diff --git a/.gitignore b/.gitignore
index 014352a..2e2f8c4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,7 @@
 .ipynb_checkpoints
 OpenMRG/notebooks/data
 OpenRainER/notebooks/data
-*.pyc
\ No newline at end of file
+OpenMesh/notebooks/data
+*.pyc
+.DS_Store
+.idea/
\ No newline at end of file
diff --git a/OpenMesh/README.md b/OpenMesh/README.md
new file mode 100644
index 0000000..14332e7
--- /dev/null
+++ b/OpenMesh/README.md
@@ -0,0 +1,69 @@
+# OpenMesh
+
+OpenMesh is a dataset of microwave link and personal weather station (PWS)
+measurements over New York City, covering January 2024. It also includes ASOS reference station
+data for validation. The raw data is sourced from two Zenodo records (jacoby_2025_OpenMesh).
+
+
+## Example files
+
+The root folder contains ready-to-use NetCDF sample files at three time windows.
+File names follow the convention: openmesh_{sensor}_{duration}.nc
+
+CML files (75 links, received signal level in dBm):
+  openmesh_cml_1d.nc       one day
+  openmesh_cml_1w.nc       one week
+  openmesh_cml_20d.nc      20 days
+
+PWS files (35 Weather Underground stations, rainfall, temperature, humidity, wind, pressure):
+  openmesh_wu_pws_1d.nc    one day
+  openmesh_wu_pws_1w.nc    one week
+  openmesh_wu_pws_20d.nc   20 days
+
+ASOS reference station files (airport weather stations, used for comparison and validation):
+  openmesh_asos_ws_20d.nc  20 days
+
+
+## Notebooks
+
+create_OpenMesh_data.ipynb
+  Downloads the raw CML and PWS data from Zenodo into notebooks/data/jacoby_2025_OpenMesh.
+  Generates sample NetCDF files for any time window using the functions in openmesh_data_io.py.
+  Also produces the root example files above. 
+
+analyze_OpenMesh_data.ipynb
+  Loads CML and PWS datasets either from the root example files or by creating them on the fly
+  from the raw data for a custom time window. Provides an overview of the dataset dimensions,
+  link/station metadata, and basic visualizations of signal level and weather variables.
+
+ASOS_opensense.ipynb
+  Downloads and processes ASOS airport station data (JFK, EWR, LGA) for the same time window
+  as the CML and PWS data. Converts the data to the opensense NetCDF format and saves it to
+  the samples folder. Used to generate the reference files for validation.
+
+
+## Helper modules
+
+openmesh_data_io.py
+  Central module for all data operations. Key functions:
+  - download_openmesh() and download_pws_wu(): download and extract Zenodo archives
+  - create_openmesh_sample() and create_pws_wu_sample(): slice a time window from raw data
+  - create_asos_sample(): fetch and convert ASOS data for a given time window
+  - create_all_datasets(): run all three in one call, with save and resample options
+  - load_cml() and load_pws_wu(): load the full raw source files into memory
+
+ws_opensense_conversion.py
+  Handles unit conversions and variable transformations for PWS data, converting raw
+  Weather Underground fields to the opensense standard format.
+
+
+## Data folder
+
+The raw downloaded data is stored in notebooks/data/jacoby_2025_OpenMesh and is not tracked
+by git. Running create_OpenMesh_data.ipynb will populate it automatically. The layout is:
+
+  raw/        full source NetCDF files downloaded from Zenodo
+  meta/       station and link metadata CSVs, and HTML map visualizations
+  archived/   original ZIP archives from Zenodo
+  examples/   example notebooks included in the Zenodo release
+  docs/       README from the Zenodo release
diff --git a/OpenMesh/notebooks/ASOS_opensense.ipynb b/OpenMesh/notebooks/ASOS_opensense.ipynb
new file mode 100644
index 0000000..ff79414
--- /dev/null
+++ b/OpenMesh/notebooks/ASOS_opensense.ipynb
@@ -0,0 +1,623 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ASOS → OpenSense Conversion\n",
+    "\n",
+    "Fetch ASOS 1-minute weather data from the IEM API and convert to\n",
+    "OpenSense-format NetCDF.\n",
+    "\n",
+    "**Source:** https://mesonet.agron.iastate.edu/request/asos/1min.phtml  \n",
+    "**Output format:** OpenSense v1.0 — dims `(id, time)`, compressed NetCDF\n",
+    "\n",
+    "| Step | Cell |\n",
+    "|------|------|\n",
+    "| 1 | Imports & config |\n",
+    "| 2 | Discover NYC stations |\n",
+    "| 3 | Fetch raw data |\n",
+    "| 4 | Inspect single station |\n",
+    "| 5 | Convert to OpenSense dataset |\n",
+    "| 6 | Save NetCDF |"
+   ],
+   "id": "daeca567da2197c2"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Imports & config"
+   ],
+   "id": "35236abe7115663d"
+  },
+  {
+   "cell_type": "code",
+   "id": "2d6b1639",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:12:50.408212Z",
+     "start_time": "2026-03-25T09:12:49.516084Z"
+    }
+   },
+   "source": [
+    "import warnings\n",
+    "from datetime import datetime\n",
+    "\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "from OpenMesh.notebooks.ws_opensense_conversion import (\n",
+    "    # paths\n",
+    "    SAMPLE_DIR,  # station discovery\n",
+    "    fetch_asos_stations_nyc,\n",
+    "    load_asos_metadata,\n",
+    "    # fetch\n",
+    "    fetch_asos_raw,\n",
+    "    # convert + save\n",
+    "    to_opensense_dataset,\n",
+    "    save_opensense_dataset,\n",
+    ")\n",
+    "\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# ── time window ───────────────────────────────────────────────\n",
+    "START_DT = datetime(2024, 1, 15)\n",
+    "END_DT   = datetime(2024, 1, 22)\n",
+    "\n",
+    "# ── variables to fetch ────────────────────────────────────────\n",
+    "# full list: print(list(ASOS_AVAILABLE_VARS.keys()))\n",
+    "VARIABLES = [\n",
+    "    'precip_amount',\n",
+    "    'precip_type',\n",
+    "    'precip_category',\n",
+    "    'temperature',\n",
+    "    'wind_speed',\n",
+    "    'wind_direction',\n",
+    "]\n",
+    "\n",
+    "print(f'Period    : {START_DT.date()}  →  {END_DT.date()}')\n",
+    "print(f'Variables : {VARIABLES}')\n",
+    "print(f'SAMPLE_DIR: {SAMPLE_DIR.resolve()}')"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Period    : 2024-01-15  →  2024-01-22\n",
+      "Variables : ['precip_amount', 'precip_type', 'precip_category', 'temperature', 'wind_speed', 'wind_direction']\n",
+      "SAMPLE_DIR: /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/samples\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75286743",
+   "metadata": {},
+   "source": [
+    "## 2. Discover NYC-area ASOS stations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "33001b8d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:12:56.205963Z",
+     "start_time": "2026-03-25T09:12:50.422589Z"
+    }
+   },
+   "source": [
+    "# queries IEM network API + saves to ASOS_META_PATH\n",
+    "# re-run to refresh; loads from CSV if already saved:\n",
+    "# meta = load_asos_metadata()\n",
+    "\n",
+    "meta     = fetch_asos_stations_nyc()\n",
+    "stations = meta.index.tolist()\n",
+    "\n",
+    "print(f'\\nAvailable ({len(stations)}): {stations}')"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Querying NY_ASOS ... 7 stations found\n",
+      "  Querying NJ_ASOS ... 5 stations found\n",
+      "  Querying CT_ASOS ... 1 stations found\n",
+      "\n",
+      "  Total : 13 stations\n",
+      "  ID       Network    Name                                    Lat      Lon   Elev\n",
+      "  ────────────────────────────────────────────────────────────────────────\n",
+      "  BDR      CT_ASOS    Bridgeport/Sikorsky                  41.158  -73.129    5.0\n",
+      "  CDW      NJ_ASOS    CALDWELL/ESSEX CO.                   40.876  -74.283   53.0\n",
+      "  TEB      NJ_ASOS    TETERBORO AIRPORT                    40.859  -74.056    3.0\n",
+      "  MMU      NJ_ASOS    MORRISTOWN MUNI                      40.799  -74.415   57.0\n",
+      "  EWR      NJ_ASOS    Newark Intl                          40.683  -74.169    2.0\n",
+      "  LDJ      NJ_ASOS    Linden                               40.617  -74.245    7.0\n",
+      "  HPN      NY_ASOS    WHITE PLAINS                         41.067  -73.707  134.0\n",
+      "  ISP      NY_ASOS    ISLIP/MACARTHUR                      40.794  -73.102   30.0\n",
+      "  LGA      NY_ASOS    New York/LaGuardia                   40.779  -73.880    9.0\n",
+      "  NYC      NY_ASOS    NEW YORK CITY                        40.779  -73.969   27.0\n",
+      "  FRG      NY_ASOS    FARMINGDALE/REPUBLC                  40.729  -73.413   25.0\n",
+      "  JRB      NY_ASOS    Manhattan - Wall Street              40.701  -74.009    0.0\n",
+      "  JFK      NY_ASOS    NEW YORK/JF KENNEDY                  40.639  -73.762    7.0\n",
+      "\n",
+      "  Saved : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/meta/ASOS_stations.csv\n",
+      "\n",
+      "Available (13): ['BDR', 'CDW', 'TEB', 'MMU', 'EWR', 'LDJ', 'HPN', 'ISP', 'LGA', 'NYC', 'FRG', 'JRB', 'JFK']\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "code",
+   "id": "0242d95b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:12:56.284993Z",
+     "start_time": "2026-03-25T09:12:56.276426Z"
+    }
+   },
+   "source": [
+    "\n",
+    "\n",
+    "asos_meta = load_asos_metadata()\n",
+    "# IDs must match the index: CDW, TEB, MMU, EWR, LDJ, LGA, NYC, JRB, JFK\n",
+    "selected_stations = ['JFK', 'EWR', 'LGA', 'NYC']\n",
+    "\n",
+    "print(f\"Selected: {selected_stations}\")\n",
+    "print(asos_meta.loc[selected_stations])"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selected: ['JFK', 'EWR', 'LGA', 'NYC']\n",
+      "                           Name  Latitude  Longitude  Elevation  Network\n",
+      "Station ID                                                              \n",
+      "JFK         NEW YORK/JF KENNEDY   40.6386   -73.7622        7.0  NY_ASOS\n",
+      "EWR                 Newark Intl   40.6827   -74.1693        2.0  NJ_ASOS\n",
+      "LGA          New York/LaGuardia   40.7794   -73.8803        9.0  NY_ASOS\n",
+      "NYC               NEW YORK CITY   40.7790   -73.9692       27.0  NY_ASOS\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e72c0dff",
+   "metadata": {},
+   "source": [
+    "## 3. Fetch raw ASOS data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "01174cd8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:13:07.349012Z",
+     "start_time": "2026-03-25T09:12:56.291043Z"
+    }
+   },
+   "source": [
+    "# If you want a shorter window, define END_DT as datetime, not string:\n",
+    "END_DT = datetime(2024, 1, 17)\n",
+    "asos_raw = fetch_asos_raw(\n",
+    "    stations=selected_stations,\n",
+    "    start_dt=START_DT,\n",
+    "    end_dt=END_DT,\n",
+    "    variables=VARIABLES,\n",
+    ")\n",
+    "\n",
+    "print(f'\\nFetched: {list(asos_raw.keys())}')"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Fetching JFK ... 2,760 records\n",
+      "  Fetching EWR ... 1,392 records\n",
+      "  Fetching LGA ... 2,880 records\n",
+      "  Fetching NYC ... 1,795 records\n",
+      "\n",
+      "Fetched: ['JFK', 'EWR', 'LGA', 'NYC']\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a5f9302",
+   "metadata": {},
+   "source": [
+    "## 4. Inspect single station"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "892fc56e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:13:07.364571Z",
+     "start_time": "2026-03-25T09:13:07.356805Z"
+    }
+   },
+   "source": [
+    "station_id = 'JFK'   # change to any fetched station\n",
+    "\n",
+    "if station_id not in asos_raw:\n",
+    "    print(f'Not in asos_raw. Available: {list(asos_raw.keys())}')\n",
+    "else:\n",
+    "    df = asos_raw[station_id]\n",
+    "\n",
+    "    # metadata\n",
+    "    if station_id in meta.index:\n",
+    "        row = meta.loc[station_id]\n",
+    "        print(f'Station  : {station_id}  —  {row[\"Name\"]}')\n",
+    "        print(f'Lat/Lon  : {row[\"Latitude\"]:.4f}, {row[\"Longitude\"]:.4f}')\n",
+    "        print(f'Elev     : {row[\"Elevation\"]:.1f} m')\n",
+    "        print(f'Network  : {row[\"Network\"]}')\n",
+    "\n",
+    "    # data\n",
+    "    print(f'\\nRecords  : {len(df):,}')\n",
+    "    print(f'Time     : {str(df.index[0])[:19]}  →  {str(df.index[-1])[:19]}')\n",
+    "    print(f'Columns  : {df.columns.tolist()}')\n",
+    "    if 'precip_amount' in df.columns:\n",
+    "        print(f'Rain sum : {df[\"precip_amount\"].sum():.2f} mm')\n",
+    "    print()\n",
+    "    print(df.head(10))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Station  : JFK  —  NEW YORK/JF KENNEDY\n",
+      "Lat/Lon  : 40.6386, -73.7622\n",
+      "Elev     : 7.0 m\n",
+      "Network  : NY_ASOS\n",
+      "\n",
+      "Records  : 2,760\n",
+      "Time     : 2024-01-15 00:00:00  →  2024-01-16 23:59:00\n",
+      "Columns  : ['station_id', 'precip_amount', 'precip_type', 'precip_category', 'temperature', 'wind_speed', 'wind_direction']\n",
+      "Rain sum : 10.16 mm\n",
+      "\n",
+      "                    station_id  precip_amount precip_type precip_category  \\\n",
+      "datetime                                                                    \n",
+      "2024-01-15 00:00:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:01:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:02:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:03:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:04:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:05:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:06:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:07:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:08:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:09:00        JFK            0.0          NP             dry   \n",
+      "\n",
+      "                     temperature  wind_speed  wind_direction  \n",
+      "datetime                                                      \n",
+      "2024-01-15 00:00:00     0.000000    10.28880             279  \n",
+      "2024-01-15 00:01:00     0.000000     9.77436             280  \n",
+      "2024-01-15 00:02:00     0.000000    10.80324             288  \n",
+      "2024-01-15 00:03:00    -0.555556    11.31768             289  \n",
+      "2024-01-15 00:04:00     0.000000    10.28880             284  \n",
+      "2024-01-15 00:05:00     0.000000     8.74548             286  \n",
+      "2024-01-15 00:06:00     0.000000     8.74548             287  \n",
+      "2024-01-15 00:07:00     0.000000    10.80324             289  \n",
+      "2024-01-15 00:08:00     0.000000    10.80324             290  \n",
+      "2024-01-15 00:09:00    -0.555556    10.28880             290  \n"
+     ]
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5c5f98d",
+   "metadata": {},
+   "source": [
+    "## 5. Convert to OpenSense dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "616402f3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:13:07.568001Z",
+     "start_time": "2026-03-25T09:13:07.368945Z"
+    }
+   },
+   "source": [
+    "ds_asos = to_opensense_dataset(\n",
+    "    data=asos_raw,\n",
+    "    source='asos',\n",
+    "    variables=VARIABLES,\n",
+    "    meta=meta,\n",
+    "    start_dt=START_DT,\n",
+    "    end_dt=END_DT,\n",
+    ")\n",
+    "\n",
+    "print(f'Shape    : {dict(ds_asos.sizes)}')\n",
+    "print(f'Time     : {str(ds_asos.time.values[0])[:19]}  →  {str(ds_asos.time.values[-1])[:19]}')\n",
+    "print(f'Stations : {ds_asos.id.values}')\n",
+    "print(f'Vars     : {list(ds_asos.data_vars)}')\n",
+    "print(ds_asos)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape    : {'id': 4, 'time': 2880}\n",
+      "Time     : 2024-01-15T00:00:00  →  2024-01-16T23:59:00\n",
+      "Stations : ['JFK' 'EWR' 'LGA' 'NYC']\n",
+      "Vars     : ['precip_amount', 'precip_type', 'precip_category', 'temperature', 'wind_speed', 'wind_direction']\n",
+      "<xarray.Dataset> Size: 576kB\n",
+      "Dimensions:          (id: 4, time: 2880)\n",
+      "Coordinates:\n",
+      "  * id               (id) <U3 48B 'JFK' 'EWR' 'LGA' 'NYC'\n",
+      "  * time             (time) datetime64[ns] 23kB 2024-01-15 ... 2024-01-16T23:...\n",
+      "    lat              (id) float64 32B 40.64 40.68 40.78 40.78\n",
+      "    lon              (id) float64 32B -73.76 -74.17 -73.88 -73.97\n",
+      "    elev             (id) float64 32B 7.0 2.0 9.0 27.0\n",
+      "Data variables:\n",
+      "    precip_amount    (id, time) float64 92kB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
+      "    precip_type      (id, time) object 92kB 'NP' 'NP' 'NP' ... 'NP' 'NP' 'NP'\n",
+      "    precip_category  (id, time) object 92kB 'dry' 'dry' 'dry' ... 'dry' 'dry'\n",
+      "    temperature      (id, time) float64 92kB 0.0 0.0 0.0 ... -2.222 -2.222\n",
+      "    wind_speed       (id, time) float64 92kB 10.29 9.774 10.8 ... 6.688 6.688\n",
+      "    wind_direction   (id, time) float64 92kB 279.0 280.0 288.0 ... 314.0 312.0\n",
+      "Attributes:\n",
+      "    title:        OpenMesh ASOS sample (multi-station)\n",
+      "    source:       asos\n",
+      "    start_date:   2024-01-15\n",
+      "    end_date:     2024-01-17\n",
+      "    time_range:   2024-01-15T00:00:00 / 2024-01-17T00:00:00\n",
+      "    conventions:  OpenSense v1.0\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86d190a8",
+   "metadata": {},
+   "source": [
+    "## 6. Save NetCDF (multi-station + one file per station)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "dd98359e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:13:07.574993Z",
+     "start_time": "2026-03-25T09:13:07.571862Z"
+    }
+   },
+   "source": [
+    "# Set Global Attributes\n",
+    "\n",
+    "# ── note on naming ────────────────────────────────────────────\n",
+    "# ASOS data keeps 'precip_amount' / 'precip_rate' intentionally —\n",
+    "# OpenSense PWS convention uses 'rainfall_amount' / 'rainfall_rate'\n",
+    "# but ASOS measures all precipitation types (rain, snow, etc.)\n",
+    "# so 'precip' is more accurate here. No renaming applied.\n",
+    "\n",
+    "GLOBAL_ATTRS = {\n",
+    "    \"title\":        \"OpenMesh ASOS reference precipitation — NYC\",\n",
+    "    \"institution\":  \"NOAA / IEM\",\n",
+    "    \"source\":       \"NOAA ASOS 1-min via Iowa Environmental Mesonet\",\n",
+    "    \"references\":   \"https://mesonet.agron.iastate.edu/request/asos/1min.phtml\",\n",
+    "    \"history\":      f\"Created {pd.Timestamp.now().strftime('%Y-%m-%d')}\",\n",
+    "    \"comment\":      \"ASOS airport stations — precip_amount/rate kept (not renamed to rainfall_* since ASOS captures all precip types)\",\n",
+    "    \"license\":      \"Public domain (NOAA)\",\n",
+    "    \"conventions\":  \"OpenSense-PWS-v1.0\",\n",
+    "    \"featureType\":  \"timeSeries\",\n",
+    "    \"sensor_type\":  \"ASOS\",\n",
+    "    \"time_zone\":    \"UTC\",\n",
+    "}\n"
+   ],
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "code",
+   "id": "bfb195ab",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:13:07.782903Z",
+     "start_time": "2026-03-25T09:13:07.579606Z"
+    }
+   },
+   "source": [
+    "# ── date range for filenames ───────────────────────────────────\n",
+    "_date_start = START_DT.strftime(\"%Y-%m-%d\")\n",
+    "_date_end   = END_DT.strftime(\"%Y-%m-%d\")\n",
+    "\n",
+    "# ── save multi-station (one flat file) + one file per station ──\n",
+    "ds_asos_sample = to_opensense_dataset(\n",
+    "    data=asos_raw, source=\"asos\",\n",
+    "    variables=VARIABLES, meta=meta,\n",
+    "    start_dt=START_DT, end_dt=END_DT,\n",
+    "    extra_attrs=GLOBAL_ATTRS,\n",
+    ")\n",
+    "path_asos = save_opensense_dataset(\n",
+    "    ds_asos_sample, output_dir=SAMPLE_DIR,\n",
+    "    filename=f\"ASOS_{_date_start}_{_date_end}.nc\",\n",
+    ")\n",
+    "\n",
+    "print(f\"  Multi-station : {path_asos}\")\n",
+    "print(f\"  Shape        : {dict(ds_asos_sample.sizes)}\")\n",
+    "print(f\"  Stations     : {list(ds_asos_sample.id.values)}\")\n",
+    "print(f\"  Vars         : {list(ds_asos_sample.data_vars)}\")\n",
+    "print(ds_asos_sample.sel(id=\"JFK\"))\n",
+    "\n",
+    "# ── one file per station (station name in filename) ───────────\n",
+    "paths_single = []\n",
+    "for sid in ds_asos_sample.id.values:\n",
+    "    ds_one = to_opensense_dataset(\n",
+    "        data={sid: asos_raw[str(sid)]}, source=\"asos\",\n",
+    "        variables=VARIABLES, meta=meta,\n",
+    "        start_dt=START_DT, end_dt=END_DT,\n",
+    "        extra_attrs=GLOBAL_ATTRS,\n",
+    "    )\n",
+    "    p = save_opensense_dataset(\n",
+    "        ds_one, output_dir=SAMPLE_DIR,\n",
+    "        filename=f\"ASOS_{sid}_{_date_start}_{_date_end}.nc\",\n",
+    "    )\n",
+    "    paths_single.append(p)\n",
+    "print(f\"  Single-station files : {[p.name for p in paths_single]}\")\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Saved : ASOS_2024-01-15_2024-01-17.nc  (1.34 MB)\n",
+      "  Multi-station : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/samples/ASOS_2024-01-15_2024-01-17.nc\n",
+      "  Shape        : {'id': 4, 'time': 2880}\n",
+      "  Stations     : [np.str_('JFK'), np.str_('EWR'), np.str_('LGA'), np.str_('NYC')]\n",
+      "  Vars         : ['precip_amount', 'precip_type', 'precip_category', 'temperature', 'wind_speed', 'wind_direction']\n",
+      "<xarray.Dataset> Size: 161kB\n",
+      "Dimensions:          (time: 2880)\n",
+      "Coordinates:\n",
+      "    id               <U3 12B 'JFK'\n",
+      "  * time             (time) datetime64[ns] 23kB 2024-01-15 ... 2024-01-16T23:...\n",
+      "    lat              float64 8B 40.64\n",
+      "    lon              float64 8B -73.76\n",
+      "    elev             float64 8B 7.0\n",
+      "Data variables:\n",
+      "    precip_amount    (time) float64 23kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
+      "    precip_type      (time) object 23kB 'NP' 'NP' 'NP' 'NP' ... 'NP' 'NP' 'NP'\n",
+      "    precip_category  (time) object 23kB 'dry' 'dry' 'dry' ... 'dry' 'dry' 'dry'\n",
+      "    temperature      (time) float64 23kB 0.0 0.0 0.0 ... -2.222 -2.222 -2.222\n",
+      "    wind_speed       (time) float64 23kB 10.29 9.774 10.8 ... 9.774 10.29 10.8\n",
+      "    wind_direction   (time) float64 23kB 279.0 280.0 288.0 ... 322.0 319.0 315.0\n",
+      "Attributes: (12/14)\n",
+      "    title:        OpenMesh ASOS reference precipitation — NYC\n",
+      "    source:       NOAA ASOS 1-min via Iowa Environmental Mesonet\n",
+      "    start_date:   2024-01-15\n",
+      "    end_date:     2024-01-17\n",
+      "    time_range:   2024-01-15T00:00:00 / 2024-01-17T00:00:00\n",
+      "    conventions:  OpenSense-PWS-v1.0\n",
+      "    ...           ...\n",
+      "    history:      Created 2026-03-25\n",
+      "    comment:      ASOS airport stations — precip_amount/rate kept (not rename...\n",
+      "    license:      Public domain (NOAA)\n",
+      "    featureType:  timeSeries\n",
+      "    sensor_type:  ASOS\n",
+      "    time_zone:    UTC\n",
+      "  Saved : ASOS_JFK_2024-01-15_2024-01-17.nc  (0.38 MB)\n",
+      "  Saved : ASOS_EWR_2024-01-15_2024-01-17.nc  (0.22 MB)\n",
+      "  Saved : ASOS_LGA_2024-01-15_2024-01-17.nc  (0.38 MB)\n",
+      "  Saved : ASOS_NYC_2024-01-15_2024-01-17.nc  (0.25 MB)\n",
+      "  Single-station files : ['ASOS_JFK_2024-01-15_2024-01-17.nc', 'ASOS_EWR_2024-01-15_2024-01-17.nc', 'ASOS_LGA_2024-01-15_2024-01-17.nc', 'ASOS_NYC_2024-01-15_2024-01-17.nc']\n"
+     ]
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cf133cd",
+   "metadata": {},
+   "source": [
+    "## 7. Reload & verify"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "b9b947f2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:14:17.558591Z",
+     "start_time": "2026-03-25T09:14:17.534663Z"
+    }
+   },
+   "source": [
+    "ds_check = xr.open_dataset(path_asos)\n",
+    "\n",
+    "print(f'Shape    : {dict(ds_check.sizes)}')\n",
+    "print(f'Stations : {ds_check.id.values}')\n",
+    "print(f'Vars     : {list(ds_check.data_vars)}')\n",
+    "print(ds_check)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape    : {'id': 4, 'time': 2880}\n",
+      "Stations : ['JFK' 'EWR' 'LGA' 'NYC']\n",
+      "Vars     : ['precip_amount', 'precip_type', 'precip_category', 'temperature', 'wind_speed', 'wind_direction']\n",
+      "<xarray.Dataset> Size: 807kB\n",
+      "Dimensions:          (id: 4, time: 2880)\n",
+      "Coordinates:\n",
+      "  * id               (id) <U3 48B 'JFK' 'EWR' 'LGA' 'NYC'\n",
+      "  * time             (time) datetime64[ns] 23kB 2024-01-15 ... 2024-01-16T23:...\n",
+      "    lat              (id) float64 32B ...\n",
+      "    lon              (id) float64 32B ...\n",
+      "    elev             (id) float64 32B ...\n",
+      "Data variables:\n",
+      "    precip_amount    (id, time) float64 92kB ...\n",
+      "    precip_type      (id, time) <U2 92kB ...\n",
+      "    precip_category  (id, time) <U7 323kB ...\n",
+      "    temperature      (id, time) float64 92kB ...\n",
+      "    wind_speed       (id, time) float64 92kB ...\n",
+      "    wind_direction   (id, time) float64 92kB ...\n",
+      "Attributes: (12/14)\n",
+      "    title:        OpenMesh ASOS reference precipitation — NYC\n",
+      "    source:       NOAA ASOS 1-min via Iowa Environmental Mesonet\n",
+      "    start_date:   2024-01-15\n",
+      "    end_date:     2024-01-17\n",
+      "    time_range:   2024-01-15T00:00:00 / 2024-01-17T00:00:00\n",
+      "    conventions:  OpenSense-PWS-v1.0\n",
+      "    ...           ...\n",
+      "    history:      Created 2026-03-25\n",
+      "    comment:      ASOS airport stations — precip_amount/rate kept (not rename...\n",
+      "    license:      Public domain (NOAA)\n",
+      "    featureType:  timeSeries\n",
+      "    sensor_type:  ASOS\n",
+      "    time_zone:    UTC\n"
+     ]
+    }
+   ],
+   "execution_count": 10
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "openmesh",
+   "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": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/OpenMesh/notebooks/analyze_OpenMesh_data.ipynb b/OpenMesh/notebooks/analyze_OpenMesh_data.ipynb
new file mode 100644
index 0000000..13b55a4
--- /dev/null
+++ b/OpenMesh/notebooks/analyze_OpenMesh_data.ipynb
@@ -0,0 +1,1854 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "837f8fda",
+   "metadata": {},
+   "source": [
+    "# OpenMesh Example Notebook\n",
+    "\n",
+    "Download, explore and analyse the **OpenMesh** dataset with the\n",
+    "[OpenSense](https://github.com/OpenSenseAction) ecosystem.\n",
+    "\n",
+    "| Resource | Link |\n",
+    "|---|---|\n",
+    "| Data — CML (Zenodo) | https://doi.org/10.5281/zenodo.15287692 |\n",
+    "| Data — PWS (Zenodo) | https://doi.org/10.5281/zenodo.17508286 |\n",
+    "| Paper (ESSD) | https://doi.org/10.5194/essd-2025-238 |\n",
+    "| Code (GitHub) | https://github.com/drorjac/OpenMesh |\n",
+    "\n",
+    "**Three complementary sources:**\n",
+    "\n",
+    "| Dataset | Source | Description |\n",
+    "|---------|--------|-------------|\n",
+    "| **CML** | NYC Mesh | RSL time-series — 75 links, 3 sublinks each |\n",
+    "| **ASOS** | NOAA | Official 1-min reference rain gauges (JFK, LGA, NYC) |\n",
+    "| **PWS** | Weather Underground | Crowdsourced personal weather stations |\n",
+    "\n",
+    "**OpenSense packages used:** [`poligrain`](https://github.com/OpenSenseAction/poligrain) (CML plotting),\n",
+    "[`pypwsqc`](https://github.com/OpenSenseAction/pypwsqc) (PWS QC),\n",
+    "[`xarray`](https://docs.xarray.dev), `matplotlib`, `pyproj`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f37295d4",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "3aa88416",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:26.200936Z",
+     "start_time": "2026-04-03T13:09:26.198053Z"
+    }
+   },
+   "source": [
+    "# Uncomment to install\n",
+    "# !pip install poligrain pypwsqc xarray netCDF4 requests tqdm pyproj matplotlib numpy pandas scipy"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aab37ca9",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "0ece6bc7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:27.988998Z",
+     "start_time": "2026-04-03T13:09:26.207344Z"
+    }
+   },
+   "source": [
+    "import warnings\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "from pyproj import Geod\n",
+    "\n",
+    "import poligrain as plg\n",
+    "\n",
+    "\n",
+    "import openmesh_data_io as omio\n",
+    "import ws_opensense_conversion as wsoc\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "%matplotlib inline\n",
+    "\n",
+    "print(f\"poligrain : {plg.__version__}\")\n",
+    "print(f\"xarray    : {xr.__version__}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "poligrain : 0.3.1\n",
+      "xarray    : 2025.9.1\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfcbbff5",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Download datasets\n",
+    "\n",
+    "Download CML and PWS datasets from their respective Zenodo records (skips if already present):\n",
+    "- **CML**: https://doi.org/10.5281/zenodo.15287692\n",
+    "- **PWS**: https://doi.org/10.5281/zenodo.17508286\n",
+    "\n",
+    "Then extract a sample for the chosen time window."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "f6390d939763dbfe",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:28.051642Z",
+     "start_time": "2026-04-03T13:09:28.046687Z"
+    }
+   },
+   "source": [
+    "SAMPLE_DIR     = omio.SAMPLE_DIR\n",
+    "ASOS_META_PATH = wsoc.ASOS_META_PATH\n",
+    "print(f\"Data dir: {omio.DATA_DIR}\")\n",
+    "\n",
+    "# Download from Zenodo — skips if files already present\n",
+    "omio.download_openmesh()   # → notebooks/data/raw/ds_openmesh.nc\n",
+    "omio.download_pws_wu()     # → notebooks/data/raw/pws_wu_os.nc\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data dir: /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh\n",
+      "  CML found locally  : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\n",
+      "  PWS found locally  : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:28.147821Z",
+     "start_time": "2026-04-03T13:09:28.064097Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Paths to pre-built example files in the main OpenMesh folder\n",
+    "ROOT_DIR  = Path(\"../\")\n",
+    "CML_FILE  = ROOT_DIR / \"openmesh_cml_20d.nc\"\n",
+    "PWS_FILE  = ROOT_DIR / \"openmesh_wu_pws_20d.nc\"\n",
+    "ASOS_FILE = ROOT_DIR / \"openmesh_asos_ws_20d.nc\"\n",
+    "# ── Mode ────────────────────────────────────────────\n",
+    "# Set READ_FROM_FILES = True to load existing .nc files from the main folder,\n",
+    "READ_FROM_FILES = True\n",
+    "\n",
+    "if READ_FROM_FILES:\n",
+    "    ds_cmls = xr.open_dataset(CML_FILE)\n",
+    "    ds_pws  = xr.open_dataset(PWS_FILE)\n",
+    "    ds_asos = xr.open_dataset(ASOS_FILE)\n",
+    "\n",
+    "    print(f\"Loaded CML : {CML_FILE}\")\n",
+    "    print(f\"Loaded PWS : {PWS_FILE}\")\n",
+    "    print(f\"Loaded ASOS: {ASOS_FILE}\")\n",
+    "\n",
+    "# lon/lat are NaN in the pre-built file (ID mismatch in the creation pipeline:\n",
+    "# data uses short IDs like \"JFK\" but the metadata CSV uses ICAO IDs like \"KJFK\").\n",
+    "# Assign the known station coordinates so spatial operations work correctly.\n",
+    "ASOS_COORDS = {\n",
+    "    \"JFK\": (40.6386, -73.7622),\n",
+    "    \"EWR\": (40.6925, -74.1687),\n",
+    "    \"LGA\": (40.7794, -73.8803),\n",
+    "    \"NYC\": (40.7790, -73.9690),\n",
+    "}\n",
+    "ds_asos = ds_asos.assign_coords(\n",
+    "    lat=(\"id\", [ASOS_COORDS[s][0] for s in ds_asos.id.values]),\n",
+    "    lon=(\"id\", [ASOS_COORDS[s][1] for s in ds_asos.id.values]),\n",
+    ")\n"
+   ],
+   "id": "cfc28bf9f7c72528",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded CML : ../openmesh_cml_20d.nc\n",
+      "Loaded PWS : ../openmesh_wu_pws_20d.nc\n",
+      "Loaded ASOS: ../openmesh_asos_ws_20d.nc\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "code",
+   "id": "532c02be",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:28.155399Z",
+     "start_time": "2026-04-03T13:09:28.152869Z"
+    }
+   },
+   "source": [
+    "CREATE_DATA = False\n",
+    "if CREATE_DATA:\n",
+    "    # Create samples for a specific time window.\n",
+    "    START_TIME = \"2024-01-15\"\n",
+    "    END_TIME   = \"2024-01-30\"\n",
+    "\n",
+    "    datasets = omio.create_all_datasets(\n",
+    "        start_time=START_TIME,\n",
+    "        end_time=END_TIME,\n",
+    "        save=True,\n",
+    "    )\n",
+    "    ds_cmls = datasets[\"cml\"]\n",
+    "    ds_pws  = datasets[\"pws\"]\n",
+    "    ds_asos = datasets[\"asos\"]"
+   ],
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de2f2604",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## CML Data — Microwave Link Signal Levels\n",
+    "\n",
+    "### Dataset structure\n",
+    "\n",
+    "OpenSense v1.0 format — dims `(cml_id, sublink_id, time)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "4ebd307e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:28.171010Z",
+     "start_time": "2026-04-03T13:09:28.164419Z"
+    }
+   },
+   "source": [
+    "print(ds_cmls)\n",
+    "\n",
+    "t0  = pd.Timestamp(ds_cmls.time.values[0])\n",
+    "t1  = pd.Timestamp(ds_cmls.time.values[-1])\n",
+    "dt  = (ds_cmls.time.values[1] - ds_cmls.time.values[0]) / np.timedelta64(1, \"s\")\n",
+    "print(f\"\\n{ds_cmls.sizes}  |  {t0.date()} → {t1.date()}  |  {dt:.0f} s  |  vars: {list(ds_cmls.data_vars)}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset> Size: 26MB\n",
+      "Dimensions:       (cml_id: 75, sublink_id: 3, time: 28801)\n",
+      "Coordinates:\n",
+      "  * time          (time) datetime64[ns] 230kB 2024-01-12 ... 2024-02-01\n",
+      "  * sublink_id    (sublink_id) object 24B 'sublink_1' 'sublink_2' 'sublink_3'\n",
+      "    site_0_lat    (cml_id) float32 300B ...\n",
+      "    site_0_lon    (cml_id) float32 300B ...\n",
+      "    site_1_lat    (cml_id) float32 300B ...\n",
+      "    site_1_lon    (cml_id) float32 300B ...\n",
+      "    length        (cml_id) float32 300B ...\n",
+      "    frequency     (cml_id, sublink_id) float32 900B ...\n",
+      "    polarization  (cml_id, sublink_id) object 2kB ...\n",
+      "  * cml_id        (cml_id) <U2 600B '1' '2' '3' '4' '5' ... '72' '73' '74' '75'\n",
+      "Data variables:\n",
+      "    rsl           (cml_id, sublink_id, time) float32 26MB ...\n",
+      "Attributes: (12/15)\n",
+      "    title:                 OpenMesh CML sample (20d), start 2024-01-12\n",
+      "    file_author:           Dror Jacoby\n",
+      "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+      "    date:                  2025-11-01\n",
+      "    source:                Community NYC Mesh Wireless Network\n",
+      "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+      "    ...                    ...\n",
+      "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+      "    Conventions:           OpenSense-CML-v1.0\n",
+      "    start_date:            2024-01-12\n",
+      "    end_date:              2024-02-01\n",
+      "    time_range:            2024-01-12T00:00:00 / 2024-02-01T00:00:00\n",
+      "    cml_subset:            all\n",
+      "\n",
+      "Frozen({'cml_id': 75, 'sublink_id': 3, 'time': 28801})  |  2024-01-12 → 2024-02-01  |  60 s  |  vars: ['rsl']\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "code",
+   "id": "9a9dd5c756f01f75",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:28.585463Z",
+     "start_time": "2026-04-03T13:09:28.180729Z"
+    }
+   },
+   "source": [
+    "ds_cmls.rsl.isel(sublink_id=0).resample(time='1h').min().plot.line(x='time', add_legend=False);"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0ec808f",
+   "metadata": {},
+   "source": [
+    "### Frequency & polarization distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "2487746e314bdbf0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:28.796395Z",
+     "start_time": "2026-04-03T13:09:28.594374Z"
+    }
+   },
+   "source": [
+    "freq_vals = ds_cmls.frequency.isel(sublink_id=0).values\n",
+    "freq_vals = freq_vals[~np.isnan(freq_vals)]\n",
+    "lengths_km = ds_cmls.length.values\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "\n",
+    "axes[0].hist(freq_vals,  bins=20, color=\"steelblue\", edgecolor=\"white\")\n",
+    "axes[0].set(xlabel=\"Frequency (MHz)\", ylabel=\"Count\", title=\"Frequency distribution\")\n",
+    "\n",
+    "axes[1].hist(lengths_km, bins=30, color=\"steelblue\", edgecolor=\"white\")\n",
+    "axes[1].axvline(np.median(lengths_km), color=\"red\", linestyle=\"--\",\n",
+    "                label=f\"Median: {np.median(lengths_km):.2f} km\")\n",
+    "axes[1].set(xlabel=\"Link length (km)\", ylabel=\"Count\", title=\"Path length distribution\")\n",
+    "axes[1].legend()\n",
+    "\n",
