diff --git a/.dvc/.gitignore b/.dvc/.gitignore
new file mode 100644
index 0000000..528f30c
--- /dev/null
+++ b/.dvc/.gitignore
@@ -0,0 +1,3 @@
+/config.local
+/tmp
+/cache
diff --git a/.dvc/config b/.dvc/config
new file mode 100644
index 0000000..cca7ddb
--- /dev/null
+++ b/.dvc/config
@@ -0,0 +1,5 @@
+[core]
+    remote = calkit
+['remote "calkit"']
+    url = https://api.calkit.io/projects/symbiotic-engineering/openflash/dvc
+    auth = custom
diff --git a/.dvcignore b/.dvcignore
new file mode 100644
index 0000000..5197305
--- /dev/null
+++ b/.dvcignore
@@ -0,0 +1,3 @@
+# Add patterns of files dvc should ignore, which could improve
+# the performance. Learn more at
+# https://dvc.org/doc/user-guide/dvcignore
diff --git a/.github/workflows/.secrets b/.github/workflows/.secrets
deleted file mode 100644
index e69de29..0000000
diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
new file mode 100644
index 0000000..09550e4
--- /dev/null
+++ b/.github/workflows/deploy-docs.yml
@@ -0,0 +1,84 @@
+name: Deploy Sphinx Docs to GitHub Pages
+
+on:
+  workflow_dispatch: # gets dispatched from PR tagging workflow or manually
+
+concurrency:
+  group: "pages"
+  cancel-in-progress: true
+
+jobs:
+  build-and-deploy:
+    runs-on: ubuntu-latest
+
+    permissions:
+      pages: write
+      id-token: write
+
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - name: Install pandoc (system-level)
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y pandoc
+
+      - name: Install Python dependencies and local package
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt 
+          pip install .
+
+      - name: Build Sphinx documentation
+        run: |
+          sphinx-build -b html docs/ docs/_build/html
+
+      - name: Copy Streamlit app to docs output root
+        run: |
+          cp docs/_static/app_streamlit.html docs/_build/html/
+
+      # Add _headers for Stlite Cross-Origin Isolation ---
+      - name: Add Cross-Origin Headers for Stlite
+        run: |
+          echo "/*" > docs/_build/html/_headers
+          echo "  Cross-Origin-Opener-Policy: same-origin" >> docs/_build/html/_headers
+          echo "  Cross-Origin-Embedder-Policy: require-corp" >> docs/_build/html/_headers
+
+      # Create ZIP Artifact for Download
+      - name: Compress Docs to ZIP
+        run: zip -r docs-preview.zip docs/_build/html
+
+      - name: Upload Docs Preview Artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: docs-preview
+          path: docs-preview.zip # Uploads the newly created .zip file
+          retention-days: 1
+
+      - name: Upload artifact # This is the Pages artifact uploader
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: docs/_build/html
+
+      - name: Get latest tag to decide whether to deploy
+        run: |
+          echo "tag=$(git describe --tags --abbrev=0)"
+          echo "tag=$(git describe --tags --abbrev=0)" >> $GITHUB_OUTPUT
+        id: get-tag
+
+      - name: Deploy to GitHub Pages
+        id: deployment
+        if: ${{ !contains(steps.get-tag.outputs.tag , 'rc') }}
+        uses: actions/deploy-pages@v4
diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml
new file mode 100644
index 0000000..f001b5c
--- /dev/null
+++ b/.github/workflows/draft-pdf.yml
@@ -0,0 +1,30 @@
+name: Draft PDF
+on:
+  push:
+    paths:
+      - 'pubs/joss/paper.md'
+      - 'pubs/joss/paper.bib'
+      - 'pubs/figs/**'  # To trigger on image changes
+      - '.github/workflows/draft-pdf.yml' # To trigger on workflow changes
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: pubs/joss/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v4
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: pubs/joss/paper.pdf
\ No newline at end of file
diff --git a/.github/workflows/pr-tagging.yml b/.github/workflows/pr-tagging.yml
new file mode 100644
index 0000000..29dff58
--- /dev/null
+++ b/.github/workflows/pr-tagging.yml
@@ -0,0 +1,97 @@
+name: PR Tagging and RC Tag Management
+
+# This workflow creates a tag for pull requests into the main branch.
+# The tag for open pull requests is of the form vX.Y.ZrcPRNUM
+# where X.Y.Z is the base version (which is incremented according to the PR title)
+# and PRNUM is the pull request number (to prevent conflicts for multiple PRs open at once).
+# By default the Z number is incremented, but if the PR title contains [major] or [minor],
+# the X or Y version numbers are incremented respectively.
+# The tag for merged pull requests is of the form vX.Y.Z with no additions.
+
+on:
+  pull_request:
+    types: [opened, reopened, synchronize, closed]
+    branches:
+      - main
+  workflow_dispatch:
+
+permissions:
+  contents: write
+  issues: write
+  actions: write
+  packages: write
+  id-token: write
+
+jobs:
+  manage-pr-tags:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Compute base version (bump patch/minor/major from PR title)
+        id: compute_base
+        run: |
+          PR_TITLE="${{ github.event.pull_request.title || '' }}"
+          base=$(bash scripts/compute_base.sh "$PR_TITLE")
+          echo "base=$base" >> $GITHUB_OUTPUT
+
+      - name: Check if rc tag already exists and if so compute rc-dev tag
+        id: check_rc
+        run: |
+          rc_tag="${{ steps.compute_base.outputs.base }}rc${{ github.event.pull_request.number }}"
+          if git rev-parse "$rc_tag" >/dev/null 2>&1; then
+            devN=$(git rev-list --count $rc_tag..HEAD)
+            rc_dev_tag="${rc_tag}dev${devN}"
+          else
+            rc_dev_tag="${rc_tag}"
+          fi
+          echo "rc_dev_tag=${rc_dev_tag}" >> $GITHUB_OUTPUT
+
+      - name: Create rc tag
+        id: create_rc
+        if: ${{ github.event.action == 'opened' || github.event.action == 'reopened' || github.event.action == 'synchronize' }}
+        uses: rickstaa/action-create-tag@v1
+        with:
+          tag:  "${{ steps.check_rc.outputs.rc_dev_tag }}"
+          message: "Release ${{ steps.check_rc.outputs.rc_dev_tag }}"
+          commit_sha: ${{ github.event.pull_request.head.sha }}
+
+      - name: Create final tag
+        if: ${{ github.event.action == 'closed' && github.event.pull_request.merged == true }}
+        uses: rickstaa/action-create-tag@v1
+        with:
+          tag: ${{ steps.compute_base.outputs.base }}
+          message: "Release ${{ steps.compute_base.outputs.base }}"
+          commit_sha: ${{ github.event.pull_request.merge_commit_sha }}
+
+      - name: Dispatch docs deploy and publish package workflows
+        if: ${{ github.event.action == 'closed' && github.event.pull_request.merged == false }} == false
+        uses: actions/github-script@v7
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          script: |
+            try {
+              const owner = context.repo.owner;
+              const repo = context.repo.repo;
+              const workflow_id_1 = 'deploy-docs.yml';
+              const workflow_id_2 = 'publish-package.yml';
+
+              let ref;
+              if (${{ github.event.action == 'closed' && github.event.pull_request.merged == true }}) {
+                ref = '${{ github.ref_name }}';
+              }
+              else { 
+                ref = '${{ github.head_ref  || github.ref }}';
+              }
+
+              const resp  = await github.rest.actions.createWorkflowDispatch({ owner, repo, workflow_id: workflow_id_1, ref });
+              const resp2 = await github.rest.actions.createWorkflowDispatch({ owner, repo, workflow_id: workflow_id_2, ref });
+              console.log('docs dispatch response', resp && resp.status ? resp.status : resp);
+              console.log('package dispatch response', resp2 && resp2.status ? resp2.status : resp2);
+            } catch (err) {
+              console.error('dispatch failed', err && err.status ? { status: err.status, message: err.message } : err);
+              throw err;
+            }
diff --git a/.github/workflows/publish-package.yml b/.github/workflows/publish-package.yml
new file mode 100644
index 0000000..4de1974
--- /dev/null
+++ b/.github/workflows/publish-package.yml
@@ -0,0 +1,109 @@
+name: Build and Publish on Tag
+
+# This workflow builds and publishes the package to PyPI and Anaconda
+# when a tag is pushed. If the tag is a release candidate (contains 'rc'),
+# it is published to TestPyPI and the Anaconda package is not uploaded.
+
+on:
+  push:
+    tags:
+      - 'v*.*.*'
+  workflow_dispatch: # gets dispatched from PR tagging workflow or manually
+  workflow_call:
+
+permissions:
+  contents: write
+  packages: write
+  id-token: write
+
+jobs:
+  determine-tag:
+    runs-on: ubuntu-latest
+    outputs:
+      publish: ${{ steps.decide.outputs.publish }}
+      tag:     ${{ steps.get-tag.outputs.tag }}
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: Get latest tag to decide whether to publish
+        id: get-tag
+        run: |
+          echo "tag=$(git describe --tags --abbrev=0)"
+          echo "tag=$(git describe --tags --abbrev=0)" >> $GITHUB_OUTPUT
+      - name: Decide whether to publish
+        id: decide
+        run: |
+          echo "publish=${{ !contains(steps.get-tag.outputs.tag, 'rc') }}" >> $GITHUB_OUTPUT
+
+  build-and-publish:
+    runs-on: ubuntu-latest
+    needs: determine-tag
+    environment:
+      name: ${{ needs.determine-tag.outputs.publish == 'true' && 'pypi' || 'testpypi' }}
+      # the fact that the environment name uses an output is the reason this workflow is split into two jobs
+      url: https://pypi.org/project/open-flash/
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Install build tools
+        run: pip install build twine
+
+      - name: Build distribution packages
+        run: python -m build
+
+      - name: Publish package to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          repository-url:  ${{ needs.determine-tag.outputs.publish == 'true' && 'https://upload.pypi.org/legacy/' || 'https://test.pypi.org/legacy/' }}
+          verbose: true
+
+      - name: Setup Miniconda
+        uses: conda-incubator/setup-miniconda@v3
+        with:
+          python-version: 3.11
+          environment-file: conda-recipe/build_env.yaml
+          auto-update-conda: false
+          auto-activate-base: false
+          show-channel-urls: true
+
+      - name: Build and upload conda packages
+        uses: uibcdf/action-build-and-upload-conda-packages@2a98398b2f382f5ead0feebda695a13474e107f8
+        id: conda-build-and-upload
+        with:
+          meta_yaml_dir: conda-recipe 
+          label: main
+          user: sea-lab
+          token: ${{ secrets.ANACONDA_TOKEN }}
+          upload: ${{ needs.determine-tag.outputs.publish }}
+
+      - name: Re-format output paths
+        id: reformat-paths
+        # Needed to have the correct newline-separated files format for the following release step
+        run: |
+          paths=$(tr ' ' '\n' <<< "${{steps.conda-build-and-upload.outputs.paths}}")
+          echo "newline-separated-paths=$paths" >> $GITHUB_OUTPUT
+
+      - name: Check validity of citation.cff
+        uses: citation-file-format/cffconvert-github-action@2.0.0
+        if: always()
+        with:
+          args: "-i ./CITATION.cff -f zenodo --validate --show-trace"
+
+      - name: Create GitHub release
+        if: ${{ needs.determine-tag.outputs.publish == 'true' }}
+        uses: softprops/action-gh-release@v2
+        with:
+          tag_name: ${{ needs.determine-tag.outputs.tag }}
+          generate_release_notes: true
+          fail_on_unmatched_files: true
+          files: ${{steps.reformat-paths.outputs.newline-separated-paths}}
diff --git a/.gitignore b/.gitignore
index af93b02..690e48e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -127,6 +127,7 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+.new_env
 
 # Spyder project settings
 .spyderproject
@@ -204,4 +205,5 @@ html/
 # mat data files
 *.mat
 
-.vscode/settings.json
+# vscode configuration files
+.vscode/
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
deleted file mode 100644
index e8ef165..0000000
--- a/.vscode/settings.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-    "python.analysis.extraPaths": [
-        "./src/python",
-        "./package/src"
-    ]
-}
\ No newline at end of file
diff --git a/CITATION.cff b/CITATION.cff
index be7f990..23ca7c6 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -1,33 +1,54 @@
 cff-version: 1.2.0
-message: "If you use this software, please cite it as below."
+title: OpenFLASH
+message: "If you use this software, please cite it using the metadata from this file."
+type: software
 authors:
-- family-names: "YOUR_NAME_HERE"
-  given-names: "YOUR_NAME_HERE"
-  orcid: "https://orcid.org/0000-0000-0000-0000"
-- family-names: "Haji"
-  given-names: "Maha N."
-  orcid: "https://orcid.org/0000-0002-2953-7253"
-title: "sea-lab-template"
-version: 1.0.0
-doi: 10.5281/zenodo.1234
-date-released: 2023-09-14
-url: "https://github.com/symbiotic-engineering/sea-lab-template"
-
-preferred-citation:
-  type: article
-  authors:
-  - family-names: "YOUR_NAME_HERE"
-    given-names: "YOUR_NAME_HERE"
-    orcid: "https://orcid.org/0000-0000-0000-0000"
+  - family-names: "Best"
+    given-names: "Hope"
+  - given-names: Rebecca
+    family-names: McCabe
+    email: rgm222@cornell.edu
+    affiliation: Cornell University
+    orcid: 'https://orcid.org/0000-0001-5108-998X'
+  - family-names: "Khanal"
+    given-names: "Kapil"
+    orcid: "https://orcid.org/0000-0002-0327-5945"
+  - family-names: "Bimali"
+    given-names: "Yinghui"
+  - family-names: "Lo"
+    given-names: "En"
+  - family-names: "Jiang"
+    given-names: "Ruiyang"
+  - family-names: "Fernandez"
+    given-names: "John"
+  - family-names: "Treacy"
+    given-names: "Collin"
+    orcid: "https://orcid.org/0009-0000-9381-2697"
   - family-names: "Haji"
     given-names: "Maha N."
     orcid: "https://orcid.org/0000-0002-2953-7253"
-  doi: "10.0000/00000"
-  journal: "Journal Title"
-  month: 9
-  start: 1 # First page number
-  end: 10 # Last page number
-  title: "My awesome research software"
-  issue: 1
-  volume: 1
-  year: 2021
+repository-code: 'https://github.com/symbiotic-engineering/OpenFLASH'
+abstract: >-
+  Open source flexible library for analytical and semi-analytical hydrodynamics
+keywords:
+  - wave-energy
+  - linear-potential-theory
+  - matched-eigenfunction-expansion
+  - hydrodynamics
+license: MIT
+
+preferred-citation:
+  type: conference-paper
+  authors:
+    - family-names: "McCabe"
+      given-names: "Rebecca"
+      orcid: "https://orcid.org/0000-0001-5108-998X"
+    - family-names: "Khanal"
+      given-names: "Kapil"
+      orcid: "https://orcid.org/0000-0002-0327-5945"
+    - family-names: "Haji"
+      given-names: "Maha N."
+      orcid: "https://orcid.org/0000-0002-2953-7253"
+  title: "Open-source toolbox for semi-analytical hydrodynamic coefficients via the matched eigenfunction expansion method"
+  year: 2024
+  doi: "10.5281/zenodo.15168251"
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000..a7da3fc
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,2 @@
+include requirements.txt
+include LICENSE
diff --git a/README.md b/README.md
index 63d5fb5..a89b3ba 100644
--- a/README.md
+++ b/README.md
@@ -1,31 +1,69 @@
-# semi-analytical-hydro
+# OpenFLASH ⚡️
+Open-source Flexible Library for Analytical and Semi-analytical Hydrodynamics 
 
-matlab: see `src/matlab/run_MEEM.m` for the symbolic and numeric code, see `test/matlab/` for some scripts to get results.
+[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
+[![Unit Tests](https://github.com/symbiotic-engineering/OpenFLASH/actions/workflows/ci.yml/badge.svg)](https://github.com/symbiotic-engineering/OpenFLASH/actions/workflows/ci.yml)
+[![codecov](https://codecov.io/gh/symbiotic-engineering/OpenFLASH/graph/badge.svg?token=BKOU81RS8Q)](https://codecov.io/gh/symbiotic-engineering/OpenFLASH)
+[![GitHub](https://img.shields.io/github/license/symbiotic-engineering/OpenFLASH)](https://github.com/symbiotic-engineering/OpenFLASH/blob/main/LICENSE)
 
-python: see `src/python/MEEM.ipynb` to assemble the matrices and get results, and `src/python/` for some helper functions.
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17453419.svg)](https://doi.org/10.5281/zenodo.17453419)
+![GitHub Release](https://img.shields.io/github/v/release/symbiotic-engineering/OpenFLASH)
+![PyPI - Version](https://img.shields.io/pypi/v/open-flash)
+![Conda Version](https://img.shields.io/conda/v/sea-lab/open-flash)
 
-unlikely to be useful now, but keeping around in case: `test/time_comparison` for time comparisons of BEM (Capytaine), and `dev\` for various fiddly matlab experiments.
 
-# to fill in
-Describe your project here
 
-Provide a citation to the corresponding paper
+## About The OpenFLASH Project
 
-Provide the authors names and emails
+The OpenFLASH project is a Python package designed for solving hydrodynamic boundary value problems using eigenfunction expansion methods. It provides a modular framework for defining complex geometries, setting up multi-domain problems, performing numerical computations, and analyzing results, particularly for linear potential flow hydrodynamics. It can be significantly faster than Boundary Element Method calculations although is restricted to certain geometries (currently axisymmetric compound cylinders).  
 
-State the license type
+When referencing this work, please reference our `citation.cff`.
 
-Describe the file structure and how to run the code
-
-Include a funding acknowledgement (this is required for most grants, ie NSF has specific template text that must go here)
+This project is licensed under the MIT License. See the `LICENSE` file for details.
 
 ## Installation
+Three common installation options are shown below. For more details, see the [installation](https://symbiotic-engineering.github.io/OpenFLASH/installation.html) section of the docs.
+
+Option 1: via pypi (recommended for users who manage environments with venv or similar)
 
-You can install the `meem` package in our github:
+```bash
+pip install open-flash
+```
 
-### Install from the `CS_group` Branch
+Option 2: via conda (recommended for users who manage environments with conda)
 
-package is only available in the `CS_group` branch for now, use the following command to install it directly from that branch:
+```bash
+conda create -n openflash-env sea-lab::open-flash
+conda activate openflash-env
+```
 
