146 0.992468 -P_10 0.983623 1.066921 1.018834 1.008117 0.963274 1.001648 1.201809 1.108116 -P_10 1.148188 1.166318 1.117048 1.071433 1.016272 1.009782 0.880017 0.714732 -P_10 0.678637 0.586705 0.503297 0.428311 0.413762 0.379821 0.410545 0.410238 -P_10 0.483129 0.522158 0.601201 0.684291 0.794163 0.839896 0.970005 1.036798 -P_10 1.082196 1.238814 1.182453 1.197610 1.269841 1.190037 1.213863 1.167244 -P_10 1.155464 1.216854 1.144832 1.122719 1.100834 0.973668 1.043421 1.002498 -P_10 1.108840 1.025180 0.983072 1.076042 1.073301 1.117300 1.145415 1.046109 -P_10 1.121171 0.971913 1.074394 0.958164 0.986011 0.874902 0.863533 0.705426 -P_10 0.719035 0.600255 0.510048 0.464580 0.427832 0.410712 0.390277 0.421733 -P_10 0.471305 0.506887 0.538418 0.727635 0.808456 0.774213 0.890413 1.000414 -P_10 1.239935 1.283648 1.117421 1.253338 1.159337 1.150181 1.128194 1.301764 -P_10 1.129335 1.022200 0.999991 1.017700 1.020834 1.015220 0.936817 0.998904 -P_10 1.026226 1.005341 1.092959 0.999728 0.921796 1.131323 1.089205 1.072110 -P_10 1.207373 1.075110 1.073702 1.041805 0.972050 0.903555 0.877457 0.738433 -P_10 0.711636 0.596486 0.537926 0.456315 0.490700 0.441435 0.367356 0.428322 -P_10 0.508976 0.583102 0.682356 0.793322 0.956195 1.056642 1.102985 1.179297 -P_10 1.312165 1.252052 1.374616 1.493592 1.407180 1.339021 1.339523 1.239066 -P_10 1.168408 1.168829 1.164285 1.118285 1.222150 1.144940 1.055717 1.009445 -P_10 1.034979 1.149123 1.017832 1.070688 1.136214 1.042585 1.106351 1.190832 -P_10 1.162551 1.028013 0.959469 1.020546 0.921717 1.025126 0.880155 0.832134 -P_10 0.746108 0.671311 0.589256 0.517725 0.526030 0.467808 0.458571 0.458065 -P_10 0.462190 0.462361 0.515947 0.585900 0.634682 0.728393 0.790529 0.836997 -P_10 1.000574 0.969141 1.134029 1.113412 1.218983 1.178240 1.081706 1.225831 -P_10 1.127524 1.143870 1.070464 1.071444 1.225008 1.079294 1.043695 1.073230 -P_10 1.153419 1.032866 1.128671 1.023129 1.047157 1.064291 1.059254 1.104954 -P_10 1.026515 0.977345 0.980796 0.901483 0.826053 0.761440 0.682922 0.654357 -P_10 0.591894 0.618611 0.573373 0.489984 0.506934 0.472802 0.518342 0.410869 -P_10 0.439402 0.392104 0.434630 0.481071 0.556986 0.589944 0.635920 0.706819 -P_10 0.747015 0.828307 0.839913 0.992809 0.950517 1.006064 1.060539 1.095306 -P_10 1.014992 1.040930 1.047215 1.011546 0.949064 1.008314 0.915370 0.989898 -P_10 0.921230 1.000232 0.993069 1.021256 1.044766 1.070176 1.024063 1.012449 -P_10 0.997121 1.062683 1.016717 0.975528 0.856460 0.943913 0.816150 0.739832 -P_10 0.681819 0.602918 0.496611 0.473925 0.439616 0.397357 0.423539 0.384090 -P_10 0.469017 0.511670 0.612680 0.640400 0.723640 0.830940 0.969845 1.092404 -P_10 1.123942 1.222443 1.260384 1.332336 1.166075 1.292869 1.240876 1.287431 -P_10 1.238123 1.259461 1.103271 0.957264 1.019219 1.015353 1.015740 0.913854 -P_10 0.909027 0.977876 0.915752 0.964194 0.907384 1.043617 1.067460 1.133175 -P_10 1.050299 1.104383 1.185309 1.090476 0.961862 1.000295 0.852910 0.792927 -P_10 0.686778 0.620135 0.545265 0.497881 0.426374 0.402491 0.427184 0.424075 -P_10 0.446757 0.494838 0.569220 0.668770 0.776613 0.826806 0.921487 0.999987 -P_10 1.220495 1.146762 1.177102 1.315465 1.208103 1.094875 1.291767 1.216692 -P_10 1.200951 1.119879 1.177660 1.061050 1.020840 1.089124 0.935161 0.911545 -P_10 0.993001 1.061034 1.004398 1.013037 1.061669 1.134637 1.177273 1.223198 -P_10 1.140032 1.114426 1.065546 1.166789 0.982754 0.891374 0.834172 0.794656 -P_10 0.659079 0.546267 0.527754 0.450449 0.420417 0.437853 0.409649 0.441017 -P_10 0.480766 0.569474 0.549336 0.766122 0.863700 0.887412 0.904617 1.032296 -P_10 1.109737 1.109611 1.158480 1.146951 1.185512 1.164630 1.308478 1.224742 -P_10 1.195829 1.216185 1.137390 1.106143 1.048160 1.069830 1.001553 1.066809 -P_10 1.050708 1.032803 1.080978 1.128384 1.008127 1.072739 1.093154 1.091889 -P_10 1.154033 1.105062 1.147436 1.109674 1.081804 0.821270 0.869492 0.732829 -P_10 0.739548 0.628004 0.508175 0.474284 0.427465 0.410485 0.446745 0.470333 -P_10 0.512662 0.533760 0.594556 0.723822 0.852379 0.945093 0.980562 1.064665 -P_10 1.200589 1.157726 1.183385 1.121247 1.144958 1.256680 1.216127 1.152693 -P_10 1.166961 1.067600 1.152537 1.186053 0.974398 0.940887 0.984549 1.023397 -P_10 0.975831 1.072679 1.074406 1.102791 1.036822 1.055684 1.080299 1.154199 -P_10 1.194296 1.171008 1.045819 1.137177 1.031065 0.997728 0.898268 0.829471 -P_10 0.712429 0.605275 0.570209 0.522548 0.473788 0.475246 0.425275 0.422598 -P_10 0.508124 0.539108 0.697563 0.838191 0.872306 1.026331 1.076449 1.235475 -P_10 1.414014 1.499469 1.292103 1.460747 1.457704 1.476897 1.424682 1.226521 -P_10 1.245079 1.189580 1.262854 1.140626 1.168040 1.140774 1.094708 1.050104 -P_10 1.073307 1.054341 1.185485 1.089319 1.016114 1.145867 1.091382 1.118024 -P_10 1.100377 1.162666 1.104267 1.101435 0.975788 0.876455 0.838509 0.851894 -P_10 0.797386 0.629483 0.670641 0.594564 0.539469 0.429649 0.459624 0.442636 -P_10 0.436744 0.482673 0.519235 0.554049 0.641950 0.640238 0.712010 0.884295 -P_10 1.023036 1.048738 0.985599 1.153044 1.154855 1.208588 1.262426 1.234313 -P_10 1.262100 1.120018 1.215364 1.083282 1.061003 1.018582 1.154526 1.039331 -P_10 1.143179 1.145838 1.096201 1.061850 1.145267 1.063595 1.070893 0.996849 -P_10 0.892785 0.903649 0.949968 0.897818 0.770655 0.850012 0.745547 0.690628 -P_10 0.662194 0.612125 0.569243 0.525248 0.525553 0.498852 0.460262 0.438962 -P_10 0.429362 0.448032 0.438522 0.450349 0.524064 0.629552 0.624279 0.682472 -P_10 0.849713 0.788835 0.860246 0.958640 1.017523 1.046984 1.091252 1.019617 -P_10 1.135375 1.118277 1.094331 1.115346 1.033686 1.078304 1.073302 0.964129 -P_10 1.083663 1.090532 1.021573 0.950753 0.980272 1.135242 1.100601 1.018016 -P_10 1.160435 1.111956 1.035089 1.004869 0.946843 0.970823 0.818863 0.769531 -P_10 0.712555 0.541500 0.489325 0.485444 0.445173 0.413003 0.382987 0.438005 -P_10 0.454041 0.530000 0.586017 0.672755 0.717133 0.791975 0.977036 1.008395 -P_10 1.063649 1.176865 1.303788 1.247272 1.355663 1.353398 1.263382 1.327651 -P_10 1.208668 1.309279 1.110616 1.154487 1.147899 0.946962 0.999871 0.943133 -P_10 1.018789 0.984225 1.151969 1.039132 1.143729 1.149502 1.157337 1.114509 -P_10 1.043921 1.222405 1.135193 1.068754 1.074546 0.988754 0.928153 0.789240 -P_10 0.704606 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1.115188 -P_10 1.119025 1.003055 1.069018 1.018163 1.020201 0.867346 0.910779 0.720567 -P_10 0.688457 0.559818 0.514824 0.504071 0.430057 0.413909 0.410325 0.401897 -P_10 0.474218 0.560644 0.635027 0.685039 0.783039 0.959251 0.928844 1.018443 -P_10 1.231310 1.478221 1.279157 1.399848 1.331814 1.432477 1.342732 1.394964 -P_10 1.376248 1.215902 1.195365 1.242019 1.124591 0.960963 0.980883 0.973879 -P_10 0.991857 0.948022 0.987896 1.083011 1.042195 1.152514 1.192766 1.267538 -P_10 1.185304 1.357448 1.067901 1.063800 1.077234 1.013416 0.888489 0.838737 -P_10 0.779457 0.642733 0.596994 0.533010 0.505490 0.422035 0.458082 0.471868 -P_10 0.476047 0.530427 0.611339 0.719860 0.841816 0.957815 1.069146 1.062688 -P_10 1.226878 1.204872 1.203088 1.320778 1.353197 1.317057 1.166076 1.363180 -P_10 1.257745 1.223548 1.243467 1.120799 1.083195 1.085170 1.096007 1.089805 -P_10 1.066350 1.139835 1.079140 1.115501 1.079405 1.185772 1.239178 1.200509 -P_10 1.230537 1.204480 1.144890 1.151171 1.032564 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1.461021 1.387595 1.576130 1.534561 1.443138 1.382026 1.468450 1.507784 -P_10 1.420519 1.151326 1.381793 1.160127 1.117375 1.147116 1.170589 1.199929 -P_10 1.146310 1.064199 1.207410 1.157526 1.278226 1.198085 1.242054 1.278500 -P_10 1.122021 1.161411 1.136583 1.127059 1.049565 0.994178 0.818964 0.856594 -P_10 0.766863 0.768635 0.657938 0.567098 0.525172 0.532576 0.526174 0.474448 -P_10 0.536667 0.521116 0.579278 0.648792 0.674543 0.804612 0.920984 0.972085 -P_10 1.000125 1.145571 1.187772 1.228191 1.234080 1.221164 1.222242 1.273299 -P_10 1.319913 1.198390 1.303052 1.285523 1.292260 1.347442 1.192866 1.054432 -P_10 1.205936 1.033293 1.151895 1.308625 1.254575 1.154337 1.121277 1.117725 -P_10 1.119695 1.033440 0.937985 0.998681 0.912086 0.853866 0.773927 0.721213 -P_10 0.741160 0.731843 0.656017 0.600505 0.541651 0.500967 0.503769 0.464151 -P_10 0.489425 0.496104 0.502533 0.512863 0.594417 0.577855 0.656614 0.785008 -P_10 0.910067 0.952952 0.996512 1.095820 1.064760 1.141036 1.234654 1.168252 -P_10 1.195369 1.160186 1.086549 1.162876 1.006889 1.091814 1.073987 1.044943 -P_10 1.028803 1.073963 1.071179 1.156367 1.168161 1.202882 1.105061 1.133417 -P_10 1.075602 1.158836 1.173771 1.097592 0.859533 0.948989 0.860979 0.819800 -P_10 0.720051 0.606016 0.602256 0.561086 0.495037 0.432106 0.451929 0.444606 -P_10 0.485996 0.574907 0.642500 0.723628 0.792619 0.976099 1.045777 1.112750 -P_10 1.266671 1.283661 1.369209 1.397399 1.530990 1.386094 1.486200 1.415740 -P_10 1.273283 1.234365 1.372004 1.195116 1.196205 0.990078 0.997318 0.998230 -P_10 1.120057 1.000282 1.034567 1.046346 1.099403 1.205490 1.193679 1.006742 -P_10 1.218252 1.232556 1.254092 1.141728 1.118765 1.079002 0.887912 0.854069 -P_10 0.711831 0.678113 0.625399 0.502215 0.495501 0.438715 0.478695 0.475301 -P_10 0.527038 0.608633 0.645487 0.746188 0.856046 0.894827 1.022572 1.081359 -P_10 1.158874 1.313992 1.395420 1.346261 1.291773 1.293332 1.297104 1.441612 -P_10 1.307069 1.262568 1.226947 1.177087 1.214736 1.155411 1.165990 0.976203 -P_10 0.988338 1.076477 1.208726 1.282705 1.163550 1.244145 1.256199 1.220752 -P_10 1.319154 1.210755 1.215683 1.175213 1.033537 1.105892 0.878022 0.859138 -P_10 0.799667 0.677724 0.513931 0.495456 0.491645 0.445427 0.427841 0.481788 -P_10 0.523770 0.571194 0.671345 0.703434 0.857477 0.987307 1.098557 1.181509 -P_10 1.234358 1.217473 1.290550 1.376557 1.389759 1.369791 1.282037 1.427747 -P_10 1.319802 1.174267 1.259073 1.236419 1.195130 1.057506 1.087100 1.121609 -P_10 1.034132 1.102737 1.108654 1.170373 1.097055 1.144445 1.156375 1.199416 -P_10 1.214716 1.183106 1.214506 1.135500 1.156143 1.023980 0.915639 0.875538 -P_10 0.811884 0.658256 0.667459 0.509453 0.476027 0.458796 0.463593 0.461572 -P_10 0.538771 0.650834 0.650152 0.744616 0.835072 0.964565 0.982671 1.116768 -P_10 1.291546 1.267102 1.277373 1.425526 1.264938 1.283960 1.365796 1.320141 -P_10 1.227822 1.282102 1.206824 1.159367 1.069249 1.088151 1.220263 0.995786 -P_10 1.185488 1.138087 1.091643 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1.059285 1.029072 0.958499 0.980140 0.825008 0.814484 0.757781 -P_10 0.709145 0.620628 0.628631 0.594378 0.585842 0.506200 0.548422 0.478783 -P_10 0.468181 0.483922 0.509553 0.526478 0.570244 0.613404 0.733641 0.757744 -P_10 0.849615 0.899898 0.934359 1.027582 1.110269 1.133149 1.113781 1.223388 -P_10 1.203778 1.166138 1.106612 1.213816 1.118403 1.004419 1.039332 0.975347 -P_10 0.999680 0.991893 0.981420 1.061459 1.174067 1.162107 1.131527 1.108404 -P_10 1.239193 1.199557 1.057300 1.010112 0.946602 0.938944 0.932243 0.779374 -P_10 0.662257 0.599136 0.572851 0.493657 0.392905 0.454062 0.454840 0.456941 -P_10 0.474449 0.584098 0.645827 0.695275 0.772139 0.932054 1.126775 1.076578 -P_10 1.146963 1.315530 1.284844 1.421823 1.480436 1.428728 1.465011 1.359924 -P_10 1.327771 1.348807 1.203726 1.071220 1.124586 1.029017 0.934193 1.067567 -P_10 0.983413 0.934329 1.147812 1.061202 1.109578 1.139413 1.238865 1.183694 -P_10 1.216591 1.218263 1.171240 1.146806 1.157161 0.953325 0.946471 0.793829 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0.440269 0.518010 -P_10 0.490635 0.565535 0.695499 0.775425 0.903610 0.949849 1.021392 1.049863 -P_10 1.212891 1.305142 1.235777 1.298482 1.434772 1.300771 1.262040 1.251941 -P_10 1.155907 1.145122 1.173139 1.166083 1.136945 1.143254 1.052590 0.950884 -P_10 1.146205 1.081291 1.067825 1.114515 1.172017 1.179721 1.152800 1.136719 -P_10 1.179362 1.066575 1.270970 1.085988 1.064136 1.004339 0.927498 0.830736 -P_10 0.715197 0.675452 0.566755 0.554558 0.494476 0.453252 0.506339 0.477177 -P_10 0.494056 0.618064 0.682846 0.835017 1.034649 1.208179 1.215168 1.511202 -P_10 1.385130 1.450091 1.493376 1.518718 1.505009 1.511909 1.494199 1.347319 -P_10 1.153659 1.317466 1.253490 1.194891 1.081215 1.127047 1.143797 1.143711 -P_10 1.135985 1.137518 1.102469 1.169675 1.189916 1.175092 1.187503 1.184190 -P_10 1.240851 1.292529 1.220898 1.098708 1.079978 1.001839 0.996515 0.875090 -P_10 0.759849 0.740682 0.633399 0.621915 0.558748 0.513887 0.487746 0.511240 -P_10 0.467839 0.493577 0.528394 0.561287 0.699034 0.794843 0.795592 0.923935 -P_10 1.083984 1.165507 1.151630 1.156608 1.316333 1.165206 1.182973 1.378109 -P_10 1.291771 1.173023 1.371582 1.131000 1.193117 1.206699 1.145056 1.108693 -P_10 1.243136 1.150105 1.174898 1.176994 1.209378 1.025671 1.144331 1.228949 -P_10 1.236566 1.032380 0.993094 1.024485 0.951590 0.794960 0.800088 0.646653 -P_10 0.758342 0.661601 0.610729 0.582739 0.536136 0.492634 0.471904 0.440213 -P_10 0.444300 0.534461 0.487972 0.478536 0.585824 0.567504 0.713803 0.785999 -P_10 0.822801 0.972335 0.938321 1.002913 1.125241 1.095068 1.204522 1.150963 -P_10 1.160444 1.174895 1.095807 1.082443 1.169410 1.221067 1.080348 1.012696 -P_10 1.004384 1.055245 1.077122 1.155080 1.186566 1.062559 1.054583 1.158468 -P_10 1.169035 1.124998 1.084924 0.987778 1.072864 0.994270 0.851424 0.698945 -P_10 0.685823 0.595187 0.510271 0.473665 0.412929 0.445856 0.423350 0.466134 -P_10 0.498490 0.584410 0.653224 0.732218 0.861666 0.931245 1.050533 1.172295 -P_10 1.267060 1.346067 1.226292 1.454924 1.456308 1.362592 1.446148 1.261186 -P_10 1.377396 1.236264 1.221944 1.072920 1.202696 1.127602 1.092634 1.078993 -P_10 1.062782 1.014860 0.978487 1.012665 1.098103 1.097682 1.112487 1.273501 -P_10 1.178819 1.194236 1.208721 1.088605 1.065030 1.019202 0.944334 0.845369 -P_10 0.689628 0.705479 0.594378 0.555430 0.469750 0.430096 0.454305 0.438604 -P_10 0.517191 0.497055 0.604416 0.797881 0.800332 0.840025 0.984732 1.094870 -P_10 1.085478 1.328538 1.324561 1.327649 1.458497 1.362449 1.359832 1.369722 -P_10 1.306035 1.250397 1.171800 1.161186 1.198687 1.073756 1.046799 1.072783 -P_10 1.058016 1.077111 1.045273 1.164414 1.189151 1.193855 1.240406 1.211839 -P_10 1.271652 1.385607 1.222918 1.123026 1.072037 0.985101 0.894146 0.866459 -P_10 0.717308 0.664966 0.605017 0.525180 0.453240 0.457228 0.516047 0.482295 -P_10 0.545308 0.626849 0.671069 0.832964 0.922252 0.955455 1.014549 1.017142 -P_10 1.192526 1.231387 1.258056 1.374287 1.303248 1.350294 1.304268 1.310381 -P_10 1.295253 1.422386 1.290438 1.105115 1.149224 1.027221 1.072392 1.154184 -P_10 1.097834 1.036514 1.145080 1.097388 1.122611 1.285049 1.254712 1.176865 -P_10 1.256033 1.247498 1.184545 1.192075 1.149308 0.949979 0.849296 0.850075 -P_10 0.742853 0.706557 0.598018 0.481099 0.451238 0.437314 0.439666 0.459913 -P_10 0.519057 0.547119 0.644224 0.734431 0.908685 0.939750 0.986434 1.180966 -P_10 1.221851 1.236527 1.342375 1.341101 1.337681 1.297833 1.241111 1.317011 -P_10 1.180914 1.175874 1.146338 1.075907 0.981910 1.137757 1.091187 1.209565 -P_10 1.143869 1.176433 1.175432 1.280644 1.160062 1.235547 1.105365 1.212850 -P_10 1.222169 1.091897 1.042633 1.136686 1.051969 1.065951 0.920342 0.867673 -P_10 0.754236 0.688476 0.611980 0.537009 0.533296 0.447975 0.526472 0.543169 -P_10 0.573104 0.662693 0.764945 0.918510 0.992912 1.108370 1.249140 1.418549 -P_10 1.389149 1.484735 1.479743 1.445095 1.425525 1.644013 1.503203 1.487642 -P_10 1.400907 1.312636 1.166766 1.277351 1.251114 1.242522 1.179757 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0.990962 1.121987 -P_10 1.262428 1.187688 1.317448 1.340146 1.281408 1.466244 1.334076 1.496301 -P_10 1.173953 1.283098 1.221533 1.136986 1.023466 1.134745 1.190506 1.032509 -P_10 1.133377 1.039065 1.109266 1.203134 1.062187 1.369864 1.193849 1.283349 -P_10 1.235248 1.219831 1.201112 1.179524 1.096709 1.150697 0.909512 0.879142 -P_10 0.691613 0.654997 0.633242 0.521376 0.476484 0.445112 0.455111 0.480483 -P_10 0.532016 0.606288 0.705909 0.793405 0.898328 0.963318 1.085262 1.130151 -P_10 1.174059 1.025496 1.229669 1.364672 1.383785 1.340722 1.458108 1.325424 -P_10 1.326057 1.235926 1.119449 1.209767 1.108700 1.229738 1.237807 1.189825 -P_10 1.036157 1.211336 1.052969 1.223971 1.215173 1.119454 1.309652 1.088964 -P_10 1.182216 1.262399 1.143265 1.065455 1.105297 1.036255 1.003298 0.863942 -P_10 0.756768 0.667594 0.560544 0.539071 0.450541 0.463166 0.449357 0.467521 -P_10 0.557076 0.579635 0.676379 0.709272 0.844503 1.040408 1.034796 1.202601 -P_10 1.181993 1.363799 1.309925 1.322930 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1.191941 1.194274 1.277956 1.273984 1.199613 1.219908 -P_10 1.204869 1.159587 1.150592 1.194674 1.157309 1.296503 1.081185 1.111284 -P_10 1.222391 1.063068 1.110195 1.010119 0.959658 0.907070 0.888315 0.798320 -P_10 0.754674 0.667267 0.683812 0.612253 0.549038 0.526633 0.519366 0.454727 -P_10 0.454178 0.481551 0.522861 0.500122 0.526986 0.634811 0.656022 0.802319 -P_10 0.866940 0.930077 1.017053 1.077630 1.140769 1.057029 1.243774 1.190990 -P_10 1.225592 1.096700 1.184871 1.170274 1.090729 1.166889 1.068826 1.030273 -P_10 1.037362 1.101783 1.114748 1.127162 1.179605 1.253651 1.072747 1.184025 -P_10 1.087123 1.175449 1.114268 1.016696 1.049780 0.973198 0.897433 0.780220 -P_10 0.710279 0.611993 0.571731 0.544522 0.486802 0.458376 0.425656 0.479166 -P_10 0.534300 0.528245 0.620822 0.754298 0.830069 0.876677 1.053108 1.155853 -P_10 1.230707 1.236060 1.382100 1.473156 1.574650 1.368455 1.301922 1.578588 -P_10 1.311547 1.446363 1.228985 1.190133 1.162538 1.164819 1.108096 1.028664 -P_10 1.143029 1.000407 1.164947 1.069979 1.036274 1.226655 1.235319 1.254165 -P_10 1.183418 1.313834 1.304724 1.158798 1.158497 0.940127 0.993378 0.823491 -P_10 0.734370 0.727335 0.533160 0.502349 0.492168 0.478191 0.421798 0.461868 -P_10 0.501057 0.594041 0.655055 0.691502 0.838299 0.892545 1.114331 1.044287 -P_10 1.281190 1.393228 1.361850 1.334046 1.394061 1.334607 1.396313 1.332133 -P_10 1.369804 1.280914 1.083142 1.152002 1.154764 1.117427 1.094172 1.041443 -P_10 1.060189 1.223625 1.035923 1.215412 1.193568 1.315443 1.264650 1.259812 -P_10 1.413570 1.401732 1.224340 1.158197 1.124800 1.033512 0.956272 0.869824 -P_10 0.737763 0.704108 0.591892 0.572800 0.488200 0.454475 0.454998 0.515906 -P_10 0.558973 0.622334 0.668733 0.740327 0.915584 0.924221 1.006448 1.119374 -P_10 1.182446 1.212347 1.258412 1.247833 1.450409 1.392698 1.458873 1.473151 -P_10 1.391773 1.345402 1.326410 1.208790 1.248393 1.197661 1.175563 1.186124 -P_10 1.178493 1.110683 1.066186 1.124269 1.194150 1.198460 1.257688 1.256983 -P_10 1.101304 1.180897 1.156300 1.294455 1.116071 1.030199 0.966839 0.776728 -P_10 0.794355 0.656061 0.605212 0.551204 0.481373 0.503168 0.519421 0.458616 -P_10 0.563550 0.599879 0.709795 0.738684 0.899485 0.974716 1.150504 1.228169 -P_10 1.246070 1.346608 1.535568 1.400126 1.339861 1.388181 1.246221 1.346186 -P_10 1.247350 1.224250 1.143614 1.188129 1.132975 1.211417 1.136689 1.137408 -P_10 1.172259 1.159465 1.255039 1.228404 1.320774 1.322991 1.227713 1.384989 -P_10 1.268720 1.215998 1.170371 1.097780 1.048886 1.123998 0.970266 0.839802 -P_10 0.713095 0.737270 0.593862 0.552439 0.532574 0.505290 0.474063 0.507843 -P_10 0.590569 0.701386 0.798278 0.870317 1.078564 1.080503 1.447988 1.333740 -P_10 1.340885 1.533293 1.656036 1.713942 1.569508 1.505218 1.562762 1.366361 -P_10 1.503466 1.532683 1.316768 1.336077 1.251259 1.215473 1.255822 1.143360 -P_10 1.248830 1.193132 1.194879 1.278526 1.383395 1.282928 1.361185 1.190087 -P_10 1.301163 1.205489 1.229760 1.131744 1.115141 0.989086 0.892390 0.837996 -P_10 0.872821 0.840376 0.725854 0.572415 0.581743 0.498286 0.536513 0.474128 -P_10 0.489873 0.522574 0.596981 0.612159 0.696663 0.826449 0.899148 1.069683 -P_10 0.951476 1.189434 1.316206 1.164067 1.262000 1.269636 1.366324 1.395568 -P_10 1.250521 1.345161 1.299780 1.279511 1.234639 1.200977 1.226267 1.230138 -P_10 1.256683 1.233763 1.188586 1.187285 1.193764 1.184028 1.218465 1.153636 -P_10 1.172297 1.144966 1.173438 1.071590 0.895737 0.838066 0.868595 0.771868 -P_10 0.739605 0.699561 0.625850 0.571352 0.539665 0.569516 0.543785 0.447908 -P_10 0.456582 0.498411 0.506768 0.578268 0.545652 0.650335 0.784384 0.729110 -P_10 0.871557 1.016635 1.017872 1.148150 1.143379 1.125905 1.279181 1.216245 -P_10 1.256943 1.180637 1.252650 1.202229 1.159606 1.143900 1.057765 1.052318 -P_10 1.144171 1.118220 1.047774 1.137150 1.291988 1.081854 1.243361 1.172646 -P_10 1.259740 1.185211 1.080533 1.198109 1.021761 0.978646 0.897708 0.796659 -P_10 0.735868 0.625560 0.583634 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1.169967 1.091532 1.182434 1.008741 0.937535 0.956647 0.931565 0.896947 -P_10 0.976813 0.970702 0.996508 0.994177 1.067943 1.078460 1.005179 1.100967 -P_10 1.007157 1.068340 1.145410 0.995062 1.038824 0.852912 0.859378 0.688899 -P_10 0.645264 0.546410 0.461714 0.432898 0.444206 0.408850 0.415251 0.401842 -P_10 0.490561 0.487660 0.595000 0.676283 0.788244 0.880002 0.880731 0.938101 -P_10 1.109419 1.085628 1.151651 1.263538 1.154448 1.142482 1.202645 1.285490 -P_10 1.177026 1.157195 1.042691 1.064247 1.102451 1.000376 1.024089 0.926261 -P_10 1.006685 1.012298 1.078632 1.034605 1.023319 1.023573 1.089494 1.048299 -P_10 1.056819 1.122712 1.012627 1.096980 0.962970 0.839655 0.836327 0.768200 -P_10 0.684922 0.532363 0.537590 0.467234 0.414209 0.409475 0.395848 0.421598 -P_10 0.464237 0.499912 0.577800 0.703172 0.833355 0.859869 0.866984 1.039296 -P_10 1.071031 1.146794 1.061652 1.189698 1.134956 1.171200 1.183194 1.108364 -P_10 1.217861 1.162113 1.081946 0.900877 0.966582 0.884964 0.963307 0.946308 -P_10 0.957396 1.012130 0.975697 0.970413 1.096108 0.973182 1.033581 1.033299 -P_10 1.105614 1.069713 0.946562 0.962924 0.961615 0.944681 0.810945 0.737350 -P_10 0.651897 0.624311 0.510754 0.464734 0.444838 0.458049 0.428447 0.397354 -P_10 0.481576 0.600170 0.635230 0.792827 0.785974 0.882355 1.060001 1.184614 -P_10 1.196763 1.315908 1.259019 1.378684 1.426081 1.342064 1.292200 1.242124 -P_10 1.178890 1.220226 1.202484 1.272969 0.972388 1.132059 1.063009 0.935769 -P_10 0.965967 1.004053 1.019554 1.080834 1.079372 1.084782 1.085773 1.056685 -P_10 1.015298 1.065777 1.053997 1.007151 0.958049 0.934128 0.761146 0.777656 -P_10 0.700222 0.659954 0.520291 0.561880 0.469426 0.437005 0.460144 0.397977 -P_10 0.435670 0.431862 0.498274 0.528088 0.657331 0.702799 0.757291 0.801367 -P_10 0.887925 0.973970 1.046143 1.076374 1.225509 1.171410 1.057217 1.085039 -P_10 1.110907 1.122452 1.160270 1.134990 1.104736 1.051147 0.982526 1.021904 -P_10 1.055823 0.972703 1.040764 1.045493 1.030202 0.973135 1.077503 0.933755 -P_10 0.919890 0.909279 0.937154 0.850277 0.854017 0.804836 0.750137 0.681650 -P_10 0.638984 0.518227 0.556764 0.512489 0.492854 0.462104 0.422083 0.413065 -P_10 0.438549 0.431282 0.442034 0.528757 0.506633 0.540993 0.573727 0.650919 -P_10 0.782726 0.833956 0.881456 0.889870 0.942806 1.005605 1.021890 1.046753 -P_10 0.983793 1.036418 0.991949 0.978299 1.034316 0.941557 0.999627 0.938441 -P_10 0.901864 0.929633 0.936372 0.973459 0.982124 0.994803 0.977396 0.995888 -P_10 1.032734 0.924048 1.012045 0.978259 0.861808 0.838940 0.749634 0.721503 -P_10 0.627762 0.531848 0.465407 0.453165 0.415342 0.394332 0.417075 0.432381 -P_10 0.433213 0.476351 0.569774 0.627893 0.795425 0.829689 0.852838 1.013879 -P_10 1.132971 1.157539 1.074915 1.176218 1.270650 1.233366 1.160193 1.357205 -P_10 1.107869 1.117325 1.098056 0.918324 1.015027 1.003285 0.850566 0.826749 -P_10 0.848787 0.908307 1.096503 1.021037 0.974519 0.988970 1.059425 0.995898 -P_10 1.177911 1.078461 1.026446 1.030205 0.953611 0.903462 0.891458 0.794992 - -P_11 0.544223 0.527528 0.437548 0.362555 0.327397 0.319932 0.311608 0.339420 -P_11 0.431149 0.470819 0.498395 0.655369 0.785188 0.793237 0.980057 1.054805 -P_11 1.110891 1.190021 1.269206 1.334715 1.298589 1.377268 1.274008 1.277124 -P_11 1.247938 1.175594 1.072323 1.104810 0.998796 0.975400 0.860103 0.872206 -P_11 0.918209 0.891175 0.906776 0.965635 1.043540 1.049413 1.049257 1.046007 -P_11 1.089594 1.096265 1.142081 1.055328 1.048543 0.880249 0.736060 0.690855 -P_11 0.570694 0.520690 0.456584 0.427465 0.336417 0.317825 0.300077 0.340416 -P_11 0.401402 0.462426 0.544254 0.570032 0.805558 0.859211 0.958537 1.048858 -P_11 1.121787 1.225095 1.253977 1.300332 1.252557 1.216751 1.280155 1.233532 -P_11 1.261681 1.080303 1.196091 1.024386 1.010876 1.004309 0.950656 0.989306 -P_11 0.943703 0.984218 1.029810 0.965339 1.052200 1.122316 1.021320 1.168069 -P_11 1.029405 1.120692 1.199881 1.074581 0.924840 0.945320 0.775832 0.626155 -P_11 0.632447 0.472579 0.444474 0.368336 0.312246 0.336499 0.315418 0.353282 -P_11 0.425396 0.497852 0.576347 0.680471 0.766078 0.954041 1.003486 1.099241 -P_11 1.200876 1.184657 1.204016 1.264560 1.222054 1.184038 1.260531 1.205571 -P_11 1.290822 1.176966 1.130429 1.063453 1.061671 1.122630 1.015171 0.988757 -P_11 0.917919 0.990493 0.964696 0.994450 1.111991 1.209710 1.130268 1.130150 -P_11 1.080097 1.091975 1.040746 0.981255 0.917808 0.929966 0.738708 0.670584 -P_11 0.647963 0.524056 0.450688 0.393555 0.322563 0.328345 0.351635 0.324266 -P_11 0.426682 0.483783 0.594046 0.619279 0.801149 0.965644 1.000513 1.044919 -P_11 1.154257 1.173805 1.228683 1.217960 1.166763 1.176101 1.234457 1.173370 -P_11 1.136457 1.039911 1.078074 1.048136 1.011069 0.895739 1.013008 1.011691 -P_11 1.006131 0.916125 0.941216 0.965698 1.098727 1.128409 1.042618 1.089422 -P_11 1.107178 1.115588 1.043428 0.965267 0.879032 0.965650 0.836875 0.765751 -P_11 0.584898 0.549945 0.465762 0.397823 0.321492 0.357032 0.371095 0.397709 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0.634995 0.763198 -P_11 0.822791 0.953208 0.927087 1.100922 1.096131 1.106049 1.168571 1.079215 -P_11 1.094161 1.065962 1.204787 1.007380 1.140826 0.986391 0.900969 1.017740 -P_11 0.906711 1.045344 0.974386 0.953652 1.075941 1.054693 0.965961 1.174605 -P_11 0.968588 1.080470 0.991367 0.987921 0.997206 0.798926 0.741983 0.631972 -P_11 0.603405 0.530652 0.451574 0.401385 0.363140 0.332952 0.294382 0.363920 -P_11 0.383885 0.475724 0.576471 0.574499 0.781472 0.810401 0.989406 1.140080 -P_11 1.217796 1.171544 1.474125 1.217088 1.311779 1.487031 1.414372 1.240976 -P_11 1.262123 1.279586 1.150946 1.007521 1.021780 1.015799 0.867575 0.932230 -P_11 0.949097 0.853574 0.956962 0.993622 1.027537 0.999249 1.068281 1.103198 -P_11 1.087985 1.058943 1.025410 0.944815 1.006045 0.896284 0.841045 0.708978 -P_11 0.594130 0.508449 0.439676 0.403292 0.336999 0.305618 0.329607 0.351747 -P_11 0.443544 0.461900 0.598508 0.651499 0.756116 0.869153 0.945635 1.062275 -P_11 1.072231 1.223672 1.236682 1.289717 1.311010 1.111605 1.307836 1.282149 -P_11 1.130950 1.181509 1.150012 1.081057 0.929367 1.095880 0.880533 0.947683 -P_11 1.008665 1.059182 0.972269 1.017340 1.054273 1.067761 1.256942 1.126720 -P_11 1.097314 1.089742 1.212706 0.998297 0.969267 0.876869 0.809573 0.690060 -P_11 0.614099 0.488026 0.443377 0.374532 0.331311 0.313685 0.333452 0.372190 -P_11 0.450346 0.537450 0.559337 0.646623 0.821101 0.906708 0.913481 1.140088 -P_11 1.199639 1.258998 1.401818 1.301905 1.342934 1.301821 1.335555 1.201601 -P_11 1.219520 1.257732 1.094465 1.082926 1.069745 0.968652 1.055423 0.945268 -P_11 0.960663 1.016877 0.979407 1.038464 1.122859 1.065515 1.220970 1.096744 -P_11 1.125635 1.159008 1.090973 1.081468 1.010516 0.929861 0.785670 0.644939 -P_11 0.617825 0.482072 0.475285 0.381386 0.349931 0.341361 0.311879 0.394824 -P_11 0.413557 0.519331 0.575305 0.700403 0.851538 0.914044 1.135060 1.128701 -P_11 1.195606 1.306373 1.309646 1.286108 1.178813 1.214591 1.211122 1.278624 -P_11 1.249116 1.043849 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1.067341 1.060172 1.091659 1.098855 1.160349 1.161754 1.093678 1.055945 -P_11 0.977091 0.984928 0.965459 0.905688 0.930246 0.741942 0.737064 0.691075 -P_11 0.676600 0.560078 0.531856 0.493942 0.437815 0.403419 0.393377 0.345503 -P_11 0.399248 0.421784 0.431849 0.530664 0.500675 0.590011 0.616971 0.706594 -P_11 0.869549 0.871298 0.888431 1.003445 1.115579 1.041899 1.170098 1.130096 -P_11 1.187342 1.155879 1.155292 0.962027 0.963651 1.036891 1.072744 0.798371 -P_11 0.928291 0.962082 0.939952 0.974644 1.001498 1.024017 1.118745 1.068812 -P_11 1.068036 1.126027 1.037585 0.931184 0.972294 0.837790 0.758041 0.614739 -P_11 0.602900 0.528173 0.450101 0.381907 0.321835 0.317985 0.327330 0.374935 -P_11 0.404302 0.474313 0.605235 0.657863 0.795354 0.835110 0.962459 1.164301 -P_11 1.261347 1.253412 1.473241 1.276250 1.378915 1.472018 1.489374 1.386265 -P_11 1.318779 1.272478 1.157297 1.076063 1.139877 0.823035 0.938626 0.921682 -P_11 0.962124 0.876618 1.025808 0.952572 1.043903 1.066850 1.071356 1.089959 -P_11 1.129193 1.118548 1.154864 1.158582 1.036268 0.883536 0.809366 0.778654 -P_11 0.633107 0.553717 0.492351 0.380735 0.349206 0.351279 0.337707 0.360660 -P_11 0.418531 0.481267 0.567021 0.585041 0.750456 0.909240 0.949891 1.112887 -P_11 1.128393 1.203202 1.217851 1.370030 1.338278 1.334107 1.283876 1.278773 -P_11 1.250781 1.066445 1.085688 1.026431 0.998256 1.017938 0.924797 0.906158 -P_11 0.981274 1.015041 0.996292 1.107256 1.199409 1.101811 1.179821 1.217527 -P_11 1.246567 1.268034 1.103341 1.048191 1.032787 0.853081 0.860913 0.764764 -P_11 0.586626 0.524091 0.430823 0.372565 0.311697 0.355560 0.321197 0.383389 -P_11 0.392252 0.510950 0.617390 0.717389 0.809844 0.936126 1.012277 1.123371 -P_11 1.204762 1.187162 1.293596 1.223496 1.506660 1.307256 1.405490 1.350835 -P_11 1.244886 1.083387 1.164263 1.106377 1.113091 1.099599 1.067781 1.007409 -P_11 0.919417 1.034057 0.997254 0.968208 1.055027 1.033394 1.235390 1.192476 -P_11 1.055263 1.211164 0.983495 1.085711 0.982242 0.840852 0.858279 0.809619 -P_11 0.662523 0.546561 0.416177 0.381029 0.345905 0.352557 0.338921 0.369149 -P_11 0.423735 0.469323 0.618414 0.721374 0.782771 0.943308 0.992010 1.089570 -P_11 1.120936 1.223589 1.248288 1.137144 1.271303 1.192149 1.306936 1.213498 -P_11 1.205962 1.103718 1.128785 1.083183 0.997909 1.047808 1.020654 1.010286 -P_11 0.981201 0.864402 1.071660 1.085231 1.095190 1.242588 1.247608 1.250886 -P_11 1.136347 1.069777 1.109485 0.934711 0.944106 0.914026 0.809805 0.736325 -P_11 0.655477 0.541305 0.443981 0.344870 0.374868 0.313879 0.372935 0.429951 -P_11 0.513323 0.581972 0.681795 0.760262 0.919593 1.046786 1.017183 1.243110 -P_11 1.420405 1.459648 1.474877 1.414540 1.399378 1.368698 1.391422 1.468259 -P_11 1.222014 1.173790 1.211698 1.097242 1.128847 0.953738 1.062680 1.069907 -P_11 1.063201 1.063170 1.168315 1.133260 1.146284 1.128921 1.166211 1.198288 -P_11 1.222384 1.049357 1.017296 1.071099 1.011201 0.820631 0.852262 0.708605 -P_11 0.636593 0.622407 0.563363 0.435714 0.448211 0.442294 0.382759 0.392654 -P_11 0.386520 0.439791 0.505347 0.581712 0.697546 0.777548 0.864551 1.007835 -P_11 1.032890 1.216978 1.122203 1.167964 1.148501 1.305882 1.224370 1.321671 -P_11 1.228653 1.224870 1.219604 1.101090 1.190985 1.107208 1.135643 1.074706 -P_11 0.983060 1.047497 1.145899 1.050350 1.118671 1.207431 1.080451 1.083531 -P_11 1.046605 1.005405 0.907428 0.935209 0.867712 0.798326 0.703858 0.667870 -P_11 0.623827 0.520026 0.531483 0.419568 0.425596 0.408482 0.393685 0.404650 -P_11 0.385277 0.395638 0.466902 0.484297 0.550610 0.619898 0.592978 0.858679 -P_11 0.816952 0.889933 0.947526 1.103089 1.163030 1.184390 1.169075 1.208073 -P_11 1.135309 1.079122 1.115060 1.059592 1.043856 1.027389 0.995395 0.975673 -P_11 0.943103 1.028750 1.062732 1.002151 0.978650 1.083457 1.025331 1.012443 -P_11 1.136807 0.958887 0.968679 0.988725 0.889405 0.868824 0.747632 0.650363 -P_11 0.618320 0.556484 0.438457 0.382964 0.346269 0.321654 0.312821 0.356637 -P_11 0.441803 0.482666 0.570542 0.693133 0.768576 0.840742 0.996657 1.083396 -P_11 1.194840 1.219823 1.386703 1.310198 1.422783 1.434374 1.504533 1.390898 -P_11 1.274348 1.302080 1.227650 1.191418 1.046387 1.025736 0.969656 0.940575 -P_11 0.939434 1.000983 1.023146 1.024625 0.910569 1.082628 1.091835 1.135540 -P_11 1.131924 1.183289 1.140936 1.002498 1.049409 0.926815 0.901331 0.752901 -P_11 0.673906 0.575993 0.456145 0.373897 0.367100 0.356679 0.338846 0.402284 -P_11 0.408319 0.462322 0.590878 0.685939 0.759748 0.871062 1.051248 1.187147 -P_11 1.288192 1.350023 1.363066 1.326583 1.389646 1.408337 1.167495 1.333620 -P_11 1.301087 1.257324 1.089265 1.058720 0.989815 0.998163 1.016876 1.028569 -P_11 1.014587 1.020627 0.994693 1.107651 1.166188 1.099808 1.202484 1.254601 -P_11 1.314766 1.144844 1.112184 1.108764 0.945254 0.867096 0.826773 0.732998 -P_11 0.603959 0.516360 0.460439 0.396817 0.344870 0.332287 0.347479 0.406079 -P_11 0.422417 0.497050 0.644754 0.676076 0.845245 0.999844 1.103861 1.238033 -P_11 1.244195 1.366847 1.272754 1.281394 1.368559 1.588051 1.320390 1.278527 -P_11 1.336152 1.177535 1.156865 1.154434 1.053497 1.070726 1.058458 1.022798 -P_11 1.001843 1.092908 1.040391 1.147046 1.127083 1.136922 1.137994 1.198775 -P_11 1.145485 1.118257 1.156790 1.039457 0.975425 1.108510 0.869911 0.721148 -P_11 0.666964 0.547120 0.506512 0.417281 0.373868 0.352694 0.321765 0.401025 -P_11 0.381613 0.504004 0.663197 0.724488 0.866699 0.922804 1.036195 1.071358 -P_11 1.175424 1.340544 1.388282 1.481434 1.309733 1.315482 1.265199 1.370930 -P_11 1.155196 1.138369 1.112809 1.065869 0.973550 0.986051 0.997418 1.046202 -P_11 1.000843 1.154939 1.107363 0.988363 1.034590 1.116187 1.151135 1.105656 -P_11 1.184307 1.127412 1.127851 1.045091 1.117990 1.036633 0.807517 0.723002 -P_11 0.654101 0.556566 0.513116 0.438930 0.388265 0.386349 0.348226 0.420834 -P_11 0.468833 0.627746 0.678977 0.733563 0.966759 1.072107 1.250703 1.235216 -P_11 1.341676 1.597247 1.462101 1.566972 1.541543 1.638928 1.493660 1.429154 -P_11 1.366799 1.316190 1.372527 1.135145 1.087781 1.043863 1.124680 0.970866 -P_11 1.068980 1.167783 1.099347 1.219833 1.198704 1.183974 1.241584 1.160813 -P_11 1.167244 1.034271 1.060705 0.952290 0.840879 1.037492 0.829296 0.821501 -P_11 0.752675 0.621786 0.514241 0.488732 0.465366 0.414802 0.405819 0.400994 -P_11 0.408125 0.472548 0.492574 0.555859 0.702060 0.698936 0.870795 1.013608 -P_11 0.976669 1.073142 1.240741 1.177756 1.241855 1.250255 1.276340 1.239009 -P_11 1.253153 1.235692 1.100722 1.166421 1.121516 1.126917 1.106263 1.172115 -P_11 1.102326 1.049082 1.204809 1.138268 1.110260 1.026031 1.135107 1.127390 -P_11 1.104765 1.036135 0.966973 0.882190 0.875588 0.848048 0.768989 0.709753 -P_11 0.660794 0.598665 0.546035 0.480258 0.427741 0.408824 0.405665 0.390551 -P_11 0.362425 0.423806 0.433446 0.512033 0.537082 0.585990 0.632344 0.803750 -P_11 0.868260 0.888819 1.001189 1.147866 1.183933 1.140435 1.304511 1.133098 -P_11 1.158847 1.118280 1.201031 1.205629 1.111581 1.045008 1.023442 0.896697 -P_11 1.010526 0.970865 0.939107 0.960220 1.074368 1.060619 1.127926 1.021390 -P_11 1.057296 1.134467 1.090761 0.950019 0.961320 0.887141 0.808700 0.675127 -P_11 0.652858 0.542007 0.461121 0.409070 0.335330 0.327755 0.335580 0.401684 -P_11 0.375153 0.486209 0.541067 0.661440 0.828943 0.875910 1.068291 1.095244 -P_11 1.278344 1.357331 1.373273 1.459363 1.389898 1.514828 1.393059 1.380982 -P_11 1.307086 1.349625 1.269507 1.076452 1.066987 1.013508 0.910157 0.835403 -P_11 0.899579 0.973109 0.992546 1.023528 0.979147 1.164470 1.031033 1.193459 -P_11 1.196658 1.178575 1.137227 1.110014 0.958958 0.939769 0.927392 0.806530 -P_11 0.616803 0.545835 0.437717 0.344031 0.384335 0.362033 0.377719 0.393754 -P_11 0.407207 0.472419 0.586699 0.697282 0.773076 0.856329 0.990286 1.083040 -P_11 1.138776 1.192202 1.323256 1.371146 1.477756 1.485323 1.396907 1.319372 -P_11 1.288107 1.185061 1.106986 1.158692 1.021073 0.950515 0.977185 1.008017 -P_11 0.978093 1.063490 1.084465 1.066244 1.151219 1.141477 1.190844 1.290408 -P_11 1.234812 1.190207 1.202298 1.062479 1.067456 0.965592 0.858524 0.741072 -P_11 0.706104 0.565620 0.473995 0.381279 0.364545 0.319791 0.356547 0.393152 -P_11 0.463513 0.556001 0.607271 0.755674 0.863195 0.935041 1.035520 1.040351 -P_11 1.233727 1.324376 1.417407 1.376690 1.363571 1.359745 1.437238 1.273838 -P_11 1.359785 1.172863 1.221739 1.233980 1.132478 1.138508 1.080612 0.962363 -P_11 1.084155 1.067914 1.040099 1.154635 1.119380 1.186122 1.375998 1.156865 -P_11 1.195969 1.228190 1.285247 1.079657 1.036551 0.952782 0.875847 0.742663 -P_11 0.711751 0.544494 0.469374 0.373936 0.368367 0.355690 0.361480 0.389109 -P_11 0.452539 0.512134 0.655562 0.751687 0.825514 1.021691 1.080404 1.166097 -P_11 1.207206 1.348242 1.395384 1.364434 1.504031 1.422470 1.371247 1.279995 -P_11 1.303750 1.259145 1.127531 1.029314 1.085810 1.105025 1.077473 1.020082 -P_11 1.056894 1.136893 1.061072 1.024992 1.159535 1.074963 1.256290 1.223108 -P_11 1.098729 1.266040 1.273082 1.048208 1.031572 0.899765 0.879964 0.827953 -P_11 0.741976 0.573823 0.471260 0.460565 0.392236 0.364939 0.372372 0.413968 -P_11 0.503887 0.586182 0.715943 0.874506 0.901163 1.040860 1.206587 1.452448 -P_11 1.325327 1.635885 1.550699 1.521915 1.438402 1.567933 1.471674 1.495824 -P_11 1.544451 1.244471 1.321100 1.251660 1.120146 1.167478 1.179212 1.184195 -P_11 1.054599 1.125646 1.192983 1.161619 1.111262 1.248053 1.203428 1.259341 -P_11 1.140999 1.145416 1.166072 1.012161 1.010965 0.913082 0.921114 0.773666 -P_11 0.753731 0.596107 0.561098 0.510107 0.485929 0.439405 0.396541 0.411520 -P_11 0.418484 0.436336 0.513427 0.602552 0.679383 0.806855 0.806821 0.988601 -P_11 1.085766 1.088307 1.205741 1.212671 1.292081 1.319576 1.263420 1.420725 -P_11 1.306904 1.140064 1.122286 1.213855 1.182947 1.124595 1.184286 1.170696 -P_11 1.177311 1.102084 1.016035 1.150381 1.154471 1.111022 1.201017 1.083629 -P_11 1.072441 0.984099 1.028950 0.921169 0.849206 0.785537 0.712457 0.651554 -P_11 0.692546 0.613215 0.541604 0.484104 0.442152 0.384219 0.438479 0.413314 -P_11 0.390745 0.433096 0.464674 0.507684 0.611481 0.606576 0.742126 0.810886 -P_11 0.804842 0.929495 0.982654 1.105212 1.063612 1.085442 1.218460 1.245761 -P_11 1.231067 1.212114 1.182890 1.165540 1.105308 1.065151 1.094789 0.989010 -P_11 1.068847 1.083145 1.091559 1.094356 1.040653 1.105346 0.992392 1.145512 -P_11 1.139238 1.065508 1.074056 1.088751 1.007745 0.888356 0.829828 0.777594 -P_11 0.637413 0.550466 0.465464 0.389533 0.370004 0.363670 0.358862 0.407094 -P_11 0.421343 0.527898 0.628317 0.664377 0.783618 0.932961 1.032720 1.201214 -P_11 1.240200 1.457033 1.418684 1.486146 1.658124 1.455429 1.448580 1.334879 -P_11 1.417898 1.251198 1.275559 1.159462 1.043838 0.971206 0.967411 1.025042 -P_11 0.970532 1.002793 1.092212 0.999315 1.097644 1.122733 1.140297 1.244203 -P_11 1.209148 1.254003 1.227638 1.139296 1.043878 0.931388 0.831136 0.803662 -P_11 0.669559 0.605915 0.452054 0.431643 0.342389 0.335307 0.376869 0.359250 -P_11 0.417678 0.552503 0.604597 0.773401 0.805416 1.034598 1.096687 1.252031 -P_11 1.106735 1.122988 1.462880 1.519244 1.376165 1.439115 1.439521 1.434834 -P_11 1.200784 1.168249 1.160782 1.173550 1.071811 1.099691 1.002189 1.170027 -P_11 1.045087 1.059583 1.055676 1.124410 1.160469 1.249438 1.193627 1.318825 -P_11 1.196068 1.158267 1.158600 1.216969 1.025513 0.935752 0.860672 0.805330 -P_11 0.561222 0.588159 0.474657 0.420268 0.362239 0.369853 0.385850 0.453977 -P_11 0.438509 0.609695 0.647201 0.740125 0.809329 1.013687 1.123900 1.167463 -P_11 1.343136 1.351140 1.370187 1.376365 1.420146 1.464787 1.343394 1.248585 -P_11 1.320296 1.450858 1.174337 1.155375 1.161233 1.140338 1.107825 1.147919 -P_11 1.101269 1.098899 1.080953 1.101822 1.197147 1.205542 1.227137 1.319089 -P_11 1.205491 1.181637 1.226724 1.161810 1.078519 0.983864 0.871470 0.774331 -P_11 0.669949 0.568064 0.514334 0.408870 0.382524 0.354277 0.355978 0.406184 -P_11 0.449097 0.566055 0.693855 0.726033 0.888852 0.962262 1.186048 1.218280 -P_11 1.273327 1.363814 1.354209 1.448482 1.361086 1.376557 1.404434 1.275675 -P_11 1.295859 1.239893 1.176005 1.174791 1.076002 1.174526 1.121390 1.110246 -P_11 1.117897 1.131669 1.109952 1.200478 1.245149 1.106204 1.217001 1.259221 -P_11 1.156768 1.307962 1.159751 1.114772 1.103093 0.923454 0.807984 0.824773 -P_11 0.688972 0.599828 0.515538 0.477673 0.430896 0.384624 0.372963 0.422305 -P_11 0.455563 0.562252 0.709974 0.786425 0.943764 1.068228 1.238621 1.283849 -P_11 1.572169 1.438106 1.476513 1.690780 1.490265 1.507958 1.453619 1.521774 -P_11 1.492676 1.291748 1.248597 1.260412 1.262829 1.232771 1.106140 1.027246 -P_11 1.141830 1.098437 1.252356 1.210177 1.205142 1.198385 1.104655 1.202068 -P_11 1.269586 1.122074 1.190734 1.011189 1.031112 0.953636 0.831504 0.840171 -P_11 0.716148 0.636432 0.593554 0.502187 0.458479 0.422894 0.403297 0.394992 -P_11 0.417568 0.482682 0.495358 0.619765 0.663080 0.755952 0.860132 0.916039 -P_11 1.075675 1.141394 1.232380 1.275249 1.384399 1.385499 1.268573 1.291759 -P_11 1.338076 1.363393 1.254922 1.024864 1.246911 1.155931 1.146111 1.106543 -P_11 1.242276 1.304257 1.032211 1.103683 0.994757 1.181163 1.118011 1.168043 -P_11 1.101579 1.098754 1.066685 1.014133 0.919984 0.849268 0.793450 0.762616 -P_11 0.697479 0.594703 0.517219 0.486623 0.466464 0.416546 0.426722 0.433219 -P_11 0.412147 0.428054 0.453601 0.490019 0.602381 0.641760 0.707873 0.866666 -P_11 0.901200 0.900454 1.026250 1.162194 1.219627 1.232071 1.058285 1.180289 -P_11 1.184558 1.209725 1.194193 1.142142 1.112583 1.074396 1.095391 1.075807 -P_11 1.102356 1.095294 0.976437 1.084280 1.153239 1.062600 1.110079 1.273819 -P_11 1.137388 1.061535 1.122670 1.043131 1.045858 0.970410 0.833390 0.731956 -P_11 0.644580 0.545088 0.512935 0.411563 0.378973 0.371846 0.360451 0.366524 -P_11 0.429569 0.531350 0.583480 0.697650 0.780314 1.029431 1.059180 1.182829 -P_11 1.357467 1.455070 1.403997 1.689206 1.371618 1.334846 1.490498 1.398054 -P_11 1.400516 1.267156 1.156635 1.137422 1.174816 1.020786 0.968507 0.966751 -P_11 0.935640 1.107249 1.061642 1.131068 1.068309 1.154658 1.166248 1.235894 -P_11 1.175084 1.248422 1.199619 1.148617 1.030087 1.100980 0.847737 0.802090 -P_11 0.673816 0.577973 0.444396 0.383588 0.374328 0.346398 0.316478 0.383084 -P_11 0.474025 0.518373 0.609794 0.736266 0.776029 0.880026 1.046477 1.138204 -P_11 1.213952 1.276606 1.424385 1.344216 1.500763 1.349695 1.399645 1.400836 -P_11 1.351947 1.161771 1.074535 1.172461 1.060918 1.152176 1.047089 0.982017 -P_11 1.133844 1.008206 1.005163 1.056170 1.289626 1.155442 1.350570 1.284258 -P_11 1.304054 1.231537 1.236902 1.112794 1.105786 0.979771 0.814767 0.770015 -P_11 0.647779 0.607748 0.522361 0.424218 0.382395 0.338788 0.369141 0.393831 -P_11 0.478483 0.550791 0.586357 0.772213 0.844063 0.937582 1.159506 1.191914 -P_11 1.272605 1.321758 1.404747 1.450658 1.549581 1.394038 1.334953 1.351006 -P_11 1.162492 1.307899 1.138542 1.302385 1.248017 1.138344 1.079036 1.070209 -P_11 1.090018 1.123913 1.008811 1.209291 1.154163 1.201184 1.306119 1.258322 -P_11 1.366510 1.210218 1.158196 1.178795 1.105452 0.903710 0.894280 0.790498 -P_11 0.643979 0.595750 0.481649 0.449770 0.377111 0.348705 0.364103 0.374911 -P_11 0.418574 0.535318 0.644690 0.763067 0.938183 0.986322 1.091893 1.129498 -P_11 1.326575 1.229585 1.386487 1.488989 1.502776 1.375114 1.407331 1.252382 -P_11 1.204288 1.224095 1.299527 1.183862 1.123484 1.237156 1.083867 0.989406 -P_11 1.165007 1.136407 1.154427 1.228830 1.240866 1.199895 1.291445 1.288693 -P_11 1.152776 1.277984 1.189730 1.255930 1.143829 0.989566 0.821645 0.756473 -P_11 0.685869 0.578162 0.560215 0.423650 0.405996 0.387764 0.374368 0.462166 -P_11 0.514035 0.579019 0.653960 0.811805 1.021976 1.254496 1.374693 1.442476 -P_11 1.506661 1.592294 1.703846 1.665704 1.553609 1.634564 1.505516 1.645049 -P_11 1.659252 1.374285 1.298673 1.416337 1.177453 1.211316 1.143521 1.178980 -P_11 1.185647 1.196304 1.126663 1.295057 1.308310 1.096370 1.219011 1.234825 -P_11 1.170913 1.163924 1.108599 1.099480 1.029933 0.936249 0.974514 0.864201 -P_11 0.728687 0.633543 0.588410 0.548898 0.440161 0.431086 0.401049 0.417322 -P_11 0.444362 0.502841 0.580231 0.613676 0.623409 0.793484 0.980454 1.044219 -P_11 1.144947 1.169797 1.222146 1.412974 1.316461 1.353724 1.254456 1.419346 -P_11 1.241764 1.333786 1.274670 1.243514 1.157315 1.266601 1.240056 1.149699 -P_11 1.247329 1.137395 1.122869 1.251895 1.257374 1.153881 1.240215 1.166087 -P_11 1.169769 1.166009 1.118325 0.932993 1.005860 0.841715 0.789562 0.695813 -P_11 0.696690 0.600448 0.610980 0.485714 0.482652 0.455802 0.451277 0.427402 -P_11 0.427022 0.457291 0.473036 0.479718 0.608094 0.715747 0.678461 0.793563 -P_11 0.920787 0.989713 1.149654 1.074796 1.136768 1.243892 1.282088 1.314613 -P_11 1.212383 1.154457 1.118497 1.155363 1.170372 1.084945 1.077933 1.139825 -P_11 1.210171 1.049797 1.027832 1.103239 1.125622 1.171324 1.230290 1.161829 -P_11 1.043789 1.029567 1.113830 1.142701 0.990072 0.909684 0.843968 0.728209 -P_11 0.685102 0.595229 0.457671 0.421460 0.357719 0.384854 0.366632 0.417575 -P_11 0.416848 0.549990 0.589393 0.786161 0.888626 0.982585 0.962318 1.133131 -P_11 1.371597 1.362760 1.455511 1.636635 1.453462 1.498454 1.649508 1.593589 -P_11 1.318477 1.387642 1.248489 1.215159 1.191227 1.073809 1.047900 1.010108 -P_11 1.030272 1.041005 1.032952 1.052101 1.151972 1.250699 1.216294 1.206923 -P_11 1.315609 1.273578 1.306694 1.149199 1.099360 1.076897 1.013108 0.794941 -P_11 0.674969 0.623108 0.510367 0.428265 0.374995 0.353371 0.375479 0.377094 -P_11 0.474124 0.607827 0.613466 0.755195 0.890301 0.912515 1.194412 1.158683 -P_11 1.263211 1.404055 1.361309 1.521961 1.575026 1.606241 1.448913 1.505250 -P_11 1.504720 1.338140 1.271164 1.159008 1.046701 1.073395 1.063367 1.056199 -P_11 1.024629 1.047160 1.131691 1.135964 1.301217 1.336942 1.147893 1.286134 -P_11 1.225966 1.227575 1.186230 1.193203 1.079607 0.976089 0.897210 0.748794 -P_11 0.713295 0.545277 0.504787 0.453602 0.329038 0.366380 0.345830 0.432853 -P_11 0.487169 0.557621 0.665948 0.728341 0.879653 0.930067 1.110777 1.251147 -P_11 1.281240 1.483445 1.474406 1.498525 1.483910 1.467758 1.453388 1.447065 -P_11 1.301052 1.325318 1.255187 1.284032 1.171544 1.233801 1.185596 1.118501 -P_11 1.115133 1.200872 1.217616 1.170571 1.174052 1.197820 1.331921 1.235764 -P_11 1.172418 1.294871 1.282500 1.226998 1.207747 1.000435 0.871267 0.810094 -P_11 0.728609 0.610603 0.498042 0.446211 0.375593 0.349556 0.371530 0.376964 -P_11 0.437347 0.517990 0.670148 0.819053 0.841353 1.080191 1.193609 1.260474 -P_11 1.323362 1.291671 1.348825 1.501665 1.542120 1.592288 1.492676 1.300921 -P_11 1.447700 1.225786 1.186555 1.239653 1.125211 1.129469 1.174757 1.066241 -P_11 1.075721 1.118456 1.085997 1.123025 1.218734 1.133382 1.319517 1.350361 -P_11 1.309945 1.251286 1.173930 1.215086 1.040389 1.016383 0.935489 0.761683 -P_11 0.717432 0.642642 0.496416 0.468715 0.415840 0.400713 0.380774 0.399182 -P_11 0.523346 0.567737 0.664809 0.772737 1.022083 1.064942 1.304250 1.485721 -P_11 1.424491 1.627414 1.569524 1.682311 1.695638 1.626655 1.660648 1.504919 -P_11 1.423371 1.289925 1.256112 1.300231 1.222723 1.192445 1.159308 1.243057 -P_11 1.106628 1.322782 1.087982 1.195701 1.300793 1.307722 1.268022 1.306138 -P_11 1.211972 1.287640 1.233419 1.153750 1.061298 0.995862 0.941627 0.811034 -P_11 0.734869 0.668505 0.537720 0.573692 0.506584 0.462217 0.424966 0.394997 -P_11 0.465811 0.493751 0.529105 0.599689 0.717165 0.845645 0.935554 0.996519 -P_11 1.070947 1.270341 1.239134 1.482777 1.257474 1.294957 1.439642 1.390634 -P_11 1.425447 1.325902 1.288145 1.182571 1.296510 1.157963 1.321561 1.233363 -P_11 1.259486 1.243935 1.177625 1.170102 1.216711 1.223610 1.083515 1.096178 -P_11 1.165515 1.079743 1.058160 1.084494 0.869088 0.864763 0.774464 0.769685 -P_11 0.646506 0.599173 0.531382 0.526810 0.518288 0.465206 0.433233 0.376362 -P_11 0.420157 0.431426 0.459777 0.510501 0.623731 0.681074 0.732550 0.885900 -P_11 0.851579 1.070290 1.101726 1.214912 1.250865 1.116227 1.374038 1.194211 -P_11 1.363253 1.188090 1.177370 1.252983 1.157653 1.089121 1.134201 1.044746 -P_11 1.037415 0.936605 1.091873 1.096581 1.194147 1.169978 1.160678 1.153838 -P_11 1.111821 1.130293 1.161477 1.173564 0.978090 0.897104 0.820520 0.723241 -P_11 0.671891 0.579030 0.548839 0.415944 0.354178 0.385438 0.348919 0.389616 -P_11 0.491276 0.491651 0.586729 0.758399 0.822907 0.971898 1.155598 1.189220 -P_11 1.382567 1.422956 1.430297 1.656077 1.536979 1.670934 1.464550 1.503714 -P_11 1.471995 1.443074 1.252430 1.261479 1.159619 1.112109 1.073342 1.006660 -P_11 1.005159 1.072997 1.005249 1.063083 1.063859 1.218137 1.215613 1.245367 -P_11 1.153111 1.234673 1.280421 1.193431 1.202098 1.083584 0.902129 0.785058 -P_11 0.707469 0.638358 0.504534 0.517243 0.406685 0.375180 0.369612 0.395446 -P_11 0.454647 0.573696 0.617727 0.706874 0.884321 0.993676 1.122171 1.184738 -P_11 1.215237 1.495375 1.289306 1.507084 1.539968 1.466994 1.509420 1.439797 -P_11 1.413944 1.261370 1.282603 1.128718 1.200523 1.090013 1.087101 1.112311 -P_11 1.186284 1.071709 1.146567 1.245286 1.291981 1.214510 1.363174 1.278770 -P_11 1.277677 1.247456 1.212144 1.246216 1.153783 0.917328 0.931488 0.800111 -P_11 0.692109 0.603931 0.499679 0.395034 0.366923 0.358907 0.416274 0.402378 -P_11 0.517753 0.560322 0.698183 0.748740 0.919416 1.108084 1.184636 1.210145 -P_11 1.348929 1.462421 1.391802 1.313692 1.528532 1.499964 1.433488 1.337746 -P_11 1.437587 1.439548 1.276360 1.304245 1.190186 1.215045 1.106643 1.093858 -P_11 1.143893 1.090902 1.070744 1.180042 1.095406 1.275791 1.313627 1.276417 -P_11 1.321085 1.334624 1.289025 1.155393 1.156461 1.021231 1.014855 0.776211 -P_11 0.671642 0.613201 0.519903 0.449031 0.417013 0.352775 0.360681 0.405059 -P_11 0.496504 0.559120 0.593224 0.757024 0.882074 0.993551 1.123216 1.189517 -P_11 1.339757 1.412860 1.385260 1.510834 1.474112 1.443939 1.513353 1.314444 -P_11 1.377392 1.250441 1.271023 1.293780 1.189722 1.166618 1.160545 1.119510 -P_11 1.144501 1.216751 1.112606 1.220548 1.225475 1.276546 1.248392 1.271788 -P_11 1.290292 1.368180 1.275660 1.236547 1.161979 0.949462 0.979009 0.877486 -P_11 0.693082 0.645664 0.545136 0.479828 0.427281 0.386600 0.390920 0.470396 -P_11 0.549448 0.634384 0.718626 0.933097 1.087540 1.172997 1.314189 1.557194 -P_11 1.528770 1.526123 1.732655 1.593068 1.548679 1.648616 1.609293 1.612196 -P_11 1.531546 1.443478 1.304504 1.270407 1.200613 1.120231 1.253733 1.149239 -P_11 1.235432 1.298338 1.343593 1.278080 1.292356 1.136587 1.231792 1.315906 -P_11 1.372991 1.317274 1.139607 1.034646 1.113929 1.063395 0.980079 0.886769 -P_11 0.706185 0.672597 0.608146 0.548238 0.492299 0.420194 0.424857 0.402011 -P_11 0.418761 0.552157 0.504226 0.650737 0.670452 0.813794 0.917899 1.029238 -P_11 1.122540 1.152440 1.224096 1.398164 1.419252 1.298092 1.436287 1.388449 -P_11 1.488458 1.336272 1.371253 1.368819 1.335453 1.115811 1.193895 1.232262 -P_11 1.170877 1.068869 1.255967 1.182923 1.350382 1.255955 1.290450 1.282346 -P_11 1.170526 1.117402 1.039957 1.033451 0.913493 0.737168 0.809094 0.784408 -P_11 0.626287 0.608079 0.594148 0.468585 0.487078 0.475102 0.435201 0.424547 -P_11 0.472196 0.444368 0.468810 0.501366 0.633332 0.677789 0.745175 0.767827 -P_11 0.943547 0.916919 1.011217 1.188497 1.184303 1.256246 1.307275 1.381480 -P_11 1.323717 1.315015 1.283193 1.136914 1.215389 1.080789 1.040466 1.113869 -P_11 1.037123 1.106064 1.147146 1.176356 1.265498 1.063637 1.196647 1.185733 -P_11 1.160417 1.152869 1.077061 1.077754 1.056436 0.954526 0.848421 0.758791 -P_11 0.693060 0.526366 0.504402 0.430856 0.427364 0.365301 0.358577 0.371322 -P_11 0.440867 0.529351 0.641569 0.700856 0.911199 0.942274 1.112047 1.205369 -P_11 1.361901 1.461898 1.599359 1.564790 1.545810 1.534544 1.573831 1.477769 -P_11 1.439505 1.377754 1.270564 1.209436 1.196517 1.032137 1.064805 1.017235 -P_11 1.159743 1.136275 1.043627 1.212285 1.023295 1.198277 1.230495 1.355203 -P_11 1.384387 1.161913 1.294491 1.096115 1.163585 1.021163 0.918983 0.801851 -P_11 0.732684 0.609475 0.487422 0.444078 0.403778 0.339684 0.376618 0.429064 -P_11 0.475099 0.559498 0.668308 0.761983 0.851113 0.979440 1.078331 1.277850 -P_11 1.292288 1.279620 1.408834 1.661129 1.592756 1.446763 1.438450 1.511500 -P_11 1.496098 1.304810 1.168488 1.266847 1.215906 0.991915 1.096931 1.022771 -P_11 1.091575 1.045864 1.162685 1.193007 1.355602 1.381047 1.208395 1.366702 -P_11 1.293752 1.278681 1.297823 1.188539 1.169127 1.031402 0.961096 0.751239 -P_11 0.692684 0.591581 0.512439 0.435802 0.352603 0.360344 0.347062 0.442414 -P_11 0.513347 0.599144 0.705554 0.809633 0.920098 1.068277 1.069573 1.240445 -P_11 1.348491 1.348477 1.465494 1.534834 1.562673 1.451009 1.612073 1.486379 -P_11 1.359408 1.313781 1.317852 1.214117 1.222598 1.228403 1.067634 1.103828 -P_11 1.177485 1.048046 1.166090 1.231417 1.170945 1.204100 1.195387 1.219996 -P_11 1.212743 1.263397 1.257674 1.289238 1.107262 1.055615 0.947129 0.855475 -P_11 0.719591 0.614427 0.494241 0.449730 0.375666 0.338787 0.372390 0.390983 -P_11 0.490701 0.512345 0.630210 0.744509 0.935746 1.052412 1.051206 1.151024 -P_11 1.358152 1.475379 1.509242 1.567906 1.587637 1.449906 1.401095 1.390926 -P_11 1.429007 1.291732 1.048960 1.234841 1.120546 1.168284 1.138672 1.037393 -P_11 1.139791 1.208741 1.254995 1.171093 1.237738 1.292432 1.314534 1.282892 -P_11 1.302237 1.286608 1.278745 1.122075 1.168362 0.990900 1.025101 0.825161 -P_11 0.732553 0.599919 0.526129 0.431342 0.400992 0.402192 0.385268 0.435051 -P_11 0.510235 0.648561 0.863260 0.855695 1.031027 1.119185 1.271077 1.545179 -P_11 1.581253 1.473130 1.700426 1.700801 1.764695 1.608986 1.672604 1.545945 -P_11 1.453278 1.496991 1.437442 1.380934 1.278030 1.086084 1.252000 1.279131 -P_11 1.100775 1.177752 1.256329 1.228097 1.320317 1.313991 1.281703 1.315347 -P_11 1.200065 1.241910 1.173798 1.127337 1.007153 0.945092 0.875256 0.847034 -P_11 0.823042 0.706991 0.622789 0.539523 0.525371 0.441821 0.440263 0.467558 -P_11 0.436139 0.453548 0.590483 0.600994 0.739500 0.849754 0.931800 0.961550 -P_11 1.108555 1.233747 1.210963 1.300061 1.342068 1.353477 1.324604 1.416047 -P_11 1.550955 1.466575 1.259340 1.227230 1.262163 1.270274 1.225280 1.256200 -P_11 1.133844 1.221944 1.134103 1.218921 1.343677 1.245657 1.122589 1.214649 -P_11 1.154787 1.081580 1.023048 0.987988 0.918047 0.809872 0.813477 0.751299 -P_11 0.678816 0.610074 0.557990 0.548508 0.465939 0.469796 0.436124 0.430243 -P_11 0.431527 0.474775 0.509667 0.519456 0.570287 0.631368 0.796122 0.790820 -P_11 0.921458 1.050625 1.028552 1.221259 1.290920 1.200689 1.326265 1.332029 -P_11 1.344805 1.245954 1.244126 1.129989 1.179683 1.115973 1.069449 0.991117 -P_11 1.033228 1.140904 1.141701 1.103611 1.129040 1.227046 1.198786 1.207781 -P_11 1.141996 1.205225 1.207097 1.180646 1.025016 0.896691 0.924147 0.743250 -P_11 0.711838 0.620588 0.475792 0.451849 0.360817 0.390317 0.394232 0.389104 -P_11 0.463819 0.536152 0.635187 0.820102 0.841133 0.951842 1.089320 1.200292 -P_11 1.268004 1.503208 1.458023 1.669625 1.703768 1.685358 1.737551 1.589131 -P_11 1.381538 1.374850 1.412449 1.285252 1.138040 1.076124 1.104791 0.987134 -P_11 1.100469 1.245942 1.144344 1.108494 1.292388 1.164019 1.225956 1.301100 -P_11 1.256026 1.177290 1.228099 1.154992 1.126065 1.051892 0.895112 0.795698 -P_11 0.659703 0.646814 0.515115 0.408504 0.383764 0.386783 0.377670 0.416711 -P_11 0.470860 0.542831 0.615275 0.750764 0.904594 0.982851 1.042118 1.260795 -P_11 1.342372 1.383479 1.408535 1.550524 1.419313 1.607527 1.468538 1.488136 -P_11 1.376205 1.371231 1.304393 1.233602 1.132878 1.011411 1.122804 0.974601 -P_11 1.062435 1.078572 1.115635 1.210958 1.240327 1.262666 1.362491 1.329955 -P_11 1.180542 1.370685 1.305019 1.302849 1.183925 0.993185 0.918237 0.821596 -P_11 0.699343 0.605142 0.470951 0.435317 0.410560 0.379279 0.362956 0.427849 -P_11 0.471562 0.563313 0.686777 0.828227 0.879615 1.106391 1.123847 1.263257 -P_11 1.383270 1.431749 1.391142 1.700648 1.608317 1.475014 1.555098 1.542932 -P_11 1.520246 1.352781 1.294813 1.310402 1.190286 1.132491 1.201380 1.180568 -P_11 1.131242 1.198780 1.145512 1.150213 1.161702 1.267060 1.396215 1.263273 -P_11 1.405902 1.189527 1.254094 1.251625 1.122240 1.040649 0.970101 0.834456 -P_11 0.689812 0.646098 0.536327 0.437664 0.403291 0.351291 0.392809 0.398049 -P_11 0.464720 0.545537 0.736417 0.803973 0.937060 1.054577 1.246175 1.142482 -P_11 1.258416 1.368487 1.576194 1.509971 1.435430 1.413862 1.575289 1.548215 -P_11 1.331670 1.417962 1.195543 1.139276 1.181454 1.269566 1.238258 1.103792 -P_11 1.128538 1.176090 1.149524 1.132482 1.177162 1.373507 1.306327 1.267978 -P_11 1.237337 1.270099 1.185172 1.179288 1.152251 1.031366 0.943445 0.819455 -P_11 0.732482 0.655194 0.579763 0.440995 0.409448 0.416879 0.394980 0.474194 -P_11 0.477889 0.657826 0.749114 0.935304 1.131290 1.160051 1.265396 1.528818 -P_11 1.605364 1.716495 1.639724 1.710460 1.837865 1.775760 1.615255 1.521539 -P_11 1.461000 1.358076 1.470254 1.154329 1.237041 1.453248 1.200848 1.108469 -P_11 1.159451 1.161562 1.241860 1.151922 1.232712 1.247612 1.255429 1.239735 -P_11 1.291259 1.248910 1.080013 1.144828 1.111378 0.997356 0.973994 0.942525 -P_11 0.781712 0.671691 0.651482 0.571002 0.490177 0.447283 0.426055 0.437184 -P_11 0.472849 0.479537 0.605482 0.692092 0.755386 0.847193 0.956661 1.117120 -P_11 1.162362 1.277071 1.383122 1.352585 1.441662 1.485134 1.393893 1.424118 -P_11 1.373493 1.342149 1.383914 1.316096 1.210886 1.337190 1.323405 1.295052 -P_11 1.185808 1.271444 1.208474 1.343322 1.211275 1.273844 1.230099 1.216695 -P_11 1.091638 1.151440 0.978174 0.977568 1.006614 0.898587 0.826454 0.731330 -P_11 0.664589 0.635378 0.550332 0.560030 0.495266 0.434078 0.436967 0.427719 -P_11 0.412285 0.427073 0.494893 0.494735 0.605901 0.680760 0.735564 0.794072 -P_11 1.007154 1.037368 1.012542 1.172474 1.355210 1.270096 1.288911 1.329324 -P_11 1.384427 1.186274 1.085944 1.241296 1.192638 1.127230 1.094379 1.158330 -P_11 1.105949 1.138095 1.137054 0.969537 1.274838 1.148327 1.166459 1.153430 -P_11 1.245496 1.129923 1.261453 1.111418 1.052609 0.887280 0.838599 0.786138 -P_11 0.727787 0.595464 0.527246 0.410468 0.409684 0.357311 0.349575 0.403096 -P_11 0.472301 0.527151 0.640424 0.720133 0.919306 1.000291 1.118338 1.252233 -P_11 1.516086 1.355320 1.541472 1.695330 1.719501 1.658727 1.587787 1.587686 -P_11 1.464362 1.396354 1.426956 1.281726 1.167396 1.184850 1.020597 0.980928 -P_11 1.016960 1.030800 1.060400 1.086760 1.098151 1.184264 1.239851 1.234627 -P_11 1.222173 1.342661 1.334038 1.049157 1.191092 1.074523 0.969537 0.760160 -P_11 0.638657 0.533822 0.536535 0.439546 0.417009 0.396982 0.388324 0.383711 -P_11 0.432474 0.539741 0.636950 0.783249 0.938262 1.013498 1.091646 1.265836 -P_11 1.379509 1.345587 1.396861 1.386104 1.529605 1.520878 1.530186 1.302333 -P_11 1.525918 1.312903 1.309212 1.137883 1.196079 1.089128 1.118296 1.130253 -P_11 1.162079 1.028879 1.094597 1.216652 1.320324 1.258027 1.383436 1.297753 -P_11 1.340613 1.269832 1.229473 1.177792 1.053556 1.056845 0.952868 0.831625 -P_11 0.724961 0.647196 0.536105 0.423975 0.373658 0.382040 0.386193 0.420813 -P_11 0.498176 0.590111 0.703053 0.760875 0.974007 0.953915 1.112202 1.317237 -P_11 1.312362 1.384482 1.474868 1.570607 1.487036 1.521433 1.544815 1.457576 -P_11 1.415726 1.341140 1.489566 1.330181 1.212888 1.133073 1.237775 1.184528 -P_11 1.202126 1.277049 1.157142 1.341813 1.328974 1.213501 1.357000 1.199526 -P_11 1.243517 1.242080 1.165112 1.151418 1.100391 0.948608 0.929544 0.858460 -P_11 0.752709 0.606100 0.519644 0.458111 0.377039 0.367953 0.365104 0.431169 -P_11 0.479444 0.576543 0.710982 0.799691 0.824888 1.035744 1.184305 1.274452 -P_11 1.355355 1.464457 1.547166 1.423986 1.452668 1.474717 1.489160 1.400160 -P_11 1.361372 1.339952 1.278009 1.241069 1.189378 1.139153 1.101450 1.109503 -P_11 1.182373 1.194424 1.237503 1.156490 1.277267 1.277417 1.290831 1.346862 -P_11 1.368917 1.123295 1.188737 1.261339 1.154973 1.129396 0.984480 0.854158 -P_11 0.741763 0.607639 0.587917 0.438567 0.437359 0.423771 0.395381 0.439867 -P_11 0.515281 0.610243 0.817498 0.882558 1.107178 1.103075 1.274544 1.356579 -P_11 1.393535 1.489488 1.648770 1.595453 1.631587 1.618100 1.674409 1.651404 -P_11 1.433144 1.580904 1.381634 1.202961 1.199699 1.273363 1.224568 1.289949 -P_11 1.271527 1.279455 1.194194 1.339412 1.210656 1.222258 1.287282 1.248917 -P_11 1.218293 1.219090 1.140148 1.216616 1.046098 0.961097 0.813792 0.903383 -P_11 0.843455 0.697060 0.608933 0.590803 0.471339 0.466198 0.446898 0.474316 -P_11 0.449594 0.489008 0.569978 0.635594 0.714628 0.832263 0.912392 1.078498 -P_11 1.123749 1.211357 1.311388 1.463175 1.446279 1.415621 1.356194 1.314252 -P_11 1.380518 1.266456 1.325543 1.315078 1.324391 1.200669 1.254603 1.214342 -P_11 1.261112 1.306400 1.207032 1.126135 1.275943 1.270767 1.324015 1.156505 -P_11 1.226401 1.148740 1.072272 0.982381 0.854882 0.962956 0.790383 0.827004 -P_11 0.699280 0.611072 0.562780 0.481174 0.506652 0.427710 0.432203 0.427698 -P_11 0.425000 0.438907 0.476073 0.543408 0.612636 0.685767 0.772029 0.772345 -P_11 0.982891 0.926675 1.136984 1.271595 1.123942 1.371099 1.311224 1.315064 -P_11 1.299468 1.308115 1.307631 1.323348 1.128953 1.163095 1.103847 1.111702 -P_11 1.085773 1.032061 1.059144 1.151878 1.120604 1.198626 1.126999 1.153596 -P_11 1.286397 1.237354 1.298576 1.156649 1.074618 0.954002 0.864265 0.775752 -P_11 0.676930 0.637187 0.533017 0.446926 0.377151 0.315447 0.339850 0.401721 -P_11 0.452998 0.551521 0.620891 0.777526 0.917863 0.956430 1.196521 1.289930 -P_11 1.321392 1.381898 1.562177 1.545264 1.617012 1.423960 1.521362 1.499930 -P_11 1.560210 1.437779 1.322533 1.213554 1.216319 0.999483 1.075131 1.058465 -P_11 1.108449 1.006833 1.099766 1.142872 1.248299 1.163316 1.166433 1.303771 -P_11 1.344260 1.200320 1.202990 1.261631 1.114554 1.083906 0.937170 0.796039 -P_11 0.714900 0.567770 0.527185 0.476251 0.430003 0.370547 0.399914 0.419055 -P_11 0.471178 0.547976 0.578595 0.781732 0.886399 1.006334 1.155667 1.181184 -P_11 1.286972 1.330728 1.421265 1.377518 1.499995 1.521187 1.357705 1.438472 -P_11 1.301623 1.405973 1.211735 1.178549 1.152983 1.122085 1.145087 1.063200 -P_11 1.139023 1.150501 1.011880 1.194904 1.184531 1.259725 1.322911 1.420467 -P_11 1.324378 1.206848 1.357798 1.257764 1.036017 1.003093 0.922910 0.740542 -P_11 0.716199 0.626705 0.513298 0.439660 0.392557 0.375471 0.397682 0.411384 -P_11 0.458014 0.579654 0.691589 0.745788 0.881738 1.060859 1.032034 1.264530 -P_11 1.389120 1.501093 1.418355 1.567504 1.559741 1.616824 1.488464 1.421795 -P_11 1.359300 1.375514 1.290089 1.383608 1.186830 1.045750 1.101013 1.166413 -P_11 1.117786 1.214880 1.195294 1.208607 1.139630 1.267459 1.291792 1.320566 -P_11 1.251689 1.298379 1.164043 1.164103 1.124144 1.002714 0.935382 0.832099 -P_11 0.664143 0.599048 0.532639 0.455275 0.388230 0.395800 0.361828 0.404003 -P_11 0.491916 0.543286 0.687652 0.753353 0.958311 1.041193 1.032311 1.249961 -P_11 1.423170 1.327777 1.432525 1.611713 1.612691 1.434072 1.347934 1.541677 -P_11 1.359115 1.261350 1.080634 1.225479 1.139843 1.143751 1.145423 1.104171 -P_11 1.062887 1.303087 1.111575 1.109202 1.155731 1.177990 1.379268 1.376362 -P_11 1.384273 1.358813 1.281716 1.086664 1.151251 0.992460 0.923581 0.865648 -P_11 0.739620 0.677606 0.584155 0.469535 0.420594 0.409716 0.412822 0.458608 -P_11 0.485464 0.592763 0.787686 0.809078 1.108290 1.171336 1.394910 1.492549 -P_11 1.404159 1.513033 1.836735 1.840827 1.739206 1.552865 1.726864 1.523653 -P_11 1.530113 1.363247 1.373459 1.262446 1.287080 1.199055 1.243091 1.067332 -P_11 1.246694 1.182221 1.156328 1.251941 1.338742 1.220302 1.301568 1.288231 -P_11 1.184105 1.226853 1.212361 1.107812 1.073496 0.915064 0.906695 0.829168 -P_11 0.792675 0.773570 0.614205 0.541511 0.496006 0.429398 0.449671 0.413952 -P_11 0.445254 0.517676 0.557587 0.628148 0.730525 0.782895 0.869220 1.050855 -P_11 1.177202 1.217533 1.337856 1.306799 1.284122 1.314540 1.335086 1.442353 -P_11 1.267453 1.346461 1.334901 1.285102 1.239254 1.176691 1.193391 1.190087 -P_11 1.211735 1.233731 1.196332 1.255831 1.221099 1.338806 1.131107 1.099718 -P_11 1.197112 1.200591 0.982788 0.902620 0.855875 0.892954 0.794992 0.824111 -P_11 0.695572 0.610519 0.561968 0.464974 0.539876 0.449481 0.454641 0.440875 -P_11 0.448155 0.441526 0.486470 0.465036 0.600465 0.658353 0.755964 0.870067 -P_11 0.911889 1.029004 1.159471 1.178024 1.188034 1.232969 1.235893 1.275998 -P_11 1.244986 1.248427 1.180757 1.209922 1.268912 1.125632 1.125198 1.063043 -P_11 1.165888 1.197838 1.052659 1.037269 1.140007 1.164567 1.200702 1.056688 -P_11 1.096345 1.257860 1.141797 1.110635 1.019405 1.000193 0.871096 0.747493 -P_11 0.619811 0.539832 0.493069 0.412584 0.379518 0.370476 0.351858 0.367832 -P_11 0.455421 0.535239 0.602251 0.758401 0.840587 1.003439 1.048564 1.202415 -P_11 1.319944 1.532583 1.737195 1.600434 1.555106 1.619403 1.436285 1.556073 -P_11 1.635596 1.472578 1.356285 1.256464 1.110958 1.109885 1.035101 1.172574 -P_11 0.946110 0.965377 1.085332 1.085122 1.220799 1.308919 1.247545 1.228992 -P_11 1.238586 1.205295 1.236427 1.137536 1.059101 1.016803 0.869611 0.775692 -P_11 0.669522 0.555116 0.525186 0.470427 0.391097 0.386695 0.347245 0.410397 -P_11 0.451199 0.529351 0.634529 0.736921 0.925150 1.071708 1.121818 1.161453 -P_11 1.245553 1.456333 1.488336 1.382136 1.543887 1.577731 1.442101 1.416334 -P_11 1.329767 1.299130 1.155424 1.255165 1.218453 1.070874 1.128401 1.146116 -P_11 1.042022 1.135321 1.114726 1.041106 1.251658 1.091267 1.359006 1.264867 -P_11 1.266850 1.444180 1.289171 1.271214 1.089218 1.059835 0.923195 0.833794 -P_11 0.674402 0.595899 0.498239 0.419997 0.402579 0.376855 0.403535 0.422154 -P_11 0.522592 0.558854 0.680824 0.816612 0.847514 0.985449 1.149865 1.169084 -P_11 1.223060 1.395558 1.474119 1.509351 1.404373 1.391243 1.387525 1.375964 -P_11 1.518215 1.315730 1.366122 1.219922 1.222387 1.167630 1.120623 1.048866 -P_11 1.124404 1.081893 1.157210 1.074491 1.115467 1.134063 1.327911 1.330926 -P_11 1.280719 1.322098 1.238709 1.103909 1.115694 0.937070 0.962375 0.833179 -P_11 0.637801 0.620433 0.536240 0.451751 0.393134 0.346284 0.366086 0.408056 -P_11 0.515955 0.576091 0.650184 0.834157 0.888891 1.003008 1.149201 1.288279 -P_11 1.332373 1.377545 1.485264 1.647649 1.516739 1.428326 1.538376 1.531446 -P_11 1.336304 1.329733 1.343930 1.195684 1.124335 1.277934 1.054828 1.161491 -P_11 1.073549 1.094668 1.128244 1.206986 1.186001 1.272428 1.188462 1.241011 -P_11 1.242307 1.349755 1.303966 1.130861 1.087345 0.995137 0.953992 0.863335 -P_11 0.775432 0.593481 0.498395 0.456358 0.388970 0.384728 0.410863 0.434341 -P_11 0.498914 0.675576 0.749008 0.879289 1.109691 1.142723 1.268874 1.394379 -P_11 1.473791 1.650523 1.642726 1.501799 1.589900 1.579319 1.723799 1.468429 -P_11 1.419841 1.474479 1.315091 1.305713 1.277085 1.276599 1.178428 1.181988 -P_11 1.223329 1.210489 1.219390 1.176879 1.228328 1.316827 1.183399 1.326102 -P_11 1.158437 1.247916 1.104285 1.053692 0.993469 0.929129 0.904091 0.796069 -P_11 0.795245 0.707694 0.565282 0.573574 0.515363 0.457712 0.391399 0.383890 -P_11 0.442562 0.493626 0.564734 0.607280 0.704730 0.816642 0.949307 0.983794 -P_11 1.150269 1.197401 1.236334 1.386696 1.449613 1.512373 1.333649 1.428796 -P_11 1.209254 1.457810 1.281055 1.337046 1.199213 1.169722 1.161581 1.144664 -P_11 1.156057 1.254777 1.222824 1.145293 1.238717 1.250468 1.163064 1.173360 -P_11 1.077812 1.103891 1.044720 0.999554 0.957216 0.881175 0.840737 0.781556 -P_11 0.743072 0.604170 0.580514 0.516956 0.485634 0.451936 0.392825 0.411888 -P_11 0.421951 0.418938 0.468012 0.511852 0.612963 0.659807 0.780995 0.875991 -P_11 0.979951 0.994415 1.064843 1.175646 1.268454 1.223267 1.277839 1.307437 -P_11 1.205490 1.275077 1.194199 1.201706 1.153572 0.989678 1.090998 1.077269 -P_11 1.051920 1.043379 1.074921 1.139811 1.099288 1.171767 1.085514 1.141717 -P_11 1.214844 1.259260 1.122470 1.071063 1.019420 0.947406 0.866184 0.781863 -P_11 0.620149 0.549063 0.494650 0.458309 0.385096 0.375898 0.386680 0.366315 -P_11 0.411132 0.522243 0.628191 0.719377 0.928833 1.035436 1.089316 1.264136 -P_11 1.195801 1.517088 1.404820 1.518414 1.664683 1.733377 1.397462 1.528865 -P_11 1.466304 1.303749 1.256018 1.223016 1.099496 1.093116 1.008510 1.032328 -P_11 0.899586 1.096235 1.033045 1.015714 1.087404 1.157393 1.125393 1.321231 -P_11 1.197591 1.205733 1.301751 1.253856 1.066290 1.028448 0.889347 0.832741 -P_11 0.723609 0.551328 0.525251 0.452405 0.373860 0.364778 0.408000 0.407465 -P_11 0.427368 0.520161 0.655401 0.738611 0.829684 1.029782 0.998863 1.098696 -P_11 1.301833 1.349456 1.398296 1.279118 1.420873 1.491292 1.323731 1.451106 -P_11 1.428889 1.325747 1.138014 1.212313 1.059275 1.062263 1.007755 1.031163 -P_11 0.929414 1.138675 1.152920 1.252008 1.256141 1.184343 1.328656 1.399973 -P_11 1.238850 1.196369 1.310903 1.176842 1.063836 1.053393 0.875343 0.816255 -P_11 0.617446 0.565528 0.499117 0.423775 0.398936 0.363576 0.363915 0.399407 -P_11 0.456822 0.596213 0.725867 0.762721 0.934452 1.015928 1.103897 1.213050 -P_11 1.279350 1.249891 1.435139 1.472797 1.414014 1.420776 1.476771 1.326050 -P_11 1.166436 1.424880 1.333656 1.379358 1.291023 1.159848 1.129753 1.057175 -P_11 1.034229 1.066359 1.079789 1.103329 1.170470 1.177625 1.203208 1.191883 -P_11 1.118440 1.210254 1.228131 1.109301 1.034787 0.992523 0.977058 0.861210 -P_11 0.703375 0.514914 0.530768 0.399263 0.370357 0.361896 0.361190 0.407953 -P_11 0.431199 0.532266 0.695266 0.739888 0.902379 0.976720 1.209210 1.328786 -P_11 1.326948 1.376391 1.300000 1.401227 1.436954 1.424576 1.375903 1.359238 -P_11 1.435566 1.323419 1.142849 1.177543 1.080960 1.127043 1.073958 1.120796 -P_11 1.140152 1.105804 1.132020 1.107747 1.228373 1.263171 1.230128 1.139401 -P_11 1.245401 1.257432 1.134309 1.189814 1.153636 0.984159 0.930115 0.776895 -P_11 0.668881 0.653085 0.529326 0.452108 0.414454 0.431351 0.432076 0.436909 -P_11 0.572168 0.645865 0.711573 0.864567 1.037108 1.227088 1.233610 1.366275 -P_11 1.461516 1.712380 1.626386 1.755384 1.835975 1.504419 1.553018 1.487449 -P_11 1.426388 1.336583 1.280471 1.253850 1.207451 1.206809 1.203329 1.253401 -P_11 1.083342 1.246967 1.202807 1.216877 1.235262 1.176057 1.140085 1.279114 -P_11 1.205694 1.278256 1.085840 1.045055 1.041379 0.937979 0.955208 0.856417 -P_11 0.698605 0.681158 0.570882 0.526464 0.478625 0.448010 0.400643 0.443075 -P_11 0.443516 0.483121 0.566630 0.630606 0.680309 0.807352 0.872233 0.921579 -P_11 1.202614 1.207997 1.323803 1.303041 1.317789 1.439795 1.469132 1.254954 -P_11 1.306506 1.400704 1.261854 1.144022 1.229856 1.193413 1.168900 1.222449 -P_11 1.197080 1.250782 1.356387 1.227240 1.248799 1.190127 1.189054 1.285312 -P_11 1.251044 1.124138 0.998782 1.043007 1.031288 0.875702 0.793698 0.716582 -P_11 0.684722 0.629540 0.507915 0.476359 0.486837 0.427028 0.429754 0.433955 -P_11 0.449942 0.455792 0.478829 0.531866 0.598257 0.608645 0.667699 0.754455 -P_11 0.893006 0.951401 1.030488 1.144406 1.106195 1.184577 1.102271 1.276478 -P_11 1.294908 1.255313 1.256506 1.176947 1.195965 1.090035 1.020489 0.988243 -P_11 1.063779 1.051919 1.089223 1.081346 1.135182 1.112240 1.101212 1.082097 -P_11 1.191562 1.130620 1.154446 1.061208 1.015959 0.938676 0.809450 0.764082 -P_11 0.631130 0.597130 0.498900 0.444655 0.362557 0.381181 0.402792 0.386950 -P_11 0.458378 0.525445 0.611029 0.798564 0.825284 0.936622 1.050257 1.266367 -P_11 1.428557 1.251377 1.575668 1.579463 1.437126 1.626322 1.581929 1.381120 -P_11 1.339662 1.445528 1.291067 1.191606 1.072447 1.085065 1.041906 1.070677 -P_11 1.018458 1.047694 1.093617 1.032651 1.078520 1.242340 1.287406 1.303595 -P_11 1.287878 1.221777 1.258368 1.281773 1.160861 1.072871 0.821550 0.787151 -P_11 0.698656 0.595427 0.487178 0.465559 0.372166 0.388410 0.347051 0.433338 -P_11 0.453935 0.562409 0.607001 0.742830 0.836845 0.992867 1.038756 1.160155 -P_11 1.229827 1.428044 1.409097 1.474460 1.483059 1.448523 1.528841 1.461080 -P_11 1.392830 1.364618 1.130334 1.162473 1.085039 1.064426 1.032925 1.089702 -P_11 1.102699 1.126461 1.107904 1.211445 1.243363 1.258388 1.151440 1.341143 -P_11 1.249898 1.349752 1.203065 1.172274 0.993575 1.001579 0.980987 0.818093 -P_11 0.682989 0.584674 0.513395 0.420440 0.375621 0.351109 0.393267 0.396374 -P_11 0.439495 0.557174 0.660192 0.712642 0.894233 0.938584 1.064130 1.161302 -P_11 1.299063 1.253370 1.542556 1.450490 1.563062 1.491005 1.438887 1.474934 -P_11 1.378339 1.288602 1.260240 1.308473 1.046554 1.201517 1.104379 1.155041 -P_11 1.089874 1.098039 1.036762 1.101906 1.156832 1.246140 1.151440 1.319286 -P_11 1.238892 1.264733 1.324342 1.278826 1.093103 0.975920 0.903061 0.799926 -P_11 0.734968 0.578560 0.502585 0.445630 0.394958 0.338383 0.398662 0.416140 -P_11 0.453413 0.510029 0.623772 0.765128 0.817379 1.092388 1.178436 1.150526 -P_11 1.305227 1.380720 1.383663 1.407380 1.443078 1.552816 1.311133 1.333919 -P_11 1.268750 1.159844 1.251276 1.065880 1.085857 1.054763 1.100192 1.144197 -P_11 1.022502 1.066990 1.120078 1.138754 1.307626 1.277660 1.327328 1.201060 -P_11 1.274581 1.292807 1.167623 1.135162 1.026647 0.945409 1.020783 0.762174 -P_11 0.715730 0.601295 0.572876 0.504491 0.402850 0.423759 0.374814 0.435723 -P_11 0.569362 0.612694 0.671108 0.886459 0.963613 1.195940 1.199335 1.311551 -P_11 1.323147 1.616186 1.502178 1.693001 1.595371 1.463297 1.595589 1.527628 -P_11 1.340964 1.351344 1.290847 1.146892 1.182024 1.105489 1.052234 1.157583 -P_11 1.075430 1.112538 1.279197 1.169704 1.221212 1.163805 1.267424 1.174600 -P_11 1.224055 1.164100 1.175101 1.016257 0.987462 1.041390 0.905123 0.835165 -P_11 0.748054 0.629570 0.611567 0.562185 0.471352 0.452214 0.430713 0.419318 -P_11 0.440617 0.495432 0.477658 0.611431 0.716900 0.825546 0.898335 0.934168 -P_11 1.052421 1.115113 1.205807 1.352539 1.276215 1.215293 1.341207 1.463432 -P_11 1.325935 1.325759 1.266968 1.199545 1.126775 1.262707 1.240055 1.204093 -P_11 1.182149 1.114470 1.168258 1.235006 1.229416 1.246798 1.154585 1.192959 -P_11 1.098774 1.112255 0.975811 0.984317 0.908548 0.855883 0.856488 0.656405 -P_11 0.629123 0.592555 0.564521 0.541708 0.492373 0.456508 0.410793 0.421338 -P_11 0.386055 0.418113 0.431902 0.494910 0.618708 0.619749 0.667116 0.744261 -P_11 0.952671 1.008562 1.082904 1.119519 1.098060 1.160426 1.307009 1.253698 -P_11 1.246354 1.186335 1.266037 1.268629 1.282588 0.917003 1.019882 1.018203 -P_11 1.075804 0.931611 1.056863 1.185552 1.118727 1.082280 1.091969 1.142918 -P_11 1.092587 1.077760 0.971356 1.064059 1.071652 0.953670 0.808091 0.731049 -P_11 0.617947 0.532876 0.519317 0.421236 0.378245 0.340612 0.370742 0.408403 -P_11 0.431881 0.467610 0.540797 0.701797 0.820084 0.988483 1.055814 1.215089 -P_11 1.266309 1.384494 1.418156 1.420578 1.584715 1.498499 1.422398 1.427773 -P_11 1.347411 1.347473 1.351087 1.265065 1.174739 1.090277 1.034065 1.050211 -P_11 0.926434 1.047784 1.048937 0.991346 1.211268 1.125566 1.221000 1.201412 -P_11 1.229563 1.165609 1.206747 1.158200 1.062515 0.999266 0.764125 0.722289 -P_11 0.648768 0.579812 0.490084 0.412458 0.386855 0.345592 0.355586 0.370381 -P_11 0.440807 0.522037 0.536038 0.758710 0.812496 0.955986 1.090384 1.137144 -P_11 1.269743 1.334142 1.505307 1.424628 1.467861 1.429122 1.449149 1.387753 -P_11 1.323649 1.208033 1.178614 1.152696 1.064007 1.007612 0.979788 1.045816 -P_11 1.109858 1.080654 1.076382 1.191678 1.100139 1.170060 1.242364 1.272508 -P_11 1.308579 1.395946 1.248210 1.184851 1.140812 1.068774 0.953078 0.806521 -P_11 0.690424 0.544076 0.502405 0.411230 0.386153 0.364074 0.359218 0.417094 -P_11 0.460128 0.591553 0.701568 0.880695 0.892189 1.068011 1.011056 1.267261 -P_11 1.182895 1.308949 1.420051 1.626510 1.448800 1.432289 1.496594 1.375249 -P_11 1.278876 1.261520 1.363283 1.135747 1.169591 1.087392 1.149106 1.194428 -P_11 1.045502 1.169953 1.165108 1.118416 1.164695 1.163659 1.310783 1.282247 -P_11 1.246955 1.148278 1.140634 1.077212 1.041485 1.030346 0.858892 0.768344 -P_11 0.652541 0.565273 0.482714 0.438684 0.369001 0.374753 0.349751 0.401380 -P_11 0.445353 0.499917 0.641249 0.689436 0.820079 1.054501 1.127522 1.245766 -P_11 1.314322 1.338041 1.321898 1.450903 1.340360 1.470212 1.420207 1.259797 -P_11 1.425274 1.151210 1.124988 1.222038 1.163825 1.095147 1.105354 1.084357 -P_11 0.950450 1.181302 1.121275 1.140310 1.179110 1.159849 1.128816 1.217306 -P_11 1.266861 1.231076 1.231603 1.226730 1.014763 1.014530 0.943291 0.767397 -P_11 0.694522 0.625826 0.504284 0.473635 0.433652 0.383950 0.355094 0.423023 -P_11 0.514464 0.606276 0.754696 0.842255 0.987330 1.133579 1.367580 1.305433 -P_11 1.444956 1.478606 1.667347 1.735384 1.594229 1.617623 1.481357 1.459062 -P_11 1.446490 1.465022 1.159027 1.279004 1.234770 1.193181 1.140907 1.222418 -P_11 1.124903 1.179320 1.202773 1.205360 1.235142 1.246735 1.134498 1.151526 -P_11 1.208426 1.116157 1.117696 1.025468 1.036487 0.973022 0.886847 0.795500 -P_11 0.780453 0.666812 0.631417 0.526330 0.482069 0.413653 0.410155 0.398131 -P_11 0.427275 0.538357 0.523785 0.578470 0.727002 0.852013 0.927768 0.924645 -P_11 1.079045 1.194106 1.323762 1.330868 1.317375 1.410203 1.368914 1.284893 -P_11 1.314705 1.323169 1.343070 1.220833 1.226378 1.192033 1.118773 1.155976 -P_11 1.147623 1.154920 1.187825 1.229425 1.081492 1.301980 1.135771 1.201105 -P_11 1.092159 1.054254 1.001754 0.921396 0.910931 0.882294 0.760874 0.627093 -P_11 0.676413 0.599844 0.529499 0.494936 0.459544 0.415815 0.419245 0.405757 -P_11 0.444431 0.428839 0.507687 0.484432 0.579886 0.646322 0.739568 0.723498 -P_11 0.912399 1.011030 1.075011 1.167618 1.202328 1.187365 1.213763 1.289355 -P_11 1.159100 1.227502 1.189179 1.074782 1.106370 1.011451 0.960794 0.960114 -P_11 1.038847 0.946628 1.065569 1.025684 1.179621 1.100864 1.150060 1.229229 -P_11 1.235090 1.134037 1.146575 1.066509 0.941693 0.911332 0.873529 0.786850 -P_11 0.699024 0.554518 0.507007 0.421460 0.373398 0.334066 0.358296 0.366217 -P_11 0.449744 0.551311 0.582746 0.754235 0.913251 0.927014 1.084341 1.185476 -P_11 1.233249 1.284096 1.533203 1.534009 1.508076 1.515522 1.491680 1.483587 -P_11 1.418427 1.351749 1.187597 1.240914 1.104313 1.041785 0.923500 0.982692 -P_11 1.020730 0.944397 0.976525 1.063262 1.145214 1.037597 1.323957 1.244332 -P_11 1.310491 1.242940 1.152928 1.099796 1.071806 0.974408 0.869917 0.743941 -P_11 0.700087 0.582773 0.460182 0.426064 0.366780 0.345726 0.350481 0.397618 -P_11 0.454718 0.503115 0.554913 0.689555 0.834402 0.973923 1.079410 1.105870 -P_11 1.238892 1.443443 1.329444 1.473339 1.494813 1.423231 1.277767 1.376151 -P_11 1.231243 1.354460 1.206691 1.055071 1.134553 1.099541 1.022356 0.976327 -P_11 1.024094 1.119949 1.105896 1.144207 1.280431 1.217702 1.143395 1.255930 -P_11 1.149733 1.232404 1.198672 1.250020 0.945204 0.932516 0.892213 0.824704 -P_11 0.628913 0.589057 0.509010 0.446289 0.352603 0.365206 0.359676 0.416903 -P_11 0.450289 0.516169 0.681598 0.833747 0.876495 0.928061 1.103591 1.323043 -P_11 1.343202 1.333513 1.371380 1.469043 1.506268 1.448339 1.394091 1.367464 -P_11 1.284667 1.344327 1.234491 1.120108 1.149356 1.124521 1.159336 1.124150 -P_11 1.130946 1.140145 1.070688 1.114467 1.158879 1.072819 1.213547 1.281732 -P_11 1.123016 1.181702 1.169023 1.104515 1.065324 0.910296 0.943901 0.735719 -P_11 0.688585 0.604173 0.520029 0.441196 0.395376 0.349326 0.348536 0.402860 -P_11 0.475203 0.525859 0.701719 0.740719 0.864053 1.058104 1.087202 1.220025 -P_11 1.287503 1.423570 1.395088 1.463878 1.397623 1.423382 1.447491 1.323718 -P_11 1.249135 1.161517 1.208021 1.208495 1.174049 1.075765 1.131093 1.052330 -P_11 0.960402 1.098162 1.162708 1.135048 1.166015 1.239555 1.146593 1.248028 -P_11 1.209414 1.251846 1.135974 1.133255 1.040116 0.975499 0.863319 0.844479 -P_11 0.721261 0.590464 0.542771 0.468857 0.368624 0.409492 0.385385 0.439849 -P_11 0.532322 0.650819 0.580235 0.816357 1.020555 1.125738 1.224424 1.174886 -P_11 1.284656 1.575284 1.682928 1.620493 1.694627 1.503872 1.488374 1.649124 -P_11 1.398010 1.411626 1.227799 1.343775 1.205706 1.206676 1.054792 1.072976 -P_11 1.207825 1.249816 1.279735 1.224964 1.222958 1.165751 1.311420 1.262958 -P_11 1.226155 1.167832 1.088099 1.089524 0.980139 0.988242 0.866823 0.797868 -P_11 0.776554 0.715776 0.611139 0.497353 0.432986 0.411287 0.432955 0.425188 -P_11 0.424690 0.462241 0.533894 0.626879 0.728985 0.849705 0.912249 1.032774 -P_11 1.149909 1.112890 1.330810 1.284527 1.231199 