+    "for ax in axes: ax.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"Frequency  : {freq_vals.min():.0f} – {freq_vals.max():.0f} MHz  |  unique: {np.unique(freq_vals)}\")\n",
+    "print(f\"Length     : {lengths_km.min():.2f} – {lengths_km.max():.2f} km  |  median: {np.median(lengths_km):.2f} km\")"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Frequency  : 5270 – 69120 MHz  |  unique: [ 5270.  5280.  5300.  5310.  5490.  5500.  5505.  5510.  5545.  5550.\n",
+      "  5560.  5570.  5700.  5710.  5750.  5760.  5770.  5795.  5810.  5815.\n",
+      "  5825. 24100. 24200. 58320. 60480. 62640. 64800. 65880. 68040. 69120.]\n",
+      "Length     : 24.50 – 6936.50 km  |  median: 1447.50 km\n"
+     ]
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cc73d27",
+   "metadata": {},
+   "source": [
+    "### CML network map\n",
+    "\n",
+    "`poligrain.scatter_lines()` draws CML paths colored by frequency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d92324ff",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:29.036897Z",
+     "start_time": "2026-04-03T13:09:28.804139Z"
+    }
+   },
+   "source": [
+    "x0, y0 = ds_cmls.site_0_lon.values, ds_cmls.site_0_lat.values\n",
+    "x1, y1 = ds_cmls.site_1_lon.values, ds_cmls.site_1_lat.values\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "lc = plg.plot_map.scatter_lines(\n",
+    "    x0=x0, y0=y0, x1=x1, y1=y1,\n",
+    "    c=ds_cmls.frequency.isel(sublink_id=0).values,\n",
+    "    s=2, cmap=plt.cm.plasma, ax=axes[0],\n",
+    ")\n",
+    "plt.colorbar(lc, ax=axes[0], label=\"Frequency (MHz)\", shrink=0.8)\n",
+    "axes[0].set(xlabel=\"Longitude\", ylabel=\"Latitude\",\n",
+    "            title=f\"CML network — {len(x0)} links, coloured by frequency\")\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "axes[1].scatter(lengths_km, freq_vals, alpha=0.6, edgecolors=\"white\", linewidths=0.3)\n",
+    "axes[1].set(xlabel=\"Link length (km)\", ylabel=\"Frequency (MHz)\",\n",
+    "            title=\"Frequency vs. path length\")\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x600 with 3 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "cell_type": "markdown",
+   "id": "354e6b25",
+   "metadata": {},
+   "source": [
+    "### Data availability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "40dbe0ec",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:29.256735Z",
+     "start_time": "2026-04-03T13:09:29.043972Z"
+    }
+   },
+   "source": [
+    "rsl = ds_cmls[\"rsl\"].isel(sublink_id=0)\n",
+    "valid = ~np.isnan(rsl)\n",
+    "avail_link = valid.mean(dim=\"time\").values\n",
+    "avail_time = valid.mean(dim=\"cml_id\")\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "axes[0].hist(avail_link * 100, bins=20, color=\"teal\", edgecolor=\"white\")\n",
+    "axes[0].axvline(avail_link.mean() * 100, color=\"red\", linestyle=\"--\",\n",
+    "                label=f\"Mean: {avail_link.mean()*100:.1f}%\")\n",
+    "axes[0].set(xlabel=\"Availability (%)\", ylabel=\"Count\", title=\"Per-link RSL availability\")\n",
+    "axes[0].legend()\n",
+    "\n",
+    "axes[1].plot(pd.DatetimeIndex(avail_time.time.values), avail_time.values * 100,\n",
+    "             color=\"steelblue\", linewidth=0.6)\n",
+    "axes[1].set(ylabel=\"Network availability (%)\", title=\"Network-wide availability over time\")\n",
+    "axes[1].xaxis.set_major_formatter(mdates.DateFormatter(\"%b %d\"))\n",
+    "\n",
+    "for ax in axes: ax.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"Mean availability : {avail_link.mean()*100:.1f}%  |  \"\n",
+    "      f\">80%: {(avail_link > 0.8).sum()} / {ds_cmls.sizes['cml_id']} links\")"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean availability : 97.0%  |  >80%: 73 / 75 links\n"
+     ]
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9a8c665",
+   "metadata": {},
+   "source": [
+    "### Excess attenuation\n",
+    "\n",
+    "$$A = \\mathrm{RSL_{baseline}} - \\mathrm{RSL}$$\n",
+    "\n",
+    "Baseline = per-link median RSL (dry-weather reference). Positive $A$ → signal drop → rain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "ff81a8c101f119dc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:29.697233Z",
+     "start_time": "2026-04-03T13:09:29.270942Z"
+    }
+   },
+   "source": [
+    "# QC — remove implausible outliers (top 0.1% of values) from both networks\n",
+    "ds_asos[\"precip_amount\"] = ds_asos[\"precip_amount\"].where(ds_asos[\"precip_amount\"] <= ds_asos[\"precip_amount\"].quantile(0.999))\n",
+    "ds_pws[\"rainfall_amount\"] = ds_pws[\"rainfall_amount\"].where(ds_pws[\"rainfall_amount\"] <= ds_pws[\"rainfall_amount\"].quantile(0.999))\n",
+    "\n",
+    "ds_asos[\"precip_amount\"].resample(time=\"1h\").sum(skipna=True).plot.line(x=\"time\", add_legend=True)\n",
+    "plt.title(\"ASOS hourly accumulated precipitation (mm)\");\n",
+    "plt.show()\n",
+    "\n",
+    "ds_pws[\"rainfall_amount\"].resample(time=\"1h\").sum(skipna=True).plot.line(x=\"time\", add_legend=False)\n",
+    "plt.title(\"PWS hourly accumulated precipitation (mm)\");\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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vT6T7B9oWGBVLdWMxMuleffVV9OnTB1arFe3atYtYSzNs2DDMnTsXP/30E5YtW4Z27drh4IMPBuALdPbt24fffvtNPyiFBjoAkJmZibPPPhtnn302AN+P4GmnnYann34a1157LQ455JA629m6deuw6xwOB5xOJwDfEEwhRMT2d+zYEUCwXqihaqsviuSmm27C8OHDsWjRIowYMQJPP/00jj32WAwcOLDO+/34448YMWIEhg4diueffx6dO3eG3W7HRx99hAcffFB/nYEairoKNqPZJhaFhYWmy3a7vc7rXS5XXJ73n//8J7788kvce++9OPLII5GXl6f/8AT2S10C9Spjx46tdZvS0lJIkgRN09C+ffuw2yNdV5vASUKk+4d+BkM/W4G23nbbbbjtttsiPn6gu2bv3r2Neo9DX5PVakXr1q1j/p7ceuutePrpp3HnnXdiyJAhaNWqFWRZxpVXXhnV+xRJXTV+HTt2DAsI6jtO1CZwuzHANcrKygq7zW63h332A9dH+uw35PsT6f6B5491X7Y0DHRI16dPH33UVW0CgcuSJUuwbNkyDBkyRL+tb9++aNOmDRYvXowlS5agQ4cOehBUl65du+Kqq67CzTffjN9++63eQKc+gYPqrl27wm7buXMnAKBNmzYAggcMt9ttGuES2t8f0JCsyEknnYR+/fph5syZyMnJwcqVK6OqD3j77bdhs9nw6aefmg6ooXNnBGqntm/fji5dukR8LOM2dcnIyAirJwJq3w+N4XA44Ha7w66v70e1vLwcn376Ke6//37cdddd+vVutxulpaVRPXfgfX/qqadqHYHUrl07eL1eSJKE3bt3h90e6braBIKVSPcP/SEO/WwF2nr33XdjzJgxER8/8P0qKipqVM3V7t270alTJ/2yoigoKSmJGCxE4/XXX8ell16q10oF7Nu3L2LdSzQCbdm1a1dYULdz5059fzVW4Hmi/UwlQqBt8XrN6Y5dV9QghxxyCIqKivDVV1/hm2++MY3UkSQJJ554IhYuXIjly5eHZXMqKytRVVUV8XED6exAxqUxsrOzcfTRR+PDDz80nfFomobXX38dnTt31rufAiOK1qxZY3qMTz75pNHtAIAbb7wR8+fPx91334127drpo4HqEpi00WKx6Nc5nc6w4fsjRoyAxWLBM888U+tjHXfcccjPz8ezzz4bccRWQPfu3bFhwwZTAFJSUoLvv/++3vY2VPfu3cP291dffVXrZyNAkiQIIcKGXL/wwgumUUwA9G1Cz3iPP/54FBQUYN26dRg0aFDEf3a7HdnZ2TjqqKPw4Ycfms6oKysrG/TZqKysxLx580zXvfnmm5BlGSeeeGKd9z344IPRq1cv/PLLL7W2NdCFe9ppp2Hx4sUxd8m+8cYbpsvvvvsuFEWJOBIvoLZ9DPjeq9D3af78+WFdLXU9RqiTTjoJAMJOFn766Sf8/vvvGD58eL2PEQ273Y6ePXti06ZNcXm8phAYeNHcQ/5TFTM61CCSJGHo0KF4//33IYQwZXQAX/fVzTffDCFEWKCzfv16nHrqqfjHP/6BIUOGoEOHDti/fz/mz5+P2bNnY+jQoTjuuOPi0s4ZM2bglFNOwbBhw3DbbbfBbrdj1qxZ+PXXX/HWW2/pZ8+nn346CgsLccUVV2Dq1KmwWq14+eWXsW3btri04+KLL8bdd9+Nr7/+GpMmTdJT1HUZNWoUHn/8cfzzn//EVVddhZKSEvz73/8O++Ho3r077rnnHjzwwANwOp248MILkZ+fj3Xr1mHfvn2YMmUKcnJy8Nhjj+HKK6/EySefjAkTJqBdu3bYuHEjfvnlF8ycOROAbyLH5557DhdffDEmTJiAkpISPPLII8jLy4vLfjC65JJLcO+99+K+++7DkCFDsG7dOsycORP5+fl13i8vLw8nnngiHn30UbRp0wbdu3fH0qVL8eKLL4ZlCfr16wcAmD17NnJzc5GRkYEePXqgdevWeOqppzBu3DiUlpZi7NixaNu2Lfbu3YtffvkFe/fu1QPHBx54ACNHjsQpp5yCf/3rX1BVFQ8//DCys7OjPttv3bo1rr32WmzduhUHHXQQFixYgOeffx7XXnstunbtWu/9n3vuOZx22mk49dRTMX78eHTq1AmlpaX4/fffsXLlSrz33nsAgKlTp+Kzzz7DiSeeiHvuuQeHHnooysrKsHDhQtx6663o3bt3nc/z4Ycfwmq14pRTTsFvv/2Ge++9F/3798f5559f631yc3PRrVs3fPzxxxg+fDgKCwv19+WMM87Ayy+/jN69e+Owww7DihUr8Oijj4ZlYg444ABkZmbijTfeQJ8+fZCTk4OOHTtGPOE5+OCDcdVVV+Gpp56CLMs47bTTsGXLFtx7773o0qULbrnllnr3Z7SGDh2Kzz77LG6PF2/Lly9H69atceihhya6KakhgYXQlCQCIwF++umnqLafNWuWACCKiorCblu9erU+ourPP/803bZ//34xbdo0cdJJJ4lOnToJu90usrOzxeGHHy6mTZsmampq6n1uAOL6668Puz50xI4QQnzzzTfipJNOEtnZ2SIzM1Mcc8wx4pNPPgm7748//iiOO+44kZ2dLTp16iTuv/9+8cILL0QcdTVq1KiI7Yr0/AHjx48XVqtVbN++vd7XF/DSSy+Jgw8+WDgcDtGzZ08xY8YM8eKLL4a1SQghXn31VXHkkUeKjIwMkZOTIwYMGBA2imXBggViyJAhIjs7W2RlZYm+ffuKhx9+2LTNK6+8Ivr06SMyMjJE3759xTvvvFPrqKtHH33UdN/ACKn33nvPdH2kz5bb7RZ33HGH6NKli8jMzBRDhgwRq1evjmrU1fbt28W5554rWrVqJXJzc8XIkSPFr7/+GnH/P/nkk6JHjx7CYrGEjexZunSpGDVqlCgsLBQ2m0106tRJjBo1Kqz98+bNE4cddpiw2+2ia9eu4qGHHqp1pF6oIUOGiEMOOUQsWbJEDBo0SDgcDtGhQwdxzz33mEZR1bZPA3755Rdx/vnni7Zt2wqbzSbat28vTjrpJH20Y8C2bdvE5ZdfLtq3by9sNpvo2LGjOP/888WePXtMzxNp1NWKFSvEmWeeKXJyckRubq648MIL9fsZX0/oyLwvvvhCDBgwQDgcDgFAfw/2798vrrjiCtG2bVuRlZUlBg8eLL755puIj/HWW2+J3r17C5vNZhrFFWk/q6oqHn74YXHQQQcJm80m2rRpIy6++GKxbdu2iPs+VOjnuTZffvmlACB+/PHHsPtnZ2eHbV/b84UeM2o71gZe6969e+t9Pk3TRLdu3cJGblHtJCHqyGcTUaN4PB50794dgwcPjjjJG6WvoUOHYt++ffj1118T3ZRaTZ48GVOmTMHevXtZ7xHisMMOw/HHH19n13AifPnllxgxYgR+++23ejN15MMaHaImsHfvXnz77be49tprsWfPHlPxLBElv0ceeQQvv/xyo4q8m8K0adNw+eWXM8hpANboEDWB+fPn47LLLkOHDh0wa9aseoeUE1FyGTlyJB599FFs3rw5YWtuhdq/fz+GDBmC6667LtFNSSnsuiIiIqK0xa4rIiIiSlsMdIiIiChtMdAhIiKitNWii5E1TcPOnTuRm5sb1wUPiYiIqOkIIVBZWYmOHTuaFhiOpEUHOjt37qx1jSAiIiJKbtu2bat3VFyLDnQC68Rs27atSaa6JyIiovirqKhAly5d9N/xurToQCfQXZWXl8dAh4iIKMVEU3bCYmQiIiJKWwx0iIiIKG0x0CEiIqK0xUCHiIiI0hYDHSIiIkpbDHSIiIgobTHQISIiorTFQIeIiIjSFgMdIiIiSlsMdIiIiChtMdAhIiKitMVAh4iIiNIWA500oWkaqisroLlciW4KERFR0mjRq5enk2cffRjFTjcGf/Ulhrz9DmwdOya6SURERAnHjE6aKHa6AQBr+h2CsvffT3BriIiIkgMDnTQk2e2JbgIREVFSYKCTZgQAycZAh4iICGCgk5aY0SEiIvJhoJNmJAhIDgY6REREAAOdNCQxo0NEROTHQCcNSTZboptARESUFBjopBkB1ugQEREFMNBJQ5KF80ASEREBDHTSjtUK+PI6RERExEAnzeR43YBgoENERAQw0ElPDHSIiIgAMNBJS0LTEt0EIiKipMBAJx0xoUNERASAgU56YtcVERERAAY6aYqBDhEREcBAJ+1IADM6REREfgx00owAGOgQERH5MdBJI5JqgyZaQzDQISIiAgBwrYA0klfeGx60Qlm5E/mJbgwREVESSOmMjqIomDRpEnr06IHMzEz07NkTU6dOhdZC55GRNd9ink53ghtCRESUJFI6o/Pwww/j2WefxSuvvIJDDjkEP//8My677DLk5+fjpptuSnTzmp+Q/P8nthlERETJIqUDnWXLluGss87CqFGjAADdu3fHW2+9hZ9//jnBLUsw1ugQEREBSPGuq8GDB+PLL7/Ehg0bAAC//PILvv32W5x++ukJblliSL7B5SxGJiIi8kvpjM6dd96J8vJy9O7dGxaLBaqq4sEHH8SFF14YcXu32w23O1jAUlFR0VxNbVaMc4iIiHxSOqPzzjvv4PXXX8ebb76JlStX4pVXXsG///1vvPLKKxG3nzFjBvLz8/V/Xbp0aeYWN7FAjY7GSIeIiAhI8UDn9ttvx1133YV//OMfOPTQQ3HJJZfglltuwYwZMyJuf/fdd6O8vFz/t23btmZucVNj1xUREZFRSndd1dTUQJbNsZrFYql1eLnD4YDD4WiOpiWUV0l0C4iIiJJDSgc6Z555Jh588EF07doVhxxyCFatWoXHH38cl19+eaKb1uxytAx0sVmx0w14vFKim0NERJQUUjrQeeqpp3DvvffiuuuuQ3FxMTp27Iirr74a9913X6Kb1uzGeo6BNdOCX4TKQIeIiMgvpQOd3NxcPPnkk3jyyScT3ZSEs8ICAOhgk1DFEh0iIiIAKV6MTOEcssRiZCIiIj8GOmnGIQFomUt9ERERhWGgk2YyZIkTBhIREfkx0ElDgqt6EhERAWCgk54Y5xAREQFgoJM2ahBcw0uwRoeIiAgAA5204ZK8+t+yxnl0iIiIAAY6acMCj/63zEU9iYiIADDQSRttUKb/XWPfmbiGEBERJREGOmlCINhdJazOBLaEiIgoeTDQSRuGuhyNbysRERHAQCeNBN9KiW8rERERAAY6acNUfsyMDhEREQAGOulDGLquBN9WIiIigIFOGmHXFRERUSj+IqYJzfhWcsJAIiIiAAx00oKmaaZAx8rly4mIiAAw0EkPQsA4vNyiqYlrCxERURJhoJMGVE2FKdABAx0iIiKAgU5aUBUNxkBHZs8VERERAAY6aUHA3HUVMqsOERFRi8VAJw14FXPXFREREfkw0EkDqqKEXMOgh4iICGCgkxaEosAY3EjsuiIiIgLAQCct+KbNMQQ6ghkdIiIigIFOWlBCMjpERETkw0AnDWiqed4chjxEREQ+DHTSgC/QCYY3rNAhIiLyYaCTBjRVgzAEOm7ZnsDWEBERJQ8GOmlAUUNqdFiMTEREBICBTlrQQpaAYJhDRETkw0AnDQjNHOgQERGRDwOdNKBqmukyQx4iIiIfBjppQHF7IJm6rhjqEBERAQx00oLKRT2JiIgiiinQsVgsKC4uDru+pKQEFoul0Y2ihlFV86KeDHmIiIh8Ygp0hIg8JZ3b7Ybdzjlcmpvi9cI8vDxhTSEiIkoq1oZs/N///hcAIEkSXnjhBeTk5Oi3qaqKr7/+Gr17945vC6levuHlQczoEBER+TQo0HniiScA+DI6zz77rKmbym63o3v37nj22Wfj20Kql8bh5URERBE1KNDZvHkzAGDYsGH48MMP0apVqyZpFDWM6lVg6oUUrDEnIiICGhjoBCxevDje7aBG8I26YnBDREQUKupA59Zbb8UDDzyA7Oxs3HrrrXVu+/jjjze6YRQ9oXoBBIvA2YlFRETkE3Wgs2rVKni9Xv3v2kgSf2abm6/rioiIiEJFHegYu6vYdZVcNIWrlxMREUXCwo40oIXMjMwlIIiIiHxiKkaurq7GQw89hC+//BLFxcX+4c1Bf/31V1waR9HRVK3+jYiIiFqgmAKdK6+8EkuXLsUll1yCDh06sC4nwTTV3HXFd4OIiMgnpkDns88+w/z583H88cfHuz0UA+FVQ7qrGOoQEREBMdbotGrVCoWFhfFuC8UotOuQYQ4REZFPTIHOAw88gPvuuw81NTXxbg/FQISudcVRV0RERABi7Lp67LHHsGnTJrRr1w7du3eHzWYz3b5y5cq4NI6io6pqoptARESUlGIKdM4+++w4N4MaQ2jmQIf5HCIiIp+YAp37778/3u2gxtBCh5cz1CEiIgI4YWBa0NTQjA4DHSIiIiDGjI4sy3XOncOakeYlwgIdIiIiAmIMdObOnWu67PV6sWrVKrzyyiuYMmVKXBpG0ZMQOuoqQQ0hIiJKMjEFOmeddVbYdWPHjsUhhxyCd955B1dccUWjG0bR01iMTEREFFFca3SOPvpofPHFF/F8SIpGSKBjgQAqdiaoMURERMkjboGO0+nEU089hc6dO8frISlaIYOuJEjAn/9LTFuIiIiSSExdV61atTIVIwshUFlZiaysLLz++utxaxxFRwhvyDUSIMf01hIREaWVmH4Nn3zySdNlWZZRVFSEo48+Gq1atYpHu6ghQoqPJYCBDhEREWIMdMaNGxfVdtdddx2mTp2KNm3axPI0FC0tdDg/MzpERERAE08Y+Prrr6OioqIpn4IAiNCUjgAgcS5IIiKiJv01FIITujQHSYTMo8OMDhEREQAuAZEeRIS1rhjoEBERMdBJCyGZMxYjExER+TDQSQdhXYQSIFsS0hQiIqJkkvKBzo4dO3DxxRejdevWyMrKwuGHH44VK1YkulnNK6xGB1AquLAqERFRk/ZvXHzxxcjLy2uyx9+/fz+OP/54DBs2DJ999hnatm2LTZs2oaCgoMmeMxmJ0KmRIcGz29m0by4REVEKiPm38JtvvsFzzz2HTZs24f3330enTp3w2muvoUePHhg8eDAA4JlnnolbQyN5+OGH0aVLF8yZM0e/rnv37k36nMkotOdKAiCFDjknIiJqgWLquvrggw9w6qmnIjMzE6tWrYLb7QYAVFZWYvr06XFtYF3mzZuHQYMG4bzzzkPbtm0xYMAAPP/887Vu73a7UVFRYfqXHiLU6DDQISIiii3QmTZtGp599lk8//zzsNls+vXHHXccVq5cGbfG1eevv/7CM888g169euHzzz/HNddcgxtvvBGvvvpqxO1nzJiB/Px8/V+XLl2ara1NSUAyXZZCLhMREbVUMQU669evx4knnhh2fV5eHsrKyhrbpqhpmoaBAwdi+vTpGDBgAK6++mpMmDCh1i6zu+++G+Xl5fq/bdu2NVtbm5IUVqODCCOxiIiIWp6YAp0OHTpg48aNYdd/++236NmzZ6Mb1ZB29O3b13Rdnz59sHXr1ojbOxwO5OXlmf6lhdCYRrDrioiICIgx0Ln66qtx00034YcffoAkSdi5cyfeeOMN3Hbbbbjuuuvi3cZaHX/88Vi/fr3pug0bNqBbt27N1oakIEWYMJCBDhERUWyjru644w6Ul5dj2LBhcLlcOPHEE+FwOHDbbbfhhhtuiHcba3XLLbfguOOOw/Tp03H++efjxx9/xOzZszF79uxma0MykMJiGoldV0RERAAk0YiVN2tqarBu3Tpomoa+ffsiJycnnm2Lyqeffoq7774bf/75J3r06IFbb70VEyZMiOq+FRUVyM/PR3l5eUp3Y71568040X6uftmpFaPzEBWZo85PYKuIiIiaRkN+vxs1p1xWVhYGDRrUmIdotDPOOANnnHFGQttAREREySmmQMflcuGpp57C4sWLUVxcDE0zj/ppziHmBISPJmfXFRERERBjoHP55Zdj0aJFGDt2LI466ihIEudtSaTQvc9iZCIiIp+YAp358+djwYIFOP744+PdHoqBiJjRiTC3DhERUQsT0/DyTp06ITc3N95toViJ0OHlEiC4ejkREVFMgc5jjz2GO++8E3///Xe820MxiNhzyECHiIgotq6rQYMGweVyoWfPnsjKyjKtdwUApaWlcWkcRSnSPDqs0SEiIoot0LnwwguxY8cOTJ8+He3atWMxcqJFqtHRmNEhIiKKKdD5/vvvsWzZMvTv3z/e7aEYRFytnF1XREREsdXo9O7dG06nM95toVhFHHXFQIeIiCimQOehhx7Cv/71LyxZsgQlJSWoqKgw/aPmFV6NI0GtcCWgJURERMklpq6rkSNHAgCGDx9uul4IAUmSoKrMJjSn0BIpCQAUJRFNISIiSioxBTqLFy+OdzuoEcJrdCTAwlFXREREMQU6Q4YMiXc7qDHC4hzOjExERAQ0INBZs2YN+vXrB1mWsWbNmjq3PeywwxrdMGqICBkdFiMTERFFH+gcfvjh2L17N9q2bYvDDz8ckiRBRFghmzU6zU+KNDkgMzpERETRBzqbN29GUVGR/jclkbBiZGZ0iIiIgAYEOt26ddP//vvvv3HcccfBajXfXVEUfP/996ZtqelJkWYJYEaHiIgotnl0hg0bFnE9q/LycgwbNqzRjaLGkiA0Di8nIiKKKdAJzJcTqqSkBNnZ2Y1uFDUWu66IiIiABg4vHzNmDABfwfH48ePhcDj021RVxZo1a3DcccfFt4UUEwnsuiIiImpQoJOfnw/Al9HJzc1FZmamfpvdbscxxxyDCRMmxLeFVK/wweUSBGt0iIiIGhbozJkzBwDQvXt33HbbbfV2U3333XcYNGiQKfND8SckRFjwioEOERFRTDU6999/f1S1OKeddhp27NgRy1NQg0SYMFBjoENERBRToBOtSBMKUvxJUvh+FszoEBERNW2gQ81D+DM6mr8uxzdhIAMdIiIiBjppIDDS35zFYaBDRETEQCeNBLsKmdEhIiICmjjQiTSpIDWBsIyOBLA+ioiIqOGBjhACf//9N5xOZ1TbUtML1OgE9re/Yidh7SEiIkoWMQU6vXr1wvbt2+vdtrKyEj179oypYRS9QN5MINh1xSCTiIgohkBHlmX06tULJSUlTdEeioGe0TFkcbgEBBERUYw1Oo888ghuv/12/Prrr/FuD8VAH3UljBkdLupJRETUoCUgAi6++GLU1NSgf//+sNvtpjWvAKC0tDQujaPoBOKbQEbHtyIEu66IiIhiCnSefPLJODeDGkU2FyMDElThTVx7iIiIkkRMgc64cePi3Q6Kg0AWR5IkqBq7roiIiGKeR2fTpk2YNGkSLrzwQhQXFwMAFi5ciN9++y1ujaOGMRYjK6zRISIiii3QWbp0KQ499FD88MMP+PDDD1FVVQUAWLNmDe6///64NpCiZxxSrgklgS0hIiJKDjEFOnfddRemTZuGRYsWwW6369cPGzYMy5Yti1vjKDpCCh9errIYmYiIKLZAZ+3atTjnnHPCri8qKuL8OglkzOh4uNYVERFRbIFOQUEBdu3aFXb9qlWr0KlTp0Y3ihom0urlf7jzEtQaIiKi5BFToPPPf/4Td955J3bv3g1JkqBpGr777jvcdtttuPTSS+PdRqqHEL630Th3jgUsRiYiIoop0HnwwQfRtWtXdOrUCVVVVejbty9OPPFEHHfccZg0aVK820hRMnZdZcgMdIiIiGKaR8dms+GNN97A1KlTsWrVKmiahgEDBqBXr17xbh9FQYqw1hURERHFGOgEHHDAAfrq5FKgUISaXWBRTw3G4eWJag0REVHyiDnQefHFF/HEE0/gzz//BAD06tULN998M6688spa7zNw4MAGPYckSZg3bx4LnKMkONKKiIjIJKZA595778UTTzyBiRMn4thjjwUALFu2DLfccgu2bNmCadOmRbzf6tWr8a9//Qs5OTn1PocQAg899BDcbncsTWxRAl1XMGR0mNAhIiKKMdB55pln8Pzzz+PCCy/Urxs9ejQOO+wwTJw4sdZABwBuv/12tG3bNqrneeyxx2JpXoujd10ZipEFIx0iIqLYAh1VVTFo0KCw64844ggoSu1LD2zevBlFRUVRP8+6devQsWPHWJrYskiBwXOGQIclU0RERLENL7/44ovxzDPPhF0/e/ZsXHTRRbXer1u3bg0qWu7SpQssFkssTWxRgqOuBDR/nY7E0eVERI3mVt045+NzMPHLiTj1/VPxzOrw3z5Kbo0qRv7f//6HY445BgCwfPlybNu2DZdeeiluvfVWfbvHH3+81sdwuVxYs2YNiouLoWnmQtrRo0fH2jQCYC3nop5ERI21ZNsSbCzbiI1lGwEAs36ZhWsPvzaxjaIGiSnQ+fXXX/URVJs2bQLgW+eqqKgIv/76q75dXdmbhQsX4tJLL8W+ffvCbpMkCarKlET0ghmdwOzI7LkiImo8wYLHlBdToLN48eKottu+fTs0TYMsh/eQ3XDDDTjvvPNw3333oV27drE0g/wk/xIQEML3TwI0hjpERESx1ehEq2/fvtiyZUvE24qLi3HrrbcyyIkzwYHlREREuiYNdOpK+Y0dOxZLlixpyqdvQSJ0XUlN+tYSERGlhEYtAdEYM2fOxHnnnYdvvvkGhx56KGw2m+n2G2+8MUEtS0GmseTM6BARxQuz5KkvYYHOm2++ic8//xyZmZlYsmSJqXBZkiQGOg0hGTM6PoJrjxERESUu0Jk0aRKmTp2Ku+66K2KxMkVPMp5w+LsLRdP2ShIREaWEJv01rGt4ucfjwQUXXMAgJx5EcK2rYI0OMzpERI0lQYJNs0ISEnLcVkBjV1aqSVgx8rhx4/DOO+805dO3IP6uKxHsTxYsRiYiajThVvHaxul4bdXNeOlxF556v1uim0QNFFPX1eWXX47//Oc/yM3NNV1fXV2NiRMn4qWXXgJQ91pVqqrikUceweeff47DDjssrBi5rhmVycy8erk/uGRGh4io0TJ3SMhXc+Ba/yO8ANpt+ivRTaIGium0/5VXXoHT6Qy73ul04tVXX9Uv17VW1dq1azFgwADIsoxff/0Vq1at0v+tXr06lma1YIZiZL1Gh4EOEVFjeWz+5XQ0LquTqhqU0amoqIAQvh/TyspKZGRk6LepqooFCxagbdu2UT1WtLMrU/3MIY2IdCUREcVAEb7liAQDnZTVoECnoKAAkiRBkiQcdNBBYbdLkoQpU6bErXHUUIYZH1ijQ0TUaCKw7qLg+oupqkGBzuLFiyGEwEknnYQPPvgAhYWF+m12ux3dunWrtSYnlMvlwlNPPYXFixdHXL185cqVDWlay+Zf68pXoRMIdZjSISJqLE3x/zZpDHRSVYMCnSFDhgAANm/ejC5dujRqaPjll1+ORYsWYezYsTjqqKM4HLpR/PtOBIuRWaNDRNR4QvUFOuy6Sl0xjbrq1q0bysrK8OOPP0bMxlx66aX1Psb8+fOxYMECHH/88bE0gQwCEwYai5G51hURUeMJPaPDQCdVxRTofPLJJ7joootQXV2N3NzcsOUbogl0OnXqFDY8nWIVvtZVHVMYERFRlITqP5iy6yplxXTa/69//QuXX345KisrUVZWhv379+v/SktLo3qMxx57DHfeeSf+/vvvWJoQ0YwZMyBJEm6++ea4PWYqMIc5nBmZiChuVA2aqwzCW53ollCMYsro7NixAzfeeCOysrJifuJBgwbB5XKhZ8+eyMrKCpswMNqAKeCnn37C7Nmzcdhhh8XcptQVaR6dhC1jRkSUNhx7q1G9cHqim0GNENOv4amnnoqff/4ZPXv2jPmJL7zwQuzYsQPTp09Hu3btGpWBqKqqwkUXXYTnn38e06ZNi/lxUpdxZmQfVbZF3pSIiKJWsGF3optAjRRToDNq1CjcfvvtWLduHQ499NCwbMzo0aPrfYzvv/8ey5YtQ//+/WNpgsn111+PUaNG4eSTT64z0HG73XC73frlioqKRj93MpBEIKPD4eVERPEkWAaQ8mIKdCZMmAAAmDp1athtkiRBVesv2urdu3fEZSQa6u2338bKlSvx008/1bvtjBkz0nJCQ/1raBheLjHQISJqNE7VkfpiKkbWNK3Wf9EEOQDw0EMP4V//+heWLFmCkpISVFRUmP5FY9u2bbjpppvw+uuvm5ajqM3dd9+N8vJy/d+2bduiep7kF/wi6sXIiWoKEVGaExzWmlISVrE6cuRIAMDw4cNN1wshos4KrVixAsXFxTjiiCP061RVxddff42ZM2fC7XabFhV1OBxwOBxxegXJI9I8Ogx1iIgaT/ZoYdcJCGbNU0hMgU6kLiuj++67r97HiMeinsOHD8fatWtN11122WXo3bs37rzzzlpXTk83kqkYmV1XRETxIivhJ92a0CBzUtaUEVOgM3fuXNNlr9eLzZs3w2q14oADDqg10FmzZg369esHWZb15STq8ttvv+Hggw+G1Rq5mbm5uejXr5/puuzsbLRu3Trs+nQmTMXIAQx0iIgaS/aGz4jMrqvUElOgs2rVqrDrKioqMH78eJxzzjm13m/AgAHYvXs3ioqKonqeY489FqtXr27UMPaWwFihE8zo8ItIRNRYkTI6iqrAZuEUHqkibjU6eXl5mDp1Ks444wxccsklEbcRQuDee++NeqJBj8fT4HYsWbKkwfdJdXo3lQBrdIiI4qD4r51Q3F7IiooNXbqjoKoCNZ0OQ+vtf4St70jJLa7FyGVlZSgvL6/19hNPPBHr16+P+vGOPfZYZGZmxqNpac4wMzJrdIiIGm3Wq7MBAP0dCq69ZwaO2adg5gonKnoDgoFOSokp0Pnvf/9ruiyEwK5du/Daa6/po6kiaYnZlubHjA4RUWNoajCQ+avQN3XJkGJfrU6eAmhc4DOlxBToPPHEE6bLsiyjqKgI48aNw9133x2XhlH0JFNGx3wdERE1jDFjY/EHPVZDEscYCFHyiynQ2bx5c7zbQXHCCQOJiBpHU4KBTKAY2WIYacUandTS6IkAtm/fjh07dsSjLRQrEczogMXIRESNYgxkLP5uKqthIKuqhg85p+QV8xIQU6dORX5+Prp164auXbuioKAADzzwACPdZiaEYYZOIZjRISJqJGHomrIqvtG/FhH5dkp+MXVd/d///R9efPFFPPTQQzj++OMhhMB3332HyZMnw+Vy4cEHH4x3O6kWmkDIvMgcdUVE1BiaYQki2X/ybgx0tAhz61DyiinQeeWVV/DCCy9g9OjR+nX9+/dHp06dcN111zHQaUaqJmAOdXwkiYEOEVEsNM1Qj+NfTNAU6HgbPscbJU5MXVelpaXo3bt32PW9e/dGaWlpoxtF0dOEMOVuOGEgEVHjCEMxcuAvUzFyFItOU/KIKdDp378/Zs6cGXb9zJkz0b9//0Y3iqKnKKq5GJlLPxARNYqmaeiz7w8cvnsNVH923FSjw66rlBJT19UjjzyCUaNG4YsvvsCxxx4LSZLw/fffY9u2bViwYEG820h1UBXFtHo5a3SIiBpH0wQO++IXAMDWwa0BmOfREREW+qTkFVNGZ8iQIdiwYQPOOecclJWVobS0FGPGjMH69etxwgknxLuNVAfNqyC4BARMUwYSEVHDqYZ1FhXVAsA8vFxTWaOTSmJe66pjx44sOk4CilcJhjQC+jw6zOgQEcVGdbv0vxWLL9BhMXLqijnQcblcWLNmDYqLi8PmzjGOxqKmpSqKoUaHw8uJiBpL9UYKdAyRjsfb3E2iRogp0Fm4cCEuvfRS7Nu3L+w2SZKgsiK92WjGjI4E6MXIrEkmIoqJ5nHrf6uyL9CRTRkdBjqpJKYanRtuuAHnnXcedu3aBU3TTP8Y5DQvVTHU6AgpWKHDeXSIiGKiGLqmVEt4jY5goJNSYgp0iouLceutt6Jdu3bxbg81UGhGh/PoEBE1jjGjo1h8HR/GGh3FzRqdVBJToDN27FgsWbIkzk2hWCimUVcSAn1WkmCgQ0QUCy1CjY5p1BWHl6eUmGp0Zs6cifPOOw/ffPMNDj30UNhsNtPtN954Y1waR/UzZnQ4vJyIqPGEEgxkAhkdq3FZCAY6KSWmQOfNN9/E559/jszMTCxZssRUDyJJEgOdZqR4PSHZm9iqkPdurcTWdSU4/OSusFhjSvQREaUF1evG9k6d4LXZ4LFl4LytHnR2GgMd1uikkpgCnUmTJmHq1Km46667IMv8UUwkr6EvWUhSzMPL353+k+9+soSBI7rFr4FERClGuN347oTBAIBWaIsJv7tNtyscXp5SYopSPB4PLrjgAgY5ScBT7TRd1ouRY6zR2betqrFNIiJKaaonWKNjt2SE3a55WIycSmKKVMaNG4d33nkn3m2hGLhdTj17Y1zHnBMGEhHFRlGCgYzXEv4z6eGiniklpq4rVVXxyCOP4PPPP8dhhx0WVoz8+OOPx6VxVD+PywUZwdXLA11XnC+QiCg2xlFXFjn8Z1L1sBg5lcQU6KxduxYDBgwAAPz666+m2zhRXfPyOp0IJFaNwQ2HlxMRxUb1BmtyLFL4z6TCiXFTSkyBzuLFi+PdDoqRx+VGBuz65eByLAx0iIhioRm6ruwRfiY1hTnzVMJq4hSnuN3BeXQkwS4rIqJGUg1LQNhgCb9d1cKuo+QV8+rlP/30E9577z1s3boVnpAK9A8//LDRDaPoGKciF0LoiRyJMSwRUUw0VUHg55GBTuqL6dfw7bffxvHHH49169Zh7ty58Hq9WLduHb766ivk5+fHu41UB83t1etxBIx1Ouy6IiKKhVCDxcb2CD+TmsbjayqJKdCZPn06nnjiCXz66aew2+34z3/+g99//x3nn38+unbtGu82Uh1Ma101foJkIqIWT2jBCQFlOTyjE1w8mVJBTIHOpk2bMGrUKACAw+FAdXU1JEnCLbfcgtmzZ8e1gVQ34TGsXs6VroiIGk0xrGVl94YfTTX2XKWUmAKdwsJCVFZWAgA6deqkDzEvKytDTU1N/FpH9dIU43wOxs4rhjpERLEwzgeYofqOpZ9nbkCppRwAJ2RNNTEFOieccAIWLVoEADj//PNx0003YcKECbjwwgsxfPjwuDaQ6qZ5VP1Lp8EY5vCLSEQUC68x0PEfVH/K2IpiWykAZnRSTUyjrmbOnAmXyzdz5N133w2bzYZvv/0WY8aMwb333hvXBlLdhGroK5bgi3aAmNe6IiJq6bwqEBhslekvPHbKTqhSIALi8TWVxBToFBYW6n/Lsow77rgDd9xxR9h2Dz30EK655hoUFBTE3ECqm9C04FpXQjCjQ0TUSKoiITAPa4Y/tnHJLqiS70xSZS1ySmnSyVamT5+O0tLSpnwKMnzjfBMG8htIRNQYqmHuHD2jY3FCDaTMmTFPKU0a6HAIXtMTWnDNcmGaRYdfRCKiWChq8Kcx039YdVmCXVeMc1ILp89NdUJAn0cHnDCQiKixNOH/aRRApj+J45Rr9K4rwZ/OlMJ3K8UJDaZ5dIIY6BARxUIVvvJVC2RY/cdSl7U62HXF42tKYaCT4lSoeh7Vl9HxBTvsuqpdWXEN5v13NXas35/ophBREtI0C/KlY9FLO16/zi1XQQt0XfH4mlIY6KS4arghS/55dCQBlkXVb9GLv2HbulJ89MSqRDeFiJKQAhnnObMw2OsbelUDAYelWu+6YkYntTRpoHPCCScgMzOzKZ+ixZOEBNn/NiowzJLMarlaVZW5E90EIkpiamBsuV8JNGiSahh1Fb7+FSWvqOfRqaioiPpB8/LyAAALFixoeIuoQSQAFsn3pVOFGqzSkRjo1EaWuW+IqHaK7DBdLoWAV4Jh1BU7Q1JJ1IFOQUEBpHp+PIUQkCQJqqrWuR3FjyRJsETI6EgNyOi0uGkAGOcQUR1UKTSjIyAkGIqRmdFJJVEHOosXL27KdlCsBGDxf+kUqDHNjCy0lhXoMKNDRHVR5fCuKwDBGh2JgU4qiTrQGTJkSFO2g2IlCb3rSoES07zIooUtUFdfZpKIWjZNspkuBwMdrnWViqIOdNasWRP1gx522GExNYZiIAGyv79YEQqM65dHq6V1XUnM6BBRHYRs/mkslbwAjIEOa3RSSdSBzuGHHw5Jkur9UWSNTjOzCL3ryiuphuHl0f+Yay2s64oJHSKqi5DMP437JSeAYI0OZ0ZOLVEHOps3b27KdlCMLBYBSyCjA69+fYNqdFpWnMOMDhHVSUhW00TzbrkMQLBGh93fqSXqQKdbt25N2Q6KlRwcXu41FCM3qOuqxWV0eJAiqs20TTvxfVkVPjz8QGRYWmbmIjTQOdJlQdHmc6Bm+04mJWZ0UkrUgU4k69atw9atW+HxeEzXjx49ulGNouhZDd83BcEuw4ZldFpYoMNjFFGt3t1dimKPgj+qXTg8LyvRzUkMw0GiGC7kVXXDYVXdUHHg54ENEtMuiklMgc5ff/2Fc845B2vXrjXV7QTOlFmj03wkS3CYoyIpMWZ0jBfSP+jh8HKi2rn9GV6lBRwLaiP5s+RboeIFaTWOwBG+G/zzk3EtwdQS07ntTTfdhB49emDPnj3IysrCb7/9hq+//hqDBg3CkiVL4txEqovNH+hoQoMKrdFdV1pLGGrOriuiWrn9BwFvCw10hBD6MaIGgGRx6rdZVd+wcwsDnZQSU0Zn2bJl+Oqrr1BUVARZliHLMgYPHowZM2bgxhtvxKpVXCyxuQSGlmtCAaRYwpyQrqsWcHCT2XVFFJEQAq5ARqeF1e4FCE3ovRNeCNgNM85bNd9EgjyEpJaY3i9VVZGTkwMAaNOmDXbu3AnAV7C8fv36+LWO6mWx+GJVVagAREwZHc2U0Un/gxtHXRFF5jZ8/1tqRkdVgoGOAsBuqH0MZHRkZnRSSkwZnX79+mHNmjXo2bMnjj76aDzyyCOw2+2YPXs2evbsGe82Uh1k/6gIVSgQkgiOFGjIWleG7qqWcGzjqCuiyNyGvuuWWqOjKpoexngB2BDcJ1bVt9gnF4BILTEFOpMmTUJ1dTUAYNq0aTjjjDNwwgknoHXr1njnnXfi2kCqW6DrShUKIBnmRW7Aj7mxRqclDDVnnEMUmTGj01IDHU0VkA1dV1ZDnjwQ6DCjk1oatAREv379IMsyTj31VP36nj17Yt26dSgtLUWrVq14ttzMJP9bqAkFApohkRPb8PKWMNScXVdEkbkMGZ2W23Wl6aOqvDBnb6xqBgDW6KSaqN+vAQMGYN++fQB8wU1JSYnp9sLCQgY5ieAfBhnI6AROPqRYu65aQkaHgQ5RRKaMTgs4FkSiqZoe3CgQphFWFiXQdcVjSCqJOtApKCjQl4HYsmULtBYxDjn5SVKwGFlIAkL/AsaW0WkJb6sxIG8JGSyiaLmZ0YGqBLuuhsgrYTHsBquaCYCBTqqJuuvq3HPPxZAhQ9ChQwdIkoRBgwbBYolckvXXX3/FrYFUNxm+UQCqUGD1/91QLXl4udAEJAsPWkRAaI1OAhuSQJqqQfa/9gKpHDkIzvwvC99vntRC902qijrQmT17NsaMGYONGzfixhtvxIQJE5Cbm9uUbaMoyFJgwkAFmcIBQzly1I9h7LpqCcPLjdXImiogcwgFEQDAyYwOVMVYgOw1r2slfH+zGDm1NGjU1ciRIwEAK1aswE033cRAJwkEzjBUoUKWLHrXVUOmKNdMo67i275kZFwCQlNb5sGcKBLW6ACaoumBjgQFkrAHb/TXPrIYObXENLx8zpw58W4HxciiFyN7YYXF0PMU4/DyFnAWZ1zUk4EOURDn0QFUVRh+GBWowrCwqb7WFaWSmAKd6upqPPTQQ/jyyy9RXFwcVpjMGp3mY8zoWCWr/g1s2Orlhr9b2Flci+iqI4oS59HxDS83ZnSEKaPDrqtUFFOgc+WVV2Lp0qW45JJL9OJkSgyLfyCkpimwSrZGL+rZEo5txteoqS2gr44oSpxHx9d1ZQtM0yEp0IyBjr/Tir94qSWmQOezzz7D/Pnzcfzxx8e7PdRAgWGOqlBgFcaq2lhXL0//g5vp9bLrikhnWuuqBRwLIjEWI0vwQgjDaFa9RoehTiqJqaaqVatWKCwsjHdbGmzGjBk48sgjkZubi7Zt2+Lss89ucYuKSjB0XQkrhGh4MXJLG15uzuik/+slihZrdHxZ3mAGwAtoxmk7/BmdlrlrUlZMgc4DDzyA++67DzU1NfFuT4MsXboU119/PZYvX45FixZBURSMGDFCX4erJZANGR2LcbLyBq11Ffy7JUwYaIx0VHZdEem4erkvoxOYJNAjqRDCBqG5IITKjE6Kiqnr6rHHHsOmTZvQrl07dO/eHTabeaK6lStXxqVx9Vm4cKHp8pw5c9C2bVusWLECJ554YrO0IdFkf6yqCRWABBGo0mnAEhCaca2rFpCuNgZzzOgQBTkNgb/irkpgSxLHmNGZ0ToXhRUa3OWzIMkFQOEVABjopJqYAp2zzz47zs2Ij/LycgCotVvN7XbD7XbrlysqKpqlXU1FUzX9C6dB9ReF+7uuuHp57QRrdIgiMWV0Sv4CcHDiGpMgxoyOIqnQPGUAAKGVxTAdKyWDmAKd+++/P97taDQhBG699VYMHjwY/fr1i7jNjBkzMGXKlGZuWdPRVFWvxRH+/icRw1fQXKKT/j/8goEOUUTGGh1VcdexZfoyZnQUSYHFUIwcOHY0pAaSEi9tJni84YYbsGbNGrz11lu1bnP33XejvLxc/7dt27ZmbGH8CVXopxYCGiRIjR9e3gJKVszzBrWAF0wUJVNGR/EmsCWJoyrB1cu9kgrZGOhABQDIDSgNoMSLOqNTWFiIDRs2oE2bNmjVqlWdXSOlpaVxaVy0Jk6ciHnz5uHrr79G586da93O4XDA4XA0Y8ualuJVIPm/cL46GxlAw884WtrMyMbXqzKjQ6QzzqOjqEoCW5I4mipg1buuFMgIzqMjhBdABmt0UkzUgc4TTzyhr2315JNPNlV7GkQIgYkTJ2Lu3LlYsmQJevTokegmNStF9eoBp4AGQDZ0XUWfrGtpMyNzeDlRZKaMTgsNdHwZHd9xVJFUWDRZz5RrwrdP2HWVWqIOdMaNGxfx70S6/vrr8eabb+Ljjz9Gbm4udu/eDQDIz89HZmZmglvX9FSvuUbH+OWLNaPTAuIc1ugQ1cLNjA5URcDhz5R7JQWyBn+HVSCjw0An1cRUjGzkdDrh9Zr7cvPy8hr7sFF55plnAABDhw41XT9nzhyMHz++WdqQSIriCQY6EJAkGbGMB9Ba2Kgr07xBnEeHSOdWVf1vbwutX9NCMjqyMAQ68P3WsUYntcS8qOedd96Jd999FyUlJWG3q4YvS1NqCfUkdVHcHn2+HC0w6iqG1cuNfTktYZ8yo0MUmalGp4UGOqoqYDHVPgZ/z5jRSU0xjbq644478NVXX2HWrFlwOBx44YUXMGXKFHTs2BGvvvpqvNtItfC6XZAl31sYmCgweKLRgAkDDcezeI26qi53o2KfMz4PFmdc64ooMpchw+mVZKAFDjE3ZnQyXBZ96g4AUJViAAx0Uk1Mgc4nn3yCWbNmYezYsbBarTjhhBMwadIkTJ8+HW+88Ua820i18Hi8hq4rTR+BBcS+1lW8uq5evvM7vDZpGdzO5Ovn5+rlRJGZanQkC1BVnMDWJIaqarAI30/jiStzARFc6kh1rwIQnJGeUkNM71Zpaak+wikvL08fTj548GB8/fXX8Wsd1cnjckPyfyGF0CDJFgj9LU3czMjGx6jYm3xZHXZdEUVmDnSsgOJKYGsSQ1OEYWkdDUIt028T+vQdlEpiCnR69uyJLVu2AAD69u2Ld999F4Av01NQUBCvtlE93O7QYmQrEEONTrwXL9eSvM6Hw8uJIjMNL5esgNryJg1UFU0vNhZCBRAM/vRARzCjk0piercuu+wy/PLLLwB8sw0HanVuueUW3H777XFtINXO6/boZx6+jI7VcMaRHBmdpCxuZkaHKCKX4buryBZAa3mBjqYKwxqCIrhQMoLHM04YmFoaPOrK6/Vi3rx5eO655wAAw4YNwx9//IGff/4ZBxxwAPr37x/3RlJkHo8X2foXUoNssRomDGxIMbJxHp3G//Ane/DA1cuJInOL0IxO8tXYNTXFo8DiXwRCCA0QHv024c/uSKzRSSkNDnRsNht+/fVX0xIQXbt2RdeuXePaMKqf1+OBrNfoCMiSVR8I2aBRAcL8txCiQaufhz2caablmB+m6RgzOi10CC1RJG7D10GRrIDWAgMdr2E4OTRABLNawYwOA51UEtO7demll+LFF1+Md1uogRSPYh51JVsh/MPN0YBARQvprmpsUkcYsiQCyZcxYY0OUThFEzCGNV7J2iK7rlRDoKMJTZ87BzAfzzSFJ0mpIqYJAz0eD1544QUsWrQIgwYNQnZ2tun2xx9/PC6No7qpbjdk5ADwn2lINkAEDlWx1ejol+XYMzqmwCkJ4wjOo0MUzh2S3VRkS4ssRtZCMzoI77rybehbX5CSX0yBzq+//oqBAwcCADZs2GC6rTFdHtQwXq9HnztHQIMsWSH8nVcSpKi7oEILhhtbkGwOJJLvrMc8vDz52keUCK6Q770iWVpk15WmGIuPI3dd+TZszlZRY8QU6CxevDje7aAYqE6XYXRAaG+VBE1osEiWeh8ntI6msV1XxoyOmoQZE+Pr/f37XThyVA9Y7fXvJ6J0FpbRkayAWp2g1iSOUIOZGi2kRseYotY8HsiSE/hrCXDgyYAtuoWkhdcLz5IPYT18CCxF7ePXcKoV824pTPF4goGOJPt7q4KjrqKtj2najE4SBjqG1+us9OK3b3YmsDVEycEd8r33yLYWWaMj/Bkd/ThhGnVl2EduF/D+5cA7FwOL7o/68T1vPQjHd1dBzBoel/ZS/RjopDDF4wlObAUJEMGvYaDrKhqhxcihlxtKS/pAx3y5uqzlredDFMoVMaPTAgMd//FL0/umfN13kmwHbAcHN/S4gT//5/t71WtRP77896cAAKvY3ui2UnQY6KQwr3GtK0nydV0Z+q+0aDuRQ2ORxo66Mk4YmJSBjq9N3Q9tDQBQOHqCSM/oWPwDGlpqjQ60QEbHfFxo3X4oZNsBwSu8huUxMgoa8ATJd0xMdwx0UphTUYJrskgyBILZimTJ6KhJWOwbCMSsDl9djupNvjYSNbdAjU4GfOvTqZIFogVOGBg4fImQE0VJtgBS8CfT6wwu9omM/AY8AwOd5sZAJ4W5VM2Q0QEkgeDMyJKvGDkaYcPLG1mNnPw1Or7/bXYGOkQBgVFXGQhmKrwtMNAJFACEHj9lSQYg68dHr9OY0WlIoEPNjYFOCnNpml6MLCQp5DyhkfPoNILx+JCcgY4/o+MPdBQGOkR6RifTn9EBAK+m1rZ52tKk4JQdRrLsD3T816uVZcEbGxDohB6pqekx0Elhbi1wloHgjMj+AEdCAzI6ou7LDWUMbpJynhp/86x23z4zzoRK1FIFMjoOQ0ZHbYEZncARK7RGR5YskCDrGR+lbK/hxoZMT8FAp7kx0ElhHmPhsST51qkyDC+Pthg57hmdJF8dPFBDxIwOUVDkjE7LC3SEFFy53Ej21+gEjm+eckOgo7hAyYuBTgrzGt4+IVkgJMQ0vDw0sGl0MbKa3IGOCM3ocNQVkT7qyg43JOHLcipqy8p2Ck1A+Je/EcL82mVJAmDRu66U6rLgjQqnqEhmMc2MTMnBK8sILFfuOwsR0LuuDGcemqZhyfu/4y9Zw+DB3dC7fZ7pccLioUbEJns2V+D374IT8CVjoBMYVmEXQE+7DKenZR3MiSIJzKMzZvEqTChbioWDB+LTth1Qsu4rDG2Vg6M6HJXgFjY9TQi9vDGsGFmv0fFx7/gl+AvakIwOV0lqdszopDCvYeFNIUuAZI5RFH/aecOyHThoZSlG/lyGSXN/DXscTcQvo/P+wz/jj+W7DY+VfNmSwMvN3FaBQ7MsKHK2vPQ8UahARme7pR1+ad0Lt37/Cp70tMPjewpx+f+uSnDrmodQBYQlUIwcMrxDkv1dV75jmkuzBW9sUEYnCU/+0hwDnRSmGN49LSSjAwS7pFw7q/Trtu8P9r+Hblfb5cZIxoxOINNlCdTqJGPBNFEzc6kaJMMoqz3u1shXfMcOIdkaPe1EKtA0Af9k82HFyBZJAgzFyG7JEbyxQTU66b8fkw0DnRSmWIKV/hoCq1uFBzpeQ7Cxr8odlrGJ9zw6RkkZ6Phfb+DDL8cxsCNKVW5NQ/uy4EmRUCVYA8XIkk3PEKczTRX6gSF0MIcsy5BgCWZ0JAdw6Pm+G1mjk9QY6KSwwJmFEALwz6NjDHQCfcweJXiWpmgC+2s85scJHV4exwRHUgY6/iYFA52ENYUoabg1gYNLgiOJNFWGzV+QKyQb3Gr6/5iLOjI6sr/rKpCR8cAGtOrmu9Ebnimv41ka31BqEAY6KUz1v3sCmv7lNH6JhD/A8YSMKtpbZT5gNW1GJ7miCONrC+wyuQWk5Inq49I0HFBWrF/WNAnWFhbo+LquapkwMLTrClagoKvvxhhrdOJZJkC1Y6CTyvxFc5rQfEs+QEATxq4rf6ATklXZW1l3oNPY4eWmx0qyjI4xppH8f1uSq4lECeHWBDoYZvv1alZYRbDryqN6It8xjWiqgCRHXgJCr9HxH0QUWIECf0Yn1hqdJDsRTFcMdFKYpGd0BDQJvl9uw9BFzes7SHlDvkzFFSGBTpxXLzdKukDHEMRJgaUgIJIu80TU3NyahlbOasNlO2z+QEdIdrjU9J8Uz9R1FZrRgX/Ulf8A6RWWYEZH8wIxLJchkuz4mK4Y6KQwSa/R0SBkGZqkQTMWIyu+g1RDu65izehEul+yBRCmrqtARkeSODsytXhuTSDHEwxmPMKuFyOLFpTRCc6jEzIzsgQY17pyqRKQ3Sa4QQwFyYKTlTYLBjqpTD/z8J1jaDBndIQ/o+MJCTZCu67qG4UVrUhBTTy7weLB3HXlz+hInB2ZyKVpcCjBYMYj2WDTvL4LLahGB7VkdCyyf2Zk/3FDlWyANTO4QZTdV5LhIMSMTvNgoJPCZH9KQkADLJIe8ATM+moj3v5xq6nrygqgOLRGJ06LekbqpkrmriuohkDnf9MAV3mCWkUtyZ+7t+H6pybhwx+XJropJi5NYJulAh5rX2Rox2JxXj8UVjtx3hfzMWq5My6BTo1HwYzPfsfqbWWNb3ATEMZAx1ijI3xHV0mS9KOsKiTAYgVk//TIihv7P/gC226cAs3trfU5VGFHmfcyeLSeAE+wmgUDnRQWKJoTwjfqSvPPphPw4/bfcM/ctah2B/uOrQBK6ht1FWNwkgqBjjESDLxOKwDvineA3z9NTJuoRZny/r9RVGLFp0teTXRTTDyaho0ZZTi3qh0u9mRha94AtNlahus+eB2XLtoUl66rp77aiOeW/oWzn/4uDi2Ov9rm0ZGFwOqMbADBaT00yT8zsjXD97/iwu7/m4iq/72N4idfrvU5Kryno0o9F8We/7Lrqpkw0ElhgWHRGnwFdMWKasroOCwKNAGUGebNsQKoDlnbKbQ+RY2xriYVAh1TV5r/dUqSBFVkA+7KBLWKWhKn4purxi5s9WzZvDxCQNJsaOM/WSqCjMxK3/wwDkVBdU1VXXePyobdyf0d01QBKUJGx+FVsdvqe7/0QCfQbWX1z5Bs6Lry/L2t1udQtE7630LhOnvNgYFOCgvMiyyEBkgCqmReAqKVzZdSrXIHZzS1QkKN2zzDqRoS6GhKHGt0kqwYOVJGBwAU5DRwiChRbGR/psCqJdmaym4VsHWHxX8MaQMJdkMXTPX+/Y1+Ckmqf5tE8p0I+d4fY42OQ1Hh1lc19x83AjGKIaOjq+OFylJwZBuU9C/wTgYMdFKYVQQnthKyb15kIDj/Q47N97/xTbYAqAnJ6IQW4qZzRsc0GaKhbarI4TTu1CwC8zZZRXIFOvbKGghrR/1yISRYXcFApyYugU5yRzpC0yDDF3xopoyOArc+kaAIbOz7X8/oBI8fdb1OSQpmxjQPA53mwEAnhVmE4QxDkgApMKun74uYafcFNFZDlscKc4YHCHZdyVb/BIQx9htHCmrUpAt0/H9IgDAEdJrIZkaHmoVF+HKxyZbRya6ogNXSWr9cCBlWT/BY4SprfLG+JckDHU0VsAhfcGcc2mHK6OiBTiBijZDRkYPrEIaSpJrg81XxmNMcGOikMFNGJ3AAkYJfRIfFnyI33MeX0QntuvIFRPYM35axBieRMzrJ1XUVKLyWJMnUdaUimxkdahay8B12AwFPssgqL4NdytUvt4YE2ZD99ZY3vr5GTvJfHE0TsEq+QEcTwdfuUFS4LL7GB46v+jDxCBmduvvoDOsRVjKj0xyS/GNHdbGFZnTgq9EJdM/YZV9AYzycWiHBqwrTJIKBGh17pi/QiTmjo4XfL9nWcgnsG0mCafp1DVnM6FCT82pePcCxaclVjGyvqUQWMvTLrSEBhkBHq2h8MXKyd12ZMzrmriunP0rTjyGB2/0ZHc0ZzNT4ZxeMzFCErlYx0GkODHRSmEXzdzVB07uujGcLNik8oxP425jVCXRd2TN8B2A15mLkVKjR8f0fmtHRRCYDHWpyJc6SYNeVsMKrKPXco/nYPS7kGIKvQsimgQladXWkuzWInOSBjtAELIFlLwz1fHZFRYXVd/QUoS8hEOjUGPdPHT+twqH/qVYxi9wcGOikMIsIpFJ9gU5gwsBAalWWwmt0Mv3pV2Odjp7R0buu0rhGJ9B1JcNUjKwJZnSo6ZU4S/SuKwAorihLXGNCWFQP8rVg/rc1JHiMv+rVzkY/hzHRIWKdmbQJaZqAxT+cyjiPjmaxwWMJyegEmu8PdERVMKNTVzwnYMzoJE+gm84Y6KQwq/97KISAgD+jYyhGlgMrDwsX/rv3fbxWtQy5Nt+BrHhHFRbOXot926ugKKFdV/EcXp5cB7PAsTW0KFJDBmt0UkBNRTk+ffJhbFmzKtFNick+5z5Tbc5Nb36H9QmcW2buyh04dsaX+H7jPrjyq9HNE+yeskPCkgFD8Opp5wAAZGfDTwT+/fl6PPXln1hXsg7jPhuHDdUzcWrxInSr+RtKknVrA4GuK//wcsOoq0cuvQ6aYckdwLfW4KQHXsGTvx2Hj6R38e2P5dg5cCTcI++DIvWodeJVj5al/7379+1YM+l97PttcxO9IgIY6KQ0YzGyL2kTmCk5uLSBRZZQXPUT2lVtgn3v18j2BzorXl2PTSv34sNHV+hzy+hdVzFmdCJlb5K1GDmsC11kMKOTApa+9iLWL/sGHzx4b6KbEpP97v2mjM6GPXtx2ZwfE9aeW95djV3lLlz28k/4pdOBKFTNPwnWdvmYM/p8uG02WGoa9v0ornRh5uKNeGzRBpS7arCyeCV6/r4TB1VvxOg9C+BOwlmBhSYg+wMc4wLJv/bqC82/awJHOQ0SrO6NGKsOwCBnFpzKALRrNxhtMjrDausEz47IAWyJCI5sa2Vpj0KlHSre+6tJXg/5MNBJYVbNXIwsIGDVHFD9mRxJE/jfLSdCQrDgLdvqC2Y8Fb6CO69heYhA11WsGZ1IZzDJl9HxtccSEukIiRmdVFBevCfRTWgUj+oxZXTsshs7yxMfYLsVDTXWDGRYfLP9Kv7FPDt4fP+77Bkocta+flPExzRMRCr7u2vyK4y3J9+swJpmyOhAhtbmXOw85Gi0qqzAVdvW+673H9JkCMAafC87eW2wycH6G+Gu5fVFmBHb7rLH6RVQJAx0Upg1YjGyrAc6miZwQFGOqQAwy1r7W27Ti5HTecJA3/9hRZHCzowONTmn4jIFOjYpeUbd2GoAm+z7wS33+JapaOsPVpwOBwo9Dasn0Qw1OJJ/GITNECslY0ZHU4W+tI6AgGTNhmbPQPt9xRhUXuy/3kcOObRJAKxyMIiJFOhoXi+sCJ9WwLwcM8UbA50UZjUOL0dweLk+/0OEzEyOLfLcHRabDIs/CIrnhIFJ13WlZ3RCb3Ew0KEmV+0xBzpWOXkCnbb7/VlezYMqxTcLcpE/0KnJyERuAwMd4xQW8I/msivBE4xkDHR8o678f4vAEHKB1uVlkPV5dfzd3yH3lYUGqxTMzGgRAh3hrYYlws+ucSg7xR8DnRQWGF5uHHUFSFD980Do1XMGmeG/8AAAq02GxT8zcqwjpSLV9iRbRgd6Rif0BjvgZaCT7JJ9Hpb6VHtcphodm9Sw7qCm1LHS302lVsOl+oZKt1J9+9vpyEBGA4fCGwMZ4V/uwu4Nvn+uZOy6UlRY9IwOAElAAlBYUQ45ML+O/wRTAiAZg1YNsMjByTyEJ/z1Ka5yWJjRaXYMdFKYvgRE4EsiBQId3xdMUvVhAjoHIh9YLVYZsqUJMjpJNrJCq60YGTbW6FCTq/I4zRkdyRc8VLsTO8xYSEDHGt9xw6VWwan4Ap0CxddWpyMDNm8Da3QMK3ML/3IXskjujI6mePU2agCEJCAJgcKKMlg083skA7DKwRFUWSEzXUcOdMogRwh0NGZ0mhQDnRQW6LoKLj7nK0oO1OgYzxwD7FrkuTCMXVfxXQIiuQKdQNAXOrxcgpVdV9TkarzmjI7dH+jsrUxwkG2V0NblO474Mjq+YeZ5qi9AcTockL0NzOh4wzM65tuTMKPj9eq1N77Eju81FJaXQdICMyb7yAKwytn6fWWYjymRuq5UTwVkhO8L41B2ij8GOilKVRT9gBnI6Ag9o+Nf+kG1mG4HAFmN/GNu7LqKPaOT/F1XgRqd8IyOhRkdanI1Xrc5o+OfnG5vomfItQBtAtkbpUrvusrWfDUnTkcG0MBAx2XI6GiqFVbF/KVLxoyOMAY6gK/rKiSjY1gXGBZbTq2PFTnQqYQcIehjjU7TYqCTopw1NYYancBXL1CM7A90/AdUzXC2IKuRix8tNhlyE2R0hCaSagbUwK4IzejIsDCjQ03O5Q0ddeX7MSyuSGygY7NoKPBnb1xqDZz+QCdT883668zIAJSGZWCMGR2PCuR6goW6Aslao+PVMzMCvrpHX41OpIyOBJuh6yqUWhP+nmqeCn0Emul6ZnSaFAOdFFVZUWVYAiJyjY4sZLg9Cqyu4Bdrv+SF++gi/N7JPJeDxSrDYvF9wTfursRxM77EdW+swNWv/Ywpn/wWVZtqq8dJpqyOntEJvwGznWdgS0lp3J5r5//9H3befU+TBXo1P/+MzedfAOeaNU3y+MnIuC+bOoAWQuCDDz7Ap59+Wud2//ttN85++jts3lf/WlBSmQe5+/thuPMEXFAzCOcpHsiZf+OFb//CmFnfYVe5uWtZ1QSuevVnTF/we8TH+6a0Eqf9vAG/VtaE3bZgyjeYf/cSLH7V9/11bl2Lty8fijWPXKRvc528Bx+KHIyWSpEnfIGIRwp2XTlEBpYu2IMzC8+G3PWMel+fkVvRkO+uxJufTUbRlefinT1/49xu23F4txthLzwJ98z5CcfdvRALv93aoMdtSpo3mCnXAP8xVfiKkdXATPO+/6xeoPvOnbU+lnN/uf53yeZN+GPYQMx+ezUkKTzQUVUFb076V8LmifqkuAyjVmzA3870zGoz0ElR5RVVhmJk49lAsOvKqlmwcs1eVOfs1m9dnOeAKLDj/cG5psezGjI6u0qd2FnuwoK1u/H5b3sw57st8EYxTLy2gEb1Js/ZSujMyF7N98W2ynbsrXJg9o8r4vI8amUlyj/4EOVz50IpLo7LY4b6++JL4FqzBluvnNAkj5/s1AYWxzbU/v37sXbtWvz8889Q6hhxdNVrK7B6WxnufL/+gLPr74fA48hBD8mOXDkfR7i7wZa7Bqu2lmHl1jI8+vl60/Y/bi7F/9btweyvI8+ce94vm7CqsgaX/brFdL2ryoPDnEB/YcEfP+6C4lHx43P3YUd1DhatCP4An+A6AG0lGSeVd0Omf1I/BVXwSDmo9PqGmGdbspAPO6xdhtb7+ozcioosxY1W7ipIFZXoAA8ctkNxsJyJ7vkDsN8iYadQcc2naxv0uE1JmDI6vpNIWQi0qiwHhArJKukZHYdLQ4Gn9mC7YnfwuLt38h3YZinEug4HR35eaNj153p8/fpL8XopDTLhty1YUVGDezbsSMjzNzUGOimqvLw6rEYH8A2/DXRdWYUF1dUeqIYhrB4ROV1ssVlg8Y+6kiKcKZdU1T/fR21z5ngjjD5IlNAJAz3+miWrZIOkeOFxNn6FZgDQaoJn2GpZWVwes9bnqqiof6M0pHibdg4aVQ1+bj2e+p9rXxR1NhZvBnKlYNeVRdgAS/B+NSF1Hftrgs9b18nGHrc56KssDWaGMmQJXrcKV034QASHP+DPVgTs/tFAEmqgFZ6GxeXV+N+Ol/H17vd8G/kXr4yWW9GQofjar2X47psBX/FuFiSoSThVgKaq5owOBFppKiyaBqgaJKscrNGRJNMEgaEkwzxmkqsGzoxMeO2Zkbf1B1duZ3hmrjnta+KTh0RhoJOiKqrc5tXL/STDzMhWYUFNjQLj+HK5lg+yL6Pj+7JZEH4AimZUSG0ZHa8rmQIdc0bH7R+FJksybIoGa5zm0tGqgwcsZe++uDwmAUILftabOqNjzOJEE+hEQwiBVobvlwwbJMPsyFkO89Bj47Dz0CCoLjWGZSXs/kAHWnhWKsP/RbBKEmyS73giS9VQLQLC2g77PSUocfu6Z2TZhl3btkfdBrdXQ5a/wF9xBGpZ8gEAkX/uE0/xBjM6EBIgGUaOaSosNtlfteM71gYmCCyRg8fHwIStkmb4eRUaXJkZUPzbayEnnLK/O8uekax7JrUx0ElR+8urDBkdQ6AjS6ZAx+n0BtMYAOCO/ENuscl6RscSIV4prqw/AKg10GnAAbqpBdep8fFowddlUwFLnLIExoyOsndvXB6TAFUJBjdKnIKP2hiDm6gCnagSFAKtDIddSdggGWZHzrab6zeMGZ3qOmYmDk2OOCuC93NI/u+gZv4eujyKntGxAbDqgU4NVNn/XJJdX/cKAHb+saH2lxbCrajI0AOdTNSoVqiiEACQGd3OanaVHgUyghkdYcyXq5o/0AkIZnR224PvTaCQ26IFg1bN5YUzIxOyxb/MjjC/lxZ/oGPLaFjWLN6kJH1fGouBTooqL6sOZnRCupoCxcg2YYXLqUAyjrqqJWNhMWV0wjUqo5PgydCM9Bodf3CoCgVC+F6bTZNg8canGE+rCXaBKfsY6MSLMbhJukAnCkLAFOgIkQEYsgGhAYvxe1fTgCUYnIZuND2jY/hxFaqKPVt3wqFndACr3qXmhfDP7yNJNmhQ9XWryrdGn9FxeTVkBgIdewaqVTs00QpAeKCTLCMzKxTVcAIZWD/QT1NhtYV2XfkyNNsN8UmgkNs0X47TA1dGBqz6ccccdAYCHYs1vFCZGo+BToqqdDr1Mw8BzTS5Q+BLZBVWuEMCHUmJ3AdstQYnDIw90IlcQ+BJwq4ryX/A0YQKCb7gz65JkJX4dIeYanT2sesqXozdVU1do9MkgQ6AVsZoRnOYMjo1IfVsxu9ddQMyo+6q4H7KkPzdx4aMjlK5F8U/rNQv2yRf9y3gOwkQ/vl94O9qUfzHEE9JWfRtUFS9RsfjyES1YoeKQKATsm1NcpwMVSgCEoKBjjGjI1QNVpusH2slSHrX1dbs4FHT5Z9V2mJYpVxyeeDKzIDDf6Kl1ZLR8brTc9RTojHQSVE1bk8woyNpMObNA/2/VljhcSmQDGcPkhr8IhnPoSw2GbJ/eLksgFyH+cyiuFEZnWQKdHz/y4ZAR5Z8QYlVWCB54lWjY8josOsqbozBjdrEgY7b8KPjjuYHKIqkhAUycowZHWRAko3BjPkHsLiy9ttMTx3y3J4aY6AjweNWIAzF1d592+D+a4dpmwDfD31gW/9IrMAxpKKq1jaEcisaMv3HG6/N4Qt0/BkdKyTYDdvWlCfH4qZVmjCcQPoyOnq2SVVhs1nMNTr+jM5fecGUTmCyRQtswfu6fBkdh79OKrTrSpIkyJDhcUWeub4pmaZsSNM1txjopCiXVzGsGxMMdKSQjI7X6YFsOJOzaMEDp2p4941LQFggoU/HPNPzRZXRqWUenaQKdPxtDGa5FMiS7+Bik22QnNEfyOtirtFhRideFGNGx9O0xcjRZHSimXbBKDNkPSRJcgCysQ6njoxOA0YvGgcAZEoSvE4PFMP301O2G8IwMsvhPxYomheynKFnPCUpEOj4Lltc0QckbkPXldtqR6W3AIBDvz3LcHJWk+AJEwOqBAw1OhKM0avQNNhCu678+2dzQYYeMARqdGRJBvyzP2suLyqzs5Hp9R1fQgMd3/ZWeF3NP2mpM8nWI2wKDHRSVI3Xq38hjYEOYK7RcXq8sBgOxrIho+OxBu9jtZm7rg4JCXRiKUa22HyPl1SBjp529i+bIVTI8B3wrZIdqI7P8HLBYuQmoXqaL6MTTaBj6mqKoo4z2/9D70Rg9J8DVs3QdVVHRie0RkcznImH1vYoruC2WRLgrSiH11Ac660ohuwM3t/qfwBFeCHkbOidLoGuK38mwtaQ7jNFRYbX993yyDKq0N50u7H7qjpJMjrVkhRci0xIEJJmKEZWYTVldCTY/BmdCrsFbs33nXerwWOI5lbhqayEW7ahKiMLWWrtgY5FssKbgIxOtSHTlySlUnHHyqcU5ZQ8hkXkfDMiA4BQXVC1YNdVpVdFYXUWAN8kYTZvJfrM+wuj3NlwFO0E9nQDADyyaAPWL/kdVyMDsgB6tzdPKLhyaxm63zU/rB05DisGH9gGX/y+B2e4HNg4MBt/tbPhii8qIAkNDgDPf7URmSWlmHhSL5z/3DJcdnx3XD3kgKbYLfXSi5E1LwA7AAV7rBJaeYHB7c7BNwVrMPvd16Hsq8CECROgSir+ueCf6FPYBw8OfjDq5zFmdDxbtmDnnXei48MPm7b532+7cc/cX/HEBf1xQq+ihr2OOiawi8l3/8Ujf25GJ9duXLR7AcrUPGTf+QtsOYVR3f3nTz7Eivkf4fz7Z6BVh07xbZufEMJcjOwPdKp+3IWyjzdBzrCi6Nr+sLXJxCdPPISyPbvwz2n/hsVa+1wndT3Xtm0fAigAAMyfPx+7d+/GmWeeCQBY/lcJpr7+JY6x/oVOchfs0Aqiyu5kC1+gs0do6O4v/u2k2XCwtA4/ir74+e/96HH3fPRok42CTBtQU4mPRQEqJCcueXs1nnnlDTy45C1UH3gsSg45Actc7QFNQ4n2Fv6x+Ajk9DgSV63/AQdu/QvofDYAIF+S4Vm8HkcWPYIj2mhYV/Y93v3sD7TK6YpuIe1TNA8UKRfZFt9Pg+QPdLzqPgA52Nh+H4ZGswM/uBI3/bUS77c9HPgTcGVswleOS9DbsMklOcBpldkQQsFTX96GKw64C/3a9Ivm0ZtMjSTrq4trkoxARsdjs2FBzx4oLC1GZ/+2B+cfqd8vc9fVcCp3IsOSDa/mhqJ5YJXtGPHwQjziVNDutKdw6a6NaJtxIIDwYmQAsEgWOCsrm/T1RVJj+Nzu2rsHkye/icNXrMLBf/6JfR064rqbbkdGxTN4ccMlaC9+Q9sH72/2NjYWMzopyqN59eGgivDqwwI1pUbvXbcLG+AGZGNqUpVwiisPmZoN8p7gYc4jCaj+EQZWACf3aYfe7XPRo0022uYG082hqtwKFv62G4om4PWq+LlXBkrzLPitqx1V/jlPyis9eG/Fdvz7f+uxu8KFGZ/9Eb8d0VD+XWHzn1FpcOPDVpv1m48p82Cu04bi4mJs2rQJy3Yuw5/7/8S8TfMaNDLEGOgAQPmCz8Luf9VrK7Cvyo3LX/6pwS9Dc5rP/Bod+Kz7CI93G4eTS5cDAAosFdj17YdR333p6y+han8pvn37tca1ow6aqppWeQ50Yzl/LQFUAa3aC/emMqiKgg3Lv0Xx5k3YtTH64dBGilKO6urdputWrAjOmn3dGytxlLYOmseFU+x/Aqh/nhtVU5Htr0yxKJL+eWiv5OFxx3/17YQA/tpbjZVby3CppQStZTt6SPnIFB5c9+sCZDldKFq7GId6OsMGK2yyHfurD8FA7yp88fsedFD7YJ/FnJG1