+Option 3: via git (recommended for developers)
+
+Note - if you are a developer outside of the SEA Lab, please create a fork and clone your fork.
+1.  **Clone the repository:**
+```bash
+git clone https://github.com/symbiotic-engineering/OpenFLASH.git
+cd OpenFLASH
+```
+2.  **Install the package:**
+```bash
+pip install -e .
+```
+3.  **Install dependencies:**
 ```bash
-pip install git+https://github.com/symbiotic-engineering/semi-analytical-hydro.git@CS_group
+pip install -r requirements.txt
+```
+
+## Usage
+
+Please see our [documentation](https://symbiotic-engineering.github.io/OpenFLASH/) and [tutorial notebook](https://symbiotic-engineering.github.io/OpenFLASH/tutorial_walk.html). The documentation provides detailed instructions and API reference for the different modules and classes within the `openflash` package.
+
+If you prefer not to utilize the package programatically, the model can also be run with an [interactive web app](http://symbiotic-engineering.github.io/OpenFLASH/app_streamlit.html) (the site may take several seconds to load).
+
+## Theory
+Please see our [equations documentation](https://symbiotic-engineering.github.io/OpenFLASH/multi_equations.html) and [references](https://symbiotic-engineering.github.io/OpenFLASH/citations.html) for mathematical background and derivations as well as validation information.
+
+## MATLAB
+We also have a MATLAB code version, although the Python package is intended as primary for future development. MATLAB only supports bodies consisting of 2 concentric cylinders, rather than the arbitrary N concentric cylinders in the Python package.
+
+See `matlab/src/run_MEEM.m` for the symbolic and numeric code, see `matlab/test/` for some scripts to get results, and `matlab/dev` for various matlab experiments.
diff --git a/calkit.yaml b/calkit.yaml
new file mode 100644
index 0000000..1828b9c
--- /dev/null
+++ b/calkit.yaml
@@ -0,0 +1,5 @@
+owner: symbiotic-engineering
+name: openflash
+title: OpenFLASH
+description:
+git_repo_url: https://github.com/symbiotic-engineering/OpenFLASH
diff --git a/conda-recipe/build_env.yaml b/conda-recipe/build_env.yaml
new file mode 100644
index 0000000..7be8487
--- /dev/null
+++ b/conda-recipe/build_env.yaml
@@ -0,0 +1,7 @@
+name: build_env
+channels:
+  - conda-forge
+dependencies:
+  - conda-build
+  - conda-verify
+  - anaconda-client
\ No newline at end of file
diff --git a/conda-recipe/meta.yaml b/conda-recipe/meta.yaml
new file mode 100644
index 0000000..1ba98db
--- /dev/null
+++ b/conda-recipe/meta.yaml
@@ -0,0 +1,40 @@
+package:
+  name: open-flash
+  version: {{ GIT_DESCRIBE_TAG }}
+
+source:
+  path: ..
+
+build:
+  noarch: python
+  # Use a two-stage build script for robustness
+  script:
+    - python -m pip install . --no-deps --ignore-installed -vv
+
+requirements:
+  host:
+    - python
+    - pip
+    - setuptools
+    - wheel
+
+  run:
+    - python
+    - numpy
+    - scipy
+    - matplotlib
+    - pandas
+    - h5netcdf
+    - xarray
+    - streamlit
+    - ipykernel
+  
+about:
+  home: https://github.com/symbiotic-engineering/semi-analytical-hydro
+  license: MIT
+  summary: "A Python package for semi-analytical hydrodynamics modeling using the matched eigenfunction method"
+
+extra:
+  recipe-maintainers:
+    - hopeonthestack
+    - rmccabe
diff --git a/debug_log.txt b/debug_log.txt
new file mode 100644
index 0000000..461b2f8
--- /dev/null
+++ b/debug_log.txt
@@ -0,0 +1,1262 @@
+========================================
+=== TESTING CONFIG: config3 ===
+========================================
+  Parameters:
+    h (depth): 1.9
+    m0 (wavenum): 1.0
+    a (radii): [0.3 0.5 1.  1.2 1.6]
+    d (drafts): [0.5 0.7 0.8 0.2 0.5]
+
+  [SUPERPOSITION START] Combining 5 active bodies...
+
+[POTENTIAL MATRIX AUDIT] Boundary 0 (CONTRACTION (Step Up))
+  Depth L vs R : 1.4000 vs 1.2000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 0 (CONTRACTION (Step Up))
+  Depth L vs R : 1.4000 vs 1.2000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 1 (CONTRACTION (Step Up))
+  Depth L vs R : 1.2000 vs 1.1000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 1 (CONTRACTION (Step Up))
+  Depth L vs R : 1.2000 vs 1.1000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 2 (EXPANSION (Step Down))
+  Depth L vs R : 1.1000 vs 1.7000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 2 (EXPANSION (Step Down))
+  Depth L vs R : 1.1000 vs 1.7000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 3 (CONTRACTION (Step Up))
+  Depth L vs R : 1.7000 vs 1.4000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 3 (CONTRACTION (Step Up))
+  Depth L vs R : 1.7000 vs 1.4000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 4 (EXPANSION (Step Down))
+  Depth L vs R : 1.4000 vs 1.9000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 4 (EXPANSION (Step Down))
+  Depth L vs R : 1.4000 vs 1.9000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+  [DEPTH CONSISTENCY CHECK]
+    Global Geometry h: 1.9
+    Region 0 (Index 0):
+      Global h (Geometry) : 1.9000
+      Global h (Domain)   : 1.9000  ✅
+      Draft d (Domain)    : 0.5000
+      Local Depth (h - d) : 1.4000
+    Region 1 (Index 1):
+      Global h (Geometry) : 1.9000
+      Global h (Domain)   : 1.9000  ✅
+      Draft d (Domain)    : 0.7000
+      Local Depth (h - d) : 1.2000
+    Region 2 (Index 2):
+      Global h (Geometry) : 1.9000
+      Global h (Domain)   : 1.9000  ✅
+      Draft d (Domain)    : 0.8000
+      Local Depth (h - d) : 1.1000
+    Region 3 (Index 3):
+      Global h (Geometry) : 1.9000
+      Global h (Domain)   : 1.9000  ✅
+      Draft d (Domain)    : 0.2000
+      Local Depth (h - d) : 1.7000
+    Region 4 (Index 4):
+      Global h (Geometry) : 1.9000
+      Global h (Domain)   : 1.9000  ✅
+      Draft d (Domain)    : 0.5000
+      Local Depth (h - d) : 1.4000
+    Region 5 (Index 5):
+      Global h (Geometry) : 1.9000
+      Global h (Domain)   : 1.9000  ✅
+      Draft d (Domain)    : 0.0000
+      Local Depth (h - d) : 1.9000
+
+  [DEBUG] Running Orthogonality Checks for 6 regions...
+
+  [ORTHOGONALITY CHECK] Region 0
+    Type: Interior (d=0.5, h=1.9)
+    Inner Products <Zm, Zn> (Expect Diag ~ h-d, Off-Diag ~ 0):
+    m=0: [  1.4000e+00   0.0000e+00   0.0000e+00  ]
+    m=1: [  0.0000e+00   1.4000e+00   0.0000e+00  ]
+    m=2: [  0.0000e+00   0.0000e+00   1.4000e+00  ]
+    (Theoretical H-d = 1.4000)
+
+  [ORTHOGONALITY CHECK] Region 1
+    Type: Interior (d=0.7, h=1.9)
+    Inner Products <Zm, Zn> (Expect Diag ~ h-d, Off-Diag ~ 0):
+    m=0: [  1.2000e+00   0.0000e+00   0.0000e+00  ]
+    m=1: [  0.0000e+00   1.2000e+00   0.0000e+00  ]
+    m=2: [  0.0000e+00   0.0000e+00   1.2000e+00  ]
+    (Theoretical H-d = 1.2000)
+
+  [ORTHOGONALITY CHECK] Region 2
+    Type: Interior (d=0.8, h=1.9)
+    Inner Products <Zm, Zn> (Expect Diag ~ h-d, Off-Diag ~ 0):
+    m=0: [  1.1000e+00   0.0000e+00   0.0000e+00  ]
+    m=1: [  0.0000e+00   1.1000e+00   0.0000e+00  ]
+    m=2: [  0.0000e+00   0.0000e+00   1.1000e+00  ]
+    (Theoretical H-d = 1.1000)
+
+  [ORTHOGONALITY CHECK] Region 3
+    Type: Interior (d=0.2, h=1.9)
+    Inner Products <Zm, Zn> (Expect Diag ~ h-d, Off-Diag ~ 0):
+    m=0: [  1.7000e+00   0.0000e+00   0.0000e+00  ]
+    m=1: [  0.0000e+00   1.7000e+00   0.0000e+00  ]
+    m=2: [  0.0000e+00   0.0000e+00   1.7000e+00  ]
+    (Theoretical H-d = 1.7000)
+
+  [ORTHOGONALITY CHECK] Region 4
+    Type: Interior (d=0.5, h=1.9)
+    Inner Products <Zm, Zn> (Expect Diag ~ h-d, Off-Diag ~ 0):
+    m=0: [  1.4000e+00   0.0000e+00   0.0000e+00  ]
+    m=1: [  0.0000e+00   1.4000e+00   0.0000e+00  ]
+    m=2: [  0.0000e+00   0.0000e+00   1.4000e+00  ]
+    (Theoretical H-d = 1.4000)
+
+  [ORTHOGONALITY CHECK] Region 5
+    Type: Exterior (d=0.0, h=1.9)
+    Inner Products <Zm, Zn> (Expect Diag ~ h-d, Off-Diag ~ 0):
+    m=0: [  1.9000e+00   0.0000e+00   0.0000e+00  ]
+    m=1: [  0.0000e+00   1.9000e+00   0.0000e+00  ]
+    m=2: [  0.0000e+00   0.0000e+00   1.9000e+00  ]
+    (Theoretical H-d = 1.9000)
+    [Diagnosing Linear System for config3]
+  [Matrix Plot saved to: /Users/hopebest/Documents/semi-analytical-hydro/package/test_artifacts/config3_matrix_A.png]
+      [Matrix Info] Size: (400, 400) | Condition Num: 1.3024e+03
+
+  [CONTINUITY DIAGNOSTIC - FLUX] config3
+    Boundary 0 (R=0.30): ✅ PASS
+      Common Height: 0.70
+      RMS Vel Diff : 1.0372e+00
+      Rel Flux Err : 0.00%
+      Step Check (Left (Reg 0)): ✅
+      Avg Leak Vel : -5.9002e-02
+      Step Height  : 0.2000
+    Boundary 1 (R=0.50): ✅ PASS
+      Common Height: 0.80
+      RMS Vel Diff : 3.0691e-01
+      Rel Flux Err : 0.00%
+      Step Check (Left (Reg 1)): ✅
+      Avg Leak Vel : -3.2938e-02
+      Step Height  : 0.1000
+    Boundary 2 (R=1.00): ✅ PASS
+      Common Height: 0.80
+      RMS Vel Diff : 8.8568e-02
+      Rel Flux Err : 0.00%
+      Step Check (Right (Reg 3)): ✅
+      Avg Leak Vel : -2.2771e-03
+      Step Height  : 0.6000
+    Boundary 3 (R=1.20): ✅ PASS
+      Common Height: 0.50
+      RMS Vel Diff : 1.3362e-02
+      Rel Flux Err : 0.00%
+      Step Check (Left (Reg 3)): ✅
+      Avg Leak Vel : -7.4906e-04
+      Step Height  : 0.3000
+    Boundary 4 (R=1.60): ✅ PASS
+      Common Height: 0.50
+      RMS Vel Diff : 3.9482e-02
+      Rel Flux Err : 0.00%
+
+  [CONTINUITY DIAGNOSTIC - POTENTIAL] config3
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.10424099+1.02163926e-02j -0.00530177-1.01874886e-04j
+  0.00376538-4.42745998e-06j] ...
+R2 coefficients (Cs[NMK:]): [-0.17924099-1.02163926e-02j -0.03344102+4.09287308e-08j
+  0.01443681-6.27625588e-08j] ...
+[DEBUG] Region 1 mask count: 0
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.08182092+1.16246992e-02j  0.00186566-2.74952994e-04j
+ -0.00064689+4.97757520e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.16363911-1.16246992e-02j -0.01673802+4.41516625e-07j
+  0.00520099-4.65967662e-07j] ...
+[DEBUG] Region 2 mask count: 0
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.02399093+8.72055745e-03j -0.00076618-5.27279376e-04j
+  0.00072391-5.17353588e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.07693211-8.72055745e-03j  0.00412443-9.54125088e-05j
+ -0.00328406+5.82005143e-05j] ...
+[DEBUG] Region 3 mask count: 0
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.0205036 +0.00908332j  0.00165125-0.00105635j -0.00050738+0.00029925j] ...
+R2 coefficients (Cs[NMK:]): [-0.08478932-9.08331560e-03j  0.00062173-3.46263894e-05j
+ -0.00098932+1.88561499e-05j] ...
+[DEBUG] Region 4 mask count: 0
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.10424099+1.02163926e-02j -0.00530177-1.01874886e-04j
+  0.00376538-4.42745998e-06j] ...
+R2 coefficients (Cs[NMK:]): [-0.17924099-1.02163926e-02j -0.03344102+4.09287308e-08j
+  0.01443681-6.27625588e-08j] ...
+[DEBUG] Region 1 mask count: 50
+Region 1 min/max R: 0.3001/0.3001
+R1n_vals max: 7.8590e-01
+R2n_vals max: 9.9960e-01
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.08182092+1.16246992e-02j  0.00186566-2.74952994e-04j
+ -0.00064689+4.97757520e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.16363911-1.16246992e-02j -0.01673802+4.41516625e-07j
+  0.00520099-4.65967662e-07j] ...
+[DEBUG] Region 2 mask count: 0
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.02399093+8.72055745e-03j -0.00076618-5.27279376e-04j
+  0.00072391-5.17353588e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.07693211-8.72055745e-03j  0.00412443-9.54125088e-05j
+ -0.00328406+5.82005143e-05j] ...
+[DEBUG] Region 3 mask count: 0
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.0205036 +0.00908332j  0.00165125-0.00105635j -0.00050738+0.00029925j] ...
+R2 coefficients (Cs[NMK:]): [-0.08478932-9.08331560e-03j  0.00062173-3.46263894e-05j
+ -0.00098932+1.88561499e-05j] ...
+[DEBUG] Region 4 mask count: 0
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.10424099+1.02163926e-02j -0.00530177-1.01874886e-04j
+  0.00376538-4.42745998e-06j] ...
+R2 coefficients (Cs[NMK:]): [-0.17924099-1.02163926e-02j -0.03344102+4.09287308e-08j
+  0.01443681-6.27625588e-08j] ...
+[DEBUG] Region 1 mask count: 50
+Region 1 min/max R: 0.4999/0.4999
+R1n_vals max: 9.9990e-01
+R2n_vals max: 4.7572e-01
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.08182092+1.16246992e-02j  0.00186566-2.74952994e-04j
+ -0.00064689+4.97757520e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.16363911-1.16246992e-02j -0.01673802+4.41516625e-07j
+  0.00520099-4.65967662e-07j] ...
+[DEBUG] Region 2 mask count: 0
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.02399093+8.72055745e-03j -0.00076618-5.27279376e-04j
+  0.00072391-5.17353588e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.07693211-8.72055745e-03j  0.00412443-9.54125088e-05j
+ -0.00328406+5.82005143e-05j] ...
+[DEBUG] Region 3 mask count: 0
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.0205036 +0.00908332j  0.00165125-0.00105635j -0.00050738+0.00029925j] ...
+R2 coefficients (Cs[NMK:]): [-0.08478932-9.08331560e-03j  0.00062173-3.46263894e-05j
+ -0.00098932+1.88561499e-05j] ...
+[DEBUG] Region 4 mask count: 0
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.10424099+1.02163926e-02j -0.00530177-1.01874886e-04j
+  0.00376538-4.42745998e-06j] ...
+R2 coefficients (Cs[NMK:]): [-0.17924099-1.02163926e-02j -0.03344102+4.09287308e-08j
+  0.01443681-6.27625588e-08j] ...
+[DEBUG] Region 1 mask count: 0
+
+[DEBUG] Region 2 coefficients:
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+ -0.00064689+4.97757520e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.16363911-1.16246992e-02j -0.01673802+4.41516625e-07j
+  0.00520099-4.65967662e-07j] ...
+[DEBUG] Region 2 mask count: 50
+Region 2 min/max R: 0.5001/0.5001
+R1n_vals max: 6.5353e-01
+R2n_vals max: 9.9963e-01
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.02399093+8.72055745e-03j -0.00076618-5.27279376e-04j
+  0.00072391-5.17353588e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.07693211-8.72055745e-03j  0.00412443-9.54125088e-05j
+ -0.00328406+5.82005143e-05j] ...
+[DEBUG] Region 3 mask count: 0
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.0205036 +0.00908332j  0.00165125-0.00105635j -0.00050738+0.00029925j] ...
+R2 coefficients (Cs[NMK:]): [-0.08478932-9.08331560e-03j  0.00062173-3.46263894e-05j
+ -0.00098932+1.88561499e-05j] ...
+[DEBUG] Region 4 mask count: 0
+
+[DEBUG] Region 1 coefficients:
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+  0.00376538-4.42745998e-06j] ...
+R2 coefficients (Cs[NMK:]): [-0.17924099-1.02163926e-02j -0.03344102+4.09287308e-08j
+  0.01443681-6.27625588e-08j] ...
+[DEBUG] Region 1 mask count: 0
+
+[DEBUG] Region 2 coefficients:
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+ -0.00064689+4.97757520e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.16363911-1.16246992e-02j -0.01673802+4.41516625e-07j
+  0.00520099-4.65967662e-07j] ...
+[DEBUG] Region 2 mask count: 50
+Region 2 min/max R: 0.9999/0.9999
+R1n_vals max: 9.9995e-01
+R2n_vals max: 3.4652e-01
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.02399093+8.72055745e-03j -0.00076618-5.27279376e-04j
+  0.00072391-5.17353588e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.07693211-8.72055745e-03j  0.00412443-9.54125088e-05j
+ -0.00328406+5.82005143e-05j] ...
+[DEBUG] Region 3 mask count: 0
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.0205036 +0.00908332j  0.00165125-0.00105635j -0.00050738+0.00029925j] ...
+R2 coefficients (Cs[NMK:]): [-0.08478932-9.08331560e-03j  0.00062173-3.46263894e-05j
+ -0.00098932+1.88561499e-05j] ...
+[DEBUG] Region 4 mask count: 0
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.10424099+1.02163926e-02j -0.00530177-1.01874886e-04j
+  0.00376538-4.42745998e-06j] ...
+R2 coefficients (Cs[NMK:]): [-0.17924099-1.02163926e-02j -0.03344102+4.09287308e-08j
+  0.01443681-6.27625588e-08j] ...
+[DEBUG] Region 1 mask count: 0
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.08182092+1.16246992e-02j  0.00186566-2.74952994e-04j
+ -0.00064689+4.97757520e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.16363911-1.16246992e-02j -0.01673802+4.41516625e-07j
+  0.00520099-4.65967662e-07j] ...
+[DEBUG] Region 2 mask count: 0
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.02399093+8.72055745e-03j -0.00076618-5.27279376e-04j
+  0.00072391-5.17353588e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.07693211-8.72055745e-03j  0.00412443-9.54125088e-05j
+ -0.00328406+5.82005143e-05j] ...
+[DEBUG] Region 3 mask count: 50
+Region 3 min/max R: 1.0001/1.0001
+R1n_vals max: 9.0889e-01
+R2n_vals max: 9.9977e-01
+🚨 Expansion region 3 has nonzero R1 coeffs at indices [ 0  1  2  3  4  5  7  8  9 10 11 13 14 15 16 20]: [ 2.39909300e-02+8.72055745e-03j -7.66182947e-04-5.27279376e-04j
+  7.23907755e-04-5.17353588e-05j -4.08699329e-04+5.19387597e-05j
+  1.97003360e-04-2.54510600e-05j -6.66381962e-05+8.28264610e-06j
+  4.44611295e-05-4.33537942e-06j -5.42973434e-05+5.12075258e-06j
+  4.70719037e-05-4.28019260e-06j -3.07344078e-05+2.69532965e-06j
+  1.18742368e-05-9.80126012e-07j -1.54517875e-05+1.36537641e-06j
+  1.97119947e-05-1.69442917e-06j -1.79019524e-05+1.50913836e-06j
+  1.18243114e-05-9.74881676e-07j -1.06700382e-05+8.90451778e-07j]
+[DEBUG] Expansion region 3 phiH stats: min=0.021663857492631958, max=0.02346278815026505
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.0205036 +0.00908332j  0.00165125-0.00105635j -0.00050738+0.00029925j] ...
+R2 coefficients (Cs[NMK:]): [-0.08478932-9.08331560e-03j  0.00062173-3.46263894e-05j
+ -0.00098932+1.88561499e-05j] ...
+[DEBUG] Region 4 mask count: 0
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.10424099+1.02163926e-02j -0.00530177-1.01874886e-04j
+  0.00376538-4.42745998e-06j] ...
+R2 coefficients (Cs[NMK:]): [-0.17924099-1.02163926e-02j -0.03344102+4.09287308e-08j
+  0.01443681-6.27625588e-08j] ...
+[DEBUG] Region 1 mask count: 0
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.08182092+1.16246992e-02j  0.00186566-2.74952994e-04j
+ -0.00064689+4.97757520e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.16363911-1.16246992e-02j -0.01673802+4.41516625e-07j
+  0.00520099-4.65967662e-07j] ...
+[DEBUG] Region 2 mask count: 0
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.02399093+8.72055745e-03j -0.00076618-5.27279376e-04j
+  0.00072391-5.17353588e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.07693211-8.72055745e-03j  0.00412443-9.54125088e-05j
+ -0.00328406+5.82005143e-05j] ...
+[DEBUG] Region 3 mask count: 50
+Region 3 min/max R: 1.1999/1.1999
+R1n_vals max: 9.9996e-01
+R2n_vals max: 6.3598e-01
+🚨 Expansion region 3 has nonzero R1 coeffs at indices [ 0  1  2  3  4  5  7  8  9 10 11 13 14 15 16 20]: [ 2.39909300e-02+8.72055745e-03j -7.66182947e-04-5.27279376e-04j
+  7.23907755e-04-5.17353588e-05j -4.08699329e-04+5.19387597e-05j
+  1.97003360e-04-2.54510600e-05j -6.66381962e-05+8.28264610e-06j
+  4.44611295e-05-4.33537942e-06j -5.42973434e-05+5.12075258e-06j
+  4.70719037e-05-4.28019260e-06j -3.07344078e-05+2.69532965e-06j
+  1.18742368e-05-9.80126012e-07j -1.54517875e-05+1.36537641e-06j
+  1.97119947e-05-1.69442917e-06j -1.79019524e-05+1.50913836e-06j
+  1.18243114e-05-9.74881676e-07j -1.06700382e-05+8.90451778e-07j]
+[DEBUG] Expansion region 3 phiH stats: min=0.025232772042699015, max=0.026844522646784053
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.0205036 +0.00908332j  0.00165125-0.00105635j -0.00050738+0.00029925j] ...
+R2 coefficients (Cs[NMK:]): [-0.08478932-9.08331560e-03j  0.00062173-3.46263894e-05j
+ -0.00098932+1.88561499e-05j] ...
+[DEBUG] Region 4 mask count: 0
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.10424099+1.02163926e-02j -0.00530177-1.01874886e-04j
+  0.00376538-4.42745998e-06j] ...
+R2 coefficients (Cs[NMK:]): [-0.17924099-1.02163926e-02j -0.03344102+4.09287308e-08j
+  0.01443681-6.27625588e-08j] ...
+[DEBUG] Region 1 mask count: 0
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.08182092+1.16246992e-02j  0.00186566-2.74952994e-04j
+ -0.00064689+4.97757520e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.16363911-1.16246992e-02j -0.01673802+4.41516625e-07j
+  0.00520099-4.65967662e-07j] ...
+[DEBUG] Region 2 mask count: 0
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.02399093+8.72055745e-03j -0.00076618-5.27279376e-04j
+  0.00072391-5.17353588e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.07693211-8.72055745e-03j  0.00412443-9.54125088e-05j
+ -0.00328406+5.82005143e-05j] ...
+[DEBUG] Region 3 mask count: 0
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.0205036 +0.00908332j  0.00165125-0.00105635j -0.00050738+0.00029925j] ...
+R2 coefficients (Cs[NMK:]): [-0.08478932-9.08331560e-03j  0.00062173-3.46263894e-05j
+ -0.00098932+1.88561499e-05j] ...
+[DEBUG] Region 4 mask count: 50
+Region 4 min/max R: 1.2001/1.2001
+R1n_vals max: 8.5620e-01
+R2n_vals max: 9.9974e-01
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.10424099+1.02163926e-02j -0.00530177-1.01874886e-04j
+  0.00376538-4.42745998e-06j] ...
+R2 coefficients (Cs[NMK:]): [-0.17924099-1.02163926e-02j -0.03344102+4.09287308e-08j
+  0.01443681-6.27625588e-08j] ...
+[DEBUG] Region 1 mask count: 0
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.08182092+1.16246992e-02j  0.00186566-2.74952994e-04j
+ -0.00064689+4.97757520e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.16363911-1.16246992e-02j -0.01673802+4.41516625e-07j
+  0.00520099-4.65967662e-07j] ...
+[DEBUG] Region 2 mask count: 0
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.02399093+8.72055745e-03j -0.00076618-5.27279376e-04j
+  0.00072391-5.17353588e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.07693211-8.72055745e-03j  0.00412443-9.54125088e-05j
+ -0.00328406+5.82005143e-05j] ...
+[DEBUG] Region 3 mask count: 0
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.0205036 +0.00908332j  0.00165125-0.00105635j -0.00050738+0.00029925j] ...
+R2 coefficients (Cs[NMK:]): [-0.08478932-9.08331560e-03j  0.00062173-3.46263894e-05j
+ -0.00098932+1.88561499e-05j] ...
+[DEBUG] Region 4 mask count: 50
+Region 4 min/max R: 1.5999/1.5999
+R1n_vals max: 9.9997e-01
+R2n_vals max: 3.5624e-01
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.10424099+1.02163926e-02j -0.00530177-1.01874886e-04j
+  0.00376538-4.42745998e-06j] ...
+R2 coefficients (Cs[NMK:]): [-0.17924099-1.02163926e-02j -0.03344102+4.09287308e-08j
+  0.01443681-6.27625588e-08j] ...
+[DEBUG] Region 1 mask count: 0
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.08182092+1.16246992e-02j  0.00186566-2.74952994e-04j
+ -0.00064689+4.97757520e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.16363911-1.16246992e-02j -0.01673802+4.41516625e-07j
+  0.00520099-4.65967662e-07j] ...
+[DEBUG] Region 2 mask count: 0
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.02399093+8.72055745e-03j -0.00076618-5.27279376e-04j
+  0.00072391-5.17353588e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.07693211-8.72055745e-03j  0.00412443-9.54125088e-05j
+ -0.00328406+5.82005143e-05j] ...
+[DEBUG] Region 3 mask count: 0
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.0205036 +0.00908332j  0.00165125-0.00105635j -0.00050738+0.00029925j] ...
+R2 coefficients (Cs[NMK:]): [-0.08478932-9.08331560e-03j  0.00062173-3.46263894e-05j
+ -0.00098932+1.88561499e-05j] ...
+[DEBUG] Region 4 mask count: 0
+
+  [MATRIX BALANCE AUDIT - 6️⃣ KILLER CHECK] config3
+
+[POTENTIAL MATRIX AUDIT] Boundary 0 (CONTRACTION (Step Up))
+  Depth L vs R : 1.4000 vs 1.2000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 0 (CONTRACTION (Step Up))
+  Depth L vs R : 1.4000 vs 1.2000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+    Boundary 0 (Project on RIGHT):
+      Matrix Imbalance: 1.3893e-17
+      ✅ PASS
+
+[POTENTIAL MATRIX AUDIT] Boundary 1 (CONTRACTION (Step Up))
+  Depth L vs R : 1.2000 vs 1.1000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 1 (CONTRACTION (Step Up))
+  Depth L vs R : 1.2000 vs 1.1000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+    Boundary 1 (Project on RIGHT):
+      Matrix Imbalance: 7.8274e-17
+      ✅ PASS
+
+[POTENTIAL MATRIX AUDIT] Boundary 2 (EXPANSION (Step Down))
+  Depth L vs R : 1.1000 vs 1.7000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 2 (EXPANSION (Step Down))
+  Depth L vs R : 1.1000 vs 1.7000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+    Boundary 2 (Project on LEFT):
+      Matrix Imbalance: 1.5505e-17
+      ✅ PASS
+
+[POTENTIAL MATRIX AUDIT] Boundary 3 (CONTRACTION (Step Up))
+  Depth L vs R : 1.7000 vs 1.4000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 3 (CONTRACTION (Step Up))
+  Depth L vs R : 1.7000 vs 1.4000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+    Boundary 3 (Project on RIGHT):
+      Matrix Imbalance: 7.1524e-18
+      ✅ PASS
+    Boundary 4: Skipping (Exterior Boundary handled by different functions)
+
+    [COEFFICIENT DECAY CHECK] config3
+      Reg 0 (Inner): Head(avg3)=1.09e-01 | Tail(avg3)=1.10e-04 | Ratio=1.01e-03
+      Reg 1 (Annulus):
+        R1: Head=3.79e-02 | Tail=1.70e-05
+        R2: Head=7.58e-02 | Tail=2.18e-04
+      Reg 2 (Annulus):
+        R1: Head=2.84e-02 | Tail=1.00e-05
+        R2: Head=6.20e-02 | Tail=5.79e-05
+      Reg 3 (Annulus):
+        R1: Head=9.06e-03 | Tail=3.34e-06
+        R2: Head=2.83e-02 | Tail=1.26e-05
+      Reg 4 (Annulus):
+        R1: Head=8.32e-03 | Tail=7.03e-06
+        R2: Head=2.90e-02 | Tail=2.19e-06
+      Reg 5 (Outer): Head(avg3)=6.25e-03 | Tail(avg3)=7.13e-06 | Ratio=1.14e-03
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.10424099+1.02163926e-02j -0.00530177-1.01874886e-04j
+  0.00376538-4.42745998e-06j] ...
+R2 coefficients (Cs[NMK:]): [-0.17924099-1.02163926e-02j -0.03344102+4.09287308e-08j
+  0.01443681-6.27625588e-08j] ...
+[DEBUG] Region 1 mask count: 150
+Region 1 min/max R: 0.326530612244898/0.4571428571428572
+R1n_vals max: 9.5519e-01
+R2n_vals max: 9.0024e-01
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.08182092+1.16246992e-02j  0.00186566-2.74952994e-04j
+ -0.00064689+4.97757520e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.16363911-1.16246992e-02j -0.01673802+4.41516625e-07j
+  0.00520099-4.65967662e-07j] ...
+[DEBUG] Region 2 mask count: 400
+Region 2 min/max R: 0.5224489795918368/0.979591836734694
+R1n_vals max: 9.8969e-01
+R2n_vals max: 9.1974e-01
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.02399093+8.72055745e-03j -0.00076618-5.27279376e-04j
+  0.00072391-5.17353588e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.07693211-8.72055745e-03j  0.00412443-9.54125088e-05j
+ -0.00328406+5.82005143e-05j] ...
+[DEBUG] Region 3 mask count: 150
+Region 3 min/max R: 1.0448979591836736/1.1755102040816328
+R1n_vals max: 9.8969e-01
+R2n_vals max: 9.0220e-01
+
+=== MODE CONTRIBUTIONS @ BAD POINT ===
+n=0:
+  R1 contrib = +2.3744e-02
+  R2 contrib = -6.2200e-03
+n=1:
+  R1 contrib = +1.0466e-03
+  R2 contrib = -3.9102e-03
+n=2:
+  R1 contrib = +9.3876e-04
+  R2 contrib = -2.2321e-03
+n=3:
+  R1 contrib = +5.0174e-04
+  R2 contrib = -4.2807e-05
+n=4:
+  R1 contrib = +2.2815e-04
+  R2 contrib = +3.0750e-04
+
+Partial sums:
+  Σ R1 = +2.645879e-02
+  Σ R2 = -1.209762e-02
+  Σ total = +1.436117e-02
+
+=== RECONSTRUCTION DECISION ===
+Region 3
+is_expansion = True
+Used contribution (R1 only) = +2.645879e-02
+Alternative contribution (R2 only) = -1.209762e-02
+🚨 Expansion region 3 has nonzero R1 coeffs at indices [ 0  1  2  3  4  5  7  8  9 10 11 13 14 15 16 20]: [ 2.39909300e-02+8.72055745e-03j -7.66182947e-04-5.27279376e-04j
+  7.23907755e-04-5.17353588e-05j -4.08699329e-04+5.19387597e-05j
+  1.97003360e-04-2.54510600e-05j -6.66381962e-05+8.28264610e-06j
+  4.44611295e-05-4.33537942e-06j -5.42973434e-05+5.12075258e-06j
+  4.70719037e-05-4.28019260e-06j -3.07344078e-05+2.69532965e-06j
+  1.18742368e-05-9.80126012e-07j -1.54517875e-05+1.36537641e-06j
+  1.97119947e-05-1.69442917e-06j -1.79019524e-05+1.50913836e-06j
+  1.18243114e-05-9.74881676e-07j -1.06700382e-05+8.90451778e-07j]
+[DEBUG] Expansion region 3 phiH stats: min=0.021770001482053235, max=0.0264062684909321
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.0205036 +0.00908332j  0.00165125-0.00105635j -0.00050738+0.00029925j] ...
+R2 coefficients (Cs[NMK:]): [-0.08478932-9.08331560e-03j  0.00062173-3.46263894e-05j
+ -0.00098932+1.88561499e-05j] ...
+[DEBUG] Region 4 mask count: 300
+Region 4 min/max R: 1.2408163265306125/1.5673469387755103
+R1n_vals max: 9.8969e-01
+R2n_vals max: 8.9838e-01
+    > Body 0 Active ([True, False, False, False, False]):
+      OpenFlash Max |phi|: 4.511758e-01
+      OpenFlash Mean |phi|: 2.665020e-02
+
+[POTENTIAL MATRIX AUDIT] Boundary 0 (CONTRACTION (Step Up))
+  Depth L vs R : 1.4000 vs 1.2000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 0 (CONTRACTION (Step Up))
+  Depth L vs R : 1.4000 vs 1.2000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 1 (CONTRACTION (Step Up))
+  Depth L vs R : 1.2000 vs 1.1000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 1 (CONTRACTION (Step Up))
+  Depth L vs R : 1.2000 vs 1.1000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 2 (EXPANSION (Step Down))
+  Depth L vs R : 1.1000 vs 1.7000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 2 (EXPANSION (Step Down))
+  Depth L vs R : 1.1000 vs 1.7000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 3 (CONTRACTION (Step Up))
+  Depth L vs R : 1.7000 vs 1.4000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 3 (CONTRACTION (Step Up))
+  Depth L vs R : 1.7000 vs 1.4000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 4 (EXPANSION (Step Down))
+  Depth L vs R : 1.4000 vs 1.9000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 4 (EXPANSION (Step Down))
+  Depth L vs R : 1.4000 vs 1.9000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+      [Matrix Info] Size: (400, 400) | Condition Num: 1.3024e+03
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [-0.07715212+1.81926991e-02j  0.10726661-1.81412287e-04j
+ -0.0149062 -7.88413780e-06j] ...
+R2 coefficients (Cs[NMK:]): [ 0.15215212-1.81926991e-02j  0.03332301+7.28832683e-08j
+ -0.01425575-1.11763554e-07j] ...
+[DEBUG] Region 1 mask count: 150
+Region 1 min/max R: 0.326530612244898/0.4571428571428572
+R1n_vals max: 9.5519e-01
+R2n_vals max: 9.0024e-01
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.1452288 +2.07005214e-02j  0.00335762-4.89618721e-04j
+ -0.00117024+8.86374784e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.29068335-2.07005214e-02j -0.03291256+7.86224591e-07j
+  0.01237595-8.29765435e-07j] ...
+[DEBUG] Region 2 mask count: 400
+Region 2 min/max R: 0.5224489795918368/0.979591836734694
+R1n_vals max: 9.8969e-01
+R2n_vals max: 9.1974e-01
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.0426734 +1.55290114e-02j -0.00138826-9.38945417e-04j
+  0.00130212-9.21270208e-05j] ...
+R2 coefficients (Cs[NMK:]): [-0.13679105-0.01552901j  0.00694515-0.0001699j  -0.00607666+0.00010364j] ...
+[DEBUG] Region 3 mask count: 150
+Region 3 min/max R: 1.0448979591836736/1.1755102040816328
+R1n_vals max: 9.8969e-01
+R2n_vals max: 9.0220e-01
+
+=== MODE CONTRIBUTIONS @ BAD POINT ===
+n=0:
+  R1 contrib = +4.2233e-02
+  R2 contrib = -1.1060e-02
+n=1:
+  R1 contrib = +1.8963e-03
+  R2 contrib = -6.5844e-03
+n=2:
+  R1 contrib = +1.6886e-03
+  R2 contrib = -4.1301e-03
+n=3:
+  R1 contrib = +9.0272e-04
+  R2 contrib = -1.1403e-04
+n=4:
+  R1 contrib = +4.1075e-04
+  R2 contrib = +5.6966e-04
+
+Partial sums:
+  Σ R1 = +4.713176e-02
+  Σ R2 = -2.131859e-02
+  Σ total = +2.581317e-02
+
+=== RECONSTRUCTION DECISION ===
+Region 3
+is_expansion = True
+Used contribution (R1 only) = +4.713176e-02
+Alternative contribution (R2 only) = -2.131859e-02
+🚨 Expansion region 3 has nonzero R1 coeffs at indices [ 0  1  2  3  4  5  6  7  8  9 10 11 13 14 15 16 19 20 21 22 25 26 27]: [ 4.26733994e-02+1.55290114e-02j -1.38826337e-03-9.38945417e-04j
+  1.30211652e-03-9.21270208e-05j -7.35317091e-04+9.24892242e-05j
+  3.54677480e-04-4.53216212e-05j -1.20059186e-04+1.47492069e-05j
+ -1.46512106e-05+1.04416467e-06j  8.01524179e-05-7.72016663e-06j
+ -9.79240548e-05+9.11870897e-06j  8.49196559e-05-7.62189347e-06j
+ -5.54631405e-05+4.79967079e-06j  2.14402220e-05-1.74534577e-06j
+ -2.78783705e-05+2.43137505e-06j  3.55743831e-05-3.01733119e-06j
+ -3.23138998e-05+2.68737716e-06j  2.13481515e-05-1.73600699e-06j
+  1.57408403e-05-1.32049168e-06j -1.92612131e-05+1.58565962e-06j
+  1.70556175e-05-1.38371245e-06j -1.06119108e-05+8.43553471e-07j
+ -1.07534899e-05+8.84437381e-07j  1.23897061e-05-1.00468885e-06j
+ -1.04525625e-05+8.37098613e-07j]
+[DEBUG] Expansion region 3 phiH stats: min=0.03870859202420018, max=0.04703685447819471
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.03640894+0.01617499j  0.00294035-0.00188107j -0.00090432+0.00053289j] ...
+R2 coefficients (Cs[NMK:]): [-0.15069465-1.61749880e-02j  0.00082029-6.16604615e-05j
+ -0.00179554+3.35778268e-05j] ...
+[DEBUG] Region 4 mask count: 300
+Region 4 min/max R: 1.2408163265306125/1.5673469387755103
+R1n_vals max: 9.8969e-01
+R2n_vals max: 8.9838e-01
+    > Body 1 Active ([False, True, False, False, False]):
+      OpenFlash Max |phi|: 2.894489e-01
+      OpenFlash Mean |phi|: 3.965724e-02
+
+[POTENTIAL MATRIX AUDIT] Boundary 0 (CONTRACTION (Step Up))
+  Depth L vs R : 1.4000 vs 1.2000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 0 (CONTRACTION (Step Up))
+  Depth L vs R : 1.4000 vs 1.2000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 1 (CONTRACTION (Step Up))
+  Depth L vs R : 1.2000 vs 1.1000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 1 (CONTRACTION (Step Up))
+  Depth L vs R : 1.2000 vs 1.1000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 2 (EXPANSION (Step Down))
+  Depth L vs R : 1.1000 vs 1.7000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 2 (EXPANSION (Step Down))
+  Depth L vs R : 1.1000 vs 1.7000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 3 (CONTRACTION (Step Up))
+  Depth L vs R : 1.7000 vs 1.4000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 3 (CONTRACTION (Step Up))
+  Depth L vs R : 1.7000 vs 1.4000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 4 (EXPANSION (Step Down))
+  Depth L vs R : 1.4000 vs 1.9000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 4 (EXPANSION (Step Down))
+  Depth L vs R : 1.4000 vs 1.9000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+      [Matrix Info] Size: (400, 400) | Condition Num: 1.3024e+03
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.40285847+8.71849431e-02j -0.07545204-8.69382812e-04j
+  0.01783992-3.77831845e-05j] ...
+R2 coefficients (Cs[NMK:]): [-4.02858466e-01-8.71849431e-02j  5.69523410e-05+3.49278772e-07j
+ -8.74519229e-05-5.35604918e-07j] ...
+[DEBUG] Region 1 mask count: 150
+Region 1 min/max R: 0.326530612244898/0.4571428571428572
+R1n_vals max: 9.5519e-01
+R2n_vals max: 9.0024e-01
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.24822097+0.09920319j  0.11290228-0.0023464j  -0.02894919+0.00042478j] ...
+R2 coefficients (Cs[NMK:]): [-0.02094824-9.92031900e-02j  0.04902183+3.76782719e-06j
+ -0.01691628-3.97648815e-06j] ...
+[DEBUG] Region 2 mask count: 400
+Region 2 min/max R: 0.5224489795918368/0.979591836734694
+R1n_vals max: 9.8969e-01
+R2n_vals max: 9.1974e-01
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.20213192+0.07441974j -0.00856456-0.00449971j  0.0073183 -0.0004415j ] ...
+R2 coefficients (Cs[NMK:]): [-0.64330839-0.07441974j  0.00804045-0.00081423j -0.03675516+0.00049667j] ...
+[DEBUG] Region 3 mask count: 150
+Region 3 min/max R: 1.0448979591836736/1.1755102040816328
+R1n_vals max: 9.8969e-01
+R2n_vals max: 9.0220e-01
+
+=== MODE CONTRIBUTIONS @ BAD POINT ===
+n=0:
+  R1 contrib = +2.0005e-01
+  R2 contrib = -5.2012e-02
+n=1:
+  R1 contrib = +1.1699e-02
+  R2 contrib = -7.6228e-03
+n=2:
+  R1 contrib = +9.4903e-03
+  R2 contrib = -2.4982e-02
+n=3:
+  R1 contrib = +5.1055e-03
+  R2 contrib = -5.5753e-03
+n=4:
+  R1 contrib = +2.3516e-03
+  R2 contrib = +2.8957e-03
+
+Partial sums:
+  Σ R1 = +2.286940e-01
+  Σ R2 = -8.729610e-02
+  Σ total = +1.413979e-01
+
+=== RECONSTRUCTION DECISION ===
+Region 3
+is_expansion = True
+Used contribution (R1 only) = +2.286940e-01
+Alternative contribution (R2 only) = -8.729610e-02
+🚨 Expansion region 3 has nonzero R1 coeffs at indices [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
+ 24 25 26 27 28 30 31 32 33 34 36 37 38 39]: [ 2.02131921e-01+7.44197423e-02j -8.56455565e-03-4.49971180e-03j
+  7.31829992e-03-4.41500683e-04j -4.15874592e-03+4.43236471e-04j
+  2.03057539e-03-2.17194983e-04j -6.96701202e-04+7.06826819e-05j
+ -8.28107042e-05+5.00395444e-06j  4.68652488e-04-3.69973848e-05j
+ -5.77052729e-04+4.36996247e-05j  5.03402454e-04-3.65264299e-05j
+ -3.30737533e-04+2.30014811e-05j  1.29289468e-04-8.36422736e-06j
+  4.73150780e-05-3.85151915e-06j -1.65431054e-04+1.16518882e-05j
+  2.12263121e-04-1.44599681e-05j -1.93545203e-04+1.28787281e-05j
+  1.28437305e-04-8.31947310e-06j -4.27708061e-05+2.55086399e-06j
+ -3.78957827e-05+2.74545017e-06j  9.40057354e-05-6.32819744e-06j
+ -1.15492272e-04+7.59896283e-06j  1.02581910e-04-6.63117059e-06j
+ -6.41007773e-05+4.04256460e-06j  1.40043209e-05-7.60616985e-07j
+  3.28301512e-05-2.25136520e-06j -6.44592277e-05+4.23849273e-06j
+  7.44967787e-05-4.81477429e-06j -6.30184162e-05+4.01163096e-06j
+  3.57302073e-05-2.21621576e-06j -2.88810381e-05+1.92252104e-06j
+  4.84558611e-05-3.13939895e-06j -5.28416028e-05+3.37696951e-06j
+  4.24397187e-05-2.67559744e-06j -2.15442336e-05+1.32026039e-06j
+  2.48050640e-05-1.62340412e-06j -3.78339642e-05+2.42895756e-06j
+  3.97062661e-05-2.52232992e-06j -3.11122863e-05+1.95616208e-06j]
+[DEBUG] Expansion region 3 phiH stats: min=0.18214356475259644, max=0.22811808594733754
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.16801139+0.07751546j  0.01409615-0.00901468j -0.00439218+0.00255379j] ...
+R2 coefficients (Cs[NMK:]): [-0.70372567-0.07751546j -0.01329951-0.0002955j  -0.00712911+0.00016092j] ...
+[DEBUG] Region 4 mask count: 300
+Region 4 min/max R: 1.2408163265306125/1.5673469387755103
+R1n_vals max: 9.8969e-01
+R2n_vals max: 8.9838e-01
+    > Body 2 Active ([False, False, True, False, False]):
+      OpenFlash Max |phi|: 4.976841e-01
+      OpenFlash Mean |phi|: 1.389281e-01
+
+[POTENTIAL MATRIX AUDIT] Boundary 0 (CONTRACTION (Step Up))
+  Depth L vs R : 1.4000 vs 1.2000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 0 (CONTRACTION (Step Up))
+  Depth L vs R : 1.4000 vs 1.2000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 1 (CONTRACTION (Step Up))
+  Depth L vs R : 1.2000 vs 1.1000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 1 (CONTRACTION (Step Up))
+  Depth L vs R : 1.2000 vs 1.1000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 2 (EXPANSION (Step Down))
+  Depth L vs R : 1.1000 vs 1.7000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 2 (EXPANSION (Step Down))
+  Depth L vs R : 1.1000 vs 1.7000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 3 (CONTRACTION (Step Up))
+  Depth L vs R : 1.7000 vs 1.4000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 3 (CONTRACTION (Step Up))
+  Depth L vs R : 1.7000 vs 1.4000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 4 (EXPANSION (Step Down))
+  Depth L vs R : 1.4000 vs 1.9000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 4 (EXPANSION (Step Down))
+  Depth L vs R : 1.4000 vs 1.9000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+      [Matrix Info] Size: (400, 400) | Condition Num: 1.3024e+03
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 8.64906776e-02+5.57974014e-02j -5.81790441e-03-5.56395406e-04j
+ -2.88602280e-05-2.41808211e-05j] ...
+R2 coefficients (Cs[NMK:]): [-8.64906776e-02-5.57974014e-02j  2.50950944e-06+2.23534560e-07j
+ -3.84859174e-06-3.42781236e-07j] ...
+[DEBUG] Region 1 mask count: 150
+Region 1 min/max R: 0.326530612244898/0.4571428571428572
+R1n_vals max: 9.5519e-01
+R2n_vals max: 9.0024e-01
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.09755681+0.06348895j -0.01559025-0.00150167j  0.00550604+0.00027185j] ...
+R2 coefficients (Cs[NMK:]): [-9.75568149e-02-6.34889468e-02j  2.74409939e-05+2.41136782e-06j
+ -2.89984171e-05-2.54490853e-06j] ...
+[DEBUG] Region 2 mask count: 400
+Region 2 min/max R: 0.5224489795918368/0.979591836734694
+R1n_vals max: 9.8969e-01
+R2n_vals max: 9.1974e-01
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [0.01401281+0.04762781j 0.1018845 -0.00287977j 0.00401804-0.00028256j] ...
+R2 coefficients (Cs[NMK:]): [0.57422248-0.04762781j 0.05015619-0.0005211j  0.00504306+0.00031787j] ...
+[DEBUG] Region 3 mask count: 150
+Region 3 min/max R: 1.0448979591836736/1.1755102040816328
+R1n_vals max: 9.8969e-01
+R2n_vals max: 9.0220e-01
+
+=== MODE CONTRIBUTIONS @ BAD POINT ===
+n=0:
+  R1 contrib = +1.3868e-02
+  R2 contrib = +4.6427e-02
+n=1:
+  R1 contrib = -1.3917e-01
+  R2 contrib = -4.7551e-02
+n=2:
+  R1 contrib = +5.2106e-03
+  R2 contrib = +3.4276e-03
+n=3:
+  R1 contrib = +1.6583e-02
+  R2 contrib = +3.3924e-03
+n=4:
+  R1 contrib = +1.3349e-02
+  R2 contrib = +7.6853e-04
+
+Partial sums:
+  Σ R1 = -9.015618e-02
+  Σ R2 = +6.464397e-03
+  Σ total = -8.369179e-02
+
+=== RECONSTRUCTION DECISION ===
+Region 3
+is_expansion = True
+Used contribution (R1 only) = -9.015618e-02
+Alternative contribution (R2 only) = +6.464397e-03
+🚨 Expansion region 3 has nonzero R1 coeffs at indices [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
+ 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39]: [ 1.40128136e-02+4.76278138e-02j  1.01884497e-01-2.87976589e-03j
+  4.01803634e-03-2.82555565e-04j -1.35078355e-02+2.83666450e-04j
+  1.15267241e-02-1.39002392e-04j -7.28716650e-03+4.52361364e-05j
+  3.13648726e-03+3.20247562e-06j  5.84118049e-05-2.36779179e-05j
+ -2.00134860e-03+2.79672775e-05j  2.72614622e-03-2.33765121e-05j
+ -2.47950189e-03+1.47206941e-05j  1.62368966e-03-5.35301321e-06j
+ -5.45057038e-04-2.46492975e-06j -4.24113069e-04+7.45707985e-06j
+  1.05984749e-03-9.25421999e-06j -1.27012770e-03+8.24224384e-06j
+  1.08751374e-03-5.32437097e-06j -6.36619017e-04+1.63252480e-06j
+  8.69201919e-05+1.75705781e-06j  3.96680342e-04-4.04997653e-06j
+ -6.94118200e-04+4.86325235e-06j  7.53296901e-04-4.24387600e-06j
+ -5.93039872e-04+2.58719673e-06j  2.87919173e-04-4.86786476e-07j
+  5.89159390e-05-1.44084888e-06j -3.46616490e-04+2.71258857e-06j
+  5.02988597e-04-3.08140241e-06j -5.00168155e-04+2.56739954e-06j
+  3.56835421e-04-1.41835363e-06j -1.27913209e-04+1.77930390e-08j
+ -1.14199872e-04+1.23039225e-06j  3.01042765e-04-2.00918068e-06j
+ -3.86232111e-04+2.16122322e-06j  3.55847851e-04-1.71235283e-06j
+ -2.29053101e-04+8.44952080e-07j  4.97083149e-05+1.66774001e-07j
+  1.27936383e-04-1.03896072e-06j -2.55081044e-04+1.55450603e-06j
+  3.01329729e-04-1.61426331e-06j -2.61492003e-04+1.25192214e-06j]
+[DEBUG] Expansion region 3 phiH stats: min=-0.13446978936454781, max=0.14987176324741533
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.09197674+0.04960904j  0.00913247-0.00576929j -0.00301771+0.0016344j ] ...
+R2 coefficients (Cs[NMK:]): [-0.40626246-0.04960904j -0.04339457-0.00018911j  0.01941254+0.00010298j] ...
+[DEBUG] Region 4 mask count: 300
+Region 4 min/max R: 1.2408163265306125/1.5673469387755103
+R1n_vals max: 9.8969e-01
+R2n_vals max: 8.9838e-01
+    > Body 3 Active ([False, False, False, True, False]):
+      OpenFlash Max |phi|: 5.746394e-01
+      OpenFlash Mean |phi|: 6.190733e-02
+
+[POTENTIAL MATRIX AUDIT] Boundary 0 (CONTRACTION (Step Up))
+  Depth L vs R : 1.4000 vs 1.2000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 0 (CONTRACTION (Step Up))
+  Depth L vs R : 1.4000 vs 1.2000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 1 (CONTRACTION (Step Up))
+  Depth L vs R : 1.2000 vs 1.1000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 1 (CONTRACTION (Step Up))
+  Depth L vs R : 1.2000 vs 1.1000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 2 (EXPANSION (Step Down))
+  Depth L vs R : 1.1000 vs 1.7000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 2 (EXPANSION (Step Down))
+  Depth L vs R : 1.1000 vs 1.7000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 3 (CONTRACTION (Step Up))
+  Depth L vs R : 1.7000 vs 1.4000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 3 (CONTRACTION (Step Up))
+  Depth L vs R : 1.7000 vs 1.4000
+  Block Side   : RIGHT (Diagonal)
+  Formulation  : CONTRACTION (Assumes R<L)
+
+[POTENTIAL MATRIX AUDIT] Boundary 4 (EXPANSION (Step Down))
+  Depth L vs R : 1.4000 vs 1.9000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+
+[POTENTIAL MATRIX AUDIT] Boundary 4 (EXPANSION (Step Down))
+  Depth L vs R : 1.4000 vs 1.9000
+  Block Side   : LEFT (Diagonal)
+  Formulation  : EXPANSION (Assumes L<R)
+      [Matrix Info] Size: (400, 400) | Condition Num: 1.3024e+03
+
+[DEBUG] Region 1 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.13569975+1.53112847e-01j -0.00859951-1.52679663e-03j
+ -0.00015372-6.63542434e-05j] ...
+R2 coefficients (Cs[NMK:]): [-1.35699746e-01-1.53112847e-01j  3.62152879e-06+6.13397970e-07j
+ -5.55380948e-06-9.40621061e-07j] ...
+[DEBUG] Region 1 mask count: 150
+Region 1 min/max R: 0.326530612244898/0.4571428571428572
+R1n_vals max: 9.5519e-01
+R2n_vals max: 9.0024e-01
+
+[DEBUG] Region 2 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.15316779+0.17421911j -0.02310061-0.00412071j  0.00682834+0.00074599j] ...
+R2 coefficients (Cs[NMK:]): [-1.53167787e-01-1.74219106e-01j  3.94154250e-05+6.61699975e-06j
+ -4.16337176e-05-6.98344689e-06j] ...
+[DEBUG] Region 2 mask count: 400
+Region 2 min/max R: 0.5224489795918368/0.979591836734694
+R1n_vals max: 9.8969e-01
+R2n_vals max: 9.1974e-01
+
+[DEBUG] Region 3 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.16162769+0.1306948j  -0.06468872-0.00790232j  0.01249465-0.00077536j] ...
+R2 coefficients (Cs[NMK:]): [-0.16162769-0.1306948j  -0.01578436-0.00142995j  0.01064959+0.00087225j] ...
+[DEBUG] Region 3 mask count: 150
+Region 3 min/max R: 1.0448979591836736/1.1755102040816328
+R1n_vals max: 9.8969e-01
+R2n_vals max: 9.0220e-01
+
+=== MODE CONTRIBUTIONS @ BAD POINT ===
+n=0:
+  R1 contrib = +1.5996e-01
+  R2 contrib = -1.3068e-02
+n=1:
+  R1 contrib = +8.8360e-02
+  R2 contrib = +1.4964e-02
+n=2:
+  R1 contrib = +1.6203e-02
+  R2 contrib = +7.2382e-03
+n=3:
+  R1 contrib = -3.7584e-03
+  R2 contrib = +1.2426e-03
+n=4:
+  R1 contrib = -7.7968e-03
+  R2 contrib = -7.9982e-04
+
+Partial sums:
+  Σ R1 = +2.529694e-01
+  Σ R2 = +9.577689e-03
+  Σ total = +2.625471e-01
+
+=== RECONSTRUCTION DECISION ===
+Region 3
+is_expansion = True
+Used contribution (R1 only) = +2.529694e-01
+Alternative contribution (R2 only) = +9.577689e-03
+🚨 Expansion region 3 has nonzero R1 coeffs at indices [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
+ 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39]: [ 1.61627695e-01+1.30694799e-01j -6.46887193e-02-7.90232417e-03j
+  1.24946527e-02-7.75356661e-04j  3.06140582e-03+7.78405026e-04j
+ -6.73244999e-03-3.81434465e-04j  5.76957994e-03+1.24131831e-04j
+ -3.29383939e-03+8.78786729e-06j  8.13019684e-04-6.49742341e-05j
+  9.81489996e-04+7.67446041e-05j -1.87499387e-03-6.41471506e-05j
+  1.94436763e-03+4.03948449e-05j -1.43049485e-03-1.46891266e-05j
+  6.36969913e-04-6.76397827e-06j  1.50628307e-04+2.04628656e-05j
+ -7.21653092e-04-2.53943720e-05j  9.69713024e-04+2.26174228e-05j
+ -8.94145674e-04-1.46105298e-05j  5.78128257e-04+4.47978783e-06j
+ -1.51888310e-04+4.82151706e-06j -2.48517928e-04-1.11134823e-05j
+  5.16807160e-04+1.33451808e-05j -5.98990905e-04-1.16455591e-05j
+  4.99356194e-04+7.09948935e-06j -2.70794653e-04-1.33578377e-06j
+  2.47637493e-04+7.44357528e-06j -3.90874461e-04-8.45563204e-06j
+  4.06998042e-04+7.04516416e-06j -3.05603760e-04-3.89208379e-06j
+  1.27634577e-04+4.88256225e-08j  6.91863138e-05+3.37630168e-06j
+ -2.27879652e-04-5.51336382e-06j  3.07721580e-04+5.93058154e-06j
+ -2.93795189e-04-4.69884278e-06j  1.98595494e-04+2.31862085e-06j
+ -5.60054969e-05+4.57642139e-07j -9.00253657e-05-2.85099717e-06j
+  1.98551880e-04+4.26569765e-06j -2.42739535e-04-4.42967673e-06j
+  2.16138829e-04+3.43538155e-06j]
+[DEBUG] Expansion region 3 phiH stats: min=0.08664395178380932, max=0.2639082199160567
+
+[DEBUG] Region 4 coefficients:
+R1 coefficients (Cs[:NMK]): [ 0.1714123 +0.13613145j  0.14100361-0.01583144j -0.03622977+0.00448492j] ...
+R2 coefficients (Cs[NMK:]): [ 0.85715913-0.13613145j  0.07554295-0.00051894j -0.02007036+0.0002826j ] ...
+[DEBUG] Region 4 mask count: 300
+Region 4 min/max R: 1.2408163265306125/1.5673469387755103
+R1n_vals max: 9.8969e-01
+R2n_vals max: 8.9838e-01
+    > Body 4 Active ([False, False, False, False, True]):
+      OpenFlash Max |phi|: 3.593327e-01
+      OpenFlash Mean |phi|: 1.393823e-01
+
+  [GEOMETRY TRANSITION ANALYSIS]
+    Global Water Depth (h): 1.9
+    Domain 0: R=[0.00, 0.30] | Depth=1.4000 | Center Start
+    Domain 1: R=[0.30, 0.50] | Depth=1.2000 | CONTRACTION (Step Up / Shallower Fluid)
+    Domain 2: R=[0.50, 1.00] | Depth=1.1000 | CONTRACTION (Step Up / Shallower Fluid)
+    Domain 3: R=[1.00, 1.20] | Depth=1.7000 | EXPANSION (Step Down / Deeper Fluid) <--- 🚨 POCKET REGION (Likely Failure Point)
+    Domain 4: R=[1.20, 1.60] | Depth=1.4000 | CONTRACTION (Step Up / Shallower Fluid)
+    Domain 5: R=[1.60, inf] | Depth=1.9000 | EXPANSION (Exit to Open Ocean)
+
+  [CONVERSION DEBUG]
+    Omega (w): 3.062791
+    Scaling Factor (1/w): 0.326500
+    Capytaine Raw Real [Min, Max]: [6.1873e-02, 1.2521e+00]
+    Capytaine Raw Imag [Min, Max]: [-1.9857e+00, 9.7727e-01]
+
+--- PHASE / CONVENTION DIAGNOSTIC ---
+  Transform 'None': Rel Error = 55.6198%
+  Transform 'Negated (-1)': Rel Error = 248.9557%
+  Transform 'Conjugate (*)': Rel Error = 147.5438%
+  Transform 'Rotated 90 (j)': Rel Error = 172.1528%
+  Transform 'Rotated -90 (-j)': Rel Error = 188.2443%
+  Transform 'Conjugate & Negated': Rel Error = 208.0945%
+  [DIAGNOSTIC] Best match is: 'None'
+
+  [MAGNITUDE CHECK] Max | |phi_of| - |phi_cap| |: 7.579661e-01
+
+  [LOCATOR] Top 3 Real Part Errors:
+    @ (R=1.176, Z=-0.233) -> Diff: 1.0227 (OF: 1.0777 vs CAP: 0.0550)
+      -> NEAR CORNER of Body 3 (a=1.2, d=0.2) dist=0.0408
+
+  [SERIES SUMMATION DIAGNOSTIC] at R=1.1755, Z=-0.2327
+    [REGION LOCATOR] r=1.1755 -> Region 3 selected
+    Region Bounds: [1.0000, 1.2000]
+    Point is in Region 3
+    Top 5 Contributing Terms:
+      Mode R1_0: 2.3744e-02+8.6307e-03j (Abs: 2.5264e-02)
+      Mode R2_0: -6.2200e-03-7.0507e-04j (Abs: 6.2599e-03)
+      Mode R2_1: -3.9102e-03+9.0456e-05j (Abs: 3.9112e-03)
+      Mode R2_2: -2.2321e-03+3.9557e-05j (Abs: 2.2324e-03)
+      Mode R1_1: 1.0466e-03+7.2023e-04j (Abs: 1.2704e-03)
+    Term Summation Check:
+      Total Sum: 0.0144+0.0086j
+    @ (R=1.176, Z=-0.271) -> Diff: 0.9272 (OF: 1.0384 vs CAP: 0.1112)
+      -> NEAR CORNER of Body 3 (a=1.2, d=0.2) dist=0.0755
+    @ (R=1.110, Z=-0.233) -> Diff: 0.8684 (OF: 1.0764 vs CAP: 0.2080)
+      -> NEAR CORNER of Body 3 (a=1.2, d=0.2) dist=0.0955
+[Debug CSV saved to: /Users/hopebest/Documents/semi-analytical-hydro/package/test_artifacts/config3_debug_data.csv]
+
+  [FINAL COMPARISON] config3
+    Omega: 3.0628
+    Max Abs OpenFlash (Real): 1.264401e+00
+    Max Abs Capytaine (Real): 6.483243e-01
+
+[Debug plot saved to: /Users/hopebest/Documents/semi-analytical-hydro/package/test_artifacts/config3_real_comparison.png]
+
+[Debug plot saved to: /Users/hopebest/Documents/semi-analytical-hydro/package/test_artifacts/config3_imag_comparison.png]
+F
+
+E           Failed: 
+E           Not equal to tolerance rtol=0.13, atol=0.01
+E           [config3] Real part mismatch
+E           Mismatched elements: 1772 / 2114 (83.8%)
+E           Max absolute difference among violations: 1.02269426
+E           Max relative difference among violations: 157.00640727
+E            ACTUAL: array([ 1.264401,  1.225748,  1.187316, ..., -0.110443, -0.110111,
+E                  -0.110001])
+E            DESIRED: array([ 0.648324,  0.616882,  0.593708, ..., -0.08108 , -0.080811,
+E                  -0.080726])
\ No newline at end of file
diff --git a/dev/MEEM_figs/.gitkeep b/dev/MEEM_figs/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/math-progress/slant-math.jpeg b/dev/math-progress/slant-math.jpeg
similarity index 100%
rename from math-progress/slant-math.jpeg
rename to dev/math-progress/slant-math.jpeg
diff --git a/math-progress/weird_region_math.pdf b/dev/math-progress/weird_region_math.pdf
similarity index 100%
rename from math-progress/weird_region_math.pdf
rename to dev/math-progress/weird_region_math.pdf
diff --git a/hydro/python/MEEM.ipynb b/dev/python/MEEM.ipynb
similarity index 100%
rename from hydro/python/MEEM.ipynb
rename to dev/python/MEEM.ipynb
diff --git a/hydro/python/Multi_MEEM_with_slant.ipynb b/dev/python/Multi_MEEM_with_slant.ipynb
similarity index 100%
rename from hydro/python/Multi_MEEM_with_slant.ipynb
rename to dev/python/Multi_MEEM_with_slant.ipynb
diff --git a/hydro/python/constants.py b/dev/python/constants.py
similarity index 100%
rename from hydro/python/constants.py
rename to dev/python/constants.py
diff --git a/dev/python/convergence-study/bicylinder_capytaine.ipynb b/dev/python/convergence-study/bicylinder_capytaine.ipynb
new file mode 100644
index 0000000..20ab423
--- /dev/null
+++ b/dev/python/convergence-study/bicylinder_capytaine.ipynb
@@ -0,0 +1,218 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This code generates capytaine data by creating a bicylindrical configuration with two cylinder meshes (separated my some minimal distance so they don't overlap).\n",
+    "\n",
+    "Pros: Less likely to have unforseen errors since this is close to Capytaine's typical usage. Does not support arbitrary cylinder amount but could be extended.\n",
+    "\n",
+    "Cons: Has covered panels that, at best, increase computation time (bad for high-resolution meshes), and at worst cause inaccurate computations (although not likely). Additionally, it cannot support configurations that are not a stack of cylinders (cross section has indents)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import capytaine as cpt\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "\n",
+    "# removes capytaine warnings from clogging outputs\n",
+    "import logging\n",
+    "logging.getLogger(\"capytaine\").setLevel(logging.ERROR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creates two full cylinders, separated by a minimal distance\n",
+    "\n",
+    "solver = cpt.BEMSolver()\n",
+    "\n",
+    "def showBemCompoundCylinder(a1,a2,d1,d2,resolution):\n",
+    "  body2 = cpt.meshes.predefined.mesh_vertical_cylinder(radius= a2,center=(0,0,0),length = d2,faces_max_radius=resolution*a2)\n",
+    "  body1 = cpt.meshes.predefined.mesh_vertical_cylinder(radius=a1,center=(0,0,0),length = d1-d2,faces_max_radius=resolution*a2)\n",
+    "  body1 = body1.translated([0,0,-d2-0.001])\n",
+    "  \n",
+    "\n",
+    "  body = body1 + body2\n",
+    "  body = cpt.FloatingBody(body)\n",
+    "  body.add_translation_dof(name='Heave')\n",
+    "  body = body.immersed_part()\n",
+    "  body.show_matplotlib()\n",
+    "\n",
+    "\n",
+    "def bemCompoundCylinder(h,a1,a2,d1,d2,resolution, w):\n",
+    "  body2 = cpt.meshes.predefined.mesh_vertical_cylinder(radius= a2,center=(0,0,0),length = d2,faces_max_radius=resolution*a2)\n",
+    "  body1 = cpt.meshes.predefined.mesh_vertical_cylinder(radius=a1,center=(0,0,0),length = d1-d2,faces_max_radius=resolution*a2)\n",
+    "  body1 = body1.translated([0,0,-d2-0.00001])\n",
+    "\n",
+    "  body = body1 + body2\n",
+    "  body = cpt.FloatingBody(body)\n",
+    "  body.add_translation_dof(name='Heave')\n",
+    "  body = body.immersed_part()\n",
+    "  #body.show_matplotlib()\n",
+    "  faces_centers = body.mesh.faces_centers\n",
+    "\n",
+    "  rad_problem = cpt.RadiationProblem(body=body,\n",
+    "                                        wavenumber = w, water_depth=h)\n",
+    "  results = solver.solve(rad_problem, keep_details = True, )\n",
+    "  dataset = cpt.assemble_dataset([results])\n",
+    "  \n",
+    "  w = 1\n",
+    "  rho = 1023 # density of our special material\n",
+    "  wave_amp = 1\n",
+    "  g = 9.81\n",
+    "  omega = np.sqrt(w*np.tanh(w*h)*g)\n",
+    "\n",
+    "  A = np.array(dataset['added_mass'])\n",
+    "  B = np.array(dataset['radiation_damping'])\n",
+    "  A_nondim = h**3 / (rho * np.pi * a2**3) * A\n",
+    "  B_nondim = h**3 / (omega * rho * np.pi * a2**3) * B\n",
+    "  return A,B, A_nondim, B_nondim, body.mesh.nb_faces\n",
+    "\n",
+    "def timeit(h,a1,a2,d1,d2, res,iter):\n",
+    "  def oneRun(res):\n",
+    "    start_time = time.time()\n",
+    "    result = bemCompoundCylinder(h,a1,a2,d1,d2,res)\n",
+    "    end_time = time.time()\n",
+    "    return end_time-start_time\n",
+    "  avg_time = np.mean([oneRun(res) for i in range(iter)])\n",
+    "  return avg_time\n",
+    "\n",
+    "# avg_time = timeit(resolution,100)#default resolution\n",
+    "\n",
+    "# print(f\"Execution time: {avg_time} seconds\") #run more than once ..second time is the actual runtime that excludes compile time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Mini Bicylinder\n",
+    "# h = 1.05\n",
+    "# a1 = 0.5\n",
+    "# a2 = 1\n",
+    "# d1 = 0.5\n",
+    "# d2 = 0.25\n",
+    "# resolution = 30\n",
+    "\n",
+    "# # Small Bicylinder\n",
+    "# h = 20.0\n",
+    "# a1 = 5.0\n",
+    "# a2 = 10.0\n",
+    "# d1 = 10.0\n",
+    "# d2 = 5.0\n",
+    "# resolution = 30\n",
+    "\n",
+    "# Big Bicylinder\n",
+    "h = 25.0\n",
+    "a1 = 10.0\n",
+    "a2 = 15.0\n",
+    "d1 = 15.0\n",
+    "d2 = 10.0\n",
+    "resolution = 30\n",
+    "\n",
+    "m0_nums = np.linspace(0.1, 5.5, 20)\n",
+    "results = [bemCompoundCylinder(h,a1,a2,d1,d2,resolution, w) for w in m0_nums]\n",
+    "\n",
+    "A_nondim = [res[2].flatten() for res in results]\n",
+    "B_nondim = [res[3].flatten() for res in results]\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(m0_nums,A_nondim, '*-')\n",
+    "plt.xlabel(\"Wavenumber m0\")\n",
+    "plt.ylabel(\"Added mass (kg)\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(m0_nums,B_nondim, '*-')\n",
+    "plt.xlabel(\"Wavenumber m0\")\n",
+    "plt.ylabel(\"Damping\")\n",
+    "plt.show()\n",
+    "\n",
+    "showBemCompoundCylinder(a1,a2,d1,d2,resolution)\n",
+    "\n",
+    "# Damping is evidently inaccurate for low wavenumbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# times = [timeit(res,10) for res in resolutions]\n",
+    "\n",
+    "# plt.figure()\n",
+    "# plt.plot(npanels,times,'*-')\n",
+    "# plt.xlabel(\"Panels (N)\")\n",
+    "# plt.ylabel(\"Time(s)\")\n",
+    "# plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_bicylinder.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_bicylinder.csv
new file mode 100644
index 0000000..55bb46b
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_bicylinder.csv
@@ -0,0 +1,21 @@
+pyCapytaineLambda_x,pyCapytaineLambda_y,pyCapytaineMu_x,pyCapytaineMu_y,pyMEEMLambda_x,pyMEEMLambda_y,pyMEEMMu_x,pyMEEMMu_y
+0.1,264.87442265,0.1,6623.80648664,0.1,977.272962187972,0.1,7175.51948041289
+0.410526315789474,0.18726147,0.410526315789474,7855.30050339,0.410526315789474,0.478830042157226,0.410526315789474,8625.53744419588
+0.721052631578947,0.00720423,0.721052631578947,7994.77030965,0.721052631578947,0.000514405889551203,0.721052631578947,8776.90250474158
+1.03157894736842,-0.06423966,1.03157894736842,8033.612716,1.03157894736842,6.86260891992925e-07,1.03157894736842,8831.54416145763
+1.34210526315789,-0.00801057,1.34210526315789,8062.06992505,1.34210526315789,1.01475213981125e-09,1.34210526315789,8859.87495433788
+1.65263157894737,0.0993163,1.65263157894737,8077.20605516,1.65263157894737,1.59470211104197e-12,1.65263157894737,8877.22677029992
+1.96315789473684,0.05153856,1.96315789473684,8094.66083974,1.96315789473684,2.60959117973024e-15,1.96315789473684,8888.94923353991
+2.27368421052632,0.23701892,2.27368421052632,8101.24605346,2.27368421052632,4.39552512362504e-18,2.27368421052632,8897.40060372674
+2.58421052631579,-0.12257184,2.58421052631579,8100.99982698,2.58421052631579,7.56580523956493e-21,2.58421052631579,8903.78290202964
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_bicylinder2.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_bicylinder2.csv
new file mode 100644
index 0000000..96b9fdd
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_bicylinder2.csv
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_bicylinder_regenerated.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_bicylinder_regenerated.csv
new file mode 100644
index 0000000..0955c2c
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_bicylinder_regenerated.csv
@@ -0,0 +1,16 @@
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_bicylinder_regenerated_v2.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_bicylinder_regenerated_v2.csv
new file mode 100644
index 0000000..9f50483
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_bicylinder_regenerated_v2.csv
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_tricylinder.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_tricylinder.csv
new file mode 100644
index 0000000..56f51ed
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_tricylinder.csv
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_tricylinder2.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_tricylinder2.csv
new file mode 100644
index 0000000..1afb7e7
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_tricylinder2.csv
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_tricylinder_regenerated.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_tricylinder_regenerated.csv
new file mode 100644
index 0000000..3e94179
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_tricylinder_regenerated.csv
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_tricylinder_regenerated_v2.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_tricylinder_regenerated_v2.csv
new file mode 100644
index 0000000..9f634b4
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/big_tricylinder_regenerated_v2.csv
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_bicylinder.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_bicylinder.csv
new file mode 100644
index 0000000..57a3289
--- /dev/null
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new file mode 100644
index 0000000..8d4f921
--- /dev/null
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_bicylinder_m0_from5_to_10.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_bicylinder_m0_from5_to_10.csv
new file mode 100644
index 0000000..75e07a1
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_bicylinder_m0_from5_to_10.csv
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_bicylinder_regenerated.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_bicylinder_regenerated.csv
new file mode 100644
index 0000000..e638aa3
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_bicylinder_regenerated.csv
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new file mode 100644
index 0000000..d0a3d18
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_bicylinder_regenerated_v2.csv
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new file mode 100644
index 0000000..e3ee178
--- /dev/null
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_tricylinder2.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_tricylinder2.csv
new file mode 100644
index 0000000..b939eee
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_tricylinder_regenerated.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_tricylinder_regenerated.csv
new file mode 100644
index 0000000..5dcb7d5
--- /dev/null
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_tricylinder_regenerated_v2.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/mini_tricylinder_regenerated_v2.csv
new file mode 100644
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new file mode 100644
index 0000000..ab1ab6a
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new file mode 100644
index 0000000..153bc97
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_bicylinder2.csv
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new file mode 100644
index 0000000..5cc161b
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_bicylinder_regenerated.csv
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_bicylinder_regenerated_v2.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_bicylinder_regenerated_v2.csv
new file mode 100644
index 0000000..9b4a6ca
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_bicylinder_regenerated_v2.csv
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_tricylinder.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_tricylinder.csv
new file mode 100644
index 0000000..cdec8dd
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_tricylinder.csv
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diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_tricylinder2.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_tricylinder2.csv
new file mode 100644
index 0000000..2f4a187
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+1.88888888888889,-0.0105487731161261,1.88888888888889,3835.61914967636,1.88888888888889,1.03254919240604e-06,1.88888888888889,3965.88748014867
+2.11111111111111,-0.00411570739575395,2.11111111111111,3843.57535893277,2.11111111111111,9.78114027565213e-08,2.11111111111111,3972.17858560236
+2.33333333333333,-0.00832528669169804,2.33333333333333,3861.63405628563,2.33333333333333,9.38494963784121e-09,2.33333333333333,3977.22763218854
+2.55555555555556,-0.00222206083799563,2.55555555555556,3847.29478316114,2.55555555555556,9.10018596264626e-10,2.55555555555556,3981.36984451619
+2.77777777777778,-5.40881915287221e-05,2.77777777777778,3847.87911757255,2.77777777777778,8.9022062210438e-11,2.77777777777778,3984.82964495281
+3,0.00136182433866574,3,3849.37854462885,3,8.77395321029461e-12,3,3987.76298404319
diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_tricylinder_regenerated.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_tricylinder_regenerated.csv
new file mode 100644
index 0000000..0bfae37
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_tricylinder_regenerated.csv
@@ -0,0 +1,16 @@
+m0,pyCapytaineMu_x,pyCapytaineMu_y,pyCapytaineLambda_y
+0.1,0.1,0.36131783243562415,0.1459762268396702
+0.5214285714285715,0.5214285714285715,0.46255358191835816,0.00042544312600659867
+0.9428571428571428,0.9428571428571428,0.4761061340086468,1.0071463511668595e-06
+1.3642857142857143,1.3642857142857143,0.4796044137238121,-1.879001396513241e-08
+1.7857142857142858,1.7857142857142858,0.48276703060107506,-1.5577212488293689e-09
+2.2071428571428573,2.2071428571428573,0.48308647835863316,-4.634057540429988e-08
+2.6285714285714286,2.6285714285714286,0.4850822906730328,1.254790132204851e-08
+3.0500000000000003,3.0500000000000003,0.48681627414822704,3.609555620365413e-08
+3.4714285714285715,3.4714285714285715,0.48647762756513496,2.0241400272852722e-08
+3.892857142857143,3.892857142857143,0.4867826386092047,-5.382320811047025e-08
+4.314285714285714,4.314285714285714,0.48669657625903895,5.668871589140076e-08
+4.735714285714286,4.735714285714286,0.4878684099916502,5.300999254845885e-08
+5.157142857142857,5.157142857142857,0.4862363139854338,1.0321142378453847e-10
+5.578571428571428,5.578571428571428,0.4858000942822257,-4.348457459300445e-08
+6.0,6.0,0.4859566497551202,1.6240356642806864e-08
diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_tricylinder_regenerated_v2.csv b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_tricylinder_regenerated_v2.csv
new file mode 100644
index 0000000..6af7108
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/csv_data/small_tricylinder_regenerated_v2.csv
@@ -0,0 +1,16 @@
+m0,pyCapytaineMu_x,pyCapytaineMu_y,pyCapytaineLambda_y
+0.1,0.1,0.6960819600749478,0.2856607976898419
+0.5214285714285715,0.5214285714285715,0.8755176536472405,0.00044853375202689175
+0.9428571428571428,0.9428571428571428,0.9044478442091868,1.7765708474059817e-06
+1.3642857142857143,1.3642857142857143,0.9117141812486097,1.0103081425665858e-05
+1.7857142857142858,1.7857142857142858,0.9176526098210427,2.1276287201292344e-05
+2.2071428571428573,2.2071428571428573,0.9184242675758878,8.195268099422315e-06
+2.6285714285714286,2.6285714285714286,0.9221150237360304,1.4058648575082619e-05
+3.0500000000000003,3.0500000000000003,0.9255441272407293,8.220614930751295e-05
+3.4714285714285715,3.4714285714285715,0.9253034970467933,1.979897570771697e-05
+3.892857142857143,3.892857142857143,0.925871547139901,-2.3879398240721578e-05
+4.314285714285714,4.314285714285714,0.925685216930584,2.6638005894316612e-05
+4.735714285714286,4.735714285714286,0.9280554415056675,2.4545232478132837e-05
+5.157142857142857,5.157142857142857,0.925061141481684,2.186358623077801e-05
+5.578571428571428,5.578571428571428,0.924133252476996,4.997389420661064e-06
+6.0,6.0,0.9244227751695389,-7.832297242824115e-06
diff --git a/dev/python/convergence-study/meem-vs-capytaine-data/displayed-figs.ipynb b/dev/python/convergence-study/meem-vs-capytaine-data/displayed-figs.ipynb
new file mode 100644
index 0000000..93993c1
--- /dev/null
+++ b/dev/python/convergence-study/meem-vs-capytaine-data/displayed-figs.ipynb
@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e2b4d458",
+   "metadata": {},
+   "source": [
+    "Python plots of the figures in the figs folder. They compare MEEM and Capytaine values across wavenumbers for a few different configurations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0aff50b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "from matplotlib.colors import to_rgb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b4a59f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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uaNKkCebMmYNJkyZh5syZkMvlWL16NXx9fbFo0SIAQEBAAI4ePYolS5awICIio1GpAIVC/PJWKMTJxiavEEhPB+7cyVuXPy43F6hbN68QePQI+OMP7bjc3Lzp9dfFCQDi44ElS/KKhoI/u3UD3n1XjH361AI9e8o0X9Lq7akfv/suMH68GJuUBLRtq70+/+N+/YDly8XY5GSgWrXCv1ABoHdv4OefxcdKJeDlpT+P3bsDv/+eN//GG+L+F6ZDB+DAgbz5oUOB588Lj23dWrsgmjcPuH+/8NiGDbULooMHvfHwoazQ2Fq1tAuivXuBs2cL366bm/b8mTPA0aOFx9rZac/fuQNculR4rLTAsaHkZPH3p0/+Xh91cVmcWIlEf5xEoh1rZiboDy4nRi+IRo0ahdDQUHTq1EmrIDp9+jQUCgU6deqkWVavXj14e3sjJiYGrVu3RkxMDBo2bAi3fO+akJAQjBw5EpcuXULTpk0RExOjtQ11TP5DcwVlZ2cjOztbM5+SkgIAUCgUUBT4S1PPF1xORWPeDMO8FU4QxA/pnBztydkZsLQU8/XsmQWOHFFCpZIgJwfIzs6LUyiADh0EzX+rcXHAjh1Szbq8bUqQnQ1ERKjw6qviB/jBgxLMmaOOFbetLgYUCmDhQiV69RJj//hDgnfflSE3F1CpdL8tVqxQYtgwsUo4elSCLl30f0R//rkS48aJsdeuSRAWpj92+nQlWrUSY+PjgS++MNcbW726Ej16qKBQKJCTI8POnfrPrGjaVAmFQtyuQgFcv65/uykpKigU4jep2AOiPzY3Ny9WEABzczPIZCh0srMTNLEA4O9vBoVCO0YqFSCTAf7+gqa9ABAUJEN6ulggFJzq1tWO7dVLimfPJJr1MpmgeezhgXx5UCA4+A6cnf1hbi7NFy/+dHKC1nbHjZPgyRNJgdcXt21lBSgUeYXClCkSPHlSeHvNzbVj584FPv644HbFSSLRjl24EJg6VXt9/sdivBi7aJFYHOqLtbHJi128GPjyS93Y/IWSOjYsTIGoqNL/bCvJ9oxaEG3atAlnzpzBqVOndNYlJCRALpfD0dFRa7mbmxsSEhI0MW4FSmj1/ItiUlJSkJmZCSsrK53Xnj9/PmbNmqWzfN++fbBW//tWQFRUlJ69pKIwb4apyHnLzZVAoZBBLs+F7N9/kh8/tsTjx1ZQKGTIzpZCoZAhJ0eGnBwpcnJkaNfuPuzsxA+uv/5yxV9/uSM7W4xRKKTIzhZ/5uTIMGbMWXh5iScc/P57LaxfXx8KhRSCoFtgzJlzFA0bPgEAxMT4YtAgS73tnjo1Fi1aiOcoHjjgheXLm+mN/c9/TuPRo4cAgOPHa+DYsZZ6Y48duwArq7sAgNOnXZGTE6g39ty5S9i9+zYA4OpVJzg4tIJUKsDMTAWZTAWZTIBMJs4/eHALu3ffAwA8eGCDpk0bataJBYB6UkGhSMDu3eJn4rNnFnjrrTr5YlT/fgGL846Oz7B7t5gzBwczjBp19t8vNO3tSqUC3NzSsXu3+LvIzZVg3jwnrfXqn1KpABsbBXbvFv/RVKmANWss8n35a08ymYDdu/O+sDdv1psyAOLhHrV8/1e/MHbgwOLHvvFG8WN79ACAm8WKtbcXp+LEAoCtbeFxSqVubFFKEnvtWvFjX1Zpf7ZlZGQUO9ZoBdG9e/cwZswYREVFwdJS/4eUMUyePBnj1f3AEHuIvLy8EBwcDPsC71yFQoGoqCh07twZ5ub6/+MhbcybYUozb7m5QFoa4OCQ9x/bxYvA339LkJoKpKWJP8XHQGqqBIsXKzUf3p9/LsW6dVJkZYnnYWRmij+VSnFjV64oULu2GPvpp1J8+WXhhxAAYNiwADRuLD6Oi5Nizx79sQ0btkObNuKX5c2bYpFUGLlcQIsWrdGxowCFQoE//7yMOnVUkMslkMvF9eJPcQoJaYFWrcTtOjtLkJOjhFwu/uedP04uB7p0aYJ69ZoAABo1Apo1y9WKNTMTH5ubAz4+r8DZ+RUA4pfqwIEKrfXqx2ZmgFQaACAAgHjoKu9jqLB9bPjvJBo6VG/KAHhozfXtW1SsSP1eW7jQn3+jJcDPNsOUVd7UR3iKw2gF0enTp5GUlIRmzfL+C1MqlThy5AhWrFiBvXv3IicnB8+fP9fqJUpMTIS7uzsAwN3dHSdPntTarvoqtPwxBa9MS0xMhL29faG9QwBgYWEBC/UZZPmYm5vr/UUVtY70Y94Mk5hojidPzDUFS8Fp+nTxCxYQu8N37ECB4kYsYADxyo9q1cTHq1cD336r/3VnzZKienXxcXKy9omZBSmV5lD/aj08xHMnLC3FwwAFfzo55cX+e8qg1vr8jxs2NNPEhoeL53lYWIiTumAxNwckEgnyf8S1bfsA8+c3zvd+K9ijlHdo6LXXxEm/vALF11f7pN6imJsX3RtQEfFv1DDMm2FKO28l2ZbRCqKOHTviwoULWssGDRqEevXqYdKkSfDy8oK5uTkOHDiAXr16AQCuXbuGu3fvIjBQ7HIODAzEvHnzkJSUBFdXVwBid5u9vT3q16+vidldoG8wKipKsw2i8iYIYkHy7Bng45O3fNs28eTKp0/F6dmzvCk5GXj4MC924kQZtmzR/xrjx4tXqABi0aLvRExAbIu6IPL3BwIDxRM0C062ttonbo4YAbz1VuFFi6WlOKmNGydOxdG2rTgVh6Nj3n4SEb0MoxVEdnZ2eOWVV7SW2djYoHr16prlgwcPxvjx41GtWjXY29tj9OjRCAwMROvWrQEAwcHBqF+/Pvr374+FCxciISEBU6dOxahRozQ9PCNGjMCKFSswceJEhIeH4+DBg/j111+xa9eu8t1hqtJUKvFKn8ePgQYN8pavWgUcPy6uS0oSp0ePxBN0pVLxhEL1FR8//1z0uRIpKeIJiwDg7i6eAFxY4WJnp33S4pAhQHCw/li5PC92/Pj8h2mKVrs2NIfEiIgqO6NfZVaUJUuWQCqVolevXsjOzkZISAi+/vprzXqZTIadO3di5MiRCAwMhI2NDcLCwjB79mxNjK+vL3bt2oVx48Zh2bJl8PT0xPfff89L7qnYsrK0ezt+/FEsch48EK/YiY8HEhPFkxplMvHqJfXJxNHRwK+/Fr5duVwsctQ9HMHB4lVR1auLV6JUqyb+VE+2tnmXqS5erMLy5frPs8mvZUtxIiIi/SpUQRQdHa01b2lpiZUrV2LlypV6n+Pj46NzSKygoKAgnNU30AMRgFu3gCtXxAHS1NOdO8A//4hFS1ZWXpGza1fhRY5EIhYz6jFWAHE8lRYtABcXcfA2V1fxsYtL3ngzakOGvLidvNqeqARyc/PGV8jOFufzrsUv/Dr+/NeIk0mpUAURUVnJzRULnGvXxOnmTWDFirzPvE8+KfpwVWKieGIwAPTqBdSrB/znP+KyGjXEycUFKHj+3ttvl8nuEJUP9eiR+Yda1jcVtT7/wE/Z2XnTi+ZfNkbfyI/FUbBgKqqAyjdvJpUiKCMDZjNmFB6vfk7+kQkFQf/j8liWf3qZZS8RayYIeMPGRry80khYEFGVtWkTsHWr+lJy3d6VyZPzho1/5RXg+vW8K4bUU82a4ii5+a8Meu+9ctsFKi5ByLuZUv4RF9Xz6elwun4dEnt78UuysJiCozoWZ1lhMSVVkp6IksSqR6ssbtFSYL25Uom3Sr43FZdEIl56qd7PF1Hft6KE3bISAA6A2L1MxSYBIH+ZArYUsCCiSkulAq5eBWJjxauzzp0TCyBnZ3H9mTPavT6WluLtDvz9xcks37t/+nRxonKgUIj3pVBf/5+WVrypqFj1GAJ6mANoVz57Z1ry947o60GRycQT5tTjIqjHSChsvjgxhjzHwkJsh7qgVPdQGNLb9YL1uTk5OBkTg5bNm8NMItH/PDX1UNDqx4Ut0/e4tGNLY5mBsYrcXBw9fhztS/YOLFUsiKhSURc9sbHAiRPi+Tr5XbiQN6LsW2+J5+w0bCge4vLy0r2HD72AQlF4QfKiQqao9flui1NmJBKtL2FBLkdGbi6sHR0hyT9gUf7H+paVJEYcAKn47cx/M6fSjFUrxiEefesUKhX2HzqETiEhMLe0LPwQUmU9z0b9Zay+50UpEhQKPMrIgBAcXOrbrtIUCqTfuWPUJrAgogorJQU4dEi8QqpGDXFZbCyQ7yJCWFsDr74qnrjcuDHw7/BTAIozuF4Vo1SKPS/6ChdDHhtyCKi4zMy0BziytX3xpC/Oykq7SMnfGwAgV6HA/t270a1bNw6WV1wKBXLs7cVLHJkzMgEsiKjCyM0FTp4EoqKAffvEHiClUhzLZ8QIMaZdO6B/f3HwwNatxd4fs6r2LhYEcWRG9TX98fFAQgIQHw/Zgwdo/fffkC1cqNsj84LDRi9FLi+8IHlRIVPU+vwDIBERGVlV+yqhSujOHfEE5927xV6h/Pz8tAuegABxHKAKTakUh5V+8ED8+fChWNTkf/z0qf7zExQKsToshBSAW6Fr8hFvAV54UVKcx4XNs3ghoiqOBRGVu5wc4O7dvHl7e2DLFrEGcHICOnUCOncWp5o1jdZM/bKyxBuAJSXlDVp061bedOdO6RxqqlYt75r+fyeliwvO3b+PRm3awEw9WmPBAsbCovKe20FEZCQsiKhcCAJw8CCwcaN4zy5/fxk++URcV60a8PXX4uGvV1/NGwCxzBv0zz/ApUvizcLS04GMDPGnesrIEM/afvJE7NFR/8zIePH2zczEgYpq1BAHK1IPWKR+XK2aGJP/JFX1vLm5eKlcITcYVikUuLd7Nxp268bzOoiIShELIipT6enATz8BX30ljgSt9s8/EmRm5r39hg4tw0YkJ4uXn50/r/0zNdXwbcpk4rDUNWuKt3EvOP3nP1Xw5CYioqqLn9hUZr79VhwB+tkzcd7WFujTR7ydRWBgLvbuLfw8mZfy+LE4ANGZM8Dp0+J0+3bhsXK5eD2+m5t4uZqNjfZkbS0ez6teXezRyf/Tzo7X8BMRVSEsiKjUqMc5U3eMODqKxVCtWsBHHwGDBuWN+Fwq9+TKygL++gv480/g1Cmx+Ml/clJ+Xl5Ao0bicblGjcSpbl0ediIiIgAsiKiUnD8P/N//AR07QnNuUM+ewM6dQJcupXReUHKyeJv5P//MK4IKG+TPzw9o1gxo3lz82bRp3t1WiYiICsGCiF5KQgIwbRqwZo04Kv3t28CkSXm3DQoNLeEGBUG8euvGDfEGZH//LT6+ckW8KVnB0XpdXYG2bYE2bcQCqEkTwMGhtHaPiIhMBAsiMkhmJrBkCTB/vjguIAC8+y6wYEEJr/hWKoFjx8T7cfz5p1gAqTdYmNq1xQJIPdWpw0vMiYjopbEgohI7eFA8H0h9us6rr4rFUbFvk5GTA0lUFBqvXAmzoUOBR4+010skgI+PWOz4+Yk/69QRX0h9Dw8iIqJSxIKISqxaNeDePcDTU+wR+uCDYlxwlZEh3pNj61bg999h9vw5aubf4FtvAW++Kd6MzNe30DF4iIiIygoLIiqW+/fFAggQT9P53//EE6itrYt4UlISsGMH8PvvYjGU715bgqsr7jRrBq8xY2DWsSOv9iIiIqNiQURFyskRrxpbtUq82WqjRuLy7t31POHaNbFa+t//gJgY7ZOgvb2BHj2AXr2Q27Ilzu/dC08WQ0REVAGwICK9bt4UB1H86y9xfu/evIJIx7VrwMcfi9fZ59e8OfDf/4qHxBo1yjsBulQGIiIiIiodLIioUL/+Kt5OIyVFvOFqZKRY1+h48gSYNUvsQsrNFQcc6tRJLIC6d887zkZERFSBsSAiLTk54qjS33wjzr/2mnhDVm/vQgK//hqYPTvv3hxvvgl88YV4OwwiIqJKhDdjIi3ffCNOEgnw6adAdHSBYkgQxPODXnkFGDdOLIYaNhRPmt6xg8UQERFVSuwhIi0ffijeF/WddwoZZTohQRyAaM8ecd7VFZg7FwgPL6V7cxARERkHCyJCRoY47I9MJk5r1xYStHs3MHCgOIiihYXYOzR5ct7dWomIiCoxHjIzcampQNeuwODB4r3IdGRni8VPaKhYDDVsKHYhzZ/PYoiIiKoMFkQmLDkZCAkBjhwBtm0TL7PXcvUq0Lo1sHSpOD96NHDypDiaNBERURXCQ2Ym6tkzsRg6dUq8rH7fPvG2YQDEE6fXrBEvN8vIAJydxeNob75p1DYTERGVFfYQmaDHj4EOHcRiyNlZvFlrixYQC6FTp8Tb1g8ZIhZDHTsC586xGCIioiqNPUQmJidHvHtGXBzg5gbs3w+8Uj0e+GK9OPri5ctioJmZeAXZhAnFuHMrERFR5WbUb7pVq1ahUaNGsLe3h729PQIDA/HHH39o1gcFBUEikWhNI0aM0NrG3bt3ERoaCmtra7i6umLChAnIzc3ViomOjkazZs1gYWGBOnXqIDIysjx2r0KKiRHvSebgIODQxD/wysRu4mjSEyeKxZClJdCnDxAbC0yaxGKIiIhMglF7iDw9PbFgwQL4+flBEASsW7cOb731Fs6ePYsGDRoAAIYOHYrZs2drnmOd7/bqSqUSoaGhcHd3x/HjxxEfH48BAwbA3Nwcn332GQDg9u3bCA0NxYgRI7BhwwYcOHAAQ4YMQY0aNRASElK+O1wBtG8PHJx7HBlzFyPg/37LW/Haa0BYGPDee4CDg/EaSEREZARGLYi6F7hl+rx587Bq1SrExsZqCiJra2u4u7sX+vx9+/bh8uXL2L9/P9zc3NCkSRPMmTMHkyZNwsyZMyGXy7F69Wr4+vpi0aJFAICAgAAcPXoUS5YsMcmCCOnpaLu0F5CWIPYMhYUBAwYAdesau2VERERGU2GOhyiVSmzatAnp6ekIDAzULN+wYQOcnZ3xyiuvYPLkycjIyNCsi4mJQcOGDeHm5qZZFhISgpSUFFy6dEkT06lTJ63XCgkJQUxMTBnvUcWRkAC88QZw6RLES+gTEoBatcTr7OfOZTFEREQmz+gnVV+4cAGBgYHIysqCra0ttm3bhvr/jnPTp08f+Pj4wMPDA+fPn8ekSZNw7do1bN26FQCQkJCgVQwB0MwnJCQUGZOSkoLMzExYWVnptCk7OxvZ2dma+ZSUFACAQqGAQqHQilXPF1xeUeTkAO+8I8OxY1IM7JeDE39/DimA3JkzIUgkgJHaXdHzVlExb4Zh3kqOOTMM82aYsspbSbZn9ILI398fcXFxSE5OxpYtWxAWFobDhw+jfv36GDZsmCauYcOGqFGjBjp27IibN2+idu3aZdam+fPnY9asWTrL9+3bp3UOU35RUVFl1p6X8e23DXHsWC1YWyvwmdMYSNNS8bxWLRy2tRVvx2FkFTVvFR3zZhjmreSYM8Mwb4Yp7bzlP6r0IkYviORyOerUqQMAaN68OU6dOoVly5bhm2++0Ylt1aoVAODvv/9G7dq14e7ujpMnT2rFJCYmAoDmvCN3d3fNsvwx9vb2hfYOAcDkyZMxfvx4zXxKSgq8vLwQHBwM+wK3q1AoFIiKikLnzp1hbm5ekl0vcz/+KMHu3eKveP3iR+j00RoAgO2KFehW4DBieavIeavImDfDMG8lx5wZhnkzTFnlTX2EpziMXhAVpFKptA5X5RcXFwcAqFGjBgAgMDAQ8+bNQ1JSElxdXQGI1aW9vb3msFtgYCB2F+gJiYqK0jpPqSALCwtYWFjoLDc3N9f7iypqnTGcOgWMGiU+njkTePvYJ+Lxs44dYda1q1Hbll9Fy1tlwbwZhnkrOebMMMybYUo7byXZllELosmTJ6Nr167w9vZGamoqNm7ciOjoaOzduxc3b97Exo0b0a1bN1SvXh3nz5/HuHHj0K5dOzRq1AgAEBwcjPr166N///5YuHAhEhISMHXqVIwaNUpT0IwYMQIrVqzAxIkTER4ejoMHD+LXX3/Frl27jLnrZSo5GXjnHfG+rP/9LzDtrfNAs/XiygULjNs4IiKiCsioBVFSUhIGDBiA+Ph4ODg4oFGjRti7dy86d+6Me/fuYf/+/Vi6dCnS09Ph5eWFXr16YerUqZrny2Qy7Ny5EyNHjkRgYCBsbGwQFhamNW6Rr68vdu3ahXHjxmHZsmXw9PTE999/X6UvuU9PB2rXBszNgR9/BKR9Jou35Xj//X/v0UFERET5GbUg+uGHH/Su8/LywuHDh1+4DR8fH51DYgUFBQXh7NmzJW5fZeXhId6SIz4ecDgbLZ48rb4VBxEREemocOcQkeFUqrw7bUilwH88BKDnJHHBsGHAvyevExERkbYKMzAjvbzwcPFE6rS0fxds3QqcPAnY2ADTphm1bURERBUZe4iqiN9/B9atE3uG+vcHWrfIBT79VFw5fjyg5/YnRERExB6iKuHJE2D4cPHx//0f0Lo1gDVrgOvXAWdn4OOPjdo+IiKiio4FURXw0Ufi7cnq1QNmz4ZYIc2YIa6cNg0oMJgkERERaeMhs0pu61Zg40bxUNm6dYClhQD0Gy5WSP7+eV1HREREpBd7iCqxR4+AESPEx5MmAS1bQqyKfvtNvMx+wwagkBG3iYiISBsLokrs6lUgNxdo0ODfI2Q3bwKjR4sr58wBmjc3avuIiIgqCx4yq8TatgUuXQKePwcsZLni5WVpaUC7dsCECcZuHhERUaXBgqgSEgRAIhEf16ghTpj9GRATI55A/eOPgExm1DYSERFVJjxkVsk8fy6eK/T77/kWxsb+e3kZgK+/Bnx8jNE0IiKiSosFUSWiUgEDBwJ//SVeap+VBfEQWb9+gFIJfPAB0LevsZtJRERU6bAgqgB27sx3u40iLFwI/O9/gFwObN4MWFoCGDtWPJnay0vsHSIiIqISY0FkZCdPAj16AE2aAMeO6Y87cACYMkV8vHw58OqrALZtA374QTyh6McfAUfHsm8wERFRFcSCyMhycgAPD7GTp21bcTyh7GztmPv3xaNhKhUwaBAwdCiAhw//fQDxirKgoPJuOhERUZXBgsjIXn8duHABCAsTrx5buBBo0QKIixPX5+QA774rDsLYtCmwciUgyVUA770n3qKjSRNxzCEiIiIyGAuiCsDBAYiMFI+AuboCFy+Kh8QWLRLXN28OODkBW7YAVlYQe4SOHRMvsf/1V/GkIiIiIjIYC6IKpEcPsRjq2VMcgdrKSqx1VqwQB2CsVQvApk3AsmXiE378EfDzM2aTiYiIqgQOzFjBuLiIPUH79gHBwXnLa9QAcPkyMGSIuOCTT4C33jJKG4mIiKoa9hAZW1QU8PSp1iKJBAgJyRuNGgCQkiJ2HaWnAx068LwhIiKiUsSCyJiSksQip1Yt4LPPxGKnMIIAhIcD164B//kP8PPP4t3siYiIqFSwIDKmxESxGEpOFgcZql1bHFwxJ0c7bvFi4LffAHNz8Xiaq6tx2ktERFRFsSAypoYNgbNngQ0bxMIoMREYNQoICAA2bhQHHjp8WBycCACWLgVatzZqk4mIiKoiFkTGJpUCffoAV66Igwy5uQG3bon3JGvWDHj/ffE+Zf36ASNHGru1REREVRILoopCLgc+/FAcsnrePHGMoXPnxF6jhg2Bb74pcJY1ERERlRYWRBWNjQ3w6adiL9HEieK199u2AdbWxm4ZERFRlcVLlSqq6tWBzz83diuIiIhMAnuIiIiIyOSxICIiIiKTx4KIiIiITB4LIiIiIjJ5Ri2IVq1ahUaNGsHe3h729vYIDAzEH3/8oVmflZWFUaNGoXr16rC1tUWvXr2QmJiotY27d+8iNDQU1tbWcHV1xYQJE5Cbm6sVEx0djWbNmsHCwgJ16tRBZGRkeeweERERVRJGLYg8PT2xYMECnD59Gn/99Rc6dOiAt956C5cuXQIAjBs3Djt27MDmzZtx+PBhPHz4ED179tQ8X6lUIjQ0FDk5OTh+/DjWrVuHyMhITJ8+XRNz+/ZthIaG4o033kBcXBzGjh2LIUOGYO/eveW+v0RERFQxGfWy++7du2vNz5s3D6tWrUJsbCw8PT3xww8/YOPGjejQoQMAYO3atQgICEBsbCxat26Nffv24fLly9i/fz/c3NzQpEkTzJkzB5MmTcLMmTMhl8uxevVq+Pr6YtGiRQCAgIAAHD16FEuWLEFISEi57zMRERFVPBVmHCKlUonNmzcjPT0dgYGBOH36NBQKBTp16qSJqVevHry9vRETE4PWrVsjJiYGDRs2hJubmyYmJCQEI0eOxKVLl9C0aVPExMRobUMdM3bsWL1tyc7ORnZ2tmY+JSUFAKBQKKBQKLRi1fMFl1PRmDfDMG+GYd5KjjkzDPNmmLLKW0m2Z/SC6MKFCwgMDERWVhZsbW2xbds21K9fH3FxcZDL5XB0dNSKd3NzQ0JCAgAgISFBqxhSr1evKyomJSUFmZmZsLKy0mnT/PnzMWvWLJ3l+/btg7WeEaOjoqKKt8OkhXkzDPNmGOat5JgzwzBvhintvGVkZBQ71ugFkb+/P+Li4pCcnIwtW7YgLCwMhw8fNmqbJk+ejPHjx2vmU1JS4OXlheDgYNjb22vFKhQKREVFoXPnzjA3Ny/vplZazJthmDfDMG8lx5wZhnkzTFnlTX2EpziMXhDJ5XLUqVMHANC8eXOcOnUKy5Ytw/vvv4+cnBw8f/5cq5coMTER7u7uAAB3d3ecPHlSa3vqq9DyxxS8Mi0xMRH29vaF9g4BgIWFBSwsLHSWm5ub6/1FFbWO9GPeDMO8GYZ5KznmzDDMm2FKO28l2VaFG4dIpVIhOzsbzZs3h7m5OQ4cOKBZd+3aNdy9exeBgYEAgMDAQFy4cAFJSUmamKioKNjb26N+/fqamPzbUMeot0FERERk1B6iyZMno2vXrvD29kZqaio2btyI6Oho7N27Fw4ODhg8eDDGjx+PatWqwd7eHqNHj0ZgYCBat24NAAgODkb9+vXRv39/LFy4EAkJCZg6dSpGjRql6eEZMWIEVqxYgYkTJyI8PBwHDx7Er7/+il27dhlz14mIiKgCMWpBlJSUhAEDBiA+Ph4ODg5o1KgR9u7di86dOwMAlixZAqlUil69eiE7OxshISH4+uuvNc+XyWTYuXMnRo4cicDAQNjY2CAsLAyzZ8/WxPj6+mLXrl0YN24cli1bBk9PT3z//fe85J6IiIg0jFoQ/fDDD0Wut7S0xMqVK7Fy5Uq9MT4+Pti9e3eR2wkKCsLZs2cNaiMRERFVfRXuHCIiIiKi8saCiIiIiEweCyIiIiIyeSyIiIiIyOSxICIiIiKTx4KIiIiITB4LIiIiIjJ5LIiIiIjI5LEgIiIiIpPHgoiIiIhMHgsiIiIiMnksiIiIiMjksSAiIiIik8eCiIiIiEweCyIiIiIyeSyIiIiIyOSxICIiIiKTx4KIiIiITB4LIiIiIjJ5LIiIiIjI5LEgIiIiIpPHgoiIiIhMHgsiIiIiMnksiIiIiMjksSAiIiIik8eCiIiIiEweCyIiIiIyeSyIiIiIyOSxICIiIiKTx4KIiIiITB4LIiIiIjJ5ZsZ88fnz52Pr1q24evUqrKys0KZNG3z++efw9/fXxAQFBeHw4cNazxs+fDhWr16tmb979y5GjhyJQ4cOwdbWFmFhYZg/fz7MzPJ2Lzo6GuPHj8elS5fg5eWFqVOnYuDAgWW+j0RE5UGpVEKhUJTa9hQKBczMzJCVlQWlUllq263qmDfDvEze5HI5pNKX798xakF0+PBhjBo1Cq+++ipyc3Px6aefIjg4GJcvX4aNjY0mbujQoZg9e7Zm3traWvNYqVQiNDQU7u7uOH78OOLj4zFgwACYm5vjs88+AwDcvn0boaGhGDFiBDZs2IADBw5gyJAhqFGjBkJCQspvh4mISpkgCEhISMDz589Lfbvu7u64d+8eJBJJqW67KmPeDPMyeZNKpfD19YVcLn+pNhi1INqzZ4/WfGRkJFxdXXH69Gm0a9dOs9za2hru7u6FbmPfvn24fPky9u/fDzc3NzRp0gRz5szBpEmTMHPmTMjlcqxevRq+vr5YtGgRACAgIABHjx7FkiVLWBARUaWmLoZcXV1hbW1dal/CKpUKaWlpsLW1LZX/vk0F82YYQ/OmUqnw8OFDxMfHw9vb+6Xe/0YtiApKTk4GAFSrVk1r+YYNG7B+/Xq4u7uje/fumDZtmqaXKCYmBg0bNoSbm5smPiQkBCNHjsSlS5fQtGlTxMTEoFOnTlrbDAkJwdixYwttR3Z2NrKzszXzKSkpAMQuvYJd0ur50uyqNgXMm2GYN8NU1bwplUo8e/YMLi4ucHJyKtVtC4KAnJwcWFhYsKejBJg3w7xM3pydnfHw4UNkZWVpnSoDlOxvvsIURCqVCmPHjsVrr72GV155RbO8T58+8PHxgYeHB86fP49Jkybh2rVr2Lp1KwDxv6P8xRAAzXxCQkKRMSkpKcjMzISVlZXWuvnz52PWrFk6bdy3b5/W4br8oqKiSrjHBDBvhmLeDFPV8mZmZgZ3d3eoVCrNP26lLTU1tUy2W9Uxb4YxJG85OTnIzMzEwYMHkZubq7UuIyOj2NupMAXRqFGjcPHiRRw9elRr+bBhwzSPGzZsiBo1aqBjx464efMmateuXSZtmTx5MsaPH6+ZT0lJgZeXF4KDg2Fvb68Vq1AoEBUVhc6dO8Pc3LxM2lMVMW+GYd4MU1XzlpWVhXv37sHOzg6Wlpalum1BEJCamgo7Ozv2dJQA82aYl8lbVlYWrKys0K5dO52/g5L8o1AhCqKIiAjs3LkTR44cgaenZ5GxrVq1AgD8/fffqF27Ntzd3XHy5EmtmMTERADQnHfk7u6uWZY/xt7eXqd3CAAsLCxgYWGhs9zc3Fzvh2lR60g/5s0wzJthqlrelEolJBIJpFJpqZ+volKpAECzfSoe5s0wL5M3qVQKiURS6N93Sf7ejfrbEgQBERER2LZtGw4ePAhfX98XPicuLg4AUKNGDQBAYGAgLly4gKSkJE1MVFQU7O3tUb9+fU3MgQMHtLYTFRWFwMDAUtoTIiKikpNIJNi+fbuxm1EqDNmXO3fuQCKRaL7bjcmoBdGoUaOwfv16bNy4EXZ2dkhISEBCQgIyMzMBADdv3sScOXNw+vRp3LlzB7///jsGDBiAdu3aoVGjRgCA4OBg1K9fH/3798e5c+ewd+9eTJ06FaNGjdL08owYMQK3bt3CxIkTcfXqVXz99df49ddfMW7cOKPtOxERvZyUlBRMmTIF9erVg6WlJdzd3dGpUyds3boVgiCUWzte5ks9Pj4eXbt2LdX2BAUF6b1oiPQz6iGzVatWARB/efmtXbsWAwcOhFwux/79+7F06VKkp6fDy8sLvXr1wtSpUzWxMpkMO3fuxMiRIxEYGAgbGxuEhYVpjVvk6+uLXbt2Ydy4cVi2bBk8PT3x/fff85J7IqJK6vnz53j99deRnJyMuXPn4tVXX4WZmRkOHz6MiRMnokOHDnB0dDR2M19I35AyVP6MfsissEk9grSXlxcOHz6MJ0+eICsrCzdu3MDChQt1Tmz28fHB7t27kZGRgUePHuHLL7/UufQuKCgIZ8+eRXZ2Nm7evMlRqomIjCgoKAgRERGIiIiAg4MDnJ2dMW3aNAiCgNmzZ2tdbazWpEkTTJs2DQDw6aef4s6dOzhx4gTCwsJQv3591K1bF0OHDkVcXBxsbW0BAD/99BNatGgBOzs7uLu7o0+fPlqnWERHR0MikWDXrl1o1KgRLC0t0bp1a1y8eBEAkJ6eDnt7e2zZskWrLdu3b4eNjQ1SU1M1p3s0bdoUMpkMb775JgDg1KlT6Ny5M5ydneHg4ID27dvjzJkzWtvJf5hJ3dO0detWvPHGG7C2tkbjxo0RExOj9ZyjR4+ibdu2sLKygpeXFz766COkp6cXO/eTJk1C3bp1YW1tjVq1amHatGlal6fPnDkTTZo0wZo1a+Dt7Q1bW1t8+OGHUCqVWLhwIdzd3eHq6op58+bpbFvd42VlZYVatWrp5O3kyZNo2rQpLC0t0aJFC5w9e1ZrvVKpxODBg+Hr6wsrKyv4+/tj2bJlxd63l8EzvoiIqhBBEJCbnl4qk7KE8SU9TLVu3TqYmZnh5MmTWLZsGRYvXozvv/8e4eHhuHLlCk6dOqWJPXv2LM6fP49BgwZBpVJh06ZN6Nu3Lzw8PHS2a2trq/mnWKFQYM6cOTh37hy2b9+OO3fuFPoP8YQJE7Bo0SKcOnUKLi4u6N69OxQKBWxsbNC7d2+sXbtWK37t2rV45513YGdnp7mwZ//+/Xjw4AF++uknAOIl5GFhYTh69ChiY2Ph5+eHbt26vfDS8ilTpuDjjz9GXFwc6tatiw8++EBzOfnNmzfRpUsX9OrVC+fPn8cvv/yCo0ePIiIioth5t7OzQ2RkJC5fvoxly5bhu+++w5IlS7Ribt68iT/++AN79uzBzz//jB9++AGhoaG4f/8+Dh8+jM8//xxTp07FiRMntJ43bdo09OrVC+fOnUPfvn3Ru3dvXLlyBQCQlpaGN998E/Xr18fp06cxc+ZMfPzxx1rPV6lU8PT0xObNm3H58mVMnz4dn376KX799ddi75/BBHqh5ORkAYCQnJyssy4nJ0fYvn27kJOTY4SWVV7Mm2GYN8NU1bxlZmYKly9fFjIzMzXLFGlpwg7AKJMiLa3YbW/fvr0QEBAgqFQqzbJJkyYJAQEBgiAIQteuXYWRI0dq1o0ePVoICgoSBEEQEhMTBQDC4sWLS5yzU6dOCQCE1NRUQRAE4dChQwIAYdOmTZqYJ0+eCFZWVsIvv/wiCIIgnDhxQpDJZMLDhw81r29mZiZER0cLgiAIt2/fFgAIZ8+eFZRKpfDs2TNBqVTqvLZSqRTs7OyEHTt2aJYBELZt26a1ne+//16z/tKlSwIA4cqVK4IgCMLgwYOFYcOGaW33zz//FKRSqeZ90L59e2HMmDHFzskXX3whNG/eXDM/Y8YMwdraWkhJSdEsCwkJEWrWrKm1X/7+/sL8+fO19mXEiBFa227VqpXm9/jNN98I1atX13q/rlq1SgAgnD59Wm/eRo0aJfTq1Utv+wv7O1Ar6vu7IPYQERGRUbRu3VprzJnAwEDcuHEDSqUSQ4cOxc8//4ysrCzk5ORg48aNCA8PB4AS9USdPn0a3bt3h7e3N+zs7NC+fXsA4k3B88t/1XG1atXg7++v6dlo2bIlGjRogHXr1gEA1q9fDx8fH61bTBUmMTERQ4cOhZ+fHxwcHGBvb4+0tDSd1y5IfdEQkHdFtfow37lz5xAZGQlbW1vNFBISApVKhdu3bxcnJfjll1/w2muvwd3dHba2tpg6dapOm2rWrAk7OzvNvJubG+rXr691Sbybm5vW4UcAOldvBwYGavJ45coVzWFJffEAsHLlSjRv3hwuLi6wtbXFt99++8KclYYKMQ4RERGVDpm1Nbqkpb30dlQqFVJTUmBnb1/scWFkekbyN0T37t1hYWGBbdu2QS6XQ6FQ4J133gEAuLi4wNHREVevXi1yG+np6QgJCUFISAg2bNgAFxcX3L17FyEhIcjJySlRe4YMGYKVK1fik08+wdq1azFo0KAXDiAYFhaGJ0+eYNmyZfDx8YGFhQUCAwNf+Nr5x85Rv4Z6nJ60tDQMHz4cH330kc7zvL29X7gfMTEx6Nu3L2bNmoWQkBA4ODhg06ZNmnt9FtYGdTsKW6ZuV2nZtGkTPv74YyxatAiBgYGws7PDF198oXNoriywICIiqkIkEgnMbGxeejsqlQoypRJmNjZlNsBgwS859Xk2MpkMgFhQrF27FnK5HL1799YMpCuVStG7d2/89NNPmDFjhs55RGlpabC0tMTVq1fx5MkTLFiwAF5eXgCAv/76q9C2xMbGagqKZ8+e4fr16wgICNCs79evHyZOnIivvvoKly9fRlhYmGad+i7rSqVSa5vHjh3D119/jW7dugEA7t27h8ePH5csSQU0a9YMly9fRp06dQx6/vHjx+Hj44MpU6Zolv3zzz8v1ab8YmNjMWDAAK35pk2bAhBvrP7TTz8hKytL00sUGxur0742bdrgww8/1Cy7efNmqbWvKDxkRkRERnH37l2MHz8e165dw88//4zly5djzJgxmvVDhgzBwYMHsWfPHs3hMrV58+bBy8sLrVq1wo8//ojLly/jxo0bWLNmDZo2bYq0tDR4e3tDLpdj+fLluHXrFn7//XfMmTOn0LbMnj0bBw4cwMWLFzFw4EA4OzujR48emvVOTk7o2bMnJkyYgODgYK27Kri6usLKygp79uxBYmKi5kblfn5++Omnn3DlyhWcOHECffv2LfTuCCUxadIkHD9+HBEREYiLi8ONGzfwv//9T+ek6kePHiEuLk5rSkxMhJ+fH+7evYtNmzbh5s2b+Oqrr7Bt27aXalN+mzdvxpo1a3D9+nXMmDEDJ0+e1LStT58+kEgkGDp0KC5fvozdu3fjyy+/1Hq+n58f/vrrL+zduxfXr1/HtGnTtE6uL0ssiIiIyCgGDBiAzMxMtGzZEqNGjcKYMWO07l/p5+eHNm3aoF69eprbNqlVq1YNsbGx6NevH+bOnYumTZuibdu2+Pnnn/HFF1/AwcEBLi4uiIyMxObNm1G/fn0sWLBA5wtYbcGCBRgzZgyaN2+OhIQE7NixQ9PzozZ48GDk5OToFGdmZmb46quv8M0338DT0xN9+/YFAPzwww949uwZmjVrhv79++Ojjz6Cq6vrS+WsUaNGOHz4MK5fv462bduiadOmmD59uk4v2caNG9G0aVOt6bvvvsN///tfjBs3DhEREWjSpAmOHz+uGcqgNMyaNQubNm1Co0aN8OOPP+Lnn3/W3DXC1tYWO3bswIULF9C0aVNMmTIFn3/+udbzhw0bhp49e+L9999Hq1at8OTJE63eojL1wtOuiVeZlQHmzTDMm2Gqat6KurrmZRV1tVRpKM6VUCqVSqhdu7awaNGiMmmDIORdZfbs2bMXxv74449C9erVhezsbL0xZZ23qupl8lZaV5nxHCIiIqpwHj16hE2bNiEhIQGDBg0yalsyMjIQHx+PBQsWYPjw4To9R1Q18JAZERFVOK6urpg9eza+/fZbODk5GbUtCxcuRL169eDu7o7JkycbtS1UdthDRERE5S46OrrI9UI53Zw1KCjoha81c+ZMzJw5s1zaQ8bDHiIiIiIyeSyIiIiIyOSxICIiIiKTx4KIiIiITB4LIiIiIjJ5LIiIiIjI5LEgIiIiMhKJRILt27cbuxkEFkRERFRJpaSkYMqUKahXrx4sLS3h7u6OTp06YevWreU2jhEA3LlzBxKJBHFxcSV+bnx8PLp27Vqq7QkKCoJEIsGCBQt01oWGhkIikXBcpUKwICIiokrn+fPnaNOmDX788UdMnjwZZ86cwZEjR/D+++9j4sSJmjvOV3Tu7u6wsLAo9e16eXkhMjJSa9mDBw9w4MAB1KhRo9RfrypgQUREROUuKCgIERERiIiIgIODA5ydnTFt2jQIgoDZs2fjlVde0XlOkyZNNHdm//TTT3Hnzh2cOHECYWFhqF+/PurWrYuhQ4ciLi4Otra2AICffvoJLVq0gJ2dHdzd3dGnTx8kJSVpthkdHQ2JRIJdu3ahUaNGsLS0ROvWrXHx4kUAQHp6Ouzt7bFlyxattmzfvh02NjZITU2Fr68vAKBp06aQyWR48803AQCnTp1C586d4ezsDAcHB7Rv3x5nzpzR2k7+Q2bqnqatW7fijTfegLW1NRo3boyYmBit5xw9ehRt27aFlZUVvLy88NFHHyE9PV0r5s0338Tjx49x7NgxzbJ169YhODgYrq6uetug5ujoqFNQVXUsiIiIqhJBANLTjTOV8DDVunXrYGZmhpMnT2LZsmVYvHgxvv/+e4SHh+PKlSs4deqUJvbs2bM4f/48Bg0aBJVKhU2bNqFv377w8PDQ2a6trS3MzMQ7UykUCsyZMwfnzp3D9u3bcefOHQwcOFDnORMmTMCiRYtw6tQpuLi4oHv37lAoFLCxsUHv3r2xdu1arfi1a9finXfegZ2dHU6ePAkA2L9/Px48eICffvoJAJCamoqwsDAcPXoUsbGx8PPzQ7du3ZCamlpkXqZMmYKPP/4YcXFxqFu3Lj744APk5uYCAG7evIkuXbqgV69eOH/+PH755RccPXoUERERWtuQy+Xo27evVrsjIyMRHh5e5GubNIFeKDk5WQAgJCcn66zLyckRtm/fLuTk5BihZZUX82YY5s0wVTVvmZmZwuXLl4XMzMy8hWlpgiCWJuU/paUVu+3t27cXAgICBJVKpVk2adIkISAgQBAEQejataswcuRIzbrRo0cLQUFBgiAIQmJiogBAWLx4cYlzdurUKQGAkJqaKgiCIBw6dEgAIGzatEkT8+TJE8HKykr45ZdfBEEQhBMnTggymUx4+PCh5vXNzMyE6OhoQRAE4fbt2wIA4ezZs4JSqRSePXsmKJVKnddWKpWCnZ2dsGPHDs0yAMK2bdu0tvP9999r1l+6dEkAIFy5ckUQBEEYPHiwMGzYMK3t/vnnn4JUKtW8D9q3by+MGTNGiIuLE+zs7IS0tDTh8OHDgqurq6BQKITGjRsLM2bMKLQNag4ODsLatWuLm9aXVlTeXqTQv4N/FfX9XRB7iIiIyChat24NiUSimQ8MDMSNGzegVCoxdOhQ/Pzzz8jKykJOTg42btyo6d0QStATdfr0aXTv3h3e3t6ws7ND+/btAQB3797VigsMDNQ8rlatGvz9/XHlyhUAQMuWLdGgQQOsW7cOALB+/Xr4+PigXbt2Rb52YmIihg4dCj8/Pzg4OMDe3h5paWk6r11Qo0aNNI/V5/uoD/OdO3cOkZGRsLW11UwhISFQqVS4ffu21nYaN24MPz8/bNmyBWvWrEH//v01PWeki5khIqpKrK2BtLSX3oxKpUJKSgrs7e0hlRbzf2dr65d+XbXu3bvDwsIC27Ztg1wuh0KhwDvvvAMAcHFxgaOjI65evVrkNtLT0xESEoKQkBBs2LABLi4uuHv3LkJCQpCTk1Oi9gwZMgQrV67EJ598grVr12LQoEFaxVxhwsLC8OTJEyxbtgw+Pj6wsLBAYGDgC1/b3Nxc81j9GiqVCgCQlpaG4cOH46OPPtJ5nre3t86y8PBwrFy5EpcvX9Yc2itIIpHoFJkKhaLINlZFLIiIiKoSiQSwsXn57ahUgFIpbqu4BVEJnThxQmtefZ6NTCYDIBYUa9euhVwuR+/evWFlZQUAkEql6N27N3766SfMmDFD5zyitLQ0WFpa4urVq3jy5AkWLFgALy8vAMBff/1VaFtiY2M1BcWzZ89w/fp1BAQEaNb369cPEydOxFdffYXLly8jLCxMs04ulwMAlEql1jaPHTuGr7/+Gt26dQMA3Lt3D48fPy5Zkgpo1qwZLl++jDp16hQrvk+fPvj444/RuHFj1K9fv9AYFxcXxMfHa+Zv3LiBjIyMl2pnZcSCiIiIjOLu3bsYP348hg8fjjNnzmD58uVYtGiRZv2QIUM0RUn+q6UAYN68eYiOjkarVq0wb948tGjRAubm5vjzzz8xf/58nDp1Ct7e3pDL5Vi+fDlGjBiBixcvYs6cOYW2Zfbs2ahevTrc3NwwZcoUODs7o0ePHpr1Tk5O6NmzJyZMmIDg4GB4enpq1rm6usLKygp79uyBh4cHcnJyYG9vDz8/P81VbikpKZgwYYKmqDPUpEmT0Lp1a0RERGDIkCGwsbHB5cuXERUVhRUrVujEOzk5IT4+XqvXqaAOHTpgxYoVCAwMhFKpxKRJk4qMr6p4DhERERnFgAEDkJmZiZYtW2LUqFEYM2YMhg0bplnv5+eHNm3aoF69emjVqpXWc6tVq4bY2Fj069cPc+fORdOmTdG2bVv8/PPP+OKLL+Dg4AAXFxdERkZi8+bNqF+/PhYsWIAvv/yy0LYsWLAAY8aMQfPmzZGQkIAdO3Zoen7UBg8ejJycHJ0rtczMzPDVV1/hm2++gaenJ/r27QsA+OGHH/Ds2TM0a9YM/fv3x0cffaRzyXtJNWrUCIcPH8b169fRtm1bNG3aFNOnTy/0ajs1R0dH2BTRa7ho0SJ4eXmhbdu2mh4l61I8/FlZsIeIiIiMwtzcHEuXLsWqVasKXS8IAh4+fIgPP/yw0PUODg6YP38+5s+fr/c1PvjgA3zwwQc62y3o9ddf14w9pM+DBw9QvXp1vPXWWzrrhgwZgiFDhmjOvQLEcYnyDx0AQHMeVGFtqVmzpk7bHB0ddZa9+uqr2Ldvn952RkdHF7kfBUfU9vDwwN69e7WWPX/+vMhtVEXFLogePnxYZAVKRERUWh49eoRNmzYhISEBgwYNMmpbMjIyEB8fjwULFmD48OE6PUdUNRT7kFmDBg2wcePGsmwLERERAPG8nNmzZ+Pbb7+Fk5OTUduycOFC1KtXD+7u7pg8ebJR20Jlp9gF0bx58zB8+HC8++67ePr0aam8+Pz58/Hqq6/Czs4Orq6u6NGjB65du6YVk5WVhVGjRqF69eqwtbVFr169kJiYqBVz9+5dhIaGwtraGq6urpgwYYJmVE+16OhoNGvWDBYWFqhTp47JDUlORFSRREdHY+nSpXrXC4KAR48eoU+fPmXajqCgIAiCAEdHR70xM2fOhEKhwIEDBzS3BKGqp9gF0Ycffojz58/jyZMnqF+/Pnbs2PHSL3748GGMGjUKsbGxiIqKgkKhQHBwsNY9WcaNG4cdO3Zg8+bNOHz4MB4+fIiePXtq1iuVSoSGhiInJwfHjx/HunXrEBkZienTp2tibt++jdDQULzxxhuIi4vD2LFjMWTIEJ1jpkRERGSaSnRSta+vLw4ePIgVK1agZ8+eCAgI0Bn1suCN64qyZ88erfnIyEi4urri9OnTaNeuHZKTk/HDDz9g48aN6NChAwDx/jEBAQGIjY1F69atsW/fPly+fBn79++Hm5sbmjRpgjlz5mDSpEmYOXMm5HI5Vq9eDV9fX83lnAEBATh69CiWLFmCkJCQkqSAiIiIqqASX2X2zz//YOvWrXBycsJbb71VqsOAJycnAxAvpwTEIdcVCgU6deqkialXrx68vb0RExOD1q1bIyYmBg0bNoSbm5smJiQkBCNHjsSlS5fQtGlTxMTEaG1DHTN27NhC25GdnY3s7GzNvPqKAYVCoTN6p3reFEf1fBnMm2GYN8NU1bwpFAoIggCVSqUZybi0qK9sUm+fiod5M8zL5E2lUkEQBCgUCs2gnmol+ZsvUTXz3Xff4f/+7//QqVMnXLp0CS4uLiV5epFUKhXGjh2L1157Da+88goAICEhAXK5XOfYrpubGxISEjQx+Ysh9Xr1uqJiUlJSkJmZqTNQ1vz58zFr1iydNu7bt0/v2AxRUVHF3FPKj3kzDPNmmKqWNzMzM7i7uyMtLa3Et6IorhfdmZ0Kx7wZxpC85eTkIDMzE0eOHNE5f7gkI24XuyDq0qULTp48iRUrVmDAgAHFb2kxjRo1ChcvXsTRo0dLfdslNXnyZIwfP14zn5KSAi8vLwQHB8Pe3l4rVqFQICoqCp07dzbJkT0NxbwZhnkzTFXNW1ZWFu7duwdbW1tYWlqW6rYFQUBqairs7OxeeM8uysO8GeZl8paVlQUrKyu0a9dO5+9AfYSnOIpdECmVSpw/f15ruPLSEhERgZ07d+LIkSNa23d3d0dOTg6eP3+u1UuUmJgId3d3TUzBG9apr0LLH1PwyrTExETY29sXOoy6hYUFLCwsdJabm5vr/TAtah3px7wZhnkzTFXLm1KphEQigVQqLf4NWItJfdhCvX0qHubNMC+TN6lUColEUujfd0n+3ov9qlFRUaVeDAmCgIiICGzbtg0HDx6Er6+v1vrmzZvD3NwcBw4c0Cy7du0a7t69i8DAQABAYGAgLly4gKSkJK222tvba25kFxgYqLUNdYx6G0RERFVFdHQ0JBJJiUebjoyMLHL4garOqOXrqFGjsH79emzcuBF2dnZISEhAQkICMjMzAYjDsg8ePBjjx4/HoUOHcPr0aQwaNAiBgYFo3bo1ACA4OBj169dH//79ce7cOezduxdTp07FqFGjNL08I0aMwK1btzBx4kRcvXoVX3/9NX799VeMGzfOaPtORESGmzlzJiQSCbp06aKz7osvvoBEIkFQUJBOfMGpXr16mpigoKBCY0aMGKGJUS+LjY3Ves3s7GxUr14dMplM69QPiUSC7du3l96OU5kx6r3M1Pevyf+mBcRL6wcOHAgAWLJkCaRSKXr16oXs7GyEhITg66+/1sTKZDLs3LkTI0eORGBgIGxsbBAWFobZs2drYnx9fbFr1y6MGzcOy5Ytg6enJ77//nteck9EVInVqFEDhw4dwv3797WOYKxZswbe3t468Q0aNMD+/fu1lhW8Unro0KFa3x8AdC6m8fLywtq1azX/mAPAtm3bYGtrW2oDF1P5M2oPkSAIhU7qYggALC0tsXLlSjx9+hTp6enYunWr5twgNR8fH+zevRsZGRl49OgRvvzyS503eVBQEM6ePYvs7GzcvHlT6zWIiKh8BQUFISIiAhEREXBwcICzszOmTZsGQRAwe/ZszdXG+TVp0gTTpk3TzLu6uiI4OBjr1q3TLDt+/DgeP36M0NBQneerr8rLPzk7O2vFWFtb68QUvJgmLCwMmzZt0hzNAMQiLCwsrEQ5ePLkCT744AP85z//gbW1NRo2bIiff/5ZJ0+jR4/G2LFj4eTkBDc3N3z33XdIT0/HoEGDYGdnhzp16uCPP/7Q2f6xY8fQqFEjWFpaonXr1jo3r42MjIS3tzesra3x9ttv48mTJ1rrb968ibfeegtubm6wtbXFq6++qlNQViU844uIqApKT8/VO2VlKYsZq0RmZvFiDbFu3TqYmZnh5MmTWLZsGRYvXozvv/8e4eHhuHLlitad4s+ePYvz58/r3Og1PDxc61ZMa9asQd++fcv0BqzNmzdHzZo18dtvvwEQbx915MgR9O/fv0TbycrKQvPmzbFr1y5cvHgRw4YNQ//+/XUuFFq3bh2cnZ1x8uRJjB49GiNHjsS7776LNm3a4MyZMwgODkb//v11LjGfMGECFi1ahFOnTsHFxQXdu3fXjMtz4sQJDB48GBEREYiLi8Mbb7yBuXPnaj0/LS0N3bp1w4EDB3D27Fl06dIF3bt3x927d0uasspBoBdKTk4WAAjJyck663JycoTt27cLOTk5RmhZ5cW8GYZ5M0xVzVtmZqZw+fJlITMzU2cdsEPv1K3bCa1Ya+vdemPbtz+mFevsvLfQuJJq3769EBAQIKhUKs2ySZMmCQEBAYIgCELXrl2FkSNHataNHj1aCAoK0szPmDFDaNy4sZCTkyO4uroKhw8fFtLS0gQ7Ozvh3LlzwpgxY4T27dtrxUulUsHGxkZrGj58uFabzM3NdWLWr1+fL68Qtm3bJixdulR44403BEEQhFmzZglvv/228OzZMwGAsGPHDkGpVGrFF1doaKjwf//3f1ptev311zXzubm5go2NjdC/f3/Nsvj4eAGAEBMTIwiCIBw6dEgAIGzatEkT8+TJE8HKykr45ZdfBEEQhA8++EDo1q2b1mu///77goODQ5Hta9CggbB8+fJi709xKZVK4dmzZ5q8lURRfwdFfX8XZNRziIiIyHS1bt1aa8yZwMBALFq0CEqlEkOHDkV4eDgWL14MqVSKjRs3YsmSJTrbMDc3R79+/bB27VrcunULdevWRaNGjQp9PX9/f/z+++9aywoeDuvbty+mTJmitazgwL4A0K9fP3zyySe4desWIiMj8dVXXxV7v9WUSiU+++wz/Prrr3jw4AFycnKQnZ2tc85S/v2RyWSoXr06GjZsqNO+/FdbA9C6krpatWrw9/fHlStXAABXrlzB22+/rROf/5ZaaWlpmDlzJnbt2oX4+Hjk5uYiMzOzyvYQsSAiIqqC0tJ0r75Sk8m0B75LSuqsE6NSqZCSkgpHR+2C4c6dDqXTwBfo3r07LCwssG3bNsjlcigUCrzzzjuFxoaHh6NVq1a4ePEiwsPD9W5TLpejTp06Rb6ug4PDC2MAoHr16njzzTcxePBgZGVloWvXriUeZfmLL77AsmXLsHTpUjRs2BA2NjYYO3aszqjjBcfSUY+5k38eQKnfKuTjjz9GVFQUvvzyS9SpUwdWVlZ45513ymxUdGNjQUREVAXZ2BT/472wWJVKBaVSBisr2QtjDXXixAmt+djYWPj5+WnuRxUWFoa1a9dCLpejd+/ehQ6kC4hXjzVo0ADnz59Hnz59Sq19LxIeHo5u3bph0qRJOvfQKo5jx47hrbfeQr9+/QCIOb9+/bpmDL2XFRsbq7na7tmzZ7h+/ToCAgIAiDc5Lyz/Bds3cOBATU9SWloa7ty5Uyptq4hYEBERkVHcvXsX48ePx/Dhw3HmzBksX74cixYt0qwfMmSI5gv82LFjRW7r4MGDUCgURQ4smJubq7nHpZpEItE6JJaRkaETY2FhAScnJ53tdenSBY8ePdI57FbQ7du3ERcXp7XMz88Pfn5+2LJlC44fPw4nJycsXrwYiYmJpVYQzZ49G9WrV4ebmxumTJkCZ2dn9OjRAwDw0Ucf4bXXXsOXX36Jt956C3v37tU6XKZu49atW9G9e3dIJBJMmzatSt+wlgUREREZxYABA5CZmYmWLVtCJpNhzJgxGDZsmGa9n58f2rRpg6dPn6JVq1ZFbsvGxuaFr3fp0iXUqFFDa5mFhQWysrI089999x2+++47rZiQkBCdYgEQi6mCl+0XJv+9MdX+/PNPTJ06Fbdu3UJISAisra0xbNgw9OjRA8nJyS/cZnEsWLAAY8aMwY0bN9CkSRPs2LFDc/Vd69at8d1332HGjBmYPn06OnXqhKlTp2LOnDma5y9evBjh4eFo06YNnJ2dMWnSpBLdG6yykQiCIBi7ERVdSkoKHBwckJycXOjNXXfv3o1u3bpVqXsklTXmzTDMm2Gqat6ysrJw+/Zt+Pr6lvrNXcVziFJgb29fJvfkCgoKQpMmTbB06VK9MYIgwM/PDx9++GGhRUVFVNZ5q6peJm9F/R0U9f1dEHuIiIiownn06BE2bdqEhIQEnbGHiMoCCyIiIqpwXF1d4ezsjG+//bbQ83eIShsLIiIiKnfR0dFFrufZHFTeeICTiIiITB4LIiIiIjJ5LIiIiIjI5LEgIiIiIpPHgoiIiIhMHgsiIiIiMnksiIiIiMjksSAiIqJKZ+bMmZBIJOjSpYvOui+++AISiQRBQUE68QWnevXqaWKCgoIKjRkxYoQmRr2s4J3hs7OzUb16dchkMhw9erTY8RKJ5IVjMlH5YEFERESVUo0aNXDo0CHcv39fa/maNWvg7e2tE9+gQQPEx8drTfmLFwAYOnSoTszChQu1Yry8vLB27VqtZdu2bYOtrW2h7SxpPBkHCyIioiooPV3/lO/m7i+MzcwsXmxJBQUFISIiAhEREXBwcICzszOmTZsGQRAwe/ZsvPLKKzrPadKkCaZNm6aZd3V1RXBwMNatW6dZdvz4cTx+/BihoaE6zzczM4O7u7vWVPBu9dbW1joxBW8KGhYWhk2bNiEzX3LWrFmDsLCwQve1uPHR0dGQSCR4/vy5ZllcXBwkEgnu3LlT6Lap9LAgIiKqgmxt9U+9emnHurrqxtjbS+Hp6YjQUIlWbM2ahW/TEOvWrYOZmRlOnjyJZcuWYfHixfj+++8RHh6OK1eu4NSpU5rYs2fP4vz58zo3eg0PD0dkZKRmfs2aNejbty/kcrlhjSqG5s2bo2bNmvjtt98AAHfv3sWRI0fQv3//Uokn42BBRERERuHl5YUlS5bA398fffv2xejRo7FkyRJ4enoiJCRE6zDT2rVr0b59e9SqVUtrG2+++SZSUlJw5MgRpKen49dff0V4eHihr3fhwgXY2tpqTfnPDwKAr7/+Widmw4YNOtsKDw/HmjVrAACRkZHo1q0bXFxc9O5rSeOp/PHmrkREVVBamv51Mpn2fFKSboxKpUJKSgocHe0B5PUSleaRm9atW0Miydt2YGAgFi1aBKVSiaFDhyI8PByLFy+GVCrFxo0bsWTJEp1tmJubo1+/fli7di1u3bqFunXrolGjRoW+nr+/P37//XetZQUPh/Xt2xdTpkzRWubm5qazrX79+uGTTz7BrVu3EBkZia+++qrIfS1pPJU/FkRERFWQjc3LxapUgFIJWFkZvt2X0b17d1hYWGDbtm2Qy+VQKBR45513Co0NDw9Hq1atcPHiRb29QwAgl8tRp06dIl/XwcHhhTEAUL16dbz55psYPHgwsrKy0LVrV6Smpr5UvFQqHrQRBEGzTKFQvLAtVDpYEBERkVGcOHFCaz42NhZ+fn6Q/duFFRYWhrVr10Iul6N3796wKlid/atBgwZo0KABzp8/jz59+pR5u9XCw8PRrVs3TJo0SdPml4lXH0KLj4+Hk5MTAPGkaiofLIiIiMgo7t69i/Hjx2P48OE4c+YMli9fjkWLFmnWDxkyBAEBAQCAY8eOFbmtgwcPQqFQwNHRUW9Mbm4uEhIStJZJJBKtQ2IZGRk6MRYWFpoCJb8uXbrg0aNHOofd9HlRfJ06deDl5YWZM2di3rx5uH79ulY+qGyxICIiIqMYMGAAMjMz0bJlS8hkMowZMwbDhg3TrPfz80ObNm3w9OlTtGrVqsht2RTjWN6lS5dQo0YNrWUWFhbIyjcOwXfffYfvvvtOKyYkJAR79uzR2Z5EItG5bL8oL4o3NzfHzz//jJEjR6JRo0Z49dVXMXfuXLz77rvFfg0yHAsiIiIyCnNzcyxduhSrVq0qdL0gCHj48CE+/PBDnXUzZ87EzJkz9W576dKlJYoHUKwRo/Of31OQo6MjlEolUlJSih1fcP1rr72G8+fPF/s1qfSwICIiogrn0aNH2LRpExISEnTGHiIqC0Ydh+jIkSPo3r07PDw8IJFIsH37dq31AwcO1LmnTMH71jx9+hR9+/aFvb09HB0dMXjwYKQVuN70/PnzaNu2LSwtLeHl5aUzDDsREVUsrq6umD17Nr799ttCz98hKm1G7SFKT09H48aNER4ejp49exYa06VLF63BuSwsLLTW9+3bF/Hx8YiKioJCocCgQYMwbNgwbNy4EQCQkpKC4OBgdOrUCatXr8aFCxcQHh4OR0dHrWPVRERUfl50eIqHiai8GbUg6tq1K7p27VpkjIWFBdzd3Qtdd+XKFezZswenTp1CixYtAADLly9Ht27d8OWXX8LDwwMbNmxATk4O1qxZA7lcjgYNGiAuLg6LFy9mQUREREQAKsGtO6Kjo+Hq6gp/f3+MHDkST5480ayLiYmBo6OjphgCgE6dOkEqlWrGt4iJiUG7du207msTEhKCa9eu4dmzZ+W3I0REZYS9KWTKSuv9X6FPqu7SpQt69uwJX19f3Lx5E59++im6du2KmJgYyGQyJCQkwNXVVes5ZmZmqFatmmYciYSEBPj6+mrFqMecSEhIKPTYdHZ2NrKzszXz6isGFAqFzqih6nmOJloyzJthmDfDVOW8CYKAtLQ0ndMJSmO76p8qlapUt12VMW+GeZm8ZWdnQxAECIKg9zu6OCp0QdS7d2/N44YNG6JRo0aoXbs2oqOj0bFjxzJ73fnz52PWrFk6y/ft2wdra+tCnxMVFVVm7anKmDfDMG+GqYp5s7OzQ3Z2NrKysiCXy7XuDVYa8vfKU/Exb4Ypad4EQcCjR4/w9OlT3LhxQ2d9RkZGsbdVoQuigmrVqgVnZ2f8/fff6NixI9zd3ZFU4K6Eubm5ePr0qea8I3d3dyQmJmrFqOf1nZs0efJkjB8/XjOfkpICLy8vBAcH64wwqlAoEBUVhc6dO8Pc3Pyl99FUMG+GYd4MU5XzJggCkpKStMa+Ka3tZmVlwdLSstSLrKqMeTPMy+TNzMwMLVq0KPRvuyR/F5WqILp//z6ePHmiGWk0MDAQz58/x+nTp9G8eXMA4vDtKpVKM6ppYGAgpkyZAoVCoUlWVFQU/P399V7KaWFhUWj3s7m5ud4P06LWkX7Mm2GYN8NU1bx5enpCqVSW6iFBhUKBI0eOoF27dlUyZ2WFeTPMy+RNLpdrboxbUEm2ZdSCKC0tDX///bdm/vbt24iLi0O1atVQrVo1zJo1C7169YK7uztu3ryJiRMnok6dOggJCQEABAQEoEuXLhg6dChWr14NhUKBiIgI9O7dGx4eHgCAPn36YNasWRg8eDAmTZqEixcvYtmyZViyZIlR9pmIqCzIZLJi3WC0JNvLzc2FpaUlv9hLgHkzTEXIm1GvMvvrr7/QtGlTNG3aFAAwfvx4NG3aFNOnT4dMJsP58+fx3//+F3Xr1sXgwYPRvHlz/Pnnn1q9Nxs2bEC9evXQsWNHdOvWDa+//jq+/fZbzXoHBwfs27cPt2/fRvPmzfF///d/mD59Oi+5JyIiIg2j9hAFBQUVebnc3r17X7iNatWqaQZh1KdRo0b4888/S9w+IiIiMg0VfhwiIiIiorLGgoiIiIhMHgsiIiIiMnksiIiIiMjksSAiIiIik8eCiIiIiEweCyIiIiIyeSyIiIiIyOSxICIiIiKTx4KIiIiITB4LIiIiIjJ5LIiIiIjI5LEgIiIiIpPHgsiIVCoBly6lYtu2eKhUgrGbQ0REZLLMjN0AU6ZUCmjS5AhycwXcvdsRXl5Wxm4SERGRSWIPkRGZm0tRu7Y1AODatTQjt4aIiMh0sSAyMn9/WwDAtWvpRm4JERGR6WJBZGT+/jYA2ENERERkTCyIjIw9RERERMbHgsjI8goi9hAREREZCwsiI1MfMrt7NxOZmUojt4aIiMg08bJ7I3N2luPzz+uhVi1rSCTGbg0REZFpYkFkZBKJBBMn1jF2M4iIiEwaD5kRERGRyWMPUQXw6FE2YmOfQyoFQkPdjN0cIiIik8Meogrg6NGn+O9/T2HmzOvGbgoREZFJYkFUAeQfi0gQeJNXIiKi8saCqAKoXdsaUimQmpqLhIRsYzeHiIjI5LAgqgAsLGTw9eVNXomIiIyFBVEFwVt4EBERGQ8LogqCN3klIiIyHqMWREeOHEH37t3h4eEBiUSC7du3a60XBAHTp09HjRo1YGVlhU6dOuHGjRtaMU+fPkXfvn1hb28PR0dHDB48GGlp2kXF+fPn0bZtW1haWsLLywsLFy4s610rMXUP0dWrLIiIiIjKm1ELovT0dDRu3BgrV64sdP3ChQvx1VdfYfXq1Thx4gRsbGwQEhKCrKwsTUzfvn1x6dIlREVFYefOnThy5AiGDRumWZ+SkoLg4GD4+Pjg9OnT+OKLLzBz5kx8++23Zb5/JREc7IKNG5viiy/qG7spREREJseoAzN27doVXbt2LXSdIAhYunQppk6dirfeegsA8OOPP8LNzQ3bt29H7969ceXKFezZswenTp1CixYtAADLly9Ht27d8OWXX8LDwwMbNmxATk4O1qxZA7lcjgYNGiAuLg6LFy/WKpyMzdfXWnNiNREREZWvCjtS9e3bt5GQkIBOnTppljk4OKBVq1aIiYlB7969ERMTA0dHR00xBACdOnWCVCrFiRMn8PbbbyMmJgbt2rWDXC7XxISEhODzzz/Hs2fP4OTkpPPa2dnZyM7Ou/w9JSUFAKBQKKBQKLRi1fMFl1PRmDfDMG+GYd5KjjkzDPNmmLLKW0m2V2ELooSEBACAm5v2rSzc3Nw06xISEuDq6qq13szMDNWqVdOK8fX11dmGel1hBdH8+fMxa9YsneX79u2DtXXhvThRUVHF2a0iXb0KXL8uwSuvCKhV66U3VymURt5MEfNmGOat5JgzwzBvhintvGVkZBQ7tsIWRMY0efJkjB8/XjOfkpICLy8vBAcHw97eXitWoVAgKioKnTt3hrm5+Uu97q+/nsfGjfGYM8cP3bpV7YqoNPNmSpg3wzBvJcecGYZ5M0xZ5U19hKc4KmxB5O7uDgBITExEjRo1NMsTExPRpEkTTUxSUpLW83Jzc/H06VPN893d3ZGYmKgVo55XxxRkYWEBCwsLneXm5uZ6f1FFrSuugAB7APH4++9Mk/lDKo28mSLmzTDMW8kxZ4Zh3gxT2nkrybYq7DhEvr6+cHd3x4EDBzTLUlJScOLECQQGBgIAAgMD8fz5c5w+fVoTc/DgQahUKrRq1UoTc+TIEa3jiFFRUfD39y/0cJkxqcci4qX3RERE5cuoBVFaWhri4uIQFxcHQDyROi4uDnfv3oVEIsHYsWMxd+5c/P7777hw4QIGDBgADw8P9OjRAwAQEBCALl26YOjQoTh58iSOHTuGiIgI9O7dGx4eHgCAPn36QC6XY/Dgwbh06RJ++eUXLFu2TOuQWEXBm7wSEREZh1EPmf3111944403NPPqIiUsLAyRkZGYOHEi0tPTMWzYMDx//hyvv/469uzZA0tLS81zNmzYgIiICHTs2BFSqRS9evXCV199pVnv4OCAffv2YdSoUWjevDmcnZ0xffr0CnXJvZqfnw0kEuD5cwUePcqBq6vuYTsiIiIqfUYtiIKCgorsCZFIJJg9ezZmz56tN6ZatWrYuHFjka/TqFEj/Pnnnwa3s7xYWcng42OFO3cyce1aGgsiIiKiclJhzyEyVbzJKxERUfljQVTBzJ3rjxMnXkPv3h7GbgoREZHJqLCX3ZuqFi0cjd0EIiIik8MeIiIiIjJ5LIgqGIVChRUrbmP06IvIyVEZuzlEREQmgQVRBWNmJsEnn1zFihV3cOtW8e/BQkRERIZjQVTBSCSSfFeaccRqIiKi8sCCqAJS38KDBREREVH5YEFUAXEsIiIiovLFgqgCYg8RERFR+WJBVAGxh4iIiKh8sSCqgOrWFXuIHj/OwdOnOUZuDRERUdXHkaorIBsbM5w48Rpq17ZBtWpyYzeHiIioymNBVEG1bOlk7CYQERGZDB4yIyIiIpPHHqIK6uLFFKxZcw/Vq8sxZYqfsZtDRERUpbGHqIJ6+DAbS5bcxoYND4zdFCIioiqPBVEFpR6L6O+/05Gby5u8EhERlSUWRBWUl5cVrKykUCgE3LmTaezmEBERVWksiCooqVQCPz+xl+jqVY5YTUREVJZYEFVgTZs6AACOHHli5JYQERFVbSyIKrDgYBcAwL59j43cEiIioqqNBVEF1rmzMwAgOVmB9PRcI7eGiIio6uI4RBWYi4sF7tzpAG9vK0gkEmM3h4iIqMpiQVTB+fhYG7sJREREVR4PmVUSubkqCIJg7GYQERFVSSyIKoFBg+Lg4hKFc+dSjN0UIiKiKokFUSXw+HEOnj9XYO/eR8ZuChERUZXEgqgSCAlRX37PgoiIiKgssCCqBNTjER09+oyX3xMREZUBFkSVgJ+fDXx8rJCTo8Lhwxy1moiIqLRV6IJo5syZkEgkWlO9evU067OysjBq1ChUr14dtra26NWrFxITE7W2cffuXYSGhsLa2hqurq6YMGECcnMrVy+LRCLRHDbjeURERESlr0IXRADQoEEDxMfHa6ajR49q1o0bNw47duzA5s2bcfjwYTx8+BA9e/bUrFcqlQgNDUVOTg6OHz+OdevWITIyEtOnTzfGrrwU3saDiIio7FT4gRnNzMzg7u6uszw5ORk//PADNm7ciA4dOgAA1q5di4CAAMTGxqJ169bYt28fLl++jP3798PNzQ1NmjTBnDlzMGnSJMycORNyuby8d8dgHTs6o00bJ3Tu7AylUoBMxpGriYiISkuFL4hu3LgBDw8PWFpaIjAwEPPnz4e3tzdOnz4NhUKBTp06aWLr1asHb29vxMTEoHXr1oiJiUHDhg3h5uamiQkJCcHIkSNx6dIlNG3atNDXzM7ORnZ2tmY+JUUc/0ehUEChUGjFqucLLi9tNjZAdHRLAIBKlQuVqkxfrsyVV96qGubNMMxbyTFnhmHeDFNWeSvJ9ip0QdSqVStERkbC398f8fHxmDVrFtq2bYuLFy8iISEBcrkcjo6OWs9xc3NDQkICACAhIUGrGFKvV6/TZ/78+Zg1a5bO8n379sHauvBbaURFRZVk1+hfzJthmDfDMG8lx5wZhnkzTGnnLSMjo9ixFbog6tq1q+Zxo0aN0KpVK/j4+ODXX3+FlZVVmb3u5MmTMX78eM18SkoKvLy8EBwcDHt7e61YhUKBqKgodO7cGebm5mXWJrXnzxWIjn6K7t1dK/Vhs/LOW1XBvBmGeSs55swwzJthyipv6iM8xVGhC6KCHB0dUbduXfz999/o3LkzcnJy8Pz5c61eosTERM05R+7u7jh58qTWNtRXoRV2XpKahYUFLCwsdJabm5vr/UUVta60qFQC6tc/hMePcxAb+xpatXIq09crD+WRt6qIeTMM81ZyzJlhmDfDlHbeSrKtCn+VWX5paWm4efMmatSogebNm8Pc3BwHDhzQrL927Rru3r2LwMBAAEBgYCAuXLiApKQkTUxUVBTs7e1Rv379cm//y5JKJWjfvhoAjlpNRERUmip0QfTxxx/j8OHDuHPnDo4fP463334bMpkMH3zwARwcHDB48GCMHz8ehw4dwunTpzFo0CAEBgaidevWAIDg4GDUr18f/fv3x7lz57B3715MnToVo0aNKrQHqDLgeERERESlr0IfMrt//z4++OADPHnyBC4uLnj99dcRGxsLFxexKFiyZAmkUil69eqF7OxshISE4Ouvv9Y8XyaTYefOnRg5ciQCAwNhY2ODsLAwzJ4921i79NI6dxb3PTb2OZKTFXBwYJcsERHRy6rQBdGmTZuKXG9paYmVK1di5cqVemN8fHywe/fu0m6a0dSsaY26dW1w/Xo6Dh58jLffrmHsJhEREVV6FfqQGRVOfdiMo1YTERGVDhZElZD6Nh48j4iIiKh0VOhDZlS4oKDqWL26oaYwIiIiopfDgsjIBEGAKicHshJc9WZra4bhw33KsFVERESmhYfMjCjnyROcee89xPXvD0EQDNqGSmXY84iIiCgPCyIjyrhzBwnbtyN+82Y8WL++xM/fuTMRTZocQXQ0T64mIiJ6GSyIjMixeXPUnTEDAHAxIgIZ//xTouf/8UcSLlxIxZQp1wzuYSIiIiIWREZX+5NP4BQYiNyUFMQNGABBqSz2c6dM8YOlpRTHjz/D7t1JL34CERERFYoFkZFJzczQ5KefILO1xdMjR3Br8eJiP9fDwxKjR9cEAEydeo3nExERERmIBVEFYFO7NhosXQoAuDplClLOnSv2cydNqgM7OzPExaXgt9/iy6iFREREVRsLogrCKzwcbj16QFAocLZvXyizsor1vOrV5fi//6sFAJg27Rpyc1Vl2UwiIqIqiQVRBSGRSNDo228hd3VF6qVLuPrpp8V+7rhxvqhe3RzXrqVjx47EMmwlERFR1cSCqAKxcHFB4zVrAAC3lyzB4wMHivU8e3tzLF3aAP/7Xwv06OFelk0kIiKqklgQVTBuoaHwHj4cABA3cCBynj0r1vP69fPEf//rDolEUpbNIyIiqpJYEFVA9Rctgo2fH7Lu38fFUaNK/PzkZAUyM4t/+T4REZGpY0FUAZnZ2KDJ+vWQyGR4+PPPuPP118V+7po1d1Gr1kGsWHGn7BpIRERUxbAgqqCcWraE/9y5AMRRrOO3bi3W82QyCZ4+VWDBgr+RnKwoyyYSERFVGSyIKrDakyaJ5xMJAs726YMnhw+/8Dn9+nkiIMAWT58qsHjxrXJoJRERUeXHgqgCk0gkaLhyJdx69IAqOxun3noLKRcuFPkcmUyC2bPrAgAWLbqFU6eel0NLiYiIKjcWRBWcRCZDs40bUe3115GbnIwTXbq88CawPXvWQIcO1ZGerkTnzrH466/n5dNYIiKiSooFUSUgs7JCi99/h12DBsh++BAnQkKQ8+SJ3nipVIL//e9VvPaaE5KTc9G58wkkJBRv5GsiIiJTxIKokpA7OaHlnj2w9PJC+rVrOPnmm1BmZOiNt7U1wx9/tEKbNk74+ONacHe3LMfWEhERVS4siCoRK09PtNqzB+ZOTngeG4vT778PVW6u3ng7OzMcOhSIKVP8yrGVRERElQ8LokrGrn59vLpzJ6SWlkjauRNnevdGbnq63ni5PO9XnJqai7ffPoVz51LKo6lERESVBguiSqhamzZo9ssvkJiZIeG333CsTRtk3L79wudNnnwF27cnomPHGFy4wKKIiIhIjQVRJeX+3/+i9cGDkLu6IvX8efzZosULbwY7d249tGhmjydPFGjXYh82fvknBEEopxYTERFVXCyIKrHqbdui7V9/waFFCyiePsWJkBDcWrq00CJHmZmJp+u+xuQH4aiDG3ieY4m+E5Lxap3f8Oef+q9YIyIiMgUsiCo5Ky8vtDlyBJ4DBkBQKnF53DicGzgQysxMAIAyIwO3lizBwVq1cHnsWMgTb2KRx0r0r/kX5MjG6VuWaNcuBj/98LeR94SIiMh4WBBVATIrKzSOjET9pUshkclw/8cfcbxtW/z9+ediITR+PLITEmDl7Y2Gq1Yh9NYlrLs1A9EzH6KLZC+q4zGqf94TKRcvAgCUSh5GIyIi02Jm7AZQ6ZBIJKg1ZgzsGzbE6ffeQ/Lp00g+fRoAYOXjgzqffgqvgQMhlcs1zwmcMRobOh/H8Xf7QXXjNo62bIkGK1fhg+/qwMfHCt26uSIkxAWurhbG2i0iIqJywYKoinHu0AFt//oLZ/v0Qc6TJ6g9YQI8BwzQKoTyq9amDYLPncTZvn3xaN8+rA9fjBjMQ0zMM2za9BASCdCihQO6dnVFt26uaNHCETKZpJz3ioiIqGyZ1CGzlStXombNmrC0tESrVq1w8uRJYzepTFjXrInXjh/HG9euwXvIEL3FkJrc2Rktd+9G3Vmz0FhyAV/i//CeZAvqWD6EIACnTiVj9uwbaN36GKZPv6Z53rNnOTh7Nhnp6foHhyQiIqoMTKaH6JdffsH48eOxevVqtGrVCkuXLkVISAiuXbsGV1dXYzfP6CQyGepOnw6nNm1gNXgw/O+uQ/+sdXiCajiDZjiN5jiLpqj2fQROnJXD0sMD0c8bYOxv4ijY/3GTol5dG9Rr6IT/eFrD1VWOzp1d4O1tBQBQqQRIJOKhPSIioorGZAqixYsXY+jQoRg0aBAAYPXq1di1axfWrFmDTz75xMitqzhcOnVChzt3kPXgAVLi4pB89iwaxMXhrbjNSLn1JSRJAh79oQIA3EZHOGAgkuGIB4kqPEhMxYE/UzXb+szjO7xe4wHM7OwQldwU88+3g51FLqzlKthYqiARMrG0+jbY2srwYU8JmjWQQ6VQ4OKNXOw9KYG5VAm5VAkzKGEuU0JuLoVMLkOr+oC3hzmkcjkepZnj8j0zmMllkMqkkMkk//6UQiKTora3GVyqm0EikSA5TcDtB0pxnVQC6b+TRCoWaW4uZqjuJP5JZGQJuJ+gBADNenVBJ5EA1RxlcHKQARIJsrJViE9SavZbXfSpaz8H+39jAeTkqBCfVMTtVmylqOZoBkgkyM0V8DBBobU+NzcXSVee4Jrj33BwkGvaq1QKeBCvKGyTAABraymcq4mxKpWA+w/1x1pZSeFSXYwVBAH3HuiPtbSUwtU572Pk3oMc6BvaSi6XwN3VXDN//2EOVKrCY83lEtTIF/sgXqH3ZH9zcwlquOXFxicqoFBox6rz9ne1v+HjbZ0Xm6SAIqfw7UqlgKdHXu9qQpICOXpiJRLA6z95sYmPFMjO1n9xgrdnXuyjJ7nIzNSTCACeHuaQ/vsefPw0FxkZ+mP/U8Ncc0j7ybNcpKfrj/VwN4eZmRj7LFmJ1FSl1vr87zVPD0vNqPfPk5VIKRCbn5uLGSwsxNjkFCWSU/THujqbwdJSjE1NU+LZc/2xLs5msPo3Ni1diafP9MdWr2YGG2sxNj1DhSdP9f/NVXOSwdZG/PvMyFTh8RP9sU6OMtjZirFZWSokPdaNVectwfsWqlcT7yGZna1C4iP927W3k8Ex32dEQlGfEXZ5nyeFfUbkZ2MjrTyfEUIu8Pix3u2UB5MoiHJycnD69GlMnjxZs0wqlaJTp06IiYnRic/OzkZ2drZmPiVFHNVZoVBAodD+xavnCy6v7Mzc3FAtJATVQkI0yxTJyUg9dw4ZN28iOz4eXvHx6BO/GUn3nuPmfeDWEys8VLkhGQ54DkfYP4xD8sO7AIC7cEAWOiArwwzQ3JPWFkgUHzU/ORNKiCeB70dHLMNYvW2bhAV4HccAAH/idSzEJACF538MlqITxAErT+JVzMF0vdsdgVUIxW4AwHk0xBR8pjd2ENagJ7YBAK7DD/+HxXpj+2ADPsAmAMA/8EYEVuqN7YnfMAiRAIAEuGEovi8kyhXATXTDLozEagDAczigP9br3W5H7MdYLAMAZMEC72KL3tjXcBSf4HMAgADgv9ihN7YFTmEGZmvme2ELclD4SfgNcR6fYYpmvi/WIwUOhcb64ToW4/808+H4AY9QeE+uF+7ia4zSzH+IlbgH70IiXeGK4/gBQzRLxmEx/kbh9/pzwHOsR3/N/CeYj0t4pdBYC2RhC97VzM/EDJxGi0JjAWAHumseL8AkHMPremM34x1YQvw8WoKxOIiOemPXoy8cIH5efY2R+APd9Mb+gHC44hEAYA0GYRt6FhIlvtdW4kN44x4AYAP6YBM+0LvdRRiPurgBAPgNPRGJQXpjP8NkNIR4desudMNqjNQbOwMz0eKlPiMKNxZL0BEHAQCn0AKzMUNvbPE/I1wxaPJkI35GiCrTZ0QNPMQ3Tp9A8YH+95YhSvLdbBIF0ePHj6FUKuHm5qa13M3NDVevXtWJnz9/PmbNmqWzfN++fbC2ttZZDgBRUVGl09jKwM1NnJo00SyqCaCmSgWkpQEZGeKUORzKzExIMjMRkpqNVk/+QHqagKwMFbIyBWRmAtlZQGa2DN6WcggSP0AmQ41sC3RJOQmFYA4FzKCAORSCGVQqCZQqwNHNGYLlK0BuLqyTHeD7+D5UkEAQJBAAqASpZt7K1gyCeXUAgFmOJaqlPQUggQAxVoD4H7IACeQWUgjmdoAgQJorh21WGvL+kcmLAwAzcwkEmfjfH1TmsMrJ1EpR/n+ApDIJBDMLsRtBJYdFTpbe1MpkgCD7t/dAMINcka0/VipAMBNjBcG8yFgzqSpfrPwFscp8sSg6VqKCYJ7X2yHPySlBrAJyFL5tc0muVqx5Tm6xY80UuZALemKRCyHfOXXmRcYqtGLNFEq9sXKdWJXeWADasbkqyFX6Y2FuDkEivqNkuUKRsYK5HIJE/u92i44VtyvGSnPxglizvFglIFcWEWtmBkGqjpUUGSsxk2liJUrpC7Zb/Nj825WqpJDnFhErk2r+5iQqWZGxUpnEoFiozIof+4K/e6kUmr/P0vyM0I590WeEqtQ/I8wluYBcXurfpRkZGS8O+pdEMIF7Nzx8+BD/+c9/cPz4cQQGBmqWT5w4EYcPH8aJEye04gvrIfLy8sLjx49hb2+vFatQKBAVFYXOnTvD3NwcVDzMm2GYN8MwbyXHnBmGeTNMWeUtJSUFzs7OSE5O1vn+LsgkeoicnZ0hk8mQmJiotTwxMRHu7u468RYWFrCw0O3SMzc31/uLKmod6ce8GYZ5MwzzVnLMmWGYN8OUdt5Ksi2TuOxeLpejefPmOJDv5qcqlQoHDhzQ6jEiIiIi02QSPUQAMH78eISFhaFFixZo2bIlli5divT0dM1VZ0RERGS6TKYgev/99/Ho0SNMnz4dCQkJaNKkCfbs2aNzojURERGZHpMpiAAgIiICERERxm4GERERVTAmcQ4RERERUVFYEBEREZHJY0FEREREJo8FEREREZk8FkRERERk8lgQERERkcljQUREREQmjwURERERmTwWRERERGTyTGqkakMJggAASElJ0VmnUCiQkZGBlJQU3tm4BJg3wzBvhmHeSo45MwzzZpiyypv6e1v9PV4UFkTFkJqaCgDw8vIyckuIiIiopFJTU+Hg4FBkjEQoTtlk4lQqFR4+fAg7OztIJBKtdSkpKfDy8sK9e/dgb29vpBZWPsybYZg3wzBvJcecGYZ5M0xZ5U0QBKSmpsLDwwNSadFnCbGHqBikUik8PT2LjLG3t+eb3wDMm2GYN8MwbyXHnBmGeTNMWeTtRT1DajypmoiIiEweCyIiIiIyeSyIXpKFhQVmzJgBCwsLYzelUmHeDMO8GYZ5KznmzDDMm2EqQt54UjURERGZPPYQERERkcljQUREREQmjwURERERmTwWRERERGTyWBAVw8qVK1GzZk1YWlqiVatWOHnyZJHxmzdvRr169WBpaYmGDRti9+7d5dTSiqUkeYuMjIREItGaLC0ty7G1xnfkyBF0794dHh4ekEgk2L59+wufEx0djWbNmsHCwgJ16tRBZGRkmbezoilp3qKjo3XeaxKJBAkJCeXT4Apg/vz5ePXVV2FnZwdXV1f06NED165de+HzTP2zzZC88bMNWLVqFRo1aqQZdDEwMBB//PFHkc8xxnuNBdEL/PLLLxg/fjxmzJiBM2fOoHHjxggJCUFSUlKh8cePH8cHH3yAwYMH4+zZs+jRowd69OiBixcvlnPLjaukeQPEEUrj4+M10z///FOOLTa+9PR0NG7cGCtXrixW/O3btxEaGoo33ngDcXFxGDt2LIYMGYK9e/eWcUsrlpLmTe3atWta7zdXV9cyamHFc/jwYYwaNQqxsbGIioqCQqFAcHAw0tPT9T6Hn22G5Q3gZ5unpycWLFiA06dP46+//kKHDh3w1ltv4dKlS4XGG+29JlCRWrZsKYwaNUozr1QqBQ8PD2H+/PmFxr/33ntCaGio1rJWrVoJw4cPL9N2VjQlzdvatWsFBweHcmpdxQdA2LZtW5ExEydOFBo0aKC17P333xdCQkLKsGUVW3HydujQIQGA8OzZs3JpU2WQlJQkABAOHz6sN4afbbqKkzd+thXOyclJ+P777wtdZ6z3GnuIipCTk4PTp0+jU6dOmmVSqRSdOnVCTExMoc+JiYnRigeAkJAQvfFVkSF5A4C0tDT4+PjAy8uryP8eSMT32stp0qQJatSogc6dO+PYsWPGbo5RJScnAwCqVaumN4bvN13FyRvAz7b8lEolNm3ahPT0dAQGBhYaY6z3GguiIjx+/BhKpRJubm5ay93c3PSeb5CQkFCi+KrIkLz5+/tjzZo1+N///of169dDpVKhTZs2uH//fnk0uVLS915LSUlBZmamkVpV8dWoUQOrV6/Gb7/9ht9++w1eXl4ICgrCmTNnjN00o1CpVBg7dixee+01vPLKK3rj+Nmmrbh542eb6MKFC7C1tYWFhQVGjBiBbdu2oX79+oXGGuu9xrvdU4UQGBio9d9CmzZtEBAQgG+++QZz5swxYsuoqvH394e/v79mvk2bNrh58yaWLFmCn376yYgtM45Ro0bh4sWLOHr0qLGbUqkUN2/8bBP5+/sjLi4OycnJ2LJlC8LCwnD48GG9RZExsIeoCM7OzpDJZEhMTNRanpiYCHd390Kf4+7uXqL4qsiQvBVkbm6Opk2b4u+//y6LJlYJ+t5r9vb2sLKyMlKrKqeWLVua5HstIiICO3fuxKFDh+Dp6VlkLD/b8pQkbwWZ6mebXC5HnTp10Lx5c8yfPx+NGzfGsmXLCo011nuNBVER5HI5mjdvjgMHDmiWqVQqHDhwQO+xz8DAQK14AIiKitIbXxUZkreClEolLly4gBo1apRVMys9vtdKT1xcnEm91wRBQEREBLZt24aDBw/C19f3hc/h+82wvBXEzzaRSqVCdnZ2oeuM9l4r01O2q4BNmzYJFhYWQmRkpHD58mVh2LBhgqOjo5CQkCAIgiD0799f+OSTTzTxx44dE8zMzIQvv/xSuHLlijBjxgzB3NxcuHDhgrF2wShKmrdZs2YJe/fuFW7evCmcPn1a6N27t2BpaSlcunTJWLtQ7lJTU4WzZ88KZ8+eFQAIixcvFs6ePSv8888/giAIwieffCL0799fE3/r1i3B2tpamDBhgnDlyhVh5cqVgkwmE/bs2WOsXTCKkuZtyZIlwvbt24UbN24IFy5cEMaMGSNIpVJh//79xtqFcjdy5EjBwcFBiI6OFuLj4zVTRkaGJoafbboMyRs/28S/wcOHDwu3b98Wzp8/L3zyySeCRCIR9u3bJwhCxXmvsSAqhuXLlwve3t6CXC4XWrZsKcTGxmrWtW/fXggLC9OK//XXX4W6desKcrlcaNCggbBr165ybnHFUJK8jR07VhPr5uYmdOvWTThz5owRWm086svBC07qPIWFhQnt27fXeU6TJk0EuVwu1KpVS1i7dm25t9vYSpq3zz//XKhdu7ZgaWkpVKtWTQgKChIOHjxonMYbSWH5AqD1/uFnmy5D8sbPNkEIDw8XfHx8BLlcLri4uAgdO3bUFEOCUHHeaxJBEISy7YMiIiIiqth4DhERERGZPBZEREREZPJYEBEREZHJY0FEREREJo8FEREREZk8FkRERERk8lgQERERkcljQUREREQmjwUREZkcpVKJNm3aoGfPnlrLk5OT4eXlhSlTphipZURkLBypmohM0vXr19GkSRN899136Nu3LwBgwIABOHfuHE6dOgW5XG7kFhJReWJBREQm66uvvsLMmTNx6dIlnDx5Eu+++y5OnTqFxo0bG7tpRFTOWBARkckSBAEdOnSATCbDhQsXMHr0aEydOtXYzSIiI2BBREQm7erVqwgICEDDhg1x5swZmJmZGbtJRGQEPKmaiEzamjVrYG1tjdu3b+P+/fvGbg4RGQl7iIjIZB0/fhzt27fHvn37MHfuXADA/v37IZFIjNwyIipv7CEiIpOUkZGBgQMHYuTIkXjjjTfwww8/4OTJk1i9erWxm0ZERsAeIiIySWPGjMHu3btx7tw5WFtbAwC++eYbfPzxx7hw4QJq1qxp3AYSUbliQUREJufw4cPo2LEjoqOj8frrr2utCwkJQW5uLg+dEZkYFkRERERk8ngOEREREZk8FkRERERk8lgQERERkcljQUREREQmjwURERERmTwWRERERGTyWBARERGRyWNBRERERCaPBRERERGZPBZEREREZPJYEBEREZHJY0FEREREJu//AaDufvrbgsqkAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Wmi9RBLRaAfn5CuTnSz+1WgW0WgFarQAHh3y4uuYBAPLyFLh2zfW/dQUxWq0AnU4BT89MPP649AdKbq4Se/fWkdcZxgrw80tD+/ZJ8n6/+y7wv0JBH9MCCxfegU4noGHD+3j22WsApILj44/bybEF8dLUpMk9REScld/f2LEhyMtTQqsV5C9r6QtbQOPG9/DuuwWfb8OHhyItTQVRLNifXqNG9/Dpp3/I8yNGdMedO8V/3tStm4ZFi/bI85GRXfDvv87Fxnp5ZeLrr3fK8+PHd8bVq27Fxrq65mD16oLbCd57ryPOnXOH9FXRyiDWzi4fHh5b5fmPP26PEycMP2v1BEFEs2a/yfOLF7dFXFztYmMBIDh4K9Rq6cvy++9bYe9e3xJju3bdBhcX6Utvw4YgbNvmX2Lss8/uhIdHNgBgy5am2Lq1QYmxv/++F76+6QCAXbua4ODBxiXGxsT8gWvXUgAAf/zREGfOBAJwLTZ29+443Lp1FwAQG+uPa9eCio2T9nUUWVm3AABHj9bFnTstS4w9cuQ4bGwSAQDHjj2GnJw2JcbGx59CjRo3AAB//ukFoH2JsadPn8HWrdcBACdPugPoWGLshQvnsXXrFQDAxYs1ADwtrxMEEYIAAFIhdunSRWzdehEAkJDgDI1Gam95f45lZWWVOdZsBdGNGzcwZswYxMTEwE5/js1CTJkyBePHj5fn09LS4Ovri9DQULjoz/GVkUajQUxMDLp37w5b/Z88VCrmrOxEEcjM1GDbtj3o0OEZ6HS2cHOD/BfzvXvAmTMCcnNhMOXlATk5Ajp21KFZMyn28mVg2TIFcnKA3FwBeXnSaW/9z8GDdXjxRenD7OxZYPhwJTQaARoNDKa8POCdd3SYMEH6Qjt9GmjduuR/x3fe0WL2bCn26lXglVdKjh09WouoKCn21i3g1VdLjh08WIdevbQAgMzM0vdbq9Zj6NUrQM5pv34lx9av74Zevfzk+ddes0F2tlBsrErljV69esnzgmADjab4WGdnN4NYR0cb3LkjvVYoRCgUkCd3dyeDWD8/JTQaEUolDOIUCqBOHXuD2B9/VMLJSb8/w/3WqKEyiN2zRwEfHx0AEffv34GHhzuUSgEKBWBvrzCIPXtWgcaNdRCEgv3pXwsCDGKTkgS0bKk1ilP8d1dr79495LNlubkCOnbUynFF9//cc92h/zvVzk5A165ag+MWft237zNw/q9u9PAQ0KdPvhxTNLZnz05w/a+mqVcP6Ns3v9h9CgLQoUMH6P92DwgAXn45BydOHEObNq1ha2tjEP/EE+3k2FatgPDw/CL71BcOQP36beTLSsHBQESEptjjCwLg49MSTk5SwdS5M/DGGxqj96af3NyawcFB+sXv2hWIjCw51t4+ECpVIAAgLAwYM6YgVvo/XTAplY2hUEiFY8+ewNixpZ2dafDfJBk+XIOYGJT7577+Ck9ZmK0gOnbsGG7duoVWhc7larVa7N+/H19++SW2b9+OvLw8pKSkGJwlSk5Ohre3NwDA29sbR44cMdiv/im0wjFFn0xLTk6Gi4tLsWeHAECtVkOtVhstt7W1feh/qEfZ1lpV9Zzl5gKpqUBWlnQ5IyvLcGrfHvDxkWJPngQ2bDCO0U/TpgEhIVLs//4HDBsmLZeu7NoC6C0fd+VKYOhQ6fWxY0DvglVGFi1SypdTbt0C5s8vObZjR4V8GUOrBY4fLzk2K0sJW1vpxoPiTqra2AC2ttJPtbog1tkZePxxabl+0sfZ2AD16xfEurgAL7xgGKuflEqgQwcFbG2lb1hHR2DKFGm5UgkIghaXL/+FwMDGUKmUaNy4IBYAvv1WitPvq/Brb2/DWP3zGfo4haLgtZOTYeyffxbEFo5TKgFbW8PYy5cLFwlFiygBhZ+JOXiw5H+LorHr1hVdV1RB7IIF0k+NRoOtW+PQq1evIr+TBbHvvVdaGwxj33jjQbFK+dUrr0hTWWLDwqSpLLEdOkhTWTRvLk1l0agR4O8P6HR30L27Era2JX/N+vpKU1l4ekpTWbi5AUUurpTI1rbgD6iyxFb0+Yvy/tw3ZV9mK4i6du2K06dPGywbNmwYmjRpgsmTJ8PX1xe2trbYtWsX+vXrBwD466+/kJCQgODgYABAcHAwPvroI9y6dQue//1PiYmJgYuLCwIDA+WYrVu3GhwnJiZG3gdRUVotkJYmFTNpadIXtP4D4/hxYMeOgnWpqYavv/pKKnQAYNUqYNSoko+zcaP0hQ5IZ1xmzSo5dtiwgtcKhXSzYnFsbKT7TvTc3IDGjaUPMbXaeHr88YJYPz9g4sSCWJVK+gDU/3zyyYLYBg2ALVuk5YVj9K8Lf3A3bCi1V7/Oxqbgr8uiatcGrlwpOQ+FOTtLOSwLW1vg448L5jUaHbZuvYRevRrKBVZhERFl2y8AdOpU9lg/vwfH6NmY/Q5PIutitl85Z2dnPPHEEwbLHB0dUatWLXn58OHDMX78eNSsWRMuLi546623EBwcjPb/feOEhoYiMDAQgwcPxqeffoqkpCRMnToVkZGR8hmeUaNG4csvv8SkSZMQERGB3bt346effsKWLVsq9w2TWYiidNkoKQlITCz4OWQIoL+1bPVq4JNPCoqajAzDfezZU3B2Ji5OOtNQksInI/VnRuztpcnBwXAqfPW1SRMgMtI4Rj+1L3SJPyQEOH9eWq5WAwqFBnv2bMNzz/WAnZ3hX0MdOgAXLpQtV/XqAZ9+WrZYV1eg0BWQUimVZf9rlYjIXCz6b5D58+dDoVCgX79+yM3NRVhYGJYsWSKvVyqV+O233zB69GgEBwfD0dER4eHhmFXoT21/f39s2bIF48aNw8KFC1GnTh0sX76cj9xXcbm5UvFRuMjp1w/w8JDWL1sGfPCBtK64hwyefLKgIMrIkAqMotRq6Yu/8BmXJ56QLke5uEjrCk8uLjB4mmfAAGDgwIL7IkrTqpXhtqVxdpYKKD2NBlCrdfKjsUREZDqLKoj27t1rMG9nZ4fFixdj8eLFJW7j5+dndEmsqJCQEJw4caI8mkiVQBSB27ftsHmzgKefhvyY7HffSWdykpKksz5FNWlScCZHpwNu3ChYV7OmdL+Ojw/g7W14xuK554DAwIKiRv+zmNvI8PTT0lQWLFCIiKoOiyqIyProdMClS8CJE9L9OSdOACdO2ODuXekM3u7dwDPPSLHZ2cC5cwXb2tpKxY2+yHF0LFj33HNAixbSOi+v4osbPVNubCQiouqJBRFVmrw86ebhOnUKLm199ZV074whAQqFDoGBAlSqgrtve/UCYmIKCqCaNUu+OVd/NoiIiKgsWBBRhcjIkB4ll874SGd/zp6V7nf59tuCp3hatJBuOA4Kku6hadkSaNYsHzdu/I4XXuhh8Mgkz+QQEVFFYUFEj+zOHenSl/5R6/37pXt59F3GF+bmZvgUV7t20tNdhR8x1mhEJCfrjLYlIiKqKCyI6KHk5ABz5wJffy3dvDxlSkE/L02aSMXQY49JZ3wKT35+hpe5eOMxERFZAhZEZBJRlHpKHj8euHatYPl/Q8cBkM4U3bpVcJ8QERGRpStDDylEknPngNBQ4MUXpWLosceA77+XOjNcscIwlsUQERFVJTxDRGU2dy6wc6f0CPuECcC775Z9DBwiIiJLxoKISqTVAunpBZ0YfvSR1BfQhx8ajoFFRERU1fGSGRXr4EFpeIvw8IJl3t7A2rUshoiIqPrhGSIy8O+/wOTJwJo10vzly8A//0idKRIRkZXR6aS+UlJSpPkHdf1fhbEgIgDSY/Tz50uXxTIzpUfjhw+X5vX9CxERURWj00mdvaWkGE6pqcbLiluXmirtozB3d2kogNq1pam4197egEpVee+zHLAgIly4ADz7LHDlijQfHAx88QXQurV520VEheh0UlfveXkFk6nz+mWA1AmYjY308wGTIIpwP30agrMzYGdXeryNTcF+9a+LTopKultDFKWbITUaw0mfh+KW6XuU1XeYJghle11omZCfD9fLl6Vu+m1sDNfn5xsev/C/U3Hte9DrwstycoyLmbS04nvJNZV+1ACNRuqN984d4PTp0rfx8Chb4VRoRAJzYkFEqFdP+qz18QE+/RQYOLDkMcKIKp1WK32w37snTffvF7wuaT41VTqt7+goTU5OBa8dHaGwt0eTxEQoTp8GXFxKjDOY7O2L/8XQaqXTqkWnrKzil5clJifHuJjRais/9/+xAdCxPHcoCCUXSw+aAONiprQixwxsAISY5cilsLOTnpApOrm6lm25nZ20n7t3gcRE4OZNaSrudWKilP/bt6Xp1KnS2+bhARsfHzxpZycNWmkmLIisUGqq1MP0+PHS54udndTZYr16gLOzuVtHD0PQFw15eQ/+Es7IeHBMZqb0BaxWmz6pVGWLy8oyLGRKKm5SUsrnL9xClAAaA8DPP5uQZAFwcJCKJlvbgmImN7dc22ZSe1Qqw8nW9sHz+r/GtVrjKT+/2OVifj4yUlPhZG8PQacrftui+9D/LI4oFhQt5lA4F/pJv0yhKPj/Joolvy5lvSiKyMnOhp2dHYSi64s7ZuH2lLbsQevV6uILHFfX8rvvx91dmpo1KzlGp5N+f4srmooWTvn5wO3bEG7fhquZO7BjQWRFdDpg1SppmI1bt6TiZ/RoaV1p/7fpAbKzC/4S0k+3bkl/SeXlSb/whb8kSnttyjqNBsjMhE1mJp4z01/ClcrJCahZE6hRQ/pZeCq6zMVFKlRKKPa0aWn4++xZ1PPwgCI7u/TCMSdHOr4oFqwrjiAUf2bJwaHks04lxdnZFRSXhYuawq8rcdybfI0Gu7duRa9evQwGXH4g/SWrwv+HH3UqWlQULQ4etFyprPBT4PkaDXY8TL6qC4WioHAKCio5TqeTPidv3kR+QgJOx8WhVeW10ggLIisRFwe8/TZw9Kg037gx0LChedtksTIzDQubosVO0XUlfUFWEoOPdqWybF++pU36y0ZKpVRUVNTk6GhcyJRU7NSoUa43aOo0GpzeuhW+vXpB8aAvLK3W+NJWXp5xoWNnx2vNRRW+NEZUlEIh3Wfk4QExMBBJRW/ermT8X1rNJSZKPUp/95007+wMREcDUVFV7gGAR5edDfz9tzTuyPXr0pSUZFz0ZGebvm9bW+mvIQ8P6bE8Dw9pXn8DauF7IMpr3tYWcHSERqVCzKFD6P7CC7B1dOSXcnlTKqVfHF5PJqrWWBBVYzdvAk88Id2GAQAREdKI9F5e5m1XhcnJkQoefbFz/bph8ZOcXPZ9qdUFhU1ZJldX8xUiGg00zs5Sm1kMERE9FBZE1Vjt2sDJk8CXXwL9+kk9T1dpublAQkLJBU9i4oP34ewM+PtLk5+fNEJtcQWOkxOLCyIiK8KCqJrz9QU++cTcrTBBTg5w9ixqHzgAxZ9/GhY8N28++GkjR8eCgqdevYJJP+/mxkKHiIiMsCCqpjQai+nrqnipqVKPkOfPG05Xr8JWp0PbkrZzcDAscIoWPDVrsuAhIiKTsSCqpkJDpft5584FAgPN1AhRlO7bKVr0nD8vne0paTNXV9z39oZb+/ZQBAQYnvFxd2fBQ0RE5Y4FUTV06RKwd6/0RKOLSyUcUKeTLmkVV/joBwQsjo8PEBBgNOXXqoU/fv8dvcrySDQREVE5YEFUDa1aJf0MC6vAUep1OuDbb4GvvpIKn5IeVRcE6exOQIB0qkpf+DRpIt3PUxxz9V5LRERWiwVRNaPVFhREEREVdJCTJ6UurmNjC5apVECjRsZnfBo1ksaAIiIismAsiKqZHTuk23Nq1QL69CnnnWdkSL06LlggVV5OTtL8c89JZ4HYGy0REVVR/AarZlaskH4OGlR+Y/lBFIFNm6SxP/75R1r28svA/PlSPz5ERERVHAuiauTOHWnUegAYNqycdnr9OvDWW8Bvv0nz/v7A4sVAz57ldAAiIiLzY0FUjTg4AEuWSLf2NG/+iDvTaIB584CZM6Ubpm1tgUmTgPfekw5ERERUjbAgqkYcHIARI6Tpkfzxh3TT9Nmz0nznzsDSpdJN0kRERNWQwpwHX7p0KYKCguDi4gIXFxcEBwfj999/l9eHhIRAEASDadSoUQb7SEhIQO/eveHg4ABPT09MnDgR+fn5BjF79+5Fq1atoFar0aBBA6zSP4ZFhu7cAYYPB55+WiqG3N2B1auBPXtYDBERUbVm1jNEderUwZw5c9CwYUOIoojVq1fj+eefx4kTJ9C0aVMAwMiRIzFr1ix5G4dCl2u0Wi169+4Nb29vHDp0CImJiRgyZAhsbW3x8ccfAwCuXbuG3r17Y9SoUVizZg127dqFESNGwMfHB2FhYZX7hivQ559LN1EPHCiNXmESnU4qfCZOBO7elZaNHAnMmfMQOyMiIqp6zFoQ9SnyXPhHH32EpUuXIi4uTi6IHBwc4O3tXez2O3bswLlz57Bz5054eXmhRYsW+OCDDzB58mRER0dDpVLhq6++gr+/P+bOnQsACAgIwIEDBzB//vxqUxBlZwMffSR1Ch0QAHTrZsLGZ89Kl8f++EOab9ZM6myxQ4eKaCoREZFFsph7iLRaLX7++WdkZmYiODhYXr5mzRr88MMP8Pb2Rp8+fTBt2jT5LFFsbCyaNWsGLy8vOT4sLAyjR4/G2bNn0bJlS8TGxqJbkQohLCwMY8eOLbEtubm5yM3NlefT0tIAABqNBhoTe1HWx5u6nSl++UVASooN/PxEdOqUX7aOnjMzofjoIygWLICQnw/R0RG66dOhi4qSbqA2Y2/RlZGz6oT5Mh1zZhrmyzTMl+kqKmem7M/sBdHp06cRHByMnJwcODk5YePGjQj8bzTSAQMGwM/PD7Vr18apU6cwefJk/PXXX9iwYQMAICkpyaAYAiDPJyUllRqTlpaG7Oxs2BfTi/Ls2bMxc+ZMo+U7duwwuGRnipiYmIfarizmzg0G4Ing4L+wbdtfD4z3OnoUQd98A4fbtwEAie3a4fSIEcj28AAqsJ2mqsicVUfMl+mYM9MwX6ZhvkxX3jnLysoqc6zZC6LGjRsjPj4eqamp+OWXXxAeHo59+/YhMDAQr7/+uhzXrFkz+Pj4oGvXrrhy5Qrq169fYW2aMmUKxo8fL8+npaXB19cXoaGhcDFxtFSNRoOYmBh0794dthUwUOn168DJk7YQBBEffFAffn6l5OXGDSjHj4fiv86KxLp1oZ0/H+59+uCZcm/Zw6vonFU3zJfpmDPTMF+mYb5MV1E501/hKQuzF0QqlQoNGjQAALRu3RpHjx7FwoUL8fXXXxvFtmvXDgBw+fJl1K9fH97e3jhy5IhBTHJyMgDI9x15e3vLywrHuLi4FHt2CADUajXUxXTzbGtr+9D/UI+ybWnWrJF+du0qoEGDEvav0QCLFgEzZgCZmdIQG++8A2HaNNg4OpZ7m8pLReWsumK+TMecmYb5Mg3zZbryzpkp+zLrY/fF0el0BvfvFBYfHw8A8PHxAQAEBwfj9OnTuHXrlhwTExMDFxcX+bJbcHAwdu3aZbCfmJgYg/uUqiqdDli5Unpd4kCusbFAmzbAhAlSMdSxI3DihPQEmQUXQ0RERJXJrGeIpkyZgp49e6Ju3bpIT0/H2rVrsXfvXmzfvh1XrlzB2rVr0atXL9SqVQunTp3CuHHj8PTTTyMoKAgAEBoaisDAQAwePBiffvopkpKSMHXqVERGRspneEaNGoUvv/wSkyZNQkREBHbv3o2ffvoJW7ZsMedbLxf37wOtWklPmb3wQjEBe/cCXbtKlVPNmsBnnwFDhwIKi6uDiYiIzMqsBdGtW7cwZMgQJCYmwtXVFUFBQdi+fTu6d++OGzduYOfOnViwYAEyMzPh6+uLfv36YerUqfL2SqUSv/32G0aPHo3g4GA4OjoiPDzcoN8if39/bNmyBePGjcPChQtRp04dLF++vFo8cl+rFrBxo1QQFXv1b+ZMqRh69lnpVJK7e6W3kYiIqCowa0H07bfflrjO19cX+/bte+A+/Pz8sHXr1lJjQkJCcOLECZPbV1UUWwwdOSKdIbKxkYbdYDFERERUIl47qaIOHwYuXy4l4NNPpZ8DBwJ16lRKm4iIiKoqFkRVVGQk0LAhsH59MSsvXQL+66sJEydWaruIiIiqIhZEVdDJk8CxY1KH0l27FhPw+eeAKEr3Dv03BAoRERGVjAVRFaR/1P7554u5NSgpSRqoFQAmTarUdhEREVVVLIiqmNxc4IcfpNfF9j30xRdSUHAw8NRTldo2IiKiqooFURWzeTNw9y7w2GNAaGiRlenpwJIl0utJkwBBqPT2ERERVUUsiKqYFSukn+HhgFJZZOWyZUBKCtC4MfDcc5XdNCIioiqLBVEVkpEhPW4PAMOGFVmZlwfMmye9njiRvVETERGZwOyDu1LZOTkBN24A+/cD/42HW+DHH4F//wV8fIBBg8zSPiIioqqKpxGqGAcHoEePIgt1OmmcMgAYOxb4bxw3IiIiKhsWRFVERobUtVCxtm4Fzp4FnJ2BN96o1HYRERFVByyIqoioKCAwENi+vZiV+mE6Ro0CXF0rtV1ERETVAe8hqgLS0oCffwaysgAXlyIrY2OBP/6Quq0eO9YczSMiIqryeIaoCli/XiqGmjQB2rcvslJ/dmjwYKB27UpvGxERUXXAgqgK0Pc9FBFRpK/FCxeA//1Pej1hQqW3i4iIqLpgQWThzp0D4uKkThgHDy6ycu5c6U7r558HAgLM0j4iIqLqgAWRhdMP5Prss4C3d6EViYnAd99JrzmIKxER0SNhQWTBNJqCmsdoINeFC6XeqTt2BDp0qPS2ERERVSd8ysyCKZXAmjXATz8BPXsWWpGaCixdKr2ePNksbSMiIqpOWBBZMIUC6NZNmgx88430LH5AANC7t1naRkREVJ3wkllVk5sLLFggveYgrkREROWC36YW6ptvpCfpL14ssmLNGuDmTanPoYEDzdI2IiKi6oaXzCyQKALz50vdDDVpAjRq9N+KwoO4jhsHqFRmayMREVF1wjNEFiguTiqGHByAV14ptOK336QVrq7A66+brX1ERETVDQsiC6Tvmfrll4uMXfbJJ9LP0aOLGdSMiIiIHhYLIguTmQmsWye9Nuh76OBB4NAh6TLZ22+bpW1ERETVFQsiC/PLL0BGBtCgAdCpU6EV+kFcw8MBHx+ztI2IiKi6YkFkYfSXy4YNKzSQ67lzwK+/SgveecdsbSMiIqquWBBZEK0WaNUK8PIChgwptOLzz6WfL7wANG5sjqYRERFVayyILIhSKT1u/++/QJ06/y385x/ghx+k1xymg4iIqEKwILJASmWhmYULpVFen34aaNfObG0iIiKqzsxaEC1duhRBQUFwcXGBi4sLgoOD8fvvv8vrc3JyEBkZiVq1asHJyQn9+vVDcnKywT4SEhLQu3dvODg4wNPTExMnTkR+fr5BzN69e9GqVSuo1Wo0aNAAq1atqoy3Z5Ljx4Hdu6W+F2UpKcDXX0uvJ00yR7OIiIisglkLojp16mDOnDk4duwY/vzzT3Tp0gXPP/88zp49CwAYN24cNm/ejJ9//hn79u3DzZs30bdvX3l7rVaL3r17Iy8vD4cOHcLq1auxatUqTJ8+XY65du0aevfujWeeeQbx8fEYO3YsRowYge3bt1f6+y3Nxx8DXbsC0dGFFn71FZCeDjzxBNCrl7maRkREVO2ZdeiOPn36GMx/9NFHWLp0KeLi4lCnTh18++23WLt2Lbp06QIAWLlyJQICAhAXF4f27dtjx44dOHfuHHbu3AkvLy+0aNECH3zwASZPnozo6GioVCp89dVX8Pf3x9y5cwEAAQEBOHDgAObPn4+wsLBKf8/FuX1beogMAPr1+29hTo50uQyQBnGVHzkjIiKi8mYxY5lptVr8/PPPyMzMRHBwMI4dOwaNRoNu3brJMU2aNEHdunURGxuL9u3bIzY2Fs2aNYOXl5ccExYWhtGjR+Ps2bNo2bIlYmNjDfahjxk7dmyJbcnNzUVubq48n5aWBgDQaDTQaDQmvS99fGnbffedAhqNEq1a6RAYqIVGAwirV8MmKQmiry/yX3pJuo/ISpQlZ1SA+TIdc2Ya5ss0zJfpKipnpuzP7AXR6dOnERwcjJycHDg5OWHjxo0IDAxEfHw8VCoV3NzcDOK9vLyQlJQEAEhKSjIohvTr9etKi0lLS0N2djbs7e2N2jR79mzMnDnTaPmOHTvg4ODwUO8zJiam2OWiCHzxRQgAV7Rtexpbt14HtFp0nTULTgDOdO+OqyVsW92VlDMqHvNlOubMNMyXaZgv05V3zrKyssoca/aCqHHjxoiPj0dqaip++eUXhIeHY9++fWZt05QpUzB+/Hh5Pi0tDb6+vggNDYWLiWOIaTQaxMTEoHv37rC1tTVaf+yYgL//toGdnYgPPgiEm1sghE2bYHPzJkQ3NzT57DM0cXZ+5PdUlTwoZ2SI+TIdc2Ya5ss0zJfpKipn+is8ZWH2gkilUqFBgwYAgNatW+Po0aNYuHAhXn31VeTl5SElJcXgLFFycjK8vb0BAN7e3jhy5IjB/vRPoRWOKfpkWnJyMlxcXIo9OwQAarUaarXaaLmtre1D/0OVtO1330k/+/YV4OFhK50y+u9+JyEyErY1az7U8aqDR8m3NWK+TMecmYb5Mg3zZbryzpkp+7K4foh0Oh1yc3PRunVr2NraYteuXfK6v/76CwkJCQgODgYABAcH4/Tp07h165YcExMTAxcXFwQGBsoxhfehj9Hvw5xEETh2THotD+T6xx/A4cOAWg289ZbZ2kZERGRNzHqGaMqUKejZsyfq1q2L9PR0rF27Fnv37sX27dvh6uqK4cOHY/z48ahZsyZcXFzw1ltvITg4GO3btwcAhIaGIjAwEIMHD8ann36KpKQkTJ06FZGRkfIZnlGjRuHLL7/EpEmTEBERgd27d+Onn37Cli1bzPnWAUgPjsXFSZPc56J+ENehQ6UxPIiIiKjCmbUgunXrFoYMGYLExES4uroiKCgI27dvR/fu3QEA8+fPh0KhQL9+/ZCbm4uwsDAsWbJE3l6pVOK3337D6NGjERwcDEdHR4SHh2PWrFlyjL+/P7Zs2YJx48Zh4cKFqFOnDpYvX24xj9wLAiCfrDpzBtiyRVo4YYJZ20VERGRNzFoQffvtt6Wut7Ozw+LFi7F48eISY/z8/LB169ZS9xMSEoITJ048VBsr1WefST/79QP+u6+KiIiIKp7F3UNktW7cANaulV5zmA4iIqJKxYLIUsyfD+TnA888A7Rta+7WEBERWRUWRJbg/n3gm2+k1zw7REREVOlYEFmCpUuBzEwgKAiwkJu9iYiIrAkLInPLzi4YxHXSJA7iSkREZAYsiMztu++AW7eAunWBV14xd2uIiIisEgsic9Jqgc8/l16/8w7ALt6JiIjMggWROR06BFy+DNSsCQwfbu7WEBERWS2zD+5q1Tp1Ak6dkooiR0dzt4aIiMhqsSAyt2bNpImIiIjMhpfMiIiIyOqxICIiIiKrx4KIiIiIrB4LIiIiIrJ6LIiIiIjI6rEgIiIiIqvHgoiIiIisHgsiIiIisnosiIiIiMjqsSAiIiIiq8eCiIiIiKweCyIiIiKyeiyIiIiIyOqxICIiIiKrx4KIiIiIrB4LIiIiIrJ6LIiIiIjI6rEgIiIiIqvHgoiIiIisHgsiIiIisnosiIiIiMjqmbUgmj17Ntq2bQtnZ2d4enrihRdewF9//WUQExISAkEQDKZRo0YZxCQkJKB3795wcHCAp6cnJk6ciPz8fIOYvXv3olWrVlCr1WjQoAFWrVpV0W+PiIiIqggbcx583759iIyMRNu2bZGfn4/33nsPoaGhOHfuHBwdHeW4kSNHYtasWfK8g4OD/Fqr1aJ3797w9vbGoUOHkJiYiCFDhsDW1hYff/wxAODatWvo3bs3Ro0ahTVr1mDXrl0YMWIEfHx8EBYWVnlvmIiogmi1Wmg0mgrZt0ajgY2NDXJycqDVaivkGNUJ82W6R8mZSqWCQvHo53fMWhBt27bNYH7VqlXw9PTEsWPH8PTTT8vLHRwc4O3tXew+duzYgXPnzmHnzp3w8vJCixYt8MEHH2Dy5MmIjo6GSqXCV199BX9/f8ydOxcAEBAQgAMHDmD+/PksiIioShNFEUlJSUhJSanQY3h7e+PGjRsQBKHCjlNdMF+me5ScKRQK+Pv7Q6VSPVIbzFoQFZWamgoAqFmzpsHyNWvW4IcffoC3tzf69OmDadOmyWeJYmNj0axZM3h5ecnxYWFhGD16NM6ePYuWLVsiNjYW3bp1M9hnWFgYxo4dW2w7cnNzkZubK8+npaUBkCpYU/8C08dX1F9u1RFzZhrmy3TVKWfJyclIS0uDh4cHHBwcKuQLWBRFZGZmwtHRkV/wZcB8me5hc6bT6ZCYmIh///0Xjz32mNG2pvyOW0xBpNPpMHbsWHTs2BFPPPGEvHzAgAHw8/ND7dq1cerUKUyePBl//fUXNmzYAABISkoyKIYAyPNJSUmlxqSlpSE7Oxv29vYG62bPno2ZM2catXHHjh0Gl+tMERMT81DbWTPmzDTMl+mqes4EQYCPjw+8vb1ha2tboQWeSqWqFgVkZWG+TPewOXN0dMTNmzdx5swZ6HQ6g3VZWVll3o/FFESRkZE4c+YMDhw4YLD89ddfl183a9YMPj4+6Nq1K65cuYL69etXSFumTJmC8ePHy/NpaWnw9fVFaGgoXFxcTNqXRqNBTEwMunfvDltb2/JuarXEnJmG+TJddclZbm4uEhISULNmTaM/7MqTKIpIT0+Hs7Mzz3iUAfNlukfJma2tLVJSUvDMM89ArVYbrNNf4SkLiyiIoqKi8Ntvv2H//v2oU6dOqbHt2rUDAFy+fBn169eHt7c3jhw5YhCTnJwMAPJ9R97e3vKywjEuLi7Ffoio1WqjpAJS0h/2w/NRtrVWzJlpmC/TVfWcabVaCIIApVJZLjeVlkT/V7cgCBV6nOqC+TLdo+RMqVRCEATY2NgY/T6b8vtt1n8pURQRFRWFjRs3Yvfu3fD393/gNvHx8QAAHx8fAEBwcDBOnz6NW7duyTExMTFwcXFBYGCgHLNr1y6D/cTExCA4OLic3gkRERFVZWYtiCIjI/HDDz9g7dq1cHZ2RlJSEpKSkpCdnQ0AuHLlCj744AMcO3YM169fx6+//oohQ4bg6aefRlBQEAAgNDQUgYGBGDx4ME6ePInt27dj6tSpiIyMlM/yjBo1ClevXsWkSZNw4cIFLFmyBD/99BPGjRtntvdOREQkCAI2bdpk7maUi4d5L9evX4cgCPLJDnMya0G0dOlSpKamIiQkBD4+PvK0fv16ANINVjt37kRoaCiaNGmCd955B/369cPmzZvlfSiVSvz2229QKpUIDg7GoEGDMGTIEIN+i/z9/bFlyxbExMSgefPmmDt3LpYvX85H7omIqrC0tDS8//77aNKkCezs7ODt7Y1u3bphw4YNEEWx0trxKF/qiYmJ6NmzZ7m2JyQkpMSnqKlkZr2H6EH/YX19fbFv374H7sfPzw9bt24tNSYkJAQnTpwwqX1ERGSZUlJS8NRTTyE1NRUffvgh2rZtCxsbG+zbtw+TJk1Cly5d4ObmZu5mPlBJfexR5ePdXkREVOlCQkIQFRWFqKgouLq6wt3dHdOmTYMoipg1a5ZB9yt6LVq0wLRp0wAA7733Hq5fv47Dhw8jPDwcgYGBaNSoEUaOHIn4+Hg4OTkBAL7//nu0adMGzs7O8Pb2xoABAwzuOd27dy8EQcCWLVsQFBQEOzs7tG/fHmfOnAEAZGZmwsXFBb/88otBWzZt2gRHR0ekp6fL97+2bNkSgiCgS5cuAICjR4+ie/fucHd3h6urKzp37ozjx48b7KfwZSb9maYNGzbgmWeegYODA5o3b47Y2FiDbQ4cOIBOnTrB3t4evr6+ePvtt5GZmVnm3E+ePBmNGjWCg4MDHn/8cUybNs3gcffo6Gi0aNECK1asQN26deHk5IQ333wTWq0Wn376Kby9veHp6YmPPvrIaN/6M1729vZ4/PHHjfJ25MgRtGzZEnZ2dmjTpo3RiQqtVovhw4fD398f9vb2aNy4MRYuXFjm9/YoWBAREVUjoigiPzOz3CdtGWJMvUy1evVq2NjY4MiRI1i4cCHmzZuH5cuXIyIiAufPn8fRo0fl2BMnTuDUqVMYNmwYdDod1q1bh4EDB6J27dpG+3VycoKNjXQBRKPR4IMPPsDJkyexadMmXL9+HUOHDjXaZuLEiZg7dy6OHj0KDw8P9OnTBxqNBo6Ojujfvz9WrlxpEL9y5Uq89NJLcHZ2lp903rlzJxITE+UiID09HeHh4Thw4ADi4uLQsGFD9OrVC+np6aXm5f3338eECRMQHx+PRo0a4bXXXpPH57xy5Qp69OiBfv364dSpU1i/fj0OHDiAqKioMufd2dkZq1atwrlz57Bw4UIsW7YM8+fPN4i5cuUKfv/9d2zbtg0//vgjvv32W/Tu3Rv//PMP9u3bh08++QRTp07F4cOHDbabNm0a+vXrh5MnT2LgwIHo378/zp8/DwDIyMjAs88+i8DAQBw7dgzR0dGYMGGCwfY6nQ516tTBzz//jHPnzmH69Ol477338NNPP5X5/T00kR4oNTVVBCCmpqaavG1eXp64adMmMS8vrwJaVj0xZ6ZhvkxXXXKWnZ0tnjt3TszOzpaXaTIyxM2AWSZNRkaZ2965c2cxICBA1Ol08rLJkyeLAQEBoiiKYs+ePcXRo0fL69566y0xJCREFEVRTE5OFgGI8+bNMzlnR48eFQGI6enpoiiK4p49e0QA4rp16+SYu3fvivb29uL69etFURTFw4cPi0qlUrx586Z8fBsbG3Hv3r2iKIritWvXRADiiRMnRFEURa1WK96/f1/UarUGx9ZqtaKzs7O4efNmeRkAcePGjQb7Wb58ubz+7NmzIgDx/PnzoiiK4vDhw8XXX3/dYL9//PGHqFAo5P8HnTt3FseMGVPmnHz22Wdi69at5fkZM2aIDg4OYlpamrwsLCxMrFevnsF7aty4sTh79myD9zJq1CiDfbdr107+d/z666/FWrVqGfx/Xbp0qQhAPHbsWLE5E0VRjIyMFPv161di+4v7PdAz5fubZ4iIiMgs2rdvb9AJX3BwMC5dugStVouRI0fixx9/RE5ODvLy8rB27VpEREQAePD9p4UdO3YMffr0Qd26deHs7IzOnTsDABISEgziCnfDUrNmTTRu3Fg+s/Hkk0+iadOmWL16NQDghx9+gJ+fn8GYm8VJTk7GyJEj0bBhQ7i6usLFxQUZGRlGxy5K/xQ1UNDFjP4y38mTJ7Fq1So4OTnJU1hYGHQ6Ha5du1aWlGD9+vXo2LEjvL294eTkhKlTpxq1qV69enB2dpbnvby8EBgYaNBHkJeXl8HlRwBG3dkEBwfLeTx//rx8WbKkeABYvHgxWrduDQ8PDzg5OeGbb755YM7Kg0V0zEhEROVD6eCAHhkZ5bpPnU6H9LQ0OLu4lNppnvIhhzYqTp8+faBWq7Fx40Z5SIeXXnoJAODh4QE3NzdcuHCh1H1kZmYiLCwMYWFhWLNmDTw8PJCQkICwsDDk5eWZ1J4RI0Zg8eLFePfdd7Fy5UoMGzbsgT0qDx06FPfu3cPChQvh5+cHtVqN4ODgBx67cGeC+mPoOy7MyMjAG2+8gbfffttou7p16z7wfcTGxmLgwIGYOXMmwsLC4OrqinXr1smDnxfXBn07iltWdKiMR7Vu3TpMmDABc+fORXBwMJydnfHZZ58ZXZqrCCyIiIiqEUEQYOPoWK771Ol0UGq1sHF0LNeel4t+yenvs1EqlQCA8PBwrFy5EiqVCv3795dHFlAoFOjfvz++//57zJgxw+g+ooyMDNjZ2eHChQu4e/cu5syZA19fXwDAn3/+WWxb4uLi5ILi/v37uHjxIgICAuT1gwYNwqRJk7Bo0SKcO3cO4eHh8jr9KOtardZgn4cOHcKSJUvQq1cvAMCNGzdw584d05JURKtWrXDu3Dk0aNDgobY/dOgQ/Pz88P7778vL/v7770dqU2FxcXEYMmSIwXzLli0BAAEBAfj++++Rk5MjnyWKi4szal+HDh3w5ptvysuuXLlSbu0rDS+ZERGRWSQkJGD8+PH466+/8OOPP+KLL77AmDFj5PUjRozA7t27sW3bNvlymd5HH30EX19ftGvXDt999x3OnTuHS5cuYcWKFWjZsiUyMjJQt25dqFQqfPHFF7h69Sp+/fVXfPDBB8W2ZdasWdi1axfOnDmDoUOHwt3dHS+88IK8vkaNGujbty8mTpyI0NBQg2GmPD09YW9vj23btiE5ORmpqakAgIYNG+L777/H+fPncfjwYQwcOPCRx5ybPHkyDh06hKioKMTHx+PSpUv43//+Z3RT9e3btxEfH28wJScno2HDhkhISMC6detw5coVLFq0CBs3bnykNhX2888/Y8WKFbh48SJmzJiBI0eOyG0bMGAABEHAyJEjce7cOWzduhWff/65wfYNGzbEn3/+ie3bt+PixYuYNm2awc31FYkFERERmcWQIUOQnZ2NJ598EpGRkRgzZozBgN4NGzZEhw4d0KRJE3kcS72aNWsiLi4OgwYNwocffoiWLVuiU6dO+PHHH/HZZ5/B1dUVHh4eWLVqFX7++WcEBgZizpw5Rl/AenPmzMGYMWPQunVrJCUlYfPmzfKZH73hw4cjLy/PqDizsbHBokWL8PXXX6N27dp48cUXAQDLli3D/fv30apVKwwePBhvv/02PD09HylnQUFB2LdvHy5evIhOnTqhZcuWmD59utFZsrVr16Jly5YG07Jly/Dcc89h3LhxiIqKQosWLXDo0CG5K4PyMHPmTKxbtw5BQUH47rvv8OOPP8rDaDk5OWHz5s04ffo0WrZsiffffx+ffPKJwfavv/46+vbti1dffRXt2rXD3bt3Dc4WVagH3nZNfMqskjFnpmG+TFddclba0zXlqaSnph5FWZ6E0ul0Yv369cW5c+eW23GL0j9ldv/+/QfGfvfdd2KtWrXE3NzcUuMqIl/V3aPkrLyeMuM9REREZHFu376NdevWISkpCcOGDTNrW7KyspCYmIg5c+bgjTfeMDpzRNUDL5kREZHF8fT0xKxZs/DNN9+gRo0aZm3Lp59+iiZNmsDb2xtTpkwxa1uo4vAMERERVbq9e/eWul6spMFZQ0JCHnis6OhoREdHV0p7yHx4hoiIiIisHgsiIiIisnosiIiIiMjqsSAiIiIiq8eCiIiIiKweCyIiIiKyeiyIiIiIzEQQBGzatMnczSCwICIioioqLS0N77//Ppo0aQI7Ozt4e3ujW7du2LBhQ6X1YwQA169fhyAIiI+PN3nbxMRE9OzZs1zbExISAkEQMGfOHKN1vXv3hiAI7FepGCyIiIioyklJSUGHDh3w3XffYcqUKTh+/Dj279+PV199FZMmTZJHnLd03t7eUKvV5b5fX19frFq1ymDZv//+i127dsHHx6fcj1cdsCAiIqJKFxISgqioKERFRcHV1RXu7u6YNm0aRFHErFmz8MQTTxht06JFC3lk9vfeew/Xr1/H4cOHER4ejsDAQDRq1AgjR45EfHw8nJycAADff/892rRpA2dnZ3h7e2PAgAG4deuWvM+9e/dCEARs2bIFQUFBsLOzQ/v27XHmzBkAQGZmJlxcXPDLL78YtGXTpk1wdHREeno6/P39AQAtW7aEIAjo0qULAODo0aPo3r073N3d4erqis6dO+P48eMG+yl8yUx/pmnDhg145pln4ODggObNmyM2NtZgmwMHDqBTp06wt7eHr68v3n77bWRmZhrEPPvss7hz5w4OHjwoL1u9ejVCQ0Ph6elZYhv03NzcjAqq6o4FERFRdSKKQGameSYTL1OtXr0aNjY2OHLkCBYuXIh58+Zh+fLliIiIwPnz53H06FE59sSJEzh16hSGDRsGnU6HdevWYeDAgahdu7bRfp2cnGBjI41MpdFo8MEHH+DkyZPYtGkTrl+/jqFDhxptM3HiRMydOxdHjx6Fh4cH+vTpA41GA0dHR/Tv3x8rV640iF+5ciVeeuklODs748iRIwCAnTt3IjExUS6e0tPTER4ejgMHDiAuLg4NGzZEr169kJ6eXmpe3n//fUyYMAHx8fFo1KgRXnvtNeTn5wMArly5gh49eqBfv344deoU1q9fjwMHDiAqKspgHyqVCgMHDjRo96pVqxAREVHqsa2aSA+UmpoqAhBTU1NN3jYvL0/ctGmTmJeXVwEtq56YM9MwX6arLjnLzs4Wz507J2ZnZxcszMgQRak0qfwpI6PMbe/cubMYEBAg6nQ6ednkyZPFgIAAURRFsWfPnuLo0aPldW+99ZYYEhIiiqIoJicniwDEefPmmZyzo0ePigDE9PR0URRFcc+ePSIAcd26dXLM3bt3RXt7e3H9+vWiKIri4cOHRaVSKd68eVM+vo2Njbh3715RFEXx2rVrIgDxxIkToiiKolarFe/fvy9qtVqDY2u1WtHZ2VncvHmzvAyAuHHjRoP9LF++XF5/9uxZEYB4/vx5URRFcfjw4eLrr79usN8//vhDVCgU8v+Dzp07i2PGjBHj4+NFZ2dnMSMjQ9y3b5/o6ekpajQasXnz5uKMGTOKbYOeq6uruHLlyrKm9ZGVlLOyKPb34D+mfH/zDBEREZlF+/btIQiCPB8cHIxLly5Bq9Vi5MiR+PHHH5GTk4O8vDysXbtWPrshmnAm6tixY+jTpw/q1q0LZ2dndO7cGQCQkJBgEBccHCy/rlmzJho3bozz588DAJ588kk0bdoUq1evBgD88MMP8PPzw9NPP13qsZOTkzFy5Eg0bNgQrq6ucHFxQUZGhtGxiwoKCpJf6+/30V/mO3nyJFatWgUnJyd5CgsLg06nw7Vr1wz207x5czRs2BC//PILVqxYgcGDB8tnzsgYM0NEVJ04OAAZGeW6S51Oh7S0NLi4uEChKOXvaAeHcjtmnz59oFarsXHjRqhUKmg0Grz00ksAAA8PD7i5ueHChQul7iMzMxNhYWEICwvDmjVr4OHhgYSEBISFhSEvL8+k9owYMQKLFy/Gu+++i5UrV2LYsGEGxVxxhg4dinv37mHhwoXw8/ODWq1GcHDwA49ta2srv9YfQ6fTAQAyMjLwxhtv4O233zbarm7dukbLIiIisHjxYpw7d06+tFeUIAhGRaZGoym1jdURCyIioupEEABHx/Ldp04HaLXSfksriEx0+PBhg3n9fTZKpRIAEB4ejpUrV0KlUqF///6wt7cHACgUCvTv3x/ff/89ZsyYYXQfUUZGBuzs7HDhwgXcvXsXc+bMga+vLwDgzz//LLYtcXFxckFx//59XLx4EQEBAfL6QYMGYdKkSVi0aBHOnTuH8PBweZ1KpQIAaLVag30eOnQIS5YsQa9evQAAN27cwJ07d0xLUhGtWrXCuXPn0KBBgzLFDxgwABMmTEDz5s0RGBhYbIyHhwcSExPl+UuXLiErK+uR2lkVsSAiIiKzSEhIwPjx4/HGG2/g+PHj+OKLLzB37lx5/YgRI+SipPDTUgDw0UcfYe/evWjXrh0++ugjtGnTBra2tvjjjz8we/ZsHD16FHXr1oVKpcIXX3yBUaNG4cyZM/jggw+KbcusWbNQq1YteHl54f3334e7uzteeOEFeX2NGjXQt29fTJw4EaGhoahTp468ztPTE/b29ti2bRvq1KkDlUoFQRDQsGFD+Sm3tLQ0TJw4US7qHtbkyZPRvn17REVFYcSIEXB0dMS5c+cQExODL7/80ii+Ro0aSExMNDjrVFSXLl3w5ZdfIjg4GFqtFpMnTy41vrriPURERGQWQ4YMQXZ2Np588klERkZizJgxeP311+X1DRs2RIcOHdCkSRO0a9fOYNuaNWsiLi4OgwYNwocffoiWLVuiU6dO+PHHH/HZZ5/B1dUVHh4eWLVqFX7++WcEBgZizpw5+Pzzz4tty5w5czBmzBi0bt0aSUlJ2Lx5s3zmR2/48OHIy8szelLLxsYGixYtwtdff43atWvjxRdfBAAsW7YM9+/fR6tWrTB48GC8/fbbRo+8myooKAj79u3DxYsX0alTJ7Rs2RLTp08v9mk7PTc3NziWctZw7ty58PX1RadOneQzSg7lePmzquAZIiIiMgtbW1ssWLAAS5cuLXa9KIq4efMm3nzzzWLXu7q6Yvbs2Zg9e3aJx3jttdfw2muvGe23qKeeekrue6gk//77L2rVqoXnn3/eaN2IESMwYsQIAAX3XLVs2dKg6wAA8n1QxbWlXr16Rm1zc3MzWta2bVvs2LGjxHbu3bu31PdRtEft2rVrY/v27QbLUlJSSt1HdVTmgujmzZulVqBERETl5fbt21i3bh2SkpIwbNgws7YlKysLiYmJmDNnDt544w2jM0dUPZT5klnTpk2xdu3acj347Nmz0bZtWzg7O8PT0xMvvPAC/vrrL4OYnJwcREZGolatWnByckK/fv2QnJxsEJOQkIDevXvDwcEBnp6emDhxotyJld7evXvRqlUrqNVqNGjQwOp64CQiqko8PT0xa9YsfPPNN6hRo4ZZ2/Lpp5+iSZMm8Pb2xpQpU8zaFqo4ZS6IPvroI7zxxht4+eWXce/evXI5+L59+xAZGYm4uDjExMRAo9EgNDTUoAvycePGYfPmzfj555+xb98+3Lx5E3379pXXa7Va9O7dG3l5eTh06BBWr16NVatWYfr06XLMtWvX0Lt3bzzzzDOIj4/H2LFjMWLECKNThEREVDn27t2LBQsWlLheFEXcvn0bAwYMqNB2hISEQBRFuLm5lRgTHR0NjUaDXbt2yUOCUDVkSm+QV69eFZ955hnRy8tL/PXXX03ZtExu3bolAhD37dsniqIopqSkiLa2tuLPP/8sx5w/f14EIMbGxoqiKIpbt24VFQqFmJSUJMcsXbpUdHFxEXNzc0VRFMVJkyaJTZs2NTjWq6++KoaFhZWpXeypunIxZ6ZhvkxXXXJWWg+95elRehG2RsyX6Syhp2qTbqr29/fH7t278eWXX6Jv374ICAgw6vWy6MB1ptCPTlyzZk0AUg+jGo0G3bp1k2OaNGmCunXrIjY2Fu3bt0dsbCyaNWsGLy8vOSYsLAyjR4/G2bNn0bJlS8TGxhrsQx8zduzYYtuRm5uL3NxceT4tLQ2A1FGVqZ1V6eOtsZOrh8WcmYb5Ml11yZlGo4EoitDpdHLHfRVB/O+mXv2xqHTMl+keJWc6nQ6iKEKj0ch9WOmZ8jtu8lNmf//9NzZs2IAaNWrg+eefL7duwHU6HcaOHYuOHTvKoxwnJSVBpVIZncr08vJCUlKSHFO4GNKv168rLSYtLQ3Z2dlG/ULMnj0bM2fONGrjjh07HvpRxJiYmIfazpoxZ6ZhvkxX1XNmY2MDb29vZGRkmNzz8sN40KCkZIj5Mt3D5CwvLw/Z2dnYv3+/0f3DpnQwaVI1s2zZMrzzzjvo1q0bzp49Cw8PD1M2L1VkZCTOnDmDAwcOlNs+H9aUKVMwfvx4eT4tLQ2+vr4IDQ2Fi4uLSfvSaDSIiYlB9+7drbKjq4fBnJmG+TJddclZTk4Obty4AScnJ9jZ2VXYcURRRHp6OpydnR84XAUxXw/jUXKWk5MDe3t7PP3000a/B/orPGVR5oKoR48eOHLkCL788ksMGTKk7C0tg6ioKPz222/Yv3+/Qe+f3t7eyMvLQ0pKisFZouTkZHh7e8sxRcdn0T+FVjim6JNpycnJcHFxKbbXULVaDbVabbTc1tb2oT88H2Vba8WcmYb5Ml1Vz5lWq4UgCFAoFKWPMfaI9Jcw9Mei0jFfpnuUnCkUCgiCUOzvsym/32U+qlarxalTp8q1GBJFEVFRUdi4cSN2794Nf39/g/WtW7eGra0tdu3aJS/766+/kJCQII9MHBwcjNOnT8sjAQPSaXAXFxd53Jbg4GCDfehjCo9uTERERNarzAVRTEyMwdmb8hAZGYkffvgBa9euhbOzM5KSkpCUlITs7GwAUi+kw4cPx/jx47Fnzx4cO3YMw4YNQ3BwMNq3bw8ACA0NRWBgIAYPHoyTJ09i+/btmDp1KiIjI+WzPKNGjcLVq1cxadIkXLhwAUuWLMFPP/2EcePGlev7ISIiMre9e/dCEASTe5tetWpVqd0PVHdmPZe3dOlSpKamIiQkBD4+PvK0fv16OWb+/Pl49tln0a9fPzz99NPw9vbGhg0b5PVKpRK//fYblEolgoODMWjQIAwZMgSzZs2SY/z9/bFlyxbExMSgefPmmDt3LpYvX46wsLBKfb9ERFQ+oqOjIQgCevToYbTus88+gyAICAkJMYovOjVp0kSOCQkJKTZm1KhRcox+WVxcnMExc3NzUatWLQiCYDB0hiAI2LRpU7m9b6o4Zh3LTCxmPJmi7OzssHjxYixevLjEGD8/P2zdurXU/YSEhODEiRMmt5GIiCyTj48P9uzZg3/++cfgCsaKFStQt25do/imTZti586dBsuKPik9cuRIgz+oARg9Xezr64uVK1fKVyoAYOPGjXByciq3joup8vFuLyIiqnQhISGIiopCVFQUXF1d4e7ujmnTpkEURcyaNUvufqWwFi1aYNq0afK8p6cnQkNDsXr1annZoUOHcOfOHfTu3dtoe303BYUnd3d3gxgHBwejmKJPF4eHh2PdunXy7R2AVISFh4eblIO7d+/itddew2OPPQYHBwc0a9YMP/74o1Ge3nrrLYwdOxY1atSAl5cXli1bhszMTAwbNgzOzs5o0KABfv/9d6P9Hzx4EEFBQbCzs0P79u2NBq9dtWoV6tatCwcHB7z44ou4e/euwforV67g+eefh5eXF5ycnNC2bVujgrI6YUFERFQNZWbmlzjl5GjLHJudXbbYh7F69WrY2NjgyJEjWLhwIebNm4fly5cjIiIC58+fNxgp/sSJEzh16pTRQK8REREGY1OuWLECAwcOrNABWFu3bo169erh//7v/wBI42nu378fgwcPNmk/OTk5aN26NbZs2YIzZ87g9ddfx+DBg42enF69ejXc3d1x5MgRvPXWWxg9ejRefvlldOjQAcePH0doaCgGDx5s1OfOxIkTMXfuXBw9ehQeHh7o06eP3FHh4cOHMXz4cERFRSE+Ph7PPPMMPvzwQ4PtMzIy0KtXL+zatQsnTpxAjx490KdPHyQkJJiasqrB5D6yrRCH7qhczJlpmC/TVZeclTZkAbC5xKlXr8MGsQ4OW0uM7dz5oMGwCu7u24uNM1Xnzp3FgIAAUafTycsmT54sBgQEiKIoij179hRHjx4tr3vrrbfEkJAQeX7GjBli8+bNxby8PNHT01Pct2+fmJGRITo7O4snT54Ux4wZI3bu3NkgXqFQiI6OjgbTG2+8YdAmW1tbo5gffvihUF4hbty4UVywYIH4zDPPiKIoijNnzhRffPFF8f79+yIAcdeuXXK+9PFl1bt3b/Gdd94xaNNTTz0lz+fn54uOjo7i4MGD5WWJiYkGQ1rt2bNHBCCuW7dOjrl7965ob28vrl+/XhRFUXzttdfEXr16GRz71VdfFV1dXUttX9OmTcUvvviizO+nrKrc0B1ERETlpX379gad8AUHB2Pu3LnQarUYOXIkIiIiMG/ePCgUCqxduxbz58832oetrS0GDRqElStX4urVq2jUqBGCgoKKPV7jxo3x66+/Giwrejls4MCBeP/99w2WFR3pAAAGDRqEd999F1evXsWqVauwaNGiMr9vPa1Wi48//hg//fQT/v33X+Tl5SE3N9fonqXC70epVKJWrVpo1qyZUfsKdz8DwKBrmZo1a6Jx48Y4f/48AOD8+fN48cUXjeK3bdsmz2dkZCA6OhpbtmxBYmIi8vPzkZ2dXW3PELEgIiKqhjIyjJ++0lMqDXsCvnWre4mxCoVh7PXrXR6tYWXUp08fqNVqbNy4ESqVChqNBi+99FKxsREREWjXrh3OnDmDiIiIEvepUqnQoEGDUo/r6ur6wBgAqFWrFp599lkMHz4cOTk56Nmzp8nDTnz22WdYuHAhFixYgGbNmsHR0RFjx441GoalaOeC+k4IC88DKPdx0yZMmICYmBh8/vnnaNCgAezt7fHSSy9VyjAx5sCCiIioGnJ0LPvH+4NiC3/RmrLfBzl8+LDBfFxcHBo2bCgP0BkeHo6VK1dCpVKhf//+xY4sAEhPjzVt2hSnTp3CgAEDyq19DxIREYFevXph8uTJRoOKlsXBgwfx/PPPY9CgQQCkPF+8eFHuVPhRxcXFyU/b3b9/HxcvXkRAQAAAICAgoNj8F23f0KFD5TNJGRkZuH79erm0zRKxICIiIrNISEjA+PHj8cYbb+D48eP44osvMHfuXHn9iBEj5C/wgwcPlrqv3bt3Q6PRlNqxYH5+vjzot54gCAaXxLKysoxi1Go1atSoYbS/Hj164Pbt2w8c4/LatWuIj483WNawYUM0bNgQv/zyCw4dOoQaNWpg3rx5SE5OLreCaNasWahVqxa8vLzw/vvvw93dHS+88AIA4O2330bHjh3x+eef4/nnn8f27dsNLpfp27hhwwb06dMHgiBg2rRp5X4WypKwICIiIrMYMmQIsrOz8eSTT0KpVGLMmDF4/fXX5fUNGzZEhw4dcO/ePbRr167UfTk6Oj7weGfPnoWPj4/BMrVajZycHHl+2bJlWLZsmUFMWFiYUbEASMVU0cf2i1N4sHC9P/74A1OnTsXVq1cRFhYGBwcHvP7663jhhReQmpr6wH2WxZw5czBmzBhcunQJLVq0wObNm+Wn79q3b49ly5ZhxowZmD59Orp164apU6figw8+kLefN28eIiIi0KFDB7i7u2Py5MkmDZZa1QiiWIbeEa1cWloaXF1dkZqa+lCj3W/duhW9evWq0oNIVibmzDTMl+mqS85ycnJw7do1+Pv7V+ho9zqdDmlpaXBxcSm3wUpDQkLQokULLFiwoMQYURTRsGFDvPnmm8UWFZaqIvJV3T1Kzkr7PTDl+5tniIiIyOLcvn0b69atQ1JSklHfQ0QVgQURERFZHE9PT7i7u+Obb74p9v4dovLGgoiIiCpd4QFQi8O7Oaiy8eImERERWT0WRERERGT1WBARERGR1WNBRERERFaPBRERERFZPRZEREREZPVYEBEREZHVY0FERERVTnR0NARBQI8ePYzWffbZZxAEASEhIUbxRacmTZrIMSEhIcXGjBo1So7RLys6Mnxubi5q1aoFQRAM+lgyNZ7MhwURERFVST4+PtizZw/++ecfg+UrVqxA3bp1jeKbNm2KxMREg+nAgQMGMSNHjjSK+fTTTw1ifH19sXLlSoNlGzduhJOTU7HtNDWezIMFERFRNZSZWfJUaHD3B8ZmZ5ct1lQhISGIiopCVFQUXF1d4e7ujmnTpkEURcyaNQtPPPGE0TYtWrTAtGnT5HlPT0+EhoZi9erV8rJDhw7hzp076N27t9H2NjY28Pb2NpiKjlbv4OBgFFN0UNDw8HCsW7cO2YWSs2LFCoSHhxf7Xssav3fvXgiCgJSUFHlZfHw8BEHA9evXi903lR8WRERE1ZCTU8lTv36GsZ6eJcf27GkYW69e8XEPY/Xq1bCxscGRI0ewcOFCzJs3D8uXL0dERATOnz+Po0ePyrEnTpzAqVOnjAZ6jYiIwKpVq+T5FStWYODAgVCpVA/XqDJo3bo16tWrh//7v/8DACQkJGD//v0YPHhwucSTebAgIiIis/D19cX8+fPRuHFjDBw4EG+99Rbmz5+POnXqICwszOAy08qVK9G5c2c8/vjjBvt49tlnkZaWhv379yMzMxM//fQTIiIiij3e6dOn4eTkZDAVvj8IAJYsWWIUs2bNGqN9RUREYMWKFQCAVatWoVevXvDw8CjxvZoaT5WPg7sSEVVDGRklr1MqDedv3So5VlHkz+byvHLTvn17CIIgzwcHB2Pu3LnQarUYOXIkIiIiMG/ePCgUCqxduxbz58832oetrS0GDRqElStX4urVq2jUqBGCgoKKPV7jxo3x66+/Giwrejls4MCBeP/99w2WeXl5Ge1r0KBBePfdd3H16lWsWrUKixYtKvW9mhpPlY8FERFRNeToWH6xOt3D7fdR9OnTB2q1Ghs3boRKpYJGo8FLL71UbGxERATatWuHM2fOlHh2CABUKhUaNGhQ6nFdXV0fGAMAtWrVwrPPPovhw4cjJycHPXv2RHp6+iPFK/6rPkVRlJdpNJoHtoXKBwsiIiIyi8OHDxvMx8XFoWHDhlD+dworPDwcK1euhEqlQv/+/WFvb1/sfpo2bYqmTZvi1KlTGDBgQIW3Wy8iIgK9evXC5MmT5TY/Srz+ElpiYiJq1KgBQLqpmioHCyIiIjKLhIQEjB8/Hm+88QaOHz+OL774AnPnzpXXjxgxAgEBAQCAgwcPlrqv3bt3Q6PRwM3NrcSY/Px8JCUlGSwTBMHgklhWVpZRjFqtlguUwnr06IHbt28bXXYryYPiGzRoAF9fX0RHR+Ojjz7CxYsXDfJBFYsFERERmcWQIUOQnZ2NJ598EkqlEmPGjMHrr78ur2/YsCE6dOiAe/fuoV27dqXuy7EM1/LOnj0LHx8fg2VqtRo5hfohWLZsGZYtW2YQExYWhm3bthntTxAEo8f2S/OgeFtbW/z4448YPXo0goKC0LZtW3z44Yd4+eWXy3wMengsiIiIyCxsbW2xYMECLF26tNj1oiji5s2bePPNN43WRUdHIzo6usR9L1iwwKR4AGXqMbrw/T1Fubm5QRRF6HQ6pKWllTm+sI4dO+LUqVNlPiaVHxZERERkcW7fvo1169YhKSnJqO8hoopg1n6I9u/fjz59+qB27doQBAGbNm0yWD906FCjMWWKjltz7949DBw4EC4uLnBzc8Pw4cORUeR501OnTqFTp06ws7ODr6+vUTfsRERkWTw9PTFr1ix88803xd6/Q1TezHqGKDMzE82bN0dERAT69u1bbEyPHj0MOudSq9UG6wcOHIjExETExMRAo9Fg2LBheP3117F27VoAQFpaGkJDQ9GtWzd89dVXOH36NCIiIuDm5mZwrZqIiCrPgy5P8TIRVTazFkQ9e/ZEz6L9whehVqvh7e1d7Lrz589j27ZtOHr0KNq0aQMA+OKLL9CrVy98/vnnqF27NtasWYO8vDysWLECKpUKTZs2RXx8PObNm8eCiIiIiABUgXuI9u7dC09PT9SoUQNdunTBhx9+iFq1agEAYmNj4ebmJhdDANCtWzcoFAocPnwYL774ImJjY/H0008bjGsTFhaGTz75BPfv3y/2VGxubi5yc3Plef3NcRqNxuROsvTx7Fyr7Jgz0zBfpqsuOcvPz4coitBqtdAV7j2xnOnP1uhvGKbSMV+me5ScabVaiKKI/Px8o99pU37HLbog6tGjB/r27Qt/f39cuXIF7733Hnr27InY2FgolUokJSXB09PTYBsbGxvUrFlT7kciKSkJ/v7+BjH6PieSkpKKLYhmz56NmTNnGi3fsWMHHBwcHuq9xMTEPNR21ow5Mw3zZbqqnjNBEODj44N79+7B2dm5wo9XWk/MZIz5Mt3D5CwrKwtZWVnYs2ePUTGVlZVV5v1YdEHUv39/+XWzZs0QFBSE+vXrY+/evejatWuFHXfKlCkYP368PJ+WlgZfX1+EhoaWuQMuPY1Gg5iYGHTv3h22trbl3dRqiTkzDfNluuqUs+TkZKSlpcHOzg4ODg4GY4OVF1EUkZmZCUdHxwrZf3XDfJnuYXOm0+mQmZmJWrVqISgoyGhb/RWesrDogqioxx9/HO7u7rh8+TK6du0Kb29v3CoyKmF+fj7u3bsn33fk7e2N5ORkgxj9fEn3JqnVaqObtwGpz4yH/fB8lG2tFXNmGubLdNUhZ4899hiUSiXu3LlTYccQRRHZ2dmwt7fnF3wZMF+me5ScKRQKPPbYYwa3xuiZ8vtdpQqif/75B3fv3pV7Gg0ODkZKSgqOHTuG1q1bA5C6b9fpdHKvpsHBwXj//feh0WjkxMTExKBx48Z8lJOIqjz9ZTNPT88KuydKo9Fg//79ePrpp6t8AVkZmC/TPUrOVCqVPDDuozBrQZSRkYHLly/L89euXUN8fDxq1qyJmjVrYubMmejXrx+8vb1x5coVTJo0CQ0aNEBYWBgAICAgAD169MDIkSPx1VdfQaPRICoqCv3790ft2rUBAAMGDMDMmTMxfPhwTJ48GWfOnMHChQsxf/58s7xnIqKKoFQqyzTA6MPuOz8/H3Z2dvyCLwPmy3SWkDOzdsz4559/omXLlmjZsiUAYPz48WjZsiWmT58OpVKJU6dO4bnnnkOjRo0wfPhwtG7dGn/88YfB5aw1a9agSZMm6Nq1K3r16oWnnnoK33zzjbze1dUVO3bswLVr19C6dWu88847mD59Oh+5JyIiIplZzxCFhISU2vnW9u3bH7iPmjVryp0wliQoKAh//PGHye0jIiIi62DWM0REREREloAFEREREVk9FkRERERk9VgQERERkdVjQURERERWjwURERERWT0WRERERGT1WBARERGR1WNBRERERFaPBRERERFZPRZEREREZPVYEBEREZHVY0FEREREVo8FkRmJoohz59KxfHkC8vN15m4OERGR1bIxdwOsmU4HBAcfRFpaPlq3dkXLlq7mbhIREZFV4hkiM1IqBQQH1wAAHDx4z8ytISIisl4siMysQwepIDp06L6ZW0JERGS9WBCZWceONQEABw+yICIiIjIXFkRm1q6dGxQKICEhG//8k23u5hAREVklFkRm5uRkg+bNXQDwshkREZG5sCCyAAWXzXhjNRERkTnwsXsLMGJEXfTq5Yn27d3M3RQiIiKrxILIAjRv7iJfNiMiIqLKx0tmREREZPVYEFmIY8dSMGXKeaxZ84+5m0JERGR1WBBZiAMH7mHOnCtYu/amuZtCRERkdVgQWQj9k2aHDt2HTieauTVERETWhQWRhWje3AUODkqkpGhw4UKGuZtDRERkVVgQWQhbWwWefNINAPsjIiIiqmwsiCwIB3olIiIyDxZEFqRjR6kg4kCvRERElcusBdH+/fvRp08f1K5dG4IgYNOmTQbrRVHE9OnT4ePjA3t7e3Tr1g2XLl0yiLl37x4GDhwIFxcXuLm5Yfjw4cjIMLwH59SpU+jUqRPs7Ozg6+uLTz/9tKLf2kMJDpYKonv38pCenm/m1hAREVkPsxZEmZmZaN68ORYvXlzs+k8//RSLFi3CV199hcOHD8PR0RFhYWHIycmRYwYOHIizZ88iJiYGv/32G/bv34/XX39dXp+WlobQ0FD4+fnh2LFj+OyzzxAdHY1vvvmmwt+fqWrUUOHy5Wdw+3YonJ3ZiTgREVFlMeu3bs+ePdGzZ89i14miiAULFmDq1Kl4/vnnAQDfffcdvLy8sGnTJvTv3x/nz5/Htm3bcPToUbRp0wYA8MUXX6BXr174/PPPUbt2baxZswZ5eXlYsWIFVCoVmjZtivj4eMybN8+gcLIU9es7mrsJREREVsdiT0Ncu3YNSUlJ6Natm7zM1dUV7dq1Q2xsLPr374/Y2Fi4ubnJxRAAdOvWDQqFAocPH8aLL76I2NhYPP3001CpVHJMWFgYPvnkE9y/fx81atQwOnZubi5yc3Pl+bS0NACARqOBRqMx6X3o403dzpoxZ6ZhvkzHnJmG+TIN82W6isqZKfuz2IIoKSkJAODl5WWw3MvLS16XlJQET09Pg/U2NjaoWbOmQYy/v7/RPvTriiuIZs+ejZkzZxot37FjBxwcHB7q/cTExJQpLjsbWLxYwJUrAhYt0sHW9qEOVy2UNWckYb5Mx5yZhvkyDfNluvLOWVZWVpljLbYgMqcpU6Zg/Pjx8nxaWhp8fX0RGhoKFxfTRqXXaDSIiYlB9+7dYVuG6kYURbz99l7cvp0HL68OaN/ezdTmV3mm5szaMV+mY85Mw3yZhvkyXUXlTH+FpywstiDy9vYGACQnJ8PHx0denpycjBYtWsgxt27dMtguPz8f9+7dk7f39vZGcnKyQYx+Xh9TlFqthlqtNlpua2v70P9QpmzboUMN/O9/yThyJA2dOnk81PGqg0fJtzVivkzHnJmG+TIN82W68s6ZKfuy2H6I/P394e3tjV27dsnL0tLScPjwYQQHBwMAgoODkZKSgmPHjskxu3fvhk6nQ7t27eSY/fv3G1xHjImJQePGjYu9XGYJ9B00sj8iIiKiymHWgigjIwPx8fGIj48HIN1IHR8fj4SEBAiCgLFjx+LDDz/Er7/+itOnT2PIkCGoXbs2XnjhBQBAQEAAevTogZEjR+LIkSM4ePAgoqKi0L9/f9SuXRsAMGDAAKhUKgwfPhxnz57F+vXrsXDhQoNLYpZGP9DrwYP3IIoc6JWIiKiimfWS2Z9//olnnnlGntcXKeHh4Vi1ahUmTZqEzMxMvP7660hJScFTTz2Fbdu2wc7OTt5mzZo1iIqKQteuXaFQKNCvXz8sWrRIXu/q6oodO3YgMjISrVu3hru7O6ZPn26Rj9zrtW7tCpVKgVu38nD1ahYfxSciIqpgZi2IQkJCSj0DIggCZs2ahVmzZpUYU7NmTaxdu7bU4wQFBeGPP/546HZWNjs7JVq3dkVs7H0cPHifBREREVEFs9ibqq1d5841odHooFZb7G1eRERE1QYLIgv18cdNMHu2YO5mEBERWQWefrBQgsBiiIiIqLKwILJwWVla3L+fZ+5mEBERVWssiCxYdPRfcHXdhvnzr5m7KURERNUaCyIL5uNjh/x8EQcP3jN3U4iIiKo1FkQWTN9j9eHDKcjP15m5NURERNUXCyIL1rSpM1xdbZCZqcWpU+nmbg4REVG1xYLIgikUAoKDpbNEhw7xshkREVFFYUFk4TjQKxERUcVjQWTh9AO9HjrEgoiIiKiisKdqC/fkk254+WUfdOhQA1qtCKWSHTYSERGVNxZEFs7JyQY//dTa3M0gIiKq1njJjIiIiKweC6IqQBRFXLqUgV9/TTJ3U4iIiKolXjKrAv75JweNGu2FUikgNTUMjo78ZyMiIipPPENUBfj62qNOHTtotSKOHk0xd3OIiIiqHRZEVQT7IyIiIqo4LIiqCH1/RBzolYiIqPyxIKoi9GeIYmNToNOJZm4NERFR9cKCqIpo3twFDg5KpKRocP58hrmbQ0REVK2wIKoibG0VaNfODQAHeiUiIipvfH67Cpk8uT7GjvXHU0/VNHdTiIiIqhUWRFVIWJinuZtARERULfGSGREREVk9FkRVzKFD9zB9+l98/J6IiKgc8ZJZFbNq1T9YtiwBOTlauW8iIiIiejQ8Q1TFdOwo9Ud06BB7rCYiIiovLIiqGH0HjX/+mYrcXK2ZW0NERFQ9sCCqYho0cISHhwq5uTocP55m7uYQERFVCyyIqhhBEAoN9Mobq4mIiMqDRRdE0dHREATBYGrSpIm8PicnB5GRkahVqxacnJzQr18/JCcnG+wjISEBvXv3hoODAzw9PTFx4kTk5+dX9lspVxzolYiIqHxZ/FNmTZs2xc6dO+V5G5uCJo8bNw5btmzBzz//DFdXV0RFRaFv3744ePAgAECr1aJ3797w9vbGoUOHkJiYiCFDhsDW1hYff/xxpb+X8qI/Q3T2LMc0IyIiKg8WXxDZ2NjA29vbaHlqaiq+/fZbrF27Fl26dAEArFy5EgEBAYiLi0P79u2xY8cOnDt3Djt37oSXlxdatGiBDz74AJMnT0Z0dDRUKlVlv51y0batG06dehqBgc7mbgoREVG1YPEF0aVLl1C7dm3Y2dkhODgYs2fPRt26dXHs2DFoNBp069ZNjm3SpAnq1q2L2NhYtG/fHrGxsWjWrBm8vLzkmLCwMIwePRpnz55Fy5Ytiz1mbm4ucnNz5fm0NOnmZY1GA41GY1L79fGmblcaQQCaNLGHTpcPna7cdmsxKiJn1RnzZTrmzDTMl2mYL9NVVM5M2Z9FF0Tt2rXDqlWr0LhxYyQmJmLmzJno1KkTzpw5g6SkJKhUKri5uRls4+XlhaSkJABAUlKSQTGkX69fV5LZs2dj5syZRst37NgBBweHh3ovMTExD7WdNWPOTMN8mY45Mw3zZRrmy3TlnbOsrKwyx1p0QdSzZ0/5dVBQENq1awc/Pz/89NNPsLe3r7DjTpkyBePHj5fn09LS4Ovri9DQULi4uJi0L41Gg5iYGHTv3h22trbl1sbr17MRHX0JKSn52LSpVbnt1xJUVM6qK+bLdMyZaZgv0zBfpquonOmv8JSFRRdERbm5uaFRo0a4fPkyunfvjry8PKSkpBicJUpOTpbvOfL29saRI0cM9qF/Cq24+5L01Go11Gq10XJbW9uH/od6lG2L4+Kiw9q1iRAEIDMTcHOrfr905Z2z6o75Mh1zZhrmyzTMl+nKO2em7MuiH7svKiMjA1euXIGPjw9at24NW1tb7Nq1S17/119/ISEhAcHBwQCA4OBgnD59Grdu3ZJjYmJi4OLigsDAwEpvf3ny8lKjfn0HiCIQF8dhPIiIiB6FRRdEEyZMwL59+3D9+nUcOnQIL774IpRKJV577TW4urpi+PDhGD9+PPbs2YNjx45h2LBhCA4ORvv27QEAoaGhCAwMxODBg3Hy5Els374dU6dORWRkZLFngKqagv6IWBARERE9Cou+ZPbPP//gtddew927d+Hh4YGnnnoKcXFx8PDwAADMnz8fCoUC/fr1Q25uLsLCwrBkyRJ5e6VSid9++w2jR49GcHAwHB0dER4ejlmzZpnrLZWrDh1q4Lvv/sGhQ+ygkYiI6FFYdEG0bt26Utfb2dlh8eLFWLx4cYkxfn5+2Lp1a3k3zSLoR74/fDgF+fk62NhY9Ak/IiIii8Vv0CosMNAZrq42yMzU4tSpdHM3h4iIqMqy6DNEVDqFQsBTT9XEjRvZSE1lB2BEREQPiwWRBRB1OgiKhztZ97//tYVSKZRzi4iIiKwLL5mZkS4vD6cjI3F23LiH3geLISIiokfHM0RmdO/gQfz931Nxbm3bos6gQQ+9r9xcLQBArVaWS9uIiIisCc8QmZH7M8+g4dSpAIBTI0ci9cSJh9rPyJEn4eq6HZs2JZdn84iIiKwGCyIzaxQdDY+ePaHLycGfffsi7+5dk/dhb69Ebq6O/RERERE9JBZEZiYolWi5Zg0c6tdH9vXrOP7aaxC1WpP20aGD1B8Re6wmIiJ6OCyILICqRg202bgRSgcH3ImJwYX33zdpe/0QHvHxacjMzK+IJhIREVVrLIgshEuzZgj69lsAwJVPPsHNX34p87a+vvaoU8cOWq2II0dSKqiFRERE1RcLIgvyWP/+ePyddwAAJ4cORfrZs2XelgO9EhERPTwWRBamyZw5qNWlC7SZmfjzxRehSUkp03b6+4h4YzUREZHpWBBZGIWNDVqtWwf7unWReekS4ocMgajTPXC7kJBaeOEFL/Tp41UJrSQiIqpeWBBZILWHB1pv2ACFWo3kzZtx6cMPH7hNUJALNm5si9Gj61V8A4mIiKoZFkQWyq11azT76isAwMXoaCRv2WLmFhEREVVfLIgsmO/QofB7801AFHFi4EBkXLpUarwoirh+PQuxsbyxmoiIyBQsiCxc0/nzUaNjR+SnpuLYiy8iPyOjxNiYmDvw99+NIUMebggQIiIia8WCyMIpVCq0/vlnqH18kH72LE4OHw5RFIuNbdvWFQBw+XIWbt3KrcxmEhERVWksiKoAOx8ftP7lFwi2tkj86Sdc/fzzYuNq1FChaVNnAMChQ7xsRkREVFYsiKqImh06oOnChQCA8+++i9s7dxYb17Ej+yMiIiIyFQuiKsRv1CjUGToU0OlwvH9/ZF2/bhTDgV6JiIhMx4KoChEEAc2WLoVr69bQ3L2LP/v2hTY72yBGP4THn3+mIjdXa45mEhERVTksiKoYpZ0d2mzYAJW7O9JOnMDpUaMMbrKuX98BHh4q5OXpcOxYqhlbSkREVHWwIKqC7OvWRav16wGFAv989x2uL14srxMEAXPmNMHPP7dCYKCzGVtJRERUdbAgqqLcu3RBwKefAgDOjRuHu3/8Ia+LiKiLl16qDTc3W3M1j4iIqEphQVSFPT5+PGr37w8xPx/HX34Z2f/+a+4mERERVUksiKowQRAQtHw5nJs1Q25yMo699BK0uVKHjH/8cRcffngJN25kP2AvRERExIKoirNxdESbjRth6+aGlLg4nB0zBgDw7rsXMG3aX9iz566ZW0hERGT5WBBVA47166Pl2rWAICDh66+R8O23hfojYgeNRERED8KCqJrw7NkTjWfNAgCcefNNNPeUzgxxCA8iIqIHY0FUjTR47z14Pf88dHl5UM9/AwBw9mw6UlI0Zm4ZERGRZbOqgmjx4sWoV68e7Ozs0K5dOxw5csTcTSpXgkKBFt99B8fGjWGfeB517O9DFIG4OJ4lIiIiKo3VFETr16/H+PHjMWPGDBw/fhzNmzdHWFgYbt26Ze6mlStbFxe02bgRSicnNMo+DgBYv/6mvD4xMQf79t3FiROpuHo1E3fu5CEvT2eu5hIREVkEG3M3oLLMmzcPI0eOxLBhwwAAX331FbZs2YIVK1bg3XffNXPrypdzQABafPcdAvt+hd3oioYnluHPl5KhUKmw+e+GiD7U1mgblY0OTmod5vS/he4tNRBUKpxIcMB3exzg4qSAs5MCzk5KuLgo4epiCxdXW7Rq7oA6j9kBgoD7qVr8/W9+wQ4FAUKh/fvWtkWtGkoAQFqGDn//owEEQR8q/fxvxqOmCPz7LzIvXkSuRomEf/+75FdoiBL9aw93G9Ryk+r6rOz/9lskVj+0iXsNJTxqSrE5uSKu3Sh0KVFuiwAIAmq6KuDlKXVsqdEAV/7Ok2Lkxhb8rOGqhI+XLSAIyM8XcelaXsH6Qm0RRRFuLkrU9rYFRBE6nYgLl3MN21vop7OTAnW8C35Fz13M1e/QIDY/Px+3Tv+L9LpnYGMr7fvClTxptUHOpB/29gL8aivlxRev5kGrr4kLxwOwVwuoV8dGfq+X/9ZAkw/D9wdAgACVCqhf11ZedyVBg7w80TD2v9e2NgIa+qvkxVcTNMjNMzy2nlIBNHq8IPb6Pxpk5xQfKwhAk/oFsQk3NcjMMo7N12iQcOxmQc4A/JOUj/QMXbF5AIDGj9tCoZDafzNJg9R0nZxTABAL/bs09LOBzX8pTrytxf1UreF+C233uK8CKlsBEEUk39XhXmqhP1D0/y8hAALgX8cGdnYKCIKA2/e0uJMiynHyb5wgzderYwN7eyUEQcDd+1rcuqffr1D0nw91H7OFo4P0u3EvRYvkO4bjIObn5yPhz1v4UzyNer72cHaW3tz9VC2SbuWjWKKI2t42cHVWAqKI1HQt/k3Uxxr/Lnu5K1DDVdpveoYW/yTmF5svESK83ZWo4Sq1NzNbh4SbJY/b6FlTIX/2ZGXr8Hcpse41FPCoKcXm5Iq4/k+hWH3O/kteTTclvDyk3408jYirCfny+nxNPhKOJ+Go9iRsbGzh5qpAbS/p/5n8GWH49mWuLgrU9pTaoNOJ+Ouqptj/j2IxnxHnL+cZxek5OihQt7aN9BkHFHxGFCUIsLcTUK9OQce+Bp8RRdipBfj7FsReup6H/BL+S6hsBdT3K4i98rcGeRqpEQrkA2buS88qCqK8vDwcO3YMU6ZMkZcpFAp069YNsbGxRvG5ubnIzc2V59PS0gAAGo0GGo1p9+Po403d7lG5P/ssIt45gjtzf0Stk3uRdFI6S5SJTngMPsiGPbLggBzYAwDy8hW4l6/Av99+gzM4BgDYhy7YiHGF9qr7b5LeywRMRWfsBwD8gafwKSaX2J6xmI+u2A0AOIo2mIUZJcaOwlL0xlYcAHAaT+A9zC4xdhhWoC82AgAuoiHewbwSY1/DWgzAjwCAv1EXUVhcYmxf/B+GYRUAIBmeGIFvS4zthS0Yja8AAClwxWD8UGJsF+zCOCwAAORAjZfxS4mxHXEA7+ITeb4PNpcY2wbJ8JnZSp7vh1+QB3WxsU/gNGbjPXl+IH5AGlyLjW2Ii5iHd+T54ViOW/AqNtYXCViCSHn+TSzGDdQtNtYTyfgWI+T5cZiHy2hYbKwrUvADBsvzU/AxzqBZsbFq5OAXvCzPR2MGjqFNsbGAL/znFORsDibjIJ4qIRb4GS/BDtLnwnyMxW50LTH2BwyEK6TPjSUYjd/Rq8TY5RgOL0hnqldgGDaib4mxi/Em6uIGAGAtXsOPGFBi7FyMRyNcAgD8H/piFYaVGPsxpqAZzgAAtqAXvsLoYqJ8ANzEDESjzX+fETvRFQsxtsT9TsYcPIWDAB78GTEGC9ANuwCU/TMCsOTPiMcAJAGwlM+Io5iBWfK8pX1G+OAmvq4xFZqhQ0t8Dw/DlO9eqyiI7ty5A61WCy8vw38kLy8vXLhwwSh+9uzZmDlzptHyHTt2wMHB4aHaEBMT81DbPZKn2qG/sx2EW27Q5ecD+fl4Kj8fT+X/Afw3r83TIjtXQHaOAlm5SnjY1ICIDkB+Phqk22JYylZka1TIyrdFllaNrHwVsnVqZGrtUNNeA1FwAQCoNEq45xT0eVT0Dw+VWgHR1hkQRdjk26BG7v1CsYXPpgAqFSDaSHlWaG3hmlvCILUCoLIFRJUTIAhQaO3gml3ygLZqtQBR7frffh3gmllKrK0IUS21V9DZwzWr5Fg7pQairZ00o1PBNa/kWHtlnhwriuqS3xsAB5s8iGpH+S/S0trrYKOB6FDwgeWalo485AAQjGIdbfIgOkndMkAQ4JKSCUE0jgMAJ5tciC615HnnlGzk6tKKjXVWZkN0rlGwbVoOXA1iC/5XOAtZEJ0L2uuUmQNXbfHvz0XINIh1zMwtMVaNPIgurtJf1IIAhywNXPNLzpvoUrBf+6z8IrFFcuLiClHI+y9WC1dN8XkAALi6QVRKfwnbZQKuuSXHCjVqQFRKuVFnKuGaXUqsqwtEpfTvoc4uLVaEwtEeolJ6f6pcodT/awo7NUQbaexD2zxFqf+HlWobiEpHKTa/9P3aqJUQbZ2k1/k2pcba2tlAVEntVWrU//0uF///UmVvA1Et/V9T5DvANaPknKnslBDta/4X6wTX9JJj1XaKMsaKUKsA0U5qr6BzgmtGKZ8RNlqIaqf/zvQ4lPp5Ym+TD9FO+rcQRfUDfu/zi/zeP8xnhDFHZS5EBxd53iUjA0LxJ2XhpMiB6FgQ65yRhVyx+HY4K7IMYp0ys+Gqk2KdhSzA2bncvyuzsrLKHCuIYrEnzaqVmzdv4rHHHsOhQ4cQHBwsL580aRL27duHw4cPG8QXd4bI19cXd+7cgYuLC0yh0WgQExOD7t27w9aWY4uVBXNmGubLdMyZaZgv0zBfpquonKWlpcHd3R2pqakP/P62ijNE7u7uUCqVSE5ONlienJwMb29vo3i1Wg212vhUoq2t7UP/Qz3KttaKOTMN82U65sw0zJdpmC/TlXfOTNmXVTxlplKp0Lp1a+zatUteptPpsGvXLoMzRkRERGSdrOIMEQCMHz8e4eHhaNOmDZ588kksWLAAmZmZ8lNnREREZL2spiB69dVXcfv2bUyfPh1JSUlo0aIFtm3bZnSjNREREVkfqymIACAqKgpRUVHmbgYRERFZGKu4h4iIiIioNCyIiIiIyOqxICIiIiKrx4KIiIiIrB4LIiIiIrJ6LIiIiIjI6rEgIiIiIqvHgoiIiIisHgsiIiIisnpW1VP1wxJFEQCQlpZm8rYajQZZWVlIS0vjqMdlxJyZhvkyHXNmGubLNMyX6SoqZ/rvbf33eGlYEJVBeno6AMDX19fMLSEiIiJTpaenw9XVtdQYQSxL2WTldDodbt68CWdnZwiCYNK2aWlp8PX1xY0bN+Di4lJBLaxemDPTMF+mY85Mw3yZhvkyXUXlTBRFpKeno3bt2lAoSr9LiGeIykChUKBOnTqPtA8XFxf+YpiIOTMN82U65sw0zJdpmC/TVUTOHnRmSI83VRMREZHVY0FEREREVo8FUQVTq9WYMWMG1Gq1uZtSZTBnpmG+TMecmYb5Mg3zZTpLyBlvqiYiIiKrxzNEREREZPVYEBEREZHVY0FEREREVo8FEREREVk9FkQVbPHixahXrx7s7OzQrl07HDlyxNxNslj79+9Hnz59ULt2bQiCgE2bNpm7SRZt9uzZaNu2LZydneHp6YkXXngBf/31l7mbZbGWLl2KoKAgueO34OBg/P777+ZuVpUxZ84cCIKAsWPHmrspFis6OhqCIBhMTZo0MXezLNq///6LQYMGoVatWrC3t0ezZs3w559/mqUtLIgq0Pr16zF+/HjMmDEDx48fR/PmzREWFoZbt26Zu2kWKTMzE82bN8fixYvN3ZQqYd++fYiMjERcXBxiYmKg0WgQGhqKzMxMczfNItWpUwdz5szBsWPH8Oeff6JLly54/vnncfbsWXM3zeIdPXoUX3/9NYKCgszdFIvXtGlTJCYmytOBAwfM3SSLdf/+fXTs2BG2trb4/fffce7cOcydOxc1atQwS3v42H0FateuHdq2bYsvv/wSgDQmmq+vL9566y28++67Zm6dZRMEARs3bsQLL7xg7qZUGbdv34anpyf27duHp59+2tzNqRJq1qyJzz77DMOHDzd3UyxWRkYGWrVqhSVLluDDDz9EixYtsGDBAnM3yyJFR0dj06ZNiI+PN3dTqoR3330XBw8exB9//GHupgDgGaIKk5eXh2PHjqFbt27yMoVCgW7duiE2NtaMLaPqKjU1FYD0JU+l02q1WLduHTIzMxEcHGzu5li0yMhI9O7d2+CzjEp26dIl1K5dG48//jgGDhyIhIQEczfJYv36669o06YNXn75ZXh6eqJly5ZYtmyZ2drDgqiC3LlzB1qtFl5eXgbLvby8kJSUZKZWUXWl0+kwduxYdOzYEU888YS5m2OxTp8+DScnJ6jVaowaNQobN25EYGCguZtlsdatW4fjx49j9uzZ5m5KldCuXTusWrUK27Ztw9KlS3Ht2jV06tQJ6enp5m6aRbp69SqWLl2Khg0bYvv27Rg9ejTefvttrF692izt4Wj3RNVAZGQkzpw5w/sVHqBx48aIj49HamoqfvnlF4SHh2Pfvn0siopx48YNjBkzBjExMbCzszN3c6qEnj17yq+DgoLQrl07+Pn54aeffuJl2WLodDq0adMGH3/8MQCgZcuWOHPmDL766iuEh4dXent4hqiCuLu7Q6lUIjk52WB5cnIyvL29zdQqqo6ioqLw22+/Yc+ePahTp465m2PRVCoVGjRogNatW2P27Nlo3rw5Fi5caO5mWaRjx47h1q1baNWqFWxsbGBjY4N9+/Zh0aJFsLGxgVarNXcTLZ6bmxsaNWqEy5cvm7spFsnHx8foj5GAgACzXWZkQVRBVCoVWrdujV27dsnLdDoddu3axXsWqFyIooioqChs3LgRu3fvhr+/v7mbVOXodDrk5uaauxkWqWvXrjh9+jTi4+PlqU2bNhg4cCDi4+OhVCrN3USLl5GRgStXrsDHx8fcTbFIHTt2NOoq5OLFi/Dz8zNLe3jJrAKNHz8e4eHhaNOmDZ588kksWLAAmZmZGDZsmLmbZpEyMjIM/pK6du0a4uPjUbNmTdStW9eMLbNMkZGRWLt2Lf73v//B2dlZvjfN1dUV9vb2Zm6d5ZkyZQp69uyJunXrIj09HWvXrsXevXuxfft2czfNIjk7Oxvdj+bo6IhatWrxPrUSTJgwAX369IGfnx9u3ryJGTNmQKlU4rXXXjN30yzSuHHj0KFDB3z88cd45ZVXcOTIEXzzzTf45ptvzNMgkSrUF198IdatW1dUqVTik08+KcbFxZm7SRZrz549IgCjKTw83NxNs0jF5QqAuHLlSnM3zSJFRESIfn5+okqlEj08PMSuXbuKO3bsMHezqpTOnTuLY8aMMXczLNarr74q+vj4iCqVSnzsscfEV199Vbx8+bK5m2XRNm/eLD7xxBOiWq0WmzRpIn7zzTdmawv7ISIiIiKrx3uIiIiIyOqxICIiIiKrx4KIiIiIrB4LIiIiIrJ6LIiIiIjI6rEgIiIiIqvHgoiIiIisHgsiIiIisnosiIjI6mi1WnTo0AF9+/Y1WJ6amgpfX1+8//77ZmoZEZkLe6omIqt08eJFtGjRAsuWLcPAgQMBAEOGDMHJkydx9OhRqFQqM7eQiCoTCyIislqLFi1CdHQ0zp49iyNHjuDll1/G0aNH0bx5c3M3jYgqGQsiIrJaoiiiS5cuUCqVOH36NN566y1MnTrV3M0iIjNgQUREVu3ChQsICAhAs2bNcPz4cdjY2Ji7SURkBrypmois2ooVK+Dg4IBr167hn3/+MXdziMhMeIaIiKzWoUOH0LlzZ+zYsQMffvghAGDnzp0QBMHMLSOiysYzRERklbKysjB06FCMHj0azzzzDL799lscOXIEX331lbmbRkRmwDNERGSVxowZg61bt+LkyZNwcHAAAHz99deYMGECTp8+jXr16pm3gURUqVgQEZHV2bdvH7p27Yq9e/fiqaeeMlgXFhaG/Px8XjojsjIsiIiIiMjq8R4iIiIisnosiIiIiMjqsSAiIiIiq8eCiIiIiKweCyIiIiKyeiyIiIiIyOqxICIiIiKrx4KIiIiIrB4LIiIiIrJ6LIiIiIjI6rEgIiIiIqvHgoiIiIis3v8D2UUXVdFpGK4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "data_folder = Path(\"csv_data\")\n",
+    "csv_files = sorted(data_folder.glob(\"*.csv\"))\n",
+    "\n",
+    "base_colors = {\n",
+    "    \"Capytaine\": \"red\",\n",
+    "    \"pyMEEM\": \"blue\",\n",
+    "    \"Yeung\": \"green\",\n",
+    "    \"matlabMEEM\": \"purple\",\n",
+    "}\n",
+    "\n",
+    "base_styles = {\n",
+    "    \"Capytaine\": \"-\",\n",
+    "    \"pyMEEM\": \"--\",\n",
+    "    \"Yeung\": \"-\",\n",
+    "    \"matlabMEEM\": \":\",\n",
+    "}\n",
+    "\n",
+    "def adjust_brightness(color_name, factor):\n",
+    "    rgb = to_rgb(color_name)\n",
+    "    adjusted = tuple(min(1, max(0, c * factor)) for c in rgb)\n",
+    "    return adjusted\n",
+    "\n",
+    "def get_color(label):\n",
+    "    for key, base_color in base_colors.items():\n",
+    "        if key in label:\n",
+    "            if \"Mu\" in label or \"AddedMass\" in label:\n",
+    "                return adjust_brightness(base_color, 1.3), base_styles[key]  # brighter\n",
+    "            elif \"Lambda\" in label or \"Damping\" in label:\n",
+    "                return adjust_brightness(base_color, 0.7), base_styles[key]  # darker\n",
+    "            else:\n",
+    "                return base_color, \"--\"\n",
+    "    return \"gray\", \"--\"  # fallback color if no match\n",
+    "\n",
+    "# Main plotting loop\n",
+    "for csv_path in csv_files:\n",
+    "    df = pd.read_csv(csv_path)\n",
+    "    fig, ax = plt.subplots()\n",
+    "    \n",
+    "    for col in df.columns:\n",
+    "        if col.endswith('_x'):\n",
+    "            label = col[:-2]\n",
+    "            x = df[col]\n",
+    "            y = df[label + '_y']\n",
+    "            color, linestyle = get_color(label)\n",
+    "            ax.plot(x, y, label=label, color=color, linestyle = linestyle)\n",
+    "    \n",
+    "    ax.set_title(csv_path.stem)\n",
+    "    ax.set_xlabel(\"X\")\n",
+    "    ax.set_ylabel(\"Y\")\n",
+    "    ax.legend()\n",
+    "    ax.grid(True)\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/dev/python/convergence-study/multicylinder_capytaine.ipynb b/dev/python/convergence-study/multicylinder_capytaine.ipynb
new file mode 100644
index 0000000..8f7c69f
--- /dev/null
+++ b/dev/python/convergence-study/multicylinder_capytaine.ipynb
@@ -0,0 +1,433 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This code creates the capytaine mesh by creating the water-facing outline of each region, creating a mask that says whether the panels associated with each region are heaving or not, then adding the region meshes together before adding the dof with the mask. The code generated capytaine data for a variety of configurations and over a range of wavenumbers (plotted in meem-vs-capytaine data).\n",
+    "\n",
+    "Pros: More arbitrary configurations, no excess panels.\n",
+    "\n",
+    "Cons: Making the theta direction panel density different between regions will create gaps. This does not appear to change the results for reasonably high resolutions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rSJf6s1tKGz7",
+    "outputId": "d58c0339-e27d-4651-99ac-f4da2155a60f"
+   },
+   "outputs": [],
+   "source": [
+    "# This generates configuration values with Capytaine.\n",
+    "\n",
+    "#!pip install capytaine #uncomment if first time running\n",
+    "\n",
+    "import capytaine as cpt\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "import sys\n",
+    "import time\n",
+    "\n",
+    "# removes capytaine warnings from clogging outputs\n",
+    "import logging\n",
+    "logging.getLogger(\"capytaine\").setLevel(logging.ERROR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "ukVJNFS8XIfE"
+   },
+   "outputs": [],
+   "source": [
+    "def save_potential_array(title, arr):\n",
+    "    file_path = title + \"-real\" + \".csv\"\n",
+    "    np.savetxt(file_path, np.real(arr), delimiter=\",\", fmt=\"%.6e\")\n",
+    "    file_path = title + \"-imag\" + \".csv\"\n",
+    "    np.savetxt(file_path, np.imag(arr), delimiter=\",\", fmt=\"%.6e\")\n",
+    "\n",
+    "# use to get rid of prints\n",
+    "def deafen(function, *args):\n",
+    "    real_stdout = sys.stdout\n",
+    "    sys.stdout = open(os.devnull, \"w\")\n",
+    "    output = function(*args)\n",
+    "    sys.stdout = real_stdout\n",
+    "    return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "solver = cpt.BEMSolver()\n",
+    "\n",
+    "def timed_solve(problem, reps):\n",
+    "    t_lst = []\n",
+    "    for i in range(reps):\n",
+    "        t0 = time.perf_counter()\n",
+    "        result = solver.solve(problem, keep_details = True)\n",
+    "        t1 = time.perf_counter()\n",
+    "        t_lst.append(t1 - t0)\n",
+    "    tdiff = sum(t_lst)/reps\n",
+    "    return result, tdiff\n",
+    "\n",
+    "def get_points(a, d): # These points define the outline of the body\n",
+    "    d_prime = d + [0]\n",
+    "    d_index = 0\n",
+    "    a_index = 0\n",
+    "    pt_lst = [(0, - d[0])]\n",
+    "    for i in range(len(a)):\n",
+    "        pt_lst.append((a[a_index], - d_prime[d_index]))\n",
+    "        d_index +=1\n",
+    "        pt_lst.append((a[a_index], - d_prime[d_index]))\n",
+    "        a_index+=1\n",
+    "    return pt_lst\n",
+    "\n",
+    "# compute number of panels along each surface given total number along the outline\n",
+    "def get_f_densities(pt_lst, total_units):\n",
+    "    face_lengths = np.array([])\n",
+    "    for i in range(len(pt_lst) - 1):\n",
+    "        p1, p2 = pt_lst[i], pt_lst[i + 1]\n",
+    "        face_length = abs(p2[0] - p1[0]) + abs(p2[1] - p1[1]) # one of these two values will be zero\n",
+    "        face_lengths = np.append(face_lengths, face_length)\n",
+    "    total_length = sum(face_lengths)\n",
+    "    each_face_densities = np.vectorize(lambda x: max(1, x/total_length * total_units))(face_lengths) # each face needs at least one panel\n",
+    "    remainders = each_face_densities % 1\n",
+    "    each_face_densities = each_face_densities.astype(int)\n",
+    "    remaining_units = total_units - sum(each_face_densities)\n",
+    "    if remaining_units < 0: # high proportion of small faces\n",
+    "        for u in range(remaining_units * -1):\n",
+    "            i = np.argmax(each_face_densities) # cut density from the largest faces\n",
+    "            each_face_densities[i] = (each_face_densities[i]) - 1\n",
+    "    else:\n",
+    "        for u in range(remaining_units): # distribute remaining units where most needed\n",
+    "            i = np.argmax(remainders)\n",
+    "            each_face_densities[i] = (each_face_densities[i]) + 1\n",
+    "            remainders[i] = 0\n",
+    "    assert sum(each_face_densities) == total_units\n",
+    "    return each_face_densities\n",
+    "    \n",
+    "def make_face(p1, p2, f_density, t_density):\n",
+    "    zarr = np.linspace(p1[1], p2[1], f_density + 1)\n",
+    "    rarr = np.linspace(p1[0], p2[0], f_density + 1)\n",
+    "    xyz = np.array([np.array([x/np.sqrt(2),y/np.sqrt(2),z]) for x,y,z in zip(rarr,rarr,zarr)])\n",
+    "    return cpt.AxialSymmetricMesh.from_profile(xyz, nphi = t_density)\n",
+    "\n",
+    "def faces_and_heaves(heaving, region, p1, p2, f_density, t_density, meshes, mask, panel_ct):\n",
+    "    mesh = make_face(p1, p2, f_density, t_density)\n",
+    "    meshes += mesh\n",
+    "    new_panels = f_density * t_density\n",
+    "    if heaving[region]:\n",
+    "        direction = [0, 0, 1]\n",
+    "    else:\n",
+    "        direction = [0, 0, 0]\n",
+    "    for i in range(new_panels):\n",
+    "        mask.append(direction)\n",
+    "    return meshes, mask, (panel_ct + new_panels)\n",
+    "\n",
+    "def get_excitation_phase(result):\n",
+    "    return np.angle((cpt.assemble_dataset([result]))[\"excitation_force\"][0][0][0])\n",
+    "\n",
+    "def make_body(pts, t_densities, f_densities, heaving):\n",
+    "    meshes = cpt.meshes.meshes.Mesh()\n",
+    "    panel_ct = 0\n",
+    "    mask = []\n",
+    "    for i in range((len(pts) - 1) // 2):\n",
+    "        p1, p2, p3 = pts[2 * i], pts[2 * i + 1], pts[2 * i + 2]\n",
+    "        # make a horizontal face\n",
+    "        meshes, mask, panel_ct = faces_and_heaves(heaving, i, p1, p2, f_densities[2 * i], t_densities[i], meshes, mask, panel_ct)\n",
+    "        # make a vertical face\n",
+    "        if p2[1] < p3[1]: # body on left\n",
+    "            region = i\n",
+    "        else: # body on right\n",
+    "            region = i + 1\n",
+    "        meshes, mask, panel_ct = faces_and_heaves(heaving, region, p2, p3, f_densities[2 * i + 1], t_densities[region], meshes, mask, panel_ct)\n",
+    "    body = deafen(cpt.FloatingBody, meshes) # unclosed boundary warnings\n",
+    "    return body, panel_ct, mask\n",
+    "\n",
+    "###################################\n",
+    "# Solving\n",
+    "def rb_solve(a, d, heaving, t_densities, face_units, m0, h, rho, reps):\n",
+    "    pt_lst = get_points(a, d)\n",
+    "    f_densities = get_f_densities(pt_lst, face_units)\n",
+    "    \n",
+    "    body, panel_count, mask = make_body(pt_lst, t_densities, f_densities, heaving)\n",
+    "    body.dofs[\"Heave\"] = mask  \n",
+    "    # body.show_matplotlib() # uncomment to show mesh\n",
+    "    \n",
+    "    rad_problem = cpt.RadiationProblem(body = body, wavenumber = m0, water_depth = h, rho = rho)\n",
+    "    result, t_diff = timed_solve(rad_problem, reps)\n",
+    "\n",
+    "    diff_problem = cpt.DiffractionProblem(body = body, wavenumber = m0, water_depth = h, rho = rho)\n",
+    "    result_d, t_diff_d = timed_solve(diff_problem, reps)\n",
+    "\n",
+    "    g = 9.81\n",
+    "    omega = np.sqrt(m0*np.tanh(m0*h)*g)\n",
+    "\n",
+    "    am = np.array(list(result.added_mass.values()))\n",
+    "    dp = np.array(list(result.radiation_damping.values()))\n",
+    "    am_nondim = h**3 / (rho * np.pi * max(a)**3) * am\n",
+    "    dp_nondim = h**3 / (omega * rho * np.pi * max(a)**3) * dp\n",
+    "\n",
+    "    #print(\"Panel Count: \", panel_count)\n",
+    "    #print(result.added_mass)\n",
+    "    #print(result.radiation_damping)\n",
+    "    #print(\"Solve Time (Radiation): \", t_diff)\n",
+    "    #print(\"Solve Time (Diffraction): \", t_diff_d)\n",
+    "    #print(\"Excitation Phase: \", get_excitation_phase(result_d))\n",
+    "    return am, dp, am_nondim, dp_nondim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Mini Bicylinder\n",
+    "# h = 1.001\n",
+    "# d = [0.25, 0.125]\n",
+    "# a = [0.125, 0.25]\n",
+    "# heaving = [1, 1]\n",
+    "\n",
+    "# # Small Bicylinder\n",
+    "# h = 1.001\n",
+    "# d = [0.5, 0.25]\n",
+    "# a = [0.5, 1.0]\n",
+    "# heaving = [1, 1]\n",
+    "\n",
+    "# # Big Bicylinder\n",
+    "# h = 1.001\n",
+    "# d = [0.75, 0.5]\n",
+    "# a = [0.5, 0.75]\n",
+    "# heaving = [1, 1]\n",
+    "\n",
+    "# # Mini Tricylinder\n",
+    "# h = 2.001\n",
+    "# d = [1.0, 0.5, 0.25]\n",
+    "# a = [0.25, 0.5, 1.0]\n",
+    "# heaving =[1, 1, 1]\n",
+    "\n",
+    "# Small Tricylinder\n",
+    "h = 20.0\n",
+    "d = [15, 10, 5]\n",
+    "a = [5, 10, 15]\n",
+    "heaving =[1, 1, 1]\n",
+    "\n",
+    "# # Big Tricylinder\n",
+    "# h = 25.0\n",
+    "# d = [20, 15, 10]\n",
+    "# a = [10, 15, 20]\n",
+    "# heaving =[1, 1, 1]\n",
+    "\n",
+    "# # Some Bicylinder\n",
+    "# h = 1.001\n",
+    "# d = [0.75, 0.25]\n",
+    "# a = [0.25, 0.75]\n",
+    "# heaving = [1, 1]\n",
+    "\n",
+    "t_densities = [50, 50, 50] # number of panels around each cylinder\n",
+    "face_units = 50 # number of panels along the outline of the configuration\n",
+    "m0 = 1\n",
+    "rho = 1023 # density of our special material\n",
+    "config = \"config0\"\n",
+    "\n",
+    "m0_nums = np.concatenate((np.linspace(0.1, 1, 20), np.linspace(1, 6, 30)))\n",
+    "\n",
+    "results = [rb_solve(a, d, heaving, t_densities, face_units, m0, h, rho, 1) for m0 in m0_nums]\n",
+    "# results = rb_solve(a, d, heaving, t_densities, face_units, m0, h, rho)\n",
+    "\n",
+    "hydro_collector_real_CPT = [res[2].flatten() for res in results]\n",
+    "hydro_collector_imag_CPT = [res[3].flatten() for res in results]\n",
+    "\n",
+    "# plt.figure()\n",
+    "# plt.plot(m0_nums,A_nondim, m0_nums,B_nondim, '*-')\n",
+    "# plt.xlabel(\"Wavenumber m0\")\n",
+    "# plt.ylabel(\"Added mass (kg)\")\n",
+    "# plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/zz/_5443rfn2v1_n4x4gqlv6jxc0000gr/T/ipykernel_29770/894598877.py:7: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  plt.legend(loc='best')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/zz/_5443rfn2v1_n4x4gqlv6jxc0000gr/T/ipykernel_29770/894598877.py:17: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  plt.legend(loc='best')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the plots\n",
+    "plt.figure(figsize=(5,5))\n",
+    "plt.plot(m0_nums, hydro_collector_real_CPT, linestyle='-')\n",
+    "plt.title('Added Mass, Capytaine')\n",
+    "plt.xlabel('m0')\n",
+    "plt.ylabel('Nondimensional Added Mass')\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(5,5))\n",
+    "plt.plot(m0_nums[10:], hydro_collector_imag_CPT[10:], linestyle='-') \n",
+    "plt.title('Nondimensional Damping, Capytaine')\n",
+    "plt.xlabel('m0')\n",
+    "plt.ylabel('Damping')\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "panel_mult = [20, 25, 30, 35, 40]\n",
+    "t_densities = [1, 1, 1] # number of panels around each cylinder\n",
+    "face_units = 1 # number of panels along the outline of the configuration\n",
+    "results = [rb_solve(a, d, heaving, [t * i for t in t_densities] , face_units*i, 1, h, rho, 1) for i in panel_mult]\n",
+    "\n",
+    "A_nondim = [res[2].flatten() for res in results]\n",
+    "B_nondim = [res[3].flatten() for res in results]\n",
+    "\n",
+    "# plt.plot(panel_mult,A_nondim, panel_mult,B_nondim, '*-')\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(panel_mult, A_nondim)\n",
+    "plt.title(\"Added Mass\")\n",
+    "plt.xlabel(\"resolution multiplier\")\n",
+    "plt.ylabel(\"Nondimensional Added Mass\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(panel_mult, B_nondim)\n",
+    "plt.title(\"Damping\")\n",
+    "plt.xlabel(\"resolution multiplier\")\n",
+    "plt.ylabel(\"Nondimensional Damping\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pyCapytaine_mu_nondim = [3701.031582011947 3733.2969738454644 3754.0258594650613 3784.1506994293472 3788.0606409779525];\n",
+      "pyCapytaine_lambda_nondim = [0.6838363667108702 -0.3675180044587952 -0.9468423979028825 -0.9534687146344872 -0.8171612989913569];\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(A_nondim)\n",
+    "# print(B_nondim)\n",
+    "\n",
+    "# Extract values from NumPy arrays\n",
+    "flat_list1 = [x.item() for x in A_nondim]\n",
+    "flat_list2 = [x.item() for x in B_nondim]\n",
+    "\n",
+    "# Convert to MATLAB-style string\n",
+    "matlab_list1 = \"pyCapytaine_mu_nondim = [\" + \" \".join(map(str, flat_list1)) + \"];\"\n",
+    "matlab_list2 = \"pyCapytaine_lambda_nondim = [\" + \" \".join(map(str, flat_list2)) + \"];\"\n",
+    "\n",
+    "# Print MATLAB-style string\n",
+    "print(matlab_list1)\n",
+    "print(matlab_list2)\n",
+    "\n",
+    "# MATLAB used to help generate the .figs"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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diff --git a/dev/python/convergence-study/nmk_per_region.ipynb b/dev/python/convergence-study/nmk_per_region.ipynb
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+++ b/dev/python/convergence-study/nmk_per_region.ipynb
@@ -0,0 +1,514 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For various configurations, MEEM is run with varying numbers of terms (NMK)\n",
+    "in each region (varying both total count and proportion in each region).\n",
+    "\n",
+    "The conclusion was that for fastest convergence to the asymptotic value,\n",
+    "the NMK should be evenly split between the regions.\n",
+    "\n",
+    "In the tested configurations, convergence was fast (less than 1% change in hydro coefficient values from incrementing NMK/region, when for single-digit NMK)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import pickle\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "sys.path.append(os.path.relpath('../'))\n",
+    "from multi_condensed import Problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "############################################ SETTING UP VARIABLE ###############################################\n",
+    "# # Mini Bicylinder\n",
+    "# h = 1.001\n",
+    "# d = [0.5, 0.25]\n",
+    "# a = [0.5, 1.0]\n",
+    "# heaving = [1, 1]\n",
+    "\n",
+    "# # Small Bicylinder\n",
+    "# h = 20.0\n",
+    "# d = [10.0, 5.0]\n",
+    "# a = [5.0, 10.0]\n",
+    "# heaving = [1, 1]\n",
+    "\n",
+    "# # Big Bicylinder\n",
+    "# h = 25.0\n",
+    "# d = [15.0, 10.0]\n",
+    "# a = [10.0, 15.0]\n",
+    "# heaving = [1, 1]\n",
+    "\n",
+    "# # Mini Tricylinder\n",
+    "# h = 2.001\n",
+    "# d = [1.0, 0.5, 0.25]\n",
+    "# a = [0.25, 0.5, 1.0]\n",
+    "# heaving =[1, 1, 1]\n",
+    "\n",
+    "# # Small Tricylinder\n",
+    "# h = 20.0\n",
+    "# d = [15, 10, 5]\n",
+    "# a = [5, 10, 15]\n",
+    "# heaving =[1, 1, 1]\n",
+    "\n",
+    "# # Big Tricylinder\n",
+    "# h = 25.0\n",
+    "# d = [20, 15, 10]\n",
+    "# a = [10, 15, 20]\n",
+    "# heaving =[1, 1, 1]\n",
+    "\n",
+    "# Some bicylinder\n",
+    "h = 1.001\n",
+    "d = [0.5, 0.25]\n",
+    "a = [0.25, 0.5]\n",
+    "heaving = [1, 1]\n",
+    "\n",
+    "m0 = 1.0\n",
+    "rho = 1023\n",
+    "####################################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "############################################## MEEM ############################################################\n",
+    "hydro_collector_real = []\n",
+    "hydro_collector_imag = []\n",
+    "hydro_nondim_real_diff = [np.nan]\n",
+    "hydro_nondim_imag_diff = [np.nan]\n",
+    "timing = []\n",
+    "loop_num = 0\n",
+    "terms = range(2, 31)  # X-axis (index of elements)\n",
+    "for i in terms:\n",
+    "    NMK = [i, i, i]\n",
+    "    start = time.perf_counter()\n",
+    "    prob = Problem(h, d, a, heaving, NMK, m0, rho)\n",
+    "    a0 = prob.a_matrix()\n",
+    "    b0 = prob.b_vector()\n",
+    "    x = prob.get_unknown_coeffs(a0, b0)\n",
+    "    am, dp = prob.hydro_coeffs(x, \"nondimensional\")\n",
+    "    hydro_collector_real.append(am)\n",
+    "    hydro_collector_imag.append(dp)\n",
+    "    end = time.perf_counter()\n",
+    "    timing.append(end - start)\n",
+    "\n",
+    "    if loop_num != 0:\n",
+    "        hydro_nondim_real_diff.append((hydro_collector_real[loop_num]-hydro_collector_real[loop_num-1])/hydro_collector_real[loop_num-1])\n",
+    "        hydro_nondim_imag_diff.append((hydro_collector_imag[loop_num]-hydro_collector_imag[loop_num-1])/hydro_collector_imag[loop_num-1])\n",
+    "    # if loop_num != 0:\n",
+    "    #     percent_diff_real = abs((186621534261.50247 - hydro_collector_real[loop_num-1])/186621534261.50247)\n",
+    "    #     percent_diff_imag = abs((3529258.9182286593 - hydro_collector_imag[loop_num-1])/3529258.9182286593)\n",
+    "    #     if percent_diff_real <= 0.001 and percent_diff_imag <= 0.001:\n",
+    "    #         break\n",
+    "    #     if times_MEEM[-1] > 11.94:\n",
+    "    #         break\n",
+    "    \n",
+    "    loop_num += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "###graphing time, difference, and added mass###\n",
+    "ticks = np.arange(0, 31, 2)\n",
+    "# Create the plots\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "\n",
+    "# Plot real parts\n",
+    "plt.subplot(5, 1, 1)  # 1 row, 2 columns, first plot\n",
+    "plt.plot(range(terms[0], terms[0]+loop_num), hydro_collector_real, 'bo-', markersize=2, label='Added Mass')\n",
+    "plt.xlabel('Number of Terms')\n",
+    "plt.xticks(ticks, fontsize=9)\n",
+    "# plt.ylabel('Added Mass (Nondimensional)')\n",
+    "plt.title('Added Mass (Nondimensional)')\n",
+    "# plt.axhline(y=0.57774782, color='purple', linestyle='--', label='Capytaine Value')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Plot the difference\n",
+    "# returns the index of the first value difference < 0.001, or None if there isn’t one\n",
+    "idx = next((i for i, v in enumerate(hydro_nondim_real_diff) if np.abs(v) < 0.001), None)\n",
+    "plt.subplot(5, 1, 2)  # 1 row, 2 columns, second plot\n",
+    "plt.plot(range(terms[0], terms[0]+loop_num), hydro_nondim_real_diff, 'ro-', markersize=2, label='% Difference')\n",
+    "plt.axvline(x=terms[idx],color='purple', linestyle='--', label='difference less than 0.001')\n",
+    "plt.xlabel('Number of Terms')\n",
+    "plt.xticks(ticks, fontsize=9)\n",
+    "# plt.ylabel('%Difference')\n",
+    "plt.title('% Add Mass Difference to the Previous One')\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Plot imag parts\n",
+    "plt.subplot(5, 1, 3)  # 1 row, 2 columns, first plot\n",
+    "plt.plot(range(terms[0], terms[0]+loop_num), hydro_collector_imag, 'bo-', markersize=2, label='Damping')\n",
+    "plt.xlabel('Number of Terms')\n",
+    "plt.xticks(ticks, fontsize=9)\n",
+    "# plt.ylabel('Added Mass (Nondimensional)')\n",
+    "plt.title('Damping (Nondimensional)')\n",
+    "# plt.axhline(y=0.19735159, color='purple', linestyle='--',label='Capytaine Value')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Plot the difference\n",
+    "idx = next((i for i, v in enumerate(hydro_nondim_imag_diff) if np.abs(v) < 0.001), None)\n",
+    "plt.subplot(5, 1, 4)  # 1 row, 2 columns, second plot\n",
+    "plt.plot(range(terms[0], terms[0]+loop_num), hydro_nondim_imag_diff, 'ro-', markersize=2, label='% Difference')\n",
+    "plt.axvline(x=terms[idx],color='purple', linestyle='--', label='difference less than 0.001')\n",
+    "plt.xlabel('Number of Terms')\n",
+    "plt.xticks(ticks, fontsize=9)\n",
+    "# plt.ylabel('%Difference')\n",
+    "plt.title('% Damping Difference to the Previous One')\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Plot running time\n",
+    "plt.subplot(5, 1, 5)  # 1 row, 2 columns, second plot\n",
+    "plt.plot(range(terms[0], terms[0]+loop_num), timing, 'go-', markersize=2, label='Running time')\n",
+    "plt.xlabel('Number of Terms')\n",
+    "plt.xticks(ticks, fontsize=9)\n",
+    "# plt.ylabel('Time it takes (sec)')\n",
+    "plt.title('Time it takes (sec)')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Show the plots\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[np.float64(0.5048487975656226), np.float64(0.4994196953219498), np.float64(0.5071541534504106), np.float64(0.5068076376567928), np.float64(0.5050358078627524), np.float64(0.5063034769412432), np.float64(0.5086174468194284), np.float64(0.5089672343443831), np.float64(0.5080065956645895), np.float64(0.5081513639233127), np.float64(0.5093917191416305), np.float64(0.509807678443169), np.float64(0.5094056753995247), np.float64(0.5094676261112178), np.float64(0.5099488697416557), np.float64(0.5101000999811246), np.float64(0.5100018896512211), np.float64(0.5100939395900582), np.float64(0.5103690055020488), np.float64(0.5104825243827926), np.float64(0.5103696700464131), np.float64(0.5103766563424532), np.float64(0.5106063534562001), np.float64(0.5107190928466055), np.float64(0.5106390023289887), np.float64(0.5106479713943807), np.float64(0.5107795113055134), np.float64(0.5108355153474284), np.float64(0.510808130385714)]\n",
+      "[np.float64(0.27571218284812515), np.float64(0.2780037326843269), np.float64(0.2769940730912289), np.float64(0.2770736269217718), np.float64(0.2775766060300609), np.float64(0.2775682798301534), np.float64(0.27725792443458896), np.float64(0.27719185386819817), np.float64(0.277324655364526), np.float64(0.2773247048849135), np.float64(0.2771850028883889), np.float64(0.27714103536481055), np.float64(0.2771972065822494), np.float64(0.27719338488502243), np.float64(0.2771263106093854), np.float64(0.2771040250685285), np.float64(0.2771278514871202), np.float64(0.27712419222383206), np.float64(0.27708567625809905), np.float64(0.27706876830257404), np.float64(0.2770845767083062), np.float64(0.27708498528622644), np.float64(0.27705739503744825), np.float64(0.27704433772377535), np.float64(0.2770554023613694), np.float64(0.2770550218121251), np.float64(0.2770366916969072), np.float64(0.2770283354240414), np.float64(0.2770345957503823)]\n",
+      "pyMEEM_mu_nondim = [0.5048487975656226 0.4994196953219498 0.5071541534504106 0.5068076376567928 0.5050358078627524 0.5063034769412432 0.5086174468194284 0.5089672343443831 0.5080065956645895 0.5081513639233127 0.5093917191416305 0.509807678443169 0.5094056753995247 0.5094676261112178 0.5099488697416557 0.5101000999811246 0.5100018896512211 0.5100939395900582 0.5103690055020488 0.5104825243827926 0.5103696700464131 0.5103766563424532 0.5106063534562001 0.5107190928466055 0.5106390023289887 0.5106479713943807 0.5107795113055134 0.5108355153474284 0.510808130385714];\n",
+      "pyMEEM_lambda_nondim = [0.27571218284812515 0.2780037326843269 0.2769940730912289 0.2770736269217718 0.2775766060300609 0.2775682798301534 0.27725792443458896 0.27719185386819817 0.277324655364526 0.2773247048849135 0.2771850028883889 0.27714103536481055 0.2771972065822494 0.27719338488502243 0.2771263106093854 0.2771040250685285 0.2771278514871202 0.27712419222383206 0.27708567625809905 0.27706876830257404 0.2770845767083062 0.27708498528622644 0.27705739503744825 0.27704433772377535 0.2770554023613694 0.2770550218121251 0.2770366916969072 0.2770283354240414 0.2770345957503823];\n"
+     ]
+    }
+   ],
+   "source": [
+    "####printing the hydro-coefficients and converting them into MATLAB format###\n",
+    "print(hydro_collector_real)\n",
+    "print(hydro_collector_imag)\n",
+    "\n",
+    "matlab_list1 = \"pyMEEM_mu_nondim = [\" + \" \".join(map(str, hydro_collector_real)) + \"];\"\n",
+    "matlab_list2 = \"pyMEEM_lambda_nondim = [\" + \" \".join(map(str, hydro_collector_imag)) + \"];\"\n",
+    "\n",
+    "# Print MATLAB format\n",
+    "print(matlab_list1)\n",
+    "print(matlab_list2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the plots\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# Plot real parts\n",
+    "plt.subplot(3, 1, 1)  # 1 row, 2 columns, first plot\n",
+    "plt.plot(range(terms[0], terms[0]+loop_num), hydro_collector_real, 'bo-', markersize=2, label='Real Part')\n",
+    "plt.xlabel('Number of Terms')\n",
+    "plt.ylabel('Real Value')\n",
+    "plt.title('Real Part of Complex Numbers')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Plot imaginary parts\n",
+    "plt.subplot(3, 1, 2)  # 1 row, 2 columns, second plot\n",
+    "plt.plot(range(terms[0], terms[0]+loop_num), hydro_collector_imag, 'ro-', markersize=2, label='Imaginary Part')\n",
+    "plt.xlabel('Number of Terms')\n",
+    "plt.ylabel('Imaginary Value')\n",
+    "plt.title('Imaginary Part of Complex Numbers')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Plot running time\n",
+    "plt.subplot(3, 1, 3)  # 1 row, 2 columns, second plot\n",
+    "plt.plot(range(terms[0], terms[0]+loop_num), timing, 'go-', markersize=2, label='Running time')\n",
+    "plt.xlabel('Number of Terms')\n",
+    "plt.ylabel('Time it takes (sec)')\n",
+    "plt.title('Time it takes (sec)')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Show the plots\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# filepath = \"nmk-per-region-data/\"\n",
+    "\n",
+    "# with open(filepath + \"h50_convergence_data.pkl\", \"rb\") as f:\n",
+    "#     real_h50, imag_h50, timing_h50 = pickle.load(f)\n",
+    "\n",
+    "# with open(filepath + \"h51_convergence_data.pkl\", \"rb\") as f:\n",
+    "#     real_h51, imag_h51, timing_h51 = pickle.load(f)\n",
+    "\n",
+    "# with open(filepath + \"h52_convergence_data.pkl\", \"rb\") as f:\n",
+    "#     real_h52, imag_h52, timing_h52 = pickle.load(f)\n",
+    "\n",
+    "# with open(filepath + \"h53_convergence_data.pkl\", \"rb\") as f:\n",
+    "#     real_h53, imag_h53, timing_h53 = pickle.load(f)\n",
+    "\n",
+    "# with open(filepath + \"h54_convergence_data.pkl\", \"rb\") as f:\n",
+    "#     real_h54, imag_h54, timing_h54 = pickle.load(f)\n",
+    "\n",
+    "# with open(filepath + \"h55_convergence_data.pkl\", \"rb\") as f:\n",
+    "#     real_h55, imag_h55, timing_h55 = pickle.load(f)\n",
+    "\n",
+    "# with open(filepath + \"h56_convergence_data.pkl\", \"rb\") as f:\n",
+    "#     real_h56, imag_h56, timing_h56 = pickle.load(f)\n",
+    "\n",
+    "# with open(filepath + \"h35_convergence_data.pkl\", \"rb\") as f:\n",
+    "#     real_h35, imag_h35, timing_h35 = pickle.load(f)\n",
+    "\n",
+    "# with open(filepath + \"h65_convergence_data.pkl\", \"rb\") as f:\n",
+    "#     real_h65, imag_h65, timing_h65 = pickle.load(f)\n",
+    "\n",
+    "# with open(filepath + \"h80_convergence_data.pkl\", \"rb\") as f:\n",
+    "#     real_h80, imag_h80, timing_h80 = pickle.load(f)\n",
+    "\n",
+    "# ## Alternative plotting needed.\n",
+    "\n",
+    "# plt.scatter(timing_h50, real_h50, label='50')\n",
+    "# plt.scatter(timing_h51, real_h51, label='51')\n",
+    "# plt.scatter(timing_h52, real_h52, label='52')\n",
+    "# plt.scatter(timing_h53, real_h53, label='53')\n",
+    "# plt.scatter(timing_h54, real_h54, label='54')\n",
+    "# plt.scatter(timing_h55, real_h55, label='55')\n",
+    "# plt.scatter(timing_h56, real_h56, label='56')\n",
+    "# plt.scatter(timing_h35, real_h35, label='35')\n",
+    "# plt.scatter(timing_h65, real_h65, label='65')\n",
+    "# plt.scatter(timing_h80, real_h80, label='80')\n",
+    "# plt.axhline(y=186621534261.50247, color='r', linestyle='--')\n",
+    "# plt.xlabel('running time')\n",
+    "# plt.ylabel('hydro_real')\n",
+    "# plt.title('hydro_real, variable height')\n",
+    "\n",
+    "# plt.legend()\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filepath = \"nmk-per-region-data/\"\n",
+    "\n",
+    "with open(filepath + \"h50_iii_conv_data.pkl\", \"rb\") as f:\n",
+    "    real_h50_iii, imag_h50_iii, timing_h50_iii = pickle.load(f)\n",
+    "\n",
+    "with open(filepath + \"h50_i2ii_conv_data.pkl\", \"rb\") as f:\n",
+    "    real_h50_i2ii, imag_h50_i2ii, timing_h50_i2ii = pickle.load(f)\n",
+    "\n",
+    "with open(filepath + \"h50_i3ii_conv_data.pkl\", \"rb\") as f:\n",
+    "    real_h50_i3ii, imag_h50_i3ii, timing_h50_i3ii = pickle.load(f)\n",
+    "\n",
+    "with open(filepath + \"h50_i4ii_conv_data.pkl\", \"rb\") as f:\n",
+    "    real_h50_i4ii, imag_h50_i4ii, timing_h50_i4ii = pickle.load(f)\n",
+    "\n",
+    "with open(filepath + \"h50_2i2ii_conv_data.pkl\", \"rb\") as f:\n",
+    "    real_h50_2i2ii, imag_h50_2i2ii, timing_h50_2i2ii = pickle.load(f)\n",
+    "\n",
+    "with open(filepath + \"h50_2ii2i_conv_data.pkl\", \"rb\") as f:\n",
+    "    real_h50_2ii2i, imag_h50_2ii2i, timing_h50_2ii2i = pickle.load(f)\n",
+    "\n",
+    "with open(filepath + \"h50_i2i2i_conv_data.pkl\", \"rb\") as f:\n",
+    "    real_h50_i2i2i, imag_h50_i2i2i, timing_h50_i2i2i = pickle.load(f)\n",
+    "\n",
+    "with open(filepath + \"h50_i3i3i_conv_data.pkl\", \"rb\") as f:\n",
+    "    real_h50_i3i3i, imag_h50_i3i3i, timing_h50_i3i3i = pickle.load(f)\n",
+    "\n",
+    "with open(filepath + \"h50_ii2i_conv_data.pkl\", \"rb\") as f:\n",
+    "    real_h50_ii2i, imag_h50_ii2i, timing_h50_ii2i = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(timing_h50_iii, real_h50_iii, label='i,i,i')\n",
+    "plt.scatter(timing_h50_ii2i, real_h50_ii2i, label='i,i,2i')\n",
+    "plt.scatter(timing_h50_i2ii, real_h50_i2ii, label='i,2i,i')\n",
+    "plt.scatter(timing_h50_i3ii, real_h50_i3ii, label='i,3i,i')\n",
+    "plt.scatter(timing_h50_i4ii, real_h50_i4ii, label='i,4i,i')\n",
+    "plt.scatter(timing_h50_2i2ii, real_h50_2i2ii, label='2i,2i,i')\n",
+    "plt.scatter(timing_h50_2ii2i, real_h50_2ii2i, label='2i,i,2i')\n",
+    "plt.scatter(timing_h50_i2i2i, real_h50_i2i2i, label='i,2i,2i')\n",
+    "plt.scatter(timing_h50_i3i3i, real_h50_i3i3i, label='i,3i,3i')\n",
+    "plt.axhline(y=186621534261.50247, color='r', linestyle='--')\n",
+    "plt.xlabel('running time')\n",
+    "plt.ylabel('hydro_real')\n",
+    "plt.title('hydro_real, iii comparison (h=50)')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(timing_h50_iii, imag_h50_iii, label='i,i,i')\n",
+    "plt.scatter(timing_h50_ii2i, imag_h50_ii2i, label='i,i,2i')\n",
+    "plt.scatter(timing_h50_i2ii, imag_h50_i2ii, label='i,2i,i')\n",
+    "plt.scatter(timing_h50_i3ii, imag_h50_i3ii, label='i,3i,i')\n",
+    "plt.scatter(timing_h50_i4ii, imag_h50_i4ii, label='i,4i,i')\n",
+    "plt.scatter(timing_h50_2i2ii, imag_h50_2i2ii, label='2i,2i,i')\n",
+    "plt.scatter(timing_h50_2ii2i, imag_h50_2ii2i, label='2i,i,2i')\n",
+    "plt.scatter(timing_h50_i2i2i, imag_h50_i2i2i, label='i,2i,2i')\n",
+    "plt.scatter(timing_h50_i3i3i, imag_h50_i3i3i, label='i,3i,3i')\n",
+    "plt.axhline(y=3529258.9182286593, color='r', linestyle='--')\n",
+    "\n",
+    "plt.xlabel('running time')\n",
+    "plt.ylabel('hydro_imag')\n",
+    "plt.title('hydro_imag, iii comparison (h=50)')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "########YEUNG MULTIPLICATION HYDRO-COEFF CALCULATION#######\n",
+    "    # hydro_coef_1_1 = (4*(h-d[0])**2-a[0]**2)/(8*a[0]*(h-d[0]))+X[0]/a[0]\n",
+    "    # W_1 = np.zeros(NMK[0]+1, dtype=complex)\n",
+    "    # for n in range(1,NMK[0]):\n",
+    "    #     W_1[n] = X[n]*(-1)**n*besseli(1,lambda_ni(n, 0)*a[0])/(lambda_ni(n,0)*besseli(0,lambda_ni(n,0)*a[0]))\n",
+    "    # hydro_coef_1_2 = (2/a[0]**2)*sum(W_1)\n",
+    "    # hydro_coef_2_1 = (a[1]**2-a[0]**2)**2/(8*a[1]**3)/(h-d[1])*(4*(h-d[1])**2-(a[1]**2+a[0]**2))\n",
+    "    # hydro_coef_2_2 = X[NMK[0]]*(a[1]**2-a[0]**2)/(2*a[1]**3)\n",
+    "    # hydro_coef_2_3 = X[NMK[0]+NMK[1]]/(2*a[1]**3)*(a[1]**2*np.log(a[1])-a[0]**2*np.log(a[0])-(a[1]**2-a[0]**2)/2)\n",
+    "    # W_2 = np.zeros(NMK[1]+1, dtype=complex)\n",
+    "    # for m in range(1,NMK[1]):\n",
+    "    #     W_2[m] = (-1)**m/lambda_ni(m, 1)*(X[NMK[0]+m]*(a[1]*besseli(1,lambda_ni(m, 1)*a[1])/besseli(0,lambda_ni(m,1)*scale) -a[0]*besseli(1,lambda_ni(m,1)*a[0])/besseli(0,lambda_ni(m,1)*scale))\n",
+    "    #                                      -X[NMK[0]+NMK[1]+m]*(a[1]*besselk(1,lambda_ni(m, 1)*a[1])/besselk(0,lambda_ni(m,1)*scale) -a[0]*besselk(1,lambda_ni(m,1)*a[0])/besselk(0,lambda_ni(m,1)*scale)))\n",
+    "    # hydro_coef_2_4 = (2/a[1]**3)*sum(W_2)\n",
+    "\n",
+    "    # hydro_coef = hydro_coef_1_1 + hydro_coef_1_2 + hydro_coef_2_1 + hydro_coef_2_2 + hydro_coef_2_3 + hydro_coef_2_4\n",
+    "    # hydro_coef_real = hydro_coef.real\n",
+    "    # hydro_coef_imag = hydro_coef.imag/omega\n",
+    "\n",
+    "    ###############"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/hydro/python/coupling.py b/dev/python/coupling.py
similarity index 100%
rename from hydro/python/coupling.py
rename to dev/python/coupling.py
diff --git a/hydro/python/equations.py b/dev/python/equations.py
similarity index 100%
rename from hydro/python/equations.py
rename to dev/python/equations.py
diff --git a/dev/python/low_m0_MEEM.ipynb b/dev/python/low_m0_MEEM.ipynb
new file mode 100644
index 0000000..8b0dab0
--- /dev/null
+++ b/dev/python/low_m0_MEEM.ipynb
@@ -0,0 +1,788 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "76293819",
+   "metadata": {},
+   "source": [
+    "This notebook implements a low frequency approximation for MEEM. The approximation uses the first radial eigenfunction(s) for each region while the vertical eigenfunction is constant (no z-dependence). The general form is shown to match the two-region form given in Chau 2012. Damping converges to a finite value while added mass goes to infinite.\n",
+    "Manual comparisons between the outputs generated in this file and via the full MEEM implementations show that the convergence between the approximation and the full version is very slow, and configuration dependent. The conclusion is that this should not be added to the final package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1b641d8b-5164-41d9-8a7e-e1a0091bdf2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import block\n",
+    "import numpy as np\n",
+    "from scipy.special import hankel1 as besselh\n",
+    "import scipy.linalg as linalg\n",
+    "from numpy import log, pi, sqrt\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3f007c8e-2adb-42be-855b-582b69ee3497",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#variable block\n",
+    "h = 1\n",
+    "d = [0.5, 0.25]\n",
+    "a = [0.5, 1]\n",
+    "heaving = [1, 0]\n",
+    "m0s = np.array(list(range(1, 101, 1))) * 1e-6\n",
+    "g = 9.81\n",
+    "rho = 1023"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3e166487-cc4a-458f-96af-7e0dc6c0af87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#useful constants block\n",
+    "bds = len(a) # number of boundaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2aa3a2f8-164a-4ada-986b-6fd79ff2f40c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def particular_potential(r, n):\n",
+    "    if n >= bds: # free surface region\n",
+    "        return 0\n",
+    "    if heaving[n]:\n",
+    "        return - (r**2)/(4 * (h - d[n]))\n",
+    "    else:\n",
+    "        return 0\n",
+    "\n",
+    "def diff_particular_potential_total(r, n):\n",
+    "    if n >= bds: # free surface region\n",
+    "        return 0\n",
+    "    if heaving[n]:\n",
+    "        return - r/2\n",
+    "    else:\n",
+    "        return 0\n",
+    "\n",
+    "def b_potential_entry(n):\n",
+    "    return - particular_potential(a[n], n) + particular_potential(a[n], n + 1)\n",
+    "\n",
+    "def b_velocity_entry(n):\n",
+    "    return - diff_particular_potential_total(a[n], n) + diff_particular_potential_total(a[n], n + 1)\n",
+    "    \n",
+    "# sets potential equation for nth boundary into the A matrix\n",
+    "def potential_row(A, n, m0):\n",
+    "    if n == 0:\n",
+    "        if bds == 1: # single cylinder\n",
+    "            A[0][0] = 1\n",
+    "            A[0][1] = - besselh(0, m0 * a[0])\n",
+    "        else:\n",
+    "            A[0][0] = 1\n",
+    "            A[0][1] = - 1\n",
+    "            A[0][2] = - log(a[0])\n",
+    "    elif n == bds - 1:\n",
+    "        A[n][2*n - 1] = 1\n",
+    "        A[n][2*n] = log(a[n])\n",
+    "        A[n][2*n + 1] = - besselh(0, m0 * a[n])\n",
+    "    else:\n",
+    "        A[n][2*n - 1] = 1\n",
+    "        A[n][2*n] = log(a[n])\n",
+    "        A[n][2*n + 1] = - 1\n",
+    "        A[n][2*n + 2] = - log(a[n])\n",
+    "\n",
+    "def velocity_row(A, n, m0):\n",
+    "    if n == 0:\n",
+    "        if bds == 1: # single cylinder\n",
+    "            A[bds][0] = 0\n",
+    "            A[bds][1] = h * m0 * besselh(1, m0 * a[0])\n",
+    "        else:\n",
+    "            A[bds][0] = 0\n",
+    "            A[bds][1] = 0\n",
+    "            A[bds][2] = - 1/a[0] * (h - d[1])\n",
+    "    elif n == bds - 1:\n",
+    "        A[bds + n][2*n - 1] = 0\n",
+    "        A[bds + n][2*n] = 1/a[n] * (h - d[n])\n",
+    "        A[bds + n][2*n + 1] = h * m0 * besselh(1, m0 * a[n])\n",
+    "    else:\n",
+    "        A[bds + n][2*n - 1] = 0\n",
+    "        A[bds + n][2*n] = 1/a[n] * (h - d[n])\n",
+    "        A[bds + n][2*n + 1] = 0\n",
+    "        A[bds + n][2*n + 2] = - 1/a[n] * (h - d[n + 1])\n",
+    "\n",
+    "def build_A(m0):\n",
+    "    A = np.zeros((2 * bds, 2 * bds), dtype = complex)\n",
+    "    for n in range(bds):\n",
+    "        potential_row(A, n, m0)\n",
+    "        velocity_row(A, n, m0)\n",
+    "    return A\n",
+    "\n",
+    "def build_B():\n",
+    "    b1 = (np.vectorize(b_potential_entry, otypes = [float]))(list(range(bds)))\n",
+    "    b2 = (np.vectorize(b_velocity_entry, otypes = [float]))(list(range(bds)))\n",
+    "    return np.concatenate([b1, b2])\n",
+    "    \n",
+    "\n",
+    "# modifies A matrix for a particular m0, all other parameters the same.\n",
+    "def A_new_m0(A, m0):\n",
+    "    A[bds-1][2*bds-1] = - besselh(0, m0 * a[-1]) \n",
+    "    A[2*bds-1][2*bds-1] = h * m0 * besselh(1, m0 * a[-1])\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "706c4ce5-0ada-472c-a626-7ca5ca0b0466",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hydro coefficient calculation functions\n",
+    "def particular_potential_int_eval(region, boundary):\n",
+    "    return -(a[boundary]**4)/(16 * (h - d[region]))\n",
+    "\n",
+    "def total_particular():\n",
+    "    accumulator = 0\n",
+    "    for region in range(bds):\n",
+    "        if heaving[region]:\n",
+    "            if region == 0:\n",
+    "                accumulator += particular_potential_int_eval(0, 0)\n",
+    "            else:\n",
+    "                accumulator += particular_potential_int_eval(region, region) - particular_potential_int_eval(region, region - 1)\n",
+    "    return accumulator\n",
+    "\n",
+    "#dphi/dz mandated to be 1 in the heaving regions, so just integrate potential * r\n",
+    "\n",
+    "def ln_potential_int_eval(bd):\n",
+    "    return (a[bd]**2/2) * (log(a[bd]) - 1/2)\n",
+    "\n",
+    "def const_potential_int_eval(bd):\n",
+    "    return (a[bd]**2/2)\n",
+    "\n",
+    "def create_c_vector():\n",
+    "    c = []\n",
+    "    for region in range(bds):\n",
+    "        if region == 0:\n",
+    "            if heaving[0]:\n",
+    "                c.append(const_potential_int_eval(0))\n",
+    "            else:\n",
+    "                c.append(0)\n",
+    "        else:\n",
+    "            if heaving[region]:\n",
+    "                c.append(const_potential_int_eval(region) - const_potential_int_eval(region - 1))\n",
+    "                c.append(ln_potential_int_eval(region) - ln_potential_int_eval(region - 1))\n",
+    "            else:\n",
+    "                c.append(0)\n",
+    "                c.append(0)\n",
+    "    return c\n",
+    "\n",
+    "def get_hydro_coeffs(X, m0):\n",
+    "    const = total_particular()\n",
+    "    c = create_c_vector()\n",
+    "    raw = np.dot(X, c) + const\n",
+    "    total = 2 * pi * rho * raw #* h**3 \n",
+    "    return np.real(total), (np.imag(total)) # * omega_from_m0(m0))\n",
+    "\n",
+    "def to_nondim(coeff, a_norm):\n",
+    "    return coeff/(pi * a_norm**3 * rho)\n",
+    "\n",
+    "def omega_from_m0(m0): # at small m0 approximation\n",
+    "    return sqrt(m0**2 * h * g)\n",
+    "\n",
+    "def get_max_heaving_radius():\n",
+    "    max_rad = a[0]\n",
+    "    for i in range(bds - 1, 0, -1):\n",
+    "        if heaving[i]:\n",
+    "            max_rad = a[i]\n",
+    "            break\n",
+    "    return max_rad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "23da91e8-3cda-4b8d-b37f-eeedb40addb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = build_A(m0s[0])\n",
+    "b = build_B()\n",
+    "\n",
+    "solutions = []\n",
+    "\n",
+    "for m0 in m0s:\n",
+    "    A_new_m0(A, m0)\n",
+    "    solutions.append(linalg.solve(A,b))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1dc992e4-d47e-43b8-89a9-dbafed4b748f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regular_hydros = []\n",
+    "nondim_am = []\n",
+    "nondim_d = []\n",
+    "a_norm = get_max_heaving_radius()\n",
+    "for i in range(len(m0s)):\n",
+    "    added_mass, damping = get_hydro_coeffs(solutions[i][:-1], m0s[i])\n",
+    "    nd_am, nd_d = to_nondim(added_mass, a_norm), to_nondim(damping, a_norm)\n",
+    "    regular_hydros.append((added_mass, damping))\n",
+    "    nondim_am.append(nd_am)\n",
+    "    nondim_d.append(nd_d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0b2473fe-0b68-4f9f-ba0f-f872f05086cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2012 solutions for low frequency\n",
+    "\n",
+    "a1, a2, d1, d2 = a[0], a[1], d[0], d[1]\n",
+    "\n",
+    "# Heaving outer cylinder\n",
+    "def oc_heaving(m0):\n",
+    "    term_1 = (a2/(2 * m0 * h)) * (1 - (a1/a2)**2) * (besselh(0, m0 * a2)) / (besselh(1, m0 * a2))\n",
+    "    alpha = term_1 + ((a2**2 - a1**2)/4 + (a1**2/2) * log(a1/a2))/(h-d2)\n",
+    "    beta = term_1 + ((a2**2)/4 - (a1**2/2) * log(a2))/(h-d2)\n",
+    "    gamma = a1**2/(2 * (h-d2))\n",
+    "    delta = (a2/(2 * m0 * h)) * (1 - (a1/a2)**2)/ (besselh(1, m0 * a2))\n",
+    "    return [alpha, beta, gamma, delta]\n",
+    "\n",
+    "def oc_heaving_hydros(m0):\n",
+    "    t1 = (a2**2 - a1**2)**2/(2 * h * m0 * a2) * (besselh(0, m0 * a2)) / (besselh(1, m0 * a2))\n",
+    "    t2 = ((a2**2 - a1**2)**2 + 2 * a1**2 * (a1**2 - a2**2 + 2 * a1**2 * log(a2/a1)))/(8 * (h - d2))\n",
+    "    val = (t1 + t2)/(a2 ** 3)\n",
+    "    return np.real(val), np.imag(val)\n",
+    "\n",
+    "# Heaving inner cylinder\n",
+    "def ic_heaving(m0):\n",
+    "    term_1 = (a1**2/(2 * m0 * h * a2)) * (besselh(0, m0 * a2)) / (besselh(1, m0 * a2))\n",
+    "    alpha = term_1 + (a1**2 * log(a2/a1))/(2 * (h-d2)) + a1**2/(4 * (h-d1))\n",
+    "    beta = term_1 + (a1**2 * log(a2))/(2 * (h-d2))\n",
+    "    gamma = -a1**2/(2 * (h-d2))\n",
+    "    delta = (a1**2/(2 * m0 * h * a2))/ (besselh(1, m0 * a2))\n",
+    "    return [alpha, beta, gamma, delta]\n",
+    "\n",
+    "def ic_heaving_hydros(m0):\n",
+    "    t1 = (besselh(0, m0 * a2))/(h * m0 * a2 * besselh(1, m0 * a2))\n",
+    "    t2 = log(a2/a1)/(h-d2) + 1/(4*(h-d1)) # It's an error that (eq. 84) has an 8 instead of a 4 here.*\n",
+    "    val = (a1/2) * (t1 + t2)\n",
+    "    return np.real(val), np.imag(val)\n",
+    "    # *If the integral in eq. 84 is correct, it evaluates to having a 4 instead of an 8 there.\n",
+    "\n",
+    "# the paper claims these as the limits (m0 -> 0) but I'm not sure                    \n",
+    "oc_heaving_lim_am = -a[1]/(2*h)*(1 - (a[0]/a[1])**2)**2 * log(m0s * a[1])\n",
+    "oc_heaving_lim_dp = len(m0s) * [pi * a[1] / (4 * h) * (1 - (a[0]/a[1])**2)**2]\n",
+    "ic_heaving_lim_am = - a[0]/(2*h) * log (m0s * a[1])\n",
+    "ic_heaving_lim_dp = pi * a[0] / (4 * h)\n",
+    "\n",
+    "oc_coeffs = []\n",
+    "ic_coeffs = []\n",
+    "\n",
+    "nondim_am_oc = []\n",
+    "nondim_d_oc = []\n",
+    "nondim_am_ic = []\n",
+    "nondim_d_ic = []\n",
+    "\n",
+    "for m0 in m0s:\n",
+    "    nd_oc_am, nd_oc_d = oc_heaving_hydros(m0)\n",
+    "    nondim_am_oc.append(nd_oc_am)\n",
+    "    nondim_d_oc.append(nd_oc_d)\n",
+    "    nd_ic_am, nd_ic_d = ic_heaving_hydros(m0)\n",
+    "    nondim_am_ic.append(nd_ic_am)\n",
+    "    nondim_d_ic.append(nd_ic_d)\n",
+    "    oc_coeffs.append(oc_heaving(m0))\n",
+    "    ic_coeffs.append(ic_heaving(m0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4fa2a631-807c-434f-afea-e7b2c37d35f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def coeff_comparisons(lst1, lst2, m0s):\n",
+    "    for i in range(len(m0s)):\n",
+    "        print(m0s[i])\n",
+    "        print(lst1[i])\n",
+    "        print(lst2[i])\n",
+    "\n",
+    "#coeff_comparisons(ic_coeffs, solutions, m0s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5ed7976f-cb69-44cb-a719-3dc25dd1310e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_compare(xs, ys1, ys2, lab1, lab2, ylab, title = None):\n",
+    "    plt.plot(xs, ys1, color = \"blue\", linestyle = \"-\", label = lab1)\n",
+    "    plt.plot(xs, ys2, color = \"red\", linestyle = \"--\", label = lab2)\n",
+    "    plt.legend()\n",
+    "    plt.xlabel(\"m0\")\n",
+    "    plt.ylabel(ylab)\n",
+    "    plt.title(title)\n",
+    "\n",
+    "plot_compare(m0s, nondim_am_ic, nondim_am, \"2012 Paper\", \"This Implementaion\", \"Nondimensional Added Mass\",\n",
+    "             title = \"2012 paper (2-cylinder) formula and this implementation, comparison\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "93664e88-d8c5-48a6-8beb-8c3abf0a2461",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Difference between 2012 paper values and implementation here')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(m0s, np.array(nondim_am_ic)-np.array(nondim_am), color = \"green\", label = \"Added mass difference\")\n",
+    "plt.plot(m0s, np.array(nondim_d)-np.array(nondim_d_ic), color = \"blue\", label = \"Damping difference\")\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"m0\")\n",
+    "plt.ylabel(\"Difference\")\n",
+    "plt.title(\"Difference between 2012 paper values and implementation here\")\n",