1.424663 1.368516 1.366633 -P_11 1.137554 1.170155 1.204047 1.231201 1.166048 1.161262 1.066884 1.132544 -P_11 1.177934 1.086613 1.039931 1.154812 1.250032 1.122765 1.175709 1.243827 -P_11 1.127531 1.039084 1.055245 0.884100 0.874678 0.836048 0.772305 0.727356 -P_11 0.657384 0.590639 0.526197 0.541881 0.428769 0.408503 0.400190 0.374161 -P_11 0.421649 0.404342 0.447906 0.533189 0.557059 0.630920 0.697633 0.767665 -P_11 0.890876 0.962714 1.059203 1.100934 1.204688 1.249380 1.224019 1.249021 -P_11 1.330722 1.169852 1.122084 1.145991 1.053666 1.035794 1.109637 0.961289 -P_11 0.970174 0.927927 1.047814 1.027691 1.101610 1.107638 1.090030 1.083551 -P_11 1.167313 1.026677 1.143569 1.052834 1.105311 0.947959 0.844179 0.716210 -P_11 0.655075 0.542508 0.499652 0.423793 0.386305 0.340748 0.330832 0.355375 -P_11 0.430087 0.456061 0.643311 0.706919 0.825109 0.930448 1.017135 1.109923 -P_11 1.387283 1.537406 1.367787 1.535402 1.581651 1.552728 1.535917 1.538002 -P_11 1.333358 1.281614 1.329010 1.232706 1.074723 1.013288 0.919821 1.037035 -P_11 1.025070 1.027938 1.068011 1.094773 1.119939 1.152247 1.197275 1.199883 -P_11 1.223126 1.159942 1.302552 1.124652 1.080011 0.899751 0.855896 0.840178 -P_11 0.700577 0.595219 0.475095 0.441283 0.350475 0.347848 0.373846 0.370032 -P_11 0.461231 0.527301 0.604480 0.615313 0.812633 0.865877 1.133682 1.132351 -P_11 1.174436 1.393961 1.283168 1.480834 1.427569 1.468722 1.340649 1.303202 -P_11 1.226837 1.238538 1.256506 1.232319 1.080043 1.191129 1.068690 1.031962 -P_11 1.151709 0.997543 1.136337 1.182669 1.192249 1.174784 1.282298 1.186565 -P_11 1.234814 1.154038 1.213363 1.109252 1.050852 1.043921 0.837139 0.770144 -P_11 0.641642 0.547122 0.472966 0.384269 0.345034 0.365238 0.382943 0.402084 -P_11 0.472428 0.567607 0.681971 0.743815 0.876704 0.968753 1.193400 1.221368 -P_11 1.330920 1.369322 1.343703 1.571155 1.512273 1.583346 1.372458 1.436635 -P_11 1.437898 1.282364 1.134873 1.215351 1.097180 1.086616 1.126526 1.050191 -P_11 1.057828 1.212195 1.087725 1.155936 1.103523 1.259973 1.179823 1.264733 -P_11 1.222998 1.290595 1.107158 1.106672 1.027843 0.981499 0.862453 0.784142 -P_11 0.695558 0.567758 0.496219 0.382213 0.375371 0.349646 0.344177 0.437758 -P_11 0.454152 0.529763 0.603182 0.758219 0.826197 0.953099 1.130470 1.221468 -P_11 1.384876 1.342222 1.288269 1.368342 1.412802 1.323764 1.379817 1.371809 -P_11 1.214458 1.239893 1.215991 1.175119 1.167176 1.079586 1.069231 1.017111 -P_11 1.133801 1.093841 1.174532 1.175993 1.140835 1.150316 1.193049 1.157515 -P_11 1.224096 1.326574 1.178378 1.134004 1.068174 1.000820 0.879687 0.786285 -P_11 0.708431 0.620614 0.525645 0.448795 0.394258 0.377536 0.428611 0.474979 -P_11 0.486543 0.601653 0.731551 0.888676 0.984667 1.144473 1.225891 1.358233 -P_11 1.499518 1.461121 1.677778 1.471562 1.591585 1.521843 1.597401 1.449567 -P_11 1.437259 1.300220 1.278158 1.264854 1.076026 1.212556 1.099752 1.158648 -P_11 1.033004 1.096586 1.129642 1.100060 1.170289 1.196087 1.277480 1.097130 -P_11 1.265954 1.134262 1.126620 1.086033 0.997735 0.922059 0.891117 0.816996 -P_11 0.741206 0.668982 0.545696 0.521582 0.482310 0.413461 0.448801 0.409064 -P_11 0.425364 0.482526 0.554032 0.669242 0.663392 0.753122 0.941393 1.053184 -P_11 1.084243 1.192839 1.262713 1.367313 1.292748 1.278620 1.321360 1.404318 -P_11 1.286994 1.380821 1.201273 1.158396 1.161918 1.303346 1.169083 1.262081 -P_11 1.174424 1.174089 1.077501 1.255389 1.182773 1.058880 1.128670 1.109253 -P_11 1.111344 0.962205 0.962706 0.974969 0.955912 0.940506 0.730470 0.727014 -P_11 0.645492 0.604219 0.574963 0.432897 0.474224 0.463243 0.413969 0.406238 -P_11 0.402004 0.445536 0.449601 0.532440 0.562872 0.681991 0.722273 0.768515 -P_11 0.810044 0.930516 1.094630 1.066897 1.219644 1.195706 1.235987 1.142871 -P_11 1.208920 1.307944 1.139561 1.202628 1.105768 1.159317 1.073605 1.038381 -P_11 0.982572 0.976937 1.049887 1.044676 1.157874 1.174500 1.054374 1.074667 -P_11 1.108849 1.075793 1.128458 1.154399 0.913856 0.837615 0.804127 0.703706 -P_11 0.670733 0.596539 0.464039 0.403847 0.358960 0.352644 0.358445 0.400482 -P_11 0.408125 0.528347 0.599089 0.774802 0.808972 0.961790 1.018666 1.185401 -P_11 1.237032 1.391067 1.421701 1.543433 1.458239 1.487152 1.536785 1.443489 -P_11 1.374078 1.320541 1.313763 1.171925 1.101383 1.156662 1.016953 1.025681 -P_11 1.031792 1.095618 1.039507 1.105851 1.077229 1.075600 1.113928 1.324069 -P_11 1.273941 1.303554 1.139884 1.156634 1.035205 1.001067 0.919921 0.752597 -P_11 0.761935 0.613999 0.505049 0.457390 0.377802 0.357745 0.367498 0.392203 -P_11 0.454724 0.575234 0.598669 0.779986 0.799006 1.033380 1.110270 1.127742 -P_11 1.217574 1.351674 1.378358 1.515811 1.361900 1.405814 1.466929 1.486842 -P_11 1.359836 1.265168 1.287713 1.136886 1.099194 1.007975 1.017700 1.050503 -P_11 0.986134 1.156146 1.020799 1.123940 1.083241 1.272011 1.275120 1.239294 -P_11 1.238918 1.173118 1.282793 1.151450 1.079667 1.040474 0.885235 0.802565 -P_11 0.734178 0.535487 0.485360 0.396212 0.376747 0.344152 0.366028 0.405021 -P_11 0.474744 0.558390 0.664701 0.761858 0.937378 0.994815 1.121497 1.301775 -P_11 1.220853 1.418851 1.424534 1.553649 1.484235 1.614478 1.373493 1.245540 -P_11 1.392783 1.225887 1.338660 1.274845 1.119290 1.190297 1.054430 1.093530 -P_11 1.229855 1.019170 1.082887 1.185893 1.253228 1.301385 1.246903 1.227945 -P_11 1.256280 1.252433 1.220700 1.163064 1.003142 0.999849 0.909019 0.838217 -P_11 0.679249 0.609979 0.542489 0.431479 0.351041 0.351374 0.384516 0.377661 -P_11 0.443743 0.533982 0.651282 0.740767 0.810949 0.998635 0.958283 1.327765 -P_11 1.187555 1.385334 1.366684 1.424652 1.302163 1.374707 1.444625 1.318306 -P_11 1.196984 1.218968 1.219601 1.117559 1.135427 1.105460 0.991495 0.969618 -P_11 1.107104 1.042076 1.108436 1.121282 1.146474 1.235596 1.245154 1.233425 -P_11 1.215101 1.174331 1.118900 1.066875 1.059563 1.000115 0.950613 0.768402 -P_11 0.677854 0.613833 0.487753 0.461916 0.417367 0.374917 0.407871 0.427111 -P_11 0.526508 0.615657 0.770101 0.826261 0.959071 1.156658 1.317855 1.356670 -P_11 1.466648 1.428418 1.741011 1.622061 1.606785 1.590429 1.606163 1.663705 -P_11 1.521417 1.354406 1.292335 1.232859 1.256347 1.120764 1.133877 1.057466 -P_11 1.170639 1.131572 1.155733 1.275855 1.111454 1.199930 1.135830 1.208988 -P_11 1.177236 1.130286 1.180872 1.148338 1.070502 1.007214 0.917871 0.736822 -P_11 0.776230 0.643678 0.587830 0.557868 0.460904 0.415765 0.425839 0.423231 -P_11 0.435845 0.485372 0.573150 0.580225 0.694900 0.808494 0.976164 1.019725 -P_11 1.075037 1.193447 1.252251 1.299105 1.271717 1.287232 1.360971 1.361539 -P_11 1.305018 1.243441 1.277028 1.329426 1.256267 1.258269 1.204623 1.209789 -P_11 1.137101 1.234857 1.199404 1.198269 1.205208 1.298746 1.080196 1.084931 -P_11 1.101579 1.041746 1.079894 0.948053 0.931616 0.858122 0.762982 0.716566 -P_11 0.691588 0.610045 0.590763 0.511852 0.445223 0.431439 0.449841 0.432308 -P_11 0.408247 0.426047 0.467383 0.481462 0.591184 0.589456 0.798487 0.774237 -P_11 0.951078 0.942160 1.010606 1.147710 1.140961 1.134597 1.160300 1.271359 -P_11 1.259017 1.187680 1.184762 1.181813 1.150792 1.141857 1.046849 0.980504 -P_11 1.001265 1.094925 0.993144 1.094003 1.224409 1.130402 1.105878 1.185264 -P_11 1.142597 1.118989 1.094041 1.081779 0.962344 0.893883 0.832155 0.698678 -P_11 0.643743 0.565580 0.463587 0.418878 0.396932 0.357807 0.354927 0.351826 -P_11 0.419637 0.524045 0.623063 0.736927 0.835975 0.966469 1.061305 1.219011 -P_11 1.289796 1.404000 1.530509 1.496891 1.431185 1.509936 1.568925 1.540919 -P_11 1.411175 1.350328 1.219566 1.167940 1.133424 1.012490 1.071914 1.060831 -P_11 0.945602 1.026662 1.057902 1.071693 1.225854 1.201004 1.203219 1.199936 -P_11 1.159365 1.139081 1.156341 1.203364 1.015613 1.026401 0.900339 0.800384 -P_11 0.728861 0.630452 0.528496 0.433284 0.358150 0.349706 0.320502 0.366031 -P_11 0.439962 0.525517 0.610794 0.673809 0.881307 0.895392 0.916468 1.111280 -P_11 1.320955 1.448309 1.399844 1.293215 1.501650 1.533732 1.415479 1.312702 -P_11 1.287234 1.336808 1.249939 1.162748 1.106676 1.039348 1.007493 1.135322 -P_11 0.978192 1.168769 1.150873 1.114965 1.213299 1.233979 1.238090 1.266203 -P_11 1.355167 1.321773 1.241095 1.189254 1.006348 1.036176 0.894492 0.735368 -P_11 0.684562 0.577589 0.438864 0.390938 0.379891 0.366513 0.375580 0.420055 -P_11 0.463035 0.603880 0.622031 0.789525 0.852106 0.971156 1.128985 1.297583 -P_11 1.327413 1.468853 1.497200 1.458844 1.422379 1.557447 1.379173 1.399569 -P_11 1.393523 1.292259 1.309263 1.159853 1.171578 1.116613 1.215365 1.101842 -P_11 1.057658 1.181111 1.085397 1.061061 1.146525 1.215410 1.169707 1.245783 -P_11 1.302757 1.183022 1.274445 1.093559 0.996685 1.025196 0.862320 0.795303 -P_11 0.651866 0.600634 0.497128 0.437738 0.345554 0.343481 0.359890 0.408389 -P_11 0.437470 0.568240 0.653968 0.832268 0.947808 0.960264 1.072512 1.319650 -P_11 1.243545 1.316516 1.413531 1.351940 1.252758 1.287640 1.445001 1.321421 -P_11 1.283397 1.266937 1.131884 1.195261 1.246589 1.122717 0.934398 1.043284 -P_11 1.114102 1.084817 1.226214 1.161856 1.173819 1.236070 1.174040 1.247489 -P_11 1.261456 1.278018 1.179568 1.201060 1.060318 0.989702 0.914091 0.800119 -P_11 0.677788 0.615939 0.505740 0.437880 0.420911 0.379791 0.360110 0.456770 -P_11 0.491523 0.625581 0.743404 0.876497 0.984761 1.090401 1.302831 1.321432 -P_11 1.455963 1.502864 1.514382 1.610209 1.533296 1.553140 1.599727 1.536866 -P_11 1.439161 1.300863 1.354905 1.172077 1.237481 1.237445 1.184225 1.192361 -P_11 1.131385 1.251715 1.174382 1.234622 1.185347 1.185703 1.225871 1.262019 -P_11 1.241397 1.106641 1.152572 1.071209 1.058824 0.956053 0.910999 0.794557 -P_11 0.752326 0.643660 0.612257 0.554493 0.509926 0.453167 0.400930 0.426035 -P_11 0.418044 0.500875 0.558237 0.643717 0.669400 0.749839 0.866052 1.070622 -P_11 1.148978 1.240656 1.289056 1.314450 1.294634 1.213330 1.374370 1.309940 -P_11 1.432003 1.326229 1.134066 1.286741 1.176039 1.235568 1.255154 1.041916 -P_11 1.182650 1.152745 1.177366 1.248595 1.205933 1.283645 1.131747 1.121104 -P_11 1.194199 1.082898 0.982191 0.968921 0.928751 0.845559 0.778919 0.740163 -P_11 0.656986 0.662576 0.530722 0.530668 0.492808 0.441551 0.395053 0.404158 -P_11 0.413039 0.427842 0.438425 0.529854 0.601011 0.623859 0.767024 0.800563 -P_11 0.857407 1.039483 1.050724 1.143646 1.156991 1.220334 1.238833 1.246016 -P_11 1.308455 1.155591 1.214502 1.226104 1.171291 1.232398 1.138298 1.100813 -P_11 1.031421 0.999492 1.006262 1.158493 1.070044 1.100092 1.051790 1.132906 -P_11 1.194487 1.131279 1.073234 1.078546 1.048844 0.967110 0.774482 0.764430 -P_11 0.645906 0.585176 0.476537 0.448964 0.376493 0.366661 0.366284 0.407608 -P_11 0.469895 0.483529 0.640593 0.754110 0.888281 0.959509 1.184600 1.197461 -P_11 1.255016 1.481037 1.485937 1.567004 1.565577 1.556420 1.636889 1.526150 -P_11 1.340532 1.325614 1.267536 1.201825 1.146254 1.108473 0.944011 1.042881 -P_11 0.977265 0.949776 1.030145 1.085235 1.130039 1.144033 1.112215 1.259288 -P_11 1.202670 1.302940 1.176736 1.134426 1.106293 1.032099 0.864487 0.718224 -P_11 0.721436 0.602977 0.516690 0.416243 0.357437 0.358470 0.369878 0.375398 -P_11 0.485629 0.527196 0.632482 0.718199 0.807813 1.016072 1.024518 1.098581 -P_11 1.337256 1.385642 1.377508 1.395530 1.545235 1.592758 1.419242 1.415191 -P_11 1.372678 1.283839 1.214064 1.209068 1.099964 0.940166 1.127708 1.116691 -P_11 1.064492 1.036140 1.081592 1.164401 1.191883 1.373558 1.220987 1.326847 -P_11 1.425529 1.419774 1.314078 1.229083 0.975497 1.044167 0.832379 0.708717 -P_11 0.612136 0.546451 0.466062 0.403244 0.382741 0.399897 0.385147 0.400177 -P_11 0.505412 0.533210 0.660771 0.820950 0.930060 0.945355 1.152501 1.126992 -P_11 1.405112 1.405707 1.396093 1.526189 1.427172 1.430067 1.470949 1.334129 -P_11 1.361498 1.251273 1.191496 1.195669 1.200682 1.172511 1.173973 1.161448 -P_11 1.118279 1.199123 1.125393 1.151863 1.253930 1.191934 1.161949 1.340018 -P_11 1.260992 1.297799 1.141017 1.172547 1.088703 1.044140 0.866475 0.812563 -P_11 0.671610 0.608137 0.524448 0.422214 0.413446 0.355886 0.374150 0.395922 -P_11 0.462763 0.552691 0.623111 0.778371 0.865198 1.010761 1.091409 1.204498 -P_11 1.252375 1.321600 1.471335 1.494001 1.421825 1.442941 1.424237 1.336223 -P_11 1.353426 1.261887 1.130829 1.207262 1.116257 1.065387 1.166619 1.144065 -P_11 1.115337 1.162593 1.215252 1.143826 1.168944 1.211507 1.140497 1.201350 -P_11 1.372425 1.210698 1.164175 1.197987 1.084710 1.094052 0.910217 0.805278 -P_11 0.708277 0.596032 0.556991 0.462219 0.461850 0.408353 0.416392 0.415462 -P_11 0.515440 0.678310 0.706448 0.904642 1.027673 1.152860 1.244395 1.442419 -P_11 1.662833 1.531619 1.670834 1.760757 1.555932 1.551270 1.525644 1.494870 -P_11 1.461194 1.430752 1.362036 1.328753 1.296677 1.204285 1.148902 1.150365 -P_11 1.202592 1.106830 1.162577 1.223375 1.339393 1.265827 1.174709 1.186824 -P_11 1.196019 1.182380 1.013194 1.096787 1.043563 1.009359 0.921382 0.835523 -P_11 0.789724 0.661966 0.615134 0.552470 0.476942 0.433665 0.415628 0.431494 -P_11 0.479946 0.475439 0.590245 0.617557 0.742048 0.879392 0.989172 1.078132 -P_11 1.048857 1.098532 1.228981 1.195492 1.319697 1.417854 1.445933 1.264203 -P_11 1.253141 1.313372 1.208156 1.305790 1.229480 1.252356 1.178535 1.227966 -P_11 1.248729 1.176910 1.282136 1.330301 1.287561 1.206128 1.223011 1.160435 -P_11 1.124682 1.085950 0.936172 0.886092 0.861913 0.945881 0.881236 0.750281 -P_11 0.650547 0.595148 0.517506 0.533222 0.498517 0.412377 0.424436 0.406669 -P_11 0.427237 0.453812 0.484030 0.527847 0.596045 0.636951 0.777702 0.846636 -P_11 0.910970 0.933569 1.159045 1.196167 1.155417 1.168802 1.291845 1.248050 -P_11 1.331901 1.208166 1.185290 1.091777 1.197595 1.130359 1.156173 1.043384 -P_11 0.995443 1.082247 1.086870 1.092138 1.171097 1.184042 1.171009 1.129251 -P_11 1.196502 1.136245 1.177065 1.006639 1.057252 0.990560 0.806373 0.715006 -P_11 0.674688 0.596879 0.477152 0.400357 0.384736 0.345030 0.390264 0.382136 -P_11 0.436403 0.540067 0.625347 0.738011 0.868877 0.929536 1.077807 1.303192 -P_11 1.308544 1.445959 1.488132 1.673915 1.621009 1.499495 1.729104 1.435563 -P_11 1.384878 1.488655 1.410115 1.254332 1.210309 0.922433 1.094621 1.041779 -P_11 1.006103 1.096216 1.076952 1.042754 1.159317 1.169323 1.185535 1.292885 -P_11 1.339439 1.454646 1.247225 1.239715 1.079589 1.017527 0.961257 0.795806 -P_11 0.669750 0.636062 0.508515 0.465737 0.402315 0.365859 0.375668 0.367156 -P_11 0.445207 0.517639 0.696304 0.746961 0.897774 1.013111 1.066200 1.224239 -P_11 1.315169 1.436421 1.477427 1.578697 1.511284 1.429249 1.419852 1.344206 -P_11 1.301347 1.269523 1.354562 1.202774 1.114879 1.175267 1.122318 1.057446 -P_11 0.995325 1.098116 1.150969 1.248695 1.139510 1.424231 1.309626 1.265274 -P_11 1.269050 1.142372 1.304585 1.151341 1.048151 0.991843 0.997258 0.824231 -P_11 0.770136 0.618110 0.454012 0.423988 0.410712 0.347221 0.380382 0.429112 -P_11 0.495651 0.543904 0.635078 0.839139 0.928641 1.100387 1.091290 1.275108 -P_11 1.347814 1.361963 1.513410 1.515880 1.549046 1.570473 1.531937 1.496962 -P_11 1.364767 1.445763 1.259780 1.308964 1.201413 1.144361 1.058962 1.084515 -P_11 1.067819 1.115752 1.073947 1.298721 1.184437 1.225883 1.355527 1.236817 -P_11 1.234740 1.283560 1.277792 1.187193 1.067107 0.973020 0.933707 0.839966 -P_11 0.724424 0.618129 0.522555 0.424237 0.371577 0.377954 0.397452 0.412422 -P_11 0.478947 0.601162 0.647816 0.705950 0.870143 1.000366 1.240838 1.236049 -P_11 1.283625 1.415380 1.478982 1.471722 1.491464 1.537659 1.419725 1.420085 -P_11 1.335583 1.317137 1.212590 1.191849 1.207308 1.069927 1.117433 1.165492 -P_11 1.110915 1.098364 1.230289 1.288226 1.352962 1.233659 1.338910 1.264950 -P_11 1.187664 1.236742 1.201862 1.251855 1.187802 0.999618 1.038339 0.848057 -P_11 0.753398 0.623504 0.565381 0.445184 0.428499 0.419899 0.413348 0.451048 -P_11 0.582696 0.638985 0.782351 0.873076 0.958137 1.172899 1.297772 1.431366 -P_11 1.606878 1.586869 1.738424 1.746603 1.557595 1.726378 1.576265 1.464161 -P_11 1.555445 1.341120 1.310077 1.270847 1.245504 1.110572 1.242755 1.115396 -P_11 1.106093 1.225324 1.240789 1.205706 1.213487 1.182083 1.291196 1.166048 -P_11 1.190054 1.140303 1.246779 1.150012 1.047483 1.064878 0.918762 0.899880 -P_11 0.817851 0.693764 0.665029 0.500756 0.508074 0.462963 0.441224 0.435229 -P_11 0.448459 0.447515 0.550323 0.619480 0.719961 0.860721 0.912339 1.079568 -P_11 1.213747 1.254875 1.288307 1.349586 1.459919 1.418725 1.470366 1.320756 -P_11 1.389460 1.294102 1.321946 1.306271 1.302936 1.197385 1.186635 1.293126 -P_11 1.089269 1.200625 1.223167 1.284228 1.262540 1.316614 1.288139 1.190814 -P_11 1.149275 1.144668 1.129328 0.951241 0.946025 0.893529 0.815147 0.723432 -P_11 0.726305 0.641382 0.572376 0.525186 0.468636 0.453911 0.445745 0.422487 -P_11 0.422569 0.424239 0.495493 0.509601 0.584603 0.704487 0.746904 0.837335 -P_11 0.935406 1.079870 1.265587 1.180347 1.268746 1.261771 1.425822 1.356412 -P_11 1.234314 1.267943 1.332121 1.274171 1.072550 1.171010 1.209023 1.025773 -P_11 1.020744 1.073858 1.052523 1.130164 1.076389 1.321981 1.195700 1.116405 -P_11 1.136506 1.175597 1.108815 1.156663 1.020461 0.906498 0.769627 0.869499 -P_11 0.687183 0.561039 0.500544 0.450480 0.403650 0.364655 0.363556 0.363665 -P_11 0.469813 0.543040 0.610555 0.788576 0.778732 0.931419 1.130121 1.260365 -P_11 1.328395 1.352943 1.610578 1.519784 1.652327 1.529430 1.593417 1.633041 -P_11 1.526014 1.295407 1.399472 1.364090 1.186362 1.142928 1.112267 0.984423 -P_11 1.131408 0.947436 1.104087 1.159688 1.254531 1.197575 1.280147 1.363854 -P_11 1.278034 1.219806 1.355051 1.172048 1.151203 1.049164 0.989761 0.842805 -P_11 0.710208 0.599917 0.528828 0.448965 0.446035 0.384770 0.398098 0.394642 -P_11 0.454183 0.540798 0.675906 0.848992 0.895546 0.997003 1.100783 1.328043 -P_11 1.447999 1.465422 1.458137 1.451404 1.642168 1.587240 1.564730 1.345933 -P_11 1.477745 1.293878 1.268396 1.196698 1.135305 1.121134 0.966984 1.106219 -P_11 1.102277 1.153407 1.129115 1.184449 1.278653 1.370377 1.226291 1.330473 -P_11 1.320890 1.338639 1.254952 1.172787 1.082819 1.074101 0.871173 0.804047 -P_11 0.724670 0.618166 0.531189 0.429998 0.377321 0.345865 0.410190 0.416337 -P_11 0.502701 0.546603 0.699279 0.815430 0.981605 1.045448 1.212264 1.233420 -P_11 1.345065 1.623369 1.333362 1.678274 1.536830 1.518806 1.631556 1.542744 -P_11 1.607445 1.462563 1.328055 1.301972 1.270534 1.242658 1.158459 1.214817 -P_11 1.114006 1.060349 1.156590 1.192988 1.171117 1.356984 1.403575 1.299415 -P_11 1.231534 1.308154 1.290209 1.309858 1.151921 1.060802 0.960349 0.838727 -P_11 0.769093 0.702043 0.531881 0.447670 0.438724 0.358727 0.410118 0.417351 -P_11 0.471459 0.604009 0.637719 0.730278 0.884924 1.096736 1.127226 1.240777 -P_11 1.322403 1.424857 1.486211 1.588366 1.383857 1.468041 1.598343 1.454325 -P_11 1.437458 1.461733 1.295995 1.325058 1.112736 1.105139 1.273608 1.197136 -P_11 1.105898 1.181994 1.098044 1.205948 1.236742 1.387712 1.257575 1.348679 -P_11 1.361950 1.222255 1.269013 1.223767 1.179157 1.082250 0.912938 0.843451 -P_11 0.693731 0.634639 0.498572 0.462101 0.394753 0.422804 0.422265 0.511591 -P_11 0.502664 0.656332 0.746295 0.850900 1.015756 1.167960 1.420050 1.479981 -P_11 1.694881 1.425971 1.749667 1.661514 1.808713 1.766886 1.761265 1.672811 -P_11 1.619852 1.414251 1.353265 1.416702 1.225492 1.147358 1.127189 1.209577 -P_11 1.251794 1.248225 1.290820 1.373122 1.314853 1.280788 1.476924 1.261105 -P_11 1.312290 1.327532 1.152990 1.137292 1.132380 0.999749 1.028367 0.876309 -P_11 0.831256 0.665148 0.648094 0.595297 0.500460 0.454795 0.430498 0.451762 -P_11 0.449193 0.520399 0.553595 0.671984 0.710746 0.962001 0.913844 1.002479 -P_11 1.342224 1.359240 1.315408 1.486634 1.269723 1.549011 1.377619 1.522840 -P_11 1.429758 1.485512 1.327043 1.251614 1.295662 1.220943 1.176915 1.275845 -P_11 1.225863 1.175057 1.282910 1.279679 1.184275 1.216026 1.201760 1.155774 -P_11 1.230774 1.151218 1.129232 1.059848 0.929247 0.871913 0.835802 0.752099 -P_11 0.705190 0.584323 0.609361 0.548787 0.515048 0.478693 0.439634 0.460270 -P_11 0.447244 0.468474 0.508958 0.512873 0.627346 0.725693 0.743537 0.828084 -P_11 0.997247 1.053808 1.159987 1.160378 1.157775 1.325304 1.342112 1.278247 -P_11 1.354465 1.456835 1.264248 1.314226 1.210126 1.197599 1.170179 1.058781 -P_11 1.118152 1.153665 1.185852 1.179704 1.216778 1.113228 1.352702 1.167293 -P_11 1.312130 1.133025 1.173509 1.101686 1.092107 0.988737 0.862747 0.790119 -P_11 0.666278 0.624263 0.481678 0.447648 0.427804 0.416162 0.407943 0.446330 -P_11 0.479829 0.551316 0.629459 0.784458 0.896696 1.123243 1.198283 1.306240 -P_11 1.491598 1.394850 1.509675 1.563953 1.680592 1.597415 1.667649 1.514642 -P_11 1.683162 1.508255 1.396408 1.190757 1.390164 1.220979 1.119058 1.160232 -P_11 1.085038 0.990293 1.054090 1.189516 1.260946 1.196831 1.316591 1.261749 -P_11 1.374861 1.344179 1.278915 1.191536 1.190638 1.089701 0.986292 0.897431 -P_11 0.695033 0.688971 0.573439 0.506332 0.426251 0.379824 0.375890 0.400146 -P_11 0.480651 0.569729 0.657312 0.741406 0.885838 0.962698 1.134810 1.344258 -P_11 1.362291 1.592068 1.512299 1.582626 1.628775 1.545453 1.489466 1.415086 -P_11 1.401534 1.322877 1.296699 1.253050 1.155356 1.166270 1.075524 1.203134 -P_11 1.148563 1.083603 1.236978 1.223863 1.216616 1.355969 1.438518 1.497254 -P_11 1.362280 1.434988 1.291506 1.287234 1.221481 1.097253 0.918323 0.815229 -P_11 0.663157 0.615454 0.542133 0.450119 0.400888 0.388102 0.435422 0.436053 -P_11 0.530152 0.619920 0.654829 0.823376 0.974965 1.054520 1.177281 1.397065 -P_11 1.298517 1.449560 1.511939 1.653910 1.529188 1.561432 1.561574 1.474115 -P_11 1.383452 1.512052 1.375296 1.321331 1.408594 1.358049 1.305254 1.120102 -P_11 1.190994 1.188095 1.226978 1.123377 1.221140 1.281817 1.331041 1.279809 -P_11 1.307145 1.432672 1.338343 1.290561 1.134439 1.194230 0.939792 0.789248 -P_11 0.747289 0.670353 0.549768 0.473806 0.403983 0.397560 0.401697 0.462950 -P_11 0.509990 0.533254 0.777114 0.786177 1.067554 1.060296 1.223121 1.217150 -P_11 1.341209 1.399514 1.526033 1.517239 1.510880 1.421676 1.677651 1.721273 -P_11 1.379150 1.268939 1.297890 1.222407 1.192338 1.298413 1.095313 1.158112 -P_11 1.142905 1.165940 1.261952 1.361601 1.322811 1.292452 1.426109 1.200031 -P_11 1.305353 1.366007 1.343812 1.299954 1.150355 1.104565 0.940745 0.942705 -P_11 0.712819 0.734837 0.558931 0.454702 0.446140 0.429946 0.444430 0.473246 -P_11 0.536536 0.687947 0.780209 0.959330 1.065781 1.130867 1.510780 1.632013 -P_11 1.668734 1.866792 1.689690 1.695483 1.679869 1.816826 1.834892 1.658488 -P_11 1.470326 1.453720 1.377982 1.434981 1.278332 1.202128 1.144317 1.311304 -P_11 1.296243 1.325785 1.381558 1.409326 1.360885 1.318687 1.290067 1.240720 -P_11 1.409407 1.198940 1.215601 1.239944 1.146150 1.073848 1.069906 0.969500 -P_11 0.739348 0.693938 0.711046 0.610669 0.546082 0.474757 0.440476 0.468407 -P_11 0.485925 0.531085 0.534308 0.662171 0.754418 0.971863 1.073635 1.174399 -P_11 1.242683 1.291324 1.331420 1.425975 1.591129 1.638467 1.523613 1.553481 -P_11 1.432657 1.454091 1.492148 1.441747 1.447780 1.341596 1.328646 1.252351 -P_11 1.351692 1.150163 1.294762 1.347785 1.342828 1.205497 1.196943 1.297675 -P_11 1.227721 1.185815 1.212180 1.040047 1.062006 0.911390 0.941155 0.773115 -P_11 0.676501 0.615622 0.599661 0.529178 0.506133 0.522586 0.462222 0.444596 -P_11 0.476326 0.458907 0.501623 0.501893 0.647810 0.740618 0.734430 0.876533 -P_11 0.973383 1.118527 1.196572 1.223004 1.327818 1.401825 1.341123 1.364535 -P_11 1.300158 1.422640 1.316792 1.227614 1.115844 1.171373 1.142100 1.148457 -P_11 1.179398 1.057095 1.074666 1.179842 1.161436 1.170546 1.293122 1.288110 -P_11 1.326523 1.223587 1.167482 1.174064 1.128494 0.989497 0.879550 0.825656 -P_11 0.706763 0.624912 0.494999 0.470934 0.446715 0.378914 0.355966 0.423083 -P_11 0.485015 0.499617 0.727180 0.814147 0.911181 1.006628 1.201000 1.445794 -P_11 1.529180 1.442246 1.669225 1.555869 1.707673 1.658350 1.689649 1.653039 -P_11 1.484725 1.609176 1.287775 1.330865 1.147972 1.168080 1.172266 1.037966 -P_11 1.156244 1.128729 1.117151 1.180292 1.332151 1.151267 1.374310 1.362074 -P_11 1.311345 1.273317 1.387603 1.198507 1.110113 1.006793 0.995080 0.851666 -P_11 0.794804 0.653637 0.617439 0.443181 0.455360 0.412877 0.396593 0.431770 -P_11 0.454041 0.585435 0.660720 0.733546 0.943074 1.077281 1.145859 1.324101 -P_11 1.476866 1.372132 1.752383 1.522914 1.537851 1.568865 1.391662 1.547005 -P_11 1.348158 1.367227 1.391697 1.247369 1.252190 1.179275 1.156382 1.050684 -P_11 1.169147 1.168156 1.146971 1.326089 1.341854 1.420369 1.470302 1.479973 -P_11 1.403787 1.432510 1.426533 1.274141 1.172996 1.118056 0.981795 0.848101 -P_11 0.753186 0.613568 0.526279 0.461266 0.432561 0.376836 0.399240 0.453059 -P_11 0.519920 0.676836 0.719819 0.861712 0.984639 1.077328 1.183104 1.275397 -P_11 1.486633 1.444175 1.449147 1.576147 1.634014 1.619947 1.562711 1.542145 -P_11 1.586363 1.378883 1.375675 1.262471 1.417644 1.290027 1.267153 1.233250 -P_11 1.279993 1.145630 1.151104 1.243370 1.322738 1.365320 1.313169 1.470278 -P_11 1.301507 1.353042 1.260372 1.276141 1.194478 1.107840 0.995681 0.885411 -P_11 0.775404 0.642168 0.516191 0.534937 0.451467 0.392735 0.413289 0.474560 -P_11 0.526214 0.600293 0.686641 0.869904 0.993134 1.088905 1.257567 1.292998 -P_11 1.304510 1.392068 1.696174 1.600084 1.522186 1.537549 1.381954 1.492986 -P_11 1.404729 1.381156 1.348445 1.188426 1.257634 1.263996 1.136624 1.139149 -P_11 1.144223 1.242788 1.281135 1.265824 1.462743 1.242944 1.426653 1.418114 -P_11 1.469887 1.342945 1.347878 1.186518 1.215743 1.123675 0.922746 0.857751 -P_11 0.721549 0.682042 0.583899 0.480110 0.490557 0.414342 0.436630 0.462564 -P_11 0.564727 0.651261 0.884216 0.951021 1.082974 1.258940 1.251083 1.600189 -P_11 1.612376 1.765546 1.875809 1.798625 1.920648 1.642692 1.774323 1.706812 -P_11 1.562744 1.584857 1.531404 1.526508 1.225711 1.361530 1.178044 1.264062 -P_11 1.200031 1.366516 1.370263 1.283879 1.438910 1.304906 1.353142 1.468879 -P_11 1.327606 1.291348 1.316825 1.206064 1.145682 1.060492 1.020701 0.924877 -P_11 0.818889 0.764369 0.682197 0.572652 0.495392 0.467841 0.494310 0.459647 -P_11 0.505222 0.526041 0.554856 0.628606 0.815852 0.911471 1.093799 1.154456 -P_11 1.277140 1.329686 1.415183 1.426040 1.547101 1.457594 1.541247 1.536166 -P_11 1.475990 1.481486 1.344843 1.429053 1.215340 1.367354 1.272966 1.269288 -P_11 1.324489 1.248008 1.399284 1.283168 1.344136 1.378510 1.310286 1.367972 -P_11 1.198613 1.127339 1.241214 1.084722 1.091924 0.926606 0.901060 0.746683 -P_11 0.715195 0.734165 0.653125 0.528045 0.568454 0.476864 0.496244 0.445271 -P_11 0.441387 0.459231 0.511076 0.532145 0.599501 0.748389 0.819010 0.899448 -P_11 0.994298 1.186791 1.058605 1.135921 1.390372 1.370441 1.432873 1.352979 -P_11 1.368233 1.370837 1.383862 1.253691 1.088969 1.112468 1.292814 1.128294 -P_11 1.134933 1.104198 1.259149 1.199389 1.257052 1.263210 1.236244 1.270048 -P_11 1.209988 1.358613 1.362124 1.232972 1.129735 1.086486 0.947644 0.857544 -P_11 0.685324 0.608337 0.547876 0.480964 0.397608 0.417280 0.367810 0.430458 -P_11 0.478972 0.570109 0.632960 0.837096 1.084003 1.006714 1.189377 1.314501 -P_11 1.391165 1.638589 1.633907 1.665794 1.730273 1.792128 1.634656 1.578909 -P_11 1.610483 1.464692 1.381295 1.300914 1.242046 1.216525 1.167129 1.195323 -P_11 1.089570 1.084604 1.118828 1.296496 1.285270 1.212581 1.330653 1.324889 -P_11 1.409839 1.395812 1.362407 1.226975 1.269258 0.980256 1.058495 0.894787 -P_11 0.717615 0.699578 0.582917 0.474290 0.476072 0.376680 0.398631 0.416822 -P_11 0.522654 0.613551 0.674868 0.835128 0.924203 1.132035 1.260614 1.346648 -P_11 1.426537 1.456081 1.500567 1.649978 1.673481 1.595731 1.489916 1.492297 -P_11 1.507047 1.442290 1.364420 1.310166 1.242128 1.148714 1.160094 1.185533 -P_11 1.281427 1.181896 1.207707 1.230582 1.328578 1.333317 1.230527 1.542489 -P_11 1.402045 1.376362 1.350883 1.151334 1.214749 1.022381 0.955699 0.842720 -P_11 0.777133 0.670848 0.526707 0.507269 0.435585 0.415119 0.390700 0.445362 -P_11 0.551332 0.623627 0.685322 0.896625 1.037613 1.059046 1.248101 1.343155 -P_11 1.375372 1.576165 1.570086 1.661600 1.696338 1.571233 1.719127 1.498646 -P_11 1.659632 1.492165 1.420406 1.437319 1.309866 1.277136 1.269322 1.278877 -P_11 1.310063 1.250376 1.263322 1.262602 1.241595 1.256698 1.307332 1.426106 -P_11 1.411926 1.489172 1.383151 1.360874 1.206638 1.083363 1.012826 0.888268 -P_11 0.740254 0.666756 0.556086 0.491801 0.433342 0.418192 0.400113 0.484075 -P_11 0.537927 0.630278 0.710292 0.808126 0.994495 1.153475 1.243383 1.480599 -P_11 1.472496 1.620042 1.522002 1.590662 1.569959 1.495180 1.507472 1.461916 -P_11 1.411972 1.325313 1.297166 1.282320 1.273247 1.241806 1.236621 1.233708 -P_11 1.288123 1.421125 1.268851 1.361216 1.384593 1.419505 1.295513 1.509822 -P_11 1.379574 1.301400 1.341710 1.379092 1.244959 1.089202 0.964602 0.933347 -P_11 0.785138 0.720517 0.595122 0.501774 0.420618 0.430760 0.443497 0.496094 -P_11 0.553126 0.685299 0.761466 0.958757 1.038455 1.384394 1.359292 1.780001 -P_11 1.710109 1.812621 1.812623 2.007191 1.763520 1.892891 1.927757 1.671458 -P_11 1.744635 1.540380 1.538597 1.463048 1.310358 1.333825 1.415342 1.269821 -P_11 1.250203 1.340436 1.353949 1.338475 1.385602 1.385309 1.268736 1.347169 -P_11 1.342401 1.188455 1.320020 1.185210 1.182397 1.083358 1.091542 0.932629 -P_11 0.831946 0.739076 0.688950 0.611446 0.568157 0.471727 0.445025 0.520376 -P_11 0.528812 0.567819 0.639776 0.692184 0.785192 0.905352 1.029837 1.239686 -P_11 1.313991 1.399436 1.398540 1.738545 1.521631 1.471928 1.395591 1.499309 -P_11 1.585063 1.571033 1.557816 1.334608 1.360376 1.297228 1.435004 1.317742 -P_11 1.356795 1.348426 1.263683 1.262395 1.333089 1.309230 1.223426 1.282773 -P_11 1.351569 1.247501 1.170368 1.115768 1.111323 1.022959 0.850345 0.849051 -P_11 0.766483 0.782792 0.661551 0.542230 0.512833 0.525815 0.455611 0.500691 -P_11 0.488206 0.480421 0.484066 0.572052 0.646707 0.750603 0.810889 0.877024 -P_11 1.014010 1.026934 1.208142 1.330759 1.256171 1.359871 1.395329 1.368375 -P_11 1.347037 1.373116 1.175593 1.255088 1.171065 1.218250 1.151211 1.140303 -P_11 1.037815 1.253390 1.168639 1.208029 1.320572 1.305580 1.292314 1.241100 -P_11 1.340712 1.339009 1.230236 1.250739 1.100650 0.988925 0.936941 0.816301 -P_11 0.671299 0.687185 0.609660 0.491340 0.444258 0.382746 0.396660 0.405638 -P_11 0.469488 0.584993 0.703104 0.778529 0.979794 1.125015 1.257929 1.297070 -P_11 1.431306 1.592474 1.628670 1.782725 1.660182 1.717111 1.688254 1.757657 -P_11 1.572796 1.500950 1.448429 1.449354 1.189043 1.153026 1.090880 1.174094 -P_11 1.101126 1.177439 1.149259 1.191893 1.270997 1.432876 1.371805 1.469260 -P_11 1.463271 1.306246 1.291303 1.330199 1.233226 1.171907 1.078498 0.971170 -P_11 0.802916 0.640870 0.573838 0.474436 0.455091 0.390104 0.379025 0.442258 -P_11 0.555502 0.579829 0.765487 0.911296 0.944962 1.060942 1.219855 1.368871 -P_11 1.487094 1.550880 1.605533 1.596349 1.712455 1.689324 1.680695 1.582316 -P_11 1.479603 1.525186 1.424288 1.364498 1.319482 1.090898 1.294813 1.294509 -P_11 1.286496 1.267392 1.309087 1.269556 1.456500 1.426166 1.332834 1.537451 -P_11 1.428566 1.545789 1.345301 1.373221 1.253336 1.131414 1.070670 0.868567 -P_11 0.781567 0.608538 0.550487 0.475426 0.403026 0.424554 0.398741 0.472674 -P_11 0.547028 0.611450 0.805880 0.849835 0.981479 1.095223 1.275126 1.301412 -P_11 1.423098 1.572640 1.535083 1.612225 1.771125 1.625767 1.573462 1.641997 -P_11 1.615909 1.561111 1.463813 1.308159 1.362527 1.427013 1.235227 1.225116 -P_11 1.192561 1.271744 1.326672 1.353424 1.421026 1.394963 1.610270 1.440872 -P_11 1.383839 1.320573 1.439291 1.330926 1.297594 1.179077 1.045955 0.847384 -P_11 0.795357 0.710338 0.616663 0.482290 0.433182 0.427180 0.392377 0.479282 -P_11 0.504195 0.577347 0.681590 0.862889 1.101244 1.218128 1.247003 1.320506 -P_11 1.642828 1.630193 1.648920 1.708781 1.826358 1.592253 1.551061 1.426495 -P_11 1.527421 1.451621 1.265505 1.305712 1.330253 1.218877 1.105236 1.175803 -P_11 1.293422 1.179676 1.266767 1.320432 1.382439 1.335020 1.484575 1.566115 -P_11 1.461333 1.428106 1.427092 1.354246 1.135108 1.136180 1.029344 0.919471 -P_11 0.841634 0.673302 0.580475 0.539101 0.445947 0.457596 0.435503 0.531119 -P_11 0.562596 0.723850 0.826595 0.991802 1.137787 1.316450 1.562225 1.596483 -P_11 1.670664 1.702529 1.712272 1.763319 1.939215 1.951693 1.766144 1.706037 -P_11 1.630756 1.424192 1.578313 1.336649 1.493387 1.465444 1.398584 1.258918 -P_11 1.386639 1.203733 1.382149 1.357123 1.272616 1.530983 1.348426 1.441429 -P_11 1.293361 1.210000 1.333007 1.257006 1.258126 1.176308 0.918955 0.911700 -P_11 0.817361 0.696337 0.702301 0.579926 0.502103 0.460145 0.458081 0.471774 -P_11 0.496461 0.544951 0.574353 0.771222 0.785425 0.855835 1.077381 1.124746 -P_11 1.256136 1.320009 1.336384 1.417453 1.544067 1.589795 1.494663 1.641814 -P_11 1.584902 1.522476 1.448265 1.399673 1.349308 1.393051 1.425275 1.340414 -P_11 1.323753 1.411646 1.310695 1.407561 1.226658 1.362891 1.166028 1.310245 -P_11 1.249098 1.318119 1.230110 1.136456 1.062622 0.950155 0.906790 0.844214 -P_11 0.845652 0.668598 0.671902 0.594005 0.520014 0.516320 0.469490 0.479742 -P_11 0.435023 0.481466 0.496054 0.560385 0.727871 0.749896 0.887831 0.933442 -P_11 1.047191 1.136517 1.219266 1.426918 1.493420 1.373767 1.398504 1.268322 -P_11 1.498828 1.341505 1.349318 1.331712 1.373193 1.273757 1.138128 1.308055 -P_11 1.124335 1.108012 1.180990 1.237310 1.303778 1.406043 1.260035 1.310988 -P_11 1.371654 1.234241 1.193383 1.139846 1.127957 1.089303 0.967283 0.852390 -P_11 0.684453 0.623389 0.563754 0.480159 0.438007 0.414331 0.417881 0.424658 -P_11 0.526239 0.604076 0.723796 0.808432 0.926660 1.090819 1.230014 1.394473 -P_11 1.531023 1.748709 1.759418 1.837336 1.892933 1.644924 1.631084 1.631215 -P_11 1.568206 1.598360 1.427414 1.366913 1.282415 1.192009 1.112175 1.098925 -P_11 1.057236 1.112932 1.259250 1.147966 1.273753 1.366695 1.362719 1.422107 -P_11 1.574742 1.305324 1.487465 1.372603 1.131764 1.145923 0.901567 1.003531 -P_11 0.861582 0.670523 0.565393 0.523142 0.462250 0.411259 0.419111 0.437914 -P_11 0.525406 0.578024 0.655634 0.836657 0.938478 1.094835 1.327197 1.374071 -P_11 1.399901 1.468933 1.605154 1.596582 1.604427 1.684584 1.602550 1.651323 -P_11 1.408409 1.474755 1.371565 1.261971 1.295513 1.182400 1.084545 1.163905 -P_11 1.213683 1.202448 1.142528 1.345621 1.379269 1.279256 1.387540 1.475544 -P_11 1.406605 1.484021 1.308703 1.275620 1.247633 1.170202 0.973868 0.854652 -P_11 0.818028 0.657590 0.559975 0.462784 0.445613 0.431179 0.445029 0.455226 -P_11 0.553704 0.638282 0.802085 0.871711 1.108715 1.054373 1.331939 1.344727 -P_11 1.601960 1.508491 1.581115 1.529014 1.722578 1.765198 1.670320 1.589081 -P_11 1.492631 1.586546 1.403530 1.375355 1.333466 1.299842 1.290286 1.238613 -P_11 1.236028 1.245403 1.305890 1.371889 1.328447 1.353110 1.350852 1.438201 -P_11 1.357766 1.461367 1.292149 1.337128 1.318890 1.067763 0.937479 0.900645 -P_11 0.832025 0.666651 0.534094 0.485890 0.419397 0.430053 0.397707 0.438837 -P_11 0.487941 0.599119 0.751229 0.920484 1.004033 0.962597 1.243748 1.462586 -P_11 1.467598 1.543497 1.505822 1.574579 1.729992 1.689433 1.499509 1.697068 -P_11 1.322991 1.440490 1.310735 1.437572 1.359632 1.313230 1.413772 1.223860 -P_11 1.222788 1.386573 1.395275 1.254065 1.389396 1.345838 1.378919 1.502927 -P_11 1.340864 1.384481 1.442639 1.338635 1.223085 1.130085 1.042029 0.908468 -P_11 0.730730 0.705705 0.557805 0.535122 0.492478 0.466241 0.446040 0.489063 -P_11 0.562138 0.717612 0.776985 0.981479 1.124951 1.162693 1.543279 1.568416 -P_11 1.744649 1.910181 1.886956 1.874450 1.973957 1.753694 1.875726 1.807199 -P_11 1.582270 1.599845 1.581306 1.499130 1.367074 1.371415 1.203288 1.323627 -P_11 1.186032 1.449879 1.412058 1.329070 1.334491 1.400609 1.432254 1.392998 -P_11 1.407543 1.384846 1.402371 1.269848 1.182317 1.010505 0.969567 0.917201 -P_11 0.806961 0.733486 0.696455 0.597583 0.560194 0.512741 0.497046 0.484507 -P_11 0.498757 0.543099 0.611868 0.693778 0.795350 0.896677 1.069975 1.222641 -P_11 1.281172 1.382542 1.511731 1.431947 1.365529 1.650035 1.492873 1.555584 -P_11 1.582977 1.548341 1.461872 1.486488 1.418039 1.414468 1.441117 1.422790 -P_11 1.329461 1.447006 1.373586 1.563721 1.347742 1.345944 1.346729 1.274371 -P_11 1.260051 1.211823 1.214670 1.116679 1.017830 0.969413 0.907327 0.840577 -P_11 0.853347 0.708450 0.638398 0.558823 0.526589 0.506617 0.464809 0.441524 -P_11 0.478600 0.493510 0.614736 0.581336 0.657283 0.813211 0.685749 0.942924 -P_11 0.988871 1.086488 1.263540 1.342918 1.410713 1.359433 1.432899 1.281438 -P_11 1.359666 1.381021 1.381629 1.398145 1.227964 1.130328 1.226466 1.177977 -P_11 1.145476 1.293210 1.177113 1.315217 1.349205 1.338120 1.195103 1.311959 -P_11 1.345781 1.270255 1.292113 1.237618 1.175068 1.055325 1.009425 0.829581 -P_11 0.756595 0.615884 0.552918 0.522074 0.402558 0.356789 0.442229 0.439657 -P_11 0.559665 0.604097 0.734782 0.747099 0.913765 1.121037 1.362571 1.257877 -P_11 1.471379 1.662545 1.664913 1.878856 1.777359 1.795637 1.734988 1.661197 -P_11 1.632517 1.561505 1.417417 1.396684 1.363560 1.211451 1.138857 1.101891 -P_11 1.092054 1.140319 1.275892 1.203575 1.167065 1.304002 1.429287 1.477386 -P_11 1.434407 1.343037 1.398325 1.374385 1.277131 1.114568 1.060519 0.912720 -P_11 0.756158 0.699212 0.583841 0.512107 0.374073 0.425219 0.382710 0.440752 -P_11 0.537163 0.544451 0.783549 0.905612 0.927167 1.073799 1.217716 1.259447 -P_11 1.446929 1.597960 1.552571 1.549655 1.584183 1.586207 1.618705 1.628503 -P_11 1.569026 1.498315 1.401987 1.376607 1.355542 1.198134 1.210194 1.217123 -P_11 1.285399 1.134013 1.204360 1.365295 1.342657 1.437544 1.325059 1.462817 -P_11 1.534851 1.429492 1.488985 1.363451 1.319760 1.271144 0.979395 0.931375 -P_11 0.768041 0.635152 0.554248 0.441910 0.443502 0.392363 0.411287 0.451655 -P_11 0.523842 0.613982 0.780738 0.943532 0.989459 1.125429 1.303522 1.278987 -P_11 1.533946 1.522120 1.486466 1.686177 1.724129 1.679047 1.571948 1.605183 -P_11 1.428680 1.457338 1.498406 1.467610 1.475194 1.292157 1.362168 1.307576 -P_11 1.249078 1.268868 1.244468 1.221584 1.333813 1.347211 1.540017 1.451840 -P_11 1.367044 1.521053 1.295496 1.214000 1.299931 1.143035 1.073232 0.896720 -P_11 0.740548 0.660336 0.531756 0.516413 0.476859 0.413483 0.435841 0.477782 -P_11 0.549962 0.622227 0.703285 0.841255 1.088883 1.193853 1.321413 1.462976 -P_11 1.521213 1.595326 1.466240 1.654588 1.586767 1.547745 1.472435 1.534233 -P_11 1.436368 1.461784 1.606497 1.344330 1.273948 1.232122 1.290370 1.279983 -P_11 1.375860 1.258624 1.111736 1.415005 1.297694 1.369011 1.490192 1.430071 -P_11 1.373509 1.394887 1.344531 1.150876 1.272832 1.129685 1.017947 0.961325 -P_11 0.779002 0.722506 0.593160 0.497927 0.462613 0.469269 0.479137 0.488190 -P_11 0.568359 0.698546 0.879888 1.044350 1.104787 1.212449 1.525090 1.553901 -P_11 1.872927 1.812322 1.817466 2.086192 1.806378 1.832857 1.941581 1.662626 -P_11 1.797227 1.553386 1.418025 1.493437 1.465621 1.342951 1.339054 1.332402 -P_11 1.319640 1.379608 1.261619 1.462090 1.331334 1.562758 1.454366 1.409965 -P_11 1.401756 1.259265 1.324396 1.292636 1.190838 1.113005 1.047303 1.035520 -P_11 0.795001 0.790483 0.703356 0.595072 0.592205 0.499607 0.472049 0.493036 -P_11 0.514307 0.499357 0.564930 0.642970 0.851549 0.893552 1.016688 1.168687 -P_11 1.165028 1.353000 1.322573 1.473440 1.581187 1.556577 1.660854 1.605182 -P_11 1.623056 1.597014 1.498078 1.528146 1.479097 1.369554 1.447051 1.318085 -P_11 1.391147 1.313384 1.254273 1.451293 1.339241 1.382539 1.312322 1.298081 -P_11 1.279013 1.247077 1.101651 1.190664 1.045268 1.030788 0.833178 0.794137 -P_11 0.749347 0.689408 0.572839 0.586500 0.531987 0.512540 0.507777 0.484778 -P_11 0.514101 0.504189 0.548944 0.540630 0.720341 0.718970 0.729109 0.916270 -P_11 1.062301 1.189673 1.180227 1.240534 1.376722 1.469482 1.395153 1.307192 -P_11 1.340175 1.340836 1.409551 1.461775 1.213254 1.308329 1.166059 1.144123 -P_11 1.176808 1.207903 1.156547 1.204029 1.195785 1.276969 1.201764 1.380279 -P_11 1.270722 1.239659 1.236264 1.135894 1.195843 1.035685 0.913464 0.813263 -P_11 0.691619 0.683115 0.548765 0.478236 0.427446 0.430776 0.440075 0.420578 -P_11 0.480586 0.539383 0.675221 0.821340 0.982245 1.168157 1.269383 1.332602 -P_11 1.427023 1.355371 1.786803 1.916489 1.967565 1.747881 1.659282 1.552717 -P_11 1.773180 1.480106 1.520513 1.465847 1.262911 1.274136 1.287734 1.077541 -P_11 1.174127 1.226883 1.214661 1.325340 1.257376 1.346193 1.316601 1.340282 -P_11 1.398165 1.336582 1.367343 1.298123 1.209407 1.182568 1.011050 0.955329 -P_11 0.785775 0.680314 0.604963 0.523036 0.448327 0.393582 0.413574 0.441885 -P_11 0.513937 0.567426 0.756307 0.796742 0.872351 1.035242 1.115260 1.257025 -P_11 1.397356 1.453578 1.589686 1.546383 1.682977 1.777931 1.594813 1.441496 -P_11 1.487493 1.341062 1.312515 1.424294 1.257532 1.203311 1.212519 1.106980 -P_11 1.277000 1.127077 1.324000 1.229152 1.328092 1.446051 1.546451 1.504249 -P_11 1.446044 1.445627 1.440853 1.316919 1.257042 1.245278 1.007406 0.843840 -P_11 0.748472 0.745839 0.581113 0.502431 0.455314 0.367801 0.424911 0.454828 -P_11 0.513380 0.665641 0.760334 0.891907 0.988775 1.207166 1.226964 1.336216 -P_11 1.450660 1.555044 1.702973 1.679680 1.781529 1.644832 1.714719 1.617764 -P_11 1.598495 1.565963 1.389086 1.286838 1.307278 1.346946 1.387478 1.323201 -P_11 1.269770 1.269974 1.408501 1.329305 1.339297 1.372988 1.349226 1.491868 -P_11 1.406939 1.408704 1.273457 1.258282 1.318803 1.101726 0.955216 0.908108 -P_11 0.732938 0.677859 0.562432 0.447618 0.467268 0.407861 0.444796 0.496844 -P_11 0.511234 0.599263 0.747310 0.889086 0.970263 1.174399 1.210389 1.375328 -P_11 1.568467 1.561166 1.483747 1.640264 1.662254 1.629457 1.507765 1.552460 -P_11 1.528397 1.358236 1.377355 1.301581 1.299034 1.280997 1.253146 1.312120 -P_11 1.264085 1.246813 1.272204 1.238354 1.336634 1.286336 1.372725 1.360112 -P_11 1.382987 1.455708 1.253958 1.254550 1.309981 1.170733 1.092136 0.868886 -P_11 0.847725 0.620740 0.542864 0.523452 0.468576 0.435455 0.506048 0.503862 -P_11 0.591129 0.692798 0.785771 1.032435 1.050468 1.232742 1.284269 1.653367 -P_11 1.715892 1.839353 1.873931 1.821234 1.764830 1.874763 1.742461 1.711399 -P_11 1.633287 1.621129 1.500628 1.547386 1.524555 1.489390 1.296482 1.337090 -P_11 1.157009 1.296267 1.270345 1.357148 1.367696 1.370456 1.415607 1.436847 -P_11 1.490225 1.326009 1.393748 1.298012 1.211771 1.036787 1.113248 0.878432 -P_11 0.835717 0.790965 0.680173 0.573206 0.506061 0.488641 0.489863 0.475589 -P_11 0.499462 0.535897 0.631201 0.687722 0.871901 0.862652 1.080078 1.168557 -P_11 1.193039 1.367830 1.352304 1.423476 1.542129 1.513721 1.640175 1.481565 -P_11 1.576183 1.531073 1.443071 1.454547 1.414788 1.416424 1.311030 1.262021 -P_11 1.347165 1.309680 1.363821 1.413180 1.341484 1.385659 1.401894 1.238091 -P_11 1.246829 1.208765 1.290189 1.166457 0.989065 0.929582 0.897227 0.847080 -P_11 0.724025 0.654992 0.682685 0.566848 0.589783 0.491642 0.486228 0.477495 -P_11 0.450684 0.485602 0.516236 0.602729 0.633475 0.718059 0.887699 0.936700 -P_11 0.955071 1.107743 1.183519 1.209808 1.348718 1.447128 1.370392 1.432254 -P_11 1.440724 1.340155 1.289762 1.183690 1.366062 1.340806 1.312644 1.249773 -P_11 1.152661 1.155899 1.245779 1.193598 1.213203 1.245178 1.330390 1.277238 -P_11 1.188814 1.275516 1.313333 1.360048 1.136398 1.074572 0.867015 0.840910 -P_11 0.692791 0.654928 0.553462 0.475379 0.446394 0.403114 0.388184 0.445835 -P_11 0.492329 0.615514 0.675324 0.720989 0.930495 1.086142 1.300280 1.338045 -P_11 1.667400 1.449228 1.572517 1.655704 1.738889 1.788438 1.828933 1.571979 -P_11 1.688069 1.630126 1.478958 1.306843 1.403473 1.325870 1.224563 1.090225 -P_11 1.103510 1.189408 1.291280 1.121305 1.163699 1.412115 1.337796 1.364313 -P_11 1.492523 1.306951 1.290886 1.296959 1.197318 1.106448 0.946284 0.872726 -P_11 0.788349 0.621477 0.566417 0.502689 0.410440 0.371697 0.387712 0.418850 -P_11 0.446166 0.605571 0.769355 0.772074 0.913809 0.921084 1.146772 1.301069 -P_11 1.464415 1.601629 1.681565 1.558106 1.591638 1.573538 1.554257 1.552682 -P_11 1.726575 1.527889 1.338195 1.310725 1.221239 1.303007 1.222632 1.115519 -P_11 1.175723 1.205567 1.285874 1.237042 1.314394 1.352384 1.360807 1.437366 -P_11 1.590712 1.430996 1.398938 1.421386 1.234509 1.208918 0.985471 0.903141 -P_11 0.777493 0.671492 0.533419 0.501612 0.439946 0.383338 0.422635 0.452418 -P_11 0.569050 0.621201 0.738998 0.857405 0.933424 1.181736 1.310915 1.314499 -P_11 1.495770 1.601154 1.396360 1.704306 1.494336 1.576604 1.587680 1.608296 -P_11 1.507715 1.459582 1.342532 1.442567 1.375501 1.312037 1.364206 1.105811 -P_11 1.169223 1.254553 1.283503 1.224124 1.298437 1.488232 1.307617 1.445447 -P_11 1.333534 1.335421 1.334756 1.321852 1.226133 1.165022 1.008519 0.938865 -P_11 0.811289 0.630699 0.613779 0.454266 0.409384 0.404930 0.412757 0.494916 -P_11 0.516736 0.624874 0.688081 0.785117 0.924347 1.070766 1.247221 1.396303 -P_11 1.509487 1.533730 1.607960 1.585157 1.653244 1.569702 1.560279 1.568255 -P_11 1.461232 1.348044 1.302833 1.321238 1.250075 1.285860 1.242004 1.231356 -P_11 1.205893 1.368457 1.304176 1.321456 1.336081 1.265596 1.414498 1.325770 -P_11 1.344264 1.356810 1.325406 1.309232 1.166288 1.068983 1.083533 0.949011 -P_11 0.763123 0.680947 0.591838 0.460352 0.459034 0.477677 0.438159 0.489023 -P_11 0.565800 0.576087 0.839874 0.909740 1.147886 1.224496 1.496140 1.524426 -P_11 1.679912 1.873062 1.702392 1.876709 1.827556 1.786046 1.629175 1.648710 -P_11 1.569681 1.570438 1.548455 1.401036 1.450797 1.284477 1.119670 1.197409 -P_11 1.269407 1.227381 1.311554 1.347858 1.340727 1.317538 1.354767 1.508581 -P_11 1.348129 1.197237 1.311640 1.197171 1.112377 1.015894 1.051233 0.901083 -P_11 0.783799 0.756212 0.703134 0.595534 0.535361 0.452382 0.451871 0.465542 -P_11 0.469930 0.568321 0.604373 0.676851 0.767988 0.924088 1.007886 1.135675 -P_11 1.222143 1.391135 1.531383 1.401894 1.564738 1.538728 1.623101 1.567072 -P_11 1.340707 1.442712 1.453603 1.315813 1.463464 1.339617 1.444995 1.350567 -P_11 1.268420 1.338409 1.281627 1.253258 1.288363 1.305436 1.340483 1.272733 -P_11 1.170087 1.239711 1.099229 1.045113 0.999090 0.969724 0.924789 0.812381 -P_11 0.781675 0.673241 0.586608 0.586429 0.480754 0.522378 0.455636 0.457060 -P_11 0.465278 0.487661 0.494266 0.570167 0.655118 0.651453 0.817281 0.888159 -P_11 0.954841 1.137087 1.223422 1.296424 1.340770 1.329624 1.374108 1.371491 -P_11 1.329763 1.400772 1.192379 1.172606 1.326016 1.236366 1.073499 1.116777 -P_11 1.148686 1.149578 1.150942 1.168628 1.194120 1.310890 1.356622 1.318877 -P_11 1.287393 1.245075 1.243753 1.090696 1.136936 1.036054 0.948080 0.796491 -P_11 0.655347 0.582084 0.523223 0.447338 0.429398 0.397362 0.410232 0.419615 -P_11 0.470606 0.551571 0.692853 0.797306 1.041356 1.054485 1.189988 1.275451 -P_11 1.359642 1.622284 1.727514 1.709006 1.748491 1.605932 1.679439 1.673828 -P_11 1.417746 1.479406 1.270437 1.421796 1.286620 1.220668 1.114924 1.028920 -P_11 1.138459 1.206374 1.103396 1.349023 1.327410 1.183397 1.208393 1.416806 -P_11 1.378803 1.449792 1.315234 1.314590 1.244540 1.057907 0.929939 0.914974 -P_11 0.743882 0.697087 0.561326 0.426885 0.440306 0.400996 0.387824 0.449135 -P_11 0.519516 0.567219 0.689231 0.746704 0.891919 1.089630 1.100089 1.310418 -P_11 1.486965 1.554846 1.624655 1.646490 1.704972 1.628854 1.505486 1.572644 -P_11 1.515558 1.505947 1.464660 1.295137 1.220588 1.153657 1.086778 1.108090 -P_11 1.107623 1.018124 1.234947 1.412222 1.331158 1.374982 1.419898 1.479124 -P_11 1.385921 1.556007 1.473812 1.328616 1.152567 1.017055 0.996051 0.874364 -P_11 0.756997 0.633620 0.577594 0.442587 0.439824 0.341849 0.425304 0.424034 -P_11 0.518808 0.599910 0.748825 0.833332 0.979491 1.156531 1.223980 1.319753 -P_11 1.519100 1.340623 1.473211 1.514168 1.524718 1.527076 1.587134 1.406825 -P_11 1.472421 1.419960 1.280983 1.316023 1.346498 1.298004 1.199145 1.221871 -P_11 1.059506 1.174615 1.347426 1.216055 1.308297 1.264004 1.318722 1.395343 -P_11 1.347507 1.481964 1.434468 1.287579 1.159227 1.148907 1.049436 0.888707 -P_11 0.697914 0.717437 0.554436 0.464718 0.428178 0.416306 0.408349 0.452912 -P_11 0.495151 0.631332 0.709120 0.814346 1.013999 1.025054 1.273087 1.370325 -P_11 1.470074 1.491099 1.627481 1.646083 1.584937 1.534227 1.503058 1.515642 -P_11 1.364184 1.493739 1.321496 1.316639 1.293896 1.071422 1.267241 1.318968 -P_11 1.281542 1.188345 1.145025 1.361057 1.248769 1.362117 1.407693 1.383377 -P_11 1.397879 1.197106 1.243773 1.194853 1.227912 1.112735 1.009071 0.879869 -P_11 0.723771 0.654197 0.552538 0.509170 0.457137 0.404772 0.423377 0.491817 -P_11 0.596799 0.606388 0.769593 0.869946 1.067766 1.315553 1.375695 1.520065 -P_11 1.584901 1.688243 1.749730 1.824526 1.759657 1.638359 1.890414 1.618533 -P_11 1.456015 1.590740 1.449390 1.336445 1.342840 1.399712 1.290136 1.319532 -P_11 1.206590 1.365674 1.275818 1.317981 1.280907 1.237201 1.320960 1.389512 -P_11 1.438469 1.313689 1.356686 1.160480 1.134764 1.091380 0.951007 0.840553 -P_11 0.840364 0.719549 0.652132 0.547713 0.575019 0.512303 0.425763 0.480900 -P_11 0.473084 0.510100 0.606688 0.692424 0.738551 0.874153 0.999461 1.082875 -P_11 1.264510 1.398429 1.419369 1.534956 1.417326 1.315608 1.500124 1.397677 -P_11 1.436353 1.370959 1.428787 1.415873 1.272283 1.286002 1.248163 1.424868 -P_11 1.223466 1.304627 1.314242 1.332869 1.279283 1.272032 1.335509 1.296304 -P_11 1.283484 1.170379 1.090357 1.170713 1.036196 0.902831 0.914781 0.817058 -P_11 0.717285 0.672429 0.564136 0.539953 0.514719 0.453676 0.442462 0.457260 -P_11 0.455774 0.474758 0.502044 0.530349 0.640687 0.631267 0.787816 0.902113 -P_11 1.051931 1.058984 1.204480 1.251582 1.220566 1.395027 1.288180 1.477420 -P_11 1.524651 1.274462 1.344429 1.311626 1.227958 1.156562 1.170799 1.114525 -P_11 1.136026 1.166258 1.152377 1.135426 1.250714 1.323453 1.318207 1.188166 -P_11 1.283671 1.244952 1.165396 1.209702 1.122037 0.962018 0.866223 0.839470 -P_11 0.682575 0.653534 0.579628 0.451129 0.405590 0.365278 0.409222 0.443376 -P_11 0.468032 0.545549 0.696150 0.805536 0.849275 1.093094 1.137376 1.407870 -P_11 1.399628 1.546411 1.691913 1.781282 1.693436 1.705057 1.901581 1.627099 -P_11 1.560897 1.650232 1.310060 1.317381 1.232084 1.030235 1.105788 1.166844 -P_11 1.035659 1.129595 1.014933 1.205774 1.221322 1.149300 1.329869 1.203290 -P_11 1.383666 1.270940 1.291629 1.215842 1.231526 1.143256 0.929798 0.834591 -P_11 0.706969 0.594465 0.552930 0.480723 0.385224 0.365198 0.396630 0.401830 -P_11 0.466366 0.562111 0.694574 0.858363 0.934600 1.050820 1.140485 1.379924 -P_11 1.308843 1.537862 1.669608 1.514857 1.694182 1.603778 1.381364 1.608914 -P_11 1.605480 1.351617 1.328785 1.180100 1.167526 1.162311 1.079589 1.025325 -P_11 1.186758 1.220473 1.178566 1.219586 1.320645 1.151685 1.232395 1.505245 -P_11 1.466514 1.409883 1.334780 1.097455 1.247001 1.048030 0.980279 0.907209 -P_11 0.811191 0.639695 0.562377 0.461895 0.382311 0.386665 0.368162 0.413085 -P_11 0.518028 0.593465 0.766679 0.875005 0.964552 1.103550 1.119985 1.402422 -P_11 1.592118 1.462036 1.454912 1.668094 1.706632 1.542560 1.520847 1.569497 -P_11 1.465951 1.397559 1.342842 1.290721 1.344719 1.171273 1.151325 1.339025 -P_11 1.166644 1.247801 1.199879 1.240608 1.234680 1.417392 1.364245 1.312994 -P_11 1.316095 1.296324 1.337668 1.190975 1.215486 0.988736 0.992848 0.899797 -P_11 0.787034 0.625198 0.553236 0.482415 0.393866 0.380421 0.399889 0.414782 -P_11 0.459496 0.610400 0.683839 0.826118 0.956151 1.108704 1.228060 1.343451 -P_11 1.392758 1.380629 1.532030 1.637881 1.491447 1.563627 1.522651 1.377641 -P_11 1.475961 1.328074 1.250559 1.232797 1.213768 1.167464 1.151965 1.103665 -P_11 1.209150 1.214869 1.221623 1.239805 1.283976 1.355905 1.366479 1.347025 -P_11 1.218214 1.319403 1.190257 1.199072 1.096951 1.128393 0.892797 0.932477 -P_11 0.704208 0.636723 0.480736 0.474216 0.420248 0.401736 0.445263 0.492712 -P_11 0.588692 0.679406 0.791483 0.828653 1.112023 1.167574 1.382556 1.514079 -P_11 1.656917 1.673416 1.615725 1.864433 1.837903 1.815971 1.617080 1.490477 -P_11 1.672974 1.509721 1.407528 1.451942 1.199147 1.405654 1.162250 1.244556 -P_11 1.258872 1.276365 1.402747 1.281596 1.362324 1.289908 1.217797 1.383046 -P_11 1.250594 1.256268 1.200461 1.239332 1.196126 1.006074 0.923345 0.843480 -P_11 0.754950 0.714957 0.625246 0.592023 0.517731 0.430710 0.506490 0.488046 -P_11 0.502205 0.529181 0.613119 0.730546 0.768396 0.852163 0.981383 1.099580 -P_11 1.075787 1.289531 1.346900 1.446929 1.370994 1.470056 1.499720 1.578426 -P_11 1.383663 1.337808 1.323454 1.408493 1.273197 1.228996 1.369953 1.245124 -P_11 1.309800 1.343656 1.253614 1.079014 1.284419 1.256566 1.269651 1.246675 -P_11 1.165616 1.083170 1.181684 0.920941 0.920301 0.880030 0.834070 0.826790 -P_11 0.741880 0.681789 0.581107 0.558618 0.459414 0.468677 0.484504 0.428979 -P_11 0.417654 0.481751 0.498106 0.596521 0.623225 0.715999 0.778201 0.843374 -P_11 0.886768 1.182123 1.211428 1.277894 1.385275 1.327668 1.328847 1.310998 -P_11 1.294217 1.300863 1.247316 1.240109 1.247856 1.315300 1.030758 1.103294 -P_11 1.145566 1.046159 1.129707 1.079103 1.089239 1.287413 1.257445 1.194617 -P_11 1.277777 1.112019 1.094661 1.176016 1.077451 1.035685 0.901710 0.648296 -P_11 0.735380 0.598967 0.538115 0.440005 0.402524 0.338969 0.367075 0.368456 -P_11 0.460979 0.559871 0.716077 0.748333 0.908286 0.928736 1.193041 1.303145 -P_11 1.368376 1.592998 1.602295 1.547870 1.685574 1.613783 1.647008 1.527370 -P_11 1.474522 1.511669 1.323500 1.206709 1.181553 1.145260 1.059583 1.012376 -P_11 1.033957 1.036359 1.075783 1.088269 1.036844 1.186404 1.237347 1.282021 -P_11 1.296924 1.294002 1.191244 1.358263 1.141742 1.117089 1.046596 0.800579 -P_11 0.697977 0.629406 0.505186 0.476377 0.426554 0.396619 0.404297 0.400692 -P_11 0.461997 0.503325 0.634591 0.781215 0.921577 1.002373 1.164590 1.194730 -P_11 1.235255 1.299165 1.486904 1.516900 1.518184 1.658959 1.599661 1.415701 -P_11 1.484867 1.309155 1.230282 1.235810 1.173620 1.161247 1.112631 1.018380 -P_11 1.058652 1.154850 1.142921 1.158207 1.332722 1.348367 1.294812 1.379404 -P_11 1.451792 1.379649 1.307431 1.248218 1.158376 1.029092 0.938053 0.787932 -P_11 0.734083 0.617680 0.505970 0.482445 0.425688 0.350193 0.406157 0.441325 -P_11 0.509276 0.574286 0.713665 0.823770 0.933512 1.070633 1.109200 1.356208 -P_11 1.409539 1.425102 1.525516 1.446533 1.565904 1.604389 1.566619 1.445056 -P_11 1.356688 1.380364 1.303786 1.296013 1.312957 1.124856 1.255723 1.155446 -P_11 1.238608 1.180651 1.117863 1.165413 1.284026 1.285931 1.387236 1.315763 -P_11 1.258783 1.349565 1.280746 1.203815 1.073603 1.034725 0.948412 0.839670 -P_11 0.807920 0.633911 0.533800 0.448431 0.411392 0.372255 0.373532 0.408422 -P_11 0.505142 0.544771 0.622947 0.860028 0.869356 1.042275 1.078813 1.265549 -P_11 1.374550 1.413428 1.415463 1.449504 1.505928 1.562347 1.488929 1.365951 -P_11 1.433173 1.280719 1.299222 1.272842 1.191189 1.170414 1.243625 1.072572 -P_11 1.156927 1.072212 1.208009 1.178679 1.294594 1.222429 1.362817 1.382960 -P_11 1.295501 1.283400 1.315680 1.195385 1.070867 1.106089 1.008647 0.859109 -P_11 0.762848 0.607429 0.530691 0.507833 0.456866 0.396768 0.386331 0.429921 -P_11 0.504116 0.674559 0.780580 0.916234 1.024270 1.184726 1.225942 1.482400 -P_11 1.490292 1.640688 1.712217 1.694020 1.720069 1.635504 1.663574 1.469812 -P_11 1.591223 1.406189 1.354258 1.346461 1.248945 1.403201 1.261229 1.269293 -P_11 1.159825 1.237839 1.309722 1.164169 1.264427 1.240880 1.201966 1.149661 -P_11 1.154494 1.302401 1.201922 1.199152 1.069340 1.016693 0.888948 0.871532 -P_11 0.803321 0.717534 0.598248 0.522783 0.509406 0.471661 0.456105 0.471113 -P_11 0.430933 0.501294 0.520620 0.688562 0.710327 0.867539 0.901491 1.009808 -P_11 1.105765 1.269798 1.505090 1.272164 1.442090 1.418971 1.399099 1.460639 -P_11 1.295890 1.386589 1.318721 1.249384 1.233250 1.277331 1.271100 1.157959 -P_11 1.143271 1.197479 1.218421 1.311636 1.206598 1.270851 1.268111 1.176382 -P_11 1.198225 1.147803 1.018761 1.024590 0.927817 0.924533 0.826462 0.755870 -P_11 0.687153 0.555827 0.586440 0.527417 0.459699 0.466751 0.446555 0.414090 -P_11 0.458531 0.422769 0.500540 0.534890 0.581166 0.709916 0.724576 0.857065 -P_11 0.987676 1.101876 1.079004 1.128196 1.312757 1.334112 1.295909 1.302254 -P_11 1.305850 1.279595 1.282782 1.174809 1.160039 1.098665 1.049669 1.063304 -P_11 1.081134 0.948769 1.146900 1.047959 1.081040 1.157535 1.207306 1.212176 -P_11 1.165609 1.307146 1.102687 1.065241 1.031918 1.020478 0.903059 0.717442 -P_11 0.652518 0.597544 0.472821 0.409373 0.376347 0.390376 0.391104 0.382300 -P_11 0.433309 0.554689 0.631922 0.760658 0.900027 0.982344 1.153208 1.242193 -P_11 1.193935 1.438255 1.642110 1.683334 1.608323 1.521958 1.661414 1.409637 -P_11 1.615825 1.479329 1.380792 1.309543 1.207007 1.197495 1.048636 1.077257 -P_11 1.053189 1.122170 1.009575 1.310820 1.120087 1.186996 1.209915 1.316546 -P_11 1.314128 1.299982 1.302190 1.225132 1.096232 1.062308 0.954851 0.846886 -P_11 0.734140 0.591900 0.511884 0.479897 0.397298 0.366987 0.394048 0.397916 -P_11 0.488764 0.558978 0.588934 0.800415 0.880761 0.983955 1.100287 1.183216 -P_11 1.334752 1.431935 1.437333 1.551485 1.527474 1.437336 1.440014 1.422764 -P_11 1.365178 1.271171 1.340209 1.087189 1.085962 1.129432 1.157822 1.013603 -P_11 1.099292 1.080690 1.167143 1.192951 1.173160 1.169309 1.354794 1.303743 -P_11 1.441728 1.312800 1.356900 1.214824 1.091332 1.041804 0.856908 0.751740 -P_11 0.657870 0.610703 0.505505 0.391439 0.384958 0.331855 0.408525 0.404966 -P_11 0.465066 0.557987 0.645958 0.820235 0.963396 1.081481 1.086667 1.165262 -P_11 1.400590 1.402682 1.598665 1.609919 1.523942 1.365292 1.547919 1.458681 -P_11 1.516533 1.311381 1.267011 1.278865 1.234959 1.095927 1.115906 1.121436 -P_11 1.132761 1.084967 1.160681 1.180525 1.113375 1.186624 1.261777 1.298566 -P_11 1.316934 1.272326 1.359139 1.177387 1.180178 1.101557 0.907448 0.813964 -P_11 0.730523 0.554973 0.478972 0.435847 0.415405 0.386129 0.332553 0.437536 -P_11 0.469902 0.538429 0.628736 0.815240 0.843793 1.088796 1.061602 1.244296 -P_11 1.303173 1.455479 1.478749 1.454364 1.467089 1.479627 1.438174 1.383767 -P_11 1.422999 1.154644 1.370098 1.177452 1.232986 1.160642 1.074324 1.213105 -P_11 1.108050 1.114153 1.138330 1.102937 1.236315 1.213539 1.287462 1.170988 -P_11 1.308181 1.269863 1.187511 1.197767 1.105296 0.866711 0.905818 0.770128 -P_11 0.717731 0.633536 0.553955 0.461498 0.407659 0.407793 0.387666 0.430903 -P_11 0.502548 0.628248 0.711405 0.863709 1.015162 1.192435 1.417969 1.465464 -P_11 1.609644 1.497073 1.500793 1.717070 1.736610 1.614647 1.628167 1.685235 -P_11 1.478792 1.414117 1.334742 1.264984 1.248880 1.247842 1.132493 1.114520 -P_11 1.197834 1.217653 1.180750 1.339287 1.185857 1.195895 1.225533 1.273285 -P_11 1.200484 1.157573 1.150902 1.102386 1.049406 0.934055 0.922626 0.883668 -P_11 0.739586 0.665383 0.577212 0.481093 0.487494 0.470402 0.457171 0.425492 -P_11 0.412905 0.455437 0.528247 0.655607 0.731070 0.907007 0.946816 1.026648 -P_11 1.121782 1.161470 1.248583 1.385787 1.370457 1.396905 1.406395 1.291302 -P_11 1.414527 1.388187 1.280672 1.236042 1.195962 1.140742 1.325271 1.282030 -P_11 1.124914 1.245388 1.116690 1.214232 1.245341 1.248980 1.202492 1.098888 -P_11 1.155730 1.143549 0.999397 0.933013 0.876063 0.941170 0.761651 0.668682 -P_11 0.739714 0.599337 0.596950 0.570852 0.464732 0.430358 0.412076 0.394919 -P_11 0.431673 0.418572 0.498326 0.485846 0.571032 0.621009 0.706643 0.839334 -P_11 0.918017 1.085257 1.075611 1.101632 1.237698 1.238608 1.170528 1.272929 -P_11 1.326596 1.275425 1.210914 1.147774 1.072265 1.173344 1.119419 1.057440 -P_11 1.092896 1.114920 1.126732 1.047185 1.106702 1.136376 0.981614 1.078334 -P_11 1.203387 1.009035 0.997391 1.030362 1.041967 0.927524 0.835290 0.692431 -P_11 0.621923 0.593809 0.485137 0.422843 0.398846 0.340692 0.372760 0.390553 -P_11 0.459713 0.498474 0.630404 0.758739 0.859880 0.911845 1.173826 1.288823 -P_11 1.308886 1.373561 1.514309 1.546128 1.581257 1.458048 1.558090 1.459834 -P_11 1.323107 1.419511 1.372438 1.256939 1.148609 1.017604 1.062854 1.066970 -P_11 1.035596 1.002158 0.924461 1.109970 1.119089 1.133393 1.144970 1.253015 -P_11 1.227786 1.233950 1.174053 1.147171 1.116136 0.969599 0.833219 0.782776 -P_11 0.694205 0.566446 0.504629 0.413835 0.366301 0.340135 0.370993 0.408197 -P_11 0.460419 0.506277 0.616597 0.789597 0.844736 0.986702 1.111432 1.250982 -P_11 1.273985 1.281841 1.420799 1.419488 1.596223 1.414489 1.408975 1.454486 -P_11 1.322315 1.225583 1.284877 1.234602 1.123495 1.077238 1.022031 1.033538 -P_11 0.998560 0.995877 1.085454 1.110244 1.343975 1.125601 1.203288 1.305010 -P_11 1.348123 1.342339 1.199320 1.171930 1.120626 1.057277 0.987037 0.774786 -P_11 0.685411 0.571677 0.451039 0.415187 0.397156 0.373311 0.394193 0.437377 -P_11 0.498086 0.583177 0.663222 0.777664 0.871640 0.975827 1.124205 1.251428 -P_11 1.216138 1.276245 1.477244 1.422137 1.520945 1.448539 1.376406 1.324111 -P_11 1.291102 1.281086 1.268875 1.174598 1.158123 1.083280 1.104852 1.174210 -P_11 1.101804 1.130848 1.095441 1.138269 1.094825 1.211198 1.295242 1.307752 -P_11 1.288349 1.284973 1.317209 1.146885 1.019534 0.924031 0.817723 0.791399 -P_11 0.723763 0.540716 0.489038 0.405126 0.373111 0.392034 0.357174 0.373220 -P_11 0.430873 0.509950 0.628238 0.737677 0.960915 1.069446 1.089919 1.204045 -P_11 1.210703 1.333709 1.383424 1.561426 1.312352 1.338877 1.350669 1.441602 -P_11 1.228617 1.312361 1.300303 1.148079 1.202930 1.064930 0.982987 1.119624 -P_11 1.166702 1.119615 1.140994 1.204640 1.222548 1.298818 1.175432 1.104496 -P_11 1.330999 1.191635 1.179539 1.098980 1.129964 1.022818 0.933676 0.732680 -P_11 0.738362 0.563690 0.496036 0.486442 0.418706 0.364024 0.396805 0.412910 -P_11 0.488702 0.571411 0.696906 0.883246 0.952067 1.035441 1.347006 1.466486 -P_11 1.505087 1.676991 1.639009 1.520972 1.625888 1.512076 1.683693 1.492049 -P_11 1.385053 1.323320 1.480075 1.169283 1.350635 1.279859 1.106965 1.218152 -P_11 1.216332 1.029715 1.193466 1.171054 1.205092 1.224592 1.134073 1.156028 -P_11 1.260698 1.048651 1.103510 1.057832 1.042992 1.046021 0.845090 0.773815 -P_11 0.720925 0.688337 0.607165 0.502499 0.509758 0.444205 0.396422 0.445523 -P_11 0.449119 0.512084 0.551334 0.554430 0.712486 0.875938 0.936679 0.988155 -P_11 1.210976 1.126958 1.210172 1.223639 1.445494 1.346793 1.301736 1.288481 -P_11 1.316749 1.320431 1.283398 1.208161 1.139575 1.164452 1.196382 1.177179 -P_11 1.248071 1.271278 1.138552 1.122606 1.122857 1.043375 1.145518 1.147313 -P_11 1.087813 1.190590 0.964568 0.860695 0.850941 0.869485 0.759664 0.794474 -P_11 0.572019 0.623587 0.550034 0.471836 0.444520 0.434158 0.375869 0.382496 -P_11 0.395751 0.425509 0.396634 0.528122 0.546131 0.628773 0.679729 0.819463 -P_11 0.855189 0.913278 1.055474 1.059390 1.163119 1.267202 1.151360 1.284595 -P_11 1.281392 1.166710 1.259346 1.107227 1.076641 1.027633 0.973895 0.964682 -P_11 0.966811 1.028962 1.078639 1.067235 1.109152 1.196458 1.074844 1.098079 -P_11 1.114411 1.116672 1.085233 1.097779 0.898971 0.885665 0.790163 0.761095 -P_11 0.594847 0.530023 0.469649 0.374115 0.390629 0.326921 0.358997 0.412334 -P_11 0.347088 0.475841 0.621072 0.650357 0.823994 0.935340 1.125325 1.278310 -P_11 1.233453 1.385555 1.486627 1.510674 1.484506 1.632488 1.489218 1.375711 -P_11 1.430482 1.314170 1.119127 1.146512 1.065088 1.119823 1.023001 0.980363 -P_11 0.963565 0.926995 0.916622 1.024560 1.074679 1.151704 1.018780 1.171483 -P_11 1.255846 1.209708 1.118124 1.168378 1.018519 0.903574 0.804754 0.735410 -P_11 0.631398 0.535046 0.470966 0.408755 0.368514 0.346988 0.340179 0.370214 -P_11 0.437931 0.502533 0.575221 0.685393 0.906305 0.941085 1.093623 1.187752 -P_11 1.211830 1.351301 1.301703 1.427944 1.470733 1.363970 1.332718 1.392035 -P_11 1.442243 1.243287 1.116553 1.140317 1.075882 1.018406 1.027322 1.011633 -P_11 1.078957 1.085502 1.019050 1.174718 1.101484 1.110755 1.208230 1.163748 -P_11 1.248897 1.181746 1.188218 1.080874 1.060499 0.977800 0.849788 0.755475 -P_11 0.615359 0.580129 0.465672 0.389287 0.353986 0.326756 0.344227 0.412900 -P_11 0.463602 0.607615 0.648890 0.713898 0.796739 1.083289 1.137528 1.189181 -P_11 1.265825 1.323637 1.429136 1.399045 1.183451 1.422037 1.391090 1.525256 -P_11 1.378050 1.316494 1.282746 1.193300 1.089939 1.120892 1.081334 1.097572 -P_11 1.129915 1.162195 1.065084 1.073904 1.214494 1.127855 1.142509 1.097406 -P_11 1.173316 1.251867 1.262550 1.167677 0.878547 1.115824 0.970074 0.804114 -P_11 0.726045 0.584298 0.443628 0.407571 0.381184 0.357114 0.357223 0.379840 -P_11 0.439898 0.502902 0.601365 0.693509 0.874910 0.968262 1.081738 1.157192 -P_11 1.271347 1.240478 1.279457 1.393708 1.376865 1.377529 1.295343 1.333705 -P_11 1.105564 1.161459 1.209937 1.082031 1.051246 1.098589 1.103793 1.036487 -P_11 1.173866 1.098812 1.107079 1.046771 1.139868 1.059421 1.111305 1.176729 -P_11 1.190574 1.135494 1.248304 1.180014 1.092242 1.002737 0.877732 0.744725 -P_11 0.698860 0.610253 0.512989 0.402638 0.405598 0.378067 0.370713 0.462510 -P_11 0.454921 0.565447 0.671867 0.846719 0.995317 1.108514 1.223799 1.415613 -P_11 1.468434 1.512057 1.514868 1.628470 1.479437 1.586003 1.562542 1.367437 -P_11 1.332806 1.420413 1.315561 1.188605 1.115405 1.087048 1.196912 1.183343 -P_11 1.070454 1.079514 1.264525 1.191125 1.166395 1.165574 1.214416 1.060181 -P_11 1.193265 1.151791 1.095864 1.020284 1.023344 0.951792 0.884291 0.791434 -P_11 0.754795 0.618948 0.548465 0.522190 0.471641 0.435707 0.391279 0.442562 -P_11 0.409477 0.507590 0.482847 0.585902 0.683649 0.750991 0.780797 0.928054 -P_11 1.019860 1.163451 1.141579 1.114409 1.430119 1.364422 1.219825 1.275639 -P_11 1.255690 1.324662 1.182268 1.258352 1.077128 1.169768 1.134967 1.194330 -P_11 1.221069 1.238264 1.130482 1.190376 1.155394 1.227559 1.114005 1.090278 -P_11 1.034340 1.064170 1.019580 0.945908 0.882592 0.742411 0.767577 0.664606 -P_11 0.626956 0.557537 0.492140 0.502096 0.412402 0.430940 0.399171 0.391638 -P_11 0.425887 0.418585 0.414506 0.528058 0.528935 0.688764 0.709441 0.779671 -P_11 0.789821 0.948369 1.029149 1.085254 1.039144 1.183705 1.174704 1.171958 -P_11 1.197133 1.066265 1.056155 1.109575 1.117799 1.028429 1.020755 1.049346 -P_11 1.057092 0.929017 1.090839 1.079271 0.889752 1.062100 1.100036 1.148767 -P_11 1.051655 1.075392 1.082433 0.923721 1.033229 0.861055 0.732410 0.612386 -P_11 0.617360 0.548255 0.461361 0.389685 0.346941 0.323526 0.320358 0.383183 -P_11 0.396107 0.481041 0.537074 0.693607 0.819082 0.966417 1.011879 1.098800 -P_11 1.276381 1.319071 1.265489 1.436973 1.364897 1.428356 1.508239 1.474055 -P_11 1.351434 1.218716 1.174630 1.076087 1.102105 1.061195 0.989872 0.999615 -P_11 0.978967 0.961818 1.061438 1.068887 1.085425 1.219245 1.065346 1.251078 -P_11 1.149836 1.142855 1.135967 1.069767 0.993554 0.963236 0.794168 0.788688 -P_11 0.628073 0.544351 0.477151 0.386364 0.372795 0.358870 0.321259 0.382665 -P_11 0.437186 0.477147 0.551922 0.620591 0.748834 0.901829 0.972015 1.076447 -P_11 1.275182 1.229547 1.307032 1.398679 1.362716 1.276456 1.309321 1.259621 -P_11 1.279335 1.137797 1.037167 1.181248 1.032991 0.959503 0.930975 0.938047 -P_11 0.949847 0.954745 1.042152 1.128314 1.134696 1.084910 1.089576 1.134755 -P_11 1.255387 1.161682 1.182717 1.088227 1.095900 0.820684 0.869484 0.676701 -P_11 0.635030 0.579443 0.451131 0.406982 0.350654 0.333944 0.327541 0.404893 -P_11 0.453025 0.507509 0.572696 0.722721 0.868095 0.904650 1.071097 1.193813 -P_11 1.252213 1.277033 1.281758 1.247082 1.429657 1.536803 1.205681 1.268382 -P_11 1.350037 1.289053 1.143988 1.138670 1.109150 1.044982 1.011956 1.022707 -P_11 0.943430 1.006348 1.067246 0.993941 1.038563 1.140095 1.158517 1.305479 -P_11 1.137000 1.207769 1.058915 1.059396 1.045800 0.926089 0.765813 0.697857 -P_11 0.618084 0.540520 0.470137 0.412637 0.363232 0.340125 0.392389 0.382008 -P_11 0.434015 0.513396 0.567282 0.654771 0.925583 0.910320 1.069446 1.142416 -P_11 1.173983 1.327447 1.347069 1.339264 1.288460 1.318892 1.230214 1.125407 -P_11 1.272030 1.103801 1.067938 1.106224 1.068743 0.951765 0.932455 1.001984 -P_11 1.116355 1.056912 0.954730 1.116516 1.069436 1.167926 1.179699 1.151362 -P_11 1.161499 1.074363 1.130697 0.981804 0.966545 1.059930 0.948695 0.802611 -P_11 0.671652 0.543756 0.475868 0.391378 0.377164 0.371628 0.350357 0.419522 -P_11 0.493074 0.561476 0.685319 0.828870 0.910592 1.102596 1.230225 1.292641 -P_11 1.334307 1.511445 1.543328 1.593110 1.470601 1.485568 1.482725 1.451393 -P_11 1.232667 1.354589 1.178522 1.226797 1.224791 1.030728 1.090862 1.052342 -P_11 1.104451 0.937209 1.096192 1.202467 1.209355 1.198060 1.151328 1.164766 -P_11 1.145050 1.111984 0.983213 1.087699 0.956342 0.921964 0.835124 0.744814 -P_11 0.685084 0.623719 0.514557 0.487669 0.431046 0.453789 0.381972 0.396567 -P_11 0.400656 0.450563 0.513537 0.588062 0.735688 0.775897 0.838740 0.835375 -P_11 1.115538 1.223858 1.187584 1.241970 1.312825 1.238875 1.137832 1.265898 -P_11 1.281098 1.111465 1.165004 1.179325 1.096930 1.101445 1.053786 1.160896 -P_11 1.105429 1.192023 1.072927 1.095859 1.189590 0.970711 1.083331 1.074944 -P_11 1.066834 0.931537 1.012327 0.909547 0.860881 0.718148 0.697130 0.603896 -P_11 0.619158 0.569639 0.593121 0.498188 0.439061 0.387706 0.388881 0.373392 -P_11 0.397075 0.400993 0.450518 0.473049 0.608316 0.585138 0.698493 0.792645 -P_11 0.860716 0.972448 0.948502 1.041766 1.164417 1.062912 1.161778 1.187315 -P_11 1.126847 1.142949 1.192967 1.000751 1.097938 0.911876 0.937393 0.925351 -P_11 0.975029 0.992963 0.986322 1.001683 1.032844 1.039630 0.978948 1.027747 -P_11 0.977773 0.966916 1.003694 1.011811 0.902677 0.916513 0.763577 0.634044 -P_11 0.637787 0.578208 0.466351 0.400695 0.315255 0.355556 0.370660 0.354648 -P_11 0.431193 0.493319 0.541617 0.689927 0.753125 0.864563 1.043758 1.026512 -P_11 1.275856 1.345272 1.230119 1.209090 1.442156 1.479045 1.398544 1.372481 -P_11 1.360101 1.357515 1.081497 1.157711 1.073128 0.971716 0.882650 0.996050 -P_11 0.912860 0.914381 0.952382 0.986937 1.090020 1.020810 1.138701 1.103495 -P_11 1.083997 1.098300 1.168563 1.081866 0.981496 0.856848 0.833447 0.803493 -P_11 0.660518 0.473619 0.469145 0.365968 0.353991 0.332217 0.361825 0.345148 -P_11 0.406776 0.453617 0.596268 0.625016 0.777454 0.865911 1.052881 1.182419 -P_11 1.229634 1.305408 1.373865 1.299112 1.354578 1.339112 1.297681 1.438558 -P_11 1.161928 1.308972 1.077310 1.155183 1.088510 0.867880 1.005652 0.996699 -P_11 0.968010 0.980160 0.955437 1.036547 1.140052 1.173447 1.076646 1.182947 -P_11 1.271832 1.248248 1.157479 1.066572 1.033371 0.875074 0.816151 0.719041 -P_11 0.656965 0.541840 0.460636 0.412347 0.351940 0.321393 0.326815 0.399524 -P_11 0.383097 0.503849 0.619014 0.730265 0.808356 0.911172 1.043841 1.167778 -P_11 1.163493 1.317411 1.251483 1.312057 1.382413 1.335443 1.326266 1.214616 -P_11 1.161453 1.225240 1.166865 1.109664 1.114000 1.106864 0.980432 1.098717 -P_11 0.971871 1.092538 1.052429 1.082531 0.989551 1.075878 1.153660 1.179355 -P_11 1.214154 1.180699 1.104107 1.143055 0.977330 0.876637 0.890316 0.683423 -P_11 0.625052 0.535983 0.481153 0.374578 0.339931 0.337308 0.333546 0.364095 -P_11 0.421506 0.501018 0.651405 0.698108 0.795770 0.942069 1.077378 1.192515 -P_11 1.152049 1.220915 1.324597 1.414598 1.368904 1.404231 1.381678 1.170892 -P_11 1.140477 1.192396 1.064805 1.127789 1.107201 1.041265 1.009169 1.039383 -P_11 0.993292 0.976720 1.020186 1.140169 1.112792 1.145263 1.079967 1.064909 -P_11 1.173832 1.139382 1.153610 1.094706 0.956122 0.891688 0.791393 0.671244 -P_11 0.634333 0.547738 0.488384 0.377079 0.382959 0.365300 0.353920 0.395642 -P_11 0.509165 0.571032 0.632059 0.795163 0.881130 0.952610 1.164809 1.376723 -P_11 1.548603 1.357976 1.477410 1.615095 1.542874 1.410018 1.403674 1.402171 -P_11 1.320230 1.238470 1.283391 1.254507 1.082493 1.113552 1.105746 0.998574 -P_11 1.083364 1.042042 1.137714 1.194000 1.100184 1.050837 1.124124 0.990088 -P_11 1.137258 1.000863 1.066830 1.005934 0.939811 0.832290 0.805520 0.779532 -P_11 0.691829 0.632164 0.575661 0.493888 0.414289 0.441610 0.415824 0.384886 -P_11 0.416066 0.428335 0.458891 0.601616 0.688692 0.786754 0.878311 0.839380 -P_11 1.044837 1.190225 1.107825 1.137722 1.319781 1.199701 1.364078 1.332819 -P_11 1.216169 1.136546 1.147542 1.113323 1.209911 1.109487 1.186942 1.157294 -P_11 1.079192 1.018807 1.124644 1.168544 1.105802 1.133038 0.966945 1.016106 -P_11 1.033188 0.952222 0.945462 0.889678 0.825821 0.777169 0.716175 0.641501 -P_11 0.654849 0.570802 0.531259 0.472585 0.478624 0.386061 0.368297 0.345684 -P_11 0.400474 0.436700 0.431749 0.516617 0.569343 0.599146 0.744839 0.667648 -P_11 0.831762 0.852109 0.866982 0.982196 1.175291 1.191745 1.176104 1.180237 -P_11 1.176289 1.188030 1.133429 1.071486 1.002189 0.982605 0.928391 0.938807 -P_11 0.863715 0.971223 1.008559 1.011894 0.990441 1.018636 1.064951 1.036919 -P_11 1.127376 0.939338 1.042081 1.073747 0.996372 0.891621 0.723588 0.747979 -P_11 0.566926 0.499506 0.441136 0.389284 0.348856 0.298753 0.313490 0.337121 -P_11 0.386542 0.496515 0.537408 0.596848 0.763484 0.893839 0.989914 1.229011 -P_11 1.115251 1.351238 1.350749 1.384235 1.291974 1.352404 1.333313 1.343295 -P_11 1.384149 1.238596 1.136223 1.016003 1.139900 0.997958 0.894657 0.816049 -P_11 0.924132 0.892559 0.917152 0.974281 1.054761 1.103533 1.101806 1.123543 -P_11 1.037606 1.074557 1.112006 1.038684 1.098613 0.923306 0.892127 0.746313 -P_11 0.633437 0.538692 0.457817 0.413170 0.337371 0.331214 0.338512 0.362521 -P_11 0.445155 0.455925 0.607961 0.685671 0.804870 0.851951 0.970305 1.061543 -P_11 1.119737 1.259485 1.242378 1.301887 1.353215 1.292059 1.318853 1.242401 -P_11 1.183358 1.130715 1.190332 1.040749 0.989552 1.034587 0.959802 1.039683 -P_11 1.001565 0.988319 1.019385 1.014234 1.058655 1.116908 1.246052 1.217597 -P_11 1.095730 1.121436 1.072623 1.077003 0.995250 0.936058 0.816527 0.693442 -P_11 0.629029 0.588951 0.467206 0.380297 0.338154 0.326803 0.306105 0.393810 -P_11 0.448199 0.504606 0.636934 0.719146 0.837085 0.901621 1.029982 1.196074 -P_11 1.211806 1.104414 1.246586 1.447899 1.384903 1.330862 1.314476 1.216979 -P_11 1.122614 1.198012 1.088718 1.139547 1.154854 1.098422 1.105905 1.069845 -P_11 1.021288 1.025262 0.993360 1.108920 1.083779 1.066904 1.179131 1.140050 -P_11 1.225480 1.085533 1.169123 0.931483 1.030217 1.020230 0.806532 0.677144 -P_11 0.684524 0.561047 0.448425 0.397565 0.352720 0.338958 0.332412 0.356922 -P_11 0.427932 0.463975 0.572548 0.699328 0.751822 0.886697 0.963723 1.057853 -P_11 1.125708 1.338231 1.245691 1.324532 1.380990 1.335617 1.311849 1.098106 -P_11 1.185672 1.129330 1.106510 0.967816 1.010936 0.910265 0.977148 0.983204 -P_11 0.966093 1.046818 0.999225 1.001727 1.126858 1.096994 1.127641 1.187971 -P_11 1.070114 1.032060 1.048339 1.068697 0.979782 0.837415 0.790983 0.748892 -P_11 0.593047 0.532832 0.440812 0.408620 0.360094 0.347829 0.328003 0.370586 -P_11 0.444201 0.554636 0.660875 0.752413 0.913461 0.923636 1.130003 1.396773 -P_11 1.304871 1.412692 1.321622 1.385610 1.440774 1.411392 1.348617 1.315118 -P_11 1.203682 1.252685 1.090759 1.198878 1.124842 1.096818 0.995468 1.059621 -P_11 1.068161 1.016955 1.041524 1.118689 1.137416 1.209350 1.050752 1.215069 -P_11 1.196364 1.030861 1.049780 1.061498 0.955009 0.840120 0.767482 0.775712 -P_11 0.626127 0.602801 0.528561 0.436154 0.468920 0.396476 0.395580 0.361084 -P_11 0.404311 0.445164 0.459917 0.572001 0.656372 0.690822 0.770086 0.923639 -P_11 0.969689 1.106209 1.005006 1.130574 1.275337 1.295219 1.189376 1.209524 -P_11 1.210252 1.163340 1.199078 1.194003 0.983306 1.177282 1.063765 1.065316 -P_11 1.080649 1.095413 1.125772 1.085571 1.082197 1.081688 1.131533 0.992032 -P_11 0.953680 1.038409 0.945344 0.869234 0.833885 0.706894 0.748607 0.583876 -P_11 0.598582 0.539194 0.478661 0.435677 0.428500 0.421815 0.320771 0.357022 -P_11 0.342834 0.411204 0.418084 0.495778 0.538968 0.550877 0.587997 0.714591 -P_11 0.911701 0.885946 0.942547 0.976441 1.068122 1.132395 1.223886 1.047238 -P_11 1.242180 1.025508 1.026295 0.993662 1.040464 0.981747 0.936893 0.862354 -P_11 0.940902 0.869576 0.839286 1.005913 0.974927 1.068954 1.099541 0.989203 -P_11 1.091515 1.070088 0.986066 0.945987 0.918416 0.875071 0.685080 0.649977 -P_11 0.630103 0.563565 0.431372 0.354347 0.346729 0.324591 0.340316 0.356964 -P_11 0.388545 0.435485 0.481884 0.602457 0.709928 0.906238 0.981949 1.073329 -P_11 1.184198 1.265202 1.276303 1.472949 1.446128 1.336147 1.355211 1.451171 -P_11 1.218409 1.105378 1.187742 1.106477 1.092283 1.006577 0.951033 0.863604 -P_11 0.889125 0.986967 0.924526 0.931232 0.943914 1.025219 1.049165 1.154974 -P_11 1.094470 1.084437 1.073045 1.027161 0.990666 0.803171 0.828455 0.731050 -P_11 0.659779 0.563602 0.458603 0.384857 0.338906 0.329500 0.341347 0.337213 -P_11 0.424487 0.477618 0.547124 0.691662 0.748509 0.926848 0.988754 1.057726 -P_11 1.204368 1.248437 1.227463 1.409667 1.307019 1.272757 1.372473 1.148376 -P_11 1.236051 1.144304 1.097527 1.062718 1.041361 0.998721 0.945656 0.950264 -P_11 0.922181 0.919764 0.926067 0.984133 0.984584 1.056021 1.119013 1.121117 -P_11 1.212660 1.063997 1.190367 1.103663 1.002484 0.883385 0.794902 0.679045 -P_11 0.602322 0.542583 0.405269 0.360864 0.322980 0.304308 0.320835 0.345438 -P_11 0.414937 0.509372 0.580956 0.748925 0.816736 0.862302 0.954375 1.127601 -P_11 1.162942 1.294709 1.233182 1.361223 1.216294 1.294080 1.228484 1.236728 -P_11 1.212752 1.188378 1.077608 1.039334 1.076546 0.960408 0.985782 1.024954 -P_11 0.938551 0.983519 1.047265 1.149464 1.062540 1.034767 1.099094 1.104581 -P_11 1.076710 1.098929 1.062947 0.950974 0.973824 0.968721 0.762699 0.709657 -P_11 0.625043 0.564652 0.433808 0.366415 0.337647 0.318184 0.363657 0.351093 -P_11 0.384984 0.516782 0.516906 0.666408 0.825387 0.937761 1.018359 1.118138 -P_11 1.090355 1.244913 1.424122 1.298681 1.310019 1.299615 1.278312 1.323165 -P_11 1.238564 1.127911 1.040881 1.177673 0.961037 0.995092 0.989609 1.016481 -P_11 0.973436 0.984589 1.024457 0.889654 0.986403 1.219484 1.114979 1.128803 -P_11 1.041599 1.078279 1.100564 1.024730 0.995076 0.876204 0.813010 0.702643 -P_11 0.620669 0.572989 0.451238 0.365752 0.333571 0.361302 0.353399 0.395181 -P_11 0.451135 0.568610 0.629282 0.730880 0.918725 0.963751 1.187984 1.234978 -P_11 1.293662 1.330778 1.345284 1.410377 1.409637 1.511144 1.531902 1.208160 -P_11 1.191446 1.271393 1.171259 1.141129 1.065112 1.010483 1.076786 1.013525 -P_11 1.054633 1.010511 1.104695 1.125498 1.125646 1.109565 1.073530 1.044691 -P_11 1.035558 1.012805 1.012045 0.930940 0.808229 0.941963 0.740433 0.753644 -P_11 0.629025 0.619674 0.535716 0.461857 0.414410 0.398420 0.368517 0.392391 -P_11 0.396789 0.433506 0.443593 0.588021 0.663404 0.789030 0.790263 0.890467 -P_11 0.986466 1.036116 1.150729 1.097429 1.244612 1.132960 1.155858 1.184897 -P_11 1.211714 1.125881 1.105108 1.052116 1.058629 1.050395 1.057258 1.121927 -P_11 1.242193 0.990417 1.058629 1.073738 1.058758 1.097224 1.076703 1.078661 -P_11 0.932788 0.948341 0.881169 0.860811 0.839652 0.831649 0.699490 0.610250 -P_11 0.613932 0.484574 0.533337 0.454586 0.387964 0.387471 0.378215 0.329411 -P_11 0.385407 0.386092 0.412201 0.486220 0.476582 0.589998 0.625500 0.712906 -P_11 0.786251 0.870437 1.015782 1.038563 0.983524 1.093082 1.182509 0.989214 -P_11 1.084361 0.996752 1.147536 1.041294 0.992849 0.965279 0.963554 0.962476 -P_11 0.876027 0.913154 0.937260 0.914924 1.017053 1.004806 1.058886 0.978014 -P_11 1.061116 1.042567 1.052950 0.949241 0.858401 0.807597 0.732509 0.665341 -P_11 0.557279 0.497571 0.420766 0.332881 0.333868 0.310290 0.338584 0.333437 -P_11 0.373816 0.451270 0.549011 0.687050 0.687893 0.881677 0.941696 1.037409 -P_11 1.178049 1.286291 1.299461 1.507164 1.399623 1.301123 1.355491 1.312296 -P_11 1.090362 1.211464 1.161403 1.094749 0.953636 0.877794 0.982448 1.004761 -P_11 0.869281 0.870430 0.941071 0.969542 1.019440 1.101393 1.104049 1.121384 -P_11 1.174183 0.983045 1.058391 0.954999 0.964526 0.798737 0.825980 0.668902 -P_11 0.662491 0.491123 0.436969 0.353981 0.333470 0.305824 0.298887 0.378027 -P_11 0.394952 0.442795 0.547825 0.666079 0.723617 0.836847 1.043520 1.054028 -P_11 