S75AWpZkdMk+GGppH3gz7QilCA8yCtoiz+HPtwS6rvxDzNdnR/Ed0FRg7Xsotbog+wM7T4YVR3pKTJudWJYDm2SBXXZg5Pb+uPe7e+t/7CbmkiXYJV+bFSEg/DU6m3v0QKXDgdL9JYj0s+mSPVjj+B1ezY3lbf+Ex18iMLDGjXZW374PBDkAsNe5DY8VPg+3YQ4lWbKiar95HzWHakOgUyNZAUhwZvne/93ZuSi37cTwks4oVNrBo57U7O2LB2Z0UpQqCWTKvrevWnIDyAQ0DUKrhuZ/W+2aDR4LTMNKbcKNDBGeY596wWEo6pGHuff/CBkSCrPsmH/jCZAAqEKgwhleD/HGD1vx+CLDD4nhYVUZcPsv2/3Pt6ci8YtmBn5c7P4fTK9aifdbL8Jxuw9CH/kQtHIDin+/qqoKj6FbocJTgXxHflTPEyhGLrzsMpTOmQN4vVDLymBt1SpsW28Ma4GFBlJaTQ0seXm1bB2Fyj2QhYo2njL9Krmm4V1uTZl6D+2qUjy+HxPNMGeMWuFBTXmZYZvYukTcnr1Q1fDDo9frhc1mQ7nTC4TECVX1TKPgVt3I9o/E8WgCCiTYALTzZsNhjVyjkmH4Ug2UypFhCGg1w/ctQ85AkVSGfLghuUtRVRD+OfWobtgtDmRYcgBRjHz/LMVGiuaFo01P2Mr9P+Z6jY5vP2YrGfC4XbA76pjvpcb3Y73Z0QqZ/nm7Mh1uZLjN9UmGgUrIlnNR5Y1PfVxjuFUXMqXWAACXpiLQya1ZggM/hGR+7So8GFNZjof6vAur+j46F2fAtacKWdZcHIDwbOIu52b8WvYNfsjZgcVtb8Wrfz6IQjUfVtkKrYELp8ZDleE5vf7spyb73px9rQoBeGE3rLiulJfCmh9dpjdZMKOTolRJwOGfrKocXkiQIaleQFRD83+57MIKVZZMgU62iDy8vH27HLQxjBxQVQ0WWYIsS7BZZLTOcYT969bavHKvJyN45FJkCR5/hijwVTcWViZq3ozA09r9H31F8y2fsFv2rUeV6w1+oZ1OJyo8weUV9jmjLyoOzIxsbV0IS77vRyeew8yNMy8D4YFPgwiBmpoytPaWwWKYfFJURbdGV3O9l0rITMiqvxhZNSxPolV5UF22X79sDHoawuPeC1UN/5Gqqqr9x9itaFDq6L7yqB5k+LOwbk0gEN+2UbLgkiMX+BQalhg4UPaYJnQzTvGSYclGkVyOA1EFyb0Tmj08W7Pf4wtcHZZMyEJBgQifhVcRHljzu0Cy+ouREajR8e3rXG8mfvvzl1pfIwCgyrcw5d8ZbZDpL671ODKQLZn3p81QWJRhCQ+6EiFX+L6jqlDhFQD8NToi0FZJ04O/ACG50MkfLCgWDV6Lpo+86imFf4Yq/ZktxSKgSho8sm/fypIVQtOa/dhozOh4LRbTuKu9rVpD1qphNRTRu9b+0Iytiw8GOilKAMiw+L5EpVYNEiRIihdCq4bw96vbhM1/YAxG7Bla5KxKVr5dnzAQADSl/i9bUY65S8vtCH6cajIkBH6WAhkdY2FleYQMUXMI1OgEzlC8whfI7Lf5/s/SMuD1z15aVVVlCm4aFOj4Aw8pKwuWojYAACWegU6EjE7MnPux15qDth7zyu2iYnctdzBzGwq4m/IQHZqdUbweCH+XVYBa6TUFOsa/G8Lj2Rcxo1NXoAMANd7az8irXdVw+OtyXAIIfMXylQzU1LLuU7YWDFjahVQ7y4YfxExLNorkMnSHC3DvhkUOb3uFd59eG2KXgAItK2wbr+aBJa+jb44JICyjk+PNwMYt9Sxe6g90tjsK9YxOla0AmaEpMIPMJAl08oWvltGlVkOC7JtHRwi4/BksAQ0i5HVIcKKNcei+Vegjr7rJ4cFkIAjy+oNJj39yxsBcOmq86+/qUeUOfq+EJEOVZCj+iQv3tWoFWS1Hvhqs2fRs+bNZ2xcPDHRSkBACVhV6RmeXwwpZ8k12J7RqqP4+ZpuwwqoCxkDHroUEGBbfgSgr1w7ZEvw4qFEUVrbNCwl0MoL3r8yQ4Q1kdPzHTGNqP5p5eZpC4GzJ5j9YueALcPbZfAc4B7JRY/cd1KqqqrCvJhic7HVG35UTCDws2dmwFvn66I2jr9RGDuk0zrwMhGd4GqRqD4rtrdHOba4PkGuiy+hUlwUDJHc9gUBjhHZdqV4vtGqPKbpSKz2m9lTvNwdv0XJ7iiMGOpV6sWjo++e7XFedzv79Jfowbpcm9EAnx5WJmloyOpla8DtWIGxwGIbUWw2BToY1G21Rhi6SF3DvhTVCVYJTqdB/ZDMsQH6kQEdyw+awA7L/+x8oRvZ34eYoGdhTsqnW1wgAqPQFOrscbfRAp8xeAIdce3dXhiUnKWZHzld9Xa8upQqQLAi8r65Mf6AjaQit+JABFBkCHcWi6ZMGZkdY9DPwHij+tXa8/kWX9UkDm3nkVWV5uemy12LVX29JXivIahkKlWBXqJqC02Uw0ElBXpeKQn+WRBUqim02X9eV4oUQ1dD8Q1itsMKqms8OrJpi6jW2OvyTW9lkyLIESQ4UNUeT0TEfuIyBTnWGDK9eoxN+32jm5WkKQviGz1v8Z6pO+H64ih1lAACHFBLoGLI4Jc7oCwWNGR1rm0CgE3ys0mrzj3ZDpvcHzBMSGp8vJlV7UGwvRFuvOSiwuKMLEkwZlPLYMijRCO26UrweqJXm67RKT5wyOrV3XXlVDZaQBTLt/pOJutajKt+/X5+YzyskKP4fUbs3F05osCLCopsiGOhkw44sp/97Y8uExXD4dshZKFBr0B4qhKtMz1g6leAoHpdapS9PkGOxw+YfXeRUgsGpF25YrBZosn9hypBi5CzVgXJnPZk+f0Znny2Y0dlvCHSMbdLbb8lEdWVlwoOdXP/z+4IRC4T/ZM0ZWINK0sLG8ssiIyyjE9jPkTjVangtml7TGOy68r0fzT2XTmWlua1eixUu/+vdn5cHWS1HK8VQ/1edHFMBNERaBDqzZs1Cjx49kJGRgSOOOALffPNNopvUpKrL3WjtP5C51WpoFhskydd1Ba1GL5azwgaHN2SqcU0zDEsHZJv5B9Linx1ZVerP6ORlms9sXIauq8pMyRDohJ+tRjMvT1MQQpgmR3PLvnbsySgDADjkHNT4h9ZWVlaaAp29DSjODQQiclYWrG38XVeGjE7o629o4BfWddWY+X8q92CPvTXa+jM6pVbfQc3urajrXjpj1qR6f2mT/VipoV1XHg/USt91cpY/7V/pQXVpHAIdd+Ri5KqqKuyrciNLMgdYmf7Ldc1eXFZaikz/j6Qxo2Pz5KJaktAa4fs7UwS7SbI0BzL9gY7sL4r3aG7fMiaSBLe3PfKFB8JVAZv/fpVK8GzdpVbrmYZW1iz//VV9/hcAUOCBxSZDlQJLQIQUI6sZqFLqCYD9gc5+eyGy/D/aJY5WyLD4unGqDG3yam69O611TRaqvfGZxypWuf45dFxqNWAYXu70ryouJA22kMOZQCZaGwp6Fcmmd10FuNXgPnapVXo2BwC8/q4r2V8A7Kxq3iHmYYGO1QpnZiY0eybKs3PDMjrCk3phQ+q1OMQ777yDm2++Gf/3f/+HVatW4YQTTsBpp52GrVu3JrppTaamwoN8zX/GplbBIllMNTqa5PtSWiQLMkN+HKSQLhPZYr49MMlfbcs5mB7LcGaTbbfAFZrRgbkY2ShhGR1N6GfVLrUKXn8twu6sMgCAVbajwOuGhgg1Oq6G1+jIWVnBritDjU7o6290oNPIjM5eQ0ZnbU4vAIBDRPejYwwmVEUx1ezEU+jaVqrXC80f6Ng65vivFHDtD/5QxBrouOvI6OytdOuBTUAw0Km962rfrgpY/J89t4BejGzVclAjyyiSygAABVnB5800fHuyNatelyNl+H54XEoVXP4f0XJvF9glNzR3Nez+rtlqJfi5cqpVerdJgc23v1ya17Q6uSo8sNpkeP3ZpWAxsm8/Z2gOOEU9AbA/0Km0BkddVVizkWHxBVfVhiDAqQTb1M5Z0KDu4aaQ7e8qdKpVAKx6xbfedQUNjrB6KjtaacETQ4/I0V9TQJkn+LpcajUUw4gz4a9mDCzoWVnSvPugssZ80uW1WOB2OKAUdEJFVk54RketfbqRZCWJROcKG+noo4/GwIED8cwzz+jX9enTB2effTZmzJhR530rKiqQn5+P8vJy5DVmaG6ILX/+ifnPzIrb44USADrL/TDIehB2VP+JL60/oJ0jB3LpVkhuCTX2CzC6dVsAwP/2b4O76lP9vq4MFY7cvsHHkmog4IUkB2YKlgABSFJ0HwvDvKHQYDENebUKFZLwnRWJCI8nRzOVbNw50BFdcaT9UOxzbcdXzufxZS/fgWZm8f2wyQ684VgO2b0PFk3ButyNCJzV5SiZ6FXZMapnyamohqQBlQVZsCgasqpcUGwWOLMDB0zzqjISYBpRUx+71wuHIYXsyrbDa2v4xHg+GlTIyIYTdnhRg0xk+WeLrkD93wupsgqSoWZIa90aaIpVmN0eyIbARWRkQsovgA1WKFBggS/gd8Ntmm9HkiyRHq1ukgq3JXx/ygCsilpr0XVd72OOVIDhtpNQo3nxv/0VGJCdjW4ZGfjd8zf+wA/+Rzffd5TtTNj9dTIl6j78UvIZAKBdRncckncC9jj/hk22o9DRAZurF2KLrQSiWsMxeWcjy5KLXys2oV+eb3LOj7fOxAG5A9Cv1fHwaB7YZTuK3fuhqmXokNUDALC64nM4uhVi976tqKgcDU3ZDU/lm+iU1QuD243BXmU3Ps18A4XVPWrddTmohgwVy9scgksWLIYkgG+HnoAL1GOhCQ1/VK9B35zDAQDFzq2wyFa0dnTEQm0BKtRKOESsn+PGO8xxGHqIA/Hzvs+xTRwEa+E6SJYKOPwlAIqnJw63HosONnOOIMt7BS7p0A4AoDo74uw/rDijyzX67Vsqf0X3XN9kiO9ufgSVmRp+7OcLGG8uvxh9vH3xS8VSbC1fB62gAHA0XzCxM7cN9mUX6Je7lO9GlrcG+TVerOjcE5r3W0yqvkm/vVpbjy+U3xr8PNc8+m9YLDF8F2vRkN/vlJ5Hx+PxYMWKFbjrrrtM148YMQLff/992PZut9u0OF9FRXSp+YaqrinDWfYxTfLYoVxqNQ5ZsxYH7fH9AAhIWHzCOP32Ea26AK2ubZa2pBqnWg3ZeSxO3zgcAODKroZNduAi9zG+DWQAocmJ2geOmLUJuU943Wfj2IEI06AkRmv/v6ZmB5Bb71ZNL9rPQC08yn64K+bAbR0OZAxCH3s39Ambozhca0sbnNT2EtN1LrUaivAF6j2yR6JHSPs8hrWl3GqNXjti9y9d4PTu0mtDACCzzImOXz2PjpIVS4aMRiDIDxQjF1nb4zLvv6LaB8MqAAz2zR58gRpobw1slTVATrD9Fs0KOICR8umJ72PwR7BOtQqwyDjyy+XIrtmj37y7nQvSoGPD7vZW9bM4fWPwskt9xnR7lVJmeAqBXE8nnL7xXwCA3CwLYAf65w1B/7wh8Xst0XL7/+kO870POcCpZQBwhGnzbPlgnGU/uEFPoQkNVZUlyC9o26imxiqlA519+/ZBVVW0a9fOdH27du2we3d4wdyMGTMwZcqUZmmbEjq6qQmowotdVRvQxVkDERjaBIEOe1dga8aB6JgZXFtGCq7u1OTtSgWaULGtehOscnfIqu8gvtW5HwcnyTDX1CNBkgKz/TblZ6y5nqdpCAj8XfU7AAt2Vm9C15y++sKQtSl2bUOmJRu5NvMkbapQsK36L2SLHBQ5OpsCFgAo8+5HVel+7LNbUendBwgJpW4vqr0VcFgy/ff/E1bY0C6jBxStBgV/r4awCUjwos3elfBaM+CucaFKBso9pci2xhZpWmUbPKoLbtUJy85VqCk6FBbJiu3Vf8Jq7YCiDBdkyQKrbGuWY2ddapQKlHicyNeqkKEUG46tQH71RmwoK0FOqwLsdW1C24weWFsjIKvmLkvJegD+rvodnbIORLFrD/6s3ILO2f2w27kZgA1W2wH6cWe324K2VgcskpbUn+tdzs3It7VBVoyfgURK6a6rnTt3olOnTvj+++9x7LHBKPvBBx/Ea6+9hj/+MC81ECmj06VLl7h3XamqiqrK5p/Km4gomWiaBo+qwW71BWGlJfvRpk34rLqKpuiBmlzLnEKU2nJyW7PrKhZt2rSBxWIJy94UFxeHZXkAwOFwwNEMfZ8WiyVhKToiomTVqlX4cZmoqSW6R7RR7HY7jjjiCCxatMh0/aJFi3DcccclqFVERESULFI6owMAt956Ky655BIMGjQIxx57LGbPno2tW7fimmuuqf/ORERElNZSPtC54IILUFJSgqlTp2LXrl3o168fFixYgG7d6h/FQEREROktpYuRG6up5tEhIiKiptOQ3++UrtEhIiIiqgsDHSIiIkpbDHSIiIgobTHQISIiorTFQIeIiIjSFgMdIiIiSlsMdIiIiChtMdAhIiKitMVAh4iIiNJWyi8B0RiBSaErKioS3BIiIiKKVuB3O5rFHVp0oFNZWQkA6NKlS4JbQkRERA1VWVmJ/Pz8Ordp0WtdaZqGnTt3Ijc3F5IkJbo5UauoqECXLl2wbds2rtHVhLifmwf3c/Phvm4e3M9mTbE/hBCorKxEx44dIct1V+G06IyOLMvo3LlzopsRs7y8PH6JmgH3c/Pgfm4+3NfNg/vZLN77o75MTgCLkYmIiChtMdAhIiKitMVAJwU5HA7cf//9cDgciW5KWuN+bh7cz82H+7p5cD+bJXp/tOhiZCIiIkpvzOgQERFR2mKgQ0RERGmLgQ4RERGlLQY6RERE1KwkScJHH33ULM/FQCdJff311zjzzDPRsWPHiB+IyZMno3fv3sjOzkarVq1w8skn44cffkhMY1NcfftakqSI/x599NHENDgFzZgxA0ceeSRyc3PRtm1bnH322Vi/fr1pmw8//BCnnnoq2rRpA0mSsHr16sQ0NsVFs6/Hjx8f9nk+5phjEtTi1BTNft6zZw/Gjx+Pjh07IisrCyNHjsSff/6ZoBbHX6TPkSRJ2LhxY6KbZsJAJ0lVV1ejf//+mDlzZsTbDzroIMycORNr167Ft99+i+7du2PEiBHYu3dvM7c09dW3r3ft2mX699JLL0GSJJx77rnN3NLUtXTpUlx//fVYvnw5Fi1aBEVRMGLECFRXV+vbVFdX4/jjj8dDDz2UwJamvmj2NQCMHDnS9LlesGBBglqcmurbz0IInH322fjrr7/w8ccfY9WqVejWrRtOPvnksPcilYV+jnbt2oUePXokullmgpIeADF37tw6tykvLxcAxBdffNE8jUpT0ezrs846S5x00knN06A0VVxcLACIpUuXht22efNmAUCsWrWq+RuWhiLt63HjxomzzjorcY1KQ6H7ef369QKA+PXXX/VtFEURhYWF4vnnn09UM+Oqrs/RvHnzxMCBA4XD4RA9evQQkydPFl6vV78dgJg1a5YYOXKkyMjIEN27dxfvvvtuk7STGZ004PF4MHv2bOTn56N///6Jbk5a27NnD+bPn48rrrgi0U1JaeXl5QCAwsLCBLck/dW2r5csWYK2bdvioIMOwoQJE1BcXJyI5qWN0P3sdrsBABkZGfo2FosFdrsd3377bfM3sBl9/vnnuPjii3HjjTdi3bp1eO655/Dyyy/jwQcfNG1377334txzz8Uvv/yCiy++GBdeeCF+//33+DeoScIniivUkmX45JNPRHZ2tpAkSXTs2FH8+OOPzd+4NFPbvg54+OGHRatWrYTT6Wy+RqUZTdPEmWeeKQYPHhzxdmZ04qe2ff3222+LTz/9VKxdu1bMmzdP9O/fXxxyyCHC5XIlqKWpLdJ+9ng8olu3buK8884TpaWlwu12ixkzZggAYsSIEQlsbfyMGzdOWCwWkZ2drf8bO3asOOGEE8T06dNN27722muiQ4cO+mUA4pprrjFtc/TRR4trr7027u1s0auXp7phw4Zh9erV2LdvH55//nmcf/75+OGHH9C2bdtENy1tvfTSS7joootMZ2nUMDfccAPWrFmT9me1yaC2fX3BBRfof/fr1w+DBg1Ct27dMH/+fIwZM6a5m5nyIu1nm82GDz74AFdccQUKCwthsVhw8skn47TTTktgS+Nv2LBheOaZZ/TL2dnZOPDAA/HTTz+ZMjiqqsLlcqGmpgZZWVkAgGOPPdb0WMcee2yTDEJgoJPCAh+oAw88EMcccwx69eqFF198EXfffXeim5aWvvnmG6xfvx7vvPNOopuSsiZOnIh58+bh66+/RufOnRPdnLTWkH3doUMHdOvWLa1GBDWXuvbzEUccgdWrV6O8vBwejwdFRUU4+uijMWjQoAS1Nv4Cv0NGmqZhypQpEYPm+k4SJUmKa/sABjppRQih9wtT/L344os44ogjWAcVAyEEJk6ciLlz52LJkiXJNyojjcSyr0tKSrBt2zZ06NChGVqYHhqyn/Pz8wEAf/75J37++Wc88MADzdXMhBg4cCDWr18fFgCFWr58OS699FLT5QEDBsS9PQx0klRVVZVpLoLNmzdj9erVKCwsROvWrfHggw9i9OjR6NChA0pKSjBr1ixs374d5513XgJbnZrq2tddu3YFAFRUVOC9997DY489lqhmprTrr78eb775Jj7++GPk5uZi9+7dAHw/AJmZmQCA0tJSbN26FTt37gQAfU6S9u3bo3379olpeAqqb19XVVVh8uTJOPfcc9GhQwds2bIF99xzD9q0aYNzzjknwa1PHdF8pt977z0UFRWha9euWLt2LW666SacffbZGDFiRCKb3uTuu+8+nHHGGejSpQvOO+88yLKMNWvWYO3atZg2bZq+3XvvvYdBgwZh8ODBeOONN/Djjz/ixRdfjH+D4l71Q3GxePFiASDs37hx44TT6RTnnHOO6Nixo7Db7aJDhw5i9OjRLEaOUV37OuC5554TmZmZoqysLHENTWGR9i8AMWfOHH2bOXPmRNzm/vvvT1i7U1F9+7qmpkaMGDFCFBUVCZvNJrp27SrGjRsntm7dmtiGp5hoPtP/+c9/ROfOnfX9PGnSJOF2uxPX6Dira3j5woULxXHHHScyMzNFXl6eOOqoo8Ts2bP12wGIp59+WpxyyinC4XCIbt26ibfeeqtJ2in5n5CIiIgo7XAeHSIiIkpbDHSIiIgobTHQISIiorTFQIeIiIjSFgMdIiIiSlsMdIiIiChtMdAhIiKipDN+/HicffbZjX4cBjpElHKWLFkCSZJQVlaW6KYQJcyMGTNw5JFHIjc3F23btsXZZ5+tzygeIITA5MmT0bFjR2RmZmLo0KH47bff9NtLS0sxceJEHHzwwcjKykLXrl1x4403ory8POJzut1uHH744ZAkqd4FOAPf09B/kyZNavRrbwgGOkSU9IYOHYqbb75Zv3zcccdh165d+hpCRC3R0qVLcf3112P58uVYtGgRFEXBiBEjUF1drW/zyCOP4PHHH8fMmTPx008/oX379jjllFNQWVkJANi5cyd27tyJf//731i7di1efvllLFy4EFdccUXE57zjjjvQsWPHBrVz/fr12LVrl/7vrrvuiv1Fx6JJ5lsmIoqjIUOGiJtuuinRzSBKasXFxQKAWLp0qRBCCE3TRPv27cVDDz2kb+NyuUR+fr549tlna32cd999V9jtduH1ek3XL1iwQPTu3Vv89ttvAoBYtWpVne0JLK+zf//+iLdv375dnH/++aKgoEAUFhaK0aNHi82bN+u3B5aYmDx5sigqKhK5ubniqquuavAyGszoEFFSGz9+PJYuXYr//Oc/eur75ZdfNnVdvfzyyygoKMCnn36qp+DHjh2L6upqvPLKK+jevTtatWqFiRMnQlVV/bE9Hg/uuOMOdOrUCdnZ2Tj66KOxZMmSxLxQokYKdDcVFhYC8C1QvHv3btMiog6HA0OGDMH3339f5+Pk5eXBag2u+71nzx5MmDABr732GrKyshrd1pqaGgwbNgw5OTn4+uuv8e233yInJwcjR46Ex+PRt/vyyy/x+++/Y/HixXjrrbcwd+5cTJkypUHPxdXLiSip/ec//8GGDRvQr18/TJ06FQBMNQYBNTU1+O9//4u3334blZWVGDNmDMaMGYOCggIsWLAAf/31F84991wMHjwYF1xwAQDgsssuw5YtW/D222+jY8eOmDt3LkaOHIm1a9eiV69ezfo6iRpDCIFbb70VgwcPRr9+/QBAX1G9Xbt2pm3btWuHv//+O+LjlJSU4IEHHsDVV19teuzx48fjmmuuwaBBg7Bly5YGta1z586my3///Tc+/vhjyLKMF154AZIkAQDmzJmDgoICLFmyRA/O7HY7XnrpJWRlZeGQQw7B1KlTcfvtt+OBBx6ALEeXq2GgQ0RJLT8/H3a7HVlZWWjfvj0A4I8//gjbzuv14plnnsEBBxwAABg7dixee+017NmzBzk5Oejbty+GDRuGxYsX44ILLsCmTZvw1ltvYfv27XrNwW233YaFCxdizpw5mD59evO9SKJGuuGGG7BmzRp8++23YbcFAokAIUTYdQBQUVGBUaNGoW/fvrj//vv165966ilUVFTg7rvvrvX5DznkED14OuGEE/DZZ5/pt33zzTfIzc3VL7dq1QorVqzAxo0bTdcDgMvlwqZNm/TL/fv3N2WQjj32WFRVVWHbtm3o1q1bre0xYqBDRGkhKytLD3IA31lr9+7dkZOTY7quuLgYALBy5UoIIXDQQQeZHsftdqN169bN02iiOJg4cSLmzZuHr7/+2pQ9CZwY7N69Gx06dNCvLy4uDsvyVFZWYuTIkcjJycHcuXNhs9n027766issX74cDofDdJ9BgwbhoosuwiuvvIIFCxbA6/UCADIzM03b9ejRAwUFBabrNE3DEUccgTfeeCPs9RQVFdX7miMFarVhoENEacF4YAZ8B8JI12maBsB3oLVYLFixYgUsFotpO2NwRJSshBCYOHEi5s6diyVLlqBHjx6m23v06IH27dtj0aJFGDBgAABfXdrSpUvx8MMP69tVVFTg1FNPhcPhwLx585CRkWF6nP/+97+YNm2afnnnzp049dRT8c477+Doo48GgKizKwEDBw7EO++8g7Zt2yIvL6/W7X755Rc4nU49eFq+fDlycnLCusPqwkCHiJKe3W43FRHHw4ABA6CqKoqLi3HCCSfE9bGJmsP111+PN998Ex9//DFyc3P1mpz8/HxkZmZCkiTcfPPNmD59Onr16oVevXph+vTpyMrKwj//+U8AvkzOiBEjUFNTg9dffx0VFRWoqKgA4MusWCwWdO3a1fS8gROBAw44oEEBh9FFF12ERx99FGeddRamTp2Kzp07Y+vWrfjwww9x++2364/r8XhwxRVXYNKkSfj7779x//3344Ybboi6PgdgoENEKaB79+744YcfsGXLFuTk5OhZmcY46KCDcNFFF+HSSy/FY489hgEDBmDfvn346quvcOihh+L000+PQ8uJms4zzzwDwDfPlNGcOXMwfvx4AL55b5xOJ6677jrs378fRx99NP73v//ptTErVqzADz/8AAA48MADTY+zefNmdO/evUnanpWVha+//hp33nknxowZg8rKSnTq1AnDhw83ZXiGDx+OXr164cQTT4Tb7cY//vEPTJ48uUHPJQkhRJzbT0QUVxs2bMC4ceP0NPacOXNw2WWXYf/+/SgoKMDLL7+Mm2++2TRT8uTJk/HRRx+ZZm8dP348ysrK8NFHHwHwFTBPmzYNr776Knbs2IHWrVvj2GOPxZQpU3DooYc274skoibBQIeIiIjSFicMJCIiorTFQIeIiIjSFgMdIiIiSlsMdIiIiChtMdAhIiKitMVAh4iIiNIWAx0iIiJKWwx0iIiIKG0x0CEiIqK0xUCHiIiI0hYDHSIiIkpbDHSIiIgobf0/lLxCNRdNuF0AAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "cell_type": "code",
+   "id": "aa35752078d27d21",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:29.922886Z",
+     "start_time": "2026-04-03T13:09:29.704149Z"
+    }
+   },
+   "source": [
+    "# remove sentinel values (-99 is a known fill value in the dataset)\n",
+    "ds_cmls[\"rsl\"] = ds_cmls.rsl.where(ds_cmls.rsl > -99)\n",
+    "\n",
+    "dt_h   = (pd.Timestamp(ds_cmls.time.values[1]) - pd.Timestamp(ds_cmls.time.values[0])).total_seconds() / 3600\n",
+    "win_5h = round(5 / dt_h)   # timesteps in 5 h — diurnal window\n",
+    "win_2h = round(2 / dt_h)   # timesteps in 2 h — noise window\n",
+    "\n",
+    "# diurnal: signal fluctuates in a daily cycle (sun heating antenna) — not rain\n",
+    "qc_diurnal = (ds_cmls.rsl.rolling(time=win_5h, center=True).std() > 2).mean(dim=\"time\") > 0.1\n",
+    "\n",
+    "# noisy: signal is randomly unstable for long periods — hardware or interference issue\n",
+    "qc_noisy = (ds_cmls.rsl.rolling(time=win_2h, center=True).std() > 0.8).mean(dim=\"time\") > 0.2\n",
+    "\n",
+    "# null bad sublinks, keep the CML structure intact\n",
+    "qc_bad = qc_diurnal | qc_noisy\n",
+    "ds_cmls_clean = ds_cmls.copy()\n",
+    "ds_cmls_clean[\"rsl\"] = ds_cmls.rsl.where(~qc_bad)\n",
+    "print(f\"Bad sublinks nulled — CMLs fully intact: {ds_cmls_clean.sizes['cml_id']}\")\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bad sublinks nulled — CMLs fully intact: 75\n"
+     ]
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e496a5c313f48f76",
+   "metadata": {},
+   "source": [
+    "### Baseline Computation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "398034409fc219cb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:32.338838Z",
+     "start_time": "2026-04-03T13:09:29.928825Z"
+    }
+   },
+   "source": [
+    "import pycomlink as pycml\n",
+    "\n",
+    "# ── config ────────────────────────────────────────────────────\n",
+    "# \"rolling\"      — rolling median, no wet mask needed\n",
+    "# \"std\"          — wet from fixed rolling std threshold, no reference\n",
+    "# \"std_adaptive\" — wet from per-link adaptive std threshold, no reference\n",
+    "# \"ref_asos\"     — wet from ASOS network mean precip (requires ds_asos)\n",
+    "BASELINE      = \"ref_asos\"\n",
+    "WINDOW_HOURS  = 6    # window duration for all methods\n",
+    "WET_STD       = 0.4  # std threshold for \"std\" method (dB)\n",
+    "WET_THRESHOLD = 0.1  # mm/window for \"ref_asos\" method\n",
+    "N_DRY         = 10   # dry samples averaged at wet event start\n",
+    "\n",
+    "dt_h   = (pd.Timestamp(ds_cmls.time.values[1]) - pd.Timestamp(ds_cmls.time.values[0])).total_seconds() / 3600\n",
+    "WINDOW = round(WINDOW_HOURS / dt_h)\n",
+    "\n",
+    "def _apply_baseline(rsl, wet):\n",
+    "    return xr.apply_ufunc(\n",
+    "        pycml.processing.baseline.baseline_constant,\n",
+    "        rsl, wet.broadcast_like(rsl),\n",
+    "        input_core_dims=[[\"time\"], [\"time\"]],\n",
+    "        output_core_dims=[[\"time\"]],\n",
+    "        vectorize=True,\n",
+    "        kwargs={\"n_average_last_dry\": N_DRY},\n",
+    "    )\n",
+    "\n",
+    "# ── baseline ──────────────────────────────────────────────────\n",
+    "if BASELINE == \"rolling\":\n",
+    "    ds_cmls[\"baseline\"] = ds_cmls.rsl.rolling(\n",
+    "        time=WINDOW, center=True, min_periods=WINDOW // 2\n",
+    "    ).median()\n",
+    "\n",
+    "elif BASELINE == \"std\":\n",
+    "    ds_cmls[\"wet\"]      = ds_cmls.rsl.rolling(time=WINDOW, center=True).std() > WET_STD\n",
+    "    ds_cmls[\"baseline\"] = _apply_baseline(ds_cmls.rsl, ds_cmls.wet)\n",
+    "\n",
+    "elif BASELINE == \"std_adaptive\":\n",
+    "    roll_std            = ds_cmls.rsl.rolling(time=WINDOW, center=True).std()\n",
+    "    ds_cmls[\"wet\"]      = roll_std > 1.1 * roll_std.quantile(0.85, dim=\"time\")\n",
+    "    ds_cmls[\"baseline\"] = _apply_baseline(ds_cmls.rsl, ds_cmls.wet)\n",
+    "\n",
+    "elif BASELINE == \"ref_asos\":\n",
+    "    rain_mean  = ds_asos[\"precip_amount\"].resample(time=\"15min\").sum(skipna=True).mean(dim=\"id\", skipna=True)\n",
+    "    wet_asos   = (rain_mean > WET_THRESHOLD).rolling(time=3, center=True).max()\n",
+    "    wet_on_cml = wet_asos.astype(int).interp(time=ds_cmls.time, method=\"nearest\").fillna(0).astype(bool)\n",
+    "    ds_cmls[\"wet\"]      = wet_on_cml\n",
+    "    ds_cmls[\"baseline\"] = _apply_baseline(ds_cmls.rsl, wet_on_cml)\n",
+    "\n",
+    "print(f\"Baseline: {BASELINE}  |  window: {WINDOW_HOURS}h = {WINDOW} samples\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline: ref_asos  |  window: 6h = 360 samples\n"
+     ]
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "cell_type": "code",
+   "id": "8dc5cb9fbb1bfb7e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:32.951801Z",
+     "start_time": "2026-04-03T13:09:32.440832Z"
+    }
+   },
+   "source": [
+    "# ── config ───────────────────────────────────────────────────\n",
+    "R_MIN   = 0.1    #  rain rates below this are set to zero\n",
+    "\n",
+    "# ── observed attenuation ─────────────────────────────────────\n",
+    "ds_cmls[\"A_obs\"] = ds_cmls[\"baseline\"] - ds_cmls.rsl\n",
+    "ds_cmls[\"A_obs\"] = ds_cmls.A_obs.where(ds_cmls.A_obs >= 0, 0)\n",
+    "\n",
+    "# ── wet antenna correction (optional) ────────────────────────\n",
+    "\n",
+    "ds_cmls[\"waa\"] = pycml.processing.wet_antenna.waa_pastorek_2021_from_A_obs(\n",
+    "    A_obs=ds_cmls.A_obs,\n",
+    "    f_Hz=ds_cmls.frequency * 1e6,\n",
+    "    pol=ds_cmls.polarization.values,\n",
+    "    L_km=ds_cmls.length / 1000,\n",
+    ")\n",
+    "ds_cmls[\"A\"] = ds_cmls.A_obs - ds_cmls.waa\n",
+    "\n",
+    "\n",
+    "ds_cmls[\"A\"] = ds_cmls.A.where(ds_cmls.A >= 0, 0)\n",
+    "\n",
+    "# ── rain rate from k-R power law ─────────────────────────────\n",
+    "ds_cmls[\"R\"] = pycml.processing.k_R_relation.calc_R_from_A(\n",
+    "    A=ds_cmls.A,\n",
+    "    L_km=ds_cmls.length.astype(float) / 1000,\n",
+    "    f_GHz=ds_cmls.frequency / 1000,\n",
+    "    pol=ds_cmls.polarization,\n",
+    "    R_min=R_MIN,\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": 14
+  },
+  {
+   "cell_type": "code",
+   "id": "da983e0aa790856b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:33.539571Z",
+     "start_time": "2026-04-03T13:09:32.958420Z"
+    }
+   },
+   "source": [
+    "# ── baseline inspection — adjust link_idx and time window to evaluate params ──\n",
+    "link_idx = 0\n",
+    "t_check  = slice(\"2024-01-20\", \"2024-01-30\")\n",
+    "\n",
+    "n_panels = 3 + (\"wet\" in ds_cmls)\n",
+    "fig, axes = plt.subplots(n_panels, 1, figsize=(14, 3 * n_panels), sharex=True)\n",
+    "\n",
+    "ds_cmls.rsl.isel(cml_id=link_idx, sublink_id=0).sel(time=t_check).plot(\n",
+    "    ax=axes[0], label=\"RSL\", alpha=0.7)\n",
+    "ds_cmls.baseline.isel(cml_id=link_idx, sublink_id=0).sel(time=t_check).plot(\n",
+    "    ax=axes[0], label=\"baseline\", linewidth=2)\n",
+    "axes[0].set(title=f\"RSL vs baseline — link {link_idx}  |  method: {BASELINE}\", ylabel=\"dBm\")\n",
+    "axes[0].legend()\n",
+    "\n",
+    "ds_cmls.A_obs.isel(cml_id=link_idx, sublink_id=0).sel(time=t_check).plot(ax=axes[1])\n",
+    "axes[1].set(title=\"A_obs\", ylabel=\"dB\")\n",
+    "\n",
+    "ds_cmls.R.isel(cml_id=link_idx, sublink_id=0).sel(time=t_check).plot(ax=axes[2])\n",
+    "axes[2].set(title=\"Rain rate R\", ylabel=\"mm/h\")\n",
+    "\n",
+    "if \"wet\" in ds_cmls:\n",
+    "    ds_cmls.wet.sel(time=t_check).plot(ax=axes[3])\n",
+    "    axes[3].set(title=\"Wet mask\", ylabel=\"wet (1/0)\")\n",
+    "\n",
+    "plt.tight_layout();"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x1200 with 4 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "cell_type": "code",
+   "id": "6dfd9a15ddaab633",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:33.783643Z",
+     "start_time": "2026-04-03T13:09:33.546906Z"
+    }
+   },
+   "source": [
+    "ds_cmls.R.resample(time=\"1h\").mean().cumsum(dim=\"time\").stack(link=(\"cml_id\", \"sublink_id\")).plot.line(x=\"time\", add_legend=False)\n",
+    "plt.title(\"Cumulative rainfall (mm)\");"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "cell_type": "code",
+   "id": "71de72adb4408c5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:33.892577Z",
+     "start_time": "2026-04-03T13:09:33.789678Z"
+    }
+   },
+   "source": [
+    "# ── filter options — set to None to skip ─────────────────────\n",
+    "LENGTH_KM  = (1, 10)   # min/max link length\n",
+    "FREQ_GHZ   = (65.0, 70.0)  # min/max frequency\n",
+    "\n",
+    "# ── build mask — True = keep ──────────────────────────────────\n",
+    "mask = xr.ones_like(ds_cmls.rsl.isel(time=0), dtype=bool)\n",
+    "\n",
+    "### filter by link length and frequency\n",
+    "if LENGTH_KM  is not None: mask = mask & (ds_cmls.length    >= LENGTH_KM[0] * 1000) & (ds_cmls.length    <= LENGTH_KM[1] * 1000)\n",
+    "if FREQ_GHZ   is not None: mask = mask & (ds_cmls.frequency >= FREQ_GHZ[0]  * 1000) & (ds_cmls.frequency <= FREQ_GHZ[1]  * 1000)\n",
+    "\n",
+    "### filter by QC flags\n",
+    "DROP_NOISY    = True\n",
+    "DROP_DIURNAL  = False\n",
+    "\n",
+    "if DROP_NOISY:    mask = mask & ~qc_noisy\n",
+    "if DROP_DIURNAL:  mask = mask & ~qc_diurnal\n",
+    "\n",
+    "ds_cmls_filtered = ds_cmls.where(mask)\n",
+    "\n",
+    "print(f\"Sublinks kept : {int(mask.sum().values)} / {ds_cmls.sizes['cml_id'] * ds_cmls.sizes['sublink_id']}\")\n",
+    "print(f\"CMLs with at least one valid sublink : {int(mask.any(dim='sublink_id').sum().values)} / {ds_cmls.sizes['cml_id']}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sublinks kept : 13 / 225\n",
+      "CMLs with at least one valid sublink : 10 / 75\n"
+     ]
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "cell_type": "code",
+   "id": "a14b55a9052536d0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:34.128586Z",
+     "start_time": "2026-04-03T13:09:33.899112Z"
+    }
+   },
+   "source": [
+    "ds_cmls_filtered.R.resample(time=\"1h\").mean().cumsum(dim=\"time\").stack(link=(\"cml_id\", \"sublink_id\")).plot.line(x=\"time\", add_legend=False);"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 18
+  },
+  {
+   "cell_type": "code",
+   "id": "c916eb24fd52df61",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:34.512045Z",
+     "start_time": "2026-04-03T13:09:34.134876Z"
+    }
+   },
+   "source": [
+    "t_check = slice(\"2024-01-27\", \"2024-01-30\")\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(14, 6), sharex=True)\n",
+    "\n",
+    "ds_cmls_filtered.rsl.sel(time=t_check).stack(link=(\"cml_id\", \"sublink_id\")).dropna(\"link\", how=\"all\").plot.line(\n",
+    "    x=\"time\", ax=axes[0], add_legend=False, linewidth=0.5, alpha=0.5)\n",
+    "axes[0].set(ylabel=\"dBm\", title=\"RSL — chosen sublinks\")\n",
+    "\n",
+    "ds_asos[\"precip_amount\"].resample(time=\"1h\").sum().sel(time=t_check).plot.line(\n",
+    "    x=\"time\", ax=axes[1], add_legend=True)\n",
+    "axes[1].set(ylabel=\"mm/h\", title=\"ASOS precipitation\")\n",
+    "\n",
+    "plt.tight_layout();"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 19
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d9cc1ca67bbbd6e",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Personal Weather Station (PWS) Data\n",
+    "\n",
+    "PWS data from Weather Underground provides dense crowdsourced rainfall observations\n",
+    "across NYC. Consumer-grade stations require quality control before use.\n",
+    "\n",
+    "### Dataset Overview"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "ef5f5fbf51d0064b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:34.628985Z",
+     "start_time": "2026-04-03T13:09:34.520005Z"
+    }
+   },
+   "source": [
+    "# data availability per station — fraction of non-NaN timesteps\n",
+    "avail = (~np.isnan(ds_pws[\"rainfall_amount\"])).mean(dim=\"time\")\n",
+    "\n",
+    "avail.plot.hist(bins=20, color=\"darkorange\", edgecolor=\"white\")\n",
+    "plt.axvline(float(avail.mean()), color=\"red\", linestyle=\"--\",\n",
+    "            label=f\"Mean: {float(avail.mean())*100:.1f}%\")\n",
+    "plt.title(\"PWS data availability\")\n",
+    "plt.xlabel(\"Availability fraction\")\n",
+    "plt.ylabel(\"Number of stations\")\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3);\n",
+    "\n",
+    "print(f\"Stations > 80% availability : {int((avail > 0.8).sum())} / {ds_pws.sizes['id']}\")\n",
+    "print(f\"Stations > 50% availability : {int((avail > 0.5).sum())} / {ds_pws.sizes['id']}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stations > 80% availability : 27 / 37\n",
+      "Stations > 50% availability : 30 / 37\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 20
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eadf7c17c312733c",
+   "metadata": {},
+   "source": [
+    "### Quality Control with `pypwsqc`\n",
+    "\n",
+    "[`pypwsqc`](https://github.com/OpenSenseAction/pypwsqc) implements QC methods\n",
+    "| Filter | What it catches |\n",
+    "|--------|----------------|\n",
+    "| **Faulty Zero (FZ)** | Station reports zero while neighbours report rain |\n",
+    "| **Suspect Zero (SZ)** | Suspiciously few non-zero readings overall |\n",
+    "| **High Accumulation (HA)** | Implausibly large values |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "c71df9cb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:34.643831Z",
+     "start_time": "2026-04-03T13:09:34.638416Z"
+    }
+   },
+   "source": [
+    "import pypwsqc\n",
+    "print(f\"pypwsqc   : {pypwsqc.__version__}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pypwsqc   : 0.2.1\n"
+     ]
+    }
+   ],
+   "execution_count": 21
+  },
+  {
+   "cell_type": "code",
+   "id": "905b1dbd1a9d6307",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:34.680615Z",
+     "start_time": "2026-04-03T13:09:34.657688Z"
+    }
+   },
+   "source": [
+    "import pypwsqc.peak_removal_filter as prf\n",
+    "\n",
+    "from pypwsqc import peak_removal_filter as prf\n",
+    "# ── 0. availability — drop stations with too little data ──────\n",
+    "MIN_AVAIL = 0.5   # keep stations with at least 50% valid data\n",
+    "avail     = (~np.isnan(ds_pws[\"rainfall_amount\"])).mean(dim=\"time\")\n",
+    "ds_pws    = ds_pws.sel(id=avail[avail >= MIN_AVAIL].id)\n",
+    "print(f\"[0] Availability filter : {ds_pws.sizes['id']} stations kept  (>= {MIN_AVAIL*100:.0f}%)\")\n",
+    "\n",
+    "# ── 1. quantile — remove implausible values ───────────────────\n",
+    "ds_pws[\"rainfall_amount\"] = ds_pws[\"rainfall_amount\"].where(\n",
+    "    ds_pws[\"rainfall_amount\"] <= ds_pws[\"rainfall_amount\"].quantile(0.999)\n",
+    ").assign_attrs(ds_pws[\"rainfall_amount\"].attrs)\n",
+    "print(f\"[1] Quantile filter     : top 0.1% values removed\")\n",
+    "\n",
+    "# ── project to UTM (needed by peak removal and IC filter) ───\n",
+    "ds_pws = prf.convert_to_utm(ds_pws, name_coord_lon=\"lon\", name_coord_lat=\"lat\", zone=18)\n",
+    "print(f\"[+] UTM projection      : x/y coords added\")\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0] Availability filter : 30 stations kept  (>= 50%)\n",
+      "[1] Quantile filter     : top 0.1% values removed\n",
+      "[+] UTM projection      : x/y coords added\n"
+     ]
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "cell_type": "code",
+   "id": "ced09c9fd9b74bc1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:36.351355Z",
+     "start_time": "2026-04-03T13:09:34.686052Z"
+    }
+   },
+   "source": [
+    "# ── 2. FZ + HI flagging ───────────────────────────────────────\n",
+    "NINT, N_STAT, HI_THRES_A, HI_THRES_B = 10, 3, 0.5, 2.0\n",
+    "rain    = ds_pws[\"rainfall_amount\"]\n",
+    "ref     = rain.median(dim=\"id\", skipna=True)\n",
+    "nbrs_nn = (~np.isnan(rain)).sum(dim=\"id\").broadcast_like(rain)\n",
+    "ds_qc   = xr.Dataset({\"rainfall\": rain, \"reference\": ref.broadcast_like(rain),\n",
+    "                       \"nbrs_not_nan\": nbrs_nn.astype(float)})\n",
+    "ds_qc   = pypwsqc.flagging.hi_filter(ds_qc, hi_thres_a=HI_THRES_A, hi_thres_b=HI_THRES_B,\n",
+    "                                      nint=NINT, n_stat=N_STAT)\n",
+    "ds_qc   = pypwsqc.flagging.fz_filter(ds_qc, nint=NINT, n_stat=N_STAT)\n",
+    "clean   = (ds_qc[\"hi_flag\"] != 1) & (ds_qc[\"fz_flag\"] != 1)\n",
+    "ds_pws[\"rainfall_amount_qc\"] = rain.where(clean)\n",
+    "print(f\"[2] FZ+HI filter        : HI={float((ds_qc['hi_flag']==1).mean())*100:.1f}%  \"\n",
+    "      f\"FZ={float((ds_qc['fz_flag']==1).mean())*100:.1f}%  flagged\")\n",
+    "\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2] FZ+HI filter        : HI=0.0%  FZ=3.8%  flagged\n"
+     ]
+    }
+   ],
+   "execution_count": 23
+  },
+  {
+   "cell_type": "code",
+   "id": "37ecafb255bb9cad",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:09:36.952974Z",
+     "start_time": "2026-04-03T13:09:36.403627Z"
+    }
+   },
+   "source": [
+    "import contextlib\n",
+    "import io\n",
+    "\n",
+    "# ── 3. peak removal — redistribute spikes after NaN gaps ─────\n",
+    "MAX_DISTANCE, N_CLOSEST           = 5000, 5\n",
+    "QUANTILE, SEQ_LEN_THRESHOLD       = 0.99, 3\n",
+    "SEQ_NAN_THRESHOLD, MIN_STATION_THRESHOLD = 0.5, 2\n",
+    "\n",
+    "ds_pr      = ds_pws[\"rainfall_amount_qc\"].to_dataset(name=\"rainfall\").assign_coords(x=ds_pws.x, y=ds_pws.y)\n",
+    "nbrs_pr    = prf.get_closest_points_to_point(ds_pr, ds_pr, max_distance=MAX_DISTANCE, n_closest=N_CLOSEST)\n",
+    "w          = prf.inverse_distance_weighting(nbrs_pr)\n",
+    "n_peaks    = 0; n_affected = 0; n_skipped = 0\n",
+    "for sid in ds_pr.id.values:\n",
+    "    with contextlib.redirect_stderr(io.StringIO()):\n",
+    "        peaks, starts, _, lens = prf.get_nan_sequences(ds_pr, sid, QUANTILE, SEQ_LEN_THRESHOLD)\n",
+    "    if not peaks: continue\n",
+    "    try:\n",
+    "        seqs  = prf.interpolate_precipitation(ds_pr, sid, nbrs_pr, w, starts, peaks, lens,\n",
+    "                    seq_nan_threshold=SEQ_NAN_THRESHOLD, min_station_threshold=MIN_STATION_THRESHOLD)\n",
+    "    except ValueError:\n",
+    "        # pypwsqc bug: irregular time coordinate causes shape mismatch between\n",
+    "        # the expected sequence length and the neighbor slice length\n",
+    "        n_skipped += 1\n",
+    "        continue\n",
+    "    print(f\"    {sid} — {len(peaks)} peak(s) redistributed\")\n",
+    "    n_peaks += len(peaks); n_affected += 1\n",
+    "    ds_pr = prf.overwrite_seq(ds_pr, sid, seqs, starts, peaks)\n",
+    "ds_pws[\"rainfall_amount_qc\"] = ds_pr[\"rainfall\"]\n",
+    "if n_skipped:\n",
+    "    print(f\"    (skipped {n_skipped} station(s) due to irregular time coordinate)\")\n",
+    "print(f\"[3] Peak removal        : {n_affected} stations  |  {n_peaks} peaks redistributed\")\n",
+    "\n"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculate weights for stations: 100%|██████████| 30/30 [00:00<00:00, 5435.62 stations/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 3/3 [00:00<00:00, 1085.11 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 2/2 [00:00<00:00, 31655.12 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1288 — 2 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 2/2 [00:00<00:00, 43464.29 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 2057.54 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 18157.16 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1896 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 1223.90 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 2117.53 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 17189.77 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1931 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 5047.30 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 1198.72 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 2/2 [00:00<00:00, 25040.62 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1918 — 2 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 2/2 [00:00<00:00, 2066.16 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 2724.90 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 11096.04 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1298 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 23172.95 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 2308.37 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 17549.39 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1388 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 1088.02 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 1268.69 neighbors/s]\n",
+      "Interpolate precipitation values for sequences:   0%|          | 0/1 [00:00<?, ? sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 1707.26 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 19239.93 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1401 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 2070.24 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 2663.05 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 2319.86 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1824 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 988.99 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 2049.25 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 14217.98 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO589 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 1222.47 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 3/3 [00:00<00:00, 1862.20 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 20867.18 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1591 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 16384.00 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 2168.16 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 18477.11 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO638 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 26886.56 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 2096.63 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 19878.22 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1796 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 27060.03 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 2122.89 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 18236.10 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1622 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 1652.60 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 1974.49 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 14513.16 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1533 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 24244.53 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 1251.19 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 6374.32 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1900 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 17549.39 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 726.48 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 2/2 [00:00<00:00, 29330.80 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1326 — 2 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 2/2 [00:00<00:00, 2272.72 sequences/s]\n",
+      "Get precipitation values from neighbors: 100%|██████████| 2/2 [00:00<00:00, 2457.84 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 5282.50 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1053 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 24385.49 sequences/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    (skipped 1 station(s) due to irregular time coordinate)\n",
+      "[3] Peak removal        : 17 stations  |  20 peaks redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 24
+  },
+  {
+   "cell_type": "code",
+   "id": "f9361a0da80b68e0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:11:32.735647Z",
+     "start_time": "2026-04-03T13:11:31.347239Z"
+    }
+   },
+   "source": [
+    "import pypwsqc.indicator_correlation as ic\n",
+    "\n",
+    "# ── 4. IC filter — drop stations uncorrelated with neighbours ─\n",
+    "ds_pws_ic  = ds_pws[\"rainfall_amount_qc\"].assign_coords(x=ds_pws.x, y=ds_pws.y)\n",
+    "ds_asos_ic = prf.convert_to_utm(ds_asos.drop_vars(\"quantile\", errors=\"ignore\"),\n",
+    "                                  name_coord_lon=\"lon\", name_coord_lat=\"lat\", zone=18)\n",
+    "da_asos_ic = ds_asos_ic[\"precip_amount\"].assign_coords(x=ds_asos_ic.x, y=ds_asos_ic.y)\n",
+    "da_asos_ic = da_asos_ic.reindex(time=ds_pws_ic.time, method=\"nearest\", tolerance=\"2min\")\n",
+    "\n",
+    "dist_ref,  indcorr_ref = ic.indicator_distance_matrix(ds_pws_ic, ds_pws_ic,  prob=0.99, min_valid_overlap=10)\n",
+    "dist_pws,  indcorr_pws = ic.indicator_distance_matrix(da_asos_ic, ds_pws_ic, prob=0.99, min_valid_overlap=10)\n",
+    "result = ic.ic_filter(indicator_correlation_matrix_ref=indcorr_ref,\n",