+    "# basically zero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cb02e216-08db-48b6-b892-1dc891ee54c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Difference (damping)')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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P/lxHRwcNGjSQ939edu3aBZlMhsmTJ0NDQ/HPQVbmo0ePIi0tDWPGjFFoM2TIEBgbG+O///7LkaVXr17y59WqVYOpqSmqV68ODw8P+fSs/88t44gRIxSejxo1CgDk72FhULaPL1++jNjYWAwbNkzhMz5o0CCYmJgota6s8YUAEB8fj1evXqFFixZ4+PAh4uPjFdrWqFFD/v0BPnzWqlWrptBP+/fvR8OGDdGgQQOFdtn32haGgIAAhVO4PTw8IAgCAgIC5NM0NTXh7u7+yc9bQXz99dcK62/WrBkyMzPlh4iOHTuGjIwMfPPNNwrzZX1mPuX169cAIN9rkp+NGzfi33//xbhx41ClShX59KxDTrmNo9LV1f3kISlljRw5Enfu3MGiRYtyjFMF/rcNr169ync5PXv2xJUrVxARESGftmXLFkilUnTq1Ek+7ePPbXJyMl69eoXGjRtDEIRC2StWkN+a4sRB2iVAUlISrK2tC215Dx48wN27d/PcdR0bG6vwvHLlygrPX758ibdv32LFihVYsWKFUsuoWLGiwvOsL2rWseysL2KtWrXyzB0eHg5BEDBp0iRMmjQpz/V+XPzk5uM/XFmqVq0q36VcWOtp1qyZ/Af79OnTcHd3h7u7O8zNzXH69GnY2NggNDQUffr0kc+j7Hvz4MEDCIKQ67YAgLa2tsLzChUq5LgOiZmZGW7cuJHvNkREREBDQyPfgf5ZP0TVqlVTmK6jowNHR8ccYxlyy2JiYgJ7e/sc04D/fUY+ln27nZycoKGhUaiXW1C2j7O2L3s7bW1tODo6KrWus2fPYsqUKQgJCUFKSorCa/Hx8QqFVvbvEvDhvfy4n548eaJQbGbJ/h4Vhux5srLm9n7m9l4W9vqz/23Jen8+HrAMAObm5koVPVmEbOOnsjt9+jQCAgLg7e2NmTNnKryWVUjkNo7w/fv3CoVGQc2fPx8rV67E9OnT4evrm2ubrG341DWJunfvju+++w5btmzBTz/9BEEQEBQUhHbt2sHY2FjeLjIyEpMnT8aePXtyvLfZC/uCKMhvTXFigSSyZ8+eIT4+PseX+3PIZDK4urri999/z/X17H/Ysn95swZ69uvXDwMHDsx1GbVr11Z4rqmpmWu7T/3RyW2933//Pby9vXNtUxj9VFjradq0KVauXImHDx/i9OnTaNasGSQSCZo2bYrTp0/Dzs4OMplMYW+Asu+NTCaDRCLBgQMHcu3b7INJC6P/C0teWT4nY/Y/+Hn9AGS/Pkx+VO3jgoqIiICnpydcXFzw+++/w97eHjo6Oti/fz8WLlyYY2B1SXovAdXez6LIWNT9YWFhASD3Qj1LaGgo/Pz8UKtWLWzbti3H3pty5coB+HCNo+x/X1+8eKGwp68gAgMD8eOPP2LYsGH45Zdf8myXtQ25jU/6mJ2dHZo1a4atW7fip59+wvnz5xEZGYm5c+fK22RmZqJNmzaIi4vDjz/+CBcXFxgYGCAqKgqDBg3K94QAZb+fBfmtKU4skESWNagxrx/qgnByckJoaCg8PT0LdHVTKysrGBkZITMzE15eXoWWCQBu3bqV5zKz/jWura39WevNOrz1sfv376NSpUoqrye//ssqfI4cOYJLly5hwoQJAD4MyF66dCns7OxgYGCA+vXry+dR9r1xcnKCIAioXLkyqlatmm/Gz+Hk5ASZTIY7d+7Azc0t1zYODg4AgLCwMIU9JmlpaXj06FGhfUY+9uDBA4U9m+Hh4ZDJZPL3MGvPQPbB6rmdmZNXPyvbx1nb/+DBA/mhUODD4N5Hjx6hTp06+W7L3r17kZqaij179ijsDcl+mFQVDg4OuX7Ow8LCCrzM4lAUV1vOen/Cw8MVPjOvX79Wao9WxYoVoaenh0ePHuX6ekREBHx8fGBtbY39+/fnWjhnfXcuX76sUAw9f/4cz549K9AJBll2796Nr776Cl27ds11UP/HHj16BEtLy08OfAc+HGb75ptvEBYWhi1btkBfX1/hMgc3b97E/fv3sWbNGoVB2UeOHPnkspX9fhbFb01h4hgkEQUHB2P69OmoXLlyoY4d6NGjB6KiorBy5cocr7179+6TZ3ZoamrC398f27dvz/X0+IJczr5evXqoXLky/vjjjxxfmqx/CVpbW6Nly5ZYvnw5Xrx4UeD17tq1S2EM0cWLF3HhwgW0a9dO5fUYGBgAyPlFBz4cmixfvjwWLlyI9PR0+RV4mzVrhoiICGzbtg0NGzZU+Nemsu9N165doampialTp+b4l7IgCPJxE5+rc+fO0NDQwLRp03L8izBrvV5eXtDR0cFff/2lkOXff/9FfHw82rdvXyhZPpb9h+Dvv/8GAPl7aGxsDEtLS/kYsCxLlizJsay83kNl+9jd3R1WVlZYtmwZ0tLS5G0CAwOVuiJ01h6Qj9cRHx+P1atXf3LevPj6+uL8+fO4ePGifNrLly+xYcOGAi+zOOT3fSooT09PaGlp5bhswqJFi5SaX1tbG+7u7rh8+XKO16Kjo9G2bVtoaGjg0KFDeRYeNWvWhIuLC1asWKGwl2Tp0qWQSCTo1q2bClv0P6dOnUKvXr3QvHlzbNiwIcc4weyuXLmCRo0aKbVsf39/aGpqYtOmTQgKCkKHDh3k7w+Q++dWEASlzpRU9vtZFL81hYl7kIrJgQMHcO/ePWRkZCAmJgbBwcE4cuQIHBwcsGfPnkK9MGP//v2xdetWDBs2DMePH0eTJk2QmZmJe/fuYevWrTh06FCO01GzmzNnDo4fPw4PDw8MGTIENWrUQFxcHK5evYqjR48iLi5OpUwaGhpYunQpOnbsCDc3NwwePBjlypXDvXv3cPv2bRw6dAjAhx/Gpk2bwtXVFUOGDIGjoyNiYmIQEhKCZ8+eITQ09JPrcnZ2RtOmTTF8+HCkpqbijz/+gIWFBcaPHy9vo+x63NzcoKmpiblz5yI+Ph5SqVR+PRvgQzG0efNmuLq6yv/VVK9ePRgYGOD+/fsK448A5d8bJycnzJgxAxMnTsTjx4/RuXNnGBkZ4dGjR9i5cye+/vprfP/99yq9B3n11c8//4zp06ejWbNm6Nq1K6RSKS5dugQ7OzvMnj0bVlZW8tOofXx84Ofnh7CwMCxZsgRffPGFwuDwwvLo0SP4+fnBx8cHISEhWL9+Pfr06aOwt+arr77CnDlz8NVXX8Hd3R2nTp3C/fv3cywraw/ezz//jF69ekFbWxsdO3ZUuo+1tbUxY8YMDB06FK1bt0bPnj3x6NEjrF69WqkxSG3btoWOjg46duyIoUOHIikpCStXroS1tXWuBboyxo8fj3Xr1sHHxwejR4+GgYEBVqxYAQcHh0+OOxNTXu/Fxz/MqrKxsZFfNiTrMxMaGooDBw7A0tJSqb1WnTp1ws8//4yEhASFMTg+Pj54+PAhxo8fjzNnzuDMmTMK623Tpo38+fz58+Hn54e2bduiV69euHXrFhYtWoSvvvoK1atXV1jfjBkzAEB+6YZ169bJl511CO3Jkyfw8/OTF1hBQUEKy6hdu7bC4afY2FjcuHEjxwkOebG2tkarVq3w+++/IzExMcdlRlxcXODk5ITvv/8eUVFRMDY2xvbt25UeZ6bs97Owf2sKVTGcKVemZZ2qmvXQ0dERbG1thTZt2gh//vlnrpeF/9zT/AXhw6nJc+fOFWrWrClIpVLBzMxMqF+/vjB16lQhPj5e3g6AMGLEiFyzx8TECCNGjBDs7e0FbW1twdbWVvD09BRWrFghb5N1mn9QUJDCvHmd5nnmzBmhTZs2gpGRkWBgYCDUrl07x+m1ERERwoABAwRbW1tBW1tbKF++vNChQwdh27ZtuebMvs758+cLv/32m2Bvby9IpVKhWbNmOW7nosp6Vq5cKTg6Ogqampo5TsldvHixAEAYPny4wjxeXl4CAOHYsWM51qvseyMIgrB9+3ahadOmgoGBgWBgYCC4uLgII0aMEMLCwuRt8vpsDBw4UHBwcMi3z7KsWrVKqFu3rjxPixYthCNHjii0WbRokeDi4iJoa2sLNjY2wvDhw3NcskHVz2n2z1/WZ//OnTtCt27dBCMjI8HMzEwYOXJkjtvvpKSkCAEBAYKJiYlgZGQk9OjRQ375hY9PIxYEQZg+fbpQvnx5QUNDI8dp5sr0sSB8uJVD5cqVBalUKri7uwunTp1S+lYje/bsEWrXri3o6uoKlSpVEubOnSusWrUqR5a8+im39dy4cUNo0aKFoKurK5QvX16YPn268O+//xb6af7ZL1GSNe/Lly8Vpg8cOFAwMDBQmKbKe6Hs+rP+5nz8PczIyBAmTZok2NraCnp6ekLr1q2Fu3fvChYWFjlu9ZGbmJgYQUtLS1i3bl2O/Hk9cnvfd+7cKbi5uQlSqVSoUKGC8Msvv+R6+5T8lpt9O/N6ZO/XpUuXqnyrkZUrVwoABCMjoxzfL0EQhDt37gheXl6CoaGhYGlpKQwZMkQIDQ3N8/IcH1Pl+6nMb40YJIIg0sg/IqJsfv31V0ydOhUvX7785EBTovy8ffsWZmZmmDFjBn7++edPtg8ICMD9+/dVukJ6SVK3bl20bNkSCxcuFDtKqcExSEREpNZyu85Q1lWYc7tdSG6mTJmCS5cu4ezZs4WYrHgcPHgQDx48wMSJE8WOUqpwDBIREam1LVu2IDAwEL6+vjA0NMSZM2ewadMmtG3bVn7yxKdUrFhR4X5/6sTHxwdJSUlixyh1WCAREZFaq127NrS0tDBv3jwkJCTIB25nDYYmKgiOQSIiIiLKhmOQiIiIiLJhgURERESUDQskIiIiomxYIBERERFlwwJJSadOnULHjh1hZ2cHiUSCXbt2Fen6MjMzMWnSJFSuXBl6enpwcnLC9OnTRbujNxERUVnC0/yVlJycjDp16uDLL79E165di3x9c+fOxdKlS7FmzRrUrFkTly9fxuDBg2FiYoJvv/22yNdPRERUlrFAUlK7du3kdxLPTWpqKn7++Wds2rQJb9++Ra1atTB37lylr+Ka3blz59CpUyf5ndIrVaqETZs2Kdy9m4iIiIoGD7EVkpEjRyIkJASbN2/GjRs30L17d/j4+ODBgwcFWl7jxo1x7Ngx+d2PQ0NDcebMmXyLNCIiIioc3INUCCIjI7F69WpERkbCzs4OAPD999/j4MGDWL16NWbNmqXyMidMmICEhAS4uLhAU1MTmZmZmDlzJvr27VvY8YmIiCgb7kEqBDdv3kRmZiaqVq0KQ0ND+ePkyZOIiIgAANy7dw8SiSTfx4QJE+TL3Lp1KzZs2ICNGzfi6tWrWLNmDRYsWIA1a9aItZlERERlBvcgFYKkpCRoamriypUr0NTUVHjN0NAQAODo6Ii7d+/muxwLCwv5///www+YMGECevXqBQBwdXXFkydPMHv2bAwcOLCQt4CIiIg+xgKpENStWxeZmZmIjY1Fs2bNcm2jo6MDFxcXpZeZkpICDQ3FHXyampqQyWSflZWIiIg+jQWSkpKSkhAeHi5//ujRI1y/fh3m5uaoWrUq+vbtiwEDBuC3335D3bp18fLlSxw7dgy1a9eWn4mmio4dO2LmzJmoWLEiatasiWvXruH333/Hl19+WZibRURERLmQCLzyoFJOnDiBVq1a5Zg+cOBABAYGIj09HTNmzMDatWsRFRUFS0tLNGzYEFOnToWrq6vK60tMTMSkSZOwc+dOxMbGws7ODr1798bkyZOho6NTGJtEREREeWCBRERERJQNz2IjIiIiyoYFEhEREVE2HKT9CTKZDM+fP4eRkREkEonYcYiIiEgJgiAgMTERdnZ2Oc4KVwYLpE94/vw57O3txY5BREREBfD06VNUqFBB5flYIH2CkZERgA8dbGxsLHIaIiIiUkZCQgLs7e3lv+OqYoH0CVmH1YyNjVkgERERqZmCDo/hIG0iIiKibFggEREREWXDAomIiIgoGxZIRERERNmwQCIiIiLKhgUSERERUTYskIiIiIiyYYFERERElA0LJCIiIqJsWCARERERZaN2BdLixYtRqVIl6OrqwsPDAxcvXsy3fVBQEFxcXKCrqwtXV1fs37+/mJISERGRulKrAmnLli347rvvMGXKFFy9ehV16tSBt7c3YmNjc21/7tw59O7dGwEBAbh27Ro6d+6Mzp0749atW8WcnIiIiNSJRBAEQewQyvLw8MAXX3yBRYsWAQBkMhns7e0xatQoTJgwIUf7nj17Ijk5Gfv27ZNPa9iwIdzc3LBs2TKl1pmQkAATExPEx8cX6s1qXya/REp6CnS1dCHVkn74r6a0wDfVIyIiov/53N9vrSLIVCTS0tJw5coVTJw4UT5NQ0MDXl5eCAkJyXWekJAQfPfddwrTvL29sWvXrjzXk5qaitTUVPnzhISEzwuehyknpmDp5aU5putp6cFQxxBGUqMP/9UxgqmuKcz0zGCm++FhoW8BawNrWOlbwdrAGtYG1rDUt4SmhmaRZCUiIipr1KZAevXqFTIzM2FjY6Mw3cbGBvfu3ct1nujo6FzbR0dH57me2bNnY+rUqZ8f+BM0JBrQ1dLF+4z3CtPfZbzDu4x3eJnyUqXlaUo0YWNoAzsjuw8PQztUNKmo8ChvXB5aGmrzlhMREYmGv5bZTJw4UWGvU0JCAuzt7Qt9PYt8F2GR7yIIgoC0zDSkZqbiXfo7pKSnICktCYlpiUhKS0JCagLevn+LN+/e4M37N3jz7g1ev3uNlykvEZsci9jkWLxOeY1MIRPPE5/jeeLzPNeppaEFBxMHOJk7wdHUEc7mzqhqURVVLarC0cwR2prahb6dRERE6khtCiRLS0toamoiJiZGYXpMTAxsbW1zncfW1lal9gAglUohlUo/P7CSJBIJpFpSSLWkMJYWbIxThiwDscmxeJH4Ql4kPUt4hsiESETGf3g8jX+KdFk6It5EIOJNRI5laEo04WjmiOpW1VHDsgZqWtdETauacLF0gZ623uduJhERkVpRmwJJR0cH9evXx7Fjx9C5c2cAHwZpHzt2DCNHjsx1nkaNGuHYsWMYM2aMfNqRI0fQqFGjYkhcfLQ0tOSH1uqjfq5tMmUf9jA9fPMQEW8i8PDNQzyIe4AHrx/g/uv7SE5P/vA87gH2hO2RzyeBBFUsqqCOTR3UsamD2ja14WbrhgrGFTignIiISi21Ootty5YtGDhwIJYvX44GDRrgjz/+wNatW3Hv3j3Y2NhgwIABKF++PGbPng3gw2n+LVq0wJw5c9C+fXts3rwZs2bNwtWrV1GrVi2l1llUZ7GVJIIg4Hnic4S9DsPdl3dx++XtD4/Y23j97nWu81jpW6FeuXqoX64+6tvVR4PyDVDBuEIxJyciIspdmTmLDfhw2v7Lly8xefJkREdHw83NDQcPHpQPxI6MjISGxv8u7dS4cWNs3LgRv/zyC3766SdUqVIFu3btUro4KiskEgnKG5dHeePyaF25tcJrMUkxCI0JxY2YGwiNCUVodCjuvLyDlykvcSjiEA5FHJK3tTOyg0d5D3iU90DDCg3xRfkvoK+tX9ybQ0RE9NnUag+SGMrCHiRVvUt/hxsxN3D1xVVceXEFl59fxs3Ym5AJMoV2WhpaqGtbF03sm6BJxSZoWrEpbA3zHv9FRERUWD7395sF0iewQFJOcloyrr64igtRF3Ah6gLOPT2X6xl11SyqoblDc7RwaIEWlVrwsBwRERUJFkhFjAVSwQiCgMj4SJx7eg5nn57FmcgzuBFzAwIUP25VzKvAs7InWldujVaVW8FS31KkxEREVJqwQCpiLJAKz5t3b3Am8gxOPTmFk09O4sqLKzkOy9UrVw9tHdvC29kbje0bQ0dTR6S0RESkzlggFTEWSEUn/n08Tj45iWMPjyH4cTBuxSreRNhA2wCtKreCr7Mv2ldtj4omFUVKSkRE6oYFUhFjgVR8opOicSTiCA4/PIzDEYcRmxyr8HpNq5poX6U9OlTtgMb2jXnvOSIiyhMLpCLGAkkcMkGG0OhQHAg/gP0P9iPkWYjC4TgLPQt0qNoBftX80NapLQx1DEVMS0REJQ0LpCLGAqlkiHsXh8MRh7Hv/j4cCD+AuHdx8tekmlK0dWqLrtW7omPVjrDQtxAxKRERlQQskIoYC6SSJ0OWgbORZ7EnbA92h+1WuLecpkQTLSu1hH91f3St3hU2hjYiJiUiIrGwQCpiLJBKNkEQcPvlbey8uxPb725HaEyo/DUNiQZaVmqJHjV6oGv1rrAysBIxKRERFScWSEWMBZJ6iYiLwI67OxB0JwiXnl+ST9eUaMLT0RN9avVBl+pdYCzle0lEVJqxQCpiLJDU16M3jxB0JwhBd4Jw+fll+XSpphQdq3VEn1p94FvFF1ItqYgpiYioKLBAKmIskEqH8LhwbL61GRtubsC9V/fk0810zdCzZk8MqDMADSs0hEQiETElEREVFhZIRYwFUukiCAJCY0Kx8eZGbLy5EVGJUfLXnM2dMaD2AAx0G8iLUhIRqTkWSEWMBVLplSnLxPHHx7E2dC123N2B5PRkAIAEErRxaoPBboPR2aUzdLV0RU5KRESqYoFUxFgglQ1JaUnYcXcHVl9fjROPT8inm+qaon/t/hhSbwhcbVzFC0hERCphgVTEWCCVPQ/fPETg9UCsCV2DyPhI+XSP8h4YUm8IetbqySt3ExGVcCyQihgLpLIrU5aJow+PYuXVldgdthsZsgwAgLHUGP1r98dw9+GoaV1T5JRERJQbFkhFjAUSAUBMUgzWhK7ByqsrER4XLp/erGIzDHcfDv8a/tDR1BExIRERfYwFUhFjgUQfkwkyBD8KxtLLS7H73m5kCpkAABsDGwytPxRD3YfCzshO5JRERMQCqYixQKK8RCVE4Z+r/2DF1RV4nvgcAKCloYVuNbphVINRaFShEa+rREQkEhZIRYwFEn1KemY6dt7bib8v/o0zkWfk07+w+wJjGo5B9xrdoa2pLWJCIqKyhwVSEWOBRKq4Hn0df1/4GxtubkBqZioAwM7IDiO/GImh7kNhrmcuckIiorKBBVIRY4FEBfEy+SWWX1mOxZcWIzopGgCgr62PgLoBGNNwDBzNHEVOSERUurFAKmIskOhzpGWmYcutLfj9/O+4Hn0dAKAh0YB/dX983/h7NCjfQNyARESlFAukIsYCiQqDIAgIfhSMBSELcDD8oHx6y0otMaHJBLR1assB3UREhYgFUhFjgUSF7VbsLSw4twAbbm6QX3zSzdYNPzb5Ed1qdIOWhpbICYmI1B8LpCLGAomKytP4p1h4fiFWXFkhv1Guk5kTJjadiP51+vPCk0REn4EFUhFjgURF7XXKayy+tBh/XfgLr9+9BgDYG9tjfJPxCKgbAD1tPZETEhGpHxZIRYwFEhWXpLQkrLiyAvPPzZef+WZjYIPxTcZjaP2hMNAxEDkhEZH6YIFUxFggUXF7n/Eeq6+txtyzc/Ek/gkAwNrAGt83+h7ffPENCyUiIiWwQCpiLJBILOmZ6VgbuhYzT8/Eo7ePAACW+pb4ofEPGPHFCBZKRET5YIFUxFggkdjSM9Ox4eYGzDg1AxFvIgB82KM0selEDK0/lGOUiIhy8bm/3xpFkKlIxMXFoW/fvjA2NoapqSkCAgKQlJSU7zwtW7aERCJReAwbNqyYEhMVDm1NbQxyG4R7I+9hdafVcDRzRGxyLMYeGgunv5yw6OIipGakih2TiKhUUZs9SO3atcOLFy+wfPlypKenY/Dgwfjiiy+wcePGPOdp2bIlqlatimnTpsmn6evrq1RJcg8SlTTpmelYE7oG009NR2R8JADAwcQBU1tORb/a/aCpoSlyQiIi8ZWJQ2x3795FjRo1cOnSJbi7uwMADh48CF9fXzx79gx2dna5zteyZUu4ubnhjz/+KPC6WSBRSZWakYp/r/2LGadm4EXSCwBAdcvqmNF6Brq4dOGVuYmoTCsTh9hCQkJgamoqL44AwMvLCxoaGrhw4UK+827YsAGWlpaoVasWJk6ciJSUlHzbp6amIiEhQeFBVBJJtaT45otvEP5tOOZ5zYO5njnuvroL/63+aPBPAwQ/ChY7IhGR2lKLAik6OhrW1tYK07S0tGBubo7o6Og85+vTpw/Wr1+P48ePY+LEiVi3bh369euX77pmz54NExMT+cPe3r5QtoGoqOhr6+OHJj/g4bcPMbn5ZBjqGOLy88vwXOsJn/U+8pvkEhGR8kQtkCZMmJBjEHX2x7179wq8/K+//hre3t5wdXVF3759sXbtWuzcuRMRERF5zjNx4kTEx8fLH0+fPi3w+omKk4muCaa2moqIbyMwqsEoaGto41DEIdRdXhd9d/TF47ePxY5IRKQ2RB2D9PLlS7x+/TrfNo6Ojli/fj3GjRuHN2/eyKdnZGRAV1cXQUFB6NKli1LrS05OhqGhIQ4ePAhvb2+l5uEYJFJXEXERmHR8Ejbd2gQA0NHUwbcNvsVPzX6CmZ6ZyOmIiIpWmRqkffnyZdSvXx8AcPjwYfj4+OQ7SDu7s2fPomnTpggNDUXt2rWVmocFEqm7qy+uYvyR8Tj26BgAwFzPHJOaT8I3X3zDG+ISUalVJgok4MNp/jExMVi2bJn8NH93d3f5af5RUVHw9PTE2rVr0aBBA0RERGDjxo3w9fWFhYUFbty4gbFjx6JChQo4efKk0utlgUSlgSAIOBh+ED8c+QG3X94GADiZOWF+m/no7NKZZ7wRUalTJs5iAz6cjebi4gJPT0/4+vqiadOmWLFihfz19PR0hIWFyc9S09HRwdGjR9G2bVu4uLhg3Lhx8Pf3x969e8XaBCLRSCQStKvSDteHXcfKjitha2iLiDcR6Lq1K1qtaYVrL66JHZGIqERRmz1IYuEeJCqNElMTMffsXPwW8hveZ7yHBBIMdBuIWa1noZxRObHjERF9tjKzB4mICo+R1AgzWs9A2Mgw9HHtAwECAq8Houqiqph7Zi5vXUJEZR4LJKIyrKJJRWzougEhASFoUL4BktKSMOHYBNRaWgt7w/aCO5iJqKxigUREaFihIUICQrCm8xrYGtoiPC4cfpv94LPBB2GvwsSOR0RU7FggEREAQEOigQF1BuD+yPuY0GQCdDR1cDjiMFyXumLC0QlISksSOyIRUbFhgURECoykRpjtNRu3v7kN3yq+SJelY+7ZuXBZ5ILNtzbzsBsRlQkskIgoV87mzvivz3/Y23svHM0cEZUYhd7be8NzrSfuvrwrdjwioiLFAomI8tWhagfc/uY2prWcBl0tXRx/fBx1ltXBxKMTkZyWLHY8IqIiwQKJiD5JV0sXk1pMwp1v7qBD1Q5Il6Vjztk5qLGkBnbd2yV2PCKiQscCiYiUVtmsMvb23ovdvXbDwcQBkfGR6LKlCzpt7oTI+Eix4xERFRoWSESkMr9qfrgz4g4mNp0ILQ0t7AnbgxqLa+D3kN+RIcsQOx4R0WdjgUREBaKvrY9ZnrNwfeh1NK3YFMnpyRh3eBy+WPkFLkVdEjseEdFnYYFERJ+lpnVNnBx0Ev90/Admuma4Hn0dDf9tiDEHx/DaSUSktlggEdFn05BoIKBeAO6NvIe+rn0hE2T488KfqLmkJvY/2C92PCIilbFAIqJCY21gjfVd1+Ng34OoZFoJkfGRaL+xPXpv743Y5Fix4xERKY0FEhEVOm9nb9wafgvjGo2DhkQDm29tRvXF1bH+xnpeiZuI1AILJCIqEgY6BljQdgEufnURdWzqIO5dHPrv7I8OmzrgafxTseMREeWLBRIRFan6dvVxacglzGw9EzqaOtj/YD9qLqmJZZeXQSbIxI5HRJQrFkhEVOS0NbXxU7OfcH3odTSq0AiJaYkY/t9wtFnXBo/ePBI7HhFRDiyQiKjYVLeqjtODT+MP7z+gp6WH4EfBcF3qiiWXlnBvEhGVKCyQiKhYaWpoYnTD0bgx/AaaVWyG5PRkjNg/Al5rvbg3iYhKDBZIRCQKZ3NnnBh0An/5/AV9bX0cf3wctZfVxoorK3imGxGJjgUSEYlGQ6KBUR6jcGPYh71JSWlJGLpvKNptaIdnCc/EjkdEZRgLJCISnZO5E04MOoHf2/4OXS1dHIo4hFpLamFt6FruTSIiUbBAIqISQUOigbGNxuLa0GtoUL4B4lPjMXDXQHQL6oaXyS/FjkdEZQwLJCIqUVwsXXD2y7OY2XomtDS0sOPuDrgudcW++/vEjkZEZQgLJCIqcbQ0tPBTs59w8auLqGlVEzHJMei4qSOG7BmCpLQkseMRURnAAomISqy65eri8teXMa7ROEggwT/X/oHbMjecf3Ze7GhEVMqxQCKiEk1XSxcL2i7A8YHHUdGkIiLeRKDpqqaYemIqMmQZYscjolKKBRIRqYUWlVogdFgo+rj2QaaQiV9P/opmq5shIi5C7GhEVAqxQCIitWGqa4oNXTdgQ9cNMJGa4Pyz83Bb7oY119fwcgBEVKhYIBGR2unj2gehw0LR3KE5ktKSMGj3IPTZ0Qdv378VOxoRlRIskIhILTmYOiB4QDBmtJoBTYkmNt/aDLdlbjgbeVbsaERUCrBAIiK1pamhiZ+b/4wzX56Bo5kjnsQ/QfPA5ph2choyZZlixyMiNaY2BdLMmTPRuHFj6Ovrw9TUVKl5BEHA5MmTUa5cOejp6cHLywsPHjwo2qBEVOwaVmiIa0OvoV/tfpAJMkw5MQWeaz15PzciKjC1KZDS0tLQvXt3DB8+XOl55s2bh7/++gvLli3DhQsXYGBgAG9vb7x//74IkxKRGIylxljXZR3Wdl4LA20DnHxyEm7L3HgFbiIqEImgZqd+BAYGYsyYMXj79m2+7QRBgJ2dHcaNG4fvv/8eABAfHw8bGxsEBgaiV69eSq0vISEBJiYmiI+Ph7Gx8efGJ6Ji8OD1A/Ta3gtXX1wFAIz2GI25XnMh1ZKKnIyIisvn/n6rzR4kVT169AjR0dHw8vKSTzMxMYGHhwdCQkLynC81NRUJCQkKDyJSL1UsquDcl+cwxmMMAODPC3+iyaomePjmobjBiEhtlNoCKTo6GgBgY2OjMN3Gxkb+Wm5mz54NExMT+cPe3r5IcxJR0ZBqSbHQZyH29t4Lcz1zXHlxBXWX18W2O9vEjkZEakDUAmnChAmQSCT5Pu7du1esmSZOnIj4+Hj54+nTp8W6fiIqXB2qdsD1odfRxL4JElIT0D2oO0b8NwLvMzgWkYjypiXmyseNG4dBgwbl28bR0bFAy7a1tQUAxMTEoFy5cvLpMTExcHNzy3M+qVQKqZTjFIhKE3sTexwfeByTj0/GnLNzsOTyEoQ8C0FQ9yA4mTuJHY+ISiBRCyQrKytYWVkVybIrV64MW1tbHDt2TF4QJSQk4MKFCyqdCUdEpYO2pjZme81Gi0ot0H9nf1yLvob6K+pjdafV6FK9i9jxiKiEUZsxSJGRkbh+/ToiIyORmZmJ69ev4/r160hKSpK3cXFxwc6dOwEAEokEY8aMwYwZM7Bnzx7cvHkTAwYMgJ2dHTp37izSVhCR2HycfXBt6DU0tm+M+NR4dN3aFd8d+g5pmWliRyOiEkRtCqTJkyejbt26mDJlCpKSklC3bl3UrVsXly9flrcJCwtDfHy8/Pn48eMxatQofP311/jiiy+QlJSEgwcPQldXV4xNIKISooJxBZwYeALjGo0DACw8vxAtAlvgaTzHHBLRB2p3HaTixusgEZVuu+/txqDdg/D2/VtY6ltik/8meDl6fXpGIirReB0kIqLP0MmlE658fQV1beviVcortF3XFjNOzYBMkIkdjYhExAKJiMo8RzNHnAs4h6/qfgUBAiYdn4SOmzoi7l2c2NGISCQqH2JLTU3FhQsX8OTJE6SkpMDKygp169ZF5cqViyqjqHiIjahsWX1tNb7Z/w3eZ7xHZdPK2N5jO+qWqyt2LCJS0ef+fitdIJ09exZ//vkn9u7di/T0dJiYmEBPTw9xcXFITU2Fo6Mjvv76awwbNgxGRkYqBympWCARlT3Xo6/Df6s/Hr55CF0tXSzvsBwD6gwQOxYRqaBYxiD5+fmhZ8+eqFSpEg4fPozExES8fv0az549Q0pKCh48eIBffvkFx44dQ9WqVXHkyBGVgxARlRRutm64POQy2jm3w/uM9xi4ayBG/DeClwIgKkOU2oO0fPlyfPnll9DW1v7kAu/cuYMXL17A09OzUAKKjXuQiMoumSDD1BNTMe3UNABAowqNsK3HNtgZ2YmcjIg+pdgOsZVVLJCIaN/9fei3ox/iU+Nha2iL7T22o7F9Y7FjEVE+eJo/EVER61C1Ay5/fRk1rWoiOikaLQNbYsWVFWLHIqIipHKBZGZmBnNz8xwPCwsLlC9fHi1atMDq1auLIisRkWiczZ1x/qvz6FajG9Jl6Ri6byi+3vs1UjNSxY5GREVA5QJp8uTJ0NDQQPv27TF16lRMnToV7du3h4aGBkaMGIGqVati+PDhWLlyZVHkJSISjaGOIbZ224rZnrMhgQQrr65EqzWt8CLxhdjRiKiQqTwGyd/fH23atMGwYcMUpi9fvhyHDx/G9u3b8ffff2PFihW4efNmoYYVA8cgEVFuDoYfRO/tvfH2/VuUNyqPnT134ovyX4gdi4j+X7GPQTp06BC8vHLep8jT0xOHDh0CAPj6+uLhw4cqhyEiUhc+zj64NOQSqltWR1RiFJqtboZ1oevEjkVEhUTlAsnc3Bx79+7NMX3v3r0wNzcHACQnJ5eqi0USEeUma1xSx6odkZqZigG7BmDcoXHIkGWIHY2IPpOWqjNMmjQJw4cPx/Hjx9GgQQMAwKVLl7B//34sW7YMAHDkyBG0aNGicJMSEZVAxlJj7Oq1C1OOT8GM0zPw+/nfcfvlbWzuthmmuqZixyOiAirQdZDOnj2LRYsWISwsDABQrVo1jBo1Co0bl77rgnAMEhEpK+h2EAbuGoh3Ge9QzaIa9vbeiyoWVcSORVQm8UKRRYwFEhGp4uqLq+i0uROeJTyDqa4ptnbbijZObcSORVTmiFIgyWQyhIeHIzY2FjKZTOG15s2bqxyiJGOBRESqik6KRpctXXD+2XloSjSx0HshRjYYCYlEInY0ojKj2Auk8+fPo0+fPnjy5AmyzyqRSJCZmalyiJKMBRIRFcT7jPcYum8o1oauBQAMdx+OP33+hLbmp+9pSUSfr9hP8x82bBjc3d1x69YtxMXF4c2bN/JHXFycygGIiEojXS1dBHYKxDyveZBAgqWXl8J3oy/evn8rdjQiUoLKe5AMDAwQGhoKZ2fnospUonAPEhF9rt33dqPvjr5ITk9GNYtq2NdnH5zNy8bfUCKxFPseJA8PD4SHh6u8IiKisqqTSyec+fIM7I3tEfY6DB7/eODk45NixyKifKh8HaRRo0Zh3LhxiI6OhqurK7S1FY+n165du9DCERGVFm62brg45CI6be6Ei1EX0WZdG/zj9w8G1BkgdjQiyoXKh9g0NHLudJJIJBAEgYO0iYg+4V36OwzaPQhbb28FAPzc7GdMazUNGhKVd+gTUT4+9/db5T1Ijx49UnklRET0gZ62Hjb5b4KzmTNmnZmFmadnIjwuHKs7rYaetp7Y8Yjo//FCkZ/APUhEVFQCrwfi671fI12WjoYVGmJ3r92wNrAWOxZRqVAs10Has2cP2rVrB21tbezZsyfftn5+fiqHKMlYIBFRUTrx+AS6bumKN+/fwNHMEfv77Ec1y2pixyJSe8VSIGloaCA6OhrW1ta5jkGSL4xjkIiIVBb2Kgy+G33x8M1DmOmaYVevXWjuULruSkBU3IrlNH+ZTAZra2v5/+f1KG3FERFRcahmWQ3nA86jYYWGePP+Ddqsa4ONNzeKHYuoTONpE0REJYCVgRWCBwTDv7o/0jLT0HdHX8w6PSvHLZ2IqHgUqEA6duwYOnToACcnJzg5OaFDhw44evRoYWcjIipT9LT1sLX7Vnzf6HsAwM/BP2PYvmHIkGWInIyo7FG5QFqyZAl8fHxgZGSE0aNHY/To0TA2Noavry8WL15cFBmJiMoMDYkG5redj0XtFkECCVZcXYFOmzshKS1J7GhEZYrKp/lXqFABEyZMwMiRIxWmL168GLNmzUJUVFShBhQbB2kTkVh23duF3tt7433Ge9QvVx/7+uyDraGt2LGI1EKx34vt7du38PHxyTG9bdu2iI+PVzmAsmbOnInGjRtDX18fpqamSs0zaNAgSCQShUdu2YmISqLOLp1xfOBxWOpb4sqLK2j0byOEvQoTOxZRmaBygeTn54edO3fmmL5792506NChUELlJi0tDd27d8fw4cNVms/HxwcvXryQPzZt2lRECYmICl/DCg0REhACJzMnPH77GE1WNcH5Z+fFjkVU6ql8q5EaNWpg5syZOHHiBBo1agQAOH/+PM6ePYtx48bhr7/+krf99ttvCy3o1KlTAQCBgYEqzSeVSmFry13SRKS+nM2dcS7gHDps7IBLzy+h9ZrW2NJtCzpW6yh2NKJSS+UxSJUrV1ZuwRIJHj58WKBQ+QkMDMSYMWPw9u3bT7YdNGgQdu3aBR0dHZiZmaF169aYMWMGLCwslF4fxyARUUmRnJaMHtt6YP+D/dCQaGB5h+X4qt5XYsciKpF4s9p8+Pj4oGvXrqhcuTIiIiLw008/oV27dggJCYGmpmau86SmpiI1NVX+PCEhobjiEhHly0DHALt77cbQvUOx6voqDNk7BFEJUZjcYjIkEonY8YhKFVEvFDlhwoQcg6izP+7du1fg5ffq1Qt+fn5wdXVF586dsW/fPly6dAknTpzIc57Zs2fDxMRE/rC3ty/w+omICpuWhhb+8fsHk5pPAgD8evJXfPPfN8iU8U4GRIVJ5UNsgiBg27ZtOH78OGJjYyGTyRRe37Fjh9LLevnyJV6/fp1vG0dHR+jo6Mifq3KILTdWVlaYMWMGhg4dmuvrue1Bsre35yE2Iipxll5aihH7R0CAgK7Vu2JD1w3Q1dIVOxZRiVDsh9jGjBmD5cuXo1WrVrCxsfms3bpWVlawsrIq8PyqevbsGV6/fo1y5crl2UYqlUIqlRZbJiKighr+xXBYGVih746+2HF3B3zW+2B3r90w0TUROxqR2lO5QFq3bh127NgBX1/fosiTp8jISMTFxSEyMhKZmZm4fv06AMDZ2RmGhoYAABcXF8yePRtdunRBUlISpk6dCn9/f9ja2iIiIgLjx4+Hs7MzvL29izU7EVFR6VajGyz0LNB5S2ecfHISzQOb42DfgyhnlPc/BIno01Qeg2RiYgJHR8eiyJKvyZMno27dupgyZQqSkpJQt25d1K1bF5cvX5a3CQsLk1+sUlNTEzdu3ICfnx+qVq2KgIAA1K9fH6dPn+YeIiIqVVpVboWTg07CxsAGN2JuoOnqpoiIixA7FpFaU3kM0po1a3Dw4EGsWrUKenp6RZWrxOBp/kSkLh6+eYg269rg4ZuHsDGwwaF+h1DHto7YsYhE8bm/3yoXSO/evUOXLl1w9uxZVKpUCdra2gqvX716VeUQJRkLJCJSJ9FJ0fBZ74PQmFCYSE2wt/deNHNoJnYsomJX7IO0Bw4ciCtXrqBfv36fPUibiIgKl62hLU4MOgG/TX44HXkabde3xdZuW3nVbSIVqbwHycDAAIcOHULTpk2LKlOJwj1IRKSO3qW/Q89tPbH3/l5oSjQR2DkQ/Wr3EzsWUbH53N9vlQdp29vbs1AgIirh9LT1sKPnDgyoMwCZQib67+yPRRcXiR2LSG2oXCD99ttvGD9+PB4/flwEcYiIqLBoaWhhdafVGNVgFABg1IFRmHFqBlQ8cEBUJql8iM3MzAwpKSnIyMiAvr5+jkHacXFxhRpQbDzERkTqThAETD05FVNPTgUAjG04Fr+1/Y1jSKlUK/ZB2n/88YfKKyEiIvFIJBL82vJXmOmaYcyhMVh4fiHi38djRccV0NTI/cbdRGWdynuQyhruQSKi0iTweiAC9gRAJsjQo2YPrOuyDjqaOp+ekUjNFPsepI+9f/8eaWlpCtNYRBARlVyD3AbBUMcQfbb3wdbbW5Gcloyg7kHQ0y79F/4lUoXKg7STk5MxcuRIWFtbw8DAAGZmZgoPIiIq2brV6IbdvXZDV0sX/z34D74bfZGYmih2LKISReUCafz48QgODsbSpUshlUrxzz//YOrUqbCzs8PatWuLIiMRERWydlXa4VC/QzDSMcKJxyfgtc4Lb969ETsWUYmh8hikihUrYu3atWjZsiWMjY1x9epVODs7Y926ddi0aRP2799fVFlFwTFIRFSaXX5+Gd7rvRH3Lg5utm443O8wrAysxI5F9NmK/UKRcXFxcHR0BPBhvFHWaf1NmzbFqVOnVA5ARETicbdzx4mBJ2BjYIPr0dfRIrAFnic+FzsWkehULpAcHR3x6NEjAICLiwu2bt0KANi7dy9MTU0LNRwRERU9VxtXnBx0EuWNyuPuq7tovro5nrx9InYsIlGpXCANHjwYoaGhAIAJEyZg8eLF0NXVxdixY/HDDz8UekAiIip61Syr4fTg06hsWhkRbyLQbHUzhMeFix2LSDSffR2kJ0+e4MqVK3B2dkbt2rULK1eJwTFIRFSWPEt4Bs+1nrj/+j7KGZZD8MBguFi6iB2LSGWf+/vNC0V+AgskIiprYpJi4LXOC7dib8HawBrHBhxDLetaYsciUkmxFEh//fWX0gv89ttvVQ5RkrFAIqKy6FXKK7RZ1wbXo6/DQs8CRwcchZutm9ixiJRWLAVS5cqVFZ6/fPkSKSkp8kHZb9++hb6+PqytrfHw4UOVQ5RkLJCIqKx68+4NvNd749LzSzDTNcOhfofwRfkvxI5FpJRiOc3/0aNH8sfMmTPh5uaGu3fvIi4uDnFxcbh79y7q1auH6dOnqxyAiIhKJjM9MxzpfwSN7Rvjzfs38FrnhfPPzosdi6hYqDwGycnJCdu2bUPdunUVpl+5cgXdunWTXwKgtOAeJCIq6xJTE9FhUwecenIKRjpGOND3AJpUbCJ2LKJ8FfuFIl+8eIGMjIwc0zMzMxETE6NyACIiKtmMpEbY32c/WlVqhcS0RHiv98apJ7wwMJVuKhdInp6eGDp0KK5evSqfduXKFQwfPhxeXl6FGo6IiEoGAx0D7OuzD16OXkhOT0a7De1w4vEJsWMRFRmVC6RVq1bB1tYW7u7ukEqlkEqlaNCgAWxsbPDPP/8URUYiIioB9LX1safXHrR1aouU9BT4bvDFsYfHxI5FVCQKfB2kBw8e4O7duwA+3HKkatWqhRqspOAYJCIiRe8z3qPrlq44EH4Aulq62Nd7HzwdPcWORaSAF4osYiyQiIhySs1Ihf9Wf/z34D/oaulib++98HLkMAsqOYplkPacOXPw7t07pRZ44cIF/PfffyoHISIi9SHVkmJ7j+3oULUD3me8R8dNHXH04VGxYxEVGqUKpDt37qBixYr45ptvcODAAbx8+VL+WkZGBm7cuIElS5agcePG6NmzJ4yMjIosMBERlQxSLSm2dd/GIolKJaUPsYWGhmLRokXYtm0bEhISoKmpCalUipSUFABA3bp18dVXX2HQoEHQ1dUt0tDFiYfYiIjyl5qRiu5B3bH3/l4ebqMSo9jHIMlkMty4cQNPnjzBu3fvYGlpCTc3N1haWqq8cnXAAomI6NM+LpL0tPSwr88+tK7cWuxYVIZxkHYRY4FERKScjwdu62np4UDfA2hRqYXYsaiMKvYraRMREeUma+B2O+d2eJfxDr4bfXH6yWmxYxEVCAskIiIqNFItKXb03AFvJ2+kpKeg3YZ2OBt5VuxYRCpTiwLp8ePHCAgIQOXKlaGnpwcnJydMmTIFaWlp+c73/v17jBgxAhYWFjA0NIS/vz/vF0dEVMR0tXSxs+dOtHFsI78tyYVnF8SORaQStSiQ7t27B5lMhuXLl+P27dtYuHAhli1bhp9++inf+caOHYu9e/ciKCgIJ0+exPPnz9G1a9diSk1EVHbpaethV69daF25tfwGt1eeXxE7FpHSCjxIOzw8HBEREWjevDn09PQgCAIkEklh58vT/PnzsXTpUjx8+DDX1+Pj42FlZYWNGzeiW7duAD4UWtWrV0dISAgaNmyo1Ho4SJuIqOCS0z7sQTodeRpmumY4PvA46tjWETsWlQHFPkj79evX8PLyQtWqVeHr64sXL14AAAICAjBu3DiVAxRUfHw8zM3N83z9ypUrSE9Ph5fX/67F4eLigooVKyIkJCTP+VJTU5GQkKDwICKigjHQMcB/ff5DowqN8Ob9G3it88Kt2FtixyL6JJULpLFjx0JLSwuRkZHQ19eXT+/ZsycOHjxYqOHyEh4ejr///htDhw7Ns010dDR0dHRgamqqMN3GxgbR0dF5zjd79myYmJjIH/b29oUVm4ioTDKSGuFA3wNwt3PHq5RX8FzriXuv7okdiyhfKhdIhw8fxty5c1GhQgWF6VWqVMGTJ09UWtaECRMgkUjyfdy7p/glioqKgo+PD7p3744hQ4aoGv+TJk6ciPj4ePnj6dOnhb4OIqKyxkTXBIf6HYKbrRtik2PhudYTEXERYsciypOWqjMkJycr7DnKEhcXB6lUqtKyxo0bh0GDBuXbxtHRUf7/z58/R6tWrdC4cWOsWLEi3/lsbW2RlpaGt2/fKuxFiomJga2tbZ7zSaVSlbeDiIg+zVzPHEf6H0HLwJa4/fI2Wq9tjdODT6OiSUWxoxHloPIepGbNmmHt2rXy5xKJBDKZDPPmzUOrVq1UWpaVlRVcXFzyfejo6AD4sOeoZcuWqF+/PlavXg0Njfyj169fH9ra2jh27Jh8WlhYGCIjI9GoUSOVchIRUeGw1LfE0QFHUcW8CiLjI9F6TWs8T3wudiyiHFQ+i+3WrVvw9PREvXr1EBwcDD8/P9y+fRtxcXE4e/YsnJycCj1kVnHk4OCANWvWQFNTU/5a1t6gqKgoeHp6Yu3atWjQoAEAYPjw4di/fz8CAwNhbGyMUaNGAQDOnTun9Lp5FhsRUeF7Gv8UzQOb4/Hbx3CxdMHJQSdhbWAtdiwqRT7391vlQ2y1atXC/fv3sWjRIhgZGSEpKQldu3bFiBEjUK5cOZUDKOPIkSMIDw9HeHh4jrFPWfVdeno6wsLCkJKSIn9t4cKF0NDQgL+/P1JTU+Ht7Y0lS5YUSUYiIlKevYk9ggcEo3lgc9x7dQ9t1rXB8YHHYa6X99nJRMWJN6v9BO5BIiIqOvdf30fz1c0RkxyDBuUb4Gj/ozCSGokdi0qBYr8O0urVqxEUFJRjelBQENasWaNyACIiKruqWlTF0QFHYa5njotRF9FxU0ekpKd8ekaiIqZygTR79mxYWlrmmG5tbY1Zs2YVSigiIio7alnXwqF+h2CkY4STT07Cf6s/UjNSxY5FZZzKBVJkZCQqV66cY7qDgwMiIyMLJRQREZUt7nbu2N93P/S09HAw/CD67OiDDFmG2LGoDFO5QLK2tsaNGzdyTA8NDYWFhUWhhCIiorKnacWm2N1rN3Q0dbDj7g58uftLyASZ2LGojFK5QOrduze+/fZbHD9+HJmZmcjMzERwcDBGjx6NXr16FUVGIiIqI9o4tcHWbluhKdHEuhvr8O2Bb8FziUgMKhdI06dPh4eHBzw9PaGnpwc9PT20bdsWrVu35hgkIiL6bJ1cOmFN5zWQQILFlxZj0vFJYkeiMqjAp/nfv38foaGh0NPTg6urKxwcHAo7W4nA0/yJiMSx5NISjNg/AgAwz2sefmjyg8iJSJ0U+4Uis1StWhVVq1Yt6OxERET5+uaLb5CQmoCJxyZi/NHxMJYaY6j7ULFjURmhcoGUmZmJwMBAHDt2DLGxsZDJFAfQBQcHF1o4IiIq2yY0nYD49/GYc3YOhv83HKa6puhZq6fYsagMULlAGj16NAIDA9G+fXvUqlULEomkKHIREREBAGZ5zkJ8ajyWXl6Kfjv7wUTXBD7OPmLHolJO5TFIlpaWWLt2LXx9fYsqU4nCMUhEROKTCTL029EPm25tgp6WHo70P4ImFZuIHYtKsGK/1YiOjg6cnZ1VXhEREVFBaUg0sKbzGrRzbod3Ge/QfmN7hEaHih2LSjGVC6Rx48bhzz//5HUpiIioWGlramNbj21oYt8E8anx8F7vjfC4cLFjUSml8iG2Ll264Pjx4zA3N0fNmjWhra2t8PqOHTsKNaDYeIiNiKhkefv+LVoGtkRoTCgqmVbC2S/Pws7ITuxYVMIU+2n+pqam6NKli8orIiIiKgymuqY41O8Qmqxqgog3EfBe741Tg07BTM9M7GhUihT4QpFlBfcgERGVTA/fPESTVU0QnRSNJvZNcLj/Yehr64sdi0qIYh+kDQAZGRk4evQoli9fjsTERADA8+fPkZSUVJDFERERqczRzBGH+h2Cqa4pzj49ix5BPZCemS52LColVC6Qnjx5AldXV3Tq1AkjRozAy5cvAQBz587F999/X+gBiYiI8lLbpjb29d4HPS09/PfgPwTsCYBMkH16RqJPULlAGj16NNzd3fHmzRvo6enJp3fp0gXHjh0r1HBERESf0qRiEwR1D4KmRBPrbqzD+CPjxY5EpYDKBdLp06fxyy+/QEdHR2F6pUqVEBUVVWjBiIiIlNW+anus7rQaAPBbyG9YcG6ByIlI3alcIMlkMmRmZuaY/uzZMxgZGRVKKCIiIlX1r9Mf89vMBwD8cOQHrA1dK3IiUmcqF0ht27bFH3/8IX8ukUiQlJSEKVOmlJnbjxARUcn0fePvMa7ROADAl7u/xP4H+0VOROpK5dP8nz59Ch8fHwiCgAcPHsDd3R0PHjyApaUlTp06BWtr66LKKgqe5k9EpF5kggyDdg3CuhvroKelh+CBwWhYoaHYsaiYfe7vd4Gug5SRkYEtW7YgNDQUSUlJqFevHvr27aswaLu0YIFERKR+0jPT0WlzJxwIPwBzPXOc/fIsXCxdxI5FxahYC6T09HS4uLhg3759qF69usorU0cskIiI1FNyWjJar22Ni1EX4WDigHMB53hLkjKkWC8Uqa2tjffv36u8EiIiouJmoGOAfb33oYp5FTyJfwLfDb6Ifx8vdixSEyoP0h4xYgTmzp2LjIyMoshDRERUaKwMrHCo3yHYGNggNCYUXbZ0QWpGqtixSA2oPAYp64KQhoaGcHV1hYGBgcLrO3bsKNSAYuMhNiIi9XftxTW0CGyBxLRE9KjZA5v8N0FDUqC7bZGa+Nzfby1VZzA1NYW/v7/KKyIiIhJL3XJ1saPnDvhu8MXW21tR3qg8fvf+XexYVIIV6Cy2soR7kIiISo+NNzei746+AIDf2/6OsY3GipyIikqxDtLOkpGRgaNHj2L58uVITEwEADx//hxJSUkFWRwREVGx6OPaB3O95gIAxh0eh6DbQSInopJK5UNsT548gY+PDyIjI5Gamoo2bdrAyMgIc+fORWpqKpYtW1YUOYmIiArFD41/wNP4p1h0aRH67ewHG0MbNHdoLnYsKmFU3oM0evRouLu7482bNwoXhswavF0UHj9+jICAAFSuXBl6enpwcnLClClTkJaWlu98LVu2hEQiUXgMGzasSDISEZF6kEgk+MPnD3Rx6YK0zDR02twJt2Nvix2LShiV9yCdPn0a586dg46OjsL0SpUqISoqqtCCfezevXuQyWRYvnw5nJ2dcevWLQwZMgTJyclYsCD/OzYPGTIE06ZNkz/X19cvkoxERKQ+NDU0saHrBrRZ1wZnn55Fuw3tcP6r87yQJMmpXCDJZDJkZmbmmP7s2TMYGRkVSqjsfHx84OPjI3/u6OiIsLAwLF269JMFkr6+PmxtbYskFxERqS89bT3s7rUbTVY1QdjrMLTf2B6nBp2CkbRofstIvah8iK1t27b4448/5M8lEgmSkpIwZcoU+Pr6Fma2fMXHx8Pc3PyT7TZs2ABLS0vUqlULEydOREpKSjGkIyIidWChb4H9fffD2sAa16Ovo3tQd6Rnposdi0oAlU/zf/bsGby9vSEIAh48eAB3d3c8ePAAlpaWOHXqFKytrYsqq1x4eDjq16+PBQsWYMiQIXm2W7FiBRwcHGBnZ4cbN27gxx9/RIMGDfK9mGVqaipSU/93ldWEhATY29vzNH8iolLsUtQltFzTEinpKQioG4CVHVdCIpGIHYs+Q7HerDZLRkYGtmzZgtDQUCQlJaFevXro27evwqBtZUyYMAFz587Nt83du3fh4vK/OzBHRUWhRYsWaNmyJf755x+V1hccHAxPT0+Eh4fDyckp1za//vorpk6dmmM6CyQiotJtb9hedN7SGTJBhumtpuOX5r+IHYk+Q7EUSPXq1cOxY8dgZmaGadOm4fvvvy+Uwc4vX77E69ev823j6OgoHxD+/PlztGzZEg0bNkRgYCA0NFQ7QpicnAxDQ0McPHgQ3t7eubbhHiQiorJr6aWl+Gb/NwCAtZ3Xon+d/iInooIqlgJJT08PDx48QIUKFaCpqYkXL14Uy6G0j0VFRaFVq1aoX78+1q9fD01NTZWXcfbsWTRt2hShoaGoXbu2UvPwStpERGXLj0d+xLxz86CtoY3D/Q+jZaWWYkeiAiiWe7G5ublh8ODBaNq0KQRBwIIFC2BoaJhr28mTJ6sc4lOioqLQsmVLODg4YMGCBXj58qX8tawz1KKiouDp6Ym1a9eiQYMGiIiIwMaNG+Hr6wsLCwvcuHEDY8eORfPmzZUujoiIqOyZ7TUbj+MfY+vtreiypQvOfXkO1a2qix2LiplSBVJgYCCmTJmCffv2QSKR4MCBA9DSyjmrRCIpkgLpyJEjCA8PR3h4OCpUqKDwWtYOsPT0dISFhcnPUtPR0cHRo0fxxx9/IDk5Gfb29vD398cvv/CYMhER5U1DooE1ndfgWcIznHt6Dr4bfXE+4DxsDG3EjkbFSOVB2hoaGoiOji72Q2xi4SE2IqKy6VXKKzT6txHC48Lxhd0XODHoBPS1ebFhdVEsN6utV68e3rx5AwCYMmVKnofXiIiISgtLfUvs77MfFnoWuPT8Evps74NMWc4LJVPppFSBdPfuXSQnJwMApk2bhqSkpCINRUREVBJUsaiC3b12Q6opxe6w3Rh/ZLzYkaiYqMUgbSIiIrE0qdgEgZ0D0Xt7b/x+/nc4mztj+BfDxY5FRUypMUhhYWGYMmUKIiIicPXqVdSoUSPPQdpXr14tkqBi4RgkIiICgJmnZuKX479AU6KJfX32wcfZ59MzkWiK/UraHKRNRERlkSAIGLx7MNaEroGRjhHOfHkGtW142ZiSqlgGaX9MJpOVmeKIiIgoi0QiwYqOK9CyUkskpiWi/cb2eJH4QuxYVESUGoO0Z88etGvXDtra2tizZ0++bf38/AolGBERUUmjo6mDHT12oNG/jRD2Ogx+m/1wctBJnv5fCil1iO3jw2r53f9MIpEgM7N0nQLJQ2xERJRdRFwEPP7xwOt3r9G1elcEdQ+ChkTlgzJUhIrlENvHh9VkMlmej9JWHBEREeXGydwJO3vuhLaGNnbc3YGfj/0sdiQqZCx3iYiICqCZQzP84/cPAGDO2TlYfW21yImoMKlUIMlkMqxatQodOnRArVq14OrqCj8/P6xduxYqngxHRESk9gbUGYCfm33YezR031CcfHxS5ERUWJQukARBgJ+fH7766itERUXB1dUVNWvWxJMnTzBo0CB06dKlKHMSERGVSNNaTUOPmj2QLktHly1d8OD1A7EjUSFQ6iw2AAgMDMSpU6dw7NgxtGrVSuG14OBgdO7cGWvXrsWAAQMKPSQREVFJpSHRQGCnQDx++xgXoy6i46aOCAkIgZmemdjR6DMovQdp06ZN+Omnn3IURwDQunVrTJgwARs2bCjUcEREROpAT1sPu3ruQgXjCgh7HYYe23ogPTNd7Fj0GZQukG7cuAEfn7wvq96uXTuEhoYWSigiIiJ1U86oHPb23gsDbQMcfXgUYw6OETsSfQalC6S4uDjY2Njk+bqNjQ3evHlTKKGIiIjUkZutG9Z3XQ8JJFhyeQkWX1wsdiQqIKULpMzMzFxvUJtFU1MTGRkZhRKKiIhIXXV26YzZnrMBAKMPjsbhiMMiJ6KCUHqQtiAIGDRoEKRSaa6vp6amFlooIiIidTa+yXjce30PgdcD0SOoBy58dQHVLKuJHYtUoHSBNHDgwE+24RlsREREH269taz9Mjx4/QBnn55Fx00dcf6r8zDXMxc7GilJqXuxlWW8FxsRERVUbHIsvlj5BSLjI+FZ2RMH+h6Atqa22LHKhGK5FxsRERGpztrAWn5m27FHxzD20FixI5GSWCAREREVodo2tbGh6wZIIMHiS4ux9NJSsSORElggERERFbFOLp0ws/VMAMCoA6MQ/ChY5ET0KSyQiIiIisGEphPQ17UvMoVMdA/qjodvHoodifLBAomIiKgYSCQSrOy4El/YfYG4d3Hw2+SHxNREsWNRHlggERERFRM9bT3s7LkT5QzL4fbL2+i3sx9kgkzsWJQLFkhERETFqLxxeezsuRNSTSn2hO3BpOBJYkeiXLBAIiIiKmYeFTywsuNKAMCsM7Ow+dZmkRNRdiyQiIiIRNC/Tn/80PgHAMCXu7/E1RdXRU5EH2OBREREJJLZnrPh4+yDdxnv0HlzZ8Qmx4odif4fCyQiIiKRaGpoYpP/JlS1qIqnCU/RbWs3pGWmiR2LwAKJiIhIVKa6ptjdazeMpcY4HXkaow+MFjsSgQUSERGR6FwsXbCx60ZIIMGyK8uw7PIysSOVeWpTIPn5+aFixYrQ1dVFuXLl0L9/fzx//jzfed6/f48RI0bAwsIChoaG8Pf3R0xMTDElJiIiUl77qu0xy3MWgA+3Izn95LTIico2tSmQWrVqha1btyIsLAzbt29HREQEunXrlu88Y8eOxd69exEUFISTJ0/i+fPn6Nq1azElJiIiUs2PTX5Ez5o9kSHLQLegbnga/1TsSGWWRBAEQewQBbFnzx507twZqamp0NbWzvF6fHw8rKyssHHjRnkhde/ePVSvXh0hISFo2LChUutJSEiAiYkJ4uPjYWxsXKjbQERElF1yWjKarGqC0JhQ1C9XH6cHn4aetp7YsdTO5/5+q80epI/FxcVhw4YNaNy4ca7FEQBcuXIF6enp8PLykk9zcXFBxYoVERISkueyU1NTkZCQoPAgIiIqLgY6BtjVaxcs9Cxw5cUVDPtvGNR0X4ZaU6sC6ccff4SBgQEsLCwQGRmJ3bt359k2OjoaOjo6MDU1VZhuY2OD6OjoPOebPXs2TExM5A97e/vCik9ERKSUSqaVsKXbFmhKNLE2dC3+uvCX2JHKHFELpAkTJkAikeT7uHfvnrz9Dz/8gGvXruHw4cPQ1NTEgAEDCr2qnjhxIuLj4+WPp095/JeIiIqfp6MnFrRdAAAYd3gcgh8Fi5yobNESc+Xjxo3DoEGD8m3j6Ogo/39LS0tYWlqiatWqqF69Ouzt7XH+/Hk0atQox3y2trZIS0vD27dvFfYixcTEwNbWNs/1SaVSSKVSlbeFiIiosI32GI2rL65i3Y116LmtJy4PuQwHUwexY5UJohZIVlZWsLKyKtC8MpkMwIcxQ7mpX78+tLW1cezYMfj7+wMAwsLCEBkZmWtBRUREVNJIJBIs77Act1/extUXV9F1a1ecGXyGg7aLgVqMQbpw4QIWLVqE69ev48mTJwgODkbv3r3h5OQkL3aioqLg4uKCixcvAgBMTEwQEBCA7777DsePH8eVK1cwePBgNGrUSOkz2IiIiMSmp62HHT12wFLfEldfXOWg7WKiFgWSvr4+duzYAU9PT1SrVg0BAQGoXbs2Tp48KT8clp6ejrCwMKSkpMjnW7hwITp06AB/f380b94ctra22LFjh1ibQUREVCAOpg7Y2m2rfND2oouLxI5U6qntdZCKC6+DREREJcXCkIX47vB30JRo4tiAY2hRqYXYkUqsMnkdJCIiorJoTMMx6OPaB5lCJroHdcezhGdiRyq1WCARERGpCYlEgpUdV8LN1g0vU17Cf6s/UjNyP1mJPg8LJCIiIjWir62PHT12wEzXDBejLuLbA9+KHalUYoFERESkZiqbVcYm/02QQIIVV1fg36v/ih2p1GGBREREpIa8nb0xvdV0AMCI/SNwKeqSyIlKFxZIREREampis4noVK0TUjNT4b/VHy+TX4odqdRggURERKSmNCQaWNN5DapaVMXThKfovb03MmQZYscqFVggERERqTETXRPs7LkTBtoGOPboGCYFTxI7UqnAAomIiEjN1bCqgVWdVgEA5pydg513d4qcSP2xQCIiIioFetTsgbENxwIABu4aiPuv74ucSL2xQCIiIiol5nrNRbOKzZCYloiuW7oiOS1Z7EhqiwUSERFRKaGtqY2t3beinGE53H55G0P2DgFvuVowLJCIiIhKEVtDW2ztvhVaGlrYdGsT/r74t9iR1BILJCIiolKmacWmWNBmAQBg3OFxCHkaInIi9cMCiYiIqBT61uNb9KjZAxmyDHQP6o7Y5FixI6kVFkhERESlkEQiwT8d/4GLpQuiEqPQe3tvZMoyxY6lNlggERERlVJGUiNs77EdBtoGCH4UjMnHJ4sdSW2wQCIiIirFaljVwD9+/wAAZp2Zhb1he0VOpB5YIBEREZVyvWr1wqgGowAA/Xf2x8M3D0VOVPKxQCIiIioDFrRdgIYVGiI+NR7dg7rjfcZ7sSOVaCyQiIiIygAdTR1s7bYVFnoWuPriKsYcHCN2pBKNBRIREVEZYW9ijw1dN0ACCZZfWY51oevEjlRisUAiIiIqQ7ydvTG5xYez2Yb9Nwy3Ym+JnKhkYoFERERUxkxqPgltHNsgJT0F3bZ2Q2JqotiRShwWSERERGWMpoYmNnTdgPJG5RH2Oow3tc0FCyQiIqIyyMrASn5T2y23t2DZ5WViRypRWCARERGVUY3tG2OO5xwAwJhDY3Dl+RWRE5UcLJCIiIjKsO8afYdO1TohLTMN3YO64+37t2JHKhFYIBEREZVhEokEqzutRmXTynj09hEG7x7M8UhggURERFTmmemZYWv3rdDR1MGue7vwx/k/xI4kOhZIREREBHc7dyz0XggAGH90PEKehoicSFwskIiIiAgAMNx9OHrW7IkMWQZ6buuJuHdxYkcSDQskIiIiAvBhPNKKjivgbO6MpwlPMXDXwDI7HkltCiQ/Pz9UrFgRurq6KFeuHPr374/nz5/nO0/Lli0hkUgUHsOGDSumxEREROrHWGqMoO5BkGpKse/+Pvwe8rvYkUShNgVSq1atsHXrVoSFhWH79u2IiIhAt27dPjnfkCFD8OLFC/lj3rx5xZCWiIhIfbnZuuFPnz8BABOOTSiT45G0xA6grLFjx8r/38HBARMmTEDnzp2Rnp4ObW3tPOfT19eHra1tcUQkIiIqNb6u/zVOPDmBzbc2o+e2nrg29Bos9C3EjlVs1GYP0sfi4uKwYcMGNG7cON/iCAA2bNgAS0tL1KpVCxMnTkRKSkq+7VNTU5GQkKDwICIiKmskEgmWd1iOKuZV8DThKQbtHlSmxiOpVYH0448/wsDAABYWFoiMjMTu3bvzbd+nTx+sX78ex48fx8SJE7Fu3Tr069cv33lmz54NExMT+cPe3r4wN4GIiEhtZB+PtPD8QrEjFRuJIGI5OGHCBMydOzffNnfv3oWLiwsA4NWrV4iLi8OTJ08wdepUmJiYYN++fZBIJEqtLzg4GJ6enggPD4eTk1OubVJTU5Gamip/npCQAHt7e8THx8PY2FjJLSMiIio9ll1ehuH/DYeWhhbODD4DjwoeYkf6pISEBJiYmBT491vUAunly5d4/fp1vm0cHR2ho6OTY/qzZ89gb2+Pc+fOoVGjRkqtLzk5GYaGhjh48CC8vb2VmudzO5iIiEjdCYKAntt6IuhOEBxMHHBt6DWY6ZmJHStfn/v7LeogbSsrK1hZWRVoXplMBgAKe3s+5fr16wCAcuXKFWidREREZZFEIsHKjitx5cUVPHzzEAF7ArC9x3alj+CoI7UYg3ThwgUsWrQI169fx5MnTxAcHIzevXvDyclJvvcoKioKLi4uuHjxIgAgIiIC06dPx5UrV/D48WPs2bMHAwYMQPPmzVG7dm0xN4eIiEjtmOiaYEu3LdDW0MbOezux+NJisSMVKbUokPT19bFjxw54enqiWrVqCAgIQO3atXHy5ElIpVIAQHp6OsLCwuRnqeno6ODo0aNo27YtXFxcMG7cOPj7+2Pv3r1ibgoREZHacrdzx/w28wEA4w6Pw9UXV0VOVHREHYOkDjgGiYiI6H8EQUCXLV2wO2w3nM2dcfXrqzCSGokdK4fP/f1Wiz1IREREVDJIJBKs6rQK9sb2CI8Lx/D/hpfK6yOxQCIiIiKVmOuZY5P/JmhKNLHh5gYEXg8UO1KhY4FEREREKmtSsQmmtZoGABh5YCTuvrwrcqLCxQKJiIiICmRC0wnwcvRCSnoKem7riXfp78SOVGhYIBEREVGBaEg0sK7LOlgbWONm7E18d+g7sSMVGhZIREREVGC2hrZY32U9JJBg2ZVl2H5nu9iRCgULJCIiIvosbZzaYHyT8QCAr/Z+hSdvn4ic6POxQCIiIqLPNr3VdDQo3wBv379F3x19kSHLEDvSZ2GBRERERJ9NW1Mbm/w3wVhqjLNPz2LayWliR/osLJCIiIioUDiaOWJ5h+UAgBmnZuDE4xPiBvoMLJCIiIio0PSq1Qtfun0JAQL67uiLVymvxI5UICyQiIiIqFD91e4vuFi64Hnic3y5+0u1vBUJCyQiIiIqVAY6Btjsvxk6mjrYe38vllxaInYklbFAIiIiokJXx7YO5nnNAwCMOzwON2NuipxINSyQiIiIqEh86/EtfKv4IjUzFb2290JKeorYkZTGAomIiIiKhEQiwepOq2FraIs7L+9g3KFxYkdSGgskIiIiKjLWBtZY23ktAGDZlWXYeXenyImUwwKJiIiIilQbpzb4ofEPAICAPQF4lvBM5ESfxgKJiIiIityM1jNQv1x9vHn/BgN2DkCmLFPsSPligURERERFTkdTBxv9N8JA2wDHHx/H/HPzxY6ULxZIREREVCyqWlTFX+3+AgBMOj4Jl6IuiZwobyyQiIiIqNgMdhuM7jW6I0OWgT47+iAxNVHsSLligURERETFRiKRYHmH5bA3tkd4XDi+Pfit2JFyxQKJiIiIipWZnhnWd10PCSQIvB6ILbe2iB0pBxZIREREVOyaOzTHz81+hoG2AdJl6WLHyUEiqOMtdotRQkICTExMEB8fD2NjY7HjEBERlRrpmel4mvAUjmaOhb7sz/395h4kIiIiEoW2pnaRFEeFgQUSERERUTYskIiIiIiyYYFERERElA0LJCIiIqJsWCARERERZaN2BVJqairc3NwgkUhw/fr1fNu+f/8eI0aMgIWFBQwNDeHv74+YmJjiCUpERERqS+0KpPHjx8POzk6ptmPHjsXevXsRFBSEkydP4vnz5+jatWsRJyQiIiJ1p1YF0oEDB3D48GEsWLDgk23j4+Px77//4vfff0fr1q1Rv359rF69GufOncP58+eLIS0RERGpK7UpkGJiYjBkyBCsW7cO+vr6n2x/5coVpKenw8vLSz7NxcUFFStWREhISJ7zpaamIiEhQeFBREREZYtaFEiCIGDQoEEYNmwY3N3dlZonOjoaOjo6MDU1VZhuY2OD6OjoPOebPXs2TExM5A97e/vPiU5ERERqSNQCacKECZBIJPk+7t27h7///huJiYmYOHFikWeaOHEi4uPj5Y+nT58W+TqJiIioZNESc+Xjxo3DoEGD8m3j6OiI4OBghISEQCqVKrzm7u6Ovn37Ys2aNTnms7W1RVpaGt6+fauwFykmJga2trZ5rk8qleZYDxEREZUtEkEQBLFDfEpkZKTCWKDnz5/D29sb27Ztg4eHBypUqJBjnvj4eFhZWWHTpk3w9/cHAISFhcHFxQUhISFo2LChUuv+3LsBExERUfH73N9vUfcgKatixYoKzw0NDQEATk5O8uIoKioKnp6eWLt2LRo0aAATExMEBATgu+++g7m5OYyNjTFq1Cg0atRI6eII+DD+CQAHaxMREamRrN/tgu4HUosCSRnp6ekICwtDSkqKfNrChQuhoaEBf39/pKamwtvbG0uWLFFpuYmJiQDAwdpERERqKDExESYmJirPpxaH2MQkk8nw/PlzGBkZQSKRFHg5CQkJsLe3x9OnT3moroixr4sP+7r4sK+LD/u6+BRlXwuCgMTERNjZ2UFDQ/Vz0krNHqSioqGhkesYp4IyNjbmF66YsK+LD/u6+LCviw/7uvgUVV8XZM9RFrW4DhIRERFRcWKBRERERJQNC6RiIpVKMWXKFF5jqRiwr4sP+7r4sK+LD/u6+JTkvuYgbSIiIqJsuAeJiIiIKBsWSERERETZsEAiIiIiyoYFEhEREVE2LJAALF68GJUqVYKuri48PDxw8eLFfNsHBQXBxcUFurq6cHV1xf79+xVeFwQBkydPRrly5aCnpwcvLy88ePBAoU1cXBz69u0LY2NjmJqaIiAgAElJSQptbty4gWbNmkFXVxf29vaYN2+eyllKGnXt65UrV6JZs2YwMzODmZkZvLy8Ppm9JFDX/v7Y5s2bIZFI0LlzZ9U2vpipc1+/ffsWI0aMQLly5SCVSlG1atUS/bdEnfv6jz/+QLVq1aCnpwd7e3uMHTsW79+/L2BPFL2S2Nfv37/HoEGD4OrqCi0trTz/Npw4cQL16tWDVCqFs7MzAgMDVdt4oYzbvHmzoKOjI6xatUq4ffu2MGTIEMHU1FSIiYnJtf3Zs2cFTU1NYd68ecKdO3eEX375RdDW1hZu3rwpbzNnzhzBxMRE2LVrlxAaGir4+fkJlStXFt69eydv4+PjI9SpU0c4f/68cPr0acHZ2Vno3bu3/PX4+HjBxsZG6Nu3r3Dr1i1h06ZNgp6enrB8+XKVspQk6tzXffr0ERYvXixcu3ZNuHv3rjBo0CDBxMREePbsWRH0VOFQ5/7O8ujRI6F8+fJCs2bNhE6dOhVe5xQyde7r1NRUwd3dXfD19RXOnDkjPHr0SDhx4oRw/fr1Iuipz6fOfb1hwwZBKpUKGzZsEB49eiQcOnRIKFeunDB27Ngi6KnPV1L7OikpSRg2bJiwYsUKwdvbO9e/DQ8fPhT09fWF7777Trhz547w999/C5qamsLBgweV3v4yXyA1aNBAGDFihPx5ZmamYGdnJ8yePTvX9j169BDat2+vMM3Dw0MYOnSoIAiCIJPJBFtbW2H+/Pny19++fStIpVJh06ZNgiAIwp07dwQAwqVLl+RtDhw4IEgkEiEqKkoQBEFYsmSJYGZmJqSmpsrb/Pjjj0K1atWUzlLSqHNfZ5eRkSEYGRkJa9asUXbzi52693dGRobQuHFj4Z9//hEGDhxYogskde7rpUuXCo6OjkJaWlpBN79YqXNfjxgxQmjdurVClu+++05o0qSJSn1QXEpqX38sr78N48ePF2rWrKkwrWfPnoK3t/cntvp/yvQhtrS0NFy5cgVeXl7yaRoaGvDy8kJISEiu84SEhCi0BwBvb295+0ePHiE6OlqhjYmJCTw8PORtQkJCYGpqCnd3d3kbLy8vaGho4MKFC/I2zZs3h46OjsJ6wsLC8ObNG6WylCTq3tfZpaSkID09Hebm5qp0Q7EpDf09bdo0WFtbIyAgoKDdUCzUva/37NmDRo0aYcSIEbCxsUGtWrUwa9YsZGZmfk63FAl17+vGjRvjypUr8sNUDx8+xP79++Hr61vgPikqJbmvlVEYv49lukB69eoVMjMzYWNjozDdxsYG0dHRuc4THR2db/us/36qjbW1tcLrWlpaMDc3V2iT2zI+XsenspQk6t7X2f3444+ws7PL8QUsKdS9v8+cOYN///0XK1euVG6DRaTuff3w4UNs27YNmZmZ2L9/PyZNmoTffvsNM2bMUK4DipG693WfPn0wbdo0NG3aFNra2nByckLLli3x008/KdcBxagk97Uy8sqSkJCAd+/eKbWMMl0gERXEnDlzsHnzZuzcuRO6urpixyl1EhMT0b9/f6xcuRKWlpZixyn1ZDIZrK2tsWLFCtSvXx89e/bEzz//jGXLlokdrdQ5ceIEZs2ahSVLluDq1avYsWMH/vvvP0yfPl3saJSLMl0gWVpaQlNTEzExMQrTY2JiYGtrm+s8tra2+bbP+u+n2sTGxiq8npGRgbi4OIU2uS3j43V8KktJou59nWXBggWYM2cODh8+jNq1a+e/0SJS5/6OiIjA48eP0bFjR2hpaUFLSwtr167Fnj17oKWlhYiICKX7oTioc18DQLly5VC1alVoamrK21SvXh3R0dFIS0v7xNYXL3Xv60mTJqF///746quv4Orqii5dumDWrFmYPXs2ZDKZcp1QTEpyXysjryzGxsbQ09NTahllukDS0dFB/fr1cezYMfk0mUyGY8eOoVGjRrnO06hRI4X2AHDkyBF5+8qVK8PW1lahTUJCAi5cuCBv06hRI7x9+xZXrlyRtwkODoZMJoOHh4e8zalTp5Cenq6wnmrVqsHMzEypLCWJuvc1AMybNw/Tp0/HwYMHFY6Pl0Tq3N8uLi64efMmrl+/Ln/4+fmhVatWuH79Ouzt7T+zdwqXOvc1ADRp0gTh4eEKP9D3799HuXLlFMbTlATq3tcpKSnQ0FD82c0qTIUSdlvUktzXyiiU30elh3OXUps3bxakUqkQGBgo3LlzR/j6668FU1NTITo6WhAEQejfv78wYcIEefuzZ88KWlpawoIFC4S7d+8KU6ZMyfU0RlNTU2H37t3CjRs3hE6dOuV6GmPdunWFCxcuCGfOnBGqVKmicBrj27dvBRsbG6F///7CrVu3hM2bNwv6+vo5TvP/VJaSRJ37es6cOYKOjo6wbds24cWLF/JHYmJiUXbZZ1Hn/s6upJ/Fps59HRkZKRgZGQkjR44UwsLChH379gnW1tbCjBkzirLLCkyd+3rKlCmCkZGRsGnTJuHhw4fC4cOHBScnJ6FHjx5F2WUFVlL7WhAE4fbt28K1a9eEjh07Ci1bthSuXbsmXLt2Tf561mn+P/zwg3D37l1h8eLFPM2/IP7++2+hYsWKgo6OjtCgQQPh/Pnz8tdatGghDBw4UKH91q1bhapVqwo6OjpCzZo1hf/++0/hdZlMJkyaNEmwsbERpFKp4OnpKYSFhSm0ef36tdC7d2/B0NBQMDY2FgYPHpzjxzY0NFRo2rSpIJVKhfLlywtz5szJkf1TWUoade1rBwcHAUCOx5QpUz6/U4qQuvZ3diW9QBIE9e7rc+fOCR4eHoJUKhUcHR2FmTNnChkZGZ/ZI0VHXfs6PT1d+PXXXwUnJydBV1dXsLe3F7755hvhzZs3n98pRaSk9nVef5M/dvz4ccHNzU3Q0dERHB0dhdWrV6u07RJBKGH79YiIiIhEVqbHIBERERHlhgUSERERUTYskIiIiIiyYYFERERElA0LJCIiIqJsWCARERERZcMCiYiIiCgbFkhERERE2bBAIiL6f5GRkWjfvj309fVhbW2NH374ARkZGWLHIiIRaIkdgIioJMjMzET79u1ha2uLc+fO4cWLFxgwYAC0tbUxa9YsseMRUTHjHiQiKpVatmyJUaNGYcyYMTAzM4ONjQ1WrlyJ5ORkDB48GEZGRnB2dsaBAwcAAIcPH8adO3ewfv16uLm5oV27dpg+fToWL16MtLQ0kbeGiIobCyQiKrXWrFkDS0tLXLx4EaNGjcLw4cPRvXt3NG7cGFevXkXbtm3Rv39/pKSkICQkBK6urrCxsZHP7+3tjYSEBNy+fVvErSAiMbBAIqJSq06dOvjll19QpUoVTJw4Ebq6urC0tMSQIUNQpUoVTJ48Ga9fv8aNGzcQHR2tUBwBkD+Pjo4WIz4RiYgFEhGVWrVr15b/v6amJiwsLODq6iqfllUAxcbGFns2IirZWCARUamlra2t8FwikShMk0gkAACZTAZbW1vExMQotM96bmtrW8RJiaikYYFERASgUaNGuHnzpsLepCNHjsDY2Bg1atQQMRkRiYEFEhERgLZt26JGjRro378/QkNDcejQIfzyyy8YMWIEpFKp2PGIqJixQCIiwocxSvv27YOmpiYaNWqEfv36YcCAAZg2bZrY0YhIBBJBEASxQxARERGVJNyDRERERJQNCyQiIiKibFggEREREWXDAomIiIgoGxZIRERERNmwQCIiIiLKhgUSERERUTYskIiIiIiyYYFERERElA0LJCIiIqJsWCARERERZcMCiYiIiCib/wPoKHAsScC8CwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(m0s, np.array(nondim_d)-np.array(ic_heaving_lim_dp), color = \"green\")\n",
+    "plt.title(\"Difference between computed and limiting (2012) value\")\n",
+    "plt.xlabel(\"m0\")\n",
+    "plt.ylabel(\"Difference (damping)\")\n",
+    "# Approaches zero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8511b980-0fd3-4c3d-b2f3-82af034c2929",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare(m0s, nondim_d_ic, nondim_d, \"2012 Value\", \"Implementation Here\", \"Nondimensional Damping\",\n",
+    "             title = \"2012 paper (2-cylinder) formula and this implementation, comparison\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "966cc5ce-ad3d-47e4-9c39-2f67f47bd671",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def low_solver():\n",
+    "    A = build_A(m0s[0])\n",
+    "    b = build_B()\n",
+    "    solutions = []\n",
+    "    for m0 in m0s:\n",
+    "        A_new_m0(A, m0)\n",
+    "        solutions.append(linalg.solve(A,b))\n",
+    "    \n",
+    "    nondim_am = []\n",
+    "    nondim_d = []\n",
+    "    a_norm = get_max_heaving_radius()\n",
+    "    for i in range(len(m0s)):\n",
+    "        added_mass, damping = get_hydro_coeffs(solutions[i][:-1], m0s[i])\n",
+    "        nd_am, nd_d = to_nondim(added_mass, a_norm), to_nondim(damping, a_norm)\n",
+    "        nondim_am.append(nd_am)\n",
+    "        nondim_d.append(nd_d)\n",
+    "    return nondim_am, nondim_d\n",
+    "\n",
+    "added_mass_various = []\n",
+    "damping_various = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fc8b2a46-19bd-4808-9f81-1f389e0d00e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.3640262463216195\n",
+      "2.5292031170945184\n",
+      "3.6799572352241605\n",
+      "4.830112987190839\n",
+      "5.98025562327502\n",
+      "0.762195003759961\n",
+      "0.7842040565083574\n",
+      "0.7846076466946342\n",
+      "0.7846134727493457\n",
+      "0.7846135488959539\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The following boxes are for visually inspecting that yes, the damping looks like it converges,\n",
+    "# Yes, the added mass is diverging.\n",
+    "\n",
+    "# config0 \n",
+    "h = 1.001\n",
+    "d = [0.5, 0.25]\n",
+    "a = [0.5, 1]\n",
+    "heaving = [1, 1]\n",
+    "m0s = [0.1, 0.01, 0.001, 0.0001, 0.00001]\n",
+    "\n",
+    "bds = len(a) # number of boundaries\n",
+    "\n",
+    "added_mass, damping = low_solver()\n",
+    "for am in added_mass:\n",
+    "    print(am)\n",
+    "for dmp in damping:\n",
+    "    print(dmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "66373c22-5c46-4b8c-8cde-0f5e4e045b79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.1856963920659827\n",
+      "2.4382407343103165\n",
+      "3.667642581077993\n",
+      "4.895719833223409\n",
+      "6.123765789492739\n",
+      "0.7875175881343128\n",
+      "0.8367400243252997\n",
+      "0.8377429134078579\n",
+      "0.8377578402971169\n",
+      "0.8377580384568498\n"
+     ]
+    }
+   ],
+   "source": [
+    "# config1\n",
+    "h = 1.5\n",
+    "d = [1.1, 0.85, 0.75, 0.4, 0.15]\n",
+    "a = [0.3, 0.5, 1, 1.2, 1.6]\n",
+    "heaving = [1, 1, 1, 1, 1]\n",
+    "m0s = [0.1, 0.01, 0.001, 0.0001, 0.00001]\n",
+    "\n",
+    "bds = len(a) # number of boundaries\n",
+    "\n",
+    "added_mass, damping = low_solver()\n",
+    "for am in added_mass:\n",
+    "    print(am)\n",
+    "for dmp in damping:\n",
+    "    print(dmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6e400361-553c-402b-b640-3bc36592e30c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.029734978422935793\n",
+      "0.13243910771566925\n",
+      "0.24907331248003647\n",
+      "0.3642637997048136\n",
+      "0.4793943904766781\n",
+      "0.039593835603309264\n",
+      "0.0762957198763721\n",
+      "0.0784988260564866\n",
+      "0.0785392254341329\n",
+      "0.07853980862220948\n"
+     ]
+    }
+   ],
+   "source": [
+    "# config2\n",
+    "h = 100\n",
+    "d = [29, 7, 4]\n",
+    "a = [3, 5, 10]\n",
+    "heaving = [1, 1, 1]\n",
+    "m0s = [0.1, 0.01, 0.001, 0.0001, 0.00001]\n",
+    "\n",
+    "bds = len(a) # number of boundaries\n",
+    "\n",
+    "added_mass, damping = low_solver()\n",
+    "for am in added_mass:\n",
+    "    print(am)\n",
+    "for dmp in damping:\n",
+    "    print(dmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2e28d720-ffd8-40ef-988e-7038039f52cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9444870489527376\n",
+      "1.9333378454614216\n",
+      "2.9039182508043244\n",
+      "3.8734529235507056\n",
+      "4.842962889026492\n",
+      "0.6217244116849838\n",
+      "0.6605842297304997\n",
+      "0.6613759842693614\n",
+      "0.6613877686556185\n",
+      "0.661387925097513\n"
+     ]
+    }
+   ],
+   "source": [
+    "# config3\n",
+    "h = 1.9\n",
+    "d = [0.5, 0.7, 0.8, 0.2, 0.5]\n",
+    "a = [0.3, 0.5, 1, 1.2, 1.6]\n",
+    "heaving = [1, 1, 1, 1, 1]\n",
+    "m0s = [0.1, 0.01, 0.001, 0.0001, 0.00001]\n",
+    "\n",
+    "bds = len(a) # number of boundaries\n",
+    "\n",
+    "added_mass, damping = low_solver()\n",
+    "for am in added_mass:\n",
+    "    print(am)\n",
+    "for dmp in damping:\n",
+    "    print(dmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "56e2c44e-548f-4926-8260-2c98b30723da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7307707149903037\n",
+      "1.3861827048000597\n",
+      "2.0334818962479835\n",
+      "2.68044450672924\n",
+      "3.3273997395265904\n",
+      "0.4287346896149781\n",
+      "0.44111478178595115\n",
+      "0.44134180126573175\n",
+      "0.44134507842150694\n",
+      "0.44134512125397407\n"
+     ]
+    }
+   ],
+   "source": [
+    "# config4 \n",
+    "h = 1.001\n",
+    "d = [0.5, 0.25]\n",
+    "a = [0.5, 1]\n",
+    "heaving = [0, 1]\n",
+    "m0s = [0.1, 0.01, 0.001, 0.0001, 0.00001]\n",
+    "\n",
+    "bds = len(a) # number of boundaries\n",
+    "\n",
+    "added_mass, damping = low_solver()\n",
+    "for am in added_mass:\n",
+    "    print(am)\n",
+    "for dmp in damping:\n",
+    "    print(dmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f1cf017c-2121-44ba-ba1d-194917f0f55b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9516871489179057\n",
+      "1.5342755843043552\n",
+      "2.1096526433691767\n",
+      "2.6847305193525153\n",
+      "3.2598018373946056\n",
+      "0.3810975018799804\n",
+      "0.3921020282541787\n",
+      "0.39230382334731706\n",
+      "0.3923067363746727\n",
+      "0.3923067744479769\n"
+     ]
+    }
+   ],
+   "source": [
+    "# config5\n",
+    "h = 1.001\n",
+    "d = [0.5, 0.25]\n",
+    "a = [0.5, 1]\n",
+    "heaving = [1, 0]\n",
+    "m0s = [0.1, 0.01, 0.001, 0.0001, 0.00001]\n",
+    "\n",
+    "bds = len(a) # number of boundaries\n",
+    "\n",
+    "added_mass, damping = low_solver()\n",
+    "for am in added_mass:\n",
+    "    print(am)\n",
+    "for dmp in damping:\n",
+    "    print(dmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ce1e8510-b9d9-40b6-9fec-c67dffb1e647",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.02295458213957543\n",
+      "0.10800387160688797\n",
+      "0.2045886565722605\n",
+      "0.2999778990430984\n",
+      "0.3953175412612793\n",
+      "0.032787655263100395\n",
+      "0.06318048562962374\n",
+      "0.06500487785737656\n",
+      "0.06503833258200545\n",
+      "0.06503881552005168\n"
+     ]
+    }
+   ],
+   "source": [
+    "# config6\n",
+    "h = 100\n",
+    "d = [29, 7, 4]\n",
+    "a = [3, 5, 10]\n",
+    "heaving = [0, 1, 1]\n",
+    "m0s = [0.1, 0.01, 0.001, 0.0001, 0.00001]\n",
+    "\n",
+    "bds = len(a) # number of boundaries\n",
+    "\n",
+    "added_mass, damping = low_solver()\n",
+    "for am in added_mass:\n",
+    "    print(am)\n",
+    "for dmp in damping:\n",
+    "    print(dmp)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/dev/python/multi_MEEM.ipynb b/dev/python/multi_MEEM.ipynb
new file mode 100644
index 0000000..2e3ad6a
--- /dev/null
+++ b/dev/python/multi_MEEM.ipynb
@@ -0,0 +1,1237 @@
+{
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import numpy as np\n",
+                "from scipy.special import hankel1 as besselh\n",
+                "from scipy.special import iv as besseli\n",
+                "from scipy.special import kv as besselk\n",
+                "import scipy.integrate as integrate\n",
+                "import scipy.linalg as linalg\n",
+                "import matplotlib.pyplot as plt\n",
+                "from math import sqrt, cosh, cos, sinh, sin, pi\n",
+                "from scipy.optimize import newton, minimize_scalar\n",
+                "from multi_constants import *\n",
+                "from multi_equations import *\n",
+                "import pandas as pd\n",
+                "from multi_condensed import Problem"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "metadata": {
+                "scrolled": true
+            },
+            "outputs": [],
+            "source": [
+                "boundary_count = len(NMK) - 1\n",
+                "for arr in [a, d, heaving]:\n",
+                "    assert len(arr) == boundary_count, \"NMK should have one more entry than a, d, and heaving, which should all have the same number of entries.\"\n",
+                "\n",
+                "for entry in heaving:\n",
+                "    assert (entry == 0 or entry == 1), \"heaving entries should be booleans.\"\n",
+                "\n",
+                "left = 0\n",
+                "for radius in a:\n",
+                "    assert radius > left, \"a entries should be increasing, and start greater than 0.\"\n",
+                "    left = radius\n",
+                "\n",
+                "for depth in d:\n",
+                "    assert depth >= 0, \"d entries should be nonnegative.\"\n",
+                "    assert depth < h, \"d entries should be less than h.\"\n",
+                "\n",
+                "for val in NMK:\n",
+                "    assert (type(val) == int and val > 0), \"NMK entries should be positive integers.\"\n",
+                "\n",
+                "assert (m0 > 0), \"m0 should be positive.\" # currently shouldn't be too large, but that will be fixed eventually\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# CREATING THE A MATRIX\n",
+                "size = NMK[0] + NMK[-1] + 2 * sum(NMK[1:len(NMK) - 1])\n",
+                "boundary_count = len(NMK) - 1\n",
+                "\n",
+                "rows = [] # collection of rows of blocks in A matrix, to be concatenated later\n",
+                "\n",
+                "## Define values/functions to help block creation\n",
+                "#Coupling integral values\n",
+                "I_nm_vals = np.zeros((max(NMK), max(NMK), boundary_count - 1), dtype = complex)\n",
+                "for bd in range(boundary_count - 1):\n",
+                "    for n in range(NMK[bd]):\n",
+                "        for m in range(NMK[bd + 1]):\n",
+                "            I_nm_vals[n][m][bd] = I_nm(n, m, bd)\n",
+                "I_mk_vals = np.zeros((NMK[boundary_count - 1], NMK[boundary_count]), dtype = complex)\n",
+                "for m in range(NMK[boundary_count - 1]):\n",
+                "    for k in range(NMK[boundary_count]):\n",
+                "        I_mk_vals[m][k]= I_mk(m, k, boundary_count - 1)\n",
+                "\n",
+                "## Functions to create blocks of certain types\n",
+                "# arguments: diagonal block on left (T/F), vectorized radial eigenfunction, boundary number\n",
+                "def p_diagonal_block(left, radfunction, bd):\n",
+                "    region = bd if left else (bd + 1)\n",
+                "    sign = 1 if left else (-1)\n",
+                "    return sign * (h - d[region]) * np.diag(radfunction(list(range(NMK[region])), a[bd], region))\n",
+                "    \n",
+                "# arguments: dense block on left (T/F), vectorized radial eigenfunction, boundary number\n",
+                "def p_dense_block(left, radfunction, bd):\n",
+                "    I_nm_array = I_nm_vals[0:NMK[bd],0:NMK[bd+1], bd]\n",
+                "    if left: # determine which is region to work in and which is adjacent\n",
+                "        region, adj = bd, bd + 1\n",
+                "        sign = 1\n",
+                "        I_nm_array = np.transpose(I_nm_array)\n",
+                "    else:\n",
+                "        region, adj = bd + 1, bd\n",
+                "        sign = -1\n",
+                "    radial_vector = radfunction(list(range(NMK[region])), a[bd], region)\n",
+                "    radial_array = np.outer((np.full((NMK[adj]), 1)), radial_vector)\n",
+                "    return sign * radial_array * I_nm_array\n",
+                "\n",
+                "def p_dense_block_e(bd):\n",
+                "    I_mk_array = I_mk_vals\n",
+                "    radial_vector = (np.vectorize(Lambda_k, otypes = [complex]))(list(range(NMK[bd+1])), a[bd])\n",
+                "    radial_array = np.outer((np.full((NMK[bd]), 1)), radial_vector)\n",
+                "    return (-1) * radial_array * I_mk_array\n",
+                "\n",
+                "#####\n",
+                "# arguments: diagonal block on left (T/F), vectorized radial eigenfunction, boundary number\n",
+                "def v_diagonal_block(left, radfunction, bd):\n",
+                "    region = bd if left else (bd + 1)\n",
+                "    sign = (-1) if left else (1)\n",
+                "    return sign * (h - d[region]) * np.diag(radfunction(list(range(NMK[region])), a[bd], region))\n",
+                "\n",
+                "# arguments: dense block on left (T/F), vectorized radial eigenfunction, boundary number\n",
+                "def v_dense_block(left, radfunction, bd):\n",
+                "    I_nm_array = I_nm_vals[0:NMK[bd],0:NMK[bd+1], bd]\n",
+                "    if left: # determine which is region to work in and which is adjacent\n",
+                "        region, adj = bd, bd + 1\n",
+                "        sign = -1\n",
+                "        I_nm_array = np.transpose(I_nm_array)\n",
+                "    else:\n",
+                "        region, adj = bd + 1, bd\n",
+                "        sign = 1\n",
+                "    radial_vector = radfunction(list(range(NMK[region])), a[bd], region)\n",
+                "    radial_array = np.outer((np.full((NMK[adj]), 1)), radial_vector)\n",
+                "    return sign * radial_array * I_nm_array\n",
+                "\n",
+                "def v_diagonal_block_e(bd):\n",
+                "    return h * np.diag((np.vectorize(diff_Lambda_k, otypes = [complex]))(list(range(NMK[bd+1])), a[bd]))\n",
+                "\n",
+                "def v_dense_block_e(radfunction, bd): # for region adjacent to e-type region\n",
+                "    I_km_array = np.transpose(I_mk_vals)\n",
+                "    radial_vector = radfunction(list(range(NMK[bd])), a[bd], bd)\n",
+                "    radial_array = np.outer((np.full((NMK[bd + 1]), 1)), radial_vector)\n",
+                "    return (-1) * radial_array * I_km_array\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "metadata": {
+                "scrolled": true
+            },
+            "outputs": [],
+            "source": [
+                "# Potential Blocks\n",
+                "col = 0\n",
+                "for bd in range(boundary_count):\n",
+                "    N = NMK[bd]\n",
+                "    M = NMK[bd + 1]\n",
+                "    if bd == (boundary_count - 1): # i-e boundary, inherently left diagonal\n",
+                "        row_height = N\n",
+                "        left_block1 = p_diagonal_block(True, np.vectorize(R_1n), bd)\n",
+                "        right_block = p_dense_block_e(bd)\n",
+                "        if bd == 0: # one cylinder\n",
+                "            rows.append(np.concatenate((left_block1,right_block), axis = 1))\n",
+                "        else:\n",
+                "            left_block2 = p_diagonal_block(True, np.vectorize(R_2n), bd)\n",
+                "            left_zeros = np.zeros((row_height, col), dtype=complex)\n",
+                "            rows.append(np.concatenate((left_zeros,left_block1,left_block2,right_block), axis = 1))\n",
+                "    elif bd == 0:\n",
+                "        left_diag = d[bd] > d[bd + 1] # which of the two regions gets diagonal entries\n",
+                "        if left_diag:\n",
+                "            row_height = N\n",
+                "            left_block = p_diagonal_block(True, np.vectorize(R_1n), 0)\n",
+                "            right_block1 = p_dense_block(False, np.vectorize(R_1n), 0)\n",
+                "            right_block2 = p_dense_block(False, np.vectorize(R_2n), 0)\n",
+                "        else:\n",
+                "            row_height = M\n",
+                "            left_block = p_dense_block(True, np.vectorize(R_1n), 0)\n",
+                "            right_block1 = p_diagonal_block(False, np.vectorize(R_1n), 0)\n",
+                "            right_block2 = p_diagonal_block(False, np.vectorize(R_2n), 0)\n",
+                "        right_zeros = np.zeros((row_height, size - (col + N + 2 * M)),dtype=complex)\n",
+                "        block_lst = [left_block, right_block1, right_block2, right_zeros]\n",
+                "        rows.append(np.concatenate(block_lst, axis = 1))\n",
+                "        col += N\n",
+                "    else: # i-i boundary\n",
+                "        left_diag = d[bd] > d[bd + 1] # which of the two regions gets diagonal entries\n",
+                "        if left_diag:\n",
+                "            row_height = N\n",
+                "            left_block1 = p_diagonal_block(True, np.vectorize(R_1n), bd)\n",
+                "            left_block2 = p_diagonal_block(True, np.vectorize(R_2n), bd)\n",
+                "            right_block1 = p_dense_block(False, np.vectorize(R_1n),  bd)\n",
+                "            right_block2 = p_dense_block(False, np.vectorize(R_2n),  bd)\n",
+                "        else:\n",
+                "            row_height = M\n",
+                "            left_block1 = p_dense_block(True, np.vectorize(R_1n),  bd)\n",
+                "            left_block2 = p_dense_block(True, np.vectorize(R_2n),  bd)\n",
+                "            right_block1 = p_diagonal_block(False, np.vectorize(R_1n),  bd)\n",
+                "            right_block2 = p_diagonal_block(False, np.vectorize(R_2n),  bd)\n",
+                "        left_zeros = np.zeros((row_height, col), dtype=complex)\n",
+                "        right_zeros = np.zeros((row_height, size - (col + 2 * N + 2 * M)),dtype=complex)\n",
+                "        block_lst = [left_zeros, left_block1, left_block2, right_block1, right_block2, right_zeros]\n",
+                "        rows.append(np.concatenate(block_lst, axis = 1))\n",
+                "        col += 2 * N\n",
+                "\n",
+                "\n",
+                "###############################\n",
+                "# Velocity Blocks\n",
+                "col = 0\n",
+                "for bd in range(boundary_count):\n",
+                "    N = NMK[bd]\n",
+                "    M = NMK[bd + 1]\n",
+                "    if bd == (boundary_count - 1): # i-e boundary, inherently left diagonal\n",
+                "        row_height = M\n",
+                "        left_block1 = v_dense_block_e(np.vectorize(diff_R_1n, otypes=[complex]), bd)\n",
+                "        right_block = v_diagonal_block_e(bd)\n",
+                "        if bd == 0: # one cylinder\n",
+                "            rows.append(np.concatenate((left_block1,right_block), axis = 1))\n",
+                "        else:\n",
+                "            left_block2 = v_dense_block_e(np.vectorize(diff_R_2n, otypes=[complex]), bd)\n",
+                "            left_zeros = np.zeros((row_height, col), dtype=complex)\n",
+                "            rows.append(np.concatenate((left_zeros,left_block1,left_block2,right_block), axis = 1))\n",
+                "    elif bd == 0:\n",
+                "        left_diag = d[bd] <= d[bd + 1] # taller fluid region gets diagonal entries\n",
+                "        if left_diag:\n",
+                "            row_height = N\n",
+                "            left_block = v_diagonal_block(True, np.vectorize(diff_R_1n, otypes=[complex]), 0)\n",
+                "            right_block1 = v_dense_block(False, np.vectorize(diff_R_1n, otypes=[complex]), 0)\n",
+                "            right_block2 = v_dense_block(False, np.vectorize(diff_R_2n, otypes=[complex]), 0)\n",
+                "        else:\n",
+                "            row_height = M\n",
+                "            left_block = v_dense_block(True, np.vectorize(diff_R_1n, otypes=[complex]), 0)\n",
+                "            right_block1 = v_diagonal_block(False, np.vectorize(diff_R_1n, otypes=[complex]), 0)\n",
+                "            right_block2 = v_diagonal_block(False, np.vectorize(diff_R_2n, otypes=[complex]), 0)\n",
+                "        right_zeros = np.zeros((row_height, size - (col + N + 2 * M)),dtype=complex)\n",
+                "        block_lst = [left_block, right_block1, right_block2, right_zeros]\n",
+                "        rows.append(np.concatenate(block_lst, axis = 1))\n",
+                "        col += N\n",
+                "    else: # i-i boundary\n",
+                "        left_diag = d[bd] <= d[bd + 1] # taller fluid region gets diagonal entries\n",
+                "        if left_diag:\n",
+                "            row_height = N\n",
+                "            left_block1 = v_diagonal_block(True, np.vectorize(diff_R_1n, otypes=[complex]), bd)\n",
+                "            left_block2 = v_diagonal_block(True, np.vectorize(diff_R_2n, otypes=[complex]), bd)\n",
+                "            right_block1 = v_dense_block(False, np.vectorize(diff_R_1n, otypes=[complex]),  bd)\n",
+                "            right_block2 = v_dense_block(False, np.vectorize(diff_R_2n, otypes=[complex]),  bd)\n",
+                "        else:\n",
+                "            row_height = M\n",
+                "            left_block1 = v_dense_block(True, np.vectorize(diff_R_1n, otypes=[complex]),  bd)\n",
+                "            left_block2 = v_dense_block(True, np.vectorize(diff_R_2n, otypes=[complex]),  bd)\n",
+                "            right_block1 = v_diagonal_block(False, np.vectorize(diff_R_1n, otypes=[complex]),  bd)\n",
+                "            right_block2 = v_diagonal_block(False, np.vectorize(diff_R_2n, otypes=[complex]),  bd)\n",
+                "        left_zeros = np.zeros((row_height, col), dtype=complex)\n",
+                "        right_zeros = np.zeros((row_height, size - (col + 2* N + 2 * M)),dtype=complex)\n",
+                "        block_lst = [left_zeros, left_block1, left_block2, right_block1, right_block2, right_zeros]\n",
+                "        rows.append(np.concatenate(block_lst, axis = 1))\n",
+                "        col += 2 * N\n",
+                "\n",
+                "## Concatenate the rows of blocks into the square A matrix\n",
+                "A = np.concatenate(rows, axis = 0)\n",
+                "\n",
+                "###########################################################################\n",
+                "# This plots a sparsity matrix\n",
+                "if False:\n",
+                "    \n",
+                " rows, cols = np.nonzero(A)\n",
+                " plt.figure(figsize=(6, 6))\n",
+                " plt.scatter(cols, rows, color='blue', marker='o', s=100) \n",
+                " plt.gca().invert_yaxis() \n",
+                " plt.xticks(range(A.shape[1]))\n",
+                " plt.yticks(range(A.shape[0]))\n",
+                "\n",
+                " cols = [NMK[0]]\n",
+                " for i in range(1, boundary_count):\n",
+                "    cols.append(cols[-1] + NMK[i])\n",
+                "    cols.append(cols[-1] + NMK[i])\n",
+                " cols.append(cols[-1] + NMK[-1])\n",
+                "\n",
+                " for val in cols:\n",
+                "    plt.axvline(val-0.5, color='black', linestyle='-', linewidth=1) \n",
+                "    plt.axhline(val-0.5, color='black', linestyle='-', linewidth=1) \n",
+                "\n",
+                " # for y in range(0, A.shape[0], 3):\n",
+                "    # plt.axhline(y-0.5, color='black', linestyle='-', linewidth=1) \n",
+                "\n",
+                " plt.grid(True)\n",
+                " plt.title('Non-Zero Entries of the Matrix')\n",
+                " plt.xlabel('Column Index')\n",
+                " plt.ylabel('Row Index')\n",
+                " plt.show()\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "b = np.zeros(size, dtype=complex)\n",
+                "\n",
+                "index = 0\n",
+                "\n",
+                "# potential matching\n",
+                "for boundary in range(boundary_count):\n",
+                "    if boundary == (boundary_count - 1): # i-e boundary\n",
+                "        for n in range(NMK[-2]):\n",
+                "            b[index] = b_potential_end_entry(n, boundary)\n",
+                "            index += 1\n",
+                "    else: # i-i boundary\n",
+                "        for n in range(NMK[boundary + (d[boundary] <= d[boundary + 1])]): # iterate over eigenfunctions for smaller h-d\n",
+                "            b[index] = b_potential_entry(n, boundary)\n",
+                "            index += 1\n",
+                "\n",
+                "# velocity matching\n",
+                "for boundary in range(boundary_count):\n",
+                "    if boundary == (boundary_count - 1): # i-e boundary\n",
+                "        for n in range(NMK[-1]):\n",
+                "            b[index] = b_velocity_end_entry(n, boundary)\n",
+                "            index += 1\n",
+                "    else: # i-i boundary\n",
+                "        for n in range(NMK[boundary + (d[boundary] > d[boundary + 1])]): # iterate over eigenfunctions for larger h-d\n",
+                "            b[index] = b_velocity_entry(n, boundary)\n",
+                "            index += 1"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "metadata": {
+                "scrolled": true
+            },
+            "outputs": [],
+            "source": [
+                "X = linalg.solve(A,b)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "real (added mass): 1290352136164.114\n",
+                        "imag (damping): 202705362.95834258\n",
+                        "real/(h^3): 1290352.136164114\n",
+                        "imag/(h^3): 202.7053629583426\n",
+                        "nondimensional, real: 401497.4013679475\n",
+                        "nondimensional, imag (no omega factor): 20.137484949464962\n",
+                        "Excitation Phase: -2.968870660013088 radians\n",
+                        "Excitation Force: 1889.3768721017243 N\n"
+                    ]
+                }
+            ],
+            "source": [
+                "# The c-vector dotted with X is the hydro coefficient integral (+ a constant, sum(hydro_p_terms))\n",
+                "c = np.zeros((size - NMK[-1]), dtype=complex)\n",
+                "col = 0\n",
+                "for n in range(NMK[0]):\n",
+                "    c[n] = heaving[0] * int_R_1n(0, n)* z_n_d(n)\n",
+                "col += NMK[0]\n",
+                "for i in range(1, boundary_count):\n",
+                "    M = NMK[i]\n",
+                "    for m in range(M):\n",
+                "        c[col + m] = heaving[i] * int_R_1n(i, m)* z_n_d(m)\n",
+                "        c[col + M + m] = heaving[i] * int_R_2n(i, m)* z_n_d(m)\n",
+                "    col += 2 * M\n",
+                "\n",
+                "hydro_p_terms = np.zeros(boundary_count, dtype=complex)\n",
+                "for i in range(boundary_count):\n",
+                "    hydro_p_terms[i] = heaving[i] * int_phi_p_i(i)\n",
+                "\n",
+                "hydro_coef = 2 * pi * (np.dot(c, X[:-NMK[-1]]) + sum(hydro_p_terms))\n",
+                "hydro_coef_real = hydro_coef.real * h**3 * rho\n",
+                "if m0 == inf: hydro_coef_imag = 0\n",
+                "else: hydro_coef_imag = hydro_coef.imag * omega * h**3 * rho\n",
+                "\n",
+                "# find maximum heaving radius\n",
+                "max_rad = a[0]\n",
+                "for i in range(boundary_count - 1, 0, -1):\n",
+                "    if heaving[i]:\n",
+                "        max_rad = a[i]\n",
+                "        break\n",
+                "\n",
+                "hydro_coef_nondim = h**3/(max_rad**3 * pi)*hydro_coef\n",
+                "\n",
+                "print(\"real (added mass):\", hydro_coef_real)\n",
+                "print(\"imag (damping):\", hydro_coef_imag)\n",
+                "print(\"real/(h^3):\", hydro_coef_real/(h**3)) # to compare with Capytaine\n",
+                "print(\"imag/(h^3):\", hydro_coef_imag/(h**3))\n",
+                "print(\"nondimensional, real:\", hydro_coef_nondim.real)\n",
+                "print(\"nondimensional, imag (no omega factor):\", hydro_coef_nondim.imag)\n",
+                "\n",
+                "print(\"Excitation Phase:\", excitation_phase(X), \"radians\")\n",
+                "print(\"Excitation Force:\", excitation_force(hydro_coef_imag), \"N\")\n",
+                "\n",
+                "# print(\"ratio\", hydro_coef_real/hydro_coef_imag)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# Split up the Cs into groups depending on which equation they belong to.\n",
+                "Cs = []\n",
+                "row = 0\n",
+                "Cs.append(X[:NMK[0]])\n",
+                "row += NMK[0]\n",
+                "for i in range(1, boundary_count):\n",
+                "    Cs.append(X[row: row + NMK[i] * 2])\n",
+                "    row += NMK[i] * 2\n",
+                "Cs.append(X[row:])"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "def make_R_Z(sharp, spatial_res): # create coordinate array for graphing\n",
+                "    rmin = (2 * a[-1] / spatial_res) if sharp else 0.0\n",
+                "    r_vec = np.linspace(rmin, 2*a[-1], spatial_res)\n",
+                "    z_vec = np.linspace(0, -h, spatial_res)\n",
+                "    if sharp: # more precise near boundaries\n",
+                "        a_eps = 1.0e-4\n",
+                "        for i in range(len(a)):\n",
+                "            r_vec = np.append(r_vec, a[i]*(1-a_eps))\n",
+                "            r_vec = np.append(r_vec, a[i]*(1+a_eps))\n",
+                "        r_vec = np.unique(r_vec)\n",
+                "        for i in range(len(d)):\n",
+                "            z_vec = np.append(z_vec, -d[i])\n",
+                "        z_vec = np.unique(z_vec)\n",
+                "    return np.meshgrid(r_vec, z_vec)\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "def phi_h_n_inner_func(n, r, z):\n",
+                "    return (Cs[0][n] * R_1n(n, r, 0)) * Z_n_i(n, z, 0)\n",
+                "\n",
+                "def phi_h_m_i_func(i, m, r, z):\n",
+                "    return (Cs[i][m] * R_1n(m, r, i) + Cs[i][NMK[i] + m] * R_2n(m, r, i)) * Z_n_i(m, z, i)\n",
+                "\n",
+                "def phi_e_k_func(k, r, z):\n",
+                "    return Cs[-1][k] * Lambda_k(k, r) * Z_k_e(k, z)\n",
+                "\n",
+                "phi_e_k_vec = np.vectorize(phi_e_k_func, otypes = [complex])\n",
+                "phi_h_n_inner_vec = np.vectorize(phi_h_n_inner_func, otypes = [complex])\n",
+                "phi_h_m_i_vec = np.vectorize(phi_h_m_i_func, otypes = [complex])\n",
+                "\n",
+                "\n",
+                "R, Z = make_R_Z(True, 50)\n",
+                "\n",
+                "regions = []\n",
+                "regions.append((R <= a[0]) & (Z < -d[0]))\n",
+                "for i in range(1, boundary_count):\n",
+                "    regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))\n",
+                "regions.append(R > a[-1])\n",
+                "\n",
+                "phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "\n",
+                "for n in range(NMK[0]):\n",
+                "    temp_phiH = phi_h_n_inner_vec(n, R[regions[0]], Z[regions[0]])\n",
+                "    phiH[regions[0]] = temp_phiH if n == 0 else phiH[regions[0]] + temp_phiH\n",
+                "\n",
+                "for i in range(1, boundary_count):\n",
+                "    for m in range(NMK[i]):\n",
+                "        temp_phiH = phi_h_m_i_vec(i, m, R[regions[i]], Z[regions[i]])\n",
+                "        phiH[regions[i]] = temp_phiH if m == 0 else phiH[regions[i]] + temp_phiH\n",
+                "\n",
+                "for k in range(NMK[-1]):\n",
+                "    temp_phiH = phi_e_k_vec(k, R[regions[-1]], Z[regions[-1]])\n",
+                "    phiH[regions[-1]] = temp_phiH if k == 0 else phiH[regions[-1]] + temp_phiH\n",
+                "\n",
+                "phi_p_i_vec = np.vectorize(phi_p_i)\n",
+                "\n",
+                "phiP[regions[0]] = heaving[0] * phi_p_i_vec(d[0], R[regions[0]], Z[regions[0]])\n",
+                "for i in range(1, boundary_count):\n",
+                "    phiP[regions[i]] = heaving[i] * phi_p_i_vec(d[i], R[regions[i]], Z[regions[i]])\n",
+                "phiP[regions[-1]] = 0\n",
+                "\n",
+                "phi = phiH + phiP\n",
+                "\n",
+                "#nanregions = []\n",
+                "#nanregions.append((R <= a[0]) & (Z > -d[0]))\n",
+                "#for i in range(1, len(a)):\n",
+                "#    nanregions.append((R > a[i-1]) & (R <= a[i]) & (Z > -d[i]))\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 11,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "def plot_potential(field, R, Z, title):\n",
+                "    plt.figure(figsize=(8, 6))\n",
+                "    plt.contourf(R, Z, field, levels=50, cmap='viridis')\n",
+                "    plt.colorbar()\n",
+                "    plt.title(title)\n",
+                "    plt.xlabel('Radial Distance (R)')\n",
+                "    plt.ylabel('Axial Distance (Z)')\n",
+                "    plt.show()\n",
+                "\n",
+                "plot_potential(np.real(phiH), R, Z, 'Homogeneous Potential')\n",
+                "plot_potential(np.imag(phiH), R, Z, 'Homogeneous Potential Imaginary')\n",
+                "\n",
+                "plot_potential(np.real(phiP), R, Z, 'Particular Potential')\n",
+                "plot_potential(np.imag(phiP), R, Z, 'Particular Potential Imaginary')\n",
+                "\n",
+                "plot_potential(np.real(phi), R, Z, 'Potential (Real Part)')\n",
+                "plot_potential(np.imag(phi), R, Z, 'Total Potential Imaginary')\n",
+                "\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "def v_r_inner_func(n, r, z):\n",
+                "    return (Cs[0][n] * diff_R_1n(n, r, 0)) * Z_n_i(n, z, 0)\n",
+                "\n",
+                "def v_r_m_i_func(i, m, r, z):\n",
+                "    return (Cs[i][m] * diff_R_1n(m, r, i) + Cs[i][NMK[i] + m] * diff_R_2n(m, r, i)) * Z_n_i(m, z, i)\n",
+                "\n",
+                "def v_r_e_k_func(k, r, z):\n",
+                "    return Cs[-1][k] * diff_Lambda_k(k, r) * Z_k_e(k, z)\n",
+                "\n",
+                "def v_z_inner_func(n, r, z):\n",
+                "    return (Cs[0][n] * R_1n(n, r, 0)) * diff_Z_n_i(n, z, 0)\n",
+                "\n",
+                "def v_z_m_i_func(i, m, r, z):\n",
+                "    return (Cs[i][m] * R_1n(m, r, i) + Cs[i][NMK[i] + m] * R_2n(m, r, i)) * diff_Z_n_i(m, z, i)\n",
+                "\n",
+                "def v_z_e_k_func(k, r, z):\n",
+                "    return Cs[-1][k] * Lambda_k(k, r) * diff_Z_k_e(k, z)\n",
+                "\n",
+                "v_r_inner_vec = np.vectorize(v_r_inner_func, otypes = [complex])\n",
+                "v_r_m_i_vec = np.vectorize(v_r_m_i_func, otypes = [complex])\n",
+                "v_r_e_k_vec = np.vectorize(v_r_e_k_func, otypes = [complex])\n",
+                "v_z_inner_vec = np.vectorize(v_z_inner_func, otypes = [complex])\n",
+                "v_z_m_i_vec = np.vectorize(v_z_m_i_func, otypes = [complex])\n",
+                "v_z_e_k_vec = np.vectorize(v_z_e_k_func, otypes = [complex])"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 13,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "vr = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "vrH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "vrP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "\n",
+                "vz = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "vzH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "vzP = np.full_like(R, np.nan + np.nan*1j, dtype=complex)\n",
+                "\n",
+                "for n in range(NMK[0]):\n",
+                "    temp_vrH = v_r_inner_vec(n, R[regions[0]], Z[regions[0]])\n",
+                "    temp_vzH = v_z_inner_vec(n, R[regions[0]], Z[regions[0]])\n",
+                "    if n == 0:\n",
+                "        vrH[regions[0]] = temp_vrH\n",
+                "        vzH[regions[0]] = temp_vzH\n",
+                "    else:\n",
+                "        vrH[regions[0]] = vrH[regions[0]] + temp_vrH\n",
+                "        vzH[regions[0]] = vzH[regions[0]] + temp_vzH\n",
+                "\n",
+                "for i in range(1, boundary_count):\n",
+                "    for m in range(NMK[i]):\n",
+                "        temp_vrH = v_r_m_i_vec(i, m, R[regions[i]], Z[regions[i]])\n",
+                "        temp_vzH = v_z_m_i_vec(i, m, R[regions[i]], Z[regions[i]])\n",
+                "        if m == 0:\n",
+                "            vrH[regions[i]] = temp_vrH\n",
+                "            vzH[regions[i]] = temp_vzH\n",
+                "        else:\n",
+                "            vrH[regions[i]] = vrH[regions[i]] + temp_vrH\n",
+                "            vzH[regions[i]] = vzH[regions[i]] + temp_vzH\n",
+                "\n",
+                "for k in range(NMK[-1]):\n",
+                "    temp_vrH = v_r_e_k_vec(k, R[regions[-1]], Z[regions[-1]])\n",
+                "    temp_vzH = v_z_e_k_vec(k, R[regions[-1]], Z[regions[-1]])\n",
+                "    if k == 0:\n",
+                "        vrH[regions[-1]] = temp_vrH\n",
+                "        vzH[regions[-1]] = temp_vzH\n",
+                "    else:\n",
+                "        vrH[regions[-1]] = vrH[regions[-1]] + temp_vrH\n",
+                "        vzH[regions[-1]] = vzH[regions[-1]] + temp_vzH\n",
+                "\n",
+                "vr_p_i_vec = np.vectorize(diff_r_phi_p_i)\n",
+                "vz_p_i_vec = np.vectorize(diff_z_phi_p_i)\n",
+                "\n",
+                "vrP[regions[0]] = heaving[0] * vr_p_i_vec(d[0], R[regions[0]], Z[regions[0]])\n",
+                "vzP[regions[0]] = heaving[0] * vz_p_i_vec(d[0], R[regions[0]], Z[regions[0]])\n",
+                "for i in range(1, boundary_count):\n",
+                "    vrP[regions[i]] = heaving[i] * vr_p_i_vec(d[i], R[regions[i]], Z[regions[i]])\n",
+                "    vzP[regions[i]] = heaving[i] * vz_p_i_vec(d[i], R[regions[i]], Z[regions[i]])\n",
+                "vrP[regions[-1]] = 0\n",
+                "vzP[regions[-1]] = 0\n",
+                "\n",
+                "vr = vrH + vrP\n",
+                "vz = vzH + vzP"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 14,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plot_potential(np.real(vr), R, Z, 'Radial Velocity - Real')\n",
+                "plot_potential(np.imag(vr), R, Z, 'Radial Velocity - Imaginary')\n",
+                "plot_potential(np.real(vz), R, Z, 'Vertical Velocity - Real')\n",
+                "plot_potential(np.imag(vz), R, Z, 'Vertical Velocity - Imaginary')"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 15,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# Format Potential Matrix for Testing:\n",
+                "R, Z = make_R_Z(False, 50)\n",
+                "\n",
+                "regions = []\n",
+                "regions.append((R <= a[0]) & (Z < -d[0]))\n",
+                "for i in range(1, boundary_count):\n",
+                "    regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))\n",
+                "regions.append(R > a[-1])\n",
+                "\n",
+                "phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "\n",
+                "for n in range(NMK[0]):\n",
+                "    temp_phiH = phi_h_n_inner_vec(n, R[regions[0]], Z[regions[0]])\n",
+                "    phiH[regions[0]] = temp_phiH if n == 0 else phiH[regions[0]] + temp_phiH\n",
+                "\n",
+                "for i in range(1, boundary_count):\n",
+                "    for m in range(NMK[i]):\n",
+                "        temp_phiH = phi_h_m_i_vec(i, m, R[regions[i]], Z[regions[i]])\n",
+                "        phiH[regions[i]] = temp_phiH if m == 0 else phiH[regions[i]] + temp_phiH\n",
+                "\n",
+                "for k in range(NMK[-1]):\n",
+                "    temp_phiH = phi_e_k_vec(k, R[regions[-1]], Z[regions[-1]])\n",
+                "    phiH[regions[-1]] = temp_phiH if k == 0 else phiH[regions[-1]] + temp_phiH\n",
+                "\n",
+                "phi_p_i_vec = np.vectorize(phi_p_i)\n",
+                "\n",
+                "phiP[regions[0]] = heaving[0] * phi_p_i_vec(d[0], R[regions[0]], Z[regions[0]])\n",
+                "for i in range(1, boundary_count):\n",
+                "    phiP[regions[i]] = heaving[i] * phi_p_i_vec(d[i], R[regions[i]], Z[regions[i]])\n",
+                "phiP[regions[-1]] = 0\n",
+                "\n",
+                "phi = phiH + phiP\n",
+                "\n",
+                "nanregions = []\n",
+                "nanregions.append((R <= a[0]) & (Z > -d[0]))\n",
+                "for i in range(1, len(a)):\n",
+                "    nanregions.append((R > a[i-1]) & (R <= a[i]) & (Z > -d[i]))"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 16,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "def interpret_file(filename, omega): # comparison with Capytaine\n",
+                "    file_path = \"test/data/\" + filename + \"-imag.csv\"\n",
+                "    df = (pd.read_csv(file_path, header=None)).transpose()\n",
+                "    real_array = (df.to_numpy()) * (-1/omega)\n",
+                "        \n",
+                "    file_path = \"test/data/\" + filename + \"-real.csv\"\n",
+                "    df = (pd.read_csv(file_path, header=None)).transpose()\n",
+                "    imag_array = (df.to_numpy()) * (1/omega)\n",
+                "\n",
+                "    return(real_array, imag_array)\n",
+                "\n",
+                "def interpret_file2(filename):\n",
+                "    file_path = \"test/data/\" + filename + \"-imag - matlab.csv\"\n",
+                "    df = (pd.read_csv(file_path, header=None))\n",
+                "    #df_flipped = df.iloc[::-1].reset_index(drop=True)\n",
+                "    imag_array = df.to_numpy()\n",
+                "        \n",
+                "    file_path = \"test/data/\" + filename + \"-real - matlab.csv\"\n",
+                "    df = (pd.read_csv(file_path, header=None))\n",
+                "    #df_flipped = df.iloc[::-1].reset_index(drop=True)\n",
+                "    real_array = df.to_numpy()\n",
+                "\n",
+                "    return(real_array, imag_array)\n",
+                "\n",
+                "def populate_column(prefer_lower, l_col, h_col, smooth):\n",
+                "    new_col = np.empty_like(l_col)\n",
+                "    if smooth:\n",
+                "        for i in range(len(l_col)):\n",
+                "            if np.isnan(l_col[i]) or np.isnan(h_col[i]):\n",
+                "                if prefer_lower:\n",
+                "                    new_col[i] = l_col[i]\n",
+                "                else:\n",
+                "                    new_col[i] = h_col[i]\n",
+                "            else:\n",
+                "                new_col[i] = ((l_col[i] + h_col[i]) / 2)\n",
+                "    else:\n",
+                "        if prefer_lower:\n",
+                "            new_col = l_col\n",
+                "        else:\n",
+                "            new_col = h_col\n",
+                "    return new_col.reshape(-1, 1)\n",
+                "\n",
+                "def sharpen(arr, smooth):\n",
+                "    # just for plotting purposes, add some points near the boundary that take on value of nearby points for sharp corners\n",
+                "    r_vec = np.linspace(0, 2*a[-1], 50)\n",
+                "    z_vec = np.linspace(0, -h, 50)\n",
+                "    a_eps = 1.0e-4\n",
+                "    for i in range(len(a)):\n",
+                "        lower = a[i]*(1-a_eps)\n",
+                "        idx = np.searchsorted(r_vec, lower)\n",
+                "        l_col = arr[:, idx - 1]\n",
+                "        h_col = arr[:, idx]\n",
+                "        if lower not in r_vec:\n",
+                "            r_vec = np.insert(r_vec, idx, lower)\n",
+                "            new_col = populate_column(True, l_col, h_col, smooth)\n",
+                "            arr = np.hstack([arr[:, :idx], new_col, arr[:, idx:]])\n",
+                "        higher = a[i]*(1+a_eps)\n",
+                "        if higher not in r_vec:\n",
+                "            idx = np.searchsorted(r_vec, higher)\n",
+                "            r_vec = np.insert(r_vec, idx, higher)\n",
+                "            new_col = populate_column(False, l_col, h_col, smooth)\n",
+                "            arr = np.hstack([arr[:, :(idx + 1)], new_col, arr[:, (idx + 1):]])\n",
+                "    return np.meshgrid(r_vec, z_vec), arr\n",
+                "\n",
+                "def plot_comparison(field, title, color):\n",
+                "    (R_new, Z_new), field = sharpen(field, True)\n",
+                "    plt.figure(figsize=(8, 6))\n",
+                "    plt.contourf(R_new, Z_new, field, levels=50, cmap = color)\n",
+                "    plt.colorbar()\n",
+                "    plt.title(title)\n",
+                "    plt.xlabel('Radial Distance (R)')\n",
+                "    plt.ylabel('Axial Distance (Z)')\n",
+                "    plt.show()\n",
+                "\n",
+                "def plot_difference(title, R, Z, arr1, arr2):\n",
+                "    arr3 = arr1 + arr2\n",
+                "    (R_new, Z_new), field = sharpen(arr3, False)\n",
+                "    plt.figure(figsize=(8, 6))\n",
+                "    plt.contourf(R_new, Z_new, field, levels=2, cmap='viridis')\n",
+                "    plt.colorbar()\n",
+                "    plt.title(title)\n",
+                "    plt.xlabel('Radial Distance (R)')\n",
+                "    plt.ylabel('Axial Distance (Z)')\n",
+                "    plt.show()\n",
+                "\n",
+                "# arguments: the name of the csv to compare with, an appropriately formatted potential array\n",
+                "# the threshold of closeness, appropriate R and Z, and omega to help with conversion\n",
+                "# tailored for 50x50 points (including nans), evenly spaced, twice the widest radius, and given height\n",
+                "\n",
+                "def potential_comparison(filename, arr, threshold, thres2, R, Z, omega, nan_mask):\n",
+                "\n",
+                "    real_arr, imag_arr = interpret_file(filename, omega)\n",
+                "    real_calc_arr = np.real(arr)\n",
+                "    imag_calc_arr = np.imag(arr)\n",
+                "\n",
+                "    plot_comparison(real_arr, 'Capytaine Potential Real', \"viridis\")\n",
+                "    plot_comparison(real_calc_arr, 'MEEM Potential Real', \"viridis\")\n",
+                "    plot_comparison(imag_arr, 'Capytaine Potential Imaginary', \"viridis\")\n",
+                "    plot_comparison(imag_calc_arr, 'MEEM Potential Imaginary', \"viridis\")\n",
+                "\n",
+                "    plot_comparison(real_arr - real_calc_arr, 'Real Potential Difference', \"plasma\")\n",
+                "    plot_comparison(imag_arr - imag_calc_arr, 'Imag Potential Difference', \"plasma\")\n",
+                "\n",
+                "    raw_fraction_real = (real_arr - real_calc_arr)/real_arr\n",
+                "    raw_fraction_imag = (imag_arr - imag_calc_arr)/imag_arr\n",
+                "\n",
+                "    fraction_real = np.where(abs(raw_fraction_real) < 0.1, raw_fraction_real, np.nan)\n",
+                "    fraction_imag = np.where(abs(raw_fraction_imag) < 1, raw_fraction_imag, np.nan)\n",
+                "\n",
+                "    plot_comparison(fraction_real, 'Fractional Real Potential Difference', \"plasma\")\n",
+                "    plot_comparison(fraction_imag, 'Fractional Imag Potential Difference', \"plasma\")\n",
+                "\n",
+                "    is_within_threshold_r = 1. * np.isclose(real_arr, np.real(arr), rtol=threshold, atol = 0.01)\n",
+                "    is_within_threshold_i = 1. * np.isclose(imag_arr, np.imag(arr), rtol=threshold, atol = 0.01)\n",
+                "\n",
+                "    iwt_r_2 = 1. * np.isclose(real_arr, np.real(arr), rtol=thres2, atol = 0.01)\n",
+                "    iwt_i_2 = 1. * np.isclose(imag_arr, np.imag(arr), rtol=thres2, atol = 0.01)\n",
+                "\n",
+                "    for i in range(len(nan_mask)):\n",
+                "        is_within_threshold_r[nan_mask[i]] = np.nan\n",
+                "        is_within_threshold_i[nan_mask[i]] = np.nan\n",
+                "        iwt_r_2[nan_mask[i]] = np.nan\n",
+                "        iwt_i_2[nan_mask[i]] = np.nan\n",
+                "        \n",
+                "    plot_difference(\"Real Potential Match\", R, Z, is_within_threshold_r, iwt_r_2) #, \" + str(100 * threshold) + \"%\"\n",
+                "    plot_difference(\"Imaginary Match\", R, Z, is_within_threshold_i, iwt_i_2)\n",
+                "\n",
+                "    match_r = np.sum(np.isnan(is_within_threshold_r)) + np.sum(is_within_threshold_r == 1)\n",
+                "    match_i = np.sum(np.isnan(is_within_threshold_i)) + np.sum(is_within_threshold_i == 1)\n",
+                "\n",
+                "    return (match_r, match_i)\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 17,
+            "metadata": {
+                "scrolled": true
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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yWrZsaVitVqNJkyZ2H3Nzteuuu84ICAgwfv31V4e3T5o0yahZs6YREBBgtz9Xx+TsI5OuHlPOR/XMmzfPbvmOHTuMu+++26hSpYphtVqNOnXqGPfee6+xZs2afJ/bnO1NmzYt3/UMwzDee+89o02bNobVajWuueYao1+/fnmej8zMTOPvf/+7Ua1aNcNiseT5+KQ33njDiI6ONsqWLWtUrFjRaNGihfH0008bv/32m22dnNfnalc/R4ZhGG+++aZRv359IzAw0O7jk65eNzs723jhhReMOnXqGFar1WjTpo2xbNkyY8CAAUadOnXstik3PjLJ2fM2cOBAIzAw0Dh48KBhGIZx7tw5Y+zYsUbDhg2N4OBgo2rVqkb79u2Nl156yUhPT7fd7+233zYaNWpk+zs5b948IyEhIc/zyEcmAf7FYhiFfMc6AK9Wt25dNW/eXMuWLXP5Pm3atNE111yjNWvWmDgyAEBpxHs6AUiSvvvuO+3cuVP9+/cv6aEAAPwQ7+kESrndu3dr27ZtevnllxUZGak+ffqU9JAAAH6ImU6glFu6dKkGDRqkjIwMvfvuu3bfxgMAgKeU2vd0zpo1S9OmTVNycrJatWqlV199VTfeeGNJDwsAAMAvlcqZzvfee08jR45UQkKCtm/frlatWikuLi7PV9QBAADAM0rlTGe7du10ww036LXXXpN05evboqKi9Pe//11jxowp4dEBAAD4n1J3IlF6erq2bdumsWPH2pYFBAQoNjbW4bd2pKWl2X07SXZ2tv744w9VqVLFra+nAwAAJccwDJ07d041atRQQEDxH+i9fPmy0tPTTdl2cHCwT7wfv9RF56lTp5SVlaXq1avbLa9evbr27duXZ/3Jkydr4sSJxTU8AABgoqNHj6pWrVrFus/Lly+rbp0KOnEyq+CVCyEiIkKHDh3y+vAsddHprrFjx2rkyJG26ykpKapdu7aOHj2q0NDQEhwZADPcXXtYSQ+hRFis1pIeAmCqzOx0JZ5aoIoVKxb7vtPT03XiZJYOfFdLoRU9O8uaei5b17b9Venp6USnt6lataoCAwN14sQJu+UnTpxQREREnvWtVqusDn4Yh4aGEp2AHwoqW/y/kAAUn5J8a1xoxQCPR6cvKXXRGRwcrOjoaK1Zs0a9evWSdOV9mmvWrNGwYaVzhgMASkzZsiU9guJx6VJJj8C/ufL3KLv0xp63KHXRKUkjR47UgAED1LZtW914442aMWOGLly4oEGDBpX00ACUtNISQShevvj3ypVQ9sXHhRJTKqOzT58++v333zV+/HglJyerdevWWrlyZZ6TiwAAKLUISnhYqYxOSRo2bBiH0wHkYZTlhBpHLJfSCl4JAPJRaqMTABwxygaZtm3LpQzTtm02b49xohjwfkQnABQTM4PWVb4cvvkxM4oJWsAziE4AyCXbWrI/FgPSMk3dvqfD118jNjdPBC3hChCdAGAnvVKwAi9nS5KyQuw/YiVnuTsCL7sXkZ6KXrPjNUdRIrY0BGuOooYr0Qp/QHQCQC45oXl1cOYsKyg8r749K6RoP2bdjdYchYnX4grVHIUJ1tIUqrkVJVoJVngLohMAcsmyWpRlDczn9kAFphl5ljubHc2PKzOn7kRrYQM1hzuhWtyBmsPdUC2tkZobweo9zmanKcvDH1J/Ltv9IzAlhegEgFzSQgPyRGWW1eIwNHMEphkFxqajwHQ1UF09rO9qoBY1TiXXA7Wk4jSHO5FKoOZV2GAlVuEI0QkAuWQFS5LlqutSVrBFgenubSt3qBYmSj1xX8fby/9HvyeiNIcrcVrSYZrD1UAlTgtGrMIRohMAcskOdvxne39GaWCaoUyrxe7WMv8fm1lXLc8tz2xqKY1SybfCVHItTgnTwilMrBKqvoPoBIBcsqyO/5xzPTDtSowG2GY97QNUUp4Ilf4M0T+35ThInR3GdxaWrr0v1HmUFuaM/OKOUqngMPWmKJUI0+LkaqgaWSYPBAUiOgEgl4wKf/45yypl/387BDjpA2cBmiO/EJXMi1GpZIL0yjad/2oxI0gl34tSiTBF6UN0AgW4dLxeSQ/Bzpls9w4lnc12fog3x+mssvne/kd2Bae3nc50fFtKVjnH28osn2fZ2YxyV13PO57zmfazGanpf16/kG5/26WMKz/aLqf/+Us9I+3Ksuz0XGemp18JLkvalf8GpFskq5SVceU5y87VBNlO+iD3zGfO+z9zv/fz6oi8Oh6dHZq/mqMYze/kJslxULoTkkW9v+NtOv61Y1aM5nAWpd4Yo7kVFKZEKXwJ0Ql4GXejMjdXAlMqODKlwoWmVLTYvLLMfmz5xaZkH5w5sXm1nOC0k24fVAHpV567gDTXnsOr5Z7xdBSfOQo6Ez4nQp3F59XbkgqOT7v7/H9IFn4W88/nragBar9d+9fI7AjN4ShGvT1Ec3MWpcQovBHRCRSgKBHorivRWLjocSUkc+QXlLbtFSIsJcdxKbkWmFLeyJTyD00pb2zmO8N5VWzmzHK6wtEh9pz3eTqSFew8PKX8Y7Ew8VnQNu3u44F49MQ3NjnfdslEqOT7ISoRo/BORCdQAFdnD4vKnWh0xJWQtO0rn6DMUZiwlBzH5ZXljh+fK5EpuReaUv6H0yX72HQ2yxmQ4fywuquchafkXnxK5s9+SkULR7NmQa9su+QiVMobor4WoTmIUZQkohMoQFFj0BXuBKMjrkRkjvxiMkd+USk5D8srtzl/vhwFpuRaZEquh2YOV2Y3i3pY3W5/dicV/Sm/w+2S66Fo9uynZE6AFnVbjrfvXREq+W6ISsQoigfRCRSgqEFYEHeC0RFXIjK3goJSyj8qr9zuflhKjuNSci0wcxQUmpJrs5slIb9ZT8mc+HRnu3nu58GZSzNnQa9sv2QjVPK/EJWIUXgW0QkUoKhRmB93g9ERVyIyR0Ex+ed6+c/u5heWkntxmcNRZF4dmDkKDE2pwNjMmeGUXJvldPaRSZL9+zqdzXba1i0gPK9sz9z4dGXbee5rUoB6YnuO91HyESr5z2H5qzmKUUK0YCnZFmV5+C1b54vpLWCeQHQCBfBEGDrjTjA64mpE/rm+a28VKGxU5nA3LiX3AlNyEJmSSycJ5Y5NqeDgdPS+TmcnDtnGFpyzL8e3F3S43baem/Ep+V6AmrE9x/vwzgiV/DtEJWIUfyI6gQIUNQydcTcY897fvfeaFhSSOQoKSin/qJSch6XkPC4l54EpuRaZuTl636YrHM1o5jfLmR9XZj0lz8Wn5P7sp7vbz3NfD89aFkeAXtnPn3/XSipAc/hziErEKP5EdAIFKGocOt9u0U5QcjUic7gSk1LBQSnlH5VS/mEpFSIucziJTGfv1cwvNh3NcBY2LvNT0KynZG58SsUz+2nbho8dhr+yH++YBc3NXw/L50aMlj5EJ1CAosahI+4G49VcDcgcroRkjoKCUio4KqX8w1IqXFzmyO+EIHdD03Ybv+fsFPQh9iUhKyTA1NnPP/dz5e+uN8RnjtwR6o8BmlvuGCVA/QvRCRSgqIHoLneDMoc7YSm5FpeSZwJTKiAyJdNCU3I/NgPTrpwc5Iij93MW9B7P/GY5bdtwYR1fY0YkFvUbldzbl/fFp1R6A1QiQn0d0QkUk8LGZA53o1JyPSwl1+JS8lBgSkWKTKlooSkVPLPpKDy9ITi9bfaxIGbNThbXrOeVfXlnfEp/Bqi/x2cOItS3EZ1AAYoai64oTFBK7kVlDlfjUnItMCXPRKbk2udomh2buRUUlPnd7kpsSt49w+mpQ+z+EJ5X9uf98SmVngCViFBfU7KflAyUAhfSrQVenLmUUSbfiyOX04PyvVwtI62M08vVstMDHV7ySA/Ie3HAkhZgd3EkIN1id3G4TprFdnF4e8afF2cC0/68uMITwemuwgSgO2exm+nqk4I8uV2ztu18n2XynHzkTbKtZWyX0sYoG2S7wDVTpkyRxWLRiBEj8l1vxowZaty4scqWLauoqCg9+eSTunz5slv7Kn1/IwE3FXYWsiCFmaXM4c5sZQ5XZy0lF2cuc7gwgym5/m1Arny8katfWenKrKarkenqfdwJTl84rO7JE4rMnJks7lnPK/v03pnPHKV1BlRycHb8+UL8Y/dzW7du1Zw5c9SyZct811u0aJHGjBmjuXPnqn379jpw4IAGDhwoi8Wi6dOnu7w/ohMwkdmHv3NzJypzmBGXkntfN+nqZ2h6MjSl/MPR2YlEnnj/pm1bXnxY3Uxmh6dUPCcZ2e/X++NTKn3v/7was5/2zp8/r379+unNN9/Uv/71r3zX3bRpkzp06KD7779fklS3bl317dtX3377rVv7JDqBAuQXjmWDMk05WccR06MyhxtxKZkTmFLxRmZ+6+fEZ0kGp6+dPFQQs2clS2LW88p+fSs+pdIboP4oNTXV7rrVapXV6vxIXXx8vLp3767Y2NgCo7N9+/b63//+py1btujGG2/UL7/8ohUrVujBBx90a4xEJ1AEhf3mnasVJiil4olKyb2wlNyLS8n1wJQ8eyKQK+szu2nOZ3YWR3hKxT/reWXfvhGfEgFa3P7IKqu0LM++B/lC1pW/41FRUXbLExISNGHCBIf3Wbx4sbZv366tW7e6tI/7779fp06d0l//+lcZhqHMzEw99thj+uc//+nWWIlOmObWdSOLdH9Pv5ey8Ie6ncdjSHCGxz7H0pHiikrJ/bCUzI1L233cPBnVE6HpquIITn+b5SxNfCk+JQ6/+7qjR48qNDTUdt3ZLOfRo0c1fPhwrV69WiEhIS5tOzExUS+88IL+85//qF27djp48KCGDx+uSZMm6dlnn3V5jEQnTGPWCTgFKcoJOpJ7M5TOzgZ3VaGiUirWsJTcj0upeAJT8vyJQK4qjo9EKmpwesuZ66VdVkgZnwlPifj0VaGhoXbR6cy2bdt08uRJXX/99bZlWVlZWr9+vV577TWlpaUpMND+d9Ozzz6rBx98UA8//LAkqUWLFrpw4YIeeeQRPfPMMwoIcO13C9EJr1DUUHRFcZ6gczVfiUupcIEpFV9kSuaHZkD6n9+Z7ug2l/ZXgrEpeTY4zZhtLYnD3nBPtrUM4emHbr31Vu3atctu2aBBg9SkSRONHj06T3BK0sWLF/OEZc56huH6zweiE6YxKySLcjKOI56Iyhxux2UhozJHScSlVLjAlIo3MotyP+nPuMyJT1+JTcn7g7M08rXZzhyEp/+pWLGimjdvbresfPnyqlKlim15//79VbNmTU2ePFmS1KNHD02fPl1t2rSxHV5/9tln1aNHD4eR6gzRCdN4Og5dVaIR6UgRwrIoUZmjJOJSKnxgSiUbma4uz7NvP4tNybzgLN5vESqZM9kd8eXwlDjcXpokJSXZzWyOGzdOFotF48aN07Fjx1StWjX16NFDzz//vFvbJTphGk/GnycV6eSc4Hx+eRXDWeGOFCUqbdsoQlxKJROYnri/J745yB9jU2KGE3kx6+m/EhMT871epkwZJSQkKCEhoUj78c4qAK7ikRlHVxQUjiZ+hqUznohKqehhKRUtLiXPnMBT0pFpG4cXxKbkO7Obtu17yaxjSfHV2c4czHqiKIhOmKbYQjG3Ir5H0hln8WhYs/O9vSCeCkrJM1Fp21YR41Iq+cDM4envQffX2JSY3Swuvh6eErOeKByiE+YxKQA9wRMzkK5sx5+jMocnwtBT2/FUYOYOy6zgvMvc2paHQ86sj0EqjuAs7bOcuflLeErMesJ1RCdKnKcC0AyejEan+/BgTNq26cGolDwXlp7eVnEcLi9MbJoRcGZ+5ibBiaJg1hOuIjphmpKKyeIIRVeYEZOS54NS8mwImrVNbzpM7nSbxCZKKcITriA6YRpvib+iMisene7PhKjM4QtxKXk+MCVzv/fcVw6h5yjO2CzpGc6S3j+Q25ns8krL9uz5Dhezszy6PTMRnShVijsg8+zfxKDMYUYEmr1tX4tMyfdCUyI2vYmvv5/TEWY7URCiEyWupEPQk3w9Ks3evhlxmcPsyLTth9gseH8EJwAHiE6YxtdisjiCsSBmB2Vx7seXA9PsSPPH0JS8I/a8YQylGbOdyA/RCZ/jDXHoruKKyeLen5lhmcPXA1MqnsjMUVpjU/KecRTEHw+tA64gOmEaX4xDR4o7GAtS3OMpjrCUfPfwuDP+Hpq2fXtJ6HnLOMBsJ5wjOuHXvC0YnSnJcRZXVOYorriUij/GCM2S4W3jAeAY0QnT+ErwmcUbHn9xB2WO4gxLqWQDrLSEpuSdceeNY8pPaTm0zmwnHCE6AXlHILqqpEIyt+KOSrt9e8EHmpem0JS8N+y8dVwAHCM64dV8KQaLyhtiMreSDEvJe2YvM62WPMvM5g2hKXlv1HnruApSWmY5AWeITpimNAVjfrwtJnMr6bCUvCOw8gtKf/14I2e8Oei8eWwFKW3ByaF1OEJ0AgXw5mjMjzcEZW7eElbFOWOZH295PiTvjzlvH19+SltsAvkhOuGXfDUUXeVtQZmbN8UUgemcL4ScL4wxP6U1OJnlhDNEJ0zj7+Hnad4ckrl5Y0B5S1zm8MbnSPKdiPOVceantAYn8vdHVnldyvRsel3K8p2/a0QnYBJficireWswSd4Xl7l54/PmS/HmS2PNT2mPTWY5kR+iE/h/vhqJ7vDGMHLEm+Myh7c+l74Wb7423vyU9uAECkJ0wjSlIeK8gbfGT368PSpzntOs//+4pKuXexNfjDZfHHN+iE3ANUQn4CW8MWgKy9uj8mrOnntvfE18Ndh8ddwFITj/xKF1FIToBEzgjbHiKb4WlLn54uviy7Hmy2MvCLFpj+CEK4hO4P/5YpCYwZejMocvv5a+Hmq+Pv6CEJt5EZxwFdEJr+TL0eDt/CEqc/j63xN/CTR/eRz5ITYdIzjhDqITpvH1IPA1/hSTV/OXv0v+FGf+9FicITTzR3DCXUQn4KX8OSId8ZewzOFvUeZvjyc/xGbBCE4UBtEJmKy0xaMz/haVV/PHKPPHx5QfYrNgxCaKgugEHCAU3efvUXk1fwwyf3xMBSE0XUdwoqiITpiGcPMPpS0mpbzxlRUS4JdB5o+PyRWEpvsITs9IzSqn9CzPptdlvnsdgDcrjSF5NXeCyx/izB8eQ1ERm+4jNuFJRCfgw4jH/JXW0Cqtj9sRQrPwCE54GtEJeBEi0n0EFs/B1QjNoiE2YRaiEzAJAek5RNWfeC7yIjI9g9iE2YhOQARiSSOkHON5cY7Q9BxiE8WF6IRpCDnkIJ4KxnNUMELTs4hNFDeiE0CREEvu4flyHZFpDmITJSWgpAcAwPsEXs52+QLHeL7cF3g50+4CzwpIyyQ4kceUKVNksVg0YsQIp+u8+eab6tixoypXrqzKlSsrNjZWW7ZscXtfzHQCpQCxYy6e38IhLM1HZCI/W7du1Zw5c9SyZct810tMTFTfvn3Vvn17hYSEaOrUqerSpYv27NmjmjVrurw/ohPwIcSNd+B1KBwis/gQmyjI+fPn1a9fP7355pv617/+le+6CxcutLv+1ltv6YMPPtCaNWvUv39/l/dJdALFgEjxbrw+nkdglgxis/RKTU21u261WmW1Wp2uHx8fr+7duys2NrbA6LzaxYsXlZGRoWuuucat+xGdQC7Eh//itTUXkVlyCE3fcSaznKyZQR7dZlpmhiQpKirKbnlCQoImTJjg8D6LFy/W9u3btXXr1kLtc/To0apRo4ZiY2Pduh/RCdPwSx7Fib9vxYfALHmEJq529OhRhYaG2q47m+U8evSohg8frtWrVyskJMTt/UyZMkWLFy9WYmKi2/cnOgH4BKKy5BCZ3oHQRH5CQ0PtotOZbdu26eTJk7r++utty7KysrR+/Xq99tprSktLU2BgoMP7vvTSS5oyZYq+/PLLAk8+csRvPjLp8OHDGjx4sOrVq6eyZcuqQYMGSkhIUHp6ut16P/zwgzp27KiQkBBFRUXpxRdfLKERA8iNj2byDld/bBHBWbJyPuaI4ISn3Hrrrdq1a5d27txpu7Rt21b9+vXTzp07nQbniy++qEmTJmnlypVq27ZtofbtNzOd+/btU3Z2tubMmaOGDRtq9+7dGjJkiC5cuKCXXnpJ0pU32Xbp0kWxsbF6/fXXtWvXLj300EOqVKmSHnnkkRJ+BIB/Ihi9F0HpnQhMmKlixYpq3ry53bLy5curSpUqtuX9+/dXzZo1NXnyZEnS1KlTNX78eC1atEh169ZVcnKyJKlChQqqUKGCy/v2m+js2rWrunbtartev3597d+/X7Nnz7ZF58KFC5Wenq65c+cqODhY1113nXbu3Knp06cTnYAbCEnfQlx6P0IT3iQpKUkBAX8eDJ89e7bS09P1t7/9zW69/E5WcsRvotORlJQUu9P5N2/erJtuuknBwcG2ZXFxcZo6darOnDmjypUr59lGWlqa0tLSbNev/kgCwB8Qkf6BuPQthCa8RWJiYr7XDx8+7JH9+G10Hjx4UK+++qptllOSkpOTVa9ePbv1qlevbrvNUXROnjxZEydONHewgIcQj6UDcembiEyUdl5/ItGYMWNksVjyvezbt8/uPseOHVPXrl3Vu3dvDRkypEj7Hzt2rFJSUmyXo0ePFml7gDPufN85J9v4P0cn9HBij+/hRCDgT14/0/nUU09p4MCB+a5Tv359259/++033XzzzWrfvr3eeOMNu/UiIiJ04sQJu2U51yMiIhxuu6BP9If/I+ZgFgLS/xCXgHNeH53VqlVTtWrVXFr32LFjuvnmmxUdHa158+bZvQlWkmJiYvTMM88oIyNDQUFXvhFg9erVaty4scND6/A+BCB8DWHp34hMwHVef3jdVceOHVPnzp1Vu3ZtvfTSS/r999+VnJxsO61fku6//34FBwdr8ODB2rNnj9577z3NnDlTI0eOLMGR+y9PHC7m8DF8BYfCS4fch8sJTsA9Xj/T6arVq1fr4MGDOnjwoGrVqmV3m2EYkqSwsDB98cUXio+PV3R0tKpWrarx48fzcUkA3EJMlh6EJTwpJaOcgjM8+93r6RkZHt2emfwmOgcOHFjgez8lqWXLltqwYYP5AwLgUwhJSEQmYCa/iU4AcIaghDNEJlB8iE4APouYhDsITKBkEZ0AvA4xiaIiMAHvQ3QCMB0RCTMRmIBvIDoBuIWAREkiMAHfRXQCpRTxCG9GXAL+h+gEfBjhCH9AYAKlA9EJlABiEaUVgQmUXkQn4ABRCBQNcQngakQnTEO4Af6PuATgKqITAJAvwhLwjLOZIQrOCPboNtMzAz26PTMRnQAAwhKA6YhOACglCEuUNpZLGX/+OSsjnzVRHIhOAPATRCVgH5rwLkQnAPgIohJwjND0DUQnAHgJohJwDZHpm4hOACgmRCVQeISm7yM6AcBDiErAswhN/0J0AoCbiEvAHESmfyM6AZRaxCNQsojM0oXoBOBXCEnAexGZpRvRCcBrEZCAbyMykRvRCcB0xCNQOhCZ+buQaVV6pme/ez0j0+LR7ZmJ6ARQIKIRwNUITLiL6AT8HMEIwBOITBQV0Ql4GSIRgDcgMuFpRCfgBPEHoLQgMFEciE6YhmgDAO9DYKKkEJ0AAPgxIhPegugEAMAPEJfwdkQnAAA+hsCELwoo6QEAAADnLJcy8lyAwpo9e7Zatmyp0NBQhYaGKiYmRp9//nm+9zl79qzi4+MVGRkpq9Wqa6+9VitWrHB738x0AgDgBYhJFIdatWppypQpatSokQzD0DvvvKOePXtqx44duu666/Ksn56erttuu03h4eFaunSpatasqSNHjqhSpUpu75voBACgGBGXMENqaqrddavVKqvVmme9Hj162F1//vnnNXv2bH3zzTcOo3Pu3Ln6448/tGnTJgUFBUmS6tatW6gxcngdAAATODosTnCWbufSg5WabvXo5Vz6le9yj4qKUlhYmO0yefLkAseTlZWlxYsX68KFC4qJiXG4zqeffqqYmBjFx8erevXqat68uV544QVlZWW5/fiZ6QQAoAgISXiDo0ePKjQ01Hbd0Sxnjl27dikmJkaXL19WhQoV9NFHH6lZs2YO1/3ll1+0du1a9evXTytWrNDBgwf1+OOPKyMjQwkJCW6NkegEAMAFxCW8Wc6JQa5o3Lixdu7cqZSUFC1dulQDBgzQV1995TA8s7OzFR4erjfeeEOBgYGKjo7WsWPHNG3aNKITAICiIC7h74KDg9WwYUNJUnR0tLZu3aqZM2dqzpw5edaNjIxUUFCQAgMDbcuaNm2q5ORkpaenKzg42OX98p5OAECp4+z9lgQnSqPs7GylpaU5vK1Dhw46ePCgsrOzbcsOHDigyMhIt4JTIjoBAH6KsATyGjt2rNavX6/Dhw9r165dGjt2rBITE9WvXz9JUv/+/TV27Fjb+kOHDtUff/yh4cOH68CBA1q+fLleeOEFxcfHu71vDq8DAHwS8Qi47+TJk+rfv7+OHz+usLAwtWzZUqtWrdJtt90mSUpKSlJAwJ9zklFRUVq1apWefPJJtWzZUjVr1tTw4cM1evRot/dNdAIAvBJRCXje22+/ne/tiYmJeZbFxMTom2++KfK+ic5CuqvTZJUJdP5xBACA/BGVQOlCdAIATEFUAsiN6AQAFApRCcAdRCcAwCGiEoAnEZ0AUMoQkwBKAtEJAH6EoAS814V0q8oEefYk5Mx0j27OVEQnAHg5QhKAPyA6AaAEEJIAShuiEwA8gIgEgPwRnQCQC/EIAOYgOgH4LQISALwH0QnA6xGPAOD7iE4ApiMaAQBEJ4B8EYwAAE8gOgE/QiACALwV0QkUA2IQAFDaEZ3wWYQcAAC+g+gsJMulDFkCA0p6GAAAwEdcziyjwAzPpldWZpZHt2cmqgkAAACmIzoBAABgOqITAAAApiM6AQAAYDqiEwAAAKYjOgEAAGA6ohMAAACmIzoBAABgOqITAAAApiM6AQAAYDqiEwAAAKbju9cBAACKweX0MgosE+TRbWal+853r7sVnWfPntVHH32kDRs26MiRI7p48aKqVaumNm3aKC4uTu3btzdrnAAAAPBhLh1e/+233/Twww8rMjJS//rXv3Tp0iW1bt1at956q2rVqqV169bptttuU7NmzfTee++ZPWYAAAD4GJdmOtu0aaMBAwZo27ZtatasmcN1Ll26pI8//lgzZszQ0aNH9Y9//MOjAwUAAIDvcik6f/zxR1WpUiXfdcqWLau+ffuqb9++On36tEcGBwAAAP/g0uH1goKzqOsDAADAv7n8kUkNGjTQK6+84vT2EydOKDAw0CODAgAAgH9xOToPHTqk0aNHa+DAgUpPT3e4jmEYHhsYAAAA/IdbHw7/0Ucfae3atbrpppt0/PjxPLdbLBaPDQwAAACeNXv2bLVs2VKhoaEKDQ1VTEyMPv/883zvs2TJEjVp0kQhISFq0aKFVqxYUah9uxWdN9xwg7Zu3aoyZcqobdu2+vbbbwu1UwAAABS/WrVqacqUKdq2bZu+++473XLLLerZs6f27NnjcP1Nmzapb9++Gjx4sHbs2KFevXqpV69e2r17t9v7dvtrMKtXr67ExETdcccd6ty5s+bNm+f2TgEAAFD8evToodtvv12NGjXStddeq+eff14VKlTQN99843D9mTNnqmvXrho1apSaNm2qSZMm6frrr9drr73m9r4L9d3rZcqU0Zw5c/TKK6/oscce0/Dhw5WZmVmYTZkiLS1NrVu3lsVi0c6dO+1u++GHH9SxY0eFhIQoKipKL774YskMEgAAwENSU1PtLmlpaQXeJysrS4sXL9aFCxcUExPjcJ3NmzcrNjbWbllcXJw2b97s9hhd/hpMR+/XfOyxx9S8eXP97W9/09dff+32zs3y9NNPq0aNGvr+++/tlqempqpLly6KjY3V66+/rl27dumhhx5SpUqV9Mgjj5TQaAEAQGmQmVZG2YFufQN5gbLTrmwvKirKbnlCQoImTJjg8D67du1STEyMLl++rAoVKuijjz5y+uU/ycnJql69ut2y6tWrKzk52e2xuvzInZ2Z/te//lVbt27VXXfd5fbOzfD555/riy++0AcffJDnjbELFy5Uenq65s6dq+DgYF133XXauXOnpk+fTnQCAACfdfToUYWGhtquW61Wp+s2btxYO3fuVEpKipYuXaoBAwboq6++chqenuJydB46dEjVqlVzeFtUVJS+/vrrEj+x6MSJExoyZIg+/vhjlStXLs/tmzdv1k033aTg4GDbsri4OE2dOlVnzpxR5cqV89wnLS3Nboo6NTXVnMEDAAAUUs7Z6K4IDg5Ww4YNJUnR0dHaunWrZs6cqTlz5uRZNyIiQidOnLBbduLECUVERLg9Rpff01mnTp18PxLJarXqpptucnsAnmIYhgYOHKjHHntMbdu2dbiOsyninNscmTx5ssLCwmyXq6evAQAAfFl2drbT94DGxMRozZo1dstWr17t9D2g+XEpOrt27er0rKbczp07p6lTp2rWrFluD8SZMWPGyGKx5HvZt2+fXn31VZ07d05jx4712L4laezYsUpJSbFdjh496tHtAwAAFJexY8dq/fr1Onz4sHbt2qWxY8cqMTFR/fr1kyT179/frqWGDx+ulStX6uWXX9a+ffs0YcIEfffddxo2bJjb+3bp8Hrv3r11zz33KCwsTD169FDbtm1Vo0YNhYSE6MyZM/rxxx+1ceNGrVixQt27d9e0adPcHogzTz31lAYOHJjvOvXr19fatWu1efPmPO9haNu2rfr166d33nnH6RSxJKfTxFarNd/3RQAAAPiKkydPqn///jp+/LjCwsLUsmVLrVq1SrfddpskKSkpSQEBf85Jtm/fXosWLdK4ceP0z3/+U40aNdLHH3+s5s2bu71vi+Hid1empaVpyZIleu+997Rx40alpKRc2YDFombNmikuLk6DBw9W06ZN3R6EJyQlJdm93/K3335TXFycli5dqnbt2qlWrVqaPXu2nnnmGZ04cUJBQUGSpH/+85/68MMPtW/fPpf2k5qaqrCwMN3a+CmVCSRGAQDwBZlZaVqz/2WlpKS4/N5HT8lphzpvPauAciEe3Xb2xcs68vCkEnlc7nL5RCKr1aoHHnhADzzwgCQpJSVFly5dUpUqVWwBV5Jq165td71ChQqSpAYNGqhWrVqSpPvvv18TJ07U4MGDNXr0aO3evVszZ87UK6+8UuzjBQAAKE0K/WFROSfW+JKwsDB98cUXio+PV3R0tKpWrarx48fzcUkAAAAm8+wnlHqRunXrOvxs0ZYtW2rDhg0lMCIAAIDSq1BfgwkAAAC4g+gEAACA6fz28DoAAIA3yc4IlNIDPb9NH1Gomc6zZ8/qrbfe0tixY/XHH39IkrZv365jx455dHAAAADwD27PdP7www+KjY1VWFiYDh8+rCFDhuiaa67Rhx9+qKSkJC1YsMCMcQIAAMCHuT3TOXLkSA0cOFA//fSTQkL+/IDT22+/XevXr/fo4AAAAOAf3I7OrVu36tFHH82zvGbNmkpOTvbIoAAAAOBf3I5Oq9Vq93WTOQ4cOKBq1ap5ZFAAAADwL25H55133qnnnntOGRkZkq5893pSUpJGjx6te+65x+MDBAAAgO9zOzpffvllnT9/XuHh4bp06ZI6deqkhg0bqmLFinr++efNGCMAAAB8nNtnr4eFhWn16tX6+uuv9f333+v8+fO6/vrrFRsba8b4AAAA4AcK/eHwHTp0UIcOHTw5FgAAAPgptw+vP/HEE/r3v/+dZ/lrr72mESNGeGJMAAAARWK5lGZ/uZxW0kMq9dyOzg8++MDhDGf79u21dOlSjwwKAAAgP3mi8qoLvI/bh9dPnz6tsLCwPMtDQ0N16tQpjwwKAADA7+IxPUAKLNQ3kOe/TR/h9kgbNmyolStX5ln++eefq379+h4ZFAAA8H/MVpYubs90jhw5UsOGDdPvv/+uW265RZK0Zs0avfzyy5oxY4anxwcAAHwY8YgcbkfnQw89pLS0ND3//POaNGmSJKlu3bqaPXu2+vfv7/EBAgAA70VUwlWF+sikoUOHaujQofr9999VtmxZVahQwdPjAgAAXoCohKcU+nM6JfFd6wAA+DiiEsXF7ROJTpw4oQcffFA1atRQmTJlFBgYaHcBAADeoaATdQhOFCe3ZzoHDhyopKQkPfvss4qMjJTFYjFjXAAAIB8EI3yN29G5ceNGbdiwQa1btzZhOAAAgKCEP3I7OqOiomQYhhljAQDArxGTKM3cfk/njBkzNGbMGB0+fNiE4QAA4Htcee8kwYnSzu2Zzj59+ujixYtq0KCBypUrp6CgILvb//jjD48NDgCAkkQoAp7jdnTyrUMAAF9HTALFz+3oHDBggBnjAACgyIhJeDNLWoAsAW6/s7HAbfqKIn04/OXLl5Wenm63LDQ0tEgDAgAgN0IS8A9uR+eFCxc0evRovf/++zp9+nSe27OysjwyMACA/yIkgdLH7TnZp59+WmvXrtXs2bNltVr11ltvaeLEiapRo4YWLFhgxhgBAF7O1bO3OYsbKL3cnun87LPPtGDBAnXu3FmDBg1Sx44d1bBhQ9WpU0cLFy5Uv379zBgnAKCYEIUAzOB2dP7xxx+qX7++pCvv38z5iKS//vWvGjp0qGdHBwAoEgISgLdw+/B6/fr1dejQIUlSkyZN9P7770u6MgNaqVIljw4OAOD+oWsOYwPwRm7PdA4aNEjff/+9OnXqpDFjxqhHjx567bXXlJGRoenTp5sxRgDweQQggNLO7ZnOJ598Uk888YQkKTY2Vvv27dOiRYu0Y8cODR8+3OMDBABvxIwjAF80efJk3XDDDapYsaLCw8PVq1cv7d+/3+X7L168WBaLRb169XJ7325H54IFC5SW9ucP0Dp16ujuu+9WkyZNOHsdgNcqyiFqIhKAv/jqq68UHx+vb775RqtXr1ZGRoa6dOmiCxcuFHjfw4cP6x//+Ic6duxYqH0X6vB6165dFR4ebrf83LlzGjRokPr371+ogQCAIwQeAHjOypUr7a7Pnz9f4eHh2rZtm2666San98vKylK/fv00ceJEbdiwQWfPnnV7325Hp2EYslgseZb/+uuvCgsLc3sAAPwLkQgAxS81NdXuutVqldVqLfB+KSkpkqRrrrkm3/Wee+45hYeHa/DgwdqwYUOhxuhydLZp00YWi0UWi0W33nqrypT5865ZWVk6dOiQunbtWqhBACg+RCEAlIyADIsCAvNO3BVJxpXtRUVF2S1OSEjQhAkT8r1rdna2RowYoQ4dOqh58+ZO19u4caPefvtt7dy5s0hDdTk6c94wunPnTsXFxalChQq224KDg1W3bl3dc889RRoMUFoRggCAojh69KhCQ0Nt112Z5YyPj9fu3bu1ceNGp+ucO3dODz74oN58801VrVq1SGN0OToTEhIkSXXr1tV9993n0oMBvB2xBwDwB6GhoXbRWZBhw4Zp2bJlWr9+vWrVquV0vZ9//lmHDx9Wjx49bMuys7MlSWXKlNH+/fvVoEEDl/bp9ns6b7nlFv3++++2AW7ZskWLFi1Ss2bN9Mgjj7i7OfggQg0AAN9kGIb+/ve/66OPPlJiYqLq1auX7/pNmjTRrl277JaNGzdO586d08yZM/Mc1s+P29F5//3365FHHtGDDz6o5ORkxcbGqnnz5lq4cKGSk5M1fvx4dzfpkyyX02Rx+wOnAAAASk58fLwWLVqkTz75RBUrVlRycrIkKSwsTGXLlpUk9e/fXzVr1tTkyZMVEhKS5/2eOd9Amd/7QB1xO5t2796tG2+8UZL0/vvvq0WLFtq0aZMWLlyo+fPnu7s5AAAAFJPZs2crJSVFnTt3VmRkpO3y3nvv2dZJSkrS8ePHPb5vt2c6MzIybO/n/PLLL3XnnXdKujL9asYAAQAA4BmGYRS4TmJiYr63F3aS0e2Zzuuuu06vv/66NmzYoNWrV9s+Jum3335TlSpVCjUIAAAA+De3o3Pq1KmaM2eOOnfurL59+6pVq1aSpE8//dR22B0AAADIze3D6507d9apU6eUmpqqypUr25Y/8sgjKleunEcHBwAAAP/gdnRKUmBgoF1wSlc+vxMAAABwxKXovP7667VmzRpVrlzZ9nWYzmzfvt1jgwMAAIB/cCk6e/bsaTtjPefrMAEAAOC6gDSLAvKZuCuUNA9vz0QuRWfOV2Be/WcAAADAFW6/p9MwDG3btk2HDx+WxWJRvXr1CjzkDgAAgNLNrehct26dBg8erCNHjtg+XDQnPOfOnaubbrrJlEECAADAt7n8OZ0HDx7UHXfcobp16+rDDz/U3r179eOPP2rJkiWqVauWbr/9dv3yyy9mjhUAAAA+yuWZzhkzZugvf/mL1qxZY7e8SZMmuuuuuxQbG6tXXnlFr776qscHCQAAAN/m8kxnYmKiRowY4fA2i8WiESNGaN26dZ4aFwAAAPyIy9GZlJSkFi1aOL29efPmOnLkiEcGBQAAAP/icnSeP38+36+5LFeunC5evOiRQQEAAMC/uHX2+o8//qjk5GSHt506dcojAwIAAID/cSs6b731VttHJeVmsVhkGAaf1QkAAACHXI7OQ4cOmTkOAAAA+DGXo7NOnTpmjgMAAMCvWTKkgEDPbtPI8Oz2zOTyiUQAAABAYRGdAAAAMB3RCQAAANMRnQAAADAd0QkAAADTuXT2eps2bVz+DM7t27cXaUAAAADwPy5FZ69evUweBgAAAPyZS9GZkJBg9jgAAADgx3hPJwAAAEzn1nevS1JWVpZeeeUVvf/++0pKSlJ6errd7X/88YfHBgcAAAD/4PZM58SJEzV9+nT16dNHKSkpGjlypO6++24FBARowoQJJgwRAAAAvs7tmc6FCxfqzTffVPfu3TVhwgT17dtXDRo0UMuWLfXNN9/oiSeeMGOcAAAAPi0wXQp07cOAXJde8Crewu2ZzuTkZLVo0UKSVKFCBaWkpEiS7rjjDi1fvtyzowMAAIBfcDs6a9WqpePHj0uSGjRooC+++EKStHXrVlmtVs+ODgAAAH7B7ei86667tGbNGknS3//+dz377LNq1KiR+vfvr4ceesjjAwQAAIDvc/s9nVOmTLH9uU+fPqpdu7Y2b96sRo0aqUePHh4dHAAAAPyD29F5tZiYGMXExHhiLAAAAPBTLkXnp59+qm7duikoKEiffvppvuveeeedHhlYYS1fvlzPPfecfvjhB4WEhKhTp076+OOPbbcnJSVp6NChWrdunSpUqKABAwZo8uTJKlOmyP0NAAAAJ1z+7vXk5GSFh4fn+z3sFotFWVlZnhqb2z744AMNGTJEL7zwgm655RZlZmZq9+7dttuzsrLUvXt3RUREaNOmTTp+/Lj69++voKAgvfDCCyU2bgAAAH/n0olE2dnZCg8Pt/3Z2aUkgzMzM1PDhw/XtGnT9Nhjj+naa69Vs2bNdO+999rW+eKLL/Tjjz/qf//7n1q3bq1u3bpp0qRJmjVrVp5vVgIAAPBH69evV48ePVSjRg1ZLBa7I8LOLFy4UK1atVK5cuUUGRmphx56SKdPn3Zrvx797vWLFy96cnNu2b59u44dO6aAgAC1adNGkZGR6tatm91M5+bNm9WiRQtVr17dtiwuLk6pqanas2ePw+2mpaUpNTXV7gIAAOCrLly4oFatWmnWrFkurf/111+rf//+Gjx4sPbs2aMlS5Zoy5YtGjJkiFv7dTs6b731Vh07dizP8m+//VatW7d2d3Me88svv0iSJkyYoHHjxmnZsmWqXLmyOnfubPs++OTkZLvglGS7npyc7HC7kydPVlhYmO0SFRVl4qMAAAAwV7du3fSvf/1Ld911l0vrb968WXXr1tUTTzyhevXq6a9//aseffRRbdmyxa39uh2dISEhatmypd577z1JVw63T5gwQR07dtTtt9/u7uYKNGbMGFkslnwv+/btU3Z2tiTpmWee0T333KPo6GjNmzdPFotFS5YsKfT+x44dq5SUFNvl6NGjnnpoAAAAHnH1Udm0tDSPbTsmJkZHjx7VihUrZBiGTpw4oaVLl7rdfW6fsr18+XLNmjVLDz30kD755BMdPnxYR44c0bJly9SlSxd3N1egp556SgMHDsx3nfr169u+JalZs2a25VarVfXr11dSUpIkKSIiIk+VnzhxwnabI1arlW9aAgAARRaYJgV6eqP/35ZXH4lNSEjQhAkTPLKLDh06aOHCherTp48uX76szMxM9ejRw+XD8zkK9TlB8fHx+vXXXzV16lSVKVNGiYmJat++fWE2VaBq1aqpWrVqBa4XHR0tq9Wq/fv3669//askKSMjQ4cPH1adOnUkXSn1559/XidPnrSdGLV69WqFhobaxSoAAIAvOXr0qEJDQ23XPTlh9uOPP2r48OEaP3684uLidPz4cY0aNUqPPfaY3n77bZe343Z0njlzRg8//LDWrFmjOXPm6KuvvlKXLl304osv6vHHH3d3cx4TGhqqxx57TAkJCYqKilKdOnU0bdo0SVLv3r0lSV26dFGzZs304IMP6sUXX1RycrLGjRun+Ph4ZjMBAIDPCg0NtYtOT5o8ebI6dOigUaNGSZJatmyp8uXLq2PHjvrXv/6lyMhIl7bjdnQ2b95c9erV044dO1SvXj0NGTJE7733nh5//HEtX75cy5cvd3eTHjNt2jSVKVNGDz74oC5duqR27dpp7dq1qly5siQpMDBQy5Yt09ChQxUTE6Py5ctrwIABeu6550pszAAAAN7s4sWLeb5EJzDwyhsFDMNweTtuR+djjz2mZ555RgEBf56D1KdPH3Xo0EGDBg1yd3MeFRQUpJdeekkvvfSS03Xq1KmjFStWFOOoAAAAvMf58+d18OBB2/VDhw5p586duuaaa1S7dm2NHTtWx44d04IFCyRJPXr00JAhQzR79mzb4fURI0boxhtvVI0aNVzer9vR+eyzzzpcXqtWLU2fPt3dzQEAAKAYfffdd7r55ptt10eOHClJGjBggObPn6/jx4/bTsKWpIEDB+rcuXN67bXX9NRTT6lSpUq65ZZbNHXqVLf2W+QvHD937pzeffddvfXWW9q2bVuJfisRAAAA8te5c+d8D4vPnz8/z7K///3v+vvf/16k/Rb6G4nWr1+vAQMGKDIyUi+99JJuueUWffPNN0UaDAAAAPyTWzOdycnJmj9/vt5++22lpqbq3nvvVVpamj7++GM+cggAAABOuTzT2aNHDzVu3Fg//PCDZsyYod9++02vvvqqmWMDAACAn3B5pvPzzz/XE088oaFDh6pRo0ZmjgkAAAB+xuWZzo0bN+rcuXOKjo5Wu3bt9Nprr+nUqVNmjg0AAAB+wuXo/Mtf/qI333xTx48f16OPPqrFixerRo0ays7O1urVq3Xu3DkzxwkAAAAf5vbZ6+XLl9dDDz2kjRs3ateuXXrqqac0ZcoUhYeH68477zRjjAAAAD4vIEMKSPfwJaOkH5XrCv2RSZLUuHFjvfjii/r111/17rvvempMAAAA8DNFis4cgYGB6tWrlz799FNPbA4AAAB+xiPRCQAAAOSH6AQAAIDpiE4AAACYjugEAACA6YhOAAAAmI7oBAAAgOmITgAAAJiO6AQAAIDpiE4AAACYrkxJDwAAAKA0CEyXAj290XRPb9A8zHQCAADAdEQnAAAATEd0AgAAwHREJwAAAExHdAIAAMB0RCcAAABMR3QCAADAdEQnAAAATEd0AgAAwHREJwAAAExHdAIAAMB0fPc6AABAMQhMMxRoGJ7daLqHt2ciZjoBAABgOqITAAAApiM6AQAAYDqiEwAAAKYjOgEAAGA6ohMAAACmIzoBAABgOqITAACgFFm/fr169OihGjVqyGKx6OOPP853/Q8//FC33XabqlWrptDQUMXExGjVqlVu75foBAAAKEUuXLigVq1aadasWS6tv379et12221asWKFtm3bpptvvlk9evTQjh073Nov30gEAABQinTr1k3dunVzef0ZM2bYXX/hhRf0ySef6LPPPlObNm1c3g7RCQAA4ONSU1PtrlutVlmtVlP2lZ2drXPnzumaa65x634cXgcAACgGgemGyqR59hL4/9+9HhUVpbCwMNtl8uTJpj2Ol156SefPn9e9997r1v2Y6QQAAPBxR48eVWhoqO26WbOcixYt0sSJE/XJJ58oPDzcrfsSnQAAAD4uNDTULjrNsHjxYj388MNasmSJYmNj3b4/h9cBAACQr3fffVeDBg3Su+++q+7duxdqG8x0AgAAlCLnz5/XwYMHbdcPHTqknTt36pprrlHt2rU1duxYHTt2TAsWLJB05ZD6gAEDNHPmTLVr107JycmSpLJlyyosLMzl/TLTCQAAUIp89913atOmje3jjkaOHKk2bdpo/PjxkqTjx48rKSnJtv4bb7yhzMxMxcfHKzIy0nYZPny4W/tlphMAAKAU6dy5swzDcHr7/Pnz7a4nJiZ6ZL/MdAIAAMB0RCcAAABMR3QCAADAdEQnAAAATEd0AgAAwHScvQ4AAFAMAtMMBWY7P2u8MIwMz27PTMx0AgAAwHREJwAAAExHdAIAAMB0RCcAAABMR3QCAADAdEQnAAAATEd0AgAAwHREJwAAAExHdAIAAMB0RCcAAABMR3QCAADAdHz3OgAAQDEIvJytwKxsj27TyPDs9szETCcAAABMR3QCAADAdEQnAAAATEd0AgAAwHREJwAAAExHdAIAAMB0RCcAAABMR3QCAADAdEQnAAAATEd0AgAAwHREJwAAAEzHd68DAAAUg8A0E757PZPvXi8RBw4cUM+ePVW1alWFhobqr3/9q9atW2e3TlJSkrp3765y5copPDxco0aNUmZmZgmNGAAAoHTwq+i84447lJmZqbVr12rbtm1q1aqV7rjjDiUnJ0uSsrKy1L17d6Wnp2vTpk165513NH/+fI0fP76ERw4AAODf/CY6T506pZ9++kljxoxRy5Yt1ahRI02ZMkUXL17U7t27JUlffPGFfvzxR/3vf/9T69at1a1bN02aNEmzZs1Senp6CT8CAAAA/+U30VmlShU1btxYCxYs0IULF5SZmak5c+YoPDxc0dHRkqTNmzerRYsWql69uu1+cXFxSk1N1Z49exxuNy0tTampqXYXAAAAuMdvTiSyWCz68ssv1atXL1WsWFEBAQEKDw/XypUrVblyZUlScnKyXXBKsl3POQR/tcmTJ2vixInmDh4AAMDPef1M55gxY2SxWPK97Nu3T4ZhKD4+XuHh4dqwYYO2bNmiXr16qUePHjp+/Hih9z927FilpKTYLkePHvXgowMAACgdvH6m86mnntLAgQPzXad+/fpau3atli1bpjNnzig0NFSS9J///EerV6/WO++8ozFjxigiIkJbtmyxu++JEyckSREREQ63bbVaZbVai/5AAAAASjGvj85q1aqpWrVqBa538eJFSVJAgP3kbUBAgLKzr3yGVUxMjJ5//nmdPHlS4eHhkqTVq1crNDRUzZo18/DIAQAAkMPrD6+7KiYmRpUrV9aAAQP0/fff68CBAxo1apQOHTqk7t27S5K6dOmiZs2a6cEHH9T333+vVatWady4cYqPj2c2EwAAlBqzZs1S3bp1FRISonbt2uU5Eny1s2fPKj4+XpGRkbJarbr22mu1YsUKt/bpN9FZtWpVrVy5UufPn9ctt9yitm3bauPGjfrkk0/UqlUrSVJgYKCWLVumwMBAxcTE6IEHHlD//v313HPPlfDoAQAAisd7772nkSNHKiEhQdu3b1erVq0UFxenkydPOlw/PT1dt912mw4fPqylS5dq//79evPNN1WzZk239uv1h9fd0bZtW61atSrfderUqeN2mQMAAPiL6dOna8iQIRo0aJAk6fXXX9fy5cs1d+5cjRkzJs/6c+fO1R9//KFNmzYpKChIklS3bl239+s3M50AAACl1dWfKZ6WluZwvfT0dG3btk2xsbG2ZQEBAYqNjdXmzZsd3ufTTz9VTEyM4uPjVb16dTVv3lwvvPCCsrKy3BqjX810AgAAeKvAy5kKLJPp0W0amVe2FxUVZbc8ISFBEyZMyLP+qVOnlJWV5fBzy/ft2+dwH7/88ovWrl2rfv36acWKFTp48KAef/xxZWRkKCEhweWxEp0AAAA+7ujRo7aPjJTk0ROks7OzFR4erjfeeEOBgYGKjo7WsWPHNG3aNKITAACgNAkNDbWLTmeqVq2qwMBA2+eU5zhx4oTTzyyPjIxUUFCQAgMDbcuaNm2q5ORkpaenKzg42KUx8p5OAACAUiI4OFjR0dFas2aNbVl2drbWrFmjmJgYh/fp0KGDDh48aPvcc0k6cOCAIiMjXQ5OiegEAAAoVUaOHKk333xT77zzjvbu3auhQ4fqwoULtrPZ+/fvr7Fjx9rWHzp0qP744w8NHz5cBw4c0PLly/XCCy8oPj7erf1yeB0AAKAU6dOnj37//XeNHz9eycnJat26tVauXGk7uSgpKcnuGx6joqK0atUqPfnkk2rZsqVq1qyp4cOHa/To0W7tl+gEAAAoZYYNG6Zhw4Y5vC0xMTHPspiYGH3zzTdF2ieH1wEAAGA6ohMAAACmIzoBAABgOqITAAAApiM6AQAAYDrOXgcAACgGAelZCsjy7HevB2RleXR7ZmKmEwAAAKYjOgEAAGA6ohMAAACmIzoBAABgOqITAAAApiM6AQAAYDqiEwAAAKYjOgEAAGA6ohMAAACmIzoBAABgOqITAAAApuO71wEAAIqB5VKGLIGene+zZGV4dHtmYqYTAAAApiM6AQAAYDqiEwAAAKYjOgEAAGA6ohMAAACmIzoBAABgOqITAAAApiM6AQAAYDqiEwAAAKYjOgEAAGA6ohMAAACm47vXAQAAioHlcposHp7us2SneXaDJmKmEwAAAKYjOgEAAGA6ohMAAACmIzoBAABgOqITAAAApiM6AQAAYDqiEwAAAKYjOgEAAEqZWbNmqW7dugoJCVG7du20ZcuWfNdfsmSJmjRpopCQELVo0UIrVqxwe59EJwAAQCny3nvvaeTIkUpISND27dvVqlUrxcXF6eTJkw7X37Rpk/r27avBgwdrx44d6tWrl3r16qXdu3e7tV+iEwAAoBSZPn26hgwZokGDBqlZs2Z6/fXXVa5cOc2dO9fh+jNnzlTXrl01atQoNW3aVJMmTdL111+v1157za39Ep0AAAA+LjU11e6Slub46zHT09O1bds2xcbG2pYFBAQoNjZWmzdvdnifzZs3260vSXFxcU7Xd4bvXgcAACgOly5LAdme3WZ2uiQpKirKbnFCQoImTJiQZ/VTp04pKytL1atXt1tevXp17du3z+EukpOTHa6fnJzs1lCJTgAAAB939OhRhYaG2q5brdYSHI1jRCcAAICPCw0NtYtOZ6pWrarAwECdOHHCbvmJEycUERHh8D4RERFure8M7+kEAAAoJYKDgxUdHa01a9bYlmVnZ2vNmjWKiYlxeJ+YmBi79SVp9erVTtd3hplOAACAUmTkyJEaMGCA2rZtqxtvvFEzZszQhQsXNGjQIElS//79VbNmTU2ePFmSNHz4cHXq1Ekvv/yyunfvrsWLF+u7777TG2+84dZ+iU4AAIBSpE+fPvr99981fvx4JScnq3Xr1lq5cqXtZKGkpCQFBPx5MLx9+/ZatGiRxo0bp3/+859q1KiRPv74YzVv3tyt/VoMwzA8+kj8XGpqqsLCwhRbZ5jKBHjfm3QBAEBemdlp+vLIa0pJSXHpvY+eZGuH8IdVJiDYo9vOzE7XlyffKpHH5S7e0wkAAADTEZ0AAAAwHdEJAAAA0xGdAAAAMB3RCQAAANPxkUkAAADFwEhLk2Hx7IcGGUa6R7dnJmY6AQAAYDqiEwAAAKYjOgEAAGA6ohMAAACmIzoBAABgOqITAAAApiM6AQAAYDqiEwAAAKYjOgEAAGA6ohMAAACmIzoBAABgOqITAAAApitT0gMAAAAoDbIvXFK2JdOz2zQyPLo9MzHTCQAAANMRnQAAADAd0QkAAADTEZ0AAAAwHdEJAAAA0xGdAAAAMB3RCQAAANP5THQ+//zzat++vcqVK6dKlSo5XCcpKUndu3dXuXLlFB4erlGjRikz0/7zsBITE3X99dfLarWqYcOGmj9/vvmDBwAAKOV8JjrT09PVu3dvDR061OHtWVlZ6t69u9LT07Vp0ya98847mj9/vsaPH29b59ChQ+revbtuvvlm7dy5UyNGjNDDDz+sVatWFdfDAAAAKJV85huJJk6cKElOZya/+OIL/fjjj/ryyy9VvXp1tW7dWpMmTdLo0aM1YcIEBQcH6/XXX1e9evX08ssvS5KaNm2qjRs36pVXXlFcXFxxPRQAAIBSx2eisyCbN29WixYtVL16dduyuLg4DR06VHv27FGbNm20efNmxcbG2t0vLi5OI0aMcLrdtLQ0paWl2a6npKRIkjKz0z37AAAAgGlyfm8bhlFyY1CG5OHdZ8p3vgbTb6IzOTnZLjgl2a4nJyfnu05qaqouXbqksmXL5tnu5MmTbbOsuSUefcNTQwcAAMXk9OnTCgsLK9Z9BgcHKyIiQhuSPzFl+xEREQoODjZl255UotE5ZswYTZ06Nd919u7dqyZNmhTTiPIaO3asRo4cabt+9uxZ1alTR0lJScX+l7YkpaamKioqSkePHlVoaGhJD6fY8Lh53KUBj5vHXRqkpKSodu3auuaaa4p93yEhITp06JDS0805ShocHKyQkBBTtu1JJRqdTz31lAYOHJjvOvXr13dpWxEREdqyZYvdshMnTthuy/lvzrLc64SGhjqc5ZQkq9Uqq9WaZ3lYWFip+seaIzQ0lMddivC4Sxced+lSWh93QEDJnEMdEhLiE2FophKNzmrVqqlatWoe2VZMTIyef/55nTx5UuHh4ZKk1atXKzQ0VM2aNbOts2LFCrv7rV69WjExMR4ZAwAAABzzmY9MSkpK0s6dO5WUlKSsrCzt3LlTO3fu1Pnz5yVJXbp0UbNmzfTggw/q+++/16pVqzRu3DjFx8fbZiofe+wx/fLLL3r66ae1b98+/ec//9H777+vJ598siQfGgAAgN/zmROJxo8fr3feecd2vU2bNpKkdevWqXPnzgoMDNSyZcs0dOhQxcTEqHz58howYICee+45233q1aun5cuX68knn9TMmTNVq1YtvfXWW259XJLValVCQoLDQ+7+jMfN4y4NeNw87tKAx126Hrc3sRgl+dkBAAAAKBV85vA6AAAAfBfRCQAAANMRnQAAADAd0QkAAADTEZ0OzJo1S3Xr1lVISIjatWuX50Pnr7ZkyRI1adJEISEhatGiRZ7PAvV2kydP1g033KCKFSsqPDxcvXr10v79+/O9z/z582WxWOwuvvahtxMmTMjzGAr69itff60lqW7dunket8ViUXx8vMP1ffW1Xr9+vXr06KEaNWrIYrHo448/trvdMAyNHz9ekZGRKlu2rGJjY/XTTz8VuF13fz4Ut/wed0ZGhkaPHq0WLVqofPnyqlGjhvr376/ffvst320W5t9KcSvo9R44cGCex9C1a9cCt+vLr7ckh//WLRaLpk2b5nSbvvB6u/J76/Lly4qPj1eVKlVUoUIF3XPPPXm+IOZqhf25ANcQnVd57733NHLkSCUkJGj79u1q1aqV4uLidPLkSYfrb9q0SX379tXgwYO1Y8cO9erVS7169dLu3buLeeSF99VXXyk+Pl7ffPONVq9erYyMDHXp0kUXLlzI936hoaE6fvy47XLkyJFiGrHnXHfddXaPYePGjU7X9YfXWpK2bt1q95hXr14tSerdu7fT+/jia33hwgW1atVKs2bNcnj7iy++qH//+996/fXX9e2336p8+fKKi4vT5cuXnW7T3Z8PJSG/x33x4kVt375dzz77rLZv364PP/xQ+/fv15133lngdt35t1ISCnq9Jalr1652j+Hdd9/Nd5u+/npLsnu8x48f19y5c2WxWHTPPffku11vf71d+b315JNP6rPPPtOSJUv01Vdf6bffftPdd9+d73YL83MBbjBg58YbbzTi4+Nt17OysowaNWoYkydPdrj+vffea3Tv3t1uWbt27YxHH33U1HGa6eTJk4Yk46uvvnK6zrx584ywsLDiG5QJEhISjFatWrm8vj++1oZhGMOHDzcaNGhgZGdnO7zdH15rScZHH31ku56dnW1EREQY06ZNsy07e/asYbVajXfffdfpdtz9+VDSrn7cjmzZssWQZBw5csTpOu7+Wylpjh73gAEDjJ49e7q1HX98vXv27Gnccsst+a7ja6+3YeT9vXX27FkjKCjIWLJkiW2dvXv3GpKMzZs3O9xGYX8uwHXMdOaSnp6ubdu2KTY21rYsICBAsbGx2rx5s8P7bN682W59SYqLi3O6vi9ISUmRJF1zzTX5rnf+/HnVqVNHUVFR6tmzp/bs2VMcw/Oon376STVq1FD9+vXVr18/JSUlOV3XH1/r9PR0/e9//9NDDz0ki8XidD1/eK1zO3TokJKTk+1ez7CwMLVr187p61mYnw++ICUlRRaLRZUqVcp3PXf+rXirxMREhYeHq3Hjxho6dKhOnz7tdF1/fL1PnDih5cuXa/DgwQWu62uv99W/t7Zt26aMjAy7169JkyaqXbu209evMD8X4B6iM5dTp04pKytL1atXt1tevXp1JScnO7xPcnKyW+t7u+zsbI0YMUIdOnRQ8+bNna7XuHFjzZ07V5988on+97//KTs7W+3bt9evv/5ajKMtmnbt2mn+/PlauXKlZs+erUOHDqljx446d+6cw/X97bWWpI8//lhnz57VwIEDna7jD6/11XJeM3dez8L8fPB2ly9f1ujRo9W3b1+FhoY6Xc/dfyveqGvXrlqwYIHWrFmjqVOn6quvvlK3bt2UlZXlcH1/fL3feecdVaxYscBDzL72ejv6vZWcnKzg4OA8/zNV0O/znHVcvQ/c4zNfg4niER8fr927dxf4/p2YmBjFxMTYrrdv315NmzbVnDlzNGnSJLOH6RHdunWz/blly5Zq166d6tSpo/fff9+lmQB/8Pbbb6tbt26qUaOG03X84bVGXhkZGbr33ntlGIZmz56d77r+8G/lvvvus/25RYsWatmypRo0aKDExETdeuutJTiy4jN37lz169evwBMBfe31dvX3FkoeM525VK1aVYGBgXnObjtx4oQiIiIc3iciIsKt9b3ZsGHDtGzZMq1bt061atVy675BQUFq06aNDh48aNLozFepUiVde+21Th+DP73WknTkyBF9+eWXevjhh926nz+81jmvmTuvZ2F+PnirnOA8cuSIVq9ene8spyMF/VvxBfXr11fVqlWdPgZ/er0lacOGDdq/f7/b/94l7369nf3eioiIUHp6us6ePWu3fkG/z3PWcfU+cA/RmUtwcLCio6O1Zs0a27Ls7GytWbPGbqYnt5iYGLv1JWn16tVO1/dGhmFo2LBh+uijj7R27VrVq1fP7W1kZWVp165dioyMNGGExeP8+fP6+eefnT4Gf3itc5s3b57Cw8PVvXt3t+7nD691vXr1FBERYfd6pqam6ttvv3X6ehbm54M3ygnOn376SV9++aWqVKni9jYK+rfiC3799VedPn3a6WPwl9c7x9tvv63o6Gi1atXK7ft64+td0O+t6OhoBQUF2b1++/fvV1JSktPXrzA/F+CmEj6RyessXrzYsFqtxvz5840ff/zReOSRR4xKlSoZycnJhmEYxoMPPmiMGTPGtv7XX39tlClTxnjppZeMvXv3GgkJCUZQUJCxa9euknoIbhs6dKgRFhZmJCYmGsePH7ddLl68aFvn6sc9ceJEY9WqVcbPP/9sbNu2zbjvvvuMkJAQY8+ePSXxEArlqaeeMhITE41Dhw4ZX3/9tREbG2tUrVrVOHnypGEY/vla58jKyjJq165tjB49Os9t/vJanzt3ztixY4exY8cOQ5Ixffp0Y8eOHbaztKdMmWJUqlTJ+OSTT4wffvjB6Nmzp1GvXj3j0qVLtm3ccsstxquvvmq7XtDPB2+Q3+NOT0837rzzTqNWrVrGzp077f69p6Wl2bZx9eMu6N+KN8jvcZ87d874xz/+YWzevNk4dOiQ8eWXXxrXX3+90ahRI+Py5cu2bfjb650jJSXFKFeunDF79myH2/DF19uV31uPPfaYUbt2bWPt2rXGd999Z8TExBgxMTF222ncuLHx4Ycf2q678nMBhUd0OvDqq68atWvXNoKDg40bb7zR+Oabb2y3derUyRgwYIDd+u+//75x7bXXGsHBwcZ1111nLF++vJhHXDSSHF7mzZtnW+fqxz1ixAjbc1S9enXj9ttvN7Zv3178gy+CPn36GJGRkUZwcLBRs2ZNo0+fPsbBgwdtt/vja51j1apVhiRj//79eW7zl9d63bp1Dv9e5zy27Oxs49lnnzWqV69uWK1W49Zbb83zfNSpU8dISEiwW5bfzwdvkN/jPnTokNN/7+vWrbNt4+rHXdC/FW+Q3+O+ePGi0aVLF6NatWpGUFCQUadOHWPIkCF54tHfXu8cc+bMMcqWLWucPXvW4TZ88fV25ffWpUuXjMcff9yoXLmyUa5cOeOuu+4yjh8/nmc7ue/jys8FFJ7FMAzDnDlUAAAA4Are0wkAAADTEZ0AAAAwHdEJAAAA0xGdAAAAMB3RCQAAANMRnQAAADAd0QkAAADTEZ0AAAAwHdEJoFh07txZI0aMsF2vW7euZsyY4fL958+fr0qVKnlkLJ7cljdas2aNmjZtqqysLLfu95e//EUffPCBSaMCUNoRnQBsBg4cKIvFIovFoqCgINWrV09PP/20Ll++7PF9bd26VY888ohHt5kzdovFovLly6tRo0YaOHCgtm3bZrdenz59dODAAZe26YuB+vTTT2vcuHEKDAyUdOUx5DwvAQEBioyMVJ8+fZSUlGR3v3HjxmnMmDHKzs4uiWED8HNEJwA7Xbt21fHjx/XLL7/olVde0Zw5c5SQkODx/VSrVk3lypXz+HbnzZun48ePa8+ePZo1a5bOnz+vdu3aacGCBbZ1ypYtq/DwcI/v2xts3LhRP//8s+655x675aGhoTp+/LiOHTumDz74QPv371fv3r3t1unWrZvOnTunzz//vDiHDKCUIDoB2LFarYqIiFBUVJR69eql2NhYrV692nb76dOn1bdvX9WsWVPlypVTixYt9O6779pt48KFC+rfv78qVKigyMhIvfzyy3n2c/Xh9enTp6tFixYqX768oqKi9Pjjj+v8+fNuj79SpUqKiIhQ3bp11aVLFy1dulT9+vXTsGHDdObMGUl5Zy+///573XzzzapYsaJCQ0MVHR2t7777TomJiRo0aJBSUlJsM4UTJkyQJP33v/9V27ZtVbFiRUVEROj+++/XyZMnbdtMTEyUxWLRmjVr1LZtW5UrV07t27fX/v377cb72Wef6YYbblBISIiqVq2qu+66y3ZbWlqa/vGPf6hmzZoqX7682rVrp8TExHwf/+LFi3XbbbcpJCTEbrnFYlFERIQiIyPVvn17DR48WFu2bFFqaqptncDAQN1+++1avHixO085ALiE6ATg1O7du7Vp0yYFBwfbll2+fFnR0dFavny5du/erUceeUQPPvigtmzZYltn1KhR+uqrr/TJJ5/oiy++UGJiorZv357vvgICAvTvf/9be/bs0TvvvKO1a9fq6aef9sjjePLJJ3Xu3Dm7eM6tX79+qlWrlrZu3apt27ZpzJgxCgoKUvv27TVjxgzbLOHx48f1j3/8Q5KUkZGhSZMm6fvvv9fHH3+sw4cPa+DAgXm2/cwzz+jll1/Wd999pzJlyuihhx6y3bZ8+XLddddduv3227Vjxw6tWbNGN954o+32YcOGafPmzVq8eLF++OEH9e7dW127dtVPP/3k9LFu2LBBbdu2zff5OHnypD766CMFBgbaDsHnuPHGG7Vhw4Z87w8AhWIAwP8bMGCAERgYaJQvX96wWq2GJCMgIMBYunRpvvfr3r278dRTTxmGYRjnzp0zgoODjffff992++nTp42yZcsaw4cPty2rU6eO8corrzjd5pIlS4wqVarYrs+bN88ICwvLdxySjI8++ijP8kuXLhmSjKlTpzrcVsWKFY358+c73KYr+zUMw9i6dashyTh37pxhGIaxbt06Q5Lx5Zdf2tZZvny5Icm4dOmSYRiGERMTY/Tr18/h9o4cOWIEBgYax44ds1t+6623GmPHjnU6jrCwMGPBggV5HoMko3z58ka5cuUMSYYk44knnshz/08++cQICAgwsrKyCnzMAOCOMiVWuwC80s0336zZs2frwoULeuWVV1SmTBm79wdmZWXphRde0Pvvv69jx44pPT1daWlptvdn/vzzz0pPT1e7du1s97nmmmvUuHHjfPf75ZdfavLkydq3b59SU1OVmZmpy5cv6+LFi0V+76dhGJKuHGJ2ZOTIkXr44Yf13//+V7Gxserdu7caNGiQ7za3bdumCRMm6Pvvv9eZM2dsJ98kJSWpWbNmtvVatmxp+3NkZKSkKzONtWvX1s6dOzVkyBCH29+1a5eysrJ07bXX2i1PS0tTlSpVnI7r0qVLeQ6tS1LFihW1fft2ZWRk6PPPP9fChQv1/PPP51mvbNmyys7OVlpamsqWLZvPMwAA7uHwOgA75cuXV8OGDdWqVSvNnTtX3377rd5++23b7dOmTdPMmTM1evRorVu3Tjt37lRcXJzS09MLvc/Dhw/rjjvuUMuWLfXBBx9o27ZtmjVrliQVabs59u7dK0mqV6+ew9snTJigPXv2qHv37lq7dq2aNWumjz76yOn2Lly4oLi4OIWGhmrhwoXaunWrbf2rxxsUFGT7c0705gRqflF3/vx5BQYGatu2bdq5c6ftsnfvXs2cOdPp/apWrWp772puAQEBatiwoZo2baqRI0fqL3/5i4YOHZpnvT/++EPly5cnOAF4HNEJwKmAgAD985//1Lhx43Tp0iVJ0tdff62ePXvqgQceUKtWrVS/fn27jx9q0KCBgoKC9O2339qWnTlzJt+PKNq2bZuys7P18ssv6y9/+YuuvfZa/fbbbx57HDnvy4yNjXW6zrXXXqsnn3xSX3zxhe6++27NmzdPkhQcHJzn8y737dun06dPa8qUKerYsaOaNGlidxKRq1q2bKk1a9Y4vK1NmzbKysrSyZMn1bBhQ7tLRESE0222adNGP/74Y4H7HjNmjN57770877XdvXu32rRp494DAQAXEJ0A8tW7d28FBgbaZh4bNWqk1atXa9OmTdq7d68effRRnThxwrZ+hQoVNHjwYI0aNUpr167V7t27NXDgQAUEOP9x07BhQ2VkZOjVV1/VL7/8ov/+9796/fXXCzXes2fPKjk5WUeOHNHq1av1t7/9TYsWLdLs2bMdft7mpUuXNGzYMCUmJurIkSP6+uuvtXXrVjVt2lTSlbPsz58/rzVr1ujUqVO6ePGiateureDgYNt4P/30U02aNMntsSYkJOjdd99VQkKC9u7dq127dmnq1KmSrkRwv3791L9/f3344Yc6dOiQtmzZosmTJ2v58uVOtxkXF6eNGzcWuO+oqCjdddddGj9+vN3yDRs2qEuXLm4/FgAoUEm/qRSA9xgwYIDRs2fPPMsnT55sVKtWzTh//rxx+vRpo2fPnkaFChWM8PBwY9y4cUb//v3t7nfu3DnjgQceMMqVK2dUr17dePHFF41OnTrleyLR9OnTjcjISKNs2bJGXFycsWDBAkOScebMGcMwXD+RKOcSEhJiNGjQwBgwYICxbds2u/VybystLc247777jKioKCM4ONioUaOGMWzYMNvJPoZhGI899phRpUoVQ5KRkJBgGIZhLFq0yKhbt65htVqNmJgY49NPPzUkGTt27DAM488TiXLGbxiGsWPHDkOScejQIduyDz74wGjdurURHBxsVK1a1bj77rttt6Wnpxvjx4836tatawQFBRmRkZHGXXfdZfzwww9On4PTp08bISEhxr59+xw+3tw2b95sSDK+/fZbwzAM49dffzWCgoKMo0eP5vc0A0ChWAzj/99hDwDwC6NGjVJqaqrmzJnj1v1Gjx6tM2fO6I033jBpZABKMw6vA4CfeeaZZ1SnTh23v84yPDy8UG8TAABXMNMJAAAA0zHTCQAAANMRnQAAADAd0QkAAADTEZ0AAAAwHdEJAAAA0xGdAAAAMB3RCQAAANMRnQAAADAd0QkAAADT/R+zd3Cke6x8YAAAAABJRU5ErkJggg==",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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GDhwo6eL7RFNSUjR+/HhzJwcAviao6P/p9zrZOaWPQfmV9r+lgoqZBsrO56JUkiZOnKgRI0aoS5cu6tq1q+bMmaOzZ89q5MiRZk8NgNl8IZJQsSrL/6YqSxxXlp8HKh2fjNIhQ4bot99+09SpU5WRkaGOHTvq888/L/LhJwA+yNl/MCvLP/CAs4hBVHI+GaWSNH78eF6uB1B2vvYPPBEOwMN8NkoBwJGCoACPP4ZftmfOVehRVSHCCWegSiNKAaCCVUT4lqRKRrEzPBnOBC/gcUQpABRyoU6Q/LMvSJLyg+x/RVq3e5rFw4/j7ij22sgtzB3BS9gCJSJKAaCQ/EA/SdX+/7+X3hYg/5yynVfGlaA1gtzzq9nTcWtVnsj1iaC1Kk/YErTwAUQpABRSEOinAgdBWvh2v1LC1FG4Xrrq6oqyrtCWJW4rKmStyhq0PhWzEkHrJbIKcmQUuPfk+acLvOcErEQpABRyoYQgtSruF6c1Vh2tshbHmZVXV4K2vG8xcCVkKzpgC3M1Zn0uYgsjaFFFEKUAUEhubYv8C/VL/v+3j79d0xSNzmo5BSWusEpyuMLqbMA6+7YBZwPWHe+PrSoBKzkfsT4dr44QtKhARCkAFJIfaJFkXHJdyg+U/HMubs8PsA9X/xyj2BXWaoVisizR+sc8So9XV97v6ky8uvODXc4ErNnhKhGvblXWoCVmfRZRCgCFFARIkqXQnwvfZpGfrUWMQrdYiuzHGrAlvR2g2iURWZ5olUoO17J8QKu0cHX32QhKC9fKEK1WxKsHlSVmCVmvQJQCQCF5taTqZy7+2frSfUGACsWo1cUQ9c8xbOOkP17mt66wXsoaq1LxwXpprFqVFK0VHaxSydHqidNnVaVotXImXglXN3AmZPM9Pw2UD1EKAIXkB0h+AX+skuYX+q9/7h+B+sf1P1ZPLw1UK7v3qDqI1cKhKrm2umpVXLCWFqsX51T1g1WqmtEqEa6AFVEKlOL8kRinx2YWuP8lpEwXm+BEQVCpY04V1Cx5HxdqlXh7Zr7j+zvafvJCsMOxWXn288zMq1FkzJk8+3+szxa6fjbX/rbsvOq2P+fk/vGr7UKuvySpIM//j8E5f0SYJddPfrkXQ9Ev1yI5CNKiIVrM9ULBWTg0Lw1V/0v6wplQtR3PJQFZXKRaOYpVZ0L1j7kVvX9ZQ9W2z2Li0dNfTlBStFbWYLUqKVwJVngL954sC/BCmQU5Tl/K/hjFX0pyoiCoyKU4pwpq2i4O93Whlu1SZH75Ne0uxd1W2MkLwUWCNCsvyHax28clQXomL8AuSM/mBZQrSO1cEqTlVThc7bYX8/J94fuUuN8S7l/YhUA/p05jVZj1XKylvYe1OPmBfnYXd8kPqmZ3qUhGUDW7S1VSEBTg8AI4Mm3aNFksFrtLmzZtih3/+uuvq2fPnqpbt67q1q2r+Ph4bdq0yW5MYmJikX327dvX5blVrb95gAlcXal0lTMrm8UpbcXT9hilrHxKxa9+lnabsyuhtn25uCJq2+ZkiFqVtEJaOEbtVknl6L2jjrc5wxqWjlY9HZ9qyvH9i9tHYYXDtLTV08IKh6krK6iFFQ7T8q6i2u3XpK95lRyvqlb21dRLFRemrKziiiuu0Nq1a23Xq1UrPgdTU1M1bNgwde/eXUFBQZo1a5b69OmjXbt2qVGjRrZxffv21cKFC23XAwNd/8AaUQqUojzRWBJng9IRZyLTqqSgdHZMceEpFR+fknMBanVpiF4aoZJ9iEpFY/TSlVFbkLppdbSkgJSK+0DUxbgsLiqdiVPrPqTS41SqPIEqeU+kSt4RqhKxiosRGhER4dTYd9991+76G2+8oRUrViglJUXDhw+3bQ8MDHR6n8XOq1z3BnxAeeKxOK5E5aWciUxXx5cUnZLr4WnlKEAdrYJKzkWo5EKI2u5QdHVU+mNltPCfLw1K/1wnX2oPKH9UeiJOpT8C1ZU4ldwTqJLnVlEl8yNV8p5QlYjVqi4rK8vuemBgYLGrlT///LOioqIUFBSkuLg4zZgxQ02aNHHqcc6dO6e8vDzVq1fPbntqaqrCwsJUt25dXXfddXrqqadUv359l46BKAVKUZ6AvJSrQVne+5cWm1LJwWl7XBfD08qVAJWci1DJiRCVnIrRwtfL+hL9pYpbLbUqadVU8nycSpUjUCXvjFTJu0JVIlbdKatAcvdX1Z/5//1FR0fbbU9KStK0adOKjO/WrZsWLVqk1q1b68iRI5o+fbp69uypnTt3qnbt2qU+3qRJkxQVFaX4+Hjbtr59++qWW25RTEyM9u7dq0ceeUT9+vVTWlqa/P0d/H4uBlEKlKK8Iemu/TgTmIU5E5tSycEplRydUvHhKbkWn5LjAJUcf2CppBC1cvTe0cIcbXMH64eeiotTZ4LS1TgtbX+FVYZAlTy7iipVnkiVioZqVY5UK2K1cjl48KBCQkJs14tbJe3Xr5/tz7GxserWrZuaNm2q999/X6NGjSrxMWbOnKmlS5cqNTVVQUF//BszdOhQ25/bt2+v2NhYtWjRQqmpqerdu7fTx0CUAqUoT0y6GpLFcTYwrUoLTavSglMqOTql4sNTKj4+JdcCVHIuQq1cjVFnVklLjUMHL+E7s2oquSdOnd3fpcr68r7kuUCVfC9SJe8IVYlYNUtISIhdlDqrTp06uuyyy7Rnz54Sxz377LOaOXOm1q5dq9jY2BLHNm/eXA0aNNCePXuIUsCdyhKWrkZkSZwNTCtnQtOqtOCUSo5OqeTwlIqPT8nFAJWcilCr4l6mLzqu2OlJcv59pcUpbdVU8lyclrbPwsqzeiq5N1Alz6+iSpUrUiXvDlWJWK2szpw5o7179+quu+4qdszs2bP19NNPa/Xq1erSpUup+zx06JBOnDihyMhIl+ZClAKlcCUwXQ3I0rgSmFbOhKZUemxalSc6peLDUyohPqViA1Qq/hP0rr5E7ygUHUWfMyFYmtJWTaXS328qyeFXmpa2T6lsq6dS5QtUyXciVfLOl/0v5ShWCVXPefDBB3XTTTepadOm+vXXX5WUlCR/f38NGzZMkjR8+HA1atRIM2bMkCTNmjVLU6dO1ZIlS9SsWTNlZGRIkmrVqqVatWrpzJkzmj59um699VZFRERo7969evjhh9WyZUslJCS4NDeiFCiFM6FZlngsjbNxaXcfJ0NTKj02pdKDUyo5OqVSwlMqU3xKpcWmayFq5Y74hL2CQD+3hGlh+YF+HgtTu8f5/yCsDHFqVThSvTFQrS4NVSLVfQ4dOqRhw4bpxIkTatiwoXr06KGNGzeqYcOGkqT09HT5+f3xu3fevHnKzc3VX/7yF7v9WD9I5e/vrx07dmjx4sU6deqUoqKi1KdPHz355JMun6uUKAVK4e7gLEts2u7rQnRKzoWnlTsCVCpfhEqln0e0MsSosyuszj5+WVRUQF8I9CvTamlhVTlMpcoZp5LvBKpEpLrT0qVLS7w9NTXV7vr+/ftLHF+jRg2tXr26nLO6iCgFSlFSRAZXzy11jNOP42JwWrkSnpJz8Sm5KUClUiNUKvuKqHO3F39bcWHnl1v0a0NL2p+7gtSVl9irGk+FqeS5l/OLPF4ljVPpj0D19ji1IlK9E1EKlIO737/piKvRKTkfnpJz8Sk5GaBSuVdCpfKH6MUxxd/mzCqj9f6lfVDJjCCt6LcZuGO1VPJMmEoVu2oqVY04lXwnUCUi1VsQpUApyhOUpSlLcEquRafkfHhKLsSnVO5V0MI8HaKSazHqzD5ZIXWdJ8NUqrhVU6lyx6nku4Eq2UcqgVp1EKWAB1VUdEquhafk/viU3Begzo8p/bHKEqLl3acr+3M1SM36MJa7Vkslz4WpVPGrplLlj1OJQJWkgnzf+T9/VRVRCpSirGFZnLIEp+R6dEouhqfkdHxK7g1Q18Y5NcwjMerMfj0ZpN7E02EqVeyqqVQ14lTyvfefVia/FwQpp8D537POOOvu7y01EVEKeIgn3td5KZejU/JIeFq58rWdZkSos/sr7oT5Ff0Je1cf39PcuVoqeTZMJXNWTaWqF6cSgYrKgSgFSlHWlc3CKmSV08qF6LSqDPH5x3jnx7ozREvat6OvEC3vY/jyKmlhFRGmUsWvmkr2J+MnUIHSEaXwmD9/9kiFPVZ5X2J3R3gW5rbTKTlSAdEplSUmXRt/8T6ujXdlpdCd+64ML9mbvUqK8skPqlbpw9SKl/dhFqIUlZ7Z7+ks60vrVp58X2dhFRGe5bmfJwO0Ih+jXI9nUpCyKmveS/l2c6hCYSoRp6h4RCk8xt0x6Yi7VjjLG55W7jqXZ3HKEp5WlTlArdzxzUrufozyPm55g7AyBak7309q5cmX7iujqham0sU4JUxREYhSeIy7XxJ3RYVHZnEq6KV2q7KGZ3nuW1EBWp7HK0vsOvpGp6oUo+6YQ2GeCFJUHayaoiIQpTCduwLSWeX+xHpgMf84e/BT7YWVJzzLe//yfLq8IiPUHY9X5sf1shiVPBekvrZKalUVV0utWDWFJxGl8BhPxGa5Vy5L40xYuvlE8o6YGZ4X71+uu5crrCo6QsvzmHaPXwli1B3zuBQrpJ5R1cNUYtUU7keUwmM8HpDFKeP7NYtTnrh0pLzB6M79mBmf5X18syPUNg8vjVHJs0Hqq6ukhVXlMJVYNYX7EaXwHDfHoTu5OzQvZfZK5x/7cctuKvzDQZ6Yg7tPbF9ZYlSqekEK78GqKdyJKEWl4+lgdIW74tBz+3PfvtwVSW55KbwSRHBxyhOA7j7XqKdO9USQVpyqvlpqxaop3IEohceYEZfujj4zH9/tK3tu3J+3xrDk/tDzxEnvq3qM8tK9PW8KU4lV05JkFtRQXoF739p2riDfrfszE1EKjzE7EJ1VkfP02OqdB/brM1HsgcDz1LcvefIk+AQpALMRpaiyKmv0VqXwlDwzX3fPtbJHqG3fVWhV1KoiX6o3M0jN/janknjDKmlhvJSPsiJK4TGVNRpd4anAvJSnv9e8KoWyRyK5ioWoRIyiaiNMURZEKbxSRcWkMzwdnFaePOaqtEorVcx3vVfFVVGp4j/EZHaQVuYVUgD2iFJ4TGUKw/JyFCD5ASXf7k7esGLr0Wj2wIeT8h18zSgh6jyzY1SqGkHqbS/dF8ZqKVxFlMLnuO1k5ZX4tEXFqYhVW08fU0W9FO/Jn1VFhKjkuzEqVY0gBWCPKIXHVNTL1pWRGavEFfnzrojj83S4VeTPq6IiVDLvHKOVJUYlgrQyYbUUriBKAQcq01sPzIr7ivwZVNX3gJb4eBUYopK5J7wnSAG4A1GKSq8yBaI7VIYVZFNWcis40rw9QiXzv3mpMsWoVPWC1JvfT1oYq6VwFlEKj/GmmLw0cC79EExlCE3J/J+5GWFm5s/eF0NUqnwxKlW9IAVQFFEKn+OOiKnoEDI7NgszI8Rsj10Jfg6EaOVSVWPUV1ZJYe/3/GCdv+De9Dqf7z3/WyJK4TGVISAqu8oUm4WZGZ6S761+XqoyRKgVMep+vhakvHQPZxGlgIdU1uCUKkd4Wfl6gEqVK0KlyhuiEjEKeDOiFChGZY7KklSW0CqsMqyaV7afCyHqmqocoxJBCjiDKEWVVFWDsTwqW1RdqjKEZ2GV7edV2SJUqvwhKlX9GJV8O0h56R6uIErhMb4Yjq6obNFUmsoWnVaV8edYGQPUqiqEqOQdMSr5dpACriJKATeqjIHkrMoanVaV/WdbWUO0qkSo5D0hKhGjEqukcB1RCp9S2cPGEyp7bBZWlZ4fItR9vClGJYIUKCuiFB5TlQKjqqhKgelIVfnfROHgvBDoV2RbZUOIVg7EKFA+RClgkqoemI5Ulei0ciY0K2OMVsUItfLGGJUI0kvx0j3KgigF3MwbY7OwqhaeVpUxLp1VlSNU8t4QlYhRRwhSlBVRCp/n7RHpqqoanYURoOby5gi1IkYdI0hRHkQpPIbYq1y8ITYvVZXj08obIlQiREGQOiMzv6Zy892bXufzvefnTpQCVZw3xmZh3hCeVt4SoFa+EKISMVoaYhTuQpQClYi3B2ZxvCk8rbwtQCXfiVArYrR0BCnciSgFPMRXA9MRb4zOS3ljhEqEKIpHkMLdiFL4LKLRPXwhOC9FgHofYtQ1BCk8gSiFxxB9VZcvhqbkvbFZHF+OUIkQLQtiFJ5ElAI+wldD81K+Fp6F+XqESoRoeRCk8DSiFKiiiMyS+XJ8SgRoYYRo+RCjqCh+Zk8AwEXVcgpcuuAiv5wChxdf459TYHfxdf7ZF2wXlI0l+wJB6gNmzpwpi8WiCRMmFDtm0aJFslgsdpegoCC7MYZhaOrUqYqMjFSNGjUUHx+vn3/+2aW5sFIKeADR6F6+GJklITodI0DdgxD1HZs3b9aCBQsUGxtb6tiQkBDt3r3bdt1isdjdPnv2bL300ktavHixYmJi9PjjjyshIUE//PBDkYAtDlEKn0Q0Vi5EZ/EI0JIRou5DjPqWM2fO6I477tDrr7+up556qtTxFotFERERDm8zDENz5szRY489pgEDBkiS3n77bYWHh+vjjz/W0KFDnZoTL9/DY1x9OboiL6g4xb287ssvtTty6cvvvAzvWOGX5QlS9+Bleu+QlZVld8nJySlx/Lhx49S/f3/Fx8c7tf8zZ86oadOmio6O1oABA7Rr1y7bbfv27VNGRobdvkJDQ9WtWzelpaU5fQyslAIoE2Ky7IhN5xGenkOIVrys/BrKza/u1n1m5+dJkqKjo+22JyUladq0aQ7vs3TpUm3dulWbN2926jFat26tt956S7GxscrMzNSzzz6r7t27a9euXWrcuLEyMjIkSeHh4Xb3Cw8Pt93mDKIUgA2h6V7EZ9kQop5FjHqngwcPKiQkxHY9MDCw2HH33XefkpOTnX6vZ1xcnOLi4mzXu3fvrssvv1wLFizQk08+Wb6JF0KUAl6MyKwYxGfZEaAVhxj1biEhIXZRWpwtW7bo2LFj+tOf/mTblp+fr//+97965ZVXlJOTI39//xL3Ub16dXXq1El79uyRJNt7TY8eParIyEjbuKNHj6pjx45OHwNRClRiRGXlQ4CWDxFasQhRXKp37976/vvv7baNHDlSbdq00aRJk0oNUulixH7//fe64YYbJEkxMTGKiIhQSkqKLUKzsrL0zTffaOzYsU7PjSgFPIiorBoITc8hQs1BjKI4tWvXVrt27ey2BQcHq379+rbtw4cPV6NGjTRjxgxJ0hNPPKErr7xSLVu21KlTp/TMM8/owIED+utf/ypJtvOcPvXUU2rVqpXtlFBRUVEaOHCg03MjSuHzCEffQHh6HgFqLkIU7pKeni4/vz9O0HTy5EmNHj1aGRkZqlu3rjp37qyvv/5abdu2tY15+OGHdfbsWY0ZM0anTp1Sjx499Pnnnzv9vlVJshiGYbj1SLxcVlaWQkNDlZmZ6dR7N3xZr76zzJ4CfAzhWbGIUPMRos67kJ+jdd/PMuXfb2s7PLaxj4JqufnT92fy9NSVa7yiS7zmPKX79+/XqFGjFBMToxo1aqhFixZKSkpSbm6u3bgdO3aoZ8+eCgoKUnR0tGbPnm3SjAG4qrhzeXJOT8+79PygBKl5rOcVJUjhbbzm5fuffvpJBQUFWrBggVq2bKmdO3dq9OjROnv2rJ599llJF/+fSp8+fRQfH6/58+fr+++/19133606depozJgxJh8B4NuIysqD4Kx8CFD4Aq+J0r59+6pv3762682bN9fu3bs1b948W5S+++67ys3N1VtvvaWAgABdccUV2r59u55//nmiFPAQYrPyIj4rN0IUvsZrotSRzMxM1atXz3Y9LS1NV199tQICAmzbEhISNGvWLJ08eVJ169Ytso+cnBy7r+rKysry7KSBKoDQrHoI0KqBEIUv89oo3bNnj15++WXbKqkkZWRkKCYmxm6c9SuxrJ8ou9SMGTM0ffp0z04WMBGB6V2Iz6qHEPUdpy4EK/CCez/olHMhz637M1Ol/6DT5MmTZbFYSrz89NNPdvc5fPiw+vbtq8GDB2v06NHlevwpU6YoMzPTdjl48GC59gd4QmkfAOLDQd7F0YeO+PBR1VH4g0oEKfCHSr9S+sADDygxMbHEMc2bN7f9+ddff9W1116r7t2767XXXrMbFxERoaNHj9pts163fkXWpQIDA4v9/ligPAhClITA9C7EJ1C6Sh+lDRs2VMOGDZ0ae/jwYV177bXq3LmzFi5caHfiV0mKi4vTo48+qry8PFWvfnH5PDk5Wa1bt3b40j18F8GIikJ8eiciFHBdpY9SZx0+fFi9evVS06ZN9eyzz+q3336z3WZdBb399ts1ffp0jRo1SpMmTdLOnTv14osv6oUXXjBr2nARsYiqiPD0fkQoUH5eE6XJycnas2eP9uzZo8aNG9vdZv3SqtDQUK1Zs0bjxo1T586d1aBBA02dOpXTQXkIAQlfQ3z6DiIUcD+vidLExMRS33sqSbGxsfryyy89PyEAXo0A9S1EKOB5XhOlAOAOxCYkIhQwA1EKwGcQnHCEAAUqB6IUQJVGaMIVBChQeRGlACoVIhPuRIQCVQdRCsCjiExUBOITqPqIUgAuITJhNgIUVdXpvEDl5AW4dZ+5eZX+G+OdRpQCPozARGVHgAK+gygFqjjCEt6CAAV8G1EKmISYhK8iPgE4QpTC5xGHgGcQnwBcQZTCY4g9wDcQnwDcgSgFAJSK8ATgaUQpAEAS4QnAXEQpAPgQwhO+yi871+wpoBREKQB4EaIT+EPhEC0wcR5wDlEKAFUM4Qk4xmpo1UaUAkAlQ3QCzqtKIZp1oYaqu/lrRvMu+Lt1f2YiSgGgghGdQNlVpQiFa4hSAHAzohNwL0LUNxClAFBGxCfgGUSobyJKAeASl8amEVSNAAU8iAiFRJQC8AHlDUqCFHAvIhSOEKUAKjWCEKj6iFA4gygF4HGEJeBbiFCUBVEKwCmEJYDiEKFwB6IU8BFEJQB3IEDhKUQpUEkRkQAqAyIUFYUoBUpAGALwJQQozESUwmMIOgCo3IjQinUmL0DV8wLcus+8PLfuzlREKQAAPoAARWVHlAIA4GUIUFRFRCkAAFUU8QlvQpQCAFAFEKDwdkQpAACVCPEJX0WUAgBgAuITsOdn9gQAAPB2ftm5RS6AGebNm6fY2FiFhIQoJCREcXFx+uyzz4od36tXL1ksliKX/v3728YkJiYWub1v374uz42VUgAA3ITYRGXXuHFjzZw5U61atZJhGFq8eLEGDBigbdu26Yorrigy/sMPP1Ru7h//uz5x4oQ6dOigwYMH243r27evFi5caLseGBjo8tyIUgAAXEB4oiq76aab7K4//fTTmjdvnjZu3OgwSuvVq2d3fenSpapZs2aRKA0MDFRERES55kaUAgDgAPGJqiQrK8vuemBgYKmrlfn5+Vq+fLnOnj2ruLg4px7nzTff1NChQxUcHGy3PTU1VWFhYapbt66uu+46PfXUU6pfv75Lx0CUAgB8FuGJinQur7qquflrRi/kGZKk6Ohou+1JSUmaNm2aw/t8//33iouLU3Z2tmrVqqWPPvpIbdu2LfWxNm3apJ07d+rNN9+02963b1/dcsstiomJ0d69e/XII4+oX79+SktLk7+/v9PHQpQCALwa4QlfcPDgQYWEhNiul7RK2rp1a23fvl2ZmZn64IMPNGLECH3xxRelhumbb76p9u3bq2vXrnbbhw4davtz+/btFRsbqxYtWig1NVW9e/d2+hj49D0AoEpz9Ml2PuUOX2P9NL31UlKUBgQEqGXLlurcubNmzJihDh066MUXXyxx/2fPntXSpUs1atSoUufSvHlzNWjQQHv27HHpGFgpLaNBPf6pav6uf7IMAOAawhLwrIKCAuXk5JQ4Zvny5crJydGdd95Z6v4OHTqkEydOKDIy0qV5EKUAANMRnkDFmDJlivr166cmTZro9OnTWrJkiVJTU7V69WpJ0vDhw9WoUSPNmDHD7n5vvvmmBg4cWOTDS2fOnNH06dN16623KiIiQnv37tXDDz+sli1bKiEhwaW5EaUAAI8jOoHK4dixYxo+fLiOHDmi0NBQxcbGavXq1br++uslSenp6fLzs3935+7du7VhwwatWbOmyP78/f21Y8cOLV68WKdOnVJUVJT69OmjJ5980uVzlRKlAIByIzqBquHST85fKjU1tci21q1byzAMh+Nr1KhhW2UtL6IUAFAighNARSBKAcCHEZwAKguiFAC8ELEJoKohSgGgCiE2AXgrohQATEZoAgBRCgBuR2QCcORcXoD8cwPcus/8PMefiq+KiFIAKCPiEwDchygFgEtcGpsFQQEEKAB4GFEKoEqriFgkSAHA84hSAKYg9AAAhRGlAFxGUAIA3I0oBbwcAQkAqAqIUsAkxCIAAH8gSoFSEI8AAHgeUVpGftm58vO3mD0NAAAAr+Bn9gQAAAAAVkoBAAAqQHZedfnnVXfrPvPzCty6PzOxUgoAAADTEaUAAAAwHVEKAAAA0xGlAAAAMB1RCgAAANMRpQAAADAdUQoAAADTEaUAAAAwHVEKAAAA07n0jU6nTp3SRx99pC+//FIHDhzQuXPn1LBhQ3Xq1EkJCQnq3r27p+YJAAAAL+bUSumvv/6qv/71r4qMjNRTTz2l8+fPq2PHjurdu7caN26s9evX6/rrr1fbtm21bNkyT88ZAAAAXsapldJOnTppxIgR2rJli9q2betwzPnz5/Xxxx9rzpw5OnjwoB588EG3ThQAAKAqy8n1l381l16kLlV+7gW37s9MTv1kfvjhB9WvX7/EMTVq1NCwYcM0bNgwnThxwi2TAwAAgG9w6uX70oK0vOMBAADg25z+9H2LFi30wgsvFHv70aNH5e/v75ZJAQAAwLc4HaX79u3TpEmTlJiYqNzcXIdjDMNw28QAAADgO1w6T+lHH32kdevW6eqrr9aRI0eK3G6xWNw2MQAAAPgOl6L0z3/+szZv3qxq1aqpS5cu+uabbzw1LwAAAPgQl7/RKTw8XKmpqbrxxhvVq1cvLVy40BPzAgAAgA8p09eMVqtWTQsWLNALL7yge+65R/fdd58uXKg858nKyclRx44dZbFYtH37drvbduzYoZ49eyooKEjR0dGaPXu2OZMEAACAjdNR6uj9ovfcc49SUlK0bNkyDRgwwK0TK4+HH35YUVFRRbZnZWWpT58+atq0qbZs2aJnnnlG06ZN02uvvWbCLAEAAGDldJQW98n6Hj16aPPmzW6bUHl99tlnWrNmjZ599tkit7377rvKzc3VW2+9pSuuuEJDhw7VP/7xDz3//PMmzBQAAABWLp0SqmHDhg5vi46O1ldffaX169e7bWJlcfToUY0ePVrvvPOOatasWeT2tLQ0XX311QoICLBtS0hI0O7du3Xy5EmH+8zJyVFWVpbdBQAAAO7ldJQ2bdq0xFM+BQYG6uqrr3bLpMrCMAwlJibqnnvuUZcuXRyOycjIUHh4uN026/WMjAyH95kxY4ZCQ0Ntl+joaPdOHAAA+IT8XH9dcPMlP9d7vrjIqSjt27evNm7cWOq406dPa9asWZo7d265J2Y1efJkWSyWEi8//fSTXn75ZZ0+fVpTpkxx22NL0pQpU5SZmWm7HDx40K37BwAAgFTNmUGDBw/WrbfeqtDQUN10003q0qWLoqKiFBQUpJMnT+qHH37Qhg0b9J///Ef9+/fXM88847YJPvDAA0pMTCxxTPPmzbVu3TqlpaUpMDDQ7rYuXbrojjvu0OLFixUREaGjR4/a3W69HhER4XDfgYGBRfYJAAAA93IqSkeNGqU777xTy5cv17Jly/Taa68pMzNT0sVP5bdt21YJCQnavHmzLr/8crdOsGHDhsW+l7Wwl156SU899ZTt+q+//qqEhAQtW7ZM3bp1kyTFxcXp0UcfVV5enqpXry5JSk5OVuvWrVW3bl23zhsAAADOcypKpYsrhnfeeafuvPNOSVJmZqbOnz+v+vXr2wLPTE2aNLG7XqtWLUlSixYt1LhxY0nS7bffrunTp2vUqFGaNGmSdu7cqRdffFEvvPBChc8XAAAAf3A6Si9l/eBPVRIaGqo1a9Zo3Lhx6ty5sxo0aKCpU6dqzJgxZk8NAADAp5U5Siu7Zs2aOTy3amxsrL788ksTZgQAAIDilOlrRgEAAFD1zJs3T7GxsQoJCVFISIji4uL02WeflXif5cuXq02bNgoKClL79u31n//8x+52wzA0depURUZGqkaNGoqPj9fPP//s8tyIUgAAAB/RuHFjzZw5U1u2bNG3336r6667TgMGDNCuXbscjv/66681bNgwjRo1Stu2bdPAgQM1cOBA7dy50zZm9uzZeumllzR//nx98803Cg4OVkJCgrKzs12am8Uo7vtD4VBWVpZCQ0MV3+p+VfPnVFEAAFQFF/JztPbnF5SZmamQkJAKfWxrOzR9/XH51Qxy674LzmXrwOgny3Vc9erV0zPPPKNRo0YVuW3IkCE6e/asVq5cadt25ZVXqmPHjpo/f74Mw1BUVJQeeOABPfjgg5Iufhg+PDxcixYt0tChQ52eR5lWSk+dOqU33nhDU6ZM0e+//y5J2rp1qw4fPlyW3QEAAKAcLv1K9JycnFLvk5+fr6VLl+rs2bOKi4tzOCYtLU3x8fF22xISEpSWlibp4tfQZ2Rk2I0JDQ1Vt27dbGOc5fIHnXbs2KH4+HiFhoZq//79Gj16tOrVq6cPP/xQ6enpevvtt13dJQAAgNcruOAv5bn3a0ELLlzc36Vfg56UlKRp06Y5vM/333+vuLg4ZWdnq1atWvroo4/Utm1bh2OL+4p269ezW/9b0hhnuRylEydOVGJiombPnq3atWvbtt9www26/fbbXd0dAAAAyungwYN2L9+X9G2UrVu31vbt25WZmakPPvhAI0aM0BdffFFsmFYUl1++37x5s/72t78V2d6oUSOXixgAAADlZ/00vfVSUpQGBASoZcuW6ty5s2bMmKEOHTroxRdfdDi2uK9ot349u/W/JY1xlstRGhgYqKysrCLb//e//zn1daAAAACoPAoKCop9D2pcXJxSUlLstiUnJ9vegxoTE6OIiAi7MVlZWfrmm2+KfZ9qcVx++f7mm2/WE088offff1+SZLFYlJ6erkmTJunWW291dXcAAACoIFOmTFG/fv3UpEkTnT59WkuWLFFqaqpWr14tSRo+fLgaNWqkGTNmSJLuu+8+XXPNNXruuefUv39/LV26VN9++61ee+01SRc7cMKECXrqqafUqlUrxcTE6PHHH1dUVJQGDhzo0txcjtLnnntOf/nLXxQWFqbz58/rmmuuUUZGhuLi4vT000+7ujsAAABUkGPHjmn48OE6cuSIQkNDFRsbq9WrV+v666+XJKWnp8vP748X0rt3764lS5boscce0yOPPKJWrVrp448/Vrt27WxjHn74YZ09e1ZjxozRqVOn1KNHD33++ecKCnLt9FdlPk/pV199pe+++05nzpzRn/70pyKnC/BWnKcUAICqpzKcpzR63jT51XDzeUrPZ+vg2GmmHJe7ubxSanXVVVfpqquucudcAAAA4KNc/qDTP/7xD7300ktFtr/yyiuaMGGCO+YEAAAAH+NylK5YscLhCmn37t31wQcfuGVSAAAA5Zad88fFiW84grlcfvn+xIkTCg0NLbI9JCREx48fd8ukAAAAnJJNbHoLl1dKW7Zsqc8//7zI9s8++0zNmzd3y6QAAAAk2a92OrrAa5Tpa0bHjx+v3377Tdddd50kKSUlRc8995zmzJnj7vkBAABv5ytxmeMn+bm8Hlj6Pr2Ey1F69913KycnR08//bSefPJJSVKzZs00b948DR8+3O0TBAAAVZyvRCfKpUynhBo7dqzGjh2r3377TTVq1FCtWrXcPS8AAFBVEJ1wgzKfp1QS33UPAIAvIDpRAVx+I8LRo0d11113KSoqStWqVZO/v7/dBQAAVDF8mAiVgMsrpYmJiUpPT9fjjz+uyMhIWSwWT8wLAAC4A1GJKsLlKN2wYYO+/PJLdezY0QPTAQAATiM44UVcjtLo6GgZhuGJuQAAACuCEz7G5feUzpkzR5MnT9b+/fs9MB0AALxcae/f5H2c8FEur5QOGTJE586dU4sWLVSzZk1Vr17d7vbff//dbZMDAKDKICSBcnE5SvnWJgCAzyE4AY9zOUpHjBjhiXkAAFDxiE1UIEuunyz+7v1aUEuuD3/NaGHZ2dnKzc212xYSElKuCQEAUC6EJlAluRylZ8+e1aRJk/T+++/rxIkTRW7Pz893y8QAALAhNAGv5/Ka78MPP6x169Zp3rx5CgwM1BtvvKHp06crKipKb7/9tifmCADwNs5+Ap1PogM+w+WV0n//+996++231atXL40cOVI9e/ZUy5Yt1bRpU7377ru64447PDFPAEBlRjgCKCeXo/T3339X8+bNJV18/6j1FFA9evTQ2LFj3Ts7AEDFIzABmMDll++bN2+uffv2SZLatGmj999/X9LFFdQ6deq4dXIAgHJw9SVyXioHYCKXV0pHjhyp7777Ttdcc40mT56sm266Sa+88ory8vL0/PPPe2KOAOC7iEQAPsLlKL3//vttf46Pj9dPP/2kLVu2qGXLloqNjXXr5ADAqxCYAFAsl6P07bff1pAhQxQYGChJatq0qZo2barc3Fy9/fbbGj58uNsnCQBuU1IYBgWW/b4AgHJx+T2lI0eOVGZmZpHtp0+f1siRI90yKQAol7K+X5L3WgKAaVxeKTUMQxaLpcj2Q4cOKTQ01C2TAgAiEAB8i9NR2qlTJ1ksFlksFvXu3VvVqv1x1/z8fO3bt099+/b1yCQBVAFEJACUyC/PIj//ogt75ZLn5v2ZyOkoHThwoCRp+/btSkhIUK1atWy3BQQEqFmzZrr11lvdPkEAbkQ4AgAqKaejNCkpSZLUrFkzDR061PZBJwBlRCACAGDj8ntKr7vuOv32229q3LixJGnTpk1asmSJ2rZtqzFjxrh9goBbEYIAAFRKLkfp7bffrjFjxuiuu+5SRkaG4uPj1a5dO7377rvKyMjQ1KlTPTFPVBSiDQAAmMDlKN25c6e6du0qSXr//ffVvn17ffXVV1qzZo3uuece34nSnJwynFALAAAAjricVXl5ebb3k65du1Y333yzJKlNmzY6cuSIe2cHAAAAn+BylF5xxRWaP3++vvzySyUnJ9tOA/Xrr7+qfv36bp8gAAAAvJ/LUTpr1iwtWLBAvXr10rBhw9ShQwdJ0qeffmp7WR8AAABwhcvvKe3Vq5eOHz+urKws1a1b17Z9zJgxqlmzplsnBwAAAN/gcpRKkr+/v12QShfPXwoAAACUhVNR+qc//UkpKSmqW7eu7etGi7N161a3TQ4AAMBb+OVa5Ofn5q8FzfWxrxkdMGCA7RP31q8bBQAAANzFqSi1fsXopX8GAAAA3MHl95QahqEtW7Zo//79slgsiomJKfUlfQAAAKAkLkXp+vXrNWrUKB04cECGYUiSLUzfeustXX311R6ZJAAAALyb0+cp3bNnj2688UY1a9ZMH374oX788Uf98MMPWr58uRo3bqwbbrhBv/zyiyfnCgAAAC/l9ErpnDlzdOWVVyolJcVue5s2bTRo0CDFx8frhRde0Msvv+z2SQIAAMC7Ob1SmpqaqgkTJji8zWKxaMKECVq/fr275gUAAAAf4nSUpqenq3379sXe3q5dOx04cMAtkwIAAID7zZgxQ3/+859Vu3ZthYWFaeDAgdq9e3eJ93n99dfVs2dP1a1bV3Xr1lV8fLw2bdpkNyYxMVEWi8Xu0rdvX5fm5nSUnjlzpsSvEa1Zs6bOnTvn0oMDAACg4nzxxRcaN26cNm7cqOTkZOXl5alPnz46e/ZssfdJTU3VsGHDtH79eqWlpSk6Olp9+vTR4cOH7cb17dtXR44csV3ee+89l+bm0qfvf/jhB2VkZDi87fjx4y49MAAAACrW559/bnd90aJFCgsL05YtW4o9i9K7775rd/2NN97QihUrlJKSouHDh9u2BwYGKiIiosxzcylKe/fubTsVVGEWi0WGYXCuUgAAABNkZWXZXQ8MDLR9G2dJMjMzJUn16tVz+rHOnTunvLy8IvdJTU1VWFiY6tatq+uuu05PPfWU6tev7/R+nY7Sffv2Ob1TAAAA2LPkSn5Ov3HSOUbuxf9GR0fbbU9KStK0adNKvG9BQYEmTJigq666Su3atXP6MSdNmqSoqCjFx8fbtvXt21e33HKLYmJitHfvXj3yyCPq16+f0tLS5O/v79R+nY7Spk2bOj1ZAAAAVJyDBw8qJCTEdt2ZVdJx48Zp586d2rBhg9OPM3PmTC1dulSpqakKCgqybR86dKjtz+3bt1dsbKxatGih1NRU9e7d26l9u7nXAQAAUNFCQkLsLqVF6fjx47Vy5UqtX79ejRs3duoxnn32Wc2cOVNr1qxRbGxsiWObN2+uBg0aaM+ePU4fg0vvKQUAAEDVZRiG7r33Xn300UdKTU1VTEyMU/ebPXu2nn76aa1evVpdunQpdfyhQ4d04sQJRUZGOj03VkoBAAB8xLhx4/Svf/1LS5YsUe3atZWRkaGMjAydP3/eNmb48OGaMmWK7fqsWbP0+OOP66233lKzZs1s9zlz5oyki6cNfeihh7Rx40bt379fKSkpGjBggFq2bKmEhASn50aUAgAA+Ih58+YpMzNTvXr1UmRkpO2ybNky25j09HQdOXLE7j65ubn6y1/+YnefZ599VpLk7++vHTt26Oabb9Zll12mUaNGqXPnzvryyy+dem+rFS/fAwAA+AhHp/a8VGpqqt31/fv3lzi+Ro0aWr16dTlmdZFTUdqpUyenz0G6devWck0IAAAAvsepKB04cKCHpwEAAABf5lSUJiUleXoeAAAA8GF80AkAAACmc/mDTvn5+XrhhRf0/vvvKz09Xbm5uXa3//77726bHAAAgLfwz5P83b0cmOfm/ZnI5R/N9OnT9fzzz2vIkCHKzMzUxIkTdcstt8jPz6/U71gFAAAAHHE5St999129/vrreuCBB1StWjUNGzZMb7zxhqZOnaqNGzd6Yo4AAADwci5HaUZGhtq3by9JqlWrljIzMyVJN954o1atWuXe2QEAAMAnuByljRs3tp3lv0WLFlqzZo0kafPmzS6dtR8AAACwcjlKBw0apJSUFEnSvffeq8cff1ytWrXS8OHDdffdd7t9ggAAAPB+Ln/6fubMmbY/DxkyRE2aNFFaWppatWqlm266ya2TAwAAgG9wOUovFRcXp7i4OHfMBQAAAD7KqSj99NNP1a9fP1WvXl2ffvppiWNvvvlmt0ysrFatWqUnnnhCO3bsUFBQkK655hp9/PHHttvT09M1duxYrV+/XrVq1dKIESM0Y8YMVatW7j4HAABAGTlVYgMHDlRGRobCwsI0cODAYsdZLBbl5+e7a24uW7FihUaPHq1//vOfuu6663ThwgXt3LnTdnt+fr769++viIgIff311zpy5IiGDx+u6tWr65///Kdp8wYAAPB1TkVpQUGBwz9XJhcuXNB9992nZ555RqNGjbJtb9u2re3Pa9as0Q8//KC1a9cqPDxcHTt21JNPPqlJkyZp2rRpCggIMGPqAAAAPs+tX3Z17tw5d+7OJVu3btXhw4fl5+enTp06KTIyUv369bNbKU1LS1P79u0VHh5u25aQkKCsrCzt2rXL4X5zcnKUlZVldwEAAIB7uRylvXv31uHDh4ts/+abb9SxY0d3zKlMfvnlF0nStGnT9Nhjj2nlypWqW7euevXqpd9//13SxRP/Fw5SSbbrGRkZDvc7Y8YMhYaG2i7R0dEePAoAAOCt/HI9c/EWLkdpUFCQYmNjtWzZMkkXX86fNm2aevbsqRtuuMHtE5w8ebIsFkuJl59++sn2toJHH31Ut956qzp37qyFCxfKYrFo+fLlZX78KVOmKDMz03Y5ePCguw4NAAAA/8/lj5yvWrVKc+fO1d13361PPvlE+/fv14EDB7Ry5Ur16dPH7RN84IEHlJiYWOKY5s2b275lqvB7SAMDA9W8eXOlp6dLkiIiIrRp0ya7+x49etR2myOBgYF8UxUAAICHlek8SOPGjdOhQ4c0a9YsVatWTampqerevbu75yZJatiwoRo2bFjquM6dOyswMFC7d+9Wjx49JEl5eXnav3+/mjZtKuniOVWffvppHTt2TGFhYZKk5ORkhYSE2MUsAAAAKpbLL9+fPHlSt956q+bNm6cFCxbotttuU58+ffTqq696Yn5OCwkJ0T333KOkpCStWbNGu3fv1tixYyVJgwcPliT16dNHbdu21V133aXvvvtOq1ev1mOPPaZx48axGgoAAGAil1dK27Vrp5iYGG3btk0xMTEaPXq0li1bpr///e9atWqVVq1a5Yl5OuWZZ55RtWrVdNddd+n8+fPq1q2b1q1bp7p160qS/P39tXLlSo0dO1ZxcXEKDg7WiBEj9MQTT5g2ZwAAAJQhSu+55x49+uij8vP7Y5F1yJAhuuqqqzRy5Ei3Ts5V1atX17PPPqtnn3222DFNmzbVf/7znwqcFQAAAErjcpQ+/vjjDrc3btxYzz//fLknBAAAAN9T7pPnnz59Wq+99pq6du1q6nlKAQAAUHWVOUr/+9//asSIEYqMjNSzzz6r6667Ths3bnTn3AAAAOAjXHr5PiMjQ4sWLdKbb76prKws3XbbbcrJydHHH3/MKZUAAABQZk6vlN50001q3bq1duzYoTlz5ujXX3/Vyy+/7Mm5AQAAeA3/XM9cvIXTK6WfffaZ/vGPf2js2LFq1aqVJ+cEAAAAH+P0SumGDRt0+vRpde7cWd26ddMrr7yi48ePe3JuAAAA8BFOR+mVV16p119/XUeOHNHf/vY3LV26VFFRUSooKFBycrJOnz7tyXkCAADAi7n86fvg4GDdfffd2rBhg77//ns98MADmjlzpsLCwnTzzTd7Yo4AAADwcuU6T2nr1q01e/ZsHTp0SO+995675gQAAAAfU+6T50sXv1N+4MCB+vTTT92xOwAAAPgYt0QpAAAAUB5EKQAAAExHlAIAAMB0RCkAAABMR5QCAADAdE5/zSgAAADKzi9P8rO4d59Gnnv3ZyZWSgEAAGA6ohQAAACmI0oBAABgOqIUAAAApiNKAQAAYDqiFAAAAKYjSgEAAGA6ohQAAMCH/Pe//9VNN92kqKgoWSwWffzxxyWOT01NlcViKXLJyMiwGzd37lw1a9ZMQUFB6tatmzZt2uTSvIhSAAAAH3L27Fl16NBBc+fOdel+u3fv1pEjR2yXsLAw223Lli3TxIkTlZSUpK1bt6pDhw5KSEjQsWPHnN4/3+gEAADgQ/r166d+/fq5fL+wsDDVqVPH4W3PP/+8Ro8erZEjR0qS5s+fr1WrVumtt97S5MmTndo/K6UAAABVXFZWlt0lJyfH7Y/RsWNHRUZG6vrrr9dXX31l256bm6stW7YoPj7ets3Pz0/x8fFKS0tzev9EKQAAQAXwzzE8cpGk6OhohYaG2i4zZsxw27wjIyM1f/58rVixQitWrFB0dLR69eqlrVu3SpKOHz+u/Px8hYeH290vPDy8yPtOS8LL9wAAAFXcwYMHFRISYrseGBjotn23bt1arVu3tl3v3r279u7dqxdeeEHvvPOO2x6HKAUAAKjiQkJC7KLU07p27aoNGzZIkho0aCB/f38dPXrUbszRo0cVERHh9D55+R4AAAAu2b59uyIjIyVJAQEB6ty5s1JSUmy3FxQUKCUlRXFxcU7vk5VSAAAAH3LmzBnt2bPHdn3fvn3avn276tWrpyZNmmjKlCk6fPiw3n77bUnSnDlzFBMToyuuuELZ2dl64403tG7dOq1Zs8a2j4kTJ2rEiBHq0qWLunbtqjlz5ujs2bO2T+M7gygFAADwId9++62uvfZa2/WJEydKkkaMGKFFixbpyJEjSk9Pt92em5urBx54QIcPH1bNmjUVGxurtWvX2u1jyJAh+u233zR16lRlZGSoY8eO+vzzz4t8+KkkFsMwDDccn8/IyspSaGio4pv8XdX83PcmYgAA4DkXCnK0Nv1VZWZmVuh7L6U/2qHjnU/LPyDIrfvOz83W9n89aspxuRvvKQUAAIDpiFIAAACYjigFAACA6YhSAAAAmI5P3wMAAFQA/1zJ3907zXX3Ds3DSikAAABMR5QCAADAdEQpAAAATEeUAgAAwHREKQAAAExHlAIAAMB0RCkAAABMR5QCAADAdEQpAAAATEeUAgAAwHREKQAAAExXzewJAAAA+AK/XEP+huHWfRp57t2fmVgpBQAAgOmIUgAAAJiOKAUAAIDpiFIAAACYjigFAACA6YhSAAAAmI4oBQAAgOmIUgAAAJiOKAUAAIDpiFIAAACYjq8ZBQAAqADVcgpUraDAvTvNc/P+TMRKKQAAAExHlAIAAMB0RCkAAABMR5QCAADAdEQpAAAATEeUAgAAwHREKQAAAExHlAIAAMB0RCkAAABMR5QCAADAdEQpAAAATFfN7AkAAAD4Ar/cAvkVuPe76v0uuHd/ZmKlFAAAAKYjSgEAAGA6ohQAAACmI0oBAABgOqIUAAAApiNKAQAAYDqvitL//e9/GjBggBo0aKCQkBD16NFD69evtxuTnp6u/v37q2bNmgoLC9NDDz2kCxcumDRjAACAijd37lw1a9ZMQUFB6tatmzZt2lTs2F69eslisRS59O/f3zYmMTGxyO19+/Z1aU5eFaU33nijLly4oHXr1mnLli3q0KGDbrzxRmVkZEiS8vPz1b9/f+Xm5urrr7/W4sWLtWjRIk2dOtXkmQMAAFSMZcuWaeLEiUpKStLWrVvVoUMHJSQk6NixYw7Hf/jhhzpy5IjtsnPnTvn7+2vw4MF24/r27Ws37r333nNpXl4TpcePH9fPP/+syZMnKzY2Vq1atdLMmTN17tw57dy5U5K0Zs0a/fDDD/rXv/6ljh07ql+/fnryySc1d+5c5ebmmnwEAAAAnvf8889r9OjRGjlypNq2bav58+erZs2aeuuttxyOr1evniIiImyX5ORk1axZs0iUBgYG2o2rW7euS/PymiitX7++Wrdurbfffltnz57VhQsXtGDBAoWFhalz586SpLS0NLVv317h4eG2+yUkJCgrK0u7du1yuN+cnBxlZWXZXQAAACqTS1slJyfH4bjc3Fxt2bJF8fHxtm1+fn6Kj49XWlqaU4/15ptvaujQoQoODrbbnpqaqrCwMLVu3Vpjx47ViRMnXDoGr/maUYvForVr12rgwIGqXbu2/Pz8FBYWps8//9xW6hkZGXZBKsl23foS/6VmzJih6dOne3byAADA6/nnFMg/371fC2r8/9eMRkdH221PSkrStGnTiow/fvy48vPzHfbQTz/9VOrjbdq0STt37tSbb75pt71v37665ZZbFBMTo7179+qRRx5Rv379lJaWJn9/f6eOpdJH6eTJkzVr1qwSx/z4449q3bq1xo0bp7CwMH355ZeqUaOG3njjDd10003avHmzIiMjy/T4U6ZM0cSJE23Xs7KyijzxAAAAZjp48KBCQkJs1wMDAz3yOG+++abat2+vrl272m0fOnSo7c/t27dXbGysWrRoodTUVPXu3dupfVf6KH3ggQeUmJhY4pjmzZtr3bp1WrlypU6ePGl7Ul599VUlJydr8eLFmjx5siIiIop8uuzo0aOSpIiICIf7DgwM9NgTCwAA4A4hISF2UVqcBg0ayN/f39Y/VkePHi22hazOnj2rpUuX6oknnij1cZo3b64GDRpoz5493hOlDRs2VMOGDUsdd+7cOUkX3xdRmJ+fnwoKLi5tx8XF6emnn9axY8cUFhYmSUpOTlZISIjatm3r5pkDAABULgEBAercubNSUlI0cOBASVJBQYFSUlI0fvz4Eu+7fPly5eTk6M477yz1cQ4dOqQTJ0649Eq113zQKS4uTnXr1tWIESP03Xff6X//+58eeugh7du3z3YerT59+qht27a666679N1332n16tV67LHHNG7cOFZDAQCAT5g4caJef/11LV68WD/++KPGjh2rs2fPauTIkZKk4cOHa8qUKUXu9+abb2rgwIGqX7++3fYzZ87ooYce0saNG7V//36lpKRowIABatmypRISEpyeV6VfKXVWgwYN9Pnnn+vRRx/Vddddp7y8PF1xxRX65JNP1KFDB0mSv7+/Vq5cqbFjxyouLk7BwcEaMWKEU8vQAAAA3mDIkCH67bffNHXqVGVkZKhjx476/PPPbR9+Sk9PL/LK8+7du7VhwwatWbOmyP78/f21Y8cOLV68WKdOnVJUVJT69OmjJ5980qVFP4thGEb5Ds23ZGVlKTQ0VPFN/q5qfqyuAgBQFVwoyNHa9FeVmZnp1Hsv3cnaDlf3mKpq1YLcuu8LF7L13w1PmHJc7uY1L98DAACg6iJKAQAAYDqiFAAAAKYjSgEAAGA6ohQAAACm85pTQgEAAFRm/tkX5F/tglv3aVxw7/7MxEopAAAATEeUAgAAwHREKQAAAExHlAIAAMB0RCkAAABMR5QCAADAdEQpAAAATEeUAgAAwHREKQAAAExHlAIAAMB0fM0oAABABbDk5Mvi5q8FteTnu3V/ZmKlFAAAAKYjSgEAAGA6ohQAAACmI0oBAABgOqIUAAAApiNKAQAAYDqiFAAAAKYjSgEAAGA6ohQAAACmI0oBAABgOqIUAAAApqtm9gQAAAB8gV92rvz8Le7dZ36uW/dnJlZKAQAAYDqiFAAAAKYjSgEAAGA6ohQAAACmI0oBAABgOqIUAAAApiNKAQAAYDqiFAAAAKYjSgEAAGA6ohQAAACmI0oBAABgumpmTwAAAMAn5OS4fzmwIMfNOzQPK6UAAAAwHVEKAAAA0xGlAAAAMB1RCgAAANMRpQAAADAdUQoAAADTEaUAAAA+Zu7cuWrWrJmCgoLUrVs3bdq0qcTxy5cvV5s2bRQUFKT27dvrP//5j93thmFo6tSpioyMVI0aNRQfH6+ff/7ZpTkRpQAAAD5k2bJlmjhxopKSkrR161Z16NBBCQkJOnbsmMPxX3/9tYYNG6ZRo0Zp27ZtGjhwoAYOHKidO3faxsyePVsvvfSS5s+fr2+++UbBwcFKSEhQdna20/OyGIZhlPvofEhWVpZCQ0MV3+TvquYXaPZ0AACAEy4U5Ght+qvKzMxUSEhIhT62J9uhLMfVrVs3/fnPf9Yrr7wiSSooKFB0dLTuvfdeTZ48ucj4IUOG6OzZs1q5cqVt25VXXqmOHTtq/vz5MgxDUVFReuCBB/Tggw9KkjIzMxUeHq5FixZp6NChTs2LlVIAAIAqLisry+6Sk+P4m55yc3O1ZcsWxcfH27b5+fkpPj5eaWlpDu+TlpZmN16SEhISbOP37dunjIwMuzGhoaHq1q1bsft0hCgFAACoCNm5UnaOmy+5kqTo6GiFhobaLjNmzHA4hePHjys/P1/h4eF228PDw5WRkeHwPhkZGSWOt/7XlX06Us3pkQAAAKiUDh48aPfyfWBg1XuLISulAAAAVVxISIjdpbgobdCggfz9/XX06FG77UePHlVERITD+0RERJQ43vpfV/bpCFEKAADgIwICAtS5c2elpKTYthUUFCglJUVxcXEO7xMXF2c3XpKSk5Nt42NiYhQREWE3JisrS998802x+3SEl+8BAAB8yMSJEzVixAh16dJFXbt21Zw5c3T27FmNHDlSkjR8+HA1atTI9r7U++67T9dcc42ee+459e/fX0uXLtW3336r1157TZJksVg0YcIEPfXUU2rVqpViYmL0+OOPKyoqSgMHDnR6XkQpAACADxkyZIh+++03TZ06VRkZGerYsaM+//xz2weV0tPT5ef3x4vp3bt315IlS/TYY4/pkUceUatWrfTxxx+rXbt2tjEPP/ywzp49qzFjxujUqVPq0aOHPv/8cwUFBTk9L85T6iLOUwoAQNVTKc5TGvZXVfMLcOu+LxTkau2xN0w5LnfjPaUAAAAwHVEKAAAA0xGlAAAAMB1RCgAAANMRpQAAADAdp4QCAACoAMb58zIs+e7dp5Hr1v2ZiZVSAAAAmI4oBQAAgOmIUgAAAJiOKAUAAIDpiFIAAACYjigFAACA6YhSAAAAmI4oBQAAgOmIUgAAAJiOKAUAAIDp+JpRAACAClBwPkcFlgL37tPIc+v+zMRKKQAAAExHlAIAAMB0RCkAAABMR5QCAADAdEQpAAAATEeUAgAAwHREKQAAAExXZaL06aefVvfu3VWzZk3VqVPH4Zj09HT1799fNWvWVFhYmB566CFduHDBbkxqaqr+9Kc/KTAwUC1bttSiRYs8P3kAAACUqMpEaW5urgYPHqyxY8c6vD0/P1/9+/dXbm6uvv76ay1evFiLFi3S1KlTbWP27dun/v3769prr9X27ds1YcIE/fWvf9Xq1asr6jAAAADgQJX5Rqfp06dLUrErm2vWrNEPP/ygtWvXKjw8XB07dtSTTz6pSZMmadq0aQoICND8+fMVExOj5557TpJ0+eWXa8OGDXrhhReUkJBQUYcCAACAS1SZKC1NWlqa2rdvr/DwcNu2hIQEjR07Vrt27VKnTp2Ulpam+Ph4u/slJCRowoQJxe43JydHOTk5tuuZmZmSpAsFue49AAAA4DHWf7cNwzBvDsqT3PzwF+Q9XzPqNVGakZFhF6SSbNczMjJKHJOVlaXz58+rRo0aRfY7Y8YM2yptYamH3nDX1AEAQAU5ceKEQkNDK/QxAwICFBERoS8zPvHI/iMiIhQQEOCRfVckU6N08uTJmjVrVoljfvzxR7Vp06aCZlTUlClTNHHiRNv1U6dOqWnTpkpPT6/w/1GbKSsrS9HR0Tp48KBCQkLMnk6F4bg5bl/AcXPcviAzM1NNmjRRvXr1Kvyxg4KCtG/fPuXmeuZV1oCAAAUFBXlk3xXJ1Ch94IEHlJiYWOKY5s2bO7WviIgIbdq0yW7b0aNHbbdZ/2vdVnhMSEiIw1VSSQoMDFRgYGCR7aGhoT71l9kqJCSE4/YhHLdv4bh9i68et5+fOZ/xDgoK8opw9CRTo7Rhw4Zq2LChW/YVFxenp59+WseOHVNYWJgkKTk5WSEhIWrbtq1tzH/+8x+7+yUnJysuLs4tcwAAAEDZVJlTQqWnp2v79u1KT09Xfn6+tm/fru3bt+vMmTOSpD59+qht27a666679N1332n16tV67LHHNG7cONtK5z333KNffvlFDz/8sH766Se9+uqrev/993X//febeWgAAAA+r8p80Gnq1KlavHix7XqnTp0kSevXr1evXr3k7++vlStXauzYsYqLi1NwcLBGjBihJ554wnafmJgYrVq1Svfff79efPFFNW7cWG+88YZLp4MKDAxUUlKSw5f0vRnHzXH7Ao6b4/YFHLdvHXdVYjHMPDcCAAAAoCr08j0AAAC8F1EKAAAA0xGlAAAAMB1RCgAAANMRpQ7MnTtXzZo1U1BQkLp161bkpPyXWr58udq0aaOgoCC1b9++yLlQK7sZM2boz3/+s2rXrq2wsDANHDhQu3fvLvE+ixYtksVisbtUtZMCT5s2rcgxlPbtYVX9uZakZs2aFTlui8WicePGORxfVZ/r//73v7rpppsUFRUli8Wijz/+2O52wzA0depURUZGqkaNGoqPj9fPP/9c6n5d/f1Q0Uo67ry8PE2aNEnt27dXcHCwoqKiNHz4cP36668l7rMsf1cqWmnPd2JiYpFj6Nu3b6n7rcrPtySHf9ctFoueeeaZYvdZFZ5vZ/7dys7O1rhx41S/fn3VqlVLt956a5Ev0LlUWX8vwD2I0kssW7ZMEydOVFJSkrZu3aoOHTooISFBx44dczj+66+/1rBhwzRq1Cht27ZNAwcO1MCBA7Vz584KnnnZffHFFxo3bpw2btyo5ORk5eXlqU+fPjp79myJ9wsJCdGRI0dslwMHDlTQjN3niiuusDuGDRs2FDvWG55rSdq8ebPdMScnJ0uSBg8eXOx9quJzffbsWXXo0EFz5851ePvs2bP10ksvaf78+frmm28UHByshIQEZWdnF7tPV38/mKGk4z537py2bt2qxx9/XFu3btWHH36o3bt36+abby51v678XTFDac+3JPXt29fuGN57770S91nVn29Jdsd75MgRvfXWW7JYLLr11ltL3G9lf76d+Xfr/vvv17///W8tX75cX3zxhX799VfdcsstJe63LL8X4EYG7HTt2tUYN26c7Xp+fr4RFRVlzJgxw+H42267zejfv7/dtm7duhl/+9vfPDpPTzp27Jghyfjiiy+KHbNw4UIjNDS04iblAUlJSUaHDh2cHu+Nz7VhGMZ9991ntGjRwigoKHB4uzc815KMjz76yHa9oKDAiIiIMJ555hnbtlOnThmBgYHGe++9V+x+XP39YLZLj9uRTZs2GZKMAwcOFDvG1b8rZnN03CNGjDAGDBjg0n688fkeMGCAcd1115U4pqo934ZR9N+tU6dOGdWrVzeWL19uG/Pjjz8akoy0tDSH+yjr7wW4DyulheTm5mrLli2Kj4+3bfPz81N8fLzS0tIc3ictLc1uvCQlJCQUO74qyMzMlCTVq1evxHFnzpxR06ZNFR0drQEDBmjXrl0VMT23+vnnnxUVFaXmzZvrjjvuUHp6erFjvfG5zs3N1b/+9S/dfffdslgsxY7zhue6sH379ikjI8Pu+QwNDVW3bt2KfT7L8vuhKsjMzJTFYlGdOnVKHOfK35XKKjU1VWFhYWrdurXGjh2rEydOFDvWG5/vo0ePatWqVRo1alSpY6va833pv1tbtmxRXl6e3fPXpk0bNWnSpNjnryy/F+BeRGkhx48fV35+vsLDw+22h4eHKyMjw+F9MjIyXBpf2RUUFGjChAm66qqr1K5du2LHtW7dWm+99ZY++eQT/etf/1JBQYG6d++uQ4cOVeBsy6dbt25atGiRPv/8c82bN0/79u1Tz549dfr0aYfjve25lqSPP/5Yp06dUmJiYrFjvOG5vpT1OXPl+SzL74fKLjs7W5MmTdKwYcMUEhJS7DhX/65URn379tXbb7+tlJQUzZo1S1988YX69eun/Px8h+O98flevHixateuXepL2FXt+Xb071ZGRoYCAgKK/J+t0v49t45x9j5wryrzNaOoGOPGjdPOnTtLff9QXFyc4uLibNe7d++uyy+/XAsWLNCTTz7p6Wm6Rb9+/Wx/jo2NVbdu3dS0aVO9//77Tq0keIM333xT/fr1U1RUVLFjvOG5RlF5eXm67bbbZBiG5s2bV+JYb/i7MnToUNuf27dvr9jYWLVo0UKpqanq3bu3iTOrOG+99ZbuuOOOUj+oWNWeb2f/3ULlx0ppIQ0aNJC/v3+RT+cdPXpUERERDu8TERHh0vjKbPz48Vq5cqXWr1+vxo0bu3Tf6tWrq1OnTtqzZ4+HZud5derU0WWXXVbsMXjTcy1JBw4c0Nq1a/XXv/7Vpft5w3Ntfc5ceT7L8vuhsrIG6YEDB5ScnFziKqkjpf1dqQqaN2+uBg0aFHsM3vR8S9KXX36p3bt3u/z3Xarcz3dx/25FREQoNzdXp06dshtf2r/n1jHO3gfuRZQWEhAQoM6dOyslJcW2raCgQCkpKXYrRYXFxcXZjZek5OTkYsdXRoZhaPz48froo4+0bt06xcTEuLyP/Px8ff/994qMjPTADCvGmTNntHfv3mKPwRue68IWLlyosLAw9e/f36X7ecNzHRMTo4iICLvnMysrS998802xz2dZfj9URtYg/fnnn7V27VrVr1/f5X2U9nelKjh06JBOnDhR7DF4y/Nt9eabb6pz587q0KGDy/etjM93af9ude7cWdWrV7d7/nbv3q309PRin7+y/F6Am5n8QatKZ+nSpUZgYKCxaNEi44cffjDGjBlj1KlTx8jIyDAMwzDuuusuY/LkybbxX331lVGtWjXj2WefNX788UcjKSnJqF69uvH999+bdQguGzt2rBEaGmqkpqYaR44csV3OnTtnG3PpcU+fPt1YvXq1sXfvXmPLli3G0KFDjaCgIGPXrl1mHEKZPPDAA0Zqaqqxb98+46uvvjLi4+ONBg0aGMeOHTMMwzufa6v8/HyjSZMmxqRJk4rc5i3P9enTp41t27YZ27ZtMyQZzz//vLFt2zbbp8xnzpxp1KlTx/jkk0+MHTt2GAMGDDBiYmKM8+fP2/Zx3XXXGS+//LLtemm/HyqDko47NzfXuPnmm43GjRsb27dvt/v7npOTY9vHpcdd2t+VyqCk4z59+rTx4IMPGmlpaca+ffuMtWvXGn/605+MVq1aGdnZ2bZ9eNvzbZWZmWnUrFnTmDdvnsN9VMXn25l/t+655x6jSZMmxrp164xvv/3WiIuLM+Li4uz207p1a+PDDz+0XXfm9wI8hyh14OWXXzaaNGliBAQEGF27djU2btxou+2aa64xRowYYTf+/fffNy677DIjICDAuOKKK4xVq1ZV8IzLR5LDy8KFC21jLj3uCRMm2H5G4eHhxg033GBs3bq14idfDkOGDDEiIyONgIAAo1GjRsaQIUOMPXv22G73xufaavXq1YYkY/fu3UVu85bnev369Q7/d209toKCAuPxxx83wsPDjcDAQKN3795Ffh5NmzY1kpKS7LaV9PuhMijpuPft21fs3/f169fb9nHpcZf2d6UyKOm4z507Z/Tp08do2LChUb16daNp06bG6NGji8Sltz3fVgsWLDBq1KhhnDp1yuE+quLz7cy/W+fPnzf+/ve/G3Xr1jVq1qxpDBo0yDhy5EiR/RS+jzO/F+A5FsMwDM+swQIAAADO4T2lAAAAMB1RCgAAANMRpQAAADAdUQoAAADTEaUAAAAwHVEKAAAA0xGlAAAAMB1RCgAAANMRpQAqRK9evTRhwgTb9WbNmmnOnDlO33/RokWqU6eOW+bizn1VRikpKbr88suVn5/v0v2uvPJKrVixwkOzAoCSEaUAbBITE2WxWGSxWFS9enXFxMTo4YcfVnZ2ttsfa/PmzRozZoxb92mdu8ViUXBwsFq1aqXExERt2bLFbtyQIUP0v//9z6l9VsWAffjhh/XYY4/J399f0sVjsP5c/Pz8FBkZqSFDhig9Pd3ufo899pgmT56sgoICM6YNwMcRpQDs9O3bV0eOHNEvv/yiF154QQsWLFBSUpLbH6dhw4aqWbOm2/e7cOFCHTlyRLt27dLcuXN15swZdevWTW+//bZtTI0aNRQWFub2x64MNmzYoL179+rWW2+12x4SEqIjR47o8OHDWrFihXbv3q3BgwfbjenXr59Onz6tzz77rCKnDACSiFIAlwgMDFRERISio6M1cOBAxcfHKzk52Xb7iRMnNGzYMDVq1Eg1a9ZU+/bt9d5779nt4+zZsxo+fLhq1aqlyMhIPffcc0Ue59KX759//nm1b99ewcHBio6O1t///nedOXPG5fnXqVNHERERatasmfr06aMPPvhAd9xxh8aPH6+TJ09KKrr6+d133+naa69V7dq1FRISos6dO+vbb79VamqqRo4cqczMTNtK47Rp0yRJ77zzjrp06aLatWsrIiJCt99+u44dO2bbZ2pqqiwWi1JSUtSlSxfVrFlT3bt31+7du+3m++9//1t//vOfFRQUpAYNGmjQoEG223JycvTggw+qUaNGCg4OVrdu3ZSamlri8S9dulTXX3+9goKC7LZbLBZFREQoMjJS3bt316hRo7Rp0yZlZWXZxvj7++uGG27Q0qVLXfmRA4BbEKUAirVz5059/fXXCggIsG3Lzs5W586dtWrVKu3cuVNjxozRXXfdpU2bNtnGPPTQQ/riiy/0ySefaM2aNUpNTdXWrVtLfCw/Pz+99NJL2rVrlxYvXqx169bp4Ycfdstx3H///Tp9+rRdXBd2xx13qHHjxtq8ebO2bNmiyZMnq3r16urevbvmzJljW2U8cuSIHnzwQUlSXl6ennzySX333Xf6+OOPtX//fiUmJhbZ96OPPqrnnntO3377rapVq6a7777bdtuqVas0aNAg3XDDDdq2bZtSUlLUtWtX2+3jx49XWlqali5dqh07dmjw4MHq27evfv7552KP9csvv1SXLl1K/HkcO3ZMH330kfz9/W0v8Vt17dpVX375ZYn3BwCPMADg/40YMcLw9/c3goODjcDAQEOS4efnZ3zwwQcl3q9///7GAw88YBiGYZw+fdoICAgw3n//fdvtJ06cMGrUqGHcd999tm1NmzY1XnjhhWL3uXz5cqN+/fq26wsXLjRCQ0NLnIck46OPPiqy/fz584YkY9asWQ73Vbt2bWPRokUO9+nM4xqGYWzevNmQZJw+fdowDMNYv369IclYu3atbcyqVasMScb58+cNwzCMuLg444477nC4vwMHDhj+/v7G4cOH7bb37t3bmDJlSrHzCA0NNd5+++0ixyDJCA4ONmrWrGlIMiQZ//jHP4rc/5NPPjH8/PyM/Pz8Uo8ZANypmmk1DKBSuvbaazVv3jydPXtWL7zwgqpVq2b3/sT8/Hz985//1Pvvv6/Dhw8rNzdXOTk5tveH7t27V7m5uerWrZvtPvXq1VPr1q1LfNy1a9dqxowZ+umnn5SVlaULFy4oOztb586dK/d7Tw3DkHTxJWxHJk6cqL/+9a965513FB8fr8GDB6tFixYl7nPLli2aNm2avvvuO508edL24aD09HS1bdvWNi42Ntb258jISEkXVyqbNGmi7du3a/To0Q73//333ys/P1+XXXaZ3facnBzVr1+/2HmdP3++yEv3klS7dm1t3bpVeXl5+uyzz/Tuu+/q6aefLjKuRo0aKigoUE5OjmrUqFHCTwAA3IuX7wHYCQ4OVsuWLdWhQwe99dZb+uabb/Tmm2/abn/mmWf04osvatKkSVq/fr22b9+uhIQE5ebmlvkx9+/frxtvvFGxsbFasWKFtmzZorlz50pSufZr9eOPP0qSYmJiHN4+bdo07dq1S/3799e6devUtm1bffTRR8Xu7+zZs0pISFBISIjeffddbd682Tb+0vlWr17d9mdrFFsDtqToO3PmjPz9/bVlyxZt377ddvnxxx/14osvFnu/Bg0a2N47W5ifn59atmypyy+/XBMnTtSVV16psWPHFhn3+++/Kzg4mCAFUOGIUgDF8vPz0yOPPKLHHntM58+flyR99dVXGjBggO6880516NBBzZs3tzu9UosWLVS9enV98803tm0nT54s8RRMW7ZsUUFBgZ577jldeeWVuuyyy/Trr7+67Tis7wuNj48vdsxll12m+++/X2vWrNEtt9yihQsXSpICAgKKnO/zp59+0okTJzRz5kz17NlTbdq0sfuQk7NiY2OVkpLi8LZOnTopPz9fx44dU8uWLe0uERERxe6zU6dO+uGHH0p97MmTJ2vZsmVF3uu7c+dOderUybUDAQA3IEoBlGjw4MHy9/e3rVy2atVKycnJ+vrrr/Xjjz/qb3/7m44ePWobX6tWLY0aNUoPPfSQ1q1bp507dyoxMVF+fsX/umnZsqXy8vL08ssv65dfftE777yj+fPnl2m+p06dUkZGhg4cOKDk5GT95S9/0ZIlSzRv3jyH5xs9f/68xo8fr9TUVB04cEBfffWVNm/erMsvv1zSxbMEnDlzRikpKTp+/LjOnTunJk2aKCAgwDbfTz/9VE8++aTLc01KStJ7772npKQk/fjjj/r+++81a9YsSRcj+Y477tDw4cP14Ycfat++fdq0aZNmzJihVatWFbvPhIQEbdiwodTHjo6O1qBBgzR16lS77V9++aX69Onj8rEAQLmZ/aZWAJXHiBEjjAEDBhTZPmPGDKNhw4bGmTNnjBMnThgDBgwwatWqZYSFhRmPPfaYMXz4cLv7nT592rjzzjuNmjVrGuHh4cbs2bONa665psQPOj3//PNGZGSkUaNGDSMhIcF4++23DUnGyZMnDcNw/oNO1ktQUJDRokULY8SIEcaWLVvsxhXeV05OjjF06FAjOjraCAgIMKKioozx48fbPoxkGIZxzz33GPXr1zckGUlJSYZhGMaSJUuMZs2aGYGBgUZcXJzx6aefGpKMbdu2GYbxxwedrPM3DMPYtm2bIcnYt2+fbduKFSuMjh07GgEBAUaDBg2MW265xXZbbm6uMXXqVKNZs2ZG9erVjcjISGPQoEHGjh07iv0ZnDhxwggKCjJ++uknh8dbWFpamiHJ+OabbwzDMIxDhw4Z1atXNw4ePFjSjxkAPMJiGP//CQAAgFd46KGHlJWVpQULFrh0v0mTJunkyZN67bXXPDQzACgeL98DgJd59NFH1bRpU5e/LjQsLKxMb0MAAHdgpRQAAACmY6UUAAAApiNKAQAAYDqiFAAAAKYjSgEAAGA6ohQAAACmI0oBAABgOqIUAAAApiNKAQAAYDqiFAAAAKb7PyozSx07XMplAAAAAElFTkSuQmCC",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "(np.int64(2427), np.int64(2500))"
+                        ]
+                    },
+                    "execution_count": 17,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "potential_comparison(\"config6\", phi, 0.01, 0.03, R, Z, omega, nanregions)"
+            ]
+        },
+        {
+            "cell_type": "raw",
+            "metadata": {
+                "vscode": {
+                    "languageId": "raw"
+                }
+            },
+            "source": [
+                "# Runs everything before the comparison with the condensed.py file instead\n",
+                "\n",
+                "alpha = Problem(h, d, a, heaving, NMK, m0, rho)\n",
+                "alpha_a = alpha.a_matrix()\n",
+                "alpha_b = alpha.b_vector()\n",
+                "alpha_x = alpha.get_unknown_coeffs(alpha_a, alpha_b)\n",
+                "am0, dp0 = alpha.hydro_coeffs(alpha_x, \"umerc\")\n",
+                "am1, dp1 = alpha.hydro_coeffs(alpha_x, \"capytaine\")\n",
+                "am2, dp2 = alpha.hydro_coeffs(alpha_x, \"nondimensional\")\n",
+                "print(\"real (added mass):\", am0)\n",
+                "print(\"imag (damping):\", dp0)\n",
+                "print(\"real/(h^3):\", am1)\n",
+                "print(\"imag/(h^3):\", dp1)\n",
+                "print(\"nondimensional, real:\", am2)\n",
+                "print(\"nondimensional, imag (no omega factor):\", dp2)\n",
+                "print(\"Excitation Phase:\", alpha.excitation_phase(alpha_x))\n",
+                "print(\"Excitation Force:\", alpha.excitation_force(dp0))\n",
+                "\n",
+                "alpha_cs = alpha.reformat_coeffs(alpha_x)\n",
+                "alpha.plot_potentials(alpha_cs)\n",
+                "alpha.plot_velocities(alpha_cs)"
+            ]
+        },
+        {
+            "cell_type": "raw",
+            "metadata": {
+                "vscode": {
+                    "languageId": "raw"
+                }
+            },
+            "source": [
+                "# Run Test File\n",
+                "\n",
+                "# %run test/multi_test.py"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 18,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "R, Z = make_R_Z(False, 50)\n",
+                "\n",
+                "regions = []\n",
+                "regions.append((R <= a[0]) & (Z < -d[0]))\n",
+                "for i in range(1, boundary_count):\n",
+                "    regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))\n",
+                "regions.append(R > a[-1])\n",
+                "\n",
+                "vr = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "vrH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "vrP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "\n",
+                "vz = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "vzH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+                "vzP = np.full_like(R, np.nan + np.nan*1j, dtype=complex)\n",
+                "\n",
+                "for n in range(NMK[0]):\n",
+                "    temp_vrH = v_r_inner_vec(n, R[regions[0]], Z[regions[0]])\n",
+                "    temp_vzH = v_z_inner_vec(n, R[regions[0]], Z[regions[0]])\n",
+                "    if n == 0:\n",
+                "        vrH[regions[0]] = temp_vrH\n",
+                "        vzH[regions[0]] = temp_vzH\n",
+                "    else:\n",
+                "        vrH[regions[0]] = vrH[regions[0]] + temp_vrH\n",
+                "        vzH[regions[0]] = vzH[regions[0]] + temp_vzH\n",
+                "\n",
+                "for i in range(1, boundary_count):\n",
+                "    for m in range(NMK[i]):\n",
+                "        temp_vrH = v_r_m_i_vec(i, m, R[regions[i]], Z[regions[i]])\n",
+                "        temp_vzH = v_z_m_i_vec(i, m, R[regions[i]], Z[regions[i]])\n",
+                "        if m == 0:\n",
+                "            vrH[regions[i]] = temp_vrH\n",
+                "            vzH[regions[i]] = temp_vzH\n",
+                "        else:\n",
+                "            vrH[regions[i]] = vrH[regions[i]] + temp_vrH\n",
+                "            vzH[regions[i]] = vzH[regions[i]] + temp_vzH\n",
+                "\n",
+                "for k in range(NMK[-1]):\n",
+                "    temp_vrH = v_r_e_k_vec(k, R[regions[-1]], Z[regions[-1]])\n",
+                "    temp_vzH = v_z_e_k_vec(k, R[regions[-1]], Z[regions[-1]])\n",
+                "    if k == 0:\n",
+                "        vrH[regions[-1]] = temp_vrH\n",
+                "        vzH[regions[-1]] = temp_vzH\n",
+                "    else:\n",
+                "        vrH[regions[-1]] = vrH[regions[-1]] + temp_vrH\n",
+                "        vzH[regions[-1]] = vzH[regions[-1]] + temp_vzH\n",
+                "\n",
+                "vr_p_i_vec = np.vectorize(diff_r_phi_p_i)\n",
+                "vz_p_i_vec = np.vectorize(diff_z_phi_p_i)\n",
+                "\n",
+                "vrP[regions[0]] = heaving[0] * vr_p_i_vec(d[0], R[regions[0]], Z[regions[0]])\n",
+                "vzP[regions[0]] = heaving[0] * vz_p_i_vec(d[0], R[regions[0]], Z[regions[0]])\n",
+                "for i in range(1, boundary_count):\n",
+                "    vrP[regions[i]] = heaving[i] * vr_p_i_vec(d[i], R[regions[i]], Z[regions[i]])\n",
+                "    vzP[regions[i]] = heaving[i] * vz_p_i_vec(d[i], R[regions[i]], Z[regions[i]])\n",
+                "vrP[regions[-1]] = 0\n",
+                "vzP[regions[-1]] = 0\n",
+                "\n",
+                "vr = vrH + vrP\n",
+                "vz = vzH + vzP\n",
+                "\n",
+                "def plot_velocity_stream(v_r, v_z, R, Z, title):\n",
+                "    Z = (Z[:, 0])[::-1]         # reverse y\n",
+                "    v_r = v_r[::-1, :]      # flip u vertically\n",
+                "    v_z = v_z[::-1, :]      # flip v vertically\n",
+                "    plt.figure(figsize=(8, 6))\n",
+                "    plt.streamplot(R[0, :], Z, v_r, v_z, color='magenta', density=2)\n",
+                "    plt.title(title)\n",
+                "    plt.xlabel('Radial Distance (R)')\n",
+                "    plt.ylabel('Axial Distance (Z)')\n",
+                "    plt.show()\n",
+                "\n",
+                "def plot_velocity_quiver(v_r, v_z, R, Z, title):\n",
+                "    plt.figure(figsize=(8, 6))\n",
+                "    # plt.quiver(R, Z, v_r, v_z, color = \"blue\") # Can be messy at times but captures magnitudes with arrow length well.\n",
+                "    magnitude = np.hypot(v_r, v_z)\n",
+                "    plt.quiver(R, Z, v_r, v_z, magnitude, cmap = \"viridis\", scale_units='xy', scale=1)\n",
+                "    plt.title(title)\n",
+                "    plt.xlabel('Radial Distance (R)')\n",
+                "    plt.ylabel('Axial Distance (Z)')\n",
+                "    plt.show()\n",
+                "\n",
+                "# These can't run with unevenly spaced sample points, but they're also not very important.\n",
+                "plot_velocity_stream(np.real(vr), np.real(vz), R, Z, 'Real Velocity')\n",
+                "plot_velocity_quiver(np.real(vr), np.real(vz), R, Z, 'Real Velocity')"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 19,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# Run this code block for a quick visual of our results converted to the Capytaine conventions.\n",
+                "# Capytaine should output something that is off by a factor of (-) i * omega. The following graphs do that:\n",
+                "# Figure out why vrc factor is different from phic and vrz (no negative sign) (likely due to times i vs divide by i)\n",
+                "\n",
+                "#phic = - phi * 1j * omega\n",
+                "#vrc = vr * 1j * omega\n",
+                "#vzc = - vz * 1j * omega\n",
+                "\n",
+                "#R, Z = make_R_Z(False, 50) # phi was redefined into this coordinate array earlier\n",
+                "#plot_potential(np.real(phic), R, Z, 'Capytaine Potential Real')\n",
+                "#plot_potential(np.imag(phic), R, Z, 'Total Potential Imaginary')\n",
+                "\n",
+                "#R, Z = make_R_Z(True, 50) # \"sharp\" coordinate array\n",
+                "#plot_potential(np.real(vrc), R, Z, 'Radial Velocity - Real') \n",
+                "#plot_potential(np.imag(vrc), R, Z, 'Radial Velocity - Imaginary')\n",
+                "#plot_potential(np.real(vzc), R, Z, 'Vertical Velocity - Real')\n",
+                "#plot_potential(np.imag(vzc), R, Z, 'Vertical Velocity - Imaginary')\n"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": ".venv",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.11.12"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 4
+}
diff --git a/dev/python/multi_condensed.py b/dev/python/multi_condensed.py
new file mode 100644
index 0000000..8c82034
--- /dev/null
+++ b/dev/python/multi_condensed.py
@@ -0,0 +1,969 @@
+# For notebooks that don't deal with all the inner MEEM workings, and don't want to duplicate A-matrix, b-vector code, etc.
+# Useful for just generating hydro coefficients, test cases.
+# This file may not reflect recent edits to multi_equations, multi-MEEM.
+
+import numpy as np
+from scipy.special import hankel1 as besselh
+from scipy.special import iv as besseli
+from scipy.special import kv as besselk
+from scipy.special import ive as besselie
+from scipy.special import kve as besselke
+import scipy.integrate as integrate
+import scipy.linalg as linalg
+import matplotlib.pyplot as plt
+from numpy import sqrt, cosh, cos, sinh, sin, pi, exp, inf
+from scipy.optimize import newton, minimize_scalar, root_scalar
+import scipy as sp
+import pandas as pd
+
+g = 9.81
+
+# generic helper function
+def insert_submatrix(mat, submat, row, col):
+        mat = mat.copy()  # Avoid modifying original A
+        rows, cols = submat.shape
+        mat[row:row+rows, col:col+cols] = submat
+        return mat
+
+class Problem:
+    def __init__(self, h, d, a, heaving, NMK, m0, rho, scale = None):
+        self.h = h
+        self.d = d
+        self.a = a
+        self.heaving = heaving
+        self.NMK = NMK
+        self.m0 = m0
+        self.rho = rho
+        self.scale = a if scale is None else scale
+        self.size = NMK[0] + NMK[-1] + 2 * sum(NMK[1:len(NMK) - 1])
+        self.boundary_count = len(NMK) - 1
+        self.m_k = self.m_k_array()
+
+    def angular_freq(self, m0): # omega
+        if m0 == inf:
+            return inf
+        else:
+            return sqrt(m0 * np.tanh(m0 * self.h) * g)
+        
+    def wavenumber(self, omega): # m0
+        m0_err = (lambda m0: (m0 * np.tanh(self.h * m0) - omega ** 2 / g))
+        return (root_scalar(m0_err, x0 = 2, method="newton")).root
+    
+    def lambda_ni(self, n, i): # factor used often in calculations
+        return n * pi / (self.h - self.d[i])
+    
+    #############################################
+    def m_k_entry(self, k):
+      m0 = self.m0
+      h = self.h
+      if k == 0: return m0
+      elif m0 == inf:
+          return ((k - 1/2) * pi)/h
+
+      m_k_h_err = (lambda m_k_h: (m_k_h * np.tan(m_k_h) + m0 * h * np.tanh(m0 * h)))
+      k_idx = k
+
+      # becca's version of bounds from MDOcean Matlab code
+      m_k_h_lower = pi * (k_idx - 1/2) + (pi/180)* np.finfo(float).eps * (2**(np.floor(np.log(180*(k_idx- 1/2)) / np.log(2))) + 1)
+      m_k_h_upper = pi * k_idx
+
+      m_k_initial_guess = pi * (k_idx - 1/2) + np.finfo(float).eps
+      result = root_scalar(m_k_h_err, x0=m_k_initial_guess, method="newton", bracket=[m_k_h_lower, m_k_h_upper])
+
+      m_k_val = result.root / h
+      return m_k_val
+    
+    def m_k_array(self): # create an array of m_k values for each k to avoid recomputation
+        m_k = (np.vectorize(self.m_k_entry, otypes = [float]))(list(range(self.NMK[-1])))
+        return m_k
+    
+    def N_k(self, k):
+        h, m0, m_k = self.h, self.m0, self.m_k
+        if m0 == inf: return 1/2
+        elif k == 0:
+            return 1 / 2 * (1 + sinh(2 * m0 * h) / (2 * m0 * h))
+        else:
+            return 1 / 2 * (1 + sin(2 * m_k[k] * h) / (2 * m_k[k] * h))
+    
+    #############################################
+    # vertical eigenvector coupling computation
+    def I_nm(self, n, m, i): # coupling integral for two i-type regions
+        h, d = self.h, self.d
+        dj = max(d[i], d[i+1]) # integration bounds at -h and -d
+        if n == 0 and m == 0:
+            return h - dj
+        lambda1 = self.lambda_ni(n, i)
+        lambda2 = self.lambda_ni(m, i + 1)
+        if n == 0 and m >= 1:
+            if dj == d[i+1]:
+                return 0
+            else:
+                return sqrt(2) * sin(lambda2 * (h - dj)) / lambda2
+        if n >= 1 and m == 0:
+            if dj == d[i]:
+                return 0
+            else:
+                return sqrt(2) * sin(lambda1 * (h - dj)) / lambda1
+        else:
+            frac1 = sin((lambda1 + lambda2)*(h-dj))/(lambda1 + lambda2)
+            if lambda1 == lambda2:
+                frac2 = (h - dj)
+            else:
+                frac2 = sin((lambda1 - lambda2)*(h-dj))/(lambda1 - lambda2)
+            return frac1 + frac2
+
+    def I_mk(self, m, k, i): # coupling integral for i and e-type regions
+        h, m0, m_k, N_k = self.h, self.m0, self.m_k, self.N_k
+        dj = self.d[i]
+        if m == 0 and k == 0:
+            if m0 == inf: return 0
+            elif m0 * h < 14:
+                return (1/sqrt(N_k(0))) * sinh(m0 * (h - dj)) / m0
+            else: # high m0h approximation
+                return sqrt(2 * h / m0) * (exp(- m0 * dj) - exp(m0 * dj - 2 * m0 * h))
+        if m == 0 and k >= 1:
+            return (1/sqrt(N_k(k))) * sin(m_k[k] * (h - dj)) / m_k[k]
+        if m >= 1 and k == 0:
+            if m0 == inf: return 0
+            elif m0 * h < 14:
+                num = (-1)**m * sqrt(2) * (1/sqrt(N_k(0))) * m0 * sinh(m0 * (h - dj))
+            else: # high m0h approximation
+                num = (-1)**m * 2 * sqrt(h * m0 ** 3) *(exp(- m0 * dj) - exp(m0 * dj - 2 * m0 * h))
+            denom = (m0**2 + self.lambda_ni(m, i) **2)
+            return num/denom
+        else:
+            lambda1 = self.lambda_ni(m, i)
+            if abs(m_k[k]) == lambda1:
+                return sqrt(2/N_k(k)) * (h - dj)/2
+            else:
+                frac1 = sin((m_k[k] + lambda1)*(h-dj))/(m_k[k] + lambda1)
+                frac2 = sin((m_k[k] - lambda1)*(h-dj))/(m_k[k] - lambda1)
+                return sqrt(2/N_k(k)) * (frac1 + frac2)/2
+
+    def I_nm_vals(self): # Computes all necessary I_nm
+      NMK, boundary_count = self.NMK, self.boundary_count        
+      I_nm_vals = np.zeros((max(NMK), max(NMK), boundary_count - 1), dtype = complex)
+      for bd in range(boundary_count - 1):
+          for n in range(NMK[bd]):
+              for m in range(NMK[bd + 1]):
+                  I_nm_vals[n][m][bd] = self.I_nm(n, m, bd)
+      return I_nm_vals
+    
+    def I_mk_vals(self): # Computes all necessary I_mk
+      NMK, boundary_count = self.NMK, self.boundary_count        
+      I_mk_vals = np.zeros((NMK[boundary_count - 1], NMK[boundary_count]), dtype = complex)
+      for m in range(NMK[boundary_count - 1]):
+          for k in range(NMK[boundary_count]):
+              I_mk_vals[m][k]= self.I_mk(m, k, boundary_count - 1)
+      return I_mk_vals
+
+    #############################################
+    def b_vector(self):
+        b = np.zeros(self.size, dtype=complex)
+        index = 0
+        d, boundary_count, NMK = self.d, self.boundary_count, self.NMK
+
+        # potential matching
+        for boundary in range(boundary_count):
+            if boundary == (boundary_count - 1): # i-e boundary
+                for n in range(NMK[-2]):
+                    b[index] = self.b_potential_end_entry(n, boundary)
+                    index += 1
+            else: # i-i boundary
+                for n in range(self.NMK[boundary + (d[boundary] <= d[boundary + 1])]): # iterate over eigenfunctions for smaller h-d
+                    b[index] = self.b_potential_entry(n, boundary)
+                    index += 1
+
+        # velocity matching
+        for boundary in range(boundary_count):
+            if boundary == (boundary_count - 1): # i-e boundary
+                for n in range(NMK[-1]):
+                    b[index] = self.b_velocity_end_entry(n, boundary)
+                    index += 1
+            else: # i-i boundary
+                for n in range(NMK[boundary + (d[boundary] > d[boundary + 1])]): # iterate over eigenfunctions for larger h-d
+                    b[index] = self.b_velocity_entry(n, boundary)
+                    index += 1
+        return b
+    
+    def b_potential_entry(self, n, i): # for two i-type regions
+        #(integrate over shorter fluid, use shorter fluid eigenfunction)
+        h, d, a, heaving = self.h, self.d, self.a, self.heaving
+        j = i + (d[i] <= d[i+1]) # index of shorter fluid
+        constant = (heaving[i+1] / (h - d[i+1]) - heaving[i] / (h - d[i]))
+        if n == 0:
+            return constant * 1/2 * ((h - d[j])**3/3 - (h-d[j]) * a[i]**2/2)
+        else:
+            return sqrt(2) * (h - d[j]) * constant * ((-1) ** n)/(self.lambda_ni(n, j) ** 2)
+
+    def b_potential_end_entry(self, n, i): # between i and e-type regions
+        h, d, a, heaving = self.h, self.d, self.a, self.heaving
+        constant = - heaving[i] / (h - d[i])
+        if n == 0:
+            return constant * 1/2 * ((h - d[i])**3/3 - (h-d[i]) * a[i]**2/2)
+        else:
+            return sqrt(2) * (h - d[i]) * constant * ((-1) ** n)/(self.lambda_ni(n, i) ** 2)
+
+    def b_velocity_entry(self, n, i): # for two i-type regions
+        h, d, a, heaving = self.h, self.d, self.a, self.heaving
+        if n == 0:
+            return (heaving[i+1] - heaving[i]) * (a[i]/2)
+        if d[i] > d[i + 1]: #using i+1's vertical eigenvectors
+            if heaving[i]:
+                num = - sqrt(2) * a[i] * sin(self.lambda_ni(n, i+1) * (h-d[i]))
+                denom = (2 * (h - d[i]) * self.lambda_ni(n, i+1))
+                return num/denom
+            else: return 0
+        else: #using i's vertical eigenvectors
+            if heaving[i+1]:
+                num = sqrt(2) * a[i] * sin(self.lambda_ni(n, i) * (h-d[i+1]))
+                denom = (2 * (h - d[i+1]) * self.lambda_ni(n, i))
+                return num/denom
+            else: return 0
+
+    def b_velocity_end_entry(self, k, i): # between i and e-type regions
+        h, d, a, heaving, m0, m_k = self.h, self.d, self.a, self.heaving, self.m0, self.m_k
+        constant = - heaving[i] * a[i]/(2 * (h - d[i]))
+        if k == 0:
+            if m0 == inf: return 0
+            elif m0 * h < 14:
+                return constant * (1/sqrt(self.N_k(0))) * sinh(m0 * (h - d[i])) / m0
+            else: # high m0h approximation
+                return constant * sqrt(2 * h / m0) * (exp(- m0 * d[i]) - exp(m0 * d[i] - 2 * m0 * h))
+        else:
+            return constant * (1/sqrt(self.N_k(k))) * sin(m_k[k] * (h - d[i])) / m_k[k]
+        
+    #############################################
+    # Eigenfunctions and derivatives, inner regions
+    # The "Bessel I" radial eigenfunction
+    def R_1n(self, n, r, i):
+        scale, lambda_ni = self.scale, self.lambda_ni
+        if n == 0:
+            return 0.5
+        elif n >= 1:
+            if r == scale[i]:
+                return 1
+            else:
+                return besselie(0, lambda_ni(n, i) * r) / besselie(0, lambda_ni(n, i) * scale[i]) * exp(lambda_ni(n, i) * (r - scale[i]))
+        else: 
+            raise ValueError("Invalid value for n")
+
+    # Bessel I, differentiated wrt r
+    def diff_R_1n(self, n, r, i):
+        scale, lambda_ni = self.scale, self.lambda_ni
+        if n == 0:
+            return 0
+        else:
+            top = lambda_ni(n, i) * besselie(1, lambda_ni(n, i) * r)
+            bottom = besselie(0, lambda_ni(n, i) * scale[i])
+            return top / bottom * exp(lambda_ni(n, i) * (r - scale[i]))
+
+    # The "Bessel K" radial eigenfunction
+    def R_2n(self, n, r, i):
+        scale, lambda_ni = self.scale, self.lambda_ni
+        if i == 0:
+            raise ValueError("i cannot be 0")  # this shouldn't be called for i=0, innermost region.
+        elif n == 0:
+            return 0.5 * np.log(r / self.a[i])
+        else:
+            if r == scale[i]:
+                return 1
+            else:
+                return besselke(0, lambda_ni(n, i) * r) / besselke(0, lambda_ni(n, i) * scale[i]) * exp(lambda_ni(n, i) * (scale[i] - r))
+
+    # Bessel K, differentiated wrt r
+    def diff_R_2n(self, n, r, i):
+        scale, lambda_ni = self.scale, self.lambda_ni
+        if n == 0:
+            return 1 / (2 * r)
+        else:
+            top = - lambda_ni(n, i) * besselke(1, lambda_ni(n, i) * r)
+            bottom = besselke(0, lambda_ni(n, i) * scale[i])
+            return top / bottom * exp(lambda_ni(n, i) * (scale[i] - r))
+
+    # i-region vertical eigenfunction
+    def Z_n_i(self, n, z, i):
+        if n == 0:
+            return 1
+        else:
+            return np.sqrt(2) * np.cos(self.lambda_ni(n, i) * (z + self.h))
+
+    # i-region vertical eigenfunction, differentiated wrt z
+    def diff_Z_n_i(self, n, z, i):
+        if n == 0:
+            return 0
+        else:
+            lambda0 = self.lambda_ni(n, i)
+            return - lambda0 * np.sqrt(2) * np.sin(lambda0 * (z + self.h))
+
+    #############################################
+    # Eigenfunctions and derivatives, outer region
+    # e-region radial eigenfunction
+    def Lambda_k(self, k, r):
+        m0, m_k, scale = self.m0, self.m_k, self.scale
+        if k == 0:
+            if m0 == inf:
+            # the true limit is not well-defined, but whatever value this returns will be multiplied by zero
+                return 1
+            else:
+                if r == scale[-1]:
+                    return 1
+                else:
+                    return besselh(0, m0 * r) / besselh(0, m0 * scale[-1])
+        else:
+            if r == scale[-1]:
+                return 1
+            else:
+                return besselke(0, m_k[k] * r) / besselke(0, m_k[k] * scale[-1]) * exp(m_k[k] * (scale[-1] - r))
+
+    # e-region radial eigenfunction, differentiated wrt r
+    def diff_Lambda_k(self, k, r):
+        m0, m_k, scale = self.m0, self.m_k, self.scale
+        if k == 0:
+            if m0 == inf:
+            # the true limit is not well-defined, but this makes the assigned coefficient zero
+                return 1
+            else:
+                numerator = -(m0 * besselh(1, m0 * r))
+                denominator = besselh(0, m0 * scale[-1])
+                return numerator / denominator
+        else:
+            numerator = -(m_k[k] * besselke(1, m_k[k] * r))
+            denominator = besselke(0, m_k[k] * scale[-1])
+            return numerator / denominator * exp(m_k[k] * (scale[-1] - r))
+
+    # e-region vertical eigenfunction
+    def Z_k_e(self, k, z):
+        h, m0, m_k, N_k = self.h, self.m0, self.m_k, self.N_k
+        if k == 0:
+            if m0 == inf: return 0
+            elif m0 * h < 14:
+                return 1 / sqrt(N_k(k)) * cosh(m0 * (z + h))
+            else: # high m0h approximation
+                return sqrt(2 * m0 * h) * (exp(m0 * z) + exp(-m0 * (z + 2*h)))
+        else:
+            return 1 / sqrt(N_k(k)) * cos(m_k[k] * (z + h))
+
+    # e-region vertical eigenfunction, differentiated wrt z
+    def diff_Z_k_e(self, k, z):
+        h, m0, m_k, N_k = self.h, self.m0, self.m_k, self.N_k
+        if k == 0:
+            if m0 == inf: return 0
+            elif m0 * h < 14:
+                return 1 / sqrt(N_k(k)) * m0 * sinh(m0 * (z + h))
+            else: # high m0h approximation
+                return m0 * sqrt(2 * h * m0) * (exp(m0 * z) - exp(-m0 * (z + 2*h)))
+        else:
+            return -1 / sqrt(N_k(k)) * m_k[k] * sin(m_k[k] * (z + h))
+        
+    #############################################
+    # A matrix calculations
+    def a_matrix(self):
+        d, NMK, boundary_count, size = self.d, self.NMK, self.boundary_count, self.size
+        # localize eigenfunctions
+        R_1n, R_2n, diff_R_1n, diff_R_2n = self.R_1n, self.R_2n, self.diff_R_1n, self.diff_R_2n
+        # localize block functions
+        p_diagonal_block = self.p_diagonal_block
+        p_dense_block, p_dense_block_e = self.p_dense_block, self.p_dense_block_e
+        v_diagonal_block, v_diagonal_block_e = self.v_diagonal_block, self.v_diagonal_block_e
+        v_dense_block, v_dense_block_e = self.v_dense_block, self.v_dense_block_e
+
+        # compute the coupling integrals and store values
+        I_nm_vals = self.I_nm_vals()
+        I_mk_vals = self.I_mk_vals()
+
+        rows = [] # collection of rows of blocks in A matrix, to be concatenated later
+
+        # Potential Blocks
+        col = 0
+        for bd in range(boundary_count):
+            N = NMK[bd]
+            M = NMK[bd + 1]
+            if bd == (boundary_count - 1): # i-e boundary, inherently left diagonal
+                row_height = N
+                left_block1 = p_diagonal_block(True, np.vectorize(R_1n), bd)
+                right_block = p_dense_block_e(bd, I_mk_vals)
+                if bd == 0: # one cylinder
+                    rows.append(np.concatenate((left_block1,right_block), axis = 1))
+                else:
+                    left_block2 = p_diagonal_block(True, np.vectorize(R_2n), bd)
+                    left_zeros = np.zeros((row_height, col), dtype=complex)
+                    rows.append(np.concatenate((left_zeros,left_block1,left_block2,right_block), axis = 1))
+            elif bd == 0:
+                left_diag = d[bd] > d[bd + 1] # which of the two regions gets diagonal entries
+                if left_diag:
+                    row_height = N
+                    left_block = p_diagonal_block(True, np.vectorize(R_1n), 0)
+                    right_block1 = p_dense_block(False, np.vectorize(R_1n), 0, I_nm_vals)
+                    right_block2 = p_dense_block(False, np.vectorize(R_2n), 0, I_nm_vals)
+                else:
+                    row_height = M
+                    left_block = p_dense_block(True, np.vectorize(R_1n), 0, I_nm_vals)
+                    right_block1 = p_diagonal_block(False, np.vectorize(R_1n), 0)
+                    right_block2 = p_diagonal_block(False, np.vectorize(R_2n), 0)
+                right_zeros = np.zeros((row_height, size - (col + N + 2 * M)),dtype=complex)
+                block_lst = [left_block, right_block1, right_block2, right_zeros]
+                rows.append(np.concatenate(block_lst, axis = 1))
+                col += N
+            else: # i-i boundary
+                left_diag = d[bd] > d[bd + 1] # which of the two regions gets diagonal entries
+                if left_diag:
+                    row_height = N
+                    left_block1 = p_diagonal_block(True, np.vectorize(R_1n), bd)
+                    left_block2 = p_diagonal_block(True, np.vectorize(R_2n), bd)
+                    right_block1 = p_dense_block(False, np.vectorize(R_1n),  bd, I_nm_vals)
+                    right_block2 = p_dense_block(False, np.vectorize(R_2n),  bd, I_nm_vals)
+                else:
+                    row_height = M
+                    left_block1 = p_dense_block(True, np.vectorize(R_1n),  bd, I_nm_vals)
+                    left_block2 = p_dense_block(True, np.vectorize(R_2n),  bd, I_nm_vals)
+                    right_block1 = p_diagonal_block(False, np.vectorize(R_1n),  bd)
+                    right_block2 = p_diagonal_block(False, np.vectorize(R_2n),  bd)
+                left_zeros = np.zeros((row_height, col), dtype=complex)
+                right_zeros = np.zeros((row_height, size - (col + 2 * N + 2 * M)),dtype=complex)
+                block_lst = [left_zeros, left_block1, left_block2, right_block1, right_block2, right_zeros]
+                rows.append(np.concatenate(block_lst, axis = 1))
+                col += 2 * N
+
+        # Velocity Blocks
+        col = 0
+        for bd in range(boundary_count):
+            N = NMK[bd]
+            M = NMK[bd + 1]
+            if bd == (boundary_count - 1): # i-e boundary, inherently left diagonal
+                row_height = M
+                left_block1 = v_dense_block_e(np.vectorize(diff_R_1n, otypes=[complex]), bd, I_mk_vals)
+                right_block = v_diagonal_block_e(bd)
+                if bd == 0: # one cylinder
+                    rows.append(np.concatenate((left_block1,right_block), axis = 1))
+                else:
+                    left_block2 = v_dense_block_e(np.vectorize(diff_R_2n, otypes=[complex]), bd, I_mk_vals)
+                    left_zeros = np.zeros((row_height, col), dtype=complex)
+                    rows.append(np.concatenate((left_zeros,left_block1,left_block2,right_block), axis = 1))
+            elif bd == 0:
+                left_diag = d[bd] <= d[bd + 1] # taller fluid region gets diagonal entries
+                if left_diag:
+                    row_height = N
+                    left_block = v_diagonal_block(True, np.vectorize(diff_R_1n, otypes=[complex]), 0)
+                    right_block1 = v_dense_block(False, np.vectorize(diff_R_1n, otypes=[complex]), 0, I_nm_vals)
+                    right_block2 = v_dense_block(False, np.vectorize(diff_R_2n, otypes=[complex]), 0, I_nm_vals)
+                else:
+                    row_height = M
+                    left_block = v_dense_block(True, np.vectorize(diff_R_1n, otypes=[complex]), 0, I_nm_vals)
+                    right_block1 = v_diagonal_block(False, np.vectorize(diff_R_1n, otypes=[complex]), 0)
+                    right_block2 = v_diagonal_block(False, np.vectorize(diff_R_2n, otypes=[complex]), 0)
+                right_zeros = np.zeros((row_height, size - (col + N + 2 * M)),dtype=complex)
+                block_lst = [left_block, right_block1, right_block2, right_zeros]
+                rows.append(np.concatenate(block_lst, axis = 1))
+                col += N
+            else: # i-i boundary
+                left_diag = d[bd] <= d[bd + 1] # taller fluid region gets diagonal entries
+                if left_diag:
+                    row_height = N
+                    left_block1 = v_diagonal_block(True, np.vectorize(diff_R_1n, otypes=[complex]), bd)
+                    left_block2 = v_diagonal_block(True, np.vectorize(diff_R_2n, otypes=[complex]), bd)
+                    right_block1 = v_dense_block(False, np.vectorize(diff_R_1n, otypes=[complex]),  bd, I_nm_vals)
+                    right_block2 = v_dense_block(False, np.vectorize(diff_R_2n, otypes=[complex]),  bd, I_nm_vals)
+                else:
+                    row_height = M
+                    left_block1 = v_dense_block(True, np.vectorize(diff_R_1n, otypes=[complex]),  bd, I_nm_vals)
+                    left_block2 = v_dense_block(True, np.vectorize(diff_R_2n, otypes=[complex]),  bd, I_nm_vals)
+                    right_block1 = v_diagonal_block(False, np.vectorize(diff_R_1n, otypes=[complex]),  bd)
+                    right_block2 = v_diagonal_block(False, np.vectorize(diff_R_2n, otypes=[complex]),  bd)
+                left_zeros = np.zeros((row_height, col), dtype=complex)
+                right_zeros = np.zeros((row_height, size - (col + 2* N + 2 * M)),dtype=complex)
+                block_lst = [left_zeros, left_block1, left_block2, right_block1, right_block2, right_zeros]
+                rows.append(np.concatenate(block_lst, axis = 1))
+                col += 2 * N
+
+        ## Concatenate the rows of blocks into the square A matrix
+        return np.concatenate(rows, axis = 0)
+
+    ### Blocks in the A matrix
+    ## Potential blocks
+    # arguments: diagonal block on left (T/F), vectorized radial eigenfunction, boundary number
+    def p_diagonal_block(self, left, radfunction, bd):
+        h, d, a, NMK = self.h, self.d, self.a, self.NMK
+        region = bd if left else (bd + 1)
+        sign = 1 if left else (-1)
+        return sign * (h - d[region]) * np.diag(radfunction(list(range(NMK[region])), a[bd], region))
+        
+    # arguments: dense block on left (T/F), vectorized radial eigenfunction, boundary number
+    def p_dense_block(self, left, radfunction, bd, I_nm_vals):
+        a, NMK = self.a, self.NMK
+        I_nm_array = I_nm_vals[0:NMK[bd],0:NMK[bd+1], bd]
+        if left: # determine which is region to work in and which is adjacent
+            region, adj = bd, bd + 1
+            sign = 1
+            I_nm_array = np.transpose(I_nm_array)
+        else:
+            region, adj = bd + 1, bd
+            sign = -1
+        radial_vector = radfunction(list(range(NMK[region])), a[bd], region)
+        radial_array = np.outer((np.full((NMK[adj]), 1)), radial_vector)
+        return sign * radial_array * I_nm_array
+
+    def p_dense_block_e(self, bd, I_mk_vals):
+        a, NMK = self.a, self.NMK
+        I_mk_array = I_mk_vals
+        radial_vector = (np.vectorize(self.Lambda_k, otypes = [complex]))(list(range(NMK[bd+1])), a[bd])
+        radial_array = np.outer((np.full((NMK[bd]), 1)), radial_vector)
+        return (-1) * radial_array * I_mk_array
+
+    ## Velocity blocks
+    # arguments: diagonal block on left (T/F), vectorized radial eigenfunction, boundary number
+    def v_diagonal_block(self, left, radfunction, bd):
+        h, d, a, NMK = self.h, self.d, self.a, self.NMK
+        region = bd if left else (bd + 1)
+        sign = (-1) if left else (1)
+        return sign * (h - d[region]) * np.diag(radfunction(list(range(NMK[region])), a[bd], region))
+
+    # arguments: dense block on left (T/F), vectorized radial eigenfunction, boundary number
+    def v_dense_block(self, left, radfunction, bd, I_nm_vals):
+        a, NMK = self.a, self.NMK
+        I_nm_array = I_nm_vals[0:NMK[bd],0:NMK[bd+1], bd]
+        if left: # determine which is region to work in and which is adjacent
+            region, adj = bd, bd + 1
+            sign = -1
+            I_nm_array = np.transpose(I_nm_array)
+        else:
+            region, adj = bd + 1, bd
+            sign = 1
+        radial_vector = radfunction(list(range(NMK[region])), a[bd], region)
+        radial_array = np.outer((np.full((NMK[adj]), 1)), radial_vector)
+        return sign * radial_array * I_nm_array
+
+    def v_diagonal_block_e(self, bd):
+        h, a, NMK = self.h, self.a, self.NMK
+        return h * np.diag((np.vectorize(self.diff_Lambda_k, otypes = [complex]))(list(range(NMK[bd+1])), a[bd]))
+
+    def v_dense_block_e(self, radfunction, bd, I_mk_vals): # for region adjacent to e-type region
+        I_km_array = np.transpose(I_mk_vals)
+        a, NMK = self.a, self.NMK
+        radial_vector = radfunction(list(range(NMK[bd])), a[bd], bd)
+        radial_array = np.outer((np.full((NMK[bd + 1]), 1)), radial_vector)
+        return (-1) * radial_array * I_km_array
+    
+    #############################################
+    # Solver, returns a single vector with all unknown coefficients
+    # This output is used for hydro coefficient computations
+    def get_unknown_coeffs(self, a, b):
+        return linalg.solve(a,b)
+    
+    # Input: Single vector of the coefficients
+    # Output: List of lists, where each list contains the coefficients for that region.
+    # This output is used for plotting.
+    def reformat_coeffs(self, x):
+        NMK, boundary_count = self.NMK, self.boundary_count
+        cs = []
+        row = 0
+        cs.append(x[:NMK[0]])
+        row += NMK[0]
+        for i in range(1, boundary_count):
+            cs.append(x[row: row + NMK[i] * 2])
+            row += NMK[i] * 2
+        cs.append(x[row:])
+        return cs
+
+    #############################################
+    # Hydro coefficient computation
+    def hydro_coeffs(self, x, convention):
+        heaving, NMK, boundary_count = self.heaving, self.NMK, self.boundary_count
+        # Build c-vector
+        c = np.zeros((self.size - NMK[-1]), dtype=complex)
+        col = 0
+        for n in range(NMK[0]):
+            c[n] = heaving[0] * self.int_R_1n(0, n)* self.z_n_d(n)
+        col += NMK[0]
+        for i in range(1, boundary_count):
+            M = NMK[i]
+            for m in range(M):
+                c[col + m] = heaving[i] * self.int_R_1n(i, m)* self.z_n_d(m)
+                c[col + M + m] = heaving[i] * self.int_R_2n(i, m)* self.z_n_d(m)
+            col += 2 * M
+
+        hydro_p_terms = np.zeros(boundary_count, dtype=complex)
+        for i in range(boundary_count):
+            hydro_p_terms[i] = heaving[i] * self.int_phi_p_i(i)
+
+        hydro_coef = 2 * pi * (np.dot(c, x[:-NMK[-1]]) + sum(hydro_p_terms))
+
+        if convention == "nondimensional":
+            # find maximum heaving radius
+            max_rad = self.a[0]
+            for i in range(boundary_count - 1, 0, -1):
+                if heaving[i]:
+                    max_rad = self.a[i]
+                    break
+            hydro_coef_nondim = self.h**3/(max_rad**3 * pi)*hydro_coef
+            added_mass = hydro_coef_nondim.real
+            damping = hydro_coef_nondim.imag
+        elif convention == "umerc":
+            added_mass = hydro_coef.real * self.h**3 * self.rho
+            if self.m0 == inf: damping = 0
+            else: damping = hydro_coef.imag * self.angular_freq(self.m0) * self.h**3 * self.rho
+        elif convention == "capytaine":
+            added_mass = hydro_coef.real * self.rho
+            if self.m0 == inf: damping = 0
+            else: damping = hydro_coef.imag * self.angular_freq(self.m0) * self.rho
+        else:
+            raise ValueError("Allowed conventions are nondimensional, umerc, and capytaine.")
+        return added_mass, damping
+
+    # Some intermediate integrals
+    # integrating R_1n * r in region i
+    def int_R_1n(self, i, n):
+        a, scale = self.a, self.scale
+        if n == 0:
+            inner = (0 if i == 0 else a[i-1]) # central region has inner radius 0
+            return a[i]**2/4 - inner**2/4
+        else:
+            lambda0 = self.lambda_ni(n, i)
+            bottom = lambda0 * besselie(0, lambda0 * scale[i])
+            if i == 0: inner_term = 0
+            else: inner_term = (a[i-1] * besselie(1, lambda0 * a[i-1]) / bottom) * exp(lambda0 * (a[i-1] - scale[i]))
+            outer_term = (a[i] * besselie(1, lambda0 * a[i]) / bottom) * exp(lambda0 * (a[i] - scale[i]))
+            return outer_term - inner_term
+
+    # integrating R_2n * r in region i
+    def int_R_2n(self, i, n):
+        a, scale = self.a, self.scale
+        if i == 0:
+            raise ValueError("i cannot be 0")
+        lambda0 = self.lambda_ni(n, i)
+        if n == 0:
+            return (a[i-1]**2 * (2*np.log(a[i]/a[i-1]) + 1) - a[i]**2)/8
+        else:
+            outer_term = a[i] * besselke(1, lambda0 * a[i])
+            inner_term = a[i-1] * besselke(1, lambda0 * a[i-1])
+            bottom = - lambda0 * besselke(0, lambda0 * scale[i])
+            return (outer_term / bottom) * exp(lambda0 * (scale[i] - a[i])) - (inner_term/bottom)* exp(lambda0 * (scale[i] - a[i-1]))
+
+    # integrating phi_p_i * d_phi_p_i/dz * r *d_r at z=d[i]
+    # where phi_p_i is the particular solution in a heaving region i
+    def int_phi_p_i(self, i):
+        h, d, a = self.h, self.d, self.a
+        denom = 16 * (h - d[i])
+        if i == 0:
+            num = a[i]**2*(4*(h-d[i])**2-a[i]**2)
+        else:
+            num = (a[i]**2*(4*(h-d[i])**2-a[i]**2) - a[i-1]**2*(4*(h-d[i])**2-a[i-1]**2))
+        return num/denom
+
+    # evaluate an interior region vertical eigenfunction at its top boundary
+    def z_n_d(self, n):
+        if n ==0:
+            return 1
+        else:
+            return sqrt(2)*(-1)**n
+
+    #############################################
+    def excitation_phase(self, x):
+        # from x, access the first coefficient of the e-region expansion
+        return -(pi/2) + np.angle(x[-self.NMK[-1]]) - np.angle(besselh(0, self.m0 * self.scale[-1]))
+    
+    def excitation_force(self, damping):
+        # Chau 2012 eq 98
+        m0, h, omega = self.m0, self.h, self.angular_freq(self.m0)
+        const = np.tanh(m0 * h) + m0 * h * (1 - (np.tanh(m0 * h))**2)
+        return sqrt((2 * const * self.rho * (g ** 2) * damping)/(omega * m0)) ** (1/2)
+    
+    #############################################
+    # Graphics functions
+    
+    def make_R_Z(self, sharp, spatial_res): # create coordinate array for graphing
+        rmin = (2 * self.a[-1] / spatial_res) if sharp else 0.0
+        r_vec = np.linspace(rmin, 2*self.a[-1], spatial_res)
+        z_vec = np.linspace(0, -self.h, spatial_res)
+        if sharp: # more precise near boundaries
+            a_eps = 1.0e-4
+            for i in range(len(self.a)):
+                r_vec = np.append(r_vec, self.a[i]*(1-a_eps))
+                r_vec = np.append(r_vec, self.a[i]*(1+a_eps))
+            r_vec = np.unique(r_vec)
+            for i in range(len(self.d)):
+                z_vec = np.append(z_vec, -self.d[i])
+            z_vec = np.unique(z_vec)
+        return np.meshgrid(r_vec, z_vec)
+    
+    def plot_pv(self, field, R, Z, title):
+        plt.figure(figsize=(8, 6))
+        plt.contourf(R, Z, field, levels=50, cmap='viridis')
+        plt.colorbar()
+        plt.title(title)
+        plt.xlabel('Radial Distance (R)')
+        plt.ylabel('Axial Distance (Z)')
+        plt.show()
+
+    def generate_potential_plot_array(self, cs):
+        h, d, a, heaving, NMK = self.h, self.d, self.a, self.heaving, self.NMK
+        R_1n, R_2n, Lambda_k= self.R_1n, self.R_2n, self.Lambda_k
+        Z_n_i, Z_k_e = self.Z_n_i, self.Z_k_e
+
+        def phi_h_n_inner_func(n, r, z):
+            return (cs[0][n] * R_1n(n, r, 0)) * Z_n_i(n, z, 0)
+        def phi_h_m_i_func(i, m, r, z):
+            return (cs[i][m] * R_1n(m, r, i) + cs[i][NMK[i] + m] * R_2n(m, r, i)) * Z_n_i(m, z, i)
+        def phi_e_k_func(k, r, z):
+            return cs[-1][k] * Lambda_k(k, r) * Z_k_e(k, z)
+        def phi_p_i(d, r, z): # particular solution
+            return (1 / (2* (h - d))) * ((z + h) ** 2 - (r**2) / 2)
+
+        phi_e_k_vec = np.vectorize(phi_e_k_func, otypes = [complex])
+        phi_h_n_inner_vec = np.vectorize(phi_h_n_inner_func, otypes = [complex])
+        phi_h_m_i_vec = np.vectorize(phi_h_m_i_func, otypes = [complex])
+        phi_p_i_vec = np.vectorize(phi_p_i)
+
+        R, Z = self.make_R_Z(True, 50)
+
+        regions = []
+        regions.append((R <= a[0]) & (Z < -d[0]))
+        for i in range(1, self.boundary_count):
+            regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))
+        regions.append(R > a[-1])
+
+        phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+        phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+        phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+
+        for n in range(NMK[0]):
+            temp_phiH = phi_h_n_inner_vec(n, R[regions[0]], Z[regions[0]])
+            phiH[regions[0]] = temp_phiH if n == 0 else phiH[regions[0]] + temp_phiH
+
+        for i in range(1, self.boundary_count):
+            for m in range(NMK[i]):
+                temp_phiH = phi_h_m_i_vec(i, m, R[regions[i]], Z[regions[i]])
+                phiH[regions[i]] = temp_phiH if m == 0 else phiH[regions[i]] + temp_phiH
+
+        for k in range(NMK[-1]):
+            temp_phiH = phi_e_k_vec(k, R[regions[-1]], Z[regions[-1]])
+            phiH[regions[-1]] = temp_phiH if k == 0 else phiH[regions[-1]] + temp_phiH
+
+        phiP[regions[0]] = heaving[0] * phi_p_i_vec(d[0], R[regions[0]], Z[regions[0]])
+        for i in range(1, self.boundary_count):
+            phiP[regions[i]] = heaving[i] * phi_p_i_vec(d[i], R[regions[i]], Z[regions[i]])
+        phiP[regions[-1]] = 0
+
+        phi = phiH + phiP
+        return phi, phiH, phiP
+
+    def generate_velocity_plot_array(self, cs):
+        h, d, a, heaving, NMK = self.h, self.d, self.a, self.heaving, self.NMK
+        R_1n, R_2n, Lambda_k= self.R_1n, self.R_2n, self.Lambda_k
+        diff_R_1n, diff_R_2n, diff_Lambda_k= self.diff_R_1n, self.diff_R_2n, self.diff_Lambda_k
+        Z_n_i, Z_k_e = self.Z_n_i, self.Z_k_e
+        diff_Z_n_i, diff_Z_k_e = self.diff_Z_n_i, self.diff_Z_k_e
+
+        def diff_r_phi_p_i(d, r, z): 
+            return (- r / (2* (h - d)))
+        def diff_z_phi_p_i(d, r, z): 
+            return ((z+h) / (h - d))
+        def v_r_inner_func(n, r, z):
+            return (cs[0][n] * diff_R_1n(n, r, 0)) * Z_n_i(n, z, 0)
+        def v_r_m_i_func(i, m, r, z):
+            return (cs[i][m] * diff_R_1n(m, r, i) + cs[i][NMK[i] + m] * diff_R_2n(m, r, i)) * Z_n_i(m, z, i)
+        def v_r_e_k_func(k, r, z):
+            return cs[-1][k] * diff_Lambda_k(k, r) * Z_k_e(k, z)
+        def v_z_inner_func(n, r, z):
+            return (cs[0][n] * R_1n(n, r, 0)) * diff_Z_n_i(n, z, 0)
+        def v_z_m_i_func(i, m, r, z):
+            return (cs[i][m] * R_1n(m, r, i) + cs[i][NMK[i] + m] * R_2n(m, r, i)) * diff_Z_n_i(m, z, i)
+        def v_z_e_k_func(k, r, z):
+            return cs[-1][k] * Lambda_k(k, r) * diff_Z_k_e(k, z)
+
+        vr_p_i_vec = np.vectorize(diff_r_phi_p_i)
+        vz_p_i_vec = np.vectorize(diff_z_phi_p_i)
+        v_r_inner_vec = np.vectorize(v_r_inner_func, otypes = [complex])
+        v_r_m_i_vec = np.vectorize(v_r_m_i_func, otypes = [complex])
+        v_r_e_k_vec = np.vectorize(v_r_e_k_func, otypes = [complex])
+        v_z_inner_vec = np.vectorize(v_z_inner_func, otypes = [complex])
+        v_z_m_i_vec = np.vectorize(v_z_m_i_func, otypes = [complex])
+        v_z_e_k_vec = np.vectorize(v_z_e_k_func, otypes = [complex])
+
+        R, Z = self.make_R_Z(True, 50)
+
+        regions = []
+        regions.append((R <= a[0]) & (Z < -d[0]))
+        for i in range(1, self.boundary_count):
+            regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))
+        regions.append(R > a[-1])
+
+        vrH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+        vrP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+        vzH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+        vzP = np.full_like(R, np.nan + np.nan*1j, dtype=complex)
+
+        for n in range(NMK[0]):
+            temp_vrH = v_r_inner_vec(n, R[regions[0]], Z[regions[0]])
+            temp_vzH = v_z_inner_vec(n, R[regions[0]], Z[regions[0]])
+            if n == 0:
+                vrH[regions[0]] = temp_vrH
+                vzH[regions[0]] = temp_vzH
+            else:
+                vrH[regions[0]] = vrH[regions[0]] + temp_vrH
+                vzH[regions[0]] = vzH[regions[0]] + temp_vzH
+
+        for i in range(1, self.boundary_count):
+            for m in range(NMK[i]):
+                temp_vrH = v_r_m_i_vec(i, m, R[regions[i]], Z[regions[i]])
+                temp_vzH = v_z_m_i_vec(i, m, R[regions[i]], Z[regions[i]])
+                if m == 0:
+                    vrH[regions[i]] = temp_vrH
+                    vzH[regions[i]] = temp_vzH
+                else:
+                    vrH[regions[i]] = vrH[regions[i]] + temp_vrH
+                    vzH[regions[i]] = vzH[regions[i]] + temp_vzH
+
+        for k in range(NMK[-1]):
+            temp_vrH = v_r_e_k_vec(k, R[regions[-1]], Z[regions[-1]])
+            temp_vzH = v_z_e_k_vec(k, R[regions[-1]], Z[regions[-1]])
+            if k == 0:
+                vrH[regions[-1]] = temp_vrH
+                vzH[regions[-1]] = temp_vzH
+            else:
+                vrH[regions[-1]] = vrH[regions[-1]] + temp_vrH
+                vzH[regions[-1]] = vzH[regions[-1]] + temp_vzH
+
+        vrP[regions[0]] = heaving[0] * vr_p_i_vec(d[0], R[regions[0]], Z[regions[0]])
+        vzP[regions[0]] = heaving[0] * vz_p_i_vec(d[0], R[regions[0]], Z[regions[0]])
+        for i in range(1, self.boundary_count):
+            vrP[regions[i]] = heaving[i] * vr_p_i_vec(d[i], R[regions[i]], Z[regions[i]])
+            vzP[regions[i]] = heaving[i] * vz_p_i_vec(d[i], R[regions[i]], Z[regions[i]])
+        vrP[regions[-1]] = 0
+        vzP[regions[-1]] = 0
+
+        vr = vrH + vrP
+        vz = vzH + vzP
+
+        return vr, vz
+    
+    def plot_potentials(self, cs):
+        R, Z = self.make_R_Z(True, 50)
+        phi, phiH, phiP = self.generate_potential_plot_array(cs)
+        self.plot_pv(np.real(phiH), R, Z, 'Homogeneous Potential')
+        self.plot_pv(np.imag(phiH), R, Z, 'Homogeneous Potential Imaginary')
+
+        self.plot_pv(np.real(phiP), R, Z, 'Particular Potential')
+        self.plot_pv(np.imag(phiP), R, Z, 'Particular Potential Imaginary')
+
+        self.plot_pv(np.real(phi), R, Z, 'Potential (Real Part)')
+        self.plot_pv(np.imag(phi), R, Z, 'Total Potential Imaginary')
+
+    def plot_velocities(self, cs):
+        R, Z = self.make_R_Z(True, 50)
+        vr, vz = self.generate_velocity_plot_array(cs)
+        
+        self.plot_pv(np.real(vr), R, Z, 'Radial Velocity - Real')
+        self.plot_pv(np.imag(vr), R, Z, 'Radial Velocity - Imaginary')
+        self.plot_pv(np.real(vz), R, Z, 'Vertical Velocity - Real')
+        self.plot_pv(np.imag(vz), R, Z, 'Vertical Velocity - Imaginary')
+
+    #############################################
+    # Format a 50 x 50 array of potentials for testing
+    def config_potential_array(self, cs):
+        boundary_count, NMK, heaving = self.boundary_count, self.NMK, self.heaving
+        a, d, h, = self.a, self.d, self.h
+
+        R_1n, R_2n, Lambda_k= self.R_1n, self.R_2n, self.Lambda_k
+        Z_n_i, Z_k_e = self.Z_n_i, self.Z_k_e
+
+        def phi_h_n_inner_func(n, r, z):
+            return (cs[0][n] * R_1n(n, r, 0)) * Z_n_i(n, z, 0)
+        def phi_h_m_i_func(i, m, r, z):
+            return (cs[i][m] * R_1n(m, r, i) + cs[i][NMK[i] + m] * R_2n(m, r, i)) * Z_n_i(m, z, i)
+        def phi_e_k_func(k, r, z):
+            return cs[-1][k] * Lambda_k(k, r) * Z_k_e(k, z)
+        def phi_p_i(d, r, z): # particular solution
+            return (1 / (2* (h- d))) * ((z + h) ** 2 - (r**2) / 2)
+
+        phi_e_k_vec = np.vectorize(phi_e_k_func, otypes = [complex])
+        phi_h_n_inner_vec = np.vectorize(phi_h_n_inner_func, otypes = [complex])
+        phi_h_m_i_vec = np.vectorize(phi_h_m_i_func, otypes = [complex])
+        phi_p_i_vec = np.vectorize(phi_p_i)
+
+        R, Z = self.make_R_Z(False, 50)
+
+        regions = []
+        regions.append((R <= a[0]) & (Z < -d[0]))
+        for i in range(1, boundary_count):
+            regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))
+        regions.append(R > a[-1])
+
+        phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+        phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+        phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+
+        for n in range(NMK[0]):
+            temp_phiH = phi_h_n_inner_vec(n, R[regions[0]], Z[regions[0]])
+            phiH[regions[0]] = temp_phiH if n == 0 else phiH[regions[0]] + temp_phiH
+
+        for i in range(1, boundary_count):
+            for m in range(NMK[i]):
+                temp_phiH = phi_h_m_i_vec(i, m, R[regions[i]], Z[regions[i]])
+                phiH[regions[i]] = temp_phiH if m == 0 else phiH[regions[i]] + temp_phiH
+
+        for k in range(NMK[-1]):
+            temp_phiH = phi_e_k_vec(k, R[regions[-1]], Z[regions[-1]])
+            phiH[regions[-1]] = temp_phiH if k == 0 else phiH[regions[-1]] + temp_phiH
+
+        phi_p_i_vec = np.vectorize(phi_p_i)
+
+        phiP[regions[0]] = heaving[0] * phi_p_i_vec(d[0], R[regions[0]], Z[regions[0]])
+        for i in range(1, boundary_count):
+            phiP[regions[i]] = heaving[i] * phi_p_i_vec(d[i], R[regions[i]], Z[regions[i]])
+        phiP[regions[-1]] = 0
+
+        phi = phiH + phiP
+
+        nanregions = []
+        nanregions.append((R <= a[0]) & (Z > -d[0]))
+        for i in range(1, len(a)):
+            nanregions.append((R > a[i-1]) & (R <= a[i]) & (Z > -d[i]))
+
+        return R, Z, phi, nanregions
+    
+    #############################################
+    # Capabilities beyond the multi-constants/equations/MEEM format
+    
+    def change_m0(self, new_m0):
+        self.m0 = new_m0
+        self.m_k = self.m_k_array()
+
+    # Given an A matrix for the same configuration/NMK but possibly different m0, return this problem's A matrix.
+    # This reduces computation.
+    def a_matrix_from_old(self, a_matrix):
+        d = self.d
+        I_mk_vals = self.I_mk_vals()
+        # insert potential block
+        row = 0
+        for i in range(self.boundary_count - 1):
+            row += self.NMK[i + (d[i] <= d[i + 1])]
+        col = self.size - self.NMK[-1]
+        submat = self.p_dense_block_e(-1, I_mk_vals)
+        intermediate_matrix = insert_submatrix(a_matrix, submat, row, col)
+
+        # insert velocity blocks
+        row = self.size - self.NMK[-1]
+        left_block1 = self.v_dense_block_e(np.vectorize(self.diff_R_1n, otypes=[complex]), -1, I_mk_vals)
+        right_block = self.v_diagonal_block_e(-1)
+        if self.boundary_count == 1:
+            col = 0
+            submat = np.concatenate((left_block1,right_block), axis = 1)
+        else:
+            col = self.size - (self.NMK[-1] + 2 * self.NMK[-2])
+            left_block2 = self.v_dense_block_e(np.vectorize(self.diff_R_2n, otypes=[complex]), -1, I_mk_vals)
+            submat = np.concatenate((left_block1,left_block2,right_block), axis = 1)
+        final_matrix = insert_submatrix(intermediate_matrix, submat, row, col)
+        return final_matrix
+
+    # Given a b vector for the same configuration/NMK but possibly different m0, return this problem's b vector.
+    def b_vector_from_old(self, b_vector):
+        b_vector = b_vector.copy()
+        index = len(b_vector) - self.NMK[-1]
+        for k in range(self.NMK[-1]):
+            b_vector[index] = self.b_velocity_end_entry(k, -1)
+            index += 1
+        return b_vector
+        
+        
+        
diff --git a/dev/python/multi_constants.py b/dev/python/multi_constants.py
new file mode 100644
index 0000000..3ff4ae3
--- /dev/null
+++ b/dev/python/multi_constants.py
@@ -0,0 +1,13 @@
+from numpy import inf
+# Constants
+h = 100
+d = [29, 7, 4]
+a = [3, 5, 10]
+heaving = [0, 1, 1]
+# 0/false if not heaving, 1/true if yes heaving
+NMK = [100, 100, 100, 100] # Number of terms in approximation of each region (including e).
+# All computations assume at least 2 regions.
+
+m0 = 1
+g = 9.81
+rho = 1023
diff --git a/hydro/python/multi_equations.py b/dev/python/multi_equations.py
similarity index 69%
rename from hydro/python/multi_equations.py
rename to dev/python/multi_equations.py
index 67cb3ae..ea925db 100644
--- a/hydro/python/multi_equations.py
+++ b/dev/python/multi_equations.py
@@ -2,21 +2,34 @@
 from scipy.special import hankel1 as besselh
 from scipy.special import iv as besseli
 from scipy.special import kv as besselk
+from scipy.special import ive as besselie
+from scipy.special import kve as besselke
 import scipy.integrate as integrate
 import scipy.linalg as linalg
 import matplotlib.pyplot as plt
-from numpy import sqrt, cosh, cos, sinh, sin, pi, exp
+from numpy import sqrt, cosh, cos, sinh, sin, pi, exp, inf
 from scipy.optimize import newton, minimize_scalar, root_scalar
 import scipy as sp
 from multi_constants import *
 
-omega = sqrt(m0 * np.tanh(m0 * h) * g)
+def m0_to_omega(m0):
+    if m0 == inf:
+        return inf
+    else:
+        return sqrt(m0 * np.tanh(m0 * h) * g)
+
+omega = m0_to_omega(m0)
 
 def wavenumber(omega):
     m0_err = (lambda m0: (m0 * np.tanh(h * m0) - omega ** 2 / g))
     return (root_scalar(m0_err, x0 = 2, method="newton")).root
 
-scale = np.mean([[0]+a[0:-1], a], axis = 0)
+scale = a #np.append((np.mean([[0]+a[0:-1], a], axis = 0)), a[-1])
+
+# After which the k = 0 e-region eigenfunction is well approximated by its limiting form.
+# Empirically the true and approximating form differ by a fraction of < 1e-10 after this.
+# The limiting form is used to prevent inf/inf errors.
+LARGE_M0H = 14
 
 def lambda_ni(n, i): # factor used often in calculations
     return n * pi / (h - d[i])
@@ -24,8 +37,9 @@ def lambda_ni(n, i): # factor used often in calculations
 #############################################
 # creating a m_k function, used often in calculations
 def m_k_entry(k):
-    # m_k_mat = np.zeros((len(m0_vec), 1))
     if k == 0: return m0
+    elif m0 == inf:
+        return ((k - 1/2) * pi)/h
 
     m_k_h_err = (
         lambda m_k_h: (m_k_h * np.tan(m_k_h) + m0 * h * np.tanh(m0 * h))
@@ -93,14 +107,16 @@ def I_nm(n, m, i): # coupling integral for two i-type regions
 def I_mk(m, k, i): # coupling integral for i and e-type regions
     dj = d[i]
     if m == 0 and k == 0:
-        if m0 * h < 14:
+        if m0 == inf: return 0
+        elif m0 * h < LARGE_M0H:
             return (1/sqrt(N_k(0))) * sinh(m0 * (h - dj)) / m0
         else: # high m0h approximation
             return sqrt(2 * h / m0) * (exp(- m0 * dj) - exp(m0 * dj - 2 * m0 * h))
     if m == 0 and k >= 1:
         return (1/sqrt(N_k(k))) * sin(m_k[k] * (h - dj)) / m_k[k]
     if m >= 1 and k == 0:
-        if m0 * h < 14:
+        if m0 == inf: return 0
+        elif m0 * h < LARGE_M0H:
             num = (-1)**m * sqrt(2) * (1/sqrt(N_k(0))) * m0 * sinh(m0 * (h - dj))
         else: # high m0h approximation
             num = (-1)**m * 2 * sqrt(h * m0 ** 3) *(exp(- m0 * dj) - exp(m0 * dj - 2 * m0 * h))
@@ -109,11 +125,11 @@ def I_mk(m, k, i): # coupling integral for i and e-type regions
     else:
         lambda1 = lambda_ni(m, i)
         if abs(m_k[k]) == lambda1:
-            return (h - dj)/2
+            return sqrt(2/N_k(k)) * (h - dj)/2
         else:
             frac1 = sin((m_k[k] + lambda1)*(h-dj))/(m_k[k] + lambda1)
             frac2 = sin((m_k[k] - lambda1)*(h-dj))/(m_k[k] - lambda1)
-            return sqrt(2)/2 * (1/sqrt(N_k(k))) * (frac1 + frac2)
+            return sqrt(2/N_k(k)) * (frac1 + frac2)/2
             
 #############################################
 # b-vector computation
@@ -121,7 +137,7 @@ def I_mk(m, k, i): # coupling integral for i and e-type regions
 def b_potential_entry(n, i): # for two i-type regions
     #(integrate over shorter fluid, use shorter fluid eigenfunction)
     
-    j = i + (d[i] < d[i+1]) # index of shorter fluid
+    j = i + (d[i] <= d[i+1]) # index of shorter fluid
     constant = (heaving[i+1] / (h - d[i+1]) - heaving[i] / (h - d[i]))
     if n == 0:
         return constant * 1/2 * ((h - d[j])**3/3 - (h-d[j]) * a[i]**2/2)
@@ -154,7 +170,8 @@ def b_velocity_entry(n, i): # for two i-type regions
 def b_velocity_end_entry(k, i): # between i and e-type regions
     constant = - heaving[i] * a[i]/(2 * (h - d[i]))
     if k == 0:
-        if m0 * h < 14:
+        if m0 == inf: return 0
+        elif m0 * h < LARGE_M0H:
             return constant * (1/sqrt(N_k(0))) * sinh(m0 * (h - d[i])) / m0
         else: # high m0h approximation
             return constant * sqrt(2 * h / m0) * (exp(- m0 * d[i]) - exp(m0 * d[i] - 2 * m0 * h))
@@ -179,7 +196,10 @@ def R_1n(n, r, i):
     if n == 0:
         return 0.5
     elif n >= 1:
-        return besseli(0, lambda_ni(n, i) * r) / besseli(0, lambda_ni(n, i) * scale[i])
+        if r == scale[i]: # Saves bessel function eval
+            return 1
+        else:
+            return besselie(0, lambda_ni(n, i) * r) / besselie(0, lambda_ni(n, i) * scale[i]) * exp(lambda_ni(n, i) * (r - scale[i]))
     else: 
         raise ValueError("Invalid value for n")
 
@@ -188,9 +208,9 @@ def diff_R_1n(n, r, i):
     if n == 0:
         return 0
     else:
-        top = lambda_ni(n, i) * besseli(1, lambda_ni(n, i) * r)
-        bottom = besseli(0, lambda_ni(n, i) * scale[i])
-        return top / bottom
+        top = lambda_ni(n, i) * besselie(1, lambda_ni(n, i) * r)
+        bottom = besselie(0, lambda_ni(n, i) * scale[i])
+        return top / bottom * exp(lambda_ni(n, i) * (r - scale[i]))
 
 #############################################
 # The "Bessel K" radial eigenfunction
@@ -200,7 +220,10 @@ def R_2n(n, r, i):
     elif n == 0:
         return 0.5 * np.log(r / a[i])
     else:
-        return besselk(0, lambda_ni(n, i) * r) / besselk(0, lambda_ni(n, i) * scale[i])
+        if r == scale[i]:  # Saves bessel function eval
+            return 1
+        else:
+            return besselke(0, lambda_ni(n, i) * r) / besselke(0, lambda_ni(n, i) * scale[i]) * exp(lambda_ni(n, i) * (scale[i] - r))
 
 
 # Differentiate wrt r
@@ -208,9 +231,9 @@ def diff_R_2n(n, r, i):
     if n == 0:
         return 1 / (2 * r)
     else:
-        top = - lambda_ni(n, i) * besselk(1, lambda_ni(n, i) * r)
-        bottom = besselk(0, lambda_ni(n, i) * scale[i])
-        return top / bottom
+        top = - lambda_ni(n, i) * besselke(1, lambda_ni(n, i) * r)
+        bottom = besselke(0, lambda_ni(n, i) * scale[i])
+        return top / bottom * exp(lambda_ni(n, i) * (scale[i] - r))
 
 
 #############################################
@@ -232,25 +255,41 @@ def diff_Z_n_i(n, z, i):
 # Region e radial eigenfunction
 def Lambda_k(k, r):
     if k == 0:
-        return besselh(0, m0 * r) / besselh(0, m0 * scale[-1])
+        if m0 == inf:
+        # the true limit is not well-defined, but whatever value this returns will be multiplied by zero
+            return 1
+        else:
+            if r == scale[-1]:  # Saves bessel function eval
+                return 1
+            else:
+                return besselh(0, m0 * r) / besselh(0, m0 * scale[-1])
     else:
-        return besselk(0, m_k[k] * r) / besselk(0, m_k[k] * scale[-1])
+        if r == scale[-1]:  # Saves bessel function eval
+            return 1
+        else:
+            return besselke(0, m_k[k] * r) / besselke(0, m_k[k] * scale[-1]) * exp(m_k[k] * (scale[-1] - r))
 
 # Differentiate wrt r
 def diff_Lambda_k(k, r):
     if k == 0:
-        numerator = -(m0 * besselh(1, m0 * r))
-        denominator = besselh(0, m0 * scale[-1])
+        if m0 == inf:
+        # the true limit is not well-defined, but this makes the assigned coefficient zero
+            return 1
+        else:
+            numerator = -(m0 * besselh(1, m0 * r))
+            denominator = besselh(0, m0 * scale[-1])
+            return numerator / denominator
     else:
-        numerator = -(m_k[k] * besselk(1, m_k[k] * r))
-        denominator = besselk(0, m_k[k] * scale[-1])
-    return numerator / denominator
+        numerator = -(m_k[k] * besselke(1, m_k[k] * r))
+        denominator = besselke(0, m_k[k] * scale[-1])
+        return numerator / denominator * exp(m_k[k] * (scale[-1] - r))
 
 
 #############################################
 # Equation 2.34 in analytical methods book, also eq 16 in Seah and Yeung 2006:
 def N_k(k):
-    if k == 0:
+    if m0 == inf: return 1/2
+    elif k == 0:
         return 1 / 2 * (1 + sinh(2 * m0 * h) / (2 * m0 * h))
     else:
         return 1 / 2 * (1 + sin(2 * m_k[k] * h) / (2 * m_k[k] * h))
@@ -260,7 +299,8 @@ def N_k(k):
 # e-region vertical eigenfunctions
 def Z_k_e(k, z):
     if k == 0:
-        if m0 * h < 14:
+        if m0 == inf: return 0
+        elif m0 * h < LARGE_M0H:
             return 1 / sqrt(N_k(k)) * cosh(m0 * (z + h))
         else: # high m0h approximation
             return sqrt(2 * m0 * h) * (exp(m0 * z) + exp(-m0 * (z + 2*h)))
@@ -269,7 +309,8 @@ def Z_k_e(k, z):
 
 def diff_Z_k_e(k, z):
     if k == 0:
-        if m0 * h < 14:
+        if m0 == inf: return 0
+        elif m0 * h < LARGE_M0H:
             return 1 / sqrt(N_k(k)) * m0 * sinh(m0 * (z + h))
         else: # high m0h approximation
             return m0 * sqrt(2 * h * m0) * (exp(m0 * z) - exp(-m0 * (z + 2*h)))
@@ -286,10 +327,11 @@ def int_R_1n(i, n):
         return a[i]**2/4 - inner**2/4
     else:
         lambda0 = lambda_ni(n, i)
-        inner_term = (0 if i == 0 else a[i-1] * besseli(1, lambda0 * a[i-1])) # central region has inner radius 0
-        top = a[i] * besseli(1, lambda0 * a[i]) - inner_term
-        bottom = lambda0 * besseli(0, lambda0 * scale[i])
-        return top / bottom
+        bottom = lambda0 * besselie(0, lambda0 * scale[i])
+        if i == 0: inner_term = 0
+        else: inner_term = (a[i-1] * besselie(1, lambda0 * a[i-1]) / bottom) * exp(lambda0 * (a[i-1] - scale[i]))
+        outer_term = (a[i] * besselie(1, lambda0 * a[i]) / bottom) * exp(lambda0 * (a[i] - scale[i]))
+        return outer_term - inner_term
 
 #integrating R_2n * r
 def int_R_2n(i, n):
@@ -299,9 +341,10 @@ def int_R_2n(i, n):
     if n == 0:
         return (a[i-1]**2 * (2*np.log(a[i]/a[i-1]) + 1) - a[i]**2)/8
     else:
-        top = a[i] * besselk(1, lambda0 * a[i]) - a[i-1] * besselk(1, lambda0 * a[i-1])
-        bottom = - lambda0 * besselk(0, lambda0 * scale[i])
-        return top / bottom
+        outer_term = a[i] * besselke(1, lambda0 * a[i])
+        inner_term = a[i-1] * besselke(1, lambda0 * a[i-1])
+        bottom = - lambda0 * besselke(0, lambda0 * scale[i])
+        return (outer_term / bottom) * exp(lambda0 * (scale[i] - a[i])) - (inner_term/bottom)* exp(lambda0 * (scale[i] - a[i-1]))
 
 #integrating phi_p_i * d_phi_p_i/dz * r *d_r at z=d[i]
 def int_phi_p_i(i):
@@ -318,3 +361,13 @@ def z_n_d(n):
         return 1
     else:
         return sqrt(2)*(-1)**n
+
+#############################################
+def excitation_phase(x): # x-vector of unknown coefficients
+    coeff = x[-NMK[-1]] # first coefficient of e-region expansion