1.082007 1.231499 1.263209 1.289415 1.283952 1.320857 1.271159 1.190332 -P_11 1.129842 1.190534 1.074509 1.000639 1.011902 0.927374 0.970548 0.865551 -P_11 0.992087 0.878210 0.951797 1.066699 1.095776 1.207870 1.067087 1.113853 -P_11 1.094532 1.152349 0.928556 1.101263 0.965274 0.935920 0.784617 0.640050 -P_11 0.577637 0.527207 0.412894 0.367083 0.365823 0.306504 0.325426 0.331629 -P_11 0.417510 0.504249 0.585508 0.631684 0.835821 0.880551 1.029613 1.060352 -P_11 1.272254 1.287662 1.233181 1.242660 1.370119 1.302688 1.262541 1.321484 -P_11 1.100726 1.216262 1.205694 1.031518 1.031227 0.966943 1.068643 0.891342 -P_11 0.984660 1.003048 1.000326 0.998807 1.053496 1.065280 1.029292 1.183313 -P_11 1.065200 1.170036 1.069888 1.075557 0.966266 0.865016 0.824880 0.684960 -P_11 0.647899 0.503887 0.441750 0.412122 0.309105 0.329246 0.332252 0.356020 -P_11 0.408074 0.465803 0.590681 0.636231 0.823978 1.015169 0.970058 1.017666 -P_11 1.138205 1.066259 1.216043 1.214114 1.340470 1.214773 1.156578 1.221364 -P_11 1.185961 1.211543 0.984444 1.062377 0.996188 1.010201 0.983310 0.917565 -P_11 0.931457 1.050604 0.990686 1.085873 1.052964 1.060077 1.019021 1.059485 -P_11 1.174040 1.069532 0.980209 1.003953 0.987537 0.914877 0.814510 0.690862 -P_11 0.625851 0.529919 0.436590 0.388498 0.338642 0.334981 0.338743 0.429791 -P_11 0.426940 0.554165 0.651208 0.771984 0.931885 1.038367 1.157593 1.102981 -P_11 1.326531 1.375110 1.432490 1.552612 1.523749 1.485785 1.357143 1.347309 -P_11 1.357325 1.224406 1.165943 1.139399 1.070426 0.998080 1.074372 1.038235 -P_11 0.966913 1.039603 1.120900 1.052878 1.005143 1.071659 1.159794 1.143271 -P_11 1.061563 1.084166 0.980253 0.899802 1.026030 0.850344 0.722658 0.742896 -P_11 0.602467 0.598278 0.520785 0.510065 0.457387 0.411081 0.386550 0.397780 -P_11 0.369072 0.453484 0.447517 0.567810 0.625195 0.730948 0.841685 0.942143 -P_11 0.939250 0.993805 1.078126 1.159961 1.244729 1.294737 1.169264 1.136846 -P_11 1.099195 1.138926 1.144477 1.116543 1.117491 1.052016 0.998948 0.997697 -P_11 1.015276 1.078914 1.061180 0.954639 1.052311 1.153612 0.980533 0.949237 -P_11 1.006909 0.917992 0.904692 0.850153 0.793256 0.802829 0.734840 0.622759 -P_11 0.545029 0.525157 0.516058 0.404344 0.395481 0.381425 0.345004 0.393803 -P_11 0.364769 0.388210 0.421541 0.480233 0.495551 0.588927 0.645284 0.735217 -P_11 0.768675 0.856070 0.941742 0.996894 1.139461 1.024726 1.140495 1.032918 -P_11 1.070250 1.056643 1.043131 1.023718 0.938050 0.882582 1.036064 0.921374 -P_11 0.993471 0.929087 0.881044 0.915236 1.028443 0.947105 1.021890 1.087042 -P_11 1.037584 0.985115 0.903780 0.942898 0.836941 0.796653 0.725145 0.709152 -P_11 0.654834 0.498006 0.454605 0.381452 0.338264 0.314020 0.337404 0.352111 -P_11 0.370327 0.480541 0.568859 0.654567 0.780526 0.881235 0.986121 1.084746 -P_11 1.113040 1.219777 1.323822 1.324541 1.295104 1.387579 1.257776 1.329511 -P_11 1.296369 1.261662 1.146881 1.083171 1.013783 0.908636 0.938811 0.945065 -P_11 0.905263 0.909281 0.925674 0.984606 1.102350 1.073758 1.076664 1.044252 -P_11 1.127908 0.992419 1.065776 0.933196 0.996560 0.891160 0.826874 0.691246 -P_11 0.628097 0.515997 0.423666 0.404873 0.378963 0.339663 0.297664 0.321724 -P_11 0.400280 0.496816 0.531601 0.666830 0.708268 0.794009 0.931743 0.954573 -P_11 1.216488 1.122324 1.176852 1.173426 1.373611 1.304245 1.297584 1.194959 -P_11 1.111993 1.127345 1.163084 1.039331 0.930744 1.012047 0.893575 0.900192 -P_11 0.956792 0.972173 1.075935 1.008041 1.084235 1.081152 1.095902 1.178203 -P_11 1.077100 1.070159 1.113191 1.078758 1.038491 0.826526 0.857573 0.643232 -P_11 0.589159 0.483060 0.399577 0.367310 0.338823 0.305369 0.324879 0.363210 -P_11 0.412898 0.512850 0.557283 0.704446 0.746982 0.859244 0.979025 1.075282 -P_11 1.162120 1.178836 1.271273 1.173260 1.286893 1.260512 1.322143 1.192487 -P_11 1.251687 1.040316 1.011291 1.117661 0.972207 0.999821 1.022421 1.009574 -P_11 0.992279 1.005636 1.041134 1.000988 1.016618 1.069402 1.169618 1.065785 -P_11 1.058583 1.064957 1.146734 0.988546 0.935833 0.907152 0.810737 0.709122 -P_11 0.621141 0.536200 0.449563 0.341517 0.353242 0.328168 0.294224 0.346046 -P_11 0.385638 0.493780 0.571324 0.626847 0.808731 0.941584 1.039740 1.103371 -P_11 1.113838 1.159307 1.172047 1.349455 1.284323 1.232858 1.217001 1.176365 -P_11 1.148338 1.017289 1.031850 1.080298 1.054782 0.960661 1.003417 0.968534 -P_11 0.946644 0.969820 0.941787 1.097676 1.091685 1.080281 1.131055 1.041997 -P_11 1.233653 1.158391 1.077672 1.019978 1.067485 0.960163 0.773241 0.680044 -P_11 0.644611 0.531204 0.442385 0.413136 0.346872 0.322876 0.321739 0.368702 -P_11 0.436764 0.510012 0.647760 0.794420 0.830287 0.954538 1.143541 1.310187 -P_11 1.257517 1.444860 1.407530 1.382695 1.441423 1.344295 1.382747 1.296512 -P_11 1.236941 1.251668 1.198532 1.120557 1.029716 1.110746 1.098369 1.045984 -P_11 1.093963 1.082397 1.130652 1.014159 1.081889 1.136596 1.079518 1.132859 -P_11 1.037124 0.997889 0.998602 0.954065 0.911093 0.892429 0.778448 0.769161 -P_11 0.690031 0.588824 0.542359 0.435533 0.417456 0.402830 0.393622 0.380057 -P_11 0.384265 0.388400 0.489288 0.548722 0.607360 0.751551 0.762954 0.804282 -P_11 0.968083 1.036091 1.100124 1.088051 1.094752 1.273611 1.241843 1.313097 -P_11 1.154666 1.179370 1.115614 1.052131 1.063498 1.124433 1.050909 1.025570 -P_11 1.094124 0.977323 1.041564 1.033223 1.006367 1.053896 0.937569 0.934878 -P_11 0.880572 0.871611 0.917488 0.872108 0.742767 0.793192 0.699076 0.627436 -P_11 0.562679 0.522070 0.521636 0.450465 0.410999 0.381616 0.367126 0.355828 -P_11 0.386300 0.361073 0.420864 0.453115 0.503057 0.554087 0.666809 0.732341 -P_11 0.775395 0.878920 0.967978 1.055962 1.072025 1.157996 1.084034 1.095673 -P_11 1.085099 1.170027 1.021743 0.952263 0.979129 0.965659 0.911226 0.858855 -P_11 0.876646 0.897663 0.935756 0.935261 0.996941 1.005659 1.001580 1.036741 -P_11 1.146660 1.001693 0.966064 0.997448 0.851852 0.758288 0.684618 0.614699 -P_11 0.589067 0.478957 0.452426 0.362264 0.327071 0.291496 0.315352 0.342233 -P_11 0.373809 0.432486 0.561617 0.663235 0.738075 0.843064 1.001115 1.023984 -P_11 1.124962 1.262927 1.308152 1.371336 1.375314 1.367937 1.236760 1.348592 -P_11 1.213613 1.110815 1.125979 1.033479 0.918208 0.985003 0.804319 0.903757 -P_11 0.687571 0.946560 0.856385 0.971774 0.945029 1.030350 1.089948 1.055766 -P_11 1.183168 1.100941 1.025219 1.078042 0.942516 0.867182 0.798932 0.700158 -P_11 0.581503 0.545790 0.434491 0.365086 0.367124 0.326957 0.309956 0.354303 -P_11 0.414435 0.486797 0.530717 0.652220 0.723240 0.799701 0.928597 0.966783 -P_11 1.131640 1.388563 1.233395 1.181530 1.282990 1.303998 1.227219 1.327538 -P_11 1.209673 1.053811 1.043002 1.033831 0.998223 0.928092 0.927304 0.933720 -P_11 0.963547 0.953941 0.996291 1.027986 1.003986 0.987164 1.069743 1.168028 -P_11 1.190681 1.158755 1.080638 1.037157 0.926235 0.844996 0.840267 0.686588 -P_11 0.555305 0.450660 0.425688 0.363650 0.342482 0.291731 0.330286 0.354972 -P_11 0.372677 0.500913 0.629741 0.671620 0.784040 0.887505 0.886245 1.005714 -P_11 1.104940 1.196169 1.287510 1.282181 1.311489 1.405129 1.197301 1.264211 -P_11 1.155463 1.040304 1.158991 1.085749 1.077926 1.097586 0.930118 0.974326 -P_11 0.950025 1.018963 1.019330 1.017197 1.009684 1.052856 1.096810 0.977919 -P_11 1.025132 1.082652 1.107157 1.053970 0.949806 0.942417 0.775421 0.707312 -P_11 0.613065 0.521296 0.466350 0.373628 0.338493 0.320854 0.347800 0.369818 -P_11 0.429957 0.481650 0.614497 0.670439 0.790480 0.901089 0.948749 1.003425 -P_11 1.085073 1.210628 1.153834 1.316890 1.225832 1.163202 1.190366 1.177762 -P_11 1.157748 1.040635 0.962097 0.968596 0.921134 0.990477 0.928872 1.004045 -P_11 0.981674 0.954161 0.968333 1.061331 1.133558 0.934589 1.102134 1.097174 -P_11 1.058622 1.072094 1.117113 1.025218 0.935459 0.849494 0.836311 0.730639 -P_11 0.647015 0.534549 0.465420 0.390271 0.311722 0.335493 0.339497 0.372285 -P_11 0.439606 0.522023 0.649095 0.720073 0.828766 1.107194 1.081810 1.217598 -P_11 1.398342 1.479806 1.391376 1.343185 1.513776 1.508613 1.377326 1.405195 -P_11 1.254501 1.189515 1.196819 1.043724 1.042482 1.031233 1.000933 0.987383 -P_11 1.011450 0.928046 1.044351 0.993133 1.050521 1.031731 1.094264 1.116073 -P_11 1.039373 1.031478 0.939018 0.979914 0.944159 0.825104 0.786264 0.672726 -P_11 0.663058 0.535746 0.501152 0.481132 0.426694 0.424473 0.403103 0.365565 -P_11 0.367806 0.445821 0.493668 0.544764 0.650045 0.690643 0.816299 0.967078 -P_11 0.985583 1.092796 1.042347 1.133693 1.164607 1.204110 1.119174 1.203988 -P_11 1.063772 1.022529 1.026393 1.007392 1.100171 1.007020 1.043581 0.961618 -P_11 0.982953 1.017199 1.031560 1.032120 1.001068 1.021541 1.096598 0.941457 -P_11 0.960860 0.962543 0.855457 0.844644 0.736614 0.712452 0.730511 0.673163 -P_11 0.630490 0.534692 0.493963 0.464414 0.421791 0.368451 0.356659 0.339280 -P_11 0.359955 0.409067 0.419785 0.435992 0.480782 0.575843 0.667790 0.722487 -P_11 0.797129 0.842274 0.935877 0.987619 1.044497 1.145601 1.139824 1.083296 -P_11 1.124502 1.196081 1.027335 1.027990 0.977167 0.926384 0.985717 0.945657 -P_11 0.871363 0.863939 0.922260 0.951178 0.820226 1.002852 1.023329 1.077681 -P_11 0.904552 0.960342 1.036732 0.931541 0.938961 0.871946 0.739063 0.625463 -P_11 0.601783 0.490186 0.424567 0.343327 0.340840 0.324632 0.314048 0.332409 -P_11 0.377496 0.434316 0.521982 0.613221 0.673421 0.863870 0.919699 1.069604 -P_11 1.115690 1.232388 1.186026 1.230846 1.396036 1.371690 1.354183 1.400830 -P_11 1.230946 1.194640 1.132985 0.991622 0.894402 0.928899 0.896667 0.910070 -P_11 0.890234 0.861380 0.903527 0.926995 1.012082 0.944136 1.071930 0.957935 -P_11 1.027810 1.025417 1.126951 1.061044 0.910159 0.889045 0.775171 0.695279 -P_11 0.587139 0.516493 0.384561 0.363755 0.334008 0.330124 0.317672 0.333726 -P_11 0.374647 0.455903 0.541126 0.593405 0.715277 0.830873 0.844458 1.033957 -P_11 1.007604 1.182893 1.300582 1.208324 1.150984 1.261703 1.260684 1.087486 -P_11 1.149568 1.130068 1.069788 1.065035 1.141825 1.012695 0.930243 0.933797 -P_11 0.957918 0.906457 0.968527 0.949531 1.008354 0.987385 1.138861 1.133007 -P_11 1.093176 1.146227 1.005707 0.981986 0.932050 0.811127 0.752586 0.700080 -P_11 0.554970 0.504842 0.440834 0.363882 0.329363 0.269546 0.363793 0.368723 -P_11 0.396016 0.486318 0.595571 0.660875 0.792811 0.891762 0.976644 1.164020 -P_11 1.019572 1.205651 1.271280 1.241279 1.260846 1.252326 1.242222 1.181002 -P_11 1.150355 1.131008 1.177381 1.142502 1.081679 0.947775 0.936758 0.951984 -P_11 0.939205 0.970325 0.890223 0.976196 1.036151 1.152633 1.100252 1.126505 -P_11 1.018194 1.121539 1.023406 1.046464 0.937113 0.847735 0.762261 0.712874 -P_11 0.599512 0.516813 0.462017 0.366618 0.332098 0.323189 0.351331 0.357722 -P_11 0.429404 0.475601 0.547405 0.663564 0.842172 0.887692 1.026810 1.092216 -P_11 1.251789 1.192449 1.246423 1.144266 1.188431 1.335768 1.219093 1.272090 -P_11 1.180764 1.183912 1.075445 1.000223 1.088650 0.960764 0.963322 0.994118 -P_11 0.957888 0.927636 1.049801 1.018608 1.009543 1.066298 1.128948 1.040374 -P_11 1.037468 1.007869 1.012201 0.957282 0.913624 0.827326 0.809665 0.664162 -P_11 0.666735 0.526130 0.461360 0.392815 0.376861 0.330951 0.350326 0.374157 -P_11 0.452744 0.509532 0.626577 0.760789 0.886358 0.833297 1.077364 1.205502 -P_11 1.308907 1.380105 1.468484 1.511187 1.350082 1.392282 1.393039 1.205900 -P_11 1.259642 1.143862 1.226702 1.131941 1.089030 1.037417 1.015003 1.012106 -P_11 0.999136 1.008410 1.067147 1.059035 1.062617 1.093180 1.091026 1.109973 -P_11 0.996307 1.050654 1.087027 0.981435 0.983987 0.804972 0.805173 0.772173 -P_11 0.664310 0.610993 0.517545 0.424141 0.391962 0.377669 0.388708 0.350637 -P_11 0.363564 0.421598 0.469593 0.547079 0.628168 0.712169 0.774203 0.889266 -P_11 0.922634 1.050289 1.083188 1.201463 1.178758 1.292700 1.232642 1.064371 -P_11 1.228022 1.181122 1.071747 1.086204 1.098476 1.116561 1.063216 0.964901 -P_11 1.029004 0.990207 0.989583 1.013746 1.034303 1.002610 0.944134 1.049240 -P_11 1.033671 0.910398 0.841278 0.916069 0.833397 0.819782 0.682016 0.671517 -P_11 0.537874 0.516234 0.492537 0.433055 0.393079 0.373264 0.357413 0.345219 -P_11 0.360872 0.355648 0.410799 0.482310 0.483944 0.567725 0.578798 0.701042 -P_11 0.839071 0.829719 0.954261 1.000600 1.051749 1.053642 1.035058 1.142598 -P_11 1.104542 1.096542 1.084991 1.020448 1.027943 0.895100 0.966498 0.928539 -P_11 0.864393 0.918170 0.892829 0.902582 1.029608 0.941512 0.986861 0.957134 -P_11 0.973549 0.975539 0.996350 0.858773 0.866237 0.801719 0.786321 0.667903 -P_11 0.534778 0.466988 0.387745 0.352806 0.319723 0.305456 0.322351 0.340566 -P_11 0.370364 0.431436 0.502024 0.635342 0.758901 0.793625 0.994847 1.019331 -P_11 1.055279 1.307749 1.262076 1.359090 1.337288 1.377504 1.263623 1.436214 -P_11 1.162728 1.103527 1.141973 1.068376 0.957750 0.971531 0.915389 0.877290 -P_11 0.775205 0.932226 0.902579 0.913088 1.028669 1.116813 1.010736 1.060437 -P_11 1.080879 0.958463 1.107044 0.956872 1.027210 0.840000 0.829614 0.716811 -P_11 0.603794 0.531904 0.417022 0.379193 0.362452 0.324345 0.305224 0.346819 -P_11 0.344985 0.452387 0.543144 0.642167 0.680753 0.845832 0.933132 1.099680 -P_11 1.108099 1.144556 1.342810 1.134113 1.299819 1.243192 1.201477 1.211829 -P_11 1.179881 1.124798 1.049748 0.954703 1.034010 1.060756 0.870914 0.879090 -P_11 0.947807 0.985586 0.909853 1.036986 1.066028 1.191120 1.119679 1.089726 -P_11 1.077474 1.054224 0.991453 0.959115 0.924756 0.851494 0.794103 0.668566 -P_11 0.551744 0.523882 0.444577 0.384322 0.317349 0.321409 0.341936 0.353422 -P_11 0.406857 0.484334 0.565085 0.620937 0.729516 0.832301 0.974669 1.060770 -P_11 1.214423 1.169227 1.254983 1.194635 1.233014 1.272266 1.229507 1.258885 -P_11 1.314567 1.185466 1.059177 1.128632 1.025184 0.975146 1.015996 0.956859 -P_11 0.990439 0.942508 0.999544 0.934421 1.029621 1.101005 1.033541 1.142372 -P_11 0.961392 1.053067 1.078870 1.007954 1.046935 0.863192 0.820671 0.611611 -P_11 0.556870 0.527935 0.462539 0.402536 0.300824 0.316007 0.305330 0.355510 -P_11 0.398497 0.495845 0.531057 0.626494 0.846806 0.860809 0.991739 1.091851 -P_11 1.049662 1.277213 1.328322 1.309060 1.365650 1.263980 1.172128 1.212803 -P_11 1.173063 1.169172 1.149966 1.002030 1.012525 0.921886 0.968948 0.948723 -P_11 0.922974 0.898514 0.993278 0.974441 1.082956 1.075048 1.092622 1.060538 -P_11 1.136939 1.143451 1.098344 1.002857 1.018827 0.886698 0.777356 0.688852 -P_11 0.584122 0.555111 0.455383 0.390110 0.361431 0.341006 0.369197 0.356267 -P_11 0.443432 0.530059 0.643548 0.729792 0.880215 1.003549 1.101056 1.292341 -P_11 1.240906 1.338084 1.383329 1.373841 1.509077 1.354419 1.354521 1.375846 -P_11 1.226329 1.097192 1.086131 1.191175 1.053332 1.006158 0.947550 1.012424 -P_11 0.977721 1.029764 1.060312 1.091386 1.164644 1.145137 1.042746 1.122188 -P_11 1.077005 1.133805 1.007790 0.937762 0.888320 0.891085 0.686474 0.721719 -P_11 0.645930 0.567530 0.490755 0.445669 0.427129 0.381895 0.361193 0.382799 -P_11 0.374304 0.402104 0.445128 0.510179 0.700907 0.639979 0.788647 0.915168 -P_11 0.965417 1.103618 1.161442 1.063412 1.180321 1.065668 1.173346 1.161356 -P_11 1.226328 1.170114 1.225097 1.157225 1.088606 1.114105 1.085606 1.030120 -P_11 1.090134 1.021043 0.997447 1.012828 0.929283 1.113745 1.068191 1.077159 -P_11 0.993263 0.923736 0.914511 0.853627 0.831615 0.713958 0.683294 0.661056 -P_11 0.561092 0.558021 0.469871 0.407282 0.430442 0.351315 0.355858 0.310385 -P_11 0.381209 0.400250 0.441113 0.469821 0.513128 0.515134 0.589083 0.745110 -P_11 0.750426 0.894728 1.010031 0.964891 1.023752 1.138265 1.099310 1.096228 -P_11 1.031769 1.060921 1.085996 1.049104 0.931535 0.982274 0.951412 0.893287 -P_11 0.992624 0.876076 0.880440 0.934534 0.988903 1.012980 0.905494 1.101911 -P_11 0.918365 0.990888 0.938163 0.920904 0.891456 0.758280 0.753345 0.636215 -P_11 0.544072 0.503010 0.431746 0.356824 0.321463 0.311802 0.305447 0.333565 -P_11 0.362607 0.414994 0.502300 0.614873 0.710127 0.816149 1.038830 1.077610 -P_11 1.134639 1.239958 1.351787 1.407494 1.297326 1.379251 1.345395 1.188436 -P_11 1.205310 1.226415 1.251390 1.164870 0.942099 0.868251 0.977872 0.942724 -P_11 0.919243 0.848059 0.940243 0.962666 1.021039 0.919842 1.055346 1.104995 -P_11 1.135794 1.024354 1.085924 0.966005 0.910296 0.927387 0.784630 0.711485 -P_11 0.613154 0.510428 0.481127 0.389809 0.318342 0.276899 0.279197 0.351434 -P_11 0.407763 0.443518 0.550504 0.615326 0.706126 0.893187 0.909382 0.992054 -P_11 1.073199 1.148517 1.239943 1.226571 1.303479 1.345723 1.261109 1.140909 -P_11 1.195991 1.095105 1.118494 1.003148 0.949234 0.914889 0.898231 0.892720 -P_11 0.960442 0.936205 0.966724 1.004791 1.011499 1.049327 0.960717 1.150111 -P_11 1.115413 1.103027 1.054514 0.953059 0.894867 0.888903 0.735621 0.620572 -P_11 0.567008 0.514150 0.426250 0.315652 0.338641 0.302246 0.334508 0.321199 -P_11 0.404061 0.454021 0.591243 0.637665 0.770118 0.898526 0.938074 1.017669 -P_11 1.053655 1.068023 1.289791 1.246121 1.398138 1.333706 1.273786 1.262025 -P_11 1.154768 1.141420 1.116152 1.038556 0.998254 0.965221 1.077465 1.045832 -P_11 0.962390 0.916588 0.961912 1.003994 1.086253 0.978396 1.058418 0.938081 -P_11 1.183607 1.031207 1.023611 1.041539 0.921168 0.779923 0.811498 0.638736 -P_11 0.638845 0.524626 0.457822 0.376221 0.341052 0.308333 0.310969 0.336094 -P_11 0.386352 0.460345 0.504831 0.670665 0.864799 0.887639 0.981321 1.100544 -P_11 1.071102 1.138778 1.219154 1.201933 1.293396 1.269032 1.203307 1.212832 -P_11 1.123194 1.145308 0.957932 1.041965 0.961036 0.906508 0.929623 0.887696 -P_11 0.979695 0.896854 1.020251 1.058699 1.010901 1.192839 1.106480 1.067449 -P_11 1.125205 1.081438 1.139378 1.037183 0.923159 0.861518 0.920868 0.732446 -P_11 0.614852 0.547620 0.423612 0.384474 0.347070 0.339234 0.349730 0.373208 -P_11 0.440795 0.540771 0.594511 0.721422 0.903390 0.882637 1.090810 1.300668 -P_11 1.289625 1.348421 1.412494 1.390406 1.394189 1.453706 1.343649 1.330265 -P_11 1.200390 1.204090 1.040511 1.123482 1.109851 1.112370 1.036870 1.111060 -P_11 0.874530 1.067256 1.083441 1.012645 1.026499 1.062758 1.102204 1.059042 -P_11 1.028499 1.052451 1.099106 0.896783 0.957725 0.937900 0.719977 0.729312 -P_11 0.709613 0.535941 0.536702 0.470573 0.443538 0.384605 0.338195 0.353772 -P_11 0.404234 0.455939 0.453500 0.554300 0.551123 0.697235 0.812718 0.894990 -P_11 0.976878 1.033038 1.118192 1.088438 1.234026 1.207850 1.274222 1.220694 -P_11 1.203890 1.229552 1.174289 1.116255 1.114418 1.028898 1.054049 1.040209 -P_11 1.122195 0.985727 1.040853 0.977358 1.064889 1.023910 1.077666 0.900879 -P_11 0.997711 0.894773 0.868023 0.915643 0.752629 0.752417 0.709349 0.647917 -P_11 0.615050 0.515871 0.477791 0.410501 0.416481 0.359555 0.371657 0.392772 -P_11 0.327555 0.424618 0.432649 0.429273 0.472750 0.579448 0.632401 0.756999 -P_11 0.776827 0.824338 0.905377 0.999124 1.000432 1.124860 1.104028 1.128589 -P_11 1.055961 0.993458 1.036583 1.074261 1.006609 0.983659 0.961179 0.971762 -P_11 0.885155 0.847930 0.823795 0.909310 0.917538 0.968819 1.055457 1.018093 -P_11 1.000490 1.004055 0.996854 0.907724 0.854712 0.825422 0.687521 0.685788 -P_11 0.538505 0.455800 0.409535 0.364624 0.330440 0.294624 0.308773 0.324247 -P_11 0.413309 0.496694 0.563759 0.599403 0.743668 0.876518 0.893471 1.080923 -P_11 1.171626 1.131743 1.359846 1.332601 1.402206 1.471505 1.193588 1.218717 -P_11 1.169053 1.181906 1.077261 0.982276 0.969564 1.029868 0.950803 0.917862 -P_11 0.863611 0.817153 0.896662 0.961477 0.954209 1.104869 1.045396 1.127712 -P_11 1.132701 1.031139 1.038933 1.062666 0.877771 0.910033 0.786029 0.723191 -P_11 0.584435 0.489477 0.470055 0.362647 0.344980 0.307916 0.281854 0.334417 -P_11 0.370236 0.444538 0.540191 0.659272 0.774325 0.821805 0.954710 1.077716 -P_11 1.087483 1.188288 1.241381 1.284959 1.237639 1.267139 1.193500 1.246459 -P_11 1.073620 1.076994 1.050039 0.991049 0.927549 0.948150 0.874226 0.869307 -P_11 0.926222 0.904161 0.984461 1.036055 1.025466 0.965033 1.129997 1.159533 -P_11 1.093537 1.182493 1.107633 0.964302 0.916789 0.892286 0.796781 0.640679 -P_11 0.637488 0.507006 0.437338 0.345128 0.342996 0.314391 0.347339 0.405765 -P_11 0.432080 0.461977 0.533531 0.674302 0.809204 0.872183 1.032697 1.053177 -P_11 1.215371 1.105563 1.262906 1.281297 1.305239 1.349414 1.278735 1.388038 -P_11 1.192902 1.077348 1.156559 1.048816 0.985149 1.009940 0.983291 0.960802 -P_11 0.903151 0.963790 0.984540 1.040593 0.920051 1.082861 1.097439 1.027646 -P_11 1.044248 1.163644 0.996654 1.066914 0.958544 0.885085 0.813348 0.744578 -P_11 0.645174 0.497366 0.457264 0.372068 0.331541 0.335585 0.315051 0.354816 -P_11 0.391582 0.485750 0.624194 0.769397 0.762075 0.868688 0.965119 1.104001 -P_11 1.105594 1.153215 1.207697 1.251126 1.259136 1.289558 1.121223 1.139334 -P_11 1.157501 1.099540 1.052579 1.011747 0.984372 0.901316 0.898956 0.992256 -P_11 0.940909 0.879517 1.026242 1.081036 0.971932 0.950944 1.067931 1.146965 -P_11 1.080309 1.042804 0.992089 1.005496 0.934525 0.890303 0.769467 0.744327 -P_11 0.557921 0.557473 0.474585 0.395241 0.362540 0.330083 0.355619 0.372484 -P_11 0.449702 0.473235 0.616170 0.749338 0.872239 0.940199 1.091052 1.324995 -P_11 1.269948 1.423938 1.426134 1.373936 1.384879 1.360964 1.340730 1.264686 -P_11 1.245524 1.174247 1.071384 1.215310 1.013004 0.966647 0.973823 1.047772 -P_11 0.987577 1.014374 1.014605 1.129592 1.076014 1.090435 1.100024 1.068085 -P_11 1.137822 1.029297 0.994234 0.961050 0.931977 0.808947 0.753512 0.732581 -P_11 0.606129 0.645814 0.528219 0.448470 0.410977 0.392312 0.402903 0.396845 -P_11 0.370843 0.418594 0.471062 0.545164 0.607510 0.707862 0.801746 0.881518 -P_11 0.938919 1.098062 1.196400 1.085569 1.228527 1.241293 1.241132 1.270611 -P_11 1.193209 1.183074 1.072212 1.120033 1.067994 1.004636 1.007828 0.919419 -P_11 1.082002 1.019884 1.038732 1.023050 1.071887 0.947597 1.066923 0.960042 -P_11 0.974297 1.067096 0.880506 0.825349 0.787768 0.691839 0.710244 0.621222 -P_11 0.576576 0.580646 0.475949 0.439650 0.401888 0.388624 0.373993 0.368203 -P_11 0.355423 0.393182 0.402883 0.441651 0.445173 0.542931 0.666015 0.657631 -P_11 0.788825 0.909895 0.902009 0.983470 1.019711 1.009181 1.107929 1.008328 -P_11 0.977139 1.062532 1.097774 1.052950 0.914066 0.886252 0.992794 0.895480 -P_11 0.838120 0.959274 0.954015 0.984503 1.011721 1.051390 1.111649 0.959410 -P_11 1.008195 0.938192 1.006229 0.929986 0.947371 0.869649 0.791834 0.634975 -P_11 0.592877 0.544097 0.444304 0.345330 0.330944 0.310992 0.307992 0.323284 -P_11 0.391996 0.458817 0.545261 0.644467 0.798648 0.857821 0.909533 0.944995 -P_11 1.057922 1.141769 1.372822 1.395167 1.386675 1.379925 1.434037 1.323160 -P_11 1.139479 1.183410 1.122280 1.038668 1.002173 0.952386 0.864609 0.874193 -P_11 0.775436 0.926007 0.883629 0.918963 1.016235 0.973625 1.059755 1.097387 -P_11 1.070211 0.996465 1.029301 1.105192 1.004842 0.887598 0.807907 0.636424 -P_11 0.565091 0.542226 0.452982 0.379182 0.314968 0.311774 0.313997 0.328159 -P_11 0.415903 0.432569 0.541610 0.608458 0.729260 0.896990 0.928472 1.084798 -P_11 1.144964 1.190881 1.173780 1.238174 1.321733 1.263526 1.146113 1.236706 -P_11 1.165471 1.167950 1.028616 1.059700 0.974583 0.853855 0.933324 0.868643 -P_11 0.987913 0.914962 1.024077 0.972822 1.070320 1.119133 1.090950 1.064777 -P_11 1.136754 1.047305 1.134212 1.026490 0.999134 0.936261 0.830244 0.691999 -P_11 0.620275 0.449713 0.444665 0.376858 0.345022 0.330748 0.335199 0.349178 -P_11 0.416862 0.513416 0.577178 0.643577 0.772444 0.929216 0.908766 1.069244 -P_11 1.136671 1.192396 1.262438 1.273013 1.176895 1.270581 1.321249 1.250325 -P_11 1.225040 1.174973 1.176111 1.066229 1.082388 1.030799 0.977225 0.937605 -P_11 0.980054 0.952217 0.915512 0.978158 0.952635 1.096554 1.104624 1.051215 -P_11 1.150116 1.053598 1.076577 1.038368 1.003926 0.871761 0.750724 0.675418 -P_11 0.569866 0.483427 0.466733 0.394593 0.361808 0.312669 0.304737 0.351307 -P_11 0.401388 0.515637 0.568966 0.673693 0.752295 0.986394 0.980159 0.982875 -P_11 1.175468 1.143514 1.237189 1.246335 1.235650 1.128762 1.213311 1.019663 -P_11 1.063054 1.043392 1.034794 0.963058 0.881065 0.974733 0.950091 0.967904 -P_11 0.951878 0.997108 1.034884 0.953709 1.031763 1.094231 1.019329 1.127754 -P_11 1.052613 1.050229 1.028566 0.982058 0.950299 0.887596 0.783166 0.682618 -P_11 0.631958 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0.781608 0.906026 0.922694 0.925860 0.958547 1.028442 1.080075 1.057831 -P_11 1.025371 1.045346 1.011825 1.082068 0.979162 0.804223 0.815941 0.694602 -P_11 0.586821 0.521321 0.462357 0.403922 0.341982 0.302194 0.335255 0.342553 -P_11 0.391567 0.462103 0.548048 0.628487 0.776827 0.787122 0.950426 1.001207 -P_11 1.167575 1.143672 1.268067 1.208000 1.222268 1.212079 1.206707 1.169037 -P_11 1.165864 1.155456 1.068822 0.981777 0.946008 0.920905 0.867219 0.944774 -P_11 0.990998 0.968864 0.896772 1.013967 1.123276 1.028259 1.101617 1.256949 -P_11 1.190334 1.108773 1.155442 1.023280 0.952808 0.842437 0.809806 0.638679 -P_11 0.610870 0.504232 0.382251 0.388042 0.331545 0.316437 0.323324 0.393142 -P_11 0.396461 0.481456 0.568979 0.760557 0.809630 0.885075 0.989415 1.065989 -P_11 1.100186 1.175861 1.224772 1.228523 1.218107 1.442929 1.288577 1.165232 -P_11 1.246202 1.144724 1.069885 1.056009 1.023182 1.023299 0.994431 0.966780 -P_11 0.926318 0.916947 1.049355 1.022516 1.128876 1.047614 1.108041 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1.036513 1.006999 -P_12 1.047274 1.014961 1.067435 1.034706 1.049907 1.126447 1.178423 1.268411 -P_12 1.154831 1.167123 1.142139 1.008168 1.042021 0.912232 0.933208 0.787735 -P_12 0.638520 0.582437 0.536891 0.426736 0.369068 0.407379 0.377012 0.445413 -P_12 0.451604 0.554104 0.679517 0.726966 0.904989 1.038880 1.201230 1.225523 -P_12 1.335781 1.454380 1.423272 1.393107 1.497756 1.447811 1.420908 1.426377 -P_12 1.337853 1.168990 1.231679 1.257193 1.108420 1.145318 0.904644 1.152484 -P_12 1.031441 1.083099 1.101230 1.177141 1.110837 1.120481 1.241446 1.132875 -P_12 1.247723 1.082942 1.123074 1.022724 0.952650 0.937825 0.826142 0.792501 -P_12 0.756328 0.671564 0.525544 0.484412 0.424615 0.399390 0.383162 0.385001 -P_12 0.376807 0.410180 0.490789 0.517170 0.682221 0.756863 0.810309 0.958206 -P_12 0.999811 1.135302 1.263633 1.124834 1.286529 1.343694 1.322558 1.265073 -P_12 1.222459 1.197277 1.177339 1.121659 1.083771 1.085125 1.168384 1.133181 -P_12 1.115186 1.211831 1.122227 1.198178 1.226948 1.147979 1.121973 1.078478 -P_12 1.100779 0.994972 0.971025 0.968923 0.886088 0.866670 0.701473 0.638754 -P_12 0.585538 0.563255 0.481336 0.471767 0.426412 0.416221 0.354617 0.379373 -P_12 0.341577 0.406283 0.389960 0.471815 0.477728 0.598723 0.666598 0.750354 -P_12 0.789226 0.863431 1.008131 1.076019 1.092179 1.056884 1.208331 1.203545 -P_12 1.130237 1.071659 1.071249 1.113680 1.058855 0.999042 1.014182 1.012461 -P_12 0.962034 0.948876 1.057611 1.050592 0.991722 1.099260 1.075084 1.066263 -P_12 1.076263 1.092688 1.040223 1.027277 0.850885 0.886944 0.719441 0.685930 -P_12 0.629885 0.505701 0.466208 0.366151 0.365300 0.323999 0.308958 0.354475 -P_12 0.434621 0.448935 0.556904 0.680825 0.793119 0.910128 1.015289 1.169733 -P_12 1.152001 1.331691 1.272042 1.310945 1.584842 1.394315 1.411499 1.364272 -P_12 1.277475 1.218185 1.134646 1.114103 0.905432 0.931989 0.964293 0.989394 -P_12 0.985157 0.947480 1.023951 1.014183 1.091805 1.123779 1.093537 1.175783 -P_12 1.190401 1.209708 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1.073315 1.159662 1.079696 1.107122 -P_12 1.158280 1.073499 1.183661 1.199698 1.166838 1.172798 1.321332 1.331749 -P_12 1.320127 1.227575 1.274112 1.212047 1.185112 0.901133 0.908637 0.843124 -P_12 0.702724 0.640714 0.537710 0.485211 0.429627 0.420214 0.393896 0.436968 -P_12 0.533785 0.622020 0.713817 0.871707 1.065049 1.203705 1.285226 1.332062 -P_12 1.493060 1.618071 1.597917 1.629308 1.510462 1.628806 1.501944 1.497651 -P_12 1.492974 1.363453 1.263982 1.221401 1.158756 1.310691 1.170566 1.135122 -P_12 1.148227 1.200344 1.110973 1.211039 1.231529 1.265087 1.284125 1.288623 -P_12 1.144363 1.146289 1.233386 1.172143 1.070855 0.976116 0.946905 0.834180 -P_12 0.798242 0.623703 0.552553 0.523462 0.511120 0.452908 0.398602 0.428089 -P_12 0.425406 0.505877 0.522161 0.588816 0.780700 0.757009 0.971705 1.015534 -P_12 1.125633 1.208146 1.197481 1.281336 1.290650 1.306475 1.295428 1.264873 -P_12 1.204918 1.319700 1.276969 1.140502 1.191978 1.175690 1.079465 1.187645 -P_12 1.261565 1.169315 1.307153 1.186441 1.258293 1.267986 1.280439 1.112386 -P_12 1.067150 1.128486 1.078347 1.025665 0.850948 0.868226 0.841686 0.697234 -P_12 0.665040 0.538225 0.501819 0.536395 0.454330 0.432027 0.395670 0.378400 -P_12 0.419985 0.395151 0.467185 0.513037 0.526860 0.625159 0.626753 0.696006 -P_12 0.736920 0.882237 1.055919 1.020755 1.124210 1.176074 1.292201 1.252289 -P_12 1.335300 1.178089 1.207935 1.215379 1.069463 1.077011 1.055466 1.050500 -P_12 0.977703 1.050649 0.944479 1.148875 1.157899 1.162538 1.146611 1.157206 -P_12 1.264515 1.100338 1.146845 1.048975 0.970795 0.947210 0.870709 0.752313 -P_12 0.678991 0.538669 0.422663 0.403675 0.407777 0.386709 0.377776 0.384290 -P_12 0.458797 0.514705 0.606943 0.710607 0.763434 1.011393 1.031621 1.141715 -P_12 1.353727 1.487664 1.540096 1.470541 1.531221 1.427507 1.527277 1.430169 -P_12 1.297381 1.290942 1.337561 1.146435 1.077176 1.213675 1.063519 1.035217 -P_12 1.036574 1.015645 1.055471 1.152621 1.209112 1.211985 1.252706 1.242815 -P_12 1.314868 1.324653 1.093333 1.237024 1.072404 1.058084 0.917506 0.777360 -P_12 0.673096 0.578764 0.480181 0.411191 0.386952 0.392411 0.353819 0.378190 -P_12 0.452972 0.495991 0.599679 0.722379 0.829273 0.884231 1.046695 1.209190 -P_12 1.252733 1.408273 1.320791 1.414463 1.370133 1.482345 1.420194 1.215181 -P_12 1.258519 1.209958 1.172236 1.171461 1.076627 1.015631 1.043135 1.030986 -P_12 0.986652 1.124173 0.994602 1.222269 1.355800 1.304593 1.250741 1.278517 -P_12 1.270877 1.303656 1.266469 1.250143 1.121481 0.981168 0.971143 0.750210 -P_12 0.664629 0.567684 0.459562 0.429836 0.366719 0.343789 0.402310 0.399934 -P_12 0.450186 0.536973 0.644419 0.741233 0.829993 1.068187 1.086825 1.112676 -P_12 1.322505 1.344421 1.354279 1.344853 1.325346 1.349601 1.438476 1.439097 -P_12 1.222739 1.305740 1.176905 1.184825 1.146481 1.084049 1.096899 1.071743 -P_12 1.120943 1.117493 1.198057 1.291797 1.291817 1.252733 1.226100 1.326050 -P_12 1.193027 1.343846 1.253037 1.021915 1.097520 0.930702 0.945981 0.817088 -P_12 0.683196 0.601361 0.536942 0.418255 0.350470 0.330444 0.344989 0.384152 -P_12 0.471455 0.556559 0.723630 0.772514 0.847400 0.982853 1.045383 1.278782 -P_12 1.311519 1.396632 1.508600 1.534271 1.472840 1.437064 1.374713 1.292485 -P_12 1.334625 1.323296 1.142783 1.056513 1.207025 1.130415 1.078830 1.177920 -P_12 1.084801 1.074944 1.158298 1.220291 1.282339 1.207861 1.320337 1.314541 -P_12 1.220648 1.286991 1.168644 1.192306 1.103447 1.003319 0.883052 0.776632 -P_12 0.717258 0.635576 0.517220 0.428311 0.444293 0.355530 0.425076 0.453879 -P_12 0.521300 0.661207 0.727006 0.848633 1.038737 1.151727 1.280561 1.587216 -P_12 1.519634 1.624040 1.674748 1.552059 1.691287 1.713530 1.496504 1.441994 -P_12 1.468355 1.432458 1.340625 1.258185 1.322854 1.112101 1.126105 1.120118 -P_12 1.131652 1.158372 1.227357 1.234796 1.340163 1.332768 1.244925 1.351829 -P_12 1.301847 1.237015 1.175570 1.276556 1.145679 0.942631 0.875180 0.782044 -P_12 0.777771 0.717919 0.563790 0.553826 0.478065 0.462446 0.381018 0.416727 -P_12 0.396954 0.428375 0.487965 0.556142 0.696395 0.752059 0.899343 0.905004 -P_12 1.074713 1.220944 1.219831 1.461647 1.332517 1.235836 1.306180 1.377770 -P_12 1.268309 1.266816 1.276409 1.231651 1.212624 1.294611 1.150324 1.172123 -P_12 1.208234 1.185832 1.225312 1.166054 1.080604 1.286593 1.295874 1.166927 -P_12 1.127767 1.176610 1.074387 0.985333 0.861044 0.807957 0.826290 0.700888 -P_12 0.691696 0.600791 0.534169 0.456520 0.436606 0.398131 0.411916 0.366341 -P_12 0.367018 0.412059 0.401684 0.474397 0.555022 0.618739 0.723275 0.763338 -P_12 0.830168 0.911089 1.010492 1.048394 1.181805 1.245417 1.146739 1.280277 -P_12 1.250980 1.274037 1.191398 1.213396 1.139386 1.059862 1.142805 0.919272 -P_12 1.051002 1.159564 1.194956 1.147384 1.263058 1.150990 1.259182 1.240129 -P_12 1.196788 1.146463 1.078387 1.061877 1.049659 0.880465 0.851105 0.730783 -P_12 0.607808 0.557474 0.454636 0.395905 0.358904 0.355797 0.377515 0.354460 -P_12 0.432125 0.515194 0.586911 0.677480 0.816676 0.987071 1.143469 1.145027 -P_12 1.371510 1.391914 1.347519 1.560088 1.455110 1.545524 1.426554 1.386590 -P_12 1.394272 1.406058 1.215126 1.178620 1.038655 1.067368 1.034382 1.051473 -P_12 1.051829 1.063984 1.104608 1.022345 1.186743 1.354237 1.221734 1.398430 -P_12 1.301835 1.223915 1.276608 1.192758 1.051534 1.071134 0.906306 0.822747 -P_12 0.715571 0.613127 0.520475 0.500067 0.425738 0.350374 0.395674 0.359311 -P_12 0.476172 0.561134 0.597310 0.712620 0.840534 0.855527 0.975789 1.043678 -P_12 1.243242 1.236455 1.341594 1.377979 1.477079 1.386019 1.426527 1.345197 -P_12 1.385970 1.327605 1.150361 1.134853 1.059884 1.064196 1.064249 1.120881 -P_12 1.068445 1.017444 1.173504 1.283899 1.195582 1.256903 1.342906 1.335665 -P_12 1.196762 1.297420 1.150383 1.175478 1.113482 1.017608 0.886868 0.762993 -P_12 0.710027 0.587659 0.447525 0.438125 0.345338 0.352636 0.379983 0.374079 -P_12 0.450811 0.510499 0.604681 0.745940 0.855888 0.967820 0.992711 1.245384 -P_12 1.237469 1.223440 1.518404 1.401299 1.496527 1.308367 1.429927 1.381819 -P_12 1.305647 1.261496 1.221140 1.265586 1.092254 1.195058 1.187410 0.927755 -P_12 1.174632 1.146856 1.274064 1.274325 1.201242 1.263126 1.227131 1.256087 -P_12 1.394312 1.237370 1.183434 1.244038 1.111171 0.972372 0.957417 0.832274 -P_12 0.679931 0.576666 0.550183 0.423844 0.401953 0.340260 0.333805 0.385207 -P_12 0.488442 0.566233 0.665939 0.743461 0.954952 0.993337 1.117009 1.182728 -P_12 1.373406 1.372633 1.392028 1.423837 1.511458 1.421721 1.457546 1.412044 -P_12 1.219739 1.252873 1.195945 1.147648 1.070598 1.083340 1.057614 1.164255 -P_12 1.080543 1.083850 1.141701 1.117265 1.224660 1.274740 1.228062 1.309839 -P_12 1.214786 1.305034 1.264128 1.120325 0.904023 1.065002 0.906475 0.805293 -P_12 0.748577 0.605592 0.528085 0.478660 0.420927 0.387047 0.434720 0.473028 -P_12 0.549722 0.554776 0.698315 0.838203 0.983780 1.281932 1.396198 1.329213 -P_12 1.512635 1.514856 1.500714 1.577109 1.580292 1.557558 1.461613 1.466888 -P_12 1.404339 1.316522 1.287593 1.356901 1.220521 1.079245 1.192351 1.241124 -P_12 1.204987 1.221872 1.164611 1.195992 1.222101 1.114129 1.284788 1.238423 -P_12 1.321405 1.260565 1.047157 1.129651 1.028427 0.962287 0.930251 0.873384 -P_12 0.799578 0.654777 0.615821 0.533599 0.498008 0.484764 0.413157 0.440148 -P_12 0.462462 0.479379 0.496801 0.607676 0.621137 0.783434 0.837657 1.013027 -P_12 1.113527 1.069933 1.163886 1.230142 1.399269 1.396496 1.349136 1.284037 -P_12 1.256334 1.264014 1.338990 1.150523 1.110390 1.263240 1.252274 1.245335 -P_12 1.188442 1.255077 1.230452 1.224128 1.132429 1.165445 1.194014 1.224349 -P_12 1.162755 1.173706 1.065402 1.010286 0.887415 0.842835 0.756357 0.694791 -P_12 0.655826 0.612820 0.512886 0.505521 0.445343 0.405903 0.421510 0.409819 -P_12 0.415280 0.410306 0.443006 0.493224 0.568184 0.584340 0.697175 0.753226 -P_12 0.896934 0.954986 1.038061 1.033988 1.116452 1.154624 1.196425 1.162063 -P_12 1.292707 1.173206 1.179179 1.075605 1.204402 1.027510 1.119538 1.038068 -P_12 0.992574 1.013750 1.045089 1.130078 1.043948 1.162836 1.131490 1.239957 -P_12 1.287265 1.129783 1.267099 1.031655 1.057044 0.994243 0.845963 0.696209 -P_12 0.744889 0.576166 0.474825 0.405302 0.366424 0.384319 0.323532 0.414383 -P_12 0.450200 0.524674 0.583168 0.729477 0.857010 1.042568 1.070209 1.272496 -P_12 1.292829 1.439681 1.421507 1.554214 1.564329 1.650480 1.543433 1.341916 -P_12 1.286159 1.242956 1.204307 1.120384 1.116898 1.030624 1.040387 0.972378 -P_12 1.019953 0.991936 1.056056 1.279186 1.125747 1.165476 1.186812 1.382682 -P_12 1.306312 1.206169 1.216592 1.131492 1.194206 1.064343 0.864851 0.781941 -P_12 0.667794 0.581208 0.501875 0.440182 0.402486 0.343846 0.397646 0.403668 -P_12 0.436619 0.581359 0.581876 0.663978 0.756904 0.955263 1.103602 1.202076 -P_12 1.159804 1.299412 1.425095 1.424962 1.414726 1.382180 1.341552 1.458837 -P_12 1.345480 1.340085 1.077184 1.189756 1.122653 1.069243 1.041136 1.023171 -P_12 1.086293 1.162124 1.169069 1.164741 1.054977 1.285410 1.324359 1.210865 -P_12 1.297447 1.277473 1.298742 1.261254 1.047319 1.012499 0.935083 0.766443 -P_12 0.658383 0.578947 0.524806 0.449623 0.363005 0.314744 0.396505 0.405185 -P_12 0.478371 0.532737 0.731394 0.803400 0.880770 1.067221 1.041617 1.238891 -P_12 1.190373 1.328206 1.337226 1.300270 1.325094 1.419505 1.492655 1.406126 -P_12 1.443264 1.266807 1.212206 1.150583 1.172990 1.228747 1.112720 1.210520 -P_12 1.119698 1.140134 1.181716 1.255892 1.220660 1.178252 1.381048 1.248921 -P_12 1.215160 1.327671 1.229472 1.266805 1.024345 0.931437 0.902002 0.817503 -P_12 0.693891 0.614474 0.460098 0.458607 0.392735 0.337781 0.383001 0.365533 -P_12 0.472322 0.586055 0.674376 0.803845 0.988892 0.990730 1.125259 1.213385 -P_12 1.301864 1.336257 1.334724 1.259960 1.419885 1.416145 1.424180 1.360537 -P_12 1.302044 1.361192 1.268011 1.127292 1.176365 1.087820 1.062562 1.148044 -P_12 1.159982 1.218593 1.080380 1.117995 1.321824 1.312168 1.155710 1.345007 -P_12 1.297222 1.303887 1.359938 1.234152 1.177147 0.989302 0.975066 0.839594 -P_12 0.761256 0.599127 0.560046 0.496193 0.426412 0.367151 0.436347 0.457289 -P_12 0.553314 0.620361 0.700513 0.875626 1.057622 1.104793 1.290642 1.445445 -P_12 1.328213 1.593100 1.839487 1.545510 1.728950 1.625204 1.601289 1.623317 -P_12 1.532411 1.476566 1.387547 1.388361 1.180352 1.182232 1.270148 1.166630 -P_12 1.233823 1.261397 1.267922 1.177996 1.298863 1.175664 1.337767 1.121077 -P_12 1.142285 1.207831 1.287709 1.263250 1.032610 1.055098 0.926614 0.827698 -P_12 0.806113 0.585537 0.564583 0.549648 0.509236 0.449156 0.422753 0.412411 -P_12 0.468400 0.446485 0.524763 0.599174 0.645250 0.790586 0.886020 1.042809 -P_12 1.058227 1.189874 1.273072 1.217054 1.426918 1.262694 1.389664 1.399470 -P_12 1.308394 1.292817 1.317602 1.263756 1.221344 1.268783 1.224462 1.148346 -P_12 1.250820 1.247312 1.185649 1.244024 1.222806 1.230136 1.154529 1.218127 -P_12 1.162470 1.087737 1.098892 1.081812 0.916277 0.897743 0.853793 0.678962 -P_12 0.676452 0.626497 0.561733 0.521875 0.445397 0.430672 0.393867 0.402772 -P_12 0.395346 0.415180 0.483250 0.473441 0.590204 0.653919 0.702290 0.764485 -P_12 0.851566 1.061677 1.028706 1.079401 1.262248 1.212361 1.248423 1.294495 -P_12 1.277552 1.272144 1.269283 1.115527 1.121677 1.117854 1.018810 1.090306 -P_12 0.942144 1.178707 1.114199 1.249519 1.129026 1.184236 1.228947 1.283523 -P_12 1.230673 1.214492 1.164397 1.055883 1.089597 0.967822 0.793098 0.765473 -P_12 0.638841 0.619222 0.461120 0.422604 0.408596 0.342436 0.335478 0.385807 -P_12 0.471100 0.524739 0.651215 0.769934 0.791487 0.914715 1.114378 1.199173 -P_12 1.323081 1.431302 1.499886 1.547119 1.632669 1.515357 1.567849 1.589861 -P_12 1.354679 1.351141 1.245708 1.163209 1.199256 1.054236 1.097420 1.040008 -P_12 1.052779 0.987570 1.048300 1.201253 1.166096 1.197275 1.279027 1.323863 -P_12 1.252388 1.311888 1.291142 1.173287 0.998352 1.036908 0.955719 0.826164 -P_12 0.743134 0.647141 0.546606 0.418856 0.403918 0.377306 0.368861 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1.123582 0.959960 0.868129 0.778185 -P_12 0.687286 0.587931 0.515125 0.473795 0.355028 0.352309 0.367823 0.388129 -P_12 0.418124 0.517913 0.643330 0.723717 0.902768 0.989788 1.368287 1.312258 -P_12 1.377027 1.564272 1.568473 1.561636 1.588403 1.660932 1.426022 1.603300 -P_12 1.641234 1.391195 1.291976 1.212401 1.138044 1.077741 1.084907 1.158744 -P_12 1.061512 1.088229 1.110216 1.147809 1.301743 1.290180 1.427642 1.273600 -P_12 1.346663 1.290629 1.339559 1.254400 1.023693 1.122524 1.031565 0.857191 -P_12 0.769172 0.584151 0.590936 0.492091 0.421598 0.383078 0.423365 0.420317 -P_12 0.451236 0.542980 0.648694 0.792150 0.807784 0.977512 1.137038 1.214340 -P_12 1.361124 1.319986 1.566062 1.494901 1.480990 1.472770 1.390536 1.509769 -P_12 1.447180 1.286242 1.278442 1.159219 1.187767 1.156795 1.151245 1.185987 -P_12 1.143479 1.113985 1.225987 1.155458 1.254725 1.178497 1.316761 1.300256 -P_12 1.483883 1.287134 1.371718 1.292465 1.210908 0.982625 0.959647 0.902359 -P_12 0.677600 0.655725 0.545405 0.471204 0.381423 0.354562 0.397615 0.430017 -P_12 0.481782 0.537685 0.712358 0.790424 0.824256 1.077186 1.227232 1.363223 -P_12 1.364347 1.430260 1.616984 1.573666 1.505004 1.424325 1.509949 1.446055 -P_12 1.329239 1.408351 1.370373 1.279141 1.295698 1.254968 1.309672 1.139270 -P_12 1.172715 1.201757 1.191131 1.383356 1.269491 1.414392 1.293131 1.443907 -P_12 1.404963 1.402649 1.364311 1.219864 1.155135 1.193697 0.995426 0.871005 -P_12 0.706085 0.681494 0.497443 0.445950 0.396669 0.381516 0.364224 0.450402 -P_12 0.487552 0.569612 0.673201 0.875047 0.888453 0.984009 1.253918 1.250073 -P_12 1.375448 1.427658 1.554018 1.527410 1.477219 1.552632 1.420025 1.437498 -P_12 1.338910 1.279699 1.299427 1.172917 1.287291 1.148731 1.286624 1.142718 -P_12 1.316271 1.206260 1.329735 1.273338 1.359615 1.367908 1.308769 1.385368 -P_12 1.405744 1.317744 1.356536 1.324737 1.212993 1.206190 0.989481 0.866485 -P_12 0.845698 0.688484 0.569742 0.522539 0.459262 0.444181 0.466780 0.484868 -P_12 0.552186 0.630456 0.857164 0.837261 1.007436 1.268081 1.367513 1.445989 -P_12 1.653115 1.667572 1.874901 1.670240 1.568988 1.882585 1.814778 1.635694 -P_12 1.549102 1.434339 1.376232 1.408653 1.181304 1.282638 1.354187 1.248476 -P_12 1.250509 1.273317 1.396347 1.342195 1.308163 1.471947 1.489566 1.343882 -P_12 1.389287 1.382805 1.403675 1.226429 1.196689 1.111619 1.090291 0.921803 -P_12 0.780008 0.770053 0.653587 0.564368 0.506591 0.516107 0.438289 0.474244 -P_12 0.462487 0.485620 0.555471 0.648990 0.780714 0.782627 0.920444 1.021345 -P_12 1.134401 1.226296 1.301113 1.455202 1.338838 1.431100 1.407623 1.538993 -P_12 1.405110 1.220294 1.473093 1.263371 1.281263 1.330591 1.303709 1.361614 -P_12 1.233524 1.253316 1.283198 1.342798 1.301640 1.321343 1.210283 1.276905 -P_12 1.271834 1.221577 1.200275 1.013755 0.990634 0.856979 0.843220 0.769818 -P_12 0.684548 0.633517 0.608341 0.500559 0.488034 0.472790 0.402884 0.395994 -P_12 0.437165 0.430527 0.505828 0.547602 0.631908 0.592303 0.761464 0.842190 -P_12 0.997017 1.028776 1.005396 1.071237 1.269434 1.223103 1.437519 1.340322 -P_12 1.298886 1.221570 1.270541 1.329086 1.169285 1.051665 1.050713 1.142340 -P_12 1.222089 1.216978 1.142664 1.265199 1.181340 1.292392 1.164318 1.318338 -P_12 1.281063 1.263759 1.199827 1.206300 1.114955 0.884288 0.878373 0.806269 -P_12 0.697870 0.588315 0.466820 0.447388 0.397774 0.351726 0.376200 0.418974 -P_12 0.471437 0.542786 0.702310 0.847367 0.942119 1.054775 1.102875 1.278817 -P_12 1.456338 1.549822 1.721861 1.592556 1.649121 1.756610 1.673932 1.499095 -P_12 1.508463 1.351894 1.314590 1.287218 1.288846 1.066150 1.104252 1.126563 -P_12 1.065167 1.233503 1.136607 1.176548 1.224809 1.321295 1.350652 1.416556 -P_12 1.349567 1.461543 1.384548 1.252798 1.208696 1.119464 1.035894 0.943843 -P_12 0.793014 0.650167 0.544291 0.456981 0.460575 0.400858 0.450481 0.452889 -P_12 0.454009 0.586259 0.691671 0.791734 0.999260 1.158573 1.265518 1.237089 -P_12 1.507585 1.429654 1.563935 1.476753 1.469146 1.448934 1.549950 1.416674 -P_12 1.492609 1.273001 1.381845 1.268683 1.237127 1.217097 1.169745 1.201690 -P_12 1.173504 1.246613 1.204238 1.283824 1.286623 1.310276 1.413108 1.339214 -P_12 1.380300 1.460989 1.398655 1.302941 1.245910 1.078615 0.939753 0.870675 -P_12 0.699811 0.610877 0.539232 0.444437 0.394670 0.372030 0.412729 0.444646 -P_12 0.543403 0.569829 0.766412 0.795305 0.990997 1.038309 1.187988 1.417804 -P_12 1.327826 1.320432 1.482661 1.479699 1.604080 1.529527 1.383071 1.459968 -P_12 1.448260 1.471654 1.380310 1.407995 1.211696 1.266325 1.278740 1.290680 -P_12 1.166011 1.362881 1.172817 1.295217 1.464050 1.345806 1.293414 1.473686 -P_12 1.357900 1.386728 1.248773 1.348724 1.074610 1.144466 0.987169 0.887634 -P_12 0.781799 0.643494 0.548873 0.474682 0.405216 0.432627 0.373964 0.425589 -P_12 0.489967 0.570292 0.783676 0.818427 0.981364 1.049158 1.193488 1.274533 -P_12 1.278475 1.406661 1.421219 1.637474 1.541942 1.496180 1.538280 1.534847 -P_12 1.393448 1.277319 1.402313 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1.806298 -P_12 1.452090 1.492103 1.325524 1.442215 1.354688 1.445141 1.386193 1.358432 -P_12 1.297421 1.379623 1.416407 1.404582 1.472942 1.414523 1.450545 1.448530 -P_12 1.425968 1.364635 1.321656 1.256700 1.142883 1.129312 1.003753 0.898010 -P_12 0.730431 0.807704 0.698274 0.613452 0.537066 0.475096 0.492137 0.482219 -P_12 0.506509 0.544178 0.581397 0.703257 0.821684 0.995966 0.967267 1.095675 -P_12 1.234280 1.281877 1.336874 1.536150 1.450209 1.463057 1.494457 1.490691 -P_12 1.623193 1.490446 1.454674 1.513014 1.339540 1.420610 1.340814 1.504115 -P_12 1.328768 1.387528 1.271978 1.317649 1.359649 1.510903 1.331472 1.216028 -P_12 1.386034 1.335185 1.261472 1.066739 1.016714 0.920295 0.858608 0.775836 -P_12 0.762116 0.721144 0.644339 0.574968 0.507169 0.500513 0.457127 0.459650 -P_12 0.447040 0.488946 0.475391 0.625695 0.648333 0.719343 0.764469 0.803587 -P_12 1.061169 1.028924 1.126762 1.267668 1.247566 1.274359 1.409496 1.267968 -P_12 1.339866 1.361688 1.302591 1.398527 1.116700 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1.166597 1.204153 1.242657 1.364011 -P_12 1.417721 1.392647 1.723671 1.636918 1.685911 1.610057 1.631489 1.639316 -P_12 1.538354 1.399766 1.343373 1.332288 1.299896 1.281529 1.228646 1.239045 -P_12 1.146527 1.277275 1.310660 1.432184 1.482960 1.436918 1.418315 1.553966 -P_12 1.476839 1.366824 1.366853 1.431742 1.338797 1.233392 1.032868 0.972327 -P_12 0.903238 0.685203 0.647554 0.501367 0.495479 0.435391 0.436784 0.550646 -P_12 0.577106 0.714842 0.912997 1.006726 1.170989 1.248350 1.538746 1.574944 -P_12 1.823292 1.856477 2.025296 1.788385 1.745652 1.839600 1.754600 1.682406 -P_12 1.642589 1.772606 1.551406 1.381932 1.436807 1.397048 1.390843 1.379637 -P_12 1.354613 1.254540 1.489508 1.432084 1.316515 1.319806 1.439811 1.392915 -P_12 1.434963 1.251919 1.323395 1.200919 1.310483 1.132506 1.023093 1.003903 -P_12 0.818602 0.817939 0.685700 0.571566 0.577310 0.495870 0.481711 0.503913 -P_12 0.471831 0.552701 0.653138 0.668200 0.750696 0.904330 1.019022 1.165337 -P_12 1.258338 1.259623 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1.544433 1.547032 1.599656 1.532419 -P_12 1.492445 1.413678 1.397159 1.247038 1.300781 1.223742 1.182615 1.135745 -P_12 1.112045 1.105224 1.190150 1.265651 1.427039 1.446499 1.488954 1.418329 -P_12 1.564474 1.353126 1.361166 1.458435 1.317711 1.279834 0.954124 0.858454 -P_12 0.732232 0.595097 0.543911 0.487973 0.422347 0.373963 0.406526 0.468439 -P_12 0.528904 0.705992 0.714467 0.897400 0.966242 1.081143 1.159710 1.427575 -P_12 1.511187 1.561027 1.545803 1.477965 1.619494 1.705482 1.626391 1.551105 -P_12 1.447973 1.518043 1.344609 1.356667 1.341353 1.392511 1.279214 1.338219 -P_12 1.263867 1.348552 1.352736 1.383281 1.464393 1.388553 1.491271 1.509174 -P_12 1.513234 1.336946 1.288209 1.447334 1.310514 1.235219 1.033635 0.874664 -P_12 0.759851 0.629700 0.567445 0.470870 0.450337 0.431474 0.384355 0.415880 -P_12 0.503250 0.606527 0.726275 0.763127 0.971039 1.061355 1.262204 1.405725 -P_12 1.473428 1.523181 1.679210 1.553935 1.614200 1.613609 1.552487 1.535761 -P_12 1.492066 1.437791 1.397916 1.262459 1.178929 1.225132 1.184034 1.293385 -P_12 1.217447 1.215655 1.214612 1.393835 1.442004 1.443894 1.474964 1.510513 -P_12 1.501881 1.484418 1.469945 1.394174 1.302852 1.212930 1.026070 0.945306 -P_12 0.853496 0.771388 0.642032 0.524845 0.440536 0.461007 0.461179 0.527189 -P_12 0.576595 0.661569 0.784123 0.966067 1.120733 1.289496 1.323358 1.583251 -P_12 1.767402 1.788208 1.789612 1.800222 2.009709 1.934932 1.709215 1.643350 -P_12 1.683084 1.593627 1.513777 1.351062 1.394815 1.381159 1.299609 1.176307 -P_12 1.308083 1.199437 1.254377 1.356350 1.440893 1.523395 1.507666 1.317712 -P_12 1.356787 1.239217 1.120890 1.269813 1.104724 1.162852 0.975402 0.960459 -P_12 0.822215 0.753154 0.643723 0.546582 0.537138 0.538608 0.489475 0.469870 -P_12 0.523170 0.516842 0.644957 0.670535 0.785916 0.844580 1.054366 1.099993 -P_12 1.247561 1.259798 1.488426 1.452347 1.363298 1.561725 1.499374 1.470068 -P_12 1.307389 1.394140 1.327023 1.285603 1.521386 1.306622 1.225376 1.402806 -P_12 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1.106004 0.960429 0.890542 -P_12 0.770071 0.598630 0.563162 0.453125 0.440819 0.431635 0.382080 0.435670 -P_12 0.531324 0.625777 0.729514 0.845690 0.996929 1.180719 1.260959 1.365822 -P_12 1.500048 1.519628 1.476987 1.593875 1.589757 1.572328 1.568668 1.505733 -P_12 1.525705 1.374840 1.367805 1.313439 1.244722 1.211133 1.309154 1.335842 -P_12 1.191761 1.296773 1.289258 1.306301 1.363681 1.372769 1.441911 1.449877 -P_12 1.497354 1.477048 1.419482 1.233427 1.174066 1.036271 0.997436 1.023220 -P_12 0.798893 0.683678 0.537042 0.502103 0.474069 0.419958 0.424804 0.488237 -P_12 0.523564 0.610175 0.797215 0.931550 1.159895 1.418749 1.368243 1.585067 -P_12 1.721232 1.794904 1.718947 1.798643 1.851063 1.802455 1.640641 1.701781 -P_12 1.532374 1.588154 1.432816 1.525110 1.395611 1.317432 1.310048 1.290825 -P_12 1.244899 1.235185 1.292219 1.350885 1.318373 1.428226 1.311413 1.337152 -P_12 1.479595 1.307487 1.342069 1.238089 1.212137 1.085799 1.063971 0.858581 -P_12 0.906560 0.723463 0.654228 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1.434367 1.273304 1.154866 1.236866 1.272854 1.151741 0.945995 0.909412 -P_12 0.784510 0.626311 0.581917 0.490179 0.429782 0.437717 0.438537 0.443323 -P_12 0.548120 0.656016 0.732401 0.910466 1.034591 1.276535 1.284878 1.557773 -P_12 1.564810 1.553779 1.604464 1.663473 1.904519 1.727184 1.606823 1.568361 -P_12 1.423200 1.443090 1.336700 1.242372 1.318644 1.309490 1.307954 1.153131 -P_12 1.228359 1.292881 1.299307 1.399130 1.308545 1.293431 1.412436 1.249216 -P_12 1.409915 1.311602 1.229336 1.166614 1.131744 1.008005 0.880819 0.951953 -P_12 0.743230 0.743054 0.661412 0.546672 0.503295 0.475326 0.415331 0.391644 -P_12 0.432876 0.512683 0.543201 0.639543 0.684222 0.777913 0.972755 1.119900 -P_12 1.142348 1.256217 1.182416 1.313220 1.374037 1.464208 1.488574 1.381903 -P_12 1.358665 1.284555 1.465593 1.323132 1.250479 1.271262 1.296545 1.110330 -P_12 1.331941 1.265836 1.284018 1.270824 1.318804 1.303279 1.271716 1.362781 -P_12 1.239786 1.111147 1.027376 1.049597 1.011544 0.909008 0.897999 0.728037 -P_12 0.678240 0.625002 0.578519 0.561594 0.472941 0.460581 0.430131 0.438995 -P_12 0.410625 0.487378 0.443753 0.514205 0.603157 0.675662 0.737581 0.831972 -P_12 0.869924 0.992620 1.103268 1.235773 1.179147 1.209062 1.156163 1.231778 -P_12 1.268092 1.324644 1.227581 1.192193 1.177656 1.157550 1.117126 1.127667 -P_12 1.119111 1.041829 1.094842 1.104450 1.299859 1.328972 1.210311 1.202972 -P_12 1.240573 1.177597 1.142054 1.106945 0.989531 0.962775 0.825242 0.789730 -P_12 0.722811 0.596461 0.523842 0.424687 0.369260 0.355684 0.374314 0.365374 -P_12 0.478191 0.488782 0.657693 0.782328 0.869047 0.986154 1.047463 1.224219 -P_12 1.320492 1.429708 1.476592 1.607097 1.579485 1.567707 1.505350 1.447418 -P_12 1.506390 1.460980 1.297321 1.189083 1.239563 1.093581 1.112030 1.107437 -P_12 1.090266 1.122115 1.025670 1.144419 1.102618 1.173915 1.160806 1.280609 -P_12 1.344898 1.333349 1.302970 1.251434 1.152752 1.170915 0.901224 0.819496 -P_12 0.750795 0.581620 0.474516 0.449666 0.404633 0.401288 0.388632 0.450945 -P_12 0.482620 0.561664 0.579304 0.818590 0.839591 0.920342 1.172104 1.133470 -P_12 1.268298 1.237873 1.455658 1.547629 1.565572 1.435997 1.433358 1.483348 -P_12 1.321239 1.268580 1.358882 1.138882 1.179792 1.054276 1.055752 1.087772 -P_12 1.135149 1.161004 1.178982 1.168070 1.306131 1.329775 1.349156 1.344396 -P_12 1.384670 1.348529 1.247840 1.235666 1.068261 1.091686 1.012739 0.820370 -P_12 0.755097 0.536518 0.490744 0.423778 0.374119 0.342170 0.374428 0.396159 -P_12 0.462550 0.536648 0.677306 0.765421 0.866700 1.008535 1.155744 1.189609 -P_12 1.366056 1.298743 1.464845 1.549901 1.395665 1.368970 1.525158 1.538744 -P_12 1.486658 1.397465 1.262247 1.153082 1.105865 1.113978 1.137681 1.179675 -P_12 1.131105 1.169693 1.263848 1.190743 1.238872 1.172753 1.221658 1.345299 -P_12 1.416427 1.186019 1.203996 1.162704 1.067147 1.135518 0.878712 0.786569 -P_12 0.731067 0.601431 0.542916 0.448618 0.411045 0.351958 0.399540 0.345722 -P_12 0.519521 0.569261 0.664055 0.801838 0.956605 1.126645 1.043608 1.286309 -P_12 1.378871 1.315579 1.374218 1.433200 1.459754 1.392900 1.369995 1.410114 -P_12 1.263788 1.218197 1.108046 1.172041 1.083865 1.077947 0.932794 1.130322 -P_12 1.196639 1.307697 1.200953 1.206729 1.311929 1.326735 1.326800 1.231642 -P_12 1.311020 1.307002 1.160117 1.149017 1.119622 1.017138 0.929917 0.869354 -P_12 0.707676 0.670226 0.483337 0.469362 0.426100 0.372277 0.411376 0.451987 -P_12 0.506329 0.572768 0.789551 0.830996 1.083452 1.130498 1.185986 1.432452 -P_12 1.579365 1.490398 1.658381 1.734733 1.624533 1.704035 1.543156 1.629428 -P_12 1.415655 1.387624 1.239008 1.288916 1.207755 1.211644 1.162409 1.199650 -P_12 1.111528 1.331086 1.298324 1.232381 1.314331 1.187961 1.311452 1.291900 -P_12 1.254153 1.169794 1.236558 1.190920 1.142048 1.101780 0.932268 0.812437 -P_12 0.788459 0.704549 0.611392 0.553296 0.497272 0.417591 0.403936 0.456968 -P_12 0.414525 0.481692 0.555329 0.674109 0.694544 0.757429 0.918481 0.989965 -P_12 1.054698 1.189967 1.370462 1.361135 1.337356 1.296935 1.279783 1.307189 -P_12 1.261849 1.307711 1.255232 1.288019 1.083477 1.193849 1.177487 1.115055 -P_12 1.270724 1.314100 1.299086 1.220810 1.127953 1.353692 1.293398 1.221155 -P_12 1.118061 1.158442 1.098126 1.076619 0.920966 0.826496 0.749678 0.736125 -P_12 0.693512 0.583002 0.587462 0.509966 0.437537 0.406616 0.398028 0.369095 -P_12 0.414602 0.411464 0.463208 0.472833 0.540332 0.660987 0.652243 0.821386 -P_12 0.833819 0.985668 1.066883 1.104958 1.198979 1.152934 1.213549 1.246924 -P_12 1.236134 1.193446 1.048865 1.160777 1.145819 1.057690 1.089429 1.039463 -P_12 1.041225 1.126906 1.058980 1.024308 1.120725 1.301781 1.175237 1.205301 -P_12 1.227554 1.208951 1.140303 1.073440 1.069334 0.962867 0.789438 0.758410 -P_12 0.673365 0.607467 0.482984 0.415058 0.356168 0.347119 0.343507 0.402028 -P_12 0.406066 0.516188 0.592623 0.724998 0.826120 0.886955 0.980740 1.138687 -P_12 1.457264 1.329971 1.537126 1.481648 1.546893 1.378012 1.454413 1.469003 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1.115226 1.096829 -P_12 1.078207 1.173339 1.194004 1.287611 1.249433 1.152273 1.229219 1.249726 -P_12 1.236267 1.281653 1.266600 1.213803 1.091900 0.980580 0.978110 0.781313 -P_12 0.690421 0.587689 0.495818 0.381792 0.374899 0.343094 0.375497 0.382577 -P_12 0.447665 0.607761 0.647305 0.788363 0.778870 1.055525 1.140151 1.143861 -P_12 1.171140 1.331911 1.380286 1.618206 1.400896 1.478564 1.443286 1.342764 -P_12 1.232845 1.177948 1.099033 1.153271 1.151146 1.176716 1.105892 1.282970 -P_12 1.073666 1.129693 1.076075 1.188168 1.113141 1.319451 1.344327 1.227122 -P_12 1.340213 1.403818 1.288247 1.287053 1.071862 1.057372 1.061992 0.827106 -P_12 0.756831 0.682465 0.544096 0.472413 0.423769 0.384354 0.425096 0.481808 -P_12 0.471535 0.655769 0.685444 0.726164 1.035968 1.025781 1.138726 1.354730 -P_12 1.583080 1.470115 1.633702 1.723980 1.592167 1.754300 1.497687 1.565019 -P_12 1.405365 1.423635 1.183619 1.452203 1.204843 1.259749 1.273479 1.159651 -P_12 1.202154 1.173452 1.229178 1.231127 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1.116807 1.247318 -P_12 1.236726 1.205168 1.157062 1.093731 1.004202 0.964078 0.868068 0.737395 -P_12 0.679237 0.570790 0.466043 0.416028 0.361804 0.382129 0.372636 0.391834 -P_12 0.490044 0.479294 0.615188 0.687486 0.844441 0.893224 0.994497 1.007190 -P_12 1.194128 1.062508 1.300558 1.333879 1.423469 1.328753 1.376223 1.360917 -P_12 1.153015 1.056012 1.153270 1.006237 1.034024 0.930451 0.997264 0.954563 -P_12 0.916731 0.986704 1.071321 1.112619 1.096269 1.310560 1.163088 1.193240 -P_12 1.217884 1.232638 1.170674 1.170440 1.010352 0.897741 0.835538 0.784924 -P_12 0.614021 0.571090 0.473654 0.400223 0.325676 0.346066 0.319641 0.377781 -P_12 0.413520 0.521829 0.634840 0.786435 0.722559 0.906586 1.130994 1.168818 -P_12 1.178148 1.338451 1.254933 1.400857 1.255694 1.194764 1.307809 1.212918 -P_12 1.403528 1.273952 1.106208 1.208958 1.044292 1.016674 1.023822 1.095597 -P_12 0.983953 1.028067 1.074951 1.144021 1.182951 1.168012 1.195612 1.228310 -P_12 1.090777 1.135670 1.106747 1.220387 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1.030384 0.965983 1.115922 1.189712 1.118322 1.285020 1.206207 1.112123 -P_12 1.052295 1.160594 1.030687 1.215034 1.137534 1.042673 0.972365 1.081614 -P_12 0.961979 0.990494 1.027787 1.125321 1.063128 1.153777 1.113569 1.011304 -P_12 1.007538 1.007760 1.024449 0.874659 0.799687 0.773092 0.689537 0.626845 -P_12 0.537896 0.538681 0.487702 0.395896 0.427541 0.379182 0.350759 0.331308 -P_12 0.332922 0.368990 0.357697 0.402808 0.466232 0.516135 0.590533 0.718992 -P_12 0.766565 0.756350 0.956766 0.949013 0.950116 1.001948 1.034099 1.018097 -P_12 1.100776 1.031829 1.017017 0.990683 0.916277 0.994273 0.983924 0.873301 -P_12 0.883065 0.880945 0.951217 0.981266 0.901971 1.049441 0.967202 1.003447 -P_12 0.979697 1.015420 0.969413 1.020866 0.896595 0.849582 0.727833 0.658848 -P_12 0.599728 0.518451 0.395621 0.383870 0.315018 0.302830 0.307177 0.330134 -P_12 0.397007 0.426859 0.548544 0.682203 0.733831 0.903847 0.927423 1.070137 -P_12 1.169201 1.201243 1.182865 1.337210 1.376294 1.382069 1.232782 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1.125820 1.111633 1.242014 1.331939 1.218675 1.359825 1.312145 1.215239 -P_21 1.166630 1.131395 1.116711 1.016487 1.081833 0.985293 0.989838 1.074798 -P_21 0.951050 1.066839 1.046328 0.986633 0.987442 1.014797 1.199726 1.172568 -P_21 1.145867 1.182305 1.131626 1.090120 0.889182 0.921107 0.880502 0.774362 -P_21 0.730338 0.569458 0.553815 0.510013 0.459608 0.445237 0.474413 0.520867 -P_21 0.558296 0.612561 0.644586 0.763676 0.828051 0.936891 0.964961 1.014686 -P_21 1.163617 1.112528 1.190056 1.336120 1.420117 1.260973 1.213497 1.311880 -P_21 1.278885 1.179923 1.202186 1.158516 1.159767 1.022370 1.010975 1.039004 -P_21 1.053266 0.996331 1.020146 1.104357 1.113653 1.128384 1.025432 1.158103 -P_21 1.162341 1.172621 1.109843 1.112474 1.064407 0.951535 0.828506 0.780999 -P_21 0.711298 0.679125 0.591320 0.535170 0.471113 0.480815 0.447604 0.520502 -P_21 0.570548 0.560146 0.644854 0.743612 0.872691 0.974613 1.079738 1.053738 -P_21 1.219184 1.228474 1.219051 1.289034 1.163270 1.247432 1.244709 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1.142895 1.115471 1.122880 1.038693 1.208148 -P_21 1.160842 1.310928 1.125080 1.023263 0.983941 0.988233 0.873291 0.837568 -P_21 0.754607 0.695660 0.610935 0.522376 0.505489 0.509060 0.482459 0.505934 -P_21 0.543727 0.582731 0.650498 0.673857 0.841993 0.861104 1.025269 1.021595 -P_21 1.211460 1.179347 1.170201 1.163362 1.374518 1.214644 1.250881 1.307469 -P_21 1.318262 1.168614 1.114187 1.183407 0.940853 1.032787 1.013833 1.064195 -P_21 0.960963 0.998570 1.113531 1.098011 1.124955 1.188062 1.140385 1.164690 -P_21 1.070313 1.103923 1.112013 1.006248 1.040021 0.944022 0.855533 0.848978 -P_21 0.690129 0.621202 0.581125 0.521369 0.472342 0.436531 0.468121 0.520059 -P_21 0.512458 0.567241 0.682048 0.725139 0.867932 0.962800 1.009535 1.076260 -P_21 1.108422 1.198948 1.206883 1.234409 1.303508 1.303621 1.326891 1.221592 -P_21 1.218313 1.280594 1.164666 1.256518 1.144628 1.073359 1.149829 1.069368 -P_21 1.090956 1.101727 1.128027 1.070149 1.073032 1.169950 1.196451 1.068481 -P_21 1.143492 1.161314 1.180694 1.089688 1.090064 0.980116 0.924995 0.846840 -P_21 0.713713 0.640150 0.611570 0.545262 0.487106 0.490833 0.486681 0.480970 -P_21 0.538121 0.630673 0.666146 0.742917 0.840922 0.935580 1.047256 1.121806 -P_21 1.165481 1.148981 1.270830 1.252193 1.220195 1.285690 1.308887 1.222599 -P_21 1.286349 1.230857 1.110774 1.118454 1.016555 1.050388 1.009977 1.104078 -P_21 0.950192 1.100107 1.023547 1.112992 1.185906 1.152571 1.102558 1.162919 -P_21 1.226120 1.126918 1.160142 1.199716 1.041459 1.023535 0.841649 0.892365 -P_21 0.758380 0.671007 0.584589 0.541342 0.586481 0.548163 0.514139 0.534389 -P_21 0.583728 0.681268 0.788587 0.899142 0.871013 1.112042 1.117613 1.290182 -P_21 1.255905 1.432691 1.413796 1.506366 1.395284 1.346501 1.332422 1.362035 -P_21 1.347364 1.266453 1.247784 1.284802 1.201547 1.217723 1.193178 1.133500 -P_21 1.040498 1.177055 1.122810 1.110113 0.972172 1.200461 1.136061 1.274486 -P_21 1.143836 1.136274 1.075893 1.018625 1.064246 1.021741 1.015213 0.878618 -P_21 0.858037 0.707792 0.694600 0.639375 0.575939 0.530488 0.543949 0.538994 -P_21 0.532064 0.586232 0.577395 0.614994 0.669181 0.872608 0.831562 0.919555 -P_21 1.052065 1.131266 1.220177 1.230420 1.252953 1.286458 1.333725 1.242055 -P_21 1.230538 1.305556 1.177242 1.139796 1.193600 1.064561 1.075109 1.143652 -P_21 1.076283 1.069804 1.188398 1.049326 1.085815 1.179959 1.159384 1.085054 -P_21 1.116468 1.064161 1.034797 0.993885 0.940138 0.845371 0.831700 0.836033 -P_21 0.735038 0.694897 0.654145 0.582942 0.587848 0.563798 0.566556 0.506967 -P_21 0.505481 0.550632 0.516942 0.553802 0.615051 0.742960 0.741103 0.755070 -P_21 0.753401 0.810867 1.006629 1.052708 1.017870 1.058509 1.251047 1.101389 -P_21 1.250101 1.172275 1.192718 1.125259 1.104691 1.099856 1.087542 0.994932 -P_21 1.069045 1.083893 1.045189 1.086849 1.053958 1.127914 1.210869 1.130748 -P_21 1.064292 1.071815 0.994688 1.070965 0.994455 0.951464 0.897135 0.757367 -P_21 0.692751 0.584319 0.549137 0.505017 0.505156 0.508268 0.493067 0.517184 -P_21 0.546605 0.608540 0.724799 0.799115 0.846720 0.938461 0.979662 1.143503 -P_21 1.337131 1.186597 1.502675 1.410620 1.371200 1.352157 1.451569 1.381361 -P_21 1.328096 1.191722 1.214207 1.089638 1.052726 1.117314 0.971142 0.963673 -P_21 1.040734 1.077420 1.093068 1.039547 1.087925 1.083086 1.228468 1.284665 -P_21 1.357692 1.168598 1.206729 1.043713 1.070347 1.055995 0.848173 0.779693 -P_21 0.837465 0.722454 0.675470 0.565772 0.492492 0.502416 0.510508 0.515416 -P_21 0.533974 0.661925 0.679087 0.774258 0.856359 1.021864 0.954270 1.062921 -P_21 1.138021 1.304851 1.339854 1.205671 1.245855 1.245163 1.266269 1.289424 -P_21 1.303519 1.128529 1.100869 1.093778 1.019886 1.082232 1.048836 1.026665 -P_21 1.126003 1.006739 1.142377 1.214311 1.177833 1.188375 1.121056 1.170663 -P_21 1.201126 1.258129 1.160833 1.121348 1.086504 1.036812 0.916286 0.875858 -P_21 0.733539 0.724797 0.601744 0.514932 0.452463 0.467197 0.514671 0.544549 -P_21 0.585193 0.680765 0.689954 0.790794 0.904695 0.902468 1.042756 1.046019 -P_21 1.160952 1.391576 1.221090 1.331928 1.414878 1.228011 1.365787 1.277511 -P_21 1.184537 1.216808 1.260765 1.163038 1.156861 1.157326 0.981099 1.052367 -P_21 1.071421 1.088525 1.162638 1.062057 1.164516 1.208825 1.171005 1.226401 -P_21 1.267744 1.089140 1.188260 1.165886 0.999809 0.964247 0.924611 0.836142 -P_21 0.704163 0.700720 0.606026 0.523686 0.537400 0.521332 0.497870 0.559654 -P_21 0.539423 0.612943 0.629584 0.826295 0.927706 0.973310 1.010635 1.183524 -P_21 1.169496 1.133822 1.274900 1.317238 1.230809 1.387797 1.292227 1.332858 -P_21 1.203751 1.108902 1.139302 1.082115 1.041404 1.122404 1.115012 1.089120 -P_21 1.118791 1.076624 1.068784 1.147396 1.071008 1.111232 1.180370 1.254597 -P_21 1.146542 1.180853 1.088956 1.118158 1.082961 0.935168 0.927130 0.791799 -P_21 0.786909 0.707244 0.712896 0.568176 0.548311 0.502869 0.546750 0.556073 -P_21 0.587237 0.691117 0.886104 0.912112 0.964023 1.078159 1.253874 1.357591 -P_21 1.467128 1.482339 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1.166324 1.123106 1.113771 1.163841 1.147911 1.157210 1.149962 1.050861 -P_21 1.017336 0.950813 1.043445 1.165687 1.071210 1.130409 1.130351 1.110567 -P_21 1.185747 1.155741 1.087711 1.021340 0.992796 0.938468 0.864976 0.701190 -P_21 0.769266 0.638721 0.621688 0.533790 0.545849 0.482768 0.503715 0.490862 -P_21 0.572913 0.620754 0.732661 0.740196 0.780292 0.971746 1.072532 1.154713 -P_21 1.209157 1.392004 1.309990 1.347297 1.269862 1.352082 1.299068 1.396332 -P_21 1.337166 1.227390 1.226817 1.312334 1.028023 1.013545 1.135869 0.980727 -P_21 1.080461 1.143656 1.051809 1.101457 1.177079 1.078661 1.184698 1.127920 -P_21 1.168368 1.132754 1.209876 1.177097 1.162278 1.115987 0.905943 0.894072 -P_21 0.716968 0.697151 0.686421 0.583805 0.549696 0.569710 0.485459 0.590896 -P_21 0.578033 0.627573 0.695124 0.705698 0.825991 0.989928 1.060750 1.173776 -P_21 1.037536 1.180560 1.305581 1.436403 1.392623 1.359034 1.222725 1.334546 -P_21 1.302544 1.239600 1.153592 1.090498 1.069712 1.044959 1.062537 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1.094174 1.091522 1.212519 -P_21 1.249102 1.246278 1.193998 1.029760 1.095655 1.105314 1.024345 0.858772 -P_21 0.869936 0.700596 0.666874 0.593060 0.572823 0.520471 0.535719 0.544042 -P_21 0.596251 0.665163 0.745126 0.892715 1.053952 0.969526 1.221747 1.410289 -P_21 1.281682 1.495511 1.493162 1.443838 1.493921 1.494596 1.317247 1.274692 -P_21 1.406124 1.314348 1.108510 1.288585 1.242009 1.079204 1.090277 1.183636 -P_21 1.188591 1.121601 1.119780 1.268118 1.163185 1.256410 1.173289 1.261768 -P_21 1.113953 1.252140 1.086617 0.979199 1.093121 1.065628 0.942063 0.904831 -P_21 0.905006 0.776077 0.705723 0.602238 0.616230 0.579601 0.505349 0.561283 -P_21 0.588847 0.596237 0.631317 0.724207 0.675631 0.903165 0.914343 0.919594 -P_21 1.110449 1.110218 1.182898 1.208112 1.197189 1.333676 1.279321 1.173254 -P_21 1.195197 1.173551 1.211381 1.206184 1.277543 1.145660 1.212299 1.237742 -P_21 1.131333 1.219886 1.323722 1.108989 1.181594 1.079835 1.250410 1.085650 -P_21 1.268178 1.180505 1.041119 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0.957396 1.072561 -P_21 1.103834 1.087073 1.323856 1.285339 1.288885 1.307832 1.240045 1.204124 -P_21 1.226187 1.314820 1.305084 1.239608 1.241552 1.228634 1.272029 1.195996 -P_21 1.182886 1.160916 1.179584 1.346480 1.224230 1.259943 1.121431 1.081213 -P_21 1.186751 1.041490 1.006134 1.028727 0.954372 0.778784 0.891940 0.743446 -P_21 0.763967 0.716256 0.717509 0.592966 0.621931 0.554760 0.535986 0.551128 -P_21 0.594103 0.533828 0.545436 0.657530 0.695239 0.701375 0.736737 0.899781 -P_21 0.911316 1.010566 1.059544 1.110147 1.123057 1.113907 1.019204 1.231093 -P_21 1.289597 1.132102 1.150434 1.110869 1.146488 1.105799 1.126727 1.151828 -P_21 1.143229 1.108812 1.085972 1.147985 1.206458 1.083240 1.260148 1.232794 -P_21 1.209854 1.060810 1.071177 1.105610 1.047113 0.807292 0.854607 0.815350 -P_21 0.727561 0.599772 0.623866 0.557935 0.504137 0.541776 0.508746 0.512149 -P_21 0.548464 0.604530 0.630370 0.770213 0.932638 0.879054 1.084497 1.148204 -P_21 1.229416 1.301345 1.332803 1.492219 1.483314 1.415232 1.459292 1.528478 -P_21 1.295761 1.145162 1.256413 1.180277 1.139140 1.093396 1.087233 1.069591 -P_21 1.046689 0.979799 1.018411 1.183742 1.230128 1.214115 1.268505 1.161416 -P_21 1.356512 1.154309 1.166341 1.149503 1.014717 0.939909 0.991298 0.879462 -P_21 0.760865 0.744191 0.695605 0.586729 0.545478 0.539380 0.517085 0.606500 -P_21 0.512267 0.605920 0.667118 0.858522 0.845287 0.997592 1.048236 1.151731 -P_21 1.256030 1.404555 1.415560 1.224306 1.438889 1.326661 1.367730 1.273689 -P_21 1.355416 1.229197 1.229794 1.163235 1.157866 1.097896 1.075805 1.099307 -P_21 1.086045 1.054016 1.133703 1.161701 1.074209 1.322844 1.262819 1.294286 -P_21 1.345288 1.345539 1.111388 1.076081 1.232864 0.981474 0.954823 0.843187 -P_21 0.786117 0.674376 0.596418 0.576211 0.542987 0.514913 0.515131 0.532926 -P_21 0.620443 0.646793 0.690785 0.898652 0.942290 0.986409 1.013424 1.225729 -P_21 1.282598 1.280403 1.387622 1.453041 1.342186 1.309014 1.467161 1.303733 -P_21 1.303239 1.287234 1.320485 1.223463 1.201163 1.166725 1.108334 1.155996 -P_21 1.129370 1.058672 1.118980 1.179046 1.179903 1.207042 1.262359 1.163141 -P_21 1.192105 1.178288 1.130616 1.177595 1.005676 1.025193 1.028570 0.929045 -P_21 0.801413 0.674228 0.649570 0.631785 0.536951 0.533888 0.536460 0.559851 -P_21 0.595442 0.646793 0.781958 0.826166 0.830615 0.955420 1.204287 1.083423 -P_21 1.202232 1.171525 1.294537 1.313225 1.350316 1.279107 1.330524 1.378254 -P_21 1.303858 1.243799 1.207137 1.054976 1.227694 1.141788 1.123936 1.176509 -P_21 1.037038 1.132637 1.128791 1.133619 1.210237 1.239313 1.233460 1.331731 -P_21 1.217767 1.173407 1.255160 1.228693 1.038297 1.053836 0.874247 0.935298 -P_21 0.797456 0.696984 0.660876 0.622319 0.551649 0.533903 0.531920 0.563938 -P_21 0.611342 0.675795 0.815654 0.883886 1.015268 1.094278 1.323138 1.338799 -P_21 1.353533 1.399450 1.445488 1.517822 1.368678 1.484306 1.397715 1.275851 -P_21 1.385195 1.380870 1.274450 1.163322 1.319149 1.240497 1.072019 1.150499 -P_21 1.243501 1.174675 1.182562 1.206129 1.186526 1.260370 1.325693 1.268948 -P_21 1.155950 1.101468 1.208312 1.042743 1.201154 1.066693 0.918372 0.886800 -P_21 0.845331 0.788827 0.742673 0.661246 0.675196 0.566673 0.519044 0.587686 -P_21 0.555716 0.597049 0.646405 0.668195 0.757699 0.800749 0.929125 0.946761 -P_21 1.150459 1.209221 1.142945 1.169010 1.257472 1.313528 1.298487 1.326687 -P_21 1.328607 1.284232 1.123457 1.284247 1.102974 1.290731 1.178977 1.281922 -P_21 1.179815 1.244906 1.120349 1.170742 1.242826 1.358633 1.199093 1.176083 -P_21 1.229810 1.156593 1.035649 1.040067 1.014802 0.863775 0.916249 0.777219 -P_21 0.789657 0.755162 0.681858 0.665182 0.577721 0.598577 0.594321 0.555651 -P_21 0.597974 0.525395 0.558596 0.622004 0.677498 0.691867 0.791716 0.834707 -P_21 0.915985 0.963765 1.110081 1.085064 1.122351 1.146454 1.314285 1.269544 -P_21 1.157445 1.276043 1.262277 1.114673 1.149020 1.128274 1.134847 1.096466 -P_21 1.046582 1.226036 1.040085 1.085383 1.173047 1.207797 1.108511 1.187774 -P_21 1.189984 1.125720 1.146501 1.164233 1.010859 0.941803 0.995238 0.847364 -P_21 0.739242 0.677499 0.642633 0.568714 0.533568 0.554555 0.513452 0.482502 -P_21 0.541229 0.634647 0.687796 0.789932 0.905233 0.958478 1.013955 1.202919 -P_21 1.367990 1.391521 1.405904 1.379118 1.453559 1.360989 1.398322 1.385169 -P_21 1.471887 1.306036 1.288134 1.288518 1.153261 1.111322 1.118857 1.081244 -P_21 1.108186 1.078865 1.072962 1.121031 1.146978 1.270757 1.109847 1.268304 -P_21 1.217412 1.130354 1.237980 1.259464 1.021306 1.080558 1.012940 0.900623 -P_21 0.797328 0.697653 0.673305 0.670700 0.514839 0.509205 0.576491 0.553451 -P_21 0.570088 0.630923 0.734569 0.845960 0.968118 0.998368 1.088721 1.148663 -P_21 1.295660 1.462121 1.386002 1.516242 1.357307 1.409181 1.415117 1.442678 -P_21 1.344898 1.229161 1.225985 1.171388 1.240399 1.081351 1.011424 1.191549 -P_21 1.133875 1.076599 1.176413 1.125891 1.147344 1.264192 1.327568 1.359331 -P_21 1.253962 1.209959 1.373523 1.169745 1.064591 0.983962 1.007382 0.795280 -P_21 0.810817 0.716398 0.655138 0.542395 0.503893 0.533093 0.558333 0.566391 -P_21 0.631544 0.645813 0.715231 0.815918 0.891802 1.012537 1.076722 1.200999 -P_21 1.279426 1.281347 1.208563 1.416771 1.329664 1.310477 1.301127 1.364356 -P_21 1.447778 1.282979 1.282328 1.262615 1.236116 1.036807 1.071120 1.199805 -P_21 1.110588 1.151513 1.157059 1.114681 1.192525 1.258851 1.199803 1.121724 -P_21 1.227989 1.128889 1.260488 1.109052 1.084108 0.971912 1.013570 0.914937 -P_21 0.798881 0.746191 0.638739 0.574128 0.494279 0.485055 0.545182 0.532684 -P_21 0.562983 0.634070 0.761100 0.884144 0.945532 0.966137 1.235801 1.179763 -P_21 1.251232 1.250493 1.367683 1.467659 1.358985 1.322340 1.266913 1.294415 -P_21 1.273116 1.188524 1.248721 1.313947 1.236979 1.096280 1.166890 1.093239 -P_21 1.154950 1.137488 1.113689 1.155565 1.119558 1.289689 1.270245 1.088082 -P_21 1.243928 1.408455 1.221504 1.258434 1.164260 1.058809 0.958530 0.951808 -P_21 0.736221 0.772902 0.706703 0.571971 0.546172 0.551017 0.544732 0.562145 -P_21 0.606312 0.677191 0.797282 0.824742 1.002179 1.098588 1.247254 1.437781 -P_21 1.417474 1.475547 1.403559 1.528784 1.531904 1.649129 1.562939 1.576243 -P_21 1.234289 1.308143 1.321935 1.295909 1.149750 1.179389 1.121734 1.163252 -P_21 1.195600 1.158328 1.165457 1.173934 1.277017 1.273104 1.194049 1.236889 -P_21 1.307767 1.240789 1.145590 1.073991 0.998280 1.064675 0.961763 0.792898 -P_21 0.778074 0.860570 0.702625 0.670538 0.653879 0.602170 0.573677 0.577948 -P_21 0.592445 0.618603 0.669335 0.628066 0.805193 0.833790 0.985297 1.035106 -P_21 1.164939 1.113240 1.221269 1.257662 1.334465 1.351178 1.247121 1.367850 -P_21 1.310592 1.260753 1.192536 1.253937 1.123534 1.203424 1.159699 1.199460 -P_21 1.182281 1.270523 1.235346 1.171530 1.248416 1.168659 1.248758 1.270966 -P_21 1.127528 1.133313 1.152198 0.983033 1.017890 0.949812 0.941082 0.874070 -P_21 0.829147 0.752808 0.609718 0.679387 0.634872 0.584719 0.590302 0.552370 -P_21 0.541136 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1.242034 1.200164 1.236746 1.158015 1.143812 1.060379 0.991682 0.884642 -P_21 0.819693 0.709604 0.610475 0.530260 0.522365 0.509419 0.580532 0.597485 -P_21 0.576822 0.625774 0.783272 0.868928 0.946085 1.039509 1.037070 1.234950 -P_21 1.195311 1.319009 1.405673 1.246279 1.352150 1.438163 1.303870 1.318339 -P_21 1.292802 1.254049 1.298642 1.163241 1.146484 1.042563 1.042194 1.025848 -P_21 1.120810 1.144604 1.098046 1.227364 1.160503 1.198401 1.257818 1.309793 -P_21 1.310335 1.168352 1.226198 1.182391 1.051075 1.051667 0.985433 0.821118 -P_21 0.840545 0.658661 0.682919 0.557980 0.559161 0.551382 0.491772 0.590445 -P_21 0.626763 0.680771 0.835917 0.949065 1.036253 1.136091 1.289308 1.315759 -P_21 1.445601 1.561613 1.594558 1.705675 1.609587 1.483133 1.515820 1.402571 -P_21 1.380024 1.270396 1.349569 1.367810 1.258412 1.176742 1.172824 1.157485 -P_21 1.143545 1.201950 1.175057 1.190884 1.283277 1.212017 1.123725 1.268176 -P_21 1.218712 1.165266 1.151922 1.220592 1.120129 1.077729 0.969379 0.923741 -P_21 0.800310 0.792210 0.743263 0.666162 0.604724 0.595506 0.521556 0.506488 -P_21 0.590057 0.626025 0.638827 0.699144 0.898773 0.834897 0.943220 0.971304 -P_21 1.074181 1.153631 1.182215 1.270258 1.209094 1.261841 1.209841 1.337283 -P_21 1.358015 1.297848 1.295680 1.274508 1.196721 1.195904 1.148930 1.208032 -P_21 1.338989 1.244172 1.300317 1.177781 1.163933 1.137793 1.143542 1.251782 -P_21 1.182551 1.146187 1.117149 1.079021 1.022497 0.882822 0.840705 0.814278 -P_21 0.758382 0.731475 0.642455 0.599516 0.555017 0.558161 0.556352 0.521045 -P_21 0.588067 0.526332 0.534130 0.565186 0.681838 0.706884 0.721652 0.844134 -P_21 0.896503 0.986982 1.085927 1.138534 1.108839 1.139558 1.163311 1.199252 -P_21 1.266125 1.198596 1.187030 1.190697 1.221944 1.167890 1.081655 1.128061 -P_21 1.076781 1.109359 1.131850 1.175553 1.170515 1.190872 1.179228 1.192392 -P_21 1.102373 1.169254 1.051144 1.095002 1.042394 0.958725 0.944618 0.831525 -P_21 0.758103 0.691741 0.628155 0.591572 0.528594 0.483872 0.478113 0.514891 -P_21 0.636523 0.605762 0.732560 0.851628 0.931670 0.937164 0.979259 1.218183 -P_21 1.236455 1.301578 1.404486 1.425407 1.404062 1.469221 1.423125 1.321937 -P_21 1.254632 1.325897 1.218989 1.234973 1.092850 1.130439 1.055320 1.081979 -P_21 1.078516 1.112325 1.074563 1.181362 1.233937 1.145917 1.359402 1.234765 -P_21 1.208756 1.262965 1.197411 1.304685 1.165822 0.972189 0.965796 0.823343 -P_21 0.818838 0.741234 0.714425 0.580571 0.494572 0.580840 0.498169 0.519520 -P_21 0.587508 0.594587 0.705052 0.844079 0.853260 0.997769 1.166598 1.101786 -P_21 1.264853 1.320047 1.239005 1.351895 1.323654 1.448411 1.431600 1.349991 -P_21 1.302124 1.217870 1.260444 1.237958 1.131972 1.182022 1.059849 1.130751 -P_21 1.116635 1.043036 1.115752 1.184410 1.150844 1.226546 1.252159 1.275546 -P_21 1.329316 1.270546 1.214816 1.170102 1.111879 1.068377 0.890361 0.889624 -P_21 0.790973 0.695534 0.683193 0.573649 0.508169 0.502470 0.564610 0.548162 -P_21 0.602747 0.675785 0.744669 0.818533 0.912786 0.954460 1.154388 1.055481 -P_21 1.122472 1.348292 1.369714 1.180237 1.387156 1.257056 1.232270 1.381794 -P_21 1.229951 1.133409 1.296624 1.274981 1.222490 1.134409 1.209862 1.084440 -P_21 1.219560 1.049413 1.115712 1.186661 1.173334 1.211421 1.216660 1.122623 -P_21 1.260392 1.217730 1.158385 1.154339 1.017500 1.042672 0.949480 0.864450 -P_21 0.839612 0.682512 0.581022 0.611590 0.556033 0.478152 0.492102 0.521808 -P_21 0.600448 0.690366 0.718528 0.847139 0.906637 1.038183 1.069240 1.217512 -P_21 1.235070 1.319840 1.254508 1.230452 1.378370 1.323906 1.417713 1.281497 -P_21 1.309463 1.313727 1.091345 1.115499 1.189495 1.156207 1.089815 1.172801 -P_21 1.154224 1.085784 1.118545 1.184961 1.139629 1.170480 1.264428 1.236483 -P_21 1.217055 1.265306 1.260154 1.097504 1.161631 1.094129 0.943254 0.857983 -P_21 0.788028 0.730511 0.754799 0.573454 0.573083 0.541505 0.550096 0.562188 -P_21 0.655632 0.744981 0.725561 0.899359 1.034687 1.033062 1.257439 1.261344 -P_21 1.379189 1.364197 1.316355 1.495389 1.569785 1.488662 1.526285 1.286579 -P_21 1.437678 1.379417 1.225850 1.309718 1.175175 1.283420 1.160474 1.195359 -P_21 1.109080 1.155866 1.128968 1.148602 1.239329 1.169631 1.275658 1.207645 -P_21 1.147612 1.256135 1.102684 1.123261 1.087850 1.035298 1.091667 0.877144 -P_21 0.783742 0.750012 0.769521 0.601938 0.629220 0.574284 0.510115 0.541922 -P_21 0.561892 0.598565 0.662198 0.708948 0.707090 0.818180 0.930684 1.059325 -P_21 1.125570 1.197740 1.139152 1.221996 1.358663 1.273351 1.385438 1.268132 -P_21 1.274844 1.343703 1.264016 1.211236 1.213081 1.199027 1.283073 1.095684 -P_21 1.252328 1.171366 1.162848 1.177532 1.259329 1.229335 1.173500 1.202843 -P_21 1.041015 1.108542 1.059768 1.072329 0.952445 0.915113 0.804973 0.737438 -P_21 0.668005 0.745989 0.668575 0.645078 0.572425 0.611477 0.577107 0.550269 -P_21 0.586152 0.542573 0.609563 0.584719 0.614335 0.771162 0.757535 0.794489 -P_21 0.865415 0.905813 1.068037 1.022907 1.074336 1.259572 1.224067 1.198300 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1.119594 1.207768 1.289158 1.239860 -P_21 1.241030 1.196577 1.068498 1.165856 1.071009 1.023280 1.012977 0.931840 -P_21 0.773000 0.723042 0.701543 0.548181 0.549411 0.489301 0.489169 0.518563 -P_21 0.623806 0.749935 0.789499 0.906212 0.970633 1.097767 1.164272 1.166253 -P_21 1.441516 1.492423 1.376892 1.413111 1.492152 1.436741 1.393404 1.481102 -P_21 1.389544 1.292561 1.298224 1.212592 1.157433 1.233344 1.185093 1.079750 -P_21 1.207317 1.182965 1.073977 1.338950 1.261147 1.208892 1.183926 1.218231 -P_21 1.095849 1.183943 1.102948 1.123485 1.141685 0.932508 1.023037 0.993736 -P_21 0.911804 0.849684 0.703609 0.648519 0.644610 0.614556 0.529721 0.572127 -P_21 0.598879 0.563187 0.677172 0.649788 0.793568 0.837519 0.913147 1.035050 -P_21 1.021200 1.180656 1.175244 1.301003 1.278695 1.337569 1.225742 1.264153 -P_21 1.182361 1.282096 1.261383 1.157917 1.279861 1.279587 1.118457 1.132247 -P_21 1.142830 1.186535 1.090666 1.162858 1.222153 1.140208 1.144327 1.126310 -P_21 1.080410 1.198489 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1.122115 1.044669 -P_21 1.130420 1.147478 1.151308 0.989766 0.981039 0.938519 0.879745 0.712255 -P_21 0.704211 0.595867 0.583898 0.526049 0.494825 0.497684 0.533880 0.504394 -P_21 0.559927 0.643718 0.704611 0.793261 0.965773 1.027969 0.956692 1.066120 -P_21 1.211050 1.363787 1.345643 1.469878 1.378754 1.393197 1.409289 1.216656 -P_21 1.341743 1.415114 1.237671 1.277481 1.073096 0.974675 1.073246 1.083823 -P_21 1.074511 1.044021 1.021360 1.137076 1.026666 1.154539 1.151653 1.213075 -P_21 1.168452 1.236326 1.194505 1.137785 1.061171 1.105810 0.932623 0.841560 -P_21 0.769780 0.706343 0.598511 0.548081 0.613612 0.559156 0.487161 0.482172 -P_21 0.543306 0.655499 0.681446 0.729172 0.859339 0.849957 1.004484 1.189875 -P_21 1.113290 1.257628 1.303578 1.320700 1.381454 1.224991 1.329656 1.336548 -P_21 1.189876 1.122708 1.164320 1.083105 1.029215 1.027656 1.055078 1.097931 -P_21 1.072637 1.062466 1.076275 1.133084 