+    "                      distance_correlation_matrix_ref=dist_ref,\n",
+    "                      indicator_correlation_matrix=indcorr_pws,\n",
+    "                      distance_matrix=dist_pws)\n",
+    "\n",
+    "good_stations = result.id_neighbor.values[result[\"indcorr_good\"].values]\n",
+    "bad_stations  = result.id_neighbor.values[~result[\"indcorr_good\"].values]\n",
+    "ds_pws = ds_pws.sel(id=good_stations)\n",
+    "print(f\"[4] IC filter           : {len(bad_stations)} stations dropped  |  {ds_pws.sizes['id']} remaining\")\n",
+    "print(f\"    Dropped: {list(bad_stations)}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 30/30 [00:01<00:00, 28.64it/s]\n",
+      "100%|██████████| 4/4 [00:00<00:00, 28.30it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4] IC filter           : 3 stations dropped  |  27 remaining\n",
+      "    Dropped: [np.str_('KNYNEWYO1472'), np.str_('KNYNEWYO1238'), np.str_('KNYNEWYO1805')]\n"
+     ]
+    }
+   ],
+   "execution_count": 25
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe524523201419ca",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Multi-Sensor Overview: CML + PWS + ASOS\n",
+    "\n",
+    "All three sources aligned over their common time window.\n",
+    "All aggregations use xarray `.resample()` — no raw pandas conversion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "f5d21d336da49594",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:11:39.805395Z",
+     "start_time": "2026-04-03T13:11:39.171145Z"
+    }
+   },
+   "source": [
+    "# ── config ────────────────────────────────────────────────────\n",
+    "RESAMPLE = \"1h\"  # common aggregation for all sensors\n",
+    "\n",
+    "# common time window\n",
+    "t0 = max(pd.Timestamp(ds_cmls.time.values[0]),  pd.Timestamp(ds_pws.time.values[0]),  pd.Timestamp(ds_asos.time.values[0]))\n",
+    "t1 = min(pd.Timestamp(ds_cmls.time.values[-1]), pd.Timestamp(ds_pws.time.values[-1]), pd.Timestamp(ds_asos.time.values[-1]))\n",
+    "\n",
+    "cml_r  = ds_cmls_filtered.R.resample(time=RESAMPLE).mean().mean(dim=[\"cml_id\",\"sublink_id\"], skipna=True).sel(time=slice(t0,t1))\n",
+    "pws_r  = ds_pws[\"rainfall_amount_qc\"].resample(time=RESAMPLE).sum(skipna=True).mean(dim=\"id\", skipna=True).sel(time=slice(t0,t1))\n",
+    "asos_r = ds_asos[\"precip_amount\"].resample(time=RESAMPLE).sum(skipna=True).mean(dim=\"id\", skipna=True).sel(time=slice(t0,t1))\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
+    "\n",
+    "# ── time series ───────────────────────────────────────────────\n",
+    "ax = axes[0, 0]\n",
+    "asos_r.plot(ax=ax, label=\"ASOS\", linewidth=1.5)\n",
+    "pws_r.plot(ax=ax,  label=\"PWS\",  linewidth=1.5)\n",
+    "cml_r.plot(ax=ax,  label=\"CML\",  linewidth=1.5, color=\"black\")\n",
+    "ax.set(title=f\"Network mean rainfall ({RESAMPLE})\", ylabel=\"mm/h\")\n",
+    "ax.legend(); ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "# ── cumulative ────────────────────────────────────────────────\n",
+    "ax = axes[0, 1]\n",
+    "asos_r.cumsum().plot(ax=ax, label=\"ASOS\", linewidth=1.5)\n",
+    "pws_r.cumsum().plot(ax=ax,  label=\"PWS\",  linewidth=1.5)\n",
+    "cml_r.cumsum().plot(ax=ax,  label=\"CML\",  linewidth=1.5, color=\"black\")\n",
+    "ax.set(title=\"Cumulative rainfall\", ylabel=\"mm\")\n",
+    "ax.legend(); ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "# ── CML vs ASOS hexbin ────────────────────────────────────────\n",
+    "ax = axes[1, 0]\n",
+    "ax.hexbin(asos_r.values, cml_r.values, gridsize=30, bins=\"log\", cmap=\"Blues\")\n",
+    "ax.set(title=\"CML vs ASOS\", xlabel=\"ASOS (mm/h)\", ylabel=\"CML (mm/h)\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "# ── PWS vs ASOS hexbin ───────────────────────────────────────\n",
+    "ax = axes[1, 1]\n",
+    "ax.hexbin(asos_r.values, pws_r.values, gridsize=30, bins=\"log\", cmap=\"Oranges\")\n",
+    "ax.set(title=\"PWS vs ASOS\", xlabel=\"ASOS (mm/h)\", ylabel=\"PWS (mm/h)\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout();"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x1000 with 4 Axes>"
+      ],
+      "image/png": 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bhwoU1cZWSruXe72KiXHX/AAAAAAAAIBK3n//fV1++eXBtgj33HOP3SE5Um5xefAb0S0SSNoiPKi0daDKbRCC7REklXvpawsAAAAAAA6vqKhIF1xwgXw+nyZOnKjXXntNSUlJdoflSBtyCiRJ7VrEy1O1mK54v/+SpC3qiKStA5nmgTYIsZV+F5SVl9sQDQAAAAAAcJrXX39d27ZtU8eOHfX444/L5SJFVF/fbc+VJB3ZLjX0Dl+5VJrnv07SFnXET6QDVa60DWmPUO61IRoAAAAAAOA0//73vyVJF198sZKTk22Oxtm+35EvSerTtkrSNlBlK0nxLSIXEKICSVsHqlxpG9oegaQtAAAAAAA4tN27d2vx4sWSpIkTJ9ocjfN9n+2vpj2yatK2aI//MqGV5GZaKdQNSVsHqpy0dbukQNq2nPYIAAAAAADgMJ5++ml5vV4NGjRIffr0sTscR/P6TP2QfZBK20DSNjEtwlEhGpC0daDK7RFchmS4/GnbMtojAAAANLo5c+bIMAxNnTo1uMyyLM2YMUPt2rVTQkKCRowYobVr19oXJAAAB+Hz+fTII49Ikv74xz/aHI3z/bSrUGVeU0lxbnVqnRh6Z7DStnXkA4PjkbR1oMqVti5Dclc0C/f6SNoCAAA0pjVr1uiJJ57QgAEDQpbff//9evDBB/XII49ozZo1ysrK0ujRo5Wfn29TpAAA1OzVV1/Vpk2b1Lp1a1ojhMGj7/4oSRrUqZVcLiP0zuK9/ksqbVEPNNRwoMpJW0MK/lJgIjIAAIDGU1BQoIkTJ+of//iHZs2aFVxuWZYeeugh3XbbbRo/frwk/9dOMzMztXDhQk2aNMmukAEACLFnzx5NmTJFkr/KNjEx8TCPiIz3N+zSff/7Qdm5JXaHUieWpL2FZXIZ0s2n9a6+Au0R0AAkbR2oansEl0HSFgAAoLFNnjxZZ5xxhkaNGhWStN24caOys7M1ZsyY4DKPx6Phw4fro48+OmjStrS0VKWlpcHbeXn+SUxM0wz5kD5amKYpy7Ki8rU1R4xn9GAso8vBxnP9+vV6+eWX9eSTT2rHjh3q3bu3brnlFtvHPbe4XPcs+l4vf7HN1jga6vcnd1XfdinV9qdRuEeGJCuhlaw67mt+NhtPpPdtfbdD0taBQiptDclV0R6hzMtEZAAAAI3h+eef1+eff641a9ZUuy87O1uSlJmZGbI8MzNTmzdvPuhzzpkzRzNnzqy2fNeuXSopcValUW2Ypqnc3FxZlhU8f4VzMZ7Rg7GMLjWN5+bNm3XKKaeoqKhIkpSWlqa//e1vKigoUEFBgW2xen2WJr+0Xl9tL5Ah6bdHpevs/umq2mGgqYtxGerY0qOcnJxq97XYu00JkgpMjwpruP9Q+NlsPJHet/Vtl0XS1oGq97T1/0bzeX12hQQAABC1tmzZomuvvVZLlixRfHz8QdczjNB3mZZlVVtW2fTp0zVt2rTg7by8PHXs2FHp6elKTU096OOcyjRNGYah9PR03nxGAcYzejCW0aWm8Zw5c6aKiop05JFH6uqrr9bFF1+spKQkmyOV5vzvB321vUDJnhjNv/RYHdO5ld0hhZ1hFkqSkjI6Kykjo06P5Wez8UR63x7q/PFQSNo6UNX2CIGkbVk5lbYAAADh9tlnnyknJ0fHHHNMcJnP59N7772nRx55ROvWrZPkr7ht27ZtcJ2cnJxq1beVeTweeTyeastdLlfUvjkzDCOqX19zw3hGD8ay7izL0s6dO1VWVibLsoLv0+2+XlZWppycHKWkpMjlcik7O1vz58+XJD366KMaMWJEo+6X2srJL9HTH/m/jfLAuUfpuK5R2vO1YiIyV1IbqR4/X/xsNp5I7tv6boOkrQMddCIyLz1tAQAAwu3UU0/VN998E7LssssuU+/evXXzzTerW7duysrK0tKlSzVo0CBJUllZmVauXKm5c+faETIQNq+++qrWr18fTAhV/ldQUKDk5ORDVpSj6WMsa8+yLG3btk3ffPONvvnmG+Xm5todUq0NHTpUw4cPtzuMoGc/3qwyn6mjO7XUaf2y7A6n8QQnImttbxxwJJK2DlS50tYwjGClrbec9ggAAADhlpKSon79+oUsS0pKUlpaWnD51KlTNXv2bPXo0UM9evTQ7NmzlZiYqAkTJtgRMhAWq1at0m9+8xu7wwCaLJfLpbi4OEn+9+aBpLed12NjY+VyuYLf5IiNjdVpp52m6667rskk5YvKvHp2lb/K9sqTu9kcTSMr2ue/TIzSSmI0KpK2DhSotA005w4mbam0BQAAsMVNN92k4uJiXX311dq3b58GDx6sJUuWKCUlxe7QgHp74YUXJEl9+/bVscceG0wOBb7mWVxcrISEhCaTCEL9WJbFWNZBWlqa+vfvr/79+6t3797BpG1TYZqmcnJylJGR0WS/Uv/SZ1u1v6hcnVonakzfKK6y9ZVLpRXV2CRtUQ8kbR0okLQN/Dl1GYH2CPS0BQAAiIQVK1aE3DYMQzNmzNCMGTNsiQcIN9M09eKLL0qS7r33Xp111lnV7m/qiSHUDmOJSPKZlp78YKMk6fITuwSL0KJSkb+frQyXFN/C3ljgSPxGdqBAe4SqlbblXtojAAAAAGi41atXa+vWrUpOTtbYsWPtDgdAlPjwx93atKdIqfExOvfYjnaH07gC/WwTWkkut72xwJFI2jpQ1fYIhqyK5dbBHgIAAAAAtfbZZ59JkkaOHKn4+HibowEQLV77crsk6ayB7ZXkifIvfweTtkxChvohaetAwfYIFUnbQHsES6ZdIUH+cbn88sv1wAMP2B0KAAAA0CD5+fmSpDZt2tgcCYBoUVLu09trsyVJvx7YzuZoIqC4YhKyhFb2xgHHivKPNaJT1fYIgeStz0fS1k5vvfWW5s+fL0m64YYbbI4GAAAAqL9A0jY5OdnmSABEixXrdqmg1Kv2LRN0TKdmkMgs2e+/TGhpZxRwsCZTaTtnzhwZhqGpU6faHUqTV709QmA57RHstH79ertDAAAAAMKioKBAkpSSkmJzJACixee/+CtPT+mdIVc0T0AWULzff0mlLeqpSSRt16xZoyeeeEIDBgywOxRHCLZHqLgd+F1H0tZe27dvtzsEAAAAICyotAUQbt9tz5Mk9W2XanMkERKotI1vaWcUcDDbk7YFBQWaOHGi/vGPf6hVKz59qI2q7REClz7TZ1NEkKRt27bZHQIAAAAQFlTaAggny7L0/Q5/0vbI5pK0DVbatrQzCjiY7T1tJ0+erDPOOEOjRo3SrFmzDrluaWmpSktLg7fz8vw/8KZpBqtPo41pmrIsK+T1eb1eSTUkbX3Rux+coHKlbU3jUNNYwrkYz+jBWEYXxrNxRHq/Mn6A/ai0BRBOu/JLtaewTC5D6pnZTD4MCkxERqUt6snWpO3zzz+vzz//XGvWrKnV+nPmzNHMmTOrLd+1a5dKSkrCHV6TYJqmcnNzZVmWXC5/YfSuXbskSUbFDGSBNgm5ubnKycmxI0xI2rJlS/B6TeNQ01jCuRjP6MFYRhfGs3FEer8GkkUA7EOlLYBwWltRZdstPVnxsW6bo4kQJiJDA9mWtN2yZYuuvfZaLVmyRPHx8bV6zPTp0zVt2rTg7by8PHXs2FHp6elKTY3O8nrTNGUYhtLT04Nvknbv3i2p0kRkFZeJSUnKyMiwI0xIys7ODl6vaRxqGks4F+MZPRjL6MJ4No5I79fanhsCaDxU2gIIp2BrhLbRmbupUaA9ApW2qCfbkrafffaZcnJydMwxxwSX+Xw+vffee3rkkUdUWloqtzv00xePxyOPx1PtuVwuV1S/MTMMo8bXWLU9gmUpqvdDU2ZZloqLi4O3DzYOBxtLOBPjGT0Yy+jCeDaOSO5Xxg6wXyBpS6UtgHD4fLO/VUCz6WcrVaq0Zf4m1I9tSdtTTz1V33zzTciyyy67TL1799bNN99cLWGLAwJ93gJtEZiIzH6B/soAAABANAi0R6DSFkBDFZV59f4G/zeGh/dMtzmaCGIiMjSQbUnblJQU9evXL2RZUlKS0tLSqi1HKMuyJFWvtDVNy6aIsHXr1uB1qoMAAADgdFTaAgiX99bvUqnXVKfWieqd1Ux+p5jmgUpb2iOgnsguOVCg0jbY07ZiuY+Zlm1TOWnLjNcAAABwMp/PF2z9RaUtgIZ6e+1OSdKYIzODE6pHvbJ8yarIDVBpi3qyrdK2JitWrLA7BEcItkcIVtpaIcsRedu3bw+5bVlW8/ljBAAAgKgSaI0gUWkLoGF25BbrzW92SJJO759lczQRFGiN4PZIsQm2hgLnotLWgaq3RzBCliPydu/eHXKbsQAAAIBTBZK2MTExNU4EDQC19bd3flSZ19TxXVvr6E7NaEIuJiFDGJC0daDq7REClbYkCu1SNWlL1TMAAACcKtDPNjk5mW+PAai3jbsL9Z9Pt0iSbhzbq3n9PmESMoQBSVsHCrZHqLjtrrhCT1v7UGkLAACAaBGotKU1AoCG+OvS9fKZlkb2StdxXVrbHU5kMQkZwoCkrQNVbY8Q+LCK6k777Nq1K+Q2YwEAAACnqlxpCwD18fXW/Xr9K//cLzeM7WVzNDYo3ue/pNIWDdCkJiJD7Zg+n6QDvWxdgfYIVHfahkpbAAAARAsqbQHU1Za9RcrOKwnenvH6WknS2QPbqW+7FnaFZZ9AewQqbdEAJG0dyPR5JR2osA1OREZ1p23oaQsAAIBoQaUtgHfX5ei77XmSpJ15JVr18x6VeWt+n1vus7Rtf3G15anxMbrtjCMbNc4mq6giR5DUxt444GgkbR3IMgOVtv7bhuGv6vT5SBTahUpbAAAARItA0pZKW6B5+nZbri6bv6ZOj3G7DHVqnRiceyfGbWja6F5KT/GEP0AnKAwkbdPtjQOORtLWgQ60R/DfDjQmtkSi0A5er1f79u0LWUalLQAAAJwq0B6BSlugefrz2+skSf3ap+rItqlKiHVr6BFtlJYUd9DHHJGRrJaJB7+/2SmsmPeGpC0agKStAwXbI1TcDiRvqbS1x549e6otI2kLAAAAp6LSFmi+vvhln1au36UYl6FHJxytzmlJdofkTAU5/kuStmgA1+FXQVMT6F0brLStaI9gWSQK7RBojVD5pJb2CAAAAHAqKm2B5uvfn/wiSTprYHsStg1RSE9bNBxJWwcyTX+l7YGkrf+SnK09AknbjIyM4DIqbQEAAOBUTEQGNE95JeVa9PV2SdKEwR1tjsbBLIv2CAgLkrYOFGyPEOxpWzERGYlCW9SUtKXSFgAAAE4VSNqmpqbaHAmASHrjq+0qKTfVIyNZR3dqZXc4zlWSK5nl/utU2qIBSNo6kGX6E4KBCttA8tak1NYWu3b5P0Gj0hYAAADRIC8vTxJJW6C5efWLbZKk3x3bUUYg0YC6C7RGiEuRYhPsjQWORtLWgaq2R3AHkrYkCm0RqLRNTz/wtQcqbQEAAOBUTEQGND/b9hdrzaZ9MgzpzKPa2R2OswVaIyTTGgENE9OQB5umqR9//FE5OTnVEobDhg1rUGA4ONPnkyQFPvcyFJiIjEShHfbs2SNJSktLk2EYsiyLBDoAAAAci0pboPl54yt/L9vBXVsrq0W8zdE4HP1sESb1TtquWrVKEyZM0ObNm6slCw3DkK8isYjwsyraILgqvq5woNKWpK0dKs+uS9IWAAAATkfSFmheSsp9euajTZKkswa2tzeYaEDSFmFS76TtVVddpWOPPVZvvvmm2rZtS7+TCApU2gZ72lZU2pIotEdhYaEkKSkpSS6XS6ZpUvUMAA1U5jX1Q3ae+rVrIZeLcwwAiCTaIwDNy8JPftH23BJlpcbrN4NI2jZYMGnLJGRomHonbTds2KAXX3xRRxxxRDjjQS2YPn9P20Ce3GVUJG1JFNqictI28OEFCXQAaJgb/vuVXv9qu64f3VNTTu1hdzgA0KxQaQs0H5Zl6Z/v/yxJuubUHoqPddscURSg0hZhUu+JyAYPHqwff/wxnLGgliwz0B5BIZckCu0RSNomJyfL5fL/SFFpCwAN83pFX7Un3vvZ5kgAoHkpKytTaWmpJJK2QHOweU+RtueWKM7t0vijqbINi4Ic/yVJWzRQnSptv/766+D1KVOm6Prrr1d2drb69++v2NjYkHUHDBgQnghRjWn6K22DSdvARGT0tLVF1fYIEgl0AAiX2Jh6f74MAKiHQGsEifYIQHOweuNeSdJRHVtQZRsuef7iA6W0tTcOOF6dkrYDBw4MTrQUcPnllwevB+5jIrLGFehpG+jwF6y0tUgU2qGm9ghU2gJAeMTQzxYAIirQGiEhIUExMfXupgfAIVZv8idtj+/a2uZIokjeNv9lCyqX0TB1+iu8cePGxooDdRCoqA28j3UHetr6SNragUpbAGg8sW4qbQEgkgKVtrRGAJqHQKXt8V3TbI4kSvjKpfxs//XUDvbGAserU9K2c+fOuvXWW3X22Wfr+OOPb6yYcBim6a+0DSRtAzVIicXb7AmomWMiMgBoPLFuKm3RMCUlJfr73/+ud999Vzk5OdX+Rn/++ec2RQY0TYFKW1ojANEvJ69Ev+wtksuQjuncyu5wokN+tiRLcsXS0xYNVufvu+zYsUPjxo2T2+3WmWeeqbPOOkujRo2Sx+NpjPhQA9PnrbjmfyMbSN7GlOXX/AA0qpoqbWmPAADhQaUtGuryyy/X0qVLdc455+j4448PfsAKoGaBpC2VtkD0W7vD//PePT1ZyR7aoYRFoDVCalvJxXksGqbOP5Xz58+XZVn64IMP9MYbb+j666/Xtm3bNHr0aP3617/WuHHj1KZNm8aIFRWsit61gTcdgbce5UacTRE1X5ZlUWkLAI2IpC0a6s0339Rbb72lE0880e5QAEegPQLQfHy33Z+07dOWn/ewyd3qv6Q1AsKgXu+EDMPQySefrPvvv18//PCDVq9erRNOOEH/+Mc/1L59ew0bNkwPPPCAtm3j6/qNITgRWUW2NlBpazERWcQVFxcHq2qptAWA8KM9Ahqqffv2fM0bqAPaIwDNx/cVlbZHtiNpGzZMQoYwCkv5Sp8+fXTTTTfpww8/1JYtW3TJJZfo/fff13PPPReOp0cVgZ62gaxtIHlLdWfkBapsJSkxMZGJyAAgzKi0RUP95S9/0c0336zNmzfbHQrgCLRHAJqP73ZQaRt2edv9l6kkbdFwYW9akpGRoSuuuEJXXHFFuJ8aFQIVtZbhfyNrBJdT3RlpgaRtfHy83G53sD0CYwEA4RFDpS0a6Nhjj1VJSYm6deumxMRExcbGhty/d+9emyIDmqZAewQqbYHoVlTm1cbd/vezfdry8x42wfYIJG3RcPVO2jITr30C7REkl1Yf82e5Fv1JEu0R7FC5n60kKm0BIMyotEVDXXDBBdq2bZtmz56tzMxMJiIDDoNKW6B5WJedL8uS2iR7lJESb3c40YP2CAijeidtmYnXPqavIiFoGNrR8QwVxNwpaSeJQhtUTdoyERkANFzlbyvEkbRFA3300Uf6+OOPddRRR9kdCuAIJG2B5uGzzfskSf3b87MeVvnZ/suUtvbGgahQ76QtM/HaJ1BR6zL8SULDFbzDvqCaqYNV2tIeAQDqr9R74IMv2iOgoXr37q3i4mK7wwAcg/YIQPPw4Y+7JUlDu7exOZIoYllS0R7/9aR0e2NBVKh3+Qoz8drnQHsEQ0bwf6o77UClLQCEX2n5gd+htEdAQ9133326/vrrtWLFCu3Zs0d5eXkh/wCEotIWiH5lXlOfbPT3dB96RJrN0USRsgLJV+a/nsh+RcPVu9I2MBPv448/rs6dO4czJhxGoNLWMAy5DEMKTn5FojDSqLQFgPAr8fqC12m/hIY67bTTJEmnnHJKyPFkWZYMw5DP5zvYQ4FmiaQtEP2+2rpfRWU+tU6KU58sftbDJlBlG5MgxSXaGwuiQr2TtszEa59Apa1h+FskuIJJWxKFkcZEZAAQfiXlB5JopsnfNjTMu+++a3cIgKPQHgGIfivW5UiShnRLk8vFB+RhE0jaUmWLMKl30paZeO0TTAgahr+nbSBpS6Iw4g7WHoEEOgDUX+Wetj6Stmig4cOHq6SkRF9//bVycnLq9cHqvHnzNG/ePG3atEmS1LdvX9155506/fTTJfn/7s+cOVNPPPGE9u3bp8GDB+vRRx9V3759w/lSgIig0haIbl6fqZc+2yZJGtsvy+ZookxRRfFiYmt740DUqHfSlpl47WNZgUpbQ4ZBotBOVNoCQPhVrrT18bcNDbR48WJdfPHF2r17d7X7atseoUOHDrrvvvt0xBFHSJKefvppnXXWWfriiy/Ut29f3X///XrwwQe1YMEC9ezZU7NmzdLo0aO1bt06qhXhOCRtgej23oZdys4rUavEWI3tm2l3ONGFSluEWb1n92AmXvv4fAeStq5KlbYkCiOPicgAIPwqV9rSHgEN9ac//UnnnnuuduzYIdM0Q/7Vtp/tmWeeqV/96lfq2bOnevbsqXvvvVfJyclatWqVLMvSQw89pNtuu03jx49Xv3799PTTT6uoqEgLFy5s5FcHhJdlWbRHAKLYO9/v1G2vfCtJGn90B3li3DZHFGUCSdukNvbGgahR70rbwEy89957r/r371+tpy2fzDaewBsMl2HIZSg4EZlIFEYcE5EBQPhRaYtwysnJ0bRp05SZGZ5qIp/Pp//+978qLCzUkCFDtHHjRmVnZ2vMmDHBdTwej4YPH66PPvpIkyZNOuhzlZaWqrS0NHg7UOEYSCpHG9M0ZVlWVL62aFFcXCyv1ytJSk5OPuRYMZ7Rg7GMLjWNp2lauumlb/TyF/62CJ1bJ+qKE7sw5mFmFO6WIclKaB2W9pX8bDaeSO/b+m6n3klbZuK1j+nzD7ZhBBK3Fe0RxBvbSKM9AgCEX0k5PW0RPuecc45WrFih7t27N+h5vvnmGw0ZMkQlJSVKTk7WK6+8oiOPPFIfffSRJFVLCmdmZmrz5s2HfM45c+Zo5syZ1Zbv2rVLJSUlDYq3KTJNU7m5ubIsK3jOhKalchuRoqKiQx6HjGf0YCyjS03jufLH/Xr5i21yG9IFR2fq9ye0k6s0Tzk5eTZHG11S92xVoqQCy6PCnJwGPx8/m40n0vs28C2Wuqp30paZeO3j8/k//Xa5XFKlSlsShZFXUFAgiYnIACCcSr0HPvg1+X2KBnrkkUd07rnn6v3336/x22HXXHNNrZ6nV69e+vLLL7V//3699NJLuuSSS7Ry5crg/VUn5Q0UMhzK9OnTNW3atODtvLw8dezYUenp6VH5rTXTNGUYhtLT03nz2UQFqr2Tk5OVlXXoCYoYz+jBWEaXmsbzv6/8LEm6clg33TS2l53hRTXDKpIkJaV3UlJGRoOfj5/NxhPpfRsfH1+vx9U7aRuOmXhRP6HtEQy5XCQK7UKlLQCEX+VKW36doqEWLlyot99+WwkJCVqxYkVIItUwjFonbePi4oITkR177LFas2aNHn74Yd18882SpOzsbLVt2za4fk5OzmFbMng8Hnk8nmrLXS5X1L45Mwwjql+f0wUKElJTU2s1Roxn9GAso8v+Yq/Wrt8tl8vQV1ty9enmfYp1G7rsxK6McWMq2itJciW1kcK0n/nZbDyR3Lf13Ua9k7bhmIkX9ROciMzl72kbrO7knW3EHWwiMhLoAFA/eSXl2pl34Ou49LRFQ91+++26++67dcstt4T1pNyyLJWWlqpr167KysrS0qVLNWjQIElSWVmZVq5cqblz54Zte0AkMAkZ4HxfbtmvS55eq/zS0JzMpUO7KDO1ftV+qKXARGSJafbGgahR76RtYCbeO++8M2wTO6B2gu0Rgv1sSRTapbi4WJKUmJgoiUpbAGgIy7J0wux3VFRWqT0CPW3RQGVlZTrvvPMalLC99dZbdfrpp6tjx47Kz8/X888/rxUrVmjx4sUyDENTp07V7Nmz1aNHD/Xo0UOzZ89WYmKiJkyYEMZXAjS+QHuEaGzPATQHH/+0R79/eo0Ky3xq1zJe6ckeeWLcmjC4k84a2M7u8KJfMGnbxt44EDXqnbQN90y8qL0DE5G5ZOhAotCySBRGWnl5uSQF++MZ9BcGgHor9ZohCVuJSls03CWXXKIXXnhBt956a72fY+fOnbrooou0Y8cOtWjRQgMGDNDixYs1evRoSdJNN92k4uJiXX311dq3b58GDx6sJUuWUK0IxyFpCzjXu+tydNWzn6nUa+rYjilacMUJSo6Pszus5sM0pWJ/ewQqbREu9U7ahmsmXtSdz6xoj2AY/iRhsD0Cb2wjrWrS9kACnbEAgLoqKa/eWolKWzSUz+fT/fffr7ffflsDBgyoNhHZgw8+eNjnePLJJw95v2EYmjFjhmbMmNGQUAHb0R4BcJbiMp/+tnyDlqzN1uY9RfKalk7pna67RnVQYly90z2oj6I9UqCQLqGVvbEgatT7pzhcM/Gi7nyBStuKnrYu+qjaxuv1t6qIifH/KNEeAQDqr9Rb/XcnlbZoqG+++SbYa/bbb78Nua/ypGQAqLQFnCQ7t0QXPvmJfswpCC47Y0BbPXjuAO3bU33uITSy3ev8l626SDFUOCM86p20DcdMvPPmzdO8efO0adMmSVLfvn1155136vTTT69vWM1CSE9bV6VKW9ojRNzB2iOQQAeAuqup0tbHnzY00Lvvvmt3CIBjkLQFmr49BaX6v/d+1itfbNOu/FJlpnp02xlHqkdGsnpnpfBe1C47v/NfZhxpbxyIKvVO2oZjJt4OHTrovvvu0xFHHCFJevrpp3XWWWfpiy++UN++fesbWtTzVelpS6LQPlTaAkD41FRpS3sEAIgc2iMA9vD6zJBvF23ZW6w1m/bKW+U8aOveIv3n0y3aV+QvHuraJknPXnG8OrRKDK5DXsAmOYGkbR9740BUqXfSNhwz8Z555pkht++9917NmzdPq1atqjFpW1paqtLS0uDtwCfBpmlGbZLMNE1ZlhXy+rwVlbaGy5BkhbRHiNb90FQFKm3dbrdM0wwm0H0+X7WxqGks4VyMZ/RgLJuOolJvtWW+Ov6NZzwbR6T3K+MH2INKW6D21u/M14KPNqmwhvOXuthTUKZPNu5Rua/2ydbeWSmaOqqHRvTKUHysu0HbR5jkfO+/pNIWYVTvpG04ZuKtzOfz6b///a8KCws1ZMiQGteZM2eOZs6cWW35rl27VFJSEpY4mhrTNJWbmyvLsoIJ8tKK12oYhvbv2y+z4pM0n8+rnJwc22JtjsrKyiT5qxJycnLk8/m/2rtv375qY1HTWMK5GM/owVg2Hdm78nW+e7kSVaqnfP5WSWXeuv1tYzwbR6T3a6DaD0BkkbQFaueZjzfpnkXf1SnRWhexbkPHd22t1PjQuYOSPDEa2StDY/pmKtbNeU6TYVmVkrZU2iJ86p20DcdMvJJ/coghQ4aopKREycnJeuWVV3TkkTV/MjF9+nRNmzYteDsvL08dO3ZUenp61J5YBKo309PTg2+SXG7/sBmGobS0VoqpdDsjI8O2WJujQJI2IyNDGRkZiovzNxxPSUmpNhY1jSWci/GMHoxl05Gwz9R9sf+UJL3pG6ydai3DcNfpbxvj2TgivV/j4+MbfRsAqqM9AnB4j777o/78tn/SqVN7Z2joEW0a9HxxMS4N6dZamanxIcs8MVTQOkbeNqk0V3LFSGk97I4GUaTeSdtwzcTbq1cvffnll9q/f79eeuklXXLJJVq5cmWNiVuPxyOPx1Ntucvliuo3ZoZhhLxGsyJRaLhccrtcFW0SJMukqijSAu0RPB5PyBgFxqyqqmMJZ2M8owdj2TT4yg58a6aFUaidVmv56lHZyXg2jkjuV8YOsAeVtpFRUu7T8h9ytHrjXnmbQDsYy5KKi4uVkLBLdXgr3yztzi/T4rXZkqSpo3ro2lN71Cn/gcMoyZUKd0um1//PKT553H/ZppcUE2dvLIgq9U7ahmsm3ri4uOBEZMcee6zWrFmjhx9+WP/3f/8XluePRoHqTsNwyWUYlSYis/8PfnPDRGQAED7lZcXB67Hy/35lIjIAiBySto3v/Q27dNsr3+qXvUV2h4IGuPVXvfWHYd3tDiN67P1Zene2tPYVZyVrKzNc0sjwtA8FAuqdtG0slmWFTDaG6nwVCUGXy5Bh+JO3kvwfkSKiApW2gfYgRqVJ4QAAdeMtPfAGNlH+cwEfv08BIGJoj9B49haWadai7/TyF9skSRkpHp0xoK1aJMQe5pGNz7IsFRYWKikpiarRWji6UysN65ludxjOt+lD6esXpOyvpZ3fSb6KPFBcsuSOk1xuSQ45HuMSpRG3Sn3G2R0JooytSdtbb71Vp59+ujp27Kj8/Hw9//zzWrFihRYvXmxnWE2ez+dP2gYrbSvaI5i8sY04Km0BIHy8pQcqbUd2jdeanyUfv04BIGIOVmn70U+79f6G3SHLLMtSUWGREpP2keg7jH2FZfrft9nKLS6XYUiXDOmiG8b2UrKnadRQmaapnJwcZWRk0J4Gjc+ypA8fkpbNCF3ebYQ0aqbUbmDkYwKaKFv/SuzcuVMXXXSRduzYoRYtWmjAgAFavHixRo8ebWdYTZ5p+tsjyDAqeg5R3WkHy7KCSVsqbQGg4XzlB3ra/ubIFrr/Zz6QBIBIsSyrxkrbFetydNmCNXypLwx6Z6Vozvj+GtSpld2hAPbwlkpvTpO++Jf/dv9zpSPPklp1lTL7iqbKQChbk7ZPPvmknZt3rEBPW7ncchmG3BWfhtLTNrKC46ADSVsqbQGg/nyVKm3d5QWSWslHT1sAiIj8/Pxg4UGLFi0kSYu+3q5bX/5GliWd3KONemYeSOZalqWioiIlJiZSaXsYsW6XTujWWicd0UYxbipZ0YxYln9isXVvSju+8rdE2L3O3//1tPukwZPsjhBo0prG9zFQJ4FkoSW3XEbl6k47o2p+Av1spQPtEQJjQdIWAOrOLDvQ09aftGUiMgCIlP3790vyTxSdkJCgt9dm608Lv5AkHdu5lf55ybHyxLiD6/OVeiCKmT5p+T3SusVS4S5JVkXCofKlVPFfDfcFLk3JVxb63Enp0tmPSz1GRe71AA5F0taBfBVfybdcbhmGIcPFRGR2qJy0rVppS3sEAKg7y3tgItLEbR/pkdgl+ps10caIAKD52LdvnySpZcuWMgxDz6/+RZI0/uj2mjO+f0jCFkCUWzVP+uCv4Xu+jCOlnmOlxDbSwAlSYuvwPTcQxUjaOlCw0tZwV3Szrai0pbozogL9bCUmIgOA+irzmvohO0/92rWQWX6gPULC5nc0zi1lWrmSLrEvQABoJgKVtq1atdKeglK9VzHx2OSRR5CwBZqTvRuld+72Xz/ldqnn6f52BoYhyTjIpQ5+f1IbKTbBjlcCOB5JWwcKJAStip62hqsiaWtnUM3QodojUGkLALVzw3+/0utfbddNp/VSi3J/pe2fPyzV82vLtXhiorIS99gcIQA0D4GkbcuWLfXWt9nymZb6tU9V9/RkewMDEFmfPiX5SqUuJ0sn38DkYICNaD7kQIFKW9NwyWUYchlUd9ohUGnrdruDyVoqbQGgbl7/arsk6ZHlP0pef6XtTctK9fkOU9PfKdV+K8nO8ACg2ajcHuGf7/8sSTp7YHs7QwIQad4y6avn/NdP+CMJW8BmJG0dyBdICBoxMgwFK22ptY2sQKVtoJ+tRKUtANRXSblPKi8NWbZik1f7rWR+pwJABAQqbfNMjzbvKVJaUpwuOL6TvUEBiKz1FROPJWdKPcbYHQ3Q7JG0daADlbZuGYaClbZMRBZZgUrbyklbKm0BoH5MSzJ8JTIr/S37aZ+lPWaiTP68AUCjCyRttxX5ixCuHnmEkjx00wOalc+f8V8OnCC5Yw+9LoBGR9LWgXy+ip62hr+nrQKJQpK2ERWotA30s5VI2gJAg3hLlVsSuuiLbWXykbUFgEYXaI9QYHkkSaf0zrAzHACRlrtV+ukd//VBF9kbCwBJJG0dKVhpG5iIjK/k26KmSlvGAgDqz+0r0Z7i0N+fP2zdx4eSABABgUpbMy5RMS5DHVsx2zvQrHz2tGSZUueTpLTudkcDQCRtHSlQxWkaMXIZB6o7aY8QWVTaAkB4Gb4y7SkK/f3pLfdSaQsAERBI2ro8yeqUlqgYN28VgWZj80fSBw/6rx97mb2xAAjiL7EDBSYis4wYyZAMUd1pByYiA4Dwcpul1SptLdMnH79TAaDRBdojuOKT1a1Nss3RAIgYy5JemyyZXqnveKnfb+2OCEAFkrYOFOhp66tojxCo7iRRGFmB9ghU2gJA/cVVquRym6XaU1Tlb5lpyuJXKgA0umClbXyyuqUn2RsMgMjZ+7P/nztO+vXfpIpCJAD2I2nrQMFKW8VUTERGdacdqLQFgIaLjz1wKhJTU6WtZVJpCwAREKy09SSpaxuStkCz8fO7/ssOx0ueFHtjARCCpK0DBZK2pquip61Bpa0dqLQFgIaLj3UHr8eYZdUqbS3TR09bAIiAA5W2SepG0hZoPn5e6b/sNtzeOABUQ9LWgcyKN6+mESPDMGQE2yPYGVXzU1OlLUlbAKibkKStVVZjpa3JHzgAaFRer1cFBQWS/O0RutIeAWgeTFPa9L7/ercRtoYCoDqStg4UrLQ13DKMyl/JJ1EYSYFKW9ojAED9VW6PEFcpadvC419mmSaVtgDQyAJVtpLUqkVLpSd77AsGQOTsXi8V75Nik6R2g+yOBkAVJG0dyOerqLR1xYRORGZnUM1QoNKW9ggAUH+emAOVth6jPNgeITO54m8bSVsAaHSBpK0Rl6CebVsECxEARLmd3/ovM/tK7thDrwsg4kjaOlCg0tZn+HvaGoGetrypjSgmIgOAhhta+oHei7tWA4yf5NGBStvMTj0k+f+20R4BABrXgUnIktUjM9nmaABETPbX/sus/vbGAaBGMYdfBU1NoOLIZ8TKZRgyXBWJQmptI4qJyACg4aYXzJFc0hNxD2qflRystM1IaylJMpmIDAAa3Y4dOyRJ7qSW6pHB7PFAs5H9jf+SpC3QJFFp60C+ShORSZLLVfHVUiqRIopKWwAInySVyKPyA5W2aa0k+X+fUmkLAI1r+/btkiR3ShqVtkBzkl3RHiFrgL1xAKgRSVsHCrx59bdHMA60R+BNbURRaQsA4RMjn9xmqYr8n4epTesWkvy/T338SgWARrV5yxZJkju5tXpmUmkLNAv5O6XCHMlwSRl97I4GQA1I2jrQgfYI/p62rkB7BHK2EVVTpS1JWwCoH5dMmeVlwdupKf5KL5OJyACg0a376RdJUmLLdGWkeGyOBkBE7PjKf9m6uxSXaG8sAGpE0taBgklbV2ilLV8fjaxApS3tEQCg4dwyZXr9SVvDMJSYkCCJ9ggAEAkbf/FX2nbu2CF4Pgsgym3+wH/ZcbC9cQA4KCYic6DKE5EZhmRUVHdSahtZgUpb2iMAQMPFGKZ85f7fnQkJ8Yqp+ECMSlsAaHyBnra9u3e2ORIAEbPxff9l15PtjQPAQVFp60C+iveupitWhmHIFehpa2NMzRGVtgAQXl6fT5KUmJiomNg4Sf4PKn38TgWARrVvV7Yk6eg+3WyOBEBElORKO770X+9C0hZoqkjaOlCg4sg0/BWegUpbi+rOiKLSFgDCKzAJWUJCgtxu/+9Wy7JkUmkLAI2moLBI5UX5kqQTB/SwORoAEbH5I8ky/f1sW7S3OxoAB0HS1mksS2aw0tZfheRyU2lrh5omIqPSFgDqr7jc/7szISEx2B7BR3sEAGhUn6z9UZJkxHg06IgONkcDICJ+WOS/7DrM3jgAHBJJW6fxlctXUcTpc/nf0JIotEegPQKVtgAQHsX+X6tKSEgItkewaI8AAI3qvS/WS5ISW7ZRjJu3h0DUK94vffOS//pR59saCoBD46+y05jlIT1tJckI9LTlPW1E1VRpS9IWAOovUGmbmJgod0yg0tYSv1IBoHHklZTr6bf8M8i3bdfO5mgANDqfV/rwYclbLGX0lToOtjsiAIcQc/hV0KRUqrQ13RXtEVyBYSRrG0k1VdpS9QwA9Ve5py0TkQHNW2lpqT7//POQD8Irn19VPdcK5+1oeW6fz6d169Zp27ZtKigoUGFhYbXLHbv3K3/vLknSmGEnCECU8nmlxTdLX/9HKs3zLzvuCqni/SuApomkrcNYvvJgajbQ09Zw+X/RMlFLZFFpCwDhVewN9LQ9kLQ1mYgMaHZ27typk046ST/++KPdoTQbp44br9mz7rE7DACNZe3L0pp/+q8ntpGGTJaOuczemAAcFklbhzHLS4PXXS53xSVdLuwQqLRlIjIACI/iSpW2ldsjMBEZ0HxYlqXf/OY3+vHHH9WiRQtlZGSE3G9UqQrj9qFvd+nSRUcccYSSk5OVnJyspKSk4OWS9fv1/Bc56tmpvZbO/G21xwKIEqZPev8v/usn3yCNmC65SQUBTsBPqsP4ykuC1wPJ2mB1J4nCiApU2jIRGQCER02Vtj5TtEcAmpGdO3fq448/lmEYWrNmjXr06GF3SFHHsizNevN7vbLDlCcrVZN/PYCELRBNtn2m1A8ek1GwRTIk5e+Ucn+RPC2kE68hYQs4CD+tDuMrO1BpawQqbCsmIqOlbWTVVGkbSNpSaQsAdReotE1MTFRMHO0RgOYoPz9fkpSSkkLCtpEs+nqHnvxgoyRp4uBO+u3RHWyOCEDYfL9Ixn8uUqJVpYjI00Ia96AU38KeuADUC0lbh6lcaet2u0MuLbK2EVVTpW2gSoFKWwA4PNO0VLnBT1H5gUpbd4w/aetlIjKgWSkoKJAkJScn2xxJdMorKdfMN76TJF17ag9dN7qnzREBCJu1r0qvTJJhmSrpcorijrtELnec5I6TOg+R4pLsjhBAHZG0dRhfefVKWxd9VG3BRGQA0DA+KzRpW+z/AkP19ghU2gLNRqDSlqRt43jl823aXVCqrm2SdPXI7naHA6Ch9vwkffNfad1b0o6vJElWz9O0f8RflJHVTmL+G8DRSNo6TOX2CG6Xf/iMip405GwjK9AeoaZKWxLoAHB4PtNSbKXbxZUrbYNJW0um6bMhOgB2CFTapqSk2BxJ9LEsS8+t/kWSdMmQzvLEuG2OCEC9WZb0wYPSu3Mks6K/lOGShl4ja+Rt0u699sYHICz42MVhTF958LpR0RbB5SJRaAcqbQGgYapOoBmotE1MTAxW2npNyfR5Ix0aEGLOnDk67rjjlJKSooyMDJ199tlat25dyDqWZWnGjBlq166dEhISNGLECK1du9amiJ2LStvG8/kv+/VDdr48MS79ZhB9bAFHMk3/xGLL7pLeudufsO06XDrrMemGDdLomZKL2jwgWvDT7DChlbb+ZK1RMREZKdvIqmkiMiptAaD2vKYl0zLkMvy/Myv3tI2Ji69YR5JJ0hb2WrlypSZPnqzjjjtOXq9Xt912m8aMGaPvvvtOSUn+HoH333+/HnzwQS1YsEA9e/bUrFmzNHr0aK1bt46q0Tqg0rZx5JeU68YX/V+dPmNAW7VIjD3MIwDY6svnpI/+LuVvlyp/46i8+EBlrSSdfr90/B+kivehAKILSVuH8Xn9SVu3ceD3sstVMREZicKIqmkiMiptAaD2TNOSTy655H8zUrmnbWAiMp8lWV6StrDX4sWLQ27Pnz9fGRkZ+uyzzzRs2DBZlqWHHnpIt912m8aPHy9Jevrpp5WZmamFCxdq0qRJdoTtSExEFn5fbdmvG1/8Sj/vKlRWarxu/VUfu0MCokdpgVSwUyreL3lLDru6fGXS7g1SWUGlZeVS3jYpd6s/KVu0R9q97uDPYbiklHbSSVOl469s6CsA0ISRtHUYX3mZJH9LhMAEZAfaI9gWVrNUU6VtIGlLAh0ADs9nWjJ1oDKkOKTS1iPJX41rUWmLJiY3N1eS1Lp1a0nSxo0blZ2drTFjxgTX8Xg8Gj58uD766KODJm1LS0tVWnrgW1R5eXmS/B/+RuMHwKZpyrKsQ762wD5ISkqKyn0Qae9v2KVJ//pcJeWm0pLiNO/CQWqdGBuWfVub8YQzMJb19NXzMt68TkZtkrV1ZBkuWcNukvr8WorxHLjDHSelZB1ogVDDmDGe0YOxbDyR3rf13Q5JW4cJJG3dhoJJW8Og0tYONVXaBtoj8EsVAA7PZ1ly68Dvy8qVtjHBicjkr0ABmgjLsjRt2jSddNJJ6tevnyQpOztbkpSZmRmybmZmpjZv3nzQ55ozZ45mzpxZbfmuXbtUUhL+JIDdTNNUbm6uLMsKftBdVU5OjiTJ7XYHr6N+cku8uupf36ik3NSQLqmaMbarWsSVhW2/1mY84QyMZd3Fbf1Ird6aIsP0yoxNlOVpJatycvVgDEPe1E4yE1pXWuaWLylTZnKWzNgkWXHJ8rbuKTOp4m9K5c+uvZJKDz3JGOMZPRjLxhPpfRvo2V9XJG0dxlfuP4F3uYwD7RFiAklbu6JqnpiIDAAaxvRZijEO/L4M9LRNTEyUu+IDMSYiQ1Pzpz/9SV9//bU++OCDavcZVXoKWpZVbVll06dP17Rp04K38/Ly1LFjR6Wnpys1NTV8QTcRpmnKMAylp6cf9A2Sz+dvl5KRkaGMjIxIhhd13vpok4rLTfXMTNb8y4coLia8b0prM55wBsayjrK/kbH0GhmmV1a/c6TfPCHDMFTbrrJxjRoc4xlNGMvGE+l9Gx8fX6/HkbR1GNPrTxQaRqWkbaA9AlORRVSgPUJNlbZUPQPA4XmrVNAWV9xMSEgI/m71mpJl+ao+FLDFlClT9Prrr+u9995Thw4dgsuzsrIk+Stu27ZtG1yek5NTrfq2Mo/HI4+nemWWy+WK2jdnhmEc8vUVFhZKklJTU6N2H0SCZVl6Yc1WSdKFJ3RWfFzjvO073HjCOeo1lj6vlLdVKiuSLJ9kmf5JsyzLf9tX7p80y+dVcNrs4Psk6+DXg+sd7HqVx5g+qWS/tOsHqSTX31vWW+r/V+1xtXi+Q103fVL2N/7X12mojLMfk+F2136fRQg/m9GDsWw8kdy39d0GSVuH8XkretoaB3raug3/MJInjCwqbQGgYcwqE4wVeyv1tK1I2vosVbzZA+xjWZamTJmiV155RStWrFDXrl1D7u/atauysrK0dOlSDRo0SJJUVlamlStXau7cuXaE7FhMRBYe72/YrXU78xUf69JZA9s3/AnLS/xJMcs8kJgzfXLn7pJc+ysScw1sZeMtlYr3+bfjLfU/nxWhc2pfmX/yp8o91GuT2KvVfYHrplS01z9pVcFOqTi3hueo9NiQOGq4r8GPOXCfISmzjo9p9rqfIv32ydB+swAQZrYmbefMmaOXX35ZP/zwgxISEjR06FDNnTtXvXr1sjOsJq2micgMd2DyK9vCapZqmoiMSlsAqL2qbQ8qV9q6K6pWTOvAB5aAXSZPnqyFCxfqtddeU0pKSrCHbYsWLZSQkCDDMDR16lTNnj1bPXr0UI8ePTR79mwlJiZqwoQJNkfvLIGebyRt629vYZnueO1bSdIFx3dSi4TYwzziICxL+mGR9O4cKec7VU3WuSSlNyxUNBG1/Vp/jdweKT5VMlwV/9wVl4bkjvXf73L7bwe2FGwbU/l2He+ret2TKqX3lJIy/InUmHj/9o1K1W21fa7DXW/dzb8tAGhktiZtV65cqcmTJ+u4446T1+vVbbfdpjFjxui7775TUlKSnaE1WZUrbY3g3xvaI9ihponIqLQFgNozq7ZH8B7oaVv5d2vgA0vALvPmzZMkjRgxImT5/Pnzdemll0qSbrrpJhUXF+vqq6/Wvn37NHjwYC1ZskQpKSkRjtbZApW2B9tveSXl2ry7KJIhOYJpWfpq634t/jZbn2zcK59pKSs1XtNG1zOxVF4iLbpO+mphpYVGRfLNn5izDJcsw5DhipER46mYzb4B6T93rJTYWopvIcUkSO4YfwIwElxuKbGNPwap7sm9Gh9Tw+MTW0nJmf5/Ca0r3afQ65Wft7Huq3S/aZravWeP2rRpI1cgyRny2JofJ8Plfx18bRsAGoWtSdvFixeH3J4/f74yMjL02WefadiwYTZF1bT5KhKFhutAo/PK1UiInJoqbQNJWyptAeDwKlfaWpalohp62kqSSaUtbFabv+uGYWjGjBmaMWNG4wcUxapW2haX+bS/uEzv/rBLi9dm6+Ofdqvcx3nW4fTOStG9v+mvlPh6VNmWl0jPnS/9/K4/KXfSddLxk6TkjJBEnmWaysnJUUZGhgySds5mmjJLYqSUDBKwANCENKmetrm5uZKk1q1b13h/aWmpSktLg7fz8vIk+T8ZjNbKRtM0ZVlW8PV5q/S0NU3zwFc+LCo8IylQaetyuartd5/PV21Z1bGEszGe0YOxtI+v/MDf9NJKc415PJ7gt0gkyVdWUuvxYTwbR6T3K+PXfAUqbcuMOF35zKda+t3Oauukp3gU62rQF7qjUruWCRrTN1Nj+2apc9ohvrVY9UOIyrdL86T/XupP2MYmSef/y9+7EwAARFyTSdpalqVp06bppJNOUr9+/WpcZ86cOZo5c2a15bt27VJJSUljh2gL0zSVm5sry7LkcrlUkOdPbBuGIdPnVU5OjkqK/a/dkqWcnBw7w21Wysr8CfT8/Pzgfg/MeFxcXFxtLKqOJZyN8YwejKV99u7ZE7xeXKlTQn5+fkhlY2FBXq3/vjGejSPS+zVQbYnmJ5C0nb10k7YqLbj8qI4tNbYiIdk9vZb9br2l0s5vpf1bwhukt1Tasso/cVVJnpS7VcrP9s8mH1CfSaLqdF+V+y1L2in/v+U1PLauYhOlif+RupzUsOcBAAD11mSStn/605/09ddf64MPPjjoOtOnT9e0adOCt/Py8tSxY0elp6crNTU1EmFGnGmaMgxD6enpcrlc+inOP2SGYSg2NlYZGRlKTvX3/DItKSMjw85wmxWfz39inpGREdzvgeMwLi6u2lhUHUs4G+MZPRhL++zZcyARG+hn63a71b59++DvWEmKjXHX+u8b49k4Ir1f4+PjG30baJoCCftNuT4lt3HplatPVPf0ZMXF1HDcWZY/Kbtvs1S0259ELd4nFe2Tdn4j7fxOMsurPw6H1rKT9LtnpXYD7Y4EAIBmrUkkbadMmaLXX39d7733njp06HDQ9TwejzweT7XlLpcrqt+YGYYRfI2WN9DT1hVc7nZXDKOlqN4PTU2gPYLH4wnu90B/4YNVIlUeSzgf4xk9GEubVKpKC/SzTUxMlKvib1xwtfLyOo0N49k4IrlfGbvmqby8PNgKzYhL0Kl9MtWnbZXCjI8ekb75ryTLn6Dd/8uhnzShtdSmp3+iqbAxpLYD/DPIxyVJLTpKKW0PTGIVXK0WEzkd9r4q99dyYqkG3RffIsz7CwAA1IetSVvLsjRlyhS98sorWrFihbp27WpnOI4QmIjM39PWv+zARGRMyhBJgYnIKk+WE0gyMBEZABxe5YnIisv9vzcTEhIk+X+fxrgkrymZPiYiA5qDQGsESXLFxeuso9qFrvDLKmnJ7Qr56n9MgpTZV0pqIyWmSQmtJE+qlN5LajfIXzVaLdEJAADQ9NmatJ08ebIWLlyo1157TSkpKcrOzpYktWjRIvimDaF8vgOVtq6KE9BgotC2qJqnmpK2gcogJlABgMOzKiVti6okbSXJ7TLkNS35yknaAs1BMGnrjtFJset16s8rpY0Vd/rKpQ1LJFlS399IAyf6q0HbHyvFR2ebNAAA0LzZmrSdN2+eJGnEiBEhy+fPn69LL7008gE5gFmRKFRIpa1/GCnujKxAv8VApbN0IGlLpS0AHF7lStt9FfOJtmrVKrjM7TIkWTK9JG2B5iCQtPXExurpuDlyf1bDh+AtO0vj/uqvqAUAAIhitrdHQN34Aj1tDVfwq16V+6giMizLClbTVk7aBqqeqbQFgMOrXGm7u8j/e7NNmzbBZTEVn06aPiYSApqDwCRk6Z4yxShW6jHGX0kbkN5TOmKU5EmxKUIAAIDIaRITkaH2KidtA5W2RqC6066gmqHKSdmaKm1J2gLA4VVOxu4p8v8VS0tLCy5zVfyhC/RzBxDdApW2LeIslSlGcef9W4qJszkqAAAAezA1r8P4AlVJLiPY09YVrLS1K6rmJ9AaQaq50paqZwCoBfNApe2e4upJ2wOVtrRHAJqDwsJCSVJirFRkJJOwBQAAzRpJW4epqdLW5TqQtCVZGBkHS9pSaQsAtWdV+l1aU6Wt213xO9XrFYDoV1xcLElKjDVU7E6yORoAAAB7kbR1GF9wIjKXDFX0tA0kbWXJJGcbEYdL2pI8B4DDqzwR2e6KStvKPW3dgT7hJG2BZiGQtE2IlUrdyTZHAwAAYC+Stg5jVnyV1DBcgXnI5A5Ud1JpGzGHa49ApS0AHJ5lHrqnrdtNewSgOSkqKpIkJcQYKoshaQsAAJo3krYO4/NWJAtdrmBPW1XqaUulbWRQaQsADWcdpqdt8ENJH5W2QHNQudK2nKQtAABo5kjaOkwwWWi4VPFeVjEVPf8sSSbJwoig0hYAwqAiGbshro/2xLaTdLCkbXn1xwKIOsGkbYwhX1yKzdEAAADYi6Stw1SeiCzQ09blipFElW0kVU7aBqprK18naQsAh2dVJG1NubV79x5JoT1tYyraI1j0tAWahQNJW8mMS7U5GgAAAHuRtHWYYLLQdaCnrSvmQKWnj2RhRHgrEggxMTEhywOVtrRHAIBasPy/S4u8B5I1lSttXbRHAJqVA+0RDMlDpS0AAGjeSNo6TLCC03AHe9q6K1V6mpTbRkQgeV65NYJEpS0A1EWg0nZfsf93ZkxMjFJSDiRq3G7aIwDNSeVKWyOeSlsAANC8kbR1GF+g2sgw5ApU2rqotI20wyVtqbQFgFqomIhsX5H/b1daWlrwGwtSpZ7tlVrSAIhegaRtYqwhV0ILm6MBAACwF0lbhznQHsEdfGPrch/4ir6PStuIOFjSlonI8P/s3XeYXGX9/vH7TN2+aZtKKiRCEkogCEEQEAmgNCmCUgIq6hcEkZ+isSZIERUVRZAIBEQlFgig9JIChkAIBJIQQoD0ttlNtu/U8/z+ODOzO9leZqfs+3Vde+3uzJmZZ5+zO3vmns/5PAC6IBba7m10nlOb97OVaI8A9DdN7REkTwGhLQAA6N8IbbNMPCw0litRaZvUHiFKWNgXqLQFgF5gO8+l+xqcz8372UqSJ/YcS2gL9A9N7REseQsGpHcwAAAAaUZom2USlbZWU6Vt88WwaI/QN6i0BYCei/e0rWpwPu8f2sZ72hqb0BboDxoaGiQ5lba+wgHpHQwAAECaEdpmmWgk3h7B1WpP2wh9//oEC5EBQM9Zxgljt+8LSJLGjh2bdL3bFa+05X8b0B80r7T1Fw1M82gAAADSi9A2y9ixU0mN3LIU72nbFBzanJbfJzqqtKU9AgB0zMT+p22tdKrrJkyYkHR9/DnWjlBpC/QHzStt84sJbQEAQP9GaJtlovGetS6X4q1sm1fa0h6hb1BpCwC9INb2YPve1kNbjycW2hoqbYH+oL6+XpJTaVtAaAsAAPo5QtssE41X2jbraWs1C21ZiKxvsBAZAPScZUdljNH2SieoaVFpG39OjRDaAv1BY6zS1uNxy+MvSPNoAAAA0ovQNsskFiJzueWyWmmPYBMW9gUWIgOAXmBHtLfRqD4QliSNHz8+6Wp3bKHNeGsgALmtsdEJbeXNl2LHVAAAAP0VoW2WibdHMJY7sRCZ5WrajRFe2PYJKm0BoOcsO6qP9jnPl6NGjVJeXl7S9W537DmV/21AvxAIOIsSykeVLQAAAKFtlon3rDWWWwU+pwLJoqdtn6PSFgB6zpiIPt7nPF/u3xpBktzueKUtC5EB/UEgGHS+8BWmdyAAAAAZgNA2y9iJSluXivxOYOiKvaiVJEN7hD7BQmQA0HOW3X5o640tRGZ4TgVyXjQaVSTev9pfnN7BAAAAZABC2ywTX4jMbl5pazXtxigLkfWJjiptaY8AAB2zTFSbqpz/W/v3s5Wa3pS0o7RHAHJdY2Nj0zeEtgAAAIS22SYRyro8KoxV2ia1R+CFbZ+g0hYAes6yI6oPO29ylZSUtLje4yG0BfqLhoaGxNeG0BYAAIDQNqvYUUVjBZy25VahP97TtnmlLS9s+wILkQFAz1kmqlDs35bP52txvSf2HGsMb4QBuS5eaet3S2EfoS0AAAChbTaxI0p0P3C5VRhrjyDLkhXfhMVa+kQ8tI1XgcWxEBkAdIEdaTe0dVNpC/Qb8dA23yuFPUVpHg0AAED6EdpmEzsiO1bBacutAl+sytNyyRVLbW2bF7Z9gUpbAOg5l4kqFDuFxO/3t7jeGw9teSMMyHmJ0NZjEdoCAACI0Da7RMNN7RFcTe0RZLlkxUPbCKFtX+hoITICBgDomGV30B7B6/yfMzynAjmveaVtxEt7BAAAAELbbGJHE+0RTLOetpLVrNKWF7Z9IRJx2lCwEBkAdJ9lIgrGuvq02h7BTaUt0F/EQ9sCr0VoCwAAIELb7GJHEpW2cnlU2Kw9QrynbSQaTsfI+p3mlbavf1yps+96VW9v2Ud7BADoAqtZe4TWQluvxytJslmIDMh5Te0RJNtXkubRAAAApB+hbTZpHtpaLhW00h7BRAkL+0Lz0Paiecv17rZq/b9/vkN7BADoAqenrfN16+0RYqEtz6lAzmtqj2DJ9lNpCwAAQGibTexwoj2CLFezSttm7RFMJC1D629a62kbitpU2gJAF1gm0m5o644vRBYltAVyXfNKW0OlLQAAAKFtVrGjisbCQMvlUoGvWaVtbJMoC5H1iXho63I1hbbjhxRSaQsAXdBhe4RYpS1vhAG5r6G+XpJTaas8Km0BAAAIbbOJHVFjrGWtx+eXzxPbfc3bIxAW9ol4aBuym4KEEaV5VNoCQBe47A7aI8QqbaO2zfMqkOMa66olOZW2Vl5pmkcDAACQfoS22SQaViDW/SAvL6/ZFU3tEaI27RH6Qjy0bQg3hQhRW1TaAkAXWLIToa3f729xvdfrBLnGthW1CW2BXNZYWyVJ8ntccvsK0zsYAACADEBom03siAIR50Vrfn5+0+WWlWiPYKK0R+gLrYW2Ebuppy2hLQB0zGUi7bZH8PjiC5EZRQhtgZwWqK+RJHm8Hvl97g62BgAAyH2EttnEjqoxVkibn9+s0tay5IpXeBrCwr4QD23rQk3zHWYhMgDoEpeJKthOewSvJxbaGiOb51UgpwUb6iRJltsrv4eXKAAAABwRZRM7nKi0LcjLT7oq3tPWZiGyPtFUadsU2oYihvYIANAFLtNBT9tYe4Qo7RGAnBdoqJUkWR5f07oNAAAA/RhHRNmk2UJkhYX7hbbxTQyhbV+Ih7aNkaYQgUpbAOiiaESR2HtcrYW28T63tm0IbYEcF2iolyQZt19+D+0RAAAACG2zSbOetoX5BUlXuai07VPx0DbYbLojtk2lLQB0QfPFM1sLbeOXRaKEtkCuCzTGQluPn/YIAAAAIrTNLnYk0dO2uCg5tE20R6CnbZ+Ih7aymv6EwhFDpS0AdIEdCSe+bj20dfq3R22jKM+rQE4LNDZIkmyPn/YIAAAAIrTNLtGmStuiguT2CPFKW2NTadsXWgttQ1EqbQGgK+xI+5W2Hq+zEFnENuJpFchtgUBAEu0RAAAA4ghts0mznrYlxYX7XemEhVFe1faJeGhruZpeVETspp62hLYA0DE76vxTc7tdcrtbhjReX1NP2wjPq0izpUuX6qyzztLIkSNlWZYef/zxpOuNMZozZ45Gjhyp/Px8nXTSSVq7dm16BpuFAsGQ8wXtEQAAACQR2maVaDiocOw1a0lh6+0RTJRK277QvNJ2aLETKtAeAegf3t5cqQWvru2zv/N/rtiq1z+u7JPH6mvxSluPx9vq9R5v7PmVSltkgPr6eh1++OG66667Wr3+l7/8pX7zm9/orrvu0ooVKzR8+HCdeuqpqq2t7eORZqdgLLQ1tEcAAACQJHnSPQB0XjB22pjUMrSNt0eIEtr2icQ8u1waM6hA5bVBhWmPAPQL9Q9forPDb2nd0Fc0edInUvpYmyrqdeOj72r0oHy9cuNnUvpY6WBHndDW6209tPX6ndA2ahvOJEHanXHGGTrjjDNavc4Yo9/97nf60Y9+pPPOO0+S9NBDD2nYsGH6+9//rm984xut3i4YDCoYDCa+r6mpkeQcR+TisYRt2zLGtPqzxSttjdsvr4tjqWzQ3v5EdmFf5hb2Z+5gX6ZOX89tdx8nraHt0qVL9atf/UorV67Uzp07tXDhQp177rnpHFJGa4wt0CBJhfv1tI1ltjL8MfeJRHsEywlt39y8T6GoTaUt0A8cHHlPBVZQ9VtWSykObSvqnDBnb10opY+TLpHYQmRthbYer9PnNhyVotFwq9sAmWDjxo3atWuXZs6cmbjM7/frxBNP1LJly9oMbW+77TbNnTu3xeV79uxJ9HjNJbZtq7q6WsY0nZ0U1xALbaNuv2qq9iraQF/bTNfe/kR2YV/mFvZn7mBfpk5fz213z7xKa2gbP83syiuv1Pnnn5/OoWSFQKBRkuR2WcrPS16wxRWr8DSGStu+kFRpO9ipeo5EDZW2QI6zo7ZKTJ1kScG61LcsqA85zzUN4aiMaXqOyRXxStvWFiGTJK8vT5IUtpMXLQMyza5duyRJw4YNS7p82LBh2rx5c5u3mz17tm644YbE9zU1NRo9erTKyspUUlKSmsGmkW07ZyWVlZW1eIEUDDvPd7YnT6OGD6NFQhZob38iu7Avcwv7M3ewL1Onr+c2Ly+vW7dLa2jb3mlmaKmx0Qlt/R6XvO79fqlir+Nt2iP0iaaetm6NjYW2YSptgZxXW1ulUsv5+w/X7k354zUEnaDSGCkYsZXnzaHKM2MSz6VeXxuVtrGFyCK2UTRKaIvMt/8bKx292eL3++WPtQFpzuVy5eyLM8uyWv35giHnb9z25Mvvdefcm1S5qq39iezDvswt7M/cwb5Mnb6c2+4+Rlb1tO1vfb+k5D4bDQ1OewSvxyXPfr2+4oe10WgkZ+cik4TDzmm6luVSWZHzYisUtRNhbWu/k/SjyS3sz9zRlX1Zu3e3SmNfRxv2pnz/1waaWgLUB8PyuXMoxLAjikSd50yfz9/qXLqbtUeIhIOdmm/+NlMjW/p+pcvw4cMlORW3I0aMSFxeXl7eovoWrQvEKm0tbwGBLQAAgLIstO1vfb+k5D4blXudqi6fx62GulqVl5cntou3R6ivb0i6HKlRW1vnfOFyKdzgvHkQikRVWdl0uvTu3buTXnTQjya3sD9zR1f25a6tH+mA2NfRuoqUP9/urqxKfL11R7nCJa23EchK4UbFuj/I7XK3Opc1sefaiC3tq6xQeVHHpxXxt5ka2dL3K13Gjx+v4cOH64UXXtC0adMkSaFQSEuWLNHtt9+e5tFlh3hoK39B+xsCAAD0E1kV2va3vl9Scp+NDS7ntFivx62yQQM0dOjQxHbxbDDP70+6HKnhiS2aY1kujR4xVNL7itrJvez2741CP5rcwv7MHV3Zl3vWNy0I5o/Upfz51uVvCq4KSgZo6NCilD5eXzKNVQrFKm3zCwpancthw52KxbAtlRQXdWq++dtMjWzp+5VKdXV1+vDDDxPfb9y4UatWrdKgQYM0ZswYXX/99br11ls1ceJETZw4UbfeeqsKCgr05S9/OY2jzh7BiFNdbfly53kOAACgJ7IqtO2Pfb+kpj4boaBTTez1uOTzepJ+ZqvZQmS5PBeZIhxpWoisJM8JcCO2kWUlz/3++4J+NLmF/Zk7Orsvw3VNfWy9kZqU7/vGUNMp4oGInVO/a6FIOFFpm5+f3+rPFl+gLGIbGbvz/9/420yNbOj7lUpvvvmmTj755MT38UKCWbNm6cEHH9SNN96oxsZGXX311dq3b5+OOeYYPf/88youLk7XkLOGMUaBsPMmjuUvTPNoAAAAMkNWhbb9XWMgHtp65N2vr2G80jbbesBlq1BsFXOXyy1/s4WBmi8Dx2JkQO6J1je1QMkL16T88eqCTYtvNYZya6HJcDiYCG39/tbbPnhjZzWEo5JhITKk2UknndTu/3bLsjRnzhzNmTOn7waVI0KhprMYLD+VtgAAAFKaQ9uOTjNDskDAOaD1eNzye/ar4Ix9NoS2fSIcdsIDj8ctn7tpX0SaTT8BOpB7TMO+xNeFdq1s28jlSt2COQ2hpqCyIZxboW0oEFAw4gRgfl/Ls2gkyeNxDlMitmTscKvbAMh+zdemcOXnZsszAACArkpraNvRaWZIFq+09Xg88rr3Ow0/3h6BoLBPRGKVtl63O6nq2W5WgEOlLZB7rEBTe4RSq141gbAGFKRucbD6ZtW1/brS1pbsCJW2QK4KNDYmvrb8tJMAAACQ0hzadnSaGZI1r7TdP7RVoj1Cbr2oz1ShWE9bj8cjd7Mqu0iz1JZKWyD3eAJVia9LVa8ddaHUhrbN2iM05FhoGwk1hbbx3rX7i4e2tpGi4VCr2wDIfoF6Z9HFPI9k+fLTPBoAAIDMkHmrPKBNjcGgJMnjbafS1hAU9oVILLT1et2yLCvRIiFqmlXdEtoCOccbamqPUGAFta+mNqWP1xBsVmmbY+0RIuGgQlHnja62Qtt4ewRJCocCrW4DIPsF6qslOaGtvIS2AAAAEqFtVgkEnX5+HrcnqY+q1NTT1o7m1ov6TBWOt0eIBQrxFglR2iMAOS0vkrz4WO3e8pQ+Xn2o+UJkudUeIBwKdbrSVnJCXgC5KVjvPLfmeSy5PXlpHg0AAEBmILTNIvFKW7fHI69nv4VvYt/S07ZvxHvaejxuSZI3tjAc7RGA3FYQqU76vr6mMqWP17wlQq61R4hGOm6P0LzStvnq8gByS6DOCW19bkt+rzvNowEAAMgMhLZZpDFWaev2eFtU2sbbI9hUd/aJeHsEX6wKzOOKh7ZNYTqVtkDuKbKddgh27J2ySN3e9jbvsbpmPW1zrj1CqKk9gt/vb3WbpErbEJW2QK4KxCptfR6X/B5engAAAEiEtlklEHSqjJxK2/3aI8QXIovm1umzmSoSa0MRb4/gi7VHoNIWyGF2VEWqlyRVuoc6lzVbmCwVGpqHtrlWaRvuuD2Cy+VK/H+jPQKQuwINzhtiPo9LPkJbAAAASYS2WaUh4IS2rnYqbSnu7Bvx9gi+/dojNI9pqbQFcku0oUouOX/XVXmjJElWYF97N+kR2zZqCOd2e4RgB6GtJHldzv+3cJD2CECuCtQ7oa3X41K+j/YIAAAAEqFtVgnEFqFxe7zyulvfdcam0rYvxCtt4+0R4vuj+dnLVNoCuaVun7PoWK3JVzR/iCTJFahp7yY90hiOJr0Rl3uVtuFEe4T2Qlt3LLSl0hbIXYEG5ywGr8etAkJbAAAASYS2WaWhWU/b+IvYOCve05agsE9E4+0RvE57BI+L9ghArquJhbbVVpFcvkLnwnB9yh6vPpT8Jlyu9bS1O7EQmSR5Yu1nohEqbYFcFWysk+Qs8Frg83SwNQAAQP9AaJtFAqFYaOtt+eLWSrRH4JT8vpCotI21R4j3XwtHbblii5KxL4Dc0lC9R5JU7yqRfAWSJFekMXWPF0wOaRtCuXUmhelsaBt/U4yFyICcFWhskCR53G7le6m0BQAAkAhts0pj7AW7p5UXt/GFWoydW5VYmSoaiYW2sWqQpvYIhqpnIEcFYqFto6dErlhoa6UwtG1RaZtr7REioU61R4hX2kbC4T4ZF4C+F2+P4PZ4aI8AAAAQQ2ibRYKxF+wer7/FdYmFyAgK+0Q0Fo77Yz1t45VgVNoCuStcVylJCnkHyOV32iO4Iw0pe7z6FpW2uRXamki4U5W27thzKj1tgdwVr7R1uz0sRAYAABBDaJtFmiptW4a2cVR39o1oxNkXvlhP2+btEai0BXJTtH6v8zlvoDzx0DYaSNnj5X5P21AitPX72/6/1tTTlkpbIFcFAs5ZCy6Pl562AAAAMYS2WSQQe8HubeXFbVNPW4LCvhBfiMzvTW6PEImaRKUtoS2QW6xGJ7Q1+YPkznNCW2809T1tBxQ4Ff251h5B0c61R0hU2oZYiAzIVcmhLZW2AAAAEqFtVomHth5vXovrXImetgSFfSEadebZlwhtnR0Qoj0CkLPcgX2SJFfBQPnyiiRJXjt1p+zXBZ3K0iFFzht1OdceIRrq3EJksTfFolEqbYFcFWiMnbXg8dEeAQAAIIbQNosEQk5Q6M1rGdqKU/L7lL1/T1s37RGAXOcLVUuSvMWD5StwQlu/aUzZGzR7652QctSAfElOe4ScejMoGlawUz1tYwuR9Wal7Y5V0uJfSOHUtbcA0HnBoPMGmOWm0hYAACCO0DaLBCKx9gi+1iptaY/Ql+xYe4S8WN81X7PQ1uNxLotEIq3fGEBWyos4oW1ecZn8+cWSpHwFFQin5nm3ss4JMUYPyk9clqrHSgfTyfYI8TfFIr3Z0/alm6TFt0kbnu+9+wTQbYFYaCu3TwVeetoCAABIhLZZJVFp689vcV0ss6W6s48kKm19TqVtvD3CrU+/L9sV6z/ZmLpelwD6XpFdI0lqNF6t27hDkpSnkL5472u675WPe/3x9tY7laWjBhQkLmsI5c6bQVY03Kn2CG63U3XX3kJkj7yxRRf+aZn21XeyGre+3PncUNG57QGkVCAQC209ftojAAAAxBDaZgljjAIRpyLJ629ZaWspfkp+bvU8zFR2rKdt3n4LkUlSo+282CC0BXKHbRuVmFpJ0szzZunIz1+p9yuiKrCCWr29Wjc/tU71wd4NVCtiAeSQIp9K8503g8prU9dDt89Fw2qI5bD5+S3fjIxL9LRtJ7Rd8MYWrdi0T8s/ruzcYwecAF7Bus5tDyClGoPO853x+GmPAAAAEENomyXivb4kyZNX0OJ6V7znX4RK277QVGnbMrS1PM6iQYS2QO6oqq1ToRVUTdBoX5XTJmH5tqjyFdSR1gcarGq99H55rz7m3nrneX9wkU8HDXV66H6wu7ZXHyOdLDukmqDzZmRpaWmb27ld8dC27VC8PlayW9fZ4DxYm/wZQFrVxxYii3gKle8ltAUAAJAIbbNGINC0WIo7r+WL2/jiV6Fw7pw6mw61gbDW7axJumx3TUCbK+uTLou3odi/PYIkWV7nNF9CWyB3VFXuliS9vqPpjTGPSxpg1esx/xy94b9aS95+r1cfs7LOqTwbXOjXpGFOaLthd+5UhrrscCK0LSkpaXM7j6fj9giNsdC2IdSJs02MkYKx5/lQ7swnkM2q6pxjJlMwMFGIAAAA0N8R2maJeGhrSZK/qMX18YXIwlHaI/TEuX/8n8648xWt3LxXktOW4ry7l+mMO19RbaApMDCxStv8WB/G5EpbQlsg19Tuc6poX93WVAG2t9EkvnZbRp//+BYFwr3zHGyMUWWsPcKgQp8mDnUWPsulSttwKKRA7H3G9kJbd6I9Qttz2xib9/rO9PwNN0p2bLtgTfvbAugTVbHnO6twcJpHAgAAkDkIbbNEPADM90rytdMegdC2Rz7a41TUPrtmlyTnlNvtVY1qCEW1ubIhsZ2JVdrm+WmPAPQHoVqnV+rrO5sqbSsbTNI2n3G9pU1bt/XK49UFIwrF2t0MLvJp0jAntN1QnjuVoYFmz5HFxcVtbueJL0QWbbvSNr5AW6f6CjcPaulpC2SE6kbn+NUqLEvzSAAAADIHoW2WCNQ7LyzzPJaMt5XQNl5pS0/bbotEm+YuP9ardm9d00rkO6ubWlQ0Vdq20h6BSlsg54QbqmSM0dvbmvqLV7byJ75j28Zeeby9saqzfK9bBT5Poj3C5sr6XqvmTbfGWA/LPJ9XHo+nze3cseuSetpuf0u665PSW3+RbRv5wzU6ylqv+mAn5ibQPLTNncplIGuFG1UVcI7BrNLhaR4MAABA5iC0zRKNdVWSpHyPZHnzWlyfWKiFSttua74quzdWuVxR33TZzupGVTeE9f1/v5voaZuXCG1pjwDkmv+8s0NznlyrqG0UbaxRRYNReW1TtWdFoOW/0Mqdm3rlsSvi/WyLnOeTsmK/SvO9so30ud+/0qLPdjZqiIW2hYX57W4Xb48QiYe2oQbpzydLFeulF36qQCSqX3nv1aP+uSqrervjB25eaUtPWyDtglW7E61SvCXD0jsYAACADEJomyUC9c5q5XkeS5anZWhrueKVtoS23bWzuilkrY2dYtu80nZHVUB3L/lQ/3hzq2Sc0Dbf74S2blfzSlvaIwC54Lof3aJf/r+vauXH5YoGarSlOrkdQkUrf+L1e7b2ymPHK20HFzqhrWVZmjZmgCTp4z31euSN3nmcdGoMOm+KFRe0H9p63E6lrR2NpTrL72660thqCEV1oLVDkjSw7qOOHzhQ3fQ1lbZA2lWXO21lLEnFpS0X2wUAAOivCG2zRGOd8yLT57Hk9bhbXB9vj0BP2+5r3v6gusGppqtsVmm7q7pRu2LbxHvaxtso1ASaTtu1vE7I0tDQ1AMXQPapeWW+Gj98XSteWyoTqNXWmuT2M5WNpsVtotXbe+WxK+uc557BRf7EZb++8HBNHeUs2LW9KvvfFGoMOMF0UWHLlj/NueM9beNvSm59venKUIMag2ENtpzq2YJgeccPTE9bIKNUxULbQr9LhXm+NI8GAAAgcxDaZolAvVMN5Pe6kk7Fj3OzEFmP7axqFto2xkPbZpW21QENiQco8Upbn/PiYk+z1gq0RwCyXyhiKxA7fb9+y7uyQrXaUu383Y8cOVKStLfZQmRVRRMViBj5A+U97jlr20bvbHPeqBtU2BRgDCny65snHihJ2plDoW1xUWG728XfqIzasTfH9rzfdKUdVmjfdg2wnHYRxaE9HT9w8+ra5gEugLSo2uNUyhfleZTva1mYAAAA0F8R2maJxjrnhaXX7Zav2aJXca5EaMtCZN2VVGkbD22btUfYVR1QIByVMaYptM1z2iNE7aZ5pz0CkP0qaxvVEHZC2ep9lXKF6hLtEY444ghJUlVstfN9jUaH3/6+ptxdpyF2pT4s71n15g3/XKVH3tgiqak9QtyIUqeVQPPnq2zVGIyFtsXth7ZJlbaheqnKmZuoy3mutXa9m9i2JFLR8QMH9utpa1pWTAPoO1UVToV8gd+rAi+hLQAAQByhbZYINDghgM/rbqPS1rnMtm1FbV6AdkfznrZVsdB2b31yaFvVEE4EtpKUH1uI7NrPTNRBQ53V3V1U2gJZb/v2bYmva6sq5Q7VJdojxEPbmkBUEdvoF68GtaWiXh/vM/I27FFFXbC1u+yUqoaQ/vvuTklSntelUycnL8ozcoDT03x3TSDrn+sbAs7zbHFRUbvbeTyxN8eiUaligySpwpRoq/8gSZK3/J3EtoOilR0/cPPqWjsiRbI/AAeyWVWlE9rm5flUQKUtAABAAqFtloiHth63Wz5Py90W72lrjFFDKNLienRsR7PKtZpYaNs8fAlFbX1cUZ8U2nq9Tk/b0YMK9OINJ+rKT42jPQKQA3bvaFroq76mSp5IU6Xt4YcfnrhufYWt37/R9OaO6vepLtj95+Dn1+5WxDY6eHix3v/5GZo+blDS9WVFfrksKWKbHoXDmaAhNk8lxcXtbueKt0eIRqU96yVJH5mR2mY7c5NXsSax7WC7E6FtYL+WCPS1BdKqep/zd+vz5yXWCgAAAAChbdYI1MdCW6+n1UrbeHsEyxg1hPbrp9hYlerh5YRdzSptq1uptJWkD8trE4uQSU2n7cYV+T2yvLRHALJdxc4tia8b6mrki9YnetpOmDBBJSXOgmDPfhhRs3UIFayvUV1D9//2/7vaqbI987ARrV7vcbs0rMSptt2RxX1tbbvpDcbSkvZDW2+s0taORhXa9Z4kaYM9ShsCAyRJ/j2rE9sOUG3HlbP797Glry2QVlVV+yRJ3rwCKm0BAACaIbTNEoHGBkmSx+2Rt9VK29hlxlZ98yqv5fdIt4+V3n+qL4aZtSJRO2kxsbpgRJGonehpG69uDkeNZDeF4vuHtgU+D5W2QA7YV7Ez8XVjXZ2sUK121jqVtmPGjNHgwYMlSe/sTu4jXtFgy67Z3a3HtG2j1z5yerKePnV4m9uNKHVC211Z3Nc2GLFVH3SeSwcMKG13W4/HqbyLRKNq3B4Lbc0B2hwZKEkqCSf3sbWrd7X/4IHq5O9DVNoC6VRV5fxNuvyEtgAAAM0R2maJxgZnZWyXx9PqQmSWFb9sv0rbLcudz9veTPEIs9u+hrD2bw9Z3RhOVNoeMrxZJZhpu9K20O8mtAVyQNWepuC1oaFetTU1MpL8Pq/Kyso0aJBzav47u5PPbNhdZ2TV7VR31AYjzhtDclqutCW+GNmOLA5tawJhNcRC28EDB7S7rTsW2tq2nehf+4E5QDvN4Fa3D1Vta/XyhBaVtrUdDxhAylTVxP4G/UUqoD0CAABAAqFtlggEnADQ5fW23x5BJrmfYkOsv1+gKtVDzGrxcHZggVdFfucFw/aqRoWiTkA7eWRTJZhpJ7Qt8NEeAcgFtfuaqjcDjY2qqHLeOBs5Ypgsy0qEtu/uV2m7u95I9cmVn51V3eC0ZcnzuuT3tF1tFq+03ZnF7RGqG8OqCzpzV1o6oN1t45W23nCtCgK7FTQevW0fpO1thbb7trf/4PS0BTJKVa3z/GrnlWhEbLFFAAAAENpmjXgA6HK3HtpaiYXI7OSFyOr3xO5gX8rHmM0qYwv6DC7yqzTf6Z/48R7nRUShz60JQwqbNm63p61blscJbdduqdDWvQ2pHDaQPeyotHZhtwPNvlZbvTfxdUMgoMpYz+sxow+QJA0dOjRp+yOOOEKSE9q6GzuxGFYr4r20489BbRkxwKm03ZnFlbbNQ9uSjkJbr3P2gj/qhKurzEEKyK8PzGg1Gl+L7aPVHVQ6xytr3bHbvvOIVLOj84MH0Kuq6pzn16ivVGPaOcsAAACgvyG0zRLxSlvL40/0V20uHtpaxiT6BEpqCkhYjKxdlbFK20GFvkRgsma702NteGleUuWHLzb9lmU1a0vhaN7TNhhs1J9f+TjVQweyw9qF0r+ukH4/TQrVp3s0yWq2y7NnbdJFDbVNfU8DgZDKa5w3dsaNHSdJmjJlStL2Rx11lCSpvN6WJ9i9N8nioe2A/JZBZHOjBzqh7caKDJvHLqhqCKsu1MnQNlZpG479a1tuH6Iffe4Q2S6fVtifSGwXr7w1HQWw8fYIJSOdz+89Li34smRMmzcBkCLGqKrOeQMqmDdYo2JvSgEAAIDQNmsEAk5gYHl88rXaHqGVhcjsaFN7BCpt2xWvtB1S1BTartjszNmkYcWJ05ElqdjvzPX+Vbba8baKXY2J0NaEQ2rZfRjop7a/5XwO1kjP/Sg9Y3jtj9Jj35AioabLjJH1+2ka8uh50t6NiYsb6pr6nAaCYW2tdgLGMePGS2qqrI2Lh7a764z8wb3qjurGsIwxKvG3vxDPxGFOj+2P9tQpun8z7ixR3RBQTdAZe3GHlbbOc3Ik9rMutydr6oCIxiy/Q49uHZjY7j17rPNFbTuVtuHGpv+LA8c3Xb7jbWnTK137IQD0XKBa1Y3OOzKuAQcoz8tCZAAAAHGEtlkiHtrK42+1PcLA2OrbdqBW9fGFyBr3STLNvkZb9rZSafvO1ipJTkASX/hHkopipbbx6i9J0qpHpHknaeLS6+WKtUcwkVCr+wrob+qDEW3f+lHTBWsf6/tBGCM990Pp3QX68KUHZGJVlR+sWSHLdipc17zxUiIEbaxv6nPaEIxoS41z+ZhxEyS1DG2nTZsmyWmPkBeu6tYQqxvD2r3gh3rxlssUDAbb3G7MoAL5PS4FI7a2ZGkLlsbaqkRoWzJwaLvbejzOc3I41pnmHftA/eKn39eiRS/r9w/9N7HdejPa2b5+V9t3tud9ZzHJgsEtW3Us+0MXfwoAPVazQxUNznPBsOEj0zwYAACAzEKilCUCsRfwxuOX192yfnP48GGSpFBdjRrilbbxfrYSC5F1oCIW2g4u9GtgYXI/yUnDijS02K/YWm8tK22DtdKLP5MkFW15SQd5nXk3kZD2NoQE9Hd3LfpQFVs/aLogUC2F+jhsDDS1O/jfKy/qb69vUXVjWI/986HE5U/97y09u8YJ/Boamhb5qgkabYlX2o4ZI0kaPnx40t2PHu0EhnvqjfJD3XuTbFfFXgW3rFbVjo363//+J2OMli1bpnXr1smsuF96+RYp3Ci3y9KBZUWSpA27azu418wUrtqh2lguXTK4rN1tvbFK23BU2m0GKK+wRO+sejtxfZ3JU4Pxa4V9sLN9w+6272z3e87noZOlo2Y5X487wfn80cvOGSoA+kzNjg3OAo6SJk48MM2jAQAAyCyEtlmiMeiEf8bj15Bif4vrR4xyAoP6urqmStvmVUSBal6MtmNvXSy0LfJp+thBSddNGlYsj9ulocVOi4R4pW0itH3zAamuKSS4PG+JJMlEgqqobbtaDugvPthVqzFWefKF7Z3C3l2h+rb7ktZsT3x5hOsj3fSf97R1b4OOV1P4N87apQ9iIWhDoGmRr/qwtKnKCW3j4ez+/azLypzgMWqkaH332iNs3bYt8fWaNWv09NNP61Of+pQmT56s8y//prT0l9JfzpHCAU0aFgtty+vauruM1rDro/h5IBowYEC727o98fYI0iYzXCNK81Rd3RTCf6rxtzo5eIc+Nk6Q7m8sb/l7EA5Iz/9Eevth5/uhk6WjrpS+sVS6bKFkuSQ7ItXt93sKIKU+WOM8Bw8s9GrS6GFpHg0AAEBmIbTNEo1B5/Rd252X1F81bvgB4yRJ1fWBpp62zSttpaRKMySrrHfC1cGFfn12cvKLhnGDCyUpsRhZ4f6h7c53nM9TviBJOte/MnHbiursDFSA3lS9r0IDLedvYaeJvSnSLETtFbvXSr8YKz1zYxuDaHq8qdZG+aN1Wr9ll452rU9cPs7arZ3VToVtYyCcdPPY+zqJ0FZKXozM5/NpQInTazZY281K2x1NC2i98cYbeuWVph6rj78fUVXASFtfl9Y9qUnDncf6IEsrbfdtcyqvy4p9ystr+T+tOa/PeaMybBtttodpaIE7KbStrAtptwapwnJ+tzx2UFZwv/93K+dLy34vbXnN+X7YZMntkUYcLrm9UvEI5/Le/r0E0K731znV78MHl2j0wII0jwYAACCzENpmiYZYgODJK1SBz9Pi+uFjJkqS9taFVR+KhbbxxVbi6Gvbpsr9eto2X+zN53G+Hhnra1vgdSrsEqFtfPGiqefLjP2USrxNFc179tWkeug57Z7FH+nMP7yiM//wis6/Z5lWbuZ3OBu5a7ZIkmrcA/WxHQ/HernS9qOXJTssbXi+xVVR2+g/r6xoGo9l9EnXOr3xvxfltyKJy8e6dmlndUCBcFR1+4W2kjSgwKOioqLE9wsWLNCRRx6pJ554QpI0bJjTm7WutqZbC4Tt3tU0J2+88YbWrFmT+N5IWlY/zvlm+0pNGlqsM12vacc7L+v7/35XdpYtSFa9a7MkacTgog62bL4QmbTJDJOndnuiJ7EkRWqc6tjiwiLtNc79uRv2q5iNL4QXN3Ry8vclo2ID2yYAfWf9h84xVOmgIRo9iNAWAACgOULbLBGIBbGFxSWtXj9igvMCtKLBVuXeWLC1f6VtY1Wqhpf1KmNldEOKfJKkOy8+QpI0a8bYxDZTRjlzXxBbGX7YsFhF7r5NzueB42Qdd528bkvxtsOV1XVJ4QI6r7IuqF8/v15rttdozfYardy8T79/aUO6h4UuagxFNSjoVC9aA8dpl5xqSNPrlbaxXqX7NkvhxqSrXt9YqU0bP0i67LOutzRk3ypJ0kfF0yVJI6292ltVrd01AdUFW7aTGTkwP+n7qVOnauXKlTr77LMlSRMnOm+ebd8bUF1dfZd/hL3lTaHthg0b9Oqrr0qSJgxy3qi7eP6HOunBeu1at1zT8nfqLt8f9C//TXrlzbf1/q7sqrit3uP8rCPLBnWwpeT1Os/L4ai02QxXYPfHSddHqp2AdsSAfO02AyVJrvr9+tqWr0v+fughyd+XxkJbKm2BPrVuo3OGQd6QEZo4tOM3cQAAAPoTQtss0RgLbUvaCG0Hj5qgeHHojo3vO1/svzJ2gCrF1oSjtqobnaq6QYVOOHDGoSP04g0navbnml7Yf/2ECXrquuM1MOCEDYcffrjTcqIx1r9y4Dhp4kxFSw5Qfmwts2Aw0NRjGF3y3NrditpGnxhWrN9edLgk6X8fVmhfPYu7ZZOd1Y2JfrYFww7Ubg2WJNVXbOndByqPhbYyUkVyuP/Uuzs10nLOPHg16rQ0mOl+U8e4nCBv17ATFfE67QY81Zu0fkeVqgPOmy15zU5smDB8QLtDOGTyVEnS+kpb9dXtLIbVhqqK5NvEWwB84/85Cx3W1jdqyeaofvf4mxpcsVLba2xVBYxu9C7QhvLsCm1r9jr/n0aOHN7Blk2hbcSWNpuh+ujNpUnXR2JzffgBpdoda7/hbh7ahhubfj9cHmniaZK/OPlBEpW2ORTafrSoxd8CkGne3+4cmxaPOFADY8dgAAAAcBDaZolgxFkEp3TgwFavd3k8GlbkpAvB8o+1dW+D9lXsSN6osUp6+kbp0aukaKTlnfQzUdto7Y5qvfqhEx64LGlAQdMLhoOGFinP605873G7NGVkqVavXi0pFtruc07xVcEQJwRwueSecJLyPU6prYmEEoucoWueWu38/p47bZS+MO0AHTKiRBHb6Pn3dqV5ZOiKndUBTbCcNzrcg8crXOiEdI2VPTsNfW99SHXx/t12VNrzftOVFU1VtZGorWfX7NIIOaHt4/bxanCXaLBVq0+7nb/lyMhPKlrqVNUPj+zQ6nXrFHvK1ZhBTf1WjzhkQrtjmvSJT0iSPqiMKlhdroq6oBrbedMmEI6qvLZpwbPavU64HW8HIElDBxbrCxdelHS7v7/TqI9ffEAH/7FOJz9Ur3Nc/9PuzeuVTfZVOYH06NFjO9hS8rudAD1sG72z5gO99tLTcrlcOvPMMyVJ0Vil7dRRpdqVqLSNtUdYcZ90y3DJRKXCodKP90iX/LPlg5Qe4HyuyYH2CLYtPfMD6eFzpYfOkqItW30AmSAajerDPc6ZEQd8YlqaRwMAAJB5CG2zRGPYSRAGtRHaSk0LZfnrduiEXy7SBx87fcKiJnau/o63pTfulVb/U/p4Ucs7qK+Udq1xXvD1tv/dKS38PykS7P377qbfvfiBPv/7V3XlfKfX5cACn9wuq4NbSe+84yw8dthhh0n7Yv1sB45r2mD8CYlKWxMOqqI+c37mbFFRF9RrHzkh2+cPdXqgnnmY8/m/7/ZyL1Sk1I6qRh3tigWqo6bLP9AJx0wPKhrLawM6+deLdcE9y5xervs2SZGm8LN5gPv6xr2qrA9plNupiN9qD1X12JmJ64PGo4KxRyg6eJIkaZrrQ2145TFJUkmeW7trmv5+v/6DX7U7rk/EQtv1lbY2b92q429/WV/7y4o2t7/2kbf1qV+8rLU7nACzscppafOtG5oWUxs2qFgHHXSQjjzySA0a5FSRbq0x+vIfX1ddSFq1y9aGvbYKti5ud2yZxBijiqoGSdLYAyd2uL03FmKHo9KuVx+VJP3sZz/TBRdcIKmpPcLEoUXaHWu/4V/3b+dsk2dnN93R8EMlVxuHPblUabv6X9Lr9zhf1+6UPnwxveMB2rBm5XIFI1KBVzrosKPTPRwAAICMk/bQ9u6779b48eOVl5eno446Kmm1bDQJxkLbIYMGt7nN8EGx03sb9ihfAU2xNkmSPjCx1c5X/6tp45UPSpUfOV8bI704V/rNIdKfPiXdfaxU3qxqradqdkgvzpHe+bu07j9SxYdpr/yxbaN/velUVA0vydOYQQX66gnj9aMf/UjTpk3Txx9/3OrtGhsb9cEHThWfE9pucq5oHtqOOz5RaZsfqU30y0XnPbtml2wjHXZAqcYMdhYm+VwsvF32UaX2ZnOLhFCD1LA33aNwqlNf+rm09FfOc0CK1JZv0ZoNWzXxD3U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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 26
+  },
+  {
+   "cell_type": "code",
+   "id": "c1aff0185ba36b1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:11:46.892254Z",
+     "start_time": "2026-04-03T13:11:45.163745Z"
+    }
+   },
+   "source": [
+    "RESAMPLE = \"10min\"\n",
+    "\n",
+    "A_da      = ds_cmls[\"A\"].mean(dim=\"sublink_id\").resample(time=RESAMPLE).mean()\n",
+    "pws_plot  = ds_pws[\"rainfall_amount_qc\"].resample(time=RESAMPLE).sum(skipna=True)\n",
+    "asos_plot = ds_asos[\"precip_amount\"].resample(time=RESAMPLE).sum(skipna=True)\n",
+    "\n",
+    "fig, axes = plt.subplots(3, 1, figsize=(16, 12), sharex=True)\n",
+    "\n",
+    "im0 = axes[0].pcolormesh(A_da.time.values, np.arange(A_da.sizes[\"cml_id\"]),\n",
+    "                          A_da.values, cmap=\"Blues\",\n",
+    "                          vmin=0, vmax=float(np.nanpercentile(A_da.values, 99)))\n",
+    "plt.colorbar(im0, ax=axes[0], label=\"A (dB)\", shrink=0.8)\n",
+    "axes[0].set(ylabel=\"CML index\", title=f\"Network-wide excess attenuation A ({RESAMPLE})\")\n",
+    "\n",
+    "im1 = axes[1].pcolormesh(pws_plot.time.values, np.arange(pws_plot.sizes[\"id\"]),\n",
+    "                          pws_plot.values, cmap=\"Greens\",\n",
+    "                          vmin=0, vmax=float(np.nanpercentile(pws_plot.values, 99)))\n",
+    "plt.colorbar(im1, ax=axes[1], label=\"mm\", shrink=0.8)\n",
+    "axes[1].set(ylabel=\"PWS index\", title=f\"PWS rainfall ({RESAMPLE})\")\n",
+    "\n",
+    "im2 = axes[2].pcolormesh(asos_plot.time.values, np.arange(asos_plot.sizes[\"id\"]),\n",
+    "                          asos_plot.values, cmap=\"Oranges\",\n",
+    "                          vmin=0, vmax=float(np.nanpercentile(asos_plot.values, 99)))\n",
+    "plt.colorbar(im2, ax=axes[2], label=\"mm\", shrink=0.8)\n",
+    "axes[2].set(ylabel=\"ASOS index\", title=f\"ASOS precipitation ({RESAMPLE})\")\n",
+    "axes[2].xaxis.set_major_formatter(mdates.DateFormatter(\"%b %d\"))\n",
+    "\n",
+    "for ax in axes: ax.grid(True, alpha=0.3)\n",
+    "plt.tight_layout();"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1600x1200 with 6 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 27
+  },
+  {
+   "cell_type": "code",
+   "id": "02e96e27",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:11:50.068882Z",
+     "start_time": "2026-04-03T13:11:49.847171Z"
+    }
+   },
+   "source": [
+    "plg.plot_map.plot_plg(\n",
+    "    da_cmls=ds_cmls_filtered.isel(sublink_id=0).R.resample(time=\"1h\").mean().sum(dim=\"time\"),\n",
+    "    da_gauges=ds_pws[\"rainfall_amount_qc\"].resample(time=\"1h\").sum(skipna=True).sum(dim=\"time\"),\n",
+    "    vmin=0,\n",
+    "    vmax=30,\n",
+    "    cmap=\"turbo\",\n",
+    "    colorbar_label=\"Cumulative rainfall (mm)\",\n",
+    ")\n",
+    "plt.title(\"Cumulative rainfall — CML links + PWS stations\")\n",
+    "plt.gcf().autofmt_xdate(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 28
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b1954a0c0d1817b",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Cross-Dataset Validation: CML vs. ASOS\n",
+    "\n",
+    "### Nearest ASOS Station per CML\n",
+    "\n",
+    "For each CML midpoint we find the nearest ASOS station using `pyproj`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cdcc089b4e044bf7",
+   "metadata": {},
+   "source": [
+    "### CML Attenuation vs. ASOS Rainfall\n",
+    "\n",
+    "Hourly mean attenuation A for CMLs nearest to the wettest ASOS station,\n",
+    "compared against the station's hourly rainfall. Both aggregated via\n",
+    "xarray `.resample()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a217f1395350ec47",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:11:54.013981Z",
+     "start_time": "2026-04-03T13:11:53.700575Z"
+    }
+   },
+   "source": [
+    "ds_asos_proj = prf.convert_to_utm(ds_asos.drop_vars(\"quantile\", errors=\"ignore\"),\n",
+    "                                   name_coord_lon=\"lon\", name_coord_lat=\"lat\", zone=18)\n",
+    "\n",
+    "ds_cmls.coords[\"site_0_x\"], ds_cmls.coords[\"site_0_y\"] = plg.spatial.project_point_coordinates(ds_cmls.site_0_lon, ds_cmls.site_0_lat, \"EPSG:32618\")\n",
+    "ds_cmls.coords[\"site_1_x\"], ds_cmls.coords[\"site_1_y\"] = plg.spatial.project_point_coordinates(ds_cmls.site_1_lon, ds_cmls.site_1_lat, \"EPSG:32618\")\n",
+    "\n",
+    "closest  = plg.spatial.get_closest_points_to_line(ds_cmls, ds_asos_proj, max_distance=20000, n_closest=1)\n",
+    "R_1h     = ds_cmls_filtered.R.isel(sublink_id=0).resample(time=\"1h\").mean()\n",
+    "\n",
+    "asos_1h  = ds_asos[\"precip_amount\"].resample(time=\"1h\").sum(skipna=True)\n",
+    "\n",
+    "pairs = [(R_1h.sel(cml_id=c).values, asos_1h.sel(id=closest.sel(cml_id=c).neighbor_id.values[0])\n",
+    "          .reindex(time=R_1h.time, method=\"nearest\").values)\n",
+    "         for c in closest.cml_id.values if closest.sel(cml_id=c).neighbor_id.values[0] is not None]\n",
+    "\n",
+    "R_all, asos_all = [np.concatenate([p[i] for p in pairs]) for i in [0, 1]]\n",
+    "mask = ~np.isnan(R_all) & ~np.isnan(asos_all)\n",
+    "\n",
+    "plt.hexbin(asos_all[mask], R_all[mask], gridsize=40, bins=\"log\", cmap=\"Blues\", extent=[0, 5, 0, 5])\n",
+    "plt.plot([0, 5], [0, 5], \"r--\")\n",
+    "plt.xlabel(\"ASOS (mm/h)\"); plt.ylabel(\"CML R (mm/h)\"); plt.title(\"CML vs nearest ASOS\");\n"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 29
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b116ea81",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Save Processed Datasets\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "9f63dee2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-03T13:12:03.065348Z",
+     "start_time": "2026-04-03T13:12:00.330986Z"
+    }
+   },
+   "source": [
+    "save = True\n",
+    "\n",
+    "if save:\n",
+    "    samples_dir = omio.SAMPLE_DIR\n",
+    "    samples_dir.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "    t0 = pd.Timestamp(ds_cmls.time.values[0])\n",
+    "    t1 = pd.Timestamp(ds_cmls.time.values[-1])\n",
+    "    nd = max(1, int(np.ceil((t1 - t0).total_seconds() / 86400)))\n",
+    "    tag = f\"{nd}d_{t0.strftime('%Y%m%d')}_{t1.strftime('%Y%m%d')}\"\n",
+    "\n",
+    "    omio.save_compressed_netcdf(ds_cmls, samples_dir / f\"openmesh_cml_{tag}_processed.nc\")\n",
+    "    omio.save_compressed_netcdf(ds_pws,  samples_dir / f\"openmesh_wu_pws_{tag}_qc.nc\")\n",
+    "    omio.save_compressed_netcdf(ds_asos, samples_dir / f\"openmesh_asos_ws_{tag}_reference.nc\")\n",
+    "\n",
+    "    print(\"Saved to\", samples_dir.resolve())\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved to /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/samples\n"
+     ]
+    }
+   ],
+   "execution_count": 30
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7cdf254",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Summary\n",
+    "\n",
+    "> **Usage example only.** This notebook is intended as a demonstration of how to work with the OpenMesh datasets. Parameters, thresholds, and methods should be adapted for real analysis.\n",
+    "\n",
+    "This notebook demonstrated how to:\n",
+    "\n",
+    "1. **Download** the OpenMesh CML and PWS datasets from Zenodo programmatically\n",
+    "2. **Load** all three datasets with a single unified function\n",
+    "3. **Explore** the CML network structure, RSL signal levels, frequency/polarization distribution, and data availability\n",
+    "4. **Map** the CML network and PWS stations using `poligrain` (`scatter_lines`, `plot_lines`, `plot_plg`)\n",
+    "5. **QC** CML data by removing sentinel values and implausible outliers\n",
+    "6. **Compute** baseline RSL using `pycomlink` (rolling or constant method)\n",
+    "7. **Derive** excess attenuation $A = \\mathrm{RSL_{baseline}} - \\mathrm{RSL}$ and rain rate $R$\n",
+    "8. **Filter** CML links by length and frequency band\n",
+    "9. **Check** PWS data availability per station\n",
+    "10. **Apply QC** to PWS data using `pypwsqc` (FZ, HI, peak removal, and IC filters)\n",
+    "11. **Fetch** NOAA ASOS reference data via the IEM API and convert to xarray\n",
+    "12. **Compare** all three sources (CML, PWS, ASOS) over a common time window\n",
+    "13. **Validate** CML attenuation against the nearest ASOS reference station\n",
+    "14. **Save** processed datasets as compressed NetCDF using `omio.save_compressed_netcdf`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "openmesh",
+   "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": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/OpenMesh/notebooks/create_OpenMesh_data.ipynb b/OpenMesh/notebooks/create_OpenMesh_data.ipynb
new file mode 100644
index 0000000..912e023
--- /dev/null
+++ b/OpenMesh/notebooks/create_OpenMesh_data.ipynb
@@ -0,0 +1,3078 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "43c8be9c",
+   "metadata": {},
+   "source": [
+    "# Download and process OpenMesh dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11984e03",
+   "metadata": {},
+   "source": [
+    "This notebook \n",
+    "* downloads the dataset [OpenMesh (Jacoby et al. 2025)](https://doi.org/10.5281/zenodo.15287692)\n",
+    "* creates sample datasets for different time periods\n",
+    "* loads and inspects CML (Commercial Microwave Link) data\n",
+    "* loads and inspects PWS (Personal Weather Station) data\n",
+    "* fetches and converts ASOS reference data to OpenSense format\n",
+    "* plots comparisons of CML, PWS and ASOS reference data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "8f4ee475",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:02.728991Z",
+     "start_time": "2026-03-25T10:52:02.726080Z"
+    }
+   },
+   "source": [
+    "# Uncomment to install\n",
+    "# !pip install xarray netCDF4 requests tqdm numpy pandas poligrain pypwsqc pyproj matplotlib"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "code",
+   "id": "779e6250",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:03.885828Z",
+     "start_time": "2026-03-25T10:52:02.732827Z"
+    }
+   },
+   "source": [
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings\n",
+    "\n",
+    "import openmesh_data_io as omio\n",
+    "import ws_opensense_conversion as wsoc\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
+   ],
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_config",
+   "metadata": {},
+   "source": [
+    "## Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "cell_config",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:03.939057Z",
+     "start_time": "2026-03-25T10:52:03.937174Z"
+    }
+   },
+   "source": "DATA_DIR       = \"data/jacoby_2025_OpenMesh/\"\nSAMPLE_DIR     = \"data/samples/\"\nCML_PATH       = \"data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\"\nPWS_PATH       = \"data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\"\nASOS_META_PATH = \"data/jacoby_2025_OpenMesh/meta/ASOS_stations.csv\"",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bd9bd75",
+   "metadata": {},
+   "source": [
+    "## Download and transform OpenMesh dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "14316229",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:03.946848Z",
+     "start_time": "2026-03-25T10:52:03.944468Z"
+    }
+   },
+   "source": [
+    "omio.download_openmesh(\n",
+    "    output_dir=DATA_DIR\n",
+    ");"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  CML found locally  : data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6280d99b",
+   "metadata": {},
+   "source": [
+    "## Load CML dataset"
+   ]
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:03.962088Z",
+     "start_time": "2026-03-25T10:52:03.959293Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "print(\"DATA_DIR:\", omio.DATA_DIR)\n",
+    "raw = omio.DATA_DIR / \"raw\"\n",
+    "if raw.exists():\n",
+    "  print(\"Files in raw/:\", list(raw.iterdir()))\n",
+    "else:\n",
+    "  print(\"raw/ folder does not exist!\")"
+   ],
+   "id": "3b49954f3ad19ef5",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DATA_DIR: /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh\n",
+      "Files in raw/: [PosixPath('/Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc'), PosixPath('/Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_opensense_sample_jan.nc'), PosixPath('/Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc')]\n"
+     ]
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "code",
+   "id": "3257673a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.429506Z",
+     "start_time": "2026-03-25T10:52:03.971904Z"
+    }
+   },
+   "source": [
+    "ds_cml = omio.load_cml()\n",
+    "print(ds_cml)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Loaded CML        : {'cml_id': 75, 'sublink_id': 3, 'time': 354241}  ←  data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\n",
+      "<xarray.Dataset> Size: 322MB\n",
+      "Dimensions:       (cml_id: 75, sublink_id: 3, time: 354241)\n",
+      "Coordinates:\n",
+      "  * time          (time) datetime64[ns] 3MB 2023-10-29 ... 2024-07-01\n",
+      "  * sublink_id    (sublink_id) object 24B 'sublink_1' 'sublink_2' 'sublink_3'\n",
+      "    site_0_lat    (cml_id) float32 300B 40.7 40.66 40.73 ... 40.66 40.73 40.65\n",
+      "    site_0_lon    (cml_id) float32 300B -73.94 -74.0 -73.95 ... -73.95 -74.0\n",
+      "    site_1_lat    (cml_id) float32 300B 40.69 40.68 40.72 ... 40.66 40.73 40.64\n",
+      "    site_1_lon    (cml_id) float32 300B -73.92 -73.99 -73.98 ... -73.95 -73.95\n",
+      "    length        (cml_id) float32 300B 2.178e+03 2.955e+03 ... 122.4 4.506e+03\n",
+      "    frequency     (cml_id, sublink_id) float32 900B 6.804e+04 6.804e+04 ... nan\n",
+      "    polarization  (cml_id, sublink_id) object 2kB 'v' 'v' 'v' ... 'v' 'v' 'v'\n",
+      "  * cml_id        (cml_id) <U2 600B '1' '2' '3' '4' '5' ... '72' '73' '74' '75'\n",
+      "Data variables:\n",
+      "    rsl           (cml_id, sublink_id, time) float32 319MB -49.0 -49.0 ... nan\n",
+      "Attributes:\n",
+      "    title:                 OpenMesh\n",
+      "    file_author:           Dror Jacoby\n",
+      "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+      "    date:                  2025-11-01\n",
+      "    source:                Community NYC Mesh Wireless Network\n",
+      "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+      "    naming_convention:     OpenSense-1.0\n",
+      "    license_restrictions:  CC BY 4.0 – https://creativecommons.org/licenses/b...\n",
+      "    reference:             https://doi.org/10.5281/zenodo.15287692\n",
+      "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+      "    Conventions:           OpenSense-CML-v1.0\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_slice_select",
+   "metadata": {},
+   "source": [
+    "### Slice & select CML data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d397dd43",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.453539Z",
+     "start_time": "2026-03-25T10:52:04.436922Z"
+    }
+   },
+   "source": [
+    "# ── CML selection ─────────────────────────────────────────────\n",
+    "cml_ids     = [\"5\", \"7\", \"15\", \"42\"]       # None = all 75\n",
+    "sublink_ids = [\"sublink_1\"]   # None = all sublinks \n",
+    "\n",
+    "# ── Slice Time window ─────────────────────────────────────────────────────\n",
+    "ds_cml_jan = ds_cml.sel(\n",
+    "    time=slice(\"2024-01-01\", \"2024-02-01\"),\n",
+    "    cml_id=cml_ids,\n",
+    "    sublink_id=sublink_ids,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# ── Inspect ───────────────────────────────────────────────────\n",
+    "print(f\"Time     : {str(ds_cml_jan.time.values[0])[:19]}  →  {str(ds_cml_jan.time.values[-1])[:19]}\")\n",
+    "print(f\"CML IDs  : {ds_cml_jan.cml_id.values}\")\n",
+    "print(f\"Sublinks : {ds_cml_jan.sublink_id.values}\")\n",
+    "print(f\"Shape    : {dict(ds_cml_jan.sizes)}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time     : 2024-01-01T00:00:00  →  2024-02-01T23:59:00\n",
+      "CML IDs  : ['5' '7' '15' '42']\n",
+      "Sublinks : ['sublink_1']\n",
+      "Shape    : {'cml_id': 4, 'sublink_id': 1, 'time': 46080}\n"
+     ]
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad2ac0b8",
+   "metadata": {},
+   "source": [
+    "## Create CML samples\n",
+    "\n",
+    "Samples are **saved to disk by default** (`→ data/samples/`), returning the file path.\n",
+    "Use `return_type=\"sample\"` to get the `xr.Dataset` in memory instead.\n",
+    "\n",
+    "### In-memory sample (no disk write)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "be23ecef",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.486878Z",
+     "start_time": "2026-03-25T10:52:04.461948Z"
+    }
+   },
+   "source": [
+    "ds_sample_custom = omio.create_openmesh_sample(\n",
+    "    ds=ds_cml,\n",
+    "    start_time= \"2024-01-15\",\n",
+    "    end_time  = \"2024-01-25\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    "    return_type=\"sample\",\n",
+    "    save = False\n",
+    ")\n",
+    "\n",
+    "ds_sample_custom"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-25 00:00:00  (10d)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_10d.nc\n",
+      "  Timesteps: 14,401\n",
+      "  CML count: 75\n",
+      "  Save     : skipped (save=False)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset> Size: 13MB\n",
+       "Dimensions:       (cml_id: 75, sublink_id: 3, time: 14401)\n",
+       "Coordinates:\n",
+       "  * time          (time) datetime64[ns] 115kB 2024-01-15 ... 2024-01-25\n",
+       "  * sublink_id    (sublink_id) object 24B 'sublink_1' 'sublink_2' 'sublink_3'\n",
+       "    site_0_lat    (cml_id) float32 300B 40.7 40.66 40.73 ... 40.66 40.73 40.65\n",
+       "    site_0_lon    (cml_id) float32 300B -73.94 -74.0 -73.95 ... -73.95 -74.0\n",
+       "    site_1_lat    (cml_id) float32 300B 40.69 40.68 40.72 ... 40.66 40.73 40.64\n",
+       "    site_1_lon    (cml_id) float32 300B -73.92 -73.99 -73.98 ... -73.95 -73.95\n",
+       "    length        (cml_id) float32 300B 2.178e+03 2.955e+03 ... 122.4 4.506e+03\n",
+       "    frequency     (cml_id, sublink_id) float32 900B 6.804e+04 6.804e+04 ... nan\n",
+       "    polarization  (cml_id, sublink_id) object 2kB 'v' 'v' 'v' ... 'v' 'v' 'v'\n",
+       "  * cml_id        (cml_id) <U2 600B '1' '2' '3' '4' '5' ... '72' '73' '74' '75'\n",
+       "Data variables:\n",
+       "    rsl           (cml_id, sublink_id, time) float32 13MB -50.0 -50.0 ... nan\n",
+       "Attributes: (12/15)\n",
+       "    title:                 OpenMesh CML sample (10d), start 2024-01-15\n",
+       "    file_author:           Dror Jacoby\n",
+       "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+       "    date:                  2025-11-01\n",
+       "    source:                Community NYC Mesh Wireless Network\n",
+       "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+       "    ...                    ...\n",
+       "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+       "    Conventions:           OpenSense-CML-v1.0\n",
+       "    start_date:            2024-01-15\n",
+       "    end_date:              2024-01-25\n",
+       "    time_range:            2024-01-15T00:00:00 / 2024-01-25T00:00:00\n",
+       "    cml_subset:            all"
+      ],
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+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 13MB\n",
+       "Dimensions:       (cml_id: 75, sublink_id: 3, time: 14401)\n",
+       "Coordinates:\n",
+       "  * time          (time) datetime64[ns] 115kB 2024-01-15 ... 2024-01-25\n",
+       "  * sublink_id    (sublink_id) object 24B &#x27;sublink_1&#x27; &#x27;sublink_2&#x27; &#x27;sublink_3&#x27;\n",
+       "    site_0_lat    (cml_id) float32 300B 40.7 40.66 40.73 ... 40.66 40.73 40.65\n",
+       "    site_0_lon    (cml_id) float32 300B -73.94 -74.0 -73.95 ... -73.95 -74.0\n",
+       "    site_1_lat    (cml_id) float32 300B 40.69 40.68 40.72 ... 40.66 40.73 40.64\n",
+       "    site_1_lon    (cml_id) float32 300B -73.92 -73.99 -73.98 ... -73.95 -73.95\n",
+       "    length        (cml_id) float32 300B 2.178e+03 2.955e+03 ... 122.4 4.506e+03\n",
+       "    frequency     (cml_id, sublink_id) float32 900B 6.804e+04 6.804e+04 ... nan\n",
+       "    polarization  (cml_id, sublink_id) object 2kB &#x27;v&#x27; &#x27;v&#x27; &#x27;v&#x27; ... &#x27;v&#x27; &#x27;v&#x27; &#x27;v&#x27;\n",
+       "  * cml_id        (cml_id) &lt;U2 600B &#x27;1&#x27; &#x27;2&#x27; &#x27;3&#x27; &#x27;4&#x27; &#x27;5&#x27; ... &#x27;72&#x27; &#x27;73&#x27; &#x27;74&#x27; &#x27;75&#x27;\n",
+       "Data variables:\n",
+       "    rsl           (cml_id, sublink_id, time) float32 13MB -50.0 -50.0 ... nan\n",
+       "Attributes: (12/15)\n",
+       "    title:                 OpenMesh CML sample (10d), start 2024-01-15\n",
+       "    file_author:           Dror Jacoby\n",
+       "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+       "    date:                  2025-11-01\n",
+       "    source:                Community NYC Mesh Wireless Network\n",
+       "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+       "    ...                    ...\n",
+       "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+       "    Conventions:           OpenSense-CML-v1.0\n",
+       "    start_date:            2024-01-15\n",
+       "    end_date:              2024-01-25\n",
+       "    time_range:            2024-01-15T00:00:00 / 2024-01-25T00:00:00\n",
+       "    cml_subset:            all</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-503c4b16-1975-40c2-a09c-7d65b17460d4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-503c4b16-1975-40c2-a09c-7d65b17460d4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>cml_id</span>: 75</li><li><span class='xr-has-index'>sublink_id</span>: 3</li><li><span class='xr-has-index'>time</span>: 14401</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-35194312-9b80-4810-bcac-f323fda2ac3e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35194312-9b80-4810-bcac-f323fda2ac3e' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-15 ... 2024-01-25</div><input id='attrs-0b9a5a46-3ddf-4228-83db-4d460fc4064f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b9a5a46-3ddf-4228-83db-4d460fc4064f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a33ebc6-f802-43f3-ab5c-ed758f96f677' class='xr-var-data-in' type='checkbox'><label for='data-7a33ebc6-f802-43f3-ab5c-ed758f96f677' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time_utc</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-15T00:00:00.000000000&#x27;, &#x27;2024-01-15T00:01:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-15T00:02:00.000000000&#x27;, ..., &#x27;2024-01-24T23:58:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-24T23:59:00.000000000&#x27;, &#x27;2024-01-25T00:00:00.000000000&#x27;],\n",
+       "      shape=(14401,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sublink_id</span></div><div class='xr-var-dims'>(sublink_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;sublink_1&#x27; &#x27;sublink_2&#x27; &#x27;sublink_3&#x27;</div><input id='attrs-bcdc543d-e254-460d-89b8-060d1f1298b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcdc543d-e254-460d-89b8-060d1f1298b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9443a6f0-ce37-4c1a-aff0-780076e84446' class='xr-var-data-in' type='checkbox'><label for='data-9443a6f0-ce37-4c1a-aff0-780076e84446' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sublink_identifier</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;sublink_1&#x27;, &#x27;sublink_2&#x27;, &#x27;sublink_3&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_0_lat</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>40.7 40.66 40.73 ... 40.73 40.65</div><input id='attrs-ae8bb065-2f3a-4ac3-844f-a9807372eb33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae8bb065-2f3a-4ac3-844f-a9807372eb33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fff192c7-aa33-44ee-9ae1-a8b30f82c1a3' class='xr-var-data-in' type='checkbox'><label for='data-fff192c7-aa33-44ee-9ae1-a8b30f82c1a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_0_latitude</dd></dl></div><div class='xr-var-data'><pre>array([40.696095, 40.657784, 40.725693, 40.657654, 40.727554, 40.73676 ,\n",
+       "       40.727554, 40.715588, 40.68601 , 40.660015, 40.830387, 40.658028,\n",
+       "       40.71111 , 40.713284, 40.7199  , 40.725903, 40.6726  , 40.72128 ,\n",
+       "       40.672546, 40.65801 , 40.606663, 40.815357, 40.6458  , 40.72579 ,\n",
+       "       40.725616, 40.729553, 40.68602 , 40.686214, 40.6726  , 40.7161  ,\n",
+       "       40.672554, 40.635117, 40.683   , 40.672558, 40.693817, 40.6726  ,\n",
+       "       40.657867, 40.672527, 40.686016, 40.674458, 40.686016, 40.7161  ,\n",
+       "       40.7199  , 40.815334, 40.695812, 40.7109  , 40.696053, 40.725735,\n",
+       "       40.6579  , 40.711086, 40.696037, 40.70845 , 40.715515, 40.686016,\n",
+       "       40.7085  , 40.725735, 40.711086, 40.724674, 40.672684, 40.69604 ,\n",
+       "       40.672684, 40.67543 , 40.682983, 40.672684, 40.696033, 40.71397 ,\n",
+       "       40.6579  , 40.7137  , 40.672684, 40.672684, 40.736717, 40.715614,\n",
+       "       40.6579  , 40.725616, 40.64778 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_0_lon</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-73.94 -74.0 ... -73.95 -74.0</div><input id='attrs-ce61a5ab-0319-4fa8-8e2a-3f52d3b56483' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce61a5ab-0319-4fa8-8e2a-3f52d3b56483' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce9e4ce9-2b54-48e4-8a6d-1714befcb1a6' class='xr-var-data-in' type='checkbox'><label for='data-ce9e4ce9-2b54-48e4-8a6d-1714befcb1a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_0_longitude</dd></dl></div><div class='xr-var-data'><pre>array([-73.93975 , -74.004845, -73.95414 , -74.00518 , -73.946045,\n",
+       "       -73.95441 , -73.946045, -73.9844  , -73.9176  , -73.913605,\n",
+       "       -73.941025, -74.00477 , -74.00129 , -73.99037 , -73.98802 ,\n",
+       "       -73.983635, -73.9642  , -73.94671 , -73.96445 , -74.004745,\n",
+       "       -73.992935, -73.9503  , -74.0134  , -73.98355 , -73.95407 ,\n",
+       "       -73.945946, -73.917496, -73.91747 , -73.96419 , -73.986   ,\n",
+       "       -73.964455, -73.950554, -73.946   , -73.96443 , -73.906364,\n",
+       "       -73.964195, -74.004906, -73.964   , -73.917496, -73.9403  ,\n",
+       "       -73.917496, -73.986   , -73.98802 , -73.95034 , -73.9397  ,\n",
+       "       -74.0012  , -73.93978 , -73.98368 , -74.0049  , -74.001305,\n",
+       "       -73.939766, -73.92189 , -73.98438 , -73.917496, -73.92195 ,\n",
+       "       -73.98368 , -74.0013  , -73.98073 , -73.96446 , -73.93979 ,\n",
+       "       -73.96446 , -73.98058 , -73.99392 , -73.96386 , -73.93979 ,\n",
+       "       -73.92912 , -74.0049  , -73.93552 , -73.96386 , -73.96386 ,\n",
+       "       -73.95465 , -73.98442 , -74.0049  , -73.95407 , -74.00077 ],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_1_lat</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>40.69 40.68 40.72 ... 40.73 40.64</div><input id='attrs-0e4f534c-f385-422c-a43a-9cca4aed39b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e4f534c-f385-422c-a43a-9cca4aed39b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f050acc0-2700-4b95-9a9e-a76a62b84af0' class='xr-var-data-in' type='checkbox'><label for='data-f050acc0-2700-4b95-9a9e-a76a62b84af0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_1_latitude</dd></dl></div><div class='xr-var-data'><pre>array([40.6862  , 40.682995, 40.715588, 40.646   , 40.729553, 40.733902,\n",
+       "       40.725616, 40.7274  , 40.680214, 40.68582 , 40.815594, 40.66764 ,\n",
+       "       40.71609 , 40.71281 , 40.725616, 40.72067 , 40.680122, 40.725616,\n",
+       "       40.658012, 40.696133, 40.601913, 40.830326, 40.64593 , 40.724674,\n",
+       "       40.7296  , 40.73391 , 40.69389 , 40.7136  , 40.67813 , 40.724674,\n",
+       "       40.660744, 40.672516, 40.696   , 40.655624, 40.70538 , 40.67351 ,\n",
+       "       40.67543 , 40.689587, 40.68582 , 40.696003, 40.705837, 40.725616,\n",
+       "       40.724674, 40.79355 , 40.674442, 40.711456, 40.6875  , 40.71088 ,\n",
+       "       40.664997, 40.71281 , 40.689   , 40.70538 , 40.71898 , 40.67673 ,\n",
+       "       40.71363 , 40.72847 , 40.715645, 40.724   , 40.667133, 40.693817,\n",
+       "       40.672047, 40.6577  , 40.681797, 40.683826, 40.691147, 40.71363 ,\n",
+       "       40.6566  , 40.70538 , 40.696   , 40.6828  , 40.73505 , 40.690742,\n",
+       "       40.65964 , 40.726616, 40.635002], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_1_lon</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-73.92 -73.99 ... -73.95 -73.95</div><input id='attrs-436dff49-46a8-46a4-ab63-a85f51823e08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-436dff49-46a8-46a4-ab63-a85f51823e08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f7eb136-c74f-4a7d-8f33-494da70bca3a' class='xr-var-data-in' type='checkbox'><label for='data-3f7eb136-c74f-4a7d-8f33-494da70bca3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_1_longitude</dd></dl></div><div class='xr-var-data'><pre>array([-73.9175  , -73.99379 , -73.9844  , -74.01368 , -73.945946,\n",
+       "       -73.95061 , -73.9471  , -73.94617 , -73.954796, -73.917274,\n",
+       "       -73.950005, -73.982414, -73.98606 , -73.99474 , -73.95407 ,\n",
+       "       -73.98805 , -73.96321 , -73.9471  , -74.004715, -73.93976 ,\n",
+       "       -74.001045, -73.9408  , -74.01363 , -73.98073 , -73.9459  ,\n",