+    return -(pi/2) + np.angle(coeff) - np.angle(besselh(0, m0 * scale[-1]))
+    
+def excitation_force(damping):
+    # Chau 2012 eq 98
+    const = np.tanh(m0 * h) + m0 * h * (1 - (np.tanh(m0 * h))**2)
+    return ( (2 * const * rho * (g ** 2) * damping) / (omega * m0) ) ** (1/2)
\ No newline at end of file
diff --git a/dev/python/old_assembly.py b/dev/python/old_assembly.py
new file mode 100644
index 0000000..9f38dd8
--- /dev/null
+++ b/dev/python/old_assembly.py
@@ -0,0 +1,601 @@
+from functools import partial
+import numpy as np
+from scipy.special import hankel1 as besselh
+from scipy.special import iv as besseli
+from scipy.special import kv as besselk
+from scipy.special import ive as besselie
+from scipy.special import kve as besselke
+import scipy.integrate as integrate
+import scipy.linalg as linalg
+import matplotlib.pyplot as plt
+from numpy import sqrt, cosh, cos, sinh, sin, pi, exp, inf
+from scipy.optimize import newton, minimize_scalar, root_scalar
+import scipy as sp
+
+LARGE_M0H = 14
+def diff_z_phi_p_i_old(d, z, h): 
+    return ((z+h) / (h - d))
+def diff_r_phi_p_i_old(d, r, h): 
+    return (- r / (2* (h - d)))
+def diff_Z_n_i_old(n, z, i, h, d):
+    if n == 0:
+        return 0
+    else:
+        lambda0 = lambda_ni_old(n, i, h, d)
+        return - lambda0 * np.sqrt(2) * np.sin(lambda0 * (z + h))
+def diff_Z_k_e_old(k, z, NMK, m0, h):
+    m_k = m_k_old(NMK, m0, h)
+    N_k = N_k_old(k, m0, h, m_k)
+    if k == 0:
+        if m0 == inf: return 0
+        elif m0 * h < LARGE_M0H:
+            return 1 / sqrt(N_k(k)) * m0 * sinh(m0 * (z + h))
+        else: # high m0h approximation
+            return m0 * sqrt(2 * h * m0) * (exp(m0 * z) - exp(-m0 * (z + 2*h)))
+    else:
+        return -1 / sqrt(N_k(k)) * m_k[k] * sin(m_k[k] * (z + h))
+def phi_p_i_old(d, r, z, h): 
+    return (1 / (2* (h - d))) * ((z + h) ** 2 - (r**2) / 2)
+def make_R_Z_old(a, h, d, sharp, spatial_res): # create coordinate array for graphing
+    rmin = (2 * a[-1] / spatial_res) if sharp else 0.0
+    r_vec = np.linspace(rmin, 2*a[-1], spatial_res)
+    z_vec = np.linspace(0, -h, spatial_res)
+    if sharp: # more precise near boundaries
+        a_eps = 1.0e-4
+        for i in range(len(a)):
+            r_vec = np.append(r_vec, a[i]*(1-a_eps))
+            r_vec = np.append(r_vec, a[i]*(1+a_eps))
+        r_vec = np.unique(r_vec)
+        for i in range(len(d)):
+            z_vec = np.append(z_vec, -d[i])
+        z_vec = np.unique(z_vec)
+    return np.meshgrid(r_vec, z_vec)
+def Z_k_e_old(k, z, m0, h, NMK):
+    m_k = m_k_old(NMK, m0, h)
+    if k == 0:
+        if m0 == inf: return 0
+        elif m0 * h < LARGE_M0H:
+            return 1 / sqrt(N_k_old(k, m0, h, m_k)) * cosh(m0 * (z + h))
+        else: # high m0h approximation
+            return sqrt(2 * m0 * h) * (exp(m0 * z) + exp(-m0 * (z + 2*h)))
+    else:
+        return 1 / sqrt(N_k_old(k, m0, h, m_k)) * cos(m_k[k] * (z + h))
+def R_2n_old(n, r, i, a, h, d):
+    if i == 0:
+        raise ValueError("i cannot be 0")  # this shouldn't be called for i=0, innermost region.
+    elif n == 0:
+        return 0.5 * np.log(r / a[i])
+    else:
+        if r == scale_old(a)[i]:  # Saves bessel function eval
+            return 1
+        else:
+            return besselke(0, lambda_ni_old(n, i, h, d) * r) / besselke(0, lambda_ni_old(n, i, h, d) * scale_old(a)[i]) * exp(lambda_ni_old(n, i, h, d) * (scale_old(a)[i] - r))
+def Z_n_i_old(n, z, i, h, d):
+    if n == 0:
+        return 1
+    else:
+        return np.sqrt(2) * np.cos(lambda_ni_old(n, i, h, d) * (z + h))
+    
+def R_1n_old(n, r, i, a, h, d):
+    if n == 0:
+        return 0.5
+    elif n >= 1:
+        if r == scale_old(a)[i]: # Saves bessel function eval
+            return 1
+        else:
+            return besselie(0, lambda_ni_old(n, i, h, d) * r) / besselie(0, lambda_ni_old(n, i, h, d) * scale_old(a)[i]) * exp(lambda_ni_old(n, i, h, d) * (r - scale_old(a)[i]))
+    else: 
+        raise ValueError("Invalid value for n")
+
+def Lambda_k_old_wrapped(m0, a, NMK, h):
+    def func(k, r):
+        return Lambda_k_old(k, r, m0, a, NMK, h)
+    return np.vectorize(func, otypes=[complex])
+    
+def Lambda_k_old(k, r, m0, a, NMK, h):
+    m_k = m_k_old(NMK, m0, h)
+    if k == 0:
+        if m0 == inf:
+        # the true limit is not well-defined, but whatever value this returns will be multiplied by zero
+            return 1
+        else:
+            if r == scale_old(a)[-1]:  # Saves bessel function eval
+                return 1
+            else:
+                return besselh(0, m0 * r) / besselh(0, m0 * scale_old(a)[-1])
+    else:
+        if r == scale_old(a)[-1]:  # Saves bessel function eval
+            return 1
+        else:
+            return besselke(0, m_k[k] * r) / besselke(0, m_k[k] * scale_old(a)[-1]) * exp(m_k[k] * (scale_old(a)[-1] - r))
+        
+def diff_Lambda_k_old(k, r, m0, a, NMK, h):
+    m_k = m_k_old(NMK, m0, h)
+    if k == 0:
+        if m0 == inf:
+        # the true limit is not well-defined, but this makes the assigned coefficient zero
+            return 1
+        else:
+            numerator = -(m0 * besselh(1, m0 * r))
+            denominator = besselh(0, m0 * scale_old(a)[-1])
+            return numerator / denominator
+    else:
+        numerator = -(m_k[k] * besselke(1, m_k[k] * r))
+        denominator = besselke(0, m_k[k] * scale_old(a)[-1])
+        return numerator / denominator * exp(m_k[k] * (scale_old(a)[-1] - r))
+def b_potential_entry_old(n,i, d, heaving, h, a):
+    j = i + (d[i] <= d[i+1]) # index of shorter fluid
+    constant = (heaving[i+1] / (h - d[i+1]) - heaving[i] / (h - d[i]))
+    if n == 0:
+        return constant * 1/2 * ((h - d[j])**3/3 - (h-d[j]) * a[i]**2/2)
+    else:
+        return sqrt(2) * (h - d[j]) * constant * ((-1) ** n)/(lambda_ni_old(n, j, h, d) ** 2)
+def b_potential_end_entry_old(n,i, h, d, heaving, a):
+    constant = - heaving[i] / (h - d[i])
+    if n == 0:
+        return constant * 1/2 * ((h - d[i])**3/3 - (h-d[i]) * a[i]**2/2)
+    else:
+        return sqrt(2) * (h - d[i]) * constant * ((-1) ** n)/(lambda_ni_old(n, i, h, d) ** 2)
+def b_velocity_entry_old(n, i, heaving, a, d, h): # for two i-type regions
+    if n == 0:
+        return (heaving[i+1] - heaving[i]) * (a[i]/2)
+    if d[i] > d[i + 1]: #using i+1's vertical eigenvectors
+        if heaving[i]:
+            num = - sqrt(2) * a[i] * sin(lambda_ni_old(n, i+1, h, d) * (h-d[i]))
+            denom = (2 * (h - d[i]) * lambda_ni_old(n, i+1, h, d))
+            return num/denom
+        else: return 0
+    else: #using i's vertical eigenvectors
+        if heaving[i+1]:
+            num = sqrt(2) * a[i] * sin(lambda_ni_old(n, i, h, d) * (h-d[i+1]))
+            denom = (2 * (h - d[i+1]) * lambda_ni_old(n, i, h, d))
+            return num/denom
+        else: return 0
+def b_velocity_end_entry_old(k, i, heaving, a, h, d, m0, NMK): # between i and e-type regions
+    constant = - heaving[i] * a[i]/(2 * (h - d[i]))
+    m_k = m_k_old(NMK, m0, h)
+    if k == 0:
+        if m0 == inf: return 0
+        elif m0 * h < LARGE_M0H:
+            return constant * (1/sqrt(N_k_old(0, m0, h, m_k))) * sinh(m0 * (h - d[i])) / m0
+        else: # high m0h approximation
+            return constant * sqrt(2 * h / m0) * (exp(- m0 * d[i]) - exp(m0 * d[i] - 2 * m0 * h))
+    else:
+        return constant * (1/sqrt(N_k_old(k, m0, h, m_k))) * sin(m_k[k] * (h - d[i])) / m_k[k]
+def diff_R_2n_old(n, r, i, h, d, a):
+    if n == 0:
+        return 1 / (2 * r)
+    else:
+        top = - lambda_ni_old(n, i, h, d) * besselke(1, lambda_ni_old(n, i, h, d) * r)
+        bottom = besselke(0, lambda_ni_old(n, i, h, d) * scale_old(a)[i])
+        return top / bottom * exp(lambda_ni_old(n, i, h, d) * (scale_old(a)[i] - r))
+def diff_R_1n_old(n, r, i, h, d, a):
+    if n == 0:
+        return 0
+    else:
+        top = lambda_ni_old(n, i, h, d) * besselie(1, lambda_ni_old(n, i, h, d) * r)
+        bottom = besselie(0, lambda_ni_old(n, i, h, d) * scale_old(a)[i])
+        return top / bottom * exp(lambda_ni_old(n, i, h, d) * (r - scale_old(a)[i]))
+def scale_old(a):
+    return a
+def R_1n_old(n, r, i, h, d, a):
+    if n == 0:
+        return 0.5
+    elif n >= 1:
+        if r == scale_old(a)[i]: # Saves bessel function eval
+            return 1
+        else:
+            return besselie(0, lambda_ni_old(n, i, h, d) * r) / besselie(0, lambda_ni_old(n, i, h, d) * scale_old(a)[i]) * exp(lambda_ni_old(n, i, h, d) * (r - scale_old(a)[i]))
+    else: 
+        raise ValueError("Invalid value for n")
+def R_2n_old(n, r, i, a, h, d):
+    if i == 0:
+        raise ValueError("i cannot be 0")  # this shouldn't be called for i=0, innermost region.
+    elif n == 0:
+        return 0.5 * np.log(r / a[i])
+    else:
+        if r == scale_old(a)[i]:  # Saves bessel function eval
+            return 1
+        else:
+            return besselke(0, lambda_ni_old(n, i, h, d) * r) / besselke(0, lambda_ni_old(n, i, h, d) * scale_old(a)[i]) * exp(lambda_ni_old(n, i, h, d) * (scale_old(a)[i] - r))
+def I_nm_old(n, m, i, h, d):
+    dj = max(d[i], d[i+1]) # integration bounds at -h and -d
+    if n == 0 and m == 0:
+        return h - dj
+    lambda1 = lambda_ni_old(n, i, h, d)
+    lambda2 = lambda_ni_old(m, i + 1, h, d)
+    if n == 0 and m >= 1:
+        if dj == d[i+1]:
+            return 0
+        else:
+            return sqrt(2) * sin(lambda2 * (h - dj)) / lambda2
+    if n >= 1 and m == 0:
+        if dj == d[i]:
+            return 0
+        else:
+            return sqrt(2) * sin(lambda1 * (h - dj)) / lambda1
+    else:
+        frac1 = sin((lambda1 + lambda2)*(h-dj))/(lambda1 + lambda2)
+        if lambda1 == lambda2:
+            frac2 = (h - dj)
+        else:
+            frac2 = sin((lambda1 - lambda2)*(h-dj))/(lambda1 - lambda2)
+        return frac1 + frac2
+    
+def N_k_old(k, m0, h, m_k):
+    if m0 == inf: return 1/2
+    elif k == 0:
+        return 1 / 2 * (1 + sinh(2 * m0 * h) / (2 * m0 * h))
+    else:
+        return 1 / 2 * (1 + sin(2 * m_k[k] * h) / (2 * m_k[k] * h))
+
+def m_k_entry_old(k, m0, h):
+    if k == 0: return m0
+    elif m0 == inf:
+        return ((k - 1/2) * pi)/h
+
+    m_k_h_err = (
+        lambda m_k_h: (m_k_h * np.tan(m_k_h) + m0 * h * np.tanh(m0 * h))
+    )
+    k_idx = k
+
+    # # original version of bounds in python
+    m_k_h_lower = pi * (k_idx - 1/2) + np.finfo(float).eps
+    m_k_h_upper = pi * k_idx - np.finfo(float).eps
+    # x_0 =  (m_k_upper - m_k_lower) / 2
+    
+    # becca's version of bounds from MDOcean Matlab code
+    m_k_h_lower = pi * (k_idx - 1/2) + (pi/180)* np.finfo(float).eps * (2**(np.floor(np.log(180*(k_idx- 1/2)) / np.log(2))) + 1)
+    m_k_h_upper = pi * k_idx
+
+    m_k_initial_guess = pi * (k_idx - 1/2) + np.finfo(float).eps
+    result = root_scalar(m_k_h_err, x0=m_k_initial_guess, method="newton", bracket=[m_k_h_lower, m_k_h_upper])
+    # result = minimize_scalar(
+        # m_k_h_err, bounds=(m_k_h_lower, m_k_h_upper), method="bounded"
+    # )
+
+    m_k_val = result.root / h
+
+    shouldnt_be_int = np.round(m0 * m_k_val / np.pi - 0.5, 4)
+    # not_repeated = np.unique(m_k_val) == m_k_val
+    assert np.all(shouldnt_be_int != np.floor(shouldnt_be_int))
+
+        # m_k_mat[freq_idx, :] = m_k_vec
+    return m_k_val
+
+# create an array of m_k values for each k to avoid recomputation
+def m_k_old(NMK, m0, h):
+    func = np.vectorize(lambda k: m_k_entry_old(k, m0, h), otypes=[float])
+    return func(range(NMK[-1]))
+
+def lambda_ni_old(n, i, h, d):
+    return n * pi / (h - d[i])
+
+def I_mk_old(m,k,i, d, h, m0, NMK):
+    m_k = m_k_old(NMK, m0, h)
+    dj = d[i]
+    if m == 0 and k == 0:
+        if m0 == inf: return 0
+        elif m0 * h < LARGE_M0H:
+            return (1/sqrt(N_k_old(0, m0, h, m_k))) * sinh(m0 * (h - dj)) / m0
+        else: # high m0h approximation
+            return sqrt(2 * h / m0) * (exp(- m0 * dj) - exp(m0 * dj - 2 * m0 * h))
+    if m == 0 and k >= 1:
+        return (1/sqrt(N_k_old(k, m0, h, m_k))) * sin(m_k[k] * (h - dj)) / m_k[k]
+    if m >= 1 and k == 0:
+        if m0 == inf: return 0
+        elif m0 * h < LARGE_M0H:
+            num = (-1)**m * sqrt(2) * (1/sqrt(N_k_old(0, m0, h, m_k))) * m0 * sinh(m0 * (h - dj))
+        else: # high m0h approximation
+            num = (-1)**m * 2 * sqrt(h * m0 ** 3) *(exp(- m0 * dj) - exp(m0 * dj - 2 * m0 * h))
+        denom = (m0**2 + lambda_ni_old(m, i, h, d) **2)
+        return num/denom
+    else:
+        lambda1 = lambda_ni_old(m, i, h, d)
+        if abs(m_k[k]) == lambda1:
+            return sqrt(2/N_k_old(k, m0, h, m_k)) * (h - dj)/2
+        else:
+            frac1 = sin((m_k[k] + lambda1)*(h-dj))/(m_k[k] + lambda1)
+            frac2 = sin((m_k[k] - lambda1)*(h-dj))/(m_k[k] - lambda1)
+            return sqrt(2/N_k_old(k, m0, h, m_k)) * (frac1 + frac2)/2
+def debug_block(block, name, bd):
+    max_val = np.max(np.abs(block))
+    if max_val > 1e6:  # suspiciously large
+        print(f"WARNING: Large values detected in {name} at boundary {bd}, max abs={max_val}")
+        print(block)
+    else:
+        print(f"{name} at boundary {bd} max abs={max_val}")
+        
+def assemble_old_A_and_b(h, d, a, NMK, heaving, m0):
+    left = 0
+    for radius in a:
+        assert radius > left, "a entries should be increasing, and start greater than 0."
+        left = radius
+
+    for depth in d:
+        assert depth >= 0, "d entries should be nonnegative."
+        assert depth < h, "d entries should be less than h."
+
+    for val in NMK:
+        assert (type(val) == int and val > 0), "NMK entries should be positive integers."
+
+    # CREATING THE A MATRIX
+    size = NMK[0] + NMK[-1] + 2 * sum(NMK[1:len(NMK) - 1])
+    boundary_count = len(NMK) - 1
+
+    rows = [] # collection of rows of blocks in A matrix, to be concatenated later
+
+    ## Define values/functions to help block creation
+    #Coupling integral values
+    I_nm_vals = np.zeros((max(NMK), max(NMK), boundary_count - 1), dtype = complex)
+    for bd in range(boundary_count - 1):
+        for n in range(NMK[bd]):
+            for m in range(NMK[bd + 1]):
+                I_nm_vals[n][m][bd] = I_nm_old(n, m, bd, h, d)
+    I_mk_vals = np.zeros((NMK[boundary_count - 1], NMK[boundary_count]), dtype = complex)
+    for m in range(NMK[boundary_count - 1]):
+        for k in range(NMK[boundary_count]):
+            I_mk_vals[m][k]= I_mk_old(m, k, boundary_count - 1, d, h, m0, NMK)
+
+    ## Functions to create blocks of certain types
+    # arguments: diagonal block on left (T/F), vectorized radial eigenfunction, boundary number
+    def p_diagonal_block(left, radfunction, bd):
+        region = bd if left else (bd + 1)
+        sign = 1 if left else (-1)
+        return sign * (h - d[region]) * np.diag(radfunction(list(range(NMK[region])), a[bd], region))
+        
+    # arguments: dense block on left (T/F), vectorized radial eigenfunction, boundary number
+    def p_dense_block(left, radfunction, bd):
+        I_nm_array = I_nm_vals[0:NMK[bd],0:NMK[bd+1], bd]
+        if left: # determine which is region to work in and which is adjacent
+            region, adj = bd, bd + 1
+            sign = 1
+            I_nm_array = np.transpose(I_nm_array)
+        else:
+            region, adj = bd + 1, bd
+            sign = -1
+        radial_vector = radfunction(list(range(NMK[region])), a[bd], region)
+        radial_array = np.outer((np.full((NMK[adj]), 1)), radial_vector)
+        return sign * radial_array * I_nm_array
+
+    def p_dense_block_e(bd):
+        I_mk_array = I_mk_vals
+        vectorized_func = Lambda_k_old_wrapped(m0, a, NMK, h)
+        radial_vector = vectorized_func(list(range(NMK[bd+1])), a[bd])
+        radial_array = np.outer((np.full((NMK[bd]), 1)), radial_vector)
+        return (-1) * radial_array * I_mk_array
+
+    #####
+    # arguments: diagonal block on left (T/F), vectorized radial eigenfunction, boundary number
+    def v_diagonal_block(left, radfunction, bd):
+        region = bd if left else (bd + 1)
+        sign = (-1) if left else (1)
+        return sign * (h - d[region]) * np.diag(radfunction(list(range(NMK[region])), a[bd], region))
+
+    # arguments: dense block on left (T/F), vectorized radial eigenfunction, boundary number
+    def v_dense_block(left, radfunction, bd):
+        I_nm_array = I_nm_vals[0:NMK[bd],0:NMK[bd+1], bd]
+        if left: # determine which is region to work in and which is adjacent
+            region, adj = bd, bd + 1
+            sign = -1
+            I_nm_array = np.transpose(I_nm_array)
+        else:
+            region, adj = bd + 1, bd
+            sign = 1
+        radial_vector = radfunction(list(range(NMK[region])), a[bd], region)
+        radial_array = np.outer((np.full((NMK[adj]), 1)), radial_vector)
+        return sign * radial_array * I_nm_array
+    
+    diff_Lambda_k_func = np.vectorize(
+        partial(diff_Lambda_k_old, m0=m0, a=a, NMK=NMK, h=h),
+        otypes=[complex]
+    )
+    test_k = 0
+    test_r = a[bd] # Assuming bd is 2, so a[2] = 10
+
+    # Get direct output from diff_Lambda_k_old
+    direct_dlk_old = diff_Lambda_k_old(test_k, test_r, m0, a, NMK, h)
+    print(f"DEBUG_direct: diff_Lambda_k_old({test_k}, {test_r}, m0={m0}) -> {direct_dlk_old}")
+
+    # Get output from the vectorized function
+    vectorized_dlk = diff_Lambda_k_func([test_k], test_r) # Pass test_k in a list for vectorize
+    print(f"DEBUG_vectorized: diff_Lambda_k_func([{test_k}], {test_r}) -> {vectorized_dlk}")
+
+    # Check if they are close
+    print(f"DEBUG_comparison: direct vs vectorized close? {np.isclose(direct_dlk_old, vectorized_dlk[0])}")
+    def v_diagonal_block_e(bd):
+        vectorized_diff_Lambda_k_func = np.vectorize(
+            partial(diff_Lambda_k_old, m0=m0, a=a, NMK=NMK, h=h),
+            otypes=[complex]
+        )
+        
+        # Calculate the diagonal elements by applying the vectorized function
+        # 'a[bd]' is the fixed 'r' value for this boundary (radius)
+        diagonal_elements = vectorized_diff_Lambda_k_func(list(range(NMK[bd+1])), a[bd]) # NMK[bd+1] is M
+        
+        # Create the diagonal matrix and ensure complex dtype
+        return h * np.diag(diagonal_elements).astype(complex)
+
+    def v_dense_block_e(radfunction, bd): # for region adjacent to e-type region
+        I_km_array = np.transpose(I_mk_vals)
+        radial_vector = radfunction(list(range(NMK[bd])), a[bd], bd)
+        radial_array = np.outer((np.full((NMK[bd + 1]), 1)), radial_vector)
+        if bd == 2:
+            print(f"DEBUG: v_dense_block_e OLD (bd={bd}")
+            print(f"  I_km_array shape: {I_km_array.shape}") # Transposed!
+            print(f"  radial_vector shape: {radial_vector.shape}") # For the k-th element
+        return (-1) * radial_array * I_km_array
+    R_1n_func = np.vectorize(partial(R_1n_old, h=h, d=d, a=a))
+    R_2n_func = np.vectorize(partial(R_2n_old,  a=a, h=h, d=d))
+    diff_R_1n_func = np.vectorize(partial(diff_R_1n_old, h=h, d=d, a=a), otypes=[complex])
+    diff_R_2n_func = np.vectorize(partial(diff_R_2n_old, h=h, d=d, a=a), otypes=[complex])
+    # Potential Blocks
+    col = 0
+    row_start = 0
+    for bd in range(boundary_count):
+        N = NMK[bd]
+        M = NMK[bd + 1]
+        if bd == (boundary_count - 1): # i-e boundary, inherently left diagonal
+            row_height = N
+            # print(f"[OLD] Adding potential block at bd={bd}, rows {row_start}-{row_start + row_height}, cols {col}-{col + N + M}")
+            left_block1 = p_diagonal_block(True, np.vectorize(R_1n_func), bd)
+            print(f"At boundary {bd}, adding p_diagonal_block with shape {left_block1.shape}, max abs val: {np.max(np.abs(left_block1))}")
+            print(f"Block values: {left_block1}")
+            right_block = p_dense_block_e(bd)
+            debug_block(right_block, "p_dense_block_e", bd)
+            if bd == 0: # one cylinder
+                rows.append(np.concatenate((left_block1,right_block), axis = 1))
+            else:
+                left_block2 = p_diagonal_block(True, np.vectorize(R_2n_func), bd)
+                left_zeros = np.zeros((row_height, col), dtype=complex)
+                rows.append(np.concatenate((left_zeros,left_block1,left_block2,right_block), axis = 1))
+        elif bd == 0:
+            left_diag = d[bd] > d[bd + 1] # which of the two regions gets diagonal entries
+            if left_diag:
+                row_height = N
+                # print(f"[OLD] Adding potential block at bd={bd}, rows {row_start}-{row_start + row_height}, cols {col}-{col + N + 2*M}")
+                left_block = p_diagonal_block(True, np.vectorize(R_1n_func), 0)
+                right_block1 = p_dense_block(False, np.vectorize(R_1n_func), 0)
+                right_block2 = p_dense_block(False, np.vectorize(R_2n_func), 0)
+            else:
+                row_height = M
+                # print(f"[OLD] Adding potential block at bd={bd}, rows {row_start}-{row_start + row_height}, cols {col}-{col + N + 2*M}")
+                left_block = p_dense_block(True, np.vectorize(R_1n_func), 0)
+                right_block1 = p_diagonal_block(False, np.vectorize(R_1n_func), 0)
+                right_block2 = p_diagonal_block(False, np.vectorize(R_2n_func), 0)
+            right_zeros = np.zeros((row_height, size - (col + N + 2 * M)),dtype=complex)
+            block_lst = [left_block, right_block1, right_block2, right_zeros]
+            rows.append(np.concatenate(block_lst, axis = 1))
+            col += N
+        else: # i-i boundary
+            left_diag = d[bd] > d[bd + 1] # which of the two regions gets diagonal entries
+            if left_diag:
+                row_height = N
+                # print(f"[OLD] Adding potential block at bd={bd}, rows {row_start}-{row_start + row_height}, cols {col}-{col + 2*N + 2*M}")
+                left_block1 = p_diagonal_block(True, np.vectorize(R_1n_func), bd)
+                print(f"At boundary {bd}, adding p_diagonal_block with shape {left_block1.shape}, max abs val: {np.max(np.abs(left_block1))}")
+                print(f"Block values: {left_block1}")
+                left_block2 = p_diagonal_block(True, np.vectorize(R_2n_func), bd)
+                right_block1 = p_dense_block(False, np.vectorize(R_1n_func),  bd)
+                right_block2 = p_dense_block(False, np.vectorize(R_2n_func),  bd)
+            else:
+                row_height = M
+                # print(f"[OLD] Adding potential block at bd={bd}, rows {row_start}-{row_start + row_height}, cols {col}-{col + 2*N + 2*M}")
+                left_block1 = p_dense_block(True, np.vectorize(R_1n_func),  bd)
+                print(f"At boundary {bd}, adding p_dense_block with shape {left_block1.shape}, max abs val: {np.max(np.abs(left_block1))}")
+                print(f"Block values: {left_block1}")
+                left_block2 = p_dense_block(True, np.vectorize(R_2n_func),  bd)
+                right_block1 = p_diagonal_block(False, np.vectorize(R_1n_func),  bd)
+                right_block2 = p_diagonal_block(False, np.vectorize(R_2n_func),  bd)
+            left_zeros = np.zeros((row_height, col), dtype=complex)
+            right_zeros = np.zeros((row_height, size - (col + 2 * N + 2 * M)),dtype=complex)
+            block_lst = [left_zeros, left_block1, left_block2, right_block1, right_block2, right_zeros]
+            rows.append(np.concatenate(block_lst, axis = 1))
+            col += 2 * N
+        row_start += row_height
+
+
+    ###############################
+    # Velocity Blocks
+     # Compute row_start offset for velocity blocks safely
+    row_start = 0
+    for i in range(boundary_count):
+        if i < boundary_count - 1:
+            if d[i] > d[i + 1]:
+                row_start += NMK[i]
+            else:
+                row_start += NMK[i + 1]
+        else:
+            # For last boundary, no d[i+1], just add NMK[i]
+            row_start += NMK[i]
+    col = 0
+    for bd in range(boundary_count):
+        N = NMK[bd]
+        M = NMK[bd + 1]
+        if bd == (boundary_count - 1): # i-e boundary, inherently left diagonal
+            row_height = M
+            print(f"[OLD] Adding velocity block at bd={bd}, rows {row_start}-{row_start + row_height}, cols {col}-{col + N + M}")
+            left_block1 = v_dense_block_e(np.vectorize(diff_R_1n_func, otypes=[complex]), bd)
+            right_block = v_diagonal_block_e(bd)
+            print(f"right_block[local_m=1, local_k=0]: {right_block[1, 0]}")
+            debug_block(left_block1, "v_dense_block_e (left_block1)", bd)
+            debug_block(right_block, "v_diagonal_block_e (right_block)", bd)
+            if bd == 0: # one cylinder
+                rows.append(np.concatenate((left_block1,right_block), axis = 1))
+            else:
+                left_block2 = v_dense_block_e(np.vectorize(diff_R_2n_func, otypes=[complex]), bd)
+                left_zeros = np.zeros((row_height, col), dtype=complex)
+                rows.append(np.concatenate((left_zeros,left_block1,left_block2,right_block), axis = 1))
+        elif bd == 0:
+            left_diag = d[bd] <= d[bd + 1] # taller fluid region gets diagonal entries
+            if left_diag:
+                row_height = N
+                print(f"[OLD] Adding velocity block at bd={bd}, rows {row_start}-{row_start + row_height}, cols {col}-{col + N + 2*M}")
+                left_block = v_diagonal_block(True, np.vectorize(diff_R_1n_func, otypes=[complex]), 0)
+                right_block1 = v_dense_block(False, np.vectorize(diff_R_1n_func, otypes=[complex]), 0)
+                right_block2 = v_dense_block(False, np.vectorize(diff_R_2n_func, otypes=[complex]), 0)
+            else:
+                row_height = M
+                print(f"[OLD] Adding velocity block at bd={bd}, rows {row_start}-{row_start + row_height}, cols {col}-{col + N + 2*M}")
+                left_block = v_dense_block(True, np.vectorize(diff_R_1n_func, otypes=[complex]), 0)
+                right_block1 = v_diagonal_block(False, np.vectorize(diff_R_1n_func, otypes=[complex]), 0)
+                right_block2 = v_diagonal_block(False, np.vectorize(diff_R_2n_func, otypes=[complex]), 0)
+            right_zeros = np.zeros((row_height, size - (col + N + 2 * M)),dtype=complex)
+            block_lst = [left_block, right_block1, right_block2, right_zeros]
+            rows.append(np.concatenate(block_lst, axis = 1))
+            col += N
+        else: # i-i boundary
+            left_diag = d[bd] <= d[bd + 1] # taller fluid region gets diagonal entries
+            if left_diag:
+                row_height = N
+                print(f"[OLD] Adding velocity block at bd={bd}, rows {row_start}-{row_start + row_height}, cols {col}-{col + 2*N + 2*M}")
+                left_block1 = v_diagonal_block(True, np.vectorize(diff_R_1n_func, otypes=[complex]), bd)
+                debug_block(left_block1, "v_diagonal_block (left_block1)", bd)
+                left_block2 = v_diagonal_block(True, np.vectorize(diff_R_2n_func, otypes=[complex]), bd)
+                debug_block(left_block2, "v_diagonal_block (left_block2)", bd)
+                right_block1 = v_dense_block(False, np.vectorize(diff_R_1n_func, otypes=[complex]),  bd)
+                debug_block(right_block1, "v_dense_block (right_block1)", bd)
+                right_block2 = v_dense_block(False, np.vectorize(diff_R_2n_func, otypes=[complex]),  bd)
+                debug_block(right_block2, "v_dense_block (right_block2)", bd)
+            else:
+                row_height = M
+                print(f"[OLD] Adding velocity block at bd={bd}, rows {row_start}-{row_start + row_height}, cols {col}-{col + 2*N + 2*M}")
+                left_block1 = v_dense_block(True, np.vectorize(diff_R_1n_func, otypes=[complex]),  bd)
+                left_block2 = v_dense_block(True, np.vectorize(diff_R_2n_func, otypes=[complex]),  bd)
+                right_block1 = v_diagonal_block(False, np.vectorize(diff_R_1n_func, otypes=[complex]),  bd)
+                right_block2 = v_diagonal_block(False, np.vectorize(diff_R_2n_func, otypes=[complex]),  bd)
+            left_zeros = np.zeros((row_height, col), dtype=complex)
+            right_zeros = np.zeros((row_height, size - (col + 2* N + 2 * M)),dtype=complex)
+            block_lst = [left_zeros, left_block1, left_block2, right_block1, right_block2, right_zeros]
+            rows.append(np.concatenate(block_lst, axis = 1))
+            col += 2 * N
+        row_start += row_height
+
+    ## Concatenate the rows of blocks into the square A matrix
+    A = np.concatenate(rows, axis = 0)
+    b = np.zeros(size, dtype=complex)
+
+    index = 0
+
+    # potential matching
+    for boundary in range(boundary_count):
+        if boundary == (boundary_count - 1): # i-e boundary
+            for n in range(NMK[-2]):
+                b[index] = b_potential_end_entry_old(n, boundary, h, d, heaving, a)
+                index += 1
+        else: # i-i boundary
+            for n in range(NMK[boundary + (d[boundary] <= d[boundary + 1])]): # iterate over eigenfunctions for smaller h-d
+                b[index] = b_potential_entry_old(n, boundary, d, heaving, h, a)
+                index += 1
+
+    # velocity matching
+    for boundary in range(boundary_count):
+        if boundary == (boundary_count - 1): # i-e boundary
+            for n in range(NMK[-1]):
+                b[index] = b_velocity_end_entry_old(n, boundary, heaving, a, h, d, m0, NMK)
+                index += 1
+        else: # i-i boundary
+            for n in range(NMK[boundary + (d[boundary] > d[boundary + 1])]): # iterate over eigenfunctions for larger h-d
+                b[index] = b_velocity_entry_old(n, boundary, heaving, a, d, h)
+                index += 1
+    return A, b
\ No newline at end of file
diff --git a/dev/python/slants/Multi_MEEM_with_slant.ipynb b/dev/python/slants/Multi_MEEM_with_slant.ipynb
new file mode 100644
index 0000000..82df141
--- /dev/null
+++ b/dev/python/slants/Multi_MEEM_with_slant.ipynb
@@ -0,0 +1,346 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "sys.path.append(os.path.relpath('../'))\n",
+    "import numpy as np\n",
+    "from multi_condensed import Problem\n",
+    "from math import sqrt, cosh, cos, sinh, sin, pi\n",
+    "from scipy.optimize import newton, minimize_scalar\n",
+    "from multi_condensed import Problem\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# constants block\n",
+    "h = 50.0\n",
+    "d = np.array([29.0, 7.0, 5.5, 4.0])\n",
+    "a = np.array([3.0, 5.0, 7.5, 10.0])\n",
+    "heaving = [0, 1, 1, 1] # 0/false if not heaving, 1/true if yes heaving\n",
+    "slant = [0, 1, 1, 1] # 0/false if not slanted, 1/true if yes slanted\n",
+    "NMK = [10, 10, 10, 10, 10]\n",
+    "omega = 0.4\n",
+    "# # omega = np.linspace(0.1, 1.5, 15)\n",
+    "rho = 1023\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prob = Problem(h, d, a, heaving, NMK, 1, rho)\n",
+    "prob.change_m0(prob.wavenumber(omega))\n",
+    "a_matrix = prob.a_matrix()\n",
+    "b_vector = prob.b_vector()\n",
+    "x = prob.get_unknown_coeffs(a_matrix, b_vector)\n",
+    "Cs = prob.reformat_coeffs(x)\n",
+    "\n",
+    "am, dp = prob.hydro_coeffs(x, \"nondimensional\")\n",
+    "boundary_count = prob.boundary_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def phi_h_n_inner_func(n, r, z):\n",
+    "    return (Cs[0][n] * prob.R_1n(n, r, 0)) * prob.Z_n_i(n, z, 0)\n",
+    "\n",
+    "def phi_h_m_i_func(i, m, r, z):\n",
+    "    return (Cs[i][m] * prob.R_1n(m, r, i) + Cs[i][NMK[i] + m] * prob.R_2n(m, r, i)) * prob.Z_n_i(m, z, i)\n",
+    "\n",
+    "def phi_e_k_func(k, r, z):\n",
+    "    return Cs[-1][k] * prob.Lambda_k(k, r) * prob.Z_k_e(k, z)\n",
+    "\n",
+    "def phi_p_i(d, r, z): # particular solution\n",
+    "            return (1 / (2* (h - d))) * ((z + h) ** 2 - (r**2) / 2)\n",
+    "\n",
+    "phi_e_k_vec = np.vectorize(phi_e_k_func, otypes = [complex])\n",
+    "phi_h_n_inner_vec = np.vectorize(phi_h_n_inner_func, otypes = [complex])\n",
+    "phi_h_m_i_vec = np.vectorize(phi_h_m_i_func, otypes = [complex])\n",
+    "phi_p_i_vec = np.vectorize(phi_p_i)\n",
+    "\n",
+    "spatial_res=50\n",
+    "r_vec = np.linspace(2 * a[-1] / spatial_res, 2*a[-1], spatial_res)\n",
+    "z_vec = np.linspace(-h, 0, spatial_res)\n",
+    "\n",
+    "#add values at the radii\n",
+    "a_eps = 1.0e-4\n",
+    "for i in range(len(a)):\n",
+    "    r_vec = np.append(r_vec, a[i]*(1-a_eps))\n",
+    "    r_vec = np.append(r_vec, a[i]*(1+a_eps))\n",
+    "r_vec = np.unique(r_vec)\n",
+    "\n",
+    "for i in range(len(d)):\n",
+    "    z_vec = np.append(z_vec, -d[i]*(1-a_eps))\n",
+    "    z_vec = np.append(z_vec, -d[i]*(1+a_eps))\n",
+    "z_vec = np.unique(z_vec)\n",
+    "\n",
+    "R, Z = np.meshgrid(r_vec, z_vec)\n",
+    " \n",
+    "\n",
+    "regions = []\n",
+    "regions.append((R <= a[0]) & (Z < -d[0]))\n",
+    "for i in range(1, boundary_count):\n",
+    "    regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))\n",
+    "regions.append(R > a[-1])\n",
+    "\n",
+    "phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+    "phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+    "phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+    "\n",
+    "for n in range(NMK[0]):\n",
+    "    temp_phiH = phi_h_n_inner_vec(n, R[regions[0]], Z[regions[0]])\n",
+    "    phiH[regions[0]] = temp_phiH if n == 0 else phiH[regions[0]] + temp_phiH\n",
+    "\n",
+    "for i in range(1, boundary_count):\n",
+    "    for m in range(NMK[i]):\n",
+    "        temp_phiH = phi_h_m_i_vec(i, m, R[regions[i]], Z[regions[i]])\n",
+    "        phiH[regions[i]] = temp_phiH if m == 0 else phiH[regions[i]] + temp_phiH\n",
+    "\n",
+    "for k in range(NMK[-1]):\n",
+    "    temp_phiH = phi_e_k_vec(k, R[regions[-1]], Z[regions[-1]])\n",
+    "    phiH[regions[-1]] = temp_phiH if k == 0 else phiH[regions[-1]] + temp_phiH\n",
+    "\n",
+    "phiP[regions[0]] = heaving[0] * phi_p_i_vec(d[0], R[regions[0]], Z[regions[0]])\n",
+    "for i in range(1, boundary_count):\n",
+    "    phiP[regions[i]] = heaving[i] * phi_p_i_vec(d[i], R[regions[i]], Z[regions[i]])\n",
+    "phiP[regions[-1]] = 0\n",
+    "\n",
+    "phi = phiH + phiP\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Goal: Solve for hydrocoefficients when the body is slanted\n",
+    "\n",
+    "Approach: Using the the potentials at the two corners to estimate d_phi/d_z by applying trigonometry.\n",
+    "\n",
+    "Problem: The the values for hydrocoefficients are too small"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "real 64.92695158565381\n",
+      "imag 157.8334888059197\n",
+      "(8115868.948206726+7891674.440295986j)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/zz/_5443rfn2v1_n4x4gqlv6jxc0000gr/T/ipykernel_91360/507150699.py:28: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  vel_z[i] = (phi_top_corner1 - phi_bttm_corner1)*(d[i-1]-d[i])/((d[i-1]-d[i])**2+(a[i]-a[i-1])**2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "hydro_terms = np.zeros((prob.size - NMK[-1]), dtype=complex)\n",
+    "vel_z = np.zeros(len(NMK)-1, dtype=complex)\n",
+    "\n",
+    "col = 0\n",
+    "region_indx = 0\n",
+    "for n in range(NMK[0]):\n",
+    "    if slant[0]:\n",
+    "        #need definition\n",
+    "        pass \n",
+    "    else:\n",
+    "        hydro_terms[n] = prob.int_R_1n(0, n)* x[n] * prob.z_n_d(n)\n",
+    "col += NMK[0]\n",
+    "region_indx += 1\n",
+    "for i in range(1, boundary_count):\n",
+    "    M = NMK[i]\n",
+    "    if slant[i]:\n",
+    "        #bottom_corner\n",
+    "        r_index = np.where(r_vec == a[i-1]*(1+a_eps))\n",
+    "        z_index = np.where(z_vec == -d[i-1]*(1-a_eps))\n",
+    "        phi_bttm_corner1 = phi[z_index, r_index]\n",
+    "\n",
+    "        #top__corner\n",
+    "        r_index = np.where(r_vec == a[i]*(1-a_eps))\n",
+    "        z_index = np.where(z_vec == -d[i]*(1+a_eps))\n",
+    "        phi_top_corner1 = phi[z_index, r_index]\n",
+    "        \n",
+    "        #slant velocity z component approximation\n",
+    "        vel_z[i] = (phi_top_corner1 - phi_bttm_corner1)*(d[i-1]-d[i])/((d[i-1]-d[i])**2+(a[i]-a[i-1])**2)\n",
+    "        \n",
+    "        for m in range(M):\n",
+    "            hydro_terms[col + m] = vel_z[i] * prob.int_R_1n(i, m)* x[col + m] * prob.z_n_d(m)\n",
+    "            hydro_terms[col + M + m] = vel_z[i] * prob.int_R_2n(i, m)* x[col + M + m] * prob.z_n_d(m)\n",
+    "    else:\n",
+    "        for m in range(M):\n",
+    "            hydro_terms[col + m] = prob.int_R_1n(i, m)* x[col + m] * prob.z_n_d(m)\n",
+    "            hydro_terms[col + M + m] = prob.int_R_2n(i, m)* x[col + M + m] * prob.z_n_d(m)\n",
+    "    col += 2 * M\n",
+    "    region_indx += 1\n",
+    "\n",
+    "hydro_p_terms = np.zeros(boundary_count, dtype=complex)\n",
+    "for i in range(boundary_count):\n",
+    "    if not heaving[i]:\n",
+    "        hydro_p_terms[i] = 0\n",
+    "    elif slant[i]:\n",
+    "        hydro_p_terms[i] = vel_z[i] * prob.int_phi_p_i(i)\n",
+    "    else:\n",
+    "        hydro_p_terms[i] = prob.int_phi_p_i(i)\n",
+    "\n",
+    "\n",
+    "hydro_coef =2*pi*(sum(hydro_terms) + sum(hydro_p_terms))\n",
+    "hydro_coef_real = hydro_coef.real\n",
+    "hydro_coef_imag = hydro_coef.imag/omega\n",
+    "\n",
+    "# find maximum heaving radius\n",
+    "max_rad = a[0]\n",
+    "for i in range(boundary_count - 1, 0, -1):\n",
+    "    if heaving[i]:\n",
+    "        max_rad = a[i]\n",
+    "        break\n",
+    "\n",
+    "hydro_coef_dim = h**3*hydro_coef\n",
+    "\n",
+    "print(\"real\", hydro_coef_real)\n",
+    "print(\"imag\", hydro_coef_imag)\n",
+    "print(hydro_coef_dim)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "real 18.42499488038034\n",
+      "imag 99.0193608653291\n",
+      "(733.107252913849+1575.9420743517305j)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/zz/_5443rfn2v1_n4x4gqlv6jxc0000gr/T/ipykernel_91360/2242581120.py:32: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  delta_phi[i] = phi_top_corner - phi_bttm_corner\n"
+     ]
+    }
+   ],
+   "source": [
+    "hydro_terms = np.zeros((prob.size - NMK[-1]), dtype=complex)\n",
+    "cos_theta = np.zeros(len(NMK)-1, dtype=complex)\n",
+    "sin_theta = np.zeros(len(NMK)-1, dtype=complex)\n",
+    "delta_phi = np.zeros(len(NMK)-1, dtype=complex)\n",
+    "\n",
+    "col = 0\n",
+    "region_indx = 0\n",
+    "for n in range(NMK[0]):\n",
+    "    if slant[0]:\n",
+    "        #need definition\n",
+    "        pass \n",
+    "    else:\n",
+    "        hydro_terms[n] = prob.int_R_1n(0, n)* x[n] * prob.z_n_d(n)\n",
+    "col += NMK[0]\n",
+    "region_indx += 1\n",
+    "for i in range(1, boundary_count):\n",
+    "    M = NMK[i]\n",
+    "    if slant[i]:\n",
+    "        #slant velocity z component approximation\n",
+    "        cos_theta[i] = (d[i-1]-d[i])/((d[i-1]-d[i])**2+(a[i]-a[i-1])**2)\n",
+    "        sin_theta[i] = (a[i]-a[i-1])/((d[i-1]-d[i])**2+(a[i]-a[i-1])**2)\n",
+    "\n",
+    "        #bottom_corner\n",
+    "        r_index = np.where(r_vec == a[i-1]*(1+a_eps))\n",
+    "        z_index = np.where(z_vec == -d[i-1]*(1-a_eps))\n",
+    "        phi_bttm_corner = phi[z_index, r_index]\n",
+    "\n",
+    "        #top__corner\n",
+    "        r_index = np.where(r_vec == a[i]*(1-a_eps))\n",
+    "        z_index = np.where(z_vec == -d[i]*(1+a_eps))\n",
+    "        phi_top_corner = phi[z_index, r_index]\n",
+    "        delta_phi[i] = phi_top_corner - phi_bttm_corner\n",
+    "\n",
+    "        for m in range(M):\n",
+    "            hydro_terms[col + m] = delta_phi[i]*cos_theta[i]*sin_theta[i] * prob.int_R_1n(i, m)* x[col + m] * prob.z_n_d(m)\n",
+    "            hydro_terms[col + M + m] = delta_phi[i]*cos_theta[i]*sin_theta[i] * prob.int_R_2n(i, m)* x[col + M + m] * prob.z_n_d(m)\n",
+    "    else:\n",
+    "        for m in range(M):\n",
+    "            hydro_terms[col + m] = prob.int_R_1n(i, m)* x[col + m] * prob.z_n_d(m)\n",
+    "            hydro_terms[col + M + m] = prob.int_R_2n(i, m)* x[col + M + m] * prob.z_n_d(m)\n",
+    "    col += 2 * M\n",
+    "    region_indx += 1\n",
+    "\n",
+    "hydro_p_terms = np.zeros(boundary_count, dtype=complex)\n",
+    "for i in range(boundary_count):\n",
+    "    if not heaving[i]:\n",
+    "        hydro_p_terms[i] = 0\n",
+    "    elif slant[i]:\n",
+    "        hydro_p_terms[i] = delta_phi[i]*cos_theta[i]*sin_theta[i]*prob.int_phi_p_i(i)\n",
+    "    else:\n",
+    "        hydro_p_terms[i] = prob.int_phi_p_i(i)\n",
+    "\n",
+    "\n",
+    "hydro_coef =2*pi*(sum(hydro_terms) + sum(hydro_p_terms))\n",
+    "hydro_coef_real = hydro_coef.real\n",
+    "hydro_coef_imag = hydro_coef.imag/omega\n",
+    "\n",
+    "# find maximum heaving radius\n",
+    "max_rad = a[0]\n",
+    "for i in range(boundary_count - 1, 0, -1):\n",
+    "    if heaving[i]:\n",
+    "        max_rad = a[i]\n",
+    "        break\n",
+    "\n",
+    "hydro_coef_nondim = h**3/(max_rad**3 * pi)*hydro_coef\n",
+    "\n",
+    "print(\"real\", hydro_coef_real)\n",
+    "print(\"imag\", hydro_coef_imag)\n",
+    "print(hydro_coef_nondim)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/dev/python/slants/capytaine_generator.py b/dev/python/slants/capytaine_generator.py
new file mode 100644
index 0000000..3613549
--- /dev/null
+++ b/dev/python/slants/capytaine_generator.py
@@ -0,0 +1,290 @@
+# These functions are used in many files, 
+
+import capytaine as cpt
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+import time
+import sys, os
+
+# removes capytaine warnings from clogging outputs
+import logging
+
+class CapytaineSlantSolver:
+    
+    # arguments for whether or not to display mesh, panel count, hydro coefficients, computation time, or excitation phase
+    def __init__(self, mesh, panel_count, hydros, times, phase):
+      self.show_mesh = mesh
+      self.show_pc = panel_count
+      self.show_hydros = hydros
+      self.show_times = times
+      self.show_phase = phase
+      self.solver = cpt.BEMSolver()
+      logging.getLogger("capytaine").setLevel(logging.ERROR)
+
+    # use to get rid of prints
+    def __deafen(self, function, *args, **kwargs):
+        real_stdout = sys.stdout
+        try:
+            sys.stdout = open(os.devnull, "w")
+            output = function(*args, **kwargs)
+        finally:
+            sys.stdout = real_stdout
+        return output
+
+    def __timed_solve(self, problem, reps):
+        t_lst = []
+        for i in range(reps):
+            t0 = time.perf_counter()
+            result = self.solver.solve(problem, keep_details = True)
+            t1 = time.perf_counter()
+            t_lst.append(t1 - t0)
+        tdiff = sum(t_lst)/reps
+        return result, tdiff
+
+    def __get_points(self, a, d_in, d_out): # These points define the outline of the body
+        pt_lst = [(0, - d_in[0])]
+        for i in range(len(a)):
+            pt_lst.append((a[i], - d_out[i]))
+            if i < (len(a) - 1): # not last body region
+                if d_out[i] != d_in[i + 1]: # vertical face exists
+                    pt_lst.append((a[i], - d_in[i + 1]))
+            else: # need vertical face to water surface
+                pt_lst.append((a[i], 0))
+        return pt_lst
+
+    # compute number of panels along each surface given total number along the outline
+    def __get_f_densities(self, pt_lst, total_units):
+        face_lengths = np.array([])
+        for i in range(len(pt_lst) - 1):
+            p1, p2 = pt_lst[i], pt_lst[i + 1]
+            face_length = math.sqrt((p2[0] - p1[0]) ** 2 + (p2[1] - p1[1]) ** 2) # one of these two values will be zero
+            face_lengths = np.append(face_lengths, face_length)
+        total_length = sum(face_lengths)
+        each_face_densities = np.vectorize(lambda x: max(1, x/total_length * total_units))(face_lengths) # each face needs at least one panel
+        remainders = each_face_densities % 1
+        each_face_densities = each_face_densities.astype(int)
+        remaining_units = total_units - sum(each_face_densities)
+        if remaining_units < 0: # high proportion of small faces
+            for u in range(remaining_units * -1):
+                i = np.argmax(each_face_densities) # cut density from the largest faces
+                each_face_densities[i] = (each_face_densities[i]) - 1
+        else:
+            for u in range(remaining_units): # distribute remaining units where most needed
+                i = np.argmax(remainders)
+                each_face_densities[i] = (each_face_densities[i]) + 1
+                remainders[i] = 0
+        assert sum(each_face_densities) == total_units
+        return each_face_densities
+
+    def __make_face(self, p1, p2, f_density, t_density):
+        zarr = np.linspace(p1[1], p2[1], f_density + 1)
+        rarr = np.linspace(p1[0], p2[0], f_density + 1)
+        xyz = np.array([np.array([x/np.sqrt(2),y/np.sqrt(2),z]) for x,y,z in zip(rarr,rarr,zarr)])
+        return cpt.AxialSymmetricMesh.from_profile(xyz, nphi = t_density)
+
+    def __faces_and_heaves(self, heave_status, p1, p2, f_density, t_density, meshes, mask, panel_ct):
+        mesh = self.__make_face(p1, p2, f_density, t_density)
+        meshes += mesh
+        new_panels = f_density * t_density
+        if heave_status:
+            direction = [0, 0, 1]
+        else:
+            direction = [0, 0, 0]
+        for i in range(new_panels):
+            mask.append(direction)
+        return meshes, mask, (panel_ct + new_panels)
+
+    def get_excitation_phase(self, result):
+        return np.angle((cpt.assemble_dataset([result]))["excitation_force"][0][0][0])
+
+    def __make_body(self, pts, t_densities, f_densities, heaving):
+        meshes = cpt.meshes.meshes.Mesh()
+        panel_ct = 0
+        mask = []
+        heave_region = -1
+        for i in range(len(pts) - 1):
+            p1, p2 = pts[i], pts[i + 1]
+            if p1[0] != p2[0]: # face spans some horizontal distance
+                heave_region += 1 # advance to next region
+                # make a horizontal face
+                meshes, mask, panel_ct = self.__faces_and_heaves(heaving[heave_region], p1, p2, f_densities[i], t_densities[heave_region], meshes, mask, panel_ct)
+            else: # make a vertical face
+                if p1[1] <= p2[1]: # body on inside
+                  j = heave_region # defer to variables of inner region
+                else: # body on outside
+                  j = heave_region + 1 # defer to variables of outer region
+                meshes, mask, panel_ct = self.__faces_and_heaves(heaving[j], p1, p2, f_densities[i], t_densities[j], meshes, mask, panel_ct)
+        body = self.__deafen(cpt.FloatingBody, mesh = meshes) # unclosed boundary warnings
+        # , lid_mesh = meshes.generate_lid() # consider adding lid mesh to above function
+        return body, panel_ct, mask
+
+    def construct_and_solve(self, a, d_in, d_out, heaving, t_densities, face_units, h, m0, rho, reps, f_densities = None):
+        pt_lst = self.__get_points(a, d_in, d_out)
+        if f_densities is None:
+            f_densities = self.__get_f_densities(pt_lst, face_units)
+        
+        body, panel_count, mask = self.__make_body(pt_lst, t_densities, f_densities, heaving)
+        body.dofs["Heave"] = mask  
+        if self.show_mesh: body.show_matplotlib()
+        
+        rad_problem = cpt.RadiationProblem(body = body, wavenumber = m0, water_depth = h, rho = rho)
+        result, t_diff = self.__timed_solve(rad_problem, reps)
+
+        diff_problem = cpt.DiffractionProblem(body = body, wavenumber = m0, water_depth = h, rho = rho)
+        result_d, t_diff_d = self.__timed_solve(diff_problem, reps)
+
+        if self.show_pc: print("Panel Count: ", panel_count)
+        if self.show_hydros:
+          print(result.added_mass)
+          print(result.radiation_damping)
+        if self.show_times:
+          print("Solve Time (Radiation): ", t_diff)
+          print("Solve Time (Diffraction): ", t_diff_d)
+        if self.show_phase: print("Excitation Phase: ", self.get_excitation_phase(result_d))
+        return result, t_diff, result_d, t_diff_d, panel_count
+    
+    def __get_region(self, a, r):
+        # assumes r >= 0
+        # returns -1 if in outermost, extends-to-infinity region
+        region = 0
+        for rad in a:
+            if r <= rad:
+                return region
+            else:
+                region += 1
+        return -1
+    
+    def __above_line(self, p1, p2, x, y):
+        x1, y1 = p1
+        x2, y2 = p2
+
+        if x2 == x1:
+            raise ValueError(f"The line defined by points {p1} and {p2} is vertical.")
+
+        slope = (y2 - y1) / (x2 - x1)
+        y_on_line = y1 + slope * (x - x1)
+
+        return y > y_on_line
+    
+    
+    # given a body definable by a, d_in, d_out, returns a function that says whether or not a given point is in the body.
+    # that function assumes points are at/below the surface.
+    def get_body_bounds_from_regions(self, a, d_in, d_out):
+        pt_lst = self.__get_points(a, d_in, d_out)
+
+        def is_inside(r, z):
+            region = self. __get_region(a, r)
+            if region == -1: return False
+            inner_rad = 0 if region == 0 else a[region - 1]
+            return self.__above_line((inner_rad, - d_in[region]), (a[region], -d_out[region]), r, z)
+        
+        return is_inside
+        
+    def __get_points_and_mask(self, h, a, d_in, d_out, res):
+
+        is_inside = self.get_body_bounds_from_regions(a, d_in, d_out)
+
+        R_range = np.linspace(0.0, 2 * a[-1], num = res)
+        Z_range = np.linspace(0, -h, num = res) 
+        R, Z = np.meshgrid(R_range, Z_range)
+
+        # Flatten R and Z into 1D arrays
+        r_flat = R.ravel()
+        z_flat = Z.ravel()
+
+        # Create a validity mask by applying not is_inside(r, z) to each point
+        valid_mask = np.array([(not is_inside(r, z)) for r, z in zip(r_flat, z_flat)])
+
+        # Create full 3D points for valid locations (assuming y = y_value)
+        valid_points = np.column_stack((r_flat[valid_mask],
+                                        np.full(np.sum(valid_mask), 0),
+                                        z_flat[valid_mask]))
+        
+        return R, Z, valid_mask, valid_points
+    
+    def get_potential_array(self, h, a, d_in, d_out, res, rad_result):
+        R, Z, valid_mask, valid_points = self.__get_points_and_mask(h, a, d_in, d_out, res)
+
+        valid_results = self.solver.compute_potential(valid_points, rad_result)
+
+        # Build output array filled with NaNs, alter valid points to finite.
+        result = np.full(R.size, np.nan + np.nan*1j)
+        result[valid_mask] = valid_results
+
+        return R, Z, result.reshape(R.shape)
+
+    def get_velocity_arrays(self, h, a, d_in, d_out, res, rad_result):
+        R, Z, valid_mask, valid_points = self.__get_points_and_mask(h, a, d_in, d_out, res)
+
+        valid_results = self.solver.compute_velocity(valid_points, rad_result)
+
+        # Extract columns: r (or x) = computed[:, 0], z = computed[:, 2]
+        r_vel_flat = np.full(R.size, np.nan + np.nan*1j)
+        z_vel_flat = np.full(R.size, np.nan + np.nan*1j)
+
+        r_vel_flat[valid_mask] = valid_results[:, 0]
+        z_vel_flat[valid_mask] = valid_results[:, 2]
+
+        # Reshape to original grid shape
+        r_vel = r_vel_flat.reshape(R.shape)
+        z_vel = z_vel_flat.reshape(R.shape)
+
+        return R, Z, r_vel, z_vel
+    
+    def __plot_contour(self, R, Z, data, color_label, title):
+        plt.contourf(R, Z, data, cmap='viridis', levels = 50)
+        plt.colorbar(label = color_label)
+        plt.contour(R, Z, data, colors='black', linestyles='solid', linewidths=0.05, levels=50)
+        plt.xlabel('R')
+        plt.ylabel('Z')
+        plt.title(title)
+        plt.show()
+
+    def plot_potential(self, h, a, d_in, d_out, res, rad_result, MEEM_convention = False):
+        # Also returns arrays, no recomputation necessary
+        R, Z, potential_array = self.get_potential_array(h, a, d_in, d_out, res, rad_result)
+
+        if MEEM_convention:
+            omega = rad_result.omega
+            potential_array = potential_array * 1j / omega
+
+        real_phi = np.real(potential_array)
+        imag_phi = np.imag(potential_array)
+
+        self.__plot_contour(R, Z, real_phi, "Potential", "Real Potential with Capytaine")
+        self.__plot_contour(R, Z, imag_phi, "Potential", "Imaginary Potential with Capytaine")
+
+        return real_phi, imag_phi
+
+    def plot_velocities(self, h, a, d_in, d_out, res, rad_result, MEEM_convention = False):
+        R, Z, vr, vz = self.get_velocity_arrays(h, a, d_in, d_out, res, rad_result)
+
+        if MEEM_convention:
+            omega = rad_result.omega
+            vr = vr * 1j / omega
+            vz = vz * 1j / omega
+
+        real_vr = np.real(vr)
+        imag_vr = np.imag(vr)
+        real_vz = np.real(vz)
+        imag_vz = np.imag(vz)
+
+        self.__plot_contour(R, Z, real_vr, "Radial Velocity", "Real radial velocity with Capytaine")
+        self.__plot_contour(R, Z, imag_vr, "Radial Velocity", "Imag radial velocity with Capytaine")
+        self.__plot_contour(R, Z, real_vz, "Vertical Velocity", "Real vertical velocity with Capytaine")
+        self.__plot_contour(R, Z, imag_vz, "Vetical Velocity", "Imag vertical velocity with Capytaine")
+
+        return real_vr, imag_vr, real_vz, imag_vz
+    
+    def plot_from_array(self, h, a, data, color_lab = None, title = None):
+        res = len(data)
+        R_range = np.linspace(0.0, 2 * a[-1], num = res)
+        Z_range = np.linspace(0, -h, num = res) 
+        R, Z = np.meshgrid(R_range, Z_range)
+        self.__plot_contour(R, Z, data, color_lab, title)
+
+    
+
+    
+    
\ No newline at end of file
diff --git a/dev/python/slants/capytaine_slant.ipynb b/dev/python/slants/capytaine_slant.ipynb
new file mode 100644
index 0000000..6212374
--- /dev/null
+++ b/dev/python/slants/capytaine_slant.ipynb
@@ -0,0 +1,568 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rSJf6s1tKGz7",
+    "outputId": "d58c0339-e27d-4651-99ac-f4da2155a60f"
+   },
+   "outputs": [],
+   "source": [
+    "# This generates configuration values with Capytaine.\n",
+    "# This is a notebook to workshop doing slants with Capytaine.\n",
+    "# Outputs are not used anywhere.\n",
+    "\n",
+    "#!pip install capytaine #uncomment if first time running\n",
+    "\n",
+    "import capytaine as cpt\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "m0_nums = np.concatenate((np.linspace(0.1, 1, 10), np.linspace(1, 6, 10)))\n",
+    "print(len(m0_nums))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/enlo/miniconda3/envs/meem/lib/python3.10/site-packages/capytaine/matrices/block_toeplitz.py:218: RuntimeWarning: divide by zero encountered in matmul\n",
+      "  fft_of_result = blocks_of_diagonalization @ fft_of_vector\n",
+      "/Users/enlo/miniconda3/envs/meem/lib/python3.10/site-packages/capytaine/matrices/block_toeplitz.py:218: RuntimeWarning: overflow encountered in matmul\n",
+      "  fft_of_result = blocks_of_diagonalization @ fft_of_vector\n",
+      "/Users/enlo/miniconda3/envs/meem/lib/python3.10/site-packages/capytaine/matrices/block_toeplitz.py:218: RuntimeWarning: invalid value encountered in matmul\n",
+      "  fft_of_result = blocks_of_diagonalization @ fft_of_vector\n",
+      "/Users/enlo/miniconda3/envs/meem/lib/python3.10/site-packages/scipy/sparse/linalg/_isolve/iterative.py:769: RuntimeWarning: divide by zero encountered in matmul\n",
+      "  x += y @ v[:col+1, :]\n",
+      "/Users/enlo/miniconda3/envs/meem/lib/python3.10/site-packages/scipy/sparse/linalg/_isolve/iterative.py:769: RuntimeWarning: overflow encountered in matmul\n",
+      "  x += y @ v[:col+1, :]\n",
+      "/Users/enlo/miniconda3/envs/meem/lib/python3.10/site-packages/scipy/sparse/linalg/_isolve/iterative.py:769: RuntimeWarning: invalid value encountered in matmul\n",
+      "  x += y @ v[:col+1, :]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[02:17:37] </span><span style=\"color: #808000; text-decoration-color: #808000\">WARNING </span> Mesh resolution for <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RadiationProblem</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">body</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"axisymmetric_mesh\"</span><span style=\"font-weight: bold\">)</span>,         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.889</span>, <span style=\"color: #808000; text-decoration-color: #808000\">water_depth</span>=<span style=\"color: #800080; text-decoration-color: #800080\">inf</span>, <span style=\"color: #808000; text-decoration-color: #808000\">radiating_dof</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Heave'</span><span style=\"font-weight: bold\">)</span>:                                     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         The resolution of the mesh of the body <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"axisymmetric_mesh\"</span><span style=\"font-weight: bold\">)</span> might be    \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         insufficient for <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.888888888888889</span>.                                                 \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         This warning appears because the largest panel of this mesh has radius <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.178</span> &gt; wavelength/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>.   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[02:17:37]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m Mesh resolution for \u001b[1;35mRadiationProblem\u001b[0m\u001b[1m(\u001b[0m\u001b[33mbody\u001b[0m=\u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m\"axisymmetric_mesh\"\u001b[0m\u001b[1m)\u001b[0m,         \n",
+       "\u001b[2;36m           \u001b[0m         \u001b[33mwavenumber\u001b[0m=\u001b[1;36m4\u001b[0m\u001b[1;36m.889\u001b[0m, \u001b[33mwater_depth\u001b[0m=\u001b[35minf\u001b[0m, \u001b[33mradiating_dof\u001b[0m=\u001b[32m'Heave'\u001b[0m\u001b[1m)\u001b[0m:                                     \n",
+       "\u001b[2;36m           \u001b[0m         The resolution of the mesh of the body \u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m\"axisymmetric_mesh\"\u001b[0m\u001b[1m)\u001b[0m might be    \n",
+       "\u001b[2;36m           \u001b[0m         insufficient for \u001b[33mwavenumber\u001b[0m=\u001b[1;36m4\u001b[0m\u001b[1;36m.888888888888889\u001b[0m.                                                 \n",
+       "\u001b[2;36m           \u001b[0m         This warning appears because the largest panel of this mesh has radius \u001b[1;36m0.178\u001b[0m > wavelength/\u001b[1;36m8\u001b[0m.   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #808000; text-decoration-color: #808000\">WARNING </span> Mesh resolution for <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RadiationProblem</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">body</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"axisymmetric_mesh\"</span><span style=\"font-weight: bold\">)</span>,         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.444</span>, <span style=\"color: #808000; text-decoration-color: #808000\">water_depth</span>=<span style=\"color: #800080; text-decoration-color: #800080\">inf</span>, <span style=\"color: #808000; text-decoration-color: #808000\">radiating_dof</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Heave'</span><span style=\"font-weight: bold\">)</span>:                                     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         The resolution of the mesh of the body <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"axisymmetric_mesh\"</span><span style=\"font-weight: bold\">)</span> might be    \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         insufficient for <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.444444444444445</span>.                                                 \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         This warning appears because the largest panel of this mesh has radius <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.178</span> &gt; wavelength/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>.   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m Mesh resolution for \u001b[1;35mRadiationProblem\u001b[0m\u001b[1m(\u001b[0m\u001b[33mbody\u001b[0m=\u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m\"axisymmetric_mesh\"\u001b[0m\u001b[1m)\u001b[0m,         \n",
+       "\u001b[2;36m           \u001b[0m         \u001b[33mwavenumber\u001b[0m=\u001b[1;36m5\u001b[0m\u001b[1;36m.444\u001b[0m, \u001b[33mwater_depth\u001b[0m=\u001b[35minf\u001b[0m, \u001b[33mradiating_dof\u001b[0m=\u001b[32m'Heave'\u001b[0m\u001b[1m)\u001b[0m:                                     \n",
+       "\u001b[2;36m           \u001b[0m         The resolution of the mesh of the body \u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m\"axisymmetric_mesh\"\u001b[0m\u001b[1m)\u001b[0m might be    \n",
+       "\u001b[2;36m           \u001b[0m         insufficient for \u001b[33mwavenumber\u001b[0m=\u001b[1;36m5\u001b[0m\u001b[1;36m.444444444444445\u001b[0m.                                                 \n",
+       "\u001b[2;36m           \u001b[0m         This warning appears because the largest panel of this mesh has radius \u001b[1;36m0.178\u001b[0m > wavelength/\u001b[1;36m8\u001b[0m.   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[02:17:38] </span><span style=\"color: #808000; text-decoration-color: #808000\">WARNING </span> Mesh resolution for <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RadiationProblem</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">body</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"axisymmetric_mesh\"</span><span style=\"font-weight: bold\">)</span>,         \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.000</span>, <span style=\"color: #808000; text-decoration-color: #808000\">water_depth</span>=<span style=\"color: #800080; text-decoration-color: #800080\">inf</span>, <span style=\"color: #808000; text-decoration-color: #808000\">radiating_dof</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Heave'</span><span style=\"font-weight: bold\">)</span>:                                     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         The resolution of the mesh of the body <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"axisymmetric_mesh\"</span><span style=\"font-weight: bold\">)</span> might be    \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         insufficient for <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.0</span>.                                                               \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         This warning appears because the largest panel of this mesh has radius <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.178</span> &gt; wavelength/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>.   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[02:17:38]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m Mesh resolution for \u001b[1;35mRadiationProblem\u001b[0m\u001b[1m(\u001b[0m\u001b[33mbody\u001b[0m=\u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m\"axisymmetric_mesh\"\u001b[0m\u001b[1m)\u001b[0m,         \n",
+       "\u001b[2;36m           \u001b[0m         \u001b[33mwavenumber\u001b[0m=\u001b[1;36m6\u001b[0m\u001b[1;36m.000\u001b[0m, \u001b[33mwater_depth\u001b[0m=\u001b[35minf\u001b[0m, \u001b[33mradiating_dof\u001b[0m=\u001b[32m'Heave'\u001b[0m\u001b[1m)\u001b[0m:                                     \n",
+       "\u001b[2;36m           \u001b[0m         The resolution of the mesh of the body \u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m\"axisymmetric_mesh\"\u001b[0m\u001b[1m)\u001b[0m might be    \n",
+       "\u001b[2;36m           \u001b[0m         insufficient for \u001b[33mwavenumber\u001b[0m=\u001b[1;36m6\u001b[0m\u001b[1;36m.0\u001b[0m.                                                               \n",
+       "\u001b[2;36m           \u001b[0m         This warning appears because the largest panel of this mesh has radius \u001b[1;36m0.178\u001b[0m > wavelength/\u001b[1;36m8\u001b[0m.   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Profile of the axisymmetric body\n",
+    "def shape(z):\n",
+    "    if z >= -0.25:\n",
+    "        return 0.5\n",
+    "    elif z < -0.25 and z >= -0.499:\n",
+    "        return z + 0.75\n",
+    "    elif z < -0.4999 and z <= -0.5:\n",
+    "        return 5000*z+2500 #approximating a the bottom surface\n",
+    "\n",
+    "# Generate the mesh and display it with VTK.\n",
+    "buoy = cpt.FloatingBody(\n",
+    "    mesh=cpt.AxialSymmetricMesh.from_profile(shape, z_range=np.linspace(-0.5, 0, 30), nphi=40)\n",
+    ")\n",
+    "buoy.add_translation_dof(name=\"Heave\")\n",
+    "buoy.show_matplotlib()\n",
+    "\n",
+    "# Set up problems\n",
+    "m0_nums = np.concatenate((np.linspace(0.1, 1, 10), np.linspace(1, 6, 10)))\n",
+    "problems = [cpt.RadiationProblem(body=buoy, radiating_dof='Heave', wavenumber = m0)\n",
+    "            for m0 in m0_nums]\n",
+    "\n",
+    "# Solve the problems using the axial symmetry\n",
+    "solver = cpt.BEMSolver(engine=cpt.HierarchicalToeplitzMatrixEngine())\n",
+    "results = [solver.solve(pb) for pb in problems]\n",
+    "dataset = cpt.io.xarray.assemble_dataset(results)\n",
+    "\n",
+    "h = 1.001\n",
+    "rho = 1023  # density of our special material\n",
+    "g = 9.81\n",
+    "omega = dataset['omega']\n",
+    "A = dataset['added_mass'].sel(radiating_dof='Heave',influenced_dof='Heave')\n",
+    "B = dataset['radiation_damping'].sel(radiating_dof='Heave', influenced_dof='Heave')\n",
+    "A_nondim = h**3 / (rho * np.pi * 0.5**3) * A                # 0.5 is the radius of the slant object\n",
+    "B_nondim = h**3 / (omega * rho * np.pi * 0.5**3) * B        # 0.5 is the radius of the slant object\n",
+    "# Plot results\n",
+    "plt.figure()\n",
+    "plt.plot(m0_nums[1:], A_nondim, label=\"Added mass\")\n",
+    "plt.plot( m0_nums[1:], B_nondim, label=\"Radiation damping\")\n",
+    "plt.xlabel('wave number (m0)')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def body_from_profile(x,y,z,nphi):\n",
+    "    xyz = np.array([np.array([x/math.sqrt(2),y/math.sqrt(2),z]) for x,y,z in zip(x,y,z)])    # /sqrt(2) to account for the scaling\n",
+    "    body = cpt.FloatingBody(cpt.AxialSymmetricMesh.from_profile(xyz, nphi=nphi))\n",
+    "    return body\n",
+    "\n",
+    "def make_slant(d1, d2, a1, a2, res):\n",
+    "\n",
+    "    #normal vectors have to be facing outwards\n",
+    "    z1 = np.linspace(-d1,-d2,res)\n",
+    "    x1 = np.linspace(a1, a2, res) \n",
+    "    y1 = np.linspace(a1, a2,res)\n",
+    "    bottom_frustum = body_from_profile(x1,y1,z1,res**2)\n",
+    "\n",
+    "    z2 = np.linspace(-d1, -d1, res)\n",
+    "    x2 = np.linspace(0.1001, a1, res)\n",
+    "    y2 = np.linspace(0.1001, a1, res)\n",
+    "    bottom_surface = body_from_profile(x2,y2,z2,res**2)\n",
+    "\n",
+    "    z3 = np.linspace(-d2, 0, 1+int(res/2))\n",
+    "    x3 = np.full_like(z3, a2)\n",
+    "    y3 = np.full_like(z3, a2)\n",
+    "    outer_surface = body_from_profile(x3,y3,z3,res**2)\n",
+    "\n",
+    "    z4 = np.linspace(0,0,res)\n",
+    "    x4 = np.linspace(a2, 0.1001, res)\n",
+    "    y4 = np.linspace(a2, 0.1001, res)\n",
+    "    top_surface = body_from_profile(x4,y4,z4, res**2)\n",
+    "\n",
+    "    z5 = np.linspace(0, -d1, 1+int(res/2))\n",
+    "    x5 = np.full_like(z5, 0.1001)\n",
+    "    y5 = np.full_like(z5, 0.1001)\n",
+    "    inner_surface = body_from_profile(x5,y5,z5,res**2)\n",
+    "\n",
+    "    RM3 = bottom_frustum + outer_surface + top_surface + inner_surface + bottom_surface\n",
+    "\n",
+    "    \n",
+    "    print('RM3 created')\n",
+    "    RM3.show_matplotlib()\n",
+    "\n",
+    "    return RM3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def solve_slant(a, d, h, m0, res):\n",
+    "    rho = 1023 # density of our special material\n",
+    "    g = 9.81\n",
+    "    omega = np.sqrt(m0*np.tanh(m0*h)*g)\n",
+    "\n",
+    "    slant = make_slant(d[0], d[1], a[0], a[1], res)\n",
+    "    slant.add_translation_dof(name=\"Heave\")\n",
+    "    slant = slant.immersed_part() # removes points above z = 0\n",
+    "\n",
+    "    solver = cpt.BEMSolver()\n",
+    "    rad_problem = cpt.RadiationProblem(body = slant, wavenumber = m0, water_depth = h, rho = rho)\n",
+    "    results = solver.solve(rad_problem, keep_details = True)\n",
+    "\n",
+    "    A = np.array(list(results.added_mass.values()))\n",
+    "    B = np.array(list(results.radiation_damping.values()))\n",
+    "    A_nondim = h**3 / (rho * np.pi * max(a)**3) * A\n",
+    "    B_nondim = h**3 / (omega * rho * np.pi * max(a)**3) * B\n",
+    "    return A, B, A_nondim, B_nondim\n",
+    "\n",
+    "h = 1.001\n",
+    "a = [0.25, 0.5]\n",
+    "d = [0.5, 0.25]\n",
+    "m0_nums = np.concatenate((np.linspace(0.1, 1, 10), np.linspace(1, 6, 10)))\n",
+    "res = 10\n",
+    "\n",
+    "results = [solve_slant(a, d, h, m0, res) for m0 in m0_nums]\n",
+    "\n",
+    "hydro_collector_real_CPT = [res[2].flatten() for res in results]\n",
+    "hydro_collector_imag_CPT = [res[3].flatten() for res in results]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the plots\n",
+    "plt.figure(figsize=(5,5))\n",
+    "plt.plot(m0_nums, hydro_collector_real_CPT, label='A-Mass Capytaine',  linestyle=':')\n",
+    "plt.plot(m0_nums, hydro_collector_imag_CPT, label='Damping Capytaine',  linestyle=':') \n",
+    "\n",
+    "plt.xlabel('m0')\n",
+    "plt.ylabel('')\n",
+    "plt.title('Slant Hydro Coeffs MEEM vs Capytaine ')\n",
+    "plt.legend(loc='best')\n",
+    "plt.grid(True)\n",
+    "plt.ylim(-0.02, 1.5)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def body_from_profile(x,y,z,nphi):\n",
+    "    xyz = np.array([np.array([x/np.sqrt(2),y/np.sqrt(2),z]) for x,y,z in zip(x,y,z)])    # /sqrt(2) to account for the scaling\n",
+    "    body = cpt.FloatingBody(cpt.AxialSymmetricMesh.from_profile(xyz, nphi=nphi))\n",
+    "    return body\n",
+    "\n",
+    "def make_face(p1, p2, f_density, t_density):\n",
+    "    zarr = np.linspace(p1[1], p2[1], f_density + 1)\n",
+    "    rarr = np.linspace(p1[0], p2[0], f_density + 1)\n",
+    "    return body_from_profile(rarr, rarr, zarr, t_density)\n",
+    "\n",
+    "def get_points(a, d): # These points define the outline of the body\n",
+    "    d_prime = d + [0]\n",
+    "    d_index = 0\n",
+    "    a_index = 0\n",
+    "    pt_lst = [(0, - d[0])]\n",
+    "    for i in range(len(a)):\n",
+    "        pt_lst.append((a[a_index], - d_prime[d_index]))\n",
+    "        d_index +=1\n",
+    "        pt_lst.append((a[a_index], - d_prime[d_index]))\n",
+    "        a_index+=1\n",
+    "    return pt_lst\n",
+    "\n",
+    "# compute number of panels along each surface given total number along the outline\n",
+    "def get_f_densities(pt_lst, total_units):\n",
+    "    face_lengths = np.array([])\n",
+    "    for i in range(len(pt_lst) - 1):\n",
+    "        p1, p2 = pt_lst[i], pt_lst[i + 1]\n",
+    "        face_length = abs(p2[0] - p1[0]) + abs(p2[1] - p1[1]) # one of these two values will be zero\n",
+    "        face_lengths = np.append(face_lengths, face_length)\n",
+    "    total_length = sum(face_lengths)\n",
+    "    each_face_densities = np.vectorize(lambda x: max(1, x/total_length * total_units))(face_lengths) # each face needs at least one panel\n",
+    "    remainders = each_face_densities % 1\n",
+    "    each_face_densities = each_face_densities.astype(int)\n",
+    "    remaining_units = total_units - sum(each_face_densities)\n",
+    "    if remaining_units < 0: # high proportion of small faces\n",
+    "        for u in range(remaining_units * -1):\n",
+    "            i = np.argmax(each_face_densities) # cut density from the largest faces\n",
+    "            each_face_densities[i] = (each_face_densities[i]) - 1\n",
+    "    else:\n",
+    "        for u in range(remaining_units): # distribute remaining units where most needed\n",
+    "            i = np.argmax(remainders)\n",
+    "            each_face_densities[i] = (each_face_densities[i]) + 1\n",
+    "            remainders[i] = 0\n",
+    "    assert sum(each_face_densities) == total_units\n",
+    "    return each_face_densities\n",
+    "    \n",
+    "def make_body(a, d, heaving, t_densities, face_units): \n",
+    "    pts = get_points(a,d)\n",
+    "    f_densities = get_f_densities(pts, face_units)\n",
+    "    faces_and_heaves = []\n",
+    "    panel_ct = 0\n",
+    "    for i in range((len(pts) - 1) // 2):\n",
+    "        p1, p2, p3 = pts[2 * i], pts[2 * i + 1], pts[2 * i + 2]\n",
+    "        # make a horizontal face\n",
+    "        h_face = make_face(p1, p2, f_densities[2 * i], t_densities[i])\n",
+    "        h_heave = heaving[i]\n",
+    "        faces_and_heaves.append((h_face, h_heave))\n",
+    "        panel_ct += f_densities[2 * i] * t_densities[i]\n",
+    "        # make a vertical face\n",
+    "        if p2[1] < p3[1]: # body on left\n",
+    "            region = i\n",
+    "        else: # body on right\n",
+    "            region = i + 1\n",
+    "        v_face = make_face(p2, p3, f_densities[2 * i + 1], t_densities[region])\n",
+    "        faces_and_heaves.append((v_face, heaving[region]))\n",
+    "        panel_ct += f_densities[2 * i + 1] * t_densities[region]\n",
+    "    body = add_heaves(faces_and_heaves)\n",
+    "    return body, panel_ct\n",
+    "    \n",
+    "\n",
+    "def add_heaves(faces_and_heaves):\n",
+    "    hcreate = False\n",
+    "    screate = False\n",
+    "    for fh in faces_and_heaves: # Splits list of faces into those that are heaving and those that are not.\n",
+    "        if fh[1]: #fh of the form (face, heaving)\n",
+    "            if not hcreate:\n",
+    "                heaving_body = fh[0]\n",
+    "                hcreate = True\n",
+    "            else:\n",
+    "                heaving_body = heaving_body + fh[0]\n",
+    "        else:\n",
+    "            if not screate:\n",
+    "                still_body = fh[0]\n",
+    "                screate = True\n",
+    "            else:\n",
+    "                still_body = still_body + fh[0]\n",
+    "    if hcreate: # Adds heave dof to the heaving collection\n",
+    "        heaving_body.add_translation_dof(name='Heave')\n",
+    "        if screate:\n",
+    "            return (heaving_body + still_body)\n",
+    "        else:\n",
+    "            return (heaving_body)\n",
+    "    else:\n",
+    "        return (still_body)\n",
+    "\n",
+    "###################################\n",
+    "# Solving\n",
+    "solver = cpt.BEMSolver()\n",
+    "\n",
+    "def rb_solve(a, d, heaving, t_densities, face_units, m0, h, rho):\n",
+    "    body, panel_count = make_body(a, d, heaving, t_densities, face_units)\n",
+    "    body = body.immersed_part() # removes points above z = 0\n",
+    "    # body.show_matplotlib()\n",
+    "    \n",
+    "    rad_problem = cpt.RadiationProblem(body = body, wavenumber = m0, water_depth = h, rho = rho)\n",
+    "    results = solver.solve(rad_problem, keep_details = True)\n",
+    "\n",
+    "    rho = 1023 # density of our special material\n",
+    "    wave_amp = 1\n",
+    "    g = 9.81\n",
+    "    omega = np.sqrt(m0*np.tanh(m0*h)*g)\n",
+    "\n",
+    "    A = np.array(list(results.added_mass.values()))\n",
+    "    B = np.array(list(results.radiation_damping.values()))\n",
+    "    A_nondim = h**3 / (rho * np.pi * max(a)**3) * A\n",
+    "    B_nondim = h**3 / (omega * rho * np.pi * max(a)**3) * B\n",
+    "    # print(results.added_mass)\n",
+    "    # print(type(results.added_mass))\n",
+    "    # print(results.radiation_damping)\n",
+    "    print(\"# of Panels: \", panel_count)\n",
+    "    # return A, B, A_nondim, B_nondim\n",
+    "    return A, B, A_nondim, B_nondim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Mini Bicylinder\n",
+    "# h = 1.001\n",
+    "# d = [0.25, 0.125]\n",
+    "# a = [0.125, 0.25]\n",
+    "# heaving = [1, 1]\n",
+    "\n",
+    "# # Small Bicylinder\n",
+    "# h = 1.001\n",
+    "# d = [0.5, 0.25]\n",
+    "# a = [0.5, 1.0]\n",
+    "# heaving = [1, 1]\n",
+    "\n",
+    "# # Big Bicylinder\n",
+    "# h = 1.001\n",
+    "# d = [0.75, 0.5]\n",
+    "# a = [0.5, 0.75]\n",
+    "# heaving = [1, 1]\n",
+    "\n",
+    "# # Mini Tricylinder\n",
+    "# h = 2.001\n",
+    "# d = [1.0, 0.5, 0.25]\n",
+    "# a = [0.25, 0.5, 1.0]\n",
+    "# heaving =[1, 1, 1]\n",
+    "\n",
+    "# # Small Tricylinder\n",
+    "# h = 20.0\n",
+    "# d = [15, 10, 5]\n",
+    "# a = [5, 10, 15]\n",
+    "# heaving =[1, 1, 1]\n",
+    "\n",
+    "# # Big Tricylinder\n",
+    "# h = 25.0\n",
+    "# d = [20, 15, 10]\n",
+    "# a = [10, 15, 20]\n",
+    "# heaving =[1, 1, 1]\n",
+    "\n",
+    "# Some Bicylinder\n",
+    "\n",
+    "h = 1.001\n",
+    "d = [0.75, 0.25]\n",
+    "a = [0.25, 0.75]\n",
+    "heaving = [1, 1]\n",
+    "\n",
+    "t_densities = [20, 40, 60] # number of panels around each cylinder\n",
+    "face_units = 60 # number of panels along the outline of the configuration\n",
+    "m0 = 1\n",
+    "rho = 1023 # density of our special material\n",
+    "config = \"config0\"\n",
+    "\n",
+    "# m0_nums = np.concatenate((np.linspace(0.1, 1, 20), np.linspace(1, 6, 30)))\n",
+    "\n",
+    "# results = [rb_solve(a, d, heaving, t_densities, face_units, m0, h, rho) for m0 in m0_nums]\n",
+    "results = rb_solve(a, d, heaving, t_densities, face_units, m0, h, rho)\n",
+    "\n",
+    "\n",
+    "# A_nondim = [res[2].flatten() for res in results]\n",
+    "# B_nondim = [res[3].flatten() for res in results]\n",
+    "\n",
+    "# plt.figure()\n",
+    "# plt.plot(m0_nums,A_nondim, m0_nums,B_nondim, '*-')\n",
+    "# plt.xlabel(\"Wavenumber m0\")\n",
+    "# plt.ylabel(\"Added mass (kg)\")\n",
+    "# plt.show()\n",
+    "\n",
+    "# panel_mult = [20,30,40]\n",
+    "# results = [rb_solve(a, d, heaving, t_densities*i, face_units*i, 2, h, rho) for i in panel_mult]\n",
+    "\n",
+    "# A_nondim = [res[2].flatten() for res in results]\n",
+    "# B_nondim = [res[3].flatten() for res in results]\n",
+    "\n",
+    "# plt.figure()\n",
+    "# plt.plot(panel_mult,A_nondim, panel_mult,B_nondim, '*-')\n",
+    "# plt.xlabel(\"resolution multiplier\")\n",
+    "# plt.ylabel(\"Added mass (kg)\")\n",
+    "# plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(A_nondim)\n",
+    "# print(B_nondim)\n",
+    "\n",
+    "# Extract values from NumPy arrays\n",
+    "flat_list1 = [x.item() for x in A_nondim]\n",
+    "flat_list2 = [x.item() for x in B_nondim]\n",
+    "\n",
+    "# Convert to MATLAB-style string\n",
+    "matlab_list1 = \"pyCapytaine_mu_nondim = [\" + \" \".join(map(str, flat_list1)) + \"];\"\n",
+    "matlab_list2 = \"pyCapytaine_lambda_nondim = [\" + \" \".join(map(str, flat_list2)) + \"];\"\n",
+    "\n",
+    "# Print MATLAB code\n",
+    "print(matlab_list1)\n",
+    "print(matlab_list2)\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/dev/python/slants/pseudo_slant_comparison.ipynb b/dev/python/slants/pseudo_slant_comparison.ipynb
new file mode 100644
index 0000000..f61d3a8
--- /dev/null
+++ b/dev/python/slants/pseudo_slant_comparison.ipynb
@@ -0,0 +1,433 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from numpy import sqrt\n",
+    "import time\n",
+    "\n",
+    "import capytaine as cpt\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "sys.path.append(os.path.relpath('../'))\n",
+    "from multi_condensed import Problem\n",
+    "\n",
+    "# removes capytaine warnings from clogging outputs\n",
+    "import logging\n",
+    "logging.getLogger(\"capytaine\").setLevel(logging.ERROR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m0_nums_MEEM = np.concatenate((np.linspace(0.1, 1, 20), np.linspace(1, 6, 20)))\n",
+    "\n",
+    "# Some slant bicylinder\n",
+    "\n",
+    "def makeSlant(d1, d2, a1, a2, resolu, terms):\n",
+    "    d = np.linspace(d1, d2, resolu)\n",
+    "    a = a1 + (d1-d) * (a2-a1) / (d1-d2)\n",
+    "    heaving = np.ones(resolu, dtype=int)\n",
+    "    NMK = np.full(resolu+1, terms) \n",
+    "    return d.tolist(), a.tolist(), heaving.tolist(), NMK.tolist()\n",
+    "\n",
+    "resolu = 25 #number of mini cylinders the slant object has\n",
+    "terms = 50 #number of terms in each region\n",
+    "d, a, heaving, NMK = makeSlant(0.5, 0.25, 0.25, 0.5, resolu, terms)\n",
+    "\n",
+    "# m0 = 1.0\n",
+    "h = 1.001\n",
+    "g = 9.81\n",
+    "rho = 1023\n",
+    "####################################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "############################################## MEEM ############################################################\n",
+    "hydro_collector_real_MEEM = []\n",
+    "hydro_collector_imag_MEEM = []\n",
+    "hydro_nondim_real_diff = [np.nan]\n",
+    "hydro_nondim_imag_diff = [np.nan]\n",
+    "times_MEEM = []\n",
+    "loop_num = 0\n",
+    "\n",
+    "for m0 in m0_nums_MEEM:\n",
+    "    start = time.perf_counter()\n",
+    "    prob = Problem(h, d, a, heaving, NMK, m0, rho)\n",
+    "    a0 = prob.a_matrix()\n",
+    "    b0 = prob.b_vector()\n",
+    "    x = prob.get_unknown_coeffs(a0, b0)\n",
+    "    am, dp = prob.hydro_coeffs(x, \"nondimensional\")\n",
+    "    hydro_collector_real_MEEM.append(am)\n",
+    "    hydro_collector_imag_MEEM.append(dp)\n",
+    "    end = time.perf_counter()\n",
+    "    times_MEEM.append(end - start)\n",
+    "\n",
+    "    if loop_num != 0:\n",
+    "        hydro_nondim_real_diff.append((hydro_collector_real_MEEM[loop_num]-hydro_collector_real_MEEM[loop_num-1])/hydro_collector_real_MEEM[loop_num-1])\n",
+    "        hydro_nondim_imag_diff.append((hydro_collector_imag_MEEM[loop_num]-hydro_collector_imag_MEEM[loop_num-1])/hydro_collector_imag_MEEM[loop_num-1])\n",
+    "    # if loop_num != 0:\n",
+    "    #     percent_diff_real = abs((186621534261.50247 - hydro_collector_real[loop_num-1])/186621534261.50247)\n",
+    "    #     percent_diff_imag = abs((3529258.9182286593 - hydro_collector_imag[loop_num-1])/3529258.9182286593)\n",
+    "    #     if percent_diff_real <= 0.001 and percent_diff_imag <= 0.001:\n",
+    "    #         break\n",
+    "    #     if times_MEEM[-1] > 11.94:\n",
+    "    #         break\n",
+    "    \n",
+    "    loop_num += 1\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n",
+      "Panel Count:  2500\n"
+     ]
+    }
+   ],
+   "source": [
+    "def deafen(function, *args):\n",
+    "    real_stdout = sys.stdout\n",
+    "    sys.stdout = open(os.devnull, \"w\")\n",
+    "    output = function(*args)\n",
+    "    sys.stdout = real_stdout\n",
+    "    return output\n",
+    "\n",
+    "solver = cpt.BEMSolver(engine=cpt.HierarchicalToeplitzMatrixEngine())\n",
+    "\n",
+    "def timed_solve(problem, reps):\n",
+    "    t_lst = []\n",
+    "    for i in range(reps):\n",
+    "        t0 = time.perf_counter()\n",
+    "        result = solver.solve(problem, keep_details = True)\n",
+    "        t1 = time.perf_counter()\n",
+    "        t_lst.append(t1 - t0)\n",
+    "    tdiff = sum(t_lst)/reps\n",
+    "    return result, tdiff\n",
+    "\n",
+    "def get_points(a, d): # These points define the outline of the body\n",
+    "    d_prime = d + [0]\n",
+    "    d_index = 0\n",
+    "    a_index = 0\n",
+    "    pt_lst = [(0, - d[0])]\n",
+    "    for i in range(len(a)):\n",
+    "        pt_lst.append((a[a_index], - d_prime[d_index]))\n",
+    "        d_index +=1\n",
+    "        pt_lst.append((a[a_index], - d_prime[d_index]))\n",
+    "        a_index+=1\n",
+    "    return pt_lst\n",
+    "\n",
+    "# compute number of panels along each surface given total number along the outline\n",
+    "def get_f_densities(pt_lst, total_units):\n",
+    "    face_lengths = np.array([])\n",
+    "    for i in range(len(pt_lst) - 1):\n",
+    "        p1, p2 = pt_lst[i], pt_lst[i + 1]\n",
+    "        face_length = abs(p2[0] - p1[0]) + abs(p2[1] - p1[1]) # one of these two values will be zero\n",
+    "        face_lengths = np.append(face_lengths, face_length)\n",
+    "    total_length = sum(face_lengths)\n",
+    "    each_face_densities = np.vectorize(lambda x: max(1, x/total_length * total_units))(face_lengths) # each face needs at least one panel\n",
+    "    remainders = each_face_densities % 1\n",
+    "    each_face_densities = each_face_densities.astype(int)\n",
+    "    remaining_units = total_units - sum(each_face_densities)\n",
+    "    if remaining_units < 0: # high proportion of small faces\n",
+    "        for u in range(remaining_units * -1):\n",
+    "            i = np.argmax(each_face_densities) # cut density from the largest faces\n",
+    "            each_face_densities[i] = (each_face_densities[i]) - 1\n",
+    "    else:\n",
+    "        for u in range(remaining_units): # distribute remaining units where most needed\n",
+    "            i = np.argmax(remainders)\n",
+    "            each_face_densities[i] = (each_face_densities[i]) + 1\n",
+    "            remainders[i] = 0\n",
+    "    assert sum(each_face_densities) == total_units\n",
+    "    return each_face_densities\n",
+    "    \n",
+    "def make_face(p1, p2, f_density, t_density):\n",
+    "    zarr = np.linspace(p1[1], p2[1], f_density + 1)\n",
+    "    rarr = np.linspace(p1[0], p2[0], f_density + 1)\n",
+    "    xyz = np.array([np.array([x/np.sqrt(2),y/np.sqrt(2),z]) for x,y,z in zip(rarr,rarr,zarr)])\n",
+    "    return cpt.AxialSymmetricMesh.from_profile(xyz, nphi = t_density)\n",
+    "\n",
+    "def faces_and_heaves(heaving, region, p1, p2, f_density, t_density, meshes, mask, panel_ct):\n",
+    "    mesh = make_face(p1, p2, f_density, t_density)\n",
+    "    meshes += mesh\n",
+    "    new_panels = f_density * t_density\n",
+    "    if heaving[region]:\n",
+    "        direction = [0, 0, 1]\n",
+    "    else:\n",
+    "        direction = [0, 0, 0]\n",
+    "    for i in range(new_panels):\n",
+    "        mask.append(direction)\n",
+    "    return meshes, mask, (panel_ct + new_panels)\n",
+    "\n",
+    "def get_excitation_phase(result):\n",
+    "    return np.angle((cpt.assemble_dataset([result]))[\"excitation_force\"][0][0][0])\n",
+    "\n",
+    "def make_body(pts, t_densities, f_densities, heaving):\n",
+    "    meshes = cpt.meshes.meshes.Mesh()\n",
+    "    panel_ct = 0\n",
+    "    mask = []\n",
+    "    for i in range((len(pts) - 1) // 2):\n",
+    "        p1, p2, p3 = pts[2 * i], pts[2 * i + 1], pts[2 * i + 2]\n",
+    "        # make a horizontal face\n",
+    "        meshes, mask, panel_ct = faces_and_heaves(heaving, i, p1, p2, f_densities[2 * i], t_densities[i], meshes, mask, panel_ct)\n",
+    "        # make a vertical face\n",
+    "        if p2[1] < p3[1]: # body on left\n",
+    "            region = i\n",
+    "        else: # body on right\n",
+    "            region = i + 1\n",
+    "        meshes, mask, panel_ct = faces_and_heaves(heaving, region, p2, p3, f_densities[2 * i + 1], t_densities[region], meshes, mask, panel_ct)\n",
+    "    body = deafen(cpt.FloatingBody, meshes) # unclosed boundary warnings\n",
+    "    return body, panel_ct, mask\n",
+    "\n",
+    "###################################\n",
+    "# Solving\n",
+    "def rb_solve(a, d, heaving, t_densities, face_units, m0, h, rho, reps):\n",
+    "    pt_lst = get_points(a, d)\n",
+    "    f_densities = get_f_densities(pt_lst, face_units)\n",
+    "    \n",
+    "    body, panel_count, mask = make_body(pt_lst, t_densities, f_densities, heaving)\n",
+    "    body.dofs[\"Heave\"] = mask  \n",
+    "    # body.show_matplotlib() # uncomment to show mesh\n",
+    "    \n",
+    "    rad_problem = cpt.RadiationProblem(body = body, wavenumber = m0, water_depth = h, rho = rho)\n",
+    "    result, t_diff = timed_solve(rad_problem, reps)\n",
+    "\n",
+    "    #diff_problem = cpt.DiffractionProblem(body = body, wavenumber = m0, water_depth = h, rho = rho)\n",
+    "    #result_d, t_diff_d = timed_solve(diff_problem, reps)\n",
+    "\n",
+    "    g = 9.81\n",
+    "    omega = np.sqrt(m0*np.tanh(m0*h)*g)\n",
+    "\n",
+    "    am = np.array(list(result.added_mass.values()))\n",
+    "    dp = np.array(list(result.radiation_damping.values()))\n",
+    "    am_nondim = h**3 / (rho * np.pi * max(a)**3) * am\n",
+    "    dp_nondim = h**3 / (omega * rho * np.pi * max(a)**3) * dp\n",
+    "\n",
+    "    print(\"Panel Count: \", panel_count)\n",
+    "    #print(result.added_mass)\n",
+    "    #print(result.radiation_damping)\n",
+    "    #print(\"Solve Time (Radiation): \", t_diff)\n",
+    "    #print(\"Solve Time (Diffraction): \", t_diff_d)\n",
+    "    #print(\"Excitation Phase: \", get_excitation_phase(result_d))\n",
+    "    return am, dp, am_nondim, dp_nondim\n",
+    "\n",
+    "t_densities = np.full(resolu, 50).tolist() # number of panels around each cylinder\n",
+    "face_units = 50 # number of panels along the outline of the configuration\n",
+    "m0_nums_CPT = np.concatenate((np.linspace(0.1, 1, 5), np.linspace(1, 6, 10)))\n",
+    "results = [rb_solve(a, d, heaving, t_densities, face_units, m0, h, rho, 1) for m0 in m0_nums_CPT]\n",
+    "\n",
+    "hydro_collector_real_CPT = [res[2].flatten() for res in results]\n",
+    "hydro_collector_imag_CPT = [res[3].flatten() for res in results]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "############################################## CAPYTAINE Real Slant ############################################################\n",
+    "\n",
+    "# Profile of the axisymmetric body\n",
+    "def shape(z):\n",
+    "    if z >= -0.25:\n",
+    "        return 0.5\n",
+    "    elif z < -0.25 and z >= -0.49999:\n",
+    "        return z + 0.75\n",
+    "    elif z < -0.49999 and z <= -0.5:\n",
+    "        return 25000*z+12500 #approximating a the bottom surface\n",
+    "\n",
+    "# Generate the mesh and display it with VTK.\n",
+    "buoy = cpt.FloatingBody(\n",
+    "    mesh=cpt.AxialSymmetricMesh.from_profile(shape, z_range=np.linspace(-0.5, 0, 100), nphi=100)\n",
+    ")\n",
+    "buoy.add_translation_dof(name=\"Heave\")\n",
+    "buoy.show_matplotlib()\n",
+    "\n",
+    "# Set up problems\n",
+    "m0_nums_CPT_slant = np.concatenate((np.linspace(0.1, 1, 20), np.linspace(1, 6, 20)))\n",
+    "problems = [cpt.RadiationProblem(body=buoy, radiating_dof='Heave', wavenumber = m0, water_depth = h, rho = rho)\n",
+    "            for m0 in m0_nums_CPT_slant]\n",
+    "\n",
+    "# Solve the problems using the axial symmetry\n",
+    "solver = cpt.BEMSolver(engine=cpt.HierarchicalToeplitzMatrixEngine())\n",
+    "\n",
+    "results = []\n",
+    "times_CPT = []\n",
+    "for pb in problems:\n",
+    "    t0 = time.perf_counter()\n",
+    "    res = solver.solve(pb)\n",
+    "    t1 = time.perf_counter()\n",
+    "\n",
+    "    results.append(res)\n",
+    "    times_CPT.append(t1 - t0)\n",
+    "    \n",
+    "dataset = cpt.io.xarray.assemble_dataset(results)\n",
+    "\n",
+    "h = 1.001\n",
+    "rho = 1023  # density of our special material\n",
+    "g = 9.81\n",
+    "omega = dataset['omega']\n",
+    "# omega = sqrt(m0 * np.tanh(m0 * h) * g)\n",
+    "A = dataset['added_mass'].sel(radiating_dof='Heave',influenced_dof='Heave')\n",
+    "B = dataset['radiation_damping'].sel(radiating_dof='Heave', influenced_dof='Heave')\n",
+    "A_nondim = h**3 / (rho * np.pi * 0.5**3) * A                # 0.5 is the radius of the slant object\n",
+    "B_nondim = h**3 / (omega * rho * np.pi * 0.5**3) * B        # 0.5 is the radius of the slant object\n",
+    "# Plot results\n",
+    "plt.figure()\n",
+    "plt.plot(m0_nums_CPT_slant[1:], A_nondim, label=\"Added mass\")\n",
+    "plt.plot( m0_nums_CPT_slant[1:], B_nondim, label=\"Radiation damping\")\n",
+    "plt.xlabel('wave number (m0)')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the hydro comparison plots\n",
+    "plt.figure(figsize=(5,5))\n",
+    "plt.plot(m0_nums_MEEM, hydro_collector_real_MEEM, label='A-Mass MEEM', linestyle='-')\n",
+    "plt.plot(m0_nums_MEEM, hydro_collector_imag_MEEM, label='Damping MEEM', linestyle='-')\n",
+    "plt.plot(m0_nums_CPT_slant[1:], A_nondim, label=\"Added mass\", linestyle=':') #somehow m0_nums_CPT is one element shorter\n",
+    "plt.plot( m0_nums_CPT_slant[1:], B_nondim, label=\"Radiation damping\",linestyle=':') #somehow m0_nums_CPT is one element shorter\n",
+    "plt.plot(m0_nums_CPT, hydro_collector_real_CPT, label='A-Mass Capytaine',  linestyle=':')\n",
+    "plt.plot(m0_nums_CPT, hydro_collector_imag_CPT, label='Damping Capytaine',  linestyle=':') \n",
+    "\n",
+    "plt.xlabel('m0')\n",
+    "plt.ylabel('')\n",
+    "plt.title('Hydro Coeffs: Pseudo Slant MEEM vs Slant Capytaine \\n(our omega formula)')\n",
+    "plt.legend(loc='best')\n",
+    "plt.grid(True)\n",
+    "plt.ylim(-0.02, 1.5)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "###graphing time difference###\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.plot(times_MEEM, label='time MEEM')\n",
+    "plt.plot(times_CPT, label='time CPT')\n",
+    "plt.xlabel('Iteration index')\n",
+    "plt.ylabel('Time (s)')\n",
+    "plt.title('MEEM vs CPT solve time per iteration')\n",
+    "plt.ylim(-0.02, 5)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Not pictured: despite having less panels, the cpt pseudo slant takes much longer to run than cpt slant\n",
+    "# This is likely an inefficiency in our code."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/hydro/python/test/data/Total-Potential-imag-matlab.csv b/dev/python/test/data/Total-Potential-imag-matlab.csv
similarity index 100%
rename from hydro/python/test/data/Total-Potential-imag-matlab.csv
rename to dev/python/test/data/Total-Potential-imag-matlab.csv
diff --git a/hydro/python/test/data/Total-Potential-real-matlab.csv b/dev/python/test/data/Total-Potential-real-matlab.csv
similarity index 100%
rename from hydro/python/test/data/Total-Potential-real-matlab.csv
rename to dev/python/test/data/Total-Potential-real-matlab.csv
diff --git a/dev/python/test/data/WAMIT_exc_phase.csv b/dev/python/test/data/WAMIT_exc_phase.csv
new file mode 100644
index 0000000..b57ad0a
--- /dev/null
+++ b/dev/python/test/data/WAMIT_exc_phase.csv
@@ -0,0 +1,261 @@
+omega (rad/s),excitation phase (rad)
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diff --git a/dev/python/test/data/config-key.md b/dev/python/test/data/config-key.md
new file mode 100644
index 0000000..b2e995d
--- /dev/null
+++ b/dev/python/test/data/config-key.md
@@ -0,0 +1,192 @@
+<<<<<<< HEAD
+Parenthetical values are matching points (real, imag), with rtol = 0.03, real atol = 0.01, imag atol = 0.0001. <br> 
+Other values are what was inputted into capytaine for the corresponding configurations, and what the hydro coefficient outputs were. <br>
+Note: Capytaine convention does not multiply hydro coefficients by a factor of h^3.<br>
+The hydro coefficients/point matching in double parenthesis was data given by the code at the bottom of test_potential.py,
+which matches better sometimes.
+=======
+Match is the number of matching potential points for (real, imag), with rtol = 0.01, real/imag atol = 0.01 (2500 max).<br> 
+t_densities and face_units determined the number of panels in the Capytaine mesh.  <br>
+Capytaine added_mass, radiation_damping, and excitation_phase are listed first.<br>
+MEEM added_mass, radiation_damping and excitation_phase with 100 coefficients per region, converted to the capytaine convention, are listed second. <br>
+(Capytaine convention does not multiply hydro coefficients by a factor of h^3).<br>
+>>>>>>> origin/main
+
+config0:<br>
+h = 1.001 <br>
+d = [0.5, 0.25] <br>
+a = [0.5, 1] <br>
+heaving = [1, 1] <br>
+<<<<<<< HEAD
+m0 = 1 <br>
+g = 9.81 <br>
+rho = 1023 <br>
+zdensities = [10, 10] <br>
+rdensities = [20, 20] <br>
+tdensities = [50, 100] <br>
+added_mass = 1620.53, radiation_damping = 3221.55, (2500, 2500) <br>
+((added_mass = 1586.99, radiation_damping = 3192.55, (2500, 2500)))
+=======
+t_densities = [50, 100] <br>
+face_units = 90 <br>
+panels = 6750 <br>
+m0 = 1 <br>
+rho = 1023 <br>
+g = 9.81 <br>
+CPT: added_mass = 1585.87, radiation_damping = 3187.09, excitation_phase = -0.519 <br>
+MEEM: added_mass = 1600.79, radiation_damping = 3222.35, excitation_phase = -0.521 <br>
+Match: (2500, 2500)
+>>>>>>> origin/main
+
+config1: <br>
+h = 1.5 <br>
+d = [1.1, 0.85, 0.75, 0.4, 0.15] <br>
+a = [0.3, 0.5, 1, 1.2, 1.6] <br>
+heaving = [1, 1, 1, 1, 1] <br>
+<<<<<<< HEAD
+m0 = 1 <br>
+g = 9.81 <br>
+rho = 1023 <br>
+zdensities = [20, 10, 30, 20, 15]<br>
+rdensities = [10, 10, 20, 10, 15]<br>
+tdensities = [40, 50, 70, 80, 100]<br>
+added_mass = 4760.37, radiation_damping = 11539.05, (2500,2500)<br>
+((added_mass = 4684.70, radiation_damping = 11442.87, (2499, 1825)))
+=======
+t_densities = [30, 50, 100, 120, 160] <br>
+face_units = 93 <br>
+panels = 8930 <br>
+m0 = 1 <br>
+rho = 1023 <br>
+g = 9.81 <br>
+CPT: added_mass = 4740.37, radiation_damping = 11657.30, excitation_phase = -1.063 <br>
+MEEM: added_mass = 4792.01, radiation_damping = 11683.90, excitation_phase =  -1.068<br>
+Match: (2500, 2500)
+>>>>>>> origin/main
+
+config2:<br>
+h = 100<br>
+d = [29, 7, 4]<br>
+a = [3, 5, 10]<br>
+heaving = [1, 1, 1]<br> 
+<<<<<<< HEAD
+m0 = 1<br>
+g = 9.81<br>
+rho = 1023<br>
+zdensities = [40, 10, 10]<br>
+rdensities = [15, 10, 25]<br>
+tdensities = [50, 80, 200]<br>
+added_mass = 1386873.78, radiation_damping = 283.92, (2450, 2439)<br>
+((added_mass = 1355730.94, radiation_damping = 184.13, (2464, 2455)))
+=======
+t_densities = [30, 50, 100]<br> 
+face_units = 110 <br> 
+panels = 5330 <br>
+m0 = 1 <br>
+rho = 1023 <br>
+g = 9.81 <br>
+CPT: added_mass = 1341617.60, radiation_damping = 200.79, excitation_phase = -2.965 <br>
+MEEM: added_mass = 1367585.14, radiation_damping = 204.74, excitation_phase = -2.969 <br>
+Match: (2399, 2500)
+>>>>>>> origin/main
+
+config3:<br>
+h = 1.9<br>
+d = [0.5, 0.7, 0.8, 0.2, 0.5]<br>
+a = [0.3, 0.5, 1, 1.2, 1.6]<br>
+heaving = [1, 1, 1, 1, 1]<br>
+<<<<<<< HEAD
+m0 = 1<br>
+g = 9.81<br>
+rho = 1023<br>
+zdensities = [15, 10, 30, 15, 25]<br>
+rdensities = [10, 10, 20, 10, 15]<br>
+tdensities = [40, 50, 70, 80, 100]<br>
+added_mass = 3470.42, radiation_damping = 6124.83, (621, 408) <br>
+((added_mass = 5863.80, radiation_damping = 6745.27, (2478, 698)))
+=======
+t_densities = [30, 50, 100, 120, 160] <br>
+face_units = 105 <br>
+panels = 11660 <br>
+m0 = 1 <br>
+rho = 1023 <br>
+g = 9.81 <br>
+CPT: added_mass = 5881.40, radiation_damping = 6869.47, excitation_phase = -1.044 <br>
+MEEM: added_mass = 6057.48, radiation_damping = 6886.69, excitation_phase = -1.052 <br>
+Match: (2372, 2462)
+>>>>>>> origin/main
+
+config4:<br>
+h = 1.001 <br>
+d = [0.5, 0.25] <br>
+a = [0.5, 1] <br>
+heaving = [0, 1] <br>
+<<<<<<< HEAD
+m0 = 1<br>
+g = 9.81<br>
+rho = 1023<br>
+zdensities = [10, 10] <br>
+rdensities = [20, 20] <br>
+tdensities = [50, 100] <br>
+added_mass = 943.31, radiation_damping = 1847.53, (2500, 2500)
+=======
+t_densities = [50, 100] <br>
+face_units = 90 <br>
+panels = 6750 <br>
+m0 = 1 <br>
+rho = 1023 <br>
+g = 9.81 <br>
+CPT: added_mass = 924.62, radiation_damping = 1836.05, excitation_phase = -0.519 <br>
+MEEM: added_mass = 931.66, radiation_damping = 1852.36, excitation_phase = -0.521 <br>
+Match: (2500, 2500)
+>>>>>>> origin/main
+
+config5:<br>
+h = 1.001 <br>
+d = [0.5, 0.25] <br>
+a = [0.5, 1] <br>
+heaving = [1, 0] <br>
+<<<<<<< HEAD
+m0 = 1<br>
+g = 9.81<br>
+rho = 1023<br>
+zdensities = [10, 10] <br>
+rdensities = [20, 20] <br>
+tdensities = [50, 100] <br>
+added_mass = 291.39, radiation_damping = 189.86, (2500, 2500)
+=======
+t_densities = [50, 100] <br>
+face_units = 90 <br>
+panels = 6750 <br>
+m0 = 1 <br>
+rho = 1023 <br>
+g = 9.81 <br>
+CPT: added_mass = 286.15, radiation_damping = 185.21, excitation_phase = -0.519 <br>
+MEEM: added_mass = 288.36, radiation_damping = 188.42, excitation_phase = -0.521 <br>
+Match: (2500, 2500)
+>>>>>>> origin/main
+
+config6:<br>
+h = 100<br>
+d = [29, 7, 4]<br>
+a = [3, 5, 10]<br>
+heaving = [0, 1, 1]<br> 
+<<<<<<< HEAD
+m0 = 1<br>
+g = 9.81<br>
+rho = 1023<br>
+zdensities = [40, 10, 10]<br>
+rdensities = [15, 10, 25]<br>
+tdensities = [50, 80, 200]<br>
+added_mass = 1306746.66, radiation_damping = 279.84, (2479,2439)
+=======
+t_densities = [30, 50, 100]<br> 
+face_units = 110 <br> 
+panels = 5330 <br>
+m0 = 1 <br>
+rho = 1023 <br>
+g = 9.81 <br>
+CPT: added_mass = 1265164.44, radiation_damping = 198.74, excitation_phase = -2.965 <br>
+MEEM: added_mass = 1290352.14, radiation_damping = 202.71, excitation_phase = -2.969 <br>
+Match: (2427, 2500)
+>>>>>>> origin/main
diff --git a/hydro/python/test/data/config0-imag.csv b/dev/python/test/data/config0-imag.csv
similarity index 100%
rename from hydro/python/test/data/config0-imag.csv
rename to dev/python/test/data/config0-imag.csv
diff --git a/hydro/python/test/data/config0-real.csv b/dev/python/test/data/config0-real.csv
similarity index 100%
rename from hydro/python/test/data/config0-real.csv
rename to dev/python/test/data/config0-real.csv
diff --git a/hydro/python/test/data/config1-imag.csv b/dev/python/test/data/config1-imag.csv
similarity index 100%
rename from hydro/python/test/data/config1-imag.csv
rename to dev/python/test/data/config1-imag.csv
diff --git a/hydro/python/test/data/config1-real.csv b/dev/python/test/data/config1-real.csv
similarity index 100%
rename from hydro/python/test/data/config1-real.csv
rename to dev/python/test/data/config1-real.csv
diff --git a/hydro/python/test/data/config2-imag.csv b/dev/python/test/data/config2-imag.csv
similarity index 100%
rename from hydro/python/test/data/config2-imag.csv
rename to dev/python/test/data/config2-imag.csv
diff --git a/hydro/python/test/data/config2-real.csv b/dev/python/test/data/config2-real.csv
similarity index 100%
rename from hydro/python/test/data/config2-real.csv
rename to dev/python/test/data/config2-real.csv
diff --git a/hydro/python/test/data/config3-imag.csv b/dev/python/test/data/config3-imag.csv
similarity index 100%
rename from hydro/python/test/data/config3-imag.csv
rename to dev/python/test/data/config3-imag.csv
diff --git a/hydro/python/test/data/config3-real.csv b/dev/python/test/data/config3-real.csv
similarity index 100%
rename from hydro/python/test/data/config3-real.csv
rename to dev/python/test/data/config3-real.csv
diff --git a/hydro/python/test/data/config4-imag.csv b/dev/python/test/data/config4-imag.csv
similarity index 100%
rename from hydro/python/test/data/config4-imag.csv
rename to dev/python/test/data/config4-imag.csv
diff --git a/hydro/python/test/data/config4-real.csv b/dev/python/test/data/config4-real.csv
similarity index 100%
rename from hydro/python/test/data/config4-real.csv
rename to dev/python/test/data/config4-real.csv
diff --git a/hydro/python/test/data/config5-imag.csv b/dev/python/test/data/config5-imag.csv
similarity index 100%
rename from hydro/python/test/data/config5-imag.csv
rename to dev/python/test/data/config5-imag.csv
diff --git a/hydro/python/test/data/config5-real.csv b/dev/python/test/data/config5-real.csv
similarity index 100%
rename from hydro/python/test/data/config5-real.csv
rename to dev/python/test/data/config5-real.csv
diff --git a/dev/python/test/data/config6-imag.csv b/dev/python/test/data/config6-imag.csv
new file mode 100644
index 0000000..d516827
--- /dev/null
+++ b/dev/python/test/data/config6-imag.csv
@@ -0,0 +1,50 @@
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diff --git a/dev/python/test/data/config6-real.csv b/dev/python/test/data/config6-real.csv
new file mode 100644
index 0000000..1334452
--- /dev/null
+++ b/dev/python/test/data/config6-real.csv
@@ -0,0 +1,50 @@
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diff --git a/dev/python/test/data/test_potential_generator.ipynb b/dev/python/test/data/test_potential_generator.ipynb
new file mode 100644
index 0000000..5400de2
--- /dev/null
+++ b/dev/python/test/data/test_potential_generator.ipynb
@@ -0,0 +1,701 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rSJf6s1tKGz7",
+    "outputId": "d58c0339-e27d-4651-99ac-f4da2155a60f"
+   },
+   "outputs": [],
+   "source": [
+    "# This generates configuration values with Capytaine.\n",
+    "\n",
+    "#!pip install capytaine #uncomment if first time running\n",
+    "\n",
+    "import capytaine as cpt\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import copy\n",
+    "from capytaine.bem.airy_waves import airy_waves_potential, airy_waves_velocity, froude_krylov_force\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "ukVJNFS8XIfE"
+   },
+   "outputs": [],
+   "source": [
+    "def save_potential_array(title, arr):\n",
+    "    file_path = title + \"-real\" + \".csv\"\n",
+    "    np.savetxt(file_path, np.real(arr), delimiter=\",\", fmt=\"%.6e\")\n",
+    "    file_path = title + \"-imag\" + \".csv\"\n",
+    "    np.savetxt(file_path, np.imag(arr), delimiter=\",\", fmt=\"%.6e\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Making the body\n",
+    "def body_from_profile(x,y,z,nphi):\n",
+    "    xyz = np.array([np.array([x/np.sqrt(2),y/np.sqrt(2),z]) for x,y,z in zip(x,y,z)])    # /sqrt(2) to account for the scaling\n",
+    "    body = cpt.FloatingBody(cpt.AxialSymmetricMesh.from_profile(xyz, nphi=nphi))\n",
+    "    return body\n",
+    "\n",
+    "def make_surface(ztop, zbot, rin, rout, fdensity, tdensity):\n",
+    "    zarr = np.linspace(- zbot, -ztop, fdensity)\n",
+    "    rarr = np.linspace( rin, rout, fdensity)\n",
+    "    return body_from_profile(rarr, rarr, zarr, tdensity)\n",
+    "\n",
+    "def make_shell(top, bottom, inner, outer, zdensity, rdensity, tdensity):\n",
+    "    outer_surface = make_surface(top, bottom, outer, outer, zdensity, tdensity)\n",
+    "    bottom_surface = make_surface(bottom, bottom, inner, outer, rdensity, tdensity)\n",
+    "    top_surface = make_surface(top, top, inner, outer, rdensity, tdensity)\n",
+    "    bod = outer_surface + bottom_surface + top_surface\n",
+    "    if inner > 0:\n",
+    "        inner_surface = make_surface(top, bottom, inner, inner, zdensity, tdensity)\n",
+    "        bod = bod + inner_surface\n",
+    "    return bod\n",
+    "\n",
+    "def make_bodies(attribute_lst): # Returns a list of shells, given parameters for each\n",
+    "    bod_lst = []\n",
+    "    for att in attribute_lst:\n",
+    "        bod_lst.append(make_shell(att[\"top\"], att[\"bottom\"], att[\"inner\"], att[\"outer\"], att[\"zdensity\"], att[\"rdensity\"], att[\"tdensity\"]))\n",
+    "    return bod_lst\n",
+    "\n",
+    "def add_heaves(bod_lst, heaving):\n",
+    "    hcreate = False\n",
+    "    screate = False\n",
+    "    for i in range(len(heaving)): # Splits list of shells into those that are heaving and those that are not.\n",
+    "        if heaving[i]:\n",
+    "            if not hcreate:\n",
+    "                heaving_body = bod_lst[i]\n",
+    "                hcreate = True\n",
+    "            else:\n",
+    "                heaving_body = heaving_body + bod_lst[i]\n",
+    "        else:\n",
+    "            if not screate:\n",
+    "                still_body = bod_lst[i]\n",
+    "                screate = True\n",
+    "            else:\n",
+    "                still_body = still_body + bod_lst[i]\n",
+    "    if hcreate: # Adds heave dof to the heaving collection\n",
+    "        heaving_body.add_translation_dof(name='Heave')\n",
+    "        if screate:\n",
+    "            return (heaving_body + still_body)\n",
+    "        else:\n",
+    "            return (heaving_body)\n",
+    "    else:\n",
+    "        return (still_body)\n",
+    "\n",
+    "# getting an attribute list from the current multi-meem input setup\n",
+    "def gen_to_att_lst(d, a, zdensities, rdensities, tdensities):\n",
+    "    ct = len(d)\n",
+    "    tops = [0] * ct\n",
+    "    bottoms = d\n",
+    "    inners = [0] + a[:-1]\n",
+    "    outers = a\n",
+    "    att_lst = []\n",
+    "    key_lst = [\"top\", \"bottom\", \"inner\", \"outer\", \"zdensity\", \"rdensity\", \"tdensity\"]\n",
+    "    for i in range(ct):\n",
+    "        vals = [tops[i], bottoms[i], inners[i], outers[i], zdensities[i], rdensities[i], tdensities[i]]\n",
+    "        att = {}\n",
+    "        for j in range(len(key_lst)):\n",
+    "            att[key_lst[j]] = vals[j]\n",
+    "        att_lst.append(att)\n",
+    "\n",
+    "    return att_lst\n",
+    "\n",
+    "###################################\n",
+    "# Solving\n",
+    "solver = cpt.BEMSolver()\n",
+    "\n",
+    "def rb_solve(d, a, zdensities, rdensities, tdensities, heaving, m0, h, rho):\n",
+    "    att_lst = gen_to_att_lst(d, a, zdensities, rdensities, tdensities)\n",
+    "    bod_lst = make_bodies(att_lst)\n",
+    "    body = add_heaves(bod_lst, heaving)\n",
+    "    body = body.immersed_part() # removes points above z = 0\n",
+    "    body.show_matplotlib()\n",
+    "    \n",
+    "    rad_problem = cpt.RadiationProblem(body = body, wavenumber = m0, water_depth = h, rho = rho)\n",
+    "    results = solver.solve(rad_problem, keep_details = True)\n",
+    "    print(results.added_mass)\n",
+    "    print(results.radiation_damping)\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 410
+    },
+    "id": "fYujEq2e3QqD",
+    "outputId": "75513233-38a3-46e6-d3bc-991bddeea24f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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A/SicE9LQ0ABvb+8u13LrrLItKCjAoUOHEB8fjyFDhjjoq93pB+QKXsrmdMOHD2+zRFB3AYLbJXDLb9fYSF1dHSwWCxoaGhAcHHzGEhq6o7A1Go3bHKSKigrHdQJoAnTq1CnodLoO3Zpdlb5w8wESQLoKEO4Aqb3mfCogdU9UAOonsdvtsFqtKCoqQlFREaZMmdLlWm4dWTBWqxWZmZk4deoUxo8f7ygZA3hWjNTdsZU5SM3NzTh48CDq6+sxbdo0+Pv7d2m/XRHX2EhKSgqMRiOsVqtTOZz2XFGnm/S0FafMQYqLi4MQArW1tdi/fz9qampQUFDgVHiV3Zo9ARx9leza02QHjg+xKAGpqKgIDQ0NiI+PV9uXd1FUAOpjcc3t0el03ZoddgRAtbW1DmU8c+bMVjkk3bWAeNvKykqkpKQgMDAQM2bM6LFq0F0VnU4HPz8/xMXFASAKOM/8u0Jo6A/pbdeYRqOBv78/NBoNhg0bBm9vbwctvqSkBFlZWTAajT2Sg9RXFlBvs+2UgNTY2Ij6+noAULvFdlFUAOpDcdcquydqubnbXgiB/Px8HD58GAMGDEBiYqLbl7I7NG62nnJycpCdnY2hQ4ciPj7+tHzZzGYzYmJiWhEauIK1p4SGvmLY9WUxUqVbc+DAgbDZbKiurnbKQTKbzU4Mu87mIJ2pFlB7YrPZHADDorYv90xUAOojUeb2KH3MPZFI6qoMrVYrMjIyUF5ejgkTJjjcUm1t39Xj2+12h1UxZcoURzzmdJG2QMJTQkNPV2jorPR3MVKdTtdmDtKJEydQV1cHX19fJ0Bqqy17X1tAfXUsdwV63fVCUtuXuxcVgHpZOsrtYRdcV8UVwGpqapCSkgKz2YyZM2d22Kmzqy646upq5ObmQqvVIikpqV8UdHviKZOwPUIDV2hgCykgIKCXRt1a+tIC6kjay0HKzs5GQ0ODUw5SQECAkzLuKwuor9h2NputXVezsoYdoLYvdycqAPWidCa3p6dccEII5OXl4ciRIxg0aBAGDRrUqYfYUwtICIHc3FxkZWUhODgYGo3mtAOf7oo7RetaocHX1xdCCFRVVfUaoaGvqi0AXXNZueYgKSnfmZmZaG5u7vO27Z5QsHviWJ4wK9sDJNfmfP8rgKQCUC8Jz3I6mpH1BADZbDakpKSgqqoKkyZNcrhMOrt9ZxWd1WpFeno6KisrMXnyZNTU1KC8vLxL4+6rmX1PiLJCA0CEBq4iwXlUvUVoOJMqFHh5eSEqKgpRUVGtKlmUl5c7nlNlDlJPn19fAhCXyuqqKAHJtReSKyD9+9//xtSpUzFx4sQeGfvpIioA9bCwy41Zbh25A7oLQHV1dY66ajNnzvTYGumsBeTq2jMajaitrT0ryt14KmazGeHh4cjPz8esWbOcCA05OTmOdgrdrdDQlxZQbxQJVVayKCoqQl5eHkJCQhwxJI1G4wTc3t7e3R7H6WwBdSTKenSugPS3v/0N/v7+KgCp0rZ0pZxOVwFICIGTJ08iKysLALpUyw3oGICUbLqBAwciMTHRcU7dBc/elL5yWfQmoeFMrlLt7jgGgwFxcXGIi4uD3W53AHd5eTmOHTsGvV7fivLt6bh6GhTak+5aQB2JEpDq6+s9KhR8pogKQD0kXS2no4zhdHab5uZmpKWloba2FuPHj8f+/fu7PO72OqparVYcOnQIZWVlmDhxIkJCQpx+7y6Fu7elNy2ItvbdFUJDW8wx4MxywXV0HKWy1mq18Pf3h7+/PxISEhzAXVlZieLiYgdwKwGpI0INcGZbQG2JEAIWi8WjWopniqgA1E1xze3pSi033k9ntqusrERqair8/f2RlJTkWN7VF68tC4hrxnGbBncJiN0BoP8VaYvQUFlZ2WGFhjPZBecqHdGw3eUgMXDn5eXh0KFD8Pb2dgIkdwy0vqJ7A71vASnFYrHA19e3T47Vl6ICUDfEbrejtLQUVqsVwcHBXXoYeZuOAEQIgZycHBw7dgxDhgxBQkICNBqNA/i6qqzckRAKCwuRkZGBhIQEDB48uN3Kwl05rhDCUcYkJCSkV6oQnK6sIXeEBmbYuVZo6Av3Zn9ZQB2JTqdDSEiIw+puaWlxAFJOTg7S09Pd5iCdjRYQoAKQKgpRZjuXlJSgubm53WTP9kQJQG1JU1MT0tLSYLFYMHXqVKc8lM5s354oLSBlmwbXmnFtbespANlsNmRkZKCsrAw+Pj7Iyclx+P456bEzrpbOSG9bED2htF2LhVosFkdyb0VFBQAgPT3dcX16uuVEXyVudtcyMRgMCAsLczyTSkvy6NGjaGxshJ+fnwOEuEpBb0pfHIOP09DQAB8fn14/Vl+LCkAeiivRQK/Xo6mpqcv7YwBpKw5TUVGB1NRUBAYGIikpqZXbgV/q7hQUZcWXkpICnU6HpKSkTik6TwHIYrHgwIEDMBgMmDZtmsNdWV1djYqKChQUFCAzM9PR4ZOrWLcXIzmbRKPRwNfXF76+voiPj0dhYSHy8/Ph4+OD4uJiZGVl9XiFhr4gOvBxetIycbUkOQcpPz8fFosFW7duhb+/v+M58vPz63HLqK+sLYvFAgBqDOh/Xdy1ytbpdG2CR2eEmS7u2hocO3YMOTk5GDZsGOLi4twqira29+T4DQ0N2LlzJ2JjYzF06FCPeql0FoCKi4uRnp7uOAZfS6YsBwUFASBXi+vMlmMkniiS09UF54loNBoYDAYMHDgQAwcOhNVqdYC1K6Gho1I4bUlfAVBvx2Y4B6m5uRm1tbUYNGhQm910g4KC4Ovr2+3x9FUMiAFIdcH9j0p75XR6gorsuo+mpiakpqaisbGxU20NulpOx263o7i42MGm49lkZ6UzAGS325GVlYX8/HyMHj0akZGRjuXuxGAwOGXXu4uRKHNs2ssdOdMJEq7j1+v1TnERpRsqKyvLQWjg69OZCg1nqgXUlnBcxrWbrsViccrV0mg0ToQGT3OQmLnaFy64+vp6mEymfq8w3xuiAlAH0lFuT3s05s6KEoDKyspw8ODBNjuJdrR9Z6W+vh6pqaloampCQECAx+DTmeM2NjYiNTUVLS0tmDFjRpd82K4xkrq6OlRUVKC0tBTZ2dkwGAxO8aOzqSxQR+DQHqGhoKCgU7P+s8UCUh7HFeiUrk1lDpLyOXLNQerIBc3PfV8AUF1dXY8k6Z6OogJQO9KZ3J7uFhMFJIhlZWXh5MmTGDFiBGJiYjzOJeqsnDp1CmlpaYiKioKfnx8KCwu7NO72LKCKigqkpKQgNDQUkyZN6pE4jjLpMyEhwdEuoKKiwkHVZZdUY2Njr84YT8d2DO0RGpQVGpSEhr60gPpCWXcmLqPMQRowYADsdruj7YQyebi9HKS+bPtQV1d3VhIQABWA3IonuT094YLTaDQ4dOgQAGD69OkeBxs7GwOy2+04evQocnNzMXr0aERFRaGoqKhHGtKxCCFw4sQJZGdntxu76glxbRegLBrKSY01NTVO8aMzaRbZHZBzJTS4JnoyocHX19dRt7A3rce+tIA8ney4xiE51uYuB4mJMa5tVXpTmIJ9Jj27nRUVgFyEg+NKemp7N767JITS0lI0NjYiJCQEEyZM6JKl0JkYUGNjI1JSUmC1WjFjxgxHQLO7BAblcVtaWpCWloaamppWdHF32/a0uLqk2GKqqKhAbm4uALSq0Xa6v9Q9NT7XRE9WskVFRbDb7di2bVu3CQ3tSV8xxmw2W7eB1DXWpsxBOn78OCwWi6MsTnl5eYfVLLorZ2sZHkAFIIe4tsruCHhYumoBKa0RLy8vR1/5rkhHYygtLcXBgwcRERGBESNGOLlCeqolNxcr9fb2Pi36A3FJe2UX1NraWlRUVDjaTSspzcHBwR677PqqTE5vCCtZrVaL2tpaTJo0qduEhvakr/sB9aS45iA1NTWhqKgIJ06ccKpmweDt7+/fo+5Gbvx3NooKQGhNNOgs+ABds4AaGhqQkpICu92OGTNmID09vdfacmdnZ+PEiRMYOXIkYmJiWq3T3XpuXKw0MzOzVbHS00k0Go2T359LvSgpzdxMjWu09VWWe0fj7k3hyVZPEBrak75ywfUF0JlMJgQEBMBoNCIpKcmp7URhYSGsVqujDxK3nejOmCwWixoDOlvFXW6PJ+KpBVRSUoL09HRERkZi+PDh0Ol0vdKWm6ncTU1N7caVunNsjpUdOXKkw9bf7sbc29IesLqWemlubkZFRQUqKiqQmZmJlpYWBAQEOOJH/eGD76tq2O6O0RVCQ0fHOVMtoLaOwxMU12tVX1/vAKS8vLxug3d9fb1qAZ1twlZPXl4eIiIioNfru/Syd5aGbbfbceTIERQUFGDUqFGIiopy2kdPWkDl5eVITU3tFJW7qzGg+vp6HDx4EEKITldO6Evx9F4ajUZERkYiMjLSSYlUVFTgxIkTrXr89IX0VTHSjq5Ve4QGpTtTeX1cXbB9ZQH1VXJoW8fRaDTw8fGBj48PYmNjHakDrv2ilIDUEcVaZcGdZcLg09LSgvT0dISGhnaZsqtsHNXWQ1RfX4+UlBQAQFJSUquAYndziZQtHY4fP47jx49j+PDhiI2N7ZRy8VTRMY07LCwMFouly+BzOrrqgNZKxG63O+JHTNM1Go2wWq04depUm5WZe2osvSldsbLaqlzdXoWGs9kCak/c9Yuqra1FZWVlp3OQVBfcWSTK3B7utd5d6wNo+4EsKipCRkYGYmJiMGzYMLcvR3eIALx9c3Mzfv31V9TX13eqekJXji2EwNGjR3Hy5EmMGjUKQUFBXc4h6gvpKQtCq9UiICAAAQEBDgZZQUEBcnJyHJWZlQH7gICAHlGCfWEB9YRl4s6d6VpOSavVorS0FF5eXt0mNLQnfcm262r1e36WOBbJ1iRPbry8vBAUFASdTgez2QyLxeLkMfFEPv74Y7z99tsoLi7GuHHj8OGHH2Lq1Klu1/3yyy9x++23Oy0zmUxobGx0/C+EwPPPP4/PPvsMVVVVmDlzJj755BMMGTKkS+P7nwEgZW6PslV2d2nUDDqulXFtNhsOHz6MoqIijBkzpt1KA921gKxWK44fP46QkBDMmDHDo9l4ZwG4ubnZUR6IY0r8YPZVMuPpInq93kFTnjZtGpqamhzxo4yMDFitVqf4SFfbTfRnDKg74o7QsH//fkcjxd6oy8ZyullAHYlOp2szB+nHH3/E66+/DrPZjNGjR2Pp0qWYM2dOp13A3377LR599FH85S9/wbRp0/D+++/jwgsvxJEjRxylrlzF398fR44ccfzvel/eeust/OlPf8I//vEPDBw4EM8++ywuvPBCHDp0yG3PsI7kfwKA7HY7rFar23I63QUgd+0Q6urqkJqaCq1Wi5kzZ3booupOW+4TJ06gqqoKYWFhGD9+fJfcKR3NtCsrK5GSkoKgoCCnXCU+1ukIQH0xHj6GyWRCVFQUoqKi3AbsWcn0dLuJnpC+uHdmsxl6vR4JCQkICQlx1GWrqKjoEqGhPTndLaCORJmD9MQTT+D222/HFVdcAb1ej6effhpHjhzB3LlzsXHjxg739cc//hF33XWXw6r5y1/+gpUrV+KLL77Ak08+6XYbjUbjqNfoKkIIvP/++3jmmWdw6aWXAgD++c9/IiIiAkuXLsV1113n+fl6vMUZJJ3J7ekJBppGo3GAGDdzi4+Px5AhQzr1kHZlDMqkz5CQEAQEBHRJkbRnAQkhcPLkSRw9etSpCZ5yW15PFRJ3AXue0XK7CW9vbwcYtZfweaZaQG0dhyd+rnXZ3OVntUdoaE/ONAuoIwkPD0dAQABuvvlm3HXXXSgqKsLhw4c73I5d8k899ZRjmVarxfnnn4+dO3e2uV1dXZ2jRfrEiRPx2muvYdSoUQCAnJwcFBcX4/zzz3esHxAQgGnTpmHnzp0qACnFtZxOW7k93bWAeB8MCKdOncK4cePaNHHdiacAVF1djZSUFPj6+iIpKQlZWVnd7gfkKlarFenp6aisrMTkyZPdmv3dBaDeBq7TARiVs/tBgwY5suorKiqc2k20lfB5tgBQW7Em1/iashU3Exp8fHwcYNRRhYYz3QJyJxaLxZFGwZZ2R1JWVgabzdbK9R8REdEmgA0bNgxffPEFxo4di+rqarzzzjtISkpCRkYGYmNjUVxc7NiH6z75N0/lrAQgZW5PR/WaegKAACA1NRVeXl6YOXOmx75QrVbrAMr2RAiB3NxcZGVlITExEQMHDnScX3fK6fC++XtdXR0OHDgAk8mEpKSkNl1GXQUgBuuKiopWrRV6SlpaWlBQUID9+/fDbrc7guJcH668vBxpaWmOoqY8WeFPfn4+QkJCYDabHTN3rVYLrVaLwsJChIaGwtvbG1FRUfD29oavry98fHxQXV2NgQMHYsSIEYiPj0dCQoKjVh3QOqveXcInA1Zzc3OfgHR/Val2J+4IDW0BdlBQUCvCR19aQH3VKLGvSvHMmDEDM2bMcPyflJSEESNG4NNPP8XLL7/cK8c8qwCovb49bUl3CABCCBQUFMBqtSI8PByjR4/u0sPfmYraSotk0qRJTkqtO9UMXFl8RUVFSE9PR0JCAoYMGdLu9eNtPTl2bW0tDhw44Ais1tTUOLleQkJCHMrFlUxht9uRm5uL1NRUZGZm4vjx48jIyEBOTg58fX3Q2FiH5uYmtLRY0dJC22g0gLvhabWA3U6Jwa6nyOsXFha2+VtVVRW0WiA9/SCEkMv1esB1LqHVAgaDBna7gF5vQFhYFEJCQhAZGYm4uDgMHDgQAwYMwPjx4+Hv74+KigqUlZWhqqrKcW8ZpHs6fnS69wMyGo1u+0OxS9NmsyEwMNDhzjyTas51Rjim6GmB4tDQUOh0OpSUlDgtLykpaTPG4yoGgwETJkxAdnY2ADi2KykpcbLCSkpKMH78eI/Gx3LWAFBHfXvakq5aQFarFRkZGSgvL4eXlxeio6O7/OB3ZMFwnTWz2ezWIukOiPI1stlsOHLkCAoLCzvtQuRtO2t9MbgNGDDA4Y4KDg7GgAEDYLVaUVVVhfz8fKxbtw5paWnIz89HSUkJiosL0NBQD7udQEMeHzAYgOZmstrYJS8EKX0GBZ2OPkLQ9jab837aw8/2fnMdC99+g4G2U96Spibakc3WguLiXBQX5yI9HQ6g1OlofaNRA7PZC/7+oQgODkZ0dDQiIiIwevRoDBo0CH5+fk7xo+7GIfrbBeepuKvQoCQ0AEBWVhZCQ0N7teBsX8WAgK7lARmNRkyaNAkbN27EZZddBoDGvHHjRixZsqRT+7DZbEhLS8OCBQsAAAMHDkRkZCQ2btzoAJyamhrs3r0b9913n0fjYzkrAIjLyXfW6lFKV/r5MCB4eXkhKSnJ4ebpqrRFBGALq6M6a1qtFi2sybpwbADYt28fAPeJsh1JZ7qichUIBreKigqsXr0a27dvx8GDB3Hs2FHU1lY7WQ96PWAykaLn5TqdVPZWK4EPi81Gy5V6oaWFliuHyJeQl2m1tIw/yt9cL7fdDieLRykMOLw/5Tj49trtzmNmsLJa+dwEmpoaUF6eh/LyPKSmpjqsKp0O8Pb2QkBAKKKiojBo0CBMmjQJ559/PsLCwuDv7++xsu2rBNHeOI4roaG5udlR1duV0MCu3p6yWvoyBlRfX++xBQQAjz76KG699VZMnjwZU6dOxfvvvw+LxeJgxd1yyy2IiYnB66+/DgB46aWXMH36dAwePBhVVVV4++23cfLkSdx5550A6Hr/9re/xSuvvIIhQ4Y4aNjR0dEOkPNUzmgAYpcbs9y6UsvNEwtICIG8vDwcOXLECRC6m8fjDgStVisOHTqEsrIyTJw40eETdyfdSaatrKwEAPj4+GD06NEezeqY2NEeADU1NWHDhg1Yv349srOz8csvvwCwoqXF7lDiAQGksBlk9HpSti0tgMWiPJ5U7ELQX3alscXjat3w/hiclACjvGVKUFF+Vz5OGo3ct3Jf/JfHz5aWUnQ6WsZeRT42W2QaDeDlRefc1OR8TIC20+sBIRpRW5uP0tJ87N27Fz/88C2eegrw9jYjLCwKY8aMwfnnn49LLrkEQUFBHb4PZ5oF1BkZMGCA471mQkNubi4OHTrkEaGhPXHN++stsVqtaGxs7FIlhGuvvRalpaV47rnnUFxcjPHjx2PNmjUOEkFubq4TiFZWVuKuu+5CcXExgoKCMGnSJOzYsQMjR450rPP444/DYrHg7rvvRlVVFWbNmoU1a9Z0KQcIADTidKAKdUGEEGhsbHSiV3flAU9PT4fRaMTQoUPbXa+lpQUZGRmorKzE2LFjnQBhz549iI6ORmxsrMfHB4CCggIUFBQ4MpTr6uqQkpICg8GAcePGdXhzjx8/jtraWowbN67Tx1SW7bHZbJgzZ06X8i/WrVuHmTNnOl6QxsZGfPjhh9i4cSNycnJw6lQ+GhpI2/JpNDdLRQxIhcvxGlbG7HLTaklJK60jjYasIzoXuQ8GJ61WghIreXfiLmajFAa4zgi70Rgk+fhWqzwvvd7Z0mpubm2d8bWxWunYXl50rk1NACelsxVoswH+/tKSqqhwHs+QIcMxe/Zs3HrrrRg7dmyrMefk5KCxsREjRozo3El2QYQQ2Lx5M2bOnNmrOVCNjY3YsWMHzj33XLe6QEloqKys7JDQ0J7s27cP8fHxHrFduyKVlZVISEhwEHbONjkjLSC2ejZv3oxJkyYhMDCwy/vqjAXEtGcfHx+3MZjutuVWxoA4jyghIQGDBw/ulTwizki3WCwODn9X5yENDQ344osvsHbtWqSm7kdFRQVsNlJ+ej3g50fKkd1lrOybm0lx2u1ScQtByzUaoKEB8PGRAKDRSBBhi0ZRIeS/10G66NiKUVo6fCkZnADaN0+CeX3lpeRjKy0n3t7VmmLwUQKfkgTBFo7SW8rj5WugBE6Wxkb6aLXSJcmuxfBwAh2+rl5eEqy9vICyssP47LPD+Oyzz+DlpUVoaDQGDRqEm2++GVdffXWfWEDK5o69fZz2JqLtERq4jQITGjqq0NBXZAfLf10AajXs00Bcc3u6q/h5H23FT5SJmEras6v0RDVrm82G9PR0B6OEabqd3b6zx6+ursaBAwfg7+/vKNvjiQvPbrfjp59+wpdffonNmzfCZiNN6eUFRESQMq2vp9m6zQaUlRHQ+PqS0mxulkq4upr2yRM7q5WUbHMzKWSl+81kon3U1tK2BoMEAY4H2WzO8RUAMBolCChJCmxduQIMXU9nV5tSXF13vG9eX2nZsQXDVlZDg1xHo5EgYjBIwNZo6LyV8SmjUY5fpwPq6uQxTp2i7c1mCWIM5hYLHdPbm/bh5WVHSUk+Cgvz8csvv2DJkrsRFhaD6dOn4/HHH8fw4cM78QR4Ljy56W2F7SkodERoUOZwuRIa+ioGVF9fDy8vr9OiN1VvyBkDQK6tsrnjZU8kkTa6TqVBVkJ6ejpqamraTMRk6W4MqLm5GRaLBVqttkutDToDIMr4lSuYdhTHOXr0KD755BOsWbMG+fk5aG4W8PICAgNp9q3Xk6I9eZIUaGws/V9YSP/X1NBHqwXCwui32lqa1et0wH/DUNBoaJ8mE4ETu6M0GgIWZWzEbielyt9Z+QJyuRKQtFpnywNwJgm4vt98OdtbriRHsLgCIG/PVpYr2DGo1NfLZUoryMtLxsSam+m6MeiaTBKgDAYCppYWeW0IdOhTWwsUF8t9BwcDfn4CNls+fvjhB/zwww8wGnXw8wvEk08+idtvv73HAvZ9aQF1FRQ8qdAQFBQEq9XaJ6DArRhOt1JXPSWnPQApy+m4stx6IonUHXhUVlYiNTUVfn5+nWov3R1LrLi4GIcPH4ZWq8W0adO6XGG3PQCx2WzIyMhok9Dgbvtdu3bhvffew4YNa9DU1ASrFRgwAIiLIzCpqwPKy2ndlhZSkoGBpCTz8mg5/9/QIC0WTkswm8nyqaykbRnEGIwA6RJjEGJrhWMqbFFoNDTL12ik5cXWEe9XCPruLi4kRGtwYnEHWkpgU5Ii+JjKx4m/m83O1hKDKZMmTCb5G1uCAQG0Xk1N6+P7+0ugLSuT5ycEuT19fOj3wkLaHwOf0UgThLIyICeHjuPnR5OB5mYbamvL8dhjj+Hppx9DVFQcFi5chEceeaTTuSPu5HS1gNqT9io05ObmoqWlBYcOHXLQvbtDaGhPzuZeQMBpDkAd5fb0VBkdBg8hBHJycpCdnY2hQ4e2qn3WlnTFBaekJg8aNKgVI8UTae/4FosFBw4cgMFgQFJSkltCA2+/du1afPTRR9i1aytqaxuh1wNjxwJHjpDSO3GC1g8NBRISpGVjsZCSq6oC4uMJGBoaCKTYQrBaSQn7+8t4EAOIzUbKk2f77Bprbqa/FgspSWVsp7mZEzxpe6W7jmMxSndYQwMdl4P1gLPVpLz0yrgNL3cFLWUMiMfLIMCuNOVyPlcWjv0oCQtarTwPBmOtVhIyWlpoX5GRZHmyC9NolPG1+HiyprgyCseM7HaaPJSUANnZdB0CA2nfyioqWi0QFUWTA4slD5988gk++eQT6PU6XH31NXj88ccxcOBAj57VjmIzPSW9mZvjWqFh8+bNiI2NhcVi6VSFhq4Kd0NVLaA+ls60yu4pALLZbGhubsbBgwdhsVgwdepUj4gNno6jvr4eqamp4G6iLS0tOMHavQvSFgCVlJQgLS0NsbGxGDp0qNsXIj09HS+99BIOHNiDlhYbbDZg0iQCk7Iy4MABWm/YMODwYVJQ/JvZDAwaJN07NhtZPwYDzbIbG0kZchC9sZGUJVtGTU2kNPlvYyPAxhnP2lta6Ji1tXLMJpMkNgDOdGwGD6u1tXuMwY0BQhnPcZ28soWjXM5uPI7XMLONrSTXHB8W5foMBjYbKX+O79TVOVtRbN2w+43BRgi61gYDWX0MWgy2ubm0vo8PLaupkW7DvDy6Z6GhBOhVVUBpKf1mNFIMLziY1jt0SB6byA42/Pvf/8Z33/0boaHhWLhwEe69917ExsZ26CLqKwp2XxEDuAFleHi4g5DU2NjoYNd5SmhoTywWS5+U4ekvOe0AyJNyOj0FQE1NTdi+fTsCAwORlJTkcXdLTxJBuZtoZGQkhg8fDp1Oh9ra2h5NZLXb7cjKykJ+fj5Gjx7dyn1SUVGBV155BT/88C1KS8uh0wEzZgAHD5IiTEkhRTd+PM3cS0uBo0dpWw6o+/iQYsvIIIU5fDgpSXb5HD9O68fGktVTV0cKr6UFKCqibSIjpXVQXU37ZreeRkNg1NhI27L7jEkKfLpNTVIRAxLIlImgSsYbB+mVote7Bw7A/XJ3+wBkPAdwduvx2Pk7W0MMyoC05Ox2uras/JuaJPjodAQU/NdgkC7NlhYCNLOZPno93QsGNV9f2q+vL92HnBwJirGxFJs7fJjAx8uLAKq+Xl7n0lKyoKKigLKyU/jiiy/w5ZdfwMvLG0uWLMHll1/uSPZ0tbL7shtqX7X9BpxdilwNxZXQoGzD3RahoT2pq6s7axlwwGkGQJ6W09HpdJ0q4tne8UpKSmCxWDBy5EjExcV16QHuDAnBbrfj6NGjyM3NxejRo51qKfUEi4797I2NjUhNTUVLSwtmzJjh5D9esWIFnnzySZw8mY2WFmDUKHKl5eYC27eTMpo2Ddi3jxTP/v203ciRpHhKSwkg2C2m19PyujpSXqzMTp0iIOAYBCvVgQOlG89gICACyKKJjQXy82V+jxBkZcnrR4qVzldSjZl15yocO+LvbBUBUvEC0hLiGBUvZ9eZ0qPD+1ECWls0b15mMtHx6uudqdVMHuA8JyYPBAY6g44yxuTvL3Ofiovlb3o9AYvZTPvKy5OUdL2eqNq+vjQpYPAHaP3ERCAtja69jw/dh6IiCYxGIxATA0RHA8eOAXv2OFtGBkM93nrrLfz5zx9gxoxzcOONNyI6Otox8w8KCjrrLCB+V9ty93lKaGivQsPZ3I4bOI0ASNkqu7fruAGUoX/w4EHU1dXBy8sL8fHxXdoPj6M9AHEFBdcZDQNIV3MyGMAqKiqQkpKC0NBQTJo0CXq9HvX19XjhhRfw1Vd/R2VlLYKDgdGjybV2+DApkSFDgMGDCYj27SNFnZhICkerpfU4hjBmDAFMSQkpzZISUk4hIaQ0i4ud6dQ8G29ooP0xYaCxkWbZHM/Jz6dzYcuBkyvZ2mAGGECK0tubltfUSLIB6x62WpRxF7aelNUI2CXGyaDK2I/SvcfL2KJhajfrH96nu/ykpib68Pg4juPlJV2HVqvcd1UVLffyco51hYbSBIBJGgwAfn4Urykvl9YjA2BCAo01O5v2xaw5u50mAzk5QGoqkR2Cg51dqQYDrRMZSevk5NCYDAbpFuVjJSUBBkMTNmxYj40b1yMoKAQ33XQTFi5ciIaGBnh7e8Nut6OqquqsaccNdJ7V1x6hIS8vz1GhgcFISWior6/vMgB50o77s88+wz//+U+kp6cDACZNmoTXXnvNaf3bbrsN//jHP5y2u/DCC7FmzZoujQ84DQDINbfHk3I6XbWAysvLkZqaipCQEIwdOxYHDx70eB9Kac+CKSsrQ2pqKsLDwzFy5Ei3syZe1tUgqkajcTSgGjZsGOLi4pCdnY0FCxagoCAPdjtZNr6+NNs9cIC+R0WRay07m/7GxACzZtEMmplser107xQX0/KgIGDiRFJK5eUSdIYPJ1dOVRUBA8eA2FKqrydQYGuAyQVBQbRuSwv95QB8UBABjdVKVhZXUKivdyYdANKCYUYZgwBbUwxkOl1rQgBde9o/z+5ZlFYRA5Q7t1xzs/u4ERMNlODj709jUca1dDoaK1stSno6uy1NJgKUxkbaPjYWKCig6825QE1NtI6fH9HimeWm07WOB4WF0cRBrwfS02X8adAgehb27AGysmj7wEC5PbtHR46ksezYQfsICSFyRHl5OT744AP861//wN1334crr7wShYWFTu24uaCqt7d3j1lHfVUglI/T1XG7EhpaWloc7jomNFgsFmzevBmlpaXw9/f3+BietuPesmULrr/+egdR6c0338T8+fORkZGBmJgYx3oXXXQR/v73vzv+725li34txeOa2+MpUyY7Oxv19fVuS4y0dbxjx47hxIkTGD58OGJjY1FXV4fdu3c7dfnzVFxL6QAErNnZ2Thx4gRGjBjRbpkeq9WKDRs2YN68eR7Hn1paWrB//35UVlZixowZOHLkCB566EGkpaVCp3MOxNvt5HaLjycgKiggxRUQQLNrVuCBgcDkyfR7To5U5lotuXNqaggETCaKEx08SOvwkxQZSTP2qipSnGxpBAWRkrJYaB9MRbZaab9GIy0rK3N2YwESpBobpXUA0DrK8BvHOxgQ2LPBIMRuO6Z28z5YMSvrwLkTdm0xsPC2DDLMfuNKBHx8TsDlc2KigbJqga8v7aO6Wl4bvV7m9BiNdG14rBoNgY6XlwRp/s1opP35+5OV2tQkwTkkhO5PYyMBDOc0hYeT5btrF12DkBDax8mT0vUYHEz3vLCQiApGI23HrlaAjnnBBWTxpqQA3t4GzJp1Lv72t79Bq9U6kj2rqqqg1+sdYNTddhO5ubmoqanB6NGju7yPzkhNTQ1SU1Mxe/bsXtl/Y2Mj9u3bhw8++ABbtmyBzWbDeeedh/PPPx/z5s3D+PHjO7T0pk2bhilTpuCjjz4CQLovLi4ODz74YJvtuJVis9kQFBSEjz76CLfccgsAsoCqqqqwdOnSbp8jS79YQJ1pld0Z8cQFx26w5uZmTJ8+3VFdtqdyiZQWUFNTE1JTU9HU1OR0rPa2B9CmFdWWcG8do9GIDRs24JFHfosTJ07Azw+46Saa1aamSuWr1RKlOiODcnoWLyYg4pkyEwzq64ENG0ixzZ5NLrcjR0hhceb94MGkDPfsobEoWXJlZWQVmc3k7jt5ksCoqopm9ToduXeam8ntp9EQALKSi42l7VlhajS0LQNDQwMpUFbeTC5gyrEyb4aTXc1mmazJsR1Xd5sSrBjUlPMBjtO4VjtgtyILVypQkgwAur5c9YBBrKWFxtbSImNe7JrT6WRibn09XXsGTyYbeHsTyPPYmOoeEEDLcnIkOCYkEFicPEmTBn7lDAayXlNSaAyRkcSIO3CALFy2ombOpGdqwwYZPzp+XMbuAgKAiy8G9u4FfvyRxnHhhcCePS3YuHEdBg2Kw4IFl+LDDz9EfHw8bDabo1250hXV1WKhZ0s7bi8vL8yaNQuzZs3C/fffDy8vL4wZMwYbN27Exx9/jGPHjrW7fVfbcSulvr7e0S5FKVu2bEF4eDiCgoJw3nnn4ZVXXmm3UHJH0m8uOKvV2i3wAToPHqWlpTh48CDCw8MdsREWjr905+FVjqO8vBwHDx5EcHAwJk6c2KkXyNO+OgBZXYcOHUJpaSleeOF55ObmQa+nGWpwMPDVV6RQr7+eAv+7dkklpdOR0jpxgtwwl15KAJOVJV1RzLTasIEU0KxZZLmkpNBsOieHlBTnljBLjl1WrABTU0k5DRlC63Dw/dgxWi80lBR2UxPFdpjKzdLYSOuYTDLXxWqVylqvlzEQ3gfP6JVWjbJ8jdINp6w6oMwR4mRXZbxIWWjUbqexMWi77l+ZBAtI5l5AgHTHMVA2NNA1MZtlYVVmrHGODo9TryeAMJnoHpSUyOPExZHSLyqSlg27yxITCXROnpRMOpuNXKNFRXJSEhtLccBTp2i/ZjNw3nlEUlm5kvY/dizt6/hxCU6XXELPyjff0PEuuQRYvx7YuJHGbzIBixYB33+/DKtXL8cll1yBDz74wGH9JCYmOlxRFRUVyMrKQlNTEwICAhyxET8/v3bf0bOxHXd9fT2GDx+Ohx56CA899FCn4sRdacftKk888QSio6OdPEMXXXQRrrjiCgwcOBDHjh3D008/jYsvvhg7d+7sMiD3CwB11Ca7s9JRKR4l82zkyJFOvkwWZfylO4mgNpsNx44dw/Hjxx1xmM4CqydttW02GzIzM7Fz50589NGHyMrKQlgYsGQJ8Je/kA++qope+IEDgX//m5TalVfS8p9/ltUCuN7aTz+Rslu8mEDp4EGpIPV6AoBt22ibKVNI4R44QEqqsJCsHwaiU6dI2dbUkPKOj6fvPGljxR0aKuvBsbXR0EDg6etL6zQ1ScvAy4sUW0sLnSNbR3a7M1uOyQ1cFYFjULwuu9g4/4atJ9facyxKhh0r7fp6ZxICkxKUNG/OVdJq5T6EcK70wGw8s5n2odUSkDJgWiz0G5MwQkPpt9xc2p7B0ceHrj/TqJUVH8aNo0lAWRldF6bE86NWVUX3LyaGgOfkSWmhXXghgcqyZXTsKVPIuklPp98DAoCFC4HkZOBf/yIyyuLFBFSrV9Pxg4OBqVNpWXIycPnlgE4n8P33P2LZsh8xevR4bNiwAUajEQaDoVWx0IqKClRUVCDvv7MSZfzIlcp8tlhASnHthtoXbMI33ngD33zzDbZs2eJEqb/uuusc38eMGYOxY8ciMTERW7Zswbx587p0rH6zgHriQrZnATU0NCA1NRVWq9Ut80y5D4AUe3d6gzQ2NiI/Px9Tp05FQECAx/voDADV19dj06ZNuOuuu1BZWQk/P+C3v6Ug8CefkLK6807gb38jpZeZKXN0li4l5b1wISmPdeskIYAV1k8/0feFCwlI9u6lbU6dIoUaFUXKrLmZLBqNhmba2dlyNj15MgHRyZOS3RYfT9ZSZSUpvOZm+u7nRzPuujpZG44D6lFR9H9FBQFJczPFpADntg5+fjKWwvGYqirn62a1kquKwYcVMLPC6B5K1xfgnMMDOLsxOZGV2XNMf+aEWX60mWTAukpJ9eZtw8NpfJwQCkjw8vKSVSUAAhGDgUCkoYHGGBJCoFVTQ8DNeVBmM93btDRaJyBATgIAWnfQILKAt28nC5jP/dxzgc2bgRUr6D5MnQr88ossmxQYSOD044/A//0f7ScpCVi1iu69ELQsPJz2/csvwC230G/8jDGbLiUlBQkJ0fjDH15o1anTbDYjJiYGMTExEEI4qMylpaU4evQojEajU/zobLSAukLD7k477nfeeQdvvPEGNmzY0GFsfdCgQQgNDUV2dvaZB0A9IW2x4DjZMyIiAiNGjGh3tsIuwK7GgSorK5GRkeGoauApiYClIwAqLi7GQw89hDVrkmG1kp/IYgHee49e6HvvJRfXRx+RArznHmD5cnKvHDok3WAbNpCCmj2bZs3Ll5NCrqmRs9rly0mJXHABKf/t20nh5eaS8omIIPdLQwMpMCFICZaU0PHMZkps5byT/Hw4aslFRhKglZaSgq6sJEU1YAB953eGE1lDQghkSktJabHi4tteW0vrsKXBSavsfmJXmjIuBEjaNcdk6utlgU9AVpTm/7nattEoAYiJAq50by6bYzLJOJXJJKnX1dUS0Dgvh1153t50jY1Gum48TmXMh5cDtC8mC+TlScvGaiWrJiyMriUXijWZyIVmMtF9ZQIFQOSCAwcIfAYMoOdl7Vq6J1otHWfWLJrMfPstseDCw+mZOnGC9jNhAt2DzEyaCNx3H/Drr8A//yktK2XriN//Hli3rhlPP/00PvzwA/z1r59jzpw5rZ5/jUYDf39/+Pv7Y8CAAU5U5pMnTyIjIwN6vR7e3t4oLy/vkXblbUlfWkBciscT6Wo77rfeeguvvvoq1q5di8mTJ3d4nPz8fJSXlzvlNHoqZ5UFpKyvNmrUqE5dGE/cX0oRQuDEiRPIzs5GfHw8cnNzuww+QNsAJITAZ599hmeeeRI1NfVYvJhe/Pffl1UA6uuBP/+ZlNK115JS+dvfaJbNZIS0NBkXiIsjRbN1K+X1TJlCM9PqaslAi44mRdTSAkyfTm6eTZvo94oKUlDBwQR61dWkHBsapBWxbRuNf8oUii9YrZJN5etLyu7QIVpeXi5jDsOG0T45sF9VJZljzBbz9qZx1dXRekoXHCDzYzjeZbFIsGGAsNtb07Fde/VwmRulKEkFHAvi/TLLjUGKc6EMBjrnykqZeMpgx+NhYkVDA7k1AZlXFBND17+qSsbHDAZan+nR+fmyCnZsLAHDiRMUs2NwS0qi89mzx7mWHecYpafT8zBoEE1CGFTCw2n52rXkRps6lY61e7e0EM89l+7n3r1Edvjtb8mC+uQTaQHyhCAsDHj4YeDLL4FXX6V7/sILwOuvl+CSSy5BVFQkdu/e0245LFcqM/e4stvtOHz4MFpaWlrFj3rKfdWXFlBXi5F62o77zTffxHPPPYevv/4aAwYMQPF/g4+cUFtXV4cXX3wRV155JSIjI3Hs2DE8/vjjGDx4MC688MIun98ZbwExANXX1yMlJQUAWlUA8GQ/nZGWlhakpaWhpqYGU6ZMgdFo7FYtN8A9AJWVlWHChPEoKyuDVgv84Q/ka1++nJTA5ZcDb71FADRgAAWlv/+elMOYMZT78+9/k9JZuJAUwOrVMk4QFkbK7osvSGldey0BUXk5ubu0WtpvaiopxmHDyJ22fj1ZQ8ePE8ttzBgCDc5P4diFnx/Nfm02Ar28PBlH+fVXGk98PO2L6cZZWXJ2X19Pyq+5WTLKGhrI6omIIFeUsnoCu8VsNhlrMRjoN85l4ooIypwfZW4Px3OUVaoBybZjy4r3w+4ktoi4CZ8ycbalha6p0SiJIEFB8n+uelBWJskI3IBu6FBynbGLUKMh4OfrmZlJx7fbSfHHxNDkggkCAFk8p06Rq9ZolInAfJ0tFnpWoqMp3nPoEP0WFUWfPXvoel5wAT0vu3ZJULn0UiIabNxIE6PrriNgef99eXyzmY4xbBhwww1ktT/3HE1sRowgUHvlFbqe0dHAqVPFGDZsEF555U3cddddnXp/jEYjTCYT/P39ERcX5xQ/OnnyJDQajQOMOH7UVekrC4hL+nTEonUnnrbj/uSTT9Dc3IyrrrrKaT/PP/88XnjhBeh0Ohw8eBD/+Mc/UFVVhejoaMyfPx8vv/xyt6jz/ZYHxPXeuiMWiwXbtm3DuHHjkJ6ejujoaAwfPtzj2cnPP/+MMWPGtKIcuhPujurr64sxY8bAaDSisbERW7ZswYUXXtjlWda2bdswdOhQRwD2s88+w2OPPQKr1YpRowgEeEY8aRK9rKmppHQefBD48EOasY4aRQqLFaqXF/Cb3xA7qayMYjQTJpCFxLNxb29SmsXF5IK77joKGhcWSubXkCHkiqmuJuU/ciRZSDy7ZnDKyiJQ4HhJbCz9LSpybpsdEkLbVVc758bExpKy4sRTnY6UtZ8fLSstlZaH8snl2ITVKhMmlS4wd6LREIgBBBZ2u6zO0NAg4zsajWwSx7qAq2sHBEjChLKSAo+J3Xg6nUy25XgYn5+SWq3TyXJHPEa2LoUgsD90SAISt2ZITJQ1/PiexMTQtaitlTlBTG8HaOwTJtAzsnatJFLEx9M+jh+ndebOJYJCUZHMz7r+eooB2WwEJJMmAR9/LF2XfD4WC20/Ywbw7rt0/ueeS2WeqqqkFTZ3LrnyAgOB118H/v53Ar6RI0fiP//5T6e8Gcw+dc25U5bCqaysRHV1NUwmk1P8yBPvxfHjx9HU1NSrbcwBAqCEhASsX7++Uy6xM1H6DYA4D6g7YrFYsHXrVuj1erdFNzsr27Ztw7Bhw9rtQtpeQ7fm5mZs2rQJ559/fpeJDDt27MCgQYPg5+eHiy66CPv27cWYMeQj/93vSPFecQVZH6yMeIacmUmK4umnyTf/66+kRMxmYkaxQrr8clJSOTlk2Vx9NfDBB84B9+hoAhC9nmar27eTIuJ8l1GjCMhKSgi4Ro2SLh27nWI8kyfTcbisixCkgKqqyA3H8Ra9noCtspLOj0u8xMTQ2MvKZGM6q5UUbny87GPDtG12i7E7LDiYxsdVodkqY0uJQck12ZUVOgtft46EQQSQ+TscpwoIoGMy4YP3ryROMEmgooLAgt14XNGaCQocD/L1lfsKDaXrxPE7BnQG6ZAQsmLS0mgfnK+TlET3Y8cOCZxcwZxzgebOJWIBXzudjlhuP/xA47rwQnoGvv1WXjuuBSgEPV+hoQRMOh1ZO1x0RKslV198PFlPAwcCzz5Lrrj8fGDePDpvdiG+8sobuP/++9u9DwcOHEBERASio6PbXc9qtTriRxUVFQ4rQ5l/1N4kNjs7G3a7HUOHDm33ON0VIQTCwsKQmpraa91q+1vOWADiPjd1dXVISkrqUrkKlh07diAxMbEVb57FarUiPT0dlZWVGDduXCtLyWazYf369TjvvPO63EVy165dKCgowG233fjf8vektJOTyQL44x+BN98kP/3cuaTUNmyQvXQCAkihaLXAk0+SdbRyJc3YL7iAXHP8TiUm0qw3I4NmnPfeC3z6qXOMIjZWWkBXXklW1cGD0iIaP56UZU4O7TMhQbp9OCFy3jyKE1RUSAWdlETbsSKy28kVyK44jkGx8k5IkEmTXl7OriOdjhRlczMpKw7m82PFwMAFPmtrZeVspngrm92x9cD/K11ubK0oWWw8Jo1Gxr8CAuj3qippaQBybFznjV2KwcF0fXi8QtB4Q0Jk4i4XduWkVCUJwcuLnpWKCmml6XQE4gMGADt3ynF4ewPz59P15DgbM/Fqa+maJCaSRfPNN5I1GBxMdO4NG2gfV19NwJCaKoFnyhSyksxmIh7k5QHffScrdNfUyGfnjjso/piVRcmt110HPPUUgeqoUfRcMtiGh9M9TkkB5syZ+9/Ore7fsV9//RUxMTEeT0SbmpocYFRRUdFhK4UjR45Ap9Nh8ODBHh3HU2lqakJYWBjy8/PdppCcDXJGAlBhYSEyMjIQGxuLkydPYu7cuW4brXVWdu/ejbi4OLczJ642YDabMXbsWLf+TiEE1q5d261x3H333fj6638iOppeul9/lQoxPl5WOH7zTXJ9JSeTgrn3XrJ8eKauLOt/112kuD7/nP6/+25yvSmz/EePppdbpwPuv59mvEyr1unIXVRRQcpyzhxSCr/8ItlTEyfSONPSaL8+PqRImK0GEGDu2eNcEXrsWDpGRoZUYszQ2r9fFsTkfbDiDw8nRWmxOOfwREZS3IqtHk5aVSakshgMMqjPNdg4N0rJSOPrD8jry7e/qYkUK1cpcK0PxxYes75aWgic/P3l9eSxs4UkBLk28/IIENiFFx5On2PHaD0G+IQE+p0TiPV6eiYGDybLhi0Xo1EmipaXS1eutzftT6ulCcXQocDXX8teS0OG0NjS0siSufpqikEqSR0TJ9KxgoPJUl+xgkCP865aWqQb8ZVXiHRQXQ3ceCON/9VX6Xp5eTk/Hw89RJbWqVPAo4/SObz6KhAcHIDk5NVuy+3s3bsXCQkJbmuddVaEEKivr3eAUVVVFbRarQOMgoODcfz4cZjNZgwcOLDLx+mMVFRUYMCAAaiqqupSaseZIGcUAHESZklJCcaMGYPw8HCsXbsWs2bN6lbJ8n379iEiIgJxcXGOZUIIFBQUIDMzEwMHDkRiYmK78Z2ujqO5uRlz587F/v37sWgRcPPNwO2304v41FMEOPxicrDZZiMwCQ4mv7rZTBbSq69K5hJbMkxIuO464I03SLHdeisBwtGj0hU1eDBt29JCzLmiImDLFvl7cLC0IoYPJ+WUnCyBYeJEcg3t2SPZZTodKdzaWhknyspyrqPGuoJzjTg/hoHM15f2w64pdtHp9aSo2TpQtsg2mchaqK6msSjdYzzrd40hdSQdueM4hMCPNLvKuLabMj7EwMItFDh3RrkvpnGPHUsgrQR1jtvs3Onc02f0aLpnFou01i65hCzhpibahi03BvxZs+haL18uz3HaNLq2RUXkGktKIuBhYBo4kMadkUEW0/330/NXUOB8nXQ6GusVVwDPP0///+EPFG/atk0+n3y9YmPJev/d7+j/Tz4hUszy5fTshITIwqcvvPAKHnroIad7sHv3biQmJiI0NLTzN7YDsdvtqKmpccSPampqoNVq4evri/j4eAQFBfVKK24AyMvLw6hRo9DS0tJrx+hv6TcAEkKgua0uYG6krq4OKSkp0Ov1GDdunIPFsnHjRkyZMqVbLrgDBw4gKCgIAwYMAEAut0OHDqGsrAxjx47t1AO9YcMGTJs2zSPGCj1gw9HSYkNEBL34ycnkG//0U0reKygAnniC3FPffSfjIaxU/fyAxx4jcGlsBJ55hrb5+99J+Vss0n0E0LpffUXrzJ5Ns9Cvv5buJg6q19cD559PyuaLL6Ty51yU0lJync2dS+49VjxjxxJD65dfJAuM65CVlNB+GTC4Vw73FmJWWXAwuZ549mw0khXI27NVw0H7+nppabBCVoJFQACNublZWgBcCYHBiN1+gHPyKf/ldZQECHZpNjXJBFF2o3HCLYvSMgVI2Qohk2v1egKClhY6n6AgyeRjRZ2YSN+5pBGXMmLgYbeVRkOuLVbyfn4SwAECvgsvpBjd9u3y2s+fTwq+oYEsIp2OaNUcK7v4YrLMy8oIlM47j6waV/C028lqqa6m5zgoiCY1f/6zczyPJxmPPkr7f/ddsmS/+AJ45BFKjg0Pl88CxwADA8n9d/vtd+C9995zHHvnzp0YNmxYp8hEXRWr1YoDBw5Ap9OhubkZDQ0NjvhRcHBwj7abOHz4MObOnYva2to+o333tZz2AKS0RBISEjB48GCnm7FlyxaMGzcOQUFBXR5Lamoq/Pz8MGjQIAfQGQwGjBs3rtMutU2bNmHSpEmdNpV37dqFiy++ADpdCyZPJgUCkDLw96eXV6ejTPPPPiPywXnnUX7FNdfQSzxypPTDC0FB3tJSsiauu45iSE8+KZlKv/wi64aNHEnbHTxIIHPnnTRLVfa8MRqJeTZ8OMWB3nlHMtkMBnLPFRbSeosWkcuEldngwcSMWrNGMsw0Glp2/LiMe2g0ZNE0N8tKzxyD0ukIeFnhR0bSLF7Z1E5JoEhIoP+5wKmyp45rPMb1cVFWUGAmHIvNRkpZWTKHhQHelQHHsaXmZllGyGika6HMW+J7FxxMcRJu2cBN5rgcEt8LPmcuCJuZKS07m03G0oxGOuapUxJ4goOJRLB9O4EY36uFC+k+abU0oThwQBZ/FYJiNl99JeOBOTkyB4jvdXY2Xbc//pGspZ9/pvsqBI2B7+FFF9Gz7OVFyakff0wkhJkzaQL2xz9KEFVW9n7zTQK7ujraf3IybT9jxgysXLkSer0e27dvx6hRo9rNH+oJUZIdlK24KyoqoGw3ERQU1GG78vZk3759uPbaa1FSUtInJXj6Q05rWLVarUhLS0NWVhbGjx+PoUOHtpoJdLcrKu/DZrOhsLAQO3fuRFhYGKZMmeJRPMeTXKKvv/4a8+efi8DAFmzZIrP/H3uMXFnMZGpuptycdevIKho7lphsISHkxmBX1zXXkPI9ckRWq161ipL9hg2jF5ZrEF51Fc2aDx8m3z7HIJ59lqyEP/6RLBgusKnVEnPt1VdJwbzwAlk+NpvMwQkPp+x4jYYID1otKalvv6V933wzzVq1WlJuNTW0XmgoLSsooHFPm0b70mpp3ydOEGCxcVteLl2MPCsPDycLhwucMviwNeDnR7/7+dH5MSW6osL5oyzfU1tL584fvs7cOryyUn4YfDh/hynt3MeIj8mtLaqqCFjYyho0iM6vrExaBsycYyIDswM5b4jzmnJyZKyF9VNVFQGC1SpbYURFETEAAP7xD5oAGI3kfgMopjh3Lm2zaRPtIySELHAhaBJ01VU0hh9+ILKBvz/dG0CuExxMcccDB+g8+L5yfpAQBHxMrLnqKrrWZjO5bt9/X8bzAHr2P/qIzvWNNyiHbdAgWq7X08Rp586diI2NRn19fZ+W4uE8IG7FPWrUKMyaNQuTJk1CUFAQysvLsW/fPmzfvh2HDh1CUVERmnj21kk527uhAv1oAQFo94bU1tYiJSUFJpMJY8eObRMMmL7cVQo2ABw6dAgVFRVoampyxJY8la1bt2L48OEdUrnvu+8+fPnll9BqyeL4+GNSPp9/Tgy3998nxfDcczJ5NCZGdifVammWy0yov/6VlMb//R+B1xVXEJgwFnKAX6ull/mLL0iBzJhBiur//k+63zjXxm4n+ndmJgEZ74tjAAYDBYnXr5dVDlwJC5Mny6KmHNO58kpZ+gcgJbJgAY2fl3Fge/duWa4GoHMOC5P15BikOZlVqyWA4JwcZqVxrIMrxre0SMacsq+QMvlUqcPY/cY0ZFb0vJ2yAZySdMDtJficuJuoySTr27F4e8sWCj4+shW6MlaktCKDgui38nJ5DSIjCYwPHnQmr1x4IRFPmFQRFESAlJFB1yQmhr7zMzJ3Lh1706bWvwPkpsvOponA/Pnkpn3sMcm+Uz53b71Fk5/UVLr38+dT7JKvvTKd4M47iXnX0kJxn88/pzyjRYvotyuvlBRyvvdCEGvuyBEgLCwC77//J8yePdvj0jWeyp49exx10NoTu92O6upqB6Ghtra2zc6n7mTVqlV46aWXkJGR0dOncNpIvwJQc3MzXA+vzLfpTPB/9+7diI2N7TJN0WKxYPfu3QDInO9qhnRHVO7m5ma89dZbeP31VxAWJpMNOVHRbCbFNG8esdoWLqRla9aQJbNrF5ET9u8noHIFmLFjqf7bI4+QEvvXvwgkjh0jxV1aKtlPcXFEm126lH574QXajmf6rPhsNsp0nzwZePll5wA7u2euv56U6bJlktUVGEjnVllJxyotdVa4l15Ks27uzWMwkHJas0YqFo2Gqj3s2iWBl8Fv3DjaH+cUKZMfBwyg/2tqJKWb83KUotVKq4zPpb6eAMzXVwINPSOylQK7h7Ra6TLz9XVux8DXEKBtvLykwi0slNsHBNAnMJCuFVt3Oh3tc/p0Ut5FRbLiAvfz4XsfE0NAs327BJ6RIymG889/yvhRQgIdv7SUvlsskuKt0VAy88qV9LxERsqOtnwuv/898Kc/0XW/5x5iu504Id1wDAxDhgBvv03PqsVCz+5XX1G8icfC53/uueR6e/FFsma+/56sorw8YtytWUPj4PvDY7n0Ujr377+nsSxYQBMvP78A7N69t1uT0c7Irl27MHToUI9jTcrOpxUVFWhsbIS/v78jfuTabuL777/Hp59+6tBPZ6WIfpSmpibR2Njo+NTV1Yldu3aJ1atXi4KCAqff2vps27ZNZGVldWpd18+JEyfEihUrxObNm8WePXu6tA/+bNmyReTk5Lj9raSkRDz99NNCr4eYMgUiNxciJATCbIZ46SWIwEAIQH70evo7Zw5EVBR9f/ddiHfegdDpIMaNg1ixAsLLi/739ZXbaTQQ06ZBBATQsjfegHjuOVpvwACISZNoHYCWDRsGYTRCGAwQH3xAx9Rq6QPIvxEREH/9K0RYGP1vNjv/Pn48xO9/L8eu0dA6cXH03c+PjqPRyG2mTIEwmZzPfdIkiAsugEhMpDEBtI5WS/sIDJTjj4yk4/LxvLzkfgwGiJgYuX1gIISPD4S3N41DeUzXj1ZL65hMtE8+nrv1+DqazbRvHx95jkOGyOvFY+R9jx4NMWGCPEedTu7v3HPldfH3p/s9Z47ch15Px5k1S45Dq4WYORNi7lznsY0cKZ+TIUPoeDwOX196/vz9aZm/P63Hz9LEiRC/+x395uND+9do5DpDh9I11ukgnn4a4tVXabm3Nz1r/Dzy82Ey0XXaupXOUaOBuOsuiLffltdAef0MBjofjYbeg4wMiNhY2s/WrRC33Ubb/eY3EM8+y/faIHJzc0VNTU2vfVavXi3y8vK6vZ+SkhKRmZkpduzYIVasWCGSk5PFtm3bRHp6uti2bZt4//33xbnnntsl3frRRx+JhIQEYTKZxNSpU8Xu3bvbXf+7774Tw4YNEyaTSYwePVqsXLnS6Xe73S6effZZERkZKby8vMS8efNEVlZWl8amlNMGgEpKSsS6devE1q1bRXV1dacV/86dO0VmZqZHYFFfXy8OHDggVqxYIU6cOCEyMzPFrl27ugVAv/zyi8jOznZa1tDQILKyssSdd97peImys0mZm0wQW7bIl+iRRyD+9S96aQcOhBgzxhkovL3pe1gYxNKlpIzNZoj16yEuv5x+u/BCiOHDJQjo9VIpT5oEsWoVKR2TCeKFF0gps6IyGOR2N9xAYGQ00vZ+fnIcAMR990HMmEHjCwmRihGgdV95hY6jHH9iIu1fp6OPEojCw+n4Wq08RkQExKWXkpL28XFW4iNGQERHy3NTgmVEBJ0XXy++Dv7+pLyio+ka8nG8vOhjNtN14TG6jlWnkwrUbJYgZjJBBAfTOURHS0BRHtvXV55DVJTzNfP3p98ZEPh6RUXRNZ43j9bj6zN8OMSiRRKIeKIyeLDztRg9mv4ajRDx8c7AMn48PW8MXMox63T0bMyfL++nt7e8X2YzxG9/S9v4+ED8+CPEqFHyGeJ7rtdDTJ4Mccst9P+0aRA7d9K5Gwx0Dspnjq+HTgfx1FP0LGu1BGpffkm/DR0KcfQoXW+djp4Dnc55/BoNRExMlKiqquo1AFqxYoUoKCjo0X1WV1eL/Px8kZaWJv79738Lg8EgfH19RWxsrPj6669FSUlJp/XqN998I4xGo/jiiy9ERkaGuOuuu0RgYGCb+9i+fbvQ6XTirbfeEocOHRLPPPOMMBgMIi0tzbHOG2+8IQICAsTSpUtFamqqWLx4sRg4cKBoaGjoFgb0KwA1NzeLhoYGceTIEZGcnCwyMjJEQ0ODR4p/z549Ij09vdPrV1ZWis2bN4tNmzaJiooK0djYKLKyssT27du7BUDbt293ssQsFovYs2eP+PTTT51eEK2WXrrQUKk0br4ZYscOUmaDBkHk59NfrRbiww9pJsoWBb/cBgMp0ogIWvbb30L88INUOF9/LddXKgWAFDsf+957IR5+2Hm2ykrMywvik09IuQIQs2fTeqycw8IgrrqKxuLtLWf7rOzuv59m3fw/QJYZj2PkSIgHHiClqlSErLRZqV10ER2brRGdjq7ftGm0/5AQ52MkJtL5xcSQAlWeG38YoPz8aF8hIRBBQfL8/fzkh5cFBpLyUx4vKspZiTJ48X7j4mh95fHNZtpu2DDnbU0muhbz59O1Ud6LwYPJEuLtzz8f4vbbnYEWoMkLn5dygqHTQdxzD8Q557QGbYDG+O23NGYGLiWIXn01HQ8g62bRIudnisHHZKIJ0qhRdMzXX4e49lo5Bt6vTkfX8uKL6f8pUyAOHKDnw9cXIjOT7jtAlld4uLNlzseePZssZq0W4uWXIb75hn6bMWNGrwHQ8uXLRXFxca9aWYWFheLKK68Uw4cPFxMmTBBarVaMGzdOVFZWdqhXp06dKh544AHH/zabTURHR4vXX3/d7frXXHONWLhwodOyadOmiXvuuUcIQdZPZGSkePvttx2/V1VVCZPJJP797393QfNL6VcAslgsYufOnWL16tWiqKioS4p///79IjU1tVPr5ubmipUrV4pff/1VWCwWx/Ls7Gzxyy+/dAuAdu7cKQ4fPiwaGxtFeXm52Lhxo1i7dq0IDPQTQUEQBw9KpXL11a2VEr/IU6dKUPn6a4g336SX67LLII4cIeUTGkrAoVS6PFPXaMhtEhFB2/3jHwROWi1ZVbGxcjujUR5r1CiI7dulEh4+XI5LqyVlOX487X/yZFIYSiU2ebKcKU+d6nxOo0fTR2mZRUbKY4eEQCxZQspRCdYmE1kVvN3YseS6UYIcuzXHj3cGN/6NgYiVJLvhlOu5+/j4kCL09ZXXwNWyUY6Tzx2gCcDQoRK4GbADA2k8kyfTefE+jUY6h0suka5TrZb2FxoqgT00lJT5pZfKiQVbjcHB8rvyGnp5Qbz2WmugYnfbJZdAXHONMxjx9t7eEMuXS2sqMtIZQGJiaBKg0UBcdx2ty27LWbOk5cb75rF9+SXdS4MB4vvvIV58kdaZPFla80prmK3DCy6g/0eMgNizh65nYCBEWRk9O3o9WflvvknrLV68uMeBobq6WixdulScOnWqVwGopqZGPPLII+LOO+8UQghx6tQp8dNPPwm73d6uTm1qahI6nU785z//cVp+yy23iMWLF7vdJi4uTrz33ntOy5577jkxduxYIYQQx44dEwDEgQMHnNY555xzxEMPPdR5he9G+hWAtm7dKrZv3y5qamq6rPhTU1PF/v37O3S5paamiuTkZHH8+PFWv+fk5IjNmzd3C4D27NkjMjIyxMmTJ8WKFStESkqKGD58qNDryV/9+OP0Urz0Eil6g4Fmbzt20AsbEEAzPdcZHr/Ed9xBCsHfHyI9XbpI3niDYi86Hc22fXycLRgGlMmTIXbvpt/9/Cim5O/v7LpgBXrzzaTkNBoa06RJcp96PR1Hp6N9P/uss0vNx0cq0cmT6Tvvd9w4cu+wYmGfP1tJej3ErbdCXHGFc2zEz4/Aky26oCBSQkrgZQU9YYJUWkpQ4HMMDSXFGR7u7EYzm+l6KV1vPONmq4/X4+28vKT7jcemVPTKsY0bR2Nj4OJrMGECPQd8/X195bHZ7TZ0KMVK2ALidU0mGb+79lqIP/xBxntCQyEWL3YGy0GD5DbXXEPf+Rje3nQcjYasEo6pKGM+fK3efZdA0WSiCY7SBae0uEaNki7E++6DWLlSTj6+/VbeH7ZS+RiRkdIl+p//EMgZDPQePf88rfPQQxDbttF24eESRDUaeX80GogPPvigR0GhsrJSLF26VJSXl/c6AN11113it7/9rUc6taCgQAAQO3bscFr+2GOPialTp7rdxmAwiK+//tpp2ccffyzCw8OFEOSiAyAKCwud1rn66qvFNddc49H4XKVf84DGjRuHiRMndrmAJ9Bx/k1jYyP27t2L0tJSzJgxw229N51O53FDOlfRaDSOTqyjR4/G3XffjcOHs6DVEqPtrbeI7SQEUVfDwohu+sADlEuydCnlxtjtVIqEa2TFxtJ2f/87sbQsFqIpr1tHjK+qKuqvMno0Za0HBBBT6PXXJW0ZIOr1zJm0j8GDKSu9pobK/bz9tmyR4O1NrKWVK2lZczMxsQIDiQVlMBBLyWYjavArr9D3hx6ivJDGRtnf5tdf6XtAAOUXhYYSmy4ggBILfX2JmZWdTeuPGkVU3J9+Iir6b35Dy2triRLu70/VG/R6ymNiVpVWSxTw+nrKQeEkxqYmYomFhtJ5aTSy11FZGZ0TAAfV2cdHVjrw8ZGlcph95+0tm+FptXR9m5sp14VZdlYrjTM0FA76eUAAXUNOGubcKe7ds2sXsQV1OmLTMZtw2jRieVVUUDJyejodw2yGg/H22GPEGPv2W6LwX3utzCFavZrGzdTq0lKiYdvtVFWjqYlYjAEBdB7Dh8sit6+8Igu5AkSffvdd2ubll2XrizvuIJq2ry9dg6AgYkQGB1O+0R//SPfyk08oR8hgoGt/3XWyvFBDA+WK/eY3dMx776V9tLRQSsHy5XSt582jVhQmEz2LnLt06hSdD1eA12go32niRODxxx9FAZeb6AFhXdMX+Ub19fXw9vbu9eP0q3QLvropLS0t3bI6Ghsb2yUQ5Ofni1WrVom9e/c6udxcPwUFBWLdunVdHkNVVZVYvXq1WLlypSgvLxdbt24VOh3NJB9+WJIIXF1mPFOLjibXg1ZLbobKSrJOwsMhiook++ijjyQrKDJSBqI5PsSzyKuuohmiRgNx000Qu3ZJd9vs2c4WAc/cvbzIHRITQ+N44AGa5SrdMjz+W26heIDyHHh/vr5ErFCeK7uTALoeS5bIgP/vf08zex47uwqZGThgAM2e2RLRasmNuGgRuQWVllJoKLk5IyKcZ9SDBpHFxPENvZ7OWzlb53X54+0tiR6u6yi/KwPpISEUgxk40NmS9fGh68rXin8LCCALiK0e/Ncdd8UVZIUqY2L8G0Dn/8EHFBsE6HwnT5bXWaOh52jYMGnFKc917Fi67vivRePnJ7c1GiFuvJGOHRBAFg7HwZT3WqulZ3b8eBrfv/8t2WxxcTKup7wPWi3diwceoP+ZERoWRs97aal8vmfPpuupdMXxMxYfT9dArydr7O67ab1vv6U4KP5rxWdnMyMvvsesklOnTomlS5eK6urqXreALrvsMvHaa695pFNVF5wH0hMA5I5A0NDQINLS0kRycnIrZpq7T1FRkVi9enWXjl9YWChWrVol1q9fL/bt2yfq6+tFaGiQiIiAqKiQtOZ9+8j9BJA7jhV4fLwz/ZWVhEZDL/KwYbTsnnuky2HuXIi6Ohk4/+gjqVyZGcb+dmZ8aTQQf/ub9KO/+CK5bZQMO3b7+PmRWwyg9f/6V+mGUhIaNBpSVj4+tJ/Jk+V+/P0hVq+W8QPlMQBS0u+9JxlhCxaQ+03p6hs4kAgF7CK65RbpqsN/XXFXXklKUOnuGj2a4lCBgc7LAUk5Dg6ma8PjMhqle03pUlRSgr28JJXcbKZ9MLstPl5+V17HkSMJ/JQAkJAAcd55NIngY+n19EzwBEEZy2ElPHs2UfHDwmibgQNp/AwwZjPRodmd+Ne/Enjz/h57rDWjkb/fdRexHwECy+BgZ3DlY86eTbFJJqHcfbcEPeU+9XqKL3EcLSsLIilJutIeeojWS0yk59wVaPR6GsfEifTbnXdCfPwx/XbbbQQuZjMBWmMjufvMZnJPMz19+nQZP5s+fXqPgEJRUZFYvnx5r4NPTU2NuOCCC8SHH37osV6dOnWqWLJkieN/m80mYmJi2iUhLFq0yGnZjBkzWpEQ3nnnHcfv1dXVZz4JoScA6NixY+Lnn392/F9dXS22bt0q1q9fL0pLSzu1j1OnTomVK1d6dNyGhgaRmZkpkpOTRVZWljh48KDYt2+fmDBhgmN2d9VV9PA/8gjEiROkvM47D8JqlQy2ykoCFK2WGDwXXkjbzJpFv7sqCr2eLCZWJI8/TsCm0UA8+SS9gEYjWRWPP956dm8w0O9DhtA2YWEQx4/LYP/995NiZ6WtjA1ddRX54wE6j9BQ59m5crb7zDP0u0ZD43vnHfo9OFjGiHj9++6j8Wo0pFBefNEZiAIDZRxAoyEigtKqYstx6FBn5erjI2NCzExTWlocSOf9hoXR+Pg6KUkNQUGSOGIwEIgwwPE5c74SQFYGx0YYvGNjKbbGuV1s0d19N/3GEwglicBgoMnHo4/K/Shp5Ho9nfvdd0vwWL9enve4cfLe8rkwUy4qikCJ77PyXIxGIq8EB5Ni//Zb2g9fV+W9Dg0la12vp8nQ/ffT/t58E+LPf5bW3pAh7q2i0FA5MUpKgli3jq7DsGEQDQ1kTXl707ty222073vukTlgvr7uiSU6HZE+Bg+m3zZu3CgKCwu7Zb0UFBSIlStX9gkAJSUliS+++MJjvfrNN98Ik8kkvvzyS3Ho0CFx9913i8DAQFFcXCyEEOLmm28WTz75pGP97du3C71eL9555x2RmZkpnn/+ebc07MDAQLFs2TJx8OBBcemll575NGyr1dptADpx4oTYtGmTwxpZvXq12LVrl6irq+v0PsrLy8Xy5cs7vX5tba3YuXOnWLNmjSgpKRGNjY3i0KFDYtmyZY6guesLzbPi4GDpXrroIogHH5QKOy+PlNjcuQRSERH0cv76K82iNRpyLTB7TPmiGQzO9OAnn5Sz6R9+gPjTn2gM8+cTyLnmX7Ci50D3/PkQy5bRftkdyOsaDDR7Z0vnvfecXU5K0Lr7bhmInjsXYvNmqcgXLGhN616wQNK6X3yRzl9JCJg3T4Ivs9uYNAAQgLGV40pC4HwkJka4o54r75fSNeZuPWVSaFSUM6tNua6vL8TChTK5lMHz6qsltdyVwefjQ+4lnpBwYqzSQvj0UwI6Zkl+/z3tT6ul/Smfv6uuIkWs1dK9M5mk9WUyycnECy9ISyg4mK6lErx4/xddJNlrl1xCBAN+xtn15woIvr6Sth8VBbF/P90PX1+I2lqypnkidv31tM2AAdItrLRm+dwYeBYvpn3zhIddcUuWkJuPKO7DRHJysli1apXYuXOnOHLkiCgtLfUIFHJzc8Xq1av7BIDGjRsnvvvuuy7p1g8//FDEx8cLo9Eopk6dKnbt2uX4bc6cOeLWW291Wv+7774TQ4cOFUajUYwaNarNRNSIiAhhMpnEvHnzxJEjR7o0NqWc8QCUl5cn1q1bJw4dOuSwRjzNJaqqqhJLly7t1HalpaVi3bp1Ytu2baKmRrL3Dh8+LMaMGSNMJsrjYRfDZ5/RbE2jIWUyfbpz1ruSEcYvWHy8nG3efDMpIrZ2SktJCcyYAXHqlHTDPfaYZLwplaZO55zguGQJgYnJRKy4BQvot0WLJLtNqbABmsnzbPOuu0j5uOa+AGTlvPmmTKQ0m6WS5sx6vZ7GuXYt5bJoNEShTUpyviYTJkj32M03E5gqz+ucc0g5KYGe4xlK5c/VGJRWRViYtDgCAiQbkGNDbBEpk1DZMmGmHx8nLEy6Sfm4AQHyeHFxcsw8JgZ510kD3/uPP5bbuVoLY8dSwrKPD53vW2+1TkLlazhjhpwcJCRAnDwpQWzUKLJwABoDW6CuoBEZSc+d0UjXbNs2Oj+tlsDD1fLl87r2WvkMHDxIAKHXQ6SmEnMTIKtn5ky5DbtyXV1xgwfTe6PRkBv2gw9ag8u0aRBNTWTdhYRA2Gx0TKORJnZsIX7++efi5MmT4sCBA2Ljxo1i6dKlYsOGDeLXX38VOTk5orKysl1QOHHihFi3bl2vg091dbUYMmRIKyA40yUnJ8fpWTnjAejkyZNi+fLlYu3atQ5rxNNPTU2NWLp0aYdW07Fjx0RycrJIT09vBVY//fSTACiL22IhhXXRRRBC0IsYG0svyJw59LLl5spg7Ftv0exPo5HJle5iQkwTBijOwS6eRx+l/AedjvZZWUnrjhlDgMCxDXZ9Kd0sPKu96ir6PyyMSAs865w9m15w19gPQMFyzm2Ki5MW0+jRRJ5ISKB9PPSQzEtiZcPnd/315LZh6/C116SS1GhIKTGwjhkjYxp8XYYNI/Di68LWREKCcx7J2LGtE2X5e3S0VKpMz9ZonAGdYyBmswQ+5bXk5Nlhw5x/8/aW58N/OUlV6Wa84AIqmaR0ffJvBgNZsHzuISEy7sXnHRsrKzqkphK4c3LmDz/Q97AwGrvrxIGv0cUXS3r1N9+QMtdo6FlQPnvKba69VhIRNmygcwDICrn3XmlJcf6Za54Wu+C0WnKtPvww/XbDDXQenK7Q2EjgHB5O79TChbT95s1yEnX++RKMvb3p41qZQqfTOCn68vJycfToUbF7926xevVqsXz5cvHzzz+LtLQ0UVBQ0Mpdd+zYMbFhw4Y+AaCoqCjxyy+/9KeK7nGxWq2iqKjI8elXALLZbN0Cn+LiYrFq1SqxbNkyUVtb2+X9WCwWsXTpUieLxvX3ffv2iZUrV4q8vDy365hMJieFodWSouCZ9qRJpEA0GgqcFxSQsjj3XHqh4uNpttnQIC2MtDT5Ej/+OCl8nnmGhLSeHbMSZGCZOZNcVgAporQ0Wufyy8l9YzDQy88xAeW+OEdn+nTJXvrHP2TSJbu/eH0lY+u110jRabUQ330nZ+EXXUQBbKVLRznLv/FGCRJPPUUKUQksnOPk50c5L+xG0usJQBYtco6NxMbSdVUSKNhaMRppf64KSqmc3c3ulb8zKYEnBOwOYtekkkWo1dJYGOh4H48/Lq1LXs7EjKgoiF9+oWsPyKRY3nbqVHJ/AZQntnWrzBX7z3+k29T1PIODZXzx7rtlvCg4uDVbk4912WV0P7Raqo7x6afSmuP6c0xocSUU8PPDlQ0WLKDnQqcjr4DFQuOJjYWw2+mdMBrpOHxtxo+XRBvOX3J3X0JDyZWs1xNx4dxz6ThffSUBftWqVW0q/aKiIpGRkSG2bt3q1l139OhRsXnz5j4BoICAAPHrr7/2p4ruVWloaDgzAaihoUEcPnxYJCcni9TUVLFs2bJuAVlDQ4NYunSpqKysbPVbRUWF2LRpk9i8ebPb3xsbKQ6l05EV8eqr9HIMGULWjtK15k6JmUySPRUSQkqFFeX770tLxmajZUFB5C+/+mpab+NGOTO+/XapJIKCWit6ZRyK3U4LFkj3xGOPkctQr6fA7+TJrcfNrpKEBPrt8svJytLrWysvo5GOpdGQMty4kc7Ry4tcObfeKplV4eFyWy8vCUQTJ1K9MWX8Q3k9b72VAFl5nTmJlt1eQ4dKcoHSTcWxCFbSynvC106vl0DE7jemmytr1PG2ej3dLyUQDh8u3XpsRf3+986K2sdHWgm33SZL1LjGpkJDSXmHhtJ+9u+XZXHCwyXRQumW1Olo8vKHP9Cy++8nVpprkq5ORxOWW2+lZVdcIa10Hp/rM8XxR55ozZhBydZMBc/Pp2dxyBCI6mq6FyYTWe08iYmPl+DdHuCPHy+rMbz4Iu3fYKDEbibiHDhA8SiAXJnZ2bT8hhsI6AICIEaPHt0pEKiqqmrlrlu9erVYvXp1p9x13flUVVUJvV7fI3GW01Wuv/76Mw+AlASA4uJij+I37X2Sk5NFWVmZ0zIu3bN//35RX1/f5rY33nij0GiI6cZU0fXr6WXQaKieW00NvSw33UQsH7OZZs2PPy5jClyexN0LyP55DhjzjPiSS2Ts45tvJB21rEwqj6++kt8XLKD4idLXrnRVcXwqNlYqlUWLJMide66cyWo0EFVVkmSwYwedq0ZDYHj99VJxK8+Bg/YAuc8OHaJlISHkalK6CpWEhttvl64l10KkYWHSGuDz4bgBX1O2CBgoXGfpDHoMzpwDxKDD5BLlNsrryCw8Bj5mGjIQDRggySRskZ13Hn338pJxMCXzjnOavv1WVgEYMEAWq3XNkRkwgNxYGg1NICor6T76+5MCvuEG2mb9enldBg+WhBWmc7vbd2KiZHbOmUNxPC8vAoYNG8jyMJloUsJxx6AgCeiuz7Qyh+2yywhcfHwoXWDePNp3URERdHQ6imFxrbcffiAXHUBEHnY7T5kii576+xMZh+vIzZghn62uWDHl5eUOV11n3HXd+Zw6dUoArasPnC3y8ssvi+Dg4P4FILvd7hFIlJSUiLVr1zqV76mtre1U/Kajz6pVqxwxJGUekbvSPa4fg0EngoPpxQ8MJEX100/0Muh0RBzgUjwpKRDJyfT9m2/IFafTkXKx2WgmeNVVlEMUFkZK7bvv5Czx7rvlzDEurnVrAWVcgZUsu6E4bycxkdb57jtJw339dQrqarWkuJS121gB8YxauZyDyJdcQhRwk4msuJYWOo6PDwEzF6S84w7J3FLObnU6UpQJCbTsoYeoZhhTt9kCYvcm143jatRK4OH6ZAx+4eHOJAC+TkxycA18d/RRMsKUgMTLlfEutsCUYDVwIClD3oeyvlp8vLRIhw6VwK8EUr4O8+fLGNmxY7RPo5Fo9S+9ROvdd5+0aLy9aWyuVagZ0Pm5mDaNkko1GnoOXn9duhynTpWWJic/u1ppTG9ny3fJEun2W7OG9hEQQM/7fffReGprafKm0dA6TN++9FJ5DcLD5fPGcTmlleoONJXj4rwsAufJXQKGtLQ0sWPHDoe77tChQ2Lbtm0iOTlZrFy5ssvsOtfP8ePHBQBRU1PTnyq6V+SHH34QBoNBbNiw4cwAIG5rkJycLA4dOuRk7dTX1zsyk7sDQGvWrBFFRUWipqbGozyib7/91i0AKBUif2f3FYMBV2sG6KVkX/yll0I88QR9v+sumunp9cSmE4JmdzNn0vexY8kiys6m2bRWS0H988+XFs+YMVIBuRuXTudcSWHwYEn1vvRSmp0CVHDyvvtkfEVZUFOphL295Qx42DByKXEMqKmJQDo2FiInhyw2piRz0N/1+s2aJeNUt95Ks3z+jfOaeNwcLGelxMrWFUyVeUSuH7ZC2FJyXdeVCux6z/l318C9sneR8nhGI7WwYAsrMNAZbJipZzBQxYFrrqHjbNxIsT2+zpwIzG5E13ut0dCzwHXTLruMLBd2zTJpREkWcAXm4GCaWAG0LyaNjB1LE5DERALRmhqykkwmiuXwpGDhQpnkHBFB95zfDaOx9fPkCvDh4dJde+GFsibiLbdIC/G22yA2baJ9XXEFPXOhoRSHFYKA2sfHq0vAkJKSInbv3t2muy4lJUVs2rRJLF26VKxfv77T7Dp3QKfRaITVau1PFd3jkpaWJry9vcUzzzzT/ySEzgBQXV2dw+wtLCx0u86yZctEeXl5twBo/fr14vDhw2LNmjUe5RFddtllQq8n//Yzz9ALsGIFxB//SN+ffppo1FotvXwPPUQv29ChFLxl6i/P4tv7KCnNXl4yn8jbm5QBB+hvuIH+ennJdg4AWV9cZPK770h58Av7wAP0Ug8d2tr6cVXOWi2BIvvaP/iASvDrdKQQ7rpLutpcG87xTN7XV7r4hg+na2Q0kpLYtUsmQE6Z4jwWnukCkqHnDrSUbj9XWq/reNyBS1tKUGnZuAKva66Q8i8DmdksXYExMa2ZWqz8771Xtj948EECaICuaWioM5OQt1NWsFiwgNiVALlCKyromk+eTIH+ceNoIiOEjGVNnCgnLldcIZ/hpCRZlT00lMbCFSMGD3YuaMrL23qGeR88UVm0iNyx3t4Ux5k1i+7pihUUq9LpaHL1r3/R9tu20XMMkGVWVUXrXH01nVdkJJ2bEGQlh4XR93vvpf3abHReGg3EkSNHPAag/fv3i3379nXKXZednS327NkjVq9eLZYtW+aRu27Xrl3C19dX2Gy2/lTRPS5///vfnZ+J/hxMRwDEOTcdNalbsWKFOHXqVJfBp6GhweHTPXz4sEfxpJCQQDF7Nj3kkybRrM5up5deqyVg4hpZqakUJwHIh22x0Mvz+utSEdxyC7nswsPJItq8mV5yLrkzdqzMBOd8kgkTZN6IO/+9O1aTMtAeFCQtliFD5Ew9KYm6TXI84aGHpLKy2ej4QUFEkeUM9ePHyXUGUHn+48clFfu770hZjR0r80JcFT4DlEZDs/4pU2R+zMMPS/BSAjZbcO6Aw/XcXWnO7SlKd7Rj1/XaUrbu1nV3X0aPplgVQEwxptYzC08Jmgz+06ZJK+bWW6UVc9VVNNuPiKCJhBAUS+HGhRdcQGO48056VgGyLJXuTWbMKa+PO4uZn4ORI2Xpm3POoQkXTw6WLaN7OGECuZrHj6fnVAiKhfr40PeXX6bx5+bKHJ8//UnmCy1cKF3YAwZQXhgD3jnnyAoUQ4fK2CB3HGZmnrLZIJ9bV8rz7Nu3T+zfv9+jbaqrq0VxcbFH7roNGzaIiIiIDtsvnOnSrwAkhGhTsWdnZ4vk5GSRlpbWISB0p58QtwFftmyZSEtL82jbkpISh/+cy9dwK2h2ZXBLZIDcZuzamj9fKoHFi2Vn1EsvJcDSamkW+/339ALNnUsVEWJiyGKorSVCgK8vVU3gpMLCQnLTABSH2riRvv/+9xTI5tjB889Lv/9VV8lOn4MGtU2EcP2wm4s7emq1pCB4+/PPlwm1S5YQ/RyghNVVq+j7++/T7NxoJEXz5z/TOCIiZAzN9bisKPk7Vz5wVfZtWXGd/XQEUh192gMnTnhVApVeT4qT2yLceCO55gCKnf30E31PTJRdSY1GAiQGEY6XuZIs3I1Bo6HtJ0yQ1O/775d5VR99JKsivPuurNpx113yeRs3jgABoHfg3HPlxGb4cGmxhoXJVhFsGfN67blDO5ogRETIoqXjx0t386xZsrfQjBnk0uZ1PvqILWyzxwC0e/dukZKS0q34TlVVlcjNzW3TXXfq1CmxfPlyMWjQoG4DUHl5ubjhhhuEn5+fCAgIEL/5zW9EbW1tu+svWbJEDB06VHh5eYm4uDjx4IMPiqqqKqf13N2TrtSF63cAUrblbmyknJu9e/eKVatWifz8/E4Bwbp16zq9rvJTVlYm1q9fL3755Rfx888/d6pwqfLD5uTChfJhnzCBFK/BQMp3zBhn5hcratcAdmcVoStzi2fV7MfnWA4Xw1SWemGrQdmNNTyc4k/sGlmwgJSGvz/NrtldcsMNkn33ySekhACy2Ljt8oQJ9OIzKLiWg2lPkfA1YpZSUJC09sLDiZbM1s/06TI5lhWnKzB2FkS7+2nvHiotHlc3ntksrc6oKAnUvr6SQq+MxbgjO/Dy6GhJIEhKkqSD884jWr3BQMsPHKAJwsiRZKUvWkT3VAiZc8WuPXcWXGdAITRUxqvGj5dMwIsvlvXeFi6UrMy5c2kSYjDQ8/PDD2QlDRxIrti5c+l65OfTNmYzjf2TT2i/1dVk3QHEysvJoe/PP09EGIOB3k0h6JkfM4a+c1tzT8Fjx44dIi0trVsA1J67btmyZcLHx0ckJiaK8PBwsX///m654S666CIxbtw4sWvXLrF161YxePBgcf3117e5flpamrjiiivE8uXLRXZ2tti4caMYMmSIuPLKK53WAyD+/ve/OyWVdqUuXL/2A3KVuro67Ny5E/X19UhKSkJoaGintuuoJ5A7KSoqws6dOxEREYEpU6bAYDB4vI+MjAwAwF//KnvXvPce8OOP1MvkpZeAgwepb8uSJUBhIfVLuewy6oEyciT1YKmvBxYsoD4nubnA/ffTvtavpx4sQlBfle++o14x995L/U4MBmD+fOo15OUFDB0K/Pa31I/G3596roSGUm+ZefOoRwsADBpE+wQAo5H649jtNI5ffqHvNTV0jPJy6rny9dfUx0Wnox41QUG0/dtvA4sX0/cPP5Tn/qc/UU8a/X971hQVUd+Z+fOp30xUFJCQQOeVkED7vf566pMDAAMHAiUlNM7qauptY7VSv5xdu+i7EDQ+rZa2NxjorxCyP09vC19HV9Fo5Eevp78sdjvd/7o6+v/UKfrdy4vOa9486vVkMFDvnUWLaB/r1wMvvkjrHDoEbN0qn42dO2lfU6dS7yg/P7oud95JvaKKi4Hx46knVH4+jWHCBOoXVVcHnDhB+73lFuDhh+n3Cy6ge6rT0bpffw1ERlLfnTVrgBkz6J7u2QPccAONcdky6h3U0gK89ho9K0IASUnATTfRGH18qCeWTkfPWVQU9aIqLqZnMCSE7n1aGu2ztpb2W15O1+2NN6g3khDUT2vTJtrviy9SLyutFvjnP+n98/Oj3598ktY/coT6bRUWct8oz/qA2e126HQ6j7bpSAwGA8LDwzF8+HDMmTMH69evx5gxY9Dc3Iw5c+YgMjISN954I/Lz8z3ab2ZmJtasWYPPP/8c06ZNw6xZs/Dhhx/im2++QWFhodttRo8ejR9//BGXXHIJEhMTcd555+HVV19FcnIyrFar07qBgYGIjIx0fLy8vDw/eY8hq4eFLaDjx487Ekvby7lx99myZUun6NKNjcSa279/v1ixYoXIzc11LN+9e7c4dOiQR8edOXOm0OspEY6tgO+/p5gOQEHgpUula4NL1SxaROv5+dEs8V//ooB8cDDlQDAb7uOPZW7Ls8+SGwsgH77SP/7KK7TfSZNo5sf1z556iqwHb29K/OM8lQcekHTW666TrrHzz6eYD/5rzdx0k6RHL1woGXtXXSWtoUsvlQy1efNk/GjyZJkhP3QozcY1Gprtcx6Svz/NzDmzfcoUeYyJE2VezogRkp7NVcQB2qavLJ2OPkxmYNcfx0qULDweL7su2SXFrSB4fWbPMc1cuY0yQZaXs6WkPC7g7Npqi2jR1ndXS7u716Y39qv8tEe/bu+zZcsWj6yVn3/+WRw+fLhHLSB3n/fee0/MmzdPNDc3i61bt4pnn31W1NR4Rsn+29/+JgIDA52WtbS0CJ1OJ3766adO7+ezzz4ToaGhTssAiOjoaBESEiKmTJki/va3v3XJXdjvANTQ0CB+/fVXsXLlSidA8OSzdetWcfTo0Q7Xq6qqEps3bxabNm0SFRUVTr/t27fP4xiQRqPpkZfA3cddDk5XP65xhu7sg+Nc7DrjIp0aDQGdsjU1u9O8vGQFbG6Mx64eZoLp9bLFgV5PLqXISNp+wABZjiYyUvYY8vJyboHdnnJyR3jo7U9bmf0MVFxaiMkWRiMB+0MPybyv55+XLTCefVay4ZYskYzGG2+kuJrRSFT399+nfU+eTPEOLnPzl7+QC8pohPj8c1lB4YMP5IQmIoLah2g05I7905/oOs+bRxUpwsJoQrBsGe3L35/YkDxR+s9/KPkTIDfZjz/S9+eeo7JCRiPFs3bsoG3nz4fYs4fu8ZAhVNnhnHPomAcPypyon38mdyJAE7bDh+n766+T281sJmJOXh5dw/nzKYl1zBjad2mpLGVVUUEEGQDihx9+8AgYNm/eLLKysnodgF599dU2G8h1Vl599VUxdOjQVsvDwsLEn//8507to7S0VMTHx4unn37aaflLL70ktm3bJvbv3y/eeOMNYTKZxAcffODxGPvVBSeEwL59+1BTU4OkpCSEs//FQ9Hr9R26z8rKyrB9+3b4+flh2rRprVrdarVaj81xIQSGDSN3xO23k+m/YQPwwguAEMC//kVuLAD4/e/JZQKQS2X3bnJnjBpFratnzybXQ2oqcOut5H7JyCA3ArsOvv6atv/iC2q/DZALIjeXWiKfdx65FsaNIzfbqVPkhvP1pbbOzz1H2xw7Rq49APj4Y2qPDQCXXgr84Q/0PTGR3CpGI22flETLY2KofTO3DG9oINegENT2+9dfafmbb1ILaK2WWkaXlZGrbcwYcrVceCG58TZsIPejRkPuonHjyBUUF0fuE4DcRBUV5I4pK6P23AC1/66vp+9KF5fyO0DuJHbZKcV1PXfSmXXa29bdYykEXReLhdapqaH7abXS+f3pT3RPm5vpOh44QOv/9a/khgOAbdvk94oKclO2tFBr8kmT6BgTJtDzYTLR/WTXaWAgtdKOj6ft77iDXH4Aub7ee4+Ot2oVtVpvbgY2bgSuuorGd/Qo3dPMTLr/11wDrF1LLrXbb6dnCgCeeQZ45BH6/umnwIMP0jmuXEm/cQv1d9+le1xURK6zU6fo3q5bR88QQO44i4W+c5t5dnHGx8tW57Gx1Da9sZHer+Bg2i40FIiOpmfBYKDnmK5dhUf31Gaz9bgLzp1YLBb4+vq6/e3JJ5+ERqNp93P48OFuj6GmpgYLFy7EyJEj8cILLzj99uyzz2LmzJmYMGECnnjiCTz++ON4++23PT+Ix5DVw1JWVuaxy831s2fPHpGRkeH2t4aGBpGRkdFhd9TU1FSxf/9+j44LwEHBfuklmt0JQXRjgEqHHDxI3z//HKK5mb7feSetFxdHBABuShcURJTaRYtoX++9J2eW990nk+4mTiTXFSBZU/jvjJuzw5ltxHkZzDbyxEpjFw67zrjOmBCSan3qlGRn3X+/rIacmEjj1WjIjcRN0Tij3rX6gKs7xsuLrB6djq7LjTfSGJj15esr992RK66t8+wJi6itfXTEQlPSzQMDZZXqCRPIutHp6Nl64gnJCmQ3KFuOrlUw3Lm6mFDAxBKmIQ8ZIi3USy+VpJSLL5YkhmuvJSuGGYoff0zXfdQoet4jI2kfDz0kn8Hrr5cu5OnTZXJsfDytw0QSZq65PpOeXG/XZ5ldmPyXc6z0ehonW+dKN+6dd97pkWWybt06ceLEiV63gB566CFx9913u9WZp06dEpmZme1+mpqauuWCq6mpETNmzBDz5s3rFLlgxYoVAoBobGzsnOL/r/Q7APVEV9Rff/1VpKamtlpeU1Mjtm/fLtauXdthnlB6errYu3evxwAUHEwvZ2wsvQRcMBGQmd5ctoSrGbP/vj0feHsuOGWxTn9/AiQvL1JOV1xBriwvL1IMs2fTsd56i4APoO9cs27JEqqdpdWSG+iDD0ghBgYSvZZL5Q8fLhl1vr5t1/dSfgICpHvtggtonDod5fMwC+rBB8kdotGQIty3j86J3TN8TGa9uSZ5GgwysdP1uimX9bT7ra08H/7b1rXhiUBgIP2NjCQl7a4CgNEoY2IRETIBdPJkcjWNGEHPXW0txfE0GprA/O53tM3TTxNQaLXkQrvjDpkmwL2ZeELQ3rnyBIZjUyaTrK/GEwKu8n3PPTKv6ZNPZC+r1aspd0yrpeRoIeg5uvde+j5gAO2nqoqo3H5+lIDKLrjVq8m1CFAC9Y8/0r5uvplcc2YzTcpef52ubUwMXYf4eBrjrbfK1iHnny/HOHz4cI+AYfXq1SI3N7fXAejOO+8Ujz76aLd066FDhwQAsW/fPseytWvXCo1GIwoKCtrcrrq6WkyfPl3MmTNHWCyWTh3rlVdeEUFBQR6P8awAoJSUlFbWS0lJiVizZo3YsWNHp1o1ZGZmil27dnkMQOzL5yDykCEyKD91quxoOXEiKV2ulP366/TShYZC/POfpGy9vamEyO9+Ry/XoUNEVgCIzHDqFH1/9FF6aUNDpQU2bx79v2MHAaLBQMl7XHNs6lQ5Li5+2h4I8kyZy6MMHy5p3BdcQH5+o5GWf/89KcPQUPLH33kn/dbURC0cAMpgZ7/94sVUNUKjIaXGlo2rRWQ2y6KbMTFkXXHF7eho52oAnICrHH9bsS9e7m7m3R6ougMJd/tobxm3mnZ3zPBwIn4YDHTezz8vrc64OPfVF0wmAjGdjhT6I4+Q9Wk2U4IwJ4ZmZkLs3Uvfv/qKKMpGI01ShKC4Gk8C+Dl5+22Z4/Pb39Lzq9EQgWXxYtlQLy6ucwDPwMyTJZ6YcadWbqkREEDP6MsvS6DYvp3iXAAldBcX0/evv6bxe3nRuQpB13HxYvo+bZpMfr3nHtqmvp5iTfReTvQIGFauXCkKCgp6HYCuv/568eyzz3Zbv1500UViwoQJYvfu3WLbtm1iyJAhTjTs/Px8MWzYMLF7924hBIHPtGnTxJgxY0R2drYTzZrLAi1fvlx89tlnIi0tTRw9elT8+c9/Ft7e3uK5557zeHxnBQClpaU5rJeGhgZx9OhRt3Xj2vscOXJE7Nixo9PHrKurEwC5iYSA+OILeqArKylhFCDl29hIL9ptt9F6iYlkJQlB2ezBwfT91Vfp5SwtlSVQnnhCFmIcOlRmnJtMZAEp3Q2uytBVIfj5keuE62ndeaeskvyXvxBoGY1kjTCj78YbpavP319myScm0pivvlpms//xj6REsrLI3cigxwDIylKpjNmiGTVKJuKOH0+dWmNiSDlddJF7a4tnv1w/TumOYveWaw5NWzk5/LcjVhaDclvleNwdS2l9Kceo09F9SEqSuTzKWnh6vcwTGjWKAu9mMzEdt2+nmb3BQBYCEzTcVU5gIB42TDIXX31VtmbYvBmivNwZsF2fHS6bw4QPbjUC0HPz5Zd07HvvpX5WUVE0zr17aeJlNtM6XEX93ntlfcEJE2S5JR+f1i0u2gIxZgT6+EhvQEiITF6OjqZnPDCQrMevviLg1OnIyuIE7WnTpnkEDMuXL3fUjOzNz+LFi8Ubb7zRbf1aXl4urr/+euHr6yv8/f3F7bff7pSIyh1KN2/eLIQQYvPmzW1e+5ycHCGEEKtXrxbjx48Xvr6+wsfHR4wbN0785S9/6VK+0lkBQIcOHRK7du0SFotF7NmzR6xatUoUFBR4tI/s7GyxdevWTq3LCaw8o7XbZTLc5s0QR4/S98svp8x+dllwmXqtlpSGl5csKeJa28vd7DsiQq572WWkrA0Gmimyq+KDD4iJBJAl1dRE4DNpEjGXBgyg4158sbRoAgJk3xjX43J9L4OB/PtciPKf/6RZJkDKMDi4tatLq6XlXOTy6qtJ+QE0062uJhAMDKTZLbvSXGMb06fL6gC33EIzdCXFmJWWr69zKSJ3rk4GJyUQKJWuUonztrw+/8brKvfr6iLk9dv6/4ILpEtLp6PrxIr06afJZcSKX3k9zWZZDf3cc8lN5etLSbtWq6xi8OWXMjl65kyyUN1Zd8rmbiNHyu6il1wC8fe/S5ffgw/Sdfb2pgmIa8xNCbw82eBitd7eRPePjaXt1qyRLeuPH6cEWYAsaZuNjvnII1SaJySErJhVqwhE/fzIMuOJzXXXyYnZoEH0fLOb0x2YKser0xEAbdq0SaSkpIi8vDxRVVXVJihUV1eLpUuXilOnTvU6AM2bN0989NFH/a2ee136HYB6oi33kSNHxM8//yw2btwofv75Z1FVVeXxPnJycsSWLVs6XO/EiRNixYoVIjU11fEwK2Md7bkiON/DYKAXaOpU+RJxW4TFi4nW6u9PL35JCeXQcFHFZ56h7VtaJJX0hRdkrayYGFJGrAwY5FytI7NZulqGDKFSPQEB5K5Yt06C2803yyx9ruTtOvPnQqtJSXSct94i5QVQxYTqagLP8HBqzcC5QpGRzu2+ucCqXk/nw9RgrhSgzHPhahPcVE7pVuMW0QwkPG7leBmwADqmaxFTvmZtudMiI+V1VFoafExXy0fZghuQCnzAAHK1uboLGcy+/17mad15JwGEK5AYDGQ9cWmnm24iwktAAAFPSwttp9WSdfn++xLMuUSNl5eMXSo/XCyX+zclJJD71c+PyvDk59NzbDLRPedGepdeKvsb+fq6v5ZaLW3HlnFCgiylc+GFVABVpyOLqamJnp/582Wh0cBAmvxxEeCTJ6X34ccfaRujkbwI+/fTNZo4Ubq89XqI22+/XRw+fFjs2LFDrFixQqxYsUJs375dHD58uBXQVFZWiqVLl4qysrJeB6AZM2aIL7/8sr/Vc6/LWQFA+/fvF8uWLRMpKSldZtTl5uaKjRs3tvl7fX29SE1NFcnJyeLEiROisbHR8SLNmUMzOi8vmqn/4x8EAiEhEBkZ5Ds3Gml298MPtM2ePTTDA0j52+30gk2ZQgrjvPMI1F5+WRYdHTmSXiIO/rpzAXFRxsREesEeekjW8HriCXoRdToa85dfyljQ8OHuXV3MgPPyIkX+0kuyVExqqjyf3/+eXDnBwdLnfvnlNJ5zz23dYoAV+dVXS9/8lVdSnIiVunL2r9GQIuHGacr9cS4NH4/XnzZN1iLjeJNGQ+ccFuYMOsrj+flJa4MBnK0szntSnkN8vGxa59qzh2fkrMzHj5eWFP/lMT/8MN0vtnwYwDhuct99shBrZqYEpoULCXzclWkaOJAmOBoNsRRtNnouExLIPcxtzH19yR2m0dDE5x//kBXL77yztatTaQWyG3jkSDnB+uQTOb5jx8htCNDkZONGWmfuXLL2vL3pfk6a1D4hgi0rziljFy0zP//5T1lB/ORJWTX7hx/oeTSZCDyFIIDUaiH+/Oc/O1k4+fn5IjU1VWzevFksW7ZMrFu3Tuzbt89Ro407J/c2AI0dO1Z8//33/ayde1/OaACqr68XKSkpYvny5WLdunXdArH8/Hyxfv16t7/V1MgeQcquqayAXn6ZHurzzqOZvtJSsdmotDxAtao2b6bvEyfSDJRfXm417K6sP38CA2nmz0roww8JSLy86CXnMvMHDhCzDSAlzF0sWdG4m41GRJDCMBoJQFavpsA019JiNp8SoPz9CTgvuUReB2YauSrpceNkqf+rryZg5irc33xDFg4DhVKxXXwxxZSYpKCseq3TkYU1erRzsukDDzgXuwwNlQVilduydQWQomUQcW0trtPRtWfCCbvAuNEdnytfG25KqCRy3Hqrc+2+2Fi6N1qt7InDit1kIjLKiBG0bP58uj5KwkV8vATtujpSwt7e5Aq+7DJnRqbSqmMAmTKF2loDZHVccIF7koO3N5EENBqa1KxZQ6AaEkLFbTlJ9oILpKvVFQSVfZFmzJBxxbfeIqDQ6ciK515X55xDzL7p0+lcf/xRxoxuuIGsI647x9fZHVjxJC08XNLEp08n65op6EuXLm0TBCoqKsSxY8fEnj17xJo1a8SyZcvE0qVLRVpamigsLOzRDqiurr7ExESxevXqftbOvS/9DkBdacvd2EhVDX7++WexYcMGcfToUbFhw4ZuAVBRUZFYs2ZNq+XMptu5c2erHkGsMLhSNb/4N94oy+oHBEj6tetLwspGryflvHAhLb/1VmL36HSyidvYsaRMhCBrQ6ejzHK2HoYNc24oplSeOh29rBoNseWee46+z5lDzLr4eNkWmdlJri67ESNIWRsMBHTsg+cq2rweA5hWS7PrH3+k38PCyBV3/fW07jnnyJgAu6/YdRgeTjN8JelCOZaICAItZXO/sDAJ6KwE4+PlNWWlFBsradCjRrUGJZ5ls/uSlScrM+V10etJkQcH0xiUQKLVktuLj8dg+NJLpGR53EYjWahMt2b3JIPXhAkQn35K64WEUIwvLMw9mN51F1kBOh0p6aYmsoD0eiLJXHKJVNxKwONq7nxfvvhCMhxvuMF9zyVm3/G1/PRTGitPhp56itZ96CGyvPR6Wr+t4rG+vtIFOG4cuSVNJkorEILuVVQUTei4EvexYzJ+9O67RHowmWgi+MYb9O4FBdEzz4DjCrKZmZmdBoWcnByxbNkysXXrVrF8+XKxatUqsWvXLnH06FFRXl7eowAUEREhtm3b1t/qudfljASgwsJCsWrVKrFnzx5RV1cnCgsLxdq1a7sFQKdOnRIrV650WsZsuszMTLdsOqVSdAUWriIdEEAujBEjSBEvW0auFq2WArB79tA2L78su4Vy58YFC+iFeuopORvmRm3u4jBTppAS0OlodsttjX/3O5pRBgTQ59JLpTvK1W0zahS9tCYTxR9uuknOXJl553reCxbIni5DhhDN9dxzad9ffUXWFAMJt05ga+qee0jxGAyUeMu0bWVAn9d98kmp2Pi3qVMlC0xp1bAy4/WGDCFAc40FsXuNFXB4uNw2Jkb2WQoOlst9fSUj0FUxjxwpLQY+1ty5MrbB47nuOrpufC20WgLnOXPoe3y8dL3yfh54QDYXvO02epbYMuR4FgPbggXymfn3vyG2bpX1Ai0WCcyLFtHz4BrbMxjoWRk+nP6/6ioiPHh700TnwQdlQ0Tl9dTrZY6TXk+eAGbMHT4MsWULje+55yjWaDTSM3frrdLt6GrR8CTKy4smKUYjHePLL6XlVlEhy/6sWEHvksFAsVUh6F5OmEBEjddek89uRUVFp4GhoKBArFixQtTUUEuFEydOiP3794sNGzaIpUuXio0bN4qUlBSRm5vbLpmhMwDk5+cnDhw40M/auffljAKghoYGkZmZKZKTk0VWVpYDFEpKSsSqVau6BUBlZWVi+fLlorGRWkLs27evQzad8iV55BGagUVGkhISgl4qLy+atX31Fa23aRMFbjUaUjhbt5Ly40Q6BhdXi4ktJbOZZpXTp9MLtmsXxF//Sut8/jn1A9LryWWzaJH7Cgg6HbmtuHX2smXkytFqScFzJrtyDHo9KSNmVk2aJN0iej25FvkcJ0+m8+KYinI/Oh3lMTG9fMgQom5zbgyPk8kJgwaRQuExseKYMUNaLHFxzm5FtvjYvcWdV9kKCQoiNxLTnxm0XWfmbX10OgJ7LpAaGirjYny9Z86U1Q0YqHx8WoNlTAxZtLwt18T7059ktYdrr5UxK72erKV775Xkg59/JqtAq6X7yoVm+dgxMZJ+X1JCkwNl0iwDz333yfpw11xDgO2u2kBMDJFlmMhQV0fXICSEXGlMblHS7l1dmzfdJKt8bN5M8VC9nt4jq5XGOnAgxP/9n/QmXHABTS7c5XsxCYgBn/OH/vIX2rdWS1aREBRH5HP2BBjy8vLE6tWr3f5WWloqjhw5Inbu3ClWrlwpkpOTxbZt20RmZqYoKSnx6DhVVVVCq9WKo0eP9rd67nXpdwDqTFvuxsZGUVtbK3bu3CnWrFkjSkpKWoFHcnJytwCIGS4VFRVi06ZNYvPmzaKysrLdbbgYqU5HL64QFFTV6ykfiOM9t95Kbgh+KUNCWtOuNRqacc6cSS/HwIGU+8EFH202qnqt1VJSZ2oqbTd8OMVJWJEorQaDgWbRrHyWL6f9m0xERuCumv7+0o3GMamBA2kf559PFFijUbrXlB0zhw2T8aH775eWH58XkybmzqXZb1gY7efTT2UMgvfFFHWjkX576y1pqTGIcswmJITGr7yGvr4UL4mIcFawyjjBrFmk0NzlunC5GgaMgAA5y2eigzuihl5Plgn3QlKCjvLv6NHONG+OT7DVctVVzmWVdDpyqc2aJQkFnAzKk5ShQ2Xc7dprKQWAGXrPPUdK23UCABAo/+1vdH2NRkkB5/s/aJAEjM2bqYoCEzsGDXJ+zvicRo4kANLryXVst5MV5uUl3bYajaT/81iUOVtz5sjn8q9/JUvGbJaJpZMm0X3OySFPAkB/77yT9hER0doy4+vp7y8nDfzxBBhOnDgh1q1b1ykLpqCgQBw8eFBs2bJFLFu2TKxdu1bs27dPHD9+vEOrq6ioSAAQxcXF/a2ee13OCADi1tzbtm0TNTU1bYKHJ620XT81NTVi6dKlYuXKleLXX3/tFJtOr9c7AchVV0mXBRMKlAqWlcq8ebLtwZVXQqSlkQLgBM/f/IZeyqIiiORk+WKed56zS4K/m0wyfjJ0KG0zeDAp3oYGmQ0/aZKM47jOIpOSSOHr9bTtxx9LC8YVKB98UOYADR0qy7Dw7PzSS2VMJC1NJpmOHUuxMmUHU61WKsxRowjseAbP42RryGCga/Dii3Lmq9fTOd1zj9yO3V+jRxOAuoKNckYeFycVFrfE5vMePZr+9/GhbUaNkpaa0UiWAFcqUJ4Ps+jGjCHQUJIURo6kGKGSHKDXE0CzpRYZSddQee2vuYZAm4kSDzwgae98rSZOpHH5+NB15OTmoUPpunMuFQMAX6f4eNnmesoU2rc7Rp3BQNd+/Hha9uWXFHfixE9lrMhkknGX6dOpRJCPD13TlhbZEO6DD2T7emZbKicPfO2GDZNsviVLZMWD4cMJ6G64gX7LyqJqIBoNJduuWkVjGTaM1lFOTjwFoOPHj4sNGzZ47FJjMsPevXvF2rVrxbJly8SWLVtEWlqaKCgoaEVmOHbsmAAg6urq+ls997qc9gB07NgxkZycLNLT09sEGAYPV5KAJ6699PR0sXTpUnH48OFOb+fn5+ekmNl3HxFBL/Zrr5Gy1miIVMBuhvPPp5dm9myazbW00IvIiobdB0q/PLvfGOBmz6aEU3Yx2WyUjKrRkN9buQ/lLDAxkV5kgOInFRX0cup0BFBcUgggZRAeTv8/8YQcI1sLfN4+PnSuzGS76SZSOIGB9PJ//bVMcOR9jxghqc6vvkrJhXxsf39nwAgNpdYD993n7J4cOZJcQEyRZsUycSK5dziwz/GDuDg6V07qHTdOKuOOPu7iElFRZBFwIc+QENqv0vUUGUmWCZM2lAr10Ued6dNaLa171VVyH3PnOpM8IiKIxjx7tnRRcpFQflbMZhmvuugieu4CAqSL7+qraX2jkb6zpaPVysA9QPf088+l21IZyDca6byZZVhRIeOFTz0lE2ldSRu+vnTuGg1NJmpr6TqGhVGFAo7xvf46gR0TR1xzlJTkk4UL6TgzZ5LFNGECPUO1teTKBCi3rqFBVoPoCgAdPXpUbNq0qcuxHf4UFxeLQ4cOiW3btonk5GQHmSErK0vk5eWJ1NRUodVqHaVvuiqetuMWQog5c+a0evbvuecep3VOnjwpFixYIMxmswgLCxO///3vRUtLS5fG2O8AJIRwq9w5DrNy5UqRl5fXLhBYLBaxdOlSUV1d7TH41NXVOVx7zPHv7LZxcXFON8rfn16in3+m/x9/nHItoqLI9BeC3FRaLc0yOTCtzNZnfzZn+193HcVW9HoCFbudlJLRSG44rtXl7+9sseh0Mt4wezbFnkJDSYns3Cl76ihnrToduXs4l2LuXFliRzlr5Jnzxx87x4GUvycmkotHGVSOipKuvYkTKQitrOqtdJ3o9cTkeucdqeD5Go0dK10pRiMpKJ2O9sGgowSqwYPJ6mTwVs7secbNyaI8Q+Y4WnS0DPCHh9Mx2CJy54obPZquG1ukysD+yJEyj0kJOgsXSqo87/OCCyhhdNAgabWwhcT34g9/IMYax4oefFDGgviY/ExotQTgcXHyGGazbJk9ZQoxyZTECuV9//BDmthoNMRMY4vJFaCDg2k/YWH0TB4/ThY4b3fppa3BXK+n63rJJdIatNtlj6RjxyiXR6OheNlXX8m+UkzBVz57TNefMUP2osrJkf2EugpAhw8fFj///HO3Acg13nPy5Elx4MABsXHjRhEZGSni4+OFwWAQmzZtEk1NTV3Wq5624xaCAOiuu+5yqgFXXV3t+N1qtYrRo0eL888/Xxw4cECsWrVKhIaGiqeeeqpLYzwtAciTOAxbMBy/8QR8uKTO1q1bRU0N1XlS5vl09Bk/fryTQtBqiWYrBLnMTCZ6YRgIOKmRH34/P5rZceY/14C7805ikg0aRC9xU5N0o02dSq4fdwysGTPIjcb12qxWGSy+4ALZCI7H6+9P45kzh/I7goJIYV1zjXPCpY8PBdS1WrKyuEKB0jrj2MEHH8icFc50v+giaSm9+y5tz/TmpCRna8rbmyyiG2+UcR9OPGRLhy3Na6+V8SceR2QkWSVsRSnzhljxMsAPHy5n9ePGEWgwbdlolNYAgxozqSZMkPlUw4bRWLiCg/L+JiRIyvTQoXI/DJw6HU1CeHlAgAQiPqfwcLqnt90mz4WBiBXvsGE0wZg3T16b//yHzk9Jt2aW2eDBxJBUFnA1meR9eOMN+p3zgJSJtDodjSUqiv5fupSeXSYnKO8RAxxT7FtaCFS0WsqJe+stmSPGgK28PnzNn3mGrtHgwfQusEt3yxZi1un1NFFbv57ug68vES1c68q5I5p4AhZstfQkALl+MjMzxZIlS4SXl5eIiIgQvr6+YvHixSItLc0jncqVsPfu3etYtnr1atFRJew5c+aIhx9+uM3fV61aJbRarVN86pNPPhH+/v5dAsvTAoC4LXdjY6PIy8sTK1euFPv37/eoqkFycrIoLS3t9PonTpwQycnJ4uDBgw7X3sqVKzts26D8XH755a0eaFaUSovB31+6QN55h2apGg25N/LyaP0hQwi4WPG+9JKzG02pZAcPlooqKYncH6NGkRLJyJDxJbPZOegdFUUzV42GlJTFIgtExsTILHSdjgCH+8LEx1NmuzIAbzLJ5M7LL5ftAJQzeC7hM2gQueS4jlxCgvNMmIP4b7whSQYREa2TI7la8sMPS8uA3UozZtA14eKefB6BgQQqY8bIHJhhw6Qyd6Wzs2XDLbNZMbuCi/ITFkbnxmytoCDZ6VUJfpMn0/jHjZO/8fOyZAm54/h+McgoySoLFtB94HNREkfYWnnySVqHA/tBQURtV1YYULIiH3mE3F2uLEn+u2ULPZMaDVnHI0bI62s0SkLC0KFEvGF69/3303jd7XfAAJqomM30vaqKYkkAgdK770qrSAnanNbAz++yZfTe+PkRu+/TT2m9Tz4hd5u3N11TZY5edwAoPT1d7Nixo1cBiMMJiYmJwmq1igMHDog33nhDnDx50iOd2tVeQHPmzBGhoaEiJCREjBo1Sjz55JNOLRmeffZZMW7cOKdtjh8/LgCI/fv3ezRGIU4jAGpoaBBpaWkiOTlZHD9+3GNX2urVq0VxcXGH63FJnRUrVoiTJ086/bZmzRpRVFTU6WM+8MADTrNVfsmioshlcdll9NKkphJzTaOhZS0tNIs2m6k22rXX0rYhIXKWykoiPp7+nz+fZo2BgbReQwNZVwC5NqZOdXZ3sbKMiyP3xcCBpDB++UUWLuVgPJ9DRASBYkwM/XbHHc7ts729pTUTHEw+emX1hgEDpEvO29s5lmMwEECMHCmVObtUuH6Y0UgW28MPOwPu0KFEPVfSh/n8LryQzl1pjQF0nZKSCMS4aoBSkfn6SuvH35/WGThQuvvCwki5DRkil3l70znGxsqGZuzucX0GBg+mezZ1qryHPDa2dBYtkhYGs+zuuYdcoPHxrcGX3ZcBAXTtH3nE2cXGOUUAPYPz5jlfk9tuo/0r3Yd8DC8vekbnz5djUU6qQkPJ2uGyN6+9RhMkpYXF4Dl1KpEBOO9s2zayqpndyeWROAeLr++CBRTL5HbkWVnkYuS44nPPORNw+Ny4BBaz4AoL6Z3QaCg1orCw7dbtngBDamqq2LVrV68D0P/93/+JsWPHdkundrUd96effirWrFkjDh48KP71r3+JmJgYcfnllzt+v+uuu8T8+fOdtrFYLAKAWLVqlcfjPC0AqKZGlrrxxIpRftatW9dhBezq6mrxyy+/iA0bNrh1ta1bt07k5+d3+ph/+tOf3D7UAAWKy8okSYD7kTC1mYtrus6+580j5aLR0IyZ68Lp9RRb+r//k8qQ2WfMhDv/fNlrpahINn275hoZIOaXlzP7TSayTu64g5YnJpKFoswfuvtumUty662klNnyMBhothscTOvHxjort5AQ8r1zRW1mvT35pGTDJSbSrJfpt8p8jhtvlLP3gADZO0bJpDObSQGx1cBj5evKcS5vbxr7mDF0bdmNxlXG27qXyg/nFg0cKPvgjB1L1s+gQdLlplSSISF0b3x8JPWafw8KIgtSyeoD6Dwfe4wAWVkzLjSUniN2L82YQbG2adOcjxkYKK/bhRfKag0MvmvWyJJCDDYMRuPHUyUKdn3yvvmeDxpEbmJmBv76q8x54ngZpwSMGUM5S/zcNzbKHk+PPy6rd7taSWPGkJXEybNNTXKismoVgRpblbfe2tolrddL9y/XuOsuAO3fv1/s3bu31wHo008/FUlJSW515RNPPNHhM5qZmdllAHKVjRs3CgAiOztbCHEWApDdbhebNm0Su3bt6jKLrbGxUWzcuFHk5ua2+XtxcbFYs2ZNu8fpaB+unx9//LGVBcQvX2wsKV5OoORZH79kw4ZJ99eVV1JyakwMvcDl5TIedN11spOpqwvIx4cC1ZMm0Qv3ww+y0GNAgHy5eTsu16PXEztozRoZ62Grh8/nxhtlX6ABA1oHjxcskO6W4GDZIZPPb8kS6VZhBbNokSQ/cPD99ddbVy3geBXPzGfMkPXd+PhmM7mD5s+na8TUZ77WbMFMniyZde7qwel0tD5bbMqCpEFB9OHMfo1G5ky5awHu7U0KmdtQBwbKOJ7rcc89l2JvyokIW5JsjbGFOm8eWYrKjrpBQaSg+X7q9XQf3nnH2d3G1gEDJ9fj42NecQXFe4xGur7MCuTxPvYY7VOvp+t3ww0SXBiMGPifeYbykHx86LgXXihjXTodPSPccuPPfyYLh9tL7N8vKyYkJkpAZlo7x1HvuIPAKCSEPkVFVDUboBykHTtkv6ykpNZlhLoDQPv27RP79+/vdQD64x//2ErJs/RFO26lcN+zNWvWCCHOUhdcVVVVt3J4GhsbxZYtW0ROTk6r5coGdW2V1OloH219MjMz250ps7LhLPZbb6UCjgYDKQWbTSZT/vSTrFTt6yt7rrDST0igF9FopCoDL71EyxcvphgQZ/aHhDi74ebNk0y5adOo9huXc1FSd3U6Wv7FFzIZ87LL5KydY0jPPCNpu1wWhbcPD6fz40x4zuO4+GKphDhuwbNuV9cUK8cRIwh4b73VuTqEry/t79prnam57H6cOJGU+7nnOjd402hkuwdlTbKYGArWjxpFsRlWkOPG0b74w2SR8eMJRIcPJyuFc5TCwmROkBJowsMJrGfPpu2Cg50TL6dMIet0zhy6vspJwG9+Q646BgWzmdxozADka3LPPeTa5Gup05ELeOpUZ3BLSpIJp8rvbEGvXi1jaIMHSyuLf//xR0kWuPFGGU9UPuecC7Z7t2ynMHUqgYNrpXDe9ttviYav1RLANTdLYP3DH+iY7mJJM2bQBIw9BzabbAGxbZukn7dl/XgKQLt37xYpKSm9DkAvv/yyuOyyy7qlU7vajttVtm3bJgCI1NRUIYQkIZSUlDjW+fTTT4W/v79obGz0eJynBQA1Nzd3C3waGxvF1q1bRXZ2ttMyi8Ui9u7d2+kGde720d7n1KlTbi0g5cvy6adUhHPwYFIgBQWyV84111C2N7+MrsrrgQdkOZTPPyd/Ntcty8yUhStdt5s5kzLouWbZ3r2yJYMrE8jPjzLdOV4wciTNNpXEh2eekVWPBw6Us3NWgr/7nYzb8Da+vpJQEBlJykmprMaMoY9yPzExpKwfflhuqyxWevnlMo+D3XucUxIXJ2fi/DsrUKVlMmtW69YFnf24IyEoe9kkJZHFO3x4a6Yarzt1Klm+7KZT5vhMnkxKfcgQ5/s6axZd4yFD5P7CwmRsi5fFxUkLh8e6aBEBAVtwnDR63XV0bb28nBOcAXJ9Ll8uC9POmyfvA8e3/vIXGruPD01aZs9ufa4AXffKSply8OqrRN9n963yudXpyNLj9uDffUfFRk0mmrClpsrJU2KiPB8l2QQgq62lhQDKNT2gOwC0c+dOcfDgwV4HoCeffFLcdNNN3darnrbjzs7OFi+99JKj/cSyZcvEoEGDxDnnnOPYhmnY8+fPFykpKWLNmjUiLCzszKZh90RX1B07djglkVZWVjqo3J1tULdjxw5x5MiRTh+zvLy8TWXFD77RSD5y7nMTGEizc26mxkqWg9Gff05MIEAm6nHZ/kceodmgMsbACjk6mvIlzjlHrsutsZWBez7u55/TLJmpttOmyTHrdKSE/vAH6UtfsMCZURcTQ0DKxTr5N2YteXnRzJ5L8fA6Dz4oC5XymBYtIktOyYoLCZEdNZVWWmQkKfprriFFxWNmNllcHC1n4DaZWsd3GOxNJhof5/9ER8vJQHy8/MTFSWshKEjmAwUGui9KymDKLEZuDBce7tztlc9t3jyaTHD8hPOkEhIkqPOHLZzBg1uX9VHS87k8D19TPz9S/kzNZuX9wgsS8LjiNG+n1ZJb9/rrJSHmggucj7l4sax8MXMmWfFsRboSYhYsIOvdbCarLiWFwEKrpfFyTIpjk5MmyW7CubmUjKzRkMXX1ERjNZkoRUFJOVcCYFsTB/7k5eV1uq3Ctm3bREZGRq8D0IMPPijuvffebutVT9tx5+bminPOOUcEBwcLk8kkBg8eLB577DGnPCAhhDhx4oS4+OKLhdlsFqGhoeJ3v/tdlxNRNUIIgX4Wq9UKm83WrX2kpqbC19cXiYmJKCsrQ2pqKiIjIzFixAhotdpO7SMlJQUBAQEYOHBgh+ueOnUKBw8exKJFi6DRAMqrqNMBfDoaDWA00sdiAex2Wj5/PlBTA+zZA7z3HjB3LjB7Nq1/5Ajw+efAM88ACQm0TVERHUOjAebNA7Zvp2Ns2wb8/DPwu98B8fHAK68A994LNDbS73o9EBUFFBQAF18MvPkmcMUVwNGjwPDhQF4eUFdHY/b1Bd59F3j9dSAnB5g+HaiupvFYrTTup54Cjh0DvvuOzslgoPPSaOgzbhxw553Ao4/SNhoNEBwMPPEE8Ne/0r70ehrbkiXAvn3Azp20n5YW4LzzgIEDgb/9jY6n1wPh4bQsOhooLqb1rVb6zWoFBg8GIiKAgACgsBDIyKB98fZmM+DlJf9WVABlZcDo0bRMqwXq6+lc8/NpOy8vWg7IvzU1gJ8fHc9konNoaaExFRfTGFtaaF8NDXQPhKDtzWZg2jQaT2UlfYqKaD3+/eKLgaoqIDWVjsXbA/T77Nl03y0WWmYwAHffDSxfTvfXbqf7uGABXeejR2md5ma6VzodHW/KFLq3H35I47HbgXPPpWv/yitAUxMwcyY9WxoN/T5uHPDpp8Dll9O4r7iCjnHoEI1fowHuuAP44Qe6vm+9BYwfDyxaRGP19QXKy+U9u/56upaffUbn/c03QFISkJ5Oz0VaGh3fZqP9e3vTczpyJLB3L53D4cPAsmX0fMyeTfflX/+i92bVKvfvovI91ev1WLp0KQAgODgYISEhCA4OhslkalM/hIeHIzo6ukP90B357W9/i6CgILzzzju9epzTQroIrj0qPWEB7du3T6SmpoqMjAyRnJwsjh075vE+9u7dK9LT09tdp6GhwXEMnkHwR2niu1YP0OnIlfXKK9LNYbHQrFGno8KjXKper3dOotNqyTUxYwZ9f/ppiIMHZVzid79zblnt5SVdamFhEOnp0gUXFOTcwlqjoXEx0SEwkNZlS0CnI3fH229L1h3HPnh8Xl5EJnjiCWfrb84citUw5ZYtMGVbaaXbh5lUPLbBg2lcwcHO7D1lkumMGc4Wjskkex/5+JAlMXMmuYgSElrXwVNeYw6Ycz01b2/5YeKBkrru+tHpyNI591yyfsaNk9aF0pID6Fwvukj+rrwWvP6QIWTR8rXW62UJHy5iyhbsM8+Q+015X2+6iejrymRco5HcYbztb34jG+txjG3tWrIgtVpZDUJ5Xz78kNpvGAx0Lx57TCbi6nRkHbJFGxtLbrQlS2j74cNlPT1e//LLZRmnzz4jl7G3N+1n2TI5FqUlr9HQtf7nP+n8oqPJvb10afsuceVn7NixoqqqSuTl5YmUlBSxadMmsXTpUrFhwwZx4MABcfLkSae2Cps3bxZZWVm9bgFde+214vnnn+9vtdwncloAUE+05f7111/F2rVrxdq1az1KJlV+9u/fL1JTU9v8va6uTuzYscPpGO4e7Lb8zgEBlDD32mv0/+LF5GvnQLYrU+qllyTZgBP9rriC/p80SdbiYorsrbeSMjEYiLm0erVUDAMGOBfFnDyZXGjcRuA3vyFlrWTNPfWUrGEWHk6gpgxqjx1LCoD7zvC5zp8vy+oEB0uA4L/cZ0aZO8IBe6ZYc1Vo3i8DFxMa+Dc+J6ORgHXKlLZBhvsR8Vji4ui6DB5M4+GyLiEhMkY1ZgwtHz1aJuIOHy7p6jEx5L5zpVHzR1m/j5vKuQIZuwq1WgIcrsbAbrcFCwhMlCDm5UXgzoQE3te0abLVBV+niRMpFsm5MvzbDTdIdllcXOsyQffcQ43/mMwxYYIzEMTFUeLzpEn0/+zZMjeNn5EhQ2Rs5sYbyZXGE41bbiGQ5nMymcgVqdfT5OrIEXoGtVp6pioq5PkqXdFMkLn8cvk+KIHc3XcA4ptvvmml/MvKykRWVpbYtWtXq7YK69evF8eOHet1AFq0aJF46623+lst94mcFQBUVlYmVq5c6ejV0dX9pKamiv3797d5DGXZHl6+cOFCt2CjfOC1WlLIBgMp5Mcec44dKJly99xDDKLgYFIyW7dSJrtWS7NTVk58jDvukH1Vpk6lFsdz59L/oaGSkqvRkDL8/nv5+9ChkmLLCnTkSCIBcDWAe+6RiosV4osvkkJkC4lBhOMH3t5U5JJzTxgozjuPZuTK6xIa+v/sXXV4VNfTfje7cfdAhDgkxIND8aLB6i20pUKpUCrwq1MXaKlSpy3S0qLF3SE4cWKQkBDf6MZl5X5/zHfu3Y1nk00CzTzPPoTdK+eee++8Z2bemSEaOqt9x8bK4jIAKXAWH2HjdHIiy4bFZtStIDYmBggsjuDtLRzH17f5/J/W4gXqylk9HygsTAiym5vT2NUTitl9ZnPh7i5U7raxIaWqXsDUzY3mlyludk5DQ4HWz6wyBuisi6utrXDdkycL88UsnGeeEbrosvFNmUJ5WWwuhgzRBJr+/Wkx89hjgnXOYn9svl55hawPVifvnXcoXsTGrq9Pc8S6zG7eTOw3Fof7+mtNcouBgcD+mzOHYqHMcvrhB3BJSXQuCwuyrlgeWnPvYksWUHFxcatAoN5W4eTJk9yuXbu4gwcPclFRUVxGRgZXVlamEwCaMGFCh3J1bme57QGIldQ5c+YMd/HixU4BWUJCAnf16tUm32dlZXH79u3j4uLimtC4t2zZ0kSBNbcCY6wttort109IrAsLo3IkzIWydCnlU7B2zgyc2KpyyhShdYK5OVU3WL2aXjpDQwI79dX1//5HVpdEQkrrhx9o1coUlZ4egQ7L2A8JoVL7bMXPlOInnwi111xcBGXOxjVyJLlnGB2XsbzeeIOsM7ZaZe6lZcsEWi9TlHfdRaCqDgwsT4kp6WnTBAo2O6Z6kzlzcwLZxswstj2zqBhINbY+2bww0G18TxnwMjp149+Y5cNAYdw4gXknEgngxLa3tyfAZqWK1MfCAvqsG6i6O00sJgLHsGGa5AYjI7JuGNWc7WNsLFDdnZzIlcsqkLPctV27aO7EYqFhofqY5s8nVzGrXqCerwQQIFy+TGw+9txs2EDnY/NkZCRYMoGBVCh03jw6xoQJ5HZTv//q8//RR+SiMzam64uPF2rKNQc4jd3i6s9CR4Hh4MGDXFxcHHf58mXu4MGD3J49e7izZ89yiYmJXEFBQbvJDG19hg8fzm3YsKGn1XK3yG0LQDU1NVxsbCy3b98+Lisri0tJSeHOnz/fKQBKSkriLl26xP+ftWlg8Z6W3HItWUDsRWCKmCk+tlJOShISTr28CIRYcqd6XxQ9PXrRDx0SqLgvvwzu6lUhuVI9TiIWE6Pp8GGhVtfMmQQq6mMICBD6+lhYECWc1ZFjVtZDDwmK0dKSysSoFw81M6O41iuvCG4kkYgUzquvNm0+5u9Pbib1a2OgMn688H8GlpMnk9JrDOqsEoKhIVlWM2dqKmE2PhYjUW/T7exMIMtW1H5+ZJF4edH/3dxIwbLt+/encTOLJDSUwJa5r/z86MOu1daW5ko9+RgQKi8YGxM4sooA6hYts3z19cmdxtx7bA6nTCHriOXJMAB+5hlyvVlYaCarrlqlWU2BJdPOmycA06hRgluMgeuyZfS8MCuUgT8rqGtuTm69r78Wnm11FhtAlnlcnGAtPfII0bfV76WZmQByy5YRk5PFpL74QqgE3xyo/PorWVp6ekLeXHNg05wFZGNj02Fg2L9/P5eTk8NbR/n5+dy1a9e406dPc7t37+YOHTrEXblypV1N51r7BAQEcDt27Ohptdwt0isAqCNtuevqNEvqlJSUcHV1dVxaWhp39uzZTgFQamoqD2Lq8Z62ygOpK5nmYjnqq0OJhFaa1tb0Mp8+TfRS9ZeGbW9kRNYNIwj4+pIPndWOMzHRfOmMjKgZHSu74+hI+wcFCYpFIqH9Ge37wQcJqJjyYAmbrPIBS0hlsSh2nOBgsrrYSpZZC46OwurY0FAz4M3mwsZGaGpmYiJct6mpoOimThWUIrOCzMwIDNXzaNSVCnMbursTAEskNOcTJpAF0lz1gsYfNkeMrq4+9sZEgsYflrRrakpgOny4sJBwcREK0qrf36FDyWXHFg3MemTPw9Ch5PpiOV/smq2tyaps3OLbyYkWJ8xyYs/jffeRFaxOFjA1pYoE6qV2mBuVjcHVlYDoueeaPmssnjZsGLhz5+ieMdekustWLKaeUD/+SPfX2JjAY8ECYdwMGMVioVTQL7/QOZ2caLG1ZAn97uioCag+PjS36l6Clu4t+/vrr7/uMDDs3buXy8/Pb/a3srIy7ubNm9zVq1f5pnOnT5/mEhISuLy8vHZbR+Xl5ZyHhwd35MiRnlbL3SK9goatUqkgZ7zZNkQmkyEmJgbW1tYICAiARCIBAOTn5yMzMxMjR47Uehw5OTnIz8+Hn58fYmJiYGxsjKCgIBgYGLS637Jly/DDDz9AJKL/sxlllFOJhOig9fVEKeU4ICiI6KxFRfR/sZiosnI58O23gJ0dsHAhHWfrVtpu8WLaVl+f6LTsHC+9BPj5AUuX0vnefhtwcqLtGQ2VUYcTE+nvn38GNm0C1q0T6Kk2NjS+khLAxwf44AOix+7eLVzLk08CN28Cp07ReD09gbQ0ouqqVLTd4MFEBV6zhijDHEc02rvuAgIDic7LKOmmpvTdgAHAtm0CTV1Pj2jVSiVRkseNI3pvYqJAN9bXB+ztAQsLouSamQFDhhBNPCtLOBZA2zOquJUV7SuV0tg8Pel8BQV0rsGDabuaGrpWFxdhLCkpdD57e/r/rVu0rbU13bvaWjomo60zsbYm6nN1NVHPa2sBW1s6X0MDbSMSER3a0lKT4qxS0W+BgTQHeXlEOy8upnNaWQGVlbStuzuQmUnfe3rSNdXXE628uprm6IkngJMnifL8/68PXnmF6Mxff03HZ+e1thYo5TNmAM8/D7zwAtH32f329ibavlwOvPwyMHUqsGABPbPW1kQ5Z89hQADd55deAo4epf//9BPw6KM0l+xaTUxovMOGAbt20btw5AgwaxZR9CdOpPED9AwpFMI8sueH0a/Zs6supaWlvO5oj3Ach5MnT2LkyJEwNjZuc/uamhqUlpaipKQEZWVlkEgksLW1ha2tLaytraGvr9/ieXx9fbFz585O6bLbRW4bAOI4DtnZ2UhNTYWPjw8GDBgAEdP4oLyc69evY8yYMVqPIy8vD+np6aivr4erqyt8fX01ztGSlJaWauQGsJeX/Q3Q/83MSKlZWJDisbcnJSgSUb7E119TfsW1a6Qkli+nFy49nfJAmHLT06McnvXrgUWLgPh4YORIyrOZO5cAgSnbESMob8bYGPjwQ3pZ33iDzm9gQMrJ3JwUmIUF5ROZmgKffEKKQyQi5TF9OnDoEF2Hvz8weTLw3XdCnoatLSkdsZgApqaG9jU3p/yQpCTg8GHaXiwmBXPffZR7c+gQjUMkonGGhADOzrR9WZkwzyYmBKJlZYCXFwFFXh4QHS3Mt0hEyra+nubKy4v+L5NRjk9YGI2pvJzmmS0I2rn+0RiLmRntV1lJ4NrQIBzXxoaApK6OFDGgqQRFIlKMU6bQ2NLSgMJCIe8GoEXArVv09+DB9BxUVgrPwIABpKDj4ug3uZzm1t0duPtu4MQJAlB2XhMTArhTp0i5u7gQ6ERHC8+shwc9AzExwMaNdEyWyzVwIOWtSSS0jURCeUPsnvbrR/ftyhV6tr//nvJ5PvsMfA6bXC6c67XX6BmYOZPmaMkSet5nz6b7x94fkYjO8dprlGM2ciQ9A0FBdO1GRvTsFhcLuW+NAQegMdLcilBeXt6h+61SqXDq1CmMGTOmzQVpc/vKZDIekGpqamBhYcHnHpmbm/N6huM4ODs749y5cwgKCurQeW5L6TnjS5C22nJXV1dzly9f5g4cOMDl5eU1u01ubi53+PBhrd1vtbW13MWLF7ldu3ZxmZmZHd4faOqeUWcANXYXBQQI2zMaq4cHudhYHx6Wl8L2MTamYqNLlghumGPHhGZ1zLXj4CC4JGbNoqCxehyncZ+aOXOIHcfGwcgHIpFAwXV0JFfha68J+zEX0fvvazK2mJtt3jzBVWJmJlTDnjJFYOcxIgAgxJvU500iIffT3Xdr/sY+LM5iZERzOny4UKLGy0uzy2rjeyOR0H6WlgKLSiQSimaymI6ZGbnG7OwEN5y5ueACbS7GwHJoRo4UimyyigguLnRe9f1sbIR75OBA26lX/NbTIzr2I48I+WDM1eTmRi6y++7TZN05OxMt+sknBVcnY1yuXk3jYveHxXHs7IT9J0+mMjuMBMPih59+KrhHPTyIzq/OztTXJ+ILc+dNmkTMTkZ8Uc9rYs/YgQOUS8Zyi7ZtEypxqMcFRSKaxwcfFMrwqI+Zuf3U3XCNXXJvvfVWh91vJSUlfNPLzpIMCgsLuZSUFO7cuXPc3r17uf3793MXLlzg9uzZwyUmJnIikYhLT0/vlE7taDvuxjmN6p+tW7fy2zX3+z///KP1OHs9ALHuqKdOnWq1pE5BQQF34MABrcCnsrKSO3fuHHfw4EGtQeyll17ib0hLMSH1QC57eSUSUpJPPikoJXVfu0RCL+LmzfTi6esTCJw4Ifj52fFZ0NzNjbpDvvCCEKgfPlzzJbW1pVpbjz8uKJbBgzXPzYpffv+9kMvCrm3uXEFh6uuT8mfAxq7TzIyU2MiRmsrWxIRAZcKEprkzpqbCdQQEkJJkx2WxGXt7GuuoUUJsSD0IzebDwECgRbPx+PrScdncMUIBK91ja0sEgTFjBMqzhwcRJ+66S0j4HTmSYh9sGzMzikUMGCAE1RuDjFhM4xk6lMCE1cfz9BTiJmyOLSyIGTdnTtM4j76+wPJrvFBxcRGaFTIFDdB1v/WWQI1nz8WiRQQo6qVsxGIiZYSFCZT2559v2n592TJafLCk6YgIIRmZgd348eDee09Y1KxYAW7/fk1aPbsGgLa/cUMgqgwbRvlAjBoOCLXrGKmDtWVnvze3IGCgZGFhqhVoFBUVcbt27eoyphv7yGQyLisri4uJieHuvvtuTiQScQC4V199lYuMjNS6xE1H23ErFAqNNtz5+fncBx98wJmZmWkAFwBu3bp1GtvV1tZqNUaO6+UA1JHuqEVFRdzevXs7DBwsvycyMpLLzs7mDh06pLUVZWdn3WSVrW4VqFNvWa0upqhEIrIA2MsbHAwuIUFQ8uHhVIA0IkJTCbCVq5ERrWr/+ENgxE2cqFmQUSwmivOqVUKLgeBgYrapB3D9/anHDGsXwSi5DNxYYJkpNfX8DUNDUiL/+5+gsBjIBAQQ4C1cKCgPsZiAdcQIUpyDBmlakoaGQs0vQKgszejEbD6NjUmhM+qxhwdd+8SJwkpcfaXcklXUWGk1RzpoScGZmZFlwMgHrMZecLBglTGihPq+enpkqU6fLtT9Uz83SxwWi2ke/P01rVi2gJgzR9MSYdZaeLhmEmpwMAEhOx5A8/bmm/R8sPOLxTTXX34pkFQcHIhxp07ICAykPKGZMwWr8uWXhX5JjIl47Jiw6HB2Jrr1iy9qEjOY1SsSEfHl4kXhnqq37Q4PF0g9rAV8S/dI/e/ly5drBRQFBQXcnj17uhR8mvucPn2aA8A98MADnK2tLWdpadlhRpy27bgbS0hICPfkk09qfAeA27lzZ4fG05r0CgDiOK6JO6yjJXVKS0u5Xbt2daitw61bt7h9+/bxbbmlUqnWVlRdXR23Zs2aZhUbAx915g5AbhaxmFbv6tUEWDVgKyuyZFhZe3XriDGZpk6l0vOsWrWrK+VdMBccs7B+/lloGW1vT7lAM2ZoKpKlSylJkbnF/Pw0S+GLRPSyv/girWQZRZcp1VdeIdcIs/DYcYcMIXBlrCl1sHrsMXKvsX2Ym27wYHLdPPigJtiwbZhrzdRU6Hw6dWrrq19TU9qP0XwZY87LS+iZY2tLCn7wYMHqs7Oj7wYNEsrSODuTtePiItCZzc01rR71sdjaCiAUGkorez8/AYjVrWbGkhSLCZi9vDTzf9hi5cEH6Xe2CBGLaUxLl9Lvbm6a1uUrrxB4DBigOTZTU7JU2Tm8vYnezGj1AF33Tz/RvWfWi7+/sHhin2efpeoePj70f1dXYdHEzjl/Pj3XzNKdOpUK9np7a84D6/Hk6EiuRFtbuh4GYuz5lkgENqb6c9f4GRCLobULLS8vj9u3b5/OASg2NpaTSCScQqHgFAoFd/nyZS4nJ6dDurQregFdvXqVA8CdO3dO43t6Fvpztra23NChQ7nff/+dU6lUHRqfxvG03rOLRd0d1rjcTXs+FRXUS726urrNbdXbf6vHe4qLi7WyotQ/jVe26n+ruxvUXxi2Qpw8mf52dCS3G8ufUI8FMGV26pTgYjM2BrdypUDnZkpn9mxy4wCkKJ59luiwrO4Wowy/+qqwynRyokoHY8dqKtL77ycfPQNKRkvW16fyQEx5mZkJ7j4DAwFYDA1JqbPcG3Y+NjbmQnFyopW0+u9sVezvT2DDKnMzBaQ+5wxonJ1JcbNaa6z6wPjxQm0xe3s6H1tJN87jamkxAdD1urpqNqAbMICsrvBw4bxmZnQ/zc2bWl9WVjRXvr7CwsPbW7OBnUhEv8+fT4rcxUXTOjI1pfvMrGf137y9CTBYlQb2vY8PWa5PPNE0NvT11zQ/bKzDhgnPH9t//HjqzDtzplD2iN0Ptq2NDSUlf/mlsPAYOlQoOwXQwmL7dsojU3fRbdsmLNZMTena2XvDxuDhIXS+ZVZ5c++euufhmWee0RoYsrOzuQMHDugcgCIjIzlLS8tOKfWu6Ib63HPPcX5+fk2+//DDD7nIyEguOjqaW7lyJWdoaMh9++23Wo+11wBQfX09V1RUxB05coSLjIzkKio6VlKnurqa27VrV5v7sXjPkSNHmuT3lJWVddiKavx57rnnmlVc6taLuguL5dSoNwdjL98jjwh9ZSQSelF//VVYHUdEUAdIptT19Gjfhx8WMtzHjiWfO1uFshfT1lZY4ZuZ0cr4xx+F4DIb77vvCmVjJBJSZo3L7ZuakqvrzTc1V9dGRqSMH3uMjs8IA3p6pHyGDiXwUq8xxwDHwYGUO3PXTJokxHLYuQ0NhbwjVkPMyopch8OHC0Db0v0wMKB5YsmQzI1mZyd8GHja2QktGNi+LGG0pXMwxT5hAhEvxowR7qeTEx1L3R3HclruvZfccSwxVh0cTUyE+IixMW3P3GgsB4e5Vl1dhXvh6ioQLRwdhfkOCqIYDSNYMHfu88+T5T14sKYLmVmNzPJ46imyuFmrdDa3QUHCMx0SAm7fPnqe2WKicYLyXXeRFTRlilAphFm5rELGoEG0/7BhQskfNjZmXbb0sbOz1igs2tFPZmYmd/jwYZ0D0OHDh7n+/fs3C0Dd1Y67pqaGs7S05FavXt3mtitWrOBcXFzap+SbkV5BwwaAW7duISEhAe7u7vD29m4X/VldOI7D4cOHMW7cuBZ5+lVVVYiOjoapqSmCgoKacPEbGhpw4sQJTJkypd0tHBrvHxkZiZdffhnXr1/XKP8uFhOllOUFWVhQvg2jmTo5ESX22jWiWDs4EF1WqaTfGeXU05Nyd7ZtI5orO4eVFdGCc3KInrtqFeWbfPcdndPKiui++vpEcTUwIKru/fcT3fbECRojxxGtdeRI+k5Pj6jMDzxA/790icakr08l+0eMIArw7t1EQ9bTo+M8+ihtt3cv5a4w6q2tLdFuy8spF6WiguaD4wBXV6IBOzgQJfn0aYGSrK9P12djQ9RiCwsq3y+XU9uBrCxhrgE6pqEh0cHFYqIHs/YRt24RDXnoUPpNIqF8mbQ0aplgakrHUCppDP370z1RKukcly8LrScUCvrcvEntAjw9aX4bGogy3dAg7KcuYjHNQ20t7ZeZSXlh+vp0PEZb7tePqObOzjQ32dlE3a+ro+OIRMDjj9P1ZGRQnlJVldDCYPFi2v/yZbpu1iLD0hKYP59ya27epDGGh9O9ZPekoYHy0RYvpn1++onuJcfR70uWUCuIjAw63iuv0PV88YVAh/bwIAr/r7/SvDzyCOX0LFtGz7pYTPfQyIjGplJROxE9PeDHH+kaH3wQ+PdfYexKJeUXmZlRGwpXV5q/5oQ9d9u3b8eUKVM6/E4zKS4uxs2bNzFs2DCtj9EeOXbsGF5//XWkpqY20YFFRUUoKSlpdX9PT0/89ddfWLZsGcrUchgUCgWMjIywbds2zJs3r9Vj/Pnnn3jqqaeQm5sLe3v7Vrfdv38/IiIiUFdX12Ibi9akVwCQSqXCuXPn4O7uDgcHB62Pc/ToUYwcORJmZmZNfpNKpYiPj28V4BQKBY4dO4ZJkya1mCjWklRWViI6Ohocx8HExARjx47V+J0BEMvVMDCgl6iwkJQbS+RjClcspp4pf/9NiuCRRygP46uvSEEYGpISYjk85ubUc8fWFnj/fTqulxcp7KgoIVdj0CDKLzp5ko7NcZQ3kptLx5P8f68eZ2d68U1NqTdRXp4AUM8/T3kbu3fTPqamNO7aWuprdPkyKX4DAzp2URHlbQwcSGChUtH4HR3puPr6wLRpwMWLpIQBQXH060dK3cqKxnbggGbOjt7/99JheUeDBgm9f2QySlz19yflV1pKwMdxTQGho6K+uFAXKytSiI6OtM3p0/Svnx/NT02NkECqnqtibk5KPDcXCA2lay0pofvI+v8AlCMVEEDXcvUq/c6ux9KS8orMzYEdO+hamQwcSEBQUkKLF6bsHRwouVgmo7nNzhYSOI2NgTFjqAdRTQ3dh8WLgf37qVcPQMd44w26199/T+Py9qaE1E8+EXKb9PWBLVtom+PH6f6/+SYtlp59lo6vr0+5aj/8QM8FIACUpydd37VrtE91tXDtKhXlHbF8q8ZiaWnJJ6+LxWKt7ndhYSGysrIwZMgQrfZvr+zevRtffvklYmJitD5GcnIy/P39cfXqVYSHhwMAjhw5gmnTpiEnJ6fNfkbjx4+HnZ0dtm/f3ua5PvnkE3z55ZcoLS3Vaqy9AoAAsh46O5QTJ04gPDwclpaW/HccxyEtLQ2ZmZkICgqCo6Nji/szK2r8+PEwMjJq93kLCgqQkJAADw8P1NbWwtDQEB9//DG2bt3KKyq20mbZ2qampBRZBYGgIFIOUillvFdVAdev03bV1bSvgQE1elu/niwdiQS4915ayX74ISkka2tqyPXbb6TsWaLom2/S399/T8ceMIBWoqtWCY3PTE2Bxx6j39ato2x8hsNyOSWfRkYKwDd6NO2ze7dmsqOfH12Dvj41HAOEpFQfHzq+kRFVWWCAK5GQgnF1pXnIzqak0exsQbGIRHQtbGVtZkbnz84WKhYUFwvWARNWNYF9SktpnPb29JtYTIq/pobAiiXxikSUSGlpScdmyi4vT5hDpZKuva6OlDhLrpfLBYBiYGpnR/s4OQHnz9PCISCA/pXJBIuJGd+DBlECak0NKe26OiEhGSArMDycFhOmpgR6OTl0DImE7kFMDG1nYUFWnkJBjQunTqXniFnYrFrGww+TYv/7b6HRoIUFPV+7dtH9r6+nihPBwVSVgF1fUBDwv//RWLdsoXGOG0f3af9+oRrC4sWUcPrSS2RxubvT83D9umAlz59PFpdCQdtevkzPAUvg1dOj50kqpWcmO7v5d9PMzAj79x9GbW0tGhoaYGVlxVckMDExafc7np+fj/z8fISFhbV7H21k06ZN2LRpE86ePdup40yfPh1SqRQ///wz5HI5nnjiCQwZMgR///03ACA3NxeTJk3Cxo0bNay6tLQ0+Pr64sCBA5g2bZrGMffu3QupVIoRI0bAyMgIR48exfLly7F8+XJ88MEHWo2z1wCQXC6HSr12ihZy+vRpBAQEwNbWlj9mfHw8qqurERYW1qxl1FiOHDmC0aNHw5T5YVqR5sAtOTkZIpEIgwYNwtixY3H58mUAQkUB5oJjpV1YRnpODn0nk9FL6OQEjB1Lq1WlksrsHDsGJCcLivj++0mJGxiQG2bKFFrN1tTQMRwdaSW6Zg0d38aGVpu2trRqZStnDw/guedI6URFkZIOCaHjJCcLIGFuTivioUPJHbhnj2AV+frSeOvrye3G3Issu37sWLrW48fJGmKuRwcHUvoODmTZnT5Niou5+czNCSisrGhsLCM/NZUUJSCMwdCQFDSrmJCRQfv5+Ajut8JCWkX7+grzX1ND526PMEBWKGh+ra3pvpmb0wKgqorcePX19KmoIPeQkRGNoa6uackYLy8CG5WKXIkJCXQdYrFQykgkEko1zZxJc5iVRQpYvWrA00/TM5SeTnNUVSX89uSTNLeXLpE7Ti6neaqvJ1CysKB95HIaz4MP0gIiP19Q/I8+SsCzcSMQG0vzEBhIY9bXp2tQKqmawaOPEoicOUPfDxtGVi4DIhsbssbLyshVXF9PlRGKi8n9LBIJrmNLS3on0tLoOzs7Aiz2DJuZCdeqLrt378aECRPAcRxfHqe4uBgymQxGRkZ8F9S2rKOcnByUlJQgODi4fQ+KlrJ27VocPnwYhw8f7tRxSktLsWTJEuzduxd6enq499578d133/E6MDMzEx4eHjh58iTGjx/P7/fWW2/hr7/+QmZmZpMwxKFDh/Dmm28iLS0NHMfB29sbzz33HBYtWqRVyAK4wwAoMjISvr6+cHBwQGVlJWJiYlqM97Qkx48fx9ChQ2FhYdHqdgqFAvHx8aisrERYWBjMzc0BAKmpqVAqlfD390ddXR28vAagpKRcwwJicRaJRFC2Y8fSC11TQwqzoIBeTE9PWt2LRLRCnTWLAIW1r54/n4Bg925B4Q8dSueJiyNl/NBDBBzffkvfse3Gj6e/T5ygsfj70wr4/HlasbKaa+PHU6mZ6Ghy06i3ww4KovEePEhjNzQkZXbrFl1nSAjtp1SSAu3Xj0CgthaYM4dAhbXBVo9/3HUXASVAY87IEOZeX58UtJUVKS2plMbu7EznLyqi0j+Vlc3fOxanYm5RiYSUl+j/a7ox76xIRGOVSOh7Zq2ymm/qCpe5rZhyZWJhQddTW0sLhLo6oWV3UhIdy8SEvld3v7K569dPaCHO3KSsFE9YGFlIJSWk2EtKBIVsY0PnMzUFzp4la5jVR2PPUXk57Xf9Os27nh6BjosL3WfWij0ggJ7H0lIBRC0sqAyUkxNZQczaBggc+vWj6xOLqW34qFFUticlha6zf39a9Hz5peBSZrEyNo6//6bzDx1K+7CSQba29G6YmNA12NvT2FQqTbfoww8/jF9++aXZZ0CpVKKsrAwlJSUoKSlp0zrKyspCRUUFAgICWtAIXSNff/014uLisGPHDp2ep7dIrwEghUIBpfqbq4VcuHAB7u7uEIlEWhMaTp48idDQUFhZWbW4TXV1NWJiYmBoaIjg4GCN2lA3btxAXV0dAgMDAQApKSkICQkBICgnsZhe3LIyAgobG1Ie3t5CjGTECHopT5+m4y5eTNZOeblQ4JG5Y2bNopf03XdpWwMDcpctWAD8+SeBg0RCq0SZjBQEs1C8vAjEamrIbVdSIsQ3nnuOXBuHDpEisLEhcsLx46T4mUVnYkKKMCiIwODgQcG1Y25O1oa7O+3/+++aRSKtrAiwHBzIlcQKl8pk4OeMFdK0sCBlaGZG2+TmEoA1XrcwYGAxJAMDwQJibsWgIAGExGKKc9jZ0fHVH5fjx2n1PWSIcMyGBnJFOTuTS40REWQyWiw4ONC9qaujeaqrI6BRf7z19WlsWVmkQIODSRFXVNA9YJYwc6H6+BAg5eTQWE1MaJwsPmRhQbGj06dpvHZ2dO+YO27UKIqHyWQ0vqIiGnP//lR/8OZNAh22OHFyIguG42gxcuOG4GY1NKSY3alT9Dw6OdGzolAAq1fT9erpCa67L7+kBYmVFbmQV60SAN/bm+Kba9YQiISF0XWuW0fHcHCgcyiV9HduLs2tnx9ZnOpuWWYJAoCDgz3S0tJbfIfVhVlHDIzUrSNbW1tYWVkhKysLtbW18Pf3b9cxtZWPP/4YBQUF2LBhg07P01vkjgKgS5cuQSKRoKysDIGBga3Ge1qSM2fOYPDgwbwbr7EUFxcjLi4Ozs7O8PX1bWJ63rx5E5WVlRqm+ptvvonvvvsaBgaC/15fn15Itmq9+25yVdTX0wqUBW8HDaJioyoVuTUMDIgRpFKRe624mNx07OVbtoyO+ddfwirawYEqD7NVbv/+FOsxNKTCoXl5gsunqorAJDVVcJNMnEgKe+NGskQUCtp+5EiyPCoqyApjMRCFghRJWBh9x9xuLK7CFLqjIymdCxeE35l7krm3rKwIAJnfv6ZGmGtGmDA3p+2NjQXL8sIFug4rK5pLptRFopYLVXby8eNFX58UpJMTjbmhgaxKpZLchzU1NM/V1UK8igG2szPtq69PgGBmRseRSoUK4kxcXCgOl59PCp5VvmbXs2gRWYE3bpArtapKiEcGBhJgXbpEbq2GBppve3uyiGxthZhUv370vKhUwB9/CIsDQ0MClxMnCKRqamhB89xzVG2dWe4ODvRcGhgQiSYzk/5+4w3aLypKYO2xuM7o0WS5ARTjCQyk+WCgdfMmzRGzxuRygXHo5OSAa9eSOlw0lIlCoYBMJtOwjgwMDGBoaAg/P78OxY46Km+++SZUKhV+ZBTAO1zuGACSy+U4c+YMRCIRhg0b1q54T3Ny7tw5+Pj4NGHjcRyHzMxMpKWlwd/fH87Ozs3un5mZibKyMoSGhmp8v3r1arz77ju8i8DDg1Zz9fWCJTN8OAVbOY5WpfHxpBwAChqfPEkvoKcnKRSxmJTA0qUU12CWyrRpZP0kJFCF7YYG2m/+fFrhr10rVNieMIFe2uPHBZqrnR1ZUIMHU9zp9GkhDiEWEwvu5k0CNVbqv18/UoL9+tGHVag2NCSlWllJq+5x40jBMbabgYFg8bi50RxUV5M7kjGhWPzDwkKgm5ubE8jY2NB8ZGcLQXZ1UScgiMV0HgZszAUnFtN8Mio8A0qA5tDQkKw45uJRqYT4A7OAlEpSgswVpO5uZSIS0bEGDKD7L5EQeJ85Q3Pg4EBAXF0tXIdIRKAwcSI9IyUltOhgFc/lcjpOSAhZUaWlZJ0VFwvntrKiBY6tLV3nmTOC9eriQs+aQkHP3rVrAiiKxcCLL1J166tX6V6zxcvu3XQsZjHNmUMV3f/9lwCpvp6uSaEg19tXX9H9cXame29qSs+EmRlVS9+6lb63tyf3259/EmA6OtL15ebStuXl9CwMG0bXqadH95aRQczNjREXlwg7O7vWX/R2CrOOUlJSUFdXh4aGhibWkbbMuuZk6dKlsLOzwxdffNFlx+zN0msASKlUQtHc0rQdwuI9SqUSLi4u8PHx0XoczI3Xr18/jbElJiaipKSkTfdcdnY2pFJps3TNt956C1999RUAern79yfFaWRELrfISPYS0WqSKfOYGFI2zz5L7pfoaDreihW0goyNFV7oYcPI1VJdTQrJyooAxMeHVsKGhrS6vPdeanewZ4/QkmD6dPrtwAE6D3OBKJUEHp6eZAFxHI0xOJhWppGR5O9nq3hLS7LcPDzo7z//FFxF+voEAAMGkHKprydgZe4tgBSOuTkpzPR0GsPw4XT81FQhgM5EX5+uy8SE5iErS2CBGRrS7wYGNMacHLLmmBVSU0Ofjjx6LEbD8nQYMDKmWWwsuUSZW6i+ns5165YAhuoxH0BwhwYHE9BUVBBYJiQI94ed092dLMjISLIWvLzovMzCMTOjhcbFizSH9vZ035jbdPhwGsPp0zRfDQ00nzY2wLx5dM+2b9ekQi9YQOc9fJgAiYERezYYCUBPj2KO8+bRQoctbDw8yI186RKBlFhMCyKJhNxtYjEtiAIDyVpkxByAnoVp0wTLPzRUs4WEsTHd35oaIDLyEvz8/Np/M9spKSkpMDAwgJubG8rKyvjWCg0NDbC2tubJDJ21jp566ikEBATgXeZPv8PltgegxhRoIyOjTgHQ5cuX4ezszFs4dXV1PCc/NDS0TXp2bm4ucnJyMHz48GZ/nzVrFo4ePQpvb3JFMKuD44BJkwRCwIMPAvv2Ce4OHx9a9bq4kNK7coX28fMjX/3GjQQwZmaUINjQQO4Slrf2/vu0St69G/jnH8FPr1IROyoxkZSDnh6tUqdNo7/XrqVtWPB32DA6561bBFSMIq5QkNXECBTHj5MSZZaEiQmRGRwcSLFcvEiAyawQKyu6NmtrUoQqFdF+GzeVMzIiBSmV0n5DhtCxjY1JGSYlNU1KVRd18gH7W6Ui5WllRcdXJyHk5wtAzCwgxp5TT6BkK/DmxNiYrMqGBrJMGBmBMfBkMlrhM5IAE3NzAvLoaDpGaCjtn59Pc6dOfIiIoHufny8ktTJ3r1hM7Lj6enqGrl0T8qEAAumFC8kVxiwgExM6JktglUjoek1NCWA8PcnivnxZALaPPyYw/Ocfes7YHH7xBcUFjx4VWHceHjRGhYKe+6tX6Vk1MiIizKVLdC8dHAj4WB8ihYLONWwYbQOwhnQSHD58go+3drUkJibC1NQU7u7u/HftiR111Dp6+OGHMWHCBCxbtqyLr6B3ym0LQBzH4fr168jOzkZQUBAcHBw0KNDaSlRUFOzt7fmVTmxsLOzs7DB48OB2UQ0LCgqQkZHRajfD559/HuvW/QEPDyGr38eHFA3z0SuVZBVVVdGL6OhIL+affwp5Mc89R/kWLHgbEyMwhMRiamqXkkIrXUZ7njWLgr7//APs3EnHEYvJ/TV9Or3wW7fSOdkLP3w4ueouXiS/vFIpNGQrLCRlUFMjdKg0MiKlIRLR+QcNIsBguWpGRkJHz+pqcgGVlJDlwJIn2VSbmJCyMjenj5kZKcrMTM3Gf2wfZi1YW9O+jIBgYEDWBKNxM1aiWExK+eZNUoQMBNjn6lXBajA0FM6Zn0/jHTVKmCf2iY+na3dzI2VbUyNUR2AsPyYWFrSdsTEtKgIDBXZXSQkBjXpek4cHga5IRPN34wYBukgkLBQGD6Z7UllJ1iXLn2G5XpMn0zkzM+l3Zo1ZWJBlbGVF1x0VRc8fex7NzOg5OH2aFhcmJrRQqqwkwPHwINeksTF1T2XnZ0y/hx6iRcaHH9K1DBtGrsEff6TrNTKiZ2HTJvqbAaiBAVHPDx2isY4cSVYSE2NjCU6dOqcTy4dJQkICrKys4Orq2uI2CoVCg1knl8t568jW1rZdnVRnz56Nhx56CIsXL+7K4fda6TUA1JG23A0NDYiPj0dtbS1CQ0P5eM/169chl8sxePBgrccRGxsLS0tL6OvrIzk5Gb6+vnBzc2s3k66wsBA3btzA6NGjW9ympqYGL730EjZt+pNXGnp6ZCEcO0ZKIiKCAIIpQlbxYMIE+jc2lr5/6CFS5r//Ti/swIFEe92zh3z9bEq/+YbAg9GlWdxpwwZa4W7cSIqYkQBKS+lYEgkBC6MoDx1K7p3ffhPAQiym3/z9SQkZGpILhzG5DA3JDcSSMC0tyZ3CrDuAtrGyot8tLEjBuLrSfmlpmp1RxWIhNmJqStfCPufOkZIbOFAgH5SWCqyrtp529fgP+5eBXEfeFImElHz//gR6cjmB0MWLBEQDBxL4VlTQfWBJyez8bm40nwyMzp4VutdWVwvWj4MD3Y8TJ2jxMWAAgWlFhTD3AwcSKDo50bNy44ZQOSIoiIDg99+FGF9DA83lzJnCvizex3FkHc+fTyCxbZtgqTo4kKVlbEzVDHJy6HjPPUfgtWYNXatKRQm0paXkGvTyIndfbS1tP2ECHXvfPnpW7rqL/lZ/BQMDWUdbPfz22x+455572n9ztJDY2FjY29u3GPttLBzHobq6mnfVyWQyGBsb8666lqyjSZMm4aWXXsL8+fO7+hJ6pdx2AMRK3pibmyMoKEijr3t6ejqqq6s71co2Li4ONTU1qKmpQUhISItsuJakuLgYSUlJTUrxMCktLUVMTAz69++PM2fO4KWXXoK5OSkpFiNQKuklnTePVqI5ObQCHzSILBfGdBo/nhS1QkHWzvr1ggLx9AReeIEspmvXaBtW9kQkIrosC5gPH05uPF9fAsCvvxaC2/36kSXm40NjOX5cqPPFSui4upKlc+aM4NpjllxEBLmfkpJoRa3O0tLTI0VkY0OfwkKy4tRLqhgZEcjY2BAwlZWRgh0/nsaQlSVUJlAXVv6FxX/kctrG2ZmUOrN+xGJSyPX1ZEWqV0EQiQjoVSpyXzICAssFSkgg4HV01LSAKiroXAYGAjmh8djMzIjNxaopVFeTRXLzJgGJXK4Zm2IWob4+PRfFxQSuubkE5Cy51ciIrA9ra8Edx2rfAUIyqaEhAXtcHD0HjO6tp0dWSX09WWTMTTp+PAGRvj7VBdy4UagoYWVF42Glo5RKsoAtLOi+M+ZhSAiN/Z13aCz29sTUO3KEni1DQyIkrF8vWIumpvRM3XWXUOJp1CgCcjMzU/zzz1YYGhrqvEZbdHQ0+vfvDycnJ632b691NGLECHzyySeYO3eu1mP95JNPsH//fsTGxsLAwAAy9ZVeC8JxHN577z2sXbsWMpkMo0ePxk8//aQRzigtLcWLL76okdz67bffak34Am4zAMrPz8e1a9fg4eEBLy+vJlZJSwy09kpDQwPOnTsHjuMwYsQIrQKKZWVliIuL08guZpKdnY2UlBQMGjSIN+W/+uorvP/+2zA05BAQQMwuU1NS+qdOkSIpLhaA6cknKfh8/TqthpcsEYABoNXjmDECvZq5h5YupVUkc135+5NCmTOHzsncNBMn0mp7+nSKOezcSUqYlWRRKEiZzZlDyosVgTQzI5CytiYXjZWVkHDJfnd2plV4ZCStlu3tiUnFbruBAa2ey8sJ+AIC6BrlciIfsJiZOlmBVT8wM6PrbWggK83QkI7H/mXMLDc3IfZSW6tZlUAbYZahublARrC0pPiYtTWRClg+UH29UDRUIhFICupvIHOV3nMPja28nD4ymRAzYaBkZ0fuOAcHekbOnCHgY/ccEKpQcBwp+oICwY2or0/PCit5dPkyLXbYb9On0zj69SNX2rff0vj19enePPkkjfHbb4VagRMm0D7795MVLhIRsWXuXAKWhATaLjSUnoV9++jesaKxcjkthIYMITAzMqJFzK5ddA2MwBESEoJjx46hrKwM2dnZOq/RduXKFQwYMKBTtSqZMOuIgVF5eTlOnz6N/Px8HDt2DBs2bMD06dO1Pv57770HKysr5OTk4Pfff28XAK1atQqfffYZNmzYAA8PD6xYsQIJCQlISkri497Tp09Hfn4+fvnlF768z9ChQ/nyPtrIbQFAzcV7mpPs7GwUFBRg6NChHT5/RUUFYmJiIBKJYGtrq7Ubr7y8HFFRUZg4cSL/nUqlQkpKCvLz8xEaGgobGxuNfTIyMjBu3BgUFZXA1JQUo54erTT37KFV6MyZ9G9kpJCN7uxML76dHVGjP/6YnY9KmowcSZYOUzpz59LqeM8eUpANDbR6nz+frKBdu8hFxyoI3HUX7RMeTivZ9esJrABSBI6OFG9wdyfFdOAArf5ZnpCLC1lH/fqRcjl/nlhtLNudUY8dHAQLZ9s2QRGpVxI3NibFyujarq60P1sjZGTQGFsKI6pXPWDxH2bFFRQIiaGApgXEinMOGCBYP4yMUFBA/zJgbkxGaByjYmWF5HKyXKZOpXtQXS18WGkd9eswNSXr18eH5j87mxYQxcVkETKrkpUhKiujOE91NVk5JSVCDtiAATRXbA6vXaN7xorSOjgQEB08SB/GhpNIhDptn39OSdFHjwrPAscBK1dS6Z0bNwRGYmUlPQdpaXSP77uPKmMDtDi47z7an1nvTzxBiwXGtvTwoL/9/OgYcjkwf/58/PTTTwC6r0bbpUuX4O3t3WGPSHtEoVDgyJEj+Oeff7B3714YGBhg0qRJmDFjBiIiIuDm5qbVcdevX4+XX365TQDiOA79+/fHsmXLsHz5cgCkxxwdHbF+/Xo89NBDfIHTK1eu8GB/6NAhzJgxo10FTluSXgNAHMehoZnlaENDA+Li4lBXV4ewsLBWa7Tl5eUhKysLI0aM6NC5GZPO09MTSqUSDQ0NWpfcqKqqwoULF3D33XdrjL++vh5hYWEtWlUKhQLTp0/H2bNnoa9PyiYpiVbzV67Qy2luTmylv/8miyUggF7g9evpJdXXJ8Vjakr12OrrhVI+I0eSJVNbS4pmzBhKVh0wgBSMXE4v+2OPkYKxs6NtmZtu4EByoWzZQkBXVye4TljeC1PM995LY710SSi9wtxdDQ2kTAMCaHWbmEjXyZQoK7rKYkUWFgL5YPt2QamrK2iWwKpUClYIIx4YGtJ5L1+mlbWbmyYA1dXR6t7ams6pXlpHqSSgaGigfRndm1lVaWlklUybJlS3Zv/eukXWhJ+fUKy0urplMgKrCOHpSWSU3FyKzVRVEdCUl9P9YPuIRESaYMVbi4vpPkqlQizFwICsDx8f+j4ri8bM4kOmpgSCU6dSLOjGDYrtnT1L5zEwoPv+0EN0/YsWCXE7CwtazMyaRS5bFpPs35/caBIJJamySgnPPEPn//NP2t/Xl+btxx8F0GYVra2syJLauZOOM2OGYN09+eQzWL16NT9vubm5KC4u1nmNtvPnz8PPzw/W1tY6OwfHcXBycsJff/2FtLQ0HDx4ENOmTcPrr7+u1fHaC0A3b96El5cXYmJiNFiE48aNQ0hICL799lv88ccfnWrx0JL0agBiVklz8Z7mRCqVIi0trVUCQONzpqWl4datW7xllZ6ejqqqKq0f6JqaGpw9exZTp07l+w+ZmZm1a/wAcN999+HQoX2wtCQFw/rsTJtG7qrMTArapqeTAq+rI5/45MlU/l6ppBd56VJSYhs2kEJi8R+FggLOzHU2YQKB2s2bBEjse3t7UiTDh5NLbdcu+k2pJMU1fjzFCoYOpRX32rUC9RYgBWhpSaDm7EzHT0wUCq4CmlaQiQmtxgcPps/163SN6vXcmMXCesEw+rWpKSkoc3NS2lIpKWRGPmhNWNyjceyHeXcZELFVPosDsX8bU6cbi5GRYAXa2tL8MzJCdTVZMtXVpKgZW07daurXj+bD0ZGAo7yc3JFSqaarzdCQQD0wUCiHxGJgrHqEjQ2548zM6F6vWUOgsH8/LQLYcZhbzcaG7um+ffQMMZfnd9+RFfbHH0LOFUDEg9OniUXJFh2GhuTK276dxjJkCFnCbMESHk5u31WraM6nT6c5OH6c/s8Yb05O9ti1ay+8vb0BACKRCHp6esjNzUV5eTlf+kpXEhkZiaCgoDZrRHZGlEolrK2tkZGRoUH31lbaC0Dnz5/H6NGjkZeXp5H/+MADD0AkEmHLli349NNPsWHDBqSmpmrs6+DggA8++ADPPfecVmNsWyP2kLB4j6enJzw9PdvFQhOLxe2upqBQKBAXF4fq6mqMGDGCD6SJxeJOFUXV09MDx3GQSqVISEiAm5sbfHx82s2iW7t2LXbs2IEPP3wfW7cWY+5cwTUWEEAgsn49bTt5Mrk3/vyTXlL2gstkwHvvkfK7+25yuVVXA2+/TQrznnvI7fbJJ6QIT54UKLAff0yAJJORUlAoSHHOmyf0EnJ3J1fQwYOkRKyt6TeAVtBeXmRxXLhALsOoKMECkkgoLmJnR8c1MqJYVHw87Z+YSEBrZESrbFdX+vfqVfp30iQCllu3SBGzGJOeHlkChw9rkg/UWzT4+wuWj3oO0OXLNP5+/ZonIchkZEmokxA4jlbrKSl0XENDTStILiflrFCQZZKZ2ZSMABAwe3vTnOjpCa642FiBgs0YcEwcHekeZGVRZQmOI0ssNZVIHOzxVSqpSvro0fTsFBYSS/DAAfr9xRdpjlxdyYU7YwZZ11u2UM02hYLAavRoel5eeYXA7KmnaO48PYGPPiJXXGQkJZoaGdFiafZs4IMPaBG0Zw89W6GhBHqs2sbTTxOZ4fPPaTy+vgQ8HEcWemQkPdf3338//vjjDwDkzmYfpVKJ+vp6cBwHhUIBPT09rasytyVKpVJnx2ZS/f9MEVbYWF3eeOMNrFq1qtX9k5OTO5WC0hPSawCIKWiVSoXr168jJycHwcHBHQr6tReAqqurER0dDSMjI4wcOVKjUnZHQKw5YQ9pfHw8Bg8e3GHfqJ6eHjw9PZGZmYVFixZh8+ZNkEjI0khIICth9mx6qQ8don2efpoUw6ZN5PYaN46AautWWr0CtNJ84AFa0e7dKyjuBQtolblpE7k8/v6bFKGfHymQzEzy92/aJOxjbEx9X1ju0e7dREgQiWgspqZkiYwZQ//v35/GP3IkKbSYGNqerlcAAmdnAi/Wv6a4mBR8cjIppdJSihExV5i5Oe1TXEwgMWQI7afueispoViNoSEpw5aIBykp9GlORCKB5NGcMKaXqalQJog1kszLI9BkRUkZIaGmhqy8ujpyfSUkaFo+xsaCu/Gee+j62KewkABHpSKLQyIhAH3gAbonwcF0j1evpt9WrhRKB+nrC3GzL78ky/jYMdr+xx+F4p6zZlGOT24uPUdPPkn7SCT0bI0eTc/ghx8KVtjy5WTN/vgjPXdiMZ3rtdfIovr3XwIe1lH144+FliFff00AGhBAz+rGjXQf163bpMEIY++XSqWCVCpFbm4u/P39eRCi+0XWEfu3K0SlUnVpyZ3mhAFQc6yyZcuWYeHCha3u7+npqdV5GbNPKpVqWEBSqZR3yTk5OaGQ9T/5f1EoFCgtLdWaGQj0IhccQPGT2NhY1NfXIzQ0tF09edSlsrISly5dwuTJk1vcpqioCHFxcXBxcWm2mGheXh6ys7NbrGTQmqhUKly7dg15eXkYMmSIVvWoGl/D2bNnERExFfX1KsybR8qTWR4PPUTKiNX2YlWMy8rI9eTvT6va1avpZW5ooJXzs8+SUv7mG0HJhYcTGBka0sqYUWcNDcmKmjuXlObSpaRkWRsJMzNyx7m7U7O7J5+k2Ed8PCl/tuoXi0k5zptH8QY3NwKU+Hiy1lhdMHVvAVOWLIcnPFxwubFGfZmZBBwtrRmYe0giEYgMBgaCFcQSPj096Xf1HCCRiI7PXGXq1g+LQ928KVCR6+sFK4jFNdi26mJkRIpYJCKAmjaNtquqEoqUlpfTfWTHYnPo7k5K2s+PFgUcRwuHxEQC9suXCdwZq4xZOHfdRc+CgQFZzFu3CuMxNhZaNOTkENV/4ECWZ0Ogwqwja2uhuyyrblFQQPeRsfrc3Gjh8dtvZKnq6dHxFi4koIuMpHM+/TTdu6NHaaxBQXQsqRS4++6p+Ouvv1qMmebn5yMpKQlBQUGwt7fnLSKO46BSqTSaWzLLSFswUqlUOHXqFMaMGaN1gdP2SFpaGkaMGIHa2touAc6OkhCWL1/OV2CoqKiAg4NDExKCtl1WW5JeYwEplUpcuHAB5ubmCA0NbVe8pLG0Zr2oFxNtzTLR1gKqr69HTEwM777rKHgy0dPT03ABjhkzBiUlFXjppZewceMfkEg4PP88KfstW2il/fbb5Hq6dIncQs8+S0ru998JfAB6+V1cyG//1lvC+V55hcDk33/J7QKQYhw6lNwymzeTC2j/fiEAPmsWkRVu3CD34LlzgqW1bRtRyBcvpvhRQwORFX7+mVb5MTHCipnVk2MU73//peupqqJg+Y0b9PnnH1JurPQKE1ZoVKmk2JKDgyb92tBQALlRo0jpK5V0/MpK+jDXlkwm5MMwIgLHEaCIRDQe9a6qzMXHjOeAAKE1OWu9UFdHFoqtLSllZgHV1BDAsGKl+/cL18RiRsHBdIxjx+he33UX/T89XbAKS0sJrBjpk5X8GTeOjn3qFDHOIiPp799/p+3Yq3XffbSIqa4ma/qnnwj0WJHUl16iY0VGEmDV1RFAhITQoqGujuq4McvxrrvIajpyhH4H6B58/DG1CH/9dfo/Pdd0XYaGtCjZuZOeDXt7Gxw8+A/GjBnT4juSl5eHlJQUBAcH84s8dYBhAKRUKjVcdmw7kUjUIetIfV9dSlVVFUxNTTvUPqY5ycrKQmlpKbKysqBUKhEbGwsA8Pb25q2rQYMG4bPPPsO8efMgEonw8ssv4+OPP4aPjw9Pw+7fvz9vffr5+WHatGlYtGgR32V1yZIleOihh7QGH6CXWUBSqRSWlpZa34D6+nqcPHkSU6ZM0XhYlEolrl27htLSUoSFhWm07G4sRUVFSE1NbfUFaCwVFRWIjo6GtbU1AgICcPz48XZ3VW0stbW1OH36NKZOnQoAGi9PTk4OHnzwQSQkUMBkxgxapd66Ra4UqZSUMssteeQRUnyffCIE2+fNoxX3tm208mQtFx55hNwq+/cLK2uA3Frz5pEFsGsXkRpY8JsVNp0yhSyAZ5+lgHlRESkyZv14eVG8YtYssqBqawm0Llwgt0turiYjzMiILBI/P4oLfP45ZdM/+6wQ68nJoUD4/v2k5ENDSZGWlJByb4mOzbL9meuP5RWZmTWN/+jp0bFYoc7GFhCzglg9v9bWLYaGBA4ODuQ2dHSkz6efkjL+5huBtnzrFn1OnyaLwcmJLF22LtHXFyxAAwPgs88IFAoKSIlfvUpgzRJZTUwEK2j6dPrt889pbq9fp2OamxOI6ukR6/KJJ4g4UFJC5/D2JtALDKRrYCQBZ2e67vJyISGZleI5e5bco6zMz2OP0XOxaRPN/bRpZNGxnnH33fcQ1q5d26qiz8nJwfXr1xESEtIknaElYdYRAyam8trrqquvr8e5c+cwfvx4nYLQuXPn8PTTTyM7O7tTILRw4cJm+wmpdz8ViURYt24d79Zjiai//vorZDIZxowZgx9//BG+vr78/m11WdVGehUANTQ0oDPDUSgUOHbsGCZNmsTHdWpraxETEwM9PT2EhobCkC3BWpDS0lIkJCRg3Lhx7Tono3B7eXnBw8MDIpGo3V1VmxMGonfffTfvTmj8guzYsQOLFi1Efb0Ko0YRk+3330kB2dvTSvPUKVqJsnjHM88QQB05IlCxMzNpBcxiEA0NpESKi0nhjBhBAeuCAlIyzs4EFv/7H7lUdu4klw+zHACyembMENoqHDlCYMPcZOz29u9PoBEaSlZWaSldw9Wr5EJKTxd64DQu9GlpSWPx8CDFFxlJxVYDAgRyg4EBnevll2l1v327Zr4N+2zaROd+4IGmFGyVihR6fj6Bp3pjO/b3/v0Ehh99JMSu1D8PPEALgu3bCUSkUs1/N24khc1xmjXfmHVXX0/gHxZGcxocTHORkkLV0CsqaByM7WdgQG4yfX0a98GDdP+YFcQARyymRcv06QQEmZm07YkTQsfbwYOJfODgQHG+bduE+Y+IoP2OHSMLtaGB7unChUIpnoICOs8rrxCw/v67UMz12WfJpVddDYwZMw7r169vM96blZWFtLQ0hIaGak2HbmwdtcdVV1NTg0uXLmHChAlanbO9cuTIEbz99ttIaSkYeQdKrwKgzrbl5jgOhw8fxvjx42FkZISysjLExMTAwcEB/v7+7Vq9NJdI2tK50tPTkZGR0YQs0Z6uqi2JXC7H8ePH+dUWW6E1FpVKhVWrVuHrrz9HdXUDpk8XSuFQUy6K5aSkCOVR3NzINaZQkCuOZb1PnEhlVAoKKD7AmJbOzhQAHz+e3FjbtwtAIhaTdTR1KgHVpUtEWjAzozEwq8Dbm6i/f/5JK+DJk2llHxUl1CxjIMma5fn5EcD5+NAqPSKC4lNBQXT+zEwCQmbtNO42qi6skObAgTQ2RhIwMyMr4uBBcr+9/HLTKtliMSne2Fhyc7KmZ+r/HjpEcZzJk2l1X15O11RZKVTMbiyMTm5gQNtbWZGry9eXLAxvbxrH9u3kQr3vPrrm1FSBls5ynziO7t9ddxHJo7aWQHPbNrJYGHCbmND9Z+uqX36hRcn+/TSXrKKDkRHN6w8/kEv0yBGBDVdVRSA4eTK53vLyhOrhJiYEZps30zPl7U3HFYtpHkQiOrerq8DiHDRoINav39AuCnVmZiYyMjK0fq9akvZYRzU1NYiOjm6xvFZXyc6dO/Htt98iKipKp+fpTXJHARBAq4hRo0ahrKwMKSkpGDhwIFxdXdtt0jZOJG1OFAoFEhISUFFRgbCwsCa0ydOnTyMgIECrrGmlUomjR4/CxcUFjo6OsLKyahU4FQoFXn/9daxfvxZ1dUqeUvvbb6TgzcyESgosj4i54rZvJ6shJ4eUp4EBrfRPniQF6eBAK+b6elLWM2dSLODxxwlkWD8atmJWKGjFvGABKatDh8jNduuWkN2vp0fKacgQWs0HBVFMKjkZuP9+ApjsbBozK1zJkkxtbCiO5eFBiszFhSylX3+la3FxIWsmO5s+eXkEMPn5dJ7aWoGFxkCLFeVkPX7Y28D+ZY+jer6Q+r/sWKwNt6mpUJbH0pLmgHUCdXEh66yujqyfggICAQ8PmpP0dKFJIRPWgsLRkbYJCSHAZ6206+ooBsTaGQAC1Vwup7m96y467rlzdG/T0wWCgq8vlVUaMYIWBb/9RtsqFASM48eTFZedTZY1O763N1k7RkYU7ykvJzfjpEn0nOzaRS5eltszejSx43JzASMjfWzcuAkzZ85s1zuRkZGBW7duISwsTKd5OOrxInXrqLKyEsnJyRg1apROXXB//fUXNm/ejNOnT+vsHL1N7jgAOnbsGGxtbVFaWqpVMVGWSDplypRmQau2thbR0dGQSCQIDQ1tlhUTGRmJgQMHwp5xcdshbPWlVCpRUlKCwsJCFP1/VU47OzvY29vD1ta2RXJGTU0NZs6cidjYK2ho4DBpksBcio0lhTprFlkT+/YJ7jl7e/L3+/vT6vXUKaHe15w5tE9dHf125YrAfhs8mFww48YRCG3dSu4YlpgpEpFyYyvz558nJeToSC623FwCKWa5MJYbi/v4+JCbUCajMQ8eLBTYLCkRKkg3jvWwQp82NnRteXn0YRWZGYOO/b1ypUA1bs4C+vJLArFDh5q3gL78ksCTKeGKCuEjkxEANzRAox27uqhX9nZyIreYry8B5vXrRHP+5htaJCQlEdstO1sARravry8RLcaOpXN9+SWBjbm5AEympjSnpqYEWGvXEnnl8GEaO2MGchy5JpOSaF4SE4UCs4MHE916xw5yz7JkXAsLarH9ww8U72NMSWdnAXBdXV2wcuWqdhfa5DgON2/eRHZ2NsLDw5vNj9GVMABSKBRITEyEUqnkq6PoguYNAL/88guOHz+OgwcPdtkxe7v0KgDqbFvuhoYGnDx5EsbGxhgyZIhWxURbIjIAaLdL7/z58/Dy8oKjo2O7zsliPQx8GUuH4ziUl5ejqKgIhYWFqKurg42NDezt7WFvb99sPKuurg5vv/02/vprHaqq6hEcTEQFLy9SYjU15Ip59lmKIZibExAoFARCjz1GORkyGSmyykpSMKNGkdX02mukHMvL6XgNDaS0wsMpHvTmmxTXOXhQqFvGmtaJxUKeR3AwKbPKSiqomp1NyrG4mBSXuutKIqEV//DhQn05Fxf6HDsGvPsuxaXEYgKbwkIhNyg7W+hHox7bUY9HtSXMjdeaqCe4Mqq3kZFQR23qVLqGAQPoXjg5kcU0dCiBxt13k6WYkSFYQoyBx3Gaibms38+HH9J3rJnc5cu0HyBYacOH07FHj6a5iIwk4CguFkoXBQeTNcUSS2NjhcZvrGV3SAhZvmw8ZmZ0TbNmAcuWEcixNhJPP0339YsvaFtPTw98+eVXmDJlSvsmHEKVkry8PISHh3cq0K2tqFQqJCQk8G1fWJJ5V9O8mXz11Ve4du0atrFg239A7hgAYkw0hUKBgICATpVNb0xkAKjmVFJSUrv6A128eBFubm7toicyq4fdhtYe4urqahQVFaGoqAjl5eUwNzeHg4MD7O3tm9A3VSoVvvrqK3z33VeQycphY0NWiL4+xX+YKy44mJhp166RlZObSwra3JzK5nt4UBzk6FFSWqwz6pw5FHtQqej3M2dI8atbP2PHkmvHw4OsJScnUsZSKQEhe/KYUffSS4L1o69PMQ+2Ujc3p3MzKnNjQ5klf9rbk5VlZ0ef48dJOf/9N23DLCC2NnnsMVL4u3c3ZbYpFLSij4wkFx9r762vL8RxXnuNLMPDhzWtH/b57jsCkpEjhVptpaVNY0P6+gQy5uY0T+7uBMQHD1LMrKyMLK2UFCKMpKZqtlhgZY9GjCC32Y4dFMOZNo3uDSvhZWZGwFdaStecmkrnOHOGFhMsrvTmm7TQOHyYjiWTESDZ29NzUVJCzxHrlmpkRJUzDh8m5h49Jw54+umnMWLECH7hZGdn12ZXYVZ8WCqVIjw8XOuUhs6ISqVCfHw86urqEB4erqEL1IkMDJCYaEPzZvLhhx+iuLgY69at67Lr6O1yRwCQetkeqVQKT09PrQFIpVLhyJEjPJGB4zikpqYiNze33S69K1euoF+/fnBxcWl1O3U2Tktkg5akoaGBB6OSkhIYGhryllHjuNHnn3+OTZv+QkZGOjiOXFoREeSWYgVCTU0p2D1lCpEXWO6aSkUr43vuoVX0ffeRsqutFVxKgwfTavirrygRleM0rR+AlJSrKyW0BgVRDCErixTfP//QitnQUJMJZmRESjUtjVby999PK3JnZxpXTg4Bw9atZEmoVEISbl2d0AROPbbTXmExHla5uyMWEyC48Bhl296eYioMIF1dySL634trHl8AAGQ6SURBVP/oul54gZQ569+Tnk4uMPXeSGIx3ScHB5q/06cJqNasIaslOprIIDdvCv2YLCzI6pw6leYwPZ0IBFFRQkzOxkawcDdsoFiRgQHdOyMjsn6mTyc3o6OjQPd3dxcsZlbg1MREHzNnzsWqVavg6OjItx4oLi7mF06mpqY8GDVOu+A4DikpKSguLkZ4eLhWXozOSmvg09L2zSXBqgNRe8DojTfegEgkwvfff98l13E7SK8CIG3act+4cQNZWVl8MdHLly/D2dm53Z0Lm5PDhw/jrrvugr6+PuLi4lBbW9tmJW51iYqKgp2dHQYMGNDq2LUFn8bC4kYMkIDm40a5ubl46623cPDgHlRXN0Bfn4LG99xDxSkvXxYAwMpKoEX/+y8pNbmcVurBweTCs7Gh1fPx4wQGLPOfxX5GjCAlFRVFLDL2HjPgEotJkcpktKLeupXAJT+f9omLI/BJTRWC6uqWj1hMK/ryclr1e3sLNGxmAf36K63IL1yg/Wtr6XxlZfT54QdSqE89JdC92b8qFdUiy80lujpLbjUyEv79+2+69o0bac5YkdSGBlLKjz5KY33hBYF6nZdH11hYqFn5ARCSaC0tCRyysogJFxpK15uaSpZQUpLQHFB9LtzciOCRkUF1/t5/n0A+MpLGIxIJDfref5+Oe+kSseFYrBAgav/DD9N227cT2BHAUM7YmDE0r7t3sxbtJnjnnRVYsmRJq8pWLpejuLgYxcXFKPn/4JSdnR3s7OxgY2ODGzduoKysDOHh4e1qYd3V0lHwaW7/1mjerVlHL774IhwdHdus+XYnyW0LQHK5HPHx8aiurkZYWBjvI46KioK9vb3WPTQAIjIEBQUhNTUVxsbGCA4O7tCDyNp6e3h4NPmNkQ1YzKez4NPc8duKG6lUKnz77bf46KMPoVA0QKkkC2LBAlK877wjlJQxNydraeZMAolXXxWsJoAU3rRp5I575BFaLdfUaFo/5uYEbGFhtH+/fhTYPnOGQOb6dcH9BZCS7NeP6psNHEiunjlzKPjd0EAWQnIy5S9FR9O/lpZCFQL1pm3NCXN3mZoSAMjlZCWokw9YPOfGDXI9Dh1K52btFBgJobCQ/m6rKjZz3RkaElBYWwvN+0aOpAB+v34EpiwR9cQJAkALC6H9OUAuNFYrz8GBSAr29uRGZVZQaqpQVsjKiqzOadMIcL7/nhYOzMphnW8nTxbqDQYH04IEoIXGkCHkXhs5ko5dWgqYmhpg3LjJePfdd7WqRq3+rBYXF6Oqqgp6enoYMGAAnJycuqQqQEeks+DT0jHbmwT75JNPIigoCCtWrOj0eW8XuS0BiBUTbQ4cWlP+7ZXjx49DpVLB1dUVAwcO7PBLEB8fD1NTU3h5eWl83xLZQJfSXNxILBajoqKCb13+2WefYefOHSgsLIJIRMpZLCaG2JYtpNRYp009PbKaXniBAOTAAXLrsB40jo6k6EaOJPdZVBQx7k6don1Z7MPIiEgP4eGkMBMSaEWflERWV0oKrf7Ly5uy3fT1KU7CevucOUNuw/BwwfoxMqIV/yef0PnfeYeUrUxG7j5Wcy0qisY+cKDgLmNEBY4TGsS5u2s2sWOfpCQ6zn33EaAw9p29PY3jyScJcH77jY6lnoiak0OWB7NI1N9EQ0Oar9paAndGv2bJwMnJdG0cJ1ieenoEqs7OtAgoKiI69OXLBHTnz2vSzseOpd5Njo4Edvv20ZwD9N3MmeS6i46m5yAzkxFJgrFs2TLce++9XfKMshqKFRUVcHZ2hkwmQ2lpKQwNDXnryNraWqfFQHUBPs2dozXr6OGHH8bUqVPxyiuvdPm5e6v0KgBqT1tuVkzU1dUVvr6+TRR4QkICjIyMNHqZt1c4jkNWVhaSk5Ph4eGBgQMHdvgYAHDt2jUYGBholLHoCNlAV1JbW4v4+HhU/X/afOO40c2bN/Hxxx9jz55dqK+Xa7Df+vUjV9zOnYKitrEhhtXdd5Piv/deUsKs+RogtBePjCS3zvLlpPTPniXlzaodMDExIVeavz+58nx9yT02Zw5ZYklJ5JbLzqY8mpISOh/LsG9J+vfXTES1sKC/T52i1TyLhzWmYR84QNbaK680T8M+dowU/YgRBG7l5UISqno+DxPmAmOfkhIa2913UyzN35+smpIScov+8w8BemP6tb4+/W1oSPXXJkwgQE5JIcDYtYuOoVQSaNrYCLGcixfp96FD6W9WVsnLi66hupqYdVu3kqtQXx9wd/fGqFGj8cUXX3QpKYAp/traWoSHh/NpDUqlEqWlpby7rqGhAba2tjwgtUVk0GYMugSf5s4JgAej6upqBAQE4OGHH+a7vf4X5LYBII7jkJGRgfT09FaLiSYlJUFPT6/DfTFUKhWSk5MhlUohFovh7+/foTwedUlOToZIJOLH0JXxHm2FdWblOA7BwcGQSCQoLS1FYWEhiouLwXGcRtwoMTER3333HU6cOIbi4mKoVAQGmZmUXzRsGMUArl0TsvNZ24iFC4m4EBND1kdCAilppjzNzUkZstYBmzfTdq+9Rtump5OVUFMjMLMUCqFt9oAB9HF1JYX/449U7HTYMLJ6iopoJZ+bS4r25k0KwLNCoCwZVaEQxs5K9zRORmWWkETSNAmV9e9RKEj5m5gQqLGurFZWZDlYWVGsyd6eFH1REQFvZiYF9lkLilu3CAyZsPPY2NDx/f3pOvz96ZyzZtGxbWzIjckYiCYmQiHV334jS+ziRbKCLl2ia5dIaNEwdiwtMOrqKNdpzx7628BABF9ffzz66GN4+umnu1ThM1EqlYiPj0dDQwPCwsJaVPwcx6GqqooHo/LycpiZmfHPq4WFhdbvVE+AT2Opra3Ffffdh7KyMuzYsaOJ5+ROltsCgFgx0bKyMoSGhrZaTDQ1NRUKhQKDBw9u93kbGhoQExMDhUKBsLAwxMbGwsPDQ2smXWpqKpRKJd+npKfBp7q6mu8sGxAQ0MSVoe6LLyoqQk1NDWxsbODg4AA7OzuUl5fju+++w549u3HrVgaUSopDjB9PcYOBA8kNtno1gQSr8mxiQgpzzBhiyIWFUeLqhQu0Ui8t1UzOdHGhuM+gQQId28yMzuHvTy697GxS3hUVAiWb5aYw0dOjmJCtLW1XWEgkAktLgYbNklF//JEU//r1mpYPs4S+/55cg9u3C5aPuhW0Zg0B3CuvNE1CLSsjgG7cfpsJS0IViwlgXFzI+gsIoOs/epT66Hz0EbncGP2azRkDKB8fiu1MnEh/p6dTImpqqgCiEgkB0ZAhNO9RUeS23L+fjg0A5ubGcHX1wMyZM/HOO+9oVZG+vcKqNCuVSoSGhnZI8Tc0NPCkm5KSEohEIh6MbGxs2n0sBj719fWtAqAupa6uDg899BAqKipw+PDhVnXbnSi9CoCaa8vNiomKxWKEhIS0WUw0LS0NNTU1fHyjLamsrER0dDQsLCwQGBgIiUTSaSZdWloaamtrMXjwYJ2RDdorpaWlfP8jb2/vdo2hubiRvb09HBwcoK+vjx07dmDLli2IibmCsrJyqFTkequqorjB88+TsjxyhJhV6nkvdnYERKGhZP3070/xmchIiuHk5ZFVo57rI5EQYAQFCZYPS0bNyaHzvfsujSE7W2CYlZSQu66qSiApqBcbbS+1mvVMak7YdLIGeOpJqCYmdH5jY3JPurmRZejsTGMtKCArZuJEqgGXmUnbZ2UJlhkDKRMTisv4+ND8DRlCcSc/P4q3Xb5Mbjo2zwYGQmmkadMIbM+fJ5djVBSjaYvg4uKBKVOmYPHixRouY12KQqFAbGwsOI7TuvUKE5VKhfLycp7mXVNTAysrKx6QTExMWqylGBcX16b1pUupr6/HggULUFBQgGPHjmldYPV2ll4NQKWlpYiNjYWjoyP8/PzaFTfJyMiATCZDaGhom9sWFhYiLi4OHh4e8PLy4h/UzjLp0tPTUVFRoVG6oyfAJy8vD8nJyRg4cGCbOUktSVv5RhUVFdiwYQP27NmDqKhLUCg4PnN/2DDKHRoxghhydnakfNPSBHKBujzwgFCKh5XhiYsjN5WlJSlhBk7MymKibvWwsja2thQPiY4mC8zWtmk5nmefJcV97JgmAYH9+9VXFKA/eFAzAZURMl54gaycTZuaJqGWl1MSLaObs5hVc7EhExMak50dAZWPD4Hpvn3EPmP062vXaLzq+UGmpnS9ISEUC/L3pxpwUVHk3mPFlQ0MRLC0tIG7uycCAgLw6KOPtpjIrCuRy+UaC8quJhbU1tbyYFRWVsY/r4zIwPpt9TT4yOVyPPbYY8jMzMSJEye0qht5J0ivBaCsrCykpqZi4MCBHQKCrKwsFBYWYsiQIa2eh8WTAgMDm7jaYmJiYG1tDXd3d62u4datW0hPT4ezszMcHBw65aPWRlgNLZYf1VUPNwsMtxQ3kkgkKC4uxvbt23H48GHExkajrKyYX5WbmRGDLiSErB9zcwKY77+n1T9LcFU3gm1syBrw9qYkVmb9ODrS98ePU0+dceMIGFiF7MpKzaoJLJajjbSnFI+6MAo3o6tTORoCF0dHstwGDKC4z+LFRLK4+24hCfXmTfpkZQk5SSIRWVJWVgTOQUEU9xo/nnKNoqKoIkN8vABwVPjVF6NHj8a8efOgr68PW1tb+Pn5oaGhgVfU6owze3t7XlF3tcjlckRHR0NfXx/BwcE6b3HN8uNY7EihUMDGxga1/+/DHDJkSI+Aj0KhwFNPPYWkpCScPHmyzTYUd7L0OgCqq6vjyQAdaTrFpK2W2urxpJaq67ZEo27P+Fkci/moi4uLIRaL+ZWmrl5uJiqVComJibwVqKsaWm3FjXJycpCdnY3AwEBcv34dn3/+OdLS0lBRUYaKChkPSqampFiLi6mUS1AQfXfzJtGxr12j+JK+PilhVtmACSMo+PkRSDH6M/vs2kWB9bNnCQBraoSPTEbdOjMzyWJQzyFiiahHjtBYliwhq44loRob0+fbb4ns8O+/NG4jIzo2s4CefZbOe++9FLtiSahFRXT+xtdiZETbOzoSGKenExvN0ZHGmZwsxINu3BD2NTGRwN7eGQEBARg3bhxmz54NV1dXAORSjYqKgoODQ7NpBWxhwZ5XhUIBW1tb3nLoijbUDQ0NiI6OhpGREYKCgrqdBcpxHCoqKnDt2jXU19dDqVTCwsKCZ9V11yJRqVTi2WefRVRUFE6dOqV1nPlOkV4FQHK5HBcuXOADk9pkQkulUqSnp2PUqFFNfqurq0NMTAxEIlGrzekSExOhr6/fIZ94S2QDlUqFsrIyvrq1UqmEnZ0dHBwcWq1urY0wpptKpWpXvKwrhcWNCgsLUV5eDpFIBBcXFzg7O8PMzKxJnbr4+HgcPHgQFy9eRFxcLGSyYt7tBZAyHjCAgOXYMWLXLV8uWD5ZWaSIr1whdhdbRLISPAxMWhNDQwFo+vdvSsEWi8ltVl1NY1GnX7Pj19S0HB9ioqcnABdLQrW3Jwto504iSDz/PB2HlePJzCTQzMzUvA5DQxFMTS3Qv78rxGIx7r//fjzyyCMtFr5lMU5nZ2cNN3NLwnEcKisreTCqrKyEhYUFD0aN72V7pL6+HtHR0TAxMUFgYGCPpCA0drtxHKdRkUFPT48Ho65+L5kolUosXboUkZGROHXqVKeqtdwp0qsAiNVdc3Nz09o8Ly4uRnJyMu666y6N78vLyxEdHQ07OzsMHjy41Zcg+f9pQX5+fu06p3oPkdbIBmwVxhR1bW1tm9Wt2yttMd26Q1h1ivr6ejg7O6OsrKzNOnXqolAocOXKFZw7dw7x8fG4ceMG8vNzUVpa0sSFZmBAlGwbG6J733svBebVrR9jY6rIvGWLUBRUJqMPaxp38iT9f+TIptWyVSoCgKoqchmyJnLqHVHPnyeAeu45OiezwFie0axZFJ9ZsUJIRJVKCdhu3SJLprEYGIhgZGQIfX1T6OnpYebMmRg2bBgmTpzIWzXtEZlMhpiYGLi7u2udmF1fX8+DUUlJCQwMDHgwsrGxaRNM6urqEB0dDXNz8zbfO11JWzEflUoFmUzGA1JNTQ2sra01iAxdMYZXX30VR48exalTp1ot0/Vfkl4FQEDn23KXlZUhLi6O730OkFsuMTER3t7ecHd3b3MFd/36dcjl8jap3Oo9fICOkw3UrYaKigpYWFhoBIXbK+ya+/fvDx8fnx4hPDDr0tDQEEFBQfwKUt29U1RUBJVKxYNRR1aarC9LQkICkpOT+T4xUqkUhYV50NfXg0KharYDKSC4zdQJCObmFNCXyaggaHM07MhIcoMtWNCUhl1fTxZYRQVRxCsqCKzUK32ri54eYGCgBwMDAxgamsHc3AIymQxjxoxBcHAwQkJCMGTIENjZ2Wl5FwRhBB4fH58OgVZr0jg5VC6XaySHNl5A1dXV4erVq7C2toa/v3+PPJfaEA5qamo0iAzGxsY8GLXVILKlMbzxxhvYs2cPTp48+Z/K82lL7jgAqqiowJUrVzBp0iSNYqXBwcHtTixNT09HdXV1q1TuxpUNOst0YytNxjYzMTHhwag1/3R+fj6SkpI6xXTrrFRWViImJoYPcLf0grYUN2KA1JlkR7lczheOtbGxQXZ2NrKyspCWlobz58/D1dUVVVVVqK6uRk1NDerq6lBbW4vychnq6mphbm4KjmP1uoS6XXK5HAqFCiYmRtDTE0MkEkMslkAkEkMikaCqqgoSiQRubm58mSOxWIz+/fvD2NgYFRUV8PHxwbBhwxAQEAA7OzudW6dFRUVISEjAoEGD2tUSRBthyaHMOiovL+dddfb29hCLxYiKiuKfidsFfBqLQqHQiI8plcoOxcdUKhVWrFiBrVu34uTJk91Gdb9dpNcBUGe7olZXVyMyMhKTJk3iy86oFyttj2RmZvJJr80JIxsolUqd5PcoFAq+KyojMbA8HEZi0BXTraNSUlKC+Ph4DBgwAB4eHlpZgI3zjezt7TsUa2AxhsbWV3cKx3FITk5GSUmJRuV0FgNk18lKyjAF1tVxuoKCAiQmJiIgIKDdDRG7Qurr63nLiCpnqGBqagofHx/Y2Nh0u0tYF1Tr5uJj7Jm1s7ODubl5k9YSH330EdatW4eTJ0/C39+/02O40+SOA6C6ujqcOnUKZmZmMDQ0RHBwcIdZPK1Rubu7soG6AissLORXYPX19XybiJ7oFgkIeUZ+fn6dXmkzWnBhYWGH4kasMC1z8/RUjCEhIYGvzN6SJcd64zAwYm5Xdp2dzcXJzc1FamoqgoKCusSNp41UVVXxbjcDAwOd13FrTrorz6e+vl6jIoNYLIaNjQ3i4+MxY8YM/PLLL/jpp59w4sQJraqF/xfkjgOgwsJCREdHw8XFRWuFlJubi9zcXAwbNkzj+/aSDXQlHMehtLQUiYmJkMvl4DiOpz53lsTQ0XFkZGTg1q1bOrG+2hs3Ki8vR0xMDJydndtd5aGrRaFQIC4uDgqFAqGhoR1a7DCrQT3JV9tcnFu3buHmzZsICQnpsYz6yspKREVFwcXFhWfcNQe6ZmZm/P1sbDV0VtpbX66rhS0Ur127hkWLFiE/Px96enp45ZVX8Oyzz8LT07NbxnG7yR0FQNnZ2UhOToZKpeI7mmojBQUFyMjIwMiRIwF0nmzQVVJdXY3Y2FiYmZkhICAAdXV1Gi4sbUkMHRGVSsV3rAwNDYW5ublOzsOkpbiRsbEx8vLy4OPj06neT50RVkNQIpHwBV61leZAV91V11qhzoyMDGRlZbVZJ1GXUlFRgejoaLi5ubWqbNUTYEtKSiCRSDTquHXGVcfARy6Xd7i+XFcJx3H44Ycf8NFHH+Hpp59GQkICzpw5g4EDB/JJuH0iSK8DIG3acqtUKqSmpiIvLw+hoaG4cuUKxowZo7USLioqQmpqKsaMGdPlZANtpS2mG1tNFxYWorS0FMbGxnzcqKuS7BQKBU+zDg0N1bkrpTmprq5Geno6pFIpAGi4sLTJUdFWGL3Y1NS0y3Nb1On6RUVFqK6uhpWVFX+djBbMSDb5+fkIDw/vMVcsS3Hw8PDoUPUQZjUwQKqvr4eNjQ0PSB15vpRKpYYl2lPgs3btWrz33ns4ePAgn4tYUVGBqKgoTJgwodvH1NvltgcguVyO2NhYvqKtiYkJjh8/jqFDhzZb5aA9UlJSgmvXrmHs2LE9Xska6DjTjZEYmALT09PjLaP25G40J/X19YiJiYG+vj6CgoJ67AXPzMxEZmYmgoODYWZmxiuv4uLidseNOiss7tRdDC/1+malpaUwMTGBnZ0dqqurUVlZiSFDhnRJroo2wnKNvLy8OmWJMlcdu86OtFzoLeCzYcMGvPHGG9i7dy/GjRvX7WO4HeW2BqCqqip+FaruAjl16hSCg4O19oXLZDJER0dj7NixPQo+6ky3wMBArQLLLZEYWMmc9riNqqqq+Pp4PRXoZ0nKUqkUoaGhTRYXzbmwdFFxoqfjTgqFAkVFRUhLS0NdXR0kEolWeVVdIbrINWLSuOUCq1TArpO56noL+GzatAmvvvoq9uzZg4kTJ3b7GG5X6XUA1N623KwzqpubWxOX1NmzZ+Hn56c1E6iiogIXLlyAr68vHBwcesTVpFKpkJSUhLKyMoSEhHRJrIXRSFlZoOrq6jbzcMrKyhAbGwtXV9d2lXLRhbCWzZWVlQgLC2uzRJOu8o1Ya4uOupq6UtQ7iIaGhqK2tpa/zsaVNXT53JaUlCAuLg4DBw7UeUkZVqmAWbp1dXWwtraGra0tCgsLwXEcwsLCeox+v23bNixZsgTbt2/HtGnTun0Mt7PcdgDEqk3fuHGjxc6o58+fh5eXV4fzIBjZQKFQICcnB1KpVKNCgYODQ7e4OrqrpltNTY1G/TYWT3FwcICpqSkKCgqQlJQEX1/fHktyZQmmrD6gNoUxuyLfqLCwENeuXdNpcmdb0lYTt8bXqSu2GUt09fPzQ79+/brkmB2R6upqFBYWIjMzEwqFAqamprxFb2lp2a2LpF27dmHRokXYvHkzZs2a1W3nvVPktgIgVumZMbCsrKya3e7SpUtwdXXtkKJgyaWMgcfIBqxCAQvus4fdwcFBJ0HvmpoaxMTE8Ey37krgY31/2HWKxWIoFAp4e3tjwIABPWL56CLBVJ2FVVxcDAMDAz4+1lLciOXXBAQE9FjpfBbrFIlECAkJaXMu1F1YxcXFXcY2KywsREJCQrcnuqqLutstMDCQt3aLi4shEol45qCuXZL79u3DE088gT///BP33HOPzs5zJ0uvA6CW2nKzILhKpWo12Q8Arl69CgcHh3YHRdWTS0UiUYsxDrlcrpEsyQozOjg4wMrKqtNKujfUdGMZ/VKpFFZWVpDJZNDT0+OvU1sSQ0elOxJM2xM3yszMREZGBoKDgzvcGqSrhLUyYEDcUfBorRqDvb19u61KVmUhMDCwx4BYHXwau91Yd1QGRqyoKLtObarrtySHDx/GggUL8Mcff+DBBx/ssuP+1+S2ACCWY2BlZYXAwMA2X8CYmBhYWVm1qwKwtpUN1JuzFRUV8SsvbZU0Y7r5+vp2eUC3vaJUKpGQkICamhq+HQbzv7PrlMvl/Eq6tfyUzkhPBPqbixsZGhqioaEBwcHBPVZZgNG9mUXcWSBWr+FWVFSk0W6htWoM+fn5SE5O7tEqC62BT3PCXMzFxcUoKyuDiYkJf52dcdWdOHECDz30EH766ScsWLCgx9ixd4L0egAqKChAQkICPD094enp2a6bHR8fDxMTE3h7e7e6XVeV1WmspFlDr/YwzdSrCmjLdOsKaWho0HDxNAcs6rWwCgsL20Vi6Kiw+IK3t3ePJZiy0jqM8lxZWQkzMzPeVddd+Ua1tbWIiorSaTXpxu0WmqOyMxdkcHBwj9Uc7Cj4NBbWJJLVqgOg0f+nvQupM2fO4P7778e3336LJ554og98Oim9DoBYW26O45Ceno6MjAwEBQV1yN+clJQEsViMgQMHtngO9ZhPV9KsW+r5w5SXurtDF0w3bYT1ErKwsMDgwYPb7eJhK8yioiLIZDKYm5trVGLo6Jyy2nKDBw/usU6RrGOuel23xtn7+vr6Gq5XXbgHWYqBo6MjfH19u61bZ2OXpLGxMaqqqhAUFNQr3W7aSHPWbnv6/1y4cAHz5s3DqlWr8Oyzz/aBTxdIrwSg2tpaJCQkoLy8HGFhYR1WzKmpqVAqlc1Wn22JbKArYYydwsJCVFZWwsrKiq9qnZqayucvdGf3UnWRyWSIjY3tdNypcTFRIyMjXkm35e5onGDaU7EWVtdNqVQiJCSk2dhIS3Ej5pLsiqA3czm7urq22+rvamF5Vzk5OTAyMkJdXV2z1Rh0LV0NPs1J4/4/LNGXdYA1MDDAlStXMGfOHHz44Yd48cUX+8Cni6TXAVBtbS0uXrwIsVisNe02LS0NtbW1TSrQNi6r090Jlax2W35+PsrLyyGRSODq6gonJ6dOV0LWRhi1uKsTCZVKpUY7CRYfa46B1VaCaXeJNnXdmLXLXK9dkW/EKgv0ZK4RAGRkZCAzMxNhYWGwtLTUyDfqynhKa9Id4NNYWBWR4uJixMTEYNmyZQgICEBcXBzeeOMNrFixog98ulB6HQAplUqkpqbCw8NDa4DIyMhAeXk5QkJC+O+6u41CS8IsDmYZMLeOkZERT+/uqtptrQlr1qZrarF6EmFhYaEGicHGxgapqantTjDVldTV1SEqKopvZ67tc9c4r6qjcSOW3NmTeVes+kZ2djbCw8Ob9T6ol3pi8RT1BUZXAAXLeVKpVAgNDe2RJFOlUol169bhtddeg7W1NYqLizFmzBhERERg6dKlfYVFu0B6HQABFBjtjGRlZaGoqAjh4eEAwCeXAj1XTBRomemmVCp5F0BRUVGzDei6SlgBS1a4tTurJzMGFnNJVlVVQSwWw8PDA/369eux4qa6qOvW0bgRs0Z7KrkTAB93zc3NbXdxU3Xqc1FREV+loDNWYG8AH4BiydOnT8fzzz+P999/H9nZ2di3bx8uX76MdevW9VlCXSC9EoA625ab9fMZOnSozsgGHRHGdMvMzGyTxspyNpiSZr1wWG5KZ8vVJyYmoqKigi/c2hPCEkxZcmRJSQlPYlCvxKDre8Xo3ur9a3QhbcWNioqKkJyc3KOJruqVtYcMGaJ1JfnOVmPoLeBz/fp1TJ8+HQsXLsSnn37aBzY6kjsSgAoKCnDz5k0MHz6828gGLQljupWWlna4f446W0cqlaK+vp6nd9vb23fIBcAy6TmOazHA3h3SUoJpcx1RmUtSFzEGVkjTy8sLAwYM6NJjtyaNWZLV1dUAAFdXV7i7u/eIFcjicMxr0FULE3ZP2Ue9cKq1tXWTxVRvAZ/09HRMnz4dDzzwAFavXt0jxXf/K3JHAlBhYSFSUlIwYsSIVisb6FpYHTOFQoGQkJBOKRdWrl7dfWVtbc2DUWvHrq2t1ehd013lfRpLexNMGYmBraRFIhFfoaCzTcsAQCqV8u6unqrrBgCZmZm4efMm+vfvj8rKSg2LQVelnhoLq3xRWlqK8PBwncXhGldjkMvlGg33xGJxrwCfzMxMTJ8+HbNmzcJ3333XBz46ll4JQJ3pisoUNWPSsVW0tbV1t1pArKabrpR+bW0tD0bNFRJlUl5ejtjYWDg6OmLgwIE95krQNsGUxRgY04yVkWFJvh0NBPeGum7qgf6wsDCe+ddS3EibFt3tHUdiYiLKy8sRHh7ebdZXc9UYxGIxJBIJgoKCuoWE05zk5uZiypQpmDJlCn766acun+8ffvgBX3zxBQoKChAcHIw1a9Zg2LBhzW47fvx4nD59usn3M2bMwP79+wEACxcuxIYNGzR+nzp1Kg4dOtSl49al3FEApM50A4hxJpVKUVRUBI7jeDDSdT0zxnTr169ftyQRqhcSLSkpgYmJCRwcHKCvr4+0tDRe6fcU+HRVgqk6iaGoqIi3AhnwtqVAe0NdN47jcP36dUilUoSFhbUY6Nd1vhFrcVFVVYXw8PAey0NTKpWIjo5GfX09TExMUFZW1m2NBdUlPz8f06ZNw5gxY/Dbb791+YJxy5YteOyxx/Dzzz9j+PDh+Oabb7Bt2zakpqY2uxAqLS1FQ0MD//+SkhIEBwfjt99+w8KFCwEQAEmlUqxbt47fztDQUOs+aD0hdwwAMaJBc2QDjuP4UjmFhYVQKBSws7ODo6NjpwP7jYUVbOypmm4KhQLFxcW4desWKioqoK+vj379+vGr6O4EIV0nmDbOTWmJ9qzO/FO3OLpbmLurpKSkQ7GWxnGjzuYbsVJDNTU1CA8P77F4YHMxH+Z+ZZagOvB2pGROR0QqlWLGjBkICwvDxo0bdeKiHj58OIYOHYrvv/8eAN0DV1dXvPjii3jjjTfa3P+bb77Bu+++i/z8fN7DsXDhQshkMuzatavLx9td0isBqCNdUVlZHbZ9W2QD9eTBwsJC1NXV8fGFzhTXVGe6BQYGwt7eXqvjdFYYlTY7OxtBQUFQqVS84gKgUTBVl7Gg7k4wlcvlPBipt+e2s7NDfn4+ysrKEBYWpjW7q7OibnG0Vc29LWlcAqkjcSOlUon4+Hi+hX1vAp/Gog68rIEis3jt7Oy6hCxRXFyMGTNmwN/fH3///bdOYk8NDQ0wMTHB9u3bMXfuXP77xx9/HDKZDLt3727zGIGBgRg5ciR+/fVX/ruFCxdi165dMDAwgLW1NSZOnIiPP/64x+r1aSO3NQB1trIBixdJpVKN4pqOjo4dKlOvUqn4lW1HmW5dKaxfkkwma6JsG1uBLCG0I625OzIO1sE0NDS02+nezH0llUpRUFAAjuPg6OiIfv366Rx4WxqPrpR+R+JGrLKAXC5HWFhYjyVSast26+pqDKWlpYiIiIC7uzu2bt2qMzDOy8uDs7Mzzp8/j5EjR/Lfv/baazh9+jQuXbrU6v6XL1/G8OHDcenSJY2Y0ebNm2FiYgIPDw+kp6fjrbfegpmZGS5cuNBjRKOOym0LQCy/R6lUdll+T01NDa+gKyoq+LptrcUXupLp1hlRH0dbteVaas3N3FediQd0RQfTrhBW102hUMDDw4NnYNXX1/MunY5S2Tszjpa6mHaltBY3sra2RkJCAjiO61GWWVdRrdWrWxcVFUFPT0/DVdeWAi4vL8esWbPg6OiIf//9V6cxsM4C0OLFi3HhwgXEx8e3ut3Nmzfh5eWFY8eOYdKkSV0ydl1LzzyFnRRdldUxMTGBu7s73N3dUVdXx4PR9evXm23LXVNTg9jYWBgbG7erS6WuhPWMae84RCIRLCwsYGFhAW9vbx548/LykJKSAktLS/5aO0LLVe9g2pPzoV7XLTw8HBKJBA4ODvD19eWp7FlZWUhKSuIXGV3dsAwgJRkTEwOxWIzw8HCdr0pZBQ17e3sN99XNmzdRXV0NiUQCDw8PKBSKHitt01VUa319fTg5OcHJyYkv91RcXIzr16+jvr6ej5HZ2dk1WRRWVlZi3rx5sLGxwY4dO3ROwGA0c6lUqvG9VCptk5RTXV2NzZs348MPP2zzPJ6enrCzs0NaWtptA0C90gJqqy13d1c2aMwyMzU1hYWFBQoLC9GvX78epTdXVlYiJiYGdnZ2GDRoUKcZQ6xgamFhIcrKytrdgrw7Opi2R1jOU3vqujVHYuiqHJyGhgZERUXB2Ni4R3OvGAgCFP9jVSe6O99IqVQiJiZG5xYYx3EaMTKWW1VTUwMjIyMEBgbivvvug0Qiwb59+7otJjh8+HAMGzYMa9asAUB6zM3NDUuWLGmVhLB+/Xo8++yzyM3NbTO2k5OTAzc3N+zatQuzZ8/u0vHrSm4bAOoo2UBXIpfL+SC/SCSCsbFxtxYRVZeSkhLEx8fzVltXn1u9BTkL7DdXnaAnOpg2J6yHjp2dXYfrurXWbt3S0rJDgMqKm7L+Sj0Fxi218u7ufCN18AkLC+tWMGbX+ttvv+Hbb7+FUqmEnZ0dvv/+e8ycObPb6OdbtmzB448/jl9++QXDhg3DN998g61btyIlJQWOjo547LHH4OzsjM8++0xjv7vuugvOzs7YvHmzxvdVVVX44IMPcO+998LJyQnp6el47bXXUFlZiYSEhB6j1XdUbgsAakw26CnwYbTijIwMBAYGwsbGhm87UFRUxLt6WMFJXY6R5db4+/t3S/HKllosGBkZISMjAz4+Pj3WwRTo2rpu6rEUdfZge+ILNTU1iIqK6vLiph0VZoGZmJggMDCwRVBRKpV87UFd0J4Z+ABAaGhoj1mCdXV1uO+++5Cbm4uxY8fi8OHDKCsrw9KlS/HJJ590yxi+//57PhE1JCQE3333HYYPHw6AEk/d3d2xfv16fvvU1FQMGjQIR44cwd13361xrNraWsydOxcxMTGQyWTo378/pkyZgo8++qhDzTt7WnolAKm35e4tbRTaYrqpVCqUlpbycSOmoLs68ZVl0WdlZfVYQiXzuWdkZKC0tBR6eno88Nra2nZ7fIG1MdBFXTdWj4/dV/V6fHZ2dhoki6qqKkRFRaFfv36dau7XWamvr0dUVBTMzMw61F6iJdpze8o9NSe9BXzq6+uxYMECSKVSHD16FNbW1uA4DnFxcaisrMRdd93VI+Pqk14OQL0FfDrKdOM4TqOitVKp7JKK1uog2FoWva5FPcE0MDAQ+vr6GnlVLSloXUh31nVTr8fHSsgwEoOxsTESExN7tIspILj/LC0tMXjw4E6No7P5Rr0BfORyOR577DHcunULx48fv61yZP4L0msBqL6+vsfbKACaTLfAwMAOr+7VE19ZRWuWf2Nvb9/u4ykUCj6XJDQ0tMfo3m0lmLIaX41bkLenVE5HJScnB9evX++xxF9G2MjLy0NFRQUMDQ3h7OzcbYH9xlJbW4uoqCjY2Nh0ufuvo/lGvQF8FAoFnnzySaSkpODEiRM9VvuvT1qWXglA27ZtQ21tLaZMmdKuHiK6ElbTzcnJqUuYbo0bslVXV2u0V2jJWqirq0NsbCz09fXb3S5aF9LRBFPGMissLOT7/aiXyumMsFhcSEhIj9a+Ki4uRnx8PLy8vGBgYMBXYtDX1+evtTvqmbHYE2ND6vKdaS1uxPKNgJ4FH6VSicWLFyMmJgYnT57sVA3CPtGd9EoA+v777/Hdd98hJycHkydPxty5czF9+vRu7d5ZUFCApKSkDldv7oiot1eorKxs1t9eVVWFmJgYflXb060ltE0wVaeyl5aWwtjYmHfndIQ9qN44rTtK/LQmUqkUiYmJ8Pf311BwjRNCOY7jrd6urj0I0HMUFRUFR0fHbil+qy7N9TeSSCTw9PTscB5ZV4lSqcSLL76Ic+fO4dSpU3B2du7yc3SksvX69evxxBNPaHxnaGiIuro6/v8cx+G9997D2rVrIZPJMHr0aPz000/w8fHp8rH3JumVAAQIBRO3b9+Of//9l0+umjNnDmbOnKmzwpqNmW7d5dpRT3yVyWSwsLCAmZkZCgoKMGDAgB6NK6gnmAYFBXXaAlMoFBqMOvW2Ga1ZCywGVlpa2qN13QDwSbttPSPqJAbWspr1welIuaeWhBEf+vfv36MUeOZ2UyqVcHR0RHFxsUbcqL0dUTsrKpUKr7zyCo4fP46TJ0/qpNlgRytbr1+/Hi+99BJSU1P570QikQZbbdWqVfjss8+wYcMGeHh4YMWKFUhISEBSUlKPudu7Q3otAKkLqyLMwCgpKQnjxo3DnDlzMGvWLNjZ2XXJg60e5A8JCemx1XVDQwNfvRkA77rqrlbV6qLrBFN19mBr1oJSqeQrOHe2mGdnJTs7Gzdu3EBwcHCHgtqMxKAeI2NVJ+zt7TtcM6+yshJRUVE9TnxgLRVEIpGG261xbpWu841UKhVef/117N27F6dOnYKnp2eXHp9JRytbr1+/Hi+//DJkMlmzx+M4Dv3798eyZcuwfPlyAJRW4OjoiPXr1+Ohhx7SyXX0BrktAEhdOI5DWloaD0axsbEYPXo05syZg9mzZ8PJyUmrF1EulyM+Ph5yubxHa7qpW2BBQUGwtLTUSAY1MjKCg4MDHB0ddb6i7O4E0+Yoz3Z2drC1teXBWNf11NoSdm9CQ0NhZWXVqWMxEkNRURFKS0v5qhPtsRbKy8sRHR0Nd3d3eHh4dGocnRGFQoGYmJgm4NNY2EKDXa9SqeTjn12Rb6RSqfDOO+9g27ZtOHXqlM5cV9pUtl6/fj2efvppODs7Q6VSISwsDJ9++ikGDx4MQKjhFhMTg5CQEH6/cePGISQkBN9++61OrqU3yG0HQOrClPWOHTuwc+dOXLp0CcOHD8ecOXMwZ84cuLi4tEtp1tbWIiYmRmumW1eJSqVCamoqCgsLm41vKJVKDTDSZeKrenBdF26MtoQRNvLz85GdnQ2VSsUXTHVwcOj2TG/W5iInJwfh4eFdXvGcWQvqJAYWI2vslpTJZIiJiYGnp2eP3BsmDHz09PQQEhLS7thWa20WtKnJx3EcPvzwQ2zYsAEnT56En5+fNpfTLtGmsOiFCxdw48YNBAUFoby8HKtXr8aZM2eQmJgIFxcXnD9/HqNHj0ZeXp5GUvkDDzwAkUiELVu26Ox6elpuawBSF47jkJubi3///Rc7duzAuXPnEBYWhrlz52LOnDktlqpRZ7r5+vr2WJCfleyvra1FaGhomy+hSqXSqMLAEl8dHR077d7oqg6mnRX1um5eXl48+LIW5I2Lw+pKGPW8sLAQ4eHhOo89NXZLqlQqXjmLxWLEx8fDx8enRxoeMtEWfJqTlvKN2mMJchyHlStX4ueff8aJEycQGBio9TjaI52tbA3QYsPPzw8PP/wwPvroo/80AN2W1bCbE5FIBBcXFyxduhQvvvgiCgoKsGvXLuzYsQPvvfceAgICeDBiWerbt2+HmZkZ/P39e7SMTH19PWJjYyEWizF06NB2uSP09PT4l5RVJigsLERiYqLWia/qCaYhISE9mrTH6rrZ29vztGJTU1MMGDAA9fX1fBwlLS0NpqamPPh2df4Nx3FISkpCWVkZhg4d2i2sLtZawM7OjndLFhUVITU1FfX19TAzM4Oenh4aGhp6pN1FV4IPQFXoBwwYgAEDBmjEjW7dutVq3IjjOHz99df44YcfcPz4cZ2DD9C5ytZM9PX1ERoairS0NADg95NKpRoAJJVKNVxyd6LcMRZQS8JxHEpKSrB7925s374dJ06cgI+PD6ysrHD16lVs3rwZU6ZM6bHxVVdXIyYmhs9c76wF1jiO0tDQ0K7Gc93dwbQ16Uhdt+ZcV13lluzKLqadFeYSZfGeoqIiVFRUdIrEoI10Nfi0Js3FjaysrBAVFYU5c+Zg8+bNWLlyJQ4fPtwiBVoXom1layZKpRKDBw/GjBkz8NVXX/EkhOXLl2PZsmUAgIqKCjg4OPSREO4k4TgORUVFuP/++3Hp0iVwHAcPDw/Mnj0b8+bNa7Vooy6Euf90FeRvnPhaU1Oj0XiOrZ57uoOpurC6btrkX7H8m8ZuSVZYsyP3lnUPbWho6NHW1QBQWFiIhISEJi7Ruro63lpgJAZm+eqCoNKd4NNYWNzo2rVrWLx4MW7dugWRSISlS5fi5Zdf7lYPRkcrW3/44YcYMWIEvL29IZPJ8MUXX2DXrl2IioqCv78/AKJhr1y5UoOGHR8ff8fTsO8YF1x7pLq6GvPnz0d5eTnS0tJgYWGBffv2YceOHZg8eTKcnJx4MAoLC9MpGLEkRl368kUiEczNzfkYCkt8zcnJQXJyMqytrWFra4vCwkJwHIehQ4f2qKLtbF039YZs6m7JlJSUDrUgVygUiI2NBcdxCA8P71HWHZuTgICAJlWOjYyM4OLiAhcXFygUCh6Mrl692uWU554EH4CeZUtLS4waNQrLli3Da6+9hqeeegqxsbHw8vJCQEAADh061C2VoB988EEUFRXh3Xff5Stbq587KytLY77LysqwaNEiFBQUwNraGuHh4Th//jwPPgDFkKqrq/HMM89AJpNhzJgxOHTo0B0NPsB/zAJSKBT47LPP8PLLLzdhMVVVVeHgwYP4999/sX//flhbW2P27NmYM2cOhg8f3qUv3K1bt5Cent5jNcwACvDn5+cjIyMDKpUKFhYWcHJy6rHsdV3WdVNvQV5YWIja2tpmLUGAXHrR0dF82aOeKiUDAPn5+UhOTu7wnKi7rgoLCzVIDNpUK+9p8GHCcRw2bdqEZcuWYc+ePZgwYQIAoLS0FEePHuWD9n1y+8h/CoDaK7W1tThy5Ah27NiBffv2wcjICLNnz8bcuXMxatQorWnaHMfh+vXr/KqpO0sLNRb1BFMvLy+eUVdaWgozMzONLqi6lMbEh+6o69a4BBKLo1hZWSEpKanNHjrdIazSQkeTXRuLejHcoqIiHnwZILVFZ+9N4LNt2zYsWbIEO3bswNSpU3tkHH3StdIHQG1IfX09jh8/jh07dmD37t3Q09PDrFmzMHfuXIwdO7bd7hmlUskHtHs6ztJagqlcLtdoP67e8bWr4wrqdd3CwsK6PLemPcKSQfPz81FeXg6JRAI3Nzc4Ojp2e9UJJswaDAkJ6fJ+T+qVGBiJgcWNGj+TvQV8AGDnzp145plnsGXLFkRERPTYOPqka6UPgDogcrkcp0+fxvbt27Fr1y7I5XJERERgzpw5mDBhQouryYaGBsTGxgIAQkJCejTO0pEEU1azTSqVajDMHB0dNVpyayPqdd3Cw8N7FJCZNWhjYwMrKyu+5YChoSEcHR1hb2/f6ettr2RlZSE9Pb1brEF1OntpaSlMTEw0GHWxsbG9Anz27duHJ554An/99RfmzZvXY+Pok66XPgDSUhQKBSIjI3kwqqqqwvTp0zF37lxMnjyZj6OwFb6lpSUCAgJ69EXuTIJpcwwzZhl1NMjdm+q6VVZWIjo6ukkxT/UW5EVFRRpdX3VRxwwQyvyEhYV1u3u2cYFYpVIJAwMD+Pn5dZhB2JVy6NAhPProo1i3bh0eeOABnZyjI5Wt165di40bN+LatWsAgPDwcHz66aca2y9cuBAbNmzQ2G/q1Kk4dOiQTsZ/O0sfAHWBKJVKXLx4kS8JVFxcjKlTp8LHxwc//vgj3n77bbzwwgs9FiBVj7MEBQV1OsGUMcykUinPoFNvP94ayDKGmUql6vG6bswVOWDAgFbrqalUKo0Ot+pBfZaY2FlhbdbDwsJ6NAdLoVAgOjqaJ6YwMFIvENtdpapOnDiBhx56CD///DPmz5+vk/eno5Wt58+fj9GjR2PUqFEwMjLCqlWrsHPnTiQmJvJtHxYuXAipVIp169bx+xkaGvZo36reKn0A1MWiUqlw9epVrFy5Ert27YJYLMaMGTMwZ84czJgxo9uVi64TTDuS+NrQ0IDo6GgYGBj0OMOstLQUcXFx8PLy6lAOiXplgsYtyO3t7TsMqLquMdcRYeAjkUj4+9O4309NTY1GOwld1eQ7c+YM7r//fnz33XdYuHChzhZvHa1s3ViUSiWsra3x/fff47HHHgNAACSTybBr1y6djPlOkv9UHlB3iJ6eHq5evYojR45gy5Yt8PHxwfbt27F69Wo8//zzmDRpEmbPno2IiIguLyDaWFiCaUVFBYYOHaqTOItIJIKVlRWsrKzg4+ODqqoqSKVS3Lx5E4mJibxyNjMzQ0JCAszNzREQENCjDLOioiIkJCRg4MCBHW5Wpn693t7ePKMuKysLSUlJzTYVbElYZfe8vDwMGTJE54zD1qQ58AGE/BtLS0v+elkb8pSUFL4mn729fZfVyDt//jweeOABrF69Wqfg09DQgKioKLz55pv8d3p6epg8eTIuXLjQrmPU1NRALpc3IYucOnWKd9dOnDgRH3/8cY+Wtuqt0mcBdbEoFAo88MADWLZsGUaPHs1/z2qKsTYSycnJGD9+PObMmYOIiIgu62nEpLMdTLtCmHLOz89HdXU1DA0N4eHh0SPVrJm0ltjZWamtreUtwfLy8iZ9nNSFUfKlUmm3FDhtTVoCn7aEkRhYOwltu9yqy5UrVzBnzhx89NFHWLJkiU4XaF1RWPT555/H4cOHkZiYyC84Nm/eDBMTE3h4eCA9PR1vvfUWzMzMcOHChR61+nuj9AFQDwijHzMwiouLw5gxY/ieRo6Ojp168bq6g2lnhMVZnJycYGRkhKKiIpSXl/O5N92Z+NreLqZdIeotyEtKSniGGbMGU1NTUVxc3OMMQG3Bp7njNO5yy8CovaSNmJgYRERE4J133sGrr76q85hpZwFo5cqV+Pzzz3Hq1CkEBQW1uB3r93Ps2DFMmjSpy8Z/J0gfAPWwcByHjIwMnsBw+fJljBgxgu9p5Ozs3KEXUdcdTDsiLdV1q6+v5y2FsrIymJmZwdHRsVlLoaskKysLaWlpOsmtaUvUy+QUFxfz3/v5+cHR0bHH7lFXgU9jYaQNBsCMxMBIG80tiBISEjBjxgwsX74cb7zxRrcQdrRpLsdk9erV+Pjjj3Hs2DEMGTKkzXPZ29vj448/xuLFi7ti6HeM9AFQLxKO45CTk4N///0X//77L86dO4fw8HC+jcSAAQPa7JLZnR1MWxPm6vL399coMd9YuiPxNSMjA5mZmT1Cb1YXFpOTyWSwtrZGSUkJALSbQdiVoivwaSzqZZBY8zlWBkkikcDJyQlJSUmYPn06XnjhBbz33nvd+txqU9n6888/xyeffILDhw9jxIgRbZ4jJycHbm5u2LVrF2bPnt2l47/dpQ+AeqlwHIeCggLs3LkTO3bswJkzZxAYGIg5c+Zg7ty5TQCGBdZ7qoOpumhb162xpWBgYMCDkTaJoOpB/p6qtMBEvbVDeHg4DA0NwXEcXzC1sLAQcrmcJ23Y2dnpjKLeXeDTnNTU1KCwsBA3b97EvHnzMGDAABQUFGD+/Pn4+eefu33R1NHK1qtWrcK7776Lv//+WyPGa2ZmBjMzM1RVVeGDDz7AvffeCycnJ6Snp+O1115DZWUlEhISeiz22VulD4BuA+E4DsXFxXyDvRMnTmDQoEF8fTpWKuiff/5p1drojnF2VV039cTXwsLCDieCchyHlJQUFBcXIywsrEeD/CqViu92Gx4e3iwhpHHrDHVLoSvpzj0JPo0lMjIS999/P2xtbZGXlwcPDw/MnTsXS5cu7dbn+Pvvv+cTUUNCQvDdd99h+PDhAIDx48fD3d0d69evBwC4u7vj1q1bTY7x3nvv4f3330dtbS3mzp2LmJgYyGQy9O/fH1OmTMFHH33ULZW6bzfpA6DbTDiOQ1lZGfbs2YPt27fj0KFDUKlUmDt3Ll577bUeozjrsq5b40RQ9cTX5rL0VSoVkpKSIJPJEB4e3iPVvZmwvkJyuRxhYWHttmqYpdCemm3tFblcjpiYmF4BPpmZmZg2bRrmzJmDb7/9FjU1NTh8+DB2796NlStXatWOo09uP+kDoNtUVCoVXn31Vfzzzz9YunQpoqOjcejQIfTr14/vaRQaGtotYNSddd1YIiirwsD6/Dg6OvJgpF7mpyddHkqlErGxsTwVXluXGiNtMLqzqampBqOuPW6r3gQ+2dnZmDZtGqZOnYoff/yxR4kyfdKz0gdAt6n8+uuvWL16NQ4dOgRPT08A1NPowIED+Pfff3HgwAHY2NjwlbuHDRumE6XD6rrV1tYiNDS0W+u6NdfnRywWQywW9wp6c0xMDAAgNDS0y6jwrAU5i5MZGhq2GSfrTeCTn5+PqVOnYuzYsVi7dm1fXsx/XPoA6DYVuVyOioqKFrOra2pqNHoamZiY8A32OtPTqPEY4uLiekVdN7lcjqioKMjlckgkEp3FUNo7lpiYGIjFYp1WklYvmFpcXMy3IGeMOj09PX4s+vr6CAoK6lGFL5VKMX36dAwZMgQbNmzoA58+6QOg/4LU1dXxRIU9e/ZALBYjIiIC8+bNw1133aUVcPSmum7NjYVVJZBKpXwMhbVW0GVMqKc6qqq3IGe5NzY2NqiqqoKxsXGP36Pi4mLMmDEDgwcPxqZNm3SWHN2RytYAsG3bNqxYsQKZmZnw8fHBqlWrMGPGDP53juPw3nvvYe3atZDJZBg9ejR++ukn+Pj46GT8/zXpA6D/mMjlcpw6dYpvI6FQKBAREYG5c+di/Pjx7bIUamtrER0d3SvqutXX1yMqKgqmpqYtdjFlTedY4mtrJXI6IwwIjYyMEBQU1GPzwnEcSktLkZiYCIVCAY7jWmxB3h1SWlqKmTNnwtPTE1u3btWZpdzRytbnz5/H2LFj8dlnnyEiIgJ///03Vq1ahejoaAQEBAAg2vVnn32GDRs2wMPDAytWrEBCQgKSkpJ6tI3InSJ9APQfFtbTaNu2bdi1axeqq6sxY8YMzJ07F5MmTWrWUqiqqkJ0dDTs7e0xaNCgHk12ra2tRVRUFKytreHn59cuhd/Q0IDi4mJIpVK+RA6rwtDegH5z0h4g7C5hVhizCGtqangArqyshJWVFQ9GumYIymQyzJo1C/369cOOHTt06grtaGXrBx98ENXV1di3bx//3YgRIxASEoKff/4ZHMehf//+WLZsGZYvXw6Akr0dHR2xfv16PPTQQzq7lv+K3Hb0k9LSUsyfPx8WFhawsrLCU089haqqqha3z8zMhEgkavazbds2frvmft+8eXN3XFKPiUQiwfjx4/HDDz8gKysL+/btg6OjI1577TV4eHjg8ccfx86dO1FdXQ0AOHnyJP766y84Ozv3OPhUV1fjypUrsLOz61DJIQMDA/Tv3x+hoaEYP348PD09+WOdO3cO169fh0wmQ0fWZXV1dbh69SrMzc17Hfjo6enBzMwMHh4eGD58OMaMGQMHBwcUFhbi3LlzuHjxIm7evNnqO6StVFRU4J577oGdnR22b9+uU/Bhla0nT57Mf9dWZesLFy5obA9Q4zi2fUZGBgoKCjS2sbS0xPDhw9tdLbtPWpfbrh3D/PnzkZ+fj6NHj0Iul+OJJ57AM888g7///rvZ7V1dXZGfn6/x3a+//oovvvgC06dP1/h+3bp1mDZtGv9/KyurLh9/bxWxWIwxY8ZgzJgx+PLLL3HlyhXs2LED77//Pp555hkEBgYiJiYGy5Ytg5eXV4+OtbKyElFRUXBxcYGXl5fWQMhKwTg5OWkE9BmBgLnprKysWgQVdSvM39+/R0G5OfBpLEZGRnBzc4ObmxtvDRYWFiIjIwNGRkb8NWtbzZpJVVUV7rvvPpiYmGDnzp06d1exxnmNkz0dHR2RkpLS7D4FBQXNbl9QUMD/zr5raZs+6ZzcVgCUnJyMQ4cO4cqVK3wBwDVr1mDGjBlYvXp1s8lrYrG4SfvpnTt34oEHHmjSf8XKyqrDrarvRNHT08Pw4cMxfPhwrFy5EqtXr8Y777wDW1tbfPXVV4iPj8fs2bMxc+ZMnfc0aiwymQwxMTFwd3dvtYtpR0UdcFjiq1QqRUJCQouJr7W1tbh69SpsbW3h5+fX68GnsTBrsH///hrVrKOjo/n5sLe373AL8pqaGjzwwAPQ09PDnj17epQO3ye9W24rF9yFCxdgZWWlUX128uTJ0NPTa1fvDgCIiopCbGwsnnrqqSa/vfDCC7Czs8OwYcPwxx9/dMgNc6fKH3/8gY8++gi7du1CXl4erl69iiFDhuD777+Hh4cH5s2bh/Xr16O4uFjn81VaWoro6Gh4e3t3Kfg0Fj09Pdja2sLf3x9jx45FcHAwJBIJUlJScPr0aSQkJCArKwuXL1+Gvb39bQk+jUUikcDR0RGBgYEYN24c/P39+fp1p0+fxrVr13h2XWtSV1eHhx9+GPX19di3b1+3NdljrdGlUqnG91KptMVFpZOTU6vbs387csw+6ZjcVgBUUFDQhM0ikUhgY2PTbpP4999/h5+fH0aNGqXx/YcffoitW7fi6NGjuPfee/H888/zFXL/y2JjY4ODBw9ixowZEIlECAgIwPvvv4+4uDgkJCRg/Pjx+OOPP+Dl5YWIiAisXbsWBQUFXQ5GRUVFiI2NxaBBg+Dq6tqlx25NRCIRrK2tMXDgQIwZMwbh4eEQi8VITU2FXC5HXV0dCgoKIJfLu21M6tIV4NNY9PT0+Nja2LFj+YaG169fx6lTpxAXF4e8vLwm11xfX48FCxZAJpPhwIED3dp+3sDAAOHh4Th+/Dj/nUqlwvHjxzV6/ajLyJEjNbYHgKNHj/Lbe3h4wMnJSWObiooKXLp0qcVj9knHpFew4N544w2sWrWq1W2Sk5Px77//YsOGDUhNTdX4zcHBAR988AGee+65Vo9RW1uLfv36YcWKFVi2bFmr27777rtYt24dsrOz23cR/2HhOA43b97kexpduXIFI0eO5Hsa9e/fv1MWQkFBAZKSkjB48OAeL+jI4k/Ozs5wcnLi2WVVVVU81dnBwaFbqM66AJ/WpLmCqfX19UhMTMQ999yDd955B1lZWTh+/HiPtJ/uaGXr8+fPY9y4cVi5ciVmzpyJzZs349NPP21Cw165cqUGDTs+Pr6Pht1F0isAqKioiO+N0pJ4enrir7/+wrJly1BWVsZ/r1AoYGRkhG3btmHevHmtHuPPP//EU089hdzc3DbbBOzfvx8RERGoq6vrK6HeAeE4DtnZ2XxPo/Pnz2PIkCE8GLXV06ixsNYOQUFBsLOz0+HI25aKigpER0fDzc2NL3/EpHHxUEZ1dnBw0Imi6m7waU5qampw4sQJfPTRR7h27RqMjY3xv//9D48++ii8vb27fTxAxypbA5SI+s477/CJqJ9//nmziai//vorZDIZxowZgx9//BG+vr7dfWl3pPQKAGqvJCcnw9/fH1evXkV4eDgA4MiRI5g2bRpycnLarKA7fvx4nhLalnzyySf48ssvUVpa2iVj/y8Kx3HIz8/nexqdPXsWQUFBPBi11TTv1q1buHnzZqdbO3SFlJeXIzo6Gh4eHnB3d29125YSXx0dHbskIN8bwIeJQqHA4sWLcfXqVSxevBgnTpzA8ePHMWjQIFy+fLlv8dYnrcptBUAAMH36dEilUvz88888DXvIkCE8DTs3NxeTJk3Cxo0bNUpwpKWlwdfXFwcOHNCgWgPA3r17IZVKMWLECBgZGeHo0aNYvnw5li9fjg8++KBbr+9OFdbTiIHRyZMnMWjQIL7BnnpekUqlwvXr11FQUIDQ0NAe7WIKAGVlZYiNjYWXl5dGa/H2SENDg0bHV20qWatLbwIfpVKJJUuW4MKFCzh58iScnZ0BkKV48eJFTJkypcfG1ie3h9x2AFRaWoolS5Zg79690NPTw7333ovvvvuOZ9tkZmbCw8MDJ0+exPjx4/n93nrrLfz111/IzMxs8tIeOnQIb775JtLS0sBxHNzd3WFiYoKUlBT+HN9++22rjJ7x48fj9OnTGt8tXrwYP//8M///rKwsPPfcczh58iTMzMzw+OOP47PPPtNZXazeKqyn0e7du7Fjxw4cO3YMHh4evGX0008/QSqV4s8//+w2FlVLUlpaitjYWPj6+sLFxaVTx2qukjWrwtCevBtWcNXQ0LDHwUelUuHll1/GiRMncOrUqQ4Dc5/0CXAbAlB3yPTp05Gfn49ffvmFt7KGDh3aYrIrQADk6+uLDz/8kP/OxMSEZwIplUqEhITAyckJX3zxBfLz8/HYY49h0aJF+PTTT3V+Tb1ZysvLsXfvXuzYsQN79+4FADzyyCNYtGhRt/U0ak5KSkoQFxeHgQMH8qv7rhL1xNeioiKNPCRra+smYMTAp6frzAEEPq+99hr279+PkydPNomH9UmftFf6AKiRsDiTerLroUOHMGPGjFbjTOPHj0dISAi++eabZn8/ePAgIiIikJeXxzO5fv75Z7z++usoKirq9gKRvU3kcjkWLlyIy5cv4+WXX8aZM2dw4MAB2Nra8q3Hhw4d2m0VnYuKipCQkAA/Pz+dt4dWqVR8+/GioiJwHMeDkY2NDZRKZa8Cn3feeQfbt2/HyZMn+6pC90mn5LbKA+oO6Uyy66ZNm2BnZ4eAgAC8+eabqKmp0ThuYGCgBo146tSpqKioQGJiYtdfyG0m33zzDa5du4bIyEi88MIL2LJlC6RSKb7++muUlpbinnvugZ+fH5YvX46zZ89CoVDobCyFhYWIj4/H4MGDdQ4+QNO8G9Y6ITk5GadOnUJkZCREIhEGDx7co+DDcRw++ugjbNmyBceOHetW8OloDcjS0lK8+OKLGDhwIIyNjeHm5oalS5eivLxcY7v/Yg3I3iT/reBDO0TbZNdHHnkEAwYMQP/+/REfH4/XX38dqamp+Pfff/njNldTiv32X5elS5fi6aef1mC7mZiYYN68eZg3bx7q6upw7Ngx7NixA4888gj09fX5nkZjxozpshL/BQUFSExMRGBgYLMl/HUtLPHV2toaHh4euHLlCjiOg1wux5kzZ2BnZwcHBwfY2dl1awNAjuOwcuVKrFu3DidOnMCgQYO67dxAx2tA5uXlIS8vD6tXr4a/vz9u3bqFZ599Fnl5eU1YsJ2tAblx40a88soryMvL02D9zZ07F+bm5vjzzz87dLz/kvxnAKi9ya7ayjPPPMP/HRgYiH79+mHSpElIT0/v8eKdt4MYGhq2Stk1MjJCREQEIiIiIJfLcfLkSWzfvh1PPvkklEolZs6ciXnz5mH8+PFauzPz8vKQkpKC4ODgHs85Ymw3U1NTBAUFQSQSobq6GlKpFJmZmUhMTISNjQ3fZE+XLlyO4/DVV1/hxx9/xPHjx/kkze4SbWpABgQEYMeOHfz/vby88Mknn2DBggVQKBQaxJ/O1oC8//77sXTpUuzZswf3338/ALKi9+/fjyNHjmh93P+C/GdccMuWLUNycnKrH09PTzg5OaGwsFBjX4VCgdLS0g49pCz5LS0tDUDLdafYb33SftHX18eUKVPw66+/Ijc3F9u2bYOpqSleeOEFuLu7Y9GiRdi3bx/q6urafczc3NxeBT6NYz4ikQhmZmbw8vLCyJEjMWrUKNjY2CA3NxdnzpzB1atXkZWV1aFrbo9wHIc1a9bg66+/xqFDhxASEtKlx2+PdEUNSIDILhYWFk1Yp52tAWlsbIxHHnkE69at47/766+/4ObmpsHE7ZOm8p+xgOzt7dusfgBQfSiZTIaoqCg+2fXEiRNQqVQ8qLRHYmNjAYCPIYwcORKffPIJCgsLedfO0aNHYWFhAX9//w5eTZ8wkUgkmDBhAiZMmIA1a9bg/Pnz2LFjB/73v/+hrKwM06ZNw5w5czBlypQWu59mZ2fjxo0bCA0N7fGE1/ay3UxMTODu7g53d3fU1dXxVRiuX78OCwsLnsTQmcRXjuPwyy+/YOXKlTh48CCGDh2q9bE6I11RA7K4uBgfffSRhqcCoBqQEydOhImJCY4cOYLnn38eVVVVWLp0aYfGuGjRIgwdOhS5ublwdnbG+vXrsXDhwh4tUntbCNcnTWTatGlcaGgod+nSJS4yMpLz8fHhHn74Yf73nJwcbuDAgdylS5c4juO4tLQ07sMPP+SuXr3KZWRkcLt37+Y8PT25sWPH8vsoFAouICCAmzJlChcbG8tt27aNMzQ05AwMDDhLS0vuySef5CorK1scU0lJCbdkyRLO19eXMzIy4lxdXbkXX3yRk8lkGtsBaPL5559/uniGer8olUruwoUL3PLlyzkvLy/OxMSEmzt3Lrdu3TquoKCAq66u5qqrq7n169dze/fu5XJzc/nveuojk8m448ePc+fPn+cqKyu1OkZZWRmXmprKnT17ltu9ezd37NgxLj4+npNKpVxVVVW7j1NVVcWtWbOGMzc3586cOaOTe/T66683+7yqf5KTk7lPPvmE8/X1bbK/vb099+OPP7Z5nvLycm7YsGHctGnTuIaGhla3XbFiBefi4qLV9YSFhXGffvopd/XqVU5PT4/LysrS6jj/JemjYTcjHU12zc7OxoIFC3Dt2jVUV1fD1dUV8+bNwzvvvKNREfjWrVt47rnncOrUKSgUClhZWWHnzp3gOK7NXKNr167hvffew8KFCzWCqkFBQRpBVZFI1GxQ9b9cOFGlUiE2Nhbbt2/Hv//+i1u3bmHy5MmQy+WIjIzEyZMnMXjw4B4dI7N8jI2Nu6yrauPE1/Y2nOM4Dn/99ReWL1+OPXv2YMKECZ0eS3PSHTUgKysrMXXqVJiYmGDfvn1tvgedqQH5008/4ZtvvsHdd9+NGzdu4PDhwx3a/78ofQDUA6JtrlFj2bZtGxYsWIDq6mrery0SibBz507MnTtXV8O/rYXjOCQkJGDp0qU8vXnixImYO3cuIiIiYGNj0+1uE12AT2NRKpUaYCSRSDQ6vrJr5jgOW7duxYsvvogdO3Zg6tSpXT6Wjoq2NSArKiowdepUGBoa4sCBA+1yR3amBmR5eTnf3G/jxo148MEHO3yM/5r8Z0gIvUl6e1D1TpfNmzcjJSUFsbGxSEhIwNixY/Hbb7/B09MTs2bNwm+//QapVNot89bQ0KBz8AGo46t6wzk/Pz8olUrExcXhzJkzePPNN7Fjxw5s2bIFS5YswZYtW3oF+ACAn58fpk2bhkWLFuHy5cs4d+4clixZgoceeogHn9zcXL4AKkDgM2XKFFRXV+P3339HRUUFCgoKUFBQwDfV27t3L3777Tdcu3YNaWlp+Omnn/Dpp5/ixRdf1GqclpaWuPfee2FmZta3AGyn/GdICL1Jboeg6p0qNTU1iI2NxenTpzFw4EAAwNtvv4233noLN2/exPbt2/H333/j1VdfxahRozBnzhzMnj270z2NmpOGhgZER0frHHwaC0t8tbOzw6BBg1BWVoZNmzZhyZIlqKiowPjx4yGXy1FTU9Nr2mmz8U2aNEnDLc5ELpcjNTWVT/6Ojo7mF3ONW0NkZGTA3d0d+vr6+OGHH/DKK6+A4zh4e3vjq6++wqJFi7QeZ25uLubPn99XBbyd0ueC60LprsZ6FRUVuPvuu2FjY4M9e/a0mpDY11iv48JxHLKysvieRhcuXMDQoUP5kkBubm6dBqOeAp+W5NChQ1iwYAHefvttVFZWYseOHcjLy8PPP/+MRx99tEfHdjtIWVkZTp06hfvuuw9JSUn84qZPWpc+AOpCudOCqn1CYJSXl8e3kYiMjERwcDBfudvLy6vDYNTbwOf48eN4+OGH8csvv+CRRx6BSCQCx3G4du0aLC0t+ypdt0Pc3d1RVlaGFStWYPny5T09nNtG+gCoB+R2Car2iaZwHIfCwkLs2rULO3bswKlTp+Dn58f3NBo4cGCbYMRiPiYmJr0CfM6cOYP7778fa9asweOPP96Xt9In3Sp9ANRD0tHGeiyoWlNTg507d2okVdrb20MsFvc11utG4TgOpaWlfE+j48ePw9PTE3PmzMG8efPg7+/fBFx6G/icO3cO9957L1avXo1Fixb1gU+fdL90X8pRn6hLSUkJ9/DDD3NmZmachYUF98QTT2gkomZkZHAAuJMnT3Icx3EnT55sMVkvIyOD4ziOO3jwIOfs7MyJRCIOAGdiYsK9/vrrnFKpbHEcW7du5QYOHMgZGhpyAQEB3P79+zV+V6lU3IoVKzgnJyfOyMiImzRpEnf9+vUun4/bXcrKyriNGzdyc+fO5YyNjTlvb29u+fLl3NmzZ7nKykouLS2NW7hwIRcZGal1kmlXfk6ePMlZWFhwa9as4VQqVbfNU0lJCffII49w5ubm7UrA5jiOGzduXJNnfvHixRrb3Lp1i5sxYwZnbGzM2dvbc8uXL+fkcrkuL6VPukD6AOgOks2bN3MGBgbcH3/8wSUmJnKLFi3irKysOKlU2uz2586d48RiMff5559zSUlJ3DvvvMPp6+tzCQkJ/DYrV67kLC0tuV27dnFxcXHc7NmzOQ8PD662tra7Luu2k4qKCu6ff/7h7r//fs7MzIxzcXHhrKysuPDwcK64uLjHwefs2bOclZUV9+WXX3Yr+HAcVRkJDg7mLl68yJ09e5bz9vbWqDLSnIwbN45btGgRl5+fz3/Ky8v531mVkcmTJ3MxMTHcgQMHODs7O+7NN9/U9eX0SSelD4DuIBk2bBj3wgsv8P9XKpVc//79uc8++6zZ7R944AFu5syZGt8NHz6cX12qVCrOycmJ++KLL/jfZTIZZ2ho+J8s76ON3Lp1ixswYADn4uLCWVhYcC4uLtxzzz3HHT58mKuoqOh28Llw4QJnbW3NffbZZ90OPklJSRwA7sqVK/x3Bw8e5EQiEZebm9vifuPGjeNeeumlFn8/cOAAp6enxxUUFPDf/fTTT5yFhQVXX1/fJWPvE91IXyLqHSIsvjB58mT+Oz09PUyePBkXLlxodp8LFy5obA9Qkzy2fUZGBgoKCjS2sbS0xPDhw1s8Zp8IUlxcjFmzZmHYsGG4efMmpFIpfvzxR9TU1OChhx6Cr68vXnrpJZw6dQpyuVzn40lKSsKsWbPw8ssv4/XXX+/2mE9fs8c+aSx9iah3iBQXF0OpVDbb9C4lJaXZfVpqkseSYdm/rW3TJy2LgYEB5s2bh7fffhv6+vrQ19fHrFmzMGvWLDQ0NPA9jRYuXAiO4/ieRuPGjevy/j6pqamIiIjAM888gxUrVvQI4aCv2WOfNJY+C6hP+kRHYmFhgffff7/ZRGEDAwNMnToVa9euRV5eHrZs2QITExM899xz8PDwwDPPPIMDBw50SX+ftLQ0REREYMGCBfjoo4+6HHzeeOONZltbq39aWgS1R5555hlMnToVgYGBmD9/PjZu3IidO3ciPT29C6+iT3pC+iygO0Ts7OwgFoubbXrXUsO7lprkse3Zv1KplO9rxP7fE43J7lSRSCSYOHEiJk6ciDVr1uDcuXPYsWMHXn31VZSXl2v0NOpoaZzMzExERETg3nvvxeeff64T6veyZcuwcOHCVrfRVbNHLy8vODk58TXgmPQ1e7xNpKeDUH3SdTJs2DBuyZIl/P+VSiXn7OzcKgkhIiJC47uRI0c2ISGsXr2a/728vLyPhNBNolQqufPnz3PLli3jPD09OVNTU27u3Lnc+vXrNXoatfRJSUnhBgwYwC1evLhVKn53CSMhXL16lf/u8OHDbZIQGktkZCQHgIuLi+M4TiAhqLM9f/nlF87CwoKrq6vrugvoky6XPgC6g2Tz5s2coaEht379ei4pKYl75plnOCsrK54d9Oijj3JvvPEGv/25c+c4iUTCrV69mktOTubee++9ZmnYVlZW3O7du7n4+Hhuzpw5fTTsHhClUsldvXqVe+ONN/imhBEREdzatWu53NzcJs3m0tLSOC8vL+7JJ5/kFApFTw+fl+5o9njo0CHO3t6+j4Z9G0gfAN1hsmbNGs7NzY0zMDDghg0bxl28eJH/bdy4cdzjjz+usf3WrVs53/9r795CotyiOID/O+aMoZl3xRDrZGeS8papqJGR5O0hhR4SNK3MB0MhKjMhCzNMSyhI0spxMCysMe0GmWXZxS6SUV4ow1LEyBDFSi3zss7DOc7py0uNY37jnPV70u2eb/YoupyZvfb/r79IIpHQ0qVLx21Etba2JqlUSv7+/tTY2EjZ2dlkb29PUqmUPD09VX8wxnLq1ClauXIlmZiYkImJCfn7+4+aHx0dParZMDAwUPNviA4aHh6m2tpaSklJoWXLlpFEIqHAwEDKycmh1tZWevv2LclkMoqMjNSq4kOkfgN2a2srrVq1iszMzEgqlZKDgwMlJiYK+oCIiFpaWig4OJjmzJlDFhYWtHPnTm5EnQH4KB6mtvPnzyMqKgq5ubnw8vLCsWPHoFQq0djYOGqXEwBERETA19cXPj4+MDAwQGZmJkpLS9HQ0ID58+cDADZt2oQPHz5AoVCobieVSmFqajptj2smIiK8evUKFy9eRElJCWpra2FgYIC1a9dCqVSOyopiTJtwAWJq8/LygoeHB7KzswH8E3ltZ2eHhIQE7Nmz56e3HxoagqmpKbKzsxEVFQXgnwLU3d2NS5cu/c6l6zQiwuvXr7F3714UFhby6edM6/E2bKaWyTS8/qivrw8DAwMwMzMTjFdWVsLKygoymQxxcXE/jbZgQrNmzYJMJoNSqeTiw2YELkBMLRM1vP5q019SUhJsbW0FRSwoKAhnzpxBRUUFMjMzcffuXQQHB6vikxljuodfIGbTKiMjA0VFRaisrBSE6YWHh6s+dnJygrOzMxYtWoTKykr4+/uLsVTG2G/Gz4CYWibT8DoiKysLGRkZKC8vh7Oz84Rz//zzT1hYWKCpqUnjNTPGtBMXIKYWiUQCd3d3VFRUqMaGh4dRUVEBb2/vcW93+PBhpKWloaysTHAY5Xja2trQ2dkpOIGBaY+uri5ERETA2NgYJiYmiImJQU9Pz7jzW1paxj2mR6lUquaN9fWioqLpeEhMDGLuAWczk7oNrxkZGSSRSKi4uFiQ6TLS//H582fatWsXPXr0iJqbm+nWrVu0fPlyWrx4MX39+lWtniOFQjGqn0gqlQrmcMie5tTN9RkcHBT87N+/f0+pqalkZGQk6AMCQAqFQjCPm551FxcgNinqNLza29uPmeS6f/9+IiLq6+ujgIAAsrS0JH19fbK3t6fY2Fhqb29XO2RPoVCQsbGx4A/Y9zkxRByyp6nJ5vr8yNXVlbZs2SIYA0ClpaVTtVSm5bgAMa2mbsieQqGgefPmjXs9DtnTnFwuJxMTE8HYwMAA6enpUUlJyS9d4+nTpwSAqqqqBOMAyNbWlszNzcnDw4Pkcvm0B+ex6cPvATGtNdmeo56eHtjb28POzg6hoaGCUDIO2dPcZHN9vieXy+Ho6AgfHx/B+IEDB3DhwgXcvHkT69evx7Zt23D8+PEpWzvTLlyAdEBHRwdsbGyQnp6uGnv48CEkEolgs8BMM5meI5lMhvz8fFy+fBmFhYUYHh6Gj48P2traAHDI3kR+d67PiC9fvuDcuXOIiYkZ9bWUlBT4+vrCzc0NSUlJ2L17N44cOaLxfTLtxH1AOsDS0hL5+fkICwtDQEAAZDIZNm7ciPj4+P9dD423t7dgN56Pjw8cHR1x8uRJpKWlibgy7TdduT7FxcXo6+tTHcM0ES8vL6SlpaG/v59Pd9BBXIB0REhICGJjYxEREYEVK1bA0NAQhw4dEntZGtGk52iEvr4+3NzcVP1EHLI3PktLS1haWv50nre3N7q7u1FTUwN3d3cAwO3btzE8PKwKi5uIXC7HunXrfum+nj9/DlNTUy4+OopfgtMhWVlZGBwchFKpxNmzZ2f8L+1ke46+NzQ0hLq6OlWxWbhwIWxsbATX/PTpE548efLL1/y/c3R0RFBQEGJjY1FdXY2qqirEx8cjPDwctra2AIB3795hyZIlo5JKm5qacO/ePWzdunXUda9evYq8vDzU19ejqakJOTk5SE9PR0JCwrQ8LiYCsXdBsKlTV1dHBgYGpKenR1euXBF7OVNC3Z6j1NRUunHjBr1584ZqamooPDycDAwMqKGhQTWHQ/Y0p26uz4jk5GSys7MbM6H1+vXr5OrqSkZGRmRoaEguLi6Um5urFWmu7PfgAqQj+vv7ycXFhaKjoyk9PZ2srKzG7ZWZadTpOdq+fbtqrrW1NYWEhNCzZ88E1xsvZI+I1Gp69fPzG7O/KSQkRDWHg/YYGx/nAemIxMREFBcX48WLFzAyMoKfnx/mzZuHa9euib20GUPdoL2uri58+/ZN9XlnZydcXFyQl5enejOfg/YYm4DYFZBp7s6dOzR79my6f/++aqy5uZmMjY3pxIkTIq5sZlG36fVHR48epblz51JPT49qLDo6mkJDQ6d6qYzpBN4FpwNWr16NgYEBwdiCBQvw8eNHkVY084w0vSYnJ6vG1A3ak8vlCA8Ph6GhoWB8JGjP1NQUa9aswcGDB2Fubj6l62dsJuJdcIxB86C96upq1NfXj9rdxUF7jI2PnwExNgXkcjmcnJzg6ekpGOegPcbGx8+AGINmTa+9vb0oKioa82iZH3HQHmP/4QLEGDRrelUqlejv70dkZORP74eD9hj7Dxcgxv61Y8cOnD59GgUFBXj58iXi4uLQ29uLzZs3AwCioqIEmxRGyOVyhIWFjdpY0NPTg8TERDx+/BgtLS2oqKhAaGgoHBwcEBgYOC2PiTFtxu8BMfavDRs2oKOjA/v27UN7eztcXV1RVlam2pjQ2tqKP/4Q/s/W2NiIBw8eoLy8fNT19PT0UFtbi4KCAnR3d8PW1hYBAQFIS0ub8cckMTYVuBGVMcaYKPglOMYYY6LgAsQYY0wUXIAYY4yJggsQY4wxUXABYowxJgouQIwxxkTBBYgxxpgouAAxxhgTBRcgxhhjouACxBhjTBRcgBhjjImCCxBjjDFR/A2uwjTvK/5LNAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/capytaine/bodies/bodies.py:1143: RuntimeWarning: divide by zero encountered in scalar divide\n",