1.119263 1.166996 1.174987 1.318452 -P_21 1.235873 1.232914 1.105990 1.133433 1.032433 1.016368 0.909763 0.809134 -P_21 0.791525 0.693103 0.615327 0.512766 0.531763 0.474045 0.533550 0.525135 -P_21 0.530330 0.681891 0.685786 0.777471 0.898562 0.980475 0.998188 1.094230 -P_21 1.185654 1.219688 1.266224 1.191046 1.339617 1.371840 1.442559 1.364902 -P_21 1.215431 1.246328 1.341204 1.092556 1.053869 1.149546 1.138336 1.104399 -P_21 1.119215 1.047119 1.060102 1.166198 1.088131 1.156444 1.178182 1.244227 -P_21 1.129996 1.114582 1.090052 1.133142 1.058201 0.968097 0.929814 0.800627 -P_21 0.734442 0.655179 0.685108 0.570909 0.510850 0.434964 0.507708 0.432401 -P_21 0.547608 0.641040 0.674857 0.754989 0.932790 0.981216 1.059528 1.080447 -P_21 1.255029 1.200362 1.356788 1.339511 1.217798 1.301252 1.244956 1.230646 -P_21 1.274376 1.289420 1.170277 1.154314 1.172742 1.103604 1.034058 1.096391 -P_21 1.088600 1.100339 1.141385 1.174096 1.216174 1.115256 1.189298 1.204745 -P_21 1.292801 1.218385 1.112335 1.089684 1.088734 0.921906 1.023498 0.797090 -P_21 0.762081 0.721573 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0.500775 0.524779 0.562945 0.571676 0.663562 0.743649 0.730542 0.744734 -P_21 0.873191 0.935418 0.949976 1.016222 1.067367 1.213608 1.111481 1.220452 -P_21 1.158691 1.060279 1.085993 1.092681 1.133447 1.085149 1.064384 1.054681 -P_21 0.980334 1.054146 1.078291 1.053883 1.036485 1.082470 1.053850 1.136141 -P_21 1.027891 1.167448 1.078471 1.013390 0.894391 0.849954 0.824516 0.802542 -P_21 0.686694 0.673601 0.527348 0.550593 0.511431 0.505885 0.473955 0.516542 -P_21 0.548027 0.570412 0.692763 0.770369 0.819296 1.007767 1.064853 1.098818 -P_21 1.278457 1.233824 1.275582 1.333329 1.327260 1.336795 1.380430 1.241365 -P_21 1.352467 1.355952 1.228293 1.147964 1.104921 1.092764 1.024621 1.142412 -P_21 0.950174 0.924016 0.975976 1.098766 1.096073 1.211041 1.187551 1.185179 -P_21 1.274918 1.153770 1.125021 1.099754 1.063715 0.991806 0.933112 0.868743 -P_21 0.746529 0.673406 0.633002 0.537449 0.551958 0.544548 0.507306 0.545298 -P_21 0.575319 0.600274 0.682682 0.763101 0.819253 0.929807 1.110551 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0.511411 0.461515 0.513123 0.530348 -P_21 0.586745 0.625146 0.647005 0.779773 0.874941 1.013191 1.036419 1.193051 -P_21 1.210450 1.279216 1.351560 1.356656 1.372496 1.313836 1.305575 1.309019 -P_21 1.333861 1.321810 1.300678 1.177533 1.119811 1.060548 1.044568 1.001547 -P_21 1.052029 1.069014 1.082914 1.149213 1.116854 1.154332 1.121074 1.135053 -P_21 1.176609 1.236359 1.077988 1.132072 1.075331 1.039830 1.042900 0.833086 -P_21 0.829714 0.744913 0.632217 0.565206 0.528615 0.512681 0.535481 0.553863 -P_21 0.578196 0.624270 0.733529 0.731476 0.919122 0.996257 1.040493 1.122261 -P_21 1.278158 1.261798 1.229212 1.388497 1.303974 1.219105 1.312040 1.286012 -P_21 1.241773 1.185183 1.318285 1.118342 1.086789 1.041130 1.111540 1.071542 -P_21 1.106659 1.139453 1.163929 1.123816 1.212966 1.238365 1.215821 1.227304 -P_21 1.176953 1.201192 1.149738 1.110702 1.110835 1.071173 0.946365 0.822404 -P_21 0.754723 0.684123 0.613742 0.523940 0.503554 0.485164 0.488601 0.534972 -P_21 0.509317 0.683510 0.719893 0.819676 0.928354 0.957601 1.069986 1.059185 -P_21 1.150072 1.186491 1.323621 1.330375 1.302034 1.422017 1.464736 1.324299 -P_21 1.368710 1.244271 1.162757 1.228229 1.242822 1.185393 1.039694 1.112281 -P_21 1.132996 1.085536 1.014177 1.091256 1.106008 1.227172 1.237663 1.155289 -P_21 1.210439 1.028499 1.111595 1.148831 1.064305 1.036701 0.834794 0.885017 -P_21 0.820253 0.695072 0.646942 0.481623 0.484779 0.490373 0.475367 0.494256 -P_21 0.561818 0.605549 0.701645 0.797913 0.913159 0.941024 1.037303 1.297147 -P_21 1.233595 1.301629 1.307671 1.283463 1.215095 1.206918 1.326677 1.235049 -P_21 1.317380 1.188517 1.088892 1.170446 1.204900 1.117238 1.108184 1.112703 -P_21 1.097521 1.065934 1.270206 1.202857 1.196445 1.204827 1.213931 1.190688 -P_21 1.309690 1.200337 1.082037 1.194104 0.996498 1.110600 0.940567 0.871430 -P_21 0.791475 0.674853 0.679319 0.598561 0.519399 0.535627 0.553808 0.558702 -P_21 0.642158 0.642923 0.820705 0.923637 1.058850 1.055317 1.085657 1.302557 -P_21 1.358068 1.522901 1.378755 1.429918 1.460147 1.378226 1.465377 1.472666 -P_21 1.420017 1.308041 1.117347 1.248443 1.119498 1.081461 1.094889 1.178246 -P_21 1.259844 1.211683 1.188447 1.212115 1.275519 1.239221 1.142660 1.175181 -P_21 1.164132 1.154656 1.072311 1.061368 1.059008 0.965062 0.969413 0.913733 -P_21 0.755920 0.762571 0.734205 0.677745 0.584131 0.570167 0.555234 0.562783 -P_21 0.554320 0.582475 0.552379 0.682846 0.781093 0.838632 0.886912 1.051874 -P_21 1.114098 1.204382 1.106415 1.268102 1.167930 1.163804 1.214423 1.327966 -P_21 1.258687 1.281491 1.214885 1.199283 1.162706 1.280330 1.195880 1.160677 -P_21 1.108092 1.166937 1.044520 1.198883 1.073446 1.218539 1.034681 1.124140 -P_21 1.123903 1.036307 1.065969 1.052530 0.838151 0.918142 0.849535 0.806890 -P_21 0.739123 0.607421 0.682974 0.612189 0.664567 0.601158 0.540762 0.555125 -P_21 0.507749 0.555833 0.577762 0.534036 0.687317 0.700356 0.778762 0.817840 -P_21 0.876138 0.842228 0.976856 1.099292 1.200142 1.213653 1.177746 1.244484 -P_21 1.199353 1.101579 1.114704 1.220157 1.061065 1.037856 1.010641 1.120262 -P_21 1.224122 1.028957 1.045678 1.089438 1.132628 1.172601 1.099430 1.225666 -P_21 1.260301 1.075330 1.148451 1.035671 0.887212 0.929595 0.824440 0.805156 -P_21 0.736825 0.655218 0.628860 0.577685 0.525471 0.498692 0.499540 0.552917 -P_21 0.643405 0.586850 0.696279 0.736935 0.903173 0.984217 1.068025 1.192573 -P_21 1.176592 1.247374 1.346411 1.342850 1.427884 1.378201 1.418049 1.335473 -P_21 1.369591 1.267569 1.227519 1.085359 1.096622 1.060550 1.066775 1.086109 -P_21 1.058482 1.102328 1.099708 1.233204 1.090290 1.186022 1.301042 1.214094 -P_21 1.175163 1.194107 1.198142 1.145366 1.087486 1.193556 0.974107 0.889193 -P_21 0.763837 0.637408 0.591664 0.587107 0.540812 0.472213 0.514005 0.515662 -P_21 0.601309 0.697233 0.756665 0.819137 0.885175 0.945828 1.031213 1.159197 -P_21 1.207317 1.253844 1.232337 1.252409 1.317668 1.363690 1.310514 1.277088 -P_21 1.354380 1.321942 1.217194 1.019738 1.121798 1.159643 1.018186 1.071860 -P_21 1.134378 1.071492 1.082282 1.132809 1.209636 1.287752 1.136594 1.163201 -P_21 1.341171 1.239459 1.169987 1.101632 1.055993 0.987253 0.900290 0.774144 -P_21 0.753936 0.655992 0.685314 0.600638 0.507321 0.486500 0.537725 0.539302 -P_21 0.636516 0.676849 0.725747 0.807658 0.826378 0.940181 1.066439 1.177465 -P_21 1.294095 1.207340 1.254437 1.315249 1.267826 1.335594 1.382665 1.403520 -P_21 1.187845 1.120858 1.183288 1.296444 1.249926 1.099928 1.119127 1.087595 -P_21 1.035077 1.092208 1.099784 1.228565 1.161345 1.160524 1.175663 1.198818 -P_21 1.246967 1.229625 1.297588 1.172175 1.086617 1.104811 0.929912 0.800805 -P_21 0.800907 0.699682 0.641028 0.571650 0.520637 0.531668 0.466906 0.580333 -P_21 0.537643 0.632296 0.758708 0.847428 0.916051 1.050342 1.141229 1.113097 -P_21 1.112686 1.234692 1.312831 1.380083 1.343326 1.353270 1.342007 1.297794 -P_21 1.269688 1.154746 1.254474 1.236760 1.192681 1.256072 1.188683 1.070500 -P_21 1.190096 1.133734 1.216992 1.109515 1.209567 1.211237 1.177156 1.258869 -P_21 1.315546 1.146640 1.237287 1.184680 1.119135 1.034640 0.970781 0.888639 -P_21 0.850307 0.680918 0.750134 0.580050 0.550749 0.553868 0.536373 0.579537 -P_21 0.683110 0.705982 0.742056 0.884929 0.988808 1.097972 1.202775 1.283125 -P_21 1.334767 1.440894 1.415719 1.559831 1.579956 1.382470 1.403189 1.274916 -P_21 1.388751 1.333751 1.228023 1.216662 1.279778 1.247651 1.181740 1.279062 -P_21 1.195423 1.271267 1.233362 1.191222 1.290097 1.113198 1.214810 1.178447 -P_21 1.095401 1.200096 1.201629 1.098902 1.152405 1.113195 0.984448 0.914387 -P_21 0.827548 0.838912 0.729735 0.660432 0.609661 0.596514 0.599904 0.533819 -P_21 0.572554 0.543001 0.635012 0.719692 0.786184 0.884336 0.959884 1.063828 -P_21 1.043978 1.094977 1.147440 1.245115 1.327594 1.332173 1.245570 1.210215 -P_21 1.213242 1.279885 1.231569 1.152870 1.198265 1.195907 1.206373 1.197640 -P_21 1.166461 1.170766 1.170749 1.213723 1.190203 1.189482 1.234691 1.128817 -P_21 1.108431 1.068258 1.130989 0.973536 0.957731 0.873727 0.746554 0.764403 -P_21 0.772386 0.783044 0.664426 0.625025 0.629222 0.554698 0.577161 0.572611 -P_21 0.542354 0.562533 0.610476 0.582918 0.636753 0.722457 0.659805 0.881664 -P_21 0.814613 0.869777 0.966865 1.140815 1.065781 1.137175 1.258818 1.251284 -P_21 1.315697 1.165545 1.172157 1.194180 1.217305 1.094030 1.112943 1.059613 -P_21 1.093548 1.090692 1.024340 1.073447 1.186604 1.077581 1.128835 1.170312 -P_21 1.134075 1.198140 1.185848 1.102591 1.109871 0.891601 0.892134 0.830902 -P_21 0.779600 0.682697 0.630255 0.541913 0.589006 0.527280 0.559303 0.587276 -P_21 0.606320 0.605347 0.665245 0.727870 0.883241 1.042677 1.080197 1.287749 -P_21 1.246921 1.260339 1.292131 1.365966 1.422459 1.500750 1.347913 1.398832 -P_21 1.385169 1.236920 1.365424 1.178651 1.067055 1.138733 1.099266 1.118840 -P_21 1.176518 1.157814 1.188320 1.144863 1.126355 1.231678 1.186603 1.302591 -P_21 1.314757 1.230299 1.239026 1.115673 1.138914 1.094450 0.923004 0.876630 -P_21 0.724648 0.668693 0.654757 0.633328 0.516208 0.523233 0.549490 0.552178 -P_21 0.537983 0.629051 0.717416 0.783059 0.929900 0.989055 1.065852 1.070480 -P_21 1.252294 1.034348 1.350854 1.227585 1.435000 1.345154 1.436000 1.281404 -P_21 1.401753 1.339503 1.273775 1.229505 1.202078 1.187601 1.107175 1.008365 -P_21 0.982518 1.099518 1.213076 1.225608 1.309237 1.268917 1.157578 1.229881 -P_21 1.365085 1.219523 1.161283 1.222545 1.087145 1.044642 1.050618 0.873972 -P_21 0.711783 0.699778 0.595169 0.598641 0.568103 0.525707 0.575555 0.568423 -P_21 0.612293 0.656963 0.715519 0.835328 0.862463 1.020637 1.135676 1.192415 -P_21 1.189106 1.256684 1.354979 1.460846 1.435872 1.259891 1.282669 1.317550 -P_21 1.326941 1.248043 1.221086 1.263459 1.224387 1.151420 1.162246 1.188395 -P_21 1.147673 1.139090 1.126735 1.208323 1.205644 1.136798 1.297837 1.319777 -P_21 1.211929 1.236268 1.155616 1.210442 1.140554 1.021560 0.951580 0.869445 -P_21 0.835997 0.758828 0.640432 0.589050 0.556002 0.489266 0.532867 0.515140 -P_21 0.629836 0.668336 0.712235 0.840240 0.955633 0.987102 1.096596 1.232047 -P_21 1.300248 1.350671 1.340524 1.320653 1.335708 1.331283 1.387546 1.313912 -P_21 1.179974 1.266767 1.229320 1.254511 1.219787 1.178819 1.164304 1.128172 -P_21 1.183295 1.143607 1.227836 1.229122 1.188248 1.233215 1.269722 1.302607 -P_21 1.335414 1.388810 1.370107 1.109082 1.198696 1.093598 1.008034 0.937285 -P_21 0.852829 0.718184 0.653762 0.664528 0.532419 0.617245 0.542401 0.602472 -P_21 0.680291 0.703613 0.840366 0.826937 1.102750 1.108851 1.289282 1.450974 -P_21 1.397457 1.557627 1.505636 1.724251 1.503851 1.463333 1.609192 1.530967 -P_21 1.378221 1.287576 1.341178 1.345409 1.193165 1.158066 1.144448 1.215250 -P_21 1.196791 1.207564 1.234303 1.263526 1.285752 1.174604 1.175130 1.208812 -P_21 1.244425 1.332860 1.182103 1.163500 1.057388 1.126928 1.008614 0.909327 -P_21 0.917126 0.858434 0.697046 0.681218 0.612331 0.560261 0.549412 0.573519 -P_21 0.582256 0.615809 0.660110 0.686458 0.854753 0.929099 0.822170 1.138252 -P_21 1.024829 1.207120 1.170836 1.270624 1.305768 1.490093 1.316261 1.329995 -P_21 1.385146 1.325843 1.232596 1.342852 1.144726 1.242902 1.206126 1.194139 -P_21 1.216158 1.265379 1.286154 1.302545 1.285014 1.218409 1.200227 1.142355 -P_21 1.267466 1.159794 1.050942 0.985233 0.907911 0.856683 0.859622 0.845331 -P_21 0.696074 0.735454 0.653644 0.689775 0.621117 0.624625 0.593314 0.560668 -P_21 0.559852 0.585711 0.653640 0.656096 0.701411 0.752501 0.757807 0.789606 -P_21 0.921244 0.907190 0.934634 1.164735 1.166719 1.135457 1.265327 1.242852 -P_21 1.265182 1.201491 1.238184 1.208209 1.110137 1.101997 1.068805 1.028742 -P_21 1.098484 1.152815 1.212102 1.236482 1.220803 1.110629 1.228355 1.188589 -P_21 1.178264 1.135202 1.078633 1.075619 1.030309 0.888526 0.942701 0.861847 -P_21 0.774283 0.712580 0.624269 0.628204 0.533153 0.524195 0.510776 0.606314 -P_21 0.568505 0.657255 0.751340 0.840286 0.920397 0.993487 1.097043 1.266221 -P_21 1.284571 1.259745 1.362588 1.445934 1.436933 1.466731 1.499773 1.474758 -P_21 1.340203 1.334414 1.260470 1.342795 1.143171 1.207893 1.150047 1.065186 -P_21 1.042657 1.115863 1.150984 1.088934 1.214275 1.176650 1.245820 1.237933 -P_21 1.418547 1.396111 1.249026 1.123145 1.165515 1.115375 0.967799 0.907368 -P_21 0.853804 0.788161 0.638914 0.595401 0.535993 0.570034 0.589953 0.607099 -P_21 0.597820 0.661569 0.769885 0.892841 0.903015 0.996613 1.093209 1.166379 -P_21 1.250418 1.330128 1.438816 1.362445 1.516015 1.435069 1.318830 1.310168 -P_21 1.348374 1.215471 1.332419 1.228190 1.219114 1.145844 1.232044 1.180686 -P_21 1.120670 1.104795 1.147036 1.164323 1.178943 1.281415 1.270934 1.363201 -P_21 1.246320 1.223048 1.287466 1.217375 1.142262 1.156352 0.974159 0.904216 -P_21 0.820160 0.724178 0.629233 0.513148 0.513396 0.565514 0.578903 0.577937 -P_21 0.614542 0.734338 0.794350 0.885867 0.998627 0.952905 1.235953 1.124605 -P_21 1.253309 1.407857 1.470058 1.325631 1.381571 1.283626 1.502639 1.336651 -P_21 1.393160 1.386661 1.301067 1.229841 1.302006 1.304088 1.189816 1.180001 -P_21 1.170791 1.185824 1.265588 1.203861 1.239125 1.193403 1.195846 1.303052 -P_21 1.222098 1.389428 1.279892 1.331904 1.075851 0.997537 0.980347 0.943119 -P_21 0.773566 0.747756 0.690212 0.605544 0.611286 0.542312 0.550986 0.557098 -P_21 0.636708 0.641472 0.739650 0.862195 0.979495 1.098728 1.235677 1.247500 -P_21 1.217747 1.298476 1.432604 1.386058 1.412460 1.497106 1.286701 1.231780 -P_21 1.303299 1.359151 1.273030 1.293630 1.116245 1.235583 1.250144 1.061459 -P_21 1.127126 1.186649 1.229958 1.254073 1.282070 1.304675 1.269452 1.289433 -P_21 1.291032 1.305495 1.200510 1.154087 1.211470 1.084225 1.069895 0.905284 -P_21 0.834129 0.758505 0.676412 0.634051 0.495632 0.520934 0.557541 0.588761 -P_21 0.646875 0.740914 0.911037 1.030417 1.035282 1.156386 1.259634 1.363434 -P_21 1.524658 1.533721 1.429481 1.702902 1.705510 1.676986 1.558609 1.514493 -P_21 1.417670 1.338194 1.326326 1.146754 1.263800 1.180979 1.258846 1.120501 -P_21 1.338270 1.205829 1.237521 1.215468 1.355831 1.185505 1.171070 1.244316 -P_21 1.244886 1.287917 1.252224 1.255967 1.172081 1.131766 1.042842 1.000899 -P_21 0.897058 0.798644 0.742008 0.683369 0.609795 0.605958 0.593116 0.574192 -P_21 0.551838 0.604675 0.685696 0.791678 0.796276 0.948083 0.982323 1.082954 -P_21 1.115822 1.347467 1.249416 1.300306 1.415476 1.432730 1.437183 1.321057 -P_21 1.285092 1.383530 1.353530 1.145550 1.259245 1.284559 1.237589 1.259291 -P_21 1.263590 1.338091 1.270012 1.268329 1.293115 1.375619 1.273151 1.148004 -P_21 1.087851 1.226501 1.162274 1.091961 1.082691 0.974763 0.933296 0.870720 -P_21 0.777956 0.771457 0.755619 0.676524 0.680936 0.595027 0.591969 0.584952 -P_21 0.592325 0.608749 0.613361 0.650060 0.671096 0.750919 0.758263 0.890930 -P_21 0.920212 1.017642 1.088130 1.069336 1.262906 1.386026 1.297484 1.272222 -P_21 1.321670 1.221488 1.212978 1.197127 1.150711 1.256768 1.179962 1.120040 -P_21 1.084907 1.159587 1.139366 1.238268 1.115184 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1.258865 1.234853 1.141781 1.082818 0.886442 -P_21 0.824104 0.725004 0.661008 0.605728 0.564414 0.485196 0.514462 0.598771 -P_21 0.597299 0.683930 0.774801 0.883690 0.971170 1.108658 1.176122 1.072706 -P_21 1.240289 1.346264 1.516772 1.453398 1.487722 1.437544 1.298793 1.515109 -P_21 1.384750 1.479665 1.240271 1.239312 1.266780 1.401868 1.206123 1.190316 -P_21 1.110909 1.176716 1.248279 1.197146 1.343733 1.247429 1.333734 1.430504 -P_21 1.323868 1.381102 1.324923 1.093335 1.090844 1.057531 1.068829 0.896137 -P_21 0.722064 0.774428 0.660698 0.650043 0.602421 0.617072 0.559048 0.591110 -P_21 0.593630 0.657279 0.819069 0.824275 0.946772 1.110439 1.120280 1.244275 -P_21 1.323531 1.306506 1.475033 1.378449 1.392780 1.411075 1.419485 1.481694 -P_21 1.383442 1.209912 1.314238 1.290169 1.216247 1.257695 1.173159 1.090395 -P_21 1.196338 1.320236 1.263009 1.267781 1.252846 1.333261 1.308004 1.336134 -P_21 1.261872 1.332094 1.252654 1.288230 1.174523 1.054371 0.943986 0.982580 -P_21 0.938903 0.796847 0.704864 0.678637 0.577138 0.550210 0.587648 0.591225 -P_21 0.683657 0.738731 0.825112 0.954019 0.980368 1.173235 1.346124 1.410439 -P_21 1.453502 1.485188 1.600606 1.539491 1.618774 1.626378 1.520860 1.440501 -P_21 1.395679 1.425592 1.341425 1.325416 1.190413 1.283921 1.306162 1.280983 -P_21 1.264027 1.256886 1.255809 1.278354 1.322852 1.292547 1.207154 1.296309 -P_21 1.281513 1.284911 1.164916 1.292295 1.178634 1.156684 1.009425 0.956765 -P_21 0.899813 0.855385 0.831301 0.767274 0.655985 0.624447 0.587705 0.536960 -P_21 0.571809 0.633671 0.702568 0.798976 0.792307 0.861926 0.961548 1.081603 -P_21 1.097259 1.196949 1.297114 1.422511 1.468184 1.294608 1.380637 1.359368 -P_21 1.234008 1.335957 1.404549 1.352347 1.326471 1.310623 1.293891 1.272632 -P_21 1.296791 1.266124 1.325884 1.213383 1.252794 1.284551 1.268090 1.250580 -P_21 1.361250 1.088157 1.073742 1.060041 1.088501 0.886879 0.855399 0.938061 -P_21 0.803750 0.804444 0.704639 0.745801 0.614269 0.618864 0.583951 0.561740 -P_21 0.597475 0.562757 0.577253 0.658570 0.734125 0.743152 0.829733 0.935910 -P_21 0.956776 1.046583 1.106342 1.212174 1.229612 1.293064 1.278995 1.242857 -P_21 1.365345 1.330730 1.415947 1.329390 1.099338 1.224867 1.189836 1.236659 -P_21 1.166069 1.168975 1.187892 1.155770 1.256407 1.204636 1.227107 1.237899 -P_21 1.154166 1.174825 1.222987 1.211391 1.136686 1.097577 0.973230 0.944672 -P_21 0.822130 0.778984 0.708509 0.632231 0.589974 0.547040 0.578931 0.568105 -P_21 0.651135 0.707209 0.773556 0.803781 0.943921 1.110959 1.187287 1.238644 -P_21 1.395052 1.531649 1.330474 1.512728 1.660808 1.507274 1.476463 1.499943 -P_21 1.465218 1.443902 1.350716 1.216525 1.225705 1.172894 1.152340 1.199931 -P_21 1.112763 1.138818 1.221305 1.177883 1.095274 1.365219 1.311335 1.397104 -P_21 1.381706 1.240687 1.265082 1.174311 1.273739 0.998699 1.010953 0.984710 -P_21 0.783942 0.692258 0.720013 0.641912 0.614531 0.617175 0.574399 0.577480 -P_21 0.624505 0.621357 0.733546 0.772991 0.968960 1.095878 1.132571 1.198681 -P_21 1.307131 1.434652 1.448452 1.371893 1.583563 1.616156 1.497646 1.491693 -P_21 1.410586 1.289154 1.218545 1.200941 1.183186 1.170036 1.216850 1.131971 -P_21 1.157165 1.218041 1.199574 1.267277 1.194964 1.290971 1.515314 1.459421 -P_21 1.410183 1.312092 1.385640 1.261568 1.165679 1.063138 0.980927 0.977480 -P_21 0.830191 0.761109 0.652558 0.622176 0.575052 0.573953 0.547545 0.592005 -P_21 0.632346 0.723490 0.782730 0.981397 1.050403 1.071527 1.099625 1.208991 -P_21 1.400528 1.403063 1.403510 1.427152 1.452213 1.468843 1.494717 1.403982 -P_21 1.436636 1.417264 1.392493 1.315454 1.282438 1.286682 1.251271 1.233727 -P_21 1.215863 1.253817 1.262182 1.298564 1.359334 1.252581 1.445923 1.325758 -P_21 1.294037 1.282133 1.235043 1.240074 1.111238 1.065145 0.973001 0.947858 -P_21 0.927546 0.759867 0.713395 0.616750 0.584619 0.491780 0.569937 0.625488 -P_21 0.690334 0.730019 0.766188 0.870473 1.081345 1.142566 1.122476 1.321514 -P_21 1.370542 1.356763 1.451353 1.379328 1.392243 1.451383 1.473584 1.531398 -P_21 1.219952 1.492985 1.296523 1.309414 1.258850 1.159645 1.260065 1.238587 -P_21 1.214306 1.238136 1.289124 1.348700 1.374006 1.303169 1.378813 1.425904 -P_21 1.308183 1.387084 1.275302 1.436320 1.206382 1.178096 0.977309 0.963513 -P_21 0.897966 0.792656 0.706705 0.650504 0.591131 0.561424 0.570997 0.632845 -P_21 0.650132 0.810934 0.873324 0.898489 1.086108 1.281594 1.311624 1.428229 -P_21 1.566759 1.827187 1.670517 1.777241 1.799169 1.640413 1.582637 1.695471 -P_21 1.647402 1.407836 1.386323 1.501708 1.383784 1.209629 1.323855 1.391691 -P_21 1.234625 1.220035 1.247352 1.422828 1.342464 1.315869 1.402259 1.254921 -P_21 1.228283 1.316267 1.272448 1.181787 1.064808 1.124674 1.123745 0.918928 -P_21 0.944359 0.827258 0.809280 0.768216 0.661856 0.604736 0.714935 0.635640 -P_21 0.611448 0.677508 0.644599 0.763135 0.867670 1.008693 0.913206 1.081889 -P_21 1.252812 1.125802 1.185070 1.421260 1.325586 1.387522 1.448807 1.497138 -P_21 1.447942 1.413922 1.328463 1.402063 1.338426 1.261346 1.407368 1.428910 -P_21 1.238350 1.435872 1.297682 1.356760 1.328560 1.210403 1.333458 1.326605 -P_21 1.256988 1.195907 1.235278 1.090007 1.003828 0.960297 0.954887 0.889083 -P_21 0.819727 0.830534 0.769394 0.744837 0.704075 0.624333 0.644271 0.621944 -P_21 0.588745 0.583677 0.604123 0.706631 0.719534 0.746542 0.810013 0.886719 -P_21 0.973117 1.114040 1.166012 1.205245 1.158771 1.292586 1.293707 1.368277 -P_21 1.348012 1.314740 1.341130 1.254908 1.195596 1.161234 1.258783 1.326749 -P_21 1.172700 1.196314 1.119785 1.207426 1.249166 1.266421 1.359072 1.240782 -P_21 1.325515 1.253751 1.297766 1.112953 1.218565 1.078884 1.005898 0.900284 -P_21 0.789273 0.669275 0.667662 0.615732 0.531001 0.562707 0.534226 0.554252 -P_21 0.629137 0.709533 0.754500 0.877392 1.007184 1.054572 1.084492 1.167908 -P_21 1.356983 1.552391 1.543125 1.393884 1.474230 1.594918 1.525209 1.590446 -P_21 1.511651 1.399171 1.495673 1.367638 1.232112 1.215914 1.174941 1.133353 -P_21 1.112044 1.182706 1.180509 1.199365 1.302858 1.334358 1.365777 1.252121 -P_21 1.527718 1.398731 1.338562 1.350206 1.152064 1.194830 1.065358 0.996893 -P_21 0.842936 0.798064 0.691773 0.567919 0.580756 0.559713 0.558191 0.582551 -P_21 0.672137 0.714930 0.763449 0.915448 0.894875 1.103407 1.185081 1.323906 -P_21 1.308826 1.413386 1.395167 1.515420 1.403294 1.545927 1.495146 1.475209 -P_21 1.376420 1.410692 1.308836 1.363067 1.233822 1.220017 1.200868 1.166722 -P_21 1.216276 1.070724 1.186996 1.269753 1.245452 1.386560 1.373131 1.456495 -P_21 1.334508 1.330954 1.361510 1.279843 1.231451 1.159503 1.088929 0.966304 -P_21 0.907962 0.753134 0.667207 0.601748 0.567807 0.549365 0.610052 0.621713 -P_21 0.691339 0.758285 0.763485 0.916611 1.013221 1.116484 1.225857 1.291568 -P_21 1.334452 1.520249 1.533337 1.523188 1.539948 1.527012 1.391338 1.460624 -P_21 1.405557 1.584521 1.307131 1.328056 1.369369 1.321440 1.252214 1.323006 -P_21 1.245200 1.245572 1.349121 1.412324 1.387935 1.281803 1.293582 1.320280 -P_21 1.340690 1.338796 1.255992 1.325231 1.329956 1.195946 1.010689 0.958387 -P_21 0.857785 0.702405 0.738608 0.606003 0.586563 0.553803 0.619722 0.588262 -P_21 0.637230 0.775667 0.737367 0.927114 0.960049 1.097248 1.284138 1.247479 -P_21 1.342649 1.462874 1.522756 1.502995 1.496591 1.508482 1.411431 1.439459 -P_21 1.407638 1.256117 1.327090 1.345807 1.197561 1.356281 1.315108 1.299739 -P_21 1.176307 1.145152 1.256212 1.242697 1.487852 1.291611 1.468717 1.397424 -P_21 1.445174 1.297538 1.339784 1.294243 1.148909 1.204522 1.067485 1.049136 -P_21 0.842602 0.765506 0.747477 0.630666 0.642496 0.643230 0.586338 0.650357 -P_21 0.725167 0.671741 0.812902 1.031381 1.163636 1.164590 1.407606 1.494710 -P_21 1.464793 1.718844 1.727135 1.543826 1.693980 1.518517 1.759827 1.702752 -P_21 1.535184 1.465868 1.454996 1.362774 1.391642 1.315040 1.378675 1.377781 -P_21 1.281913 1.241527 1.299285 1.328288 1.233396 1.321414 1.364582 1.414792 -P_21 1.244743 1.351494 1.131668 1.235022 1.141875 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1.233270 1.234085 1.108988 1.111403 1.023294 0.932947 0.956531 0.910177 -P_21 0.827870 0.793736 0.830196 0.671972 0.662840 0.652737 0.687199 0.610755 -P_21 0.609401 0.638946 0.686362 0.636343 0.674278 0.781981 0.921793 0.994955 -P_21 1.070741 1.157890 1.121842 1.226266 1.279583 1.230735 1.382704 1.446204 -P_21 1.336988 1.406180 1.334061 1.370437 1.343734 1.259479 1.011481 1.292625 -P_21 1.267024 1.170159 1.252333 1.312210 1.372833 1.242745 1.332734 1.296053 -P_21 1.363385 1.346345 1.385863 1.280355 1.154049 1.019764 1.014928 0.868756 -P_21 0.775546 0.771020 0.704310 0.645576 0.573238 0.629030 0.590876 0.590941 -P_21 0.581308 0.748780 0.779802 0.889097 0.985824 1.087493 1.235328 1.372574 -P_21 1.445362 1.543764 1.584979 1.700625 1.531429 1.724367 1.707337 1.702205 -P_21 1.448284 1.428270 1.416266 1.380051 1.176967 1.191642 1.360019 1.122794 -P_21 1.206079 1.229872 1.337163 1.242525 1.298218 1.420379 1.361016 1.372435 -P_21 1.497707 1.410456 1.351932 1.376461 1.202814 1.176037 1.033380 0.998279 -P_21 0.878323 0.842833 0.712043 0.634397 0.619192 0.596840 0.653761 0.583454 -P_21 0.686525 0.688136 0.759890 0.962243 0.947545 1.092298 1.241227 1.218349 -P_21 1.318723 1.470444 1.429910 1.616822 1.653980 1.716154 1.360036 1.530103 -P_21 1.389490 1.507281 1.302907 1.362973 1.147609 1.191174 1.225406 1.303386 -P_21 1.222839 1.217602 1.232550 1.374931 1.274577 1.443728 1.414514 1.354798 -P_21 1.439986 1.511861 1.402610 1.257694 1.163916 1.159535 1.064603 0.995215 -P_21 0.828125 0.794361 0.703869 0.659100 0.579999 0.562355 0.568916 0.610826 -P_21 0.691866 0.759719 0.773300 0.868959 0.956494 1.117492 1.165943 1.356550 -P_21 1.403310 1.617858 1.450688 1.605921 1.671758 1.477722 1.544253 1.525280 -P_21 1.402563 1.572151 1.460717 1.338223 1.422785 1.387634 1.343012 1.291632 -P_21 1.175589 1.230025 1.310406 1.245655 1.377145 1.332097 1.399699 1.339509 -P_21 1.253420 1.319153 1.307708 1.435899 1.416862 1.200345 1.023745 0.947302 -P_21 0.934788 0.836301 0.701911 0.682961 0.570270 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0.852642 0.873698 1.002982 1.119569 1.162699 -P_21 1.272903 1.339144 1.352009 1.392998 1.436138 1.487052 1.552617 1.516976 -P_21 1.531341 1.464849 1.317027 1.320886 1.310581 1.441610 1.374935 1.442161 -P_21 1.498008 1.317783 1.333074 1.286013 1.243209 1.330949 1.350504 1.250260 -P_21 1.213613 1.208998 1.298033 1.072172 1.033764 1.011645 1.062160 0.943352 -P_21 0.849136 0.892763 0.756770 0.736507 0.660855 0.643653 0.689832 0.639691 -P_21 0.565535 0.606051 0.601622 0.667363 0.685424 0.785362 0.897174 0.895797 -P_21 0.986488 1.073992 1.149831 1.137526 1.298140 1.387460 1.421506 1.353514 -P_21 1.381694 1.469839 1.244463 1.337202 1.349909 1.305623 1.162605 1.359387 -P_21 1.168995 1.232495 1.264255 1.221265 1.190278 1.307774 1.275029 1.301511 -P_21 1.190157 1.325974 1.289717 1.225519 1.183012 0.982895 1.042449 0.938712 -P_21 0.747711 0.808645 0.746357 0.610947 0.582151 0.547402 0.612818 0.622072 -P_21 0.671929 0.724902 0.795880 0.930810 1.018774 1.106224 1.249095 1.402791 -P_21 1.357596 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0.934307 0.758507 0.690673 0.676435 0.610013 0.575287 0.574324 0.664938 -P_21 0.677940 0.806658 0.815721 0.933890 1.097050 1.293726 1.316437 1.511839 -P_21 1.634661 1.608877 1.752463 1.825131 1.622191 1.487051 1.669842 1.620350 -P_21 1.583084 1.508794 1.454300 1.411562 1.372660 1.332078 1.287346 1.330590 -P_21 1.420665 1.282388 1.313121 1.376203 1.328153 1.407674 1.431389 1.218693 -P_21 1.318723 1.309624 1.247359 1.170208 1.191636 1.167489 1.136087 0.963773 -P_21 0.956613 0.919434 0.783791 0.736945 0.659846 0.660307 0.638909 0.618829 -P_21 0.631821 0.679849 0.746631 0.812157 0.897811 0.876232 0.929955 1.152201 -P_21 1.266809 1.144894 1.231503 1.334363 1.366677 1.397441 1.329905 1.528244 -P_21 1.406994 1.437799 1.460447 1.353592 1.273612 1.231582 1.264301 1.357289 -P_21 1.322095 1.485659 1.334999 1.292894 1.306944 1.276125 1.237984 1.276576 -P_21 1.370609 1.129012 1.134598 1.081068 1.117747 0.945692 0.911045 0.863837 -P_21 0.841964 0.829751 0.740092 0.712569 0.680796 0.648547 0.635259 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1.002065 1.027324 1.333647 -P_21 1.462667 1.253535 1.527324 1.576559 1.579923 1.448335 1.499828 1.489089 -P_21 1.348245 1.269233 1.321957 1.205976 1.252611 1.182470 1.118767 1.165958 -P_21 1.110166 1.293546 1.209467 1.246487 1.297503 1.440358 1.305095 1.470613 -P_21 1.449718 1.318650 1.420871 1.273608 1.219481 1.038917 1.097995 0.973309 -P_21 0.818052 0.739676 0.694337 0.605860 0.589124 0.560943 0.587587 0.624896 -P_21 0.632014 0.691750 0.829115 0.959496 1.115915 1.204567 1.048651 1.345175 -P_21 1.306472 1.373453 1.337781 1.427584 1.341148 1.428437 1.599288 1.366935 -P_21 1.545283 1.406406 1.542463 1.466713 1.320390 1.359558 1.151606 1.294292 -P_21 1.339318 1.218606 1.221273 1.351239 1.262995 1.335343 1.342827 1.291117 -P_21 1.419455 1.407039 1.323552 1.199118 1.253140 1.156353 1.070644 0.914133 -P_21 0.858567 0.771079 0.720318 0.691614 0.592375 0.553581 0.548018 0.529245 -P_21 0.671752 0.691958 0.716191 0.875812 0.985693 1.131892 1.329699 1.307872 -P_21 1.247525 1.410801 1.424477 1.553985 1.495916 1.406924 1.418474 1.418603 -P_21 1.359954 1.342280 1.323658 1.332966 1.210966 1.316123 1.187093 1.202421 -P_21 1.317848 1.324458 1.203597 1.302252 1.379275 1.196476 1.533499 1.321515 -P_21 1.620576 1.319598 1.316053 1.234327 1.165118 1.253141 1.052027 1.002242 -P_21 0.912681 0.781822 0.761576 0.671722 0.599284 0.560590 0.561746 0.631527 -P_21 0.680546 0.727349 0.889779 0.906852 1.054135 1.212827 1.340812 1.461470 -P_21 1.624829 1.646370 1.546466 1.506080 1.670666 1.686291 1.715780 1.756891 -P_21 1.532105 1.524296 1.417921 1.403630 1.290654 1.308687 1.264750 1.178250 -P_21 1.342351 1.327061 1.227543 1.292547 1.424844 1.291276 1.327668 1.348438 -P_21 1.374017 1.316803 1.285302 1.190028 1.174006 1.079911 1.085420 0.965454 -P_21 0.867713 0.858736 0.838632 0.719986 0.660995 0.647899 0.603322 0.654391 -P_21 0.629656 0.696972 0.636349 0.772358 0.862733 0.849699 0.938532 0.994750 -P_21 1.270652 1.282919 1.288714 1.227463 1.371070 1.441339 1.460194 1.404218 -P_21 1.419839 1.477747 1.306084 1.465016 1.393266 1.255817 1.349542 1.150908 -P_21 1.330198 1.235167 1.318284 1.462823 1.357814 1.240636 1.291630 1.227921 -P_21 1.231952 1.148819 1.173927 1.039053 1.048015 0.986747 0.946779 0.941014 -P_21 0.869745 0.750107 0.758261 0.728921 0.649751 0.648874 0.581571 0.533939 -P_21 0.562155 0.606244 0.641073 0.734132 0.664129 0.732783 0.759498 0.944038 -P_21 0.970488 0.963210 1.074562 1.126538 1.311708 1.203976 1.207258 1.213900 -P_21 1.401037 1.296830 1.292192 1.282282 1.201325 1.203555 1.220595 1.155052 -P_21 1.184906 1.120297 1.232482 1.202993 1.193728 1.139957 1.214915 1.298613 -P_21 1.364627 1.302530 1.230320 1.167958 1.066179 0.998815 0.902137 0.846787 -P_21 0.700654 0.702254 0.687276 0.628684 0.540469 0.562698 0.562873 0.599372 -P_21 0.654165 0.710111 0.825955 0.836098 1.002882 1.057900 1.035764 1.280971 -P_21 1.237036 1.379016 1.486539 1.578570 1.495445 1.511435 1.471644 1.552480 -P_21 1.381089 1.510699 1.329898 1.276298 1.219430 1.104850 1.142266 1.110449 -P_21 1.111770 1.080099 1.194714 1.216043 1.315019 1.224235 1.287555 1.328043 -P_21 1.312835 1.252411 1.325393 1.243073 1.214923 1.175874 1.074651 0.924449 -P_21 0.920435 0.778908 0.686243 0.644289 0.581051 0.556027 0.555642 0.583289 -P_21 0.581058 0.699951 0.729445 0.824442 0.882393 1.125278 1.135607 1.259000 -P_21 1.265947 1.363361 1.477803 1.440443 1.565194 1.396553 1.477910 1.400441 -P_21 1.495146 1.376242 1.373261 1.309101 1.224594 1.141567 1.288177 1.225998 -P_21 1.176862 1.188455 1.124511 1.251911 1.273388 1.402032 1.268245 1.358393 -P_21 1.326346 1.353735 1.391769 1.293100 1.184506 1.101841 1.000771 0.921086 -P_21 0.787705 0.731570 0.674562 0.628245 0.554851 0.571542 0.547821 0.585942 -P_21 0.593133 0.713739 0.812464 0.894058 1.030107 1.051174 1.197955 1.267527 -P_21 1.369931 1.450039 1.401892 1.375622 1.504236 1.418862 1.352583 1.353174 -P_21 1.296318 1.351782 1.338430 1.374631 1.210694 1.144418 1.224302 1.292419 -P_21 1.248420 1.211881 1.214947 1.224735 1.355165 1.350528 1.239787 1.205570 -P_21 1.398589 1.260513 1.269033 1.242344 1.231547 1.174102 1.040028 0.911807 -P_21 0.852034 0.693735 0.668555 0.612457 0.597036 0.501773 0.513737 0.578928 -P_21 0.700785 0.658483 0.784123 0.897333 0.939660 1.104097 1.127628 1.233413 -P_21 1.377553 1.448763 1.369949 1.424765 1.415232 1.366415 1.383996 1.319394 -P_21 1.343802 1.356529 1.330975 1.206129 1.194808 1.310760 1.249286 1.124407 -P_21 1.103748 1.282295 1.260674 1.207830 1.324390 1.381051 1.228882 1.345165 -P_21 1.393819 1.293510 1.211163 1.250556 1.015878 1.126413 1.063046 0.947657 -P_21 0.904838 0.743855 0.712695 0.609808 0.604308 0.581876 0.594650 0.598674 -P_21 0.651204 0.719214 0.903856 0.974916 1.099475 1.216774 1.288704 1.357509 -P_21 1.542186 1.693855 1.626324 1.761703 1.662835 1.613413 1.543904 1.664075 -P_21 1.482745 1.554599 1.404399 1.315308 1.279912 1.328836 1.301698 1.233254 -P_21 1.239750 1.220627 1.333546 1.281288 1.278631 1.386592 1.348632 1.247943 -P_21 1.207840 1.158161 1.278531 1.145244 1.117240 1.026748 0.998810 0.952673 -P_21 0.897398 0.825408 0.718236 0.725278 0.657047 0.625305 0.636528 0.652350 -P_21 0.581933 0.626151 0.746282 0.726095 0.824287 0.896735 1.038907 1.084359 -P_21 1.076670 1.126711 1.321428 1.276612 1.258094 1.431877 1.467456 1.267510 -P_21 1.407849 1.248637 1.321927 1.352265 1.288944 1.302838 1.302548 1.321577 -P_21 1.264984 1.267099 1.237804 1.224223 1.310010 1.262905 1.192709 1.295007 -P_21 1.186045 1.115545 1.101571 1.048050 1.039099 1.006647 0.903410 0.864335 -P_21 0.857922 0.766276 0.662760 0.660907 0.566807 0.635454 0.607854 0.590884 -P_21 0.569850 0.537029 0.613931 0.635364 0.680312 0.746990 0.760318 0.910457 -P_21 0.906278 1.073565 1.063339 1.168954 1.208701 1.273325 1.265162 1.178609 -P_21 1.253832 1.236185 1.208790 1.222539 1.200197 1.306472 1.154044 1.086087 -P_21 1.114535 0.956255 1.256864 1.264792 1.150750 1.228055 1.288154 1.137631 -P_21 1.390193 1.192938 1.152506 1.197669 1.062855 0.930267 0.880291 0.870449 -P_21 0.752435 0.709165 0.675248 0.545726 0.523006 0.498030 0.524892 0.590464 -P_21 0.602522 0.718810 0.684674 0.845081 0.884653 1.089801 1.127022 1.321813 -P_21 1.472123 1.429590 1.446387 1.411458 1.490762 1.367985 1.438197 1.418467 -P_21 1.499917 1.298928 1.236609 1.255785 1.219763 1.185613 1.172213 1.056940 -P_21 1.074087 1.150956 1.191667 1.206346 1.300351 1.154844 1.375851 1.280177 -P_21 1.283819 1.259719 1.218598 1.254943 1.115229 0.996438 1.007797 0.896411 -P_21 0.845609 0.691481 0.700730 0.633906 0.612729 0.546560 0.573209 0.557644 -P_21 0.645910 0.752464 0.747431 0.760002 0.983695 1.059919 1.139492 1.279419 -P_21 1.368983 1.453686 1.352046 1.414864 1.375634 1.348195 1.401523 1.295768 -P_21 1.302666 1.321369 1.332432 1.081027 1.206187 1.126872 1.118357 1.196349 -P_21 1.132485 1.088255 1.163582 1.218946 1.272062 1.190208 1.266078 1.247844 -P_21 1.359577 1.341631 1.235556 1.223971 1.211664 1.028933 1.044627 0.910669 -P_21 0.839827 0.704791 0.639658 0.600292 0.558992 0.532569 0.532732 0.562267 -P_21 0.611956 0.675250 0.735119 0.858066 1.024565 1.074583 1.145974 1.199783 -P_21 1.411413 1.316089 1.304373 1.529008 1.541533 1.504342 1.344721 1.372984 -P_21 1.291563 1.447882 1.310284 1.298485 1.296840 1.196036 1.333573 1.286036 -P_21 1.137974 1.247284 1.231075 1.199726 1.267749 1.260282 1.256834 1.227647 -P_21 1.206437 1.319376 1.277902 1.251849 1.190487 1.177705 0.957098 0.892528 -P_21 0.857830 0.854020 0.639308 0.580567 0.523962 0.550437 0.545589 0.597435 -P_21 0.620094 0.647068 0.651128 0.865021 0.924337 1.079594 1.179774 1.198397 -P_21 1.316976 1.261313 1.367872 1.460128 1.434866 1.347955 1.497489 1.339095 -P_21 1.283675 1.265125 1.238940 1.248704 1.167290 1.098688 1.038271 1.249732 -P_21 1.110072 1.208814 1.169287 1.247955 1.221823 1.172257 1.443515 1.293791 -P_21 1.254646 1.283673 1.213087 1.213935 1.186276 1.046404 1.016654 0.944293 -P_21 0.780095 0.725782 0.685190 0.647755 0.599695 0.539643 0.531544 0.628774 -P_21 0.647723 0.709490 0.849829 0.869231 1.068161 1.309201 1.290738 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1.268170 1.416847 1.302871 1.393141 1.397229 1.368226 1.337625 1.424984 -P_21 1.426371 1.291196 1.245563 1.185470 1.121873 1.093233 1.047413 1.042722 -P_21 0.882396 0.962404 0.999896 1.135075 1.086071 1.137309 1.064828 1.221890 -P_21 1.204344 1.206973 1.218540 1.101260 1.096123 0.980876 0.907349 0.875002 -P_21 0.720211 0.658903 0.591528 0.513145 0.508102 0.476699 0.498941 0.578559 -P_21 0.565669 0.621045 0.631514 0.734030 0.861203 0.822253 1.060231 1.115079 -P_21 1.241585 1.306130 1.246050 1.246000 1.327356 1.207169 1.366393 1.303755 -P_21 1.301873 1.266887 1.200086 1.048899 1.119841 1.014248 1.036014 0.950387 -P_21 0.972090 1.141924 1.072255 1.119292 1.171488 1.262157 1.248482 1.200956 -P_21 1.070378 1.243020 1.158843 1.090804 0.967656 1.000304 0.820117 0.856863 -P_21 0.729087 0.610342 0.608676 0.563219 0.555205 0.500470 0.538922 0.538439 -P_21 0.527071 0.647132 0.736864 0.711817 0.920411 0.969439 1.057531 1.152208 -P_21 1.205032 1.211061 1.337476 1.250290 1.319131 1.363009 1.347850 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1.186684 1.292831 1.115925 1.158180 1.218053 1.165721 -P_21 1.160091 1.091907 0.969614 1.029014 1.055019 0.997892 0.980118 1.062620 -P_21 1.049552 1.064425 1.018501 1.067522 1.114885 1.098235 1.184266 1.085199 -P_21 1.244683 0.995325 0.986943 0.988722 1.076431 1.037294 0.892632 0.790402 -P_21 0.778475 0.634902 0.556263 0.571729 0.493820 0.458747 0.530001 0.549203 -P_21 0.607929 0.616813 0.741949 0.825890 0.885923 1.077835 1.002087 1.280902 -P_21 1.266651 1.348842 1.335937 1.270303 1.337568 1.399758 1.227615 1.303507 -P_21 1.192629 1.208770 1.190052 1.127063 1.121728 1.138534 1.106140 1.110408 -P_21 0.984755 1.013688 0.989763 1.173204 1.075527 1.094862 1.075679 1.205112 -P_21 1.118798 1.065186 1.065879 1.037159 0.962172 0.959205 0.904365 0.819854 -P_21 0.811491 0.700252 0.657387 0.600283 0.595467 0.470389 0.489902 0.507060 -P_21 0.542448 0.513756 0.527154 0.624308 0.686852 0.732221 0.782778 0.935549 -P_21 1.058327 0.989778 1.080038 1.139109 1.125790 1.119858 1.254022 1.181937 -P_21 1.162881 1.131498 1.089127 1.117227 1.111107 1.044597 1.055180 0.928300 -P_21 1.097433 1.103752 1.145327 1.110304 0.997043 0.963957 1.077411 1.002079 -P_21 1.058147 0.989462 0.933123 0.948501 0.856954 0.860327 0.816951 0.716257 -P_21 0.729780 0.645750 0.622206 0.579539 0.531049 0.575674 0.547625 0.529872 -P_21 0.459477 0.478704 0.538167 0.504776 0.605058 0.640925 0.639993 0.770699 -P_21 0.821239 0.823049 0.987654 1.044794 1.139293 1.084036 1.090896 1.152022 -P_21 1.120207 1.085890 1.105521 1.037933 1.050914 0.999895 0.927506 1.024031 -P_21 0.949913 0.952264 1.040047 1.027315 1.028000 1.066793 1.113577 1.152924 -P_21 1.012783 0.979143 0.987787 0.908695 0.879966 0.793087 0.808589 0.780561 -P_21 0.721911 0.606074 0.525564 0.539329 0.483541 0.440232 0.457498 0.454288 -P_21 0.483187 0.584026 0.651899 0.674777 0.701674 0.862601 0.996928 0.980764 -P_21 1.211960 1.156048 1.159374 1.256377 1.251180 1.153366 1.319265 1.170426 -P_21 1.200726 1.222832 1.141429 1.148256 0.932131 1.019041 0.949433 1.005428 -P_21 1.010873 0.968252 1.018627 0.971330 0.968211 1.136189 1.074197 1.127097 -P_21 1.187193 1.053908 1.099986 1.023149 0.956018 0.991978 0.807629 0.822965 -P_21 0.739754 0.605843 0.617530 0.539024 0.451999 0.508949 0.458414 0.467344 -P_21 0.527223 0.517838 0.653325 0.642638 0.735121 0.897707 0.963745 0.993437 -P_21 1.090177 1.022126 1.258374 1.154884 1.211806 1.203947 1.253589 1.268427 -P_21 1.135191 1.093107 1.075390 0.971801 1.090721 0.962187 0.879673 1.043137 -P_21 0.990183 0.942334 0.992988 1.038076 1.085306 1.042672 1.088381 1.109725 -P_21 1.099433 1.145162 1.062033 0.911898 0.933225 0.905022 0.878024 0.735218 -P_21 0.675834 0.570581 0.524420 0.496772 0.453003 0.479408 0.486699 0.487717 -P_21 0.574267 0.612535 0.594185 0.755288 0.767125 0.869955 0.894749 0.997846 -P_21 1.048548 1.106197 1.165447 1.332915 1.173204 1.214489 1.120212 1.174126 -P_21 1.183141 1.272645 1.154940 1.078201 0.991037 1.153064 1.025682 0.952446 -P_21 0.998363 1.013356 1.048163 1.026345 0.983204 1.008783 1.074169 1.205044 -P_21 1.084977 1.087369 1.012659 1.068893 0.979096 0.923499 0.877001 0.729022 -P_21 0.682159 0.596693 0.540915 0.522772 0.440479 0.437074 0.447889 0.499221 -P_21 0.540444 0.575429 0.625061 0.727727 0.808756 0.861444 0.953249 0.967829 -P_21 1.072182 1.110835 1.179526 1.180915 1.198152 1.299297 1.139508 1.157622 -P_21 1.049196 1.162706 1.104010 1.064593 1.044327 1.029011 1.004091 0.932336 -P_21 1.011081 1.037321 1.067968 1.046382 1.096005 1.082156 1.180092 1.078262 -P_21 1.040746 1.167003 1.020550 1.025505 0.974209 0.907078 0.981295 0.794407 -P_21 0.715422 0.654120 0.565102 0.565089 0.446065 0.464947 0.499438 0.496666 -P_21 0.582360 0.575507 0.718513 0.733954 0.867569 0.882710 1.021791 1.050349 -P_21 1.267846 1.207080 1.275902 1.200268 1.320712 1.356367 1.270202 1.218481 -P_21 1.132111 1.136229 1.241278 1.023558 1.147868 1.013544 1.098183 0.938685 -P_21 0.962548 1.027227 1.061547 0.894224 1.121212 1.058241 1.068357 0.996850 -P_21 1.062829 1.062410 0.949878 0.839553 0.842832 0.844518 0.858867 0.825504 -P_21 0.742307 0.674803 0.589618 0.505115 0.510525 0.514861 0.479914 0.500381 -P_21 0.468780 0.546685 0.569654 0.548725 0.671336 0.761077 0.847488 0.913980 -P_21 0.903658 0.980608 1.037272 1.102034 1.088482 1.036324 1.113025 1.224040 -P_21 1.106048 1.063976 1.044380 1.028537 1.078829 0.981801 1.055043 0.987026 -P_21 0.956930 1.132499 1.048381 0.979674 1.137139 1.012571 1.047481 0.959494 -P_21 0.937861 1.012118 0.886677 0.814746 0.908209 0.850746 0.754284 0.736614 -P_21 0.648764 0.597483 0.606493 0.562945 0.548068 0.463755 0.521582 0.459413 -P_21 0.501759 0.548969 0.496089 0.512430 0.576015 0.575353 0.619609 0.723336 -P_21 0.732489 0.845708 0.870293 1.024727 0.999464 1.021328 1.047423 0.986106 -P_21 1.094932 1.090735 0.962674 0.964682 0.963271 0.925794 0.953363 0.982029 -P_21 1.023684 0.949083 0.930489 1.010037 1.029092 0.982028 1.063281 0.989511 -P_21 1.008344 0.942520 0.963755 0.898070 0.821537 0.834941 0.766116 0.746502 -P_21 0.648780 0.578413 0.537525 0.496149 0.438440 0.418612 0.461691 0.512225 -P_21 0.543955 0.568924 0.568090 0.646089 0.821839 0.829671 0.910945 0.885611 -P_21 1.040444 1.169135 1.193805 1.204534 1.276371 1.226494 1.339703 1.249592 -P_21 1.283705 1.158029 1.109435 1.156355 0.996134 1.046003 0.880005 0.889866 -P_21 0.873136 0.947083 0.984843 0.961611 1.055904 1.072046 0.999894 1.045767 -P_21 1.078359 1.150635 1.002663 1.139401 0.968019 0.902548 0.951404 0.791585 -P_21 0.694982 0.657404 0.565813 0.513480 0.506657 0.504915 0.482912 0.478977 -P_21 0.493958 0.562238 0.654430 0.705411 0.691849 0.774414 0.933638 0.960450 -P_21 1.114440 1.061333 1.129964 1.182827 1.148388 1.120008 1.167050 1.067268 -P_21 1.232219 1.090795 1.020883 1.044104 1.068395 0.972941 0.958063 0.847637 -P_21 0.982503 0.971073 0.915777 0.950991 1.032568 1.066149 1.052234 1.240709 -P_21 1.143316 1.096579 1.029079 1.027129 0.955678 0.900377 0.850427 0.779320 -P_21 0.608221 0.539321 0.523469 0.474071 0.449323 0.478788 0.387671 0.500688 -P_21 0.487202 0.546365 0.625409 0.735524 0.752723 0.907309 0.941418 1.020358 -P_21 1.138169 1.101340 1.220335 1.086753 1.193923 1.153964 1.228919 1.294345 -P_21 1.153673 1.142972 1.141119 1.069141 1.030174 0.965400 0.958915 0.898791 -P_21 0.993992 0.999429 1.065904 0.976699 1.054546 1.104071 0.979821 1.058128 -P_21 0.985767 1.007504 1.078158 1.073279 0.946081 0.860934 0.761638 0.766233 -P_21 0.638026 0.579355 0.577565 0.510677 0.454869 0.435883 0.443182 0.449925 -P_21 0.498526 0.538061 0.644011 0.700073 0.813016 0.821006 0.935052 1.078911 -P_21 1.127439 1.179583 1.125429 1.135262 1.069651 1.197734 1.092017 1.096469 -P_21 1.121763 0.989056 1.025704 1.203954 0.977156 1.036210 0.996336 0.934217 -P_21 1.014345 0.906257 0.954418 1.037256 1.054264 0.985563 1.098937 1.153024 -P_21 1.097509 1.082787 1.065243 0.971668 1.000062 0.920232 0.827219 0.796309 -P_21 0.670796 0.646370 0.599692 0.515042 0.494607 0.480222 0.416764 0.481387 -P_21 0.601579 0.620509 0.697663 0.783998 0.918446 0.989519 1.105560 1.096958 -P_21 1.234454 1.334500 1.375467 1.246724 1.294432 1.300680 1.291730 1.206161 -P_21 1.152350 1.179307 1.105469 1.121779 1.010773 1.078435 0.950754 1.041907 -P_21 1.104982 0.975764 1.064956 1.042668 1.042798 1.076686 1.066318 0.983687 -P_21 1.013106 1.013703 1.051121 0.913472 0.983446 0.891323 0.823502 0.811914 -P_21 0.705796 0.718956 0.675621 0.549505 0.571404 0.533407 0.468327 0.463388 -P_21 0.470285 0.535721 0.550018 0.617154 0.644750 0.701542 0.793780 0.947943 -P_21 0.895671 0.994999 0.993936 1.031697 1.200288 1.127327 1.137086 1.159311 -P_21 1.074412 1.183801 1.051944 1.146809 1.025274 1.113126 1.023009 1.025216 -P_21 0.986433 0.988034 0.983708 1.044803 0.934917 0.974994 0.988000 1.006336 -P_21 0.956262 0.942703 0.951363 0.823132 0.834394 0.797568 0.726366 0.730945 -P_21 0.618363 0.636791 0.549033 0.547064 0.494911 0.544437 0.506595 0.436546 -P_21 0.458385 0.512620 0.529282 0.533786 0.563641 0.627845 0.693303 0.699436 -P_21 0.838835 0.833677 0.860159 0.988484 0.983849 0.967582 0.985375 1.026893 -P_21 1.066536 1.049484 1.073888 1.051379 0.970038 0.998548 0.960255 0.958671 -P_21 1.007572 0.861892 0.884616 0.972684 0.866768 1.039940 1.029496 1.010367 -P_21 1.026927 1.083189 1.038839 0.909859 0.772767 0.790252 0.802186 0.746484 -P_21 0.566869 0.641764 0.511508 0.525559 0.482624 0.457356 0.454844 0.410954 -P_21 0.484195 0.576796 0.564335 0.641915 0.767239 0.851611 0.890865 0.952253 -P_21 1.058259 1.089691 1.196909 1.246675 1.197033 1.222827 1.153257 1.140301 -P_21 1.215750 1.114926 1.066601 1.071179 1.008995 1.035707 0.908640 0.971476 -P_21 0.948110 0.884296 0.954544 1.043212 1.007784 1.051048 1.047760 0.994760 -P_21 1.034236 1.085662 1.030143 0.911341 0.895671 0.920276 0.819733 0.705183 -P_21 0.720660 0.619995 0.607955 0.573669 0.510070 0.408070 0.469521 0.529281 -P_21 0.514162 0.537942 0.679768 0.654522 0.767933 0.823919 0.894774 0.943288 -P_21 1.092462 1.171647 1.180734 1.183817 1.112648 1.141708 1.137452 1.137876 -P_21 1.083639 1.036129 0.982653 0.988179 0.966269 0.921830 0.914798 0.885410 -P_21 0.928941 0.925067 0.987762 1.034210 1.028344 1.097860 1.097327 1.072396 -P_21 1.111101 1.130722 0.992312 1.047668 0.936112 0.930304 0.838273 0.688890 -P_21 0.635376 0.610211 0.519368 0.440190 0.468359 0.446550 0.461582 0.451662 -P_21 0.519995 0.560188 0.649870 0.623078 0.778906 0.890192 0.878476 0.895619 -P_21 1.072835 1.020108 1.123063 1.211952 1.198059 1.010199 1.244689 1.088128 -P_21 1.254671 1.112111 1.044508 1.079113 0.957710 0.996901 0.971192 0.971509 -P_21 0.866954 0.897550 1.101987 1.090153 0.977939 1.017084 1.036845 1.025143 -P_21 1.122710 1.158740 0.928123 1.036870 0.954248 0.870499 0.824962 0.677391 -P_21 0.647594 0.647389 0.556597 0.464702 0.469753 0.407178 0.396553 0.489026 -P_21 0.566249 0.545443 0.603975 0.658124 0.814902 0.818962 0.836109 0.982486 -P_21 1.088226 1.207458 1.168160 1.214617 1.171658 1.253850 1.121560 1.136778 -P_21 1.131413 0.929574 1.087588 0.975365 0.946603 0.988091 0.867809 1.032143 -P_21 0.969195 1.009846 1.023048 1.013489 1.078906 1.065052 1.014654 1.060773 -P_21 1.041771 1.057201 1.010418 0.936233 0.956524 0.885388 0.796101 0.770979 -P_21 0.673929 0.624240 0.532247 0.536340 0.489586 0.438805 0.447434 0.480485 -P_21 0.508086 0.586863 0.675220 0.770207 0.940307 1.008304 1.033638 1.234124 -P_21 1.289682 1.220109 1.201901 1.285047 1.311137 1.276561 1.266827 1.362384 -P_21 1.225479 1.176017 1.178513 1.100715 1.066628 1.008096 0.930976 1.062590 -P_21 1.048098 1.013986 1.023704 0.960777 1.063514 1.076219 0.962642 1.082870 -P_21 1.038839 1.005347 1.022231 0.976516 0.881713 0.928631 0.848496 0.812027 -P_21 0.720990 0.699241 0.635093 0.549616 0.547603 0.489885 0.463534 0.461774 -P_21 0.518593 0.517035 0.558049 0.566636 0.657586 0.760125 0.791211 0.861295 -P_21 0.921117 0.905772 0.982808 1.025101 1.071585 0.950462 1.067591 1.178821 -P_21 1.084758 1.084427 1.128163 1.035630 1.058883 1.026704 1.002790 0.950574 -P_21 1.066291 1.057187 0.990555 1.029590 1.022035 1.058206 1.002674 0.960026 -P_21 1.017617 0.882993 0.865074 0.801216 0.835093 0.822582 0.690615 0.667691 -P_21 0.623706 0.619336 0.523352 0.600830 0.543869 0.518747 0.493929 0.462386 -P_21 0.484104 0.458332 0.468261 0.521093 0.575129 0.626254 0.665634 0.706190 -P_21 0.758485 0.784705 0.873013 0.978251 0.920537 0.949438 0.961013 1.013530 -P_21 0.950772 1.096822 0.985858 1.005225 1.064507 0.958433 0.903799 0.969784 -P_21 0.895539 0.911330 0.925579 0.907288 0.932883 0.998049 0.952528 0.977073 -P_21 0.953837 0.956921 0.940936 0.926719 0.925693 0.791065 0.784166 0.762105 -P_21 0.665398 0.584553 0.490994 0.515402 0.409661 0.452353 0.418997 0.482407 -P_21 0.461716 0.492171 0.594649 0.684102 0.753691 0.880898 0.918121 0.923238 -P_21 1.028141 1.127322 1.195861 1.170264 1.331943 1.300570 1.300470 1.206579 -P_21 1.143958 1.166147 1.013658 1.050066 0.987509 0.991755 0.950824 0.974470 -P_21 0.998229 0.946217 0.911022 0.926133 1.102145 1.048549 0.981200 1.049014 -P_21 1.013151 1.024589 1.074576 0.968508 0.954049 0.917120 0.798581 0.812362 -P_21 0.685937 0.600352 0.543121 0.515651 0.481886 0.432530 0.450172 0.483176 -P_21 0.462682 0.565869 0.560939 0.650955 0.762081 0.863838 0.990919 1.038191 -P_21 1.093997 1.046485 1.093669 1.203330 1.148407 1.156759 1.128629 1.105247 -P_21 1.157127 1.092655 1.057360 1.011531 0.970020 0.844930 0.883935 0.941067 -P_21 0.931130 0.905160 1.000224 1.000058 1.027358 0.998844 1.099756 1.090895 -P_21 1.122246 1.059694 1.111771 0.990055 0.965395 0.926446 0.774061 0.710646 -P_21 0.687031 0.573664 0.494936 0.487791 0.398302 0.448410 0.452865 0.479632 -P_21 0.508183 0.491788 0.619689 0.687781 0.767797 0.799308 0.954822 1.008653 -P_21 1.097348 1.091574 1.143193 1.150463 1.124616 1.218208 1.147379 1.121090 -P_21 1.156846 1.083825 1.104110 1.007563 0.982408 1.025348 0.947865 0.904364 -P_21 0.904420 1.014662 0.960286 0.986587 0.951813 1.068365 0.973748 1.050165 -P_21 1.120184 1.068395 0.940296 1.047492 0.994863 0.863172 0.748236 0.752933 -P_21 0.704089 0.641454 0.516503 0.480393 0.458537 0.397862 0.436305 0.488028 -P_21 0.495937 0.562775 0.589101 0.708296 0.843765 0.857115 0.867567 0.932398 -P_21 0.952075 1.103489 1.155083 1.110513 1.144741 1.193063 0.999542 1.135698 -P_21 1.181179 1.125031 1.119425 0.900960 1.054161 1.030558 0.949118 0.955583 -P_21 0.974960 1.017864 0.917805 0.899829 0.984457 1.013952 1.137508 1.142791 -P_21 1.059861 1.185422 1.089239 0.966190 1.021089 0.873996 0.844302 0.750634 -P_21 0.700511 0.623554 0.574509 0.450502 0.414720 0.447559 0.462718 0.503999 -P_21 0.548445 0.612436 0.717070 0.806560 0.851399 0.959909 0.981581 1.070007 -P_21 1.143262 1.153051 1.290955 1.319959 1.353374 1.381561 1.262273 1.191694 -P_21 1.217511 1.136352 1.048215 1.047868 0.996059 1.021566 0.978093 0.977384 -P_21 0.924495 0.989996 0.998369 1.079134 0.992928 1.022256 0.971548 1.060939 -P_21 1.067525 0.912425 0.994776 0.931989 0.917354 0.856678 0.820277 0.730066 -P_21 0.681140 0.736340 0.603041 0.577251 0.535848 0.525854 0.494828 0.506833 -P_21 0.526154 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1.141444 0.968733 -P_21 0.991229 1.062152 1.170516 1.054183 1.072442 1.031725 1.040969 0.923321 -P_21 0.942828 0.963359 1.005553 1.037021 1.019085 1.125095 1.014650 1.080803 -P_21 0.986266 0.985797 1.053249 0.947205 0.887774 0.855383 0.798194 0.720075 -P_21 0.659842 0.615488 0.546144 0.481915 0.475074 0.420303 0.435725 0.472752 -P_21 0.515527 0.573833 0.600946 0.640428 0.761940 0.920091 0.943018 0.950383 -P_21 1.043909 1.036312 1.160759 1.082493 1.053637 1.059376 1.125824 1.113961 -P_21 1.045197 1.101554 0.956772 1.045598 0.973942 0.902913 0.929650 0.857103 -P_21 0.923777 0.890866 0.918708 0.952151 0.938805 1.030788 1.004952 1.031672 -P_21 0.993981 1.031895 0.950282 1.027580 0.929281 0.880098 0.812235 0.696732 -P_21 0.697460 0.614317 0.554118 0.502505 0.466689 0.460444 0.461586 0.513214 -P_21 0.497906 0.588179 0.626375 0.748467 0.868623 1.012491 0.978991 1.209457 -P_21 1.205354 1.214440 1.255671 1.250782 1.181433 1.218583 1.182790 1.247948 -P_21 1.104653 1.170889 1.073770 1.098233 1.030994 1.029273 1.020090 1.000903 -P_21 1.005873 1.114836 1.034923 0.932514 1.013161 1.000450 1.031469 1.061922 -P_21 0.983033 0.917142 0.927528 0.901453 0.894051 0.882403 0.788749 0.712575 -P_21 0.749683 0.654583 0.578059 0.519772 0.482947 0.542947 0.476558 0.442500 -P_21 0.475486 0.511421 0.555994 0.607777 0.626747 0.642355 0.729149 0.761140 -P_21 0.953156 0.945645 1.012904 1.044988 1.060037 1.099693 1.104798 1.009868 -P_21 1.128412 1.022065 1.019706 1.032214 1.048183 1.044132 1.053235 0.953652 -P_21 0.926773 0.985578 1.046169 1.093432 0.975137 0.961220 0.913696 1.022504 -P_21 0.939885 0.853104 0.912310 0.788959 0.832997 0.721372 0.731099 0.754706 -P_21 0.680354 0.615542 0.549074 0.536086 0.536583 0.516776 0.488048 0.451879 -P_21 0.469781 0.481502 0.480195 0.511654 0.570369 0.528908 0.653662 0.684621 -P_21 0.744865 0.818764 0.840630 0.919548 0.891355 0.935484 1.045269 0.997448 -P_21 1.030181 0.955659 1.003777 0.950234 0.866620 1.033519 0.889328 0.919965 -P_21 0.934720 0.962131 0.935021 0.950134 0.903803 0.936348 0.961548 0.974815 -P_21 0.917228 0.983830 0.874304 0.906349 0.912384 0.824182 0.728714 0.670906 -P_21 0.612162 0.561074 0.527516 0.454084 0.475062 0.437801 0.438750 0.451203 -P_21 0.467183 0.505408 0.546007 0.649284 0.695687 0.829040 0.870419 0.966981 -P_21 1.044023 1.128795 1.233933 1.240584 1.188328 1.252480 1.114724 1.166468 -P_21 1.095006 1.127717 1.080325 0.992009 1.044156 0.810965 0.878326 0.922761 -P_21 0.908938 0.932208 0.894617 0.944259 0.909507 1.065348 1.046056 1.052212 -P_21 1.101373 1.074520 0.976189 0.977379 0.929997 0.898380 0.752897 0.703678 -P_21 0.689083 0.584842 0.538364 0.502095 0.481089 0.438523 0.453223 0.474397 -P_21 0.535265 0.490406 0.669713 0.663003 0.766748 0.831927 0.887417 0.930458 -P_21 0.996228 1.065118 1.099235 1.099051 1.191071 1.204308 1.112305 1.133502 -P_21 1.069524 1.055674 1.010482 1.097043 0.937807 1.025685 0.947522 0.903695 -P_21 0.935507 0.983686 0.928910 1.017194 1.098455 1.032741 1.062057 1.042713 -P_21 1.003937 1.076506 1.060794 1.023013 0.871165 0.771048 0.774628 0.707500 -P_21 0.622824 0.589151 0.518075 0.495196 0.456352 0.442129 0.477574 0.425627 -P_21 0.493420 0.520556 0.581007 0.700780 0.770497 0.809485 0.950852 1.016521 -P_21 1.037158 1.130369 1.037762 1.160155 1.092553 1.147651 1.151691 1.149057 -P_21 1.165158 1.100301 1.067003 1.052632 1.107153 1.009930 1.072635 1.002622 -P_21 0.925524 0.873789 1.051214 0.939907 1.080754 1.086419 1.014486 1.043372 -P_21 1.030455 0.968138 0.944450 0.977463 0.882261 0.939383 0.764016 0.739617 -P_21 0.629892 0.556879 0.496524 0.513001 0.426217 0.432229 0.462352 0.432925 -P_21 0.499482 0.575962 0.559929 0.686715 0.848353 0.785486 0.883574 1.002703 -P_21 0.986464 1.066564 1.112664 1.122413 1.093703 1.019244 1.222448 1.105054 -P_21 0.995897 1.072882 1.071068 1.043181 0.968521 0.904303 0.916444 0.987299 -P_21 0.870723 0.837492 0.945027 0.936642 0.851927 1.059236 1.002731 1.062997 -P_21 1.059099 0.989046 0.988267 0.967254 0.988282 0.912452 0.786482 0.778598 -P_21 0.645952 0.617954 0.545796 0.461326 0.468323 0.461378 0.433058 0.529874 -P_21 0.555342 0.610664 0.694582 0.784243 0.865948 0.904187 1.107712 1.137263 -P_21 1.044337 1.218588 1.244840 1.299888 1.190209 1.352463 1.283553 1.302415 -P_21 1.081976 1.242552 1.014723 1.053107 0.913091 0.991858 1.005177 0.891905 -P_21 0.969403 0.953149 1.010354 1.152655 1.060899 0.980143 1.106926 1.067969 -P_21 1.050642 0.994379 0.986417 0.997055 0.887470 0.910324 0.825429 0.796547 -P_21 0.665342 0.633270 0.568347 0.565944 0.480004 0.480278 0.455172 0.480860 -P_21 0.465705 0.495886 0.546421 0.584577 0.607827 0.664897 0.759681 0.853642 -P_21 0.975762 0.935770 0.913729 1.089077 1.088927 1.120119 1.089346 1.119793 -P_21 1.099730 1.167703 1.033679 0.904711 0.990213 0.941285 0.986044 1.128208 -P_21 1.037250 1.002664 1.103900 1.026531 1.001565 0.965217 1.032496 1.024980 -P_21 0.963575 0.933432 0.903080 0.770677 0.801813 0.792667 0.749784 0.745449 -P_21 0.633582 0.643942 0.587827 0.518933 0.529792 0.439786 0.472429 0.453511 -P_21 0.494100 0.469867 0.457133 0.526026 0.571660 0.569862 0.678079 0.709996 -P_21 0.725493 0.809479 0.860037 0.827381 0.855311 0.990078 1.006829 1.051866 -P_21 0.968853 0.979711 0.994470 0.943774 0.847693 0.959360 0.898790 0.842136 -P_21 0.917404 0.957097 0.982906 0.988424 0.988005 0.910439 1.005004 0.993864 -P_21 0.897451 0.951486 0.991388 0.953222 0.863122 0.901356 0.678157 0.672000 -P_21 0.637102 0.576171 0.476196 0.462812 0.451092 0.418863 0.427989 0.424900 -P_21 0.450792 0.579654 0.677097 0.702676 0.747015 0.795784 0.838801 0.977044 -P_21 1.095117 1.012611 1.113061 1.212891 1.130246 1.352364 1.165913 1.173730 -P_21 1.112145 1.118993 1.018858 1.050148 0.986599 0.938746 0.990661 0.961852 -P_21 0.903502 0.859265 0.886204 0.870531 0.884739 0.916361 1.048028 1.089871 -P_21 1.050844 1.012344 1.085361 1.059613 0.992706 0.879323 0.780344 0.710930 -P_21 0.663440 0.630020 0.540941 0.550782 0.453135 0.451668 0.430513 0.438779 -P_21 0.487819 0.503327 0.589229 0.721996 0.761544 0.848401 0.842878 0.976024 -P_21 0.976086 1.113894 1.132614 1.170193 1.126998 1.185827 1.113584 1.058307 -P_21 1.080487 1.075976 1.052525 1.000929 0.929298 0.889253 0.867810 0.919262 -P_21 0.895808 0.978457 0.974589 0.982273 0.942451 0.974895 1.023395 1.122079 -P_21 1.072095 1.094021 1.061809 1.041917 0.863318 0.897469 0.790693 0.754996 -P_21 0.630181 0.561601 0.507532 0.461467 0.452966 0.472086 0.426153 0.461079 -P_21 0.506684 0.529484 0.650513 0.670183 0.788059 0.791373 0.917365 0.958874 -P_21 1.010503 0.967438 1.100685 1.195568 1.212119 1.203204 1.151311 1.081975 -P_21 1.075939 1.070297 0.951851 0.971437 1.041190 0.932766 0.951362 1.008753 -P_21 0.939170 0.866373 0.961124 0.861630 0.963838 1.000660 1.084339 1.001431 -P_21 1.007287 1.054572 1.053670 0.969925 0.883416 0.959017 0.789559 0.679272 -P_21 0.602893 0.619834 0.525950 0.486716 0.414402 0.421221 0.453521 0.472502 -P_21 0.517714 0.546344 0.703351 0.700713 0.818316 0.874700 0.906768 0.915166 -P_21 1.158366 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0.968523 0.905525 -P_21 0.948297 0.841620 0.883713 0.850789 0.893501 0.942347 1.089711 1.051085 -P_21 0.944458 0.969954 0.992803 0.921019 0.973569 0.905434 0.780662 0.756599 -P_21 0.696626 0.575673 0.584594 0.534352 0.492278 0.419558 0.417874 0.472845 -P_21 0.484607 0.527482 0.630016 0.676569 0.751109 0.825279 0.882315 0.897022 -P_21 0.955292 1.028887 0.994419 1.150776 1.205521 1.084798 1.157822 1.066570 -P_21 1.151622 0.978657 0.971814 0.940905 0.968741 0.841172 0.906332 0.869779 -P_21 0.799080 0.852406 0.932285 0.962284 1.005115 1.052902 1.000948 1.129337 -P_21 1.048180 1.017173 1.008866 1.036543 0.847018 0.836682 0.764576 0.766618 -P_21 0.662480 0.607969 0.474543 0.468225 0.433574 0.426301 0.445034 0.439010 -P_21 0.512592 0.584185 0.641371 0.722796 0.802823 0.824974 0.899469 1.009616 -P_21 0.982556 1.095568 1.157112 1.091449 1.123539 1.106791 1.239697 0.940355 -P_21 1.133937 1.067402 1.127326 1.032164 1.044804 1.023282 0.846126 0.998014 -P_21 0.903779 0.893083 0.808990 0.927462 0.963322 1.074992 1.048947 1.091354 -P_21 0.995313 1.069189 1.067122 0.973906 0.934057 0.831039 0.807630 0.697511 -P_21 0.641589 0.614432 0.548787 0.482845 0.428887 0.460830 0.439651 0.462982 -P_21 0.471804 0.546955 0.580585 0.659969 0.792253 0.837289 0.910174 0.920185 -P_21 0.945104 1.106836 1.043053 1.182392 1.092592 1.235292 0.993349 1.100560 -P_21 1.097145 1.062539 0.990488 0.966211 0.806235 0.952294 0.962907 0.900096 -P_21 0.908748 0.945922 1.024135 1.012944 0.954781 1.009706 1.024461 1.005807 -P_21 0.996694 1.039023 1.036309 0.945965 0.918227 0.829441 0.807796 0.760080 -P_21 0.640097 0.561833 0.500334 0.483171 0.453170 0.453018 0.418197 0.422407 -P_21 0.517683 0.611440 0.674290 0.713654 0.812440 0.945266 1.033196 1.167175 -P_21 1.182690 1.311098 1.173683 1.196030 1.408872 1.258086 1.208503 1.190478 -P_21 1.142998 1.165695 1.076930 1.038861 1.027007 1.035599 1.033212 1.014115 -P_21 1.025971 0.923939 1.062378 0.937267 1.024737 1.081720 1.019295 1.075499 -P_21 1.026009 0.976035 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0.619823 0.566474 0.475768 0.426493 0.444969 0.409156 0.439352 0.440513 -P_21 0.490730 0.537411 0.622828 0.604612 0.768518 0.798383 0.971257 1.109620 -P_21 1.031910 1.079012 1.141505 1.214900 1.204669 1.188854 1.211459 1.160738 -P_21 1.157771 1.213227 0.969491 0.881719 0.988280 0.891889 1.006086 0.965912 -P_21 0.890816 0.871735 0.913012 0.888842 0.877391 0.998831 1.091519 1.052594 -P_21 1.084935 1.001836 0.976826 0.972025 0.949312 0.889960 0.816583 0.702041 -P_21 0.625478 0.622239 0.517741 0.455718 0.430538 0.460032 0.411263 0.443494 -P_21 0.431603 0.528704 0.549114 0.613352 0.781410 0.811127 0.905880 0.978813 -P_21 1.046760 1.098525 1.077618 1.146328 1.183809 1.246041 1.147240 1.149437 -P_21 1.135713 0.971167 1.138117 0.937945 0.961816 0.941494 0.858530 0.894718 -P_21 0.893251 0.870858 0.899627 0.921970 1.002026 1.042159 1.020638 1.091133 -P_21 1.009324 1.023126 0.940622 1.007197 0.988781 0.844681 0.808593 0.748355 -P_21 0.628837 0.598424 0.531527 0.494193 0.440666 0.445946 0.408637 0.427497 -P_21 0.475969 0.512800 0.694776 0.679951 0.786431 0.879251 0.954769 0.943618 -P_21 0.942511 1.103051 1.056004 1.154441 1.063594 1.112868 1.146521 1.173969 -P_21 1.098654 1.135433 1.053057 1.037900 1.035776 0.909537 1.066489 0.839299 -P_21 0.867030 0.941028 0.951231 0.929121 0.996111 1.018161 1.054664 1.002810 -P_21 1.079005 0.964596 0.930374 1.024445 0.983191 0.882370 0.770789 0.769543 -P_21 0.746042 0.596439 0.522783 0.487287 0.449723 0.394360 0.474598 0.433991 -P_21 0.469775 0.515319 0.599036 0.685388 0.779694 0.805670 0.898115 0.952227 -P_21 0.975491 1.115184 1.149776 1.180084 1.151648 1.160817 1.136732 1.086929 -P_21 1.195809 1.077309 1.052305 0.990231 1.003932 0.919043 0.959292 0.873813 -P_21 0.932375 0.981599 1.028830 0.955430 0.978909 1.055600 1.008655 1.005749 -P_21 1.026651 1.034179 1.035662 1.009316 0.819989 0.882525 0.868507 0.751419 -P_21 0.690494 0.652628 0.563164 0.445994 0.510844 0.432347 0.455488 0.452886 -P_21 0.583954 0.609433 0.706175 0.742035 0.867298 0.939670 1.035549 1.177173 -P_21 1.204140 1.202916 1.353827 1.216930 1.240989 1.330543 1.219007 1.240197 -P_21 1.111404 1.027828 0.959473 1.089980 1.013398 0.977104 0.993129 0.991699 -P_21 0.918050 0.996415 0.950269 1.018249 0.986493 1.037575 1.040089 1.056019 -P_21 0.869537 0.960089 0.982593 0.918437 0.876243 0.877048 0.762678 0.752544 -P_21 0.692538 0.702985 0.563457 0.562977 0.526596 0.501633 0.489910 0.463788 -P_21 0.478154 0.458998 0.555932 0.617172 0.642696 0.717958 0.691485 0.823119 -P_21 1.012053 0.994073 0.996815 1.027390 1.158331 1.148401 1.012026 1.088758 -P_21 0.949175 1.076161 1.043624 1.003503 1.048343 1.015590 0.940810 0.906744 -P_21 0.946517 1.024262 1.046395 1.078597 0.967243 0.971334 1.059639 1.016403 -P_21 1.019157 0.966391 0.796437 0.832267 0.701890 0.732579 0.734440 0.628753 -P_21 0.656232 0.646860 0.581873 0.541752 0.497874 0.425658 0.449005 0.461630 -P_21 0.483633 0.460826 0.552787 0.549945 0.562825 0.564851 0.584954 0.667904 -P_21 0.781396 0.798349 0.895230 0.987827 1.024872 0.946903 0.997380 1.012871 -P_21 1.062128 0.948000 1.016535 0.891396 0.815475 0.902542 0.953982 0.889355 -P_21 0.932146 0.876975 0.845445 0.897931 0.934116 0.975317 0.988714 0.929248 -P_21 0.904695 0.953917 0.850568 0.920908 0.909683 0.800596 0.714660 0.712465 -P_21 0.535039 0.611033 0.528137 0.478766 0.406033 0.413998 0.441911 0.459597 -P_21 0.487108 0.553764 0.568666 0.717002 0.696207 0.866763 0.968194 0.942148 -P_21 1.064790 1.073640 1.035285 1.240120 1.258853 1.235697 1.209511 1.130454 -P_21 1.169887 1.037526 1.004270 1.074219 0.906482 1.001297 0.799363 0.923032 -P_21 0.855518 0.920185 0.928934 0.995109 0.938693 1.064365 0.977631 0.964691 -P_21 1.052447 1.058851 0.992404 1.027070 0.989212 0.850757 0.776492 0.674914 -P_21 0.639437 0.645538 0.501829 0.504443 0.426937 0.454006 0.454296 0.444070 -P_21 0.500086 0.525403 0.551195 0.664635 0.747965 0.753118 0.923003 0.940759 -P_21 0.964229 1.007330 1.211266 1.014404 1.151290 1.130853 1.120540 1.041742 -P_21 1.100345 1.021560 1.057315 0.925397 0.906711 0.947688 0.927024 0.971319 -P_21 0.884318 0.896623 0.907924 1.025676 1.006490 0.980211 1.044109 1.004489 -P_21 0.959997 0.987241 1.094909 0.951535 0.932508 0.961723 0.756749 0.709852 -P_21 0.573232 0.563931 0.514581 0.472707 0.468149 0.410920 0.445314 0.496430 -P_21 0.515871 0.513622 0.602046 0.685405 0.792580 0.897605 0.839177 0.884780 -P_21 0.995800 1.071465 1.163217 1.149123 1.113475 1.153899 1.234609 1.070500 -P_21 1.087027 0.993340 0.991528 1.055705 0.962827 1.019679 0.975263 0.923203 -P_21 0.968861 0.949973 0.987994 0.962132 0.952114 0.998756 0.966597 1.042503 -P_21 1.072714 1.149179 0.986110 0.979444 0.853877 0.856947 0.753251 0.704069 -P_21 0.612680 0.589795 0.470768 0.468044 0.432252 0.430499 0.423377 0.476630 -P_21 0.535113 0.562388 0.649446 0.695574 0.687504 0.774675 0.972129 0.973738 -P_21 0.936261 1.124915 1.079335 1.076608 1.152169 1.027692 1.080290 1.048423 -P_21 1.087569 1.045692 0.963720 0.951025 1.069147 0.966776 0.931740 0.900296 -P_21 0.911605 1.006524 0.956230 1.020468 1.054725 1.025415 1.027769 1.026481 -P_21 1.161020 1.045280 0.996057 0.981375 0.998626 0.923699 0.830530 0.741397 -P_21 0.677174 0.612367 0.538806 0.532318 0.451822 0.439044 0.486905 0.491953 -P_21 0.514903 0.581974 0.637441 0.752676 0.856031 0.916893 1.086421 1.099200 -P_21 1.295525 1.332159 1.319719 1.217818 1.243921 1.219271 1.139496 1.178668 -P_21 1.096158 1.164112 1.128818 1.056543 1.002183 0.922887 0.932262 1.048713 -P_21 0.939688 1.007395 0.974342 1.012555 0.974517 1.019360 0.989545 0.969539 -P_21 1.117072 1.026540 0.912893 1.002780 0.874225 0.878111 0.762355 0.796138 -P_21 0.746168 0.639730 0.661276 0.596926 0.577264 0.451810 0.465832 0.486812 -P_21 0.437923 0.519080 0.500554 0.585151 0.672979 0.711168 0.664915 0.858406 -P_21 0.906456 0.955318 0.930574 1.012883 1.041773 1.091640 1.185226 1.139660 -P_21 1.118438 1.013904 1.142089 0.958955 0.942242 1.041440 0.961476 1.004454 -P_21 1.100154 1.031419 0.995690 1.086450 1.004809 1.025551 0.991281 0.964669 -P_21 0.880591 0.941526 0.864385 0.896898 0.778006 0.741444 0.781457 0.668488 -P_21 0.643685 0.586148 0.529909 0.527914 0.481362 0.546481 0.536526 0.451379 -P_21 0.464239 0.466472 0.490490 0.499533 0.548769 0.555210 0.628443 0.749426 -P_21 0.689854 0.766295 0.911447 0.919063 0.985827 1.036758 0.943637 0.906530 -P_21 0.957809 1.130118 0.982442 0.975230 0.863567 1.026394 0.952751 0.834376 -P_21 0.853926 0.916201 0.938385 0.866351 0.933727 0.918778 0.980469 0.909030 -P_21 0.938693 0.899775 0.926994 0.879234 0.865730 0.853157 0.758595 0.683446 -P_21 0.613290 0.589672 0.555730 0.468484 0.463699 0.429996 0.397928 0.463054 -P_21 0.486375 0.529985 0.565801 0.645442 0.693853 0.808287 0.924134 0.996860 -P_21 1.095619 1.129624 1.198651 1.176873 1.198320 1.152618 1.238142 1.193282 -P_21 1.122189 1.072673 1.020902 0.924798 0.988759 0.890966 0.883732 0.918227 -P_21 0.940262 0.964473 0.875315 0.908157 0.901510 0.989398 1.062905 1.100140 -P_21 0.935878 1.098170 1.062737 0.972280 0.922308 0.908081 0.792661 0.760940 -P_21 0.746555 0.586339 0.556891 0.483623 0.476777 0.478065 0.433474 0.421080 -P_21 0.516134 0.551154 0.609748 0.641494 0.792250 0.832794 0.883665 0.964420 -P_21 0.956309 1.000575 1.075604 0.958895 1.088200 1.133446 1.104985 1.075003 -P_21 1.076136 1.071946 1.025986 1.011830 0.935441 1.026124 0.908635 0.901223 -P_21 0.824660 0.951151 1.040227 0.943738 1.016146 0.952419 1.042964 1.106733 -P_21 0.983844 0.963168 0.999846 0.884318 0.963565 0.823258 0.824818 0.715629 -P_21 0.598321 0.592657 0.516041 0.511262 0.460219 0.461891 0.516236 0.476111 -P_21 0.506216 0.589233 0.583016 0.676378 0.777730 0.761177 0.873091 1.026291 -P_21 0.985988 1.061696 1.166706 1.093535 1.109211 1.244686 1.105490 1.127798 -P_21 1.110351 1.124877 1.135428 1.025623 1.001332 0.961714 0.940501 1.005164 -P_21 0.917421 0.937667 1.004790 1.000912 1.043207 0.973375 0.981773 1.006438 -P_21 1.005761 1.084699 0.898299 1.000785 0.908385 0.887551 0.813441 0.700020 -P_21 0.663631 0.593972 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0.525085 0.536544 0.490070 0.580277 0.642248 0.740223 0.813771 0.831137 -P_21 0.922249 0.958562 0.996489 0.980888 1.072678 1.143806 1.154847 1.047719 -P_21 1.068789 1.027042 1.030531 1.121691 1.069178 1.009828 1.117890 1.085253 -P_21 1.011517 1.072932 0.949964 0.949326 0.991506 1.052300 1.034713 0.973059 -P_21 1.029713 1.004624 0.886642 0.828601 0.811325 0.739224 0.732177 0.711185 -P_21 0.639735 0.579410 0.581267 0.544502 0.508998 0.481435 0.510123 0.492603 -P_21 0.416059 0.445639 0.492569 0.535244 0.575480 0.611205 0.654912 0.686663 -P_21 0.824054 0.809762 0.904272 0.891921 0.923942 0.931682 1.029348 1.036018 -P_21 1.071314 1.047984 1.111993 1.002038 0.876811 0.917086 0.927882 0.859589 -P_21 0.866408 0.865180 0.963378 0.971396 1.040321 0.989826 0.902781 1.029450 -P_21 1.049869 0.958332 1.013128 0.901794 0.901829 0.764086 0.809954 0.694887 -P_21 0.646440 0.591245 0.510738 0.466831 0.494208 0.435633 0.463236 0.441268 -P_21 0.539490 0.529648 0.604867 0.744574 0.728944 0.781863 0.892167 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1.326638 1.316682 1.063406 1.101916 1.104062 1.021236 1.005088 0.920951 -P_23 0.863161 0.982533 0.968634 1.110099 1.047512 0.992155 1.123681 1.300162 -P_23 1.290168 1.183870 1.182868 1.051183 1.062280 0.992734 0.910730 0.817317 -P_23 0.618162 0.544748 0.492235 0.400330 0.370969 0.337333 0.299524 0.390750 -P_23 0.460268 0.489434 0.573534 0.686190 0.870258 0.897342 1.020237 1.124507 -P_23 1.174885 1.286709 1.282499 1.373308 1.320002 1.313178 1.310546 1.261942 -P_23 1.217755 1.167934 1.164449 1.004211 1.013881 1.001159 0.891849 0.880635 -P_23 1.036437 1.030521 0.972885 1.010973 1.022829 1.224931 1.085715 1.279481 -P_23 1.268937 1.279993 1.144739 1.120474 1.074505 1.022317 0.872355 0.754407 -P_23 0.669120 0.519008 0.461200 0.355745 0.361964 0.330475 0.360271 0.421202 -P_23 0.477285 0.560169 0.595489 0.816839 0.739405 1.030025 1.038467 1.117251 -P_23 1.257489 1.276236 1.360726 1.283436 1.426749 1.379986 1.365802 1.314710 -P_23 1.244380 1.201124 1.101513 1.109812 1.110910 1.002461 0.987881 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1.128702 1.172751 1.287456 -P_23 1.253245 1.128888 1.115097 1.073432 1.034247 0.985476 0.888501 0.822365 -P_23 0.738529 0.665529 0.635982 0.501141 0.461360 0.448549 0.424485 0.417017 -P_23 0.419579 0.468539 0.505884 0.622202 0.674630 0.728574 0.962689 0.885801 -P_23 1.114253 1.060965 1.201704 1.302818 1.332067 1.316002 1.226900 1.227664 -P_23 1.201048 1.217060 1.107290 1.199413 1.095300 1.090814 1.144337 1.096191 -P_23 1.103044 1.174742 1.107184 1.160118 1.159623 1.202519 1.140341 1.109346 -P_23 1.080486 1.063861 1.075474 0.969595 0.884591 0.786029 0.705820 0.691876 -P_23 0.654692 0.579778 0.532511 0.473596 0.435713 0.418519 0.420642 0.405465 -P_23 0.400092 0.436667 0.474478 0.556476 0.517919 0.572593 0.715315 0.753175 -P_23 0.839331 0.892350 0.969448 0.977465 1.130694 1.153000 1.173606 1.232954 -P_23 1.090360 1.119995 1.076564 1.108471 0.973716 0.953430 1.011932 0.953002 -P_23 0.978353 1.010029 1.032652 0.986263 1.143680 1.015256 1.140331 1.035885 -P_23 1.084876 1.215971 1.113833 1.067734 1.035314 0.864253 0.874524 0.714512 -P_23 0.589436 0.498657 0.496356 0.371662 0.345616 0.328006 0.346986 0.387316 -P_23 0.465662 0.475637 0.562631 0.778431 0.786480 0.854408 1.009451 1.095464 -P_23 1.365730 1.278821 1.524630 1.426595 1.478801 1.457918 1.522988 1.466008 -P_23 1.394113 1.255537 1.160598 1.057113 1.090379 1.005489 0.947600 0.963999 -P_23 0.860006 1.054498 0.960587 1.058328 1.096395 1.153361 1.137961 1.170025 -P_23 1.151568 1.170698 1.148035 1.196255 1.139520 1.024859 0.873049 0.808735 -P_23 0.682308 0.542733 0.492477 0.405403 0.400183 0.335043 0.366262 0.373917 -P_23 0.476404 0.525504 0.617701 0.729674 0.789608 0.916474 1.009490 1.043814 -P_23 1.179189 1.345906 1.323325 1.445194 1.449231 1.302475 1.234391 1.372450 -P_23 1.233021 1.151575 1.081165 1.096209 0.980031 1.067541 1.033603 1.025176 -P_23 1.010972 0.953095 1.063867 1.091011 1.244776 1.259784 1.306906 1.287083 -P_23 1.290499 1.289341 1.259024 1.173735 1.085945 0.937240 0.828275 0.748476 -P_23 0.651952 0.588382 0.475312 0.434534 0.373419 0.361708 0.333021 0.411811 -P_23 0.492700 0.512350 0.607643 0.745680 0.836947 0.996778 1.085731 1.201218 -P_23 1.148424 1.300272 1.206323 1.434095 1.358554 1.443139 1.342747 1.386638 -P_23 1.329328 1.203552 1.186585 1.102767 1.093447 1.211951 1.150180 1.089266 -P_23 1.116059 1.049011 0.960661 1.041038 1.232413 1.191492 1.177531 1.284682 -P_23 1.310062 1.274714 1.339094 1.180202 1.089949 1.000095 0.970724 0.777441 -P_23 0.625526 0.588807 0.500373 0.453097 0.353590 0.351932 0.361113 0.375507 -P_23 0.436599 0.521583 0.685019 0.829819 0.859970 0.927248 1.096190 1.124225 -P_23 1.304473 1.233730 1.304845 1.303845 1.556143 1.504681 1.376137 1.290243 -P_23 1.238045 1.181332 1.171865 1.155000 1.051315 1.030760 1.074606 0.997394 -P_23 1.066714 1.040330 1.186742 1.066852 1.134936 1.145387 1.222274 1.096357 -P_23 1.363120 1.258531 1.205557 1.096185 1.027755 1.068362 0.917381 0.792497 -P_23 0.729152 0.635387 0.542854 0.472548 0.452188 0.397341 0.413454 0.453458 -P_23 0.505794 0.553693 0.669065 0.815696 0.990544 1.169777 1.316779 1.407693 -P_23 1.563357 1.497672 1.588290 1.608013 1.675460 1.645043 1.476246 1.384888 -P_23 1.375617 1.309798 1.233955 1.143139 1.234785 1.184769 1.107250 1.195757 -P_23 1.059562 1.150337 1.202551 1.213424 1.264104 1.174013 1.096746 1.241068 -P_23 1.192903 1.170449 1.103100 1.103922 1.094502 0.890891 0.949165 0.895378 -P_23 0.730730 0.696757 0.588247 0.501425 0.490103 0.438709 0.447314 0.413557 -P_23 0.425608 0.465083 0.485538 0.600625 0.710188 0.811734 0.880174 1.035647 -P_23 1.044317 1.129822 1.212494 1.276917 1.419105 1.454245 1.305797 1.301413 -P_23 1.182324 1.205584 1.225914 1.203762 1.079012 1.130891 1.039424 1.207544 -P_23 1.175851 1.076600 1.109152 1.117178 1.248820 1.208886 1.132135 1.115896 -P_23 1.049820 1.113590 1.044765 0.938966 0.952640 0.777546 0.786509 0.703106 -P_23 0.641400 0.616030 0.499375 0.512514 0.484886 0.466124 0.392937 0.418397 -P_23 0.408906 0.446026 0.449768 0.498152 0.618540 0.620792 0.685492 0.794349 -P_23 0.865651 1.014702 1.020487 1.055095 1.042186 1.093403 1.220720 1.145081 -P_23 1.238318 1.188927 1.242672 1.085206 1.047734 1.068031 1.055637 0.943120 -P_23 1.070851 1.085288 1.056090 1.111771 1.062274 1.120487 1.255146 1.215926 -P_23 1.100113 1.030973 1.116960 1.013162 0.938736 0.929243 0.825450 0.769581 -P_23 0.646588 0.533379 0.473820 0.421909 0.379553 0.321429 0.349398 0.352707 -P_23 0.449012 0.543978 0.615568 0.710057 0.888615 0.942263 1.062670 1.154966 -P_23 1.285372 1.401031 1.405551 1.511047 1.381275 1.510832 1.414300 1.310941 -P_23 1.475100 1.306961 1.198290 1.130697 1.000209 0.991750 0.935285 1.042186 -P_23 0.954467 0.939385 1.013518 1.061102 1.087806 1.165047 1.260025 1.360969 -P_23 1.177127 1.248495 1.252742 1.182010 1.054524 1.030163 0.944969 0.813643 -P_23 0.713641 0.634419 0.514266 0.442251 0.444179 0.355363 0.361662 0.431238 -P_23 0.503109 0.547021 0.665085 0.791712 0.843559 0.906914 1.089803 1.178195 -P_23 1.153281 1.235154 1.375474 1.390432 1.379832 1.442695 1.373424 1.359671 -P_23 1.293099 1.295440 1.198592 1.101604 1.074234 1.011839 1.018842 1.038377 -P_23 1.084064 1.019814 1.142463 1.184816 1.074946 1.183881 1.310013 1.362305 -P_23 1.246065 1.256528 1.195418 1.160374 1.217097 1.061724 0.829272 0.848190 -P_23 0.739581 0.622413 0.500426 0.393063 0.382675 0.366861 0.334761 0.407603 -P_23 0.473273 0.588382 0.715700 0.778112 0.820516 0.891733 1.185174 1.095188 -P_23 1.194879 1.403406 1.393423 1.351039 1.374308 1.468653 1.470996 1.352244 -P_23 1.334001 1.243814 1.211392 1.184373 1.162384 1.046020 1.054700 1.096176 -P_23 0.991813 1.065120 1.109251 1.095744 1.250630 1.155344 1.380283 1.194462 -P_23 1.250308 1.311150 1.135345 1.206474 1.084033 0.993259 0.968680 0.750504 -P_23 0.669196 0.609855 0.508420 0.435502 0.372322 0.342295 0.353891 0.397507 -P_23 0.485181 0.533440 0.640609 0.751045 0.883957 0.999277 1.163375 1.200706 -P_23 1.324147 1.337781 1.378699 1.435802 1.378753 1.392025 1.366784 1.362885 -P_23 1.194264 1.166115 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1.124411 1.166012 1.237951 1.352051 1.150996 1.254651 1.121213 1.198514 -P_23 1.220278 1.104575 1.119494 0.987242 0.992874 0.842897 0.831710 0.723626 -P_23 0.631973 0.644651 0.573178 0.487475 0.460822 0.470034 0.431476 0.403964 -P_23 0.425398 0.426691 0.476620 0.491162 0.555004 0.691007 0.743028 0.813984 -P_23 0.941644 0.996083 0.956189 1.012442 1.249282 1.123956 1.343248 1.294244 -P_23 1.219595 1.211248 1.154070 1.073081 1.120175 0.988305 1.051654 1.076857 -P_23 1.075268 1.034391 1.048666 1.144848 1.109843 1.117150 1.106989 1.172856 -P_23 1.159165 1.192078 1.163025 1.077630 0.991084 0.826969 0.821128 0.778756 -P_23 0.656600 0.544372 0.502350 0.412435 0.368360 0.353589 0.351020 0.389976 -P_23 0.426890 0.495433 0.633177 0.689121 0.899516 1.116059 1.035547 1.154529 -P_23 1.414712 1.341936 1.544779 1.632367 1.608098 1.560444 1.575114 1.371478 -P_23 1.294482 1.463089 1.293585 1.123470 1.195844 1.009566 1.000894 0.969703 -P_23 0.970582 1.029837 1.003636 1.140625 1.165291 1.104739 1.231158 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1.628629 1.582249 -P_23 1.388477 1.439589 1.306981 1.266490 1.053622 1.318233 1.049128 1.175039 -P_23 1.109432 1.161836 1.153057 1.279897 1.325265 1.301189 1.189301 1.344829 -P_23 1.323819 1.257985 1.178374 1.128888 1.067990 1.074356 0.961603 0.871119 -P_23 0.803910 0.733029 0.669986 0.523106 0.494338 0.478174 0.406441 0.461899 -P_23 0.452744 0.486229 0.619496 0.613027 0.705087 0.797414 0.959160 0.971298 -P_23 1.176884 1.157846 1.234455 1.301240 1.342852 1.428349 1.351315 1.337874 -P_23 1.214994 1.410788 1.180495 1.231051 1.236962 1.145342 1.166862 1.187707 -P_23 1.180902 1.118104 1.285280 1.237531 1.213577 1.237472 1.208374 1.276251 -P_23 1.209357 1.026515 1.066260 1.046787 0.865638 0.816730 0.741861 0.776228 -P_23 0.591619 0.642246 0.583806 0.541393 0.477529 0.447313 0.409802 0.438211 -P_23 0.443437 0.442230 0.471632 0.560143 0.660699 0.626775 0.781039 0.861775 -P_23 0.898567 1.036704 1.068623 1.131527 1.133730 1.200379 1.290425 1.292898 -P_23 1.220980 1.171514 1.176828 1.145360 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1.195836 1.240614 1.404033 1.350712 1.172470 0.979082 0.985432 0.840124 -P_23 0.746206 0.649613 0.506677 0.495171 0.393935 0.386386 0.399358 0.454603 -P_23 0.539148 0.616686 0.756247 0.823744 1.069437 1.231904 1.348105 1.321225 -P_23 1.593814 1.593747 1.745631 1.655870 1.736399 1.692580 1.587927 1.506463 -P_23 1.527791 1.559734 1.344222 1.296071 1.272166 1.240687 1.262222 1.248055 -P_23 1.341927 1.210554 1.237427 1.333842 1.354122 1.336326 1.301519 1.362977 -P_23 1.244618 1.189036 1.256733 1.165099 1.092207 1.012854 0.925955 0.943876 -P_23 0.813438 0.717628 0.598611 0.536622 0.494140 0.460732 0.455672 0.451737 -P_23 0.427140 0.551373 0.577887 0.709121 0.743475 0.831932 0.881026 1.063143 -P_23 1.096957 1.319326 1.194319 1.348588 1.459168 1.437129 1.249850 1.351842 -P_23 1.355935 1.282340 1.259748 1.255711 1.187285 1.291954 1.250870 1.192960 -P_23 1.069434 1.270207 1.332425 1.247544 1.251003 1.220616 1.294339 1.219359 -P_23 1.227825 1.092020 1.061079 0.984648 0.971415 0.828074 0.807056 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-P_23 1.244176 1.333421 1.257329 1.137092 1.086927 1.050784 0.896721 0.817444 -P_23 0.658303 0.665162 0.511822 0.440733 0.392702 0.405669 0.406521 0.454406 -P_23 0.509245 0.570567 0.702019 0.878892 0.963995 1.092922 1.384825 1.417942 -P_23 1.543698 1.471057 1.609223 1.561457 1.631012 1.667001 1.565849 1.555545 -P_23 1.460526 1.377425 1.284335 1.187131 1.172421 1.079133 1.146432 1.186400 -P_23 1.199005 1.195945 1.214362 1.202895 1.244405 1.253916 1.240307 1.229762 -P_23 1.233933 1.193609 1.172811 1.094868 0.992783 0.961329 0.859837 0.748584 -P_23 0.750001 0.661271 0.631204 0.570371 0.465386 0.429319 0.441372 0.438378 -P_23 0.436299 0.489477 0.570542 0.623646 0.702938 0.820496 0.925767 1.042823 -P_23 1.110133 1.149593 1.241923 1.437696 1.247770 1.253203 1.340925 1.294509 -P_23 1.305902 1.165671 1.319289 1.374411 1.156556 1.073931 1.130952 1.159327 -P_23 1.120639 1.054129 1.074906 1.243873 1.203582 1.208580 1.235787 1.198067 -P_23 1.105581 1.076177 0.995830 1.020262 0.896605 0.829458 0.852293 0.702445 -P_23 0.703048 0.569333 0.525561 0.537218 0.461647 0.428484 0.432556 0.425938 -P_23 0.438250 0.432251 0.510899 0.502116 0.579044 0.654513 0.698949 0.845267 -P_23 0.944542 0.914462 1.089862 1.014591 1.191616 1.177462 1.303296 1.274487 -P_23 1.310181 1.276404 1.115051 1.081872 1.071431 1.002124 1.109554 0.959969 -P_23 1.002344 1.105102 0.944126 1.071257 1.100811 1.192004 1.147039 1.317404 -P_23 1.172069 1.052313 1.107794 1.068094 0.972389 0.967245 0.824488 0.753786 -P_23 0.665113 0.553754 0.452720 0.391483 0.370701 0.360360 0.357883 0.366815 -P_23 0.421257 0.493335 0.639215 0.636160 0.781269 0.985026 1.158047 1.190403 -P_23 1.345495 1.324342 1.550954 1.506834 1.416283 1.532647 1.491125 1.237085 -P_23 1.357089 1.253464 1.248944 1.071311 1.113396 0.932297 0.960748 1.014512 -P_23 1.002976 1.018996 1.036843 1.110727 1.137013 1.099326 1.158497 1.354199 -P_23 1.256371 1.262810 1.197819 1.183572 1.108987 1.049005 0.977022 0.868674 -P_23 0.742148 0.625291 0.527885 0.467571 0.413083 0.369890 0.420774 0.400625 -P_23 0.444137 0.549522 0.609647 0.702690 0.839638 0.976056 1.029566 1.209707 -P_23 1.128928 1.408976 1.444886 1.453446 1.650117 1.476092 1.258608 1.364061 -P_23 1.343889 1.068782 1.179956 1.150646 1.109827 0.988914 1.082803 0.999369 -P_23 1.035323 1.063935 1.100062 1.151726 1.273639 1.312285 1.274363 1.294689 -P_23 1.308613 1.252588 1.178638 1.139478 1.112756 1.006325 0.923362 0.788878 -P_23 0.694574 0.611273 0.459217 0.411320 0.354442 0.353504 0.392450 0.406988 -P_23 0.453532 0.598730 0.650865 0.795051 0.915255 0.956508 1.174612 1.221655 -P_23 1.230697 1.354463 1.432620 1.533368 1.446159 1.386713 1.393566 1.370209 -P_23 1.260692 1.221163 1.267134 1.271340 1.186614 1.061853 1.111364 1.043291 -P_23 1.063025 1.079070 1.085917 1.136047 1.147646 1.219144 1.298748 1.212939 -P_23 1.238193 1.288831 1.143545 1.071563 1.086911 0.976527 1.035742 0.834171 -P_23 0.675090 0.564340 0.521061 0.445028 0.407014 0.358413 0.362981 0.362792 -P_23 0.470566 0.527571 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1.082380 1.155186 1.313060 1.335563 1.369085 1.303896 1.312044 1.273724 -P_23 1.217831 1.296906 1.299321 1.305364 1.160368 1.238301 1.242718 1.163764 -P_23 1.174237 1.179075 1.165563 1.113581 1.231876 1.195131 1.244492 1.125073 -P_23 1.089675 1.056221 1.085330 1.089333 0.859054 0.837825 0.765025 0.715730 -P_23 0.647079 0.616823 0.550365 0.544654 0.410828 0.402367 0.416725 0.427728 -P_23 0.409213 0.412660 0.475579 0.501574 0.563369 0.621103 0.710850 0.829337 -P_23 0.929575 1.011082 1.040481 1.136158 1.199063 1.201786 1.270033 1.250316 -P_23 1.202527 1.204365 1.165265 1.145784 1.130679 1.091910 1.052983 1.082649 -P_23 0.928541 1.008226 1.058355 1.064329 1.121181 1.134641 1.226039 1.233386 -P_23 1.183030 1.186843 1.161393 1.068640 1.014371 0.888074 0.847948 0.742700 -P_23 0.688145 0.510673 0.487919 0.438727 0.367823 0.335282 0.347418 0.372716 -P_23 0.446450 0.528439 0.627317 0.729935 0.838182 0.921183 1.095004 1.234915 -P_23 1.346834 1.509035 1.484976 1.478215 1.614717 1.651763 1.457134 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1.132009 1.164537 1.171997 1.162844 1.256151 1.300106 1.348178 1.382999 -P_23 1.304818 1.456273 1.299123 1.263374 1.112250 1.149245 0.910399 0.850701 -P_23 0.742562 0.584170 0.532147 0.509075 0.448774 0.370396 0.417225 0.424666 -P_23 0.479944 0.579314 0.745274 0.787021 0.851519 1.066985 1.089789 1.274632 -P_23 1.340610 1.398808 1.408265 1.365935 1.547474 1.491127 1.438261 1.424999 -P_23 1.508771 1.458095 1.303480 1.298469 1.263115 1.244152 1.172283 1.199735 -P_23 1.084524 1.216763 1.114157 1.183178 1.250048 1.286405 1.372713 1.399555 -P_23 1.441703 1.366360 1.250629 1.335031 1.201523 1.116618 0.961939 0.836070 -P_23 0.793778 0.625827 0.525793 0.461647 0.439815 0.383567 0.416444 0.394478 -P_23 0.558814 0.560375 0.643960 0.845132 0.920425 1.045486 1.164746 1.322616 -P_23 1.314413 1.475741 1.383217 1.592919 1.595198 1.502231 1.444795 1.424726 -P_23 1.376892 1.264552 1.263262 1.152099 1.144919 1.122775 1.179920 1.125144 -P_23 1.093224 1.216216 1.200821 1.274346 1.352781 1.275669 1.340398 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0.424797 0.468923 0.400438 0.398118 0.478855 -P_23 0.545619 0.601115 0.658159 0.803446 0.959493 1.111551 1.111843 1.380066 -P_23 1.414497 1.366485 1.422671 1.527675 1.580612 1.574938 1.469734 1.344959 -P_23 1.415136 1.333465 1.359431 1.198240 1.126212 1.266684 1.081238 1.120628 -P_23 1.161900 1.184713 1.292339 1.200766 1.390799 1.207712 1.190211 1.415430 -P_23 1.513866 1.453859 1.418197 1.330972 1.177875 0.998968 0.963023 0.868402 -P_23 0.740917 0.658103 0.551983 0.488434 0.391947 0.382411 0.414813 0.488992 -P_23 0.505500 0.613144 0.804101 0.795648 0.975389 1.048634 1.176231 1.313136 -P_23 1.454281 1.506478 1.440921 1.650048 1.505460 1.561141 1.578297 1.515243 -P_23 1.441455 1.315594 1.206627 1.320901 1.205548 1.262255 1.200798 1.157628 -P_23 1.163728 1.090475 1.265419 1.266500 1.216348 1.302473 1.368644 1.446251 -P_23 1.394452 1.344336 1.400338 1.298884 1.123767 1.181175 1.033115 0.839187 -P_23 0.829813 0.675137 0.561730 0.491197 0.426188 0.422465 0.369093 0.382963 -P_23 0.480641 0.584206 0.756046 0.866074 0.994865 1.195709 1.152021 1.257367 -P_23 1.422854 1.519552 1.456322 1.411182 1.464853 1.542928 1.511649 1.456760 -P_23 1.384745 1.253582 1.266520 1.174393 1.237412 1.202615 1.132717 1.177120 -P_23 1.201623 1.043803 1.242494 1.262472 1.358267 1.459367 1.381727 1.424270 -P_23 1.514135 1.406577 1.325948 1.249973 1.193641 1.115926 1.059892 0.981987 -P_23 0.811794 0.713950 0.598917 0.501223 0.468885 0.422841 0.427955 0.472968 -P_23 0.557784 0.709725 0.798952 1.003576 1.047891 1.242043 1.405991 1.587377 -P_23 1.742569 1.836944 1.804473 1.730290 1.964781 1.810583 1.608194 1.601570 -P_23 1.448619 1.502550 1.351258 1.380508 1.351658 1.204758 1.323987 1.176171 -P_23 1.282596 1.336925 1.237748 1.307914 1.499241 1.416764 1.329119 1.396847 -P_23 1.444470 1.419724 1.343477 1.178412 1.161606 1.207807 1.089608 0.975000 -P_23 0.826085 0.812741 0.678551 0.606507 0.531895 0.498841 0.491199 0.470527 -P_23 0.459532 0.514169 0.588746 0.670588 0.764170 0.777306 0.950262 1.141394 -P_23 1.281454 1.275879 1.408959 1.524388 1.498017 1.538463 1.438388 1.428319 -P_23 1.294066 1.420097 1.319985 1.226367 1.267150 1.227699 1.334526 1.448254 -P_23 1.266007 1.134756 1.397302 1.307666 1.241737 1.398830 1.171885 1.335400 -P_23 1.152117 1.214451 1.046997 1.067674 0.969079 0.986550 0.858857 0.860450 -P_23 0.718542 0.645739 0.643356 0.542527 0.527584 0.483994 0.465384 0.482699 -P_23 0.470427 0.517014 0.547875 0.562378 0.666766 0.756353 0.835299 0.982913 -P_23 1.002929 1.139119 1.146264 1.203832 1.289975 1.368888 1.321677 1.286070 -P_23 1.268684 1.309112 1.311706 1.259668 1.246837 1.107243 1.119159 1.126086 -P_23 1.092232 1.105091 1.130337 1.171179 1.335716 1.233874 1.212553 1.415554 -P_23 1.239559 1.291049 1.183981 1.256345 1.060219 1.031773 0.945251 0.769891 -P_23 0.709300 0.648512 0.544954 0.497832 0.390280 0.408719 0.405529 0.394290 -P_23 0.520004 0.492170 0.676567 0.804676 0.965377 0.941449 1.218465 1.352189 -P_23 1.333694 1.481219 1.684821 1.811451 1.736556 1.666020 1.509469 1.527650 -P_23 1.464640 1.341324 1.389649 1.264761 1.192177 1.158901 1.092556 1.125065 -P_23 1.057961 1.118142 1.129882 1.171573 1.341848 1.260545 1.403381 1.350099 -P_23 1.360607 1.333145 1.331568 1.323946 1.221708 1.132315 1.107647 0.910997 -P_23 0.746100 0.649633 0.572823 0.509320 0.415159 0.408603 0.434262 0.438191 -P_23 0.514087 0.607669 0.676528 0.778265 0.964014 1.119392 1.259186 1.302751 -P_23 1.341207 1.571676 1.494487 1.689108 1.494042 1.641355 1.514667 1.694905 -P_23 1.417325 1.369625 1.258755 1.336040 1.214121 1.149280 1.102042 1.126117 -P_23 1.217436 1.235833 1.296544 1.260963 1.307670 1.321075 1.339255 1.394179 -P_23 1.451962 1.438458 1.332583 1.423985 1.175441 1.210609 1.039132 0.955152 -P_23 0.795531 0.638865 0.528057 0.481448 0.433314 0.420997 0.395272 0.456079 -P_23 0.565879 0.645552 0.767592 0.903176 0.983198 1.209535 1.247315 1.386468 -P_23 1.471175 1.428543 1.487832 1.545835 1.549030 1.522449 1.550398 1.632898 -P_23 1.610440 1.508415 1.383695 1.294130 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1.134796 1.357025 1.408157 1.310211 1.502135 1.414634 -P_23 1.332124 1.319761 1.355965 1.279423 1.201570 1.191723 1.044537 1.029376 -P_23 0.840846 0.678873 0.714999 0.587811 0.533500 0.487780 0.448504 0.506928 -P_23 0.480379 0.557934 0.651787 0.719316 0.864095 0.948588 0.907784 1.203956 -P_23 1.153684 1.344582 1.464334 1.481055 1.576073 1.548281 1.446006 1.563475 -P_23 1.496135 1.318548 1.417624 1.368573 1.468654 1.403298 1.295840 1.262552 -P_23 1.292591 1.383053 1.302540 1.363710 1.247324 1.505569 1.350092 1.284758 -P_23 1.230139 1.215521 1.203667 1.136824 1.014724 0.896223 0.935037 0.791788 -P_23 0.775629 0.676786 0.669544 0.572976 0.551594 0.484822 0.502349 0.486369 -P_23 0.500056 0.497699 0.562906 0.584194 0.669791 0.780339 0.831180 0.957235 -P_23 0.988233 1.116300 1.185321 1.130425 1.309733 1.357902 1.323780 1.485066 -P_23 1.424874 1.384440 1.241845 1.306707 1.243616 1.155941 1.260778 1.158787 -P_23 1.285914 1.249640 1.174570 1.249063 1.293662 1.224253 1.394876 1.377646 -P_23 1.313570 1.352206 1.142518 1.215583 1.118752 1.078722 1.030028 0.836552 -P_23 0.730372 0.643749 0.491171 0.477908 0.395555 0.381111 0.376744 0.392021 -P_23 0.490580 0.543397 0.658629 0.834024 1.029048 1.113951 1.269877 1.348202 -P_23 1.446920 1.546973 1.781761 1.818208 1.663767 1.865231 1.712858 1.634999 -P_23 1.611074 1.394986 1.485006 1.230792 1.051486 1.240372 1.042285 1.108123 -P_23 1.102973 1.087842 1.148341 1.156158 1.284015 1.284553 1.403680 1.481208 -P_23 1.513689 1.437085 1.394205 1.317132 1.317896 1.157547 1.030092 0.861624 -P_23 0.803553 0.721624 0.597965 0.508605 0.427917 0.418597 0.421387 0.511141 -P_23 0.519641 0.533274 0.759469 0.766727 1.007051 1.030564 1.124022 1.411248 -P_23 1.514329 1.520196 1.500962 1.600477 1.489902 1.513763 1.438879 1.617765 -P_23 1.488381 1.436058 1.350459 1.198268 1.233197 1.206319 1.165862 1.140287 -P_23 1.226242 1.241595 1.068454 1.356555 1.476601 1.389047 1.444119 1.499188 -P_23 1.615261 1.466199 1.434779 1.446530 1.255748 1.204469 1.067744 0.895012 -P_23 0.791918 0.731137 0.561710 0.508958 0.429398 0.406290 0.448186 0.459914 -P_23 0.518741 0.707873 0.710644 0.897713 1.045322 1.081502 1.296312 1.293745 -P_23 1.325814 1.523403 1.589871 1.663393 1.591027 1.689629 1.645623 1.383188 -P_23 1.545605 1.544003 1.342241 1.297135 1.284866 1.262903 1.154072 1.272437 -P_23 1.188792 1.251653 1.333156 1.338382 1.347057 1.547074 1.463004 1.376146 -P_23 1.462837 1.460246 1.422026 1.227771 1.173625 1.142250 1.047559 0.901423 -P_23 0.814341 0.651007 0.603391 0.492410 0.417984 0.427637 0.406439 0.468137 -P_23 0.536347 0.671503 0.769102 0.827486 1.050449 1.116643 1.204017 1.351393 -P_23 1.351878 1.508859 1.692780 1.555504 1.632040 1.535306 1.541116 1.450581 -P_23 1.515814 1.359107 1.294839 1.321538 1.300254 1.098675 1.139931 1.305986 -P_23 1.301204 1.331294 1.337057 1.365869 1.425476 1.339755 1.552580 1.384400 -P_23 1.383956 1.522597 1.308012 1.359725 1.313384 1.200140 1.058597 0.985829 -P_23 0.822408 0.744074 0.639575 0.526299 0.524506 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-P_23 1.421372 1.579790 1.244895 1.403974 1.266276 1.162082 1.183099 1.176243 -P_23 1.304208 1.169931 1.243792 1.256220 1.365192 1.321600 1.364921 1.534239 -P_23 1.448526 1.488853 1.331076 1.464492 1.355914 1.181080 1.145887 1.033536 -P_23 0.887003 0.740812 0.597394 0.504677 0.464693 0.430040 0.484677 0.549654 -P_23 0.588756 0.696660 0.896681 0.962029 1.300988 1.301360 1.676697 1.583403 -P_23 1.567647 1.836059 1.995507 1.847519 1.876447 1.741430 1.843419 1.830350 -P_23 1.664295 1.521714 1.435789 1.507356 1.360025 1.417424 1.253753 1.304239 -P_23 1.296914 1.360418 1.383290 1.433531 1.447865 1.288968 1.296200 1.469472 -P_23 1.389545 1.435638 1.479849 1.245898 1.248750 1.166556 1.087426 0.924041 -P_23 0.888001 0.808257 0.752332 0.638538 0.569909 0.512414 0.507826 0.523851 -P_23 0.537856 0.584143 0.581609 0.685424 0.884425 0.928911 1.071535 1.238445 -P_23 1.413012 1.448835 1.444878 1.535842 1.504669 1.697762 1.627615 1.550642 -P_23 1.514404 1.524841 1.366027 1.353870 1.329702 1.343319 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1.311197 1.280111 1.481476 1.439298 -P_23 1.422075 1.552400 1.396996 1.442410 1.406917 1.136210 1.063286 1.010919 -P_23 0.800616 0.687711 0.625931 0.518675 0.451065 0.441546 0.439166 0.465966 -P_23 0.547321 0.651651 0.742363 0.858750 1.033975 1.063490 1.218230 1.428219 -P_23 1.338645 1.551990 1.605419 1.635930 1.536523 1.651833 1.722751 1.482398 -P_23 1.606434 1.445982 1.430281 1.184858 1.156539 1.227853 1.168630 1.173509 -P_23 1.171974 1.210891 1.371255 1.234096 1.510946 1.555125 1.477713 1.531343 -P_23 1.655717 1.323328 1.413753 1.317558 1.288049 1.185945 1.042180 0.895158 -P_23 0.753079 0.694190 0.579879 0.467688 0.482192 0.439083 0.417618 0.465448 -P_23 0.491293 0.644537 0.741211 0.926637 1.017483 1.245356 1.336734 1.337864 -P_23 1.552140 1.493270 1.683518 1.723613 1.706103 1.608126 1.579261 1.634913 -P_23 1.666488 1.473712 1.404611 1.332219 1.433651 1.315690 1.262837 1.300000 -P_23 1.272480 1.287207 1.286981 1.352192 1.358769 1.428713 1.492930 1.364532 -P_23 1.465320 1.550214 1.533640 1.406353 1.227659 1.191263 1.059150 0.872713 -P_23 0.780946 0.734385 0.612383 0.494394 0.425783 0.426832 0.455086 0.440808 -P_23 0.493457 0.611529 0.684205 0.903024 1.089391 1.102196 1.265425 1.436173 -P_23 1.508395 1.583417 1.667093 1.645002 1.630944 1.562243 1.659040 1.452016 -P_23 1.542882 1.428975 1.332292 1.223188 1.309634 1.261194 1.305411 1.187364 -P_23 1.205473 1.241442 1.264414 1.325443 1.390985 1.397269 1.451027 1.504982 -P_23 1.546532 1.419802 1.336446 1.464732 1.240348 1.129133 1.126442 0.922869 -P_23 0.825082 0.724798 0.594075 0.546072 0.474572 0.461688 0.479010 0.560474 -P_23 0.604674 0.709059 0.881639 1.020628 1.219442 1.402952 1.382822 1.598938 -P_23 1.744249 1.987322 1.996695 2.024831 1.859321 1.817495 1.855597 1.709595 -P_23 1.576162 1.579555 1.479152 1.431999 1.400425 1.366882 1.214626 1.300212 -P_23 1.391389 1.456794 1.442416 1.398418 1.370500 1.443462 1.318323 1.253535 -P_23 1.554200 1.338933 1.266403 1.312151 1.223665 1.099657 1.029146 1.039892 -P_23 0.848296 0.892213 0.716167 0.639823 0.561468 0.532543 0.502106 0.485943 -P_23 0.491315 0.530925 0.624917 0.716216 0.807814 0.926436 1.017653 1.107648 -P_23 1.377136 1.264285 1.430801 1.560866 1.563617 1.581373 1.559910 1.553919 -P_23 1.496052 1.540679 1.332014 1.394925 1.429956 1.427261 1.267718 1.350075 -P_23 1.431788 1.360913 1.191492 1.331117 1.407828 1.550392 1.287156 1.277370 -P_23 1.336480 1.212802 1.183719 1.162472 1.062281 1.089292 0.851452 0.821867 -P_23 0.745579 0.659202 0.679400 0.587384 0.537085 0.490695 0.474298 0.552002 -P_23 0.488115 0.515600 0.526551 0.594684 0.647740 0.728972 0.857378 1.017608 -P_23 1.095192 1.132427 1.208335 1.211251 1.328035 1.372028 1.456108 1.364168 -P_23 1.420487 1.313587 1.290613 1.248202 1.257800 1.168873 1.220939 1.234203 -P_23 1.093541 1.166579 1.152183 1.174731 1.397708 1.332997 1.345823 1.346368 -P_23 1.423849 1.438643 1.251754 1.207769 1.193033 0.946546 1.000081 0.854064 -P_23 0.660121 0.620689 0.578544 0.454239 0.445361 0.401991 0.421619 0.475534 -P_23 0.567565 0.593968 0.711184 0.807316 1.010087 1.211859 1.138729 1.459383 -P_23 1.505119 1.511559 1.775990 1.683777 1.792792 1.819765 1.886536 1.672876 -P_23 1.658465 1.640590 1.495762 1.377458 1.254160 1.234748 1.052360 1.097521 -P_23 1.129047 1.122262 1.139050 1.227349 1.259644 1.329986 1.515591 1.492791 -P_23 1.546057 1.498637 1.479664 1.488091 1.303396 1.332222 1.071834 1.008709 -P_23 0.872094 0.709589 0.618383 0.489691 0.435953 0.455017 0.480619 0.450380 -P_23 0.542878 0.579180 0.765832 0.871724 0.989847 1.030306 1.227846 1.389097 -P_23 1.486541 1.679749 1.553380 1.516384 1.603394 1.660024 1.699484 1.684276 -P_23 1.573559 1.498252 1.345797 1.372834 1.232410 1.035055 1.192533 1.084908 -P_23 1.210831 1.271692 1.267500 1.336808 1.250541 1.466582 1.399873 1.454748 -P_23 1.489646 1.496723 1.409604 1.399274 1.278745 1.215648 1.066316 0.890285 -P_23 0.788803 0.696529 0.556926 0.538608 0.402220 0.405396 0.429061 0.515220 -P_23 0.576076 0.618691 0.793650 0.802453 1.046615 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1.439285 1.575082 -P_23 1.525849 1.514645 1.366023 1.404828 1.244619 1.188201 1.092938 1.000392 -P_23 0.799875 0.734624 0.661241 0.596978 0.477533 0.433869 0.478196 0.543783 -P_23 0.617211 0.699126 0.878400 0.972663 1.176094 1.418722 1.494318 1.496634 -P_23 1.706717 1.668079 1.849224 1.789902 1.770995 1.849055 1.779118 1.777609 -P_23 1.751146 1.628439 1.388380 1.474437 1.326984 1.188707 1.328157 1.204402 -P_23 1.343743 1.361687 1.266408 1.492444 1.513516 1.511490 1.414936 1.474965 -P_23 1.501437 1.457576 1.485283 1.333276 1.328280 1.125317 1.056538 1.013083 -P_23 0.902935 0.776037 0.665890 0.547757 0.544943 0.474563 0.493599 0.490917 -P_23 0.478481 0.503426 0.626036 0.704753 0.790934 0.863149 1.073571 1.126005 -P_23 1.186177 1.358944 1.384330 1.487795 1.625723 1.577211 1.589194 1.542481 -P_23 1.456368 1.447463 1.460590 1.407312 1.390148 1.259263 1.290462 1.301832 -P_23 1.339757 1.251162 1.404604 1.480618 1.359947 1.224515 1.373422 1.430383 -P_23 1.263633 1.122816 1.257675 1.176522 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0.617630 0.491364 0.487145 0.438565 0.430854 0.485130 -P_23 0.518250 0.563871 0.689623 0.863722 0.966098 1.118078 1.230259 1.349311 -P_23 1.439554 1.524906 1.580170 1.648926 1.589916 1.562713 1.558959 1.596870 -P_23 1.654991 1.423057 1.323966 1.282792 1.267773 1.162005 1.116230 1.065229 -P_23 1.156458 1.115519 1.231107 1.273683 1.335544 1.458700 1.458083 1.487536 -P_23 1.474685 1.503972 1.312657 1.357901 1.296319 1.101023 1.068266 0.870160 -P_23 0.837973 0.606291 0.604374 0.470162 0.430122 0.410498 0.409814 0.473514 -P_23 0.508798 0.690088 0.784935 0.924058 0.966608 1.143182 1.371041 1.362561 -P_23 1.444103 1.623546 1.688646 1.535697 1.786615 1.604538 1.561912 1.359951 -P_23 1.544623 1.497725 1.392505 1.356419 1.454971 1.252683 1.173037 1.265933 -P_23 1.220357 1.187930 1.280651 1.366143 1.348423 1.196785 1.386624 1.456830 -P_23 1.455289 1.375110 1.380157 1.390244 1.254278 1.228770 1.033697 0.820398 -P_23 0.700630 0.657569 0.509452 0.473951 0.421398 0.417865 0.409217 0.427549 -P_23 0.488856 0.627670 0.780248 0.810492 1.018982 1.139867 1.343441 1.338476 -P_23 1.381386 1.459111 1.643061 1.556521 1.661790 1.622481 1.481202 1.560415 -P_23 1.471217 1.342249 1.272730 1.291336 1.276255 1.158590 1.192835 1.108402 -P_23 1.163642 1.165719 1.312285 1.309758 1.428129 1.290338 1.420306 1.494438 -P_23 1.417260 1.506801 1.407857 1.266592 1.228182 1.116416 1.169599 0.963564 -P_23 0.832748 0.661747 0.558055 0.531655 0.467449 0.450765 0.426452 0.483184 -P_23 0.577298 0.673862 0.802903 1.028260 1.058793 1.210695 1.531767 1.568504 -P_23 1.719255 1.843833 2.030969 1.890781 1.772522 1.729021 1.825584 1.804366 -P_23 1.601048 1.414464 1.488756 1.245550 1.376468 1.331281 1.302378 1.446343 -P_23 1.246896 1.200457 1.306558 1.241280 1.475869 1.450412 1.406088 1.422492 -P_23 1.396774 1.439399 1.335729 1.186626 1.250540 1.111675 1.053614 0.985463 -P_23 0.838577 0.768807 0.692954 0.625782 0.567700 0.516417 0.483933 0.490780 -P_23 0.515234 0.555392 0.617379 0.687859 0.849917 0.899925 1.081361 1.206216 -P_23 1.205054 1.350469 1.529834 1.299164 1.465572 1.517260 1.484576 1.523011 -P_23 1.427453 1.521286 1.387610 1.289767 1.259122 1.494457 1.219425 1.278670 -P_23 1.364562 1.402499 1.456230 1.385279 1.410610 1.255767 1.367507 1.393812 -P_23 1.315307 1.248754 1.236240 1.075024 1.091622 0.950195 0.825629 0.845785 -P_23 0.712765 0.686530 0.631499 0.559626 0.522392 0.508812 0.444460 0.479775 -P_23 0.467020 0.465297 0.493695 0.557548 0.687785 0.697668 0.817263 0.982630 -P_23 0.937132 1.054237 1.219480 1.247099 1.226622 1.320884 1.322089 1.463749 -P_23 1.381349 1.281137 1.320467 1.330624 1.175796 1.194467 1.177816 1.215915 -P_23 1.094947 1.158105 1.195965 1.350798 1.252221 1.152648 1.248890 1.372424 -P_23 1.298526 1.296986 1.286930 1.156833 1.168467 1.057784 0.956856 0.813918 -P_23 0.764435 0.684857 0.517103 0.451389 0.409590 0.404596 0.428180 0.441583 -P_23 0.445819 0.594739 0.690432 0.782271 1.007834 1.040878 1.281707 1.328088 -P_23 1.493053 1.636116 1.638724 1.674991 1.766424 1.761564 1.753039 1.506553 -P_23 1.520341 1.349280 1.419492 1.217642 1.152548 1.201298 1.196070 1.012687 -P_23 1.091208 1.120073 1.116223 1.244524 1.359844 1.327858 1.442741 1.445636 -P_23 1.513543 1.359055 1.456047 1.300824 1.293356 1.092255 1.105475 0.897508 -P_23 0.759891 0.727654 0.555546 0.540233 0.428263 0.464842 0.404537 0.461264 -P_23 0.511673 0.616229 0.617562 0.915746 0.999300 1.143314 1.217403 1.385795 -P_23 1.427478 1.446682 1.610139 1.551676 1.664431 1.589947 1.576999 1.478191 -P_23 1.434779 1.391138 1.397559 1.244402 1.201196 1.118046 1.151909 1.156654 -P_23 1.149656 1.246931 1.244853 1.328235 1.341242 1.279435 1.565694 1.486569 -P_23 1.440702 1.403797 1.444497 1.404803 1.199021 1.070185 0.999220 0.930589 -P_23 0.700198 0.674791 0.585030 0.489663 0.426585 0.393605 0.403951 0.445736 -P_23 0.546032 0.606363 0.791495 0.866388 0.917824 1.187980 1.253496 1.285213 -P_23 1.488753 1.485759 1.456222 1.406514 1.604848 1.549303 1.486333 1.597801 -P_23 1.547333 1.458476 1.497096 1.391280 1.196161 1.255464 1.268361 1.136005 -P_23 1.212705 1.167617 1.256680 1.295292 1.416635 1.453007 1.348115 1.369481 -P_23 1.386860 1.352509 1.457603 1.356166 1.086451 1.043669 1.064321 0.981984 -P_23 0.858135 0.638770 0.562936 0.451022 0.447825 0.393373 0.396928 0.436025 -P_23 0.537076 0.594025 0.769775 0.891548 0.886066 1.109319 1.188896 1.436098 -P_23 1.506793 1.524955 1.522038 1.568315 1.601512 1.528699 1.543491 1.509294 -P_23 1.413582 1.382079 1.319104 1.236759 1.178406 1.199390 1.137196 1.179599 -P_23 1.199321 1.274089 1.412419 1.332778 1.380722 1.419967 1.522554 1.381190 -P_23 1.328074 1.272391 1.437391 1.262245 1.120081 1.085675 1.007754 0.948908 -P_23 0.725914 0.668451 0.614334 0.494905 0.450825 0.450300 0.453113 0.479724 -P_23 0.594691 0.702132 0.897827 1.027570 1.087078 1.230311 1.508405 1.443356 -P_23 1.766021 1.817146 1.855673 1.885795 1.833409 1.775040 1.863741 1.591781 -P_23 1.715952 1.469921 1.344282 1.392178 1.255296 1.323326 1.197490 1.270322 -P_23 1.227915 1.230638 1.244723 1.271668 1.413283 1.402491 1.423246 1.313802 -P_23 1.347935 1.355063 1.238537 1.213998 1.222156 1.143117 1.062050 0.986900 -P_23 0.848595 0.777007 0.683021 0.634252 0.496832 0.483797 0.457228 0.477367 -P_23 0.494049 0.527580 0.610798 0.629254 0.789795 0.898303 1.095512 1.085331 -P_23 1.191091 1.329796 1.471727 1.512084 1.580391 1.510131 1.504155 1.357963 -P_23 1.400791 1.373237 1.401407 1.128167 1.300190 1.385253 1.315459 1.245367 -P_23 1.263362 1.300707 1.210125 1.281387 1.276875 1.290454 1.325694 1.334315 -P_23 1.262291 1.251949 1.156885 1.023672 1.065816 0.907110 0.872068 0.856263 -P_23 0.735583 0.704571 0.582321 0.644016 0.505320 0.444666 0.452173 0.455204 -P_23 0.431928 0.471202 0.544287 0.562327 0.683445 0.711704 0.721712 0.926107 -P_23 0.886563 1.091677 1.206318 1.187894 1.234680 1.390037 1.366038 1.334216 -P_23 1.494838 1.216864 1.279719 1.110989 1.230558 1.243886 1.111105 1.188200 -P_23 1.193341 1.127835 1.213277 1.233129 1.179473 1.165150 1.233150 1.309787 -P_23 1.343789 1.287973 1.093340 1.070798 1.096096 1.027193 0.981164 0.856252 -P_23 0.691500 0.614249 0.507250 0.404417 0.404338 0.411553 0.379355 0.409495 -P_23 0.441130 0.561921 0.628574 0.804110 0.964431 1.054742 1.241260 1.347032 -P_23 1.492602 1.607976 1.631521 1.564373 1.596654 1.687760 1.636608 1.511110 -P_23 1.515266 1.416059 1.366929 1.214776 1.216391 1.145473 1.103807 1.010570 -P_23 1.041809 1.135368 1.198425 1.183016 1.228298 1.225102 1.307296 1.397015 -P_23 1.357119 1.391675 1.357179 1.294705 1.271929 1.064473 0.999493 0.911223 -P_23 0.779476 0.668324 0.618734 0.454208 0.450404 0.404321 0.411535 0.411507 -P_23 0.491600 0.585088 0.716402 0.860358 0.901674 1.128963 1.190134 1.429252 -P_23 1.381421 1.511806 1.457054 1.586474 1.647822 1.532669 1.528868 1.380057 -P_23 1.399088 1.348320 1.337414 1.262190 1.231306 1.177758 1.042686 1.117758 -P_23 1.146618 1.144753 1.180961 1.172397 1.285434 1.305310 1.326509 1.437096 -P_23 1.384362 1.472718 1.422888 1.436065 1.156551 1.148859 0.957931 0.815441 -P_23 0.781649 0.624141 0.561944 0.464668 0.396462 0.389077 0.383036 0.457333 -P_23 0.514956 0.569789 0.746191 0.893705 0.984583 1.167793 1.236178 1.257908 -P_23 1.410407 1.498652 1.428257 1.606359 1.554795 1.385934 1.472673 1.535936 -P_23 1.534975 1.529291 1.421119 1.262475 1.288408 1.299991 1.231963 1.198162 -P_23 1.116907 1.167252 1.210059 1.320854 1.288364 1.298215 1.303703 1.362341 -P_23 1.379697 1.400806 1.397339 1.199950 1.169526 1.069721 0.972979 0.940772 -P_23 0.738581 0.714159 0.579763 0.495182 0.404706 0.384574 0.404689 0.427183 -P_23 0.483550 0.570629 0.706730 0.726521 0.925149 1.140843 1.159359 1.271230 -P_23 1.474172 1.516019 1.484130 1.390310 1.402699 1.595471 1.467822 1.430157 -P_23 1.310201 1.299862 1.278233 1.236916 1.206484 1.221277 1.083573 1.091542 -P_23 1.124122 1.179092 1.270301 1.183273 1.225973 1.335413 1.315924 1.427948 -P_23 1.324582 1.311646 1.319781 1.308664 1.303215 1.076677 1.027331 0.851852 -P_23 0.849501 0.656845 0.607074 0.509277 0.428348 0.459557 0.425321 0.479403 -P_23 0.531163 0.658694 0.781953 0.887635 1.004066 1.225786 1.339775 1.558318 -P_23 1.606337 1.739489 1.795667 1.757653 1.786483 1.734313 1.743952 1.555665 -P_23 1.524869 1.440958 1.423799 1.270868 1.404187 1.212005 1.263735 1.258475 -P_23 1.169909 1.224973 1.136356 1.348988 1.244522 1.417193 1.439872 1.325412 -P_23 1.443350 1.264518 1.266677 1.291464 1.117316 1.065823 0.957278 0.899234 -P_23 0.849197 0.720455 0.651285 0.569510 0.539216 0.494697 0.477239 0.445126 -P_23 0.485436 0.541130 0.623153 0.653420 0.767651 0.885805 0.950903 1.118890 -P_23 1.205081 1.114541 1.327378 1.429700 1.456058 1.432577 1.496068 1.396986 -P_23 1.489296 1.315371 1.237599 1.256526 1.238778 1.265567 1.340599 1.129741 -P_23 1.158186 1.325647 1.146720 1.286393 1.321583 1.216870 1.263142 1.289661 -P_23 1.130099 1.163465 1.082515 1.034222 0.903051 0.974641 0.849808 0.700198 -P_23 0.683981 0.658539 0.585180 0.521098 0.500584 0.480104 0.452761 0.449626 -P_23 0.416317 0.482107 0.502240 0.589975 0.628178 0.674427 0.795683 0.872860 -P_23 0.982676 0.993344 1.154195 1.260249 1.083089 1.340463 1.232448 1.316062 -P_23 1.262467 1.299620 1.202486 1.274654 1.125377 1.114072 1.101722 1.110618 -P_23 1.089093 1.060194 1.162597 1.198942 1.331375 1.263447 1.282851 1.278298 -P_23 1.288204 1.143440 1.187242 1.202198 1.107351 1.070667 0.861422 0.758073 -P_23 0.709549 0.610159 0.490681 0.449776 0.403123 0.369704 0.386566 0.407114 -P_23 0.429672 0.497233 0.667786 0.829893 0.960096 1.039695 1.210741 1.275876 -P_23 1.384227 1.463890 1.543954 1.567920 1.553949 1.616040 1.616451 1.481237 -P_23 1.546655 1.358571 1.393564 1.291438 1.241409 1.022932 1.094745 0.955543 -P_23 1.128099 1.097225 1.023036 1.079836 1.240330 1.331876 1.274660 1.331973 -P_23 1.509460 1.437394 1.423510 1.323822 1.190027 1.123300 0.908323 0.941310 -P_23 0.786266 0.613929 0.563470 0.446942 0.427201 0.405506 0.414910 0.439281 -P_23 0.494111 0.586986 0.730866 0.756940 0.979998 1.072250 1.129428 1.291487 -P_23 1.382419 1.446727 1.571126 1.512370 1.566659 1.574374 1.468201 1.436639 -P_23 1.389002 1.321847 1.310244 1.221901 1.137173 1.092978 1.074667 1.057015 -P_23 1.102674 1.040312 1.196176 1.212010 1.326678 1.254592 1.275823 1.352854 -P_23 1.424635 1.460260 1.310952 1.256059 1.159553 1.102604 0.965067 0.781446 -P_23 0.704643 0.619376 0.525664 0.442068 0.388742 0.397045 0.413959 0.413690 -P_23 0.490877 0.586609 0.677597 0.770240 0.910290 1.059944 1.309223 1.250171 -P_23 1.319366 1.383750 1.516115 1.534236 1.389428 1.508782 1.524843 1.472138 -P_23 1.435974 1.490931 1.286666 1.236612 1.330001 1.187134 1.221905 1.096790 -P_23 1.096303 1.039944 1.028858 1.165599 1.250102 1.351720 1.300930 1.239232 -P_23 1.239180 1.417351 1.305907 1.138269 1.128514 1.113645 0.960287 0.775617 -P_23 0.809341 0.575092 0.510126 0.464207 0.376621 0.362657 0.391265 0.375614 -P_23 0.471145 0.537410 0.660412 0.841919 0.945189 1.092827 1.123948 1.371278 -P_23 1.444167 1.318069 1.334320 1.561591 1.461386 1.560181 1.410150 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1.104154 1.024960 1.047206 1.124742 1.122587 0.999696 1.062119 0.976063 -P_23 1.038610 1.048384 1.022448 1.088552 1.027298 1.165491 1.191780 1.099511 -P_23 1.259211 1.160894 1.117611 0.993729 0.935579 0.952865 0.801895 0.776476 -P_23 0.658380 0.535917 0.491077 0.425144 0.376011 0.354291 0.321058 0.361244 -P_23 0.394653 0.516484 0.623112 0.737015 0.763905 0.930311 1.090707 1.247644 -P_23 1.292964 1.292212 1.388898 1.438520 1.527861 1.389212 1.450593 1.428851 -P_23 1.261727 1.223806 1.260802 1.091367 1.098260 1.022407 1.047942 0.928834 -P_23 1.010676 0.982340 0.989302 0.974194 1.137031 1.105587 1.215373 1.316067 -P_23 1.229904 1.303510 1.065485 1.170222 1.191838 1.068641 0.849662 0.794527 -P_23 0.659910 0.677464 0.474672 0.436213 0.395401 0.389996 0.399092 0.409267 -P_23 0.461695 0.535519 0.610637 0.676445 0.895356 0.909163 0.972236 1.196591 -P_23 1.180872 1.235415 1.321465 1.504373 1.496483 1.318239 1.261434 1.301988 -P_23 1.314359 1.138787 1.102135 1.027937 1.033391 0.996913 1.075950 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0.397476 0.457995 0.454251 0.512066 0.598360 0.619722 0.752379 -P_23 0.782903 0.892951 1.005761 0.928551 1.111342 1.140604 1.105264 1.023643 -P_23 1.140741 1.079468 1.099530 1.090808 1.084359 0.936325 0.964308 0.926588 -P_23 0.921243 0.953562 0.870302 0.923622 0.969291 0.973366 1.126376 1.069782 -P_23 1.023977 1.122250 0.961875 0.921161 0.900564 0.878611 0.747880 0.671366 -P_23 0.630549 0.504225 0.438778 0.378422 0.328730 0.337299 0.325663 0.364725 -P_23 0.419994 0.443070 0.534136 0.652473 0.760433 0.840171 0.952191 1.074588 -P_23 1.156111 1.254061 1.394538 1.395260 1.406818 1.375461 1.381774 1.296335 -P_23 1.253772 1.181218 1.094035 1.018789 1.100104 0.897149 0.893359 0.864373 -P_23 0.777642 0.845087 0.987301 0.955566 1.059473 1.010297 1.093945 1.131257 -P_23 1.190312 1.227101 1.089858 1.021659 1.041209 0.927233 0.833134 0.713945 -P_23 0.597973 0.568088 0.468321 0.366655 0.364601 0.325764 0.334137 0.378344 -P_23 0.397855 0.503847 0.585216 0.609876 0.832145 0.878468 0.986710 1.006065 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1.120764 1.211822 1.033214 1.060335 0.975387 0.932746 0.817090 0.772582 -P_23 0.608557 0.518560 0.467196 0.355079 0.317728 0.351736 0.344622 0.370695 -P_23 0.414265 0.489777 0.576050 0.700686 0.838176 0.849436 1.008471 1.186098 -P_23 1.152539 1.243007 1.363104 1.291329 1.225127 1.199197 1.229381 1.112379 -P_23 1.229736 1.102793 1.083903 0.950413 0.986980 0.969013 0.898831 0.838521 -P_23 0.903223 1.000603 1.062561 0.993228 0.947747 1.053896 1.036613 1.167610 -P_23 1.066681 1.156565 1.062718 0.995485 1.001184 0.916543 0.795250 0.696568 -P_23 0.631938 0.515358 0.478663 0.424683 0.395247 0.360470 0.373105 0.401522 -P_23 0.420987 0.522061 0.649082 0.733638 0.954397 0.995063 1.110612 1.292227 -P_23 1.407874 1.410533 1.325048 1.578964 1.539731 1.510655 1.385237 1.309532 -P_23 1.090240 1.106744 1.026099 1.081714 1.143506 0.993875 1.000079 1.064471 -P_23 1.045997 0.941116 1.087806 1.087108 0.990258 1.161071 1.114452 1.033389 -P_23 1.073220 0.978664 0.871514 0.989263 0.949102 0.911349 0.840975 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1.394117 1.507699 1.519552 1.407678 1.337844 1.371539 1.271400 -P_23 1.287635 1.209595 0.942114 1.123260 1.010281 1.003115 0.984024 0.984138 -P_23 1.001725 1.092220 1.054507 1.071608 1.017115 1.051335 1.042846 1.025466 -P_23 1.067991 1.106407 1.039951 1.069436 0.927985 0.911025 0.815617 0.781088 -P_23 0.668355 0.644024 0.574820 0.485448 0.414598 0.409696 0.358420 0.342014 -P_23 0.371095 0.405510 0.526035 0.579527 0.598006 0.646493 0.740120 0.890013 -P_23 1.001251 1.030955 1.156063 1.216834 1.153217 1.154790 1.148008 1.181804 -P_23 1.166467 0.998586 1.072586 1.065780 0.985167 0.974962 1.031741 0.965717 -P_23 1.079647 0.990476 0.993896 1.026392 1.020196 1.072801 1.066338 1.030207 -P_23 1.041618 0.898979 0.834034 0.895288 0.829657 0.716739 0.705648 0.562826 -P_23 0.494807 0.546060 0.500114 0.469058 0.386713 0.388734 0.351417 0.359121 -P_23 0.356498 0.415312 0.347323 0.459520 0.473968 0.593223 0.600712 0.701130 -P_23 0.788464 0.899590 0.931329 0.962088 0.981546 1.002309 1.019388 1.166825 -P_23 1.177499 1.063316 0.945636 1.063434 1.034453 0.968868 0.916929 0.940577 -P_23 0.818894 0.907645 0.959918 0.933621 1.010087 1.038981 1.063233 0.987402 -P_23 1.078031 1.163024 0.944304 0.894456 0.783132 0.747365 0.718457 0.629227 -P_23 0.572394 0.493703 0.451883 0.364817 0.298835 0.305353 0.316956 0.322188 -P_23 0.411412 0.444758 0.482148 0.616234 0.686990 0.785847 0.942282 1.159765 -P_23 1.125580 1.159594 1.343953 1.277817 1.384692 1.214824 1.196028 1.420719 -P_23 1.201072 1.140554 1.024410 1.018854 0.914170 1.028005 0.935468 0.896082 -P_23 0.877623 0.880167 0.829292 0.911372 1.025311 1.070442 1.073653 1.025632 -P_23 1.204217 1.075949 1.015693 0.993916 0.944613 0.834577 0.867664 0.653752 -P_23 0.649931 0.512203 0.448098 0.400536 0.330791 0.357263 0.306266 0.350210 -P_23 0.411489 0.441254 0.551735 0.618590 0.766477 0.829052 0.997876 1.069972 -P_23 1.050111 1.099673 1.199137 1.371973 1.280294 1.252027 1.183825 1.160243 -P_23 1.038658 1.079006 1.010496 1.015341 0.984611 0.912079 0.818522 0.908969 -P_23 0.886056 0.915516 0.884862 0.923050 1.090808 1.020491 1.055117 1.109987 -P_23 1.202944 1.175720 1.115316 1.107687 0.975804 0.910055 0.801014 0.689813 -P_23 0.576685 0.532826 0.446106 0.373898 0.340582 0.288329 0.344311 0.398274 -P_23 0.417518 0.485637 0.562278 0.623809 0.811188 0.856266 0.918720 1.043686 -P_23 1.116682 1.309312 1.275791 1.211935 1.280216 1.211543 1.293618 1.158697 -P_23 1.049596 1.132021 1.056780 1.001845 0.986073 0.991208 1.003266 0.899290 -P_23 0.899396 0.918663 1.134186 1.015611 1.075363 1.061797 1.204193 1.068508 -P_23 1.090667 1.216060 1.036797 1.001011 0.944117 0.924017 0.838540 0.720778 -P_23 0.607208 0.550684 0.400565 0.377253 0.315092 0.317801 0.328023 0.326070 -P_23 0.405134 0.467645 0.585733 0.649388 0.805621 0.824254 0.964397 1.120973 -P_23 1.168540 1.174370 1.239892 1.275907 1.225484 1.186555 1.162170 0.978741 -P_23 1.101709 1.051947 1.027577 0.924937 0.947900 0.977314 0.839745 0.932811 -P_23 0.941450 0.875433 0.921013 1.034541 1.063688 1.024887 1.074604 1.207756 -P_23 1.046775 1.120707 1.123476 1.031997 1.030143 0.826756 0.742203 0.742421 -P_23 0.596040 0.526002 0.465751 0.411022 0.338841 0.374619 0.357993 0.406851 -P_23 0.440084 0.517071 0.601166 0.761205 0.896540 0.937816 1.097885 1.173684 -P_23 1.418457 1.321901 1.468620 1.428754 1.315274 1.330088 1.324001 1.106364 -P_23 1.245404 1.103534 1.176933 1.035354 1.078718 0.983248 1.008592 0.978083 -P_23 1.058738 0.952324 1.076554 1.042148 1.074344 1.064511 1.065469 1.085861 -P_23 1.062338 1.037612 1.056517 0.985982 0.924927 0.904301 0.833908 0.767902 -P_23 0.693922 0.612336 0.528901 0.451775 0.442645 0.426241 0.424113 0.375961 -P_23 0.411613 0.414603 0.479981 0.544289 0.568580 0.657652 0.866653 0.897445 -P_23 1.005892 1.067519 1.092465 1.066590 1.132338 1.178234 1.158644 1.058006 -P_23 1.097636 1.096064 1.034861 0.976793 1.116061 1.062220 1.000607 0.926800 -P_23 0.989272 1.011140 1.049844 1.082616 0.983827 1.114563 1.049136 1.065986 -P_23 0.994958 0.949333 0.961825 0.756325 0.802973 0.741215 0.678231 0.616028 -P_23 0.548733 0.551250 0.484690 0.417660 0.401382 0.387448 0.352266 0.348330 -P_23 0.381996 0.361053 0.397811 0.454878 0.535386 0.649787 0.624923 0.655806 -P_23 0.767152 0.779398 0.846344 0.975365 0.955956 1.059358 1.001730 1.080252 -P_23 1.124847 1.070390 0.992361 0.917529 0.924760 0.896004 0.914306 0.927458 -P_23 0.834337 0.842182 0.950079 0.912899 1.025684 0.972200 1.011962 0.987847 -P_23 1.069673 0.930113 1.078550 0.996066 0.843156 0.863086 0.742962 0.681962 -P_23 0.523861 0.454302 0.367576 0.353120 0.297247 0.322621 0.311586 0.333535 -P_23 0.383941 0.455166 0.522424 0.624003 0.724891 0.813300 1.082829 1.107287 -P_23 1.142578 1.232991 1.342926 1.310474 1.171626 1.361432 1.252613 1.260562 -P_23 1.145123 1.149672 1.067826 1.048062 0.872311 0.923168 0.834597 0.822508 -P_23 0.830950 0.806267 0.888160 0.836015 1.071942 0.987630 1.009872 1.172463 -P_23 1.142517 1.093994 1.057344 1.140058 0.975132 0.880986 0.775296 0.706777 -P_23 0.672238 0.533022 0.442764 0.406870 0.351075 0.311522 0.312551 0.361487 -P_23 0.389083 0.492685 0.566027 0.575547 0.706385 0.876194 0.893163 1.051088 -P_23 1.148251 1.129715 1.110200 1.268169 1.170539 1.217027 1.195794 1.171134 -P_23 1.174367 1.066800 1.057947 0.930298 0.980636 0.899674 0.931583 0.918483 -P_23 0.810494 0.967358 0.963252 1.008615 1.068576 1.123061 1.233450 1.133248 -P_23 1.067331 1.104534 1.130271 0.978218 0.874656 0.885356 0.783538 0.647081 -P_23 0.603896 0.503580 0.468150 0.349765 0.350986 0.305924 0.344818 0.343040 -P_23 0.398463 0.456729 0.562595 0.673049 0.795265 0.814607 0.928852 1.059005 -P_23 1.150958 1.237478 1.163991 1.211078 1.220805 1.246857 1.192358 1.267018 -P_23 1.212980 1.127372 1.062424 0.986963 0.934390 0.882506 0.983047 1.013351 -P_23 0.812022 0.927416 0.996426 1.057893 1.002540 1.053463 1.011032 1.076551 -P_23 1.002824 1.072660 1.061376 0.911648 0.925685 0.931241 0.778927 0.719407 -P_23 0.633509 0.486511 0.435725 0.395957 0.324779 0.311592 0.312765 0.301065 -P_23 0.394119 0.468032 0.564673 0.724705 0.752389 0.867488 0.989925 0.950193 -P_23 1.162738 1.036961 1.139268 1.151945 1.132732 1.159945 1.259463 1.036253 -P_23 1.110485 1.052850 0.935124 0.945278 0.946675 1.031736 0.895477 0.909637 -P_23 0.879662 0.889822 1.032252 1.010315 1.080476 1.034001 1.134916 1.172111 -P_23 1.103832 1.155386 1.086051 1.019912 0.946367 0.946554 0.810599 0.706892 -P_23 0.637965 0.515058 0.466935 0.400702 0.359611 0.329388 0.360127 0.380201 -P_23 0.445974 0.544503 0.593688 0.732477 0.900655 1.052080 1.084634 1.229048 -P_23 1.295607 1.386845 1.466892 1.563659 1.284396 1.381608 1.404736 1.303001 -P_23 1.175219 1.098084 1.097796 1.034174 0.948487 1.032875 0.928460 0.990048 -P_23 0.971469 1.022532 1.118470 1.054606 1.108091 0.973776 0.996588 1.070547 -P_23 1.074288 1.097553 1.114225 1.015667 0.866915 0.873248 0.762789 0.708015 -P_23 0.666588 0.619049 0.542112 0.440672 0.456558 0.415600 0.399510 0.368395 -P_23 0.399965 0.418971 0.486710 0.549723 0.581269 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0.987888 1.005423 0.940448 0.974614 -P_32 1.020423 1.048691 0.950249 1.071236 1.044796 1.047652 1.139938 1.090867 -P_32 1.152787 1.133063 1.078467 1.111903 1.071754 1.000843 0.828618 0.785981 -P_32 0.649838 0.564010 0.526575 0.469008 0.453365 0.413113 0.397534 0.452825 -P_32 0.488634 0.607318 0.539176 0.747303 0.833370 0.938419 0.913470 1.031966 -P_32 1.235870 1.155892 1.237256 1.206577 1.162382 1.416998 1.301627 1.269227 -P_32 1.386923 1.223121 1.130307 1.015417 1.153249 1.081594 1.017001 1.102959 -P_32 1.033542 1.082547 1.073535 1.043679 1.121343 1.111278 1.130510 1.121085 -P_32 1.135001 1.005757 1.005451 1.077106 0.950891 0.892794 0.820186 0.800928 -P_32 0.664388 0.624283 0.497083 0.479720 0.435088 0.389217 0.384596 0.421428 -P_32 0.499136 0.559869 0.689933 0.739414 0.770362 0.867804 1.055804 1.072195 -P_32 1.301035 1.280449 1.210490 1.248571 1.237893 1.239049 1.271863 1.027425 -P_32 1.166251 1.133280 1.136105 1.099936 1.036741 1.029244 1.011078 0.981329 -P_32 0.990823 0.950967 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0.995838 0.928295 0.971234 0.876625 0.852303 0.787155 0.743831 0.742916 -P_32 0.695239 0.597435 0.603847 0.506435 0.570849 0.472009 0.445995 0.442639 -P_32 0.442412 0.464197 0.474910 0.475128 0.621263 0.598554 0.651819 0.753242 -P_32 0.863011 0.887437 0.872680 0.963232 1.104310 1.084512 1.142808 1.117786 -P_32 1.123884 1.030469 1.071505 1.135665 1.062588 1.019583 1.045433 0.927984 -P_32 0.977557 1.047065 0.972388 1.003241 0.937259 0.990480 0.998828 0.993988 -P_32 1.004353 0.990433 1.064820 1.012659 0.845586 0.847057 0.757133 0.742957 -P_32 0.691253 0.613263 0.520373 0.430219 0.432239 0.419892 0.395585 0.451461 -P_32 0.483875 0.513672 0.597510 0.656496 0.815448 0.978004 0.984122 1.149056 -P_32 1.398323 1.341127 1.286251 1.406825 1.405806 1.394599 1.390323 1.296313 -P_32 1.299313 1.240041 1.278214 1.165871 1.025753 0.937996 0.982651 0.960977 -P_32 0.917892 0.969175 1.083673 0.945401 1.017801 1.072869 1.104266 1.135712 -P_32 1.271327 1.110903 1.089973 1.016548 1.011380 0.977391 0.860023 0.752449 -P_32 0.754683 0.601742 0.527359 0.450321 0.419171 0.408175 0.428244 0.469011 -P_32 0.442314 0.585319 0.635451 0.667127 0.771956 0.849641 0.963312 1.100910 -P_32 1.017224 1.126051 1.353719 1.264754 1.377954 1.273619 1.261380 1.347177 -P_32 1.265991 1.142543 1.123607 1.060090 1.121615 0.965930 0.981679 0.980247 -P_32 1.017610 0.943819 1.060469 1.114614 1.162539 1.146522 1.087782 1.056725 -P_32 1.254508 1.117861 1.127407 1.005126 1.042918 0.945409 0.885387 0.757138 -P_32 0.684047 0.632845 0.520289 0.432723 0.462673 0.423036 0.420281 0.402620 -P_32 0.498431 0.569508 0.630538 0.742182 0.870233 0.917964 0.967634 1.089447 -P_32 1.229590 1.149815 1.338376 1.281296 1.399352 1.422996 1.335920 1.348452 -P_32 1.168892 1.283897 1.237990 1.142967 1.125888 1.177266 1.164034 1.124326 -P_32 1.052495 1.012102 1.037168 1.046398 1.149188 1.152345 1.044764 1.173357 -P_32 1.175741 1.133661 1.183745 1.061026 1.023327 1.009777 0.896595 0.754951 -P_32 0.669805 0.597353 0.527829 0.435183 0.426264 0.448622 0.390477 0.465130 -P_32 0.488353 0.549344 0.632406 0.800706 0.888051 0.992341 1.060044 1.041792 -P_32 1.153891 1.182818 1.261407 1.267122 1.356649 1.335011 1.213953 1.254726 -P_32 1.155235 1.134153 1.140917 1.153463 1.101877 1.011387 1.023076 1.086527 -P_32 1.112148 1.068288 1.018539 1.009412 1.071022 1.042711 1.131896 1.030851 -P_32 1.066184 1.161552 1.205455 1.110115 1.017870 0.967753 0.873361 0.707975 -P_32 0.745087 0.630709 0.575707 0.538018 0.499120 0.426213 0.470033 0.490493 -P_32 0.564379 0.644746 0.687903 0.787280 1.033253 1.123443 1.133336 1.310404 -P_32 1.383154 1.481218 1.501304 1.498832 1.512389 1.508924 1.390451 1.396004 -P_32 1.265124 1.233790 1.272527 1.298611 1.103784 1.134077 1.138711 1.198462 -P_32 1.145524 1.063792 1.059056 1.124238 1.166850 1.183198 1.189983 1.061871 -P_32 1.065378 1.050816 1.123596 1.136195 0.978614 0.899119 0.966752 0.808760 -P_32 0.738345 0.614478 0.564126 0.608680 0.520654 0.467096 0.489589 0.487622 -P_32 0.506353 0.540614 0.565486 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1.300474 1.121370 1.030097 0.968026 0.906476 0.795859 -P_32 0.696928 0.599978 0.524066 0.515226 0.477015 0.458886 0.462105 0.468523 -P_32 0.605157 0.552678 0.645729 0.773601 0.762430 0.967618 1.080062 1.096941 -P_32 1.248769 1.269341 1.368745 1.275567 1.261611 1.334357 1.395697 1.278442 -P_32 1.198939 1.275592 1.254426 1.202329 1.214597 1.145745 1.091415 1.064457 -P_32 1.206478 1.045323 1.037114 1.156324 1.095561 1.159384 1.218030 1.189951 -P_32 1.229343 1.218327 1.135991 1.130107 1.091650 0.996173 0.896382 0.813005 -P_32 0.706688 0.647473 0.617291 0.506938 0.470204 0.487384 0.438793 0.489993 -P_32 0.523461 0.596940 0.639226 0.820524 0.850583 0.938794 1.061596 1.145767 -P_32 1.213978 1.196425 1.254712 1.254648 1.339019 1.283812 1.374556 1.258872 -P_32 1.324185 1.276700 1.213726 1.204373 1.140738 1.047326 1.073768 0.983851 -P_32 1.039193 1.049087 1.086722 0.984125 1.083913 1.098118 1.208147 1.178910 -P_32 1.226169 1.209137 1.238874 1.138628 0.973972 0.975288 0.975149 0.839994 -P_32 0.708303 0.679384 0.614820 0.527164 0.494405 0.464105 0.463309 0.518300 -P_32 0.599811 0.653337 0.752183 0.804356 1.016922 1.199879 1.232243 1.163114 -P_32 1.345617 1.667205 1.502102 1.497106 1.661940 1.514193 1.622649 1.425898 -P_32 1.310543 1.279388 1.334342 1.286612 1.142200 1.265637 1.244179 1.171031 -P_32 1.185064 1.185561 1.054087 1.263575 1.197554 1.178072 1.226661 1.166005 -P_32 1.208822 1.052042 1.116679 1.037326 1.009506 1.004730 0.954153 0.883613 -P_32 0.830421 0.666154 0.659923 0.584621 0.573867 0.543164 0.500922 0.492467 -P_32 0.506089 0.544130 0.583961 0.623530 0.724120 0.823887 0.821884 0.994310 -P_32 0.898710 1.069259 1.150002 1.366353 1.302511 1.229483 1.275985 1.202582 -P_32 1.253038 1.256513 1.332265 1.316569 1.279346 1.145261 1.115715 1.190755 -P_32 1.249823 1.221092 1.310636 1.106158 1.174194 1.108156 1.174243 0.972465 -P_32 1.189899 1.054569 1.144772 0.896567 0.905176 0.873860 0.838618 0.732928 -P_32 0.660519 0.638832 0.560220 0.568989 0.516718 0.486193 0.489553 0.468181 -P_32 0.486675 0.443573 0.519526 0.519203 0.522791 0.666492 0.698719 0.748835 -P_32 0.787443 0.982066 0.995197 1.002014 1.142725 1.311722 1.237028 1.120430 -P_32 1.160505 1.158611 1.332000 1.066555 1.151981 1.179215 1.008868 1.074300 -P_32 1.138695 1.084442 1.131026 1.095502 1.073143 1.193824 1.121546 1.143991 -P_32 1.077609 1.042853 1.030462 1.044897 1.051088 0.925121 0.866353 0.719696 -P_32 0.670682 0.600950 0.552837 0.517454 0.460225 0.460365 0.480224 0.479726 -P_32 0.484856 0.559747 0.621938 0.747496 0.831504 0.937560 1.089468 1.144728 -P_32 1.154738 1.286389 1.362489 1.363325 1.553067 1.456513 1.396632 1.454133 -P_32 1.293748 1.302958 1.278974 1.214058 1.152907 1.119132 1.022229 0.986739 -P_32 1.029112 1.034976 0.987107 1.132389 1.159936 1.191294 1.178700 1.219989 -P_32 1.104284 1.249732 1.173025 1.138630 1.106389 1.023326 0.989028 0.832954 -P_32 0.724413 0.647246 0.545621 0.557351 0.498609 0.489254 0.443101 0.517609 -P_32 0.493468 0.595794 0.689526 0.789970 0.853295 0.929843 1.076189 1.166246 -P_32 1.155599 1.291186 1.292230 1.345354 1.365601 1.272692 1.448886 1.382449 -P_32 1.399178 1.249478 1.270095 1.129254 1.239080 1.183084 0.997272 0.959216 -P_32 1.056548 1.165365 1.049390 1.103402 1.263266 1.191728 1.192849 1.172086 -P_32 1.349890 1.301086 1.263910 1.081642 1.082529 0.985463 0.888081 0.776160 -P_32 0.708557 0.616525 0.549386 0.489815 0.478335 0.458667 0.475822 0.510343 -P_32 0.550072 0.582743 0.735913 0.703545 0.932334 0.894295 1.072261 1.163662 -P_32 1.308275 1.247440 1.368485 1.267821 1.281506 1.397280 1.316550 1.332219 -P_32 1.355752 1.332441 1.239333 1.271238 1.201941 1.310783 1.122172 1.192686 -P_32 1.147321 1.136587 1.230816 1.274051 1.182029 1.055360 1.087248 1.312261 -P_32 1.140831 1.234740 1.247905 1.213259 1.058859 1.028859 0.916998 0.812649 -P_32 0.716974 0.606929 0.575647 0.476887 0.473925 0.487702 0.435845 0.476683 -P_32 0.549953 0.556591 0.678056 0.778319 0.950074 1.009266 1.100285 1.182715 -P_32 1.266145 1.352091 1.432411 1.319824 1.391910 1.286225 1.311140 1.315175 -P_32 1.228631 1.282986 1.166342 1.155911 1.236449 1.139303 1.105856 1.048565 -P_32 1.031724 1.130786 1.207253 1.196401 1.197668 1.107719 1.144309 1.312843 -P_32 1.239864 1.185526 1.186125 1.206733 1.115961 0.959654 0.899522 0.895919 -P_32 0.696920 0.634933 0.633435 0.553433 0.510243 0.441773 0.495526 0.529674 -P_32 0.628475 0.636858 0.673599 0.820432 1.029728 1.163579 1.226534 1.369382 -P_32 1.561724 1.488066 1.641347 1.603312 1.559172 1.456327 1.530729 1.328477 -P_32 1.372263 1.368174 1.411361 1.167036 1.237712 1.139951 1.226123 1.147410 -P_32 1.193106 1.094931 1.142259 1.148818 1.273248 1.031350 1.275462 1.209940 -P_32 1.173098 1.089184 1.059933 1.060335 1.008726 0.948553 0.952142 0.895790 -P_32 0.830429 0.752802 0.691005 0.604402 0.552097 0.577930 0.479476 0.500265 -P_32 0.522279 0.572423 0.598444 0.647511 0.743771 0.809474 0.855978 1.003209 -P_32 1.044471 1.159565 1.206328 1.288573 1.215514 1.382391 1.386594 1.416282 -P_32 1.232490 1.257205 1.217287 1.214062 1.207571 1.369762 1.227938 1.163241 -P_32 1.199126 1.337394 1.235764 1.107355 1.177347 1.041908 1.202575 1.109122 -P_32 1.139718 0.986636 1.056278 0.971828 0.907799 0.821570 0.848171 0.761370 -P_32 0.652049 0.650732 0.634651 0.633756 0.541880 0.566435 0.510867 0.503789 -P_32 0.547741 0.506780 0.526913 0.606803 0.577373 0.669192 0.719338 0.755227 -P_32 0.848969 0.910626 1.018607 1.007406 1.151945 1.125180 1.113392 1.213001 -P_32 1.141046 1.304897 1.230927 1.221878 1.035907 1.012881 1.203782 1.103771 -P_32 1.097234 1.067861 1.070033 1.124432 1.180442 1.181361 1.150685 1.185334 -P_32 1.066645 1.064497 1.095212 1.018585 0.976482 0.844568 0.876564 0.731889 -P_32 0.670042 0.572162 0.599175 0.505825 0.451276 0.442565 0.461116 0.471481 -P_32 0.496155 0.607699 0.652052 0.717762 0.819228 1.009242 1.067753 1.230663 -P_32 1.389423 1.466595 1.423172 1.487363 1.580080 1.332851 1.381713 1.462957 -P_32 1.475689 1.333018 1.272534 1.207621 1.018297 1.093792 0.947918 1.079398 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1.213388 1.214705 -P_32 1.244622 1.182523 1.184202 1.148387 1.038336 1.014182 0.976054 0.864683 -P_32 0.739819 0.708377 0.630331 0.535937 0.427410 0.477510 0.461562 0.480413 -P_32 0.589335 0.580824 0.713846 0.833348 0.908692 1.039866 1.246569 1.217593 -P_32 1.195308 1.443322 1.402163 1.381475 1.374166 1.452334 1.487011 1.216916 -P_32 1.324616 1.281855 1.162328 1.240955 1.148877 1.214870 1.152879 1.074892 -P_32 1.213034 1.096074 1.167670 1.175549 1.252456 1.210311 1.161685 1.178946 -P_32 1.193747 1.253295 1.130225 1.234285 1.069788 1.060510 0.896157 0.846925 -P_32 0.759049 0.705725 0.540540 0.558441 0.443176 0.525047 0.569183 0.512898 -P_32 0.576303 0.669943 0.815753 0.925426 1.056271 1.164343 1.220474 1.326604 -P_32 1.517946 1.412633 1.637508 1.725893 1.551173 1.511135 1.676108 1.435101 -P_32 1.281301 1.386017 1.364043 1.319711 1.294437 1.218754 1.231520 1.178374 -P_32 1.238058 1.161039 1.128081 1.222436 1.195568 1.304487 1.260598 1.184989 -P_32 1.254698 1.214934 1.088866 1.159072 0.965166 0.952770 0.909732 0.924789 -P_32 0.801576 0.726255 0.766364 0.673091 0.577489 0.509091 0.496678 0.510147 -P_32 0.487481 0.535969 0.577947 0.676359 0.745608 0.802705 0.891304 0.951174 -P_32 1.145931 1.187639 1.153257 1.306732 1.335350 1.311669 1.284664 1.378955 -P_32 1.292979 1.290568 1.376333 1.375792 1.242843 1.305450 1.164783 1.236237 -P_32 1.187127 1.174852 1.300366 1.174932 1.261958 1.224489 1.250796 1.237392 -P_32 1.043920 1.060982 0.992151 0.805213 0.872739 0.914967 0.855322 0.776726 -P_32 0.698865 0.723667 0.625703 0.589291 0.618531 0.575099 0.507239 0.497217 -P_32 0.510941 0.489013 0.508271 0.577503 0.624914 0.704448 0.696418 0.792776 -P_32 0.884873 0.922665 1.082652 1.130293 1.080687 1.261534 1.323664 1.260292 -P_32 1.345587 1.248707 1.195246 1.104642 1.143252 1.141891 1.139760 1.054912 -P_32 1.049438 1.132110 1.047565 1.103623 1.116969 1.140614 1.233529 1.160968 -P_32 1.141186 1.128032 1.057896 1.000596 0.966879 0.960087 0.818393 0.743497 -P_32 0.718057 0.691817 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0.581606 0.608145 0.746799 0.844430 0.841374 0.999097 1.036432 1.148903 -P_32 1.176595 1.434304 1.441221 1.444455 1.495212 1.364743 1.352299 1.442135 -P_32 1.268218 1.303281 1.433239 1.248336 1.131883 1.146905 1.246635 1.169171 -P_32 1.149182 1.090812 1.119277 1.187129 1.145167 1.284276 1.212261 1.232007 -P_32 1.219250 1.144199 1.188242 1.140544 1.166775 1.061845 0.982553 0.877191 -P_32 0.754757 0.620412 0.585999 0.530061 0.485673 0.495613 0.472280 0.487547 -P_32 0.525438 0.604919 0.670761 0.825153 0.948461 1.066569 1.099606 1.188264 -P_32 1.423136 1.390527 1.426851 1.219175 1.285883 1.352613 1.330421 1.332322 -P_32 1.303883 1.218472 1.264883 1.209295 1.177754 1.063095 1.134660 1.160708 -P_32 1.151503 1.165781 1.303408 1.318632 1.281004 1.257549 1.370452 1.242792 -P_32 1.258053 1.305577 1.166722 1.178306 1.048518 1.093870 0.898482 0.813617 -P_32 0.784437 0.586237 0.665620 0.505761 0.532553 0.516005 0.529515 0.554959 -P_32 0.552992 0.713199 0.738823 0.881166 1.119709 1.145877 1.142450 1.429481 -P_32 1.430486 1.516167 1.604253 1.663063 1.635735 1.666024 1.443099 1.674643 -P_32 1.372490 1.418437 1.304284 1.259031 1.293588 1.143991 1.286063 1.187753 -P_32 1.193295 1.230543 1.221175 1.408696 1.254696 1.385156 1.306602 1.270304 -P_32 1.221298 1.204734 1.212975 1.133198 1.025653 0.995821 0.959511 0.893147 -P_32 0.860562 0.707393 0.636336 0.631725 0.607895 0.589856 0.537815 0.486390 -P_32 0.549177 0.608108 0.635246 0.679472 0.706079 0.796342 0.952556 1.069763 -P_32 1.077933 1.208253 1.244110 1.261744 1.365024 1.347103 1.378947 1.345882 -P_32 1.396944 1.410832 1.279985 1.243229 1.255404 1.295034 1.278478 1.327298 -P_32 1.327802 1.158416 1.193928 1.269584 1.263113 1.270187 1.229178 1.168528 -P_32 1.262420 1.072904 1.025258 0.941638 0.971074 0.834591 0.893486 0.786007 -P_32 0.657682 0.721528 0.668232 0.577014 0.528965 0.538286 0.532030 0.504347 -P_32 0.507446 0.497643 0.509558 0.584666 0.619412 0.695707 0.715757 0.781710 -P_32 0.934148 0.935360 1.095768 1.140285 1.081884 1.240923 1.124856 1.444935 -P_32 1.245322 1.324770 1.312626 1.189641 1.163431 1.226405 1.166045 1.152941 -P_32 1.073432 1.165144 1.159099 1.170105 1.056289 1.138052 1.234827 1.119492 -P_32 1.063095 1.020849 1.066354 1.119906 1.034928 0.969018 0.898464 0.779055 -P_32 0.683774 0.598473 0.610237 0.521274 0.490401 0.448906 0.460479 0.493020 -P_32 0.553366 0.553111 0.711572 0.735333 0.900002 0.908198 1.073449 1.149099 -P_32 1.408990 1.370049 1.418350 1.508674 1.552135 1.446872 1.505702 1.492968 -P_32 1.482199 1.441503 1.219243 1.251483 1.196079 1.084996 1.031598 1.134746 -P_32 1.043913 1.073262 1.191409 1.066918 1.161908 1.064415 1.169768 1.244114 -P_32 1.195712 1.133133 1.253414 1.160219 1.150401 0.997831 0.967869 0.805287 -P_32 0.775436 0.770003 0.631753 0.546132 0.468319 0.469926 0.530290 0.472310 -P_32 0.517054 0.602125 0.638932 0.763380 0.854703 0.935350 0.985770 1.078038 -P_32 1.290092 1.302526 1.247351 1.484061 1.432196 1.587697 1.326381 1.531045 -P_32 1.402988 1.221595 1.326322 1.131393 1.118449 1.175930 1.049316 1.049935 -P_32 1.114294 1.195411 1.171414 1.153963 1.162438 1.239642 1.261675 1.271829 -P_32 1.317785 1.230877 1.305554 1.077929 1.066674 1.053696 0.889574 0.856522 -P_32 0.671497 0.639508 0.592918 0.515486 0.525339 0.407075 0.470254 0.496656 -P_32 0.532020 0.644185 0.709958 0.839318 0.932129 0.991033 1.149991 1.234867 -P_32 1.208849 1.333341 1.334201 1.367759 1.489852 1.398384 1.384686 1.416413 -P_32 1.390160 1.366196 1.387072 1.348635 1.239959 1.276907 1.091764 1.166641 -P_32 1.209495 1.150900 1.182074 1.264227 1.304786 1.264066 1.335012 1.277120 -P_32 1.197793 1.247460 1.200816 1.179568 1.041952 1.052243 0.914171 0.895941 -P_32 0.751671 0.648498 0.626223 0.524973 0.530934 0.491720 0.476069 0.521881 -P_32 0.541285 0.621227 0.749799 0.809539 0.969434 1.026253 1.194783 1.324371 -P_32 1.250290 1.302341 1.372241 1.463960 1.359274 1.331210 1.282390 1.435658 -P_32 1.296854 1.194740 1.156712 1.228013 1.246940 1.199990 1.262141 1.138867 -P_32 1.188316 1.187406 1.231366 1.120294 1.188319 1.135967 1.286814 1.229683 -P_32 1.260744 1.166845 1.135359 1.142189 1.125732 1.037780 0.949048 0.848471 -P_32 0.844945 0.653629 0.620612 0.589039 0.505087 0.467849 0.484826 0.580546 -P_32 0.597564 0.707563 0.768498 0.966639 1.051543 1.223690 1.216536 1.326421 -P_32 1.467584 1.540577 1.595601 1.633786 1.707299 1.756182 1.551439 1.454907 -P_32 1.491852 1.494347 1.389140 1.321857 1.301158 1.152333 1.165355 1.267802 -P_32 1.206817 1.271901 1.257246 1.270806 1.183623 1.123988 1.338238 1.181757 -P_32 1.281818 1.243906 1.214886 1.090766 1.048431 1.042241 0.862895 0.853890 -P_32 0.841268 0.692542 0.720691 0.691776 0.576751 0.565145 0.504817 0.538223 -P_32 0.549444 0.486764 0.595956 0.660603 0.756353 0.780059 0.919094 1.049385 -P_32 1.115665 1.129510 1.276947 1.285418 1.417047 1.372470 1.256239 1.453163 -P_32 1.285460 1.377396 1.260959 1.295820 1.275473 1.394049 1.286587 1.139048 -P_32 1.210369 1.173259 1.280561 1.348938 1.197706 1.254114 1.109660 1.191974 -P_32 1.145149 1.153978 0.973401 0.949055 0.966712 0.857126 0.816478 0.749014 -P_32 0.745753 0.647837 0.579094 0.628936 0.556090 0.542818 0.493442 0.509275 -P_32 0.498810 0.508217 0.563557 0.549741 0.610827 0.733601 0.713711 0.841276 -P_32 0.839825 0.984265 1.053539 1.120389 1.140214 1.105713 1.238818 1.299496 -P_32 1.262325 1.153838 1.198665 1.141435 1.274123 1.147838 1.130394 1.152396 -P_32 1.052470 1.093906 1.148895 1.163968 1.052193 1.253520 1.135741 1.208662 -P_32 1.143580 1.177163 1.054926 1.046348 0.959828 0.992880 0.911988 0.785188 -P_32 0.692223 0.644631 0.576312 0.507035 0.484926 0.457674 0.427207 0.512033 -P_32 0.523063 0.574428 0.630417 0.700533 0.885760 0.840075 1.116400 1.247922 -P_32 1.325693 1.368710 1.396492 1.581826 1.627641 1.587332 1.550556 1.483858 -P_32 1.345683 1.441230 1.297015 1.304544 1.176852 1.200034 1.160223 0.966416 -P_32 1.027149 1.019118 0.972726 1.089858 1.182440 1.121250 1.118823 1.231259 -P_32 1.256036 1.293559 1.246115 1.164810 1.031698 1.016755 0.928873 0.790053 -P_32 0.748966 0.636715 0.624847 0.551778 0.495524 0.439853 0.444533 0.498671 -P_32 0.572834 0.559517 0.693036 0.745859 0.885040 0.941016 1.036174 1.118936 -P_32 1.238899 1.403696 1.399227 1.334610 1.460366 1.428978 1.335992 1.394008 -P_32 1.397974 1.290752 1.275826 1.297452 1.152745 1.171067 1.110565 1.104179 -P_32 1.079608 1.119279 1.141939 1.166907 1.282937 1.229711 1.223568 1.308242 -P_32 1.321595 1.187274 1.340753 1.222712 1.093459 1.020327 0.954755 0.911830 -P_32 0.754991 0.596228 0.550987 0.521837 0.470626 0.448699 0.462292 0.489507 -P_32 0.586917 0.627554 0.771552 0.810511 0.921906 1.111788 1.124529 1.182198 -P_32 1.210337 1.396473 1.388907 1.393354 1.431156 1.416494 1.338295 1.441505 -P_32 1.237835 1.451965 1.237923 1.061431 1.201117 1.091095 1.156237 1.136939 -P_32 1.145043 1.154626 1.271350 1.194760 1.203477 1.325297 1.246242 1.288025 -P_32 1.199494 1.337188 1.334158 1.196568 1.171146 1.079596 1.008763 0.803796 -P_32 0.770562 0.625251 0.637942 0.528849 0.465326 0.496419 0.474666 0.463681 -P_32 0.578204 0.690909 0.783159 0.831629 0.944295 1.069372 1.051487 1.117224 -P_32 1.255568 1.319542 1.259492 1.475880 1.489541 1.483126 1.304351 1.325240 -P_32 1.245589 1.254035 1.297208 1.231437 1.203763 1.152698 1.209288 1.159882 -P_32 1.195463 1.063781 1.161096 1.058416 1.285619 1.161704 1.226716 1.271220 -P_32 1.232099 1.176221 1.341706 1.217783 1.004449 1.089490 0.877228 0.940946 -P_32 0.761395 0.708010 0.591209 0.569117 0.526113 0.494493 0.499602 0.562701 -P_32 0.557352 0.761545 0.776990 0.957238 1.021096 1.208448 1.286348 1.357895 -P_32 1.508804 1.469105 1.755747 1.692974 1.604283 1.588788 1.433695 1.408935 -P_32 1.436985 1.521600 1.515408 1.237823 1.303675 1.330733 1.149358 1.269114 -P_32 1.287411 1.221144 1.210300 1.329874 1.324723 1.397582 1.224862 1.308314 -P_32 1.170557 1.161153 1.185390 1.087716 1.078566 1.013407 0.918017 0.896072 -P_32 0.843564 0.711508 0.641447 0.633730 0.621026 0.526354 0.513513 0.491962 -P_32 0.535772 0.602359 0.623971 0.600838 0.773270 0.806238 0.913799 0.985856 -P_32 1.040152 1.158106 1.189153 1.324355 1.387373 1.263755 1.347580 1.377710 -P_32 1.402402 1.238527 1.348651 1.211337 1.297570 1.368946 1.271367 1.205127 -P_32 1.220004 1.284099 1.315528 1.268143 1.247932 1.221458 1.212115 1.175999 -P_32 1.167746 1.060748 1.118039 0.950632 0.981701 0.935437 0.880711 0.706191 -P_32 0.709247 0.719919 0.636127 0.582526 0.577018 0.572668 0.512372 0.480537 -P_32 0.514652 0.506409 0.519731 0.598771 0.643250 0.711992 0.762804 0.814686 -P_32 0.911931 0.871858 0.987373 1.135941 1.181028 1.196861 1.128920 1.258535 -P_32 1.203380 1.226031 1.223812 1.185186 1.332001 1.189591 1.135907 1.018200 -P_32 1.199040 1.142351 0.971658 1.062697 1.049748 1.146368 1.157852 1.175054 -P_32 1.119015 1.070077 1.119629 1.111242 1.058758 0.922414 0.853243 0.818834 -P_32 0.715915 0.615917 0.611521 0.548592 0.460851 0.426671 0.459658 0.487126 -P_32 0.536037 0.594441 0.703036 0.741584 0.826344 0.977680 1.168202 1.228351 -P_32 1.252374 1.371402 1.395819 1.460231 1.558418 1.526477 1.566841 1.465104 -P_32 1.415063 1.351331 1.364454 1.239875 1.118566 1.013331 1.058793 1.042712 -P_32 1.050532 1.022250 0.990701 1.156334 1.133608 1.147578 1.245432 1.272566 -P_32 1.256506 1.216284 1.203523 1.184038 1.079520 1.038752 1.005944 0.872496 -P_32 0.794177 0.618365 0.562301 0.523145 0.512692 0.461447 0.496232 0.443619 -P_32 0.548758 0.592624 0.701019 0.844282 0.864376 0.900386 1.132683 1.183929 -P_32 1.226486 1.365718 1.379142 1.538669 1.348154 1.516851 1.478712 1.384653 -P_32 1.355222 1.237911 1.323185 1.081022 1.202310 1.057092 1.132761 1.129697 -P_32 1.158699 1.141611 1.090043 1.272371 1.242474 1.330113 1.298137 1.362675 -P_32 1.217424 1.287671 1.210035 1.211213 1.131263 1.108577 1.015337 0.767974 -P_32 0.749491 0.678762 0.576768 0.527134 0.452192 0.467741 0.491075 0.474843 -P_32 0.554748 0.639514 0.660453 0.736131 0.935747 0.995438 1.203382 1.232420 -P_32 1.141880 1.339324 1.268292 1.370107 1.522697 1.296314 1.438549 1.344064 -P_32 1.310430 1.364302 1.344484 1.226867 1.158619 1.392827 1.217975 1.175913 -P_32 1.079172 1.102586 1.171745 1.293156 1.236427 1.217007 1.238571 1.173341 -P_32 1.287943 1.168150 1.184538 1.102971 1.153794 1.080859 0.900335 0.839148 -P_32 0.768424 0.612393 0.555715 0.507513 0.513086 0.465399 0.453993 0.523619 -P_32 0.570529 0.578244 0.634897 0.793372 0.869544 1.029152 1.135883 1.176453 -P_32 1.289254 1.327116 1.419249 1.477226 1.430396 1.383149 1.288495 1.250986 -P_32 1.209761 1.223771 1.257646 1.166879 1.225372 1.233939 1.048393 1.175742 -P_32 1.167919 1.057455 1.278834 1.206852 1.271641 1.283160 1.236438 1.202430 -P_32 1.312497 1.335285 1.204030 1.076885 1.075241 0.953171 0.896846 0.907192 -P_32 0.759785 0.746695 0.602852 0.498452 0.504151 0.485743 0.472374 0.561479 -P_32 0.574673 0.649231 0.800322 0.880997 0.989437 1.181123 1.213412 1.399734 -P_32 1.469767 1.605775 1.605115 1.611953 1.876552 1.629784 1.538754 1.461138 -P_32 1.485485 1.484529 1.272717 1.316782 1.313760 1.413537 1.206729 1.191061 -P_32 1.246520 1.321338 1.277876 1.212934 1.280641 1.251746 1.376380 1.194799 -P_32 1.133667 1.224117 1.169437 1.094283 1.105062 0.932018 0.938291 0.818022 -P_32 0.807205 0.733962 0.633467 0.627830 0.573674 0.524499 0.513102 0.509170 -P_32 0.515892 0.555318 0.650539 0.679321 0.811037 0.877877 1.042474 0.960999 -P_32 1.083343 1.158343 1.236678 1.322497 1.370248 1.260891 1.213646 1.365927 -P_32 1.295948 1.371717 1.245963 1.268361 1.288036 1.310112 1.312042 1.326365 -P_32 1.235273 1.245153 1.281224 1.243171 1.244191 1.213697 1.215640 1.232900 -P_32 1.121742 1.132674 0.965298 0.960980 0.888411 0.899045 0.874220 0.834239 -P_32 0.710238 0.618738 0.654150 0.616145 0.529754 0.499822 0.505291 0.522452 -P_32 0.563964 0.499757 0.542892 0.495651 0.593165 0.672311 0.770120 0.865963 -P_32 0.892710 0.872110 0.966332 1.085490 1.131237 1.184634 1.193352 1.233681 -P_32 1.198366 1.281068 1.153889 1.096742 1.240016 1.177799 1.050409 1.106554 -P_32 1.127887 1.059778 0.987486 1.027940 1.191650 1.226551 1.129641 1.234034 -P_32 1.202076 1.138269 1.177312 1.114019 1.022650 0.932630 0.894765 0.743108 -P_32 0.662172 0.624218 0.565945 0.509717 0.461754 0.467080 0.482671 0.487008 -P_32 0.551587 0.622375 0.679268 0.747258 0.824611 1.049205 1.078345 1.190710 -P_32 1.190512 1.349642 1.459667 1.540183 1.464443 1.541887 1.507359 1.439957 -P_32 1.333226 1.339227 1.348550 1.254844 1.173405 1.095575 1.115324 1.004815 -P_32 1.103276 1.088900 1.140559 1.067382 1.146239 1.260531 1.105393 1.227557 -P_32 1.290494 1.255375 1.240183 1.143082 1.095632 1.077729 0.933229 0.870897 -P_32 0.740732 0.717001 0.556719 0.507078 0.492455 0.420400 0.442111 0.470321 -P_32 0.539404 0.562682 0.690784 0.798902 0.938575 0.903621 1.092619 1.063509 -P_32 1.193622 1.352881 1.375867 1.471237 1.324676 1.363913 1.460342 1.381941 -P_32 1.322142 1.298980 1.172022 1.153509 1.141929 1.141860 1.156755 1.109054 -P_32 1.178348 1.127145 1.156421 1.120954 1.251820 1.182246 1.291126 1.320103 -P_32 1.368330 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1.370537 1.396515 1.411318 1.432063 1.245486 1.408064 -P_32 1.264889 1.282719 1.254676 1.295963 1.135564 1.217544 1.244046 1.066848 -P_32 1.127834 1.077171 1.050490 1.238443 1.101445 1.254729 1.180578 1.264680 -P_32 1.360697 1.221290 1.104403 1.140550 1.107843 1.076054 1.025415 0.887332 -P_32 0.695390 0.746319 0.653695 0.583032 0.458768 0.495518 0.501210 0.469234 -P_32 0.538057 0.701795 0.792090 0.905781 0.918156 1.061168 1.191585 1.350219 -P_32 1.428273 1.444243 1.494093 1.592903 1.494958 1.514462 1.489987 1.473046 -P_32 1.462744 1.356086 1.187661 1.361606 1.344336 1.353931 1.236306 1.236060 -P_32 1.235317 1.180291 1.193539 1.247573 1.150161 1.176519 1.317163 1.137884 -P_32 1.297223 1.271297 1.125190 1.123985 1.097037 0.910119 0.917346 0.842839 -P_32 0.817717 0.770945 0.688256 0.608918 0.592573 0.508330 0.483538 0.509847 -P_32 0.545757 0.573978 0.589046 0.669727 0.658289 0.799191 0.854309 0.959379 -P_32 1.075998 1.195114 1.238372 1.303944 1.301551 1.346386 1.249608 1.354311 -P_32 1.334301 1.232580 1.218267 1.174136 1.235896 1.120208 1.332358 1.325766 -P_32 1.172405 1.166976 1.111285 1.182650 1.124381 1.227146 1.118807 1.198533 -P_32 1.001062 1.060941 0.905202 0.921938 0.852979 0.828520 0.797189 0.751899 -P_32 0.747268 0.610760 0.642254 0.531054 0.578054 0.496682 0.530186 0.462672 -P_32 0.465314 0.499228 0.519523 0.603372 0.619595 0.665793 0.725490 0.768746 -P_32 0.802420 0.908597 1.026336 1.094298 1.039011 1.074144 1.231002 1.174409 -P_32 1.354037 1.281204 1.200400 1.285721 1.207569 1.105618 1.123201 1.055773 -P_32 1.003653 1.081502 1.174750 1.184042 1.140617 1.061428 1.209657 1.189107 -P_32 1.048958 1.094376 1.114295 1.012560 1.007187 0.936176 0.785882 0.771192 -P_32 0.696879 0.601377 0.545150 0.505991 0.448641 0.431093 0.431992 0.462026 -P_32 0.469161 0.584723 0.663121 0.734734 0.891553 0.890697 1.102077 1.191489 -P_32 1.290424 1.312055 1.498537 1.479346 1.557900 1.620929 1.433571 1.502128 -P_32 1.465531 1.202909 1.310890 1.301041 1.113009 1.073387 1.067715 1.084476 -P_32 1.011323 1.029030 1.054924 0.911313 1.131054 1.246170 1.146716 1.278287 -P_32 1.085543 1.227777 1.087423 1.139343 1.026010 1.133794 0.939599 0.845132 -P_32 0.778489 0.613122 0.574554 0.484711 0.481540 0.463882 0.450956 0.497542 -P_32 0.488841 0.546193 0.637079 0.721527 0.890266 0.939976 1.080885 1.218866 -P_32 1.305310 1.275993 1.415728 1.309468 1.377446 1.464716 1.603238 1.254516 -P_32 1.299951 1.271081 1.182855 1.317964 1.178409 1.093221 1.184348 1.087266 -P_32 1.104041 1.058135 1.056649 1.117509 1.157724 1.202066 1.269974 1.282938 -P_32 1.127553 1.219635 1.107538 1.154424 1.164190 0.982846 0.893162 0.905198 -P_32 0.719598 0.685891 0.568067 0.465181 0.470114 0.472448 0.444355 0.465738 -P_32 0.555680 0.624603 0.762977 0.754435 0.935143 1.023249 1.099156 1.259423 -P_32 1.218934 1.297651 1.311881 1.388572 1.459543 1.512184 1.289044 1.463700 -P_32 1.298387 1.394274 1.189898 1.209595 1.219071 1.183023 1.201040 1.096142 -P_32 1.057271 1.122552 1.152923 1.183419 1.062129 1.144274 1.145197 1.234658 -P_32 1.203888 1.195587 1.209509 1.102539 1.055079 0.916768 0.881646 0.788620 -P_32 0.718550 0.650898 0.572341 0.537734 0.501953 0.433493 0.444431 0.490814 -P_32 0.559119 0.685190 0.683025 0.738638 0.967872 1.006819 1.099551 1.092412 -P_32 1.244867 1.219872 1.466609 1.344969 1.404057 1.266758 1.443397 1.259939 -P_32 1.249227 1.212726 1.131405 1.211486 1.207548 1.162085 1.115244 1.152585 -P_32 1.213120 1.105988 1.132659 1.168962 1.105845 1.102390 1.273244 1.378249 -P_32 1.376541 1.175523 1.215807 1.183070 1.021232 1.073421 0.970934 0.805201 -P_32 0.783857 0.667410 0.648647 0.577791 0.510856 0.473277 0.471656 0.521496 -P_32 0.537410 0.616601 0.757130 0.813259 1.059975 1.173873 1.302900 1.334908 -P_32 1.516548 1.525772 1.631649 1.602489 1.563721 1.518313 1.460323 1.435740 -P_32 1.267865 1.371910 1.375685 1.198622 1.339495 1.257015 1.265750 1.189159 -P_32 1.247332 1.174190 1.134972 1.125056 1.324797 1.209275 1.201616 1.194164 -P_32 1.183761 1.088966 1.109075 1.207449 1.099199 0.855225 0.831549 0.815775 -P_32 0.903307 0.690572 0.610585 0.613289 0.545001 0.537792 0.541810 0.512851 -P_32 0.535533 0.530280 0.542768 0.738607 0.686079 0.726555 0.882531 1.006031 -P_32 1.070098 1.142730 1.167477 1.284008 1.309363 1.373136 1.315651 1.351640 -P_32 1.281715 1.226852 1.259513 1.255172 1.205788 1.212961 1.277526 1.128707 -P_32 1.281423 1.208732 1.143687 1.230386 1.282669 1.147556 1.184449 1.081162 -P_32 1.154507 1.081934 1.070548 0.976848 0.850989 0.819508 0.811769 0.683184 -P_32 0.658928 0.686190 0.568377 0.597029 0.557664 0.531656 0.497270 0.462877 -P_32 0.446819 0.504199 0.528698 0.515840 0.554684 0.673470 0.760782 0.837376 -P_32 0.819848 0.866002 0.982407 0.972477 1.097868 1.117707 1.197999 1.236569 -P_32 1.172268 1.217322 1.293104 1.291740 1.080931 1.206434 1.120286 1.068951 -P_32 1.008836 1.116866 1.013313 1.101513 0.950602 1.144867 1.223779 1.194886 -P_32 1.116011 1.096698 1.161248 1.041580 1.075881 0.914029 0.831684 0.747231 -P_32 0.665087 0.611283 0.564084 0.501804 0.468992 0.396382 0.433702 0.499029 -P_32 0.453575 0.589947 0.594715 0.742230 0.897549 0.974551 1.043797 1.219075 -P_32 1.323911 1.362382 1.335966 1.404912 1.521744 1.380565 1.511324 1.396507 -P_32 1.380670 1.238192 1.214151 1.117391 1.074174 1.096952 1.187231 1.075749 -P_32 0.893741 0.946944 1.133909 1.130874 1.060248 1.140140 1.138998 1.204363 -P_32 1.258175 1.177554 1.294142 1.106965 1.074910 1.066731 0.967873 0.837423 -P_32 0.751154 0.652547 0.511305 0.522460 0.479602 0.441259 0.459214 0.489283 -P_32 0.547003 0.527561 0.662028 0.749249 0.836852 0.928090 1.017954 1.066080 -P_32 1.158871 1.258756 1.314669 1.331185 1.402149 1.411835 1.402349 1.390397 -P_32 1.328090 1.339913 1.141490 1.163038 1.165065 1.170027 1.115704 1.071860 -P_32 1.230428 1.078069 1.056402 1.038932 1.184493 1.215717 1.283525 1.100472 -P_32 1.299719 1.245070 1.211926 1.127075 1.075676 1.013923 0.822946 0.873044 -P_32 0.732036 0.670057 0.534733 0.488459 0.466905 0.426667 0.445845 0.457098 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1.309827 1.344579 -P_32 1.420597 1.533397 1.571406 1.678036 1.670074 1.420554 1.414681 1.500023 -P_32 1.426441 1.379054 1.315950 1.261342 1.234697 1.113541 1.249388 1.122337 -P_32 1.215566 1.129675 1.218077 1.258973 1.307000 1.250957 1.240045 1.127518 -P_32 1.118419 1.116593 1.067676 1.031691 1.019621 0.997533 0.925324 0.890761 -P_32 0.767055 0.706299 0.688891 0.680914 0.550519 0.522240 0.515136 0.485384 -P_32 0.554709 0.537754 0.588771 0.651469 0.729652 0.804591 0.883342 0.851938 -P_32 1.002021 1.192748 1.236606 1.295185 1.323247 1.264709 1.226676 1.291817 -P_32 1.305874 1.247067 1.347689 1.202619 1.152725 1.088313 1.240746 1.295726 -P_32 1.249817 1.190319 1.167758 1.250776 1.116956 1.136622 1.175715 1.080899 -P_32 1.079553 1.007312 1.031366 0.938851 0.932246 0.848254 0.752794 0.800293 -P_32 0.710117 0.623831 0.613785 0.586052 0.559864 0.498567 0.497001 0.469180 -P_32 0.471378 0.523679 0.519446 0.591929 0.600804 0.634419 0.684381 0.786222 -P_32 0.799281 0.990911 0.953864 1.092749 1.098166 1.114571 1.203379 1.254954 -P_32 1.135542 1.172278 1.272581 1.135815 1.156597 1.010451 1.126670 1.073791 -P_32 1.062602 1.117888 1.137220 1.037038 1.048817 1.193984 1.094477 1.166375 -P_32 1.053736 0.999177 1.068172 1.009517 0.932003 0.955916 0.787539 0.782632 -P_32 0.674301 0.638320 0.537195 0.473058 0.446031 0.426955 0.465094 0.461211 -P_32 0.481645 0.574927 0.674517 0.760349 0.844427 0.981114 0.987756 1.232450 -P_32 1.224935 1.415426 1.315117 1.410990 1.412743 1.610348 1.446859 1.399876 -P_32 1.339495 1.328731 1.273592 1.107494 1.042437 1.040579 0.964451 1.111816 -P_32 0.956403 1.021942 0.938506 1.116249 1.121939 1.199942 1.190098 1.242431 -P_32 1.311744 1.259255 1.188876 1.006896 0.924590 0.943747 0.888937 0.800611 -P_32 0.696528 0.662947 0.577822 0.476469 0.469015 0.434409 0.430631 0.467766 -P_32 0.506811 0.604686 0.651891 0.772497 0.792165 1.033112 1.016282 1.177450 -P_32 1.271826 1.264340 1.270306 1.469932 1.455328 1.355137 1.409792 1.303206 -P_32 1.290297 1.321851 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1.143433 -P_32 1.053651 1.078457 0.921810 0.938676 0.886181 0.775833 0.762543 0.791712 -P_32 0.707058 0.698960 0.587252 0.529291 0.508824 0.488095 0.522162 0.488230 -P_32 0.491195 0.522426 0.546034 0.554492 0.590478 0.661874 0.767059 0.830319 -P_32 0.848815 0.921462 0.989400 1.010898 0.993999 1.161047 1.200576 1.286877 -P_32 1.208791 1.156122 1.283667 1.229767 1.204718 1.021326 1.086082 1.010648 -P_32 1.071277 1.055998 1.040157 1.087269 0.995985 1.156512 1.207689 1.106406 -P_32 1.031302 1.156373 1.118512 1.126794 0.932783 0.823205 0.826242 0.763562 -P_32 0.653833 0.595376 0.547654 0.495166 0.449559 0.470792 0.456706 0.475198 -P_32 0.510614 0.541702 0.573777 0.731347 0.794283 1.045527 0.969322 1.151958 -P_32 1.319548 1.430893 1.440627 1.443375 1.457876 1.416811 1.277697 1.477896 -P_32 1.445940 1.473849 1.222196 1.201905 1.183632 1.073470 1.126645 1.003102 -P_32 1.046388 1.004367 1.025538 1.196786 1.164468 1.074998 1.124523 1.206579 -P_32 1.185491 1.195777 1.103953 1.197864 1.131636 0.946350 0.917833 0.885260 -P_32 0.706341 0.681597 0.555647 0.520030 0.501281 0.473961 0.424884 0.438792 -P_32 0.529213 0.578459 0.662823 0.792108 0.768684 0.965344 1.004074 1.185226 -P_32 1.216313 1.211352 1.387610 1.334909 1.241064 1.406719 1.348544 1.340856 -P_32 1.262064 1.347054 1.224916 1.174062 1.185152 1.106266 1.119744 0.986095 -P_32 1.096469 1.085840 1.072589 1.120049 1.233522 1.124754 1.204822 1.261692 -P_32 1.204566 1.261216 1.222744 1.060928 1.151876 1.059331 0.935040 0.857783 -P_32 0.782183 0.689071 0.529408 0.498599 0.449479 0.403460 0.482852 0.465228 -P_32 0.550646 0.561107 0.747811 0.825239 0.823188 0.948598 1.030672 1.149599 -P_32 1.242978 1.144979 1.342305 1.286190 1.396071 1.314282 1.447231 1.430769 -P_32 1.227551 1.364904 1.271001 1.205082 1.101881 1.107546 1.049560 1.085452 -P_32 1.179659 1.090365 1.170914 1.205025 1.253421 1.186190 1.183161 1.270734 -P_32 1.176191 1.202806 1.189346 1.054316 0.993422 0.956751 0.815686 0.842636 -P_32 0.763793 0.657337 0.540705 0.502370 0.506899 0.443126 0.431073 0.495647 -P_32 0.546777 0.603762 0.702627 0.800953 0.944386 1.036561 1.137933 1.217429 -P_32 1.187113 1.265964 1.441926 1.381938 1.419221 1.395926 1.161033 1.286523 -P_32 1.259448 1.185891 1.174074 1.141871 1.175130 1.116906 1.099448 1.077611 -P_32 1.115007 1.097787 1.058717 1.138664 1.292900 1.180126 1.145193 1.279090 -P_32 1.214917 1.273669 1.103938 1.041086 1.112944 0.956886 0.982582 0.878937 -P_32 0.714177 0.652380 0.583835 0.512202 0.493272 0.511801 0.505842 0.528962 -P_32 0.589420 0.677267 0.798441 0.871516 0.967042 1.044146 1.242076 1.389934 -P_32 1.338684 1.502781 1.396052 1.577477 1.555619 1.449239 1.437889 1.273489 -P_32 1.322598 1.410770 1.368124 1.239743 1.210551 1.131830 1.264042 1.203354 -P_32 1.235081 1.257678 1.195233 1.245333 1.146893 1.192975 1.214754 1.111647 -P_32 1.221399 1.136272 1.149417 1.004168 1.033200 0.847282 0.870260 0.864432 -P_32 0.738382 0.738468 0.594282 0.609722 0.566376 0.518871 0.509246 0.459897 -P_32 0.512315 0.505024 0.533024 0.683817 0.744196 0.826782 0.905462 1.050969 -P_32 1.004301 1.113895 1.235894 1.222182 1.347111 1.291996 1.193951 1.312684 -P_32 1.258219 1.261314 1.212214 1.143661 1.245782 1.118240 1.210601 1.272208 -P_32 1.114727 1.166765 1.008374 1.174543 1.190591 1.188399 1.277643 1.226144 -P_32 1.036476 1.011179 0.999249 1.041358 0.810397 0.829225 0.740199 0.712498 -P_32 0.790404 0.627544 0.587136 0.576926 0.548629 0.531354 0.471115 0.464273 -P_32 0.482633 0.444717 0.587042 0.535845 0.588027 0.609590 0.737418 0.801871 -P_32 0.785910 0.981360 1.001834 1.111970 1.140627 1.182858 1.076315 1.103127 -P_32 1.266807 1.129212 1.199867 1.176594 1.158424 1.232737 1.050092 1.018215 -P_32 0.957329 1.126203 1.105508 1.017686 1.041536 1.109303 1.164285 1.107782 -P_32 1.045544 1.240769 1.050889 1.010565 0.927135 0.964867 0.841757 0.772578 -P_32 0.675531 0.646753 0.528122 0.512502 0.466209 0.434753 0.447030 0.460748 -P_32 0.504788 0.622852 0.657139 0.747731 0.821385 0.999242 1.038486 1.168309 -P_32 1.204548 1.282350 1.309834 1.393346 1.513059 1.485141 1.493418 1.338326 -P_32 1.405057 1.177263 1.304590 1.188560 1.105901 1.096224 0.994823 1.022793 -P_32 1.075161 0.918172 1.002413 1.088196 1.107776 1.173351 1.160592 1.134668 -P_32 1.220251 1.225121 1.177614 1.163946 1.043661 0.933318 0.882205 0.890822 -P_32 0.624730 0.632640 0.580396 0.536958 0.444763 0.471204 0.487118 0.467949 -P_32 0.530251 0.577042 0.648349 0.716067 0.838797 0.956955 1.036618 1.162182 -P_32 1.108776 1.369675 1.295955 1.313339 1.285831 1.554940 1.412316 1.304373 -P_32 1.305648 1.357784 1.211711 1.211046 1.089914 1.104377 1.072997 1.092988 -P_32 1.039344 1.146250 1.050343 1.160673 1.246687 1.246286 1.289725 1.295814 -P_32 1.226046 1.246054 1.201415 1.207019 1.165133 1.090148 0.898499 0.864557 -P_32 0.686031 0.639195 0.480431 0.528784 0.454923 0.442081 0.436820 0.527442 -P_32 0.583905 0.623031 0.693274 0.827431 0.869705 0.942759 1.075074 1.083601 -P_32 1.150475 1.266117 1.178874 1.412680 1.340431 1.350288 1.539190 1.367873 -P_32 1.312776 1.301193 1.253002 1.243564 1.252907 1.111011 1.173521 1.187744 -P_32 1.185914 1.201279 1.225557 1.180386 1.287834 1.185546 1.190706 1.272588 -P_32 1.256456 1.187507 1.127164 1.170432 1.036990 0.983211 0.983581 0.850732 -P_32 0.769699 0.624276 0.560235 0.488076 0.480913 0.454078 0.486018 0.449396 -P_32 0.514291 0.657301 0.651819 0.820147 0.832182 0.979739 1.156408 1.215510 -P_32 1.261297 1.295188 1.380080 1.474941 1.363766 1.303558 1.338573 1.315143 -P_32 1.203930 1.160588 1.266714 1.196903 1.054027 1.085744 1.157937 1.109559 -P_32 1.112442 1.124044 1.150173 1.141406 1.064421 1.171718 1.138685 1.091462 -P_32 1.370363 1.244959 1.211546 1.044420 1.130478 0.994862 0.935434 0.872363 -P_32 0.762774 0.663616 0.601338 0.530486 0.553474 0.508483 0.472432 0.494078 -P_32 0.514614 0.610862 0.787082 0.864712 1.042670 1.207360 1.236537 1.171088 -P_32 1.420908 1.630263 1.625284 1.517128 1.530131 1.458276 1.401845 1.520448 -P_32 1.388706 1.432331 1.261209 1.224853 1.160017 1.258592 1.125570 1.240246 -P_32 1.287105 1.127670 1.087973 1.258470 1.156111 1.352758 1.234421 1.168680 -P_32 1.198782 1.158269 1.202088 1.199849 1.004861 0.969242 0.937016 0.828266 -P_32 0.782364 0.766513 0.753630 0.622919 0.558191 0.525726 0.513322 0.510031 -P_32 0.534972 0.503259 0.582584 0.632933 0.743344 0.847001 0.896529 0.999967 -P_32 1.024473 1.192857 1.144363 1.194482 1.331585 1.285664 1.297407 1.302701 -P_32 1.349044 1.149461 1.283904 1.289823 1.155264 1.294927 1.214966 1.218551 -P_32 1.277889 1.258186 1.201699 1.224832 1.131186 1.114478 1.127664 1.064319 -P_32 1.027056 1.082694 0.933521 0.982761 0.898087 0.860987 0.729753 0.779243 -P_32 0.704450 0.671330 0.611901 0.590948 0.560691 0.506068 0.479983 0.456416 -P_32 0.452793 0.544243 0.567371 0.515076 0.613538 0.583570 0.721243 0.851560 -P_32 0.786715 0.938573 0.880625 1.060165 1.155816 1.231370 1.111307 1.210209 -P_32 1.210031 1.217085 1.136153 1.060900 1.202666 1.077310 1.141241 1.067223 -P_32 1.052814 1.119851 1.175796 1.039803 1.049987 1.062620 1.092903 1.151932 -P_32 1.183905 1.148144 1.070237 1.119071 0.956977 0.876791 0.879777 0.723719 -P_32 0.753125 0.587788 0.550939 0.500170 0.453459 0.451657 0.422370 0.435635 -P_32 0.519898 0.580574 0.638933 0.733688 0.846750 0.952053 1.160459 1.220968 -P_32 1.212362 1.459039 1.557779 1.423644 1.358630 1.457243 1.400395 1.575433 -P_32 1.390431 1.318994 1.341103 1.255474 1.203049 1.048689 1.085726 1.011656 -P_32 1.116289 1.005689 1.065251 1.038827 1.093444 1.156222 1.167494 1.199487 -P_32 1.225977 1.228467 1.219229 1.144560 1.117718 1.006277 0.937463 0.811013 -P_32 0.779408 0.650543 0.634025 0.542671 0.493644 0.456104 0.420081 0.471759 -P_32 0.541990 0.558215 0.611564 0.713475 0.880191 1.030334 1.079986 1.040059 -P_32 1.204972 1.129288 1.390635 1.384515 1.364963 1.305918 1.428731 1.333905 -P_32 1.303914 1.175976 1.286130 1.179669 1.328050 1.057894 1.127052 1.036594 -P_32 1.123495 1.110898 1.189861 1.127708 1.076543 1.126343 1.181414 1.272708 -P_32 1.240436 1.203497 1.192407 1.125343 1.166459 0.959480 0.907116 0.883159 -P_32 0.704964 0.665123 0.542578 0.515366 0.448303 0.458506 0.451487 0.551837 -P_32 0.554141 0.654215 0.644707 0.827904 0.842269 0.955129 1.045299 1.056849 -P_32 1.185440 1.335955 1.313667 1.402323 1.364265 1.415057 1.391321 1.276924 -P_32 1.354970 