+       "       -73.95059 , -73.90638 , -73.9355  , -73.962456, -73.98073 ,\n",
+       "       -73.96041 , -73.96444 , -73.9398  , -73.956795, -73.93057 ,\n",
+       "       -73.96408 , -73.98058 , -73.9512  , -73.9173  , -73.93987 ,\n",
+       "       -73.91528 , -73.95407 , -73.98073 , -73.96564 , -73.94032 ,\n",
+       "       -73.99449 , -73.9474  , -74.0012  , -73.99054 , -73.9948  ,\n",
+       "       -73.939   , -73.93057 , -73.9585  , -73.93087 , -73.93564 ,\n",
+       "       -73.9831  , -73.98454 , -73.9783  , -73.94681 , -73.906364,\n",
+       "       -73.95537 , -74.0051  , -73.994736, -73.95053 , -73.94496 ,\n",
+       "       -73.93564 , -73.9993  , -73.93057 , -73.9398  , -73.9459  ,\n",
+       "       -73.9582  , -73.9701  , -73.99854 , -73.95469 , -73.95021 ],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>length</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.178e+03 2.955e+03 ... 4.506e+03</div><input id='attrs-515356d3-f01d-46b0-aca5-bb6fd326d20f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-515356d3-f01d-46b0-aca5-bb6fd326d20f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f1c4853-0548-42c2-bfe6-7e362ef9017c' class='xr-var-data-in' type='checkbox'><label for='data-0f1c4853-0548-42c2-bfe6-7e362ef9017c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>distance_between_pair_of_antennas</dd></dl></div><div class='xr-var-data'><pre>array([2178.1     , 2954.633   , 2786.53    , 1480.4     ,  222.1     ,\n",
+       "        451.8     ,  232.6     , 3486.6     , 3201.9673  , 2882.7     ,\n",
+       "       1808.9     , 2171.1     , 1400.6     ,  373.3     , 2937.8     ,\n",
+       "        690.7     ,  839.7     ,  482.8     , 3768.2     , 6936.5     ,\n",
+       "        865.6     , 1845.1     ,   24.5     ,  268.4     ,  818.82947 ,\n",
+       "        623.      , 1283.1     , 3401.3     ,  631.4     , 1051.4     ,\n",
+       "       1355.5     , 4316.1     , 1535.8     , 1987.9     , 2412.3335  ,\n",
+       "        101.5     , 2834.5     , 2181.4     ,   26.932957, 2392.7     ,\n",
+       "       2209.      , 2891.0608  ,  813.2     , 2743.331   , 2373.7     ,\n",
+       "        570.4     , 1147.5     , 2216.4     , 1447.5     ,  582.3     ,\n",
+       "        784.2     ,  809.1     , 2220.      , 1530.5     , 1287.1569  ,\n",
+       "        307.7     , 1503.4     ,  218.6     , 1614.8     , 2836.2     ,\n",
+       "        771.8     , 2859.2     ,  149.      , 1673.2     ,  696.7     ,\n",
+       "        552.3     ,  495.1     , 1013.6     , 3292.4     , 1888.7     ,\n",
+       "        352.4     , 3015.2     ,  571.5     ,  122.4     , 4505.6     ],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frequency</span></div><div class='xr-var-dims'>(cml_id, sublink_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>6.804e+04 6.804e+04 ... nan nan</div><input id='attrs-e7d62457-558e-4f0c-92e3-bdad861a90a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7d62457-558e-4f0c-92e3-bdad861a90a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4155a5db-cc9b-4aea-8bcf-4653db42011d' class='xr-var-data-in' type='checkbox'><label for='data-4155a5db-cc9b-4aea-8bcf-4653db42011d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>MHz</dd><dt><span>long_name :</span></dt><dd>sublink_frequency</dd></dl></div><div class='xr-var-data'><pre>array([[68040., 68040.,  5765.],\n",
+       "       [ 5545., 24100., 24200.],\n",
+       "       [68040.,  5760., 68040.],\n",
+       "       [24200., 24200.,  5300.],\n",
+       "       [60480., 60480.,    nan],\n",
+       "       [58320., 58320.,    nan],\n",
+       "       [58320., 58320.,    nan],\n",
+       "       [68040.,  5760.,    nan],\n",
+       "       [ 5710.,  5700.,    nan],\n",
+       "       [ 5505.,  5495.,    nan],\n",
+       "       [58320., 58320.,    nan],\n",
+       "       [65880., 65880.,    nan],\n",
+       "       [24100., 24200.,    nan],\n",
+       "       [ 5795.,  5795.,    nan],\n",
+       "       [ 5560.,  5560.,    nan],\n",
+       "       [62640., 62640.,    nan],\n",
+       "       [68040., 68040.,    nan],\n",
+       "       [64800., 64800.,    nan],\n",
+       "       [24100., 24100.,    nan],\n",
+       "       [69120., 65880.,    nan],\n",
+       "...\n",
+       "       [ 5570.,    nan,    nan],\n",
+       "       [ 5810.,    nan,    nan],\n",
+       "       [ 5710.,    nan,    nan],\n",
+       "       [ 5490.,    nan,    nan],\n",
+       "       [ 5490.,    nan,    nan],\n",
+       "       [ 5490.,    nan,    nan],\n",
+       "       [ 5310.,    nan,    nan],\n",
+       "       [ 5490.,    nan,    nan],\n",
+       "       [ 5510.,    nan,    nan],\n",
+       "       [ 5560.,    nan,    nan],\n",
+       "       [ 5560.,    nan,    nan],\n",
+       "       [ 5490.,    nan,    nan],\n",
+       "       [ 5560.,    nan,    nan],\n",
+       "       [ 5560.,    nan,    nan],\n",
+       "       [ 5510.,    nan,    nan],\n",
+       "       [ 5510.,    nan,    nan],\n",
+       "       [ 5710.,    nan,    nan],\n",
+       "       [ 5490.,    nan,    nan],\n",
+       "       [ 5700.,    nan,    nan],\n",
+       "       [ 5760.,    nan,    nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polarization</span></div><div class='xr-var-dims'>(cml_id, sublink_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;v&#x27; &#x27;v&#x27; &#x27;v&#x27; &#x27;v&#x27; ... &#x27;v&#x27; &#x27;v&#x27; &#x27;v&#x27; &#x27;v&#x27;</div><input id='attrs-c5d04b03-1fa3-4cba-8f1c-facd473c6ac0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5d04b03-1fa3-4cba-8f1c-facd473c6ac0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67408ce0-91f6-4f19-87cc-95096351c38b' class='xr-var-data-in' type='checkbox'><label for='data-67408ce0-91f6-4f19-87cc-95096351c38b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>no units</dd><dt><span>long_name :</span></dt><dd>sublink_polarization</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "...\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
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+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cml_id</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;1&#x27; &#x27;2&#x27; &#x27;3&#x27; &#x27;4&#x27; ... &#x27;73&#x27; &#x27;74&#x27; &#x27;75&#x27;</div><input id='attrs-0694db14-2934-4de9-ab2e-5488dc3e8452' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0694db14-2934-4de9-ab2e-5488dc3e8452' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf0d310d-aff5-4bd5-8cde-e3fb36276528' class='xr-var-data-in' type='checkbox'><label for='data-bf0d310d-aff5-4bd5-8cde-e3fb36276528' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>commercial_microwave_link_identifier</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1&#x27;, &#x27;2&#x27;, &#x27;3&#x27;, &#x27;4&#x27;, &#x27;5&#x27;, &#x27;6&#x27;, &#x27;7&#x27;, &#x27;8&#x27;, &#x27;9&#x27;, &#x27;10&#x27;, &#x27;11&#x27;, &#x27;12&#x27;, &#x27;13&#x27;,\n",
+       "       &#x27;14&#x27;, &#x27;15&#x27;, &#x27;16&#x27;, &#x27;17&#x27;, &#x27;18&#x27;, &#x27;19&#x27;, &#x27;20&#x27;, &#x27;21&#x27;, &#x27;22&#x27;, &#x27;23&#x27;, &#x27;24&#x27;, &#x27;25&#x27;,\n",
+       "       &#x27;26&#x27;, &#x27;27&#x27;, &#x27;28&#x27;, &#x27;29&#x27;, &#x27;30&#x27;, &#x27;31&#x27;, &#x27;32&#x27;, &#x27;33&#x27;, &#x27;34&#x27;, &#x27;35&#x27;, &#x27;36&#x27;, &#x27;37&#x27;,\n",
+       "       &#x27;38&#x27;, &#x27;39&#x27;, &#x27;40&#x27;, &#x27;41&#x27;, &#x27;42&#x27;, &#x27;43&#x27;, &#x27;44&#x27;, &#x27;45&#x27;, &#x27;46&#x27;, &#x27;47&#x27;, &#x27;48&#x27;, &#x27;49&#x27;,\n",
+       "       &#x27;50&#x27;, &#x27;51&#x27;, &#x27;52&#x27;, &#x27;53&#x27;, &#x27;54&#x27;, &#x27;55&#x27;, &#x27;56&#x27;, &#x27;57&#x27;, &#x27;58&#x27;, &#x27;59&#x27;, &#x27;60&#x27;, &#x27;61&#x27;,\n",
+       "       &#x27;62&#x27;, &#x27;63&#x27;, &#x27;64&#x27;, &#x27;65&#x27;, &#x27;66&#x27;, &#x27;67&#x27;, &#x27;68&#x27;, &#x27;69&#x27;, &#x27;70&#x27;, &#x27;71&#x27;, &#x27;72&#x27;, &#x27;73&#x27;,\n",
+       "       &#x27;74&#x27;, &#x27;75&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c1b42bcf-a6d5-4870-a35e-0b72d6eb7e94' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c1b42bcf-a6d5-4870-a35e-0b72d6eb7e94' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>rsl</span></div><div class='xr-var-dims'>(cml_id, sublink_id, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-50.0 -50.0 -50.0 ... nan nan nan</div><input id='attrs-1d995459-95a7-4422-82e5-c980c7741fa5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d995459-95a7-4422-82e5-c980c7741fa5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51653508-6b4e-4d45-99cb-65ee6621d8ad' class='xr-var-data-in' type='checkbox'><label for='data-51653508-6b4e-4d45-99cb-65ee6621d8ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dBm</dd><dt><span>long_name :</span></dt><dd>received_signal_level</dd><dt><span>sampling :</span></dt><dd>instantaneous</dd></dl></div><div class='xr-var-data'><pre>array([[[-50., -50., -50., ..., -52., -52., -52.],\n",
+       "        [-49., -49., -49., ..., -52., -52., -52.],\n",
+       "        [-72., -73., -72., ..., -73., -72., -72.]],\n",
+       "\n",
+       "       [[-63., -64., -64., ..., -63., -63., -64.],\n",
+       "        [-60., -60., -59., ..., -60., -60., -59.],\n",
+       "        [-58., -58., -58., ..., -65., -65., -64.]],\n",
+       "\n",
+       "       [[-53., -54., -53., ..., -57., -56., -56.],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [-54., -55., -54., ..., -58., -57., -56.]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[-61., -61., -61., ..., -62., -61., -61.],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
+       "\n",
+       "       [[-52., -52., -51., ..., -53., -52., -52.],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
+       "\n",
+       "       [[-66., -67., -68., ..., -68., -68., -68.],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]],\n",
+       "      shape=(75, 3, 14401), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e1a259b2-6749-4845-a00b-78aa00e9960d' class='xr-section-summary-in' type='checkbox'  ><label for='section-e1a259b2-6749-4845-a00b-78aa00e9960d' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>OpenMesh CML sample (10d), start 2024-01-15</dd><dt><span>file_author :</span></dt><dd>Dror Jacoby</dd><dt><span>institution :</span></dt><dd>Cellular Environmental Monitoring (CellEnMon) Lab, School of Electrical Engineering, Tel-Aviv University; Wireless and Mobile Networking (WiMNet) Lab, Department of Electrical Engineering, Columbia University</dd><dt><span>date :</span></dt><dd>2025-11-01</dd><dt><span>source :</span></dt><dd>Community NYC Mesh Wireless Network</dd><dt><span>history :</span></dt><dd>2025-11-01: Updated metadata, converted netCDF → OpenSense-1.0 CML format</dd><dt><span>naming_convention :</span></dt><dd>OpenSense-1.0</dd><dt><span>license_restrictions :</span></dt><dd>CC BY 4.0 – https://creativecommons.org/licenses/by/4.0/</dd><dt><span>reference :</span></dt><dd>https://doi.org/10.5281/zenodo.15287692</dd><dt><span>comment :</span></dt><dd>OpenMesh: Wireless Signal Dataset for Opportunistic Urban Weather Sensing in New York City. Data covers the period 2023-10-29 to 2024-07-01 (UTC). All timestamps are in UTC. Signal levels are in dBm. This dataset is described in the associated publication: https://essd.copernicus.org/preprints/essd-2025-238/</dd><dt><span>Conventions :</span></dt><dd>OpenSense-CML-v1.0</dd><dt><span>start_date :</span></dt><dd>2024-01-15</dd><dt><span>end_date :</span></dt><dd>2024-01-25</dd><dt><span>time_range :</span></dt><dd>2024-01-15T00:00:00 / 2024-01-25T00:00:00</dd><dt><span>cml_subset :</span></dt><dd>all</dd></dl></div></li></ul></div></div>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76123ca2",
+   "metadata": {},
+   "source": [
+    "### Create & Save samples to disk\n",
+    "\n",
+    "Ways to create and save a CML sample file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "f9c6e75c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.568698Z",
+     "start_time": "2026-03-25T10:52:04.505213Z"
+    }
+   },
+   "source": [
+    "sep = \"═\" * 60\n",
+    "\n",
+    "# Option A: from an already-loaded dataset → returns saved file path\n",
+    "print(f\"\\n{sep}\")\n",
+    "print(\"  [A] From loaded dataset (ds_cml) — 1 week, all links\")\n",
+    "print(f\"{sep}\")\n",
+    "path_1w = omio.create_openmesh_sample(\n",
+    "    ds=ds_cml,\n",
+    "    time_interval=\"1w\",\n",
+    "    start_time=\"2024-01-16\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    ")\n",
+    "\n",
+    "# Option B: standalone (reads from disk) → returns saved file path\n",
+    "print(f\"\\n{sep}\")\n",
+    "print(\"  [B] Standalone (reads from disk) — 1 day, all links\")\n",
+    "print(f\"{sep}\")\n",
+    "path_1d = omio.create_openmesh_sample(\n",
+    "    time_interval=\"1d\",\n",
+    "    start_time=\"2024-01-16\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    ")\n",
+    "\n",
+    "# Option C: custom window + specific links → returns file (!) \n",
+    "print(f\"\\n{sep}\")\n",
+    "print(\"  [C] 1-hour at 12:00 PM, subset of links\")\n",
+    "print(f\"{sep}\")\n",
+    "cml_sample_1h = omio.create_openmesh_sample(\n",
+    "    ds=ds_cml,\n",
+    "    start_time=\"2024-01-16T12:00:00\",\n",
+    "    end_time=\"2024-01-16T13:00:00\",\n",
+    "    cml_ids=[\"1\", \"5\", \"7\", \"15\", \"42\"],\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    "    return_type=\"sample\",\n",
+    ")\n",
+    "\n",
+    "cml_sample_1h\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  [A] From loaded dataset (ds_cml) — 1 week, all links\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-16 00:00:00  →  2024-01-23 00:00:00  (1w)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_1w.nc\n",
+      "  Timesteps: 10,081\n",
+      "  CML count: 75\n",
+      "  Exists   : openmesh_cml_1w.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  [B] Standalone (reads from disk) — 1 day, all links\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\n",
+      "  Window   : 2024-01-16 00:00:00  →  2024-01-17 00:00:00  (1d)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_1d.nc\n",
+      "  Timesteps: 1,441\n",
+      "  CML count: 75\n",
+      "  Exists   : openmesh_cml_1d.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  [C] 1-hour at 12:00 PM, subset of links\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-16 12:00:00  →  2024-01-16 13:00:00  (1h)\n",
+      "  CML IDs  : ['1', '5', '7', '15', '42']\n",
+      "  Output   : openmesh_cml_1h_cmls5.nc\n",
+      "  Timesteps: 61\n",
+      "  CML count: 5\n",
+      "  Exists   : openmesh_cml_1h_cmls5.nc  (skipped — pass replace=True to overwrite)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset> Size: 4kB\n",
+       "Dimensions:       (cml_id: 5, sublink_id: 3, time: 61)\n",
+       "Coordinates:\n",
+       "  * time          (time) datetime64[ns] 488B 2024-01-16T12:00:00 ... 2024-01-...\n",
+       "  * sublink_id    (sublink_id) object 24B 'sublink_1' 'sublink_2' 'sublink_3'\n",
+       "    site_0_lat    (cml_id) float32 20B ...\n",
+       "    site_0_lon    (cml_id) float32 20B ...\n",
+       "    site_1_lat    (cml_id) float32 20B ...\n",
+       "    site_1_lon    (cml_id) float32 20B ...\n",
+       "    length        (cml_id) float32 20B ...\n",
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+       "    polarization  (cml_id, sublink_id) object 120B ...\n",
+       "  * cml_id        (cml_id) <U2 40B '1' '5' '7' '15' '42'\n",
+       "Data variables:\n",
+       "    rsl           (cml_id, sublink_id, time) float32 4kB ...\n",
+       "Attributes: (12/15)\n",
+       "    title:                 OpenMesh CML sample (1h), start 2024-01-16\n",
+       "    file_author:           Dror Jacoby\n",
+       "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+       "    date:                  2025-11-01\n",
+       "    source:                Community NYC Mesh Wireless Network\n",
+       "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+       "    ...                    ...\n",
+       "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+       "    Conventions:           OpenSense-CML-v1.0\n",
+       "    start_date:            2024-01-16\n",
+       "    end_date:              2024-01-16\n",
+       "    time_range:            2024-01-16T12:00:00 / 2024-01-16T13:00:00\n",
+       "    cml_subset:            1,5,7,15,42"
+      ],
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+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 4kB\n",
+       "Dimensions:       (cml_id: 5, sublink_id: 3, time: 61)\n",
+       "Coordinates:\n",
+       "  * time          (time) datetime64[ns] 488B 2024-01-16T12:00:00 ... 2024-01-...\n",
+       "  * sublink_id    (sublink_id) object 24B &#x27;sublink_1&#x27; &#x27;sublink_2&#x27; &#x27;sublink_3&#x27;\n",
+       "    site_0_lat    (cml_id) float32 20B ...\n",
+       "    site_0_lon    (cml_id) float32 20B ...\n",
+       "    site_1_lat    (cml_id) float32 20B ...\n",
+       "    site_1_lon    (cml_id) float32 20B ...\n",
+       "    length        (cml_id) float32 20B ...\n",
+       "    frequency     (cml_id, sublink_id) float32 60B ...\n",
+       "    polarization  (cml_id, sublink_id) object 120B ...\n",
+       "  * cml_id        (cml_id) &lt;U2 40B &#x27;1&#x27; &#x27;5&#x27; &#x27;7&#x27; &#x27;15&#x27; &#x27;42&#x27;\n",
+       "Data variables:\n",
+       "    rsl           (cml_id, sublink_id, time) float32 4kB ...\n",
+       "Attributes: (12/15)\n",
+       "    title:                 OpenMesh CML sample (1h), start 2024-01-16\n",
+       "    file_author:           Dror Jacoby\n",
+       "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+       "    date:                  2025-11-01\n",
+       "    source:                Community NYC Mesh Wireless Network\n",
+       "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+       "    ...                    ...\n",
+       "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+       "    Conventions:           OpenSense-CML-v1.0\n",
+       "    start_date:            2024-01-16\n",
+       "    end_date:              2024-01-16\n",
+       "    time_range:            2024-01-16T12:00:00 / 2024-01-16T13:00:00\n",
+       "    cml_subset:            1,5,7,15,42</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-72cb3fd6-983b-40ae-bbde-aa21f8f7fe1a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-72cb3fd6-983b-40ae-bbde-aa21f8f7fe1a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>cml_id</span>: 5</li><li><span class='xr-has-index'>sublink_id</span>: 3</li><li><span class='xr-has-index'>time</span>: 61</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d035d1b7-e2d3-42e3-9bf0-395268f90f1d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d035d1b7-e2d3-42e3-9bf0-395268f90f1d' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-16T12:00:00 ... 2024-01-...</div><input id='attrs-23ea024f-f7e8-4ed6-a9ea-0d6275a30fd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23ea024f-f7e8-4ed6-a9ea-0d6275a30fd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36d23d2d-f669-438e-b91f-e3989611b5e6' class='xr-var-data-in' type='checkbox'><label for='data-36d23d2d-f669-438e-b91f-e3989611b5e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time_utc</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-16T12:00:00.000000000&#x27;, &#x27;2024-01-16T12:01:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:02:00.000000000&#x27;, &#x27;2024-01-16T12:03:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:04:00.000000000&#x27;, &#x27;2024-01-16T12:05:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:06:00.000000000&#x27;, &#x27;2024-01-16T12:07:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:08:00.000000000&#x27;, &#x27;2024-01-16T12:09:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:10:00.000000000&#x27;, &#x27;2024-01-16T12:11:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:12:00.000000000&#x27;, &#x27;2024-01-16T12:13:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:14:00.000000000&#x27;, &#x27;2024-01-16T12:15:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:16:00.000000000&#x27;, &#x27;2024-01-16T12:17:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:18:00.000000000&#x27;, &#x27;2024-01-16T12:19:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:20:00.000000000&#x27;, &#x27;2024-01-16T12:21:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:22:00.000000000&#x27;, &#x27;2024-01-16T12:23:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:24:00.000000000&#x27;, &#x27;2024-01-16T12:25:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:26:00.000000000&#x27;, &#x27;2024-01-16T12:27:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:28:00.000000000&#x27;, &#x27;2024-01-16T12:29:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:30:00.000000000&#x27;, &#x27;2024-01-16T12:31:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:32:00.000000000&#x27;, &#x27;2024-01-16T12:33:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:34:00.000000000&#x27;, &#x27;2024-01-16T12:35:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:36:00.000000000&#x27;, &#x27;2024-01-16T12:37:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:38:00.000000000&#x27;, &#x27;2024-01-16T12:39:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:40:00.000000000&#x27;, &#x27;2024-01-16T12:41:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:42:00.000000000&#x27;, &#x27;2024-01-16T12:43:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:44:00.000000000&#x27;, &#x27;2024-01-16T12:45:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:46:00.000000000&#x27;, &#x27;2024-01-16T12:47:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:48:00.000000000&#x27;, &#x27;2024-01-16T12:49:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:50:00.000000000&#x27;, &#x27;2024-01-16T12:51:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:52:00.000000000&#x27;, &#x27;2024-01-16T12:53:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:54:00.000000000&#x27;, &#x27;2024-01-16T12:55:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:56:00.000000000&#x27;, &#x27;2024-01-16T12:57:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:58:00.000000000&#x27;, &#x27;2024-01-16T12:59:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T13:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sublink_id</span></div><div class='xr-var-dims'>(sublink_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;sublink_1&#x27; &#x27;sublink_2&#x27; &#x27;sublink_3&#x27;</div><input id='attrs-9293c0db-c016-4afd-ac81-e6b69d470d7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9293c0db-c016-4afd-ac81-e6b69d470d7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0616a038-74fb-4628-89db-8805724046b0' class='xr-var-data-in' type='checkbox'><label for='data-0616a038-74fb-4628-89db-8805724046b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sublink_identifier</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;sublink_1&#x27;, &#x27;sublink_2&#x27;, &#x27;sublink_3&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_0_lat</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8a997513-b52e-4e3a-bc1b-2b478c545949' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a997513-b52e-4e3a-bc1b-2b478c545949' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6e81138-7100-4821-8701-1ad919281a8b' class='xr-var-data-in' type='checkbox'><label for='data-c6e81138-7100-4821-8701-1ad919281a8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_0_latitude</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_0_lon</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dcc55bae-c7c6-4b3b-a324-d8e75bd0e9ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcc55bae-c7c6-4b3b-a324-d8e75bd0e9ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e97e0417-f540-4041-a181-7b17547ded87' class='xr-var-data-in' type='checkbox'><label for='data-e97e0417-f540-4041-a181-7b17547ded87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_0_longitude</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_1_lat</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-08f8d15e-bfbb-4f75-9940-c4aa5ea2bf1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08f8d15e-bfbb-4f75-9940-c4aa5ea2bf1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39405e4f-7d79-4dba-bd1c-c4b2451ca9bc' class='xr-var-data-in' type='checkbox'><label for='data-39405e4f-7d79-4dba-bd1c-c4b2451ca9bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_1_latitude</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_1_lon</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c7139547-73ec-405e-be7c-9311a77530f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7139547-73ec-405e-be7c-9311a77530f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd91ee20-40d4-4618-ace4-dc1af89d33d5' class='xr-var-data-in' type='checkbox'><label for='data-fd91ee20-40d4-4618-ace4-dc1af89d33d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_1_longitude</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>length</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03bb07c5-c1b7-48bb-a33f-e699b26c90ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03bb07c5-c1b7-48bb-a33f-e699b26c90ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65413607-86b8-48ef-b92c-e90cbc9470a5' class='xr-var-data-in' type='checkbox'><label for='data-65413607-86b8-48ef-b92c-e90cbc9470a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>distance_between_pair_of_antennas</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frequency</span></div><div class='xr-var-dims'>(cml_id, sublink_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd22df7a-6764-4055-b66b-5b3f739b1191' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd22df7a-6764-4055-b66b-5b3f739b1191' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f044aadf-fcfa-4f01-a24a-e5fcbf0d3f33' class='xr-var-data-in' type='checkbox'><label for='data-f044aadf-fcfa-4f01-a24a-e5fcbf0d3f33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>MHz</dd><dt><span>long_name :</span></dt><dd>sublink_frequency</dd></dl></div><div class='xr-var-data'><pre>[15 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polarization</span></div><div class='xr-var-dims'>(cml_id, sublink_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5286927c-18a1-446f-9e88-ffbe9f1cf2c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5286927c-18a1-446f-9e88-ffbe9f1cf2c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d090344-d60c-4318-8ab6-03f2663de4e5' class='xr-var-data-in' type='checkbox'><label for='data-1d090344-d60c-4318-8ab6-03f2663de4e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>no units</dd><dt><span>long_name :</span></dt><dd>sublink_polarization</dd></dl></div><div class='xr-var-data'><pre>[15 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cml_id</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;1&#x27; &#x27;5&#x27; &#x27;7&#x27; &#x27;15&#x27; &#x27;42&#x27;</div><input id='attrs-60ec6b9c-0810-43af-a712-62dac459e74c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60ec6b9c-0810-43af-a712-62dac459e74c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25986eef-9b52-44f3-aaae-a141a8dfba8f' class='xr-var-data-in' type='checkbox'><label for='data-25986eef-9b52-44f3-aaae-a141a8dfba8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>commercial_microwave_link_identifier</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1&#x27;, &#x27;5&#x27;, &#x27;7&#x27;, &#x27;15&#x27;, &#x27;42&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-98519b2c-99bb-48f3-add2-325eb7a5c5de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98519b2c-99bb-48f3-add2-325eb7a5c5de' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>rsl</span></div><div class='xr-var-dims'>(cml_id, sublink_id, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-18c01e09-3fdf-4f15-9ebc-96e702099733' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18c01e09-3fdf-4f15-9ebc-96e702099733' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45f2ddad-92e4-40a4-b3e6-7271727b0ead' class='xr-var-data-in' type='checkbox'><label for='data-45f2ddad-92e4-40a4-b3e6-7271727b0ead' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dBm</dd><dt><span>long_name :</span></dt><dd>received_signal_level</dd><dt><span>sampling :</span></dt><dd>instantaneous</dd></dl></div><div class='xr-var-data'><pre>[915 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-71b3e7da-4801-45d8-bfa9-e2908bdd2b8f' class='xr-section-summary-in' type='checkbox'  ><label for='section-71b3e7da-4801-45d8-bfa9-e2908bdd2b8f' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>OpenMesh CML sample (1h), start 2024-01-16</dd><dt><span>file_author :</span></dt><dd>Dror Jacoby</dd><dt><span>institution :</span></dt><dd>Cellular Environmental Monitoring (CellEnMon) Lab, School of Electrical Engineering, Tel-Aviv University; Wireless and Mobile Networking (WiMNet) Lab, Department of Electrical Engineering, Columbia University</dd><dt><span>date :</span></dt><dd>2025-11-01</dd><dt><span>source :</span></dt><dd>Community NYC Mesh Wireless Network</dd><dt><span>history :</span></dt><dd>2025-11-01: Updated metadata, converted netCDF → OpenSense-1.0 CML format</dd><dt><span>naming_convention :</span></dt><dd>OpenSense-1.0</dd><dt><span>license_restrictions :</span></dt><dd>CC BY 4.0 – https://creativecommons.org/licenses/by/4.0/</dd><dt><span>reference :</span></dt><dd>https://doi.org/10.5281/zenodo.15287692</dd><dt><span>comment :</span></dt><dd>OpenMesh: Wireless Signal Dataset for Opportunistic Urban Weather Sensing in New York City. Data covers the period 2023-10-29 to 2024-07-01 (UTC). All timestamps are in UTC. Signal levels are in dBm. This dataset is described in the associated publication: https://essd.copernicus.org/preprints/essd-2025-238/</dd><dt><span>Conventions :</span></dt><dd>OpenSense-CML-v1.0</dd><dt><span>start_date :</span></dt><dd>2024-01-16</dd><dt><span>end_date :</span></dt><dd>2024-01-16</dd><dt><span>time_range :</span></dt><dd>2024-01-16T12:00:00 / 2024-01-16T13:00:00</dd><dt><span>cml_subset :</span></dt><dd>1,5,7,15,42</dd></dl></div></li></ul></div></div>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71b030a7",
+   "metadata": {},
+   "source": [
+    "### Batch creation (shortcut)\n",
+    "\n",
+    "`create_openmesh_samples_batch` creates multiple time-interval samples in one call — a shortcut instead of calling `create_openmesh_sample` repeatedly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "3a9524cc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.595965Z",
+     "start_time": "2026-03-25T10:52:04.587966Z"
+    }
+   },
+   "source": [
+    "batch_samples = omio.create_openmesh_samples_batch(\n",
+    "    intervals=[\"1h\", \"1d\", \"1w\", \"1mo\"],\n",
+    "    start_time=\"2024-01-16\",\n",
+    "    ds=ds_cml,\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    ")\n",
+    "\n",
+    "batch_samples\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Interval : 1h\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-16 00:00:00  →  2024-01-16 01:00:00  (1h)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_1h.nc\n",
+      "  Timesteps: 61\n",
+      "  CML count: 75\n",
+      "  Exists   : openmesh_cml_1h.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Interval : 1d\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-16 00:00:00  →  2024-01-17 00:00:00  (1d)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_1d.nc\n",
+      "  Timesteps: 1,441\n",
+      "  CML count: 75\n",
+      "  Exists   : openmesh_cml_1d.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Interval : 1w\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-16 00:00:00  →  2024-01-23 00:00:00  (1w)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_1w.nc\n",
+      "  Timesteps: 10,081\n",
+      "  CML count: 75\n",
+      "  Exists   : openmesh_cml_1w.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Interval : 1mo\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-16 00:00:00  →  2024-02-15 00:00:00  (1mo)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_1mo.nc\n",
+      "  Timesteps: 43,201\n",
+      "  CML count: 75\n",
+      "  Exists   : openmesh_cml_1mo.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Batch complete:\n",
+      "    1h     → openmesh_cml_1h.nc\n",
+      "    1d     → openmesh_cml_1d.nc\n",
+      "    1w     → openmesh_cml_1w.nc\n",
+      "    1mo    → openmesh_cml_1mo.nc\n",
+      "───────────────────────────────────────────────────────\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'1h': PosixPath('data/samples/openmesh_cml_1h.nc'),\n",
+       " '1d': PosixPath('data/samples/openmesh_cml_1d.nc'),\n",
+       " '1w': PosixPath('data/samples/openmesh_cml_1w.nc'),\n",
+       " '1mo': PosixPath('data/samples/openmesh_cml_1mo.nc')}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c12356e0",
+   "metadata": {},
+   "source": [
+    "## Load a saved CML sample & inspect\n",
+    "\n",
+    "Re-open the 1-week sample (`path_1w`) that was saved to disk above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "c171717a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.622918Z",
+     "start_time": "2026-03-25T10:52:04.608344Z"
+    }
+   },
+   "source": [
+    "ds_sample = omio.load_cml_sample(path_1w)\n",
+    "print(ds_sample)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Loaded CML sample : {'cml_id': 75, 'sublink_id': 3, 'time': 10081}  ←  openmesh_cml_1w.nc\n",
+      "<xarray.Dataset> Size: 9MB\n",
+      "Dimensions:       (cml_id: 75, sublink_id: 3, time: 10081)\n",
+      "Coordinates:\n",
+      "  * time          (time) datetime64[ns] 81kB 2024-01-16 ... 2024-01-23\n",
+      "  * sublink_id    (sublink_id) object 24B 'sublink_1' 'sublink_2' 'sublink_3'\n",
+      "    site_0_lat    (cml_id) float32 300B ...\n",
+      "    site_0_lon    (cml_id) float32 300B ...\n",
+      "    site_1_lat    (cml_id) float32 300B ...\n",
+      "    site_1_lon    (cml_id) float32 300B ...\n",
+      "    length        (cml_id) float32 300B ...\n",
+      "    frequency     (cml_id, sublink_id) float32 900B ...\n",
+      "    polarization  (cml_id, sublink_id) object 2kB ...\n",
+      "  * cml_id        (cml_id) <U2 600B '1' '2' '3' '4' '5' ... '72' '73' '74' '75'\n",
+      "Data variables:\n",
+      "    rsl           (cml_id, sublink_id, time) float32 9MB ...\n",
+      "Attributes: (12/15)\n",
+      "    title:                 OpenMesh CML sample (1w), start 2024-01-16\n",
+      "    file_author:           Dror Jacoby\n",
+      "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+      "    date:                  2025-11-01\n",
+      "    source:                Community NYC Mesh Wireless Network\n",
+      "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+      "    ...                    ...\n",
+      "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+      "    Conventions:           OpenSense-CML-v1.0\n",
+      "    start_date:            2024-01-16\n",
+      "    end_date:              2024-01-23\n",
+      "    time_range:            2024-01-16T00:00:00 / 2024-01-23T00:00:00\n",
+      "    cml_subset:            all\n"
+     ]
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_pws_section",
+   "metadata": {},
+   "source": [
+    "## PWS (Personal Weather Stations)\n",
+    "\n",
+    "Download, load and create samples from the Weather Underground PWS network data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ceb1e249",
+   "metadata": {},
+   "source": [
+    "### Download PWS data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "952b0b2a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.631854Z",
+     "start_time": "2026-03-25T10:52:04.629084Z"
+    }
+   },
+   "source": [
+    "omio.download_pws_wu(\n",
+    "    output_dir=DATA_DIR\n",
+    ");"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  PWS found locally  : data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n"
+     ]
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_pws_load",
+   "metadata": {},
+   "source": [
+    "### Load full PWS dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "b56043fc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.771498Z",
+     "start_time": "2026-03-25T10:52:04.644615Z"
+    }
+   },
+   "source": [
+    "import netCDF4 as nc\n",
+    "\n",
+    "pws_path = PWS_PATH\n",
+    "root        = nc.Dataset(pws_path)\n",
+    "group_names = list(root.groups.keys())\n",
+    "first       = root.groups[group_names[0]]\n",
+    "\n",
+    "print(f\"Total stations     : {len(group_names)}\")\n",
+    "print(f\"First 5 stations   : {group_names[:5]}\")\n",
+    "print(f\"Variables          : {list(first.variables.keys())}\")\n",
+    "print(f\"Dimensions         : { {k: v.size for k, v in first.dimensions.items()} }\")\n",