+      "  p = np.hypot(1/x_span, 1/y_span)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Heave': 3427.430789509558}\n",
+      "{'Heave': 6711.690040932826}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#original  - compound cylinder\n",
+    "h = 1.001\n",
+    "d = [0.5, 0.25]\n",
+    "a = [0.5, 1]\n",
+    "w = 1\n",
+    "rho = 1023 # density of our special material\n",
+    "zdensities = [10, 10]\n",
+    "rdensities = [20, 20]\n",
+    "tdensities = [50, 100]\n",
+    "config = \"config0\"\n",
+    "heaving = [1, 1]\n",
+    "\n",
+    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "gV2Sd-xRL_Z5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Heave': 9507.973834727813}\n",
+      "{'Heave': 23299.787375615415}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#staircase  - compound cylinder\n",
+    "h = 1.5\n",
+    "d = [1.1, 0.85, 0.75, 0.4, 0.15]\n",
+    "a = [0.3, 0.5, 1, 1.2, 1.6]\n",
+    "w = 1\n",
+    "rho = 1023 # density of our special material\n",
+    "zdensities = [20, 10, 30, 20, 15]\n",
+    "rdensities = [10, 10, 20, 10, 15]\n",
+    "tdensities = [40, 50, 70, 80, 100]\n",
+    "config = \"config1\"\n",
+    "heaving = [1, 1, 1, 1, 1]\n",
+    "\n",
+    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "G4aw1fAAb7Vh"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NQqMBnn76aTz99NPQaoHY2DikpS3GbbfdhrPOOmvOBZTBYtkAcOtaZS205bqYjo2NqV1Mgxwq2QQpOI5Dd3c3qqqqkJqaipUrV/q0qARCoVmj0WBmZgYHDx70y21mNpvx97//Hffffz9GRoZgtQqrqngYRgL+wN2x0teUMPzEl9gVqbBt0tftdvltUsKRuv+oXcAYLrnkEmg0wKJFi7BkSRbuuOMOnHjiiX68h+AhG1+sLHddTMfGxpy6mIrJR20ktzBQySYIYbVaUVVVhb6+PqxZswapqak+/ziUrrOxWCxoaWmBzWZDUVGR126z+vp63HbbbSgvL8XAwCC/cErXE3fxFOmCLEcobJvcW2aLt/i1QLjDPBGPHOmIrwebnxwJiglIann19fWjr68fZ599NgAgMzMTO3fuxEUXXeRVK4lgIht30jme4KmLaVNTEyoqKhAZGenkdlPJZ36gZqMFGUZHR1FaWgqDwYD169cjIiLCr3E6OjowOjqKTZs2zXlOzG0WFhaG6elpnHTSSW73b21txTXXXIM9ez6Dw+Hb18tTZpfc6764qaTwxg0mdwz774nP52KlyZ2XzVX83sTnEO+j1QKpqYtx2mmn4eGHH3bp7ty3bx9Wrly5YOnqYoyNjaG2thbHHXec4mNLu5iOjY05dTFl5KN2MQ0MVMsmSOBwONDW1ob6+npkZ2dj2bJlc9KTUsKNxnEcOjo60NzcjOXLlyMuLg4VFRWy+/b29uL+++/Hv/71AqanzeA4WhAZxIuju4XSEznIve7qbcrtK1385xIHElsqrsZiSQb+nEtKhFJ3m/QcLL7EHjsc9Lk899xzeO6555CQkIBHH30U55xzjmSO/lsTSiOQyQqeupjW19ejrKyMVxxnLbTVLqbKQCWbIIDZbEZFRQXGxsaQn5+PxMTEOX+55+pGs1gsqKmpwcTEBIqKihAXF4fx8fFZBPa73/0Ojz76KAYHB2eNIV4QXT12t/D7Y3W4siTEGWZKw9O85VxlruDKXebLHOSO02iAkZERXHrppQgJ0eCMM87GHXfcgby8vKBzo81XZpxcF1Oz2YyRkRGMj487dTFlVo9KPv5DJZsFBMdxGBwcRFlZGWJiYlBSUqJYx8q5WDauss2YKoHJZMLFF1+Mt99+W3Q+WuRcuXS8gTurw1t3lKt9PM2DFXmK95fOX6cTxnc3ntyxbG5y28Xb7Hbv36t4P0+Wo/icFguHN954A2+88QZSUxfhzDO/jvXr13s+4TxgIa0sRj7p6em86Ov09DQOHjyIiYkJ9PX1OXUxZSnZahdT76CSzQLB4XCgoaEBLS0tCA8PV7yAzx/LhrnNWlpasHz5cmRlZTn9iPr6+nDjjTeiqakJgHyg2xNZiBdIKTF5m0XmacH2FuJxPV0qVy4+XzPQXO3rbi6uYk+uMutcWY5y4/X19ePZZ5/F3/++C9///sX44x//uKA1N/Np2XgCUxzXaDTIzc1FZGQk3+5C2sWUud3UFtquoZLNAsBoNKK0tBQzMzNYtmwZhoeHFf+B+Uo2FosF1dXVmJycxObNm3nhRADo7OzExRdfjLKygy5Tcj0F4+UWUlfE5K7aX+6u3xeiEe/r6fJ4Igl328Wve5Oo4G4uvqZoe2M9SV+z2x34+9//jr///e+47LLL8Ic//GFBFv1gKjBlcDgc0Ol0Tl1MWVtzk8nEk8/hw4edupgmJSXx5KNCJZt5Bcdx6OnpQVVVFZKTk5Gfn4/h4WHZeMdc4YsbbXR0FJWVlbwrj7nN2tvbceWVV2Lv3j0eF3y2gLnKmJKfo/y+7qYttyh78zZ9URJwVVDqb76FXD2NJ7jbX/yauxRrT9vYNZe+tmvXLvz977tw111346abbvJ+0gogmJIVAPrNurK2xF1MWQttcRfTzs5O2O12vpdPUlLSV7qF9lfzXS8AbDYbampq0NPTg1WrVmHx4sV83xmlZWUA7ywbV24zs9mM8847D7t3f+TRfeNtxpQwL3nCcHUJlEoddrVwy8VhxK49uWPFa6GrceXiT74QjtS1Jn3N0/mlkCN+uYxA8bh33303fvObX+Hvf/8nzjjjDO9ONEcEkxsNmC2f4w6+djFl5PNV6WKqks08YHx8HIcOHYJWq0VxcTEiIyP51wLR5MybcV25ze6991789rcPwGZzv4q5uisWvyYHX9OOA5E9JoY7nvfGdeaKkFyRhC/WnitS91Z01FOhqKu5io8zm2248MLvIi0tHR9++BHS0tJcD6QAgs2Nxm4E/SEEuS6mrNEf62IKgG8kl5SUdEy30FbJJoBwOBzo6OhAXV0dMjMzsXz58llfpECRjTs3mthtdtxxx8FgMKClpQWnnHIy+vsHvjzefTaYu8VL/JrcwreQ8CeRwB3cxZrk4CnbzBuCkzunp2QNT3OUS1DQ6wGbjebc3d2DtWvzcOONt+CXv/yl6zc4RxzNlo0niBv9sbFZI7nBwcFZXUwZ+czn9Xjsscfw0EMPoa+vDxs2bMCf/vQnFBUVKTK2SjYBgsViQUVFBYaHh7Fx40YkJSXJ+qLn07Jx5TbbuXMnHnvsMQCzLRbmZvKUDSY+1vmczv/Fx/uSVeZvEoAUniwC8T6envsythjuLAlfIXd9pXAXD2OEIpeubrM572u1Ag899Fv89a9P4PPP9yEzM9O/SbtBsMVsGPkFYk7edDHdvXs3ysrKkJ2djYsuuginnHJKwK7PSy+9hJtuuglPPPEEtmzZgkcffRSnn346GhsbkZKSMufxVbJRGBzH8e2aIyIiUFJS4lafKpCWjXhc5jabmpri3WYmkwn5+fno6OgAMJsstNrZz8U1G2yhYpDbl0HuztzbQLa77XPd191xnp57G8T39HogLD532Wfiz1lKKJ5iUBwHjI1NYOPGNfjd7/6IHTt2KDrvYLNs7Hb7vM1HrotpREQEzGYzdu3aheeffx6JiYnYvn07zjzzTFx66aWKnv+RRx7BlVdeiR/84AcAgCeeeAJvvfUWnnnmGdx6661zHj94PtVjAA6HA42Njdi3j+76CgoKPAohBtKyYW600dFR7N27FxqNBiUlJYiLi8Mnn3yCRYuS0dHRAZ1Ovo5EOi3pc/FC5U5YU+kbMSXGMxjmPg5zD8q52L1JifYF7uYqVcz2NL4c+Yght7ZKP3urFbjxxhvwta99TdHvb7DFbBaS/HQ6HYqKivDAAw8gOzsb//jHP/DSSy9h5cqVqKmpUfRcFosFpaWlOPXUU/ltWq0Wp556Kvbt26fIOVTLRiGYTCaUlZXBZDJh8+bNXosaMrJRWjKEZbm1t7fPcps99thj2LlzJ4DZ1okrl46c1eOqZgaQt2QCnVnmDTRfNkNTqtUPK1SVs2S8bRftDdy9Z1c1Se7gLnmAKR1IC22l79FuB0pLD2DJkgw0NDQhKirKu5O7QTBaNgsdsOc4DkajEQkJCTjhhBNwwgknKH6OoaEh2O12LFq0yGn7okWL0NDQoMg5gudTPUrBcRyOHDmC3bt3w2AwoKSkxCf1XPZFVlp822azgeM4dHV1oaioiO89c8899+Dmm+WJBvCOaKSLkDd3xtKxxcrJSkE6D7lyBm8Ij1krWq3w526eYskYMVwpDgQDvClodTic3xPLpgOE92GzAZOTU8jOzkBPT48C8wrOmM1Cw2g0KkLmC4mFv4pHMex2O6qqqlBaWorly5dj/fr1MBgMPo3BvshKuiJGR0dRVlYGACguLuazX2699Vb85jcP8MF5KdG4+o37qsQsLQCVgzeBbV/hqtCUwZ2lodEIrjWOE/rfsDgFu9PX6+WvE1ucpZASnvh6BKq2z9U1l26XkqPcDbxcbY5OJ1wTBovFgbVrV6G6unpON07BsrgzMPWAhYbJZHIqmVAaSUlJ0Ol06O/vd9re39+P1NRURc4RPJ/qUYbJyUl8+umnGB4eRnFxMTIzM/36kShJNhzHoa2tDYcOHcKSJUsACJbTddddhz/+8VFecFK6HkiFKF1ZHnq9836eFiyFDTavIX0/7mAw0L5Wq/y1YUTCcUTQLFYjBcfRWGK4KqQEnMlprsQj/pxcfZU8kTFTOvAEMeGw68ySSbZtOw4vvfQSX8BsMpl8Ip9gi9nMZ4KAKzBlgkBaNiEhISgoKMCHH37odN4PP/wQxcXFipxDjdn4CI7j0NnZiZqaGqSnpyMvL29Odz5KkY3FYkFVVRWMRiOKiooQERGB5uZmcByH2267Dbt2PQ3A+6p+VwWIUmvI3WLKxnF3Hn8gjh1IhT1dWS7umq75G7+Rez/SsaQp3mLXpfhY6XX1Fd4qGUivg78N5sT1QmwM9vzqq6/CRx99jP7+fjQ1NSEkJAQJCQl8ppU7ZXPVspkNRtjR0dEBPc9NN92Eyy67DIWFhSgqKsKjjz4Ko9HIZ6fNFSrZ+ACr1YrKykoMDAxg/fr1SElJmbN/WaPRzEpT9hWjo6OoqKhAXFwcSkpKYDAY+MrnF198EX/4w+9dLvKu1Jbl4jnA3AhDKU+hq7Rhd2rHrgoZ/Q3gSwsfxXOTjis+t3RfVwWvShWfemPNiCF3k+HqxkOcYMCuA32fOJxxxuno6+sHx3F8c7LDhw+jrq4OERERiI+PR0JCAuLi4pxcz8EWswkGy8ZoNAJAwGM23/ve9zA4OIg777wTfX192LhxI/73v//NShrwFyrZeAlWOxMaGoqSkhKEh4crNra/6c8cx6G9vR2tra1YsWIFlixZwv9QtVotRkdH8cMf/hCAa0UAuQVNr5dfhJVos+ztIuouu0t6p87eh3jhl85Venldkamvc2ULrDgG5a5ttVylv9hKY248pTP4XMFgmG2NyakciAlFDtLXzeYZbNu2DXv37kViYiISExMB0A0bI5+2tjYYjUZER0fzVk8wLO5iBIOlZTKZoNfrFet15Q4//vGP8eMf/zggY6tk4wEOhwMtLS1oaGhAWloa1q5dG5B2AL6KcUrdZiwJgEGj0eDaa6/58rGzO8cVmQC0SLtbUDwtwHJ3wb52n5TWirCFTzymWEqFwVWBqat5yoHVHIlJhGWnsXiNp7Gki7JcGrh4H6mV5kufHV8g99nJuRDZ+aX722zy5AQIcS0xYdbU1ODpp5/GFVdcwe9nMBiQnJyM5ORkAMDMzAxGR0cxOjqKxsZGmM1mWCwWzMzMID4+HjExMQu62AdD6vPU1BQiIyODyuLzByrZuMH09DTKy8sxOTmJmJgYxMbGBuSL76tlI+c2k+InP/kJpqfNAJzvoPV61xYN4J5Q5BYaT/58T2PKjSWXIs3AxhUv5nKdKcXjSWtG5Cw0tkjKWUMsK41lo0nJWDyedL6urBo2htx1kc5fiXodV9JArlyJriwqm83ZZSZX4yMm7JtvvhEXXnghIiIiZOcVGhqK1NRUpKamguM4HDx4EDExMTAajeju7obD4UBsbCzvdouKiprXRTcYLBtGNkc7VLKRAcdxGBgYQHl5OWJjY1FSUoLa2tqAVPoD3pONO7eZGEajEU899aTTNpY95EqpGfBMCFarvD9fTjNMemcsDZTLvz/nBRZwP185i0eads2Ol7MuGFzFreTmJ2f1Sa+HtEhWatW5s/TYe5KStSuLwlvIXUNXVqw0rVn6mnR+UrD3xr5zhYWFqKur8zhH9l1OTExEcnIyX8zILJ+Ojg5eqJL9sU6agUKwJAgc7TU2gEo2s2C321FXV4fOzk7k5eUhIyMDWq0WOp1uQcnGk9tMjK1bt/KLPSD86N2dQuxic+VGc7Uoy925s+3iRcmTZcPce0w6h81f7NZxpVogJjw5EhIjEMF4V/Ei9l/8unRezsF1+fEZ0bizRnyBVuv6c3bnSgVojsxCdnft2Bx7e7tRWVmJDRs2eJyXOPVZo9HMak7GJPqZSrJer3fKdPMkD+Ur7HY730xwoWA0GgNOqvMBlWxEmJqawqFDh2C327F161anVMNANTljY7sjm5GREVRWVrp1mzGYTCa0tTXzle9Sl5P7ebgnJLaQuyMdV64VVxATjNg1IyY/No60DkhKbNIEAnH8wJUrytV2cZ2R3HuQXiu5eJGYYKREJBXDlFoubGzpdrkEBF/hjtTERO+OgNl7cEU40ozB8847j+/f4g7u3FZiif7s7GxeJXlkZAQ9PT1oaGhAWFiYU6bbXIkiWNxoqmVzjIDjOHR3d6OqqgqpqalYuXLlrNatC2HZsCLNtrY2t24zMc477zwAQpzBHaQxAXHXSldwF/egObs/XgppkFw8H1dWlqt5uqsfkeuxI90u97orq83VIismITZ3qVUjZ5lYrfKuMvZcnHUnPY8v8EQi7rIUxRBfF2YluXMPDg0NYnJy0mOtiC+pz1KVZJvNxme6dXR08Is02ycuLs7nlszB4EYzGo1qzOZYgNVqRXV1NY4cOYI1a9YgNTVV9suu1Wphm2vlnQvIkY0vbjMxPvnkE6/PK7UUpNvcgS2a0niIr5AjFLYYu6rzkZunlDjdEY+3C6k/26TBfbZNPAdXRGW1uiZYaSae3LjewF28Tq/3LS7ELCBGOAxy2WwcB1x22WV49dVX3Y45F0tCr9cjKSkJSUlJAOg3xOI9zc3NMJvNiI6O5t1uMTExHokkGFKxVbI5BjA6OorS0lJeQNNVxgxAd1EWiyUg85CSDXObxcfHe3SbidHZ2Sm73d3dLIvnsP++QBoz8eV4tr80GQBwv+DJxRPYgiceQ3wOV1ZNoOBKEZtZLu7mwCwE5kqUc2mJM9mUcKmxtdTT3OTAcULiCBuL3YRIbwDEUiiux1NOriYkJASLFi3iixLNZjNGR0cxMjKC3t5e2Gy2WZlu0nMHgxvtWBDhBL6iZONwONDW1ob6+npkZ2dj2bJlHu9wdDpdwGI2zEUndpvl5eUhMzPTp6Dgj370I6fn7lwibAEQD89iMv68TfHi4o2V5M6NxSCtsJembbPnrt6jNEHB05yUgiuLQ+wSA4g05Nxh7HMRx93EEMeT/Pm8xNahlIz9vT7i1HP2HWAExOap0XgePJCLe1hYGNLS0pCWlgaO42AymXjLp6urCxzHOWW6RUZGBkWdjWrZHKUwm82oqKjA2NgY8vPzkZiY6NWCHqgmZ2xsi8WCQ4cOYXp62ie3mRh79+4FIN8SgC0A7HfM3CaAc+DfmxRlV5DGObyBXBaZ2OphECcmsNflpF+kYFbCfJCMeK7uCEA8b1dfKfb+2KItHdebuIoYUrdeoOAuW5DjgI8++ggnn3yym+PnR65Go9EgMjISkZGRyMjIAMdxmJqawsjICIaHh9Ha2srfBI6OjiIqKkpR1RBfYDQa+SLYoxlfGbLhOA6Dg4MoLy9HdHQ0SkpKfJJ/CKRlY7Va0dHRgeTkZGzatMnnIKZ4HHao9K5SXN3NILZCxLEGqa89UAu1WMBRSiyMfKTWiSuXmdwcNRrBNTQf7jMGT4u5L8F9Znn44jKTe6+uEh08HTfX16Tnf+qpp1ySDbuZWwi3lUajQXR0NKKjo5GVlQWHw4GJiQlUVlZibGwMPT09CA0NdbJ85kM+BiCyWbp06bycK5D4SpCNw+FAQ0ODUzGkr1/oQFg2zG02MjKCxMREbNiwYU53dSx9WPwcEKwBV9ljYotCfCxLPZa+xsZkx7P/vi7o4rt08X/xdpbpxO7qpXOVKg/LZZLNp1UjB2/Swb0peJVCp6PPZ2bG874L9Rrg/N7279/vZhzaaaFjJGwOcXFx0Gq1WLlyJSIjI2cJikZGRjpluvnay8pbBLqXzXzhmCcbo9GI0tJSzMzMYMuWLX65pwDlLZuZmRlUVVVhenoaixYtQnh4+JzdB3K1J2y7u7oKV4sfG8/bokx/3TruICUgaTaaeG5it2CAjFC/IH2f4qQMVhfk77WTEo03x7I5sPMGmozF44+Njbncj93MBVPxIosh6XQ6WUHRkZERtLa2wmQy8YKiCQkJiI2NVSzWo9bZBDk4jkNPTw+qqqqQnJyM/Pz8Od15KGnZiLPNNm3ahJaWloDFgzyBLTbim0lPdSjSffw9rz9wdZmkdUVyKcTz6UqTgsVexGnZvnzk0oQBsXvN1+wxYLakDvvvTh7IXzh/n1zfBSykG00OHMe5TBBwJyhaX18Pi8XCZ7rNVVBUlasJYthsNr5T4KpVq7B48eI53y0pYdm4yjZjCQJzxVwWCeaKkqbc+nIufxZztggzC8rT8SwrTbpYSus65DLCfLUexMkL3h7rKh7jSh7GVbaZ2FJjRComKrEbca5WnPj8bD2ci7UlBiNIuaSO2fMILsvGF7eeVFB0enqaJx8mKBoXF8eTjy+Como2WpBifHwchw4dglarRXFxsWIf0lwtG7HbbMuWLYiJiVFsbAZXiwPb7mvVuTeLjT8LsvhYdocuPpYtslKZf3FsR0w4bEET78/28bboUc4F6U9AXHp95XTa2PGuxDXFSR2ePi8l3YWMsMVWmLefqfhzApxjaN7WQrMam2AhG/ab9NUdptFoEBERgYiICKSnp0MsKDoyMoL29nY+JsTcbq7c6OzYQHfpnA8cM2TjcDjQ0dGBuro6ZGZmYvny5Yrmx8/FshkeHkZVVRXvNpNmmynpotPrBctEumDKVbeLwQLy3sBbWRN3EJOgXF8a8VykKbXi36VUrl/c60Y6PzlVZX/nLT4n4L6bJYOYYOSIRky04vHFBZIM/hKNOwIWu1W9sVSlafVsbH8UqoOxSycwd7eeN4KiBoPBKdNNLCi6EEWd2dnZs4rEH3jgAdx6661+j3lMkI3FYkFFRQX6+vqQk5OD3Nxcxb+0/ghxchyH1tZWtLe3uy3SVJJspH1eGOTcSa4SClwVGzJ4Sjjwx8KRO05aeS+GO102qd6a3JjS+ft6By8H8ZxcXT/x+5DuIy1aFZMns9aYtWAw+E823kgNMSUDbyAuLnVHMp6+G8FQrS8GI79ANEuUCoqOj49jdHTUSVD00KFDiI6OXrB+Nvfeey+uvPJK/vlcraujmmw4juPbNUdERCAmJiZgUtw6nQ4cx4HjOK/Gd+c2kyJQBaPiDC62YHkbw3A3HXcS9P5YOq4aoTFYrd5ZInLHihc4d22jAc89Y5h7yRu3kKe5SuNO0oZkYgtMej6OA3wN8blKv/ZUP+MJLN7mjSXjOZtROakaJTBfumg6nQ4JCQlISEgAIAiKvv3223jqqadgNpvxve99D2eeeSZOPvlkHH/88fPiVouOjkZqaqpi4wXPJ+sjHA4HGhsbsW/fPmRmZqKgoAAhISEBbQMAuM+mYRgeHsbevXt5zTV3RMPGDmQ2msPhrEBM55Tfl6k/A/KLjavFVm5fXzlf6k4TexvZ5RFnTrH5MHhSofbUNpqRGiuIZWBqC77EH8Rgd/5iiMcxGJyJhp0LmE1aOp33rk4xmKtLbrurObvLRAQES8xdiwbp5+Ru8Q42N9pCWVpMUPS+++7DZ599BgD46U9/iomJCVx//fV44IEH5mUev/nNb5CYmIhNmzbhoYcemrMQ8VFp2ZhMJpSVlcFkMmHz5s28xHig9csAuCUFsdts5cqVyMjI8OrHEyiykfr7mQvGW9FNd3EdVxlUrp57M1dAXqJfnG0mPb+0d4zYpSNOXpDOSbyf9P2J04zZNm8bjclZTK7cVWzOzCoQJw3IBdo91Q95+gxcZYvJzY9ZV3LHeGMhieNYYrerO3WMYHOjBYMumslkAgBceumlvEvLOpeWrV7ihhtuQH5+PhISErB3717cdtttOHLkCB555BG/xzyqyIbjOPT19aGiogIJCQmzFJEDSTaeLBtf3GZyYytJNiwGIleMyRYscSKBFO56ycjFa1wtsOJjPLtQ5APtcu+DPZc2IBO/P+kc2P7u4jniuUjbFLiC9Bq5UpiWuwZybjS2jogz7rwlbvGNhdxzKfmy87hKGHB3UyF174nHZEQtF8NxV+sWbGQTDPMxGo0ICQlxum7+1gveeuutePDBB93uU19fj5UrV+Kmm27it61fvx4hISG46qqr8MADD/gt03PUkI3dbkdtbS26urqwcuVKpKenz/oiBJJsWKBQjhRYtllCQoJf2mZKN2ZjC7fBINQ2SBcOTwFgV4QjtwB5uuS+Eo80ruQqXiO1agDnhVMag5EjhrlkpUkbmknn4smqYGOwz0hqiXpDjFLIWSju5uDq/XuKX4mPERMQIzRXlqC7Nh7BFrMJhsZpLDlACffizp07sWPHDrf75OTkyG7fsmULbDYbOjo6kJeX59f5jwqymZycxKFDh8BxHIqLi12mAQaSbOTG99dtJkUg3Gha7ewuj1KIF1vposOyn6SQW0RcBbvl4G0qrRhyloyrOYlfly6Wcsf7qwrt6j1Kt4n3k3ss5+4Tp28HGnK6d+LvjhjuurMyd5k0ZVuKuLg4N3MJrphNMDROUzITTax44CsqKiqg1WqRkpLi9/mDmmw4jkNnZydqamqQnp6OvLw8t3cagWxwBjiTwszMDCorK2E2m312m7kb119I37d4OHG6rLhBlthFJa3Ad7VN7k5Yuih6imG4g7tFn40jHtOdZeKN1cIaf/lSM+RqPZSLA4mvhavHcpaRr0TjanF3F7cSvy5+7upz95Qw4Oo9iREaGgqTySRbxBgMbisxgmE+TIRzPkl43759OHDgAE466SRER0dj3759uPHGG3HxxRfz8XF/ELRkY7VaUVlZiYGBAaxfvx4pKSkeL/h8WTbDw8OorKxEYmIi8vPz/W4JwDBXshkbG0NFRYXH/djCyjLOvLlUclaG9Fh3d+GuzjEX95U0LsL+e0pLdlfI6G3FvLToUjqOXJzIm2vgL8mIz80gF7eR28/Vc+lnI30uLTB1Fd+SI8CEhAQcOHAAoaGhfHvm+Ph4hISEBMXiLkYwJAgshFRNaGgoXnzxRdx9992YmZnB0qVLceONNzrFcfxBUJLNyMgISktLERoaipKSEq+bFgWabDQaDbq7uzEwMDAnt5kU/pINx3Ho6upCU1MTcnNzPew7W/NMznXmTVaZ9BJ7K3gpXniV8hqKkwE8Ve/r9fKEwmRhxFpk0sVavAa6IgQ5ApVaBOJYiJwKghJwRyjSGwNPNw7ShBBxerbcvMXJAXLfgVWrVuGEE07g5fo7OztRW1uLqKgoGAwGOByOoFjkgeCwbBZC8Tk/P99tKwh/EVRk43A40NLSgsbGRuTk5CAnJ8enDzuQZDMzMwOz2QybzYatW7cqWlTlD9nYbDbU1tZiZGQEBQUFfEGYO4gXUJYAIHc3Lr0zlkuPlS6s4kXL1X7+fjS+ZGOJIZ2jp0ssTpoQtytg18BThppc/EeuIJPBXQGqHKQFn4DvFqJUwFQ8BznLUO6zlBYIs3kZDEKaOLM0pe8xPT19lly/xWLhWzMbjUZ89tlnvGJyQkICoqOjFySWEwxkc6z0sgGCiGymp6dRXl6OyclJFBYWIj4+3ucvWKDIhrnNdDodcnNzFa/eZWTjrTrB1NQUysvLecvP21RE6Y+fZakBs+/kxQufWKBRvE08nquUX2/iKZ4WTF+D92wO0kvJEiE8xYU0GufYFtvu6XxycR8pgSjx9ZRao1J46yL1JqtQGt9j1qH0db1esNjE9UlSyFngISEhWLRoEUwmEyIiIrB06VKMjIzwBASAJ574+HhFej95g2CwsI4VxWcgCMiG4zgMDAygvLwcsbGxKCkpQUhIiF9jKU02HMehpaUFHR0dWLVqFY4cOaLY2GKwuydvyKa3txe1tbXIyspCbm6uT3decunMcpleYsKRLqKuFmCpNSSGtG5GevfvjXSK3Hnc1bG4yqZzdccthqv34WlecmPKBds9ndeX1+W2eaPR5g0hiY9llouYhFlihTQLTVwjJMXq1atdno/jOOh0Ol4xOSMjAxzHYXJyEiMjI+jv70dTU5NsvCcQCAbL5lhpnAYsMNnY7XbU19fzudsZGRlz+nCVJBuz2YyqqirMzMzwbrOBgYGAWE7iglFX75+1tu7t7cWGDRv8TkEUp96yWAVbKNkCJK7Yl8ZxXLmq3N3Ry+mxebtwuttH+tibinn2/pSEXKGrGJ7m5GpMb/d1Zxm6InJPcRrxsYBzrMmVa5Udy9xxrhIEXEFucddoNIiJiUFMTAwvWikX72HaYkp2yLTb7X4XMCoF1bJRAFNTUygtLVU0BqIU2QwNDaGqqgpJSUlO2WZKF18ysB+Yq7Gnp6dRUVEBjuNQUlLitjDOu/MJLjS20DD/vZRoXB0vTpn2pCDg7nhfZW3cwVXqr1xCgFw1vT/wNbVbClfWmat5eWpjIP78xOO4e5+uBEHZeMytKD6HHNGw12w210Wh7koEvKmzcRXvGRkZQX19PaxWq2LxnmCwbEwmExYtWrSgc1AKC0I2HMehsrISUVFRWLly5ZxThxnmSjZSt1l6errTF9WfNgPewB3ZDA4OoqqqCqmpqVi5cqUid23iIk6py0NO4obm6JpgpJdEvK+nxVFuoXV1p+5NzY8UrhZZdncu59bzBnI1NWJ4ox8nnZ+7WhlvY0euPj8Gd+QodoexOIyUeKQyNVK4Ixoaw70Qp69SLCzes2jRIrAOmUrFe4JBQWAhetkECgtCNhqNBgUFBfxjpcDIxttAuxhybjO58QMjmKmBRqNxGltMfKtXr0Z6erqi55RK17hK2xUTjJiU3BGMtKDUG8jFcKTjSl1lcou5dCx3lpPYghP3knG1PyMYT9l1rtxqnghNLsVY7jg569NT+wQGd/EcKZHLEY+na8tcaP5YfHOVqxF3yFQi3hMMCgIq2ShxYr1ecStBrMzsyx2JK7eZFIGybNjYjGwsFgsqKysxPT2teJo1g5xagHS71IKRk12RHi8eX85378rqcWWlSLe5W7AZGcnt72p89rq0l4zU6nFlxUnhyz2OdEF2lT4ufU3OevHUPkEu+89d4oD4+yC1eNgY4rot8XfG34JdpeVq5hrvCQY3mko2QQr2RfE2ZdGT20xu/Ln2dHAFRjajo6OoqKhAXFwcSkpKfHIxhoeHY3p6ek7zkFoPcmKX0kvgKmYjJyTpyupxVbMjhbv6Hrn5i8f3NgvN1ULsS6ac3GvSMaRzcVd342reriwadzVG7q6fHPnJWTPs/UhvPPzJ5BPmFdjF3dd4j81mU91oCmLByCYQefK+NDgzm82orKyExWLx2noItGXT29uL7u5uLF++HFlZWT5fo+zsbNTX13vcTywzIr6bB5wzy8QLljurRu6SyC2C4gXXXZ2NO1ePL7UhUrCMO7nsubnCF4L0NE8G8Wvi6yn3uQCuyVtujmwMd5+dq9esVvek7e9PZL5Vnz3Fe2w2Gw4fPgyr1Tqv9T1iqNloQQqNRuNVkoDYbVZQUOC19RComI3NZoPNZkNvby9f0OoPzjnnHK/IhtXbAMJdqsMBJCYCw8PCIsjeqpxVI661kLvccougKwvHU6zI1Wtyz9kxruo8GOnNNYuMwVOigKv4jVy6sbt4iFxtlHQ/T2Tuioyk19CV8Z6RAfT1ec4m9DfDcCHdVnLxnr179yIiIsIp3iNONghUfQ8Dx3EwGo3z0gJ6PnBMkQ3gPiPN4XCgtbWVd5tlZGT4NHYgLJvJyUleRHPVqlVzUlW9+uqrvW4ZK6390GqBkRF6HhsLTE0JLhSWQCANGgPyi6x4IXf12F1A252bRxoYd0UsrpIe2NwZ4boiNU8Qx6T8+Uq4k4WRgysXmasaHldk7m3shkGjoZuQoSGgu3t2coK/9VJyCKYWAxqNBhzHYfHixYiNjZ3X+h4xVLkaBRCoL5UrsvHHbebt2P6CqQFkZ2djYGBgzl9WX/LxxXfj7O47OZkWlbExIDycUlhDQoCJCedFSby4y93FuooBSKVy5LaLVQukrzFIz+fqTtzTV0zcRppderaASmM/bD9AIF1P4TtXKcByrjzpvu6KMKXk4A2JS61V6ThyFlJoKBAZSd8JnU6QqvFkbbqCJ/2+YAjIiyGez3zW94ihxmyCGHKE4K/bTG5sJdxodrsdDQ0N6Ovrw8aNG5GcnIzh4WFFxvZW1FMu5XVoiB5HRwNftj7H2rXAwYNAfDwwOsrmz84l3OUy8pKShauiRVcWj/RuWS6wLucCk1vYxWoBri6JONNMfD7WDEx8LHt/3txv6HTyROPqeOm+0vm6I3hvSNxV/Y5UoZl9puedB/zf/5FblV3vqChgfFzYT6w64Q1uv/12t68fTZ06A1nfw2Cz2WA2m1WyCVaIyYapSHd2dvrlNpNCCTeayWRCRUUFNBqNU/sEpVx0Z555Jt566y23++j1wqLHFhdWFa7VAkajsNiWldH/pUuJbNLSACYRJ3blyJGFO9++K4sHcJ8C7GqbVDiTQc4acwdP2V/eZKS5O95VbYrU0pHO111jMjHRunNhMstGXDPDXg8NBWZmgPPPB/7zH+DVVwWFichIIpnJSWFMb1PBxfjBD37g9vVgsmw4jvN6Pt7W9/ga75mamgKAYyZmExyfrIJgZGM2m3Hw4EEMDAxg69atcyYaNvZcrI+BgQHs27cPcXFx2LJli1OfHqVaQ//jH//wuI+4OI9ZJixBgE2BZRydeCI9Ly+n/yyktHq1vJtKvM1VwaB0P2nRuJzVIgZLY5aO584Lye7CAxkS8DQHRipyc/PkGnRnjIutIncuTDnXJPs8zzqL/r/0Er2P7GxyoXKcYM0sWTJbW85bz29UVJRHj0IwxWzYb9Ef1zar78nOzkZ+fj5OOOEE5OXlwWAwoLOzE3v27MEXX3yBlpYWjIyMuLzJNH3pXjhWLJsFI5tAxmzGx8f5TBIliyL9tT44jkNTUxMqKyuxatUqrF69etYdk1JkExER4fH9yhU+6nTkMgGEBchuBz79lBaXb3+btrFkN7OZjjvvvNljsXOIf6fidUZ6dy91IUl/33LuKOnizBIZXK0N7C5eHHtREsxalFbZi+EqTVs6H+aKFEOutknusTuw82g05B4FgG3b6P+rr9I469cL18psJoJKTaV9OjroP7tBAbxPBvjDH/7gcZ9gsmzY71yJ+bB4T25uLoqKirBt2zZkZWXBarWivr4en332GcrLy9HR0YGJiQlwX15Uo9GIsLCwgNb63H///bzeYlxcnOw+XV1dOOussxAREYGUlBTcfPPNftUbBscnqxAcDgdMJhMOHz6MvLw8rFu3TjHdNcA/y2ZmZgYHDx5Ef38/iouLsXjxYtn9lCIbAHj++efdvi6NizDXSHIyLSKMdKKjgbAwWqRefZW2XXQR/f/SLY3du+n/734nHMNccq5iCXJuJDGkC63c4stcf3Lvzd36IFYvYFXw/kKjIZJhWWnuPj53X0M5lQRp3Eo6T3fxGKkFJ4493XAD/We5JP/9L71+wgk0pslE16W3lz5LjYbSnfV64bsghji5whW0Wi3OP/989zshuGI27LcYiPmweM+qVatQUlKCoqIiJCcn85mpn332Ga644go8+eSTAVedtlgsOP/883HNNdfIvm6323HWWWfBYrFg79692LVrF5577jnceeedPp8rOD5ZBcDcZjMzM8jMzFRcSwzw3bIZHR3F3r17ERISguLiYrfmsJJkc/rpp7u0bthvR1wFzu74Bwdpe3w8LSCTkwDjxvh4WnBeeIGef//79N9spmNvuYWe33cfjbFunetF3xP/e5sWLEcULPbhaXFnriu2WDLLxBN0OvozGISsNHdfCancixgsnVwKubmLr4GUJOUKXdnnGxlJ7rBHH6XnH31E+3z8Mf3/wQ9o35YWet7eTv+Li+nzF8d4wsOd06bZvDz9JF5gXxoPCDbLRqvVBtytx+I9GRkZWLduHY4//nisX78eqamp+PjjjzE+Po6cnBxceeWVeOmllzDOfJoK4Z577sGNN96IdevWyb7+3nvvoa6uDs8//zw2btyIM888E/fddx8ee+wxWCwWn851TLjRBgcH8fnnnyMiIgKpqakBMzuZZcN58B1wHIf29nYcOnQIOTk52LBhg0cLS0myAfBlEsLs7eJ4h9gKYAuYXk+pznY73cm2ttLrK1bQgrN4MR2/axdtP/dcGmfZMtr+05/SWBkZtDidcQbdRYvvzj0pEMhdKrn3wuIwUrAF0NO6xfZj8QytlkiEkY/BQP/ZY3Fmm6cqenfvRXp+KaRfAzm3p7sYWEoKXfOlS4FTT6V9f/Yzmn9nJ+3z29/S///8h/4za/bSS+n/nj3C+2VdAcbGhPcjTql2h5iYGJzFAkIeEGwxm4UgPo1Gg7i4ONx///349a9/jby8PDz55JOIjY3FAw88gOrq6nmdz759+7Bu3TqnsorTTz8dExMTqK2t9Wms4LiN8BMOhwNNTU2oqKjAypUrsW7dOhgMhoBKyrDzuoLVakVFRQU6OzuxefNmr2VnlCab9PR0nHPOubKviYPF4qBxRIRzVpTFQokAHAfs30/bUlNp37VraVFmHrsNG2j75ZfT//ffJ5fMvn00Znw88OSTtO/69e5dL3JWgKtL6Gq7OMXaGzBrh5GIzUaPWT2N1epsEXkCs5isVvn9Xc1LXGgq3le87knHi4qibf/3f/R89Wo6b1sbbbPbgdxcGvfWW2mfm28Wss0A4IIL6P+zz9JYKSn0fQCEYt/s7NlxMU9f7c8++8z9DiIEmxstGHTRoqOjcfrpp+Phhx9GRUUFtrEg2zyhr69vVv0ee97X1+fTWAv6yc7lLkacbVZcXMy7zQIplilWlZbD5OQk9u3bB5vNhpKSEpcBNzkoSTYcx6GtrQ2XX365bH8Q6W+ILTpmMz1OTqbtDgdQU0PbLryQtn0pdsBbBN/5Dj1/5RX6X15OY91xB1k2VistViMjwNVX0z7f+ha5dnbsIIsJINUCd1lOru4f3CUFMHLw1kXmCd6Owaw4d/c8rqrv5cCITmwdbtxIi/+SJcBPfkLbWBnLJ59QBllUFPC1r5GFumwZXd/bb6frtWYNjXn66XTMrl20fcMGIYY3NUXny8yk/8zFxtyugPs41eWXX46lS5d69R45jgsqsgmG9gL+toS+9dZbwdqWuPpraGgIwIzdIzg+WR8hdptJYyGB0i8D3At9dnd3Y//+/Vi8eDEKCwt91k1Sat42mw0VFRXo6upCUVERnnrqqVn7SKfPYg/srrq/n7YnJFD9hcNBKbEA+fgBoLmZ/u/bR///9Cf639REi+h995FltGkT+f9DQymOo9MBv/oVLaBvvEFxAYOBLCeHA7jkErKCDAZg5UrnBV6ONNhzd0TAXGpzXTu8qbExGDwTjZz1wiAumGWWxeLFRMpRUUBpKW3LyKBr19UF3H8/XVdWdHvDDUBWFhF6RQUwPQ28+y6N/f3v0/9ly2jfF1+k+Z58Mr0/JlM0Okqp8BwHHD5M44eFObtg2TzlyH7RokW48cYbMTk56dHtDAg3cF91N5oYJpPJr668O3fuRH19vdu/nJwcr8ZKTU1FP1sQvgR7nsrSFL3EUUU2DocDjY2NTm4zqamrtKSMGCxgKCYFu92OmpoaNDY2YuPGjcjNzfXrB6OEZTM1NeVkWcXGxmLx4sX485//LDqPULciDvICZNnodORC0enIGomJocWRJQg8/TTte9VV9H90lBarG2+k57/4BT3PyqIF9fPPyf9vtxPZ2O3Ayy8LQfPBQSKeVavo+Pp6sohiYiiIzXE05po1dMyZZzrHQcQSLK6IR5z2zMhAaej1dD2ZFeIK7JpLXVFs8f/ud4GSEsoEq6ujbVu30jUxm4GiItr29tt07cPDyQXmcAAPPUSvPfkkudBGRyn+ZjCQ+8xgECzT//6X/v/oR3R92tpoHp2dNLecHEGU1WAQYlriIlHAucCTISQkBB9++CHGx8dRVlaGPXv2oLa2FkeOHMHMzIzsdQlk9pc/8LZNSSDhr1RNcnIyVq5c6fbP25vh4uJiVFdXY2BggN/2/vvvIyYmBqtXr/ZpXkeNG425zQYHB53cZlIE0o0GOGekmUwmHDhwAJOTkygpKUEy8z/5Oe5cyKavrw/79u3DokWLnCwrjUaDc845B/fccw8AZ/VftoCwxACG4WHaJzKSCMdmI5+/3U4uFZ0OeOwx2veyy2ixys6mMW6/XVAciIkBNm+mO3O7Xchku+46Ou/xx5ObR6sFvpweamtJoWBggNQKAOC99yj+o9cDDzxA5//5zynoDQB33UVzjYsTFmM5SBMB5kI6zFJiacbSsaRrJvMm5eZS0N7hIBJetQrIywP++ld6PSyMSMJoJMsOoLTzujr6HI4/nrY9/TS9Hh5Ork6NhtLS9XpySUZGkkvs+OPpXL/5Df1nrjBmif7nP3Rsby89/9a36Dq1tAiqA8zFOTNDFiq7lsDsOiGNBmhubsaSJUuwfv16HH/88Vi3bh3Cw8PR09ODzz//HAcOHEBzczOGh4f53xKzfoKFbILBspkPXbSuri7eE2K321FRUYGKigpeveC0007D6tWrcckll6CyshLvvvsufvnLX+K6667zOS07OD5ZD2Bus8jISI8pxIG0bNj4DocDAwMD2Lt3L+Lj42epAfgDf8mGWXs1NTVYv349VqxY4UTibNyf//znuPfee2e5k5jAJhOlFC+aZjPFb7Ra0kdjWWZ2O7nHdDrg8cdp35ISGuO88+j/J59Q9lJTk5DN9dprtG9kJO3z1lvAO+/Q+Ts66Ny33UZjRUUB119P+9fVkbtoelpIRPjrX4U4EUvLzcoSguR//jOwcyc9fvVVys7KzAR+/Wt6Hxs2CAWNxcVEjFotkJ9P842OpkA7QIst8zqsXk1xEp2OrJCoKDr2hhtoXmefDVx7rbCwn3wyke0XXwjHMxJ9/HF6Xw0NwCmn0LZdu2iht9uJqAEi8G9/m64bK8a88kq6LiMjlDHocNB4Nhu9L42GrJgPPqCx0tPp/d1wA712ww2CnBDHUfxMo6FryvTxWPb88DBti4ggwpFm2Im/tgcPHnJSLtdqtYiLi0NOTg4KCwtx/PHHY+nSpbDb7WhsbMSnn36K8vJyHD58GMGEYEkQCLTi85133olNmzbhrrvuwtTUFDZt2oRNmzbh0KFDAGi9e/PNN6HT6VBcXIyLL74Yl156Ke69916fzxXUZCN2m61atQpr1671+AUIZMwGIEuho6MDlZWVWLNmDVatWqXIHZA/ZGOxWFBaWspL8sipPjOpdAC45ZZbcM8990led64b4TihYtzhICvD4SA5E44DDhwQXrPbhcSBf/yDxmptpVjBtdfSIjw9DfT0UPzme9+jfS+8kMjhrLPorh4A/v1vOv+dd5LrzWKhu2hGQGeeSXP8y1+E9O3BQcHaGRykxZUt5HfcQXMCaAGdmRGy4gBa9E89lR7/7GdAYSHN6YMPiFxOPRU4dIjOf8YZVKcCAPfeC3zjG3T+nTtpvNhYwXIZHyeCBSgz74svyGpgRu/rr1PGl04HvPkmEXp4uHBt/vUvsi6io4moACKRl14ia+cvf6E5rVpFMZVVq4iU9XqAhedefZWue2wsva+oKGD5cnrtN7+h65yXJwhuajRE3hxH20NCiLTGxuiYnBw6ZnLSOR7FZI4AIDRUj8rKSqxgGR8uYDAYkJKSgpUrV6K4uBhbtmxBcnIyJiYmAACff/65R5fbfCAYEgTmg2yee+45PjlD/Ld9+3Z+n6ysLLz99tswmUwYHBzEww8/7FexvIbzJnoXINhsNpdWyPT0NCorK2Gz2bBx40avzcmRkRFUVVU5XSylMDMzg927dyMkJASFhYWKmrg9PT3o6elBkTs/kAjj4+MoLy9HbGysW6WE/fv3IysrC2lsFQbw0EMP4Y477uCfi0UfxY+Tk8mlIy7ku/Za4I9/FDKWsrPJKvnTn2jRCwsTRDHZopidTckAGzcC1dVCfQxbqFtbqVdKZiZlR+3fTwsdG0scNP/e92gx/cY3yMr6wx8oWeGUUwRX0gMP0FhTUzR/sYq1r0rFriA3hti1ZrWSRTM0RPtdcAGR37e/DRQUAL/8JV237dtpbqecQm7GmBiKs7DxNBoiFZ2O3s+bb5Je3Y4dRFyMEPR6ciMODRHR/uUvdP3MZkG52eEgy+vNN4Gvf50sS4DOsX07ufWSk4kcNRqyBpnoKktqYOOICSc8PAxNTS2IYQU5fmBqagqHDh3Cxo0bMTIygpGREUxMTCAyMpLvGRMXFzdv1kZHRweMRiPWrFkzL+eTw44dO5Cfn+9RLftoQVBaNoODg9i7dy+ioqI8us2kCJQbbWRkBHv37oVWq8WKFSsU96X6ok7Q3d2NL774AllZWdi4caPbuww5i+nmm2/Gc889B4Nh9sev0Qgul8FBussND6eFTKMhogEEF9XAAO178820EP3kJ3RMSgotSFVVRDQAcNJJtO1Pf6J9jUZB9iYjg8YZGwOSkoggWOzo7LPJYnE4yO1mt5O1wuS2CgpoUZyaojt6rZZiQd/4Br2+bx/FStatE4jw8suFmpOf/pRe1+nIygkJIVfZtdfS68uXU/EqQDGqbdvoHP/8J12r5cuJRB0OWuh/9zuyEl5/ncg2MpL2A8gyZFbSkiUUmO/rE2InaWm0b3Q0WUV2O1k6iYn03s44g47dtYssjYgIIX2ZSQY9+ihdu95eIq7ISEoECAkBGhvpPP/7H+175ZUsziJkIkZG0k3AkSP0PsUyNVLtt0WLFqG7u3dORANQzEan07l0uTU0NPAaYp2dnV5nufmLYEkQOFYUn4EgIxt/3GZSKO1GYzUrpaWlWLZsWcDMWq1W6/HH43A4+My3/Px8LF261GOShdiNJsYFF1yAhoZmxMQQabLkFIeDFj+HgwjGYiFSYK61pUtpUWYV6Cw+kZ5Oi9aDD9KCtHQp3Rnn5lIsRaMRFtnf/Iael5RQ3AMQYh7l5WTpTE4KOmtDQ5SGrdPR6yEhVOnOdNpWr6Y5jI2RO0irBZ55RnCjrV1Li2x1NbmEmGzL++/T621tREJi6ZrISMH1FRpKhAsQoRmNgl7c+DhZZeecQ6/fcw/w4x/TOQoLab5jY8AjjwgZfsz9dNdddF3XraPsMo4j1yJzP552Go351FMU17HbhTjSa6/RNWekABAR6nTkNtPryQLKz6fP8Kmn6HNhCgJ33knv4eWX6fnAAB173HGCRcismZgYoS6IEY9OB5x77rloampWRH9QTj1A7HITa4j5kuU2l/kEgxvNn9TnYEXQZKNNT0/jiy++4LPNXAlWegKzbJS467FarSgvL+drVpYsWQK9Xh+QmJCnmM309DQOHDiAiYkJlJSU8B0D5zJueno6+voGsGnTZjCZI8btGo3QvyQsTIhDLFpEi96WLfTaww/T9oICWoxOO40Wtf37aRHs66OFmuMEPS6Oo8X9ww+FDDWzmRa3W26hO3W9nmpCHA7qqVNfT+ddupQWz//9j7KwALJgUlPJimAN4O68kxZfgIpJo6KIpBYvFlKuW1po4fzwQyEb649/pLm0tBAp6vW00L/yCj1+6y0KzFut9H6MRnrM0slXrKBr43CQusKGDWS1dXfT/M8/nyw1FnOanKTxWPzokUeEBIElS2jbnXeSpRYbS2QAUNyrq4uub2Mj7X/fffT/rLPo8/vkE7Kk7HayFkNDqU5KqwXuvts5Vfzss2nOu3cLRb7MWBkcFJS6p6fpd/v66//FLqZZpAA8Le5iDTGW5bZ27VqEhYWhu7vbZZbbXOaz0JaNyWRSLRulwTK7/HGbScG+IHP9sk1MTGDv3r1wOBx8zQrguxint3A37vDwMPbt24fo6GifM988kZhWq8Vnn32GXbt28fGatDShfmVmhhb3rVtp26FDQj2J3S5oabHMsO5uIqcLL6QFbnpaKABlbq3vf58WsuOOE+7U//EPoRj0vffo8dKltOjddpsgp3LPPcKi19JCr//yl5SEUFNDbiWOozv5Tz+lYxwOIrzERHKTGQw0v3vvpddeeYUWe4OByCMhgVxXTGL/e98jkrHZKN5x9dW0cB84QFbbkiWCSy4nR8jge+01isv09hLhAOT++tvfaPHetYviNQaDQDYvv0yP4+MF193TT9M1GRujLDutllx3kZFkPf3whzTG66/T/n/7G807KYn2S0oSSPrxx+n65ObSvizW9Z//0DFLlgiyRUymZtUqQcEgNzcXhw8fwYms0ZFC8NWS0Gq1iI+Px7Jly7B582be5Waz2Zxcbl1dXZiamvL55jMYEgSmpqYCniAwn1jQBAHWz6Grqwtr1qzx25oRw2634/3338dJJ53ktzx3d3c3X2Wbk5PjZIFVVFQgNjbWaxkObzE6OoqKigqcdNJJ/DaO49DR0YGWlhasXLkSmZmZPo/ry3wtFgu2bNmChoZ6PitNLH8UEkIV/iybymajxbari+6U775b6BrJ7o5TUylW8+yzzgkCAC3+aWlEEu3ttDAWFZEVw0QfWTCa3VlfeSUR09e/TlbDXXfRov+d7xA5nHMOEU1SEj2fnqZ5s2w0wLsEAVZ7xGI8gPtOmuwY9hqrT2EivaeeStbYN79JC/+f/0xWGFNV2L6dyCc1lVxabK6soJb19HnmGcogu+kmKt40GoX3FBND7q9HH6UY2pIldI7xcaFm5qSTgM8+I6L/7DPhPFu2kMsvLExIOmDJHzQPDZ566hl8mzU2UhhDQ0Noa2vzOkHGHTiOg8lk4hMNxsbGoNPp+E6ZCQkJHteGmpoaREdHIysra87z8Qccx2H16tV44YUXFCf2hcKCUndvby+Ghobm5DaTwp2kjCfY7XZUV1ejqakJmzZtwrJly2b5kQNp2YgtEJvNhsrKSl7Q0x+ikRvXHUJCQlBeXo5PPtmNsDADTzQJCbSQORx0l63VCnpcQ0O0yP7qV7Sg7dhBd8ZsgezoIKIBaKG124kgQkPJhcQkmpYuFdJqMzJokf2yrgwnnCD0X/nXv2jcV1+lcQCyQgYH6djUVDr23HPJjcaSFFavpiLIjg6a/7nnAqyFx8UXC3GlSy8lwkxOFuIwGRlCx9JvfYtIDqCAPLMa3nyTFvNf/5pciw4HJSasXk2xr5ISOoa55AC6Rt3dFEf55z9pXqw4NS6O4jg2G3DFFfT+jUbBNfjII0SmUVFEHCwFHCDrjwlxTk7Seb79bbrmrCbns89ovhdcQMe2tNAxk5OU3KHR0LXS6YCvf/3r6OsbDBjRAMoqPms0GkRGRiIzMxMbNmzA8ccfjzVr1ji53Dx1ygyGBAF/5WqCFQtKNhkZGdi6dauimV0ajcavjDSj0Yj9+/fDaDSipKQESUlJsvsFqo5HTApsLhaLBcXFxT4JekohldfxBlu2bEFnZze+/e1v80HtyEhalFgc4eGHaXG8+GJ6vngxLV5PPkmuocxMujNetIhqN7RaclsB5OYByAV03XX0+NJLaWEtLyfrx2qlhZTJ2jCubWqiRfPss4XkgsRE2n9ighIUmBuNqRwsX07WU2srHQeQ9cTkYGw2oZ3AypVkCS1aRMWegOCOAmjhDg2lRbiujiwmlm4M0HtkmXorVhDRDQ2R1afXk87Y1BQt+NdfT9ZPfj4Vozoc5BJjmWeXX07j/OY3QhyHueM++4xuAFh6ut1O7j2WIAAQWebm0ufx6qs0174+obhTpyPZGoeDrl1oKO3PlEnS0tJw8GAZXnzxRZ+1/nxFIAPyci43aadMVkXPXG7BkiCgxmwUAiMGpaHX630iGyb1kpCQgKKiIoSFhbncN9CWTX9/P/bt24fk5GQUFhbOuVOfN1luUvT29uKLL77Ar371K4yNTeLUU0/lNcwYATBvxxNP0PPVq2nxYnIqVVW0uI+NEVE5HEIVvcVCY+3dK6Qvh4fTIn/99UIL6rPPJiI4dIgUDAAiNYuFBCm7u2nbpZcSCebk0CLNcZQlx+I8559P1haznGw2umsvLaU5v/46Pec4spZMJiISls5dWSm4Dl94gYpHbTaKr5hMZHEMDdG5UlOJZDiOrI9ly4goOzromB/+kFx+ej0t7lYrkes3v0nH33YbuRStVqGC/7776HonJ5MbDKAkgNFR2relha7vz35G749l6b32GpGzzUaWUmSkYK39/vdC3YxOR5aX2UzJBpGRkXj99f9DY2MjcllwJ8CYz8XdYDA4dcrcvHkzEhMTMTo6ikOHDuHzzz/H1NQUJiYmfG4QphSsVissFotKNkohUAqv3hKCw+FAQ0MDampqsHbtWq/UAAJp2XAch6qqKqxduxZ5eXnzrkzArkddXR02btyIpUuXwmAw4M0338TIyARKSo7nVZ0nJmjxPv98WrSYqnBPD/n9TzuNFlmLhRZrgDKqAAq4JydTSvLGjbTtuedoUfzd7wTJmYICIqCf/YxiNQC57ziOFtryckEnrb2dFtaQELpzf+stWsQBIqjYWLJQfvc7ob3BH/9Ic37nHYoBGQw038REWpRZq+cLLxTqez7+mFKbQ0Mp8SEzk6wecVbe8uU0h8pKsgqPHKHYDEA1RiyA/+c/C+KnhYX0+muvEbEsWiR0Q921i8i2v58C/DodkZheTyoDF15I7+nDD2n/Bx+kfRYvpphYZialSU9Nkcqz3U7ErNPRfB0OylwLCwvHv//9bxw5csQpdjgfWKj2AlKX2wknnMAXco6OjmLPnj0eXW6BANMmC7Q22nxiQRMEOI4LyJ3Dnj17sGLFCqSkpLjcx2w2o7KyElarFZs2bfI666OlpQUmkwnr169XarqwWCyoqKjAyMgIiouL+cw3JdDQ0ACO47CKySq7mUNlZSVmZmbcXg+bzYZTTz0VFRWlsFqtSEqiBfWGG8hK0WppwWYp06GhRBQPPkh6alVVQnA+Lo6sgC++IDdXQQFlPnV1kUXE5HNYTxqbjeIszz1HZPa1r9HC/8YbZBGNjpKszb//TVaBzUZzYXNicJcgwNSVmTuKJSqwfj8sUUEuQUA8vkZDlsTkJJ1/yxaS4fn614n0/vlPem3JEoqJlZSQ9ZSTQzEcNj+mDDA9TXN74AG6nvffT645VmSp09H5JiZonB/8gMi8rU0QMeU4yiosKyMV7cpKOk9UVAz+9a9/4Xhmli4ADh8+jNHRUUV/V3PB/v37sXz5ckRHR2N0dJRPNrBarYiLi+MTDSIjIwNy03z48GGsWbMGVqtVkTqmYEBQpD4rDU8xm+HhYezduxfh4eEoLi72Kb1QactmYmIC+/bt492JSgcEvbFs2Bz0ej22bt3q9nro9Xrcf//9aGpqxS233IaQkAw4HJQBpdWSRZCURPGEmRkiogcfpGPPPZfuom+8kRbGqSla+ABaGB0OWhRXrKDHrM4nP5/GBIhoZmbI+vnxj2nb9dcLkiwbN9IYF1xABGizkYWzYQMlAezfT/M85RQh3sQUCjQaIjGtlubPXIWJiYIA5kknCQ3ffvELspgyM4U41AMPCO+3tpZiQMnJ5PYCyPJjKdkREZTY0NhIGXYsYyw8nAjoH/+g+d9yC81hakpQur79dqHgctMmInYWN7r5ZvpfV0cktXgxzTskhMjHYiFX4OLFWXj66WfR3d29oEQDBEcRpRgsQSAkJMSjy62urg59fX2K3jizgs5guiZzxbHzTkRwRTYcx6G1tRVlZWVYvny5bD8cT1AyZtPT04MDBw4gIyMDG7/0JyntovOUIHDkyBGnOXhzF8VUCe666y40N7dg//6DOOusb8LhIKtiaIgW2DVriFQ2bnQuJHzxRRpn5UoqWAQojmGz0d02a0HNRDNDQgTBzqYmGnP7dnLhAbRIT0/TYnzHHbQI//WvFFgHKJ5UXU3urJ/+lObQ3y/EWRITiTT0eiIhpiDNupBmZZFFANA8kpKEbDlGiizj7oEHaA4cR8H8ujo67y9+Qcc0Ngp6aTt20LmKiijLzm6nep2EBLKiWO3OLbcQSVmtNFeAEhx0OsE9ZjYLMSZ2r3D55ZRZNjpKLrbpaWB6Ohbnn38BBgZG8OmnnyqWBTpXBBvZyM3HlcstNDQUhw8fVtTlxmpsgqWZnBJYUPssUBdSjmysViuqqqowNTWFoqIiv11VSlg2LDZy5MgRbNq0CUlJSXwQX2mycZUgwHEcmpqacPjwYWzYsMGty1EKqQTOunXr8O9//xsOhwMvv/wybr75ZrS1DfAtm1mR4Cef0B32zAwtfDU1ggpATAzdnV92Gb22axft+7//kfXDbhpZPx3WugCgRm7PPkvjMtXp73+fnv/nP+Ru+/RTej4wQGM1N9O+ej25tFiLhZ/+lOZaXk56YhoNufkOHqTzMjkclgY+M0PEwgpXY2Mp9tPeTskGf/uboDywdi0RR38/WS3p6TROVRXVL7H3wu5/2Pt7+GGqrzEaKUbT1kZuN4uFyIypF1x7LQlw7thB6c/M2jIYdNi27Thcd9112L59O18UrGS68VzBcVzQzAXwTkGAZbmxTDeLxcK72+rq6mCz2fx2uR1rac9AEFg2gfiCSclmfHwce/fuBQAnNQB/MFfLxmw244svvsDY2BiKi4v5FGuNRqNIt04p5Ma0Wq0oLS1Ff38/tm7d6hPRAK6tJa1Wi+9973vo6urCyMgEdu7ciZiYeD4L7eyzaaH+1rcoVpGdLVgML7xAi+cTT9BdPkALamgopUf/9Ke07Yor6P/QEMVAWIJAQwMt8Oz3uX+/0Chs7VqyCJKTadEOC6O7fpaU8P77lA1mMJB1kZxMhZOMTy+5hEjPbqeUY5Yg0NdHLqpVq4S+PiedRJacTkekYjKRZbN9O73+618TSdnt1FmTxX2Y5fbmm2SJZWYKBPTss2QRdXbSsTqdoABw3nmkzGAwCO65e+4hotq2bRs++OADtLd34s9//jNSUlKwf/9+7N+/H01NTbysfzAgmCwbjuP8UhAICQlBamoqVq9ejeOOO25OLrdj0bJZ0AQBgALTSk+hpqYGoaGhX0prHEZjYyOWLVvmlXClJ/T396O1tRUlrErPB4yMjKCiogJJSUlYs2bNrDunDz74AFu2bFE03bGjowOjo6PYtGkTAPoSl5WVITIyEuvXr4eB3Rb7AOZ2c9UtVQqbzYY//OEPeOutt3Dw4F6+Gj4qirKwPvmEyOHkk4mEhocFqRSAFuaQECKja68lkjjxRLJeLr6Ynt99N1ktp55KtSNMj42dSxzMlyYIiBUC4uOp4j4khB4fOULEEhpKwfewMCHpwFWCAOsRFBZGZKPVAuvXk4X29a/T9jffJPdXYiIVhW7cCPz97+R6ZKKbAJFGQgLNKTGRanh27qQaouuuExIWtFogPj4eS5Zk4/HHH3fZstdms2F0dBTDw8MYHByE1Wrl77wTExMRERGxIAtcU1MTNBoNlrOCpgWEw+HAJ598gm3btilWX2S32zE+Ps5bPlNTU4iKinJqnyAmt5dffhlPPvkkDrAGUscAguNWQmFotVpYrVZUV1ejpaUF+fn5s2Rn5jK2r5YNk50pLS1Fbm6uy1hRoC0bVsOTmpqK/Px8v4iGjenLDYJer8dVV12Fu+++G7t378WBAwdw2mmnQauNweef02eydSsRQ2gopQnb7bTQ6/W0ALMEgWeeITfbO+8IAf777hNUik88ke7yL7qIalZYHGjTJkoC+N//BHVjFvM5+WShar6ggP6HhFB6MUCZbUy1JD+fCJHjKDEhMpJiM0ze//77SU2BVeDn5tJ5WQHq0BAF55mC8uQkudFeeIHmxbTSUlIoTdpmozHj4ohw7rmHxv7xj+n95uWtxIMPPoj29k60t3di9+7dbnvD6/V6vkf9ihUrEBkZicTERIyMjODgwYPYu3cv6uvrMTAwACtj6nnAQqU+y4H9vpWcj06nQ0JCAnJzc1FUVIRt27ZhyZIlsFgsqKurw6efforKykp0dXWhrKxsXnTR7r//fpSUlCAiIsJl4bhGo5n19yILuvqIBc+pcyWBPxfY7XYMDAwgKioKJSUlbos0fYWv6gQ2mw21tbUYGRlBYWGhU8tcKQJFNna7Hc3Nzejo6MC6deuQyvoF+AlfP7PBwUFUVlZiyZIlWL58OTQaDd544w1YrVZYrVZ8/PHH+O9//4tXX30VdXUTvDXCFvXwcHIr9fZSoL+oiBbxnBzqYMnaIGg0FCTXaIR4BUAEYrPR3f+DDwqK1uw8OTnk8hoeJgI6cIBqZa68khqKLV9OZFhdTSnMBw8KatZaLREEa0TGZGSYECbDrbcSmVRVCaoIF11ECRWbN1N8aMcOSmr4yU/o/bAEhyuvpPOEhIRgzZqNyM3NxT333CPbmdVX6HQ6ZGZmIjMzE3a7HWNjYxgZGUF7eztqa2sRExPDWz3R0dEBs3qCKX7EfoOBJD/mcktNTQXHcTAajRgZGUFtbS0uueQSGAwGxMXF4cUXX8Spp57qUtFkLrBYLDj//PNRXFyMp59+2uV+zz77LM5gjZQAvxVNFtyNZrVaFV1g+/r6UFVVhfDwcBx33HGKf2HGx8dRWlqKk1nPXjcwmUwoKyuDwWDAxo0bPaoBfPrpp1i9erWiX6yuri40N1PPkfz8fEVcdKWlpUhOTsYSpl3jAmIhUTmhVavVKusb7+7uxuOPP47m5mbs3r0bNts0xDfZGg1V6hcWksvs17+mDLfBQUF1+hvfoAX6lVfI2vniCwrmp6fTPiEhwqLPlAVYzxZXYHU/zChloqMAWT+joxTD0ekoe2znTnLxRUVREkJxMc21oYFI5uc/J6slJITIyWwWzqPX65GTk4vo6Gjs3LkTp556quKSMX19fejp6UFBQYHs62azGSMjIxgeHsbo6Cg0Gg3v9vFGzNIX1NbWIjIyEtnZ2YqN6S9MJhMOHDgw74WtDEajEddffz1qamoQFhaGqqoqbNq0CR9//HFAFAWee+45/PSnP8UYU2AVQaPR4LXXXsM5THpiDlhwy0YpsMZrPT09SE1NDZhEuLdutIGBAVRVVSE9Pd1rNQClLRuj0YjW1lZwHIfi4mLFFitv9Nbsdjtqa2sxPDzsc/ZfRkYG7r//fqdtIyMjeOmll9DY2Ih3330XPT09eOcdWph/8QthP3aaqiqh+VlRERWKGo1kCZ1yCmW9nXkmJSt8/DG5rN56ixb8pCRKTvjnP4lELryQXHZnnUWk9fzzFKgfHiYLKzGRMs9YsH5oiCwfs5kIbnJSkIlhc9XpiHiolUMYSkpKEBUVhR//+McoKiqaF5eSpwywsLAwLF68GIsXL4bD4cDk5CSGh4d5VXQWc0hMTERsbOyc5hxMCQILPReWXh0XF4ennnoKAwMD2LNnz4JJ11x33XX44Q9/iJycHFx99dX4wQ9+4JcVekyQjdlsRkVFBex2O4qLizE8PIwBpiaoMDylPrNanvb2dp/bJihJNsx1FR8fD7PZrOhdsSc32szMDMq+rNYsLi5WxI2ZkJCAa5hMM4CYmBg+OA4ICQSLF1NadGcnxUz0enJtsfuDkhJa6J94gv4ASo1mSgWpqWShfPABxYoA0k579116zFK3AbKgALKUzjuPHv/1r/RfrydRUpuNHvf3C3EXdi42p5tuuh53sIrMeYQv6cZarRaxsbGIjY1FTk6OU5pvbW0t7HY7L+GfmJjoU88lNheVbAQYjUY+ZpOSkoLz2BdsnnHvvffi5JNPRkREBN577z1ce+21mJqawg033ODzWAtONnP10w4NDaGyshIpKSlYvXo1dDodxsfHA6ZhxILjcl9IVstjNBqxdetWn+9ElCAbjuPQ3t6O1tZWrFmzBnq9Hs2sCEQhuEsQGB8fR3l5ORISEmQz7pQG66vDJG2KiijlmdWZhIdTxf9rrwnHMPXkiQn6Ky6m1OKBAbJUbDayWmJiyGoZHiaLadEi2ic6mh63tQmyNix7jmW63XgjpVQbjaSc8Mc/UpJBbe3sLDZ/EzXmirks8NKYw9TUFEZGRjAwMIDm5maEhYUhMTERCQkJiI+P9/g9CKaYTTC0FzAajX6502+99VY8yCQsXKC+vh4rV670ajzxTdCmTZtgNBrx0EMPHZ1k4y/EFsSqVauQwbTX4XsQ3xewL6GUbCYnJ1FeXo7IyEgUFxf7tYDMlWxsNhtqamowNjbGu66GhoYCokogRzZHjhxBTU2NYmnmniBux5yZSdYMy+piitCRkRQXee01Si1++mlqfWyxUGAfoNoZhspKcs0B5P6KjSWyGRujuAoTIWVCpBoNeH24r32Ngvvf+AZln33wgSBEarcT0Wi1QlEoQwfrUDbPUKqQUqPRIDo6mm82ZrPZMDY2huHhYTQ1NWFmZoYvbkxMTJStHwkGa4IhGOZiNBr9EuHcuXMnduzY4XafnJwcP2dF7Ufuu+8+zMzM+ByzOyrJxmKxoKqqCiaTCVu2bEEMa5b+JQLVBoCNDdDdD5N26e3tRW1tLZYuXSrbcM1bzEWdgCUjhISEoKSkhHebBSLbTzomx3FoaWlBZ2enT2oEc13omJYYQCnTBgMlA3z0EVXSA2TtsLhnVBRZPrt3U+X9TTcRAb36KsnMHDxIVk5bm9Dfhf2eEhLotddeoyLOp58mHbWtWynWYzCQtcTeUnU1WUE9PdTDBiBX23vvCU3hGNxlKAYSgara1+v1SEpK4u/MWdfM4eFhdHR0QKfT8VZPQkICDAZD0LnRgsGy8SdGk5ycjGQWrAwAKioqEB8f71dyyIKTja9f9rGxMVRUVCAmJsalBeFrPxtfwH4QDofDKSlh48aNc/6Q/bVsmCtx8eLFs5IRApFOLSYbm82G6upqTExMKF6Q6gksOwygtGiAYjK7d1PF/iefkIzMccfRax9/TNaK8D7o/znnUPX9wYMkb/PKK6Qw8PLLZDElJFCa8kMPkVtu+XJKgZZ+dQ8fFtQPLr+ciC4sjMZ/4w2yklhatBgL2Xp4PlxXERERiIiIQEZGBhwOB59e3dnZyadXm81mTE9PB4VsTaCSi3zBfMjVkNLHCLq6umC321FRUQEAyM3NRVRUFP773//yKiNhYWF4//338etf/xo/Y2qwPmLBycZbcBzHqwHk5uYiOzvb5ZcykG401vBtenoaVVVVsNlsfGHUXOErMXAch87OTjQ3N89yJfo7pjdgY5pMJpSXl8NgMCia7eYL2FeAVeq3tNDz44+nxd9uJymYd9+l/jGMnC69lDLUACqyZC41b2A0EjlZreQaY2ISH3wg6Jldfz1ZWA4HJR1wHJGZ3NdyIa4bsDB6ZFqtlrdoAEomGRkZQVNTEzo7O9HV1eWkaKBkerW3CAY32tTUVMBv3O68807s2rWLf85URj7++GNs374dBoMBjz32GG688UZwHIfc3Fw88sgjuJI1l/IRRwXZiAsjCwoK+C+qKwSSbBgqKiqQmJiItWvXKmZy+0IM4tTizZs3u60ADoQbbXp6Gvv27UNaWhpWrlzp149zrgudOGaTmkrWzQsv0La+PtoeE0Mkk5NDEi///Cdpqu3bR5lhAKkCMOTmCgR2wQXC+E88Qa2mzWYK/L//Pm2fmRFiPCefTJX9551HRZqffEL719VRUkBdHZGR9Ku5UDGbYAjKh4aGIi0tDR0dHVixYgX0ej1GRkbQ29uLxsZGRERE8C43qaRLoLDQCQKsyDPQCgLPPfccnnvuOZevn3HGGU7FnHPFgjtJPX3Zp6amsG/fPszMzKCkpMQj0QBC7EPpRZbjON7kTEtLw/r16xX9UnpLNtPT0zhw4ABMJhNKSkrcVvQGwrKZmppCf38/li9fjtWrVy/YXaA4ZhMSQvGV7dvJmnjiCXKhTUwIrrPkZOCEE+hxQwNZNwClK2/YQI+XL6exALJgmMUTHi64zvLySNssKoriNizZLzdXKPisq6N6m9ZWsnjsdqrrkcsC91UIVSkEg8uKgeM46HQ6xMbGYunSpSgsLMS2bduwdOlSWK1W1NfX85Iuhw8fhslkUvz3zRAMlo3JZDqmWkIDQW7ZsOwmJnPi7ReAEYA4iD9XiC2J0NBQpKSkKP5D9YYYmJgnS/X2dE2UJBsWoxodHfVKQSDQEMdsBgaIZEpKgL17KZi/fz8t9l96B7B3r1ClDwgWzOWXU3+Yykrg7bcpYeDyy0mdICOD0p937AB++1sinTVrqMBTLOAJUDLAq6/S4+99TxDjPPNMsoRYx00pFqqnTDCRjdwCbzAYkJKSgpSUFCdJl6GhIbS0tCA0NNQpvVrJ33owJAgcay0GgpJsWL+X3t5en3utAMqTjclkQkVFBbRaLYqLi1FaWhoQN527LDpmVTU1NSEvL8/rhZ650ea6sFitVlRUVGBmZgbp6emK3FWyefl/vLDgm81EPq2ttO3EEyll2WQiS+TDD6mbKMM11wgtCBobAV80J81mir/YbBSzOe002v7WW4Jlc+21RDBhYVRoynrkyClFL0RcAgg+snE3F41Gg6ioKERFRWHJkiWw2+18u+bW1lZMT08jNjaWj/VERUX5/d4W2rJhxKpaNgpD+oWYnp5GRUUFOI7zO/DO1EmVIITBwUFUVVU5xSYC4ZoCBLVqKRwOB+rq6jAwMOBRzFNuTGBuCwtrSxAVFYWtW7eivb0dZrGJsECw2YSYSmIiaaO99BJtGxgg0omJoX4w6emUTfbKK2TxfPCBkILMlAIAUpxmH+2OHYJL7W9/o4ZnZjNZP2+/Tdt7eoTGbieeSCR2wQXUf+ejj+g1RmYNDTSe9NJ1sUyFeUYwkY2vqc86nc4pvXp6eppPr+7s7ORVltmfL0kYC002FosFNpvtmCObBY/ZiDE4OIi9e/ciOjoaW7Zs8duMZBljcyEbVjRaUVGBlStXOrmsApWAIGfZmM1mHDhwAJOTkygpKfG5JoMtJv6S4+DgIPbv34/U1FRs2rQJer3e5xYDgYI4ZmMwkBWxbRuRxVNPkWtsaopiLwARDrNCGhqAH/yAHj/8sEA4ycmC9XHkiJBSrdVSPEejodYD//wnne+MM4QMuDVrBPJrbKT6nsZGIh27nXrtyMVsdDodJicn5/2aBhPZzHWBDw8PR3p6OtavX4/jjz9+VrvmgwcPorW1FWNjY17p+i2kG8345Rc20AkC840Ft2wAoSiwo6MDq1ev9roplzvMhRBYL5zJycl5LRqVWkyjo6N81pu/0i9iy8YXuFNsViLDjXVDtNvt/F2tr4sNWyc1GqrgdzioyPLQIVKELi0lVxlr7/LFF841Luz4G26g7LXaWnK3/d//kVXz7rtCzOaKK6g9QWgoNTq78ELKOBOjt5fiPAB1JWUxm5NPJpWC2FiB+MQICwtDWVkZfzfO4hCBlrEJlkJK5k5Vai5y7ZqHh4cxMjKC6upqOBwOJ6tHquO20JbN1NQUNBqNGrNRGlarFYcOHcL09LRfemKu4C/ZMNmZiIgIl7Ujc6n0dwcx2Rw+fBgNDQ1Yvnw5srKy/L4DFReheguHw4Ha2loMDQ3JKjbPlWwY0bCxWIEsmy9zg3r6wbMOlRwndM9sbxdiNiYTxW1Wr6aCTnHM5qc/FWI2HR2uu27KgUnQsHTmb32Ltr/+OhGMRkPutLfeooLQ9HSybKqq5M+zevVq5OfnY3x8nK+yr6urQ0xMDBITE+ccg3CFYLFs2GcfqLmEhIQgLS0NaWlp4DgOk5OTGBkZQV9fH5qamhAeHu6UXr3QCgIs7TkYbgSUxIKTDUt3ZC4aJcf1lWxY9lt2djZyc3NdfvkDadnY7XbU1dXhyJEjyM/PR2Ji4pzGZO/BW3KYmZlBeXk535ZATrF5LmTDREzZD1qv18PhcMBut/Pp6uLFh5GP3A/PbhdqXGJiqG7mlVfIlTU0RIt+dDTFcRITgXvvJcvj4EGKuTArY8UKYczNmwVCuPpqIWbzzDOkDmCx0BjMgunqEmI/J55IWWyXXUa9av73P6rDaWmh45qayDKSxmx6enr4WFx8fDxyc3NhNpv5u3FxDEJJqydYyIZ9l+ZjcdVoNIiJiUFMTAyys7Od2mQ3NDTAarXyRJOQkLAgbbIZ2QTDZ6MkgoJs8vLyFPdX+0I2DocDTU1N6O7u9ir7LVCWjd1ux8TEBKxWK4qLixUxo5mV4M18mWJzfHy822JVb8eTglk0zE3BfkxiFxojHLaf+DNk+7F9xYKWej2lJa9bR2TyzDP0enq6QCrZ2eTeOniQYjZ3302B/3vuISJpaCC3FysIbW4WYjA2G7V3PnyYCkR/8QvKODv9dOpvExtLtTqMmxsbqb6nrY3GBagGqKxsNtmkpaXNulZhYWFIT09Heno6HA5HQKyeYCGb+eiM6QqsTXZycjI4joPJZEJlZSVMJhMOHjwIg8HglF49Hwrdx2LaMxAEZBMoeEs2MzMzqKyshMViQXFxsVdBuUBYNuPj42htbYVGo8HWrVvnvVi0r68P1dXVXik2+5MgwKwZKdHIjQ04q2uLrR6byA/FVJcBsmq0WhLarKykYH1FBZHDsmW0T1mZkJos7rj585+TddLQQLpq//0vxWw++IBiNklJFJ958EGydAoKKIZz/fXO+mi9vcA779Dj006j8cPDSTrnwAEiJLE2G4Mn17E4BqGk1RNsZLPQc9FoNIiMjITBYEBWVhYSExMXpE321NSUatkcTfCGbJioZ1xcHPLz87124ymdjdbT04O6ujqkpqZifHxccX+xO7eXP4rNvrjRWPCXXS93RCMHqdUjju+I62xsNlrcOzpoG8tK6+4G1q+fHbO55RYhZtPdPVsc0x2sVqCmRmgbcP75tP0//3GO2fzf/1GNzeLFND92jNx79AVKWT3BkiDAamyCZXFlN0RMnZq5ssVtsg8fPhywNtn+thcIdgQF2QRCv8sTIbAAvCdRTzlotVpYWHHFHMAq8nt7e7Fx40YAkO0DPle4smz8VWz29vNiJKOUT16aseZwCDGbyEhyl/33v+RSGxqi7VFRFLiPigLuuIMyzL74glSYmRRNbq5wjpISIWZz/fVCzGbXLiITq5XGe/112t7eLrQwOPFE4JJLgB/+ELjzTpK0YTEbs5n2DQub7UYbYZ3X/Lwm/lo9wWLZBAvpMbhKfZa2yZ6YmMDIyIhTm2x2nefSJlt1ox1lcEU2drsd9fX1GBgY8DsAr0TMxmKxoKKiAhaLBVu3bkVkZCRGRkYCEguSi7FMT0+jrKzML8Vmb8hGnAjgqzXj/TyEOhutlghn9WqKyTz/PG1btEgI4OfkkNz/F1+QRfLrX5Mu2u23UzuBpiYac3SU9q+tFVSczWZyqQEU+7n5Zspo+/rXqWg0IYHcayyLtqGBztvVRfPQaKjVQWXlbLJRsv+IL1ZPMAhxAsEhCCqGN6nPWq0WcXFxiIuLU7xNtmrZHGWQI5vp6WmUl5dDo9GgpKRENtPKG8w1ZjMxMYHy8nLExMQ4ue8CqUwgJofR0VGUl5dj0aJFWLVqlc93YJ7m6SoRQEmQ20V4T0YjEcOmTeSuysujVOMjR6gnDUByMeIbRkYkv/gFWSy//S2wZw9ZJDt2UDEmi9lccw01WDMYSJDz2muBnTsFywqgmA2zqLZvJ5daZCTV/pSV0WO5mE2g5Go8WT0OhwNWqxUGg2Fe6npcYaHrWqTwJ/VZyTbZKtkEEIFYjHQ6nZP0y/DwMCoqKpCamurXAisd219SYOnVOTk5yMnJcXrvgSQbNi4z+X3RV5PCnWXjbSKAEmB1NnRecn+xOExxMS327e1kcXzwAfDHPwrH3nortZEGiCR88eJarVRfY7dTR84LLqDtL74oxGxuuIEy3HJzyboSHyOFkin/7iC1eg4dOgS9Xj9vdT2uEGxkM9fmaXJtspmOm7hNNiMfaTKASjZHGXQ6HcxmMziOQ3t7O1pbW102GPNnbF8tG47j0NTUhMOHD7sMxAeKbJgbrb6+Hr29vXOu35Ejm7kmAvgDjnNuMWCxUNtlvZ500gCyZN56i7bddhslCuzfT7EdlhK9fLkw5rZtQszmJz8R2kI/9xy52mw2UnZm6s5tbYLb7aSTSO356qspnfrf/yaV57Y2ig8dPkxuNqny87ScFHSAodVqodfrsWjRIqSlpc1LXY8rBFPMRmk1A8A5vRpwbpPd1tYGvV7Pu9oiIiICLsLZ0dGB++67Dx999BH6+vqwePFiXHzxxbj99tud3OlVVVW47rrrcPDgQSQnJ+P666/HLbfc4vd5j2myYUrF4+PjspXw/sJXUrBarXzu/tatW13etQSSbJqbm/lCzbkGH6VkI00EmI/MInYudrm0WkoCWLmSZGr+/W+yMJKTqacNQDGbhAQim6oqEuZ84gmycl57jWpjOI5UBwDahxkdJhPV2QDA0qXATTdRzOZb3wKefJLOs3mzELOpqSEyKy8XrJ2iIorlSLlFKoc0XxAnCMxHXY8rBFPMht0sBVJBwFWb7DfeeAN33nknoqOjsX79euzfvx+bN29WfC4NDQ1wOBx48sknkZubi5qaGlx55ZUwGo14+OGHAZCr/7TTTsOpp56KJ554AtXV1bj88ssRFxeHH0l1mrxEcNxOBAA2mw0jIyN822aliAbwzbKZnJzEvn37oNFoUFxc7NY8ZrEVJQlnamoKRqORr99RqlBUSD8W4jPiiv9Ag7meWK2N2UyWzdq1ZM3k5xNRDAyQGwughZ9ljnGcULB5++3AuefS4z17hBTpjz8m1xdAMRpWp7N1K3XkZCTC0N1N2W4AWUhGIykYnHAC9bKJiBC6g8q9l/mGq2w0FuvJzc3Fli1bUFxcjNTUVExOTqKsrAyff/456urq0N/fL6tS7iuCyY023wWmrE12bm4ubrjhBlRUVCAzMxPj4+P4+te/juTkZFxzzTWKnvOMM87As88+i9NOOw05OTn45je/iZ/97Gd4lZnrAF544QVYLBY888wzWLNmDS644ALccMMNeOSRR/w+b1BYNkovTv39/Whra0NISAgKCwsXpMkZm0dVVZVH+RvxuIByP77BwUFUVlYiJCQEWVlZii1q4h45gU4EcAWhEJCsG42GXFxtbUQkhYWUZlxfT0WVb70F/OUvwvG33UbtAQD670vMxmKhTDWHwzlm889/CgR0883A009TcWlSEqVANzTI1/MEG9lIEWir56tMNlJkZ2cjNTUV55xzDq6++mocOnQIhw8fDvh5x8fHnbog79u3DyeccIKTW+3000/Hgw8+iNHRUZ/V54EgIRulII6LZGVlYXh4OGDJB+4sG7GK9bp165CamurVuP6IZro6v1ixuaenR9E6JmaBLRTRiCFOfQaATz+lxwMD9Dw8nIorNRpSCtizB/j8cyIfVmezcqUw3vHHCzGbG24Q5GdYzMZuJ/21V16h7S0tgtvta18jC+naa4H77qOUaKORCNBoJPcZqwdyfg8L067BnzqbQKgZBEu9DyAkByzkfJg2mk6nw5YtW7Bly5aAnq+lpQV/+tOfeBcaQIoiS5cuddpv0Zcugr6+Pr/IJjhuJxSAxWLBoUOHMDAwgK1btyIxMTEgYpmAe8vGZrOhvLwcvb292Lp1q9dEw8YF5kY2DocDNTU16OjoQFFRERYvXqxoLIgtjBaLBc3NzRgfH1/Q3jbsbWk0FLPZvJm2vf46sG8fLfAsHTk7W6j2r6ggVxhAJJSXR485jtoVAGS1VFTQY5OJ5GYAiv089hiR2nnnUXwGoJgMSygoLaVjSktpDL2esuLksu2P5k6dzOpZt24d30cmJCQEHR0d2LNnD0pLS9HR0eG2X0+wWTYLPRd/s9FuvfVWPl7q6q+BCfV9iZ6eHpxxxhk4//zzceWVVyr1FmQRFJbNXL/wTEAyNjYWxcXF0Ov1sFqtASMbV5aN0WhEWVkZwsLCfC6UBOZONq4Um5VSaGDxpMjISKxZswZDQ0OoqqoCx3F818SkpKR5qdcwGAyYmZnhn1utFLdZvpxcZ8uXE1mMjQGpqURGZWX0mN6Lc52NTkdFnp9+SmrNl10m1NkkJwPXXUevs5jNtddStpr4q9vRQeKdACkR6PVEUJs2kdstJETIXBMj2N1o3sJfqycYFniGhW4vwFpC+0M2O3fuxI4dO9zuk5OTwz/u7e3FSSedhJKSEvz1r3912i81NRX9/f1O29hzX26gxQgKspkLWN2IVEAyUN00AcGyEf9YWXwkMzMTy5cv9/vH468VMjExgbKyMlnFZiUsG2kigLiAbXx8HENDQ+js7ERtbS1iY2ORlJSE5OTkgAkKCplvgkaaxSLEbNatI5dVRQW5t15/nbp3Mtx6K8B+S11dvmmjzcyQEoDDQf9ZzOaFFwRxz1/9ijqAFhRQksD0NGW7yZ1noRbaQLuvvI31WCyWoHOjLST8JRtxerUn9PT04KSTTkJBQQGeffbZWe+5uLgYt99+O1/0CwDvv/8+8vLy/HKhAUcx2bC6kb6+Ptm6kUCSjViRWKvVoq2tDW1tbbM6Wvo7tq/E4Emx2d+WAAzu4jMajYaX7WB3s0NDQxgcHOSTNJKTk5GUlOSxctoXSN8Ps1T27yfi6eujhT00lDTNtFoimAMHKMvsvfeEmM26dcI4TMATIDcb83A9/TQVaToczjGb1lbB7XbWWdQm+vrrKcPtd7+j7LP+fpKumZ4m4pFTEVgIzGesxJ3VMzQ0BI1Gg7q6unnrUuoKC23ZAIEv6uzp6cH27duRlZWFhx9+GIOsKA2C1XLRRRfhnnvuwRVXXIGf//znqKmpwR/+8Af8/ve/9/u8QUE2vn7hzWYz7y4qKSmR1R1ii3YgflDsLmBmZgaNjY0YHx+XbR/t79jeEoM4EcGdYrM/LQHE5/AlESAsLAwZGRnIyMiA3W7nF5P6+npYLBYkJiby7jZ/5YIAcbtrYVt0NDVC++ILcoUB1FTtyBF6nJ4OfOc7RDYHD5Iu2qOPEjG89hqlOet0Ank0NAiuNosFWLKEXGW5uZRtdtVVNN7vfkfuts2bBeHOzz4jt96hQzRmaChlpnV1zSYbi8Ui6dMzP3fWC1lMKbZ6WltbMTk5ycd6FlLNYKEtm7m40bzF+++/j5aWFrS0tMwqcmfrRGxsLN577z1cd911KCgoQFJSEu68806/a2yAICEbXzA8PIzKykqkpKRg1apVLu9C2Ha73a64T5yNXVpaitDQUJSUlPgcn3EFb60QsWKzp3ba/rjRWGrzXKRndDqdU2OqqakpDA0N4ciRI2hoaEBUVBTvbouJifFp/NDQ0FnK20YjEUJDAxFCTQ0t7OnpZO0cOgRkZQn7szXlZz+j1+vqgE8+Ad5/n9SbP/iAjk1JIW20X/+ajtm6lfrbXHONszZaSwvV8gBUW6PXU9rzmjXkQtPphMw1MdjNAOvV46lDqVIIliwwjuMQHh6O3NzceetS6goLHT8ym81wOBwBVRDYsWOHx9gOAKxfvx6fffaZYuc9ashGnM67cuVKZDJ1RRcIJNkMf7lixMbGYu3atYp+Ob1xo/mq2OxrgoCYZABlpGfEelFLly6FxWLB8PAwBgcHUVZWBq1Wy1s8iYmJbj8zu90u2+LBYqEF3+Gg7LLYWLJyvvlN0i37+98FS+hnPxMKPNvaKMHAWzCLhePIQvrOd2j7888LltDvf08tDQoLicimpoTkASlCQ0OdNOU8dShVCsFENuL3tpBqBq7aC8wXjF/mxXvTxPFoQ1CQjacvi81mQ01NDUZHR7F582bExcV5HJMtkErGbcSEp9VqsXTpUsUXAE9WiD+Kzb665pTsQeMKISEhSEtLQ1paGi/ZMTQ0hNbWVlRXVyM+Pp63esSqBzMzM6ioqJAlz5AQCthznFCoqdcTCfzrX5R1VlZG3TQ//VSI2RQUCGNs3SoQ0lVXCW6xp54SyOo//6E/gNxqTA7nO98hi+YnPyEpmzvuIEtmbIzIRqslVx/bXwy51tiMeAJl9QQL2biLkwSqS6m7uSykZTM1NQWtVutTS4KjBUFBNu5gNBpRXl6OkJAQlJSU+FSToGSSgN1uR01NDUZGRrB582aUl5cHJAHBHTH4q9jsLekuVKEmk+xISEjAihUrYDKZMDQ0hKGhITQ3NyM8PBxJSUmIiopCa2srEhISEBISMsu6iYqimM2BA9TemeNIIqa9nayLpCTqZ/POO1SDs2sXZYzdey+RR1UVjcEKQtvbBUkbh4NcdD09ZDXdfjtw6aUkvPnrX5N7buNGIRvtf/8jS+vgQSK88HBy7bH4kafrAcxujc0ISCmrJ5jIxluSCLTVs9AxG1bQGQyfi9IIGrKRc/UMDAygqqoKGRkZWLFihc9fAp1O59Sz3l+wPjharZYnvLn2tHEFObIRd/T0R7HZmwQBsUWz0BXUERERWLJkCZYsWcJr3HV3d6Orq4snztDQUEyxrmhfYmKCdNDi4oSYjdlMRZhaLWWqiRWe2Vu87jpSDaiqonjNBx9QzObddynwn5JCSs7330/HbNlCr192mfO86+qE5IIzzySSSUujubS10bFDQ85N37yBnNXDPqu5WD3BRDb+LPCBsHoWOhtNJZt5hjjLau3atUhjcrs+QomOmiMjI6ioqJjltlJibDlIyYYpV8/MzPit2OzOWlIiESCQYAW6o6OjWL16NaKiojA0NORU0MlgtVIg3molSyMhgdxll11GyswvvyykNf/kJ4JsTH39bCVmd5iaItkbjiMC++Y3afs//0mWEMeRvM0111DMxmSiFOixMd+JRgolrZ5gIRulsuKUsHoW2o3GyOZYRNCRjcViQVVVFS/HP5esjLm40TiOQ1dXF5qammTdVvNh2UxNTaGsrAyRkZHYunWr34kOrjLcApEIoCTYTUd3dzc2bdrECwXGxsbKzjMsjFo7OxzUOyYsjCyav/yF0px/+Uuydl55hWRkmGG0bZswRn6+YPH84AeCG42NATj3s+nsFFSld+wgV9pNNxHBXXMN1fuwOhuNBoiPF6wfJeDK6hEnG7D9mGQJs3SDhWwC0WJgLmoGC6XoABzblk1waESAFsSJiQns27cPWq0WxcXFc07/89eNxvTFWltbUVhYKBsfCaRlY7fbMTg4iP3792PRokVOraP9HVOu2RlblNg+wfQFt9vtqK6uRn9/PzZv3uykSAtAtkYnKooC/FotZaF98glZOdXVgnbaWWfRvrt3U0YaQH1tCgvpsbgmd2BAaMJmMFD7AoD+v/46Pb7oIqqpAYBVqwSievllOvfBgxTniYmheFIgPTRarRY6nQ4hISEICwtDaGgo9Hq9k3CqzWaD1Wp1cr0tNObDmvBWw81sNi+4COex2KUTCCLLpqenx2W7ZH/hDyGwglEAKCkpcVl4GCjLRqPRYHh4mFdsnqsiATDbjRbMbjOArNvKykpwHIeioiLZ1O6oqCinymeA3FSxseQ+y82lGMrUFLB+PS3ye/YAq1cL+7O3fdll5FI7dIg6eH74IcVk3nqL6mwWLQKuvJIkaLRayl5jrjMxysoECZzvfpfOuXQpxW1YO4PBQRojAPcps+DO6pn8srLUZrNBp9MFJLXaW8y368qd1TMyMoKxsTGYzeYFUTOYmppS3WiBxvj4ODZu3Oi1to838NWyGR0dRUVFBRITE7FmzRq3gcJAWDYOhwMTExOYmZnxOsXbG4iTL4KlNYArsOzD6OjoWRpvYphY3rIINhsRzMwMEURiIgX8f/5z4IEHgDffpHYDAAX8WW1NWZl8OrIrjI+T3A1AMaHTTqPHL71EqdIOB5HWt79Nbrn+fkqBtlqF/jvzDXGsZ2xsDDU1NcjOzkZoaOiCFJSKsdDuPHGsp6KiAhEREdBqtQuiZqDGbOYBa9asUdxS8CVmc/jwYTQ0NGDFihVYsmSJV43OlJwvU2y2Wq1YvHixYkQDCJaN+C8YiWZ0dBSVlZVYvHgxli9f7nZ+ckQfGUndMm02qn+JiyMr4v77gd/+lupeOjuBZ56hAkvWNfOMM4Qx1qwRXF0XXig8/sMfgD/+kR6//jr9aTRksbD64htuoDYGN98MfOMblAjQ2Unxo0DFbHwFU+pevnw5Xxi9EAWlYix0UF4MjuMQFRWFxYsXL4iawbHsRguOTzhA8IZsHA4H6urq0NTUhPz8fGRlZXm1CCtp2bBYVXh4OFJTUxX/4bEEgWBJbZbDkSNHUFZWhmXLlmHFihUe5yenQxcRQWnJBgPFaT75hIhnzx5a6ENCgJNOon3fe48sHgB44gkhSWD1aiItgNKmmWEcE0MFmwBZKx9/TGRy8cVCK+jsbCHT7G9/o0Zrhw5RunNCArnUFnJN7evrQ2VlJVatWuWkwKHVaqHX6xESEsL/iWM9NpsNFouFb9sRCIs+WMhGqiCgRL8eX6BaNkcpPJENq0a32WwuBT1dQSnLhik2s1hVU1OTIrVBDIxcJiYmUFtbi+TkZCQmJi6Yqq4UHMehvb2dFxNNSkry6ji5u7+REcpAS0ykmE1jI/WPOf54qnn56COK3zAwPjvnHHJ17dlDrrBPPiESee01IWZzxRXA3XfTOBs20JhS7NlDVgxAMR6NhpIJIiKIcDiO/ut0RETzicOHD6O5udnjNZ6vglIxFlIQVAp3xDcfagZGo9FvCf9gR9CQTaDaN8tpaAFCwzW5/i/ejj1X2X45xWYles+Iz+FwOBAfH4/8/HwMDQ2hvb0dNTU1iI+P50UyF0oag7WJGB4exubNm33KPpyQCbLY7ZTaPD1NlsjxxwNvvEE6ZTfeSNbI++/TvpdeKuiYffaZ0M3THdiN6+CgkPr8/vukSADQttBQiskcOAAUFxPZtLURmZnNZNnMJ9FwHIe2tjZ0dXUhPz/fZ/esu4JSd6nVviAQqc/+wpeizkCoGRiNRo+6j0crgoZsAgFXlk1PTw/q6uqQm5uL7Oxsv77oWq3WJZF5gjvFZqXcc+JEAJ1Ox9+RLV++HNPT0xgcHMTg4CCampoQERHBE4+rGhalYbVaUVVVBavViqKiIp/bDci5LGJjiTTsdhLkTEsj6+KnPwVuuQW4807KWPvd7yhgPzJCr7NW0QBZRIyEvvlNIWbz0EMkSwNQosGbb9JrAwN0TF8fcNdd5IL7xS+oRTRA1lV7O2W7aTTU9ZPJ4QQaHMehsbER/f39KCwsnHMpgSurR6zlBsCJdLwhnmBzoy2kmsGx7EYLjk84QJCSDbuTbmhowKZNm2Qbjfkytj+kMD09jQMHDsBiscjWEilh2YgXAbn4THh4OJYsWYKCggJs374dy5Yt412Ku3fvRk1NDfr7+xV154kxPT2NgwcPQqvVorCw0K++NnKuoNBQWuTDwqip2e7dRDz//S+9rtFQzxmALJ7bbiNr5aWXhIyyE04giRqAiIMZAqmpAikddxzFhOx2spCYlZOUJFg/Dz1EFk55OWW6LV5M485XJprD4UBtbS2GhoZ8thq9hVarhcFgQGhoKB/rERMRi/XYbDa3sZ5gIhul5uJvrMdkMgWMbDo6OnDFFVdg6dKlCA8Px7Jly3DXXXc53TR3dHTwNwviv/3798/5/F8Zy8ZisaCiooJf5P2RfRHDn5iNN4rNcyEbVhXO5uVNIoBer8eiRYuwaNEi3hUwODjIqy8nJCTwnTaVcLeNj4/z8j/+6N0xyCUIDA5S4WRiIumhdXSQ8OU3v0nWyjvvAJs2zR7r+ONJeeC996gj5+7dFLP55z8pZpOaSnU3d91FxLRyJYlxMrC14t13hbbUt95K24qLyX1mNJJszdAQzSVAPA6A7s6rqqpgNpuxefNmn8Rr/YUvVo80tTqYYjaBaDHgjdUzMDCAsbExjI2NBSwbraGhAQ6HA08++SRyc3NRU1ODK6+8EkajEQ8//LDTvh988AHWrFnDP/dVj1EOQUM2gYrZ2O12TExMoLy8HDExMXOuxheP7QspeKvY7C/ZSFsDsDsSXyD+UaxYsQJGoxFDQ0Po7+9HY2MjIiMjeXebr83OABJWrampwbJly7xKL3cHaUEnQ00NWRwhIUBJCak5v/QScMEFVM3/+ee037e/LWSdvf02pUwDruMpHCdoqfX0kPYZQBbSSy/R47feIqvK4SDLKieHhD+rqihxgMVsAkk0TEsPAAoLCxcsEcRdQak0ySBYYjaMFOdLzUAc66mtrcVf/vIXtLe344EHHsDhw4dx5plnYuPGjYrN54wzzsAZojz/nJwcNDY24vHHH59FNomJiXyLaKUQHLcTAYJOp4PZbMaBAweQkZGBjRs3KqZ75K1lw1x3jY2NyM/P99gawB+yEQdrxXeOc0VkZCSysrJQWFiIE088EdnZ2Xzjtk8//RS1tbUYGBjweB04jkNnZydqamqwdu1ar9PL3UHuBxgfTy6ryUmyVLq7iSS++11Kh779duDBB2lfg4EsH60W+OEPSagToPRl1sTwlFMExYFf/1pwqf3vf9SpU6+n861aRdsffpj01/R6GgeggtGmJkpaYG0OAoWZmRkcOnQIer0e+fn5QZNxKJbRCQkJcZLRGR8f57+/gUqt9hbsRm0+VZ/ZDd5VV12FiooKZGZm4swzz0RZWRm2b9+Oa665JqDnHx8fnyUFBQDf/OY3kZKSgm3btuGNN95Q5FzHLNlwHIcjR47AbDZjw4YNWLZsmaJ3T97U8FitVpSVlWFoaAjFxcVemaK+kg27Y2SBzUDdIRoMBqSlpWHdunU48cQTsW7dOuj1ejQ1NeGTTz5BeXk5uru7YTabnY5j7RE6OjpQUFDAZ93NFXIZOwYDSclERwO9vWTFOByCFcJxFMwHgBdeoJiNw0FZat/+Nm0//3whPTonRyCSvDwS1gSA00+nhACbjaRunnmGtotd7cyNxtocLF1KyQFyVs3Q0NCc0+hZHCw6OhobNmxYUJl8d2AWj8FggMlkQk1NDXJzc3kXLdNvY7Ge+SQesft5oWCxWPCd73wHr7zyCoaGhvCrX/0qYOdqaWnBn/70J1x11VX8tqioKPzud7/Dyy+/jLfeegvbtm3DOeecowjhHJNuNKvVisrKSkxNTcFgMCi2wInhyY1mNBpRWlqKyMhIFBcXe21R+RILWihFALlmZ4ODgzhy5AgaGhoQFRWF5ORkJCQkoL29HWazGUVFRYqmWEtjblotpRcvWUIWzoYNlG3W0EDqzXo91c2wOhgxli8nknrlFcpU27OHBDafekqI2Zx/PikQPP44nSM2VjieEch//iOoTj/yCG0780ygq4seG400J2mdTX19PSwWi1N8zJekicnJSZSVlWHRokXIy8sLCpeUJzAlgxUrViDjy4wMJXv1+ANxGvdCQawgEBIS4pV816233ooHmcnuAvX19Vi5ciX/vKenB2eccQbOP/98XHnllfz2pKQk3HTTTfzzzZs3o7e3Fw899BC+KScI6AOChmyUwuTkJMrLyxEZGYkNGzagtLQ0IOdxRwpDQ0O8SexNNbx0XE93c/4kAgQKGo0GkZGRiIyMRHZ2NiwWCx/naWtrg0ajQWpqKqamppyyleaKI5KWlw4Huamqq4lYsrKIQOrqgL17KYOsoYHaRgNEAowwXnyRSAKgIlA568NiEcQ0W1tJvoYd+7e/0eOPP6aOnBxHlk9yMmWhdXZSKrbFIl9ns23bNhiNxlmEzdpiu4uPjY2Noby8HFlZWXPKrpxPDAwMoLq6GqtXr3bqVbUQBaVi2O32edWEk4LjOL/kanbu3IkdzPfrAjk5Ofzj3t5enHTSSSgpKcFfWd8MN9iyZQveZwVqc8AxRTb9/f2oqqpCdnY2cnNzYTKZAqLMDMhbNiw20dzcjNWrVyM9Pd3ncT2RjRKJAIFESEgIoqOj0dLSgrS0NCxatAjDw8NoaGiAxWJBYmIif/c+lywpuWOTkqh+xmwmYmEZnVu3UsLAzTeTG23HDtIza2sjYmKtBgCyiBi2bhUeP/igEO/56CPKWNPriThOOYUy2R5/nLLf7r+fikoBKhhtbycC0+mo66e0NbRGo0FUVBSioqKwdOlSWCwWDA8PY2hoCGVlZdBqtfx1S0xM5K1kOZ2zYMeRI0dQV1eHdevWefQ4+Nurx18sdAq2yWTitdl8AUva8QY9PT046aSTUFBQgGeffdar91tRUeF3A0sxgoZs5rJgiqvx161bx2dR6HS6gGWYSC0baV2Dv0Ka7shG/GNbyDswdxgaGkJ1dbXTnXZycjLy8vL4u/eenh7U19cjOjqa/6H4qqabm5uLDz74AIDQ/VKjAdato8SAkRGhF81vfyscx2Khjz9OsjS//CVZP488QhbKVVcRUe3eTdZQVBQVcObnA2efDdxzD7nUnn2WXrv0UjrmvfeEAlCOo6QDgAQ/tVqK+QwMeNcRNCQkBGlpaUhLS+OzlYaGhvh09Pj4eISEhKC/vx9r165VPGsoUOjp6UFjY6NPskQMc0mt9hbB0BIakJdiUgI9PT3Yvn07srKy8PDDDztldLLv0K5duxASEoJNX9YIvPrqq3jmmWfwN2a+zwFBQzb+wmazoaqqCpOTk7Oq8dkdoL9Vwe4gtmyYYrPD4UBxcbFfRYoMrsgm2FsDAJTe3djYOMs9AsjfvQ8ODmJoaAgdHR0wGAy8xZOQkODx8xJnWnGcUM2fmkruseXLiXz27aOKfq2W6maam+kY5nYDiDSWLaPHt91Gbrfduym7LD2dlAi+8Q167Z57iLDEl5+pR//tb6QYwHHAv/9N2y64gLqChoaSdTMx4VtraHE6OlN/aG5uRl9fHzQaDVpbWzE+Po7k5GTExcUF5Q0IAHR1daGlpQUbN26UzX7yFb6kVrP9PF2bQKwTvsBoNEKv1wesLur9999HS0sLWlpa+DgZg7iw9L777kNnZyf0ej1WrlyJl156Cd/5znfmfP6gIhtx3xVvYDQaUVZWhrCwMBQXF89qssW+OHa7XfE0UGbZMI21uLg4rFu3bs53RnJkE+xEw3Ecmpub0dvbi/z8fK+EBENCQvhaA7vdjtHRUQwODqKurg42m83J3SbXPK2HBVC+hN1OhFJbSwt7cjK1C9i3j+Ilubnkvnr5Zdr/5JOFVObHH6c4DEAur7Exemw2UyEmQNuqquhxbS0VeAJUBPqXv9Dj0lLKhAPIqoqJoXM4HBRLcjjmVtDJcRx6e3t5LbmoqCiMjIzw1qTD4XBKMpC7bgsBJrTqjzabN/Bk9XibZLDQbjQmVROoOezYscNjbOeyyy7DZZddFpDzBxXZ+IKBgQFUVVW5DcKzL1Yg4jbsi/3FF18o2l2UkY202Rl7LdiIxm63o6amBpOTk9i8ebNfUhs6nQ5JSUlISkrCypUrMTU1hcHBQRw+fJgXNGTuNtafXY7QUlJokTeZiBBYzCY9nWI2P/0pxWG+9S2q7K+uJovooYcEAmAtCACh7QBAvWxYP5s9e6hAVKslq+jccylR4KmniNh++1uhlcBbb1EGnEZDRJOSQmnZvkKsc8aIht5zClJSUsBxHCYnJ2ddN5ZkEOimX67m3Nraiu7ubkW02byF1OrxtldPINQDfMGx3KUTOArJhqnYtrW1Ye3atR4DV3q9XnGy4TgOHR0dAKBY62YG8Y9E/D/YEgEAQQJIo9G4bN/sKzQaDaKjoxEdHY2cnByYzWYMDQ1hcHAQbW1tCA0NRXJyspMbQPtlm2WbjWIjExO0oH/Z3Rs330xuK44j0gGoxfPeveQa6+igos4HHiCLpbKSmqN9+9tknTz7LLB9O6VD/+hH1D7gkUfIirnoIkqtfvFFwbXGcURoAGWw6fVkZbW2UhM1X8HigePj4y5TyDUaDWJiYhATE8Nr3Q0NDfFuSr1ezxNPQkJCwBdVjuPQ1NSEvr4+FBYWLlhDMDl3GyMeqdVjs9mCwrI5VhFUZOPJjcbUksfHx7FlyxZZfSwpfOnW6Q3EcwCU0QwSgy0CVquVfxyMfnjWvjkmJsZjC+25ICwsDBkZGcjIyIDdbsfIyAgGBwfRzfRlILinhoZIFy0iAti4kSya//6X3GQcR8WXLPWZdc5kYN6dHTvI9fb660REixcT2Zx4IumjMeUABo4jsgIoW62lhVx6X3xB2668khQHNBoiQ28SBMTwV+csNDTUSRJldHQUQ0NDaGxsxMzMDN9iQinNOzE4jnNqHTFXHUKlIOduE/9NTEzwau6BSK32BEY2wXZTqRSCimzcwWQyoaysDCEhISgpKfH6LlqpJmcAeKkWvV6P4uJifPzxx4pbTeyLduTIESxatGheRBR9xcjICCorK5GZmam4MoM76HQ63p0mjcHZbGThNDZSzGb1akG9ua+P3FdmM/Dhh7TtrLOoJgagVGWWjnzwINXFALSNfbw9PcKxBw8CP/4xPX7sMeDPfyYy6eig8/T3k+ssLk7QSisvp/mFhAjuPU9QSueMpU4nJiY6FeGKNe+Y1TPXFhOs8+34+DgKCwsXrFeSNxCTSVtbGwYGBrBhwwZotVqfYj1KQbVsggBDQ0N8b/q8vDyfPnCl3GhMsTklJQWrV6/m9Z6UlNNgVl1ubi6OHDmCpqYmxMbGIiUlBcnJyUFxh9jb28tXI/tTR6QU5Nynqalk3VgsFLNhVkRYGC3yV11FUjOnnkrdOQ8dIhJ5/nlh3+9+Vxjv7LOFx3/7m1C8WVZG6dEA1eucdx7w6KMUs6mupmZtDM8/Lyg9A5S4IMltkMXMzAyf/LJ+/XrFLEdpEa7VauVrehixiWt6fCE4h8OB6upqmEwmFBYWBuWNkhxYczlxXGm+C0oBlWzmFdI7KhYbaWlpwapVq2al63kDJdxoTLF5xYoVTmrFSllNTBGAmfPZ2dlYunQpzGYz3+SsubmZb3KWkpLil+ryXOfIfpQbN25U3H3oK3KZyJkIZjMt/gBZJ0wV4NJLhZgNww03UPbYzTcTMf3736Rn9re/kQvsr38FrruOpG9+9StKArj2WuBrXwOuvloQ5jzvPOD73yey0WjIEnI4hILQ8XEiuw0byIU3NuY59ZlZ8XFxcfyNTaBgMBiQmpqK1NRUcBzH1/Swjq6xsbG8u82di4e5+2ZmZlBQUBA0mXCe0NraisOHD6OgoMApgWG+C0oBShBYqNjWfCCoyEYMluU0MjIypyLJuZANE5FkKb3SBVYJy8ZVe12A4hWZmZnIzMyEzWbD8PAwBgcH+apy5lIKdMCXuUZGR0edMqEWEqwAjkGrpULOqCj6W7WK5Gqee4762NhslKK8Zw/t39UlNDJjdToApURHRRHZ/OAHFLP51a+ILEpKZs/DZCLSAoCdOykxQacT0qevv55aEJjNREQSndJZYDpnqampPksdzRUajQZxcXGIi4tDbm4upqen+SSD1tZWhIaG8u62+Ph4fnG12WyoqKiAw+FAQUFB0KhNewIjGk8JDPNRUArAL6maowlBSTbT09MoLy+HVqtFSUnJnMxxf8mGiXlOT09j69atsubtXC0bqfSMuy+otMnZ2NgYBgcH0dDQAKvV6rEuxV+w62Cz2VBUVBQ0rpHDEkVNh4MW+a4uIp5VqwTts95eiqVER5MKs0ZDiQBsHb/pJqEw8513BLHOmhohltPYCPzrX/R4926hayeTltLpgJkZOm9bGx2blERzsVrJqtHpSBlawpM8gk3nLDw8nL/ZYckZQ0NDqK2thc1m48VYe3t7YTAYFOsVFWgwK52lZPu6wPtaUOot8QSyS2cwIKi+GRqNBiMjI3wnR1fdLH2BP2TDikUjIiKwdetWl3dqc7Fs5lKoKVVdltalMNdHSkrKnOI8JpOJFzXdtGlTUMnWy/UFWrxY0Earq6MUaAa9ntKUzzmHMsuuuQb49FNyoX38sdBv5rrrhGPE9W//+hf9abWkQjAwQNvXriULaOdO4IknKHngT38S6nb+/GeaT2gokU58PJENS9dmGBwcRHV1ddDqnImTM1gtVF9fH5qbm+FwOBAdHY3Ozk4kJSXNu4vXF7Dan56eHhQUFMzZklCqoBQgN5qvMj5HE4KKbLq7u1FdXY2VK1cq9oPzlWx8UWz217IRWzRzLdSUq0thcZ6WlhY+zuNrptHY2BgvwDff7hxvsGjRolnbJiYo9Tk6mmpamFVyyilCLIW9jfPOI0mbm24iN9gbbxBhfPIJ/d19N/Cb35B18sMfUnLBL39JMaEf/Qi4916SrTn9dNJQYxgfp3OxPjhaLWW9FRZSSvXg4GyiYeKUa9asOSp0zjQaDUJCQjA4OIikpCTk5eXxVk9XVxe0Wq1TTU+wWDtMQ7G3t1cRopGDvwWlAN3kLl26VPE5BQuC41vwJWJjY1FYWOiV3Im38JYQ/FFs9sdqCnQPGldxHuaW9CbO09/fj9raWuTm5nrsLLpQMDEdGRHGxynrLDaWOmWeeSYF7js7ydL44x9JVBOgVGQ5d1ZmJsDafpxyCllLAOmjyX0t+/upjgagRmomE7nLFi+mdgU33USutpERIiG7fXZyQH19vV/ilAuF6elplJaWOiUwLF68GIsXL+ZdvENDQ2hubsb09LRTTc9CZVSKiaawsHBe3FW+FJRqtVrVjTafiImJUTwvX6/XY2Zmxu0+LAA+ODjoUzKCL240d4kAgYKrOA8r7GN3nyzOwwi3ra0N69at81q2fCHQ398/a1toKKUZDw6SsCbzfjY2Us3N4sXUMlqnA265RTjuoouEeprnnhNcZJ98IhR7HjwoNER7803gS8FpPP88/dfrKfU6JYXiMx98QAQ1PU2xnNpaOm9MDCUPiAknUJphgYDJZEJpaSkvLST9Dss11mMKEE1NTYiIiOClieZLOJRp9zE1g4VY0D316mE1VcuXL5/3uc0XNJwvypcBBrvoSqKtrQ2Tk5PYIG5UIsLMzAwqKipgt9uRn5/vk2IzE+D0ZPr6kggwH+A4jo/zDA4OYnJyErGxsXA4HJienkZ+fr5X6gwLiY8++ghniwthQB00h4dpcbfZyBIZHSXyyc4GLr8cuPBCyir71a+oLcCnnxJJjI3RMQ6Hs4tLDOYN0umoOVtTE8nYXH89Sdu8/jqN98QTVMOzerWgkRYVRW6+zExqgaDTCXEdaWZdsGJqagqlpaV+Z8oxS5tluDkcDqeankCkS4uJpqCgICgtB7vdjp07d+L111/Hv/71L5xyyikLPaWAIPh0UBSGO1fXxMQE9u3bh9DQUGzZssXn1gDeWDZiiyZYetCwOE9OTg62bNmC4uJiWK1WGI1G2Gw21NTUoLm5GWNjYz6pcM8n5BaNkRGK16xbR4s787StW0cLu1iiprAQYKrpu3eTLprNRlbQrl20/T//AT7/nB7feSeNb7NR/GbvXtpeVERkBZAV091N+6xfT+dKTBRiNgDFkcREc7RgYmIChw4dQkZGht8xPGZpr1mzBieccALy8/MRGRmJzs5OfPrpp/jiiy/Q3t6OyclJRb53Un22YCQah8OB2267Df/73/+wf//+Y5ZogCBzowUCrsimr68P1dXVc1Js9hQPCvbWAABgNptRVVWFsLAwFBUVAYBfcZ75hlgbjYGRidlMlfoXXEAkEh1N1s2zzxKBAGTVuALL7s7KEmI2rt52czPwj3/Q4wsvFPZl3Tt//GOKG7F0aofD+142wQKWkr106VJkZ2crMqZGo0FsbCxiY2OxbNkyXnB1aGgIbW1tCAkJ4d1t/nzvGNEMDAygsLAwKNQ3pHA4HLjjjjvw2muv4eOPP3Zq3XwsIqjIJhCLsZRsWOpje3s71q9fL5vV5MvYriybQCcCKIGJiQmUl5fz6azM6vIlzjPfYHGlxsbGWa8xa6a+nkiCfTSffQbk5FCGmMlE7q/f/15Y9E8+WVCDfvBBIWX6pZeE9s4ffijIzLzwAsV2AOC118i9ptGQakB8PFlKTz9NLrP+fnLrsY6dKSkUEzpaCIeVIgQ6JVsquMqEQ1k7cXGfHk8eCNaOYXBwEAUFBUFJNBzH4Ve/+hX+9a9/4eOPP8aKFSsWekoBR1DFbDiOg8VblUIvMTAwgObmZhx33HGw2+2orq7G2NjYLHkKf9DY2AibzYY1a9bw25j0DCO4YGwNAAh1HTk5OcjKyvI4R1dxHnGfmUBD3NPFYDDga1/7mtPrWVkUs7FaaYFnjcpaWkgF+pJLKG6zeTMRxltvUffONWvI8jAaiXSsVue4jUZDf6Gh5CqLjKTxPvuMrJmrr6banbffpqLQZ54B/u//SIMNIEtn0SIqLl2yhFxtTNoGoFhIMH5HhoaGUFVVhby8vAXTweM4DkajkU8yGB8fR2RkJE880nR+MdEEqxAox3F48MEH8fjjj+Ojjz7CunXrFnpK84KgsmwCAWbZMFUCnU7nk2q0p7HF5BhsiQCuwFr0rlmzxmvLzlWfmYGBgTnV83gLJl80NTWFoqIitLe3z9qnr4+IYM0aEsrUaCjwv2oVPT5yRIjjxMVRfcw//0mutXffpeZqlZWUFv2971EGWloaZbLdcw+9HhsLXHEFtZpOSaHEA/ZWOzvpvBYLEY1ORx1CDx+mOfX2ksKBtFvnnj17nOpSguF709/fj5qamgWv/RG3E2fCoczdVl5eDo1G43TtWlpaMDQ0FNRE8+ijj+LPf/4zPvzww68M0QBBRjaBcqNZrVbs27fPSbFZCYhjNuJEgGB1mzE/9pEjR+acbit2e/hbz+MtWFoox3HYvHkzQkJCMC3TGIbxPksU+OEPqTjzuOPICnn9dbI4ACIZdr/hjwgEI62DBymtGSBVAvbVuvpqykq7/HLgvvsElWhpQScArFq1CoODg6ivrw+o9JC3YEWm69atQ0pKyryf3x0MBgPS0tKQlpYGh8PBC4e2traiqqoKWq0WWVlZiqqxKwWO4/DYY4/h4YcfxrvvvotNmzYt9JTmFUHlRgOo+6OSU2ppaeFVo8WKzUqgs7MTw8PD2LRpU9AnAjAXotFoxKZNmwLmxxbHeQYHBzEzM4PExESkpKT4tXiazWZeOmjdunU8cZWVleH444932jcpiVKd7Xaq7r/sMorNdHeTdbN1K1kh779P1gXLstfrySIaHwe+/nVKMPjoI+Dii6kR21//SkKcaWlUwCntSRMeTu61iy4iMnn9dbKQli2jMV56SThXRoZznxxASH2Wc1XKtcQOJLq7u9HU1IQNGzYsuLK3t2DN2oaGhpCeno7x8XGMjo7yXV2TkpKchEMXao5PPfUU7rrrLrzzzjsokVN1PcYRVJaNkmC+W5a1lJWVpfg5mGUT7ETDaom0Wi2KiooCqsorLeozGo0YGBiYpdvmTZxncnIS5eXlfAGheLGQU5mIiqJFX68n4mB9ZVpb6X92NmWHbdgAvPoqcOAA1ducfDJpqY2PU0ozi/e88godp9dTSjRLEEhKIjfZ3/8O/OxnRFAnn0waaa++SvuIiz51OnKj1dYKkjlybQakrsqZmRkMDg7yGVquVJeVAivo3bRpk6IqHoEEx3G8InlRURGfPGC32/maHiYcKq7pmU9BWY7jsGvXLtx555148803v5JEAxyjZCNWbC4oKMCBAwd4MlAKHMdBp9NhcnIS3d3dSE5O9rlOZz4wNTWF8vJyxMfHB7w3ihRif7uvcR7WDdSVArL0fWi1FBuJiKC6mpoaisscPkzBe4OBstBaWmh/jhM6ef7+95RtdsMNREAVFRSz2buXstoyMoC77qLizbg4Sqm++WYiG3FYYP9+cqvNzFAtjk4HnHQStTXIzCSy6e0l8vLGeA8NDZVtiV1bWwu73a5oQWRbWxs6OzuRn5+PWCaXHeQQE01hYaHT70+n0yElJQUpKSngOA6Tk5MYGhrib3qio6N5qyc6OjpgN4kcx+GFF17Az3/+c7zxxhs44YQTAnKeowHHHNkwxebw8HBs3bqV/xLZ7XbFFlqWCJCYmIglS5bgyJEjaGxsVExtWSkMDw+jqqoKS5Ys8buWSEm4ivNUVFRAo9HwxGO1Wj12A5UqTTgcRDgmEwXhQ0NJMPMXv6DYzdNPU9xm927a/9FHKa4D+J6KbLGQpQNQYgHTW7vjDiISh4OUn6+/HvjGN4hsDh6kfbRa/9KexarLbPEcHBxEZ2cnamtr/c4MlGqGzTVDc77AiGZsbGwW0Uih0WgQExODmJgY3mJk372Ojg7o9Xq+picxMVGxWjKO4/Dyyy/jpptuwiuvvIKTTjpJkXGPVgRdzMZqtfod3GOKzRkZGcjLy4NGowHHcXj33Xexfft2RSwPV4WazOUxMDCAkZERPj0zJSUloHdOrtDT04OGhgasWrUKi1llYpBCHOc5cuQIrFYrYmNjkZ6ejuTkZNm79p6enlm1CRkZ5OriOIrBXHABkUx7O7nOzjiD4i5/+ANZQNPTsxf+1avpf10ducZCQymdOTOTOm42Nzvvr9cTaY2OAvffD3R0AC++SHI1eXnAWWdRSjRA58rMpJiNOBttrnI1TOl7aGgIIyMjCAsL491t7vTHmKt5YGAgaKVc5MBxHGprazE+Po6CgoI5/a4dDgdf08NijGLh0LlktL3++uv40Y9+hBdffBHf+MY3/B7nWMExYdm4U2xmEjFKtW92FZ8RuzxsNhvvLjp06BAMBgNPPIEWH2RFq4cPH8amTZuQkJAQsHMpBa1Wi/j4eAwNDYHjOKxduxZms5lvxy131y4Xd9LriRBSUigp4OmnaftHH9H/qChyb/3hD1Rn099PWWNXX03us/JysoxYUWdpqaAcMD4uFHiuWUOB/9tuA26/Hdi2jdpFb9tG6c8OB9XaAFTLA1BG3Oefk4tNaYiVvlmsgtVRORwOnngSExP568aC6qwTbjCmCcuBEc3ExAQKCwvnHHvRarVITExEYmIi8vLy+Jqe/v5+NDY28q5eVtPj7W/3zTffxJVXXonnn39eJZovcdSTDVNsZrIUcoHNubSGFp/H29RmvV7P93V3OBwYGRnBwMAAqqurwXEckpKSkJKSoqjJDpCrkP0Qg6V9szdwOBz8neqWLVt4F+TSpUv5OM/g4CBaW1sRHh6O5OTkWT1SdDqyKsLCSC1gYoIy0A4coLoYvR4oK6PYjPgYgBqmrVtH/995h+I9559PWWtpaWSN3HwzvZ6QQK0LLr2UyEb8Nfj3v2n8mRngoYdo2zXXAH/7m9A1dGBASI8OBKSxiomJCQwODqK9vR01NTWIi4tDUlISRkdHYTKZPLqgggnsezI5OYmCgoKABPkjIyMRGRmJrKwsWK1WPk5WWVnJ/3bZn6tEm3fffReXX345nnnmGZx77rmKz/FoRdCRjS/uJovFgvLyctjtdhQXF7u8O5sL2UgVAfzpqsm+nBzHYXx8HAMDA2hqappzWrAYFouF/0EUFRUtSH2GP7BaraiqquLbTkvn7SrOU1dX57Sf3U6LuNlMgXiNhoQ29+6l+MxNNxGJXH897X/LLWQBAeTyGhz0fs79/RSrAchCeuwxevzYY0LtzrvvkrWzfj25z5h1ZTDQ8/koAxHrj+Xm5mJ6ehoDAwNob2+H1WpFREQEDh8+zMv9L3RMzx0cDgdf1BsoopHCYDDw0k3stzs0NISOjg6nOFliYiIiIyOh1Wrx0Ucf4ZJLLsETTzyB7373uwGf49GEoIvZ2Gw2r4hhYmICZWVliIuLc6q/kMNnn32GVatW+dycStqDRknpGSbDMTAwwNdUxMXFISUlBcnJyT65NYxGI8rLyxEdHY21a9cGjVimJ5jNZpSXlyM0NBTr16/3qaPj1NTULPWD7GyybgBa1M8+m1KRm5pIouacc4gALr6YhDonJijYL/4FREfTsSMjZCHpdBSniYggQhO7wXQ62jczk/Z5800633/+QyS3di2wZQtZVzodHZ+dTXEl1jkUmL8WA3a7HZWVlbBarVi/fj1v9QwNDQGAk7stWLprAs5EU1hYGBQ3UmKL+7PPPsOjjz6K1atXY9++fXj00Ufxox/9KKjJeyEQPN8oH9Df34+qqiqvFZv9sWzE8ZlAtAaQSwtmxNPU1ISoqCieeKKioly+x9HRUVRWVmLx4sVYvnz5UfMFZynZCQkJWLVqlc/XV27BsVpp8V+3juIvrOblmWcEteWtW2nb448T2Vx+OWWOsayy9evJ1TUyQvuzaSUm0mtvvUWEdfPNwGmnkULB5s2UfMBqIP+/vTMPi7Lc//972EVZZUfZFDQX1pA0rTyZiIAMlpl2XNL6dk5pv0xLO0fTTpqpHeNYaupV0eK5Sh1cUhFzIU3MBRhEEVQUUbYBZRu22Z7fH8+5H2YQkWVmnmfwfl0XVziDzO0087zn/tyfz/utUrGRBAC7awLYuZy0NPbsBzDOzkYblUrFuTBERETAwsICffr00fnUTkqVubm53CF5Vz/46BsiNA0NDYIRGkB3xx0YGIj6+nqsX78ednZ2eO+995CamgqxWIy5c+fyvVTBIDix6ehi2V3H5q6KDR/RADY2NvDx8YGPjw+USiU3RX7r1i1YW1tzwqNd7iC2IkFBQQZ15NU3xLJ+4MCBGDRoULezUbQxN2d3DObm7K7F3h6YOpWNFVi/nv2Z9HTWELMtzzzD/r2DB4EdO9gy3LRpbFeZlxe7c3nzTfb8pX9/ICyMPQ8ikC7szz5jw9Oam1vbodetY4WJUF398LgCQ6FUKjlfwLCwsAd2viKRCI6OjnB0dERgYCAaGxu519+1a9c6NL40JBqNBrm5uWhsbERERIRghKYtV69exebNm7Fx40a8/fbbuHLlCg4ePIisrCwqNloITmwehrZjc1RUVJeSJLsiNkLIoLG0tOQy3bW7i3JycjjjQY1GA5lMZlLZ9QDrwn358mUEBQVhAJmq7AZtd0JqdWup6swZdkfy5JOs2Bw9yh7ql5UB//43+/Nz5rSGnqWksHHSAGvc2d5LRalk/z7ARhh8/TX7/b//3bpbSU1ld1ZWVmwZLTycbShgGHZXY2bGDoEqFO0/hiFQKBTIzMyEjY0NgoODO1VitbW1ha+vL3dI3tb3TrvcZqiSrakITXZ2NsRiMVasWIEFCxZAJBJh5MiRj5XBZmcR3JmNWq2Gqk2MobZjc2hoaJcPB8kEfUfBT6QRQMhmmmQmoKCgAA0NDTqGlx11xwiFO3fu4Pr16xgxYoReDB7bzoUEBAA3b7YOTj79NDtQmZfHfh8Tw7YqDx/OztOUlrKBaw9LzSTXt/ZSL8g11tGRLdulpwPnzwNffcWafaanszsgH5/WIdOWltYzG42mVXAMdWZDfOX69euHESNG9LgUTOahyFlFc3OzTrlNX11tGo0Gly5dQnNzM8LDwwUrNLm5uZg8eTKWLFmCZcuWCe56ITT49zJvQ9v/YdXV1Th79izs7e0RGRnZrS6UR+1siMiQeAAhCg3ACnFRURHMzMwwduxYjBo1Cn379kVRURF+//13ZGVl4c6dO2hubuZ7qTqQKfXCwkKEh4cbzEm4vp7tQhs/nhUbEt28fHmrYJAB+ZUrgU2bWKHJyGC71QDW3+y559jvn3qKHewE2F3S0qXs96+/ztreqNXA4sWtZTJyLVco2CYEgD3/AdjzHaC1683QO5umpiZcvHgRDg4OGDlypF7OHInvXVBQEJ5++mk89dRTcHZ2Rnl5Of744w/8+eefKCwsRG1tbbfNdDUaDXJyctDc3CzoHU1eXh7i4uKwcOFCKjSdRNBltJKSEuTl5SEwMLBTAV8PoyOxMZUMGrK769OnD5588knuzMLOzg6DBg3i6uzl5eUoKCjg3ILd3Nx4nQwnc1DV1dWIjIw06FrIhdzKinUQWL6cnYU5eJAtgx040NpNVl/PlrwAdjaHmBEsWcLuhNLT2VkZT092dyIWs+c269axRpzkZaJWs4OcANtscOUKK3Slpext27axJbuaGvbPTU26szmGgFg2ubq6ck4ahoDMpGjnzFRWVqK4uLhbMRNEaFpaWhARESHYnXpBQQHi4uLwxhtvYOXKlVRoOokgxUbbsTksLKzHZxIPExttoRFqoiYA1NbWQiqVwt3dHUFBQe0KonadXaFQcNY5N2/e5AYh3dzcYG9vb7R/p0qlwqVLl6BQKDBq1CiDz0bY2LBikpbGfu/qyt5+/jxAjHYPH2Yv9q+/3mqi+cUX7KE+wJpwlpez32t7ppWWst5qAPtfkt22YgX7XzMz1rXA1ZUtzf32G7sbIiW606fZ0puDAyt05OWo7/8XcrkcmZmZ8PLywuDBg432/7ptzgyxgCFx4iTW2dXVtd3XgVqt5l4rQhaawsJCxMXF4dVXX8Xq1asFe80QIoI7s1EoFLhw4QKampoQHh6ul0/C169fR0tLC0aMGMHdZipCQw7UBw0a1K08HjIIKZPJUFVVxRk6urm5GTTjo6WlBdnZ2bC0tERISIhB5jbavjYGD2bLW5aW7E7G358VhZwctrT2wgvsgKenJxsLcO0aGz9gbd0qNp3B3JwVs4YGYOhQ1lV62zb2cdatYxsFUlPZMhwRQHt7tpEgIIB1oiZnNs7Ozrhz545eng8yezZw4EBBGK8CrfNkpLutrq6Oc1wmbf1kR6NUKhEeHi5YoSkqKkJMTAzi4+OxadMmwVZBhIrgdjYtLS2wsLDAU089pbcXnbm5Odd0YAqNAAC7zuLiYhQWFvboQN3CwoKbgiafOGUyGWdTr22doy9BIEOmDg4OGD58uMHelFZWVlwst0jUWkabNAn49dfWUtYrr7DlK+3Zlr/+ld21vPoq27124QLrkbZpE1sK27aNdY12dGRbp198kf07iYms+8Abb7DC8dprbBv0tm3s72toYL+ef55dk50dK2Tjx7OhasXF7OOTnc3atWv18lyQdnJ/f/8OG2GMjfY8mb+/PxQKBVduI47LAPseFfKOpqSkBLGxsZg0aRIVmm4iuGesX79+CA0N1euLztzcHBqNxmQaATQaDQoKClBUVISIiAi9HagT08EnnngC48aNQ3h4OGxsbFBYWIjff/8d2dnZKCkp4S7g3aG2thYXLlyAu7u7XjqgOuKVV17hvmeY1hbk+np2t7J1K/tnmYy94O/ezQ5XAmxMc319698lRwp/+Qu7AwJYT7UFC9jvIyLYjra2lJezHW8AMG4c24mmUgG+vuzv/c9/2PtI2U27jmBmBsycObMHzwDLvXv3kJWVhcGDBwtKaNrDysoKXl5eCAkJwbhx42BlZcVVGTIyMpCTk9Pj16C+KSsrw+TJkzF+/Hhs2bKFCk03EdzOxhCQnY0pNAKoVCrk5uaiqakJo0aNMtj0trZvVmBgIGedU1JSwjktk0HSzmbzEKfhwYMHw8fHxyDr1mbz5s344YcfuD87ObGuAOnprYf/ADtn8/zzrOVMTQ0rLP/6V+v9CxawRp0Am85JROvPP9k5GYB1JCAu0nv3tg5tkjRQc3O2kUAkYlubv/kGGD26dXeVk8Pe5+nJeqsBwDvvvNvj1yF5zocOHSr4KAltyBmNmZkZnn76aZibm3OR2MTt29iR2O1RUVGBuLg4REVFYceOHSZjBSVEBHdmA7ClNH1SWlqKmzdvctt0Ie5mAHYuQiqVwtLSEsHBwbyVFEg+SmVlJZfNQ5yEH2adc/fuXRQUFGDEiBGddnbQB7t27cIbb7wGtZo9s7l+nY0SkMtZcWlsZEUjLq71bMXFpdWY8+hRVlAaGtgdSUctyWZm7Je5OWvieecOO7sTF8c6QuflAR9/zDYH7NjBlt6AViG6dYs9R7p7F+jb1wElJaU9+rdXVFTg8uXLRn/Oe4parYZUKoVGo0FYWFi75duWlhau3Hbv3j1YWVlxwmPIs0ZtqqqqMHnyZAwbNgz//e9/BeUXZ4oI8tkjoWf6gGEY2NnZQa1W4/Tp09wZRXs29XxSX1+P7OxsrszF585LOx9Fu6X1woULsLS0fMA65+bNmyguLuYlP+fll19GY2Mj3nnnbZBz9hdeYM9HSEDa+PGskNTVtXaHhYUBo0axYnPkCHDpEtuhdvEi+/W3vwHbt7OiIhazw6ALFrA7k2XLgLlzWXGbMIG1tAHYM6OSEnZn9OKLrMgEB7O7ovBwVmxu3WJvT0s7ioaGhm43wJSWliI/Px/BwcFwJW13JoBarUZ2djYYhnmo0ABsPpS3tze8vb0fGolN3NQNMYtz//59TJkyBYGBgfjpp58Eda0wVXr1M0iaAKytrTFmzBiuVEQswom9/8PSII1FVVUVcnNz4evrC39/f0HtvLRbWrXf9JcuXeLuVygUvGbXz507FxUVFVi9mq2NFRezO5DNm4Hp09kZmtxcdsfh68v+nWPH2N1GW6ys2J0PwAqFpyf7fd++ujk05LNQVha7cwLYnRPAlsvEYlbwli5lGxSystj7nJyccOTIESiVSvz555+wsbHhugM76ztGnBhCQkLQn7h/mgBEaAB0KDRteVgkdnFxMfLy8nTC9WxtbXv8/qmtrYVYLIa3tzd+/vlnwQ6WmhqCLKMpFIoe7WzaZtC019pM2jFlMhnq6uo4e383NzejhkmR8tOwYcPgSa5sJoBKpUJWVhYaGxthbm4OhULBeWa5urryUgL89ddf8de/zoBKxUAkYtuc/9//Yy3+Y2NZ4ejfnzXLBFq70/r3Z7vGiorYjrPmZjYm4J132NvXrGEP//39gR9+YMtujY2t7dJWVqxrwIwZrH9abi57rvPMM+zjkyaBIUOGICMjg3t9aefzEJt/8vw9zHfs9u3buHnzJsLCwuDo6Gi4J1PPqFQqZGdnQyQStWsG2l20rf7v378Pa2tr7jnsTipufX09EhISYG9vjwMHDphMsJwpIEixUSqVXIZMV+mOIwCx95fJZKipqYGdnR0nPIaaeGcYBtevX0dpaSlCQkLaTRgVKgqFAlKpFGZmZtwMDTnclclkkMvlnGeWscVbpVIhPj4eZ86c4s5fdu1iz1TGjAF+/JGdedm+nT3f2bCB9TYrKWGbB6yt2VJb25efmRl7X0MD24gQHMw2InzwAZtZ8+KL7IzPBx+wty9ezJbeAMDS0gxJSV926ACs0Wi4YL3KykouWE9bvG/evIk7d+4gPDy8S0a0fEOExszMDKGhoQY7ZNc2ra2qquIisR+VrEloaGjA1KlTYWlpiV9//ZVX543eSK8SG+3W5u62NWtP39+7d69Th+NdRa1W4/Lly6ivr0dYWJhJvagbGxu5oLbhw4e3e+FoamrinkMi3trWOcYoE8pkMsyZM0dHdMLD2YP7iAjg++/Z0thLL7FlsLw81m4mJ4cVIXK7pydbelu7lj2n8fRkXQNmzWLLc0lJwIAB7M9/8w3rAk1CRC0tRfjHPz7CBx980KW1tzcIaWVlBZVKhZEjR5rUGY2xhKYt2pHYlZWVaGhogKOjo065TZvGxkZMmzYNarUahw8fNplIdVOi15zZ6CsawMrKijuYVKlUqKqqQkVFBYqKirhcma7U19tCdgUikcik4psBdkI9OzsbHh4eCAoKeui/v0+fPlw2DxniI3HE3Tmj6A5ubm5ITU1Fc3Mz1q9fj//85wtIpQpERLD3f/cd68DM/rt0518ehVrNdpQBrN8a8T2bP59toXZzc8OOHTswgQz1dBHtQUg/Pz/k5eVBJpPB3t4ely5dMtpz2FNIqZW4tRuzbbi9SGxSbrt+/TpsbW2hUCjQ3NyMMWPGYNasWWhubkZaWhoVGgPRK3Y2pBHAkI4AZItOyhzm5uac8HS2Nkwm6+3t7R+6KxAq9+7dQ05ODgICArptitr2OSRmjW5ubnB2djZYB15xcTFu3LiBgQMHYteuXfjyyy+hVDahubk1W8DCgi2fhYez3WIXLrCWNn36sLM3gYFsd1tOju7vtrAA7Owc0a+fHdatW4f4+Hi9/TsYhuFMTCMiItCnTx+d57Cz5zx8QALbLCwsEBISIph1Aa1nZbt27cKnn36KxsZG2Nvb4/PPP8eLL75oUiVKU0KQYkMGMB9FZxoBDIG27YtMJgPDMDoXzfbeWPfv30dOTk6P0in5orS0FFevXtVrEwPJRiHCo1QqubZ0FxcXvbSakmRXYujatltOLpdjx44dkMlkyM3Nxfnz5+Hk5ITGxgbU1VXD1rYPRCIRGhqaYG/vCEdHJ1RXV+Ppp59GVFQUgoOD8Ze//MUgIknikOVyOef00N7PkDhnmUzGGV6S59DQxqcPQ8hCo41SqcSsWbNw5coVxMXF4dixY7h+/Trmzp2L7du3G+xxT506hQ0bNiAzMxNlZWXYu3cvxGIxdz/DMFi5ciV27NiBmpoaPP3009i6dSsCAwMNtiZjYLJi07YRgC8zTZLfXlFRAZlM1u5Fk1yshw4dCm9vb6OvsbswDIOioiIUFRUhODjYYG22pJ2VCE9DQwN30XyYS3Bnfmd+fj4qKysRHh5uUqURkkrb1NTU6UyX9s55+IiZUCqVyMrKgpWVVaeTQflApVJh3rx5yM/Px8mTJ7lzsMLCQpSWlmLcuHEGe+zU1FScOXMGERERmDp16gNis27dOqxduxbff/89/P39sWLFCuTm5iIvL8+ku+NMUmz00QhgCLQvmjKZDE1NTbCxsUFzc7PJTXmTmIeKigqEh4fDjqSOGYHGxkZOeGpra2Fvb88JT2cummRXUF9fj/DwcINZ/hgCMl2vVqsRFhbW7RbylpYWHRcIcs6jPYyrb7SFJiQkRLCWUGq1Gm+++Says7Nx8uRJeJB8cB4QiUQ6YsMwDLy8vLB48WIsWbIEADv34+7ujuTkZB0/QFNDkGLTXjQ0QV+NAIaGRNtWV1fD2toajY2NcHJy4i6aQv6EQrrlSAmHz4u19kXz3r17sLW15Z7D9rJ51Go1cnJyuEFTU2rAIJ1bQNeGHh+FMc55lEolMjMzYW1tLXihWbhwIc6cOYP09HTeKw1txebmzZsYNGgQsrOzERoayv3cs88+i9DQUPyHDG2ZICbVjWaMRgB9oFQqkZOTA5VKhTFjxsDa2pprB9ZO0iQNBp01ujQGSqUSUqkUDMMgMjKS94u1tbU1BgwYgAEDBnDdgZWVlVyXExEeJycnbkLdzMxMJ83UFCC7ApL/o8/yk3YzC8MwqKmpQWVlJa5du6aXcx4iNDY2NggODhas0Gg0Grz33ns4deoUTp48ybvQtEf5/5L72lZB3N3duftMFZN4N7ZtBBCy0JA5lL59++pMSmu3A5NP6zKZDDdu3OBmedzd3XlztwVao6dtbW0xcuRIwdXbLSws4OHhAQ8PD2g0Gs465/Lly1z3oq2tLYKDg01KaFpaWpCVlYU+ffoY/GItEong5OQEJycnzu27srKSc/vu6jmPQqHg1j5y5EhBC83SpUuRlpaG9PR0+BLfIorREOQ7UvtiK5RGgM5QU1MDqVQKT0/PDudQtD+tE6NL4tlmY2PDfQo1ZoQzMQJ1dXXF0KFDBfscE8zMzLjJcB8fH1y8eJEbfDx9+rTO9D3fu7OOaG5uRmZmJtcOb8yLddtgM+2S5c2bNx95zqNQKJCZmcl9OBGy0Cxfvhz79u1Deno6AgIC+F7SQyHnRxUVFTqdnxUVFTplNVNEkGJD0D6fEYlEgn0xA+yL4cqVK13OcmlrdElq69plIhLhbCgBIG3Zfn5+8PPzE7zQaFNfX4+srCx4enpyraHk0zrJRSG+d66uroJqFmhqakJmZiacnJwwbNgw3p937Q9B2tYvOf8bLtI+51Gr1SYhNAzD4JNPPsHPP/+M9PR0wbcP+/v7w8PDA8ePH+fEpa6uDufOncPf//53fhfXQwQrNqbSCMAwDGeO2FMrEW1xIWUimUzGOSyT+/Q5AFleXo68vDyTC98CgOrqakil0gdEUvvTOsnmkclkuHbtGvr168eVifRlP9QdGhoakJmZCTc3NwwZMkRwr+9HnfOIRCLY2toiKChI0ELz2Wef4dtvv8XJkycxdOhQvpcEgJ3vunHjBvfnW7duQSqVwtnZGT4+Pnj33XexevVqBAYGcq3PXl5eOu3Rpoggu9HOnj0La2trDB48GObm5oJ7IxI0Gg03yxEWFmawyWPyZict1SqVSmeWp7tnK7dv30ZhYSGCg4PhQnz1TQSSUBkUFIQBAwZ06u9olyyrqqo4h2DiAmHMkmVWVha8vLwwePBgwb6+26O5uRkXL16Eubk5zM3NdeZ5+EzUbAvDMPjiiy/wxRdf6OwShEB6ejrGjx//wO1z5sxBcnIyN9S5fft21NTUYOzYsdiyZQuCgoJ4WK3+EKTYLF26FElJSQgMDERCQgISExPxxBNPCOJFTFCpVLh06RJaWloQGhpqtPIMMRgkwtPc3KyTy9OZuQyGYXDt2jWUl5cjNDSUtxya7kKGZHsyu0Syecg8D4BHukDog9raWmRlZcHPzw/+/v4GeQxD0dLSgszMTM6E1czMjJd5nkfBMAy++uorfPbZZzh69CgiIyONvgbKgwhSbMgn+V9//RUSiQRHjx6Fj48PJzx814ibm5uRnZ0Na2trXjufyNQ4ER65XP7IyXsy8FhXV4fw8HBBtV13BuJzps/gsLY7R6VSyQl4Z6zpOwsp+xF/OVOiPaFpi/Y5j7aAG9O3jWEYbN++HR9//DFSU1MxevRogz8mpXMIUmzaUldXh0OHDkEikeDIkSNwc3NDQkICxGIxIiIijCo8dXV1kEqlcHFxwdChQwVVr25qauIumLW1tXBwcODq7n369OHmf8h0upC7tNryKJ8zfT6OXC7ndjwkm6enw7jEyLQrZT+h0NLSgosXL8LBwQHDhw/v1G5F+5ynsrISzc3NcHZ25sTHEL5tDMMgOTkZH374IQ4ePIhnnnlG749B6T4mITbaNDQ0IDU1FRKJBIcOHYKTkxOmTJmChIQEREVFGfTTEzkn6InzsbFoaWnhhKe6uhp9+/aFQqGAra0twsPDBTdD0xF8+py1FfDuBOuR180TTzxhUmmsQGtrdleEpi0d+bbp65yHYRjs3LkTixcvxoEDB9o9E6Hwi8mJjTZNTU04evQoJBIJDh48CBsbG8THxyMxMRFjxozRa3mL5L4PHz7cpDzOALZ8k5OTw8U39+nTh7tg2tnZCVo0heRzph2sR84nHjUTVV5ejitXrpicNx7QKjSOjo56bc3W9zkPwzDYvXs3FixYAIlEgujoaL2sk6JfTFpstFEoFDh27BgkEgkOHDgAkUiEuLg4JCYmYty4cd0uGZHD9LKyMoSGhppU7jvQek7g4+ODgICABzJlLC0tdXJ5hCQ8QvY5I5ko5KJpZmamMxNlZmaG0tJS5Ofnm2S3H+k6M/QMkD7Oefbu3Ys333wTP//8M+Li4gyyTkrP6TVio41SqcTvv/+OPXv2YN++fVAoFIiLi4NYLMb48eM7XS8mVu8NDQ0ICwszucN0Mmj6sHMCjUajIzwikQiurq5wd3fnLph8QTJRSJywkO1nSL4R2fWo1WrY2tpCLpcjODjYpGKcgdZhU2dnZ6N2gXbnnOfgwYN47bXX8NNPPyExMdEo66R0j14pNtqo1Wr88ccf2LNnD/bu3Qu5XI6YmBiIxWJMmDDhoWWZlpYWSKVS7mKnr44kY0HKfiNGjICbm9sjf147zIxcMEkrsLETIJubm5GVlSVYj7aOINEMd+/ehbW1NRQKhcEPxvUJX0LTHqTTUvucx8XFBfX19Rg5ciSOHj2K2bNn49tvv8XLL7/M2zopnaPXi402arUaf/75JyQSCfbu3YuqqipER0cjISEB0dHR3MHz5cuXUVVVBRcXFwwbNkxQHWePgmEY3LhxAyUlJd0u+2nP8lRUVKClpUVniNSQwtvQ0ICsrCzuYmdqz/3Nmzdx584dhIeHw97enjsYl8lkqKurg4ODAyfiQtspNzU14eLFi1ynpZBKqi0tLaiqqkJubi5mzJgBe3t71NTUYNmyZVi5cqWgd74UlsdKbLTRaDTIzMzkdjx3797FCy+8gIEDB+K7777Dhg0bMGfOHEG94R6FRqPhMuvDw8P1ks6o3Qosk8l0UjTd3Nz0eo5SV1eH7OxszufMlJ57hmFw/fp1lJWVISIiot2OOW237/v376Nv376c8PDdqEGExtXVVZD2Odqkpqbi1VdfxZNPPomCggIwDIO4uDj8+9//NliabHusWrUKH3/8sc5tQ4YMQX5+vtHWYEo8tmKjDQk6W758OQ4fPgwzMzNMnDgRCQkJiI2NNagJpr4gjgYKhQJhYWEGK9eQFE3ySZ2YXLq5ufUoEI40Mvj7+8PPz09/CzYCpDW7qqoKERERndqxKJVKnUAzS0tLHescY+7oTEloMjIyMHXqVHz++ed44403oNFocPbsWRw+fBgff/yxUcvdq1atwp49e3Ds2DHuNgsLC5NrBjEWVGzAis2KFSuwZcsWSCQSeHh4YM+ePUhJSUFeXh6effZZiMVixMXFwcXFRXBvxpaWFmRnZ3PBW8YqKWibXFZXV3drBgXons+ZUCC7yZqaGkRERHSrNVvbdLWyshIMw3BlS0OflzU2NnKGoB3FYgiBCxcuICEhAatXr8bbb7/N+1pXrVqFffv2QSqV8roOU4GKDdiL9bx587B8+XI88cQT3O3k/IMIj1QqxdNPPw2xWIwpU6bA3d2d9xc8OeMgLap8nXFoz6Boxze7u7t36K6sD58zviAzQHK5HBEREXrZTTIMg9raWm732NLS0mXvu87S2NiIixcvwt3dXfBCk52djbi4OKxYsQKLFi0SxFpXrVqFDRs2wMHBATY2Nhg9ejTWrl3bpYiRxwkqNp2EYRgUFRVBIpEgJSUF58+fx1NPPYWEhAQkJCTA29vb6G8AEtbm7e0tKPdgEt9MSkRWVlbcjsfBwYFbJ3Gd1qfPmbFQq9WcEauhZoDa877TVzYPiTgwBaHJzc3F5MmTsWTJEixbtkwwa01NTYVcLseQIUNQVlaGjz/+GCUlJbh8+TLs7Oz4Xp7goGLTDRiGwd27d5GSkoKUlBScOXMGERERnPAYI4CMlJ66GtZmbLTdlWUyGTf8SAQpPDzc5Fyn1Wo1pFIp5zFnrHOCpqYmbgaluroa/fr10ylbdvY1R4TGw8ND8I0YeXl5iImJwYIFC/DRRx8Jeq01NTXw9fXFxo0bMX/+fL6XIzio2PQQhmFQXl6OvXv3QiKR4NSpUxg5ciQnPIZ4M9+9exfXrl0zOescMvxYUFCAhoYGWFhY6ATCmcI8jVKphFQqhUgk4nXYVKlUcsJDsnna2z22paGhARcvXjSJLJ2CggLExMRg3rx5WLNmjaDXSoiMjMSECROwdu1avpciOKjY6BGGYVBVVYX9+/dDIpHgxIkTCAoK4hyqezokR+Y4iouLERoaCicnJz2u3vBo+5yFhYVBoVBwOx6FQqEzyyPEuQmFQoGsrCwuWkIo4tjW8oU4QRDLF3KOZ0pCc+PGDcTExGDGjBlYv369ScxbyeVy+Pj4YNWqVXjnnXf4Xo7goGJjIIj1xoEDByCRSPDbb7/B19eXE56uZvJoNBpcvXoV9+7dM7rzsT7Qbs1ue8bRdpansbFRJ5dHCJ5oLS0tOq4GQr34EScI0qyhVCrh4uICe3t7FBUVYcCAARg0aJCghaaoqAiTJk1CQkIC/vOf/wj2uV6yZAni4+Ph6+uL0tJSrFy5ElKpFHl5eSZnUWQMqNgYibq6Ohw8eJDL5PHw8OCEJzw8vMM3FDmMbm5uRlhYWI/mWfhAoVDoWP88ateifSheX1+vlzyZnqBts29KjhIMw6C+vh4lJSUoKSkBwzDo378/N88jROucO3fuYNKkSYiOjsaWLVsE/Vy/8sorOHXqFO7duwdXV1eMHTsWa9aswaBBg/hemiChYsMDcrmcy+Q5fPgwl8kjFosxatQonfIM8WgzNzdHSEiIyXm0EZ+zvn37YsSIEV0uPZFDcZlMhpqaGtjb23NnE8aweyFzKP379+fdK6w7yOVyXLx4EQMHDoSnpyf3XNbW1nLPJcmU4ZuysjJER0fjmWeewY4dOwRTpqToByo2PNPY2KiTyWNra4v4+HiIxWI4OTlxh6MTJkwQ9Ke89tC3z5n2GQ+xeyHC09EsT3eRy+XIysoyifbg9qivr0dmZiYGDhz4wKftttk8JOPI1dX1odk8hqSiogIxMTGIjIxEcnIyFZpeCBUbAdHc3Izjx48jJSUFEokEdXV1GDRoED7//HM899xzJrWrqaurQ1ZWlsFmgJRKpc4sj7W1Ndzd3TsMMusK5EJtCmcc7UHWT3KMOoJk85Dn0tzcnBMeY0RNVFVVYfLkyRg+fDh27twpyOYQSs+hYiNA0tLSMG3aNLzyyiswMzPDvn37oFQqERcXh4SEhC5l8vCBsX3O2gbCkYtld33GamtrkZWVBT8/P/j7+xto1YaDCI2vr2+X10/a08lzSaImSGebvoXg/v37iI2NRUBAAHbt2mVSH6goXYOKjcC4e/cuhg0bhm3btmHGjBkA2E+eJJNn3759kMvlmDx5MhISEjrM5OEDmUyGy5cvY8iQIfD29jb645OLZUVFBeczpp3L8yjhuX//PqRSqeCHZR8G2VF2R2jaoh01UVlZiaamJr12CdbU1CA+Ph6enp5ISUkRRNchxXBQsREgpaWl8PLyavc+kslDohHu3bvHtYlGR0fzetArNJ8z0n5OznmUSqWO8LT9lF5VVYVLly7xJpQ9pa6uDpmZmQbbUbYNM3NwcOB2kF39wFNXVwexWAwHBwfs37/f5DosKV2Hio0Jo9FocPHiRU54SktL8cILLyAhIQExMTGwt7c32lqIz1loaCicnZ2N9ridhbQBE+FpamrSMbisrq5Gbm4uhg0bBk9PT76X22UMLTRtIY7flZWVXW7WkMvlmDp1KqysrLimGErvh4pNL0Gj0SAnJ4cTnps3b+L555/nMnkcHR0NcsjNMAwKCwtx9+5dhIWFmYzPmVwuR2VlJSoqKlBfXw8A8Pb2xqBBgwR9HtYe5IwpICAAvr6+Rn980qxBrHMsLS11zsy0X3eNjY146aWXoNFocPjwYZMbTqZ0Hyo2vRCGYZCXl8dFI1y9ehXPPfccl8nTv39/vQgPwzA6rgZCmNXoKiUlJcjPz4enpycaGhpQW1vbo/KQseFbaNpCjFdJWzXACkxNTQ0mTpyI+fPno6GhAUeOHDHqzpvCP1RsejkkrpgIT05ODsaOHYuEhIQeZfJo+5yFh4cL/qLcHsXFxVzpj/jMtY1u7q6zsjGoqalBdnY2Bg0aJMhmBnJmJpFIsHbtWshkMtjb22Pt2rV4+eWXeS+3bt68GRs2bEB5eTlCQkLw5ZdfYtSoUbyuqTdDxeYxgmEY3Lp1i8vkuXDhAkaPHo0pU6Z0KZNHpVIhJycHKpUKYWFhJtlFdOvWLRQVFXUYcUCclUkgnI2NDSc8fAw+aiN0odFGoVBg1qxZuHbtGqZOnYqjR4/i0qVLmDJlCiQSCS9r+uWXXzB79mx8/fXXiIqKQlJSEnbv3o2CggK4ubnxsqbeDhWbxxSGYXDnzh0ukycjIwNPPvkkF43g6+vb7sWU+JwR+xxTG8DTPmOKiIjodMiVWq3WGSLVjkcw1HnYwyBCM3jwYAwcONBoj9sdVCoV5s2bh4KCApw4cYIzqLx9+zaXVcMHUVFRiIyMxFdffQWA3akPHDgQCxcuxLJly3hZU2+Hig0FDMOgrKwMe/fuRUpKCk6dOoXg4GBOeIgDwM2bN3Hp0iX4+/t3y+eMbxiGwbVr11BRUdEj52yNRqMTCAdAJ5fHkBP31dXVyM7ORmBgoEkIzZtvvomcnBycOHECHh4efC8JAPuBydbWFnv27IFYLOZunzNnDmpqarB//37+FteLoWJD0YFk8hDhOXHiBIYOHYqnnnoKKSkpmDp1Kr744gtBnV10Bu1mhoiICL212xJLfyI8arVaJ5dHn4JMhCYoKAgDBgzQ2+81BGq1GgsWLMDZs2eRnp7+0LkxPigtLYW3tzcyMjIwevRo7vYPPvgAv//+O86dO8fj6novpuXs2A3WrFmDMWPGwNbWFo6Oju3+THFxMWJjY2Fraws3Nze8//77UKlUxl2oQCDBW//3f/+H1NRUlJeXY+rUqfj+++9RXV2NU6dO4ZNPPsGlS5eg0Wj4Xm6n0Gg0uHLlCqqrqxEZGanXuQ4zMzM4Oztj6NChGDduHMLDw2FjY4MbN24gPT0dOTk5KCsrg1Kp7NHj3L9/32SERqPRYNGiRfjjjz9w7NgxQQkNhT9Mq+DeDRQKBaZNm4bRo0fjm2++eeB+tVqN2NhYeHh4ICMjA2VlZZg9ezYsLS3x6aef8rBi4SASiZCbm4ukpCSsWbMGb7zxBpfJ8/zzz8PT0xNTpkxBYmIiwsLCBOlKrdFokJubi8bGRjz55JMGnaERiURwcHCAg4MDBg8ezE3c3759G1euXOGsXtzc3LrUVEEsdEzB2UCj0eCDDz7Ab7/9hvT0dEE2L5AdZ0VFhc7tFRUVgin19UYemzJacnIy3n33XdTU1Ojcnpqairi4OJSWlnIWK19//TWWLl2KyspKk+y00ifr1q2Dm5sbXnvtNZ3b5XI5Dh8+DIlEgtTUVDg7OyM+Ph6JiYmIjIwUxHmOWq1GTk5Ou+mgxqaxsZErtRGrF3d3d7i6unbYNm5qQrN8+XLs2bMHJ0+eRGBgIN9LeihRUVEYNWoUvvzySwDs2n18fLBgwQLaIGAgHnux+eijj3DgwAFIpVLutlu3biEgIABZWVkICwsz7kJNkMbGRqSlpUEikeDQoUOwtbXlwuBGjx7NS8eaSqWCVCoFwzAIDQ0VlJswsXqRyWSorq6GnZ2dziwP4d69e8jJycHQoUMFX4piGAb/+te/8MMPP+DkyZMYOnQo30vqkF9++QVz5szBtm3bMGrUKCQlJWHXrl3Iz88XhK9fb6TXl9EeRXl5+QMvLvLn8vJyPpZkctja2iIxMRGJiYlobm7GsWPHkJKSgpkzZ8LCwoLb8YwdO9YoF32lUons7GyYm5sjLCxMELssbWxsbDBw4EAMHDgQCoWCa6m+efMmF2JmbW2Na9eumYRXG8Mw+Oyzz/Ddd99xDSVCZ/r06aisrMRHH32E8vJyhIaG4siRI1RoDIjwiuydYNmyZRCJRB1+5efn873MxxIbGxvExcXh22+/RXl5OX788UeYm5tj3rx5GDRoEN566y2kpaWhpaXFII+vUCiQmZkJS0tLhIaGCk5o2mJlZQUvLy+Ehobi2WefRUBAAKqrq5Gfnw9zc3PU1dWhpqYGQi1AMAyDjRs3YsuWLTh69ChGjBjB95I6zYIFC3D79m20tLTg3LlziIqK4ntJvRqT3NksXrwYc+fO7fBnHpVOSPDw8MD58+d1biMHh/SwsGdYWlpi4sSJmDhxIjZv3ow//vgDu3fvxoIFC9DQ0IDY2FgkJCTg+eef14vdTUtLCzIzM9GvXz+MGDFCkA0LHWFhYQELCwvU19dj2LBhsLKygkwmg1QqhUgk4kptxkjP7AwMw+DLL7/EF198gbS0NISGhvK9JIqAeezPbEiDQFlZGWdTsX37drz//vuQyWQm5wBsCqjVapw9e5ZzqK6urkZ0dDTEYjEmTpzYLUPPpqYmZGZmwtHREcOGDRPExbirVFZWcjEH2h902pvl0c7l4WP3xjAMtm3bhn/9619ITU3VmVehUNqj14tNcXEx7t+/jwMHDmDDhg04ffo0AGDw4MHo168f1Go1QkND4eXlhfXr16O8vByzZs3C66+//ti3PhsDjUaDCxcucMJTVlaGF154AWKxGJMmTeqUM3BjYyMyMzPh4uKCoUOHmtzAKcAKzaVLlzB8+PAOd9QMw6C2tpYTHoVCoZPLY4xmDIZh8N133+Ef//gHDh06hHHjxhn8MSmmT68Xm7lz5+L7779/4PaTJ0/iueeeA8D6NP39739Heno6+vbtizlz5uCzzz4zOd8vU0ej0UAqlXLCc+vWLUyYMIHL5HFwcHhASORyOTIzM+Hp6YnAwECTFpquJpwyDAO5XM4JT0NDg47wGKLVm2EY/PTTT1iyZAkOHDiA8ePH6/0xKL2TXi82FNOEYRhcuXKFi0bIz8/H+PHjIRaLERsbi/79++PMmTPIyMjAiy++iICAAJMUGplMhtzcXL1EaTc0NHAt1XV1dXB0dOTOefQRu8wwDHbt2oWFCxciJSUFEydO7PHvpDw+ULGhCB5ioCmRSCCRSLhdwNWrVzF//nysX7/epIVm5MiRere1b25u5nY8NTU1sLe353Y83Q25S0lJwd/+9jf88ssviI2N1et6TZkffvgBixYtQmlpqc4Zr1gshp2dHX788UceVyccqNjwiJ+fH27fvq1z29q1a+kEcwcwDIP//ve/mD9/Ptzc3FBaWooxY8ZwmTxeXl4mITwVFRW4fPmyQYSmLQqFQieXp2/fvtyOp1+/fp16vg4ePIjXXnsNO3fu1HFKprDNKZ6entixYwemTZsGgP0g4e3tjaNHj9JS4/+gYsMjfn5+mD9/Pt544w3uNjs7O5OMVzYWR44cwUsvvYTNmzdj9uzZuHPnDhcGd/bsWS6TRywWw8fHR5DCQ4QmODiYy3cxFiqVSieXx8rKihOe9s7EAPY5nzVrFpKTk7mLKUWXt956C0VFRTh8+DAAYOPGjdi8eTNu3LghyNcgH1Cx4RE/Pz+8++67ePfdd/leislw5swZlJSU4OWXX9a5nWEYlJaWctEIp0+fRnBwMMRiMRISEjBo0CBBvOn5FJq2qNVq3Lt3DzKZDJWVlTA3N+fmeJycnGBlZYXjx49jxowZ2LZtG2bOnCmI51CIZGdnIzIyErdv34a3tzeCg4Mxbdo0rFixgu+lCQYqNjzi5+eH5uZmKJVK+Pj4YObMmVi0aBHtgushDMOgsrKSEx7i1UWEh6/26PLycly5ckUQQtMWjUaD6upqyGQy7Nu3D5s2bcLw4cORnZ2NTZs24fXXXxek0AipFB0REYGXXnoJEydOxKhRo1BUVCT4gDtjQsWGRzZu3Ijw8HA4OzsjIyMDH374IV577TVs3LiR76X1GhiGQXV1Nfbv3w+JRIJjx44hICCAK7UNHz7cKAOgZWVluHr1KoKDg+Hi4mLwx+sJKpUKX331FVauXAkHBwe0tLQgNjYWU6dORWJioqBMTYVUit66dSuSkpLwwgsv4Pr160hLSzP6GoQMFRs9s2zZMqxbt67Dn7l69Wq7ZoXffvst3nzzTcjlcupcYCBqa2vx66+/QiKRIC0tDd7e3pzwhIaGGkR4iNCEhISgf//+ev/9+ub8+fNISEjAmjVr8NZbbyEnJ4fbIaanpwtq5y2kUnRtbS28vLygUqnwww8/YPr06XwvSVBQsdEzlZWVuHfvXoc/ExAQ0O7A3ZUrVzBixAjk5+djyJAhhloi5X/U19frZPK4uLjoZPLoQ3hMTWiysrIQHx+PFStWYNGiRYIsnWkjtFL07NmzcejQoQfaoCkmasQpZFxdXbtdj5dKpTAzMzN4KyyFxc7ODtOnT8f06dPR2NiII0eOQCKRIDExEf369dPJ5OmO/1hpaSny8/NNRmguXbqEKVOmYOnSpSYhNADwzjvvPFCKLisr460UXVJSgldffZUKTTvQnQ1PnD17FufOncP48eNhZ2eHs2fPYtGiRYiJiWnXXodiPJqbm/Hbb78hJSUF+/fvh5WVFeLj4yEWizudyUOEJjQ0FM7OzkZYdc/Iy8tDTEwMFi5ciBUrVvAqNKZYiq6urkZ6ejpeeukl5OXl0cpEO1Cx4YmsrCy89dZbyM/PR0tLC/z9/TFr1iy899579FORgFAoFDh58iQkEgn27dsHjUaDuLg4iMViPPfcc+2WQ0tKSlBQUGAyQlNQUICYmBjMnz8fq1ev5n1HY4qlaD8/P1RXV2PFihVYsmSJ0R7XlKBiQ6F0EpVKhdOnT2P37t3Yt28fmpqaEBsbiylTpmDChAmwsbHB5s2b4eDggMmTJ5uE0Ny4cQMxMTGYOXMm1q1bZ5LRDNrs3LkTs2fPRlVVFZycnPheDkULKjYUSjdQq9XIyMjAnj17sG/fPtTU1MDf3x9Xr15FcnIyEhIS+F7iIykqKsKkSZMgFouRlJRkckJDS9GmBRWbx5jNmzdjw4YNKC8vR0hICL788kuMGjWK72WZHBqNBh9++CGSkpLg7OyM+vp6nUweOzs7vpf4AHfu3EF0dDQmTZqELVu2mJzQALQUbWpQsXlM+eWXXzB79mx8/fXXiIqKQlJSEnbv3o2CggLaDddFtm7dimXLluHw4cMYPXo0srOzuWiE4uJiLpNn8uTJD/UfMyZlZWWIjo7Gs88+i+3bt/OS9El5/KBi85gSFRWFyMhIfPXVVwDYT+cDBw7EwoULqet0F/n6668RHByMMWPG6NzOMAwuX77MCc+1a9cwfvx4JCQkIC4uDs7OzkYXnoqKCsTExCAyMhLJyclUaChGg4rNY4hCoYCtrS327NmjYxc/Z84c1NTUYP/+/fwtrpfCMAwKCgq4TJ7c3Fw888wzSEhIQHx8PNzc3AwuPJWVlYiNjcWIESPw008/CcoJgNL7Mb1CLaXHVFVVQa1WP5AM6e7ujvLycp5W1bsRiUQYOnQo/vnPfyIzMxNXr17FxIkTsXPnTgQGBiImJgZbt25FSUkJDPH57/79+4iPj0dQUBB+/PFHKjQUo0PFhkIxMiKRCIMHD8bSpUvx559/orCwEGKxGHv37sUTTzyBCRMmYNOmTbh9+7ZehKempgYJCQnw9fXFzz//LCgjTcrjAxWbxxAXFxeYm5ujoqJC5/aKigp4eHjwtKrHE5FIBF9fX7z33ns4ffo0bt++jZkzZyItLQ3BwcF49tlnsXHjRhQWFnZLeOrq6pCYmAhXV1fs3r273UFICsUYULF5DLGyskJERASOHz/O3abRaHD8+HGMHj2ax5U93ohEInh7e2PhwoU4ceIE7ty5g9dffx2nTp1CREQExowZg3Xr1iE/P79TwiOXy/HSSy+hX79+2Lt3L2xsbIzwr6BQ2oc2CDym/PLLL5gzZw62bduGUaNGISkpCbt27UJ+fv4DZzkUfmEYBvfv39fJ5Bk8eDCmTJmCxMREDBs27IE5mcbGRrz44osAgEOHDqFfv358LJ1C4aBi8xjz1VdfcUOdoaGh2LRpE6KiovheFqUDGIbRyeQ5evQoBgwYwGXyhISEQKFQYPr06WhoaMCRI0dgb2/P97IpFCo2FIopU19fj0OHDkEikeDIkSPo378/zMzM4OjoiBMnTsDR0ZHvJVIoAKjYUCi9hoaGBuzduxerV69Geno6bfagCAraIEDhnVWrVkEkEul8tZdVQumYvn374q9//Svy8/MFIzRr1qzBmDFjYGtr+9BdVnFxMWJjY2Fraws3Nze8//77UKlUxl0oxeDQyS6KIBg+fDiOHTvG/ZkOHfYOFAoFpk2bhtGjR+Obb7554H61Wo3Y2Fh4eHggIyMDZWVlmD17NiwtLfHpp5/ysGKKoaDvaIogsLCwEMyncYr++PjjjwEAycnJ7d5/9OhR5OXl4dixY3B3d0doaCg++eQTLF26FKtWraJzQb0IWkajCILr16/Dy8sLAQEBePXVV1FcXMz3kihG4OzZsxg5cqROu310dDTq6upw5coVHldG0TdUbCi8ExUVheTkZBw5cgRbt27FrVu3MG7cONTX1/O9NIqBKS8vb9ejj9xH6T1QsaHwTkxMDKZNm4bg4GBER0fj8OHDqKmpwa5du/heGqUdli1b9kBDR9uv/Px8vpdJERj0zIYiOBwdHREUFIQbN27wvRRKOyxevBhz587t8GcCAgI69bs8PDxw/vx5nduIZx89w+tdULGhCA65XI7CwkLMmjWL76VQ2sHV1RWurq56+V2jR4/GmjVrIJPJuITY3377Dfb29hg2bJheHoMiDGgZjcI7S5Yswe+//46ioiJkZGQgMTER5ubmmDFjBt9Lo/SQ4uJiSKVSFBcXQ61WQyqVQiqVQi6XAwAmTpyIYcOGYdasWcjJyUFaWhqWL1+Ot99+G9bW1jyvnqJXGIrJI5PJGHd3d2bNmjXcbWfOnGEsLS2ZY8eO8biyzjF9+nTG09OTsbKyYry9vZnp06czN27c4HtZFD0wZ84cBsADXydPnuR+pqioiImJiWH69OnDuLi4MIsXL2aUSiV/i6YYBGpX00s4fPgwxGIxMjIyMGTIEISGhiIhIQEbN27ke2kUCoVCvdF6E2+//TaOHTuGJ598Erm5ubhw4QItRVAoFEFAz2x6EZ9//jlUKhV2796NnTt3UqHpAqdOnUJ8fDy8vLwgEomwb98+nfsZhsFHH30ET09P9OnTBxMmTMD169f5WSyFYoJQselFFBYWorS0FBqNBkVFRXwvx6RoaGhASEgINm/e3O7969evx6ZNm/D111/j3Llz6Nu3L6Kjo9Hc3GzklVIopgkto/USFAoFRo0ahdDQUAwZMgRJSUnIzc3l2kkpnUckEmHv3r0Qi8UA2F2Nl5cXFi9ejCVLlgAAamtr4e7ujuTkZLzyyis8rpZCMQ3ozqaX8M9//hO1tbXYtGkTli5diqCgIMybN4/vZfUKbt26hfLyckyYMIG7zcHBAVFRUTh79iyPK6NQTAcqNr2A9PR0JCUl4ccff4S9vT3MzMzw448/4vTp09i6dSvfyzN5iEdXex5e1L+LQukc1EGgF/Dcc89BqVTq3Obn54fa2lqeVkShUCi60J0NhfIIiEcX8ewiVFRUUP8uCqWTULGhUB6Bv78/PDw8cPz4ce62uro6nDt3DqNHj+ZxZRSK6UDLaBQKWPNPbZfpW7duQSqVwtnZGT4+Pnj33XexevVqBAYGwt/fHytWrICXlxfXsUahUDqGtj5TKGCbLMaPH//A7XPmzEFycjIYhsHKlSuxfft21NTUYOzYsdiyZQuCgoJ4WC2FYnpQsaFQKBSKwaFnNhQKhUIxOFRsKBQKhWJwqNhQKBQKxeBQsaFQKBSKwaFiQ6FQKBSDQ8WGQqFQKAaHig2FQqFQDA4VGwqFQqEYHCo2FAqFQjE4VGwoFAqFYnCo2FAoFArF4FCxoVAoFIrB+f99Q0z9MR/IQwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Heave': 3481289.873851666}\n",
+      "{'Heave': -409392.0163770599}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#really tall  - compound cylinder\n",
+    "h = 100\n",
+    "d = [29, 7, 4]\n",
+    "a = [3, 5, 10]\n",
+    "w = 1\n",
+    "rho = 1023 # density of our special material\n",
+    "zdensities = [40, 10, 10]\n",
+    "rdensities = [15, 10, 25]\n",
+    "tdensities = [50, 80, 200]\n",
+    "config = \"config2\"\n",
+    "heaving = [1, 1, 1]\n",
+    "\n",
+    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "AjeiVwZRPpcy"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Heave': 5844.703334473447}\n",
+      "{'Heave': 13509.03353109275}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#indents  - compound cylinder\n",
+    "h = 1.9\n",
+    "d = [0.5, 0.7, 0.8, 0.2, 0.5]\n",
+    "a = [0.3, 0.5, 1, 1.2, 1.6]\n",
+    "w = 1\n",
+    "rho = 1023 # density of our special material\n",
+    "zdensities = [15, 10, 30, 15, 25]\n",
+    "rdensities = [10, 10, 20, 10, 15]\n",
+    "tdensities = [40, 50, 70, 80, 100]\n",
+    "config = \"config3\"\n",
+    "heaving = [1, 1, 1, 1, 1]\n",
+    "\n",
+    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh__Heave': 1969.6883142754816}\n",
+      "{'axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh__Heave': 3865.1910610424184}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#original - only outer heaving\n",
+    "h = 1.001\n",
+    "d = [0.5, 0.25]\n",
+    "a = [0.5, 1]\n",
+    "w = 1\n",
+    "rho = 1023 # density of our special material\n",
+    "zdensities = [10, 10]\n",
+    "rdensities = [20, 20]\n",
+    "tdensities = [50, 100]\n",
+    "config = \"config4\"\n",
+    "heaving = [0, 1]\n",
+    "\n",
+    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh__Heave': 525.0198998057563}\n",
+      "{'axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh__Heave': 388.05322984841234}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#original - only inner heaving\n",
+    "h = 1.001\n",
+    "d = [0.5, 0.25]\n",
+    "a = [0.5, 1]\n",
+    "w = 1\n",
+    "rho = 1023 # density of our special material\n",
+    "zdensities = [10, 10]\n",
+    "rdensities = [20, 20]\n",
+    "tdensities = [50, 100]\n",
+    "config = \"config5\"\n",
+    "heaving = [1, 0]\n",
+    "\n",
+    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NQqMBnn76aTz99NPQaoHY2DikpS3GbbfdhrPOOmvOBZTBYtkAcOtaZS205bqYjo2NqV1Mgxwq2QQpOI5Dd3c3qqqqkJqaipUrV/q0qARCoVmj0WBmZgYHDx70y21mNpvx97//Hffffz9GRoZgtQqrqngYRgL+wN2x0teUMPzEl9gVqbBt0tftdvltUsKRuv+oXcAYLrnkEmg0wKJFi7BkSRbuuOMOnHjiiX68h+AhG1+sLHddTMfGxpy6mIrJR20ktzBQySYIYbVaUVVVhb6+PqxZswapqak+/ziUrrOxWCxoaWmBzWZDUVGR126z+vp63HbbbSgvL8XAwCC/cErXE3fxFOmCLEcobJvcW2aLt/i1QLjDPBGPHOmIrwebnxwJiglIann19fWjr68fZ599NgAgMzMTO3fuxEUXXeRVK4lgIht30jme4KmLaVNTEyoqKhAZGenkdlPJZ36gZqMFGUZHR1FaWgqDwYD169cjIiLCr3E6OjowOjqKTZs2zXlOzG0WFhaG6elpnHTSSW73b21txTXXXIM9ez6Dw+Hb18tTZpfc6764qaTwxg0mdwz774nP52KlyZ2XzVX83sTnEO+j1QKpqYtx2mmn4eGHH3bp7ty3bx9Wrly5YOnqYoyNjaG2thbHHXec4mNLu5iOjY05dTFl5KN2MQ0MVMsmSOBwONDW1ob6+npkZ2dj2bJlc9KTUsKNxnEcOjo60NzcjOXLlyMuLg4VFRWy+/b29uL+++/Hv/71AqanzeA4WhAZxIuju4XSEznIve7qbcrtK1385xIHElsqrsZiSQb+nEtKhFJ3m/QcLL7EHjsc9Lk899xzeO6555CQkIBHH30U55xzjmSO/lsTSiOQyQqeupjW19ejrKyMVxxnLbTVLqbKQCWbIIDZbEZFRQXGxsaQn5+PxMTEOX+55+pGs1gsqKmpwcTEBIqKihAXF4fx8fFZBPa73/0Ojz76KAYHB2eNIV4QXT12t/D7Y3W4siTEGWZKw9O85VxlruDKXebLHOSO02iAkZERXHrppQgJ0eCMM87GHXfcgby8vKBzo81XZpxcF1Oz2YyRkRGMj487dTFlVo9KPv5DJZsFBMdxGBwcRFlZGWJiYlBSUqJYx8q5WDauss2YKoHJZMLFF1+Mt99+W3Q+WuRcuXS8gTurw1t3lKt9PM2DFXmK95fOX6cTxnc3ntyxbG5y28Xb7Hbv36t4P0+Wo/icFguHN954A2+88QZSUxfhzDO/jvXr13s+4TxgIa0sRj7p6em86Ov09DQOHjyIiYkJ9PX1OXUxZSnZahdT76CSzQLB4XCgoaEBLS0tCA8PV7yAzx/LhrnNWlpasHz5cmRlZTn9iPr6+nDjjTeiqakJgHyg2xNZiBdIKTF5m0XmacH2FuJxPV0qVy4+XzPQXO3rbi6uYk+uMutcWY5y4/X19ePZZ5/F3/++C9///sX44x//uKA1N/Np2XgCUxzXaDTIzc1FZGQk3+5C2sWUud3UFtquoZLNAsBoNKK0tBQzMzNYtmwZhoeHFf+B+Uo2FosF1dXVmJycxObNm3nhRADo7OzExRdfjLKygy5Tcj0F4+UWUlfE5K7aX+6u3xeiEe/r6fJ4Igl328Wve5Oo4G4uvqZoe2M9SV+z2x34+9//jr///e+47LLL8Ic//GFBFv1gKjBlcDgc0Ol0Tl1MWVtzk8nEk8/hw4edupgmJSXx5KNCJZt5Bcdx6OnpQVVVFZKTk5Gfn4/h4WHZeMdc4YsbbXR0FJWVlbwrj7nN2tvbceWVV2Lv3j0eF3y2gLnKmJKfo/y+7qYttyh78zZ9URJwVVDqb76FXD2NJ7jbX/yauxRrT9vYNZe+tmvXLvz977tw111346abbvJ+0gogmJIVAPrNurK2xF1MWQttcRfTzs5O2O12vpdPUlLSV7qF9lfzXS8AbDYbampq0NPTg1WrVmHx4sV83xmlZWUA7ywbV24zs9mM8847D7t3f+TRfeNtxpQwL3nCcHUJlEoddrVwy8VhxK49uWPFa6GrceXiT74QjtS1Jn3N0/mlkCN+uYxA8bh33303fvObX+Hvf/8nzjjjDO9ONEcEkxsNmC2f4w6+djFl5PNV6WKqks08YHx8HIcOHYJWq0VxcTEiIyP51wLR5MybcV25ze6991789rcPwGZzv4q5uisWvyYHX9OOA5E9JoY7nvfGdeaKkFyRhC/WnitS91Z01FOhqKu5io8zm2248MLvIi0tHR9++BHS0tJcD6QAgs2Nxm4E/SEEuS6mrNEf62IKgG8kl5SUdEy30FbJJoBwOBzo6OhAXV0dMjMzsXz58llfpECRjTs3mthtdtxxx8FgMKClpQWnnHIy+vsHvjzefTaYu8VL/JrcwreQ8CeRwB3cxZrk4CnbzBuCkzunp2QNT3OUS1DQ6wGbjebc3d2DtWvzcOONt+CXv/yl6zc4RxzNlo0niBv9sbFZI7nBwcFZXUwZ+czn9Xjsscfw0EMPoa+vDxs2bMCf/vQnFBUVKTK2SjYBgsViQUVFBYaHh7Fx40YkJSXJ+qLn07Jx5TbbuXMnHnvsMQCzLRbmZvKUDSY+1vmczv/Fx/uSVeZvEoAUniwC8T6envsythjuLAlfIXd9pXAXD2OEIpeubrM572u1Ag899Fv89a9P4PPP9yEzM9O/SbtBsMVsGPkFYk7edDHdvXs3ysrKkJ2djYsuuginnHJKwK7PSy+9hJtuuglPPPEEtmzZgkcffRSnn346GhsbkZKSMufxVbJRGBzH8e2aIyIiUFJS4lafKpCWjXhc5jabmpri3WYmkwn5+fno6OgAMJsstNrZz8U1G2yhYpDbl0HuztzbQLa77XPd191xnp57G8T39HogLD532Wfiz1lKKJ5iUBwHjI1NYOPGNfjd7/6IHTt2KDrvYLNs7Hb7vM1HrotpREQEzGYzdu3aheeffx6JiYnYvn07zjzzTFx66aWKnv+RRx7BlVdeiR/84AcAgCeeeAJvvfUWnnnmGdx6661zHj94PtVjAA6HA42Njdi3j+76CgoKPAohBtKyYW600dFR7N27FxqNBiUlJYiLi8Mnn3yCRYuS0dHRAZ1Ovo5EOi3pc/FC5U5YU+kbMSXGMxjmPg5zD8q52L1JifYF7uYqVcz2NL4c+Yght7ZKP3urFbjxxhvwta99TdHvb7DFbBaS/HQ6HYqKivDAAw8gOzsb//jHP/DSSy9h5cqVqKmpUfRcFosFpaWlOPXUU/ltWq0Wp556Kvbt26fIOVTLRiGYTCaUlZXBZDJh8+bNXosaMrJRWjKEZbm1t7fPcps99thj2LlzJ4DZ1okrl46c1eOqZgaQt2QCnVnmDTRfNkNTqtUPK1SVs2S8bRftDdy9Z1c1Se7gLnmAKR1IC22l79FuB0pLD2DJkgw0NDQhKirKu5O7QTBaNgsdsOc4DkajEQkJCTjhhBNwwgknKH6OoaEh2O12LFq0yGn7okWL0NDQoMg5gudTPUrBcRyOHDmC3bt3w2AwoKSkxCf1XPZFVlp822azgeM4dHV1oaioiO89c8899+Dmm+WJBvCOaKSLkDd3xtKxxcrJSkE6D7lyBm8Ij1krWq3w526eYskYMVwpDgQDvClodTic3xPLpgOE92GzAZOTU8jOzkBPT48C8wrOmM1Cw2g0KkLmC4mFv4pHMex2O6qqqlBaWorly5dj/fr1MBgMPo3BvshKuiJGR0dRVlYGACguLuazX2699Vb85jcP8MF5KdG4+o37qsQsLQCVgzeBbV/hqtCUwZ2lodEIrjWOE/rfsDgFu9PX6+WvE1ucpZASnvh6BKq2z9U1l26XkqPcDbxcbY5OJ1wTBovFgbVrV6G6unpON07BsrgzMPWAhYbJZHIqmVAaSUlJ0Ol06O/vd9re39+P1NRURc4RPJ/qUYbJyUl8+umnGB4eRnFxMTIzM/36kShJNhzHoa2tDYcOHcKSJUsACJbTddddhz/+8VFecFK6HkiFKF1ZHnq9836eFiyFDTavIX0/7mAw0L5Wq/y1YUTCcUTQLFYjBcfRWGK4KqQEnMlprsQj/pxcfZU8kTFTOvAEMeGw68ySSbZtOw4vvfQSX8BsMpl8Ip9gi9nMZ4KAKzBlgkBaNiEhISgoKMCHH37odN4PP/wQxcXFipxDjdn4CI7j0NnZiZqaGqSnpyMvL29Odz5KkY3FYkFVVRWMRiOKiooQERGB5uZmcByH2267Dbt2PQ3A+6p+VwWIUmvI3WLKxnF3Hn8gjh1IhT1dWS7umq75G7+Rez/SsaQp3mLXpfhY6XX1Fd4qGUivg78N5sT1QmwM9vzqq6/CRx99jP7+fjQ1NSEkJAQJCQl8ppU7ZXPVspkNRtjR0dEBPc9NN92Eyy67DIWFhSgqKsKjjz4Ko9HIZ6fNFSrZ+ACr1YrKykoMDAxg/fr1SElJmbN/WaPRzEpT9hWjo6OoqKhAXFwcSkpKYDAY+MrnF198EX/4w+9dLvKu1Jbl4jnA3AhDKU+hq7Rhd2rHrgoZ/Q3gSwsfxXOTjis+t3RfVwWvShWfemPNiCF3k+HqxkOcYMCuA32fOJxxxuno6+sHx3F8c7LDhw+jrq4OERERiI+PR0JCAuLi4pxcz8EWswkGy8ZoNAJAwGM23/ve9zA4OIg777wTfX192LhxI/73v//NShrwFyrZeAlWOxMaGoqSkhKEh4crNra/6c8cx6G9vR2tra1YsWIFlixZwv9QtVotRkdH8cMf/hCAa0UAuQVNr5dfhJVos+ztIuouu0t6p87eh3jhl85Venldkamvc2ULrDgG5a5ttVylv9hKY248pTP4XMFgmG2NyakciAlFDtLXzeYZbNu2DXv37kViYiISExMB0A0bI5+2tjYYjUZER0fzVk8wLO5iBIOlZTKZoNfrFet15Q4//vGP8eMf/zggY6tk4wEOhwMtLS1oaGhAWloa1q5dG5B2AL6KcUrdZiwJgEGj0eDaa6/58rGzO8cVmQC0SLtbUDwtwHJ3wb52n5TWirCFTzymWEqFwVWBqat5yoHVHIlJhGWnsXiNp7Gki7JcGrh4H6mV5kufHV8g99nJuRDZ+aX722zy5AQIcS0xYdbU1ODpp5/GFVdcwe9nMBiQnJyM5ORkAMDMzAxGR0cxOjqKxsZGmM1mWCwWzMzMID4+HjExMQu62AdD6vPU1BQiIyODyuLzByrZuMH09DTKy8sxOTmJmJgYxMbGBuSL76tlI+c2k+InP/kJpqfNAJzvoPV61xYN4J5Q5BYaT/58T2PKjSWXIs3AxhUv5nKdKcXjSWtG5Cw0tkjKWUMsK41lo0nJWDyedL6urBo2htx1kc5fiXodV9JArlyJriwqm83ZZSZX4yMm7JtvvhEXXnghIiIiZOcVGhqK1NRUpKamguM4HDx4EDExMTAajeju7obD4UBsbCzvdouKiprXRTcYLBtGNkc7VLKRAcdxGBgYQHl5OWJjY1FSUoLa2tqAVPoD3pONO7eZGEajEU899aTTNpY95EqpGfBMCFarvD9fTjNMemcsDZTLvz/nBRZwP185i0eads2Ol7MuGFzFreTmJ2f1Sa+HtEhWatW5s/TYe5KStSuLwlvIXUNXVqw0rVn6mnR+UrD3xr5zhYWFqKur8zhH9l1OTExEcnIyX8zILJ+Ojg5eqJL9sU6agUKwJAgc7TU2gEo2s2C321FXV4fOzk7k5eUhIyMDWq0WOp1uQcnGk9tMjK1bt/KLPSD86N2dQuxic+VGc7Uoy925s+3iRcmTZcPce0w6h81f7NZxpVogJjw5EhIjEMF4V/Ei9l/8unRezsF1+fEZ0bizRnyBVuv6c3bnSgVojsxCdnft2Bx7e7tRWVmJDRs2eJyXOPVZo9HMak7GJPqZSrJer3fKdPMkD+Ur7HY730xwoWA0GgNOqvMBlWxEmJqawqFDh2C327F161anVMNANTljY7sjm5GREVRWVrp1mzGYTCa0tTXzle9Sl5P7ebgnJLaQuyMdV64VVxATjNg1IyY/No60DkhKbNIEAnH8wJUrytV2cZ2R3HuQXiu5eJGYYKREJBXDlFoubGzpdrkEBF/hjtTERO+OgNl7cEU40ozB8847j+/f4g7u3FZiif7s7GxeJXlkZAQ9PT1oaGhAWFiYU6bbXIkiWNxoqmVzjIDjOHR3d6OqqgqpqalYuXLlrNatC2HZsCLNtrY2t24zMc477zwAQpzBHaQxAXHXSldwF/egObs/XgppkFw8H1dWlqt5uqsfkeuxI90u97orq83VIismITZ3qVUjZ5lYrfKuMvZcnHUnPY8v8EQi7rIUxRBfF2YluXMPDg0NYnJy0mOtiC+pz1KVZJvNxme6dXR08Is02ycuLs7nlszB4EYzGo1qzOZYgNVqRXV1NY4cOYI1a9YgNTVV9suu1Wphm2vlnQvIkY0vbjMxPvnkE6/PK7UUpNvcgS2a0niIr5AjFLYYu6rzkZunlDjdEY+3C6k/26TBfbZNPAdXRGW1uiZYaSae3LjewF28Tq/3LS7ELCBGOAxy2WwcB1x22WV49dVX3Y45F0tCr9cjKSkJSUlJAOg3xOI9zc3NMJvNiI6O5t1uMTExHokkGFKxVbI5BjA6OorS0lJeQNNVxgxAd1EWiyUg85CSDXObxcfHe3SbidHZ2Sm73d3dLIvnsP++QBoz8eV4tr80GQBwv+DJxRPYgiceQ3wOV1ZNoOBKEZtZLu7mwCwE5kqUc2mJM9mUcKmxtdTT3OTAcULiCBuL3YRIbwDEUiiux1NOriYkJASLFi3iixLNZjNGR0cxMjKC3t5e2Gy2WZlu0nMHgxvtWBDhBL6iZONwONDW1ob6+npkZ2dj2bJlHu9wdDpdwGI2zEUndpvl5eUhMzPTp6Dgj370I6fn7lwibAEQD89iMv68TfHi4o2V5M6NxSCtsJembbPnrt6jNEHB05yUgiuLQ+wSA4g05Nxh7HMRx93EEMeT/Pm8xNahlIz9vT7i1HP2HWAExOap0XgePJCLe1hYGNLS0pCWlgaO42AymXjLp6urCxzHOWW6RUZGBkWdjWrZHKUwm82oqKjA2NgY8vPzkZiY6NWCHqgmZ2xsi8WCQ4cOYXp62ie3mRh79+4FIN8SgC0A7HfM3CaAc+DfmxRlV5DGObyBXBaZ2OphECcmsNflpF+kYFbCfJCMeK7uCEA8b1dfKfb+2KItHdebuIoYUrdeoOAuW5DjgI8++ggnn3yym+PnR65Go9EgMjISkZGRyMjIAMdxmJqawsjICIaHh9Ha2srfBI6OjiIqKkpR1RBfYDQa+SLYoxlfGbLhOA6Dg4MoLy9HdHQ0SkpKfJJ/CKRlY7Va0dHRgeTkZGzatMnnIKZ4HHao9K5SXN3NILZCxLEGqa89UAu1WMBRSiyMfKTWiSuXmdwcNRrBNTQf7jMGT4u5L8F9Znn44jKTe6+uEh08HTfX16Tnf+qpp1ySDbuZWwi3lUajQXR0NKKjo5GVlQWHw4GJiQlUVlZibGwMPT09CA0NdbJ85kM+BiCyWbp06bycK5D4SpCNw+FAQ0ODUzGkr1/oQFg2zG02MjKCxMREbNiwYU53dSx9WPwcEKwBV9ljYotCfCxLPZa+xsZkx7P/vi7o4rt08X/xdpbpxO7qpXOVKg/LZZLNp1UjB2/Swb0peJVCp6PPZ2bG874L9Rrg/N7279/vZhzaaaFjJGwOcXFx0Gq1WLlyJSIjI2cJikZGRjpluvnay8pbBLqXzXzhmCcbo9GI0tJSzMzMYMuWLX65pwDlLZuZmRlUVVVhenoaixYtQnh4+JzdB3K1J2y7u7oKV4sfG8/bokx/3TruICUgaTaaeG5it2CAjFC/IH2f4qQMVhfk77WTEo03x7I5sPMGmozF44+Njbncj93MBVPxIosh6XQ6WUHRkZERtLa2wmQy8YKiCQkJiI2NVSzWo9bZBDk4jkNPTw+qqqqQnJyM/Pz8Od15KGnZiLPNNm3ahJaWloDFgzyBLTbim0lPdSjSffw9rz9wdZmkdUVyKcTz6UqTgsVexGnZvnzk0oQBsXvN1+wxYLakDvvvTh7IXzh/n1zfBSykG00OHMe5TBBwJyhaX18Pi8XCZ7rNVVBUlasJYthsNr5T4KpVq7B48eI53y0pYdm4yjZjCQJzxVwWCeaKkqbc+nIufxZztggzC8rT8SwrTbpYSus65DLCfLUexMkL3h7rKh7jSh7GVbaZ2FJjRComKrEbca5WnPj8bD2ci7UlBiNIuaSO2fMILsvGF7eeVFB0enqaJx8mKBoXF8eTjy+Como2WpBifHwchw4dglarRXFxsWIf0lwtG7HbbMuWLYiJiVFsbAZXiwPb7mvVuTeLjT8LsvhYdocuPpYtslKZf3FsR0w4bEET78/28bboUc4F6U9AXHp95XTa2PGuxDXFSR2ePi8l3YWMsMVWmLefqfhzApxjaN7WQrMam2AhG/ab9NUdptFoEBERgYiICKSnp0MsKDoyMoL29nY+JsTcbq7c6OzYQHfpnA8cM2TjcDjQ0dGBuro6ZGZmYvny5Yrmx8/FshkeHkZVVRXvNpNmmynpotPrBctEumDKVbeLwQLy3sBbWRN3EJOgXF8a8VykKbXi36VUrl/c60Y6PzlVZX/nLT4n4L6bJYOYYOSIRky04vHFBZIM/hKNOwIWu1W9sVSlafVsbH8UqoOxSycwd7eeN4KiBoPBKdNNLCi6EEWd2dnZs4rEH3jgAdx6661+j3lMkI3FYkFFRQX6+vqQk5OD3Nxcxb+0/ghxchyH1tZWtLe3uy3SVJJspH1eGOTcSa4SClwVGzJ4Sjjwx8KRO05aeS+GO102qd6a3JjS+ft6By8H8ZxcXT/x+5DuIy1aFZMns9aYtWAw+E823kgNMSUDbyAuLnVHMp6+G8FQrS8GI79ANEuUCoqOj49jdHTUSVD00KFDiI6OXrB+Nvfeey+uvPJK/vlcraujmmw4juPbNUdERCAmJiZgUtw6nQ4cx4HjOK/Gd+c2kyJQBaPiDC62YHkbw3A3HXcS9P5YOq4aoTFYrd5ZInLHihc4d22jAc89Y5h7yRu3kKe5SuNO0oZkYgtMej6OA3wN8blKv/ZUP+MJLN7mjSXjOZtROakaJTBfumg6nQ4JCQlISEgAIAiKvv3223jqqadgNpvxve99D2eeeSZOPvlkHH/88fPiVouOjkZqaqpi4wXPJ+sjHA4HGhsbsW/fPmRmZqKgoAAhISEBbQMAuM+mYRgeHsbevXt5zTV3RMPGDmQ2msPhrEBM55Tfl6k/A/KLjavFVm5fXzlf6k4TexvZ5RFnTrH5MHhSofbUNpqRGiuIZWBqC77EH8Rgd/5iiMcxGJyJhp0LmE1aOp33rk4xmKtLbrurObvLRAQES8xdiwbp5+Ru8Q42N9pCWVpMUPS+++7DZ599BgD46U9/iomJCVx//fV44IEH5mUev/nNb5CYmIhNmzbhoYcemrMQ8VFp2ZhMJpSVlcFkMmHz5s28xHig9csAuCUFsdts5cqVyMjI8OrHEyiykfr7mQvGW9FNd3EdVxlUrp57M1dAXqJfnG0mPb+0d4zYpSNOXpDOSbyf9P2J04zZNm8bjclZTK7cVWzOzCoQJw3IBdo91Q95+gxcZYvJzY9ZV3LHeGMhieNYYrerO3WMYHOjBYMumslkAgBceumlvEvLOpeWrV7ihhtuQH5+PhISErB3717cdtttOHLkCB555BG/xzyqyIbjOPT19aGiogIJCQmzFJEDSTaeLBtf3GZyYytJNiwGIleMyRYscSKBFO56ycjFa1wtsOJjPLtQ5APtcu+DPZc2IBO/P+kc2P7u4jniuUjbFLiC9Bq5UpiWuwZybjS2jogz7rwlbvGNhdxzKfmy87hKGHB3UyF174nHZEQtF8NxV+sWbGQTDPMxGo0ICQlxum7+1gveeuutePDBB93uU19fj5UrV+Kmm27it61fvx4hISG46qqr8MADD/gt03PUkI3dbkdtbS26urqwcuVKpKenz/oiBJJsWKBQjhRYtllCQoJf2mZKN2ZjC7fBINQ2SBcOTwFgV4QjtwB5uuS+Eo80ruQqXiO1agDnhVMag5EjhrlkpUkbmknn4smqYGOwz0hqiXpDjFLIWSju5uDq/XuKX4mPERMQIzRXlqC7Nh7BFrMJhsZpLDlACffizp07sWPHDrf75OTkyG7fsmULbDYbOjo6kJeX59f5jwqymZycxKFDh8BxHIqLi12mAQaSbOTG99dtJkUg3Gha7ewuj1KIF1vposOyn6SQW0RcBbvl4G0qrRhyloyrOYlfly6Wcsf7qwrt6j1Kt4n3k3ss5+4Tp28HGnK6d+LvjhjuurMyd5k0ZVuKuLg4N3MJrphNMDROUzITTax44CsqKiqg1WqRkpLi9/mDmmw4jkNnZydqamqQnp6OvLw8t3cagWxwBjiTwszMDCorK2E2m312m7kb119I37d4OHG6rLhBlthFJa3Ad7VN7k5Yuih6imG4g7tFn40jHtOdZeKN1cIaf/lSM+RqPZSLA4mvhavHcpaRr0TjanF3F7cSvy5+7upz95Qw4Oo9iREaGgqTySRbxBgMbisxgmE+TIRzPkl43759OHDgAE466SRER0dj3759uPHGG3HxxRfz8XF/ELRkY7VaUVlZiYGBAaxfvx4pKSkeL/h8WTbDw8OorKxEYmIi8vPz/W4JwDBXshkbG0NFRYXH/djCyjLOvLlUclaG9Fh3d+GuzjEX95U0LsL+e0pLdlfI6G3FvLToUjqOXJzIm2vgL8mIz80gF7eR28/Vc+lnI30uLTB1Fd+SI8CEhAQcOHAAoaGhfHvm+Ph4hISEBMXiLkYwJAgshFRNaGgoXnzxRdx9992YmZnB0qVLceONNzrFcfxBUJLNyMgISktLERoaipKSEq+bFgWabDQaDbq7uzEwMDAnt5kU/pINx3Ho6upCU1MTcnNzPew7W/NMznXmTVaZ9BJ7K3gpXniV8hqKkwE8Ve/r9fKEwmRhxFpk0sVavAa6IgQ5ApVaBOJYiJwKghJwRyjSGwNPNw7ShBBxerbcvMXJAXLfgVWrVuGEE07g5fo7OztRW1uLqKgoGAwGOByOoFjkgeCwbBZC8Tk/P99tKwh/EVRk43A40NLSgsbGRuTk5CAnJ8enDzuQZDMzMwOz2QybzYatW7cqWlTlD9nYbDbU1tZiZGQEBQUFfEGYO4gXUJYAIHc3Lr0zlkuPlS6s4kXL1X7+fjS+ZGOJIZ2jp0ssTpoQtytg18BThppc/EeuIJPBXQGqHKQFn4DvFqJUwFQ8BznLUO6zlBYIs3kZDEKaOLM0pe8xPT19lly/xWLhWzMbjUZ89tlnvGJyQkICoqOjFySWEwxkc6z0sgGCiGymp6dRXl6OyclJFBYWIj4+3ucvWKDIhrnNdDodcnNzFa/eZWTjrTrB1NQUysvLecvP21RE6Y+fZakBs+/kxQufWKBRvE08nquUX2/iKZ4WTF+D92wO0kvJEiE8xYU0GufYFtvu6XxycR8pgSjx9ZRao1J46yL1JqtQGt9j1qH0db1esNjE9UlSyFngISEhWLRoEUwmEyIiIrB06VKMjIzwBASAJ574+HhFej95g2CwsI4VxWcgCMiG4zgMDAygvLwcsbGxKCkpQUhIiF9jKU02HMehpaUFHR0dWLVqFY4cOaLY2GKwuydvyKa3txe1tbXIyspCbm6uT3decunMcpleYsKRLqKuFmCpNSSGtG5GevfvjXSK3Hnc1bG4yqZzdccthqv34WlecmPKBds9ndeX1+W2eaPR5g0hiY9llouYhFlihTQLTVwjJMXq1atdno/jOOh0Ol4xOSMjAxzHYXJyEiMjI+jv70dTU5NsvCcQCAbL5lhpnAYsMNnY7XbU19fzudsZGRlz+nCVJBuz2YyqqirMzMzwbrOBgYGAWE7iglFX75+1tu7t7cWGDRv8TkEUp96yWAVbKNkCJK7Yl8ZxXLmq3N3Ry+mxebtwuttH+tibinn2/pSEXKGrGJ7m5GpMb/d1Zxm6InJPcRrxsYBzrMmVa5Udy9xxrhIEXEFucddoNIiJiUFMTAwvWikX72HaYkp2yLTb7X4XMCoF1bJRAFNTUygtLVU0BqIU2QwNDaGqqgpJSUlO2WZKF18ysB+Yq7Gnp6dRUVEBjuNQUlLitjDOu/MJLjS20DD/vZRoXB0vTpn2pCDg7nhfZW3cwVXqr1xCgFw1vT/wNbVbClfWmat5eWpjIP78xOO4e5+uBEHZeMytKD6HHNGw12w210Wh7koEvKmzcRXvGRkZQX19PaxWq2LxnmCwbEwmExYtWrSgc1AKC0I2HMehsrISUVFRWLly5ZxThxnmSjZSt1l6errTF9WfNgPewB3ZDA4OoqqqCqmpqVi5cqUid23iIk6py0NO4obm6JpgpJdEvK+nxVFuoXV1p+5NzY8UrhZZdncu59bzBnI1NWJ4ox8nnZ+7WhlvY0euPj8Gd+QodoexOIyUeKQyNVK4Ixoaw70Qp69SLCzes2jRIrAOmUrFe4JBQWAhetkECgtCNhqNBgUFBfxjpcDIxttAuxhybjO58QMjmKmBRqNxGltMfKtXr0Z6erqi55RK17hK2xUTjJiU3BGMtKDUG8jFcKTjSl1lcou5dCx3lpPYghP3knG1PyMYT9l1rtxqnghNLsVY7jg569NT+wQGd/EcKZHLEY+na8tcaP5YfHOVqxF3yFQi3hMMCgIq2ShxYr1ecStBrMzsyx2JK7eZFIGybNjYjGwsFgsqKysxPT2teJo1g5xagHS71IKRk12RHi8eX85378rqcWWlSLe5W7AZGcnt72p89rq0l4zU6nFlxUnhyz2OdEF2lT4ufU3OevHUPkEu+89d4oD4+yC1eNgY4rot8XfG34JdpeVq5hrvCQY3mko2QQr2RfE2ZdGT20xu/Ln2dHAFRjajo6OoqKhAXFwcSkpKfHIxhoeHY3p6ek7zkFoPcmKX0kvgKmYjJyTpyupxVbMjhbv6Hrn5i8f3NgvN1ULsS6ac3GvSMaRzcVd342reriwadzVG7q6fHPnJWTPs/UhvPPzJ5BPmFdjF3dd4j81mU91oCmLByCYQefK+NDgzm82orKyExWLx2noItGXT29uL7u5uLF++HFlZWT5fo+zsbNTX13vcTywzIr6bB5wzy8QLljurRu6SyC2C4gXXXZ2NO1ePL7UhUrCMO7nsubnCF4L0NE8G8Wvi6yn3uQCuyVtujmwMd5+dq9esVvek7e9PZL5Vnz3Fe2w2Gw4fPgyr1Tqv9T1iqNloQQqNRuNVkoDYbVZQUOC19RComI3NZoPNZkNvby9f0OoPzjnnHK/IhtXbAMJdqsMBJCYCw8PCIsjeqpxVI661kLvccougKwvHU6zI1Wtyz9kxruo8GOnNNYuMwVOigKv4jVy6sbt4iFxtlHQ/T2Tuioyk19CV8Z6RAfT1ec4m9DfDcCHdVnLxnr179yIiIsIp3iNONghUfQ8Dx3EwGo3z0gJ6PnBMkQ3gPiPN4XCgtbWVd5tlZGT4NHYgLJvJyUleRHPVqlVzUlW9+uqrvW4ZK6390GqBkRF6HhsLTE0JLhSWQCANGgPyi6x4IXf12F1A252bRxoYd0UsrpIe2NwZ4boiNU8Qx6T8+Uq4k4WRgysXmasaHldk7m3shkGjoZuQoSGgu3t2coK/9VJyCKYWAxqNBhzHYfHixYiNjZ3X+h4xVLkaBRCoL5UrsvHHbebt2P6CqQFkZ2djYGBgzl9WX/LxxXfj7O47OZkWlbExIDycUlhDQoCJCedFSby4y93FuooBSKVy5LaLVQukrzFIz+fqTtzTV0zcRppderaASmM/bD9AIF1P4TtXKcByrjzpvu6KMKXk4A2JS61V6ThyFlJoKBAZSd8JnU6QqvFkbbqCJ/2+YAjIiyGez3zW94ihxmyCGHKE4K/bTG5sJdxodrsdDQ0N6Ovrw8aNG5GcnIzh4WFFxvZW1FMu5XVoiB5HRwNftj7H2rXAwYNAfDwwOsrmz84l3OUy8pKShauiRVcWj/RuWS6wLucCk1vYxWoBri6JONNMfD7WDEx8LHt/3txv6HTyROPqeOm+0vm6I3hvSNxV/Y5UoZl9puedB/zf/5FblV3vqChgfFzYT6w64Q1uv/12t68fTZ06A1nfw2Cz2WA2m1WyCVaIyYapSHd2dvrlNpNCCTeayWRCRUUFNBqNU/sEpVx0Z555Jt566y23++j1wqLHFhdWFa7VAkajsNiWldH/pUuJbNLSACYRJ3blyJGFO9++K4sHcJ8C7GqbVDiTQc4acwdP2V/eZKS5O95VbYrU0pHO111jMjHRunNhMstGXDPDXg8NBWZmgPPPB/7zH+DVVwWFichIIpnJSWFMb1PBxfjBD37g9vVgsmw4jvN6Pt7W9/ga75mamgKAYyZmExyfrIJgZGM2m3Hw4EEMDAxg69atcyYaNvZcrI+BgQHs27cPcXFx2LJli1OfHqVaQ//jH//wuI+4OI9ZJixBgE2BZRydeCI9Ly+n/yyktHq1vJtKvM1VwaB0P2nRuJzVIgZLY5aO584Lye7CAxkS8DQHRipyc/PkGnRnjIutIncuTDnXJPs8zzqL/r/0Er2P7GxyoXKcYM0sWTJbW85bz29UVJRHj0IwxWzYb9Ef1zar78nOzkZ+fj5OOOEE5OXlwWAwoLOzE3v27MEXX3yBlpYWjIyMuLzJNH3pXjhWLJsFI5tAxmzGx8f5TBIliyL9tT44jkNTUxMqKyuxatUqrF69etYdk1JkExER4fH9yhU+6nTkMgGEBchuBz79lBaXb3+btrFkN7OZjjvvvNljsXOIf6fidUZ6dy91IUl/33LuKOnizBIZXK0N7C5eHHtREsxalFbZi+EqTVs6H+aKFEOutknusTuw82g05B4FgG3b6P+rr9I469cL18psJoJKTaV9OjroP7tBAbxPBvjDH/7gcZ9gsmzY71yJ+bB4T25uLoqKirBt2zZkZWXBarWivr4en332GcrLy9HR0YGJiQlwX15Uo9GIsLCwgNb63H///bzeYlxcnOw+XV1dOOussxAREYGUlBTcfPPNftUbBscnqxAcDgdMJhMOHz6MvLw8rFu3TjHdNcA/y2ZmZgYHDx5Ef38/iouLsXjxYtn9lCIbAHj++efdvi6NizDXSHIyLSKMdKKjgbAwWqRefZW2XXQR/f/SLY3du+n/734nHMNccq5iCXJuJDGkC63c4stcf3Lvzd36IFYvYFXw/kKjIZJhWWnuPj53X0M5lQRp3Eo6T3fxGKkFJ4493XAD/We5JP/9L71+wgk0pslE16W3lz5LjYbSnfV64bsghji5whW0Wi3OP/989zshuGI27LcYiPmweM+qVatQUlKCoqIiJCcn85mpn332Ga644go8+eSTAVedtlgsOP/883HNNdfIvm6323HWWWfBYrFg79692LVrF5577jnceeedPp8rOD5ZBcDcZjMzM8jMzFRcSwzw3bIZHR3F3r17ERISguLiYrfmsJJkc/rpp7u0bthvR1wFzu74Bwdpe3w8LSCTkwDjxvh4WnBeeIGef//79N9spmNvuYWe33cfjbFunetF3xP/e5sWLEcULPbhaXFnriu2WDLLxBN0OvozGISsNHdfCancixgsnVwKubmLr4GUJOUKXdnnGxlJ7rBHH6XnH31E+3z8Mf3/wQ9o35YWet7eTv+Li+nzF8d4wsOd06bZvDz9JF5gXxoPCDbLRqvVBtytx+I9GRkZWLduHY4//nisX78eqamp+PjjjzE+Po6cnBxceeWVeOmllzDOfJoK4Z577sGNN96IdevWyb7+3nvvoa6uDs8//zw2btyIM888E/fddx8ee+wxWCwWn851TLjRBgcH8fnnnyMiIgKpqakBMzuZZcN58B1wHIf29nYcOnQIOTk52LBhg0cLS0myAfBlEsLs7eJ4h9gKYAuYXk+pznY73cm2ttLrK1bQgrN4MR2/axdtP/dcGmfZMtr+05/SWBkZtDidcQbdRYvvzj0pEMhdKrn3wuIwUrAF0NO6xfZj8QytlkiEkY/BQP/ZY3Fmm6cqenfvRXp+KaRfAzm3p7sYWEoKXfOlS4FTT6V9f/Yzmn9nJ+3z29/S///8h/4za/bSS+n/nj3C+2VdAcbGhPcjTql2h5iYGJzFAkIeEGwxm4UgPo1Gg7i4ONx///349a9/jby8PDz55JOIjY3FAw88gOrq6nmdz759+7Bu3TqnsorTTz8dExMTqK2t9Wms4LiN8BMOhwNNTU2oqKjAypUrsW7dOhgMhoBKyrDzuoLVakVFRQU6OzuxefNmr2VnlCab9PR0nHPOubKviYPF4qBxRIRzVpTFQokAHAfs30/bUlNp37VraVFmHrsNG2j75ZfT//ffJ5fMvn00Znw88OSTtO/69e5dL3JWgKtL6Gq7OMXaGzBrh5GIzUaPWT2N1epsEXkCs5isVvn9Xc1LXGgq3le87knHi4qibf/3f/R89Wo6b1sbbbPbgdxcGvfWW2mfm28Wss0A4IIL6P+zz9JYKSn0fQCEYt/s7NlxMU9f7c8++8z9DiIEmxstGHTRoqOjcfrpp+Phhx9GRUUFtrEg2zyhr69vVv0ee97X1+fTWAv6yc7lLkacbVZcXMy7zQIplilWlZbD5OQk9u3bB5vNhpKSEpcBNzkoSTYcx6GtrQ2XX365bH8Q6W+ILTpmMz1OTqbtDgdQU0PbLryQtn0pdsBbBN/5Dj1/5RX6X15OY91xB1k2VistViMjwNVX0z7f+ha5dnbsIIsJINUCd1lOru4f3CUFMHLw1kXmCd6Owaw4d/c8rqrv5cCITmwdbtxIi/+SJcBPfkLbWBnLJ59QBllUFPC1r5GFumwZXd/bb6frtWYNjXn66XTMrl20fcMGIYY3NUXny8yk/8zFxtyugPs41eWXX46lS5d69R45jgsqsgmG9gL+toS+9dZbwdqWuPpraGgIwIzdIzg+WR8hdptJYyGB0i8D3At9dnd3Y//+/Vi8eDEKCwt91k1Sat42mw0VFRXo6upCUVERnnrqqVn7SKfPYg/srrq/n7YnJFD9hcNBKbEA+fgBoLmZ/u/bR///9Cf639REi+h995FltGkT+f9DQymOo9MBv/oVLaBvvEFxAYOBLCeHA7jkErKCDAZg5UrnBV6ONNhzd0TAXGpzXTu8qbExGDwTjZz1wiAumGWWxeLFRMpRUUBpKW3LyKBr19UF3H8/XVdWdHvDDUBWFhF6RQUwPQ28+y6N/f3v0/9ly2jfF1+k+Z58Mr0/JlM0Okqp8BwHHD5M44eFObtg2TzlyH7RokW48cYbMTk56dHtDAg3cF91N5oYJpPJr668O3fuRH19vdu/nJwcr8ZKTU1FP1sQvgR7nsrSFL3EUUU2DocDjY2NTm4zqamrtKSMGCxgKCYFu92OmpoaNDY2YuPGjcjNzfXrB6OEZTM1NeVkWcXGxmLx4sX485//LDqPULciDvICZNnodORC0enIGomJocWRJQg8/TTte9VV9H90lBarG2+k57/4BT3PyqIF9fPPyf9vtxPZ2O3Ayy8LQfPBQSKeVavo+Pp6sohiYiiIzXE05po1dMyZZzrHQcQSLK6IR5z2zMhAaej1dD2ZFeIK7JpLXVFs8f/ud4GSEsoEq6ujbVu30jUxm4GiItr29tt07cPDyQXmcAAPPUSvPfkkudBGRyn+ZjCQ+8xgECzT//6X/v/oR3R92tpoHp2dNLecHEGU1WAQYlriIlHAucCTISQkBB9++CHGx8dRVlaGPXv2oLa2FkeOHMHMzIzsdQlk9pc/8LZNSSDhr1RNcnIyVq5c6fbP25vh4uJiVFdXY2BggN/2/vvvIyYmBqtXr/ZpXkeNG425zQYHB53cZlIE0o0GOGekmUwmHDhwAJOTkygpKUEy8z/5Oe5cyKavrw/79u3DokWLnCwrjUaDc845B/fccw8AZ/VftoCwxACG4WHaJzKSCMdmI5+/3U4uFZ0OeOwx2veyy2ixys6mMW6/XVAciIkBNm+mO3O7Xchku+46Ou/xx5ObR6sFvpweamtJoWBggNQKAOC99yj+o9cDDzxA5//5zynoDQB33UVzjYsTFmM5SBMB5kI6zFJiacbSsaRrJvMm5eZS0N7hIBJetQrIywP++ld6PSyMSMJoJMsOoLTzujr6HI4/nrY9/TS9Hh5Ork6NhtLS9XpySUZGkkvs+OPpXL/5Df1nrjBmif7nP3Rsby89/9a36Dq1tAiqA8zFOTNDFiq7lsDsOiGNBmhubsaSJUuwfv16HH/88Vi3bh3Cw8PR09ODzz//HAcOHEBzczOGh4f53xKzfoKFbILBspkPXbSuri7eE2K321FRUYGKigpeveC0007D6tWrcckll6CyshLvvvsufvnLX+K6667zOS07OD5ZD2Bus8jISI8pxIG0bNj4DocDAwMD2Lt3L+Lj42epAfgDf8mGWXs1NTVYv349VqxY4UTibNyf//znuPfee2e5k5jAJhOlFC+aZjPFb7Ra0kdjWWZ2O7nHdDrg8cdp35ISGuO88+j/J59Q9lJTk5DN9dprtG9kJO3z1lvAO+/Q+Ts66Ny33UZjRUUB119P+9fVkbtoelpIRPjrX4U4EUvLzcoSguR//jOwcyc9fvVVys7KzAR+/Wt6Hxs2CAWNxcVEjFotkJ9P842OpkA7QIst8zqsXk1xEp2OrJCoKDr2hhtoXmefDVx7rbCwn3wyke0XXwjHMxJ9/HF6Xw0NwCmn0LZdu2iht9uJqAEi8G9/m64bK8a88kq6LiMjlDHocNB4Nhu9L42GrJgPPqCx0tPp/d1wA712ww2CnBDHUfxMo6FryvTxWPb88DBti4ggwpFm2Im/tgcPHnJSLtdqtYiLi0NOTg4KCwtx/PHHY+nSpbDb7WhsbMSnn36K8vJyHD58GMGEYEkQCLTi85133olNmzbhrrvuwtTUFDZt2oRNmzbh0KFDAGi9e/PNN6HT6VBcXIyLL74Yl156Ke69916fzxXUZCN2m61atQpr1671+AUIZMwGIEuho6MDlZWVWLNmDVatWqXIHZA/ZGOxWFBaWspL8sipPjOpdAC45ZZbcM8990led64b4TihYtzhICvD4SA5E44DDhwQXrPbhcSBf/yDxmptpVjBtdfSIjw9DfT0UPzme9+jfS+8kMjhrLPorh4A/v1vOv+dd5LrzWKhu2hGQGeeSXP8y1+E9O3BQcHaGRykxZUt5HfcQXMCaAGdmRGy4gBa9E89lR7/7GdAYSHN6YMPiFxOPRU4dIjOf8YZVKcCAPfeC3zjG3T+nTtpvNhYwXIZHyeCBSgz74svyGpgRu/rr1PGl04HvPkmEXp4uHBt/vUvsi6io4moACKRl14ia+cvf6E5rVpFMZVVq4iU9XqAhedefZWue2wsva+oKGD5cnrtN7+h65yXJwhuajRE3hxH20NCiLTGxuiYnBw6ZnLSOR7FZI4AIDRUj8rKSqxgGR8uYDAYkJKSgpUrV6K4uBhbtmxBcnIyJiYmAACff/65R5fbfCAYEgTmg2yee+45PjlD/Ld9+3Z+n6ysLLz99tswmUwYHBzEww8/7FexvIbzJnoXINhsNpdWyPT0NCorK2Gz2bBx40avzcmRkRFUVVU5XSylMDMzg927dyMkJASFhYWKmrg9PT3o6elBkTs/kAjj4+MoLy9HbGysW6WE/fv3IysrC2lsFQbw0EMP4Y477uCfi0UfxY+Tk8mlIy7ku/Za4I9/FDKWsrPJKvnTn2jRCwsTRDHZopidTckAGzcC1dVCfQxbqFtbqVdKZiZlR+3fTwsdG0scNP/e92gx/cY3yMr6wx8oWeGUUwRX0gMP0FhTUzR/sYq1r0rFriA3hti1ZrWSRTM0RPtdcAGR37e/DRQUAL/8JV237dtpbqecQm7GmBiKs7DxNBoiFZ2O3s+bb5Je3Y4dRFyMEPR6ciMODRHR/uUvdP3MZkG52eEgy+vNN4Gvf50sS4DOsX07ufWSk4kcNRqyBpnoKktqYOOICSc8PAxNTS2IYQU5fmBqagqHDh3Cxo0bMTIygpGREUxMTCAyMpLvGRMXFzdv1kZHRweMRiPWrFkzL+eTw44dO5Cfn+9RLftoQVBaNoODg9i7dy+ioqI8us2kCJQbbWRkBHv37oVWq8WKFSsU96X6ok7Q3d2NL774AllZWdi4caPbuww5i+nmm2/Gc889B4Nh9sev0Qgul8FBussND6eFTKMhogEEF9XAAO178820EP3kJ3RMSgotSFVVRDQAcNJJtO1Pf6J9jUZB9iYjg8YZGwOSkoggWOzo7LPJYnE4yO1mt5O1wuS2CgpoUZyaojt6rZZiQd/4Br2+bx/FStatE4jw8suFmpOf/pRe1+nIygkJIVfZtdfS68uXU/EqQDGqbdvoHP/8J12r5cuJRB0OWuh/9zuyEl5/ncg2MpL2A8gyZFbSkiUUmO/rE2InaWm0b3Q0WUV2O1k6iYn03s44g47dtYssjYgIIX2ZSQY9+ihdu95eIq7ISEoECAkBGhvpPP/7H+175ZUsziJkIkZG0k3AkSP0PsUyNVLtt0WLFqG7u3dORANQzEan07l0uTU0NPAaYp2dnV5nufmLYEkQOFYUn4EgIxt/3GZSKO1GYzUrpaWlWLZsWcDMWq1W6/HH43A4+My3/Px8LF261GOShdiNJsYFF1yAhoZmxMQQabLkFIeDFj+HgwjGYiFSYK61pUtpUWYV6Cw+kZ5Oi9aDD9KCtHQp3Rnn5lIsRaMRFtnf/Iael5RQ3AMQYh7l5WTpTE4KOmtDQ5SGrdPR6yEhVOnOdNpWr6Y5jI2RO0irBZ55RnCjrV1Li2x1NbmEmGzL++/T621tREJi6ZrISMH1FRpKhAsQoRmNgl7c+DhZZeecQ6/fcw/w4x/TOQoLab5jY8AjjwgZfsz9dNdddF3XraPsMo4j1yJzP552Go351FMU17HbhTjSa6/RNWekABAR6nTkNtPryQLKz6fP8Kmn6HNhCgJ33knv4eWX6fnAAB173HGCRcismZgYoS6IEY9OB5x77rloampWRH9QTj1A7HITa4j5kuU2l/kEgxvNn9TnYEXQZKNNT0/jiy++4LPNXAlWegKzbJS467FarSgvL+drVpYsWQK9Xh+QmJCnmM309DQOHDiAiYkJlJSU8B0D5zJueno6+voGsGnTZjCZI8btGo3QvyQsTIhDLFpEi96WLfTaww/T9oICWoxOO40Wtf37aRHs66OFmuMEPS6Oo8X9ww+FDDWzmRa3W26hO3W9nmpCHA7qqVNfT+ddupQWz//9j7KwALJgUlPJimAN4O68kxZfgIpJo6KIpBYvFlKuW1po4fzwQyEb649/pLm0tBAp6vW00L/yCj1+6y0KzFut9H6MRnrM0slXrKBr43CQusKGDWS1dXfT/M8/nyw1FnOanKTxWPzokUeEBIElS2jbnXeSpRYbS2QAUNyrq4uub2Mj7X/fffT/rLPo8/vkE7Kk7HayFkNDqU5KqwXuvts5Vfzss2nOu3cLRb7MWBkcFJS6p6fpd/v66//FLqZZpAA8Le5iDTGW5bZ27VqEhYWhu7vbZZbbXOaz0JaNyWRSLRulwTK7/HGbScG+IHP9sk1MTGDv3r1wOBx8zQrguxint3A37vDwMPbt24fo6GifM988kZhWq8Vnn32GXbt28fGatDShfmVmhhb3rVtp26FDQj2J3S5oabHMsO5uIqcLL6QFbnpaKABlbq3vf58WsuOOE+7U//EPoRj0vffo8dKltOjddpsgp3LPPcKi19JCr//yl5SEUFNDbiWOozv5Tz+lYxwOIrzERHKTGQw0v3vvpddeeYUWe4OByCMhgVxXTGL/e98jkrHZKN5x9dW0cB84QFbbkiWCSy4nR8jge+01isv09hLhAOT++tvfaPHetYviNQaDQDYvv0yP4+MF193TT9M1GRujLDutllx3kZFkPf3whzTG66/T/n/7G807KYn2S0oSSPrxx+n65ObSvizW9Z//0DFLlgiyRUymZtUqQcEgNzcXhw8fwYms0ZFC8NWS0Gq1iI+Px7Jly7B582be5Waz2Zxcbl1dXZiamvL55jMYEgSmpqYCniAwn1jQBAHWz6Grqwtr1qzx25oRw2634/3338dJJ53ktzx3d3c3X2Wbk5PjZIFVVFQgNjbWaxkObzE6OoqKigqcdNJJ/DaO49DR0YGWlhasXLkSmZmZPo/ry3wtFgu2bNmChoZ6PitNLH8UEkIV/iybymajxbari+6U775b6BrJ7o5TUylW8+yzzgkCAC3+aWlEEu3ttDAWFZEVw0QfWTCa3VlfeSUR09e/TlbDXXfRov+d7xA5nHMOEU1SEj2fnqZ5s2w0wLsEAVZ7xGI8gPtOmuwY9hqrT2EivaeeStbYN79JC/+f/0xWGFNV2L6dyCc1lVxabK6soJb19HnmGcogu+kmKt40GoX3FBND7q9HH6UY2pIldI7xcaFm5qSTgM8+I6L/7DPhPFu2kMsvLExIOmDJHzQPDZ566hl8mzU2UhhDQ0Noa2vzOkHGHTiOg8lk4hMNxsbGoNPp+E6ZCQkJHteGmpoaREdHIysra87z8Qccx2H16tV44YUXFCf2hcKCUndvby+Ghobm5DaTwp2kjCfY7XZUV1ejqakJmzZtwrJly2b5kQNp2YgtEJvNhsrKSl7Q0x+ikRvXHUJCQlBeXo5PPtmNsDADTzQJCbSQORx0l63VCnpcQ0O0yP7qV7Sg7dhBd8ZsgezoIKIBaKG124kgQkPJhcQkmpYuFdJqMzJokf2yrgwnnCD0X/nXv2jcV1+lcQCyQgYH6djUVDr23HPJjcaSFFavpiLIjg6a/7nnAqyFx8UXC3GlSy8lwkxOFuIwGRlCx9JvfYtIDqCAPLMa3nyTFvNf/5pciw4HJSasXk2xr5ISOoa55AC6Rt3dFEf55z9pXqw4NS6O4jg2G3DFFfT+jUbBNfjII0SmUVFEHCwFHCDrjwlxTk7Seb79bbrmrCbns89ovhdcQMe2tNAxk5OU3KHR0LXS6YCvf/3r6OsbDBjRAMoqPms0GkRGRiIzMxMbNmzA8ccfjzVr1ji53Dx1ygyGBAF/5WqCFQtKNhkZGdi6dauimV0ajcavjDSj0Yj9+/fDaDSipKQESUlJsvsFqo5HTApsLhaLBcXFxT4JekohldfxBlu2bEFnZze+/e1v80HtyEhalFgc4eGHaXG8+GJ6vngxLV5PPkmuocxMujNetIhqN7RaclsB5OYByAV03XX0+NJLaWEtLyfrx2qlhZTJ2jCubWqiRfPss4XkgsRE2n9ighIUmBuNqRwsX07WU2srHQeQ9cTkYGw2oZ3AypVkCS1aRMWegOCOAmjhDg2lRbiujiwmlm4M0HtkmXorVhDRDQ2R1afXk87Y1BQt+NdfT9ZPfj4Vozoc5BJjmWeXX07j/OY3QhyHueM++4xuAFh6ut1O7j2WIAAQWebm0ufx6qs0174+obhTpyPZGoeDrl1oKO3PlEnS0tJw8GAZXnzxRZ+1/nxFIAPyci43aadMVkXPXG7BkiCgxmwUAiMGpaHX630iGyb1kpCQgKKiIoSFhbncN9CWTX9/P/bt24fk5GQUFhbOuVOfN1luUvT29uKLL77Ar371K4yNTeLUU0/lNcwYATBvxxNP0PPVq2nxYnIqVVW0uI+NEVE5HEIVvcVCY+3dK6Qvh4fTIn/99UIL6rPPJiI4dIgUDAAiNYuFBCm7u2nbpZcSCebk0CLNcZQlx+I8559P1haznGw2umsvLaU5v/46Pec4spZMJiISls5dWSm4Dl94gYpHbTaKr5hMZHEMDdG5UlOJZDiOrI9ly4goOzromB/+kFx+ej0t7lYrkes3v0nH33YbuRStVqGC/7776HonJ5MbDKAkgNFR2relha7vz35G749l6b32GpGzzUaWUmSkYK39/vdC3YxOR5aX2UzJBpGRkXj99f9DY2MjcllwJ8CYz8XdYDA4dcrcvHkzEhMTMTo6ikOHDuHzzz/H1NQUJiYmfG4QphSsVissFotKNkohUAqv3hKCw+FAQ0MDampqsHbtWq/UAAJp2XAch6qqKqxduxZ5eXnzrkzArkddXR02btyIpUuXwmAw4M0338TIyARKSo7nVZ0nJmjxPv98WrSYqnBPD/n9TzuNFlmLhRZrgDKqAAq4JydTSvLGjbTtuedoUfzd7wTJmYICIqCf/YxiNQC57ziOFtryckEnrb2dFtaQELpzf+stWsQBIqjYWLJQfvc7ob3BH/9Ic37nHYoBGQw038REWpRZq+cLLxTqez7+mFKbQ0Mp8SEzk6wecVbe8uU0h8pKsgqPHKHYDEA1RiyA/+c/C+KnhYX0+muvEbEsWiR0Q921i8i2v58C/DodkZheTyoDF15I7+nDD2n/Bx+kfRYvpphYZialSU9Nkcqz3U7ErNPRfB0OylwLCwvHv//9bxw5csQpdjgfWKj2AlKX2wknnMAXco6OjmLPnj0eXW6BANMmC7Q22nxiQRMEOI4LyJ3Dnj17sGLFCqSkpLjcx2w2o7KyElarFZs2bfI666OlpQUmkwnr169XarqwWCyoqKjAyMgIiouL+cw3JdDQ0ACO47CKySq7mUNlZSVmZmbcXg+bzYZTTz0VFRWlsFqtSEqiBfWGG8hK0WppwWYp06GhRBQPPkh6alVVQnA+Lo6sgC++IDdXQQFlPnV1kUXE5HNYTxqbjeIszz1HZPa1r9HC/8YbZBGNjpKszb//TVaBzUZzYXNicJcgwNSVmTuKJSqwfj8sUUEuQUA8vkZDlsTkJJ1/yxaS4fn614n0/vlPem3JEoqJlZSQ9ZSTQzEcNj+mDDA9TXN74AG6nvffT645VmSp09H5JiZonB/8gMi8rU0QMeU4yiosKyMV7cpKOk9UVAz+9a9/4Xhmli4ADh8+jNHRUUV/V3PB/v37sXz5ckRHR2N0dJRPNrBarYiLi+MTDSIjIwNy03z48GGsWbMGVqtVkTqmYEBQpD4rDU8xm+HhYezduxfh4eEoLi72Kb1QactmYmIC+/bt492JSgcEvbFs2Bz0ej22bt3q9nro9Xrcf//9aGpqxS233IaQkAw4HJQBpdWSRZCURPGEmRkiogcfpGPPPZfuom+8kRbGqSla+ABaGB0OWhRXrKDHrM4nP5/GBIhoZmbI+vnxj2nb9dcLkiwbN9IYF1xABGizkYWzYQMlAezfT/M85RQh3sQUCjQaIjGtlubPXIWJiYIA5kknCQ3ffvELspgyM4U41AMPCO+3tpZiQMnJ5PYCyPJjKdkREZTY0NhIGXYsYyw8nAjoH/+g+d9yC81hakpQur79dqHgctMmInYWN7r5ZvpfV0cktXgxzTskhMjHYiFX4OLFWXj66WfR3d29oEQDBEcRpRgsQSAkJMSjy62urg59fX2K3jizgs5guiZzxbHzTkRwRTYcx6G1tRVlZWVYvny5bD8cT1AyZtPT04MDBw4gIyMDG7/0JyntovOUIHDkyBGnOXhzF8VUCe666y40N7dg//6DOOusb8LhIKtiaIgW2DVriFQ2bnQuJHzxRRpn5UoqWAQojmGz0d02a0HNRDNDQgTBzqYmGnP7dnLhAbRIT0/TYnzHHbQI//WvFFgHKJ5UXU3urJ/+lObQ3y/EWRITiTT0eiIhpiDNupBmZZFFANA8kpKEbDlGiizj7oEHaA4cR8H8ujo67y9+Qcc0Ngp6aTt20LmKiijLzm6nep2EBLKiWO3OLbcQSVmtNFeAEhx0OsE9ZjYLMSZ2r3D55ZRZNjpKLrbpaWB6Ohbnn38BBgZG8OmnnyqWBTpXBBvZyM3HlcstNDQUhw8fVtTlxmpsgqWZnBJYUPssUBdSjmysViuqqqowNTWFoqIiv11VSlg2LDZy5MgRbNq0CUlJSXwQX2mycZUgwHEcmpqacPjwYWzYsMGty1EKqQTOunXr8O9//xsOhwMvv/wybr75ZrS1DfAtm1mR4Cef0B32zAwtfDU1ggpATAzdnV92Gb22axft+7//kfXDbhpZPx3WugCgRm7PPkvjMtXp73+fnv/nP+Ru+/RTej4wQGM1N9O+ej25tFiLhZ/+lOZaXk56YhoNufkOHqTzMjkclgY+M0PEwgpXY2Mp9tPeTskGf/uboDywdi0RR38/WS3p6TROVRXVL7H3wu5/2Pt7+GGqrzEaKUbT1kZuN4uFyIypF1x7LQlw7thB6c/M2jIYdNi27Thcd9112L59O18UrGS68VzBcVzQzAXwTkGAZbmxTDeLxcK72+rq6mCz2fx2uR1rac9AEFg2gfiCSclmfHwce/fuBQAnNQB/MFfLxmw244svvsDY2BiKi4v5FGuNRqNIt04p5Ma0Wq0oLS1Ff38/tm7d6hPRAK6tJa1Wi+9973vo6urCyMgEdu7ciZiYeD4L7eyzaaH+1rcoVpGdLVgML7xAi+cTT9BdPkALamgopUf/9Ke07Yor6P/QEMVAWIJAQwMt8Oz3uX+/0Chs7VqyCJKTadEOC6O7fpaU8P77lA1mMJB1kZxMhZOMTy+5hEjPbqeUY5Yg0NdHLqpVq4S+PiedRJacTkekYjKRZbN9O73+618TSdnt1FmTxX2Y5fbmm2SJZWYKBPTss2QRdXbSsTqdoABw3nmkzGAwCO65e+4hotq2bRs++OADtLd34s9//jNSUlKwf/9+7N+/H01NTbysfzAgmCwbjuP8UhAICQlBamoqVq9ejeOOO25OLrdj0bJZ0AQBgALTSk+hpqYGoaGhX0prHEZjYyOWLVvmlXClJ/T396O1tRUlrErPB4yMjKCiogJJSUlYs2bNrDunDz74AFu2bFE03bGjowOjo6PYtGkTAPoSl5WVITIyEuvXr4eB3Rb7AOZ2c9UtVQqbzYY//OEPeOutt3Dw4F6+Gj4qirKwPvmEyOHkk4mEhocFqRSAFuaQECKja68lkjjxRLJeLr6Ynt99N1ktp55KtSNMj42dSxzMlyYIiBUC4uOp4j4khB4fOULEEhpKwfewMCHpwFWCAOsRFBZGZKPVAuvXk4X29a/T9jffJPdXYiIVhW7cCPz97+R6ZKKbAJFGQgLNKTGRanh27qQaouuuExIWtFogPj4eS5Zk4/HHH3fZstdms2F0dBTDw8MYHByE1Wrl77wTExMRERGxIAtcU1MTNBoNlrOCpgWEw+HAJ598gm3btilWX2S32zE+Ps5bPlNTU4iKinJqnyAmt5dffhlPPvkkDrAGUscAguNWQmFotVpYrVZUV1ejpaUF+fn5s2Rn5jK2r5YNk50pLS1Fbm6uy1hRoC0bVsOTmpqK/Px8v4iGjenLDYJer8dVV12Fu+++G7t378WBAwdw2mmnQauNweef02eydSsRQ2gopQnb7bTQ6/W0ALMEgWeeITfbO+8IAf777hNUik88ke7yL7qIalZYHGjTJkoC+N//BHVjFvM5+WShar6ggP6HhFB6MUCZbUy1JD+fCJHjKDEhMpJiM0ze//77SU2BVeDn5tJ5WQHq0BAF55mC8uQkudFeeIHmxbTSUlIoTdpmozHj4ohw7rmHxv7xj+n95uWtxIMPPoj29k60t3di9+7dbnvD6/V6vkf9ihUrEBkZicTERIyMjODgwYPYu3cv6uvrMTAwACtj6nnAQqU+y4H9vpWcj06nQ0JCAnJzc1FUVIRt27ZhyZIlsFgsqKurw6efforKykp0dXWhrKxsXnTR7r//fpSUlCAiIsJl4bhGo5n19yILuvqIBc+pcyWBPxfY7XYMDAwgKioKJSUlbos0fYWv6gQ2mw21tbUYGRlBYWGhU8tcKQJFNna7Hc3Nzejo6MC6deuQyvoF+AlfP7PBwUFUVlZiyZIlWL58OTQaDd544w1YrVZYrVZ8/PHH+O9//4tXX30VdXUTvDXCFvXwcHIr9fZSoL+oiBbxnBzqYMnaIGg0FCTXaIR4BUAEYrPR3f+DDwqK1uw8OTnk8hoeJgI6cIBqZa68khqKLV9OZFhdTSnMBw8KatZaLREEa0TGZGSYECbDrbcSmVRVCaoIF11ECRWbN1N8aMcOSmr4yU/o/bAEhyuvpPOEhIRgzZqNyM3NxT333CPbmdVX6HQ6ZGZmIjMzE3a7HWNjYxgZGUF7eztqa2sRExPDWz3R0dEBs3qCKX7EfoOBJD/mcktNTQXHcTAajRgZGUFtbS0uueQSGAwGxMXF4cUXX8Spp57qUtFkLrBYLDj//PNRXFyMp59+2uV+zz77LM5gjZQAvxVNFtyNZrVaFV1g+/r6UFVVhfDwcBx33HGKf2HGx8dRWlqKk1nPXjcwmUwoKyuDwWDAxo0bPaoBfPrpp1i9erWiX6yuri40N1PPkfz8fEVcdKWlpUhOTsYSpl3jAmIhUTmhVavVKusb7+7uxuOPP47m5mbs3r0bNts0xDfZGg1V6hcWksvs17+mDLfBQUF1+hvfoAX6lVfI2vniCwrmp6fTPiEhwqLPlAVYzxZXYHU/zChloqMAWT+joxTD0ekoe2znTnLxRUVREkJxMc21oYFI5uc/J6slJITIyWwWzqPX65GTk4vo6Gjs3LkTp556quKSMX19fejp6UFBQYHs62azGSMjIxgeHsbo6Cg0Gg3v9vFGzNIX1NbWIjIyEtnZ2YqN6S9MJhMOHDgw74WtDEajEddffz1qamoQFhaGqqoqbNq0CR9//HFAFAWee+45/PSnP8UYU2AVQaPR4LXXXsM5THpiDlhwy0YpsMZrPT09SE1NDZhEuLdutIGBAVRVVSE9Pd1rNQClLRuj0YjW1lZwHIfi4mLFFitv9Nbsdjtqa2sxPDzsc/ZfRkYG7r//fqdtIyMjeOmll9DY2Ih3330XPT09eOcdWph/8QthP3aaqiqh+VlRERWKGo1kCZ1yCmW9nXkmJSt8/DG5rN56ixb8pCRKTvjnP4lELryQXHZnnUWk9fzzFKgfHiYLKzGRMs9YsH5oiCwfs5kIbnJSkIlhc9XpiHiolUMYSkpKEBUVhR//+McoKiqaF5eSpwywsLAwLF68GIsXL4bD4cDk5CSGh4d5VXQWc0hMTERsbOyc5hxMCQILPReWXh0XF4ennnoKAwMD2LNnz4JJ11x33XX44Q9/iJycHFx99dX4wQ9+4JcVekyQjdlsRkVFBex2O4qLizE8PIwBpiaoMDylPrNanvb2dp/bJihJNsx1FR8fD7PZrOhdsSc32szMDMq+rNYsLi5WxI2ZkJCAa5hMM4CYmBg+OA4ICQSLF1NadGcnxUz0enJtsfuDkhJa6J94gv4ASo1mSgWpqWShfPABxYoA0k579116zFK3AbKgALKUzjuPHv/1r/RfrydRUpuNHvf3C3EXdi42p5tuuh53sIrMeYQv6cZarRaxsbGIjY1FTk6OU5pvbW0t7HY7L+GfmJjoU88lNheVbAQYjUY+ZpOSkoLz2BdsnnHvvffi5JNPRkREBN577z1ce+21mJqawg033ODzWAtONnP10w4NDaGyshIpKSlYvXo1dDodxsfHA6ZhxILjcl9IVstjNBqxdetWn+9ElCAbjuPQ3t6O1tZWrFmzBnq9Hs2sCEQhuEsQGB8fR3l5ORISEmQz7pQG66vDJG2KiijlmdWZhIdTxf9rrwnHMPXkiQn6Ky6m1OKBAbJUbDayWmJiyGoZHiaLadEi2ic6mh63tQmyNix7jmW63XgjpVQbjaSc8Mc/UpJBbe3sLDZ/EzXmirks8NKYw9TUFEZGRjAwMIDm5maEhYUhMTERCQkJiI+P9/g9CKaYTTC0FzAajX6502+99VY8yCQsXKC+vh4rV670ajzxTdCmTZtgNBrx0EMPHZ1k4y/EFsSqVauQwbTX4XsQ3xewL6GUbCYnJ1FeXo7IyEgUFxf7tYDMlWxsNhtqamowNjbGu66GhoYCokogRzZHjhxBTU2NYmnmniBux5yZSdYMy+piitCRkRQXee01Si1++mlqfWyxUGAfoNoZhspKcs0B5P6KjSWyGRujuAoTIWVCpBoNeH24r32Ngvvf+AZln33wgSBEarcT0Wi1QlEoQwfrUDbPUKqQUqPRIDo6mm82ZrPZMDY2huHhYTQ1NWFmZoYvbkxMTJStHwkGa4IhGOZiNBr9EuHcuXMnduzY4XafnJwcP2dF7Ufuu+8+zMzM+ByzOyrJxmKxoKqqCiaTCVu2bEEMa5b+JQLVBoCNDdDdD5N26e3tRW1tLZYuXSrbcM1bzEWdgCUjhISEoKSkhHebBSLbTzomx3FoaWlBZ2enT2oEc13omJYYQCnTBgMlA3z0EVXSA2TtsLhnVBRZPrt3U+X9TTcRAb36KsnMHDxIVk5bm9Dfhf2eEhLotddeoyLOp58mHbWtWynWYzCQtcTeUnU1WUE9PdTDBiBX23vvCU3hGNxlKAYSgara1+v1SEpK4u/MWdfM4eFhdHR0QKfT8VZPQkICDAZD0LnRgsGy8SdGk5ycjGQWrAwAKioqEB8f71dyyIKTja9f9rGxMVRUVCAmJsalBeFrPxtfwH4QDofDKSlh48aNc/6Q/bVsmCtx8eLFs5IRApFOLSYbm82G6upqTExMKF6Q6gksOwygtGiAYjK7d1PF/iefkIzMccfRax9/TNaK8D7o/znnUPX9wYMkb/PKK6Qw8PLLZDElJFCa8kMPkVtu+XJKgZZ+dQ8fFtQPLr+ciC4sjMZ/4w2yklhatBgL2Xp4PlxXERERiIiIQEZGBhwOB59e3dnZyadXm81mTE9PB4VsTaCSi3zBfMjVkNLHCLq6umC321FRUQEAyM3NRVRUFP773//yKiNhYWF4//338etf/xo/Y2qwPmLBycZbcBzHqwHk5uYiOzvb5ZcykG401vBtenoaVVVVsNlsfGHUXOErMXAch87OTjQ3N89yJfo7pjdgY5pMJpSXl8NgMCia7eYL2FeAVeq3tNDz44+nxd9uJymYd9+l/jGMnC69lDLUACqyZC41b2A0EjlZreQaY2ISH3wg6Jldfz1ZWA4HJR1wHJGZ3NdyIa4bsDB6ZFqtlrdoAEomGRkZQVNTEzo7O9HV1eWkaKBkerW3CAY32tTUVMBv3O68807s2rWLf85URj7++GNs374dBoMBjz32GG688UZwHIfc3Fw88sgjuJI1l/IRRwXZiAsjCwoK+C+qKwSSbBgqKiqQmJiItWvXKmZy+0IM4tTizZs3u60ADoQbbXp6Gvv27UNaWhpWrlzp149zrgudOGaTmkrWzQsv0La+PtoeE0Mkk5NDEi///Cdpqu3bR5lhAKkCMOTmCgR2wQXC+E88Qa2mzWYK/L//Pm2fmRFiPCefTJX9551HRZqffEL719VRUkBdHZGR9Ku5UDGbYAjKh4aGIi0tDR0dHVixYgX0ej1GRkbQ29uLxsZGRERE8C43qaRLoLDQCQKsyDPQCgLPPfccnnvuOZevn3HGGU7FnHPFgjtJPX3Zp6amsG/fPszMzKCkpMQj0QBC7EPpRZbjON7kTEtLw/r16xX9UnpLNtPT0zhw4ABMJhNKSkrcVvQGwrKZmppCf38/li9fjtWrVy/YXaA4ZhMSQvGV7dvJmnjiCXKhTUwIrrPkZOCEE+hxQwNZNwClK2/YQI+XL6exALJgmMUTHi64zvLySNssKoriNizZLzdXKPisq6N6m9ZWsnjsdqrrkcsC91UIVSkEg8uKgeM46HQ6xMbGYunSpSgsLMS2bduwdOlSWK1W1NfX85Iuhw8fhslkUvz3zRAMlo3JZDqmWkIDQW7ZsOwmJnPi7ReAEYA4iD9XiC2J0NBQpKSkKP5D9YYYmJgnS/X2dE2UJBsWoxodHfVKQSDQEMdsBgaIZEpKgL17KZi/fz8t9l96B7B3r1ClDwgWzOWXU3+Yykrg7bcpYeDyy0mdICOD0p937AB++1sinTVrqMBTLOAJUDLAq6/S4+99TxDjPPNMsoRYx00pFqqnTDCRjdwCbzAYkJKSgpSUFCdJl6GhIbS0tCA0NNQpvVrJ33owJAgcay0GgpJsWL+X3t5en3utAMqTjclkQkVFBbRaLYqLi1FaWhoQN527LDpmVTU1NSEvL8/rhZ650ea6sFitVlRUVGBmZgbp6emK3FWyefl/vLDgm81EPq2ttO3EEyll2WQiS+TDD6mbKMM11wgtCBobAV80J81mir/YbBSzOe002v7WW4Jlc+21RDBhYVRoynrkyClFL0RcAgg+snE3F41Gg6ioKERFRWHJkiWw2+18u+bW1lZMT08jNjaWj/VERUX5/d4W2rJhxKpaNgpD+oWYnp5GRUUFOI7zO/DO1EmVIITBwUFUVVU5xSYC4ZoCBLVqKRwOB+rq6jAwMOBRzFNuTGBuCwtrSxAVFYWtW7eivb0dZrGJsECw2YSYSmIiaaO99BJtGxgg0omJoX4w6emUTfbKK2TxfPCBkILMlAIAUpxmH+2OHYJL7W9/o4ZnZjNZP2+/Tdt7eoTGbieeSCR2wQXUf+ejj+g1RmYNDTSe9NJ1sUyFeUYwkY2vqc86nc4pvXp6eppPr+7s7ORVltmfL0kYC002FosFNpvtmCObBY/ZiDE4OIi9e/ciOjoaW7Zs8duMZBljcyEbVjRaUVGBlStXOrmsApWAIGfZmM1mHDhwAJOTkygpKfG5JoMtJv6S4+DgIPbv34/U1FRs2rQJer3e5xYDgYI4ZmMwkBWxbRuRxVNPkWtsaopiLwARDrNCGhqAH/yAHj/8sEA4ycmC9XHkiJBSrdVSPEejodYD//wnne+MM4QMuDVrBPJrbKT6nsZGIh27nXrtyMVsdDodJicn5/2aBhPZzHWBDw8PR3p6OtavX4/jjz9+VrvmgwcPorW1FWNjY17p+i2kG8345Rc20AkC840Ft2wAoSiwo6MDq1ev9roplzvMhRBYL5zJycl5LRqVWkyjo6N81pu/0i9iy8YXuFNsViLDjXVDtNvt/F2tr4sNWyc1GqrgdzioyPLQIVKELi0lVxlr7/LFF841Luz4G26g7LXaWnK3/d//kVXz7rtCzOaKK6g9QWgoNTq78ELKOBOjt5fiPAB1JWUxm5NPJpWC2FiB+MQICwtDWVkZfzfO4hCBlrEJlkJK5k5Vai5y7ZqHh4cxMjKC6upqOBwOJ6tHquO20JbN1NQUNBqNGrNRGlarFYcOHcL09LRfemKu4C/ZMNmZiIgIl7Ujc6n0dwcx2Rw+fBgNDQ1Yvnw5srKy/L4DFReheguHw4Ha2loMDQ3JKjbPlWwY0bCxWIEsmy9zg3r6wbMOlRwndM9sbxdiNiYTxW1Wr6aCTnHM5qc/FWI2HR2uu27KgUnQsHTmb32Ltr/+OhGMRkPutLfeooLQ9HSybKqq5M+zevVq5OfnY3x8nK+yr6urQ0xMDBITE+ccg3CFYLFs2GcfqLmEhIQgLS0NaWlp4DgOk5OTGBkZQV9fH5qamhAeHu6UXr3QCgIs7TkYbgSUxIKTDUt3ZC4aJcf1lWxY9lt2djZyc3NdfvkDadnY7XbU1dXhyJEjyM/PR2Ji4pzGZO/BW3KYmZlBeXk535ZATrF5LmTDREzZD1qv18PhcMBut/Pp6uLFh5GP3A/PbhdqXGJiqG7mlVfIlTU0RIt+dDTFcRITgXvvJcvj4EGKuTArY8UKYczNmwVCuPpqIWbzzDOkDmCx0BjMgunqEmI/J55IWWyXXUa9av73P6rDaWmh45qayDKSxmx6enr4WFx8fDxyc3NhNpv5u3FxDEJJqydYyIZ9l+ZjcdVoNIiJiUFMTAyys7Od2mQ3NDTAarXyRJOQkLAgbbIZ2QTDZ6MkgoJs8vLyFPdX+0I2DocDTU1N6O7u9ir7LVCWjd1ux8TEBKxWK4qLixUxo5mV4M18mWJzfHy822JVb8eTglk0zE3BfkxiFxojHLaf+DNk+7F9xYKWej2lJa9bR2TyzDP0enq6QCrZ2eTeOniQYjZ3302B/3vuISJpaCC3FysIbW4WYjA2G7V3PnyYCkR/8QvKODv9dOpvExtLtTqMmxsbqb6nrY3GBagGqKxsNtmkpaXNulZhYWFIT09Heno6HA5HQKyeYCGb+eiM6QqsTXZycjI4joPJZEJlZSVMJhMOHjwIg8HglF49Hwrdx2LaMxAEZBMoeEs2MzMzqKyshMViQXFxsVdBuUBYNuPj42htbYVGo8HWrVvnvVi0r68P1dXVXik2+5MgwKwZKdHIjQ04q2uLrR6byA/FVJcBsmq0WhLarKykYH1FBZHDsmW0T1mZkJos7rj585+TddLQQLpq//0vxWw++IBiNklJFJ958EGydAoKKIZz/fXO+mi9vcA779Dj006j8cPDSTrnwAEiJLE2G4Mn17E4BqGk1RNsZLPQc9FoNIiMjITBYEBWVhYSExMXpE321NSUatkcTfCGbJioZ1xcHPLz87124ymdjdbT04O6ujqkpqZifHxccX+xO7eXP4rNvrjRWPCXXS93RCMHqdUjju+I62xsNlrcOzpoG8tK6+4G1q+fHbO55RYhZtPdPVsc0x2sVqCmRmgbcP75tP0//3GO2fzf/1GNzeLFND92jNx79AVKWT3BkiDAamyCZXFlN0RMnZq5ssVtsg8fPhywNtn+thcIdgQF2QRCv8sTIbAAvCdRTzlotVpYWHHFHMAq8nt7e7Fx40YAkO0DPle4smz8VWz29vNiJKOUT16aseZwCDGbyEhyl/33v+RSGxqi7VFRFLiPigLuuIMyzL74glSYmRRNbq5wjpISIWZz/fVCzGbXLiITq5XGe/112t7eLrQwOPFE4JJLgB/+ELjzTpK0YTEbs5n2DQub7UYbYZ3X/Lwm/lo9wWLZBAvpMbhKfZa2yZ6YmMDIyIhTm2x2nefSJlt1ox1lcEU2drsd9fX1GBgY8DsAr0TMxmKxoKKiAhaLBVu3bkVkZCRGRkYCEguSi7FMT0+jrKzML8Vmb8hGnAjgqzXj/TyEOhutlghn9WqKyTz/PG1btEgI4OfkkNz/F1+QRfLrX5Mu2u23UzuBpiYac3SU9q+tFVSczWZyqQEU+7n5Zspo+/rXqWg0IYHcayyLtqGBztvVRfPQaKjVQWXlbLJRsv+IL1ZPMAhxAsEhCCqGN6nPWq0WcXFxiIuLU7xNtmrZHGWQI5vp6WmUl5dDo9GgpKRENtPKG8w1ZjMxMYHy8nLExMQ4ue8CqUwgJofR0VGUl5dj0aJFWLVqlc93YJ7m6SoRQEmQ20V4T0YjEcOmTeSuysujVOMjR6gnDUByMeIbRkYkv/gFWSy//S2wZw9ZJDt2UDEmi9lccw01WDMYSJDz2muBnTsFywqgmA2zqLZvJ5daZCTV/pSV0WO5mE2g5Go8WT0OhwNWqxUGg2Fe6npcYaHrWqTwJ/VZyTbZKtkEEIFYjHQ6nZP0y/DwMCoqKpCamurXAisd219SYOnVOTk5yMnJcXrvgSQbNi4z+X3RV5PCnWXjbSKAEmB1NnRecn+xOExxMS327e1kcXzwAfDHPwrH3nortZEGiCR88eJarVRfY7dTR84LLqDtL74oxGxuuIEy3HJzyboSHyOFkin/7iC1eg4dOgS9Xj9vdT2uEGxkM9fmaXJtspmOm7hNNiMfaTKASjZHGXQ6HcxmMziOQ3t7O1pbW102GPNnbF8tG47j0NTUhMOHD7sMxAeKbJgbrb6+Hr29vXOu35Ejm7kmAvgDjnNuMWCxUNtlvZ500gCyZN56i7bddhslCuzfT7EdlhK9fLkw5rZtQszmJz8R2kI/9xy52mw2UnZm6s5tbYLb7aSTSO356qspnfrf/yaV57Y2ig8dPkxuNqny87ScFHSAodVqodfrsWjRIqSlpc1LXY8rBFPMRmk1A8A5vRpwbpPd1tYGvV7Pu9oiIiICLsLZ0dGB++67Dx999BH6+vqwePFiXHzxxbj99tud3OlVVVW47rrrcPDgQSQnJ+P666/HLbfc4vd5j2myYUrF4+PjspXw/sJXUrBarXzu/tatW13etQSSbJqbm/lCzbkGH6VkI00EmI/MInYudrm0WkoCWLmSZGr+/W+yMJKTqacNQDGbhAQim6oqEuZ84gmycl57jWpjOI5UBwDahxkdJhPV2QDA0qXATTdRzOZb3wKefJLOs3mzELOpqSEyKy8XrJ2iIorlSLlFKoc0XxAnCMxHXY8rBFPMht0sBVJBwFWb7DfeeAN33nknoqOjsX79euzfvx+bN29WfC4NDQ1wOBx48sknkZubi5qaGlx55ZUwGo14+OGHAZCr/7TTTsOpp56KJ554AtXV1bj88ssRFxeHH0l1mrxEcNxOBAA2mw0jIyN822aliAbwzbKZnJzEvn37oNFoUFxc7NY8ZrEVJQlnamoKRqORr99RqlBUSD8W4jPiiv9Ag7meWK2N2UyWzdq1ZM3k5xNRDAyQGwughZ9ljnGcULB5++3AuefS4z17hBTpjz8m1xdAMRpWp7N1K3XkZCTC0N1N2W4AWUhGIykYnHAC9bKJiBC6g8q9l/mGq2w0FuvJzc3Fli1bUFxcjNTUVExOTqKsrAyff/456urq0N/fL6tS7iuCyY023wWmrE12bm4ubrjhBlRUVCAzMxPj4+P4+te/juTkZFxzzTWKnvOMM87As88+i9NOOw05OTn45je/iZ/97Gd4lZnrAF544QVYLBY888wzWLNmDS644ALccMMNeOSRR/w+b1BYNkovTv39/Whra0NISAgKCwsXpMkZm0dVVZVH+RvxuIByP77BwUFUVlYiJCQEWVlZii1q4h45gU4EcAWhEJCsG42GXFxtbUQkhYWUZlxfT0WVb70F/OUvwvG33UbtAQD670vMxmKhTDWHwzlm889/CgR0883A009TcWlSEqVANzTI1/MEG9lIEWir56tMNlJkZ2cjNTUV55xzDq6++mocOnQIhw8fDvh5x8fHnbog79u3DyeccIKTW+3000/Hgw8+iNHRUZ/V54EgIRulII6LZGVlYXh4OGDJB+4sG7GK9bp165CamurVuP6IZro6v1ixuaenR9E6JmaBLRTRiCFOfQaATz+lxwMD9Dw8nIorNRpSCtizB/j8cyIfVmezcqUw3vHHCzGbG24Q5GdYzMZuJ/21V16h7S0tgtvta18jC+naa4H77qOUaKORCNBoJPcZqwdyfg8L067BnzqbQKgZBEu9DyAkByzkfJg2mk6nw5YtW7Bly5aAnq+lpQV/+tOfeBcaQIoiS5cuddpv0Zcugr6+Pr/IJjhuJxSAxWLBoUOHMDAwgK1btyIxMTEgYpmAe8vGZrOhvLwcvb292Lp1q9dEw8YF5kY2DocDNTU16OjoQFFRERYvXqxoLIgtjBaLBc3NzRgfH1/Q3jbsbWk0FLPZvJm2vf46sG8fLfAsHTk7W6j2r6ggVxhAJJSXR485jtoVAGS1VFTQY5OJ5GYAiv089hiR2nnnUXwGoJgMSygoLaVjSktpDL2esuLksu2P5k6dzOpZt24d30cmJCQEHR0d2LNnD0pLS9HR0eG2X0+wWTYLPRd/s9FuvfVWPl7q6q+BCfV9iZ6eHpxxxhk4//zzceWVVyr1FmQRFJbNXL/wTEAyNjYWxcXF0Ov1sFqtASMbV5aN0WhEWVkZwsLCfC6UBOZONq4Um5VSaGDxpMjISKxZswZDQ0OoqqoCx3F818SkpKR5qdcwGAyYmZnhn1utFLdZvpxcZ8uXE1mMjQGpqURGZWX0mN6Lc52NTkdFnp9+SmrNl10m1NkkJwPXXUevs5jNtddStpr4q9vRQeKdACkR6PVEUJs2kdstJETIXBMj2N1o3sJfqycYFniGhW4vwFpC+0M2O3fuxI4dO9zuk5OTwz/u7e3FSSedhJKSEvz1r3912i81NRX9/f1O29hzX26gxQgKspkLWN2IVEAyUN00AcGyEf9YWXwkMzMTy5cv9/vH468VMjExgbKyMlnFZiUsG2kigLiAbXx8HENDQ+js7ERtbS1iY2ORlJSE5OTkgAkKCplvgkaaxSLEbNatI5dVRQW5t15/nbp3Mtx6K8B+S11dvmmjzcyQEoDDQf9ZzOaFFwRxz1/9ijqAFhRQksD0NGW7yZ1noRbaQLuvvI31WCyWoHOjLST8JRtxerUn9PT04KSTTkJBQQGeffbZWe+5uLgYt99+O1/0CwDvv/8+8vLy/HKhAUcx2bC6kb6+Ptm6kUCSjViRWKvVoq2tDW1tbbM6Wvo7tq/E4Emx2d+WAAzu4jMajYaX7WB3s0NDQxgcHOSTNJKTk5GUlOSxctoXSN8Ps1T27yfi6eujhT00lDTNtFoimAMHKMvsvfeEmM26dcI4TMATIDcb83A9/TQVaToczjGb1lbB7XbWWdQm+vrrKcPtd7+j7LP+fpKumZ4m4pFTEVgIzGesxJ3VMzQ0BI1Gg7q6unnrUuoKC23ZAIEv6uzp6cH27duRlZWFhx9+GIOsKA2C1XLRRRfhnnvuwRVXXIGf//znqKmpwR/+8Af8/ve/9/u8QUE2vn7hzWYz7y4qKSmR1R1ii3YgflDsLmBmZgaNjY0YHx+XbR/t79jeEoM4EcGdYrM/LQHE5/AlESAsLAwZGRnIyMiA3W7nF5P6+npYLBYkJiby7jZ/5YIAcbtrYVt0NDVC++ILcoUB1FTtyBF6nJ4OfOc7RDYHD5Iu2qOPEjG89hqlOet0Ank0NAiuNosFWLKEXGW5uZRtdtVVNN7vfkfuts2bBeHOzz4jt96hQzRmaChlpnV1zSYbi8Ui6dMzP3fWC1lMKbZ6WltbMTk5ycd6FlLNYKEtm7m40bzF+++/j5aWFrS0tMwqcmfrRGxsLN577z1cd911KCgoQFJSEu68806/a2yAICEbXzA8PIzKykqkpKRg1apVLu9C2Ha73a64T5yNXVpaitDQUJSUlPgcn3EFb60QsWKzp3ba/rjRWGrzXKRndDqdU2OqqakpDA0N4ciRI2hoaEBUVBTvbouJifFp/NDQ0FnK20YjEUJDAxFCTQ0t7OnpZO0cOgRkZQn7szXlZz+j1+vqgE8+Ad5/n9SbP/iAjk1JIW20X/+ajtm6lfrbXHONszZaSwvV8gBUW6PXU9rzmjXkQtPphMw1MdjNAOvV46lDqVIIliwwjuMQHh6O3NzceetS6goLHT8ym81wOBwBVRDYsWOHx9gOAKxfvx6fffaZYuc9ashGnM67cuVKZDJ1RRcIJNkMf7lixMbGYu3atYp+Ob1xo/mq2OxrgoCYZABlpGfEelFLly6FxWLB8PAwBgcHUVZWBq1Wy1s8iYmJbj8zu90u2+LBYqEF3+Gg7LLYWLJyvvlN0i37+98FS+hnPxMKPNvaKMHAWzCLhePIQvrOd2j7888LltDvf08tDQoLicimpoTkASlCQ0OdNOU8dShVCsFENuL3tpBqBq7aC8wXjF/mxXvTxPFoQ1CQjacvi81mQ01NDUZHR7F582bExcV5HJMtkErGbcSEp9VqsXTpUsUXAE9WiD+Kzb665pTsQeMKISEhSEtLQ1paGi/ZMTQ0hNbWVlRXVyM+Pp63esSqBzMzM6ioqJAlz5AQCthznFCoqdcTCfzrX5R1VlZG3TQ//VSI2RQUCGNs3SoQ0lVXCW6xp54SyOo//6E/gNxqTA7nO98hi+YnPyEpmzvuIEtmbIzIRqslVx/bXwy51tiMeAJl9QQL2biLkwSqS6m7uSykZTM1NQWtVutTS4KjBUFBNu5gNBpRXl6OkJAQlJSU+FSToGSSgN1uR01NDUZGRrB582aUl5cHJAHBHTH4q9jsLekuVKEmk+xISEjAihUrYDKZMDQ0hKGhITQ3NyM8PBxJSUmIiopCa2srEhISEBISMsu6iYqimM2BA9TemeNIIqa9nayLpCTqZ/POO1SDs2sXZYzdey+RR1UVjcEKQtvbBUkbh4NcdD09ZDXdfjtw6aUkvPnrX5N7buNGIRvtf/8jS+vgQSK88HBy7bH4kafrAcxujc0ISCmrJ5jIxluSCLTVs9AxG1bQGQyfi9IIGrKRc/UMDAygqqoKGRkZWLFihc9fAp1O59Sz3l+wPjharZYnvLn2tHEFObIRd/T0R7HZmwQBsUWz0BXUERERWLJkCZYsWcJr3HV3d6Orq4snztDQUEyxrmhfYmKCdNDi4oSYjdlMRZhaLWWqiRWe2Vu87jpSDaiqonjNBx9QzObddynwn5JCSs7330/HbNlCr192mfO86+qE5IIzzySSSUujubS10bFDQ85N37yBnNXDPqu5WD3BRDb+LPCBsHoWOhtNJZt5hjjLau3atUhjcrs+QomOmiMjI6ioqJjltlJibDlIyYYpV8/MzPit2OzOWlIiESCQYAW6o6OjWL16NaKiojA0NORU0MlgtVIg3molSyMhgdxll11GyswvvyykNf/kJ4JsTH39bCVmd5iaItkbjiMC++Y3afs//0mWEMeRvM0111DMxmSiFOixMd+JRgolrZ5gIRulsuKUsHoW2o3GyOZYRNCRjcViQVVVFS/HP5esjLm40TiOQ1dXF5qammTdVvNh2UxNTaGsrAyRkZHYunWr34kOrjLcApEIoCTYTUd3dzc2bdrECwXGxsbKzjMsjFo7OxzUOyYsjCyav/yF0px/+Uuydl55hWRkmGG0bZswRn6+YPH84AeCG42NATj3s+nsFFSld+wgV9pNNxHBXXMN1fuwOhuNBoiPF6wfJeDK6hEnG7D9mGQJs3SDhWwC0WJgLmoGC6XoABzblk1waESAFsSJiQns27cPWq0WxcXFc07/89eNxvTFWltbUVhYKBsfCaRlY7fbMTg4iP3792PRokVOraP9HVOu2RlblNg+wfQFt9vtqK6uRn9/PzZv3uykSAtAtkYnKooC/FotZaF98glZOdXVgnbaWWfRvrt3U0YaQH1tCgvpsbgmd2BAaMJmMFD7AoD+v/46Pb7oIqqpAYBVqwSievllOvfBgxTniYmheFIgPTRarRY6nQ4hISEICwtDaGgo9Hq9k3CqzWaD1Wp1cr0tNObDmvBWw81sNi+4COex2KUTCCLLpqenx2W7ZH/hDyGwglEAKCkpcVl4GCjLRqPRYHh4mFdsnqsiATDbjRbMbjOArNvKykpwHIeioiLZ1O6oqCinymeA3FSxseQ+y82lGMrUFLB+PS3ye/YAq1cL+7O3fdll5FI7dIg6eH74IcVk3nqL6mwWLQKuvJIkaLRayl5jrjMxysoECZzvfpfOuXQpxW1YO4PBQRojAPcps+DO6pn8srLUZrNBp9MFJLXaW8y368qd1TMyMoKxsTGYzeYFUTOYmppS3WiBxvj4ODZu3Oi1to838NWyGR0dRUVFBRITE7FmzRq3gcJAWDYOhwMTExOYmZnxOsXbG4iTL4KlNYArsOzD6OjoWRpvYphY3rIINhsRzMwMEURiIgX8f/5z4IEHgDffpHYDAAX8WW1NWZl8OrIrjI+T3A1AMaHTTqPHL71EqdIOB5HWt79Nbrn+fkqBtlqF/jvzDXGsZ2xsDDU1NcjOzkZoaOiCFJSKsdDuPHGsp6KiAhEREdBqtQuiZqDGbOYBa9asUdxS8CVmc/jwYTQ0NGDFihVYsmSJV43OlJwvU2y2Wq1YvHixYkQDCJaN+C8YiWZ0dBSVlZVYvHgxli9f7nZ+ckQfGUndMm02qn+JiyMr4v77gd/+lupeOjuBZ56hAkvWNfOMM4Qx1qwRXF0XXig8/sMfgD/+kR6//jr9aTRksbD64htuoDYGN98MfOMblAjQ2Unxo0DFbHwFU+pevnw5Xxi9EAWlYix0UF4MjuMQFRWFxYsXL4iawbHsRguOTzhA8IZsHA4H6urq0NTUhPz8fGRlZXm1CCtp2bBYVXh4OFJTUxX/4bEEgWBJbZbDkSNHUFZWhmXLlmHFihUe5yenQxcRQWnJBgPFaT75hIhnzx5a6ENCgJNOon3fe48sHgB44gkhSWD1aiItgNKmmWEcE0MFmwBZKx9/TGRy8cVCK+jsbCHT7G9/o0Zrhw5RunNCArnUFnJN7evrQ2VlJVatWuWkwKHVaqHX6xESEsL/iWM9NpsNFouFb9sRCIs+WMhGqiCgRL8eX6BaNkcpPJENq0a32WwuBT1dQSnLhik2s1hVU1OTIrVBDIxcJiYmUFtbi+TkZCQmJi6Yqq4UHMehvb2dFxNNSkry6ji5u7+REcpAS0ykmE1jI/WPOf54qnn56COK3zAwPjvnHHJ17dlDrrBPPiESee01IWZzxRXA3XfTOBs20JhS7NlDVgxAMR6NhpIJIiKIcDiO/ut0RETzicOHD6O5udnjNZ6vglIxFlIQVAp3xDcfagZGo9FvCf9gR9CQTaDaN8tpaAFCwzW5/i/ejj1X2X45xWYles+Iz+FwOBAfH4/8/HwMDQ2hvb0dNTU1iI+P50UyF0oag7WJGB4exubNm33KPpyQCbLY7ZTaPD1NlsjxxwNvvEE6ZTfeSNbI++/TvpdeKuiYffaZ0M3THdiN6+CgkPr8/vukSADQttBQiskcOAAUFxPZtLURmZnNZNnMJ9FwHIe2tjZ0dXUhPz/fZ/esu4JSd6nVviAQqc/+wpeizkCoGRiNRo+6j0crgoZsAgFXlk1PTw/q6uqQm5uL7Oxsv77oWq3WJZF5gjvFZqXcc+JEAJ1Ox9+RLV++HNPT0xgcHMTg4CCampoQERHBE4+rGhalYbVaUVVVBavViqKiIp/bDci5LGJjiTTsdhLkTEsj6+KnPwVuuQW4807KWPvd7yhgPzJCr7NW0QBZRIyEvvlNIWbz0EMkSwNQosGbb9JrAwN0TF8fcNdd5IL7xS+oRTRA1lV7O2W7aTTU9ZPJ4QQaHMehsbER/f39KCwsnHMpgSurR6zlBsCJdLwhnmBzoy2kmsGx7EYLjk84QJCSDbuTbmhowKZNm2Qbjfkytj+kMD09jQMHDsBiscjWEilh2YgXAbn4THh4OJYsWYKCggJs374dy5Yt412Ku3fvRk1NDfr7+xV154kxPT2NgwcPQqvVorCw0K++NnKuoNBQWuTDwqip2e7dRDz//S+9rtFQzxmALJ7bbiNr5aWXhIyyE04giRqAiIMZAqmpAikddxzFhOx2spCYlZOUJFg/Dz1EFk55OWW6LV5M485XJprD4UBtbS2GhoZ8thq9hVarhcFgQGhoKB/rERMRi/XYbDa3sZ5gIhul5uJvrMdkMgWMbDo6OnDFFVdg6dKlCA8Px7Jly3DXXXc53TR3dHTwNwviv/3798/5/F8Zy8ZisaCiooJf5P2RfRHDn5iNN4rNcyEbVhXO5uVNIoBer8eiRYuwaNEi3hUwODjIqy8nJCTwnTaVcLeNj4/z8j/+6N0xyCUIDA5S4WRiIumhdXSQ8OU3v0nWyjvvAJs2zR7r+ONJeeC996gj5+7dFLP55z8pZpOaSnU3d91FxLRyJYlxMrC14t13hbbUt95K24qLyX1mNJJszdAQzSVAPA6A7s6rqqpgNpuxefNmn8Rr/YUvVo80tTqYYjaBaDHgjdUzMDCAsbExjI2NBSwbraGhAQ6HA08++SRyc3NRU1ODK6+8EkajEQ8//LDTvh988AHWrFnDP/dVj1EOQUM2gYrZ2O12TExMoLy8HDExMXOuxheP7QspeKvY7C/ZSFsDsDsSXyD+UaxYsQJGoxFDQ0Po7+9HY2MjIiMjeXebr83OABJWrampwbJly7xKL3cHaUEnQ00NWRwhIUBJCak5v/QScMEFVM3/+ee037e/LWSdvf02pUwDruMpHCdoqfX0kPYZQBbSSy/R47feIqvK4SDLKieHhD+rqihxgMVsAkk0TEsPAAoLCxcsEcRdQak0ySBYYjaMFOdLzUAc66mtrcVf/vIXtLe344EHHsDhw4dx5plnYuPGjYrN54wzzsAZojz/nJwcNDY24vHHH59FNomJiXyLaKUQHLcTAYJOp4PZbMaBAweQkZGBjRs3KqZ75K1lw1x3jY2NyM/P99gawB+yEQdrxXeOc0VkZCSysrJQWFiIE088EdnZ2Xzjtk8//RS1tbUYGBjweB04jkNnZydqamqwdu1ar9PL3UHuBxgfTy6ryUmyVLq7iSS++11Kh779duDBB2lfg4EsH60W+OEPSagToPRl1sTwlFMExYFf/1pwqf3vf9SpU6+n861aRdsffpj01/R6GgeggtGmJkpaYG0OAoWZmRkcOnQIer0e+fn5QZNxKJbRCQkJcZLRGR8f57+/gUqt9hbsRm0+VZ/ZDd5VV12FiooKZGZm4swzz0RZWRm2b9+Oa665JqDnHx8fnyUFBQDf/OY3kZKSgm3btuGNN95Q5FzHLNlwHIcjR47AbDZjw4YNWLZsmaJ3T97U8FitVpSVlWFoaAjFxcVemaK+kg27Y2SBzUDdIRoMBqSlpWHdunU48cQTsW7dOuj1ejQ1NeGTTz5BeXk5uru7YTabnY5j7RE6OjpQUFDAZ93NFXIZOwYDSclERwO9vWTFOByCFcJxFMwHgBdeoJiNw0FZat/+Nm0//3whPTonRyCSvDwS1gSA00+nhACbjaRunnmGtotd7cyNxtocLF1KyQFyVs3Q0NCc0+hZHCw6OhobNmxYUJl8d2AWj8FggMlkQk1NDXJzc3kXLdNvY7Ge+SQesft5oWCxWPCd73wHr7zyCoaGhvCrX/0qYOdqaWnBn/70J1x11VX8tqioKPzud7/Dyy+/jLfeegvbtm3DOeecowjhHJNuNKvVisrKSkxNTcFgMCi2wInhyY1mNBpRWlqKyMhIFBcXe21R+RILWihFALlmZ4ODgzhy5AgaGhoQFRWF5ORkJCQkoL29HWazGUVFRYqmWEtjblotpRcvWUIWzoYNlG3W0EDqzXo91c2wOhgxli8nknrlFcpU27OHBDafekqI2Zx/PikQPP44nSM2VjieEch//iOoTj/yCG0780ygq4seG400J2mdTX19PSwWi1N8zJekicnJSZSVlWHRokXIy8sLCpeUJzAlgxUrViDjy4wMJXv1+ANxGvdCQawgEBIS4pV816233ooHmcnuAvX19Vi5ciX/vKenB2eccQbOP/98XHnllfz2pKQk3HTTTfzzzZs3o7e3Fw899BC+KScI6AOChmyUwuTkJMrLyxEZGYkNGzagtLQ0IOdxRwpDQ0O8SexNNbx0XE93c/4kAgQKGo0GkZGRiIyMRHZ2NiwWCx/naWtrg0ajQWpqKqamppyyleaKI5KWlw4Huamqq4lYsrKIQOrqgL17KYOsoYHaRgNEAowwXnyRSAKgIlA568NiEcQ0W1tJvoYd+7e/0eOPP6aOnBxHlk9yMmWhdXZSKrbFIl9ns23bNhiNxlmEzdpiu4uPjY2Noby8HFlZWXPKrpxPDAwMoLq6GqtXr3bqVbUQBaVi2O32edWEk4LjOL/kanbu3IkdzPfrAjk5Ofzj3t5enHTSSSgpKcFfWd8MN9iyZQveZwVqc8AxRTb9/f2oqqpCdnY2cnNzYTKZAqLMDMhbNiw20dzcjNWrVyM9Pd3ncT2RjRKJAIFESEgIoqOj0dLSgrS0NCxatAjDw8NoaGiAxWJBYmIif/c+lywpuWOTkqh+xmwmYmEZnVu3UsLAzTeTG23HDtIza2sjYmKtBgCyiBi2bhUeP/igEO/56CPKWNPriThOOYUy2R5/nLLf7r+fikoBKhhtbycC0+mo66e0NbRGo0FUVBSioqKwdOlSWCwWDA8PY2hoCGVlZdBqtfx1S0xM5K1kOZ2zYMeRI0dQV1eHdevWefQ4+Nurx18sdAq2yWTitdl8AUva8QY9PT046aSTUFBQgGeffdar91tRUeF3A0sxgoZs5rJgiqvx161bx2dR6HS6gGWYSC0baV2Dv0Ka7shG/GNbyDswdxgaGkJ1dbXTnXZycjLy8vL4u/eenh7U19cjOjqa/6H4qqabm5uLDz74AIDQ/VKjAdato8SAkRGhF81vfyscx2Khjz9OsjS//CVZP488QhbKVVcRUe3eTdZQVBQVcObnA2efDdxzD7nUnn2WXrv0UjrmvfeEAlCOo6QDgAQ/tVqK+QwMeNcRNCQkBGlpaUhLS+OzlYaGhvh09Pj4eISEhKC/vx9r165VPGsoUOjp6UFjY6NPskQMc0mt9hbB0BIakJdiUgI9PT3Yvn07srKy8PDDDztldLLv0K5duxASEoJNX9YIvPrqq3jmmWfwN2a+zwFBQzb+wmazoaqqCpOTk7Oq8dkdoL9Vwe4gtmyYYrPD4UBxcbFfRYoMrsgm2FsDAJTe3djYOMs9AsjfvQ8ODmJoaAgdHR0wGAy8xZOQkODx8xJnWnGcUM2fmkruseXLiXz27aOKfq2W6maam+kY5nYDiDSWLaPHt91Gbrfduym7LD2dlAi+8Q167Z57iLDEl5+pR//tb6QYwHHAv/9N2y64gLqChoaSdTMx4VtraHE6OlN/aG5uRl9fHzQaDVpbWzE+Po7k5GTExcUF5Q0IAHR1daGlpQUbN26UzX7yFb6kVrP9PF2bQKwTvsBoNEKv1wesLur9999HS0sLWlpa+DgZg7iw9L777kNnZyf0ej1WrlyJl156Cd/5znfmfP6gIhtx3xVvYDQaUVZWhrCwMBQXF89qssW+OHa7XfE0UGbZMI21uLg4rFu3bs53RnJkE+xEw3Ecmpub0dvbi/z8fK+EBENCQvhaA7vdjtHRUQwODqKurg42m83J3SbXPK2HBVC+hN1OhFJbSwt7cjK1C9i3j+Ilubnkvnr5Zdr/5JOFVObHH6c4DEAur7Exemw2UyEmQNuqquhxbS0VeAJUBPqXv9Dj0lLKhAPIqoqJoXM4HBRLcjjmVtDJcRx6e3t5LbmoqCiMjIzw1qTD4XBKMpC7bgsBJrTqjzabN/Bk9XibZLDQbjQmVROoOezYscNjbOeyyy7DZZddFpDzBxXZ+IKBgQFUVVW5DcKzL1Yg4jbsi/3FF18o2l2UkY202Rl7LdiIxm63o6amBpOTk9i8ebNfUhs6nQ5JSUlISkrCypUrMTU1hcHBQRw+fJgXNGTuNtafXY7QUlJokTeZiBBYzCY9nWI2P/0pxWG+9S2q7K+uJovooYcEAmAtCACh7QBAvWxYP5s9e6hAVKslq+jccylR4KmniNh++1uhlcBbb1EGnEZDRJOSQmnZvkKsc8aIht5zClJSUsBxHCYnJ2ddN5ZkEOimX67m3Nraiu7ubkW02byF1OrxtldPINQDfMGx3KUTOArJhqnYtrW1Ye3atR4DV3q9XnGy4TgOHR0dAKBY62YG8Y9E/D/YEgEAQQJIo9G4bN/sKzQaDaKjoxEdHY2cnByYzWYMDQ1hcHAQbW1tCA0NRXJyspMbQPtlm2WbjWIjExO0oH/Z3Rs330xuK44j0gGoxfPeveQa6+igos4HHiCLpbKSmqN9+9tknTz7LLB9O6VD/+hH1D7gkUfIirnoIkqtfvFFwbXGcURoAGWw6fVkZbW2UhM1X8HigePj4y5TyDUaDWJiYhATE8Nr3Q0NDfFuSr1ezxNPQkJCwBdVjuPQ1NSEvr4+FBYWLlhDMDl3GyMeqdVjs9mCwrI5VhFUZOPJjcbUksfHx7FlyxZZfSwpfOnW6Q3EcwCU0QwSgy0CVquVfxyMfnjWvjkmJsZjC+25ICwsDBkZGcjIyIDdbsfIyAgGBwfRzfRlILinhoZIFy0iAti4kSya//6X3GQcR8WXLPWZdc5kYN6dHTvI9fb660REixcT2Zx4IumjMeUABo4jsgIoW62lhVx6X3xB2668khQHNBoiQ28SBMTwV+csNDTUSRJldHQUQ0NDaGxsxMzMDN9iQinNOzE4jnNqHTFXHUKlIOduE/9NTEzwau6BSK32BEY2wXZTqRSCimzcwWQyoaysDCEhISgpKfH6LlqpJmcAeKkWvV6P4uJifPzxx4pbTeyLduTIESxatGheRBR9xcjICCorK5GZmam4MoM76HQ63p0mjcHZbGThNDZSzGb1akG9ua+P3FdmM/Dhh7TtrLOoJgagVGWWjnzwINXFALSNfbw9PcKxBw8CP/4xPX7sMeDPfyYy6eig8/T3k+ssLk7QSisvp/mFhAjuPU9QSueMpU4nJiY6FeGKNe+Y1TPXFhOs8+34+DgKCwsXrFeSNxCTSVtbGwYGBrBhwwZotVqfYj1KQbVsggBDQ0N8b/q8vDyfPnCl3GhMsTklJQWrV6/m9Z6UlNNgVl1ubi6OHDmCpqYmxMbGIiUlBcnJyUFxh9jb28tXI/tTR6QU5Nynqalk3VgsFLNhVkRYGC3yV11FUjOnnkrdOQ8dIhJ5/nlh3+9+Vxjv7LOFx3/7m1C8WVZG6dEA1eucdx7w6KMUs6mupmZtDM8/Lyg9A5S4IMltkMXMzAyf/LJ+/XrFLEdpEa7VauVrehixiWt6fCE4h8OB6upqmEwmFBYWBuWNkhxYczlxXGm+C0oBlWzmFdI7KhYbaWlpwapVq2al63kDJdxoTLF5xYoVTmrFSllNTBGAmfPZ2dlYunQpzGYz3+SsubmZb3KWkpLil+ryXOfIfpQbN25U3H3oK3KZyJkIZjMt/gBZJ0wV4NJLhZgNww03UPbYzTcTMf3736Rn9re/kQvsr38FrruOpG9+9StKArj2WuBrXwOuvloQ5jzvPOD73yey0WjIEnI4hILQ8XEiuw0byIU3NuY59ZlZ8XFxcfyNTaBgMBiQmpqK1NRUcBzH1/Swjq6xsbG8u82di4e5+2ZmZlBQUBA0mXCe0NraisOHD6OgoMApgWG+C0oBShBYqNjWfCCoyEYMluU0MjIypyLJuZANE5FkKb3SBVYJy8ZVe12A4hWZmZnIzMyEzWbD8PAwBgcH+apy5lIKdMCXuUZGR0edMqEWEqwAjkGrpULOqCj6W7WK5Gqee4762NhslKK8Zw/t39UlNDJjdToApURHRRHZ/OAHFLP51a+ILEpKZs/DZCLSAoCdOykxQacT0qevv55aEJjNREQSndJZYDpnqampPksdzRUajQZxcXGIi4tDbm4upqen+SSD1tZWhIaG8u62+Ph4fnG12WyoqKiAw+FAQUFB0KhNewIjGk8JDPNRUArAL6maowlBSTbT09MoLy+HVqtFSUnJnMxxf8mGiXlOT09j69atsubtXC0bqfSMuy+otMnZ2NgYBgcH0dDQAKvV6rEuxV+w62Cz2VBUVBQ0rpHDEkVNh4MW+a4uIp5VqwTts95eiqVER5MKs0ZDiQBsHb/pJqEw8513BLHOmhohltPYCPzrX/R4926hayeTltLpgJkZOm9bGx2blERzsVrJqtHpSBlawpM8gk3nLDw8nL/ZYckZQ0NDqK2thc1m48VYe3t7YTAYFOsVFWgwK52lZPu6wPtaUOot8QSyS2cwIKi+GRqNBiMjI3wnR1fdLH2BP2TDikUjIiKwdetWl3dqc7Fs5lKoKVVdltalMNdHSkrKnOI8JpOJFzXdtGlTUMnWy/UFWrxY0Earq6MUaAa9ntKUzzmHMsuuuQb49FNyoX38sdBv5rrrhGPE9W//+hf9abWkQjAwQNvXriULaOdO4IknKHngT38S6nb+/GeaT2gokU58PJENS9dmGBwcRHV1ddDqnImTM1gtVF9fH5qbm+FwOBAdHY3Ozk4kJSXNu4vXF7Dan56eHhQUFMzZklCqoBQgN5qvMj5HE4KKbLq7u1FdXY2VK1cq9oPzlWx8UWz217IRWzRzLdSUq0thcZ6WlhY+zuNrptHY2BgvwDff7hxvsGjRolnbJiYo9Tk6mmpamFVyyilCLIW9jfPOI0mbm24iN9gbbxBhfPIJ/d19N/Cb35B18sMfUnLBL39JMaEf/Qi4916SrTn9dNJQYxgfp3OxPjhaLWW9FRZSSvXg4GyiYeKUa9asOSp0zjQaDUJCQjA4OIikpCTk5eXxVk9XVxe0Wq1TTU+wWDtMQ7G3t1cRopGDvwWlAN3kLl26VPE5BQuC41vwJWJjY1FYWOiV3Im38JYQ/FFs9sdqCnQPGldxHuaW9CbO09/fj9raWuTm5nrsLLpQMDEdGRHGxynrLDaWOmWeeSYF7js7ydL44x9JVBOgVGQ5d1ZmJsDafpxyCllLAOmjyX0t+/upjgagRmomE7nLFi+mdgU33USutpERIiG7fXZyQH19vV/ilAuF6elplJaWOiUwLF68GIsXL+ZdvENDQ2hubsb09LRTTc9CZVSKiaawsHBe3FW+FJRqtVrVjTafiImJUTwvX6/XY2Zmxu0+LAA+ODjoUzKCL240d4kAgYKrOA8r7GN3nyzOwwi3ra0N69at81q2fCHQ398/a1toKKUZDw6SsCbzfjY2Us3N4sXUMlqnA265RTjuoouEeprnnhNcZJ98IhR7HjwoNER7803gS8FpPP88/dfrKfU6JYXiMx98QAQ1PU2xnNpaOm9MDCUPiAknUJphgYDJZEJpaSkvLST9Dss11mMKEE1NTYiIiOClieZLOJRp9zE1g4VY0D316mE1VcuXL5/3uc0XNJwvypcBBrvoSqKtrQ2Tk5PYIG5UIsLMzAwqKipgt9uRn5/vk2IzE+D0ZPr6kggwH+A4jo/zDA4OYnJyErGxsXA4HJienkZ+fr5X6gwLiY8++ghniwthQB00h4dpcbfZyBIZHSXyyc4GLr8cuPBCyir71a+oLcCnnxJJjI3RMQ6Hs4tLDOYN0umoOVtTE8nYXH89Sdu8/jqN98QTVMOzerWgkRYVRW6+zExqgaDTCXEdaWZdsGJqagqlpaV+Z8oxS5tluDkcDqeankCkS4uJpqCgICgtB7vdjp07d+L111/Hv/71L5xyyikLPaWAIPh0UBSGO1fXxMQE9u3bh9DQUGzZssXn1gDeWDZiiyZYetCwOE9OTg62bNmC4uJiWK1WGI1G2Gw21NTUoLm5GWNjYz6pcM8n5BaNkRGK16xbR4s787StW0cLu1iiprAQYKrpu3eTLprNRlbQrl20/T//AT7/nB7feSeNb7NR/GbvXtpeVERkBZAV091N+6xfT+dKTBRiNgDFkcREc7RgYmIChw4dQkZGht8xPGZpr1mzBieccALy8/MRGRmJzs5OfPrpp/jiiy/Q3t6OyclJRb53Un22YCQah8OB2267Df/73/+wf//+Y5ZogCBzowUCrsimr68P1dXVc1Js9hQPCvbWAABgNptRVVWFsLAwFBUVAYBfcZ75hlgbjYGRidlMlfoXXEAkEh1N1s2zzxKBAGTVuALL7s7KEmI2rt52czPwj3/Q4wsvFPZl3Tt//GOKG7F0aofD+142wQKWkr106VJkZ2crMqZGo0FsbCxiY2OxbNkyXnB1aGgIbW1tCAkJ4d1t/nzvGNEMDAygsLAwKNQ3pHA4HLjjjjvw2muv4eOPP3Zq3XwsIqjIJhCLsZRsWOpje3s71q9fL5vV5MvYriybQCcCKIGJiQmUl5fz6azM6vIlzjPfYHGlxsbGWa8xa6a+nkiCfTSffQbk5FCGmMlE7q/f/15Y9E8+WVCDfvBBIWX6pZeE9s4ffijIzLzwAsV2AOC118i9ptGQakB8PFlKTz9NLrP+fnLrsY6dKSkUEzpaCIeVIgQ6JVsquMqEQ1k7cXGfHk8eCNaOYXBwEAUFBUFJNBzH4Ve/+hX+9a9/4eOPP8aKFSsWekoBR1DFbDiOg8VblUIvMTAwgObmZhx33HGw2+2orq7G2NjYLHkKf9DY2AibzYY1a9bw25j0DCO4YGwNAAh1HTk5OcjKyvI4R1dxHnGfmUBD3NPFYDDga1/7mtPrWVkUs7FaaYFnjcpaWkgF+pJLKG6zeTMRxltvUffONWvI8jAaiXSsVue4jUZDf6Gh5CqLjKTxPvuMrJmrr6banbffpqLQZ54B/u//SIMNIEtn0SIqLl2yhFxtTNoGoFhIMH5HhoaGUFVVhby8vAXTweM4DkajkU8yGB8fR2RkJE880nR+MdEEqxAox3F48MEH8fjjj+Ojjz7CunXrFnpK84KgsmwCAWbZMFUCnU7nk2q0p7HF5BhsiQCuwFr0rlmzxmvLzlWfmYGBgTnV83gLJl80NTWFoqIitLe3z9qnr4+IYM0aEsrUaCjwv2oVPT5yRIjjxMVRfcw//0mutXffpeZqlZWUFv2971EGWloaZbLdcw+9HhsLXHEFtZpOSaHEA/ZWOzvpvBYLEY1ORx1CDx+mOfX2ksKBtFvnnj17nOpSguF709/fj5qamgWv/RG3E2fCoczdVl5eDo1G43TtWlpaMDQ0FNRE8+ijj+LPf/4zPvzww68M0QBBRjaBcqNZrVbs27fPSbFZCYhjNuJEgGB1mzE/9pEjR+acbit2e/hbz+MtWFoox3HYvHkzQkJCMC3TGIbxPksU+OEPqTjzuOPICnn9dbI4ACIZdr/hjwgEI62DBymtGSBVAvbVuvpqykq7/HLgvvsElWhpQScArFq1CoODg6ivrw+o9JC3YEWm69atQ0pKyryf3x0MBgPS0tKQlpYGh8PBC4e2traiqqoKWq0WWVlZiqqxKwWO4/DYY4/h4YcfxrvvvotNmzYt9JTmFUHlRgOo+6OSU2ppaeFVo8WKzUqgs7MTw8PD2LRpU9AnAjAXotFoxKZNmwLmxxbHeQYHBzEzM4PExESkpKT4tXiazWZeOmjdunU8cZWVleH444932jcpiVKd7Xaq7r/sMorNdHeTdbN1K1kh779P1gXLstfrySIaHwe+/nVKMPjoI+Dii6kR21//SkKcaWlUwCntSRMeTu61iy4iMnn9dbKQli2jMV56SThXRoZznxxASH2Wc1XKtcQOJLq7u9HU1IQNGzYsuLK3t2DN2oaGhpCeno7x8XGMjo7yXV2TkpKchEMXao5PPfUU7rrrLrzzzjsokVN1PcYRVJaNkmC+W5a1lJWVpfg5mGUT7ETDaom0Wi2KiooCqsorLeozGo0YGBiYpdvmTZxncnIS5eXlfAGheLGQU5mIiqJFX68n4mB9ZVpb6X92NmWHbdgAvPoqcOAA1ducfDJpqY2PU0ozi/e88godp9dTSjRLEEhKIjfZ3/8O/OxnRFAnn0waaa++SvuIiz51OnKj1dYKkjlybQakrsqZmRkMDg7yGVquVJeVAivo3bRpk6IqHoEEx3G8InlRURGfPGC32/maHiYcKq7pmU9BWY7jsGvXLtx555148803v5JEAxyjZCNWbC4oKMCBAwd4MlAKHMdBp9NhcnIS3d3dSE5O9rlOZz4wNTWF8vJyxMfHB7w3ihRif7uvcR7WDdSVArL0fWi1FBuJiKC6mpoaisscPkzBe4OBstBaWmh/jhM6ef7+95RtdsMNREAVFRSz2buXstoyMoC77qLizbg4Sqm++WYiG3FYYP9+cqvNzFAtjk4HnHQStTXIzCSy6e0l8vLGeA8NDZVtiV1bWwu73a5oQWRbWxs6OzuRn5+PWCaXHeQQE01hYaHT70+n0yElJQUpKSngOA6Tk5MYGhrib3qio6N5qyc6OjpgN4kcx+GFF17Az3/+c7zxxhs44YQTAnKeowHHHNkwxebw8HBs3bqV/xLZ7XbFFlqWCJCYmIglS5bgyJEjaGxsVExtWSkMDw+jqqoKS5Ys8buWSEm4ivNUVFRAo9HwxGO1Wj12A5UqTTgcRDgmEwXhQ0NJMPMXv6DYzdNPU9xm927a/9FHKa4D+J6KbLGQpQNQYgHTW7vjDiISh4OUn6+/HvjGN4hsDh6kfbRa/9KexarLbPEcHBxEZ2cnamtr/c4MlGqGzTVDc77AiGZsbGwW0Uih0WgQExODmJgY3mJk372Ojg7o9Xq+picxMVGxWjKO4/Dyyy/jpptuwiuvvIKTTjpJkXGPVgRdzMZqtfod3GOKzRkZGcjLy4NGowHHcXj33Xexfft2RSwPV4WazOUxMDCAkZERPj0zJSUloHdOrtDT04OGhgasWrUKi1llYpBCHOc5cuQIrFYrYmNjkZ6ejuTkZNm79p6enlm1CRkZ5OriOIrBXHABkUx7O7nOzjiD4i5/+ANZQNPTsxf+1avpf10ducZCQymdOTOTOm42Nzvvr9cTaY2OAvffD3R0AC++SHI1eXnAWWdRSjRA58rMpJiNOBttrnI1TOl7aGgIIyMjCAsL491t7vTHmKt5YGAgaKVc5MBxHGprazE+Po6CgoI5/a4dDgdf08NijGLh0LlktL3++uv40Y9+hBdffBHf+MY3/B7nWMExYdm4U2xmEjFKtW92FZ8RuzxsNhvvLjp06BAMBgNPPIEWH2RFq4cPH8amTZuQkJAQsHMpBa1Wi/j4eAwNDYHjOKxduxZms5lvxy131y4Xd9LriRBSUigp4OmnaftHH9H/qChyb/3hD1Rn099PWWNXX03us/JysoxYUWdpqaAcMD4uFHiuWUOB/9tuA26/Hdi2jdpFb9tG6c8OB9XaAFTLA1BG3Oefk4tNaYiVvlmsgtVRORwOnngSExP568aC6qwTbjCmCcuBEc3ExAQKCwvnHHvRarVITExEYmIi8vLy+Jqe/v5+NDY28q5eVtPj7W/3zTffxJVXXonnn39eJZovcdSTDVNsZrIUcoHNubSGFp/H29RmvV7P93V3OBwYGRnBwMAAqqurwXEckpKSkJKSoqjJDpCrkP0Qg6V9szdwOBz8neqWLVt4F+TSpUv5OM/g4CBaW1sRHh6O5OTkWT1SdDqyKsLCSC1gYoIy0A4coLoYvR4oK6PYjPgYgBqmrVtH/995h+I9559PWWtpaWSN3HwzvZ6QQK0LLr2UyEb8Nfj3v2n8mRngoYdo2zXXAH/7m9A1dGBASI8OBKSxiomJCQwODqK9vR01NTWIi4tDUlISRkdHYTKZPLqgggnsezI5OYmCgoKABPkjIyMRGRmJrKwsWK1WPk5WWVnJ/3bZn6tEm3fffReXX345nnnmGZx77rmKz/FoRdCRjS/uJovFgvLyctjtdhQXF7u8O5sL2UgVAfzpqsm+nBzHYXx8HAMDA2hqappzWrAYFouF/0EUFRUtSH2GP7BaraiqquLbTkvn7SrOU1dX57Sf3U6LuNlMgXiNhoQ29+6l+MxNNxGJXH897X/LLWQBAeTyGhz0fs79/RSrAchCeuwxevzYY0LtzrvvkrWzfj25z5h1ZTDQ8/koAxHrj+Xm5mJ6ehoDAwNob2+H1WpFREQEDh8+zMv9L3RMzx0cDgdf1BsoopHCYDDw0k3stzs0NISOjg6nOFliYiIiIyOh1Wrx0Ucf4ZJLLsETTzyB7373uwGf49GEoIvZ2Gw2r4hhYmICZWVliIuLc6q/kMNnn32GVatW+dycStqDRknpGSbDMTAwwNdUxMXFISUlBcnJyT65NYxGI8rLyxEdHY21a9cGjVimJ5jNZpSXlyM0NBTr16/3qaPj1NTULPWD7GyybgBa1M8+m1KRm5pIouacc4gALr6YhDonJijYL/4FREfTsSMjZCHpdBSniYggQhO7wXQ62jczk/Z5800633/+QyS3di2wZQtZVzodHZ+dTXEl1jkUmL8WA3a7HZWVlbBarVi/fj1v9QwNDQGAk7stWLprAs5EU1hYGBQ3UmKL+7PPPsOjjz6K1atXY9++fXj00Ufxox/9KKjJeyEQPN8oH9Df34+qqiqvFZv9sWzE8ZlAtAaQSwtmxNPU1ISoqCieeKKioly+x9HRUVRWVmLx4sVYvnz5UfMFZynZCQkJWLVqlc/XV27BsVpp8V+3juIvrOblmWcEteWtW2nb448T2Vx+OWWOsayy9evJ1TUyQvuzaSUm0mtvvUWEdfPNwGmnkULB5s2UfMBqIP+/vTMPi7Lc//972EVZZUfZFDQX1pA0rTyZiIAMlpl2XNL6dk5pv0xLO0fTTpqpHeNYaupV0eK5Sh1cUhFzIU3MBRhEEVQUUbYBZRu22Z7fH8+5H2YQkWVmnmfwfl0XVziDzO0087zn/tyfz/utUrGRBAC7awLYuZy0NPbsBzDOzkYblUrFuTBERETAwsICffr00fnUTkqVubm53CF5Vz/46BsiNA0NDYIRGkB3xx0YGIj6+nqsX78ednZ2eO+995CamgqxWIy5c+fyvVTBIDix6ehi2V3H5q6KDR/RADY2NvDx8YGPjw+USiU3RX7r1i1YW1tzwqNd7iC2IkFBQQZ15NU3xLJ+4MCBGDRoULezUbQxN2d3DObm7K7F3h6YOpWNFVi/nv2Z9HTWELMtzzzD/r2DB4EdO9gy3LRpbFeZlxe7c3nzTfb8pX9/ICyMPQ8ikC7szz5jw9Oam1vbodetY4WJUF398LgCQ6FUKjlfwLCwsAd2viKRCI6OjnB0dERgYCAaGxu519+1a9c6NL40JBqNBrm5uWhsbERERIRghKYtV69exebNm7Fx40a8/fbbuHLlCg4ePIisrCwqNloITmwehrZjc1RUVJeSJLsiNkLIoLG0tOQy3bW7i3JycjjjQY1GA5lMZlLZ9QDrwn358mUEBQVhAJmq7AZtd0JqdWup6swZdkfy5JOs2Bw9yh7ql5UB//43+/Nz5rSGnqWksHHSAGvc2d5LRalk/z7ARhh8/TX7/b//3bpbSU1ld1ZWVmwZLTycbShgGHZXY2bGDoEqFO0/hiFQKBTIzMyEjY0NgoODO1VitbW1ha+vL3dI3tb3TrvcZqiSrakITXZ2NsRiMVasWIEFCxZAJBJh5MiRj5XBZmcR3JmNWq2Gqk2MobZjc2hoaJcPB8kEfUfBT6QRQMhmmmQmoKCgAA0NDTqGlx11xwiFO3fu4Pr16xgxYoReDB7bzoUEBAA3b7YOTj79NDtQmZfHfh8Tw7YqDx/OztOUlrKBaw9LzSTXt/ZSL8g11tGRLdulpwPnzwNffcWafaanszsgH5/WIdOWltYzG42mVXAMdWZDfOX69euHESNG9LgUTOahyFlFc3OzTrlNX11tGo0Gly5dQnNzM8LDwwUrNLm5uZg8eTKWLFmCZcuWCe56ITT49zJvQ9v/YdXV1Th79izs7e0RGRnZrS6UR+1siMiQeAAhCg3ACnFRURHMzMwwduxYjBo1Cn379kVRURF+//13ZGVl4c6dO2hubuZ7qTqQKfXCwkKEh4cbzEm4vp7tQhs/nhUbEt28fHmrYJAB+ZUrgU2bWKHJyGC71QDW3+y559jvn3qKHewE2F3S0qXs96+/ztreqNXA4sWtZTJyLVco2CYEgD3/AdjzHaC1683QO5umpiZcvHgRDg4OGDlypF7OHInvXVBQEJ5++mk89dRTcHZ2Rnl5Of744w/8+eefKCwsRG1tbbfNdDUaDXJyctDc3CzoHU1eXh7i4uKwcOFCKjSdRNBltJKSEuTl5SEwMLBTAV8PoyOxMZUMGrK769OnD5588knuzMLOzg6DBg3i6uzl5eUoKCjg3ILd3Nx4nQwnc1DV1dWIjIw06FrIhdzKinUQWL6cnYU5eJAtgx040NpNVl/PlrwAdjaHmBEsWcLuhNLT2VkZT092dyIWs+c269axRpzkZaJWs4OcANtscOUKK3Slpext27axJbuaGvbPTU26szmGgFg2ubq6ck4ahoDMpGjnzFRWVqK4uLhbMRNEaFpaWhARESHYnXpBQQHi4uLwxhtvYOXKlVRoOokgxUbbsTksLKzHZxIPExttoRFqoiYA1NbWQiqVwt3dHUFBQe0KonadXaFQcNY5N2/e5AYh3dzcYG9vb7R/p0qlwqVLl6BQKDBq1CiDz0bY2LBikpbGfu/qyt5+/jxAjHYPH2Yv9q+/3mqi+cUX7KE+wJpwlpez32t7ppWWst5qAPtfkt22YgX7XzMz1rXA1ZUtzf32G7sbIiW606fZ0puDAyt05OWo7/8XcrkcmZmZ8PLywuDBg432/7ptzgyxgCFx4iTW2dXVtd3XgVqt5l4rQhaawsJCxMXF4dVXX8Xq1asFe80QIoI7s1EoFLhw4QKampoQHh6ul0/C169fR0tLC0aMGMHdZipCQw7UBw0a1K08HjIIKZPJUFVVxRk6urm5GTTjo6WlBdnZ2bC0tERISIhB5jbavjYGD2bLW5aW7E7G358VhZwctrT2wgvsgKenJxsLcO0aGz9gbd0qNp3B3JwVs4YGYOhQ1lV62zb2cdatYxsFUlPZMhwRQHt7tpEgIIB1oiZnNs7Ozrhz545eng8yezZw4EBBGK8CrfNkpLutrq6Oc1wmbf1kR6NUKhEeHi5YoSkqKkJMTAzi4+OxadMmwVZBhIrgdjYtLS2wsLDAU089pbcXnbm5Odd0YAqNAAC7zuLiYhQWFvboQN3CwoKbgiafOGUyGWdTr22doy9BIEOmDg4OGD58uMHelFZWVlwst0jUWkabNAn49dfWUtYrr7DlK+3Zlr/+ld21vPoq27124QLrkbZpE1sK27aNdY12dGRbp198kf07iYms+8Abb7DC8dprbBv0tm3s72toYL+ef55dk50dK2Tjx7OhasXF7OOTnc3atWv18lyQdnJ/f/8OG2GMjfY8mb+/PxQKBVduI47LAPseFfKOpqSkBLGxsZg0aRIVmm4iuGesX79+CA0N1euLztzcHBqNxmQaATQaDQoKClBUVISIiAi9HagT08EnnngC48aNQ3h4OGxsbFBYWIjff/8d2dnZKCkp4S7g3aG2thYXLlyAu7u7XjqgOuKVV17hvmeY1hbk+np2t7J1K/tnmYy94O/ezQ5XAmxMc319698lRwp/+Qu7AwJYT7UFC9jvIyLYjra2lJezHW8AMG4c24mmUgG+vuzv/c9/2PtI2U27jmBmBsycObMHzwDLvXv3kJWVhcGDBwtKaNrDysoKXl5eCAkJwbhx42BlZcVVGTIyMpCTk9Pj16C+KSsrw+TJkzF+/Hhs2bKFCk03EdzOxhCQnY0pNAKoVCrk5uaiqakJo0aNMtj0trZvVmBgIGedU1JSwjktk0HSzmbzEKfhwYMHw8fHxyDr1mbz5s344YcfuD87ObGuAOnprYf/ADtn8/zzrOVMTQ0rLP/6V+v9CxawRp0Am85JROvPP9k5GYB1JCAu0nv3tg5tkjRQc3O2kUAkYlubv/kGGD26dXeVk8Pe5+nJeqsBwDvvvNvj1yF5zocOHSr4KAltyBmNmZkZnn76aZibm3OR2MTt29iR2O1RUVGBuLg4REVFYceOHSZjBSVEBHdmA7ClNH1SWlqKmzdvctt0Ie5mAHYuQiqVwtLSEsHBwbyVFEg+SmVlJZfNQ5yEH2adc/fuXRQUFGDEiBGddnbQB7t27cIbb7wGtZo9s7l+nY0SkMtZcWlsZEUjLq71bMXFpdWY8+hRVlAaGtgdSUctyWZm7Je5OWvieecOO7sTF8c6QuflAR9/zDYH7NjBlt6AViG6dYs9R7p7F+jb1wElJaU9+rdXVFTg8uXLRn/Oe4parYZUKoVGo0FYWFi75duWlhau3Hbv3j1YWVlxwmPIs0ZtqqqqMHnyZAwbNgz//e9/BeUXZ4oI8tkjoWf6gGEY2NnZQa1W4/Tp09wZRXs29XxSX1+P7OxsrszF585LOx9Fu6X1woULsLS0fMA65+bNmyguLuYlP+fll19GY2Mj3nnnbZBz9hdeYM9HSEDa+PGskNTVtXaHhYUBo0axYnPkCHDpEtuhdvEi+/W3vwHbt7OiIhazw6ALFrA7k2XLgLlzWXGbMIG1tAHYM6OSEnZn9OKLrMgEB7O7ovBwVmxu3WJvT0s7ioaGhm43wJSWliI/Px/BwcFwJW13JoBarUZ2djYYhnmo0ABsPpS3tze8vb0fGolN3NQNMYtz//59TJkyBYGBgfjpp58Eda0wVXr1M0iaAKytrTFmzBiuVEQswom9/8PSII1FVVUVcnNz4evrC39/f0HtvLRbWrXf9JcuXeLuVygUvGbXz507FxUVFVi9mq2NFRezO5DNm4Hp09kZmtxcdsfh68v+nWPH2N1GW6ys2J0PwAqFpyf7fd++ujk05LNQVha7cwLYnRPAlsvEYlbwli5lGxSystj7nJyccOTIESiVSvz555+wsbHhugM76ztGnBhCQkLQn7h/mgBEaAB0KDRteVgkdnFxMfLy8nTC9WxtbXv8/qmtrYVYLIa3tzd+/vlnwQ6WmhqCLKMpFIoe7WzaZtC019pM2jFlMhnq6uo4e383NzejhkmR8tOwYcPgSa5sJoBKpUJWVhYaGxthbm4OhULBeWa5urryUgL89ddf8de/zoBKxUAkYtuc/9//Yy3+Y2NZ4ejfnzXLBFq70/r3Z7vGiorYjrPmZjYm4J132NvXrGEP//39gR9+YMtujY2t7dJWVqxrwIwZrH9abi57rvPMM+zjkyaBIUOGICMjg3t9aefzEJt/8vw9zHfs9u3buHnzJsLCwuDo6Gi4J1PPqFQqZGdnQyQStWsG2l20rf7v378Pa2tr7jnsTipufX09EhISYG9vjwMHDphMsJwpIEixUSqVXIZMV+mOIwCx95fJZKipqYGdnR0nPIaaeGcYBtevX0dpaSlCQkLaTRgVKgqFAlKpFGZmZtwMDTnclclkkMvlnGeWscVbpVIhPj4eZ86c4s5fdu1iz1TGjAF+/JGdedm+nT3f2bCB9TYrKWGbB6yt2VJb25efmRl7X0MD24gQHMw2InzwAZtZ8+KL7IzPBx+wty9ezJbeAMDS0gxJSV926ACs0Wi4YL3KykouWE9bvG/evIk7d+4gPDy8S0a0fEOExszMDKGhoQY7ZNc2ra2qquIisR+VrEloaGjA1KlTYWlpiV9//ZVX543eSK8SG+3W5u62NWtP39+7d69Th+NdRa1W4/Lly6ivr0dYWJhJvagbGxu5oLbhw4e3e+FoamrinkMi3trWOcYoE8pkMsyZM0dHdMLD2YP7iAjg++/Z0thLL7FlsLw81m4mJ4cVIXK7pydbelu7lj2n8fRkXQNmzWLLc0lJwIAB7M9/8w3rAk1CRC0tRfjHPz7CBx980KW1tzcIaWVlBZVKhZEjR5rUGY2xhKYt2pHYlZWVaGhogKOjo065TZvGxkZMmzYNarUahw8fNplIdVOi15zZ6CsawMrKijuYVKlUqKqqQkVFBYqKirhcma7U19tCdgUikcik4psBdkI9OzsbHh4eCAoKeui/v0+fPlw2DxniI3HE3Tmj6A5ubm5ITU1Fc3Mz1q9fj//85wtIpQpERLD3f/cd68DM/rt0518ehVrNdpQBrN8a8T2bP59toXZzc8OOHTswgQz1dBHtQUg/Pz/k5eVBJpPB3t4ely5dMtpz2FNIqZW4tRuzbbi9SGxSbrt+/TpsbW2hUCjQ3NyMMWPGYNasWWhubkZaWhoVGgPRK3Y2pBHAkI4AZItOyhzm5uac8HS2Nkwm6+3t7R+6KxAq9+7dQ05ODgICArptitr2OSRmjW5ubnB2djZYB15xcTFu3LiBgQMHYteuXfjyyy+hVDahubk1W8DCgi2fhYez3WIXLrCWNn36sLM3gYFsd1tOju7vtrAA7Owc0a+fHdatW4f4+Hi9/TsYhuFMTCMiItCnTx+d57Cz5zx8QALbLCwsEBISIph1Aa1nZbt27cKnn36KxsZG2Nvb4/PPP8eLL75oUiVKU0KQYkMGMB9FZxoBDIG27YtMJgPDMDoXzfbeWPfv30dOTk6P0in5orS0FFevXtVrEwPJRiHCo1QqubZ0FxcXvbSakmRXYujatltOLpdjx44dkMlkyM3Nxfnz5+Hk5ITGxgbU1VXD1rYPRCIRGhqaYG/vCEdHJ1RXV+Ppp59GVFQUgoOD8Ze//MUgIknikOVyOef00N7PkDhnmUzGGV6S59DQxqcPQ8hCo41SqcSsWbNw5coVxMXF4dixY7h+/Trmzp2L7du3G+xxT506hQ0bNiAzMxNlZWXYu3cvxGIxdz/DMFi5ciV27NiBmpoaPP3009i6dSsCAwMNtiZjYLJi07YRgC8zTZLfXlFRAZlM1u5Fk1yshw4dCm9vb6OvsbswDIOioiIUFRUhODjYYG22pJ2VCE9DQwN30XyYS3Bnfmd+fj4qKysRHh5uUqURkkrb1NTU6UyX9s55+IiZUCqVyMrKgpWVVaeTQflApVJh3rx5yM/Px8mTJ7lzsMLCQpSWlmLcuHEGe+zU1FScOXMGERERmDp16gNis27dOqxduxbff/89/P39sWLFCuTm5iIvL8+ku+NMUmz00QhgCLQvmjKZDE1NTbCxsUFzc7PJTXmTmIeKigqEh4fDjqSOGYHGxkZOeGpra2Fvb88JT2cummRXUF9fj/DwcINZ/hgCMl2vVqsRFhbW7RbylpYWHRcIcs6jPYyrb7SFJiQkRLCWUGq1Gm+++Says7Nx8uRJeJB8cB4QiUQ6YsMwDLy8vLB48WIsWbIEADv34+7ujuTkZB0/QFNDkGLTXjQ0QV+NAIaGRNtWV1fD2toajY2NcHJy4i6aQv6EQrrlSAmHz4u19kXz3r17sLW15Z7D9rJ51Go1cnJyuEFTU2rAIJ1bQNeGHh+FMc55lEolMjMzYW1tLXihWbhwIc6cOYP09HTeKw1txebmzZsYNGgQsrOzERoayv3cs88+i9DQUPyHDG2ZICbVjWaMRgB9oFQqkZOTA5VKhTFjxsDa2pprB9ZO0iQNBp01ujQGSqUSUqkUDMMgMjKS94u1tbU1BgwYgAEDBnDdgZWVlVyXExEeJycnbkLdzMxMJ83UFCC7ApL/o8/yk3YzC8MwqKmpQWVlJa5du6aXcx4iNDY2NggODhas0Gg0Grz33ns4deoUTp48ybvQtEf5/5L72lZB3N3duftMFZN4N7ZtBBCy0JA5lL59++pMSmu3A5NP6zKZDDdu3OBmedzd3XlztwVao6dtbW0xcuRIwdXbLSws4OHhAQ8PD2g0Gs465/Lly1z3oq2tLYKDg01KaFpaWpCVlYU+ffoY/GItEong5OQEJycnzu27srKSc/vu6jmPQqHg1j5y5EhBC83SpUuRlpaG9PR0+BLfIorREOQ7UvtiK5RGgM5QU1MDqVQKT0/PDudQtD+tE6NL4tlmY2PDfQo1ZoQzMQJ1dXXF0KFDBfscE8zMzLjJcB8fH1y8eJEbfDx9+rTO9D3fu7OOaG5uRmZmJtcOb8yLddtgM+2S5c2bNx95zqNQKJCZmcl9OBGy0Cxfvhz79u1Deno6AgIC+F7SQyHnRxUVFTqdnxUVFTplNVNEkGJD0D6fEYlEgn0xA+yL4cqVK13OcmlrdElq69plIhLhbCgBIG3Zfn5+8PPzE7zQaFNfX4+srCx4enpyraHk0zrJRSG+d66uroJqFmhqakJmZiacnJwwbNgw3p937Q9B2tYvOf8bLtI+51Gr1SYhNAzD4JNPPsHPP/+M9PR0wbcP+/v7w8PDA8ePH+fEpa6uDufOncPf//53fhfXQwQrNqbSCMAwDGeO2FMrEW1xIWUimUzGOSyT+/Q5AFleXo68vDyTC98CgOrqakil0gdEUvvTOsnmkclkuHbtGvr168eVifRlP9QdGhoakJmZCTc3NwwZMkRwr+9HnfOIRCLY2toiKChI0ELz2Wef4dtvv8XJkycxdOhQvpcEgJ3vunHjBvfnW7duQSqVwtnZGT4+Pnj33XexevVqBAYGcq3PXl5eOu3Rpoggu9HOnj0La2trDB48GObm5oJ7IxI0Gg03yxEWFmawyWPyZict1SqVSmeWp7tnK7dv30ZhYSGCg4PhQnz1TQSSUBkUFIQBAwZ06u9olyyrqqo4h2DiAmHMkmVWVha8vLwwePBgwb6+26O5uRkXL16Eubk5zM3NdeZ5+EzUbAvDMPjiiy/wxRdf6OwShEB6ejrGjx//wO1z5sxBcnIyN9S5fft21NTUYOzYsdiyZQuCgoJ4WK3+EKTYLF26FElJSQgMDERCQgISExPxxBNPCOJFTFCpVLh06RJaWloQGhpqtPIMMRgkwtPc3KyTy9OZuQyGYXDt2jWUl5cjNDSUtxya7kKGZHsyu0Syecg8D4BHukDog9raWmRlZcHPzw/+/v4GeQxD0dLSgszMTM6E1czMjJd5nkfBMAy++uorfPbZZzh69CgiIyONvgbKgwhSbMgn+V9//RUSiQRHjx6Fj48PJzx814ibm5uRnZ0Na2trXjufyNQ4ER65XP7IyXsy8FhXV4fw8HBBtV13BuJzps/gsLY7R6VSyQl4Z6zpOwsp+xF/OVOiPaFpi/Y5j7aAG9O3jWEYbN++HR9//DFSU1MxevRogz8mpXMIUmzaUldXh0OHDkEikeDIkSNwc3NDQkICxGIxIiIijCo8dXV1kEqlcHFxwdChQwVVr25qauIumLW1tXBwcODq7n369OHmf8h0upC7tNryKJ8zfT6OXC7ndjwkm6enw7jEyLQrZT+h0NLSgosXL8LBwQHDhw/v1G5F+5ynsrISzc3NcHZ25sTHEL5tDMMgOTkZH374IQ4ePIhnnnlG749B6T4mITbaNDQ0IDU1FRKJBIcOHYKTkxOmTJmChIQEREVFGfTTEzkn6InzsbFoaWnhhKe6uhp9+/aFQqGAra0twsPDBTdD0xF8+py1FfDuBOuR180TTzxhUmmsQGtrdleEpi0d+bbp65yHYRjs3LkTixcvxoEDB9o9E6Hwi8mJjTZNTU04evQoJBIJDh48CBsbG8THxyMxMRFjxozRa3mL5L4PHz7cpDzOALZ8k5OTw8U39+nTh7tg2tnZCVo0heRzph2sR84nHjUTVV5ejitXrpicNx7QKjSOjo56bc3W9zkPwzDYvXs3FixYAIlEgujoaL2sk6JfTFpstFEoFDh27BgkEgkOHDgAkUiEuLg4JCYmYty4cd0uGZHD9LKyMoSGhppU7jvQek7g4+ODgICABzJlLC0tdXJ5hCQ8QvY5I5ko5KJpZmamMxNlZmaG0tJS5Ofnm2S3H+k6M/QMkD7Oefbu3Ys333wTP//8M+Li4gyyTkrP6TVio41SqcTvv/+OPXv2YN++fVAoFIiLi4NYLMb48eM7XS8mVu8NDQ0ICwszucN0Mmj6sHMCjUajIzwikQiurq5wd3fnLph8QTJRSJywkO1nSL4R2fWo1WrY2tpCLpcjODjYpGKcgdZhU2dnZ6N2gXbnnOfgwYN47bXX8NNPPyExMdEo66R0j14pNtqo1Wr88ccf2LNnD/bu3Qu5XI6YmBiIxWJMmDDhoWWZlpYWSKVS7mKnr44kY0HKfiNGjICbm9sjf147zIxcMEkrsLETIJubm5GVlSVYj7aOINEMd+/ehbW1NRQKhcEPxvUJX0LTHqTTUvucx8XFBfX19Rg5ciSOHj2K2bNn49tvv8XLL7/M2zopnaPXi402arUaf/75JyQSCfbu3YuqqipER0cjISEB0dHR3MHz5cuXUVVVBRcXFwwbNkxQHWePgmEY3LhxAyUlJd0u+2nP8lRUVKClpUVniNSQwtvQ0ICsrCzuYmdqz/3Nmzdx584dhIeHw97enjsYl8lkqKurg4ODAyfiQtspNzU14eLFi1ynpZBKqi0tLaiqqkJubi5mzJgBe3t71NTUYNmyZVi5cqWgd74UlsdKbLTRaDTIzMzkdjx3797FCy+8gIEDB+K7777Dhg0bMGfOHEG94R6FRqPhMuvDw8P1ks6o3Qosk8l0UjTd3Nz0eo5SV1eH7OxszufMlJ57hmFw/fp1lJWVISIiot2OOW237/v376Nv376c8PDdqEGExtXVVZD2Odqkpqbi1VdfxZNPPomCggIwDIO4uDj8+9//NliabHusWrUKH3/8sc5tQ4YMQX5+vtHWYEo8tmKjDQk6W758OQ4fPgwzMzNMnDgRCQkJiI2NNagJpr4gjgYKhQJhYWEGK9eQFE3ySZ2YXLq5ufUoEI40Mvj7+8PPz09/CzYCpDW7qqoKERERndqxKJVKnUAzS0tLHescY+7oTEloMjIyMHXqVHz++ed44403oNFocPbsWRw+fBgff/yxUcvdq1atwp49e3Ds2DHuNgsLC5NrBjEWVGzAis2KFSuwZcsWSCQSeHh4YM+ePUhJSUFeXh6effZZiMVixMXFwcXFRXBvxpaWFmRnZ3PBW8YqKWibXFZXV3drBgXons+ZUCC7yZqaGkRERHSrNVvbdLWyshIMw3BlS0OflzU2NnKGoB3FYgiBCxcuICEhAatXr8bbb7/N+1pXrVqFffv2QSqV8roOU4GKDdiL9bx587B8+XI88cQT3O3k/IMIj1QqxdNPPw2xWIwpU6bA3d2d9xc8OeMgLap8nXFoz6Boxze7u7t36K6sD58zviAzQHK5HBEREXrZTTIMg9raWm732NLS0mXvu87S2NiIixcvwt3dXfBCk52djbi4OKxYsQKLFi0SxFpXrVqFDRs2wMHBATY2Nhg9ejTWrl3bpYiRxwkqNp2EYRgUFRVBIpEgJSUF58+fx1NPPYWEhAQkJCTA29vb6G8AEtbm7e0tKPdgEt9MSkRWVlbcjsfBwYFbJ3Gd1qfPmbFQq9WcEauhZoDa877TVzYPiTgwBaHJzc3F5MmTsWTJEixbtkwwa01NTYVcLseQIUNQVlaGjz/+GCUlJbh8+TLs7Oz4Xp7goGLTDRiGwd27d5GSkoKUlBScOXMGERERnPAYI4CMlJ66GtZmbLTdlWUyGTf8SAQpPDzc5Fyn1Wo1pFIp5zFnrHOCpqYmbgaluroa/fr10ylbdvY1R4TGw8ND8I0YeXl5iImJwYIFC/DRRx8Jeq01NTXw9fXFxo0bMX/+fL6XIzio2PQQhmFQXl6OvXv3QiKR4NSpUxg5ciQnPIZ4M9+9exfXrl0zOescMvxYUFCAhoYGWFhY6ATCmcI8jVKphFQqhUgk4nXYVKlUcsJDsnna2z22paGhARcvXjSJLJ2CggLExMRg3rx5WLNmjaDXSoiMjMSECROwdu1avpciOKjY6BGGYVBVVYX9+/dDIpHgxIkTCAoK4hyqezokR+Y4iouLERoaCicnJz2u3vBo+5yFhYVBoVBwOx6FQqEzyyPEuQmFQoGsrCwuWkIo4tjW8oU4QRDLF3KOZ0pCc+PGDcTExGDGjBlYv369ScxbyeVy+Pj4YNWqVXjnnXf4Xo7goGJjIIj1xoEDByCRSPDbb7/B19eXE56uZvJoNBpcvXoV9+7dM7rzsT7Qbs1ue8bRdpansbFRJ5dHCJ5oLS0tOq4GQr34EScI0qyhVCrh4uICe3t7FBUVYcCAARg0aJCghaaoqAiTJk1CQkIC/vOf/wj2uV6yZAni4+Ph6+uL0tJSrFy5ElKpFHl5eSZnUWQMqNgYibq6Ohw8eJDL5PHw8OCEJzw8vMM3FDmMbm5uRlhYWI/mWfhAoVDoWP88ateifSheX1+vlzyZnqBts29KjhIMw6C+vh4lJSUoKSkBwzDo378/N88jROucO3fuYNKkSYiOjsaWLVsE/Vy/8sorOHXqFO7duwdXV1eMHTsWa9aswaBBg/hemiChYsMDcrmcy+Q5fPgwl8kjFosxatQonfIM8WgzNzdHSEiIyXm0EZ+zvn37YsSIEV0uPZFDcZlMhpqaGtjb23NnE8aweyFzKP379+fdK6w7yOVyXLx4EQMHDoSnpyf3XNbW1nLPJcmU4ZuysjJER0fjmWeewY4dOwRTpqToByo2PNPY2KiTyWNra4v4+HiIxWI4OTlxh6MTJkwQ9Ke89tC3z5n2GQ+xeyHC09EsT3eRy+XIysoyifbg9qivr0dmZiYGDhz4wKftttk8JOPI1dX1odk8hqSiogIxMTGIjIxEcnIyFZpeCBUbAdHc3Izjx48jJSUFEokEdXV1GDRoED7//HM899xzJrWrqaurQ1ZWlsFmgJRKpc4sj7W1Ndzd3TsMMusK5EJtCmcc7UHWT3KMOoJk85Dn0tzcnBMeY0RNVFVVYfLkyRg+fDh27twpyOYQSs+hYiNA0tLSMG3aNLzyyiswMzPDvn37oFQqERcXh4SEhC5l8vCBsX3O2gbCkYtld33GamtrkZWVBT8/P/j7+xto1YaDCI2vr2+X10/a08lzSaImSGebvoXg/v37iI2NRUBAAHbt2mVSH6goXYOKjcC4e/cuhg0bhm3btmHGjBkA2E+eJJNn3759kMvlmDx5MhISEjrM5OEDmUyGy5cvY8iQIfD29jb645OLZUVFBeczpp3L8yjhuX//PqRSqeCHZR8G2VF2R2jaoh01UVlZiaamJr12CdbU1CA+Ph6enp5ISUkRRNchxXBQsREgpaWl8PLyavc+kslDohHu3bvHtYlGR0fzetArNJ8z0n5OznmUSqWO8LT9lF5VVYVLly7xJpQ9pa6uDpmZmQbbUbYNM3NwcOB2kF39wFNXVwexWAwHBwfs37/f5DosKV2Hio0Jo9FocPHiRU54SktL8cILLyAhIQExMTGwt7c32lqIz1loaCicnZ2N9ridhbQBE+FpamrSMbisrq5Gbm4uhg0bBk9PT76X22UMLTRtIY7flZWVXW7WkMvlmDp1KqysrLimGErvh4pNL0Gj0SAnJ4cTnps3b+L555/nMnkcHR0NcsjNMAwKCwtx9+5dhIWFmYzPmVwuR2VlJSoqKlBfXw8A8Pb2xqBBgwR9HtYe5IwpICAAvr6+Rn980qxBrHMsLS11zsy0X3eNjY146aWXoNFocPjwYZMbTqZ0Hyo2vRCGYZCXl8dFI1y9ehXPPfccl8nTv39/vQgPwzA6rgZCmNXoKiUlJcjPz4enpycaGhpQW1vbo/KQseFbaNpCjFdJWzXACkxNTQ0mTpyI+fPno6GhAUeOHDHqzpvCP1RsejkkrpgIT05ODsaOHYuEhIQeZfJo+5yFh4cL/qLcHsXFxVzpj/jMtY1u7q6zsjGoqalBdnY2Bg0aJMhmBnJmJpFIsHbtWshkMtjb22Pt2rV4+eWXeS+3bt68GRs2bEB5eTlCQkLw5ZdfYtSoUbyuqTdDxeYxgmEY3Lp1i8vkuXDhAkaPHo0pU6Z0KZNHpVIhJycHKpUKYWFhJtlFdOvWLRQVFXUYcUCclUkgnI2NDSc8fAw+aiN0odFGoVBg1qxZuHbtGqZOnYqjR4/i0qVLmDJlCiQSCS9r+uWXXzB79mx8/fXXiIqKQlJSEnbv3o2CggK4ubnxsqbeDhWbxxSGYXDnzh0ukycjIwNPPvkkF43g6+vb7sWU+JwR+xxTG8DTPmOKiIjodMiVWq3WGSLVjkcw1HnYwyBCM3jwYAwcONBoj9sdVCoV5s2bh4KCApw4cYIzqLx9+zaXVcMHUVFRiIyMxFdffQWA3akPHDgQCxcuxLJly3hZU2+Hig0FDMOgrKwMe/fuRUpKCk6dOoXg4GBOeIgDwM2bN3Hp0iX4+/t3y+eMbxiGwbVr11BRUdEj52yNRqMTCAdAJ5fHkBP31dXVyM7ORmBgoEkIzZtvvomcnBycOHECHh4efC8JAPuBydbWFnv27IFYLOZunzNnDmpqarB//37+FteLoWJD0YFk8hDhOXHiBIYOHYqnnnoKKSkpmDp1Kr744gtBnV10Bu1mhoiICL212xJLfyI8arVaJ5dHn4JMhCYoKAgDBgzQ2+81BGq1GgsWLMDZs2eRnp7+0LkxPigtLYW3tzcyMjIwevRo7vYPPvgAv//+O86dO8fj6novpuXs2A3WrFmDMWPGwNbWFo6Oju3+THFxMWJjY2Fraws3Nze8//77UKlUxl2oQCDBW//3f/+H1NRUlJeXY+rUqfj+++9RXV2NU6dO4ZNPPsGlS5eg0Wj4Xm6n0Gg0uHLlCqqrqxEZGanXuQ4zMzM4Oztj6NChGDduHMLDw2FjY4MbN24gPT0dOTk5KCsrg1Kp7NHj3L9/32SERqPRYNGiRfjjjz9w7NgxQQkNhT9Mq+DeDRQKBaZNm4bRo0fjm2++eeB+tVqN2NhYeHh4ICMjA2VlZZg9ezYsLS3x6aef8rBi4SASiZCbm4ukpCSsWbMGb7zxBpfJ8/zzz8PT0xNTpkxBYmIiwsLCBOlKrdFokJubi8bGRjz55JMGnaERiURwcHCAg4MDBg8ezE3c3759G1euXOGsXtzc3LrUVEEsdEzB2UCj0eCDDz7Ab7/9hvT0dEE2L5AdZ0VFhc7tFRUVgin19UYemzJacnIy3n33XdTU1Ojcnpqairi4OJSWlnIWK19//TWWLl2KyspKk+y00ifr1q2Dm5sbXnvtNZ3b5XI5Dh8+DIlEgtTUVDg7OyM+Ph6JiYmIjIwUxHmOWq1GTk5Ou+mgxqaxsZErtRGrF3d3d7i6unbYNm5qQrN8+XLs2bMHJ0+eRGBgIN9LeihRUVEYNWoUvvzySwDs2n18fLBgwQLaIGAgHnux+eijj3DgwAFIpVLutlu3biEgIABZWVkICwsz7kJNkMbGRqSlpUEikeDQoUOwtbXlwuBGjx7NS8eaSqWCVCoFwzAIDQ0VlJswsXqRyWSorq6GnZ2dziwP4d69e8jJycHQoUMFX4piGAb/+te/8MMPP+DkyZMYOnQo30vqkF9++QVz5szBtm3bMGrUKCQlJWHXrl3Iz88XhK9fb6TXl9EeRXl5+QMvLvLn8vJyPpZkctja2iIxMRGJiYlobm7GsWPHkJKSgpkzZ8LCwoLb8YwdO9YoF32lUons7GyYm5sjLCxMELssbWxsbDBw4EAMHDgQCoWCa6m+efMmF2JmbW2Na9eumYRXG8Mw+Oyzz/Ddd99xDSVCZ/r06aisrMRHH32E8vJyhIaG4siRI1RoDIjwiuydYNmyZRCJRB1+5efn873MxxIbGxvExcXh22+/RXl5OX788UeYm5tj3rx5GDRoEN566y2kpaWhpaXFII+vUCiQmZkJS0tLhIaGCk5o2mJlZQUvLy+Ehobi2WefRUBAAKqrq5Gfnw9zc3PU1dWhpqYGQi1AMAyDjRs3YsuWLTh69ChGjBjB95I6zYIFC3D79m20tLTg3LlziIqK4ntJvRqT3NksXrwYc+fO7fBnHpVOSPDw8MD58+d1biMHh/SwsGdYWlpi4sSJmDhxIjZv3ow//vgDu3fvxoIFC9DQ0IDY2FgkJCTg+eef14vdTUtLCzIzM9GvXz+MGDFCkA0LHWFhYQELCwvU19dj2LBhsLKygkwmg1QqhUgk4kptxkjP7AwMw+DLL7/EF198gbS0NISGhvK9JIqAeezPbEiDQFlZGWdTsX37drz//vuQyWQm5wBsCqjVapw9e5ZzqK6urkZ0dDTEYjEmTpzYLUPPpqYmZGZmwtHREcOGDRPExbirVFZWcjEH2h902pvl0c7l4WP3xjAMtm3bhn/9619ITU3VmVehUNqj14tNcXEx7t+/jwMHDmDDhg04ffo0AGDw4MHo168f1Go1QkND4eXlhfXr16O8vByzZs3C66+//ti3PhsDjUaDCxcucMJTVlaGF154AWKxGJMmTeqUM3BjYyMyMzPh4uKCoUOHmtzAKcAKzaVLlzB8+PAOd9QMw6C2tpYTHoVCoZPLY4xmDIZh8N133+Ef//gHDh06hHHjxhn8MSmmT68Xm7lz5+L7779/4PaTJ0/iueeeA8D6NP39739Heno6+vbtizlz5uCzzz4zOd8vU0ej0UAqlXLCc+vWLUyYMIHL5HFwcHhASORyOTIzM+Hp6YnAwECTFpquJpwyDAO5XM4JT0NDg47wGKLVm2EY/PTTT1iyZAkOHDiA8ePH6/0xKL2TXi82FNOEYRhcuXKFi0bIz8/H+PHjIRaLERsbi/79++PMmTPIyMjAiy++iICAAJMUGplMhtzcXL1EaTc0NHAt1XV1dXB0dOTOefQRu8wwDHbt2oWFCxciJSUFEydO7PHvpDw+ULGhCB5ioCmRSCCRSLhdwNWrVzF//nysX7/epIVm5MiRere1b25u5nY8NTU1sLe353Y83Q25S0lJwd/+9jf88ssviI2N1et6TZkffvgBixYtQmlpqc4Zr1gshp2dHX788UceVyccqNjwiJ+fH27fvq1z29q1a+kEcwcwDIP//ve/mD9/Ptzc3FBaWooxY8ZwmTxeXl4mITwVFRW4fPmyQYSmLQqFQieXp2/fvtyOp1+/fp16vg4ePIjXXnsNO3fu1HFKprDNKZ6entixYwemTZsGgP0g4e3tjaNHj9JS4/+gYsMjfn5+mD9/Pt544w3uNjs7O5OMVzYWR44cwUsvvYTNmzdj9uzZuHPnDhcGd/bsWS6TRywWw8fHR5DCQ4QmODiYy3cxFiqVSieXx8rKihOe9s7EAPY5nzVrFpKTk7mLKUWXt956C0VFRTh8+DAAYOPGjdi8eTNu3LghyNcgH1Cx4RE/Pz+8++67ePfdd/leislw5swZlJSU4OWXX9a5nWEYlJaWctEIp0+fRnBwMMRiMRISEjBo0CBBvOn5FJq2qNVq3Lt3DzKZDJWVlTA3N+fmeJycnGBlZYXjx49jxowZ2LZtG2bOnCmI51CIZGdnIzIyErdv34a3tzeCg4Mxbdo0rFixgu+lCQYqNjzi5+eH5uZmKJVK+Pj4YObMmVi0aBHtgushDMOgsrKSEx7i1UWEh6/26PLycly5ckUQQtMWjUaD6upqyGQy7Nu3D5s2bcLw4cORnZ2NTZs24fXXXxek0AipFB0REYGXXnoJEydOxKhRo1BUVCT4gDtjQsWGRzZu3Ijw8HA4OzsjIyMDH374IV577TVs3LiR76X1GhiGQXV1Nfbv3w+JRIJjx44hICCAK7UNHz7cKAOgZWVluHr1KoKDg+Hi4mLwx+sJKpUKX331FVauXAkHBwe0tLQgNjYWU6dORWJioqBMTYVUit66dSuSkpLwwgsv4Pr160hLSzP6GoQMFRs9s2zZMqxbt67Dn7l69Wq7ZoXffvst3nzzTcjlcupcYCBqa2vx66+/QiKRIC0tDd7e3pzwhIaGGkR4iNCEhISgf//+ev/9+ub8+fNISEjAmjVr8NZbbyEnJ4fbIaanpwtq5y2kUnRtbS28vLygUqnwww8/YPr06XwvSVBQsdEzlZWVuHfvXoc/ExAQ0O7A3ZUrVzBixAjk5+djyJAhhloi5X/U19frZPK4uLjoZPLoQ3hMTWiysrIQHx+PFStWYNGiRYIsnWkjtFL07NmzcejQoQfaoCkmasQpZFxdXbtdj5dKpTAzMzN4KyyFxc7ODtOnT8f06dPR2NiII0eOQCKRIDExEf369dPJ5OmO/1hpaSny8/NNRmguXbqEKVOmYOnSpSYhNADwzjvvPFCKLisr460UXVJSgldffZUKTTvQnQ1PnD17FufOncP48eNhZ2eHs2fPYtGiRYiJiWnXXodiPJqbm/Hbb78hJSUF+/fvh5WVFeLj4yEWizudyUOEJjQ0FM7OzkZYdc/Iy8tDTEwMFi5ciBUrVvAqNKZYiq6urkZ6ejpeeukl5OXl0cpEO1Cx4YmsrCy89dZbyM/PR0tLC/z9/TFr1iy899579FORgFAoFDh58iQkEgn27dsHjUaDuLg4iMViPPfcc+2WQ0tKSlBQUGAyQlNQUICYmBjMnz8fq1ev5n1HY4qlaD8/P1RXV2PFihVYsmSJ0R7XlKBiQ6F0EpVKhdOnT2P37t3Yt28fmpqaEBsbiylTpmDChAmwsbHB5s2b4eDggMmTJ5uE0Ny4cQMxMTGYOXMm1q1bZ5LRDNrs3LkTs2fPRlVVFZycnPheDkULKjYUSjdQq9XIyMjAnj17sG/fPtTU1MDf3x9Xr15FcnIyEhIS+F7iIykqKsKkSZMgFouRlJRkckJDS9GmBRWbx5jNmzdjw4YNKC8vR0hICL788kuMGjWK72WZHBqNBh9++CGSkpLg7OyM+vp6nUweOzs7vpf4AHfu3EF0dDQmTZqELVu2mJzQALQUbWpQsXlM+eWXXzB79mx8/fXXiIqKQlJSEnbv3o2CggLaDddFtm7dimXLluHw4cMYPXo0srOzuWiE4uJiLpNn8uTJD/UfMyZlZWWIjo7Gs88+i+3bt/OS9El5/KBi85gSFRWFyMhIfPXVVwDYT+cDBw7EwoULqet0F/n6668RHByMMWPG6NzOMAwuX77MCc+1a9cwfvx4JCQkIC4uDs7OzkYXnoqKCsTExCAyMhLJyclUaChGg4rNY4hCoYCtrS327NmjYxc/Z84c1NTUYP/+/fwtrpfCMAwKCgq4TJ7c3Fw888wzSEhIQHx8PNzc3AwuPJWVlYiNjcWIESPw008/CcoJgNL7Mb1CLaXHVFVVQa1WP5AM6e7ujvLycp5W1bsRiUQYOnQo/vnPfyIzMxNXr17FxIkTsXPnTgQGBiImJgZbt25FSUkJDPH57/79+4iPj0dQUBB+/PFHKjQUo0PFhkIxMiKRCIMHD8bSpUvx559/orCwEGKxGHv37sUTTzyBCRMmYNOmTbh9+7ZehKempgYJCQnw9fXFzz//LCgjTcrjAxWbxxAXFxeYm5ujoqJC5/aKigp4eHjwtKrHE5FIBF9fX7z33ns4ffo0bt++jZkzZyItLQ3BwcF49tlnsXHjRhQWFnZLeOrq6pCYmAhXV1fs3r273UFICsUYULF5DLGyskJERASOHz/O3abRaHD8+HGMHj2ax5U93ohEInh7e2PhwoU4ceIE7ty5g9dffx2nTp1CREQExowZg3Xr1iE/P79TwiOXy/HSSy+hX79+2Lt3L2xsbIzwr6BQ2oc2CDym/PLLL5gzZw62bduGUaNGISkpCbt27UJ+fv4DZzkUfmEYBvfv39fJ5Bk8eDCmTJmCxMREDBs27IE5mcbGRrz44osAgEOHDqFfv358LJ1C4aBi8xjz1VdfcUOdoaGh2LRpE6KiovheFqUDGIbRyeQ5evQoBgwYwGXyhISEQKFQYPr06WhoaMCRI0dgb2/P97IpFCo2FIopU19fj0OHDkEikeDIkSPo378/zMzM4OjoiBMnTsDR0ZHvJVIoAKjYUCi9hoaGBuzduxerV69Geno6bfagCAraIEDhnVWrVkEkEul8tZdVQumYvn374q9//Svy8/MFIzRr1qzBmDFjYGtr+9BdVnFxMWJjY2Fraws3Nze8//77UKlUxl0oxeDQyS6KIBg+fDiOHTvG/ZkOHfYOFAoFpk2bhtGjR+Obb7554H61Wo3Y2Fh4eHggIyMDZWVlmD17NiwtLfHpp5/ysGKKoaDvaIogsLCwEMyncYr++PjjjwEAycnJ7d5/9OhR5OXl4dixY3B3d0doaCg++eQTLF26FKtWraJzQb0IWkajCILr16/Dy8sLAQEBePXVV1FcXMz3kihG4OzZsxg5cqROu310dDTq6upw5coVHldG0TdUbCi8ExUVheTkZBw5cgRbt27FrVu3MG7cONTX1/O9NIqBKS8vb9ejj9xH6T1QsaHwTkxMDKZNm4bg4GBER0fj8OHDqKmpwa5du/heGqUdli1b9kBDR9uv/Px8vpdJERj0zIYiOBwdHREUFIQbN27wvRRKOyxevBhz587t8GcCAgI69bs8PDxw/vx5nduIZx89w+tdULGhCA65XI7CwkLMmjWL76VQ2sHV1RWurq56+V2jR4/GmjVrIJPJuITY3377Dfb29hg2bJheHoMiDGgZjcI7S5Yswe+//46ioiJkZGQgMTER5ubmmDFjBt9Lo/SQ4uJiSKVSFBcXQ61WQyqVQiqVQi6XAwAmTpyIYcOGYdasWcjJyUFaWhqWL1+Ot99+G9bW1jyvnqJXGIrJI5PJGHd3d2bNmjXcbWfOnGEsLS2ZY8eO8biyzjF9+nTG09OTsbKyYry9vZnp06czN27c4HtZFD0wZ84cBsADXydPnuR+pqioiImJiWH69OnDuLi4MIsXL2aUSiV/i6YYBGpX00s4fPgwxGIxMjIyMGTIEISGhiIhIQEbN27ke2kUCoVCvdF6E2+//TaOHTuGJ598Erm5ubhw4QItRVAoFEFAz2x6EZ9//jlUKhV2796NnTt3UqHpAqdOnUJ8fDy8vLwgEomwb98+nfsZhsFHH30ET09P9OnTBxMmTMD169f5WSyFYoJQselFFBYWorS0FBqNBkVFRXwvx6RoaGhASEgINm/e3O7969evx6ZNm/D111/j3Llz6Nu3L6Kjo9Hc3GzklVIopgkto/USFAoFRo0ahdDQUAwZMgRJSUnIzc3l2kkpnUckEmHv3r0Qi8UA2F2Nl5cXFi9ejCVLlgAAamtr4e7ujuTkZLzyyis8rpZCMQ3ozqaX8M9//hO1tbXYtGkTli5diqCgIMybN4/vZfUKbt26hfLyckyYMIG7zcHBAVFRUTh79iyPK6NQTAcqNr2A9PR0JCUl4ccff4S9vT3MzMzw448/4vTp09i6dSvfyzN5iEdXex5e1L+LQukc1EGgF/Dcc89BqVTq3Obn54fa2lqeVkShUCi60J0NhfIIiEcX8ewiVFRUUP8uCqWTULGhUB6Bv78/PDw8cPz4ce62uro6nDt3DqNHj+ZxZRSK6UDLaBQKWPNPbZfpW7duQSqVwtnZGT4+Pnj33XexevVqBAYGwt/fHytWrICXlxfXsUahUDqGtj5TKGCbLMaPH//A7XPmzEFycjIYhsHKlSuxfft21NTUYOzYsdiyZQuCgoJ4WC2FYnpQsaFQKBSKwaFnNhQKhUIxOFRsKBQKhWJwqNhQKBQKxeBQsaFQKBSKwaFiQ6FQKBSDQ8WGQqFQKAaHig2FQqFQDA4VGwqFQqEYHCo2FAqFQjE4VGwoFAqFYnCo2FAoFArF4FCxoVAoFIrB+f99Q0z9MR/IQwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh__Heave': 3094533.7524889}\n",
+      "{'axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh__Heave': -323009.9292756434}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#really tall  - spar not heaving\n",
+    "h = 100\n",
+    "d = [29, 7, 4]\n",
+    "a = [3, 5, 10]\n",
+    "w = 1\n",
+    "rho = 1023 # density of our special material\n",
+    "zdensities = [40, 10, 10]\n",
+    "rdensities = [15, 10, 25]\n",
+    "tdensities = [50, 80, 200]\n",
+    "config = \"config6\"\n",
+    "heaving = [0, 1, 1]\n",
+    "\n",
+    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "Y__0sy0z_D-7"
+   },
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Green function returned a NaN in the interaction matrix.\nIt could be due to overlapping panels.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 22\u001b[0m\n\u001b[1;32m     20\u001b[0m points[:, \u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m=\u001b[39m Z\u001b[38;5;241m.\u001b[39mravel()\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m#need cartesian here\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m phi_inc \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_potential\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m#rad problem\u001b[39;00m\n\u001b[1;32m     24\u001b[0m regions \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     25\u001b[0m regions\u001b[38;5;241m.\u001b[39mappend((R \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m a[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;241m&\u001b[39m (Z \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m-\u001b[39md[\u001b[38;5;241m0\u001b[39m]))\n",
+      "File \u001b[0;32m~/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/capytaine/bem/solver.py:317\u001b[0m, in \u001b[0;36mBEMSolver.compute_potential\u001b[0;34m(self, points, result)\u001b[0m\n\u001b[1;32m    312\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39msources \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    313\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124mThe values of the sources of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m cannot been found.\u001b[39m\n\u001b[1;32m    314\u001b[0m \u001b[38;5;124m    They probably have not been stored by the solver because the option keep_details=True have not been set or the direct method has been used.\u001b[39m\n\u001b[1;32m    315\u001b[0m \u001b[38;5;124m    Please re-run the resolution with the indirect method and keep_details=True.\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m)\n\u001b[0;32m--> 317\u001b[0m S, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgreen_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresult\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmesh_including_lid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresult\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfree_surface\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresult\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwater_depth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresult\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencounter_wavenumber\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    318\u001b[0m potential \u001b[38;5;241m=\u001b[39m S \u001b[38;5;241m@\u001b[39m result\u001b[38;5;241m.\u001b[39msources  \u001b[38;5;66;03m# Sum the contributions of all panels in the mesh\u001b[39;00m\n\u001b[1;32m    319\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m potential\u001b[38;5;241m.\u001b[39mreshape(output_shape)\n",
+      "File \u001b[0;32m~/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/capytaine/green_functions/delhommeau.py:419\u001b[0m, in \u001b[0;36mDelhommeau.evaluate\u001b[0;34m(self, mesh1, mesh2, free_surface, water_depth, wavenumber, adjoint_double_layer, early_dot_product)\u001b[0m\n\u001b[1;32m    403\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfortran_core\u001b[38;5;241m.\u001b[39mmatrices\u001b[38;5;241m.\u001b[39mbuild_matrices(\n\u001b[1;32m    404\u001b[0m     collocation_points,  early_dot_product_normals,\n\u001b[1;32m    405\u001b[0m     mesh2\u001b[38;5;241m.\u001b[39mvertices,      mesh2\u001b[38;5;241m.\u001b[39mfaces \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    415\u001b[0m     S, K\n\u001b[1;32m    416\u001b[0m )\n\u001b[1;32m    418\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(np\u001b[38;5;241m.\u001b[39misnan(S)) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(np\u001b[38;5;241m.\u001b[39misnan(K)):\n\u001b[0;32m--> 419\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGreen function returned a NaN in the interaction matrix.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    420\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIt could be due to overlapping panels.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    422\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m early_dot_product: K \u001b[38;5;241m=\u001b[39m K\u001b[38;5;241m.\u001b[39mreshape((nb_collocation_points, mesh2\u001b[38;5;241m.\u001b[39mnb_faces))\n\u001b[1;32m    424\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m S, K\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Green function returned a NaN in the interaction matrix.\nIt could be due to overlapping panels."
+     ]
+    }
+   ],
+   "source": [
+    "# Get potentials\n",
+    "# Define the ranges for R and Z\n",
+    "R_range = np.linspace(0.0, 2*a[-1], num=50)\n",
+    "theta_range = np.linspace(-np.pi, np.pi, num=4)\n",
+    "Z_range = np.linspace(0, -h, num=50) #h\n",
+    "\n",
+    "# Create mesh grids for R, theta, and Z\n",
+    "R, theta, Z = np.meshgrid(R_range, theta_range, Z_range, indexing='ij')\n",
+    "\n",
+    "# Convert cylindrical coordinates to Cartesian coordinates for capytaine\n",
+    "X = R * np.cos(theta)\n",
+    "Y = R * np.sin(theta)\n",
+    "Z = Z\n",
+    "# Create an array of shape (N, 3)\n",
+    "points = np.zeros((R.size, 3))\n",
+    "\n",
+    "# Assign the values of R, Z, and y to the array\n",
+    "points[:, 0] = X.ravel()\n",
+    "points[:, 1] = Y.ravel()\n",
+    "points[:, 2] = Z.ravel()\n",
+    "#need cartesian here\n",
+    "phi_inc = solver.compute_potential(points,result) #rad problem\n",
+    "\n",
+    "regions = []\n",
+    "regions.append((R <= a[0]) & (Z > -d[0]))\n",
+    "for i in range(1, len(a)):\n",
+    "    regions.append((R > a[i-1]) & (R <= a[i]) & (Z > -d[i]))\n",
+    "regions.append(R > a[-1])\n",
+    "\n",
+    "# Apply masks to create a blank plot in specified regions\n",
+    "phi_inc = phi_inc.reshape((50,4,50))\n",
+    "\n",
+    "for i in range(len(a)):\n",
+    "  phi_inc[regions[i]] = np.nan\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "rNvMTwcSHgNT"
+   },
+   "outputs": [],
+   "source": [
+    "# Get velocities\n",
+    "vel_inc = solver.compute_velocity(points,result)\n",
+    "velx_inc = vel_inc[:,0].reshape((50,4,50))\n",
+    "vely_inc = vel_inc[:,1].reshape((50,4,50))\n",
+    "velz_inc = vel_inc[:,2].reshape((50,4,50))\n",
+    "for i in range(len(a)):\n",
+    "  velx_inc[regions[i]] = np.nan\n",
+    "  vely_inc[regions[i]] = np.nan\n",
+    "  velz_inc[regions[i]] = np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "-7Zut1alw6zS",
+    "outputId": "5cf0dff0-4741-408a-864a-e37a20e9caef"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot potentials and velocities\n",
+    "# The slicing limits the y-value to 0 because we only care about the x-z (r-z) plane.\n",
+    "plt.contourf(R[:, 0, :], Z[:, 0, :], phi_inc[:, 0, :], cmap='viridis', levels = 50)\n",
+    "plt.colorbar(label='Potential')\n",
+    "plt.contour(R[:, 0, :], Z[:, 0, :], phi_inc[:, 0, :], colors='black', linestyles='solid', linewidths=0.05,levels=50)\n",
+    "\n",
+    "# Add labels and title\n",
+    "plt.xlabel('R')\n",
+    "plt.ylabel('Z')\n",
+    "plt.title('Contour Plot of Re(Potential) using BEM')\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "imag_phi_inc = np.imag(phi_inc[:, 0, :])\n",
+    "\n",
+    "nan_mask = np.isnan(np.real(phi_inc[:, 0, :]))\n",
+    "\n",
+    "np.imag(phi_inc[:, 0, :])[nan_mask] = np.nan\n",
+    "\n",
+    "plt.contourf(R[:, 0, :], Z[:, 0, :], imag_phi_inc, cmap='viridis', levels = 50)\n",
+    "plt.colorbar(label='Potential')\n",
+    "plt.contour(R[:, 0, :], Z[:, 0, :], imag_phi_inc, colors='black', linestyles='solid', linewidths=0.05,levels=50)\n",
+    "\n",
+    "\n",
+    "# Add labels and title\n",
+    "plt.xlabel('R')\n",
+    "plt.ylabel('Z')\n",
+    "plt.title('Contour Plot of Im(Potential) using BEM')\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "def plot_vel(data, title):\n",
+    "  plt.contourf(R[:, 0, :], Z[:, 0, :], data[:, 0, :], cmap='viridis', levels = 50)\n",
+    "  plt.colorbar(label='V')\n",
+    "  plt.contour(R[:, 0, :], Z[:, 0, :], data[:, 0, :], colors='black', linestyles='solid', linewidths=0.05,levels=50)\n",
+    "\n",
+    "  # Add labels and title\n",
+    "  plt.xlabel('R')\n",
+    "  plt.ylabel('Z')\n",
+    "  plt.title(title)\n",
+    "\n",
+    "  plt.show()\n",
+    "\n",
+    "nan_mask = np.isnan(np.real(velx_inc))\n",
+    "\n",
+    "velx_imag = np.imag(velx_inc)\n",
+    "velz_imag = np.imag(velz_inc)\n",
+    "\n",
+    "velx_imag[nan_mask] = np.nan\n",
+    "velz_imag[nan_mask] = np.nan\n",
+    "\n",
+    "plot_vel(velx_inc, \"Contour Plot of Re(Vx) using BEM\")\n",
+    "plot_vel(velx_imag, \"Contour Plot of Im(Vx) using BEM\")\n",
+    "plot_vel(velz_inc, \"Contour Plot of Re(Vz) using BEM\")\n",
+    "plot_vel(velz_imag, \"Contour Plot of Im(Vz) using BEM\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "sa0mkZllZw_V",
+    "outputId": "b67eb2b3-636d-4bcb-b73a-74f5f7a5e070"
+   },
+   "outputs": [],
+   "source": [
+    "save_potential_array(config, phi_inc[:, 0, :])\n",
+    "# WARNING: This overwrites existing files with the same name. Ensure that <config> is correct before running."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(result.added_mass)\n",
+    "print(result.radiation_damping)\n",
+    "print((result.added_mass)[\"Heave\"]/(result.radiation_damping)[\"Heave\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code blocks below are draft code or old code, not to run but may contain useful content for future reference if something bugs out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This code cannot handle non-heaving regions, but matches significantly better for radiation_damping.\n",
+    "# It also underestimates instead of overestimates added_mass wrt to the MEEM file.\n",
+    "\n",
+    "def body_from_profile(x,y,z,nphi):\n",
+    "    xyz = np.array([np.array([x/np.sqrt(2),y/np.sqrt(2),z]) for x,y,z in zip(x,y,z)])    # /sqrt(2) to account for the scaling\n",
+    "    body = cpt.FloatingBody(cpt.AxialSymmetricMesh.from_profile(xyz, nphi=nphi))\n",
+    "    return body\n",
+    "\n",
+    "def make_surface(ztop, zbot, rin, rout, fdensity, tdensity):\n",
+    "    zarr = np.linspace(- zbot, -ztop, fdensity)\n",
+    "    rarr = np.linspace( rin, rout, fdensity)\n",
+    "    return body_from_profile(rarr, rarr, zarr, tdensity)\n",
+    "\n",
+    "def make_body(d, a, zdensities, rdensities, tdensities):\n",
+    "    # top_surface = make_surface(0, 0, 0, a[-1], fdensity, cdensity)\n",
+    "    \n",
+    "    bot_surface = make_surface(d[0], d[0], 0, a[0], rdensities[0], tdensities[0])\n",
+    "\n",
+    "    outer_surface = make_surface(0 , d[-1], a[-1], a[-1], zdensities[-1], tdensities[-1])\n",
+    "\n",
+    "    bod = bot_surface + outer_surface # + top_surface\n",
+    "\n",
+    "    for i in range(1, len(a)):\n",
+    "      # make sides\n",
+    "        side = make_surface( d[i] , d[i-1], a[i-1], a[i-1], zdensities[i-1], tdensities[i-1])\n",
+    "      # make bottoms\n",
+    "        bot = make_surface( d[i] , d[i], a[i-1], a[i], rdensities[i], tdensities[i])\n",
+    "        bod = bod + side + bot\n",
+    "\n",
+    "    return bod\n",
+    "\n",
+    "solver = cpt.BEMSolver()\n",
+    "def rb_solve(d, a, zdensities, rdensities, tdensities, rho):\n",
+    "    body = make_body(d, a, zdensities, rdensities, tdensities)\n",
+    "    body.add_translation_dof(name='Heave')\n",
+    "    body = body.immersed_part()\n",
+    "    body.show_matplotlib()\n",
+    "    \n",
+    "    rad_problem = cpt.RadiationProblem(body=body, wavenumber = w, water_depth=h, rho = rho)\n",
+    "    results = solver.solve(rad_problem, keep_details = True)\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv (3.12.1)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/dev/python/test/excitation_phase.ipynb b/dev/python/test/excitation_phase.ipynb
new file mode 100644
index 0000000..6df71c9
--- /dev/null
+++ b/dev/python/test/excitation_phase.ipynb
@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2894845d",
+   "metadata": {},
+   "source": [
+    "Check that MEEM code for excitation phase is correct by comparing it with WAMIT data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1e7135db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "sys.path.append(os.path.relpath('../'))\n",
+    "from multi_condensed import Problem\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "57f154f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Basic import\n",
+    "df = pd.read_csv(\"data/WAMIT_exc_phase.csv\")\n",
+    "wamit_phases = df[\"excitation phase (rad)\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "aa29c484",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solving and Plotting Functions\n",
+    "\n",
+    "def modify_and_solve(prob, a_matrix, b_vector, m0):\n",
+    "  prob.change_m0(m0)\n",
+    "  a_matrix = prob.a_matrix_from_old(a_matrix)\n",
+    "  b_vector = prob.b_vector_from_old(b_vector)\n",
+    "  x = prob.get_unknown_coeffs(a_matrix, b_vector)\n",
+    "  return prob.excitation_phase(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a8d4f515",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = 300\n",
+    "a = [3, 10]\n",
+    "d = [35, 2]\n",
+    "omegas = [0.02 * entry for entry in list(range(1, 261))]\n",
+    "rho = 1023\n",
+    "heaving = [1, 1]\n",
+    "NMK = [50, 50, 50]\n",
+    "\n",
+    "prob = Problem(h, d, a, heaving, NMK, 1, rho)\n",
+    "m0s = [prob.wavenumber(omega) for omega in omegas]\n",
+    "a_matrix = prob.a_matrix()\n",
+    "b_vector = prob.b_vector()\n",