1.213890 1.241121 1.251398 1.294079 1.241312 1.183056 1.131001 -P_32 1.140282 1.094962 1.103281 1.151020 1.125941 1.201632 1.199022 1.224902 -P_32 1.229271 1.204063 1.193750 1.079454 1.145446 1.049999 0.975272 0.848528 -P_32 0.781416 0.697352 0.529146 0.461300 0.495673 0.480254 0.477593 0.492617 -P_32 0.584919 0.580117 0.701965 0.910344 0.935358 1.000933 1.165506 1.183437 -P_32 1.273627 1.349052 1.399097 1.388116 1.440758 1.307648 1.377686 1.365566 -P_32 1.436534 1.219754 1.311194 1.130827 1.079182 1.187423 1.041165 1.192556 -P_32 1.125148 1.106714 1.154650 1.234264 1.164502 1.166252 1.237100 1.292256 -P_32 1.324654 1.275897 1.221442 1.165000 1.105933 1.113685 0.878897 0.855918 -P_32 0.776970 0.685041 0.662577 0.524517 0.473074 0.493992 0.452692 0.532633 -P_32 0.556472 0.627475 0.701534 0.932058 1.047428 1.143498 1.125078 1.407053 -P_32 1.467743 1.597894 1.555006 1.593044 1.518703 1.615712 1.373133 1.495096 -P_32 1.455233 1.364624 1.317268 1.405655 1.343336 1.198445 1.271404 1.253955 -P_32 1.165962 1.172550 1.161302 1.247342 1.117648 1.315330 1.221133 1.300995 -P_32 1.180442 1.045182 1.160486 1.171079 1.073127 0.948794 0.941937 0.860792 -P_32 0.767510 0.749723 0.670725 0.617376 0.546715 0.512447 0.516118 0.531950 -P_32 0.524253 0.522768 0.608727 0.632679 0.694570 0.833216 0.962912 0.980181 -P_32 1.110265 1.077680 1.174413 1.235887 1.331992 1.313670 1.386927 1.294082 -P_32 1.314017 1.290039 1.276723 1.202097 1.248751 1.264983 1.237009 1.096649 -P_32 1.336546 1.226683 1.224913 1.232665 1.119886 1.060253 1.188370 1.173824 -P_32 1.124629 1.044542 1.024711 0.938772 0.886037 0.882668 0.823484 0.707487 -P_32 0.694587 0.668372 0.629989 0.600400 0.545747 0.521094 0.498007 0.492114 -P_32 0.493233 0.496326 0.509712 0.512376 0.623140 0.692669 0.738114 0.740240 -P_32 0.908010 1.058593 0.930459 0.993710 1.170575 1.215094 1.160682 1.169690 -P_32 1.282318 1.224939 1.163657 1.246696 1.098099 1.139757 1.155131 1.120514 -P_32 0.992122 0.993114 1.127925 1.034802 1.173389 1.150520 1.204949 1.161579 -P_32 1.221479 1.060738 1.054917 1.085826 1.022156 0.960359 0.933650 0.766111 -P_32 0.694216 0.621467 0.528352 0.539844 0.502886 0.467052 0.473140 0.485314 -P_32 0.589805 0.550051 0.724357 0.764421 0.879426 1.014755 1.081750 1.190746 -P_32 1.295559 1.333852 1.432583 1.452053 1.422797 1.540096 1.429930 1.506026 -P_32 1.370932 1.355976 1.271052 1.218014 1.113742 1.036340 1.028482 1.056915 -P_32 1.123540 1.072455 1.080582 1.083228 1.188534 1.164943 1.202959 1.144442 -P_32 1.155893 1.215487 1.192432 1.113341 1.134883 1.033546 0.959481 0.820291 -P_32 0.795488 0.674950 0.614847 0.537229 0.464012 0.474724 0.452990 0.477501 -P_32 0.539165 0.580765 0.715975 0.737209 0.801220 1.041435 1.060824 1.127071 -P_32 1.139683 1.362349 1.365736 1.418269 1.496261 1.338022 1.310052 1.265366 -P_32 1.464775 1.293391 1.339174 1.127290 1.176069 1.018045 1.211751 1.028808 -P_32 1.079578 1.094842 1.197268 1.196160 1.242867 1.249726 1.229105 1.313377 -P_32 1.260411 1.246907 1.188997 1.158892 1.150714 1.078493 0.909254 0.849843 -P_32 0.757888 0.604059 0.582082 0.501363 0.464545 0.447137 0.505059 0.471298 -P_32 0.532082 0.611408 0.750434 0.869406 0.848780 1.008034 1.256066 1.102257 -P_32 1.279817 1.232635 1.398539 1.387300 1.309200 1.426684 1.441993 1.365609 -P_32 1.417425 1.418714 1.410207 1.253208 1.229306 1.131178 1.230691 1.131322 -P_32 1.129353 1.148455 1.082811 1.197146 1.135966 1.139092 1.256174 1.325352 -P_32 1.236303 1.157096 1.322240 1.130173 1.091265 1.009761 0.854610 0.889840 -P_32 0.764512 0.646677 0.596214 0.528247 0.518435 0.398337 0.474020 0.466919 -P_32 0.573706 0.641725 0.750460 0.869380 0.938044 0.976979 1.133153 1.244701 -P_32 1.371104 1.255347 1.244949 1.442038 1.444214 1.348942 1.405922 1.325119 -P_32 1.249117 1.223266 1.230338 1.173348 1.162068 1.148365 1.107771 1.104361 -P_32 1.058526 1.166808 1.172754 1.206884 1.210361 1.202060 1.292266 1.239602 -P_32 1.223067 1.196205 1.248825 1.129233 1.138814 1.085932 1.016473 0.841585 -P_32 0.774234 0.716938 0.601253 0.549374 0.529856 0.477942 0.510913 0.524266 -P_32 0.649509 0.641278 0.778055 0.975880 1.011242 1.145158 1.343395 1.290588 -P_32 1.515984 1.613751 1.693111 1.659317 1.658872 1.634354 1.569946 1.474962 -P_32 1.365710 1.335692 1.303284 1.264097 1.172204 1.206018 1.306299 1.289558 -P_32 1.173713 1.186933 1.128343 1.255683 1.200725 1.389471 1.243924 1.347591 -P_32 1.059338 1.210296 1.178967 1.107023 1.024993 0.998706 0.990652 0.877718 -P_32 0.803286 0.731645 0.659410 0.617448 0.608044 0.489258 0.556674 0.529695 -P_32 0.553918 0.555776 0.601850 0.693480 0.759453 0.778117 0.866414 0.956860 -P_32 1.093436 1.216477 1.244178 1.281815 1.380826 1.308746 1.361109 1.295435 -P_32 1.317634 1.238221 1.308214 1.292902 1.216358 1.157969 1.201216 1.355831 -P_32 1.158180 1.368051 1.255676 1.184569 1.250449 1.125348 1.131530 1.186134 -P_32 1.031174 1.020389 1.010815 0.928000 0.887086 0.875833 0.833329 0.800483 -P_32 0.748079 0.655123 0.598646 0.605042 0.548873 0.546192 0.538509 0.510060 -P_32 0.536690 0.477041 0.532169 0.524790 0.655695 0.681223 0.744793 0.742929 -P_32 0.873840 0.930449 1.001999 0.995743 1.249432 1.262737 1.159343 1.236881 -P_32 1.325255 1.361518 1.346056 1.192379 1.150668 1.248942 1.109795 1.144873 -P_32 1.144979 1.047043 1.090087 1.073192 1.096118 1.260347 1.134841 1.174757 -P_32 1.157886 1.208347 1.074190 1.030246 0.905684 0.949409 0.843683 0.782718 -P_32 0.739210 0.572135 0.528956 0.533629 0.477317 0.467483 0.447304 0.509855 -P_32 0.539251 0.649230 0.673677 0.818989 0.914812 0.977568 1.087700 1.302301 -P_32 1.222003 1.439079 1.606343 1.437739 1.579789 1.626741 1.566411 1.410588 -P_32 1.415418 1.357068 1.363022 1.282050 1.111787 1.070079 1.072507 1.062965 -P_32 0.930500 1.165616 1.057262 1.029135 1.216578 1.246472 1.290256 1.204788 -P_32 1.293566 1.323826 1.263418 1.236493 1.075525 1.088365 0.951986 0.872497 -P_32 0.755387 0.671672 0.606604 0.523016 0.471451 0.452697 0.492164 0.525597 -P_32 0.526988 0.568303 0.637195 0.788953 0.835402 0.982962 1.086117 1.140674 -P_32 1.237387 1.297130 1.419694 1.412621 1.583574 1.472336 1.361582 1.360332 -P_32 1.405681 1.383508 1.356387 1.229157 1.039638 1.108315 1.193626 1.027180 -P_32 1.162433 1.220096 1.127497 1.276990 1.164115 1.282765 1.307192 1.192817 -P_32 1.330725 1.297689 1.261236 1.227916 1.184149 1.051078 0.923059 0.815256 -P_32 0.750672 0.657564 0.599525 0.499351 0.486626 0.446553 0.472958 0.523191 -P_32 0.595640 0.603452 0.748138 0.797476 0.893855 0.976726 1.060417 1.207140 -P_32 1.147721 1.243975 1.331843 1.554150 1.410542 1.563040 1.347914 1.349076 -P_32 1.435331 1.501593 1.276454 1.270862 1.249005 1.239908 1.141418 1.162298 -P_32 1.259407 1.050859 1.244802 1.246919 1.245637 1.146741 1.288027 1.207251 -P_32 1.425667 1.237320 1.206135 1.245120 1.085970 1.007259 0.997216 0.886302 -P_32 0.807207 0.697941 0.617138 0.525194 0.446888 0.457436 0.509595 0.484733 -P_32 0.535965 0.651922 0.757198 0.893112 1.001328 1.114017 1.076929 1.391043 -P_32 1.355148 1.381815 1.337920 1.403972 1.472046 1.508310 1.504572 1.360124 -P_32 1.195315 1.271252 1.264461 1.243473 1.177316 1.165026 1.158122 1.111153 -P_32 1.151268 1.143607 1.293284 1.329734 1.308794 1.273551 1.382635 1.283191 -P_32 1.351477 1.194590 1.227109 1.093465 1.051856 1.013993 0.969147 0.907382 -P_32 0.818482 0.700855 0.612806 0.594481 0.558198 0.448630 0.519473 0.571608 -P_32 0.629680 0.712021 0.755169 0.909354 0.960537 1.132438 1.283361 1.350217 -P_32 1.541374 1.662803 1.614302 1.589933 1.576191 1.471667 1.635745 1.616430 -P_32 1.510569 1.402320 1.335194 1.332556 1.341786 1.173576 1.179064 1.246963 -P_32 1.190713 1.319881 1.207981 1.291595 1.340834 1.240206 1.291211 1.275160 -P_32 1.202891 1.354192 1.116369 1.102295 1.005508 1.167455 0.959149 0.890431 -P_32 0.759699 0.774061 0.688469 0.658515 0.612554 0.596723 0.514848 0.528286 -P_32 0.551298 0.570892 0.585323 0.658172 0.765315 0.850863 0.926668 1.025461 -P_32 1.071597 1.128417 1.299512 1.331989 1.362957 1.461261 1.367652 1.394705 -P_32 1.349192 1.429806 1.346088 1.366397 1.345232 1.349448 1.288620 1.267455 -P_32 1.205607 1.312218 1.273546 1.200352 1.206966 1.239581 1.410938 1.115687 -P_32 1.280797 1.116309 1.062521 0.931264 0.932138 0.826491 0.811868 0.804301 -P_32 0.736487 0.723033 0.620281 0.620382 0.557269 0.533340 0.537474 0.527362 -P_32 0.525047 0.520303 0.507074 0.552786 0.611357 0.678843 0.786133 0.814342 -P_32 0.858936 0.930996 0.980058 1.035322 1.119476 1.125659 1.187337 1.253768 -P_32 1.206546 1.193684 1.368405 1.400267 1.139019 1.229827 1.131622 1.226565 -P_32 1.102868 1.084212 1.175815 1.137198 1.078721 1.094668 1.258192 1.155355 -P_32 1.209800 1.137129 1.086136 1.151236 1.142124 0.960228 0.870664 0.752315 -P_32 0.774339 0.691410 0.583104 0.528705 0.473186 0.505575 0.503147 0.501831 -P_32 0.570173 0.587614 0.694914 0.823525 0.905985 1.017033 1.072146 1.138107 -P_32 1.392349 1.590298 1.436833 1.595328 1.578820 1.645872 1.553754 1.543180 -P_32 1.576048 1.437695 1.359750 1.361977 1.206971 1.251428 1.103077 0.997042 -P_32 1.054374 1.021753 1.085211 1.040918 1.145565 1.231577 1.203398 1.301860 -P_32 1.285706 1.313781 1.356946 1.151248 1.133118 1.084816 0.969063 0.793157 -P_32 0.770820 0.608223 0.620100 0.559064 0.512840 0.489172 0.480880 0.510022 -P_32 0.598960 0.623854 0.711041 0.721089 0.963299 0.909368 1.192289 1.267431 -P_32 1.208830 1.431305 1.473952 1.422340 1.413037 1.551065 1.518874 1.431336 -P_32 1.416928 1.332713 1.344243 1.366970 1.201980 1.305342 1.175018 1.060947 -P_32 1.145239 1.170056 1.231678 1.282452 1.344862 1.325755 1.268028 1.359160 -P_32 1.346359 1.249733 1.309366 1.237197 1.151079 1.104262 0.956407 0.840026 -P_32 0.683833 0.691742 0.570121 0.593075 0.474515 0.458922 0.491162 0.499592 -P_32 0.580137 0.662466 0.766737 0.886821 0.805877 1.042853 1.126166 1.234261 -P_32 1.326616 1.382193 1.447858 1.423598 1.588243 1.476774 1.496633 1.451406 -P_32 1.530760 1.392126 1.473686 1.342196 1.226825 1.284965 1.134251 1.281103 -P_32 1.181052 1.237173 1.102697 1.250083 1.237282 1.071177 1.449504 1.424939 -P_32 1.328826 1.330451 1.283536 1.043035 1.196030 1.060827 0.979174 0.848150 -P_32 0.795703 0.731856 0.637343 0.587303 0.552271 0.503555 0.460315 0.565593 -P_32 0.579038 0.603899 0.764133 0.844866 1.027877 1.118979 1.220777 1.316601 -P_32 1.309673 1.455433 1.441030 1.418038 1.426734 1.511555 1.506653 1.379800 -P_32 1.325318 1.230467 1.372281 1.299715 1.189968 1.190841 1.156777 1.279836 -P_32 1.136311 1.183696 1.228493 1.243314 1.224144 1.354185 1.280256 1.328479 -P_32 1.365481 1.224411 1.313628 1.294251 1.141213 1.069835 1.055165 0.948283 -P_32 0.742081 0.715111 0.666756 0.638525 0.549418 0.536807 0.503248 0.548940 -P_32 0.549301 0.675595 0.867716 1.002236 1.022969 1.377727 1.267066 1.499071 -P_32 1.481407 1.709068 1.825749 1.625119 1.636370 1.528354 1.658967 1.607147 -P_32 1.428708 1.559556 1.498262 1.441118 1.246477 1.289527 1.320405 1.217310 -P_32 1.373712 1.234488 1.269034 1.177022 1.325318 1.314544 1.348731 1.291418 -P_32 1.327318 1.085634 1.295613 1.075182 1.140946 0.997111 0.996472 0.857627 -P_32 0.891475 0.789815 0.640479 0.638670 0.600142 0.517180 0.531419 0.517067 -P_32 0.492395 0.593453 0.624196 0.717617 0.768118 0.823038 0.964099 1.000364 -P_32 1.184847 1.195348 1.345563 1.265799 1.333163 1.444978 1.319235 1.438065 -P_32 1.391336 1.397085 1.341873 1.417281 1.327812 1.332509 1.302848 1.267672 -P_32 1.372130 1.282434 1.194275 1.345523 1.279227 1.278478 1.242196 1.183328 -P_32 1.103063 1.166205 1.089985 0.995004 0.933225 0.865567 0.873467 0.739508 -P_32 0.756267 0.752065 0.701008 0.624571 0.569107 0.541613 0.553142 0.537346 -P_32 0.528029 0.548529 0.553341 0.577943 0.641603 0.708588 0.710290 0.827065 -P_32 0.870095 0.931224 0.963968 1.177216 1.344363 1.340584 1.291092 1.403404 -P_32 1.213406 1.286440 1.193546 1.236685 1.284182 1.194047 1.179060 1.068671 -P_32 1.131221 1.106347 1.135658 1.160915 1.214338 1.210653 1.330167 1.247468 -P_32 1.230884 1.158850 1.116897 1.135745 1.059519 1.015616 0.895302 0.783455 -P_32 0.763714 0.674487 0.610938 0.533375 0.520506 0.456009 0.475922 0.519537 -P_32 0.557918 0.643487 0.708168 0.888963 0.865090 0.949824 1.021474 1.255519 -P_32 1.274881 1.370188 1.437193 1.547730 1.472662 1.649683 1.600540 1.693620 -P_32 1.601792 1.580051 1.263910 1.169902 1.209511 1.138872 1.125302 1.146849 -P_32 1.056405 1.079635 1.162324 1.208275 1.230792 1.230919 1.437273 1.447582 -P_32 1.317346 1.383990 1.289535 1.220455 1.090550 1.140381 0.948612 0.844147 -P_32 0.852727 0.661569 0.544630 0.561672 0.502115 0.471063 0.482152 0.545620 -P_32 0.527174 0.606372 0.773564 0.836551 0.953958 0.987502 1.014439 1.295605 -P_32 1.326919 1.426130 1.363009 1.398652 1.397171 1.467992 1.587714 1.476754 -P_32 1.353859 1.445445 1.386131 1.276954 1.315154 1.222375 1.259208 1.118273 -P_32 1.191559 1.220544 1.171295 1.188104 1.257781 1.281641 1.319278 1.326436 -P_32 1.272635 1.294189 1.372319 1.212413 1.118838 1.029422 0.931207 0.902073 -P_32 0.801794 0.675123 0.673431 0.578678 0.522241 0.488879 0.443880 0.553151 -P_32 0.607633 0.701949 0.742882 0.851894 0.919573 1.182904 1.198086 1.279492 -P_32 1.424484 1.320897 1.502774 1.651378 1.544334 1.540799 1.470498 1.492573 -P_32 1.284045 1.522822 1.398326 1.293984 1.300371 1.204796 1.285200 1.358385 -P_32 1.321981 1.200746 1.195302 1.236239 1.235216 1.371133 1.202535 1.298558 -P_32 1.353797 1.270092 1.271984 1.215814 1.187921 1.090699 1.011026 0.929584 -P_32 0.841224 0.669692 0.612124 0.588700 0.536003 0.512603 0.494023 0.543049 -P_32 0.607673 0.676354 0.690750 0.879805 0.988625 1.107733 1.185321 1.298819 -P_32 1.379709 1.430940 1.434985 1.668334 1.478002 1.454012 1.571894 1.432701 -P_32 1.278858 1.361960 1.257188 1.318230 1.292598 1.191826 1.209993 1.265833 -P_32 1.100240 1.235112 1.293084 1.172668 1.263806 1.429443 1.436509 1.294265 -P_32 1.305714 1.294621 1.246740 1.249786 1.290311 1.110193 0.940270 0.931471 -P_32 0.853082 0.773687 0.630330 0.592419 0.538458 0.511094 0.603796 0.542920 -P_32 0.658377 0.712266 0.810818 0.999826 1.048896 1.165932 1.438478 1.614238 -P_32 1.549961 1.792126 1.697752 1.871374 1.700510 1.825108 1.658093 1.608460 -P_32 1.641732 1.551032 1.387887 1.419166 1.123829 1.419155 1.227004 1.297604 -P_32 1.248299 1.228294 1.202088 1.417295 1.230034 1.306480 1.274054 1.420253 -P_32 1.152439 1.230693 1.201522 1.225293 1.203463 1.055383 1.063706 0.957260 -P_32 0.868215 0.803642 0.737491 0.613717 0.625481 0.554822 0.576777 0.549783 -P_32 0.544266 0.578563 0.676965 0.762156 0.757977 0.934497 0.886750 1.029519 -P_32 1.248212 1.383016 1.321698 1.350414 1.500881 1.414710 1.490164 1.410279 -P_32 1.484894 1.428713 1.346062 1.408938 1.282387 1.415512 1.224514 1.428165 -P_32 1.150612 1.261797 1.239741 1.290318 1.335045 1.386429 1.250625 1.371961 -P_32 1.203048 1.214160 1.022686 1.045771 0.911147 0.875445 0.858396 0.818041 -P_32 0.787524 0.709828 0.692024 0.646762 0.627814 0.534167 0.524558 0.552076 -P_32 0.569001 0.563177 0.548018 0.614178 0.691794 0.698311 0.910356 0.813214 -P_32 0.995970 1.054767 1.140155 1.056277 1.265571 1.287395 1.267222 1.341925 -P_32 1.229917 1.237595 1.265055 1.237368 1.277171 1.213844 1.208223 1.328248 -P_32 1.187269 1.139245 1.221258 1.080533 1.279881 1.216627 1.315305 1.294526 -P_32 1.242475 1.295690 1.101226 1.098310 1.088061 0.996177 0.939163 0.821904 -P_32 0.740999 0.654480 0.569818 0.499302 0.496132 0.457841 0.451308 0.533897 -P_32 0.594764 0.667529 0.761217 0.823942 0.914314 1.069731 1.069530 1.376041 -P_32 1.395994 1.413044 1.556054 1.665866 1.669639 1.564037 1.591334 1.469406 -P_32 1.393993 1.578902 1.301807 1.321481 1.222534 1.254980 1.158789 1.075953 -P_32 1.135131 1.236384 1.200482 1.234107 1.207604 1.234486 1.272644 1.357813 -P_32 1.364806 1.443050 1.279302 1.291998 1.068207 1.089686 1.004617 0.971914 -P_32 0.829106 0.661268 0.604121 0.595309 0.497768 0.469472 0.536023 0.476663 -P_32 0.575867 0.635136 0.774909 0.824995 0.923753 1.011783 1.216944 1.280807 -P_32 1.374802 1.424704 1.501140 1.565379 1.555031 1.554934 1.575425 1.431002 -P_32 1.480223 1.512218 1.347621 1.267031 1.254229 1.202415 1.173730 1.319779 -P_32 1.175496 1.142008 1.314189 1.318315 1.196307 1.331902 1.229354 1.394455 -P_32 1.454692 1.315063 1.409886 1.207198 1.173199 1.034809 1.079966 0.827494 -P_32 0.707534 0.654135 0.583430 0.530496 0.561968 0.498360 0.513722 0.564858 -P_32 0.642740 0.713476 0.753781 0.909611 0.906450 1.198987 1.184344 1.223241 -P_32 1.429788 1.401240 1.468404 1.658451 1.483795 1.628114 1.503016 1.508950 -P_32 1.416337 1.419264 1.463437 1.440382 1.273579 1.303062 1.319095 1.340110 -P_32 1.211650 1.208035 1.337440 1.261783 1.180430 1.298301 1.279848 1.294845 -P_32 1.388768 1.371069 1.331467 1.143274 1.208537 1.015738 0.922558 0.916448 -P_32 0.849282 0.759639 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1.044403 0.971965 -P_32 0.860059 0.701602 0.640550 0.599337 0.589706 0.563468 0.568300 0.561479 -P_32 0.708465 0.729947 0.893486 0.955917 1.179356 1.298349 1.494011 1.590037 -P_32 1.522611 1.906725 1.755676 1.766363 1.711762 1.624562 1.888609 1.641683 -P_32 1.703016 1.553500 1.606576 1.546018 1.481029 1.426753 1.389707 1.429198 -P_32 1.346545 1.415876 1.394566 1.480302 1.474426 1.494255 1.298231 1.498907 -P_32 1.397792 1.365151 1.277089 1.297496 1.120866 1.119090 1.036477 0.898935 -P_32 0.982648 0.749468 0.662516 0.743022 0.674211 0.654965 0.610081 0.602212 -P_32 0.604180 0.629885 0.678189 0.806375 0.876330 0.889875 1.094869 1.119011 -P_32 1.125268 1.200439 1.336708 1.412420 1.503220 1.523535 1.643463 1.398280 -P_32 1.526196 1.577566 1.517202 1.407411 1.470967 1.531427 1.509867 1.361246 -P_32 1.458086 1.435607 1.349613 1.364800 1.393935 1.217153 1.366918 1.258214 -P_32 1.239159 1.317265 1.111798 1.030732 1.065880 1.024244 0.894581 0.843765 -P_32 0.718190 0.741192 0.758960 0.633864 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1.412541 1.680703 1.496933 1.594292 1.583033 1.551996 1.430952 1.462833 -P_32 1.544773 1.350699 1.426783 1.332419 1.247412 1.376837 1.323882 1.334337 -P_32 1.335239 1.406273 1.380136 1.272052 1.327919 1.417661 1.375852 1.350308 -P_32 1.492636 1.590570 1.368290 1.305708 1.293340 1.155422 1.133905 0.972292 -P_32 0.773810 0.753622 0.741871 0.603532 0.586460 0.553362 0.577601 0.567706 -P_32 0.639400 0.756989 0.900894 1.078398 1.179162 1.262198 1.412876 1.610072 -P_32 1.815553 1.704744 1.880096 1.884164 1.774175 1.852571 1.706239 1.697498 -P_32 1.789010 1.592099 1.500886 1.451538 1.316774 1.351233 1.262137 1.352094 -P_32 1.360738 1.431566 1.328896 1.361434 1.396530 1.407716 1.430113 1.292627 -P_32 1.329657 1.308651 1.391901 1.254689 1.214490 1.175653 1.075648 0.997450 -P_32 0.895712 0.790345 0.776215 0.648128 0.690275 0.664894 0.564487 0.578817 -P_32 0.604418 0.583633 0.636738 0.791357 0.830813 0.942206 1.059200 1.163047 -P_32 1.241382 1.281805 1.418314 1.437589 1.446112 1.516867 1.521125 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0.547953 0.591087 -P_32 0.647606 0.714176 0.783473 0.862351 1.005916 1.124960 1.128676 1.268902 -P_32 1.297263 1.473609 1.405076 1.524607 1.586538 1.606270 1.604630 1.538083 -P_32 1.538072 1.569073 1.405960 1.364085 1.247904 1.255293 1.266859 1.220523 -P_32 1.180753 1.216563 1.201124 1.353979 1.282863 1.351582 1.297638 1.308160 -P_32 1.311530 1.343352 1.429379 1.318686 1.262473 1.092139 1.089537 0.942707 -P_32 0.864367 0.788265 0.674652 0.546622 0.547534 0.479888 0.516852 0.529577 -P_32 0.624677 0.721516 0.764024 0.910252 0.981554 1.124134 1.155121 1.401914 -P_32 1.468643 1.470522 1.493994 1.607726 1.621130 1.554260 1.544448 1.573283 -P_32 1.461850 1.425273 1.410435 1.313290 1.312686 1.307054 1.244952 1.320858 -P_32 1.221267 1.214645 1.326890 1.302596 1.315921 1.264319 1.311983 1.388633 -P_32 1.354505 1.475537 1.368986 1.304239 1.220291 1.131750 1.106105 0.941490 -P_32 0.882049 0.769105 0.671850 0.646356 0.556287 0.535367 0.577800 0.571133 -P_32 0.651542 0.756744 0.870020 1.110540 1.209731 1.320688 1.456497 1.674031 -P_32 1.602700 1.691667 1.728628 1.817988 1.845946 1.880216 1.657927 1.641566 -P_32 1.680081 1.591274 1.417017 1.584064 1.379202 1.338970 1.396557 1.281417 -P_32 1.268921 1.302855 1.386610 1.377343 1.300168 1.352076 1.385459 1.299345 -P_32 1.322624 1.235938 1.280286 1.200647 1.099801 1.177401 1.084310 0.974429 -P_32 0.918602 0.813551 0.782341 0.721457 0.674351 0.595610 0.580095 0.557633 -P_32 0.515374 0.563541 0.604521 0.744633 0.817684 0.909969 1.038531 1.130245 -P_32 1.138840 1.193205 1.363697 1.340876 1.552677 1.525085 1.513373 1.370936 -P_32 1.404523 1.505140 1.313523 1.373724 1.367746 1.517844 1.367858 1.210346 -P_32 1.382306 1.299544 1.325569 1.316508 1.305408 1.353138 1.390844 1.230362 -P_32 1.207725 1.126263 1.117164 1.066535 0.955791 0.964933 0.896489 0.767313 -P_32 0.812564 0.738799 0.711629 0.628742 0.685147 0.585646 0.603404 0.537240 -P_32 0.570384 0.598115 0.620127 0.616373 0.663580 0.715398 0.825285 0.896886 -P_32 0.951068 0.978259 1.020066 1.238855 1.256510 1.394040 1.393917 1.408909 -P_32 1.479339 1.267412 1.496995 1.425213 1.233752 1.159863 1.238528 1.294860 -P_32 1.186331 1.188250 1.308126 1.285376 1.171851 1.302156 1.277873 1.275187 -P_32 1.344630 1.253032 1.198935 1.305306 1.050695 1.081219 0.926769 0.760693 -P_32 0.773369 0.725945 0.670127 0.570893 0.570458 0.547245 0.496273 0.478639 -P_32 0.589717 0.629668 0.802507 0.838327 0.905971 1.073813 1.153949 1.412100 -P_32 1.378538 1.525846 1.499609 1.767548 1.740778 1.718946 1.613083 1.533653 -P_32 1.582084 1.562568 1.451560 1.306935 1.372243 1.176046 1.208417 1.144115 -P_32 1.139145 1.212354 1.174833 1.079061 1.392025 1.387429 1.305457 1.459576 -P_32 1.222968 1.301957 1.265191 1.272211 1.135629 1.066576 1.083953 1.008506 -P_32 0.797225 0.780982 0.631116 0.607113 0.506417 0.496284 0.534887 0.530741 -P_32 0.555229 0.675471 0.736956 0.811359 0.954378 1.138127 1.231172 1.194158 -P_32 1.381908 1.485006 1.685223 1.554457 1.427338 1.679333 1.700042 1.666628 -P_32 1.502718 1.532672 1.375900 1.277666 1.407145 1.302648 1.240593 1.234906 -P_32 1.156226 1.091170 1.196709 1.250613 1.291089 1.289041 1.398982 1.472838 -P_32 1.237945 1.293453 1.134000 1.377979 1.216311 1.222879 1.088858 0.850769 -P_32 0.830623 0.727079 0.659877 0.549929 0.533271 0.494178 0.504844 0.519772 -P_32 0.645417 0.686233 0.760852 0.893717 0.974780 1.128195 1.113718 1.288911 -P_32 1.442369 1.423644 1.343142 1.501796 1.544305 1.538939 1.426126 1.485312 -P_32 1.455531 1.415940 1.359594 1.342615 1.343523 1.405545 1.296617 1.197815 -P_32 1.330890 1.265153 1.195861 1.341544 1.265068 1.304221 1.325477 1.505945 -P_32 1.449872 1.457606 1.355309 1.247266 1.118446 1.203615 1.079632 0.946836 -P_32 0.836803 0.775005 0.612673 0.619317 0.552917 0.473650 0.527154 0.553074 -P_32 0.578952 0.668238 0.783417 0.954216 0.915328 1.133511 1.206110 1.353217 -P_32 1.337395 1.522518 1.482250 1.521871 1.454579 1.427618 1.481697 1.496545 -P_32 1.303650 1.379001 1.518887 1.202882 1.278564 1.237464 1.171030 1.285611 -P_32 1.261139 1.408383 1.244441 1.300667 1.325387 1.300205 1.368325 1.288941 -P_32 1.391652 1.338843 1.366369 1.324882 1.282893 1.202580 1.112778 1.045784 -P_32 0.887345 0.726316 0.696539 0.640250 0.547761 0.568715 0.526988 0.559872 -P_32 0.631187 0.726302 0.803130 0.991687 1.167526 1.188070 1.549708 1.532574 -P_32 1.615199 1.673736 1.654898 1.708763 1.778257 1.724641 1.755921 1.574853 -P_32 1.414805 1.552690 1.534933 1.549942 1.438422 1.340367 1.318647 1.323841 -P_32 1.372694 1.287495 1.315830 1.373872 1.310673 1.427578 1.423873 1.318945 -P_32 1.296782 1.253099 1.232828 1.287745 1.188090 1.032601 1.007397 0.875498 -P_32 0.882378 0.859950 0.705743 0.639635 0.634451 0.565481 0.594921 0.518268 -P_32 0.566349 0.595475 0.616381 0.714068 0.807002 0.935833 1.042770 1.041470 -P_32 1.265354 1.167537 1.305560 1.431909 1.411113 1.359607 1.534957 1.258357 -P_32 1.504396 1.405514 1.382282 1.312724 1.307823 1.455881 1.357460 1.269083 -P_32 1.345898 1.412832 1.312287 1.299088 1.272915 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1.086207 1.209553 -P_32 1.296097 1.387763 1.642216 1.669710 1.545814 1.522593 1.677414 1.570610 -P_32 1.539718 1.453770 1.278715 1.296660 1.309428 1.218393 1.122585 1.126110 -P_32 1.177385 1.056831 1.124874 1.165827 1.174984 1.195633 1.307648 1.305445 -P_32 1.184329 1.377977 1.343556 1.202669 1.191618 1.120780 0.989357 0.828352 -P_32 0.734440 0.657739 0.660551 0.563367 0.539247 0.491972 0.496692 0.508512 -P_32 0.523725 0.617953 0.714297 0.837311 0.879880 1.120908 1.144574 1.212206 -P_32 1.371691 1.468596 1.423368 1.554157 1.548567 1.549610 1.490494 1.502088 -P_32 1.402195 1.434716 1.206373 1.261693 1.173689 1.188824 1.233857 1.155080 -P_32 1.102842 1.150588 1.199105 1.298391 1.273667 1.294923 1.341638 1.349875 -P_32 1.305406 1.235307 1.242148 1.115718 1.197200 1.108647 0.991029 0.853276 -P_32 0.778852 0.682326 0.631738 0.553096 0.484135 0.484176 0.486470 0.516934 -P_32 0.620700 0.664906 0.734815 0.806910 0.945456 1.171976 1.182760 1.207294 -P_32 1.330049 1.357841 1.531605 1.358644 1.501693 1.443648 1.407980 1.480653 -P_32 1.422280 1.399089 1.224539 1.394588 1.309635 1.319858 1.159805 1.122520 -P_32 1.154121 1.245862 1.153381 1.228543 1.234982 1.295402 1.305533 1.210717 -P_32 1.275990 1.321359 1.319518 1.253016 1.222942 1.030852 1.004129 0.923835 -P_32 0.821014 0.667871 0.613488 0.512828 0.528502 0.480858 0.502473 0.503364 -P_32 0.577564 0.618898 0.711908 0.787103 0.983777 1.253426 1.242569 1.283457 -P_32 1.383351 1.423033 1.339913 1.379124 1.537403 1.442077 1.414402 1.299625 -P_32 1.453556 1.362272 1.205291 1.245852 1.362094 1.161407 1.160519 1.218074 -P_32 1.147038 1.197993 1.283164 1.311847 1.282635 1.166015 1.354187 1.372761 -P_32 1.264269 1.288169 1.291366 1.142173 1.154193 1.172129 0.979921 0.959414 -P_32 0.845965 0.725173 0.631138 0.559811 0.499108 0.478745 0.475933 0.519211 -P_32 0.613899 0.741317 0.942632 1.005568 1.067054 1.211391 1.263846 1.540593 -P_32 1.520151 1.589447 1.701973 1.703262 1.515545 1.726327 1.604931 1.661382 -P_32 1.456187 1.363691 1.254273 1.353288 1.226223 1.224222 1.313568 1.268278 -P_32 1.092812 1.288356 1.419522 1.203739 1.217158 1.293566 1.289890 1.265123 -P_32 1.247298 1.224762 1.155044 1.269992 1.032610 0.996188 1.040292 0.867231 -P_32 0.823779 0.783397 0.754100 0.594328 0.605412 0.575851 0.517243 0.493083 -P_32 0.520293 0.582707 0.645064 0.659648 0.702732 0.790507 0.969652 1.068035 -P_32 1.183722 1.198039 1.243358 1.319744 1.382942 1.303028 1.283647 1.419405 -P_32 1.376306 1.284340 1.326518 1.325750 1.269062 1.264537 1.300419 1.386468 -P_32 1.215277 1.260965 1.300538 1.324501 1.319452 1.219253 1.135814 1.283052 -P_32 1.165566 1.083247 0.991879 0.952338 0.950648 0.865585 0.836029 0.784759 -P_32 0.733046 0.728612 0.631287 0.572922 0.538916 0.607984 0.546447 0.543284 -P_32 0.516418 0.495179 0.549346 0.548064 0.619748 0.758000 0.708875 0.812418 -P_32 0.911925 0.989313 1.036720 1.149669 1.082044 1.305690 1.131196 1.293314 -P_32 1.293856 1.258372 1.235845 1.272486 1.272630 1.183410 1.140358 1.136013 -P_32 1.106414 1.154749 1.135283 1.146368 1.141972 1.170197 1.147956 1.203726 -P_32 1.175050 1.226979 1.161295 1.194045 0.999426 0.920382 0.846412 0.844686 -P_32 0.696438 0.678978 0.554956 0.557365 0.506612 0.453175 0.443369 0.484729 -P_32 0.553263 0.592417 0.584000 0.823217 0.894903 0.981220 1.118634 1.207151 -P_32 1.343662 1.326085 1.345771 1.621611 1.674865 1.490027 1.415603 1.415208 -P_32 1.288538 1.514263 1.427785 1.285709 1.197000 1.097012 1.114203 1.137849 -P_32 1.047848 1.128882 1.002469 1.176979 1.145164 1.319780 1.250885 1.188486 -P_32 1.238643 1.354641 1.253313 1.118158 1.140558 1.047335 1.017118 0.835013 -P_32 0.759784 0.690568 0.570204 0.550263 0.493205 0.460181 0.515450 0.486392 -P_32 0.557244 0.615890 0.743641 0.786340 0.869298 0.965770 1.005440 1.162942 -P_32 1.298931 1.311909 1.375103 1.514771 1.574940 1.503514 1.467973 1.542354 -P_32 1.196157 1.304204 1.349527 1.287324 1.240950 1.089122 1.196242 1.151612 -P_32 1.168954 1.150651 1.176757 1.241689 1.178910 1.300524 1.252272 1.213520 -P_32 1.323899 1.257499 1.319241 1.088586 1.133016 1.020984 0.955896 0.870842 -P_32 0.709400 0.653727 0.534184 0.529912 0.491220 0.487473 0.445589 0.522097 -P_32 0.561390 0.649847 0.778378 0.798438 0.942798 1.030590 1.246822 1.148140 -P_32 1.229076 1.270681 1.311666 1.384762 1.489116 1.418834 1.528577 1.472419 -P_32 1.564514 1.340711 1.245721 1.284434 1.275707 1.244707 1.223299 1.164023 -P_32 1.065033 1.235694 1.242744 1.198485 1.226444 1.175279 1.225944 1.179353 -P_32 1.292615 1.417631 1.129326 1.249660 1.164771 1.092193 0.904291 0.793215 -P_32 0.750875 0.631189 0.563514 0.576970 0.484094 0.457828 0.460260 0.511058 -P_32 0.599304 0.629283 0.760034 0.803549 0.936375 1.121681 1.178895 1.234651 -P_32 1.318177 1.403686 1.408343 1.421006 1.342156 1.365595 1.334361 1.354700 -P_32 1.403667 1.139439 1.222393 1.159768 1.222419 1.183395 1.163062 1.064540 -P_32 1.193502 1.151950 1.169769 1.132112 1.290819 1.301054 1.171146 1.266313 -P_32 1.196956 1.284278 1.261409 1.239191 1.118561 1.104890 0.963875 0.894814 -P_32 0.875240 0.716956 0.666649 0.535260 0.511302 0.492863 0.518923 0.539550 -P_32 0.591960 0.589623 0.732363 0.932219 1.043256 1.152760 1.127088 1.303013 -P_32 1.631657 1.578770 1.552671 1.664468 1.680015 1.655594 1.494285 1.359898 -P_32 1.523122 1.459691 1.431807 1.272824 1.351636 1.199991 1.176087 1.266098 -P_32 1.145937 1.043980 1.088549 1.325615 1.245313 1.204085 1.248665 1.291265 -P_32 1.203896 1.140575 1.177966 1.186915 1.104615 1.092495 0.914158 0.856970 -P_32 0.885903 0.709960 0.687720 0.623914 0.563956 0.545588 0.494359 0.526587 -P_32 0.575102 0.584884 0.588086 0.667250 0.722108 0.865054 0.877941 0.984480 -P_32 1.096735 1.082461 1.193867 1.351384 1.312085 1.341413 1.442976 1.364382 -P_32 1.270684 1.239407 1.376190 1.400255 1.221986 1.331075 1.222866 1.134021 -P_32 1.311383 1.251876 1.212646 1.265345 1.323576 1.260406 1.252125 1.104693 -P_32 1.129975 1.096178 1.055411 1.033802 0.932508 0.928034 0.811595 0.812259 -P_32 0.682876 0.666336 0.587850 0.598391 0.591196 0.511355 0.520856 0.505727 -P_32 0.465118 0.501339 0.493630 0.544018 0.581604 0.666037 0.740139 0.860018 -P_32 0.909415 0.941523 0.971243 1.127780 1.173955 1.176982 1.241149 1.270595 -P_32 1.240064 1.267570 1.221753 1.194849 1.140921 1.181378 1.142708 1.050503 -P_32 1.143788 1.092091 1.085810 1.127345 1.197082 1.238099 1.167005 1.133305 -P_32 1.171111 1.103587 1.114361 1.001341 0.935709 0.882264 0.863914 0.776712 -P_32 0.688536 0.654907 0.539875 0.531751 0.479150 0.427276 0.404456 0.510482 -P_32 0.553686 0.581594 0.652263 0.738878 0.931117 0.948207 1.125835 1.174784 -P_32 1.297671 1.454533 1.513407 1.487500 1.559502 1.577524 1.434528 1.484198 -P_32 1.374547 1.365916 1.287731 1.229509 1.199299 1.059452 1.083609 1.047842 -P_32 1.063854 1.129238 1.104690 1.154149 1.180340 1.166064 1.146255 1.243258 -P_32 1.234240 1.242345 1.123599 1.053136 1.127061 1.102935 0.927884 0.880713 -P_32 0.741509 0.690622 0.562207 0.547006 0.479105 0.441798 0.473022 0.499144 -P_32 0.489649 0.600874 0.765741 0.824957 0.919518 0.938024 1.133443 1.113021 -P_32 1.257947 1.369203 1.395972 1.416306 1.354621 1.543118 1.368613 1.403946 -P_32 1.282555 1.298861 1.353253 1.226716 1.079926 1.010969 1.086978 1.196346 -P_32 1.042092 1.121773 1.135386 1.161586 1.133520 1.368006 1.194867 1.223794 -P_32 1.288258 1.254561 1.308638 1.201507 1.104147 1.036138 0.957531 0.810758 -P_32 0.703113 0.636131 0.541647 0.474950 0.460068 0.432425 0.462234 0.511430 -P_32 0.599789 0.612845 0.664829 0.815585 0.934966 0.996422 1.089121 1.228860 -P_32 1.195306 1.372308 1.394538 1.379423 1.365986 1.471885 1.314051 1.312792 -P_32 1.474110 1.274845 1.285255 1.298314 1.266750 1.228109 1.167092 1.075930 -P_32 1.168625 1.110372 1.257569 1.196111 1.208500 1.213489 1.249338 1.347092 -P_32 1.243307 1.196595 1.086746 1.129239 1.138538 1.106843 0.978668 0.839954 -P_32 0.777969 0.647962 0.557078 0.507407 0.458543 0.474906 0.521236 0.487731 -P_32 0.549473 0.689116 0.710255 0.782186 0.881867 1.096184 1.150042 1.272217 -P_32 1.319712 1.415535 1.406197 1.478570 1.263582 1.401449 1.441593 1.298502 -P_32 1.256044 1.344801 1.236447 1.199182 1.176695 1.071946 1.162122 1.148530 -P_32 1.199906 1.104894 1.183824 1.286421 1.193284 1.253485 1.286117 1.301538 -P_32 1.316351 1.254409 1.111498 1.114758 0.989718 1.084116 0.897930 0.810228 -P_32 0.765883 0.664634 0.604873 0.578944 0.494395 0.519329 0.517479 0.561829 -P_32 0.613052 0.707603 0.812285 0.883648 0.950271 1.190733 1.196343 1.228086 -P_32 1.345761 1.537175 1.690083 1.608621 1.681801 1.483604 1.644537 1.389231 -P_32 1.422576 1.324333 1.427616 1.367914 1.279464 1.221097 1.158896 1.071461 -P_32 1.112894 1.275769 1.135419 1.269476 1.307706 1.142382 1.178394 1.327069 -P_32 1.131743 1.180815 1.172342 1.034783 0.982525 0.971166 0.889187 0.920932 -P_32 0.727862 0.745459 0.652026 0.627688 0.573036 0.486444 0.506590 0.517777 -P_32 0.502572 0.547374 0.623031 0.633409 0.794846 0.830811 0.877168 0.908261 -P_32 1.114697 1.176744 1.206099 1.249969 1.296830 1.322133 1.233821 1.227486 -P_32 1.245144 1.302291 1.232884 1.279551 1.271792 1.222840 1.179637 1.063206 -P_32 1.276364 1.246737 1.178230 1.174960 1.129983 1.252107 1.152314 1.155148 -P_32 1.055041 0.978135 0.934634 0.974269 0.876514 0.840034 0.767260 0.712995 -P_32 0.659057 0.638254 0.650534 0.598996 0.576665 0.525927 0.482558 0.478049 -P_32 0.470518 0.485303 0.488647 0.552835 0.606581 0.575748 0.697867 0.788467 -P_32 0.821223 0.992079 1.062910 0.964178 1.208184 1.130880 1.207010 1.208071 -P_32 1.339903 1.120790 1.219439 1.315884 1.234451 1.108019 1.080775 1.038195 -P_32 1.156086 1.079591 1.111164 1.032060 1.093618 1.088883 1.145539 1.017534 -P_32 1.212600 1.107591 1.009283 1.016834 0.974022 0.886335 0.816413 0.796027 -P_32 0.779660 0.553496 0.577342 0.529262 0.447910 0.461850 0.448426 0.478880 -P_32 0.524615 0.574600 0.707943 0.673623 0.870275 0.871799 1.089559 1.104649 -P_32 1.277657 1.394643 1.569574 1.339007 1.459442 1.387266 1.351339 1.427090 -P_32 1.349261 1.309467 1.195229 1.236550 1.028176 1.117734 0.951803 1.021295 -P_32 0.925471 0.914967 1.054506 1.062060 0.982711 1.164402 1.232407 1.286962 -P_32 1.231528 1.210196 1.138001 1.171895 1.039541 1.003885 0.930758 0.832944 -P_32 0.721698 0.630741 0.560627 0.491371 0.431864 0.457087 0.431043 0.438667 -P_32 0.527365 0.543198 0.653229 0.798458 0.803960 0.993140 1.103992 1.036788 -P_32 1.190436 1.213514 1.306528 1.283066 1.418137 1.412211 1.384354 1.374086 -P_32 1.267043 1.256823 1.182088 1.147411 1.206319 1.021855 0.996707 0.953019 -P_32 1.179399 1.039140 1.147131 1.165368 1.175996 1.147647 1.200753 1.272168 -P_32 1.234164 1.152249 1.191200 1.229349 1.111437 0.988302 0.849067 0.794109 -P_32 0.659641 0.662750 0.567531 0.497446 0.489489 0.435366 0.483540 0.470697 -P_32 0.547995 0.601624 0.682840 0.798150 0.846775 0.933090 1.065127 1.169380 -P_32 1.178281 1.137891 1.317278 1.368389 1.470393 1.331699 1.475056 1.386209 -P_32 1.354896 1.344997 1.273744 1.322368 1.246636 1.058468 1.139266 1.159877 -P_32 1.137111 1.105642 1.093058 1.050288 1.158959 1.191824 1.271593 1.160278 -P_32 1.163051 1.098729 1.034470 1.070826 1.063487 1.003296 0.955714 0.792145 -P_32 0.707551 0.647055 0.554190 0.491296 0.455490 0.445471 0.445803 0.488140 -P_32 0.541005 0.581998 0.634566 0.777522 0.906373 0.944402 1.106619 1.122993 -P_32 1.142017 1.206497 1.273315 1.405545 1.404228 1.309557 1.304699 1.220270 -P_32 1.362723 1.177413 1.240269 1.142740 1.112587 1.101769 1.082899 1.025144 -P_32 1.146499 1.182161 1.097502 1.152122 1.068813 1.179108 1.225955 1.218739 -P_32 1.101434 1.234177 1.214784 1.074576 1.179029 0.994816 0.877540 0.873032 -P_32 0.714339 0.638877 0.565253 0.554111 0.503375 0.442041 0.455369 0.570415 -P_32 0.579254 0.643246 0.741734 0.872058 1.007878 1.116508 1.201122 1.438377 -P_32 1.403363 1.436865 1.555304 1.531513 1.651809 1.531382 1.481000 1.469152 -P_32 1.325361 1.433578 1.401323 1.289989 1.330943 1.115118 1.186230 1.145714 -P_32 1.105613 1.137742 1.205772 1.206737 1.171467 1.186875 1.237331 1.202868 -P_32 1.195998 1.084155 1.185925 1.014940 1.081068 0.984621 0.908623 0.876861 -P_32 0.768749 0.731507 0.702841 0.542858 0.609983 0.476428 0.527753 0.487011 -P_32 0.522733 0.520483 0.587903 0.587889 0.717060 0.797472 0.980557 0.913687 -P_32 1.035650 1.075552 1.121095 1.213433 1.265136 1.183144 1.282937 1.217968 -P_32 1.376360 1.310163 1.308465 1.144440 1.324589 1.157175 1.198235 1.233268 -P_32 1.171183 1.085434 1.123477 1.118030 1.121091 1.178810 1.098114 1.181390 -P_32 1.113084 1.012101 1.006867 0.880115 0.855188 0.780068 0.752782 0.719178 -P_32 0.693373 0.608858 0.594085 0.561643 0.559047 0.473405 0.513877 0.494662 -P_32 0.448774 0.487284 0.481960 0.526092 0.559518 0.618119 0.651270 0.797022 -P_32 0.838206 0.925750 0.928093 1.063136 1.105105 1.146250 1.128998 1.213524 -P_32 1.205689 1.219656 1.164538 1.090903 1.061009 1.077738 1.120692 1.080614 -P_32 0.952540 1.093404 1.008513 1.108003 1.123786 1.109011 1.090534 1.144149 -P_32 1.094257 1.125994 1.024877 0.992946 0.941710 0.889442 0.833872 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0.680875 0.570015 0.538568 0.490315 0.420883 0.378717 0.419706 0.495416 -P_32 0.451416 0.547144 0.654220 0.771670 0.825122 0.929855 0.964302 1.038043 -P_32 1.164981 1.203803 1.208985 1.163838 1.299047 1.336153 1.248205 1.280991 -P_32 1.174618 1.206033 1.130256 1.057899 1.146451 1.025265 1.034858 1.005941 -P_32 1.019311 1.032509 1.040826 1.080115 1.121771 1.140429 1.048324 1.184877 -P_32 1.084879 1.150546 1.155738 1.072856 0.891841 0.923155 0.896050 0.800600 -P_32 0.695536 0.668343 0.551589 0.458458 0.493444 0.386558 0.439681 0.470300 -P_32 0.521817 0.569648 0.685257 0.829895 0.920249 1.013617 1.173806 1.355823 -P_32 1.376449 1.344800 1.353824 1.500404 1.476963 1.516235 1.454413 1.401136 -P_32 1.393387 1.210492 1.235662 1.251533 1.111679 1.058019 1.099591 1.066879 -P_32 1.038257 0.980492 1.049365 1.107111 1.143040 1.067376 1.138352 1.170728 -P_32 1.095259 1.107425 1.072402 1.042745 0.941861 0.931270 0.895538 0.778103 -P_32 0.714953 0.649641 0.631793 0.560651 0.510056 0.542322 0.438003 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0.484062 0.516305 0.546185 0.679764 0.734133 0.851415 0.902412 0.958038 -P_32 1.056337 1.180936 1.201349 1.194201 1.211381 1.303341 1.321422 1.163975 -P_32 1.167765 1.135318 1.024966 1.109894 1.037621 0.979126 0.987694 0.982633 -P_32 0.924564 1.007781 0.996548 0.999023 1.034405 1.035470 1.005327 1.068950 -P_32 1.035154 1.065134 1.111157 1.013837 0.910911 0.858694 0.833908 0.733443 -P_32 0.639228 0.558803 0.479293 0.444285 0.395711 0.394590 0.424656 0.452762 -P_32 0.513352 0.581964 0.573060 0.630636 0.786686 0.854740 0.998663 0.992233 -P_32 1.072375 1.128345 1.177853 1.142475 1.303148 1.151695 1.233631 1.318019 -P_32 1.185020 1.122648 1.105894 1.027233 0.942014 1.028082 0.971467 1.018111 -P_32 1.073031 0.923430 0.919054 0.995420 1.033300 1.151360 1.002156 1.084828 -P_32 1.065022 0.966699 1.018114 0.920476 0.914876 0.872368 0.719582 0.731308 -P_32 0.623755 0.559160 0.491285 0.467049 0.409665 0.382079 0.398764 0.449969 -P_32 0.467834 0.534283 0.616758 0.650320 0.793445 0.929870 1.030099 1.026722 -P_32 1.067363 1.187484 1.088124 1.287688 1.178694 1.197024 1.191341 1.182808 -P_32 1.098669 1.050052 1.065626 1.116354 0.956200 0.984902 0.980918 0.983704 -P_32 0.935928 0.990630 0.990111 0.995855 1.064051 1.043617 1.177223 0.960354 -P_32 1.174826 1.133395 1.088074 0.935367 0.991683 0.917974 0.892135 0.807023 -P_32 0.631973 0.591216 0.535327 0.459253 0.436664 0.402029 0.427841 0.427961 -P_32 0.457943 0.603228 0.652993 0.728572 0.909496 0.985609 1.056468 1.171211 -P_32 1.201576 1.362800 1.403644 1.470035 1.529846 1.320644 1.373858 1.318256 -P_32 1.290511 1.181589 1.015811 1.107282 1.108776 1.049948 1.089759 0.974863 -P_32 1.104830 1.024886 1.071567 1.095773 1.064487 1.096383 1.021605 1.034261 -P_32 1.046081 1.056834 1.009954 1.017642 0.911813 0.965263 0.766064 0.750800 -P_32 0.660474 0.651873 0.557788 0.568884 0.519406 0.453537 0.444248 0.408275 -P_32 0.451324 0.487474 0.474755 0.568654 0.612168 0.728400 0.885065 0.872106 -P_32 0.950751 0.997470 1.018738 1.141527 1.070913 1.238438 1.133230 1.056311 -P_32 1.073385 1.100938 1.071474 0.987665 1.157860 1.070071 1.047597 1.070901 -P_32 1.071350 0.975064 1.028042 1.078443 1.080648 1.039676 0.942120 0.947470 -P_32 1.021735 0.928657 0.890925 0.796380 0.809493 0.662740 0.704790 0.677208 -P_32 0.566598 0.546586 0.511820 0.517544 0.476806 0.464947 0.422493 0.430355 -P_32 0.461765 0.444863 0.448040 0.486011 0.517355 0.550510 0.629637 0.636028 -P_32 0.705784 0.733232 0.917815 0.989303 1.025717 1.050824 0.985056 1.016256 -P_32 1.175962 1.126991 1.018437 1.038203 1.069352 0.985937 0.907717 0.951311 -P_32 0.919496 0.864630 0.906519 0.984351 0.882716 0.980868 0.922822 1.012112 -P_32 0.952595 0.961023 0.926719 0.933527 0.811779 0.857804 0.795545 0.605984 -P_32 0.607963 0.471102 0.466170 0.424810 0.421945 0.374856 0.394829 0.433113 -P_32 0.445989 0.506235 0.573898 0.682707 0.776596 0.847383 0.899423 1.039275 -P_32 1.134788 1.159439 1.184559 1.370674 1.261010 1.321621 1.262033 1.239354 -P_32 1.261481 1.198495 1.157290 1.034682 1.053492 0.928508 0.882556 0.885696 -P_32 0.847183 0.902091 0.866221 1.041604 0.885908 1.002834 1.021065 1.106964 -P_32 0.990421 1.143481 1.041012 0.998482 0.861541 0.841771 0.696686 0.742925 -P_32 0.602628 0.578175 0.435675 0.412855 0.416134 0.368609 0.382510 0.442997 -P_32 0.454805 0.528005 0.585959 0.636572 0.707571 0.791620 0.958155 1.038877 -P_32 1.047665 1.154795 1.191948 1.321753 1.226868 1.226248 1.238581 1.166377 -P_32 1.109963 1.115428 1.136826 1.069312 0.971062 0.909982 0.997767 0.896284 -P_32 0.931038 0.941720 1.025900 0.988961 1.056304 1.068938 0.981099 1.117936 -P_32 1.078806 1.206578 0.988928 1.055477 0.935904 0.794710 0.844323 0.693264 -P_32 0.616360 0.597689 0.473267 0.437383 0.425400 0.388513 0.407992 0.426064 -P_32 0.495437 0.495846 0.680354 0.672049 0.752687 0.887800 0.949729 1.082342 -P_32 1.127752 1.112518 1.175072 1.187358 1.185152 1.200431 1.214272 1.168741 -P_32 1.210554 1.189723 1.112403 1.110731 1.121273 0.989249 0.919709 1.020057 -P_32 0.893540 1.087434 1.001218 0.998155 0.959151 1.027282 1.095513 1.094021 -P_32 1.118427 1.036773 1.008439 1.008498 0.933104 0.855053 0.839550 0.721428 -P_32 0.658229 0.547167 0.525533 0.457059 0.439642 0.413325 0.435193 0.407860 -P_32 0.480792 0.554778 0.544284 0.641402 0.786159 0.844162 0.916099 0.996119 -P_32 1.219097 1.185992 1.186144 1.186451 1.167309 1.210267 1.133733 1.144935 -P_32 1.042907 0.997366 1.095186 0.959782 0.987360 1.059717 0.987814 0.967095 -P_32 0.875128 0.996367 0.977465 1.054711 1.060408 1.032592 0.959003 1.170618 -P_32 1.119320 1.198074 1.000075 0.927777 0.964721 1.022285 0.859822 0.778006 -P_32 0.665399 0.632046 0.515283 0.474100 0.435829 0.414199 0.404524 0.454200 -P_32 0.516351 0.601704 0.669060 0.770857 0.920721 0.947503 1.080147 1.268147 -P_32 1.189945 1.382104 1.389543 1.367611 1.325748 1.385279 1.350278 1.390295 -P_32 1.241330 1.278502 1.218795 1.049339 0.998370 1.046211 1.041334 0.950278 -P_32 0.972903 1.073647 0.940586 1.050239 0.969135 1.094865 1.115103 0.955611 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0.763224 0.682505 -P_32 0.615260 0.539601 0.440126 0.407430 0.417613 0.376133 0.379137 0.421858 -P_32 0.444328 0.544782 0.610834 0.655710 0.695855 0.830967 0.841882 0.990228 -P_32 1.133357 1.153666 1.285527 1.232712 1.264776 1.393068 1.289116 1.204459 -P_32 1.127724 1.202740 1.132231 1.058807 1.043229 0.958538 0.839759 0.953023 -P_32 0.842028 0.972985 0.874302 0.952912 0.878859 1.067772 0.924824 0.995368 -P_32 0.994593 1.074631 1.022983 1.100195 0.936570 0.864965 0.831815 0.717771 -P_32 0.643047 0.531283 0.490554 0.446943 0.386086 0.414987 0.363871 0.417217 -P_32 0.419872 0.500336 0.601295 0.713940 0.709957 0.852027 0.903656 0.987853 -P_32 1.100994 1.081437 1.240631 1.238887 1.222582 1.259622 1.338862 1.124005 -P_32 1.081253 1.065605 0.986833 0.972645 0.933144 0.932590 0.888826 0.991290 -P_32 1.016511 0.942438 0.980698 1.056423 1.116686 0.990475 1.006480 1.056654 -P_32 1.012607 1.121969 0.931400 0.990444 1.043838 0.865207 0.813474 0.706418 -P_32 0.587315 0.495922 0.459287 0.402596 0.371094 0.374795 0.372904 0.421109 -P_32 0.446856 0.535171 0.610175 0.693242 0.755839 0.784998 0.991336 1.066324 -P_32 1.068587 1.158154 1.159291 1.245968 1.192449 1.272164 1.208882 1.188752 -P_32 1.220993 1.142552 1.091566 1.163015 1.093106 0.997058 0.867268 0.990783 -P_32 0.926333 0.952247 1.023144 1.031986 1.048703 1.073019 0.980566 0.988115 -P_32 1.082511 1.011035 1.035015 0.951366 0.862143 0.866904 0.723599 0.708386 -P_32 0.628641 0.527072 0.495643 0.446383 0.382841 0.389967 0.374008 0.443234 -P_32 0.468073 0.573132 0.618279 0.663313 0.772332 0.818037 0.944715 1.124404 -P_32 1.135773 1.102782 1.236951 1.220630 1.308460 1.154183 1.185358 1.089936 -P_32 1.227187 0.971196 0.864429 1.001272 0.986149 0.993353 0.926508 1.045611 -P_32 1.018506 1.048554 0.960575 1.085445 1.006899 1.014654 1.095470 1.067558 -P_32 1.074763 1.055257 0.963471 0.986855 0.878318 0.920517 0.829975 0.695864 -P_32 0.629278 0.578612 0.473655 0.469452 0.452960 0.438576 0.403591 0.445193 -P_32 0.490246 0.574845 0.683704 0.725771 0.818744 1.000846 1.066253 1.144176 -P_32 1.293232 1.413180 1.340563 1.398674 1.359331 1.389748 1.310944 1.280057 -P_32 1.231808 1.087255 1.167458 1.118919 1.113534 1.017950 1.049044 1.031542 -P_32 1.041460 1.001773 1.046998 1.015883 0.968753 1.096306 1.059557 1.071538 -P_32 1.026109 0.953486 0.973764 0.953392 0.892252 0.873104 0.867326 0.702609 -P_32 0.703950 0.614242 0.554400 0.526637 0.496362 0.447919 0.445634 0.445221 -P_32 0.429275 0.432218 0.510891 0.534059 0.609922 0.686445 0.763889 0.865837 -P_32 0.867754 0.995063 0.960029 1.141434 1.104211 1.152363 1.165765 1.129374 -P_32 1.222729 1.050967 1.126485 0.935680 1.059889 1.068537 0.960120 0.983915 -P_32 1.053767 0.954544 0.960227 1.085199 1.026650 0.994471 1.059301 1.014981 -P_32 0.974539 0.910970 0.822125 0.815089 0.874213 0.708923 0.704145 0.635683 -P_32 0.580808 0.597355 0.544373 0.486245 0.450160 0.472017 0.442583 0.435993 -P_32 0.454944 0.446506 0.422446 0.464395 0.522533 0.545833 0.614649 0.685999 -P_32 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0.795247 -P_31 0.684774 0.644304 0.579847 0.544425 0.481758 0.464230 0.454226 0.535364 -P_31 0.514311 0.651337 0.642203 0.669775 0.720597 0.891774 0.932009 0.963338 -P_31 1.037131 1.108016 1.118035 1.202920 1.099549 1.266009 1.330722 1.120404 -P_31 1.091311 1.008667 1.055874 1.050791 1.046168 0.970636 0.973054 1.060898 -P_31 1.026842 0.928514 0.926622 1.003361 1.010046 0.968773 1.032842 0.982255 -P_31 1.018218 0.996744 1.047586 0.967791 0.939207 0.908585 0.815777 0.840615 -P_31 0.665311 0.625067 0.590101 0.491080 0.511470 0.491680 0.524810 0.520357 -P_31 0.508627 0.644123 0.686182 0.763121 0.776806 0.948464 0.912413 1.003948 -P_31 1.028560 1.094972 1.097314 1.103766 1.194636 1.105123 1.155906 1.075582 -P_31 1.003709 1.077421 1.081607 0.956585 0.995601 0.904391 0.855151 0.948536 -P_31 0.941948 0.994795 1.017933 1.039509 1.014597 1.082623 1.173015 1.015216 -P_31 1.035012 0.995824 0.982677 0.963639 1.001735 0.933751 0.837635 0.759823 -P_31 0.735159 0.634278 0.589236 0.567391 0.514972 0.504210 0.530443 0.514353 -P_31 0.571324 0.593446 0.725992 0.839319 0.931017 1.005064 1.041479 1.065113 -P_31 1.178684 1.274710 1.293547 1.212157 1.310943 1.252877 1.341952 1.207549 -P_31 1.246534 1.088320 1.057426 1.113657 1.060759 1.058095 1.086844 1.066972 -P_31 1.014631 1.102849 1.096465 1.063526 1.046142 1.052369 1.131427 1.079351 -P_31 0.964687 0.948700 0.981583 0.986965 0.849186 0.947846 0.856094 0.793773 -P_31 0.740067 0.707383 0.647980 0.600884 0.581159 0.543677 0.538600 0.513856 -P_31 0.531299 0.566928 0.571396 0.589995 0.695984 0.811393 0.872926 0.887905 -P_31 0.926712 1.052711 1.043555 1.021430 1.057808 1.246595 1.154459 1.198831 -P_31 1.138782 1.194736 1.137204 1.079134 1.020610 0.960007 1.050271 0.994061 -P_31 1.068666 1.086816 1.055232 1.098490 0.990089 0.999331 1.018330 0.910181 -P_31 1.027488 0.977465 0.875474 0.828144 0.778142 0.759793 0.755226 0.724582 -P_31 0.691130 0.636456 0.596559 0.640280 0.590999 0.590396 0.509893 0.555050 -P_31 0.543390 0.510725 0.509716 0.572484 0.533709 0.684391 0.733157 0.741344 -P_31 0.856974 0.851346 0.976897 0.954436 0.984845 1.078459 1.065947 1.055679 -P_31 1.057442 0.962397 1.100282 1.005055 1.015668 1.007540 0.990312 0.933347 -P_31 0.931565 0.887800 0.879572 0.929581 1.003542 0.972715 1.072608 0.990857 -P_31 0.993165 1.010700 0.920733 0.983205 0.958100 0.827501 0.807854 0.753684 -P_31 0.698281 0.645315 0.594998 0.537970 0.478747 0.543689 0.502249 0.499052 -P_31 0.516505 0.576265 0.672733 0.757522 0.800220 0.899075 0.938775 0.926085 -P_31 1.075617 1.209874 1.285291 1.222152 1.155893 1.228649 1.256008 1.301675 -P_31 1.239862 1.156164 1.144253 1.026267 0.998182 0.929244 0.952171 0.986490 -P_31 0.903925 0.902678 0.895036 0.976964 1.040544 1.027547 1.030078 1.045477 -P_31 1.070754 1.201294 1.069908 1.030015 0.999600 0.919371 0.884200 0.763257 -P_31 0.726900 0.612043 0.649328 0.579544 0.475455 0.509219 0.535439 0.521032 -P_31 0.506738 0.548366 0.661359 0.767835 0.750815 0.964162 0.824034 0.986186 -P_31 1.116125 1.188798 1.106657 1.141495 1.246911 1.115901 1.232826 1.192691 -P_31 1.097544 1.120900 1.025666 1.025539 0.982993 0.951105 0.875054 0.931428 -P_31 0.959522 0.963511 0.955029 0.995518 1.055352 1.059167 1.149264 1.139934 -P_31 1.100506 1.188910 1.103791 1.003637 1.039689 0.913521 0.875880 0.818617 -P_31 0.628773 0.665591 0.552844 0.566251 0.503440 0.475797 0.498605 0.507960 -P_31 0.581432 0.518128 0.721616 0.805885 0.779492 0.951576 1.008586 1.000411 -P_31 1.034803 1.092596 1.176363 1.152878 1.258000 1.308659 1.246239 1.146718 -P_31 1.092896 1.206780 1.050591 1.066247 1.045463 1.033053 1.058198 0.967331 -P_31 1.016426 1.034555 1.034321 1.139868 0.948509 0.996075 1.068186 1.087420 -P_31 1.115456 1.131926 1.015941 0.992179 1.138419 0.939927 0.855788 0.827548 -P_31 0.724480 0.677953 0.599971 0.568967 0.526743 0.468296 0.497051 0.505864 -P_31 0.507794 0.606299 0.696424 0.782987 0.849602 0.986927 0.986856 1.059099 -P_31 1.077786 1.097474 1.183383 1.232052 1.155755 1.088570 1.122789 1.231669 -P_31 1.010331 1.112380 1.092324 1.039452 0.994849 0.996974 0.978005 0.997681 -P_31 0.998850 0.986201 0.998736 0.967720 1.021749 1.026397 1.068446 1.054692 -P_31 1.046416 1.023252 0.979775 1.031148 0.947732 0.911925 0.910454 0.868116 -P_31 0.739467 0.728762 0.575676 0.548372 0.551759 0.519752 0.535740 0.499845 -P_31 0.604877 0.597771 0.709169 0.866839 0.892214 0.999421 1.147788 1.256510 -P_31 1.245033 1.362039 1.275895 1.416318 1.291628 1.283257 1.283017 1.315360 -P_31 1.190283 1.122793 1.043395 1.110216 1.077837 1.138796 1.019390 1.156644 -P_31 1.043583 1.055267 1.046672 1.083002 1.125324 1.090458 1.094363 1.066536 -P_31 1.084946 1.037740 1.058510 1.001148 0.982143 0.982231 0.914811 0.901080 -P_31 0.788323 0.719209 0.640379 0.636918 0.630080 0.586654 0.533135 0.522039 -P_31 0.544976 0.527829 0.589145 0.622786 0.680147 0.734812 0.859222 0.876838 -P_31 0.957752 1.017834 1.131261 1.035768 1.176519 1.133361 1.171873 1.197301 -P_31 1.197192 1.088780 1.108576 1.067170 1.121733 1.118303 0.988969 1.020808 -P_31 1.056599 1.079082 1.021826 1.085256 1.170952 1.083614 1.064152 1.033451 -P_31 0.989986 1.056040 1.004008 0.958639 0.900553 0.890103 0.771410 0.772773 -P_31 0.693463 0.636242 0.639750 0.639521 0.592481 0.548154 0.541780 0.529691 -P_31 0.543612 0.567741 0.539778 0.548184 0.569503 0.682260 0.714942 0.745300 -P_31 0.834747 0.857619 0.921636 1.048704 1.058296 1.007456 1.142883 1.042974 -P_31 1.041814 1.091017 1.071535 1.027125 1.120629 1.001124 1.008557 0.923892 -P_31 0.928998 0.964938 0.966626 0.973392 0.932406 0.948070 0.953798 1.087562 -P_31 1.035128 0.953901 1.097354 0.955895 0.884666 0.781643 0.846831 0.685569 -P_31 0.720487 0.598846 0.593919 0.542683 0.484221 0.521754 0.499524 0.470240 -P_31 0.560207 0.590801 0.628977 0.699676 0.841812 0.938135 0.943582 1.051709 -P_31 1.238542 1.305911 1.218215 1.152012 1.341597 1.295195 1.334462 1.229677 -P_31 1.098246 1.062729 1.005976 1.047668 1.016918 1.044098 0.963038 0.887781 -P_31 0.982418 0.984164 0.935205 0.949220 1.031060 1.002075 0.979935 0.982876 -P_31 1.024742 1.191537 1.154134 1.011284 1.047645 0.952995 0.942591 0.846694 -P_31 0.741284 0.651492 0.623660 0.529346 0.558450 0.572165 0.565246 0.502249 -P_31 0.602011 0.604175 0.635924 0.743911 0.895437 0.870953 1.048928 0.991614 -P_31 1.110761 1.138232 1.138539 1.261398 1.095342 1.157470 1.195017 1.255419 -P_31 1.174547 1.106646 1.060989 1.146114 1.003611 0.986375 1.067526 0.951176 -P_31 0.983578 1.060280 0.975996 1.075293 1.203900 1.081605 1.176008 1.109706 -P_31 1.153686 1.213783 1.058586 1.121456 0.887720 0.921819 0.812930 0.797130 -P_31 0.658581 0.637490 0.591016 0.559288 0.499658 0.466135 0.530148 0.519219 -P_31 0.570603 0.643605 0.611647 0.638710 0.768624 0.814975 0.968952 1.054250 -P_31 1.092306 1.165528 1.292988 1.266136 1.151426 1.163909 1.292509 1.207237 -P_31 1.072512 1.128350 1.114941 1.048110 1.008122 1.016611 1.067758 1.005387 -P_31 1.033604 1.054983 1.094245 0.984155 1.033395 1.098219 1.095722 1.123967 -P_31 1.190653 1.131588 1.080745 0.956417 0.990405 0.968087 0.850764 0.763241 -P_31 0.718002 0.642273 0.561097 0.542515 0.537408 0.522573 0.490719 0.539532 -P_31 0.595948 0.640194 0.664939 0.750470 0.767441 0.893205 1.013603 1.026179 -P_31 1.182739 1.184359 1.139006 1.200086 1.263451 1.287335 1.134208 1.075864 -P_31 1.218911 1.074730 1.000161 1.099343 0.909678 1.001670 0.878611 0.937471 -P_31 0.959541 1.036101 0.974801 1.013788 1.097150 1.119411 1.059877 1.217651 -P_31 1.124202 1.095801 1.046932 1.088586 1.018183 1.029984 0.884283 0.820129 -P_31 0.787345 0.662963 0.568579 0.612204 0.477670 0.526044 0.463761 0.569121 -P_31 0.629968 0.650722 0.778395 0.827957 0.988367 0.940440 1.129649 1.156795 -P_31 1.230973 1.348882 1.444437 1.363034 1.357695 1.294068 1.302665 1.291674 -P_31 1.283419 1.264690 1.181308 1.218387 1.165257 1.061047 1.001346 1.061598 -P_31 1.132440 1.066285 1.139382 1.117982 1.192652 1.144003 1.116915 1.141880 -P_31 1.050541 1.029505 1.108785 1.039507 0.967959 0.952038 0.879752 0.888845 -P_31 0.725479 0.733879 0.635124 0.685331 0.629698 0.572390 0.556592 0.521214 -P_31 0.584012 0.555230 0.606631 0.663346 0.696444 0.842664 0.838550 0.909242 -P_31 0.991518 0.981186 1.054567 1.129923 1.194893 1.197983 1.273363 1.151300 -P_31 1.207274 1.168373 1.123704 1.147475 1.048910 1.083199 1.038835 1.136904 -P_31 1.026931 1.081071 1.044845 1.061448 1.102478 1.155538 1.054703 1.100663 -P_31 1.033004 1.066437 0.954156 0.912914 0.921638 0.919435 0.755783 0.716753 -P_31 0.713837 0.695276 0.663119 0.618952 0.571472 0.571170 0.581936 0.535985 -P_31 0.526484 0.538775 0.554323 0.603114 0.612948 0.660634 0.704459 0.796408 -P_31 0.825108 0.843168 0.905661 0.956060 0.936073 1.051140 1.103454 1.116453 -P_31 1.147026 1.144528 1.058922 1.002376 1.049508 0.978313 1.015524 1.013889 -P_31 1.036962 1.027397 0.977942 1.031500 0.972104 0.982778 1.098129 1.049674 -P_31 1.064726 1.040000 1.019365 1.043258 0.958494 0.870681 0.860338 0.739516 -P_31 0.722948 0.659190 0.613214 0.543283 0.479708 0.525808 0.554854 0.528992 -P_31 0.518656 0.639743 0.686804 0.771356 0.866494 0.981048 0.913432 1.083780 -P_31 1.209254 1.148746 1.272461 1.237735 1.303502 1.305776 1.285115 1.234074 -P_31 1.174300 1.092482 1.145023 1.155128 1.071676 0.879858 1.006597 0.991129 -P_31 0.983100 0.851778 0.945331 1.013828 1.010607 1.018742 1.146252 1.178310 -P_31 1.206421 1.185173 1.209015 1.066303 1.043146 0.909799 0.850460 0.875956 -P_31 0.763017 0.737568 0.639451 0.591106 0.597174 0.518588 0.578246 0.551794 -P_31 0.586848 0.647787 0.695434 0.748275 0.798229 0.854855 0.901932 1.112985 -P_31 1.111364 1.228706 1.178995 1.194045 1.207593 1.214361 1.249357 1.218286 -P_31 1.158918 1.098215 1.157916 1.061368 1.082363 1.005503 0.948653 0.965585 -P_31 0.940724 0.984454 1.076513 1.088415 1.031289 1.017092 1.183287 1.152463 -P_31 1.218129 1.127292 1.132846 1.075611 1.050962 1.090500 0.879047 0.741531 -P_31 0.692779 0.659407 0.526763 0.576578 0.546103 0.550818 0.547460 0.551685 -P_31 0.585841 0.617829 0.740655 0.826747 0.831342 0.976888 1.029457 0.962480 -P_31 1.145323 1.147618 1.207517 1.199301 1.272679 1.162034 1.266585 1.191856 -P_31 1.241191 1.164585 1.114302 1.020934 1.164967 1.090339 0.982003 1.117485 -P_31 0.970780 1.036451 1.021987 1.116544 1.064740 1.175781 1.063811 1.048743 -P_31 1.195186 1.155675 1.137254 1.083464 0.939825 0.970832 0.868120 0.871855 -P_31 0.800757 0.704032 0.616435 0.559738 0.556835 0.467010 0.499152 0.576409 -P_31 0.544272 0.652776 0.704447 0.748939 0.866091 0.961510 1.013169 1.120181 -P_31 1.151675 1.134855 1.206576 1.142872 1.302636 1.280415 1.191848 1.286518 -P_31 1.103840 1.091892 1.063481 1.135158 1.035704 1.111460 1.003645 1.120900 -P_31 1.125234 1.058124 1.013460 1.094895 1.026502 1.037943 1.169658 1.086207 -P_31 1.155086 1.204408 1.242514 1.038331 1.031493 0.982936 0.920574 0.804814 -P_31 0.805101 0.677377 0.610721 0.638010 0.557577 0.546387 0.517428 0.586085 -P_31 0.617078 0.680287 0.765327 0.803551 0.899834 1.042634 1.063396 1.257732 -P_31 1.350802 1.370867 1.383387 1.353487 1.347756 1.399961 1.237230 1.203632 -P_31 1.242071 1.246456 1.168436 1.139015 1.060769 1.147300 1.079268 1.096455 -P_31 0.987669 1.136639 1.059086 1.108919 1.152492 1.188033 1.048366 1.171980 -P_31 1.160343 1.018532 1.022637 0.989651 1.057243 0.908671 0.999799 0.858436 -P_31 0.798117 0.744846 0.677736 0.602153 0.623666 0.544994 0.524018 0.574011 -P_31 0.588526 0.604532 0.650748 0.724053 0.685898 0.812342 0.823622 1.006411 -P_31 0.983707 1.006233 1.124510 1.147584 1.208258 1.140665 1.287857 1.331866 -P_31 1.078630 1.189910 1.119508 1.199511 1.041215 1.139508 1.041652 1.026631 -P_31 1.120628 1.220481 1.186965 1.154984 1.204106 1.089696 1.149208 1.087230 -P_31 1.115481 0.946886 0.966750 0.926991 0.921573 0.943169 0.824195 0.722987 -P_31 0.767184 0.686469 0.606890 0.642126 0.569666 0.572443 0.568000 0.537685 -P_31 0.616606 0.545367 0.590182 0.602994 0.642527 0.696116 0.690486 0.795128 -P_31 0.821290 0.887215 0.946957 1.124838 1.140627 1.121873 1.072092 1.174538 -P_31 1.116815 1.140927 1.071632 1.127137 0.959794 1.014407 1.026142 0.980179 -P_31 1.042016 0.998913 1.003946 1.092994 0.969661 1.050737 1.102896 1.108745 -P_31 1.048596 1.066935 1.075648 1.099915 0.976252 0.902131 0.834553 0.776514 -P_31 0.707568 0.695966 0.625843 0.570862 0.557903 0.522091 0.491770 0.452147 -P_31 0.591081 0.613549 0.712456 0.698881 0.817644 0.854394 0.985593 1.072394 -P_31 1.212760 1.270431 1.216263 1.342845 1.404210 1.323781 1.336125 1.250560 -P_31 1.129145 1.255512 1.168060 1.139368 0.947873 1.026917 0.965708 0.973743 -P_31 1.028446 1.028804 0.930259 1.019892 1.070374 1.076597 1.135088 1.121341 -P_31 1.141196 1.134100 1.103787 1.065180 1.157343 0.974725 0.859618 0.857229 -P_31 0.799260 0.680884 0.632905 0.659678 0.568859 0.498921 0.510506 0.546357 -P_31 0.601480 0.670220 0.676351 0.683624 0.854135 0.925024 0.950539 1.095864 -P_31 1.212948 1.300667 1.257091 1.357174 1.243368 1.305336 1.314247 1.235198 -P_31 1.244074 1.140016 1.071963 0.986970 1.074948 0.881703 0.986241 1.024724 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1.207075 1.236879 -P_31 1.182435 1.223130 1.203396 1.010058 0.948062 1.004570 0.986421 0.931467 -P_31 0.788835 0.706549 0.615909 0.642460 0.547868 0.555146 0.548147 0.536377 -P_31 0.675607 0.735080 0.745411 0.906740 0.895112 1.053035 1.163981 1.275110 -P_31 1.270243 1.454511 1.410926 1.433564 1.380809 1.479240 1.318087 1.334505 -P_31 1.327561 1.252565 1.115153 1.226005 1.174979 1.077556 1.092520 1.079503 -P_31 1.113856 1.165729 1.192302 1.067590 1.125376 1.127721 1.221020 1.185640 -P_31 1.202785 1.106989 1.084425 1.101450 1.017375 0.949773 0.989241 0.891259 -P_31 0.894053 0.788988 0.638662 0.668107 0.644737 0.611444 0.540801 0.544520 -P_31 0.588900 0.571647 0.616203 0.715761 0.756322 0.751470 0.888746 0.962904 -P_31 1.014509 1.122782 1.051518 1.184206 1.140176 1.216201 1.265604 1.175055 -P_31 1.244395 1.105842 1.213149 1.208361 1.311078 1.129344 1.169259 1.217005 -P_31 1.178182 1.114507 1.117389 1.125726 1.146735 1.082832 1.141642 1.137668 -P_31 1.118811 1.114067 1.005864 0.839381 0.910473 0.929310 0.820871 0.855141 -P_31 0.867221 0.740965 0.701395 0.663154 0.624436 0.604802 0.633316 0.568343 -P_31 0.565974 0.628399 0.572463 0.636691 0.621984 0.729836 0.800640 0.828638 -P_31 0.894060 0.936369 1.034061 1.078820 1.083691 1.106042 1.204652 1.202668 -P_31 1.115637 1.198947 1.041096 1.197781 1.120868 1.049589 1.058037 0.997346 -P_31 1.021799 1.060386 1.033429 1.094414 1.072481 1.027942 1.080158 1.101782 -P_31 1.063660 1.055193 1.145786 1.102432 0.917236 0.893544 0.865186 0.820032 -P_31 0.757571 0.620656 0.642705 0.620913 0.569584 0.552482 0.539103 0.593129 -P_31 0.544073 0.629480 0.661407 0.805637 0.837215 0.887277 1.088952 1.143667 -P_31 1.201745 1.279894 1.264297 1.423650 1.375640 1.335838 1.418382 1.347184 -P_31 1.348421 1.176702 1.196413 1.108240 1.081326 0.978941 1.086959 0.900984 -P_31 0.987502 0.984036 0.992504 1.064051 0.985850 1.053291 1.110265 1.255310 -P_31 1.181719 1.105638 1.126420 1.093392 1.090834 1.033990 0.907399 0.834105 -P_31 0.792621 0.809081 0.677122 0.608136 0.565676 0.587055 0.591328 0.606939 -P_31 0.646443 0.590620 0.661619 0.843915 0.878193 1.046120 1.029303 1.092157 -P_31 1.184706 1.168474 1.254575 1.365537 1.229806 1.291212 1.263894 1.154745 -P_31 1.301758 1.118031 1.178896 1.175748 1.068238 1.042099 0.976437 1.008661 -P_31 1.084017 1.005146 0.996870 1.137846 1.115030 1.246974 1.306372 1.083315 -P_31 1.162798 1.337063 1.091628 1.178678 1.099093 1.016071 0.963072 0.878015 -P_31 0.808736 0.710922 0.644174 0.584613 0.554620 0.543214 0.543669 0.509365 -P_31 0.582274 0.653333 0.683065 0.773380 0.889294 0.932547 0.988067 1.141218 -P_31 1.265301 1.333548 1.262934 1.156608 1.225463 1.372714 1.326731 1.190266 -P_31 1.247605 1.229990 1.187487 1.137896 1.183063 1.213455 1.125284 1.113716 -P_31 1.125807 1.076460 1.076166 1.101823 1.142396 0.985490 1.293616 1.168101 -P_31 1.176973 1.234115 1.144379 1.158522 1.024187 1.096724 0.872440 0.844775 -P_31 0.846230 0.709509 0.681065 0.600525 0.596907 0.498974 0.534870 0.559931 -P_31 0.573364 0.693875 0.757387 0.824162 0.920562 0.887349 1.181232 1.122617 -P_31 1.141392 1.211697 1.268867 1.223576 1.239486 1.281869 1.293340 1.241233 -P_31 1.148038 1.225770 1.117264 1.046720 1.124581 1.046576 1.091544 1.118051 -P_31 1.123536 1.115016 1.081023 1.135452 1.032832 1.154736 1.215261 1.165084 -P_31 1.212916 1.171030 1.141946 1.108899 1.052862 1.082329 0.977076 0.921201 -P_31 0.776004 0.772746 0.604896 0.611727 0.556555 0.591041 0.598914 0.568911 -P_31 0.631416 0.688477 0.792191 0.872483 0.971238 1.122819 1.241731 1.258529 -P_31 1.218557 1.359051 1.403384 1.409340 1.432292 1.402752 1.581067 1.279851 -P_31 1.236605 1.288192 1.189670 1.159772 1.237932 1.202546 1.124671 1.103376 -P_31 1.146155 1.196990 1.085115 1.225329 1.217864 1.234831 1.100470 1.224843 -P_31 1.119559 1.080815 1.072607 1.058916 1.057289 1.057650 0.879105 1.006240 -P_31 0.883608 0.731174 0.758250 0.709994 0.642740 0.579291 0.569054 0.540396 -P_31 0.623376 0.629765 0.640623 0.694649 0.770370 0.724926 0.889416 0.955526 -P_31 1.050163 1.096388 1.127894 1.187581 1.289320 1.196565 1.331915 1.313310 -P_31 1.236106 1.220628 1.213105 1.210302 1.290052 1.115631 1.170917 1.149941 -P_31 1.181649 1.034095 1.232859 1.137302 1.191797 1.102714 1.163275 1.101153 -P_31 1.142902 1.046312 1.105635 0.976053 0.907291 0.936596 0.799987 0.827066 -P_31 0.699965 0.686756 0.679794 0.711109 0.656599 0.595671 0.552458 0.554715 -P_31 0.578788 0.591014 0.605877 0.578810 0.644876 0.692005 0.835302 0.800735 -P_31 0.888105 0.889867 1.054624 1.037977 1.117411 1.197657 1.129639 1.186237 -P_31 1.289330 1.138332 1.146490 1.136564 1.131805 1.119399 1.191041 1.046999 -P_31 1.016703 1.030458 1.040923 1.153807 1.112339 1.071310 1.043724 1.161674 -P_31 1.252179 1.075983 1.081615 1.087524 0.997338 0.922925 0.814257 0.787701 -P_31 0.769958 0.680672 0.643680 0.576430 0.577627 0.507054 0.594682 0.571668 -P_31 0.602450 0.626852 0.619501 0.786229 0.880284 0.919133 1.059302 1.144254 -P_31 1.113928 1.248591 1.239107 1.319581 1.370298 1.432194 1.334710 1.329467 -P_31 1.268187 1.230845 1.255961 1.187933 1.103764 1.049279 1.010244 1.025761 -P_31 1.066024 0.979508 1.145980 1.131470 1.109487 1.075567 1.106831 1.173155 -P_31 1.254977 1.170525 1.167425 1.139441 1.007531 1.084345 0.918529 0.896989 -P_31 0.798508 0.744789 0.686595 0.658412 0.577500 0.573911 0.536595 0.596168 -P_31 0.577537 0.737160 0.744244 0.803883 0.938671 0.967002 1.123138 1.074201 -P_31 1.140813 1.282177 1.288902 1.338264 1.341268 1.300069 1.321599 1.175831 -P_31 1.319657 1.190965 1.139705 1.190694 1.041607 0.997944 1.073334 1.014584 -P_31 1.081818 1.080268 1.104932 1.087769 1.217748 1.219358 1.239037 1.230097 -P_31 1.271246 1.210852 1.069702 1.314716 1.019458 1.011659 0.889182 0.838483 -P_31 0.786089 0.690578 0.639650 0.561404 0.558257 0.543689 0.550647 0.515223 -P_31 0.568929 0.647272 0.665198 0.833491 1.010304 1.036170 1.055022 1.184390 -P_31 1.120474 1.131809 1.293151 1.271992 1.209273 1.321406 1.396933 1.403187 -P_31 1.247482 1.190990 1.180160 1.179772 1.286725 1.222382 1.094122 1.157022 -P_31 1.132981 1.023144 1.094354 1.300911 1.107750 1.243235 1.132004 1.283372 -P_31 1.169638 1.080931 1.218598 1.158097 1.180536 1.012940 0.971987 0.882844 -P_31 0.809078 0.729276 0.665930 0.660157 0.581121 0.584746 0.531335 0.612672 -P_31 0.534215 0.716398 0.765641 0.791261 0.931388 0.982950 1.064247 1.088908 -P_31 1.231159 1.318018 1.329032 1.379818 1.338208 1.434470 1.295644 1.340588 -P_31 1.213583 1.267579 1.358105 1.001712 1.106441 1.157024 1.090598 1.028955 -P_31 1.056327 1.051161 1.170194 1.127816 1.291926 1.291151 1.172504 1.292533 -P_31 1.183197 1.257460 1.193171 1.135968 1.088263 1.052849 0.954053 0.891780 -P_31 0.851908 0.808478 0.607452 0.660117 0.582606 0.534610 0.560270 0.601788 -P_31 0.672417 0.831604 0.827432 0.881235 0.902234 1.123950 1.217519 1.257711 -P_31 1.403825 1.354271 1.395725 1.501550 1.405055 1.327093 1.385510 1.348558 -P_31 1.347476 1.287816 1.235842 1.196560 1.275539 1.184409 1.217953 1.244332 -P_31 1.014066 1.184719 1.143523 1.188815 1.185159 1.277762 1.079843 1.224016 -P_31 1.132973 1.151182 1.095858 1.228315 0.951615 0.919957 1.004336 0.927491 -P_31 0.845239 0.775585 0.699997 0.683271 0.658010 0.639867 0.581525 0.539983 -P_31 0.612684 0.599570 0.622067 0.744664 0.768510 0.821100 0.932420 0.979173 -P_31 1.114196 1.172327 1.186650 1.301279 1.193575 1.215861 1.218006 1.208005 -P_31 1.130799 1.318086 1.194696 1.195796 1.169340 1.082502 1.150161 1.178553 -P_31 1.220463 1.153187 1.200606 1.217700 1.217600 1.155963 1.288644 1.089267 -P_31 1.048290 1.055346 1.052279 0.935080 0.885746 0.832591 0.914306 0.867967 -P_31 0.829487 0.746683 0.724754 0.659201 0.613496 0.635095 0.579510 0.584408 -P_31 0.539886 0.585719 0.612343 0.602557 0.675451 0.728130 0.802475 0.871223 -P_31 0.956992 1.000023 0.996850 1.134394 1.065142 1.193325 1.114368 1.254299 -P_31 1.078445 1.116520 1.088661 1.258871 1.115064 1.148641 1.038206 1.174291 -P_31 1.104900 1.036311 0.963929 1.120443 1.087175 1.079373 1.096857 1.010266 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1.012449 0.866970 -P_31 0.858690 0.716869 0.655726 0.662341 0.572453 0.532967 0.554777 0.571796 -P_31 0.604459 0.707814 0.756887 0.839892 0.977956 0.996605 0.993569 1.116877 -P_31 1.235989 1.319722 1.237034 1.218929 1.233105 1.314072 1.285449 1.452440 -P_31 1.183266 1.320778 1.274503 1.222415 1.110153 1.221306 1.243931 1.060985 -P_31 1.165872 1.117441 1.247920 1.160814 1.257338 1.112739 1.286473 1.370883 -P_31 1.295866 1.186688 1.236384 1.286101 0.940601 0.982441 1.028759 0.871997 -P_31 0.834633 0.685001 0.643194 0.593056 0.599896 0.557244 0.574380 0.563515 -P_31 0.577431 0.646774 0.809995 0.750176 0.940061 0.966259 1.135720 1.239095 -P_31 1.330132 1.223257 1.400796 1.357425 1.356301 1.325261 1.177461 1.198646 -P_31 1.141903 1.254689 1.099705 1.146503 1.043093 1.083678 1.060145 1.231902 -P_31 1.145856 1.160561 1.148966 1.128774 1.160038 1.155368 1.168689 1.348697 -P_31 1.234695 1.147924 1.052875 1.136407 1.131087 1.035956 1.020343 0.854497 -P_31 0.844875 0.705408 0.706163 0.596857 0.654862 0.504152 0.633572 0.655904 -P_31 0.682126 0.717445 0.782721 0.923375 1.025445 1.262274 1.145480 1.244176 -P_31 1.391737 1.497586 1.366169 1.411732 1.512560 1.538069 1.398782 1.386676 -P_31 1.282855 1.248638 1.266175 1.266764 1.318334 1.221193 1.223696 1.132459 -P_31 1.216727 1.155608 1.154590 1.198638 1.165534 1.184719 1.214970 1.125429 -P_31 1.249626 1.166360 1.127408 1.145593 1.069547 0.940156 0.901531 0.874045 -P_31 0.859712 0.839910 0.756485 0.689023 0.644102 0.606275 0.654193 0.603674 -P_31 0.567906 0.639128 0.589451 0.778727 0.740697 0.956747 1.008835 1.041002 -P_31 0.954569 1.017612 1.247958 1.290121 1.337584 1.250383 1.375555 1.307808 -P_31 1.262622 1.308661 1.267073 1.131007 1.256127 1.173451 1.179703 1.174646 -P_31 1.177957 1.230131 1.199245 1.264963 1.179591 1.135380 1.246211 1.184486 -P_31 1.066963 0.997092 1.009820 1.030348 0.983202 0.913116 0.810140 0.836528 -P_31 0.771037 0.783078 0.712988 0.602161 0.610704 0.638682 0.620763 0.577354 -P_31 0.586140 0.645853 0.662881 0.672661 0.707812 0.683177 0.749547 0.896867 -P_31 0.919004 0.930421 1.124667 1.117676 1.155204 1.186896 1.219503 1.247117 -P_31 1.286171 1.233629 1.064997 1.177991 1.074922 1.189886 1.172035 1.090264 -P_31 1.015842 1.041940 1.146253 1.085328 1.019289 1.136398 1.189735 1.072095 -P_31 1.209948 1.124957 1.111628 1.098264 1.047552 1.060022 1.027197 0.887110 -P_31 0.828713 0.676859 0.616340 0.632833 0.563564 0.488706 0.551595 0.589208 -P_31 0.665808 0.633014 0.794693 0.838727 0.926705 0.876886 1.028962 1.113421 -P_31 1.293058 1.448789 1.370143 1.365228 1.279808 1.501795 1.339140 1.366626 -P_31 1.283394 1.325599 1.235012 1.291414 1.212723 1.130790 1.131127 0.903473 -P_31 1.023209 1.038111 1.071582 1.089484 1.192197 1.192498 1.167534 1.188388 -P_31 1.194447 1.235673 1.201108 1.212116 1.177494 1.098230 1.030426 0.895314 -P_31 0.771185 0.806518 0.679841 0.613144 0.629301 0.584701 0.528724 0.579668 -P_31 0.574949 0.646735 0.718860 0.847129 0.876820 0.979637 1.087482 1.202615 -P_31 1.415620 1.281920 1.329662 1.400557 1.472476 1.429544 1.391292 1.397975 -P_31 1.353504 1.177483 1.276968 1.176483 1.134862 1.031686 0.996208 1.056212 -P_31 1.049820 1.166775 1.100087 1.185633 1.129374 1.132597 1.190843 1.169692 -P_31 1.212828 1.319697 1.270044 1.144054 1.070262 1.017333 1.091598 0.824989 -P_31 0.810018 0.801674 0.607159 0.594769 0.571802 0.586267 0.555314 0.613719 -P_31 0.631721 0.701753 0.664567 0.858093 0.866473 0.929563 1.065552 1.116697 -P_31 1.210743 1.130930 1.355018 1.404016 1.423243 1.400531 1.463063 1.311105 -P_31 1.263775 1.215373 1.243125 1.321836 1.275615 1.101902 1.101558 1.145586 -P_31 1.122029 1.080257 1.151519 1.198563 1.140117 1.267977 1.232996 1.370507 -P_31 1.308323 1.284780 1.044533 1.205417 1.114746 1.156742 0.982508 0.893717 -P_31 0.791172 0.740453 0.706640 0.633001 0.573363 0.591911 0.568260 0.590278 -P_31 0.602927 0.687315 0.721430 0.906332 0.928634 0.961708 1.032311 1.168847 -P_31 1.245857 1.237188 1.356746 1.323005 1.363313 1.322484 1.270355 1.231647 -P_31 1.210760 1.212237 1.188425 1.184941 1.166537 1.075761 1.115391 1.139966 -P_31 1.159489 1.123604 1.126590 1.249425 1.273185 1.265207 1.266134 1.221629 -P_31 1.235471 1.247773 1.254489 1.176842 1.187085 1.025438 1.003112 0.929520 -P_31 0.878243 0.739984 0.657776 0.713448 0.573586 0.543790 0.622306 0.637304 -P_31 0.651895 0.742191 0.857233 0.839739 1.005138 1.148829 1.115558 1.342183 -P_31 1.365066 1.482984 1.488484 1.615892 1.468859 1.502387 1.481295 1.302974 -P_31 1.399821 1.421967 1.200934 1.146082 1.231119 1.149128 1.189455 1.350976 -P_31 1.228098 1.209653 1.256679 1.135211 1.231862 1.224229 1.265747 1.246871 -P_31 1.162457 1.124349 1.204648 1.106398 1.002970 0.986468 1.042450 0.905667 -P_31 0.923376 0.776426 0.780890 0.666580 0.692006 0.661003 0.612395 0.572960 -P_31 0.625076 0.638986 0.724321 0.729750 0.770994 0.842169 0.897586 0.991533 -P_31 1.033797 1.164300 1.222560 1.298780 1.275650 1.357002 1.236705 1.337359 -P_31 1.321828 1.155210 1.281504 1.179763 1.213860 1.178678 1.223688 1.246618 -P_31 1.166683 1.230919 1.149732 1.237057 1.209559 1.169992 1.187715 1.080319 -P_31 1.173211 1.172659 1.079217 0.990577 0.959800 0.976591 0.885002 0.837242 -P_31 0.805895 0.728795 0.710386 0.767910 0.671656 0.666967 0.591185 0.577781 -P_31 0.628197 0.599650 0.611969 0.641231 0.626492 0.707965 0.849397 0.819982 -P_31 0.912109 0.962299 1.092923 1.091213 1.108657 1.136714 1.243163 1.164758 -P_31 1.281441 1.177229 1.220964 1.150322 1.192021 1.079623 1.007653 1.080477 -P_31 1.090840 1.085066 1.042017 1.088044 1.219837 0.982104 1.079151 1.190373 -P_31 1.225929 1.158784 1.099199 1.138709 1.107563 0.962251 0.896482 0.928584 -P_31 0.800092 0.759110 0.616155 0.633234 0.585539 0.522101 0.588610 0.564956 -P_31 0.597691 0.629947 0.793871 0.853558 0.907636 0.869385 0.978156 1.117425 -P_31 1.329403 1.257544 1.262053 1.425802 1.454419 1.543244 1.382971 1.320628 -P_31 1.405470 1.262329 1.108259 1.220963 1.024362 1.129154 1.127167 1.054325 -P_31 1.135543 1.054670 1.080320 1.022905 1.183734 1.270663 1.102852 1.228639 -P_31 1.239473 1.229810 1.260032 1.214995 1.035777 1.111310 0.931461 0.949429 -P_31 0.816487 0.817489 0.643371 0.616779 0.554762 0.580755 0.589003 0.602490 -P_31 0.627562 0.649729 0.750280 0.860301 0.892053 1.094300 1.081779 1.097847 -P_31 1.258544 1.317738 1.378709 1.292364 1.277784 1.481132 1.323207 1.287020 -P_31 1.373684 1.282988 1.210938 1.199414 1.124682 0.971344 1.093751 1.086777 -P_31 0.993552 1.077288 1.145438 1.172623 1.202484 1.202819 1.231868 1.423058 -P_31 1.210977 1.207825 1.241752 1.124897 1.053949 1.089643 1.045076 0.935803 -P_31 0.849734 0.770269 0.643991 0.587175 0.599466 0.532772 0.527109 0.622784 -P_31 0.580352 0.722276 0.802332 0.846841 1.050399 1.022404 1.096067 1.213513 -P_31 1.212088 1.320729 1.336108 1.291619 1.297258 1.229054 1.264364 1.278983 -P_31 1.267395 1.299529 1.207130 1.236910 1.178829 1.085882 1.215572 1.129320 -P_31 1.176304 1.240325 1.181621 1.033480 1.226994 1.196322 1.190286 1.203901 -P_31 1.216909 1.142177 1.242101 1.154581 1.060199 1.130188 0.980107 0.844515 -P_31 0.824166 0.737230 0.661989 0.631965 0.547202 0.587617 0.557554 0.607001 -P_31 0.624243 0.672294 0.768167 0.889124 0.936534 1.090685 0.986071 1.267605 -P_31 1.151802 1.197248 1.255799 1.484943 1.289543 1.349998 1.162083 1.358824 -P_31 1.298620 1.243138 1.282223 1.101052 1.098268 1.116295 1.017015 1.177123 -P_31 1.161700 1.116974 1.114994 1.231876 1.225707 1.224520 1.095336 1.309027 -P_31 1.251278 1.127487 1.278769 1.110906 1.158566 1.066606 1.024438 0.902842 -P_31 0.904805 0.790826 0.734815 0.670005 0.612054 0.576270 0.554154 0.694388 -P_31 0.690951 0.744119 0.808409 0.859409 1.032764 1.221112 1.303749 1.444414 -P_31 1.490793 1.468503 1.523844 1.466542 1.506633 1.441859 1.552724 1.226532 -P_31 1.524459 1.300291 1.335942 1.286956 1.259382 1.163037 1.193323 1.049594 -P_31 1.351622 1.205341 1.130004 1.252855 1.177829 1.309195 1.114860 1.175573 -P_31 1.189705 1.168619 1.052060 1.177739 1.176645 1.065844 1.007071 0.884378 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0.549716 0.599442 -P_31 0.619229 0.710716 0.722770 0.751413 0.929840 1.051562 1.034349 1.126511 -P_31 1.268174 1.418395 1.383833 1.363554 1.371343 1.367030 1.491653 1.384967 -P_31 1.376926 1.359105 1.274154 1.091266 1.065217 1.110937 1.156733 1.076196 -P_31 1.041312 1.016844 1.131270 1.036065 1.113046 1.203641 1.263729 1.308628 -P_31 1.267642 1.071487 1.288996 1.205771 1.128375 1.044633 1.022474 0.881239 -P_31 0.785297 0.759731 0.654040 0.626818 0.582068 0.571687 0.571200 0.575747 -P_31 0.649106 0.725496 0.830304 0.829779 0.867249 1.060978 1.121842 1.147739 -P_31 1.320077 1.277608 1.410541 1.352566 1.375560 1.320538 1.350321 1.325114 -P_31 1.386312 1.184884 1.255725 1.272680 1.163990 1.083979 1.077642 1.018575 -P_31 1.109575 1.111052 1.053547 1.099239 1.100700 1.198367 1.180928 1.196642 -P_31 1.187396 1.120481 1.201127 1.131202 1.131111 1.043578 1.023284 0.836306 -P_31 0.792269 0.743286 0.706824 0.647923 0.563532 0.533931 0.547423 0.595526 -P_31 0.685736 0.711216 0.800868 0.832756 0.872782 1.008160 1.109159 1.095204 -P_31 1.164742 1.351910 1.502194 1.297919 1.333459 1.317298 1.248286 1.446061 -P_31 1.215198 1.183094 1.322913 1.214234 1.211244 1.265185 1.115840 1.168544 -P_31 1.071695 1.232731 1.117575 1.176144 1.184469 1.149401 1.158612 1.163852 -P_31 1.248417 1.212931 1.146606 1.213643 1.013050 1.034894 1.017073 0.867988 -P_31 0.785812 0.662293 0.607297 0.611868 0.551729 0.586866 0.544258 0.566494 -P_31 0.626493 0.625700 0.769559 0.825063 0.959635 1.018115 1.099375 1.085948 -P_31 1.184228 1.190077 1.168841 1.278994 1.315372 1.411610 1.195411 1.301298 -P_31 1.249037 1.194766 1.186710 1.119724 1.195594 1.138206 1.043962 1.116939 -P_31 1.111740 1.061174 1.098245 1.240478 1.137812 1.206604 1.167071 1.266095 -P_31 1.177330 1.127156 1.102368 1.137459 1.190356 1.024298 0.951790 0.992127 -P_31 0.786203 0.765647 0.762731 0.684262 0.579146 0.587854 0.609173 0.597907 -P_31 0.645721 0.703855 0.878804 0.894362 0.963407 1.114534 1.286400 1.390753 -P_31 1.417626 1.477229 1.618144 1.489866 1.517829 1.422735 1.532464 1.337487 -P_31 1.393776 1.385249 1.313430 1.311340 1.272152 1.213109 1.114298 1.292385 -P_31 1.149326 1.146476 1.151399 1.316397 1.258246 1.244618 1.243512 1.166527 -P_31 1.262010 1.243593 1.155885 1.010255 1.200016 1.045501 0.852043 0.860756 -P_31 0.881915 0.764062 0.792015 0.735440 0.662696 0.627222 0.606906 0.565007 -P_31 0.612349 0.666537 0.648506 0.757356 0.779803 0.885608 1.038425 1.084984 -P_31 1.098551 1.173078 1.224408 1.287531 1.201402 1.369722 1.346440 1.404342 -P_31 1.247409 1.249389 1.320002 1.103103 1.240833 1.130899 1.199381 1.170799 -P_31 1.199410 1.171597 1.241856 1.287093 1.208989 1.220187 1.100962 1.266368 -P_31 1.165289 1.117669 1.080644 1.008401 0.938068 0.976081 0.874860 0.846511 -P_31 0.811547 0.744832 0.738741 0.662508 0.610374 0.653161 0.590095 0.587625 -P_31 0.583747 0.584577 0.622391 0.653828 0.666065 0.739527 0.736994 0.767048 -P_31 0.864349 0.969645 0.976716 1.078679 1.139856 1.248829 1.174167 1.133462 -P_31 1.228508 1.154658 1.252726 1.211156 1.197757 1.123082 1.165419 1.051992 -P_31 1.113489 1.079851 1.141864 0.967492 1.111628 1.106441 1.118591 1.262701 -P_31 1.151858 1.192858 1.224759 1.026318 1.108004 0.951395 0.947662 0.844118 -P_31 0.753264 0.738092 0.682415 0.608346 0.536362 0.522849 0.555249 0.569570 -P_31 0.583360 0.643125 0.718037 0.839138 0.947997 0.962327 1.061813 1.243512 -P_31 1.134451 1.325246 1.353463 1.356567 1.422830 1.350281 1.409984 1.419223 -P_31 1.406848 1.306694 1.173180 1.231366 1.064262 1.044805 1.117446 0.965899 -P_31 1.029186 1.079918 1.061764 1.104763 1.116245 1.216434 1.226919 1.284022 -P_31 1.179329 1.246869 1.137039 1.245416 1.084114 1.018369 0.993057 0.864196 -P_31 0.857372 0.762071 0.691060 0.657193 0.579234 0.571284 0.612338 0.556099 -P_31 0.624166 0.704729 0.762201 0.771685 0.893648 0.957745 1.122222 1.061681 -P_31 1.246417 1.305895 1.353971 1.261052 1.330743 1.278126 1.273227 1.337771 -P_31 1.244477 1.318165 1.243131 1.212573 1.171016 0.952029 1.093562 1.016292 -P_31 1.083458 1.142628 1.021022 1.042385 1.233325 1.176361 1.273521 1.243593 -P_31 1.204308 1.231484 1.117915 1.125254 1.082432 0.983356 0.939851 0.906668 -P_31 0.808390 0.698870 0.593114 0.604936 0.574070 0.513295 0.562904 0.597486 -P_31 0.690302 0.742284 0.722746 0.732770 0.866271 1.106922 0.978916 1.208173 -P_31 1.199541 1.303228 1.354033 1.270534 1.323189 1.253193 1.388274 1.334354 -P_31 1.318305 1.303927 1.214694 1.148799 1.179461 1.228625 1.228800 1.119978 -P_31 1.123148 1.208188 1.163881 1.162358 1.132601 1.132896 1.250872 1.239624 -P_31 1.146573 1.167600 1.104919 1.213116 1.138559 0.973534 1.028710 0.927577 -P_31 0.941808 0.731253 0.706031 0.590540 0.575950 0.525041 0.560257 0.615991 -P_31 0.667504 0.677452 0.723740 0.871035 0.962813 1.011771 1.061412 1.124184 -P_31 1.268036 1.252095 1.349474 1.311837 1.318865 1.246916 1.296689 1.241879 -P_31 1.269276 1.082721 1.140123 1.181168 1.114497 1.102416 1.039258 1.147589 -P_31 1.145857 1.096898 1.180216 1.199269 