+    "print(f\"Station ID format  : KNYNEWYO#### (K=USA, NY=state, NEWYO=NYC, ####=number)\")\n",
+    "root.close()"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total stations     : 37\n",
+      "First 5 stations   : ['KNYNEWYO1472', 'KNYNEWYO1659', 'KNYNEWYO1288', 'KNYNEWYO1896', 'KNYNEWYO1921']\n",
+      "Variables          : ['rainfall_rate', 'rainfall_amount', 'temperature', 'relative_humidity', 'wind_velocity', 'wind_direction', 'air_pressure', 'time', 'id', 'lat', 'lon', 'elev']\n",
+      "Dimensions         : {'id': 1, 'time': 60339}\n",
+      "Station ID format  : KNYNEWYO#### (K=USA, NY=state, NEWYO=NYC, ####=number)\n"
+     ]
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_create_pws_fixed",
+   "metadata": {},
+   "source": [
+    "### Create PWS samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e9615296",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:13.869436Z",
+     "start_time": "2026-03-25T10:52:04.778375Z"
+    }
+   },
+   "source": [
+    "sep = \"═\" * 60\n",
+    "\n",
+    "# Option A: 1-day sample\n",
+    "print(f\"\\n{sep}\")\n",
+    "print(\"  [A] PWS — 1 day\")\n",
+    "print(f\"{sep}\")\n",
+    "path_pws_1d = omio.create_pws_sample(\n",
+    "    time_interval=\"1d\",\n",
+    "    start_time=\"2024-01-15\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    ")\n",
+    "\n",
+    "# Option B: 1-week sample\n",
+    "print(f\"\\n{sep}\")\n",
+    "print(\"  [B] PWS — 1 week\")\n",
+    "print(f\"{sep}\")\n",
+    "path_pws_1w = omio.create_pws_sample(\n",
+    "    time_interval=\"1w\",\n",
+    "    start_time=\"2024-01-15\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    ")\n",
+    "\n",
+    "# Option C: custom window\n",
+    "print(f\"\\n{sep}\")\n",
+    "print(\"  [C] PWS — custom window\")\n",
+    "print(f\"{sep}\")\n",
+    "path_pws_custom = omio.create_pws_sample(\n",
+    "    start_time=\"2024-01-15\",\n",
+    "    end_time=\"2024-02-15\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    ")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  [A] PWS — 1 day\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-16 00:00:00  (1d)\n",
+      "  Stations : 37 groups\n",
+      "  Output   : openmesh_wu_pws_1d.nc\n",
+      "  Loaded   : 35 stations, 287 timesteps\n",
+      "  Exists   : openmesh_wu_pws_1d.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  [B] PWS — 1 week\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-22 00:00:00  (1w)\n",
+      "  Stations : 37 groups\n",
+      "  Output   : openmesh_wu_pws_1w.nc\n",
+      "  Loaded   : 35 stations, 2,012 timesteps\n",
+      "  Exists   : openmesh_wu_pws_1w.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  [C] PWS — custom window\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-02-15 00:00:00  (31d)\n",
+      "  Stations : 37 groups\n",
+      "  Output   : openmesh_wu_pws_31d.nc\n",
+      "  Loaded   : 37 stations, 7,127 timesteps\n",
+      "  Exists   : openmesh_wu_pws_31d.nc  (skipped — pass replace=True to overwrite)\n"
+     ]
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_pws_inmemory",
+   "metadata": {},
+   "source": [
+    "### Load PWS sample in memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "37895cc5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:16.834091Z",
+     "start_time": "2026-03-25T10:52:13.893880Z"
+    }
+   },
+   "source": [
+    "ds_pws_sample = omio.create_pws_sample(\n",
+    "    time_interval=\"2w\",\n",
+    "    start_time=\"2024-01-15\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    "    return_type=\"sample\",\n",
+    ")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-29 00:00:00  (2w)\n",
+      "  Stations : 37 groups\n",
+      "  Output   : openmesh_wu_pws_2w.nc\n",
+      "  Loaded   : 35 stations, 3,840 timesteps\n",
+      "  Exists   : openmesh_wu_pws_2w.nc  (skipped — pass replace=True to overwrite)\n"
+     ]
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "cell_type": "code",
+   "id": "802d8974",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:16.866302Z",
+     "start_time": "2026-03-25T10:52:16.855129Z"
+    }
+   },
+   "source": [
+    "ds_pws_sample"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset> Size: 8MB\n",
+       "Dimensions:            (id: 35, time: 3840)\n",
+       "Coordinates:\n",
+       "  * id                 (id) <U12 2kB 'KNYNEWYO1472' ... 'KNYNEWYO1053'\n",
+       "  * time               (time) datetime64[ns] 31kB 2024-01-15T00:04:00 ... 202...\n",
+       "    lat                (id) float64 280B ...\n",
+       "    lon                (id) float64 280B ...\n",
+       "    elev               (id) float64 280B ...\n",
+       "Data variables:\n",
+       "    rainfall_rate      (id, time) float64 1MB ...\n",
+       "    rainfall_amount    (id, time) float64 1MB ...\n",
+       "    temperature        (id, time) float64 1MB ...\n",
+       "    relative_humidity  (id, time) float64 1MB ...\n",
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+       "    wind_direction     (id, time) float64 1MB ...\n",
+       "    air_pressure       (id, time) float64 1MB ...\n",
+       "Attributes:\n",
+       "    title:        OpenMesh PWS sample (2w), start 2024-01-15\n",
+       "    start_date:   2024-01-15\n",
+       "    end_date:     2024-01-29\n",
+       "    time_range:   2024-01-15T00:00:00 / 2024-01-29T00:00:00\n",
+       "    source:       https://zenodo.org/records/17508286\n",
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+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 8MB\n",
+       "Dimensions:            (id: 35, time: 3840)\n",
+       "Coordinates:\n",
+       "  * id                 (id) &lt;U12 2kB &#x27;KNYNEWYO1472&#x27; ... &#x27;KNYNEWYO1053&#x27;\n",
+       "  * time               (time) datetime64[ns] 31kB 2024-01-15T00:04:00 ... 202...\n",
+       "    lat                (id) float64 280B ...\n",
+       "    lon                (id) float64 280B ...\n",
+       "    elev               (id) float64 280B ...\n",
+       "Data variables:\n",
+       "    rainfall_rate      (id, time) float64 1MB ...\n",
+       "    rainfall_amount    (id, time) float64 1MB ...\n",
+       "    temperature        (id, time) float64 1MB ...\n",
+       "    relative_humidity  (id, time) float64 1MB ...\n",
+       "    wind_velocity      (id, time) float64 1MB ...\n",
+       "    wind_direction     (id, time) float64 1MB ...\n",
+       "    air_pressure       (id, time) float64 1MB ...\n",
+       "Attributes:\n",
+       "    title:        OpenMesh PWS sample (2w), start 2024-01-15\n",
+       "    start_date:   2024-01-15\n",
+       "    end_date:     2024-01-29\n",
+       "    time_range:   2024-01-15T00:00:00 / 2024-01-29T00:00:00\n",
+       "    source:       https://zenodo.org/records/17508286\n",
+       "    conventions:  OpenSense v1.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bd30a986-9fec-4566-b799-84197d8222aa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd30a986-9fec-4566-b799-84197d8222aa' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>id</span>: 35</li><li><span class='xr-has-index'>time</span>: 3840</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ebb888ef-969e-472b-8d67-2a4a2c518fd7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ebb888ef-969e-472b-8d67-2a4a2c518fd7' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>id</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;KNYNEWYO1472&#x27; ... &#x27;KNYNEWYO1053&#x27;</div><input id='attrs-6b47abdf-bba7-4f56-8e3e-397ca869393b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b47abdf-bba7-4f56-8e3e-397ca869393b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63198e6f-8a9a-4a4e-8f20-3e26a053e60b' class='xr-var-data-in' type='checkbox'><label for='data-63198e6f-8a9a-4a4e-8f20-3e26a053e60b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;KNYNEWYO1472&#x27;, &#x27;KNYNEWYO1659&#x27;, &#x27;KNYNEWYO1288&#x27;, &#x27;KNYNEWYO1896&#x27;,\n",
+       "       &#x27;KNYNEWYO1921&#x27;, &#x27;KNYNEWYO1931&#x27;, &#x27;KNYNEWYO1313&#x27;, &#x27;KNYNEWYO1918&#x27;,\n",
+       "       &#x27;KNYNEWYO1298&#x27;, &#x27;KNYNEWYO1388&#x27;, &#x27;KNYNEWYO1606&#x27;, &#x27;KNYNEWYO1348&#x27;,\n",
+       "       &#x27;KNYNEWYO1238&#x27;, &#x27;KNYNEWYO1401&#x27;, &#x27;KNYNEWYO1824&#x27;, &#x27;KNYNEWYO343&#x27;,\n",
+       "       &#x27;KNYNEWYO1626&#x27;, &#x27;KNYNEWYO1747&#x27;, &#x27;KNYNEWYO589&#x27;, &#x27;KNYNEWYO1591&#x27;,\n",
+       "       &#x27;KNYNEWYO638&#x27;, &#x27;KNYNEWYO1796&#x27;, &#x27;KNYNEWYO1805&#x27;, &#x27;KNYNEWYO1622&#x27;,\n",
+       "       &#x27;KNYNEWYO1024&#x27;, &#x27;KNYNEWYO1533&#x27;, &#x27;KNYNEWYO1651&#x27;, &#x27;KNYNEWYO1120&#x27;,\n",
+       "       &#x27;KNYNEWYO1900&#x27;, &#x27;KNYNEWYO505&#x27;, &#x27;KNYNEWYO1326&#x27;, &#x27;KNYNEWYO1681&#x27;,\n",
+       "       &#x27;KNYNEWYO1867&#x27;, &#x27;KNYNEWYO1127&#x27;, &#x27;KNYNEWYO1053&#x27;], dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-15T00:04:00 ... 2024-01-...</div><input id='attrs-3907c01d-bd61-4b67-8272-9094ec33d046' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3907c01d-bd61-4b67-8272-9094ec33d046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e715b4c0-9c74-491b-a102-8b2f7f1eeb83' class='xr-var-data-in' type='checkbox'><label for='data-e715b4c0-9c74-491b-a102-8b2f7f1eeb83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-15T00:04:00.000000000&#x27;, &#x27;2024-01-15T00:09:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-15T00:14:00.000000000&#x27;, ..., &#x27;2024-01-28T23:49:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-28T23:54:00.000000000&#x27;, &#x27;2024-01-28T23:59:00.000000000&#x27;],\n",
+       "      shape=(3840,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c71f0c58-fc14-41c7-b161-70ac87de6a8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c71f0c58-fc14-41c7-b161-70ac87de6a8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86d570ea-3f78-4350-bdeb-00a513937d4b' class='xr-var-data-in' type='checkbox'><label for='data-86d570ea-3f78-4350-bdeb-00a513937d4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[35 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df8f002b-7cc6-4b80-84f0-26eb52d39e9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df8f002b-7cc6-4b80-84f0-26eb52d39e9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-705ccc9b-b255-4c05-b11f-94022a3798fa' class='xr-var-data-in' type='checkbox'><label for='data-705ccc9b-b255-4c05-b11f-94022a3798fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[35 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elev</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6646969b-0c4e-46ec-9c40-fa646d10065f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6646969b-0c4e-46ec-9c40-fa646d10065f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad61f472-bb9b-46ed-9ada-2d8de233fd40' class='xr-var-data-in' type='checkbox'><label for='data-ad61f472-bb9b-46ed-9ada-2d8de233fd40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[35 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3abfa068-8b50-4a84-979f-d4659b3490b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3abfa068-8b50-4a84-979f-d4659b3490b1' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>rainfall_rate</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f885ac53-0395-4afd-a761-8a2bb66db8cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f885ac53-0395-4afd-a761-8a2bb66db8cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d56553e0-4161-4aa2-b837-a4cba7e14d0c' class='xr-var-data-in' type='checkbox'><label for='data-d56553e0-4161-4aa2-b837-a4cba7e14d0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rainfall_amount</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b63701f5-ad92-4896-9566-94e8d809869f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b63701f5-ad92-4896-9566-94e8d809869f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a70db820-d0d8-4820-abbf-859fa7da5115' class='xr-var-data-in' type='checkbox'><label for='data-a70db820-d0d8-4820-abbf-859fa7da5115' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm</dd><dt><span>long_name :</span></dt><dd>rainfall_amount_per_time_unit</dd></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bdbeaee4-6cd7-4484-9fcf-195ec09ea63b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdbeaee4-6cd7-4484-9fcf-195ec09ea63b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40416808-4cc8-4cfb-8c24-93de79a79d31' class='xr-var-data-in' type='checkbox'><label for='data-40416808-4cc8-4cfb-8c24-93de79a79d31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_celsius</dd></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_humidity</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e167d919-e840-49eb-a008-e9cefec77b98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e167d919-e840-49eb-a008-e9cefec77b98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a812137-a64d-41c6-b656-4558a86590d7' class='xr-var-data-in' type='checkbox'><label for='data-3a812137-a64d-41c6-b656-4558a86590d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_velocity</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-960e7892-3ea2-4ee7-884c-f3494448c68d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-960e7892-3ea2-4ee7-884c-f3494448c68d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d866c732-875b-4aad-af44-362413116106' class='xr-var-data-in' type='checkbox'><label for='data-d866c732-875b-4aad-af44-362413116106' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_direction</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2deb8745-76df-4a88-8268-f5495907d7e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2deb8745-76df-4a88-8268-f5495907d7e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ba6b068-f1ae-4440-aa4c-32d83234002a' class='xr-var-data-in' type='checkbox'><label for='data-6ba6b068-f1ae-4440-aa4c-32d83234002a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_pressure</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-808a2b89-a518-4c18-a248-863067626f44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-808a2b89-a518-4c18-a248-863067626f44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-def1cf97-2ad1-4a18-8845-fef3faf08a9c' class='xr-var-data-in' type='checkbox'><label for='data-def1cf97-2ad1-4a18-8845-fef3faf08a9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>hPa</dd></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-45cc6bef-6f2e-4f49-911a-51c716f14587' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45cc6bef-6f2e-4f49-911a-51c716f14587' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>OpenMesh PWS sample (2w), start 2024-01-15</dd><dt><span>start_date :</span></dt><dd>2024-01-15</dd><dt><span>end_date :</span></dt><dd>2024-01-29</dd><dt><span>time_range :</span></dt><dd>2024-01-15T00:00:00 / 2024-01-29T00:00:00</dd><dt><span>source :</span></dt><dd>https://zenodo.org/records/17508286</dd><dt><span>conventions :</span></dt><dd>OpenSense v1.0</dd></dl></div></li></ul></div></div>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_pws_station",
+   "metadata": {},
+   "source": [
+    "### Inspect a single PWS station"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "ba112f92",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:54:19.103615Z",
+     "start_time": "2026-03-25T10:54:19.094652Z"
+    }
+   },
+   "source": [
+    "# ── single station ────────────────────────────────────────────\n",
+    "station_id = \"KNYNEWYO1472\"\n",
+    "\n",
+    "ds_pws_station = ds_pws_sample.sel(id=station_id)\n",
+    "\n",
+    "print(f\"Station  : {station_id}\")\n",
+    "print(f\"Lat/Lon  : {float(ds_pws_station.lat):.4f}, {float(ds_pws_station.lon):.4f}\")\n",
+    "print(f\"Elev     : {float(ds_pws_station.elev):.1f} m\")\n",
+    "print(f\"Time     : {str(ds_pws_station.time.values[0])[:19]}  →  {str(ds_pws_station.time.values[-1])[:19]}\")\n",
+    "print(f\"Shape    : {dict(ds_pws_station.sizes)}\")\n",
+    "print(ds_pws_station)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Station  : KNYNEWYO1472\n",
+      "Lat/Lon  : 40.8200, -73.9400\n",
+      "Elev     : 66.0 m\n",
+      "Time     : 2024-01-15T00:04:00  →  2024-01-28T23:59:00\n",
+      "Shape    : {'time': 3840}\n",
+      "<xarray.Dataset> Size: 246kB\n",
+      "Dimensions:            (time: 3840)\n",
+      "Coordinates:\n",
+      "    id                 <U12 48B 'KNYNEWYO1472'\n",
+      "  * time               (time) datetime64[ns] 31kB 2024-01-15T00:04:00 ... 202...\n",
+      "    lat                float64 8B 40.82\n",
+      "    lon                float64 8B -73.94\n",
+      "    elev               float64 8B 66.0\n",
+      "Data variables:\n",
+      "    rainfall_rate      (time) float64 31kB ...\n",
+      "    rainfall_amount    (time) float64 31kB ...\n",
+      "    temperature        (time) float64 31kB ...\n",
+      "    relative_humidity  (time) float64 31kB ...\n",
+      "    wind_velocity      (time) float64 31kB ...\n",
+      "    wind_direction     (time) float64 31kB ...\n",
+      "    air_pressure       (time) float64 31kB ...\n",
+      "Attributes:\n",
+      "    title:        OpenMesh PWS sample (2w), start 2024-01-15\n",
+      "    start_date:   2024-01-15\n",
+      "    end_date:     2024-01-29\n",
+      "    time_range:   2024-01-15T00:00:00 / 2024-01-29T00:00:00\n",
+      "    source:       https://zenodo.org/records/17508286\n",
+      "    conventions:  OpenSense v1.0\n"
+     ]
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f9c6bee",
+   "metadata": {},
+   "source": [
+    "## ASOS reference data\n",
+    "\n",
+    "Fetch 1-minute ASOS precipitation from the IEM API for the same period\n",
+    "as the CML samples and save a small OpenSense-style sample NetCDF."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0e928cf",
+   "metadata": {},
+   "source": [
+    "### Load ASOS station metadata\n",
+    "\n",
+    "Load or discover ASOS stations in the NYC metro area. If a local CSV exists it is used directly; otherwise stations are fetched from the IEM GeoJSON API."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_asos_metadata",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "id": "58db427b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:54:23.967554Z",
+     "start_time": "2026-03-25T10:54:23.961473Z"
+    }
+   },
+   "source": [
+    "# Fetch ASOS observations & save as OpenSense NetCDF\n",
+    "\n",
+    "# Fetch 1-minute data from the IEM API, convert to an OpenSense-format `xr.Dataset`, and write a compressed NetCDF sample.\n",
+    "from pathlib import Path\n",
+    "\n",
+    "meta = wsoc.load_asos_metadata(ASOS_META_PATH) if Path(ASOS_META_PATH).exists() else wsoc.fetch_asos_stations_nyc()\n",
+    "print(f\"  Stations ({len(meta)}): {meta.index.tolist()}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Stations (3): ['KNYC', 'KJFK', 'KLGA']\n"
+     ]
+    }
+   ],
+   "execution_count": 23
+  },
+  {
+   "cell_type": "code",
+   "id": "93613121",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:55:13.537670Z",
+     "start_time": "2026-03-25T10:54:26.442631Z"
+    }
+   },
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "start_dt  = datetime(2024, 1, 15)\n",
+    "end_dt    = datetime(2024, 1, 30)\n",
+    "stations  = [\"JFK\", \"EWR\", \"LGA\", \"NYC\"]\n",
+    "variables = [\"precip_amount\", \"precip_rate\", \"temperature\", \"wind_speed\"]\n",
+    "\n",
+    "asos_raw = wsoc.fetch_asos_raw(start_dt, end_dt, stations, variables=variables)\n",
+    "ds_asos  = wsoc.to_opensense_dataset(asos_raw, source=\"asos\", meta=meta, start_dt=start_dt, end_dt=end_dt)\n",
+    "path     = wsoc.save_opensense_dataset(ds_asos, output_dir=SAMPLE_DIR)\n",
+    "\n",
+    "print(f\"  Shape : {dict(ds_asos.sizes)}\")\n",
+    "print(f\"  Time  : {str(ds_asos.time.values[0])[:19]}  →  {str(ds_asos.time.values[-1])[:19]}\")\n",
+    "print(f\"  Vars  : {list(ds_asos.data_vars)}\")\n",
+    "print(ds_asos)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Fetching JFK ... 20,831 records\n",
+      "  Fetching EWR ... 9,834 records\n",
+      "  Fetching LGA ... 20,938 records\n",
+      "  Fetching NYC ... 18,674 records\n",
+      "  Exists : ASOS_2024-01-15_2024-01-30.nc  (skipped — pass replace=True to overwrite)\n",
+      "  Shape : {'id': 4, 'time': 21066}\n",
+      "  Time  : 2024-01-15T00:00:00  →  2024-01-29T23:59:00\n",
+      "  Vars  : ['precip_amount', 'precip_rate', 'temperature', 'wind_speed']\n",
+      "<xarray.Dataset> Size: 3MB\n",
+      "Dimensions:        (id: 4, time: 21066)\n",
+      "Coordinates:\n",
+      "  * id             (id) <U3 48B 'JFK' 'EWR' 'LGA' 'NYC'\n",
+      "  * time           (time) datetime64[ns] 169kB 2024-01-15 ... 2024-01-29T23:5...\n",
+      "    lat            (id) float64 32B nan nan nan nan\n",
+      "    lon            (id) float64 32B nan nan nan nan\n",
+      "    elev           (id) float64 32B nan nan nan nan\n",
+      "Data variables:\n",
+      "    precip_amount  (id, time) float64 674kB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
+      "    precip_rate    (id, time) float64 674kB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
+      "    temperature    (id, time) float64 674kB 0.0 0.0 0.0 ... 2.778 2.778 2.778\n",
+      "    wind_speed     (id, time) float64 674kB 10.29 9.774 10.8 ... 4.116 3.601\n",
+      "Attributes:\n",
+      "    title:        OpenMesh ASOS sample (multi-station)\n",
+      "    source:       asos\n",
+      "    start_date:   2024-01-15\n",
+      "    end_date:     2024-01-30\n",
+      "    time_range:   2024-01-15T00:00:00 / 2024-01-30T00:00:00\n",
+      "    conventions:  OpenSense v1.0\n"
+     ]
+    }
+   ],
+   "execution_count": 24
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_create_all",
+   "metadata": {},
+   "source": [
+    "## Create all datasets\n",
+    "\n",
+    "Helper function that creates CML, PWS and ASOS samples for a given time window in one call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "b74941d3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:55:52.721005Z",
+     "start_time": "2026-03-25T10:55:19.742284Z"
+    }
+   },
+   "source": [
+    "START_TIME = \"2024-01-15\"\n",
+    "END_TIME   = \"2024-01-25\"\n",
+    "# choose sensor id (none for all ids)\n",
+    "CML_IDS  = None\n",
+    "PWS_IDS  = None #[\"KNYNEWYO1942\", \"KNYNEWYO1432\", \"KNYNEWYO1622\", \"KNYNEWYO1472\"]\n",
+    "ASOS_IDS = [\"LGA\", \"NYC\", \"JFK\", \"EWR\"]\n",
+    "\n",
+    "samples = omio.create_all_datasets(\n",
+    "    start_time=START_TIME,\n",
+    "    end_time=END_TIME,\n",
+    "    cml_ids=CML_IDS,\n",
+    "    pws_ids=PWS_IDS,\n",
+    "    asos_ids=ASOS_IDS,\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    "    include_date=False,\n",
+    "    save=   True\n",
+    ")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  CML\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-25 00:00:00  (10d)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_10d.nc\n",
+      "  Timesteps: 14,401\n",
+      "  CML count: 75\n",
+      "  Saved    : openmesh_cml_10d.nc  (0.46 MB)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  PWS\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-25 00:00:00  (10d)\n",
+      "  Stations : 37 groups\n",
+      "  Output   : openmesh_wu_pws_10d.nc\n",
+      "  Loaded   : 35 stations, 2,849 timesteps\n",
+      "  Saved    : openmesh_wu_pws_10d.nc  (1.04 MB)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  ASOS\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-25 00:00:00  (10d)\n",
+      "  Stations : ['LGA', 'NYC', 'JFK', 'EWR']\n",
+      "  Variables: ['precip_amount', 'precip_rate', 'temperature', 'wind_speed']\n",
+      "  Output   : openmesh_asos_ws_10d.nc\n",
+      "  Fetching LGA ... 14,336 records\n",
+      "  Fetching NYC ... 12,078 records\n",
+      "  Fetching JFK ... 14,230 records\n",
+      "  Fetching EWR ... 4,449 records\n",
+      "  Loaded   : 4 stations, 14,400 timesteps\n",
+      "  Saved    : openmesh_asos_ws_10d.nc  (0.30 MB)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Summary:\n",
+      "    cml    → openmesh_cml_10d.nc\n",
+      "    pws    → openmesh_wu_pws_10d.nc\n",
+      "    asos   → openmesh_asos_ws_10d.nc\n",
+      "───────────────────────────────────────────────────────\n"
+     ]
+    }
+   ],
+   "execution_count": 26
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_plot_overview",
+   "metadata": {},
+   "source": [
+    "### Overview plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "59f81c8d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:57:33.511749Z",
+     "start_time": "2026-03-25T10:57:32.760881Z"
+    }
+   },
+   "source": [
+    "import matplotlib.dates as mdates\n",
+    "\n",
+    "# ── load samples ──────────────────────────────────────────────\n",
+    "ds_cml  = omio.load_cml_sample(samples[\"cml\"])\n",
+    "ds_pws  = omio.load_pws_sample(samples[\"pws\"])\n",
+    "ds_asos = xr.open_dataset(samples[\"asos\"])\n",
+    "\n",
+    "# ── plot ──────────────────────────────────────────────────────\n",
+    "fig, axes = plt.subplots(3, 1, figsize=(14, 10), sharex=True)\n",
+    "\n",
+    "# CML RSL\n",
+    "ax = axes[0]\n",
+    "for sublink in ds_cml.sublink_id.values:\n",
+    "    rsl = ds_cml.rsl.sel(cml_id=\"22\", sublink_id=sublink)\n",
+    "    ax.plot(ds_cml.time.values, rsl.values, lw=0.8, label=sublink)\n",
+    "ax.set_ylabel(\"RSL (dBm)\")\n",
+    "ax.set_title(\"CML 22 — Received Signal Level\")\n",
+    "ax.legend(fontsize=8, loc=\"upper right\")\n",
+    "ax.grid(alpha=0.3)\n",
+    "\n",
+    "# PWS precipitation\n",
+    "ax = axes[1]\n",
+    "for i, sid in enumerate(ds_pws.id.values):\n",
+    "    rain = ds_pws[\"rainfall_amount\"].sel(id=sid)\n",
+    "    label = str(sid) if i < 5 else None\n",
+    "    ax.plot(ds_pws.time.values, rain.values, lw=0.5, alpha=0.6, label=label)\n",
+    "ax.set_ylabel(\"Precip (mm)\")\n",
+    "ax.set_ylim(0, 2)\n",
+    "ax.set_title(f\"PWS — Rainfall Amount ({len(ds_pws.id)} stations)\")\n",
+    "ax.legend(fontsize=7, loc=\"upper right\", ncol=2)\n",
+    "ax.grid(alpha=0.3)\n",
+    "\n",
+    "# ASOS precipitation\n",
+    "ax = axes[2]\n",
+    "for sid in ds_asos.id.values:\n",
+    "    precip = ds_asos[\"precip_amount\"].sel(id=sid)\n",
+    "    ax.plot(ds_asos.time.values, precip.values, lw=0.8, label=str(sid))\n",
+    "ax.set_ylabel(\"Precip (mm)\")\n",
+    "ax.set_ylim(0, 2)\n",
+    "ax.set_title(\"ASOS — Precipitation Amount\")\n",
+    "ax.legend(fontsize=8, loc=\"upper right\")\n",
+    "ax.grid(alpha=0.3)\n",
+    "\n",
+    "axes[-1].xaxis.set_major_formatter(mdates.DateFormatter(\"%m-%d\"))\n",
+    "axes[-1].xaxis.set_major_locator(mdates.DayLocator())\n",
+    "plt.setp(axes[-1].xaxis.get_majorticklabels(), rotation=45, ha=\"right\")\n",
+    "\n",
+    "fig.suptitle(\"OpenMesh — CML / PWS / ASOS overview\", fontsize=13)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Loaded CML sample : {'cml_id': 75, 'sublink_id': 3, 'time': 14401}  ←  openmesh_cml_10d.nc\n",
+      "  Loaded PWS sample : {'id': 35, 'time': 2849}  ←  openmesh_wu_pws_10d.nc\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x1000 with 3 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 27
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24e943d5",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Summary\n",
+    "\n",
+    "All three data sources have been downloaded, sampled and plotted:\n",
+    "\n",
+    "| Source | Description | Format |\n",
+    "|--------|-------------|--------|\n",
+    "| **CML** | 75 commercial microwave links, 15-min resolution | `xr.Dataset` (cml_id, sublink_id, time) |\n",
+    "| **PWS** | ~200 Weather Underground personal weather stations, 5-min resolution | `xr.Dataset` (id, time) |\n",
+    "| **ASOS** | NOAA airport stations (JFK, EWR, LGA, NYC), 1-min resolution | `xr.Dataset` (id, time) |\n",
+    "\n",
+    "Sample files are saved under `data/samples/` and can be loaded independently with `omio.load_cml_sample()`, `omio.load_pws_sample()`, or `xr.open_dataset()`."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "openmesh",
+   "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": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/OpenMesh/notebooks/openmesh_data_io.py b/OpenMesh/notebooks/openmesh_data_io.py
new file mode 100644
index 0000000..05ffa26
--- /dev/null
+++ b/OpenMesh/notebooks/openmesh_data_io.py
@@ -0,0 +1,953 @@
+"""
+openmesh_data_io.py
+===================
+Download, load, and create samples from the OpenMesh CML and PWS datasets.
+
+Zenodo records
+--------------
+CML : https://zenodo.org/records/15287692
+PWS : https://zenodo.org/records/17508286
+"""
+
+from __future__ import annotations
+
+import shutil
+import tempfile
+import warnings
+import zipfile
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import List, Optional, Union
+
+import numpy as np
+import pandas as pd
+import requests
+import xarray as xr
+
+try:
+    import netCDF4 as nc4
+    _HAS_NC4 = True
+except ImportError:
+    _HAS_NC4 = False
+
+try:
+    from tqdm.auto import tqdm as _tqdm
+except ImportError:
+    class _tqdm:
+        def __init__(self, total=None, **kw): self.n = 0; self.total = total
+        def update(self, n=1): self.n += n
+        def __enter__(self): return self
+        def __exit__(self, *a): pass
+
+
+# ---------------------------------------------------------------------------
+# Constants  (all paths relative to the notebook / script working directory)
+# ---------------------------------------------------------------------------
+
+DATA_DIR   = Path(__file__).parent / "data" / "jacoby_2025_OpenMesh"   # absolute — stable regardless of CWD
+SAMPLE_DIR = Path(__file__).parent / "data" / "samples"
+CML_PATH   = DATA_DIR / "raw" / "ds_openmesh.nc"
+PWS_PATH   = DATA_DIR / "raw" / "pws_wu_os.nc"
+
+INTERVAL_DURATIONS: dict[str, timedelta] = {
+    "1h":  timedelta(hours=1),
+    "1d":  timedelta(days=1),
+    "1w":  timedelta(weeks=1),
+    "1mo": timedelta(days=30),
+}
+# Arbitrary intervals like "10d", "2w", "6h" are also accepted by _resolve_time_window.
+
+DEFAULT_START_TIME = "2024-01-15T00:00:00"
+
+_ZENODO = {
+    "cml": {  # Zenodo record 15287692 — CML (NYC Mesh wireless links, RSL time-series)
+        "url":      "https://zenodo.org/records/15287692/files/OpenMesh.zip?download=1",
+        "filename": "OpenMesh.zip",
+    },
+    "pws": {  # Zenodo record 17508286 — PWS (Weather Underground NYC, crowd-sourced stations)
+        "url":      "https://zenodo.org/records/17508286/files/PWS_NYC_WU.zip?download=1",
+        "filename": "PWS_NYC_WU.zip",
+    },
+}
+
+_PWS_VARS = [
+    "rainfall_rate", "rainfall_amount", "temperature",
+    "relative_humidity", "wind_velocity", "wind_direction", "air_pressure",
+]
+
+# Variables that should be summed (not averaged) when resampling.
+# Rates (mm/h) are averaged, only accumulations (mm) are summed.
+_ACCUMULATION_VARS = {
+    "rainfall_amount", "precip_amount",
+}
+
+
+# ---------------------------------------------------------------------------
+# Private helpers
+# ---------------------------------------------------------------------------
+
+def _rel(path: Path) -> Path:
+    """Return path relative to cwd; fall back to filename only to avoid leaking home dir."""
+    try:
+        return path.relative_to(Path.cwd())
+    except ValueError:
+        return Path(path.name)
+
+
+def _download_file(url: str, dest: Path, chunk_size: int = 8192) -> Path:
+    dest = Path(dest)
+    if dest.exists():
+        print(f"  Already exists : {dest.name} ({dest.stat().st_size / 1e6:.1f} MB) — skipping.")
+        return dest
+    print(f"  Downloading    : {dest.name} ...")
+    dest.parent.mkdir(parents=True, exist_ok=True)
+    resp  = requests.get(url, stream=True, timeout=120)
+    resp.raise_for_status()
+    total = int(resp.headers.get("content-length", 0))
+    with open(dest, "wb") as fh:
+        with _tqdm(total=total, unit="B", unit_scale=True, desc=dest.name) as pbar:
+            for chunk in resp.iter_content(chunk_size=chunk_size):
+                if chunk:
+                    fh.write(chunk)
+                    pbar.update(len(chunk))
+    print(f"  Saved          : {dest.name} ({dest.stat().st_size / 1e6:.1f} MB)")
+    return dest
+
+
+def _resample_ds(ds: xr.Dataset, rule: str) -> xr.Dataset:
+    """Resample a dataset: sum for accumulation vars, mean for all others.
+    Non-time coordinates (lat, lon, elev) are restored after resampling."""
+    parts = {}
+    for var in ds.data_vars:
+        if var in _ACCUMULATION_VARS:
+            parts[var] = ds[var].resample(time=rule).sum(skipna=True)
+        else:
+            parts[var] = ds[var].resample(time=rule).mean(skipna=True)
+    ds_r = xr.Dataset(parts)
+    # Restore non-time coordinates (lat, lon, elev) dropped by resample
+    for coord in ds.coords:
+        if "time" not in ds[coord].dims and coord not in ds_r.coords:
+            ds_r = ds_r.assign_coords({coord: ds[coord]})
+    return ds_r
+
+
+def _compress_encoding(ds: xr.Dataset, level: int = 4) -> dict:
+    return {
+        name: {"zlib": True, "complevel": level}
+        for name, var in ds.data_vars.items()
+        if var.dtype.kind in {"f", "i", "u"}
+    }
+
+
+def save_compressed_netcdf(
+    ds: xr.Dataset,
+    path: Union[str, Path],
+    *,
+    compression_level: int = 4,
+    mode: str = "w",
+) -> Path:
+    """Write *ds* to NetCDF with zlib compression using the netCDF4 engine.
+
+    Matches the encoding used by :func:`create_openmesh_sample`,
+    :func:`create_pws_sample`, and :func:`create_asos_sample` (only float/int
+    data variables are compressed; requires ``netcdf4`` installed).
+    """
+    path = Path(path)
+    ds.to_netcdf(
+        path,
+        mode=mode,
+        encoding=_compress_encoding(ds, compression_level),
+        engine="netcdf4",
+    )
+    return path
+
+
+def _infer_interval_label(start: datetime, end: datetime) -> str:
+    total_seconds = int((end - start).total_seconds())
+    if total_seconds % 3600 == 0:
+        h = total_seconds // 3600
+        if h % 720 == 0: return f"{h // 720}mo"
+        if h % 24  == 0: return f"{h // 24}d"
+        return f"{h}h"
+    return f"{total_seconds // 60}min"
+
+
+def _parse_interval(time_interval: str) -> timedelta:
+    """Parse an interval string into a timedelta.
+
+    Accepts the fixed shortcuts in INTERVAL_DURATIONS *and* arbitrary
+    ``Nh`` / ``Nd`` / ``Nw`` / ``Nmo`` patterns (e.g. ``"10d"``, ``"48h"``).
+    """
+    import re
+    if time_interval in INTERVAL_DURATIONS:
+        return INTERVAL_DURATIONS[time_interval]
+    m = re.fullmatch(r"(\d+)(h|d|w|mo)", time_interval)
+    if m:
+        n, unit = int(m.group(1)), m.group(2)
+        if unit == "h":  return timedelta(hours=n)
+        if unit == "d":  return timedelta(days=n)
+        if unit == "w":  return timedelta(weeks=n)
+        if unit == "mo": return timedelta(days=30 * n)
+    raise ValueError(
+        f"Unknown time_interval '{time_interval}'. "
+        f"Use a fixed shortcut {list(INTERVAL_DURATIONS)} "
+        f"or an arbitrary pattern like '10d', '48h', '2w', '3mo'."
+    )
+
+
+def _resolve_time_window(
+    start_time: str,
+    time_interval: Optional[str],
+    end_time: Optional[str],
+) -> tuple[datetime, datetime, str]:
+    start_dt = datetime.fromisoformat(start_time)
+    if end_time is not None:
+        end_dt = datetime.fromisoformat(end_time)
+        label  = _infer_interval_label(start_dt, end_dt)
+    elif time_interval is not None:
+        end_dt = start_dt + _parse_interval(time_interval)
+        label  = time_interval
+    else:
+        raise ValueError("Provide either time_interval or end_time.")
+    return start_dt, end_dt, label
+
+
+def _resolve_source(
+    src_path: Union[str, Path, None],
+    default_path: Path,
+    mode: str,
+    download_fn,
+) -> Path:
+    path = Path(src_path) if src_path is not None else default_path
+    if mode == "download":
+        if not path.exists():
+            print("  Source not found — downloading ...")
+            download_fn()
+        else:
+            print(f"  Source   : {_rel(path)}  (exists, skipping download)")
+    elif mode == "local":
+        if not path.exists():
+            raise FileNotFoundError(
+                f"File not found: {path}\n"
+                f"Tip: use mode='download' to fetch it automatically."
+            )
+    else:
+        raise ValueError(f"Unknown mode '{mode}'. Choose 'local' or 'download'.")
+    return path
+
+
+# ---------------------------------------------------------------------------
+# Download
+# ---------------------------------------------------------------------------
+
+def download_openmesh(output_dir: Union[str, Path] = DATA_DIR) -> Path:
+    """Download and extract the OpenMesh CML dataset from Zenodo.