+    "exc_phases = [modify_and_solve(prob, a_matrix, b_vector, m0) for m0 in m0s]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0e24effa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'MEEM - WAMIT excitation phase comparison')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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vv/46kiTliZRs166djmWzVq1aeHh4FPm6E8iI6acyjp+fH+3atWPRokWkpaWRnZ1Nnz59CtymUqVKtGvXrtC+bW1ti9ROg729PX379mXRokU0bNiQy5cvF+tmn5qaqhMZZGtrq53S0IeXlxdRUVE6iktMTIz2T7Bp06Y63zk4ONCwYUPt9kuXLiUzM5OYmBjOnDmjXd+oUSMWLlyonSp5nJiYGMLDw1m0aBFeXl4EBATQtm3bIu9naTh9+jRAvuM9Pi3YrFkzRo4cyQ8//EDHjh3z3GzOnj1LjRo1dBzNS8uOHTv46KOP2LVrV54bdHJysvYGXlQ0CkKNGjXyfBceHp4nrYGTk1Oe68bb27vIjupVq1bNo9BpIgMvXLhAQECAdn1uBVEzDqAzVlxcHB988AGbNm0iJSVFp71GyQsLC+O1117j66+/ZuHChbRo0YLu3bvz7LPPao/X6dOnkSSJatWq6ZU7t3LxOLdu3SIlJaXQtAoXL16kUaNGedZHRERov8/dx+P7r5E1KCgoz3q1Wk1ycrLOVIy+falevTppaWncunWLgIAA0tPTmTJlCvPmzePq1as6/iz6lOTcEY/50apVK3r37s3EiROZNm0arVu3pmfPngwcOFAbIXXx4kUCAwNxd3fP91jk5vFjAcW77gQyQqkRMHDgQIYPH86NGzfo1KlTiUNsDSXLrFmzmDBhArVr1yYyMrLI23711VdMnDhR+zkkJKTQZFnNmzdn1qxZJCUlsWPHDp1Q6KZNmzJ37lwyMzPZvn079erV03nSX7hwISDf+PVx7tw5KleurPe7gQMHMnPmTNzd3Xn66ad1/HSMiVqtBmS/mtw3Vw2PKycZGRlah9uzZ8+SlpaWxwfBkJw9e5YnnniC8PBwvv76a4KCgnBwcOCvv/5i2rRpWvmNiSmjXPIbS3PjTUpKolWrVnh4ePDxxx9TpUoVnJycOHjwIG+//bbO8Zg6dSpDhgxh1apV/PPPP4wdO5YpU6awe/duKlWqhFqt1uZS0jeum5ubcXayAPLb/8KOS3EYM2YM8+bNY9y4cTRp0gRPT09UKhX9+/fXez3ltuzkh0ql4vfff2f37t2sWbOG9evXM2zYMKZOncru3btLdCwNuc9lGaHUCOjVqxcjRoxg9+7dOg6KStC8eXOCg4PZsmULn3/+ebG2HTRokI7Zuyh/Ts2bN2fmzJls3LiRQ4cOaaNyQFZq0tPT+fPPPzl37hy9e/fWfnf+/Hl27tzJyy+/TKtWrXT6VKvVPPfccyxatIgPPvhA77gDBw5k/PjxXL9+nV9++aVY+1kaNOZtf3//IlnRPvroI44fP85XX33F22+/zTvvvKPjQF6lShX27NlDZmZmgU/6j5Pf1NSaNWvIyMhg9erVOk+u+qZGijq9FRISAsDJkyfzWKhOnjyp/d5QnDlzBkmSdOQ7deoUQLHD7rds2cKdO3dYsWIFLVu21K5/PDGkhpo1a1KzZk0++OADdu7cSbNmzZg1axaffvopVapUQZIkwsLC8uSUKgw/Pz88PDw4duxYge1CQkI4efJknvWaaTJDH2uN5TE3p06dwsXFRWtt+/333xk8eDBTp07Vtnnw4EGpEj9qaNy4MY0bN2bSpEksWrSIZ555hiVLlvDCCy8QEhLCxo0buXfvno61xljHQiAjfGoEuLm5MXPmTCZMmEC3bt0UlUWlUvHdd9/x0Ucf8dxzzxVr28qVK9OuXTvtkp8FJTcaJejrr78mMzNTx1ITGhpKhQoV+OKLL3TawiMrzVtvvUWfPn10ln79+tGqVSttG31UqVKFb775hilTpuhMaRmbjh074uHhweTJk/X6AuQOJ96zZw9fffUV48aN4/XXX+fNN99k+vTpbN26Vdumd+/e3L59m+nTp+fpq6AnTE0en8dvLJqn1cenCObNm6e3j6LcmOrXr4+/vz+zZs3SCQv++++/OX78uMGjzq5du8bKlSu1n1NSUvj555+pU6eOXutYQeg7Hg8fPmTGjBk67VJSUvL4m9SsWRMbGxvtPj/11FPY2toyceLEPOdGkiTu3LmTrxw2Njb07NmTNWvW6C3toOmvc+fO7N27l127dmm/u3//PrNnzyY0NLRYlteisGvXLh2/mMuXL7Nq1So6dOigPXa2trZ59vf7778vUoh+fiQmJubps06dOgDa4925c2eys7Pz/DamTZuGSqUqMEmnoOQIS40AgMGDBxe57alTp/SWNChfvrxOyGpWVla+pQ969eqVb4K6Hj160KNHjyLLUxqCg4MJCgpi165dhIaGEhgYqPN906ZNWb58OSqVSkdJWrhwIXXq1Mkz96+he/fujBkzhoMHD1K3bl29bV555RXD7UgR8fDwYObMmTz33HPUrVuX/v374+fnx6VLl/jzzz9p1qwZ06dP58GDBwwePJhq1aoxadIkACZOnMiaNWsYOnQoR48exdXVlUGDBvHzzz/z2muvsXfvXlq0aMH9+/fZuHEjo0aNyvc8apw733//ffr374+9vT3dunWjQ4cOODg40K1bN0aMGEFqaio//vgj/v7+XL9+PU8fM2fO5NNPP6Vq1ar4+/vr9RWyt7fn888/Z+jQobRq1YoBAwZoQ7pDQ0N59dVXDXqMq1evzvPPP8++ffsoX748c+fO5ebNm3oVs8Jo2rQp3t7eDB48mLFjx6JSqfjll1/y3FA3bdrEyy+/TN++falevTpZWVn88ssv2Nraai2MVapU4dNPP+Xdd9/lwoUL9OzZE3d3d86fP8/KlSt58cUXeeONN/KVZfLkyfzzzz+0atWKF198kYiICK5fv86yZcvYvn07Xl5evPPOO9oUEWPHjsXHx4cFCxZw/vx5li9fbvBp1ujoaDp27KgT0g3oTEN37dqVX375BU9PTyIjI9m1axcbN24sVZj0ggULmDFjBr169aJKlSrcu3ePH3/8EQ8PDzp37gxAt27daNOmDe+//z4XLlygdu3a/PPPP6xatYpx48bpTXcgMAAmjrYSmAG5Q7oLorgh3bnDawsK6SZXaGTukO6CMEZIt4YBAwZIgDRw4MA833399dcSIEVERGjXHThwQAKkDz/8MN8+L1y4IAHSq6++KkmSbkh3QWDkkG4Nmzdvljp27Ch5enpKTk5OUpUqVaQhQ4Zow2M1ocV79uzR2W7//v2SnZ2dNHLkSO26tLQ06f3335fCwsIke3t7KSAgQOrTp4909uxZnf3KHdItSZL0ySefSBUrVpRsbGx09mH16tVSrVq1JCcnJyk0NFT6/PPPtaHjuffzxo0bUpcuXSR3d3ed6+/xkG4NS5culWJiYiRHR0fJx8dHeuaZZ6QrV67otBk8eLDk6uqa53hpzl9haH4z69evl2rVqiU5OjpK4eHhea7v/H6D+mTfsWOH1LhxY8nZ2VkKDAyU3nrrLWn9+vU67c6dOycNGzZMqlKliuTk5CT5+PhIbdq0kTZu3JhHxuXLl0vNmzeXXF1dJVdXVyk8PFwaPXq0dPLkyUL37+LFi9KgQYMkPz8/ydHRUapcubI0evRoKSMjQ9vm7NmzUp8+fSQvLy/JyclJatiwobR27Vq9+1nU46Lv96P5rfz6669StWrVJEdHRykmJibPeU9MTJSGDh0q+fr6Sm5ublLHjh2lEydOSCEhIdLgwYMLHTv3d5rr7+DBg9KAAQOk4OBgydHRUfL395e6du2qE14uSXI4+6uvvioFBgZK9vb2UrVq1aQvv/xSJ4w/9748zuMyCgpHJUnCC0kgEAgMQWhoKNHR0axdu1ZpUawelUrF6NGj9U59CsouwqdGIBAIBAKBVSCUGoFAIBAIBFaBUGoEAoFAIBBYBcKnRiAQCAQCgVUgLDUCgUAgEAisAqHUCAQCgUAgsArKVPI9tVrNtWvXcHd3L3EFYYFAIBAIBKZFkiTu3btHYGBggUkcy5RSc+3atXwzwAoEAoFAIDBvLl++TKVKlfL9vkwpNZqiYpcvX8bDw0NhaQQCgUAgEBSFlJQUgoKCdIqD6qNMKTWaKScPDw+h1AgEAoFAYGEU5joiHIUFAoFAIBBYBUKpEQgEAoFAYBUIpUYgEAgEAoFVUKZ8aopKdnY2mZmZSoshEBQJe3t7bG1tlRZDIBAIFEcoNbmQJIkbN26QlJSktCgCQbHw8vIiICBA5F8SCARlGqHU5EKj0Pj7++Pi4iJuEAKzR5Ik0tLSSEhIAKBChQoKSyQQCATKIZSaHLKzs7UKTbly5ZQWRyAoMs7OzgAkJCTg7+8vpqIEAkGZRTgK56DxoXFxcVFYEoGg+GiuW+ELJhAIyjJCqXkMMeUksETEdSsQCARCqREIBAKBQGAlCKVGIBAIBAKBVSCUGgtn1qxZuLu7k5WVpV2XmpqKvb09rVu31mm7ZcsWVCoVZ8+eBWDXrl3Y2trSpUuXPP1euHABlUqFra0tV69e1fnu+vXr2NnZoVKpuHDhgk772NhYJkyYgEqlKnARCAQCgcDQCKXGwmnTpg2pqans379fu27btm0EBASwZ88eHjx4oF2/efNmgoODqVKlCgBz5sxhzJgx/Pfff1y7dk1v/xUrVuTnn3/WWbdgwQIqVqyYr0xvvPEG169f1y6VKlXi448/1lknMAHZD0GSlJZCIBAITIZQaiycGjVqUKFCBbZs2aJdt2XLFnr06EFYWBi7d+/WWd+mTRtAtuYsXbqUkSNH0qVLF+bPn6+3/8GDBzNv3jyddfPmzWPw4MH5yuTm5kZAQIB2sbW1xd3dXWedwMjcOwO/e8K+l5SWRCAQCEyGUGryQ5Ig674ySzGfrtu0acPmzZu1nzdv3kzr1q1p1aqVdn16ejp79uzRKjW//fYb4eHh1KhRg2effZa5c+ci6Rm3e/fuJCYmsn37dgC2b99OYmIi3bp1K+mRFZiCm5sh+wGcmQ3JJ5SWRlAYkgSJsZCVprQk5o+khrsHQS3SFwjyIpLv5Ud2GvzmpszY/VLBzrXIzdu0acO4cePIysoiPT2dQ4cO0apVKzIzM5k1axYg+89kZGRolZo5c+bw7LPPAvDkk0+SnJzM1q1b8/jh2Nvba5We5s2bM3fuXJ599lns7e0Ns68C42Dv8ej98S+g8VzlZBEUzvX1sKUT+LeEdluVlsa8OT4VYt+CaiOhwQylpRGYGcJSYwW0bt2a+/fvs2/fPrZt20b16tXx8/OjVatWWr+aLVu2ULlyZYKDgzl58iR79+5lwIABANjZ2fH0008zZ84cvf0PGzaMZcuWcePGDZYtW8awYcNMuXuCkpDb6nb+F7h/STlZBIVzaZn8mvAfqLOVlcXciZskv56eqawcArPEYiw1M2fOZObMmdpom6ioKMaPH0+nTp2MM6Cti2wxUQLb4mU1rlq1KpUqVWLz5s0kJibSqlUrAAIDAwkKCmLnzp1s3ryZtm3bArKVJisri8DAQG0fkiTh6OjI9OnT8fT01Om/Zs2ahIeHM2DAACIiIoiOjiY2NrZ0+ygwMupHb6UsODUdYr5QThxBwfjUg3M51rS7B8C3obLymDPetWXlD2TlXURTCnJhMZaaSpUq8dlnn3HgwAH2799P27Zt6dGjB3FxccYZUKWSp4CUWErwI23Tpg1btmxhy5YtOlNILVu25O+//2bv3r20adOGrKwsfv75Z6ZOnUpsbKx2OXz4MIGBgSxevFhv/8OGDWPLli3CSmMpPO4fdf5n4YNgzqhy/RXf2KCcHJaAZ/Sj9w9uKCeHwCyxGKWmW7dudO7cmWrVqlG9enUmTZqEm5ubTnRPWaZNmzZs376d2NhYraUGoFWrVvzvf//j4cOHtGnThrVr15KYmMjzzz9PdHS0ztK7d+98p6CGDx/OrVu3eOGFF0y1S4JSkaPU+LcGJ394cBOurVNUIkFBPFJCpev/KCiHBWDr9Oj93UPKySEwSyxGqclNdnY2S5Ys4f79+zRp0iTfdhkZGaSkpOgs1kqbNm1IT0+natWqlC9fXru+VatW3Lt3Txv6PWfOHNq1a5dnigmgd+/e7N+/nyNHjuT5zs7ODl9fX+zsLGbGsmwj5Uw/2ThAqOwQrp3eEJgfOZa1uVuG4vvUCnZtu6+wQGZMbitkYtlSav7+G8aPB7W68LZlFYu6Qx09epQmTZrw4MED3NzcWLlyJZGRkfm2nzJlChMnTjShhMoRGhqqNyQ7JCREZ/2aNWvy7aNhw4Y6bfX1p6FOnTo63+c3PqD1gxKYkpxzobKBykPhxNdwdS2k3wTn8gVvKjA9OUro8z/KimfTliJvYv7Ix+pmsj9xGzNpG11Icyuic2f5NSgIhg9XVhZzxaIsNTVq1CA2NpY9e/YwcuRIBg8eTHx8fL7t3333XZKTk7XL5cuXTSitQKAgGksNKvCKhnKNcxyGv1dULEF+yBqMs4PIU1MoOdreE5P/5YlXJ7BsmcLyKMDGjUpLYL5YlFLj4OBA1apVqVevHlOmTKF27dp8++23+bZ3dHTEw8NDZxEIygYaS02O03nkW/LrqemQab3TsBZLjhLavPp27ap8KpcIciw1cVdkE820r7MKamyVxMeVvX0uKhal1DyOWq0mIyNDaTEEAjNEM3eR8xOv1AM8IiAzWeT3MEvk8zXxheXaNVs3PcivcdnmsXm5g4cs+jZWIsYNKlu+RMXBYnxq3n33XTp16kRwcDD37t1j0aJFbNmyhfXr1ystmkBgfmimnzSWGpUNdyt+xDtfJ2PvaM8Pax+CrYNy8gkeQ75RN2lwn7XvD8E28wbNm7wDtFZUKvNEPlaDWizg522DycgoO0pNsN9VLt2qSM2KB4EGSotjlliMUpOQkMCgQYO4fv06np6e1KpVi/Xr19O+fXulRRMIzBDN0+yjnEep3k/x42Z7HO0f8M3Fv7EL6yHylpkLuXygunTKhIvr4X5jhFKjh5xj9f2QN/hl+3NIkg23boGfn8JymYCDU7uRlXoVnwotgRFKi2OWWIxSk1/+FIFAoAcpV/RTDmrkel0ZmU4EN2jByj+hcWMlhBPkRT5fO45U59rDRtRT76LyrW0Ky2SuyMfKIziK97pPJigwDQeHyQrLZBrKedzl0OVAVi2vQbPyEBWltETmh8UoNQKBoDjkin7KIbcrwo27Pkz/Np3GjZ1NK5ZAPzkn54uFXVi9vS5DWn6I36Yk2jzIolMX8Teti3ysTqR0pm+jRVQpfwY3x3GAv6JSmQRJ4rPV7/Dbnqf5IvghUVFiCvlxys5kpEBQltBjqXk878lvvztwQ2SZNxNkJVTKUUJXH+zBl2teZdnCO0oKZZ7kTD/1eXsIdd6LZc+ZRnBnn8JCmYb3fn2T3/Y8DUBcrIhi1IdQagQCqySvT41GqXFzfUiTajvJzLJl9v9ERWizIOfkqNXyX/ITDU4CsHmreBLPi3ysJEm+tg9djOHn+WUjUmzev321741V9tDSEUqNQDFOnjxJQEAA9+7dU1oUs+X27dv4+/tz5cqV4m34ePQTj1Kr29ja83KXXwCYNSODTFHn0gzQtdS0aC5ha5PFhWveiITcj5FzbWuUmjcXfcXgD3pz65aSQpkGtfTolh1/2kOUS9CDUGosnCFDhqBSqXjppZfyfDd69GhUKhVDhgzJ0/7x5cknn9S2CQ0N1dvms88+A+SyByqVCltbW65evaoz5vXr17Gzs0OlUhVaHuHdd99lzJgxuLu7A7BlyxZUKhVRUVFkZ+taELy8vJg/f34eGZcsWZKn36ioKFQqVZ7233zzjfazJEm88cYbeHh4sGXLlgLlLCnz58/PcwydnJx02kiSxPjx46lQoQLOzs60a9eO06dPa7/39fVl0KBBfPTRR8Uc/bE8NeSakVKp6DM4jACv61xPcGHFcvHPqDiSrvXBPbA6DSrLUypbNoosw7rkWLUk3dC9A/utv66ElGuf0x44CIVXD0KpsQKCgoJYsmQJ6enp2nUPHjxg0aJFBAcH52n/5JNPcv36dZ1l8eLFOm0+/vjjPG3GjBmj06ZixYr8/PPPOusWLFhAxYoVC5X50qVLrF27Vkfh0nDu3Lk8/eojKCiIefPm6azbvXs3N27cwNXVNd/tsrOzef755/n555/ZvHkzrVu3LnSskuLh4aFzDC9evKjz/RdffMF3333HrFmz2LNnD66urnTs2JEHDx6Z04cOHcrChQu5e/du0QfWY6kJC4PLl+HYMXCIHMGIdgsA+H5qYsl3UGAgcpSaHEuNjYsvbeocBmDzutuKSWWWPKYAVvKRy98c2G3917FGkbO3fQjIv2WBLkKpsQLq1q1LUFAQK1as0K5bsWIFwcHBxMTE5Gnv6OhIQECAzuLt7a3Txt3dPU+bxxWFwYMH51Eq5s2bx+DBgwuV+bfffqN27dp6FaAxY8bw0UcfFZot+plnnmHr1q06Nb3mzp3LM888k2818YyMDPr27cvGjRvZtm0b9erVK1TW0qBSqXSOYe4K6pIk8c033/DBBx/Qo0cPatWqxc8//8y1a9f4448/tO2ioqIIDAxk5cqVxRg5r0+NvT1UqiQvOHgy4sVshreZzQ8jxpdqHwUGIEcJVavl86VSQZtWcir8zdvdRHHL3Ginn+SPDcJly+aBven5bWE1aBS5mkFHAYg7LKx4jyOUmkK4fz//5cGDordNTy9a25IybNgwHQVj7ty5DB06tOQdFoHu3buTmJjI9u1yvZrt27eTmJhIt27dCt1227Zt1K9fX+9348aNIysri++/L7j4Yvny5enYsSMLFsgWh7S0NJYuXcqwYcP0tk9NTaVLly7Ex8ezY8cOatSoUWD/CxcuxM3NrcBl27aCc4mkpqYSEhJCUFAQPXr0IC6Xd9/58+e5ceMG7dq1067z9PSkUaNG7Nq1S6efhg0bFjqWDnqinx6nQqN+zH5hBLVdZ0Ha1XzbCUyBfL5ef2YTc+dCkybQrGMY9rYPyXig5rYw1uRC41Qt3+Dr15QtNAcO52+dtRY0lrzXO09l8/uteenpwwpLZH4IpaYQ3NzyX3r31m3r759/206ddNuGhupvV1KeffZZtm/fzsWLF7l48SI7duzg2Wef1dt27dq1eW7OkyfrJq96++23C72B29vb8+yzzzJ37lxAVqSeffZZ7O3tC5X34sWLBAYG6v3OxcWFjz76iClTppCcnFxgP8OGDWP+/PlIksTvv/9OlSpVqFOnjt62n3zyCbGxsWzbto2goKBCZezevTuxsbEFLvkpZiBXlZ87dy6rVq3i119/Ra1W07RpU63T742ceOrc1hvN5xuPxVoHBgbmmboqmLx5aq5fh3Hj4MMPc1Z4VAO/FvKT7/nCp/sERiTH+tCh8QmGDoWqVcElpDmnvg7nxg9++LldV1hAMyLnWI3sf4R33oFuneWpmEvXvaxe+dNEx9WPvknryK14I2pAPY7I6mQl+Pn50aVLF+0NvkuXLvj6+upt26ZNG2bO1C1q6OPjo/P5zTffzOPvom+qaNiwYTRt2pTJkyezbNkydu3aRVZW4RVk09PT8zjN5ub5559n6tSpfP7553kUrtx06dKFESNG8N9//zF37tx8rTQAHTp0YOPGjUyePJlp06YVKqO7u7vWibkkNGnShCZNmmg/N23alIiICP73v//xySefFKsvZ2dn0tKKYWqW8k4/3b4N334rK9/a4SsP5fD+ZL6ZW50GvSRGjRZ1E5Qh7/nCwZPQap6QCCRsg5B+SghmhuRYtYYdhPBWcCuYagGnOH2jOgcOQMeOCotnRA5/1YLsjFSCatSFG0CycKp5HKHUFEJqav7f2drqfk5IyL+tzWM2MWN4rQ8bNoyXX34ZgB9++CHfdq6urlStWrXAvnx9fQttA1CzZk3Cw8MZMGAAERERREdHExsbW+h2vr6+JCbm79hnZ2fHpEmTGDJkiHaf8mv33HPP8dFHH7Fnz54C/U6eeOIJxowZQ48ePVCr1Xz77bcFyrhw4UJGjCi4vsrff/9NixYtCmyjwd7enpiYGM6cOQNAQEAAADdv3qRChQradjdv3sxjbbp79y5+xSpuo6dMQl7fYQjuy94LB5j/b292nE9j5CgXUQ9KCXKU0N3HKnMnAerWhQoVkC1pibFICdsguJ84N0CeyD6vWnw18FlcHVNpWHcpoP9hzhoI8b0MmUng+yxLVtpxYGULxkwFPfEgZRah1BRCAUE0JmtbVJ588kkePnyISqWiowkfV4YNG8aoUaPyWH8KIiYmhvj4+ALb9O3bly+//JKJEycWOv5XX33F008/ncfh+XE6dOjAmjVr6N69O5Ik8d133+Xbtnv37jRq1KjA/ooS6aUhOzubo0eP0rlzZwDCwsIICAjg33//1SoxKSkp7Nmzh5EjR+pse+zYseJFaemJftIYb3QUbHs3BjzjxOs/p3D6nAebNsETTxR9GIGhkM/XO9O7sfUgLF0K/fqB5NeCoa/VZd3RrmzdDYW4gZUNcq7tc1c8ybaFoCB3urc+DvdOQ9YhwJqLHOf8iL1qMfWvbuw/14DGT0sEBwttV4NQaqwIW1tbjh8/rn2fHxkZGXl8Nuzs7HSmq+7du5enjYuLCx4eHnn6Gz58OH379sXLy6vIsnbs2JEXXniB7OzsAmX97LPPClXQIiIiuH37Ni4uLkUau127dqxdu5Zu3bqhVquZPn263nalnX76+OOPady4MVWrViUpKYkvv/ySixcv8sILLwByZNS4ceP49NNPqVatGmFhYXz44YcEBgbSs2dPbT9paWkcOHCgwGm4vBSUp0a3pVu9sTzXYiEzNoxk1rd3eOKJcsXfWUHpkHRDujXnSOXfggu3T3AzyZctG9OoUaNo17h1Ix+rVs/15soN2L8f6nnHyEpN4iGoYL1KzfuL3yfzocQ7TaKJrrSb/ecaEHf4Hr375P1fLqsIR2Erw8PDQ6/ikZt169ZRoUIFnaV58+Y6bTQJ4XIvb731lt7+NApRfmHU+ujUqRN2dnZs3LixwHZt27albdu2hfrplCtXDmfnohdnbNu2LX/++Sfz589n9OjRSEaImU1MTGT48OFERETQuXNnUlJS2LlzJ5GRkdo2b731FmPGjOHFF1+kQYMGpKamsm7dOh1/o1WrVhEcHFzkaS6gQEtNnikM12BeGnQNgD/+8uS68ElVAN0yCVprmnPAo3w1/yQpIJcZ8lhGYZUK8I5h2Z4+vDUpguKkc7I0vl77Ml/++Rb30pyJqiKnUD4WK8K6dZDKEMnJyRIgJScn5/kuPT1dio+Pl9LT0xWQrGwyffp0qUOHDkqLYfY0atRIWrhwYYFt8ly/Rz+RpIVI0u4XtG327ZMkkKSgID0dJB2XmlXfJoEkffJhouGEFxSNg29J0kKkZrXPSSBJy5c/+mrrjCkSSFL5cimSWq2ciGbDf09J0kKkCn6pEkjSoUOSJF1bL1ULOCmBJK1fr7SAxsPB7oEEknQx/qL091efSiBJkVVuKS2WSSjo/p0bYakRKMaIESNo2bKlqP1UALdv3+app55iwIABxduwgCrdep1NPcN5qddWAGb/qCJb1Lk0Mbqp/3Ofo0atK+Jkn87NO+7kzC6XbbRTdTIqFeBTn3phBwA4sMd6LRca65SNjYqoWnIOkFMXvHj4UEmpzAuh1AgUw87Ojvfff79UfivWjq+vL2+99RaqYoe95M1TU7MmnDgBmzbp36LP4BDqhe3npXY/8jBD1IMyKY9NqeR25nYMaUvT6jsB2LKxFBk6rQY900+OPtSrIedx2r/beh+StD5XNioq1QjGwzmZrGw7Tp1SWDAzQig1AoE1oidPjZOTHD1TpYr+TZyq9WL/Z215r9ObON8rRvZigQHQrWeko8O6VKRNjJyJevN6669vVCiSbkZhzbGqV1c2Lx6IdVRELFPw6PpQofKKJqqSfF3EHRUPIRqEUiMQWCWFl0nIg50rhDwtvz831/AiCfIn50b9xqBtfP+9bFXLzRNt1dSvvI+Y4H0KCGduaBRA+ZPGqlW3sZxA9OI1L+7cUUIu46OWNI7kKnCrzI8vjuHyd5Xo1/mswpKZDyKk+zEkUTlOYIHkuW6lvNNPFy/C7Nng6wuvvppPR5WH8fDkAlb9lkVqXDpDXyh6RJmgNMjnq0/7OKiT99smHaqzz7EhuIaA1DMfx6gyQs61/WL/k6Ta16FcTgYCz9A6VC1/mjM3q3Fgv0SHjtZ3jDRusCqVDdjYEhWtgrtXIeUYeFZTWDrzQFhqctDUKypWKnqBwEzQXLeP6m7ltdRcvgyTJ0OBORJ9G/P3qeH0+2Yh77yDcEA0FYUVIC3fCmzs4f5FuHfGdHKZJfKxmvT6Ib79FnISc4NPDPXCDgJw5ECSMqIZmWNf1OToZ9GU8825Xjyj5deko8oJZWYIS00Otra2eHl5kZBT68DFxaUEzpkCgWmRJIm0tDQSEhLw8vLKlcgwr09NgdFPGlQqOvcNI3DGVa7dqcgff8iZbQXGRrY+7I+rSGoi1KoFOuXY7FzBtxmpl/ZxenMsMT3K8FO5nhxMANg68ckLS/is/9uE9PwS6Gty0YxNVMU4QAI7ed8zXWvz8bKPiftfBxaug2Kk6rJahFKTC00tnoSCijgJBGaIl5eX9voF9P7xa2o/PV6H7HHsqz/DC21+4uMVHzLz+/v062eEmh4CXXI0zuEf9yT2JKxbl7cwY9y9vtR58R/cXTK4nVT4ebRe5GN1PcEFyUku0KrJ+1mtZgCcuQh390GI9Sk1j1tg7cpFMnNjPe6k+nLiBMTEKCiamSCUmlyoVCoqVKiAv78/mZmZSosjEBQJe3t7PaUmil4mIQ/OFRje/xyfrsxmy3ZXTpyA8HBDSSvQj26ZBH0KS40G4Tjbp5N4z4PDsRIxdcuoJTnnQq7Rvif3UuH0adDW3vWpK78mHlJGNiOSnSXxyfKPUCHxVicVzk6g8pYjoP470YpjRzKJibEvvCMrRyg1erC1tS2wHpFAYPYUp0yCHiq1HES3umtYdaAn//vhPtO+F9Yao5Jzvh4PU86NXUAjWoRv5a/Yzmz5J5GYuj55G5UJ9OSp0eAdw+xNw1l3tDuvT5Vo1tx6FL/sLDUTV0wA4JUZd3EGcA4kOuRv/jvRirhDiTDYX0kRzYIya8AUCKybvD41RZ1+AsC/tTbD8PwFNgj/eWOjP0xZBztXWteTQ3c3l+UkfI/lqdE5Vl7RbIp7gpV7u7JtU7ICwhkPtfpRhKN2n1UqoqqlAHDsiIm8+s/Mhn+awoPbphmvmAilRiCwRopbJuFxVCo6PNeWML9zRFQ4wrUL1pul1TzQlEnQhOzqb9WmtZxg7r895cpwKQuNpUb+pHOsbJ2oF3kVgP1WVi4hd9oGlc2jnY6Kkl/jTpqogvveEXB7Fxx+xzTjFRMx/SQQWCN68tQ0agQHD8qZhYuCTVAXDn7XBC/1XrCfBYwwuJiCHKQCbtS5iGkRhqdLEsmpXsTGQr16phHPrJAKtmrVq5MBwIFY6woFktSPsgarcj2sRNWRa0BduOZDaiq4uZlIoBTzrM0gLDUCgVWS11Lj7i5HR0REFLELlQ1etXPiuUWGYSOjWyYhvylC24DmNK++HYCtG8uq9Sz/4p8AdRt5AnDhmrdVZRbWmX6yfbTTviFhlPe8gbNDOhcumFCg9KsmHKzoCKVGILBK8vrUlIjQZ0Flx92Lp9mx7lyppRLkQ46l5o0he/n8cwgLy6edYzle7PYP3w9+mR6Nt5pOPnOigOKfAF4hkVQtfxqQLZPWQn6WGjzCOTQphntzvYiONGHUbvo1041VDMT0k0BgjeiJfjpzBn77DQIDYciQIvbjXJ7998bS/OVJeLpncuUG2IuoUSMgK6HP9zkKkZ0LbNm9pwOcmAqOaUBXE8hmbsjHali/C2Q5Vcb18cA87zrUC1snl0vYm0b79ibyNTEyuS01uX1qcKlEBb97kPVQzjbtWVRTbCnJfmCacYqJsNQIBFZJ3jw1J0/C++/DDz8Ur6fa7Vvj5ZJEwl131q7OMpyIgkdIec9XvlToIL/e2JBru7KEvM+zpsTy00/g5fXY1w5e1KtxHkf7ByTduGVy6YyFq4vEno8bsntiIxwccik1KhV45CSSSjmhjHBmhFBqBAJrpIA8NcXNRGsf8iRD2iwD4KcZ5hnGafHknK/DJ/3Ztw9SUwto69eCS3er8tOfHfnnj8umkc+c0OME/zijBhzh3k/ufDZquWlkMgF2dmoaVtlHo6p7sbHV/RFfy2jCwOkLadevjvEFcfR99N4MrTVCqREIrJL889QUu6SZjT3PD5HDY9dt8eNyGbyPGh/5fPUa24OGDeHYsQKa2jmzMPYdhv/0E/+bWQYrjkoSkgSJyY4kJT26rnPjGhiJvV2WdWUW1rHK6f6IXf1DWLxrIP/uDiMpychy2Hs+ep92xciDFR+h1AgE1khp89Q8RrXW3WgVsQW12pb5PxZkRhCUiEKcXx+nZWs5XPm/vX5lcAZKjSSp8KnZGW9v9Ec4+eQUQbIipSY9Xc2Xa9/gqz9fR3rs1u1ZqTKVfOSnjbg4Iwsi5dIi08zvCUcoNQKBVZLXRF/S6ScAPCN5oesWAObMydb7dCwoDbpZcgtTPBu0j8LZIY3byZ4cjytjfk6SpK2RBfkcK+8YZm0cQa2Ri/jqC+uo45d6D95a/CVvLvqKPFNvHhFEV5LNe8eOGlnLlXJlfRSWGoFAYBIkA04/5dD7uUp4uiRx9aYLR4+UOfOAkdEtaFnYOXLwi6ZJ9X0AbF1nnvlCjIektWhBPkq6cyApmRU4erkWe3ZYRz4fneinxy8QtypEB8UDEHfYyJZUHaVGWGoEAoFJMOz0E4Bzjb78/upArnxfidqV9pVSPoEOjxW0LNSaZmNLq/ryDWXrJvNz1jQqklpHqdF7PatU1Kst18c6cNA6Mpdo8tSoVGqd3zUAtg5EVZWd+I1eAyq3UnNfKDUCgcAU6Il+atUKtm2D6dNL2KeDJ+06+1DeM0FkGDY4uqn/i6J4tmolN9q6x7+M+dVI2hpZkL8CWLeBnJ/m/BUP7t41hVzGRTt9rFLrvUCio+TffNzJItZBKbEgYvpJIBCYnLx5T3x9oXlzqF27FN1WHia/XlzM/WTrKhioKNp6RkW01AAN21bGwS6Dm4meXLpYhrSaolhqAO+QcML85CzYsbEmkMvIqLM1lhr95zqilicOdhn4eiQVnBKg9JI8eiumnwQCgWko5VxTfpRvzamkNrT+aBWNGz4sYxYCYyLfKF4bdpjx46F8+cK3cK5Yl3/f78Sd/5UjxMc8iwsaB11H4XwVQO861A2V6yQcPGD5Jc01VbptVPq99F0Dw0md40bcjB7GLWqpFj41BmHKlCk0aNAAd3d3/P396dmzJydPnlRaLIHAPNGToOzkSfjmG1i5shT9qmzwr92BvWcbcuyUF3v2lEZIgZacG9Ybzx9l4kQICCjCNraONG+ejbdrEiRsM6p45oWEjUrNgF436N8f7PJzmXGvRt0qcnzzwT0pphPPSDxy9M/nScIzWs7Nkxyvq3gYmtzTTw/vQqZ5pXiwGKVm69atjB49mt27d7NhwwYyMzPp0KED9+/fV1o0gcD80JOn5sABePXV4pdJeByv2v3p2ygnw/BM64gsUZ4Shqb5NZdfb5UhpUZS42j/kEUz4li8GJzycyGxsaV+nfuEBx4nyOeSSUU0Bv5+mWx6rw3r3smn3pdbFbB1gux0uH/eeIJIjylM904bb6wSYDFKzbp16xgyZAhRUVHUrl2b+fPnc+nSJQ4cOKC0aAKBGZLXUqN50itRnprcuIXywlNyUrMlyxy5J/Sa0pOjhB4/401cHGRkFHE7vxZ8vOJDWr4wmvNGvI+ZF3kV9vzo0NGW419G8vnzpdTkzQAnR4k2UVtoHZmPAmtjy66rT9Pgw7106m7E+SeNUuMSLL+mHDfeWCXAYpSax0lOTgbAx8dHYUkEAjNET56a0oZ056Z59wZUCzjF/XQHlv8uMvGVHvnkNOvfnehoOHeuiJv5NWXdkSfZFt+Qrf8kGk88c0JSI0nw8KENmZmF1PQs11B+vbPXJKIZF401L//btotvJfafa8Ceg55G9HfLkcMzSn41syKaFqnUqNVqxo0bR7NmzYiOjs63XUZGBikpKTqLQFA2MHyemtyogp9iSOslAMz/Mbn0HZZ1tGUS5I9FtqbZe9CqjnxT2bqxjCg1SCSleeEY3AoHB8gqKKFyjlKjTowj5a5luyokJ8EP/4zip01D821To6YPNqpsElOcuX7dSIJoLDVeOffeZGGpKTWjR4/m2LFjLFmypMB2U6ZMwdPTU7sEBQWZSEKBQGH0OAobbPoJwM6Z5wamo1Kp2brLu+iWBUE+6IZ0F0fxbNVcnqvautPD4FKZJZJUpJBuAFwq8tP21/F84S6vjDYvh9biknDLhpcX/MDrv0zJt41T+UiqBcg+LkapASVJj/5btJYaodSUipdffpm1a9eyefNmKlWqVGDbd999l+TkZO1yWZQXFpQZjGupAQhq0ou3u37OL6OHEuAjrDWlQpNRuATnqFm7CtjaZHH+mm/ZqKBexDw1GvwqlSP1gTsHD1nc7U4HTZkEG5sCpnu9oomqJGszx44YoSZY7mKWGkvNvdOgNp/6YxZzliVJ4uWXX2blypVs2rSJsLCwQrdxdHTEw8NDZxEIygaGr/2Uh3INmDLiV55tOh+XWwVbTQWFUfzkexrcw5po87H8t8myp1iKRtEyCmuo29AdgLgzPjyw4IoSmjw1Bf58nSsSFXwWgLhYI7hb5I58cg0DW2dQP4RU8/FStxilZvTo0fz6668sWrQId3d3bty4wY0bN0hPT1daNIHA/NBTJqFjR1i3DiZONNAYKtWjDMPn5hmo0zKKNqOw/LFYiqdzeVrVPgLA1n/uGFgwc6QY009Apcga+LrfIjvblmPHjCyaEZFyLDX55qmRvyQ6XJ5mi4szhgN/rj5t7MCjhvzejJyFLUapmTlzJsnJybRu3ZoKFSpol6VLlyotmkBghuSdfqpUSVZs6tUz4DChz3LrXnm+XNCCie8mGLDjskYxC1o+Rqum9/F2vYuTugzMP0lqHUtNYah8Yh5lFt5nuQ/B2umnfDIKa4iOtqVK+TNUqWCEayG3pUZlCx7h8nsz8quxmPKlksjHLhAUHT2OwkbBuTynswfz1uLPcXXO4PX3MW6Kdmsl5/9t3LCTPHSOorgz5U929+J2gC82/k2AHYaXz6yQijdN5+RL3Wqn+edoRw7uToKRzkaVzlgUyVIDRNb15czX1SCwK7DGwEI8rtREyO+FpUYgEBiXvD41x4/DTz/Bv/8adqQm3Zrm5KxxZPky83EYtCzk8zX5zaN89RUUN/2WXYXm2NhIcHcfZFmuNaJISGoc7TPo0SmR7t2LtkndmvKUjCU7C6uLqNTgVVN+TTbCXFt+lhozCuu23DMsEAjyR0+ZhC1bYPhwmDHDsEOpKnZmcJvlAMz/McmwnZcV9PhAFQu3yuBcASk7k6Rz1p5lXcLHLZE/fj1X5Dpm9Rs60KXOWno0tVwrVmjQQ9a+0YVfx44quGFOqLWUeoH0FAOn+1Y/ptR4aiw1xwvJgmg6hFIjEFgjeqafip3YrajY2PPcM9moVGq27PLlwgUD918mkE/OhStuXLhQSEI5fahUHLg7iJBXLtKscw2DS2dW6MmWXRhhNcNY+2Y33u/xiXFkMgEeHtl0ifmL9rX/K7ihYznm73qFciPuMGK4ocO9cvvzqMC9mvzglJkMD24aeKySIZQagcAqyWupMXhIdy6Cm/WibeQmAH6eY9lJzhQhRwmt0rIzYWFw61bxuwiJqsrlO8HEn/Ur0faWQwn8xbxj5NfkOMh+aHCJTIJUeJkEDZ5+3iTe9yE+3tAyZD+SQaWSC2i65qRXMRNnYaHUCARWiXFrP+XBK4ohneVCe/PnZ2kVKEFRKUVIdw6+NeoTVUn2o/hvS3YhrS0YSeLirWDsy9fBy6uI27iGgL0X1++UI37vWWNKZzTu3LVh/tbB/L4rnyrduYiMdgTg+BkPw/4WtUqN7aN1ZuYsLJQagcAa0eOjYUxLDUCvZ4Lx97hJo8o7uZdiHvPrlkPJk+9p8axJq8hdAGzdYM35auSQ7qwsFZmZRdxEpWLp4VcJfPk6I8Z6G1U6Y3H+gh1DZ8/ntfmFJ5qqEhWAg10GaQ8cuXjRgELoU2o0fjVm4iwslBqBwCrRKBV5yyQY3KcmB9eIPlyeUY3FI7rgmbXPOINYKzmVpzWUSPG0saVV49sA/LfNyKH8SiJJSBRf+atZW7ZeHIrztkhLovb3W0ieGgC7cuHUqHASwLBTUHotNeaVq0YoNQKBNaJnHsPYlhocPHEIy4mxFRmGi0dxijQWQMu2rgAcOVmORKst2i2hVsu3ruIcpxoxFXF2SON+uiOnTxtJNCNS5JBuAI9wbQ2ouCMGdBbWBiDkUh3E9JNAIDA+eZ0pu3eHFStg3DgjDlt5KABx2w+xf7cFF9oxObpZckuq1ARE1KVGhRNIkg3b/rPSKUBJXSJLja1vHeqExAJw8IDlmWo0CWhtiqLUOHgRGSJnFI4/YkDHfb3TTzmWmrQrkGngEPISIJQagcAa0ZOnpmpV6NULGjQw4rjl2zB7+ztEv76bt15LMuJAVsZjlpoSTxGWa8DglgsZ2/FbQnwN6UxhTpTMUoNHDWJC5RpZh/ZaXlV5TYqYIllqgIa1btOx1jpiqp4znBAapcYml1Lj4A1O5eX3KScNN1YJsZgyCQKBoDiYqEzC46hseLKHP6pZajbvCuDiRQgJMa0IlomESiUxavBFJJcQHB1L2I2tI+8+vxVubYMAN+B5QwppHuTyqSmWUmNjT92oW7ABDu63vLBuKWfqx8amaEpNx3apdAzpBJHvAA0NJIQeSw3IU1APbsp+NeXqG2asEiIsNQKBNaInQVl8PCxaBHv2GHfo4Ga9aB2xBYBF86zWscOwSGrsbLP5YXI8M2aAi0sp+vJvIb8mbDOIaOaHGheHNNq1vk/r1sXbsm5OupqDR93NJQFukdH6xFFEwbUOvAb0dclXqTGfcglCqREIrJK800+rV8Mzz8D//mfkod1CebaTnKr/l1+yLO7moQx5o9VKjF8L0h86sXVzBmctMyVLwUgSwb6X2bD6OitWFG/TqHr+jHtyGt+OmkG2haXyCa+axtIx/Zj2/KSibZDjwHv36jXDOY3rcxQG8MjJYn1PeQ9sodQIBNaInjw1xg7pzk3v50JwtH/A8XN+xB6yPKdM0yMhSZBwx5Fbt0pZRsevKS/N/R+t31/M/B+Vd9w0PCWfWnXwr8W0517juQZTsbMw5wu/cg/p13gZnRsU0QLnEc7wH2dTbuAefpptoEKz+Vlq3KvJr/dOGWacUiCUGoHAKsn75G/0kO5ceEZ1pXu9vwH4dfYV4w9o6Uhq7me4Ur5Wa/z9Ib00hbbtPWgeIzsJb9tihRW79TjBFxnvWoAKHtyA9BsGFcv4FLPmlUslKvkmAAaMgMpXqakuv947k8uaowxCqREIrJK8eWpMaanBzoVne18HYP0/KjEFVSiGyVOjoUUL+STvifUiI6N0fZkfEkcvReNZMYTw8GJuaudKumMttp1ozh+LrxlFOmNxM8GW33b35Z9DzYu2gUpFVPUUAOLjDaRo5KfUuIXK67LTIF3Z4yqUGoHAGtFTpduUlhqAJwfW449Xe3Dgkzqo1CJnTYFIasOEdOdQo0EN/DwSeJDhwIEDpZTN3JDUZKntSEmxISWl+Jsfvt2dlp9sY8SbNSxK2T563JWnv/+N1+e+XeRtIiPlayr+lKth9lVfSDeAjT24VZbfK+xXI5QagcAa0WOiN6mlBnCo0IAeLQ7gqLoLNzaaZlCLRTJI8j3t9v7NaV59O2CNU1BSqWpk1arvhY0qm4S7rly/bmDRjEixku/lUC3aFzvbTFLTHLl82RBC5OMoDI/8alKU9asRSo1AYJUob6lBZQNBvQCQLq20uGgTk5Ir9woYQPF0DqBFLTm8dtumEpgzzJlcGYVLci27BNYkoqJ8bA4eNKRgxkWdXYwyCTnY+0ZSPUBWMgxSAyq/6SfI5VcjLDUCgcDQ6MlT07s3/PILDBpkQjkq9eKHf0ZRtdcH/LZEaDX5o9ZmyQXDKJ4tmsvHe8deD4ss4Jg/jzIKl0j5846hbqiszRzcZzkOR48srcWYR/Kq+agG1FEDREAVqNSYRwSUhQW1CQSCopF3+qlWLXkxKf4tuZl6gHM3w/h13h0GPFPOxAJYCIa21AB1mgYz7dlxNG90D5hT+g7NhhJmFNbg5EtMtfP8sh0O7UsFSpq+2bRoC1oWNfkegEslutb/F3+PBGJqdAYql06IgpQaD/Ow1AilRiCwRvQ4CiuCjR3P9L3LJ8th/RYvEhLA319ZkcwSSY2D7UMGP30TybG8QSw1doHNGddpqOzEqZ4ONs6l79QcyOVUXdLjVLeW7Lh+8LC9oaQyOlKOUlMsS41KxaDuxxh0ezbU8sGoSo3GUpN6FtRZYKOMeiGmnwQCqySvpSY+HlatgmPHTCtJjZZtaFB5L9nZtixdnGnawS0GCQ+Xe8z//iwLFhioS7cq4BQA6ky4s9dAnZoHro73adIok7p1S7Z9nQZuAFy65sHt2wYUzIg88okrZhiTV035NckAP3xtUk89qoNLENg4ytdb2qXSj1VChFIjEFgleX1qfvkFevaEOaaeiSj/BM+1XSPLMM/KnFYNRikSyuWHSkWaxxMs+G8Qr77hZFHhy/mSsxPRQXHs3JLE8uUl68YzOJJ5Lw5hx1dD8PAwoHxGpE5UMnNfHMqHA4r5A/aMJvWBK/v2PChRCLwOBVlqVDbgXlV+r2AElJh+EgisEYXLJOhgY8vTA114dW4W+w6X4+RJqFHDxDKYO5IatVpFWqotNi6lLGiZC5VfU4b/9AKZ2Q6MmQiVSzn7oDi5s9WWRgH0jmFIq6fkqTnb2YBDqUUzNkGB6QxtNR98ilkF2yuaRuP3EH81inWh0LFjKYQoSKkBCHsOMu7KVhuFEJYagcAqyfvkb/KQ7lz41+9Lx1rrAVg4L9n0Apg9EtcSA3EPa4Cnp+F6dQ5uSv3K+wHYttUaos9ym5tKcSG7hoC9lzxVkmKIWGcTUNDUT0F4RhNZUd7H+KOlTIJZmFIT+TbEfA5eUaUbpxQIpUYgsEb0hHQrZqkBcK/K8J47eaH1j3SpVczSymUBqZQRPfnhWZMWEbI/zbZNhirVrCA5N/bdpxsRGOZF69Yl7EelIsWhKfO2DmHCR5YxL3cjwYE/D3Vm9/Ho4m3o5EtkiOzjEhdbyvmnwpQaM6BUf28Z1ldURCCwEswg+d5j9HyuOj8Of5FGbpNKWYbaGlGXKktuvtjY0qJREgDbtpvvjajoyNfNg0wnrl+3ISGh5D1luNZl2Ox5TPw+pvS+JiZgx35vun71J2/+NKbY20bWkC008fGl/N2V1FpkQool2d9//83gwYOpXLky9vb2uLi44OHhQatWrZg0aRLXrllWgTCBwGopoEyCUkoNwX3Azk0O+by1TSEhzBRJKnWYcn40a+2JSqXm1AVvbt40bN+mR76IDXGs/KpUJ6icbMGIjS2tXManRCHdOURFy6Hr8ac9Svc8YS2WmpUrV1K9enWGDRuGnZ0db7/9NitWrGD9+vX89NNPtGrVio0bN1K5cmVeeuklbt26ZWy5BQJBgeRvqVFk+gnAzhUp+Gn2nm3AG+PSePhQITnMErW29pOhz4931QZEV5LDebdvs3ALWY6lwCDHyrvOo8zCB8w/5XJpLK3Vavpja5NFcqozpbI9WIBSU6Topy+++IJp06bRqVMnbPRcRf369QPg6tWrfP/99/z666+8+uqrhpVUIBAUHT2Wmn79ICICYmIUkglQhw6l59dhXE8KpE3/B3Tp7qScMOaEsXxqAMo1oEX4PI5ersXxw3egj6+BBzAlOZYaQxwrj3Dqhq1k1YGeHNybCph3bHdJClpqcCwfRdXyZzh5PZz4eKhYsaRCWIlSs2vXriJ1VrFiRT777LNSCSQQCAxBXkfhpk3lRUlsyzelX/N5fLt2GIvnJ9Cle7CyApkNpas8XSC2jrw39B8mPPUhfu0/A5438AAmJMdSY5CpOht76kYnwu9w6JD5W7BKnHwPwDOSsR3fJCPLkWrBHwAlLFeiVWqsxKdGIBBYCHry1JgFKhUD+twD4I91/qSlKSyPuSCpcXW8T58eyfTsafjuK0aE4+dxGxIs3ZdJvqFrpp9Ke3nXqSv7mhw/40Z6eun6Mjal8omzc2VUr/W82ukbQr2OlEIIzf+KhVtqXnvttSJ3+PXXX5dYGIFAYCg0T3OPnluOH4erV+UEbEomYWv4ZD3C/M5x/lZl1q7Ool9/kQMUJAK8brJswVUMmqhGg38LiJ9i+Q7aOXd2d6d71K6tpkaN0j2XV6weiq/7LW7f8+P4cUpcdsEUlMZRGJDLJaSek8sllG9TQiGsZPrp0KFDOp8PHjxIVlYWNXLSgp46dQpbW1vq1atneAkFAkHx0fNY9913MGsWfPQRTJigjFgAKr8m9G8xnSkrXmHJz3fo17+8csKYC8YuQOrblFX7e/D9Py/T9lAK731k3v4j+SMfp6bVdxF7IAtsS5cJWOUTw99vdSIoMIPydY8aQkCjUb/mHaYPHk2lyj5AMbMKA9luNYm7dI4TFxzp+24JLT7WotRs3rxZ+/7rr7/G3d2dBQsW4O3tDUBiYiJDhw6lRYsWxpFSIBAUk7w3SUWT7+XGxpb+vZKYsgL++teH5GTjGCcsC0k+P5LKOGqNgye3suvyb1w7Ml0TLFepyR2PbAi/Du9a1K98EJAg/QY4B5S+TyNRo3IyNTrMgIB2Jdo+y7UmMe9NQC3Z0nwIBAaWoBMLUGqKfVVMnTqVKVOmaBUaAG9vbz799FOmTp1qUOEEAkEJMcc8Nbmo2aYxkRXj8HG9w8l4UbkbSSL+aiQ23uH4Gik4qUUL+VrYE+uNxeZNzV37yRDqn50reOQUIkuMLX1/RiWv839xcCwfSdWAMwDEx5VwCssaHYVTUlL05qG5desW9+7dM4hQAoGgtJhhnppcqCo8wbqPBnP5u4o0rLRGaXHMALXRku9pqN6gBv4eN8l4aM/+/cYZw/jIN+NNcW2oVsOGnGwipeK+UyPeWzqJHs9WJtuMy2NdT3BiS3wrjp0PLVkH7tWIqiTXgCpxuQQLcBQu9t9br169GDp0KCtWrODKlStcuXKF5cuX8/zzz/PUU08ZQ0aBQFBc9JhlzMlSg40dQQ3aYWujhrPzlJZGeSQjhnTnoPJvQfMa2wHYtsXMQ33yRb6I76W7c+aMisuXS9+jU/kovl3/Cqs3V+f06dL3Zyz+2lyBNpO28M5PJQzJt3UkMuw6APFHUkvWhzVOP82aNYtOnToxcOBAQkJCCAkJYeDAgTz55JPMmDHDGDIKBIJikzf6yayUGoDKQwHIvrqOa2ctPn9/KTFi8j0NzgG0qHUCgG2bLNSqrslTg+EUQFvfOtQKksOczblcgtYnriR5anKIqiHnUIiLL+FFZo1KjYuLCzNmzODOnTscOnSIQ4cOcffuXWbMmIGrq6sxZBQIBMVFT54ac5p+AsCjBpuvvkzF0Zd5ul8Z96uR1KjVximTkJsWzbMA2LHX3aynWvJHk6dGjnExiALoXYc6IbEAHNpvvs5GmpDu0uxzZJR83OLPeJasBpQFKDUlThDh6upKrVq1DCmLQCAwGHktNQMGQK1a0Ly5MhLpo3rjhiSk+HPzoA2XLkFwmU0wbAJLDVC7SQgVva9Qq+plEhObGM0p2WhIBiyToMHJj5hqF+BfOLQ/DXA0QKeGR62NXiy5paZGrXLYqLJJTHHlxg2oUKGYHViAo3CJlJr9+/fz22+/cenSJR4+VpVuxYoVBhFMH//99x9ffvklBw4c4Pr166xcuZKexki/KRBYPHnnmjp0kBdzomL99rQM/4+tx1uz9Jdk3ny/jMZ2S8Z3FAawC2zO5e+DUNk6gHcyYGm1tzTTT7KlwFBWrZjasqXw0BEnJMmMpmhzYYgk4U5+1Zg+5GUqBaTj4TG/FEKYr6Wm2JfEkiVLaNq0KcePH2flypVkZmYSFxfHpk2b8DRyson79+9Tu3ZtfvjhB6OOIxBYPMZO5mYonAPo324fAEsWm6/p3/hIeDin0LljOm3bGnEYtyqonANA/RDu7DXiQEYix1KjmaozlPIRXdcLW5ssbieWsoq1EdEUtCxtEc+R7WbRLXoBrg4liICygOmnYis1kydPZtq0aaxZswYHBwe+/fZbTpw4Qb9+/Qg2su24U6dOfPrpp/Tq1cuo4wgElk/ePDWnTsHOnZjdn3af/i7Y2WZyMM6fU6eUlkYpJKoGnOXPFXf5+WcjDqNSySUTgOtxh0rmV6EosrLu5nSfqlWhUiXD9OpcoSbhgSdwd07lwgXD9Glo1Ooc5+hSOArj4AVOOQkGk08Uf3trVGrOnj1Lly5dAHBwcOD+/fuoVCpeffVVZs+ebXABS0NGRgYpKSk6i0BQJpDyJuqaMAGaNYOlSxWRKF98az5J++gNACz+pYxWuDShZU1drgWRb8YR2PYVzp0z+nCGJee67lx3A6dPw6+/Gqhf7zps+aA1ST+Vo1njh4W3V4Cmda/zef+3eKb9f6XqJ8m2Psv3PsX8uSVwzrdGpcbb21ubZK9ixYocO3YMgKSkJNLMrOTulClT8PT01C5BQUFKiyQQmAY9N0mzKZPwOO5V6P/EDgCWLMqwQOuBIchrWTMWNuWb4+2aCMC2repCWpsbpcuqmy+uofj6ZGPDQ0g5bti+DURMxC3e6vYlPVuWLnPi+XtN6fPtcl6fHFP835oFOAoXW7KWLVuyYYP8VNW3b19eeeUVhg8fzoABA3jiiScMLmBpePfdd0lOTtYulw2RqUkgsAjMu0zC4/TsX57XO3/FL+NeVVoUZZDU7DnTEJdy5alZ08hjedWiRaTsT7Nt010jD2ZgDOEtqw+VCrzryO8TDxXYVDkMo9CF1/REpVJzN9mFhITiimCFjsLTp0+nf//+ALz//vu89tpr3Lx5k969ezNnzhyDC1gaHB0d8fDw0FkEgjKBHkuN2eWpyYVH9NN89ew71C+3AFVKvNLiKEJWth3p6SoePDDyQDa2NG+YBMCOnWZ4MRSIfGNfe6AztWvDqFGG6znLvR5Pf7eE6m06k5RkuH4NxY0EJ/adrc+566UruulcvjqV/eV5x/ji/tSsbfopKyuLtWvXYmurCaez4Z133mH16tVMnTpVp8ilQCBQEsuy1OBcHgJlXz3OlbGyCcbIvVIITVvLkaonz/tw+7bxxzMYOcr6nVQfjhzBoD5Bdn612H2mMacv+XP4sOH6NRSL1lan4fh9jP+plAWvPMKJqhgHQNzRrOJta21KjZ2dHS+99BIPjP4ooZ/U1FRiY2OJzcllff78eWJjY7l06ZIi8ggE5ouZ137SR5VhbIprw/NvxLB/bzH/bC2ZnBu1KTIKa/Cp2oCIQPkxfecOS3Ji0iiARjhWPjHEhMpTT4cOmp+vkTaku7T77FyRyKCcat1Hilkuw9qUGoCGDRtqlQpTs3//fmJiYoiJiQHgtddeIyYmhvHjxysij0BgtljY9BMAgZ35aeso5m4ayOK515WWxoSY3lJDuQY0D98FwI7NiSYY0EBIukqNQY+VRzgxoUcBOLTvvgE7Ngza329pQroBVCqiqicDEHespJYac/0TKUFG4VGjRvHaa69x+fJl6tWrl6fekzFLJ7Ru3VqrrQoEgoLIO/00cCDUrQsNGigkUmHY2NOv6zUW74Blf7jx5QwzVsAMinESyhWIrRPdW5/CzmYG7WLCgE4mGNQQ5GQUNkb2ZRt76kQlwXKIjTW/wliGtLRGRsidxZ9yKaYQ5u8oXGylRuMkPHbsWO06lUqFJEmoVCqyLbNKmkBgXejJU9OnjzKiFIcn+4ThNvEel296s3ePROMm5jpXZkCMUHm6KHTtZkvXKqMhbDAWo9RoMgrnFLQ09LGKqZtT8PG0OxkZ4GhGZaDUBgz8iqztzrKxfYhsWAVJ+rzofVrA9FOxlZrz588bQw6BQGBQLKRMwmM4hbaje73VLNrxNL/9kkDjJuWVFskEyDdqL5ckWrXMIjSsxHWGi4d/C4ifAre2mWY8g5Az/WSkOllB4SH4uN3hbmo5jh2DevUM239peJRnqvSzFU5+1ejT6G3wqVe8vwhrVGpCQkKMIYdAIDAkUt7pp3Pn4N49CAoCHx+F5CoMO2f6db7Aoh3w+0pHvppeBqagciw1dcMOsWXjA7B3M824vk3JyHTkwMHy2HndomFLP9OMWxpyjpWL0wMqVDD8dazyiaFRlT1cTwnm/v1ow3ZeSgzq6O8RIb+mnKBYFTwtQKkp0t/F7t27i9xhWloacXFxJRZIIBAYgrzTT2PGQJ06sGqVIgIVmY5PBePulMLlG17s2aO0NKYg15O3KR0wHTz5Yfskmk3cyaefmGdpgLzIx+qZliu5dg3mGTr637sWf77ZlUOf1qRlg5sG7rx0NI+5zPheE+nW3ADx5u5VOH4tmi9WjuKXOcVIwGgBjsJFkuy5556jY8eOLFu2jPv39XuFx8fH895771GlShUOHDhgUCEFAkEx0Tr0WUiemlw4Ve5E93prqV7hJEnXrigtjvHRCX4w7clp1kx+3bnPU+uzYdYYK6OwBjtXVJ7V5fdmllm4Zf1LTOwzgR4tDaDU2Niz71oX3l7yBXPmFudYmr+jcJGUmvj4eLp06cIHH3yAl5cXUVFRtG/fnm7dutG8eXN8fX2pW7cu58+f559//mHQoEHGllsgEBRIXkuN2dZ+ehwHL/737hJOfBlOp3BDVSw0Z+Qbxb/H2uIb4ES7dqYbOaZ5CE726dxJduPkSdONW3I017URL2JvOWXIw4Qj5qXo6XlQKQ2REXJ/8Sedir6R2kqmn+zt7Rk7diwnT55k165dDB8+nOjoaCpWrEjr1q353//+x7Vr11i8eDE1jV64RCAQFEjuJ/9cT7SGjJ4wNq7Vu8hyXlmptCjGJ+d8ZWQ5cueOyqQp+h0qNqNhFbkO1I6t5lWQWD/ysfp9VxeaNQNjpCiTvGJo8fF/uDd8ldOnDd9/SUm440z8lQhu3PE0SH8RNeWyQbfuunDrVhE3sgCfmmI7CtevX5/69esbQxaBQGAQck9nWN70EwCVesC+kTy4fpgz+24Q3aB09W7MG93cKya1pDlXoHn0Sv470Yodm+/ywkvFzFtianKsFdfuBrBzp+z0bmhUPjFkZtnzMNOe2FioUcPwY5SEHxbX5+P/vcao3lv4oXPp+3MNrEGo33ku3AojPh5atSrCRhag1Ji7IVogEBSXfCw1FjP9BOAcwMHEwfiPTODJbm7mNQ1gaLS5V0yYfC8XzZpkALBjdzGmIZTCmBmFNXjXoU5ILACH9mcYYYCSIeU8rBgipBsAz2htDaj4uCL+wDRKjRmrDuYrmUAgKCG5/6Asc/oJILJ5PVQqias33di1S2lpjIlu7hVTK51NWsuh3Kcv+XLTvAJ+9GDEjMIanPyIqSbnY4s9YD5Tcmq1gffZvRqRlWRHqqLXgLISR2GBQGBBSPpDhAcOhHfegchIBWQqAU41nqZ7vbUALPulqJP+FoimoKVClhrvqo348YUX2DGxFT6eyhQrLjIaqxbyTdVYCmBM7UwADh1xxFwq82gLWhrq+rCxI6paEgDxx4oY0q+x1NiYr1JjotSVAoHAdOgPEX7+edNLUiqc/OjX5RK/bodlK+z52mprQRk3S26huFflhU5r4cFNSNkLTi1NLEBx0Bwr4yqA0XW9sVFlk3DHhevXITDQOOMUB03wkyF/A13a3eKgbwzhHQcAbxVBCCv3qXnwwMy1eoGgLCLlmn6ylLmmfOjQrw4ezslcu+XFru2ZSotjHCRNmYRk6teHiAgTj69SgV8L+X2CmZdMMJFVyyUwivDAEwDExhpnjOKiNoLS6xsaRkxoLM4PYosohBUqNWq1mk8++YSKFSvi5ubGuXPnAPjwww+ZM2eOwQUUCATFRX/005UrcOYMpKaaXqKS4hjanh4N/wHgtwWXFZbGWMg36jbRW9m3D376yfQSZJdrycIdAxn9QS3S000/ftGRr21H+0w8PcHFWMFa3jF0qfMnfRv9jqebeSjTmukngzkKA3jmlIJIPlZEIazQUfjTTz9l/vz5fPHFFzg4OGjXR0dH85MSv0aBQKBLPpaa/v2hWjX45x8FZCopNnb063EHgN9Xe1tnFJRkgoRyhWBTvjlvLPqKGau6sX9vduEbKEXOtT22+68kJcGsWUYaxzWULwZP4bexfWlWM95IgxSPlnXP80aXL2lV95zhOvWKZtX+7oz4egxrVmUVYQMrdBT++eefmT17Ns888wy2to92rHbt2pw4ccKgwgkEgpJgBXlqctH+qRp82vd9/v2wEzYU5Y/X0lA+LE3lXYtmNeQkfNs33lBMjsIxkQKoUoF3Hfm9mZRL6NL8OF8OfIturY4brlOXYP471Y7Z/w5n0/rkwttbo0/N1atXqVq1ap71arWazEzzMNMJBGWbgjMKW5qzrWNQC97vN4tw3z1wa7vS4hieHG1z9f5uhITIUWomx8aWZnVlZWbHNnMubmlCzdy7DpIEZ49cwjzcR/OWPik1KhURVVMAiI8rwgODBUQ/FfvvLTIykm3b8jqT/f7778TExBhEKIFAUAok/XlqLNVSg40dVOouv79sjWUT5BOTmuHOpUsoliumeUvZnWDnQV/znebLubYXbu5K+/bwzTdGHMs7hlrvHKFqr/Hs3WvEcYrI3WQnLtwKISnV2aD9RkTI19/xU0VIvmiNlprx48fz8ssv8/nnn6NWq1mxYgXDhw9n0qRJjDdGIQ6BQFBM9OepsVilBqBSL1bt706/V9qzZ7eZJA4xFDk3agllku9pqNO8Ci6O90m85475ehLI5/7czSA2boTjBpyJyYNPDJX9Zf+VQweV1/Im/q8dYeMu8OWCNgbtN6KmKwCXb3hyr7AcfNboKNyjRw/WrFnDxo0bcXV1Zfz48Rw/fpw1a9bQvn17Y8goEAiKg6Q/T42lZRTWIaA9y/YNYNnOriz7xezT3hYTE6T+LwL25etqi1tu35SkjBCFIZkmTw0AHuHEhB0FIHa/8iGD2jxGBtYnfILCKO8pTz0WqsxKVugoDNCiRQs2bNhAQkICaWlpbN++nQ4dOhhaNoFAUBIKmX6yNJ8aAOyceaqjHNK9/A8Hs8nyahA0uVfU8o1CMaXT3o1mNeWy1CcO31ZIiEIwpVXLxp46kYkAHDqkvKVG6xNn6OvDM4LIinKEV6E1oKxx+uny5ctcuXJF+3nv3r2MGzeO2bNnG1QwgUBQUvRPP/XvD2PGQGio6SUyBE/2DsbZIY0L13w4ZB4BKQZC11KjpNL58qATXJtega9Hz1VOiALRFP80jQIYU1dOuh93yp0MhWtbGm362K0yERXlGlCXz6UUIoQVKjUDBw5k8+bNANy4cYN27dqxd+9e3n//fT7++GODCygQCIqL/uinN96A776znNpPj+NS5Uk61VkPwPJFZmpJKAmmqDxdRAKqh1PB+wbcMQPPWH1oLDUmKv4ZHBGCt+tdsrJsiVc4XY3RlBobeyYM/pXkHz344KU9hQhhhdFPx44do2HDhgD89ttv1KxZk507d7Jw4ULmz59vaPkEAkFx0U4/WaLzTAE4eNK7vfxEuWKFwrIYFPl8ebkkExkJISEKilKukfx6d99j05jmgsZSYxoFUOUTQ52QWADFrYPaKt1GUOT8ggPwcLkHKYU41Vijo3BmZiaOjo4AbNy4ke7d5VDL8PBwrl+/bljpBAJBCdD/SJeQANeuobgZvTR07V0eB7sMTpz3VfzJ2WDkPIL3bLSOuDiYOVNBWTyjWHe0O50+XcJH79xVUJD8yCkVoJKwswNbYxsMvGryTLNFvNt9MnXC7xh5sILRph00hiLnES6/phQSTmaNjsJRUVHMmjWLbdu2sWHDBp588kkArl27Rrly5QwuoEAgKCb5WGqeeAIqVoTtFpy/ziO8M+2jN1C/8j4Sr1lLFJQZxdrb2HFHqse6I534e53Swugh59r+ePBPZGbCtGlGHs/ejee7b2fy0+9TN3S/kQcrmFYxpxjZbgb1ooxgPPCI4L2lk3hixPCCw+St0afm888/53//+x+tW7dmwIAB1K5dG4DVq1drp6UEAoGSaG6Suj9vi85To8G5PCsmfcW+TxrSLOg3paUxDGY2Xdi8hXzDOhTnTVqawsLkwQhZdQvDOyeprMLlEgZ23MuMoaPp3OKs4Tv3jGDL8dZsiq3HkSMFtLNGpaZ169bcvn2b27dvM3fuIw/5F198kVlGqy4mEAiKjKT/j99SyyQ8jkNYN/nNFWvJLiyfr6U7exEZCa+8oqw0wTWjqOh9haxsW7PIpKuDpECyJe8Ybib788/6bO4qOiNnRIXOI4KIQNmfJv5wATl5rFGpAbC1tcXb21tnXWhoKP7+/gYRSiAQlAb9T/5WYakBCOoFQPKFWI4fTlJWFkOQc6O+e8+b48chV8YMRVD5N6dZ9R0AbN9ibqYa+SKev6ELPXuCSWJTvOvw5Ofr6Pj6+/z3nwnGy4eUVAdupfiS9sDB8J3buRBZWZ7OPX7sfv7ttEqN+T4ZlUiy33//nX79+tG4cWPq1q2rswgEAoWRrHj6CcCtMmtPvYz/S9cZNtSciy8WFfMJ6QbAyZdmteQpjh1bC8ubb2JyLuJjFyqzahWmcRb3jqF28GEAYg8o52U/dmp//EfeYvqSBkbpPzJcLmh5/EQBVhhrdBT+7rvvGDp0KOXLl+fQoUM0bNiQcuXKce7cOTp16mQMGQUCQbEoePpJ8ZumAajXMpTMbHt2H/JX3LJRekwbplwUmjWTL5Zd+z3MrLhlTvZlEyXfA8DJjzpVzwNw+IBy5RK0DyVG6l9TA+rkeS+y8ivYbY3TTzNmzGD27Nl8//33ODg48NZbb7FhwwbGjh1LcnKyMWQUCATFQfs0pd9SY+k+NQAVYtrTtNpOAFb+bsEx6pAroZzyGYU11G4aSnnPG9SvelRhP5LH0NZ+Mm3xzzq1ZItg7BF70wyoB+3v10j6RHB4JVwc75OZZce5c/kJYYVKzaVLl2jatCkAzs7O3Msp6/ncc8+xePFiw0onEAhKgH5LTd++MGwYlC9veokMjldNnmouOzgsX1pIanezx8ymnwC7Cs25Nj2QjW81w9erAB8Lk6Or1JjqWNWu5wbAhaseJCWZZszH0SbfM1L/Nj7RhFc4gb9nAjdv5GOes0alJiAggLs5qntwcDC7d+8G4Pz580hWVWVOILBQ8okQmTwZ5syBqlUVkMnQqFQ89ZT8dtveciQkKCtOqdAUtDSj6SdcQ7BxqwhSFtwuJHW+KXnsWJnKUuMdGkFwuYsABYc8GxFjW2pwr8a2CU9wc0Z5WtS9pEeAXIqONTkKt23bltWrVwMwdOhQXn31Vdq3b8/TTz9Nr169DC6gQCAoLtrco4pKYWxCG7amXth+1GobVq3MzwnAAsi5W3m4pBISAn5+CssDsmbl1wKAxLP7FBYmNxpLjYkVQO9H5RIOx2YX3NZIqI1tnbKxx8Uvp0ZH8rG83+soNeZrqbEr7gazZ89GneM5Nnr0aMqVK8fOnTvp3r07I0aMMLiAAoGgmOQT5pSSIn/l5maC9PKmwLcxTzX5nAPn67N8SSLDR5iDNlAS5PM1rN1yhn3/pcKyPCLDqxW1+47n5PVwbt0CX1+lJeKR/xGmnX7CNZQRHd6mR71VtGn5JhBhooEf8cgnzog77RkNSUcg6ShU7PqYALmUOWtSamxsbLDJZfPr378//fv3N6hQAoGgNOjPUxMdDZcvw759UL++6aUyOCob+ve5j7vdGHr2cQU+U1qikqFEQrki4FixGTaqnCioHVl061Hs24URkO/s346exbRlPUx3yFQqOj9xCxKWgUcrlFBqWtU5jtPDk4SHeRhtjNtSA577/FkuptTi2NnHpvesVakBSEpKYu/evSQkJGitNhoGDRpkEMEEAkEJsfY8Nbmo3LglY9I7QmZ5UE8yosOBMTHT6ULPSJrUWMTxa5Hs3JRAtx6BSkukvYht7SRsTR2I5F0HErbklEsYbOLBYWSvDYysuwTqfmO0MTwDw/g37gkysx24cgWCg3N9aa1KzZo1a3jmmWdITU3Fw8MDVa5/SJVKJZQagUBx9Ec/WVNItxb/1mDvCQ9uwu1d4N9caYlKgHxiFmx6iumfQLduMH68wiIBqGxoWj+RuZth1y5l/EjyonmIVuAi9o5h79kG7N8dQjd/CAoy8fj5pGowJPa+4VQLOE381Sji49QEB+caS0epMd8/kWJL9vrrrzNs2DBSU1NJSkoiMTFRu9w1q4QGAkEZJZ/pDGtKvqfF1oGs8t35cdMLdOvjzX1zij4uKjnn63qiH/v3w4ULyoqTm6YtXADYe8SfzEyFhQGtZv7TXx155hlYtcqEY/vE8NqvXzP6+1f5b6vpMxI+yLAjLcOZrCwjKhRulYmsKJfpPn7ksbxzFuIoXOyjc/XqVcaOHYuLi4sx5BEIBKVG/3SGVVpqANuQHkxZ/S5rt0Wx4R9LTCthfnlqNNRoFIWXSyLpGY4cjjWHYyvfWPccD2fRIoiLM+HQHuHUCT0KQOw+0+dG6j/hFVyHpTFneZTxBrGxJyJMzo8Qf+SxJwQLmX4q9t9bx44d2b9/vzFkKRI//PADoaGhODk50ahRI/aaXRlZgUBp9DvPWKWlBlBVfJKeDdYC8MeyRIWlKQGPZRQ2p/NjU64uTarLeWp2bbmtsDRoNXNFcvrY2FM7/A4Ahw+ZPou1qXziImrI+xZ//DH1QKvUqMzrIn2MIvnUaPLSAHTp0oU333yT+Ph4atasib29rrdW9+7dDSthLpYuXcprr73GrFmzaNSoEd988w0dO3bk5MmTokK4QKBB0u93YK2WGuxc6fnkDab9BWv+ciIrC+zMIVCnyOjeqM3q/Ng60KvVISp6XaZmUBjQTmGBlCmToKFOHXnc2GOuSJJp7+2m2ufIKPnHc/ysp+4+WkA2YSiiUtOzZ8886z7++OM861QqFdnZxnMo+/rrrxk+fDhDhw4FYNasWfz555/MnTuXd955x2jjCgSWhf5HuqeeguRk8PRUQCQj07RjOL7ut7id7Me2bdCmjdISFQNtPSPzs9QADH/2BsMbvgfVXkFxpUbh7MvR9XyxUWVzK9GN69ch0IQBYY+CGo2709Vr+uLjdocala5z/340bm4aATRKjTlp3XkpklLzeNi2Ejx8+JADBw7w7rvvatfZ2NjQrl07du3apXebjIwMMjIemQlTUow0D3pmNj8vcuPY2QrYqCRsbNTY2EjYqCRUKrCxkXhv6Ebs7eXj+Oe2CI6fL69tpwIc7LNwdX6Iq/NDOjc7jrOTnCE1Nc0BB98IHKoPABvliqkJLAhJf56aWbNML4qpsAvpSre6q5i3dSh//JZEmzZeSotUDHJu1GboUwNAuYby6x1zmOpX1lLjXKEWNSqc5Pi1SA4fNq1So8kobGSdBmf/atye5YvK2R/cbj76QhuAYAWWGnPg9u3bZGdnU/6xanzly5fnxIkTereZMmUKEydONL5wl1fwx5oXWbk//8fDtxv3wN5BVrCWLPuZX3d0y7ftrVm+OLvLc7fvLfiW7/8ZhIfLPfx97lDeJ4XQgFtUqZhAlYoJ9Gx5AA/3TAjsAkFPmb0WLTAB+eSpsWocfej5xDnmbYU/VtnwzQwzVA7yI+d8uThm4OcH7u4Ky/M45RqSmWVH7D4VXsEPqRbuoKAwCvrUAHjVpHbIGo5fiyR2fyqdOrkVvo2BMFmeKY9weYwHCZBxFxx9cgSwoumnx/n333+ZNm0ax4/LoV8RERGMGzeOdu2Unm/V5d133+W1117Tfk5JSSHIGMkFgvvSs3smVSK2opZUqNWqR6857+2iXgU7WdNt3soVG+8D2u+ys214mGnL/XQH0h7Y41prBORYau7YyBXRU9LcSUlz58yVAHYcqa4d+voPAXh43YRz8/l5/zscuVKPutXP0Dw6juCIEAh/7dFFKSgj6M9TownJtbOzoBt+MWjfoyIeU5KpWv40iYn18bGUyz7nCfjNPj/z5pxXFRZGD+5VeW3xD0xf9yKvXbvJ1B8ULPOudao2cZkEDfZuvNHvd0Y+MZPafT8CnjDZ0I8KWhp5p+3dwCUI0i6jTjqBTfmmOQJYqVIzY8YMXnnlFfr06cMrr7wCwO7du+ncuTPTpk1j9OjRBhcSwNfXF1tbW27evKmz/ubNmwQEBOjdxtHREUdHR6PIo0OV5xn0YWGNmmjfjYiBgqtkTdK++/UvmH4rlYTD67h1M4vrt1w5d8WTs5e9uHLDjfKt34SMG3B6Fsu3Nmb1wR7abYPKXaJ5+L+0axDPk40OE9i4L4Q8Xbae4Msi+Uw/+frK9Z9OnYJq1UwvlrFxrtqN6z9UwMUxHZyvARWUFqmImGlGYQ0qFY1ikpi+DnbuVDisO+fOPvut2Uz/rSVOTqYXoV4DB7i4DbL3YUqlpnnNeFylC4QEGj+dyr9n+jNy6guEVXVh/bacldaq1EyePJlp06bx8ssva9eNHTuWZs2aMXnyZKMpNQ4ODtSrV49///1X67isVqv5999/dWSxNlQq8PZ3w7t9H2robfG6/BLxNs/dPkFw9An2HvblYJw3l+8Es3hHMIt3gJN9OomzvXFyHwM2TuDbGGpOAK9o0+2MwESUnTIJOrhUxCWwpuz7cWUVVHtJaYmKiPmfmKbNZX++g3HlePAARZQJGVlhd3HKwsVLIRG8Y+DiEkiMNemw7z+zGNqug8bzjT6Wm28Ap29UJzUrVwI+C3EULrZ0SUlJPPnkk3nWd+jQgeTkZD1bGI7XXnuNH3/8kQULFnD8+HFGjhzJ/fv3tdFQZRonX/qMas73C8LZE+tLUpIt/25UM/71yzSMSaJNo6s4uTpCxh1Iv8pz7/bg4xd+48L31WC5HxyZAJmmTyglMAZlqEzC4wQ9BcCNw1tItJSUNTmWtR//7kXr1jB9urLi6COsVnX8PW7yMNOegwcVFMQcNHPvOizf+xSvfdXRtMn/8vldG4OI2nKI5PXbniQlaYa3DEfhYv+9de/enZUrV+ZZv2rVKrp27apnC8Px9NNP89VXXzF+/Hjq1KlDbGws69aty+M8LABXV2j7hA0Tvwpiz0EvVm+uCj2vwJMHOV9tD7/ueI6Pln9M5VdO8uTHv7Bi3hEyV1SH41MhK11p8QWlIZ8aMdaafE+HSr0Yu+BbAvstYsGcNKWlKSLyzercjUps3Qpnzyosjh5U/k1pWn0nADu33lNQEvlY/e+PJxg+HDZtUkAE7xhmb3qRaauGsn3rA5MNK6lNp9B5BFahovcVAHJcZy1m+qnYSk1kZCSTJk2iS5cufPrpp3z66ad07dqVSZMmER0dzXfffaddjMHLL7/MxYsXycjIYM+ePTRq1Mgo41gbdnaAvTv4xOAX0ZD58+GJNplIkg3rjzxJ729WEDryAJ9/nEDSknpwZjaozaHYi6D46P/zKxOWGo/qVA1NRZJs+ON3JW++xUCTpwaFnF+LgoM3TWpeAGDXfwpadHMU9n8P1uSnn3LdcE2Jkx91qsia5+F9pjMHtn39c1TPSCxdG2b8wTwiiKwYD0D8sZz7gIUoNcX2qZkzZw7e3t7Ex8cTHx+vXe/l5cWcOXO0n1UqFWPHjjWMlAKD4uYGgwfD4MH2nD0Lc+bA3LkS125W5J0ln+PvkcBQmxEQ/yXU+lg4F1sa+TgKlwlLDdCjpwOvzIZt+3y5fVt2kDZvdCN6zFXpbNpEgnmwc5+bybPpPiInpFutrAJYJ/o+/AGxh003pkkjvpz8iQg6z4ZjcPxwEuBnMT41xVZqzp8/bww5BApRpQpMngwTJqhYtAgW/qpm4Mv14dSfkHqGAwu/IqTyLHxbvinnwrH2O6JVULCjsLneNA1FSKO2xIQe5NCFuqxdlcGQ500QAVkatPWM5Cdgc/2J1WsRxOR+79K03m3gR2WEkMxDAawdI19TR054o1abRg5tjJwp9lmlIrJaEvwNx+Mty1Jj5X9vgqLi4ABDhsCGjTY41hwN3c+RGTGZp79fRuVha5gwdj/317SDm1uVFlVQGJJ+h8KuXaFbN3B2Nr1IJsU7hp5NtgDwx293lZWlSOTcqDFvS41zcFPe7fEZrYLmospSagpK4eR7OVSvUxEn+3TupzuZzAdKa50ydkrhHGpFZ1MvbD+RwRfkFdbqKCwoI9i7cb3cu3hUCObeAw8mrphAjSELWPjxbKR/O8LdA0pLKMgX/dNPy5fD6tVQrpzpJTIpKhU9u8tPl/9s9SHN3P2FNZYatZmWSdDgUhFcw+Sb2y39pWmMj3n4H9mViyI66BgAh2NNU0ZIMlGZBA1Nmtmz/9MGfPlCjn+ssNQILJ3gYNh/wI7ffoPQkCyuJlbi2RkLaTbyIw7NegG29YFkJTz1BAVSFsskPEbN1o0J8ztHeoYj/6zLUlqcQpDPl4N9Fi4ustXUXLnn2p7fdvfliy8V0iY0BS3VClc0d69KnZAjABw/YtxUJho0tZ9MZanBM0J+Tcn5jxdKjcAasLGBvn3h+Ak7Jk0CV1c1u043pcGH+zi28wT8FQ37RoPa3G8cZQnT5bMwV1T+zRnfbxrzRwymZcQOpcUpmJwb9Wcv/Mj9+/DxxwrLUwDJjs14+vvfeO+7J0hNVUIC3YKWilm1bOz4cNAyrk4P5IMXt5tkSJOn6PGQlZqspLOkpmRbjKOweUsnMBucnOC99+DUKRv69oXOHdKIalxN/kM+PQP2PJ8r6kagKHry1EgS2NvLVoCEBIXkMiU2tgx5Lp3BLX/GJ/U3paUpBMuxrFWKiqKSz2Wy1bbs26tAyYSca3v++Hlcvgz9+5teBA3B1f0I9L6OKuWoScZrEnGMJ2v9TXm/hyYZD9dQJqz4BJdBd/ns0xSLsdSUqKBlUlISe/fuJSEhAbVa90Y2aNAggwgmME8CA+G33+DBAw9UTivh8goS173I119U5b1R/XFu9Cl4VC+8I4ERyWupkSTIyjGmma3PhqGp1AvOzoErf0D9781XacgnBN8s8axJ0+qr+G13ELu23KFNWxPHy+eYK8p5pUMl0w6dB02JmaRjJhnu6xHfwe1dUDdv8lujYGOLdzl7MrMdiD9612IchYut1KxZs4ZnnnmG1NRUPDw8UOX6h1SpVEKpKSNoa78EPcXotXVZvDqU5Xvj+XX0M9R9og5EjwdXI1REFxSOHju1lOuh2lyjawxOwBNcSa7Okj+74XTyEi+/E6q0RPkgn5wfVvXkz6nwzDPyYpbYOtCk9lV+2w07t2coIIAZTa16RvP9+pfZcKI370yDpk2NPJ4CJSIiw2WH++On7C3GUlPsv7fXX3+dYcOGkZqaSlJSEomJidrl7l1LCJ8UGJpBI0MJKJ/J8WuRNBq/k6++80RaUwNubFRatDKK5slfd/pJQ5mx1Ng6cSDpBd5c9JV8TSpcYDpfcgSLuxjK33/D6dMKy1MITZvIN7ddB71Mf0xzrAUzl7di3DjYt8/E4+fGK5otx1uzZm9rdu/KNsGAeX/XxiYiWs7/cPqiFw8zLMNSU+yjc/XqVcaOHYuLi/HLnwssgyefhKPH7HnqKcjKtufNRV/R88vFJP45GG6ZuZOmNaLniS73LHGZsdQA7XuF4WSfzsXr3hw9Yq5ajW5Ej7krnXWayDla7ia7cuqUqUeXz+HKrXX49ls4edLU4+fCJZjaoScAOLzf+CU5mr76I67DUtmwzc/oY2moWLUi7k4pZGfbcuZ8ToIrc53GzaHY0nXs2JH9+/cbQxaBBePrC7//DjNmgIODxOqDPYh5ZwfHfn4d7ipZ1rcsktdHo0xaagCXKh1oX/NfAFYvM1MP6cdqP5m70ulQoT71K8v3gP17TR31aB5lEjSD14mSw7ljY42vMKdlOJKW4apVfk2ByiuCiIpySHf8abecleZtqSm2T02XLl148803iY+Pp2bNmtjb2+t83717d4MJJ7AsVCoYORIaNVLRt6+ah/dcCHA7A5s7QJt/wKeu0iKWDfTkqSmrSg32HnRrc441B2HN6iw++FRpgfRhJmHKRcWtCj8MH4K3w0WCOq8AGphubDMpk6Chdh35Fhp/xoOMDHA0YkUO7T6bUqdwr05E4FL2nm3E8RMqaIT1KTXDhw8H4GM9yRRUKhXZ2aaYWxSYM3XrwsGDNlw+54Lvzcpwdx+sb4A6+Dls6k8FR2tPaas0eZ0pVSpo105WbuxKFPNouXTt7QtTYe/Rily/DhUqKC3RY2gSykkKJ5QrKioVtep5wrUrcGs7lDOhUmMmZRI0BEeE4OWSSFKaN8ePQ506xhtLq/Sa0knazpk2Mcd4kLmEyApJ8jozV2qK/fNRq9X5LkKhEWjw9IToGDdosw6CevPzf8/QfsRz3F3VGx4mKS2edaMNvXz05+foCBs2wMaNUNbc4SrEtKdB5b0A/Ln8tsLS6MPCLDUAfi3k14Rtph33MUuN0sdK5RNDnZBYAGIPGXcKSq211Jh2pwf3jGfJmAH0rvervMLalBqBoFg4+pAa8zuvL5vDprgnaDpuFueXvACZiqQjLSNo6/kqKoXZ4ORH95ZHcHZI4+YZJT1L8+ExJVTpG3WR8G/BnC3D6PH6cLZuMaUDtpn5H3nVpHbIEVwdU0lMMG6RT8UUOU25BG0+HqUPesGUSLqtW7fSrVs3qlatStWqVenevTvbtplYYxdYDG5usGmzPUEVH3LyejhNxk7nwE9vQPYDpUWzUkyfz8LcGTPqIbdn+fJ+t4+UFkUP8vma8/YPqNXw9tsKi1MUfOqz9URbVu/vxKa/75huXMmMHIUB7Jz59PlfSfnJg1efNW65hEeucibeaY8I1GoV5696k/rA1fosNb/++ivt2rXDxcWFsWPHMnbsWJydnXniiSdYtGiRMWQUWAE1a8KuPQ7UirrPzeQAWr36FX9/Ph4SDystmvUh5c1nkZwMXl7g7Q0ZSuRMUxjP6m1xcUyXfUCyzK1s9yMfKJXKDG7URcHWkSYxNwHYtcOUDyfytb14yiJOnoSOHU04dD64BdbAxkaCxENGHadBtWO0DN+Kl4eJ3Tw8wmk6YSeVXz3PlvjWJvZULj7FVmomTZrEF198wdKlS7VKzdKlS/nss8/45JNPjCGjwEqoWBG27XSlXctE7me40e3Dycx5+3s4/AHmmxnNAsknT01yMiQlWchN09B41ACXIFBnkHR6l9LS6GJJZRJy0bSZXE5896FymMydMufarlQ+herVwd3dROMWhHeM/JoYa9RhFrz2Hls/bE2dmveNOk4ePCMI8z8PQPzVSOuz1Jw7d45u3brlWd+9e3fOnz9vEKEE1ouHB/y5wZvn+qeQrbbj7M3KEDcJjgmF2HAUnKdGcT8EJVCpOJs1kJpvHyGyZX0eK1mnMPLJ+W55V/r2hbVrFRaniEQ1ro6rYyr30pw5ftxEg+op1qo43jG8u2QykQOn8NdfRhxHjwXWJDiWIzL4IgDHr0VYn1ITFBTEv//+m2f9xo0bCQoStX4EhePgAAsWefDbbzDpM1d55dGP4PjXygpmLYg8NXqpVKseF26Hcv22JwcOKC1NLnJuVnviq/H773DmjMLyFBG7Co1pUFmuU7D7v2QTjSpfyDN/a8z778OJEyYatiC863D5bhDHr1Tj0H5jTsUp5ysXUU22Dh2/GoHVOQq//vrrjB07lpEjR/LLL7/wyy+/8NJLLzFu3DjeeOMNY8gosEJUKujbF1QRr0CtT3nw0JHZU08gnZ6ttGhWQN48NWW1TEJuHIPb0LHmegBWLzd+WvsiI5lX7pUiY+9B4yhZA9uzPclEg8rHav6aekyebCYKoKMPtatcAuDIAeNFQDV4dRn+I29y4LDp59wiomTrTPzVSCSszFIzcuRIlixZwtGjRxk3bhzjxo3j2LFjLF26lBEjRhhDRoGVI0W+x9PzDzJizmxGjVSjPvur0iJZNnp8NISlBnDypXvLowCsWZ2psDC5scA8NTk0bpCOk306makmioDSJCo0l+inHGrVfAjA4aPGy2x5O8WLWyn+ZGWb/qmkWqQPtjZZ3HvgwbU7viYfvziU6Oj06tWL7du3c+fOHe7cucP27dvp0aOHoWUTlBFUNip6PBeBSqVm1r8v8fywLLIvrFBaLAum4Omnskznbi7YqLI5fNyHixeVliYHS8sonItOXRxJ+cmD+ePeNdGIZpanJofa9eS6SKcvepFmpOA6zT6rFNDkHHyrU7W8bBaLv1jJ5OMXBzO5JARlnWHPq/j1F7C1zWb+f0MY9GwG2ZfXKS2WhZJ3+snODpo0kZeyjG+tjjStvhOAtaseKiyNBsu11DhUqI+9XRbc2Wsazdnc8tTkUL5qdfw8ElCrbYiLM84YmkKWps4oDIBnBENbzeO9HpMIKm8q/6mSUSSlxsfHh9u35fTi3t7e+Pj45LsIBCVl4DM2LF0M9nZZLNoxgOGDbqK+vlVpsSwPPREi5crBzp3yUqbxqk33xnKStNXLExUWRoPGp8a8btRFwqsW2DhCZhLZSaZwcDGvMgkaVD51qB0s59w6fMg4U5t6MjWYDpcg3u45nUn9PiA81Eyr3edQpAnAadOm4Z6TEGDatGmKmL8EZYPefW1ZTCb9+mczb8tgfF78jq/mOYNvQ6VFsyDyWmoEOahU9OgBx84t4KnOd4FXlZbI7OoZFQsbe/69OJxXpo+g8lxnVm808niSeU4/4RJE/aorSEjxxyHLEwg1+BCPHMkV2GmVjZzrKfGQ2Yd0F0mpGTx4sPb9kCFDjCWLQABA7772zE97yNiX0+hVdylsmQBPbAHvWgpLZiEo+khn/lRv1oIF6S3A3gvUL4ONvcISyedr8cezyG7QAHulxSkm7gFhxF2J5sa9VCTJ2JedeToKo1Ix5eW1TLn5KjT6CXje4EMorfRK7hFcO3uTUwfCaWPGz5jFVvlsbW1JSMhrfrpz5w62tuatwQksh+cGO3D2jIpmzSR4mAib20OKGRYjNEvyTj9dvSpndK5SRSGRzAnfJuDkD5lJcHOL0tJoLTWODtm4uGBxSk2dJoE42j/gTrIbZ88aebAchX3Z1OUcPAiNGhl5vOLgk5NZ+K5xyiXUDo2nXth+XF2V8fq/79aaSmOu0nbUK9y9q4gIRaLYSo2UjzNYRkYGDg4OpRZIINDgU94NWv8F3jHEnqjAjDcWQKrIWl0oUt7pp8xMuHZNXso8NrZIgT04eD6GyZ+kmS7Ff77kjVazJBwq1Kdu6EEAdu/MMvJo8rGqEZZITIyZlEnQkFMuIfv2YTKN4Fbz94fPsv/TBlSvqkw6Ardaw6gUKDvXm0XSw3woclD9d999B8jhZD/99BNubm7a77Kzs/nvv/8IDw83vISCso2DF1erb6BNXzuS7nvi6vImgyePA5eKSktmxuSfp8ZsfBAUJqtCb9pN+YzE+z60HKCmeQsFD0zOyZm2uAOxM+H556FlS+XEKTZuVWhc/R92nW7K7v/u8uwgf+ONZY5lEjR41Wbg9IWs3N+LZcslunYz9DyRwtPKNrZERNly5Zqs1DRtqowYhVFkpWbatGmAbKmZNWuWzlSTg4MDoaGhzJo1y/ASCso8gZXLMXx4Kl9+Ay9Mn0yA7yt0fO9bM/CFMFMKKJNgNj4ICmNfqQ2dYv5g0fZ+rPntOs1bKKkkyzfqfw9E8OcOaN3awpQalYrG9RLhT9i929iDyRfyrKUx3HGE556D4GBjj1lEPKpjY3OYB5nOHDmQSNdu3obtX6naT7kID4cNGzBdra8SUGSlRlOssk2bNqxYsQJvbwOfMIEgH1Qq+GyqG9eup7JwqRu9P/2CrdV/pV6/oUqLZqbkXyZBKDU52DrQvf0NFm2H1Wvt+Px7BWXR5l6xsDIJuWjc1BmAwyd8SEsDFxcjDZRzY5/2S0NOXZCVP7NRamzsqV3jBgt3wOGD6YBh75Exb2wg8Z47G8MzqKrQ7VczGWPO00/FVvk2b94sFBqBybGxgbk/u/FE0+vcz3Cjy4udOH9E+NfoRY+JXkw/5aVjr2BsbbI4caE8584pKIgmpBszi+gpBkFRNWhR4z+eabWGe0Ytq6VJVCh/MrdjVStK9jk5Eudo8L4v3qrExduhZGUpt9MREfKrVVhqcnPlyhVWr17NpUuXePhQNyvn11+LSssC4+DgACv+CqBlvbMcPluFjp0usHPvXXwriqSPuuS11JjrTUBJvMLb0rzGDrYeb8WfK+4w5o1yCkli+ZYalW8D/htfXv7gnQR4GmcgM80orKF2PdlideqCN+np4OxsuL41Id02tspOPwGcPw8PHoCTk2Ki5EuxlZp///2X7t27U7lyZU6cOEF0dDQXLlxAkiTq1q1rDBkFAi0enir+WudEkyZXCPY+g/3uT6DbanAw0p+oJaJHg3FwgFq1zCxaRGnsPeja/Jis1KxOU1ypMbuEcsXByR9cQ+H+Bbi7HwKeMNJAulYtcztWAVUq4+9xk4SU8sTFQf36huvbHJTegAB4912oWtV868kV+5J49913eeONNzh69ChOTk4sX76cy5cv06pVK/r27WsMGQUCHQKrVuS/Ten89cEgPDP+g61dIOu+0mKZEXkdCkND4fBh2L5dEYHMli5d5GN06Ji7UcJwi4SZVp4uNuUakq224dguI5ZL0GQUNtfsy17R1Ao+AsDhQ4bNFaC1vyoY+aVSweTJMGyYYa1QhqTYR+f48eMMGjQIADs7O9LT03Fzc+Pjjz/m888/N7iAAoE+QmpWw6HjX3JW2Fs72P/jW5D9QGmxzAMx11RkwpvGsG18c67MqI69rVIJa8z8Rl1EHnq2wHfEbWo+NYIrV4w1iq4CaG6WGlxD6FB7C081WE6g91WDdq2dfrKx0AvERBT7knB1ddX60VSoUIGzuVJIaopeCgQmwbsOUuu/efnnWTQY9QO/ffItqJV63DYn8uapEehHVa4+zaPjsFffgrsHlBEiRwld/sVcbt6Ep55SRozS4lCxGWH+svP+7p1GUhC1tZ9kzE4BVNnw5nMbWT6uD50a7jFo149qPxm022KTkgJbt8JGY9f5KiHFVmoaN27M9hwbdufOnXn99deZNGkSw4YNo3HjxgYXUCAoCJVfY5zCOgMwePJY9i+YrLBEZoCePDUnT0K1atCsmUIymSs2dlr/D+naeoX8BGQl1Mv9Af7+5ul8WSS8atG4ek5m4f+M9YArn6AV3/3F9u2PHFfNCq+a8mvSUYN2Gx54iojAeBwcldVqtm6Vcym9+aaiYuRLsZWar7/+mkY5BTcmTpzIE088wdKlSwkNDWXOnDkGF1AgKIzPpwfRuW0CDzKd6fH6C1zdb6aPECYjb/TTgwdw5gzKhi6bKxU68u6SyVTpOIzDhxUYX48SapHY2NK4bhIAu4xmqZEVwHo179CsmZk6vnvWRJLg0omr3Degq9+Bz1oQ/2UUFSuqC29sRDSK5MmTj/JfmRPF/hVVrlyZWrXkasmurq7MmjWLI0eOsHz5ckJCQgwuoEBQGLa2sHilP5GVb3AtsSI9B/iRlpyqtFjKIeXNtCfcbAqgYnfir0Zy/kZF/vwjWQEB5PP19a+tGTUKDhmnHqJJaNLCFYADcb48lu3DQORV2M0O7zo0n7idkKfnsHmzAfuVzGNaOSxMjqZMT4dLlxQVRS8lUmru3LmTZ31SUhKVK1c2iFD6mDRpEk2bNsXFxQUvLy+jjSOwTDw8YM3fHpRzv8v+M7UZ3jcWSflKhQqh+eN/9PMWGYULwLk8XVrIFeD/XJVm+vFzNM6VW2oyc6acA8RSqVovGh+3O2Q8dOBwrDHm8uQ+Zy+N5Jtv4NYtIwxRWrzraH2LDh8w5PVkHhY9Ozt5KhvMMwlfsY/OhQsXyNZzs8jIyODqVcN6e+fm4cOH9O3bl5EjRxptDIFlU7m6C8sXXMTWJotFG5qzd/4X5ptMwajkNcuIjMIF07mHnOdo9+HyCtwocxLKmYkjaGlQ+TagcdW9AOzeaoQDmWOtGP9dQ159Fa5fN/wQpcbBk1pV5XvhkYOGS69c8819RL11jFu3bAtvbGQ0mYXNsVxCkZPvrV69Wvt+/fr1eHo+SnaWnZ3Nv//+S2hoqEGFy83EiRMBmD9/vtHGEFg+rXrF8MMn+6mUNJFGzmvhjBdUK2OKsJ7Cd2L6qWAqNWhP7eBYDl+qw7o193humAmdNTRlEqxB8bR14rlOu2hUZRctoxoCXQ08gG5GYXM9VrVrynNvh4+WKGm/XuKuRCJJNmSrrxmsz5JiFUpNz549AVCpVAwePFjnO3t7e0JDQ5k6dapBhSstGRkZZGRkaD+npKQoKI3AVIx4rz6caAsH10LsO1CxO7goWYXZxOjRYMT0UyG4VaZL4x85fKkOf664bVqlxoosNQD9e6fAyW/BfwwGV2oeUwDN9VjVqitfP6cvehuswKekvT6U32mNs7BFTz+p1WrUajXBwcEkJCRoP6vVajIyMjh58iRduxpaKy8dU6ZMwdPTU7sEBQUpLZLAVFQfC+UaceGaNyMHHCXzYVmahsrrTOnoKKc2N6Ix1eLp0kmeVl//nz9ZWSYcWHujtuzke1rKNZRf7+w1fN/aPDXmfawCqlXDzyMBtdqGuLjS95d7Ft0clJrmzWHWLDDHfLvFNt6dP38eX19fgwz+zjvvoFKpClxOlMK+9e6775KcnKxdLl++bBC5BRaAjS2ZdX+k7aRNzFr9JG8N3V52/Gv0VOmuXRtOn5ZzTAj006hDOHVDDzCw2W+k3jNlrKpuRmFznVIpMuUacTPZnz/+CSLuiKGTYVrG9JPKJ4ZaQXK5hCOHMgppXTi5/7psbJVXaoKDYcQIaNJEaUnyUqTpp++++44XX3wRJycnvvvuuwLbjh07tsiDv/766wwZMqTANqWJqHJ0dMTR0fAl4AWWgb1fTaZ9vIGeIyvzzaIWNKyzigFv9lBaLBNgAWGvZoht+SYc+MwHstOAukBt0wysqTxtLZYat8q8t2wqczc/y3vSNSZ9HWi4vi1k+gnnCjzdYip1Qw9SK6wzEF2q7szNUmPOFEmpmTZtGs888wxOTk5MmzYt33YqlapYSo2fnx9+fn5Fbi8QFJceL7Xn3UN7mTK7IcM/fIJ6rY9TvUGE0mIZFzPJZ2Fx2DpC+dZw7S+4/g94m0ipyclTs+bbX8mo+jb+/iYa1lioVDSOucPczbB7l6EtXrqWGrO9v6tUDO97DG58DWFelFapUWdLaH7PKjMxT8XHw+7dEBUFOfl4zYIiKTXncyVOOK9QEoVLly5x9+5dLl26RHZ2NrGxsQBUrVoVNzc3RWQSWAafzPh/e3ceFlXZPnD8Owz7vrogiyBuCLgCbrmv+Wq2+Os1NS0zy8xMLS0zNStNK7XUyupVezO1NLM3M3NJzQ1xR01MzV1zQUFF2eb8/hhACGSd4cwc7s91cQFnzjlzczgz557nPM/9xLAzPoHf9kXy+L9t2JGQhaOz+sMizadgPYt9++Cpp4yFs1auVCksa1CtC5ln17Lj14vU9aViEozsj+HVq6RCzQp4vgrQvKUtfAi7DvqQlWUskGkS2Qn7D59tIsO3CzUsuf+/ZyRc+hVuHDLBzhSCfU+jKDr0esu43s2fD7Nnw6hRVpjUWII333yTRYsW5f7euHFjAH777TfatWunUlTCGuj18PV3fjRqcoX9J+sy5uldzFkao3ZYZlTw9tPNm3DggHG6BFGE6l146MNV/Ly/B3Ns0nlhhH0FPKn2bheGR9fE1fEmt1LdOHIEIiNNtWfjsWrf4jKEmGqfZuIZwdWbPhzcZEeTMChPzVg7W4VTs7P/YE/LmDjaUod1l7od69FHH+W9Qro8T58+nT59+pgkqMIsXLgQRVEKfElCI0rCv1Y1vpppnGxve5wtqfvmqhyRGRXSUdji+yBYCvd6tI0yTgD108rrFfOc2f+vD75qySuvGDt0Wzt9lRhiamUX4dtqhqq61pAAekTQ/u3f6PjKB2zbVt6d5bmNZyFzhOUkNZY2rLvUR2fLli08+OCDBZZ3796dLVu2mCQoIcyh29NdWTV7CTsmtcD5j+Fw4Re1QzKTgm/8UqemhHQ6evQwvi3+tt3bpBMS3p/x/7VwVSPef98y59MpNUc/mtc3foTf+fsN0+03OwH88tsw5s83tkBaLI9wooKMI6AO7C5noPlGblrGizinVs2pU8Z5oCxFqZOaW7duYW9fsEnWzs5OitsJi9frxX/jUP9p4y/xz0FmhVy1KlYhzTKaqFZbQcLbtCDY9xRp6XZsXF8B84dlX6g1M/opW/NmxtfWzl12Jtyr8UQePrEpQ4dCUpIJd21qts40rJ09XcK+8rVW3bljIHrCLqIn7OJummW8iP38wNvb+N5y7Jja0dxT6qMTGRnJsmXLCixfunQp4eHhJglKCLPR6aDxDDIdQ3lz0dO8/uwOtSMyA7n9VB66Kq3p0XQjAKtXXKqAZ9RYnZpsrdu68N/n+/PjlAmm2+k/hr9b+rGKamDsxHYgoXyJXWaGwu6T0ew+GZ17nqhNp7vXWmNJ/WpK3VF4woQJPPLII5w4cYIOHToAsGHDBpYsWcJ3331n8gCFMDk7V7ZkfsOUlcYu+516HaND7zoqB2VCitx+KhcbPT263mTeWvj5VycUxdzHLSepMf6mlf+RV2hD+rceDg7VQPnERH+YdVVfbtjEEYBjpzy5cwecnMq2HyXP7SdLqlNTvz5s325Z/WpKnef27NmTH374gePHjzNs2DBGjx7NuXPnWL9+fe78UEJYug7/F8vQh9YDMHCIO9evVWRdfHMrmNTY20P16sYmY1G8dj3r4Gh3h7N/e3PksJkrUecUlLPw0v+l5t0YdHq4ewnunDfNPpX8CaClt9RUq1UTX7cr5Z4uQTHc6yhso7ecP/qFF2DdOuN3S1GmId09evSgR48epo5FiAr1wReRbNx1nD8vhvHCk0f4ZrVGbp8WMvqpbVu4oP7kvlbDuWZb5g8ZRp2qR6hX47+AOVvyrKP0f6nZunA+owNL10aScfAG46YFmGCn2f2PLL34XjadVwQNgw6w4XAnDuw30KxZ2f65BoNlttRkV1axKKU+wpMmTcJgKFglMjk5mb59+5okKCEqgotvVb6e8wd6m0yW/BzON//5W+2QTMSKhr1aKltnBjxyhtiwXegv/2re59LahJZ5nM9qz5hvPuD9z0JMM/WalbXU4FabYV3mM3/wEDo0L/unCiVPUmNJLTWWqNRH58svv6R169acPHkyd9mmTZuIjIzkxIkTJg1OCHOLefhfTOj/NQDDRrhw5rQGJr0spKVGlEH1Lsbvl8yd1BjPuTWffc+hQ5b56besGsZWw942jWs3XMhzySiH/J2qLT4BtLHjkU6JDOnwBSGe+8q8G0ttqQH49lsYP95yWoJL/a538OBBAgICaNSoEZ9//jmvvPIKXbp0YcCAAWzfvt0cMQphPjod42e2IjYsjvQMG/b/vEbtiEygYEvNtm3QsiU884w6EVmlap1Zl9CJZyY/yvpfzdnnyvj/CgtOpkEDcHY241NVMAf/ZjQK3g/Arp0mmAcqO2Ff+fkuvv8ePDzKv0uz88ie9ym57NMl6HQKvm5X8HG9is7GspKat9+Gd981TsViCUrdp8bLy4tvv/2W119/naFDh2Jra8uaNWvo2LGjOeITwuxsvWuz+OP5ZB56kroef8KpxVDTim+lFjKMJikJduyArAoou6IZXo1Ytf/ffPnbQGwDL9KpS3XzPI+i4aFp7uHE1p7PrhOxxG29Tt9+PuXcofHc7t3tbzBFF52K4BnBro3R7N/vxyOvgq9v6Xfh453FlU+zJyLTWdaghvr1ISHBOKzbErralql9+uOPP2b27Nn07duX0NBQRowYwYEDB0wdmxAVplbXIdRt2xFQYMcAuLpT7ZDKIecTsdSpKRedDd07XANgzTon0/QJKZRxxx8ubMrEiXDeRAOFLIKNnpiGxmO4K84EGbU13lr1jGTw/C8ZOuMZ4uLKuI+8J5+FvYhzatVYyrDuUp8Z3bp1Y/LkySxatIjFixezb98+2rRpQ/PmzZk+fbo5YhTC/HQ6aDYHgv6P3/9oweOP3CDjbrraUZVNERWFLez90OK17xGAg91dzlz0NOObtvGfM+urxrz1Fly8aK7nUUdsC2Phub2HvUgv90tKwWDQ8fWKGixejAn2VwE8InKnSzh4oKyJXd5bd5b1Ira0iS1LndRkZWVx8OBBHnvsMQCcnJz45JNPWL58OTNnzjR5gEJUGJ0Nt8Pn8fCsH/j2927MGGutfcTuX3zP4keLWBjnsO60rWec027NyivmeRIl/zBlrf2PwhqF4uWShMEAx4+Xc2eKQkaWHQNeakT//pBqyrkyzcUliKiaiQAc2HOrTLu4egXaTtlEp3fXWdwnk7wTW5qvNbPkSv3yWbduHf7+/gWW9+jRg4SEBJMEJYRaXLx9mDXZ+JFj0tyWHNp5St2AykJm6TYdey+6P2C8Eq/5yVzzhGmzonAOnW8s2ya2IuULL8LrlHfmQ0O+aQKsIgHU2dAw3Dih5YEDZfvn3r0LW462ZcvRNqaMzCTq1DGes0lJcPWq2tGUsU/N77//Tv/+/WnRogXns28A//e//+WopbQ/CVEO/V5qSa+WO8jIsmfQk2lkZljAx49SkWkSTKl7Ly8Aft/jz62yfdAumkYntMzlHEj9WtdxtLsN18s5REZRMCj3LlvWcqyiGuoB+POUC3fvln77nCHdOp3lvRc5OUHNmsafLaFfTamTmhUrVtC1a1ecnJzYt28faWlpgLH43rvvvmvyAIWoaDobHZ8uqoGny3X2/FmXGa/tUTuk0ilkNI2tLbi7g6urSjFZsTqt2xLid5Jafsc5k2iOAo3anNAyl04HPjHGn6/tKufOFOtrqQGq1wrE2/UaWVn6Ml34c+Z+stGZYFi8GSxbBidPQuvWakdShqTm7bff5tNPP+Xzzz/Hzu7ezKOtWrVi7969Jg1OCLVUDwviownGGbwnzY7k0G4LaFctsZxPc/de3r17Q3IyrNFCGZ4KpnPxZ9+nz3JkRgPCPX40/RMo2r79BJDpEcvz/5lH04ceIjm5HDtSDLlzZIH1HCudVyRRgdmdhQ+WfvucuZ8ssaUGIDoaQkIsI8ksdQiJiYm0aVPwvp6Hhwc3btwwRUxCWIT+Y7rwr5gtpGc68NXMHWqHUwoavjqqxKP2A8YfLq0zw96taz6jsrCtGs3ag13ZeyyE+Pjy7Mk6W2rwasSkRyex8fX29H6w9Fldzu0nS22psSSlPiWqVavG8UK6sG/dupXQ0FCTBCWEJdDpbfnsP14sem4Q7z3YC87/rHZIJaMUrFMjyqmaccqEtLNbuJVi4gqG2U00vy74hbg40OTbqE80sWHGIi1x28oxZEkxWGWfGhy8adv0FO0bbMJDKX1Nt3uteJbZUnP1KkyebBmzdZf6XW/IkCG89NJLxMXFodPpuHDhAosXL2bMmDE8//zz5ohRCNX4N4jkySG+xjfP+Ochwxw9RU2skPsYGzdC584wbpxKMVk7n2gmrZyK91Mn+Pyjc6bdd3YS2jgiiZgYbU2TkMvei5jwvwDYtf1mOXak4GR/h8Xz/uDrr419xayGVyPj96TSd5bWYcDZ4TbODuUdPWY+kybBvHlw21yDBEuo1EnNuHHjeOKJJ+jYsSO3bt2iTZs2PPPMMwwdOpQXX3zRHDEKoa6oyeBSkxtXkpk1br1F1GIoWsHRTxcuwPr1IN3eysjGFo+qVUhNc2HNz6aea6JgHygtio02lveP2+NSjteQgr1tBk88epV+/UCvN1l45ufVmG+29eXVt2tz7VrpNg0NyeD2f1z5e75lNuP5+t6b/uHYMXVjKfWrSKfTMX78eJKSkjh06BA7d+7kypUrTJkyxRzxCaE+WxfSG35Gk/F7efnD3iz+1CTTDZuP1Kkxi+7/MjahbN4dYOJPo8Z/zuyF4Uyfbqz3oUWNm/tiq8/g72uunD1bxp3k3lq1whPZuzFvrniLGYsfpNSzClnBLWVLmS6hzEfI3t6e8PBwYmJicJVxokLj7Gt24dlHjT0cXx7nydXLGSpHVJSCLTU5SY3VdKy0QHVbRlPT7y/SM+z5bZ0JS9lmX7DenNWYsWO1m9Q41WiaOwIobmcZm2oUhbQMe1b+7MMPP1hGBdsS82p8bwTU/lJOSmkFn0ryVhZWk7zFCVFCo6e3JzLoMFdTvBk9JFHtcIogxffMQedei+7NtgHw88rLJtyz8f+l2eJ7ObwaEhsWT4jfSdJSyjrlhIHkVA8eeao+Dz9sZcfKOZDImsZBNgl7b5Rq0zNn9Tw4fTVPzP7CDIGZRk5Ljdo1eCWpEaKE7Nyq8PnMU+h0Br76MYL1P15QO6TCFXH7SVpqyqd7xxQA1qx3M10rQSWoUwOA3pGPRi7k5Kxa9O/8W9n2oSj56tRYFZ2OqHBjC9/Bg6U7eVJSYM2BB1mf0M4MgZmGtNQIYYViH36Q4Q+tAmDoc5B62xLbv6Wlxlw6/KsG9rZpnLrgQ6LJGuuM/xzNVhTOw7ZKM+MPZa4srGAwGA+QNR6nyEaOABw+5kFWKfqbW/qQbriX1Jw+Tan+NlOzwtNCCBXpdLwzN4oA73OcvOjPtHFlKA9qboW01Oh0xuGvVjVaxAK5hLRlRNePmfbvsXjanzfNTrOvWJUi8cyeLsFwJZ709DJsn6eisDUep1oR/jjZp3InzZ4TJ0q+XU5FYRsLTmqCgiAx0dgnTM33GUlqhCglN/9azJ28k0FtFjC88eNwt6z9A8yl4H2Mp5+GjAz44Qd1ItIMe09mvLiCsT2nU01Za6KdVp6WGnxieG3pu/g8uopFC0rZWRbI21JjjUmN3ieCBgGHATiUUPIExWAFLTU2NsYZu/PMnqROHOo+vRDWqdewh1gwdhZVHBNh7yi1w8lPKXj7SZhQdWN1YS7+apr95bTUaL1PDYB7HWxs7bhx24u4rWWYBCpPS41VJn/u9fny2SGc+SiQh7uXfHJUS5/Q0pJY42khhPps7CDmc0AHp77m74Tf1Y4oj4K3n4QJVe/C5WQ/Fn/nyYH9prjIGC9Y6xdv4bffwM/PBLu0VDobYhoZO1vH7SpL9mbdLTXYOhEVfodAn3PoUg6VeDNDlvEcseSWGksh73pClJVvDFd8X6Xn+z/SpH09bpan+rspFdJS88svxpm6p09XJSJt8Ynh9e9m0P+jT1k03wS3HrP7QLWJvUa7duDgUP5dWrKYFk4AHP7TswyvGQVv1yTmzzzD3LkmD61ieEYYv98oeVKT01JjlYlcBZOkRohycGs2gj8uhHPhmh+Tx5p4TqAyK5jUnDwJq1ZRzhmSBQA2dnRta7x1sPZXU7yF5tx3qhxvx9XrhxPocwZFsWHPnlJurBhwc7rFkIE3GDzYLOGZXZZrJJNWTOSR5x7gVgmnkmvW6DbKYh2nP4s1b3AaUDleRUKYiaO3Px+PM87ePeuz6hwq+Ycv8ylk9FOlGFlTgTo96IONLosjJ/zKXvI/R/b/a+6iUObOhVQTFiu2SD4xxNbKnrF7e1opN7b+jkd6nwg+3fAcKzc35fDhkm6V3VJjY71/d0WRpEaIcur+TA8ejl5JlkHPC88mW0Dpdpkmwdy86rYhppax1sqva0p7Yf4nBUWB4RMaMXy4+rMcm51TdWLqGYv87NqeUrptFYXUNCfWbnBj40YzxFYRPCLuTZdwoIR9sqTzf4nJW5wQ5eUayqzxcTjZp7JlhweL/6vyvFBKwWYZaakxMbcwujY1tjas/V85J2tSlNzh3FA5/ketm9+mY4P1tAwvbdOmgfPXa9DtsZo8/LBZQjM/tzAig44AkLCvZJ2KEv+057FZ3/Hi52+ZMzJNkKRGCBMI6j6WCX1mAjBmVBoppfwAalo5n+pkmgSz0eno2tnYQrN+i3s5K6ga8pX+rwxJTYu2nqx/vTOje84r3YZ5EkCrPZdtbImqZ0yED+4v2Qegq9f0rIh/jF/2tTVnZJpgraeFEJbF3ovRE+tQu9oxXPSXOZVwUr1YCplESFpqTC+6fRiezte5nuJCQkI5dvSPlhqrvViXRnZl4VJPl6AYMChWPKQ7W1Sk8QWZ8IdziW5XGwwy+qmkKsPLR4gKYR/2GKtnvM+R6fWJSn3y3m2gCpfzvAVbauRN0XRsa7Rn5ahHuTCnOo3qlGfKBCX3Qg2V5H/k3QzQcfXv2xw7WJph8RpoqQHqN/REb5NJUrIzF0owL25uS6sU3yuWFZ8WQlgYnY7aD7+Bg5MdXNkGxz9XJ45CMpiRIyEtDf7zH3VC0iQHb9q1TqW61yW4tK7s+1EMla+lxs6NJftH4/fcVZ57vjRZnKKJlhrHKuHUqXYMR/u7nDpV/PpSp6bkKsPLR4iK4xIEUe+QmaVn7rRjbPrlqgpBFBwpodeDvb3687JoTrXOxu/lmjKhErbUABFRxhmr4/e7lbxPUp4E0KqPk2cEG8d34NZ/PGjVvPiZPXMqCktLTfEkqRHC1OoM5731HzP8Px8wfFgqmWWZt688CqlTI8ykehf++3t/Og8byqofynjBUQzY26az+r97+ekncHIybYiWKrxZAC4Ot7iV6sDRoyXdShu3n3AOpJrfHfS6dLj5Z7Gr32t8Vb1ehMWzitPi1KlTDB48mJCQEJycnKhVqxYTJ04kvUxz1wthZjZ6np/4AN6u1zj8VxDzp1d0Rb6CLTWrVkG/fvC5SnfENMu3OXvPxLL+YFt++r6sQ7sV9DYGHuyURI8eYGtr0ggtlr5KNM1CdwMQt7Ok9VoMVPe6yMxpSbxlzaObdTrwyJkuofhe5nL7qeSsIqk5evQoBoOBzz77jMOHDzNz5kw+/fRTXn/9dbVDE6JQ3qERvPXCZgAmTAsk6VpFfsIq2Kfm8GH45huIi6vAMCoDGzu6tL0OwNp1dmUsvFhJC6t5RhIbZkxqdm0raQ0EBV+3a4wcfpNnnzVfaBXhln1T+s/7L026tyGjmJHd7VomcetLF+I+6FsxwVkxq0hqunXrxoIFC+jSpQuhoaH06tWLMWPG8P3336sdmhD3NXR8KxoEHCbppgeTxpZndEwpKQVHP8mQbvNp2yMUB7u7nL3kUYrbKHkoBtIz7fjPEn8WLKCcNW+siI0dMQ2NrVtxcaWtrGsVl64iOVetzY97erHvqD/HjhW9rt5GwcUxFWfH8lav1j6rPTOSk5Px9vYucp20tDRSUlLyfQlRUWzdqjJ73AYA5i2ozh9/VNQzF2ypkeJ75uMc1p0H6m4FYO2qsnQMV0hNc2bwqPo8/TQV3wdLRbEt7AFISPQo4ZxXBm7ecWXrDgd27zZraGZn4x1JRKDx1vTBg8WtLf3kSsoqj9Dx48f5+OOPGTp0aJHrTZ06FQ8Pj9yvwMDACopQCKOOgx6iV9MfyTLoee3lyxXzpIW01EidGjNy8KZryxMArF1dsrL3+Sj5KwpXpsQzILwuox98n/kjppbs1p2icPRCPR7oXIVHHjF7eOaVZw6ohP1F9w89eNiFgZ8uZNLi5ysiMqum6stn3Lhx6HS6Ir+O/qM99/z583Tr1o0+ffowZMiQIvf/2muvkZycnPt1ttzT6QpRSi7BvDcugc4Rv/Jm12cgswKmYC6ionBlumBWpK7/cgdgc3x17t4t7dYKBkPlG9INgE8M7/d7hadjpuBSolsrSm4CaPXnsqMvkbXOAHBw360iVz130YGvfh/I6vg2FRGZVVO1n/3o0aMZNGhQkeuEhobm/nzhwgXat29Py5YtmT9/frH7d3BwwMHBobxhClEu9R4awa+24ZB6DhImQ+P3zPyM95+lu1JdMCtQRIc21Kp6nLCqx7lyNprA2j4l31hRKm1LDa6hYO8N6Ulw4yD4RBe9vlbq1GSLijB2oEo4XHQBqZw6NTKku3iqJjV+fn74+fmVaN3z58/Tvn17mjZtyoIFC7CpVK98YdXs3KDZPNjSCxJnkR7yEvae/uZ7vkLq1EhSY146lxoc+6IRNikHwG4J8O+Sb/yPisKV6n+k05Hp2Zw926/yx5kUBo0rboN7hQq1cAmIaGxs4TtzwY3kZPDwKHy93NdvBcVlzazitDh//jzt2rUjKCiI999/nytXrnDp0iUuXbqkdmhClExAT1Kcu/DyomlENbIlzayDGAq21Lz2GiQlwXvmbiSqxGxqZFcXvlTa6sJK5U1qgJuOrWk+MY6nXuvItWtFrJh9ZddSS41XcD1C/E4SHnSCoi5n9ya0lJaa4lhFUrNu3TqOHz/Ohg0bCAgIoHr16rlfQlgLfcRolu18nMTTVZg725x9awomNU5O4OUFLi5mfNrKLnvKhPOHDpKSXJqLjzbmMyorr5CG1K5mHNNc9Igm7SU1eDfmzw9rc/i9cOrWvn+xmpziezY2ktQUxyqSmkGDBqEoSqFfQlgLl1qdeXuQsS/YlLd1JJW1AG1xZJoEdfg9QL95SwgYspvvF18s+XaKAU/nG3z7xZ8sW2a+8CyWTzTRofEAxO8sopd19vu9lm4/4RKC3sEVDOmQfP+aD/fqTMk1rzhaOC2EsA46HQNfakhk4EFu3HTinTfNltXkPGHukhUr4Nln4bvvzPSUAmydCA0xdvws3dBuBUf7NPr0vkGfPuYJzaI5+hFd7yQA8TuKOm7GK3tNv1O889YdXn65AmIzN50OvBoBoCTtu+9qudMkVERMVk6SGiEqkD64N9OHLwdgznxX/jpphll3C2mp2bXLOO/Tzp2mfzpxT9cuxsvO+m3Vcj9dFyu3rlDlvWRFNzXeeonf63j/lbIv7IE+53h9bBrPPVcRkZnfxay2NH9zBzWa975vrZ5/dbjE5U/8+N9br1ZscFZIkhohKpJOR9dhz9A5agPpGfa8Ofq0GZ7k/nVqNNEPwYLFdm2Am2MKV5M92L+npBPuKty+68yyH3xYscKs4Vmsxs190dtkcvGKG+fvO6NInixRQ7dWfWuGsfdUEy5e9eD0fd4OHBwy8XO/ipf77YoNzgpp58wQwkroXIOYOt747rVqrR/Xrpq4tUa5f50aTfRDsGB2fpG0i9gBwLpVJSz2qShcvenLv58JoX9/MwZnwZwDmtAg4DAA8bvu01yRfRLfvOPKvn16EhMrKjrzsvOLpH4NY3+ahINF/+2VuTWvpOQtTggVNO39KF8+N5xj74fhc+dHE+9d6tSoRmdD59bGsbnr1pW0U6dBO1Vyy8q7CdP7vsamN9rSufWF+6xkPJ5xJ2JpEuuinf5H7vWICkoA4ODewisL7z7oybAFc/lo5WMVGZlVqqwvISHUZe/B0895UM3zbzj8DiWb+KaEimipkaTG/Do/aKygtnVfIHfulGADpXIP6QbA1pmubS/Stv4WXO7GFb5Odt8jTQ3pBrB1IjLMOFou4T7TJfx50plP1g/jx52tKzIyqyRJjRBqqfsS6J0gaTd7/veLCfOagkmNzP1Uceq2imVkt5ksePYpdGklmbVbW6X/y8wnxvj92q77rKDBId3ZohoYq3EePGRf6OOG3PNDhnQXR0OnhRBWxrEKSoM3eHjm9zR7qDu//pRsmv3KNAmq0jlXZ+bw/9C35RIckzcUv4G01Bj5xLBi1yO8PKUpp04V8rhWW2qAyIZOABw77VnohKhKdkVhG0lqiiVJjRAq0tUfQ0gNY22O11+5XvJhwEUq2FLz1ltw7hy8KiNCK0b1Lsbvl9aVYOV70yRoqfWh1HxieH/1GGZ934dtvxf2QtBgReFs/nVq0rjmXnpEb+XGjYKPy+jFkqvMLyEh1Ke357Vp4bg5prA3sSYrvi5FJdr7KaRZxsMDatS4/4R5wsSqdWHPX02YOqc2Vy4X8+laMUhLDYB7ONFh+wGI3369kBW0e/tJ5xXB3neasmpkd6pVLZjQ5Rbfk5aaYmnotBDCOvnVa8bof/8PgDcm6MjMLO8ec94U5eWtmioP8MznX/L612NZ92NxiapCdc+LLPzkInPnVkh0lslGT0xDY5Xt+F2FvAg0fPsJ11pg4wBZd+DWXwUelrmfSk7e9YSwAKPerIWv2xWOnanGwvmFfUothUJaar77DkaOhLVry7drUUK2znSONRZSWffzjaLXVRQ8nFMY+MRNnnjC/KFZsuhYY0fZfYe9Cib32ed1WLXjvP6awqBBFRubWdnYgkc4igLXTx0t8LDBoMFEzkwkqRHCAriFNOf1fsbZDKdMUUgvaTHaQhVsqdmwAWbPNk6XICpGp07GK9C636sUM7JNJiDNUbtxCO5OydxJs+fw4X8+ajyI9fwTeecdGDaswsMzq8PXuuL73FUiOj1Q4LH/e/Akf82qyRdjPlIhMusiryIhLMRzr9SjuucFbLJSOPlHOVprCmmpkY6GFe+BB+vgYHeX81d9OXq4iCxVUUhJdeOnNS6sX19x8VkiG78YmoXuBiB+5z+OmZJ3mgTtnciBdaqSdMuHC5fdSfrHXLeuzhnU9DtNNZ8bqsRmTSSpEcJCOIV0ZN20kSS+X4d6upnl2JMU37METv5RtK5nbBoresoEA6eu1qTn49Ur7TQJuZwDia59BIAj+/5Z4ydnmgQ3EhPhbAlnobAW7gF1CfY9BcChQ/98VKZJKClJaoSwFDodDbo/jr1tBiR+DBkpZduP1KmxDDobOrc8A8C6dUWM1VdkSHcunY4R/fdw9qMAPnjp2/yPZZ/Xvx7qSr16aK//kUcEkYHG6RISDmTke2jHXl9e+WY6X/3aUY3IrEplfwkJYVkCHwb3emTcucWiGb8XWoireDKhpaXo3EUPwO4EnyJqEEnxvbz869YmwOc8uqR/dgDLqVOjvSHdADgHEBl8DIBD+/J/oNl/xJv3V7/Cqm2xakRmVWzVDkAIkYfOBsLH0bVnIL8d6UCycwYjRtqVcifSp8ZSNGpTj81vtKF5+BFs+BvQF1xJMUhLTV73nS4hp06N8Rhq7lzW6YioayzEmZCQPwOW12/JyUtICEtT8wn+3fZXAKa+m05qaim3VwqOfpLbT+qw8Y6iTdQB7JVrcOPgfdaSlpp8fJqxbMf/0XPSTBbMzzPBY06dGrR7rCKjjH/boaOu+UbM5dapkeJ7xZKkRghLY2PHoGHB1PT7i0tXXPj0k6xS7qBgBjNtGiQmwuDBpgtTlICNHnxbGn++8nvh6ygyoWU+9l4kJrXip3092bA2722Y7NtPaPT2E1A30peuUb/wdPe1pKXdW26Q86PENHhaCGH97OsNYkKf2QBMm5rB7dul2LiQlppq1aBOHfDxMV2MomQyvdvw4qKPCO/6SKHz+oCCgtx+yiumqbEzWfyePLNWZ5/XBoNGbz8B9lXC+WVsdz7sNwpHx3vLcya01OLfbGryEhLCEtk6MeDZGtSqepwr1xyZN7c0M13KvSZLYlu9NesSOvPH6QB+21jY7QOFYN/TzJ2ZzFtvVXh4FqlZSy8Ajp32zZMI5rTUaDgB9Igwfr/9F2TczF18r6O/3H4qjhZPCyE0wS58KG8+9j4A098rRWuNUnD007JlMH48bN9u2hhFCfjE0DnqNwDWrS6kqKJioKrHZYYNvcuAARUcm4XyDYsixO8kAHt2Z5/P2ed1Xf8TvPQSPPywWtGZkaMvOFbj+m1PEveczF0sHYVLTpIaISyVnTtPPFWV0ConiArcz9XLGcVvA4XWqfnhB3j3XYiPN32Yohh6Bzq3/huAdeuLuCrJNAn3eDUiutYeAOK3ZSeC2ed1s7D9zJoFzz+vUmxmtibxKbyfvc7jTwfkLhv40BEOvdeA955fomJk1kFeRUJYMNsGLxI/rQsbXm1O8J1ZJdyqYEtNzic9TTbZW4F2XX3R22Ry/IwXp07leSC79eHGbQ82brInLk6V8CyP3oHoBhcAiN+Z00RZOarq1g13BuCP4x5kZH+O8fG6Q4OAIwRUKedkt5WAvMUJYckcffFuO8H4c8JEuHmiBBvJNAmWxr12O5qH7QRg3do8LW7ZrQ8Hz0TRsbsHTz6pRnSWKTpawck+FX3mtewlxpP4dpor587BtWv339aa1Qz3x8XhFukZthw/nr2wkNZXUTg5QkJYupCBULUDl5NceXvU7uJn8C7kDVDuyavMowGdG2cnNavzzlZYCTq/llHrDj6kfOHOt68ONy7IPq+XbnuEwEAYOFDF4MzIxiuCBgHGKcoTjLMmsHWPP5NWTGTV701VjMw6yEtICEun02FoNJOWk7cz4YvH+eqzS8VsULBZRqZJUJlOR6f2dwnxO0lN76P3lucUlJM6JAXoq0Rjq8+CpL1gyOBeAqjdId0AeDbInQPq0H7jrbete6oz+ftJ/Li1iZqRWQV5ixPCCtj4RDHskU0ATH3PhszMIlbOHf0kFYUtScvONTk5qxbTHx+VZ2lO6X/tFpQrM/c6YOcBWXfISjpcIAHU7LGydSEy7CIACfuN5cSl+F7JafW0EEJzhr4aia/bFU6er8KShUV1KCh4r0luP6lPV72T8Yfre+HuleylOZM0ykWrAJ0NG04NIfyVw/R6zJvcBFDD0yTkiKhvvMd86Ihx3jf5UFJyktQIYSVcasYy+v9WAvDO25lk3W/2hELq1MycCfv2waOPmjdGUQSnauDZkMwsPYnbsidrzJ3PSJKawrhXD+CPC+Hs3OedW1VXyZ7QUrMtNUDDJk4832keY/79E5Bn9KLM/VQsDZ8WQmjPsFdr4eWSROLpqqxYknKftQp2FA4JgUaNwNfX7CGKIpyhDz5Dr9HkX52yO3xntz4Y5PZTYaKiq2KnTycpxZW/TtsClSMB9K0ZyrynXmBoh3lAnpYaOT+KJYdICCviXrsDLz38HQBvv5WabybfXIW01AjLENAwBge7NFLvOrBzh5LbUlOn+jGmT8tg5Eh147M0DtUa0jD4AADxexwAUCpD/yPP7OkSbhwCRZFZuktBy6eFENqj0zHiVX/83C/TquZqUpMLa60pfJqEd9+FgwcrJEpxHzZVW9OxwSYANvx8lZz/VU2/07wyxsCgQaqFZpnc6hATtheA+DhjP5PwoD8ZMgQ6dlQzMDNzq0NquhvxiXXZteUShqyc1in5oFIcW7UDEEKUjldED84saIzj3YNw4Sp4js2/QiF1ahYtgjVrwN8foqIqMFiRn60THVucY+kO2LA+g8lT8sxCLS1rBdnoiY64DL9C/F57aA/tIuJo94bagZmZ3p4l+0byzEdv0emBKyx8cw//qjEav8guQDu1o7No0lIjhLXR2eDYaLTx56MfQuadf6wg0yRYso7d3ACIO1CFW7eM/5jrtz2J22XDkSNqRmaZoqON5/OeI/5kGWwqTceSiAbGc+PQH07UqJJMdK3d1Kwh0yQUp3KcHUJoTc2+4BLMnj8CeG/cvnvL83ayKaT4nrReqy+kWQwhfifJzLJly2bjP2ZrYmuat7SV20+FqNe4Os1C43ksdiU377hxN92B69chNVXtyMyrQSN3AC5ddeXqdafspfICLo4kNUJYIxs7znlNJmbCLsZ90JJDCTnJTN6OhFJ8zyJ5RtKx4XYA1q821hvSfEG5ctD7NCR+SgwLn+2Hp0sy89Y8ibc3PPus2pGZl6t/PUL8TgIwd1k00//3Chvj66ocleWTl5AQViqgxaM8EvMjANOnXDYuvE9Ljdx+siA6G/r2OsOkRybS/4FlgBTfK5JnJOj0ub8qlaD4HgCeEbnTJUxf0JqxS6fz07ZIlYOyfPIWJ4S1snNl7FDjm9433/ty+jTk1qiBfH0PpKXGsnToEcDER9+iicfnwL1pEuT/UwhbJ/CMJDNLz/FLtSpPAuhSk4jgYwCk3jV2KNf6n2wKktQIYcWa9ehAp4h1ZGXp+WBGev6WmkI6Cmv+QmAtqnc2fk89A9xrfZCWtMKdy+yC+zMpRI5LID3TeIHX/LHS2RBZN/mfi0QxrOYQ9erVi6CgIBwdHalevToDBgzgwoULaoclhLr8WjHu8a8B+OJLG65cKTyp+fhj+P13jdf2sCZO1Um2bc738Q/zzba+leeWShnVqF8He9t07mY4kXDG2K+kMhyrlrFpvP/EaJqG7Aak+F5JWE1S0759e7799lsSExNZsWIFJ06c4LHHHlM7LCHUpdPR4eEGNA3ZzZ27tsyZm+clnedjXUQEtG4Nfn4qxCgK9fuFwTw663smLJ+CwaD9+YzKQ+cbQ7PsC/uuY8Z+JZUhqQmq58/oHh/Stt5mQFpqSsJqDtHLL79M8+bNCQ4OpmXLlowbN46dO3eSkZGhdmhCqEpXeyjjHv6IAO+zBDjvzfuIajGJ4rV9MBi9TSYnL9fCxfEOkybBU0+pHZWFcg+nWagxqTl5KQioJAmgpzGBM1SGqSFMxCorCiclJbF48WJatmyJnZ2d2uEIoS57Dx7uF0SvhrWw96kDObfh83yU/fZbuHAB/vUvCAtTJ0yRn1toK2LDdrH9WEsu3qjOxPfUjsiC2ehzW2oAnngCYmNVjKeieERw+koQS3b0BeRjSklYVd43duxYXFxc8PHx4cyZM6xatarI9dPS0khJScn3JYQW6cNHYO9gC8mH8yy99/KePRtefhkOHar42MR92DrTMeYEABsOtVc5GMvXrPFdAOz06Xz5JQwZonJAFcGxKt/seoa/k6vh73WewQ/vLn6bSk7VpGbcuHHodLoiv44ePZq7/iuvvMK+ffv49ddf0ev1PPnkk7mzlxZm6tSpeHh45H4FBgZWxJ8lRMVzrAK1hpCRacuS7f9m/aGOhdapqQz9EKxJx07GvjQrdvXm4EE4dUrdeCxZcJNofFyvkpFlT0KC2tFUEJ2OyPrG0sk+rteoFSjTJBRHpxSVFZjZlStXuHbtWpHrhIaGYm9vX2D5uXPnCAwMZPv27bRo0aLQbdPS0khLS8v9PSUlhcDAQJKTk3F3dy9f8EJYmttneO+ZTxi3dCrRobuIS2yEztb42mneHOLiYNUq6NVL5ThFrrRLB/CuGUZqmgsAnTvDr7+qHJSlykpn8vPrUFzD6Pd8XUJDQa8vfjNrd/p/b1Kz11vY6dO5veMt7KLfVjskVaSkpODh4VHs9VvVPjV+fn74lXE4hiH7o2fepOWfHBwccHBwKNP+hbA6LkE8NSCViSvuEn8yhq3bMnmgrfGhnI8u0tHQsjhUa8gD0adZu9WY1EhLWhH09kyc34OJE6FOHRg2DObOVTso8wuqWwOAjCx7Fq9pzKBolQOycFbxFhcXF8ecOXPYv38/p0+fZuPGjfTt25datWrdt5VGiMqoSuthPNn6vwB8MPPex1i5/WS5ZswLZvJk48+SdBavslXH1nndmxph+qIHVIzEOljFS8jZ2Znvv/+ejh07UrduXQYPHkxUVBSbN2+Wlhgh8nKvy6h3YgD48Ucdf/5pXCwtNZYrMhKCjKOUK82FujzOGIswc/euunFUGI8GNAreB0DXlidUDsbyWcWQ7sjISDZu3Kh2GEJYhXotGtKjB6xeDTNnwrx50lJj6STpLLlFi4zfDxxQN44KY+/BurcG8b9tTejzVG1A7k4URV5CQmjQmDHG7wsWwNWrxsTml1+gWTN14xKF+/ln4/fVq9WNw5rs2aN2BBXHN7AGT7VdiKuLFJstjiQ1QmhQ27bQpAlERxuTmubNoWtX8PVVOzJRmNBQ4/dWrdSNwxpMmGD8PmOGunFUqMDeoLMFb/lUUhxVh3RXtJIOCRNCC5KTwcND7ShESWRmwuLFxiHd/v5qR2PZDAZITIR69SrZ7dSsu6B3VDsK1VjFkG4hhPnkTWiWL4fr16FHD7loWiJbWxg4UO0orIONDdSvr3YUKqjECU1pSFIjhMZdvQp9+hh/3rBBkhohhHZJnxohNCw9HcLD7/0uo2uEEFomb3FCaJi9PfTte+/3StUHQQhR6UhSI4TGvfjivZ/T09WLQwghzE2SGiE0LiwMxo0zDu9u2VLtaIQQwnyko7AQlcDUqWpHIIQQ5ictNUIIIYTQBElqhBBCCKEJktQIIYQQQhMkqRFCCCGEJkhSI4QQQghNkKRGCCGEEJogSY0QQgghNEGSGiGEEEJogiQ1QgghhNAESWqEEEIIoQmS1AghhBBCEySpEUIIIYQmSFIjhBBCCE2QpEYIIYQQmmCrdgAVSVEUAFJSUlSORAghhBAllXPdzrmO30+lSmpu3rwJQGBgoMqRCCGEEKK0bt68iYeHx30f1ynFpT0aYjAYuHDhAm5ubuh0OpPsMyUlhcDAQM6ePYu7u7tJ9imM5Niajxxb85Fjaz5ybM3H0o+toijcvHkTf39/bGzu33OmUrXU2NjYEBAQYJZ9u7u7W+SJoAVybM1Hjq35yLE1Hzm25mPJx7aoFpoc0lFYCCGEEJogSY0QQgghNEGSmnJycHBg4sSJODg4qB2K5sixNR85tuYjx9Z85Niaj1aObaXqKCyEEEII7ZKWGiGEEEJogiQ1QgghhNAESWqEEEIIoQmS1AghhBBCEySpKYe5c+dSs2ZNHB0diY2NZdeuXWqHpAlbtmyhZ8+e+Pv7o9Pp+OGHH9QOSROmTp1KdHQ0bm5uVKlShd69e5OYmKh2WJrwySefEBUVlVu4rEWLFqxZs0btsDRp2rRp6HQ6Ro4cqXYomjBp0iR0Ol2+r3r16qkdVplJUlNGy5YtY9SoUUycOJG9e/fSsGFDunbtyuXLl9UOzerdvn2bhg0bMnfuXLVD0ZTNmzfzwgsvsHPnTtatW0dGRgZdunTh9u3baodm9QICApg2bRp79uxh9+7ddOjQgYceeojDhw+rHZqmxMfH89lnnxEVFaV2KJrSoEEDLl68mPu1detWtUMqMxnSXUaxsbFER0czZ84cwDivVGBgIC+++CLjxo1TOTrt0Ol0rFy5kt69e6sdiuZcuXKFKlWqsHnzZtq0aaN2OJrj7e3NjBkzGDx4sNqhaMKtW7do0qQJ8+bN4+2336ZRo0bMmjVL7bCs3qRJk/jhhx/Yv3+/2qGYhLTUlEF6ejp79uyhU6dOuctsbGzo1KkTO3bsUDEyIUouOTkZMF58helkZWWxdOlSbt++TYsWLdQORzNeeOEFevToke99V5jGn3/+ib+/P6GhofTr148zZ86oHVKZVaoJLU3l6tWrZGVlUbVq1XzLq1atytGjR1WKSoiSMxgMjBw5klatWhEREaF2OJqQkJBAixYtuHv3Lq6urqxcuZLw8HC1w9KEpUuXsnfvXuLj49UORXNiY2NZuHAhdevW5eLFi0yePJkHHniAQ4cO4ebmpnZ4pSZJjRCV0AsvvMChQ4es+t65palbty779+8nOTmZ5cuXM3DgQDZv3iyJTTmdPXuWl156iXXr1uHo6Kh2OJrTvXv33J+joqKIjY0lODiYb7/91ipvnUpSUwa+vr7o9Xr+/vvvfMv//vtvqlWrplJUQpTM8OHD+emnn9iyZQsBAQFqh6MZ9vb2hIWFAdC0aVPi4+OZPXs2n332mcqRWbc9e/Zw+fJlmjRpkrssKyuLLVu2MGfOHNLS0tDr9SpGqC2enp7UqVOH48ePqx1KmUifmjKwt7enadOmbNiwIXeZwWBgw4YNcg9dWCxFURg+fDgrV65k48aNhISEqB2SphkMBtLS0tQOw+p17NiRhIQE9u/fn/vVrFkz+vXrx/79+yWhMbFbt25x4sQJqlevrnYoZSItNWU0atQoBg4cSLNmzYiJiWHWrFncvn2bp556Su3QrN6tW7fyfUr466+/2L9/P97e3gQFBakYmXV74YUX+Oabb1i1ahVubm5cunQJAA8PD5ycnFSOzrq99tprdO/enaCgIG7evMk333zDpk2bWLt2rdqhWT03N7cC/b5cXFzw8fGR/mAmMGbMGHr27ElwcDAXLlxg4sSJ6PV6+vbtq3ZoZSJJTRk9/vjjXLlyhTfffJNLly7RqFEjfvnllwKdh0Xp7d69m/bt2+f+PmrUKAAGDhzIwoULVYrK+n3yyScAtGvXLt/yBQsWMGjQoIoPSEMuX77Mk08+ycWLF/Hw8CAqKoq1a9fSuXNntUMTokjnzp2jb9++XLt2DT8/P1q3bs3OnTvx8/NTO7QykTo1QgghhNAE6VMjhBBCCE2QpEYIIYQQmiBJjRBCCCE0QZIaIYQQQmiCJDVCCCGE0ARJaoQQQgihCZLUCCGEEEITJKkRQmjegAEDePfdd82y70mTJtGoUaMyb9+8eXNWrFhhuoCEqMQkqRFCaNqBAwf4+eefGTFiRIU+7+TJk+nfv3+x673xxhuMGzcOg8FQAVEJoW2S1AghNO3jjz+mT58+uLq63ned9PR0kz/vqlWr6NWrV7Hrde/enZs3b7JmzRqTxyBEZSNJjRAiV1paGiNGjKBKlSo4OjrSunVr4uPjcx/ftGkTOp2OtWvX0rhxY5ycnOjQoQOXL19mzZo11K9fH3d3d5544glSU1NztzMYDEydOpWQkBCcnJxo2LAhy5cvz/fcP/74I7Vr18bR0ZH27duzaNEidDodN27cAODatWv07duXGjVq4OzsTGRkJEuWLCny78nKymL58uX07Nkz3/KaNWsyZcoUnnzySdzd3Xn22WcBGDt2LHXq1MHZ2ZnQ0FAmTJhARkZGvm2nTZtG1apVcXNzY/Dgwdy9e7fA8549e5bDhw/TrVs3FEVh0qRJBAUF4eDggL+/f75WI71ez4MPPsjSpUuL/FuEECWgCCFEthEjRij+/v7Kzz//rBw+fFgZOHCg4uXlpVy7dk1RFEX57bffFEBp3ry5snXrVmXv3r1KWFiY0rZtW6VLly7K3r17lS1btig+Pj7KtGnTcvf79ttvK/Xq1VN++eUX5cSJE8qCBQsUBwcHZdOmTYqiKMrJkycVOzs7ZcyYMcrRo0eVJUuWKDVq1FAA5fr164qiKMq5c+eUGTNmKPv27VNOnDihfPTRR4per1fi4uLu+/fs3btXAZRLly7lWx4cHKy4u7sr77//vnL8+HHl+PHjiqIoypQpU5Rt27Ypf/31l/Ljjz8qVatWVd57773c7ZYtW6Y4ODgoX3zxhXL06FFl/Pjxipubm9KwYcN8+58zZ47SpUsXRVEU5bvvvlPc3d2Vn3/+WTl9+rQSFxenzJ8/P9/6n3zyiRIcHFzyf5QQolCS1AghFEVRlFu3bil2dnbK4sWLc5elp6cr/v7+yvTp0xVFuZfUrF+/PnedqVOnKoBy4sSJ3GVDhw5VunbtqiiKoty9e1dxdnZWtm/fnu/5Bg8erPTt21dRFEUZO3asEhERke/x8ePH50tqCtOjRw9l9OjR93185cqVil6vVwwGQ77lwcHBSu/eve+7XY4ZM2YoTZs2zf29RYsWyrBhw/KtExsbWyCp6dy5szJnzhxFURTlgw8+UOrUqaOkp6ff93lWrVql2NjYKFlZWcXGJIS4P7n9JIQA4MSJE2RkZNCqVavcZXZ2dsTExPDHH3/kWzcqKir356pVq+bersm77PLlywAcP36c1NRUOnfujKura+7XV199xYkTJwBITEwkOjo633PExMTk+z0rK4spU6YQGRmJt7c3rq6urF27ljNnztz3b7pz5w4ODg7odLoCjzVr1qzAsmXLltGqVSuqVauGq6srb7zxRr79//HHH8TGxubbpkWLFvl+T0lJYfPmzbn9afr06cOdO3cIDQ1lyJAhrFy5kszMzHzbODk5YTAYSEtLu+/fIoQonq3aAQghrI+dnV3uzzqdLt/vOctyRvPcunULgNWrV1OjRo186zk4OJT4OWfMmMHs2bOZNWsWkZGRuLi4MHLkyCI7+fr6+pKamkp6ejr29vb5HnNxccn3+44dO+jXrx+TJ0+ma9eueHh4sHTpUj744IMSxwiwZs0awsPDCQwMBCAwMJDExETWr1/PunXrGDZsGDNmzGDz5s25xy0pKQkXFxecnJxK9VxCiPykpUYIAUCtWrWwt7dn27ZtucsyMjKIj48nPDy8zPsNDw/HwcGBM2fOEBYWlu8r58Jft25ddu/enW+7vB2UAbZt28ZDDz1E//79adiwIaGhoRw7dqzI586pH3PkyJFi49y+fTvBwcGMHz+eZs2aUbt2bU6fPp1vnfr16xMXF5dv2c6dO/P9vmrVKh566KF8y5ycnOjZsycfffQRmzZtYseOHSQkJOQ+fujQIRo3blxsjEKIoklLjRACMLZcPP/887zyyit4e3sTFBTE9OnTSU1NZfDgwWXer5ubG2PGjOHll1/GYDDQunVrkpOT2bZtG+7u7gwcOJChQ4fy4YcfMnbsWAYPHsz+/ftZuHAhQO6to9q1a7N8+XK2b9+Ol5cXH374IX///XeRCZefnx9NmjRh69atxRbIq127NmfOnGHp0qVER0ezevVqVq5cmW+dl156iUGDBtGsWTNatWrF4sWLOXz4cO6tt8zMTNasWcOYMWNyt1m4cCFZWVnExsbi7OzM119/jZOTE8HBwbnr/P7773Tp0qU0h1UIUQhpqRFC5Jo2bRqPPvooAwYMoEmTJhw/fpy1a9fi5eVVrv1OmTKFCRMmMHXqVOrXr0+3bt1YvXo1ISEhAISEhLB8+XK+//57oqKi+OSTTxg/fjxw7xbVG2+8QZMmTejatSvt2rWjWrVq9O7du9jnfuaZZ1i8eHGx6/Xq1YuXX36Z4cOH06hRI7Zv386ECRPyrfP4448zYcIEXn31VZo2bcrp06d5/vnncx/fvHkzrq6uNGnSJHeZp6cnn3/+Oa1atSIqKor169fzv//9Dx8fHwDOnz/P9u3beeqpp4qNUQhRNJ2iKIraQQghxD+98847fPrpp5w9e7Zc+7lz5w5169Zl2bJlBTr1mtqIESPIzMxk3rx5Jd5m7NixXL9+nfnz55sxMiEqB7n9JISwCPPmzSM6OhofHx+2bdvGjBkzGD58eLn36+TkxFdffcXVq1dNEGXRIiIiSp04ValShVGjRpkpIiEqF2mpEUJYhJdffplly5aRlJREUFAQAwYM4LXXXsPWVj57CSFKRpIaIYQQQmiCdBQWQgghhCZIUiOEEEIITZCkRgghhBCaIEmNEEIIITRBkhohhBBCaIIkNUIIIYTQBElqhBBCCKEJktQIIYQQQhMkqRFCCCGEJvw/T7dE1jfIKSkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wamit_negative_phases = [-1 * entry for entry in wamit_phases]\n",
+    "plt.plot(omegas, wamit_negative_phases, label = \"WAMIT\", linestyle = \"-\", color = \"orange\")\n",
+    "plt.plot(omegas, exc_phases, label = \"MEEM (NMK = 50)\", linestyle = \"--\", color = \"blue\")\n",
+    "\n",
+    "plt.xlabel(\"omega (rad/s)\")\n",
+    "plt.ylabel(\"excitation phase (rad)\")\n",
+    "plt.legend()\n",
+    "plt.title(\"MEEM - WAMIT excitation phase comparison\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9326bd19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'difference in MEEM - WAMIT excitation phases')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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45ddHSUkJbt++jSZNmsDLy8tofXh5eeHMmTO4ePFilW2y5D3U3LLoLsZPNiYtLQ0FBQVo2rRppduaN29eaQe+FwkJCRBCYNasWZg1a1aV86SmpqJhw4bS9fDwcKPbDTvoww8/XOX9K0YnhvEx5Xl7e+POnTvS9cTERAQHB5scC3Hx4kUIIapcT0DNB1GWLySra58czz77LIYPH46UlBR8//33ePfdd03Ob29vX+3p1xW1adNG9ryGtixevBgnT57EmjVrMHToUNkHx7KyMmnwuYGPj0+lM7vKi4uLw3fffYcTJ07A0dERWVlZ6NatGwCga9euuH79Oi5fvoykpCSUlpYaHRQzMzPx888/Y+LEiUbjLbp164ZNmzbhr7/+QrNmzap9nu+//z5effVV7N+/H//85z9lPcd7lZaWhszMTCxbtgzLli2rch7DQH3gbqT41VdfSWPmVqxYYfR6JCYmAkCtjr/KyMjAvHnzsG7dOqO2AEBWVlaNlpmcnIzY2NhK01u2bCndXv45VNy3vL29AUD2vlVxX4+MjISdnV2lcTBy9uGCggIsWLAAK1aswLVr1yCEkG4rvz7efPNNPPnkk2jWrBlat26NPn36YPjw4VKsZ8l7qLll0V0saqjGDKeDTp06tdpeoPKnugLGn3DKL2PVqlVVjndwcDDeRGvrjCO9Xg+NRoOtW7dWucyK30siV3XtK/+mJ0f//v2h1WoxcuRIFBUVYciQITVqT22IjY1FZGQkXnnlFSQlJVU5JqM6V65cqVTI7tixw+Qpw+XH1Tg5OcHHxwctWrQAALRr1w4uLi7Yu3cvkpKSjOYHgA0bNqCoqAjvv/8+3n///UrLXr16tXTackXDhg3D9OnTMW7cOPj6+qJ3796yn+e9MOwDzz//fLVnaFU8cG3btg3A3V6TixcvVlrHtW3IkCHYv38/Xn/9dbRr1w5ubm7Q6/Xo06eP0Wnhdam29i2D6gpzOY8zadIkrFixAq+88gq6dOkifVHp0KFDjdZH9+7dkZiYiB9++AG//vorvvzyS3z44Yf4/PPPMXbsWIveQ80ti+5iUWNj/P394ezsXGUX5YULF2r1sSIiIgDc7dWw5JN/eYZu3oCAgBovo6plbtu2DRkZGdX21kRGRkIIgfDw8Go/uSvJ2dkZAwYMwDfffIO+fftKgy6VMmzYMMyfPx8tW7ZEu3btZN8vKCio0tkZ5U+brkqHDh2kwkWr1aJLly7SAcjBwQGdOnXCvn37kJSUhICAAKPXb/Xq1WjdunWV0eMXX3yBNWvWVFvUhIaGolu3bti5cyfGjx9fqaCuK/7+/nB3d0dZWZmsfeDPP//Em2++idGjR+PEiRMYO3YsTp06BU9PTwD/26dOnz5d6UOFKdUd5O/cuYPff/8d8+bNw+zZs6XpVb3HWBJvhYWFVfmedP78een22lSx+EtISIBer6/Rtypv3LgRI0eONCqcCwsLq/zSQR8fH4wePRqjR49Gbm4uunfvjrlz52Ls2LEWv4eaWhbdxTE1Nsbe3h7x8fH4/vvvkZKSIk0/d+6c9OmutgQEBKBnz5744osvcOPGjUq3V4wdqhIfHw8PDw+88847KCkpqdEyKnrqqacghKjy4GX4tDVo0CDY29tj3rx5lT7pCSGMTlVVytSpUzFnzpxqu6Xr09ixYzFnzpwqez9M0el06NWrl9HFEBtUx8HBAbGxsdi3bx/27dsnjacx6Nq1K3bv3o2DBw9KsRRwt1do9+7dGDJkCJ5++ulKl9GjRyMhIcHk2TLz58/HnDlzpPEW9cHe3l76/qHTp09Xur38PlBSUoJRo0YhODgYH3/8MVauXIlbt27h1Vdflebp3bs33N3dsWDBgkpny5nq1TCMH6p4YDb0XFS870cffVRpGYbvnZLzjcL9+vXD//3f/+HAgQPStLy8PCxbtgyNGzdGVFSU2WVY4tNPPzW6vmTJEgBA3759LV6Wvb19pfWxZMmSSl8tUfF9xM3NDU2aNEFRUREAy95DzS2L7mJPjQ2aN28efvnlFzz44IOYMGECSktLsWTJErRq1Qp//vlnrT7Wp59+iri4OLRp0wbjxo1DREQEbt26hQMHDuDq1as4efKkyft7eHhg6dKlGD58ODp06IChQ4fC398fKSkp2LJlC7p164ZPPvnEojY99NBDGD58OBYvXoyLFy9KXeR79uzBQw89hIkTJyIyMhLz58/H9OnTcfnyZQwYMADu7u5ISkrCd999h7/97W+YOnXqvayae9a2bVuzvRoGpaWl+Oabb6q8beDAgUZfcrhnz54qTw2v+CVy5YWFhUnfWVIf4uLipMHa5QsX4G5Rs2DBAmk+gzVr1kAIgf79+1e5zH79+sHBwQGrV6+uciwHAPTo0UP6Tqb6tHDhQuzYsQOxsbEYN24coqKikJGRgWPHjuG3335DRkYGgLtF14kTJ/D777/D3d0d0dHRmD17NmbOnImnn34a/fr1g4eHBz788EOMHTsWnTp1wrPPPgtvb2+cPHkS+fn5+Prrr6tsg7OzM6KiorB+/Xo0a9YMPj4+aN26NVq3bo3u3bvj3XffRUlJCRo2bIhff/1Viv/KM5xqP2PGDAwdOhSOjo544oknqvySzWnTpmHt2rXo27cvJk+eDB8fH3z99ddISkrCpk2bav3bh5OSktC/f3/06dMHBw4cwDfffINnn31W9j5W3uOPP45Vq1bB09MTUVFROHDgAH777TfplG+DqKgo9OzZEx07doSPjw+OHDmCjRs3Gn1bu9z3UDnLIvCUblu1a9cu0bFjR+Hk5CQiIiLE559/Lp3aWN69ntIthBCJiYlixIgRIigoSDg6OoqGDRuKxx9/XGzcuFGax9zpvzt27BDx8fHC09NT6HQ6ERkZKUaNGiWOHDkizVPVqbVCiCqfV2lpqVi0aJFo0aKFcHJyEv7+/qJv377i6NGjRvNt2rRJxMXFCVdXV+Hq6ipatGghXnrpJXHhwoUq21nx+VQ8pbuqUy579OghevToYXJ5QvzvlG5TLD2lu3wbzZ3SXf5U3uqeS1XroLZP6RZCiG3btgkAwsHBQeTl5Rnddvv2baHRaAQAcejQIWl6mzZtRGhoqMnl9uzZUwQEBIiSkpJqt/WK6uOUbiGEuHXrlnjppZdESEiIcHR0FEFBQeKRRx4Ry5YtE0IIcfToUeHg4CAmTZpkdL/S0lLRqVMnERwcLO7cuSNN//HHH0XXrl2Fs7Oz8PDwEJ07dxZr1641el7lT+kWQoj9+/dL7xvln8PVq1fFwIEDhZeXl/D09BSDBw8W169fr/J5vvXWW6Jhw4bCzs7OaPur+F4jxN33jqefflp4eXkJnU4nOnfuLH766Sejeap77zG8fitWrDC5Xg37zNmzZ8XTTz8t3N3dhbe3t5g4cWKlU96r2wcrtv3OnTti9OjRws/PT7i5uYn4+Hhx/vz5SvPNnz9fdO7cWXh5eQlnZ2fRokUL8fbbb4vi4uJK68Hce6jcZd3vNELUcJQVERGRlZs7dy7mzZuHtLQ0xcemUd3jmBoiIiKyCSxqiIiIyCawqCEiIiKbwDE1REREZBPYU0NEREQ2gUUNERER2YT76sv39Ho9rl+/Dnd39xr/Wi0RERHVLyEEcnJyEBwcbPKLGe+roub69esICQlRuhlERERUA1euXEGjRo2qvf2+Kmrc3d0B3F0pHh4eCreGiIiI5MjOzkZISIh0HK/OfVXUGCInDw8PFjVEREQqY27oCAcKExERkU1gUUNEREQ2gUUNERER2QQWNURERGQTWNQQERGRTWBRQ0RERDaBRQ0RERHZBBY1REREZBNY1BAREZFNYFFDRERENoFFDREREdkEFjVERERkE1jUEBER2QC90BtdF0KguKy4ynnL9GXILMysh1bVL9X9Svenn36KRYsW4ebNm2jbti2WLFmCzp07K90sIiKboRd62Gks+8xbXFaM2/m30cC9QaXbSvWluFNwBxkFGcgpzkFxWTGKy4pRUlYCBzsHaB200Npr4ebkBl8XX3jrvGFvZy+1ZeflnUjLS0NL/5ZoHdAadho7ZBRk4MTNE2gT0AbFZcXYf2U/QjxD0Cm4E65kX8Hha4dx7MYxeOo88UyrZ+Ds6IykO0m4dOcSrmZfRXp+OtLy05Cen478knwUlhaisLQQpfpSONo7wsHOAU72TvDUesLb2Rs+Oh8EuAYg1DMUoZ6hcHNyw+Zzm3H85nE0922ODg06IDowGufSz+Hg1YNo7tsckT6ROHT1EPak7MH/Xfs/hHqGol/TfsgoyMDZtLM4m3YWPs4+GN1uNEI8Q3Ar9xZu5d1CRkEG8kvypYuAgL3GHnYaO2gdtPDUesJT6wkvnRcCXAOQV5KHfx//N5LuJCHSJxIt/VqikUcj/JH0B86ln0NLv5boFtIN3UK74Xb+bexM3ok9yXuQVZSFDg06INI7EmfTziLcOxxDWw2FgMDlzMu4nHkZN3Jv4Hb+bdwuuA290GP5E8vxcPjDtbKd1QWNEEIo3Qi51q9fjxEjRuDzzz9HbGwsPvroI2zYsAEXLlxAQECA2ftnZ2fD09MTWVlZ8PDwqIcW0/1ECAGNRmN2vlJ9KRzsTH+eyCzMREpWClKyUnAj5wbaBbVDp4adqpx3/5X92Hh2I5wdnOHn4gc/Fz88GPYgGns1rjRvdlE2fkn4BbuTdyPEIwTRgdE4duMYcotz0bdpX3hoPXA69TRO3TqF9Px09G/eH49GPorb+bdxK+8WUvNSkVuci4KSAhSUFtw9ANg5wtHeEU72TvDSecHX2Re+Lr4IcA2Ah9YDOy/vxLaEbWjo0RBtA9siOjAaaflp+O3Sb/B38UfboLY4fuM40vLT8Ej4I/B18cWJmydw4uYJXM2+it6RvfFoxKO4XXAb17Kv4VrONWQWZiK7KBtZhVkoKC1A+bcxF0cXuGvd4e7kDm9nbwS7B6Ohe0PoHHT49sy3OHz9MJr4NEF0YDRa+bfC2bSz2JuyF019m6KxV2PsS9mHUn0pekf2RlFZEf66/Re6hnRF28C2yC/Jx8WMi0jISMD1nOu4mXsTN3NvwtHOEX/r+Dd0DO5YaZ1fvH0RHx38CDfzbiLAJQD+rv4IdA1EuHc4Ir0j0dirMbQOWgB3Pz3/9NdP+PHCj8gozECXRl2QWZiJozeOIsovCrGNYnEm9Qw0Gg2eavkU/Fz8cDHjIi7evoikzCSpPTdzbyK7KBt5JXnIK85DUVkRNNDATmMHO40dXBxd4Km7e2D0cfZBQ4+GaOTeCN7O3vjvX//Frsu7EOIZIr1eV7Kv4I+kPxDsHowo/ygcunoImYWZ6BXRCzoHHY7eOIpTt06hRF+C6MBoxIXE4VrONSRnJSMlKwUZBRlm94vyNNDAx9kHwe7ByCrKQkpWinTb6Haj8dWTX+Hpb5/GpnObKt3X0c4RJfoSix6P5JvYaSKW9FtS748r9/itqqImNjYWnTp1wieffAIA0Ov1CAkJwaRJkzBt2jSz96+rouZOwR2U6kulNwxzFzkHvvLyivPw88Wf4WjviDDPMIR6hsLH2cdoOUII3Cm8A2+dt8XLVxshBBLvJMLF0QVBbkFGnyiFELiVdwt+Ln6w09jhxM0TKCotQsfgjsgoyMCZ1DNoG9QWfi5+yCvOw6nUUzh58yTOpZ9Dal4q+jTpg4EtBiK3OBepealIzUtFXkme0UHcwc4BDnYOcLRzhJuTG27k3sDiQ4tx4fYFNHRviDCvMDT2aoxmPs2gc9Bh8/nNSM1LRfug9kjOSsbha4cR4R2BKP8oHL1xFHqhx+NNH4fWQYszaWdwJvUM0vLTKj3vriFdEewejMLSQhSUFKBMlCGnKAdHbxytNK+/iz9uTr1ptG6KSosQuTgS13Ku1c0LUwUne6dqu7/VxlvnjTuFd0zOE+YZBgGBUn0pyvRlcLR3xPWc65VigfI00OCF9i/gy/5fYsbvM/DO3ndqu+lWw1PrCQ+tB7QOWjjZO8HBzgGl+lIUlRahuKz4bqFalFXpfl46LzTyaITTqafRJqAN/hz/Jzot74Qj148AuLsOowOjcTnzMrKKsuBo54jowGjEBMcgISMBfyT9AQBo5NEIEd4RCPEMkQpMPxc/uDu5Q+ughc5BB3uNPUr1pSjRl6C4rBhZhVnIKMjAncI7uJF7A1eyriAlKwW38m4hLjQOfSL7ICEjAcdvHseft/5EsHswejbuiXPp55CSlYJOwZ3wYOiDeKDRAziVegp7kvegoUdDRPlHoYVfC/x560+sObUGeqFHoFsgAl0D4evsCzcnN7g4usDZ0RkaaKAXepSJMhSWFiKrMAtZRVm4U3AHt/JuoaC0AANbDMTD4Q8jMSMR59PPIykzCe2D2qN7WHf8eetP7E3ZiwNXD8BT54meYT3Ro3EPNPJohJ8v/ozb+bfRwq8FDl49iF8Sf4GXzgthnnffx4Ldg+Hn4ofN5zZj1Z+rMCFmAj597NN63W4AGyxqiouL4eLigo0bN2LAgAHS9JEjRyIzMxM//PBDpfsUFRWhqKhIup6dnY2QkJBaL2r6fNMH2xK3WXSfioWOk70TXB1d4erkChdHF7g6usLNyQ0eWg/suLyj0icdV0dXhHqGItInEg3dG2L7pe24dOcSAl0D0blhZ3Ru2BnXsq/h4LWDaObbDG0D2+LEzRMoE2Xo36w/XJ1ckZyZjG6h3RDbMBYajQb5JfnSJzxDt2dxWTGc7J2ki6fWEwGuAfB29pYOmIWlhdidvBseWg+0DWwLZ0dnAEBOUQ52Je/CgSsHEOEdgR6NeyA5Mxknb53E6dTTCHQNxMh2I+Gl88L1nOu4nnMd6fnpyCvOQ25xbqVuV8Ob4JrTa3DsxjEAgIOdAxq6N0S4dzjCvcJx4OoBnE8/D52DDu5O7lJxoLXXoqisSFr3oZ6hSM5MhoD1bv5+Ln4I8QiBr4svdl3eVe2nTwc7BwxrPUxaj4ZPr8Uzi+Fo7yjNdyXrCkI/CgUATOo8CclZyTiXdg7tgtpB66DFzxd/hhACbQLboLV/azjZO2H1qdVIy0+Dg50DAl0DEeAaAHetO5wdnOHi6AJ7u/9/ACgrQVFZETILM6Wu6uyibACAh9YDA1sMxJ3COzh58ySSs5LhYOeA7mHdcTv/9t0DVWAb+Ln4YXfybpSUlaCpb1O0C2oHH50PNp7biPT8dNhp7NDArQEaejSEr7MvPLQe8NB6wNnBGRqNBhpoICCQX5KPnOIc5BTl4HbBbVzPuY4bOTdQoi9Bm4A26N+8P1KyUnDy1kmcSzuHBu4N0Cu8FxLuJCAlKwVdGnWBgMDvl36Hu9Yd4V7h2JuyV9p+fJ190cSnCUI9QxHkFoQgtyCcTTuLdafXoUyUVfkaPd7sccRHxiM9Px2peam4kXsDl+5cQmJGIvJK8uDi6IK8f+Zh2KZhWHd6HeIj4/FI+CM4cPUA3LXu6BzcGUduHMHZtLNo5d8KWUVZ2PLXFpTqSxHuHY4mPk0Q6R2JYPdgBLkFIdA1EF46L7g6ucLV0RU6Bx0ExN0Do74MeSV5yCrMQnZRNtLz03Et5xquZV/DzbybaBfYDoNbDUZqXipO3jyJP2/9CU+dJ/o06YMbOTdw4fYFdGjQAV46L2xP3A4A6BjcER0bdISnzhMbzmzApTuXEOYVhjDPMIR5hSHQNRDezt5meygBoKSsBBkFGUjNS8W1nGsoKStBr4he2H9lP3qt6oXWAa1xavwpxCyLwdEbR/HNwG/wWLPH4KXzQklZCZIykxDqGQqdg05aZnZRNrT2WqlHjCw3b+c8zN01Fy92fBGfP/55vT++3KJGNWNq0tPTUVZWhsDAQKPpgYGBOH/+fJX3WbBgAebNm1fnbavJgVEv9Eaf3gpLC6WDQFXCvcLh7+qP5Mxk3Mq7hbySPJxLP4dz6eeM5ruVdwv//eu/+O9f/5Wmnbh5At+e+Va6vvncZqP7BLsHI7c41+TjV2SvsYe/qz8aezXGhfQL0idYBzsHtA5oDVdHVxy6dgil+lKTy1m4b6HsxyzP0c4RZaIMpfpSJGclIzkrGTuxU7rdkI+7ObnB2cEZaflp0ECDRh6NcCX7Ci5nXgYANHBrgLZBbdHKvxV0Djqs+nMVUrJSYKexg7+LP/xd/eHu5A5nR2c4OzjDwc4BZaIMJWUlKNGXILc4F3qhx9BWQzGw5UDczL2J5MxkJGUm4cLtC0jPT0d8ZDxa+LXAiZsn4Ovsix6Ne+Bs2lkkZiSiQ4MOKNGXYMtfW+Bo74hW/q3QOqA1Wvi1gKuTq/R8rmVfww8XfoAQAs6OztInSgGBriFdEep5t1jJLMyUipqK26XhupO9Exb3XVzleq0YoS3qvQjZRdnw0nlZPMYirzgPN3JvINg9GC6OLtL0rMIs2NvZw83JrdJ98kvyoRd6o9sW912M2wW34efiJ+ugWBW90COnKAceWg+j5yd37EhWYRYu3bmExl6N4e3sXeU8/+r1LyRnJcPR7u54DEPB563zRrh3eJX3uZx5GeEfh0vxmeH/fk37YXLsZJNtKiotgp3GzqhwrU0t/Fqge1h3k/NUNbbixZgX7+lxHe0d7/ZYuAWiTWAbabrhdTOsI8P7p4+zD7x0XtJ9m/k2q7RMDy2HG9wrwxinMn3Vhbu1UE1RUxPTp0/HlClTpOuGnpratu35bRBCSJ+ELLmU6ctQJspQXFaM/JJ85BXnSTl4TnEO7hTcQYR3BPo17SdtVIWlhbiSdQXJWclSlt4+qD16R/bGX7f/wqFrh3Dk+hF467zxYNiDOJN6BhduX0DbwLYo0Zfgxws/wk5jhyC3IPx26Tdcz7kuPRdnB2d46bzg4ugCF0cXONo7olRfiuKyYhSVFuFO4R1kFmaiTJRJ2T1wtzgo1ZciLT8NJ26ekJYX7hWO7mHdcSbtDI5eP4oI7wi0DWqL1v6tcezmMfx88WcAQKBroNTN6a51h6vj3R4rO42dtK4KSwuRV5KHtoFt8feYv8NL54UbOTdwJfsKEjMSkZCRgAjvCDzZ4kmk5qUiLS8NMcExcLJ3QkJGAnycfeDr4ovkzGRcunMJUf5RCHQzLpLffOhNZBZmwlPrKa1vS0R4R6BrSNcqb+sd2Vv6u4lPE6PbekX0Mrnchh4NMaHTBLOPX/4AXdWZEBXnqahidOlg5wAfZx+zj1sVVyfXSs8TADx1ntXep3zxY+Bo74ggt6AatcHATmNX5ePKLdQ8dZ5o36C9yXkaejREQ4+GFrVLg/9/oP7/Bafhfzntut96HQzrxLBdG9aVrcft1sJe87+B29ZMNUWNn58f7O3tcevWLaPpt27dQlBQ1W94Wq0WWm397PiG7m9LP83WhM5Bh6a+TdHUt2mlg2EXly7oEtLF+A6tjK/O7jFb+juzMBOnU0/Dz8UPDdwaVPokW5XismKk56fjes51XM68DF9nX3QP6w47jR2uZl/FketHkFmYie5h3RHpEyndr6pPxQUlBdKZBjUR4hmCEM+QSoWEl87L6BNbU9+m0t9hXne7xKtip7Gr8UHcGhgOkgBQMVk2vBmVn4eUZdgfKvY+8DWqrFIBKKNIp9pjWM/VRazWQjVFjZOTEzp27Ijff/9dGlOj1+vx+++/Y+LEico2TsW8dF6IC42z6D5O9k4Idg9GsHswYoJjjG4zFBlVqerNxzD+hmqH0eDxauInfrK1HobXQup9EHyNqlNd/MQCsH6UP8XemqmmqAGAKVOmYOTIkYiJiUHnzp3x0UcfIS8vD6NHj1a6aURW4V7jJ6pf9xI/3W8YPymLPTV14JlnnkFaWhpmz56Nmzdvol27dvjll18qDR4mul8xflIXxk/yMX5SFsfU1JGJEycybiKqBuMndWH8JB/jJ2Wp5ewnlrhENoTxk7owfpKP8ZOy1BI/cc8hsiGMn9Sl4rdh8zWqHuMnZaklfuLWQGRDGD+pS8XXi/FT9Rg/KUvqqWH8RET1hfGTulTsWTO8ZnyNKmP8pCy1nNLNPYfIhjB+Uhej+On//wP4GlWF8ZOyDPETx9QQUb1h/KQuFX+HivFT9Rg/KYvxExEpwvAmz/jJ+jF+ko/xk7IYPxGRIip+ojXgJ1vrw/hJPsZPyuIp3USkiIpv/gb8ZGt9GD/Jx/hJWTylm4gUUbGb3oCfbK0P4yf5GD8pi2NqiEgRjJ/Ug/GTfIyflMUxNUSkCMZP6sH4ST7GT8riKd1EpAjGT+rB+Ek+xk/KYvxERIpg/KQejJ/kY/ykLMZPRKQIxk/qwfhJPsZPyuIp3USkCMZP6sH4ST7GT8riKd1EpAjGT+pR8QdIGT9Vj/GTsgzxE8fUEFG9YvykHhV/q4sH6uoxflIW4yciUgTjJ3UqHz+x8KyM8ZOyGD8RkSIYP6lL+YM146fqMX5SFk/pJiJFMH5Sl/KvFw/U1WP8pCye0k1EimD8pC7lD9aMn6rH+ElZHFNDRIpg/KQujJ/kYfykLI6pISJFMH5SF8ZP8jB+UhZP6SYiRTB+UhfGT/IwflIW4yciUgTjJ3Vh/CQP4ydlMX4iIkUwflIXxk/yGH1RYfleLRaA9YKndBORIhg/qQvjJ3mq/UVzrqt6wVO6iUgRjJ/UhfGTPBV//JNFev0yxE8cU0NE9Yrxk7owfpKn4u9ksUivX9X1AFsb7jlENobxk7owfpKn2l8057qqFzylm4gUwfhJXRg/yVMxfjJgkV4/eEo3ESmC8ZO6MH6Sp/x2W/7AygKwfvCUbiJSBOMndWH8JE/F+MmA66p+GOInwLoLG767EdkYxk/qwvhJnvLrpPy4Dhbp9aO6otLacGsgsjGMn9Sl/OtlOFjwQF0Z4ydlGeInwLoHC3PPIbIxjJ/UpXzPmuE1YuFZGeMnZZVf/9Y8WJjvbkQ2hvGTujB+kofxk7I4poaIFMH4SV0YP8nD+ElZRj01jJ+IqL4wflIXxk/yMH5SVvkxNeypIaJ6w/hJXRg/ycP4SVnl4yeOqSGiesP4SV0YP8nD+ElZ5dcze2pqwdtvv42uXbvCxcUFXl5eSjeHyGoxflIXxk/yVHdQ5bqqHxqN5n8/lcAxNfeuuLgYgwcPxvjx45VuCpFVY/ykLoyf5DHqqWH8pAg1/P6Tg9INkGvevHkAgJUrVyrbECIrx/hJXRg/yaeBBgKC8ZNC7DX2KEWpVcdPqilqaqKoqAhFRUXS9ezsbAVbQ1Q/GD+pC+Mn+ew0digTZYyfFGJvZw+UMX5SzIIFC+Dp6SldQkJClG4SUZ1j/KQujJ/kM2zbjJ+UUd0HJmui6NYwbdo0aDQak5fz58/XePnTp09HVlaWdLly5Uottp7IOjF+UhfGT/IZ1hXjJ2UYvquGY2qq8dprr2HUqFEm54mIiKjx8rVaLbRabY3vT6RGjJ/UxfB6MH4yr6ptm+uq/qjh7CdFixp/f3/4+/sr2QQim8P4SV0MrxfjJ/Mqxk9cT/XL8AV81hw/qWagcEpKCjIyMpCSkoKysjKcOHECANCkSRO4ubkp2zgiK8L4SV0YP8lXMX7itly/GD/VotmzZ+Prr7+Wrrdv3x4AsGPHDvTs2VOhVhFZH8ZP6sL4Sb6K2zZ7auoXBwrXopUrV0o7ffkLCxoiY4yf1IXxk3wV4ycW6PXLED9Z85gabhFENsZc/MQDgXVh/CQf4ydlsaeGiOqdufiJBwLrwvhJPsZPylLDmBoWNUQ2hvGTujB+ko/xk7LUcEo3twgiG8P4SV0YP8nH+ElZajilm3sOkY1h/KQujJ/kY/ykLMZPRFTvGD+pC+Mn+Rg/KYsDhYmo3jF+UpeqXi++RlVj/KQsntJNRPWO8ZO6VDX4kq9R1Rg/KUvaVhk/EVF9YfykLlKkwl+eNovxk7IMY2oYPxFRvWH8pC6G16v8gYKvUdUYPymL8RMR1Tuz8RN7AawK4yf5GD8piwOFiajemY2feMC0Koyf5GP8pCye0k1E9Y7xk7owfpKP8ZOy2FNDRPWO8ZO6MH6Sj/GTsjimhojqHeMndWH8JB/jJ2XxlG4iqneMn9SF8ZN8jJ+UxVO6iajeMX5SF8ZP8jF+UhbjJyKqd4yf1IXxk3zlfycLYI9WfeNAYSKqd4yf1IXxk3xS/KRn/KQEntJNRPWO8ZO6MH6Sr+JAVW7L9auqbdXasKghsjGMn9SF8ZN8jJ+UZRhTw/iJiOoN4yd1YfwkH+MnZTF+IqJ6x/hJXRg/ycf4SVkcKExE9Y7xk7pUFT9R1Rg/KYundBNRvWP8pC4VIxW+PtVj/KQs9tQQUb1j/KQujFTk47pSFsfUEFG9k3pqGD+pQsVIha9P9Rg/KUsqahg/EVF9kcbUMH5SBcZP8jF+UhbjJyKqd4yf1IWRinxcV8qSBgozfiKi+sL4SV0YP8nH+ElZauipcbBkZr1ej127dmHPnj1ITk5Gfn4+/P390b59e/Tq1QshISF11U4ikonxk7owfpKP8ZOybGZMTUFBAebPn4+QkBD069cPW7duRWZmJuzt7ZGQkIA5c+YgPDwc/fr1w8GDB+u6zURkAuMndWGkIh/XlbJspqemWbNm6NKlC5YvX45HH30Ujo6OleZJTk7GmjVrMHToUMyYMQPjxo2r9cYSkXmMn9SF8ZN8jJ+UpYYxNbKKml9//RUtW7Y0OU9YWBimT5+OqVOnIiUlpVYaR0SWY/ykLoyf5GP8pCybiZ/MFTTlOTo6IjIyssYNIqJ7w/hJXRipyMd1pSybiZ/+/PNP2QuMjo6ucWOI6N4xflIXxk/yMX5Sls3ET+3atYNGo4EQwuwOV1ZmvU+W6H7A+EldpEhFMH4yp+K6YgFYv9TQUyNr70lKSsKlS5eQlJSETZs2ITw8HJ999hmOHz+O48eP47PPPkNkZCQ2bdpU1+0lIjOqjZ/A+MkaSZGKnpGKOVxXylLDmBpZPTVhYWHS34MHD8bixYvRr18/aVp0dDRCQkIwa9YsDBgwoNYbSUTyMX5SF8PrxUjFPMZPylJD/GTxFnHq1CmEh4dXmh4eHo6zZ8/WSqOIqOaqjZ8E4ydrZHi9GKmYx/hJWTYTP5XXsmVLLFiwAMXFxdK04uJiLFiwwKKzpIiobjB+UhdGKvJxXSnLZuKn8j7//HM88cQTaNSokXSm059//gmNRoP//ve/td5AIrIM4yd1YfwkH+MnZUkfmGBDPTWdO3fGpUuXMH/+fERHRyM6Ohpvv/02Ll26hM6dO9dFG3H58mWMGTMG4eHhcHZ2RmRkJObMmWPUW0REdzF+UhfGT/IxflKWNKbGlnpqAMDV1RV/+9vfarst1Tp//jz0ej2++OILNGnSBKdPn8a4ceOQl5eH9957r97aQaQGjJ/UhZGKfFxXylLDmJoaFTUAcPbsWaSkpFTqLenfv/89N6qiPn36oE+fPtL1iIgIXLhwAUuXLmVRQ1QB4yd1YfwkH+MnZUljaqz47CeLi5pLly5h4MCBOHXqlPSFfEC5LtR6+vK9rKws+Pj4mJynqKgIRUVF0vXs7Oy6bhaR4hg/qQvjJ/kYPylLDfGTxe9uL7/8MsLDw5GamgoXFxecOXMGu3fvRkxMDHbu3FkHTawsISEBS5YswYsvvmhyvgULFsDT01O6hISE1Ev7iJTE+EldGKnIx3WlLDXETxYXNQcOHMCbb74JPz8/2NnZwc7ODnFxcViwYAEmT55s0bKmTZsGjUZj8nL+/Hmj+1y7dg19+vTB4MGDMW7cOJPLnz59OrKysqTLlStXLH26RKrD+EldGD/Jx/hJWTYZP5WVlcHd3R0A4Ofnh+vXr6N58+YICwvDhQsXLFrWa6+9hlGjRpmcJyIiQvr7+vXreOihh9C1a1csW7bM7PK1Wi20Wq1FbSJSO8ZP6lLpl6dZdFaL8ZOy1NBTY3FR07p1a5w8eRLh4eGIjY3Fu+++CycnJyxbtsyoAJHD398f/v7+sua9du0aHnroIXTs2BErVqyAnR3fmImqwvhJXaQxNYxUzGL8pCw1jKmxuKiZOXMm8vLyAABvvvkmHn/8cTz44IPw9fXF+vXra72BwN2CpmfPnggLC8N7772HtLQ06bagoKA6eUwitWL8pC6Mn+Rj/KQsm4yf4uPjpb+bNGmC8+fPIyMjA97e3nX2Zrl9+3YkJCQgISEBjRo1Mrqt4hs30f2O8ZO6MH6Sj/GTstQQP1n07lZSUgIHBwecPn3aaLqPj0+dblyjRo2CEKLKCxEZY/ykLoyf5GP8pCw1xE8WFTWOjo4IDQ2tt++iISLLMX5SF8ZP8jF+UpbN9dQAwIwZM/DPf/4TGRkZddEeIrpHjJ/UhfGTfIyflGWTY2o++eQTJCQkIDg4GGFhYXB1dTW6/dixY7XWOCKyHOMndWH8JB/jJ2UZ4idr7qmxuKgZMGBAHTSDiGqLFD+B8ZMaMH6Sj/GTsioWldbI4qJmzpw5ddEOIqolUvwkGD+pAeMn+Rg/KUsN8ZOsdzeeZUSkHoyf1IXxk3yMn5RlMwOFW7VqhXXr1qG4uNjkfBcvXsT48eOxcOHCWmkcEVmO8ZO6MH6Sj/GTstRwSres+GnJkiV44403MGHCBDz66KOIiYlBcHAwdDod7ty5g7Nnz2Lv3r04c+YMJk6ciPHjx9d1u4moGoyf1IXxk3yMn5Slhp4aWUXNI488giNHjmDv3r1Yv349Vq9ejeTkZBQUFMDPzw/t27fHiBEj8Nxzz8Hb27uu20xEJjB+UhfGT/IxflKWGsbUWDRQOC4uDnFxcXXVFiKqBYyf1IXxk3yMn5SlhviJWwSRjWH8pC6Mn+Rj/KQsNcRPfHcjsjGMn9SF8ZN8jJ+UpYb4iUUNkY1h/KQujJ/k47pSFntqiKjeMX5SF8ZP8km9WlxXiuCYGiKqd4yf1IXxk3yMn5RliJ9srqcmMTERM2fOxLBhw5CamgoA2Lp1K86cOVOrjSMiyzF+UhdGKvJxXSmrYq+iNbJ4i9i1axfatGmDQ4cOYfPmzcjNzQUAnDx5kr8LRWQFGD+pC+Mn+SrFT+ypqVc2GT9NmzYN8+fPx/bt2+Hk5CRNf/jhh3Hw4MFabRwRWY7xk7owfpKvUvzEArBe2eRA4VOnTmHgwIGVpgcEBCA9Pb1WGkVENWcufmJPjXVhpCIf15WybPKUbi8vL9y4caPS9OPHj6Nhw4a10igiqjlz8RM/3VoXxk/ySds2ex0VYYifbKqnZujQoXjjjTdw8+ZNaDQa6PV67Nu3D1OnTsWIESPqoo1EZAHGT+pSsYjh61O9ij0zLADrV8X4zxpZXNS88847aNGiBUJCQpCbm4uoqCh0794dXbt2xcyZM+uijURkAcZP6lKxiOHrUz2uK2WpIX6y6ActAcDJyQnLly/H7NmzcerUKeTm5qJ9+/Zo2rRpXbSPiCzE+Eld2PsgH3u1lKWGgcIWFzUGISEhCAkJqc22EFEtYPykLjxQy8cCUFk2eUr3U089hX/961+Vpr/77rsYPHhwrTSKiGqO8ZO6MFKRj+tKWWroqbF4i9i9ezf69etXaXrfvn2xe/fuWmkUEdUc4yd1Ye+DfOzVUpYaxtRYXNTk5uYafemegaOjI7Kzs2ulUURUc4yf1IUHavlYACrLED8BlT80WQuLi5o2bdpg/fr1laavW7cOUVFRtdIoIqo5xk/qwkhFPq4rZZVf39baW2PxQOFZs2Zh0KBBSExMxMMPPwwA+P3337F27Vps2LCh1htIRJZh/KQu7H2Qj71ayjLET8DdwcIOdjU+16jOWNyiJ554At9//z3eeecdbNy4Ec7OzoiOjsZvv/2GHj161EUbicgCjJ/UhQdq+VgAKqv8+rfWwcI1KrMee+wxPPbYY7XdFiKqBYyf1IWRinxcV8oqP6bGZuIng+LiYqSmpkKvN67WQkND77lRRFRzjJ/Uhb0P8rFXS1nl4yeb6am5ePEiXnjhBezfv99ouhACGo0GZWXWWb0R3S8YP6kLD9TysQBUltFAYSv9Aj6Li5pRo0bBwcEBP/30Exo0aMCNisjKMH5SF0Yq8nFdKat8/GQzPTUnTpzA0aNH0aJFi7poDxHdI8ZP6sLeB/nYq6Ws8uvbWsfUWFzmRkVFIT09vS7aQkS1gPGTuvBALR8LQGVpNBrpNbDW+MniouZf//oX/vGPf2Dnzp24ffs2srOzjS5EpCzGT+rCSEU+rivlWfvvP1kcP/Xq1QsA8MgjjxhN50BhIuvA+Eld2PsgH3u1lGevsUcpSq02frK4qNmxY0ddtIOIagnjJ3XhgVo+FoDKs7ezB8psqKeG3xpMZN0YP6kLIxX5uK6UZ3NjagBgz549eP7559G1a1dcu3YNALBq1Srs3bu3VhtHRJZj/KQu7H2Qj71ayjN8AZ+1xk8WFzWbNm1CfHw8nJ2dcezYMRQVFQEAsrKy8M4779R6A4nIMoyf1IUHavlYACrP2gcKW1zUzJ8/H59//jmWL18OR0dHaXq3bt1w7NixWm1cef3790doaCh0Oh0aNGiA4cOH4/r163X2eERqxfhJXRipyMd1pTzDF/DZTPx04cIFdO/evdJ0T09PZGZm1kabqvTQQw/h22+/xYULF7Bp0yYkJibi6aefrrPHI1Irxk/qUqn3gT011WKvlvIM8ZO19tRYPFA4KCgICQkJaNy4sdH0vXv3IiIiorbaVcmrr74q/R0WFoZp06ZhwIABKCkpMeoxIrrfMX5Sl0oHahad1WL8pDxpoLCVjqmxuKgZN24cXn75ZXz11VfQaDS4fv06Dhw4gKlTp2LWrFl10cZKMjIysHr1anTt2tVkQVNUVCSN+QHALwek+wLjJ3VhpCIf15XyDPGTzfTUTJs2DXq9Ho888gjy8/PRvXt3aLVaTJ06FZMmTaqLNkreeOMNfPLJJ8jPz8cDDzyAn376yeT8CxYswLx58+q0TUTWhvGTujB+ko/xk/Js6pTusrIy7NmzBy+99BIyMjJw+vRpHDx4EGlpaXjrrbcsfvBp06ZBo9GYvJw/f16a//XXX8fx48fx66+/wt7eHiNGjKj0xl3e9OnTkZWVJV2uXLlicRuJ1Ka6+MlwnQcC68L4ST7GT8qz9lO6Leqpsbe3R+/evXHu3Dl4eXkhKirqnh78tddew6hRo0zOU36cjp+fH/z8/NCsWTO0bNkSISEhOHjwILp06VLlfbVaLbRa7T21kUhtqoufDNfZZW9dKr4efH2qx/hJeTYXP7Vu3RqXLl1CeHj4PT+4v78//P39a3Rfvf7uCi0/ZoaIGD+pTcUDNXvSqsf4SXnm4qdr2dfQ0KNhfTbJiMVFzfz58zF16lS89dZb6NixI1xdXY1u9/DwqLXGGRw6dAiHDx9GXFwcvL29kZiYiFmzZiEyMrLaXhqi+xXjJ3Vh/CQf4yflGeKn9Px0HLl+BFp7LZzsnXAt5xreP/A+tidux8VJFxHmFaZI+ywuavr16wfg7pfhld+g6vJXul1cXLB582bMmTMHeXl5aNCgAfr06YOZM2cyXiKqgPGTujB+ko/xk/IM63zQt4OqvX3H5R0Y1W5UPbbqf1TxK91t2rTBH3/8Ue+PS6RGjJ/UhfGTfIyflGcYUwMAWnstPHWeKC4rhgYaDI4ajNe7vY4mPk0Uax9/pZvIxjB+UhfGT/IxflJe+ddgetx0zOk5R8HWVMZf6SayMYyf1IXxk3yMn5R3Ped/v7k4ou0IBVtSNf5KN5GNYfykLoyf5GP8pLzUvFTp73Dvez8Lurap5le6iUge/vaTujB+ko/xk/Ja+rUEAMzuPlvhllTN4jE1Sv1KNxHJU1X8VL7Xhl321oXxk3yMn5T33TPf4dC1QxgePVzpplRJNb/STUTyVBU/lS9w+OnWujB+ko/xk/Ka+zVHc7/mSjejWhaXuYZf6T506JD0K92rV6/G1KlTMX78+LpoIxFZoKr4qfzfPBBYl4oHavY+VI/xE5mjql/pJiLzGD+pCw/U8jF+InNkFTV//vknWrduDTs7O2g0GsyYMQOvv/46EhISkJubi6ioKLi5udV1W4lIBsZP6sL4ST7GT2SOrDK3ffv2SE9PB3D3V7Nv374NJycnREVFoXPnzixoiKwI4yd1YfwkH3u1yBxZe4+XlxeSkpIAAJcvX5Z+IZuIrA/jJ3XhgVo+xk9kjqz46amnnkKPHj3QoEEDaDQaxMTEwN7evsp5L126VKsNJCLLMH5SF8ZP8jF+InNkFTXLli3DoEGDkJCQgMmTJ2PcuHFwd3ev67YRUQ0wflIXxk/ysVeLzJE9ULh3797o06cPjh49ipdffplFDZGVYvykLjxQy8f4icyxeKDwrl27UFxcXKeNIqKaY/ykLoyf5GP8ROZwoDCRjWH8pC6Mn+RjrxaZw4HCRDaG8ZO68EAtH+MnMocDhYlsDOMndWH8JB/jJzJH9s8k9OnTBwA4UJjIyjF+UhfGT/KxV4vMsfi3n1asWFEX7SCiWsL4SV14oJaP8ROZI6uoGTRoEFauXAkPDw8MGjTI5LybN2+ulYYRUc0wflIXxk/yMX4ic2QVNZ6entLG5OnpWacNIqJ7Y/j0Wr6QKR8/kXVh/CQfe7XIHFlFTfnIifETkXUr/+lVCAGNRiP12vCAaX14oJaP8ROZwy2CyMaUPygaemsM/7O73vowfpKP8ROZI6unpn379rI/PRw7duyeGkRE96b8p1e90MNOYyfFT+wFsD6Mn+RjrxaZI6uoGTBggPR3YWEhPvvsM0RFRaFLly4AgIMHD+LMmTOYMGFCnTSSiOSrGD+V/58HTOvDA7V8jJ/IHFlFzZw5c6S/x44di8mTJ+Ott96qNM+VK1dqt3VEZDHGT+rC+Ek+xk9kjsVl7oYNGzBixIhK059//nls2rSpVhpFRDVXMX4q/z97AawP4yf52KtF5li89zg7O2Pfvn2Vpu/btw86na5WGkVENcf4SV14oJaP8ROZY/E3Cr/yyisYP348jh07hs6dOwMADh06hK+++gqzZs2q9QYSkWUYP6kL4yf5GD+RORYXNdOmTUNERAQ+/vhjfPPNNwCAli1bYsWKFRgyZEitN5CILMP4SV0YP8nHXi0yx+KiBgCGDBnCAobISjF+UhceqOVj/ETmcIsgsjGMn9SF8ZN8jJ/IHBY1RDaG8ZO6MH6Sj71aZA73HiIbw/hJXXiglo/xE5nDLYLIxjB+UhfGT/IxfiJzalzUFBcX48KFCygtLa3N9hDRPWL8pC6Mn+RjrxaZY/Hek5+fjzFjxsDFxQWtWrVCSkoKAGDSpElYuHBhrTeQiCzD+EldeKCWj/ETmWPxFjF9+nScPHkSO3fuNPoG4V69emH9+vW12jgishzjJ3Vh/CQf4ycyx+Lvqfn++++xfv16PPDAA0YbWKtWrZCYmFirjSOimtFAAwFRKX7iJ1vrw/hJPvZqkTkW7z1paWkICAioND0vL48bGJGVMOyLFeMn7qPWhwdq+Rg/kTkWbxExMTHYsmWLdN2wA3755Zfo0qVL7bWMiGrM8ObP+Mn6MX6Sj/ETmWNx/PTOO++gb9++OHv2LEpLS/Hxxx/j7Nmz2L9/P3bt2lUXbTRSVFSE2NhYnDx5EsePH0e7du3q/DGJ1MZOY4cyUcb4SQUYP8nHXi0yx+K9Jy4uDidOnEBpaSnatGmDX3/9FQEBAThw4AA6duxYF2008o9//APBwcF1/jhEasb4ST14oJaP8ROZU6MftIyMjMTy5ctruy1mbd26Fb/++is2bdqErVu31vvjE6kF4yf1YPwkH+MnMsfiMvfYsWM4deqUdP2HH37AgAED8M9//hPFxcW12rjybt26hXHjxmHVqlVwcXGRdZ+ioiJkZ2cbXYjuB4ZPsIyfrB/jJ/nYq0XmWLz3vPjii/jrr78AAJcuXcIzzzwDFxcXbNiwAf/4xz9qvYHA3a7zUaNG4e9//ztiYmJk32/BggXw9PSULiEhIXXSPiJrw/hJPXiglo/xE5lj8Rbx119/SYNzN2zYgB49emDNmjVYuXIlNm3aZNGypk2bBo1GY/Jy/vx5LFmyBDk5OZg+fbpFy58+fTqysrKky5UrVyy6P5FaMX5SD8ZP8jF+InMsHlMjhIBef7cr+7fffsPjjz8OAAgJCUF6erpFy3rttdcwatQok/NERETgjz/+wIEDB6DVao1ui4mJwXPPPYevv/66yvtqtdpK9yG6HzB+Ug/GT/KxV4vMsbioiYmJwfz589GrVy/s2rULS5cuBQAkJSUhMDDQomX5+/vD39/f7HyLFy/G/PnzpevXr19HfHw81q9fj9jYWMueANF9gPGTelTqqeFrVC3GT2SOxUXNRx99hOeeew7ff/89ZsyYgSZNmgAANm7ciK5du9Z6AwEgNDTU6LqbmxuAu2dhNWrUqE4ek0jNGD+pByMV+biuyByLi5ro6Gijs58MFi1aBHt7+1ppFBHdG8ZP6mKnseNrJAPjJzKnRt9TU5Xyv9hd1xo3bix1pxNRZYyf1KV8jwNfo+oxfiJzLC5qysrK8OGHH+Lbb79FSkpKpe+mycjIqLXGEVHNMH5SF41Gg///EvE1MoHxE5ljcZk7b948fPDBB3jmmWeQlZWFKVOmYNCgQbCzs8PcuXProIlEZCnGT+pS/nXha1Q9xk9kjsV7z+rVq7F8+XK89tprcHBwwLBhw/Dll19i9uzZOHjwYF20kYgsxPhJXRg/ycP4icyxeIu4efMm2rRpA+DuWUhZWVkAgMcffxxbtmyp3dYRUY0wflKX8oUMX6PqMX4icywuaho1aoQbN24AuHtK9a+//goAOHz4ML/ojshKMH5SF8ZP8jB+InMs3nsGDhyI33//HQAwadIkzJo1C02bNsWIESPwwgsv1HoDichyjJ/UhfGTPIyfyByLz35auHCh9PczzzyD0NBQHDhwAE2bNsUTTzxRq40jopph/KQujJ/kYfxE5tzz99R06dIFXbp0qY22EFEtYfykLoyf5GH8RObUqKi5ePEiduzYgdTUVOnHLQ1mz55dKw0joppj/KQujJ/kYfxE5lhc1Cxfvhzjx4+Hn58fgoKCjLtNNRoWNURWgPGTujB+kofxE5ljcVEzf/58vP3223jjjTfqoj1EVAsYP6kL4yd5GD+RORbvPXfu3MHgwYProi1EVEsYP6kL4yd5GD+RORZvEYMHD5a+m4aIrBPjJ3Vh/CQP4ycyR1b8tHjxYunvJk2aYNasWTh48CDatGkDR0dHo3knT55cuy0kIosZPsEaemgYP1k3xk/yMH4ic2QVNR9++KHRdTc3N+zatQu7du0ymq7RaFjUEFkBw5u9oZhh/GTdGD/Jw/iJzJFV1CQlJdV1O4ioFjF+UhfGT/IwfiJz7qnMFUJInwCJyHowflIXxk/yMH4ic2q09/z73/9G69atodPpoNPp0Lp1a3z55Ze13TYiqiHGT+rC+Ekexk9kjsXfUzN79mx88MEHmDRpkvTzCAcOHMCrr76KlJQUvPnmm7XeSCKyDOMndWH8JA/jJzLH4qJm6dKlWL58OYYNGyZN69+/P6KjozFp0iQWNURWgPGTujB+kqfiuuG6ooos3iJKSkoQExNTaXrHjh1RWlpaK40ionvD+EldGD/JU7FnhuuKKrK4qBk+fDiWLl1aafqyZcvw3HPP1UqjiOjeMH5SF8ZP8jB+InNq9Cvd//73v/Hrr7/igQceAAAcOnQIKSkpGDFiBKZMmSLN98EHH9ROK4nIIoyf1IXxkzwcKEzmWFzUnD59Gh06dAAAJCYmAgD8/Pzg5+eH06dPS/OxW5BIOYyf1IXxkzyVemq4rqgCi4uaHTt21EU7iKgWMX5SF8ZP8mmg4fZM1WLfHZENYvykLoyf5OO6IlO4RRDZIMZP6sL4ST6jXi2uK6qARQ2RDWL8pC7lD87sfTDNqADk9kwVcO8hskGMn9Sl/OvCA7VpRuuKPTVUAd/hiGwQ4yd1Yfwkn2H9sPijqrCoIbJBjJ/UhfGTfIZtmMUfVYV7D5ENYvykLoyf5DOsK27LVBVuFUQ2iPGTujB+ko/xE5nCoobIBjF+UhfGT/IxfiJTuPcQ2SDGT+rC+Ek+xk9kCrcKIhvE+EldGD/Jx/iJTGFRQ2SDGD+pC+Mn+Rg/kSnce4hsEOMndWH8JB/jJzKFWwWRDWL8pC6Mn+Rj/ESmsKghskGMn9SF8ZN8jJ/IFO49RDaI8ZO6MH6Sj/ETmcKtgsgGVRs/8YBplRg/ycf4iUxRTVHTuHFjaDQao8vChQuVbhaRVao2fuIB0yoxfpKP8ROZ4qB0Ayzx5ptvYty4cdJ1d3d3BVtDZL0YP6kL4yf5GD+RKaoqatzd3REUFKR0M4isHuMndWH8JB/jJzJFVaXuwoUL4evri/bt22PRokUoLS1VuklEVonxk7owfpKP8ROZopqemsmTJ6NDhw7w8fHB/v37MX36dNy4cQMffPBBtfcpKipCUVGRdD07O7s+mkqkOMZP6sL4ST7GT2SKolvFtGnTKg3+rXg5f/48AGDKlCno2bMnoqOj8fe//x3vv/8+lixZYlS0VLRgwQJ4enpKl5CQkPp6akSKYvykLoyf5GP8RKYo2lPz2muvYdSoUSbniYiIqHJ6bGwsSktLcfnyZTRv3rzKeaZPn44pU6ZI17Ozs1nY0H2B8ZO6MH6Sj/ETmaJoUePv7w9/f/8a3ffEiROws7NDQEBAtfNotVpotdqaNo9ItRg/qQvjJ/kYP5EpqhhTc+DAARw6dAgPPfQQ3N3dceDAAbz66qt4/vnn4e3trXTziKwO4yd1YfwkH+MnMkUVRY1Wq8W6deswd+5cFBUVITw8HK+++qpRtERE/8P4SV3K9zqwB8I0xk9kiiqKmg4dOuDgwYNKN4NINRg/qUv5AzR7IExj/ESmcKsgskGMn9SF8ZN8jJ/IFBY1RDaI8ZO6cKCwfIyfyBQWNUQ2iPGTuhjFTzxYm8T4iUzhVkFkgxg/qYvU+8DXxyzGT2QKixoiG1Rd/MRPt9aJvQ/yMX4iU7gHEdmg6uInHgisk9T7wNfHLBaAZAq3CiIbZPg0y/hJHRg/ycf4iUxhUUNkgwxv/Iyf1IG9D/IxfiJTuAcR2SDGT+rC+Ek+FoBkCrcKIhvE+EldGD/Jx/iJTGFRQ2SDGD+pC3sf5GP8RKZwDyKyQYyf1IXxk3wsAMkUbhVENojxk7owfpKP8ROZwqKGyAYxflIX9j7Ix/iJTOEeRGSDGD+pC+Mn+VgAkincKohsEOMndWH8JB/jJzKFRQ2RDWL8pC7sfZCP8ROZwj2IyAYxflIXHqjlYwFIpnCrILJBleInMH6yZoxU5OO6IlNY1BDZoErxk2D8ZM3Y+yAfe7XIFO5BRDaI8ZO68EAtHwtAMoVbBZENYvykLoxU5OO6IlNY1BDZIMZP6sLeB/nYq0WmcA8iskGMn9SFB2r5WACSKdwqiGwQ4yd1YaQiH9cVmcKihsgGMX5SF/Y+yMdeLTKFexCRDWL8pC48UMvHApBM4VZBZIMYP6kLIxX5uK7IFBY1RDaI8ZO6sPdBPvZqkSncg4hsEOMndeGBWj4WgGQKtwoiG8T4SV0YqcjHdUWmsKghskGMn9SFvQ/ysVeLTOEeRGSDGD+pCw/U8rEAJFO4VRDZIMZP6sJIRT6uKzKFRQ2RDWL8pC7sfZCPvVpkCvcgIhskxU9g/KQGPFDLxwKQTOFWQWSDGD+pCyMV+biuyBQWNUQ2SIqfBOMnNWDvg3zs1SJTuAcR2SDGT+rCA7V8LADJFG4VRDaI8ZO6MFKRj+uKTGFRQ2SDGD+pC3sf5GOvFpnCPYjIBjF+UhceqOVjAUimqGqr2LJlC2JjY+Hs7Axvb28MGDBA6SYRWSXGT+rCSEU+risyxUHpBsi1adMmjBs3Du+88w4efvhhlJaW4vTp00o3i8gqMX5SF/Y+yMdeLTJFFUVNaWkpXn75ZSxatAhjxoyRpkdFRSnYKiLrxfhJXXiglo8FIJmiiq3i2LFjuHbtGuzs7NC+fXs0aNAAffv2ZU8NUTUYP6kLIxX5pAKQ64qqoIqi5tKlSwCAuXPnYubMmfjpp5/g7e2Nnj17IiMjo9r7FRUVITs72+hCdD9g/KQu7H2QTyoA2atFVVB0D5o2bRo0Go3Jy/nz56HX3/20OWPGDDz11FPo2LEjVqxYAY1Ggw0bNlS7/AULFsDT01O6hISE1NdTI1IU4yd1YfwkHwtAMkXRMTWvvfYaRo0aZXKeiIgI3LhxA4DxGBqtVouIiAikpKRUe9/p06djypQp0vXs7GwWNnRfYPykLoZihgdq8xg/kSmKFjX+/v7w9/c3O1/Hjh2h1Wpx4cIFxMXFAQBKSkpw+fJlhIWFVXs/rVYLrVZba+0lUgvGT+pieF14oDaP44/IFFWc/eTh4YG///3vmDNnDkJCQhAWFoZFixYBAAYPHqxw64isD+MndWH8JB/jJzJFFUUNACxatAgODg4YPnw4CgoKEBsbiz/++APe3t5KN43I6jB+UhceqOVjAUimqKaocXR0xHvvvYf33ntP6aYQWT3GT+rCSEU+risyhe9wRDaI8ZO6sPdBPvZqkSncKohsEOMndeGBWj4WgGQK9yAiG8T4SV0YqcjHdUWm8B2OyAYxflIX9j7Ix14tMoVbBZENYvykLjxQy8cCkEzhHkRkgxg/qQsjFfm4rsgUvsMR2SBD8ZJZmIl9KfuQUXD3h1/56dY6sfdBPunbl7muqAosaohskJO9EwDg+M3jiFsRh1t5twAA/i7mf5aE6l8TnyYAgEjvSIVbYv16Nu4JX2dfPBz+sNJNISukEYZ+6ftAdnY2PD09kZWVBQ8PD6WbQ1RncopyMGnrJBy+fhipeal4JPwRjO0wFr0ieindNKrGpTuXEOoZCgc71XwnqmKEEOypuc/IPX6zqCEiIiKrJvf4zfiJiIiIbAKLGiIiIrIJLGqIiIjIJrCoISIiIpvAooaIiIhsAosaIiIisgksaoiIiMgmsKghIiIim8CihoiIiGwCixoiIiKyCSxqiIiIyCawqCEiIiKbwKKGiIiIbAKLGiIiIrIJDko3oD4JIQDc/QlzIiIiUgfDcdtwHK/OfVXU5OTkAABCQkIUbgkRERFZKicnB56entXerhHmyh4botfrcf36dbi7u0Oj0dTKMrOzsxESEoIrV67Aw8OjVpZJd3Hd1h2u27rDdVt3uG7rjrWvWyEEcnJyEBwcDDu76kfO3Fc9NXZ2dmjUqFGdLNvDw8MqNwRbwHVbd7hu6w7Xbd3huq071rxuTfXQGHCgMBEREdkEFjVERERkE1jU3COtVos5c+ZAq9Uq3RSbw3Vbd7hu6w7Xbd3huq07trJu76uBwkRERGS72FNDRERENoFFDREREdkEFjVERERkE1jUEBERkU1gUXMPPv30UzRu3Bg6nQ6xsbH4v//7P6WbZBN2796NJ554AsHBwdBoNPj++++VbpJNWLBgATp16gR3d3cEBARgwIABuHDhgtLNsglLly5FdHS09MVlXbp0wdatW5Vulk1auHAhNBoNXnnlFaWbYhPmzp0LjUZjdGnRooXSzaoxFjU1tH79ekyZMgVz5szBsWPH0LZtW8THxyM1NVXppqleXl4e2rZti08//VTpptiUXbt24aWXXsLBgwexfft2lJSUoHfv3sjLy1O6aarXqFEjLFy4EEePHsWRI0fw8MMP48knn8SZM2eUbppNOXz4ML744gtER0cr3RSb0qpVK9y4cUO67N27V+km1RhP6a6h2NhYdOrUCZ988gmAu78rFRISgkmTJmHatGkKt852aDQafPfddxgwYIDSTbE5aWlpCAgIwK5du9C9e3elm2NzfHx8sGjRIowZM0bpptiE3NxcdOjQAZ999hnmz5+Pdu3a4aOPPlK6Wao3d+5cfP/99zhx4oTSTakV7KmpgeLiYhw9ehS9evWSptnZ2aFXr144cOCAgi0jki8rKwvA3YMv1Z6ysjKsW7cOeXl56NKli9LNsRkvvfQSHnvsMaP3XaodFy9eRHBwMCIiIvDcc88hJSVF6SbV2H31g5a1JT09HWVlZQgMDDSaHhgYiPPnzyvUKiL59Ho9XnnlFXTr1g2tW7dWujk24dSpU+jSpQsKCwvh5uaG7777DlFRUUo3yyasW7cOx44dw+HDh5Vuis2JjY3FypUr0bx5c9y4cQPz5s3Dgw8+iNOnT8Pd3V3p5lmMRQ3Rfeill17C6dOnVZ2dW5vmzZvjxIkTyMrKwsaNGzFy5Ejs2rWLhc09unLlCl5++WVs374dOp1O6ebYnL59+0p/R0dHIzY2FmFhYfj2229VGZ2yqKkBPz8/2Nvb49atW0bTb926haCgIIVaRSTPxIkT8dNPP2H37t1o1KiR0s2xGU5OTmjSpAkAoGPHjjh8+DA+/vhjfPHFFwq3TN2OHj2K1NRUdOjQQZpWVlaG3bt345NPPkFRURHs7e0VbKFt8fLyQrNmzZCQkKB0U2qEY2pqwMnJCR07dsTvv/8uTdPr9fj999+ZoZPVEkJg4sSJ+O677/DHH38gPDxc6SbZNL1ej6KiIqWboXqPPPIITp06hRMnTkiXmJgYPPfcczhx4gQLmlqWm5uLxMRENGjQQOmm1Ah7ampoypQpGDlyJGJiYtC5c2d89NFHyMvLw+jRo5Vumurl5uYafUpISkrCiRMn4OPjg9DQUAVbpm4vvfQS1qxZgx9++AHu7u64efMmAMDT0xPOzs4Kt07dpk+fjr59+yI0NBQ5OTlYs2YNdu7ciW3btindNNVzd3evNO7L1dUVvr6+HA9WC6ZOnYonnngCYWFhuH79OubMmQN7e3sMGzZM6abVCIuaGnrmmWeQlpaG2bNn4+bNm2jXrh1++eWXSoOHyXJHjhzBQw89JF2fMmUKAGDkyJFYuXKlQq1Sv6VLlwIAevbsaTR9xYoVGDVqVP03yIakpqZixIgRuHHjBjw9PREdHY1t27bh0UcfVbppRCZdvXoVw4YNw+3bt+Hv74+4uDgcPHgQ/v7+SjetRvg9NURERGQTOKaGiIiIbAKLGiIiIrIJLGqIiIjIJrCoISIiIpvAooaIiIhsAosaIiIisgksaoiIiMgmsKghIps3fPhwvPPOO3Wy7Llz56Jdu3Y1vv8DDzyATZs21V6DiO5jLGqIyKadPHkSP//8MyZPnlyvjztv3jw8//zzZuebOXMmpk2bBr1eXw+tIrJtLGqIyKYtWbIEgwcPhpubW7XzFBcX1/rj/vDDD+jfv7/Z+fr27YucnBxs3bq11ttAdL9hUUNEkqKiIkyePBkBAQHQ6XSIi4vD4cOHpdt37twJjUaDbdu2oX379nB2dsbDDz+M1NRUbN26FS1btoSHhweeffZZ5OfnS/fT6/VYsGABwsPD4ezsjLZt22Ljxo1Gj/3jjz+iadOm0Ol0eOihh/D1119Do9EgMzMTAHD79m0MGzYMDRs2hIuLC9q0aYO1a9eafD5lZWXYuHEjnnjiCaPpjRs3xltvvYURI0bAw8MDf/vb3wAAb7zxBpo1awYXFxdERERg1qxZKCkpMbrvwoULERgYCHd3d4wZMwaFhYWVHvfKlSs4c+YM+vTpAyEE5s6di9DQUGi1WgQHBxv1Gtnb26Nfv35Yt26dyedCRDIIIqL/b/LkySI4OFj8/PPP4syZM2LkyJHC29tb3L59WwghxI4dOwQA8cADD4i9e/eKY8eOiSZNmogePXqI3r17i2PHjondu3cLX19fsXDhQmm58+fPFy1atBC//PKLSExMFCtWrBBarVbs3LlTCCHEpUuXhKOjo5g6dao4f/68WLt2rWjYsKEAIO7cuSOEEOLq1ati0aJF4vjx4yIxMVEsXrxY2Nvbi0OHDlX7fI4dOyYAiJs3bxpNDwsLEx4eHuK9994TCQkJIiEhQQghxFtvvSX27dsnkpKSxI8//igCAwPFv/71L+l+69evF1qtVnz55Zfi/PnzYsaMGcLd3V20bdvWaPmffPKJ6N27txBCiA0bNggPDw/x888/i+TkZHHo0CGxbNkyo/mXLl0qwsLC5L9QRFQlFjVEJIQQIjc3Vzg6OorVq1dL04qLi0VwcLB49913hRD/K2p+++03aZ4FCxYIACIxMVGa9uKLL4r4+HghhBCFhYXCxcVF7N+/3+jxxowZI4YNGyaEEOKNN94QrVu3Nrp9xowZRkVNVR577DHx2muvVXv7d999J+zt7YVerzeaHhYWJgYMGFDt/QwWLVokOnbsKF3v0qWLmDBhgtE8sbGxlYqaRx99VHzyySdCCCHef/990axZM1FcXFzt4/zwww/Czs5OlJWVmW0TEVWP8RMRAQASExNRUlKCbt26SdMcHR3RuXNnnDt3zmje6Oho6e/AwEAprik/LTU1FQCQkJCA/Px8PProo3Bzc5Mu//nPf5CYmAgAuHDhAjp16mT0GJ07dza6XlZWhrfeegtt2rSBj48P3NzcsG3bNqSkpFT7nAoKCqDVaqHRaCrdFhMTU2na+vXr0a1bNwQFBcHNzQ0zZ840Wv65c+cQGxtrdJ8uXboYXc/OzsauXbuk8TSDBw9GQUEBIiIiMG7cOHz33XcoLS01uo+zszP0ej2KioqqfS5EZJ6D0g0gIvVxdHSU/tZoNEbXDdMMZ/Pk5uYCALZs2YKGDRsazafVamU/5qJFi/Dxxx/jo48+Qps2beDq6opXXnnF5CBfPz8/5Ofno7i4GE5OTka3ubq6Gl0/cOAAnnvuOcybNw/x8fHw9PTEunXr8P7778tuIwBs3boVUVFRCAkJAQCEhITgwoUL+O2337B9+3ZMmDABixYtwq5du6T1lpGRAVdXVzg7O1v0WERkjD01RAQAiIyMhJOTE/bt2ydNKykpweHDhxEVFVXj5UZFRUGr1SIlJQVNmjQxuhgO/M2bN8eRI0eM7ld+gDIA7Nu3D08++SSef/55tG3bFhEREfjrr79MPrbh+2POnj1rtp379+9HWFgYZsyYgZiYGDRt2hTJyclG87Rs2RKHDh0ymnbw4EGj6z/88AOefPJJo2nOzs544oknsHjxYuzcuRMHDhzAqVOnpNtPnz6N9u3bm20jEZnGnhoiAnC352L8+PF4/fXX4ePjg9DQULz77rvIz8/HmDFjarxcd3d3TJ06Fa+++ir0ej3i4uKQlZWFffv2wcPDAyNHjsSLL76IDz74AG+88QbGjBmDEydOYOXKlQAgRUdNmzbFxo0bsX//fnh7e+ODDz7ArVu3TBZc/v7+6NChA/bu3Wv2C/KaNm2KlJQUrFu3Dp06dcKWLVvw3XffGc3z8ssvY9SoUYiJiUG3bt2wevVqnDlzRoreSktLsXXrVkydOlW6z8qVK1FWVobY2Fi4uLjgm2++gbOzM8LCwqR59uzZg969e1uyWomoCuypISLJwoUL8dRTT2H48OHo0KEDEhISsG3bNnh7e9/Tct966y3MmjULCxYsQMuWLdGnTx9s2bIF4eHhAIDw8HBs3LgRmzdvRnR0NJYuXYoZM2YA+F9ENXPmTHTo0AHx8fHo2bMngoKCMGDAALOPPXbsWKxevdrsfP3798err76KiRMnol27dti/fz9mzZplNM8zzzyDWbNm4R//+Ac6duyI5ORkjB8/Xrp9165dcHNzQ4cOHaRpXl5eWL58Obp164bo6Gj89ttv+O9//wtfX18AwLVr17B//36MHj3abBuJyDSNEEIo3QgioorefvttfP7557hy5co9LaegoADNmzfH+vXrKw3qrW2TJ09GaWkpPvvsM9n3eeONN3Dnzh0sW7asDltGdH9g/EREVuGzzz5Dp06d4Ovri3379mHRokWYOHHiPS/X2dkZ//nPf5Cenl4LrTStdevWFhdOAQEBmDJlSh21iOj+wp4aIrIKr776KtavX4+MjAyEhoZi+PDhmD59Ohwc+NmLiORhUUNEREQ2gQOFiYiIyCawqCEiIiKbwKKGiIiIbAKLGiIiIrIJLGqIiIjIJrCoISIiIpvAooaIiIhsAosaIiIisgksaoiIiMgm/D+0AMSFZn81vgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff = [exc_phases[i] - wamit_negative_phases[i] for i in range(260)]\n",
+    "\n",
+    "plt.plot(omegas, diff, color = \"green\")\n",
+    "\n",
+    "plt.xlabel(\"omega (rad/s)\")\n",
+    "plt.ylabel(\"phase difference (rad)\")\n",
+    "plt.title(\"difference in MEEM - WAMIT excitation phases\")\n",
+    "\n",
+    "# basically zero, except when it's 2pi."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/dev/python/test/infinite_m0_MEEM.ipynb b/dev/python/test/infinite_m0_MEEM.ipynb
new file mode 100644
index 0000000..4857df2
--- /dev/null
+++ b/dev/python/test/infinite_m0_MEEM.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3c53632b",
+   "metadata": {},
+   "source": [
+    "For various configurations, compute and plot hydro coefficients for high, finite m0 \n",
+    "and infinite m0. The expected behavior is that hydro coefficients converge as m0 increases,\n",
+    "and confirms the accuracy of the m0 = inf implementation.\n",
+    "Damping is set to zero in the code (the imaginary part computes to 0, but typical hydro coefficient computation involves multiplying by omega = inf, leading to errors. But it should be zero)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5ef36883",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from numpy import inf\n",
+    "from multi_condensed import Problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e96004ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m0s = [1e3, 3e3, 1e4, 3e4, 1e5, 3e5, 1e6]\n",
+    "rho = 1023\n",
+    "\n",
+    "# note that m0 * h > 1e8 leads to breakdown of the m_k solver\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "666dee16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_log_x_with_hline(x, y, v, coeff):\n",
+    "    plt.figure()\n",
+    "\n",
+    "    # Plot y vs x on a log-x axis\n",
+    "    plt.semilogx(x, y, label=coeff, marker='o')\n",
+    "\n",
+    "    # Add horizontal reference line at y = v\n",
+    "    plt.axhline(y=v, color='orange', linestyle='--', label='m0 = inf')\n",
+    "\n",
+    "    # Labeling\n",
+    "    plt.xlabel(\"m0\")\n",
+    "    plt.ylabel(coeff)\n",
+    "    plt.title(f\"{coeff} at increasing wavenumbers\")\n",
+    "    plt.legend()\n",
+    "    plt.grid(True, which=\"both\", linestyle=':', linewidth=0.5)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "def plot_am_dp(m0s, ams, dps, inf_am, inf_dp):\n",
+    "    plot_log_x_with_hline(m0s, ams, inf_am, \"Added Mass\")\n",
+    "    plot_log_x_with_hline(m0s, dps, inf_dp, \"Damping\")\n",
+    "\n",
+    "def solve_MEEM(h, d, a, heaving, m0, rho):\n",
+    "    NMK = [100] * (len(heaving) + 1)\n",
+    "    prob = Problem(h, d, a, heaving, NMK, m0, rho)\n",
+    "    a_matrix = prob.a_matrix()\n",
+    "    b_vector = prob.b_vector()\n",
+    "    x = prob.get_unknown_coeffs(a_matrix, b_vector)\n",
+    "    am, dp = prob.hydro_coeffs(x, \"umerc\")\n",
+    "    return am, dp\n",
+    "\n",
+    "def solve_for_all_m0s(h, d, a, heaving, m0s, rho):\n",
+    "    ams = []\n",
+    "    dps = []\n",
+    "    for m0 in m0s:\n",
+    "        am, dp = solve_MEEM(h, d, a, heaving, m0, rho)\n",
+    "        ams.append(am)\n",
+    "        dps.append(dp)\n",
+    "    inf_am, inf_dp = solve_MEEM(h, d, a, heaving, inf, rho)\n",
+    "    return ams, dps, inf_am, inf_dp\n",
+    "\n",
+    "def solve_and_plot(h, d, a, heaving, m0s, rho):\n",
+    "    ams, dps, inf_am, inf_dp = solve_for_all_m0s(h, d, a, heaving, m0s, rho)\n",
+    "    plot_am_dp(m0s, ams, dps, inf_am, inf_dp)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d91b7670",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/Bimali/Desktop/SEALab/semi-analytical-hydro/hydro/python/multi_condensed.py:551: LinAlgWarning: Ill-conditioned matrix (rcond=5.00553e-94): result may not be accurate.\n",
+      "  return linalg.solve(a,b)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# config0 \n",
+    "h = 1.001\n",
+    "d = [0.5, 0.25]\n",
+    "a = [0.5, 1]\n",
+    "heaving = [1, 1]\n",
+    "solve_and_plot(h, d, a, heaving, m0s, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "eacd0740",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/Bimali/Desktop/SEALab/semi-analytical-hydro/hydro/python/multi_condensed.py:551: LinAlgWarning: Ill-conditioned matrix (rcond=2.52058e-94): result may not be accurate.\n",
+      "  return linalg.solve(a,b)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# config1\n",
+    "h = 1.5\n",
+    "d = [1.1, 0.85, 0.75, 0.4, 0.15]\n",
+    "a = [0.3, 0.5, 1, 1.2, 1.6]\n",
+    "heaving = [1, 1, 1, 1, 1]\n",
+    "solve_and_plot(h, d, a, heaving, m0s, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a1bcc40a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# config2\n",
+    "h = 100\n",
+    "d = [29, 7, 4]\n",
+    "a = [3, 5, 10]\n",
+    "heaving = [1, 1, 1]\n",
+    "solve_and_plot(h, d, a, heaving, m0s, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8e436e0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/Bimali/Desktop/SEALab/semi-analytical-hydro/hydro/python/multi_condensed.py:551: LinAlgWarning: Ill-conditioned matrix (rcond=3.23334e-65): result may not be accurate.\n",
+      "  return linalg.solve(a,b)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# config3\n",
+    "h = 1.9\n",
+    "d = [0.5, 0.7, 0.8, 0.2, 0.5]\n",
+    "a = [0.3, 0.5, 1, 1.2, 1.6]\n",
+    "heaving = [1, 1, 1, 1, 1]\n",
+    "solve_and_plot(h, d, a, heaving, m0s, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fe929a2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# config4 \n",
+    "h = 1.001\n",
+    "d = [0.5, 0.25]\n",
+    "a = [0.5, 1]\n",
+    "heaving = [0, 1]\n",
+    "solve_and_plot(h, d, a, heaving, m0s, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dbe8963d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# config5\n",
+    "h = 1.001\n",
+    "d = [0.5, 0.25]\n",
+    "a = [0.5, 1]\n",
+    "heaving = [1, 0]\n",
+    "solve_and_plot(h, d, a, heaving, m0s, rho)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "47742f4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# config6\n",
+    "h = 100\n",
+    "d = [29, 7, 4]\n",
+    "a = [3, 5, 10]\n",
+    "heaving = [0, 1, 1]\n",
+    "solve_and_plot(h, d, a, heaving, m0s, rho)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/dev/python/test/m0_mod_confirmation.ipynb b/dev/python/test/m0_mod_confirmation.ipynb
new file mode 100644
index 0000000..fc50798
--- /dev/null
+++ b/dev/python/test/m0_mod_confirmation.ipynb
@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0ead241b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# notebook to confirm that modifying only part of the A matrix/b vector when changing m0 works.\n",
+    "# extent of time difference between the two is unclear.\n",
+    "import sys\n",
+    "import os\n",
+    "sys.path.append(os.path.relpath('../'))\n",
+    "import numpy as np\n",
+    "from multi_condensed import Problem\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9970e5ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = 100\n",
+    "d = [29, 7, 4]\n",
+    "a = [3, 5, 10]\n",
+    "heaving = [0, 1, 1]\n",
+    "NMK = [100, 100, 100, 100]\n",
+    "\n",
+    "m0s = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
+    "rho = 1023\n",
+    "\n",
+    "def modify_and_solve(prob, a_matrix, b_vector, m0):\n",
+    "  prob.change_m0(m0)\n",
+    "  a_matrix = prob.a_matrix_from_old(a_matrix)\n",
+    "  b_vector = prob.b_vector_from_old(b_vector)\n",
+    "  x = prob.get_unknown_coeffs(a_matrix, b_vector)\n",
+    "  am, dp = prob.hydro_coeffs(x, \"nondimensional\")\n",
+    "  return am, dp\n",
+    "\n",
+    "def create_and_solve(m0):\n",
+    "  prob = Problem(h, d, a, heaving, NMK, m0, rho)\n",
+    "  a_matrix = prob.a_matrix()\n",
+    "  b_vector = prob.b_vector()\n",
+    "  x = prob.get_unknown_coeffs(a_matrix, b_vector)\n",
+    "  am, dp = prob.hydro_coeffs(x, \"nondimensional\")\n",
+    "  return am, dp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "579911c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5318210260011256 9.564884053019341 10.276806697016582\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n",
+      "0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "am_modified = []\n",
+    "dp_modified = []\n",
+    "am_recreate = []\n",
+    "dp_recreate = []\n",
+    "\n",
+    "start = time.perf_counter()\n",
+    "prob = Problem(h, d, a, heaving, NMK, 1, rho)\n",
+    "a0 = prob.a_matrix()\n",
+    "b0 = prob.b_vector()\n",
+    "end = time.perf_counter()\n",
+    "tsingle = end - start\n",
+    "\n",
+    "start = time.perf_counter()\n",
+    "for m0 in m0s:\n",
+    "  am, dp = modify_and_solve(prob, a0, b0, m0)\n",
+    "  am_modified.append(am)\n",
+    "  dp_modified.append(dp)\n",
+    "end = time.perf_counter()\n",
+    "tmod = end - start\n",
+    "\n",
+    "start = time.perf_counter()\n",
+    "for m0 in m0s:\n",
+    "  am, dp = create_and_solve(m0)\n",
+    "  am_recreate.append(am)\n",
+    "  dp_recreate.append(dp)\n",
+    "end = time.perf_counter()\n",
+    "tcreate = end - start\n",
+    "\n",
+    "print(tsingle, tmod, tcreate)\n",
+    "\n",
+    "for i in range(len(am_modified)):\n",
+    "  print(am_modified[i] - am_recreate[i])\n",
+    "\n",
+    "for i in range(len(dp_modified)):\n",
+    "  print(dp_modified[i] - dp_recreate[i])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f669ff83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialize: 0.4658776489959564\n",
+      "Full A-matrix: 0.17097103298874572\n",
+      "Full b-vector 0.0020133999933023006\n",
+      "Matrix Solve: 0.017734749999362975\n",
+      "Hydro Coeff Computation: 0.007879554003011435\n",
+      "Changing m0: 0.4006503580021672\n",
+      "Changing A-matrix: 0.08661461900919676\n",
+      "Changing b-vector: 0.0007198549865279347\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Breakdown of how long each step takes.\n",
+    "start = time.perf_counter()\n",
+    "prob = Problem(h, d, a, heaving, NMK, 1, rho)\n",
+    "end = time.perf_counter()\n",
+    "print(\"Initialize:\", end - start)\n",
+    "\n",
+    "start = time.perf_counter()\n",
+    "a0 = prob.a_matrix()\n",
+    "end = time.perf_counter()\n",
+    "print(\"Full A-matrix:\", end - start)\n",
+    "\n",
+    "start = time.perf_counter()\n",
+    "b0 = prob.b_vector()\n",
+    "end = time.perf_counter()\n",
+    "print(\"Full b-vector\", end - start)\n",
+    "\n",
+    "start = time.perf_counter()\n",
+    "x = prob.get_unknown_coeffs(a0, b0)\n",
+    "end = time.perf_counter()\n",
+    "print(\"Matrix Solve:\", end - start)\n",
+    "\n",
+    "start = time.perf_counter()\n",
+    "am, dp = prob.hydro_coeffs(x, \"nondimensional\")\n",
+    "end = time.perf_counter()\n",
+    "print(\"Hydro Coeff Computation:\", end - start)\n",
+    "\n",
+    "start = time.perf_counter()\n",
+    "prob.change_m0(3)\n",
+    "end = time.perf_counter()\n",
+    "print(\"Changing m0:\", end - start)\n",
+    "\n",
+    "start = time.perf_counter()\n",
+    "a_matrix = prob.a_matrix_from_old(a0)\n",
+    "end = time.perf_counter()\n",
+    "print(\"Changing A-matrix:\", end - start)\n",
+    "\n",
+    "start = time.perf_counter()\n",
+    "b_vector = prob.b_vector_from_old(b0)\n",
+    "end = time.perf_counter()\n",
+    "print(\"Changing b-vector:\", end - start)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/hydro/python/test/multi_test.py b/dev/python/test/multi_test.py
similarity index 72%
rename from hydro/python/test/multi_test.py
rename to dev/python/test/multi_test.py
index f7b4892..02fe08e 100644
--- a/hydro/python/test/multi_test.py
+++ b/dev/python/test/multi_test.py
@@ -1,8 +1,12 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-import pytest
+# import pytest
 from multi_test_functions import MultiEvaluator
+import sys
+import os
+sys.path.append(os.path.relpath('../'))
+from multi_condensed import Problem
 
 def interpret_capytaine_file(filename, omega):
     file_path = "test/data/" + filename + "-imag.csv"
@@ -16,11 +20,11 @@ def interpret_capytaine_file(filename, omega):
     return(real_array, imag_array)
 
 def interpret_matlab_file(filename):
-    file_path = "test/data/" + filename + "-imag - matlab.csv"
+    file_path = "data/" + filename + "-imag-matlab.csv"
     df = (pd.read_csv(file_path, header=None))
     imag_array = df.to_numpy()
         
-    file_path = "test/data/" + filename + "-real - matlab.csv"
+    file_path = "data/" + filename + "-real-matlab.csv"
     df = (pd.read_csv(file_path, header=None))
     real_array = df.to_numpy()
     return(real_array, imag_array)
@@ -64,19 +68,19 @@ def potential_comparison(filename, filetype, arr, rtol, atol, R, Z, omega, nan_m
     return (match_r, match_i, is_within_threshold_r, is_within_threshold_i)
 
 def test_config1():
-    # Requires: h, d, a, heaving, m0, g, rho, NMK
-    config = MultiEvaluator(h = 1.001, d = [0.5, 0.25], a = [0.5, 1], heaving = [1, 1], m0 = 1, g = 9.81, rho = 1023, NMK = [50, 50, 50])
-    A = config.A_matrix()
-    b = config.b_vector()
-    coeffs = config.Ab_coefficients(A,b)
-    hydrocs = config.hydro_coefficients(coeffs)
-    R, Z, phi, nanregions = config.potential_matrix()
-
+    # Requires: h, d, a, heaving, m0, rho, NMK
+    prob = Problem(h = 1.001, d = [0.5, 0.25], a = [0.5, 1], heaving = [1, 1], NMK = [50, 50, 50], m0 = 1, rho = 1023 )
+    a0 = prob.a_matrix()
+    b0 = prob.b_vector()
+    x = prob.get_unknown_coeffs(a0, b0)
+    hydrocs = prob.hydro_coeffs(x, "nondimensional")
     
-    assert fractional_diff(hydrocs[2], 0.4995) <= 0.001
-    assert fractional_diff(hydrocs[3], 0.3679) <= 0.001
+    assert fractional_diff(hydrocs[0], 0.4995) <= 0.001
+    assert fractional_diff(hydrocs[1], 0.3679) <= 0.001
+
+    R, Z, phi, nanregions = prob.config_potential_array(prob.reformat_coeffs(x))
     
-    sum1, sum2, thres1, thres2 = potential_comparison("Total Potential", "matlab", phi, 0.01, 0.01, R, Z, config.omega, nanregions)
+    sum1, sum2, thres1, thres2 = potential_comparison("Total-Potential", "matlab", phi, 0.01, 0.01, R, Z, prob.angular_freq(1), nanregions)
     assert sum1 == 2500
     assert sum2 == 2500
 
diff --git a/hydro/python/test/multi_test_functions.py b/dev/python/test/multi_test_functions.py
similarity index 100%
rename from hydro/python/test/multi_test_functions.py
rename to dev/python/test/multi_test_functions.py
diff --git a/dev/python/test/test_potential_generator.ipynb b/dev/python/test/test_potential_generator.ipynb
new file mode 100644
index 0000000..0e1e3bb
--- /dev/null
+++ b/dev/python/test/test_potential_generator.ipynb
@@ -0,0 +1,889 @@
+{
+   "cells": [
+      {
+         "cell_type": "code",
+         "execution_count": 1,
+         "metadata": {
+            "colab": {
+               "base_uri": "https://localhost:8080/"
+            },
+            "id": "rSJf6s1tKGz7",
+            "outputId": "d58c0339-e27d-4651-99ac-f4da2155a60f"
+         },
+         "outputs": [],
+         "source": [
+            "# This generates configuration values with Capytaine.\n",
+            "\n",
+            "#!pip install capytaine #uncomment if first time running\n",
+            "\n",
+            "import capytaine as cpt\n",
+            "import numpy as np\n",
+            "import matplotlib.pyplot as plt\n",
+            "from capytaine.bem.airy_waves import airy_waves_potential, airy_waves_velocity, froude_krylov_force\n",
+            "import time\n",
+            "\n",
+            "import os\n",
+            "import sys"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 2,
+         "metadata": {
+            "id": "ukVJNFS8XIfE"
+         },
+         "outputs": [],
+         "source": [
+            "def save_potential_array(title, arr):\n",
+            "    file_path = \"data/\" + title + \"-real\" + \".csv\"\n",
+            "    np.savetxt(file_path, np.real(arr), delimiter=\",\", fmt=\"%.6e\")\n",
+            "    file_path = \"data/\" + title + \"-imag\" + \".csv\"\n",
+            "    np.savetxt(file_path, np.imag(arr), delimiter=\",\", fmt=\"%.6e\")\n",
+            "\n",
+            "# use to get rid of prints\n",
+            "def deafen(function, *args):\n",
+            "    real_stdout = sys.stdout\n",
+            "    sys.stdout = open(os.devnull, \"w\")\n",
+            "    output = function(*args)\n",
+            "    sys.stdout = real_stdout\n",
+            "    return output"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 3,
+         "metadata": {},
+         "outputs": [],
+         "source": [
+            "solver = cpt.BEMSolver()\n",
+            "\n",
+            "def timed_solve(problem, reps):\n",
+            "    t_lst = []\n",
+            "    for i in range(reps):\n",
+            "        t0 = time.perf_counter()\n",
+            "        result = solver.solve(problem, keep_details = True)\n",
+            "        t1 = time.perf_counter()\n",
+            "        t_lst.append(t1 - t0)\n",
+            "    tdiff = sum(t_lst)/reps\n",
+            "    return result, tdiff\n",
+            "\n",
+            "def get_points(a, d): # These points define the outline of the body\n",
+            "    d_prime = d + [0]\n",
+            "    d_index = 0\n",
+            "    a_index = 0\n",
+            "    pt_lst = [(0, - d[0])]\n",
+            "    for i in range(len(a)):\n",
+            "        pt_lst.append((a[a_index], - d_prime[d_index]))\n",
+            "        d_index +=1\n",
+            "        pt_lst.append((a[a_index], - d_prime[d_index]))\n",
+            "        a_index+=1\n",
+            "    return pt_lst\n",
+            "\n",
+            "# compute number of panels along each surface given total number along the outline\n",
+            "def get_f_densities(pt_lst, total_units):\n",
+            "    face_lengths = np.array([])\n",
+            "    for i in range(len(pt_lst) - 1):\n",
+            "        p1, p2 = pt_lst[i], pt_lst[i + 1]\n",
+            "        face_length = abs(p2[0] - p1[0]) + abs(p2[1] - p1[1]) # one of these two values will be zero\n",
+            "        face_lengths = np.append(face_lengths, face_length)\n",
+            "    total_length = sum(face_lengths)\n",
+            "    each_face_densities = np.vectorize(lambda x: max(1, x/total_length * total_units))(face_lengths) # each face needs at least one panel\n",
+            "    remainders = each_face_densities % 1\n",
+            "    each_face_densities = each_face_densities.astype(int)\n",
+            "    remaining_units = total_units - sum(each_face_densities)\n",
+            "    if remaining_units < 0: # high proportion of small faces\n",
+            "        for u in range(remaining_units * -1):\n",
+            "            i = np.argmax(each_face_densities) # cut density from the largest faces\n",
+            "            each_face_densities[i] = (each_face_densities[i]) - 1\n",
+            "    else:\n",
+            "        for u in range(remaining_units): # distribute remaining units where most needed\n",
+            "            i = np.argmax(remainders)\n",
+            "            each_face_densities[i] = (each_face_densities[i]) + 1\n",
+            "            remainders[i] = 0\n",
+            "    assert sum(each_face_densities) == total_units\n",
+            "    return each_face_densities\n",
+            "\n",
+            "def make_face(p1, p2, f_density, t_density):\n",
+            "    zarr = np.linspace(p1[1], p2[1], f_density + 1)\n",
+            "    rarr = np.linspace(p1[0], p2[0], f_density + 1)\n",
+            "    xyz = np.array([np.array([x/np.sqrt(2),y/np.sqrt(2),z]) for x,y,z in zip(rarr,rarr,zarr)])\n",
+            "    return cpt.AxialSymmetricMesh.from_profile(xyz, nphi = t_density)\n",
+            "\n",
+            "def faces_and_heaves(heaving, region, p1, p2, f_density, t_density, meshes, mask, panel_ct):\n",
+            "    mesh = make_face(p1, p2, f_density, t_density)\n",
+            "    meshes += mesh\n",
+            "    new_panels = f_density * t_density\n",
+            "    if heaving[region]:\n",
+            "        direction = [0, 0, 1]\n",
+            "    else:\n",
+            "        direction = [0, 0, 0]\n",
+            "    for i in range(new_panels):\n",
+            "        mask.append(direction)\n",
+            "    return meshes, mask, (panel_ct + new_panels)\n",
+            "\n",
+            "def get_excitation_phase(result):\n",
+            "    return np.angle((cpt.assemble_dataset([result]))[\"excitation_force\"][0][0][0])\n",
+            "\n",
+            "def make_body(pts, t_densities, f_densities, heaving):\n",
+            "    meshes = cpt.meshes.meshes.Mesh()\n",
+            "    panel_ct = 0\n",
+            "    mask = []\n",
+            "    for i in range((len(pts) - 1) // 2):\n",
+            "        p1, p2, p3 = pts[2 * i], pts[2 * i + 1], pts[2 * i + 2]\n",
+            "        # make a horizontal face\n",
+            "        meshes, mask, panel_ct = faces_and_heaves(heaving, i, p1, p2, f_densities[2 * i], t_densities[i], meshes, mask, panel_ct)\n",
+            "        # make a vertical face\n",
+            "        if p2[1] < p3[1]: # body on left\n",
+            "            region = i\n",
+            "        else: # body on right\n",
+            "            region = i + 1\n",
+            "        meshes, mask, panel_ct = faces_and_heaves(heaving, region, p2, p3, f_densities[2 * i + 1], t_densities[region], meshes, mask, panel_ct)\n",
+            "    body = deafen(cpt.FloatingBody, meshes) # unclosed boundary warnings\n",
+            "    return body, panel_ct, mask\n",
+            "\n",
+            "def construct_and_solve(a, d, heaving, t_densities, face_units, h, m0, rho, reps):\n",
+            "    pt_lst = get_points(a, d)\n",
+            "    f_densities = get_f_densities(pt_lst, face_units)\n",
+            "    \n",
+            "    body, panel_count, mask = make_body(pt_lst, t_densities, f_densities, heaving)\n",
+            "    body.dofs[\"Heave\"] = mask  \n",
+            "    body.show_matplotlib()\n",
+            "    \n",
+            "    rad_problem = cpt.RadiationProblem(body = body, wavenumber = m0, water_depth = h, rho = rho)\n",
+            "    solver = cpt.BEMSolver()\n",
+            "    result, t_diff = timed_solve(rad_problem, reps)\n",
+            "\n",
+            "    diff_problem = cpt.DiffractionProblem(body = body, wavenumber = m0, water_depth = h, rho = rho)\n",
+            "    result_d, t_diff_d = timed_solve(diff_problem, reps)\n",
+            "\n",
+            "    print(\"Panel Count: \", panel_count)\n",
+            "    print(result.added_mass)\n",
+            "    print(result.radiation_damping)\n",
+            "    print(\"Solve Time (Radiation): \", t_diff)\n",
+            "    print(\"Solve Time (Diffraction): \", t_diff_d)\n",
+            "    print(\"Excitation Phase: \", get_excitation_phase(result_d))\n",
+            "    return result, t_diff, result_d, t_diff_d, panel_count"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 4,
+         "metadata": {},
+         "outputs": [
+            {
+               "data": {
+                  "image/png": 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",
+                  "text/plain": [
+                     "<Figure size 640x480 with 1 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Panel Count:  6750\n",
+                  "{'Heave': 1586.2410564342385}\n",
+                  "{'Heave': 3186.3017275343254}\n",
+                  "Solve Time (Radiation):  56.05556566599989\n",
+                  "Solve Time (Diffraction):  0.08915816701482981\n",
+                  "Excitation Phase:  -0.5194497776393395\n"
+               ]
+            }
+         ],
+         "source": [
+            "# original - two cylinder\n",
+            "h = 1.001\n",
+            "d = [0.5, 0.25]\n",
+            "a = [0.5, 1]\n",
+            "heaving = [1, 1]\n",
+            "t_densities = [50, 100] # number of panels around each cylinder\n",
+            "face_units = 90 # number of panels along the outline of the configuration\n",
+            "m0 = 1\n",
+            "rho = 1023 # density of our special material\n",
+            "config = \"config0\"\n",
+            "reps = 1 # 1st solve always significantly longer than others\n",
+            "\n",
+            "result_r, solve_time_r, result_d, solve_time_d, panel_count_alt = construct_and_solve(a, d, heaving, t_densities, face_units, h, m0, rho, reps)"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 5,
+         "metadata": {},
+         "outputs": [
+            {
+               "data": {
+                  "image/png": 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",
+                  "text/plain": [
+                     "<Figure size 640x480 with 1 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Panel Count:  8930\n",
+                  "{'Heave': 4739.441619430274}\n",
+                  "{'Heave': 11662.810903111465}\n",
+                  "Solve Time (Radiation):  146.99396358401282\n",
+                  "Solve Time (Diffraction):  0.1805250830075238\n",
+                  "Excitation Phase:  -1.0626678333131123\n"
+               ]
+            }
+         ],
+         "source": [
+            "#staircase  - compound cylinder\n",
+            "h = 1.5\n",
+            "d = [1.1, 0.85, 0.75, 0.4, 0.15]\n",
+            "a = [0.3, 0.5, 1, 1.2, 1.6]\n",
+            "heaving = [1, 1, 1, 1, 1]\n",
+            "t_densities = [30, 50, 100, 120, 160]\n",
+            "face_units = 93\n",
+            "m0 = 1\n",
+            "rho = 1023\n",
+            "config = \"config1\"\n",
+            "reps = 1\n",
+            "\n",
+            "result_r, solve_time_r, result_d, solve_time_d, panel_count_alt = construct_and_solve(a, d, heaving, t_densities, face_units, h, m0, rho, reps)"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 6,
+         "metadata": {},
+         "outputs": [
+            {
+               "data": {
+                  "image/png": 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",
+                  "text/plain": [
+                     "<Figure size 640x480 with 1 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "data": {
+                  "text/html": [
+                     "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[15:22:03] </span><span style=\"color: #808000; text-decoration-color: #808000\">WARNING </span> Irregular frequencies for <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RadiationProblem</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">body</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>,                              \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"collection_of_meshes_28\"</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.000</span>, <span style=\"color: #808000; text-decoration-color: #808000\">water_depth</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100.0</span>, <span style=\"color: #808000; text-decoration-color: #808000\">radiating_dof</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Heave'</span>,   \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #808000; text-decoration-color: #808000\">rho</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1023.0</span><span style=\"font-weight: bold\">)</span>:                                                                                   \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         The body <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"collection_of_meshes_28\"</span><span style=\"font-weight: bold\">)</span> might display irregular frequencies \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         for <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>.                                                                              \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         Setting a lid for the floating body is recommended.                                            \n",
+                     "</pre>\n"
+                  ],
+                  "text/plain": [
+                     "\u001b[2;36m[15:22:03]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m Irregular frequencies for \u001b[1;35mRadiationProblem\u001b[0m\u001b[1m(\u001b[0m\u001b[33mbody\u001b[0m=\u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m,                              \n",
+                     "\u001b[2;36m           \u001b[0m         \u001b[33mname\u001b[0m=\u001b[32m\"collection_of_meshes_28\"\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mwavenumber\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1;36m.000\u001b[0m, \u001b[33mwater_depth\u001b[0m=\u001b[1;36m100\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33mradiating_dof\u001b[0m=\u001b[32m'Heave'\u001b[0m,   \n",
+                     "\u001b[2;36m           \u001b[0m         \u001b[33mrho\u001b[0m=\u001b[1;36m1023\u001b[0m\u001b[1;36m.0\u001b[0m\u001b[1m)\u001b[0m:                                                                                   \n",
+                     "\u001b[2;36m           \u001b[0m         The body \u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m\"collection_of_meshes_28\"\u001b[0m\u001b[1m)\u001b[0m might display irregular frequencies \n",
+                     "\u001b[2;36m           \u001b[0m         for \u001b[33mwavenumber\u001b[0m=\u001b[1;36m1\u001b[0m.                                                                              \n",
+                     "\u001b[2;36m           \u001b[0m         Setting a lid for the floating body is recommended.                                            \n"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "data": {
+                  "text/html": [
+                     "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[15:22:35] </span><span style=\"color: #808000; text-decoration-color: #808000\">WARNING </span> Irregular frequencies for <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">DiffractionProblem</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">body</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>,                            \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"collection_of_meshes_28\"</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.000</span>, <span style=\"color: #808000; text-decoration-color: #808000\">water_depth</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100.0</span>, <span style=\"color: #808000; text-decoration-color: #808000\">wave_direction</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>,    \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #808000; text-decoration-color: #808000\">rho</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1023.0</span><span style=\"font-weight: bold\">)</span>:                                                                                   \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         The body <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"collection_of_meshes_28\"</span><span style=\"font-weight: bold\">)</span> might display irregular frequencies \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         for <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>.                                                                              \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         Setting a lid for the floating body is recommended.                                            \n",
+                     "</pre>\n"
+                  ],
+                  "text/plain": [
+                     "\u001b[2;36m[15:22:35]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m Irregular frequencies for \u001b[1;35mDiffractionProblem\u001b[0m\u001b[1m(\u001b[0m\u001b[33mbody\u001b[0m=\u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m,                            \n",
+                     "\u001b[2;36m           \u001b[0m         \u001b[33mname\u001b[0m=\u001b[32m\"collection_of_meshes_28\"\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mwavenumber\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1;36m.000\u001b[0m, \u001b[33mwater_depth\u001b[0m=\u001b[1;36m100\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33mwave_direction\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.000\u001b[0m,    \n",
+                     "\u001b[2;36m           \u001b[0m         \u001b[33mrho\u001b[0m=\u001b[1;36m1023\u001b[0m\u001b[1;36m.0\u001b[0m\u001b[1m)\u001b[0m:                                                                                   \n",
+                     "\u001b[2;36m           \u001b[0m         The body \u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m\"collection_of_meshes_28\"\u001b[0m\u001b[1m)\u001b[0m might display irregular frequencies \n",
+                     "\u001b[2;36m           \u001b[0m         for \u001b[33mwavenumber\u001b[0m=\u001b[1;36m1\u001b[0m.                                                                              \n",
+                     "\u001b[2;36m           \u001b[0m         Setting a lid for the floating body is recommended.                                            \n"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Panel Count:  5330\n",
+                  "{'Heave': 1341745.8381018587}\n",
+                  "{'Heave': 222.1023180983429}\n",
+                  "Solve Time (Radiation):  31.674926709005376\n",
+                  "Solve Time (Diffraction):  0.06724579198635183\n",
+                  "Excitation Phase:  -2.963436544344762\n"
+               ]
+            }
+         ],
+         "source": [
+            "#tall  - compound cylinder\n",
+            "h = 100\n",
+            "d = [29, 7, 4]\n",
+            "a = [3, 5, 10]\n",
+            "heaving = [1, 1, 1]\n",
+            "t_densities = [30, 50, 100]\n",
+            "face_units = 110\n",
+            "m0 = 1\n",
+            "rho = 1023\n",
+            "config = \"config2\"\n",
+            "reps = 1\n",
+            "\n",
+            "result_r, solve_time_r, result_d, solve_time_d, panel_count_alt = construct_and_solve(a, d, heaving, t_densities, face_units, h, m0, rho, reps)"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 7,
+         "metadata": {},
+         "outputs": [
+            {
+               "data": {
+                  "image/png": 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",
+                  "text/plain": [
+                     "<Figure size 640x480 with 1 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Panel Count:  11660\n",
+                  "{'Heave': 5881.6050262025565}\n",
+                  "{'Heave': 6875.158124571653}\n",
+                  "Solve Time (Radiation):  170.15780250000535\n",
+                  "Solve Time (Diffraction):  0.3370624579838477\n",
+                  "Excitation Phase:  -1.0437407701107935\n"
+               ]
+            }
+         ],
+         "source": [
+            "#indents  - compound cylinder\n",
+            "h = 1.9\n",
+            "d = [0.5, 0.7, 0.8, 0.2, 0.5]\n",
+            "a = [0.3, 0.5, 1, 1.2, 1.6]\n",
+            "heaving = [1, 1, 1, 1, 1]\n",
+            "t_densities = [30, 50, 100, 120, 160]\n",
+            "face_units = 105\n",
+            "m0 = 1\n",
+            "rho = 1023\n",
+            "config = \"config3\"\n",
+            "reps = 1\n",
+            "\n",
+            "result_r, solve_time_r, result_d, solve_time_d, panel_count_alt = construct_and_solve(a, d, heaving, t_densities, face_units, h, m0, rho, reps)"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 8,
+         "metadata": {},
+         "outputs": [
+            {
+               "data": {
+                  "image/png": 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",
+                  "text/plain": [
+                     "<Figure size 640x480 with 1 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Panel Count:  6750\n",
+                  "{'Heave': 924.6499405282137}\n",
+                  "{'Heave': 1835.453237409865}\n",
+                  "Solve Time (Radiation):  54.50835541600827\n",
+                  "Solve Time (Diffraction):  0.09106395798153244\n",
+                  "Excitation Phase:  -0.5194543676491261\n"
+               ]
+            }
+         ],
+         "source": [
+            "#original - only outer heaving\n",
+            "h = 1.001\n",
+            "d = [0.5, 0.25]\n",
+            "a = [0.5, 1]\n",
+            "heaving = [0, 1]\n",
+            "t_densities = [50, 100]\n",
+            "face_units = 90\n",
+            "m0 = 1\n",
+            "rho = 1023\n",
+            "config = \"config4\"\n",
+            "reps = 1\n",
+            "\n",
+            "result_r, solve_time_r, result_d, solve_time_d, panel_count_alt = construct_and_solve(a, d, heaving, t_densities, face_units, h, m0, rho, reps)"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 9,
+         "metadata": {},
+         "outputs": [
+            {
+               "data": {
+                  "image/png": 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ybWRwcBBOpxPDw8PIyMg4bR0axjNgWywWZQxSd3e3/z4BNAE6deoUbDZbSLVmtJgKNR+gE0i0BKEipGDF+UxCGh9MApom+Hw+eDwetLa2orW1FWeddVbUudxCSTAejwc1NTU4deoU1q1b508ZA0SWjFR1bjEGyeVyoaKiAkNDQ9i8eTNSUlKiajcayLaRsrIyxMTEwOPxBKTDCaaKmmmYaClOjEGaO3cuNE3DwMAADh48iP7+fjQ3NwckXmW15kQQx1QFu060swPbhxgiIbW2tmJ4eBjz5s0zy5dHCZOAphhybI/NZhvX7DAUAQ0MDPgH461bt46JIRmvBMTH9vT0oKysDGlpaSguLp6wbNDRwmazITk5GXPnzgVALuA884/GoWE6MNmqMYvFgpSUFFgsFixbtgwJCQl+t/j29nbU1tYiJiZmQmKQpkoCmmxvO5GQRkZGMDQ0BABmtdgoYRLQFEJVKnsicrmpjtc0DU1NTTh8+DAWLFiARYsWKT/K8bhxs/R0/PhxHD16FEuXLsW8efNm5McWHx+POXPmjHFo4AzWkTo0TJWH3VQmIxXVmgUFBfB6vejr6wuIQYqPjw/wsAs3Bul0lYCCwev1+gmGYZYvjwwmAU0RxNgeUcc8EYGk8mDo8XhQVVWFrq4urF+/3q+WMjo+2vP7fD6/VHHWWWf57TEzBUYkEalDw0RnaAgX052M1GazGcYgNTQ0YHBwEElJSQGEZFSWfaoloKk6lypBr6oWklm+XA2TgCYZoWJ7WAUXLWQC6+/vR1lZGeLj47F169aQlTqjVcH19fWhsbERVqsVJSUl0zJAB0OknoTBHBo4QwNLSKmpqZPU67GYSgkoFILFIB09ehTDw8MBMUipqakBg/FUSUBT5W3n9XqDqprFHHaAWb5cBZOAJhHhxPZMlApO0zScPHkSR44cwcKFC7Fw4cKwXuJIJSBN09DY2Ija2lpkZGTAYrHMOPIZL1QDrZyhISkpCZqmobe3d9IcGqYq2wIQncpKjkESXb5ramrgcrmmvGx7JC7YE3GuSDwrgxGSXJzvg0JIJgFNEniWE2pGNhEE5PV6UVZWht7eXhQVFflVJuEeH+5A5/F4UFlZiZ6eHmzcuBH9/f3o6uqKqt9TNbOfCIgZGgByaOAsEhxHNVkODadThoK4uDjMmjULs2bNGpPJoqury/+eijFIE319U0lAnCorWoiEJNdCkgnpL3/5CzZt2oQNGzZMSN9nCkwCmmCwyo293EKpA8ZLQIODg/68alu3bo1YGglXApJVezExMRgYGDgj0t1Eivj4eOTk5KCpqQnbtm0LcGg4fvy4v5zCeDM0TKUENBlJQsVMFq2trTh58iQyMzP9NiSLxRJA3AkJCePux0yWgEJBzEcnE9If//hHpKSkmARkwhjRpNOJloA0TcOJEydQW1sLAFHlcgNCE5DoTVdQUIBFixb5r2m85DmZmCqVxWQ6NJzOWapV53E4HJg7dy7mzp0Ln8/nJ+6uri7U19fDbrePcfmOtF8TTQrBMF4JKBREQhoaGoooUfDpApOAJgjRptMRbTjhHuNyuXDo0CEMDAxg3bp1OHjwYNT9DlZR1ePxoLq6Gp2dndiwYQMyMzMDto/XhXuyMZkShFHb0Tg0GHmOAaeXCi7UecTB2mq1IiUlBSkpKZg/f76fuHt6etDW1uYnbpGQQjnUAKe3BGQETdPgdDojyqV4usAkoHFCju2JJpcbtxPOcT09PSgvL0dKSgpKSkr866P98IwkIM4Zx2UaVAGI4yGgDwqMHBp6enpCZmg4nVVwMkK5YatikJi4T548ierqaiQkJAQQksoDbarcvYHJl4BEOJ1OJCUlTcm5phImAY0DPp8PHR0d8Hg8yMjIiOpl5GNCEYimaTh+/Djq6+uxZMkSzJ8/HxaLxU980Q5WKieElpYWVFVVYf78+Vi8eHHQzMLRnFfTNH8ak8zMzEnJQjBTvYZUDg3sYSdnaJgK9eZ0SUChYLPZkJmZ6Ze63W63n5COHz+OyspKZQzSmSgBASYBmRAgRju3t7fD5XIFDfYMBpGAjDA6OopDhw7B6XRi06ZNAXEo4RwfDKIEJJZpkHPGGR0bKQF5vV5UVVWhs7MTiYmJOH78uF/3z0GP4ahawsFkSxATMWjLyUKdTqc/uLe7uxsAUFlZ6b8/E11yYqoCN8crmTgcDmRnZ/vfSVGSrKurw8jICJKTk/0kxFkKJhNTcQ4+z/DwMBITEyf9XFMNk4AihOxoYLfbMTo6GnV7TCBGdpju7m6Ul5cjLS0NJSUlY9QO/FGPJ6EoD3xlZWWw2WwoKSkJa6CLlICcTidKS0vhcDiwefNmv7qyr68P3d3daG5uRk1Njb/CJ2exDmYjOZNgsViQlJSEpKQkzJs3Dy0tLWhqakJiYiLa2tpQW1s74RkapsLRgc8zkZKJLElyDFJTUxOcTid27dqFlJQU/3uUnJw84ZLRVElbTqcTAEwb0AcdqlLZNpvNkDzCAXu6qMoa1NfX4/jx41i2bBnmzp2rHCiMjo/k/MPDw9izZw/y8/OxdOnSiGqphEtAbW1tqKys9J+D7yW7LKenpwMgVYs8s2UbSSQDyUxVwUUCi8UCh8OBgoICFBQUwOPx+MladmgIlQrHCFNFQJNtm+EYJJfLhYGBASxcuNCwmm56ejqSkpLG3Z+psgExAZkquA8ogqXTmQhXZLmN0dFRlJeXY2RkJKyyBtGm0/H5fGhra/N70/FsMlyEQ0A+nw+1tbVoamrCqlWrkJeX51+vgsPhCIiuV9lIxBibYLEjp7uDhNx/u90eYBcR1VC1tbV+hwa+P+FkaDhdJSAjsF1GrqbrdDoDYrUsFkuAQ0OkMUjsuToVKrihoSHExsZOe4b5yYBJQCEQKrYnmBtzuBAJqLOzExUVFYaVREMdHy6GhoZQXl6O0dFRpKamRkw+4Zx3ZGQE5eXlcLvdKC4ujkqHLdtIBgcH0d3djY6ODhw9ehQOhyPAfnQmpQUKRQ7BHBqam5vDmvWfKRKQeB6Z6ETVphiDJL5HcgxSKBU0v/dTQUCDg4MTEqQ7E2ESUBCEE9sz3mSigE5itbW1OHHiBJYvX445c+ZEHEsULk6dOoVDhw5h1qxZSE5ORktLS1T9DiYBdXd3o6ysDFlZWSgqKpoQO44Y9Dl//nx/uYDu7m6/qy6rpEZGRiZ1xjgTyzEEc2gQMzSIDg1TKQFNxWAdjl1GjEFasGABfD6fv+yEGDwcLAZpKss+DA4OnpEOCIBJQEpEEtszESo4i8WC6upqAMCWLVsiNjaGawPy+Xyoq6tDY2MjVq1ahVmzZqG1tXVCCtIxNE1DQ0MDjh49GtR2NRGQywWISUM5qLG/vz/AfnQ6zSLHQ3KyQ4Mc6MkODUlJSf68hZMpPU6lBBTpZEe2Q7KtTRWDxI4xclmVyQS7YJ9O7264MAlIAhvHRffUYA9+vE4IHR0dGBkZQWZmJtavXx+VpBCODWhkZARlZWXweDwoLi72GzTH68AgntftduPQoUPo7+8f4y6uOnaiIaukWGLq7u5GY2MjAIzJ0TbTP+qJ6p8c6MmDbGtrK3w+H95+++1xOzQEw1R5jHm93nETqWxrE2OQjh07BqfT6U+L09XVFTKbxXhxpqbhAUwC8kMulR2KeBjRSkCiNBIXF+evKx8NQvWho6MDFRUVyM3NxfLlywNUIRNVkpuTlSYkJMyI+kCc0l6sgjowMIDu7m5/uWnRpTkjIyNild1UpcmZDPAga7VaMTAwgKKionE7NATDVNcDmkjIMUijo6NobW1FQ0NDQDYLJu+UlJQJVTdy4b8zESYBYayjQbjkA0QnAQ0PD6OsrAw+nw/FxcWorKyctLLcR48eRUNDA1asWIE5c+aM2We8+dw4WWlNTc2YZKUzCRaLJUDvz6leRJdmLqbGOdqmKso9VL8nEzzZmgiHhmCYKhXcVBBdbGwsUlNTERMTg5KSkoCyEy0tLfB4PP46SFx2Yjx9cjqdpg3oTIUqticSRCoBtbe3o7KyEnl5eSgsLITNZpuUstzsyj06OhrUrjSec7Ot7MiRIyFLf6v6PNkIRqxyqheXy4Xu7m50d3ejpqYGbrcbqampfvvRdOjgpyobtuoc0Tg0hDrP6SoBGZ2HJyjyvRoaGvIT0smTJ8dN3kNDQ6YEdKaBpZ6TJ08iNzcXdrs9qo89XDdsn8+HI0eOoLm5GStXrsSsWbMC2phICairqwvl5eVhuXJHawMaGhpCRUUFNE0LO3PCVCLSZxkTE4O8vDzk5eUFDCLd3d1oaGgYU+NnKjBVyUhD3atgDg2iOlO8P7IKdqokoKkKDjU6j8ViQWJiIhITE5Gfn+8PHZDrRYmEFMrF2vSCO8PA5ON2u1FZWYmsrKyoXXbFwlFGL9HQ0BDKysoAACUlJWMMiuONJRJLOhw7dgzHjh1DYWEh8vPzwxpcIh3o2I07OzsbTqczavKZiao6YOwg4vP5/PYjdtONiYmBx+PBqVOnDDMzT1RfJhPRSFlGmauDZWg4kyWgYFDVixoYGEBPT0/YMUimCu4Mghjbw7XWxyt9AMYvZGtrK6qqqjBnzhwsW7ZM+XGMxxGAj3e5XDhw4ACGhobCyp4Qzbk1TUNdXR1OnDiBlStXIj09PeoYoqnAREkQVqsVqampSE1N9XuQNTc34/jx4/7MzKLBPjU1dUIGwamQgCZCMlGpM+V0SlarFR0dHYiLixu3Q0MwTKW3XbTZ7/ldYlskS5M8uYmLi0N6ejpsNhvi4+PhdDoDNCaR4IEHHsC9996LtrY2rF27Fr/97W+xadMm5b5//vOf8fnPfz5gXWxsLEZGRvy/NU3D7bffjocffhi9vb3YunUrHnzwQSxZsiSq/n1gCEiM7RFLZY/XjZpJR86M6/V6cfjwYbS2tmL16tVBMw2MVwLyeDw4duwYMjMzUVxcHNFsPFwCdrlc/vRAbFPiF3OqghlnCux2u99NefPmzRgdHfXbj6qqquDxeALsI9GWm5hOG9B4oHJoOHjwoL+Q4mTkZWPMNAkoFGw2m2EM0tNPP42f/vSniI+Px6pVq/Dss8/i3HPPDVsF/Le//Q033ngjHnroIWzevBn33Xcftm/fjiNHjvhTXclISUnBkSNH/L/l53LPPffg/vvvx6OPPoqCggL88Ic/xPbt21FdXa2sGRYKHwgC8vl88Hg8ynQ64yUgVTmEwcFBlJeXw2q1YuvWrSFVVOMpy93Q0IDe3l5kZ2dj3bp1UalTQs20e3p6UFZWhvT09IBYJT7XTCSgqegPnyM2NhazZs3CrFmzlAZ7HmQmutzERGAqnl18fDzsdjvmz5+PzMxMf1627u7uqBwagmGmS0ChIMYgfe9738PnP/95fOITn4Ddbsctt9yCI0eO4EMf+hB27NgRsq1f/epXuO666/xSzUMPPYQXXngBjzzyCL7//e8rj7FYLP58jTI0TcN9992HW2+9FR/72McAAI899hhyc3Px7LPP4tOf/nTk1xvxEacRwontmQgPNIvF4icxLuY2b948LFmyJKyXNJo+iEGfmZmZSE1NjWogCSYBaZqGEydOoK6uLqAInngs72eCoDLY84yWy00kJCT4yShYwOfpKgEZnYcnfnJeNlV8VjCHhmA43SSgUMjJyUFqaio+97nP4brrrkNraysOHz4c8jhWyf/gBz/wr7NarbjggguwZ88ew+MGBwf9JdI3bNiAu+++GytXrgQAHD9+HG1tbbjgggv8+6empmLz5s3Ys2ePSUAi5HQ6RrE945WAuA0mhFOnTmHt2rWGIq4KkRJQX18fysrKkJSUhJKSEtTW1o67HpAMj8eDyspK9PT0YOPGjUqxf7wENNnENROIUZzdL1y40B9V393dHVBuwijg80whICNbk2xfE0txs0NDYmKin4xCZWg43SUgFZxOpz+MgiXtUOjs7ITX6x2j+s/NzTUksGXLluGRRx7BmjVr0NfXh1/84hcoKSlBVVUV8vPz0dbW5m9DbpO3RYozkoDE2J5Q+ZomgoAAoLy8HHFxcdi6dWvEulCr1eonymDQNA2NjY2ora3FokWLUFBQ4L++8aTT4bb5/8HBQZSWliI2NhYlJSWGKqNoCYjJuru7e0xphYkCSx4c8Ot0OjE4OIiBgQE4nU709vbi3XffRWFhod8xxe12w+PxwOVyobS0FEuXLkViYqI/Vov/6urq/Mli9+/fj/j4eCQkJCA+Ph6Dg4PIy8tDfn6+0qtJjqpXBXzycS6Xa0pIerqyVKugcmgwIuz09PQxDh9TKQFNVaHEqUrFU1xcjOLiYv/vkpISLF++HL///e9x5513Tso5zygCCla3xwjjcQDQNA3Nzc3weDzIycnBqlWronr5w8moLUokRUVF/gScwPiyGchefK2traisrMT8+fOxZMmSoPePj43k3AMDAygtLfUbVvv7+wNUL5mZmf7BRXam0DQNvb29aGlpQXNzM1paWlBdXf1+1u1MdHa2o6urA11dPejpGXh/cAWCde+FF14w3FZVVaVcb7MBRq+MaltsrAPp6SnQNA1paelYs2aDP+aI/7Kzs7FmzRpYrVZ0d3ejs7MTvb29/mfLJD3R9qOZXg8oJiZGWR+KVZperxdpaWl+debplHMuHLBNMdIExVlZWbDZbGhvbw9Y397ebmjjkeFwOLB+/XocPXoUAPzHtbe3B0hh7e3tWLduXUT9Y5wxBBSqbo8RopWAPB4Pqqqq0NXVhbi4OMyePTvqFz+UBMN51uLj45USyXhIlO+R1+vFkSNH0NLSErYKkY8NV/picluwYIFfHZWRkYEFCxbA4/Ggt7cX7e3teOutt3D8+HF0d3ejq6sLdXVHcPToEbjdXgwNjS1/HoxkohUgQhFXsONkjI660dbWBZsNaG/vxpEj9Ybny85Ox4IFCzB//iKkpqYiOzsbOTk5yMrKQnJyMpKSkgLsR+O1Q0y3Ci5SqDI0iA4NAFBbW4usrKxJTTg7VTYgILo4oJiYGBQVFWHHjh244oorAFCfd+zYga9//ethteH1enHo0CFccsklAICCggLk5eVhx44dfsLp7+/Hvn378NWvfjWi/jHOCALidPLhSj0ioqnnw4QQFxeHkpISHDx4cNyODEa53NhwHSzPmtVqhdvtjvrcALB//34A6kDZUAinKipngWByGxoaQmVlJQ4fPoyamhocOPAeDhx4D0NDoxEP/FYrDd48Hni9wDgrZATtQzRt22zUpt0OqLStTEAdHT3o6OjBe++VBhzr9QLJyfFYuHAh5s4tQG5uLmbNmoWlS5dizZo1yMzMREpKSsSD7VQFiE7GeWSHBpfL5c/qLTs0sKp3oqSWqbQBDQ0NRSwBAcCNN96If//3f8fGjRuxadMm3HfffXA6nX6vuGuuuQZz5szBT3/6UwDAHXfcgS1btmDx4sXo7e3FvffeixMnTuBLX/oSALrf3/rWt3DXXXdhyZIlfjfs2bNn+0kuUpzWBMQqN/ZyiyaXWyQSkKZpOHnyJI4cORJACOON41GRoMfjQXV1NTo7O7Fhwwa/TlyF8QTT9vT0AAASExOxatWqiGZ17NgRjIBGR0exe/duVFVVYWBgADfeeCM8nmGcONEMrze8PovEogKv51tgsdBAb7FMDBnJCEZOvM1q1fvt81E/rFY1+Yiw2Wg/cT7Br/TAwDDKy6tQXq6rBq1WICbGgSVLFmPhQiKjjRs3oqioKKz6R6ebBBQOFixY4P+u2aGhsbER1dXVETk0BIMc9zdZ8Hg8GBkZiSoTwlVXXYWOjg7cdtttaGtrw7p16/Dyyy/7nQgaGxsDSLSnpwfXXXcd2trakJ6ejqKiIuzevRsrVqzw73PzzTfD6XTi+uuvR29vL7Zt24aXX345qhgg4DQmIE3TMDIy4v+AoiEfIHz1ldvtRlVVFXp6esYQwkSl0mEMDg6irKwMDocDJSUlIR9uNJkUxLQ9ALB06dKoPiiZgDweD15//XXs378fNTU1ePfdd9DYGLrondWqE4YMHrwBwOEITSqapg/0fBwTEkDH+nzRq+dk2GzUNksxLOVESnwOBxGPfA9kUhPJyecDRkbcOHSoBocO1eD//u//3m/LjoSEeFxyyWXYtm0bPvzhDyuzoU+Vqzf1f3IlBrlK6XgdGkKda6rKcQOISgICgK9//euGKredO3cG/P71r3+NX//610Hbs1gsuOOOO3DHHXdE1R8ZpyUBsdTzxhtvoKioCGlpaVG3FY4ExN5UiYmJShvMeMtyiwTEcUTz58/H4sWLJyWOiCPSnU6n34c/WicGj8eDvXv34sCBA3j77bewZ89uDAwMwWpVD8A81smn8/lomxERcVs8+DocgURjBD5O3s9iCSQOo+NUt1/TdLJh6YZhpGJTgSU1bsdIi6ppdL0ej3ofq3WsdOV2e9DXN4C//OUv+Mtf/gIAyM/Pxfr1Z2H58hW4+OKLUVRUNCUEJBZ3nOzzBCulEsyhgcsosENDqAwNU+Xs4HQ6AcDMhj0TIMf2jHfg5zaM7CdiIKbo9ixjIrJZe71eVFZW+j1K2E033OPDPX9fXx9KS0uRkpLiT9sTiQpP0zQcOnQIL730Eh588AF0d3fB5TImcJ7V68fTkgdecfDWNH17KEmH21S1E951hHeMah+bzZhkwuFxlsZEQjEay1hDZERONhvdI1V/ZI+8pqZ2NDX9Ey+99E/cc889yMvLxIYNm7F582ZkZmZG9M5FgqmUgCI5RyiHBjGGS3ZomCob0NDQEOLi4mZEbarJwGlDQHKpbK54ORFBpGKyPYbL5UJlZSX6+/sNAzEZ41XBuVwuOJ1OWK3WqEobhEMgov1KJtNQdpyuri7s2LEDr7zyCl577SW0tXUp9+PvUeyK202DqNcbODh7PDQIywQlHgeEJiKPZ6yaTZZKJhqRts3SFjtKGDkhiGDi8Xj0/0XwNYukzWDikfsp3+u2ti68+OKLePHFF3H77bdjwYK5mDevADfffDO2bt06YYPeVEpA0ZJCJBka0tPT4fF4pkwFF20uwdMBM56AxHQ6spfbRASRqsijp6cH5eXlSE5ODqu89Hgksba2Nhw+fBhWqxWbN2+OOsNuMALxer2oqqoydGhQHd/Y2IjnnnsOjz32CKqqqpWDrqxmYzUaD7YMJgmrdazEwwRlJJG43XqbRo9apWabTCeEYODrZmcCgPrg8QS/BlEyBIKr8Vgdp3rkRoQurhfPJZ6noeEkGhpO4q233kJ6ejLOPvtDuOSSS3HppZciNTXVuEMhMFMloGAIlqGhsbERbrcb1dXVfnfv8Tg0BMOZXAsImOEEFCq2Z6LS6DB5aJqG48eP4+jRo1i6dOmY3GdGiEYFJ7omL1y4cIxHSiQIdn6n04nS0tKgDg18fG1tLf7v//4Pzz77dxw4UK5sTxy0RDsJ/8/2DPlSmJxUNhKWhozsJ9wmQANpOBJOMCcEbm8iiIntVqJEwsQXyasZ6voB3atORTBM0qptdvtYNWgoW1VPzwCee+55PPfc83A4bMjJycU3vvFNXHHFFcjLy4voXQ1lm5koTKZjgOzQ8MYbbyA/Px9Op3PcDg3BwNVQTQloihFOqeyJIiCv1wuXy4WKigo4nU5s2rQpIseGSPsxNDSE8vJycDVRt9uNhoaGyDv/PowIqL29HYcOHUJ+fj6WLl2q/CDa29vx7LPP4oYbrseJEyfHDMpsY+BBkFVC4uDFqiW+BaIdRx74mGzE/fh/r1cfiOWBlPcVbT/cn2AI5YQAGDsa8H6qbazqYzLka2d1owpyW+zVZtRHEUaSDaBLnKprVLl+q9bJz1T87XZ70dzcgu9///u4447bsGlTMS655FJceOGFyM3NDakimioX7KlyDOAClDk5OX6HpJGREX8G9EgdGoLB6XROSRqe6cKMI6BI0ulMFAGNjo7inXfeQVpaGkpKSiKubhlJIChXE83Ly0NhYSFsNhsGBgYmNJCVpZmmpiasWrVqTOqN4eFhPP/883jyycfx6quvjYnHEcnE6x2rOpLVSSxRyPsZ2X80TR98ZSITjwvmKMDHibE2kTjyiW1H+gpF64Qgqr4sFrpOfmxGE3f29gtGPsEIT2UHkvsok4/q+tjrb2jIhZ0738TOnW8iKSke8+cvxJe//GWcddZZ/mBPWcqeymqoU1X2GwhUKXI2FNmhQSzDbeTQEAyDg4NnrAccMMMIKNJ0OjabLawknsHO197eDqfTiRUrVmDu3LmTFkvk8/lQV1eHxsZGrFq1KiCX0kR40bGefWRkBOXl5XC73SguLg7QH9fU1OC++36NJ598HG53cNIRByWOwxG7KBMTD+jyYMZkJarR6H7Qn5E9g9sI5YTAUhOrlfj/cbwWIRGtEwJgHIwqv3asanS71eTEkpPqvhjFIbFkKXKB/Lzk58z3U1QD8vUPDg6jqqoK3/zmN7FqVSG2b78UZ511lj9VEA+2Z5oExN+qkbovUoeGYBkazuRy3MAMIiCxVPZk53EDKEK/oqICg4ODiIuLw7x586Jqh/sRjEBkUpBnNEwg0cZkMIF1d3e/n5gzC0VFRbDb7XC73Xjuuefw+9//F956652A40RVmEwosqrN5xs7WKkumY+TycrIxsEDopFUITohGEk5onqQwU4Ak+0RZ9QX0QnB49GJ2Og1EdWWwWKCjFSUvM1IeglH7Sb3hY/j+xcsP15l5WFUVh5GUlI8iou34YorrsCSJUswPDyMhIQE+Hw+9Pb2njHluIHwvfqCOTScPHnSn6GByUh0aBgaGoqagCIpx/3www/jscceQ2VlJQCgqKgId999d8D+1157LR599NGA47Zv346XX345qv4BM4CA5NieSDIaRCsBdXV1oby8HJmZmVizZg0qKioibkNEMAmms7MT5eXlyMnJwYoVK5SzJl4XrRHVYrH4C1AtW7YMc+fORXd3N/7jP/4Dr7/+Crq7+6Xz6WoxWfIRZ8Cyqk0mJSNvKhUJsVSiGvR4Jh/KCYEDMsORcNguw+ORSAgzzQmBg2GNiEW8z0bkxAG8KnuPrIpT3WsxsJYhk5lIRrKkxO/J4OAw3njjVbz66qs477xz8OUv34D8/Hw0NzcHlONmKSkhIWHCpKOpyk7A54m237JDg9vt9qvr2KHB6XTijTfeQEdHB1JSUiI+R6TluHfu3Imrr77a76j085//HBdddBGqqqoCMmhcfPHF+NOf/uT/Pd7s7NNKQHJsT6SeMmy/ieR89fX1aGhoQGFhIfLz8zE4ODhhjgwiNE3D0aNH0dDQgOXLlyM/P9/weLkkQiRwu92ora2F1+tFcXEx+vv7cfPN38Uf//hwQOZoccAQuyqrzWRbjvxbRSJGxGLk8Sb3R96mMrirnBDYjhIM4jWL1z0RTghiJoRwnBBkMCmK91aFYPFS4jnl+ym6gcv7y+Qju8gHc0pQxSzJNjkAeOONt/DGG29h6dKFuOyyK/D973/fP9h2dXWhvr4edrs9QGU3ngHtdC1G53A4AjI0jIyMYP/+/WhsbMTOnTvh9Xr9lUjPP/98rFu3LuT5Iy3H/cQTTwT8/sMf/oCnn34aO3bswDXXXONfHxsbG3Y5h3AwLQQUTqnscBCJCo7VYC6XC1u2bPHnVpqoWCJRAhodHUV5eTlGR0cDzhXseCD8sgYMrq0TExODuro6/P3vT+Gvf/0rPJ6x1yPPekV1iizpsBs1Sx0q+0woEhLVevLMmrcbSTxs91DN6MXzAfoM/3RwQmCIUpyo3pIhpuoJRj7B+iL2RxVPFQ75AMZOCuL//Bz4Wvgdq609hl/96le4//77cPfdP8O1116LefPmwev1+suVi6qoaJOFninluOPi4rBt2zZs27YNN9xwA+Li4rB69Wrs2LEDDzzwAOrrx5bzEBFtOW4RQ0ND/nIpInbu3ImcnBykp6fjwx/+MO66666giZJDYdokII/HMy7yAcInj46ODlRUVCAnJ8dvG2Gw/WU8L6/Yj66uLlRUVCAjIwMbNmwI6wOKtK4OADQ3N6O6uhp2ux2/+91v8fe/Py31aay6RCQamQB44OBBS7wV4qASzB3b4xk7U2c1mMp1m/dXOSGIEkUwbzix3zM9EwIwNtO1uI8Idr6QnQYYfD9V5MPPQCYf2Q4UDvnIhKV6juK+vE2WiigY14ebb74Z9933C3znO9/H5z73Ob/0s2jRIr901N3djdraWoyOjiI1NdVvG0lOTg76jZ6uElAwDA0NobCwEN/85jfxzW9+Myw7cTTluGV873vfw+zZs3HBBRf411188cX4xCc+gYKCAtTX1+OWW27BRz7yEezZsydqQp4WAgpVJjtchErFI3qerVixQpkNWLS/jCcQ1Ov1or6+HseOHfPbYcIl1kjKanu9XtTU1KCmpgavvfYqHn/88QA3atnwLndBzlAQzB4kSzSqmCAZbrc+eDKC2XhCOSGIg1g0TgjhZEJgG4x4r1RELF6PeF7VNlUmBNkuproGlROCTCShnBDkbSpVnIp85H1Udh6V/Q4IfLYi0YnvFO/T0nIKN954I+6996e48sqrcOedd8Jms41RRQ0PD6O7uxvd3d04efIkAATYj2RX5jNFAhIhV0OdCm/Cn/3sZ/jrX/+KnTt3BrjUf/rTn/b/v3r1aqxZswaLFi3Czp07cf7550d1rmmTgCbiRgaTgIaHh1FeXg6Px6P0PBPbAGhgH09tkJGRETQ1NWHTpk1RpS0Jh4CGhoawZ88e/OxnP8W77+7D6Kg+0vBgId5W0TYguuYaST4qJwTZHVsVdyJLPRzHo7LxROOEwOfmc8lOCCrvrGBOAKJEIpIME5zswcfH8HlkpwPRoYH7oTp/MDIdrxOCUTCqSgWqIh9xIsL7iOdmyZIhq3ONXMvl+DHx2NbWDvzud7/Dzp2v4Wc/+wXOOeecgOPj4+MxZ84czJkzB5qm+V2ZOzo6UFdXh5iYmAD70ZkoAUXjhj2ecty/+MUv8LOf/QyvvfYa1qxZE3TfhQsXIisrC0ePHo2agKbmLk4SjLzgTp06hd27dyMpKSko+QC640O0dqCenh5UVVX5sxpEmzMrFAG1t7fj5z//Ob7whc9h1663/eTDAykPGCy5AIFZBzyeQLdghkrdJvIwS0kMsX0GSz0iRJWYCJWxWt4mtiVLJm73WON4uGMBE4+YJ47VXFyHJ5iEwqTBx7B7uc1GfQ63H3yPxf1ldZp4f9gJQRXbAxjHShnFCsnko3IIkddZrYEu5rJ3nKpt1TNWuYNXVh7GZZddhs985mp/fSoZFosFKSkpWLBgAdavX49zzjkHhYWFcDgcOHHiBN5++220tbWht7cXXV1d47brBsNUSkCciicSiOW4GVyOu7i42PC4e+65B3feeSdefvllbNy4MeR5mpqa0NXVFRDTGCnOKAlIzK+2cuXKsG5MJOovEZqmoaGhAUePHsW8efPQ2NgYcQYFEUZ90DQNL7/8Mn70ox+ivLxyzHaVZxsTD3trGUGUfMQZrcopQXZakKUVJiGenfNgLToJyP0W9xfPJ87wjaQCPoZn38FiVHhfUdKZiPFJHNyZlFlKCfY6yQ4aRv0WpRLxHojkEOz+yNcoTlLEtkR1o5EkahQLpLL18P8qexD3QbwG/v/551/AP//5Aj760Y/h4YcfDlqIUVVsjt28Dx8+DLfbPcZ+NFHqq6mUgKJNRhppOe6f//znuO222/Dkk09iwYIFaGtrAwB/QO3g4CB+/OMf48orr0ReXh7q6+tx8803Y/Hixdi+fXvU1zftcUDjgUhAQ0NDKCsrA4AxGQAiaSccuN1uHDp0CP39/TjrrLMQExMzrlxugJqABgYG8KlPfRI7d74p9DVwAJFtN+J+bNw3Ur/JLsDBPOHkc4ciIT6GyYv3FdsMVQtHnHXLkI3qofbjvkzWxFgcYJkYg52L74sRcTJBqSAGtKq8GlUu2+I9kNfxcSrPRFWmBJGMjBwSRNWdiqCYC3id2P//+7//Q1VVOR544PdBZ+wiYmJiEBsbi5SUFMydOzfAfnTixAlYLBY/GbH9KFpMlQTEKX2iqYYaaTnuBx98EC6XC5/85CcD2rn99tvxox/9CDabDRUVFXj00UfR29uL2bNn46KLLsKdd945Ltf5M4KA2traUFlZidmzZ6OwsDDi2UkkBMTVUZOSkvylGrg0+HiqS8rpfHbu3ImvfOVLaGhoCthP/Fh5cFB5KYmDvUgm4npVDBBDZYAXZ8lAcCcEVRJSus6x0oFKQuJ9jR5LMKcE8Xge4IwkklCSU6QQPQ+DuZGL+6omELxddb9U/TUido7jku1ARoRk5G4NGAevytuMJCHRQUOc6Ij7cZtHjzZg+/btuO666/CjH/0orEFYzKKSkJCAhIQE5OfnwygVjmg/ikR7MdNtQIxIynGHmkDHx8fjX//6V1T9CIbTWgUH0CyhsrJSmXQzXIRDQMEKuvHLOB5HBpaAXC4XfvCDH+CBBx4QtgVKLzwwqwYHI/Wb0e2WPeHEQUVFUDywioMgEw73M1hwqKofRkRjRA4sOQQb3Jl8wlGHTTREr8JQZQ+MvNEA3dnBKGBXhPzs+HjZI1J2umCwu7t8LENFRjJ5iNcvrxf7Jk90jDI4+HyUIubxxx/D//7v33HuueeOvXABRsQgp8LxeDz+VDjHjx9HZWUlkpOTA+KPQrl7z2Qb0OmE01YC4jo3ALBp06ao0lUwQtmAPB4PKisr0dPTg6KiojHBWaIr93j6cOzYMWzYsBbd3X0B20RHAsBY4tH7o0tGTC4imcg2HXHwktUwsqqNZ9Ti7FyecYv9GI/KK1g+tGAkx+Qjxg9NtKQTCkx8NltoF3JZsuT+8vEymJjEZ67aV+WCL0qbfE/4fZEdWlQqNUB/H1jVKPdbfK/kfqgIWTy/KK3zcnh4FJdffjl++MMf4sYbbzQc/MP1grPb7cjKykJWVhYAChzn+KPq6mqEKqXg9XqnhIBcLhdcLldUKrjTBaclAbW0tKCqqsqfSidUxdJQCCYBcbaB+Ph4lJSUKPWd0WYyELF7927cddeP/elzxEFeJBJV8Kc8uBpJNeK+ou1Itg3JpCNLVeHEGIULI1uJqowDoEtgwcC2IyP7RLj9UkkVkYKfXbDsCfxcZcmAl3IfeHBWtSNC7rd83/jdktVhKonMaJuRs4Fsa5TVtrxdlH6M3is+/s4778SuXTvx8MOPjAmypD5G54bN6WXy8vKgaRqGhob89iMupcBklJGRAa/XO+4xJxw4nU4AMCWgmQIOwmxvb8fatWuRk5ODxsbGcbtcGuVya25uRk1NDQoKCrBo0SJDteF4XLm9Xi+++93v4r/+67+k9YH7qewuoo5f/thFdYecFYH/l8lJ5QnHbalccGW+DTUAqmBk/zEa+IPZheT+RQImNtExQFRXyeqjSOYaXi+pKY0kQm5bpW5jsjHyaBMRTOJUqfrENkSpSmVnVPVXJA9RIlM5HoiOKPJ2ccLE74P4Xojv8M6du7ByZSH+8pf/xYUXXijdq/HHAVksFiQmJiIxMdFfSqG/vx/d3d1obW3FkSNHYLVa/d5h6enpUavdQ4EJyCzHMAmI1AY0ODiIsrIy2O12lJSU+L1YQmVDCAcyAXk8HlRXV6OzsxPr16/3i+qh2ohUAurt7cWGDevQ0tL2fhvq+AyRaERpSI7x4Q8XCJRqgqniZNIBAt2zjeJ4ALXqSIZMJCpSka/b6NUI5jXG7YQiKLk9JllZo8JGex7PRPsJD+ah7EsiVF6FIowcDvgZhXMt8n0Tf4dyyVfFEMk2RVmqMTqXPMnh7TLpAGOlH94mP0fxf5fLiyuvvBIPP/wwrrrqKuEaJj4Q1Wq1Ii0tzV8h2ePxoLS0FBaLBfX19RgeHvbbjzIyMia03ARXQ50qh4fpwIyXgERJZP78+Vi8eHHAA5noZKJMdA6Hw5+aPNI2wsGxY8dw+eUfMSQfEbJOnvdTkZG4Thw8xFmtrMKT1V0yYRipU+Tf8ixbpT6SZ9MqCShSqUg8d7jSjxgXxOeTPcWAsbYmPg/bmfh6Q0l7fP+NJLRgHn3hSJKqfUJlCldJ06r2VGRkZNcRVaqq+B+RiEQbFN8f8d1myATIfb3uuutQV1eHW265xf/9TfZgbbfbYbfbkZubi9mzZweU4pbLTaSnp4csVx4MHAM0Fel3pgszmoBESWTdunXIzs4es894q6JyG16v129bmjdvHpYsWRLRyxwJEe7evRtXXXUlOjp6/OtkF1VRolHF5sjHiaeWCULllSRC5akUDeSgStWsO5RXWLB+hHLLls9vBDFDQ6g4IhlM5kz0okQU6vGLhvtg3n0q1WY4z0ZlLws2JxK3y/vKsWYM0cFFJn2xj7LEI6oZ+f2WJ1TcNr8jospOnDiJkvs999yDl156ETt3vjklBET91Z0QVKW4u7u7x5SbYPtRJDEzZ3o1VGAGq+AGBgZQVlaG2NjYoJLIRElA7e3tGB0d9duWomkjHFfuBx98EN/97ncgJhClbbSUm5DjI8a2OZa05Bgh8QOWY0Lk+A3RSMwze5nw5IDUwPtgLCEZ35fA30b7h3JnDkVsssQzztfGT0Qs2YTTB5UjiQjVZ6GqMRRKuhHVhAyRHGSiM3LBlvcTyUbeJr4/4v+iOlP2ShSlH1ktJ7v/y/3gYw8dqsS1116Dz3/+i1NCQEZu2GIp7nnz5sHn86Gvrw/d3d1oampCTU2NYeVTFVgFZ0pAkwSLxQJNGn3EeJtQxn9g/ATkdDr9aSdE21KkCGUDcrlc+N///V/ceOONAevFD0sVFCrP0PljFN1neV/xg5Y93hhMVAzZlVfcTzynPLsNF9EIp5FIB7wtlPQjxgV5PBPrki0OsOHE/RjZsozsPeHYzeT95PshStL8nqgkaSDwnTSSmkUykUMBjDzj+L2RHQ7EY2TpRwU5vgmgND7t7e146qmn1QdNIMKVtKxWK9LT05Genh5QbkKsfJqSkuK3H8nlJpxO5xntAQdgZiUj9Xg8KC8vR319PTZs2IDFixeHZP/xEFBbWxv27NmD+Ph4ZGVljSs9RzAbUF9fHx5//HF8+cvXA1AP4KrZs5hfTFyq1Ec8IZNdZFXtyQOdaj1LUQzZDViEeB55sAwVYG7kgi1DZWRnhLL9sIQCBLe1jAfszi6eSwXZuUHVV9UxKo81+TijeBxuQ27T6LeROtdosi6fV5RyZG84cX+eSKlcseXnaRQTJeLdd/dj+/YLMTIyEnzHcSLaOCAuN7Fs2TIUFxdjy5YtyMvLg9PpRHl5OXbt2oWKigo0NjaioqICAwMDUavgHnjgASxYsABxcXHYvHkz3n333aD7P/XUUygsLPQXv3vxxRcDtmuahttuuw2zZs1CfHw8LrjgAtTV1UXVNxEzhoD6+vqwe/duuN1ulJSUhF1lLxoC8vl8qKmpCcigMJ4YHkCtgtM0DY2NjXj44Yfx7W//h1/tJro0i6oOakc/3kglIQeiqtaJg7KYHZthpH4xip0RBz15ABUHR9lzbpy3NeAcRnahUJNRHivkIm0TDSYh0X1bhWAEFA5UJKqK+RHvvSpbOUPsi5gRQWxDdrlWkZR4rGj3ESGuFyUi/i1OpngpTpB4KY7/Yv8PH67FRz96+RjNykRiolLxcLmJVatW4eyzz8b69euRmpqKQ4cO4bzzzsOtt96Kuro6/OUvf8GpU6fCbvdvf/sbbrzxRtx+++04ePAg1q5di+3btxu2sXv3blx99dX44he/iNLSUlxxxRW44oorUFmpJz++5557cP/99+Ohhx7Cvn37kJiYiO3bt4+b7C3aZD6pEHC73fB6vWhsbERtbS0WLlyIhQsXRqTzrKioQEJCAhYvXhzW/sPDwygrK4OmaVi3bh0SEhLQ2NiIjo4OFBUVRXspOHDgALKzszFv3jwA9JJWVVWhvr4eV199ddBjZfWWaMORVS3BVBSyfUd0HVYRgVH+LhGyOibUPpFs4+3irFnuV7B13CfA2J7Dqjcmh6mCUaVXcbvKjmOUSFRWqcn7hUoiKj47VYJR1TMW38tw3hXZrqOCyvajIs9w1gVzorj55ptx6623qjsxTuzcuRObNm1CQkLCpLQPkAfcN77xDVRVVSE+Ph7l5eVYvXo1du7c6XcJN8LmzZtx1lln4Xe/+x0AmnDPnTsX3/jGN/D9739/zP5XXXUVnE4n/vnPf/rXbdmyBevWrcNDDz0ETdMwe/Zs3HTTTfjOd74DgASG3Nxc/PnPfw4oVBcpplUCcrvdKCsrw7Fjx7Bx48aQ9h4VIokD4jpBKSkp2Lx5s/8FCseBIBREFZzT6cTevXsxMDCA3/72PgBjVSgMUbUkzg5V0o64XpxFs8QjD7KixKKaMRqprSKNq5Nn9OK5Qs32w/VcM0Iw7zOR3KaSfOSZvQpizJYII+eCUBKPDJUKS7VNlsLFgGWj9kSvNYacS06GqNoVzyNLO0y28vnFc6num/x877nnHjz00EPGHYoSmqZNibcdOzJs27YNBw8eRFtbG26//faQ9cZcLhcOHDgQUErbarXiggsuwJ49e5TH7NmzJ2B/ANi+fbt//+PHj6OtrS1gn9TUVGzevNmwzXAxrQR08OBB+Hw+bN26Fenp6VG1EY4KjusElZeXY/ny5Vi5cmWADnciPOm4jfb2duzZsweZmZn4y1+exO7d+wAEuqHKOnPZlVr1UcrreQBjVZusq+ePXCQh8Zwy2K0Y0B0Y5ONk9ZpIVOL3qJqpipBtRqG+5Wjdw5mAppJ8gMC4K6Nrk51BGCqVooqsQt0P+TkZqdyM2hTdpMXnLLarcrkG1AQlqoNVKt9wXNhlNbXquYr37+abb8bu3buDNxwheJI5FbngBgcH/U4I2dnZ+PjHPx5ygt7Z2Qmv1zsmVVFubq7f2UpGW1tb0P15GUmb4WJaCWjt2rXYsGHDuPIqhSKPkZERvPfee+jo6EBxcTFmz56tbGO8NiCLxYJTp07h0KFDWLVqFR566CH84Q9/DNgnmIuy7GAAGKfjEYP1RAlDlGzkj1R0KhDJRhwkjRwYxAHIyOFAti8ZedrJ16j6rUr9YvSIjR5bMK+5YGCVHUuVdntgvE+4kG17qv6F64igGuuM3g3e38iTUJZ4VBKz3L5o+xFJSVR/yX0X1XDiREpU46muj8lWJF15Imb0/vLx4jVdddUn0d/fP/ZmRAkea6bC3XtoaGhS1XwzAdNKQPHx8eP2cQ9GQJ2dndi9ezcSEhKCluYerwQ0MjKCzs5ODA0Nobi4GB6PJ0D8V8VkAIEfsGzgFT8wkURkg7HYjujFpvJeUjkYGEkg4oxTNRjx+mCeaeEi3OBR1TmCqd+A8AmIB2eRbPhPDDbleJ9wwK7sRp5tRghl/5AHXZVULUL0lpRVwSpCMVKnGU2ORNuS6LrP62TvN5Gc+BmKbcuEI0+8+H++L8HUvH19g7jppm+rN0YBnqhOBQFF44adlZUFm82G9vb2gPXt7e2G5Wry8vKC7s/LSNoMFzPGCy5aGCUSraurQ2lpKZYuXYrVq1cHFZnHYwPq7u7G7t27/ek5EhIS8MUvXvt+u9wf45kmEKhaULmqiseJ9hxR0pGlH9GbSNxPVK2JxCS2I+8ru2Qb2XvEDAPywC96YcnbVM4U8uMycl0PRgbhkI9IPCKMBmAmoXA0MKpnGA3Ea1RJdvJ1ymQl/q8iDrkNIylFlGiYzMT3RpX5QPbEFM/LpCcSoXiMSDKq+y1LPkb429+ewh133GG8QwRgD7ipCA6NphpqTEwMioqKsGPHDv86n8+HHTt2GFaXLS4uDtgfAF599VX//gUFBcjLywvYp7+/H/v27Qu7Yq0RzggCElPxjI6OYv/+/Whra8OWLVuQn58fVhuRquA0TcPx48dx4MABLF682K8f/f73v48333wbgLGOWvzYVB5D4gcuDxK8TjTq8jYx1gUIlKREiUclEYnEoZIeRInISPVmNKDJxwRzo+XtkRrdRfAAHc6cQpR4WH3DQZVeLzkFuN2Bjgwql/lgfYl0rAr2KqokVlkFJvZTniyo7DpyKQixLfG8DJGIROmc16lsmaKEL6/j91oVmxZMPayyoxmpSn/xi1+guroag4OD43LRnspidNGm4rnxxhvx8MMP49FHH0VNTQ2++tWvwul04vOf/zwA4JprrsEPfvAD//7/8R//gZdffhm//OUvcfjwYfzoRz/C/v37/dVULRYLvvWtb+Guu+7Cc889h0OHDuGaa67B7NmzccUVV4zrGqc9E8J4IUpA3d3dKC8vR3p6OtavXx92mvRIVXButxuVlZXo6+vDpk2bkJqaimPHjqGjowO/+c1vgh4r6+rFj1WMVAd0iUd0y2aILtq8FGeMrA4R21SV6xaPNSpuJ6o5WK1klKbfyF1avr0yWYmpf1SR+pFANWjJEFVsvG8wWxP3k/vGRCBPEuRziJKmDNVxqmsOlXZHhlFQabCsF4B+z/kaRdLg5y86t6iyHYiqN9nBQF4nE4n8fvO7LLtu8/4qlZ5K08Dv1H333YfPfOYzsNls/nQ4keZnm+py3NFkQrjqqqvQ0dGB2267DW1tbVi3bh1efvll/yS5sbEx4BpKSkrw5JNP4tZbb8Utt9yCJUuW4Nlnn8WqVav8+9x8881wOp24/vrr0dvbi23btuHll18OO1mzEaY1Dsjr9WK8iUQ7OjpQU1OD/Px81NfXY9myZZg7d25E5DYyMoKdO3di+/btIY/jAnUJCQlYs2aN34GioaEBd999Nx577LExkkIkBvFgMRQy4ajiNMR18v5A4GCiGixU/VBtl2OXjOwxRueR7Rah4liM7odKWgoVG8TtM4lwvI7K1hLs62C1Yig3b6OYH0B9H5gMw0Gw+ySnwVHF9Bjtr7JZBtsuk4lRfI44oVG9s/L7afRuqdoNZw65a9cuzJs3z19wbmBgwF/Xh/OzBZNwuru7UVtbiy1btoQ+2TigaRo2btyI++67D5dccsmknmsq0dDQgIKCAv/vGZ0NOxz4fD6MjIzg5MmTfmkkUvBswOv1BpWampubUV1drcxRd+rUKTz22GMA1OqjYAOb+PGIH5E8A+SPWyYhdoNVzRRFlZ0YGCkOAOKxrI4RCc5IIhLbDOaMwAOFylNOVuEwwpmXBPOAC0YIoppGJIZIp2L8HMKZ66jIjINVxX1U74aKZFVQScmqdmWHBZGIeZ0s8Yiu1qpgVXG7TD7yNbHkLec5FM8rvhcyqYltqL4bFbgPF154IU6dOhWQn43J6PDhw3C73UhNTfVLR6py3FMlAQ0ODp5x5bjnzp2L1tZW/+/TWgXX29uLqqoqaJqGkpISOEIlHjMAz3iM7EBiJVajshDXXPO5sCQdUS1A51ar4WQVGUO2A8m2GZ5tyx+srLJjWwdDJBvZbiT2TY4X0u/R2Lbk9ap7IdqrVLNusU15JmwkdYWSXORnYARZypP3Fw3owfrOz0N1PnGdPFFRnVd+H8IhanGw534D6kShYp9kUpIJjPsie3Ea3Qexno/83EQilI8Xz8Nkx5MkVWiD6j7y75GRETQ0NGDBggUAKD9bbm4ucnNzIZfjbmhoGKOum0ob0NDQ0BlXjsFms/k950ZGRk5PCUjTNJw4cQJ1dXWYN28eGhoaoiYfIFACkjE0NISysjJYLBbDbNmDg4NoaDjxft9C9T3wAw/mlcTb5bQ8ooeRrGbjGb1KxaGSiICxg5rsfcTEI/aNIfZJlIjE/2V1Hhv7jdzSgbGEwwOPPCCrBqxgKXAimfPI16ka7FU2KxVpTDZkW5R4X4ycYYyCR2WJh9sQpTGV3YfvreweLUs88n0SzxnONhEyaamkOxn33nsvHnjggTHrVeW45XIKbC/q6uoKqa4bD7i20JmcDfsLX/jC6UdAogPAWWedhbi4OBw/fhyapkUtUVksFqUjAgeWzpo1C4WFhYai99/+9rf321HPmEXJKBhBqVQVPBDIRMMfnaiCUBXwktV4KvIy6jOTlmxzkomJ/1epf7hv4nrR405lswLUaj35/vA+MoGKzhsyZJXieMHnVcVYqSBeg2o/uW9G+dxkghb/N7LVGEk8jFC2Pu6zTF78DGTiF99HWWWnsk+q7EDyZEk+Xj5GhNFz+J//+R/ccsstmDNnjnoHcJtjyykcPXo0bHXdeOByueDxeM44FRzjrrvuwr/+9a/TSwXX19eHsrIyJCUloaSkBDExMXC/P+UPZb8JBZGANE3D0aNH0dDQgJUrVyqzJ4j42te+9v5x+rpQqhVAH4BFlZY8mPIgK36sRg4H4jp5EJZnk7JuPZgRN5REJP5v5LxgVPhOvC9GA5LqPHz/VDC633xuQL+n4dp9gnm6serTSPIyUk9GSoTi5xJMelZJq5FKPPKERSXxqI5VSTzyOxlMFQcESliiZBkTA7hcY4kynGcoE+mPfvQjPPzww6EPFOBwOJCQkACv14uVK1diaGgIPT09fnWd1Wr1k1Gk3nUyBgcHAeCMlICefvpp3HHHHXjppZdODwlILFK3aNEiFBQU+MmLReDxEhAnE3W5XCgvL8fIyAi2bNkScgZy8ODBgN+h7A+ynSghARgd1T2qeJDhD1v8kHmAFolKloRURMMQB3j+sEUbjEhs8qxZtOmI+8mGY3G7kVutPPAazcxVhBTK24n7ZASRlC0WPXiWCdno2YlEL8ZJcV+CuUpzcK/HE5lrucqOp4KK6OV3J9RxMtEYqc/kiQgQqDZTOZWIbYnkI3v/iRI3999uJ9KJjaXvRD5eJkqVDUj1TT7zzDMRExBA44zNZgtQ1+Xn5/vVdT09PWhubvZXP2UyilRd53Q6YbFYzrhUPJWVlbjmmmvwve99DytXrpz5BOTxeFBVVYXu7m4UFRUhIyMjYDtHJXs8nnHNOGw2G/r6+lBeXo60tLSw44jEFOZAeIZt/vAcDmBoSCchdsHlaHu3W21gBQLJR/6YxW3ybFH0pBMHK3GGLA4wKjWeOAAYBa2K16uyeRmpomTJTpYeZPIxmg2L91CELJmKx/K1qEiMzy1Lf8EgSqHhSjuyY4ncFv9v5IggSiIqxwOV44BKKgsm8RhNAvj9lO174nHiO6bygnQ4Au2ULhdJPqOjev/kSZTsICHbolTfpMvlwsDAQMQqLiMnBFFdt3DhQn/1U1bXuVwupKWlha2u4yDUM60c9/79+zE0NIS77roLd9111/TGAWmaBpfLZbidY27i4+OxZs0aQ4J57bXXsGnTJqSkpETdj507d8LtdmPp0qWYP39+2A8+Pj4eRrdQHLhVRlTRqM4fJM/++AMcHSWCGhoC4uMBrv8UypYAjCUh1cChmpEaedIBgW7Ychvy/6rtclvBPADlY1WecGJ/je6BiogiRbhxJryv1Wp8Th6/ZDtVOLFPMoycSVReZaoaQcDY+y4ey++mkeTC76tqm9gP3le8Zu6vw6FPZkRi5H6Jz5ddw/kea5peaDDSkexrX/safvrTn0Z0TG1tLaxWa9j1xwAaX4aHh/3edT09PSHVde+++y4+85nPoLW19YwjIRFT49BugGA3tqmpCXv37sWsWbOwcePGoNLNeJKJchlwl8uFgoICLFiwIOwHPjw8bEg+QCBBiB9mYiJ9PLm59DHFx+veYayG8Hp11dzQEJCcDAwP09Ji0duw2fSBRJ6Y8SDCZMYzXwZ/7EyOfLzoSSf7XYi3mdVXqpm2eAt5cAoGIy87WWIR+8lwuwPvgwifTydTnl1Hg3AGN7tdv89G5ONw6M83GPkw2TL4f/k+Gnkyyv0VB3aRAEXC4smKfM9lDzZ+7/g4WW0rXgMfz/9z4vu4OJ18uG23myQeJhOeoPGnn5FB6xMT9XfZYtGfa6TP9tFHH43sAJAKLtI4IFal5efnY82aNTj77LOxevVqxMfHo7m5Ge+88w727duHuro6dHV1YWRkZMJcsLu7u/GZz3wGKSkpSEtLwxe/+EW/fclo/2984xtYtmwZ4uPjMW/ePHzzm99EX1/fmGuS//76179G3L9pV8FZLJaAQdzr9aK6uhodHR1Yv349srKyQrYRLQENDg6itLQUsbGxSEtLU7pYB4Ns/wkHiYlEJHl5wKlTQFYW0NMDpKQATid9oEND+sfEg+fAAC37+4mEWHU3MkLHiKQh6vN5vcczVhIS1R+yTYkHDFHFJ67jgUd2iJDVfvK5uF88CKtm1vJgLM6ARRKSDe9s01FJEawW4oGTZ8/hShwqAhLtLKGkF57di8TE90x1rOzhJqqYVFBJksFsPOI6lQpOtCOqPNFUKjXuq3ifLRZ6P0dGaKLF7/jIiN6OOEnidzYjA+juBrKzgfZ2+l7a2uh76ewM/E5YGuK2wrEBAQg6EBuBbUDjgdVqRVpaGtLS0sao6yoqKvD//t//Q15eHgYHB1FWVoa1a9dGTHoMlqJeffVVuN1ufP7zn8f111+PJ598Url/S0sLWlpa8Itf/AIrVqzAiRMn8JWvfAUtLS34+9//HrDvn/70J1x88cX+36EqtaowrSo4gHSx3AW+4Q6HA2vXrg07z9A777wTkBA0HLS2tqKyshLz58/HkiVLUFpaiszMTMyfPz/sNh544AHcdNNNYe8P6IMpD5YAEYnLpX9MLOn09gKpqbSMi9PVb6K0JKokAGo7NpbaYK8h+RjRniOSiygJyXwuq/Nk1YqKvADj/+XfRql4ZM8/8VjVrJv7E8rlXbwuWSUm91McXHkcCOW0IMIovY7KUYAnHuK+RqpK1cAqrxPVbLKKzEilGYwYZQcX+V2R1WMOB72LorMKEGhX4u+A39ekJJpopafTJC0xkYhLVqeKEpLXS8cPDo5VbQdDX19fRCquiooKZGRkhJXkOBr4fD5UV1fjnnvuwc6dO+H1ehEXF4cLL7wQP//5zyM6b01NDVasWIH33nsPGzduBAC8/PLLuOSSS9DU1BTSu5fx1FNP4bOf/SycTqffLm6xWPDMM8+MOxnptKrgRLS0tGDPnj3Izs72x/eEi0gkIH7AVVVVWLt2LZYuXQqjOKBQePbZZ4NuF42h8fH0MWZkBA54bjd9NCMj9DcwQPv29AD5+UQ+CxaQOm7BAvoI09P1j4wlEY9HN9Qygblc+r5xcYEDC/dNlIJ4gGWiAQL7CuizfXF/0RuKbUSAvp88qIq/+TziOfk84kSTz8tgSUgVf8wDj8MROgiUnwE7fLBaiv8XiZ5Va263scu1CO6val+eOKjUbrJqTqVak4kd0O+9OJ6Kjhv8fLhfslMHT4r4emXPRN7f4dDPw4TCajK253g8dJ9GR/XnwRMDtvtnZtJvVsu5XNR+Tw8d29VF65nAANIUWCzAokXU5pIltH3uXPqOsrL0dyOcycHx48dD7yRgslPxWK1WrFq1Cueeey6KiorQ1dWFf/zjH1i0aFHEacb27NmDtLQ0P/kAwAUXXACr1Yp9+/aF3U5fXx9SUlLGOGV97WtfQ1ZWFjZt2oRHHnkkqDnCCNOugmNCaGtrw9q1a5GTkxNxG+GSx8jICEpLS8Gpe0QXx2gI6O233w66XbQBieqFmBj6kLq66IPp6qJZHmsEBgboI2tpoY+2oYEkocZGYNYsoKODPt7+fiKWoSHab2REJwpWcfT00D5OJ7XhdNJgwdKU6AChch6QVXBAoM5f/BZl5wWjIEpxdi2q32T1nTyYqlRdbP8R7Wy85JkyD4qhHm+w7ycS+4I4CKsgkwq3L0sjKqlPlBzk9uX4KSY4USIUSU8l+aqcCfgZs9qNnyVLKuwiLbbJUmtcnK5ybm2lSVVTk65Sczh09bLs+p2SQt9EXh6p4ebOpW9gyRKgvh5YsQKoqaGJWX09aRI6O9XOEEbo7e0Nb0fw9fkmlYAYbANyOBzYtm0btm3bFnEbbW1tY8ZTu92OjIyMsEtpd3Z24s4778T1118fsP6OO+7Ahz/8YSQkJOCVV17BDTfcgMHBQXzzm9+MqI/TKgFpmob9+/ejv78fJSUlUZEPQDc1FHl0dnbinXfeQXJyMjZv3jzGv57jgCJBuIzPNhybjfTaCxbQB7VyJX0w69fTh1ZURB9vYSEtRY9zp5M+qrY2mil2dtJHz0ZbvhwuUJiYqBMLz0L7+2lQHhqi9TExtIyNHSuBiPYRnr3KaimZmEQ7lCh5iKoQUSoQZ9YM0WFBbFvcLnvHs3rLyGue6/k4HKGdIYwQ6tVgYzjbeVSvI+/DEpF4/TL5yAQjOgeI22SnAV4HBE6ARPIRn41MPnytsuRot1Pf4+J0sunv198vsb2YGNovKUmXwltbqY2mJmq7s5P2d7tpX59Pf99nzdLVeA4H0NdH73NLC62rr6d7VVNDv0+epONGR2k/gMgrHJzkg8PERNiAwkGwNDzf//73lU4A4t/hw4fH3Yf+/n5ceumlWLFiBX70ox8FbPvhD3+IrVu3Yv369fje976Hm2++Gffee2/E55h2L7jly5dj8+bNETsAiAgmvWiahvr6epSWlmLZsmVYtWqV8gUab1luIyQn6wMpT7Zqa+ljLC+nj6+sjD66sjKa3R0/DsyfT6SUm0sfcloa7RsfHxhfMjRE/3d10cfa1gbMnk3nWriQzjtvHp0vMTEw27XHo3vbJSTQ78xMnZx45iySjzzQ87Zg3nJAoPQkHsv7Gn3TTH4iVCTEhBksJaBoO3A4IiMk1VyDB2VuU47bEsGeYLJ3nJEdxsjNXeyL6IEmOleopCY5hkzcxhDvBbcXE0P7uFyBKjVAJw72E8rN1fszMkLE4fHQu8gSLqvqZs+mc6xeTW2uWUOTsxUriKySk+mdHh2l76CvjySp0VHdnpWYSP1LSyPCyc6mNufMGTuhMkJZWVnonQRMVTLSwcFBQwK66aabUFNTE/Rv4cKFyMvLw6lTpwKO9Xg86O7uDllKe2BgABdffDGSk5PxzDPPhMy1uXnzZjQ1NWFUnI2EgWm3AaWmpo5bpJWrojJcLhcOHjyIpqYmbN68OagBbzIIKCmJPtp16+hjXbmSPkD2c0hO1get9nb6sOrr6SNrbKQPbnCQSCYlhdbPnUv7r1lDbSxYQMelpuqDW3s7fYjHj9O2kyeJuFwuIhomIh4UAV0q6u6m3yMj1H+HQx80ZPDgJTtCiB8+q3hkF3Bg7GxclHxkO4U8mBi5drNKzuiVYuIW7T5MJHxPWF3Kv/k+ifvxQC9KfUZg12vVPjL5iE4Oqu08gQAC751IKuI2ceIggvfjawF06SE2VneKER1ObDZ6Z+12IhCXiyYsnZ20raODjh8a0s9js1F7iYn0DtvtQE4OORfk5QHV1bS9ooLOwxN3fg99Pt1WVFhI64qL6ffKlfSNFBSQNJabS5oGVmeHk0TghRdeCL2TgKkqxzA0NGRIQNnZ2SgsLAz6FxMTg+LiYvT29uLAgQP+Y19//XX4fD5s3rzZ8Nz9/f246KKLEBMTg+eeey4se3xZWRnS09MjTgYw7QQ0EVCRR19fH3bv3g3OYh0qSHUyCIjjFYaG9GA5j4c+Yq+XpB2vlwjK5yPDqtdLHxx3pb+ftjU20sdcU0PEUFFB+zc20vFDQ8Dy5TrBWSx0HpZinE7d4cHppO2jozoZzp2rx1gA9D+r/VjKYkcGceATdf5MFOIAqZJggLGOCfKtF50dxN8iWDpTSV9MLOHMgplI+BrYNsUSInt98X6cqiYYWMoC1DFB7Agik49K0pIlHiBQZSY7Ccjgdvk+sdRls+kOAF5v4HvBbbIknJCg22s8HqC5mbazVK9pNAkC6L0EiCDcbpKQnE56j5xOfbLV2kpLl0uXRnNz6R1eupQmTatW0TnXrweOHQM2bQL276ff771HZLZ/P/WzvJzua10dnX9gQPloAhAsEF6FqZKAoi3HLWL58uW4+OKLcd111+Hdd9/FO++8g69//ev49Kc/7feAa25uRmFhId59910AOvk4nU788Y9/RH9/P9ra2tDW1uYfH59//nn84Q9/QGVlJY4ePYoHH3wQd999N77xjW9E3MczjoA4b9y7776LefPmYf369WGVaojUBhROJddTp+hjPXaMPujqaiKF8nJg8WLg4EHgwguBQ4eACy4ATpwAzjuPSOecc+ij2ryZPs6FC6nNxETdgaChgT78Q4foQz9yhAYPnpGmptJ5Fyyg/bKzdTUIt8E6+ZMn6XxsMI6JId09g9UqPEDxwM6u//xN8uAsztzlIEa63/q6YJ5wKicEEWLMiwwmkXCJKJhJL1wHHyYelbqNoUpcymQte8DxK2nkGi7eH76n7OQgS0H87Nlhg1VrTOQsMeTn65ORkRHqq8tFz50lz6QkOmb2bGpz0SJ6b3Ny6H2PiQEqK6kvjY3ULscC+XxENCzF+HykivP56J3jd2hkhN7LkRF6x10uIhtNI3W1z6dLXTxxs1qp/8nJJBlJmbvGINL8kVMlAU0EAQHAE088gcLCQpx//vm45JJLsG3bNvz3f/+3f7vb7caRI0cw9L7YevDgQezbtw+HDh3C4sWLMWvWLP8f28scDgceeOABFBcXY926dfj973+PX/3qV7j99tsj7t8ZRUBerxeHDh1CXV0dNmzYgIULF4bt4x+JBDQ4OIg9e/YE3Sc1lUgjLo4+SoA+6P5++r++npY7dtBH8/rr9OG9/TYNXBUV9IGdOqWrgEQyWbGCtufn6yo4UWoaHaUZJkBElZKiOz64XMDGjTQ4LFigu4nzbP/UKWq7s5N+JyQEElh8vE4qg4PheY+JKjUgcIAUiUZFMuL3biQJBbP/iEQU7eQ1FAGxwRwwtgWxOk+0RfF62W1YtvuI90AlZYrSj5zKRnR44Ak/r0tK0gna6aT17e20z+Cg7nLNWQgKCnSp1u0mx4DRUSIdr5feGY6RysykdpYv1ydTXi+wdSudY/Nmkuhnz6bJWGwskRY7GmiaLmF5PCRJ+XykOdA04OyzaXn55dRucTH1ZeVKkn4KC0mVV1Rk/NzYEerYsWPo6+sLOgnVNG1KJaCJyISdkZGBJ598EgMDA+jr68MjjzwS0O6CBQugaRo+9KEPAQA+9KEPQdM05R8X8bv44otRWlqKgYEBf+zml7/85aiIedoJaCLyHNlsNoyOjmLv3r0YHh5GSUkJMvntj6CNcAiora0Ne/fuDeqxt2kTGU3XriWJoriYPtaPfIQ+oMsvp+XWrbRcuFCf/fHg1NtLH92JE7SsraUPs6qKiKC6mj6spiZq1+kkacrhoHbtdl1q4gh0gHTsFgt98B4P2Yk8Hvr47XaamQK6Ks5q1Y/t7dUDZVm64pmsPJsXZ+7A2EERCDxGfHdFe5FoC2GoSAigeyfGOMkQpQ52Qoh2LLFaA50Q2K5kRFSi84cIleQjxweJbtsqNZyo5hSdEfhYsc98noQEapNVamI7ubl03PLltE9aGk1K7HaSSERi8HqJnLxeIieWZgB6T71e3XPzwAFa7tlDbbz3nu7ZCejqOouFVMOJiUQiaWm6Om/1alL/rVsHlJbSN7BjB/1+9VXq+yuv0MTvpZeordJS4+e4aNEizJ49G0NDQygvL8fbb7+NQ4cOoaWlBSOsJngfTE7TbQM6kzDtBDQRcDqd6O3tRWZmZsRBrIxQBOTz+XDkyBEcOnQIq1atwrJlywz33b+fPuTnnydVwEsvkSfaK6+QTvvtt2lZU0OSTGcnfew2G7BsGX2ES5aQ1MIebPwx2+36DPXAAfqQX3mFljt20EfK5DI4SIMOE1FhIe03ezYt2RYF0IAyMqIH//X10cDi81FfbTby1GP7Ac+Wm5vHxl2IUfuiS69KclGp3+RBXCYzQJeaVG7aYnYDGWKMENt9bLZAJwQmJ9EZgSUcXi8GW6r6LCJYrSBWhTFkt2rZHVsc+1S2IdkZQXQiEO1AQ0Mk/bD7s9erS8Fs36mtpe0DAzrRzp1LfTzrLFpXUkLvzpw5JGnHxZG6zGolW4ym6Q4FohSzdCktzzmHlldcQecuKSGSWrWK3rG8PGp/dJTeyZMnScKvqqJrKC+nbWVltGxvDyRUdmCYO1f9bNasWYPZs2dj1apVOPvss7Fu3TokJSWhtbUVe/bswd69e1FbW4uuri5/7bHTxQZ0OuC0JiCfz4fDhw+jqakJ8fHxQauWhkIwG5DL5cKBAwdw6tQpFBcXB3VhTE/XB123mz7eoSHSg/f3k+QyMEAfd3c3SThdXfSRdXaSJNLZSUTU309qipER4Pzz6aP61Kfow7rsMlqyWmPePF09NjxMfWltpY/5yBHdwyg+ngaY+fPpAz/3XH0gYZUcQCoTzj9YU0PLpqbA9DhpaYEBoDLkwEiVC7Y4QIqSDxOSaL9gbzsGq5Hk8YBViaJajKF6PTjDATsdiNkORGcEcb0M2RlCVsnJYDtRKLWbeG9FNZvKG46XstehaAsSz8cqtp4e+j08rNuIeMLDjjJsm2lupiVPfnbvpn60tNBvjvthd+iUFLJ3ZmbSBMjrJWmmqYne2XffpYnQ88/rkzarlSZtViuwaxf1raFB7zPHuLH6eM0a6vfWrSTZffjD9J6fdx7159xz6VtSqWhXs7gG0sakpKSgoKAARUVFOPvss7Fo0SL/5JPV7k1NTRgcHAw7DjBSfBDKcTOmnYCiVcGNjIzgvffeQ2dnJ5YvXz4hrtwqCYi96ex2O4qLi0O+FBkZ9OHNmUOD/Ny5pJrIyqJlYqKeBTsjQ894zVJNZSUtX3yRPsCnn6bB5v2q33jqKfr9+us0g62ro3ONjpI9KCuLfs+eTedJT6c/h0M3KmsafdA+H/Dmm7Tcty9Q5ZecTEv2zOPYCnYt1zSdoABdMpLdgOWMCXJqH3GdaKsAxrpaq1y5Wa0lxuOIJCYO/sEko/FCPK/q3OL94IwYwciH9xWDT4284USSFs/FEG1sokqTJ0krV9L/eXk0YMfF0eTF4SDbDqAb+1lNpmkktQM0OdI04NOfpmexejW9Z3Y7DfynTtEk69gxmnzV1ND6xkY9/RRLYJpG34zHQ8Ti9RKheDzAJz9Jy499jJas2l6xgt7/ggKa7M2eTW3m59N5kpLoXVWleRQJSIbdbve7PBcXF2PNmjWwWCzo7e3F/v378c4776C6uhrt7e1+6WiiMDg4eMaW4xYx7QQUDbq7u7F7927Ex8djy5YtSExMHLcLtYqA2Jtu/vz5WLduXUiPmTVryKZyxRU0kF9+Oc0YL7uMpJ3LL6cZ3Cc+QR/6Zz9LH9hXvkIf3pe+RMuPf5wGk5IS+tAWL9bjIXg2PjREbXV30zna2+mvpYUkqJYW+jh7eoiAhobINuX1AtdcQwPSZZfRQLR+PZ2XHRosFn1WfPw47dPcTIPU4CCRU0JCIFkw0cgGcXl+wYOkGNAqEo0q5keOd5Ej+YGxRKOCKvPCREGVtUF1bnbF5/8BNfnIdh8jexlLjqIzgsrrUFZXivaoykrdVsP9S0ggqSI1laShzEzdRZpzs7Fk/MYbRFZPPUW/OUMVSy1Op+4tOWcOEdyyZdT26tXkpLNmDU2QmLxWraL+rFhB6rbFi2nSlZVFUlJCQuDkzGIBnnySrvevf6X+PPEEnZNDfY4eHftcwk3uabFYEBMTA5vNhrVr1+Kcc87BihUrEBMTgxMnTmDXrl147733cOzYMfT29kbkUavCRJVjmOk4rQhI0zQcP34cBw4cwOLFi7F69WrY7fYJieGxWq3+NrxeLyorK/3edGIJ8GCoqKDlQw/RgPDQQ/pvnw94+GH6wB97jJZPPUXLl16i5f79uo2H66XEx9PHm5BAH2FiInmwJSXRzDUlhVQRiYn0QXNtoZiYwMwLAA0UAJ3fbtfPyy6tPT26iyxA7TocetR6UpJu/+HYIIYqol6WCOSBNFg6HlFSEu1I9Hz0/2UtSCitiKyGmiio3KJDQVRDAoH3TVZXitKNSg0nSz+qTBGqvnG/V66k5Zw5gR6Og4P0XnR2kiQzMqLb/zweIg+fjwiFpRWfD7jqKlrecAOt/3//jyZdmzbRu5OVRVLJ6Ci1yxOo+npa1tXROY8coeXRo3o/ODib45W8XiJJTaN++HxkowJokmWz0flFxxxGDAdChQHRA44Lyi1evBibNm3C1q1bkZ+fj+HhYRw6dAi7du1CRUUFmpubMcw68TBhquCmEOGq4NxuN8rKynDixAls2rQJ8+bN8x87EQRks9ng8/kwNDSEffv2YWBgAMXFxRF506Wmks48Lo7EfdaDAzSD5LxX7FXGag2eKXI8z7vv0vK99+jjevdd+nj37dOJKj6eZqA5OWSYXb6cCOf882mQuPZaGsi++lUikc99jj7Aj36U+sUf6MKF1GZSkh4bxG7YJ0/SR84z4/5+PTWLEeTIfdG5QKX+EtPxqAiLpSqjeJ9gQa4zBTLxMuT7yP0WnS5CpeEBAtVwsjMCkzfb6mTpkFWHVVX0W3QqSUig9yIhgaTjlBRyHkhIoPeGPQ4tFj3W5513aPn3v9N5//u/6f178EFaz1IS78eTo+5uPQsFZ40HdMLYsIHOc+659JttoFdcQcvzzqPl+vWBnqXsYBETQ/emoGDscwgXwfLAxcbGYtasWVi5ciW2bduGDRs2ICUlBe3t7di7dy/27NmD2tpadHZ2howhHB4ehs/nM1VwMwUDAwPYs2cPvF4vSkpKxqQlZwIaj1GQX6w9e/YgNTU1qvx0AwPAlVfSQH7VVUQi//7v9AHwTPDmm2n5n/9JH8TXv05LVsdddhn9ZvXb8uWBXjxxcbobrM9HM0OvV7fh/N//0e///u9Aqetvf6Ptr7xC26uqaDk8rNt4vF760H0+3bmhqIiOmz+f2gmWL1YOiuSBlL832bWY14k2Gzbmi15bwew2su0lGCaTmIJJQCoCEqUaQJ1yR1TXcTsqO48q1kp0zVZJqkwcTEwxMfSOWSw0kRoZof50d9M70tZGkkd9PUkfDQ3UN477GRqiNrxekqCtVpJGrFZ6pxwOIom4OOCSS0hi/+hHidSuuIJ+f+Qj1Ofzz9edB3p7gW3biBiLi8mRZv16mpQtXkxS/ezZ5IKdnk6204QE+g5iYogIWS0HkKdotAg3E7bFYkFycjIWLFiADRs24Oyzz8bixYuhaRrq6uqwa9cuHDx4ECdOnMDAwMCYcYuDQk0JaAagubkZe/fuxezZs1FUVKQUmZk8opWCNE1D4/tTuEWLFmHlypVhv2gifD7ST3u9wL/+Rb937qTlO+/Qh8reQ6w/F7MCA3q6m6ws+nDmz6eBZcUK+og3bSI9/LnnkhR0zjk0S92wgQaOZcvoQ5w7lz7u9HTaHhen11KJi9NdtFta6LxHjtCSi7y+n5kDpaV03uPHadnaqh5kmTTkWb3soRUqGzan0THKiC07Joh2J8YkOSeFBdW5VSl2ZFKS75vsms1tMyEzMYkSkmxbEr0Q5ddZVkW6XHqMDwersvC/eDG1zTbEiy+m5dVX0/KrX9UnUR4PeaO53TSp4ZxynMF9ZITeNaeTvoX+fuCtt4ho3niD1Gw7d9Lvt9+m37t3kxPD7t3UxsGDulquu5ve4b4+2pe9ToeH6Zwul25PG69AEW0WBHZmWLZsGYqLi7F582bk5OSgr68PBw8e9DsztLW1oa+vD4ODg7BarVGFk4iItBw3QIGocmbtr3zlKwH7NDY24tJLL0VCQgJycnLw3e9+N6zMMEpoMwAjIyNj/pxOp7Z//37thRde0E6ePKncR9z32Wef1fr6+oLup/obHBzU9uzZo7388svas88+q/X09IR9bHx8vAZg3H8WC/3Z7dCsVmgOBzSbTV/yepuN9uOlw0Hr4+JoXXIyLTMy6Ji8PNpnwQJarlhBy40bocXEQPvQh2h5ySW0/qMfpeM+8Qla8u/zzqN2N25U911eZ7Op/5f/7PbA48V97Xb9f6s18Dj5t7gvt6Pql90+dt+J/HM41Od1OILfI/magvVRbF/8n4/hdngbn8dqHXvf5L+4uMAl95vfP7ud1vEynDZVf9yn+HhapqfTcs4cWi5eTMvVq2m5eTMtzzuPlpdeSstPfYqW115Ly298g5Y/+AEtf/KTwOXHPz62L/39/WH/HT58WHvzzTcjOibUX29vr3bixAmttLRU27Fjh5aXl6fNmzdPczgc2uuvv66Njo5GPa5efPHF2tq1a7W9e/dqu3bt0hYvXqxdffXVQY8599xzteuuu05rbW31//X19fm3ezwebdWqVdoFF1yglZaWai+++KKWlZWl/eAHP4iqjzNSAhLtMCUlJcjOzg66P89KIpWAOKWOx+NBSUlJgCNCOBBroPMMPDWVMiDMmUOqgtxcUmNlZpLdJTWVlsnJNKNkp4L4eDouJoY8gBwOUr/Z7aR3t9vJJdrh0IMBs7NpZss5vFgaGBjQI8w9Hl1dUl1Ny/37aYa6cyctX3yR1j/3HM0U//EPOv6556i9t96i66uoGKsOU834xUBUVTwKoBvaRVURe+ABYx0RRNtPKAcj2VMuGrCKShXsagQjT1x5vax+E50JgMB7Kp5bjIsS7T7AWC860asQ0J05gk3g3W49zRK/2lziIDeX3r3cXJKg8/JIos7Pp33mz6d3nsMO8vMp7mf2bPoOcnJIrZyeTil0kpNJUoqPB7ZsIam+qIiucfVqXYVnseh2G64gzeUfWKLhd4MlNw4P4AwLHOLwzDPG1x4OJiMNj9VqRXp6OhYtWoSzzjoLb7zxBj760Y/CZrPh6quvRmZmJj72sY+hkuMzwkRNTQ1efvll/OEPf8DmzZuxbds2/Pa3v8Vf//pXtLDawwAJCQnIy8vz/4mJnF955RVUV1fj8ccfx7p16/CRj3wEd955Jx544IGIE7sCM0QFJ6qyOjo6/KVkw7XDRFNSu62tDXv27EFubi42btzod7GMxH0yl93FoA8GfX30wQwOEqEMDdEHODpKRMPxCx4PfYheL+m8vV7SjXu95Ibt8VBchcdDbtMeD3D99TRIfPObtLzpJlr/gx/QcbffToPMHXfQ8s47qV//+Z+0vPFGWrLb97XX0vKqqxBg0N2+nY7/0Ieo3U2b6DznnafOtqw/h7FxOyJ4AJQDLOVgS9Xx8mMRCUkkLvl5iPB61etl8HXIf+HAyAYk/xb7IccDiQ4XIuGIajeR5BnifRXvibiPbAMSwZMBMTB3cJAG9p4eUp91dOgZrfv6SCXb2Uk2mpYW+t3QQMvaWtqvvJxUaPv2kcpt1y76LnbupLZfe43Ox16ZL7xAfXvuObqmf/yD+vXUU7T8y19o+fjjdC/Yq/NPf6LlH/5Ay9//npYPPhj+8wuGqaiGOmfOHFxwwQWYM2cOmpubsWvXrrCy+csYTznuJ554AllZWVi1ahV+8IMf+G1S3O7q1asDxr7t27ejv78fVezJEgEsmjadGnOCy+WCz+fD0aNH0dDQgJUrV/rThYeLN954A+vXrw+QSlTw+Xyoq6vDyZMnx9zInTt3Yu3atUgX00AHwdVXX41nFNMqHmC4eiSXLBaXLhdJO/LS7dZLW7N+X8xurFrywCMvuR/yUgZvk9uQ2w5FPrKRXNxfHBzlfsv7i3YNo//l9lWxNDJk4jPaR1wC+j0LNb/h5yTeY9EDTQX5Ooy2yX3noF95u9E9NXr2MlJTiSRSUkiSTk4mEkpKIuJJSCAiEt9tflf5/GKwKwcJswfo8DC1wSXie3vJU627mySpzk69XtCsWURkc+bo2TsaGsjD7dgx8jitqyNJq6aGNAeVlXqeuI0b9dINRmNuP2cHDgPHjx/H8PAwVqxYEfYx0eD555/Hz3/+c5SXl0fdxt13341HH30UR9i4+z5ycnLw4x//GF/96leVx/33f/835s+fj9mzZ6OiogLf+973sGnTJvzjH/8AAFx//fU4ceIE/vWvf/mP4ZilF198ER/5yEci6ueMkIBGR0exf/9+tLW1YcuWLRGTDxCeKzafp6OjA1u2bAkgHyAwFigcLF26VLmeP8DMTPjTmmgaqS80jT4sn49UCj4fqSt8PkpN4vXqReY4C8GSJTS4LltGyxUraLlyJS3XrqXl+vW0ZM81llxY4tq6lZbnnktLjjC/8EJafuQjtLz0Uj3inCUyr1ev9yJCdCQQZ9ey27QYyyOSj80WuD8wNjBVXM+Q1Wwy+Ri5Z4dSp6lUb+Go4kSvMgaTRDQxibIkJHu/ya7pots6Q1R/ymmMjK5haIjaHh6mtkZHaenx6CpecXLDz5fLu8fH0/8crJyaSmSWlkbfQUYGqemys4lIsrNJxZyZSSrn1FQ9vm31aiK+NWuorbVrackFHjdsIALcsIHOWVREy40bacn56lgFN14vyKkqxx2sGupkl+O+/vrrsX37dqxevRqf+cxn8Nhjj+GZZ55BPafvn2BMOwFpGqVDdzgcKC4ujtr3PRQB9fb2Ys+ePYiJicGWLVuUDzhSFdxyzkVigHPOoeV55wUuP/zhwOX556vXGy25HY7lWbKElmwqY0dBjn/jD5DfIQ6Y3buXlm++SR/njh00oPzrX7Tk/Fz/+AcNLn19xqouo0BUoywHoqpI5VIsth/NNy/bjXhdqLb4/Hxdont1MO1LOHE+QOD5RQmFiZjPJRIqewyKpMQQJT+WZFUJO1glF2wQZuKIiSEi4SJxovqP3fb7+0lqZzftri6SmE6donWtrWSDaWmhGKHmZnKhbmoi6aSxkbzaTp4klVxrK3nBdXSQSq6zk1RyXV30HnZ3U+aDnh5a9veTSm5wkH4PDdF7OjxMtp7RUTpueFiPsxuvrmcmlGKYinLcIrhy6tH300jk5eWhnQeU98G/I2mXETy3zBTAYrFgw4YNiI2NDTsoVQUjAtI0DU1NTTh8+DAWL16MBQsWGJ4nUjtSMFHcYqFYBEB3uX7vPVq+/jotX3yRln//Oy05dcgjj9Dy4Yf1jAp2O/C739HvX/2K2v/972m/J5+k5T//Scs336Qlu1RzsB+7fPNgkpxMH3J+Pqkyli4lctq8mVQW555LbX34w9TnDRv0No3AaheejcsqJpVqUExwyuvEwUJUNwL6oMuEJmYSEFP9yOBzGqmjRAKQVZehoGpTluxk5wNxm3i8qO4UVWvydTJRsa2IJR4xwav4DGQVmQy3WzfgDwxQG2KJi+RkOjYhgdZnZhIBzJqlq8yam0myP3mSVGcnT9L7deIEST7iMj+fts+eTcQ0Zw4tc3OJkHJyaPKUmUnElJ5ORJSYSP2z23VXa4D67vMR6Vmteh67iYLX6424gF00cDqdSDCoJ56dnR3SKQtAQDnuovcLIoVTjltGWVkZAGDWrFn+dn/yk5/g1KlT/pI0r776KlJSUqJSTU67BASQ18V46wKpyCPSlDqRElBBkLBqDs4DdOJhG93x47TkTAijo7RkyYW9fDg3G6vgODljcTG1/6EP0X7bt9Pyox+l5ac+RcvPfpaW115LS1bPFhbSklNNccoelpBKS+njfucd+pCZMMMhH4bFog+4YoAkq3F40OTHIavlxMGYt/O+4iAuSgoqbznV4zYagEUJQZaAeHuwa1fFQMnHq84tShhAoFQk299EQhL7KJO0TOAMUU0qg+/9ggW0TE/XJwMuFw3wTidJNr29NLFpa6N4nuZmej/a2sjp4NQpmsx0dNB+PT1EDAMD1MbICL1/nPDWYqH32m6niU9MDGkGYmPpvY2Lo1yK8fEU7B0fT3FI8fEU7J2QAHzxi9TmDTfQbx6nJ8pvYCqcEICJKUYXTTnu+vp63HnnnThw4AAaGhrw3HPP4ZprrsE555yDNWvWAAAuuugirFixAp/73OdQXl6Of/3rX7j11lvxta99DbGxsRH3c0YQ0EQVpRODoYaHh7Fv3z4MDg6GXaAuUgIK92Vct45mj+edR7rtiy6iJf8uLibdNweTchGuggJa5uXRkvXpo6P04Z08SR9saSl9ZBzl/eyz9EFzUsZHH6X179sRwcVcxTQoXLMmJYU+/sxM+pjz82kgys/X3b1F8KPjgVE0hIuSEBOO6FrNg6hKLSevEwdzlXqNIQd3yhoTI1KSwUQpe6sZHasiIDmfm+hAIQfTAmNdrkUXalX/uC2ZfBiiF5yo4uNrkfvPhMeJbFma4jIKn/gELb/1LTr+y1+mNq+6ipYXXEBtcdqchQupzdhYaovrAtXX6ymmRkfJ621oCPjzn4mcnniClk8+SRL6X/9KxPXccyRxvfEGbS8vJxVbezsRmsNBRJmZSe3xBC+YVr2vry/sDCpTZQOaqDxwkZbjjomJwWuvvYaLLroIhYWFuOmmm3DllVfi+eef9x9js9nwz3/+EzabDcXFxfjsZz+La665BnfccUdUfZwRXnAejyfqLAaM8vJyJCUlYdGiRejs7ER5eTny8vIiKtVQVlaG1NTUoJIN49SpU6ioqMBll12m3C56hfHHyIHN7OY6OkofuMulq5TY2MsDIA/SVqtxnAmgG5nZSyk3l1RuBQWkA1+zhnLNlZQQAX3kI6RjP+88SmOyeDGpCjMySO/ucAQOaCqvOxUSE2lwkO0VfA2iSojPwfdKVB0BunpNXCfbTVSec8E8y+R95XvIRCeqBZkwxOcyHhidn69T7r/ovCFCVlvyNfA7xyTNHmypqXrqHBUKCylLwdy5ZKNJSdFLyMtgUnO7da+2uXNpUrR1K0nP115LpHLHHcBttwF33QXceivwwx9SiMA3vwncfz9JMI8+SgT3j3+MVfkuWEAqu+RkIiLxXZLfKSbRcJ/RP9/XW2dkZCAzMxMZGRmGM/mysjLk5ORE5SQVCb71rW8hPT0dv/jFLyb1PDMBM0ICmgiwBFRfX4/S0lIUFhaGnVJHbCMUEWqahvr6epSXl2MlpxCWIOrf6Ria9dXUkCH2yBH63dKiqzP6+/VkoOw5Z7eT55vNRnaZmBhSQ8TE0EcbE0OzUYeDPmyLBfjud+lD/OhHqb3sbJoltrTQrHD3blr/7LO0/p//JCKsqqL+shcU54Zbt46W69fTkj34VMjIoIFo0SLdq89qJeJlTy1xcGX7At8z8X8gcHAVVXFMCEZxQqHmMuFkDVHZSkJJQCKMJsqiJCS2xedjuxGrMBnyPef9xH5yotiUlMD7PjJCJMFVbo1QV0fvUlMTHTc8rKdzmjOHpOA5c+gdYIk8IYHOZbeT3QbQE43++c+0/NGPaDtPkn/zG1o+9xwtWfXLERTva3v8zjZchPGGG2h5yy10nd/6Fi2/+EW6F//2b+HHegHA/PnzsW3bNqxduxYJCQlobm7GO++8g3fffRf19fXo6ekJcEqKNhVPpPigZMIGZggBTYQKzmKxoK2tDU1NTdi8eTPmcBrqCBCKgDweD0pLS/3nYMOcjGA2hvPPpw+Wa/J8+9u0TQwe9XppvctF+7lcFKTqclEshMtFdiSXizyA3G7g3nvpvHfcQcv/+i9q9623aNnRoRuSLRYih5gYculOSaFlUpJezuHkSdqvrIz6y55zrNYYe+9IxTJrFvUtP59Ij13Qk5NpQGTPKgar6uSsCDyw8jrZXiSel9sxUsvJZBBsDFHF3bBEZPSaqspoi/E3cnAurxOlOplgZHuYKrbIbqfnZbPR/Xa76bn295MairOuDw/TJMfr1e198rWwxMrlDFJT6Zr7+4m4mpuJYE6doknT8LAeszMyQlJLbCy57KekkH0yJUV3oZ4zh/qZlkaTHKtV907j+kGPPEL7sLPN/ffT+ocfpuX//i8tuUrqqVOBHoNcw4rvn+o6Rdx9992wWq1ITU3FwoULsXHjRmzbtg3z5s3D6OgoKisrA8oqeDyeKVPBfRBqAQEzhIDGi8HBQbS1tcHn86G4uDjiqGFGMALitD1erzfgHOxhEg68Xj179dy5tORZa309DSpcI+jXv6ZjbruNPsbbbqOP6Xe/o/VMLFy50m4nAklOJq+i7GyKq8jNJRtTQgKpRvr6iCT446+poQHhyBG99ovXS38sBRUV0ZLjj2RwJUuA9PEWCxGY2x3oeef1Uv98Pr1MBRe2YylHtHvwty56rgWzF6mKusn7AIHtiBA98EQCYZWOKqaHBzqjzAYiuYgODuI6+ViRwFR2ndhY+ktJoXucm0tkn55OqjObjdSoPp/uis+2mPh43blAvEd8XnaUGRyk3/Pm0cRh+XI6N9dv49Ldx49TH155RZeoBwbILtnfT/sMDlJIwsgI8LWv0fE33UTv/nXXUVsf+xgtN2+m87IWPDWV2gN05x0moL/8hfr/+OP0+9VXx97zYNLQdvbeERATE4O8vDysWLEioKxCW1sbnE4namtrUVdXh66urnGbDYxgSkCnETilTnJyMtLT0yMqMCXDarUq44BOnTqFvXv3Iicnx5+2h/Hd7343onM0NNCHfM899KH84hf0mx0F2GPu1CkanDWNnBBiYvSqkRdeSMvPfpY+wC99idRo//7vRDALFpCk0t1NhHDwIA0Gb79Ng8DJk/SBcpajuXNpIC0s1Gu/OBzUB0D3fuOgapuNZr5MDA4HEUpaGnnwxcToqhmPh87J8SMdHdT35mayOw0OUn/tdlIPiTYvBscSMSGJxMTbRUKRg1hFF2YgMLZHBkseKiO9kUedTHz8ColxULKUxCpHMbCTj2HCZNLj55SVFehdyLE4rPpi6YaDni0WsqMARACcFqqnhyQdvnezZtE5ly6ldrOydPtkUxMdV1tL7TU0UNtDQ5TDLSODMhBkZFA7CQn0fDkL9qFDdJ7/+R/q189+Rud44AHaZ+dOOpfdTssLLqDl5z5H57/pJjruRz+ie3PDDbTvJz9Jy02bxj6TcHkh1FghllUoKipCbGwsZs+eDa/Xi8OHD2PXrl0oLy/HyZMnMTQ0FLYzQyh8UKqhAqcxAfl8Phw+fBiVlZVYu3YtcnJyIgoiVUGWgDRNw9GjR/32nmXLlo1RFxo5IQD6AC0OXqJ3Fbu7stvp1VfTYH7jjXTMt75FH+EXv0gD+OWXk1olJoaWO3fS4P7II9Tu/ffTknXw3d262is7mwhi1SqaVZ53Hg0i7KRgtdIg43TSoMPxFZmZenYGq5UGJ6+XJBu+pv5+IqDeXpopDw/T4OTzUS0XgM6jadQfdk9nKayhgfbt6aEBLCVFn7Ez+LGwJCWr0cRBXCYY1WvBKj8RskFfbMsoFZGKqFTbVFVP5Tgg0W09KYl+z59PzyQpSZcmh4f1XG1WKw34sbFEDCkpdExfH00oqqvJjrdrFxHMO++QBCRmaOG4wtpaaq+nR7fx5OYSucTG6rWo2tqIJPbupWdfV6d7Uvb1UViAy0VecjEx5FwQG0vvhN1O7wqnnKqro3M//TQt77qL9vnJT+hcDz5I9+T11wPfgblzqY28vMCJRriItNYXQGNOZmYmCgsLUVJSgrPOOgvp6eno7OzEvn37sGfPHhw5ciSsonPBYEpAU4xIbUCcUqezsxPFxcXIycmB3W4ft0gsEhDbe5qbm7FlyxZDe0+wwDQmGh4cxcv0eGjA5jLFLhcN7m63nrX60Ufp2J/9jAajn/yEjuUa9y0tNDD5fDS4xMTQx+9wAF/4Aq3/whdIysjLo/M1NNBvnnkeO6b3MTVVV7fExdEsubtbd/lmlZoYZMrSyt69dN633iLS2rmTVHbvvksqvOZmvabRokU0aHKmZZ7tu1x6uWV2iOBYjjlzdNWZLE0AgfFF4jp6ruLzCvYG6J5VRs9ThiiNMYGwh5iKnMS4Hllqs9t10u3ro32amui300n3TtNILQboJL9uHZFSdjYRQm8v7V9XR8/40CFqq7ZWJ3ox6DY/n553crKu/mPHGJakExJIFXbBBdTPiy6i923xYto/IYE81QAiE02jpKCilMO5EK+8kt6lb3yDzn/ddXTeSy6h+8BFGBMTdSmc7UQPPUT3ge1DTFyyHS3UkHLXXXcF30EBMROCxWJBYmIi5s2bh/Xr1+Occ87B0qVLYbFY/EXnSktLceLECQwODoYtHWkaleP+IFRDBWYIAUWC3t5e7N69259Sh0VVOQ4oGjABsb2HbUqhXoYvf/nLIdtWxXp0ddHvX/6SPrz/+i/6/cortL2tjQZ/n48Gb6uVpCCrlWaXPh+p3dxuncTq6+n3449TW1wK+f2AZgwPkzRis9FgYrXq1VuTkmh7XR21deQIDSSjo6Rys9spzxZAM2SOFWGC5YG7q4sGpepq2qesTC9ENjBA251OnXDPPZfOs3GjrnLk+8Wz/tZW3Q3a4aD+x8QEqq6AsbE3QKBXWaiccao4IVmFJz5HMfBTjFMSUwyJA6OYtJPd8rlcNLvmsyeXzUZkHhdHwchM3uwivW8f7bN7Ny05Y39vr05yXMl22zZad845tMzM1NWdjY2079AQ9YG90DjVU3a2buDnWLPXX6d3ZGCA+n755dTWJz9J7W7ZoqsCObsCk8hvf0v374EHaJ9XXw20fXFuw2uuod9f+hLds8svp+NXrQq8pyppUiRYFRl9/vOfH7syCDRNC+oFZ7PZkJWVhaVLl/qLzmVnZ6O3txf79+/HO++8g5qaGpw6dQruYPEUIBWcKQHNMGgaVS197733UFBQgLVr1wZIH5EGkapgtVoxMjLit/cYVWCVcfvtt4fVPg9SckCg16urus47jz4urjbJwX9z59IH2tFBA9Lf/06/H3yQBrX/+R9q7/hx/YNktRcP8BdfrEsKHg/NTAF9FtnaSjNZ9oYCaDDw+XRSKSuj4wcGSGJil20mqs9+lgbUT36SBsxzz6XrXbSI2ouJ0WNLqqtpuXMn9Xn/fjqmrY0ILiGBroEN5zzLHR7WB2weuFjyUDk0iPcZCLz/KiO/vE6Vwkb8X3SMEPfjWCdRNSj2gVWRbjfdS5ZkRTf4vj4i7/p6ur9Hj+reaZpGUgXX6YmJIekhNpbIg9VynG3daiUJVdN0JwXOx5udrZNsdTXt+957tI49ANevp2MXLqS+xcfrNXeefpqOeeYZWtbV0fObN08PUvV6iZi8XlrP4zDbJF96iZ7hE09Q2489RpOR11/XHS3i4vTKrYDag1AOgVAh0pQ6LMGE6wWXkJCA/Px8rF27Fueccw5WrFgBh8OB48eP4+2338aBAwdw/Phx9Pf3j5GOTC+4KUYoFZzX68WhQ4dw9OhRFBUVKfO5jZeANE3DqVOn4HQ6sWrVKqW9xwgZwYIrMHYGJsd/ADqxHD9Og/+rr5IB+amnSAfParf9+2ng6u7WVWGcVXvZMhpsFi6kD7ezk2bO77xDH/S//kXHsDeRx0MDn8VC0khGBv2flkbxSna77hXF5SLsdr28d38/7S/mJnzqKT03l9utD2w8q738chqALrxQjzPyePRiY+xW29ND13nsGP1mlRQLo+nputpQHGxUUofVamybkWEU6xPMvCg7OIjnYilRDrDl8zBh9ffTeo6J4YHd4yEysljIQ8xup0mJw6FLv5/+ND2Dc88lNdvSpUQemZmkAnU4SFoS7VB8je3tdM9aWvQsGDk5+rNmm5DHQ674cXFEgsuW0bY5c+i9cTjo2bAHZUcHTVIOHqRtO3aQTaq0lKT1wUFS/S1aRO/m4sW69MtEzWl/TpygPhw4QCTMUrE8WQhXdXrLLbcYP0wD8NgSTRyQ1WpFRkYGFi9ejM2bN6O4uBizZs3C4OAgSktL8fbbb6Oqqgo7d+5EU1PThNiAIi3H3dDQAKPs2k899ZR/P9X2v/71r9F3NKo6qhMMn89nWPa6u7tbe/3117WdO3dqvb29hvu1tbVpL774YsQluUdGRrSBgQHtnXfe0V566SXtX//6V1Rt/Md//IcGqEtt85+4zWodW6KYj+Gyx+K+Viu0pCRomZnQUlOpvHZSErQLL6Sy2p/+NO27fDktY2MDS0FbLNDS0mjd+vW0vOgi2jZ3bmA/LEKJ8IUL6dzcrs2m//HvnBx1iWwugc3lmy0WvbSzzUYlmWNiqIR4fDy0+fPpmtasoeXGjbR+2TJqIz098Ly8NCqFzX+qktjT8cfXLq9PTaXlwoW0XLKElosW0TI7m5aJifr18HWHU76bS2qLzyYmhpb5+YFl4MV3c/ZsWm7cSNtmzQp8RxwO/d6uWkXLK66gc2zdSttmzw581/m9AqDl5uqlsq1WKq3tcEC7/HK61vXr6d7k5dH7rLqfqv+D/VkskZXh5r+Ojg7t2Wef1fr6+ia8JHdjY6NWWlqqXXjhhZrFYtEAaDfeeKP29ttva263O6oxNdJy3B6PJ6AMd2trq/bjH/9YS0pK0gYGBvz7AdD+9Kc/Bew3PDwcVR81TdNmNAGdPHlSe+GFF7SDBw9qQ0NDQQmgo6NDe/755yMmjs7OTu3VV1/V3n77be3kyZPayy+/HBUBjYyMaHFxMSEHoHAHq9xc+lAvu4w++n//d/p4rrmGtp9zjj4o8cDOAzIP8CtW0LbzztMHErsdWkJCYH9sNmhxcfT/nDn0OzOT9mPyEwe6mBg6r8NB/4sEa7XScRYLEZPDAe2ss6jNpUtpe1ZW4EAUavDkwZH7yP3n38HuK9+bYAQ1kX/BrknVNyYRkdT5erjPfK/sdiLm+Hgi7fR0updz5tDzWraMJhmbNtHz2bYt8JnxJEYeuGNiqO2MDLqnFot6MhIfT8fPmUPreMn7i+9hcjK1tWkT/f7IR2i5aRPtm5Wl3yuZAJn4PvUpnSTlb0ictEXyXX3nO9+Jiija2tq05557bkLJR/X35ptvagC0f/u3f9MyMzO11NRU7emnn45oPK2urtYAaO+9955/3UsvvaRZLBatubk57HbWrVunfeELXwhYB0B75plnIupPMMxIFZymUbqbsrIyFBYWYsWKFSFFX1bBaRH44re3t2Pv3r3Izc3123vG48p9772/DLpd1bRsRBdVIwCpVNLSSMdus5Gtx24nwzOge4r5fKTOsNtJhcLBposXk40lJobsNx6PHokeH092AtbpFxaSt5rVSiovLkpmtdI5LBbSxbtcusGa7RyapgfVDg/T71On6Hys97/6atr+5S/T9ptuomu44QZa/r//R8tLLqHtXECPk2FyuRHZgzbYI5PjbCJ0uIwY4frBsJdcUhIdw0UJOeBz3Tq67xs30jNds4bWFxTQvcjOpmeQkkL2nIQEciaIiyNnBK9XTzoL6GrRwUE6L9/D2Fg9E0VvL9mbMjJo3ZIl1IeEBP25Dg3RO5KVRUsu/pafT0suTDcwQG0dPEh9evllurbKSrL/OBy078qV1J/58+mccXG6+vHvf6e22BNQtPeoMlYwRBWj/LyjUb8B5AE3FWl4UlNTYbfb8eSTT6K9vR2vvvpqROUTgPGV42YcOHAAZWVl+OIXvzhm29e+9jVkZWVh06ZNeOSRR8YV/zQjkpEC5FoNUIbWQ4cOYWBgAOvXrw87q4HL5cLrr7+OCy+8MKShUHs/vqehoQGrV6/2F1Ji77cLL7ww6uuIY9emIBA/GvF/thPIqVxEg6vl/cDP9HQaMFatIr36RReRnWj1anK7FbMAsHtyXh7p+tevJ336ggU0aIkfKw+gHEzocNAgxZ5VYpJUq5UGQLZH8Yev8hgTXaRFzzm2hajeQqP18jFilgJ5/QcZst1Jjpdhcs7K0osNMlHxM0pM1OOuYmLoPeCEt2Iy0MREWrd+Pdkpi4vJ/sRltcWksRYLTV5WriQb45VXUu2sbdvIdpWVRcdwX2QYfT8qMhJx/fXXR53gs6+vD4cOHcI2DmybJFRUVODSSy9FT09PxOEpjGjLcYu44YYbsHPnTlSzp9D7uPPOO/HhD38YCQkJeOWVV3D77bfjnnvuwTe/+c2o+jojJCCApKCBgYEA9+dIUuow6YRyRHC73Th48CBaW1uxZcuWgCp+0UhRMuSHa2TQ5kFA/F927QX0ASQtjdZfcgnNROfPp4+4ro6WL75I7VRX6yTAmQUyM+mvs5OOO3CABoKmJt1lGCDpxmajma/TSeceGdEzEPt8REY2m+5lxi62RjEycsyOWDr8rLMCS4Zv2xa8ZPiGDbq0wF5YPOCIAb52ux6LMwWT1rDBMUxs3Od1gF6biauGsNdjYSFd5/r1NOnYsIEG9dWraQJRWEhOB/Pn036yhyWfQyRlOZ1RT4/ukWaz6YXnODuBy0XvQW8vSdnDw7R0OEiCA+h98XpJ4klMJPJZvJicK5KSqH8AOZtwpnGuHP3MM7TuwAFqmwOhRfCcUiQZOc1RsM82OTkR99xzj/EOITCVpRiMPOAmuxw3Y3h4GE8++aRS+vnhD3+IrVu3Yv369fje976Hm2++Gffee2/U54rMF3ES0draikOHDmHBggVYvHhxxOzP4nEwAhocHMTBgweRmJiI4uJiOByOgO38gmmaFtXsw+Vy4fLLL8drr72Gurq6gI+CvZ3kWbscP8LSjygFAXqaleefpwGCy2lzfAarv+bNI6mmqIj2WbeOVG/87Zw8qZ8rI4NIiXODcR43riTJAwVXoExJoX6Ikpo4++R4G4YsDfH/nNiUB73mZlpyHMvu3dTezp00CL70ErX17ru05IBHduqx2/W2NI3+j1SqGi843kQ1A+daS7JkGBOjqy5HRuj4ri7axs+Jx5PSUtrOz1JOxCm+L2L8E6A/R37nRkfp/jocupceoKfPGRigd6K/nyY+fX36eU6coH3q63U3fJuNCLOhQc/d5vXS5EjT6P05cYJCDN58k7zgystJVdzZqWe3cDqNJRjZhV70gAt27xl/+tOj41KhTWU5biMCuummm3AtV5c0wESU4/773/+OoaEhXHPNNSH33bx5M+68806Mjo6evgXpfD4fGhsbsXbtWixZsiSqwd9isQR1xW5vb8eePXswa9YsbNiwYQz5AOGRmBFYerNarfjjH/8IIHCgkwMcRXdf0X2YoYpP4X05HQ+g5/pavJgGku5uGjzefZd+86DO5JCaqgc0dnYSCfX00GDD6Wm4bbYRsQQ0OBhIPuKgxn+iupDjR+RrGx4m0jhwgH7X19N+nACTM3JnZtKyoID6nJUFf+wLn0MkH9Xsn1WD8vqJhhh7IoL7J5/XZqMBnycgPh/ZPywWkhZsNsqc7nAAV1xBy/PPp/VFRbQ/u0oDge+LHJTJ68TsDOKEwe3WSzfw/lzeuqeH7je7v3M2dc5Fx+7jDQ2kDuYS3fwMY2L0icKuXXQc24Us78dCsfu2+O7wvRN/i7Fd8kRHzoIgbk9NTcWGDRvGFaYxldVQExMTlWNgdnY2CgsLg/7FxMQElONmRFKO+49//CM++tGPhlX6u6ysDOnp6VGRDzBDCMhqtWLz5s3+GuPRQkVAmqahrq4OFRUVWLNmTVCCC1eNJ6OtrQ179+7FnDlzkJmZibS0NPzbv/3bmP3kbAgs8dA59SW/57xNTHnDx42O6o4FRUWkS2eS6OwMdB4A9IDA4WE9lsNm06tUckbtjAzaJyGBlqzr59gMiyUwdQ5LdmICT1lNIg4asv2BwWUbFi6kQc3hIHuV10vSgMulS3sej56Tjo3pnMpGBVXQqPgc5P9DQTURVhEtZ1CQHU14IOXCdKzGYonoxAnavmMHLV94gfbbs4fI4uBBamNoSM8HJ18fv0fioC5KqGKhP4tFL93AakvxXWM1bHw8PQMObmWVH5eUr6mh/p06BX9Mz+goqQs5q4PNRvv09pKkDuhSH0OU3MTf4v0X3x2Z+MWJUFycA8899xxqa2uxa9culJWV+ZOHRoKpqgU0ODg4LeW4GUePHsVbb72FL33pS2Paff755/GHP/wBlZWVOHr0KB588EHcfffd+MY3vhF1X2eMCs5isYzL9gKMTcfjdrtRUVEBp9OJ4uLikA/WYrHAarWGTUCiM8OaNWuQm5uLmpoa+Hw+PPbYY2hoaBjzgOk8akM5DxJiJL24lAdJ/vDLy2npculZqPmDb2khG0FtLa0bHQ1UWSQm6u0kJ+t1g4aGAvOciVU8uY+yMwHvywOCeIyobhTX8/+sUuLklDyQZmeTanDuXLINxMcTMTJxiuOIigDlwFJVVmyjCqVG4MFdHPjELAeA/izFeyTen6QkXTIYGKD9+/roGQwPkzRRU0OSam0tDfj87FhVxvdU7IeobhOfjSozgKzqlQkqMVGXVjWN/rda6Z2Kj9ftNDk5epbzoSG9n42NNKE5dIjsik1NNPnhCsDy5y464MgqaPF+G3nA8W9x3d/+9nesX78emqZhaGgI3d3d6OzsxNGjRxEXF+evgpqenh5UxTZVNqChoSEkJCSMu50nnngCX//613H++efDarXiyiuvxP2cQA9jy3EzHnnkEeTn5+Oiiy4a06bD4cADDzyAb3/729A0DYsXL8avfvUrXHfddVH3c8Z4wbnd7nG5QAPA22+/jaVLlyInJwcDAwMoLS1FYmIi1qxZo1S5qbBjxw6cddZZIR0gPB4PKioqMDAwgA0bNvjzxR05cgRerxcrVqzAyMgIFiyYi97egTG2h1CecEYflwj5I+WBNDs7sAQCq8P4mNRUGuyysmhwZ1uAOBCzis3t1o9X9VNUyYk2CRXJiP+zNGTE9TzLVbXNJBQXp9tO+B6Jgy175fHAKksDEwXRvse53kJ5+bFXmsVCEkVHB5GMOB6Ialf5uGDeg9wn1eRAvAd8r1Rel3x8TIzuau/16jYj8Zp9PpogcE0izg8n2mlUkwPxtyihBbPdReIBd/XVV+P3v/+98v54vV709PSgq6sLXV1dcLlcSEtLQ2ZmJjIzM8eQQGNjI/r7+7Fq1Sr1DZ8g/PrXv0Z5eTme5vxYZzhmlAQ0XrAKrq2tLWqHBqOaQCKcTidKS0sRGxuL4uLigHxxVqsVrvenhnFxcdi5cxfWrVs3ZjARPzp5oBD3k3/zAC4O3tyOWBmSBymbjaQi9kbSNBogEhKIfKxWnWRYw8D5y9h2Ic9IjQY1MdWLinxEEhEHJz5GvO2qfGyAngLIZtNLmPNgyGotcbYvz/xDueqGC/F+iBKOrBLjc4vqMLZjcc2d9nZdwhD7KD5zJjWufcQShPwseLIgv1M8yWDwM+X+iBIqn99q1dWsvGTSZ+mI70FLC23v7aV94uPp/7w83blFZSeTpX2VFCQ+M5VqV/VMc3KyDcmHjqfkoVlZWX7pqKura4x0xCp1sxz35GDGENBEwGq1oqWlBT09PX6VWKQIlVOus7MT5eXlmDNnDpYuXTrmpbTZbAEEVlhYiG9/+9v49a9/PUaFJs74xAFAnu2JHxgP4KoPUSYim40GOi7R3Nenn4PVPzxr59k3G83FAVOWVEQ1mjyD5u18flkFx/8zeDZtpAKTiYoTmYr3ThyIjM4nDv4qtWekYKlA9L5jyIO/KPXxuqGhsf0Qn61I3GLBPXabF0lKfHeMJAKVt5w8sRBVWKIEJUq9IyO6Y0FSEpEo3wOeTPG7FROj24PE65TfFXmdrGpT3UfV+8bIzc1GVVUNwgWXVuDyCh6PB729vejq6sKRI0fgcrkQExOD2NjYCVORGeGDlIgUOIMIyO12Y3BwEBaLBVu2bIl6FmFEQJqmoaGhAUePHsWKFSswh2tKS1BJUD/96U+RmZmJW2+99f19xs7MxY9eNeADaluFKJWIwX7s2soqHbaZMLHxPhyXMjSkD2SyKk62bfBMnK9FPq88qMj9Fgc1mXxCSYIiRIIxumfB/heDM2WEE+AoX6dMdHwcX5+RNMLPXEU64m9RYpH7IUoYqveE+yP3VSQhPlZWIfJ18L6iuzbbHEdGyH7Fnowejx7ILL7bKpWxShJW2fFUkwvxflgsQEJCLPbs2Teuysh2u32MdHT48GGMjIxg3759Y6SjibQNOZ1OZGVlTVh7Mx0zhoDGo4Jje4/VakV+fv64RFiVE4LX60VVVRW6urpw1llnIS0tzfB4IwL7zne+g+7ubvzqV78aM0NWOR+Ig7T8AernGjtLFGfS4kAkkhir11ivL+4jnkc1uIqqIe4jf38qUpJdZOWBXTUgRQrRFVteLzsHiJCloUgherhxewwmRRVxiR5wYltc50h8pjKpidKOfC3cpkxe4rMT3zXRRiaSJZOQODkSzyv2kb0k2ZNO0wKvS3X9oqSumvTI91K8BrFNlT3p9dffmtABXJSO0tPTMW/ePPT09KC7u9svHaWnp/udGcYrHTmdThQUFExQ72c+ZgwBRQu29xQUFGCYlejjgKxCGxkZQWlpKQCguLg4ZKqdYF50d999N/bt24d33nlH+UEZqWTE36KxVla9ietEyUKlwgPGDtryoMUDZSijt6zblweFYLp6mYRFBDOyMzjIUwYTQzD1mmgHEd3D+ViW9kQXb9EWEcyWJKtTRXCb8jZ+hqK0ID4TI+lGPp5JQJRIRTIWJR5x8q46FgiUFGVbmkpqlycuct9liUecBKkkL3kSw+2K62JjbXj11TewfPly9c0ZJ9gGZLfbkZ2djezs7ADbUUdHB+rq6sYtHQ0NDZkquNMBmqahtrYWJ0+exNq1a5GTk4OampqwXaiNIEowPT09KCsrQ1ZWFlauXBmWEVImMBk7duzADTfcgEceeSRAjy8PwLJaSo6J4A9X/vBVg1Mom4esVhOhIh+VvUoFebAUb4vKLiKrx+T+yOfiQVV1/kikKTk2Se6TOFDzNYs2MiOo3IkZLPHIqjjRO05FBvJvEfJ7JE5YRDuSKPEYOZiI5xHbkVV54oSF9xGJU5bMVM4m8jpZyuPzGak74+JsePPN3ZNGPgCgyoSgsh2xZ93hw4fhdrv90lFmZibi5Sy6CphOCNOESFRwLpcLFRUVGB4eDrD32Gy2kOVuQ4EJqKmpCTU1NVi6dCnmzZsXdv/C8aL7xS9+Abfbjf/hMqYYa2cRPzhZ+pH352NUtiKVMVyGSg2mUvuIfRMhz4rlc4lSlLxNpX6RB2bVeTkORvW4+V6EIgiWQhjBpBomBfHcoqu4EWR7hrxNNfFQEbcsxapc4rmfcsyWqm3xuQYjDJF4ZKcPQB1HxATGz0FFMuKxKglRviZxKd+zhx7646SSD507tBecLB05nU50d3f7paP4+Hi/qs5IOvqgEdDk+xVOMMSUN3Jw6USU5bZYLGhra8ORI0ewYcMGzJ8/P2I37mB96O7uxp49e3DjjTfiN7/5jXQsLeUPTjWYAIER4fwhyzE/KtuQfDlidgBxpqwyesvqNT6n2Be5D+IgLUtp8mCrkuLYViX3WXWbWToJJf3wvRbtI6FUaryPOPCHEoqDkRMP2PLzkFVjTEDyPqJrPz8DIPAZyhCJgaUZ+RgmONnVXJxoiNcgur7LhK4iFzl9EPcf0LfJ1yveDz4uJSURL7zwAvLz89UXO4GINBecxWJBUlIS5s2bh/Xr1+Pss8/GokWL4PV6cfjwYezatQvl5eVoamoKMB1MhBfcT37yE5SUlCAhISGovVqEpmm47bbbMGvWLMTHx+OCCy5AHUeFv49Iq6yGgxkjAYWD1tZWVFZWoqCgAIsWLRpDDOMlIJfLhe7ubmiahuLi4qgMisFUcCdPnsThw4dRWFiIuXPnYvny5XA6nbj99lvgdgcOgCrvM3kmqZoRigOBrK5jBFPbiQOXSuJQ2XqMHBfkfshtys4KDHHQUtl4WPVmZOAOIYAGEEc45CODB3+WoEK5cgeTgkQJV9xfJhCVZCTbjzjLNqtTVS76vK/oci1KPEz2KpsLP3+VRCZ6z4nSFE9SRGlPNQFRTbZUUhBj3bp1eO2119DT04OTnLl1EjHeOCCVdNTV1YVTp06hrq4Ob775JlpbW9HR0RF1XjWGy+XCpz71KRQXF/vzUobCPffcg/vvvx+PPvooCgoK8MMf/hDbt29HdXW13+79mc98Bq2trXj11Vfhdrvx+c9/Htdffz2efPLJqPt6WkhAmqbhyJEjqKqqwtq1aw2DS+VUPJGgv78fe/bsgc1mQ25ubtTeLCoVnM/nQ3V1NWpra1FUVIS5c+f6t914442oqKhBTk5mwDHyBycbgXmAkp0RVLNUEaLkIA+c8gAnDoLygC/OVMVjIvFqk2OgVP2RZ9E2KSuCCNGrKxh4Iss2kEjIhyEeF46ALDo3yOtFCYDBZCCvCzYJZ8lF7KMoVdvtgdcOjL3n4kRIVFGqHE1kKU2UwMTz8P+iGlPss5FEzn3n43m/z3zmM3jrrbf8BSSnIkB0IrNhs3Q0f/58bNiwAWeffTY2bdrkd2j4xCc+gcsuuwz/9V//hUZOmBcBfvzjH+Pb3/42Vq9eHdb+mqbhvvvuw6233oqPfexjWLNmDR577DG0tLTg2WefBQDU1NTg5Zdfxh/+8Ads3rwZ27Ztw29/+1v89a9/RUtLS8R9ZMwYAjJSc7lcLuzfvx+nTp1CcXFx0ISl0UpAbW1t2LdvH/Lz85GXl4fxZCeS++ByuXDgwAF0d3ejuLgYGRkZY44pKCjAsWMncPbZZwMY++GJKjHumujhxkuVnUacqapm2oxQ3lbyNy7uK/ZVvw9jHSlEyJ5VqvPJZMKzcKNHbOSEIZ6f3dBll/JoIJKQzRZcHSc6cKjake1RvF6+r0ZEZnSM+Izld4b3lx07xGNVKkLRw1CcCKmcC/h/hnhu8d0VIaucxXf5+uuvx4MPPij0cWrKJExmJgS73Y5LLrkEjz32GGJjY/HEE0/gnHPOwVNPPYW//OUvk3JOEcePH0dbWxsuuOAC/7rU1FRs3rwZe94vqzsRVVZVmNEquP7+fpSWliI5ORnFxcWwGym230ekBMTJRE+cOOH3pKuvrx+XGk+UgLj+UFJSErZs2RK0/3a7Ha+++io++clP4sUX/wlg7Acoz9RVKiwjd1hZDQcEd06Q7TLyuVWpXeS2VUGFqvOp4jlUdhzRpVeGSlqSwdKTbEgX24+UkMSBNpTzg+hBJ18Dq7FUXnGyy74stfDkg/+C3Wv5umXVqqzqE0lI9oIU9xdtkKp7a+QhJ95D8drkdvLysvHss89j8eLFcLlcEBMHn24SULBzDA8PY8OGDfjEJz6Bm2++eVLPx2h7vwa6nDkmNzfXv62trW3M5N9utyMjI8O/TzSYMRKQjNbWVr9Usn79+pDkA0RGQB6PJ6AyKt/cUG7UoWC1WqFpGtrb27F3717k5eWF3X8AePjhh3H//b9DTk7W+/0J1KWrbCViXIcIlZcU7y/+L6vdVG7b8ixZ9j5TGcnFmBmbTbdRyMQie3TxtYnnZFuLSsIRVTxGYFIzIp/xgK9VpUqTEYycZLUWoHYoMJJyOIefSBQqEheP5fPKko/4PGW1rtxPmeDkmCm5z6IHpOzoovLy/NSnPoW6umNYuXIlHA6Hnwi8Xi9GR0ehaRo8Hs+4vttQmAqic75fE4MTG4uYqmqoU40ZIwGxCs7n86G2thZNTU1+qSRchEtATqcTBw8eRFxc3JjKqON1ZOCXtKKiAitXrvTX34jk+IULF6KhoRHXXXcdnnzyCQBjYyJUrraq4FRer3KPNspEIKqrVGQhq9ZU0pXsem008MvxNHKcCe+jOod4/mC2JlE9pnL7Fc89HohEG6w/KpUj902WeAA9N5/sfSfeD00znhSIkiE/h2Du3/weyZKV3FexDVHiNXKAUD0/2bYp7h8TAzzyyOP4+Mc/LlyT9f1r8qG9vR3Nzc1YsWKFn4To/pB0xMuJwFRIQExAKjfscKuhRgOuktre3o5Zs2b517e3t2PdunX+fcZTZdUIM4aAAMrnVlZWhtHRURQXF0fsjmi320OSR0dHB8rLy5Gfn2+YTDRaAvL5fP5ZyPr166NKCcIqPK6seu211+Kyy7ZjdNQXQDC8FAcNlWE5mA1IlmqCxeaIXlMM1cBiJKlomjq+x8gzjPvEA6aRaksmOhVEV+PxkkwwiN5xwUiIpQ4jVRwP/JxHTZZuVWQDjLWbieo4lSpOJjpRncbXI3oLqkjI6DrFWB4VuQD6dlXc0/bt2/H4448bOgO1t7ejuroaa9asQXZ2Nnw+H7xeLzRN8/+v3xer/y8a+Hw+aJo2JQQUExOjLB3DHnSTgYKCAuTl5WHHjh1+wunv78e+ffvw1a9+FQACqqwWFRUBiKzKqhFmjArO6/Viz549sNvt2LJlS1S+8MHIQ9M0HD9+HGVlZVixYgUKCwuVL2S0BDQ6Oop3333X7xcfrS+/7EW3bds2dHX144tf/CLsdsv7++jqOFGakdUY8qzZyAakIoxQNiBVHA6rC1Vu0zabcdCoEdg91yjrgOzRpQITs6os9mRAJORg4x2TlGofvh6321hdyOo2+ThRihLBUomswmXpUt5PbFN2RJD3F1VuokpNtNmJalL+X5wYMLKzM/Dyy//CP/7xD0PyaWlpQU1NDdauXesflK1WKxwOB2JiYvx/NpsNFosFPp8PHo8HLpcLHo8HXq83InUd7zvZKrjBwUHDctyRoLGxEWVlZWhsbITX60VZWRnKysoCYnYKCwvxzDPPACCJ8Vvf+hbuuusuPPfcczh06BCuueYazJ49G1dccQWA8KqsRoMZIwHZbDasWbMGqampUT8Am83mnwGJL4vX60VlZSW6u7uxadMmpKamGrYRTiYDGf39/Th48CDS09OxatUq7NixI2p9NJ+fPfH4//vuuw/f+c53cNVVV6GiouL9ffUB2si1mmGUEcFIDcdQtaciLJWKTJSGZKIJx2WaY4CMXgcjtY4IWfXHEoqonpL356VsgxKDV8U+sTQhSg6hHn840QLBbFvBEn7IQcByP2UnA2CsBCxOXmRHBNkRQpasVDZJ0dFA5XxgtwMf//in3k9RZTzQNzU1oba2FuvWrVN6lAI6UbDEwhIRf0v8bYarquMJ6VTYgCai1MNtt92GRx991P97/fr1AIA33ngDH/rQhwBQ4cy+vj7/PjfffDOcTieuv/569Pb2Ytu2bXj55ZcDcl+GqrIaDWYMAQFAenr6uF2ggUCD4fDwsD9TdklJScggr0glIE6GumjRIhQUFMBisYxLjSfquPlj4Q9k3rx52LNnD55++mlcd921GB0dO8rJqjm6pkCCUklN8jGq33JbgDpAVTxWRVTBbDZWqy4tqWrtyOfgAS3c14YJKZLHw6pDo2NU1x8KbJdiqUUFJgyxD6H6qDqPqIpjCTWYDQgYqyKUHWBU6jfRw1CWvsXnI9+rc889F3/+859D2nsbGxtx9OhRrF+/Hunp6UH3FSGq3/i7YkIKR1Xn9Xon1J5kBE7DM14J6M9//jP+/Oc/B91HHmctFgvuuOMO3HHHHYbHZGRkjCvoVIUZo4IDIssHp4JIQAAlE92zZw9SUlKwadOmsCKMwyUPduE+dOgQ1q5di4ULF/r7H40UxeCXnEuUqz6IK6+8Ep2dfbj11luRmBijdM9WSTiylCIODCpvuFBxOfLgxm0aeauxTUO1jdV0rC4zgsMRmDIm0lgedhJwOHQ1Fl9bMElLvj67XW+HpYpIxievN5DUuBSDDL42vicq1RtvZ/IWj2VJJJQnHaCTkOjyrfKGk/dnGF2/KG2K+xcWLsPu3Xvx4osvhiSfhoYG1NfXY8OGDRGRz9i+WGGz2RATE4O4uDjExsbCbrf7vVdZVed2u/0kNVWxRh+0YnTADCOg8YJnKR6PBydPnsT+/fuxePHisDNZA+ERkMfjQVlZGZqbmwNcuBmh8sEFA/fz6NGj6OvrM5QIrVYrfvCDH6ClpQNf+cpXEBen/kCCuWLzNpX6TCYfWcoIZgNSecRZLMY1e/h8RrdMHFjd7vDVVw7HWBuH10vHs4QlBqSG4x3H18fSHWcOYDIEdEISyTIUmIwsFr3fRvux4V7l+s6SibxNZQPi88k2HXEfmYSAsS7YYm43lT1IXO/xAHFxDvzv//4vDhw4GFa0/vHjx9HQ0ICioqKwc5uFC7YdxcbG+h0AxImsx+PB6OjouCaV4eKDlogUOMMICKAXqq6uDrW1tdiwYUNEmaz5eNEGI2N4eBj79u2Dy+VCcXGx0mc/mlgiVrf5fD6sW7cOPp8PFRUVeOutt1BZWYn29nZlmiG73Y5f/vKXOHmyDZs2bUJMjCXA6w3QBxnRBiQOPjKxBF5LoJRhJOGoPLpCxe6EylwgOgOF4nMe8MVBj8lhvONGpII5q7lksuQ+BpsLsYcbD/xGE2+Z8FTbVJkVmLzEeCs5rkeOZ+K+qDIg8PnE/1W2K48HmDs3H0888QS6unpx6aWXGt8Efz801NfX48SJEygqKkJKSkrIY8YDrvcjOjJYrVY0NTUhISFBKR1NJCbKBnQ6YUbZgMargnO5XPB6vRgYGBhXMlFN06Bp2pj+9PT0oLS0FDk5OVixYoWhVBXpbEkkHwDIysryJy3s6+tDR0cHjh49isrKSmRkZPhdMkWVYkJCAt544w2MjIzgP//zP/H443/C4ODo+9cUaA9SuUirtoXjgBDKBiTHeYRyPuBZOg/g4rEiZGN6OFJRpJAzAIwX8n2TAzNliCTCDhmqvhi5uANqZwvZ3gME2nT4+YjPn0mIz69qQ+y3iIULC/DLX/4KF110kfpCFWAVd0tLCzZu3DjlkgF/w4cPH4bL5cL69ev9arqJdvNmfBAloBlFQOMBe6LZbDYsXbo06pmEypEBAJqbm1FdXR1WfaBIVHBsEGWJSzynxWJBWloa0tLSsGTJEjidTnR0dKC1tRWHDx9GcnIycnJykJ2d7XffjIuLwy9/+Uvce++9+NWvfoX77/8Venr63m9vbMqbcL3hVIMxS0eyDcjIySBYbAw7FAS7bSwRsF0j0tJPrI4TZ//BYohYLadyAxftRixBhGpPbluWjgDjyYFoA5I9zHi7SuI0igOSPdn43KJqVUVC4jsgk5CYNcNmAzIzc/ClL30JW7ZsQUZGBpqampCVlRWyqjAXm2xvb8fGjRunxS7CGoiRkREUFRUFxOaIjgzy5JG96qJxWhgcHDQJ6HQEl2lYuHAh2tvbx9UWvzRerxcOhwOcibu5uRkbNmxAZmZmyDbCVcGJ3jj84gYDV19csGABXC4XOjo60NHRgWPHjiE2NtYvGaWlpcFqteI73/kOvvOd7+Cee+7BE088juPH68cEedI1qw3M+j0Zq6oJ5iGnsgGp4oPEdoJJMExqkajTWM0kBrFOhDqOIbejcgQR7VahIF6/GIAqg9sSyx8wRA832abF61mdKmY7EPsv24BkEpIDRzlwlvubkODApZdegZ///OfIzc0Flx7o7Oz0T5wSExORnZ2NrKysMWEXmqbh8OHD6OzsxMaNG6dFJRWMfABjN2+ZjEQiCoeMPmjluIEZRkCRquA0TUNdXR0aGxv9aXu6urqidgAA9BmMz+eD2+1GeXm5v/JquC9HOBJQpOQjIyYmBnPmzMGcOXPg9Xr9dek5RojVeJmZmbj55ptx8803o7m5Gbfccgteeuk5OJ2uMXp/YOysWHZAUEWuA/qAKCJYHJBR0Kp4XjF4NVSwqShBBGs3GkSjglORXTiu14B+zUZpjgD9+kRpjiG63Itgux/fe+4n7yuSjPhbJiFZ8tE0IDExAbfe+kN8/etfHyPFJyUlISkpCQsWLIDb7UZnZyc6OztRVlYGgN7VrKwsZGRkoK6uDj09Pdi4cWNYJawnGqHIR4Vgbt6RSEdOp3NMQtAzHTOKgCKB2+1GRUUFnE7nmLLc462KarPZMDg4iCNHjiA+Ph5btmwJ60UUjzeSgNi+xC9mNOSjOl9OTg5ycnJC2o0effRR+Hw+/OY3v8Gdd94BwGXopq2yAcnqNVHlJl5GsDigYDYgMf1OMNKRCWqcldj9EN2xxcFdlVYmUsjSoZHajSGez8gGJG4PxwbE91aeMMhxPTLpyLE9Hg+QmBiDc8+9ALfddlvYtWccDgdmzZqFWbNmBbyrx48fx6FDh2C1WjF//ny/RDHebyMSREM+MsYTBGvagE4TcDLR+Pj4CU8mCtDLUV5ejrlz52LZsmURfwRGTghG+uKJRDh2I5vNhrVr16K2lkru/vSnP8UzzzyNU6c6/O0YkY/KBqTKNKCyZYSyAbF7swxROuO2RQkjHFJQ2X9ULuMqklF5+AGBqkHR/Tjc1092+LBajXPlBVO98XbZBiTGaIUTVCxLNiobkM0GrFq1FjfddBOuvPLK8C7UAPyupqSkYHh4GF6vF3PmzEFvby9OnDiB2NhYv3SUnp4+qbE4E0E+KkQSBDswMGAS0HQinMGYk4nOnTsXS5cuHXPMeKqiapqGxsZGuN1uFBQUYNmyZVG1o1LBBXM2mEyIdqPh4WFUVFT4U3AcPnwY2dnZ+M///E/ce++9OHbs2Pv5oJ7F6Kg+EsrGdsB4QFPF8/CgGY0NiM/NUlM4g7tKJTeR9h+GSGAycTAZGMU/yRCvTey77PjB90mUFMX+BLMBqTzfZBWr/By8XpKuFixYjC1bivHLX/5yQu0UPPAPDw+/H0YQ8/55veju7kZnZydqamrgcrmQmZnpJ6RQjgzR9GGiyUeGKB3xJJTJyOl0Yu/evSgoKJiUc89UWLTx5L6ZYLDdRQVOJlpfXx+0zEF1dTWsVisKCwsjPndNTQ3a29ths9mwYsWKqLPP1tTUwGKx+PswXnvPRMDlcqG8vByapmHt2rWw2+3o7u7GqVOn0NnZCU3TAuxGVVVVuP/++/H666+hs7MzIGhVJeGo1EnijFzlQff/2zvzuKjK/Y9/2FfZZBNUNhFRdlRySU3NhVCwsixNbVFb1NtNM7tpVpZLebuVlpaZ2m1RAfdcUoRMJZUdATEURFlmhmXYl1me3x/+nnNnhhmYfQY879eLl3LmzJnnDDPne57n+/1+PpJ36LJIvk5P9gZ0OU7buR+KtkqxVR2n5AxL0evLywFJvp68vBy996HBS14pvaWlCYYOHY4XXliIV155RasXfIpIJEJeXh46OzsRFRWl8MJPCEFzczOTO2poaIC9vT3zeXVwcFD7O6Wv4NMdbW1tePrpp1FfX4/k5GQEBATofQyGolcEIComWl9fj8jIyG7FRIuLiyEUCjFixAilX7ezsxPZ2dkQCoWIiopCTk4OI1GuDsXFxRCJRIxPiaGDT0tLC+MsGxIS0mUpQ3ItnsfjobW1FS4uLnB3d4erqysaGhrw1Vdf4fjxY7h7t1Rub5Dsco0inTBVckCyx5d9TbpkpY0AIS/3Q/+VLD/WNA8kiWTPU3f70FmIoj4gQH4OqLsAJu897dfPBt7ePpg1axbWrVuntImiOlCVZpFIhMjISJUu/J2dnUzRTW1tLUxMTJhg5OLiovSxaPDp6OjoNgDqkvb2dsybNw+NjY04e/Zst9e2vohRBSBCCDo7O6W2UTFRMzMzRERE9KjnVlJSgtbWVoSFhSn1mk1NTcjKyoKDgwNCQ0Nhbm6Oa9euMRVm6lBSUoK2tjaMGDFCq8UG6lBXV8f4Hw0ZMkSpMdC8EY/HQ0NDA/r16wc3Nze4u7vDwsICycnJOHjwILKzr6O+vqFLJZWiGYsyOSB5n0bJAEdnRap+aunFXtkeIEkU5YCA/x2PXqtVOa7sa0gWVSh6bUU5ILqvsj1YD5QoTDBwoB+mTZuGZcuWYejQoaoNWk2olBUhRCW3YHmIxWI0NDSgpqaGuXlycnJiApKtra3cz7xYLEZubm6Psy9d0tHRgQULFqC6uhrnz5/XSOOut2JUOSBZ6urqkJOTAw8PDwQHByuVN1GlCIHL5SI3Nxd+fn4ICAhgPqiaFjKYmJiA+o4Auik2UAbqmxIUFISBAwcq/TxF/UalpaWwsrJCdHQ0ZsyYAScnJzQ2NmL//v04fvw4MjOvQiwmcpfnFOV5lMkB0X+VzQHJ68HRsC5FIbKNopLQoCerBiEPyaDSXXVcdzkgegx5wVwofLCs5ujoAl9ff4SEhOCFF16QamTWBwKBgLmhjIyM1LiwwNTUFM7OznB2dkZgYCDa2tqYYHT79m2mP44WMtACIUMHH4FAgMWLF+P+/fu4cOHCQxl8ACOeAZWXl6O4uBhBQUEYPHiw0scoLy8Hl8vFyJEju30dmk8KDQ3tstSWnZ0NZ2dn+Pr6qnUOd+/exe3bt+Ht7Q13d3eN1qjVgRCCO3fuoLy8HGFhYUo1zyoDTQwryhuZm5ujpqYGSUlJOHv2LHJyslBfX9OtEZ2iwCB5Ny9vuUh2396QA6LFEaqMUxnHV0XjMzUFhgwZinHjxmHOnDmwsLBA//79ERwcjM7OTuZCXVdXx1Scubm5MRdqbSMQCJCVlQULCwuEh4frXGGa9sfR3JFQKISLiwva2toAACNHjjRI8BEKhXj55ZdRWFiI1NTUHpXA+zJGF4Da29uZYoDuTKcUUVlZiXv37im0iZXMJ0VFRckVOMzLy4OdnZ3KyUBaZi0QCJg16pqaGqZPR5dfbopYLEZBQQH4fD4iIyN1VtbZU97o/v37uHfvHkJDQ3Hr1i18+umnuHXrFpqbG9DYyFdYGWaIHJBs/kfyXoEm52VzQNr41mgzB2Rraw43N2+EhIRg4sSJmD17NgYNGgTgwZJqZmYm3N3d5bYV0BsL+nkVCoXo378/M3OglWma0NnZiaysLFhbWyMsLExvVaAUQggaGxtx48YNdHR0QCQSwcHBgamq09dNokgkwquvvorMzEykpaWpnWfuKxhVABIIBEhPT2cSk+p0QnM4HNy+fRtjx47t8lh7ezuys7NhYmKCyMhIhfmkgoICWFhYqLQmrqjYQCwWo76+HlwuFzweDyKRCK6urnB3d2dmDdqCVrpRRW1l/I+0Bc0bcblcNDQ0wMTEBAMHDoS3t3cXky2a/D19+jT++usv5ObmgM+vUdh8SvMg6uaAaDJe8pqnSr+OIXNAssHXysoEdnYO8PIaBDMzM8ydOxfPP/+8wg56muP09vaWWmZWBCEETU1NTDBqamqCg4MDE4zUMUzr6OhAVlYWbG1tERoaqvfgA3TN+RBCmJlRbW0tTE1NmWCk7e8lRSQSYeXKlbh06RLS0tLUzjH3JYwqAFHdtcGDB6s9Pad9A48++qjU9oaGBmRlZcHV1bVHf6CioiIAD3zQlUFScqO7fA+9C6MX6ra2NoXq1qrSU6WbPqDqFB0dHfD29kZ9fT1qa2vl6tTJQygU4vr167h8+TLy8vLw999/o6qqArW1tSrNOlTVYVPmeOr0EKmSA5LE0tIE1tZWMDOzgbm5OZ544gmMHj0akydPZmY1ysDn85GdnQ1fX1+1+0s6OjqYYFRbWwtLS0smGLm4uPQYTNrb25GVlYV+/fqp5MulTXrK+YjFYvD5fCYgtba2wtnZWaqQQRtjeOutt3Du3DmkpaXBx8dH42P2BYwqAAEP7uI1GVJ9fT1yc3MxadIkZltlZSUKCgowZMgQ+Pr69ngHd+vWLQgEgh5LuamsDi02oDpPyiI5a2hsbISDg4NaSWF6zl5eXggMDDRIwQOdXVpZWSEsLIy5g5Rc3uHxeBCLxUwwUuVOUygUoqCgAPn5+SgqKsKdO3dw7949cDgccLmVsLAwhVAo1pokjyTa7AOytDSFpaUlrKzs0a+fAxoaGjB27FhEREQgIiICI0eOhKurq8avRQt4AgMDVQpa3SHZHFpTUwOBQCDVHCp7A9Xe3o6MjAw4Oztj+PDhBvlcqlNw0NrayuTH6uvrYWNjwwSj7m6guhvD2rVrcfz4caSmpj5UfT490ecCUGNjI65fv44pU6Z0EStVtrH09u3baGlp6baUW1bZQNXgIwu906S9Dba2tkww6m59uqqqCoWFhSpXummTpqYmZGdnMwluRV9QRXkjGpA0aXaUFI51cXHBvXv3UF5ejpKSEly5cgWDBg1Cc3MzWlpa0Nraivb2drS1taGhgY/29jb062cHQsT/f1MhZm4uBAIBhEIxbG2tYWpqBhMTM5iZmcPExAzm5uZobm6Gubk5Bg8ezMgcmZmZwcvLCzY2NmhsbERgYCBGjx6NkJAQuLq66nx2yuPxkJ+fj2HDhils2NYU2hxKZ0cNDQ3MUp2bmxvMzMyQmZnJfCZ6S/CRRSgUSuXHRCKRSvkxsViM9evX49ChQ0hNTdVbqXtvwegCkEAgUMrKQBEtLS24dOkSpkyZgry8PDQ3NyMqKkqlZHxZWRnT9CoPSVMqXZRYC4VC1NbWMtVmZmZmTB8OLWLQVaWbqtTW1iIvLw8+Pj7w8/NTawYo22/k5uamUq6B5hhkZ1/6hBCCoqIi1NbWIioqipnB0hwgPU8qKUMvYNrO01VXV6OgoAAhISF6VVbu6OhgZkYPlDPEsLOzQ2BgIFxcXPS+JKyLUmt5+TH6mXV1dUW/fv26WEts3LgRe/fuRWpqKoYPH67xGPoafS4Atbe3Iy0tDfb29rCyskJ4eLjKVTzdlXLrW9lA8gLG5XKZO7COjg60tbWpHFy1Ce0zCg4O1vhOm5YFc7lclfJGVJiWLvMYKseQn5+PlpYWREVFKZzJUW8cGozosis9T2oqqC4VFRUoLi5GWFiYVpbx1KG5uZlZdrO0tERNTY1Oddzkoa8+n46ODilFBjMzM7i4uCAvLw+xsbH49ttvsXPnTly4cEFptfCHjT4XgLhcLrKysjBw4EC1L0gVFRWoqKjA6NGjpbYrW2ygKwghqKurQ0FBAQQCAQghTOmzpkUMqo6jtLQUd+/e1cnsS9m8UUNDA7Kzs+Ht7a20yoO2EQqFyM3NhVAoRGRkpEo3O3TWQC9gmvTi3L17F3fu3EFERITBmhqbmpqQmZmJgQMHMhV38oKuvb098/eUnTVoirL6ctqG3ijeuHEDS5YsQVVVFUxNTfHPf/4Tr776Kvz9/fUyjt5GnwpA9+7dQ1FREcRiMSZNmqT2nVZ1dTVKS0sxZswYAJoXG2iLlpYW5OTkwN7eHiEhIWhvb5dawlK3iEEVxGIx41gZGRmJfv366eR1KIryRjY2NqisrERgYKBKjcrahGoImpubMwKv6iIv6Eou1XUn1FlaWory8vIedRJ1SWNjI7KysjB48OBuL7aSDbC1tbUwNzeX0nHTZKmOBh+BQKCyvpy2IITg66+/xsaNG/HKK68gPz8fFy9eRFBQENOEy/I/jC4ASUrYKItYLEZxcTEqKysRGRmJ69evY/z48WpfhHk8HoqLizF+/HitFxuoS0+VbvRumsvloq6uDjY2NkzeSFtNdkKhkCmzjoyM1PlSijxaWlpw+/ZtxnpdcglLnR4VdaHlxXZ2dlrvbZEs1+fxeGhpaYGTkxNznrQsmBbZVFVVITo62mBLsbTFwc/PTyX1EDproAGpo6MDLi4uTEBS5fMlEomkZqKGCj67d+/Ghg0bcPr0aaYXsbGxEZmZmXjsscf0PiZjp9cHIIFAgJycHEbR1tbWFikpKRg1apRclQNlqK2txY0bNzBhwgSDK1kDqle60SIGegEzNTVlZkbK9G7Io6OjA9nZ2bCwsEBYWJjBvuBlZWUoKytDeHg47O3tmYtXTU2N0nkjTaF5J31VeEnqm9XV1cHW1haurq5oaWlBU1MTRo4cqZVeFXWgvUYBAQEazUTpUh09T1UsF4wl+Ozfvx9r167FiRMnMHHiRL2PoTfSqwNQc3MzcxcquQSSlpaG8PBwtdfC+Xw+srKyMGHCBIMGH8lKt9DQULUSy4qKGKhkjjLLRs3NzYw+nqES/bRJmcPhIDIyssvNhbwlLF0oThg67yQUChm79fb2dpibm6vVV6UNdNFrRJG1XKBKBfQ86VKdsQSfn3/+GW+99RaOHz+OyZMn630MvRWjC0AikUgpR1PqjDp48OAuS1J//vkngoOD1a4EamxsRHp6OoYOHQp3d3eDLDWJxWIUFhaivr4eERERWsm10DJSKgvU0tLSYx9OfX09cnJyMGjQIKWkXHSBWCzGjRs30NTUhKioqB4lmnTVb0StLVRdatImkg6ikZGRaGtrY85TVllDl5/b2tpa5ObmIigoSOeSMlSpgM5029vb4ezsjP79+4PL5YIQgqioKIOV3ycmJmL58uVISkrCjBkz9D6G3kyvC0BUbfrvv/9W6Ix65coVBAQEqNwHQYsNhEIh7t+/Dw6HI6VQ4O7urpelDn1purW2tkrpt9F8iru7O+zs7FBdXY3CwkIMHTrUYE2utMGU6gOqI4ypjX4jLpeLGzdu6LS5syd6MnGTPU9dVZvRRtfg4GAMGDBAK8dUhZaWFnC5XJSVlUEoFMLOzo6Z0Ts6Our1Juno0aNYsmQJDhw4gFmzZuntdfsKvSoAUaVnWoHl5OQkd7+rV69i0KBBKl0oaHMprcCjxQZUoYAm9+mH3d3dXSdJ79bWVmRnZzOVbvpq4KO+P/Q8zczMIBQKMWTIEPj4+Bhk5qOLBlPJKqyamhpYWloy+TFFeSPaXxMSEmIw6Xya6zQxMUFERESP74XkElZNTY3Wqs24XC7y8/P13ugqieSyW2hoKDPbrampgYmJCVM5qOslyZMnT+LFF1/Ef//7Xzz55JM6e52+jNEFIEW23DQJLhaLu232A4CMjAy4u7srnRSVbC41MTFRmOMQCARSzZJUmNHd3R1OTk4aX6SNQdONdvRzOBw4OTmBz+fD1NSUOU91ixhURR8NpsrkjcrKylBaWorw8HCVrUG0BbUyoIFY1eDRnRqDm5ub0rNKqrIQGhpqsEAsGXxkl92oOyoNRlRUlJ6nOur6ijh79iwWLFiAH374Ac8++6zWjvuw0SsCEO0xcHJyQmhoaI9fwOzsbDg5OSmlAKyusoGkORuPx2PuvNS9SNNKt6FDh2o9oassIpEI+fn5aG1tZeww6Po7PU+BQMDcSXfXn6IJhkj0y8sbWVlZobOzE+Hh4QZTFqDl3nRGrGkgltRw4/F4UnYL3akxVFVVoaioyKAqC90FH3nQJeaamhrU19fD1taWOU9NluouXLiAefPmYefOnViwYIHBqmP7AkYfgKqrq5Gfnw9/f3/4+/sr9cfOy8uDra0thgwZ0u1+2pLVkb1IU0MvZSrNJFUF1K100wadnZ1SSzzyAoukFhaXy1WqiEFVaH5hyJAhBmswpdI6tOS5qakJ9vb2zFKdvvqN2trakJmZqVM1aVm7BXml7HQJMjw83GCag6oGH1moSSTVqgMg5f+j7I3UxYsXMXfuXHz55Zd48cUX2eCjIUYXgKgtNyEEt2/fRmlpKcLCwlRaby4sLISZmRmCgoIUvoZkzkebZdaKPH/oxUtyuUMXlW7qQL2EHBwcMGLECKWXeOgdJo/HA5/PR79+/aSUGFR9T6m23IgRIwzmFEkdcyV13WS79y0sLKSWXnWxPEhbDDw8PDB06FC9uXXKLkna2NigubkZYWFhRrnspg7yZrvK+P+kp6djzpw52Lp1K1599VU2+GgBowxAbW1tyM/PR0NDA6KiolS+MBcXF0MkEslVn1VUbKAraMUOl8tFU1MTnJycGFXr4uJipn9Bn+6lkvD5fOTk5Gicd5IVE7W2tmYu0j0td8g2mBoq10J13UQiESIiIuTmRhTljeiSpDaS3nTJedCgQUrP+rUN7bu6f/8+rK2t0d7eLleNQddoO/jIQ9b/hzb6UgdYS0tLXL9+HfHx8fjoo4+wYsUKNvhoCaMLQG1tbfjrr79gZmamdtltSUkJ2trauijQysrq6Luhkmq3VVVVoaGhAebm5hg0aBA8PT01VkJWB1parO1GQpFIJGUnQfNj8iqwemow1Rfq6LrR2S5detVGvxFVFjBkrxEAlJaWoqysDFFRUXB0dJTqN9JmPqU79BF8ZKEqIjU1NcjOzsaqVasQEhKC3NxcrF27FuvXr2eDjxYxugAkEolQXFwMPz8/tQNEaWkpGhoaEBERwWzTt42CIuiMg84M6LKOtbU1U96tLe227qBmbbouLZZsIuRyuVJFDC4uLiguLla6wVRXtLe3IzMzk7EzV/dzJ9tXpWreiDZ3GrLviqpv3Lt3D9HR0XJXHySlnmg+RfIGQxuBgvY8icViREZGGqTJVCQSYe/evVizZg2cnZ1RU1OD8ePHIy4uDitXrmSFRbWA0QUg4EFiVBPKy8vB4/EQHR0NAExzKWA4MVFAcaWbSCRilgB4PJ5cAzptQQUsqXCrPtWTaQUWXZJsbm6GmZkZ/Pz8MGDAAIOJm+pC103VvBGdjRqquRMAk3etqKhQWtxUsvSZx+MxKgWazAKNIfgAD3LJM2fOxOuvv44PPvgA9+7dw8mTJ3Ht2jXs3buXnQlpAaMMQJraclM/n1GjRums2EAVaKVbWVlZj2WstGeDXqSpFw7tTdFUrr6goACNjY2McKshoA2mtDmytraWKWKQVGLQ9d+KlntL+tfogp7yRjweD0VFRQZtdJVU1h45cqTaSvKaqjEYS/C5desWZs6cicWLF2PTpk1ssNERfTIAVVdX486dO4iJidFbsYEiaKVbXV2dyv45ktU6HA4HHR0dTHm3m5ubSksAtJOeEKIwwa4PFDWYynNEpUuSusgxUCHNgIAA+Pj4aPXY3SFbJdnS0gIAGDRoEHx9fQ0yC6R5OLpqoK0bE/o3pT+SwqnOzs5dbqaMJfjcvn0bM2fOxDPPPINt27YZRHz3YaFPBiAul4ubN2/ikUce6VbZQNdQHTOhUIiIiAiNLi5Url5y+crZ2ZkJRt0du62tTcq7Rl/yPrIo22BKixjonbSJiQmjUKCpaRkAcDgcZrnLULpuAFBWVoY7d+7Ay8sLTU1NUjMGXUk9yUKVL+rq6hAdHa2zPJysGoNAIJAy3DMzMzOK4FNWVoaZM2di1qxZ+Oqrr9jgo2OMMgBp4opKL9S0ko7eRTs7O+t1BkQ13XR10W9ra2OCkTwhUUpDQwNycnLg4eGBoKAggy0lqNtgSnMMtNKMysjQJl9VE8HGoOsmmeiPiopiKv8U5Y3UsehWdhwFBQVoaGhAdHS03mZf8tQYzMzMYG5ujrCwML0U4cijoqIC06ZNw7Rp07Bz506tv99ff/01PvvsM1RXVyM8PBzbt2/H6NGj5e47adIk/PHHH122x8bG4rfffgMALF68GPv375d6fPr06Thz5oxWx61L+lQAkqx0Ax5UnHE4HPB4PBBCmGCkaz0zWuk2YMAAvTQRSgqJ1tbWwtbWFu7u7rCwsEBJSQlz0TdU8NFWg6lkEQOPx2NmgTTw9nQBNQZdN0IIbt26BQ6Hg6ioKIWJfl33G1GLi+bmZkRHRxusD00kEiErKwsdHR2wtbVFfX293owFJamqqsKMGTMwfvx4fP/991q/YTx48CAWLlyIXbt2ISYmBl988QUSExNRXFws90aorq4OnZ2dzO+1tbUIDw/H999/j8WLFwN4EIA4HA727t3L7GdlZaW2D5oh6DMBiBYayCs2IIQwUjlcLhdCoRCurq7w8PDQOLEvCxVsNJSmm1AoRE1NDe7evYvGxkZYWFhgwIABzF20PoOQrhtMZXtTFJU9S1b+Sc449A1d7qqtrVUp1yKbN9K034hKDbW2tiI6Otpg+UB5OR+6/EpngpKBVxXJHFXgcDiIjY1FVFQUfvzxR50sUcfExGDUqFHYsWMHgAd/g0GDBmHFihVYu3Ztj8//4osv8P7776OqqopZ4Vi8eDH4fD6OHj2q9fHqC6MMQKq4olJZHbp/T8UGks2DXC4X7e3tTH5BE3FNyUq30NBQuLm5qXUcTaGltPfu3UNYWBjEYjFz4QIgJZiqy1yQvhtMBQIBE4wk7bldXV1RVVWF+vp6REVFqV3dpSmSM46e1Nx7QlYCSZW8kUgkQl5eHmNhb0zBRxbJwEsNFOmM19XVVSvFEjU1NYiNjcXw4cPxyy+/6CT31NnZCVtbWyQlJSEhIYHZvmjRIvD5fBw7dqzHY4SGhmLMmDH47rvvmG2LFy/G0aNHYWlpCWdnZ0yePBkff/yxwfT61KFXByBNlQ1ovojD4UiJa3p4eKgkUy8Wi5k7W1Ur3bQJ9Uvi8/ldLrays0DaEKqKNbcq46AOppGRkXov96bLVxwOB9XV1SCEwMPDAwMGDNB54FU0Hl1d9FXJG1FlAYFAgKioKIM1Uqpb7aZtNYa6ujrExcXB19cXhw4d0lkwrqyshLe3N65cuYIxY8Yw29esWYM//vgDV69e7fb5165dQ0xMDK5evSqVMzpw4ABsbW3h5+eH27dv41//+hfs7e2Rnp5usEIjVem1AYj294hEIq3197S2tjIX6MbGRka3rbv8gjYr3TRBchw9acspsuamy1ea5AO04WCqDaium1AohJ+fH1OB1dHRwSzpqFrKrsk4FLmYapPu8kbOzs7Iz88HIcSgVWbaKrWWVLfm8XgwNTWVWqrr6QLc0NCAWbNmwcPDA4cPH9ZpDkzTALRs2TKkp6cjLy+v2/3u3LmDgIAAnD9/HlOmTNHK2HWNYT6FGqIrWR1bW1v4+vrC19cX7e3tTDC6deuWXFvu1tZW5OTkwMbGRimXSl1BPWOUHYeJiQkcHBzg4OCAIUOGMIG3srISN2/ehKOjI3OuqpTlSjqYGvL9kNR1i46Ohrm5Odzd3TF06FCmlL28vByFhYXMTYa2DcuABxfJ7OxsmJmZITo6Wud3pVRBw83NTWr56s6dO2hpaYG5uTn8/PwgFAoNJm2jrVJrCwsLeHp6wtPTk5F7qqmpwa1bt9DR0cHkyFxdXbvcFDY1NWHOnDlwcXFBcnKyzgswaJk5h8OR2s7hcHosymlpacGBAwfw0Ucf9fg6/v7+cHV1RUlJSa8JQEY5A+rJllvfygayVWZ2dnZwcHAAl8vFgAEDDFre3NTUhOzsbLi6umLYsGEaVwxRwVQul4v6+nqlLcj14WCqDLTnSRldN3lFDNrqwens7ERmZiZsbGwM2ntFgyDwIP9HVSf03W8kEomQnZ2t8xkYIUQqR0Z7q1pbW2FtbY3Q0FA8/fTTMDc3x8mTJ/WWE4yJicHo0aOxfft2AA+uY4MHD8by5cu7LULYt28fXn31VVRUVPSY27l//z4GDx6Mo0ePYvbs2Vodv67oNQFI1WIDXSEQCJgkv4mJCWxsbPQqIipJbW0t8vLymFmbtl9b0oKcJvblqRMYwsFUHtRDx9XVVWVdt+7s1h0dHVUKqFTclPorGSoYK7Ly1ne/kWTwiYqK0mswpuf6/fff48svv4RIJIKrqyt27NiBJ554Qm/l5wcPHsSiRYvw7bffYvTo0fjiiy9w6NAh3Lx5Ex4eHli4cCG8vb2xefNmqec9+uij8Pb2xoEDB6S2Nzc348MPP8RTTz0FT09P3L59G2vWrEFTUxPy8/MNVlavKr0iAMkWGxgq+NCy4tLSUoSGhsLFxYWxHeDxeMxSDxWc1OUYaW/N8OHD9SJeqchiwdraGqWlpQgMDDSYgymgXV03yVyKZPWgMvmF1tZWZGZmal3cVFXoDMzW1hahoaEKg4pIJGK0B3VR9kyDDwBERkYabCbY3t6Op59+GhUVFZgwYQLOnj2L+vp6rFy5Ep988olexrBjxw6mETUiIgJfffUVYmJiADxoPPX19cW+ffuY/YuLizFs2DD8/vvvePzxx6WO1dbWhoSEBGRnZ4PP58PLywvTpk3Dxo0bVTLvNDRGGYAkbbmNxUahp0o3sViMuro6Jm9EL9DabnylXfTl5eUGa6ika+6lpaWoq6uDqakpE3j79++v9/wCtTHQha4b1eOjf1dJPT5XV1epIovm5mZkZmZiwIABGpn7aUpHRwcyMzNhb2+vkr2EorJnZeSe5GEswaejowMLFiwAh8PBuXPn4OzsDEIIcnNz0dTUhEcffdQg42Ix8gBkLMFH1Uo3QoiUorVIJNKKorVkEOyui17XSDaYhoaGwsLCQqqvStEFWhfoU9dNUo+PSsjQIgYbGxsUFBQY1MUU+N/yn6OjI0aMGKHRODTtNzKG4CMQCLBw4ULcvXsXKSkpvapH5mHAaANQR0eHwW0UAOlKt9DQUJXv7iUbX6miNe2/cXNzU/p4QqGQ6SWJjIw0WLl3Tw2mVONL1oJcGakcVbl//z5u3bplsMZfWrBRWVmJxsZGWFlZwdvbW2+JfVna2tqQmZkJFxcXrS//qdpvZAzBRygU4qWXXsLNmzdx4cIFg2n/sSjGKANQYmIi2traMG3aNKU8RHQF1XTz9PTUSqWbrCFbS0uLlL2CotlCe3s7cnJyYGFhobRdtC5QtcGUVplxuVzG70dSKkcTaC4uIiLCoNpXNTU1yMvLQ0BAACwtLRklBgsLC+Zc9aFnRnNPtBpSl9+Z7vJGtN8IMGzwEYlEWLZsGbKzs5GamqqRBiGL7jDKALRjxw589dVXuH//PqZOnYqEhATMnDlTr+6d1dXVKCwsVFm9WRUk7RWamprkrrc3NzcjOzubuas1tLWEug2mkqXsdXV1sLGxYZZzVKkelDRO04fET3dwOBwUFBRg+PDhUhc42YZQQggz69W29iDw4HOUmZkJDw8PvYjfSiLP38jc3Bz+/v4q95FpC5FIhBUrVuDy5ctIS0uDt7e31l9DFWXrffv24cUXX5TaZmVlhfb2duZ3Qgg2bNiA3bt3g8/nY9y4cdi5cycCAwO1PnZjwigDEPA/wcSkpCQcPnyYaa6Kj4/HE088oTNhTdlKN30t7Ug2vvL5fDg4OMDe3h7V1dXw8fExaF5BssE0LCxM4xmYUCiUqqiTtM3obrZAc2B1dXUG1XUDwDTt9vQZkSxioJbV1AdHFbknRdDCBy8vL4OWwNNlN5FIBA8PD9TU1EjljZR1RNUUsViMf/7zn0hJSUFqaqpOzAZVVbbet28f/vGPf6C4uJjZZmJiIlWttnXrVmzevBn79++Hn58f1q9fj/z8fBQWFhpsuV0fGG0AkoSqCNNgVFhYiIkTJyI+Ph6zZs2Cq6urVj7Ykkn+iIgIg91dd3Z2MurNAJilK31ZVUui6wZTyerB7mYLIpGIUXDWVMxTU+7du4e///4b4eHhKiW1aRGDZI6Mqk64ubmprJnX1NSEzMxMgxc+UEsFExMTqWU32d4qXfcbicVivPPOOzhx4gTS0tLg7++v1eNTVFW23rdvH958803w+Xy5xyOEwMvLC6tWrcLq1asBPGgr8PDwwL59+zBv3jydnIcx0CsCkCSEEJSUlDDBKCcnB+PGjUN8fDxmz54NT09Ptb6IAoEAeXl5EAgEBtV0k5yBhYWFwdHRUaoZ1NraGu7u7vDw8ND5HaW+G0zllTy7urqif//+TDDWtZ5aT9C/TWRkJJycnDQ6Fi1i4PF4qKurY1QnlJktNDQ0ICsrC76+vvDz89NoHJogFAqRnZ3dJfjIQm806PmKRCIm/6mNfiOxWIx169YhMTERaWlpOlu6UkfZet++fXjllVfg7e0NsViMqKgobNq0CSNGjADwPw237OxsREREMM+bOHEiIiIi8OWXX+rkXIyBXheAJKEX6+TkZBw5cgRXr15FTEwM4uPjER8fj4EDByp10Wxra0N2drbalW7aQiwWo7i4GFwuV25+QyQSSQUjXTa+SibXdbGM0RO0YKOqqgr37t2DWCxmBFPd3d313ulNbS7u37+P6OhorSue09mCZBEDzZHJLkvy+XxkZ2fD39/fIH8bCg0+pqamiIiIUDq31Z3NgjqafIQQfPTRR9i/fz9SU1MRHByszukohTrCounp6fj7778RFhaGhoYGbNu2DRcvXkRBQQEGDhyIK1euYNy4caisrJRqKn/mmWdgYmKCgwcP6ux8DE2vDkCSEEJQUVGBw4cPIzk5GZcvX0ZUVBQSEhIQHx+vUKpGstJt6NChBkvyU8n+trY2REZG9vglFIvFUioMtPHVw8ND4+UNbTmYaoqkrltAQAATfKkFuaw4rK6gpedcLhfR0dE6zz3JLkuKxWLm4mxmZoa8vDwEBgYaxPCQom7wkYeifiNlZoKEEGzZsgW7du3ChQsXEBoaqvY4lEFTZWvgwc1GcHAwnnvuOWzcuPGhDkC9Ug1bHiYmJhg4cCBWrlyJFStWoLq6GkePHkVycjI2bNiAkJAQJhjRLvWkpCTY29tj+PDhBpWR6ejoQE5ODszMzDBq1CilliNMTU2ZLylVJuByuSgoKFC78VWywTQiIsKgTXtU183NzY0pK7azs4OPjw86OjqYPEpJSQns7OyY4Kvt/htCCAoLC1FfX49Ro0bppaqLWgu4uroyy5I8Hg/FxcXo6OiAvb09TE1N0dnZaRC7C20GH+CBCr2Pjw98fHyk8kZ3797tNm9ECMF//vMffP3110hJSdF58AE0U7amWFhYIDIyEiUlJQDAPI/D4UgFIA6HI7Uk1xfpMzMgRRBCUFtbi2PHjiEpKQkXLlxAYGAgnJyckJGRgQMHDmDatGkGG19LSwuys7OZznVNZ2CyeZTOzk6ljOf07WDaHarouslbutLWsqQ2XUw1hS6J0nwPj8dDY2OjRkUM6qDt4NMd8vJGTk5OyMzMRHx8PA4cOIAtW7bg7NmzCkugdYG6ytYUkUiEESNGIDY2Fp9//jlThLB69WqsWrUKANDY2Ah3d3e2CKEvQQgBj8fD3LlzcfXqVRBC4Ofnh9mzZ2POnDndijbqArr8p6skv2zja2trq5TxHL17NrSDqSRU102d/ivafyO7LEmFNVX521L30M7OToNaVwMAl8tFfn5+lyXR9vZ2ZrZAixjozFcXBSr6DD6y0LzRjRs3sGzZMty9excmJiZYuXIl3nzzTb2uYKiqbP3RRx/hkUcewZAhQ8Dn8/HZZ5/h6NGjyMzMxPDhwwE8KMPesmWLVBl2Xl5eny/D7jNLcMrQ0tKC+fPno6GhASUlJXBwcMDJkyeRnJyMqVOnwtPTkwlGUVFROg1GtIlRl2v5JiYm6NevH5NDoY2v9+/fR1FREZydndG/f39wuVwQQjBq1CiDXmg11XWTNGSTXJa8efOmShbkQqEQOTk5IIQgOjraoFV39D0JCQnponJsbW2NgQMHYuDAgRAKhUwwysjI0HrJsyGDD/Dgs+zo6IixY8di1apVWLNmDV5++WXk5OQgICAAISEhOHPmjF6UoJ999lnweDy8//77jLK15GuXl5dLvd/19fVYsmQJqqur4ezsjOjoaFy5coUJPsCDHFJLSwuWLl0KPp+P8ePH48yZM306+AAP2QxIKBRi8+bNePPNN7tUMTU3N+P06dM4fPgwfvvtNzg7O2P27NmIj49HTEyMVr9wd+/exe3btw2mYQY8SPBXVVWhtLQUYrEYDg4O8PT0NFj3ui513SQtyLlcLtra2uTOBIEHS3pZWVmM7JGhpGQAoKqqCkVFRSq/J5JLV1wuV6qIQR21ckMHHwohBD///DNWrVqF48eP47HHHgMA1NXV4dy5c0zSnqX38FAFIGVpa2vD77//juTkZJw8eRLW1taYPXs2EhISMHbsWLXLtAkhuHXrFnPXpE9pIVkkG0wDAgKYirq6ujrY29tLuaDqEtnCB33ouslKINE8ipOTEwoLC3v00NEHVGlB1WZXWSTFcHk8HhN8aUDqqZzdmIJPYmIili9fjuTkZEyfPt0g42DRLmwA6oGOjg6kpKQgOTkZx44dg6mpKWbNmoWEhARMmDBB6eUZkUjEJLQNnWfprsFUIBBI2Y9LOr5qO68gqesWFRWl9d4aZaDNoFVVVWhoaIC5uTkGDx4MDw8PvatOUOhsMCIiQut+T5JKDLSIgeaNZD+TxhJ8AODIkSNYunQpDh48iLi4OIONg0W7sAFIBQQCAf744w8kJSXh6NGjEAgEiIuLQ3x8PB577DGFd5OdnZ3IyckBAERERBg0z6JKgynVbONwOFIVZh4eHlKW3OogqesWHR1t0IBMZ4MuLi5wcnJiLAesrKzg4eEBNzc3jc9XWcrLy3H79m29zAYly9nr6upga2srVVGXk5NjFMHn5MmTePHFF/HTTz9hzpw5BhsHi/ZhA5CaCIVCXLp0iQlGzc3NmDlzJhISEjB16lQmj0Lv8B0dHRESEmLQL7ImDabyKszozEjVJLcx6bo1NTUhKyuri5inpAU5j8eTcn3VhY4Z8D+Zn6ioKL0vz8oKxIpEIlhaWiI4OFjlCkJtcubMGbzwwgvYu3cvnnnmGZ28hirK1rt378aPP/6IGzduAACio6OxadMmqf0XL16M/fv3Sz1v+vTpOHPmjE7G35thA5AWEIlE+OuvvxhJoJqaGkyfPh2BgYH45ptv8N577+GNN94wWIJUMs8SFhamcYMprTDjcDhMBZ2k/Xh3QZZWmInFYoPrutGlSB8fn2711MRisZTDrWRSnzYmagq1WY+KijJoD5ZQKERWVhZTmEKDkaRArL6kqi5cuIB58+Zh165dmD9/vk6+P6oqW8+fPx/jxo3D2LFjYW1tja1bt+LIkSMoKChgbB8WL14MDoeDvXv3Ms+zsrIyqG+VscIGIC0jFouRkZGBLVu24OjRozAzM0NsbCzi4+MRGxur94uLrhtMVWl87ezsRFZWFiwtLQ1eYVZXV4fc3FwEBASo1EMiqUwga0Hu5uamckDVtcacKtDgY25uzvx9ZP1+WltbpewkdKXJd/HiRcydOxdfffUVFi9erLObN1WVrWURiURwdnbGjh07sHDhQgAPAhCfz8fRo0d1Mua+xEPVB6QPTE1NkZGRgd9//x0HDx5EYGAgkpKSsG3bNrz++uuYMmUKZs+ejbi4OK0LiMpCG0wbGxsxatQoneRZTExM4OTkBCcnJwQGBqK5uRkcDgd37txBQUEBc3G2t7dHfn4++vXrh5CQEINWmPF4POTn5yMoKEhlszLJ8x0yZAhTUVdeXo7CwkK5poKKoMrulZWVGDlypM4rDrtDXvAB/td/4+joyJwvtSG/efMmo8nn5uamNY28K1eu4JlnnsG2bdt0Gnw6OzuRmZmJd999l9lmamqKqVOnIj09XaljtLa2QiAQdCkWSUtLY5ZrJ0+ejI8//tig0lbGCjsD0jJCoRDPPPMMVq1ahXHjxjHbqaYYtZEoKirCpEmTEB8fj7i4OK15GlE0dTDVBvTiXFVVhZaWFlhZWcHPz88gataU7ho7NaWtrY2ZCTY0NHTxcZKEluRzOBy9CJx2h6Lg0xO0iIHaSajrcivJ9evXER8fj40bN2L58uU6vUHThrDo66+/jrNnz6KgoIC54Thw4ABsbW3h5+eH27dv41//+hfs7e2Rnp5u0Fm/McIGIANAy49pMMrNzcX48eMZTyMPDw+NvnjadjDVBJpn8fT0hLW1NXg8HhoaGpjeG302virrYqoNJC3Ia2trmQozOhssLi5GTU2NwSsA1Q0+8o4j63JLg5GyRRvZ2dmIi4vDunXr8NZbb+k8Z6ppANqyZQs+/fRTpKWlISwsTOF+1O/n/PnzmDJlitbG3xdgA5CBIYSgtLSUKWC4du0aHnnkEcbTyNvbW6Uvoq4dTFVBka5bR0cHM1Oor6+Hvb09PDw85M4UtEV5eTlKSkp00lvTE5IyOTU1Ncz24OBgeHh4GOxvpK3gIwst2qABmBYx0KINeTdE+fn5iI2NxerVq7F27Vq9FOyoYy5H2bZtGz7++GOcP38eI0eO7PG13Nzc8PHHH2PZsmXaGHqfgQ1ARgQhBPfv38fhw4dx+PBhXL58GdHR0YyNhI+PT48umfp0MO0OutQ1fPhwKYl5WfTR+FpaWoqysjKDlDdLQnNyfD4fzs7OqK2tBQClKwi1ia6CjyySMkjUfI7KIJmbm8PT0xOFhYWYOXMm3njjDWzYsEGvn1t1lK0//fRTfPLJJzh79iweeeSRHl/j/v37GDx4MI4ePYrZs2drdfy9HTYAGSmEEFRXV+PIkSNITk7GxYsXERoaivj4eCQkJHQJMDSxbigHU0nU1XWTnSlYWloywUidRlDJJL+hlBYoktYO0dHRsLKyAiGEEUzlcrkQCARM0Yarq6vOStT1FXzk0draCi6Xizt37mDOnDnw8fFBdXU15s+fj127dun9pklVZeutW7fi/fffxy+//CKV47W3t4e9vT2am5vx4Ycf4qmnnoKnpydu376NNWvWoKmpCfn5+QbLfRorbADqBRBCUFNTwxjsXbhwAcOGDWP06ahU0K+//trtbEMf49SWrptk4yuXy1W5EZQQgps3b6KmpgZRUVEGTfKLxWLG7TY6OlpuQYisdYbkTEGb5c6GDD6yXLp0CXPnzkX//v1RWVkJPz8/JCQkYOXKlXr9HO/YsYNpRI2IiMBXX32FmJgYAMCkSZPg6+uLffv2AQB8fX1x9+7dLsfYsGEDPvjgA7S1tSEhIQHZ2dng8/nw8vLCtGnTsHHjRr0odfc22ADUyyCEoL6+HsePH0dSUhLOnDkDsViMhIQErFmzxmAlzrrUdZNtBJVsfJXXpS8Wi1FYWAg+n4/o6GiDqHtTqK+QQCBAVFSU0rMaOlNQRrNNWQQCAbKzs40i+JSVlWHGjBmIj4/Hl19+idbWVpw9exbHjh3Dli1b1LLjYOl9sAGolyIWi/HWW2/h119/xcqVK5GVlYUzZ85gwIABjKdRZGSkXoKRPnXdaCMoVWGgPj8eHh5MMJKU+THkkodIJEJOTg5TCq/ukhot2qDlznZ2dlIVdcosWxlT8Ll37x5mzJiB6dOn45tvvjFooQyLYWEDUC/lu+++w7Zt23DmzBn4+/sDeOBpdOrUKRw+fBinTp2Ci4sLo9w9evRonVx0qK5bW1sbIiMj9arrJs/nx8zMDGZmZkZR3pydnQ0AiIyM1FopPLUgp3kyKyurHvNkxhR8qqqqMH36dEyYMAG7d+9m+2IectgA1EsRCARobGxU2F3d2toq5Wlka2vLGOxp4mkkO4bc3Fyj0HUTCATIzMyEQCCAubm5znIoyo4lOzsbZmZmOlWSlhRMrampYSzIaUWdqakpMxYLCwuEhYUZ9ILP4XAwc+ZMjBw5Evv372eDDwsbgB4G2tvbmUKF48ePw8zMDHFxcZgzZw4effRRtQKHMem6yRsLVSXgcDhMDoVaK+gyJ2QoR1VJC3Lae+Pi4oLm5mbY2NgY/G9UU1OD2NhYjBgxAj///LPOmqNVUbYGgMTERKxfvx5lZWUIDAzE1q1bERsbyzxOCMGGDRuwe/du8Pl8jBs3Djt37kRgYKBOxv+wwQaghwyBQIC0tDTGRkIoFCIuLg4JCQmYNGmSUjOFtrY2ZGVlGYWuW0dHBzIzM2FnZ6fQxZSaztHG1+4kcjSBBkJra2uEhYUZ7H0hhKCurg4FBQUQCoUghCi0INcHdXV1eOKJJ+Dv749Dhw7pbKasqrL1lStXMGHCBGzevBlxcXH45ZdfsHXrVmRlZSEkJATAg7LrzZs3Y//+/fDz88P69euRn5+PwsJCg9qI9BXYAPQQQz2NEhMTcfToUbS0tCA2NhYJCQmYMmWK3JlCc3MzsrKy4ObmhmHDhhm02bWtrQ2ZmZlwdnZGcHCwUhf8zs5O1NTUgMPhMBI5VIVB2YS+PJQJhPqCzsLojLC1tZUJwE1NTXBycmKCka4rBPl8PmbNmoUBAwYgOTlZp0uhqipbP/vss2hpacHJkyeZbY888ggiIiKwa9cuEELg5eWFVatWYfXq1QAeNHt7eHhg3759mDdvns7O5WGh15Wf1NXVYf78+XBwcICTkxNefvllNDc3K9y/rKwMJiYmcn8SExOZ/eQ9fuDAAX2cksEwNzfHpEmT8PXXX6O8vBwnT56Eh4cH1qxZAz8/PyxatAhHjhxBS0sLACA1NRU//fQTvL29DR58WlpacP36dbi6uqokOWRpaQkvLy9ERkZi0qRJ8Pf3Z451+fJl3Lp1C3w+H6rcl7W3tyMjIwP9+vUzuuBjamoKe3t7+Pn5ISYmBuPHj4e7uzu4XC4uX76Mv/76C3fu3On2O6QujY2NePLJJ+Hq6oqkpCSdBh+qbD116lRmW0/K1unp6VL7Aw+M4+j+paWlqK6ultrH0dERMTExSqtls3RPr7NjmD9/PqqqqnDu3DkIBAK8+OKLWLp0KX755Re5+w8aNAhVVVVS27777jt89tlnmDlzptT2vXv3YsaMGczvTk5OWh+/sWJmZobx48dj/Pjx+Pe//43r168jOTkZH3zwAZYuXYrQ0FBkZ2dj1apVCAgIMOhYm5qakJmZiYEDByIgIEDtQEilYDw9PaUS+rSAgC7TOTk5KQwqkrOw4cOHGzQoyws+slhbW2Pw4MEYPHgwMxvkcrkoLS2FtbU1c87qqllTmpub8fTTT8PW1hZHjhzR+XIVNc6Tbfb08PDAzZs35T6nurpa7v7V1dXM43Sbon1YNKNXBaCioiKcOXMG169fZwQAt2/fjtjYWGzbtk1u85qZmVkX++kjR47gmWee6eK/4uTkpLJVdV/E1NQUMTExiImJwZYtW7Bt2zasW7cO/fv3x+eff468vDzMnj0bTzzxhM49jWTh8/nIzs6Gr69vty6mqiIZcGjjK4fDQX5+vsLG17a2NmRkZKB///4IDg42+uAjC50Nenl5SalZZ2VlMe+Hm5ubyhbkra2teOaZZ2Bqaorjx48btByexbjpVUtw6enpcHJyklKfnTp1KkxNTZXy7gCAzMxM5OTk4OWXX+7y2BtvvAFXV1eMHj0aP/zwg0rLMH2VH374ARs3bsTRo0dRWVmJjIwMjBw5Ejt27ICfnx/mzJmDffv2oaamRufvV11dHbKysjBkyBCtBh9ZTE1N0b9/fwwfPhwTJkxAeHg4zM3NcfPmTfzxxx/Iz89HeXk5rl27Bjc3t14ZfGQxNzeHh4cHQkNDMXHiRAwfPpzRr/vjjz9w48YNprquO9rb2/Hcc8+ho6MDJ0+e1JvJHrVG53A4Uts5HI7Cm0pPT89u96f/qnJMFtXoVQGourq6SzWLubk5XFxclJ4S79mzB8HBwRg7dqzU9o8++giHDh3CuXPn8NRTT+H1119nFHIfZlxcXHD69GnExsbCxMQEISEh+OCDD5Cbm4v8/HxMmjQJP/zwAwICAhAXF4fdu3ejurpa68GIx+MhJycHw4YNw6BBg7R67O4wMTGBs7MzgoKCMH78eERHR8PMzAzFxcUQCARob29HdXU1BAKB3sYkiTaCjyympqZMbm3ChAmMoeGtW7eQlpaG3NxcVFZWdjnnjo4OLFiwAHw+H6dOndKr/bylpSWio6ORkpLCbBOLxUhJSZHy+pFkzJgxUvsDwLlz55j9/fz84OnpKbVPY2Mjrl69qvCYLKphFFVwa9euxdatW7vdp6ioCIcPH8b+/ftRXFws9Zi7uzs+/PBDvPbaa90eo62tDQMGDMD69euxatWqbvd9//33sXfvXty7d0+5k3iIIYTgzp07jKfR9evXMWbMGMbTyMvLS6MZQnV1NQoLCzFixAiDCzrS/JO3tzc8PT2Z6rLm5mam1Nnd3V0vpc66CD7dIU8wtaOjAwUFBXjyySexbt06lJeXIyUlxSD206oqW1+5cgUTJ07Eli1b8MQTT+DAgQPYtGlTlzLsLVu2SJVh5+XlsWXYWsIoAhCPx2O8URTh7++Pn376CatWrUJ9fT2zXSgUwtraGomJiZgzZ063x/jvf/+Ll19+GRUVFT3aBPz222+Ii4tDe3s7K6GuAoQQ3Lt3j/E0unLlCkaOHMkEo548jWSh1g5hYWFwdXXV4ch7prGxEVlZWRg8eDAjf0SRFQ+lpc7u7u46uVDpO/jIo7W1FRcuXMDGjRtx48YN2NjY4O2338YLL7yAIUOG6H08gGrK1sCDRtR169Yxjaiffvqp3EbU7777Dnw+H+PHj8c333yDoUOH6vvU+iRGEYCUpaioCMOHD0dGRgaio6MBAL///jtmzJiB+/fv96igO2nSJKYktCc++eQT/Pvf/0ZdXZ1Wxv4wQghBVVUV42n0559/IiwsjAlGPZnm3b17F3fu3NHY2kEbNDQ0ICsrC35+fvD19e12X0WNrx4eHlpJyBtD8KEIhUIsW7YMGRkZWLZsGS5cuICUlBQMGzYM165dY2/eWLqlVwUgAJg5cyY4HA527drFlGGPHDmSKcOuqKjAlClT8OOPP0pJcJSUlGDo0KE4deqUVKk1AJw4cQIcDgePPPIIrK2tce7cOaxevRqrV6/Ghx9+qNfz66tQTyMajFJTUzFs2DDGYE+yr0gsFuPWrVuorq5GZGSkQV1MAaC+vh45OTkICAiQshZXhs7OTinHV3WUrCUxpuAjEomwfPlypKenIzU1Fd7e3gAezBT/+usvTJs2zWBjY+kd9LoAVFdXh+XLl+PEiRMwNTXFU089ha+++oqptikrK4Ofnx9SU1MxadIk5nn/+te/8NNPP6GsrKzLl/bMmTN49913UVJSAkIIfH19YWtri5s3bzKv8eWXX3Zb0TNp0iT88ccfUtuWLVuGXbt2Mb+Xl5fjtddeQ2pqKuzt7bFo0SJs3rxZZ7pYxgr1NDp27BiSk5Nx/vx5+Pn5MTOjnTt3gsPh4L///a/eqqgUUVdXh5ycHAwdOhQDBw7U6FjylKypCoMyfTdUcNXKysrgwUcsFuPNN9/EhQsXkJaWpnJgZmEBemEA0gczZ85EVVUVvv32W2aWNWrUKIXNrsCDADR06FB89NFHzDZbW1umEkgkEiEiIgKenp747LPPUFVVhYULF2LJkiXYtGmTzs/JmGloaMCJEyeQnJyMEydOAACef/55LFmyRG+eRvKora1Fbm4ugoKCmLt7bSHZ+Mrj8aT6kJydnbsEIxp8DK0zBzwIPmvWrMFvv/2G1NTULvkwFhZlYQOQDDTPJNnseubMGcTGxnabZ5o0aRIiIiLwxRdfyH389OnTiIuLQ2VlJVPJtWvXLrzzzjvg8Xh6F4g0NgQCARYvXoxr167hzTffxMWLF3Hq1Cn079+fsR4fNWqU3hSdeTwe8vPzERwcrHN7aLFYzNiP83g8EEKYYOTi4gKRSGRUwWfdunVISkpCamoqqwrNohG9qg9IH2jS7Przzz/D1dUVISEhePfdd9Ha2ip13NDQUKky4unTp6OxsREFBQXaP5FexhdffIEbN27g0qVLeOONN3Dw4EFwOBz85z//QV1dHZ588kkEBwdj9erV+PPPPyEUCnU2Fi6Xi7y8PIwYMULnwQfo2ndDrROKioqQlpaGS5cuwcTEBCNGjDBo8CGEYOPGjTh48CDOnz+v1+CjqgZkXV0dVqxYgaCgINjY2GDw4MFYuXIlGhoapPZ7GDUgjYmHK/mgBOo2uz7//PPw8fGBl5cX8vLy8M4776C4uBiHDx9mjitPU4o+9rCzcuVKvPLKK1LVbra2tpgzZw7mzJmD9vZ2nD9/HsnJyXj++edhYWHBeBqNHz9eaxL/1dXVKCgoQGhoqFwJf11DG1+dnZ3h5+eH69evgxACgUCAixcvwtXVFe7u7nB1ddWrASAhBFu2bMHevXtx4cIFDBs2TG+vDaiuAVlZWYnKykps27YNw4cPx927d/Hqq6+isrKySxWsphqQP/74I/75z3+isrJSquovISEB/fr1w3//+1+Vjvcw8dAEIGWbXdVl6dKlzP9DQ0MxYMAATJkyBbdv3za4eGdvwMrKqtuSXWtra8TFxSEuLg4CgQCpqalISkrCSy+9BJFIhCeeeAJz5szBpEmT1F7OrKysxM2bNxEeHm7wniNa7WZnZ4ewsDCYmJigpaUFHA4HZWVlKCgogIuLC2Oyp8slXEIIPv/8c3zzzTdISUlhmjT1hToakCEhIUhOTmZ+DwgIwCeffIIFCxZAKBRKFf5oqgE5d+5crFy5EsePH8fcuXMBPJhF//bbb/j999/VPu7DwEOzBLdq1SoUFRV1++Pv7w9PT09wuVyp5wqFQtTV1an0IaXNbyUlJQAU607Rx1iUx8LCAtOmTcN3332HiooKJCYmws7ODm+88QZ8fX2xZMkSnDx5Eu3t7Uofs6KiwqiCj2zOx8TEBPb29ggICMCYMWMwduxYuLi4oKKiAhcvXkRGRgbKy8tVOmdlIIRg+/bt+M9//oMzZ84gIiJCq8dXBm1oQAIPil0cHBy6VJ1qqgFpY2OD559/Hnv37mW2/fTTTxg8eLBUJS5LVx6aGZCbm1uP6gfAA30oPp+PzMxMptn1woULEIvFTFBRhpycHABgcghjxozBJ598Ai6XyyztnDt3Dg4ODhg+fLiKZ8NCMTc3x2OPPYbHHnsM27dvx5UrV5CcnIy3334b9fX1mDFjBuLj4zFt2jSF7qf37t3D33//jcjISIM3vCpb7WZrawtfX1/4+vqivb2dUWG4desWHBwcmCIGTRpfCSH49ttvsWXLFpw+fRqjRo1S+1iaoA0NyJqaGmzcuFFqpQJ4oAE5efJk2Nra4vfff8frr7+O5uZmrFy5UqUxLlmyBKNGjUJFRQW8vb2xb98+LF682KAitb0CwtKFGTNmkMjISHL16lVy6dIlEhgYSJ577jnm8fv375OgoCBy9epVQgghJSUl5KOPPiIZGRmktLSUHDt2jPj7+5MJEyYwzxEKhSQkJIRMmzaN5OTkkMTERGJlZUUsLS2Jo6Mjeemll0hTU5PCMdXW1pLly5eToUOHEmtrazJo0CCyYsUKwufzpfYD0OXn119/1fI7ZPyIRCKSnp5OVq9eTQICAoitrS1JSEgge/fuJdXV1aSlpYW0tLSQffv2kRMnTpCKigpmm6F++Hw+SUlJIVeuXCFNTU1qHaO+vp4UFxeTP//8kxw7doycP3+e5OXlEQ6HQ5qbm5U+TnNzM9m+fTvp168fuXjxok7+Ru+8847cz6vkT1FREfnkk0/I0KFDuzzfzc2NfPPNNz2+TkNDAxk9ejSZMWMG6ezs7Hbf9evXk4EDB6p1PlFRUWTTpk0kIyODmJqakvLycrWO8zDBlmHLQdVm13v37mHBggW4ceMGWlpaMGjQIMyZMwfr1q2TUgS+e/cuXnvtNaSlpUEoFMLJyQlHjhwBIaTHXqMbN25gw4YNWLx4sVRSNSwsTCqpamJiIjep+jALJ4rFYuTk5CApKQmHDx/G3bt3MXXqVAgEAly6dAmpqakYMWKEQcdIZ7riuZAAABIcSURBVD42NjZac1WVbXxV1nCOEIKffvoJq1evxvHjx/HYY49pPBZ56EMDsqmpCdOnT4etrS1OnjzZ4/dAEw3InTt34osvvsDjjz+Ov//+G2fPnlXp+Q8jbAAyAOr2GsmSmJiIBQsWoKWlhVnXNjExwZEjR5CQkKCr4fdqCCHIz8/HypUrmfLmyZMnIyEhAXFxcXBxcdH7sokugo8sIpFIKhiZm5tLOb7ScyaE4NChQ1ixYgWSk5Mxffp0rY9FVdTVgGxsbMT06dNhZWWFU6dOKbUcqYkGZENDA2Pu9+OPP+LZZ59V+RgPGw9NEYIxYexJ1b7OgQMHcPPmTeTk5CA/Px8TJkzA999/D39/f8yaNQvff/89OByOXt63zs5OnQcf4IHjq6ThXHBwMEQiEXJzc3Hx4kW8++67SE5OxsGDB7F8+XIcPHjQKIIPAAQHB2PGjBlYsmQJrl27hsuXL2P58uWYN28eE3wqKioYAVTgQfCZNm0aWlpasGfPHjQ2NqK6uhrV1dWMqd6JEyfw/fff48aNGygpKcHOnTuxadMmrFixQq1xOjo64qmnnoK9vT17A6gkD00RgjHRG5KqfZXW1lbk5OTgjz/+QFBQEADgvffew7/+9S/cuXMHSUlJ+OWXX/DWW29h7NixiI+Px+zZszX2NJJHZ2cnsrKydB58ZKGNr66urhg2bBjq6+vx888/Y/ny5WhsbMSkSZMgEAjQ2tpqNHbadHxTpkyRWhanCAQCFBcXM83fWVlZzM2crDVEaWkpfH19YWFhga+//hr//Oc/QQjBkCFD8Pnnn2PJkiVqj7OiogLz589nVcCVhF2C0yL6MtZrbGzE448/DhcXFxw/frzbhkTWWE91CCEoLy9nPI3S09MxatQoRhJo8ODBGgcjQwUfRZw5cwYLFizAe++9h6amJiQnJ6OyshK7du3CCy+8YNCx9Qbq6+uRlpaGp59+GoWFhczNDUv3sAFIi/S1pCrLg2BUWVnJ2EhcunQJ4eHhjHJ3QECAysHI2IJPSkoKnnvuOXz77bd4/vnnYWJiAkIIbty4AUdHR1bpWgl8fX1RX1+P9evXY/Xq1YYeTq+BDUAGoLckVVmkIYSAy+Xi6NGjSE5ORlpaGoKDgxlPo6CgoB6DEc352NraGkXwuXjxIubOnYvt27dj0aJFbN8Ki15hA5CBUNVYjyZVW1tbceTIEammSjc3N5iZmbHGenqEEIK6ujrG0yglJQX+/v6Ij4/HnDlzMHz48C7BxdiCz+XLl/HUU09h27ZtWLJkCRt8WPSP/lqOWCSpra0lzz33HLG3tycODg7kxRdflGpELS0tJQBIamoqIYSQ1NRUhc16paWlhBBCTp8+Tby9vYmJiQkBQGxtbck777xDRCKRwnEcOnSIBAUFESsrKxISEkJ+++03qcfFYjFZv3498fT0JNbW1mTKlCnk1q1bWn8/ejv19fXkxx9/JAkJCcTGxoYMGTKErF69mvz555+kqamJlJSUkMWLF5NLly6p3WSqzZ/U1FTi4OBAtm/fTsRisd7ep9raWvL888+Tfv36KdWATQghEydO7PKZX7ZsmdQ+d+/eJbGxscTGxoa4ubmR1atXE4FAoMtTYdECbADqQxw4cIBYWlqSH374gRQUFJAlS5YQJycnwuFw5O5/+fJlYmZmRj799FNSWFhI1q1bRywsLEh+fj6zz5YtW4ijoyM5evQoyc3NJbNnzyZ+fn6kra1NX6fV62hsbCS//vormTt3LrG3tycDBw4kTk5OJDo6mtTU1Bg8+Pz555/EycmJ/Pvf/9Zr8CHkgcpIeHg4+euvv8iff/5JhgwZIqUyIo+JEyeSJUuWkKqqKuanoaGBeZyqjEydOpVkZ2eTU6dOEVdXV/Luu+/q+nRYNIQNQH2I0aNHkzfeeIP5XSQSES8vL7J582a5+z/zzDPkiSeekNoWExPD3F2KxWLi6elJPvvsM+ZxPp9PrKysHkp5H3W4e/cu8fHxIQMHDiQODg5k4MCB5LXXXiNnz54ljY2Neg8+6enpxNnZmWzevFnvwaewsJAAINevX2e2nT59mpiYmJCKigqFz5s4cSL5xz/+ofDxU6dOEVNTU1JdXc1s27lzJ3FwcCAdHR1aGTuLbmAbUfsINL8wdepUZpupqSmmTp2K9PR0uc9JT0+X2h94YJJH9y8tLUV1dbXUPo6OjoiJiVF4TJb/UVNTg1mzZmH06NG4c+cOOBwOvvnmG7S2tmLevHkYOnQo/vGPfyAtLQ0CgUDn4yksLMSsWbPw5ptv4p133tF7zoc1e2SRhW1E7SPU1NRAJBLJNb27efOm3OcoMsmjzbD03+72YVGMpaUl5syZg/feew8WFhawsLDArFmzMGvWLHR2djKeRosXLwYhhPE0mjhxotb9fYqLixEXF4elS5di/fr1Bik4YM0eWWRhZ0AsLDrCwcEBH3zwgdxGYUtLS0yfPh27d+9GZWUlDh48CFtbW7z22mvw8/PD0qVLcerUKa34+5SUlCAuLg4LFizAxo0btR581q5dK9faWvJH0U2QMixduhTTp09HaGgo5s+fjx9//BFHjhzB7du3tXgWLIaAnQH1EVxdXWFmZibX9E6R4Z0ikzy6P/2Xw+Ewvkb0d0MYk/VVzM3NMXnyZEyePBnbt2/H5cuXkZycjLfeegsNDQ1SnkaqSuOUlZUhLi4OTz31FD799FOdlH6vWrUKixcv7nYfXZk9BgQEwNPTk9GAo7Bmj70EQyehWLTH6NGjyfLly5nfRSIR8fb27rYIIS4uTmrbmDFjuhQhbNu2jXm8oaGBLULQEyKRiFy5coWsWrWK+Pv7Ezs7O5KQkED27dsn5Wmk6OfmzZvEx8eHLFu2rNtSfH1BixAyMjKYbWfPnu2xCEGWS5cuEQAkNzeXEPK/IgTJas9vv/2WODg4kPb2du2dAIvWYQNQH+LAgQPEysqK7Nu3jxQWFpKlS5cSJycnpjrohRdeIGvXrmX2v3z5MjE3Nyfbtm0jRUVFZMOGDXLLsJ2cnMixY8dIXl4eiY+PZ8uwDYBIJCIZGRlk7dq1jClhXFwc2b17N6moqOhiNldSUkICAgLISy+9RIRCoaGHz6APs8czZ84QNzc3tgy7F8AGoD7G9u3byeDBg4mlpSUZPXo0+euvv5jHJk6cSBYtWiS1/6FDh8jQoUOJpaUlGTFihMJGVA8PD2JlZUWmTJlCiouLyY4dO4iPjw+xsrIio0ePZi4Y8vjuu+/I+PHjiZOTE3FyciJTpkzpsv+iRYu6NBtOnz5d8zekDyIWi0leXh5Zv349CQkJIZaWlmT69Olk586dpLy8nNy5c4cEBQWRBQsWGFXwIUT1Buzy8nIyYcIE4uLiQqysrMiQIUPI22+/LdUHRAghZWVlZObMmcTGxoa4urqSVatWsY2ovQBWiodFZQ4ePIiFCxdi165diImJwRdffIHExEQUFxd3qXICgPnz52PcuHEYO3YsrK2tsXXrVhw5cgQFBQXw9vYGACxevBgcDgd79+5lnmdlZQVnZ2e9nVdvhBCCmzdvIjk5GYcPH0ZeXh6sra3x+OOPIzExsYtXFAuLMcEGIBaViYmJwahRo7Bjxw4ADyyvBw0ahBUrVmDt2rU9Pl8kEsHZ2Rk7duzAwoULATwIQHw+H0ePHtXl0Ps0hBDcunUL69atw08//cSqn7MYPWwZNotKqNPwKktraysEAgFcXFyktqelpcHd3R1BQUF47bXXerS2YJHGxMQEQUFBSExMZIMPS6+ADUAsKtFdw6uyTX/vvPMOvLy8pILYjBkz8OOPPyIlJQVbt27FH3/8gZkzZzL2ySwsLH0PdoGYRa9s2bIFBw4cQFpampSZ3rx585j/h4aGIiwsDAEBAUhLS8OUKVMMMVQWFhYdw86AWFRCnYZXyrZt27Blyxb8/vvvCAsL63Zff39/uLq6oqSkROMxs7CwGCdsAGJRCUtLS0RHRyMlJYXZJhaLkZKSgjFjxih83qeffoqNGzfizJkzUmKUirh//z5qa2ulFBhYjIe6ujrMnz8fDg4OcHJywssvv4zm5maF+5eVlSmU6UlMTGT2k/f4gQMH9HFKLIbAkDXgLL0TVRtet2zZQiwtLUlSUpKUpwvt/2hqaiKrV68m6enppLS0lJw/f55ERUWRwMBA0t7erlLP0d69e7v0E1lZWUntw5rsaY6qvj5CoVDqb19VVUU+/PBDYm9vL9UHBIDs3btXaj+26bnvwgYgFrVQpeHVx8dHrpPrhg0bCCGEtLa2kmnTphE3NzdiYWFBfHx8yJIlS0h1dbXKJnt79+4lDg4OUhcwSZ8YQliTPU1R19dHloiICPLSSy9JbQNAjhw5oq2hshg5bABiMWpUNdnbu3cvcXR0VHg81mRPc/bs2UOcnJyktgkEAmJmZkYOHz6s1DEyMjIIAHL58mWp7QCIl5cX6d+/Pxk1ahTZs2eP3o3zWPQHmwNiMVrU7Tlqbm6Gj48PBg0ahPj4eClTMtZkT3PU9fWRZM+ePQgODsbYsWOltn/00Uc4dOgQzp07h6eeegqvv/46tm/frrWxsxgXbADqA/B4PHh6emLTpk3MtitXrsDS0lKqWKC3oU7PUVBQEH744QccO3YMP/30E8RiMcaOHYv79+8DYE32ukPXvj6UtrY2/PLLL3j55Ze7PLZ+/XqMGzcOkZGReOedd7BmzRp89tlnGr8mi3HC9gH1Adzc3PDDDz8gISEB06ZNQ1BQEF544QUsX778oeuhGTNmjFQ13tixYxEcHIxvv/0WGzduNODIjB99+fokJSWhtbWVkWHqjpiYGGzcuBEdHR2sukMfhA1AfYTY2FgsWbIE8+fPx8iRI2FnZ4fNmzcbelgaoUnPEcXCwgKRkZFMPxFrsqcYNzc3uLm59bjfmDFjwOfzkZmZiejoaADAhQsXIBaLGbO47tizZw9mz56t1Gvl5OTA2dmZDT59FHYJrg+xbds2CIVCJCYm4ueff+71X1p1e44kEYlEyM/PZ4KNn58fPD09pY7Z2NiIq1evKn3Mh53g4GDMmDEDS5YswbVr13D58mUsX74c8+bNg5eXFwCgoqICw4YN6+JUWlJSgosXL+KVV17pctwTJ07g+++/x40bN1BSUoKdO3di06ZNWLFihV7Oi8UAGLoKgkV75OfnE2tra2JmZkaOHz9u6OFoBVV7jj788ENy9uxZcvv2bZKZmUnmzZtHrK2tSUFBAbMPa7KnOar6+lDeffddMmjQILkOradPnyYRERHE3t6e2NnZkfDwcLJr1y6jcHNl0Q1sAOojdHR0kPDwcLJo0SKyadMm4u7urrBXprehSs/Rm2++yezr4eFBYmNjSVZWltTxFJnsEUJUanqdOHGi3P6m2NhYZh/WaI+FRTGsH1Af4e2330ZSUhJyc3Nhb2+PiRMnwtHRESdPnjT00HoNqhrt1dXVobOzk/m9trYW4eHh+P7775lkPmu0x8LSDYaOgCyak5qaSszNzcmff/7JbCstLSUODg7km2++MeDIeheqNr3K8p///If069ePNDc3M9sWLVpE4uPjtT1UFpY+AVsF1weYNGkSBAKB1DZfX180NDQYaES9D9r0+u677zLbVDXa27NnD+bNmwc7Ozup7dRoz9nZGZMnT8bHH3+M/v37a3X8LCy9EbYKjoUFmhvtXbt2DTdu3OhS3cUa7bGwKIadAbGwaIE9e/YgNDQUo0ePltrOGu2xsCiGnQGxsECzpteWlhYcOHBArrSMLKzRHgvL/2ADEAsLNGt6TUxMREdHBxYsWNDj67BGeyws/4MNQCws/89bb72F3bt3Y//+/SgqKsJrr72GlpYWvPjiiwCAhQsXShUpUPbs2YOEhIQuhQXNzc14++238ddff6GsrAwpKSmIj4/HkCFDMH36dL2cEwuLMcPmgFhY/p9nn30WPB4P77//PqqrqxEREYEzZ84whQnl5eUwNZW+ZysuLsalS5fw+++/dzmemZkZ8vLysH//fvD5fHh5eWHatGnYuHFjr5dJYmHRBmwjKgsLCwuLQWCX4FhYWFhYDAIbgFhYWFhYDAIbgFhYWFhYDAIbgFhYWFhYDAIbgFhYWFhYDAIbgFhYWFhYDAIbgFhYWFhYDAIbgFhYWFhYDAIbgFhYWFhYDAIbgFhYWFhYDAIbgFhYWFhYDAIbgFhYWFhYDML/ASpXKQ3YE5AOAAAAAElFTkSuQmCC",
+                  "text/plain": [
+                     "<Figure size 640x480 with 1 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Panel Count:  6750\n",
+                  "{'Heave': 286.15543998913375}\n",
+                  "{'Heave': 185.08145074428117}\n",
+                  "Solve Time (Radiation):  0.470053333992837\n",
+                  "Solve Time (Diffraction):  0.3286764999793377\n",
+                  "Excitation Phase:  -0.5194353495383882\n"
+               ]
+            }
+         ],
+         "source": [
+            "#original - only inner heaving\n",
+            "h = 1.001\n",
+            "d = [0.5, 0.25]\n",
+            "a = [0.5, 1]\n",
+            "heaving = [1, 0]\n",
+            "t_densities = [50, 100]\n",
+            "face_units = 90\n",
+            "m0 = 1\n",
+            "rho = 1023\n",
+            "config = \"config5\"\n",
+            "reps = 1\n",
+            "\n",
+            "result_r, solve_time_r, result_d, solve_time_d, panel_count_alt = construct_and_solve(a, d, heaving, t_densities, face_units, h, m0, rho, reps)"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 10,
+         "metadata": {},
+         "outputs": [
+            {
+               "data": {
+                  "image/png": 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",
+                  "text/plain": [
+                     "<Figure size 640x480 with 1 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "data": {
+                  "text/html": [
+                     "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[15:26:27] </span><span style=\"color: #808000; text-decoration-color: #808000\">WARNING </span> Irregular frequencies for <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">RadiationProblem</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">body</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>,                              \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"collection_of_meshes_68\"</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.000</span>, <span style=\"color: #808000; text-decoration-color: #808000\">water_depth</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100.0</span>, <span style=\"color: #808000; text-decoration-color: #808000\">radiating_dof</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Heave'</span>,   \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #808000; text-decoration-color: #808000\">rho</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1023.0</span><span style=\"font-weight: bold\">)</span>:                                                                                   \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         The body <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"collection_of_meshes_68\"</span><span style=\"font-weight: bold\">)</span> might display irregular frequencies \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         for <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>.                                                                              \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         Setting a lid for the floating body is recommended.                                            \n",
+                     "</pre>\n"
+                  ],
+                  "text/plain": [
+                     "\u001b[2;36m[15:26:27]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m Irregular frequencies for \u001b[1;35mRadiationProblem\u001b[0m\u001b[1m(\u001b[0m\u001b[33mbody\u001b[0m=\u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m,                              \n",
+                     "\u001b[2;36m           \u001b[0m         \u001b[33mname\u001b[0m=\u001b[32m\"collection_of_meshes_68\"\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mwavenumber\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1;36m.000\u001b[0m, \u001b[33mwater_depth\u001b[0m=\u001b[1;36m100\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33mradiating_dof\u001b[0m=\u001b[32m'Heave'\u001b[0m,   \n",
+                     "\u001b[2;36m           \u001b[0m         \u001b[33mrho\u001b[0m=\u001b[1;36m1023\u001b[0m\u001b[1;36m.0\u001b[0m\u001b[1m)\u001b[0m:                                                                                   \n",
+                     "\u001b[2;36m           \u001b[0m         The body \u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m\"collection_of_meshes_68\"\u001b[0m\u001b[1m)\u001b[0m might display irregular frequencies \n",
+                     "\u001b[2;36m           \u001b[0m         for \u001b[33mwavenumber\u001b[0m=\u001b[1;36m1\u001b[0m.                                                                              \n",
+                     "\u001b[2;36m           \u001b[0m         Setting a lid for the floating body is recommended.                                            \n"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "data": {
+                  "text/html": [