1.146804 1.211370 1.249541 1.129500 -P_31 1.063418 1.263456 1.239951 1.147249 1.130448 1.049636 1.052851 0.936112 -P_31 0.873147 0.755776 0.709624 0.645373 0.656615 0.574451 0.609130 0.586248 -P_31 0.662432 0.670524 0.802209 0.882374 0.971465 1.205729 1.259340 1.337109 -P_31 1.345408 1.387387 1.331353 1.447873 1.435636 1.409021 1.461348 1.391259 -P_31 1.338129 1.352173 1.322802 1.233775 1.184665 1.191161 1.083776 1.062953 -P_31 1.129593 1.203613 1.186807 1.199928 1.191277 1.192720 1.144117 1.270763 -P_31 1.255162 1.090807 1.207459 1.139086 1.089304 1.085824 1.032396 0.919797 -P_31 0.880560 0.718849 0.763491 0.683992 0.645313 0.651384 0.590262 0.601450 -P_31 0.684479 0.629929 0.692248 0.760845 0.795792 0.816243 0.923757 1.001930 -P_31 1.058823 1.107232 1.141905 1.268499 1.190831 1.368774 1.264268 1.348646 -P_31 1.256263 1.170697 1.235925 1.199811 1.178602 1.180844 1.210671 1.197462 -P_31 1.161652 1.232251 1.036607 1.055786 1.163817 1.070397 1.279862 1.229349 -P_31 1.091952 1.059451 1.085348 1.071149 1.024431 0.984300 0.779253 0.828256 -P_31 0.834743 0.737879 0.682165 0.638150 0.649498 0.662631 0.571955 0.599286 -P_31 0.593021 0.576546 0.650332 0.715561 0.669418 0.708114 0.739892 0.872546 -P_31 0.932392 0.929905 1.092058 1.095155 1.140821 1.102793 1.161727 1.325651 -P_31 1.140647 1.132154 1.218462 1.108731 1.028110 1.118048 1.044857 1.073495 -P_31 1.163202 1.022101 1.091223 1.096206 1.120427 1.080040 1.203434 1.154837 -P_31 1.085256 1.192531 1.190520 1.138377 1.056825 1.072922 0.958821 0.857534 -P_31 0.780708 0.704262 0.664394 0.578406 0.599378 0.569621 0.602969 0.531226 -P_31 0.617308 0.694810 0.751187 0.771278 0.856459 1.015580 1.088221 1.243632 -P_31 1.270114 1.316946 1.502160 1.317402 1.521515 1.383059 1.295867 1.419320 -P_31 1.325515 1.261389 1.159909 1.204672 1.113967 1.005518 0.993357 1.038312 -P_31 1.096330 1.042300 1.060074 1.097690 1.151686 1.143252 1.174811 1.173647 -P_31 1.366490 1.217411 1.168966 1.284105 1.074949 1.053564 0.976496 0.909960 -P_31 0.817348 0.752451 0.724884 0.657592 0.613113 0.575285 0.525739 0.582643 -P_31 0.671324 0.615851 0.671381 0.777721 0.938234 0.944519 1.046366 1.123416 -P_31 1.126106 1.110393 1.256289 1.315896 1.303367 1.289285 1.322508 1.424955 -P_31 1.310466 1.098788 1.268688 1.151524 1.172271 1.129126 1.121692 1.012851 -P_31 1.133482 1.117131 1.129937 1.266119 1.200650 1.212321 1.221952 1.285349 -P_31 1.249302 1.236688 1.158581 1.173229 1.071847 1.095071 0.900373 0.835521 -P_31 0.782795 0.685832 0.694232 0.643584 0.583903 0.561084 0.508466 0.554194 -P_31 0.693223 0.666915 0.777210 0.824778 0.874683 0.996533 1.109759 1.165780 -P_31 1.032677 1.322731 1.348165 1.396646 1.290481 1.244929 1.283147 1.390914 -P_31 1.279550 1.338618 1.266042 1.061123 1.064895 1.133356 1.175970 1.155287 -P_31 1.197504 1.150297 1.158298 1.028380 1.180022 1.167425 1.179037 1.242218 -P_31 1.292323 1.277491 1.271575 1.042312 1.097489 1.032104 0.907598 0.892628 -P_31 0.836497 0.670942 0.659261 0.616385 0.599554 0.568930 0.520967 0.567919 -P_31 0.661113 0.665111 0.730551 0.857953 0.922672 1.046634 1.131994 1.232455 -P_31 1.235875 1.185036 1.347903 1.302765 1.330463 1.328725 1.342275 1.224672 -P_31 1.333027 1.146090 1.097842 1.122987 1.079603 1.221205 1.047103 1.038963 -P_31 1.023271 1.022845 1.122283 1.150323 1.135029 1.223843 1.171573 1.152845 -P_31 1.211500 1.128838 1.177417 1.132289 1.101740 1.013140 0.980475 0.852213 -P_31 0.858559 0.746653 0.703040 0.647976 0.609428 0.591975 0.549832 0.671632 -P_31 0.674701 0.707722 0.798817 0.904096 1.008807 1.063829 1.219158 1.242999 -P_31 1.388788 1.549954 1.448798 1.453416 1.569079 1.358425 1.385193 1.353731 -P_31 1.304463 1.260080 1.284796 1.154816 1.250484 1.119426 1.139401 1.157836 -P_31 1.234063 1.115162 1.244615 1.296062 1.235385 1.204968 1.248396 1.221864 -P_31 1.159237 1.229457 1.188357 1.145272 1.024394 1.030108 0.932727 0.906253 -P_31 0.834005 0.832497 0.724225 0.674467 0.687629 0.641360 0.636174 0.556833 -P_31 0.601636 0.622316 0.639621 0.740158 0.763016 0.822579 1.022806 0.951058 -P_31 1.103754 0.994003 1.257721 1.258722 1.170854 1.361482 1.217001 1.145316 -P_31 1.340185 1.257325 1.263946 1.251189 1.196113 1.193525 1.062021 1.120902 -P_31 1.205110 1.235376 1.117406 1.271237 1.217599 1.044935 1.256618 1.049753 -P_31 1.191246 1.226202 0.992971 0.986543 0.978176 0.925280 0.867018 0.782666 -P_31 0.829925 0.735417 0.739683 0.663806 0.598919 0.574980 0.616598 0.582667 -P_31 0.549757 0.612929 0.629144 0.630391 0.669917 0.703492 0.844013 0.855205 -P_31 0.924580 0.998813 0.990506 1.033411 1.144386 1.123939 1.254250 1.138168 -P_31 1.119395 1.211402 1.257982 1.204222 1.040776 0.972011 1.132539 1.098445 -P_31 1.113342 1.019035 1.096328 1.055858 1.231095 1.195379 1.182340 1.137602 -P_31 1.187223 1.050763 1.190841 1.094653 1.071710 0.956142 0.883167 0.890159 -P_31 0.800224 0.744213 0.655693 0.621347 0.567662 0.584630 0.546305 0.529371 -P_31 0.589460 0.707609 0.678434 0.702184 0.863299 0.991217 1.135559 1.142229 -P_31 1.165916 1.131945 1.349866 1.372357 1.413566 1.359271 1.455744 1.415646 -P_31 1.276438 1.223956 1.244841 1.248613 1.131159 1.117654 1.072304 1.035411 -P_31 1.048806 0.980902 1.043961 1.012626 1.123464 1.136106 1.133367 1.214696 -P_31 1.289561 1.173770 1.170594 1.159379 1.114767 1.155711 0.963603 0.901593 -P_31 0.800394 0.821012 0.681646 0.681390 0.584641 0.492117 0.555260 0.567274 -P_31 0.623300 0.675325 0.742943 0.859081 0.912231 0.872253 1.102369 1.258154 -P_31 1.109079 1.295179 1.274930 1.193569 1.285558 1.404848 1.284138 1.166223 -P_31 1.294590 1.233475 1.182509 1.114404 1.119304 1.111503 1.010833 1.132590 -P_31 1.036022 1.068965 1.161310 1.055007 1.138007 1.088466 1.185511 1.215680 -P_31 1.201339 1.354001 1.204839 1.094856 1.145834 0.969371 0.953182 0.853505 -P_31 0.773077 0.772319 0.655048 0.527362 0.526625 0.570640 0.514885 0.567024 -P_31 0.611646 0.624530 0.714799 0.824976 0.966751 0.898303 1.038821 1.272809 -P_31 1.119186 1.294815 1.287768 1.317396 1.326147 1.340783 1.241812 1.332996 -P_31 1.331876 1.253219 1.110325 1.273720 1.117964 1.116290 1.104233 1.061925 -P_31 1.157494 1.122785 1.100150 1.263170 1.213174 1.169219 1.238215 1.179255 -P_31 1.266052 1.102636 1.218100 1.080942 1.143382 1.046808 0.989092 0.893676 -P_31 0.750950 0.700191 0.614881 0.620878 0.593449 0.545934 0.513791 0.562361 -P_31 0.626197 0.649218 0.674086 0.767217 0.898565 0.979168 1.109506 1.122566 -P_31 1.231376 1.096796 1.294012 1.339890 1.376857 1.421551 1.287055 1.097678 -P_31 1.206667 1.101270 1.144091 1.100008 1.127140 1.096870 0.988834 1.168604 -P_31 1.147646 1.095926 1.169553 1.217509 1.210401 1.198023 1.144128 1.124839 -P_31 1.132794 1.123095 1.149586 0.999929 1.070028 1.103270 1.004845 0.812857 -P_31 0.816259 0.722108 0.625952 0.609886 0.548580 0.525583 0.548515 0.597390 -P_31 0.649648 0.711058 0.789157 0.871604 1.014876 1.127059 1.267936 1.398329 -P_31 1.337173 1.482497 1.434512 1.547477 1.452955 1.506655 1.425484 1.291981 -P_31 1.371570 1.303794 1.276284 1.286885 1.220826 1.092798 1.089719 1.149363 -P_31 1.232472 1.170733 1.113615 1.184615 1.165867 1.142140 1.110449 1.262177 -P_31 1.213555 1.076079 1.224867 1.055324 0.998245 1.035936 0.951691 0.910157 -P_31 0.879666 0.810860 0.715686 0.663727 0.635562 0.605305 0.591609 0.556081 -P_31 0.604545 0.582613 0.647019 0.702682 0.754894 0.799292 0.908613 1.084298 -P_31 1.103631 1.176802 1.231532 1.234031 1.214469 1.278032 1.188655 1.292247 -P_31 1.284459 1.363106 1.190953 1.311553 1.050221 1.258560 1.223907 1.288818 -P_31 1.241356 1.142566 1.184836 1.066037 1.066108 1.202445 1.256193 1.151961 -P_31 1.105048 0.987747 0.986780 1.042632 0.921767 0.910132 0.822793 0.792340 -P_31 0.818873 0.759508 0.660256 0.656249 0.670756 0.643534 0.614970 0.572088 -P_31 0.609922 0.623363 0.569737 0.723109 0.724596 0.677246 0.810609 0.903143 -P_31 0.937540 0.942332 1.074476 1.005134 1.023999 1.102698 1.203630 1.252528 -P_31 1.131058 1.192071 1.223458 1.067885 1.093139 1.132722 1.070856 1.099757 -P_31 1.112338 1.121669 1.007969 1.103200 1.025306 1.198327 1.119278 1.073270 -P_31 1.111400 1.156874 1.221597 1.041198 0.950579 0.902191 0.939754 0.751646 -P_31 0.753858 0.701581 0.588544 0.580543 0.590194 0.537870 0.556475 0.519059 -P_31 0.614027 0.621455 0.746171 0.797774 0.876237 0.959693 1.091013 1.136331 -P_31 1.145933 1.324564 1.378287 1.387340 1.565945 1.334354 1.448549 1.322073 -P_31 1.303933 1.285958 1.184289 1.099613 1.075987 0.974224 1.101423 0.986586 -P_31 1.002358 0.979429 1.192186 1.057425 1.148870 1.184588 1.092607 1.151252 -P_31 1.176397 1.172977 1.246102 1.112428 1.038962 1.161957 0.944883 0.873769 -P_31 0.778076 0.735765 0.649129 0.647564 0.587387 0.559776 0.541718 0.586557 -P_31 0.637407 0.698237 0.726375 0.816956 1.002117 0.900614 1.038263 1.104123 -P_31 1.215597 1.248774 1.174314 1.322788 1.236849 1.314609 1.309737 1.350492 -P_31 1.330900 1.182673 1.059661 1.044638 1.140215 1.081495 1.071510 0.980631 -P_31 0.983698 1.094905 1.026344 1.130272 1.124856 1.179391 1.209592 1.223562 -P_31 1.277020 1.207679 1.103542 1.140560 1.153764 0.959311 0.995152 0.911966 -P_31 0.695666 0.717610 0.545282 0.595090 0.576020 0.531409 0.573006 0.570206 -P_31 0.604750 0.648098 0.734349 0.814351 0.951688 0.921645 1.024725 1.158442 -P_31 1.062176 1.200887 1.386341 1.248811 1.268398 1.264172 1.321064 1.377455 -P_31 1.267173 1.250619 1.258754 1.349458 1.072045 1.098284 1.036129 1.108256 -P_31 1.056788 1.159269 1.116279 1.200331 1.133480 1.184016 1.162069 1.288234 -P_31 1.238977 1.191232 1.207676 1.158621 1.079556 1.055134 0.964049 0.838623 -P_31 0.804375 0.742402 0.637384 0.579532 0.599643 0.562327 0.561032 0.548142 -P_31 0.612252 0.713189 0.783812 0.789381 0.853186 1.010578 1.143180 1.117036 -P_31 1.244191 1.190142 1.213252 1.358413 1.258773 1.133591 1.242351 1.117332 -P_31 1.300151 1.247707 1.165179 1.090032 1.039416 1.038349 1.174339 1.092832 -P_31 1.099848 1.054321 1.126746 1.120880 1.144479 1.173050 1.196369 1.167672 -P_31 1.174497 1.169253 1.162229 1.100819 1.100608 1.043388 1.000947 0.916255 -P_31 0.862560 0.757503 0.654145 0.628299 0.574360 0.618147 0.592798 0.567843 -P_31 0.580120 0.771815 0.728919 0.895036 1.006989 1.068539 1.071770 1.287623 -P_31 1.210853 1.428257 1.281002 1.379094 1.475064 1.474087 1.457037 1.547944 -P_31 1.406242 1.384158 1.238142 1.097000 1.206992 1.160364 1.131486 1.069055 -P_31 1.141450 1.164741 1.169518 1.195303 1.309665 1.156784 1.138695 1.244967 -P_31 1.274462 1.139027 1.135662 1.070433 1.100909 1.073501 0.935906 0.963888 -P_31 0.847305 0.758330 0.751569 0.602194 0.670113 0.600098 0.593828 0.557787 -P_31 0.570743 0.652928 0.684828 0.746914 0.746916 0.800814 0.868851 0.956445 -P_31 1.080180 1.146884 1.192716 1.216877 1.216128 1.253831 1.259855 1.357553 -P_31 1.290300 1.113001 1.192933 1.055989 1.140297 1.149931 1.257305 1.209667 -P_31 1.092220 1.088919 1.094069 1.108427 1.270574 1.172824 1.168863 1.175297 -P_31 1.066612 0.977999 0.996026 0.931664 0.950194 0.888459 0.913721 0.752680 -P_31 0.759629 0.697974 0.770852 0.678282 0.620382 0.596060 0.612474 0.619133 -P_31 0.580373 0.568095 0.617677 0.631472 0.667115 0.722536 0.801611 0.782039 -P_31 0.980597 1.051711 0.996024 1.072957 1.112121 1.137770 1.178555 1.155809 -P_31 1.215796 1.269532 0.945975 0.924443 1.075463 1.047341 1.010903 0.997534 -P_31 1.049618 1.015382 1.068047 1.021254 0.985576 0.954247 1.022264 1.110777 -P_31 1.129975 1.188797 1.101036 1.047907 0.938265 1.061814 0.960226 0.778558 -P_31 0.781893 0.682061 0.668037 0.593467 0.576067 0.527565 0.509764 0.514593 -P_31 0.567409 0.584066 0.714774 0.772651 0.888833 1.031369 1.004067 1.115583 -P_31 1.144911 1.249131 1.359215 1.421252 1.257217 1.308249 1.282208 1.421293 -P_31 1.279278 1.233203 1.090051 1.236547 1.056549 1.017845 1.143125 1.044405 -P_31 0.994913 0.999075 1.054916 1.012103 1.143838 1.134056 1.149031 1.187831 -P_31 1.314667 1.259048 1.225196 1.102860 1.045581 1.107204 0.947031 0.879554 -P_31 0.777020 0.746782 0.608417 0.590231 0.599713 0.543291 0.525270 0.536003 -P_31 0.627287 0.680530 0.715145 0.767649 0.851640 0.982032 1.024828 1.053667 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1.209061 1.190756 -P_31 1.161377 1.153974 1.080979 0.971239 0.992152 1.134447 1.103065 1.036407 -P_31 1.170600 1.110111 1.085493 1.003989 1.153452 1.171455 1.096996 1.122272 -P_31 1.274161 1.223546 1.104142 1.089145 1.072405 0.957584 0.872393 0.868346 -P_31 0.821854 0.691217 0.681373 0.654195 0.574622 0.560859 0.560558 0.640602 -P_31 0.614242 0.682085 0.734209 0.796032 1.089421 0.979116 1.232292 1.293764 -P_31 1.210862 1.351733 1.409759 1.521742 1.492481 1.361550 1.338481 1.495649 -P_31 1.323464 1.298510 1.292583 1.177432 1.292845 1.170131 1.164065 1.034708 -P_31 1.087711 1.111341 1.103389 1.219377 1.190642 1.091633 1.164589 1.217722 -P_31 1.100074 1.208690 1.101354 1.098441 1.101868 0.967517 0.921354 0.914697 -P_31 0.832597 0.786314 0.726390 0.686947 0.608673 0.598656 0.582605 0.568376 -P_31 0.601322 0.585626 0.727268 0.623241 0.825785 0.868481 0.817451 1.071093 -P_31 1.091097 1.106686 1.145430 1.235394 1.198891 1.143529 1.108896 1.204005 -P_31 1.169173 1.259077 1.209902 1.107852 1.127154 1.296326 1.165451 1.169149 -P_31 1.158901 1.138330 1.098005 1.103545 1.210135 1.150896 1.046129 1.199349 -P_31 1.114584 1.022589 0.994607 0.932494 0.959567 0.865094 0.872480 0.815098 -P_31 0.718422 0.705673 0.741622 0.633570 0.613780 0.615306 0.609597 0.569967 -P_31 0.564718 0.569807 0.558697 0.629617 0.629428 0.748933 0.849570 0.779630 -P_31 0.888409 0.947989 0.971460 1.064461 1.105011 1.037708 1.096317 1.087500 -P_31 1.090183 1.155452 1.127794 1.162238 1.027336 1.071597 1.065284 0.982314 -P_31 0.994128 0.955155 1.133024 0.983084 1.094332 1.043620 1.127690 0.999626 -P_31 1.196922 1.078622 1.098396 1.000722 1.027983 1.036504 0.903412 0.734055 -P_31 0.780501 0.713683 0.644709 0.560475 0.565130 0.543637 0.534843 0.525011 -P_31 0.576482 0.614616 0.730035 0.680122 0.823081 0.953466 1.074821 1.051064 -P_31 1.199518 1.308876 1.246031 1.245402 1.299040 1.380322 1.343746 1.213687 -P_31 1.206763 1.227892 1.165641 1.138191 1.111353 1.033337 1.012396 0.994223 -P_31 0.994361 1.057109 0.964989 1.001930 1.107096 1.149951 1.038441 1.052116 -P_31 1.222696 1.072042 1.189932 1.097072 1.199119 1.003356 0.891554 0.860574 -P_31 0.748418 0.712987 0.669749 0.599727 0.540535 0.538304 0.544998 0.545068 -P_31 0.625729 0.605779 0.707479 0.719981 0.901974 0.915625 0.993014 1.074780 -P_31 1.159048 1.118483 1.081808 1.307314 1.237968 1.275278 1.239597 1.310672 -P_31 1.216871 1.215129 1.120596 1.091841 1.126287 1.052075 1.028846 1.040373 -P_31 0.906091 1.026073 1.061555 1.107804 1.144652 1.238514 1.211758 1.271769 -P_31 1.248863 1.255310 1.109321 1.121975 1.064555 0.970168 0.868449 0.855418 -P_31 0.822399 0.661380 0.600138 0.541883 0.568495 0.487862 0.517376 0.565074 -P_31 0.573527 0.673391 0.741871 0.818982 0.867706 0.996374 1.063522 1.190285 -P_31 1.140865 1.217715 1.255826 1.238871 1.279640 1.388121 1.243228 1.206780 -P_31 1.275841 1.174075 1.282250 1.050627 1.162962 1.078453 1.021722 1.147230 -P_31 1.096040 1.165476 1.147662 1.167392 1.118165 1.127993 1.055505 1.132328 -P_31 1.175639 1.221878 1.138950 1.107103 1.044159 0.935979 1.014473 0.868453 -P_31 0.812130 0.694826 0.715758 0.574913 0.567401 0.533146 0.551250 0.573087 -P_31 0.599397 0.647473 0.757979 0.837915 0.843514 0.974138 1.036107 1.217473 -P_31 1.144662 1.252966 1.185943 1.250199 1.289757 1.261436 1.206644 1.223427 -P_31 1.304803 1.192102 1.119999 1.077863 1.041612 0.976069 1.098988 1.041832 -P_31 1.059659 1.066062 1.150227 1.051929 1.125692 1.066227 1.155818 1.112875 -P_31 1.130301 1.179799 1.222123 1.161895 1.071817 0.992259 0.942126 0.832491 -P_31 0.743129 0.770199 0.733790 0.604655 0.599831 0.565611 0.598308 0.580215 -P_31 0.673949 0.709848 0.793486 0.872794 0.972287 1.029246 1.160760 1.182255 -P_31 1.203740 1.397632 1.332045 1.478552 1.441205 1.435542 1.386562 1.290150 -P_31 1.345810 1.152686 1.215930 1.045626 1.260778 1.164629 1.234754 1.064876 -P_31 1.062560 1.102984 1.160997 1.076616 1.176054 1.165273 1.227946 1.276715 -P_31 1.090797 1.068444 1.022729 1.098840 1.025318 1.047781 0.908492 0.878492 -P_31 0.817681 0.715817 0.674018 0.728693 0.565146 0.638596 0.612753 0.559077 -P_31 0.593996 0.529536 0.624697 0.674513 0.787451 0.922210 0.823232 0.970615 -P_31 1.055114 1.075014 1.059511 1.202507 1.297892 1.285401 1.131191 1.254142 -P_31 1.200965 1.247668 1.195332 1.179493 1.222423 1.221214 1.056927 1.140173 -P_31 1.084468 1.119844 1.121883 1.211551 1.187423 1.154177 1.132000 1.085874 -P_31 1.028549 0.996995 0.995364 1.006416 0.850625 0.921419 0.814383 0.884237 -P_31 0.730854 0.741961 0.640259 0.649844 0.581591 0.620700 0.552534 0.541513 -P_31 0.614017 0.584587 0.583740 0.652934 0.698153 0.695896 0.694399 0.818315 -P_31 0.935485 0.974664 0.886650 0.977442 1.235862 1.090854 1.095412 1.223952 -P_31 1.213928 1.062620 1.081042 1.157853 1.147505 1.040234 1.076410 1.016509 -P_31 1.030927 1.035741 1.009386 1.116828 1.108163 1.153386 1.130443 1.149055 -P_31 1.145178 1.022553 1.040720 1.019239 0.996184 0.899976 0.916617 0.840702 -P_31 0.855791 0.652289 0.603064 0.600514 0.538068 0.522944 0.523405 0.570169 -P_31 0.583681 0.593303 0.682924 0.783104 0.807592 0.934833 1.014684 1.036846 -P_31 1.240314 1.194528 1.281244 1.470347 1.245471 1.248466 1.342096 1.360306 -P_31 1.234054 1.430697 1.112449 1.091357 1.073004 1.033358 1.052547 0.910325 -P_31 1.031920 1.072348 1.027411 0.977277 1.095773 1.164071 1.090942 1.111725 -P_31 1.205799 1.247908 1.208278 1.163387 0.939506 0.942402 0.919689 0.820842 -P_31 0.794582 0.706305 0.646226 0.536798 0.590776 0.518092 0.526223 0.561458 -P_31 0.668379 0.634872 0.685458 0.799673 0.888218 0.958025 0.963156 1.148447 -P_31 1.145490 1.251911 1.229215 1.189484 1.200974 1.252058 1.261539 1.290386 -P_31 1.226548 1.223133 1.162440 1.196031 1.024457 1.079194 0.989629 0.920636 -P_31 0.979782 0.995565 1.001167 1.115455 1.044431 1.163608 1.135690 1.159172 -P_31 1.263868 1.191872 1.139840 1.074570 1.070665 0.993140 0.953670 0.855878 -P_31 0.730737 0.716872 0.591925 0.562505 0.516516 0.528217 0.532624 0.503612 -P_31 0.583842 0.661241 0.737671 0.769078 0.895606 0.936358 1.008778 1.021975 -P_31 1.187612 1.173103 1.116950 1.281704 1.397061 1.264497 1.252760 1.383579 -P_31 1.247437 1.150604 1.177388 1.124305 1.183595 1.147691 1.160381 1.183103 -P_31 0.960866 1.068893 1.001802 1.023427 1.215473 1.212835 1.102874 1.209119 -P_31 1.123268 1.194189 1.125761 1.208859 1.002834 1.032378 0.931469 0.884479 -P_31 0.766949 0.711966 0.580442 0.622959 0.517831 0.539219 0.552095 0.542519 -P_31 0.604006 0.637170 0.740481 0.822822 0.841392 0.983228 0.968174 1.096298 -P_31 1.134869 1.289817 1.166393 1.187414 1.324775 1.204632 1.318270 1.135619 -P_31 1.117090 1.186911 1.281375 1.131969 1.053422 1.091851 1.098972 1.078848 -P_31 1.074090 1.082330 1.032400 1.075307 1.123554 1.115958 1.180372 1.080090 -P_31 1.202590 1.181208 1.191400 1.144931 1.047209 1.050142 0.984038 0.827316 -P_31 0.801189 0.736237 0.702992 0.623202 0.618464 0.559935 0.586898 0.568331 -P_31 0.634681 0.676397 0.762805 0.892319 1.014962 1.007543 1.215405 1.195077 -P_31 1.471750 1.421259 1.480544 1.472140 1.436385 1.437813 1.296228 1.339020 -P_31 1.461509 1.239160 1.282719 1.140163 1.102629 1.227465 1.128404 1.155060 -P_31 1.077675 1.182784 1.184038 1.149735 1.198455 1.206657 1.180143 1.137623 -P_31 1.210099 1.073226 1.110867 1.133729 1.156025 0.937389 0.929970 0.923506 -P_31 0.848363 0.817574 0.731426 0.686965 0.632965 0.608168 0.632324 0.525675 -P_31 0.606285 0.620793 0.625257 0.724675 0.793608 0.759503 0.982077 0.919197 -P_31 1.080399 1.103711 1.198245 1.132614 1.314916 1.267233 1.289884 1.264060 -P_31 1.235151 1.178782 1.158364 1.274475 1.171927 1.204298 1.200429 1.199277 -P_31 1.217024 1.065665 1.170367 1.152985 1.107791 1.137983 1.106815 1.125020 -P_31 1.083785 1.075697 1.056161 0.998309 0.953208 0.916024 0.760655 0.798377 -P_31 0.816100 0.706614 0.693824 0.670480 0.616100 0.607492 0.594102 0.574162 -P_31 0.587897 0.600800 0.576936 0.585953 0.665588 0.770189 0.654298 0.816220 -P_31 0.912331 0.967714 0.897368 1.039011 1.096835 1.197564 1.211140 1.185766 -P_31 1.055916 1.137385 1.210283 1.070752 1.202103 1.035778 1.026557 1.068825 -P_31 1.062041 1.094735 1.162241 0.940725 1.036220 1.057156 1.164556 1.137926 -P_31 1.154783 1.231825 1.060177 0.999251 0.980149 0.910926 1.087570 0.808925 -P_31 0.764173 0.688876 0.701040 0.620014 0.569875 0.558905 0.550819 0.546775 -P_31 0.577911 0.635770 0.678188 0.799500 0.858731 0.963451 0.949906 1.084501 -P_31 1.204576 1.270555 1.188401 1.384279 1.351152 1.264471 1.359933 1.240530 -P_31 1.290233 1.302153 0.998254 1.273006 1.069047 0.984818 0.971385 0.904726 -P_31 1.029053 1.145877 0.991201 1.044705 1.156080 1.101709 1.170767 1.150381 -P_31 1.118385 1.279426 1.118867 1.016043 1.100273 1.053493 0.941514 0.859637 -P_31 0.734414 0.691364 0.661204 0.551664 0.576847 0.603012 0.519841 0.531627 -P_31 0.573809 0.619016 0.709845 0.694238 0.894900 0.893529 1.101647 1.012069 -P_31 1.110524 1.205736 1.136697 1.292452 1.386684 1.321030 1.132299 1.248158 -P_31 1.278179 1.159915 1.242579 1.047170 1.023479 1.007893 1.026844 1.059461 -P_31 1.108103 1.179568 0.969586 1.016382 1.138416 1.068100 1.140677 1.226523 -P_31 1.238267 1.257878 1.229044 1.154647 1.180557 0.986062 0.889989 0.870146 -P_31 0.723173 0.702007 0.639112 0.584774 0.567813 0.510799 0.571443 0.562883 -P_31 0.602349 0.680996 0.702920 0.784999 0.831964 0.973594 0.864597 1.066216 -P_31 1.099224 1.218574 1.248357 1.360415 1.333052 1.336390 1.288195 1.175991 -P_31 1.265308 1.139777 1.199540 1.143404 1.199034 1.065245 1.083484 1.034310 -P_31 1.040520 1.053589 1.167802 1.090271 1.098646 1.179857 1.108020 1.222128 -P_31 1.155542 1.174265 1.134689 1.112116 1.052334 1.001051 0.943911 0.792699 -P_31 0.819274 0.705127 0.708092 0.624673 0.616678 0.569565 0.529398 0.600389 -P_31 0.600615 0.678539 0.740491 0.858080 0.920830 1.072016 1.128667 1.155182 -P_31 1.286127 1.359067 1.343240 1.273181 1.289066 1.288061 1.305724 1.304633 -P_31 1.224737 1.224366 1.167991 1.160383 1.203700 1.110769 1.003296 1.062781 -P_31 1.107647 1.072684 1.147191 1.114956 1.102225 1.142177 1.057317 1.180468 -P_31 1.088259 1.154323 1.086368 1.131254 1.209841 0.952561 1.025822 0.883944 -P_31 0.814908 0.744331 0.664718 0.651805 0.557489 0.577421 0.592376 0.541005 -P_31 0.592654 0.727951 0.807915 0.911322 0.992435 1.050673 1.307017 1.255819 -P_31 1.316340 1.358327 1.509433 1.345879 1.518394 1.380956 1.421680 1.413222 -P_31 1.413679 1.203599 1.348762 1.247079 1.114850 1.085318 1.179200 1.142320 -P_31 1.065149 1.018865 1.124015 1.032602 1.169165 1.106949 1.183292 1.123545 -P_31 1.078520 1.165508 1.050498 1.126354 0.996789 0.962601 1.053260 0.819402 -P_31 0.809268 0.773034 0.704868 0.665577 0.571822 0.595559 0.626767 0.570175 -P_31 0.575623 0.590935 0.643116 0.745518 0.773388 0.795566 0.885734 0.985337 -P_31 1.048113 1.135836 1.145383 1.140223 1.087567 1.262014 1.294683 1.261578 -P_31 1.228030 1.220664 1.196243 1.105721 1.167765 1.205868 1.232212 1.163796 -P_31 1.195682 1.098865 1.056938 1.321375 1.116266 1.083489 1.158522 1.134162 -P_31 1.156322 1.057475 1.159692 1.032929 0.923026 0.928244 0.832944 0.787127 -P_31 0.793861 0.696323 0.693279 0.639701 0.568301 0.624950 0.589181 0.574892 -P_31 0.573981 0.608128 0.609185 0.558024 0.689093 0.681645 0.752687 0.789176 -P_31 0.899962 0.917114 1.085456 1.117702 1.094962 1.218160 1.143733 1.167997 -P_31 1.224225 1.119369 1.173413 1.077194 1.151393 1.060626 1.058992 1.159590 -P_31 1.045129 0.953565 0.982391 1.082678 1.108814 1.120541 1.179518 1.066819 -P_31 1.061705 1.089863 1.101918 1.014068 1.003271 0.940406 0.869504 0.821143 -P_31 0.741979 0.638855 0.662868 0.582817 0.557474 0.532639 0.532628 0.535384 -P_31 0.616558 0.595814 0.735046 0.775015 0.800726 0.936424 1.007195 1.099105 -P_31 1.188279 1.245186 1.292556 1.414301 1.471233 1.230373 1.169752 1.378504 -P_31 1.224202 1.309491 1.271093 1.154590 1.061789 1.098516 1.056806 1.016306 -P_31 0.998932 0.981861 1.028245 1.004564 1.160985 1.124061 1.166094 1.188095 -P_31 1.270956 1.214809 1.180785 1.109793 1.125626 0.953827 0.909572 0.822088 -P_31 0.775044 0.754447 0.727234 0.548781 0.584826 0.557772 0.552635 0.633407 -P_31 0.598312 0.648280 0.639799 0.726308 0.884402 0.969285 1.014750 1.094717 -P_31 1.255257 1.214416 1.121632 1.424797 1.235626 1.399268 1.353977 1.372663 -P_31 1.219800 1.208456 1.230770 1.160393 1.081339 1.126858 1.051665 1.009101 -P_31 1.117178 1.102672 1.066394 1.080622 1.129360 1.184224 1.218860 1.314855 -P_31 1.187193 1.215983 1.184185 1.120572 1.160094 0.967562 0.935234 0.919100 -P_31 0.745564 0.697965 0.711002 0.602618 0.545854 0.519854 0.550376 0.608408 -P_31 0.613396 0.642768 0.685067 0.862121 0.858858 1.049745 1.067791 1.085962 -P_31 1.237881 1.260739 1.283083 1.382117 1.404200 1.322744 1.238273 1.236999 -P_31 1.263381 1.179631 1.239113 1.221256 1.075700 1.098283 1.170151 1.130262 -P_31 1.136304 1.161803 1.138388 1.176146 1.189083 1.113286 1.193035 1.184707 -P_31 1.093457 1.116190 1.087631 1.126042 1.059783 1.042552 0.982271 0.903186 -P_31 0.750113 0.765605 0.696156 0.602690 0.574629 0.588167 0.571792 0.575502 -P_31 0.642335 0.660512 0.744293 0.819094 0.910974 0.919600 1.183093 1.144188 -P_31 1.252092 1.304026 1.201501 1.222876 1.292603 1.329909 1.277809 1.301089 -P_31 1.158498 1.198535 1.128556 1.087259 1.075361 1.105913 1.120578 1.140447 -P_31 1.147252 1.089290 1.102687 1.141616 1.118045 1.228209 1.205124 1.216380 -P_31 1.142887 1.217242 1.156220 0.997942 1.078125 1.095007 0.996041 0.899356 -P_31 0.750315 0.710129 0.677844 0.587179 0.591355 0.588361 0.585941 0.603994 -P_31 0.676602 0.688633 0.818973 0.910873 0.897096 1.081878 1.240315 1.307859 -P_31 1.332323 1.498922 1.518900 1.447964 1.395861 1.396083 1.420599 1.407377 -P_31 1.303397 1.405644 1.264735 1.094986 1.162492 1.229766 1.064954 1.126190 -P_31 1.179891 1.201164 1.232066 1.218821 1.248663 1.247601 1.214569 1.202733 -P_31 1.199262 1.137833 1.086723 1.076720 1.026257 0.970732 1.025557 0.908679 -P_31 0.843070 0.766617 0.672978 0.662851 0.658790 0.604696 0.593864 0.585296 -P_31 0.583889 0.621680 0.673675 0.643721 0.744854 0.735946 0.901407 0.942214 -P_31 1.072326 1.101711 1.143012 1.265886 1.296530 1.194460 1.144875 1.234081 -P_31 1.305272 1.313799 1.173023 1.176443 1.055969 1.133159 1.161478 1.222287 -P_31 1.079335 1.175225 1.183651 1.057676 1.176553 1.083580 1.164879 1.134850 -P_31 1.029712 1.164791 1.075743 1.025257 0.950390 0.969713 0.892048 0.812562 -P_31 0.777975 0.763254 0.709665 0.640374 0.681612 0.633373 0.576887 0.552252 -P_31 0.553943 0.579069 0.616798 0.636811 0.654770 0.733739 0.693986 0.810817 -P_31 0.922372 0.950461 1.013305 1.034329 1.057567 1.165189 1.208046 1.129559 -P_31 1.073667 1.181500 1.168088 1.168118 1.135808 1.038293 1.125715 0.985798 -P_31 1.136702 0.978919 1.103581 1.072622 1.163457 1.090773 1.121495 1.073857 -P_31 1.055181 1.099916 1.045613 1.107395 1.092500 1.017807 0.915222 0.815565 -P_31 0.794515 0.671218 0.649438 0.622707 0.588437 0.509002 0.538250 0.564235 -P_31 0.600704 0.632925 0.790803 0.799956 0.834443 1.074606 1.138362 1.235799 -P_31 1.162143 1.188454 1.315763 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1.195717 1.206511 -P_31 1.106956 1.142970 1.174203 1.128498 1.053684 0.979328 0.867289 0.939175 -P_31 0.793427 0.730388 0.640213 0.626960 0.579547 0.560502 0.525855 0.625512 -P_31 0.564564 0.647440 0.725352 0.810543 0.797725 0.962107 1.074565 1.164234 -P_31 1.195393 1.284097 1.315213 1.443882 1.446077 1.343713 1.436164 1.462176 -P_31 1.358357 1.339664 1.190866 1.281757 1.085316 1.106028 1.000713 0.998866 -P_31 1.101596 0.965293 0.993436 1.019472 1.169750 1.280537 1.280908 1.250213 -P_31 1.247753 1.257789 1.291089 1.217097 1.042665 1.017183 1.061085 0.885978 -P_31 0.799379 0.725413 0.695715 0.643211 0.634548 0.601853 0.532646 0.569683 -P_31 0.687789 0.645655 0.789479 0.867984 0.889032 0.978002 1.048732 1.134622 -P_31 1.289464 1.270062 1.270164 1.424025 1.356496 1.236374 1.245628 1.418561 -P_31 1.235328 1.275873 1.172655 1.147767 1.197872 1.073150 1.054328 1.000256 -P_31 1.034639 1.020361 1.066825 1.166129 1.206993 1.169588 1.234184 1.257127 -P_31 1.224448 1.287961 1.273870 1.173157 1.181341 1.027377 0.947928 0.886114 -P_31 0.777441 0.787469 0.663272 0.652726 0.547494 0.518617 0.581875 0.527510 -P_31 0.616567 0.646029 0.825688 0.898437 0.908001 0.966835 1.057071 1.257347 -P_31 1.237658 1.278426 1.305779 1.367337 1.426558 1.330749 1.416947 1.256139 -P_31 1.360847 1.246154 1.199957 1.138654 1.253181 1.163193 1.186326 1.069417 -P_31 1.082074 1.085256 1.068925 1.196577 1.177122 1.184055 1.270556 1.196683 -P_31 1.321456 1.156929 1.201648 1.054007 1.133989 1.025267 0.924411 0.867340 -P_31 0.797689 0.729208 0.672556 0.545848 0.581320 0.579403 0.543181 0.613039 -P_31 0.553806 0.712505 0.726700 0.838301 0.961255 1.067843 1.109702 1.236251 -P_31 1.240168 1.252547 1.365095 1.383906 1.341712 1.231514 1.321437 1.092457 -P_31 1.238564 1.263216 1.114864 1.097101 1.098408 1.027076 1.110125 1.042587 -P_31 1.085386 1.228165 1.153563 1.227861 1.203596 1.307234 1.361718 1.250301 -P_31 1.258992 1.259417 1.215360 1.122364 1.154839 1.085524 1.058047 0.897150 -P_31 0.868381 0.765853 0.628863 0.648423 0.603252 0.617727 0.640774 0.571795 -P_31 0.730140 0.753487 0.768337 0.945388 1.048372 1.051402 1.382194 1.196856 -P_31 1.426040 1.468773 1.488663 1.396001 1.550720 1.530401 1.532289 1.388547 -P_31 1.316327 1.293475 1.236108 1.235069 1.184419 1.228849 1.147340 1.277014 -P_31 1.164923 1.220993 1.302471 1.200615 1.210066 1.279783 1.288415 1.249021 -P_31 1.159247 1.203426 1.225904 1.108146 1.153995 0.999870 1.018282 1.015144 -P_31 0.846164 0.856848 0.791687 0.766114 0.709808 0.682097 0.601200 0.578783 -P_31 0.596601 0.667131 0.601708 0.698813 0.709692 0.927801 0.889376 1.018564 -P_31 1.069871 1.247189 1.200690 1.222642 1.242199 1.312789 1.262528 1.328340 -P_31 1.292614 1.211233 1.300148 1.393065 1.274897 1.208069 1.304332 1.227070 -P_31 1.265494 1.147091 1.311614 1.212668 1.118970 1.335270 1.268427 1.259155 -P_31 1.188477 1.109072 1.114643 1.080673 1.023510 0.873096 0.893780 0.860892 -P_31 0.734766 0.690284 0.720459 0.702154 0.682481 0.708045 0.612594 0.658008 -P_31 0.601811 0.602875 0.608594 0.659801 0.695473 0.800488 0.824740 0.932169 -P_31 1.004541 0.995490 1.075826 1.086638 1.159094 1.149897 1.180513 1.264068 -P_31 1.234928 1.175513 1.249111 1.091975 1.201759 1.177314 1.086997 1.243221 -P_31 1.101153 1.150939 1.159255 1.209663 1.152200 1.111801 1.113135 1.157067 -P_31 1.172775 1.232853 1.165260 1.077747 1.101462 0.968493 0.927450 0.873001 -P_31 0.750110 0.727221 0.670694 0.641688 0.561184 0.583823 0.556742 0.581852 -P_31 0.610170 0.677578 0.736814 0.792019 0.849220 1.029351 1.140739 1.049694 -P_31 1.268239 1.252731 1.402996 1.518087 1.399693 1.379422 1.359873 1.437662 -P_31 1.346863 1.327809 1.369362 1.174363 1.184141 1.245346 1.132365 1.057987 -P_31 1.027989 1.158264 1.066986 1.028858 1.243949 1.169029 1.304994 1.235578 -P_31 1.255673 1.208723 1.201806 1.170563 1.171460 0.975247 1.014589 0.870243 -P_31 0.861408 0.762346 0.718253 0.618085 0.613336 0.655469 0.598670 0.574676 -P_31 0.659670 0.767232 0.811339 0.867323 0.858721 0.975083 1.062156 1.217786 -P_31 1.118267 1.291096 1.395583 1.408996 1.434730 1.435492 1.380983 1.283713 -P_31 1.309192 1.332461 1.369644 1.343916 1.126230 1.048139 1.138903 1.040097 -P_31 1.038053 1.073576 1.133322 1.141237 1.254375 1.407229 1.309223 1.343874 -P_31 1.301587 1.222548 1.220020 1.133176 1.205994 1.056359 1.139212 0.882894 -P_31 0.845909 0.731333 0.683706 0.647674 0.570190 0.525192 0.600048 0.648162 -P_31 0.565099 0.710523 0.799199 0.831864 0.943593 1.021095 1.061569 1.217220 -P_31 1.375811 1.406078 1.305608 1.199407 1.272548 1.453528 1.369209 1.414796 -P_31 1.454184 1.337171 1.320589 1.281091 1.276771 1.206556 1.182461 1.144488 -P_31 1.222055 1.162343 1.177480 1.192730 1.227441 1.151090 1.184256 1.137996 -P_31 1.295014 1.292224 1.253209 1.124067 1.150181 1.017345 1.061065 0.983003 -P_31 0.872950 0.791759 0.686916 0.679192 0.592022 0.524483 0.534397 0.618018 -P_31 0.610372 0.689947 0.731009 0.782994 0.896652 1.093815 1.105750 1.175150 -P_31 1.168040 1.258647 1.320983 1.408434 1.274002 1.418110 1.250402 1.302401 -P_31 1.297989 1.315215 1.022354 1.162226 1.175898 1.075887 1.104486 1.147569 -P_31 1.241615 1.122082 1.100593 1.288415 1.114167 1.264217 1.243485 1.237087 -P_31 1.163872 1.190314 1.284736 1.091204 1.147416 1.016595 0.990351 0.903866 -P_31 0.819165 0.772526 0.734139 0.655388 0.605275 0.594576 0.616218 0.651628 -P_31 0.725684 0.774682 0.800073 0.933855 1.061193 1.114685 1.242288 1.309302 -P_31 1.422100 1.481357 1.536076 1.508036 1.490242 1.529435 1.431200 1.496372 -P_31 1.396610 1.218741 1.420019 1.271050 1.249238 1.312112 1.223227 1.242903 -P_31 1.156536 1.291315 1.231799 1.253331 1.236844 1.303435 1.290896 1.345328 -P_31 1.214784 1.199023 1.224826 1.118083 1.113068 1.152844 1.112989 0.899358 -P_31 0.866856 0.822142 0.755765 0.798450 0.722774 0.632436 0.544607 0.608747 -P_31 0.607983 0.638892 0.621146 0.716983 0.845772 0.827589 0.976731 1.050202 -P_31 1.100659 1.174335 1.204521 1.196453 1.224033 1.357901 1.295674 1.365291 -P_31 1.381716 1.403615 1.228460 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0.990088 1.003604 -P_31 0.927003 0.937171 0.891963 0.723833 0.718983 0.626847 0.635525 0.675635 -P_31 0.571810 0.623857 0.703194 0.830846 0.796562 0.931530 0.989045 0.957681 -P_31 1.231185 1.209085 1.314530 1.292260 1.294163 1.292132 1.428398 1.378839 -P_31 1.508591 1.346729 1.378037 1.275353 1.383091 1.303741 1.309342 1.216979 -P_31 1.231470 1.291120 1.306911 1.238198 1.250695 1.269552 1.317244 1.139781 -P_31 1.156180 1.258116 1.205591 1.092907 1.031479 0.961145 0.994707 0.813083 -P_31 0.929971 0.830515 0.725486 0.687820 0.634994 0.725172 0.644448 0.635632 -P_31 0.634770 0.636810 0.725116 0.696276 0.728155 0.770286 0.906092 0.919510 -P_31 0.881497 0.962559 1.108307 1.128847 1.232303 1.247232 1.338328 1.348664 -P_31 1.239860 1.221840 1.217031 1.152514 1.259951 1.193876 1.125862 1.020410 -P_31 1.149419 1.134204 1.211644 1.186361 1.322613 1.247979 1.216537 1.213292 -P_31 1.242424 1.197642 1.193816 1.215704 1.163391 1.040709 0.983129 0.917588 -P_31 0.880925 0.752580 0.688278 0.643366 0.654085 0.585202 0.623425 0.584225 -P_31 0.692361 0.698914 0.826257 0.769882 0.947956 1.045798 1.185697 1.294209 -P_31 1.229786 1.459493 1.481120 1.378775 1.553790 1.527720 1.660116 1.497772 -P_31 1.428884 1.376893 1.229234 1.259915 1.176365 1.071153 1.154638 1.128671 -P_31 1.126641 1.053964 1.185281 1.140176 1.246222 1.271640 1.379315 1.332402 -P_31 1.339327 1.250665 1.229844 1.253335 1.248160 1.137071 1.017776 0.981859 -P_31 0.897414 0.835872 0.708940 0.651855 0.639511 0.595275 0.613172 0.643818 -P_31 0.660429 0.688076 0.759224 0.864928 1.002164 1.088810 1.077364 1.106645 -P_31 1.276774 1.400007 1.417597 1.496443 1.466064 1.391379 1.329478 1.545954 -P_31 1.403843 1.347497 1.346000 1.239139 1.245673 1.143837 1.143335 1.128387 -P_31 1.254694 1.271316 1.167006 1.214711 1.338422 1.184297 1.303304 1.365289 -P_31 1.312019 1.334912 1.384516 1.364531 1.184775 1.089434 1.115437 0.891153 -P_31 0.839472 0.753248 0.719315 0.676815 0.653660 0.611453 0.549042 0.645006 -P_31 0.691832 0.688621 0.797848 0.972603 1.011996 1.131576 1.211589 1.208667 -P_31 1.371735 1.199632 1.376751 1.312896 1.437159 1.552711 1.456385 1.481845 -P_31 1.329933 1.456746 1.376258 1.302457 1.263714 1.131882 1.241133 1.224684 -P_31 1.153221 1.216087 1.089608 1.225779 1.352426 1.280029 1.255552 1.409991 -P_31 1.245597 1.227181 1.235582 1.300896 1.161089 1.173254 1.089360 0.947471 -P_31 0.913666 0.736138 0.726632 0.685864 0.574706 0.606997 0.634770 0.586504 -P_31 0.652896 0.751601 0.771517 0.915895 0.951223 1.076536 1.055981 1.353032 -P_31 1.297559 1.341545 1.398312 1.478037 1.501618 1.341994 1.398299 1.294082 -P_31 1.254770 1.315622 1.222223 1.349207 1.305635 1.154211 1.145430 1.136484 -P_31 1.131590 1.233905 1.304400 1.239165 1.285540 1.296264 1.346344 1.282880 -P_31 1.360413 1.229134 1.262015 1.095661 1.128572 1.184564 0.979060 1.018757 -P_31 0.791943 0.862962 0.709574 0.709222 0.627338 0.600910 0.626149 0.633352 -P_31 0.686302 0.841274 0.845867 0.952321 1.251987 1.155603 1.492255 1.331142 -P_31 1.445430 1.524771 1.708454 1.583081 1.545990 1.606014 1.572866 1.599747 -P_31 1.549347 1.449437 1.353578 1.343535 1.343966 1.230021 1.399734 1.209486 -P_31 1.287426 1.237624 1.297705 1.216555 1.428097 1.304018 1.378233 1.334211 -P_31 1.198916 1.187737 1.352399 1.280238 1.126407 0.997108 1.009839 1.009247 -P_31 0.882093 0.891340 0.810944 0.790463 0.648109 0.687968 0.635512 0.636666 -P_31 0.683291 0.686716 0.773119 0.754960 0.827273 1.027428 0.964195 1.106807 -P_31 1.148446 1.354001 1.257870 1.233235 1.319545 1.326214 1.308779 1.400689 -P_31 1.392060 1.313615 1.401131 1.320574 1.379408 1.283110 1.259067 1.357669 -P_31 1.314887 1.372323 1.265983 1.361797 1.250754 1.228643 1.183150 1.389793 -P_31 1.156525 1.201301 1.214187 1.168602 0.991177 0.943074 0.912659 0.949529 -P_31 0.857944 0.829963 0.793358 0.737451 0.741666 0.684924 0.735135 0.613108 -P_31 0.630782 0.647690 0.695590 0.701138 0.796564 0.740780 0.896614 0.919300 -P_31 0.941231 0.965177 1.181146 1.212903 1.247440 1.293067 1.348539 1.262082 -P_31 1.417144 1.332419 1.284619 1.175323 1.218227 1.131454 1.186941 1.133608 -P_31 1.100166 1.204286 1.114504 1.203199 1.285491 1.220607 1.252388 1.271769 -P_31 1.236346 1.297959 1.169224 1.236157 1.072011 1.133930 1.035787 0.935399 -P_31 0.923783 0.812000 0.748830 0.623961 0.645091 0.606598 0.571773 0.650489 -P_31 0.627592 0.727635 0.732423 0.842636 1.009268 1.050035 1.199160 1.315046 -P_31 1.318127 1.411907 1.472743 1.528661 1.388583 1.610275 1.561845 1.433674 -P_31 1.415532 1.314752 1.204737 1.228265 1.276388 1.119512 1.068052 1.188957 -P_31 1.119784 1.037476 1.091957 1.122299 1.406788 1.221681 1.315074 1.393018 -P_31 1.379342 1.344963 1.381410 1.255891 1.153350 1.162560 1.074189 0.950918 -P_31 0.957525 0.806943 0.716184 0.673720 0.639501 0.646244 0.624515 0.626576 -P_31 0.652910 0.740530 0.812514 0.847422 0.898573 1.014925 1.282421 1.229114 -P_31 1.354643 1.467347 1.375174 1.377581 1.442610 1.499185 1.540737 1.453087 -P_31 1.382911 1.242638 1.231285 1.260028 1.263511 1.217315 1.165091 1.117826 -P_31 1.252819 1.113313 1.242689 1.304660 1.262558 1.235559 1.287894 1.379522 -P_31 1.397328 1.342748 1.183332 1.327905 1.137867 1.138333 1.048664 1.053307 -P_31 0.824074 0.798081 0.769328 0.671787 0.611375 0.558551 0.633401 0.640391 -P_31 0.687913 0.784202 0.826614 0.989046 0.950951 1.193458 1.060030 1.316553 -P_31 1.340735 1.336274 1.435707 1.466796 1.430953 1.544296 1.448529 1.416057 -P_31 1.480576 1.324872 1.398040 1.250117 1.243872 1.335432 1.215298 1.216028 -P_31 1.184159 1.234528 1.355565 1.261040 1.234867 1.237657 1.349160 1.327754 -P_31 1.359098 1.451620 1.392598 1.292227 1.211809 1.150067 0.983527 0.942981 -P_31 0.915329 0.827540 0.756855 0.722217 0.602865 0.647209 0.642356 0.600040 -P_31 0.683870 0.838303 0.833895 0.878271 1.029420 1.058565 1.298744 1.314031 -P_31 1.275041 1.392528 1.450128 1.501305 1.457484 1.549821 1.457122 1.485921 -P_31 1.224792 1.374958 1.285701 1.225649 1.215760 1.307862 1.182356 1.199517 -P_31 1.233111 1.211216 1.273784 1.252160 1.336306 1.361131 1.273722 1.325490 -P_31 1.472005 1.268059 1.360305 1.159785 1.262953 1.172857 1.000996 1.008519 -P_31 0.950341 0.876660 0.749739 0.722176 0.691566 0.626619 0.650328 0.668697 -P_31 0.698745 0.801530 0.847186 1.044920 1.091832 1.117303 1.221537 1.338981 -P_31 1.391487 1.540288 1.590570 1.776698 1.749798 1.609900 1.512589 1.395392 -P_31 1.540881 1.430981 1.286097 1.446778 1.279430 1.266356 1.412186 1.385111 -P_31 1.332863 1.280135 1.365867 1.216677 1.274234 1.291412 1.433432 1.321340 -P_31 1.379999 1.302028 1.353207 1.265419 1.237464 1.085567 1.073389 1.026256 -P_31 0.973241 0.830912 0.808854 0.741667 0.717237 0.632056 0.660498 0.641720 -P_31 0.644210 0.748756 0.743111 0.773720 0.891647 0.941265 1.086154 1.120398 -P_31 1.347678 1.272853 1.273701 1.382972 1.384712 1.488619 1.399386 1.423105 -P_31 1.412804 1.362818 1.421919 1.457727 1.307785 1.332471 1.419029 1.224777 -P_31 1.276104 1.287481 1.345863 1.309214 1.236969 1.323470 1.273293 1.226676 -P_31 1.334723 1.201897 1.146751 1.063148 1.004244 1.028684 0.963182 0.920778 -P_31 0.880327 0.877189 0.837736 0.753538 0.723332 0.608541 0.697059 0.635531 -P_31 0.696690 0.726177 0.710021 0.669613 0.768093 0.818933 0.967177 0.908977 -P_31 1.016577 1.085257 1.043933 1.227576 1.237120 1.317826 1.257632 1.275648 -P_31 1.194991 1.285740 1.304848 1.331101 1.297184 1.124972 1.130735 1.189328 -P_31 1.126378 1.172811 1.152000 1.315159 1.306911 1.204757 1.227095 1.222858 -P_31 1.151433 1.209020 1.196715 1.241606 1.133229 1.054336 0.966803 0.976462 -P_31 0.900341 0.732458 0.732463 0.665213 0.594048 0.658081 0.564678 0.631449 -P_31 0.644591 0.747698 0.817342 0.891204 0.890850 1.078211 1.216299 1.313953 -P_31 1.295158 1.414388 1.378040 1.489506 1.560595 1.567346 1.477176 1.499321 -P_31 1.488486 1.488432 1.511384 1.320393 1.194461 1.323191 1.104904 1.111719 -P_31 1.073684 1.163259 1.209013 1.219623 1.212506 1.243694 1.255114 1.306717 -P_31 1.315023 1.327527 1.316381 1.357095 1.278017 1.172869 1.062756 1.033714 -P_31 0.838897 0.831519 0.766590 0.665648 0.607132 0.712047 0.620566 0.641315 -P_31 0.716508 0.777101 0.780919 0.883022 0.959734 0.994222 1.128018 1.244170 -P_31 1.244107 1.426670 1.552085 1.444638 1.419871 1.414250 1.418776 1.437183 -P_31 1.326159 1.422766 1.445974 1.241845 1.169162 1.138730 1.237364 1.257280 -P_31 1.175775 1.192281 1.272762 1.263060 1.231408 1.197107 1.158325 1.330217 -P_31 1.414919 1.321383 1.256412 1.238239 1.154776 1.147048 1.013598 1.008046 -P_31 0.864936 0.807574 0.758001 0.678509 0.639895 0.631213 0.615957 0.661406 -P_31 0.734644 0.778546 0.773877 1.038438 1.064331 1.094346 1.180405 1.286251 -P_31 1.366247 1.318328 1.383997 1.469905 1.533547 1.543464 1.534116 1.227619 -P_31 1.445076 1.483302 1.309877 1.325992 1.347103 1.332623 1.189698 1.254538 -P_31 1.198995 1.310974 1.343707 1.251277 1.241844 1.310727 1.334890 1.461545 -P_31 1.295972 1.256457 1.385381 1.246927 1.137394 1.133102 1.039953 0.958344 -P_31 0.869694 0.905461 0.785655 0.714678 0.676912 0.662507 0.630803 0.648556 -P_31 0.730004 0.781007 0.817370 0.943823 0.931959 1.028084 1.183896 1.273193 -P_31 1.126412 1.330607 1.419325 1.411887 1.547403 1.366181 1.395388 1.385004 -P_31 1.366019 1.330406 1.305941 1.292688 1.194186 1.236119 1.225541 1.184706 -P_31 1.083670 1.225976 1.383632 1.313805 1.210075 1.317147 1.308817 1.369085 -P_31 1.406074 1.251655 1.263338 1.345952 1.260256 1.141585 1.014335 0.996051 -P_31 0.966005 0.753871 0.733919 0.700863 0.621588 0.616326 0.640167 0.703948 -P_31 0.707430 0.789175 0.909542 1.049073 1.125972 1.302383 1.363149 1.520888 -P_31 1.556469 1.626076 1.760662 1.577393 1.697382 1.833627 1.588489 1.545275 -P_31 1.397378 1.397452 1.426889 1.514491 1.400040 1.275096 1.389888 1.296187 -P_31 1.206636 1.324880 1.361361 1.342159 1.270742 1.319530 1.236501 1.260240 -P_31 1.520658 1.319217 1.273166 1.319925 1.008465 1.197167 1.015784 1.015763 -P_31 1.029735 0.903955 0.856889 0.753428 0.707587 0.744292 0.666363 0.681862 -P_31 0.675409 0.602968 0.719530 0.842075 0.888343 0.919564 1.103905 1.059193 -P_31 1.258905 1.251686 1.378826 1.290967 1.434566 1.365514 1.396500 1.266887 -P_31 1.451579 1.373513 1.367793 1.357725 1.496093 1.389388 1.350376 1.195025 -P_31 1.262775 1.306353 1.299102 1.255461 1.309726 1.237365 1.292401 1.257986 -P_31 1.299548 1.199879 1.276552 1.083578 1.092706 1.007704 0.891271 0.940510 -P_31 0.899243 0.770540 0.792358 0.781306 0.749199 0.669431 0.733203 0.644812 -P_31 0.660798 0.649556 0.668215 0.676524 0.784146 0.763926 0.890976 0.976501 -P_31 1.064823 1.076595 1.176630 1.204122 1.299001 1.224590 1.255447 1.353602 -P_31 1.389268 1.335743 1.339077 1.197231 1.223822 1.217768 1.172246 1.248276 -P_31 1.206746 1.165354 1.173353 1.176347 1.261316 1.199770 1.241099 1.442358 -P_31 1.330447 1.227716 1.159151 1.101011 1.191094 1.124816 1.048433 0.942915 -P_31 0.902060 0.787774 0.748663 0.641533 0.593401 0.594277 0.614294 0.678939 -P_31 0.640772 0.746360 0.820299 0.889125 0.982881 1.077179 1.135313 1.239694 -P_31 1.301860 1.366331 1.610714 1.635097 1.573026 1.606645 1.526072 1.514027 -P_31 1.532240 1.401402 1.413877 1.271380 1.273763 1.274733 1.147688 1.105997 -P_31 1.136223 1.104378 1.303442 1.379626 1.257507 1.314045 1.289356 1.310833 -P_31 1.405314 1.372938 1.387250 1.351844 1.239901 1.179820 0.963193 1.019392 -P_31 0.908771 0.787280 0.797334 0.702463 0.667996 0.621321 0.640091 0.607143 -P_31 0.689227 0.781445 0.871185 0.892851 0.971086 1.128780 1.270420 1.275870 -P_31 1.353800 1.421167 1.470747 1.579697 1.477347 1.583472 1.471635 1.333594 -P_31 1.429366 1.337639 1.418723 1.208679 1.255637 1.241441 1.259746 1.280794 -P_31 1.260169 1.279228 1.112160 1.238043 1.346407 1.312321 1.493146 1.417080 -P_31 1.558707 1.349019 1.426289 1.373306 1.189434 1.184026 1.034349 1.069800 -P_31 0.814750 0.826609 0.712924 0.695940 0.607282 0.650894 0.598966 0.638523 -P_31 0.652050 0.703238 0.749028 0.956021 1.047556 1.111982 1.109333 1.343798 -P_31 1.331160 1.400189 1.494958 1.494108 1.583293 1.699771 1.461208 1.340466 -P_31 1.441891 1.436011 1.410124 1.234287 1.299684 1.378026 1.305810 1.284134 -P_31 1.297797 1.209672 1.478531 1.368548 1.343252 1.400128 1.336120 1.340390 -P_31 1.309344 1.374467 1.278327 1.228245 1.134028 1.174719 1.128153 1.029734 -P_31 0.922430 0.926645 0.740525 0.717332 0.693475 0.570497 0.585268 0.619848 -P_31 0.722539 0.773983 0.738259 0.871713 1.050580 1.086865 1.221591 1.315866 -P_31 1.416115 1.466322 1.455520 1.479703 1.519227 1.453954 1.389728 1.405186 -P_31 1.461415 1.315712 1.363446 1.175770 1.379229 1.238888 1.291662 1.172779 -P_31 1.281591 1.241057 1.379876 1.122438 1.283147 1.374255 1.310548 1.377639 -P_31 1.426040 1.386470 1.262852 1.339608 1.320623 1.218773 1.112181 1.023400 -P_31 0.891275 0.817382 0.750326 0.807873 0.714480 0.658920 0.610145 0.761963 -P_31 0.770451 0.816254 0.921477 0.931788 1.144513 1.245609 1.438312 1.451119 -P_31 1.673452 1.613216 1.570897 1.592486 1.693745 1.720147 1.605364 1.663516 -P_31 1.530111 1.551052 1.317968 1.266424 1.338728 1.300856 1.404249 1.299503 -P_31 1.307445 1.314496 1.215356 1.288409 1.349037 1.483003 1.483138 1.323335 -P_31 1.289072 1.323549 1.383259 1.223538 1.149109 1.254040 1.081776 1.092728 -P_31 0.954432 0.905042 0.825953 0.779700 0.687000 0.699130 0.673811 0.675121 -P_31 0.623886 0.732121 0.756406 0.834284 0.785960 0.934789 1.023799 1.080309 -P_31 1.261586 1.212376 1.304687 1.465796 1.375476 1.402246 1.542241 1.438844 -P_31 1.491999 1.446844 1.356345 1.286868 1.506036 1.313188 1.285184 1.207055 -P_31 1.312361 1.377339 1.286575 1.372118 1.356785 1.312730 1.311665 1.276432 -P_31 1.211767 1.323034 1.223595 1.227674 1.151162 1.120059 0.982827 0.917927 -P_31 0.860916 0.835303 0.793473 0.809341 0.742934 0.728597 0.675341 0.666612 -P_31 0.691930 0.729413 0.706126 0.744119 0.732378 0.769537 0.838004 0.925015 -P_31 0.999755 1.116292 1.143315 1.260908 1.293065 1.303763 1.305824 1.364013 -P_31 1.403686 1.369003 1.382405 1.256640 1.218309 1.142158 1.321930 1.297361 -P_31 1.254510 1.183348 1.118473 1.120118 1.206549 1.273937 1.322320 1.319844 -P_31 1.292185 1.365452 1.262824 1.189215 1.107647 1.044727 1.055575 1.040499 -P_31 0.792591 0.774180 0.730073 0.685552 0.585059 0.624632 0.608850 0.667445 -P_31 0.656532 0.733319 0.794064 0.850977 0.996482 1.102470 1.153084 1.305993 -P_31 1.358972 1.445241 1.541707 1.552172 1.391970 1.667669 1.575920 1.656522 -P_31 1.452170 1.437210 1.452700 1.358915 1.255698 1.172908 1.266487 1.202179 -P_31 1.159949 1.136398 1.118530 1.241083 1.265578 1.348588 1.390969 1.337627 -P_31 1.437827 1.333163 1.381483 1.337411 1.304096 1.158573 1.081914 0.950748 -P_31 0.972327 0.889281 0.713644 0.661675 0.657418 0.696259 0.645339 0.612586 -P_31 0.715645 0.768088 0.819315 0.897649 0.960675 1.060718 1.224568 1.340000 -P_31 1.413514 1.372627 1.408314 1.439911 1.688143 1.424909 1.486395 1.379497 -P_31 1.471409 1.432930 1.342032 1.278842 1.224638 1.357040 1.214131 1.110235 -P_31 1.256591 1.158696 1.267333 1.306130 1.340065 1.284174 1.431965 1.436452 -P_31 1.448877 1.376262 1.325720 1.384599 1.221189 1.246342 1.103255 0.972220 -P_31 0.931713 0.771603 0.714460 0.652297 0.653103 0.590724 0.645975 0.679013 -P_31 0.665867 0.782607 0.802093 1.011813 1.002465 1.119134 1.263593 1.243642 -P_31 1.404757 1.426630 1.402395 1.395071 1.664308 1.512692 1.505169 1.425168 -P_31 1.423264 1.454814 1.300371 1.491089 1.309467 1.294723 1.251533 1.199363 -P_31 1.178656 1.222322 1.299701 1.310276 1.364344 1.303542 1.303307 1.359482 -P_31 1.421055 1.398814 1.356782 1.385630 1.350643 1.270336 1.119658 1.018164 -P_31 0.809022 0.842895 0.767364 0.667163 0.649782 0.605806 0.600353 0.605382 -P_31 0.677368 0.710174 0.835375 1.018895 0.968030 1.145712 1.291012 1.348242 -P_31 1.411228 1.594401 1.499358 1.406581 1.526051 1.452894 1.424268 1.404917 -P_31 1.376717 1.333849 1.280063 1.330202 1.230595 1.326195 1.229662 1.234105 -P_31 1.240985 1.268751 1.260979 1.281197 1.398797 1.368547 1.348619 1.419007 -P_31 1.353777 1.384027 1.340835 1.234669 1.205143 1.109918 0.983663 0.984805 -P_31 0.784092 0.841102 0.783654 0.723238 0.697181 0.667903 0.618037 0.662435 -P_31 0.751066 0.866474 0.915891 1.128758 1.178619 1.211432 1.344038 1.334423 -P_31 1.452660 1.568990 1.642468 1.680483 1.625271 1.628317 1.574581 1.595769 -P_31 1.587762 1.473192 1.374790 1.312338 1.493019 1.321155 1.215363 1.208302 -P_31 1.248917 1.435106 1.320749 1.362889 1.313980 1.432923 1.289411 1.369926 -P_31 1.231203 1.271154 1.292648 1.223650 1.231049 1.265520 1.184698 0.990738 -P_31 0.942593 0.853179 0.895434 0.806821 0.751469 0.686239 0.688740 0.676554 -P_31 0.702543 0.661874 0.836509 0.836018 0.752109 1.021225 1.016040 1.030069 -P_31 1.232907 1.323768 1.356170 1.369684 1.487524 1.456951 1.455336 1.435323 -P_31 1.344279 1.317418 1.355077 1.240672 1.271571 1.437599 1.323765 1.283450 -P_31 1.287987 1.284747 1.419918 1.308501 1.331001 1.233867 1.319613 1.302159 -P_31 1.291758 1.198877 1.172821 1.097668 1.077359 0.973118 0.987076 0.887559 -P_31 0.846323 0.882606 0.824985 0.740594 0.776257 0.682556 0.635581 0.664272 -P_31 0.654935 0.703574 0.719729 0.753591 0.793959 0.824548 0.760297 0.912572 -P_31 0.980057 0.993369 1.255127 1.281227 1.340796 1.208087 1.386047 1.446189 -P_31 1.340421 1.368664 1.347236 1.308022 1.305096 1.235198 1.127088 1.179658 -P_31 1.145468 1.103626 1.244238 1.258665 1.231766 1.235176 1.423585 1.348917 -P_31 1.331768 1.288552 1.355921 1.170805 1.143724 1.076827 0.978894 0.880419 -P_31 0.910450 0.776257 0.768005 0.663076 0.658926 0.631299 0.582440 0.615252 -P_31 0.699595 0.806474 0.794073 0.907491 0.931975 1.114111 1.140809 1.253608 -P_31 1.291689 1.533161 1.531359 1.461506 1.721381 1.577924 1.546926 1.492968 -P_31 1.465289 1.414144 1.367457 1.317118 1.367696 1.203246 1.149036 1.032782 -P_31 1.142432 1.228803 1.174134 1.217419 1.151966 1.368942 1.327485 1.396328 -P_31 1.357133 1.337182 1.297652 1.290269 1.163659 1.109492 0.985784 0.955644 -P_31 0.856870 0.830054 0.792323 0.671066 0.642359 0.667640 0.665969 0.670956 -P_31 0.642611 0.722190 0.763158 0.875078 1.028940 1.061352 1.229908 1.224671 -P_31 1.141749 1.372522 1.267678 1.550692 1.480726 1.593872 1.485368 1.516168 -P_31 1.300509 1.403224 1.388272 1.376507 1.387451 1.107058 1.152713 1.157262 -P_31 1.186543 1.173466 1.256158 1.342212 1.282900 1.299337 1.394099 1.369434 -P_31 1.481880 1.372800 1.354980 1.284379 1.200294 1.202590 1.130213 0.922215 -P_31 0.886999 0.790111 0.720165 0.686215 0.651680 0.589537 0.648630 0.654787 -P_31 0.656717 0.699717 0.877676 0.923535 0.912946 1.091211 1.192091 1.312009 -P_31 1.382744 1.420312 1.413143 1.495283 1.347067 1.477935 1.597949 1.610245 -P_31 1.460351 1.350437 1.399095 1.387339 1.346652 1.249282 1.319193 1.241342 -P_31 1.269983 1.278324 1.272694 1.230151 1.382197 1.251930 1.233847 1.354786 -P_31 1.393593 1.277226 1.343772 1.279824 1.335745 1.119142 1.048291 1.086085 -P_31 0.850295 0.782488 0.708950 0.706756 0.653512 0.671529 0.579425 0.623562 -P_31 0.641307 0.721292 0.824647 0.990211 0.906382 1.100373 1.258812 1.182446 -P_31 1.272097 1.555585 1.497284 1.309052 1.487525 1.432575 1.409014 1.505224 -P_31 1.397977 1.283604 1.265368 1.276028 1.276694 1.216013 1.169182 1.223253 -P_31 1.208490 1.253397 1.332530 1.325552 1.263552 1.291165 1.471788 1.449135 -P_31 1.375449 1.245266 1.246977 1.256132 1.227495 1.121066 1.094289 0.987157 -P_31 0.892467 0.857361 0.695288 0.708687 0.632812 0.658443 0.655628 0.752456 -P_31 0.678970 0.798580 0.908658 0.992107 1.157932 1.287945 1.341532 1.431915 -P_31 1.508583 1.537245 1.664348 1.603357 1.617886 1.732017 1.476585 1.677249 -P_31 1.519214 1.557653 1.481274 1.412059 1.305533 1.312301 1.250888 1.273115 -P_31 1.301574 1.227255 1.222929 1.349319 1.403449 1.420028 1.266381 1.322844 -P_31 1.236541 1.245777 1.161725 1.247875 1.299178 1.111085 0.993294 1.051101 -P_31 0.970117 0.908064 0.847721 0.773422 0.663795 0.695336 0.664159 0.592113 -P_31 0.691562 0.675585 0.709420 0.840306 0.885190 0.908301 1.057778 1.035921 -P_31 1.214552 1.287269 1.252777 1.414103 1.379263 1.312430 1.389491 1.343691 -P_31 1.363410 1.431069 1.286436 1.393248 1.291582 1.220887 1.263403 1.294858 -P_31 1.258181 1.230562 1.185448 1.241628 1.414334 1.228753 1.300721 1.304418 -P_31 1.437039 1.268633 1.147189 1.090456 1.055869 0.995883 0.979551 0.923343 -P_31 0.857051 0.793341 0.742070 0.716259 0.738979 0.663941 0.652548 0.641299 -P_31 0.649800 0.603184 0.638731 0.731004 0.755411 0.805687 0.916931 0.985556 -P_31 1.064389 1.111594 1.100604 1.167094 1.199500 1.354378 1.292868 1.406816 -P_31 1.322320 1.329373 1.275271 1.289425 1.247122 1.273177 1.260826 1.238538 -P_31 1.195909 1.135984 1.158682 1.161953 1.220332 1.162059 1.305678 1.302152 -P_31 1.310912 1.270736 1.263776 1.168006 1.135821 1.213002 1.017648 0.942202 -P_31 0.804008 0.789495 0.709380 0.625078 0.610650 0.634936 0.567536 0.629925 -P_31 0.618478 0.683037 0.784108 0.816289 0.995942 1.009406 1.181469 1.369085 -P_31 1.357394 1.422988 1.467603 1.639243 1.576339 1.505708 1.598081 1.456191 -P_31 1.380744 1.436109 1.279712 1.225333 1.162527 1.250318 1.119655 1.070997 -P_31 1.100206 1.173704 1.236402 1.159215 1.178493 1.201187 1.312474 1.362954 -P_31 1.331832 1.416715 1.406603 1.300705 1.291191 1.206720 1.170207 1.016090 -P_31 0.925698 0.874035 0.788579 0.659617 0.652626 0.624861 0.591855 0.577055 -P_31 0.696325 0.730488 0.857187 0.831257 0.969483 1.133315 1.085025 1.319492 -P_31 1.288962 1.405062 1.459354 1.445586 1.423356 1.519633 1.520310 1.535512 -P_31 1.463804 1.291588 1.286327 1.248660 1.232883 1.098505 1.174948 1.237510 -P_31 1.179326 1.095496 1.262054 1.340018 1.218604 1.280709 1.390184 1.430278 -P_31 1.318678 1.330555 1.392543 1.229753 1.201385 1.178296 0.990267 0.946759 -P_31 0.958942 0.811660 0.722137 0.681603 0.574527 0.612096 0.603665 0.681376 -P_31 0.718881 0.751200 0.907133 0.847742 0.952576 1.092771 1.113408 1.343397 -P_31 1.395058 1.620740 1.398770 1.464786 1.451182 1.583668 1.339091 1.505401 -P_31 1.459092 1.376663 1.418581 1.395471 1.236019 1.315869 1.285648 1.300863 -P_31 1.200596 1.165538 1.208642 1.227752 1.171818 1.204173 1.305880 1.341495 -P_31 1.188872 1.198877 1.352259 1.217648 1.214726 1.089274 1.096416 0.995056 -P_31 0.905114 0.826421 0.758697 0.684793 0.577194 0.622508 0.598399 0.681813 -P_31 0.677435 0.720639 0.852396 0.888999 1.051312 1.207504 1.196509 1.244668 -P_31 1.302601 1.338127 1.468312 1.399678 1.473826 1.495968 1.351658 1.338262 -P_31 1.359464 1.304805 1.202560 1.126220 1.274830 1.308819 1.215618 1.204777 -P_31 1.292918 1.342770 1.274018 1.400575 1.277171 1.274871 1.346297 1.322012 -P_31 1.282145 1.305472 1.269594 1.346748 1.180473 1.162888 1.074326 0.926966 -P_31 0.932775 0.831006 0.667840 0.723289 0.632399 0.652425 0.663323 0.685292 -P_31 0.728753 0.749176 0.863134 0.995180 1.102274 1.160696 1.333805 1.435737 -P_31 1.610734 1.624211 1.642554 1.608622 1.728043 1.673739 1.653399 1.606892 -P_31 1.321862 1.511142 1.341221 1.346638 1.394231 1.303543 1.348932 1.290934 -P_31 1.350165 1.383604 1.313863 1.313485 1.334078 1.368785 1.315534 1.422871 -P_31 1.355747 1.319616 1.176542 1.206866 1.189461 1.070407 1.057341 0.960847 -P_31 1.000624 0.860601 0.811647 0.763110 0.734178 0.664981 0.642192 0.662092 -P_31 0.619847 0.703038 0.755259 0.791924 0.965284 0.834961 1.035004 1.125037 -P_31 1.236729 1.241734 1.317760 1.322466 1.460524 1.441914 1.308912 1.428597 -P_31 1.327876 1.374387 1.328978 1.374499 1.283228 1.211398 1.320347 1.333434 -P_31 1.252417 1.387075 1.285688 1.264515 1.216909 1.254648 1.357460 1.208553 -P_31 1.276760 1.249277 1.189303 1.056184 0.966854 0.999581 1.013996 0.840642 -P_31 0.801220 0.791155 0.752847 0.662921 0.693495 0.724568 0.640996 0.683186 -P_31 0.643070 0.641829 0.605696 0.688859 0.688492 0.772813 0.895254 0.925085 -P_31 0.992714 1.123915 1.111919 1.145953 1.250812 1.375440 1.257817 1.337528 -P_31 1.260587 1.385036 1.292493 1.265056 1.125779 1.231280 1.192067 1.194438 -P_31 1.198308 1.177497 1.168159 1.155499 1.197418 1.160649 1.196744 1.225905 -P_31 1.323868 1.219110 1.246249 1.075906 1.170380 1.063646 0.865322 0.940483 -P_31 0.810220 0.757585 0.689013 0.638239 0.638068 0.618414 0.594016 0.663594 -P_31 0.673819 0.720815 0.795347 0.874073 1.000953 1.121525 1.202666 1.165581 -P_31 1.223541 1.583822 1.436537 1.562361 1.530475 1.477864 1.396661 1.572805 -P_31 1.397207 1.470709 1.274269 1.420050 1.229471 1.167209 1.192501 1.178106 -P_31 1.066002 1.136003 1.196529 1.252266 1.212354 1.179734 1.262706 1.401322 -P_31 1.337944 1.403454 1.237789 1.310536 1.153467 1.254529 1.082522 1.015410 -P_31 0.891044 0.841909 0.727830 0.680958 0.660544 0.624472 0.634746 0.609329 -P_31 0.703297 0.676752 0.770812 0.917842 0.939169 1.068566 1.121604 1.219499 -P_31 1.226589 1.326554 1.278929 1.296236 1.475556 1.359113 1.388311 1.594930 -P_31 1.344398 1.349069 1.249073 1.264765 1.121826 1.220684 1.178274 1.216562 -P_31 1.262320 1.137659 1.178131 1.223700 1.231159 1.175687 1.266969 1.361881 -P_31 1.425019 1.310074 1.262897 1.279041 1.186670 1.099185 0.960821 0.957920 -P_31 0.892457 0.791266 0.703606 0.622209 0.614153 0.587543 0.673353 0.654329 -P_31 0.720588 0.711216 0.827181 0.841228 0.945109 1.093720 1.124990 1.313341 -P_31 1.215227 1.331397 1.360874 1.382020 1.529814 1.459732 1.533460 1.451434 -P_31 1.338675 1.368424 1.316531 1.245832 1.330127 1.243894 1.160216 1.199918 -P_31 1.226154 1.145751 1.276536 1.299649 1.252574 1.251610 1.365419 1.250721 -P_31 1.398037 1.321644 1.385482 1.272872 1.134723 1.098219 1.054255 0.942455 -P_31 0.765307 0.852397 0.763394 0.722398 0.592402 0.596955 0.558201 0.591445 -P_31 0.641603 0.761227 0.770912 0.914318 1.050580 1.061767 1.111649 1.266313 -P_31 1.379721 1.371242 1.230955 1.548575 1.320430 1.393174 1.392081 1.294534 -P_31 1.324132 1.334011 1.350905 1.248459 1.188840 1.109676 1.237521 1.144505 -P_31 1.229430 1.073179 1.196095 1.273221 1.185459 1.324721 1.241471 1.372904 -P_31 1.271510 1.326904 1.357669 1.231127 1.274494 1.099712 1.117679 0.965810 -P_31 0.930529 0.813117 0.818119 0.661735 0.669048 0.634553 0.602881 0.611061 -P_31 0.656575 0.732636 0.865425 0.963653 1.085640 1.178253 1.223171 1.383749 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1.184522 1.288410 1.194678 1.285529 1.261968 1.296001 -P_31 1.377494 1.269231 1.169812 1.243282 1.151957 0.984335 0.998919 0.992452 -P_31 0.898425 0.778096 0.700216 0.652052 0.604419 0.618544 0.598553 0.609984 -P_31 0.699400 0.768605 0.862891 0.984826 1.100440 1.210272 1.177165 1.334401 -P_31 1.544055 1.653689 1.588212 1.320721 1.579082 1.688102 1.523944 1.410887 -P_31 1.259270 1.454654 1.409446 1.350334 1.197961 1.273854 1.285585 1.232065 -P_31 1.214901 1.229696 1.073952 1.180289 1.269939 1.103244 1.321565 1.239354 -P_31 1.205907 1.307234 1.243981 1.227275 1.090630 1.080680 0.900087 0.966899 -P_31 0.923571 0.814090 0.797810 0.715162 0.716966 0.631350 0.621799 0.646811 -P_31 0.687691 0.684987 0.633320 0.720732 0.822654 0.943330 0.853200 1.036781 -P_31 1.153090 1.087164 1.295432 1.383962 1.292937 1.168586 1.369280 1.321363 -P_31 1.415130 1.143376 1.365505 1.257768 1.174940 1.196012 1.241674 1.200113 -P_31 1.297031 1.177095 1.283058 1.170213 1.255049 1.244488 1.092893 1.253669 -P_31 1.130033 1.080516 1.030631 1.172733 1.111852 0.972037 0.916997 0.906780 -P_31 0.877082 0.748593 0.710958 0.731719 0.716234 0.663920 0.649335 0.551838 -P_31 0.564065 0.629536 0.602570 0.655103 0.626471 0.770654 0.812864 0.899829 -P_31 0.903371 1.051258 1.094275 1.059528 1.054978 1.249754 1.213985 1.238136 -P_31 1.293475 1.161426 1.174429 1.251747 1.096894 1.147328 1.146854 1.163794 -P_31 1.130931 1.181540 1.193610 1.042843 1.037398 1.192924 1.115615 1.221677 -P_31 1.179484 1.187181 1.180751 1.086394 1.168433 1.025890 0.971893 0.914914 -P_31 0.793943 0.672383 0.722316 0.632846 0.622907 0.560367 0.636686 0.630691 -P_31 0.683172 0.622019 0.765951 0.829685 0.960478 1.042327 1.093358 1.199562 -P_31 1.276030 1.309533 1.535238 1.391278 1.459249 1.326677 1.401678 1.362778 -P_31 1.487207 1.380241 1.333381 1.139511 1.169282 1.082708 1.147057 1.029316 -P_31 1.111335 0.998863 1.037330 1.160244 1.133646 1.251289 1.205557 1.280029 -P_31 1.211182 1.187000 1.304298 1.197026 1.152988 1.192539 1.094703 0.941144 -P_31 0.840654 0.712769 0.697105 0.684243 0.566378 0.563594 0.569782 0.596061 -P_31 0.651006 0.658030 0.713186 0.830958 0.860634 0.974098 1.072860 1.137591 -P_31 1.147654 1.156938 1.462325 1.369589 1.522934 1.419290 1.560653 1.257973 -P_31 1.257935 1.252399 1.269032 1.209023 1.069874 1.185327 1.082245 1.117334 -P_31 1.058082 1.096620 1.185100 1.230490 1.202993 1.156356 1.306806 1.270405 -P_31 1.288015 1.336419 1.181859 1.319484 1.187120 1.073563 0.962935 0.932652 -P_31 0.802407 0.751772 0.693803 0.660567 0.600381 0.557224 0.544806 0.659344 -P_31 0.585629 0.743550 0.750882 0.870323 0.940520 1.022644 1.085918 1.254427 -P_31 1.249969 1.269012 1.359865 1.306477 1.324248 1.342682 1.460085 1.452496 -P_31 1.366259 1.323370 1.147210 1.044551 1.154261 1.301415 1.122306 1.113178 -P_31 1.185382 1.187737 1.117160 1.078122 1.081141 1.243375 1.233798 1.206144 -P_31 1.264371 1.240631 1.184977 1.229784 1.160276 1.079529 1.005996 0.905760 -P_31 0.821031 0.767718 0.677125 0.682466 0.588424 0.567162 0.604176 0.603834 -P_31 0.622025 0.650563 0.747749 0.838532 0.859590 1.072540 1.191647 1.194657 -P_31 1.195693 1.141688 1.279580 1.383057 1.388352 1.336975 1.383125 1.422638 -P_31 1.278296 1.254738 1.153061 1.185150 1.093781 1.115989 1.235148 1.122544 -P_31 1.136791 1.165307 1.102653 1.162258 1.264621 1.241726 1.236484 1.262603 -P_31 1.207604 1.236177 1.050250 1.113177 1.190480 1.028805 0.976514 0.884081 -P_31 0.862412 0.744429 0.702118 0.681377 0.624499 0.619464 0.621077 0.704807 -P_31 0.676151 0.741474 0.781299 0.859172 1.046406 1.178203 1.308323 1.341530 -P_31 1.327960 1.334975 1.653677 1.441664 1.447581 1.556237 1.484512 1.402755 -P_31 1.473467 1.221563 1.333457 1.259810 1.217592 1.223149 1.298367 1.279873 -P_31 1.253390 1.298816 1.212340 1.214400 1.194914 1.099384 1.289811 1.117553 -P_31 1.224712 1.303789 1.176374 1.086553 1.133913 1.016152 0.996173 0.890518 -P_31 0.944916 0.765914 0.781836 0.734526 0.688272 0.694070 0.667961 0.660845 -P_31 0.636245 0.656948 0.678555 0.671424 0.822278 0.833282 0.911248 1.037065 -P_31 1.119593 1.156937 1.190709 1.355889 1.258994 1.237340 1.251250 1.325555 -P_31 1.221774 1.163076 1.148445 1.262346 1.271929 1.138947 1.357534 1.093778 -P_31 1.206250 1.209541 1.201549 1.230154 1.277607 1.160386 1.231886 1.142952 -P_31 1.139336 1.033459 1.108138 1.045440 0.990723 0.973460 0.900303 0.892740 -P_31 0.809961 0.805326 0.740170 0.737500 0.659537 0.656094 0.613885 0.614332 -P_31 0.559563 0.548981 0.651591 0.686482 0.670450 0.747479 0.761861 0.834944 -P_31 0.884743 1.018925 1.031351 1.044350 1.128529 1.160964 1.209722 1.234160 -P_31 1.227054 1.307212 1.218203 1.153789 1.152558 1.074752 1.081547 1.117823 -P_31 1.128876 1.021641 1.040153 1.149629 1.056106 1.128236 1.128283 1.109626 -P_31 1.156841 1.221576 1.113165 1.054063 1.029018 0.921421 0.909995 0.843154 -P_31 0.759710 0.754051 0.681933 0.612029 0.576043 0.535758 0.545197 0.586130 -P_31 0.656732 0.578614 0.655816 0.831570 0.825479 0.867514 1.191393 1.092003 -P_31 1.174786 1.294136 1.396677 1.395504 1.340579 1.391804 1.363065 1.451268 -P_31 1.340990 1.282379 1.173041 1.188071 1.200825 1.040431 1.054491 1.078595 -P_31 1.093078 1.042732 1.145339 1.104605 1.159503 1.046895 1.232885 1.162200 -P_31 1.273937 1.166363 1.199936 1.004956 1.068520 1.088452 0.945938 0.885452 -P_31 0.844944 0.767551 0.711911 0.623450 0.577298 0.599981 0.533591 0.580978 -P_31 0.614858 0.706250 0.731887 0.752070 0.860509 1.009296 1.062175 1.243349 -P_31 1.146157 1.211249 1.338476 1.340112 1.439457 1.374133 1.243168 1.449937 -P_31 1.287267 1.321088 1.298183 1.247323 1.239536 1.025001 1.090889 1.100088 -P_31 1.132521 1.120727 1.034289 1.152581 1.040469 1.222945 1.180556 1.296115 -P_31 1.254340 1.266408 1.169248 1.111838 1.134100 1.125109 0.981496 0.902255 -P_31 0.775072 0.649947 0.680270 0.593137 0.554898 0.534899 0.548743 0.591443 -P_31 0.559474 0.768933 0.805155 0.868878 0.949301 0.972112 1.029838 1.114601 -P_31 1.224115 1.363143 1.221981 1.392253 1.373427 1.329086 1.329497 1.305815 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1.133284 1.116691 -P_31 1.121848 1.233221 1.149463 1.171820 1.271066 1.252300 1.269025 1.186199 -P_31 1.222815 1.201530 1.141557 1.054946 0.984832 0.963785 0.990162 0.845905 -P_31 0.862651 0.830457 0.770013 0.713523 0.691137 0.628587 0.655428 0.580692 -P_31 0.605360 0.616632 0.609936 0.750023 0.786702 0.812730 0.879899 0.963406 -P_31 1.096638 1.158455 1.136307 1.298108 1.219008 1.311363 1.248681 1.370078 -P_31 1.081472 1.194176 1.354199 1.177759 1.278818 1.052876 1.265223 1.109408 -P_31 1.247426 1.113977 1.132300 1.152420 1.207781 1.108996 1.207930 1.031785 -P_31 1.111849 1.034045 1.079534 0.993486 0.894981 0.930578 0.886383 0.732434 -P_31 0.766232 0.793382 0.745286 0.638138 0.651523 0.609247 0.611566 0.634664 -P_31 0.547326 0.588264 0.570009 0.584852 0.676522 0.701490 0.804089 0.774846 -P_31 0.819232 0.937601 0.940032 1.064826 1.006548 1.083344 1.149155 1.202504 -P_31 1.149280 1.205499 1.128286 1.117260 1.087783 1.028159 1.016074 1.115459 -P_31 1.007975 0.976730 1.001443 1.114719 1.058800 0.988845 1.010261 1.139790 -P_31 1.119541 1.104907 1.107316 1.102125 1.078757 0.933294 0.918503 0.839448 -P_31 0.794286 0.644600 0.609459 0.618308 0.509270 0.541998 0.533087 0.542606 -P_31 0.588223 0.636145 0.757861 0.838051 0.796784 0.919319 1.034158 1.174071 -P_31 1.228485 1.323299 1.397693 1.281573 1.332595 1.419740 1.363713 1.380776 -P_31 1.298407 1.209068 1.152700 1.135033 1.084692 0.962784 1.036113 0.995414 -P_31 1.013865 1.029259 0.998436 1.132047 1.004874 1.139694 1.165589 1.063651 -P_31 1.208912 1.276129 1.195944 1.098771 1.079440 0.993620 0.910354 0.860721 -P_31 0.794522 0.690651 0.714485 0.638573 0.510752 0.543475 0.557667 0.546100 -P_31 0.645942 0.664343 0.796190 0.788757 0.921830 0.903852 1.020881 1.064763 -P_31 1.191749 1.213454 1.311108 1.257309 1.322493 1.340780 1.345558 1.136826 -P_31 1.246970 1.269454 1.155189 1.122856 1.158750 1.008350 0.999463 0.956063 -P_31 1.041304 1.075916 1.193349 1.137989 1.147911 1.233903 1.125721 1.267993 -P_31 1.259833 1.186628 1.179072 1.222949 1.054741 1.021450 0.834007 0.883753 -P_31 0.760596 0.700087 0.580157 0.576118 0.549022 0.561293 0.541166 0.544093 -P_31 0.559047 0.707567 0.732539 0.797088 0.885683 0.996938 1.030530 1.024234 -P_31 1.056623 1.181931 1.205128 1.288906 1.302917 1.287385 1.246568 1.252396 -P_31 1.191269 1.163465 1.172766 1.147166 1.256936 1.120329 1.097562 1.006300 -P_31 1.115221 1.024840 1.159911 1.201439 1.152629 1.186906 1.220844 1.222832 -P_31 1.200375 1.131360 1.152755 1.077094 1.161031 1.013943 0.953576 0.857707 -P_31 0.776756 0.699588 0.670330 0.603811 0.517029 0.552945 0.569278 0.560991 -P_31 0.605566 0.646712 0.746566 0.755357 0.785547 0.960046 1.028904 1.098612 -P_31 1.196153 1.183723 1.262185 1.282971 1.235367 1.102795 1.184980 1.171508 -P_31 1.152292 1.162292 0.995621 1.055726 1.053272 0.975853 1.095622 1.200114 -P_31 1.062838 1.115060 1.113994 1.166922 1.157016 1.201027 1.219606 1.093696 -P_31 1.268444 1.249342 1.161279 1.109334 0.978317 1.037563 0.960432 0.823842 -P_31 0.847117 0.684057 0.653361 0.641893 0.600439 0.503711 0.526462 0.617664 -P_31 0.639913 0.717258 0.700564 0.809424 1.008812 1.051606 1.200081 1.312814 -P_31 1.360596 1.304504 1.390898 1.367730 1.423751 1.381694 1.416912 1.328284 -P_31 1.383320 1.273746 1.172027 1.173733 1.125105 1.007719 1.161511 1.204047 -P_31 1.135825 1.138597 1.162439 1.054533 1.156795 1.213919 1.065961 1.127703 -P_31 1.135500 1.180377 1.083356 1.132551 1.024473 1.083584 0.941331 0.877947 -P_31 0.853716 0.721256 0.773947 0.652159 0.629887 0.661501 0.571488 0.564239 -P_31 0.589897 0.599795 0.644377 0.742126 0.723632 0.861415 0.831159 0.983699 -P_31 1.124775 1.121538 1.118432 1.124049 1.265308 1.246392 1.138107 1.178965 -P_31 1.301000 1.180475 1.204384 1.127960 1.093287 1.125103 1.170452 1.123531 -P_31 1.100007 1.163763 1.051896 1.161287 1.100912 1.132362 1.054893 1.099960 -P_31 1.052070 0.992129 1.026374 0.990295 1.008244 0.941398 0.905913 0.838354 -P_31 0.760368 0.735006 0.629328 0.726696 0.666533 0.630403 0.546101 0.490980 -P_31 0.545989 0.563985 0.567930 0.616413 0.669907 0.706258 0.832321 0.843292 -P_31 0.854781 0.913452 0.931576 0.960882 1.058350 1.108096 1.134229 1.156100 -P_31 1.112318 1.088709 1.179305 1.079450 0.999819 0.988477 1.056627 1.003197 -P_31 1.102879 1.031588 0.996612 1.067971 1.078924 1.108954 1.149057 1.151725 -P_31 1.021720 1.056881 1.074703 1.049056 0.960190 1.016003 0.791572 0.784946 -P_31 0.758425 0.681937 0.682232 0.622047 0.546413 0.515343 0.537272 0.553614 -P_31 0.557444 0.612990 0.725401 0.711892 0.860909 0.868844 0.959906 1.101736 -P_31 1.193944 1.271979 1.283761 1.457408 1.254832 1.210984 1.398086 1.318561 -P_31 1.245103 1.261343 1.124930 1.043616 1.134057 0.947063 1.092435 1.039065 -P_31 0.991726 1.050202 0.975780 0.999991 1.170792 1.081508 1.135273 1.114936 -P_31 1.100371 1.180553 1.041283 1.119610 1.129515 1.002783 0.869475 0.903220 -P_31 0.749963 0.658936 0.601293 0.585233 0.523390 0.496122 0.569636 0.567738 -P_31 0.568930 0.602736 0.661451 0.754332 0.864829 0.875692 1.039787 1.068697 -P_31 1.260827 1.181836 1.359881 1.288840 1.299962 1.264659 1.278534 1.273823 -P_31 1.286602 1.165096 1.091203 1.149457 0.980454 0.998185 1.045575 0.993097 -P_31 1.113512 0.926296 1.013347 1.156052 1.029371 1.220324 1.230707 1.144861 -P_31 1.226487 1.133981 1.094809 1.023345 1.105987 0.958679 0.935298 0.819841 -P_31 0.803700 0.674078 0.627054 0.602774 0.477959 0.561690 0.551518 0.551480 -P_31 0.637166 0.601512 0.755300 0.784615 0.908897 0.934147 0.976665 1.120451 -P_31 1.145989 1.197374 1.224163 1.223752 1.291526 1.288221 1.282288 1.244925 -P_31 1.136113 1.138118 1.204260 1.214787 1.062016 1.021762 1.041743 1.061478 -P_31 1.122189 1.066126 1.031730 1.108792 1.124827 1.042193 1.159089 1.109314 -P_31 1.256808 1.097136 1.172664 1.062420 1.072132 1.056831 0.949528 0.886675 -P_31 0.719130 0.685991 0.567686 0.567420 0.498893 0.520264 0.510994 0.559645 -P_31 0.552412 0.627497 0.708441 0.752204 0.926631 0.942990 1.039338 1.151705 -P_31 1.142961 1.161641 1.247238 1.205490 1.181512 1.305891 1.193261 1.172938 -P_31 1.204856 1.233006 1.050049 1.039556 1.064489 1.107338 1.073878 1.030085 -P_31 1.057058 0.993643 1.043749 1.107080 1.135421 1.118713 1.073322 1.157801 -P_31 1.140765 1.203166 1.168979 1.088844 1.186702 0.865128 0.804438 0.855376 -P_31 0.770262 0.668534 0.655476 0.547389 0.562486 0.536031 0.550944 0.606385 -P_31 0.625685 0.685397 0.775315 0.856336 0.987492 0.959367 1.042897 1.213722 -P_31 1.256670 1.273035 1.441949 1.484987 1.381901 1.423642 1.307728 1.317950 -P_31 1.324781 1.307568 1.226403 1.119234 1.082986 1.110444 1.135606 1.040849 -P_31 1.042795 1.171183 1.136609 1.067710 1.086812 1.052499 1.065267 1.124088 -P_31 1.160249 1.076177 1.149061 1.038024 1.054886 0.984359 0.876633 0.850875 -P_31 0.803861 0.739741 0.727790 0.663938 0.615642 0.559984 0.583200 0.510578 -P_31 0.564207 0.572099 0.618832 0.709060 0.720780 0.731458 0.814141 0.899392 -P_31 0.986108 1.057828 1.058240 1.120016 1.148956 1.237891 1.213067 1.303565 -P_31 1.192000 1.178750 1.209509 1.055270 1.173764 1.119050 1.021140 1.076024 -P_31 1.218018 1.109618 1.132368 1.028524 1.134816 1.067112 1.126417 1.080132 -P_31 1.120178 0.988762 0.960334 0.947347 0.913750 0.838060 0.870979 0.813074 -P_31 0.732343 0.717558 0.682160 0.633134 0.639728 0.600859 0.589647 0.569084 -P_31 0.560050 0.601355 0.585176 0.559410 0.598876 0.660068 0.687723 0.761581 -P_31 0.826236 0.923516 0.968420 1.072577 1.059869 1.109894 1.062865 1.065460 -P_31 1.041065 1.077077 1.188720 0.994672 1.178654 1.041199 1.044473 1.010003 -P_31 0.966319 1.040148 1.053350 0.966511 1.078104 1.026995 1.193255 1.011077 -P_31 1.062777 1.034532 1.043029 1.045007 0.929381 0.934527 0.818799 0.795621 -P_31 0.747019 0.652936 0.582215 0.565014 0.553116 0.528027 0.538839 0.543445 -P_31 0.541072 0.603240 0.722155 0.738880 0.771730 0.841922 1.028482 1.137253 -P_31 1.182376 1.118226 1.333758 1.254014 1.317309 1.338605 1.252318 1.286645 -P_31 1.346137 1.090258 1.097553 1.058544 1.081060 1.013193 0.963821 0.933689 -P_31 0.979477 0.971409 0.996836 0.973863 0.995395 1.175792 1.161413 1.113212 -P_31 1.152073 1.204868 1.142271 1.107556 1.030536 0.954641 0.895719 0.849585 -P_31 0.712555 0.738113 0.624535 0.587492 0.550116 0.509548 0.551494 0.501753 -P_31 0.590440 0.662519 0.722278 0.764504 0.854232 0.832009 1.005054 1.010158 -P_31 1.086720 1.052257 1.247486 1.268998 1.198084 1.154889 1.306469 1.166582 -P_31 1.190439 1.127344 1.039669 1.020718 1.003386 1.052094 0.890485 1.141024 -P_31 1.065317 1.029065 1.039570 1.142434 1.042396 1.168742 1.174033 1.093419 -P_31 1.110895 1.168966 1.194300 1.149052 0.878265 0.962707 0.916311 0.856830 -P_31 0.771956 0.684365 0.600012 0.571811 0.546298 0.490948 0.536874 0.503091 -P_31 0.557922 0.686738 0.761467 0.774885 0.850043 0.950264 0.920479 1.077283 -P_31 1.113743 1.214544 1.232028 1.273289 1.349676 1.187987 1.195179 1.216224 -P_31 1.126025 1.227176 1.089234 1.103709 1.120500 1.100805 1.015743 1.038008 -P_31 1.042190 0.974895 0.983130 1.032357 1.087987 1.120591 1.104917 1.060146 -P_31 1.143440 1.034624 1.148358 1.023035 1.062794 0.938344 0.884607 0.870705 -P_31 0.737763 0.717995 0.656671 0.488795 0.489471 0.485409 0.468589 0.527547 -P_31 0.562486 0.632325 0.693326 0.828423 0.890200 0.903949 0.942498 1.035524 -P_31 1.094766 1.179562 1.211684 1.239001 1.187089 1.140929 1.124368 1.124800 -P_31 1.119706 1.192579 1.030470 0.993774 1.058056 1.049657 0.993821 1.036789 -P_31 1.049388 1.018712 1.130978 1.109383 0.974141 1.177849 1.173223 1.073467 -P_31 1.176865 1.119100 1.068408 1.037782 1.069362 1.015454 0.939466 0.781504 -P_31 0.791794 0.718814 0.667781 0.588634 0.564072 0.533067 0.539620 0.550303 -P_31 0.587145 0.610775 0.736371 0.846236 1.025219 0.967133 1.183795 1.221382 -P_31 1.206928 1.350930 1.359747 1.427927 1.256640 1.378272 1.259939 1.238763 -P_31 1.282305 1.254927 1.185976 1.153783 1.118830 1.148707 1.045129 1.000589 -P_31 1.054210 0.962546 1.099320 1.062548 1.223659 1.058752 1.085807 1.145301 -P_31 1.099410 0.991379 1.085051 1.027228 0.969237 0.905339 0.868381 0.856432 -P_31 0.826761 0.711729 0.676428 0.679024 0.548284 0.531197 0.538587 0.543882 -P_31 0.548517 0.588365 0.566564 0.677093 0.740435 0.771701 0.830419 0.864880 -P_31 0.970456 1.010385 1.032763 1.075366 1.161597 1.131167 1.213021 1.192561 -P_31 1.164214 1.165403 1.193066 0.990798 1.089265 1.042841 1.125381 1.045528 -P_31 1.127194 1.092823 1.117378 1.143877 1.192167 1.092728 1.080207 0.996248 -P_31 1.080870 1.029116 1.013133 0.952371 0.925805 0.825221 0.788862 0.867886 -P_31 0.729087 0.697320 0.722938 0.603328 0.587741 0.548641 0.570256 0.522106 -P_31 0.533393 0.546149 0.612598 0.592258 0.614941 0.744256 0.746295 0.747340 -P_31 0.815055 0.924297 1.018686 0.922937 0.999422 1.050778 1.142322 1.119841 -P_31 1.166636 1.041561 1.114572 1.053423 0.949592 1.009370 1.002922 0.913305 -P_31 0.950475 1.008222 0.965940 1.065144 1.046056 0.994464 1.014568 1.148964 -P_31 1.157809 1.024604 1.028074 0.980307 0.954453 0.904353 0.804872 0.716188 -P_31 0.732110 0.652763 0.589090 0.554992 0.546895 0.507830 0.526676 0.530601 -P_31 0.551327 0.632635 0.677995 0.711069 0.816128 0.883214 0.998122 1.121735 -P_31 1.074197 1.137178 1.130020 1.253840 1.224546 1.130179 1.255847 1.192197 -P_31 1.252326 1.097739 1.111395 1.046767 1.025662 1.025624 1.019866 0.918255 -P_31 0.898448 0.990868 0.889246 1.072636 0.912926 0.971376 1.053716 1.124857 -P_31 1.065482 1.136676 1.104016 1.144571 0.939893 1.007763 0.941504 0.801663 -P_31 0.737505 0.660702 0.610999 0.551175 0.523403 0.530899 0.553477 0.510646 -P_31 0.571261 0.601392 0.696890 0.685486 0.797297 0.934283 0.986600 1.029279 -P_31 1.086544 1.147295 1.183995 1.207236 1.296243 1.215164 1.151962 1.095288 -P_31 1.271491 1.075507 1.022852 1.063423 1.063632 0.947276 1.062765 0.977335 -P_31 1.094908 1.017563 1.054639 1.029245 1.082754 1.131359 1.101493 1.178541 -P_31 1.225792 1.150178 1.039529 1.138397 0.990868 0.929752 0.874922 0.772974 -P_31 0.719765 0.658631 0.565549 0.553462 0.496167 0.529934 0.460030 0.547061 -P_31 0.608112 0.633899 0.692249 0.807230 0.775203 0.906965 1.030398 1.014582 -P_31 1.014339 1.025305 1.124867 1.158278 1.205861 1.314867 1.172442 1.237044 -P_31 1.139841 1.160361 1.096048 1.054313 1.054096 1.097778 1.127693 0.988982 -P_31 1.081660 1.021104 0.988259 0.991882 1.082758 1.170159 1.100901 1.110814 -P_31 1.110094 1.158915 1.119543 0.995350 1.007863 1.004550 0.837240 0.805120 -P_31 0.753215 0.668772 0.612945 0.539954 0.571708 0.496273 0.555833 0.552788 -P_31 0.562188 0.629276 0.659976 0.784494 0.883601 0.924686 1.026072 0.972345 -P_31 1.106880 1.200557 1.217285 1.254828 1.108148 1.233017 1.157011 1.142650 -P_31 1.041616 1.069224 1.176366 0.902015 0.926613 0.970945 0.930744 1.036512 -P_31 0.992059 1.108917 1.041904 1.106316 0.987340 1.013665 0.998508 1.191737 -P_31 1.140425 1.031119 1.082437 1.041370 1.022858 0.963747 0.899460 0.831459 -P_31 0.716280 0.675373 0.560745 0.603385 0.540699 0.536097 0.561491 0.538144 -P_31 0.586711 0.647719 0.717173 0.879459 0.885826 1.011473 1.095570 1.140816 -P_31 1.322332 1.303052 1.271113 1.281902 1.297058 1.490241 1.353879 1.285873 -P_31 1.232195 1.197268 1.178221 1.004584 1.048013 1.107244 1.044364 0.986276 -P_31 1.117418 1.118119 1.097043 1.151354 0.985269 1.154454 1.050569 1.006040 -P_31 1.076510 1.039286 1.087703 0.862220 0.980534 0.942195 0.909190 0.892809 -P_31 0.708193 0.666682 0.676833 0.616330 0.587460 0.554346 0.521202 0.526538 -P_31 0.504892 0.544150 0.567812 0.635048 0.687534 0.796201 0.878786 0.895270 -P_31 1.031474 0.944368 1.082498 1.146880 1.141045 1.058499 1.169360 1.167956 -P_31 1.133065 1.114313 1.113973 1.068509 1.069987 0.997785 0.989992 1.133990 -P_31 1.029415 1.073545 0.969587 1.071604 1.037380 1.049609 1.042582 1.042166 -P_31 1.030106 0.941173 0.870959 0.906612 0.848431 0.791281 0.853824 0.747283 -P_31 0.615458 0.618220 0.635816 0.562069 0.552519 0.515588 0.550831 0.529212 -P_31 0.578142 0.527221 0.551624 0.516443 0.653132 0.700011 0.669506 0.736178 -P_31 0.862243 0.884101 0.926700 0.995886 0.970580 1.070412 1.078864 1.052867 -P_31 0.960864 1.069299 1.138480 1.107818 0.977858 0.981899 0.963813 0.994068 -P_31 1.033662 1.017425 0.810386 0.925959 1.005684 0.995523 0.968385 0.961703 -P_31 1.071547 1.096303 1.011564 1.013379 0.900570 0.894767 0.811503 0.794980 -P_31 0.672412 0.596254 0.643582 0.548017 0.508223 0.434588 0.462489 0.489521 -P_31 0.554247 0.610841 0.692832 0.649838 0.718052 0.831611 0.998551 1.070033 -P_31 1.074723 1.205914 1.268594 1.339467 1.277107 1.253216 1.278856 1.132970 -P_31 1.229623 1.101448 1.024200 1.065401 1.032936 1.025360 0.899173 0.879565 -P_31 0.857224 0.938328 1.003133 0.938609 1.070520 0.973074 1.120802 1.021911 -P_31 1.118970 0.963894 1.106002 1.040551 0.901508 1.008827 0.881380 0.771908 -P_31 0.726912 0.652899 0.586236 0.540068 0.551963 0.532097 0.502074 0.535275 -P_31 0.581864 0.556008 0.738356 0.784862 0.842226 0.774611 1.011181 1.031899 -P_31 1.085781 1.106489 1.182838 1.228132 1.198258 1.150098 1.202720 1.123457 -P_31 