+    Skips if already present locally.
+
+    Returns
+    -------
+    Path  →  <output_dir>/raw/
+    """
+    output_dir = Path(output_dir)
+    raw_dir    = output_dir / "raw"
+    cml_nc     = raw_dir / "ds_openmesh.nc"
+
+    if cml_nc.exists():
+        print(f"  CML found locally  : {cml_nc}")
+        return raw_dir
+
+    zip_path = output_dir / "archived" / _ZENODO["cml"]["filename"]
+    _download_file(_ZENODO["cml"]["url"], zip_path)
+
+    ext_map  = {".nc": raw_dir, ".csv": output_dir / "meta",
+                ".html": output_dir / "meta" / "maps", ".ipynb": output_dir / "examples"}
+    docs_dir = output_dir / "docs"
+    for d in [*ext_map.values(), docs_dir]:
+        d.mkdir(parents=True, exist_ok=True)
+
+    with tempfile.TemporaryDirectory() as tmp:
+        with zipfile.ZipFile(zip_path, "r") as zf:
+            members = [m for m in zf.namelist() if not m.endswith("/")]
+            print(f"  Extracting {len(members)} files ...")
+            zf.extractall(tmp)
+        for f in Path(tmp).rglob("*"):
+            if f.is_file():
+                dest = ext_map.get(f.suffix.lower(), docs_dir) / f.name
+                if not dest.exists():
+                    shutil.copy2(f, dest)
+
+    print(f"  Extraction complete: {raw_dir}")
+    return raw_dir
+
+
+def download_pws_wu(output_dir: Union[str, Path] = DATA_DIR) -> Path:
+    """Download and extract the PWS Weather Underground dataset from Zenodo.
+    Skips if already present locally.
+
+    Returns
+    -------
+    Path  →  <output_dir>/raw/pws_wu_os.nc
+    """
+    output_dir = Path(output_dir)
+    pws_nc     = output_dir / "raw" / "pws_wu_os.nc"
+
+    if pws_nc.exists():
+        print(f"  PWS found locally  : {pws_nc}")
+        return pws_nc
+
+    zip_path = output_dir / "archived" / _ZENODO["pws"]["filename"]
+    _download_file(_ZENODO["pws"]["url"], zip_path)
+
+    pws_nc.parent.mkdir(parents=True, exist_ok=True)
+    print("  Extracting pws_wu_os.nc ...")
+    with zipfile.ZipFile(zip_path, "r") as zf:
+        for name in zf.namelist():
+            if name.endswith("pws_wu_os.nc"):
+                pws_nc.write_bytes(zf.read(name))
+                break
+
+    print(f"  Saved              : {_rel(pws_nc)} ({pws_nc.stat().st_size / 1e6:.1f} MB)")
+    return pws_nc
+
+
+# ---------------------------------------------------------------------------
+# CML — load
+# ---------------------------------------------------------------------------
+
+def load_cml(path: Union[str, Path, None] = None) -> xr.Dataset:
+    """Load the full CML NetCDF into memory. Defaults to CML_PATH."""
+    path = Path(path) if path is not None else CML_PATH
+    ds   = xr.load_dataset(path)   # load_dataset vs open_dataset — reads fully into memory
+    print(f"  Loaded CML        : {dict(ds.sizes)}  ←  {_rel(path)}")
+    return ds
+
+
+def load_cml_sample(path: Union[str, Path]) -> xr.Dataset:
+    """Load a CML sample NetCDF into an xarray Dataset."""
+    path = Path(path)
+    ds   = xr.open_dataset(path)
+    print(f"  Loaded CML sample : {dict(ds.sizes)}  ←  {path.name}")
+    return ds
+
+
+# ---------------------------------------------------------------------------
+# CML — sample creation
+# ---------------------------------------------------------------------------
+
+def create_openmesh_sample(
+    src_path: Union[str, Path, None] = None,
+    output_dir: Union[str, Path] = SAMPLE_DIR,
+    *,
+    ds: Optional[xr.Dataset] = None,
+    mode: str = "local",
+    start_time: str = DEFAULT_START_TIME,
+    time_interval: Optional[str] = "1d",
+    end_time: Optional[str] = None,
+    cml_ids: Optional[List[str]] = None,
+    compression_level: int = 4,
+    resample: Optional[str] = None,
+    include_date: bool = False,
+    replace: bool = False,
+    verbose: bool = True,
+    return_type: str = "path",              # "path" | "sample"
+    save: bool = True,
+) -> Union[Path, xr.Dataset]:
+    """Create a time- and/or CML-filtered sample from the OpenMesh dataset.
+
+    Parameters
+    ----------
+    src_path : path-like or None
+        Source NetCDF. Defaults to CML_PATH.
+    output_dir : path-like
+        Output directory. Defaults to SAMPLE_DIR (./data/samples).
+    ds : xr.Dataset or None
+        Already-loaded dataset — skips all file I/O when provided.
+    mode : "local" | "download"
+        "local"    — load from disk (raises FileNotFoundError if missing).
+        "download" — auto-download from Zenodo if not found locally.
+    start_time : str
+        ISO-8601 window start.
+    time_interval : str or None
+        "1h", "1d", "1w", "1mo". Ignored when end_time is given.
+    end_time : str or None
+        Explicit ISO-8601 window end. Overrides time_interval.
+    cml_ids : list of str or None
+        CML IDs to keep. None = all 75 links.
+    compression_level : int
+        zlib level 1–9.
+    resample : str or None
+        Temporal resampling rule (e.g. ``"1h"``, ``"5min"``). RSL is averaged.
+        When set, the rule is included in the filename: ``openmesh_cml_1h_1d.nc``.
+    include_date : bool
+        If True, append ``_YYYYMMDD_YYYYMMDD`` start/end dates to the filename
+        (e.g. ``openmesh_cml_1d_20240115_20240116.nc``).
+        Default False → ``openmesh_cml_1d.nc`` (canonical root copy).
+    verbose : bool
+    return_type : str
+        "path" (default) — returns file path.
+        "sample" — returns xr.Dataset.
+    save : bool
+        Write the sample to disk. Default True.
+        When ``return_type='sample'`` and ``save=False``, returns the
+        dataset without writing to disk.
+
+    Returns
+    -------
+    Path or xr.Dataset
+        If ``return_type='path'`` (default), returns the written sample file path.
+        If ``return_type='sample'``, returns the in-memory xr.Dataset instead.
+    """
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    start_dt, end_dt, label = _resolve_time_window(start_time, time_interval, end_time)
+    date_suffix   = f"_{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}" if include_date else ""
+    resample_tag  = f"_{resample}" if resample else ""
+    cml_tag       = f"_cmls{len(cml_ids)}" if cml_ids is not None else ""
+    dst_path      = output_dir / f"openmesh_cml{resample_tag}_{label}{date_suffix}{cml_tag}.nc"
+
+    _owns_ds = ds is None
+    if ds is None:
+        resolved = _resolve_source(src_path, CML_PATH, mode, download_openmesh)
+        if verbose:
+            print(f"  Source   : {resolved}")
+        ds = xr.open_dataset(resolved)
+    else:
+        if verbose:
+            print("  Source   : provided ds")
+
+    if verbose:
+        print(f"  Window   : {start_dt}  →  {end_dt}  ({label})")
+        print(f"  CML IDs  : {'all' if cml_ids is None else cml_ids}")
+        print(f"  Output   : {dst_path.name}")
+
+    ds_sample = ds.sel(time=slice(start_dt, end_dt))
+    if cml_ids is not None:
+        ds_sample = ds_sample.sel(cml_id=cml_ids)
+    if resample:
+        ds_sample = _resample_ds(ds_sample, resample)
+
+    if verbose:
+        print(f"  Timesteps: {ds_sample.sizes['time']:,}")
+        print(f"  CML count: {ds_sample.sizes['cml_id']}")
+
+    ds_sample.attrs.update({
+        "title":      f"OpenMesh CML sample ({label}), start {start_dt.strftime('%Y-%m-%d')}",
+        "start_date": start_dt.strftime("%Y-%m-%d"),
+        "end_date":   end_dt.strftime("%Y-%m-%d"),
+        "time_range": f"{start_dt.isoformat()} / {end_dt.isoformat()}",
+        "cml_subset": "all" if cml_ids is None else ",".join(cml_ids),
+    })
+
+    if save:
+        if dst_path.exists() and not replace:
+            if verbose:
+                print(f"  Exists   : {_rel(dst_path)}  (skipped — pass replace=True to overwrite)")
+            return dst_path if return_type == "path" else xr.open_dataset(dst_path)
+        try:
+            if dst_path.exists():
+                try:
+                    dst_path.unlink()
+                except PermissionError:
+                    print(f"  WARNING: could not delete existing file {dst_path} (permission). "
+                          f"Attempting to overwrite in-place.")
+
+            try:
+                save_compressed_netcdf(
+                    ds_sample, dst_path, compression_level=compression_level
+                )
+            except PermissionError as e:
+                print(f"  WARNING: could not write NetCDF file {dst_path} (permission): {e}")
+                print("           Skipping write for this interval, downstream code should handle "
+                      "missing files gracefully.")
+        except Exception as e:
+            print(f"  WARNING: unexpected error while writing {dst_path}: {e}")
+
+        if verbose and dst_path.exists():
+            print(f"  Saved    : {_rel(dst_path)}  ({dst_path.stat().st_size / 1e6:.2f} MB)")
+    else:
+        if verbose:
+            print("  Save     : skipped (save=False)")
+
+    if _owns_ds:
+        ds.close()
+
+    return ds_sample if return_type == "sample" else dst_path
+
+
+def create_openmesh_samples_batch(
+    intervals: List[str] = ("1h", "1d", "1w"),
+    start_time: str = DEFAULT_START_TIME,
+    resample: Optional[str] = None,
+    **kwargs,
+) -> dict[str, Path]:
+    """Create multiple CML samples in one call.
+
+    Parameters
+    ----------
+    intervals : list of str
+    start_time : str
+    **kwargs : forwarded to create_openmesh_sample
+
+    Returns
+    -------
+    dict  interval → Path
+    """
+    results: dict[str, Path] = {}
+    sep = "─" * 55
+
+    for interval in intervals:
+        if kwargs.get("verbose", True):
+            print(f"\n{sep}")
+            print(f"  Interval : {interval}")
+            print(sep)
+        results[interval] = create_openmesh_sample(
+            start_time=start_time, time_interval=interval, resample=resample, **kwargs
+        )
+
+    if kwargs.get("verbose", True):
+        print(f"\n{sep}")
+        print("  Batch complete:")
+        for label, p in results.items():
+            print(f"    {label:<6} → {p.name}")
+        print(sep)
+
+    return results
+
+
+# ---------------------------------------------------------------------------
+# PWS — load
+# ---------------------------------------------------------------------------
+
+def load_pws_sample(path: Union[str, Path]) -> xr.Dataset:
+    """Load a flat (id, time) PWS sample NetCDF into an xarray Dataset."""
+    path = Path(path)
+    ds   = xr.open_dataset(path)
+    print(f"  Loaded PWS sample : {dict(ds.sizes)}  ←  {path.name}")
+    return ds
+
+
+# ---------------------------------------------------------------------------
+# PWS — sample creation
+# ---------------------------------------------------------------------------
+
+def create_pws_sample(
+    src_path: Union[str, Path, None] = None,
+    output_dir: Union[str, Path] = SAMPLE_DIR,
+    *,
+    mode: str = "local",
+    start_time: str = DEFAULT_START_TIME,
+    time_interval: Optional[str] = "1d",
+    end_time: Optional[str] = None,
+    compression_level: int = 4,
+    resample: Optional[str] = None,
+    include_date: bool = False,
+    replace: bool = False,
+    verbose: bool = True,
+    return_type: str = "path",              # "path" | "sample"
+    save: bool = True,
+) -> Union[Path, xr.Dataset]:
+    """Crop PWS NetCDF (group-per-station) to a time window and save as
+    a flat (id, time) compressed sample.
+
+    Parameters
+    ----------
+    src_path : path-like or None
+        Defaults to PWS_PATH.
+    output_dir : path-like
+        Defaults to SAMPLE_DIR.
+    mode : "local" | "download"
+    start_time, time_interval, end_time : same as create_openmesh_sample
+    compression_level : int
+    verbose : bool
+
+    Returns
+    -------
+    Path or xr.Dataset
+        If ``return_type='path'`` (default), returns the written sample file path.
+        If ``return_type='sample'``, returns the in-memory xr.Dataset instead.
+    """
+    if not _HAS_NC4:
+        raise ImportError("netCDF4 is required for PWS sampling.  pip install netCDF4")
+
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    start_dt, end_dt, label = _resolve_time_window(start_time, time_interval, end_time)
+    date_suffix  = f"_{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}" if include_date else ""
+    resample_tag = f"_{resample}" if resample else ""
+    dst_path     = output_dir / f"openmesh_wu_pws{resample_tag}_{label}{date_suffix}.nc"
+
+    resolved = _resolve_source(src_path, PWS_PATH, mode, download_pws_wu)
+
+    with nc4.Dataset(str(resolved), "r") as root:
+        group_ids = list(root.groups.keys())
+
+    if verbose:
+        print(f"  Source   : {resolved}")
+        print(f"  Window   : {start_dt}  →  {end_dt}  ({label})")
+        print(f"  Stations : {len(group_ids)} groups")
+        print(f"  Output   : {dst_path.name}")
+
+    common_time: Optional[pd.DatetimeIndex] = None
+    for gid in group_ids:
+        try:
+            ds_g = xr.open_dataset(resolved, group=gid, engine="netcdf4")
+            t    = ds_g.sel(time=slice(start_dt, end_dt)).time
+            if t.sizes["time"] > 0:
+                common_time = pd.DatetimeIndex(t.values)
+                ds_g.close()
+                break
+            ds_g.close()
+        except Exception:
+            pass
+
+    if common_time is None:
+        raise RuntimeError(f"No PWS data found between {start_dt} and {end_dt}.")
+
+    n_times = len(common_time)
+    ids_out, lats_out, lons_out, elevs_out = [], [], [], []
+    var_rows: dict[str, list] = {v: [] for v in _PWS_VARS}
+    var_attrs: dict[str, dict] = {}
+    n_stations = 0
+
+    for gid in group_ids:
+        try:
+            ds_g = xr.open_dataset(resolved, group=gid, engine="netcdf4")
+            ds_c = ds_g.sel(time=slice(start_dt, end_dt))
+            if ds_c.sizes["time"] == 0:
+                ds_g.close()
+                continue
+
+            ids_out.append(gid)
+            lats_out.append(float(ds_c["lat"].values.flat[0])   if "lat"  in ds_c else np.nan)
+            lons_out.append(float(ds_c["lon"].values.flat[0])   if "lon"  in ds_c else np.nan)
+            elevs_out.append(float(ds_c["elev"].values.flat[0]) if "elev" in ds_c else np.nan)
+
+            for vname in _PWS_VARS:
+                if vname not in ds_c:
+                    var_rows[vname].append(np.full(n_times, np.nan))
+                    continue
+                if not var_attrs.get(vname):
+                    var_attrs[vname] = dict(ds_c[vname].attrs)
+                arr = ds_c[vname].values.squeeze()
+                s   = pd.Series(arr, index=pd.DatetimeIndex(ds_c.time.values))
+                var_rows[vname].append(s[~s.index.duplicated()].reindex(common_time).values)
+
+            n_stations += 1
+            ds_g.close()
+        except Exception as e:
+            warnings.warn(f"Skipping PWS group '{gid}': {e}")
+
+    if n_stations == 0:
+        raise RuntimeError(f"No PWS stations had data between {start_dt} and {end_dt}.")
+
+    if verbose:
+        print(f"  Loaded   : {n_stations} stations, {n_times:,} timesteps")
+
+    ds_out = xr.Dataset(
+        {v: (["id", "time"], np.vstack(var_rows[v]), var_attrs.get(v, {}))
+         for v in _PWS_VARS if var_rows[v]},
+        coords={
+            "id":   (["id"],   ids_out),
+            "time": (["time"], common_time),
+            "lat":  (["id"],   lats_out,  {"units": "degrees_north"}),
+            "lon":  (["id"],   lons_out,  {"units": "degrees_east"}),
+            "elev": (["id"],   elevs_out, {"units": "m"}),
+        },
+        attrs={
+            "title":       f"OpenMesh PWS sample ({label}), start {start_dt.strftime('%Y-%m-%d')}",
+            "start_date":  start_dt.strftime("%Y-%m-%d"),
+            "end_date":    end_dt.strftime("%Y-%m-%d"),
+            "time_range":  f"{start_dt.isoformat()} / {end_dt.isoformat()}",
+            "source":      "https://zenodo.org/records/17508286",
+            "conventions": "OpenSense v1.0",
+        },
+    )
+
+    if resample:
+        ds_out = _resample_ds(ds_out, resample)
+        if verbose:
+            print(f"  Resampled: {resample} → {ds_out.sizes['time']:,} timesteps")
+
+    if save:
+        if dst_path.exists() and not replace:
+            if verbose:
+                print(f"  Exists   : {_rel(dst_path)}  (skipped — pass replace=True to overwrite)")
+            return dst_path if return_type == "path" else xr.open_dataset(dst_path)
+        # Overwrite existing files explicitly; if this fails, warn but do not raise.
+        try:
+            if dst_path.exists():
+                try:
+                    dst_path.unlink()
+                except PermissionError as e:
+                    print(f"  WARNING: could not delete existing file {dst_path} (permission): {e}")
+
+            try:
+                save_compressed_netcdf(
+                    ds_out, dst_path, compression_level=compression_level
+                )
+            except PermissionError as e:
+                print(f"  WARNING: could not write PWS NetCDF file {dst_path} (permission): {e}")
+        except Exception as e:
+            print(f"  WARNING: unexpected error while writing PWS sample {dst_path}: {e}")
+
+        if verbose and dst_path.exists():
+            print(f"  Saved    : {_rel(dst_path)}  ({dst_path.stat().st_size / 1e6:.2f} MB)")
+    else:
+        if verbose:
+            print("  Save     : skipped (save=False)")
+
+    return ds_out if return_type == "sample" else dst_path
+
+
+
+# ---------------------------------------------------------------------------
+# ASOS — sample creation
+# ---------------------------------------------------------------------------
+
+def create_asos_sample(
+    output_dir: Union[str, Path] = SAMPLE_DIR,
+    *,
+    start_time: str = DEFAULT_START_TIME,
+    time_interval: Optional[str] = "1d",
+    end_time: Optional[str] = None,
+    stations: Optional[List[str]] = None,
+    variables: List[str] = ("precip_amount", "precip_rate", "temperature", "wind_speed"),
+    meta_path: Union[str, Path, None] = None,
+    compression_level: int = 4,
+    resample: Optional[str] = None,
+    include_date: bool = False,
+    replace: bool = False,
+    save: bool = True,
+    verbose: bool = True,
+    return_type: str = "path",              # "path" | "sample"
+) -> Union[Path, xr.Dataset]:
+    """Fetch ASOS 1-min data from the IEM API and save as a flat (id, time)
+    compressed NetCDF sample following the OpenMesh naming convention.
+
+    Requires a live internet connection (IEM API call).
+
+    Parameters
+    ----------
+    output_dir : path-like
+        Output directory. Defaults to SAMPLE_DIR (data/samples).
+    start_time : str
+        ISO-8601 window start. Default ``2024-01-15T00:00:00``.
+    time_interval : str or None
+        ``"1h"``, ``"1d"``, ``"1w"``, ``"1mo"``. Ignored when end_time given.
+    end_time : str or None
+        Explicit ISO-8601 window end. Overrides time_interval.
+    stations : list of str or None
+        ASOS station IDs (e.g. ``["KJFK", "KEWR"]``).
+        None → ``["JFK", "EWR", "LGA", "NYC"]`` (NYC-area defaults).
+    variables : list of str
+        Variable names from ``ws_opensense_conversion.ASOS_AVAILABLE_VARS``.
+    meta_path : path-like or None
+        Path to ASOS station metadata CSV.
+        None → ``data/meta/ASOS_stations.csv`` (or fetched from IEM if missing).
+    compression_level : int
+        zlib level 1–9.
+    include_date : bool
+        If True, append ``_YYYY-MM-DD`` start date to the filename
+        (e.g. ``openmesh_asos_1d_2024-01-15.nc``).
+        Default False → ``openmesh_asos_1d.nc``.
+    verbose : bool
+    return_type : str
+        ``"path"`` (default) — returns file path.
+        ``"sample"`` — returns the in-memory xr.Dataset.
+
+    Returns
+    -------
+    Path or xr.Dataset
+    """
+    import ws_opensense_conversion as wsoc
+
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    start_dt, end_dt, label = _resolve_time_window(start_time, time_interval, end_time)
+    date_suffix  = f"_{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}" if include_date else ""
+    resample_tag = f"_{resample}" if resample else ""
+    dst_path     = output_dir / f"openmesh_asos_ws{resample_tag}_{label}{date_suffix}.nc"
+
+    _stations = stations or ["JFK", "EWR", "LGA", "NYC"]
+
+    if verbose:
+        print(f"  Window   : {start_dt}  →  {end_dt}  ({label})")
+        print(f"  Stations : {_stations}")
+        print(f"  Variables: {list(variables)}")
+        print(f"  Output   : {dst_path.name}")
+
+    # Load or fetch station metadata
+    _meta_path = Path(meta_path) if meta_path is not None else DATA_DIR / "meta" / "ASOS_stations.csv"
+    if _meta_path.exists():
+        meta = wsoc.load_asos_metadata(_meta_path)
+    else:
+        if verbose:
+            print("  Metadata not found locally — fetching from IEM ...")
+        meta = wsoc.fetch_asos_stations_nyc(save_path=_meta_path, verbose=verbose)
+
+    # Fetch from IEM API
+    asos_raw = wsoc.fetch_asos_raw(
+        start_dt, end_dt, _stations,
+        variables=list(variables),
+        verbose=verbose,
+    )
+    if not asos_raw:
+        raise RuntimeError(
+            f"No ASOS data returned for stations {_stations} "
+            f"between {start_dt} and {end_dt}."
+        )
+
+    ds_out = wsoc.to_opensense_dataset(
+        asos_raw,
+        source="asos",
+        meta=meta,
+        start_dt=start_dt,
+        end_dt=end_dt,
+        extra_attrs={
+            "title":     f"OpenMesh ASOS sample ({label}), start {start_dt.strftime('%Y-%m-%d')}",
+            "reference": "https://zenodo.org/records/15287692",
+        },
+    )
+
+    if verbose:
+        print(f"  Loaded   : {len(asos_raw)} stations, {ds_out.sizes['time']:,} timesteps")
+
+    if resample:
+        ds_out = _resample_ds(ds_out, resample)
+        if verbose:
+            print(f"  Resampled: {resample} → {ds_out.sizes['time']:,} timesteps")
+
+    if save:
+        if dst_path.exists() and not replace:
+            if verbose:
+                print(f"  Exists   : {_rel(dst_path)}  (skipped — pass replace=True to overwrite)")
+            return dst_path if return_type == "path" else xr.open_dataset(dst_path)
+
+        try:
+            if dst_path.exists():
+                dst_path.unlink()
+            save_compressed_netcdf(
+                ds_out, dst_path, compression_level=compression_level
+            )
+            if verbose:
+                print(f"  Saved    : {_rel(dst_path)}  ({dst_path.stat().st_size / 1e6:.2f} MB)")
+        except Exception as e:
+            print(f"  WARNING: could not write ASOS sample {_rel(dst_path)}: {e}")
+    else:
+        if verbose:
+            print("  Save     : skipped (save=False)")
+
+    return ds_out if return_type == "sample" else dst_path
+
+
+# ── create_all_datasets ───────────────────────────────────────
+def create_all_datasets(
+    start_time: str,
+    end_time:   str,
+    cml_ids:    Optional[List[str]] = None,   # None = all 75
+    pws_ids:    Optional[List[str]] = None,   # None = all stations
+    asos_ids:   Optional[List[str]] = None,   # None = all in meta
+    output_dir: Union[str, Path] = SAMPLE_DIR,
+    resample: Optional[str] = None,
+    include_date: bool = False,
+    replace: bool = False,
+    save: bool = True,
+    verbose:    bool = True,
+) -> dict[str, Optional[Union[Path, "xr.Dataset"]]]:
+    """Create CML, PWS and ASOS samples for a given time window.
+
+    Parameters
+    ----------
+    save : bool
+        If True (default), write samples to disk and return Paths.
+        If False, skip disk read/write entirely — always create from
+        the raw downloaded files and return xr.Dataset objects.
+
+    Returns
+    -------
+    dict with keys "cml", "pws", "asos" → Path (save=True) or xr.Dataset (save=False)
+    """
+    from datetime import datetime
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    start_dt    = datetime.fromisoformat(start_time)
+    end_dt      = datetime.fromisoformat(end_time)
+    label        = _infer_interval_label(start_dt, end_dt)
+    date_suffix  = f"_{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}" if include_date else ""
+    resample_tag = f"_{resample}" if resample else ""
+    results      = {"cml": None, "pws": None, "asos": None}
+    sep          = "─" * 55
+
+    return_type = "path" if save else "sample"
+
+    # ── CML ──────────────────────────────────────────────────
+    print(f"\n{sep}\n  CML\n{sep}")
+    cml_tag  = f"_cmls{len(cml_ids)}" if cml_ids else ""
+    cml_path = output_dir / f"openmesh_cml{resample_tag}_{label}{date_suffix}{cml_tag}.nc"
+    if save and cml_path.exists() and not replace:
+        print(f"  exists  : {cml_path.name}  (skipped — pass replace=True to overwrite)")
+        results["cml"] = cml_path
+    else:
+        try:
+            results["cml"] = create_openmesh_sample(
+                start_time=start_time, end_time=end_time,
+                cml_ids=cml_ids, output_dir=output_dir,
+                mode="local", resample=resample, include_date=include_date,
+                replace=replace, save=save, return_type=return_type, verbose=verbose,
+            )
+        except Exception as e:
+            print(f"  FAILED  : {e}")
+
+    # ── PWS ──────────────────────────────────────────────────
+    print(f"\n{sep}\n  PWS\n{sep}")
+    pws_path = output_dir / f"openmesh_wu_pws{resample_tag}_{label}{date_suffix}.nc"
+    if save and pws_path.exists() and not replace:
+        print(f"  exists  : {pws_path.name}  (skipped — pass replace=True to overwrite)")
+        results["pws"] = pws_path
+    else:
+        try:
+            results["pws"] = create_pws_sample(
+                start_time=start_time, end_time=end_time,
+                output_dir=output_dir, mode="local",
+                resample=resample, include_date=include_date,
+                replace=replace, save=save, return_type=return_type, verbose=verbose,
+            )
+        except Exception as e:
+            print(f"  FAILED  : {e}")
+
+    # ── ASOS ─────────────────────────────────────────────────
+    print(f"\n{sep}\n  ASOS\n{sep}")
+    asos_path = output_dir / f"openmesh_asos_ws_{label}{date_suffix}.nc"
+    if save and asos_path.exists() and not replace:
+        print(f"  exists  : {asos_path.name}  (skipped — pass replace=True to overwrite)")
+        results["asos"] = asos_path
+    else:
+        try:
+            results["asos"] = create_asos_sample(
+                output_dir=output_dir,
+                start_time=start_time, end_time=end_time,
+                stations=asos_ids,
+                resample=resample, include_date=include_date,
+                replace=replace, save=save, return_type=return_type, verbose=verbose,
+            )
+        except Exception as e:
+            print(f"  FAILED  : {e}")
+
+    # ── summary ──────────────────────────────────────────────
+    print(f"\n{sep}")
+    print("  Summary:")
+    for name, val in results.items():
+        if val is None:
+            status = "NOT created"
+        elif save:
+            status = val.name
+        else:
+            status = repr(val)
+        print(f"    {name:<6} → {status}")
+    print(sep)
+
+    return results
\ No newline at end of file
diff --git a/OpenMesh/notebooks/ws_opensense_conversion.py b/OpenMesh/notebooks/ws_opensense_conversion.py
new file mode 100644
index 0000000..763b09e
--- /dev/null
+++ b/OpenMesh/notebooks/ws_opensense_conversion.py
@@ -0,0 +1,592 @@
+"""
+ws_opensense_conversion.py
+==========================
+Convert weather station data (ASOS, PWS, or custom) to OpenSense-format
+xr.Dataset and save as compressed NetCDF.
+
+Supported sources
+-----------------
+  "asos"   — NOAA ASOS 1-minute data fetched via IEM API
+  "custom" — any dict of {station_id: pd.DataFrame} with a time index
+
+Pipeline
+--------
+  1. fetch_asos_stations_nyc()          discover stations
+  2. fetch_asos_raw()                   fetch + process to metric DataFrame
+  3. to_opensense_dataset()             convert to xr.Dataset (OpenSense v1.0)
+  4. save_opensense_dataset()           save as compressed NetCDF
+
+OpenSense output format
+-----------------------
+  dims    : (id, time)
+  coords  : id, time, lat, lon, elev
+  data_vars: variable names depend on source / variables requested
+  attrs   : title, source, conventions, time_range, start_date, end_date
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+from io import StringIO
+from pathlib import Path
+from typing import Optional, Union
+
+import numpy as np
+import pandas as pd
+import requests
+import xarray as xr
+
+try:
+    from tqdm.auto import tqdm as _tqdm
+except ImportError:
+    class _tqdm:
+        def __init__(self, total=None, **kw): pass
+        def __enter__(self): return self
+        def __exit__(self, *a): pass
+        def update(self, n=1): pass
+
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+DATA_DIR        = Path(__file__).parent / "data"   # absolute — stable regardless of CWD
+SAMPLE_DIR      = DATA_DIR / "samples"
+ASOS_META_PATH  = DATA_DIR / "meta" / "ASOS_stations.csv"
+IEM_1MIN_URL    = "https://mesonet.agron.iastate.edu/cgi-bin/request/asos1min.py"
+IEM_NETWORK_URL = "https://mesonet.agron.iastate.edu/geojson/network/{network}.geojson"
+
+# All variables available from the IEM 1-min API
+ASOS_AVAILABLE_VARS = {
+    "precip_amount":       ("precip",    "mm",       "1-min precipitation amount"),
+    "precip_rate":         ("precip",    "mm/hr",    "Precipitation rate (calculated)"),
+    "precip_type":         ("ptype",     "-",        "Raw ASOS precipitation type code"),
+    "precip_category":     ("ptype",     "-",        "Simplified precip category"),
+    "temperature":         ("tmpf",      "°C",       "Air temperature"),
+    "dewpoint":            ("dwpf",      "°C",       "Dewpoint temperature"),
+    "wind_speed":          ("sknt",      "m/s",      "Wind speed"),
+    "wind_direction":      ("drct",      "degrees",  "Wind direction"),
+    "wind_gust":           ("gust_sknt", "m/s",      "Wind gust speed"),
+    "wind_gust_direction": ("gust_drct", "degrees",  "Wind gust direction"),
+}
+
+# Precipitation type mapping (METAR-based)
+PTYPE_MAP = {
+    "NP": "dry",
+    "R": "rain",  "R+": "rain",  "R-": "rain",
+    "S": "snow",  "S+": "snow",  "S-": "snow",
+    "P": "precip", "P?": "precip",
+    "M": "missing", "M ": "missing",
+    "?0": "missing", "?1": "missing", "?2": "missing", "?3": "missing",
+}
+
+
+# ---------------------------------------------------------------------------
+# Station discovery
+# ---------------------------------------------------------------------------
+
+def fetch_asos_stations_nyc(
+    lat_min: float = 40.4,
+    lat_max: float = 41.2,
+    lon_min: float = -74.5,
+    lon_max: float = -73.0,
+    networks: list[str] = ("NY_ASOS", "NJ_ASOS", "CT_ASOS"),
+    save_path: Union[str, Path, None] = ASOS_META_PATH,
+    verbose: bool = True,
+) -> pd.DataFrame:
+    """Fetch all ASOS stations within a bounding box from multiple state networks.
+
+    Queries the IEM GeoJSON API for each network and filters by bbox.
+    Default bbox covers NYC metro: Manhattan, Brooklyn, Queens, Bronx,
+    Staten Island, Newark (NJ/KEWR), JFK as the furthest east point.
+
+    Parameters
+    ----------
+    lat_min, lat_max : float   Latitude bounds  (default 40.4 – 41.0)
+    lon_min, lon_max : float   Longitude bounds (default -74.5 – -73.5)
+    networks : list of str     IEM network codes to query
+    save_path : path-like or None   Where to save CSV (None = skip)
+    verbose : bool
+
+    Returns
+    -------
+    pd.DataFrame  indexed by Station ID, columns: Name, Latitude, Longitude,
+                  Elevation, Network
+    """
+    records = []
+
+    for network in networks:
+        url = IEM_NETWORK_URL.format(network=network)
+        if verbose:
+            print(f"  Querying {network} ...", end=" ")
+        try:
+            resp     = requests.get(url, timeout=30)
+            resp.raise_for_status()
+            n_found  = 0
+            for f in resp.json()["features"]:
+                lon = f["geometry"]["coordinates"][0]
+                lat = f["geometry"]["coordinates"][1]
+                if lat_min <= lat <= lat_max and lon_min <= lon <= lon_max:
+                    p = f["properties"]
+                    records.append({
+                        "Station ID": p["sid"],
+                        "Name":       p["sname"],
+                        "Latitude":   lat,
+                        "Longitude":  lon,
+                        "Elevation":  p.get("elevation", np.nan),
+                        "Network":    network,
+                    })
+                    n_found += 1
+            if verbose:
+                print(f"{n_found} stations found")
+        except Exception as e:
+            if verbose:
+                print(f"ERROR: {e}")
+
+    if not records:
+        raise RuntimeError(
+            f"No ASOS stations found in bbox "
+            f"lat=[{lat_min},{lat_max}] lon=[{lon_min},{lon_max}]"
+        )
+
+    df = (
+        pd.DataFrame(records)
+        .set_index("Station ID")
+        .sort_values(["Network", "Latitude"], ascending=[True, False])
+    )
+    df = df[~df.index.duplicated(keep="first")]
+
+    if verbose:
+        print(f"\n  Total : {len(df)} stations")
+        print(f"  {'ID':<8} {'Network':<10} {'Name':<35} {'Lat':>7} {'Lon':>8} {'Elev':>6}")
+        print(f"  {'─'*72}")
+        for sid, row in df.iterrows():
+            print(
+                f"  {sid:<8} {row['Network']:<10} {row['Name']:<35} "
+                f"{row['Latitude']:>7.3f} {row['Longitude']:>8.3f} {row['Elevation']:>6.1f}"
+            )
+
+    if save_path is not None:
+        save_path = Path(save_path)
+        save_path.parent.mkdir(parents=True, exist_ok=True)
+        df.to_csv(save_path, index=True, index_label="Station ID")
+        if verbose:
+            print(f"\n  Saved : {save_path}")
+
+    return df
+
+
+def load_asos_metadata(path: Union[str, Path] = ASOS_META_PATH) -> pd.DataFrame:
+    """Load ASOS station metadata CSV indexed by Station ID."""
+    return pd.read_csv(Path(path), index_col="Station ID")
+
+
+# ---------------------------------------------------------------------------
+# ASOS fetch (IEM 1-min API)
+# ---------------------------------------------------------------------------
+
+def _map_precip_category(ptype) -> str:
+    if pd.isna(ptype) or str(ptype).strip() in ("", "nan", "None"):
+        return "missing"
+    s = str(ptype).strip()
+    if s in PTYPE_MAP:
+        return PTYPE_MAP[s]
+    u = s.upper()
+    if u == "NP":           return "dry"
+    if u.startswith("R"):   return "rain"
+    if u.startswith("S"):   return "snow"
+    if u.startswith("P"):   return "precip"
+    return "missing"
+
+
+def _raw_to_metric(df_raw: pd.DataFrame, station_id: str) -> pd.DataFrame:
+    """Convert raw IEM DataFrame to metric units with standardized columns."""
+    out = pd.DataFrame()
+    out["datetime"]   = pd.to_datetime(df_raw["valid(UTC)"])
+    out["station_id"] = station_id
+
+    def _num(col): return pd.to_numeric(df_raw[col], errors="coerce") if col in df_raw else None
+
+    if "tmpf"     in df_raw: out["temperature"]         = (_num("tmpf") - 32) * 5 / 9
+    if "dwpf"     in df_raw: out["dewpoint"]             = (_num("dwpf") - 32) * 5 / 9
+    if "sknt"     in df_raw: out["wind_speed"]           = _num("sknt") * 0.51444
+    if "drct"     in df_raw: out["wind_direction"]       = _num("drct")
+    if "gust_sknt"in df_raw: out["wind_gust"]            = _num("gust_sknt") * 0.51444
+    if "gust_drct"in df_raw: out["wind_gust_direction"]  = _num("gust_drct")
+    if "ptype"    in df_raw:
+        out["precip_type"]     = df_raw["ptype"].astype(str).replace("nan", None)
+        out["precip_category"] = out["precip_type"].apply(_map_precip_category)
+    if "precip"   in df_raw:
+        mm = _num("precip") * 25.4
+        out["precip_amount"] = mm
+        out["precip_rate"]   = mm * 60
+
+    return out.sort_values("datetime").reset_index(drop=True)
+
+
+def fetch_asos_raw(
+    start_dt: Union[datetime, str],
+    end_dt: Union[datetime, str],
+    stations: list[str],
+    variables: list[str] = ("precip_amount", "precip_type"),
+    verbose: bool = True,
+) -> dict[str, pd.DataFrame]:
+    """Fetch and process ASOS 1-minute data for multiple stations.
+
+    Parameters
+    ----------
+    stations : list of str   Station IDs, e.g. ["KJFK", "KEWR"]
+    start_dt, end_dt : datetime or ISO-8601 str (e.g. "2024-01-15")
+    variables : list of str
+        Output variable names (from ASOS_AVAILABLE_VARS). Controls which
+        API fields are requested and which columns appear in the output.
+        Default: precipitation only.
+        Full set: all keys of ASOS_AVAILABLE_VARS.
+    verbose : bool
+
+    Returns
+    -------
+    dict  station_id → pd.DataFrame  (time-indexed, metric units)
+    """
+    if isinstance(start_dt, str):
+        start_dt = datetime.fromisoformat(start_dt)
+    if isinstance(end_dt, str):
+        end_dt = datetime.fromisoformat(end_dt)
+    # Map requested output vars → required API fields
+    api_vars = set()
+    for v in variables:
+        if v in ASOS_AVAILABLE_VARS:
+            api_vars.add(ASOS_AVAILABLE_VARS[v][0])
+
+    api_var_str = ",".join(sorted(api_vars))
+    results: dict[str, pd.DataFrame] = {}
+
+    for sid in stations:
+        if verbose:
+            print(f"  Fetching {sid} ...", end=" ")
+        try:
+            params = {
+                "station": sid,
+                "tz":      "UTC",
+                "year1": start_dt.year,  "month1": start_dt.month,  "day1": start_dt.day,
+                "year2": end_dt.year,    "month2": end_dt.month,    "day2": end_dt.day,
+                "vars":   api_var_str,
+                "sample": "1min",
+                "what":   "download",
+                "delim":  "comma",
+            }
+            resp = requests.get(IEM_1MIN_URL, params=params, timeout=300)
+            resp.raise_for_status()
+
+            if len(resp.text) < 100:
+                if verbose: print("no data")
+                continue
+
+            df_raw = pd.read_csv(StringIO(resp.text))
+            df     = _raw_to_metric(df_raw, sid)
+
+            # Keep only requested output columns
+            keep = ["datetime", "station_id"] + [v for v in variables if v in df.columns]
+            df   = df[[c for c in keep if c in df.columns]]
+            df   = df.set_index("datetime").sort_index()
+            df   = df[~df.index.duplicated(keep="first")]
+
+            results[sid] = df
+            if verbose:
+                print(f"{len(df):,} records")
+
+        except Exception as e:
+            if verbose:
+                print(f"ERROR: {e}")
+
+    return results
+
+
+# ---------------------------------------------------------------------------
+# OpenSense conversion
+# ---------------------------------------------------------------------------
+
+def to_opensense_dataset(
+    data: dict[str, pd.DataFrame],
+    source: str = "asos",
+    variables: Optional[list[str]] = None,
+    meta: Optional[pd.DataFrame] = None,
+    lat_col: str = "Latitude",
+    lon_col: str = "Longitude",
+    elev_col: str = "Elevation",
+    start_dt: Optional[Union[datetime, str]] = None,
+    end_dt: Optional[Union[datetime, str]] = None,
+    extra_attrs: Optional[dict] = None,
+) -> xr.Dataset:
+    """Convert a dict of station DataFrames to an OpenSense-format xr.Dataset.
+
+    Parameters
+    ----------
+    data : dict  station_id → time-indexed pd.DataFrame
+    source : str  label for metadata, e.g. "asos", "pws", "custom"
+    variables : list of str or None
+        Which DataFrame columns to include. None = all numeric columns.
+    meta : pd.DataFrame or None
+        Station metadata indexed by station ID (lat/lon/elev).
+    lat_col, lon_col, elev_col : str  column names in meta
+    start_dt, end_dt : datetime or None  override time range in attrs
+    extra_attrs : dict or None  additional global attributes
+
+    Returns
+    -------
+    xr.Dataset  dims (id, time), coords (id, time, lat, lon, elev)
+    """
+    if isinstance(start_dt, str):
+        start_dt = datetime.fromisoformat(start_dt)
+    if isinstance(end_dt, str):
+        end_dt = datetime.fromisoformat(end_dt)
+
+    if not data:
+        raise ValueError("data dict is empty.")
+
+    all_times = sorted(set().union(*[df.index for df in data.values()]))
+    ids, lats, lons, elevs = [], [], [], []
+    var_arrays: dict[str, list] = {}
+
+    for sid, df in data.items():
+        ids.append(sid)
+
+        # coordinates from metadata
+        if meta is not None and sid in meta.index:
+            lats.append(float(meta.loc[sid, lat_col])   if lat_col   in meta.columns else np.nan)
+            lons.append(float(meta.loc[sid, lon_col])   if lon_col   in meta.columns else np.nan)
+            elevs.append(float(meta.loc[sid, elev_col]) if elev_col  in meta.columns else np.nan)
+        else:
+            lats.append(np.nan); lons.append(np.nan); elevs.append(np.nan)
+
+        # determine which variables to use
+        cols = variables if variables is not None else [
+            c for c in df.columns
+            if c not in ("station_id",) and pd.api.types.is_numeric_dtype(df[c])
+        ]
+
+        for col in cols:
+            if col not in var_arrays:
+                var_arrays[col] = []
+            row = df[col].reindex(all_times).values if col in df.columns else np.full(len(all_times), np.nan)
+            var_arrays[col].append(row)
+
+    t0 = start_dt or pd.to_datetime(all_times[0])
+    t1 = end_dt   or pd.to_datetime(all_times[-1])
+
+    # Build variable metadata from ASOS catalogue if available
+    def _var_attrs(col):
+        if col in ASOS_AVAILABLE_VARS:
+            _, unit, desc = ASOS_AVAILABLE_VARS[col]
+            return {"units": unit, "long_name": desc}
+        return {}
+
+    data_vars = {
+        col: (["id", "time"], np.vstack(rows), _var_attrs(col))
+        for col, rows in var_arrays.items()
+    }
+
+    attrs = {
+        "title":       f"OpenMesh {source.upper()} sample (multi-station)",
+        "source":      source,
+        "start_date":  t0.strftime("%Y-%m-%d"),
+        "end_date":    t1.strftime("%Y-%m-%d"),
+        "time_range":  f"{t0.isoformat()} / {t1.isoformat()}",
+        "conventions": "OpenSense v1.0",
+    }
+    if extra_attrs:
+        attrs.update(extra_attrs)
+
+    return xr.Dataset(
+        data_vars,
+        coords={
+            "id":   ("id",   ids),
+            "time": ("time", all_times),
+            "lat":  ("id",   lats,  {"units": "degrees_north"}),
+            "lon":  ("id",   lons,  {"units": "degrees_east"}),
+            "elev": ("id",   elevs, {"units": "m"}),
+        },
+        attrs=attrs,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Save
+# ---------------------------------------------------------------------------
+
+def _compress_encoding(ds: xr.Dataset, level: int = 4) -> dict:
+    return {
+        name: {"zlib": True, "complevel": level}
+        for name, var in ds.data_vars.items()
+        if var.dtype.kind in {"f", "i", "u"}
+    }
+
+
+def save_opensense_dataset(
+    ds: xr.Dataset,
+    output_dir: Union[str, Path] = SAMPLE_DIR,
+    filename: Optional[str] = None,
+    compression_level: int = 4,
+    replace: bool = False,
+    verbose: bool = True,
+) -> Path:
+    """Save an OpenSense xr.Dataset to a compressed NetCDF file.
+
+    Parameters
+    ----------
+    ds : xr.Dataset
+    output_dir : path-like
+    filename : str or None   auto-generated from attrs if None
+    compression_level : int
+    verbose : bool
+
+    Returns
+    -------
+    Path
+    """
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    if filename is None:
+        source    = ds.attrs.get("source", "ws").upper()
+        start_str = ds.attrs.get("start_date", "unknown")
+        end_str   = ds.attrs.get("end_date",   "unknown")
+        filename  = f"{source}_{start_str}_{end_str}.nc"
+
+    dst_path = output_dir / filename
+    if dst_path.exists():
+        if not replace:
+            if verbose:
+                print(f"  Exists : {dst_path.name}  (skipped — pass replace=True to overwrite)")
+            return dst_path
+        dst_path.unlink()
+
+    ds.to_netcdf(dst_path, encoding=_compress_encoding(ds, compression_level), engine="netcdf4")
+
+    if verbose:
+        print(f"  Saved : {dst_path.name}  ({dst_path.stat().st_size / 1e6:.2f} MB)")
+
+    return dst_path
+
+
+import netCDF4 as nc4
+from typing import Union
+
+
+def save_asos_grouped(
+    data: dict[str, pd.DataFrame],
+    meta: pd.DataFrame,
+    output_dir: Union[str, Path] = DATA_DIR / "raw",
+    filename: str = "asos_nyc.nc",
+    verbose: bool = True,
+) -> Path:
+    """Save ASOS data as grouped NetCDF mirroring pws_wu_os.nc structure exactly.
+    One group per station, dims (id=1, time), same variable layout as PWS.
+    Uses precip_amount/precip_rate instead of rainfall_* (ASOS = all precip types).
+    """
+    import netCDF4 as nc4
+
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    dst_path = output_dir / filename
+    if dst_path.exists():
+        dst_path.unlink()
+
+    # reference epoch — use earliest timestamp across all stations
+    all_times  = pd.DatetimeIndex(
+        sorted(set().union(*[df.index.tolist() for df in data.values()]))
+    )
+    epoch      = all_times[0].strftime("%Y-%m-%d %H:%M:%S")
+    time_units = f"minutes since {epoch}"
+
+    with nc4.Dataset(str(dst_path), "w", format="NETCDF4") as root:
+
+        # ── global attributes (mirrors pws_wu_os.nc) ─────────────
+        root.title              = "NOAA ASOS 1-min Precipitation Data — NYC"
+        root.file_author        = "OpenMesh"
+        root.institution        = "NOAA / Iowa Environmental Mesonet (IEM)"
+        root.date               = pd.Timestamp.now().strftime("%Y-%m-%d")
+        root.source             = "NOAA ASOS 1-min via Iowa Environmental Mesonet"
+        root.history            = f"{pd.Timestamp.now().strftime('%Y-%m-%d')}: Converted to OpenSense-PWS-1.0 netCDF4 format."
+        root.naming_convention  = "OpenSense-PWS-1.0"
+        root.license_restrictions = "Public domain (NOAA)"
+        root.reference          = "https://github.com/OpenSenseAction/OS_data_format_conventions/blob/main/netCDF_PWS.adoc"
+        root.comment            = (
+            "ASOS airport stations in NYC area. "
+            "precip_amount/precip_rate used instead of rainfall_* — "
+            "ASOS captures all precipitation types (rain, snow, etc.). "
+            "Each station stored in separate netCDF4 group. "
+            "All timestamps in UTC."
+        )
+        root.Conventions        = "OpenSense-PWS-v1.0"
+
+        for sid, df in data.items():
+            grp = root.createGroup(sid)
+            grp.createDimension("id",   1)
+            grp.createDimension("time", len(df))
+
+            # ── time ─────────────────────────────────────────────
+            t_var           = grp.createVariable("time", "i8", ("time",))
+            t_var.long_name = "time_utc"
+            t_var.calendar  = "proleptic_gregorian"
+            t_var.units     = time_units
+            t_var[:]        = nc4.date2num(
+                df.index.to_pydatetime(), units=time_units, calendar="proleptic_gregorian"
+            )
+
+            # ── id ───────────────────────────────────────────────
+            id_var           = grp.createVariable("id", str, ("id",))
+            id_var.long_name = "asos_station_identifier"
+            id_var[0]        = sid
+
+            # ── coordinates ──────────────────────────────────────
+            lat  = float(meta.loc[sid, "Latitude"])  if sid in meta.index else float("nan")
+            lon  = float(meta.loc[sid, "Longitude"]) if sid in meta.index else float("nan")
+            elev = float(meta.loc[sid, "Elevation"]) if sid in meta.index else float("nan")
+
+            lat_var           = grp.createVariable("lat",  "f8", ("id",), fill_value=float("nan"))
+            lat_var.units     = "degrees_in_WGS84_projection"
+            lat_var.long_name = "latitude"
+            lat_var[0]        = lat
+
+            lon_var           = grp.createVariable("lon",  "f8", ("id",), fill_value=float("nan"))
+            lon_var.units     = "degrees_in_WGS84_projection"
+            lon_var.long_name = "longitude"
+            lon_var[0]        = lon
+
+            elev_var           = grp.createVariable("elev", "f8", ("id",), fill_value=float("nan"))
+            elev_var.units     = "metres_above_sea"
+            elev_var.long_name = "ground_elevation_above_sea_level"
+            elev_var[0]        = elev
+
+            # ── data variables ───────────────────────────────────
+            VAR_DEFS = {
+                "precip_amount": {"units": "mm",          "long_name": "precip_amount_per_time_unit"},
+                "precip_rate":   {"units": "mm hr-1",     "long_name": "precipitation_rate"},
+                "temperature":   {"units": "degrees_celsius", "long_name": "air_temperature"},
+                "wind_speed":    {"units": "ms-1",        "long_name": "wind_speed"},
+                "wind_direction":{"units": "degrees",     "long_name": "wind_direction"},
+            }
+
+            for col, vattrs in VAR_DEFS.items():
+                if col not in df.columns:
+                    continue
+                var             = grp.createVariable(
+                    col, "f8", ("id", "time"), fill_value=float("nan")
+                )
+                var.units       = vattrs["units"]
+                var.long_name   = vattrs["long_name"]
+                var.coordinates = "elev lat lon"
+                var[0, :]       = df[col].values.astype("float64")
+
+    if verbose:
+        print(f"  Saved grouped : {dst_path}  ({dst_path.stat().st_size / 1e6:.2f} MB)")
+        with nc4.Dataset(str(dst_path), "r") as root:
+            groups = list(root.groups.keys())
+            first  = root.groups[groups[0]]
+            print(f"  Groups        : {groups}")
+            print(f"  Variables     : {list(first.variables.keys())}")
+            print(f"  Dimensions    : { {k: v.size for k, v in first.dimensions.items()} }")
+
+    return dst_path
+
diff --git a/OpenMesh/openmesh_asos_ws_20d.nc b/OpenMesh/openmesh_asos_ws_20d.nc
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diff --git a/README.md b/README.md
index 6ca0371..45b5657 100644
--- a/README.md
+++ b/README.md
@@ -17,4 +17,24 @@ The data will be downloaded by specific functions in `poligrain` (still to be ad
 
 ## Available data
 
-...to be added
+### OpenMRG
+CML and gauge data from Gothenburg, Sweden, covering a summer 2015 event. Includes received
+signal level from commercial microwave links, city rain gauges, SMHI gauges, and weather radar.
+Full dataset: Andersson et al. 2022, Zenodo.
+
+### OpenRainER
+CML and gauge data from a European network, covering an 8-day period. Includes received signal
+level from commercial microwave links and rain gauge reference measurements.
+Full dataset: Zenodo (10.5281/zenodo.10593848).
+
+### AMS PWS
+Personal weather station data from Amsterdam, Netherlands. Includes rainfall and meteorological
+measurements from citizen weather stations alongside reference gauge data.
+
+### OpenMesh
+CML and PWS data from New York City, covering January 2024. Includes received signal level from
+75 commercial microwave links (NYC Mesh network) and rainfall, temperature, humidity, wind, and
+pressure from 35 Weather Underground personal weather stations. Also includes ASOS airport
+reference station data (JFK, EWR, LGA) for validation.
+The full repository with detailed fetching functions and examples is available at
+https://github.com/drorjac/OpenMesh