+                     "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[15:26:59] </span><span style=\"color: #808000; text-decoration-color: #808000\">WARNING </span> Irregular frequencies for <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">DiffractionProblem</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">body</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>,                            \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"collection_of_meshes_68\"</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.000</span>, <span style=\"color: #808000; text-decoration-color: #808000\">water_depth</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100.0</span>, <span style=\"color: #808000; text-decoration-color: #808000\">wave_direction</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000</span>,    \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         <span style=\"color: #808000; text-decoration-color: #808000\">rho</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1023.0</span><span style=\"font-weight: bold\">)</span>:                                                                                   \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         The body <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatingBody</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>, <span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"collection_of_meshes_68\"</span><span style=\"font-weight: bold\">)</span> might display irregular frequencies \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         for <span style=\"color: #808000; text-decoration-color: #808000\">wavenumber</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>.                                                                              \n",
+                     "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         Setting a lid for the floating body is recommended.                                            \n",
+                     "</pre>\n"
+                  ],
+                  "text/plain": [
+                     "\u001b[2;36m[15:26:59]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m Irregular frequencies for \u001b[1;35mDiffractionProblem\u001b[0m\u001b[1m(\u001b[0m\u001b[33mbody\u001b[0m=\u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m,                            \n",
+                     "\u001b[2;36m           \u001b[0m         \u001b[33mname\u001b[0m=\u001b[32m\"collection_of_meshes_68\"\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mwavenumber\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1;36m.000\u001b[0m, \u001b[33mwater_depth\u001b[0m=\u001b[1;36m100\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33mwave_direction\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.000\u001b[0m,    \n",
+                     "\u001b[2;36m           \u001b[0m         \u001b[33mrho\u001b[0m=\u001b[1;36m1023\u001b[0m\u001b[1;36m.0\u001b[0m\u001b[1m)\u001b[0m:                                                                                   \n",
+                     "\u001b[2;36m           \u001b[0m         The body \u001b[1;35mFloatingBody\u001b[0m\u001b[1m(\u001b[0m\u001b[33m...\u001b[0m, \u001b[33mname\u001b[0m=\u001b[32m\"collection_of_meshes_68\"\u001b[0m\u001b[1m)\u001b[0m might display irregular frequencies \n",
+                     "\u001b[2;36m           \u001b[0m         for \u001b[33mwavenumber\u001b[0m=\u001b[1;36m1\u001b[0m.                                                                              \n",
+                     "\u001b[2;36m           \u001b[0m         Setting a lid for the floating body is recommended.                                            \n"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "name": "stdout",
+               "output_type": "stream",
+               "text": [
+                  "Panel Count:  5330\n",
+                  "{'Heave': 1265181.8940941708}\n",
+                  "{'Heave': 219.73629788683473}\n",
+                  "Solve Time (Radiation):  31.280981249990873\n",
+                  "Solve Time (Diffraction):  0.07843845800380222\n",
+                  "Excitation Phase:  -2.963439272634363\n"
+               ]
+            }
+         ],
+         "source": [
+            "#tall  - spar not heaving\n",
+            "h = 100\n",
+            "d = [29, 7, 4]\n",
+            "a = [3, 5, 10]\n",
+            "heaving = [0, 1, 1]\n",
+            "t_densities = [30, 50, 100]\n",
+            "face_units = 110\n",
+            "m0 = 1\n",
+            "rho = 1023\n",
+            "config = \"config6\"\n",
+            "reps = 1\n",
+            "\n",
+            "result_r, solve_time_r, result_d, solve_time_d, panel_count_alt = construct_and_solve(a, d, heaving, t_densities, face_units, h, m0, rho, reps)"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 11,
+         "metadata": {
+            "id": "Y__0sy0z_D-7"
+         },
+         "outputs": [],
+         "source": [
+            "# Get potentials\n",
+            "# Define the ranges for R and Z\n",
+            "R_range = np.linspace(0.0, 2*a[-1], num=50)\n",
+            "theta_range = np.linspace(-np.pi, np.pi, num=4)\n",
+            "Z_range = np.linspace(0, -h, num=50) \n",
+            "\n",
+            "# Create mesh grids for R, theta, and Z\n",
+            "R, theta, Z = np.meshgrid(R_range, theta_range, Z_range, indexing='ij')\n",
+            "\n",
+            "# Convert cylindrical coordinates to Cartesian coordinates for capytaine\n",
+            "X = R * np.cos(theta)\n",
+            "Y = R * np.sin(theta)\n",
+            "Z = Z\n",
+            "# Create an array of shape (N, 3)\n",
+            "points = np.zeros((R.size, 3))\n",
+            "\n",
+            "# Assign the values of R, Z, and y to the array\n",
+            "points[:, 0] = X.ravel()\n",
+            "points[:, 1] = Y.ravel()\n",
+            "points[:, 2] = Z.ravel()\n",
+            "#need cartesian here\n",
+            "phi_inc = solver.compute_potential(points, result_r) #rad problem\n",
+            "\n",
+            "regions = []\n",
+            "regions.append((R <= a[0]) & (Z > -d[0]))\n",
+            "for i in range(1, len(a)):\n",
+            "    regions.append((R > a[i-1]) & (R <= a[i]) & (Z > -d[i]))\n",
+            "regions.append(R > a[-1])\n",
+            "\n",
+            "# Apply masks to create a blank plot in specified regions\n",
+            "phi_inc = phi_inc.reshape((50,4,50))\n",
+            "\n",
+            "for i in range(len(a)):\n",
+            "  phi_inc[regions[i]] = np.nan\n",
+            "\n"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 12,
+         "metadata": {
+            "id": "rNvMTwcSHgNT"
+         },
+         "outputs": [],
+         "source": [
+            "# Get velocities\n",
+            "vel_inc = solver.compute_velocity(points, result_r)\n",
+            "velx_inc = vel_inc[:,0].reshape((50,4,50))\n",
+            "vely_inc = vel_inc[:,1].reshape((50,4,50))\n",
+            "velz_inc = vel_inc[:,2].reshape((50,4,50))\n",
+            "for i in range(len(a)):\n",
+            "  velx_inc[regions[i]] = np.nan\n",
+            "  vely_inc[regions[i]] = np.nan\n",
+            "  velz_inc[regions[i]] = np.nan"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 13,
+         "metadata": {
+            "colab": {
+               "base_uri": "https://localhost:8080/",
+               "height": 1000
+            },
+            "id": "-7Zut1alw6zS",
+            "outputId": "5cf0dff0-4741-408a-864a-e37a20e9caef"
+         },
+         "outputs": [
+            {
+               "name": "stderr",
+               "output_type": "stream",
+               "text": [
+                  "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/matplotlib/contour.py:1364: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+                  "  self.zmax = z.max().astype(float)\n",
+                  "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/matplotlib/contour.py:1365: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+                  "  self.zmin = z.min().astype(float)\n",
+                  "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/numpy/ma/core.py:2846: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+                  "  _data = np.array(data, dtype=dtype, copy=copy,\n"
+               ]
+            },
+            {
+               "data": {
+                  "image/png": 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",
+                  "text/plain": [
+                     "<Figure size 640x480 with 2 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "data": {
+                  "image/png": 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",
+                  "text/plain": [
+                     "<Figure size 640x480 with 2 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "data": {
+                  "image/png": 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",
+                  "text/plain": [
+                     "<Figure size 640x480 with 2 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "data": {
+                  "image/png": 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wRbU2a9YsWCwWXHPNNSgtLY1Zv3v37siVS+FfqlWvPHvwwQcBAKNHj67z64dbgaqe6EaMGAG9Xo9HHnkk6i/95cuXo7y8POq1evbsiS+++AI+ny+ybN26dThw4ECd65MsF154ITZv3hwVWp1OJ5566il07949Mr4l0fHHoygKRo4ciTfeeCPqEv3S0lKsWbMGQ4YMiRmv1FxUVcXAgQPx3//+N2GZCRMm4PDhw/jrX/8Kv9+fsEtw6tSp0DQNy5cvx1NPPQWdTocpU6bEbQHaunVrra58re13Jjx2LEyv16Nv374QQjToitWw/Px8FBUVRc3Qf+TIETz33HMN2u+RI0ewceNGZGVlJeWqvksvvRQLFizAv/71rzpfXUvUVnDMFdVaz549sWbNGlx++eXo06dP1AztGzduxNq1ayPzAZ1xxhmYNGkSnnrqKZSVleH888/H5s2bsXr1aowZMwbDhg2r8+v3798fiqLgnnvuQXl5OQwGAy644AJkZmZi7ty5WLRoEQoKCnDxxRejuLgY//rXv/D73/8+ahLEa665Bi+//DIKCgpw2WWXYffu3Xj22WfRs2fPZL1NdTZnzhw8//zzGDVqFG666Sa0a9cOq1evxp49e/DKK69EBun37NkTaWlpeOKJJ5CSkgKLxYLc3NyE48LuvPNOFBYWYsiQIbj++uuh0+nw5JNPwuv14t57723KQ6zRJZdcgr///e+w2+1xQ9/YsWNx/fXX44033kB2dnbcKShWrlyJt99+G6tWrUKXLl0AhOaduuqqq/D444/j+uuvj5TdsmULjhw5gksuuaTGutX2OzNy5EhkZWXhnHPOQceOHfH999/j0UcfxejRo5My7mjWrFl49tln8Yc//AE33ngjLBYL/t//+3/o2rUrjhw5UuuWzZdffhlWqxVCCBw8eBDLly/H0aNH8cQTT8TsY+vWrXj22Wdj9tGzZ8+447+A0Ji6hQsX1vn4iNqU5rpMkVqv//3vf2Lq1Kmie/fuQq/Xi5SUFHHOOeeIZcuWCY/HEynn9/vFokWLRI8ePYSqqiI7O1vMnTs3qowQoUvRR48eHfM6VS91F0KIf//73yInJ0coihIzLcOjjz4qTjnlFKGqqujYsaO47rrrxNGjR2P2+8ADD4jf/e53wmAwiHPOOUf897//TTgVQ10uHQcgpk+fXmO5eJf27969W1x66aUiLS1NGI1GMWjQILFu3bqYbd944w3Rt29fodPpajUtw9atW0V+fr6wWq3CbDaLYcOGiY0bN0aVSdZUDF9++WVU2fCl/7/88kvU8kmTJgmLxRK1rLS0VOh0OvHMM88kfO1x48YJAGLWrFkx6w4cOCBSU1PFH//4x5h1f/rTn4TFYhE//vhjZNns2bNF165do6aOqE5tvjNPPvmkOO+880T79u2FwWAQPXv2FLfddlvUdBGJpmKo7ff/q6++Eueee64wGAyiS5cuYunSpeKRRx4RAERJSUm1xxBvKgaLxSLy8vLESy+9FFW2pqkYKn9/K0/FkAinYqATjSREE4yYJCKqwZQpU/C///0Pn376aaO+jtfrRffu3TFnzpyoOwq0VjfffDOefPJJOByOZr+1DhGFcMwVEbUICxYswJdffpnUWdPjWblyJVRVjZrJvbVwu91Rz3/77Tc888wzGDJkCIMVUQvClisiolaif//+GDp0KPr06YPS0lIsX74cBw8exAcffFDtrZCIqGlxQDsRUStx4YUX4uWXX8ZTTz0FSZJw1llnYfny5QxWRC3MCdst+Nhjj6F79+4wGo3Izc3F5s2bm7tKRETVuvvuu/G///0PLpcLTqcTn376aZ1mUCdqDHU5nw4dOhSSJMU8Kk+ZM3ny5Jj1BQUFTXEoSXNChqsXX3wRt9xyCxYsWICtW7fijDPOQH5+Pg4fPtzcVSMiImo16no+ffXVV3Ho0KHIY/v27VAUBePGjYsqV1BQEFXu+eefb4rDSZoTcsxVbm4ufv/73+PRRx8FAGiahuzsbNx4442YM2dOM9eOiIiodWjo+fThhx/G/PnzcejQochEyZMnT0ZZWRlef/31xqx6ozrhxlz5fD5s2bIFc+fOjSyTZRkjRoyIe1sXr9cbuU8ZEPriHDlyBO3bt0/aDV+JiKhtEkKgoqICnTt3jkwI3Bg8Hk/UXQTqSwgRc24zGAwwGAwxZet6Po1n+fLlGD9+fMx9WD/66CNkZmYiPT0dF1xwAe68887IfUhbgxMuXP36668IBoPo2LFj1PKOHTtG7uVW2dKlS7Fo0aKmqh4REbVBBw4ciNw5INk8Hg96dLOi5HD8G9DXhdVqjbnH5IIFC+LOul/X82lVmzdvxvbt27F8+fKo5QUFBfjzn/+MHj16YPfu3bj99tsxatQoFBUVtZopR064cFVXc+fOxS233BJ5Xl5ejq5du+LAgQMt5t5sRC3RR7vPhNupwWqTkaxGXp3PDZ/3+EgGIRC176p/cVce9SDLEoxGCTq16VucAz4Bl1uLu65yncP11Uxm6Brpt7PXJ+BzR7+HigJoFnPjvGAd6dxu+P2h+qmqBJOldfcQuBwaxg3em5RbICXi8/lQcjiIXf/Nhi2l/q1j9goNvQYeiDm/xWu1Sobly5ejX79+GDRoUNTy8ePHR/7fr18/nH766ejZsyc++ugjDB8+vFHqkmwnXLjq0KEDFEWJufFwaWkpsrKyYsonag612WwMV0TVkFItMNkEtAoXKo/sTEmVIcv1PWFaUd9f88GggOp1IxBIHM6qStZ6vVGCnGZt0qEEHrcGr0dAkhB5/yUJMJlk6NJbRpCKK8WK8O2ejX4X3C4ReY9lGbDaWkfLRVVN8dnbUuQGhavIfmp5fqvr+bQyp9OJF154AYsXL67xdXJyctChQwfs2rWL4aql0uv1GDBgAD744AOMGTMGQGgc1QcffIAbbriheStH1MZIkgTYLAifVoQQcB4LW7GtTtEhoDbC5XWqBL1Bgk6XeENFkaCZzc1yiXQQQLJOrT6vBs+xlqfK72HV985glCClhsaxVH5tf5Lq0RQ8qhlS6vHnRs0Ne1l015fZKlf7uVPjacj5dO3atfB6vbjqqqtqfJ2ffvoJv/32Gzp16pSMajeJEy5cAcAtt9yCSZMmYeDAgRg0aBAefvhhOJ1O/OUvf2nuqhG1aZIkQaTEnvCB0B2B4/2/JkIIeAIAPG64AlqVdY3bMtUQtdlvvPCk6iWIFHNo/p/K+6uyrRdtj0s2AVUaVLxOJ1zB6Pcz/L5ZUhrSSkq1UdP5dOLEifjd736HpUuXRm23fPlyjBkzJmaQusPhwKJFizB27FhkZWVh9+7dmDVrFnr16oX8/PwmO66GOiHD1eWXX45ffvkF8+fPR0lJCfr374933303ZlAeEbV8kiRBpwIBNbarq6bTakPXN0RtA2TVcn40br1am+Cxq8yqvk9apVbSyq2iVYXXG0wS9HqJV4HXUU3n0/3798dcJVlcXIzPPvsM69evj9mfoij45ptvsHr1apSVlaFz584YOXIklixZ0mhjvxrDCTnPVUPY7XakpqaivLycY66IqvHWj6fXe1shBBzlDb/yqebXqbnlCgB0OgmqITQYnifftkcIAUOViyWOr2tY66XLoWH8uXsb9ZwRPi8dLu7W4AHtmb338fyWBCdkyxURNT6vJwh/LfqmhBAx3V+SBPgtKS2iS0cIAV9AwOJ3wuNqmm5HSZKgN0pQ2ZLSJCRJgs9gRryrJRr87kuN/0cCtTwMV0SUdDd/fCFSDmnQGxTojDLUY9MfSJIU1T2jGiXodDJkJc4pzF7/k1LVoFZVuA6VJ0yUFcBoUWICXajbUYJXTQGM9a5SnQghoAaccNq1qGWx5RKHt/A6k0VpluknqOlVCC8g6t9yVSHiTxdCdcdwRURJNfiVa5HySxAZ+g4w6FRYvT4EnQIGq4LwEGzVpEDRSQj6NJg8bgjteHCQZMBg1kGSpWpbfcLjaOKNpxEC0JtkqAa5xpaf8Ka+gAbhcEYW1PbKRaMldCzJJEkS3KoVUBu2HyEEfB4H3M5K72+cebUq06kSjOaa3zciSozhioiSpsuyOdBXOOAypMIdMCJNFTgMAdWowmj3QDHoIMsSrPAg6NcAyHCZ20E2Hm/Z0oICaoUDkizBYKndryiDRQdFPf4XuxAC+oATzrJAZFl1LVgGswzVoECzxk70WN2gVCEEfG4HtGDlZcdfr3J2MVmTH8JqIkkSfKa6TV5pDDpiWsziBS2TVYYSr8WRiBiuiCg5sm+fB6/nKHwmE/wBBW6hg0MBLAYBBCWYVR0k6GA1+FDhVCArMlSLCr3bBa1CQG/RQdYdC0h6E0yaEx43oNOHJo0Mt7KEuhZD/zfZ9IAE+D0OeJ2BqPq4I1NRhrYxpugStsZoXgecZf6Y1qrwc0WVYLLG/rqUJAkBc/zwEjW1hBDwuqJDWHh5uE6KToLJ2vwTZDoVK2CtvowQApLLAaHF/1xkRYLZytYvOnExXBFRg530l9uh+Y5CZ5TgD8jwBAzQNAUevwS7XoPRLMGjaZACEtxBFdA0WC0CjqMadMZUyAYZeqcDsk6Gzhj6teSEHsaAB0qVy7iFABRVhmrWQXLYAQF4qoxElmQJBuvxMCU0AY/dEdUNFg4DkiRB2CyQDImDgDHohOOo/9h20XWRFcBUTXALv1bQUn0IUwNOOMoCccsAzdPylYgkSfAnCJUA4A8KaOUVcdcpOgnmFhAiiRoTwxUR1ZsQAmcUzIaMChhMEvxBQApICGga/EEVwYACj1eDz+uHQ6+H3izBGwQkRYbH7oViUmHweAEPoDOlQ/V7IAUAxaCDJAGapAE+QFaOByxJkZCqD8JT7oMHxqjuN6NND0mWoAU0iIqKKn16BugMClRj9IldaALuBEHAlKpCkiQ4FUvM5JVhvoAGUe6Iu86Uoos/WD8Oty7xa4S7H4PHslfUbPYyYE5RWlQrkaxICKbEPxhjwAH70UBM6yAAWFNjLyggao0YroioXvJTJsPe2QjJEoDOKAOaDnJAgt8vIAkJAT+AgAzoZHgkCbJZg98j4FM16FMEvEYLjJ4gXJoJZrMG11E/dNYU6ANuSJoMRZXhRAoMmgc6w/FfVZImQVR4gMrdfooE1aLC53RGBsf7jl3ap5rVyHgsnb8C7nIfgOPdWbIswZCSEnvT5wShS29WIl2VAKDoZATiBAkhBCRnBapegCXJNbd0VVW1+7FyZgwGBZxVwp0QItQ118JCFwA4dda4IVIIAVeFA5oWO8rNbOUVj9S6MFxRm7R3VycIDdA04IhXg0cNfdV/cRrg15ug6CS49Va4HQEoqgyvaoHfG0TALyIDgN3lfgTMx2+266nww2tIgVTpL2tvhQ9+s7XScy/8xuM3QxNCwGf3RnUJ+ewe+I3WyH6CXj80XxABY+iMo/mDCLq8CFqOPfcFoLl9UKxG+IJ6BF0eIKhBmFIgAqF1kk5BEAZoLi8kRQZ0RmhOd2iSHr8CaOLY/49NOxCQIDQNUkCG5vVCCcqAJEH2CUg+DXJQBgQQrHBA8viguACdF9C5AtC5A5CdfkhWLwJ+L+A1QEgAJD+CigzoZcgeBSajFUFNCoUdk4Kg8EETAn5ZhuYNQNXJCBqsMJg1lLu80LXLgPTrbwh2yoJ05FforRbIOgXBoAqPwwtj+9BM3AbNg4oKFcb2x2dkN0suuA67gExLTJiQ3U547V6Y25vgVlNirsDTghqEvQJCCJjTjncvSrIEzRqbAoJeBzwVXqhGpdoB95IkwW+J3V4LCmhlodczmBXojQ3rIpMVCcIW20XnD2hwlDlCSUwCLLbat6I1B0mSEIhzQQEA+FwVcDmO38BZb5BhMDXHXSKJaofhitosv1/gqFeDqgJ+H1DmN8AtG6DKiAQrQIJXtcDnCcLn1hA8NitxwBdqboiM2RECWkCDZKp0GbsmYqYA0Bl1kLx2+Ay2yPY6ow6S24GAKRTC9DYjtN/KEbSlQpIkKAYVkixDOMqgSzHAq5ogpZgAux2QAGFOgU5vRsDhhhL0QJ9ihNevh+asgARAMasIagpktxOyCgRlE4TXBUkHQK+DQGgUtfAHIOn0EAE/grIOkAEBLxRVRdDrBXwSAqoGGYAS1ELjiVL1EKoEn+SGV+eDKgsoOkDRy1AMemhGBX6zhIBeQtAooJllSFYzlBQThCpBKBo0owahCwJ6CXpTEIpBQNarUFLMMCh+aIEgrO0NCNjt0GWlQnGWQdcuFKwAIOjxw5BmirzHXtkInRp9+2GXMEOkmeCz22FIjR5/5TNZYDJ44Cn3wpgaO0ukrMjwW1Jh1hxwlXmjAlbc75XBChgAyeeA86gPlnR9teVjX0+KfM+E1wHHUX/kasVkUnQykHo83LkrKqBpIjTmKaV1/er3VR3f5Q91LQIAROjKRVXPsFWhaUADpqqq0DjPVbK0rp8wolryyAJ2vwanokISQIVmhEPoYTUrcOqscNoDUBQJfpMVPncQfu/xYKUFBdx2P6S04y1Q7jIf/NbUqNmavQ4//CZL1DJFVeB3+qNmelYMOghNQPU64Tcca31pZ4avzA5Zr4PfYIGsKtCnm+Gv8ECVAvAbUqCmmUNdV/bQ2CFhskJSZAQcbuiCHihpoZYszeuDHHBDVgHZbICvwgOoApKqQyAAQHfsai5FgQgKSAYdNJ8fstBDSBI0OQAoegjFBxkqNH8AwqcBkKD5AEnWQTJYILkN8Bk8kDx+KB4ZqlFG0CDg0wOaSQIsJshWE2CQEVQFhEGCJmsQOgHZGISi16AzK1CMeigpZijeCghJhtksIeDwQJdmhuIog5pqCrW+ATDCi4AkRZ4DgCHohlBjg4gkS4g3bxMAuGUjVDmAgDcIXYIQ45KtMJtrF7AAwKe3QlMEHL/ZoTfroDfVPRx5jgW1oKcCXlfjhKwwLSUUUPRBJyqODc43mBXoDa0vlFSdA8zvqoC70tWiFpvCaSKoWTFcURslUBZQodMBFZoBZT49kGZDhQQ4fvPDaFVCLVbuIAI+LTJmRtMEnEd8kNKPB6uALwhZF+eyciGiugijV0XPDaQzqfA7fdD7XPDpzZAkCYZ0M4LeABSHHTqzHl7ZCDXFCM0XCLXepBjh1YxQU0PdX1JFaDyR3mKATzIg6PBAEV4oAGSrDn7JhKDTDZ0iAJ0ETVGgBLyVhyYhEBpuBEnTICQNskGB5vVCUgUkVUXQ44WkU6EpGqSgBChBQJWBICCpMmSjAcKrg9/lRdCtIWiUIMwKYDVB6GVoJgAKQoFOr0HIGiSdBsUgoBpk6GwW6CUvhNsOxWqEXniheYOwpssIlJdBTTdHvW9+hzeq1QoAAp4ADKl1nyrdb7YiWG5PGK6AUMCyWJxwHfXCnF5zwJIVCUhNDbVA/eaFpZ2+XmOcvMbQ7O+hkOWDwaw0WshyKxbg2NdbcztQ4QoFLWta3caBtSSVW7aEEHA7jo/dkmUJFhunhaCmxXBFbdJvPhUeRQ9oQHnACLm9DV53EF63BindBi8AryuIYECD31opWP0WHawAwOsIIGhLq/VrG1INkOx2+M3R+1Et+lBYCLrhVUKBQTHooBh08Du8UAJ2BCwpkPU66PU6+Cs80GleCAABow26lNA2AacHSsALVa/Arw+1hGkePxSvHYoCQJUQUCyAyw250k+4qDwztw4Qfhc0yQRFp0Lz+CAkLySbCfD4AFUAfg1CSEAgCOEHhBGQPICkAkJVIRt80Aw6iBQZMAKSTgLUICRFhlCDkGQZer0fsiqgWEwwmjQg6IZsMQCyDJ27AkKnwJIiEKjwQt/OEvV+6f0uQF+3gFF5eoV4jKl6uI96YEpPHM6ckgUWa+0DFhDqKhR6AecRe41jsarTlCELAPwmK2AKBRJneWiqClUvw2hpvVMlVB27FdAERHnF8Ztwq5wKghofwxW1SRWaEYCAAwYg3YaKI/7QrVDSQkHK4wxAC4rIgOOEwcrph96sg7sOr13dX8iq1QCf3QOD0QOvbIxaLoQAyisg62T4jVaoKaH1QghIFfbjt2Ux20IzmfsCoYAiQo1F0AGBY92OcDqOd5scu27fp+mhU47NYqkAmtAgfHZoehN0NgVCk6C5HKH756kKAk4BCYDm9UMy6SH5NGj6IIRJhvAFEHACklGDZJYh6wSg+iDpVehULyBLkKQgdKoEg1mGrAvCZJIgfEEInys0Fi3VDL3mRNAdgD7t+OB0AFDdDghZgmqp23gmn8kGxBl3FeaCEXp9EH53AKop8a+/cMBy/OaBpZ2hVq0ekhRqxZL8Tjh+88Kcrq/3tAKRkOUOhSy9UalXt2Ntheb6CgUSJXC829BkVaBTW1+3YWWyHD0thBqsNF4LgMHIwfGUfAxX1CZVaEZosoBHZ4H7iB/WVB2cUugEXnHEB1UvR4KVEPGDFQAEKo3FqgtZJ8OoOeCRY6e61tuM8Ja5YTAjKmBJkgRDmgmaPwjhsENSFfgNoavfVJspUlfY7ce3MSnw646/huRwQWgCOl14n8dW6BRI3gCEUvmuyRKERQfN4wSCx5abgYDODOH1Q2c6NhDeCGgeByAFAUVGwC9B+APQjH7IehU6xQNJr0LW6wBdEDrhgWxQAU3AZJYgyXKotcsvRVrfVL8DmqscQpEjxxamOOyQTSoUQ+yvJ6Edv2IsnsqzhCfiM1ogl9uhM1Y/TYFTskDYzHAdKYchRRc1/UK1+1dD3W7ucjsUnQRjSv1vEOg1pYRalrwOOI76oNPLMNazVay2PLrj3YY+ZwXcFUHIigRLats4XTiVKlNB+KLn3TKaOTieGq5t/LQQVeHWW+AoC0AOaJDSbHAi1A3odQVhTVdRgVDQCvo1uMr9QFpsgHId9SJQZRB7ZSLOfDxhqkWF+4gbRkv8gGVIM8Hv9EHx26HodfDpj7fcyKoCfThkuY7Ps6ToFXh1Zqipx8NIqPWq/FiFAJ0MQAYgS/Abjs/dpHn9MCjRV9iFb3oM9fivARHUEPRUAHpxbKzWsaO3AIACzeeHduxKSiFCrVOyQQdZlQAEAWhQTOEB6QJCCJh0/mPrALi8oWM0qdCZj7dKCSFCrVVBDfpUU9yxbEITkI4chdox8b1ZhCYQ9AYTrg8z2vRw/eaBpYOp2nKSJCGYmgavvQyaUUBvrv2vTJFigxJ0orzEjdSs6l+nJuGB73q/A/ZffTDZdE0SAMJTiAQ0gYqjoTm7bO0beDfpFsatt0aNS/S7K+B1B493kZoZtKjuGK6ozflX8TBI9iCELQVBAM6joVHcepMCkZ6KCoS6AV1lPig6OeqqwDCv0w+dQUGgmm4dvUWFt9yOYEpK3DBgameC3+WHzl0GWSdHpmcIC3d5BX1BKM7jISpgCs2BJasK9JWuigt6A9CFw1Z4AkxVgc8WPVYJOBb8Ku0TQsT+tB+b/wgAIEnwyWZIqgK1Shdd1f2KQBAG4YYIaoifPP3H969I0FmMUS1EQhNQPQ7AXymcShLUlNjuN9XrhBY4Pi2G2tEaU0ZoAmo4YAIwZCauv+pyhLqDFanGYAUAVskFj90HWZXrFKzCAh6t3uOv4nGrVkCUNXnLiixLQKoNIijgtDtgsbXdU0flG13rfQ6U/xZASjpnjqe6abs/IXRCemznUAT9An7ZhOCx+7QFUkJjlFxCwF3mi5QVKTYEq5yovQ4/gn4NqlEJTThZDUWvwNxOgftoBXRGHbz62JO6alahmlVoAQ2aowwAoLfq4RLmqP0olW5OLFU4jrcqITTo3auYIoPfKwv6gscDV9XXVgCf3hyZL6o6IqhB9nmheRLf2w4AIEuQdQokVY26JU1VWiAIg98V2rc7usVMkiTo4gQpvd8Vaq2rlLlkkxp33JUhUAHNp0UGr+tTE4+LMgXd8LlCx6WzHJ+tvTqmgAN+dwBeWar1oPboY3HC5w5AtarQJTEIKXY7TGnN13IkK1LMjPNtWbhVy1Fmh6yTYLW17IHw5ZqKoFb/75uD81wlDcMVtRlLv7sQqjMAWZHg1lmgmOTQLTXK/JGgEjQfb2WqfCp2l/tCM2ZbVHiNNtQQMaKY0o3wufxQKioQTIkfyGSdDGNaaHyVz+GDLlAWaomxqFFBS5Ik6G3RV7IFPP5Iy1bl/KAzqfDojZFgVpUQArLbA+HWYiY7PVYAMQOYKv1GiDPzRIjmB7xxXzKynaTIUKzxA4/qdUK4fbHbGXQxxx4+Dr3HHumGlSQJklFJOGgdAIx+FwLeYKibUK/AVIt5q4QQUJ3loTnFDEq9QpUJLrjtfgiDAmu7um9fHb27ArJBrlU4bEw1jWlri7RUG0zBUCuW0SLDYGRXIVWP4YrahAWb/gChAS5hPNYrdTweadbjY48qn+rFsa5BSQJ8ZhtkRa5TqKpMb1ah6TV4yu0wpOjhlhNf6q+3Hm+J8Tl8UIPlUesVoxI1TktnVKEzxrZW+J0+6IJVW62ijhAqpNClhBLgU021asWqKyEEDH43tED0WCfh8sYdfK6YVMhq/KsAjUFHZMxU5ZO4mqKvtqUsHKYir61X4s7GHk+4lQoATKn6hHOX1UR22OEFkh6qAMAcdMIfFDBY+Su7uTgVK5AWuoKTXYVUE/6kUqv392/+DJgSd+FJCJ2ovY4AtOCxZu9j440CKaFb0CTj71BZJ8PczghPuRd6JQifKXYsVFWVg1aY3+2H6ik/XvlwfY8Jh6+6TFMgNAHJ7YHwVdfqUP8WCcmoQm+uvrtKCAGDrwLC740Mywp9OJUKGapvkQIAk+aG3xUdg+sSpgDA4LVDC4jQGLJ6tlKFqV4H/N4gDDY1dMuZJBNCwG0PwNahbtNSNBaTRYHbEYTpBJ0rynfsCk5HmR2yIsGaemK+D1Q9hitqtW7cOgF6V3lMl1eibguvwQalynw2jfF3pzHVgIAnAJ2jSquSJMFvjr2xcFWqSQWqGWsdDl8JJ8usHMgq/V8voeaf+Nq8IfHe3oAXwn+82y78GUTVTwJ0ZhVyLQKIFtCg9zgjn60kIdJPGdRJdQpSAKB3lUdm7AaOjb2yNCwIqV4HAj4NkjH5XYCVyWV2pLSgK/TcOgukBOP8TiRaqg1mzYmyXwMwmnlVIUVjuKJW6catEwAAPnPslX6JNOXflzqjDjpj9I+X0ATgcFZaEGfM07HlPqMlYQipKXy1NFpAg97rCh2vp9IFBVUOv3KQkmQJelvdbyUTCmX2qGWSJEFvVUO3qmkgo+aEzxXqflTMCgx1nOS0rhS7HUZby7stzYk47ioeh2wB0gF4Q+OxzFYZaiu8VyMlH8MV1cszP5wd9dzrCkJSJHjV6K6wiiM+GK06uJTjy4MBDV5HADq9DJ8+es4iLaDB4zh2U1mLCqdUc9daayHJEoy2mk/GoYHobmgeLTK7+vF1xwNJNdns+GvW95xc+UVqIVFdAEBWZKhWtd5jmeKJF6LCr2VIrd/9/RLReSqgBULvQ1CVYU5r3EAlhICuogKAgMGsJPVqw2Qw+J3w8KKyKOF5yBxHypGe0bI+L2oeDFeUFAazAmd5AHrNAZ/heGBKaaeH2xGA7CuHJTU0eaeiC52g/N4gZEfoBKkzKPCpodYa87Eru7wOP3SBMgCV7hknS/CZbC3uL/lkkiSpxvFLJwIhBPRue8xkrZIkQZKlpIeo8GtWnnMMAFSLDoq18U+YBk8FAscmaDWnqi1zsHSZHQFVanMTiSaL0SzD79XYekUMV1Q/4mg5hM0W1dViSdXB7QhAdTtCN4Q9xnTsCidnmR+KKI/MO6Uajt+U1u85HrRUkwKvYoHBGvsLXAsKiIrQYO96nViTdb6qMp4pZnWc8VA6gwKXUvOYqxOBWXPA7z52dV8176MhSd15iRgCTvg9x68ylCQJxlS1yT4jc9AJrzPczSjD2MjdjPWl9zrgcWpISdc16ufR2nmNKQhU2JstXDk0A4RW/wEQTq3muxtQ7TBcUb2kd1BR9msFLDY5dInyMSarDh5nEKLMDotNB5d8fA4nS5oauo9fmR2ABINFiXQXqkYFqjH0S8HnCkB2R3f5yDoJAWMKZEWKtGy1JkIIBDxB6F32qGUxs42L6EHh4fXxBoiHW/IUnQyXnHiMVjJpQQ1BnwazcNZ4cWHCAfcANFWG0dZ0IcaoOeF3x+nLMjR+N19lQgjonRUIz9UYVCVY01tuK1D4ZuKaXkJqh5ZbT6KWhuGK6i2tgw72owGYTI7QbTmOMVoUGC2hbkIE7dDpZQTMofWSJMGaHjqZeZwB6CqFDaNFgUO2QG/Woepk50G/Bs0RO8amKkWV4dO3vNYhSZKgmnRQkzwQXWgCQb8Go88B4W38QcaSFJpRXqdP7hiqZDAEncdbw6oIqjJMqc0TDkw+B3yVBikZbS2/9UcIAam8AhIAS5quZXZRErVgDFfUILZ0HRz2IAzBCniN0XNNWVJDXy+fV0OgLBSMNFtK5Be1sco919yOAHQBe2RwtKxI8JpC95JTajmQOFDlPn3JJskS/PqmaSWqDUmWoDMo0Bna7lw7QhPQuR01XqEm9E3bChW3DkJAdVREXwdglFt061RllUOVNb3lXaVILdNjjz2G++67DyUlJTjjjDOwbNkyDBo0KGH5tWvX4o477sDevXtx0kkn4Z577sGFF14YWS+EwIIFC/Dvf/8bZWVlOOecc/D444/jpJNOaorDSQqGK2owq02B2xmE6qqA3xw7mafeIENvCN2KxlkeOknKsoSgNfomvKYqs08HAxq0qnNFHSMEICuA15QS9Ve1Tq9Al+B2MMmgBQUktxOap+ZWoqpdeonoDAq8irnFtQQlS2gGd2dksHZdSZIEQ0rLaz0Jd/FV/XjNqa0vlERClcRQRXXz4osv4pZbbsETTzyB3NxcPPzww8jPz0dxcTEyMzNjym/cuBFXXHEFli5diosuughr1qzBmDFjsHXrVpx22mkAgHvvvRePPPIIVq9ejR49euCOO+5Afn4+vvvuOxiNie9+0ZJIghOW1IndbkdqairKy8ths9mauzrN5q0fT49Z5nVr8Hk1BFNqfl+0oIDTHj3Ltk493n1YG1pQwOMIJJwtIDyLgRCATpXgrWYW9+YihEDApyHg0drs3EGhLtHQPfFa40nbEnTC4wzGTDUhSYCxBYa+ujAFnXBXBCHJEiypSqv8fFoaudwOW7vjfyi6KoIY3/+7Rj1nhM9Lb3+TA0tKAwa0VwQx+vQf61TX3Nxc/P73v8ejjz4KANA0DdnZ2bjxxhsxZ86cmPKXX345nE4n1q1bF1l29tlno3///njiiScghEDnzp0xc+ZM3HrrrQCA8vJydOzYEatWrcL48ePrfXxNiS1XlDQGkwxJBnzOCgQs1QcZWZGQUqWrxO8LdR/KigQtwQ2Qq+7DXMtxNAGfhoDdHnWClKTQQPmq3ZlNqepVk9R8jF4H/N7jrWvhcO5v4YPO60p1O+A/1oroVyWktGs7x9bcdM4KKIbWH1Dt9ujxrQaDAQZD7IVEPp8PW7Zswdy5cyPLZFnGiBEjUFRUFHffRUVFuOWWW6KW5efn4/XXXwcA7NmzByUlJRgxYkRkfWpqKnJzc1FUVMRwRScmvUFGMBAEahGwqlL1MtR2cqhVqzz0wy1JErQUa4P/otbpZVjjTMYY8GkIVsQOlA83IhktCpxK25nI9ERWtRuvakOhbG49Y6PqQggBueL4mDXZrCDF0vaOs7lJZXboraEhEM2lQjMi2ICpGFzHpmLIzs6OWr5gwQIsXLgwpvyvv/6KYDCIjh07Ri3v2LEjdu7cGfc1SkpK4pYvKSmJrA8vS1SmNWC4oqQzWRT4vRqCZXaYrDJcutp39QHRrVqaJuAsPz7uypwSPb1DQ+n0crUzYFe9orGyypOYqwYZbn3djpOSJxjQYHA7opZF3ZfwGGNKy79SLxkUZwW04LGxVAiNA2vN3ZctmaYJ4Kgdqe10UHRt4z0+cOBAVLdgvFYrqh7DFTUK1SAj1SDDVRGE5LBDS02pV+uTLEd3H7oqAkDgeKtW0Gpt1JNG1SsaE/F7g9BVVJ7DKvRv1W7IyuuAULhzt8CpI5pTMKDB6HFA06q/fU+4206SAL0iwdgKB5InQ7hlqjKDVWkzJ/qWzOCpgNctkJbZtloCbTZbrcZcdejQAYqioLS0NGp5aWkpsrKy4m6TlZVVbfnwv6WlpejUqVNUmf79+9flMJoVwxU1KnOKAiEEKo5WQFGluFcT1m1/x7+yQgi47I6Y7p3GDlzx1HfclN+nxb3iDKj+fn3h9UD97h8oBCDLqHHGetFI95CrcsvEKHpdy7w6sCUI3Z7HEfW5SADMNoXvVxOTyuyAUUJahxP3NKrX6zFgwAB88MEHGDNmDIDQgPYPPvgAN9xwQ9xt8vLy8MEHH+Dmm2+OLCssLEReXh4AoEePHsjKysIHH3wQCVN2ux2bNm3Cdddd15iHk1Qn7reCmowkSbC104W6CsvtMFrq3lWYaL/hubTCEgUuADBaZLhb2PgpVS9DbaYb82paDVcnCkCS63mbIWqwoF+D4nJEZusHAFmSYGSrVLMyBxxwVWiwpitQToAu5prccsstmDRpEgYOHIhBgwbh4YcfhtPpxF/+8hcAwMSJE/G73/0OS5cuBQD87W9/w/nnn48HHngAo0ePxgsvvID//ve/eOqppwCEft/cfPPNuPPOO3HSSSdFpmLo3LlzJMC1BgxX1GRUgwz1WFeh7LJD1Sf/Sr14gSvM7QhACoTGb1W9NYskA0FLwwfOtyZs6Wh+QgjoXA7Eu6WbQQeY2uAA+9ZM56iAX8YJ3VpV1eWXX45ffvkF8+fPR0lJCfr374933303MiB9//79kOXjf0AOHjwYa9aswbx583D77bfjpJNOwuuvvx6Z4woAZs2aBafTiWnTpqGsrAxDhgzBu+++22rmuAI4z1WdcZ6rkHjzXNWV36vB7Qr1b2jN/F5qQQFXRTDuXFOKTkKwjlc+ElWmczkQDIi43y9zStsZCN1Wqa4KBP0CJmvdW5qbcp6rF7b1hbkB81w1RV1PFIzf1GzCLVnBY1Mv6FQJvgaOyaovWZFgTYv/4xDwa/DYK+KeGDVryglx9RlVLxjQoDgdCVs+VYsMk5VzmbUm5oADnmN//BmsCnQp/Dmn2mO4omanKKExWT6vBn+ZHUZzy5rWQKfKsKbF/rUqhICrwhEz6LvyYO2AyQJFbRn3IaT60YKhQeTxhLuX9QpgSjsxr1ZsSzRNQFdRAUhAUC/Blt66TpHlmgm+YP3r7NYCNReiWmld3xxq08L3IHQ7g5DK7NDpG351YWOSJAkWW+IfISEEPE4nNE+8dbHLGMSajsHnhNcdGuiUKBCFWyp1sgQTr8Zr0/SuCgQCAooEWNN5GyBqOIYranFMFgUmS2iagqDdDghAb5ThMbSc1qzakCQp5mbUiYSDWNAdvay6X/JV1ys6wGewtuluSk0TMPqc8PtCwaeu71GEUY65/RKdWHTOCghNhCYBtsgNGqtEVBXDFbVYlacp8Ho0yMfud2U0J2cqh5akLkEskYBfg+Y+3k1ZU/Covj7Rz5vjspd49ddJgGySYTG1zptAU/PRNAGdoyLy3TZxSgtqRAxX1CoYjDIMxlDQcjtDUzmExzYFrCnsskFobJiO3YpEESa/A163BkkCdJIESxq7/KhpMFxRqxPuNgRCrRtOu+P4hJgCsNgUVEgta7JQImp8lqADbod2/M4DRrnVDUqntoHfOmrVJEmCNTV6rISjPAg5aAckwGxV4JAZtIjaonDLVJhfF7rymKi58VtIbU7lsOWsCEI+dqNnna755tEiooYRIjRmqvL4P2FgyxS1TPxWUptmqXQFkN+nIXBsUDwEYLLIcLaxgfFEbYXqqkAwEH0lhSWVU2JUp0IzIaBxnquWgOGKThhVb5LscoQGxlcWTEnhgFeiJhS+iq8qYwqv5qPWi+GKTljmKrcjEULAUR7d7QABmFM4bouooSpPhVD5Z0yRACuv4qM2huGK6BhJkpAS5/6ClcdthX//C9E259siagghBFSXA1owujuPUyHQiYbhiqgGlgQzN3tcWqRbsfJf4+Fzh9/MmzpT22P0OuD3ha7Qq/qdBzg5JxHAcEVUb0azDKM5/qSdQgg4K0J/wVdu7Qr/X1F45SK1PEII6N2xLU+hdaF/ZbMct4WXiI7jTwhRI5AkCVZb4nuVBfwicuVi1TEoABCwsNWLks8ccMDjCrU6xWttBQCDRYFO5XePqCEYroiagU6VEs7PU7nVC4i9z19omQSf2crL0gkAYPI54PNqMcvDraXhEBXUJ/7eEVHy8KeMqIWpqdULCF15hYrjt/2p3PUYfl65G1JWJHiNDGOtgTkQmnU8XqtSWHhQePjm1pIx/sUYdGKxB43wBdV6b+8J+pNYmxMbfxqJWiFZjr3tT3WCAQHhdEBU6n+sHL6qkiQpcuKuzKO3QKfnzaGr0jQBk88Bv09EvafVvceJ1mt6GdZUXlVH1JoxXBGdABRd3cJYPJomoLidCFTEDnauLF4oEFUHlTWCcCCsSx0qh5vKrUGJ9ptoe50kQWeUYDTLDEVExHBFRLUjyxJMloYFNCKiEwHb94mIiIiSiOGKiIiIKIkYroiIiIiSqM2Eq71792LKlCno0aMHTCYTevbsiQULFsDn80WV++abb3DuuefCaDQiOzsb9957bzPVmIiIiNqiNjOgfefOndA0DU8++SR69eqF7du3Y+rUqXA6nbj//vsBAHa7HSNHjsSIESPwxBNP4Ntvv8X//d//IS0tDdOmTWvmIyAiIqo/Z8CIQKD+81x5A7xgJVnaTLgqKChAQUFB5HlOTg6Ki4vx+OOPR8LVc889B5/PhxUrVkCv1+PUU0/Ftm3b8OCDDzJcERERUVK0mW7BeMrLy9GuXbvI86KiIpx33nnQ6/WRZfn5+SguLsbRo0fj7sPr9cJut0c9iIiIiBJps+Fq165dWLZsGf76179GlpWUlKBjx45R5cLPS0pK4u5n6dKlSE1NjTyys7Mbr9JERERt1JEjRzBhwgTYbDakpaVhypQpcDgcCcvv3bs3dHunOI+1a9dGysVb/8ILLzTFISXU4sPVnDlzEr654cfOnTujtvn5559RUFCAcePGYerUqQ16/blz56K8vDzyOHDgQIP2R0REdCKaMGECduzYgcLCQqxbtw6ffPJJtUNysrOzcejQoajHokWLYLVaMWrUqKiyK1eujCo3ZsyYRj6a6rX4MVczZ87E5MmTqy2Tk5MT+f/BgwcxbNgwDB48GE899VRUuaysLJSWlkYtCz/PysqKu2+DwQCDwVCPmhMREREAfP/993j33Xfx5ZdfYuDAgQCAZcuW4cILL8T999+Pzp07x2yjKErMufm1117DZZddBqvVGrU8LS0t4Xm8ObT4lquMjAyccsop1T7CY6h+/vlnDB06FAMGDMDKlSshy9GHl5eXh08++QR+//E7fxcWFqJ3795IT09v0uMiIiI6URQVFSEtLS0SrABgxIgRkGUZmzZtqtU+tmzZgm3btmHKlCkx66ZPn44OHTpg0KBBWLFiRZPcz7Q6LT5c1VY4WHXt2hX3338/fvnlF5SUlESNpbryyiuh1+sxZcoU7NixAy+++CL++c9/4pZbbmnGmhMREbUcVS/i8nq9Dd5nSUkJMjMzo5bpdDq0a9cu4ZjnqpYvX44+ffpg8ODBUcsXL16Ml156CYWFhRg7diyuv/56LFu2rMF1bogW3y1YW4WFhdi1axd27dqFLl26RK0LJ9jU1FSsX78e06dPx4ABA9ChQwfMnz+f0zAQEVGrVxEwwduAea58gVAkqHrh1oIFC7Bw4cK428yZMwf33HNPtfv9/vvv612nMLfbjTVr1uCOO+6IWVd52Zlnngmn04n77rsPN910U4Nft77aTLiaPHlyjWOzAOD000/Hp59+2vgVIiIiaoUOHDgAm80WeV7duOPajovOysrC4cOHo5YHAgEcOXKkVmOlXn75ZbhcLkycOLHGsrm5uViyZAm8Xm+zjZluM+GKiIiIGs5ms0WFq+pkZGQgIyOjxnJ5eXkoKyvDli1bMGDAAADAhg0boGkacnNza9x++fLluPjii2v1Wtu2bUN6enqzXozGcEVERESNqk+fPigoKMDUqVPxxBNPwO/344YbbsD48eMjVwr+/PPPGD58OJ5++mkMGjQosu2uXbvwySef4J133onZ71tvvYXS0lKcffbZMBqNKCwsxN13341bb721yY4tHoYrIiIianTPPfccbrjhBgwfPhyyLGPs2LF45JFHIuv9fj+Ki4vhcrmitluxYgW6dOmCkSNHxuxTVVU89thjmDFjBoQQ6NWrFx588MEGz3HZUJJo7usVWxm73Y7U1FSUl5fXutm0LXrrx9ObuwpERC2eqyKI8f2/a9RzRvi8NO3jcdBbGzCg3eHHU+evPeHPb8nQZqZiICIiImoJGK6IiIiIkohjroiIiNoAR1CFPqiv9/a+YBIrc4JjyxURERFREjFcERERESURwxURERFREjFcERERESURwxURERFREjFcERERESURp2IgIiJqA5wBI3z++k/F4A+wvSVZ+E4SERERJRHDFREREVESMVwRERERJRHDFREREVESMVwRERERJRGvFqR6kSqcECL0f6NJgkc1N2+FiIiIWgiGK6qXlFQl8n+XUwPczqj1JrMMt87U1NUiIiJqdgxX1GBmS2zvsrMiCOEMBS7NaoaiSE1dLSKiE4oroEINqPXe3h8QSazNiY3hihqFJeV4y1ZFuQuaduyJzQxJYtAiIqK2i+GKGl24C1EIgYpyF4QAFAXQrJZmrhkREVHyMVxRnX289+R6bSdJEmxpoaAVCAg4y52QZUCkMGQREVHbwakYqE7qG6yq0ukkpKYrkGXAFHAnZZ9EREQtAcMV1VqyglVllhQFTodWc0EiIqJWguGKaqUxghUREVFbxHBFNWrsYJWSKgN2Z80FiYiIWgEOaKdqNUWLlaJI0DQmfSKihnAHdPA3YJ6rQIBDNJKF5zNKqCm7Ag1GCXqvq8lej4iIqLEwXFFcTT3GymSW4XFzdmAiImr92C1YT5/tOzNqFnJqGGdFsLmrQERElBRsuaJm5XJqKDsShKqXIKdxMlEiorbqrrvuwuDBg2E2m5GWllarbSZPngxJkqIeBQUFUWWOHDmCCRMmwGazIS0tDVOmTIHD4WiEI6g9hitqFm5XKFQpCiCnWeBRzc1dJSIiakQ+nw/jxo3DddddV6ftCgoKcOjQocjj+eefj1o/YcIE7NixA4WFhVi3bh0++eQTTJs2LZlVrzN2C1KTcVYEETzW+6c3hFqqvM1bJSIiaiKLFi0CAKxatapO2xkMBmRlZcVd9/333+Pdd9/Fl19+iYEDBwIAli1bhgsvvBD3338/Onfu3KA61xdbrqjRBIMC9rIgyo+GHgajDNgsgM0Cn4EtVURELZHdbo96eL3N+2fwRx99hMzMTPTu3RvXXXcdfvvtt8i6oqIipKWlRYIVAIwYMQKyLGPTpk3NUV0AbLmiJAkEBFyVbmMjBCArAGwWSMeWcaIFIqLG4/brofPr6719wB+6Yjs7Oztq+YIFC7Bw4cKGVK3eCgoK8Oc//xk9evTA7t27cfvtt2PUqFEoKiqCoigoKSlBZmZm1DY6nQ7t2rVDSUlJs9QZYLiiWvK4Nfi8oR88SQqFp8qUY0EqTALAiRWIiFqfAwcOwGazRZ4bDIaEZefMmYN77rmn2v19//33OOWUU+pVl/Hjx0f+369fP5x++uno2bMnPvroIwwfPrxe+2wKDFcnEJ9Xg9t1PCAlIkTseoNRioSneKGJEykQEbUNNpstKlxVZ+bMmZg8eXK1ZXJycpJQq+P76tChA3bt2oXhw4cjKysLhw8fjioTCARw5MiRhOO0mgLDVT3ZyzQEAoDZIkPVV5NUmpgQAm6nQCAQG4F0qgQptebpDuIdDQeeExFRVRkZGcjIyGiy1/vpp5/w22+/oVOnTgCAvLw8lJWVYcuWLRgwYAAAYMOGDdA0Dbm5uU1Wr6oYrupJspkhpSjwOZ1wOUPdYlZb400qKoSAxy0Q8Iu4LUuVmcwyAubYAeOBRqsdERFR9fbv348jR45g//79CAaD2LZtGwCgV69esFqtAIBTTjkFS5cuxZ/+9Cc4HA4sWrQIY8eORVZWFnbv3o1Zs2ahV69eyM/PBwD06dMHBQUFmDp1Kp544gn4/X7ccMMNGD9+fLNdKQgwXDVY0BIasG3U3Cg/GuocM5okCAH4fSJmbBIQv9utNuuNJgl+Y+KuuTAOHCciopZm/vz5WL16deT5mWeeCQD48MMPMXToUABAcXExysvLAQCKouCbb77B6tWrUVZWhs6dO2PkyJFYsmRJ1Diw5557DjfccAOGDx8OWZYxduxYPPLII013YHEwXCWJSzZBSg39X3hdcMAInUWCLMempJo6EROt9zSohkRERM1n1apVNc5xJSq1SJhMJrz33ns17rddu3ZYs2ZNQ6uXVAxXjcBnMKP+F8MSERFRa8ZwRURE1AZ4gzoEAvU/rQeDvO47WThDOxEREVESMVwRERERJRHDFREREVESMVwRERERJRHDFREREVESMVwRERERJRHDFREREVEScZ4rIiKiNsDrU6DoGjDPlY93oE0WtlwRERERJRHDFREREVESMVwRERERJRHDFREREVESMVwRERERJRHDFREREVESMVwRERERJRHnuSIiImoDggEFIqDUe3utAdtSNLZcERERESVRmwxXXq8X/fv3hyRJ2LZtW9S6b775Bueeey6MRiOys7Nx7733Nk8liYiIqE1qk+Fq1qxZ6Ny5c8xyu92OkSNHolu3btiyZQvuu+8+LFy4EE899VQz1JKIiIjaojY35uo///kP1q9fj1deeQX/+c9/otY999xz8Pl8WLFiBfR6PU499VRs27YNDz74IKZNm9ZMNSYiIqK2pE21XJWWlmLq1Kl45plnYDabY9YXFRXhvPPOg16vjyzLz89HcXExjh49GnefXq8Xdrs96kFERESUSJsJV0IITJ48Gddeey0GDhwYt0xJSQk6duwYtSz8vKSkJO42S5cuRWpqauSRnZ2d3IoTERFRm9Liw9WcOXMgSVK1j507d2LZsmWoqKjA3Llzk/r6c+fORXl5eeRx4MCBpO6fiIiI2pYWP+Zq5syZmDx5crVlcnJysGHDBhQVFcFgMEStGzhwICZMmIDVq1cjKysLpaWlUevDz7OysuLu22AwxOyTiIiopQn4FMi6Bsxz5eM8V8nS4sNVRkYGMjIyaiz3yCOP4M4774w8P3jwIPLz8/Hiiy8iNzcXAJCXl4e///3v8Pv9UFUVAFBYWIjevXsjPT29cQ6AiIiITigtPlzVVteuXaOeW61WAEDPnj3RpUsXAMCVV16JRYsWYcqUKZg9eza2b9+Of/7zn3jooYeavL5ERETUNrX4MVfJlJqaivXr12PPnj0YMGAAZs6cifnz53MaBiIioka0d+9eTJkyBT169IDJZELPnj2xYMEC+Hy+hNscOXIEN954I3r37g2TyYSuXbvipptuQnl5eVS5eGOxX3jhhcY+pGq1mZarqrp37w4hRMzy008/HZ9++mkz1IiIiOjEtHPnTmiahieffBK9evXC9u3bMXXqVDidTtx///1xtzl48CAOHjyI+++/H3379sW+fftw7bXX4uDBg3j55Zejyq5cuRIFBQWR52lpaY15ODVqs+GKiIiIWoaCgoKo8JOTk4Pi4mI8/vjjCcPVaaedhldeeSXyvGfPnrjrrrtw1VVXIRAIQKc7HmHS0tISXpjWHE6obkEiIiKqXtWJs71eb6O8Tnl5Odq1a1fnbWw2W1SwAoDp06ejQ4cOGDRoEFasWBG356opseWKiIioDRBBGSJQ/zYTEQxtW3Wy7AULFmDhwoUNqVqMXbt2YdmyZQlbreL59ddfsWTJkphx0osXL8YFF1wAs9mM9evX4/rrr4fD4cBNN92U1DrXBcMVERERRRw4cAA2my3yvLq5HufMmYN77rmn2v19//33OOWUUyLPf/75ZxQUFGDcuHGYOnVqrepkt9sxevRo9O3bNybo3XHHHZH/n3nmmXA6nbjvvvsYroiIiKhlsNlsUeGqOrWd6Dvs4MGDGDZsGAYPHoynnnqqVq9RUVGBgoICpKSk4LXXXovMU5lIbm4ulixZAq/X22yTgDNcERERUb3UdqJvINRiNWzYMAwYMAArV66ELNfchWm325Gfnw+DwYA333wTRqOxxm22bduG9PT0Zr27CsMVERERNaqff/4ZQ4cORbdu3XD//ffjl19+iawLX+X3888/Y/jw4Xj66acxaNAg2O12jBw5Ei6XC88++2xkgD0QCnWKouCtt95CaWkpzj77bBiNRhQWFuLuu+/Grbfe2izHGcZwRURERI2qsLAQu3btwq5duyJ3TQkLX9nn9/tRXFwMl8sFANi6dSs2bdoEAOjVq1fUNnv27EH37t2hqioee+wxzJgxA0II9OrVCw8++GCtx3I1FoYrIiIialSTJ0+ucWxW1cm/hw4dWuOUClXnz2opOM8VERERURKx5YqIiKgt8MmA0oA2Ex/bW5KF7yQRERFREjFcERERESURwxURERFREjFcERERESURwxURERFREjFcERERESURwxURERFREnGeKyIiorbALwE6qWHbU1Kw5YqIiIgoiRiuiIiIiJKI4YqIiIgoiRiuiIiIiJKI4YqIiIgoiRiuiIiIiJKI4YqIiIgoiRiuiIiI2gA5KEEONOARPHHmudq+fXuj7r9O4Wr48OF49dVXE67/9ddfkZOT0+BKERERETWW008/Hbm5ufj3v/+NioqKpO+/TuHqww8/xGWXXYYFCxbEXR8MBrFv376kVIyIiIioMXz88cc49dRTMXPmTHTq1AmTJk3Cp59+mrT917lb8PHHH8fDDz+MP/3pT3A6nUmrCBEREVFTOPfcc7FixQocOnQIy5Ytw969e3H++efj5JNPxj333IOSkpIG7b/O4eqSSy7BF198gR07duDss8/Gjz/+2KAKEBERETUHi8WCv/zlL/j444/xv//9D+PGjcNjjz2Grl274uKLL673fus1oL1Pnz748ssvkZ2djd///vd4//33610BIiIioubWq1cv3H777Zg3bx5SUlLw9ttv13tf9b5aMDU1FW+//TamTp2KCy+8EA899FC9K0FERETUXD755BNMnjwZWVlZuO222/DnP/8Zn3/+eb33p6tLYUmSYp7/4x//QP/+/XHNNddgw4YN9a4IERERUVM5ePAgVq1ahVWrVmHXrl0YPHgwHnnkEVx22WWwWCwN2nedwpUQIu7y8ePH45RTTsGYMWMaVBkiIiKqH8knQZLrP1eV5Dtx5rkaNWoU3n//fXTo0AETJ07E//3f/6F3795J23+dwtWHH36Idu3axV3Xv39/bNmypUF9lERERESNTVVVvPzyy7jooougKErS91+ncHX++edXu759+/aYOHFigypERERE1JjefPPNRt0/b39DREREje7iiy9G165dYTQa0alTJ1x99dU4ePBgtdt4PB5Mnz4d7du3h9VqxdixY1FaWhpVZv/+/Rg9ejTMZjMyMzNx2223IRAINOah1IjhioiIiBrdsGHD8NJLL6G4uBivvPIKdu/ejUsvvbTabWbMmIG33noLa9euxccff4yDBw/iz3/+c2R9MBjE6NGj4fP5sHHjRqxevRqrVq3C/PnzG/twqiWJRKPUKS673Y7U1FS8sK0vzCnJ76clIqK2w1URxPj+36G8vBw2m61RXiN8Xuq+5C7IRmO996N5PNh7x98bta6VvfnmmxgzZgy8Xi9UVY1ZX15ejoyMDKxZsyYSwnbu3Ik+ffqgqKgIZ599Nv7zn//goosuwsGDB9GxY0cAwBNPPIHZs2fjl19+gV6vb/TjiIctV0RERNSkjhw5gueeew6DBw+OG6wAYMuWLfD7/RgxYkRk2SmnnIKuXbuiqKgIAFBUVIR+/fpFghUA5Ofnw263Y8eOHY17ENVguCIiIqIIu90e9fB6vUnb9+zZs2GxWNC+fXvs378fb7zxRsKyJSUl0Ov1SEtLi1resWPHyL3/SkpKooJVeH14XXNhuCIiImoDZL/U4AcAZGdnIzU1NfJYunRpwtecM2cOJEmq9rFz585I+dtuuw1fffUV1q9fD0VRMHHixIRzaLZmdZqKgYiIiNq2AwcORI25MhgMCcvOnDkTkydPrnZ/OTk5kf936NABHTp0wMknn4w+ffogOzsbX3zxBfLy8mK2y8rKgs/nQ1lZWVTrVWlpKbKysiJlNm/eHLVd+GrCcJnmwHBFREREETabrdYD2jMyMpCRkVGv19E0DQASdjsOGDAAqqrigw8+wNixYwEAxcXF2L9/fySM5eXl4a677sLhw4eRmZkJACgsLITNZkPfvn3rVa9kYLcgERERNapNmzbh0UcfxbZt27Bv3z5s2LABV1xxBXr27BkJSj///DNOOeWUSEtUamoqpkyZgltuuQUffvghtmzZgr/85S/Iy8vD2WefDQAYOXIk+vbti6uvvhpff/013nvvPcybNw/Tp0+vtsWtsTFcERERUaMym8149dVXMXz4cPTu3RtTpkzB6aefjo8//jgSgvx+P4qLi+FyuSLbPfTQQ7joooswduxYnHfeecjKysKrr74aWa8oCtatWwdFUZCXl4errroKEydOxOLFi5v8GCvjPFd1xHmuiIiotppynquc+Xc3eJ6rHxff3mTzXLVlbLkiIiIiSiKGKyIiIqIk4tWCREREbYDkB+QGjFYR/uTV5UTHlisiIiKiJGK4IiIiIkoihisiIiKiJGK4IiIiIkoihisiIiKiJGK4IiIiIkoiTsVARETUBsgBQG7AdAoikLy6nOjYckVERESURAxXREREREnEcEVERESURAxXREREREnEcEVERESURG0uXL399tvIzc2FyWRCeno6xowZE7V+//79GD16NMxmMzIzM3HbbbchEOAlEkRERJQcbWoqhldeeQVTp07F3XffjQsuuACBQADbt2+PrA8Ggxg9ejSysrKwceNGHDp0CBMnToSqqrj77rubseZERETUVrSZcBUIBPC3v/0N9913H6ZMmRJZ3rdv38j/169fj++++w7vv/8+OnbsiP79+2PJkiWYPXs2Fi5cCL1e3xxVJyIiajDZB8hS/bcXvuTV5UTXZroFt27dip9//hmyLOPMM89Ep06dMGrUqKiWq6KiIvTr1w8dO3aMLMvPz4fdbseOHTvi7tfr9cJut0c9iIiIiBJpM+Hqxx9/BAAsXLgQ8+bNw7p165Ceno6hQ4fiyJEjAICSkpKoYAUg8rykpCTufpcuXYrU1NTIIzs7uxGPgoiIiFq7Fh+u5syZA0mSqn3s3LkTmqYBAP7+979j7NixGDBgAFauXAlJkrB27dp6v/7cuXNRXl4eeRw4cCBZh0ZERERtUIsfczVz5kxMnjy52jI5OTk4dOgQgOgxVgaDATk5Odi/fz8AICsrC5s3b47atrS0NLIuHoPBAIPBUN/qExER0QmmxYerjIwMZGRk1FhuwIABMBgMKC4uxpAhQwAAfr8fe/fuRbdu3QAAeXl5uOuuu3D48GFkZmYCAAoLC2Gz2aJCGREREVF9tfhwVVs2mw3XXnstFixYgOzsbHTr1g333XcfAGDcuHEAgJEjR6Jv3764+uqrce+996KkpATz5s3D9OnT2TpFRERESdFmwhUA3HfffdDpdLj66qvhdruRm5uLDRs2ID09HQCgKArWrVuH6667Dnl5ebBYLJg0aRIWL17czDUnIiKitqJNhStVVXH//ffj/vvvT1imW7dueOedd5qwVkRERI1P9gNyAy5TE/7k1eVE1+KvFiQiIiJqTRiuiIiIiJKI4YqIiIgoiRiuiIiIqNFdfPHF6Nq1K4xGIzp16oSrr74aBw8eTFh+7969CScPrzw5eLz1L7zwQlMcUkIMV0RERNTohg0bhpdeegnFxcV45ZVXsHv3blx66aUJy2dnZ+PQoUNRj0WLFsFqtWLUqFFRZVeuXBlVbsyYMY18NNVrU1cLEhERUcs0Y8aMyP+7deuGOXPmYMyYMfD7/VBVNaa8oigxd0957bXXcNlll8FqtUYtT0tLS3inlebAlisiIiKKsNvtUQ+v15v01zhy5Aiee+45DB48OG6wimfLli3Ytm0bpkyZErNu+vTp6NChAwYNGoQVK1ZACJHsKtcJwxUREVEboAQAxd+ARyC0n+zsbKSmpkYeS5cuTVodZ8+eDYvFgvbt22P//v144403ar3t8uXL0adPHwwePDhq+eLFi/HSSy+hsLAQY8eOxfXXX49ly5Ylrc71wXBFREREEQcOHEB5eXnkMXfu3IRl58yZk3DQefixc+fOSPnbbrsNX331FdavXw9FUTBx4sRatTK53W6sWbMmbqvVHXfcgXPOOQdnnnkmZs+ejVmzZkVuf9dcOOaKiIiIImw2G2w2W63Kzpw5E5MnT662TE5OTuT/HTp0QIcOHXDyySejT58+yM7OxhdffIG8vLxq9/Hyyy/D5XJh4sSJNdYpNzcXS5Ysgdfrbbb7BjNcERERUb1kZGQgIyOjXttqmgYAtRrTtXz5clx88cW1eq1t27YhPT292YIVwHBFREREjWzTpk348ssvMWTIEKSnp2P37t2444470LNnz0ir1c8//4zhw4fj6aefxqBBgyLb7tq1C5988knc+wK/9dZbKC0txdlnnw2j0YjCwkLcfffduPXWW5vs2OJhuCIiIqJGZTab8eqrr2LBggVwOp3o1KkTCgoKMG/evEgLk9/vR3FxMVwuV9S2K1asQJcuXTBy5MiY/aqqisceewwzZsyAEAK9evXCgw8+iKlTpzbJcSUiiea+XrGVsdvtSE1NxQvb+sKcojR3dYiIqAVzVQQxvv93KC8vr/U4proKn5dOm3Y3FL2x3vsJ+jzY/tTtjVrXEwWvFiQiIiJKInYLEhERtQGyH5Cl+m8v/Mmry4mOLVdEREREScRwRURERJREDFdEREREScRwRURERJREDFdEREREScRwRURERJREDFdEREREScR5roiIiNoAxSegoAE3XfHxhi3JwpYrIiIioiRiuCIiIiJKIoYrIiIioiRiuCIiIiJKIoYrIiIioiRiuCIiIiJKIoYrIiIioiTiPFdERERtgOwHZKn+2wt/8upyomPLFREREVESMVwRERERJRHDFREREVESMVwRERERJRHDFREREVESMVwRERERJRGnYiAiImoDZL+AAlHv7YW//ttSNLZcERERESURwxURERFREjFcERERESURwxURERE1Ga/Xi/79+0OSJGzbtq3askOHDoUkSVGPa6+9NqrM/v37MXr0aJjNZmRmZuK2225DIBBoxCOoGQe0ExERUZOZNWsWOnfujK+//rpW5adOnYrFixdHnpvN5sj/g8EgRo8ejaysLGzcuBGHDh3CxIkToaoq7r777qTXvbbYckVERERN4j//+Q/Wr1+P+++/v9bbmM1mZGVlRR42my2ybv369fjuu+/w7LPPon///hg1ahSWLFmCxx57DD6frzEOoVYYroiIiCjCbrdHPbxeb1L2W1paiqlTp+KZZ56Jan2qyXPPPYcOHTrgtNNOw9y5c+FyuSLrioqK0K9fP3Ts2DGyLD8/H3a7HTt27EhKveuD3YJERERtgOITUETD57nKzs6OWr5gwQIsXLiwIVWDEAKTJ0/Gtddei4EDB2Lv3r212u7KK69Et27d0LlzZ3zzzTeYPXs2iouL8eqrrwIASkpKooIVgMjzkpKSBtW5IRiuiIiIKOLAgQNRXW8GgyFh2Tlz5uCee+6pdn/ff/891q9fj4qKCsydO7dOdZk2bVrk//369UOnTp0wfPhw7N69Gz179qzTvpoSwxURERFF2Gy2qHBVnZkzZ2Ly5MnVlsnJycGGDRtQVFQUE9QGDhyICRMmYPXq1bV6vdzcXADArl270LNnT2RlZWHz5s1RZUpLSwEAWVlZtdpnY2C4IiIionrJyMhARkZGjeUeeeQR3HnnnZHnBw8eRH5+Pl588cVIYKqN8NQNnTp1AgDk5eXhrrvuwuHDh5GZmQkAKCwshM1mQ9++fetwJMnFcEVERESNqmvXrlHPrVYrAKBnz57o0qULAODnn3/G8OHD8fTTT2PQoEHYvXs31qxZgwsvvBDt27fHN998gxkzZuC8887D6aefDgAYOXIk+vbti6uvvhr33nsvSkpKMG/ePEyfPr3a7szGxnBFREREzc7v96O4uDhyNaBer8f777+Phx9+GE6nE9nZ2Rg7dizmzZsX2UZRFKxbtw7XXXcd8vLyYLFYMGnSpKh5sZoDwxURERE1qe7du0NUubKx6rLs7Gx8/PHHNe6rW7dueOedd5Jex4bgPFdEREREScSWKyIiojZA9mmQhVb/7f3135aiseWKiIiIKIkYroiIiIiSiOGKiIiIKIkYroiIiIiSiOGKiIiIKIkYroiIiIiSiOGKiIiIKIk4zxUREVEboPg1KA2Y50oEOM9VsrSplqv//e9/uOSSS9ChQwfYbDYMGTIEH374YVSZ/fv3Y/To0TCbzcjMzMRtt92GQCDQTDUmIiKitqZNhauLLroIgUAAGzZswJYtW3DGGWfgoosuQklJCQAgGAxi9OjR8Pl82LhxI1avXo1Vq1Zh/vz5zVxzIiIiaivaTLj69ddf8cMPP2DOnDk4/fTTcdJJJ+Ef//gHXC4Xtm/fDgBYv349vvvuOzz77LPo378/Ro0ahSVLluCxxx6Dz+dr5iMgIiKitqDNhKv27dujd+/eePrpp+F0OhEIBPDkk08iMzMTAwYMAAAUFRWhX79+6NixY2S7/Px82O127NixI+5+vV4v7HZ71IOIiIgokTYzoF2SJLz//vsYM2YMUlJSIMsyMjMz8e677yI9PR0AUFJSEhWsAESeh7sOq1q6dCkWLVrUuJUnIiKiNqPFt1zNmTMHkiRV+9i5cyeEEJg+fToyMzPx6aefYvPmzRgzZgz++Mc/4tChQ/V+/blz56K8vDzyOHDgQBKPjoiIiNqaFt9yNXPmTEyePLnaMjk5OdiwYQPWrVuHo0ePwmazAQD+9a9/obCwEKtXr8acOXOQlZWFzZs3R21bWloKAMjKyoq7b4PBAIPB0PADISIiohNCiw9XGRkZyMjIqLGcy+UCAMhydGOcLMvQtNDcHXl5ebjrrrtw+PBhZGZmAgAKCwths9nQt2/fJNeciIio6cg+DbJW/7mqZM5zlTQtvluwtvLy8pCeno5Jkybh66+/xv/+9z/cdttt2LNnD0aPHg0AGDlyJPr27Yurr74aX3/9Nd577z3MmzcP06dPZ+sUERERJUWbCVcdOnTAu+++C4fDgQsuuAADBw7EZ599hjfeeANnnHEGAEBRFKxbtw6KoiAvLw9XXXUVJk6ciMWLFzdz7YmIiKitaPHdgnUxcOBAvPfee9WW6datG955550mqhERERGdaNpMyxURERFRS8BwRURERJREDFdEREREScRwRURERJREbWpAOxER0YlK9gYgBwP13z5Q/20pGluuiIiIiJKI4YqIiIgoiRiuiIiIqNF1794dkiRFPf7xj38kLH/kyBHceOON6N27N0wmE7p27YqbbroJ5eXlUeWq7lOSJLzwwguNfTjV4pgrIiIiahKLFy/G1KlTI89TUlISlj148CAOHjyI+++/H3379sW+fftw7bXX4uDBg3j55Zejyq5cuRIFBQWR52lpaUmve10wXBEREVGTSElJQVZWVq3KnnbaaXjllVciz3v27Im77roLV111FQKBAHS64xEmLS2t1vttCuwWJCIioibxj3/8A+3bt8eZZ56J++67D4E6XqFYXl4Om80WFawAYPr06ejQoQMGDRqEFStWQAiRzGrXGVuuiIiIKMJut0c9NxgMMBgMDd7vTTfdhLPOOgvt2rXDxo0bMXfuXBw6dAgPPvhgrbb/9ddfsWTJEkybNi1q+eLFi3HBBRfAbDZj/fr1uP766+FwOHDTTTc1uM71JYnmjnetjN1uR2pqKl7Y1hfmFKW5q0NERC2YqyKI8f2/i7S4NIbweemCfrOhU+ofggJBLzZ8e0/M8gULFmDhwoVxt5kzZw7uuSd2m8q+//57nHLKKTHLV6xYgb/+9a9wOBw1hje73Y4//OEPaNeuHd58802oqpqw7Pz587Fy5UocOHCg2n02JrZcERERUcSBAweigmB1wWfmzJmYPHlytfvLycmJuzw3NxeBQAB79+5F7969E25fUVGBgoICpKSk4LXXXqs2WIX3u2TJEni93qS0uNUHwxURERFF2Gy2WreyZWRkICMjo16vs23bNsiyjMzMzIRl7HY78vPzYTAY8Oabb8JoNNZqv+np6c0WrACGKyIiImpkRUVF2LRpE4YNG4aUlBQUFRVhxowZuOqqq5Ceng4A+PnnnzF8+HA8/fTTGDRoEOx2O0aOHAmXy4Vnn30Wdrs9Mh4sIyMDiqLgrbfeQmlpKc4++2wYjUYUFhbi7rvvxq233tqch8twRURERI3LYDDghRdewMKFC+H1etGjRw/MmDEDt9xyS6SM3+9HcXExXC4XAGDr1q3YtGkTAKBXr15R+9uzZw+6d+8OVVXx2GOPYcaMGRBCoFevXnjwwQej5tJqDgxXRERE1KjOOussfPHFF9WW6d69e9QUCkOHDq1xSoWCgoKoyUNbCs5zRURERJREDFdEREREScRuQSIiojZA9vohK/VvM5GD/iTW5sTGlisiIiKiJGK4IiIiIkoihisiIiKiJGK4IiIiIkoihisiIiKiJGK4IiIiIkoiTsVARETUFvj9QLABbSYap2JIFrZcERERESURwxURERFREjFcERERESURwxURERFREjFcERERESURwxURERFREjFcERERESUR57kiIiJqC7w+QJbqv73mS15dTnBsuSIiIiJKIoYrIiIioiRiuCIiIiJKIoYrIiIioiRiuCIiIiJKIoYrIiIioiRiuCIiIiJKIs5zRURE1BZ4fA1rMuE8V0nDlisiIiKiJGK4IiIiIkoihisiIiKiJGK4IiIioibx9ttvIzc3FyaTCenp6RgzZky15YUQmD9/Pjp16gSTyYQRI0bghx9+iCpz5MgRTJgwATabDWlpaZgyZQocDkcjHkXNGK6IiIio0b3yyiu4+uqr8Ze//AVff/01Pv/8c1x55ZXVbnPvvffikUcewRNPPIFNmzbBYrEgPz8fHo8nUmbChAnYsWMHCgsLsW7dOnzyySeYNm1aYx9OtSQhhGjWGrQydrsdqampeGFbX5hTlOauDhERtWCuiiDG9/8O5eXlsNlsjfIa4fPSiHZ/gU7W13s/Ac2H94+sbJS6BgIBdO/eHYsWLcKUKVNqtY0QAp07d8bMmTNx6623AgDKy8vRsWNHrFq1CuPHj8f333+Pvn374ssvv8TAgQMBAO+++y4uvPBC/PTTT+jcuXNSj6O22HJFREREEXa7Perh9XobvM+tW7fi559/hizLOPPMM9GpUyeMGjUK27dvT7jNnj17UFJSghEjRkSWpaamIjc3F0VFRQCAoqIipKWlRYIVAIwYMQKyLGPTpk0Nrnd9MVwRERG1AcLnhfA24OELhajs7GykpqZGHkuXLm1w3X788UcAwMKFCzFv3jysW7cO6enpGDp0KI4cORJ3m5KSEgBAx44do5Z37Ngxsq6kpASZmZlR63U6Hdq1axcp0xwYroiIiCjiwIEDKC8vjzzmzp2bsOycOXMgSVK1j507d0LTNADA3//+d4wdOxYDBgzAypUrIUkS1q5d21SH1mQ4QzsRERFF2Gy2Wo+5mjlzJiZPnlxtmZycHBw6dAgA0Ldv38hyg8GAnJwc7N+/P+52WVlZAIDS0lJ06tQpsry0tBT9+/ePlDl8+HDUdoFAAEeOHIls3xwYroiIiKheMjIykJGRUWO5AQMGwGAwoLi4GEOGDAEA+P1+7N27F926dYu7TY8ePZCVlYUPPvggEqbsdjs2bdqE6667DgCQl5eHsrIybNmyBQMGDAAAbNiwAZqmITc3NwlHWD/sFiQiIqJGZbPZcO2112LBggVYv349iouLIwFp3LhxkXKnnHIKXnvtNQCAJEm4+eabceedd+LNN9/Et99+i4kTJ6Jz586R+bH69OmDgoICTJ06FZs3b8bnn3+OG264AePHj2+2KwUBtlwRERFRE7jvvvug0+lw9dVXw+12Izc3Fxs2bEB6enqkTHFxMcrLyyPPZ82aBafTiWnTpqGsrAxDhgzBu+++C6PRGCnz3HPP4YYbbsDw4cMhyzLGjh2LRx55pEmPrSrOc1VHnOeKiIhqqynnuRpuvRI6qQHzXAkfPnCsadS6nijYLUhERESUROwWJCIiagM0tweaFKz/9sKfxNqc2NhyRURERJREDFdERERESdRqwtVdd92FwYMHw2w2Iy0tLW6Z/fv3Y/To0TCbzcjMzMRtt92GQCAQVeajjz7CWWedBYPBgF69emHVqlWNX3kiIiI6YbSacOXz+TBu3LjIvBhVBYNBjB49Gj6fDxs3bsTq1auxatUqzJ8/P1Jmz549GD16NIYNG4Zt27bh5ptvxjXXXIP33nuvqQ6DiIiI2rhWM6B90aJFAJCwpWn9+vX47rvv8P7776Njx47o378/lixZgtmzZ2PhwoXQ6/V44okn0KNHDzzwwAMAQpOPffbZZ3jooYeQn5/fVIdCREREbVirabmqSVFREfr16xd19+z8/HzY7Xbs2LEjUmbEiBFR2+Xn56OoqKhJ60pERERtV6tpuapJSUlJVLACEHleUlJSbRm73Q632w2TyRSzX6/XC6/XG3kenjnW5aj/5a5ERHRiCJ8rmmK+7gD8QANeJgBOxZAszRqu5syZg3vuuafaMt9//z1OOeWUJqpRrKVLl0a6JCv7vyHFzVAbIiJqjX777TekpqY2yr71ej2ysrLwaclbDd5XVlYW9Pr6z/JOIc0armbOnInJkydXWyYnJ6dW+8rKysLmzZujlpWWlkbWhf8NL6tcxmazxW21AoC5c+filltuiTwvKytDt27dsH///kb7QWkKdrsd2dnZOHDgQKu9zUFbOAaAx9GStIVjANrGcbSFYwBCvR1du3ZFu3btGu01jEYj9uzZA5/P1+B96fX6qPv2Uf00a7jKyMhARkZGUvaVl5eHu+66C4cPH0ZmZiYAoLCwEDabDX379o2Ueeedd6K2KywsRF5eXsL9GgwGGAyGmOWpqamt+gc+zGaztfrjaAvHAPA4WpK2cAxA2ziOtnAMACDLjTvE2Wg0MhS1IK1mQPv+/fuxbds27N+/H8FgENu2bcO2bdvgcDgAACNHjkTfvn1x9dVX4+uvv8Z7772HefPmYfr06ZFwdO211+LHH3/ErFmzsHPnTvzrX//CSy+9hBkzZjTnoREREVEb0moGtM+fPx+rV6+OPD/zzDMBAB9++CGGDh0KRVGwbt06XHfddcjLy4PFYsGkSZOwePHiyDY9evTA22+/jRkzZuCf//wnunTpgv/3//4fp2EgIiKipGk14WrVqlU1zqberVu3mG6/qoYOHYqvvvqq3vUwGAxYsGBB3K7C1qQtHEdbOAaAx9GStIVjANrGcbSFYwDaznFQ3UiiKa4PJSIiIjpBtJoxV0REREStAcMVERERURIxXBERERElEcMVERERURIxXMXx2GOPoXv37jAajcjNzY2Z+b2qtWvX4pRTToHRaES/fv1qvGKxsS1duhS///3vkZKSgszMTIwZMwbFxdXfrmfVqlWQJCnq0dwT0i1cuDCmTjXdCqmlfRbdu3ePOQZJkjB9+vS45VvK5/DJJ5/gj3/8Izp37gxJkvD6669HrRdCYP78+ejUqRNMJhNGjBiBH374ocb91vVnqyGqOwa/34/Zs2ejX79+sFgs6Ny5MyZOnIiDBw9Wu8/6fCcb8zgAYPLkyTF1KigoqHG/TflZADUfR7yfE0mScN999yXcZ1N/HrX53erxeDB9+nS0b98eVqsVY8eOjbkzSFX1/XmilovhqooXX3wRt9xyCxYsWICtW7fijDPOQH5+Pg4fPhy3/MaNG3HFFVdgypQp+OqrrzBmzBiMGTMG27dvb+KaH/fxxx9j+vTp+OKLL1BYWAi/34+RI0fC6XRWu53NZsOhQ4cij3379jVRjRM79dRTo+r02WefJSzbEj+LL7/8Mqr+hYWFAIBx48Yl3KYlfA5OpxNnnHEGHnvssbjr7733XjzyyCN44oknsGnTJlgsFuTn58Pj8STcZ11/thrzGFwuF7Zu3Yo77rgDW7duxauvvori4mJcfPHFNe63Lt/JZKjpswCAgoKCqDo9//zz1e6zqT8LoObjqFz/Q4cOYcWKFZAkCWPHjq12v035edTmd+uMGTPw1ltvYe3atfj4449x8OBB/PnPf652v/X5eaIWTlCUQYMGienTp0eeB4NB0blzZ7F06dK45S+77DIxevToqGW5ubnir3/9a6PWsy4OHz4sAIiPP/44YZmVK1eK1NTUpqtULSxYsECcccYZtS7fGj6Lv/3tb6Jnz55C07S461vi5wBAvPbaa5HnmqaJrKwscd9990WWlZWVCYPBIJ5//vmE+6nrz1YyVT2GeDZv3iwAiH379iUsU9fvZLLFO45JkyaJSy65pE77ac7PQojafR6XXHKJuOCCC6ot09yfR9XfrWVlZUJVVbF27dpIme+//14AEEVFRXH3Ud+fJ2rZ2HJVic/nw5YtWzBixIjIMlmWMWLECBQVFcXdpqioKKo8AOTn5ycs3xzKy8sBoMYbhzocDnTr1g3Z2dm45JJLsGPHjqaoXrV++OEHdO7cGTk5OZgwYQL279+fsGxL/yx8Ph+effZZ/N///R8kSUpYriV+DpXt2bMHJSUlUe91amoqcnNzE77X9fnZamrl5eWQJAlpaWnVlqvLd7KpfPTRR8jMzETv3r1x3XXX4bfffktYtjV8FqWlpXj77bcxZcqUGss25+dR9Xfrli1b4Pf7o97bU045BV27dk343tbn54laPoarSn799VcEg0F07NgxannHjh1RUlISd5uSkpI6lW9qmqbh5ptvxjnnnIPTTjstYbnevXtjxYoVeOONN/Dss89C0zQMHjwYP/30UxPWNlpubi5WrVqFd999F48//jj27NmDc889FxUVFXHLt/TP4vXXX0dZWRkmT56csExL/ByqCr+fdXmv6/Oz1ZQ8Hg9mz56NK664otqbBNf1O9kUCgoK8PTTT+ODDz7APffcg48//hijRo1CMBiMW76lfxYAsHr1aqSkpNTYndacn0e8360lJSXQ6/UxAb2mc0i4TG23oZav1dz+hupn+vTp2L59e43jEPLy8pCXlxd5PnjwYPTp0wdPPvkklixZ0tjVjGvUqFGR/59++unIzc1Ft27d8NJLL9XqL9qWZvny5Rg1ahQ6d+6csExL/BzaOr/fj8suuwxCCDz++OPVlm2J38nx48dH/t+vXz+cfvrp6NmzJz766CMMHz68WerUUCtWrMCECRNqvJijOT+P2v5upRMTW64q6dChAxRFibmyo7S0FFlZWXG3ycrKqlP5pnTDDTdg3bp1+PDDD9GlS5c6bauqKs4880zs2rWrkWpXd2lpaTj55JMT1qklfxb79u3D+++/j2uuuaZO27XEzyH8ftblva7Pz1ZTCAerffv2obCwsNpWq3hq+k42h5ycHHTo0CFhnVrqZxH26aefori4uM4/K0DTfR6JfrdmZWXB5/OhrKwsqnxN55BwmdpuQy0fw1Uler0eAwYMwAcffBBZpmkaPvjgg6jWhMry8vKiygNAYWFhwvJNQQiBG264Aa+99ho2bNiAHj161HkfwWAQ3377LTp16tQINawfh8OB3bt3J6xTS/wswlauXInMzEyMHj26Ttu1xM+hR48eyMrKinqv7XY7Nm3alPC9rs/PVmMLB6sffvgB77//Ptq3b1/nfdT0nWwOP/30E3777beEdWqJn0Vly5cvx4ABA3DGGWfUedvG/jxq+t06YMAAqKoa9d4WFxdj//79Cd/b+vw8USvQzAPqW5wXXnhBGAwGsWrVKvHdd9+JadOmibS0NFFSUiKEEOLqq68Wc+bMiZT//PPPhU6nE/fff7/4/vvvxYIFC4SqquLbb79trkMQ1113nUhNTRUfffSROHToUOThcrkiZaoex6JFi8R7770ndu/eLbZs2SLGjx8vjEaj2LFjR3McghBCiJkzZ4qPPvpI7NmzR3z++edixIgRokOHDuLw4cNCiNbxWQgRuhKra9euYvbs2THrWurnUFFRIb766ivx1VdfCQDiwQcfFF999VXkSrp//OMfIi0tTbzxxhvim2++EZdccono0aOHcLvdkX1ccMEFYtmyZZHnNf1sNeUx+Hw+cfHFF4suXbqIbdu2Rf2ceL3ehMdQ03eyqY+joqJC3HrrraKoqEjs2bNHvP/+++Kss84SJ510kvB4PAmPo6k/i5qOI6y8vFyYzWbx+OOPx91Hc38etfndeu2114quXbuKDRs2iP/+978iLy9P5OXlRe2nd+/e4tVXX408r83PE7UuDFdxLFu2THTt2lXo9XoxaNAg8cUXX0TWnX/++WLSpElR5V966SVx8sknC71eL0499VTx9ttvN3GNowGI+1i5cmWkTNXjuPnmmyPH3LFjR3HhhReKrVu3Nn3lK7n88stFp06dhF6vF7/73e/E5ZdfLnbt2hVZ3xo+CyGEeO+99wQAUVxcHLOupX4OH374YdzvULiumqaJO+64Q3Ts2FEYDAYxfPjwmOPr1q2bWLBgQdSy6n62mvIY9uzZk/Dn5MMPP0x4DDV9J5v6OFwulxg5cqTIyMgQqqqKbt26ialTp8aEpOb+LGo6jrAnn3xSmEwmUVZWFncfzf151OZ3q9vtFtdff71IT08XZrNZ/OlPfxKHDh2K2U/lbWrz80StiySEEI3TJkZERER04uGYKyIiIqIkYrgiIiIiSiKGKyIiIqIkYrgiIiIiSiKGKyIiIqIkYrgiIiIiSiKGKyIiIqIkYrgiIiIiSiKGKyKqt8mTJ0OSJEiSBFVV0aNHD8yaNQsej6e5q0ZE1Gx0zV0BImrdCgoKsHLlSvj9fmzZsgWTJk2CJEm45557mrtqRETNgi1XRNQgBoMBWVlZyM7OxpgxYzBixAgUFhY2d7WIiJoNwxURJc327duxceNG6PX65q4KEVGzYbcgETXIunXrYLVaEQgE4PV6IcsyHn300eauFhFRs2G4IqIGGTZsGB5//HE4nU489NBD0Ol0GDt2bHNXi4io2bBbkIgaxGKxoFevXjjjjDOwYsUKbNq0CcuXL2/uahERNRuGKyJKGlmWcfvtt2PevHlwu93NXR0iombBcEVESTVu3DgoioLHHnusuatCRNQsGK6IKKl0Oh1uuOEG3HvvvXA6nc1dHSKiJicJIURzV4KIiIiorWDLFREREVESMVwRERERJRHDFREREVESMVwRERERJRHDFREREVESMVwRERERJRHDFREREVESMVwRERERJRHDFREREVESMVwRERERJRHDFREREVESMVwRERERJdH/B7bOqcgjCyhsAAAAAElFTkSuQmCC",
+                  "text/plain": [
+                     "<Figure size 640x480 with 2 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "data": {
+                  "image/png": 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",
+                  "text/plain": [
+                     "<Figure size 640x480 with 2 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            },
+            {
+               "data": {
+                  "image/png": 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",
+                  "text/plain": [
+                     "<Figure size 640x480 with 2 Axes>"
+                  ]
+               },
+               "metadata": {},
+               "output_type": "display_data"
+            }
+         ],
+         "source": [
+            "# Plot potentials and velocities\n",
+            "# The slicing limits the y-value to 0 because we only care about the x-z (r-z) plane.\n",
+            "plt.contourf(R[:, 0, :], Z[:, 0, :], phi_inc[:, 0, :], cmap='viridis', levels = 50)\n",
+            "plt.colorbar(label='Potential')\n",
+            "plt.contour(R[:, 0, :], Z[:, 0, :], phi_inc[:, 0, :], colors='black', linestyles='solid', linewidths=0.05,levels=50)\n",
+            "\n",
+            "# Add labels and title\n",
+            "plt.xlabel('R')\n",
+            "plt.ylabel('Z')\n",
+            "plt.title('Contour Plot of Re(Potential) using BEM')\n",
+            "\n",
+            "plt.show()\n",
+            "\n",
+            "imag_phi_inc = np.imag(phi_inc[:, 0, :])\n",
+            "\n",
+            "nan_mask = np.isnan(np.real(phi_inc[:, 0, :]))\n",
+            "\n",
+            "np.imag(phi_inc[:, 0, :])[nan_mask] = np.nan\n",
+            "\n",
+            "plt.contourf(R[:, 0, :], Z[:, 0, :], imag_phi_inc, cmap='viridis', levels = 50)\n",
+            "plt.colorbar(label='Potential')\n",
+            "plt.contour(R[:, 0, :], Z[:, 0, :], imag_phi_inc, colors='black', linestyles='solid', linewidths=0.05,levels=50)\n",
+            "\n",
+            "\n",
+            "# Add labels and title\n",
+            "plt.xlabel('R')\n",
+            "plt.ylabel('Z')\n",
+            "plt.title('Contour Plot of Im(Potential) using BEM')\n",
+            "\n",
+            "plt.show()\n",
+            "\n",
+            "def plot_vel(data, title):\n",
+            "  plt.contourf(R[:, 0, :], Z[:, 0, :], data[:, 0, :], cmap='viridis', levels = 50)\n",
+            "  plt.colorbar(label='V')\n",
+            "  plt.contour(R[:, 0, :], Z[:, 0, :], data[:, 0, :], colors='black', linestyles='solid', linewidths=0.05,levels=50)\n",
+            "\n",
+            "  # Add labels and title\n",
+            "  plt.xlabel('R')\n",
+            "  plt.ylabel('Z')\n",
+            "  plt.title(title)\n",
+            "\n",
+            "  plt.show()\n",
+            "\n",
+            "nan_mask = np.isnan(np.real(velx_inc))\n",
+            "\n",
+            "velx_imag = np.imag(velx_inc)\n",
+            "velz_imag = np.imag(velz_inc)\n",
+            "\n",
+            "velx_imag[nan_mask] = np.nan\n",
+            "velz_imag[nan_mask] = np.nan\n",
+            "\n",
+            "plot_vel(velx_inc, \"Contour Plot of Re(Vx) using BEM\")\n",
+            "plot_vel(velx_imag, \"Contour Plot of Im(Vx) using BEM\")\n",
+            "plot_vel(velz_inc, \"Contour Plot of Re(Vz) using BEM\")\n",
+            "plot_vel(velz_imag, \"Contour Plot of Im(Vz) using BEM\")\n"
+         ]
+      },
+      {
+         "cell_type": "code",
+         "execution_count": 14,
+         "metadata": {
+            "colab": {
+               "base_uri": "https://localhost:8080/"
+            },
+            "id": "sa0mkZllZw_V",
+            "outputId": "b67eb2b3-636d-4bcb-b73a-74f5f7a5e070"
+         },
+         "outputs": [],
+         "source": [
+            "save_potential_array(config, phi_inc[:, 0, :])\n",
+            "# WARNING: This overwrites existing files with the same name. Ensure that <config> is correct before running."
+         ]
+      },
+      {
+         "cell_type": "raw",
+         "metadata": {},
+         "source": [
+            "# alternative computation that typically takes longer, but doesn't have a holey mesh warning.\n",
+            "# try this if encountering any bugs with the previous implementation\n",
+            "def make_body(a, d, heaving, t_densities, face_units): \n",
+            "    pts = get_points(a,d)\n",
+            "    f_densities = get_f_densities(pts, face_units)\n",
+            "    faces_and_heaves = []\n",
+            "    panel_ct = 0\n",
+            "    for i in range((len(pts) - 1) // 2):\n",
+            "        p1, p2, p3 = pts[2 * i], pts[2 * i + 1], pts[2 * i + 2]\n",
+            "        # make a horizontal face\n",
+            "        h_face = cpt.FloatingBody(make_face(p1, p2, f_densities[2 * i], t_densities[i]))\n",
+            "        h_heave = heaving[i]\n",
+            "        faces_and_heaves.append((h_face, h_heave))\n",
+            "        panel_ct += f_densities[2 * i] * t_densities[i]\n",
+            "        # make a vertical face\n",
+            "        if p2[1] < p3[1]: # body on left\n",
+            "            region = i\n",
+            "        else: # body on right\n",
+            "            region = i + 1\n",
+            "        v_face = cpt.FloatingBody(make_face(p2, p3, f_densities[2 * i + 1], t_densities[region]))\n",
+            "        faces_and_heaves.append((v_face, heaving[region]))\n",
+            "        panel_ct += f_densities[2 * i + 1] * t_densities[region]\n",
+            "    body = add_heaves(faces_and_heaves)\n",
+            "    return body, panel_ct\n",
+            "\n",
+            "\n",
+            "def add_heaves(faces_and_heaves):\n",
+            "    hcreate = False\n",
+            "    screate = False\n",
+            "    for fh in faces_and_heaves: # Splits list of faces into those that are heaving and those that are not.\n",
+            "        if fh[1]: #fh of the form (face, heaving)\n",
+            "            if not hcreate:\n",
+            "                heaving_body = fh[0]\n",
+            "                hcreate = True\n",
+            "            else:\n",
+            "                heaving_body = heaving_body + fh[0]\n",
+            "        else:\n",
+            "            if not screate:\n",
+            "                still_body = fh[0]\n",
+            "                screate = True\n",
+            "            else:\n",
+            "                still_body = still_body + fh[0]\n",
+            "    if hcreate: # Adds heave dof to the heaving collection\n",
+            "        heaving_body.add_translation_dof(name='Heave')\n",
+            "        if screate:\n",
+            "            return (heaving_body + still_body)\n",
+            "        else:\n",
+            "            return (heaving_body)\n",
+            "    else:\n",
+            "        return (still_body)\n",
+            "\n",
+            "###################################\n",
+            "# Solving\n",
+            "solver = cpt.BEMSolver(engine = cpt.HierarchicalToeplitzMatrixEngine())\n",
+            "\n",
+            "def rb_solve(a, d, heaving, t_densities, face_units, m0, h, rho, reps):\n",
+            "    body, panel_count = make_body(a, d, heaving, t_densities, face_units)\n",
+            "    body = body.immersed_part() # removes points above z = 0\n",
+            "    body.show_matplotlib()\n",
+            "\n",
+            "    rad_problem = cpt.RadiationProblem(body = body, wavenumber = m0, water_depth = h, rho = rho)\n",
+            "    result, t_diff = timed_solve(rad_problem, reps)\n",
+            "    print(result.added_mass)\n",
+            "    print(result.radiation_damping)\n",
+            "    print(\"# of Panels: \", panel_count)\n",
+            "    print(\"Solve Time: \", t_diff)\n",
+            "    return result, panel_count, t_diff"
+         ]
+      }
+   ],
+   "metadata": {
+      "colab": {
+         "provenance": []
+      },
+      "kernelspec": {
+         "display_name": ".venv (3.12.1)",
+         "language": "python",
+         "name": "python3"
+      },
+      "language_info": {
+         "codemirror_mode": {
+            "name": "ipython",
+            "version": 3
+         },
+         "file_extension": ".py",
+         "mimetype": "text/x-python",
+         "name": "python",
+         "nbconvert_exporter": "python",
+         "pygments_lexer": "ipython3",
+         "version": "3.12.1"
+      }
+   },
+   "nbformat": 4,
+   "nbformat_minor": 4
+}
diff --git a/test/time_comparison/bemTime.py b/dev/python/time_comparison/bemTime.py
similarity index 100%
rename from test/time_comparison/bemTime.py
rename to dev/python/time_comparison/bemTime.py
diff --git a/dev/python/time_comparison/bemTime_one_run.py b/dev/python/time_comparison/bemTime_one_run.py
new file mode 100644
index 0000000..d8518e7
--- /dev/null
+++ b/dev/python/time_comparison/bemTime_one_run.py
@@ -0,0 +1,66 @@
+#!pip install capytaine #uncomment if first time running
+#omega = 1.05
+omega = 2.769
+w = 1
+rho = 1023 # density of our special material
+wave_amp = 1
+
+import capytaine as cpt
+import numpy as np
+import matplotlib.pyplot as plt
+from capytaine.bem.airy_waves import airy_waves_potential, airy_waves_velocity, froude_krylov_force
+
+import time
+
+# In[3]:
+#make a compound cylinder
+h = 1.05
+a1 = 0.5
+a2 = 1
+d1 = 0.5
+d2 = 0.25
+#resolution = 0.4
+resolution = 0.03
+#%%
+solver = cpt.BEMSolver()
+def bemCompoundCylinder(h,a1,a2,d1,d2,resolution):
+  body2 = cpt.meshes.predefined.mesh_vertical_cylinder(radius= a2,center=(0,0,0),length = d2,faces_max_radius=resolution*a2)
+  body1 = cpt.meshes.predefined.mesh_vertical_cylinder(radius=a1,center=(0,0,0),length = d1-d2,faces_max_radius=resolution*a2)
+  body1 = body1.translated([0,0,-d2-0.001])
+
+  body = body1 + body2
+  body = cpt.FloatingBody(body)
+  body.add_translation_dof(name='Heave')
+  body = body.immersed_part()
+  #body.show_matplotlib()
+  faces_centers = body.mesh.faces_centers
+
+  rad_problem = cpt.RadiationProblem(body=body,
+                                        wavenumber = w, water_depth=h)
+  results = solver.solve(rad_problem, keep_details = True)
+  return results , body.mesh.nb_faces
+
+result = bemCompoundCylinder(h,a1,a2,d1,d2,resolution) #compile it first
+
+#%%
+#def timeit(res,iter):
+  #def oneRun(res):
+    #start_time = time.time()
+  #   result = bemCompoundCylinder(h,a1,a2,d1,d2,res)
+  #   end_time = time.time()
+  #   return end_time-start_time
+  # avg_time = np.mean([oneRun(res) for i in range(iter)])
+  # return avg_time
+
+#avg_time = timeit(resolution,1)#default resolution
+
+#print(f"Execution time: {avg_time} seconds") #run more than once ..second time is the actual runtime that excludes compile time
+
+#resolutions = np.arange(0.1,0.9,0.1)
+#resdict = {bemCompoundCylinder(h,a1,a2,d1,d2,res)[1]:timeit(res,100) for res in resolutions}
+
+#plt.figure()
+#plt.plot(list(resdict.keys()),list(resdict.values()),'*-')
+#plt.xlabel("Panels (N)")
+#plt.ylabel("Time(s)")
+#plt.show()
\ No newline at end of file
diff --git a/dev/python/time_comparison/bemTime_two_runs.py b/dev/python/time_comparison/bemTime_two_runs.py
new file mode 100644
index 0000000..469ca77
--- /dev/null
+++ b/dev/python/time_comparison/bemTime_two_runs.py
@@ -0,0 +1,66 @@
+#!pip install capytaine #uncomment if first time running
+#omega = 1.05
+omega = 2.769
+w = 1
+rho = 1023 # density of our special material
+wave_amp = 1
+
+import capytaine as cpt
+import numpy as np
+import matplotlib.pyplot as plt
+from capytaine.bem.airy_waves import airy_waves_potential, airy_waves_velocity, froude_krylov_force
+
+import time
+
+# In[3]:
+#make a compound cylinder
+h = 1.05
+a1 = 0.5
+a2 = 1
+d1 = 0.5
+d2 = 0.25
+#resolution = 0.4
+resolution = 0.03
+#%%
+solver = cpt.BEMSolver()
+def bemCompoundCylinder(h,a1,a2,d1,d2,resolution):
+  body2 = cpt.meshes.predefined.mesh_vertical_cylinder(radius= a2,center=(0,0,0),length = d2,faces_max_radius=resolution*a2)
+  body1 = cpt.meshes.predefined.mesh_vertical_cylinder(radius=a1,center=(0,0,0),length = d1-d2,faces_max_radius=resolution*a2)
+  body1 = body1.translated([0,0,-d2-0.001])
+
+  body = body1 + body2
+  body = cpt.FloatingBody(body)
+  body.add_translation_dof(name='Heave')
+  body = body.immersed_part()
+  #body.show_matplotlib()
+  faces_centers = body.mesh.faces_centers
+
+  rad_problem = cpt.RadiationProblem(body=body,
+                                        wavenumber = w, water_depth=h)
+  results = solver.solve(rad_problem, keep_details = True)
+  return results , body.mesh.nb_faces
+
+result = bemCompoundCylinder(h,a1,a2,d1,d2,resolution) #compile it first
+
+#%%
+def timeit(res,iter):
+  def oneRun(res):
+    start_time = time.time()
+    result = bemCompoundCylinder(h,a1,a2,d1,d2,res)
+    end_time = time.time()
+    return end_time-start_time
+  avg_time = np.mean([oneRun(res) for i in range(iter)])
+  return avg_time
+
+avg_time = timeit(resolution,1)#default resolution
+
+print(f"Execution time: {avg_time} seconds") #run more than once ..second time is the actual runtime that excludes compile time
+
+#resolutions = np.arange(0.1,0.9,0.1)
+#resdict = {bemCompoundCylinder(h,a1,a2,d1,d2,res)[1]:timeit(res,100) for res in resolutions}
+
+#plt.figure()
+#plt.plot(list(resdict.keys()),list(resdict.values()),'*-')
+#plt.xlabel("Panels (N)")
+#plt.ylabel("Time(s)")
+#plt.show()
\ No newline at end of file
diff --git a/test/time_comparison/time_capytaine.ipynb b/dev/python/time_comparison/time_capytaine.ipynb
similarity index 100%
rename from test/time_comparison/time_capytaine.ipynb
rename to dev/python/time_comparison/time_capytaine.ipynb
diff --git a/dev/python/two-region-MEEM/MEEM.ipynb b/dev/python/two-region-MEEM/MEEM.ipynb
new file mode 100644
index 0000000..6eac906
--- /dev/null
+++ b/dev/python/two-region-MEEM/MEEM.ipynb
@@ -0,0 +1,809 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.special import hankel1 as besselh\n",
+    "from scipy.special import iv as besseli\n",
+    "from scipy.special import kv as besselk\n",
+    "import scipy.integrate as integrate\n",
+    "import scipy.linalg as linalg\n",
+    "import matplotlib.pyplot as plt\n",
+    "from math import sqrt, cosh, cos, sinh, sin, pi\n",
+    "from scipy.optimize import newton, minimize_scalar\n",
+    "from constants import *\n",
+    "from coupling import *\n",
+    "from equations import *\n",
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CREATING THE A MATRIX\n",
+    "\n",
+    "N = 4\n",
+    "M = 4\n",
+    "K = 4\n",
+    "\n",
+    "# Initialize the A matrix with zeros as well as b vector\n",
+    "A = np.zeros((N + 2 * M + K, N + 2 * M + K), dtype=complex)\n",
+    "\n",
+    "###########################################################################\n",
+    "# Init the first row of block matrices (using d1)\n",
+    "# First block (diagonal)\n",
+    "for i in range(N): \n",
+    "    A[i][i] = (h - d1) * R_1n_1(i, a1)\n",
+    "\n",
+    "# Second and third block (dense) \n",
+    "for n in range(N): \n",
+    "    for m in range(M): \n",
+    "        try: \n",
+    "            A[n][N+m] = -R_1n_2(m, a1) * A_nm(n, m)\n",
+    "        except: \n",
+    "            print(n)\n",
+    "            print(m)\n",
+    "        A[n][N+M+m] = -R_2n_2(m, a1) * A_nm(n, m)\n",
+    "# Fourth block (zero)\n",
+    "\n",
+    "###########################################################################\n",
+    "# Init the second row of block matrices (using d2)\n",
+    "# First block (zero) \n",
+    "\n",
+    "# Second and third block (diagonal)\n",
+    "for i in range(M): \n",
+    "    A[N+i][N+i] = (h - d2) * R_1n_2(i, a2)\n",
+    "    A[N+i][N+M+i] = (h - d2) * R_2n_2(i, a2)\n",
+    "   \n",
+    "\n",
+    "# Fourth block (dense)\n",
+    "for m in range(M):\n",
+    "    for k in range(K):\n",
+    "        A[N+m][N+(2*M)+k] = -Lambda_k_r(k, a2) * A_mk(m, k)\n",
+    "        \n",
+    "\n",
+    "###########################################################################\n",
+    "# Init the third row of block matrices (using d1) \n",
+    "\n",
+    "# THERE IS A PROBLEM HERE\n",
+    "\n",
+    "# First block (dense)\n",
+    "for m in range(M): \n",
+    "    for n in range(N):\n",
+    "        A[N+M+m][n] = -diff_R_1n_1(n, a1) * A_nm(n, m)\n",
+    "\n",
+    "# Second and third blocks (diagonal)\n",
+    "for m in range(M): \n",
+    "    A[N+M+m][N+m] = (h - d2) * diff_R_1n_2(m, a1)\n",
+    "    A[N+M+m][N+M+m] = (h - d2) * diff_R_2n_2(m, a1)\n",
+    "\n",
+    "# Fourth block (zero)\n",
+    "\n",
+    "###########################################################################\n",
+    "# Init the fourth row of block matrices (using d2)\n",
+    "# First block (zero)\n",
+    "# Second and third block (dense)\n",
+    "for k in range(K): \n",
+    "    for m in range(M):\n",
+    "        # print(R_2n_2(j, a2) * A_jn(j, n))\n",
+    "        A[N+(2*M)+k][N+m] = -diff_R_1n_2(m, a2) * A_mk(m, k)\n",
+    "        A[N+(2*M)+k][N+M+m] = -diff_R_2n_2(m, a2) * A_mk(m, k)\n",
+    "\n",
+    "# Fourth block  (diagonal)\n",
+    "for k in range(K): \n",
+    "    A[N+(2*M)+k][N+(2*M)+k] = h * diff_Lambda_k_a2(k)\n",
+    "\n",
+    "\n",
+    "# A = np.where(np.abs(A) <= np.finfo(float).eps, 0, A)\n",
+    "\n",
+    "\n",
+    "rows, cols = np.nonzero(A)\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(cols, rows, color='blue', marker='o', s=100) \n",
+    "plt.gca().invert_yaxis() \n",
+    "plt.xticks(range(A.shape[1]))\n",
+    "plt.yticks(range(A.shape[0]))\n",
+    "\n",
+    "cols = [N, N + M, N + 2 * M]\n",
+    "\n",
+    "for val in cols:\n",
+    "    plt.axvline(val-0.5, color='black', linestyle='-', linewidth=1) \n",
+    "    plt.axhline(val-0.5, color='black', linestyle='-', linewidth=1) \n",
+    "\n",
+    "# for y in range(0, A.shape[0], 3):\n",
+    "    # plt.axhline(y-0.5, color='black', linestyle='-', linewidth=1) \n",
+    "\n",
+    "plt.grid(True)\n",
+    "plt.title('Non-Zero Entries of the Matrix')\n",
+    "plt.xlabel('Column Index')\n",
+    "plt.ylabel('Row Index')\n",
+    "plt.show()\n",
+    "# plt.imshow(np.real(A))\n",
+    "# plt.colorbar()\n",
+    "# print(A[:3,:3])\n",
+    "# print(R_1n_2(3, a1) * A_nm(1, 3))\n",
+    "# print(R_2n_2(3, a1) * A_nm(1, 3))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/zz/_5443rfn2v1_n4x4gqlv6jxc0000gr/T/ipykernel_41595/2478633098.py:10: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
+      "  df_complex = df.applymap(to_complex)\n"
+     ]
+    }
+   ],
+   "source": [
+    "file_path = 'values/A_values.csv'\n",
+    "df = pd.read_csv(file_path, header=None)\n",
+    "\n",
+    "def to_complex(val):\n",
+    "    try:\n",
+    "        return np.complex128(val)\n",
+    "    except ValueError:\n",
+    "        return np.nan + 1j * np.nan\n",
+    "\n",
+    "df_complex = df.applymap(to_complex)\n",
+    "A_num = df_complex.to_numpy()\n",
+    "A_num[-4][-4] = np.complex128(-0.45178 + 1.0741j)\n",
+    "\n",
+    "A_real = np.real(A_num)\n",
+    "A_imag = np.imag(A_num)\n",
+    "\n",
+    "# Save the real and imaginary parts\n",
+    "np.savetxt(\"values/A_num_real.txt\", A_real, fmt='%.6e')\n",
+    "np.savetxt(\"values/A_num_imag.txt\", A_imag, fmt='%.6e')\n",
+    "\n",
+    "\n",
+    "threshold = 0.001\n",
+    "is_within_threshold = np.isclose(A_num, A, rtol=threshold)\n",
+    "np.savetxt(\"values/A.txt\", A)\n",
+    "np.savetxt(\"values/A_match.txt\", is_within_threshold)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Find the indices where the entries do not match the threshold\n",
+    "rows, cols = np.nonzero(~is_within_threshold)\n",
+    "\n",
+    "# Plotting\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(cols, rows, color='blue', marker='o', s=100) \n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.xticks(range(is_within_threshold.shape[1]))\n",
+    "plt.yticks(range(is_within_threshold.shape[0]))\n",
+    "\n",
+    "# N = is_within_threshold.shape[1] // 3\n",
+    "# M = is_within_threshold.shape[0] // 3\n",
+    "cols = [4, 8, 12]\n",
+    "\n",
+    "for val in cols:\n",
+    "    plt.axvline(val - 0.5, color='black', linestyle='-', linewidth=1)\n",
+    "    plt.axhline(val - 0.5, color='black', linestyle='-', linewidth=1)\n",
+    "\n",
+    "plt.grid(True)\n",
+    "plt.title('Non-Zero Entries of the Matrix Not Matching the Threshold')\n",
+    "plt.xlabel('Column Index')\n",
+    "plt.ylabel('Row Index')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/zz/_5443rfn2v1_n4x4gqlv6jxc0000gr/T/ipykernel_41595/706194200.py:3: DeprecationWarning: `scipy.integrate.romberg` is deprecated as of SciPy 1.12.0and will be removed in SciPy 1.15.0. Please use`scipy.integrate.quad` instead.\n",
+      "  rhs_12 = np.array([(integrate.romberg(lambda z: phi_p_i1_i2_a1(z) * Z_n_i1(n, z),-h, -d1)) for n in range(N)])\n",
+      "/var/folders/zz/_5443rfn2v1_n4x4gqlv6jxc0000gr/T/ipykernel_41595/706194200.py:4: DeprecationWarning: `scipy.integrate.romberg` is deprecated as of SciPy 1.12.0and will be removed in SciPy 1.15.0. Please use`scipy.integrate.quad` instead.\n",
+      "  rhs_2E =np.array ([-integrate.romberg(lambda z: phi_p_a2(z) * Z_n_i2(m, z), -h, -d2) for m in range(M)]) #at a2 phi_p_i2\n",
+      "/var/folders/zz/_5443rfn2v1_n4x4gqlv6jxc0000gr/T/ipykernel_41595/706194200.py:5: DeprecationWarning: `scipy.integrate.romberg` is deprecated as of SciPy 1.12.0and will be removed in SciPy 1.15.0. Please use`scipy.integrate.quad` instead.\n",
+      "  rhs_velocity_12 = np.array([(integrate.romberg(lambda z: diff_phi_i1(a1) * Z_n_i2(m, z), -h, -d1)) - (integrate.romberg(lambda z: diff_phi_i2(a1) * Z_n_i2(m, z), -h, -d2)) for m in range(M)])\n",
+      "/var/folders/zz/_5443rfn2v1_n4x4gqlv6jxc0000gr/T/ipykernel_41595/706194200.py:6: DeprecationWarning: `scipy.integrate.romberg` is deprecated as of SciPy 1.12.0and will be removed in SciPy 1.15.0. Please use`scipy.integrate.quad` instead.\n",
+      "  rhs_velocity_2E = np.array([integrate.romberg(lambda z: diff_phi_i2(a2) * Z_n_e(k, z), -h, -d2) for k in range(K)])\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = np.zeros(N + 2 * M + K, dtype=complex)\n",
+    "\n",
+    "rhs_12 = np.array([(integrate.romberg(lambda z: phi_p_i1_i2_a1(z) * Z_n_i1(n, z),-h, -d1)) for n in range(N)])\n",
+    "rhs_2E =np.array ([-integrate.romberg(lambda z: phi_p_a2(z) * Z_n_i2(m, z), -h, -d2) for m in range(M)]) #at a2 phi_p_i2\n",
+    "rhs_velocity_12 = np.array([(integrate.romberg(lambda z: diff_phi_i1(a1) * Z_n_i2(m, z), -h, -d1)) - (integrate.romberg(lambda z: diff_phi_i2(a1) * Z_n_i2(m, z), -h, -d2)) for m in range(M)])\n",
+    "rhs_velocity_2E = np.array([integrate.romberg(lambda z: diff_phi_i2(a2) * Z_n_e(k, z), -h, -d2) for k in range(K)])\n",
+    "\n",
+    "b = np.concatenate((rhs_12, rhs_2E, rhs_velocity_12, rhs_velocity_2E))\n",
+    "\n",
+    "np.savetxt(\"values/b.txt\", b)\n",
+    "matlab_b = np.array([0.0069,\n",
+    "    0.0120,\n",
+    "   -0.0030,\n",
+    "    0.0013,\n",
+    "    0.1560,\n",
+    "    0.0808,\n",
+    "   -0.0202,\n",
+    "    0.0090,\n",
+    "         0,\n",
+    "   -0.1460,\n",
+    "    0.0732,\n",
+    "   -0.0002,\n",
+    "   -0.4622,\n",
+    "   -0.2837,\n",
+    "    0.1539,\n",
+    "   -0.0673])\n",
+    "threshold = .01\n",
+    "# is_within_threshold = np.isclose(matlab_b, b, atol=threshold)\n",
+    "np.savetxt(\"values/b_match.txt\", is_within_threshold)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.8800566118914106\n",
+      "6.153831062049789\n",
+      "9.33398599507016\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2.8801\n",
+    "print(m_k(1))\n",
+    "# 6.1538\n",
+    "print(m_k(2)) \n",
+    "# 9.3340\n",
+    "print(m_k(3)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = linalg.solve(A,b)\n",
+    "X_num = np.array([\n",
+    "   0.8701 + 0.7071j,\n",
+    "   0.5484 - 0.0294j,\n",
+    "  -3.9953 + 0.1415j,\n",
+    "  56.1169 - 1.9889j,\n",
+    "   0.7793 + 0.7119j,\n",
+    "   0.0826 - 0.0162j,\n",
+    "  -0.0242 + 0.0044j,\n",
+    "   0.0218 - 0.0037j,\n",
+    "   0.0000 + 0.0000j,\n",
+    "   0.0026 - 0.0001j,\n",
+    "  -0.0002 + 0.0000j,\n",
+    "   0.0000 - 0.0000j,\n",
+    "   0.1633 + 0.3798j,\n",
+    "   0.0307 + 0.0108j,\n",
+    "  -0.0346 + 0.0026j,\n",
+    "   0.0213 - 0.0028j,\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C_1n_1s = X[:N]\n",
+    "C_1n_2s = X[N:N+M]\n",
+    "C_2n_2s = X[N+M:N+2*M]\n",
+    "C_2n_1s = np.zeros(M)\n",
+    "B_ks = X[N+2*M:]\n",
+    "\n",
+    "phi_h_n_i1 = lambda n, r, z:  (C_1n_1s[n] * R_1n_1(n, r) + C_2n_1s(n) * R_2n_1(n)) * Z_n_i1(n, z)\n",
+    "phi_h_m_i2 = lambda m, r, z: (C_1n_2s[m] * R_1n_2(m, r) + C_2n_2s(m) * R_2n_2(m, r)) * Z_n_i2(m, z)\n",
+    "phi_e_k = lambda k, r, z: B_ks[k] * Lambda_k_r(k, r) * Z_n_e(k, z)\n",
+    "\n",
+    "\n",
+    "def phi_h_n_i1_func(n, r, z):\n",
+    "    return (C_1n_1s[n] * R_1n_1(n, r) + C_2n_1s[n] * R_2n_1(n)) * Z_n_i1(n, z)\n",
+    "\n",
+    "def phi_h_m_i2_func(m, r, z):\n",
+    "    return (C_1n_2s[m] * R_1n_2(m, r) + C_2n_2s[m] * R_2n_2(m, r)) * Z_n_i2(m, z)\n",
+    "\n",
+    "def phi_e_k_func(k, r, z):\n",
+    "    return B_ks[k] * Lambda_k_r(k, r) * Z_n_e(k, z)\n",
+    "\n",
+    "phi_h_n_i1s = np.vectorize(phi_h_n_i1_func, excluded=['n'], signature='(),(),()->()')\n",
+    "phi_h_m_i2s = np.vectorize(phi_h_m_i2_func, excluded=['m'], signature='(),(),()->()')\n",
+    "phi_e_ks = np.vectorize(phi_e_k_func, excluded=['k'], signature='(),(),()->()')\n",
+    "\n",
+    "\n",
+    "r_vec = lambda spatial_res: np.linspace(2 * a2 / spatial_res, 2*a2, spatial_res)\n",
+    "z_vec = lambda spatial_res: np.linspace(-h, 0, spatial_res)\n",
+    "R, Z = np.meshgrid(r_vec(spatial_res=50), z_vec(spatial_res=50))\n",
+    " \n",
+    "\n",
+    "regione = R > a2\n",
+    "region1 = (R <= a1) & (Z < -d1)\n",
+    "region2 = (R > a1) & (R <= a2) & (Z < -d2)\n",
+    "region_body = ~region1 & ~region2 & ~regione\n",
+    "\n",
+    "\n",
+    "phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+    "phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+    "phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
+    "\n",
+    "for n in range(N):\n",
+    "    temp_phiH = phi_h_n_i1_func(n, R[region1], Z[region1])\n",
+    "    phiH[region1] = temp_phiH if n == 0 else phiH[region1] + temp_phiH\n",
+    "\n",
+    "\n",
+    "for m in range(M):\n",
+    "    temp_phiH = phi_h_m_i2_func(m, R[region2], Z[region2])\n",
+    "    phiH[region2] = temp_phiH if m == 0 else phiH[region2] + temp_phiH\n",
+    "\n",
+    "for k in range(K):\n",
+    "    temp_phiH = phi_e_k_func(k, R[regione], Z[regione])\n",
+    "    phiH[regione] = temp_phiH if k == 0 else phiH[regione] + temp_phiH\n",
+    "\n",
+    "phi_p_i1_vec = np.vectorize(phi_p_i1)\n",
+    "phi_p_i2_vec = np.vectorize(phi_p_i2)\n",
+    "\n",
+    "phiP[region1] = phi_p_i1_vec(R[region1], Z[region1])\n",
+    "phiP[region2] = phi_p_i2_vec(R[region2], Z[region2])\n",
+    "phiP[regione] = 0\n",
+    "\n",
+    "phi = phiH + phiP\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "real 1.5520758090611653\n",
+      "imag 0.5767296214012105\n",
+      "(0.4955246799705303+0.3682600545745605j)\n"
+     ]
+    }
+   ],
+   "source": [
+    "hydro_terms = np.zeros((N+2*M), dtype=complex)\n",
+    "\n",
+    "for i in range(N):\n",
+    "    hydro_terms[i] = int_R_1n_1(i)*C_1n_1s[i]*z_n_d1_d2(i, d1)\n",
+    "for i in range(M):\n",
+    "    hydro_terms[N+i] = int_R_1n_2(i)*C_1n_2s[i]*z_n_d1_d2(i, d2)\n",
+    "    hydro_terms[N+M+i] = int_R_2n_2(i)*C_2n_2s[i]*z_n_d1_d2(i, d2)\n",
+    "\n",
+    "#when i2 is heaving\n",
+    "hydro_coef =2*pi*(sum(hydro_terms) + int_phi_p_i1_no_coef() + int_phi_p_i2_no_coef())\n",
+    "hydro_coef_real = hydro_coef.real\n",
+    "hydro_coef_imag = hydro_coef.imag/omega\n",
+    "\n",
+    "\n",
+    "hydro_coef_nondim = h**3/(a2**3 * pi)*hydro_coef\n",
+    "\n",
+    "print(\"real\", hydro_coef_real)\n",
+    "print(\"imag\", hydro_coef_imag)\n",
+    "print(hydro_coef_nondim)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "real 0.23157145089401918\n",
+      "imag 0.0698027473489804\n",
+      "(0.07393283783218098+0.044571255913188695j)\n"
+     ]
+    }
+   ],
+   "source": [
+    "##slant hydro coeffs##\n",
+    "\n",
+    "#finding phi @corner 1\n",
+    "phi_corner1_H = 0\n",
+    "for n in range(N):\n",
+    "    temp_phiH = phi_h_n_i1_func(n, a1, -d1)\n",
+    "    phi_corner1_H += temp_phiH\n",
+    "\n",
+    "phi_corner1_P = phi_p_i1(a1, -d1)\n",
+    "phi_corner1 = phi_corner1_H + phi_corner1_P\n",
+    "\n",
+    "#finding phi @corner 2\n",
+    "phi_corner2_H = 0\n",
+    "for m in range(M):\n",
+    "    temp_phiH = phi_h_m_i2_func(m, a2, -d2)\n",
+    "    phi_corner2_H += temp_phiH\n",
+    "\n",
+    "phi_corner2_P = phi_p_i2(a2, -d2)\n",
+    "phi_corner2 = phi_corner2_H + phi_corner2_P\n",
+    "\n",
+    "#slant velocity z component approximation\n",
+    "vel_z = (phi_corner2 - phi_corner1)*(d1-d2)/((d1-d2)**2+(a2-a1)**2)\n",
+    "\n",
+    "#calculating hydro coeffs\n",
+    "hydro_terms = np.zeros((N+2*M), dtype=complex)\n",
+    "\n",
+    "for i in range(N):\n",
+    "    hydro_terms[i] = int_R_1n_1(i)*C_1n_1s[i]*z_n_d1_d2(i, d1)\n",
+    "\n",
+    "for i in range(M):\n",
+    "    hydro_terms[N+i] = vel_z*int_R_1n_2(i)*C_1n_2s[i]*z_n_d1_d2(i, d2)\n",
+    "    hydro_terms[N+M+i] = vel_z*int_R_2n_2(i)*C_2n_2s[i]*z_n_d1_d2(i, d2)\n",
+    "\n",
+    "hydro_coef =2*pi*(sum(hydro_terms) + int_phi_p_i1_no_coef() + vel_z*int_phi_p_i2_no_coef())\n",
+    "hydro_coef_real = hydro_coef.real\n",
+    "hydro_coef_imag = hydro_coef.imag/omega\n",
+    "hydro_coef_nondim = h**3/(a2**3 * pi)*hydro_coef\n",
+    "\n",
+    "print(\"real\", hydro_coef_real)\n",
+    "print(\"imag\", hydro_coef_imag)\n",
+    "print(hydro_coef_nondim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_potential(field, R, Z, title):\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    plt.contourf(R, Z, field, levels=50, cmap='viridis')\n",
+    "    plt.colorbar()\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('Radial Distance (R)')\n",
+    "    plt.ylabel('Axial Distance (Z)')\n",
+    "    plt.show()\n",
+    "\n",
+    "def plot_velocity(v_r, v_z, R, Z):\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    plt.streamplot(R, Z, v_r, v_z, color='magenta', density=2)\n",
+    "    plt.title('Velocity Field')\n",
+    "    plt.xlabel('Radial Distance (R)')\n",
+    "    plt.ylabel('Axial Distance (Z)')\n",
+    "    plt.show()\n",
+    "\n",
+    "plot_potential(np.real(phiH), R, Z, 'Homogeneous Potential')\n",
+    "plot_potential(np.imag(phiH), R, Z, 'Homogeneous Potential Imaginary')\n",
+    "\n",
+    "plot_potential(np.real(phi), R, Z, 'Total Potential')\n",
+    "plot_potential(np.imag(phi), R, Z, 'Total Potential Imaginary')\n",
+    "# print(phiH)\n",
+    "# print(phiH[region2])\n",
+    "# print(phiH[regione])\n",
+    "# plot_potential(np.real(phiP), R, Z, 'Particular Potential')\n",
+    "\n",
+    "# print(np.real(phiP))\n",
+    "\n",
+    "# plot_potential(np.real(v_r), R, Z, 'Radial Velocity')\n",
+    "# plot_potential(np.real(v_z), R, Z, 'Vertical Velocity')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_matching(phi1, phi2, phi3, a1, a2, R, Z, title):\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    mask1 = R <= a1\n",
+    "    mask2 = (R > a1) & (R <= a2)\n",
+    "    mask3 = R > a2\n",
+    "\n",
+    "    # Create a combined field based on regions\n",
+    "    combined_field = np.zeros_like(R)\n",
+    "    combined_field[mask1] = phi1[mask1]\n",
+    "    combined_field[mask2] = phi2[mask2]\n",
+    "    combined_field[mask3] = phi3[mask3]\n",
+    "\n",
+    "    plt.contourf(R, Z, combined_field, levels=50, cmap='viridis')\n",
+    "    plt.colorbar()\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('Radial Distance (R)')\n",
+    "    plt.ylabel('Axial Distance (Z)')\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/matplotlib/contour.py:1515: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  self.zmax = z.max().astype(float)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/matplotlib/contour.py:1516: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  self.zmin = z.min().astype(float)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/ma/core.py:2881: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_potential(phiH, R, Z, 'Homogeneous Potential')\n",
+    "plot_potential(phiP, R, Z, 'Particular Potential')\n",
+    "plot_potential(phi, R, Z, 'Total Potential')\n",
+    "# plot_potential(v_r, R, Z, 'Radial Velocity')\n",
+    "# plot_potential(v_z, R, Z, 'Vertical Velocity')\n",
+    "# plot_velocity(np.real(v_r), np.real(v_z), R, Z)\n",
+    "\n",
+    "# # Assuming phi_p_i1_num, phi_p_i2_num, zeros are meant to show some regions with no data (using np.zeros_like(R))\n",
+    "# plot_matching(phi_i1_num, phi_i2_num, phi_e_num, a1_num, a2_num, R, Z, 'Potential')\n",
+    "# plot_matching(phi_h_i1_num, phi_h_i2_num, phi_e_num, a1_num, a2_num, R, Z, 'Homogeneous Potential')\n",
+    "# plot_matching(phi_p_i1_num, phi_p_i2_num, np.zeros_like(R), a1_num, a2_num, R, Z, 'Particular Potential')\n",
+    "# plot_matching(v_1_r_num, v_2_r_num, v_e_r_num, a1_num, a2_num, R, Z, 'Radial Velocity')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_potential(phi, R, Z, region_body, name):\n",
+    "    minphi = np.nanmin(np.real(phi))\n",
+    "    maxphi = np.nanmax(np.real(phi))\n",
+    "\n",
+    "    num_levels = 30\n",
+    "    if minphi < 0:\n",
+    "        levels = np.linspace(minphi, maxphi, num_levels)\n",
+    "    else:\n",
+    "        levels = np.linspace(minphi, maxphi, num_levels)\n",
+    "\n",
+    "    plt.figure()\n",
+    "    plt.subplot(121)\n",
+    "    contour_real = plt.contourf(R, Z, np.real(phi), levels)\n",
+    "    plt.clabel(contour_real)\n",
+    "    plt.xlabel('R')\n",
+    "    plt.ylabel('Z')\n",
+    "    plt.title(name + ' - Real')\n",
+    "    plt.colorbar()\n",
+    "\n",
+    "    imag_phi = np.imag(phi)\n",
+    "    if len(np.unique(imag_phi)) > 1:\n",
+    "        imag_phi[region_body] = np.nan\n",
+    "        plt.subplot(122)\n",
+    "        contour_imag = plt.contourf(R, Z, imag_phi, num_levels)\n",
+    "        plt.clabel(contour_imag)\n",
+    "        plt.xlabel('R')\n",
+    "        plt.ylabel('Z')\n",
+    "        plt.title(name + ' - Imaginary')\n",
+    "        plt.colorbar()\n",
+    "\n",
+    "def plot_matching(phi1, phi2, phie, a1, a2, R, Z, name):\n",
+    "    idx_a1 = np.argmin(np.abs(R - a1), axis=1)\n",
+    "    phi1_a1 = np.abs(phi1[np.arange(len(idx_a1)), idx_a1])\n",
+    "    phi2_a1 = np.abs(phi2[np.arange(len(idx_a1)), idx_a1])\n",
+    "\n",
+    "    idx_a2 = np.argmin(np.abs(R - a2), axis=1)\n",
+    "    phi2_a2 = np.abs(phi2[np.arange(len(idx_a2)), idx_a2])\n",
+    "    phie_a2 = np.abs(phie[np.arange(len(idx_a2)), idx_a2])\n",
+    "\n",
+    "    plt.figure()\n",
+    "    plt.plot(Z[idx_a1], phi1_a1, 'r--', label=name + '_1 at a_1')\n",
+    "    plt.plot(Z[idx_a1], phi2_a1, 'm-', label=name + '_2 at a_1')\n",
+    "    plt.plot(Z[idx_a2], phi2_a2, 'b-', label=name + '_2 at a_2')\n",
+    "    plt.plot(Z[idx_a2], phie_a2, 'c--', label=name + '_e at a_2')\n",
+    "    plt.legend()\n",
+    "    plt.xlabel('Z')\n",
+    "    plt.ylabel('|' + name + '|')\n",
+    "    plt.title(name + ' Matching')\n",
+    "\n",
+    "def plot_velocity(v_r, v_z, R, Z):\n",
+    "    v_tot = np.sqrt(v_r**2 + v_z**2)\n",
+    "\n",
+    "    num_levels = 10\n",
+    "    levels = np.concatenate((np.logspace(1, 6, num_levels), np.arange(0, 21, 2)))\n",
+    "\n",
+    "    plt.figure()\n",
+    "    contour_v = plt.contourf(R, Z, v_tot, levels)\n",
+    "    plt.clabel(contour_v)\n",
+    "    plt.xlabel('R')\n",
+    "    plt.ylabel('Z')\n",
+    "    plt.title('Velocity')\n",
+    "    plt.colorbar()\n",
+    "    plt.quiver(R, Z, v_r/v_tot, v_z/v_tot)\n",
+    "\n",
+    "# Example usage:\n",
+    "# plot_potential(phi, R, Z, region_body, 'Potential')\n",
+    "# plot_matching(phi1, phi2, phie, a1, a2, R, Z, 'Matching')\n",
+    "# plot_velocity(v_r, v_z, R, Z)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_potential(phiH,R,Z,region_body,'Homogeneous Potential')\n",
+    "plot_potential(phiP,R,Z,region_body,'Particular Potential')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/dev/python/two-region-MEEM/constants.py b/dev/python/two-region-MEEM/constants.py
new file mode 100644
index 0000000..0bbfaf7
--- /dev/null
+++ b/dev/python/two-region-MEEM/constants.py
@@ -0,0 +1,10 @@
+# Constants
+h = 1.001
+a1 = .5
+a2 = 1
+d1 = .5
+d2 = .25
+m0 = 1
+n = 3
+z = 6
+omega = 2
\ No newline at end of file
diff --git a/package/src/coupling.py b/dev/python/two-region-MEEM/coupling.py
similarity index 80%
rename from package/src/coupling.py
rename to dev/python/two-region-MEEM/coupling.py
index 341659c..805e568 100644
--- a/package/src/coupling.py
+++ b/dev/python/two-region-MEEM/coupling.py
@@ -91,7 +91,7 @@ def nk_sigma_helper(mk, k, m):
     return sigma1, sigma2, sigma3, sigma4, sigma5
 
 def A_mk(m, k):
-    mk = m_k(k, m0, h)
+    mk = m_k(k)
     sigma1, sigma2, sigma3, sigma4, sigma5 = nk_sigma_helper(mk, k, m)
 
     if k == 0 and m == 0:
@@ -109,6 +109,35 @@ def A_mk(m, k):
     
     return C_mk
 
+
+# def nk_sigma_helper(mk): 
+#     top = sin(2 * h * mk)
+#     bottom = 4 * h * mk
+#     sigma1 = sqrt(top/bottom + 1/2)
+#     sigma2 = sinh(m0 * (d2 - h)) 
+#     sigma3 = mk * (d2 - h)
+#     sigma4 = sq(pi) * sq(n)
+#     sigma5 = sinh(2 * h * m0)
+#     return sigma1, sigma2, sigma3, sigma4, sigma5
+
+# def A_km(k, n):
+#     mk = m_k(k)
+#     sigma1, sigma2, sigma3, sigma4, sigma5 = nk_sigma_helper(mk)
+#     if k == 0 and n == 0:
+#         return (-2 * sqrt(h) * sigma2) / (sqrt(m0) * sqrt(sigma5 + 2 * h * m0))
+#     elif 1 <= k and n == 0:
+#         return -sin(sigma3) / (mk * sigma1)
+#     elif k == 0 and 1 <= n:
+#         top = -sqrt(2) * (m0 * (d2 * cos(pi * n) * sigma2 - h * cos(pi * n) * sigma2) * (d2 - h) + pi * n * sin(pi * n) * cosh(m0 * (d2 - h)) * (d2 - h))
+#         bottom = sqrt((sigma5 / (4 * h * m0)) + 1/2) * (sq(d2) * sq(m0) - 2 * d2 * h * sq(m0) + sq(h) * sq(m0) + sigma4)
+#         return top / bottom
+#     elif 1 <= k and 1 <= n:
+#         top = -sqrt(2) * (mk * (d2 * sin(sigma3) * cos(pi * n) - h * sin(sigma3) * cos(pi * n)) * (d2 - h) - pi * n * cos(sigma3) * sin(pi * n) * (d2 - h))
+#         bottom = sigma1 * (sq(d2) * sq(mk) - 2 * d2 * h * sq(mk) + sq(h) * sq(mk) - sigma4)
+#         return top / bottom
+#     else:
+#         raise ValueError("Invalid values for n and k")
+
 def nk2_sigma_helper(mk): 
     top = sin(2 * h * mk)
     bottom = 4 * h * mk
@@ -121,7 +150,7 @@ def nk2_sigma_helper(mk):
 
 
 def A_km2(n, k):
-    mk = m_k(k, m0, h)
+    mk = m_k(k)
     sigma1, sigma2, sigma3, sigma4, sigma5 = nk2_sigma_helper(mk)
     if k == 0 and n == 0:
         return (-2 * sqrt(h) * sinh(h * m0)) / (sqrt(m0) * sqrt(sigma5 + 2 * h * m0))
@@ -137,4 +166,4 @@ def A_km2(n, k):
         return top / bottom
     else:
         raise ValueError("Invalid values for n and k")
-    
\ No newline at end of file
+    
diff --git a/package/src/equations.py b/dev/python/two-region-MEEM/equations.py
similarity index 65%
rename from package/src/equations.py
rename to dev/python/two-region-MEEM/equations.py
index 1338ff6..d9a6658 100644
--- a/package/src/equations.py
+++ b/dev/python/two-region-MEEM/equations.py
@@ -1,4 +1,3 @@
-# equation.py
 import numpy as np
 from scipy.special import hankel1 as besselh
 from scipy.special import iv as besseli
@@ -9,10 +8,11 @@
 from numpy import sqrt, cosh, cos, sinh, sin, pi
 from scipy.optimize import newton, minimize_scalar, root_scalar
 import scipy as sp
+from constants import *
 
 
 # Defining m_k function that will be used later on
-def m_k(k, m0, h):
+def m_k(k):
     # m_k_mat = np.zeros((len(m0_vec), 1))
 
     m_k_h_err = (
@@ -39,71 +39,77 @@ def m_k(k, m0, h):
         # m_k_mat[freq_idx, :] = m_k_vec
     return m_k_val
 
+
+def m_k_newton(h):
+    res = newton(lambda k: k * np.tanh(k * h) - m0**2 / 9.8, x0=1.0, tol=10 ** (-10))
+    return res
+
+
 # Equation 4:
-def lambda_n1(n, h, d1):
+def lambda_n1(n):
     return n * pi / (h - d1)
 
 
-def lambda_n2(n, h, d2):
+def lambda_n2(n):
     return n * pi / (h - d2)
 
 
 #############################################
 # Equation 5
-def phi_p_a1(z, a1):
+def phi_p_a1(z):
     return phi_p_i1(a1, z)
 
-def phi_p_a2(z, a2, h, d2):
-    return phi_p_i2(a2, z, h, d2)
+def phi_p_a2(z):
+    return phi_p_i2(a2, z)
 
-def phi_p_i1(r, z, h, d1): 
+def phi_p_i1(r, z): 
     return (1 / (2* (h - d1))) * ((z + h) ** 2 - (r**2) / 2)
 
-def phi_p_i2(r, z, h, d2): 
+def phi_p_i2(r, z): 
     return (1 / (2* (h - d2))) * ((z + h) ** 2 - (r**2) / 2)
 
-def phi_p_i1_i2_a1(z, h, a1, d1, d2):
+def phi_p_i1_i2_a1(z):
     res = ((h + z) ** 2 - a1**2 / 2) / (2 * d1 - 2 * h) - (
         (h + z) ** 2 - a1**2 / 2
     ) / (2 * d2 - 2 * h)
     return res
 
-def diff_phi_p_i2_a2(h, a2, d2):
+def diff_phi_p_i2_a2(h):
   return a2/(2*d2 - 2*h)
 
-def diff_phi_p_i1_i2_a1(z, h, a1, d1, d2): #differentiation of difference of particular solution
+def diff_phi_p_i1_i2_a1(z): #differentiation of difference of particular solution
   return ((h + z)**2 - a1**2/2)/(2*d1 - 2*h) - ((h + z)**2 - a1**2/2)/(2*d2 - 2*h) #flux/velocity at a2
 
-def diff_phi_helper(r, di, h): 
+def diff_phi_helper(r, di): 
     return -r / (2 * (h - di))
 
-def diff_phi_i1(r, d1, h): 
-    return diff_phi_helper(r, d1, h)
+def diff_phi_i1(r): 
+    return diff_phi_helper(r, d1)
 
-def diff_phi_i2(r, d2, h): 
-    return diff_phi_helper(r, d2, h)
+def diff_phi_i2(r): 
+    return diff_phi_helper(r, d2)
 
 
 #############################################
 # Equation 7: (r specifies the raidus, use a1/a2 for the radius of the cylinder you want)
-def R_1n_1(n, r, a2, h, d1):
+def R_1n_1(n, r):
     if n == 0:
         return 0.5
     elif n >= 1:
-        return besseli(0, lambda_n1(n, h, d1) * r) / besseli(0, lambda_n1(n, h, d1) * a2)
+        return besseli(0, lambda_n1(n) * r) / besseli(0, lambda_n1(n) * a2)
     else: 
         raise ValueError("Invalid value for n")
 
-def R_1n_2(n, r, a2, h, d2):
+def R_1n_2(n, r):
     if n == 0:
         return 0.5
     elif n >= 1:
-        return besseli(0, lambda_n2(n, h, d2) * r) / besseli(0, lambda_n2(n, h, d2) * a2)
+        return besseli(0, lambda_n2(n) * r) / besseli(0, lambda_n2(n) * a2)
     else: 
         raise ValueError("Invalid value for n")
 
 # Differentiating equation 7: (Once again look at changing the r's here
-def diff_R_1n_1(n, r, d1, h, a2):
+def diff_R_1n_1(n, r):
     if n == 0:
         return 0
     else:
@@ -112,7 +118,7 @@ def diff_R_1n_1(n, r, d1, h, a2):
         return top / bottom
 
 
-def diff_R_1n_2(n, r, d2, h, a2):
+def diff_R_1n_2(n, r):
     if n == 0:
         return 0
     else:
@@ -123,21 +129,24 @@ def diff_R_1n_2(n, r, d2, h, a2):
 
 #############################################
 # Equation 8:
-
+# This function is always 0 regardless of the output
+def R_2n_1(n):
+    return 0.0
 
 # My original definition
-def R_2n_2(n, r, a2, h, d2):
+def R_2n_2(n, r):
     if n == 0:
         return 0.5 * np.log(r / a2)
     else:
-        return besselk(0, lambda_n2(n, h, d2) * r) / besselk(0, lambda_n2(n, h, d2) * a2)
+        return besselk(0, lambda_n2(n) * r) / besselk(0, lambda_n2(n) * a2)
 
 
 # Differentiating equation 8:
+def diff_R_2n_1(n):
+    return 0.0
 
 
-
-def diff_R_2n_2(n, r, d2, h, a2):
+def diff_R_2n_2(n, r):
     if n == 0:
         return 1 / (2 * r)
     else:
@@ -148,106 +157,106 @@ def diff_R_2n_2(n, r, d2, h, a2):
 
 #############################################
 # Equation 9:
-def Z_n_i1(n, z, h, d1):
+def Z_n_i1(n, z):
     if n == 0:
         return 1
     else:
-        return np.sqrt(2) * np.cos(lambda_n1(n, h, d1) * (z + h))
+        return np.sqrt(2) * np.cos(lambda_n1(n) * (z + h))
 
 
-def Z_n_i2(n, z, h, d2):
+def Z_n_i2(n, z):
     if n == 0:
         return 1
     else:
-        return np.sqrt(2) * np.cos(lambda_n2(n, h, d2) * (z + h))
+        return np.sqrt(2) * np.cos(lambda_n2(n) * (z + h))
 
 
 #############################################
 # Equation 13: (m_k is a function)
-def Lambda_k_r(k, r, m0, a2, h):
+def Lambda_k_r(k, r):
     if k == 0:
         return besselh(0, m0 * r) / besselh(0, m0 * a2)
     else:
-        return besselk(0, m_k(k, m0, h) * r) / besselk(0, m_k(k, m0, h) * a2)
+        return besselk(0, m_k(k) * r) / besselk(0, m_k(k) * a2)
 
 
-def diff_Lambda_k_a2(n, m0, a2, h):
+def diff_Lambda_k_a2(n):
     if n == 0:
         numerator = -(m0 * besselh(1, m0 * a2))
         denominator = besselh(0, a2 * m0)
     else:
-        numerator = -(m_k(n, m0, h) * besselk(1, a2 * m_k(n, m0, h)))
-        denominator = besselk(0, a2 * m_k(n, m0, h))
+        numerator = -(m_k(n) * besselk(1, a2 * m_k(n)))
+        denominator = besselk(0, a2 * m_k(n))
     return numerator / denominator
 
 
 #############################################
 # Equation 2.34 in analytical methods book, also eq 16 in Seah and Yeung 2006:
-def N_k(k, m0, h):
+def N_k(k):
     if k == 0:
         return 1 / 2 * (1 + sinh(2 * m0 * h) / (2 * m0 * h))
     else:
-        return 1 / 2 * (1 + sin(2 * m_k(k, m0, h) * h) / (2 * m_k(k, m0, h) * h))
+        return 1 / 2 * (1 + sin(2 * m_k(k) * h) / (2 * m_k(k) * h))
 
 
 #############################################
 # Equation 14: (m_k is a function)
-def Z_n_e(k, z, m0, h):
+def Z_n_e(k, z):
     if k == 0:
-        return 1 / sqrt(N_k(k, m0, h)) * cosh(m0 * (z + h))
+        return 1 / sqrt(N_k(k)) * cosh(m0 * (z + h))
     else:
-        return 1 / sqrt(N_k(k, m0, h)) * cos(m_k(k, m0, h) * (z + h))
+        return 1 / sqrt(N_k(k)) * cos(m_k(k) * (z + h))
 
 #############################################
 # To calculate hydrocoefficients
 
 #differentiate with respect to z
-def diff_phi_p_i1_dz(z, h, d1):
+def diff_phi_p_i1_dz(z):
     return (h+z)/(h-d1)
 
 #differentiate with respect to z
-def diff_phi_p_i2_dz(z, h, d2):
+def diff_phi_p_i2_dz(z):
     return (h+z)/(h-d2)
 
 
 #integrating R_1n_1
-def int_R_1n_1(n, a1, a2, h, d1):
+def int_R_1n_1(n):
     if n == 0:
         return a1**2/4
     else:
-        top = a1*besseli(1, lambda_n1(n, h, d1)*a1)
-        bottom = lambda_n1(n, h, d1)*besseli(0, lambda_n1(n, h, d1)*a2)
+        top = a1*besseli(1, lambda_n1(n)*a1)
+        bottom = lambda_n1(n)*besseli(0, lambda_n1(n)*a2)
         return top/bottom
 
 #integrating R_1n_2
-def int_R_1n_2(n, a2, a1, h, d2):
+def int_R_1n_2(n):
     if n == 0:
         return a2**2/4 - a1**2/4
     else:
-        top = a2*besseli(1, lambda_n2(n, h, d2)*a2)-a1*besseli(1, lambda_n2(n, h, d2)*a1)
-        bottom = lambda_n2(n, h, d2)*besseli(0, lambda_n2(n, h, d2)*a2)
+        top = a2*besseli(1, lambda_n2(n)*a2)-a1*besseli(1, lambda_n2(n)*a1)
+        bottom = lambda_n2(n)*besseli(0, lambda_n2(n)*a2)
         return top / bottom
 
 #integrating R_2n_2
-def int_R_2n_2(n, a1, a2, h, d2):
+def int_R_2n_2(n):
     if n == 0:
         return (a1**2*(2*np.log(a2)-2*np.log(a1)+1)-a2**2)/8
     else:
-        top = a2*besselk(1, lambda_n2(n, h, d2)*a2)-a1*besselk(1, lambda_n2(n, h, d2)*a1)
-        bottom = -lambda_n2(n, h, d2)*besselk(0, lambda_n2(n, h, d2)*a2)
+        top = a2*besselk(1, lambda_n2(n)*a2)-a1*besselk(1, lambda_n2(n)*a1)
+        bottom = -lambda_n2(n)*besselk(0, lambda_n2(n)*a2)
         return top / bottom
     
 #integrating phi_p_i1 * d_phi_p_i1/dz * r *d_r at z=d1
-def int_phi_p_i1_no_coef(a1, h, d1):
+def int_phi_p_i1_no_coef():
     return a1**2*(4*(h-d1)**2-a1**2) / (16*(h-d1))
 
 #integrating phi_p_i2 * d_phi_p_i2/dz * r *d_r at z=d1
-def int_phi_p_i2_no_coef(a1, a2, h, d2):
+def int_phi_p_i2_no_coef():
     return (a2**2*(4*(h-d2)**2-a2**2) - a1**2*(4*(h-d2)**2-a1**2)) / (16*(h-d2))
 
 
-def z_n_d1_d2(n):
+def z_n_d1_d2(n, d):
     if n ==0:
         return 1
     else:
-        return sqrt(2)*(-1)**n
\ No newline at end of file
+        return sqrt(2)*(-1)**n
diff --git a/hydro/values/A.txt b/dev/python/two-region-MEEM/values/A.txt
similarity index 95%
rename from hydro/values/A.txt
rename to dev/python/two-region-MEEM/values/A.txt
index ad25d3f..b9171ce 100644
--- a/hydro/values/A.txt
+++ b/dev/python/two-region-MEEM/values/A.txt
@@ -2,15 +2,15 @@
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diff --git a/hydro/values/A_match.txt b/dev/python/two-region-MEEM/values/A_match.txt
similarity index 100%
rename from hydro/values/A_match.txt
rename to dev/python/two-region-MEEM/values/A_match.txt
diff --git a/hydro/values/A_num_imag.txt b/dev/python/two-region-MEEM/values/A_num_imag.txt
similarity index 100%
rename from hydro/values/A_num_imag.txt
rename to dev/python/two-region-MEEM/values/A_num_imag.txt
diff --git a/hydro/values/A_num_real.txt b/dev/python/two-region-MEEM/values/A_num_real.txt
similarity index 100%
rename from hydro/values/A_num_real.txt
rename to dev/python/two-region-MEEM/values/A_num_real.txt
diff --git a/hydro/values/A_values.csv b/dev/python/two-region-MEEM/values/A_values.csv
similarity index 100%
rename from hydro/values/A_values.csv
rename to dev/python/two-region-MEEM/values/A_values.csv
diff --git a/hydro/values/b.txt b/dev/python/two-region-MEEM/values/b.txt
similarity index 68%
rename from hydro/values/b.txt
rename to dev/python/two-region-MEEM/values/b.txt
index 5039ae0..f3700d5 100644
--- a/hydro/values/b.txt
+++ b/dev/python/two-region-MEEM/values/b.txt
@@ -3,13 +3,13 @@
 -2.993165870214657166e-03
 1.330295937472557701e-03
 1.559998333333333653e-01
-8.081558601251653817e-02
--2.020389650265871917e-02
-8.979509557021760416e-03
+8.081558601251656593e-02
+-2.020389650265871223e-02
+8.979509557021765620e-03
 -2.775557561562891351e-17
--1.459781247687587935e-01
+-1.459781247687587380e-01
 7.316527301817923878e-02
--2.352311108562657612e-04
+-2.352311108562664388e-04
 -4.621671557747156767e-01
 -2.837049518678329552e-01
 1.539028166376620321e-01
diff --git a/hydro/values/b_match.txt b/dev/python/two-region-MEEM/values/b_match.txt
similarity index 100%
rename from hydro/values/b_match.txt
rename to dev/python/two-region-MEEM/values/b_match.txt
diff --git a/docs/.gitkeep b/docs/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/docs/_static/SEALab_Logo_Light_202101_120ht.png b/docs/_static/SEALab_Logo_Light_202101_120ht.png
new file mode 100644
index 0000000..7c0a1df
Binary files /dev/null and b/docs/_static/SEALab_Logo_Light_202101_120ht.png differ
diff --git a/docs/_static/app_streamlit.html b/docs/_static/app_streamlit.html
new file mode 100644
index 0000000..398c6c1
--- /dev/null
+++ b/docs/_static/app_streamlit.html
@@ -0,0 +1,235 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="UTF-8" />
+    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+    <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
+    <title>Stlite app</title>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@stlite/browser@0.89.1/build/stlite.css" />
+</head>
+
+<body>
+    <div id="root"></div>
+    <script type="module">
+        import { mount } from "https://cdn.jsdelivr.net/npm/@stlite/browser@0.89.1/build/stlite.js"
+        mount(
+            {
+                requirements: ["matplotlib", "https://files.pythonhosted.org/packages/1d/20/e2d124f22516b2da7a4cd06ea83cb6e72f1cb04c9ff44a458bad8551ca20/open_flash-1.0.6-py3-none-any.whl", "xarray"],
+                entrypoint: "streamlit_app.py",
+                files: {
+                    "streamlit_app.py": `
+                    # app.py
+
+import streamlit as st
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# --- Import core MEEM modules ---
+try:
+    from openflash import *
+    from openflash.multi_equations import m_k_newton, wavenumber # Needed to convert omega to m0
+except ImportError as e:
+    st.error(f"Error importing core modules from openflash. Error: {e}")
+    st.stop()
+
+# Set print options for better visibility in console
+np.set_printoptions(threshold=np.inf, linewidth=np.inf, precision=8, suppress=True)
+
+def main():
+    st.title("OpenFLASH: MEEM Multi-Cylinder Simulation")
+    st.sidebar.header("Configuration Parameters")
+
+    # --- User Inputs for Parameters ---
+    h = st.sidebar.slider("Water Depth (h)", 0.5, 5.0, 1.001, step=0.001)
+    
+    d_input = st.sidebar.text_input("Body Step Depths (d) - comma-separated", "0.5,0.25")
+    a_input = st.sidebar.text_input("Body Radii (a) - comma-separated", "0.5,1.0")
+    heaving_input = st.sidebar.text_input("Heaving Bodies (1=True/0=False) - one per body", "1,0")
+    NMK_input = st.sidebar.text_input("Harmonics (NMK) - one per domain", "30,30,30")
+    
+    # --- UI for Single Point Test ---
+    st.sidebar.subheader("Single Frequency Test")
+    omega_single = st.sidebar.number_input("Angular Frequency (omega)", value=2.0, format="%.3f")
+    spatial_res = st.sidebar.slider("Plot Spatial Resolution", min_value=20, max_value=150, value=75, step=5)
+
+    # --- UI for Frequency Sweep ---
+    st.sidebar.subheader("Frequency Sweep for Coefficients")
+    omega_start = st.sidebar.number_input("Start Omega", value=0.1, format="%.3f")
+    omega_end = st.sidebar.number_input("End Omega", value=4.0, format="%.3f")
+    omega_steps = st.sidebar.slider("Number of Steps", min_value=10, max_value=200, value=50)
+
+
+    # --- Parse and Validate Inputs ---
+    try:
+        d_list = np.array(list(map(float, d_input.split(','))))
+        a_list = np.array(list(map(float, a_input.split(','))))
+        heaving_list = np.array(list(map(bool, heaving_input.split(','))))
+        NMK = list(map(int, NMK_input.split(',')))
+        
+        # Validation
+        if not (len(d_list) == len(a_list) == len(heaving_list)):
+            st.error("The number of depths, radii, and heaving flags must be the same (one for each body).")
+            st.stop()
+        if len(NMK) != len(a_list) + 1:
+            st.error("The number of NMK values must be one greater than the number of bodies (one for each domain).")
+            st.stop()
+    except ValueError:
+        st.error("Invalid input format. Please use comma-separated numbers.")
+        st.stop()
+
+    # --- Modern, Object-Oriented Geometry and Problem Setup ---
+    try:
+        # 1. Create Body objects
+        bodies = [
+            SteppedBody(a=np.array([a_val]), d=np.array([d_val]), slant_angle=np.array([0.0]), heaving=h_flag)
+            for a_val, d_val, h_flag in zip(a_list, d_list, heaving_list)
+        ]
+        # 2. Create Arrangement and Geometry
+        arrangement = ConcentricBodyGroup(bodies)
+        geometry = BasicRegionGeometry(arrangement, h=h, NMK=NMK)
+        # 3. Create the Problem
+        problem = MEEMProblem(geometry)
+        
+    except Exception as e:
+        st.error(f"Error during geometry setup: {e}")
+        st.stop()
+    
+    # --- Main Action Buttons ---
+    st.header("Run Analysis")
+    col1, col2 = st.columns(2)
+
+    if col1.button("Run Single Test & Plot Potentials"):
+        st.info(f"Running simulation for single omega = {omega_single:.2f}")
+        # --- Convert single omega to m0 ---
+        m0_single = wavenumber(omega_single, h)
+        problem_modes = np.where(heaving_list)[0]
+        problem.set_frequencies(np.array([omega_single]))
+        
+        # --- MEEM Engine Operations ---
+        engine = MEEMEngine(problem_list=[problem])
+        X = engine.solve_linear_system_multi(problem, m0_single)
+        
+        # Display Hydrodynamic Coefficients for the single run
+        st.subheader("Hydrodynamic Coefficients (Single Run)")
+        hydro_coefficients = engine.compute_hydrodynamic_coefficients(problem, X, m0_single)
+        if hydro_coefficients:
+            df_coeffs = pd.DataFrame(hydro_coefficients)
+            st.dataframe(df_coeffs[['mode', 'real', 'imag']])
+        else:
+            st.warning("Could not calculate hydrodynamic coefficients.")
+
+        # --- Visualize Potentials ---
+        st.subheader("Potential Field Plots")
+        potentials = engine.calculate_potentials(problem, X, m0_single, spatial_res, sharp=True)
+        R, Z, phi = potentials["R"], potentials["Z"], potentials["phi"]
+        
+        fig1, _ = engine.visualize_potential(np.real(phi), R, Z, "Total Potential (Real)")
+        st.pyplot(fig1)
+
+        fig2, _ = engine.visualize_potential(np.imag(phi), R, Z, "Total Potential (Imag)")
+        st.pyplot(fig2)
+        
+        st.success("Single frequency test complete.")
+        
+    # --- REVERTED "Run Frequency Sweep" BLOCK ---
+    if col2.button("Run Frequency Sweep & Plot Coefficients"):
+        st.info(f"Running frequency sweep for {omega_steps} steps...")
+        
+        omegas_to_run = np.linspace(omega_start, omega_end, omega_steps)
+        problem_modes = np.where(heaving_list)[0]
+        
+        # Set the frequencies and modes on the main problem object
+        problem.set_frequencies(omegas_to_run)
+        
+        # Create the engine ONCE with the main problem
+        engine = MEEMEngine(problem_list=[problem])
+        
+        with st.spinner("Running simulation..."):
+            # This single call now works correctly
+            results_obj = engine.run_and_store_results(problem_index=0)
+        st.success("Frequency sweep complete.")
+        
+        # Extract the dataset and convert to a DataFrame for plotting
+        dataset = results_obj.get_results()
+        df_results = dataset[['added_mass', 'damping']].to_dataframe().reset_index()
+
+        # --- START: FIX FOR DataFrame KeyError ---
+        # This fix is still needed to rename columns for plotting
+        data_cols = ['added_mass', 'damping', 'frequency']
+        dim_cols = [col for col in df_results.columns if col not in data_cols]
+        
+        # 2. Rename the identified dimension columns
+        if len(dim_cols) == 2:
+            rename_map = {dim_cols[0]: 'mode_i', dim_cols[1]: 'mode_j'}
+            df_results = df_results.rename(columns=rename_map)
+        elif len(dim_cols) == 0 and len(problem_modes) > 0:
+             st.error("Error: Results data does not contain mode dimensions.")
+             st.stop()
+        elif len(dim_cols) != 0:
+            st.warning(f"Unexpected number of mode dimensions ({len(dim_cols)}). Using first two.")
+            rename_map = {dim_cols[0]: 'mode_i', dim_cols[1]: 'mode_j'}
+            df_results = df_results.rename(columns=rename_map)
+        # --- END: FIX FOR KeyError ---
+
+        # --- Plotting Hydrodynamic Coefficients vs. Frequency ---
+        st.subheader("Hydrodynamic Coefficients vs. Frequency")
+        
+        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10), sharex=True)
+        
+        # This groupby should now work
+        for (mode_i, mode_j), group in df_results.groupby(['mode_i', 'mode_j']):
+            ax1.plot(group['frequency'], group['added_mass'], label=f'A({mode_i},{mode_j})')
+        ax1.set_title('Added Mass vs. Frequency')
+        ax1.set_ylabel('Added Mass')
+        ax1.legend()
+        ax1.grid(True, linestyle='--')
+
+        # This groupby should also now work
+        for (mode_i, mode_j), group in df_results.groupby(['mode_i', 'mode_j']):
+            ax2.plot(group['frequency'], group['damping'], label=f'B({mode_i},{mode_j})')
+        ax2.set_title('Damping vs. Frequency')
+        ax2.set_ylabel('Damping')
+        ax2.set_xlabel('Angular Frequency (omega)')
+        ax2.legend()
+        ax2.grid(True, linestyle='--')
+        
+        plt.tight_layout()
+        st.pyplot(fig)
+        
+        with st.expander("View Raw Data"):
+            st.dataframe(df_results)
+    # --- END: REVERTED BLOCK ---
+
+if __name__ == "__main__":
+    # Wrap main execution in a try-catch to handle potential errors gracefully
+    try:
+        main()
+    except Exception as e:
+        st.error(f"An unexpected error occurred: {e}")`,
+                    "data/logo.png": Uu("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"),
+                    "pages/🎈_subpage.py": `import streamlit as st
+
+st.title("Sub page")
+
+st.markdown("""
+_Stlite_ supports **Multi Page Apps (MPA)**, of course!
+
+Create \`pages/*.py\` like this file to add new pages.
+
+If you are new to MPA, read the official tutorial about it [🔗 here](https://docs.streamlit.io/library/get-started/multipage-apps)
+""")
+`,
+
+                },
+            },
+            document.getElementById("root")
+        )
+
+        function Uu(n) { const a = window.atob(n), i = a.length, l = new Uint8Array(i); for (let u = 0; u < i; u++)l[u] = a.charCodeAt(u); return l }
+    </script>
+</body>
+<!-- Generated from Stlite Sharing (https://edit.share.stlite.net/), and the source version is 8413a8fdd17b840f2870bdb8f43e8781722266bf -->
+
+</html>
\ No newline at end of file
diff --git a/docs/_static/domain_drawing.png b/docs/_static/domain_drawing.png
index a2d5df5..2c4ba1d 100644
Binary files a/docs/_static/domain_drawing.png and b/docs/_static/domain_drawing.png differ
diff --git a/docs/_static/domain_table.png b/docs/_static/domain_table.png
index 7066e74..e85f09f 100644
Binary files a/docs/_static/domain_table.png and b/docs/_static/domain_table.png differ
diff --git a/docs/app.py b/docs/app.py
index 55bc789..2f64635 100644
--- a/docs/app.py
+++ b/docs/app.py
@@ -1,292 +1,208 @@
+# app.py
+
 import streamlit as st
 import numpy as np
 import pandas as pd
-from scipy import linalg
 import matplotlib.pyplot as plt
-import os
-import sys
-
-# Import your custom modules
-src_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../package/src'))
-sys.path.append(src_path)
-
-from equations import *
-from multi_equations import *
-from meem_engine import MEEMEngine
-from meem_problem import MEEMProblem
-from geometry import Geometry
-
 
+# --- Import core MEEM modules ---
+try:
+    from openflash import *
+    from openflash.multi_equations import wavenumber # Needed to convert omega to m0
+    from openflash.basic_region_geometry import BasicRegionGeometry
+except ImportError as e:
+    st.error(f"Error importing core modules from openflash. Error: {e}")
+    st.stop()
 
-# Set global numpy print options
+# Set print options for better visibility in console
 np.set_printoptions(threshold=np.inf, linewidth=np.inf, precision=8, suppress=True)
 
-def plot_potential(field, R, Z, title):
-    fig, ax = plt.subplots(figsize=(8, 6))
-    c = ax.contourf(R, Z, field, levels=50, cmap='viridis')
-    plt.colorbar(c, ax=ax)
-    plt.title(title)
-    plt.xlabel("Radial Distance (R)")
-    plt.ylabel("Axial Distance (Z)")
-    st.pyplot(fig)
-
 def main():
-    st.title("MEEM Simulation")
+    st.title("OpenFLASH: MEEM Multi-Cylinder Simulation")
     st.sidebar.header("Configuration Parameters")
 
-    # Sidebar for user customization
-    st.sidebar.header("Simulation Parameters")
-
-    # User inputs for customization
-    h = st.sidebar.slider("Height (h)", 0.5, 2.0, 1.001, step=0.001)
-    d = st.sidebar.text_input("(d)", "0.5,0.25,0.25")
-    a = st.sidebar.text_input("(a)", "0.5,1,1")
-    heaving = st.sidebar.text_input("Heaving States (1=True, 0=False)", "1,1")
-    # Sidebar input for slant customization
-    slant_input = st.sidebar.text_input(
-        "Slant Vectors (e.g., 0,0,1 for each region separated by semicolon)", 
-        "0,0,1;0,0,1;0,0,1"
-    )
-
-    # Parse inputs
-    d = list(map(float, d.split(',')))
-    a = list(map(float, a.split(',')))
-    a = [val for val in a if val is not None]
-    heaving = list(map(int, heaving.split(',')))
-    slants = [
-        list(map(float, slant.split(','))) 
-        for slant in slant_input.split(';')
-    ]
-
-    # Sidebar inputs
-    NMK = st.sidebar.text_input("Number of Harmonics (NMK)", "30,30,30")
-    NMK = list(map(int, NMK.split(',')))  # Convert input to a list of integers
-    m0 = st.sidebar.number_input("Input value for m0", value=1)
-
-
-    # Ensure slants align with the number of regions
-    if len(slants) != len(NMK):
-        st.error("Please provide slant vectors for each region, separated by semicolons.")
-        return
+    # --- User Inputs for Parameters ---
+    h = st.sidebar.slider("Water Depth (h)", 0.5, 5.0, 1.001, step=0.001)
     
-    spatial_res = st.sidebar.slider("Spatial Resolution", min_value=10, max_value=100, value=50, step=5)
-    show_total = st.sidebar.checkbox("Show Total Potential Plots", value=True)
-    show_homogeneous = st.sidebar.checkbox("Show Homogeneous Potential Plots", value=True)
-    show_particular = st.sidebar.checkbox("Show Particular Potential Plots", value=True)
-    show_radial = st.sidebar.checkbox("Show Radial Velocity Potential Plots", value=True)
-    show_vertical = st.sidebar.checkbox("Show Vertical Velocity Potential Plots", value=True)
-
-    # Geometry and engine setup
-    boundary_count = len(NMK) - 1
-    domain_params = []
-    for idx in range(len(NMK)):
-        params = {
-            'number_harmonics': NMK[idx],
-            'height': h - d[idx] if idx < len(d) else h,
-            'radial_width': a[idx] if idx < len(a) else a[-1]*1.5,
-            'top_BC': None,
-            'bottom_BC': None,
-            'category': 'multi',  # Adjust category as needed
-            'di': d[idx] if idx < len(d) else 0,
-            'a': a[idx] if idx < len(a) else a[-1]*1.5,
-            'heaving': heaving[idx] if idx < len(heaving) else False,
-            # Use user input or default
-            # Set True if the region is slanted
-            'slant': slants[idx] if idx < len(slants) else [0, 0, 1]  
-        }
-        domain_params.append(params)
+    d_input = st.sidebar.text_input("Body Step Depths (d) - comma-separated", "0.5,0.25")
+    a_input = st.sidebar.text_input("Body Radii (a) - comma-separated", "0.5,1.0")
+    heaving_input = st.sidebar.text_input("Heaving Bodies (1=True/0=False) - one per body", "1,1")
+    NMK_input = st.sidebar.text_input("Harmonics (NMK) - one per domain", "30,30,30")
     
-    # Show domain parameters
-    with st.expander("View Domain Parameters"):
-        st.write("Domain Parameters:")
-        st.json(domain_params)
-
-    # Create Geometry object
-    r_coordinates = {'a': a}
-    z_coordinates = {'h': h}
-    geometry = Geometry(r_coordinates, z_coordinates, domain_params)
-
-    # Create MEEMProblem object
-    problem = MEEMProblem(geometry)
-
-    # Create MEEMEngine object
-    engine = MEEMEngine([problem])
-
-    # Solve linear system
-    A = engine.assemble_A_multi(problem, m0)
-    b = engine.assemble_b_multi(problem, m0)
-
-    # Solve the linear system A x = b
-    X = linalg.solve(A, b)
-    hydro_coefficients = engine.compute_hydrodynamic_coefficients(problem, X)
-
-    st.write("Hydrodynamic Coefficients:")
-    st.json(hydro_coefficients)
-
-    # Split up the Cs into groups depending on which equation they belong to.
-    Cs = []
-    row = 0
-    Cs.append(X[:NMK[0]])
-    row += NMK[0]
-    for i in range(1, boundary_count):
-        Cs.append(X[row: row + NMK[i] * 2])
-        row += NMK[i] * 2
-    Cs.append(X[row:])
-
-    def phi_h_n_inner_func(n, r, z):
-        return (Cs[0][n] * R_1n(n, r, 0, h, d, a)) * Z_n_i(n, z, 0, h, d)
-
-    def phi_h_m_i_func(i, m, r, z):
-        return (Cs[i][m] * R_1n(m, r, i, h, d, a) + Cs[i][NMK[i] + m] * R_2n(m, r, i, a, h, d)) * Z_n_i(m, z, i, h, d)
-
-    def phi_e_k_func(k, r, z, m0):
-        return Cs[-1][k] * Lambda_k(k, r, m0, a, NMK, h) * Z_n_e(k, z, m0, h)
-
-    # Visualization grid
-    r_vec = np.linspace(2 * a[-1] / spatial_res, 2*a[-1], spatial_res)
-    z_vec = np.linspace(-h, 0, spatial_res)
-
-    #add values at the radii
-    a_eps = 1.0e-4
-    for i in range(len(a)):
-        r_vec = np.append(r_vec, a[i]*(1-a_eps))
-        r_vec = np.append(r_vec, a[i]*(1+a_eps))
-    r_vec = np.unique(r_vec)
-
-    for i in range(len(d)):
-        z_vec = np.append(z_vec, -d[i])
-    z_vec = np.unique(z_vec)
-
-    R, Z = np.meshgrid(r_vec, z_vec)
-
-    regions = []
-    regions.append((R <= a[0]) & (Z < -d[0]))
-    for i in range(1, boundary_count):
-        regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))
-    regions.append(R > a[-1])
-
-    phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-    phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-    phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-
-    for n in range(NMK[0]):
-        temp_phiH = phi_h_n_inner_func(n, R[regions[0]], Z[regions[0]])
-        phiH[regions[0]] = temp_phiH if n == 0 else phiH[regions[0]] + temp_phiH
-
-    for i in range(1, boundary_count):
-        for m in range(NMK[i]):
-            temp_phiH = phi_h_m_i_func(i, m, R[regions[i]], Z[regions[i]])
-            phiH[regions[i]] = temp_phiH if m == 0 else phiH[regions[i]] + temp_phiH
-
-    for k in range(NMK[-1]):
-        temp_phiH = phi_e_k_func(k, R[regions[-1]], Z[regions[-1]], m0)
-        phiH[regions[-1]] = temp_phiH if k == 0 else phiH[regions[-1]] + temp_phiH
-
-    phi_p_i_vec = np.vectorize(phi_p_i)
-
-    phiP[regions[0]] = heaving[0] * phi_p_i_vec(d[0], R[regions[0]], Z[regions[0]], h)
-    for i in range(1, boundary_count):
-        phiP[regions[i]] = heaving[i] * phi_p_i_vec(d[i], R[regions[i]], Z[regions[i]], h)
-    phiP[regions[-1]] = 0
-
-    phi = phiH + phiP
-
-    def v_r_inner_func(n, r, z):
-        return (Cs[0][n] * diff_R_1n(n, r, 0, h, d, a)) * Z_n_i(n, z, 0, h, d)
-
-    def v_r_m_i_func(i, m, r, z):
-        return (Cs[i][m] * diff_R_1n(m, r, i, h, d, a) + Cs[i][NMK[i] + m] * diff_R_2n(m, r, i, h, d, a)) * Z_n_i(m, z, i, h, d)
-
-    def v_r_e_k_func(k, r, z, m0):
-        return Cs[-1][k] * diff_Lambda_k(k, r, m0, NMK, h, a) * Z_n_e(k, z, m0, h)
-
-    def v_z_inner_func(n, r, z):
-        return (Cs[0][n] * R_1n(n, r, 0, h, d, a)) * diff_Z_n_i(n, z, 0, h, d)
-
-    def v_z_m_i_func(i, m, r, z):
-        return (Cs[i][m] * R_1n(m, r, i, h, d, a) + Cs[i][NMK[i] + m] * R_2n(m, r, i, a, h, d)) * diff_Z_n_i(m, z, i, h, d)
-
-    def v_z_e_k_func(k, r, z, m0):
-        return Cs[-1][k] * Lambda_k(k, r, m0, a, NMK, h) * diff_Z_k_e(k, z, m0, h, NMK)
-
-    vr = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-    vrH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-    vrP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-
-    vz = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-    vzH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-    vzP = np.full_like(R, np.nan + np.nan*1j, dtype=complex)
-
-    for n in range(NMK[0]):
-        temp_vrH = v_r_inner_func(n, R[regions[0]], Z[regions[0]])
-        temp_vzH = v_z_inner_func(n, R[regions[0]], Z[regions[0]])
-        if n == 0:
-            vrH[regions[0]] = temp_vrH
-            vzH[regions[0]] = temp_vzH
+    # --- UI for Single Point Test ---
+    st.sidebar.subheader("Single Frequency Test")
+    omega_single = st.sidebar.number_input("Angular Frequency (omega)", value=2.0, format="%.3f")
+    spatial_res = st.sidebar.slider("Plot Spatial Resolution", min_value=20, max_value=150, value=75, step=5)
+
+    # --- UI for Frequency Sweep ---
+    st.sidebar.subheader("Frequency Sweep for Coefficients")
+    omega_start = st.sidebar.number_input("Start Omega", value=0.1, format="%.3f")
+    omega_end = st.sidebar.number_input("End Omega", value=4.0, format="%.3f")
+    omega_steps = st.sidebar.slider("Number of Steps", min_value=10, max_value=200, value=50)
+
+
+    # --- Parse and Validate Inputs ---
+    try:
+        d_list = np.array(list(map(float, d_input.split(','))))
+        a_list = np.array(list(map(float, a_input.split(','))))
+        heaving_list = np.array(list(map(bool, heaving_input.split(','))))
+        NMK = list(map(int, NMK_input.split(',')))
+        
+        # Validation
+        if not (len(d_list) == len(a_list) == len(heaving_list)):
+            st.error("The number of depths, radii, and heaving flags must be the same (one for each body/step).")
+            st.stop()
+        if len(NMK) != len(a_list) + 1:
+            st.error("The number of NMK values must be one greater than the number of steps (one for each domain).")
+            st.stop()
+    except ValueError:
+        st.error("Invalid input format. Please use comma-separated numbers.")
+        st.stop()
+
+    # --- Modern, Object-Oriented Geometry and Problem Setup ---
+    try:
+        # --- FIX: Group adjacent segments into bodies ---
+        # This logic mimics openflash_utils.py. It groups consecutive segments 
+        # with the same heaving status into a SINGLE body.
+        # e.g., heaving=[1, 1] becomes ONE body with TWO steps.
+        
+        body_map = []
+        unique_heaving_map = []
+        
+        if len(heaving_list) > 0:
+            current_body_idx = 0
+            body_map.append(current_body_idx)
+            unique_heaving_map.append(bool(heaving_list[0]))
+
+            for i in range(1, len(heaving_list)):
+                if heaving_list[i] == heaving_list[i - 1]:
+                    # Same heaving state -> same body
+                    body_map.append(current_body_idx)
+                else:
+                    # Different heaving state -> new body
+                    current_body_idx += 1
+                    body_map.append(current_body_idx)
+                    unique_heaving_map.append(bool(heaving_list[i]))
+        
+        # Use from_vectors for robust setup
+        geometry = BasicRegionGeometry.from_vectors(
+            a=a_list,
+            d=d_list,
+            h=h,
+            NMK=NMK,
+            slant_angle=np.zeros_like(a_list),
+            body_map=body_map,
+            heaving_map=unique_heaving_map
+        )
+        
+        # 3. Create the Problem
+        problem = MEEMProblem(geometry)
+        
+    except Exception as e:
+        st.error(f"Error during geometry setup: {e}")
+        st.stop()
+    
+    # --- Main Action Buttons ---
+    st.header("Run Analysis")
+    col1, col2 = st.columns(2)
+
+    if col1.button("Run Single Test & Plot Potentials"):
+        st.info(f"Running simulation for single omega = {omega_single:.2f}")
+        # --- Convert single omega to m0 ---
+        m0_single = wavenumber(omega_single, h)
+        
+        problem.set_frequencies(np.array([omega_single]))
+        
+        # --- MEEM Engine Operations ---
+        engine = MEEMEngine(problem_list=[problem])
+        X = engine.solve_linear_system_multi(problem, m0_single)
+        
+        # Display Hydrodynamic Coefficients for the single run
+        st.subheader("Hydrodynamic Coefficients (Single Run)")
+        hydro_coefficients = engine.compute_hydrodynamic_coefficients(problem, X, m0_single)
+        if hydro_coefficients:
+            df_coeffs = pd.DataFrame(hydro_coefficients)
+            st.dataframe(df_coeffs[['mode', 'real', 'imag', 'excitation_phase', 'excitation_force']])
         else:
-            vrH[regions[0]] = vrH[regions[0]] + temp_vrH
-            vzH[regions[0]] = vzH[regions[0]] + temp_vzH
-
-    for i in range(1, boundary_count):
-        for m in range(NMK[i]):
-            temp_vrH = v_r_m_i_func(i, m, R[regions[i]], Z[regions[i]])
-            temp_vzH = v_z_m_i_func(i, m, R[regions[i]], Z[regions[i]])
-            if m == 0:
-                vrH[regions[i]] = temp_vrH
-                vzH[regions[i]] = temp_vzH
-            else:
-                vrH[regions[i]] = vrH[regions[i]] + temp_vrH
-                vzH[regions[i]] = vzH[regions[i]] + temp_vzH
-
-    for k in range(NMK[-1]):
-        temp_vrH = v_r_e_k_func(k, R[regions[-1]], Z[regions[-1]], m0)
-        temp_vzH = v_z_e_k_func(k, R[regions[-1]], Z[regions[-1]], m0)
-        if k == 0:
-            vrH[regions[-1]] = temp_vrH
-            vzH[regions[-1]] = temp_vzH
+            st.warning("Could not calculate hydrodynamic coefficients.")
+
+        # --- Visualize Potentials ---
+        st.subheader("Potential Field Plots")
+        potentials = engine.calculate_potentials(problem, X, m0_single, spatial_res, sharp=True)
+        R, Z, phi = potentials["R"], potentials["Z"], potentials["phi"]
+        
+        fig1, _ = engine.visualize_potential(np.real(phi), R, Z, "Total Potential (Real)")
+        st.pyplot(fig1)
+
+        fig2, _ = engine.visualize_potential(np.imag(phi), R, Z, "Total Potential (Imag)")
+        st.pyplot(fig2)
+        
+        st.success("Single frequency test complete.")
+        
+    if col2.button("Run Frequency Sweep & Plot Coefficients"):
+        st.info(f"Running frequency sweep for {omega_steps} steps...")
+        
+        omegas_to_run = np.linspace(omega_start, omega_end, omega_steps)
+        
+        # Set the frequencies on the main problem object
+        problem.set_frequencies(omegas_to_run)
+        
+        # Create the engine ONCE with the main problem
+        engine = MEEMEngine(problem_list=[problem])
+        
+        with st.spinner("Running simulation..."):
+            results_obj = engine.run_and_store_results(problem_index=0)
+        st.success("Frequency sweep complete.")
+        
+        # Extract the dataset and convert to a DataFrame for plotting
+        dataset = results_obj.get_results()
+        df_results = dataset[['added_mass', 'damping']].to_dataframe().reset_index()
+
+        # Handle Xarray/Pandas dimension naming variations
+        data_cols = ['added_mass', 'damping', 'frequency']
+        dim_cols = [col for col in df_results.columns if col not in data_cols]
+        
+        if len(dim_cols) == 2:
+            rename_map = {dim_cols[0]: 'mode_i', dim_cols[1]: 'mode_j'}
+            df_results = df_results.rename(columns=rename_map)
+        elif len(dim_cols) != 0:
+            st.warning(f"Unexpected dimensions ({len(dim_cols)}). Renaming first two to mode_i, mode_j.")
+            if len(dim_cols) >= 2:
+                rename_map = {dim_cols[0]: 'mode_i', dim_cols[1]: 'mode_j'}
+                df_results = df_results.rename(columns=rename_map)
+
+        # --- Plotting Hydrodynamic Coefficients vs. Frequency ---
+        st.subheader("Hydrodynamic Coefficients vs. Frequency")
+        
+        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10), sharex=True)
+        
+        # Use safe iteration in case groupby keys are empty
+        if 'mode_i' in df_results.columns and 'mode_j' in df_results.columns:
+            for (mode_i, mode_j), group in df_results.groupby(['mode_i', 'mode_j']):
+                ax1.plot(group['frequency'], group['added_mass'], label=f'A({mode_i},{mode_j})')
+                ax2.plot(group['frequency'], group['damping'], label=f'B({mode_i},{mode_j})')
         else:
-            vrH[regions[-1]] = vrH[regions[-1]] + temp_vrH
-            vzH[regions[-1]] = vzH[regions[-1]] + temp_vzH
-
-    vr_p_i_vec = np.vectorize(diff_r_phi_p_i)
-    vz_p_i_vec = np.vectorize(diff_z_phi_p_i)
-
-    vrP[regions[0]] = heaving[0] * vr_p_i_vec(d[0], R[regions[0]], Z[regions[0]])
-    vzP[regions[0]] = heaving[0] * vz_p_i_vec(d[0], R[regions[0]], Z[regions[0]])
-    for i in range(1, boundary_count):
-        vrP[regions[i]] = heaving[i] * vr_p_i_vec(d[i], R[regions[i]], Z[regions[i]])
-        vzP[regions[i]] = heaving[i] * vz_p_i_vec(d[i], R[regions[i]], Z[regions[i]])
-    vrP[regions[-1]] = 0
-    vzP[regions[-1]] = 0
-
-    vr = vrH + vrP
-    vz = vzH + vzP
-
-    # Plot potentials
-    if show_total:
-        st.subheader("Total Potential")
-        plot_potential(np.real(phi), R, Z, "Real Part of Total Potential")
-        plot_potential(np.imag(phi), R, Z, "Imaginary Part of Total Potential")
-
-    if show_homogeneous:
-        st.subheader("Homogeneous Potential")
-        plot_potential(np.real(phi), R, Z, "Real Part of Homogeneous Potential")
-        plot_potential(np.imag(phi), R, Z, "Imaginary Part of Homogeneous Potential")
-
-    if show_particular:
-        st.subheader("Particular Potential")
-        plot_potential(np.real(phi), R, Z, "Real Part of Particular Potential")
-        plot_potential(np.imag(phi), R, Z, "Imaginary Part of Particular Potential")
-
-    if show_radial:
-        st.subheader("Radial Velocity Potential")
-        plot_potential(np.real(vr), R, Z, 'Radial Velocity - Real')
-        plot_potential(np.imag(vr), R, Z, 'Radial Velocity - Imaginary')
-
-    if show_vertical:
-        st.subheader("Vertical Velocity Potential")
-        plot_potential(np.real(vz), R, Z, 'Vertical Velocity - Real')
-        plot_potential(np.imag(vz), R, Z, 'Vertical Velocity - Imaginary')
+            st.warning("Could not group results by modes. Displaying raw plotting if possible.")
+
+        ax1.set_title('Added Mass vs. Frequency')
+        ax1.set_ylabel('Added Mass (kg)')
+        ax1.legend()
+        ax1.grid(True, linestyle='--')
+
+        ax2.set_title('Damping vs. Frequency')
+        ax2.set_ylabel('Damping (kg/s)')
+        ax2.set_xlabel('Angular Frequency (rad/s)')
+        ax2.legend()
+        ax2.grid(True, linestyle='--')
+        
+        plt.tight_layout()
+        st.pyplot(fig)
+        
+        with st.expander("View Raw Data"):
+            st.dataframe(df_results)
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    try:
+        main()
+    except Exception as e:
+        st.error(f"An unexpected error occurred: {e}")
\ No newline at end of file
diff --git a/docs/app_walk.rst b/docs/app_walk.rst
index 882efed..08c0e64 100644
--- a/docs/app_walk.rst
+++ b/docs/app_walk.rst
@@ -1,85 +1,96 @@
-MEEM Simulation Streamlit App
-=============================
-
-Introduction
-------------
-The MEEM Simulation app allows users to perform simulations for solving a multi-region problem with custom parameters. The app uses the MEEM (Multi-Region Eigenfunction Expansion Method) engine to compute hydrodynamic coefficients and visualize the results.
-
-Features
---------
-1. **Hydrodynamic Coefficients Calculation:** The app calculates the coefficients based on user-defined domain parameters.
-2. **Visualization:** The app provides visualizations of the radial, vertical, and total velocity potentials.
-3. **Interactive Sidebar:** Users can interactively adjust simulation parameters like height, slant vectors, spatial resolution, and harmonic values.
-
-How to Use
------------
-1. **Run the Streamlit App:**
-   Ensure that you have Streamlit installed. You can install it using:
-   
-   .. code-block:: bash
-      pip install streamlit
-
-   To launch the app, run the following command in the docs folder:
-   
-   .. code-block:: bash
-      streamlit run app.py
-
-2. **Adjust Simulation Parameters:**
-   The app features a sidebar where you can input different parameters for your simulation:
-
-   - **Water Height (h):** Adjust the height parameter. The default is set to 1.001 meters.
-   - **Body Height (d):** Enter a list of body heights separated by commas (e.g., "0.5,0.25").
-   - **Diameter (a):** Enter a list of diameters separated by commas (e.g., "0.5,1").
-   - **Heaving States:** Define whether each region is heaving (1 for True, 0 for False) by entering a comma-separated list (e.g., "1,1").
-   - **Slant Vectors:** Enter slant vectors for each region (e.g., "0,0,1;0,0,1;0,0,1").
-   - **Number of Harmonics (NMK):** Define the number of harmonics for each region, separated by commas (e.g., "30,30,30").
-   - **Spatial Resolution:** Choose the spatial resolution for the mesh grid. The range is between 10 and 100.
-   - **Checkboxes:** Select whether to display different potential plots, such as homogeneous, particular, total, radial velocity, and vertical velocity.
-
-3. **View Domain Parameters:**
-   After configuring the parameters, the app displays the domain parameters, including the number of harmonics, radial widths, and slant vectors, which are used in the simulation.
-
-4. **Run the Simulation:**
-   Once you've configured the parameters, the app automatically runs the simulation and computes the hydrodynamic coefficients.
-
-5. **Visualization:**
-   The app provides visualizations of different potential fields. These include:
-   - **Homogeneous Potential Plots**
-   - **Particular Potential Plots**
-   - **Total Potential Plots**
-   - **Radial Velocity Potential Plots**
-   - **Vertical Velocity Potential Plots**
-
-   You can interact with the plots and explore the results. The plots are generated using the Matplotlib library, with contour plots showing the variation of potential across the radial and axial distances.
-
-6. **Understanding the Results:**
-   The hydrodynamic coefficients and the potential fields are computed based on the user-defined inputs. These results are displayed in the app in a format that helps you understand the spatial variations in the velocity potentials.
-
-Code Explanation
-----------------
-The main components of the code include:
-
-1. **User Input:**
-   The app uses `st.sidebar` for user inputs, where users can adjust various parameters for the simulation.
-
-2. **Geometry and Engine Setup:**
-   The `Geometry` and `MEEMEngine` classes are used to configure the simulation domain and run the solver. The problem is set up using these objects, and the linear system is solved using SciPy's `linalg.solve()` function.
-
-3. **Potential and Velocity Calculations:**
-   The code calculates different potentials (e.g., homogeneous, particular, total) and velocities (e.g., radial, vertical) using predefined functions like `phi_h_n_inner_func`, `phi_h_m_i_func`, and others.
-
-4. **Visualization:**
-   The `plot_potential()` function creates visual representations of the computed potential fields using Matplotlib and Streamlit's `st.pyplot()`.
-
-Troubleshooting
+.. _app-module:
+
+===================
+Interactive Web App
+===================
+
+.. automodule:: app
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. _app-overview:
+
+Application Overview
+====================
+
+This Streamlit application (`app.py`) provides an interactive interface for simulating the hydrodynamic behavior of multiple bodies using the OpenFLASH engine. Users can adjust various parameters to visualize potential fields for a single frequency or compute and plot hydrodynamic coefficients over a range of frequencies.
+
+Running the App
 ---------------
-- Ensure that all necessary Python packages are installed, including `streamlit`, `numpy`, `pandas`, `scipy`, and `matplotlib`.
-- If the app does not load, check the browser console for any error messages.
-- Ensure that the correct versions of packages are being used to avoid compatibility issues.
-- If slant vectors or other inputs are misconfigured, the app will show an error and prompt you to adjust the inputs.
 
-Conclusion
-----------
-This Streamlit app provides an easy-to-use interface for running complex MEEM simulations. It allows users to customize the parameters, run simulations, and visualize the results interactively. By adjusting parameters in the sidebar, users can explore different configurations and see how they affect the hydrodynamic coefficients and velocity potentials.
+There are two ways to run the Streamlit application:
+
+**Option 1: Use the Web-Based App (Recommended)**
+
+The app is deployed and can be run directly in your web browser, with no installation required. This is the easiest way to get started.
+
+* **Interactive Streamlit App:** `Launch App <app_streamlit.html>`_
+
+**Option 2: Run the App Locally**
+
+Ensure that you have Streamlit and the `openflash` package installed.
+
+.. code-block:: bash
+
+   pip install streamlit
+
+To launch the app, run the following command from your project's root directory:
+
+.. code-block:: bash
+
+   streamlit run docs/app.py
+
+**Key Features:**
+
+* **Interactive Parameters:** Adjust water depth, body radii, step depths, and the number of harmonics.
+* **Object-Oriented Setup:** Defines the geometry by creating `SteppedBody` objects, reflecting the modern API.
+* **Single Frequency Analysis:** Solves the system for a single wave frequency and visualizes the total potential field in real time.
+* **Frequency Sweep Analysis:** Efficiently runs the simulation across a range of frequencies to compute and plot how added mass and damping coefficients change.
+
+.. _app-functions:
+
+Functions
+=========
+.. autofunction:: main
+   :noindex:
+
+The main function that sets up and runs the Streamlit application.
+
+It configures the sidebar for user inputs, parses them, and sets up the problem geometry using the object-oriented API (`SteppedBody`, `ConcentricBodyGroup`, `BasicRegionGeometry`). The interface is split into two main actions: a single frequency test and a frequency sweep.
+
+**User Inputs (Sidebar):**
+
+* **Water Depth (h):** Overall depth of the water.
+* **Body Step Depths (d):** Comma-separated list of submerged depths, one for each body.
+* **Body Radii (a):** Comma-separated list of radii, one for each body.
+* **Heaving Bodies (1/0):** Comma-separated binary list (1=True, 0=False) indicating if each body is heaving.
+* **Harmonics (NMK):** Comma-separated list specifying the number of series approximation terms for each fluid domain (number of bodies + 1).
+* **Single Frequency Test:**
+   * **Angular Frequency (omega):** The specific frequency for the potential field visualization.
+   * **Plot Spatial Resolution:** Controls the grid density for the potential plots.
+* **Frequency Sweep for Coefficients:**
+   * **Start Omega:** The beginning of the frequency range.
+   * **End Omega:** The end of the frequency range.
+   * **Number of Steps:** The number of frequencies to simulate within the range.
+
+**Simulation Workflows:**
+
+The application logic is divided into two distinct workflows, triggered by buttons in the main interface.
+
+1.  **Run Single Test & Plot Potentials:**
+
+   * Calculates the non-dimensional wavenumber (`m0`) from the user-provided `omega`.
+   * Configures the `MEEMProblem` with the single frequency and the active modes of motion.
+   * Initializes the `MEEMEngine` and calls `solve_linear_system_multi` to get the solution vector `X`.
+   * Computes and displays the hydrodynamic coefficient matrices for that single frequency.
+   * Calls `calculate_potentials` to compute the total potential field.
+   * Visualizes the real and imaginary parts of the potential field using Matplotlib.
+
+2.  **Run Frequency Sweep & Plot Coefficients:**
 
-For more information, refer to the documentation of the `MEEMEngine`, `Geometry`, and other classes used in the app.
+   * Creates an array of frequencies based on the user's start, end, and step inputs.
+   * Configures the `MEEMProblem` with the full array of frequencies and active modes.
+   * Initializes the `MEEMEngine` and calls the highly efficient `run_and_store_results` method. This single method handles the entire simulation loop internally.
+   * Extracts the computed `added_mass` and `damping` coefficients from the resulting `xarray.Dataset`.
+   * Generates and displays Matplotlib plots showing how the added mass and damping coefficients vary across the simulated frequency range.
\ No newline at end of file
diff --git a/docs/citations.rst b/docs/citations.rst
index d0f2733..1dd71b3 100644
--- a/docs/citations.rst
+++ b/docs/citations.rst
@@ -9,14 +9,22 @@ We thank Prof. R. W. Yeung and Seung-Yoon Han for discussions on the theory and
 References
 ----------
 
-1. I. K. Chatjigeorgiou, *Analytical Methods in Marine Hydrodynamics*. Cambridge: Cambridge University Press, 2018. doi: 10.1017/9781316838983.
+1. S. Bhattacharya, S. Pennock, B. Robertson, S. Hanif, M. J. E. Alam, D. Bhatnagar, D. Preziuso, and R. O’Neil, “Timing Value of Marine Renewable Energy Resources for Potential Grid Applications,” *Applied Energy*, vol. 299, p. 117281, 2021, doi: 10.1016/j.apenergy.2021.117281.
 
-2. F. P. Chau and R. W. Yeung, “Inertia and Damping of Heaving Compound Cylinders,” presented at the 25th International Workshop on Water Waves and Floating Bodies, Harbin, China, Jan. 2010. Accessed: Sep. 27, 2023. [Online]. Available: https://www.academia.edu/73219479/Inertia_and_Damping_of_Heaving_Compound_Cylinders_Fun
+2. I. K. Chatjigeorgiou, *Analytical Methods in Marine Hydrodynamics*. Cambridge: Cambridge University Press, 2018, doi: 10.1017/9781316838983.
 
-3. F. P. Chau and R. W. Yeung, “Inertia, Damping, and Wave Excitation of Heaving Coaxial Cylinders,” presented at the ASME 2012 31st International Conference on Ocean, Offshore and Arctic Engineering, American Society of Mechanical Engineers Digital Collection, Aug. 2013, pp. 803–813. doi: 10.1115/OMAE2012-83987.
+3. F. P. Chau and R. W. Yeung, “Inertia and Damping of Heaving Compound Cylinders,” presented at the 25th International Workshop on Water Waves and Floating Bodies, Harbin, China, 2010. [Online]. Available: https://www.academia.edu/73219479/Inertia_and_Damping_of_Heaving_Compound_Cylinders_Fun
 
-4. R. W. Yeung, “Added mass and damping of a vertical cylinder in finite-depth waters,” *Appl. Ocean Res.*, vol. 3, no. 3, pp. 119–133, Jul. 1981, doi: 10.1016/0141-1187(81)90101-2.
+4. F. P. Chau and R. W. Yeung, “Inertia, Damping, and Wave Excitation of Heaving Coaxial Cylinders,” in *ASME 2012 31st International Conference on Ocean, Offshore and Arctic Engineering*, 2012, pp. 803–813, doi: 10.1115/OMAE2012-83987.
 
-5. D. Son, V. Belissen, and R. W. Yeung, “Performance validation and optimization of a dual coaxial-cylinder ocean-wave energy extractor,” *Renew. Energy*, vol. 92, pp. 192–201, Jul. 2016, doi: 10.1016/j.renene.2016.01.032.
+5. F. Fusco, G. Nolan, and J. Ringwood, “Variability Reduction through Optimal Combination of Wind/Wave Resources – An Irish Case Study,” *Energy*, vol. 35, no. 1, pp. 314–325, Jan. 2010, doi: 10.1016/j.energy.2009.10.019.
 
-6. K. Kokkinowrachos, S. Mavrakos, and S. Asorakos, “Behaviour of vertical bodies of revolution in waves,” *Ocean Eng.*, vol. 13, no. 6, pp. 505–538, Jan. 1986, doi: 10.1016/0029-8018(86)90037-5.
+6. K. Kokkinowrachos, S. Mavrakos, and S. Asorakos, “Behaviour of vertical bodies of revolution in waves,” *Ocean Engineering*, vol. 13, no. 6, pp. 505–538, 1986, doi: 10.1016/0029-8018(86)90037-5.
+
+7. R. McCabe, K. Khanal, and M. Haji, “Open-source toolbox for semi-analytical hydrodynamic coefficients via the matched eigenfunction expansion method,” presented at the UMERC + METS 2024, Duluth, MN, USA, 2024. doi: 10.5281/zenodo.14504016.
+
+8. R. McCabe, K. Khanal, Y. Bimali, E. Lo, C. Treacy, and M. Haji, “Numerics of the matched eigenfunction method for computing wave forces on concentric bodies,” in preparation, 2025.
+
+9. D. Son, V. Belissen, and R. W. Yeung, “Performance validation and optimization of a dual coaxial-cylinder ocean-wave energy extractor,” *Renewable Energy*, vol. 92, pp. 192–201, Jul. 2016, doi: 10.1016/j.renene.2016.01.032.
+
+10. R. W. Yeung, “Added mass and damping of a vertical cylinder in finite-depth waters,” *Applied Ocean Research*, vol. 3, no. 3, pp. 119–133, Jul. 1981, doi: 10.1016/0141-1187(81)90101-2.
\ No newline at end of file
diff --git a/docs/conf.py b/docs/conf.py
index 4a1ba04..278fadb 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -12,26 +12,32 @@
 #
 import os
 import sys
-sys.path.insert(0, os.path.abspath('../package/src'))
-# sys.path.insert(0, os.path.abspath('.'))
+sys.path.insert(0, os.path.abspath('../package/src/openflash'))
+sys.path.insert(0, os.path.abspath('.'))
 
 
 # -- Project information -----------------------------------------------------
 
-project = 'MEEM'
-copyright = '2024, SEA Lab'
+project = 'OpenFLASH'
+copyright = '2025, SEA Lab'
 author = 'SEA Lab'
 
 # The full version, including alpha/beta/rc tags
-release = '0.1'
+from importlib.metadata import version as pkg_version, PackageNotFoundError
 
+try:
+    release = pkg_version("open-flash")
+except PackageNotFoundError:
+    release = "0+unknown"
 
 # -- General configuration ---------------------------------------------------
 
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
-extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'nbsphinx', 'sphinx.ext.mathjax', 'sphinx.ext.autodoc']
+extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'nbsphinx', 
+              'sphinx.ext.mathjax', 'sphinx_design', 'sphinx.ext.viewcode', 
+              'sphinx_tabs.tabs', 'sphinx_copybutton', 'sphinx_last_updated_by_git']
 
 # Ensure Jupyter notebooks are copied as part of the build process
 nbsphinx_allow_errors = True
@@ -50,9 +56,22 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'alabaster'
+html_theme = 'sphinx_rtd_theme'
+html_logo = '_static/SEALab_Logo_Light_202101_120ht.png'
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
\ No newline at end of file
+html_static_path = ['_static']
+
+html_context = {
+    "display_github": True,
+    "github_user": "symbiotic-engineering",
+    "github_repo": "OpenFLASH",
+    "github_version": "main",
+    "conf_py_path": "/docs/",
+}
+
+html_theme_options = {
+    'version_selector': True,
+}
\ No newline at end of file
diff --git a/docs/constants.rst b/docs/constants.rst
deleted file mode 100644
index bdb1421..0000000
--- a/docs/constants.rst
+++ /dev/null
@@ -1,38 +0,0 @@
-.. currentmodule:: package.constants
-
-Constants
-==========
-
-This module defines several physical and mathematical constants used throughout the project.
-
-Constants
-----------
-
-- **g**: Acceleration due to gravity (m/s²) — \(9.81\)
-- **pi**: The mathematical constant π — \( \pi \)
-- **h**: A project-specific constant related to the problem’s domain. 
-- **a1**: A project-specific constant that parameterizes aspects of the geometry or conditions in the problem.
-- **a2**: A project-specific constant that parameterizes aspects of the geometry or conditions in the problem.
-- **d1**: Additional constant used in defining boundary conditions or specific points in the domain.
-- **d2**: Additional constants used in defining boundary conditions or specific points in the domain.
-- **m0**: Represents a base or initial parameter, often used as a reference value in the calculations. 
-- **n**: A constant representing a fixed integer value, often used for indexing or defining specific scenarios in the model. 
-- **z**: A placeholder constant, often representing a vertical or depth-related dimension within the domain.
-- **omega**: Represents a frequency or angular velocity, typically associated with wave or rotational phenomena.
-
-Usage Example
--------------
-
-You can import and use these constants in your calculations as follows:
-
-```python
-from constants import g, pi
-```
-
-# Example of using the gravitational constant
-
-mass = 5.0  # Example mass in kg
-
-force = mass * g
-
-print("Force:", force)
diff --git a/docs/coupling.rst b/docs/coupling.rst
deleted file mode 100644
index 6d2ea70..0000000
--- a/docs/coupling.rst
+++ /dev/null
@@ -1,166 +0,0 @@
-Coupling
-========
-
-Overview
---------
-
-The `coupling` module provides functions for calculating coupling integrals used in the **Matched Eigenfunctions Method (MEEM)**. The primary functions, such as `A_nm`, `A_nm2`, `A_nj`, and `A_nj2`, compute various integral transformations. These calculations are vital for accurately modeling coupled systems in the MEEM framework.
-
-Function Definitions
---------------------
-
-.. _A_nm:
-
-A_nm
-----
-
-.. function:: A_nm(n, m)
-   
-   Computes the integral `A_nm`, representing coupling integrals between indices `n` and `m`.
-
-   **Parameters:**
-
-   - **n** (*int*): First index in the coupling integral calculation.
-   - **m** (*int*): Second index in the coupling integral calculation.
-
-   **Returns:**
-
-   - (*float*): Result of the integral for the given indices `n` and `m`.
-
-   **Raises:**
-
-   - **ValueError**: Raised if invalid indices are provided.
-
-.. code-block:: python
-
-   # Example usage
-   result = A_nm(2, 3)
-
-
-.. _A_nm2:
-
-A_nm2
------
-
-.. function:: A_nm2(j, n)
-   
-   Computes the integral `A_nm2`, applying specific transformations to indices `j` and `n`.
-
-   **Parameters:**
-
-   - **j** (*int*): First index.
-   - **n** (*int*): Second index.
-
-   **Returns:**
-
-   - (*float*): Result of the `A_nm2` integral calculation.
-
-.. _A_nj:
-
-A_nj
-----
-
-.. function:: A_nj(n, j)
-   
-   Calculates the integral `A_nj`, essential for coupling calculations.
-
-   **Parameters:**
-
-   - **n** (*int*): First index in the coupling integral.
-   - **j** (*int*): Second index in the coupling integral.
-
-   **Returns:**
-
-   - (*float*): Calculated integral for the given indices.
-
-.. _A_nj2:
-
-A_nj2
------
-
-.. function:: A_nj2(n, j)
-   
-   Computes the integral `A_nj2`, with additional transformations for `n` and `j`.
-
-   **Parameters:**
-
-   - **n** (*int*): First index.
-   - **j** (*int*): Second index.
-
-   **Returns:**
-
-   - (*float*): Calculated integral for the given indices.
-
-Helper Functions
-----------------
-
-.. _sq:
-
-sq
---
-
-.. function:: sq(x)
-   
-   Squares the input number `x`.
-
-   **Parameters:**
-
-   - **x** (*float*): Number to be squared.
-
-   **Returns:**
-
-   - (*float*): Result of squaring `x`.
-
-.. code-block:: python
-
-   # Example usage
-   result = sq(5)
-
-
-.. _nk_sigma_helper:
-
-nk_sigma_helper
----------------
-
-.. function:: nk_sigma_helper(mk, k, m)
-   
-   A helper function for `A_nm` and related calculations, handling specific transformations for values of `mk`, `k`, and `m`.
-
-   **Parameters:**
-
-   - **mk** (*float*): Coupled variable derived from `m_k`.
-   - **k** (*int*): Index in the transformation.
-   - **m** (*int*): Index in the transformation.
-
-   **Returns:**
-
-   - (*tuple*): Transformed components used in further calculations.
-
-Examples
---------
-
-The following example demonstrates a workflow using functions from the `coupling` module:
-
-.. code-block:: python
-
-   from coupling import A_nm, A_nm2, A_nj, sq
-
-   # Calculating A_nm and A_nm2
-   result_nm = A_nm(2, 3)
-   result_nm2 = A_nm2(4, 5)
-
-   # Using A_nj in further calculations
-   result_nj = A_nj(3, 6)
-
-Dependencies
-------------
-
-The following external libraries are required:
-
-- **NumPy**: For numerical operations.
-- **SciPy**: For integral transformations and mathematical operations.
-
-Notes
------
-
-Ensure all inputs fall within valid ranges to avoid `ValueError`. Index values are expected to be integers.
diff --git a/docs/domain.rst b/docs/domain.rst
deleted file mode 100644
index 98d1f28..0000000
--- a/docs/domain.rst
+++ /dev/null
@@ -1,67 +0,0 @@
-.. currentmodule:: package.domain
-
-Domain Module
-=============
-
-.. figure:: _static/domain_table.png
-   :alt: Table of domains
-   :align: center
-   :width: 100%
-
-   **Figure 1**: This table illustrates the domain characteristics.
-
-.. figure:: _static/domain_drawing.png
-   :alt: Example of how domains would look
-   :align: center
-   :width: 100%
-
-   **Figure 2**: This image illustrates the domain characteristics.
-
-This module defines the `Domain` class, which represents the characteristics of a physical domain.
-
-.. automodule:: domain
-   :members:
-   :undoc-members:
-
-Class:
---------
-
-.. autoclass:: domain.Domain
-   :members:
-   :noindex:
-   :undoc-members:
-   :show-inheritance:
-
-Attributes:
------------
-- `number_harmonics`: int — Number of harmonics in the domain.
-- `height`: float — Height of the domain.
-- `radial_width`: float — Radial width of the domain.
-- `top_BC`: float — Top boundary condition.
-- `bottom_BC`: float — Bottom boundary condition.
-- `category`: str — Category of the domain, indicating if it is 'inner', 'outer', or 'exterior'.
-- `params`: dict — Dictionary of parameters specific to the domain, such as `h`, `di`, `a1`, `a2`, `m0`.
-- `index` : int - Index of the domain in the multi-region setup.
-
-Methods:
---------
-
-.. method:: __init__(number_harmonics, height, radial_width, top_BC, bottom_BC, category, params)
-   :noindex:
-
-   Initializes the Domain class with specified parameters.
-
-   :param number_harmonics: The number of harmonics.
-   :type number_harmonics: int
-   :param height: Height of the domain.
-   :type height: float
-   :param radial_width: Radial width of the domain.
-   :type radial_width: float
-   :param top_BC: Top boundary condition.
-   :type top_BC: float
-   :param bottom_BC: Bottom boundary condition.
-   :type bottom_BC: float
-   :param category: Type of the domain (e.g., 'inner', 'outer', 'exterior').
-   :type category: str
-   :param params: Dictionary containing parameters like `h`, `di`, `a1`, `a2`, `m0`.
-   :type params: dict
\ No newline at end of file
diff --git a/docs/equations.rst b/docs/equations.rst
deleted file mode 100644
index e31a9ae..0000000
--- a/docs/equations.rst
+++ /dev/null
@@ -1,194 +0,0 @@
-.. _equations.py:
-
-Equation Module
-=====================
-
-This module defines a set of functions for solving various equations used in physics and engineering. It includes equations related to cylindrical Bessel functions, specific to the problem at hand, and integrates key mathematical concepts such as Hankel, Bessel, and modified Bessel functions.
-
-Dependencies
-------------
-
-- numpy
-- scipy (special, integrate, linalg, optimize)
-- matplotlib
-- constants (external module)
-
-Functions
----------
-
-**m_k(k)**
-   Computes the value of `m_k` using Newton's method for root finding.
-
-   :param k: Integer value for the index.
-   :return: Computed value of `m_k`.
-
-**m_k_newton(h)**
-   Solves for `m_k` using Newton's method with a given height `h`.
-
-   :param h: Height value.
-   :return: Result from Newton's method.
-
-**lambda_n1(n)**
-   Computes the first eigenvalue `lambda_n1` for the first layer.
-
-   :param n: Integer value for the index.
-   :return: Computed value of `lambda_n1`.
-
-**lambda_n2(n)**
-   Computes the second eigenvalue `lambda_n2` for the second layer.
-
-   :param n: Integer value for the index.
-   :return: Computed value of `lambda_n2`.
-
-**phi_p_a1(z)**
-   Computes the function `phi_p` for the first particular solution.
-
-   :param z: Z-coordinate.
-   :return: Computed value of `phi_p_a1`.
-
-**phi_p_a2(z)**
-   Computes the function `phi_p` for the second particular solution.
-
-   :param z: Z-coordinate.
-   :return: Computed value of `phi_p_a2`.
-
-**diff_phi_p_i2_a2(h)**
-   Differentiates the second particular solution with respect to height `h`.
-
-   :param h: Height value.
-   :return: Differentiated result.
-
-**R_1n_1(n, r)**
-   Computes the first radial function for the first layer `R_1n_1`.
-
-   :param n: Integer value for the index.
-   :param r: Radial distance.
-   :return: Computed value of `R_1n_1`.
-
-**R_1n_2(n, r)**
-   Computes the first radial function for the second layer `R_1n_2`.
-
-   :param n: Integer value for the index.
-   :param r: Radial distance.
-   :return: Computed value of `R_1n_2`.
-
-**diff_R_1n_1(n, r)**
-   Differentiates the radial function `R_1n_1` with respect to radius `r`.
-
-   :param n: Integer value for the index.
-   :param r: Radial distance.
-   :return: Differentiated result.
-
-**diff_R_1n_2(n, r)**
-   Differentiates the radial function `R_1n_2` with respect to radius `r`.
-
-   :param n: Integer value for the index.
-   :param r: Radial distance.
-   :return: Differentiated result.
-
-**R_2n_1(n)**
-   Returns 0 as per the given equation for the second radial function of the first layer.
-
-   :param n: Integer value for the index.
-   :return: 0.
-
-**R_2n_2(n, r)**
-   Computes the second radial function for the second layer `R_2n_2`.
-
-   :param n: Integer value for the index.
-   :param r: Radial distance.
-   :return: Computed value of `R_2n_2`.
-
-**diff_R_2n_2(n, r)**
-   Differentiates the radial function `R_2n_2` with respect to radius `r`.
-
-   :param n: Integer value for the index.
-   :param r: Radial distance.
-   :return: Differentiated result.
-
-**Z_n_i1(n, z)**
-   Computes the `Z_n_i1` function for the first layer.
-
-   :param n: Integer value for the index.
-   :param z: Z-coordinate.
-   :return: Computed value of `Z_n_i1`.
-
-**Z_n_i2(n, z)**
-   Computes the `Z_n_i2` function for the second layer.
-
-   :param n: Integer value for the index.
-   :param z: Z-coordinate.
-   :return: Computed value of `Z_n_i2`.
-
-**Lambda_k_r(k, r)**
-   Computes the function `Lambda_k_r` using the Bessel functions.
-
-   :param k: Integer value for the index.
-   :param r: Radial distance.
-   :return: Computed value of `Lambda_k_r`.
-
-**diff_Lambda_k_a2(n)**
-   Differentiates `Lambda_k_r` with respect to the radius `a2`.
-
-   :param n: Integer value for the index.
-   :return: Differentiated result.
-
-**N_k(k)**
-   Computes the function `N_k` based on the value of `k`.
-
-   :param k: Integer value for the index.
-   :return: Computed value of `N_k`.
-
-**Z_n_e(k, z)**
-   Computes the function `Z_n_e` for a given `k` and `z`.
-
-   :param k: Integer value for the index.
-   :param z: Z-coordinate.
-   :return: Computed value of `Z_n_e`.
-
-**diff_phi_p_i1_dz(z)**
-   Differentiates `phi_p_i1` with respect to `z`.
-
-   :param z: Z-coordinate.
-   :return: Differentiated result.
-
-**diff_phi_p_i2_dz(z)**
-   Differentiates `phi_p_i2` with respect to `z`.
-
-   :param z: Z-coordinate.
-   :return: Differentiated result.
-
-**int_R_1n_1(n)**
-   Computes the integral of `R_1n_1` for a given index `n`.
-
-   :param n: Integer value for the index.
-   :return: Computed integral.
-
-**int_R_1n_2(n)**
-   Computes the integral of `R_1n_2` for a given index `n`.
-
-   :param n: Integer value for the index.
-   :return: Computed integral.
-
-**int_R_2n_2(n)**
-   Computes the integral of `R_2n_2` for a given index `n`.
-
-   :param n: Integer value for the index.
-   :return: Computed integral.
-
-**int_phi_p_i1_no_coef()**
-   Computes the integral of `phi_p_i1` without coefficients.
-
-   :return: Computed integral.
-
-**int_phi_p_i2_no_coef()**
-   Computes the integral of `phi_p_i2` without coefficients.
-
-   :return: Computed integral.
-
-**z_n_d1_d2(n, d)**
-   Computes the value of `z_n_d1_d2` for the given index `n` and parameter `d`.
-
-   :param n: Integer value for the index.
-   :param d: Parameter for the layer.
-   :return: Computed value of `z_n_d1_d2`.
diff --git a/docs/example_results.nc b/docs/example_results.nc
deleted file mode 100644
index 97045cb..0000000
Binary files a/docs/example_results.nc and /dev/null differ
diff --git a/docs/geometry.rst b/docs/geometry.rst
deleted file mode 100644
index 01a0722..0000000
--- a/docs/geometry.rst
+++ /dev/null
@@ -1,45 +0,0 @@
-.. currentmodule:: package.geometry
-
-Geometry Module
-===============
-
-This module defines the `Geometry` class, which is responsible for creating and managing domain objects within the specified coordinates of a given geometry. Domains within the geometry are characterized by unique radial and vertical coordinates and parameterized to enable computation of eigenfunctions and potentials within each region.
-
-.. automodule:: geometry
-   :members:
-   :undoc-members:
-
-Class:
---------
-
-.. autoclass:: geometry.Geometry
-   :members:
-   :noindex:
-   :undoc-members:
-   :show-inheritance:
-
-Attributes:
------------
-- `r_coordinates`: Dict[str, float] — Dictionary of radial coordinates specifying positions within the geometry.
-- `z_coordinates`: Dict[str, float] — Dictionary of vertical coordinates that define height positions within the geometry.
-- `domain_params`: List[Dict] — A list of dictionaries where each dictionary contains parameters for initializing a `Domain` object, including harmonics, boundary conditions, and physical properties.
-
-Methods:
---------
-.. method:: __init__(r_coordinates, z_coordinates, domain_params)
-   :noindex:
-   
-   Initializes the Geometry class with the given coordinates and parameters.
-   
-   :param r_coordinates: The radial coordinates defining the radial positions within the geometry.
-   :type r_coordinates: Dict[str, float]
-   :param z_coordinates: The vertical coordinates defining height positions.
-   :type z_coordinates: Dict[str, float]
-   :param domain_params: A list of dictionaries, each containing parameters for creating a `Domain`.
-   :type domain_params: List[Dict]
-
-.. method:: make_domain_list() -> Dict[int, Domain]
-   
-   Creates a dictionary of `Domain` objects, where each key is an integer index mapping to a `Domain`.
-   
-   :returns: A dictionary of `Domain` objects by index.
diff --git a/docs/index.rst b/docs/index.rst
index 01a90b1..f66cfdf 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -3,30 +3,22 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-Welcome to MEEM's Documentation!
-================================
+Welcome to OpenFLASH's Documentation!
+======================================
 
-This documentation provides an overview of the **Multiple Expansion Eigenfunction Method (MEEM)** package, which is designed to solve mathematical problems related to eigenfunctions and domain matching. The package is structured to allow for efficient geometry management, boundary condition matching, and problem-solving functionalities.
+This documentation provides an overview of the **OpenFLASH** package, which is designed to solve mathematical problems related to eigenfunctions and domain matching. The package is structured to allow for efficient geometry management, boundary condition matching, and problem-solving functionalities.
 
 Modules Overview
 ================
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
    :caption: Contents:
 
    introduction
-   geometry
-   domain
-   meem_problem
-   meem_engine
-   results
+   installation
+   modules/index
    tutorial_walk
-   constants
-   multi_constants
-   coupling
-   equations
-   multi_equations
    app_walk
    citations
 
diff --git a/docs/installation.rst b/docs/installation.rst
new file mode 100644
index 0000000..4ab3c23
--- /dev/null
+++ b/docs/installation.rst
@@ -0,0 +1,120 @@
+.. _installation:
+
+============
+Installation
+============
+
+This section provides detailed instructions on how to set up **OpenFLASH** on your local machine.
+
+Prerequisites
+-------------
+Before you begin, ensure you have the following software installed on your system:
+
+* **Python**: Version 3.9 or higher. You can download the latest version from `python.org <https://www.python.org/downloads/>`_.
+* **pip**: The Python package installer, which typically comes bundled with Python installations.
+* **Anaconda**: Popular Python distribution for scientific computing that simplifies environment management.
+
+Recommended Setup: Virtual Environments
+---------------------------------------
+It is **strongly recommended** to use a virtual environment to manage the project's dependencies. This isolates the OpenFLASH's packages from your system-wide Python installation, preventing potential conflicts with other Python projects.
+
+Choose one of the following methods to set up your virtual environment:
+
+.. tabs::
+
+   .. tab:: Using `venv` (Python's built-in)
+
+      1.  **Create a virtual environment**:
+          Open your terminal or command prompt. Navigate to your desired location (e.g., where you plan to clone the openFlASH repository). Then, run the following command:
+
+          .. code-block:: bash
+
+              python3 -m venv openflash_project_env
+
+          This creates a new directory named `openflash_project_env` containing the virtual environment files.
+
+      2.  **Activate the virtual environment**:
+
+          * **macOS / Linux**:
+              .. code-block:: bash
+
+                  source openflash_project_env/bin/activate
+
+          * **Windows (Command Prompt)**:
+              .. code-block:: batch
+
+                  openflash_project_env\Scripts\activate.bat
+
+          * **Windows (PowerShell)**:
+              .. code-block:: powershell
+
+                  .\openflash_project_env\Scripts\Activate.ps1
+
+          Your terminal prompt should change to indicate that the virtual environment is active (e.g., `(openflash_project_env)` will appear at the beginning of your prompt).
+
+   .. tab:: Using `conda`
+
+      1.  **Create a Conda environment**:
+          If you have Anaconda installed, you can create a dedicated environment for the project:
+
+          .. code-block:: bash
+
+              conda create -n openflash_project_env python=3.9  # You can specify your preferred Python version
+
+      2.  **Activate the Conda environment**:
+
+          .. code-block:: bash
+
+              conda activate openflash_project_env
+
+          Your terminal prompt will change to show the active environment (e.g., `(openflash_project_env)`).
+
+Installation via PyPI (pip)
+---------------------------
+**OpenFLASH** can be installed directly from PyPI using `pip`.
+
+1.  **Activate your virtual environment**:
+    Ensure your chosen virtual environment (created with `venv` or `conda`) is active.
+
+2.  **Install OpenFLASH**:
+    Run the following command in your activated environment:
+
+    .. code-block:: bash
+
+        pip install open-flash
+
+    This will download and install the latest stable version of OpenFLASH and its dependencies.
+
+Installing via Conda
+---------------------
+
+You can install **OpenFLASH** directly from the `sea-lab` channel on Anaconda.org by running:
+
+.. code-block:: bash
+
+    conda install sea-lab::open-flash
+
+This will install **OpenFLASH** and all necessary dependencies into your current conda environment.
+
+.. note::
+
+   Ensure your conda environment is activated before running the command.
+
+Verification (Optional)
+-----------------------
+To quickly verify that your installation was successful and core dependencies are available, you can open a Python interpreter within your activated environment and try importing some modules:
+
+.. code-block:: python
+
+    >>> import numpy
+    >>> import scipy
+    >>> import matplotlib
+    >>> print("All core dependencies imported successfully!")
+    >>> exit()
+
+Troubleshooting
+---------------
+* ``Command python3 not found`` or similar errors: Ensure Python is correctly installed and added to your system's PATH. On some systems, ``python`` might refer to Python 2, and ``python3`` to Python 3.
+* ``pip install -r requirements.txt`` fails:
+    * Check your internet connection.
+    * For specific compilation errors related to scientific packages (e.g., ``scipy``), you may need to install system-level build tools (like ``build-essential`` on Linux or Xcode Command Line Tools on macOS) or refer to the official documentation of the problematic package.
\ No newline at end of file
diff --git a/docs/introduction.rst b/docs/introduction.rst
index 50105ca..b024893 100644
--- a/docs/introduction.rst
+++ b/docs/introduction.rst
@@ -1,39 +1,84 @@
+.. _introduction:
+
+==============
 Introduction
-============
+==============
 
-This documentation provides an overview of the **Matched Eigenfunctions Project (MEEM)**, a framework for solving boundary value problems using eigenfunction expansion methods. Designed for researchers and engineers, MEEM is ideal for fields requiring precise numerical solutions to complex physical problems.
+Welcome to **OpenFLASH**, a modern Python package for solving wave-body interaction problems for concentric cylindrical structures.
 
-Project Overview
-----------------
+OpenFLASH implements the **Matched Eigenfunction Expansion Method (MEEM)**, a powerful semi-analytical technique that offers significant performance advantages over traditional numerical methods like the Boundary Element Method (BEM). The package is designed to be user-friendly, efficient, and easily extensible for researchers, engineers, and students in marine hydrodynamics.
 
-The MEEM framework is composed of several key components:
+---
 
-- **Geometry**: Defines the spatial properties and configurations of the domains, creating domain objects with specific attributes.
-- **Domain**: Represents individual physical regions, including their unique properties and boundary conditions.
-- **MEEMProblem**: Manages multiple domains, ensuring proper matching of boundary conditions between adjacent domains.
-- **MEEMEngine**: Executes core numerical computations, constructs necessary matrices, and provides visualization tools for analysis.
-- **Results**: Class to store results in an xarray format similar to Capytaine's conventions. Provides methods to store, access, and export results to a .nc file.
+Why OpenFLASH?
+--------------
 
-Workflow
---------
+* **High Performance**: Leveraging the semi-analytical nature of MEEM, OpenFLASH is exceptionally fast. Our benchmarks show that it can compute hydrodynamic coefficients up to **10 times faster** than leading open-source BEM packages like Capytaine, especially for frequency sweep analyses. This speed is achieved by an intelligent caching system that minimizes redundant calculations.
 
-To use MEEM effectively, follow these steps:
+* **Intuitive, Object-Oriented API**: Define your physical problem intuitively by creating ``SteppedBody`` objects. The library's object-oriented structure handles the complex task of translating this physical geometry into the mathematical fluid domains required for the solver, making your code cleaner and more readable.
 
-1. **Define Geometry**: Use the Geometry class to specify the layout and properties of your physical domain.
-2. **Set Up Domains**: Create Domain objects for each region, detailing the physical characteristics and boundary conditions.
-3. **Initialize MEEMProblem**: Create an instance of MEEMProblem to manage the problem setup and perform boundary condition matching.
-4. **Run Computations**: Use MEEMEngine to execute numerical computations, assemble matrices, and visualize the results.
-5. **Store Results**: Use the Results class to store results in an xarray format similar to Capytaine's conventions. Provides methods to store, access, and export results to a .nc file.
+* **Structured Data Output**: Simulation results are not just raw numbers. OpenFLASH packages all outputs into an ``xarray.Dataset``, a powerful data structure that provides labeled dimensions (like 'frequencies', 'modes') and coordinates. This makes your data self-describing, easy to analyze with tools like Pandas, and simple to export to standard scientific formats like NetCDF.
 
-Refer to each class section for specific examples, parameter options, and code snippets.
+---
 
-Getting Started
+Quick Example
+-------------
+
+Here is a minimal example of setting up and solving a two-body problem:
+
+.. code-block:: python
+
+   import numpy as np
+   from openflash import (
+       SteppedBody, ConcentricBodyGroup, BasicRegionGeometry,
+       MEEMProblem, MEEMEngine, omega, g
+   )
+
+   # 1. Define the physical bodies
+   body1 = SteppedBody(a=np.array([5.0]), d=np.array([20.0]), heaving=True)
+   body2 = SteppedBody(a=np.array([10.0]), d=np.array([10.0]), heaving=False)
+
+   # 2. Create the geometry from the bodies
+   arrangement = ConcentricBodyGroup(bodies=[body1, body2])
+   geometry = BasicRegionGeometry(
+       body_arrangement=arrangement,
+       h=100.0,
+       NMK=[30, 30, 30]
+   )
+
+   # 3. Set up the problem with simulation parameters
+   problem = MEEMProblem(geometry)
+   problem.set_frequencies(
+       frequencies=np.array([omega(m0=1.0, h=100.0, g=g)]),
+   )
+
+   # 4. Run the engine and get results
+   engine = MEEMEngine(problem_list=[problem])
+   results = engine.run_and_store_results(problem_index=0)
+
+   # 5. Analyze the output
+   print(results.get_results())
+
+
+---
+
+Target Audience
 ---------------
+This documentation is intended for:
+
+* Researchers and students in ocean engineering and marine hydrodynamics.
 
-To begin using MEEM:
+* Engineers working on wave energy converters or offshore platforms.
 
-1. **Install Dependencies**: Ensure all required libraries are installed. Run `pip install -r requirements.txt` to install dependencies.
-2. **Import Modules**: In your Python environment, import necessary modules based on your specific problem.
-3. **Review Examples**: Check each class documentation section for code examples and application cases.
+* Developers interested in contributing to or extending the codebase.
+
+---
+
+Getting Started
+---------------
+To begin using OpenFLASH, we recommend the following steps:
 
-For additional resources, questions, or contributions, please refer to the `project repository <https://github.com/symbiotic-engineering/semi-analytical-hydro>`_ or contact the project maintainers.
+1.  **Installation**: Follow the instructions in the :doc:`installation` guide to set up the package in a virtual environment.
+2.  **Run the Tutorial**: Walk through the `Jupyter Notebook Tutorial <tutorial.html>`_ for a hands-on, interactive example of a full simulation.
+3.  **Explore the Web App**: Try the interactive Streamlit application by following the :doc:`app_walk` guide.
+4.  **API Reference**: For detailed information on specific classes and functions, refer to the individual module documentation listed in the sidebar.
\ No newline at end of file
diff --git a/docs/meem_engine.rst b/docs/meem_engine.rst
deleted file mode 100644
index d4d69e6..0000000
--- a/docs/meem_engine.rst
+++ /dev/null
@@ -1,117 +0,0 @@
-.. currentmodule:: package.meem_engine
-
-MEEMEngine Class
-================
-
-The `MEEMEngine` class manages multiple `MEEMProblem` instances and performs actions such as solving systems of equations,
-assembling matrices, and computing hydrodynamic coefficients.
-
-Methods
--------
-
-__init__(problem_list: List[MEEMProblem])
-------------------------------------------
-Initialize the `MEEMEngine` object.
-
-:param problem_list: List of `MEEMProblem` instances.
-:returns: None
-
-assemble_A(problem: MEEMProblem) -> np.ndarray
-------------------------------------------------
-Assemble the system matrix `A` for a given problem.
-
-:param problem: The `MEEMProblem` instance containing the domain information.
-:returns: Assembled matrix `A` of shape (size, size), where `size` is the total number of harmonics across all domains.
-
-assemble_A_multi(problem: MEEMProblem) -> np.ndarray
-------------------------------------------------------
-Assemble the system matrix `A` for a multi-domain problem with multiple boundary conditions.
-
-:param problem: The `MEEMProblem` instance containing the domain list and domain parameters.
-:returns: Assembled matrix `A` of shape (size, size), where `size` is the total number of harmonics across all domains.
-
-assemble_b(problem: MEEMProblem) -> np.ndarray
-------------------------------------------------
-Assemble the right-hand side vector `b` for a given problem.
-
-:param problem: The `MEEMProblem` instance.
-:returns: Assembled vector `b` as a numpy array.
-
-assemble_b_multi(problem: MEEMProblem) -> np.ndarray
-------------------------------------------------------
-Assemble the right-hand side vector `b` for a given multi-region problem.
-
-:param problem: The `MEEMProblem` instance.
-:returns: Assembled vector `b` as a numpy array.
-
-compute_hydrodynamic_coefficients(problem: MEEMProblem, X: np.ndarray) -> Dict[str, any]
------------------------------------------------------------------------------------------
-Compute the hydrodynamic coefficients for a given problem and solution vector `X`.
-
-:param problem: The `MEEMProblem` instance.
-:param X: The solution vector `X` obtained from solving `A x = b`.
-:returns: Dictionary containing the hydrodynamic coefficients and related values.
-
-Detailed Method Descriptions
------------------------------
-
-__init__(problem_list: List[MEEMProblem])
-------------------------------------------
-This method initializes the `MEEMEngine` object with a list of `MEEMProblem` instances. It is used to set up the
-problem set that the engine will manage.
-
-assemble_A(problem: MEEMProblem) -> np.ndarray
-------------------------------------------------
-This method assembles the system matrix `A` for the given problem. The matrix is constructed based on the harmonics
-and domain properties. It calculates matrix entries using the functions `equations.R_1n_1`, `equations.R_1n_2`,
-`equations.Lambda_k_r`, etc., to populate the elements of the matrix.
-
-assemble_A_multi(problem: MEEMProblem) -> np.ndarray
-------------------------------------------------------
-This method assembles the system matrix `A` for a multi-domain problem. The matrix is constructed similarly to
-`assemble_A`, but with added complexity to account for multiple boundaries and regions.
-
-assemble_b(problem: MEEMProblem) -> np.ndarray
-------------------------------------------------
-This method assembles the right-hand side vector `b` for the given problem. The vector is computed by integrating
-various functions, including `phi_p_i1_i2_a1`, `Z_n_i1`, `phi_p_a2`, and others across the domains. It considers the
-harmonics and boundary conditions to compute the vector entries.
-
-assemble_b_multi(problem: MEEMProblem) -> np.ndarray
-------------------------------------------------------
-This method assembles the right-hand side vector `b` for a multi-domain problem. It calculates entries for boundary
-conditions, velocity matching, and potential matching using the corresponding multi-domain equations.
-
-compute_hydrodynamic_coefficients(problem: MEEMProblem, X: np.ndarray) -> Dict[str, any]
------------------------------------------------------------------------------------------
-This method computes the hydrodynamic coefficients for the given problem and solution vector `X`. The coefficients are
-calculated using integrals of various functions over the domains. The method also calculates the real and imaginary
-components of the coefficients and finds the maximum heaving radius for non-dimensionalizing the coefficient.
-
-### Example Usage
-
-```python````
-from meem_engine import MEEMEngine
-from meem_problem import MEEMProblem
-
-# Define problems (Example)
-problem_1 = MEEMProblem(...)
-problem_2 = MEEMProblem(...)
-
-# Create engine with a list of problems
-engine = MEEMEngine([problem_1, problem_2])
-
-# Assemble matrix for a single problem
-matrix_A = engine.assemble_A(problem_1)
-
-# Assemble matrix for a multi-domain problem
-matrix_A_multi = engine.assemble_A_multi(problem_1)
-
-# Assemble right-hand side vector for a problem
-vector_b = engine.assemble_b(problem_1)
-
-# Assemble right-hand side vector for a multi-domain problem
-vector_b_multi = engine.assemble_b_multi(problem_1)
-
-# Compute hydrodynamic coefficients for a solution
-hydro_results = engine.compute_hydrodynamic_coefficients(problem_1, X)
diff --git a/docs/meem_problem.rst b/docs/meem_problem.rst
deleted file mode 100644
index 7bba0de..0000000
--- a/docs/meem_problem.rst
+++ /dev/null
@@ -1,47 +0,0 @@
-.. currentmodule:: package.meem_problem
-
-MEEMProblem Module
-====================
-
-This module defines the `MEEMProblem` class, which is responsible for managing individual matched eigenfunction problems. It aggregates domains and checks for boundary condition matches between them to ensure the correct mathematical problem setup.
-
-.. automodule:: meem_problem
-   :members:
-   :undoc-members:
-
-Class:
---------
-.. autoclass:: meem_problem.MEEMProblem
-   :members:
-   :noindex:
-   :undoc-members:
-   :show-inheritance:
-
-Attributes:
------------
-- `domain_list`: List[Domain] — Populated from the Geometry class, this list holds Domain instances to be checked for boundary condition matching.
-
-Methods:
---------
-.. method:: __init__(geometry: Geometry)
-   :noindex:
-
-   Initializes the MEEMProblem instance with a geometry, loading domain information from the Geometry object.
-   
-   :param geometry: An instance of the Geometry class containing domain information.
-   :type geometry: Geometry
-
-.. method:: match_domains() -> Dict[int, Dict[str, bool]]
-
-   Checks boundary condition matching between consecutive domains in the `domain_list`.
-   
-   :returns: A dictionary with information about the matching status of each domain pair, keyed by the index. The dictionary includes a boolean value for each boundary condition check (e.g., 'top_match', 'bottom_match').
-
-.. method:: perform_matching(matching_info: Dict[int, Dict[str, bool]]) -> bool
-
-   Takes matching information from `match_domains` and verifies if all domains match according to the specified boundary conditions.
-   
-   :param matching_info: A dictionary containing the matching status between domain pairs.
-   :returns: True if all domains match successfully, False otherwise.
-   
-   If matching fails, the method prints the index at which matching failed.
diff --git a/docs/meem_simulation_results.nc b/docs/meem_simulation_results.nc
new file mode 100644
index 0000000..20243b2
Binary files /dev/null and b/docs/meem_simulation_results.nc differ
diff --git a/docs/modules/basic_region_geometry.rst b/docs/modules/basic_region_geometry.rst
new file mode 100644
index 0000000..68082bd
--- /dev/null
+++ b/docs/modules/basic_region_geometry.rst
@@ -0,0 +1,81 @@
+.. _basic_region_geometry-module:
+
+============================
+Basic Region Geometry Module
+============================
+
+.. automodule:: openflash.basic_region_geometry
+
+.. _basic_region_geometry-overview:
+
+Conceptual Overview
+===================
+
+The ``BasicRegionGeometry`` class is the primary **concrete implementation** of the abstract :class:`~openflash.geometry.Geometry` class. It is designed for the most common use case: a set of simple, concentric bodies whose radii are strictly increasing from the center outwards.
+
+Its main responsibility is to take a physical description of the bodies (via a :class:`~openflash.geometry.ConcentricBodyGroup`) and automatically partition the fluid volume into a series of non-overlapping :class:`~openflash.domain.Domain` objects. This process is crucial as it translates the user-defined physical problem into the structured set of sub-regions required by the ``MEEMEngine`` for solving.
+
+Primary Usage (Object-Oriented)
+===============================
+
+The standard way to use this class is by first defining your physical objects and then passing them to the constructor.
+
+.. code-block:: python
+
+   from openflash import SteppedBody, ConcentricBodyGroup, BasicRegionGeometry
+
+   # 1. Define the physical bodies
+   body1 = SteppedBody(a=np.array([5.0]), d=np.array([20.0]), heaving=True)
+   body2 = SteppedBody(a=np.array([10.0]), d=np.array([10.0]), heaving=False)
+
+   # 2. Group the bodies into an arrangement
+   arrangement = ConcentricBodyGroup(bodies=[body1, body2])
+
+   # 3. Define other parameters
+   h = 100.0  # Total water depth
+   NMK = [30, 30, 30] # Harmonics for inner, middle, and outer domains
+
+   # 4. Create the Geometry object
+   # This object will automatically generate the fluid domains internally.
+   geometry = BasicRegionGeometry(
+       body_arrangement=arrangement,
+       h=h,
+       NMK=NMK
+   )
+
+Alternative Usage (Vector-Based)
+================================
+
+For convenience, the classmethod :meth:`~from_vectors` allows you to create a ``BasicRegionGeometry`` instance directly from NumPy arrays without explicitly creating ``SteppedBody`` objects. This can be useful for scripting or when working with data from other sources.
+
+.. code-block:: python
+
+   from openflash import BasicRegionGeometry
+   import numpy as np
+
+   # Define geometry using simple arrays
+   a_vals = np.array([5.0, 10.0])
+   d_vals = np.array([20.0, 10.0])
+   heaving_flags = [True, False]
+   h = 100.0
+   NMK = [30, 30, 30]
+
+   # Create the geometry object directly from vectors
+   geometry = BasicRegionGeometry.from_vectors(
+       a=a_vals,
+       d=d_vals,
+       h=h,
+       NMK=NMK,
+       heaving_map=heaving_flags
+   )
+
+
+.. _basic_region_geometry-api:
+
+API Reference
+=============
+
+.. autoclass:: openflash.basic_region_geometry.BasicRegionGeometry
+   :members: from_vectors, domain_list, make_fluid_domains
+   :undoc-members:
+   :show-inheritance:
\ No newline at end of file
diff --git a/docs/modules/body.rst b/docs/modules/body.rst
new file mode 100644
index 0000000..8f79890
--- /dev/null
+++ b/docs/modules/body.rst
@@ -0,0 +1,47 @@
+.. _body-module:
+
+===========
+Body Module
+===========
+
+.. automodule:: openflash.body
+
+.. _body-overview:
+
+Conceptual Overview
+===================
+
+The Body module provides the classes used to define the physical structures in the hydrodynamic simulation. These classes serve as the fundamental building blocks of the problem geometry.
+
+The user's first step in setting up a simulation is to create one or more ``Body`` objects. Each object represents a distinct physical component with its own geometric properties (like radii and depths) and dynamic properties (like whether it is heaving).
+
+Once defined, these ``Body`` objects are collected into a :class:`~openflash.geometry.BodyArrangement` (typically a :class:`~openflash.geometry.ConcentricBodyGroup`), which is then used to initialize a :class:`~openflash.geometry.Geometry` object.
+
+The most important and commonly used class in this module is the ``SteppedBody``.
+
+.. _body-api:
+
+API Reference
+=============
+
+Body (Abstract Base Class)
+--------------------------
+
+.. autoclass:: openflash.body.Body
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+   This is an **abstract base class** that defines the basic interface for all body types. You will not create instances of this class directly.
+
+---
+
+SteppedBody
+-----------
+
+.. autoclass:: openflash.body.SteppedBody
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+   This is the primary class for defining bodies with concentric, vertical-walled steps. A single ``SteppedBody`` can be composed of one or more steps, allowing for complex, tiered structures to be defined as a single entity.
diff --git a/docs/modules/domain.rst b/docs/modules/domain.rst
new file mode 100644
index 0000000..f8018b7
--- /dev/null
+++ b/docs/modules/domain.rst
@@ -0,0 +1,49 @@
+.. _domain-module:
+
+==============
+Domain Module
+==============
+
+.. automodule:: openflash.domain
+
+.. _domain-overview:
+
+Conceptual Overview
+===================
+
+The ``Domain`` class represents a single, annular (ring-shaped) region of fluid within the simulation. In the Matched Eigenfunction Expansion Method (MEEM), the entire fluid volume is subdivided into these simple domains, each characterized by a constant depth and defined by inner and outer radial boundaries.
+
+.. figure:: ../_static/domain_drawing.png
+   :alt: Example of how domains would look
+   :align: center
+   :width: 100%
+
+   **Figure 1**: A typical problem geometry is divided into multiple concentric fluid domains, including interior domains under the bodies and a final, semi-infinite exterior domain.
+
+**Role in the Workflow**
+
+It's important to understand that end-users of the OpenFLASH package **do not typically create ``Domain`` objects directly**. Instead, ``Domain`` objects are the output generated by a :ref:`geometry-module` class (like ``BasicRegionGeometry``).
+
+The typical workflow is:
+1. The user defines the physical structures as a collection of :ref:`body-module` objects.
+2. These bodies are passed to a ``Geometry`` object.
+3. The ``Geometry`` object processes the physical layout and automatically generates the corresponding list of fluid ``Domain`` objects, each with the correct boundaries and properties.
+
+Each ``Domain`` then holds the necessary information for the ``MEEMEngine`` to construct the mathematical solution within that specific region.
+
+.. figure:: ../_static/domain_table.png
+   :alt: Table of domains
+   :align: center
+   :width: 100%
+
+   **Table 2**: This table summarizes the key attributes that define each type of fluid domain.
+
+.. _domain-api:
+
+API Reference
+=============
+
+.. autoclass:: openflash.domain.Domain
+   :members:
+   :undoc-members:
+   :show-inheritance:
\ No newline at end of file
diff --git a/docs/modules/geometry.rst b/docs/modules/geometry.rst
new file mode 100644
index 0000000..ecc4bbd
--- /dev/null
+++ b/docs/modules/geometry.rst
@@ -0,0 +1,66 @@
+.. _geometry-module:
+
+===============
+Geometry Module
+===============
+
+.. automodule:: openflash.geometry
+   :no-members:
+
+.. _geometry-overview:
+
+Conceptual Overview
+===================
+
+The Geometry module provides the classes necessary to define the physical layout of the hydrodynamic problem. It acts as the bridge between the high-level description of physical objects (the :ref:`body-module`) and the low-level fluid sub-regions (the :ref:`domain-module`) used by the solver.
+
+The conceptual hierarchy is as follows:
+
+1.  **Body Objects**: You start by defining one or more physical structures using classes like ``SteppedBody``. Each ``Body`` has its own physical properties (radii, depths, heaving status).
+
+2.  **BodyArrangement**: These individual ``Body`` objects are then grouped into a ``BodyArrangement``. This class organizes the collection of bodies. For most use cases, you will use the concrete ``ConcentricBodyGroup`` class.
+
+3.  **Geometry**: Finally, a ``Geometry`` object is created from a ``BodyArrangement`` and the total water depth (`h`). The primary role of a ``Geometry`` object is to process this physical layout and generate the corresponding list of fluid ``Domain`` objects that the ``MEEMEngine`` can solve.
+
+In summary: **Bodies -> Arrangement -> Geometry -> Domains**
+
+.. _geometry-api:
+
+API Reference
+=============
+
+The module contains three key classes that work together to define the problem's spatial configuration.
+
+The Geometry Class
+------------------
+
+.. autoclass:: openflash.geometry.Geometry
+   :members: fluid_domains, make_fluid_domains
+   :undoc-members:
+   :show-inheritance:
+
+   The ``Geometry`` class is an **abstract base class**. You will not use this class directly, but rather one of its concrete implementations, such as ``openflash.basic_region_geometry.BasicRegionGeometry``. It establishes the core responsibility of turning a physical layout into a set of solvable fluid domains.
+
+---
+
+The BodyArrangement Class
+-------------------------
+
+.. autoclass:: openflash.geometry.BodyArrangement
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+   Like ``Geometry``, the ``BodyArrangement`` class is an **abstract base class**. It defines the required interface for any class that organizes a collection of ``Body`` objects.
+
+---
+
+The ConcentricBodyGroup Class
+-----------------------------
+
+.. autoclass:: openflash.geometry.ConcentricBodyGroup
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+   This is the primary **concrete class** you will use to group your ``SteppedBody`` objects for a standard concentric cylinder problem. It takes a list of ``Body`` objects and automatically concatenates their properties (like radii and depths) into single arrays that can be used by a ``Geometry`` object.
\ No newline at end of file
diff --git a/docs/modules/index.rst b/docs/modules/index.rst
new file mode 100644
index 0000000..e50764f
--- /dev/null
+++ b/docs/modules/index.rst
@@ -0,0 +1,20 @@
+Module API
+==========
+
+This section groups the core module documentation pages for OpenFLASH.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Module pages:
+
+   geometry
+   body
+   domain
+   meem_problem
+   meem_engine
+   results
+   problem_cache
+   multi_constants
+   multi_equations
+   basic_region_geometry
+
diff --git a/docs/modules/meem_engine.rst b/docs/modules/meem_engine.rst
new file mode 100644
index 0000000..303a54d
--- /dev/null
+++ b/docs/modules/meem_engine.rst
@@ -0,0 +1,60 @@
+.. _meem_engine-module:
+
+===========
+MEEM Engine
+===========
+
+.. automodule:: openflash.meem_engine
+   :no-members:
+
+.. _meem_engine-overview:
+
+Conceptual Overview
+===================
+
+The ``MEEMEngine`` is the central processing unit of the OpenFLASH package. It takes one or more :class:`~openflash.meem_problem.MEEMProblem` objects and orchestrates the entire simulation process, from assembling the mathematical system to calculating the final physical results.
+
+The engine is designed around an internal caching system (``ProblemCache``) that significantly optimizes performance, especially when running simulations over multiple frequencies. It pre-calculates parts of the system that are frequency-independent and stores functions to efficiently compute the frequency-dependent parts.
+
+Primary Workflows
+-----------------
+
+There are two primary ways to use the ``MEEMEngine``:
+
+1.  **Single Frequency Analysis**: This workflow is ideal for detailed inspection of the system at a single wave frequency. The user typically calls :meth:`~solve_linear_system_multi` to get the solution vector, then uses that vector with post-processing methods like :meth:`~calculate_potentials` or :meth:`~calculate_velocities` to generate spatial field data for visualization.
+
+2.  **Frequency Sweep Analysis**: This is the most common and powerful workflow. The user configures a ``MEEMProblem`` with a range of frequencies and then makes a single call to the :meth:`~run_and_store_results` method. The engine handles the entire loop internally, solving the system for each frequency and packaging all the hydrodynamic coefficients into a convenient :class:`~openflash.results.Results` object.
+
+.. _meem_engine-api:
+
+API Reference
+=============
+
+.. autoclass:: openflash.meem_engine.MEEMEngine
+
+   Core Solver Methods
+   -------------------
+   These are the main methods for running simulations.
+
+   .. automethod:: solve_linear_system_multi
+
+   .. automethod:: run_and_store_results
+
+
+   Post-Processing & Analysis Methods
+   ----------------------------------
+   These methods are used after solving the system to compute physical quantities.
+
+   .. automethod:: compute_hydrodynamic_coefficients
+
+   .. automethod:: calculate_potentials
+
+   .. automethod:: calculate_velocities
+
+
+   Utility & Visualization Methods
+   -------------------------------
+
+   .. automethod:: reformat_coeffs
+
+   .. automethod:: visualize_potential
\ No newline at end of file
diff --git a/docs/modules/meem_problem.rst b/docs/modules/meem_problem.rst
new file mode 100644
index 0000000..d87e50f
--- /dev/null
+++ b/docs/modules/meem_problem.rst
@@ -0,0 +1,58 @@
+.. _meem_problem-module:
+
+===================
+MEEM Problem Module
+===================
+
+.. automodule:: openflash.meem_problem
+
+.. _meem_problem-overview:
+
+Conceptual Overview
+===================
+
+The ``MEEMProblem`` class is a fundamental container in the OpenFLASH workflow. Its primary role is to bundle a fully defined **geometry** with the specific **simulation parameters** you want to investigate.
+
+Think of it as the complete "job description" for a simulation run. It holds two key pieces of information:
+
+1.  **What to Simulate:** A :class:`~openflash.geometry.Geometry` object that describes the physical layout of all the bodies and the surrounding fluid.
+2.  **How to Simulate It:** The specific wave **frequencies** and **modes of motion** (e.g., which bodies are heaving) that the :class:`~openflash.meem_engine.MEEMEngine` should solve for.
+
+You create a `MEEMProblem` instance and then pass it to the `MEEMEngine` to perform the calculations.
+
+.. _meem_problem-usage:
+
+Example Usage
+=============
+
+Creating and configuring a ``MEEMProblem`` is a straightforward process.
+
+.. code-block:: python
+
+   from openflash import MEEMProblem, BasicRegionGeometry
+   import numpy as np
+
+   # --- Assume 'geometry' is an already created BasicRegionGeometry object ---
+   # geometry = BasicRegionGeometry(...)
+
+   # 1. Create the problem instance with the geometry
+   problem = MEEMProblem(geometry)
+
+   # 2. Define the simulation parameters
+   frequencies_to_run = np.array([1.5, 2.0, 2.5]) # Frequencies in rad/s
+
+   # 3. Configure the problem with these parameters
+   problem.set_frequencies(frequencies_to_run)
+
+   # The 'problem' object is now ready to be passed to the MEEMEngine.
+
+
+.. _meem_problem-api:
+
+API Reference
+=============
+
+.. autoclass:: openflash.meem_problem.MEEMProblem
+   :members: set_frequencies
+   :undoc-members:
+   :show-inheritance:
\ No newline at end of file
diff --git a/docs/modules/multi_constants.rst b/docs/modules/multi_constants.rst
new file mode 100644
index 0000000..8f1dbac
--- /dev/null
+++ b/docs/modules/multi_constants.rst
@@ -0,0 +1,31 @@
+.. _multi_constants-module:
+
+=================
+Constants Module
+=================
+
+.. automodule:: openflash.multi_constants
+
+.. _multi_constants-overview:
+
+Conceptual Overview
+===================
+
+This module defines fundamental physical constants that are utilized throughout the OpenFLASH package. These constants are set to common default values for hydrodynamic simulations in seawater.
+
+.. _multi_constants-api:
+
+API Reference
+=============
+
+.. py:data:: g
+   :type: float
+   :value: 9.81
+
+   The acceleration due to gravity, in meters per second squared ($m/s^2$).
+
+.. py:data:: rho
+   :type: float
+   :value: 1023.0
+
+   The density of water, in kilograms per cubic meter ($kg/m^3$). The default value is typical for seawater.
\ No newline at end of file
diff --git a/docs/modules/multi_equations.rst b/docs/modules/multi_equations.rst
new file mode 100644
index 0000000..c08df92
--- /dev/null
+++ b/docs/modules/multi_equations.rst
@@ -0,0 +1,105 @@
+.. _multi_equations-module:
+
+======================
+Mathematical Equations
+======================
+
+.. automodule:: openflash.multi_equations
+
+.. _multi_equations-overview:
+
+Conceptual Overview
+===================
+
+The ``multi_equations`` module is the mathematical heart of the OpenFLASH package. It contains the Python implementations of the core analytical functions required for the Matched Eigenfunction Expansion Method (MEEM), including radial and vertical eigenfunctions, their derivatives, coupling integrals, and terms for constructing the final linear system.
+
+.. warning::
+   Most functions in this module are low-level mathematical components used internally by the :class:`~openflash.meem_engine.MEEMEngine`. The average user will typically only need to interact with the **User-Facing Utility Functions** listed below. The other sections are provided for developers and researchers interested in the underlying mathematical theory.
+
+.. _multi_equations-user-api:
+
+User-Facing Utility Functions
+=============================
+
+These are high-level helper functions that you may need to use when setting up a simulation.
+
+.. autofunction:: openflash.multi_equations.omega
+.. autofunction:: openflash.multi_equations.wavenumber
+
+.. _multi_equations-core-api:
+
+Core Mathematical Components
+============================
+
+This section details the core mathematical building blocks of the MEEM formulation. These functions are primarily called by the ``MEEMEngine`` during the matrix assembly process.
+
+Wavenumber Computations
+-----------------------
+These functions determine the wavenumbers for the exterior fluid domain.
+
+.. autofunction:: openflash.multi_equations.m_k_entry
+.. autofunction:: openflash.multi_equations.lambda_ni
+
+Coupling Integrals
+------------------
+These functions compute the integrals that couple the vertical eigenfunctions at the boundaries between adjacent fluid regions.
+
+.. autofunction:: openflash.multi_equations.I_nm
+.. autofunction:: openflash.multi_equations.I_mk
+
+Radial Eigenfunctions
+---------------------
+These functions define the radial variation of the potential in each type of fluid domain. They include the functions themselves, their derivatives, and optimized vectorized versions used for post-processing.
+
+.. rubric:: Interior Regions (Bessel I)
+
+.. autofunction:: openflash.multi_equations.R_1n
+.. autofunction:: openflash.multi_equations.diff_R_1n
+.. autofunction:: openflash.multi_equations.R_1n_vectorized
+.. autofunction:: openflash.multi_equations.diff_R_1n_vectorized
+
+.. rubric:: Intermediate Regions (Bessel K)
+
+.. autofunction:: openflash.multi_equations.R_2n
+.. autofunction:: openflash.multi_equations.diff_R_2n
+.. autofunction:: openflash.multi_equations.R_2n_vectorized
+.. autofunction:: openflash.multi_equations.diff_R_2n_vectorized
+
+.. rubric:: Exterior Region (Hankel & Bessel K)
+
+.. autofunction:: openflash.multi_equations.Lambda_k
+.. autofunction:: openflash.multi_equations.diff_Lambda_k
+.. autofunction:: openflash.multi_equations.Lambda_k_vectorized
+.. autofunction:: openflash.multi_equations.diff_Lambda_k_vectorized
+
+
+Vertical Eigenfunctions
+-----------------------
+These functions define the vertical variation of the potential in each type of fluid domain.
+
+.. rubric:: Interior & Intermediate Regions
+
+.. autofunction:: openflash.multi_equations.Z_n_i
+.. autofunction:: openflash.multi_equations.diff_Z_n_i
+.. autofunction:: openflash.multi_equations.Z_n_i_vectorized
+.. autofunction:: openflash.multi_equations.diff_Z_n_i_vectorized
+
+.. rubric:: Exterior Region
+
+.. autofunction:: openflash.multi_equations.N_k_multi
+.. autofunction:: openflash.multi_equations.Z_k_e
+.. autofunction:: openflash.multi_equations.diff_Z_k_e
+.. autofunction:: openflash.multi_equations.Z_k_e_vectorized
+.. autofunction:: openflash.multi_equations.diff_Z_k_e_vectorized
+
+
+Particular Solution & Hydrodynamic Terms
+----------------------------------------
+These functions are related to the non-homogeneous parts of the solution and the final calculation of physical coefficients.
+
+.. autofunction:: openflash.multi_equations.phi_p_i
+.. autofunction:: openflash.multi_equations.diff_r_phi_p_i
+.. autofunction:: openflash.multi_equations.diff_z_phi_p_i
+.. autofunction:: openflash.multi_equations.int_R_1n
+.. autofunction:: openflash.multi_equations.int_R_2n
+.. autofunction:: openflash.multi_equations.excitation_force
\ No newline at end of file
diff --git a/docs/modules/problem_cache.rst b/docs/modules/problem_cache.rst
new file mode 100644
index 0000000..1bcc6e3
--- /dev/null
+++ b/docs/modules/problem_cache.rst
@@ -0,0 +1,28 @@
+.. _problem_cache-module:
+
+====================
+Problem Cache Module
+====================
+
+.. automodule:: openflash.problem_cache
+
+.. _problem_cache-overview:
+
+Conceptual Overview
+===================
+
+The ``ProblemCache`` class is a crucial **internal component** designed to optimize the performance of the :class:`~openflash.meem_engine.MEEMEngine`. Its purpose is to store pre-calculated components of the mathematical system to avoid redundant computations, especially when solving a problem over a range of frequencies.
+
+.. note::
+   As an end-user of the OpenFLASH package, you will **not** need to interact with or create ``ProblemCache`` objects directly. The ``MEEMEngine`` automatically creates and manages a cache for each ``MEEMProblem`` instance it handles.
+
+How it Works
+------------
+
+When the ``MEEMEngine`` is initialized with a problem, it builds a ``ProblemCache`` that:
+
+1.  **Analyzes the System**: It identifies which parts of the governing matrices (**A**) and vectors (**b**) are constant (frequency-independent) and which parts change with the wave frequency.
+2.  **Pre-computes Templates**: It calculates the frequency-independent parts once and stores them in "template" matrices.
+3.  **Stores Calculation Logic**: For the frequency-dependent parts, it stores lightweight functions (closures) that can be quickly executed to calculate the values for any given frequency.
+
+When a user requests a solution at a new frequency, the engine simply copies the pre-computed templates and runs the stored functions to fill in the missing pieces, rather than re-building the entire system from scratch. This caching strategy is the key to the engine's efficiency during frequency sweeps.
\ No newline at end of file
diff --git a/docs/modules/results.rst b/docs/modules/results.rst
new file mode 100644
index 0000000..0fca166
--- /dev/null
+++ b/docs/modules/results.rst
@@ -0,0 +1,77 @@
+.. _results-module:
+
+==============
+Results Module
+==============
+
+.. automodule:: openflash.results
+
+.. _results-overview:
+
+Conceptual Overview
+===================
+
+The ``Results`` class is the primary container for storing, managing, and exporting all outputs from an OpenFLASH simulation. It is built on top of the powerful `xarray` library, which provides labeled, multi-dimensional arrays, making the data self-describing and easy to work with.
+
+When you run a simulation, especially a frequency sweep using the :meth:`~openflash.meem_engine.MEEMEngine.run_and_store_results` method, the engine will return a fully populated ``Results`` object.
+
+Key Features
+------------
+
+* **Structured Data:** All data is stored in an ``xarray.Dataset`` with named dimensions (like 'frequencies', 'modes', 'r', 'z') and coordinates, eliminating ambiguity.
+* **Comprehensive Storage:** Capable of storing key outputs, including hydrodynamic coefficients (added mass, damping) and detailed spatial field data (potentials, velocities).
+* **NetCDF Export:** Provides a simple method to export the entire dataset to a NetCDF (`.nc`) file, a standard format for scientific data that preserves the data's structure and labels.
+
+.. _results-usage:
+
+Example Usage
+=============
+
+The most common workflow involves receiving a ``Results`` object from the ``MEEMEngine`` and then accessing or exporting its data.
+
+.. code-block:: python
+
+   from openflash import MEEMEngine, MEEMProblem
+   import numpy as np
+
+   # --- Assume 'engine' and 'problem' are already configured ---
+   # problem.set_frequencies(np.linspace(0.5, 4.0, 50))
+
+   # 1. Run the simulation to get a populated Results object
+   results = engine.run_and_store_results(problem_index=0)
+
+   # 2. Access the underlying xarray.Dataset
+   dataset = results.get_results()
+   print("--- Accessing Added Mass Data ---")
+   print(dataset['added_mass'])
+
+   # 3. Display a summary of the dataset
+   print("\n--- Dataset Summary ---")
+   results.display_results()
+
+   # 4. Export all results to a file
+   results.export_to_netcdf("my_simulation_output.nc")
+   print("\nResults saved to my_simulation_output.nc")
+
+
+.. _results-api:
+
+API Reference
+=============
+
+.. autoclass:: openflash.results.Results
+
+   Data Storage Methods
+   --------------------
+   These methods are used by the ``MEEMEngine`` to populate the dataset. Users typically do not need to call these directly.
+
+   .. automethod:: store_hydrodynamic_coefficients
+   .. automethod:: store_all_potentials
+
+   Data Access and Export
+   ------------------------
+   These methods are the primary public interface for interacting with a populated ``Results`` object.
+
+   .. automethod:: get_results
+   .. automethod:: display_results
+   .. automethod:: export_to_netcdf
\ No newline at end of file
diff --git a/docs/multi_constants.rst b/docs/multi_constants.rst
deleted file mode 100644
index 0f3c19a..0000000
--- a/docs/multi_constants.rst
+++ /dev/null
@@ -1,40 +0,0 @@
-Multi Constants Module
-=======================
-
-This module contains the constants used in the simulation for hydrodynamic calculations.
-
-Constants
----------
-
-- **h**: `1.001`  
-  The value of h used in the simulation.
-
-- **d**: `[0.5, 0.25]`  
-  A list of depths.
-
-- **a**: `[0.5, 1]`  
-  A list of amplitude values.
-
-- **heaving**: `[1, 1]`  
-  A list indicating whether each component is heaving.  
-  `0` or `false` indicates not heaving, `1` or `true` indicates heaving.
-
-- **m0**: `1`  
-  The mass value.
-
-- **g**: `9.81`  
-  The acceleration due to gravity in m/s².
-
-- **rho**: `1023`  
-  The density of the fluid in kg/m³.
-
-- **n**: `3`  
-  The number of components in the model.
-
-- **z**: `6`  
-  A constant used in the model.
-
-- **omega**: `2.734109632312753`  
-  The angular frequency, calculated from `m0` and `g`.  
-  Formula:  
-  `omega = sqrt(m0 * g)`
diff --git a/docs/multi_equations.rst b/docs/multi_equations.rst
deleted file mode 100644
index 58b1969..0000000
--- a/docs/multi_equations.rst
+++ /dev/null
@@ -1,154 +0,0 @@
-Multi Equations 
-================
-
-This module, `multi_equations.py`, contains a variety of functions for performing computations related to multi-region eigenfunctions, vertical eigenvector coupling, Bessel functions, and other related mathematical operations. It leverages libraries such as `numpy`, `scipy`, and `matplotlib` for efficient scientific computing.
-
-Imports
--------
-
-The following libraries are imported:
-
-- `numpy` (as `np`): For numerical computations, including arrays and mathematical operations.
-- `scipy.special`: For special functions such as Bessel functions (`hankel1`, `iv`, `kv`).
-- `scipy.integrate`: For numerical integration.
-- `scipy.linalg`: For linear algebra functions.
-- `matplotlib.pyplot`: For plotting graphs.
-- `scipy.optimize`: For optimization routines.
-- `multi_constants`: Custom constants, such as `a`, `h`, and `m0`, used across the equations.
-
-Functions
----------
-
-### wavenumber(omega)
-Calculates the wavenumber for a given frequency (`omega`), using a root-finding method to solve for `m0` using the equation provided. This function is vital for determining the spatial frequency characteristics of waves.
-
-#### Parameters:
-- `omega` (float): The frequency for which the wavenumber is to be calculated.
-
-#### Returns:
-- `float`: The calculated wavenumber corresponding to the given frequency.
-
----
-
-### eigenfunction(x, m0)
-Computes the eigenfunction for a given point `x` using the specified `m0` parameter. This function is essential for generating solutions to the multi-region problem.
-
-#### Parameters:
-- `x` (float): The spatial coordinate at which to evaluate the eigenfunction.
-- `m0` (float): A parameter related to the system's eigenvalue.
-
-#### Returns:
-- `float`: The value of the eigenfunction evaluated at `x`.
-
----
-
-### vertical_eigenvector_coupling(m0, m1, z)
-Calculates the vertical coupling between two eigenvectors, indexed by `m0` and `m1`, at a given height `z`. This coupling plays a role in multi-region solutions where eigenvectors interact at different vertical levels.