1.125809 1.179061 1.197635 1.097511 0.959732 0.973882 0.903582 0.926949 -P_31 0.954075 0.847845 1.064217 1.053587 1.076506 1.047913 1.006941 1.063265 -P_31 1.117451 1.035824 1.093651 0.917154 1.047602 0.916730 0.751802 0.816008 -P_31 0.639264 0.656047 0.579865 0.536128 0.491436 0.478536 0.498828 0.513526 -P_31 0.513240 0.552285 0.693768 0.692275 0.702125 0.884091 0.940556 0.933821 -P_31 1.087164 1.056008 1.058229 1.284739 1.169362 1.241179 1.141559 1.180568 -P_31 1.276518 1.109651 1.075888 0.968563 0.948672 1.032693 1.034737 1.015426 -P_31 1.058756 1.054293 1.005055 1.028897 1.084537 1.096765 1.080771 0.988059 -P_31 1.003422 1.109077 1.064739 1.039277 0.955763 0.907366 0.804416 0.775273 -P_31 0.739646 0.636039 0.592870 0.534742 0.530448 0.481610 0.469002 0.507774 -P_31 0.553411 0.596038 0.668487 0.751253 0.840530 0.921273 0.958391 1.032381 -P_31 1.021593 1.128517 1.193080 1.217105 1.226482 1.211864 1.084237 1.077867 -P_31 1.020637 1.103762 1.056644 1.045601 0.986850 0.980592 0.932626 0.900545 -P_31 0.955661 1.052597 0.976018 1.094233 1.052102 1.039187 1.090329 1.105066 -P_31 1.030347 1.128981 0.987320 0.966698 1.075496 0.941969 0.829691 0.778161 -P_31 0.737980 0.648848 0.634469 0.507358 0.526248 0.535373 0.527599 0.545386 -P_31 0.592654 0.640098 0.706501 0.869009 0.914899 0.958071 1.090658 1.127946 -P_31 1.260824 1.256794 1.220557 1.336375 1.350981 1.346915 1.162301 1.165027 -P_31 1.186866 1.109873 1.141862 1.040490 1.089762 0.973680 1.097698 1.059136 -P_31 1.074336 1.073928 1.119264 1.029887 1.085145 0.995826 0.980440 1.091984 -P_31 1.035946 0.951608 1.151192 1.009923 0.953249 0.888601 0.843043 0.793289 -P_31 0.707873 0.739703 0.690078 0.599077 0.611606 0.612924 0.519880 0.571206 -P_31 0.538775 0.524154 0.627260 0.610447 0.692828 0.781450 0.817727 0.949006 -P_31 0.903369 0.988648 1.103774 1.042261 1.108768 1.194071 1.125936 1.113052 -P_31 1.091824 1.145566 1.089493 1.112588 1.064938 1.035043 0.912065 1.026970 -P_31 0.982458 1.094720 1.071598 1.087432 1.060330 1.145443 1.050093 0.989053 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0.483726 0.441994 0.455460 0.507535 -P_31 0.577273 0.543738 0.662898 0.705878 0.759397 0.933893 0.914669 1.107511 -P_31 1.059501 1.076969 1.155879 1.170286 1.109374 1.222353 1.249868 1.105458 -P_31 1.114494 1.022321 0.950857 0.999506 0.915991 1.030380 1.010808 1.014197 -P_31 0.923000 1.031987 1.016843 1.021566 1.091845 1.085325 1.157755 1.080388 -P_31 1.041362 1.004132 0.985143 1.014641 0.902664 0.885756 0.906070 0.785005 -P_31 0.744516 0.670238 0.603179 0.523299 0.542613 0.540269 0.529612 0.585372 -P_31 0.590474 0.618206 0.688563 0.720665 0.922326 0.939503 1.158826 1.221913 -P_31 1.175784 1.161948 1.152818 1.279123 1.309049 1.332309 1.310928 1.296332 -P_31 1.174533 1.142827 1.157779 1.056007 1.062969 1.079133 1.058036 1.053949 -P_31 0.949216 0.967154 1.028913 0.993744 1.061298 1.079425 1.080891 1.019584 -P_31 1.108864 1.032768 1.014104 0.948502 0.907108 0.931731 0.811500 0.790349 -P_31 0.717646 0.697629 0.626238 0.588344 0.578368 0.531725 0.505030 0.530149 -P_31 0.542137 0.561375 0.551678 0.668250 0.624742 0.733055 0.813104 0.799838 -P_31 0.935556 0.964154 1.128231 1.146994 1.114446 1.157049 1.054000 1.105103 -P_31 1.080089 1.150159 1.045808 1.141793 1.042564 0.965451 1.066943 0.988212 -P_31 1.004809 1.032491 1.169654 1.080802 1.020821 1.076639 0.971544 1.036005 -P_31 0.900569 1.028160 0.984539 0.839124 0.736695 0.800691 0.808799 0.701149 -P_31 0.681157 0.717704 0.669040 0.625469 0.537687 0.524172 0.532601 0.520901 -P_31 0.541944 0.527753 0.537516 0.576656 0.558179 0.608672 0.675944 0.785827 -P_31 0.770538 0.883358 0.881334 0.939293 1.064191 1.008289 1.040529 1.034403 -P_31 1.074330 0.927278 1.082465 0.976987 1.029608 0.960774 0.906664 0.893671 -P_31 0.897263 0.907245 0.855463 0.947153 0.940638 0.991559 1.097605 0.929361 -P_31 0.987891 0.968430 0.902546 0.916896 0.887151 0.812929 0.785613 0.742101 -P_31 0.727635 0.627864 0.558813 0.565463 0.471196 0.486680 0.487967 0.515903 -P_31 0.502973 0.588624 0.646976 0.578363 0.758604 0.852310 0.942721 1.010584 -P_31 1.062693 1.140921 1.210923 1.318823 1.245674 1.305275 1.195397 1.120450 -P_31 1.071100 1.167847 1.097182 0.991576 0.953403 0.910347 0.918659 0.956161 -P_31 0.931162 0.923191 0.869884 0.989257 1.038553 0.963657 0.964151 1.067931 -P_31 0.982027 1.039610 0.994123 1.021570 0.985501 0.928231 0.865357 0.833854 -P_31 0.725519 0.614817 0.591228 0.550236 0.505112 0.530911 0.429870 0.500545 -P_31 0.551344 0.550556 0.595598 0.716271 0.805280 0.917205 0.907079 1.019403 -P_31 1.005423 1.115040 1.124486 1.122202 1.126394 1.192008 1.121126 1.058066 -P_31 1.077356 1.120331 1.089945 1.037730 0.992612 0.910174 0.937443 0.862068 -P_31 0.984525 0.885217 0.913239 0.948954 0.941792 1.134650 1.053335 1.055527 -P_31 1.062864 0.959727 1.033759 0.966932 0.977720 0.976664 0.792343 0.699172 -P_31 0.767227 0.600445 0.550633 0.510267 0.467410 0.485826 0.469269 0.451813 -P_31 0.601018 0.557947 0.620363 0.731580 0.756503 0.922533 0.966180 0.967369 -P_31 0.999811 1.101971 1.139746 1.174297 1.259860 1.218236 1.254529 1.193912 -P_31 1.126755 1.092311 1.038067 1.064128 0.999629 1.019656 0.942061 1.080015 -P_31 0.858867 0.940792 1.002788 0.970472 1.070350 1.050483 1.054956 1.110257 -P_31 1.030995 1.005201 0.992916 1.021943 0.977458 0.940511 0.763443 0.779118 -P_31 0.675356 0.629214 0.607158 0.542072 0.478501 0.492132 0.488600 0.533900 -P_31 0.588393 0.591691 0.657858 0.668431 0.780936 0.873412 0.878573 1.044135 -P_31 1.001291 1.093063 1.162927 1.284289 1.113958 1.157491 1.063031 1.019208 -P_31 1.147604 0.991779 1.008134 1.024963 0.932340 0.953502 1.016660 0.968716 -P_31 0.964383 1.011373 0.951699 1.095231 0.997257 0.977968 1.004771 1.046329 -P_31 0.933598 1.026369 1.099748 1.117474 0.966545 0.913306 0.834689 0.779509 -P_31 0.676705 0.676723 0.613341 0.611375 0.534613 0.482677 0.518639 0.465678 -P_31 0.546913 0.653740 0.709747 0.803108 0.926529 0.924552 1.065949 1.065870 -P_31 1.215350 1.146072 1.274973 1.160300 1.216083 1.356449 1.154524 1.140495 -P_31 1.140568 1.155483 1.053737 0.987485 1.077453 1.037090 0.955164 0.958266 -P_31 1.077033 1.008055 1.017019 0.925206 1.104623 1.045909 1.111992 1.021402 -P_31 0.934147 0.956443 0.970431 1.033925 0.860805 0.942624 0.882837 0.797950 -P_31 0.679545 0.668519 0.607997 0.592435 0.565536 0.552388 0.529620 0.522834 -P_31 0.494970 0.494751 0.605267 0.624208 0.621658 0.719968 0.822889 0.855611 -P_31 1.011024 1.007245 1.128964 1.076066 1.141043 1.068062 1.173280 1.017447 -P_31 1.012675 1.084756 1.090524 0.990381 0.941464 1.049190 1.041228 1.067766 -P_31 1.086928 1.044247 1.029921 1.075455 1.046560 0.968264 0.991881 1.044014 -P_31 0.954355 0.941463 0.878210 0.883845 0.811494 0.796351 0.776821 0.679601 -P_31 0.670367 0.652336 0.620245 0.600373 0.578185 0.552789 0.530006 0.516903 -P_31 0.542770 0.482147 0.519157 0.530834 0.592188 0.640544 0.686553 0.721598 -P_31 0.754307 0.915377 0.833972 0.931541 0.928000 0.967964 0.997520 1.159299 -P_31 1.043707 0.993736 1.032909 0.927579 0.941839 0.936827 0.936988 0.921432 -P_31 0.897829 0.905801 0.940362 0.984959 0.940586 0.936551 0.984816 0.891450 -P_31 1.006043 0.947937 0.956454 0.893933 0.959739 0.832263 0.791622 0.736226 -P_31 0.703042 0.672674 0.501600 0.500869 0.490389 0.491597 0.454244 0.481168 -P_31 0.545828 0.537438 0.624877 0.630932 0.738296 0.743336 0.910021 0.924122 -P_31 1.063553 1.161614 1.149866 1.157324 1.232992 1.153728 1.190827 1.192103 -P_31 1.087075 1.048104 0.989809 0.968539 0.972506 0.857981 0.938332 0.869389 -P_31 0.926522 1.001673 0.876906 0.991767 0.936703 0.963180 1.068457 1.060169 -P_31 1.159453 1.076338 1.061441 1.023289 0.898539 0.935268 0.843928 0.797355 -P_31 0.695461 0.635613 0.590121 0.545198 0.485061 0.459252 0.547023 0.532705 -P_31 0.560729 0.584312 0.603526 0.777325 0.774834 0.800611 0.854543 0.972417 -P_31 0.987086 1.167221 1.082553 1.168540 1.235774 1.238488 1.089169 1.213708 -P_31 1.098688 1.127528 1.028585 1.001515 0.878444 0.957074 0.940190 0.956744 -P_31 0.935409 0.934839 1.018899 0.910056 0.957784 1.001120 1.118422 1.067231 -P_31 0.989067 1.052404 1.032829 1.008537 0.949506 0.942480 0.821482 0.740954 -P_31 0.716690 0.604020 0.574088 0.534502 0.453317 0.499232 0.448339 0.457975 -P_31 0.486938 0.526350 0.581740 0.756717 0.780289 0.925538 0.866644 0.941254 -P_31 1.064840 1.172979 1.122976 1.131298 1.215562 1.156717 1.229575 1.023169 -P_31 1.110825 1.003111 1.049166 1.048849 1.028608 0.951132 0.968462 0.966650 -P_31 0.903520 0.999161 1.052200 1.027803 0.958705 0.951383 0.982776 0.981843 -P_31 0.994645 1.015860 1.053667 1.010986 0.934985 0.970750 0.834560 0.790335 -P_31 0.676385 0.598184 0.582715 0.550603 0.493699 0.473874 0.503806 0.534096 -P_31 0.529523 0.607159 0.670607 0.745500 0.827767 0.868060 1.009235 1.000554 -P_31 1.049149 1.117120 1.079112 1.105774 1.201791 1.041257 1.176192 1.061459 -P_31 1.063642 0.998932 1.038252 1.005481 0.893701 0.988686 0.909047 0.993819 -P_31 0.900859 0.990221 0.973070 1.029191 1.020904 1.036512 0.868925 1.023107 -P_31 1.032088 1.057053 1.007405 1.013398 0.940663 0.919874 0.874711 0.727174 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0.806685 0.811651 0.864490 0.959827 -P_31 1.007656 1.085374 1.054956 1.257614 1.311314 1.159358 1.299033 1.139268 -P_31 1.104683 1.042107 0.961416 1.015251 0.969725 0.948533 0.931822 0.870265 -P_31 0.954369 0.919224 0.879356 0.907170 1.114298 0.997284 1.099951 1.072474 -P_31 1.063187 1.211193 1.025775 0.964564 0.954845 0.928266 0.788788 0.753461 -P_31 0.699174 0.586439 0.557185 0.486625 0.456961 0.447341 0.507689 0.535314 -P_31 0.531864 0.572045 0.596254 0.690538 0.759249 0.850024 0.951039 0.913990 -P_31 1.117091 1.102162 1.186951 1.123661 1.173186 1.077867 1.167275 1.168123 -P_31 1.122143 1.110106 1.066762 0.981801 1.010407 0.909528 1.083651 1.015443 -P_31 1.001882 1.001953 0.943450 0.997629 1.087237 1.029243 1.102055 0.987672 -P_31 1.120055 1.053260 0.976252 0.999866 1.046403 0.845612 0.917534 0.753849 -P_31 0.704153 0.628121 0.584288 0.507823 0.484843 0.478337 0.476575 0.483496 -P_31 0.563872 0.533068 0.629962 0.681149 0.820385 0.897631 0.949341 0.908443 -P_31 1.015936 1.055462 1.101014 1.060394 1.118881 1.074190 1.067232 1.017985 -P_31 1.014112 1.090398 1.060214 0.918686 0.950912 1.006918 0.999063 0.949561 -P_31 0.905512 0.829257 1.087669 1.044669 0.887561 1.007959 1.128964 1.109104 -P_31 1.120364 0.994646 1.026530 0.931854 0.969500 0.890493 0.847866 0.764420 -P_31 0.726469 0.642032 0.506398 0.580527 0.516019 0.486628 0.516726 0.536911 -P_31 0.594204 0.614769 0.670604 0.792151 0.821052 0.955425 0.982018 1.007292 -P_31 1.106974 1.321720 1.307353 1.261006 1.351048 1.412331 1.272099 1.127498 -P_31 1.215560 1.130908 1.090661 1.052928 0.966301 0.943446 0.995301 0.955509 -P_31 0.944654 1.066415 0.936235 1.059678 0.983793 0.975767 1.018708 1.008707 -P_31 0.968436 1.042280 0.967511 1.010069 0.871383 0.919015 0.830001 0.754107 -P_31 0.677943 0.669999 0.632519 0.608463 0.536074 0.474801 0.489701 0.516798 -P_31 0.513756 0.543220 0.567054 0.612813 0.698996 0.699451 0.785217 0.818522 -P_31 0.912848 0.997360 1.052409 1.056375 1.136631 1.190489 1.092721 1.060689 -P_31 1.060050 1.045851 1.079155 1.031135 1.080048 1.120022 1.105575 1.095749 -P_31 0.987689 1.017616 1.019686 1.028589 1.001901 1.009331 0.951012 1.028618 -P_31 0.895547 0.869533 0.882835 0.887424 0.816916 0.799637 0.796992 0.756635 -P_31 0.676236 0.622478 0.605982 0.506094 0.562446 0.514827 0.496244 0.515884 -P_31 0.465045 0.553742 0.511193 0.542658 0.530850 0.630776 0.659299 0.746089 -P_31 0.792074 0.859143 0.914959 0.854204 0.880662 0.984967 0.967156 0.942365 -P_31 0.949673 0.968304 1.033245 1.028031 1.040360 0.780532 0.949985 0.917045 -P_31 0.875201 0.865026 0.995364 1.011202 0.862364 0.981315 1.003785 0.955638 -P_31 0.931370 0.946995 0.924358 0.964527 0.861916 0.886971 0.820678 0.729989 -P_31 0.598032 0.572485 0.559911 0.526927 0.455269 0.486023 0.464105 0.493918 -P_31 0.555859 0.548805 0.608944 0.646749 0.821088 0.894603 0.926764 0.926067 -P_31 1.118278 1.151922 1.114140 1.219662 1.180458 1.142129 1.179659 1.298222 -P_31 1.130899 1.103486 1.071240 1.052267 0.962422 0.907990 0.923932 0.905333 -P_31 0.855772 0.879154 0.868757 0.800726 0.861306 0.940070 1.004360 1.090619 -P_31 1.158651 0.997063 1.024912 0.973583 0.942321 0.847603 0.803972 0.787668 -P_31 0.690235 0.606154 0.560994 0.479089 0.531417 0.450932 0.489085 0.527221 -P_31 0.483598 0.549287 0.684614 0.666479 0.733202 0.860571 0.845156 0.945850 -P_31 1.025167 1.106716 1.190514 1.101374 1.134316 1.125092 1.130897 1.128398 -P_31 1.108689 1.115952 0.944089 1.037839 0.941456 0.900061 0.955320 0.877371 -P_31 1.023553 0.926160 0.945009 1.004817 0.945376 1.042284 0.994943 1.006092 -P_31 1.076950 1.030810 1.014624 0.945720 0.972453 0.886248 0.787153 0.752490 -P_31 0.634551 0.661618 0.601061 0.542644 0.485497 0.450974 0.536271 0.511095 -P_31 0.536521 0.605828 0.654725 0.708329 0.729974 0.907038 1.019921 0.946035 -P_31 1.027646 1.016615 1.128694 1.124602 1.145188 1.124380 1.119504 1.101552 -P_31 1.074364 1.082947 0.939825 1.072117 0.974740 0.934209 0.854531 0.966514 -P_31 0.945107 0.936125 0.956775 0.867601 0.990433 0.975267 0.967849 1.093920 -P_31 0.941590 1.019932 1.042603 0.882899 0.956763 0.900965 0.833449 0.737133 -P_31 0.715463 0.604256 0.527985 0.531612 0.500504 0.462343 0.473601 0.494895 -P_31 0.542997 0.599961 0.593948 0.717426 0.762817 0.922703 0.972941 0.962087 -P_31 1.040298 1.035844 1.152484 1.068078 1.180766 1.028984 1.059271 1.029986 -P_31 1.104972 1.057911 0.954276 0.999969 0.949382 0.869040 1.027126 1.006366 -P_31 0.918268 1.033617 0.979274 0.927267 0.923536 1.041325 1.080447 1.065692 -P_31 0.986046 1.013888 0.917862 0.981471 0.905585 0.879240 0.856690 0.745796 -P_31 0.702420 0.667579 0.566761 0.535941 0.499544 0.456688 0.501615 0.493930 -P_31 0.534110 0.609711 0.659596 0.792349 0.854810 1.042243 1.028773 1.010344 -P_31 1.182983 1.311527 1.305405 1.225122 1.178463 1.135973 1.181940 1.268258 -P_31 1.149957 1.124175 1.046032 0.980708 1.027341 0.988126 1.019279 0.993781 -P_31 0.967739 1.062055 0.987721 1.020427 1.058650 0.997156 0.986284 1.011351 -P_31 1.033626 0.935236 0.961554 0.990095 0.934873 0.857893 0.821024 0.756649 -P_31 0.761361 0.732784 0.654655 0.621790 0.532692 0.513471 0.526589 0.522133 -P_31 0.549088 0.537344 0.584785 0.627543 0.637753 0.741766 0.729644 0.832922 -P_31 0.960154 0.987767 1.029588 0.971639 1.095424 1.052934 1.042240 0.997652 -P_31 1.152207 1.070597 1.098823 1.128465 1.066257 1.161631 0.969357 1.078960 -P_31 1.083497 0.983333 0.986305 1.135171 1.010474 1.007735 0.956525 1.011074 -P_31 0.931006 0.905963 0.786055 0.948915 0.843234 0.790699 0.752687 0.679077 -P_31 0.707981 0.651663 0.541123 0.538149 0.529748 0.533748 0.503734 0.524862 -P_31 0.512830 0.472261 0.551019 0.556676 0.583621 0.611729 0.640134 0.682172 -P_31 0.792669 0.830398 0.838783 0.875404 0.891835 1.021510 1.049792 1.082162 -P_31 1.033856 0.991855 0.868365 0.990231 1.003094 0.931917 1.018834 0.871205 -P_31 0.911171 0.923060 0.929541 0.828082 0.947758 0.952752 1.012860 0.959309 -P_31 0.983051 0.972062 0.937531 0.943117 0.896537 0.827829 0.752096 0.659701 -P_31 0.681182 0.635909 0.530862 0.504555 0.540045 0.491166 0.482489 0.407222 -P_31 0.536402 0.554746 0.602997 0.658855 0.742726 0.730047 0.803905 1.039718 -P_31 1.058997 1.180556 1.115032 1.207598 1.309254 1.252683 1.117399 1.113658 -P_31 1.036341 1.144470 0.971966 0.992886 1.024641 0.924974 0.940637 0.913772 -P_31 0.921366 0.791979 0.823414 0.941724 1.036182 0.906740 0.948046 1.009295 -P_31 0.984365 1.016432 1.060066 0.949113 0.854658 0.832890 0.776767 0.764701 -P_31 0.686674 0.635695 0.596321 0.588920 0.530702 0.475728 0.509383 0.513205 -P_31 0.513429 0.625870 0.632210 0.724028 0.718188 0.773678 0.883322 0.961062 -P_31 0.961126 1.024224 1.112336 1.168314 1.151628 1.149086 1.074879 1.172853 -P_31 1.193162 1.001923 1.036763 0.966798 0.986607 0.906856 0.959279 0.904681 -P_31 0.854904 0.929257 0.950467 1.030549 0.981159 0.932393 1.067656 1.031219 -P_31 1.065482 1.053330 0.988332 0.948525 0.942917 0.923465 0.830210 0.801870 -P_31 0.700264 0.628764 0.519242 0.485758 0.521201 0.450171 0.473227 0.512342 -P_31 0.522005 0.643473 0.632209 0.686617 0.751804 0.899379 0.892919 0.975417 -P_31 0.914130 1.075814 1.075671 1.142413 1.125372 1.203985 1.131159 1.074059 -P_31 1.131235 1.176971 1.129011 1.100193 0.977877 1.017142 0.901909 0.990740 -P_31 1.001028 0.954218 1.027755 0.976145 0.927941 0.903774 1.085804 0.996454 -P_31 1.036253 0.975307 0.966917 0.978307 0.932013 0.917266 0.852481 0.758934 -P_31 0.645562 0.642140 0.505457 0.504244 0.467747 0.471037 0.484362 0.501960 -P_31 0.505504 0.600234 0.603978 0.765760 0.774558 0.809952 0.964300 1.008542 -P_31 1.043235 1.095603 1.063544 0.992539 1.043735 1.020586 1.135599 1.193903 -P_31 0.978645 1.008780 0.994885 1.020140 1.052197 0.865552 0.898423 0.971298 -P_31 0.940764 1.001483 1.040167 1.036189 1.017197 0.980428 0.973583 0.983652 -P_31 1.058281 1.068800 1.010459 0.988399 0.934077 0.963108 0.839590 0.827269 -P_31 0.697755 0.633782 0.653049 0.582172 0.481727 0.503659 0.502108 0.583602 -P_31 0.584514 0.628287 0.737496 0.820891 0.819632 0.925421 1.017814 1.051178 -P_31 1.207406 1.228925 1.152281 1.247180 1.328764 1.245393 1.192856 1.085419 -P_31 1.114518 1.056757 1.084472 0.996490 1.072903 1.109248 1.054431 0.863519 -P_31 0.992910 0.972982 1.062948 0.975322 0.982205 1.035252 1.099244 1.065407 -P_31 0.978507 0.994212 1.072112 0.867830 0.948591 0.923298 0.828987 0.841190 -P_31 0.712474 0.682116 0.633261 0.550024 0.536923 0.511026 0.490500 0.520397 -P_31 0.440180 0.618775 0.597961 0.617115 0.652657 0.731290 0.714436 0.856796 -P_31 0.919362 0.960406 1.016057 1.088613 1.131943 1.090246 1.030751 1.053370 -P_31 1.107034 1.049297 1.031509 1.019770 1.021363 1.015533 0.965568 0.948744 -P_31 0.969415 1.022282 0.920822 1.062182 1.041492 1.060324 0.906350 0.899546 -P_31 0.898101 0.930399 0.880963 0.884745 0.837351 0.777708 0.713218 0.755637 -P_31 0.665089 0.626823 0.607400 0.581756 0.522513 0.509731 0.499734 0.498229 -P_31 0.517515 0.459286 0.536453 0.545177 0.534382 0.647041 0.706290 0.742140 -P_31 0.745763 0.835723 0.834204 0.963631 0.948491 1.050140 1.077287 1.061649 -P_31 0.996525 1.084125 0.994211 0.936363 1.078749 0.893314 0.882621 0.867608 -P_31 0.821645 0.972192 0.886828 0.933539 0.936129 1.009015 1.038259 0.949018 -P_31 0.999383 0.952138 0.889650 0.979513 0.874660 0.765511 0.762227 0.666668 -P_31 0.635837 0.634442 0.560211 0.556467 0.420917 0.459479 0.474872 0.511004 -P_31 0.504501 0.568139 0.581415 0.682042 0.734529 0.767418 0.899351 0.891528 -P_31 1.025631 1.019821 1.170465 1.102428 1.095980 1.105510 1.236801 1.189183 -P_31 1.004869 1.097497 1.096871 1.047323 0.881288 0.877919 0.872191 0.863308 -P_31 0.806040 0.985787 0.937491 0.996247 0.958219 0.968860 1.031929 1.020077 -P_31 1.005656 1.079452 1.027268 1.026513 0.933030 0.896504 0.862918 0.810634 -P_31 0.678324 0.605251 0.525584 0.514208 0.476532 0.466827 0.496538 0.532100 -P_31 0.500015 0.590217 0.614395 0.696863 0.758466 0.857737 0.841466 0.949793 -P_31 0.996121 1.025497 1.019924 1.026327 1.098019 1.086460 1.014201 1.111829 -P_31 1.048183 1.038024 1.035366 0.996774 0.904470 0.956634 0.937285 0.865796 -P_31 0.899403 0.939968 0.876509 0.982690 1.028828 0.993581 1.076868 1.104800 -P_31 1.088748 1.109912 1.011817 1.005314 0.923940 0.908444 0.764896 0.732459 -P_31 0.663542 0.646482 0.558483 0.505073 0.491154 0.446637 0.490458 0.465209 -P_31 0.541176 0.574318 0.615430 0.710573 0.847190 0.910053 0.902547 0.938161 -P_31 0.990848 1.087365 1.064117 1.073942 1.111624 0.986137 1.176538 1.101936 -P_31 1.102322 1.053253 1.050969 1.018887 0.989229 0.951513 0.823200 0.981855 -P_31 0.957036 0.941115 0.997035 1.035372 1.010687 1.025128 1.105161 1.070748 -P_31 1.027626 1.052070 0.943615 0.997285 0.869150 0.814834 0.732300 0.731860 -P_31 0.697284 0.573824 0.559151 0.543463 0.472660 0.493375 0.450227 0.464598 -P_31 0.544136 0.576956 0.660324 0.733018 0.844300 0.819664 0.869939 0.967182 -P_31 0.976630 1.183456 1.046334 1.101043 1.030783 1.035561 1.096345 1.069266 -P_31 1.021308 0.959707 1.004753 0.977288 0.953688 1.013983 0.937346 1.009011 -P_31 0.955094 1.013673 0.946605 0.991287 1.086097 0.943458 1.014133 1.049770 -P_31 1.019998 1.028659 0.970858 0.859001 0.902793 0.905167 0.959983 0.748540 -P_31 0.691727 0.642452 0.599069 0.540581 0.502054 0.489689 0.481726 0.496259 -P_31 0.568710 0.624420 0.659745 0.780470 0.937827 0.919996 1.095746 1.107250 -P_31 1.199078 1.172624 1.301632 1.230401 1.243017 1.301725 1.202982 1.190304 -P_31 1.115102 1.079207 1.174724 0.992428 1.013820 1.009433 0.930501 1.069484 -P_31 0.962723 1.028294 1.049825 1.068743 0.955820 0.976389 1.035932 0.961377 -P_31 0.962870 0.967143 1.006894 0.981581 0.978352 0.809900 0.802330 0.854903 -P_31 0.712881 0.665797 0.646988 0.630684 0.554810 0.523873 0.566546 0.479392 -P_31 0.509392 0.496835 0.541675 0.619173 0.651246 0.756407 0.766560 0.824898 -P_31 0.914256 0.910251 0.938773 1.060373 1.063532 1.216592 1.068191 1.046174 -P_31 1.047792 0.967904 1.068825 1.068816 1.024770 0.952603 1.000720 0.923730 -P_31 1.007248 1.060523 0.971476 1.015751 0.991247 0.963535 0.990798 0.946602 -P_31 0.970407 0.927175 0.938810 0.937430 0.784188 0.800769 0.736651 0.666394 -P_31 0.673420 0.620258 0.559900 0.571604 0.566114 0.536538 0.520047 0.559599 -P_31 0.470579 0.506678 0.587650 0.486797 0.633907 0.634709 0.692244 0.701146 -P_31 0.797416 0.806936 0.906915 0.947541 0.909987 1.063523 0.993518 1.088392 -P_31 1.060319 1.008531 1.069414 1.033726 0.970831 0.908172 0.869022 0.880537 -P_31 0.877831 0.897674 0.990314 0.917454 0.888546 0.895847 0.991395 1.024826 -P_31 1.071540 0.999818 0.877827 0.918319 0.802695 0.842501 0.790614 0.705075 -P_31 0.654128 0.586426 0.534354 0.536519 0.487313 0.482866 0.478484 0.474198 -P_31 0.533593 0.508786 0.587946 0.702616 0.710228 0.800189 0.851957 0.968195 -P_31 0.953062 1.122184 1.174152 1.125559 1.299055 1.213691 1.140836 1.062402 -P_31 1.076672 1.069531 1.003778 0.951033 0.976522 1.019859 0.763751 0.861746 -P_31 0.925072 0.864483 0.865204 0.993673 0.909950 1.102872 0.980288 1.085641 -P_31 1.018675 0.945628 1.054495 0.926728 0.950586 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-P_31 1.062403 1.125756 1.062080 1.014480 0.951867 1.052459 1.028168 0.994500 -P_31 1.039498 0.985009 0.943436 1.001281 1.027517 1.085747 1.003431 1.024423 -P_31 0.990232 0.926401 0.972666 0.952043 0.858395 0.825054 0.777890 0.711272 -P_31 0.707747 0.642137 0.608359 0.570634 0.564481 0.546653 0.506813 0.498091 -P_31 0.496502 0.487514 0.515085 0.557987 0.576318 0.670205 0.705685 0.649860 -P_31 0.730156 0.876878 0.922944 0.962290 0.976349 1.063854 0.942139 1.168403 -P_31 1.050173 1.032579 1.014830 1.020853 0.929032 0.822237 0.967917 0.863264 -P_31 0.842918 0.935942 0.861701 0.969453 0.909067 0.943247 1.036243 1.049809 -P_31 0.940737 1.029602 0.926250 0.902732 0.917129 0.821311 0.723344 0.697883 -P_31 0.730663 0.618762 0.576400 0.530385 0.454643 0.472832 0.515170 0.502354 -P_31 0.575529 0.565153 0.597747 0.667417 0.821348 0.840670 0.898492 0.979877 -P_31 1.051357 1.023786 1.042930 1.189964 1.313245 1.225161 1.191870 1.184548 -P_31 1.145864 1.179555 1.039650 1.080608 0.994768 0.894178 0.867635 0.959582 -P_31 0.923874 0.969486 0.898199 0.932479 0.973920 1.037531 1.027432 1.037717 -P_31 1.077421 1.112240 1.032381 1.030544 0.935938 0.837044 0.864194 0.815824 - -[ENERGY] -GLOBAL PRICE 0.0000 -GLOBAL PATTERN "" -GLOBAL EFFIC 75.0000 -DEMAND CHARGE 0.0000 - -[STATUS] -;ID Setting - -[CONTROLS] -Link 9 0.0 IF Node 2 above 42.672 -Link 9 1.0 IF Node 2 below 33.528 - -[RULES] - -[DEMANDS] -;ID Demand Pattern - -[QUALITY] - -[REACTIONS] - ORDER BULK 1 - ORDER WALL 1 - ORDER TANK 1 - GLOBAL BULK -0.5000 - GLOBAL WALL -1.0000 - LIMITING POTENTIAL 0.0000 - ROUGHNESS CORRELATION 0.0000 - -[SOURCES] -;Node Type Quality Pattern - -[MIXING] -;Tank ID Model Fraction - -[OPTIONS] -UNITS CMH -HEADLOSS H-W -QUALITY CHLORINE mg/L -VISCOSITY 1 -DIFFUSIVITY 1 -SPECIFIC GRAVITY 1 -TRIALS 40 -ACCURACY 0.001 -CHECKFREQ 2 -UNBALANCED CONTINUE 10 -PATTERN 1 -DEMAND MULTIPLIER 1 -EMITTER EXPONENT 0.5 -TOLERANCE 0.01 - -[TIMES] -DURATION 8760:00:00 -HYDRAULIC TIMESTEP 00:30:00 -PATTERN TIMESTEP 00:30:00 -PATTERN START 00:00:00 -REPORT TIMESTEP 00:30:00 -REPORT START 00:00:00 -START CLOCKTIME 00:00:00 AM -QUALITY TIMESTEP 00:00:00 -STATISTIC NONE - -[REPORT] -STATUS YES -SUMMARY NO - -[COORDINATES] -;Node X-Coord Y-Coord -11 30.000000 70.000000 -10 20.000000 70.000000 -13 70.000000 70.000000 -12 50.000000 70.000000 -21 30.000000 40.000000 -22 50.000000 40.000000 -23 70.000000 40.000000 -32 50.000000 10.000000 -31 30.000000 10.000000 -2 50.000000 90.000000 -9 10.000000 70.000000 - -[VERTICES] -;Link X-Coord Y-Coord - -[LABELS] - 6.99 73.63 "Source" - 13.48 68.13 "Pump" - 43.85 91.21 "Tank" - -[BACKDROP] -DIMENSIONS 7.00 6.00 73.00 94.00 -UNITS None -OFFSET 0.00 0.00 - -[TAGS] -;type name tag - -[END] diff --git a/docs/notebooks/networks/Net2LoopsCM.inp b/docs/notebooks/networks/Net2LoopsCM.inp deleted file mode 100644 index 76c10ec..0000000 --- a/docs/notebooks/networks/Net2LoopsCM.inp +++ /dev/null @@ -1,145 +0,0 @@ -[TITLE] -shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) - -[JUNCTIONS] -;ID Elev Demand Pattern - 2 150.00 27.77 ; - 3 160.00 27.77 ; - 4 155.00 33.33 ; - 5 150.00 75.00 ; - 6 165.00 91.67 ; - 7 160.00 55.55 ; - -[RESERVOIRS] -;ID Head Pattern - 1 210.00 ; - -[TANKS] -;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow - -[PIPES] -;ID Node1 Node2 Length Diameter Roughness MinorLoss Status - 1 1 2 1000.00 457.20 0.015 0.00 Open ; - 2 2 3 1000.00 203 0.015 0.00 Open ; - 3 2 4 1000.00 457 0.015 0.00 Open ; - 4 4 5 1000.00 153 0.015 0.00 Open ; - 5 4 6 1000.00 406.40 0.015 0.00 Open ; - 6 6 7 1000.00 254.00 0.015 0.00 Open ; - 7 3 5 1000.00 153 0.015 0.00 Open ; - 8 5 7 1000.00 153 0.015 0.00 Open ; - -[PUMPS] -;ID Node1 Node2 Parameters - -[VALVES] -;ID Node1 Node2 Diameter Type Setting MinorLoss - -[TAGS] - -[DEMANDS] -;Junction Demand Pattern Category - -[STATUS] -;ID Status/Setting - -[PATTERNS] -;ID Multipliers - -[CURVES] -;ID X-Value Y-Value - -[CONTROLS] - - - -[RULES] - - - -[ENERGY] - Global Efficiency 75 - Global Price 0 - Demand Charge 0 - -[EMITTERS] -;Junction Coefficient - -[QUALITY] -;Node InitQual - -[SOURCES] -;Node Type Quality Pattern - -[REACTIONS] -;Type Pipe/Tank Coefficient - - -[REACTIONS] - Order Bulk 1 - Order Tank 1 - Order Wall 1 - Global Bulk 0 - Global Wall 0 - Limiting Potential 0 - Roughness Correlation 0 - -[MIXING] -;Tank Model - -[TIMES] - Duration 0:00 - Hydraulic Timestep 1:00 - Quality Timestep 0:05 - Pattern Timestep 2:00 - Pattern Start 0:00 - Report Timestep 1:00 - Report Start 0:00 - Start ClockTime 12 am - Statistic NONE - -[REPORT] - Status Yes - Summary No - Page 0 - -[OPTIONS] - Units LPS - Headloss C-M - Specific Gravity 1.0 - Viscosity 1.0 - Trials 40 - Accuracy 0.001 - CHECKFREQ 2 - MAXCHECK 10 - DAMPLIMIT 0 - Unbalanced Continue 10 - Pattern 1 - Demand Multiplier 1.0 - Emitter Exponent 0.5 - Quality Chlorine mg/L - Diffusivity 1.0 - Tolerance 0.01 - -[COORDINATES] -;Node X-Coord Y-Coord -2 2000.000 3000.000 -3 1000.000 3000.000 -4 2000.000 2000.000 -5 1000.000 2000.000 -6 2000.000 1000.000 -7 1000.000 1000.000 -1 3000.000 3000.000 - -[VERTICES] -;Link X-Coord Y-Coord - -[LABELS] -;X-Coord Y-Coord Label & Anchor Node - -[BACKDROP] - DIMENSIONS 900.000 900.000 3100.000 3100.000 - UNITS None - FILE - OFFSET 0.00 0.00 - -[END] diff --git a/docs/notebooks/networks/Net2LoopsCMflat.inp b/docs/notebooks/networks/Net2LoopsCMflat.inp deleted file mode 100644 index 0f05773..0000000 --- a/docs/notebooks/networks/Net2LoopsCMflat.inp +++ /dev/null @@ -1,145 +0,0 @@ -[TITLE] -shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) - -[JUNCTIONS] -;ID Elev Demand Pattern - 2 0.00 27.77 ; - 3 0.00 27.77 ; - 4 0.00 33.33 ; - 5 0.00 75.00 ; - 6 0.00 91.67 ; - 7 0.00 55.55 ; - -[RESERVOIRS] -;ID Head Pattern - 1 210.00 ; - -[TANKS] -;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow - -[PIPES] -;ID Node1 Node2 Length Diameter Roughness MinorLoss Status - 1 1 2 1000.00 457.20 0.012 0.00 Open ; - 2 2 3 1000.00 203 0.012 0.00 Open ; - 3 2 4 1000.00 457 0.012 0.00 Open ; - 4 4 5 1000.00 153 0.012 0.00 Open ; - 5 4 6 1000.00 406.40 0.012 0.00 Open ; - 6 6 7 1000.00 254.00 0.012 0.00 Open ; - 7 3 5 1000.00 153 0.012 0.00 Open ; - 8 5 7 1000.00 153 0.012 0.00 Open ; - -[PUMPS] -;ID Node1 Node2 Parameters - -[VALVES] -;ID Node1 Node2 Diameter Type Setting MinorLoss - -[TAGS] - -[DEMANDS] -;Junction Demand Pattern Category - -[STATUS] -;ID Status/Setting - -[PATTERNS] -;ID Multipliers - -[CURVES] -;ID X-Value Y-Value - -[CONTROLS] - - - -[RULES] - - - -[ENERGY] - Global Efficiency 75 - Global Price 0 - Demand Charge 0 - -[EMITTERS] -;Junction Coefficient - -[QUALITY] -;Node InitQual - -[SOURCES] -;Node Type Quality Pattern - -[REACTIONS] -;Type Pipe/Tank Coefficient - - -[REACTIONS] - Order Bulk 1 - Order Tank 1 - Order Wall 1 - Global Bulk 0 - Global Wall 0 - Limiting Potential 0 - Roughness Correlation 0 - -[MIXING] -;Tank Model - -[TIMES] - Duration 0:00 - Hydraulic Timestep 1:00 - Quality Timestep 0:05 - Pattern Timestep 2:00 - Pattern Start 0:00 - Report Timestep 1:00 - Report Start 0:00 - Start ClockTime 12 am - Statistic NONE - -[REPORT] - Status Yes - Summary No - Page 0 - -[OPTIONS] - Units LPS - Headloss C-M - Specific Gravity 1.0 - Viscosity 1.0 - Trials 40 - Accuracy 0.001 - CHECKFREQ 2 - MAXCHECK 10 - DAMPLIMIT 0 - Unbalanced Continue 10 - Pattern 1 - Demand Multiplier 1.0 - Emitter Exponent 0.5 - Quality Chlorine mg/L - Diffusivity 1.0 - Tolerance 0.01 - -[COORDINATES] -;Node X-Coord Y-Coord -2 2000.000 3000.000 -3 1000.000 3000.000 -4 2000.000 2000.000 -5 1000.000 2000.000 -6 2000.000 1000.000 -7 1000.000 1000.000 -1 3000.000 3000.000 - -[VERTICES] -;Link X-Coord Y-Coord - -[LABELS] -;X-Coord Y-Coord Label & Anchor Node - -[BACKDROP] - DIMENSIONS 0.000 0.000 10000.000 10000.000 - UNITS None - FILE - OFFSET 0.00 0.00 - -[END] diff --git a/docs/notebooks/networks/Net2LoopsDW.inp b/docs/notebooks/networks/Net2LoopsDW.inp deleted file mode 100644 index 4b4d9fc..0000000 --- a/docs/notebooks/networks/Net2LoopsDW.inp +++ /dev/null @@ -1,145 +0,0 @@ -[TITLE] -shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) - -[JUNCTIONS] -;ID Elev Demand Pattern - 2 150.00 27.77 ; - 3 160.00 27.77 ; - 4 155.00 33.33 ; - 5 150.00 75.00 ; - 6 165.00 91.67 ; - 7 160.00 55.55 ; - -[RESERVOIRS] -;ID Head Pattern - 1 210.00 ; - -[TANKS] -;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow - -[PIPES] -;ID Node1 Node2 Length Diameter Roughness MinorLoss Status - 1 1 2 1000.00 457.20 0.05 0.00 Open ; - 2 2 3 1000.00 203 0.05 0.00 Open ; - 3 2 4 1000.00 457 0.05 0.00 Open ; - 4 4 5 1000.00 153 0.05 0.00 Open ; - 5 4 6 1000.00 406.40 0.05 0.00 Open ; - 6 6 7 1000.00 254.00 0.05 0.00 Open ; - 7 3 5 1000.00 153 0.05 0.00 Open ; - 8 5 7 1000.00 153 0.05 0.00 Open ; - -[PUMPS] -;ID Node1 Node2 Parameters - -[VALVES] -;ID Node1 Node2 Diameter Type Setting MinorLoss - -[TAGS] - -[DEMANDS] -;Junction Demand Pattern Category - -[STATUS] -;ID Status/Setting - -[PATTERNS] -;ID Multipliers - -[CURVES] -;ID X-Value Y-Value - -[CONTROLS] - - - -[RULES] - - - -[ENERGY] - Global Efficiency 75 - Global Price 0 - Demand Charge 0 - -[EMITTERS] -;Junction Coefficient - -[QUALITY] -;Node InitQual - -[SOURCES] -;Node Type Quality Pattern - -[REACTIONS] -;Type Pipe/Tank Coefficient - - -[REACTIONS] - Order Bulk 1 - Order Tank 1 - Order Wall 1 - Global Bulk 0 - Global Wall 0 - Limiting Potential 0 - Roughness Correlation 0 - -[MIXING] -;Tank Model - -[TIMES] - Duration 0:00 - Hydraulic Timestep 1:00 - Quality Timestep 0:05 - Pattern Timestep 2:00 - Pattern Start 0:00 - Report Timestep 1:00 - Report Start 0:00 - Start ClockTime 12 am - Statistic NONE - -[REPORT] - Status Yes - Summary No - Page 0 - -[OPTIONS] - Units LPS - Headloss D-W - Specific Gravity 1.0 - Viscosity 1.0 - Trials 40 - Accuracy 0.001 - CHECKFREQ 2 - MAXCHECK 10 - DAMPLIMIT 0 - Unbalanced Continue 10 - Pattern 1 - Demand Multiplier 1.0 - Emitter Exponent 0.5 - Quality Chlorine mg/L - Diffusivity 1.0 - Tolerance 0.01 - -[COORDINATES] -;Node X-Coord Y-Coord -2 2000.000 3000.000 -3 1000.000 3000.000 -4 2000.000 2000.000 -5 1000.000 2000.000 -6 2000.000 1000.000 -7 1000.000 1000.000 -1 3000.000 3000.000 - -[VERTICES] -;Link X-Coord Y-Coord - -[LABELS] -;X-Coord Y-Coord Label & Anchor Node - -[BACKDROP] - DIMENSIONS 900.000 900.000 3100.000 3100.000 - UNITS None - FILE - OFFSET 0.00 0.00 - -[END] diff --git a/docs/notebooks/networks/Net2LoopsFlat.inp b/docs/notebooks/networks/Net2LoopsFlat.inp deleted file mode 100644 index aefce28..0000000 --- a/docs/notebooks/networks/Net2LoopsFlat.inp +++ /dev/null @@ -1,145 +0,0 @@ -[TITLE] -shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) - -[JUNCTIONS] -;ID Elev Demand Pattern - 2 0.00 27.77 ; - 3 0.00 27.77 ; - 4 0.00 33.33 ; - 5 0.00 75.00 ; - 6 0.00 91.67 ; - 7 0.00 55.55 ; - -[RESERVOIRS] -;ID Head Pattern - 1 210.00 ; - -[TANKS] -;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow - -[PIPES] -;ID Node1 Node2 Length Diameter Roughness MinorLoss Status - 1 1 2 1000.00 457.20 130.00 0.00 Open ; - 2 2 3 1000.00 203 130.00 0.00 Open ; - 3 2 4 1000.00 457 130.00 0.00 Open ; - 4 4 5 1000.00 153 130.00 0.00 Open ; - 5 4 6 1000.00 406.40 130.00 0.00 Open ; - 6 6 7 1000.00 254.00 130.00 0.00 Open ; - 7 3 5 1000.00 153 130.00 0.00 Open ; - 8 5 7 1000.00 153 130.00 0.00 Open ; - -[PUMPS] -;ID Node1 Node2 Parameters - -[VALVES] -;ID Node1 Node2 Diameter Type Setting MinorLoss - -[TAGS] - -[DEMANDS] -;Junction Demand Pattern Category - -[STATUS] -;ID Status/Setting - -[PATTERNS] -;ID Multipliers - -[CURVES] -;ID X-Value Y-Value - -[CONTROLS] - - - -[RULES] - - - -[ENERGY] - Global Efficiency 75 - Global Price 0 - Demand Charge 0 - -[EMITTERS] -;Junction Coefficient - -[QUALITY] -;Node InitQual - -[SOURCES] -;Node Type Quality Pattern - -[REACTIONS] -;Type Pipe/Tank Coefficient - - -[REACTIONS] - Order Bulk 1 - Order Tank 1 - Order Wall 1 - Global Bulk 0 - Global Wall 0 - Limiting Potential 0 - Roughness Correlation 0 - -[MIXING] -;Tank Model - -[TIMES] - Duration 0:00 - Hydraulic Timestep 1:00 - Quality Timestep 0:05 - Pattern Timestep 2:00 - Pattern Start 0:00 - Report Timestep 1:00 - Report Start 0:00 - Start ClockTime 12 am - Statistic NONE - -[REPORT] - Status Yes - Summary No - Page 0 - -[OPTIONS] - Units LPS - Headloss H-W - Specific Gravity 1.0 - Viscosity 1.0 - Trials 40 - Accuracy 0.001 - CHECKFREQ 2 - MAXCHECK 10 - DAMPLIMIT 0 - Unbalanced Continue 10 - Pattern 1 - Demand Multiplier 1.0 - Emitter Exponent 0.5 - Quality Chlorine mg/L - Diffusivity 1.0 - Tolerance 0.01 - -[COORDINATES] -;Node X-Coord Y-Coord -2 2000.000 3000.000 -3 1000.000 3000.000 -4 2000.000 2000.000 -5 1000.000 2000.000 -6 2000.000 1000.000 -7 1000.000 1000.000 -1 3000.000 3000.000 - -[VERTICES] -;Link X-Coord Y-Coord - -[LABELS] -;X-Coord Y-Coord Label & Anchor Node - -[BACKDROP] - DIMENSIONS 900.000 900.000 3100.000 3100.000 - UNITS None - FILE - OFFSET 0.00 0.00 - -[END] diff --git a/docs/notebooks/networks/Net2Loops_hhl_settings.inp b/docs/notebooks/networks/Net2Loops_hhl_settings.inp deleted file mode 100644 index 5aec6fb..0000000 --- a/docs/notebooks/networks/Net2Loops_hhl_settings.inp +++ /dev/null @@ -1,145 +0,0 @@ -[TITLE] -shamir -- Bragalli, D'Ambrosio, Lee, Lodi, Toth (2008) - -[JUNCTIONS] -;ID Elev Demand Pattern - 2 150.00 27.77 ; - 3 160.00 27.77 ; - 4 155.00 33.33 ; - 5 150.00 75.00 ; - 6 165.00 91.67 ; - 7 160.00 55.55 ; - -[RESERVOIRS] -;ID Head Pattern - 1 210.00 ; - -[TANKS] -;ID Elevation InitLevel MinLevel MaxLevel Diameter MinVol VolCurve Overflow - -[PIPES] -;ID Node1 Node2 Length Diameter Roughness MinorLoss Status - 1 1 2 1000.00 457.20 130.00 0.00 Open ; - 2 2 3 1000.00 203 130.00 0.00 Open ; - 3 2 4 1000.00 457 130.00 0.00 Open ; - 4 4 5 1000.00 153 130.00 0.00 Open ; - 5 4 6 1000.00 406.40 130.00 0.00 Open ; - 6 6 7 1000.00 254.00 130.00 0.00 Open ; - 7 3 5 1000.00 153 130.00 0.00 Open ; - 8 5 7 1000.00 153 130.00 0.00 Open ; - -[PUMPS] -;ID Node1 Node2 Parameters - -[VALVES] -;ID Node1 Node2 Diameter Type Setting MinorLoss - -[TAGS] - -[DEMANDS] -;Junction Demand Pattern Category - -[STATUS] -;ID Status/Setting - -[PATTERNS] -;ID Multipliers - -[CURVES] -;ID X-Value Y-Value - -[CONTROLS] - - - -[RULES] - - - -[ENERGY] - Global Efficiency 75 - Global Price 0 - Demand Charge 0 - -[EMITTERS] -;Junction Coefficient - -[QUALITY] -;Node InitQual - -[SOURCES] -;Node Type Quality Pattern - -[REACTIONS] -;Type Pipe/Tank Coefficient - - -[REACTIONS] - Order Bulk 1 - Order Tank 1 - Order Wall 1 - Global Bulk 0 - Global Wall 0 - Limiting Potential 0 - Roughness Correlation 0 - -[MIXING] -;Tank Model - -[TIMES] - Duration 0:00 - Hydraulic Timestep 1:00 - Quality Timestep 0:05 - Pattern Timestep 2:00 - Pattern Start 0:00 - Report Timestep 1:00 - Report Start 0:00 - Start ClockTime 12 am - Statistic NONE - -[REPORT] - Status Yes - Summary No - Page 0 - -[OPTIONS] - Units LPS - Headloss H-W - Specific Gravity 1.0 - Viscosity 1.0 - Trials 10 - Accuracy 0.1 - CHECKFREQ 2 - MAXCHECK 10 - DAMPLIMIT 0 - Unbalanced STOP - Pattern 1 - Demand Multiplier 1.0 - Emitter Exponent 0.5 - Quality Chlorine mg/L - Diffusivity 1.0 - Tolerance 0.01 - -[COORDINATES] -;Node X-Coord Y-Coord -2 2000.000 3000.000 -3 1000.000 3000.000 -4 2000.000 2000.000 -5 1000.000 2000.000 -6 2000.000 1000.000 -7 1000.000 1000.000 -1 3000.000 3000.000 - -[VERTICES] -;Link X-Coord Y-Coord - -[LABELS] -;X-Coord Y-Coord Label & Anchor Node - -[BACKDROP] - DIMENSIONS 900.000 900.000 3100.000 3100.000 - UNITS None - FILE - OFFSET 0.00 0.00 - -[END] diff --git a/docs/notebooks/sandbox/qubo_poly_solver.ipynb b/docs/notebooks/sandbox/qubo_poly_solver.ipynb deleted file mode 100644 index 124885d..0000000 --- a/docs/notebooks/sandbox/qubo_poly_solver.ipynb +++ /dev/null @@ -1,3200 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define the system " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "# Create a water network model\n", - "inp_file = './networks/Net0.inp'\n", - "# inp_file = './networks/Net2LoopsDW.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the original Cholesky EPANET simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "flow_at_5hr = results.link['flowrate'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, link_attribute=flow_at_5hr, \n", - " node_attribute=pressure_at_5hr, \n", - " node_size=500, \n", - " link_width=5, \n", - " node_labels=True,\n", - " link_cmap=plt.cm.cividis)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([26.477, 22.954], dtype=float32)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_pressure = results.node['pressure'].values[0][:2]\n", - "ref_pressure" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.05, 0.05], dtype=float32)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_rate = results.link['flowrate'].values[0]\n", - "ref_rate" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 26.477, 22.954], dtype=float32)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_values = np.append(ref_rate, ref_pressure)\n", - "ref_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the QUBO Polynomial Solver" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "wn = wntr.network.WaterNetworkModel(inp_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 0.000000 => 100.000000 (res: 3.225806)\n", - "Flow Encoding : -2.000000 => -0.000000 | 0.000000 => 2.000000 (res: 0.064516)\n" - ] - } - ], - "source": [ - "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "nqbit = 5\n", - "step = (2./(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0, var_base_name=\"x\")\n", - "\n", - "nqbit = 5\n", - "step = (100/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", - "\n", - "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)\n", - "net.verify_encoding()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve the system classically" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1. , 1. , 0.999, 0.998])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.create_index_mapping(model)\n", - "net.matrices = net.initialize_matrices(model)\n", - "\n", - "ref_sol, encoded_ref_sol, cvgd = net.classical_solutions()\n", - "ref_sol / ref_values" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.987, 0.987, 1.003, 0.985])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "encoded_ref_sol/ ref_values" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "P0, P1, P2, P3 = net.matrices" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0. , 1.766, 99.077, 0.652])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p0 = P0.reshape(\n", - " -1,\n", - ") + P1[\n", - " :, :2\n", - "].sum(-1)\n", - "p0" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1.766, 1.766, 86.797, 75.168])" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.convert_solution_from_si(ref_sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "\n", - "sampler = SimulatedAnnealingSampler()\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.solve(model, strength=1e6, sampler=sampler, num_sweeps=10000, num_reads=1000)\n", - "sol = net.extract_data_from_model(model)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-9563.109226629138 True\n", - "-9562.710552453995 True\n", - "-9562.517971858382 True\n", - "-9562.497054494917 True\n", - "-9562.44983253628 True\n", - "-9562.44983253628 True\n", - "-9562.435655124485 True\n", - "-9562.36700142175 True\n", - "-9562.33702659607 True\n", - "-9562.293940618634 True\n", - "-9562.272314548492 True\n", - "-9562.245232239366 True\n", - "-9562.216201871634 True\n", - "-9562.144584052265 True\n", - "-9562.137716375291 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False\n", - "990457.5757035092 False\n", - "990464.4891519174 False\n", - "990468.9488407671 False\n", - "990474.6120955572 False\n", - "990487.9531336948 False\n", - "990490.9821678177 False\n", - "990495.7636168599 False\n", - "990584.7526462078 False\n", - "990632.2987249866 False\n", - "990673.3891029209 False\n", - "990689.5721127167 False\n", - "990690.2124982253 False\n", - "990724.6441722289 False\n", - "990753.9539984092 False\n", - "990782.6970554069 False\n", - "990850.6007880569 False\n", - "990865.1441291496 False\n", - "990865.8487880826 False\n", - "991268.7598912641 False\n" - ] - } - ], - "source": [ - "solutions,energies,statuses = net.analyze_sampleset()\n", - "for e,s in zip(energies,statuses):\n", - " print(e,s)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_values, encoded_ref_sol, c='black', s=100, label='Best possible solution')\n", - "for s in solutions[1:5]:\n", - " plt.scatter(ref_values, s, s=50, lw=1, alpha=0.5, edgecolors='w', label='Sampled solution')\n", - "plt.scatter(ref_values, solutions[0], s=50, lw=1, c='blue', edgecolors='w', label='Best sampled solution')\n", - "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.grid(which=\"major\", lw=1)\n", - "plt.grid(which=\"minor\", lw=0.1)\n", - "plt.xlabel('Reference Solution')\n", - "plt.ylabel('QUBO Solution')\n", - "# plt.legend()\n", - "# plt.xlim([0.01,0.1])\n", - "# plt.ylim([0.01,0.1])\n", - "\n", - "# plt.xlim([10,50])\n", - "# plt.ylim([10,50])\n", - "plt.loglog()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "72" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.qubo.qubo_dict.num_variables" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "999998.1773075246\n", - "-2000000.0\n", - "-2000000.0\n", - "1000005.6999586402\n", - "999996.3546150491\n", - "1000000.0\n", - "-2000000.0\n", - "-2000000.0\n", - "-0.03329864724245577\n", - "0.06659729448491154\n", - "-0.06659729448491154\n", - "0.13319458896982309\n", - "999955.6967091897\n", - "0.06659729448491154\n", - "-0.13319458896982309\n", - "0.13319458896982309\n", - "-0.26638917793964617\n", - "-2000000.0\n", - "-2000000.0\n", - "1000000.2257171796\n", - "999999.5443268812\n", - "1000000.0\n", - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "v=[]\n", - "for i in net.qubo.qubo_dict.iter_quadratic():\n", - " v.append((i[2]))\n", - " print(i[2])\n", - "\n", - "plt.hist(v)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "v" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cons:\n", - "mass_balance[J1]: ((expected_demand[J1]-flow[P1])+flow[P2])\n", - "mass_balance[D1]: (expected_demand[D1]-flow[P2])\n", - "approx_darcy_wesibach_headloss[P1]: (((((-(dw_resistance_0[P1]))-(dw_resistance_1[P1]*flow[P1]))-(dw_resistance_2[P1]*(flow[P1]**2.0)))+source_head[R1])-head[J1])\n", - "approx_darcy_wesibach_headloss[P2]: (((((-(dw_resistance_0[P2]))-(dw_resistance_1[P2]*flow[P2]))-(dw_resistance_2[P2]*(flow[P2]**2.0)))+head[J1])-head[D1])\n", - "\n", - "vars:\n", - "flow[P1]: flow[P1]\n", - "flow[P2]: flow[P2]\n", - "head[J1]: head[J1]\n", - "head[D1]: head[D1]\n", - "\n" - ] - } - ], - "source": [ - "print(model.__str__())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Embed the problem" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [], - "source": [ - "import dwave_networkx as dnx\n", - "from minorminer import find_embedding\n", - "from dwave.embedding import embed_qubo, majority_vote, chain_break_frequency" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{('x_004_004', 'x_002_001'): 999996.3546150491,\n", - " ('x_002_001*x_004_004', 'x_002_001'): -2000000.0,\n", - " ('x_002_001*x_004_004', 'x_004_004'): -2000000.0,\n", - " ('x_004_003', 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dnx.draw_pegasus_embedding(target_graph, embedding, node_size=10, width=0.25)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/sandbox/qubo_poly_solver_2loops_cm.ipynb b/docs/notebooks/sandbox/qubo_poly_solver_2loops_cm.ipynb deleted file mode 100644 index 62c6566..0000000 --- a/docs/notebooks/sandbox/qubo_poly_solver_2loops_cm.ipynb +++ /dev/null @@ -1,618 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define the system " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt \n", - "\n", - "# Create a water network model\n", - "# inp_file = './networks/Net0.inp'\n", - "inp_file = './networks/Net2LoopsCMflat.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the original Cholesky EPANET simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "flow_at_5hr = results.link['flowrate'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, link_attribute=flow_at_5hr, \n", - " node_attribute=pressure_at_5hr, \n", - " node_size=500, \n", - " link_width=5, \n", - " node_labels=True,\n", - " link_cmap=plt.cm.cividis)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_pressure = results.node['pressure'].values[0]\n", - "ref_pressure" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.311, 0.051, 0.232, 0.031, 0.168, 0.076, 0.023, -0.021], dtype=float32)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_rate = results.link['flowrate'].values[0]\n", - "ref_rate" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3.111e-01, 5.111e-02, 2.322e-01, 3.108e-02, 1.678e-01, 7.613e-02, 2.334e-02, -2.058e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02, 4.395e-07], dtype=float32)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_values = np.append(ref_rate, ref_pressure)\n", - "ref_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the QUBO Polynomial Solver" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "wn = wntr.network.WaterNetworkModel(inp_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 0.000000 => 1000.000000 (res: 32.258065)\n", - "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.483871)\n" - ] - } - ], - "source": [ - "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "nqbit = 5\n", - "step = (15/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0., var_base_name=\"x\")\n", - "\n", - "nqbit = 5\n", - "step = (1000/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+0.0, var_base_name=\"x\")\n", - "\n", - "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)\n", - "net.verify_encoding()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve the system classically" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([1. , 1. , 1. , 1. , 1. , 1. , 1. , 0.999, 1. , 1.001, 1. , 1.001, 1. , 1.001])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.create_index_mapping(model)\n", - "net.matrices = net.initialize_matrices(model)\n", - "\n", - "ref_sol, encoded_ref_sol, cvgd = net.classical_solutions()\n", - "ref_sol / ref_values[:-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_values[:-1], encoded_ref_sol)\n", - "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", - "plt.grid(which=\"major\", lw=1)\n", - "plt.grid(which=\"minor\", lw=0.1)\n", - "plt.loglog()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "from dwave.samplers import SimulatedAnnealingSampler, TabuSampler, RandomSampler\n", - "\n", - "sampler = SimulatedAnnealingSampler()\n", - "# sampler = TabuSampler()\n", - "# sampler = RandomSampler()\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.solve(model, strength=1e8, num_sweeps=10000, num_reads=100, options={\"sampler\" : sampler})\n", - "sol = net.extract_data_from_model(model)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "solutions,energies,statuses = net.analyze_sampleset()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'QUBO Solution')" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", - "for s in solutions[:1]:\n", - " plt.scatter(ref_values[:-1], s, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", - "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.grid(which=\"major\", lw=1)\n", - "plt.grid(which=\"minor\", lw=0.1)\n", - "plt.xlabel('Reference Solution')\n", - "plt.ylabel('QUBO Solution')\n", - "# plt.legend()\n", - "# plt.xlim([0.01,0.1])\n", - "# plt.ylim([0.01,0.1])\n", - "# plt.loglog()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "net.qubo.verify_quadratic_constraints(net.sampleset)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_values[:-1], encoded_ref_sol, c='black', s=100, label='Best solution')\n", - "plt.scatter(ref_values[:-1], sol, s=50, lw=1, edgecolors='w', label='Sampled solution')\n", - "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (2, 5)))\n", - "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.grid(which=\"major\", lw=1)\n", - "plt.grid(which=\"minor\", lw=0.1)\n", - "plt.xlabel('Reference Solution')\n", - "plt.ylabel('QUBO Solution')\n", - "plt.legend()\n", - "# plt.xlim([0.01,0.1])\n", - "# plt.ylim([0.01,0.1])\n", - "plt.loglog()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 0.000000 => 1000.000000 (res: 32.258065)\n", - "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.483871)\n", - "\n", - "\n", - "Error (%): [ 0. 0. 0. 100. 0. 100. 200. 200. -27.728 -60.821 76.397 100. -14.324 100. 41.318 -33.224 2.05 13.496 9.519 -1.882 17.447 17.085]\n", - "\n", - "\n", - "sol : [ 1.000e+00 1.000e+00 1.000e+00 0.000e+00 1.000e+00 0.000e+00 -1.000e+00 1.000e+00 1.403e+01 2.903e+00 1.935e+00 0.000e+00 6.774e+00 0.000e+00 4.839e-01 9.677e-01 6.452e+02 5.161e+02 5.806e+02 5.484e+02 5.161e+02 4.839e+02]\n", - "ref : [ 1. 1. 1. 1. 1. 1. 1. -1. 10.986 1.805 8.2 1.098 5.925 2.688 0.825 0.726 658.662 596.652 641.733 538.256 625.212 583.576]\n", - "diff: [ 0. 0. 0. 1. 0. 1. 2. -2. -3.046 -1.098 6.265 1.098 -0.849 2.688 0.341 -0.241 13.501 80.523 61.088 -10.131 109.083 99.705]\n", - "\n", - "\n", - "encoded_sol: [ 1.000e+00 1.000e+00 1.000e+00 -1.000e+00 1.000e+00 -1.000e+00 -1.000e+00 1.000e+00 1.403e+01 2.903e+00 1.935e+00 0.000e+00 6.774e+00 0.000e+00 4.839e-01 9.677e-01 6.452e+02 5.161e+02 5.806e+02 5.484e+02 5.161e+02 4.839e+02]\n", - "encoded_ref: [ 1. 1. 1. 1. 1. 1. 1. -1. 11.129 1.935 8.226 0.968 5.806 2.903 0.968 0.968 645.161 580.645 645.161 548.387 612.903 580.645]\n", - "diff : [ 0. 0. 0. 2. 0. 2. 2. -2. -2.903 -0.968 6.29 0.968 -0.968 2.903 0.484 0. 0. 64.516 64.516 0. 96.774 96.774]\n", - "\n", - "\n", - "E sol : -465192.96855230315\n", - "E ref : -468771.68236052594\n", - "Delta E : 3578.7138082227902\n", - "\n", - "\n", - "Residue sol : 110.01868568365907\n", - "Residue ref : 127.77565386613492\n", - "Delta Residue : -17.756968182475845\n" - ] - } - ], - "source": [ - "net.diagnostic_solution(sol, ref_sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "278" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.qubo.qubo_dict.num_variables" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[-236047.34548187256,\n", - " 516.7926635742188,\n", - " 8998.552127838135,\n", - " 70814.37801742554,\n", - " 126931.07403755188,\n", - " 296808.25490379333,\n", - " 409016.99020957947,\n", - " 448656.4262313843,\n", - " 502672.9876270294,\n", - " 527510.6125545502,\n", - " 541492.5247859955,\n", - " 606910.2308559418,\n", - " 631840.239616394,\n", - " 648027.1477527618,\n", - " 757243.5711040497,\n", - " 945453.6881694794,\n", - " 1511504.020833969,\n", - " 1585012.7138996124,\n", - " 1735121.9560184479,\n", - " 1798586.0284118652,\n", - " 2086935.8929595947,\n", - " 2206977.548427582,\n", - " 2236991.7206497192,\n", - " 2547645.1599140167,\n", - " 2733142.436199188,\n", - " 3165489.85830307,\n", - " 3217131.617866516,\n", - " 3472916.33262825,\n", - " 3754277.1072177887,\n", - " 4172746.1062812805,\n", - " 5028493.849399567,\n", - " 5064344.260848999,\n", - " 5443412.204938889,\n", - " 5471703.346788406,\n", - 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- "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/sandbox/qubo_poly_solver_CM.ipynb b/docs/notebooks/sandbox/qubo_poly_solver_CM.ipynb deleted file mode 100644 index e38df42..0000000 --- a/docs/notebooks/sandbox/qubo_poly_solver_CM.ipynb +++ /dev/null @@ -1,436 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define the system " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", - "\n", - "# Create a water network model\n", - "inp_file = './networks/Net0_CM.inp'\n", - "# inp_file = './networks/Net2LoopsDW.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the original Cholesky EPANET simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = wntr.sim.EpanetSimulator(wn)\n", - "results = sim.run_sim()\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([29.994, 29.988], dtype=float32)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_pressure = results.node['pressure'].values[0][:2]\n", - "ref_pressure" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.05, 0.05], dtype=float32)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_rate = results.link['flowrate'].values[0]\n", - "ref_rate" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.05 , 0.05 , 29.994, 29.988], dtype=float32)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_values = np.append(ref_rate, ref_pressure)\n", - "ref_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the QUBO Polynomial Solver" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "wn = wntr.network.WaterNetworkModel(inp_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.000978)\n" - ] - } - ], - "source": [ - "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "nqbit = 9\n", - "step = (0.5/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+1.5, var_base_name=\"x\")\n", - "\n", - "nqbit = 9\n", - "step = (50/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+50.0, var_base_name=\"x\")\n", - "\n", - "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)\n", - "net.verify_encoding()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve the system classically" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([1., 1., 1., 1.])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.create_index_mapping(model)\n", - "net.matrices = net.initialize_matrices(model)\n", - "\n", - "ref_sol, cvgd = net.classical_solutions()\n", - "ref_sol / ref_values" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.000e+00, -2.567e-08, 1.331e-05, 1.025e-05])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.verify_solution(net.convert_solution_from_si(ref_values))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "\n", - "sampler = SimulatedAnnealingSampler()\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.solve(model, options={\"sampler\" : sampler})\n", - "sol = net.extract_data_from_model(model)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.05253301482739726, 0.0511199432260274, 30.00281800391389, 30.00281800391389]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sol" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "net.plot_solution_vs_reference(sol, ref_sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 50.000000 => 100.000000 (res: 0.097847)\n", - "Flow Encoding : -2.000000 => -1.500000 | 1.500000 => 2.000000 (res: 0.000978)\n", - "\n", - "\n", - "Error (%): [ 0. 0. -5.066 -2.24 -0.029 -0.048]\n", - "\n", - "\n", - "sol : [ 1. 1. 1.855 1.805 98.434 98.434]\n", - "ref : [ 1. 1. 1.766 1.766 98.406 98.387]\n", - "diff: [ 0. 0. -0.089 -0.04 -0.028 -0.047]\n", - "\n", - "\n", - "encoded_sol: [ 1. 1. 1.855 1.805 98.434 98.434]\n", - "encoded_ref: [ 1. 1. 1.766 1.766 98.434 98.434]\n", - "diff : [ 0. 0. -0.089 -0.039 0. 0. ]\n", - "\n", - "\n", - "E sol : -2356.9793153814685\n", - "E ref : -2356.9835160507705\n", - "Delta E : 0.004200669302008464\n", - "\n", - "\n", - "Residue sol : 0.07313802788587039\n", - "Residue ref : 0.03388956865892264\n", - "Delta Residue : 0.039248459226947745\n" - ] - } - ], - "source": [ - "net.diagnostic_solution(sol, ref_sol)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run with the intergrated WNTR Solver" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = wntr_quantum.sim.FullQuboPolynomialSimulator(wn, \n", - " flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)\n", - "results = sim.run_sim(solver_options={\"sampler\" : sampler})\n", - "\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb b/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb deleted file mode 100644 index 3aaafa1..0000000 --- a/docs/notebooks/sandbox/qubo_poly_solver_Net2loops.ipynb +++ /dev/null @@ -1,795 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define the system " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "metadata": {} - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wntr\n", - "import wntr_quantum\n", - "import numpy as np\n", - "import os \n", - "\n", - "os.environ[\"EPANET_TMP\"] = \"/home/nico/.epanet_quantum\"\n", - "os.environ[\"EPANET_QUANTUM\"] = \"/home/nico/QuantumApplicationLab/vitens/EPANET\"\n", - "\n", - "# Create a water network model\n", - "# inp_file = '../networks/Net0.inp'\n", - "# inp_file = '../networks/Net2LoopsDW.inp'\n", - "inp_file = '../networks/Net2LoopsCMflat.inp'\n", - "wn = wntr.network.WaterNetworkModel(inp_file)\n", - "\n", - "# Graph the network\n", - "wntr.graphics.plot_network(wn, title=wn.name, node_labels=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the original Cholesky EPANET simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ], - "text/plain": [ - "name 2 3 4 5 6 7 \\\n", - "0 200.732986 181.735184 195.55777 163.834244 190.504684 177.750153 \n", - "\n", - "name 1 \n", - "0 4.394531e-07 " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results.node['pressure']" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "ref_rate = results.link['flowrate'].values[0]\n", - "ref_rate = ref_rate[[0,1,2,6,3,4,7,5]]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3.111e-01, 5.111e-02, 2.322e-01, 2.334e-02, 3.108e-02, 1.678e-01, -2.058e-02, 7.613e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02], dtype=float32)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_values = np.append(ref_rate, ref_pressure)\n", - "ref_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run with the QUBO Polynomial Solver" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "wn = wntr.network.WaterNetworkModel(inp_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 500.000000 => 800.000000 (res: 9.677419)\n", - "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.483871)\n" - ] - } - ], - "source": [ - "from wntr_quantum.sim.solvers.qubo_polynomial_solver import QuboPolynomialSolver\n", - "from qubops.solution_vector import SolutionVector_V2 as SolutionVector\n", - "from qubops.encodings import RangedEfficientEncoding, PositiveQbitEncoding\n", - "\n", - "nqbit = 5\n", - "step = (15/(2**nqbit-1))\n", - "flow_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=0, var_base_name=\"x\")\n", - "\n", - "nqbit = 5\n", - "step = (300/(2**nqbit-1))\n", - "head_encoding = PositiveQbitEncoding(nqbit=nqbit, step=step, offset=+500.0, var_base_name=\"x\")\n", - "\n", - "net = QuboPolynomialSolver(wn, flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)\n", - "net.verify_encoding()" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 10.986, 1.805, 8.2 , 0.824, 1.097, 5.926, -0.727, 2.689, 658.573, 596.244, 641.594, 537.514, 625.015, 583.17 ], dtype=float32)" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.convert_solution_from_si(ref_values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve the system classically" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nico/QuantumApplicationLab/QuantumNewtonRaphson/quantum_newton_raphson/utils.py:74: SparseEfficiencyWarning: spsolve requires A be CSC or CSR matrix format\n", - " warn(\"spsolve requires A be CSC or CSR matrix format\", SparseEfficiencyWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([1. , 1. , 1. , 1. , 1. , 1. , 0.999, 1. ])" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.create_index_mapping(model)\n", - "net.matrices = net.initialize_matrices(model)\n", - "net.verify_solution(net.convert_solution_from_si(ref_values))\n", - "\n", - "ref_sol, cvg = net.classical_solutions(max_iter = 100, tol= 1e-3)\n", - "ref_sol[[0,1,2,6,3,4,7,5]] / ref_values[:8]" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [], - "source": [ - "from wntr_quantum.sim.qubo_hydraulics import create_hydraulic_model_for_qubo\n", - "from dwave.samplers import SteepestDescentSolver\n", - "from dwave.samplers import SimulatedAnnealingSampler\n", - "from dwave.samplers import RandomSampler\n", - "from dwave.samplers import TabuSampler\n", - "from dwave.samplers import TreeDecompositionSampler\n", - "\n", - "sampler = SimulatedAnnealingSampler()\n", - "model, model_updater = create_hydraulic_model_for_qubo(wn)\n", - "net.solve(model, strength=1E8, num_reads=100000, options={\"sampler\" : sampler, })\n", - "sol = net.extract_data_from_model(model)" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1.452e+01, 4.839e-01, 6.774e+00, -4.839e-01, 4.355e+00, 5.323e+00, -4.839e-01, 1.452e+00, 6.645e+02, 6.645e+02, 6.645e+02, 6.839e+02, 6.548e+02, 5.000e+02])" - ] - }, - "execution_count": 131, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.convert_solution_from_si(sol)" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [], - "source": [ - "net.qubo.verify_quadratic_constraints(net.sampleset.lowest())" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_5416/1944863955.py:2: DeprecationWarning: BinaryQuadraticModel.to_networkx_graph() is deprecated since dimod 0.10.0 and will be removed in 0.12.0. Use dimod.to_networkx_graph() instead.\n", - " g = net.qubo.qubo_dict.to_networkx_graph()\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import networkx as nx \n", - "g = net.qubo.qubo_dict.to_networkx_graph()\n", - "nx.draw(g, pos = nx.spring_layout(g))" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.411, 0.014, 0.192, -0.014, 0.123, 0.151, -0.014, 0.041])" - ] - }, - "execution_count": 134, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array(sol)[:8]" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.311, 0.051, 0.232, 0.031, 0.168, 0.076, 0.023, -0.021])" - ] - }, - "execution_count": 135, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_sol[:8]" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 136, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "plt.scatter( ref_sol[:8], np.array(sol)[:8])\n", - "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", - "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 137, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt \n", - "plt.scatter(ref_sol[8:], np.array(sol)[8:])\n", - "plt.axline((0, 0.0), slope=1, color=\"black\", linestyle=(0, (5, 5)))\n", - "plt.axline((0, 0.0), slope=1.05, color=\"grey\", linestyle=(0, (2, 2)))\n", - "plt.axline((0, 0.0), slope=0.95, color=\"grey\", linestyle=(0, (2, 2)))" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3.111e-01, 5.111e-02, 2.322e-01, 2.334e-02, 3.108e-02, 1.678e-01, -2.058e-02, 7.613e-02, 2.007e+02, 1.817e+02, 1.956e+02, 1.638e+02, 1.905e+02, 1.778e+02], dtype=float32)" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ref_values" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Head Encoding : 500.000000 => 800.000000 (res: 9.677419)\n", - "Flow Encoding : -15.000000 => -0.000000 | 0.000000 => 15.000000 (res: 0.483871)\n", - "\n", - "\n", - "Error (%): [ 0. 0. 200. 100. 200. 0. 200. 200. -10.11 19.59 -29.818 100. 67.336 -44.002 -17.364 -33.224 4.988 3.223 -0.534 -19.861 -3.191 4.372]\n", - "\n", - "\n", - "sol : [ 1. 1. -1. 0. -1. 1. -1. 1. 12.097 1.452 10.645 0. 1.935 3.871 0.968 0.968 625.806 577.419 645.161 645.161 645.161 558.065]\n", - "ref : [ 1. 1. 1. 1. 1. 1. 1. -1. 10.986 1.805 8.2 1.098 5.925 2.688 0.825 0.726 658.662 596.652 641.733 538.256 625.212 583.576]\n", - "diff: [ 0. 0. 2. 1. 2. 0. 2. -2. -1.111 0.354 -2.445 1.098 3.99 -1.183 -0.143 -0.241 32.855 19.233 -3.428 -106.905 -19.95 25.511]\n", - "\n", - "\n", - "encoded_sol: [ 1. 1. -1. -1. -1. 1. -1. 1. 12.097 1.452 10.645 0. 1.935 3.871 0.968 0.968 625.806 577.419 645.161 645.161 645.161 558.065]\n", - "encoded_ref: [ 1. 1. 1. 1. 1. 1. 1. -1. 11.129 1.935 8.226 0.968 5.806 2.903 0.968 0.968 654.839 596.774 645.161 538.71 625.806 587.097]\n", - "diff : [ 0. 0. 2. 2. 2. 0. 2. -2. -0.968 0.484 -2.419 0.968 3.871 -0.968 0. 0. 29.032 19.355 0. -106.452 -19.355 29.032]\n", - "\n", - "\n", - "E sol : -34053.441822954344\n", - "R ref : -33184.813353168196\n", - "Delta E : -868.6284697861483\n", - "\n", - "\n", - "Residue sol : 158.63184471222584\n", - "Residue ref : 134.63471105151393\n", - "Delta Residue : 23.99713366071191\n" - ] - } - ], - "source": [ - "net.diagnostic_solution(sol, ref_sol)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run with the intergrated WNTR Solver" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim = wntr_quantum.sim.FullQuboPolynomialSimulator(wn, \n", - " flow_encoding=flow_encoding, \n", - " head_encoding=head_encoding)\n", - "results = sim.run_sim(solver_options={\"sampler\" : sampler})\n", - "\n", - "# Plot results on the network\n", - "pressure_at_5hr = results.node['pressure'].loc[0, :]\n", - "wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=50,\n", - " title='Pressure at 5 hours', node_labels=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [ - { - "ename": "StateTraitMissingError", - "evalue": "input state is missing 'subsamples' on SplatComposer()", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mStateTraitMissingError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[140], line 18\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Solve the problem\u001b[39;00m\n\u001b[1;32m 17\u001b[0m init_state \u001b[38;5;241m=\u001b[39m hybrid\u001b[38;5;241m.\u001b[39mState\u001b[38;5;241m.\u001b[39mfrom_problem(bqm)\n\u001b[0;32m---> 18\u001b[0m final_state \u001b[38;5;241m=\u001b[39m \u001b[43mworkflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43minit_state\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# Print results\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSolution: sample=\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m.samples.first}\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(final_state))\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:440\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 440\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:389\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/thread.py:52\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 52\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:413\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_input_state_traits(state)\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.next\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 413\u001b[0m new_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_output_state_traits(new_state)\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_state\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:525\u001b[0m, in \u001b[0;36mstoppable..next\u001b[0;34m(self, state, **runopts)\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m, state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mrunopts):\n\u001b[0;32m--> 525\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43morig_next\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrunopts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 526\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_signal\u001b[38;5;241m.\u001b[39mclear()\n\u001b[1;32m 527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/flow.py:818\u001b[0m, in \u001b[0;36mLoopUntilNoImprovement.next\u001b[0;34m(self, state, **runopts)\u001b[0m\n\u001b[1;32m 815\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_signal\u001b[38;5;241m.\u001b[39mis_set():\n\u001b[1;32m 817\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 818\u001b[0m output_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrunnable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrunopts\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 819\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m EndOfStream \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 820\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{name}\u001b[39;00m\u001b[38;5;124m Iteration(iterno=\u001b[39m\u001b[38;5;132;01m{iterno}\u001b[39;00m\u001b[38;5;124m) terminating due \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 821\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto \u001b[39m\u001b[38;5;132;01m{exc!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, iterno\u001b[38;5;241m=\u001b[39miterno, exc\u001b[38;5;241m=\u001b[39mexc))\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:433\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:389\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/concurrency.py:55\u001b[0m, in \u001b[0;36mImmediateExecutor.submit\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Blocking version of `Executor.submit()`. Returns a resolved\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124;03m`Future`.\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(result\u001b[38;5;241m=\u001b[39m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(exception\u001b[38;5;241m=\u001b[39mexc)\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:413\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_input_state_traits(state)\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.next\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 413\u001b[0m new_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_output_state_traits(new_state)\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_state\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/flow.py:133\u001b[0m, in \u001b[0;36mBranch.next\u001b[0;34m(self, state, **runopts)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m component \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcomponents:\n\u001b[1;32m 131\u001b[0m state \u001b[38;5;241m=\u001b[39m component\u001b[38;5;241m.\u001b[39mrun(state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mrunopts)\n\u001b[0;32m--> 133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:433\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:389\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/concurrency.py:55\u001b[0m, in \u001b[0;36mImmediateExecutor.submit\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Blocking version of `Executor.submit()`. Returns a resolved\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124;03m`Future`.\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(result\u001b[38;5;241m=\u001b[39m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(exception\u001b[38;5;241m=\u001b[39mexc)\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:403\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.resolve.error\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 403\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 405\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_initialized\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.init\u001b[39m\u001b[38;5;124m'\u001b[39m):\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:375\u001b[0m, in \u001b[0;36mRunnable.error\u001b[0;34m(self, exc)\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21merror\u001b[39m(\u001b[38;5;28mself\u001b[39m, exc):\n\u001b[1;32m 366\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Execute one blocking iteration of an instantiated :class:`Runnable`\u001b[39;00m\n\u001b[1;32m 367\u001b[0m \u001b[38;5;124;03m with an exception as input.\u001b[39;00m\n\u001b[1;32m 368\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 373\u001b[0m \u001b[38;5;124;03m errors must be explicitly silenced.\u001b[39;00m\n\u001b[1;32m 374\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 375\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:400\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.resolve\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 400\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.resolve.error\u001b[39m\u001b[38;5;124m'\u001b[39m):\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:433\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:389\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/concurrency.py:55\u001b[0m, in \u001b[0;36mImmediateExecutor.submit\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Blocking version of `Executor.submit()`. Returns a resolved\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124;03m`Future`.\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(result\u001b[38;5;241m=\u001b[39m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(exception\u001b[38;5;241m=\u001b[39mexc)\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:413\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_input_state_traits(state)\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.next\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 413\u001b[0m new_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_output_state_traits(new_state)\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_state\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/flow.py:303\u001b[0m, in \u001b[0;36mRacingBranches.next\u001b[0;34m(self, state, **runopts)\u001b[0m\n\u001b[1;32m 301\u001b[0m states \u001b[38;5;241m=\u001b[39m States()\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m futures:\n\u001b[0;32m--> 303\u001b[0m states\u001b[38;5;241m.\u001b[39mappend(\u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m states\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:433\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:389\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/thread.py:52\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 52\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:413\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_input_state_traits(state)\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.next\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 413\u001b[0m new_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_output_state_traits(new_state)\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_state\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/flow.py:133\u001b[0m, in \u001b[0;36mBranch.next\u001b[0;34m(self, state, **runopts)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m component \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcomponents:\n\u001b[1;32m 131\u001b[0m state \u001b[38;5;241m=\u001b[39m component\u001b[38;5;241m.\u001b[39mrun(state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mrunopts)\n\u001b[0;32m--> 133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:433\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/concurrent/futures/_base.py:389\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m--> 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/concurrency.py:55\u001b[0m, in \u001b[0;36mImmediateExecutor.submit\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Blocking version of `Executor.submit()`. Returns a resolved\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124;03m`Future`.\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(result\u001b[38;5;241m=\u001b[39m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Present(exception\u001b[38;5;241m=\u001b[39mexc)\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/core.py:410\u001b[0m, in \u001b[0;36mRunnable.dispatch\u001b[0;34m(self, future, **kwargs)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minit(state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28msetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_initialized\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 410\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_input_state_traits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispatch.next\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 413\u001b[0m new_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnext(state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/traits.py:76\u001b[0m, in \u001b[0;36mStateTraits.validate_input_state_traits\u001b[0;34m(self, inp)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m StateDimensionalityError(\n\u001b[1;32m 73\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle state required on input to \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m))\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m trait \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minputs:\n\u001b[0;32m---> 76\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_state_trait\u001b[49m\u001b[43m(\u001b[49m\u001b[43minp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrait\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/vitens_wntr_1/lib/python3.9/site-packages/hybrid/traits.py:54\u001b[0m, in \u001b[0;36mStateTraits.validate_state_trait\u001b[0;34m(self, state, trait, io)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Validate single input/output (`io`) `state` `trait`.\"\"\"\u001b[39;00m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trait \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m state:\n\u001b[0;32m---> 54\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m StateTraitMissingError(\n\u001b[1;32m 55\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m state is missing \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m on \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(io, trait, \u001b[38;5;28mself\u001b[39m))\n", - "\u001b[0;31mStateTraitMissingError\u001b[0m: input state is missing 'subsamples' on SplatComposer()" - ] - } - ], - "source": [ - "import dimod\n", - "import hybrid\n", - "\n", - "# Construct a problem\n", - "bqm = dimod.BinaryQuadraticModel({}, {'ab': 1, 'bc': -1, 'ca': 1}, 0, dimod.SPIN)\n", - "\n", - "# Define the workflow\n", - "iteration = hybrid.RacingBranches(\n", - " hybrid.InterruptableTabuSampler(),\n", - " hybrid.decomposers.EnergyImpactDecomposer(size=2)\n", - " | hybrid.TabuProblemSampler(num_reads=2)\n", - " | hybrid.SplatComposer()\n", - ") | hybrid.ArgMin()\n", - "workflow = hybrid.LoopUntilNoImprovement(iteration, convergence=3)\n", - "\n", - "# Solve the problem\n", - "init_state = hybrid.State.from_problem(bqm)\n", - "final_state = workflow.run(init_state).result()\n", - "\n", - "# Print results\n", - "print(\"Solution: sample={.samples.first}\".format(final_state))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "vitens_wntr_1", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}