-
-#### Parameters:
-- `m0` (float): The first eigenvector index.
-- `m1` (float): The second eigenvector index.
-- `z` (float): The vertical height where the coupling is evaluated.
-
-#### Returns:
-- `float`: The vertical coupling value at the specified height.
-
----
-
-### bessel_jv(n, x)
-Computes the Bessel function of the first kind of order `n` at point `x`. This is used in problems involving cylindrical symmetry, such as wave propagation in cylindrical coordinates.
-
-#### Parameters:
-- `n` (int): The order of the Bessel function.
-- `x` (float): The point at which to evaluate the Bessel function.
-
-#### Returns:
-- `float`: The value of the Bessel function at the specified point.
-
----
-
-### bessel_hankel1(n, x)
-Computes the Hankel function of the first kind of order `n` at point `x`. This function is used for modeling wave propagation in open space, particularly for radiation problems.
-
-#### Parameters:
-- `n` (int): The order of the Hankel function.
-- `x` (float): The point at which to evaluate the Hankel function.
-
-#### Returns:
-- `complex`: The value of the Hankel function at the specified point.
-
----
-
-### bessel_iv(n, x)
-Computes the modified Bessel function of the first kind of order `n` at point `x`. This function is often used in problems involving heat conduction or diffusion in cylindrical geometries.
-
-#### Parameters:
-- `n` (int): The order of the Bessel function.
-- `x` (float): The point at which to evaluate the Bessel function.
-
-#### Returns:
-- `float`: The value of the modified Bessel function at the specified point.
-
----
-
-### bessel_kv(n, x)
-Computes the modified Bessel function of the second kind of order `n` at point `x`. This function is often used in problems involving wave propagation or thermal conduction in cylindrical coordinates.
-
-#### Parameters:
-- `n` (int): The order of the Bessel function.
-- `x` (float): The point at which to evaluate the Bessel function.
-
-#### Returns:
-- `float`: The value of the modified Bessel function at the specified point.
-
----
-
-### solve_integral(func, a, b)
-Numerically integrates a function `func` over the interval `[a, b]` using scipy's `quad` method. This is used for computing integrals that arise in eigenvalue problems or coupling coefficients.
-
-#### Parameters:
-- `func` (function): The function to be integrated.
-- `a` (float): The lower bound of the integration interval.
-- `b` (float): The upper bound of the integration interval.
-
-#### Returns:
-- `float`: The result of the integration.
-
----
-
-### optimize_function(func, x0)
-Optimizes a function `func` starting from an initial guess `x0` using scipy's optimization routines. This is useful for solving for parameters like eigenvalues or minimizing error functions.
-
-#### Parameters:
-- `func` (function): The function to be optimized.
-- `x0` (float): The initial guess for the optimization.
-
-#### Returns:
-- `float`: The optimal value that minimizes the given function.
-
----
-
-### plot_eigenfunction(x_vals, eigenfunction_vals)
-Plots the eigenfunction values against the corresponding spatial coordinates using `matplotlib`. This function is useful for visualizing the solution to the eigenfunction problem.
-
-#### Parameters:
-- `x_vals` (array-like): A list or array of spatial coordinates.
-- `eigenfunction_vals` (array-like): A list or array of eigenfunction values at the corresponding coordinates.
-
-#### Returns:
-- `None`: Displays the plot.
-
----
-
-### constants_setup()
-Initializes and sets up the constants required for solving the equations, such as `a`, `h`, and `m0`. These constants are used across various functions in the module for consistency and efficiency.
-
-#### Returns:
-- `None`: Initializes constants in the module's namespace.
-
----
-
-Conclusion
-----------
-
-This module is designed to handle a variety of operations related to multi-region eigenfunction problems, providing a solid foundation for solving complex mathematical models in physics, engineering, and related fields. By utilizing standard scientific computing libraries like `numpy`, `scipy`, and `matplotlib`, this module provides efficient and accurate tools for solving problems involving waves, eigenvectors, and special functions.
diff --git a/docs/results.rst b/docs/results.rst
deleted file mode 100644
index 4195280..0000000
--- a/docs/results.rst
+++ /dev/null
@@ -1,68 +0,0 @@
-Results Class
-==============
-
-The `Results` class is designed to store results in an `xarray` format similar to Capytaine's conventions. It provides methods to store, access, and export results to a `.nc` file, with a focus on eigenfunction data (radial and vertical) across different frequencies and modes.
-
-Imports
--------
-
-The following libraries are imported:
-
-- `xarray` (as `xr`): Used for managing multi-dimensional arrays and datasets.
-- `numpy` (as `np`): Provides support for large, multi-dimensional arrays and matrices, along with mathematical functions.
-- `geometry`: The `Geometry` class, which contains domain and body information for the computational model.
-
-Class: `Results`
-----------------
-
-### __init__(self, geometry: Geometry, frequencies: np.ndarray, modes: np.ndarray)
-Initializes the `Results` class, which will store the eigenfunction results in an `xarray` Dataset.
-
-#### Parameters:
-- `geometry` (`Geometry`): A `Geometry` object that contains domain and body information used for setting up the coordinate system.
-- `frequencies` (`np.ndarray`): An array of frequency values at which the eigenfunctions are evaluated.
-- `modes` (`np.ndarray`): An array of mode shapes or identifiers corresponding to different eigenfunction modes.
-
----
-
-### store_results(self, domain_index: int, radial_data: np.ndarray, vertical_data: np.ndarray)
-Stores the radial and vertical eigenfunction results for a specific domain. The results are stored in an `xarray` Dataset.
-
-#### Parameters:
-- `domain_index` (`int`): The index of the domain, corresponding to a key in the `domain_list` of the `Geometry` object.
-- `radial_data` (`np.ndarray`): An array of radial eigenfunction values for the given domain.
-- `vertical_data` (`np.ndarray`): An array of vertical eigenfunction values for the given domain.
-
-#### Raises:
-- `ValueError`: If the domain index is not found in the `domain_list`.
-
----
-
-### export_to_netcdf(self, file_path: str)
-Exports the stored results to a NetCDF (.nc) file.
-
-#### Parameters:
-- `file_path` (`str`): The path where the `.nc` file will be saved.
-
----
-
-### get_results(self)
-Returns the stored results as an `xarray.Dataset`.
-
-#### Returns:
-- `xarray.Dataset`: The dataset containing the stored eigenfunction results.
-
----
-
-### display_results(self)
-Displays the stored results in a readable format. This method returns a string representation of the results.
-
-#### Returns:
-- `str`: A string representation of the results, or a message indicating no results are stored.
-
----
-
-Conclusion
-----------
-
-The `Results` class is designed to facilitate the storage, access, and export of eigenfunction data for computational models. It leverages the power of `xarray` to handle multi-dimensional data arrays, with support for storing results for multiple domains, frequencies, and modes. The class also includes functionality to export the results to NetCDF files for further analysis or sharing.
diff --git a/docs/tutorial_walk.ipynb b/docs/tutorial_walk.ipynb
index 6aa2fb6..4b85c78 100644
--- a/docs/tutorial_walk.ipynb
+++ b/docs/tutorial_walk.ipynb
@@ -1,478 +1,729 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Introduction\n",
-    "\n",
-    "This tutorial demonstrates the steps to simulate hydrodynamic potentials and compute coefficients using the `MEEMEngine` and related modules. \n",
-    "\n",
-    "We will:\n",
-    "1. Assemble a linear system.\n",
-    "2. Solve it to find coefficients.\n",
-    "3. Visualize hydrodynamic potentials and velocities.\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "### Required Modules\n",
-    "\n",
-    "Ensure the following libraries are installed:\n",
-    "\n",
-    "- Python 3.x\n",
-    "- NumPy\n",
-    "- SciPy\n",
-    "- Matplotlib\n",
-    "- Pandas\n",
-    "\n",
-    "Also, you must include the following custom modules:\n",
-    "``equations``, ``constants``, ``geometry``, ``meem_engine``, and ``meem_problem``.\n",
-    "\n",
-    "## Code Structure\n",
-    "\n",
-    "The main script follows these steps:\n",
-    "\n",
-    "1. Import necessary modules.\n",
-    "2. Define parameters for the domain.\n",
-    "3. Create `Geometry`, `MEEMProblem`, and `MEEMEngine` objects.\n",
-    "4. Compute coefficients and visualize results.\n",
-    "\n",
-    "## Code Walkthrough\n",
-    "\n",
-    "### 1. **Import Libraries**\n",
-    "Here's how to import required libraries and set up paths:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Import Required Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from scipy import linalg\n",
-    "from scipy.integrate import quad\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Add the source path to the system path\n",
-    "src_path = os.path.abspath(os.path.join(os.path.dirname(\"__file__\"), '../package/src'))\n",
-    "sys.path.append(src_path)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from equations import *\n",
-    "from constants import *\n",
-    "from meem_engine import MEEMEngine\n",
-    "from meem_problem import MEEMProblem\n",
-    "from geometry import Geometry\n",
-    "\n",
-    "# Set printing options for NumPy\n",
-    "np.set_printoptions(threshold=np.inf, linewidth=np.inf, precision=8, suppress=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Note:** Ensure the `src` directory contains all the required modules.\n",
-    "\n",
-    "### 2. **Define Parameters**\n",
-    "Define the simulation parameters, including the number of harmonics and domain properties:\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define Constants and Multi-Region Parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from multi_constants import *\n",
-    "from multi_equations import *\n",
-    "\n",
-    "NMK = [30, 30, 30]  # Adjust these values as needed\n",
-    "boundary_count = len(NMK) - 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create Domain Parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create domain parameters\n",
-    "domain_params = []\n",
-    "for idx in range(len(NMK)):\n",
-    "    params = {\n",
-    "        'number_harmonics': NMK[idx],\n",
-    "        'height': h - d[idx] if idx < len(d) else h,\n",
-    "        'radial_width': a[idx] if idx < len(a) else a[-1]*1.5,\n",
-    "        'top_BC': None,\n",
-    "        'bottom_BC': None,\n",
-    "        'category': 'multi',  # Adjust category as needed\n",
-    "        'di': d[idx] if idx < len(d) else 0,\n",
-    "        'a': a[idx] if idx < len(a) else a[-1]*1.5,\n",
-    "        'heaving': heaving[idx] if idx < len(heaving) else False,\n",
-    "        'slant': [0, 0, 1]  # Set True if the region is slanted\n",
-    "    }\n",
-    "    domain_params.append(params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3. **Create Objects**\n",
-    "Set up the `Geometry`, `MEEMProblem`, and `MEEMEngine` objects:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create Geometry and Problem Objects"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry object\n",
-    "r_coordinates = {'a': a}\n",
-    "z_coordinates = {'h': h}\n",
-    "geometry = Geometry(r_coordinates, z_coordinates, domain_params)\n",
-    "\n",
-    "# Create MEEMProblem object\n",
-    "problem = MEEMProblem(geometry)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4. **Assemble and Solve**\n",
-    "Assemble the linear system and solve for coefficients:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create MEEMEngine and Assemble Matrices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create MEEMEngine object\n",
-    "engine = MEEMEngine([problem])\n",
-    "\n",
-    "# Assemble A matrix and b vector using multi-region methods\n",
-    "A = engine.assemble_A_multi(problem)\n",
-    "b = engine.assemble_b_multi(problem)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Solve the Linear System and Compute Hydrodynamic Coefficients"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.02351559+0.j 0.06279153+0.j]\n",
-      "Hydrodynamic Coefficients:\n",
-      "hydro_coef: (1.5637357110637748+1.1523375596273402j)\n",
-      "hydro_coef_real: 1604.505538020095\n",
-      "hydro_coef_imag: 3232.760334266271\n",
-      "hydro_coef_nondim: (0.4992472875742309+0.3679019395307493j)\n"
-     ]
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "1d86d70a",
+            "metadata": {},
+            "source": [
+                "# Tutorial\n",
+                "\n",
+                "Welcome to this tutorial on using OpenFLASH! This guide demonstrates how to set up a multi-body hydrodynamic problem, run the simulation engine, and analyze the results.\n",
+                "\n",
+                "OpenFLASH uses the **Matched Eigenfunction Expansion Method (MEEM)** to efficiently analyze wave interactions with concentric structures, providing key hydrodynamic insights like added mass, damping, and potential fields.\n",
+                "\n",
+                "---\n",
+                "\n",
+                "## 1. Prerequisites and Setup\n",
+                "\n",
+                "Before you begin, make sure you have installed the `openflash` package. If you haven't, you can install it using `pip`. It's highly recommended to do this within a virtual environment.\n",
+                "\n",
+                "```bash\n",
+                "# Install the package from your local project directory\n",
+                "pip install open-flash\n",
+                "```"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "id": "40203f56",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "<class 'module'>\n",
+                        "['/Users/hopebest/Documents/semi-analytical-hydro/package/src/openflash']\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/package/src/openflash/__init__.py\n",
+                        "OpenFLASH modules imported successfully!\n"
+                    ]
+                }
+            ],
+            "source": [
+                "\n",
+                "import numpy as np\n",
+                "import matplotlib.pyplot as plt\n",
+                "import pandas as pd\n",
+                "import openflash\n",
+                "print(type(openflash))\n",
+                "print(openflash.__path__)\n",
+                "print(openflash.__file__)\n",
+                "\n",
+                "\n",
+                "# --- Import core modules from package ---\n",
+                "try:\n",
+                "    from openflash import *\n",
+                "    from openflash.multi_constants import g\n",
+                "    print(\"OpenFLASH modules imported successfully!\")\n",
+                "except ImportError as e:\n",
+                "    print(f\"Error importing OpenFLASH modules. Error: {e}\")\n",
+                "\n",
+                "# Set NumPy print options for better readability\n",
+                "np.set_printoptions(threshold=np.inf, linewidth=np.inf, precision=8, suppress=True)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "18f5e1f6",
+            "metadata": {},
+            "source": [
+                "---\n",
+                "\n",
+                "## 2. Defining the Problem Geometry\n",
+                "\n",
+                "We'll start by defining the physical layout of our problem. The core idea is to create `SteppedBody` objects that represent the physical structures. For this example, we'll simulate **two concentric bodies**.\n",
+                "\n",
+                "* **h**: Total water depth.\n",
+                "* **a\\_list**: A list of outer radii for each physical body.\n",
+                "* **d\\_list**: A list of submerged depths for each body's step.\n",
+                "* **heaving\\_list**: A list of boolean flags (`True`/`False`) indicating if each body is heaving.\n",
+                "* **NMK**: The number of harmonics to use in the series expansion for each fluid domain (this will be `number of bodies + 1`)."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "id": "e67e3a89",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "\n",
+                        "--- 1. Setting up the Problem ---\n",
+                        "Wave number (m0): 1.0, Angular frequency (omega): 2.7341\n"
+                    ]
+                }
+            ],
+            "source": [
+                "# ---------------------------------\n",
+                "# --- 1. Problem Setup ---\n",
+                "# ---------------------------------\n",
+                "print(\"\\n--- 1. Setting up the Problem ---\")\n",
+                "h = 1.001            # Water Depth (m)\n",
+                "d_list = [0.5, 0.25]   # Step depths (m) for inner and outer bodies\n",
+                "a_list = [0.5, 1.0]    # Radii (m) for inner and outer bodies\n",
+                "heaving_list = [True, False] # Heaving flags for each body\n",
+                "NMK = [30, 30, 30]     # Harmonics for inner, middle, and exterior domains\n",
+                "\n",
+                "m0 = 1.0    # Non-dimensional wave number\n",
+                "problem_omega = omega(m0, h, g)\n",
+                "print(f\"Wave number (m0): {m0}, Angular frequency (omega): {problem_omega:.4f}\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "97e3a7f2",
+            "metadata": {},
+            "source": [
+                "### Create Geometry and MEEM Problem Instances\n",
+                "\n",
+                "Now, we use these parameters to build our geometry in a structured, object-oriented way."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "id": "e67e3a90",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Geometry and MEEM Problem initialized.\n",
+                        "Problem configured with 1 frequency(ies) and 1 mode(s): [0].\n"
+                    ]
+                }
+            ],
+            "source": [
+                "# 1. Create a list of SteppedBody objects\n",
+                "bodies = []\n",
+                "for i in range(len(a_list)):\n",
+                "    body = SteppedBody(\n",
+                "        a=np.array([a_list[i]]),\n",
+                "        d=np.array([d_list[i]]),\n",
+                "        slant_angle=np.array([0.0]), # Assuming flat tops\n",
+                "        heaving=heaving_list[i]\n",
+                "    )\n",
+                "    bodies.append(body)\n",
+                "\n",
+                "# 2. Group the bodies into an arrangement\n",
+                "arrangement = ConcentricBodyGroup(bodies)\n",
+                "\n",
+                "# 3. Create a concrete Geometry instance\n",
+                "geometry = BasicRegionGeometry(\n",
+                "    body_arrangement=arrangement,\n",
+                "    h=h,\n",
+                "    NMK=NMK\n",
+                ")\n",
+                "\n",
+                "# 4. Create the MEEMProblem instance\n",
+                "problem = MEEMProblem(geometry)\n",
+                "\n",
+                "# 5. Set the frequencies for the problem\n",
+                "problem_frequencies = np.array([problem_omega])\n",
+                "problem.set_frequencies(problem_frequencies)\n",
+                "problem_modes = problem.modes # Get modes from geometry\n",
+                "\n",
+                "print(\"Geometry and MEEM Problem initialized.\")\n",
+                "print(f\"Problem configured with {len(problem.frequencies)} frequency(ies) and {len(problem.modes)} mode(s): {problem_modes}.\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "18f5e1f7",
+            "metadata": {},
+            "source": [
+                "---\n",
+                "\n",
+                "## 3. Running the MEEM Engine\n",
+                "\n",
+                "The `MEEMEngine` orchestrates the entire simulation. We initialize it with our problem and call the appropriate methods to solve the system. For a single frequency, we use `solve_linear_system_multi`."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "id": "e67e3a90",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "MEEMEngine initialized.\n",
+                        "\n",
+                        "--- WORKFLOW 1: Solving for a single frequency ---\n",
+                        "Linear system solved. Solution vector X shape: (120,)\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=6.46955e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n"
+                    ]
+                }
+            ],
+            "source": [
+                "# 6. Initialize the MEEM Engine\n",
+                "engine = MEEMEngine(problem_list=[problem])\n",
+                "print(\"MEEMEngine initialized.\")\n",
+                "\n",
+                "# ---------------------------------\n",
+                "# --- WORKFLOW 1: Single Solve & Plot ---\n",
+                "# ---------------------------------\n",
+                "print(\"\\n--- WORKFLOW 1: Solving for a single frequency ---\")\n",
+                "\n",
+                "# Solve the linear system. \n",
+                "# This one high-level call handles everything:\n",
+                "# 1. Calls _ensure_m_k_and_N_k_arrays\n",
+                "# 2. Calls assemble_A_multi\n",
+                "# 3. Calls assemble_b_multi\n",
+                "# 4. Solves the system for X\n",
+                "X = engine.solve_linear_system_multi(problem, m0)\n",
+                "\n",
+                "print(f\"Linear system solved. Solution vector X shape: {X.shape}\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "2d9b1c73",
+            "metadata": {},
+            "source": [
+                "---\n",
+                "\n",
+                "## 4. Analyzing and Visualizing Results\n",
+                "\n",
+                "With the solution vector `X`, we can now compute and visualize the physical results. \n",
+                "\n",
+                "### Hydrodynamic Coefficients\n",
+                "\n",
+                "The engine can compute the added mass and damping coefficient matrices from the solution vector."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "id": "4a7b0e11",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "\n",
+                        "Calculated Hydrodynamic Coefficients:\n",
+                        "   mode        real        imag  nondim_real  nondim_imag  excitation_phase  \\\n",
+                        "0     0  288.978396  189.013785     0.719333     0.172085         -0.521015   \n",
+                        "1     1  595.741621  592.674309     0.185367     0.067449         -0.521015   \n",
+                        "\n",
+                        "   excitation_force  \n",
+                        "0         63.330565  \n",
+                        "1         84.274080  \n"
+                    ]
+                }
+            ],
+            "source": [
+                "# --- Get Coefficients from X ---\n",
+                "hydro_coefficients = engine.compute_hydrodynamic_coefficients(problem, X, m0)\n",
+                "\n",
+                "if hydro_coefficients:\n",
+                "    df_coeffs = pd.DataFrame(hydro_coefficients)\n",
+                "    print(\"\\nCalculated Hydrodynamic Coefficients:\")\n",
+                "    print(df_coeffs)\n",
+                "else:\n",
+                "    print(\"Hydrodynamic coefficients could not be calculated.\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "5f3a0e1a",
+            "metadata": {},
+            "source": [
+                "### Potential Field Visualization \n",
+                "\n",
+                "Next, we'll calculate the potential field over a 2D spatial grid and visualize it."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "id": "6a7b1c2d",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/Users/hopebest/Documents/semi-analytical-hydro/package/src/openflash/multi_equations.py:347: RuntimeWarning: invalid value encountered in divide\n",
+                        "  bessel_term = (besselke(0, lambda_val * r) / denom) * exp(lambda_val * (scale(a)[i] - r))\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "\n",
+                        "Coefficients reformatted into 3 regions.\n",
+                        "  Region 0 (NMK=30): (30,) coefficients\n",
+                        "  Region 1 (NMK=30): (60,) coefficients\n",
+                        "  Region 2 (NMK=30): (30,) coefficients\n",
+                        "\n",
+                        "Calculating potential fields for visualization...\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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4MVq0aIFDhw4ZxjVs2BD//Oc/MXHiRMPyRTVq1MDZs2exceNGjBw5Eq+++qpd5zho0CCsX78eL7zwAnbu3IlHHnkElZWVOHHiBNavX49vvvkGbdu2tet9e+655/DCCy+gT58+ePLJJ3H48GF88803VaqJzZo1Q+fOnREfH49atWph//79+PTTTzFmzBiH3u97hYWFYeLEiZg2bRpSU1Px1FNP4eTJk3j//ffRrl07o4lx8fHxWLduHdLT09GuXTtUr14dvXr1svm9sUd8fDwA4B//+Af69+8PHx8f9OrVyxBy79WzZ09Mnz4dw4YNQ4cOHXDkyBGsXr3aqNJsD39/f3Tt2hXbt2/H9OnTHdqHrTp06ICaNWtiyJAhePHFF6HRaPDvf/+7Sjj39fXF1KlTMXbsWDzxxBPo27cvzp07hxUrVqBhw4ZVKtnl5eX47rvvDEviEZEbkGMJBSIl0S/NtW/fPpPPd+rUyWhpLkEQhPLycmHatGlC/fr1BR8fHyE6OlqYOHGi0bJCglB1uSI9AMLo0aONtumXMpo9e7bR9u3btwuPPPKIEBAQIAQFBQm9evUSfvnllyr7zMrKEtq0aSP4+voKDRs2FD766CPhlVdeEfz9/auM/eyzz4SOHTsK1apVE6pVqybExsYKo0ePFk6ePGnxugVBEIYMGVJlGaqysjLhnXfeEZo3by74+fkJNWvWFOLj44Vp06YJhYWFdr9vlZWVwuuvvy6EhoYKgYGBQkpKinD69OkqS0/985//FBISEoSQkBAhICBAiI2NFd566y2hrKysynnfS78014YNGyyOE4S7S3HFxsYKPj4+QkREhDBq1Cjhzz//NBpTXFws/PWvfxVCQkIEAEbvj63vjanPCUGoutyWIAjCjBkzhLp16wpardZomS5TS3O98sorQp06dYSAgADhkUceEXJycoROnToZLVll69JcgiAIn3/+uaDRaIS8vLwq52lqaa77P5/Nvfemvg5//PFHoX379kJAQIAQFRUlvPbaa4al7e5dwk4QBGHhwoVCvXr1BD8/PyEhIUH48ccfhfj4eCE1NdVo3Ndffy0AEE6dOmX1WolIHTSCIOFsEyKSVVpamlNLVxHdr7KyEs2aNUPfvn0xY8YMuU/HLJ1Oh7CwMDz99NNGbR5paWnQaDQO3aSDiJSJPbNEbuL27dtG/z916hS2bt1a5XaeRM7w8vLC9OnTsWTJEhQXF8t9OgDu3mb3/rrMqlWrcP36daPP/+PHj2Pz5s2KDuFEZD9WZoncRJ06dTB06FDDGqj/+te/UFpaioMHD5pdV5bIHWRnZ2PcuHF45plnULt2bRw4cAAff/wxmjZtitzcXPj6+sp9ikQkIU4AI3ITqamp+OSTT5Cfnw8/Pz8kJSXh7bffZpAltxcTE4Po6GgsXLgQ169fR61atTB48GDMmjWLQZbIA6iuMrtkyRLMnj0b+fn5aNWqFRYtWoSEhASz4zds2IA333wT586dQ+PGjfHOO+8YlggiIiIiInVTVc+sfumbKVOm4MCBA2jVqhVSUlKq3BJSb/fu3RgwYACGDx+OgwcPIi0tDWlpaTh69KiLz5yIiIiIpKCqymxiYiLatWuHxYsXA7g7WzU6Ohpjx47FhAkTqozv168fSkpKsHnzZsO29u3bo3Xr1sjIyHDZeRMRERGRNFTTM1tWVobc3FxMnDjRsE2r1SI5ORk5OTkmX5OTk4P09HSjbSkpKdi0aZPZ45SWlhrdPUin0+H69euoXbu2aLejJCIiImkJgoCbN28iKioKWq3r/xB9584dlJWVSbJvX19f+Pv7S7JvNVJNmL169SoqKysRERFhtD0iIgInTpww+Zr8/HyT4/Pz880eZ+bMmZg2bZrzJ0xERESyO3/+PB544AGXHvPOnTuoX6868i9XWh/sgMjISJw9e5aB9r9UE2ZdZeLEiUbV3MLCQjz44IM4f/48goKCZDwzInJGaX5LlxznplBqfZCYx9PpRNlPoc7H7tcU6/zsGn9TZ/sP3kJdgA37sz6mqNLyMUsqLD9/s8LyMYorzb9vlvZ9q8L8625XmP7RfLvc9MoMpZXmf5SXlnmZ3F5ZYXp7hYnxQqWZqmaZme3lVf+Kqa00/ZdNTZmJsSZerymv+lpthYlt9xRCK0vv4HTGdNSoUcP0eUqorKwM+ZcrcXp/NIJqiFsVLrqpQ6O251FWVsYw+1+qCbOhoaHw8vJCQUGB0faCggJERkaafE1kZKRd4wHAz88Pfn5Vv0EHBQUxzBKpWGmJeD9QiiwE1houmlerD7HOHu/Gf0NsdbuP749qdowv0vkj0Jbzqbw7ylpMvakLsDqmsDIAln7UF1f4w1IcL6oIgKWFvYorfc0+X1zuDx8zO79V4WPxOW8Tz90u94W3mYNVmAm/pWXe8DLxVEWFF7Qm9lVR5gWtifFChYnPsTKt6Q9SucZkstBWmAioZRrc/wHSlmsAEzlba2qbqYBrIjPL2SIYVEMrepilqlTzDvv6+iI+Ph5ZWVmGbTqdDllZWUhKSjL5mqSkJKPxAJCZmWl2PBF5niKh1O6HXG7qdIaHs27ofAxB1r5z8Lerwlqk80eRjeP1Qdb6OVivyBZWWqmoWqnIFlmtyJqPucXljlVkLT1nzh0LQdaUCjsqsoCZIGsnU0HWrtebCq0mtpHnUk1lFgDS09MxZMgQtG3bFgkJCViwYAFKSkowbNgwAMDgwYNRt25dzJw5EwDw0ksvoVOnTpg7dy569OiBtWvXYv/+/Vi6dKmcl0FEMpAzhDpDrDYCPUcCrJ49IRaAzSEWcJ8g6yiLbQdm2gvMBVnJ2dFeYI6t7QX20Eoz14pUQFVhtl+/frhy5QomT56M/Px8tG7dGtu2bTNM8srLyzOasdihQwesWbMGkyZNwhtvvIHGjRtj06ZNaNGihVyXQERkldgBFnBtiAXUG2StsRZkHa3KmmMuyFri7lVZovupap1ZORQVFSE4OBiFhYXsmSVSsSsX68p9CmZJEV713CHE3j0X1wRZS1VZqYKsmFVZc0EWECnM2lmVNdsra2qsqYljNrYYmKrKVpbewcn33pDl57c+O1w+WU+SCWDhTX5nLrmHqiqzRETuQMrwCjgXYAHHQiwgXzUWsB5kbWGtvcASJQRZSyStytoRZM2xNcjag1Vdz8EwS0QkIamD672cDbGA9NVYQJ4gK9eEL0cpsr3AXFXWDuaqsraytSpLnoVhlohIJK4MrnpiBFhAWdVYwH2CrKtWL5CNgquy0tyugJRIYV8VRETKJUdYNUdNIRZwzyBrjSvbCzy1Kqt0N4VSQBC3Z/amoJzvQ0rBMEtEHkFJQdRRYgVYwPEQC8gfZG3tj5U6yLqyT9YSS5O+JCVRVdYeploM1Bh6yTkMs0RECiZmgAVcG2IB+SZ6KTXIWmIpyMo66cvFVVneJIHsxTBLRKQwYgdYQLkhFnCvIGuN2H2y9rYX2E2EpbjM4U0SSCwMs0REMpMivOo5E2IBzwuyVvcv0YQvV7UXyHGDBGersnadAyu4HkncrmQiIrLqhs7H6CG2mzp/w8NRRTp/h3pj1R5klTThC1Boe4GTFVXAvqosl+MSx5IlSxATEwN/f38kJiZi7969ZsceO3YMffr0QUxMDDQaDRYsWFBlzPfff49evXohKioKGo0GmzZtqjJm6NCh0Gg0Ro/U1FSjMfpj3PuYNWuWXdfGMEtEJKH7g6vUVVgxKrGuqMaKvWKBK4KsFH2ylsjaXmAnVmWVbd26dUhPT8eUKVNw4MABtGrVCikpKbh8+bLJ8bdu3UKDBg0wa9YsREZGmhxTUlKCVq1aYcmSJRaPnZqaikuXLhken3zySZUx06dPNxozduxYu66PbQZERCKRMqha4myABRxrJwDsC7GA7dVYQJyltwDpg6w1qmwvUHBVluw3b948jBgxAsOGDQMAZGRkYMuWLVi2bBkmTJhQZXy7du3Qrl07ADD5PAB069YN3bp1s3psPz8/s4FYr0aNGlbHWMLKLBGRjUxVWV1RcTVFjFYCwPFKLMAgq6f09gKpuboqy+W47ioqKjJ6lJaWmhxXVlaG3NxcJCcnG7ZptVokJycjJydH8vPMzs5GeHg4mjRpglGjRuHatWtVxsyaNQu1a9dGmzZtMHv2bFRUVNh1DFZmicgjyFU1FZMYFVg9RwMsYH+IBVzfHwu4Z5BlVdZ2XvblIUnc1OkAkZe41q+ZHR0dbbR9ypQpmDp1apXxV69eRWVlJSIiIoy2R0RE4MSJE+Ke3H1SU1Px9NNPo379+jhz5gzeeOMNdOvWDTk5OfDyuvt5+eKLL+Lhhx9GrVq1sHv3bkycOBGXLl3CvHnzbD4OwywRkYKJGWD1XBlkxa7GArYFWZv2I2GQlYKlICvapC87KaEq66nOnz+PoKAgw//9/PxkPBvT+vfvb/h3XFwcWrZsiYYNGyI7OxtdunQBAKSnpxvGtGzZEr6+vnj++ecxc+ZMm6+JbQZERAojVgvB/ZxtKVBTkJV6LVlrQVa17QUqrMqaGuvlAW0HQUFBRg9zwS80NBReXl4oKCgw2l5QUOBUn6ojGjRogNDQUJw+fdrsmMTERFRUVODcuXM275dhlohIAaQKsIDrQyyg7iBrjRxBVontBazKqoOvry/i4+ORlZVl2KbT6ZCVlYWkpCSXnssff/yBa9euoU6dOmbHHDp0CFqtFuHh4Tbvl20GREQuJkVgvZ8zrQR6UodYQJlB1tV3+LJGzPYCubiyKktVpaenY8iQIWjbti0SEhKwYMEClJSUGFY3GDx4MOrWrYuZM2cCuDtp7JdffjH8+8KFCzh06BCqV6+ORo0aAQCKi4uNKqxnz57FoUOHUKtWLTz44IMoLi7GtGnT0KdPH0RGRuLMmTN47bXX0KhRI6SkpAAAcnJysGfPHjz++OOoUaMGcnJyMG7cODz77LOoWbOmzdfHMEtEJDFXhFc9uUIsIE01FnDdZC9A2j5ZJbQXeEJV1hNaDOzVr18/XLlyBZMnT0Z+fj5at26Nbdu2GSaF5eXlQav93+fAxYsX0aZNG8P/58yZgzlz5qBTp07Izs4GAOzfvx+PP/64YYy+93XIkCFYsWIFvLy88PPPP2PlypW4ceMGoqKi0LVrV8yYMcPQEuHn54e1a9di6tSpKC0tRf369TFu3DijPlpbaARBEBx6ZzxEUVERgoODUVhYaNRoTUTqciDvQcmP4crQei8xAizgmhALuG+QdXV7gSNVWTnCrLmqrLkwa+sdv8y9Xh9mK8vu4OjSN2T5+a3PDod+CUeNGuJ2dN68qUPrZpeZS+7ByiwRkR3kCqymyB1iAXUEWZuO5SZB1hI1VGWdXo6LVVmPxDBLRB5JSaHUHmIFWEDdIRawPcjKuXKBFCwFWamX4rKXPb2yZvfBiV9kBcMsEXkEtYZXQNwACzgXYgHPCrLWWAuySli9wCIZqrLmcOIXOYphlohIgcQOsHqurMYC6g+yckz4kr29QCT29sqaHKvyiV+FOh9U6sR9n4t1It9SzA0wzBIRKYRUARZwfYgFlB1kbSFVn6wURF2Ki1VZUhmGWSIimUgZXgHn2wkA6auxgDxBVs4+WVZlbdyHyquy5DoMs0RELiJ1eNWTK8QCDLKA69sLRJ305UZVWVZwPQfDLBGRyFwVWu/njiEWcJ8gKwVXTPqyF6uy5GoMs0REDpIrtN5LjACrxyDr3BJb1oKsItoLHKCUqqy9+62UZtekQAyzRET/pYRwais1hlhAuUHWFkrrk1XiUlxiVGXNYdsAmcMwS0QeQU1B1RwxAyzgeIgFpK3GAq4PsnL1yTpK1FvWysTsXcDYYkB2YpglIlIwsQMs4FyIBZTRVgCoI8hao4j2AjeryrKC63kYZomIFEaKAAu4PsQCDLKKby9QGFZlyREMs0REMpMqvOrJEWIBBlnFtBdYIlNV1lOW4yrW+UHQiXhDCwAlOk5tux/DLBGRi0kdXvWcDbGA9NVYQJlB1lnWgqzY7QUWuWFVluheDLNERBJyVXDVEyPAAq6pxgLi355WrCCrxD5ZVmX/hy0GdC+GWSIiEbg6tN5PbSEWcN8g62ifrKMsBlmJb5AgFjGqskprMSDXYZglIrJA7pBqjVghFlBeNRZwryBrjRImfSmpKmsOq7J0P4ZZIvIISg+l9hAzwAKOh1jAs4KsNVL1ybqqvUAu5qqyap74Ra7FMEtEpAJiB1jAtSEWkC/I2sKWICtVn6wkWJWFV5ng/AFJFRhmiYgUSooACzgXYgH1BVkxVi5QYnuBKyZ9yYVVWbIHwywRkYJIFWAB5YdYQJ4gK3WfrMvbC0RcikvqqiyX4yIxMMwSEclIyvCqJ0eIBRhkAYW1F6i8KmtviwFvLeA5GGaJiFzEFcH1Xs6GWIBB1hnWgqwS2gssUUNVVuktBjd1/qgU+Q5gt3gHsCoYZomIJODq4KonRoAFlBdiAdcGWVsork9WxElfSsOJX2QJwywRkYPkCqymiBViAQZZQIXtBZaoqCrLiV/kCAV9tRERSUdJwVNMagyxgGcHWVZl7cObJJA1DLNERCojZoAFHA+xAIPs/aQKshY5MOnLEjVUZc1hi4FnYpglIlIBsQMs4FyIBaRtKwCUF2SdZSnIWuPQpC8L1FKVNYctBnQvhlkiIoWSIsACrg+xgLxB1ha2BFkp+2QlaS9wg6osJ36RLRhmiYgURKoACzgfYgF1BllXrFwgS3uBJRaCrL23rVUaVmXpfgyzREQykTK43kuuEAtI0x8LiBtk5eyTtUYJk77srcrav3/T2znxi2zFMEtE5CKuCq96YoRYwLODrNSU3l7gCDluXcsWA8/GMEtEJDJXh9b7qSnEAsoOsqprL7BAiVVZc8xVZdW2Dm2hLgBlleJGrdu6ClH35w4YZomIbCR3SLVG7hALMMjeS5b2ApVVZbkcF4mBYZaIPILSg6ijxAqweq6qxgKeHWQdbi+wxIFJX5bIVZU1RwmVVlImhlkiIhVSSogFPC/IOsup9gIHJn1ZosSqrBgTv7TlQKXzuyGVYJglIlIJsQMs4FyIBaRtKwBcu/wWYFuQVVt7gTtXZdliQADDLBGRokkRYAHXh1hA/iArxsoFimwvcJA7V2XJs7j2K4eIiCwqrvA3eoitqCKAQdYMKe/wBUjYXuDmVVlzWJW1z5IlSxATEwN/f38kJiZi7969ZsceO3YMffr0QUxMDDQaDRYsWODQPvPz8zFo0CBERkaiWrVqePjhh/HZZ58Zjbl+/ToGDhyIoKAghISEYPjw4SguLrbr2hhmiYhkJHV41RMrxHpqkLWFkm6OAFgOsmqqyiotRKvRunXrkJ6ejilTpuDAgQNo1aoVUlJScPnyZZPjb926hQYNGmDWrFmIjIx0eJ+DBw/GyZMn8eWXX+LIkSN4+umn0bdvXxw8eNAwZuDAgTh27BgyMzOxefNmfP/99xg5cqRd18cwS0TkIvcHVynDq54YIRZwrBoL2D/RS6lBVsr2Aqc4sBSXJWqpypqjlvN0tXnz5mHEiBEYNmwYmjVrhoyMDAQGBmLZsmUmx7dr1w6zZ89G//794efn5/A+d+/ejbFjxyIhIQENGjTApEmTEBISgtzcXADA8ePHsW3bNnz00UdITExEx44dsWjRIqxduxYXL160+foYZomIRGYqtLoiuN5LzBDraDVWihUL7BmrliDr6klfSrzbl71VWbYYAEVFRUaP0tJSk+PKysqQm5uL5ORkwzatVovk5GTk5OQ4dGxb99mhQwesW7cO169fh06nw9q1a3Hnzh107twZAJCTk4OQkBC0bdvW8Jrk5GRotVrs2bPH5vPhBDAiIgtcHUKdJUaA1XNFNRaQL8jawtk+WWuUNOnLFVVZKSunSqzK3tQFoEInzR3AoqOjjbZPmTIFU6dOrTL+6tWrqKysREREhNH2iIgInDhxwqFzsHWf69evR79+/VC7dm14e3sjMDAQGzduRKNGjQDc7akNDw832oe3tzdq1aqF/Px8m8+HYZaIPILaQqm9lBBiAXUFWVf0yap90pfl19j9ElGwKnvX+fPnERQUZPi/uXYAOb355pu4ceMGtm/fjtDQUGzatAl9+/bFrl27EBcXJ9pxGGaJiFRKzAALuDbEAsoPsoptL3CQI+0FjgRWTvxyjaCgIKMwa05oaCi8vLxQUFBgtL2goMDs5C4x9nnmzBksXrwYR48eRfPmzQEArVq1wq5du7BkyRJkZGQgMjKyyiS0iooKXL9+3a5zY88sEZGK6HthGWSrcpsga40HV2XNUdr5KImvry/i4+ORlZVl2KbT6ZCVlYWkpCTJ9nnr1i0Ad3tp7+Xl5QWdTgcASEpKwo0bNwwTwgBgx44d0Ol0SExMtPl8WJklIlI4sYPrvZwJsYBygqytxAiyklPIpC8lVmXZYuCY9PR0DBkyBG3btkVCQgIWLFiAkpISDBs2DMDdJbTq1q2LmTNnArg7weuXX34x/PvChQs4dOgQqlevbuh3tbbP2NhYNGrUCM8//zzmzJmD2rVrY9OmTYYluACgadOmSE1NxYgRI5CRkYHy8nKMGTMG/fv3R1RUlM3XxzBLRKRAUgZYQPkh1t7xrlq5AFBfe4El7lCVNUdbzuCr169fP1y5cgWTJ09Gfn4+WrdujW3bthkmcOXl5RlVUC9evIg2bdoY/j9nzhzMmTMHnTp1QnZ2tk379PHxwdatWzFhwgT06tULxcXFaNSoEVauXInu3bsb9r169WqMGTMGXbp0gVarRZ8+fbBw4UK7rk8jCAI/2hYUFRUhODgYhYWFNvWmEJEy/ePnp+U+BYukDq96zoZYgEHW4vPOthe4uCrryAoGct0kwVxV1tx4oeQ29m2cJMvPb312mLO/IwKqi7yaQXEFXm37A3PJPViZJSJyMVcF13vJFWIBdQRZWzhzhy+bsCpL5BCGWSIiicgRWu8nRogF1BtkbSVGn6wSJ30ptVdWLJaquBXSHpoUhGGWiDyCEoKlK8kdYgFlBFl3by+wxJH2AkvEDKaeMvGrqNIfZZUiT2KsZHn8fgyzRERuRKwQC7iuGmvva5QWZJ3mRHuBI0txWdyfiFVZuag1+JLjGGaJiFROzAALuLYaa+9rXHl3L8C2IKu29gJXVWXlmvhFnodhlohIpcQOsYBnBVlF9Mm6eNKXJe5QlSXPxDBLRKQiUgRYQNkhFpAnyMraJwuwKgv7q7JsMfBMDLNERAonVYAFnAuxgOcGWck5OOnLUazKkpoxzBIRKYyU4dVwDCdDLODZQVbO9gK1VmWlxqqs52KYJSKSmSvCq+FYMoVYR17nsUHWwfYCR7mqKsuJXyQVhlkiIhdxZWitcmwRQizg3kHWFtaCrNOcaC/w5KoseTaGWSLyCGIGyepe/ytLyRlQbSVnNdaR14p5Zy97iNEnq7b2AleFT078IikpZ00QK65fv46BAwciKCgIISEhGD58OIqLiy2OHzt2LJo0aYKAgAA8+OCDePHFF1FYWOjCsyYid1Rc6Wt4KFVxub/h4SxnqrFSBllPai9QErluXatGJRX+KBb5UVIhzl9Z3IlqKrMDBw7EpUuXkJmZifLycgwbNgwjR47EmjVrTI6/ePEiLl68iDlz5qBZs2b4/fff8cILL+DixYv49NNPXXz2RESuIVY7AeDaaiyg7iArNVdXZV1x61qpsVrrOVQRZo8fP45t27Zh3759aNu2LQBg0aJF6N69O+bMmYOoqKgqr2nRogU+++wzw/8bNmyIt956C88++ywqKirg7a2KSycisomYIRbwnCArFrVN+nKUmMtxscWAxKKKNoOcnByEhIQYgiwAJCcnQ6vVYs+ePTbvp7CwEEFBQRaDbGlpKYqKioweRERKJGYrgZ4jrQH3v95ecgZZtbcXKKUqa+9NEojEpIowm5+fj/DwcKNt3t7eqFWrFvLz823ax9WrVzFjxgyMHDnS4riZM2ciODjY8IiOjnb4vImIxCZFgNVzNsQ60h+r9iArNVZlHRvPaq1nkTXMTpgwARqNxuLjxIkTTh+nqKgIPXr0QLNmzTB16lSLYydOnIjCwkLD4/z5804fn4jIGVIGWED51VhAuUGWVVlO/CL5ydo4+sorr2Do0KEWxzRo0ACRkZG4fPmy0faKigpcv34dkZGRFl9/8+ZNpKamokaNGti4cSN8fCx/0/Xz84Ofn59N509EJBWpguu9nAmwzuyDQfa/rARZRyd9OcpiyHXBTRKIHCVrmA0LC0NYWJjVcUlJSbhx4wZyc3MRHx8PANixYwd0Oh0SExPNvq6oqAgpKSnw8/PDl19+CX9/LmdBRMrjiuB6L7lCLCBdkLWVGGvJAjYEWRm5cl1ZUVc9YIsBOUgVU/qbNm2K1NRUjBgxAhkZGSgvL8eYMWPQv39/w0oGFy5cQJcuXbBq1SokJCSgqKgIXbt2xa1bt/Cf//zHaDJXWFgYvLzk7YMiItdyNDBW97kj+zmIRYwQ68x+pAyyYq5cIEqfLKuyRC6jijALAKtXr8aYMWPQpUsXaLVa9OnTBwsXLjQ8X15ejpMnT+LWrVsAgAMHDhhWOmjUqJHRvs6ePYuYmBiXnTsRqZfcAVQsDLIKai+wwplJX0qvyoo18cscVmU9k2rCbK1atczeIAEAYmJiIAj/+yTu3Lmz0f+JiDyR3CEW8LAgawuJJn05So1VWbWE1psVASgV6WtQr0zk1ht3wHeEiMjNiBVgnd2fvSEWkC/IikbC9gJr1FqVJXIWwywRkZtQSogF1Bdk1dBe4OhSXBaPqYCqLCd+kbMYZomIVE7sEOvsPj0yyNpCpvYCVmXJ3THMEhGpkBQBVoz9KiXI2kq0IKvC9gKxq7KOEGviF3k2hlki8gj6kBbord6fklIFWDH270iIBaQLsorqk3WSFJO+pCDnxC+2GHg2hlki8iimApuSA67UAVaMY6g1yCqlvUBNVVlXLMdFZC+GWSLyeObCnBwh1xXhVcxjSd1WACg8yErcXuCJVVmGX7IXwywRkRlSVnFdGVqlOrY7B1mbSNxeYI2aq7JiMtt3W6aT/uCkCAyzRER2kDOEOkvOEAuoK8gqob3AnauyrLKSmBhmiYg8AIOs7VzRXmCNtSArxQ0SlFCVFWttWW2ZDkqoyxZX+sC30rGvGXPKKkXdnVtgmCUicmNyh1hA2iBrD5f1ydpAqklf1l/r8EtNYlWWlIBhlog8wr2hTsmrF4hFzHYIJQdZl/fJ2kLG9gKlVGUdOgf3/7IkiTDMEpHHcYc1Z02Rop/Xk4Ksq6qyzlBSVdZVx3KkxYA8C8MsEXksdwm1SguxgAcHWYVWZaVYwYAtBqQUDLNE5PHUGmqlWlnBldVYwHOCrDXOTPpSEt5YgVxN3r+XEBEpyK0KH0UvvaU/P6nO83a5r9sEWSVyZtKX1X17WFWWLQb2W7JkCWJiYuDv74/ExETs3bvX7Nhjx46hT58+iImJgUajwYIFCxza5507dzB69GjUrl0b1atXR58+fVBQUGA0RqPRVHmsXbvWrmtjmCUiuo+UgVGp5+LqtgJA2iCrtKqss+0FnliVJfGsW7cO6enpmDJlCg4cOIBWrVohJSUFly9fNjn+1q1baNCgAWbNmoXIyEiH9zlu3Dh89dVX2LBhA7777jtcvHgRTz/9dJV9LV++HJcuXTI80tLS7Lo+hlki8giOhjWpq6HWjik1Z6uxAIOss+0FznK0KusoMW9da/E4dk78IvPmzZuHESNGYNiwYWjWrBkyMjIQGBiIZcuWmRzfrl07zJ49G/3794efn59D+ywsLMTHH3+MefPm4YknnkB8fDyWL1+O3bt346effjLaV0hICCIjIw0Pf39/u66PYZaIPIazoQ0QL2jeH5JdHZjFCrFuHWRFotSqrCuX43JFAPW0FoOioiKjR2lpqclxZWVlyM3NRXJysmGbVqtFcnIycnJyHDq2LfvMzc1FeXm50ZjY2Fg8+OCDVY47evRohIaGIiEhAcuWLYMg2Pf5wglgRORRbpf7IsDHwk9xOyihDcFeYgR6wLFqLKCyIOuC9gJrrAVZJVVlOfGrqpIKf5SJ9DWnV/7fz9/o6Gij7VOmTMHUqVOrjL969SoqKysRERFhtD0iIgInTpxw6Bxs2Wd+fj58fX0REhJSZUx+fr7h/9OnT8cTTzyBwMBAfPvtt/j73/+O4uJivPjiizafD8MsEXkcfaATK9SqhTsHWdG5qL1AjhskAOqtynLi1/+cP38eQUFBhv+bawdQujfffNPw7zZt2qCkpASzZ8+2K8yyzYCIPJZY4U7pxGgp0FNqkFXijRHkbC9Qc1WWbBMUFGT0MBdmQ0ND4eXlVWUVgYKCArOTu6yxZZ+RkZEoKyvDjRs37DpuYmIi/vjjD7NtE6YwzBKRR3PnQCt2iPWIIGsridsLrHHnqqy7tBgoha+vL+Lj45GVlWXYptPpkJWVhaSkJMn2GR8fDx8fH6MxJ0+eRF5ensXjHjp0CDVr1rSr0sw2A/I4MSvfkfsUXErjLc+f37x9K53fh7ft+/DzrTC53d/b9PZ7idlHqwRiB3RHQyxgf5C1lxL7ZG2htqqsEo7HFgPHpaenY8iQIWjbti0SEhKwYMEClJSUYNiwYQCAwYMHo27dupg5cyaAuxO8fvnlF8O/L1y4gEOHDqF69epo1KiRTfsMDg7G8OHDkZ6ejlq1aiEoKAhjx45FUlIS2rdvDwD46quvUFBQgPbt28Pf3x+ZmZl4++238eqrr9p1fQyzRCSJijIvUQKts+5UeHtEoJWiwuxMiAUcC7JSTPiymUhB1h2rsmIvx+WKAOxVzpCr169fP1y5cgWTJ09Gfn4+WrdujW3bthkmcOXl5UGr/d/n/8WLF9GmTRvD/+fMmYM5c+agU6dOyM7OtmmfADB//nxotVr06dMHpaWlSElJwfvvv2943sfHB0uWLMG4ceMgCAIaNWpkWPLLHhrB3vUPPExRURGCg4NRWFho1GhN6uVplVnAM6qz5iqzgG3VWT21BVqp2iTcKci6uiorda+sMysYSBFmHVmRwN7XWNyXmcqscOsWdmVPk+Xntz479P72b/CpJvJqBiVl+KLrMuaSe7AyS0Ruz9bqLKCelQ7cKcQCDLKG1zsRZK1xZZC1eB4iVmXZYkAAwywRuYnSMm+L1Vl7Ai1gHBaVEmylnqzGIOscqdsLrB5fQasDyD3xy6tMB9u/2kntGGaJyGPYG2j15KrWunKlBTUEWXuIHmQVMOnLHaqyjuDqBmQNwywReRRHAy3gmmqtq5cKczbEAq4LsqJP+LKVAiZ9WT2+gqqylriixcBLQa0Htyp84CPynQLLKxju78cwS0Ruw1qrgZ4zgVbPUui0FHSVsq6tGCEWUGaQVVqfLOBZVVm5WwzI8zDMEhGJTCmB1RS5QyygoiArEmduWWsLd67KMuSSLXgHMCJSNHuDj60hS6xQpyYMsv8lYp+sGO0FSqzKWtynAqqyamgxINdhmCUiycjW42gjTwm0ztyK9n7OtBUoIsjaSiXtBVJVZcW+SQKRlBhmiTyA6D/o3Yg7B1oxQyzg2hULJPtFyIXtBXKzVJWVgiM3SXB0f0T38pyvaiLyGPaGLncLtFKEWCUHWaW2Fyi5KquU5bjYYkBiYJglIsWTai3Se7lDoBU7xAKu7Y8F1BFkbSH1pC9rXF2VtYRVWZIawywRuSVHQphaA60UIRZwoyArMiVM+lJaVdZV4ZO3ryVT1Pmdm4hUo6LMC96+lXKfhs3EWIPWVaQK386EWECBQVZh7QVSU1JV1hKxb1+rRLcrvFEu8k0TKiqUea1yYpgl8hBChRYab/V+E6yo8IK3t2tC8b0hUWnBVurqsRxB1l5yBVmxeFJVli0G5AoMs0Tktmy9I5gl+vAoZ6h1RfuDsyEWcDzIyr5ygY1B1hWTvpzlzlVZthiQOQyzREQ2cGWodXXvrpqCrJL7ZMVoL1BiVVaK44lJqS0G5DoMs0QkOTn7ZsWozt5L7BYEOSediRFiAYUGWRW2Fzi9f4mqsq6c+CXm/ljJ9RwMs0SkGq7sm7WFWlc/AOStxgLqCrLuXpV1NbYYkNi4NBeRB/HUO4GJVYF0B87cAOFeqg+yNhIryEo96cuZqqyaJ36xxYAAhlkichGxJvk4GqIYaOVvKwAU1CPrwvYCuam9KusIVnI9C7+7E5HH0Ic5MXto1UDMIK/oIKvQ9gJWZZ3DYErWsDJL5GHcodXA2bVMPaVKK1ZLgR6DrP2knvRl/fiyHt5mbDEgZ3jGd3QiUgQl3Q1M7FUOlETssO7sLw+SrSOrJ3KfrK2UMOlLqqqsFDyxxeB2uS+8y31F3WdFOW8qcT/1l2iIyCOJcacpd6vQil2JBeQJspJN+FJYe4HUJFuTli0GpDAMs0Tk0dwh0EoRYgEVBFl7uLi9wKZjeVBV1hK2GJCzGGaJPJCcfbNi/slZjOosIF0YlJqUIVYVQVbB7QWsytr+OrP7cyCwspLrmdT33ZuISCJq6KOVOnTL1R8raZBVYHuBnFVZJXFVHy25N4ZZIlI1se8KptTlu1xROWaQ9QyOLsfl8PFEv+Utq69kjGGWyEMJFVpovOX5oSD2qgZS3Ob23vAoV7B1ZeuDJwdZW6mlKivVclxKn/jFFgPPxTBLRG5BikCr58pqrat7d8XoO3ZZkJWIkiZ9SU3tE7+ITGGYJSJZSLHmrJSBFhC3Wiv3hDOxJs+5NMjK2F7gqklfclZllTLxyxK2GJApDLNEHkzOVgOpSB1o9eQOo85gkLWPGDdHAFiVtRVbDMhe6v1uTESqJ9UdwVwVaNVGrBALuEeQFbO9wFOrspZYep2ntBiUVnqjokLcqFVZye9t91NGwxIREUnKI4KsHVzdXuCuVVlXXhdbDMgchlkiD6eUSThiEzO8qZkYN0Aw2p+IN72wyt4gq9D2AlsotSrramwxIEe4508xIlINKcORpwdasUOsMx8ryX9pUnB7gdxh0ZmqrBQTvzylxYBch2GWiNy2Ogt4ZqBVWjVWjX2yrmwvYFXWOkdaDLSlyrrxCUnHfX+CEZFqSP2na08JtGKHWEAFQdYOYgZZsSg5TEpRQbVY6WWLATmIYZaIFIGB1nFShFhAJUFW5D5ZW7mqvcDZqqwcE7/YRqBMS5YsQUxMDPz9/ZGYmIi9e/daHL9hwwbExsbC398fcXFx2Lp1q9HzBQUFGDp0KKKiohAYGIjU1FScOnXKaMzzzz+Phg0bIiAgAGFhYejduzdOnDhhNCYvLw89evRAYGAgwsPDMX78eFRU2FdVZ5glIgDu3WqgJ1Xok4uU1+NuQZZVWeUd31ZsMXDeunXrkJ6ejilTpuDAgQNo1aoVUlJScPnyZZPjd+/ejQEDBmD48OE4ePAg0tLSkJaWhqNHjwIABEFAWloafvvtN3zxxRc4ePAg6tWrh+TkZJSUlBj2Ex8fj+XLl+P48eP45ptvIAgCunbtalherLKyEj169EBZWRl2796NlStXYsWKFZg8ebJd16cRBIG/QllQVFSE4OBgFBYWIigoSO7TIRHErHxH7lNQLCXcQEGKdWdNHkfF69BKGcjFqJCrNcgC7tUra60q6+o7fjnaYmAuzFp6jba0AhUVd7Azd6YsP7/12aHF+vHwCvQTdd+Vt0pxtO9su64rMTER7dq1w+LFiwEAOp0O0dHRGDt2LCZMmFBlfL9+/VBSUoLNmzcbtrVv3x6tW7dGRkYGfv31VzRp0gRHjx5F8+bNDfuMjIzE22+/jeeee87kefz8889o1aoVTp8+jYYNG+Lrr79Gz549cfHiRURERAAAMjIy8Prrr+PKlSvw9fW16frcvxRDRDZTQnXWVUs/qbFKK/U5qybISoRVWVKToqIio0dpaanJcWVlZcjNzUVycrJhm1arRXJyMnJycky+Jicnx2g8AKSkpBjG64/l7+9vtE8/Pz/88MMPJvdZUlKC5cuXo379+oiOjjYcJy4uzhBk9ccpKirCsWPHrL0F/zu2zSOJiNyQ0kOt/vykPkfZgqwjZGwv8IReWVf3vDpSlbW4PwW1GJSWeaG0zFvkx92v1ejoaAQHBxseM2fONHkOV69eRWVlpVFgBICIiAjk5+ebfE1+fr7F8bGxsXjwwQcxceJE/PnnnygrK8M777yDP/74A5cuXTJ63fvvv4/q1aujevXq+Prrr5GZmWmouJo7jv45WzHMEpERT6rOGh1TQYHWVQEWcH79WD2HP28U0l5g0/5Emmwmd1VUjtveclKY+M6fP4/CwkLDY+LEiS47to+PDz7//HP8+uuvqFWrFgIDA7Fz505069YNWq3x1/TAgQNx8OBBfPfdd3jooYfQt29f3LlzR9TzEfeGwUREIqko83JZ/6zhmP8Nj3L008oRpsX6pUGJQdYeYrYXqL0qqxZckgsICgqyqWc2NDQUXl5eKCgoMNpeUFCAyMhIk6+JjIy0Oj4+Ph6HDh1CYWEhysrKEBYWhsTERLRt29bodfrKcePGjdG+fXvUrFkTGzduxIABAxAZGVllVQX9cc2dmynyl2CISHGUUJ0F5KnQAsaVUcmWvZJ4/1aPL1I11mVB1k5KbS+QuyprjaMTvxzlzi0GSuHr64v4+HhkZWUZtul0OmRlZSEpKcnka5KSkozGA0BmZqbJ8cHBwQgLC8OpU6ewf/9+9O7d2+y5CIIAQRAMPbdJSUk4cuSI0aoKmZmZCAoKQrNmzWy+Rrsrszdu3MDGjRuxa9cu/P7777h16xbCwsLQpk0bpKSkoEOHDvbukohI8e4PnLZUb5XUuqAnezUWcCzIythe4EpyrmDgDLYYKFt6ejqGDBmCtm3bIiEhAQsWLEBJSQmGDRsGABg8eDDq1q1r6Lt96aWX0KlTJ8ydOxc9evTA2rVrsX//fixdutSwzw0bNiAsLAwPPvggjhw5gpdeeglpaWno2rUrAOC3337DunXr0LVrV4SFheGPP/7ArFmzEBAQgO7duwMAunbtimbNmmHQoEF49913kZ+fj0mTJmH06NHw87N9FQibv6NcvHgRzz33HOrUqYN//vOfuH37Nlq3bo0uXbrggQcewM6dO/Hkk0+iWbNmWLdunc0nQETK5OnVWWtMVW/lrrZawyBrjFVZY2oJnWwxsF+/fv0wZ84cTJ48Ga1bt8ahQ4ewbds2w2SrvLw8o4lbHTp0wJo1a7B06VK0atUKn376KTZt2oQWLVoYxly6dAmDBg1CbGwsXnzxRQwaNAiffPKJ4Xl/f3/s2rUL3bt3R6NGjdCvXz/UqFEDu3fvRnh4OADAy8sLmzdvhpeXF5KSkvDss89i8ODBmD59ul3XZ/M6sxERERgyZAiGDh1qtvR7+/ZtbNq0CQsXLkSfPn3w6quv2nUySsR1Zt0P15m1jxLWngVct/6sOxLzFwIlB1lAnvaCu+OcH2OtKmvLPixVZq2+VgVry1p63f0tBkpYZ7bxfybAK9Df+gvsUHnrDk49O4u55B42txn88ssvqF27tsUxAQEBGDBgAAYMGIBr1645fXJERHpyTAhzBwyy0nNVVdadluMiEpPN312sBVlnxxORMiml3QBQbsuBEom15Jaey4OsneRqLxCLLVVZp/Yv0XJcrsYWAzLFru8wDRs2xPz5880+X1BQAC8v/rAhIukw0Fon9nskyy80Mi/D5cr2AjGOI9dyXK5uMSAyxa7VDM6ePYvXX38dhw8fxtKlS03eM9fGFlwiUhGhQquY3lmALQfmSBH0nQ6yHt5eYNN+JK7KWuMOLQZKXZKrssILgsiTQXUKnFwqN7u/y2zcuBE7duzAY489VuWWZQCg0ah3SRQiMk9J7QYAK7T3ErulQM/dgqwc7QVKqMqyxYDcnd3fadq1a4d9+/bB29sbbdu2xZ49e6Q4LyIiqzw90EoVYgHPDbI2H1uk9gI1V2XZYkBK4dB3q4iICGRnZ6Nnz57o3Lkzli9fLvZ5VXH9+nUMHDgQQUFBCAkJwfDhw1FcXGzTawVBQLdu3aDRaLBp0yZpT5TIjSmtOgtIG+iUTMoQK0uQVQhXTvqyBauy1im1xYBcx+HvON7e3vjggw8wf/58vPDCC3jppZdQUSHdJ9TAgQNx7NgxZGZmYvPmzfj+++8xcuRIm167YMECtj8QiUSJgRbwnCqtoquxgONBVgFVWVdP+pK7KusMV/fZssWALLFrApipQPjCCy+gRYsW+L//+z/8+OOPop3YvY4fP45t27Zh3759aNu2LQBg0aJF6N69O+bMmYOoqCizrz106BDmzp2L/fv3o06dOpKcH5GnUdqEMD19yHPHyWFSh3VPD7JKJHVVVqpAqpQWAw0DsMew6zuPuZUKOnbsiH379olyQqbk5OQgJCTEEGQBIDk5GVqt1mLP7q1bt/DXv/4VS5YsQWRkpE3HKi0tRVFRkdGDiNTFXVoP9NfBIPs/Uq1cwKqsMbYYkJrY9d3n7NmzCAsLM/lcdHQ0fvzxR+zcuVOUE7tXfn6+4T6+et7e3qhVqxby8/PNvm7cuHHo0KEDevfubfOxZs6cieDgYMMjOjra4fMmcmdKbTe4l1oDrSvDuJqCrL3kmPSllmPJMfHLUWwxIGvs+g5Ur149i72nfn5+eOyxx2ze34QJE6DRaCw+Tpw4Yc8pGnz55ZfYsWMHFixYYNfrJk6ciMLCQsPj/PnzDh2fyBOoJdCqJdS6OsSqLchK1V7g6qW4xKjKSjnxSyoubTEodb9WIzLP5p7Z1NRUTJ06Fe3bt7c47ubNm3j//fdRvXp1jB492uLYV155BUOHDrU4pkGDBoiMjMTly5eNtldUVOD69etm2wd27NiBM2fOICQkxGh7nz598OijjyI7O9vk6/z8/ODn52fxnIjof5TaP3u/e0OiUnpq5QrZov0S4sJVC9TQXiAWuauylo6vpBssqKHFoKLMC1pvkW+aoJJfzl3J5jD7zDPPoE+fPggODkavXr3Qtm1bREVFwd/fH3/++Sd++eUX/PDDD9i6dSt69OiB2bNnW91nWFiY2baFeyUlJeHGjRvIzc1FfHw8gLthVafTITEx0eRrJkyYgOeee85oW1xcHObPn49evXrZcMVE5K7kDLZyV4kVEWRV1l5gK0+vyrLFgORic5gdPnw4nn32WWzYsAHr1q3D0qVLUVhYCODuKgfNmjVDSkoK9u3bh6ZNm4p6kk2bNkVqaipGjBiBjIwMlJeXY8yYMejfv79hJYMLFy6gS5cuWLVqFRISEhAZGWmyavvggw+ifv36op4fkadTS3XWFKmDrdzhVU/UlhAXB1m52wvcqSor1/F5owSSkl1Lc/n5+eHZZ5/Fs88+CwAoLCzE7du3Ubt2bfj4+EhygnqrV6/GmDFj0KVLF2i1WvTp0wcLFy40PF9eXo6TJ0/i1q1bkp4HEZmm5kCrZ0vwNBV4lRJYzfGUICsntVRlpZr4pSTsl/U8doXZ++ln/LtCrVq1sGbNGrPPx8TEmF06TM/a80TkHHcItNYoPbjez5OCLKuy8nF1i4Ea+mXJdZQ/FZmIVEUNKxx4AtFWK9BTcI8sIG+QVUpV1urrnZj45Qy2GJDU+FOHiETHQCsv0d9/F65aoKeW9gKxiBEkpazsssWAlIw/cYhIEgy0rid6NRZwPsi6eXuBWqqy1jgThB29fa0lbDEge/CnDRFJhoHWdSR5rz00yNq+P1F35xQpJ35JhS0GJBaHv1PduHEDH330ESZOnIjr168DAA4cOIALFy6IdnJEpH6SVAvJQLL3V4YgqxRihlRbqrJKCsWmKDEIm8IWA8/l0GoGP//8M5KTkxEcHIxz585hxIgRqFWrFj7//HPk5eVh1apVYp8nkXju/SHty8qAq3jCSgeuJtkvCTIFWVZlTZN6OS6ltRhYorYWA6FS/F82hUoWB+7n0DuSnp6OoUOH4tSpU/D39zds7969O77//nvRTo5IcmVa4wdJihVacUha7XbDIGvXebAqa0SqqqylFgPe9Yvs5dB3rX379uH555+vsr1u3brIz893+qSIZMNQKzkGWsdJ3rKhkiBr9/5lmPQlFrVO/CJyJYfaDPz8/FBUVFRl+6+//oqwsDCnT4pIdmxFkBRbDuwn+S8BKuqRlbO9wObjuqgqq9SJX65uMWC/rGdz6LvXU089henTp6O8/O5XkUajQV5eHl5//XX06dNH1BMkkh3bECTBiWG2cUk1VsYgq7b2Aneqylo9PlsMSCUc+g42d+5cFBcXIzw8HLdv30anTp3QqFEj1KhRA2+99ZbY50ikHAy0omOgNc0lYV/mz2eltBeIflyVVGXZYkDuwqE2g+DgYGRmZuLHH3/E4cOHUVxcjIcffhjJyclinx+R8ugDANsPRKMPbWw9cGG4FyvIurBPVqr2AlZlxeXq6ipbDMihMKv3yCOP4JFHHhHrXIjUhaFWdJ7cS+vSCrWbB1k5KaUq6+zrldZioLYluci1HPqO9uKLL2LhwoVVti9evBgvv/yys+dEpC5sPRCVp/XSuvx6ZQ6yrqD2qqwYpGwDYIsBKY1D39U+++wzkxXZDh064NNPP3X6pIhUhxPEROfOoVZ/bS6/PgUEWSVVZcUOqGJVZeVuMXAmrCppApe2VGW/gZDDHGozuHbtGoKDg6tsDwoKwtWrV50+KSLVKtOy7UBk7tRPK1s4F/MXLYUHWSkmfSmtKivnxC9nSNFioPh+2TIt4CXy1z0LJ1U49I40atQI27Ztq7L966+/RoMGDZw+KSJVY5VWErJVM50k+3kzyJoZa+/ZWNmfm1RlrWGLASmRQ5XZ9PR0jBkzBleuXMETTzwBAMjKysLcuXOxYMECMc+PSL1YpZWM0qu1igncKg6ySqG2qqyz3KXFgDyLQ2H2b3/7G0pLS/HWW29hxowZAICYmBj861//wuDBg0U9QSJVY6CV1P2hUa5wq5jwei+FBFlHKaEqa/M4BVVl3a3FwBJLLQba0nLwO6/ncHhprlGjRmHUqFG4cuUKAgICUL16dTHPi8h9cAkvlzEVKsUOuIoMrvcSu8XFySCrtPYCNZO7SixHiwGX5CJbOLXOLACEhYWJcR5E7o9VWlkoPnyKyQ2CrNRYlXX89Rb3zRYDkpFD3/kKCgowaNAgREVFwdvbG15eXkYPIjKDk8NIKm4SZJXQXkDistRiIAUuyWXakiVLEBMTA39/fyQmJmLv3r0Wx2/YsAGxsbHw9/dHXFwctm7davR8cXExxowZgwceeAABAQFo1qwZMjIyjMZ07twZGo3G6PHCCy8Yjbn/eY1Gg7Vr19p1bQ5VZocOHYq8vDy8+eabqFOnDjQa5f32TaRorNKSmBQWZB2llLt8iVmVFYvcQVyqFgO3XZJLYdatW4f09HRkZGQgMTERCxYsQEpKCk6ePInw8PAq43fv3o0BAwZg5syZ6NmzJ9asWYO0tDQcOHAALVq0AHB3MYAdO3bgP//5D2JiYvDtt9/i73//O6KiovDUU08Z9jVixAhMnz7d8P/AwMAqx1u+fDlSU1MN/w8JCbHr+hwKsz/88AN27dqF1q1bO/JyIgIYaMl5UlT5RQiyrmovUENVli0GpATz5s3DiBEjMGzYMABARkYGtmzZgmXLlmHChAlVxr/33ntITU3F+PHjAQAzZsxAZmYmFi9ebKi+7t69G0OGDEHnzp0BACNHjsQHH3yAvXv3GoXZwMBAREZGWjy/kJAQq2Msceg7YXR0NASBa80ROY1tB+QohX7eKLG9wL792jjOg6qyznB1i4EnKSoqMnqUlpaaHFdWVobc3FwkJycbtmm1WiQnJyMnJ8fka3JycozGA0BKSorR+A4dOuDLL7/EhQsXIAgCdu7ciV9//RVdu3Y1et3q1asRGhqKFi1aYOLEibh161aV440ePRqhoaFISEjAsmXL7M6YDlVmFyxYgAkTJuCDDz5ATEyMI7sgonuxSku2kjLEqqRP1l6eXpV19vVqajFQXL9suQbwFvnz+79fp9HR0Uabp0yZgqlTp1YZfvXqVVRWViIiIsJoe0REBE6cOGHyEPn5+SbH5+fnG/6/aNEijBw5Eg888AC8vb2h1Wrx4Ycf4rHHHjOM+etf/4p69eohKioKP//8M15//XWcPHkSn3/+uWHM9OnT8cQTTyAwMNDQqlBcXIwXX3zRtvcDDobZfv364datW2jYsCECAwPh4+Nj9Pz169cd2S2RZ+MSXmQNg+zdY0nUXqDEqqwYpAzzbDGQz/nz5xEUFGT4v5+fn0uPv2jRIvz000/48ssvUa9ePXz//fcYPXo0oqKiDFXdkSNHGsbHxcWhTp066NKlC86cOYOGDRsCAN58803DmDZt2qCkpASzZ8+WPszyLl9EEmKVlu4ndUuBipbgUsuasmJVZeVuMXCmKssWA2kFBQUZhVlzQkND4eXlhYKCAqPtBQUFZvtUIyMjLY6/ffs23njjDWzcuBE9evQAALRs2RKHDh3CnDlzqrQo6CUmJgIATp8+bQizpsbMmDEDpaWlNgd0h8LskCFDHHkZEdmKVVrSU3iQdYaS2gvUWpWVc+KXM1jRdR1fX1/Ex8cjKysLaWlpAACdToesrCyMGTPG5GuSkpKQlZWFl19+2bAtMzMTSUlJAIDy8nKUl5dDqzX+/uTl5QWdzvzH9tChQwCAOnXqWBxTs2ZNuyrNTt804c6dOygrM/7qtuU3BSKyAau0nk3BbQV6Sm0vkJO7VGWtkSqQuk2/rIKkp6djyJAhaNu2LRISErBgwQKUlJQYVjcYPHgw6tati5kzZwIAXnrpJXTq1Alz585Fjx49sHbtWuzfvx9Lly4FcDfnderUCePHj0dAQADq1auH7777DqtWrcK8efMAAGfOnMGaNWvQvXt31K5dGz///DPGjRuHxx57DC1btgQAfPXVVygoKED79u3h7++PzMxMvP3223j11Vftuj6HwmxJSQlef/11rF+/HteuXavyfGUl138jEg2rtJ5HJdVYJd7hS49VWeXvn1ynX79+uHLlCiZPnoz8/Hy0bt0a27ZtM0zyysvLM6qydujQAWvWrMGkSZPwxhtvoHHjxti0aZNhjVkAWLt2LSZOnIiBAwfi+vXrqFevHt566y3DTRF8fX2xfft2Q3COjo5Gnz59MGnSJMM+fHx8sGTJEowbNw6CIKBRo0aGZcTsoREcWGNr9OjR2LlzJ2bMmIFBgwZhyZIluHDhAj744APMmjULAwcOtHeXilVUVITg4GAUFhay4uwmYj6cLfcpOI6B1v15QJB1RVVW7NvWAuLduhawXpm1qborcYuB1f1bqMxa65d1xUoGFZWl2P7rPFl+fuuzQ/ScGdAG+Iu6b93tOzj/6pvMJfdwqDL71VdfYdWqVejcuTOGDRuGRx99FI0aNUK9evWwevVqtwqzRIrCKq37csW6sQyyDhOzKivGclxyc6bFwNEga3GfbDHwaA5997x+/ToaNGgA4G7fhH4pro4dO+L7778X7+yIyDTebMF9uOpjqYB+U1fcrlaK9gK59meJs1VZZ/cvB97Clsxx6DtogwYNcPbsWQBAbGws1q9fD+Buxdbe++kSkRMYaNXLlb+QiBhkXd0nq4RJX66uyroiFEsZVrkkF7maQ20Gw4YNw+HDh9GpUydMmDABvXr1wuLFi1FeXm6YxUZELsLWA3Vx9S8gCgmySmsvUPpKAZbIXTWVqsXAHWkrNeL/Algp/y94SuNQmB03bpzh38nJyThx4gRyc3PRqFEjw3ILRORiDLXKxyCrOrZWZZW0HJdaWwy4JBc5yqHvrKtWrUJpaanh//Xq1cPTTz+N2NhYrFq1SrSTIyIHsJ9WeeT4mCgkyDp8TFZlJcMWA3I3Dn13HTZsGAoLC6tsv3nzpmEBXiKSGUOt/OT6GCgoyCqtvcCu/bq4KmsLthgQVeXQd1lBEKDRVP1m88cffyA4ONjpkyIiETHQup6cv0ioPMhKTclVWVe0GDi7tqxUHF2Siwiws2e2TZs20Gg00Gg06NKlC7y9//fyyspKnD17FqmpqaKfJBE56d5gxZ5aaSjhlwYFzPzXczTIumNVVixyV2WtkarFwOF+2XIF/+ZCorIrzKalpQEADh06hJSUFFSvXt3wnK+vL2JiYtCnTx9RT5CIRMaJYuJSQogFRA+yauiTtX//ku7eLKUsx+UstgmQUtkVZqdMmQIAiImJQf/+/eHn5yfJSRGRCzDUOkcpIRZQXJB1VXuBJ1VlbaHkFgMGYZKSQ9+Nn3jiCVy5csXw/7179+Lll1/G0qVLRTsxInIRfX+nksKZUinxvXKTIMuqrJX9uHGLgaNLchHpObTO7F//+leMHDkSgwYNQn5+PpKTk9GiRQusXr0a+fn5mDx5stjnKbsWK96DNsBf7tMgkhZ7a6tSUnC9lwThTy0VWUC6pbjctSrrjiz2y5Z54BviwRz6Ln306FEkJCQAANavX4+4uDjs3r0bq1evxooVK8Q8PyKSixKrkK6k5GtXYJB16tgKmfQlNrGW4xKDsy0GXJLLMZoyjSQPMuZQZba8vNzQL7t9+3Y89dRTAIDY2FhcunRJvLMjImXwhIqtUoPr/RS0YsG93KG9QI6qrDu0GDiDS3KRGBz67t28eXNkZGRg165dyMzMNCzHdfHiRdSuXVvUEyQihbm3YquWAGiO2q5DouDnru0FnkLu90SOJbmI7uXQd/B33nkHH3zwATp37owBAwagVatWAIAvv/zS0H5ARB5CTeFWTed6P4UGWaeO7cZVWSVN/JKzxUAq7JeleznUZtC5c2dcvXoVRUVFqFmzpmH7yJEjERgYKNrJEZEKmQuJrmpPUFtItYWCg6wr2wvkrkCS/ZQYhMn9OBRmAcDLy8soyAJ3158lIjLJHUOm1CSsXMoZZF1BiqqsrVw58UvugC/VklxE9rA5zD788MPIyspCzZo1Dbe1NefAgQOinBwRkcdy4yCr1qqsGid+qbXFgP2yZA+bw2zv3r0NKxjob2tLREQSUHiQder4LgiynlKVVTqpgjD7Zel+NodZ/a1s7/83ERGJSAVBVsntBVJRYlVW7mq1VKsYENnLoZ5ZQRCQm5uLc+fOQaPRoH79+lZbD4iIyAKpZ/YrIMi6e1VWaZTcYuAp/bLacg20XiJ/bSt0rWk52R1md+7cieHDh+P333+HINz9QtAH2mXLluGxxx4T/SSJiNyaSn44uTrIKoWYy3GJRe6qrDXOBGH2y5K97JpefPr0afTs2RMxMTH4/PPPcfz4cfzyyy/YsGEDHnjgAXTv3h2//fabVOdKROR+XBDy5O6TdZQ7VmU9/Y5fzmK/LJliV2V2wYIFaN++PbKysoy2x8bG4i9/+QuSk5Mxf/58LFq0SNSTJCJySyoKsqzKWqa0iV9StxhwSS5SErsqs9nZ2Xj55ZdNPqfRaPDyyy9j586dYpwXEZH7Ktd4TJB1lKdWZV25HyJ3YVeYzcvLQ1xcnNnnW7Rogd9//93pkyIiclsuqlIqJciqdU1ZubhDiwH7ZcnV7AqzxcXFFm9XGxgYiFu3bjl9UkREbkllQdZZrmovkKoq66ktBtb3L8+SXOyXJXPsXs3gl19+QX5+vsnnrl696vQJERG5HRf2jIoZZNXQXqAWalpblktykdrYHWa7dOliWJLrXhqNBoIgcK1ZIqJ7eWiQZVWWiFzFrjB79uxZqc6DiMi9uHgGvzsEWSknfcnJlecpd88t+2VJDnaF2Xr16kl1HkRE7kPFQdZdKb0q66oWAzmX5HKGWvtlNeWA1kvcfQrKvVzZ2DUBjIiIrFB5kGVVlqTCflmSit09s0REZIIMNwbw1CArJbHPhS0GtmGLATmDlVkiImep+A5XamR3FVeCmySwxYBIOViZJSJylIwhllVZ8SnpXOh/LPbLEoGVWSIixzDIGrhqGa67x7JzvIxVWVeuLSt3i4E1kvXLWpr8VSrTfYvJ5WwOs23atMHDDz9s04OIyG2VaxhkRcJKqOtIfdcv9ssq35IlSxATEwN/f38kJiZi7969Fsdv2LABsbGx8Pf3R1xcHLZu3Wr0vEajMfmYPXu2Ycyvv/6K3r17IzQ0FEFBQejYsSN27txptJ+8vDz06NEDgYGBCA8Px/jx41FRYd8vPza3GaSlpdm1YyIityNzb6wSl+Byl6qsmid+iUHKu36R/NatW4f09HRkZGQgMTERCxYsQEpKCk6ePInw8PAq43fv3o0BAwZg5syZ6NmzJ9asWYO0tDQcOHAALVq0AABcunTJ6DVff/01hg8fjj59+hi29ezZE40bN8aOHTsQEBCABQsWoGfPnjhz5gwiIyNRWVmJHj16IDIyErt378alS5cwePBg+Pj44O2337b5+jSCqdt5kUFRURGCg4MR/d50aAP85T4dIpKDAiZ4SRFkxajKurJXVglhVq0tBlJP/rL0emstBpYqs1b7ZS20GVTcvontFz9AYWEhgoKCLO9HZPrs0HDi2/DyFzc7VN65gzMz37DruhITE9GuXTssXrwYAKDT6RAdHY2xY8diwoQJVcb369cPJSUl2Lx5s2Fb+/bt0bp1a2RkZJg8RlpaGm7evImsrCwAwNWrVxEWFobvv/8ejz76KADg5s2bCAoKQmZmJpKTk/H111+jZ8+euHjxIiIiIgAAGRkZeP3113HlyhX4+vradH3smSUiMkfmlgI9BlllBFm1Uno/rcMUfLMEVygqKjJ6lJaWmhxXVlaG3NxcJCcnG7ZptVokJycjJyfH5GtycnKMxgNASkqK2fEFBQXYsmULhg8fbthWu3ZtNGnSBKtWrUJJSQkqKirwwQcfIDw8HPHx8YbjxMXFGYKs/jhFRUU4duyYbW8EHFzNoLKyEvPnz8f69euRl5eHsjLj7xrXr193ZLdERMqhgBALuF+QVTMxq7JKImVV1hrJ+mUVMvlLWyH+54Pw30J3dHS00fYpU6Zg6tSpVcZfvXoVlZWVRoERACIiInDixAmTx8jPzzc5Pj8/3+T4lStXokaNGnj66acN2zQaDbZv3460tDTUqFEDWq0W4eHh2LZtG2rWrGnxOPrnbOVQZXbatGmYN28e+vXrh8LCQqSnp+Ppp5+GVqs1+UYSEamGQqqxgDJ7ZJ3FquxdnrKKAUnn/PnzKCwsNDwmTpwo27ksW7YMAwcOhP89LRWCIGD06NEIDw/Hrl27sHfvXqSlpaFXr15V+m2d5VCYXb16NT788EO88sor8Pb2xoABA/DRRx9h8uTJ+Omnn0Q9QSIil1FIiAWkC7KsykpLaVVZucOuM0tycX1Zy4KCgowefn5+JseFhobCy8sLBQUFRtsLCgoQGRlp8jWRkZE2j9+1axdOnjyJ5557zmj7jh07sHnzZqxduxaPPPIIHn74Ybz//vsICAjAypUrLR5H/5ytHAqz+fn5iIuLAwBUr14dhYWFAO7OWtuyZYsjuyQiko+CqrGA+wZZpVVl7SHmHb+UxNlVDLgKgvL5+voiPj7eMDELuDsBLCsrC0lJSSZfk5SUZDQeADIzM02O//jjjxEfH49WrVoZbb916xaAu/2599JqtdDpdIbjHDlyBJcvXzY6TlBQEJo1a2bzNToUZh944AFDibhhw4b49ttvAQD79u0z+5sBEZEiKSjEAsoOsu6GLQa2HEO6sOpUvyxvlmCX9PR0fPjhh1i5ciWOHz+OUaNGoaSkBMOGDQMADB482KhN4aWXXsK2bdswd+5cnDhxAlOnTsX+/fsxZswYo/0WFRVhw4YNVaqywN2gWrNmTQwZMgSHDx/Gr7/+ivHjx+Ps2bPo0aMHAKBr165o1qwZBg0ahMOHD+Obb77BpEmTMHr0aLvypENh9i9/+YshsY8dOxZvvvkmGjdujMGDB+Nvf/ubI7skInIthVVjAeX3yLIqa+X4blq9JfXr168f5syZg8mTJ6N169Y4dOgQtm3bZphslZeXZ9TH2qFDB6xZswZLly5Fq1at8Omnn2LTpk2GNWb11q5dC0EQMGDAgCrHDA0NxbZt21BcXIwnnngCbdu2xQ8//IAvvvjCUMX18vLC5s2b4eXlhaSkJDz77LMYPHgwpk+fbtf1ibLObE5ODnJyctC4cWP06tXL2d0pCteZJXIzCguwelIGWTW2FzjyOqkmfokdZl1VmbXpOG64vqy+MluhK5V9ndnG49+Gl5/I68yW3sGp2fatM+vuHFqa635JSUlm+y6IiBRBoSEWUH6QlYO7VjmV1GIg55JcRGKyOcx++eWX6NatG3x8fPDll19aHPvUU085fWL3u379OsaOHYuvvvoKWq0Wffr0wXvvvYfq1atbfF1OTg7+8Y9/YM+ePfDy8kLr1q3xzTffICAgQPRzJCIFUnCIBdQRZOWoytp9HJVUZUkEHn6zBKrK5jCblpaG/Px8hIeHIy0tzew4jUaDykrxF0EeOHAgLl26hMzMTJSXl2PYsGEYOXIk1qxZY/Y1OTk5SE1NxcSJE7Fo0SJ4e3vj8OHDVWbWEZEbUniIBZTfIysXdw2GrlwmS+4luaxx95slkGvZHGb1yyjc/29XOH78OLZt24Z9+/ahbdu2AIBFixahe/fumDNnDqKioky+bty4cXjxxReN7jvcpEkTx06iXAOwmEukDgyyrMqKQI6qrFJaDJzevxPry7oTbRmgFflLXWBer0L0EqV+XTEx5eTkICQkxBBkASA5ORlarRZ79uwx+ZrLly9jz549CA8PR4cOHRAREYFOnTrhhx9+sHis0tLSKvc7NlDg7GciuodKvkY9Icg6fkx1799dyNkvy5slkL0cCrNdunTBhQsXqmzfs2cPWrdu7ew5VaFvb7iXt7c3atWqZfbevb/99hsAYOrUqRgxYgS2bduGhx9+GF26dMGpU6fMHmvmzJkIDg42PO6/9zGA//3AVMEPTSKPoKKvR7UEWWd5SlXW5v2xxUAc7JclExwKs/7+/mjZsiXWrVsH4G7bwdSpU/Hoo4+ie/fuNu9nwoQJ0Gg0Fh8nTpxw5BQNrRDPP/88hg0bhjZt2mD+/Plo0qQJli1bZvZ1EydONLrX8fnz5y0fSEU/RIncjsq+/tTUI8uqrOv3qZQbJUhNsn5Z8lgOLc21ZcsWLFmyBH/729/wxRdf4Ny5c/j999+xefNmdO3a1eb9vPLKKxg6dKjFMQ0aNEBkZKTRrc4AoKKiAtevXzd77946deoAQJXboTVt2hR5eXlmj+fn5+fYXcz03/h91P+NhkjxVBRg9VwRZJXSXqD2qqy7c9t+WU7+8lgOrzM7evRo/PHHH3jnnXfg7e2N7OxsdOjQwa59hIWFISwszOq4pKQk3LhxA7m5uYiPjwcA7NixAzqdDomJiSZfExMTg6ioKJw8edJo+6+//opu3brZdZ52Kdcw0BJJRYUh1lXU3l6gpKqsmlsMxMB+WVIbh9oM/vzzT/Tp0wf/+te/8MEHH6Bv377o2rUr3n//fbHPD8DdampqaipGjBiBvXv34scff8SYMWPQv39/w0oGFy5cQGxsLPbu3Qvg7hJh48ePx8KFC/Hpp5/i9OnTePPNN3HixAkMHz5ckvM04A9cInGprJ3gXtoKjar6ZOVoL3CEEqqySmwxUFtwJhKDQ5XZFi1aoH79+jh48CDq16+PESNGYN26dfj73/+OLVu2YMuWLWKfJ1avXo0xY8agS5cuhpsmLFy40PB8eXk5Tp48abSawssvv4w7d+5g3LhxuH79Olq1aoXMzEw0bNhQ9POrgm0HROJQSbgyRU09smJQalWW/kfuu3Y51S/LyV9khkNh9oUXXsA//vEPo5sP9OvXD4888giGDRsm2sndq1atWhZvkBATEwNBqBocJ0yYYLTOrMsx1BLZT8UBVs9VQZZVWRvGy3jHL7VVSq21GFjD9WVJDg6F2TfffNPk9gceeADz5s1z6oTcFntpiaxTSZiyxhODLKuyjhPrPXBFcJa7smsWJ395NIcngN3r5s2b+OSTT/DRRx8hNzdXktvZugVWaYlMc5MQC6gzyLozOSd+iUltFV5HuOPkL205oBX59lSC+71NTnPqLf7+++8xZMgQ1KlTB3PmzMETTzyBn376Saxzc18qnsxCJCp+LSiCmqqyapr4pbQAKndVlevLklTsrszm5+djxYoV+Pjjj1FUVIS+ffuitLQUmzZtqrKmK1nB1gPyVG4aYNVYlVVLnyxJT9H9spz8RRbYVZnt1asXmjRpgp9//hkLFizAxYsXsWjRIqnOzTPwBwl5EjeuxKoxyIpByVVZNbQYqGlJLrkru0Tm2FWZ/frrr/Hiiy9i1KhRaNy4sVTn5HnYS0vuzk0DrJ5agyyrsvZTa4uB0jnVL8vJXx7PrsrsDz/8gJs3byI+Ph6JiYlYvHgxrl69KtW5eR7+YCF348aVWD1PW0v2XqzKqgerquTO7Aqz7du3x4cffohLly7h+eefx9q1axEVFQWdTofMzEzcvHlTqvP0HB7ww588gId8HrsyyLIq636U1GLgbL+sNZz8RVJyaDWDatWq4W9/+xt++OEHHDlyBK+88gpmzZqF8PBwPPXUU2Kfo2fiDxpSIw8JsQCDrCeuK+vJLQbWKruc/EVycnr1syZNmuDdd9/FH3/8gU8++USMcyI9DwoGpGL6z1MP+lxVc5AVg6sDKVsMnMMWA3J3oi3l6+XlhbS0NHz55Zdi7ZL0PCgkkIp4WIDVU3uPrJztBWquyoqN78X/cPIXOUuUO4CRC3DFA1IKDwyweq4OsqzKSluVlWK/YrYYsF/WSXfkD7peFYCXyHcAg4QdHWol9ltMUvPgIEEy89BKrJ47BFlO+jLmCS0GYmC/LCkdK7NqxCotuRIDkOpbCwB5J305+lol3LpWCq6sbrNfljwBw6ya8Xa4JBUGWAM5gqwS2wvUgC0GRJ6JYVbtWKUlMTHEGnGXIMuqbFVsMbhL6n5Zazj5i8TAnll3wRBCzvDwflhT3CXIegq1rA6glvPUc7ZfljdLIFdgZdadsEpL9mB4NcsdemT11FiVlZo9VVk13yhB9f2yzkz+ulMq3nmQ4rEy644YUsgSVmEtkivIKrW9QA7uOvHLVuyXJbIPK7PuilVaupdKQ42ruVOQFYsaqrJKrP6qgar7ZYnuwTDr7hhqPRtDrM3cqbUAYFXWHDlbDNQWuhXdpqCSyV/ackAr8peioLLPI1dgmPUUDLWeRaVBRi5yBllWZZ2jtoBojVjtA64Iopz8RUrBMOtpGGrdFwOsQ9wxyKq1Kku2Yb+sFZz85XE4AcxT8Yed++CELocxyFrajyi7se+YKmwxkGt/zpK7X9Yq3saW7MDKrCdjlVa9GF6d5m49skriSS0GrJI6hpO/SEwMs8RQqyYMsaKQO8iyKmvimOqYzyMpsZbkEqNfljdLIDVhmwH9D/9crUz6jws/NqJw1yCrFEqolpoiZ4sBiUwlKxkozZIlSxATEwN/f38kJiZi7969Fsdv2LABsbGx8Pf3R1xcHLZu3Wr0vEajMfmYPXu2Ycz169cxcOBABAUFISQkBMOHD0dxcbHh+XPnzpncx08//WTXtTHMUlUMTvJjgJWE3EFWSmquyjpCCeep1iW5FN8vS6Jbt24d0tPTMWXKFBw4cACtWrVCSkoKLl++bHL87t27MWDAAAwfPhwHDx5EWloa0tLScPToUcOYS5cuGT2WLVsGjUaDPn36GMYMHDgQx44dQ2ZmJjZv3ozvv/8eI0eOrHK87du3G+0rPj7eruvTCILAvy1bUFRUhODgYETPmQFtgL/cpyMftiBIj8FVcnKHWSmrskoIs46+1pEWA3uPpfT1ZW3dlxhtBraEWanbDCz2zFqb/GWpMnvPSgYVujJsv74chYWFCAoKsrxPkemzQ8thb8PLV9zsUFl2Bz8vf8Ou60pMTES7du2wePFiAIBOp0N0dDTGjh2LCRMmVBnfr18/lJSUYPPmzYZt7du3R+vWrZGRkWHyGGlpabh58yaysrIAAMePH0ezZs2wb98+tG3bFgCwbds2dO/eHX/88QeioqJw7tw51K9fHwcPHkTr1q3teRuMsDJLtmGVUDp8b12CQdaW/YiyG8kpIcjKQSn9sk7vn5O/RFFUVGT0KC01vSRZWVkZcnNzkZycbNim1WqRnJyMnJwck6/JyckxGg8AKSkpZscXFBRgy5YtGD58uNE+QkJCDEEWAJKTk6HVarFnzx6j1z/11FMIDw9Hx44d8eWXX1q+cBMYZsk+DF7Ou7eFgO+lS8gdZKWklDVlXVmVVQK1thiIgZO/bOdVJkjyAIDo6GgEBwcbHjNnzjR5DlevXkVlZSUiIiKMtkdERCA/P9/ka/Lz8+0av3LlStSoUQNPP/200T7Cw8ONxnl7e6NWrVqG/VSvXh1z587Fhg0bsGXLFnTs2BFpaWl2B1quZkCOufcHKFsQrFNI4PBESgiyapj05Y6hi9wQJ38ZnD9/3qjNwM/PT7ZzWbZsGQYOHAh/f/taKkJDQ5Genm74f7t27XDx4kXMnj0bTz31lM37YZgl5zHYmsYAKzt3D7Jqr8q64lhqaDFw5Vq1nPzlPoKCgmzqmQ0NDYWXlxcKCgqMthcUFCAyMtLkayIjI20ev2vXLpw8eRLr1q2rso/7J5hVVFTg+vXrZo8L3O3vzczMtHhN92ObAYnLk/98zvYBRVFCkFULuUKcu7cYuPqYUve6ugTv/CU6X19fxMfHGyZmAXcngGVlZSEpKcnka5KSkozGA0BmZqbJ8R9//DHi4+PRqlWrKvu4ceMGcnNzDdt27NgBnU6HxMREs+d76NAh1KlTx6Zr02NllqRzf6Bzt6otA6tiKSXIekJVVsnsqcpKQY2tG6oOxHdMT4AiID09HUOGDEHbtm2RkJCABQsWoKSkBMOGDQMADB48GHXr1jX03b700kvo1KkT5s6dix49emDt2rXYv38/li5darTfoqIibNiwAXPnzq1yzKZNmyI1NRUjRoxARkYGysvLMWbMGPTv3x9RUVEA7vba+vr6ok2bNgCAzz//HMuWLcNHH31k1/UxzJLrqD3cMjyogicEWTE5G7iUvByXUvbtTiRdkosk069fP1y5cgWTJ09Gfn4+WrdujW3bthkmeeXl5UGr/d8f6zt06IA1a9Zg0qRJeOONN9C4cWNs2rQJLVq0MNrv2rVrIQgCBgwYYPK4q1evxpgxY9ClSxdotVr06dMHCxcuNBozY8YM/P777/D29kZsbCzWrVuH//u//7Pr+rjOrBVcZ9bFlBBwGVpVy1OCrJhVWXcOs1L1y3J9WfOcDrMirTELKGOd2TYD35JkndmDq/8hy3UpFSuzpCy2/JAWI/AysLodpQRZNZEryKqZHEtyeUy/rDVcyYDMYJgl9WEQJQVTU1VWLmquyrozjwjE5Ja4mgERqZ6nVGWV1F5AJDquZEAOYmWWiFRNSUFWLZO+xOBpd/wCxF+SS44lvpyh6MlfCl3JQFsOaEX+tiAw81fByiwRqZYnBVlPrspK2WIgBSW+v7xZArkzhlkiUiUlBVlPo8Sw5iilXwsnfxFZxzBLRKqjtCCrpqqsnJS2tiz9j+IDMVcyIAsYZolIVTwtyIrN3cOhVC0Gnt4vS6RkDLNEpBpKC7KuoLSqrDuFYSmuRYnvjxj9stYmfzmNKxmQExhmiYgcxKqsHcdmi4Hd1NQvy5UMSE4Ms0SkCqzKyk/p4VLuVQyISB4Ms0SkeEoMsqzK2nFsBc7dsef9cPd+WaVUd4kcxTBLRIrmqUHW06uySq8Cm6LGc1YFrmRAVvAOYESkWEoMsmrkCSHL3VoMxKreqmLylxvTlgvwgriVeKFcWZV9JWBllojIDp5YlXWGElsM3IVS2gOsTf6yiisZkJMYZolIkZRYlVVbn6xY3K3FgP2yriXpSgZEYJglIgVSYpB1FbGrsmwxcA21vs9Kqe46jMtyERhmiUhhlBpkWZV14LVsMSAiF2CYJSLFUGqQdRVWZd27xUDM49pSURVj8heRGjDMEhFZ4alVWSLZVzLgslxkA4ZZIlIEVmWVV5VVQ4uBmvpl3XHyl5JXMhDK2E/rKRhmiUh2Sg6yrMoSOY4rGZArMMwSkawYZN1rXVlnsF/WtcdV/UoGRP/FO4AREbkZthi4N07++i8VLMvlVSbAS+AdwKTGyiwRyYZVWVZl1c6d+2Vln/xFZCOGWSKShZKDrJqpcTkuQL3nTRLiSgZkI4ZZIqL7eHpVVg0tBvZSQ7+s2ji9kgGRSBhmicjlWJWVhidVN92xX9bdJn9ZXclAymW5SpXfT0viYZglIpdSepBlVdYzjikGOfplOfmLqCqGWSJyGaUHWXKOUlsMiMi9McwSEf2Xmm+QoNbqpiPsbTFgv6z9uJIBqQnDLBG5hNKrsq4MsmwxIHKSCtaYJddhmCUiySk9yJLzHG0xYIC+ixVhIsfxDmBE5PHUXpVlIHQ9Nd8sQYyVDCRflstN1pjVlumgFcSdkKct5wS/+7EyS0SSYlVWHdQSiKXsl1U6Na1kIOeyXOR5GGaJSDJqCLKsyopwDm7SYqDEKirZj2vMeh6GWSIiIiJSLYZZIpIEq7LqobQKqdKJ/X4prSLMZblIbRhmiUh0agiyruauLQaupLZ+WaWFVLfBZbnoPgyzROSRWJUVB/tlXUOMFQjE2o/kKxmQJJYsWYKYmBj4+/sjMTERe/futTh+w4YNiI2Nhb+/P+Li4rB169YqY44fP46nnnoKwcHBqFatGtq1a4e8vDzD888//zwaNmyIgIAAhIWFoXfv3jhx4oTRPvLy8tCjRw8EBgYiPDwc48ePR0WFfX8dYJglIlGxKluVUm+SACgvVJLjlLKSgdPcZFkuJVm3bh3S09MxZcoUHDhwAK1atUJKSgouX75scvzu3bsxYMAADB8+HAcPHkRaWhrS0tJw9OhRw5gzZ86gY8eOiI2NRXZ2Nn7++We8+eab8Pf3N4yJj4/H8uXLcfz4cXzzzTcQBAFdu3ZFZeXdX4gqKyvRo0cPlJWVYffu3Vi5ciVWrFiByZMn23V9GkEQlP3rqMyKiooQHByM6DkzoA3wt/4CIg+mliDr6qqsklsMnN2PKyuzSrmNrdhrzNoyTqxluWzZj7WeWWuVWaeX5bIWZq20GehXM6gQypBVvAaFhYUICgqyvE+R6bNDhyenwdtH3OxQUX4HuzOn4Pz580bX5efnBz8/P5OvSUxMRLt27bB48WIAgE6nQ3R0NMaOHYsJEyZUGd+vXz+UlJRg8+bNhm3t27dH69atkZGRAQDo378/fHx88O9//9vmc//555/RqlUrnD59Gg0bNsTXX3+Nnj174uLFi4iIiAAAZGRk4PXXX8eVK1fg6+tr035ZmSUiIoe4S5CVc79Kb28g53iV6+BVJvLjvzdNiI6ORnBwsOExc+ZMk+dQVlaG3NxcJCcnG7ZptVokJycjJyfH5GtycnKMxgNASkqKYbxOp8OWLVvw0EMPISUlBeHh4UhMTMSmTZvMvhclJSVYvnw56tevj+joaMNx4uLiDEFWf5yioiIcO3bM+husvx6bRxIRWaCWqqyrscXAM6k1pHIlA/U4f/48CgsLDY+JEyeaHHf16lVUVlYaBUYAiIiIQH5+vsnX5OfnWxx/+fJlFBcXY9asWUhNTcW3336Lv/zlL3j66afx3XffGb3u/fffR/Xq1VG9enV8/fXXyMzMNFRczR1H/5ytVBNmr1+/joEDByIoKAghISEYPnw4iouLLb4mPz8fgwYNQmRkJKpVq4aHH34Yn332mYvOmIiUyB0mfjGEikutwZOq8qQbJgQFBRk9zLUYSEGnu1sd7t27N8aNG4fWrVtjwoQJ6Nmzp6ENQW/gwIE4ePAgvvvuOzz00EPo27cv7ty5I+r5qCbMDhw4EMeOHUNmZiY2b96M77//HiNHjrT4msGDB+PkyZP48ssvceTIETz99NPo27cvDh486KKzJvIMrMqapuSqLKmHWCsZkGcKDQ2Fl5cXCgoKjLYXFBQgMjLS5GsiIyMtjg8NDYW3tzeaNWtmNKZp06ZGqxkAQHBwMBo3bozHHnsMn376KU6cOIGNGzdaPI7+OVupIsweP34c27Ztw0cffYTExER07NgRixYtwtq1a3Hx4kWzr9u9ezfGjh2LhIQENGjQAJMmTUJISAhyc3NdePZE7k1NQZZVWfH2o+R+WU8k1uQva7gsl/r4+voiPj4eWVlZhm06nQ5ZWVlISkoy+ZqkpCSj8QCQmZlpGO/r64t27drh5MmTRmN+/fVX1KtXz+y5CIIAQRBQ+t8KelJSEo4cOWK0qkJmZiaCgoKqBGVLVBFmc3JyEBISgrZt2xq2JScnQ6vVYs+ePWZf16FDB6xbtw7Xr1+HTqfD2rVrcefOHXTu3Nnsa0pLS1FUVGT0ICIi9VDL5C9yAG+Y4JD09HR8+OGHWLlyJY4fP45Ro0ahpKQEw4YNA3D3L9n39ty+9NJL2LZtG+bOnYsTJ05g6tSp2L9/P8aMGWMYM378eKxbtw4ffvghTp8+jcWLF+Orr77C3//+dwDAb7/9hpkzZyI3Nxd5eXnYvXs3nnnmGQQEBKB79+4AgK5du6JZs2YYNGgQDh8+jG+++QaTJk3C6NGj7Wqb8BbjTZJafn4+wsPDjbZ5e3ujVq1aFhuE169fj379+qF27drw9vZGYGAgNm7ciEaNGpl9zcyZMzFt2jTRzp3InbEqax5bDMgad+zVtboslzVcY1YS/fr1w5UrVzB58mTk5+ejdevW2LZtm2GyVV5eHrTa/9U3O3TogDVr1mDSpEl444030LhxY2zatAktWrQwjPnLX/6CjIwMzJw5Ey+++CKaNGmCzz77DB07dgQA+Pv7Y9euXViwYAH+/PNPRERE4LHHHsPu3bsNmc7LywubN2/GqFGjkJSUhGrVqmHIkCGYPn26Xdcn6zqzEyZMwDvvvGNxzPHjx/H5559j5cqVVcrZ4eHhmDZtGkaNGmXytWPHjsXevXvx9ttvIzQ0FJs2bcL8+fOxa9cuxMXFmXxNaWmpofwN3F0rLjo6muvMEpnAMGuekteWdXZfjrYYOHpcT15fFuAas0ZsXGMWUMY6s492ngJvb5HXma24g13Z02S5LqWStTL7yiuvYOjQoRbHNGjQAJGRkVXuUlFRUYHr16+bbRA+c+YMFi9ejKNHj6J58+YAgFatWmHXrl1YsmRJldl2epYWHSai/2GQNU/pVVm5/lzOP9MrG5flIrWSNcyGhYUhLCzM6rikpCTcuHEDubm5iI+PBwDs2LEDOp0OiYmJJl9z69YtADAqmwN3S9r6JSWIiEjZPH3yF1cyILJOFRPAmjZtitTUVIwYMQJ79+7Fjz/+iDFjxqB///6IiooCAFy4cAGxsbHYu3cvACA2NhaNGjXC888/j7179+LMmTOYO3cuMjMzkZaWJuPVEKkfq7KkVKz+msZQLA9tmU6SBxlTRZgFgNWrVyM2NhZdunRB9+7d0bFjRyxdutTwfHl5OU6ePGmoyPr4+GDr1q0ICwtDr1690LJlS6xatQorV640zKIjIvupKcjKQaoWA7UvyaV2Sg7JtvTLegJPumECGVPFagYAUKtWLaxZs8bs8zExMbh/Llvjxo15xy8iIgVQYhiUczUBNa5kwDVmSalUU5klIvmprSrLFgN1U2O/rBpDKpHaMcwSEYlE6S0G7o7vk7ycXmPWGt4wgcxgmCUim7Aqq37slyVZObvGLJEZDLNEREQK5MpZ61xjltSMYZaIrFJbVVYObDEwT4nXIMWdv4hIHgyzROR22GKgfmqc/CUHsW5jS6RmDLNEZBGrsu7B3ftllVA9tbXayxUPiMSlmnVmiYhsIUdVVqoWAyKl4BqzjtGWVkBbKW4/sraC/c33Y2WWiMxiVVZeSqg2EqkB7/7l2RhmiYhIMgzkRCQ1hlkiMkmNVVm2GJimtn5ZqSd/qWElA7VN2pL8hglEFjDMEhEpECua5CqqWGOWd/8iCxhmiYhI1Tw1+NuyLJdi8O5fJCGGWSKqgi0GRPZR6rJcamtXIHIEwywRkYM8oV9WrccmIs/BMEtERtRYlXU3SgqB7jb5i4jcD8MsEakeWwzIVmpYyUBpeMMEUjreAYyIDFiVtZ0aWgyISF6aMh00leL+MqCpZB/0/ViZJSJyU55QWXTHa+SkLSL7MMwSkaq5W4uBu4Qzd7kOIlI+hlkiAsAWA3t4SouBqyd/qZVSl+WyhRg3TJD77l9CKW+o4OkYZomISBG4koHtVHXDBCKJMcwSkWqrsu7WYkDSUmJlVEpu03vLW9mSFQyzRERuiD2rzuH7JyLeypYkxjBLRGQHKftl3SVAueo63OX9IiLnMMwSeTi2GJApnPxFRGrBMEtERKQQbtPnSuRCvAMYERERmcRb2TpHW1oOrZe4dUNtJftr7sfKLJEHU2uLgVzU0i+rxl5StS7L5WkrJBApEcMsEakO+2VJbRh6iaTDMEtERKJRalXYncIkb5hAZIxhlshDscWAzFHDSgZShmalBnKxiXErWyIlYJglIrKBlP2yRGJz1aoI2lIPSf6kaAyzRKQq7tgv6+mTv4hIekuWLEFMTAz8/f2RmJiIvXv3Why/YcMGxMbGwt/fH3Fxcdi6davR80OHDoVGozF6pKamGo2JiYmpMmbWrFmG58+dO1fleY1Gg59++smua2OYJfJAbDEgIncglJbKfQqqsG7dOqSnp2PKlCk4cOAAWrVqhZSUFFy+fNnk+N27d2PAgAEYPnw4Dh48iLS0NKSlpeHo0aNG41JTU3Hp0iXD45NPPqmyr+nTpxuNGTt2bJUx27dvNxoTHx9v1/UxzBIRkazUuiwXkVrMmzcPI0aMwLBhw9CsWTNkZGQgMDAQy5YtMzn+vffeQ2pqKsaPH4+mTZtixowZePjhh7F48WKjcX5+foiMjDQ8atasWWVfNWrUMBpTrVq1KmNq165tNMbHx8eu62OYJSIiImmU8TcVqRQVFRk9Ss1UqcvKypCbm4vk5GTDNq1Wi+TkZOTk5Jh8TU5OjtF4AEhJSakyPjs7G+Hh4WjSpAlGjRqFa9euVdnXrFmzULt2bbRp0wazZ89GRUXViYdPPfUUwsPD0bFjR3z55ZdWr/1+vAMYkYdRc4uBXP2ynPxlG0/r1xV7uS+PvJVtqZWlM+6ovI2gvByoFLluqLv7hRYdHW20ecqUKZg6dWqV4VevXkVlZSUiIiKMtkdERODEiRMmD5Gfn29yfH5+vuH/qampePrpp1G/fn2cOXMGb7zxBrp164acnBx4eXkBAF588UU8/PDDqFWrFnbv3o2JEyfi0qVLmDdvHgCgevXqmDt3Lh555BFotVp89tlnSEtLw6ZNm/DUU0/Z/JYwzBIRyUhpAVANy3LZy53WmCXSO3/+PIKCggz/9/Pzc+nx+/fvb/h3XFwcWrZsiYYNGyI7OxtdunQBAKSnpxvGtGzZEr6+vnj++ecxc+ZM+Pn5ITQ01GhMu3btcPHiRcyePduuMMs2AyIiN6G0YEx3qTVMa0or5T4FsiAoKMjoYS7MhoaGwsvLCwUFBUbbCwoKEBkZafI1kZGRdo0HgAYNGiA0NBSnT582OyYxMREVFRU4d+6cxTGW9mEKwywREakKb5hAZDtfX1/Ex8cjKyvLsE2n0yErKwtJSUkmX5OUlGQ0HgAyMzPNjgeAP/74A9euXUOdOnXMjjl06BC0Wi3Cw8MtjrG0D1PYZkDkQdgvS0TkedLT0zFkyBC0bdsWCQkJWLBgAUpKSjBs2DAAwODBg1G3bl3MnDkTAPDSSy+hU6dOmDt3Lnr06IG1a9di//79WLp0KQCguLgY06ZNQ58+fRAZGYkzZ87gtddeQ6NGjZCSkgLg7iSyPXv24PHHH0eNGjWQk5ODcePG4dlnnzWserBy5Ur4+vqiTZs2AIDPP/8cy5Ytw0cffWTX9THMEhERqYSXJ04SI6f169cPV65cweTJk5Gfn4/WrVtj27ZthkleeXl50Gr/98f6Dh06YM2aNZg0aRLeeOMNNG7cGJs2bUKLFi0AAF5eXvj555+xcuVK3LhxA1FRUejatStmzJhhaHfw8/PD2rVrMXXqVJSWlqJ+/foYN26cUY8sAMyYMQO///47vL29ERsbi3Xr1uH//u//7Lo+jSAI6mzmcZGioiIEBwcjes4MaAP85T4dIqewMms/qVcyUNrdv5yZAObI8R1ZY9be49jTs2rPvm3dr63jbFnNwNYwa8u+tKVVl0i6ly09s1ZvZ2ttaS4nVzOwdNOECqEMWcVrUFhYaDRRyhX02SG5/lh4a8WdmFWhK8X2s4tkuS6lYs8sEZFM2J9JcrEWZInUhGGWyEOouSpLruHqqixJwyPXqyWPxp5ZIlI8Tv6yjmGSSIFKywCtyN+/dG64GLSTWJklIiIiu1ntlyVyEYZZIiIiO6n1RgieRHf7jtynQC7CMEvkAdgv6xipVzIg6akldLLPlchxDLNERKQa7A0movsxzBIRyYChjIhIHAyzRKRoXMmAXEWKXzDU0uZApGYMs0REJAtH7v5FKmLt7l9EImGYJXJznPxFRO7I0q1sybMwzBIRqZwYfx7n3b+ISK14BzAiIhO4LBcROe1OmfhlQ94BrApWZomIiMiIprRS+oOUWglld9hGQLZhmCUiIlIBL95YgcgkhlkiIiIiUi2GWSI3pvaVDNx1jVlOmCIiEg/DLBERERGpFsMsEREREakWwywRERERqRbDLBERuSWvMkHuU3A5rQ0rHmhLK1xwJkSuwzBLRESqoJSJc54YkomUjHcAIyIiIpKAUFYKQSPuLz+CwDuA3Y+VWSKi+6jpVrZKqVYSEcmFYZbITal9jVlyHa0MhR4vhnAiEgnDLBERERGpFsMsESmSu979i4iIxMUwS0REDmPPrvNsWU6LiMxjmCUiIo/HUE6kXgyzRERERKRaDLNEREQS4M0ViFyDYZaIyIX452xyB9pSfiKTcvAOYEREREQS0N2+A52mUtx9CvxF4n6szBIRERGRajHMEhEREZFqMcwSERERkWoxzBIRERGRaqkmzL711lvo0KEDAgMDERISYtNrBEHA5MmTUadOHQQEBCA5ORmnTp2S9kSJiIiIyGVUE2bLysrwzDPPYNSoUTa/5t1338XChQuRkZGBPXv2oFq1akhJScGdO3ckPFMiIiIiZVmyZAliYmLg7++PxMRE7N271+L4DRs2IDY2Fv7+/oiLi8PWrVvNjn3hhReg0WiwYMECo+2//vorevfujdDQUAQFBaFjx47YuXOn0Zi8vDz06NEDgYGBCA8Px/jx41FRUWHXtakmzE6bNg3jxo1DXFycTeMFQcCCBQswadIk9O7dGy1btsSqVatw8eJFbNq0SdqTJSIiIlKIdevWIT09HVOmTMGBAwfQqlUrpKSk4PLlyybH7969GwMGDMDw4cNx8OBBpKWlIS0tDUePHq0yduPGjfjpp58QFRVV5bmePXuioqICO3bsQG5uLlq1aoWePXsiPz8fAFBZWYkePXqgrKwMu3fvxsqVK7FixQpMnjzZrutTTZi119mzZ5Gfn4/k5GTDtuDgYCQmJiInJ8fs60pLS1FUVGT0ICIiIlKrefPmYcSIERg2bBiaNWuGjIwMBAYGYtmyZSbHv/fee0hNTcX48ePRtGlTzJgxAw8//DAWL15sNO7ChQsYO3YsVq9eDR8fH6Pnrl69ilOnTmHChAlo2bIlGjdujFmzZuHWrVuGUPztt9/il19+wX/+8x+0bt0a3bp1w4wZM7BkyRKUlZXZfH1ue9MEfeqPiIgw2h4REWF4zpSZM2di2rRpVbbr2JpAalOpkfsMnKIpk/H8y6U7ttjrnQv2/TXO9D5s/5lR9bWOXo8D5233sey4naw9+xbKbduvreO05Trb9ldhfZzWpjHW33xNpeX9aCtteMN0VsborHziWXlesPKJW/HfD6ogyHdb4QqUAyIfvgJ3r+v+Ypufnx/8/PyqjC8rK0Nubi4mTpxo2KbVapGcnGy2uJeTk4P09HSjbSkpKUZ/2dbpdBg0aBDGjx+P5s2bV9lH7dq10aRJE6xatQoPP/ww/Pz88MEHHyA8PBzx8fGG48TFxRlltZSUFIwaNQrHjh1DmzZtrLwbd8kaZidMmIB33nnH4pjjx48jNjbWRWcETJw40egDeOHCBTRr1gwXJr3lsnMgIiIicVy7dg3BwcEuPaavry8iIyOxK/8rSfZfvXp1REdHG22bMmUKpk6dWmXs1atXUVlZabK4d+LECZP7z8/Pt1oMfOedd+Dt7Y0XX3zR5D40Gg22b9+OtLQ01KhRA1qtFuHh4di2bRtq1qxp8Tj652wla5h95ZVXMHToUItjGjRo4NC+IyMjAQAFBQWoU6eOYXtBQQFat25t9nX3/2ZTvXp1nD9/HjVq1IBGo+5Kl15RURGio6Nx/vx5BAUFyX06LuXJ1w549vXz2nntnnbtgGdff2FhIR588EHUqlXL5cf29/fH2bNn7fpTuT0EQaiSSUxVZaWSm5uL9957DwcOHDCbjQRBwOjRoxEeHo5du3YhICAAH330EXr16oV9+/YZZTNnyRpmw8LCEBYWJsm+69evj8jISGRlZRnCa1FREfbs2WPXigharRYPPPCAJOcot6CgII/75qbnydcOePb189p57Z7Ik69fq5VnepC/vz/8/f1lOfa9QkND4eXlhYKCAqPtBQUFhsLf/SIjIy2O37VrFy5fvowHH3zQ8HxlZSVeeeUVLFiwAOfOncOOHTuwefNm/Pnnn4bPvffffx+ZmZlYuXIlJkyYgMjIyCqrKuiPa+7cTFHNBLC8vDwcOnQIeXl5qKysxKFDh3Do0CEUFxcbxsTGxmLjxo0A7pa3X375Zfzzn//El19+iSNHjmDw4MGIiopCWlqaTFdBRERE5Dq+vr6Ij49HVlaWYZtOp0NWVhaSkpJMviYpKcloPABkZmYaxg8aNAg///yzIYsdOnQIUVFRGD9+PL755hsAwK1btwBU/WVCq9VCp9MZjnPkyBGjVRUyMzMRFBSEZs2a2XyNqpkANnnyZKxcudLwf31T8M6dO9G5c2cAwMmTJ1FYWGgY89prr6GkpAQjR47EjRs30LFjR2zbtk0RvykRERERuUJ6ejqGDBmCtm3bIiEhAQsWLEBJSQmGDRsGABg8eDDq1q2LmTNnAgBeeukldOrUCXPnzkWPHj2wdu1a7N+/H0uXLgVwd3JX7dq1jY7h4+ODyMhINGnSBMDdoFqzZk0MGTIEkydPRkBAAD788EOcPXsWPXr0AAB07doVzZo1w6BBg/Duu+8iPz8fkyZNwujRo+1rmxDI49y5c0eYMmWKcOfOHblPxeU8+doFwbOvn9fOa/dEnnz9nnztpixatEh48MEHBV9fXyEhIUH46aefDM916tRJGDJkiNH49evXCw899JDg6+srNG/eXNiyZYvF/derV0+YP3++0bZ9+/YJXbt2FWrVqiXUqFFDaN++vbB161ajMefOnRO6desmBAQECKGhocIrr7wilJeX23VtGkGQcc0KIiIiIiInqKZnloiIiIjofgyzRERERKRaDLNEREREpFoMs0RERESkWgyzbmLJkiWIiYmBv78/EhMTqyxCfK8PP/wQjz76KGrWrImaNWsiOTm5yvihQ4dCo9EYPVJTU6W+DIfYc+0rVqyocl33L9UmCAImT56MOnXqICAgAMnJyTh16pTUl+EQe669c+fOVa5do9EYlkgB1PNx//7779GrVy9ERUVBo9EY3S/cnOzsbMP9wRs1aoQVK1ZUGWPP+ykXe6/9888/x5NPPomwsDAEBQUhKSnJsA6k3tSpU6t83F15G3F72Hv92dnZJj/v779Vpjt+7E19PWs0GjRv3twwRi0f+5kzZ6Jdu3aoUaMGwsPDkZaWhpMnT1p93YYNGxAbGwt/f3/ExcVh69atRs+r6fs9mccw6wbWrVuH9PR0TJkyBQcOHECrVq2QkpJitAjxvbKzszFgwADs3LkTOTk5iI6ORteuXXHhwgWjcampqbh06ZLh8cknn7jicuxi77UDd++Ec+91/f7770bPv/vuu1i4cCEyMjKwZ88eVKtWDSkpKbhz547Ul2MXe6/9888/N7ruo0ePwsvLC88884zRODV83EtKStCqVSssWbLEpvH6dQ0ff/xxHDp0CC+//DKee+45o1DnyOeSHOy99u+//x5PPvkktm7ditzcXDz++OPo1asXDh48aDSuefPmRh/3H374QYrTd5q916938uRJo+sLDw83POeuH/v33nvP6JrPnz+PWrVqVfmaV8PH/rvvvsPo0aPx008/ITMzE+Xl5ejatStKSkrMvmb37t0YMGAAhg8fjoMHDyItLQ1paWk4evSoYYxavt+TFXYt5EWKlJCQIIwePdrw/8rKSiEqKkqYOXOmTa+vqKgQatSoIaxcudKwbciQIULv3r3FPlXR2Xvty5cvF4KDg83uT6fTCZGRkcLs2bMN227cuCH4+fkJn3zyiWjnLQZnP+7z588XatSoIRQXFxu2qeXjfi8AwsaNGy2Oee2114TmzZsbbevXr5+QkpJi+L+z76ccbLl2U5o1ayZMmzbN8P8pU6YIrVq1Eu/EXMSW69+5c6cAQPjzzz/NjvGUj/3GjRsFjUYjnDt3zrBNrR/7y5cvCwCE7777zuyYvn37Cj169DDalpiYKDz//POCIKjr+z1ZxsqsypWVlSE3NxfJycmGbVqtFsnJycjJybFpH7du3UJ5eTlq1apltD07Oxvh4eFo0qQJRo0ahWvXrol67s5y9NqLi4tRr149REdHo3fv3jh27JjhubNnzyI/P99on8HBwUhMTLT5/XQFMT7uH3/8Mfr3749q1aoZbVf6x90ROTk5Ru8VAKSkpBjeKzHeT7XQ6XS4efNmla/3U6dOISoqCg0aNMDAgQORl5cn0xlKo3Xr1qhTpw6efPJJ/Pjjj4btnvSx//jjj5GcnIx69eoZbVfjx15/t8/7P4/vZe3rXi3f78k6hlmVu3r1KiorKxEREWG0PSIiokpPmDmvv/46oqKijL6gU1NTsWrVKmRlZeGdd97Bd999h27duqGyslLU83eGI9fepEkTLFu2DF988QX+85//QKfToUOHDvjjjz8AwPA6Z95PV3D24753714cPXoUzz33nNF2NXzcHZGfn2/yvSoqKsLt27dF+TpSizlz5qC4uBh9+/Y1bEtMTMSKFSuwbds2/Otf/8LZs2fx6KOP4ubNmzKeqTjq1KmDjIwMfPbZZ/jss88QHR2Nzp0748CBAwDE+R6qBhcvXsTXX39d5WtejR97nU6Hl19+GY888ghatGhhdpy5r3v9x1Ut3+/JOm+5T4DkNWvWLKxduxbZ2dlGE6H69+9v+HdcXBxatmyJhg0bIjs7G126dJHjVEWRlJSEpKQkw/87dOiApk2b4oMPPsCMGTNkPDPX+vjjjxEXF4eEhASj7e76cae71qxZg2nTpuGLL74w6hnt1q2b4d8tW7ZEYmIi6tWrh/Xr12P48OFynKpomjRpYrhXPHD3a/7MmTOYP38+/v3vf8t4Zq61cuVKhISEIC0tzWi7Gj/2o0ePxtGjRxXZ20vyYGVW5UJDQ+Hl5YWCggKj7QUFBYiMjLT42jlz5mDWrFn49ttv0bJlS4tjGzRogNDQUJw+fdrpcxaLM9eu5+PjgzZt2hiuS/86Z/bpCs5ce0lJCdauXWvTDyolftwdERkZafK9CgoKQkBAgCifS0q3du1aPPfcc1i/fn2VP73eLyQkBA899JDqP+7mJCQkGK7NEz72giBg2bJlGDRoEHx9fS2OVfrHfsyYMdi8eTN27tyJBx54wOJYc1/3+o+rWr7fk3UMsyrn6+uL+Ph4ZGVlGbbpdDpkZWUZVSDv9+6772LGjBnYtm0b2rZta/U4f/zxB65du4Y6deqIct5icPTa71VZWYkjR44Yrqt+/fqIjIw02mdRURH27Nlj8z5dwZlr37BhA0pLS/Hss89aPY4SP+6OSEpKMnqvACAzM9PwXonxuaRkn3zyCYYNG4ZPPvnEaCk2c4qLi3HmzBnVf9zNOXTokOHa3P1jD9xdCeD06dM2/QKr1I+9IAgYM2YMNm7ciB07dqB+/fpWX2Pt614t3+/JBnLPQCPnrV27VvDz8xNWrFgh/PLLL8LIkSOFkJAQIT8/XxAEQRg0aJAwYcIEw/hZs2YJvr6+wqeffipcunTJ8Lh586YgCIJw8+ZN4dVXXxVycnKEs2fPCtu3bxcefvhhoXHjxsKdO3dkuUZz7L32adOmCd98841w5swZITc3V+jfv7/g7+8vHDt2zDBm1qxZQkhIiPDFF18IP//8s9C7d2+hfv36wu3bt11+fZbYe+16HTt2FPr161dlu5o+7jdv3hQOHjwoHDx4UAAgzJs3Tzh48KDw+++/C4IgCBMmTBAGDRpkGP/bb78JgYGBwvjx44Xjx48LS5YsEby8vIRt27YZxlh7P5XC3mtfvXq14O3tLSxZssTo6/3GjRuGMa+88oqQnZ0tnD17Vvjxxx+F5ORkITQ0VLh8+bLLr88ae69//vz5wqZNm4RTp04JR44cEV566SVBq9UK27dvN4xx14+93rPPPiskJiaa3KdaPvajRo0SgoODhezsbKPP41u3bhnG3P8978cffxS8vb2FOXPmCMePHxemTJki+Pj4CEeOHDGMUcv3e7KMYdZNLFq0SHjwwQcFX19fISEhQfjpp58Mz3Xq1EkYMmSI4f/16tUTAFR5TJkyRRAEQbh165bQtWtXISwsTPDx8RHq1asnjBgxQnHf2PXsufaXX37ZMDYiIkLo3r27cODAAaP96XQ64c033xQiIiIEPz8/oUuXLsLJkydddTl2sefaBUEQTpw4IQAQvv322yr7UtPHXb/c0v0P/fUOGTJE6NSpU5XXtG7dWvD19RUaNGggLF++vMp+Lb2fSmHvtXfq1MnieEG4u0xZnTp1BF9fX6Fu3bpCv379hNOnT7v2wmxk7/W/8847QsOGDQV/f3+hVq1aQufOnYUdO3ZU2a87fuwF4e5SUwEBAcLSpUtN7lMtH3tT1w3A6OvY1Pe89evXCw899JDg6+srNG/eXNiyZYvR82r6fk/maQRBECQr+xIRERERSYg9s0RERESkWgyzRERERKRaDLNEREREpFoMs0RERESkWgyzRERERKRaDLNEREREpFoMs0RERESkWgyzRERERKRaDLNEJLrOnTvj5ZdfNvw/JiYGCxYssPn1K1asQEhIiCjnIua+lCgrKwtNmzZFZWWlXa9r3749PvvsM4nOiojIdRhmiTzU0KFDodFooNFo4OPjg/r16+O1117DnTt3RD/Wvn37MHLkSFH3qT93jUaDatWqoXHjxhg6dChyc3ONxvXr1w+//vqrTftUY/B97bXXMGnSJHh5eQG4ew3690Wr1aJOnTro168f8vLyjF43adIkTJgwATqdTo7TJiISDcMskQdLTU3FpUuX8Ntvv2H+/Pn44IMPMGXKFNGPExYWhsDAQNH3u3z5cly6dAnHjh3DkiVLUFxcjMTERKxatcowJiAgAOHh4aIfWwl++OEHnDlzBn369DHaHhQUhEuXLuHChQv47LPPcPLkSTzzzDNGY7p164abN2/i66+/duUpExGJjmGWyIP5+fkhMjIS0dHRSEtLQ3JyMjIzMw3PX7t2DQMGDEDdunURGBiIuLg4fPLJJ0b7KCkpweDBg1G9enXUqVMHc+fOrXKc+9sM5s2bh7i4OFSrVg3R0dH4+9//juLiYrvPPyQkBJGRkYiJiUHXrl3x6aefYuDAgRgzZgz+/PNPAFWrrYcPH8bjjz+OGjVqICgoCPHx8di/fz+ys7MxbNgwFBYWGiqbU6dOBQD8+9//Rtu2bVGjRg1ERkbir3/9Ky5fvmzYZ3Z2NjQaDbKystC2bVsEBgaiQ4cOOHnypNH5fvXVV2jXrh38/f0RGhqKv/zlL4bnSktL8eqrr6Ju3bqoVq0aEhMTkZ2dbfH6165diyeffBL+/v5G2zUaDSIjI1GnTh106NABw4cPx969e1FUVGQY4+Xlhe7du2Pt2rX2vOVERIrDMEtEAICjR49i9+7d8PX1NWy7c+cO4uPjsWXLFhw9ehQjR47EoEGDsHfvXsOY8ePH47vvvsMXX3yBb7/9FtnZ2Thw4IDFY2m1WixcuBDHjh3DypUrsWPHDrz22muiXMe4ceNw8+ZNo1B+r4EDB+KBBx7Avn37kJubiwkTJsDHxwcdOnTAggULDFXNS5cu4dVXXwUAlJeXY8aMGTh8+DA2bdqEc+fOYejQoVX2/Y9//ANz587F/v374e3tjb/97W+G57Zs2YK//OUv6N69Ow4ePIisrCwkJCQYnh8zZgxycnKwdu1a/Pzzz3jmmWeQmpqKU6dOmb3WXbt2oW3bthbfj8uXL2Pjxo3w8vIytCLoJSQkYNeuXRZfT0SkeAIReaQhQ4YIXl5eQrVq1QQ/Pz8BgKDVaoVPP/3U4ut69OghvPLKK4IgCMLNmzcFX19fYf369Ybnr127JgQEBAgvvfSSYVu9evWE+fPnm93nhg0bhNq1axv+v3z5ciE4ONjieQAQNm7cWGX77du3BQDCO++8Y3JfNWrUEFasWGFyn7YcVxAEYd++fQIA4ebNm4IgCMLOnTsFAML27dsNY7Zs2SIAEG7fvi0IgiAkJSUJAwcONLm/33//XfDy8hIuXLhgtL1Lly7CxIkTzZ5HcHCwsGrVqirXAECoVq2aEBgYKAAQAAgvvvhildd/8cUXglarFSorK61eMxGRUnnLlqKJSHaPP/44/vWvf6GkpATz58+Ht7e3Uf9lZWUl3n77baxfvx4XLlxAWVkZSktLDf2vZ86cQVlZGRITEw2vqVWrFpo0aWLxuNu3b8fMmTNx4sQJFBUVoaKiAnfu3MGtW7ec7q0VBAHA3T+1m5Keno7nnnsO//73v5GcnIxnnnkGDRs2tLjP3NxcTJ06FYcPH8aff/5pmDSVl5eHZs2aGca1bNnS8O86deoAuFsZffDBB3Ho0CGMGDHC5P6PHDmCyspKPPTQQ0bbS0tLUbt2bbPndfv27SotBgBQo0YNHDhwAOXl5fj666+xevVqvPXWW1XGBQQEQKfTobS0FAEBARbeASIi5WKbAZEHq1atGho1aoRWrVph2bJl2LNnDz7++GPD87Nnz8Z7772H119/HTt37sShQ4eQkpKCsrIyh4957tw59OzZEy1btsRnn32G3NxcLFmyBACc2q/e8ePHAQD169c3+fzUqVNx7Ngx9OjRAzt27ECzZs2wceNGs/srKSlBSkoKgoKCsHr1auzbt88w/v7z9fHxMfxbH6b1wddSWCwuLoaXlxdyc3Nx6NAhw+P48eN47733zL4uNDTU0Bt8L61Wi0aNGqFp06ZIT09H+/btMWrUqCrjrl+/jmrVqjHIEpGqMcwSEYC7AeiNN97ApEmTcPv2bfx/e/cO0tgWhQH4vyPJqKTzGSEajAZt1HRaiI2YMiQoCEoiWGiRxkIUFFNY+ABtLHw0gkLEQouAjSEiaJQoIaDBWClBERVDRIOiomuKueZO8DHjcO8dA/8Hp9lnn332PkVYbPZaAQCv1wuTyYTm5maUl5ejsLAwocyVTqeDQqGAz+eLt0Wj0XdLYfn9fjw9PWFkZASVlZXQ6/U4OTn519bxfO61trb2zT56vR4dHR1YXl6GxWLB9PQ0AECpVL6o17q/v49IJILBwUFUV1ejpKQkIfnrV5WVlcHj8bx6z2Aw4PHxEefn5ygqKkq4cnNz3xzTYDBgb2/vp+/u7u7G/Pz8i7PMwWAQBoPhYwshIvpkGMwSUVxDQwNSUlLiO6XFxcVwu93Y2NhAKBRCW1sbzs7O4v1VKhVaW1vR2dmJlZUVBINBtLS04MuXt39aioqK8PDwgLGxMRwcHGB2dhYTExO/Nd/Ly0ucnp4iHA7D7Xajvr4eTqcT4+Pjr9aLvb29hd1ux+rqKsLhMLxeL7a3t1FaWgrge9WFWCwGj8eDi4sL3NzcID8/H0qlMj5fl8uF/v7+D8/V4XBgbm4ODocDoVAIu7u7GBoaAvA9uG5qaoLVasXi4iIODw+xtbWFgYEBLC0tvTmm0WjE+vr6T9+t0WhgNpvR19eX0L62toa6uroPr4WI6FP504d2iejPsNlsYjKZXrQPDAxIVlaWxGIxiUQiYjKZRKVSSXZ2tvT29orVak147vr6WpqbmyU9PV1ycnJkeHhYampq3k0AGx0dFbVaLWlpaWI0GmVmZkYASDQaFZFfTwB7vlJTU0Wn04nNZhO/35/Q78ex7u7upLGxUTQajSiVSsnLyxO73R5P0hIRaW9vl4yMDAEgDodDREScTqdotVr5+vWrVFVVicvlEgASCARE5J8EsOf5i4gEAgEBIIeHh/G2hYUFqaioEKVSKZmZmWKxWOL37u/vpa+vT7RarSgUClGr1WI2m2VnZ+fNbxCJRCQ1NVX29/dfXe+PNjc3BYD4fD4RETk+PhaFQiFHR0fvfWYiok/vL5G/syWIiCjpdHZ24urqCpOTkx96rqurC9FoFFNTU//RzIiI/h88ZkBElMR6enpQUFDw4b+lzc7O/q3jEkREnw13ZomIiIgoaXFnloiIiIiSFoNZIiIiIkpaDGaJiIiIKGkxmCUiIiKipMVgloiIiIiSFoNZIiIiIkpaDGaJiIiIKGkxmCUiIiKipMVgloiIiIiS1jebEaSWgqX8dgAAAABJRU5ErkJggg==",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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RERER4s8//9RpyauTJ0+K8PBw4erqKlq0aCEmTZokfvvtNwFArF27Vmo3fvx40bRp0zrfm9vp+n5WVFSIefPmibZt2woHBwcREBAgZs2aJW7evKnRTqVSiaFDh4pmzZoJABrLLxUVFYlZs2aJDh06CEdHR9GiRQvRt29fsXjxYlFeXi6E+HfJq9uXhqqGO5axqqysFC+99JLw9vYWCoVC4zO5s+3169fFxIkTRYsWLYSrq6uIiIgQp0+f1um9r83SpUuFq6trjeWo6vo7NXfuXI3+y8vLxcKFC0XXrl2lvzfBwcFi3rx5oqCgQNpv165dokePHsLZ2VkEBgaKhQsXijVr1ggA4ty5c1I7bUtefffddwKAOHv2bL3nRESNh0IIA68SICIim1JQUIB27dph0aJFiI6OtvRwahUVFQWFQiHdBIKICAAYWomIGpGFCxdi7dq1OHnypN4rNpjDqVOn0L17dxw9elTr1BYiarwYWomIiIjI6lnf/2YTEREREd3B5kJrYmIiAgMD4ezsjNDQUGRkZNTZfuvWrQgKCoKzszO6d+8u3d6RiIiIqLHQJz998skn0m2smzdvjvDw8DrbP//881AoFDWWGZSbTYXWzZs3IzY2FnFxcTh8+DB69uyJiIiIGrdTrHbw4EGMHj0a0dHROHLkCKKiohAVFYXjx4+beeRERERElqFvfkpNTcXo0aOxd+9epKWlISAgAIMHD0Z2dnaNttu3b8cvv/wCf39/U5+Gbc1pDQ0Nxb333osPP/wQwK1bCgYEBOCll17CzJkza7QfOXIkSkpK8M0330jb7rvvPvTq1QsrV64027iJiIiILEXf/HSnqqoqNG/eHB9++KHGXQ6zs7MRGhqK77//HkOHDsXUqVMxdepUU52G7dxcoLy8HJmZmZg1a5a0TalUIjw8HGlpaVr3SUtLQ2xsrMa2iIiIOu8yVFZWhrKyMum5Wq1GXl4evLy8jLqPOREREZmPEAJFRUXw9/e3yEoZN2/eRHl5uUn6FkLUyCROTk4atwGvZkh+ulNpaSkqKirg6ekpbVOr1Rg7diymT5+Orl27Gngm+rGZ0Hr16lVUVVXBx8dHY7uPjw9Onz6tdR+VSqW1vUqlqvU48fHxmDdvnvEDJiIiIou7ePEiWrVqZdZj3rx5E23buEKVa9htl+vj6uqK4uJijW1xcXGYO3dujbaG5Kc7vf766/D390d4eLi0beHChbC3t8fLL7+s/wkYyGZCq7nMmjVLozpbUFCA1q1b4+LFi3Bzc7PgyIjIGP1iEy09BCIyo6rymzj5+dto1qyZ2Y9dXl4OVW4V/vw1AG7N5K3yFhap0SHkYo1coq3KKocFCxZg06ZNSE1NhbOzMwAgMzMT77//Pg4fPmzWb6FtJrS2aNECdnZ2yMnJ0diek5MDX19frfv4+vrq1R6ovbzu5ubG0Epkw+wcnS09BCKyAEtO7XNrppQ9tEp965hLDMlP1RYvXowFCxbgxx9/RI8ePaTt+/fvR25uLlq3bi1tq6qqwrRp05CQkIDz58/rdzI6spnVAxwdHREcHIyUlBRpm1qtRkpKCsLCwrTuExYWptEeAJKTk2ttT0RERNSQGJKfAGDRokWYP38+kpKSEBISovHa2LFj8fvvv+Po0aPSw9/fH9OnT8f3339vsnOxmUorAMTGxmL8+PEICQlBnz59kJCQgJKSEkycOBEAMG7cONx1112Ij48HALzyyivo378/lixZgqFDh2LTpk349ddfsWrVKkueBhEREZHZ6JufFi5ciDlz5mDjxo0IDAyUrgVydXWFq6srvLy84OXlpXEMBwcH+Pr6olOnTiY7D5sKrSNHjsSVK1cwZ84cqFQq9OrVC0lJSdLk4qysLI0rBPv27YuNGzdi9uzZeOONN9CxY0fs2LGD97MmIiKiRkPf/LRixQqUl5fjP//5j0Y/tV3sZS42tU6rJRQWFsLd3R0FBQWc00pkw3q/uMzSQyAiM6oqv4ljq9+wyO/v6uyQe6aNSS7EatnpQqPMJTYzp5WIiIiIGi+GViIiIiKyegytRERERGT1bOpCLCIiIiJbUSTKACFvfbBIqGXtz5aw0kpEREREVo+hlYiIiIisHkMrEREREVk9hlYiIiIisnoMrURERERk9RhaiYiIiMjqMbQSERERkdVjaCUiIiIiq8fQSkRERERWj3fEIiIiIjKBIrUakPkGVkVq3hGLiIiIiMhqMbQSERERkdVjaCUiIiIiq8fQSkRERERWj6GViIiIiKweQysRERERWT2GViIiIiKyegytRERERGT1GFqJiIiIyOrxjlhEREREJlCgdkCVWt76YDHviEVEREREZL0YWomIiIjI6jG0EhEREZHVY2glIiIiIqvH0EpEREREVo+hlYiIiIisHkMrEREREVk9hlYiIiIisnoMrURERERk9XhHLCIiIiITKFA7o1JtJ2ufJeoqWfuzJay0EhEREZHVY2glIiIiIqvH0EpEREREVo+hlYiIiIisHkMrEREREVk9hlYiIiKiBi4xMRGBgYFwdnZGaGgoMjIyam37ySef4IEHHkDz5s3RvHlzhIeH12gvhMCcOXPg5+cHFxcXhIeH4+zZsyY9B4ZWIiIiogZs8+bNiI2NRVxcHA4fPoyePXsiIiICubm5WtunpqZi9OjR2Lt3L9LS0hAQEIDBgwcjOztbarNo0SJ88MEHWLlyJdLT09G0aVNERETg5s2bJjsPhlYiIiKiBmzp0qWYNGkSJk6ciC5dumDlypVo0qQJ1qxZo7X9hg0b8OKLL6JXr14ICgrCp59+CrVajZSUFAC3qqwJCQmYPXs2hg0bhh49emD9+vW4dOkSduzYYbLzYGglIiIisjGFhYUaj7KyMq3tysvLkZmZifDwcGmbUqlEeHg40tLSdDpWaWkpKioq4OnpCQA4d+4cVCqVRp/u7u4IDQ3VuU9D8I5YRERERCZQrHaBWuY7YpX+/ztiBQQEaGyPi4vD3Llza7S/evUqqqqq4OPjo7Hdx8cHp0+f1umYr7/+Ovz9/aWQqlKppD7u7LP6NVNgaCUiIiKyMRcvXoSbm5v03MnJySTHWbBgATZt2oTU1FQ4Ozub5Bi64vQAIiIiIhvj5uam8agttLZo0QJ2dnbIycnR2J6TkwNfX986j7F48WIsWLAAP/zwA3r06CFtr97PkD6NwdBKRERE1EA5OjoiODhYuogKgHRRVVhYWK37LVq0CPPnz0dSUhJCQkI0Xmvbti18fX01+iwsLER6enqdfRqL0wOIiIiIGrDY2FiMHz8eISEh6NOnDxISElBSUoKJEycCAMaNG4e77roL8fHxAICFCxdizpw52LhxIwIDA6V5qq6urnB1dYVCocDUqVPxzjvvoGPHjmjbti3eeust+Pv7IyoqymTnwdBKRERE1ICNHDkSV65cwZw5c6BSqdCrVy8kJSVJF1JlZWVBqfz3y/cVK1agvLwc//nPfzT6uf1irxkzZqCkpASTJ09Gfn4++vXrh6SkJJPOe1UIIYTJem8ACgsL4e7ujoKCAo0Jz0RkW3q/uMzSQyAiM6oqv4ljq9+wyO/v6uyw8Wg3NGkm8+oBRVX4b6/jjTKXcE4rEREREVk9hlYiIiIisnoMrURERERk9XghFhEREZEJFKhdUF4lb9S6oa6UtT9bwkorEREREVk9hlYiIiIisnoMrURERERk9RhaiYiIiMjqMbQSERERkdVjaCUiIiIiq8fQSkRERERWj6GViIiIiKweQysRERERWT3eEYuIiIjIBIqqnFEp9x2xqnhHLCIiIiIiq8XQSkRERERWj6GViIiIiKweQysRERERWT2GViIiIiKyegytRERERGT1GFqJiIiIyOoxtBIRERGR1WNoJSIiIiKrxztiEREREZlAYZULyqscZO3zZlWFrP3ZElZaiYiIiMjqMbQSERERkdVjaCUiIiIiq8fQSkRERERWj6GViIiIiKweQysRERERWT2GViIiIiKyejYTWvPy8jBmzBi4ubnBw8MD0dHRKC4urrP9Sy+9hE6dOsHFxQWtW7fGyy+/jIKCAjOOmoiIiIjkYDOhdcyYMThx4gSSk5PxzTffYN++fZg8eXKt7S9duoRLly5h8eLFOH78ONatW4ekpCRER0ebcdREREREJAebuCPWqVOnkJSUhEOHDiEkJAQAsHz5cgwZMgSLFy+Gv79/jX26deuGbdu2Sc/bt2+Pd999F08//TQqKythb28Tp05EREQ2qrjKGRUy3xGrrMpO1v5siU1UWtPS0uDh4SEFVgAIDw+HUqlEenq6zv0UFBTAzc2tzsBaVlaGwsJCjQcRERERWZZNhFaVSoWWLVtqbLO3t4enpydUKpVOfVy9ehXz58+vc0oBAMTHx8Pd3V16BAQEGDxuIiIiIpKHRUPrzJkzoVAo6nycPn3a6OMUFhZi6NCh6NKlC+bOnVtn21mzZqGgoEB6XLx40ejjExEREZFxLDqxc9q0aZgwYUKdbdq1awdfX1/k5uZqbK+srEReXh58fX3r3L+oqAiRkZFo1qwZtm/fDgeHuueWODk5wcnJSafxExEREZF5WDS0ent7w9vbu952YWFhyM/PR2ZmJoKDgwEAe/bsgVqtRmhoaK37FRYWIiIiAk5OTti1axecnZ1lGzsRERERmY9NzGnt3LkzIiMjMWnSJGRkZODAgQOIiYnBqFGjpJUDsrOzERQUhIyMDAC3AuvgwYNRUlKC1atXo7CwECqVCiqVClVVVZY8HSIiIiKzSkxMRGBgIJydnREaGirlJW1OnDiB4cOHIzAwEAqFAgkJCVrbZWdn4+mnn4aXlxdcXFzQvXt3/PrrryY6AxsJrQCwYcMGBAUFYeDAgRgyZAj69euHVatWSa9XVFTgzJkzKC0tBQAcPnwY6enpOHbsGDp06AA/Pz/pwXmqRERE1Fhs3rwZsbGxiIuLw+HDh9GzZ09ERETUmHpZrbS0FO3atcOCBQtqnYZ5/fp13H///XBwcMB3332HkydPYsmSJWjevLnJzsNmFiv19PTExo0ba309MDAQQgjp+YABAzSeExERETVGS5cuxaRJkzBx4kQAwMqVK/Htt99izZo1mDlzZo329957L+69914A0Po6ACxcuBABAQFYu3attK1t27YmGP2/bKbSSkRERES33LmmfFlZmdZ25eXlyMzMRHh4uLRNqVQiPDwcaWlpBh9/165dCAkJwYgRI9CyZUv07t0bn3zyicH96YKhlYiIiMgEiiqdUSjzo6jy1kXlAQEBGuvKx8fHax3D1atXUVVVBR8fH43tPj4+Oq91r83ff/+NFStWoGPHjvj+++/xwgsv4OWXX8Znn31mcJ/1sZnpAURERER0y8WLF+Hm5iY9N/dynWq1GiEhIfjf//4HAOjduzeOHz+OlStXYvz48SY5JiutRERERDbGzc1N41FbaG3RogXs7OyQk5OjsT0nJ6fete7r4ufnhy5dumhs69y5M7Kysgzusz4MrUREREQNlKOjI4KDg5GSkiJtU6vVSElJQVhYmMH93n///Thz5ozGtj/++ANt2rQxuM/6cHoAERERUQMWGxuL8ePHIyQkBH369EFCQgJKSkqk1QTGjRuHu+66S5oXW15ejpMnT0p/zs7OxtGjR+Hq6ooOHToAAF599VX07dsX//vf//DUU08hIyMDq1at0liOVG4MrUREREQN2MiRI3HlyhXMmTMHKpUKvXr1QlJSknRxVlZWFpTKf798v3TpEnr37i09X7x4MRYvXoz+/fsjNTUVwK1lsbZv345Zs2bh7bffRtu2bZGQkIAxY8aY7DwYWomIiIgauJiYGMTExGh9rTqIVrtz7fvaPProo3j00UflGJ5OOKeViIiIiKweQysRERERWT2GViIiIiKyepzTSkRERGQCJZWOqKh0lLXP8kqFrP3ZElZaiYiIiMjqMbQSERERkdVjaCUiIiIiq8fQSkRERERWj6GViIiIiKweQysRERERWT2GViIiIiKyegytRERERGT1GFqJiIiIyOrxjlhEREREJlBS5YRyme+IVVHFO2IREREREVktVlqp0en94jJLD4GIiIj0xEorEREREVk9hlYiIiIisnoMrURERERk9RhaiYiIiMjqMbQSERERkdVjaCUiIiIiq8fQSkRERERWj+u0EhEREZlASYUD7CvkvSNWZYWQtT9bwkorEREREVk9hlYiIiIisnoMrURERERk9RhaiYiIiMjqMbQSERERkdVjaCUiIiIiq8fQSkRERERWj6GViIiIiKweQysRERERWT3eEYuIiIjIBG5UOsC+0kHWPisr1bL2Z0tYaSUiIiIiq8fQSkRERERWj6GViIiIiKweQysRERERWT2GViIiIiKyegytRERERA1cYmIiAgMD4ezsjNDQUGRkZNTa9sSJExg+fDgCAwOhUCiQkJBQo018fDzuvfdeNGvWDC1btkRUVBTOnDljwjNgaCUiIiJq0DZv3ozY2FjExcXh8OHD6NmzJyIiIpCbm6u1fWlpKdq1a4cFCxbA19dXa5uffvoJU6ZMwS+//ILk5GRUVFRg8ODBKCkpMdl5cJ1WIiIiogZs6dKlmDRpEiZOnAgAWLlyJb799lusWbMGM2fOrNH+3nvvxb333gsAWl8HgKSkJI3n69atQ8uWLZGZmYkHH3xQ5jO4hZVWIiIiIhtTWFio8SgrK9Parry8HJmZmQgPD5e2KZVKhIeHIy0tTbbxFBQUAAA8PT1l6/NOrLQSERERmcDNCgfYlct7R6yqilt3xAoICNDYHhcXh7lz59Zof/XqVVRVVcHHx0dju4+PD06fPi3LmNRqNaZOnYr7778f3bp1k6VPbRhaiYiIiGzMxYsX4ebmJj13cnKy2FimTJmC48eP4+effzbpcRhaiYiIiGyMm5ubRmitTYsWLWBnZ4ecnByN7Tk5ObVeZKWPmJgYfPPNN9i3bx9atWpldH914ZxWIiIiogbK0dERwcHBSElJkbap1WqkpKQgLCzM4H6FEIiJicH27duxZ88etG3bVo7h1omVViIiIqIGLDY2FuPHj0dISAj69OmDhIQElJSUSKsJjBs3DnfddRfi4+MB3Lp46+TJk9Kfs7OzcfToUbi6uqJDhw4Abk0J2LhxI3bu3IlmzZpBpVIBANzd3eHi4mKS82BoJSIiImrARo4ciStXrmDOnDlQqVTo1asXkpKSpIuzsrKyoFT+++X7pUuX0Lt3b+n54sWLsXjxYvTv3x+pqakAgBUrVgAABgwYoHGstWvXYsKECSY5D4ZWIiIiogYuJiYGMTExWl+rDqLVAgMDIYSos7/6XjcFzmklIiIiIqvH0EpEREREVo+hlYiIiIisHue0EhEREZlAWaU97CrljVpVlVWy9mdLWGklIiIiIqvH0EpEREREVo+hlYiIiIisHkMrEREREVk9hlYiIiIisnoMrURERERk9RhaiYiIiMjqMbQSERERkdVjaCUiIiIiq8c7YhERERGZQFmFHewqZL4jVkWlrP3ZEr3fyfz8fGzfvh379+/HhQsXUFpaCm9vb/Tu3RsRERHo27evKcZJRERERI2YztMDLl26hGeffRZ+fn545513cOPGDfTq1QsDBw5Eq1atsHfvXgwaNAhdunTB5s2bTTlmIiIiImpkdK609u7dG+PHj0dmZia6dOmitc2NGzewY8cOJCQk4OLFi3jttddkGygRERERNV46h9aTJ0/Cy8urzjYuLi4YPXo0Ro8ejWvXrhk9OCIiIiIiQI/pAfUFVmPbExERERHVRq8lr9q3b49ly5bV+npOTg7s7OyMHhQRERER0e30Cq3nzp3D66+/jgkTJqC8vFxrGyGELAMjIiIiIqqm980Ftm/fjj179uDBBx/E5cuXa7yuUChkGRgRERERUTW9Q+u9996LQ4cOwd7eHiEhIUhPTzfFuIiIiIiIJAbdxtXHxwepqal49NFHMWDAAKxdu1bucdWQl5eHMWPGwM3NDR4eHoiOjkZxcbFO+woh8Mgjj0ChUGDHjh2mHSgRERERyc7ge4vZ29vj448/Ru/evfH888/j6NGjmDFjhpxj0zBmzBhcvnwZycnJqKiowMSJEzF58mRs3Lix3n0TEhI4bYGIiIjMqqrCDqJC3gvU1TL3Z0v0Cq3agt/zzz+Pbt264T//+Q8OHDgg28Bud+rUKSQlJeHQoUMICQkBACxfvhxDhgzB4sWL4e/vX+u+R48exZIlS/Drr7/Cz8/PJOMjIiIiItPSa3pAbSsD9OvXD4cOHZJlQNqkpaXBw8NDCqwAEB4eDqVSWeec2tLSUvz3v/9FYmIifH19dTpWWVkZCgsLNR5EREREZFl6L3nl7e2t9bWAgAAcOHAAe/fulWVgt1OpVGjZsqXGNnt7e3h6ekKlUtW636uvvoq+ffti2LBhOh8rPj4e7u7u0iMgIMDgcRMRERGRPPQKrW3atKlzbqiTkxMefPBBnfubOXMmFApFnY/Tp0/rM0TJrl27sGfPHiQkJOi136xZs1BQUCA9Ll68aNDxiYiIiEg+Os9pjYyMxNy5c3HffffV2a6oqAgfffQRXF1dMWXKlDrbTps2DRMmTKizTbt27eDr64vc3FyN7ZWVlcjLy6v1a/89e/bgr7/+goeHh8b24cOH44EHHkBqaqrW/ZycnODk5FTnmIiIiIjIvHQOrSNGjMDw4cPh7u6Oxx57DCEhIfD394ezszOuX7+OkydP4ueff8bu3bsxdOhQvPfee/X26e3tXet0g9uFhYUhPz8fmZmZCA4OBnArlKrVaoSGhmrdZ+bMmXj22Wc1tnXv3h3Lli3DY489psMZExEREZG10Dm0RkdH4+mnn8bWrVuxefNmrFq1CgUFBQBurSrQpUsXRERE4NChQ+jcubOsg+zcuTMiIyMxadIkrFy5EhUVFYiJicGoUaOklQOys7MxcOBArF+/Hn369IGvr6/WKmzr1q3Rtm1bWcdHRERERKal15JXTk5OePrpp/H0008DAAoKCnDjxg14eXnBwcHBJAOstmHDBsTExGDgwIFQKpUYPnw4PvjgA+n1iooKnDlzBqWlpSYdBxERERGZn8E3FwAgXWFvDp6ennXeSCAwMLDWJbmq1fc6EREREVkno0IrEREREWlXVWEHUc47YslFryWviIiIiIgsgaGViIiIiKweQysRERERWT2DQ2t+fj4+/fRTzJo1C3l5eQCAw4cPIzs7W7bBEREREREBBl6I9fvvvyM8PBzu7u44f/48Jk2aBE9PT3z11VfIysrC+vXr5R4nERERETViBlVaY2NjMWHCBJw9exbOzs7S9iFDhmDfvn2yDY6IiIiIjJeYmIjAwEA4OzsjNDQUGRkZdbbfunUrgoKC4OzsjO7du2P37t0arxcXFyMmJgatWrWCi4sLunTpgpUrV5ryFAwLrYcOHcJzzz1XY/tdd90FlUpl9KCIiIiISB6bN29GbGws4uLicPjwYfTs2RMRERHIzc3V2v7gwYMYPXo0oqOjceTIEURFRSEqKgrHjx+X2sTGxiIpKQn/93//h1OnTmHq1KmIiYnBrl27THYeBoVWJycnFBYW1tj+xx9/wNvb2+hBEREREZE8li5dikmTJmHixIlSRbRJkyZYs2aN1vbvv/8+IiMjMX36dHTu3Bnz58/HPffcgw8//FBqc/DgQYwfPx4DBgxAYGAgJk+ejJ49e9ZbwTWGQaH18ccfx9tvv42KigoAgEKhQFZWFl5//XUMHz5c1gESERERkabCwkKNR1lZmdZ25eXlyMzMRHh4uLRNqVQiPDwcaWlpWvdJS0vTaA8AERERGu379u2LXbt2ITs7G0II7N27F3/88QcGDx4sw9lpZ1BoXbJkCYqLi9GyZUvcuHED/fv3R4cOHdCsWTO8++67co+RiIiIyOaICqVJHgAQEBAAd3d36REfH691DFevXkVVVRV8fHw0tvv4+NQ6pVOlUtXbfvny5ejSpQtatWoFR0dHREZGIjExEQ8++KAxb1mdDFo9wN3dHcnJyThw4AB+++03FBcX45577qmRyomIiIhIfhcvXoSbm5v03MnJyazHX758OX755Rfs2rULbdq0wb59+zBlyhT4+/ubLA8aFFqr3X///bj//vvlGgsRERER6cDNzU0jtNamRYsWsLOzQ05Ojsb2nJwc+Pr6at3H19e3zvY3btzAG2+8ge3bt2Po0KEAgB49euDo0aNYvHixyUKrQdMDXn75ZXzwwQc1tn/44YeYOnWqsWMiIiIiIhk4OjoiODgYKSkp0ja1Wo2UlBSEhYVp3ScsLEyjPQAkJydL7SsqKlBRUQGlUjNG2tnZQa1Wy3wG/zIotG7btk1rhbVv37748ssvjR4UEREREckjNjYWn3zyCT777DOcOnUKL7zwAkpKSjBx4kQAwLhx4zBr1iyp/SuvvIKkpCQsWbIEp0+fxty5c/Hrr78iJiYGwK0qb//+/TF9+nSkpqbi3LlzWLduHdavX48nnnjCZOdh0PSAa9euwd3dvcZ2Nzc3XL161ehBEREREZE8Ro4ciStXrmDOnDlQqVTo1asXkpKSpIutsrKyNKqmffv2xcaNGzF79my88cYb6NixI3bs2IFu3bpJbTZt2oRZs2ZhzJgxyMvLQ5s2bfDuu+/i+eefN9l5GBRaO3TogKSkJClxV/vuu+/Qrl07WQZGRERERPKIiYmpkduqpaam1tg2YsQIjBgxotb+fH19sXbtWrmGpxODQmtsbCxiYmJw5coVPPzwwwCAlJQULFmyBAkJCXKOj4iIiIjIsND6zDPPoKysDO+++y7mz58PAAgMDMSKFSswbtw4WQdIRERERGTwklcvvPACXnjhBVy5cgUuLi5wdXWVc1xERERERBKj1mkFAG9vbznGQURERNSgKCqUUNgbtFBTnX02VgadeU5ODsaOHQt/f3/Y29vDzs5O40FEREREJCeDKq0TJkxAVlYW3nrrLfj5+UGhUMg9LiIiIiIiiUGh9eeff8b+/fvRq1cvmYdDRERERFSTQdMDAgICIISQeyxERERERFoZFFoTEhIwc+ZMnD9/XubhEBERERHVZND0gJEjR6K0tBTt27dHkyZN4ODgoPF6Xl6eLIMjIiIiIgIMDK286xURERERmZNBoXX8+PFyj4OIiIiIqFZG31zg5s2bKC8v19jm5uZmbLdERERERBKDQmtJSQlef/11bNmyBdeuXavxelVVldEDIyIiIrJlinIFFHbyrmWvKG+8a+MbtHrAjBkzsGfPHqxYsQJOTk749NNPMW/ePPj7+2P9+vVyj5GIiIiIGjmDKq1ff/011q9fjwEDBmDixIl44IEH0KFDB7Rp0wYbNmzAmDFj5B4nERERETViBlVa8/Ly0K5dOwC35q9WL3HVr18/7Nu3T77RERERERHBwNDarl07nDt3DgAQFBSELVu2ALhVgfXw8JBtcEREREREgIGhdeLEifjtt98AADNnzkRiYiKcnZ3x6quvYvr06bIOkIiIiIjIoDmtr776qvTn8PBwnD59GpmZmejQoQN69Ogh2+CIiIiIiAADK63r169HWVmZ9LxNmzZ48sknERQUxNUDiIiIiEh2Bk8PKCgoqLG9qKgIEydONHpQRERERES3Myi0CiGgUNRc3Paff/6Bu7u70YMiIiIiIrqdXnNae/fuDYVCAYVCgYEDB8Le/t/dq6qqcO7cOURGRso+SCI5qR0AZYWlR0FERA2dokoBRaXMd8Sqarx3xNIrtEZFRQEAjh49ioiICLi6ukqvOTo6IjAwEMOHD5d1gERyq2wCONac3UJERERWTK/QGhcXBwAIDAzEqFGj4OTkZJJBEZlSqb8ajgUGzYwhIiIiCzHoN/fDDz+MK1euSM8zMjIwdepUrFq1SraBEZmKummVpYdAREREejJondb//ve/mDx5MsaOHQuVSoXw8HB069YNGzZsgEqlwpw5c+Qep8WVqXqgrETe6lyhKKu/0R2K1Gqd2uWrHXRo41LPsbS/nl/VpPZ9qpy1bi/Qso+2toWVNbeVVNas6Bdr21bhqPH8RqXme3Cj/Nbzln5AJby1jpOIiIisk0Ep7Pjx4+jTpw8AYMuWLejevTsOHjyIDRs2YN26dXKOr8EpFGXSQx9FanWDD6y6MiSwEhERkW0zqNJaUVEhzWf98ccf8fjjjwMAgoKCcPnyZflGZ+MMqaTeTteQWs2UYRWoPbDqE1Zra2/KCivwb5WViIiIbJNBobVr165YuXIlhg4diuTkZMyfPx8AcOnSJXh5eck6QFtgbDi9nSWC6q3j6h9WAesPrNrCalmlPey0jo6IiIislUGhdeHChXjiiSfw3nvvYfz48ejZsycAYNeuXdK0gYamSJQBQv4rzvUNqYBuQfVWO+sOq4DhgfXOsAroHliJiIjI9hj0G3zAgAG4evUqCgsL0bx5c2n75MmT0aRJ7UGHDAupgO5B9VZb84dVwHLVVaD+wMqwSkREZNsM/k1uZ2enEViBW+u30i2GhtPbmTOoAuYJqwADKxERNQ7KcgWUSpnvYFXOO2LV65577kFKSgqaN28u3c61NocPH5ZlcNZOjmB6O31C6q329QdVwLiwCph2KgBg2guu7gysZRW3nvP7ACIiItuic2gdNmyYtGJA9e1cG5MitRqQN6PqHVJv7aNbUAVMF1YB01VXAcPmrwK6B1YiIiKyPTr/Fq++heudfybdGRJS/91XnqoqYPmwCsg7HQBgYCUiImroDPpNLoRAZmYmzp8/D4VCgbZt29Y7ZaCxMSag3tpf94oqYLmwWtd+5gisuqwQcGdgrazggldERES2Ru/QunfvXkRHR+PChQsQQgCAFFzXrFmDBx98UPZBWitjg2nN/uQPqoBxYRUwbXUVYGAlIiKi+um18Oiff/6JRx99FIGBgfjqq69w6tQpnDx5Elu3bkWrVq0wZMgQ/P3336Yaq0UVqB2Qf8fDWPlqF42HLorULtKj3v6rmtS7IkB91VVTTwfQ9YIrBlYiIiLDJSYmIjAwEM7OzggNDUVGRkad7bdu3YqgoCA4Ozuje/fu2L17d61tn3/+eSgUCiQkJMg8ak16hdaEhATcd9992LNnD4YNG4ZOnTohKCgITz75JPbu3YvQ0FAsW7bMVGO1eYaEVMCwoGrKsFrbuqu1XWylz3QAQ2/JysBKRESk3ebNmxEbG4u4uDgcPnwYPXv2REREBHJzc7W2P3jwIEaPHo3o6GgcOXIEUVFRiIqKwvHjx2u03b59O3755Rf4+/ub+jT0C62pqamYOnWq1tcUCgWmTp2KvXv3yjEum3dnQDXkq39dgypQf1UVME1YBcw3HQDQfsGVvoG1qpwBloiIGo+lS5di0qRJmDhxIrp06YKVK1eiSZMmWLNmjdb277//PiIjIzF9+nR07twZ8+fPxz333IMPP/xQo112djZeeuklbNiwAQ4O8k6Z1EavOa1ZWVno3r17ra9369YNFy5cMHpQtkLfIFofXQOqdPx6QqrUr4FzVuvbX46wCsi3QgDAwEpERI1DYWGhxnMnJydpadLblZeXIzMzE7NmzZK2KZVKhIeHIy0tTWvfaWlpiI2N1dgWERGBHTt2SM/VajXGjh2L6dOno2vXrkacie70Cq3FxcV13qa1SZMmKC0tNXpQ1qhA7YxKtbyBR9+QCugeVAHThVXAdNVVwLALrgAGViIisi6KCkAp868eUXHrvwEBARrb4+LiMHfu3Brtr169iqqqKvj4+Ghs9/HxwenTp7UeQ6VSaW2vUqmk5wsXLoS9vT1efvllA87CMHqvHnDy5EmNQd/u6tWrRg+oITMkpFaTq6oKWD6sAoZXVwEGViIioosXL8LNzU16rq3KaiqZmZl4//33cfjwYbMud6p3aB04cKC01NXtFAoFhBBcq/X/MyagAvpVVAHLhVXAsoFV200D6gusokKvqdxERERWx83NTSO01qZFixaws7NDTk6OxvacnBz4+vpq3cfX17fO9vv370dubi5at24tvV5VVYVp06YhISEB58+f1/NsdKNXaD137pxJBmGrjA2mdzJFUAWMC6uAaaurAAMrERGRqTg6OiI4OBgpKSmIiooCcGs+akpKCmJiYrTuExYWhpSUFI2L75OTkxEWFgYAGDt2LMLDwzX2iYiIwNixYzFx4kSTnAegZ2ht06aNqcZh9YrVLlDLPKdV35BazdJhFTBddRUw7IIrgIGViIhIm9jYWIwfPx4hISHo06cPEhISUFJSIgXMcePG4a677kJ8fDwA4JVXXkH//v2xZMkSDB06FJs2bcKvv/6KVatWAQC8vLzg5eWlcQwHBwf4+vqiU6dOJjsP3pDdjAwNqYB8QVWXvgwJq4Dl5q8CDKxERES1GTlyJK5cuYI5c+ZApVKhV69eSEpKki62ysrKglL57+/Jvn37YuPGjZg9ezbeeOMNdOzYETt27EC3bt0sdQoAGFpNxpiAWk3XoAroFlbr67OusApYfjoAwMBKRERkiJiYmFqnA6SmptbYNmLECIwYMULn/k01j/V2DK0ykCOgVtMnqALyVFYB81RXAesIrAqGWCIiIpvD0KqjArULyqvkf7v0DamAPFXVapYMqwADKxEREemGodXMDAmpgO5BVddjyD0VADC+ugoYtgYrwMBKRETU0OkcWnv37q3zGqyHDx82eEANiaEB9XbWElYB66yuAgysRERknZSVgLJC3j5Fpbz92RKdQ2v12l6knRwBFdAvpOpzXFOEVcA2A6uinDfAICIisjU6h9a4uDhTjsPqFVU5o9IEc1oB/YMqYJ6wCuhfXQUYWImIiEh+nNNqAYaEVEC+oAoYHlYBeaqrAAMrERER6c6g0FpVVYVly5Zhy5YtyMrKQnl5ucbreXl5sgyuoTA0pAL6TTuwVFgFjK+uAgysREREVDuDrk6ZN28eli5dipEjR6KgoACxsbF48sknoVQqMXfuXJmHaFsKqprUeOirqMpZeuiisNLZotXV2lYHkDuwVpXbyRJYFZUMsERERLbGoErrhg0b8Mknn2Do0KGYO3cuRo8ejfbt26NHjx745Zdf8PLLL8s9TqtiTOW0NoZcyCVHUAXMU10FdJ8OANS/BivAwEpERNSYGBRaVSoVunfvDgBwdXVFQUEBAODRRx/FW2+9Jd/orEhhlQvKq7SHMUOZKqgCxodVQP+5q4B55q8CDKxERESNjUHTA1q1aoXLly8DANq3b48ffvgBAHDo0CE4OdUflhqr27/21zew6jMFQJdpAPVVVw252Kq26QAMrERERGQsgyqtTzzxBFJSUhAaGoqXXnoJTz/9NFavXo2srCy8+uqrco/RJsmxbquuVVXAtJVVQL7qKmD5wKrkRVlEREQ2x6DQumDBAunPI0eOROvWrZGWloaOHTvisccek21wtkCumwoA+oXUanKEVcA8gVXX+asAK6xERGT77MoBO7l/DZXX36ShkmWd1rCwMISFhcnRldUqrnJGhcxzWgHTBVXAesIqYD2BlVVWIiIi26RzaN21axceeeQRODg4YNeuXXW2ffzxx40e2J3y8vLw0ksv4euvv4ZSqcTw4cPx/vvvw9XVtc790tLS8OabbyI9PR12dnbo1asXvv/+e7i4uMg+Rl0ZElQB84VVwDLTAQDdAuudYRVgYCUiImrodA6tUVFRUKlUaNmyJaKiomptp1AoUFVVJcfYNIwZMwaXL19GcnIyKioqMHHiREyePBkbN26sdZ+0tDRERkZi1qxZWL58Oezt7fHbb79BqTTo+jODGBpQq+kaVAHLhVWAgZWIiIhMS+fQqlartf7ZHE6dOoWkpCQcOnQIISEhAIDly5djyJAhWLx4Mfz9/bXu9+qrr+Lll1/GzJkzpW2dOnUy6ViNDamAfkEVMH1YBcw/HQAwXWBVVmg9HBEREVkx2UuOpaWlcneJtLQ0eHh4SIEVAMLDw6FUKpGenq51n9zcXKSnp6Nly5bo27cvfHx80L9/f/z88891HqusrAyFhYUajztVLz+l7WGo6qWq9K2s6rIigLHVVVsJrNowsBIRETUMBoXWgQMHIjs7u8b29PR09OrVy9gx1VA9LeF29vb28PT0hEql0rrP33//DQCYO3cuJk2ahKSkJNxzzz0YOHAgzp49W+ux4uPj4e7uLj0CAgIAAEUyBNM7GRNU5Qqr5pgOAOh3wZWhgbW+pa0YWImIiGyXQaHV2dkZPXr0wObNmwHcmi4wd+5cPPDAAxgyZIjO/cycORMKhaLOx+nTpw0ZojSF4bnnnsPEiRPRu3dvLFu2DJ06dcKaNWtq3W/WrFkoKCiQHhcvXjTo+NrcHlINmQKg6zQAOcJqXdVVfeevyrlCAMDASkRE1BgZtOTVt99+i8TERDzzzDPYuXMnzp8/jwsXLuCbb77B4MGDde5n2rRpmDBhQp1t2rVrB19fX+Tm5mpsr6ysRF5eHnx9fbXu5+fnBwDo0qWLxvbOnTsjKyur1uM5OTnJdlcvfYPpnXQJqVLbeoJqNUMrq4A81VWAgZWIiIj0Z/A6rVOmTME///yDhQsXwt7eHqmpqejbt69efXh7e8Pb27vedmFhYcjPz0dmZiaCg4MBAHv27IFarUZoaKjWfQIDA+Hv748zZ85obP/jjz/wyCOP6DVOXRgbUKvpE1QBecIqYNhUAMD6A6s2DLFERES2x6DpAdevX8fw4cOxYsUKfPzxx3jqqacwePBgfPTRR3KPD8Ct6mhkZCQmTZqEjIwMHDhwADExMRg1apS0ckB2djaCgoKQkZEB4NbSW9OnT8cHH3yAL7/8En/++SfeeustnD59GtHR0XqPoaTSscbX+4Z+1X8nXeepauyjwzQAwLipAEDd1VVbCKx3BlQGViIiMhdlhWkejZVBldZu3bqhbdu2OHLkCNq2bYtJkyZh8+bNePHFF/Htt9/i22+/lXuc2LBhA2JiYjBw4EDp5gIffPCB9HpFRQXOnDmjsXrB1KlTcfPmTbz66qvIy8tDz549kZycjPbt28s+Pn3pW1EFdK+qAsZVVgHTV1cBBlYiIiLSnUGh9fnnn8ebb76psUj/yJEjcf/992PixImyDe52np6edd5IIDAwEEKIGttnzpypsU6rpRgSUqV9ZQyrAAMrERER2R6DQutbb72ldXurVq2wdOlSowbUEBgTUKU+9AiqgOXCKmB7gdWuXOuwiIiIyIoZfCHW7YqKivDFF1/g008/RWZmpklu42qt5AioUl96BlXA9GEVkC+wagurgOGBVRtWWImIiBomo0Lrvn37sHr1amzbtg3+/v548sknkZiYKNfYrEpJlRPKK/UPlfUxVVAF6g+rgGWrq4BxgbW+pa20BVZWWYmIiGyT3qFVpVJh3bp1WL16NQoLC/HUU0+hrKwMO3bsqLEmKmlnSFAFzBdWAdsPrNowsBIREdkuvZa8euyxx9CpUyf8/vvvSEhIwKVLl7B8+XJTja3BqF6eStdlqu5U37JVGm1lqK7WdbGVrQRWzmMlIiJqWPSqtH733Xd4+eWX8cILL6Bjx46mGpPNM7SSejtdQ6rU3kLVVcD4C64A8wZWznMlIiKyPXpVWn/++WcUFRUhODgYoaGh+PDDD3H16lVTjc3q3VlBNbSSWq26oqpPVbW+mwMAt8KqodVVoO7pALVdcMXASkRERHLSK7Ted999+OSTT3D58mU899xz2LRpE/z9/aFWq5GcnIyioiJTjdPiSiocZAunt9M3qAL138WqWn1hFai/umrK6QCA4YFVG04JICIia8I7YsnLoNu4Nm3aFM888wx+/vlnHDt2DNOmTcOCBQvQsmVLPP7443KPscExJKgC8odVc0wHAGqfv2pMYDVkLdbG/A+diIjI1hkUWm/XqVMnLFq0CP/88w+++OILOcbU4NweUvUNqoC8YRUwrrpqqguuAPkCqzYMrERERLZNlpsLAICdnR2ioqIQFRUlV5c2y5BgWqMPHULq7YwNq4Bh1VXA+gJrffNYGWCJiIhsj2yhtbGSI6BKfZkgqALGhVVA/+kAAAMrERERyYuhVUc3Kh1gL2NAlfrVM6gC8oVVwDzVVUD+wKoNVwogIiJquIye00qG0XWe6u10nbMK6FZdteXAashKAQyxRETUWCUmJiIwMBDOzs4IDQ1FRkZGne23bt2KoKAgODs7o3v37ti9e7fG60IIzJkzB35+fnBxcUF4eDjOnj1rylNgaDWX29dUNXVYNdV0AMB2AiunBRAREd2yefNmxMbGIi4uDocPH0bPnj0RERGB3Nxcre0PHjyI0aNHIzo6GkeOHJGuWTp+/LjUZtGiRfjggw+wcuVKpKeno2nTpoiIiMDNmzdNdh4MrSZiTEgF/g2qcofV+qqrdc1fbSiBlQGWiIgak6VLl2LSpEmYOHEiunTpgpUrV6JJkyZYs2aN1vbvv/8+IiMjMX36dHTu3Bnz58/HPffcgw8//BDArSprQkICZs+ejWHDhqFHjx5Yv349Ll26hB07dpjsPBhaZXBnQDUkpFbTJ6gC8oRVwLDqKmDZwKoNAyoRETUGhYWFGo+ysjKt7crLy5GZmYnw8HBpm1KpRHh4ONLS0rTuk5aWptEeACIiIqT2586dg0ql0mjj7u6O0NDQWvuUA0Orjm5W1AymxgbUavpWVQHdwiqg21QAQ6cDWDqwGnLhFUMsERGZi7LSBHfEqrzVd0BAANzd3aVHfHy81jFcvXoVVVVV8PHx0dju4+MDlUqldR+VSlVn++r/6tOnHLh6gIXoE1A19tMhqAL1h1XAstVVwPSBlVVXIiJqqC5evAg3NzfpuZOTkwVHYx4MrWZk6qAKGB9WAdsLrFr7Z2AlIqIGzM3NTSO01qZFixaws7NDTk6OxvacnBz4+vpq3cfX17fO9tX/zcnJgZ+fn0abXr166XMaeuH0ABO6/Wt/QwKrrlMAqjXWwMqASkREpJ2joyOCg4ORkpIibVOr1UhJSUFYWJjWfcLCwjTaA0BycrLUvm3btvD19dVoU1hYiPT09Fr7lAMrrTIytJJaox89gipgubAK2EZgZYglIqLGLDY2FuPHj0dISAj69OmDhIQElJSUYOLEiQCAcePG4a677pLmxb7yyivo378/lixZgqFDh2LTpk349ddfsWrVKgCAQqHA1KlT8c4776Bjx45o27Yt3nrrLfj7+yMqKspk58HQagC5wqlGn3oGVUCesApYX2DVRpfAqvWYNZbIEqhy1D4+IiKihmjkyJG4cuUK5syZA5VKhV69eiEpKUm6kCorKwtK5b+/7/v27YuNGzdi9uzZeOONN9CxY0fs2LED3bp1k9rMmDEDJSUlmDx5MvLz89GvXz8kJSXB2dnZZOehEEIIk/XeABQWFsLd3R1dNs2AXRN5JzkbElQB3cIqYFx1FbBcYDVkLVZt27QFVgAMrUREjUBV+U0cW/0GCgoKdJr7Kafq7NA9+n+wc5Q3xFnyvCyNlVYzM3VQBYyvrgLWE1h1bcN5rURERA0bQ6sZGBpUAdsIq4DpAqshKwVoU11lJSIiItvE0GoCxoTUavqEVaDxBFZDLrxiYCUiIrJ9DK1GkiOgVtM3qAKmD6uA9QZWXfpiYCUiIkuxKxewg8y/hxrx7zWGVh2VVdjBTsaAejtThVXAdIG1trAKaA+stbY1IrAaMo9VWQFUOeo2NiIiIrIeDK0WYkhQBeQLq4B5A6uca7Fq26a9L1FvGyIiIrINDK1mZGhQBXQPq4BlpgMAlgus2jCwEhERNSwMrSZmTFAF5A2rgG0FVkP74zxWIiKihoehVWbGhlRAv6AK6BZWAfMGVmPWYdWnrVxtiIiIyLoxtBpBjoB6O2sMq4B+KwQA+gdWc85jtSsXqGjKO2IRERHZGoZWHVVV2EHIHFKlvi0UVgHzTAeoa7sp57ESERFRw8HQaiH6BlVA97AKNPzAqss8Vs51JSIiajgYWs3IkKBazVzVVcD2Aqsux2VgJSIism0MrSZkTEitZs7qKmCbgZXLWxERkTVSVgBKmS+jEI34dxxDq8zMHVQB3cIqYL2BVVe6BFbtx2SVlYiIyNYxtBpBjoB6O2sMq4D+gbU2tQVWOS+8qm9aAAMsERGRbWJo1VFVhR2EzCG1mr5hFbDuwKrPzQNMOY+ViIiIGg6GVgsxJKgC8oVVwHyBVZ+2cs5jZZWViIio4WBoNSNDgyqge1gFjA+stYVVwLDAauoLr7Qfk4GViIioIWFoNTFjgipg3rAKWC6w6orrsRIRETVODK0mYGxQBfQLq4DtB1ZTzmNliCUiIrJ9DK0ykCOkVrNEWAXME1j16YPTAoiIiOh2DK16kDOc3k7foAroFlYB46qrgLyBVe55rIZMC2CIJSIisk0MrToSFUooZH63TBlWAdsMrNr7rD+w6rIfAysREZmTskLADvL+7hEVjfd3GUOrBRgSVgH5qquA9QZWQy/SYkAlIiJq2BhazcTQoArIW10FLB9Y9emb0wKIiIgIYGg1KWOCKiB/WAWsI7DKOY9V+3hYdSUiImpoGFplZmxQBfQLq4Dpq6t1vW6qwKrrvgyoREREjQNDqxHkCKga/ZkgrALWHVhrI9dX/jVu91qm1n0QREREZDUYWnWkqFBCYW+iJa/0DKuA9QZWffsx5bQAIiIiajgYWi3IkmEVME1gNceFV9raaduPVVYiIqKGg6HVzAwJqoDuYRWwvsBq6nms2rDqSkRE1LAwtJqBoUFV2t+MgbWu10wdWA0NmrrsxyorERGRbWNoNRFjgypgPdVVQL7AWnv/hk0L0KUvBlYiIrIEu3IBO8E7YsmFoVVGcgRVQL+wCpg+sBqynykuvNJl3iqnBRARETVMDK1GkCukSv2ZIKwCxgdWuZa2MjQY18aQaQHK8ip5B0FERERmwdCqI0W5Ago7eUOq1LeeYRWw3sCqL1NOCyAiIqKGg6HVgiwZVnVpY0hgtdS0AK3HZJWViIiowWBotQBDwipgHYHVkD4NrZzWhmuyEhERNT6mucUT1aCoVEgPfSnLFVYTWE05j9WU0wJYZSUiIqpbXl4exowZAzc3N3h4eCA6OhrFxcV17nPz5k1MmTIFXl5ecHV1xfDhw5GTk6O17bVr19CqVSsoFArk5+frPT6GVhMyJqgC+oVVwHYCq7mnBRAREVH9xowZgxMnTiA5ORnffPMN9u3bh8mTJ9e5z6uvvoqvv/4aW7duxU8//YRLly7hySef1No2OjoaPXr0MHh8DK0yMzaoAoaFVUsFVn3by32xFKusRERExjt16hSSkpLw6aefIjQ0FP369cPy5cuxadMmXLp0Ses+BQUFWL16NZYuXYqHH34YwcHBWLt2LQ4ePIhffvlFo+2KFSuQn5+P1157zeAxMrTKQI6gWk2fsAroHlZNFVjlWEWAF18RERHpp7CwUONRVlZmVH9paWnw8PBASEiItC08PBxKpRLp6ela98nMzERFRQXCw8OlbUFBQWjdujXS0tKkbSdPnsTbb7+N9evXQ6k0PHoytOrp9oAqV1AFLFddBeQPrOaaFmDIxVd2ZQyxRERkHnblwiQPAAgICIC7u7v0iI+PN2qsKpUKLVu21Nhmb28PT09PqFSqWvdxdHSEh4eHxnYfHx9pn7KyMowePRrvvfceWrdubdQYuXqAjhRV8gXU2+lbWQV0r25ae2A1BqcFEBFRY3bx4kW4ublJz52cnLS2mzlzJhYuXFhnX6dOnZJ1bLebNWsWOnfujKefftrovhhaLcSUYVXXtpYOrOa++IpVViIiaijc3Nw0Qmttpk2bhgkTJtTZpl27dvD19UVubq7G9srKSuTl5cHX11frfr6+vigvL0d+fr5GtTUnJ0faZ8+ePTh27Bi+/PJLAIAQt36nt2jRAm+++SbmzZtX7zlUY2g1M0PCKmD9gVVfuvZjcIi9o8rKwEpERI2Rt7c3vL29620XFhaG/Px8ZGZmIjg4GMCtwKlWqxEaGqp1n+DgYDg4OCAlJQXDhw8HAJw5cwZZWVkICwsDAGzbtg03btyQ9jl06BCeeeYZ7N+/H+3bt9frXBhazcDQoCrtbwOB1dh5rLW11QWXuCIiIjJO586dERkZiUmTJmHlypWoqKhATEwMRo0aBX9/fwBAdnY2Bg4ciPXr16NPnz5wd3dHdHQ0YmNj4enpCTc3N7z00ksICwvDfffdBwA1gunVq1el4905F7Y+DK0mZG1hFZB3Wav69tMnhBp68ZXW8bDKSkREpLcNGzYgJiYGAwcOhFKpxPDhw/HBBx9Ir1dUVODMmTMoLS2Vti1btkxqW1ZWhoiICHz00UcmGR9Dq8yMDaqAfGui3q6+27LW14e557Hq2ierrERERPLw9PTExo0ba309MDBQmpNazdnZGYmJiUhMTNTpGAMGDKjRh64YWo0kR0iV+jKg0mnJwKovffoxeKoAq6xEREQNEkOrnuQMqRr9mqC6CtQfWI05jqWnBRhyIwFFeaXOYyMiIiLrwdCqI2W5AkqlCdZpNVF1FdAtsJrjwqv6+jIVVlmJiIgaDoZWC5H7oqc7WSKw6otVViIiasiU5Woo1fJee6GsbLzXcjC0mpmpwypgfGA1dD9TXHyltT/eSICIiKjRYWg1E2Oqk+YOrIaMVY5pAXItcaUNq6xERES2jaHVxIz9Kt2aAqtc81jlnBagtX9WWYmIiBocpaUHoKu8vDyMGTMGbm5u8PDwQHR0NIqLi+vcR6VSYezYsfD19UXTpk1xzz33YNu2bSYfq7Li34exfejKUoFVX8ZMC9CGVVYiIqLGwWZC65gxY3DixAkkJyfjm2++wb59+zB58uQ69xk3bhzOnDmDXbt24dixY3jyySfx1FNP4ciRI7KPT46gens/urIrt2xgNee0AFZZiYiIGi+bCK2nTp1CUlISPv30U4SGhqJfv35Yvnw5Nm3ahEuXLtW638GDB/HSSy+hT58+aNeuHWbPng0PDw9kZmbKMi65gurt/elD1zVYTXXzADmmBRjDkCqr4iarrkRERLbIJkJrWloaPDw8EBISIm0LDw+HUqlEenp6rfv17dsXmzdvRl5eHtRqNTZt2oSbN29iwIABte5TVlaGwsJCjUe120OqnOuOGtKfuQKrqddXZZWViIiIdGEToVWlUqFly5Ya2+zt7eHp6QmVSlXrflu2bEFFRQW8vLzg5OSE5557Dtu3b0eHDh1q3Sc+Ph7u7u7SIyAgAACgMFF4M+xKfXn6NuZ1U118pStWWYmIiBoXi4bWmTNnQqFQ1Pk4ffq0wf2/9dZbyM/Px48//ohff/0VsbGxeOqpp3Ds2LFa95k1axYKCgqkx8WLFw0+fl0MrdbKFViN2d+U0wJYZSUiIiJtLLrk1bRp0zBhwoQ627Rr1w6+vr7Izc3V2F5ZWYm8vDz4+vpq3e+vv/7Chx9+iOPHj6Nr164AgJ49e2L//v1ITEzEypUrte7n5OQEJycn/U9GR4bPH5X3GHLPYzVnP1wxgIiIbIGyvApKtbxFFWVl4y3SWDS0ent7w9vbu952YWFhyM/PR2ZmJoKDgwEAe/bsgVqtRmhoqNZ9SktLAQBKpWYx2c7ODmqZb6mmC2Mqn+YOrIaM1dqrrJwaQEREZNtsYk5r586dERkZiUmTJiEjIwMHDhxATEwMRo0aBX9/fwBAdnY2goKCkJGRAQAICgpChw4d8NxzzyEjIwN//fUXlixZguTkZERFRZlt7MZetGVNgZVVViIiIrIUmwitALBhwwYEBQVh4MCBGDJkCPr164dVq1ZJr1dUVODMmTNShdXBwQG7d++Gt7c3HnvsMfTo0QPr16/HZ599hiFDhph8vHKsMGArgdWSVVZdsMpKRERk+2zmNq6enp7YuHFjra8HBgZCCM3g07FjR7PcAet2ciwRpU9Y1fWYplraSt/AaswqAtqqrHdODWCVlYiIqGGymdBqreRcx1TfsKrr8Y2v+Mp7UwBdsMpKREREt2NoNYApFty3ZGA117QAU1dZdaEoN/HdEoiIiMgkGFp1pKw03d2hTDEdQNd2ck8LqI0+1VpDq6ycGkBERNRwMbRakKmqq3L1Zci0AGMvvtLG0CrrnVMDWGUlIiKyXTazekBDY+rAasp5rHJMC6i1b1ZZiYiISAtWWs3MkLAKyB9YzTUtoDbGXNzFmwkQEZEtsCurgl2VvHewErwjFpmaoWEVMH9glXNagLFVVrluJsCpAURERLaNodXEjAmrgHUFVlZZiYiIyFIYWk3E2LAKmHcOK2B4mLRklVUXrLISERHZPoZWmckRVgHTBFZjgq01VFm14QVYREREjQNDq0wsEVb1aW+qaQHmrLJyagAREVHjxdBqBLmCajVrDKyG7CdHkNWFQRdg3SyTdQxERERkHgytepI7qFazVGA19f719s8qKxEREemAoVVHduWAncI0fVsysNpqlZWIiIgaF4ZWCzJ1FbM+9QVJS1RZtdFWZeXUACIiosaFodVCTH1HKlNOC9C3aqrXRVkGLnPFqQFERGRtFBVVUKjl/f2kkPkOW7ZEaekBNEbWEFhN8XW9sSsGyIlrsxIRETUsrLSakTnWSZUjsFpTldXQqQE1cGoAERGRTWNoNQNrv4JfV3UFUEteaMWpAURERA0fpweYmDkDq6mrrPqq7VjGLHNl0AVYREREZPNYaTUROcKfNQVWa62y6oRTA4iIiGweK60mYI2B1VrIfQEWpwYQERE1DgytMlJWmD+w6srcVVZLXYDFqQFERESGycvLw5gxY+Dm5gYPDw9ER0ejuLi4zn1u3ryJKVOmwMvLC66urhg+fDhycnI02hw6dAgDBw6Eh4cHmjdvjoiICPz22296j4+hVQZyhdXqvuRub/Vf35sSpwYQERHpZMyYMThx4gSSk5PxzTffYN++fZg8eXKd+7z66qv4+uuvsXXrVvz000+4dOkSnnzySen14uJiREZGonXr1khPT8fPP/+MZs2aISIiAhUV+oUezmk1kCmqoaa6nasx/chZZbWKqQEMsURE1AAUFhZqPHdycoKTk5PB/Z06dQpJSUk4dOgQQkJCAADLly/HkCFDsHjxYvj7+9fYp6CgAKtXr8bGjRvx8MMPAwDWrl2Lzp0745dffsF9992H06dPIy8vD2+//TYCAgIAAHFxcejRowcuXLiADh066DxGVlr1JGdV9c5+TdHe0rdqrQ+nBhARUUOluFlpkgcABAQEwN3dXXrEx8cbNda0tDR4eHhIgRUAwsPDoVQqkZ6ernWfzMxMVFRUIDw8XNoWFBSE1q1bIy0tDQDQqVMneHl5YfXq1SgvL8eNGzewevVqdO7cGYGBgXqNkZVWHSkrAKUJIr4hoVGuwGrM/paushIRETVmFy9ehJubm/TcmCorAKhUKrRs2VJjm729PTw9PaFSqWrdx9HRER4eHhrbfXx8pH2aNWuG1NRUREVFYf78+QCAjh074vvvv4e9vX4xlJVWC7J4ldPSx9dSZdWVTlMD7pwKwKkBRETUQLi5uWk8agutM2fOhEKhqPNx+vRpk43zxo0biI6Oxv33349ffvkFBw4cQLdu3TB06FDcuHFDr75YabUQQwOjuaqsFuubUwOIiIhkM23aNEyYMKHONu3atYOvry9yc3M1tldWViIvLw++vr5a9/P19UV5eTny8/M1qq05OTnSPhs3bsT58+eRlpYG5f//ynrjxo1o3rw5du7ciVGjRul8LgytFmANgbW+vgwJppwaQEREZF28vb3h7e1db7uwsDDk5+cjMzMTwcHBAIA9e/ZArVYjNDRU6z7BwcFwcHBASkoKhg8fDgA4c+YMsrKyEBYWBgAoLS2FUqmEQqGQ9qt+rlbrlw84PcDMTB1YTU2uKqvZpwYQERFRrTp37ozIyEhMmjQJGRkZOHDgAGJiYjBq1Chp5YDs7GwEBQUhIyMDAODu7o7o6GjExsZi7969yMzMxMSJExEWFob77rsPADBo0CBcv34dU6ZMwalTp3DixAlMnDgR9vb2eOihh/QaI0OrGZkjeNpyldXQqQE6YYglIiKq04YNGxAUFISBAwdiyJAh6NevH1atWiW9XlFRgTNnzqC0tFTatmzZMjz66KMYPnw4HnzwQfj6+uKrr76SXg8KCsLXX3+N33//HWFhYXjggQdw6dIlJCUlwc/PT6/xcXqAmRgTWK2lymqtOJ+ViIjIeJ6enti4cWOtrwcGBkIIzSKVs7MzEhMTkZiYWOt+gwYNwqBBg4weH0OriRkbOPXZ39RVVk4NICIiIkvh9AATMmdgtSSrnxpARERENo+h1UTMHThtpcpqEay8EhER2TxODzABOQKr3NMCrIUxUwO04XxWIiKyVoqKCiiq5K0PKtSN9/ceQ6uM5KqumqJKa4oVA+razxRTAziflYiIqPFiaJWBnCFT377MUWW1pUouERERNUwMrUaQuyJqqsBq7iprreOwxNSAOyqvooyVWCIiIlvEC7EMZOnAai6GBFpjpwZoo9PUACIiImqwWGnVk7WES7mqrNbI4KWuOJ+ViIiowWJo1ZGyAlCaqC5tyWBprqkBRERERMbg9AALMySwmqvKKufUALnns+qElVciIqIGg6G1EbN0tVTX+azaGLI+Ky/CIiIisl0MrRZkzVVWOcZgCIPXZyUiIqIGjXNaLcSUgVUONjc1gFMBiIjI2twsk788qG68v+9YabUAU194pUv/lp4aQERERKQPhlYzMzSwWkvIlGscJp/PysorERFRg8LQaka2smaqpQKyKeez8iIsIiIi28bQ2sBYamqAVS11RURERA0OQ6uZGFNltZapAVaLUwGIiIgaPIZWM7CVaQFA3QGZ4ZmIiIgshaHVxMx5VypbWTWAF2ERERGRvhhayWhyzGflTQWIiIioLgytJmRtVVYiIiIiW8U7YpmINYbI+kJwQ53PyuWuiIjIIm6Wm+COWOUyd2g7WGk1ATkCqy2HxLoYM59VK85fJSIiahQYWhsAa6zqEhEREcmJoVVm1lplNWZqQF3MeVMBnVYOICIiogaJoVVGrHgaRtaVAzhdgIiIqEFiaJWJXIFV34qnOYJyQ51fS0RERLaDoZWIiIiIrB5DqwysfVpAY66UcrkrIiKihoGh1UhyBlZLhUtzHVf25a6IiIio0WBoNYKlK6wWP74ZVw7QyoCLrtQ3bphgIERERGRqvCOWgeQOjNb6Fb6px6Vt5QAiIqKGQJSXQSjk/T0qBO+IRXqwdIVTH9YahvXFNVqJiIgaN4ZWPVlLYLWWcZiCwWu0EhERUYPF0EoNB28sQERE1GAxtOrBVNVNS36F31CmDxAREVHDxtBKRERERFaPoVVHSk6z1FDbcldERERkm/Ly8jBmzBi4ubnBw8MD0dHRKC4urnOfVatWYcCAAXBzc4NCoUB+fr7G6+fPn0d0dDTatm0LFxcXtG/fHnFxcSgv138VBIZWG6TrNAVr+uqfNxYgIiKybmPGjMGJEyeQnJyMb775Bvv27cPkyZPr3Ke0tBSRkZF44403tL5++vRpqNVqfPzxxzhx4gSWLVuGlStX1tq+Llyn1cKsKVjeyZCxme3GAjrgLVyJiIh0c+rUKSQlJeHQoUMICQkBACxfvhxDhgzB4sWL4e/vr3W/qVOnAgBSU1O1vh4ZGYnIyEjpebt27XDmzBmsWLECixcv1muMrLQSERER2ZjCwkKNR5mRhZq0tDR4eHhIgRUAwsPDoVQqkZ6ebuxwNRQUFMDT01Pv/VhpJSIiIjIB9Y2bUCvk/QZSLW7NEQwICNDYHhcXh7lz5xrcr0qlQsuWLTW22dvbw9PTEyqVyuB+7/Tnn39i+fLleldZAYZWslVck5WIiBqxixcvws3NTXru5OSktd3MmTOxcOHCOvs6deqUrGOrTXZ2NiIjIzFixAhMmjRJ7/0ZWsliFOVckoGIiMgQbm5uGqG1NtOmTcOECRPqbNOuXTv4+voiNzdXY3tlZSXy8vLg6+trzFABAJcuXcJDDz2Evn37YtWqVQb1wdDaiFnzRWCmoL5xw9JDICIiMitvb294e3vX2y4sLAz5+fnIzMxEcHAwAGDPnj1Qq9UIDQ01agzZ2dl46KGHEBwcjLVr10KpNOySKl6IRURERNTIde7cGZGRkZg0aRIyMjJw4MABxMTEYNSoUdLKAdnZ2QgKCkJGRoa0n0qlwtGjR/Hnn38CAI4dO4ajR48iLy9P2mfAgAFo3bo1Fi9ejCtXrkClUhk0T9ZmQuu7776Lvn37okmTJvDw8NBpHyEE5syZAz8/P7i4uCA8PBxnz5417UD10NgqnURERGS9NmzYgKCgIAwcOBBDhgxBv379NL7Kr6iowJkzZ1BaWiptW7lyJXr37i3NUX3wwQfRu3dv7Nq1CwCQnJyMP//8EykpKWjVqhX8/Pykh75sJrSWl5djxIgReOGFF3TeZ9GiRfjggw+wcuVKpKeno2nTpoiIiMDNmzdNOFIiIiIi2+Pp6YmNGzeiqKgIBQUFWLNmDVxdXaXXAwMDIYTAgAEDpG1z586FEKLGo3oe7YQJE7S+LoT+hTubmdM6b948AMC6det0ai+EQEJCAmbPno1hw4YBANavXw8fHx/s2LEDo0aNMtVQTUrXu2E1JIryRnjSREREpMFmKq36OnfuHFQqFcLDw6Vt7u7uCA0NRVpaWq37lZWV1Viwl4iIiIgsy2YqrfqqnuDr4+Ojsd3Hx6fOyb/x8fFSVfd2VeUmmFJgwJxWoUfRUVTU3b8xrysr1bVs176IstCyXVGlva2iSnMpLIVay0mrtazTqi7XPKbQfF69ILNJPksiIrIq1T/rDfkaWi6VqABkPnwlGu+3jxYNrboueBsUFGSmEQGzZs1CbGys9Dw7OxtdunTByc/fNtsYyMRWf2XpERARkZlcu3YN7u7uZj2mo6MjfH19sV/1tUn69/X1haOjo0n6tmYWDa26LnhriOqFcHNycjSuUMvJyUGvXr1q3c/JyUnjrhKurq64ePEimjVrBoVCYdBYrE1hYSECAgJq3E2jMWjM5w407vPnufPcG9u5A437/AsKCtC6dWuD7nFvLGdnZ5w7dw7l5eX1NzaAo6MjnJ2dTdK3NbNoaNV1wVtDtG3bFr6+vkhJSZFCamFhIdLT0/VagUCpVKJVq1YmGaOl6Xo3jYaoMZ870LjPn+fOc2+MGvP5G7qQvbGcnZ0bZbA0JZu5ECsrKwtHjx5FVlYWqqqqcPToURw9ehTFxcVSm6CgIGzfvh0AoFAoMHXqVLzzzjvYtWsXjh07hnHjxsHf3x9RUVEWOgsiIiIiMoTNXIg1Z84cfPbZZ9Lz3r17AwD27t0rrRd25swZFBQUSG1mzJiBkpISTJ48Gfn5+ejXrx+SkpL4fz5ERERENsZmQuu6devqXaP1zisEFQoF3n77bbz9Ni+iup2TkxPi4uI05u42Fo353IHGff48d557Y9SYz78xn3tDpRCWXAuCiIiIiEgHNjOnlYiIiIgaL4ZWIiIiIrJ6DK1EREREZPUYWomIiIjI6jG0NhCJiYkIDAyEs7MzQkNDkZGRUWvbTz75BA888ACaN2+O5s2bIzw8vEb7CRMmQKFQaDwiIyNNfRoG0efc161bV+O87lwCTQiBOXPmwM/PDy4uLggPD8fZs2dNfRoG0efcBwwYUOPcFQoFhg4dKrWxlc993759eOyxx+Dv7w+FQoEdO3bUu09qairuueceODk5oUOHDlpXI9Hn/bQUfc/9q6++wqBBg+Dt7Q03NzeEhYXh+++/12gzd+7cGp+7OW+frQ99zz81NVXr33uVSqXRriF+9tr+PSsUCnTt2lVqYyuffXx8PO699140a9YMLVu2RFRUFM6cOVPvflu3bkVQUBCcnZ3RvXt37N69W+N1W/p5TwytDcLmzZsRGxuLuLg4HD58GD179kRERARyc3O1tk9NTcXo0aOxd+9epKWlISAgAIMHD0Z2drZGu8jISFy+fFl6fPHFF+Y4Hb3oe+7ArTvD3H5eFy5c0Hh90aJF+OCDD7By5Uqkp6ejadOmiIiIwM2bN019OnrR99y/+uorjfM+fvw47OzsMGLECI12tvC5l5SUoGfPnkhMTNSp/blz5zB06FA89NBDOHr0KKZOnYpnn31WI7wZ8nfJEvQ993379mHQoEHYvXs3MjMz8dBDD+Gxxx7DkSNHNNp17dpV43P/+eefTTF8o+l7/tXOnDmjcX4tW7aUXmuon/3777+vcc4XL16Ep6dnjX/ztvDZ//TTT5gyZQp++eUXJCcno6KiAoMHD0ZJSUmt+xw8eBCjR49GdHQ0jhw5gqioKERFReH48eNSG1v5eU//nyCb16dPHzFlyhTpeVVVlfD39xfx8fE67V9ZWSmaNWsmPvvsM2nb+PHjxbBhw+Qequz0Pfe1a9cKd3f3WvtTq9XC19dXvPfee9K2/Px84eTkJL744gvZxi0HYz/3ZcuWiWbNmoni4mJpm6187rcDILZv315nmxkzZoiuXbtqbBs5cqSIiIiQnhv7flqCLueuTZcuXcS8efOk53FxcaJnz57yDcxMdDn/vXv3CgDi+vXrtbZpLJ/99u3bhUKhEOfPn5e22epnn5ubKwCIn376qdY2Tz31lBg6dKjGttDQUPHcc88JIWzr5z3dwkqrjSsvL0dmZibCw8OlbUqlEuHh4UhLS9Opj9LSUlRUVMDT01Nje2pqKlq2bIlOnTrhhRdewLVr12Qdu7EMPffi4mK0adMGAQEBGDZsGE6cOCG9du7cOahUKo0+3d3dERoaqvP7aQ5yfO6rV6/GqFGj0LRpU43t1v65GyItLU3jvQKAiIgI6b2S4/20FWq1GkVFRTX+vZ89exb+/v5o164dxowZg6ysLAuN0DR69eoFPz8/DBo0CAcOHJC2N6bPfvXq1QgPD0ebNm00ttviZ19998s7/x7frr5/97by857+xdBq465evYqqqir4+PhobPfx8akxZ6s2r7/+Ovz9/TX+4UZGRmL9+vVISUnBwoUL8dNPP+GRRx5BVVWVrOM3hiHn3qlTJ6xZswY7d+7E//3f/0GtVqNv3774559/AEDaz5j30xyM/dwzMjJw/PhxPPvssxrbbeFzN4RKpdL6XhUWFuLGjRuy/DuyFYsXL0ZxcTGeeuopaVtoaCjWrVuHpKQkrFixAufOncMDDzyAoqIiC45UHn5+fli5ciW2bduGbdu2ISAgAAMGDMDhw4cByPMz1BZcunQJ3333XY1/87b42avVakydOhX3338/unXrVmu72v7dV3+utvLznv5lM7dxJdNYsGABNm3ahNTUVI0LkkaNGiX9uXv37ujRowfat2+P1NRUDBw40BJDlUVYWBjCwsKk53379kXnzp3x8ccfY/78+RYcmXmtXr0a3bt3R58+fTS2N9TPnW7ZuHEj5s2bh507d2rM6XzkkUekP/fo0QOhoaFo06YNtmzZgujoaEsMVTadOnVCp06dpOd9+/bFX3/9hWXLluHzzz+34MjM67PPPoOHhweioqI0ttviZz9lyhQcP37cKufekmmx0mrjWrRoATs7O+Tk5Ghsz8nJga+vb537Ll68GAsWLMAPP/yAHj161Nm2Xbt2aNGiBf7880+jxywXY869moODA3r37i2dV/V+xvRpDsace0lJCTZt2qTTLyRr/NwN4evrq/W9cnNzg4uLiyx/l6zdpk2b8Oyzz2LLli01vjK9k4eHB+6++26b/9xr06dPH+ncGsNnL4TAmjVrMHbsWDg6OtbZ1to/+5iYGHzzzTfYu3cvWrVqVWfb2v7dV3+utvLznv7F0GrjHB0dERwcjJSUFGmbWq1GSkqKRkXxTosWLcL8+fORlJSEkJCQeo/zzz//4Nq1a/Dz85Nl3HIw9NxvV1VVhWPHjknn1bZtW/j6+mr0WVhYiPT0dJ37NAdjzn3r1q0oKyvD008/Xe9xrPFzN0RYWJjGewUAycnJ0nslx98la/bFF19g4sSJ+OKLLzSWOKtNcXEx/vrrL5v/3Gtz9OhR6dwa+mcP3Lry/s8//9Tpf1St9bMXQiAmJgbbt2/Hnj170LZt23r3qe/fva38vKfbWPpKMDLepk2bhJOTk1i3bp04efKkmDx5svDw8BAqlUoIIcTYsWPFzJkzpfYLFiwQjo6O4ssvvxSXL1+WHkVFRUIIIYqKisRrr70m0tLSxLlz58SPP/4o7rnnHtGxY0dx8+ZNi5xjbfQ993nz5onvv/9e/PXXXyIzM1OMGjVKODs7ixMnTkhtFixYIDw8PMTOnTvF77//LoYNGybatm0rbty4Yfbzq4u+516tX79+YuTIkTW229LnXlRUJI4cOSKOHDkiAIilS5eKI0eOiAsXLgghhJg5c6YYO3as1P7vv/8WTZo0EdOnTxenTp0SiYmJws7OTiQlJUlt6ns/rYW+575hwwZhb28vEhMTNf695+fnS22mTZsmUlNTxblz58SBAwdEeHi4aNGihcjNzTX7+dVH3/NftmyZ2LFjhzh79qw4duyYeOWVV4RSqRQ//vij1KahfvbVnn76aREaGqq1T1v57F944QXh7u4uUlNTNf4el5aWSm3u/Jl34MABYW9vLxYvXixOnTol4uLihIODgzh27JjUxlZ+3tMtDK0NxPLly0Xr1q2Fo6Oj6NOnj/jll1+k1/r37y/Gjx8vPW/Tpo0AUOMRFxcnhBCitLRUDB48WHh7ewsHBwfRpk0bMWnSJKv7AV5Nn3OfOnWq1NbHx0cMGTJEHD58WKM/tVot3nrrLeHj4yOcnJzEwIEDxZkzZ8x1OnrR59yFEOL06dMCgPjhhx9q9GVLn3v1MkZ3PqrPd/z48aJ///419unVq5dwdHQU7dq1E2vXrq3Rb13vp7XQ99z79+9fZ3shbi3/5efnJxwdHcVdd90lRo4cKf7880/znpiO9D3/hQsXivbt2wtnZ2fh6ekpBgwYIPbs2VOj34b42QtxawknFxcXsWrVKq192spnr+28AWj8O9b2M2/Lli3i7rvvFo6OjqJr167i22+/1Xjdln7ekxAKIYQwWRmXiIiIiEgGnNNKRERERFaPoZWIiIiIrB5DKxERERFZPYZWIiIiIrJ6DK1EREREZPUYWomIiIjI6jG0EhEREZHVY2glIiIiIqvH0EpEshswYACmTp0qPQ8MDERCQoLO+69btw4eHh6yjEXOvqxRSkoKOnfujKqqKr32u++++7Bt2zYTjYqISH4MrUSN1IQJE6BQKKBQKODg4IC2bdtixowZuHnzpuzHOnToECZPnixrn9VjVygUaNq0KTp27IgJEyYgMzNTo93IkSPxxx9/6NSnLQbcGTNmYPbs2bCzswNw6xyq3xelUgk/Pz+MHDkSWVlZGvvNnj0bM2fOhFqttsSwiYj0xtBK1IhFRkbi8uXL+Pvvv7Fs2TJ8/PHHiIuLk/043t7eaNKkiez9rl27FpcvX8aJEyeQmJiI4uJihIaGYv369VIbFxcXtGzZUvZjW4Off/4Zf/31F4YPH66x3c3NDZcvX0Z2dja2bduGM2fOYMSIERptHnnkERQVFeG7774z55CJiAzG0ErUiDk5OcHX1xcBAQGIiopCeHg4kpOTpdevXbuG0aNH46677kKTJk3QvXt3fPHFFxp9lJSUYNy4cXB1dYWfnx+WLFlS4zh3Tg9YunQpunfvjqZNmyIgIAAvvvgiiouL9R6/h4cHfH19ERgYiMGDB+PLL7/EmDFjEBMTg+vXrwOoWT397bff8NBDD6FZs2Zwc3NDcHAwfv31V6SmpmLixIkoKCiQKpVz584FAHz++ecICQlBs2bN4Ovri//+97/Izc2V+kxNTYVCoUBKSgpCQkLQpEkT9O3bF2fOnNEY79dff417770Xzs7OaNGiBZ544gnptbKyMrz22mu466670LRpU4SGhiI1NbXO89+0aRMGDRoEZ2dnje0KhQK+vr7w8/ND3759ER0djYyMDBQWFkpt7OzsMGTIEGzatEmft5yIyGIYWokIAHD8+HEcPHgQjo6O0rabN28iODgY3377LY4fP47Jkydj7NixyMjIkNpMnz4dP/30E3bu3IkffvgBqampOHz4cJ3HUiqV+OCDD3DixAl89tln2LNnD2bMmCHLebz66qsoKirSCN+3GzNmDFq1aoVDhw4hMzMTM2fOhIODA/r27YuEhASpSnn58mW89tprAICKigrMnz8fv/32G3bs2IHz589jwoQJNfp+8803sWTJEvz666+wt7fHM888I7327bff4oknnsCQIUNw5MgRpKSkoE+fPtLrMTExSEtLw6ZNm/D7779jxIgRiIyMxNmzZ2s91/379yMkJKTO9yM3Nxfbt2+HnZ2dNIWgWp8+fbB///469ycishqCiBql8ePHCzs7O9G0aVPh5OQkAAilUim+/PLLOvcbOnSomDZtmhBCiKKiIuHo6Ci2bNkivX7t2jXh4uIiXnnlFWlbmzZtxLJly2rtc+vWrcLLy0t6vnbtWuHu7l7nOACI7du319h+48YNAUAsXLhQa1/NmjUT69at09qnLscVQohDhw4JAKKoqEgIIcTevXsFAPHjjz9Kbb799lsBQNy4cUMIIURYWJgYM2aM1v4uXLgg7OzsRHZ2tsb2gQMHilmzZtU6Dnd3d7F+/foa5wBANG3aVDRp0kQAEADEyy+/XGP/nTt3CqVSKaqqquo9ZyIiS7O3WFomIot76KGHsGLFCpSUlGDZsmWwt7fXmB9ZVVWF//3vf9iyZQuys7NRXl6OsrIyaX7qX3/9hfLycoSGhkr7eHp6olOnTnUe98cff0R8fDxOnz6NwsJCVFZW4ubNmygtLTV67qsQAsCtr8i1iY2NxbPPPovPP/8c4eHhGDFiBNq3b19nn5mZmZg7dy5+++03XL9+Xbp4KSsrC126dJHa9ejRQ/qzn58fgFuVztatW+Po0aOYNGmS1v6PHTuGqqoq3H333Rrby8rK4OXlVeu4bty4UWNqAAA0a9YMhw8fRkVFBb777jts2LAB7777bo12Li4uUKvVKCsrg4uLSx3vABGR5XF6AFEj1rRpU3To0AE9e/bEmjVrkJ6ejtWrV0uvv/fee3j//ffx+uuvY+/evTh69CgiIiJQXl5u8DHPnz+PRx99FD169MC2bduQmZmJxMREADCq32qnTp0CALRt21br63PnzsWJEycwdOhQ7NmzB126dMH27dtr7a+kpAQRERFwc3PDhg0bcOjQIan9neN1cHCQ/lwdmqsDbl2hsLi4GHZ2dsjMzMTRo0elx6lTp/D+++/Xul+LFi2kubu3UyqV6NChAzp37ozY2Fjcd999eOGFF2q0y8vLQ9OmTRlYicgmMLQSEYBbQeeNN97A7NmzcePGDQDAgQMHMGzYMDz99NPo2bMn2rVrp7F8VPv27eHg4ID09HRp2/Xr1+tcYiozMxNqtRpLlizBfffdh7vvvhuXLl2S7Tyq56WGh4fX2ubuu+/Gq6++ih9++AFPPvkk1q5dCwBwdHSssd7p6dOnce3aNSxYsAAPPPAAgoKCNC7C0lWPHj2QkpKi9bXevXujqqoKubm56NChg8bD19e31j579+6NkydP1nvsmTNnYvPmzTXmGh8/fhy9e/fW70SIiCyEoZWIJCNGjICdnZ1U+ezYsSOSk5Nx8OBBnDp1Cs899xxycnKk9q6uroiOjsb06dOxZ88eHD9+HBMmTIBSWfuPlg4dOqCiogLLly/H33//jc8//xwrV640aLz5+flQqVS4cOECkpOT8Z///AcbN27EihUrtK63euPGDcTExCA1NRUXLlzAgQMHcOjQIXTu3BnArVUOiouLkZKSgqtXr6K0tBStW7eGo6OjNN5du3Zh/vz5eo81Li4OX3zxBeLi4nDq1CkcO3YMCxcuBHArRI8ZMwbjxo3DV199hXPnziEjIwPx8fH49ttva+0zIiICP//8c73HDggIwBNPPIE5c+ZobN+/fz8GDx6s97kQEVmEpSfVEpFljB8/XgwbNqzG9vj4eOHt7S2Ki4vFtWvXxLBhw4Srq6to2bKlmD17thg3bpzGfkVFReLpp58WTZo0ET4+PmLRokWif//+dV6ItXTpUuHn5ydcXFxERESEWL9+vQAgrl+/LoTQ/UKs6oezs7No3769GD9+vMjMzNRod3tfZWVlYtSoUSIgIEA4OjoKf39/ERMTI10sJYQQzz//vPDy8hIARFxcnBBCiI0bN4rAwEDh5OQkwsLCxK5duwQAceTIESHEvxdiVY9fCCGOHDkiAIhz585J27Zt2yZ69eolHB0dRYsWLcSTTz4pvVZeXi7mzJkjAgMDhYODg/Dz8xNPPPGE+P3332t9D65duyacnZ3F6dOntZ7v7dLS0gQAkZ6eLoQQ4p9//hEODg7i4sWLdb3NRERWQyHE/79qgYiIbM706dNRWFiIjz/+WK/9Xn/9dVy/fh2rVq0y0ciIiOTF6QFERDbszTffRJs2bfS+HWvLli0NmuZARGQprLQSERERkdVjpZWIiIiIrB5DKxERERFZPYZWIiIiIrJ6DK1EREREZPUYWomIiIjI6jG0EhEREZHVY2glIiIiIqvH0EpEREREVo+hlYiIiIis3v8DQAMr5DDmigUAAAAASUVORK5CYII=",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 800x600 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "# --- Reformat coefficients (for plotting) ---\n",
+                "boundary_count = len(NMK) - 1\n",
+                "Cs = engine.reformat_coeffs(X, NMK, boundary_count)\n",
+                "print(f\"\\nCoefficients reformatted into {len(Cs)} regions.\")\n",
+                "for i, c_region in enumerate(Cs):\n",
+                "    print(f\"  Region {i} (NMK={NMK[i]}): {c_region.shape} coefficients\")\n",
+                "        \n",
+                "# --- Calculate and Plot Potentials from X ---\n",
+                "print(\"\\nCalculating potential fields for visualization...\")\n",
+                "potentials = engine.calculate_potentials(problem, X, m0, spatial_res=50, sharp=True)\n",
+                "\n",
+                "# Unpack\n",
+                "R = potentials[\"R\"]\n",
+                "Z = potentials[\"Z\"]\n",
+                "phiH = potentials[\"phiH\"]\n",
+                "phiP = potentials[\"phiP\"]\n",
+                "phi = potentials[\"phi\"]\n",
+                "\n",
+                "# --- Plot using built-in visualizer ---\n",
+                "engine.visualize_potential(np.real(phiH), R, Z, \"Homogeneous Potential (Real)\")\n",
+                "engine.visualize_potential(np.imag(phiH), R, Z, \"Homogeneous Potential (Imag)\")\n",
+                "engine.visualize_potential(np.real(phiP), R, Z, \"Particular Potential (Real)\")\n",
+                "engine.visualize_potential(np.imag(phiP), R, Z, \"Particular Potential (Imag)\")\n",
+                "engine.visualize_potential(np.real(phi), R, Z, \"Total Potential (Real)\")\n",
+                "engine.visualize_potential(np.imag(phi), R, Z, \"Total Potential (Imag)\")\n",
+                "        \n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "7b8a5d61",
+            "metadata": {},
+            "source": [
+                "## 5. Saving Results to NetCDF\n",
+                "\n",
+                "The `Results` class is used to store all simulation ou `xarray.Dataset` and export them to a file. This is especially useful after running a frequency sweep."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "id": "8c9d0e1f",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "\n",
+                        "--- WORKFLOW 2: Running a full sweep to store results ---\n",
+                        "Hydrodynamic coefficients stored in xarray dataset.\n",
+                        "Potentials stored in xarray dataset (batched across frequencies/modes).\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=6.46955e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "All results successfully saved to 'meem_simulation_results.nc'\n",
+                        "\n",
+                        "Contents of the final xarray Dataset:\n",
+                        "<xarray.Dataset> Size: 6kB\n",
+                        "Dimensions:             (frequency: 1, mode_i: 1, mode_j: 1, harmonics: 60,\n",
+                        "                         domain_name: 3, modes: 1)\n",
+                        "Coordinates:\n",
+                        "  * frequency           (frequency) float64 8B 2.734\n",
+                        "  * mode_i              (mode_i) int64 8B 0\n",
+                        "  * mode_j              (mode_j) int64 8B 0\n",
+                        "  * harmonics           (harmonics) int64 480B 0 1 2 3 4 5 ... 54 55 56 57 58 59\n",
+                        "  * domain_name         (domain_name) int64 24B 0 1 2\n",
+                        "  * modes               (modes) int64 8B 0\n",
+                        "Data variables:\n",
+                        "    added_mass          (frequency, mode_i, mode_j) float64 8B 289.0\n",
+                        "    damping             (frequency, mode_i, mode_j) float64 8B 189.0\n",
+                        "    potentials_real     (frequency, modes, domain_name, harmonics) float64 1kB ...\n",
+                        "    potentials_imag     (frequency, modes, domain_name, harmonics) float64 1kB ...\n",
+                        "    potential_r_coords  (domain_name, harmonics) float64 1kB 0.0 0.0 ... nan nan\n",
+                        "    potential_z_coords  (domain_name, harmonics) float64 1kB 0.0 0.0 ... nan nan\n"
+                    ]
+                }
+            ],
+            "source": [
+                "# ---------------------------------\n",
+                "# --- WORKFLOW 2: Full Sweep & Storage ---\n",
+                "# ---------------------------------\n",
+                "# This workflow is best for running multiple frequencies\n",
+                "# and saving all results to a file.\n",
+                "print(\"\\n--- WORKFLOW 2: Running a full sweep to store results ---\")\n",
+                "\n",
+                "# We already set the frequency on the 'problem' object.\n",
+                "# 'run_and_store_results' will solve for all frequencies\n",
+                "# defined in the problem list (in this case, just one).\n",
+                "results_obj = engine.run_and_store_results(problem_index=0)\n",
+                "\n",
+                "# Export the entire dataset to a NetCDF file\n",
+                "file_path = \"meem_simulation_results.nc\"\n",
+                "results_obj.export_to_netcdf(file_path)\n",
+                "print(f\"All results successfully saved to '{file_path}'\")\n",
+                "\n",
+                "# Inspect the final xarray Dataset\n",
+                "print(\"\\nContents of the final xarray Dataset:\")\n",
+                "print(results_obj.get_results())"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "id": "d275ea5c",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "\n",
+                        "--- WORKFLOW 3: Frequency Sweep & Coefficient Plotting ---\n",
+                        "Setting up 2-body problem for sweep...\n",
+                        "Sweep problem configured with 40 frequencies and 2 modes: [0 1].\n",
+                        "Running frequency sweep... (This may take a moment)\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=2.87023e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.11754e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.35782e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.59491e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.8314e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.06914e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.30955e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.5538e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.80292e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=5.05784e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=5.31944e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=5.5886e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=5.8662e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n",
+                        "/Users/hopebest/Documents/semi-analytical-hydro/.venv/lib/python3.12/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=6.15318e-30): result may not be accurate.\n",
+                        "  return f(*arrays, *other_args, **kwargs)\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Hydrodynamic coefficients stored in xarray dataset.\n",
+                        "Potentials stored in xarray dataset (batched across frequencies/modes).\n",
+                        "Sweep complete.\n",
+                        "Extracting data and plotting coefficients...\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 1000x900 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "\n",
+                        "Tutorial finished.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "# ---------------------------------\n",
+                "# --- WORKFLOW 3: Frequency Sweep & Coefficient Plotting ---\n",
+                "# ---------------------------------\n",
+                "# This workflow shows a common use case:\n",
+                "# 1. Define a problem where *all* bodies can move (heaving=[True, True])\n",
+                "# 2. Run a sweep over a range of frequencies.\n",
+                "# 3. Plot the resulting 2x2 Added Mass and Damping matrices.\n",
+                "\n",
+                "print(\"\\n--- WORKFLOW 3: Frequency Sweep & Coefficient Plotting ---\")\n",
+                "\n",
+                "# --- 3.1. Define the Problem for a Full 2-Body Sweep ---\n",
+                "# We define the problem so that *both* bodies are\n",
+                "# included as active modes (degrees of freedom).\n",
+                "print(\"Setting up 2-body problem for sweep...\")\n",
+                "\n",
+                "# Use the same geometric parameters from Section 1\n",
+                "h = 1.001            \n",
+                "d_list = [0.5, 0.25]\n",
+                "a_list = [0.5, 1.0]\n",
+                "NMK = [30, 30, 30]\n",
+                "\n",
+                "# --- KEY CHANGE ---\n",
+                "# Set heaving to [True, True] so that problem.modes becomes [0, 1].\n",
+                "# This tells run_and_store_results to solve the full 2x2 N-body problem.\n",
+                "heaving_list_sweep = [True, True] \n",
+                "\n",
+                "# 1. Create SteppedBody objects\n",
+                "bodies_sweep = []\n",
+                "for i in range(len(a_list)):\n",
+                "    body = SteppedBody(\n",
+                "        a=np.array([a_list[i]]),\n",
+                "        d=np.array([d_list[i]]),\n",
+                "        slant_angle=np.array([0.0]),\n",
+                "        heaving=heaving_list_sweep[i]\n",
+                "    )\n",
+                "    bodies_sweep.append(body)\n",
+                "\n",
+                "# 2. Create arrangement\n",
+                "arrangement_sweep = ConcentricBodyGroup(bodies_sweep)\n",
+                "\n",
+                "# 3. Create geometry\n",
+                "geometry_sweep = BasicRegionGeometry(\n",
+                "    body_arrangement=arrangement_sweep,\n",
+                "    h=h,\n",
+                "    NMK=NMK\n",
+                ")\n",
+                "\n",
+                "# 4. Create the MEEMProblem instance\n",
+                "problem_sweep = MEEMProblem(geometry_sweep)\n",
+                "\n",
+                "# 5. Set the frequencies for the sweep\n",
+                "# We will run for 40 frequencies from 0.5 to 5.0 rad/s\n",
+                "omega_sweep = np.linspace(0.5, 5.0, 40)\n",
+                "problem_sweep.set_frequencies(omega_sweep)\n",
+                "\n",
+                "print(f\"Sweep problem configured with {len(problem_sweep.frequencies)} frequencies and {len(problem_sweep.modes)} modes: {problem_sweep.modes}.\")\n",
+                "\n",
+                "# 6. Initialize a new MEEM Engine for this problem\n",
+                "engine_sweep = MEEMEngine(problem_list=[problem_sweep])\n",
+                "\n",
+                "# --- 3.2. Run the Full Sweep ---\n",
+                "print(\"Running frequency sweep... (This may take a moment)\")\n",
+                "# This one call solves the radiation problem for each mode (0 and 1)\n",
+                "# at each of the 40 frequencies.\n",
+                "results_obj_sweep = engine_sweep.run_and_store_results(problem_index=0)\n",
+                "print(\"Sweep complete.\")\n",
+                "\n",
+                "# --- 3.3. Extract and Plot Hydrodynamic Coefficients ---\n",
+                "print(\"Extracting data and plotting coefficients...\")\n",
+                "\n",
+                "# Get the xarray.Dataset\n",
+                "ds_sweep = results_obj_sweep.get_results()\n",
+                "\n",
+                "# Extract the Added Mass and Damping matrices.\n",
+                "# These are 3D arrays: (frequency, mode_j, mode_i)\n",
+                "# A[freq, 1, 0] = Added Mass on body 1 due to motion of body 0\n",
+                "A_sweep = ds_sweep.added_mass\n",
+                "B_sweep = ds_sweep.damping\n",
+                "freqs_out = ds_sweep.frequency # This is the same as omega_sweep\n",
+                "\n",
+                "# Create the plots\n",
+                "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 9), sharex=True)\n",
+                "\n",
+                "# --- Plot 1: Added Mass ---\n",
+                "ax1.plot(freqs_out, A_sweep[:, 0, 0], label=r'$A_{00}$ (Force on 0, Motion from 0)')\n",
+                "ax1.plot(freqs_out, A_sweep[:, 0, 1], label=r'$A_{01}$ (Force on 0, Motion from 1)', linestyle='--')\n",
+                "ax1.plot(freqs_out, A_sweep[:, 1, 0], label=r'$A_{10}$ (Force on 1, Motion from 0)', linestyle=':')\n",
+                "ax1.plot(freqs_out, A_sweep[:, 1, 1], label=r'$A_{11}$ (Force on 1, Motion from 1)', linestyle='-.')\n",
+                "ax1.set_ylabel('Added Mass (kg)')\n",
+                "ax1.set_title('Added Mass vs. Frequency')\n",
+                "ax1.legend()\n",
+                "ax1.grid(True, which='both', linestyle='--', alpha=0.7)\n",
+                "\n",
+                "# --- Plot 2: Damping ---\n",
+                "ax2.plot(freqs_out, B_sweep[:, 0, 0], label=r'$B_{00}$ (Force on 0, Motion from 0)')\n",
+                "ax2.plot(freqs_out, B_sweep[:, 0, 1], label=r'$B_{01}$ (Force on 0, Motion from 1)', linestyle='--')\n",
+                "ax2.plot(freqs_out, B_sweep[:, 1, 0], label=r'$B_{10}$ (Force on 1, Motion from 0)', linestyle=':')\n",
+                "ax2.plot(freqs_out, B_sweep[:, 1, 1], label=r'$B_{11}$ (Force on 1, Motion from 1)', linestyle='-.')\n",
+                "ax2.set_ylabel('Damping (N-s/m)')\n",
+                "ax2.set_title('Damping vs. Frequency')\n",
+                "ax2.set_xlabel('Frequency (rad/s)')\n",
+                "ax2.legend()\n",
+                "ax2.grid(True, which='both', linestyle='--', alpha=0.7)\n",
+                "\n",
+                "plt.tight_layout()\n",
+                "plt.show()\n",
+                "\n",
+                "\n",
+                "print(\"\\nTutorial finished.\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "7b8a5d62",
+            "metadata": {},
+            "source": [
+                "## 6. Conclusion\n",
+                "\n",
+                "This tutorial has walked you through the modern, object-oriented workflow for running a simulation with OpenFLASH. You've learned how to:\n",
+                "\n",
+                "* Define a physical problem using `SteppedBody` objects.\n",
+                "* Initialize and execute the `MEEMEngine` to solve for the system's coefficients.\n",
+                "* Extract and view hyrdodynamic coefficients.\n",
+                "* Calculate and visualzie the spatial potential field.\n",
+                "* Store all simulation results in a structured `Results` object and export to NetCDF.\n",
+                "\n",
+                "For more detailed information on specific classes and functions, please refer to the project's Sphinx documentation.\n",
+                "\n",
+                "Feel free to experiment with different parameters and geometries to explore various hydrodynamic scenarios!"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": ".venv (3.12.1)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.12.1"
+        }
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/y0/9n1rj1dx3md8kwg6n8jblkzm0000gn/T/ipykernel_70088/1204308439.py:2: LinAlgWarning: Ill-conditioned matrix (rcond=6.5447e-36): result may not be accurate.\n",
-      "  X = linalg.solve(A, b)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Solve the linear system A x = b\n",
-    "X = linalg.solve(A, b)\n",
-    "\n",
-    "# Compute hydrodynamic coefficients\n",
-    "hydro_coefficients = engine.compute_hydrodynamic_coefficients(problem, X)\n",
-    "\n",
-    "# Print hydrodynamic coefficients\n",
-    "print(\"Hydrodynamic Coefficients:\")\n",
-    "for key, value in hydro_coefficients.items():\n",
-    "    print(f\"{key}: {value}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Split Cs into Groups by Equation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Cs = []\n",
-    "row = 0\n",
-    "Cs.append(X[:NMK[0]])\n",
-    "row += NMK[0]\n",
-    "for i in range(1, boundary_count):\n",
-    "    Cs.append(X[row: row + NMK[i] * 2])\n",
-    "    row += NMK[i] * 2\n",
-    "Cs.append(X[row:])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define Potential Functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def phi_h_n_inner_func(n, r, z):\n",
-    "    return (Cs[0][n] * R_1n(n, r, 0)) * Z_n_i(n, z, 0)\n",
-    "\n",
-    "def phi_h_m_i_func(i, m, r, z):\n",
-    "    return (Cs[i][m] * R_1n(m, r, i) + Cs[i][NMK[i] + m] * R_2n(m, r, i)) * Z_n_i(m, z, i)\n",
-    "\n",
-    "def phi_e_k_func(k, r, z):\n",
-    "    return Cs[-1][k] * Lambda_k(k, r) * Z_n_e(k, z)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set Up Spatial Resolution and Grid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spatial_res = 50\n",
-    "r_vec = np.linspace(2 * a[-1] / spatial_res, 2 * a[-1], spatial_res)\n",
-    "z_vec = np.linspace(-h, 0, spatial_res)\n",
-    "\n",
-    "# Add values at the radii\n",
-    "a_eps = 1.0e-4\n",
-    "for i in range(len(a)):\n",
-    "    r_vec = np.append(r_vec, a[i] * (1 - a_eps))\n",
-    "    r_vec = np.append(r_vec, a[i] * (1 + a_eps))\n",
-    "r_vec = np.unique(r_vec)\n",
-    "\n",
-    "for i in range(len(d)):\n",
-    "    z_vec = np.append(z_vec, -d[i])\n",
-    "z_vec = np.unique(z_vec)\n",
-    "\n",
-    "R, Z = np.meshgrid(r_vec, z_vec)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "regions = []\n",
-    "regions.append((R <= a[0]) & (Z < -d[0]))\n",
-    "for i in range(1, boundary_count):\n",
-    "    regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))\n",
-    "regions.append(R > a[-1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Calculate Homogeneous and Particular Potentials"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "\n",
-    "# Homogeneous potential calculation\n",
-    "for n in range(NMK[0]):\n",
-    "        temp_phiH = phi_h_n_inner_func(n, R[regions[0]], Z[regions[0]])\n",
-    "        phiH[regions[0]] = temp_phiH if n == 0 else phiH[regions[0]] + temp_phiH\n",
-    "\n",
-    "for i in range(1, boundary_count):\n",
-    "    for m in range(NMK[i]):\n",
-    "        temp_phiH = phi_h_m_i_func(i, m, R[regions[i]], Z[regions[i]])\n",
-    "        phiH[regions[i]] = temp_phiH if m == 0 else phiH[regions[i]] + temp_phiH\n",
-    "\n",
-    "for k in range(NMK[-1]):\n",
-    "    temp_phiH = phi_e_k_func(k, R[regions[-1]], Z[regions[-1]])\n",
-    "    phiH[regions[-1]] = temp_phiH if k == 0 else phiH[regions[-1]] + temp_phiH\n",
-    "\n",
-    "# Add particular potential (if applicable)\n",
-    "phi_p_i_vec = np.vectorize(phi_p_i)\n",
-    "\n",
-    "phiP[regions[0]] = heaving[0] * phi_p_i_vec(d[0], R[regions[0]], Z[regions[0]])\n",
-    "for i in range(1, boundary_count):\n",
-    "    phiP[regions[i]] = heaving[i] * phi_p_i_vec(d[i], R[regions[i]], Z[regions[i]])\n",
-    "phiP[regions[-1]] = 0\n",
-    "\n",
-    "phi = phiH + phiP"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 5. **Visualization**\n",
-    "Generate plots for the computed potentials:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Visualize Potentials"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_potential(field, R, Z, title):\n",
-    "    plt.figure(figsize=(8, 6))\n",
-    "    plt.contourf(R, Z, field, levels=50, cmap='viridis')\n",
-    "    plt.colorbar()\n",
-    "    plt.title(title)\n",
-    "    plt.xlabel('Radial Distance (R)')\n",
-    "    plt.ylabel('Axial Distance (Z)')\n",
-    "    plt.show()\n",
-    "\n",
-    "plot_potential(np.real(phi), R, Z, 'Total Potential (Real Part)')\n",
-    "plot_potential(np.imag(phi), R, Z, 'Total Potential (Imaginary Part)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conclusion\n",
-    "This tutorial walks through MEEM simulations using `MEEMEngine`. You can also customize boundary conditions or explore additional visualizations."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+    "nbformat": 4,
+    "nbformat_minor": 5
 }
diff --git a/examples/.gitkeep b/examples/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/hydro/python/multi_MEEM.ipynb b/hydro/python/multi_MEEM.ipynb
deleted file mode 100644
index c967b4e..0000000
--- a/hydro/python/multi_MEEM.ipynb
+++ /dev/null
@@ -1,1200 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from scipy.special import hankel1 as besselh\n",
-    "from scipy.special import iv as besseli\n",
-    "from scipy.special import kv as besselk\n",
-    "import scipy.integrate as integrate\n",
-    "import scipy.linalg as linalg\n",
-    "import matplotlib.pyplot as plt\n",
-    "from math import sqrt, cosh, cos, sinh, sin, pi\n",
-    "from scipy.optimize import newton, minimize_scalar\n",
-    "from multi_constants import *\n",
-    "from multi_equations import *\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "boundary_count = len(NMK) - 1\n",
-    "for arr in [a, d, heaving]:\n",
-    "    assert len(arr) == boundary_count, \"NMK should have one more entry than a, d, and heaving, which should all have the same number of entries.\"\n",
-    "\n",
-    "for entry in heaving:\n",
-    "    assert (entry == 0 or entry == 1), \"heaving entries should be booleans.\"\n",
-    "\n",
-    "left = 0\n",
-    "for radius in a:\n",
-    "    assert radius > left, \"a entries should be increasing, and start greater than 0.\"\n",
-    "    left = radius\n",
-    "\n",
-    "for depth in d:\n",
-    "    assert depth >= 0, \"d entries should be nonnegative.\"\n",
-    "    assert depth < h, \"d entries should be less than h.\"\n",
-    "\n",
-    "for val in NMK:\n",
-    "    assert (type(val) == int and val > 0), \"NMK entries should be positive integers.\"\n",
-    "\n",
-    "assert (m0 >= 0), \"m0 should be nonnegative.\" # currently shouldn't be 0 or too large, but that will be fixed eventually\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# CREATING THE A MATRIX\n",
-    "size = NMK[0] + NMK[-1] + 2 * sum(NMK[1:len(NMK) - 1])\n",
-    "boundary_count = len(NMK) - 1\n",
-    "\n",
-    "rows = [] # collection of rows of blocks in A matrix, to be concatenated later\n",
-    "\n",
-    "## Define values/functions to help block creation\n",
-    "#Coupling integral values\n",
-    "I_nm_vals = np.zeros((max(NMK), max(NMK), boundary_count - 1), dtype = complex)\n",
-    "for bd in range(boundary_count - 1):\n",
-    "    for n in range(NMK[bd]):\n",
-    "        for m in range(NMK[bd + 1]):\n",
-    "            I_nm_vals[n][m][bd] = I_nm(n, m, bd)\n",
-    "I_mk_vals = np.zeros((NMK[boundary_count - 1], NMK[boundary_count]), dtype = complex)\n",
-    "for m in range(NMK[boundary_count - 1]):\n",
-    "    for k in range(NMK[boundary_count]):\n",
-    "        I_mk_vals[m][k]= I_mk(m, k, boundary_count - 1)\n",
-    "\n",
-    "## Functions to create blocks of certain types\n",
-    "# arguments: diagonal block on left (T/F), vectorized radial eigenfunction, boundary number\n",
-    "def p_diagonal_block(left, radfunction, bd):\n",
-    "    region = bd if left else (bd + 1)\n",
-    "    sign = 1 if left else (-1)\n",
-    "    return sign * (h - d[region]) * np.diag(radfunction(list(range(NMK[region])), a[bd], region))\n",
-    "    \n",
-    "# arguments: dense block on left (T/F), vectorized radial eigenfunction, boundary number\n",
-    "def p_dense_block(left, radfunction, bd):\n",
-    "    I_nm_array = I_nm_vals[0:NMK[bd],0:NMK[bd+1], bd]\n",
-    "    if left: # determine which is region to work in and which is adjacent\n",
-    "        region, adj = bd, bd + 1\n",
-    "        sign = 1\n",
-    "        I_nm_array = np.transpose(I_nm_array)\n",
-    "    else:\n",
-    "        region, adj = bd + 1, bd\n",
-    "        sign = -1\n",
-    "    radial_vector = radfunction(list(range(NMK[region])), a[bd], region)\n",
-    "    radial_array = np.outer((np.full((NMK[adj]), 1)), radial_vector)\n",
-    "    return sign * radial_array * I_nm_array\n",
-    "\n",
-    "def p_dense_block_e(bd):\n",
-    "    I_mk_array = I_mk_vals\n",
-    "    radial_vector = (np.vectorize(Lambda_k))(list(range(NMK[bd+1])), a[bd])\n",
-    "    radial_array = np.outer((np.full((NMK[bd]), 1)), radial_vector)\n",
-    "    return (-1) * radial_array * I_mk_array\n",
-    "\n",
-    "#####\n",
-    "# arguments: diagonal block on left (T/F), vectorized radial eigenfunction, boundary number\n",
-    "def v_diagonal_block(left, radfunction, bd):\n",
-    "    region = bd if left else (bd + 1)\n",
-    "    sign = (-1) if left else (1)\n",
-    "    return sign * (h - d[region]) * np.diag(radfunction(list(range(NMK[region])), a[bd], region))\n",
-    "\n",
-    "# arguments: dense block on left (T/F), vectorized radial eigenfunction, boundary number\n",
-    "def v_dense_block(left, radfunction, bd):\n",
-    "    I_nm_array = I_nm_vals[0:NMK[bd],0:NMK[bd+1], bd]\n",
-    "    if left: # determine which is region to work in and which is adjacent\n",
-    "        region, adj = bd, bd + 1\n",
-    "        sign = -1\n",
-    "        I_nm_array = np.transpose(I_nm_array)\n",
-    "    else:\n",
-    "        region, adj = bd + 1, bd\n",
-    "        sign = 1\n",
-    "    radial_vector = radfunction(list(range(NMK[region])), a[bd], region)\n",
-    "    radial_array = np.outer((np.full((NMK[adj]), 1)), radial_vector)\n",
-    "    return sign * radial_array * I_nm_array\n",
-    "\n",
-    "def v_diagonal_block_e(bd):\n",
-    "    return h * np.diag((np.vectorize(diff_Lambda_k))(list(range(NMK[bd+1])), a[bd]))\n",
-    "\n",
-    "def v_dense_block_e(radfunction, bd): # for region adjacent to e-type region\n",
-    "    I_km_array = np.transpose(I_mk_vals)\n",
-    "    radial_vector = radfunction(list(range(NMK[bd])), a[bd], bd)\n",
-    "    radial_array = np.outer((np.full((NMK[bd + 1]), 1)), radial_vector)\n",
-    "    return (-1) * radial_array * I_km_array\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Potential Blocks\n",
-    "col = 0\n",
-    "for bd in range(boundary_count):\n",
-    "    N = NMK[bd]\n",
-    "    M = NMK[bd + 1]\n",
-    "    if bd == (boundary_count - 1): # i-e boundary, inherently left diagonal\n",
-    "        row_height = N\n",
-    "        left_block1 = p_diagonal_block(True, np.vectorize(R_1n), bd)\n",
-    "        right_block = p_dense_block_e(bd)\n",
-    "        if bd == 0: # one cylinder\n",
-    "            rows.append(np.concatenate((left_block1,right_block), axis = 1))\n",
-    "        else:\n",
-    "            left_block2 = p_diagonal_block(True, np.vectorize(R_2n), bd)\n",
-    "            left_zeros = np.zeros((row_height, col), dtype=complex)\n",
-    "            rows.append(np.concatenate((left_zeros,left_block1,left_block2,right_block), axis = 1))\n",
-    "    elif bd == 0:\n",
-    "        left_diag = d[bd] > d[bd + 1] # which of the two regions gets diagonal entries\n",
-    "        if left_diag:\n",
-    "            row_height = N\n",
-    "            left_block = p_diagonal_block(True, np.vectorize(R_1n), 0)\n",
-    "            right_block1 = p_dense_block(False, np.vectorize(R_1n), 0)\n",
-    "            right_block2 = p_dense_block(False, np.vectorize(R_2n), 0)\n",
-    "        else:\n",
-    "            row_height = M\n",
-    "            left_block = p_dense_block(True, np.vectorize(R_1n), 0)\n",
-    "            right_block1 = p_diagonal_block(False, np.vectorize(R_1n), 0)\n",
-    "            right_block2 = p_diagonal_block(False, np.vectorize(R_2n), 0)\n",
-    "        right_zeros = np.zeros((row_height, size - (col + N + 2 * M)),dtype=complex)\n",
-    "        block_lst = [left_block, right_block1, right_block2, right_zeros]\n",
-    "        rows.append(np.concatenate(block_lst, axis = 1))\n",
-    "        col += N\n",
-    "    else: # i-i boundary\n",
-    "        left_diag = d[bd] > d[bd + 1] # which of the two regions gets diagonal entries\n",
-    "        if left_diag:\n",
-    "            row_height = N\n",
-    "            left_block1 = p_diagonal_block(True, np.vectorize(R_1n), bd)\n",
-    "            left_block2 = p_diagonal_block(True, np.vectorize(R_2n), bd)\n",
-    "            right_block1 = p_dense_block(False, np.vectorize(R_1n),  bd)\n",
-    "            right_block2 = p_dense_block(False, np.vectorize(R_2n),  bd)\n",
-    "        else:\n",
-    "            row_height = M\n",
-    "            left_block1 = p_dense_block(True, np.vectorize(R_1n),  bd)\n",
-    "            left_block2 = p_dense_block(True, np.vectorize(R_2n),  bd)\n",
-    "            right_block1 = p_diagonal_block(False, np.vectorize(R_1n),  bd)\n",
-    "            right_block2 = p_diagonal_block(False, np.vectorize(R_2n),  bd)\n",
-    "        left_zeros = np.zeros((row_height, col), dtype=complex)\n",
-    "        right_zeros = np.zeros((row_height, size - (col + 2 * N + 2 * M)),dtype=complex)\n",
-    "        block_lst = [left_zeros, left_block1, left_block2, right_block1, right_block2, right_zeros]\n",
-    "        rows.append(np.concatenate(block_lst, axis = 1))\n",
-    "        col += 2 * N\n",
-    "\n",
-    "\n",
-    "###############################\n",
-    "# Velocity Blocks\n",
-    "col = 0\n",
-    "for bd in range(boundary_count):\n",
-    "    N = NMK[bd]\n",
-    "    M = NMK[bd + 1]\n",
-    "    if bd == (boundary_count - 1): # i-e boundary, inherently left diagonal\n",
-    "        row_height = M\n",
-    "        left_block1 = v_dense_block_e(np.vectorize(diff_R_1n, otypes=[complex]), bd)\n",
-    "        right_block = v_diagonal_block_e(bd)\n",
-    "        if bd == 0: # one cylinder\n",
-    "            rows.append(np.concatenate((left_block1,right_block), axis = 1))\n",
-    "        else:\n",
-    "            left_block2 = v_dense_block_e(np.vectorize(diff_R_2n, otypes=[complex]), bd)\n",
-    "            left_zeros = np.zeros((row_height, col), dtype=complex)\n",
-    "            rows.append(np.concatenate((left_zeros,left_block1,left_block2,right_block), axis = 1))\n",
-    "    elif bd == 0:\n",
-    "        left_diag = d[bd] < d[bd + 1] # taller fluid region gets diagonal entries\n",
-    "        if left_diag:\n",
-    "            row_height = N\n",
-    "            left_block = v_diagonal_block(True, np.vectorize(diff_R_1n, otypes=[complex]), 0)\n",
-    "            right_block1 = v_dense_block(False, np.vectorize(diff_R_1n, otypes=[complex]), 0)\n",
-    "            right_block2 = v_dense_block(False, np.vectorize(diff_R_2n, otypes=[complex]), 0)\n",
-    "        else:\n",
-    "            row_height = M\n",
-    "            left_block = v_dense_block(True, np.vectorize(diff_R_1n, otypes=[complex]), 0)\n",
-    "            right_block1 = v_diagonal_block(False, np.vectorize(diff_R_1n, otypes=[complex]), 0)\n",
-    "            right_block2 = v_diagonal_block(False, np.vectorize(diff_R_2n, otypes=[complex]), 0)\n",
-    "        right_zeros = np.zeros((row_height, size - (col + N + 2 * M)),dtype=complex)\n",
-    "        block_lst = [left_block, right_block1, right_block2, right_zeros]\n",
-    "        rows.append(np.concatenate(block_lst, axis = 1))\n",
-    "        col += N\n",
-    "    else: # i-i boundary\n",
-    "        left_diag = d[bd] < d[bd + 1] # taller fluid region gets diagonal entries\n",
-    "        if left_diag:\n",
-    "            row_height = N\n",
-    "            left_block1 = v_diagonal_block(True, np.vectorize(diff_R_1n, otypes=[complex]), bd)\n",
-    "            left_block2 = v_diagonal_block(True, np.vectorize(diff_R_2n, otypes=[complex]), bd)\n",
-    "            right_block1 = v_dense_block(False, np.vectorize(diff_R_1n, otypes=[complex]),  bd)\n",
-    "            right_block2 = v_dense_block(False, np.vectorize(diff_R_2n, otypes=[complex]),  bd)\n",
-    "        else:\n",
-    "            row_height = M\n",
-    "            left_block1 = v_dense_block(True, np.vectorize(diff_R_1n, otypes=[complex]),  bd)\n",
-    "            left_block2 = v_dense_block(True, np.vectorize(diff_R_2n, otypes=[complex]),  bd)\n",
-    "            right_block1 = v_diagonal_block(False, np.vectorize(diff_R_1n, otypes=[complex]),  bd)\n",
-    "            right_block2 = v_diagonal_block(False, np.vectorize(diff_R_2n, otypes=[complex]),  bd)\n",
-    "        left_zeros = np.zeros((row_height, col), dtype=complex)\n",
-    "        right_zeros = np.zeros((row_height, size - (col + 2* N + 2 * M)),dtype=complex)\n",
-    "        block_lst = [left_zeros, left_block1, left_block2, right_block1, right_block2, right_zeros]\n",
-    "        rows.append(np.concatenate(block_lst, axis = 1))\n",
-    "        col += 2 * N\n",
-    "\n",
-    "## Concatenate the rows of blocks into the square A matrix\n",
-    "A = np.concatenate(rows, axis = 0)\n",
-    "\n",
-    "###########################################################################\n",
-    "# This plots a sparsity matrix\n",
-    "if False:\n",
-    "    \n",
-    " rows, cols = np.nonzero(A)\n",
-    " plt.figure(figsize=(6, 6))\n",
-    " plt.scatter(cols, rows, color='blue', marker='o', s=100) \n",
-    " plt.gca().invert_yaxis() \n",
-    " plt.xticks(range(A.shape[1]))\n",
-    " plt.yticks(range(A.shape[0]))\n",
-    "\n",
-    " cols = [NMK[0]]\n",
-    " for i in range(1, boundary_count):\n",
-    "    cols.append(cols[-1] + NMK[i])\n",
-    "    cols.append(cols[-1] + NMK[i])\n",
-    " cols.append(cols[-1] + NMK[-1])\n",
-    "\n",
-    " for val in cols:\n",
-    "    plt.axvline(val-0.5, color='black', linestyle='-', linewidth=1) \n",
-    "    plt.axhline(val-0.5, color='black', linestyle='-', linewidth=1) \n",
-    "\n",
-    " # for y in range(0, A.shape[0], 3):\n",
-    "    # plt.axhline(y-0.5, color='black', linestyle='-', linewidth=1) \n",
-    "\n",
-    " plt.grid(True)\n",
-    " plt.title('Non-Zero Entries of the Matrix')\n",
-    " plt.xlabel('Column Index')\n",
-    " plt.ylabel('Row Index')\n",
-    " plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = np.zeros(size, dtype=complex)\n",
-    "\n",
-    "index = 0\n",
-    "\n",
-    "# potential matching\n",
-    "for boundary in range(boundary_count):\n",
-    "    if boundary == (boundary_count - 1): # i-e boundary\n",
-    "        for n in range(NMK[-2]):\n",
-    "            b[index] = b_potential_end_entry(n, boundary)\n",
-    "            index += 1\n",
-    "    else: # i-i boundary\n",
-    "        for n in range(NMK[boundary + (d[boundary] < d[boundary + 1])]): # iterate over eigenfunctions for smaller h-d\n",
-    "            b[index] = b_potential_entry(n, boundary)\n",
-    "            index += 1\n",
-    "\n",
-    "# velocity matching\n",
-    "for boundary in range(boundary_count):\n",
-    "    if boundary == (boundary_count - 1): # i-e boundary\n",
-    "        for n in range(NMK[-1]):\n",
-    "            b[index] = b_velocity_end_entry(n, boundary)\n",
-    "            index += 1\n",
-    "    else: # i-i boundary\n",
-    "        for n in range(NMK[boundary + (d[boundary] > d[boundary + 1])]): # iterate over eigenfunctions for larger h-d\n",
-    "            b[index] = b_velocity_entry(n, boundary)\n",
-    "            index += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "X = linalg.solve(A,b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "real (added mass): 1290015950925.3684\n",
-      "imag (damping): 214471737.00572044\n",
-      "real/(h^3): 1290015.9509253683\n",
-      "imag/(h^3): 214.47173700572043\n",
-      "nondimensional, real: 401392.7962016897\n",
-      "nondimensional, imag (no omega factor): 21.306399164810813\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The c-vector dotted with X is the hydro coefficient integral (+ a constant, sum(hydro_p_terms))\n",
-    "c = np.zeros((size - NMK[-1]), dtype=complex)\n",
-    "col = 0\n",
-    "for n in range(NMK[0]):\n",
-    "    c[n] = heaving[0] * int_R_1n(0, n)* z_n_d(n)\n",
-    "col += NMK[0]\n",
-    "for i in range(1, boundary_count):\n",
-    "    M = NMK[i]\n",
-    "    for m in range(M):\n",
-    "        c[col + m] = heaving[i] * int_R_1n(i, m)* z_n_d(m)\n",
-    "        c[col + M + m] = heaving[i] * int_R_2n(i, m)* z_n_d(m)\n",
-    "    col += 2 * M\n",
-    "\n",
-    "hydro_p_terms = np.zeros(boundary_count, dtype=complex)\n",
-    "for i in range(boundary_count):\n",
-    "    hydro_p_terms[i] = heaving[i] * int_phi_p_i(i)\n",
-    "\n",
-    "hydro_coef = 2 * pi * (np.dot(c, X[:-NMK[-1]]) + sum(hydro_p_terms))\n",
-    "hydro_coef_real = hydro_coef.real * h**3 * rho\n",
-    "hydro_coef_imag = hydro_coef.imag * omega * h**3 * rho\n",
-    "\n",
-    "# find maximum heaving radius\n",
-    "max_rad = a[0]\n",
-    "for i in range(boundary_count - 1, 0, -1):\n",
-    "    if heaving[i]:\n",
-    "        max_rad = a[i]\n",
-    "        break\n",
-    "\n",
-    "hydro_coef_nondim = h**3/(max_rad**3 * pi)*hydro_coef\n",
-    "\n",
-    "print(\"real (added mass):\", hydro_coef_real)\n",
-    "print(\"imag (damping):\", hydro_coef_imag)\n",
-    "print(\"real/(h^3):\", hydro_coef_real/(h**3)) # to compare with Capytaine\n",
-    "print(\"imag/(h^3):\", hydro_coef_imag/(h**3))\n",
-    "print(\"nondimensional, real:\", hydro_coef_nondim.real)\n",
-    "print(\"nondimensional, imag (no omega factor):\", hydro_coef_nondim.imag)\n",
-    "\n",
-    "# print(\"ratio\", hydro_coef_real/hydro_coef_imag)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Split up the Cs into groups depending on which equation they belong to.\n",
-    "Cs = []\n",
-    "row = 0\n",
-    "Cs.append(X[:NMK[0]])\n",
-    "row += NMK[0]\n",
-    "for i in range(1, boundary_count):\n",
-    "    Cs.append(X[row: row + NMK[i] * 2])\n",
-    "    row += NMK[i] * 2\n",
-    "Cs.append(X[row:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_R_Z(sharp, spatial_res): # create coordinate array for graphing\n",
-    "    rmin = (2 * a[-1] / spatial_res) if sharp else 0.0\n",
-    "    r_vec = np.linspace(rmin, 2*a[-1], spatial_res)\n",
-    "    z_vec = np.linspace(0, -h, spatial_res)\n",
-    "    if sharp: # more precise near boundaries\n",
-    "        a_eps = 1.0e-4\n",
-    "        for i in range(len(a)):\n",
-    "            r_vec = np.append(r_vec, a[i]*(1-a_eps))\n",
-    "            r_vec = np.append(r_vec, a[i]*(1+a_eps))\n",
-    "        r_vec = np.unique(r_vec)\n",
-    "        for i in range(len(d)):\n",
-    "            z_vec = np.append(z_vec, -d[i])\n",
-    "        z_vec = np.unique(z_vec)\n",
-    "    return np.meshgrid(r_vec, z_vec)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def phi_h_n_inner_func(n, r, z):\n",
-    "    return (Cs[0][n] * R_1n(n, r, 0)) * Z_n_i(n, z, 0)\n",
-    "\n",
-    "def phi_h_m_i_func(i, m, r, z):\n",
-    "    return (Cs[i][m] * R_1n(m, r, i) + Cs[i][NMK[i] + m] * R_2n(m, r, i)) * Z_n_i(m, z, i)\n",
-    "\n",
-    "def phi_e_k_func(k, r, z):\n",
-    "    return Cs[-1][k] * Lambda_k(k, r) * Z_k_e(k, z)\n",
-    "\n",
-    "R, Z = make_R_Z(True, 50)\n",
-    "\n",
-    "regions = []\n",
-    "regions.append((R <= a[0]) & (Z < -d[0]))\n",
-    "for i in range(1, boundary_count):\n",
-    "    regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))\n",
-    "regions.append(R > a[-1])\n",
-    "\n",
-    "phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "\n",
-    "for n in range(NMK[0]):\n",
-    "    temp_phiH = phi_h_n_inner_func(n, R[regions[0]], Z[regions[0]])\n",
-    "    phiH[regions[0]] = temp_phiH if n == 0 else phiH[regions[0]] + temp_phiH\n",
-    "\n",
-    "for i in range(1, boundary_count):\n",
-    "    for m in range(NMK[i]):\n",
-    "        temp_phiH = phi_h_m_i_func(i, m, R[regions[i]], Z[regions[i]])\n",
-    "        phiH[regions[i]] = temp_phiH if m == 0 else phiH[regions[i]] + temp_phiH\n",
-    "\n",
-    "for k in range(NMK[-1]):\n",
-    "    temp_phiH = phi_e_k_func(k, R[regions[-1]], Z[regions[-1]])\n",
-    "    phiH[regions[-1]] = temp_phiH if k == 0 else phiH[regions[-1]] + temp_phiH\n",
-    "\n",
-    "phi_p_i_vec = np.vectorize(phi_p_i)\n",
-    "\n",
-    "phiP[regions[0]] = heaving[0] * phi_p_i_vec(d[0], R[regions[0]], Z[regions[0]])\n",
-    "for i in range(1, boundary_count):\n",
-    "    phiP[regions[i]] = heaving[i] * phi_p_i_vec(d[i], R[regions[i]], Z[regions[i]])\n",
-    "phiP[regions[-1]] = 0\n",
-    "\n",
-    "phi = phiH + phiP\n",
-    "\n",
-    "#nanregions = []\n",
-    "#nanregions.append((R <= a[0]) & (Z > -d[0]))\n",
-    "#for i in range(1, len(a)):\n",
-    "#    nanregions.append((R > a[i-1]) & (R <= a[i]) & (Z > -d[i]))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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CAACokWpkIF22bJlGjx6tSZMmKT09XVdffbVuvPFGZWRkuLs0AACAGqdGnrLv3LmzLr/8cs2fP9+2rFWrVurXr59mzJjhxsoAAABqnhp3U1N+fr527typCRMm2C3v1auXPv/88xK3ycvLs3t9YFFRkY4dO6YGDRrY3kACAACqNmOMTp48qYiICHl5Vf5J4jNnzti9yc6VfH195efnVyF9V4YaF0iPHDmiwsJChYaG2i0PDQ0t9r7tc2bMmKGpU6dWRnkAAKCCZWZmqnHjxpW6zzNnzqhJdB1l/1pYIf2HhYVp37591TaU1rhAes75I5vGmFJHOydOnKixY8fa5k+cOKGoqChlZmYqMDCwQusEUHG+zmzl7hIAVKLcU0WK75KtunXrVvq+8/Pzlf1roX7cEanAuq4dnc05WaTmsZnKz88nkFYXISEh8vb2LjYa+uuvvxYbNT3HarXKarUWWx4YGEggBaqxOi7+owCgenDn5XaBdb1cHkg9QY0LpL6+vurUqZPWrVunW2+91bZ83bp1uuWWW9xYGQDUDHW9zrq7hEpxsqjG/Yl1uwv+bHkVVU4hcFqN/G0ZO3asEhMTFRsbq7i4OL388svKyMjQ0KFD3V0agEpUU4IR3KMq/HxV1VBcFb43qFqq5k9qBRswYICOHj2qadOmKSsrS23atNEHH3yg6Ohod5cGAIDLEPxQXdTIQCpJw4YN07Bhw9xdBgAAQI3HVbUAAABwKwIpAAAA3KrGnrIHAACobLkmX17GteOBuab6Pz2AEVIAAAC4FYEUAAAAbsUpewA1VpCXd6nrThRVzPumAQDFEUgBoARlhVWJwAoArkQgBYCLcKHAKhFaAcBRBFIAqCBcEgAAjiGQAoAbEFYB4P8jkAJAFUNYBVDTEEhR493YeKS7S6ga8vLdXUGJin7/3WV9mbw8u/n3M3zt5k+Zqvk9+LPzwyoBFYAnIJAC+IP1T+GsCoVTL39/29flDacWq9X29fnhtLo6F1AJpkD1cKKoUEVFxqV9niyq/m9qIpACKM5qP3JYVQJqRYVTT/DnkVPCKYDqhkAK4MKq4OipK8OppyGcAqhuCKQAnEM4rVYIpwCqAwIpgItXxcOpRED9M8IpgKrKy90FAPAQVl/7qYrw8ve3m8pSx1J16q5oQV7etglAzTNv3jw1adJEfn5+6tSpkzZt2uTWegikACpGNQio+APBFKhZli1bptGjR2vSpElKT0/X1VdfrRtvvFEZGRluq4lACvj6ln/ChVXRgIr/j1FToGZ49tlndf/99+uBBx5Qq1at9PzzzysyMlLz5893W01cQwq4woVCaX7VuL6ySqmij5bCH7jeFKh+cnJy7OatVqus5z3iLj8/Xzt37tSECRPslvfq1Uuff/55hddYGgIpUBmcHUWtiQH2QqOmBFa34eH7gOucKqolFbn2BPWp/z0YPzIy0m75lClTlJycbLfsyJEjKiwsVGhoqN3y0NBQZWdnu7QuZxBIgarI0QBbk4KrI6f5yxFaq8NrQ90tyMubUApUYZmZmQoMDLTNnz86+mcWi8Vu3hhTbFllIpAC1ZkjwZXQWtyfgitB1DmEUqDqCgwMtAukJQkJCZG3t3ex0dBff/212KhpZeKmJsDTcVNWcf+7sYowenG46Qmovnx9fdWpUyetW7fObvm6devUtWtXN1XFCCkAyblQWpNGXAHAA40dO1aJiYmKjY1VXFycXn75ZWVkZGjo0KFuq4lACsA5hFeIU/dAdTZgwAAdPXpU06ZNU1ZWltq0aaMPPvhA0dHRbquJQApYfUpfl1dQeXV4Ip4uAABV0rBhwzRs2DB3l2FDIAXKUlZYPR/htfwu9npWgiwAVGsEUsBVnAmvEgHWlWrijVkA4EEIpLhoNzYd7+4SXMPZIOnu/RJkAaDaOllkVZGLH4yf+78H41dnBFKguilPgCbMAgCqIAIpUJO4YjSYUAvxGlEArkUgBeAcQm2NRxgF4GoEUgCVz9XX7RJwKw1hFEBFIJACqP7cdWNaDUMYBVBRCKSo8Yxv+X8NLPlnXVAJAAA1E4EUcAFXhNrzEXJRlTA6CqAiEUhx8arjadJqdK1hRYRciaAL5xFGAVQ0AilqluoYol2sooLu+Qi+noEwCqAyEEgBVIjKCr7nIwi7DmEUcL1Txqoi4+3SPk+b6v+7SiAF4FHcFYQ9CUEUQGXjX24AgCSCKAD3IZCixis677pSr2p04xPgCgRRAO5GIAXOc35ArUoIy3AlgiiAqoJAiotWHa7V87QbXKpiWCYkVz8EUQBVTdVPFEA5VIfQXN1VxZB8ITUxRBNCAVRl/LUGUONUxxB9sQiiAKoDAikAeBhCKIDqhkAKAB6AEApUDycL/VVY6OIH4xdW/99/AilqPONb9j8Mlvzq/4sOz0QIBeApCKTABVwosNYUBPOqgRAKwBMRSHHRatKNISCYuxMhFICnI5ACQBVDAAVQ0xBIAcDNCKAAajoCKQBUMgIoANgjkAJABSOAAkDZCKSo8Yp8vdxdQpXjlV/k7hKqLcInADiPQIqLxl3XnouQ7hjCJwC4BoEUABxA+ATgCieK/JVf6Nr49XvRWZf25w4EUgA4D+ETACoXgRRAjUcABQD3IpACqLEIogBQNRBIUeMVcgPPBXlz1z0AoAIRSHHRuBO75iC0A0DNFBMTowMHDtgte/TRRzVz5kyX7odACgAAgFJNmzZNQ4YMsc3XqVPH5fsgkAIAAKBUdevWVVhYWIXug/NwAAAAHiAnJ8duysvLc0m/Tz/9tBo0aKAOHTpo+vTpys/Pd0m/f8YIKQAAQCU5VeSns0WujV9n/vdg/MjISLvlU6ZMUXJycrn6HjVqlC6//HIFBwdr+/btmjhxovbt26dXXnmlXP2ej0CKGq/IyomCqsYrj7v6AcBZmZmZCgwMtM1brdYS2yUnJ2vq1Kll9pWWlqbY2FiNGTPGtqxdu3YKDg7W7bffbhs1dRUCKS4ad16jovA/CQDgvMDAQLtAWpoRI0Zo4MCBZbaJiYkpcXmXLl0kST/++COBFAAAABcnJCREISEhF7Vtenq6JCk8PNyVJRFIAQAAUNyWLVu0detW9ezZU0FBQUpLS9OYMWN08803KyoqyqX7IpACAACgGKvVqmXLlmnq1KnKy8tTdHS0hgwZokceecTl+yKQAgAAoJjLL79cW7durZR9EUhR4xX6WNxdAgAANRqBFBeNO6EBAIArkCgAAADgVoyQAgAAVJLcQqvOFvq4tM+8wgKX9ucOjJACAADArQikAAAAcCuPOWW/f/9+PfHEE9qwYYOys7MVERGhu+++W5MmTZKvr6+tXUZGhoYPH64NGzbI399fgwYN0uzZs+3aoOYp8uVOewAA3MVjAun333+voqIivfTSS2revLm++eYbDRkyRLm5uZo9e7YkqbCwUL1791bDhg21efNmHT16VIMHD5YxRnPnznXzEVQ/nvK4JMIoAADu5TGBNCEhQQkJCbb5pk2bas+ePZo/f74tkKakpGj37t3KzMxURESEJGnOnDlKSkrS9OnTFRgY6JbaAQAAajKPvob0xIkTql+/vm1+y5YtatOmjS2MSlJ8fLzy8vK0c+fOUvvJy8tTTk6O3QQAAADX8NhAunfvXs2dO1dDhw61LcvOzlZoaKhdu+DgYPn6+io7O7vUvmbMmKGgoCDbFBkZWWF1AwAA1DRVPpAmJyfLYrGUOe3YscNum4MHDyohIUH9+/fXAw88YLfOYil+vaAxpsTl50ycOFEnTpywTZmZma45OAAAAFT9a0hHjBihgQMHltkmJibG9vXBgwfVs2dPxcXF6eWXX7ZrFxYWpm3bttktO378uAoKCoqNnP6Z1WqV1Wp1vnhUW55ywxYAoGo5VeinApc/GN/bpf25Q5UPpCEhIQoJCXGo7S+//KKePXuqU6dOWrRokby87AeA4+LiNH36dGVlZSk8PFzSHzc6Wa1WderUyeW1ezpPuTud8AkAgHtV+UDqqIMHD6pHjx6KiorS7NmzdfjwYdu6sLAwSVKvXr3UunVrJSYmatasWTp27JjGjx+vIUOGcIc9AACAm3hMIE1JSdGPP/6oH3/8UY0bN7ZbZ4yRJHl7e2vNmjUaNmyYunXrZvdgfAAAALiHxwTSpKQkJSUlXbBdVFSUVq9eXfEFAQAAwCFV/i57AAAAeDaPGSEFqroi195UCQCAxyCQ4qJ5yt3pBEUAANyLU/YAAABwK0ZIAQAAKsnJAqvyCnxd2md+QfUfX6z+RwAAAIBqjUAKAAAAt+KUPVDNFXrIK1wBADUXgRQXzVPuTifQAQDgXpyyBwAAgFsRSAEAAOBWBFIAAAC4FYEUAAAAbsVNTQCK8ZQb1gAA1QOBFBfNU+5OJ3wBACrL6SJfFRS69k1NBUUu7c4tOGUPAAAAtyKQAgAAwK0IpAAAAHArAikAAADcikAKoEooqlX5EwCgbNOnT1fXrl1Vu3Zt1atXr8Q2GRkZ6tu3rwICAhQSEqKRI0cqPz/fqf3wTzIumqfcnU4wAQCgZPn5+erfv7/i4uL06quvFltfWFio3r17q2HDhtq8ebOOHj2qwYMHyxijuXPnOrwf/hQDAACgRFOnTpUkLV68uMT1KSkp2r17tzIzMxURESFJmjNnjpKSkjR9+nQFBgY6tB9O2QMAAHiAnJwcuykvL6/C97llyxa1adPGFkYlKT4+Xnl5edq5c6fD/TBCCgAAUElOn/WVz1kXPxj/7B//jYyMtFs+ZcoUJScnu3Rf58vOzlZoaKjdsuDgYPn6+io7O9vhfhghBQAA8ACZmZk6ceKEbZo4cWKJ7ZKTk2WxWMqcduzY4fB+LZbib240xpS4vDSMkAIAAHiAwMBAh67ZHDFihAYOHFhmm5iYGIf2GRYWpm3bttktO378uAoKCoqNnJaFQIqL5kl3p3vKEwMAALiQkJAQhYSEuKSvuLg4TZ8+XVlZWQoPD5f0x41OVqtVnTp1crgfD4oUwMUhjAIAULKMjAwdO3ZMGRkZKiws1K5duyRJzZs3V506ddSrVy+1bt1aiYmJmjVrlo4dO6bx48dryJAhDt9hLxFIAQAAUIrJkydryZIltvmOHTtKklJTU9WjRw95e3trzZo1GjZsmLp16yZ/f38NGjRIs2fPdmo/BFIAAACUaPHixaU+g/ScqKgorV69ulz74S57AAAAuBWBFAAAAG7FKXtcNG4GqpqKXPu8ZQAAKhyBFDUeAQ4AUFl+P1tLBWddO6Jz9myRS/tzB07ZAwAAwK0IpAAAAHArAiku2tkAd1cAAAA8AYEUAAAAbsVNTbho+U3OqNY3ftwUhErBUx0AwHMRSFEunhBGCToAALgXp+xx0To0+dndJQAAAA9AIMVFGxi63d0lAAAAD8Ap+3LY98tfVDen5mb6K/zcXQEAANXL72d9VIsH4xdTc9MUAAAAqgQCKQAAANyKU/YoF+5Qr96K+BcAAFAF8OcINR6hDAAA9+KUPQAAANyKQAoAAAC3IpACAADArQikAAAAcCtu50C5cEMQqqsiH+PuEgAA/0OcACoRIQgAarYzBT7yznftMxMLC6r/m5oIpKjxCIkAALgX15ACAADArQikAAAAcCsCKQAAANyKQAoAAAC34qYmlAs3BKEqML78HAJAdUYgBUpAwAEAoPIQSFHjET4BAHAvAikAAEAlyTtbS95nXRu/Cs8WurQ/d+CmJgAAALgVgRQAAABuxSl7eASuAwUAoPoikKJcCIJwp6Ja/PwBgCcgkAJlIPAAAFDxuIYUNV5RLVPqBABATTZ9+nR17dpVtWvXVr169UpsY7FYik0LFixwaj+MkAIAAKBE+fn56t+/v+Li4vTqq6+W2m7RokVKSEiwzQcFBTm1HwIpAACAB8jJybGbt1qtslqt5epz6tSpkqTFixeX2a5evXoKCwu76P1wyh4AAKCS5Bd4K7+glosnb0lSZGSkgoKCbNOMGTMq7bhGjBihkJAQXXHFFVqwYIGKioqc2p4RUlQYrsEEAKDyZGZmKjAw0DZf3tFRRz3xxBO67rrr5O/vr/Xr12vcuHE6cuSIHnvsMYf7cCqQnjhxQitXrtSmTZu0f/9+nT59Wg0bNlTHjh0VHx+vrl27On0QqN4InajSfPj5BFBzBAYG2gXS0iQnJ9tOxZcmLS1NsbGxDu33z8GzQ4cOkqRp06a5PpBmZWVp8uTJeuONNxQWFqYrr7xSHTp0kL+/v44dO6bU1FTNnj1b0dHRmjJligYMGOBwAUCNQkACALjZiBEjNHDgwDLbxMTEXHT/Xbp0UU5Ojg4dOqTQ0FCHtnEokLZv31733HOPtm/frjZt2pTY5vfff9e7776rZ599VpmZmRo/frzjlQPuREgEANQgISEhCgkJqbD+09PT5efnV+pjokriUCD99ttv1bBhwzLb+Pv7684779Sdd96pw4cPO1wAAAAAqqaMjAwdO3ZMGRkZKiws1K5duyRJzZs3V506dfT+++8rOztbcXFx8vf3V2pqqiZNmqQHH3zQqWtYHQqkFwqj5W0PAACAqmfy5MlasmSJbb5jx46SpNTUVPXo0UM+Pj6aN2+exo4dq6KiIjVt2lTTpk3T8OHDndqPw499atasmZ577rlS1x86dEje3t5O7RwAAABV1+LFi2WMKTb16NFDkpSQkKD09HSdPHlSubm5+vrrrzVq1CjVquXcg5wcbr1v3z49+uij2rVrlxYuXChfX99ibYzhWjw4gGs2AQDAnzj1YPyVK1cqNTVV11xzjbKysoqtt1gsLisM1YSPcX4CAAD4E6fGU6+44gqlpaXptttuU6dOnbRy5Up17ty5omoD4Eo+zr01AwDgeoVnvWTOuvZFmUUu7s8dnD6C0NBQbdy4UX379lWPHj20aNGiiqgLqDw+RTVjAgCgirqoSF2rVi299NJLeu655zR06FCNGjVKZ8+edXVtFy0vL08dOnSQxWKxPZ7gnIyMDPXt21cBAQEKCQnRyJEjlZ+f755CAQAA4Pgp+5KuDx06dKjatGmj22+/XZ999plLCyuPRx55RBEREfryyy/tlhcWFqp3795q2LChNm/erKNHj2rw4MEyxmju3LluqhYAAKBmc3iEtLQ76K+66iqlpaW5rKDy+vDDD5WSkqLZs2cXW5eSkqLdu3fr9ddfV8eOHXX99ddrzpw5WrhwoXJyctxQLQAAABwOpPv27Sv1gfeRkZH67LPPlJqa6rLCLsahQ4c0ZMgQ/etf/1Lt2rWLrd+yZYvatGmjiIgI27L4+Hjl5eVp586dpfabl5ennJwcuwkAAACu4fAp++jo6DLXW61WXXPNNeUu6GIZY5SUlKShQ4cqNjZW+/fvL9YmOztboaGhdsuCg4Pl6+ur7OzsUvueMWOGpk6d6uqSPQM3ywAAgHJyaIQ0ISFBn3/++QXbnTx5Uk8//bT++c9/lruwc5KTk2WxWMqcduzYoblz5yonJ0cTJ04ss7+SroU1xpT5DNWJEyfqxIkTtikzM7PcxwWgOEutokqdAABVg0MjpP3799cdd9yhunXr6uabb1ZsbKwiIiLk5+en48ePa/fu3dq8ebM++OAD9enTR7NmzXJZgSNGjNDAgQPLbBMTE6Mnn3xSW7duldVqtVsXGxuru+66S0uWLFFYWJi2bdtmt/748eMqKCgoNnL6Z1artVi/8CyEEwAA3MehQHr//fcrMTFR77zzjpYtW6aFCxfqt99+k/THiGPr1q0VHx+vnTt3qkWLFi4tMCQkRCEhIRds9+KLL+rJJ5+0zR88eFDx8fFatmyZ7eH9cXFxmj59urKyshQeHi7pjxudrFarOnXq5NK6UX0QRgEAlaWwwFsm39ulfRYVuLY/d3D4GlJfX18NGjRIgwYNkiSdOHFCv//+uxo0aCAfH58KK9BRUVFRdvN16tSRJDVr1kyNGzeWJPXq1UutW7dWYmKiZs2apWPHjmn8+PEaMmSIAgMDK71mAAAAXOSD8SUpKChIYWFhVSKMOsrb21tr1qyRn5+funXrpjvuuEP9+vUr8RFRAAAAqBxOvcu+OomJiSnx2alRUVFavXq1GyoCAABASTw2kKJycP0lAAAor4s+ZQ8AAAC4AiOkABzm7Vvo7hIAAB7oogLpb7/9pnfeeUd79+7V3/72N9WvX19ffPGFQkNDdckll7i6RqBCEbIAAHAvpwPpV199peuvv15BQUHav3+/hgwZovr162vlypU6cOCAli5dWhF1AgAAwEM5fQ3p2LFjlZSUpP/+97/y8/OzLb/xxhv16aefurQ4AAAAeD6nR0jT0tL00ksvFVt+ySWXKDs72yVFAQAAeCJz1kvmrGvvKXd1f+7gdCD18/NTTk5OseV79uxRw4YNXVIUUFG4XhQAgKrH6UB6yy23aNq0aXrrrbck/fEu+4yMDE2YMEG33XabywtE1UbAAwAA5eX0GO/s2bN1+PBhNWrUSL///ru6d++u5s2bq27dupo+fXpF1AgAAAAP5vQIaWBgoDZv3qwNGzboiy++UFFRkS6//HJdf/31FVEfgEpUizdvAQDc4KIfjH/ttdfq2muvdWUtgFsQwgAAcC+nT9mPHDlSL774YrHl//jHPzR69GhX1AQAAIAaxOlAunz5cnXr1q3Y8q5du+qdd95xSVEAAACoOZwOpEePHlVQUFCx5YGBgTpy5IhLigIAAEDN4fQ1pM2bN9fatWs1YsQIu+UffvihmjZt6rLC4Hm4VhMAUOMVeEm1XPwg+4Ia+GD8sWPHasSIETp8+LDtpqb169drzpw5ev75511dH6o4QiYAACgvpwPpfffdp7y8PE2fPl1PPPGEJCkmJkbz58/XPffc4/ICAQAA4Nku6rFPDz/8sB5++GEdPnxY/v7+qlOnjqvrAlAN+PqcdXcJAAAPcNHPIZXEu+vhEQhVAAC4l9NXwR46dEiJiYmKiIhQrVq15O3tbTcBAAAAznB6hDQpKUkZGRl6/PHHFR4eLovFUhF1AQAAwI3279+vJ554Qhs2bFB2drYiIiJ09913a9KkSfL19bW1y8jI0PDhw7Vhwwb5+/tr0KBBmj17tl2bC3E6kG7evFmbNm1Shw4dnN0UAAAA1cT333+voqIivfTSS2revLm++eYbDRkyRLm5uZo9e7YkqbCwUL1791bDhg21efNmHT16VIMHD5YxRnPnznV4X04H0sjISBljnN0MKBPXcQIAULUkJCQoISHBNt+0aVPt2bNH8+fPtwXSlJQU7d69W5mZmYqIiJAkzZkzR0lJSZo+fboCAwMd2pfT15A+//zzmjBhgvbv3+/spvBAvj5nXTIBAIDyycnJsZvy8vJcvo8TJ06ofv36tvktW7aoTZs2tjAqSfHx8crLy9POnTsd7tfpEdIBAwbo9OnTatasmWrXri0fHx+79ceOHXO2SwAAgJqhwCLVcvH9NwV/9BcZGWm3eMqUKUpOTnbZbvbu3au5c+dqzpw5tmXZ2dkKDQ21axccHCxfX19lZ2c73LfTgZS3MQFwB2stRtIBoCyZmZl2p8itVmuJ7ZKTkzV16tQy+0pLS1NsbKxt/uDBg0pISFD//v31wAMP2LUt6QZ3Y4xTN747HUgHDx7s7CYAzkO4AgC4WmBgoEPXbI4YMUIDBw4ss01MTIzt64MHD6pnz56Ki4vTyy+/bNcuLCxM27Zts1t2/PhxFRQUFBs5LUu5Hoz/+++/q6CgwG6ZoxevAlUF4RAAUJOEhIQoJCTEoba//PKLevbsqU6dOmnRokXy8rK//SguLk7Tp09XVlaWwsPDJf1xo5PValWnTp0crsnpm5pyc3M1YsQINWrUSHXq1FFwcLDdBAAAgOrv4MGD6tGjhyIjIzV79mwdPnxY2dnZdteG9urVS61bt1ZiYqLS09O1fv16jR8/XkOGDHFqkNLpQPrII49ow4YNmjdvnqxWq1555RVNnTpVERERWrp0qbPdAQAAoApKSUnRjz/+qA0bNqhx48YKDw+3Ted4e3trzZo18vPzU7du3XTHHXeoX79+tsdCOcrpU/bvv/++li5dqh49eui+++7T1VdfrebNmys6OlpvvPGG7rrrLme7BAAAQBWTlJSkpKSkC7aLiorS6tWry7UvpwPpsWPH1KRJE0l/XC967jFPV111lR5++OFyFYPqh+svAQBAeTl9yr5p06a2h+K3bt1ab731lqQ/Rk7r1avnytoAAABQAzg9Qnrvvffqyy+/VPfu3TVx4kT17t1bc+fO1dmzZ/Xss89WRI0AAAAewavQIq+zLn4wfqGL+3MDpwPpmDFjbF/37NlT33//vXbs2KFmzZqpffv2Li0OQPXk71tw4UYAAPyP04F06dKlGjBggO3p/1FRUYqKilJ+fr6WLl2qe+65x+VFAhWNAAUAgPs4fQ3pvffeqxMnThRbfvLkSd17770uKQqoTIRRAADcy+lAWtq7SX/++WcFBQW5pCgAAADUHA6fsu/YsaMsFossFouuu+461ar1/zctLCzUvn37lJCQUCFFAgAAwHM5HEj79esnSdq1a5fi4+NVp04d2zpfX1/FxMTotttuc3mBqN44HQ4AAC7E4UA6ZcoUSVJMTIwGDhxou6kJNRuBEwAAlJfT15Bee+21Onz4sG1++/btGj16tF5++WWXFgYAAICawelAOmjQIKWmpkqSsrOzdf3112v79u36+9//rmnTprm8QAAAAE9hybdUyFTdOf0c0m+++UZXXnmlJOmtt95S27Zt9dlnnyklJUVDhw7V5MmTXV4kgOrJvxaXdAAALszpQFpQUGC7fvTjjz/WzTffLElq2bKlsrKyXFsdUAUQqgAAqFhOn7K/7LLLtGDBAm3atEnr1q2zPerp4MGDatCggcsLBCqaf62CMicAAFCxnA6kTz/9tF566SX16NFDd955p+399atWrbKdygcAAAAc5fQp+x49eujIkSPKyclRcHCwbfmDDz6o2rVru7Q4AAAAeD6nA6kkeXt724VR6Y/nk6Lm4ZQ2AAAoL4cC6eWXX67169crODjY9grR0nzxxRcuKw4AAACez6FAesstt9jurD/3ClEAAADAFRwKpOdeG3r+1wAAAEB5OX0NqTFGO3fu1P79+2WxWNSkSZMLnsYHAFeqzbXLAKoprwKLvLxdnJkKqn8GcyqQpqam6v7779eBAwdkjJEkWyh97bXXdM0111RIkQCKI5QBADyFw4H0xx9/VJ8+fdS5c2c999xzatmypYwx2r17t1588UXddNNN+uqrr9S0adOKrBdwOYIdAADu5XAgff7559WlSxetX7/ebnnLli1166236vrrr9dzzz2nuXPnurxIAAAAeC6H39S0ceNGjR49usR1FotFo0ePVmpqqqvqAgAAQA3h8AhpRkaG2rZtW+r6Nm3a6MCBAy4pCnAVTscDAFD1ORxIT506VearQWvXrq3Tp0+7pChUHwQ+AABQXk7dZb97925lZ2eXuO7IkSMuKQgAAAA1i1OB9LrrrrM97unPLBaLjDE8ixQAAABOcziQ7tu3ryLrAAAA8HiWs5LXWdf2aVzcnzs4HEijo6Mrsg4AUO1a+e4uAQDgBk6/OhRAxSGQAQBqIgIpajxCIAAA7uXwg/EBAACAikAgBQAAgFtxyh4uxylwAACqv/379+uJJ57Qhg0blJ2drYiICN19992aNGmSfH19be1Keuzn/PnzNXToUIf35VAg7dixo8PPGP3iiy8c3jmqP8InAACe6fvvv1dRUZFeeuklNW/eXN98842GDBmi3NxczZ49267tokWLlJCQYJsPCgpyal8OBdJ+/fo51SkAAACqt4SEBLuQ2bRpU+3Zs0fz588vFkjr1aunsLCwi96XQ4F0ypQpF70DAAAAVLycnBy7eavVKqvV6tJ9nDhxQvXr1y+2fMSIEXrggQfUpEkT3X///XrwwQfl5eX4rUpcQwoAAFBJvM5KXgWu7fPcm5oiIyPtlk+ZMkXJycku28/evXs1d+5czZkzx275E088oeuuu07+/v5av369xo0bpyNHjuixxx5zuG+nA2lhYaGee+45vfXWW8rIyFB+vv01hMeOHXO2SwAotwBvrmcGULNlZmYqMDDQNl/a6GhycrKmTp1aZl9paWmKjY21zR88eFAJCQnq37+/HnjgAbu2fw6eHTp0kCRNmzatYgPp1KlT9corr2js2LF6/PHHNWnSJO3fv1/vvvuuJk+e7Gx3ACoRoQ0APFdgYKBdIC3NiBEjNHDgwDLbxMTE2L4+ePCgevbsqbi4OL388ssX7L9Lly7KycnRoUOHFBoaesH20kUE0jfeeEMLFy5U7969NXXqVN15551q1qyZ2rVrp61bt2rkyJHOdgm4FSENAFCThISEKCQkxKG2v/zyi3r27KlOnTpp0aJFDl0Xmp6eLj8/P9WrV8/hmpwOpNnZ2Wrbtq0kqU6dOjpx4oQkqU+fPnr88ced7Q4AAABV0MGDB9WjRw9FRUVp9uzZOnz4sG3duTvq33//fWVnZysuLk7+/v5KTU3VpEmT9OCDDzp1Q5XTgbRx48bKyspSVFSUmjdvrpSUFF1++eVKS0tz+Z1cAAAAcI+UlBT9+OOP+vHHH9W4cWO7dcYYSZKPj4/mzZunsWPHqqioSE2bNtW0adM0fPhwp/bldCC99dZbtX79enXu3FmjRo3SnXfeqVdffVUZGRkaM2aMs90B5cLpdgAAKkZSUpKSkpLKbHP+s0ovltOBdObMmbavb7/9djVu3Fiff/65mjdvrptvvrncBaF6IRACAIDyKvdzSLt06aIuXbq4ohYAAADUQA4F0lWrVunGG2+Uj4+PVq1aVWZbd4+SrlmzRtOmTdNXX32lgIAAXXPNNVqxYoVtfUZGhoYPH64NGzbI399fgwYN0uzZs+Xr6+vGqgEAQE3glS95WVzbp/GAk5UOv8s+OztbjRo1KvO99haLRYWFha6qzWnLly/XkCFD9NRTT+naa6+VMUZff/21bX1hYaF69+6thg0bavPmzTp69KgGDx4sY4zmzp3rtroBAABqMocCaVFRUYlfVyVnz57VqFGjNGvWLN1///225S1atLB9nZKSot27dyszM1MRERGSpDlz5igpKUnTp0936GGyAAAAcC2Xvsv+9OnTql27tiu7dNgXX3yhX375RV5eXurYsaOys7PVoUMHzZ49W5dddpkkacuWLWrTpo0tjEpSfHy88vLytHPnTvXs2bPEvvPy8pSXl2ebz8nJqdiDAVCqQJ8z7i4BAOBiTgfSHj166PXXXy/2PKpt27YpMTFRP/zwg8uKc8ZPP/0k6Y/3sz777LOKiYnRnDlz1L17d/3www+qX7++srOzi73CKjg4WL6+vsrOzi617xkzZlzwna9AVUNwAwBUF04H0sDAQLVr107z5s3TwIEDVVRUpGnTpmnGjBn661//6vICk5OTLxgG09LSbJcSTJo0SbfddpskadGiRWrcuLHefvttPfTQQ5L+uM71fMaYEpefM3HiRI0dO9Y2n5OTo8jISKePBVUTwQ0AAPdyOpCuWrVKCxYs0AMPPKBVq1Zp//79ysjI0Jo1a3T99de7vMARI0Zo4MCBZbaJiYnRyZMnJUmtW7e2LbdarWratKkyMjIk/fGaq23bttlte/z4cRUUFBQbOf0zq9XKW6gAAAAqyEVdQzp06FAdOHBATz/9tGrVqqWNGzeqa9eurq5NkhQSEqKQkJALtuvUqZOsVqv27Nmjq666SpJUUFCg/fv3Kzo6WpIUFxen6dOnKysrS+Hh4ZL+uNHJarWqU6dOFVI/AAAAyuZ0ID1+/LgeeOABrV+/Xi+99JI++eQT9erVS88884yGDRtWETU6JDAwUEOHDtWUKVMUGRmp6OhozZo1S5LUv39/SVKvXr3UunVrJSYmatasWTp27JjGjx+vIUOGcIe9i3D6GwAAOMvpQNqmTRs1adJE6enpatKkiYYMGaJly5Zp2LBhWrNmjdasWVMRdTpk1qxZqlWrlhITE/X777+rc+fO2rBhg4KDgyVJ3t7eWrNmjYYNG6Zu3brZPRgfF4cACgAAysvpQDp06FBNmjRJXl5etmUDBgxQt27ddO+997q0OGf5+Pho9uzZZQbMqKgorV69uhKrAgAA+INXgfSnCOUSpsC1/bmD04H08ccfL3F548aN9cwzz5S7IAAAANQs5c7oJ06c0Lx583T55ZcrNjbWFTUBAACgBrnoQLphwwbdfffdCg8P19y5c3XTTTdpx44drqwNAAAANYBTp+x//vlnLV68WK+99ppyc3N1xx13qKCgQMuXL7d7/icAAADgKIcD6U033aTNmzerT58+mjt3rhISEuTt7a0FCxZUZH0AqqE63jx9AQDgOIcDaUpKikaOHKmHH35Yf/nLXyqyJsBjEdQAACjO4UC6adMmvfbaa4qNjVXLli2VmJioAQMGVGRtQKUgJAIA4F4O39QUFxenhQsXKisrSw899JD+85//6JJLLlFRUZHWrVtne5c8AAAA4Ayn77KvXbu27rvvPm3evFlff/21xo0bp5kzZ6pRo0a6+eabK6JGAAAAj+B1tmKm6s7pB+P/WYsWLfTMM89oxowZev/99/Xaa6+5qi5UE5zuBgAA5eWSl1d5e3urX79+WrVqlSu6AwAAQA3i4repAgAAAM4hkAIAAMCtCKQAAABwKwIpAAAA3IpACgAAALcq12OfAMDVeJQYANQ8BFLAwxDoAKDq8jorebn4/LSp6Q/GBzwBAQ4AAPfiGlIAAAC4FYEUAAAAbsUpe5QLp7sBAEB5MUIKAAAAtyKQAgAAoEQ333yzoqKi5Ofnp/DwcCUmJurgwYN2bTIyMtS3b18FBAQoJCREI0eOVH5+vlP7IZACAACgRD179tRbb72lPXv2aPny5dq7d69uv/122/rCwkL17t1bubm52rx5s/7zn/9o+fLlGjdunFP74RpSAAAAlGjMmDG2r6OjozVhwgT169dPBQUF8vHxUUpKinbv3q3MzExFRERIkubMmaOkpCRNnz5dgYGBDu2HEVIAAAAPkJOTYzfl5eW5tP9jx47pjTfeUNeuXeXj4yNJ2rJli9q0aWMLo5IUHx+vvLw87dy50+G+CaQAAACVxDvfVMgkSZGRkQoKCrJNM2bMcEnNjz76qAICAtSgQQNlZGTovffes63Lzs5WaGioXfvg4GD5+voqOzvb4X0QSAEAADxAZmamTpw4YZsmTpxYYrvk5GRZLJYypx07dtja/+1vf1N6erpSUlLk7e2te+65R8YY23qLxVJsH8aYEpeXhmtIAXiUul48GxdAzRQYGOjQNZsjRozQwIEDy2wTExNj+zokJEQhISG69NJL1apVK0VGRmrr1q2Ki4tTWFiYtm3bZrft8ePHVVBQUGzktCwEUgBOI/QBQPV1LmBejHMjo+euT42Li9P06dOVlZWl8PBwSVJKSoqsVqs6derkcL8EUqASEeQAANXF9u3btX37dl111VUKDg7WTz/9pMmTJ6tZs2aKi4uTJPXq1UutW7dWYmKiZs2apWPHjmn8+PEaMmSIw3fYSwRSgJAIAEAJ/P39tWLFCk2ZMkW5ubkKDw9XQkKC/vOf/8hqtUqSvL29tWbNGg0bNkzdunWTv7+/Bg0apNmzZzu1LwIpAAAAimnbtq02bNhwwXZRUVFavXp1ufZFIEWVxuglAACej0CKciEwAgCA8iKQAgAAVBKvAsnL8cdzOsQUuLY/d+DB+AAAAHArAikAAADcikAKAAAAtyKQAgAAwK0IpAAAAHArAikAAADcikAKAAAAt+I5pACqtHrep91dAgCgghFIARD6AABuRSAFXIxwBwAojddZI2+LcWmf5qxr+3MHAilqPAIkAADuxU1NAAAAcCtGSFEhGHUEAACOIpCiXAieAACgvDhlDwAAALcikAIAAMCtCKQAAABwKwIpAAAA3IqbmgAAACqJV76Rl3Htg+y9Cqr/g/EZIQUAAIBbEUgBAADgVgRSAAAAuBWBFAAAAG7FTU0AaoS63r+7uwQAQCkIpAAqDaEQAFASAingwQiAAIDqgEAKOIGABwCA6xFIUeMRMgEAcC8CKQAAQCXxLjDylmvfrGQ84E1NBFKUC6OLAACgvHgOKQAAANyKQAoAAAC3IpACAADArQikAAAAKNHNN9+sqKgo+fn5KTw8XImJiTp48KBdG4vFUmxasGCBU/shkAIAAKBEPXv21FtvvaU9e/Zo+fLl2rt3r26//fZi7RYtWqSsrCzbNHjwYKf2w132AAAAKNGYMWNsX0dHR2vChAnq16+fCgoK5OPjY1tXr149hYWFXfR+GCEFAADwADk5OXZTXl6eS/s/duyY3njjDXXt2tUujErSiBEjFBISoiuuuEILFixQUVGRU30TSAEAACqJV36RvPJcPOX/Ef4iIyMVFBRkm2bMmOGSmh999FEFBASoQYMGysjI0HvvvWe3/oknntDbb7+tjz/+WAMHDtS4ceP01FNPOfd9cUmlAAAAcKvMzEydOHHCNk2cOLHEdsnJySXeiPTnaceOHbb2f/vb35Senq6UlBR5e3vrnnvukTH//+1Qjz32mOLi4tShQweNGzdO06ZN06xZs5yqnWtIAQAAPEBgYKACAwMv2G7EiBEaOHBgmW1iYmJsX4eEhCgkJESXXnqpWrVqpcjISG3dulVxcXElbtulSxfl5OTo0KFDCg0Ndah2AikAAEANci5gXoxzI6NlXZ+anp4uPz8/1atXz+F+CaQAAAAoZvv27dq+fbuuuuoqBQcH66efftLkyZPVrFkz2+jo+++/r+zsbMXFxcnf31+pqamaNGmSHnzwQVmtVof3RSAFAABAMf7+/lqxYoWmTJmi3NxchYeHKyEhQf/5z39sYdPHx0fz5s3T2LFjVVRUpKZNm2ratGkaPny4U/sikAKokupazri7BACo0dq2basNGzaU2SYhIUEJCQnl3pdH3WX/ww8/6JZbblFISIgCAwPVrVs3paam2rXJyMhQ3759FRAQoJCQEI0cOVL5+fluqhioPupazlTqBACoOTxqhLR379669NJLtWHDBvn7++v5559Xnz59tHfvXoWFhamwsFC9e/dWw4YNtXnzZh09elSDBw+WMUZz5851d/lAmQhpAABP5TGB9MiRI/rxxx/12muvqV27dpKkmTNnat68efr2228VFhamlJQU7d69W5mZmYqIiJAkzZkzR0lJSZo+fbpDj0qA5yP4AQAqind+kbydfIvRhZizru3PHTzmlH2DBg3UqlUrLV26VLm5uTp79qxeeuklhYaGqlOnTpKkLVu2qE2bNrYwKknx8fHKy8vTzp07S+07Ly+v2Ou44Dk4VQwAgHt5zAipxWLRunXrdMstt6hu3bry8vJSaGio1q5da3sOVnZ2drEHtAYHB8vX11fZ2dml9j1jxgxNnTq1IsuHkwiOAAB4jio/Quro662MMRo2bJgaNWqkTZs2afv27brlllvUp08fZWVl2fqzWCzF9mGMKXH5ORMnTrR7FVdmZmaFHGt1VNk3ujCKCQCA56nyI6SOvt5qw4YNWr16tY4fP267FnTevHlat26dlixZogkTJigsLEzbtm2z2/b48eMqKCgo89VWVqvVqYe7AgAAwHFVPpA6+nqr06dPS5K8vOwHfb28vFT0v4uH4+LiNH36dGVlZSk8PFySlJKSIqvVarvOFAAAAJWryp+yd1RcXJyCg4M1ePBgffnll/rhhx/0t7/9Tfv27VPv3r0lSb169VLr1q2VmJio9PR0rV+/XuPHj9eQIUO4wx4AAMBNPCaQhoSEaO3atTp16pSuvfZaxcbGavPmzXrvvffUvn17SZK3t7fWrFkjPz8/devWTXfccYf69eun2bNnu7l6AACAmqvKn7J3RmxsrD766KMy20RFRWn16tWVVBEAAAAuxGNGSAEAAFA9edQIKQAAQFXmlV8kLxe/qcmLNzUBAAAA5UMgBQAAgFsRSAEAAOBWBFIAAAC4FYEUAAAAbkUgBQAAgFsRSAEAAOBWBFIAAAC4FQ/GBwAAqCSWgkJZigpd22eha/tzB0ZIAdRIgV557i4BAPA/jJACqHYIkwDgWQikACoFIRIAUBoCKYBiCI8AgMpEIAWqKUIjAMBTEEgBFyIkAgDgPAIpajxCJAAA7kUghUcgVAIAUH0RSFEuBEEAAFBeBFIAAIBK4pV3Vl7eBa7ts/CsS/tzB97UBAAAALcikAIAAMCtCKQAAABwKwIpAAAA3IpACgAAgDLl5eWpQ4cOslgs2rVrl926jIwM9e3bVwEBAQoJCdHIkSOVn5/vVP/cZQ8AAIAyPfLII4qIiNCXX35pt7ywsFC9e/dWw4YNtXnzZh09elSDBw+WMUZz5851uH9GSAEAAFCqDz/8UCkpKZo9e3axdSkpKdq9e7def/11dezYUddff73mzJmjhQsXKicnx+F9EEgBAAA8QE5Ojt2Ul1f+l9ccOnRIQ4YM0b/+9S/Vrl272PotW7aoTZs2ioiIsC2Lj49XXl6edu7c6fB+CKQAAACVxJJ/tkImSYqMjFRQUJBtmjFjRrlqNcYoKSlJQ4cOVWxsbIltsrOzFRoaarcsODhYvr6+ys7OdnhfXEMKAADgATIzMxUYGGibt1qtJbZLTk7W1KlTy+wrLS1Nn3/+uXJycjRx4sQy21oslmLLjDElLi8NgRQAAMADBAYG2gXS0owYMUIDBw4ss01MTIyefPJJbd26tViwjY2N1V133aUlS5YoLCxM27Zts1t//PhxFRQUFBs5LQuBFAAAoAYJCQlRSEjIBdu9+OKLevLJJ23zBw8eVHx8vJYtW6bOnTtLkuLi4jR9+nRlZWUpPDxc0h83OlmtVnXq1MnhmgikAAAAKCYqKspuvk6dOpKkZs2aqXHjxpKkXr16qXXr1kpMTNSsWbN07NgxjR8/XkOGDHFotPYcbmoCAADARfH29taaNWvk5+enbt266Y477lC/fv1KfERUWRghBQAAwAXFxMTIGFNseVRUlFavXl2uvhkhBQAAgFsRSAEAAOBWBFIAAAC4FdeQAgAAVJb8AsnLxeOBRQWu7c8NGCEFAACAWxFIAQAA4FYEUgAAALgVgRQAAABuRSAFAACAWxFIAQAA4FYEUgAAALgVgRQAAABuxYPxAQAAKkt+geRlcW2fHvBgfAIpAI9X1+usu0sAAJSBQAqg2iNwAkD1RiAFUKURNgHA8xFIAbgNYRMAIBFIAVQQwiYAwFEEUgBOI2wCAFyJQArAhqAJAHAHAing4QiZAICqjkAKVGOETQCoZvLyXf+ezKJ8F3dY+QikQBVF2AQA1BQEUsBNCJwAAPyBQAq4GEETAADnEEgBBxE0AQCoGARS1HgETQAA3ItACo9F0AQAoHogkKJaIFwCAOC5CKSoNIRKAABQEgIpyoWQCQAAyotACgAAUEmKzvyuIkuha/s01f9NTa5+eRUAAADgFAIpAAAA3IpACgAAALcikAIAAMCtCKQAAAAoU15enjp06CCLxaJdu3bZrbNYLMWmBQsWONU/d9kDAACgTI888ogiIiL05Zdflrh+0aJFSkhIsM0HBQU51T+BFAAAAKX68MMPlZKSouXLl+vDDz8ssU29evUUFhZ20fvglD0AAIAHyMnJsZvy8vLK3eehQ4c0ZMgQ/etf/1Lt2rVLbTdixAiFhIToiiuu0IIFC1RUVOTUfhghBQAAqCQmL1/GYlzbpymQJEVGRtotnzJlipKTk8vRr1FSUpKGDh2q2NhY7d+/v8R2TzzxhK677jr5+/tr/fr1GjdunI4cOaLHHnvM4X0RSAEAADxAZmamAgMDbfNWq7XEdsnJyZo6dWqZfaWlpenzzz9XTk6OJk6cWGbbPwfPDh06SJKmTZtGIAUAAKhpAgMD7QJpaUaMGKGBAweW2SYmJkZPPvmktm7dWizYxsbG6q677tKSJUtK3LZLly7KycnRoUOHFBoa6lDtBFIAAIAaJCQkRCEhIRds9+KLL+rJJ5+0zR88eFDx8fFatmyZOnfuXOp26enp8vPzU7169RyuiUAKAACAYqKiouzm69SpI0lq1qyZGjduLEl6//33lZ2drbi4OPn7+ys1NVWTJk3Sgw8+WOolAyUhkAIAAOCi+Pj4aN68eRo7dqyKiorUtGlTTZs2TcOHD3eqn2rz2Kfp06era9euql27dqlDwBkZGerbt68CAgIUEhKikSNHKj8/367N119/re7du8vf31+XXHKJpk2bJmNce7cbAACAp4mJiZExxnbjkiQlJCQoPT1dJ0+eVG5urr7++muNGjVKtWo5N+ZZbUZI8/Pz1b9/f8XFxenVV18ttr6wsFC9e/dWw4YNtXnzZh09elSDBw+WMUZz586V9MfzuW644Qb17NlTaWlp+uGHH5SUlKSAgACNGzeusg8JAAAAqkaB9NzjCRYvXlzi+pSUFO3evVuZmZmKiIiQJM2ZM0dJSUmaPn26AgMD9cYbb+jMmTNavHixrFar2rRpox9++EHPPvusxo4dK4vFUlmHAwAAgP+pNoH0QrZs2aI2bdrYwqgkxcfHKy8vTzt37lTPnj21ZcsWde/e3e4i2/j4eE2cOFH79+9XkyZNSuw7Ly/P7m0HJ06ckCSdOuXcWwgAAID7nPu77c5L9c6qQHLx7s+qwLUduoHHBNLs7Oxiz7oKDg6Wr6+vsrOzbW1iYmLs2pzbJjs7u9RAOmPGjBIfIHvVlUdcUDkAAKhMR48eVVBQUKXu09fXV2FhYdqU/X6F9B8WFiZfX98K6bsyuDWQOvqmgNjYWIf6K+mUuzHGbvn5bc79X1JZp+snTpyosWPH2uaLiop07NgxNWjQwGNO8+fk5CgyMrLYWx5qAo6dY69pxy7V7OPn2GvmsUt/nOGMiopS/fr1K33ffn5+2rdvX7GbrV3F19dXfn5+FdJ3ZXBrIHX0TQGOCAsL07Zt2+yWHT9+XAUFBbZR0LCwMNto6Tm//vqrJJX5JgGr1VrsWVrOPOy1OnH0LQ+eiGPn2Guimnz8HHvNPHZJ8vJyz0OG/Pz8qnVorEhuDaSOvinAEXFxcZo+fbqysrIUHh4u6Y8bnaxWqzp16mRr8/e//135+fm2Ye2UlBRFREQ4HHwBAADgWtXmOaQZGRnatWuXMjIyVFhYqF27dmnXrl06deqUJKlXr15q3bq1EhMTlZ6ervXr12v8+PEaMmSI7f8CBw0aJKvVqqSkJH3zzTdauXKlnnrqKe6wBwAAcKNqc1PT5MmTtWTJEtt8x44dJUmpqanq0aOHvL29tWbNGg0bNkzdunWTv7+/Bg0apNmzZ9u2CQoK0rp16zR8+HDFxsYqODhYY8eOtbs+tKayWq2aMmWKU6/58hQcO8deE9Xk4+fYa+axSxx/VWYxvKYIAAAAblRtTtkDAADAMxFIAQAA4FYEUgAAALgVgRQAAABuRSCtAWbMmKErrrhCdevWVaNGjdSvXz/t2bOnzG02btwoi8VSbPr+++8rqWrXSE5OLnYMYWFhZW7zySefqFOnTvLz81PTpk21YMGCSqrWtWJiYkr8DIcPH15i++r+mX/66afq27evIiIiZLFY9O6779qtN8YoOTlZERER8vf3V48ePfTtt99esN/ly5erdevWslqtat26tVauXFlBR3Dxyjr2goICPfroo2rbtq0CAgIUERGhe+65RwcPHiyzz8WLF5f483DmzJkKPhrnXOhzT0pKKnYMXbp0uWC/1f1zl1Ti52exWDRr1qxS+6wun7sjf9c8+XfeExFIa4BPPvlEw4cP19atW7Vu3TqdPXtWvXr1Um5u7gW33bNnj7KysmzTX/7yl0qo2LUuu+wyu2P4+uuvS227b98+3XTTTbr66quVnp6uv//97xo5cqSWL19eiRW7Rlpamt1xr1u3TpLUv3//Mrerrp95bm6u2rdvr3/84x8lrn/mmWf07LPP6h//+IfS0tIUFhamG264QSdPniy1zy1btmjAgAFKTEzUl19+qcTERN1xxx3F3grnbmUd++nTp/XFF1/o8ccf1xdffKEVK1bohx9+0M0333zBfgMDA+1+FrKysqrcW2Yu9LlLUkJCgt0xfPDBB2X26Qmfu6Rin91rr70mi8Wi2267rcx+q8Pn7sjfNU/+nfdIBjXOr7/+aiSZTz75pNQ2qampRpI5fvx45RVWAaZMmWLat2/vcPtHHnnEtGzZ0m7ZQw89ZLp06eLiyirfqFGjTLNmzUxRUVGJ6z3lMzfGGElm5cqVtvmioiITFhZmZs6caVt25swZExQUZBYsWFBqP3fccYdJSEiwWxYfH28GDhzo8ppd5fxjL8n27duNJHPgwIFS2yxatMgEBQW5trgKVtKxDx482Nxyyy1O9eOpn/stt9xirr322jLbVMfP3Zjif9dq0u+8p2CEtAY6ceKEJKl+/foXbNuxY0eFh4fruuuuU2pqakWXViH++9//KiIiQk2aNNHAgQP1008/ldp2y5Yt6tWrl92y+Ph47dixQwUFBRVdaoXJz8/X66+/rvvuu++CbyXzhM/8fPv27VN2drbdZ2u1WtW9e3d9/vnnpW5X2s9DWdtUBydOnJDFYlG9evXKbHfq1ClFR0ercePG6tOnj9LT0yunQBfbuHGjGjVqpEsvvVRDhgzRr7/+WmZ7T/zcDx06pDVr1uj++++/YNvq+Lmf/3eN3/nqh0BawxhjNHbsWF111VVq06ZNqe3Cw8P18ssva/ny5VqxYoVatGih6667Tp9++mklVlt+nTt31tKlS/XRRx9p4cKFys7OVteuXXX06NES22dnZys0NNRuWWhoqM6ePasjR45URskV4t1339Vvv/2mpKSkUtt4ymdekuzsbEkq8bM9t6607Zzdpqo7c+aMJkyYoEGDBtleq1ySli1bavHixVq1apXefPNN+fn5qVu3bvrvf/9bidWW34033qg33nhDGzZs0Jw5c5SWlqZrr71WeXl5pW7jiZ/7kiVLVLduXf3f//1fme2q4+de0t81fuern2rz6lC4xogRI/TVV19p8+bNZbZr0aKFWrRoYZuPi4tTZmamZs+erWuuuaaiy3SZG2+80fZ127ZtFRcXp2bNmmnJkiWlvjL2/BFE87+XmV1oZLEqe/XVV3XjjTcqIiKi1Dae8pmXpaTP9kKf68VsU1UVFBRo4MCBKioq0rx588ps26VLF7ubf7p166bLL79cc+fO1YsvvljRpbrMgAEDbF+3adNGsbGxio6O1po1a8oMZ570uUvSa6+9prvuuuuC14JWx8+9rL9rNf13vjphhLQG+etf/6pVq1YpNTVVjRs3dnr7Ll26VOn/S3ZEQECA2rZtW+pxhIWFFfs/4V9//VW1atVSgwYNKqNElztw4IA+/vhjPfDAA05v6wmfuSTbkxVK+mzPHw05fztnt6mqCgoKdMcdd2jfvn1at25dmaOjJfHy8tIVV1xR7X8ewsPDFR0dXeZxeNLnLkmbNm3Snj17LurfgKr+uZf2d43f+eqHQFoDGGM0YsQIrVixQhs2bFCTJk0uqp/09HSFh4e7uLrKlZeXp++++67U44iLi7PdjX5OSkqKYmNj5ePjUxklutyiRYvUqFEj9e7d2+ltPeEzl6QmTZooLCzM7rPNz8/XJ598oq5du5a6XWk/D2VtUxWdC6P//e9/9fHHH1/U/1wZY7Rr165q//Nw9OhRZWZmlnkcnvK5n/Pqq6+qU6dOat++vdPbVtXP/UJ/12r673y15J57qVCZHn74YRMUFGQ2btxosrKybNPp06dtbSZMmGASExNt888995xZuXKl+eGHH8w333xjJkyYYCSZ5cuXu+MQLtq4cePMxo0bzU8//WS2bt1q+vTpY+rWrWv2799vjCl+3D/99JOpXbu2GTNmjNm9e7d59dVXjY+Pj3nnnXfcdQjlUlhYaKKiosyjjz5abJ2nfeYnT5406enpJj093Ugyzz77rElPT7fdST5z5kwTFBRkVqxYYb7++mtz5513mvDwcJOTk2PrIzEx0UyYMME2/9lnnxlvb28zc+ZM891335mZM2eaWrVqma1bt1b68ZWlrGMvKCgwN998s2ncuLHZtWuX3b8BeXl5tj7OP/bk5GSzdu1as3fvXpOenm7uvfdeU6tWLbNt2zZ3HGKpyjr2kydPmnHjxpnPP//c7Nu3z6Smppq4uDhzySWXePznfs6JEydM7dq1zfz580vso7p+7o78XfPk33lPRCCtASSVOC1atMjWZvDgwaZ79+62+aeffto0a9bM+Pn5meDgYHPVVVeZNWvWVH7x5TRgwAATHh5ufHx8TEREhPm///s/8+2339rWn3/cxhizceNG07FjR+Pr62tiYmJK/Ye8Ovjoo4+MJLNnz55i6zztMz/32Krzp8GDBxtj/ngMzJQpU0xYWJixWq3mmmuuMV9//bVdH927d7e1P+ftt982LVq0MD4+PqZly5ZVMqCXdez79u0r9d+A1NRUWx/nH/vo0aNNVFSU8fX1NQ0bNjS9evUyn3/+eeUf3AWUdeynT582vXr1Mg0bNjQ+Pj4mKirKDB482GRkZNj14Ymf+zkvvfSS8ff3N7/99luJfVTXz92Rv2ue/DvviSzG/O+ODQAAAMANuIYUAAAAbkUgBQAAgFsRSAEAAOBWBFIAAAC4FYEUAAAAbkUgBQAAgFsRSAEAAOBWBFIAAAC4FYEUQKVISkpSv379bPM9evTQ6NGjHd5+48aNslgs+u2338pdiyv7qor27NmjsLAwnTx50qntxo8fr5EjR1ZQVQBQOgIpAJukpCRZLBZZLBbVqlVLUVFRevjhh3X8+HGX72vFihV64oknXNpnTEyMrX5/f3/FxMTojjvu0IYNG+zade3aVVlZWQoKCrpgn9UxvE6aNEnDhw9X3bp1Jf3/Yzg3NWjQQNdee60+++wzu+0eeeQRLVq0SPv27XNH2QBqMAIpADsJCQnKysrS/v379corr+j999/XsGHDXL6f+vXr2wKTK02bNk1ZWVnas2ePli5dqnr16un666/X9OnTbW18fX0VFhYmi8Xi8v27288//6xVq1bp3nvvLbZuz549ysrK0saNG9WwYUP17t1bv/76q219o0aN1KtXLy1YsKAySwYAAikAe1arVWFhYWrcuLF69eqlAQMGKCUlxba+sLBQ999/v5o0aSJ/f3+1aNFCL7zwgl0fhYWFGjt2rOrVq6cGDRrokUcekTHGrs35p+xff/11xcbGqm7dugoLC9OgQYPswpKjzm0fFRWla665Ri+//LIef/xxTZ48WXv27JFUfNTzwIED6tu3r4KDgxUQEKDLLrtMH3zwgfbv36+ePXtKkoKDg2WxWJSUlCRJWrt2ra666irbMfbp00d79+611bF//35ZLBatWLFCPXv2VO3atdW+fXtt2bLFrt7PPvtM3bt3V+3atRUcHKz4+HjbiLQxRs8884yaNm0qf39/tW/fXu+8806Zx//WW2+pffv2aty4cbF1jRo1UlhYmNq2bavHHntMJ06c0LZt2+za3HzzzXrzzTcd/4YDgAsQSAGU6qefftLatWvl4+NjW1ZUVKTGjRvrrbfe0u7duzV58mT9/e9/11tvvWVrM2fOHL322mt69dVXtXnzZh07dkwrV64sc1/5+fl64okn9OWXX+rdd9/Vvn37bOGvvEaNGiVjjN57770S1w8fPlx5eXn69NNP9fXXX+vpp59WnTp1FBkZqeXLl0v6/6OL58J3bm6uxo4dq7S0NK1fv15eXl669dZbVVRUZNf3pEmTNH78eO3atUuXXnqp7rzzTp09e1aStGvXLl133XW67LLLtGXLFm3evFl9+/ZVYWGhJOmxxx7TokWLNH/+fH377bcaM2aM7r77bn3yySelHuunn36q2NjYMr8fp0+f1qJFiyTJ7rOVpCuvvFKZmZk6cOBAmX0AgEsZAPifwYMHG29vbxMQEGD8/PyMJCPJPPvss2VuN2zYMHPbbbfZ5sPDw83MmTNt8wUFBaZx48bmlltusS3r3r27GTVqVKl9bt++3UgyJ0+eNMYYk5qaaiSZ48ePl7pNdHS0ee6550pcFxoaah5++OES+2rbtq1JTk4ucTtH9muMMb/++quRZL7++mtjjDH79u0zkswrr7xia/Ptt98aSea7774zxhhz5513mm7dupXY36lTp4yfn5/5/PPP7Zbff//95s477yy1jvbt25tp06aVeAwBAQEmICDAWCwWI8l06tTJ5Ofn27U9ceKEkWQ2btxY5vECgCsxQgrATs+ePbVr1y5t27ZNf/3rXxUfH6+//vWvdm0WLFig2NhYNWzYUHXq1NHChQuVkZEhSTpx4oSysrIUFxdna1+rVq0Ljtqlp6frlltuUXR0tOrWrasePXpIkq3f8jLGlHrN6MiRI/Xkk0+qW7dumjJlir766qsL9rd3714NGjRITZs2VWBgoJo0aVJive3atbN9HR4eLkm2SxHOjZCWZPfu3Tpz5oxuuOEG1alTxzYtXbrU7tKA8/3+++/y8/Mrcd2mTZv0xRdf6M0331R0dLQWL15cbITU399f0h+jqABQWQikAOwEBASoefPmateunV588UXl5eVp6tSptvVvvfWWxowZo/vuu08pKSnatWuX7r33XuXn51/0PnNzc9WrVy/VqVNHr7/+utLS0myn+MvT7zlHjx7V4cOHbaHxfA888IB++uknJSYm6uuvv1ZsbKzmzp1bZp99+/bV0aNHtXDhQm3bts12Leb59f458J0LxOdO658LfyU512bNmjXatWuXbdq9e3eZ15GGhISU+lSEJk2a6NJLL9WAAQM0depU3XrrrcrLy7Nrc+zYMUlSw4YNS90HALgagRRAmaZMmaLZs2fr4MGDkv4YZevatauGDRumjh07qnnz5nYjdkFBQQoPD9fWrVtty86ePaudO3eWuo/vv/9eR44c0cyZM3X11VerZcuWF3VDU2leeOEFeXl52T0H9XyRkZEaOnSoVqxYoXHjxmnhwoWS/rgjX5Ltuk7pj4D73Xff6bHHHtN1112nVq1aXdSjsdq1a6f169eXuK5169ayWq3KyMhQ8+bN7abIyMhS++zYsaN27959wX0nJiaqqKhI8+bNs1v+zTffyMfHR5dddplzBwMA5UAgBVCmHj166LLLLtNTTz0lSWrevLl27Nihjz76SD/88IMef/xxpaWl2W0zatQozZw5UytXrtT333+vYcOGlfkcz6ioKPn6+mru3Ln66aeftGrVqot+RunJkyeVnZ2tzMxMffrpp3rwwQf15JNPavr06WrevHmJ24wePVofffSR9u3bpy+++EIbNmxQq1atJEnR0dGyWCxavXq1Dh8+rFOnTik4OFgNGjTQyy+/rB9//FEbNmzQ2LFjna514sSJSktL07Bhw/TVV1/p+++/1/z583XkyBHVrVtX48eP15gxY7RkyRLt3btX6enp+uc//6klS5aU2md8fLy2bNliF6BL4uXlpdGjR2vmzJl2p+c3bdqkq6++uszRWwBwNQIpgAsaO3asFi5cqMzMTA0dOlT/93//pwEDBqhz5846evRoseeUjhs3Tvfcc4+SkpIUFxenunXr6tZbby21/4YNG2rx4sV6++231bp1a82cOVOzZ8++qFonT56s8PBwNW/eXImJiTpx4oTWr1+vRx99tNRtCgsLNXz4cLVq1UoJCQlq0aKFbeTwkksu0dSpUzVhwgSFhoZqxIgR8vLy0n/+8x/t3LlTbdq00ZgxYzRr1iyna7300kuVkpKiL7/8UldeeaXi4uL03nvvqVatWpKkJ554QpMnT9aMGTPUqlUrxcfH6/333y/10gNJuummm+Tj46OPP/74gvu/7777VFBQoH/84x+2ZW+++aaGDBni9LEAQHlYjDnv4YAAgGpt3rx5eu+99/TRRx85td2aNWv0t7/9TV999ZUtFANAZeBfHADwMA8++KCOHz+ukydPOvU2rNzcXC1atIgwCqDSMUIKAAAAt+IaUgAAALgVgRQAAABuRSAFAACAWxFIAQAA4FYEUgAAALgVgRQAAABuRSAFAACAWxFIAQAA4FYEUgAAALjV/wMh7CYGn8Bk5AAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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eeELPP/+8evXqleW2RdkZNGiQZs6cqa+++kqdOnXSG2+8oQkTJqhfv3529el21axZU1u3blXNmjU1fPhw9ejRQz/99JMiIiL0/PPPW9sVKVJEixcv1unTpxUcHKxHHnlEH3zwgaQbN9/fsWOHSpQoodGjR6tdu3YaNmyYvv32W5tb4thjypQpatmypUJDQ/Wvf/0rywUv//T222/rqaee0owZM9S1a1etX79ea9asUdWqVW9r22aw57MbERGhUaNG6aOPPlLXrl21cuVK6z1LHXV42lE/S5n69OkjSYxq4o7s3r1bhmGoRo0aKlasmPX13Xff6fDhw5L+7167ub3s/Rz26dNHnTt3VkBAgLp27aqvvvpKv/76qyIjI83opsNZDOOmu90CAGCnrVu3qmnTplqxYsVd+0fEHo8//ri2bdumo0eP5runUuHesVgsWrt2rXr06CHpxukj/fv314EDB2xO8ZCkYsWKycfHR+np6dbgmRMvL68sF9llt73cVK9eXc8884xefPHFPPfnXuECIQCAXTZu3Ki4uDg1atRI7u7u2rdvn2bOnKnq1avneBHWvZCamqrdu3drx44dWrt2rebOnUvQxB1p2LChMjIydOLECTVv3jzbNs7OzqpVq5apdZw+fVqJiYl37XSmO0XYBADYxcPDQ1FRUZo3b54uXLggb29vdezYUTNmzDD1fq/2SkpKUpMmTeTh4aFnn31Wzz333L0uCfnAxYsXrU+Gk6QjR45o7969KlmypGrUqKH+/fvr6aef1pw5c9SwYUOdOnVK0dHRqlevnjp16uTQ7fn7++vixYsKDw/X448/Ll9fXx09elQvvfSSvL299dhjjzmkz2bjMDoAAMD/io2NVevWrbNMHzhwoJYsWaL09HRNmzZNy5Yt019//aVSpUopKChIU6ZMUb169Ry+vStXrqhHjx7as2ePzp07J19fX7Vu3VqvvfZalodd3K8e2LC5YMECzZo1S0lJSapbt67mzZuX45A4AABAQTRjxgy99NJLGjVqlObNm2fKNh7Iq9FXrVql0aNH6+WXX9aePXvUvHlzdezY0fo4MwAAgIIuPj5eH3zwgfWRvGZ5IMPm3LlzNWTIED3zzDOqXbu25s2bpwoVKui9996716UBAACY7uLFi+rfv78WLVokLy8vU7f1wF0glJaWpl27dmW5V1twcLC2bt2a7TKpqak2N069fv26zpw5o1KlSt3x4wYBAMDdYRiGLly4ID8/P4c9gMAeV69eVVpaminrNgwjSyZxdXXN8UEIw4cPV+fOndWuXTtNmzbNlJoyPXBh89SpU8rIyMhyf6uyZcsqOTk522VmzJihKVOm3I3yAACAyRITE+/6E7yuXr0qr/IldfV03h5za69ixYrp4sWLNtMmT56s8PDwLG1Xrlyp3bt3Kz4+3pRabvbAhc1MN6f/7P4jyDRx4kSNHTvW+j4lJUX+/v5KTEyUh4eHqXUCME9w/3fudQkA7qJr6Ve165vpKl68+F3fdlpamq6evqLH1veVc1HH3u81/VK61nZbmSWXZDeqmZiYqFGjRikqKuqu3arsgQub3t7eKly4cJZRzBMnTmR7N38p52FoDw8PwiaQjzk53z/3hARw99zLU+CcizrLpaiLKevOSy7ZtWuXTpw4oUaNGlmnZWRkaPPmzXr33XeVmpqa5elId+qBu0DIxcVFjRo10saNG22mb9y4UU2aNLlHVQEAAJivbdu22r9/v/bu3Wt9BQYGqn///tq7d6/Dg6b0AI5sStLYsWM1YMAABQYGKigoSB988IESEhI0dOjQe10aAACAaYoXL66AgACbaUWLFlWpUqWyTHeUBzJs9unTR6dPn9bUqVOVlJSkgIAAffnll6pYseK9Lg0AAKBAeSDDpiSFhYUpLCzsXpcBAABwT8XGxpq6/gfunE0AAADcPYRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0zywz0YHMjXrOftelwAAQIHFyCYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApiFsAgAAwDSETQAAAJiGsAkAAADTEDYBAABgGsImAAAATEPYBAAAgGmc7nUByL+a9Zx9r0sAAAD3OUY2AQAAYBrCJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApiFsAgAAwDSETQAAAJiGsAkAAADTEDYBAABgGsImAAAATEPYBAAAgGkImwAAADANYRMAAACmIWwCAADANIRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApiFsAgAAwDSETQAAAJiGsAkAAADTEDYBAABgGsImAAAATEPYBAAAgGkImwAAADANYRMAAACmIWwCAADANIRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExTYMLm0aNHNWTIEFWuXFnu7u6qWrWqJk+erLS0NJt2CQkJ6tq1q4oWLSpvb2+NHDkySxsAAAA4htO9LsBRfvnlF12/fl3vv/++qlWrpp9++kmhoaG6dOmSZs+eLUnKyMhQ586dVbp0aW3ZskWnT5/WwIEDZRiG5s+ff497AAAAUPAUmLDZoUMHdejQwfq+SpUqOnTokN577z1r2IyKitLBgweVmJgoPz8/SdKcOXMUEhKi6dOny8PD457UDgAAUFAVmMPo2UlJSVHJkiWt7+Pi4hQQEGANmpLUvn17paamateuXTmuJzU1VefPn7d5AQAA4NYKbNg8fPiw5s+fr6FDh1qnJScnq2zZsjbtvLy85OLiouTk5BzXNWPGDHl6elpfFSpUMK1uAACAguS+D5vh4eGyWCy5vnbu3GmzzPHjx9WhQwf16tVLzzzzjM08i8WSZRuGYWQ7PdPEiROVkpJifSUmJjqmcwAAAAXcfX/O5ogRI9S3b99c21SqVMn69fHjx9W6dWsFBQXpgw8+sGnn4+Oj7du320w7e/as0tPTs4x4/pOrq6tcXV3tLx4AAOABd9+HTW9vb3l7e+ep7V9//aXWrVurUaNGioiIUKFCtgO3QUFBmj59upKSkuTr6yvpxkVDrq6uatSokcNrBwAAeNDd92Ezr44fP65WrVrJ399fs2fP1smTJ63zfHx8JEnBwcGqU6eOBgwYoFmzZunMmTMaP368QkNDuRIdAADABAUmbEZFRen333/X77//rvLly9vMMwxDklS4cGFFRkYqLCxMTZs2lbu7u/r162e9NRIAAAAcq8CEzZCQEIWEhNyynb+/vzZs2GB+QQAAALj/r0YHAABA/kXYBAAAgGkImwAAADANYRMAAACmIWwCAADANIRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApiFsAgAAwDSETQAAAJiGsAkAAADTEDYBAABgGsImAAAATEPYBAAAgGkImwAAADANYRMAAACmIWwCAADANIRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExD2AQAAIBpnO51AflZcP935OTsdq/LAAAAuG8xsgkAAADTEDYBAABgGsImAAAATEPYBAAAgGkImwAAADANYRMAAACmIWwCAADANIRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApiFsAgAAwDSETQAAAJiGsAkAAADTEDYBAABgGsImAAAATEPYBAAAgGkImwAAADCNkz2NU1JStHbtWn3//fc6evSoLl++rNKlS6thw4Zq3769mjRpYladAAAAyIfyNLKZlJSk0NBQ+fr6aurUqbp06ZIaNGigtm3bqnz58oqJidG///1v1alTR6tWrTK7ZgAAAOQTeRrZrF+/vp5++mnt2LFDAQEB2ba5cuWK1q1bp7lz5yoxMVHjx493aKEAAADIf/IUNg8cOKDSpUvn2sbd3V1PPvmknnzySZ08edIhxQEAACB/y9Nh9FsFzTttDwAAgIIpz1ejV61aVW+99VaO8//++28VLlzYIUUBAACgYMhz2Dxy5IhefPFFDRw4UGlpadm2MQzDYYUBAAAg/7PrPptr165VTEyMWrRooaSkpCzzLRaLwwoDAABA/mdX2HzkkUcUHx8vJycnNWrUSNu3bzerLgAAABQAdj9BqGzZsoqNjVXXrl3VqlUrRUREmFEXAAAACoDbelylk5OT3n//fb311lsaOnSoRo0apWvXrjm6ttuWmpqqBg0ayGKxaO/evTbzEhIS1LVrVxUtWlTe3t4aOXJkjuegAgAA4M7k+XGV2Z2POXToUAUEBOiJJ57QDz/84NDC7sQLL7wgPz8/7du3z2Z6RkaGOnfurNKlS2vLli06ffq0Bg4cKMMwNH/+/HtULQAAQMGV55HNnK40b9asmeLj4x1W0J366quvFBUVpdmzZ2eZFxUVpYMHD2r58uVq2LCh2rVrpzlz5mjRokU6f/78PagWAACgYLPr1kc53ay9QoUK+uGHHxQTE+Owwm7H33//rdDQUP3P//yPihQpkmV+XFycAgIC5OfnZ53Wvn17paamateuXTmuNzU1VefPn7d5AQAA4NbyHDYrVqyY662NXF1d1aJFC4cUdTsMw1BISIiGDh2qwMDAbNskJyerbNmyNtO8vLzk4uKi5OTkHNc9Y8YMeXp6Wl8VKlRwaO0AAAAFVZ7CZocOHbR169Zbtrtw4YLeeOMN/fe//73jwjKFh4fLYrHk+tq5c6fmz5+v8+fPa+LEibmuL7vAbBhGrkF64sSJSklJsb4SExPvuF8AAAAPgjxdINSrVy/17t1bxYsXV7du3RQYGCg/Pz+5ubnp7NmzOnjwoLZs2aIvv/xSXbp00axZsxxW4IgRI9S3b99c21SqVEnTpk3Ttm3b5OrqajMvMDBQ/fv319KlS+Xj45Pl3qBnz55Venp6lhHPf3J1dc2yXgAAANxansLmkCFDNGDAAH322WdatWqVFi1apHPnzkm6MVJYp04dtW/fXrt27VLNmjUdWqC3t7e8vb1v2e6dd97RtGnTrO+PHz+u9u3ba9WqVXr00UclSUFBQZo+fbqSkpLk6+sr6cZFQ66urmrUqJFD6wYAAIAdtz5ycXFRv3791K9fP0lSSkqKrly5olKlSsnZ2dm0AvPK39/f5n2xYsUkSVWrVlX58uUlScHBwapTp44GDBigWbNm6cyZMxo/frxCQ0Pl4eFx12sGAAAo6G7rpu6S5OnpKR8fn/siaOZV4cKFFRkZKTc3NzVt2lS9e/dWjx49sr1NEgAAAO5cnkc285tKlSple29Qf39/bdiw4R5UBAAA8OC57ZFNAAAA4FYImwAAADANYRMAAACmua2wee7cOX344YeaOHGizpw5I0navXu3/vrrL4cWBwAAgPzN7guEfvzxR7Vr106enp46evSoQkNDVbJkSa1du1bHjh3TsmXLzKgTAAAA+ZDdI5tjx45VSEiIfvvtN7m5uVmnd+zYUZs3b3ZocQAAAMjf7A6b8fHxevbZZ7NML1eunJKTkx1SFAAAAAoGu8Omm5ubzp8/n2X6oUOHVLp0aYcUBQAAgILB7rDZvXt3TZ06Venp6ZJuPBs9ISFBEyZM0OOPP+7wAgEAAJB/2R02Z8+erZMnT6pMmTK6cuWKWrZsqWrVqql48eKaPn26GTUCAAAgn7L7anQPDw9t2bJF0dHR2r17t65fv66HH35Y7dq1M6M+AAAA5GO3/Wz0Nm3aqE2bNo6sBQAAAAWM3YfRR44cqXfeeSfL9HfffVejR492RE0AAAAoIOwOm6tXr1bTpk2zTG/SpIk+++wzhxQFAACAgsHusHn69Gl5enpmme7h4aFTp045pCgAAAAUDHaHzWrVqunrr7/OMv2rr75SlSpVHFIUAAAACga7LxAaO3asRowYoZMnT1ovENq0aZPmzJmjefPmObo+AAAA5GN2h83BgwcrNTVV06dP12uvvSZJqlSpkt577z09/fTTDi8QAAAA+ddt3fpo2LBhGjZsmE6ePCl3d3cVK1bM0XUBAACgALjt+2xK4lnoAAAAyJXdFwj9/fffGjBggPz8/OTk5KTChQvbvAAAAIBMdo9shoSEKCEhQa+++qp8fX1lsVjMqAsAAAAFgN1hc8uWLfr+++/VoEEDE8oBAABAQWL3YfQKFSrIMAwzagEAAEABY3fYnDdvniZMmKCjR4+aUA4AAAAKErsPo/fp00eXL19W1apVVaRIETk7O9vMP3PmjMOKAwAAQP5md9jkKUEAAADIK7vD5sCBA82oAwAAAAXQHd3U/cqVK0pPT7eZ5uHhcUcFAQAAoOCw+wKhS5cuacSIESpTpoyKFSsmLy8vmxcAAACQye6w+cILLyg6OloLFiyQq6urPvzwQ02ZMkV+fn5atmyZGTUCAAAgn7L7MPoXX3yhZcuWqVWrVho8eLCaN2+uatWqqWLFilqxYoX69+9vRp0AAADIh+we2Txz5owqV64s6cb5mZm3OmrWrJk2b97s2OoAAACQr9kdNqtUqWK9oXudOnX0ySefSLox4lmiRAlH1gYAAIB8zu6wOWjQIO3bt0+SNHHiROu5m2PGjNHzzz/v8AIBAACQf9l9zuaYMWOsX7du3Vq//PKLdu7cqapVq6p+/foOLQ4AAACOt2DBAs2aNUtJSUmqW7eu5s2bp+bNm5uyLbtHNpctW6bU1FTre39/f/Xs2VO1a9fmanQAAID73KpVqzR69Gi9/PLL2rNnj5o3b66OHTsqISHBlO3d1mH0lJSULNMvXLigQYMGOaQoAAAAmGPu3LkaMmSInnnmGdWuXVvz5s1ThQoV9N5775myPbvDpmEYslgsWab/+eef8vT0dEhRAAAAsM/58+dtXv88Ep0pLS1Nu3btUnBwsM304OBgbd261ZS68nzOZsOGDWWxWGSxWNS2bVs5Of3fohkZGTpy5Ig6dOhgSpEAAAAFwbG3asjJ2c2h67yWflWSVKFCBZvpkydPVnh4uM20U6dOKSMjQ2XLlrWZXrZsWSUnJzu0rkx5Dps9evSQJO3du1ft27dXsWLFrPNcXFxUqVIlPf744w4vEAAAALeWmJgoDw8P63tXV9cc2958lDqnI9eOkOewOXnyZElSpUqV1Ldv31w7AAAAgLvLw8PDJmxmx9vbW4ULF84yinnixIkso52OYvc5m23atNHJkyet73fs2KHRo0frgw8+cGhhAAAAcCwXFxc1atRIGzdutJm+ceNGNWnSxJRt2h02+/Xrp5iYGElScnKy2rVrpx07duill17S1KlTHV4gAAAAHGfs2LH68MMPtXjxYv38888aM2aMEhISNHToUFO2Z/dN3X/66Sf961//kiR98sknqlevnn744QdFRUVp6NChmjRpksOLBAAAgGP06dNHp0+f1tSpU5WUlKSAgAB9+eWXqlixoinbsztspqenW8/X/Pbbb9WtWzdJUq1atZSUlOTY6gAAAOBwYWFhCgsLuyvbsvswet26dbVw4UJ9//332rhxo/V2R8ePH1epUqUcXiAAAADyL7vD5htvvKH3339frVq10pNPPml9Hvr69euth9cBAAAA6TYOo7dq1UqnTp3S+fPn5eXlZZ3+n//8R0WKFHFocQAAAMjf7A6bklS4cGGboCnduP8mAAAA8E95CpsPP/ywNm3aJC8vL+tjK3Oye/duhxUHAACA/C1PYbN79+7WK9AzH1sJAAAA3Eqewmbmoypv/hoAAADIjd3nbBqGoV27duno0aOyWCyqXLnyLQ+tAwAA4MFkV9iMiYnRkCFDdOzYMRmGIUnWwLl48WK1aNHClCIBAACQP+X5Ppu///67unTpokqVKmnNmjX6+eefdfDgQX366acqX768OnXqpD/++MPMWgEAAJDP5Hlkc968eWrcuLE2bdpkM71WrVp67LHH1K5dO7311luaP3++w4sEAABA/pTnkc3Y2FiNHj0623kWi0WjR49WTEyMo+oCAABAAZDnsJmQkKB69erlOD8gIEDHjh1zSFEAAAAoGPIcNi9evJjr4yiLFCmiy5cvO6QoAAAAFAx2XY1+8OBBJScnZzvv1KlTDikIAAAABYddYbNt27bWWx79k8VikWEY3GsTAAAANvIcNo8cOWJmHQAAACiA8hw2K1asaGYdAAAAKIDyfIEQAAAAYC/CJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApsnT1egNGzbM8z00d+/efUcFAQAAoODIU9js0aOHyWUAAACgIMpT2Jw8ebLZdQAAAKAA4pxNAAAAmMauZ6NLUkZGht566y198sknSkhIUFpams38M2fOOKw4AAAA5G92j2xOmTJFc+fOVe/evZWSkqKxY8eqZ8+eKlSokMLDw00oEQAAAPmV3WFzxYoVWrRokcaPHy8nJyc9+eST+vDDDzVp0iRt27bNjBoBAACQT9kdNpOTk1WvXj1JUrFixZSSkiJJ6tKliyIjIx1bHQAAAPI1u8Nm+fLllZSUJEmqVq2aoqKiJEnx8fFydXV1bHUAAADI1+wOm4899pg2bdokSRo1apReffVVVa9eXU8//bQGDx7s8AIBAACQf9l9NfrMmTOtXz/xxBMqX768tm7dqmrVqqlbt24OLQ4AAAD5m91h82aNGzdW48aNHVELAAAACpg8hc3169erY8eOcnZ21vr163Nte69HNyMjIzV16lT9+OOPKlq0qFq0aKE1a9ZY5yckJGj48OGKjo6Wu7u7+vXrp9mzZ8vFxeUeVg0AAFAw5fnZ6MnJySpTpkyuz0m3WCzKyMhwVG12W716tUJDQ/X666+rTZs2MgxD+/fvt87PyMhQ586dVbp0aW3ZskWnT5/WwIEDZRiG5s+ff8/qBgAAKKjyFDavX7+e7df3k2vXrmnUqFGaNWuWhgwZYp1es2ZN69dRUVE6ePCgEhMT5efnJ0maM2eOQkJCNH36dHl4eNz1ugEAAAoyhz4b/fLly45cnV12796tv/76S4UKFVLDhg3l6+urjh076sCBA9Y2cXFxCggIsAZNSWrfvr1SU1O1a9euHNedmpqq8+fP27wAAABwa3aHzVatWunPP//MMn379u1q0KCBI2q6LX/88YckKTw8XK+88oo2bNggLy8vtWzZ0vq89uTkZJUtW9ZmOS8vL7m4uCg5OTnHdc+YMUOenp7WV4UKFczrCAAAQAFid9j08PDQQw89pJUrV0q6cVg9PDxcLVq0MOXioPDwcFksllxfO3futB7ef/nll/X444+rUaNGioiIkMVi0aeffmpdn8ViybINwzCynZ5p4sSJSklJsb4SExMd3k8AAICCyO5bH61fv14LFy7UM888o/Xr1+vo0aNKSEhQZGSk2rVr5/ACR4wYob59++baplKlSrpw4YIkqU6dOtbprq6uqlKlihISEiRJPj4+2r59u82yZ8+eVXp6epYRz39ydXXl6UgAAAC34bbuszl06FAdO3ZMb7zxhpycnBQbG6smTZo4ujZJkre3t7y9vW/ZrlGjRnJ1ddWhQ4fUrFkzSVJ6erqOHj2qihUrSpKCgoI0ffp0JSUlydfXV9KNi4ZcXV3VqFEjU+oHAAB4kNl9GP3s2bN6/PHH9d577+n9999X7969FRwcrAULFphRX555eHho6NChmjx5sqKionTo0CENGzZMktSrVy9JUnBwsOrUqaMBAwZoz5492rRpk8aPH6/Q0FCuRAcAADCB3SObAQEBqly5svbs2aPKlSsrNDRUq1atUlhYmCIjIxUZGWlGnXkya9YsOTk5acCAAbpy5YoeffRRRUdHy8vLS5JUuHBhRUZGKiwsTE2bNrW5qTsAAAAcz+6wOXToUL388ssqVOj/BkX79Omjpk2batCgQQ4tzl7Ozs6aPXt2ruHR399fGzZsuItVAQAAPLjsDpuvvvpqttPLly+vN998844LAgAAQMFxxzd1T0lJ0YIFC/Twww8rMDDQETUBAACggLjtsBkdHa2nnnpKvr6+mj9/vjp16qSdO3c6sjYAAADkc3YdRv/zzz+1ZMkSLV68WJcuXVLv3r2Vnp6u1atX29zfEgAAAJDsGNns1KmT6tSpo4MHD2r+/Pk6fvy45s+fb2ZtAAAAyOfyPLIZFRWlkSNHatiwYapevbqZNQEAAKCAyPPI5vfff68LFy4oMDBQjz76qN59912dPHnSzNoAAACQz+U5bAYFBWnRokVKSkrSs88+q5UrV6pcuXK6fv26Nm7caH02OQAAAJDJ7qvRixQposGDB2vLli3av3+/xo0bp5kzZ6pMmTLq1q2bGTUCAAAgn7qj+2zWrFlTb775pv788099/PHHjqoJAAAABcQd39RduvHM8R49emj9+vWOWB0AAAAKCIeETQAAACA7hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApiFsAgAAwDSETQAAAJiGsAkAAADTEDYBAABgGsImAAAATEPYBAAAgGkImwAAADANYRMAAACmIWwCAADANIRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApiFsAgAAwDSETQAAAJiGsAkAAADTEDYBAABgGsImAAAATEPYBAAAgGkImwAAADANYRMAAACmIWwCAADANIRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApiFsAgAAwDSETQAAAJiGsAkAAADTEDYBAABgGsImAAAATEPYBAAAgGkImwAAADANYRMAAACmIWwCAADANIRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQpU2Pz111/VvXt3eXt7y8PDQ02bNlVMTIxNm4SEBHXt2lVFixaVt7e3Ro4cqbS0tHtUMQAAQMFWoMJm586dde3aNUVHR2vXrl1q0KCBunTpouTkZElSRkaGOnfurEuXLmnLli1auXKlVq9erXHjxt3jygEAAAqmAhM2T506pd9//10TJkzQQw89pOrVq2vmzJm6fPmyDhw4IEmKiorSwYMHtXz5cjVs2FDt2rXTnDlztGjRIp0/f/4e9wAAAKDgKTBhs1SpUqpdu7aWLVumS5cu6dq1a3r//fdVtmxZNWrUSJIUFxengIAA+fn5WZdr3769UlNTtWvXrhzXnZqaqvPnz9u8AAAAcGtO97oAR7FYLNq4caO6d++u4sWLq1ChQipbtqy+/vprlShRQpKUnJyssmXL2izn5eUlFxcX66H27MyYMUNTpkwxs3wAAIAC6b4f2QwPD5fFYsn1tXPnThmGobCwMJUpU0bff/+9duzYoe7du6tLly5KSkqyrs9isWTZhmEY2U7PNHHiRKWkpFhfiYmJpvQVAACgoLnvRzZHjBihvn375tqmUqVKio6O1oYNG3T27Fl5eHhIkhYsWKCNGzdq6dKlmjBhgnx8fLR9+3abZc+ePav09PQsI57/5OrqKldX1zvvDAAAwAPmvg+b3t7e8vb2vmW7y5cvS5IKFbIdrC1UqJCuX78uSQoKCtL06dOVlJQkX19fSTcuGnJ1dbWe1wkAAADHue8Po+dVUFCQvLy8NHDgQO3bt0+//vqrnn/+eR05ckSdO3eWJAUHB6tOnToaMGCA9uzZo02bNmn8+PEKDQ21joYCAADAcQpM2PT29tbXX3+tixcvqk2bNgoMDNSWLVv0+eefq379+pKkwoULKzIyUm5ubmratKl69+6tHj16aPbs2fe4egAAgILpvj+Mbo/AwEB98803ubbx9/fXhg0b7lJFAAAAD7YCM7IJAACA+w9hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApiFsAgAAwDSETQAAAJiGsAkAAADTEDYBAABgGsImAAAATEPYBAAAgGkImwAAADANYRMAAACmIWwCAADANIRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApiFsAgAAwDSETQAAAJiGsAkAAADTEDYBAABgGsImAAAATEPYBAAAgGkImwAAADANYRMAAACmIWwCAADANIRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApiFsAgAAwDSETQAAAJiGsAkAAADTEDYBAABgGsImAAAATEPYBAAAgGkImwAAADANYRMAAACmIWwCAADANIRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AQAAYBrCJgAAAExD2AQAAIBpCJsAAAAwDWETAAAApsk3YXP69Olq0qSJihQpohIlSmTbJiEhQV27dlXRokXl7e2tkSNHKi0tzabN/v371bJlS7m7u6tcuXKaOnWqDMO4Cz0AAADIn1JTU9WgQQNZLBbt3bvXrmXzTdhMS0tTr169NGzYsGznZ2RkqHPnzrp06ZK2bNmilStXavXq1Ro3bpy1zfnz5/Xvf/9bfn5+io+P1/z58zV79mzNnTv3bnUDAAAg33nhhRfk5+d3W8s6ObgW00yZMkWStGTJkmznR0VF6eDBg0pMTLR+M+bMmaOQkBBNnz5dHh4eWrFiha5evaolS5bI1dVVAQEB+vXXXzV37lyNHTtWFovlbnUHAAAgX/jqq68UFRWl1atX66uvvrJ7+XwTNm8lLi5OAQEBNqm7ffv2Sk1N1a5du9S6dWvFxcWpZcuWcnV1tWkzceJEHT16VJUrV8523ampqUpNTbW+T0lJkSRdS79qUm8AAICjZf7dvpenz2Vcc3x2yFzn+fPnbaa7urraZJ7b8ffffys0NFTr1q1TkSJFbmsdBSZsJicnq2zZsjbTvLy85OLiouTkZGubSpUq2bTJXCY5OTnHsDljxgzryOo/7fpmugMqBwAAd9Pp06fl6el5V7fp4uIiHx8f7fzanOxQrFgxVahQwWba5MmTFR4eftvrNAxDISEhGjp0qAIDA3X06NHbWs89DZvh4eHZhrh/io+PV2BgYJ7Wl91hcMMwbKbf3Cbzv5vcDqFPnDhRY8eOtb6/fv26zpw5o1KlShWYQ+/nz59XhQoVlJiYKA8Pj3tdzl1F3+n7g9Z36cHuP31/MPsu3Tgy6e/vr5IlS971bbu5uenIkSNZLlx2lJvzjqQcRzXzmr+2bt2q8+fPa+LEiXdU2z0NmyNGjFDfvn1zbXPzSGROfHx8tH37dptpZ8+eVXp6unX00sfHxzrKmenEiROSlGVU9J+yG4bO6Yr4/M7Dw+OB/AUk0Xf6/mB6kPtP3x/MvktSoUL35vpoNzc3ubm53ZNt/1Ne89e0adO0bdu2LBkoMDBQ/fv319KlS/O0vXsaNr29veXt7e2QdQUFBWn69OlKSkqSr6+vpBsXDbm6uqpRo0bWNi+99JLS0tLk4uJibePn55fnUAsAAJCf5TV/vfPOO5o2bZr1/fHjx9W+fXutWrVKjz76aJ63l2/O2UxISNCZM2eUkJCgjIwM6z2eqlWrpmLFiik4OFh16tTRgAEDNGvWLJ05c0bjx49XaGio9b+3fv36acqUKQoJCdFLL72k3377Ta+//romTZpUYA6HAwAAOIK/v7/N+2LFikmSqlatqvLly+d5PfkmbE6aNMlmuLZhw4aSpJiYGLVq1UqFCxdWZGSkwsLC1LRpU7m7u6tfv36aPXu2dRlPT09t3LhRw4cPV2BgoLy8vDR27Fib8zEfVK6urpo8efIdX7WWH9F3+v4gepD7T98fzL5L9P9esRg8PgcAAAAmyTdPEAIAAED+Q9gEAACAaQibAAAAMA1hEwAAAKYhbD4AZsyYoUceeUTFixdXmTJl1KNHDx06dCjXZWJjY2WxWLK8fvnll7tUtWOEh4dn6YOPj0+uy3z33Xdq1KiR3NzcVKVKFS1cuPAuVetYlSpVynYfDh8+PNv2+X2fb968WV27dpWfn58sFovWrVtnM98wDIWHh8vPz0/u7u5q1aqVDhw4cMv1rl69WnXq1JGrq6vq1KmjtWvXmtSD25db39PT0/Xiiy+qXr16Klq0qPz8/PT000/r+PHjua5zyZIl2X4erl51/HOd78St9ntISEiWPjRu3PiW683v+11StvvPYrFo1qxZOa4zv+z3vPxdK8g/8/kNYfMB8N1332n48OHatm2bNm7cqGvXrik4OFiXLl265bKHDh1SUlKS9VW9evW7ULFj1a1b16YP+/fvz7HtkSNH1KlTJzVv3lx79uzRSy+9pJEjR2r16tV3sWLHiI+Pt+n3xo0bJUm9evXKdbn8us8vXbqk+vXr69133812/ptvvqm5c+fq3XffVXx8vHx8fPTvf/9bFy5cyHGdcXFx6tOnjwYMGKB9+/ZpwIAB6t27d5anld1rufX98uXL2r17t1599VXt3r1ba9as0a+//qpu3brdcr0eHh42n4WkpKT74ukn/3Sr/S5JHTp0sOnDl19+mes6C8J+l5Rl3y1evFgWi0WPP/54ruvND/s9L3/XCvLPfL5j4IFz4sQJQ5Lx3Xff5dgmJibGkGScPXv27hVmgsmTJxv169fPc/sXXnjBqFWrls20Z5991mjcuLGDK7v7Ro0aZVStWtW4fv16tvMLyj43DMOQZKxdu9b6/vr164aPj48xc+ZM67SrV68anp6exsKFC3NcT+/evY0OHTrYTGvfvr3Rt29fh9fsKDf3PTs7duwwJBnHjh3LsU1ERITh6enp2OJMll3fBw4caHTv3t2u9RTU/d69e3ejTZs2ubbJj/vdMLL+XXuQfubzA0Y2H0ApKSmSpJIlS96ybcOGDeXr66u2bdsqJibG7NJM8dtvv8nPz0+VK1dW37599ccff+TYNi4uTsHBwTbT2rdvr507dyo9Pd3sUk2Tlpam5cuXa/Dgwbd8WlZB2Oc3O3LkiJKTk232raurq1q2bKmtW7fmuFxOn4fclskPUlJSZLFYVKJEiVzbXbx4URUrVlT58uXVpUsX7dmz5+4U6GCxsbEqU6aMatSoodDQUJ04cSLX9gVxv//999+KjIzUkCFDbtk2P+73m/+u8TN/fyFsPmAMw9DYsWPVrFkzBQQE5NjO19dXH3zwgVavXq01a9aoZs2aatu2rTZv3nwXq71zjz76qJYtW6ZvvvlGixYtUnJyspo0aaLTp09n2z45OVlly5a1mVa2bFldu3ZNp06duhslm2LdunU6d+6cQkJCcmxTUPZ5dpKTkyUp232bOS+n5exd5n539epVTZgwQf369bM+yjc7tWrV0pIlS7R+/Xp9/PHHcnNzU9OmTfXbb7/dxWrvXMeOHbVixQpFR0drzpw5io+PV5s2bZSamprjMgVxvy9dulTFixdXz549c22XH/d7dn/X+Jm/v+Sbx1XCMUaMGKEff/xRW7ZsybVdzZo1VbNmTev7oKAgJSYmavbs2WrRooXZZTpMx44drV/Xq1dPQUFBqlq1qpYuXZrjY0pvHvkz/vchW7caEbyfffTRR+rYsaP8/PxybFNQ9nlustu3t9qvt7PM/So9PV19+/bV9evXtWDBglzbNm7c2OZCmqZNm+rhhx/W/Pnz9c4775hdqsP06dPH+nVAQIACAwNVsWJFRUZG5hq8CtJ+l6TFixerf//+tzz3Mj/u99z+rj3oP/P3C0Y2HyDPPfec1q9fr5iYGJUvX97u5Rs3bnxf/3ebF0WLFlW9evVy7IePj0+W/2BPnDghJycnlSpV6m6U6HDHjh3Tt99+q2eeecbuZQvCPpdkvQNBdvv25lGMm5ezd5n7VXp6unr37q0jR45o48aNuY5qZqdQoUJ65JFH8v3nwdfXVxUrVsy1HwVpv0vS999/r0OHDt3W74D7fb/n9HeNn/n7C2HzAWAYhkaMGKE1a9YoOjpalStXvq317NmzR76+vg6u7u5KTU3Vzz//nGM/goKCrFdtZ4qKilJgYKCcnZ3vRokOFxERoTJlyqhz5852L1sQ9rkkVa5cWT4+Pjb7Ni0tTd99952aNGmS43I5fR5yW+Z+lBk0f/vtN3377be39Y+TYRjau3dvvv88nD59WomJibn2o6Ds90wfffSRGjVqpPr169u97P2632/1d+1B/5m/79yb65JwNw0bNszw9PQ0YmNjjaSkJOvr8uXL1jYTJkwwBgwYYH3/1ltvGWvXrjV+/fVX46effjImTJhgSDJWr159L7pw28aNG2fExsYaf/zxh7Ft2zajS5cuRvHixY2jR48ahpG133/88YdRpEgRY8yYMcbBgweNjz76yHB2djY+++yze9WFO5KRkWH4+/sbL774YpZ5BW2fX7hwwdizZ4+xZ88eQ5Ixd+5cY8+ePdYrrmfOnGl4enoaa9asMfbv3288+eSThq+vr3H+/HnrOgYMGGBMmDDB+v6HH34wChcubMycOdP4+eefjZkzZxpOTk7Gtm3b7nr/cpNb39PT041u3boZ5cuXN/bu3WvzOyA1NdW6jpv7Hh4ebnz99dfG4cOHjT179hiDBg0ynJycjO3bt9+LLuYot75fuHDBGDdunLF161bjyJEjRkxMjBEUFGSUK1euwO/3TCkpKUaRIkWM9957L9t15Nf9npe/awX5Zz6/IWw+ACRl+4qIiLC2GThwoNGyZUvr+zfeeMOoWrWq4ebmZnh5eRnNmjUzIiMj737xd6hPnz6Gr6+v4ezsbPj5+Rk9e/Y0Dhw4YJ1/c78NwzBiY2ONhg0bGi4uLkalSpVy/CWdH3zzzTeGJOPQoUNZ5hW0fZ5566abXwMHDjQM48atUCZPnmz4+PgYrq6uRosWLYz9+/fbrKNly5bW9pk+/fRTo2bNmoazs7NRq1at+zJ859b3I0eO5Pg7ICYmxrqOm/s+evRow9/f33BxcTFKly5tBAcHG1u3br37nbuF3Pp++fJlIzg42ChdurTh7Oxs+Pv7GwMHDjQSEhJs1lEQ93um999/33B3dzfOnTuX7Try637Py9+1gvwzn99YDON/r34AAAAAHIxzNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMA1hEwAAAKYhbAIAAMA0hE0AAACYhrAJAAAA0xA2AdwVISEh6tGjh/V9q1atNHr06DwvHxsbK4vFonPnzt1xLY5c1/3o0KFD8vHx0YULF+xabvz48Ro5cqRJVQF4UBE2AViFhITIYrHIYrHIyclJ/v7+GjZsmM6ePevwba1Zs0avvfaaQ9dZqVIla/3u7u6qVKmSevfurejoaJt2TZo0UVJSkjw9PW+5zvwYTF9++WUNHz5cxYsXl/R/fch8lSpVSm3atNEPP/xgs9wLL7ygiIgIHTly5F6UDaCAImwCsNGhQwclJSXp6NGj+vDDD/XFF18oLCzM4dspWbKkNQw50tSpU5WUlKRDhw5p2bJlKlGihNq1a6fp06db27i4uMjHx0cWi8Xh27/X/vzzT61fv16DBg3KMu/QoUNKSkpSbGysSpcurc6dO+vEiRPW+WXKlFFwcLAWLlx4N0sGUMARNgHYcHV1lY+Pj8qXL6/g4GD16dNHUVFR1vkZGRkaMmSIKleuLHd3d9WsWVNvv/22zToyMjI0duxYlShRQqVKldILL7wgwzBs2tx8GH358uUKDAxU8eLF5ePjo379+tkEobzKXN7f318tWrTQBx98oFdffVWTJk3SoUOHJGUdrTx27Ji6du0qLy8vFS1aVHXr1tWXX36po0ePqnXr1pIkLy8vWSwWhYSESJK+/vprNWvWzNrHLl266PDhw9Y6jh49KovFojVr1qh169YqUqSI6tevr7i4OJt6f/jhB7Vs2VJFihSRl5eX2rdvbx1JNgxDb775pqpUqSJ3d3fVr19fn332Wa79/+STT1S/fn2VL18+y7wyZcrIx8dH9erV0yuvvKKUlBRt377dpk23bt308ccf5/0bDgC3QNgEkKM//vhDX3/9tZydna3Trl+/rvLly+uTTz7RwYMHNWnSJL300kv65JNPrG3mzJmjxYsX66OPPtKWLVt05swZrV27NtdtpaWl6bXXXtO+ffu0bt06HTlyxBrs7tSoUaNkGIY+//zzbOcPHz5cqamp2rx5s/bv36833nhDxYoVU4UKFbR69WpJ/zcqmBmsL126pLFjxyo+Pl6bNm1SoUKF9Nhjj+n69es263755Zc1fvx47d27VzVq1NCTTz6pa9euSZL27t2rtm3bqm7duoqLi9OWLVvUtWtXZWRkSJJeeeUVRURE6L333tOBAwc0ZswYPfXUU/ruu+9y7OvmzZsVGBiY6/fj8uXLioiIkCSbfStJ//rXv5SYmKhjx47lug4AyDMDAP7XwIEDjcKFCxtFixY13NzcDEmGJGPu3Lm5LhcWFmY8/vjj1ve+vr7GzJkzre/T09ON8uXLG927d7dOa9mypTFq1Kgc17ljxw5DknHhwgXDMAwjJibGkGScPXs2x2UqVqxovPXWW9nOK1u2rDFs2LBs11WvXj0jPDw82+Xysl3DMIwTJ04Ykoz9+/cbhmEYR44cMSQZH374obXNgQMHDEnGzz//bBiGYTz55JNG06ZNs13fxYsXDTc3N2Pr1q0204cMGWI8+eSTOdZRv359Y+rUqdn2oWjRokbRokUNi8ViSDIaNWpkpKWl2bRNSUkxJBmxsbG59hcA8oqRTQA2Wrdurb1792r79u167rnn1L59ez333HM2bRYuXKjAwECVLl1axYoV06JFi5SQkCBJSklJUVJSkoKCgqztnZycbjnatmfPHnXv3l0VK1ZU8eLF1apVK0myrvdOGYaR4zmaI0eO1LRp09S0aVNNnjxZP/744y3Xd/jwYfXr109VqlSRh4eHKleunG29Dz30kPVrX19fSbKeHpA5spmdgwcP6urVq/r3v/+tYsWKWV/Lli2zOVx/sytXrsjNzS3bed9//712796tjz/+WBUrVtSSJUuyjGy6u7tLujH6CQCOQNgEYKNo0aKqVq2aHnroIb3zzjtKTU3VlClTrPM/+eQTjRkzRoMHD1ZUVJT27t2rQYMGKS0t7ba3eenSJQUHB6tYsWJavny54uPjrYfd72S9mU6fPq2TJ09aA+HNnnnmGf3xxx8aMGCA9u/fr8DAQM2fPz/XdXbt2lWnT5/WokWLtH37duu5jzfX+88wlxl2Mw+1Zwa77GS2iYyM1N69e62vgwcP5nrepre3d453D6hcubJq1KihPn36aMqUKXrssceUmppq0+bMmTOSpNKlS+e4DQCwB2ETQK4mT56s2bNn6/jx45JujI41adJEYWFhatiwoapVq2Yz0ubp6SlfX19t27bNOu3atWvatWtXjtv45ZdfdOrUKc2cOVPNmzdXrVq1buvioJy8/fbbKlSokM19Pm9WoUIFDR06VGvWrNG4ceO0aNEiSTeuXJdkPY9SuhFef/75Z73yyitq27atateufVu3h3rooYe0adOmbOfVqVNHrq6uSkhIULVq1WxeFSpUyHGdDRs21MGDB2+57QEDBuj69etasGCBzfSffvpJzs7Oqlu3rn2dAYAcEDYB5KpVq1aqW7euXn/9dUlStWrVtHPnTn3zzTf69ddf9eqrryo+Pt5mmVGjRmnmzJlau3atfvnlF4WFheV6n0p/f3+5uLho/vz5+uOPP7R+/frbvgfnhQsXlJycrMTERG3evFn/+c9/NG3aNE2fPl3VqlXLdpnRo0frm2++0ZEjR7R7925FR0erdu3akqSKFSvKYrFow4YNOnnypC5evCgvLy+VKlVKH3zwgX7//XdFR0dr7Nixdtc6ceJExcfHKywsTD/++KN++eUXvffeezp16pSKFy+u8ePHa8yYMVq6dKkOHz6sPXv26L///a+WLl2a4zrbt2+vuLg4m3CcnUKFCmn06NGaOXOmzSHz77//Xs2bN8911BUA7EHYBHBLY8eO1aJFi5SYmKihQ4eqZ8+e6tOnjx599FGdPn06y304x40bp6efflohISEKCgpS8eLF9dhjj+W4/tKlS2vJkiX69NNPVadOHc2cOVOzZ8++rVonTZokX19fVatWTQMGDFBKSoo2bdqkF198McdlMjIyNHz4cNWuXVsdOnRQzZo1rSN+5cqV05QpUzRhwgSVLVtWI0aMUKFChbRy5Urt2rVLAQEBGjNmjGbNmmV3rTVq1FBUVJT27dunf/3rXwoKCtLnn38uJycnSdJrr72mSZMmacaMGapdu7bat2+vL774IsfTASSpU6dOcnZ21rfffnvL7Q8ePFjp6el69913rdM+/vhjhYaG2t0XAMiJxTBuuvkdACBfW7BggT7//HN98803di0XGRmp559/Xj/++KM18ALAneK3CQAUMP/5z3909uxZXbhwwa6nNF26dEkREREETQAOxcgmAAAATMM5mwAAADANYRMAAACmIWwCAADANIRNAAAAmIawCQAAANMQNgEAAGAawiYAAABMQ9gEAACAaQibAAAAMM3/B1MKPqDFLfG1AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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1phJrmO1WWcXWMNvNk4qjIuxuvmRERZS5BZqSqEiHN8CZKVOmyGKx2N1iY2Odrr9s2TL16dNH9evXV3R0tBITE/XJJ5/YrbNo0aIy27RYLDp58qTb4wu8P4WAh7k7U1kZrsxuOlPRrKftNcqZ/SxV3ixoRY87mg2VHM+ISmVnRaWyESqVDVGp/BiVXAhSyWmU/u+/Doetkkj7x4ojy8brmevY1rVanM5+lsZpeSFcGqfOtnG60+PU1fOvSvazqZW9qMCZcerJK2edGae+mFE9naM4DYSZ1TM5i1NmWCFJrVu31po1a2z3w8PDna67ceNG9enTR0888YTOOussLVy4UIMHD9bWrVvVvn1723rR0dHas2eP3XOjotz/t48wRcirSjRWxNWodMaV2CxVUXS6so6z+JTcC9BSngpRyT5Gpf8FqV2M2lauXJS6ozJxKrkXqJJ7kepOoEqeiVTJPlQ9fXlXf4eqFDyxKhGsOCUiIqLcWdLTPfvss3b3n3jiCa1YsULvv/++XZhWNPPq8tiqvAUgwFU1Hp1xJyodcSU03V23vPCUnMen5H6ASq5FaKnyZkVLOZ0dlWwxWspZlHpSeXEqlR+VrgSqq9sqVdlZVMk7kSoFZ6hKwRWrEsEaLPLy8uzuW61WWa1lz1jwww8/qFGjRrJarerUqZOeeOIJnXvuuS69RklJiY4ePaqzzz7bbvmxY8cUHx+v4uJiXXzxxXrsscfswtVVhClCXlUD8kzuBKUntlFRbJYqLzptr1uJ+JQcB6jtMQch6mhGVHI+K3o6V4NUchyllZktdfRxvm08TuJU8l+gSuaIVMm7s6mS44OqzBKrgRyqpQhWz8srkTx9aftj/91ekyZN7JZPnjxZU6ZMsVvWqVMnLV68WOeff77++OMPPf744+rSpYu++eYb1a1bt8LXevrpp3X8+HENGzbMtuyCCy7QokWL1LZtW+Xl5Wnu3Lnq2rWrdu7cqfPOO8+tfbEYhuHa33KwycvLU0xMjHJzcxUdHe3v4aCKnv/uSo9tyxNR6mpolnIlOKXyo1MqPzwl9+OzlKsRKjkOUaniGJXsg1RyPUqdBaE76zpbv8zzXYhKdw7GcjVST+dupJ6uqpF6Jm+EqsPXMVEgBkOslsfswVpUnK81P8zxy7/fpe2w/ZuGqlXbswcRHjtaoo6t/1BWVpbdfjmbMT3d8ePH1aJFC91///2aMGFCuesuWbJEt912m1asWKHevXs7Xa+kpESXXHKJunXrpueee86tfWHGFCHPHzHpiKuBebqKYrNURdEplR+eUuXiU3IcoKVcmhG1bajiGJXODNGqzZSeqaKZ04pep6Lvn5a+huRaoLo7iypV/vuoUtVPQ3Umb3/sb3sdk3z8LwXfVwDOxAyrf0VHR7sd3DVr1lTbtm31ww8/lLve0qVLNWrUKL377rvlRqkkhYWF6dJLL61wm44Qpgh5VYnKysSkI64G5ulcic1SFUWnVH54SuXHp+R+gJZyJ0SlimPU0TJPRGmp8uJUKv+jfcn1mKxMoLqy3VJV+ai/lCc/8pe8/7G/7XVMFKpS8MeqRLCaWX5+vr799ltdccUVTtdZsmSJbr31Vi1ZskSDBg2qcJuGYSgjI0Nt27Z1ezyEKUKeN2cqXeFOYJZyJTRt61YQnFLF0SmVH55SJePTtnHXI1Qq/0CmMx8rLxLdPYdpKVfitKLX9kagurPd05k9UqXQClUpNGJVIlj9YeLEiRo8eLCaNm2q/fv36/HHH1deXp5GjBghSUpOTtZvv/2mxYsXSzoVpTfffLPmzp2rzp07KycnR5JUvXp1xcTESJKmTp2qzp0767zzzlNeXp6ee+45ZWRk6IUXXnB7fIQpQp47kVmZiCyPO4Fpe44LoVnKleCUKo5OqfzwlCofn6WcRajkXojaP+b89Sobpf5SUQyXWd+Frw04UmQNq9L3UKVTkerp76OWhqovvpdaGqpmCNTTnR6rwRippzszWAlVz/n11191ww036MCBA6pfv746d+6szz//XPHx8ZKk7OxsZWZm2tZ/+eWXVVRUpHHjxmncuHG25SNGjNCiRYskSUeOHNHtt9+unJwcxcTEqH379tq4caMuu+wyt8fHwU+VwMFPwWXQxrs9vs3KBKftuW6Ep+R6fEqeCdBS3gxRqeLTO3kzSCv66N8T27Bty414dDemKxOmpaoap6U8HailfHXglO31TByCwR6pzlQmVoP94Kdg6BJmTAE3+DI4S3k6PEt5LEClCiNU8m6Innq8/Nf3VEz6K0oro7KzppJnZk4l78yeSqdmUH0Zp2adRZVCayb1dMyqBifCFDiDtz9eP5070Sm5F56S6/EpuRigkkciVHLthPdVjVHJwyHpwW0FAk/GqeT52VNfHSxl95qnRaDZI1UiVBF4CFOEPFdD1JsfsZfyZnhKno3PUq5EqOSZED21jksvV2FEuhOQrn6EHkxRGmh8PYMqmXsWtVRpqIZSoJYiVAMTYQqcwdUA9UV4Sl6Mz1J+ilD31nNpNY/PaLrznU63tluJj9cD7WAtR7z1sX4pf8SpFFiBKoVmpEqnQrWkmMNqzI4wRcir7InjnfFFfErmCFDJvWvQezpEJe/MZnorSKXQjdJSwRqn0qlANXOclgrlWVQzOFQSpfwSzx78dNzT1zj1I8IUIc/b3/OUfBSeklvxWcpbEeru+v6OUXe2W5ltS76PUk8cXOWp75n6kr/jVDL37GkpZlFhRoQp4IC3v+tZyozxWcosESq5H2femh11d9u216hkIAbTTOnpvD1rKvk3TqXAClSJWVSYB2GKSrv0o4d89lqV+Uj9dOWHpmt/DMw86yn5Jj4r/xz31vd2LFYm+Cp7YBNR6j/+jlMpcD7eL0Wgwt8IU/hdVaPzdJX5mL2Uz8JT8ml8SpWLyco+r1Izij4IxcqGnq+DVPJMlHrqHKmB9jG+GQVanEoEKvyHMEWleTIonalKaJ6ush+1S76PTykwAvTU8yr1NN/OWPrwtaTgCVLJe1Hq7Y/xzSgQ41Q6FajEKXyJMEWleSoaK6MqoVmq0sFZyg/hWaqyIVmV51Yp1nwdhz6eHZX8H6RVHcOZmClFKWZP4UuEKbzKEwHprioHp1Sl6JSqHp6Sf+Lzf8+v/HOrElm+nBWt6mtKnglBs82SSt6N0lCcLS0VqLOmpZg9hS8Qpqg0b0SnR6KyPFUMTskz0SlVPR49sY2qXqnIb0Hop9e1vb5JglQKrCj1BzMcAHW6YIhTidlTeA9hikrzekQ644G4dMRTwSl5Jjo9tR2PhFgVt+HPAPbE60vmilHJ80EqeT9KQ3m29HSBHqcSgQrvIUxReV4KRE/wZGSW8lRsemN7nrpGuyfCyQwh7LFxeCj+zB6kUvDNlJpdMMSpxMf78DzCFD7njWisDE+Hpre266nolDwbSMEWw6XMGKOS94JU8k2UMlsavJg9hScRpqg0Xwemt0LSn6/ryaCSPB9DwR7FkueDzxsnxg/0KIVjwTJrWorZU9fkllRXYYlnvwp3oqTYo9vzJ8IUleavUHSHr8fo6dA8kzeiJ5TiWPJO5HnrKk3BEqTMloYO4hRVRZjClMwYvd6OTsm7l6H0xvgDIpS9FHeBGKOS72dI/R2lZjoiH0DFCFNUmhnj0VW+iMwz+eLa597aL2+N3SuxHGAhKnk/RqXQC1LJ/FEaTB/jn45ZU1QFYYqA44+oLI8vgrNUIH5VoJTXotmLUeft95Yg9R6zRykAxwhTVJrZAtFTfBmap/PVz9Pb++f1eCZEK+SvA5rMEKUEqTkwa4rKIkwRdPwVls74OuB9tf+BHKC21/DF94Z9FKNSaAepFFhRGqwf4wNVRZii0swWgL7iz5liX//MfTaL66N489XPLxRiVDJPkEqBFaWhgllTVAZhipBjxq8gBPvXB0r5Mth8+TP15X6VIkj/hyg1L+IU7iJM4VdmjERPMMtssl/OPuDjSPPHz9ofISr5/2T4ZgtSKTCjlI/xAecIU1RasEalZJ6wdMQMP3d/hZm/3hd/7a/k/xiVzBmkUmBGKYDyEaYIKmYOSleYITpL+TPG/P0+hnqIliJIPY/ZUhwqrqm/ijybX38VB8/vK8IUlebveAgUZorN0/kzvk5nht9H/v5ZEKOuCeQglYhSwBWEKeAms4amI/4OrtOZIUAlc/xMzBSikrljVAr8IJVCN0o58AnuIkwRsgIpMB0xQ2CdySzxWcosPyOzhahk/hiVCFIgFBGmMJ1AD8aqMEtIlcds8SmZ7+dmxhCVAiNGpeAIUokoZbYUlUGYotJCOSBdZbZgcoUZw7OUWX+eZg1RKXBiVAqeIJWIUqCyCFOgAmaNocowc3SWMvvP28wRKgVWiErBFaMSQQpUFWGKoGH2oPGmQAjO0wXKe2X2CJUCL0RLBVuQSkTp6fgYH5VFmKLSAiUuAlGgheaZAvH3RiBEqBS4ISoFZ4xKBCngSYQp4EWBHpjOBGJ4ni5QIlQK7BCVgjdGSxGlZTFbiqogTAEXBGtgOhPo4SkFVnyeLtBDVAr+GJUIUmeIUlQVYYqgFmpB6apgCM9SgRqgpYIhRCViFEQpPIMwRaURfeYUTNF5ukAPUCl4IrRUKMSoRJC6gih1XW5xDRUUeza//ioOnp8/YQoEgGCNzTMFQ3yWCrYIlUInREsRpBUjSOFphCngY6ESmY4EU3ieLhgjtBQxCmeIUngDYQq4KZTDsiLBGp6nC+YIlUIvREsRpO4hSuEthCmCFgHpWaEQnacL9gAtFaohKhGjlUWUwpsIU1Qa4Rf4Qi02HQmVAJVCO0JLEaOVR5DCFwhTIIgQmo6FUnyejhD9H4K0aohS+AphCpgYoemaUA3P0xGhZRGjVUeQwtfC/D0AIJRE5Je4dYO9sPwSh7dQFJ5fYnfDqRA9/YbKs5wsIkpDxIwZM2SxWHTPPfc4XWf9+vWyWCxlbt99953deikpKUpISJDValVCQoKWL1/u9niYMQXcQCx6V6hGZkUIT+cIUM8iRkPLtm3b9Morr+iiiy5yaf09e/YoOjradr9+/fq2X6elpWn48OF67LHHdM0112j58uUaNmyYNm/erE6dOrk8JsIUQYuINB/Cs3wEqGuIUc8jSEPPsWPHdNNNN+nVV1/V448/7tJzGjRooLPOOsvhY88++6z69Omj5ORkSVJycrI2bNigZ599VkuWLHF5XHyUj0pz92NpX9/gW84+Zg/1j9ydOfOjeKLUOT6i9x4+sg8ueXl5drf8/Hyn644bN06DBg1S7969Xd5++/btFRcXp169emndunV2j6Wlpalv3752y/r166ctW7a4tQ/MmAIoF0FZNQSn+4hP7yNG/SevuLoKiqt5dJsniwslSU2aNLFbPnnyZE2ZMqXM+m+//ba+/PJLbdu2zaXtx8XF6ZVXXlGHDh2Un5+v//u//1OvXr20fv16devWTZKUk5Ojhg0b2j2vYcOGysnJcWtfCFMgBBGbnkeAVh4h6jsEaXDLysqy+w6o1Wp1uM748eOVmpqqqKgol7bbqlUrtWrVynY/MTFRWVlZmj17ti1MJclisdg9zzCMMssqQpgCAY7I9B3i0zMIUd8iRkNHdHS0XZg6smPHDu3fv18dOnSwLSsuLtbGjRv1/PPPKz8/X+Hh4RW+VufOnfXmm2/a7sfGxpaZHd2/f3+ZWdSKEKaAnxGW5kJ8eh4h6h8EKRzp1auXdu3aZbfslltu0QUXXKAHHnjApSiVpPT0dMXFxdnuJyYmavXq1br33ntty1JTU9WlSxe3xkeYAlVAVAYuAtQ7iFD/IkZRkdq1a6tNmzZ2y2rWrKm6devalicnJ+u3337T4sWLJZ064r5Zs2Zq3bq1CgoK9OabbyolJUUpKSm2bYwfP17dunXTrFmzNGTIEK1YsUJr1qzR5s2b3RofYYqQQUSGDqLTdwhRcyBI4UnZ2dnKzMy03S8oKNDEiRP122+/qXr16mrdurVWrVqlgQMH2tbp0qWL3n77bT3yyCOaNGmSWrRooaVLl7p1DlNJshiGYXhsT0JEXl6eYmJilJubW+F3OYJZj/6z/D0EhCjC0z+IUHMhRt1XVJyvtbtm+eXf79J2eOTzvoqq5eGj8o8V6vHOqUHRJUFzHtN9+/Zp1KhRat68uapXr64WLVpo8uTJKigosFsvMzNTgwcPVs2aNVWvXj3dfffdZdYB4F+OzvHJ+T5968xzhxKl5lB63lGiFMEqaD7K/+6771RSUqKXX35ZLVu21Ndff63Ro0fr+PHjmj17tqRTR50NGjRI9evX1+bNm3Xw4EGNGDFChmFo3rx5ft4DIDQQluZDdJobEYpQEjRh2r9/f/Xv3992/9xzz9WePXs0f/58W5impqZq9+7dysrKUqNGjSRJTz/9tEaOHKnp06cH/PQ34E8EZ2AgQgMDMYpQFTRh6khubq7OPvts2/20tDS1adPGFqXSqctl5efna8eOHerZs6fD7eTn59td1isvL897gwZMgtAMbARoYCFEgVOCNkx/+uknzZs3T08//bRtmaPLZdWpU0eRkZHlXjJrxowZmjp1qtfGCngbkRm8CNDARYyGpiNFNWUt8uzBT/lFhR7dnj+Z/uCnKVOmyGKxlHvbvn273XN+//139e/fX9ddd51uu+02u8ccXRqroktmJScnKzc313bLysryzM4BbqjogCAOFgpejg5E4oCkwHP6gUtEKeCY6WdM77zzTl1//fXlrtOsWTPbr3///Xf17NlTiYmJeuWVV+zWi42N1datW+2WHT58WIWFheVeMstqtTq83ixQEaIQriIygw/xCbjP9GFar1491atXz6V1f/vtN/Xs2VMdOnTQwoULFRZmPyGcmJio6dOnKzs723YZrdTUVFmtVrtrxiJ0EZLwJuIz+BGjQNWYPkxd9fvvv6tHjx5q2rSpZs+erT///NP2WGxsrCSpb9++SkhIUFJSkp566ikdOnRIEydO1OjRozki3wSIQgQLAjR0EKKAZwVNmKampurHH3/Ujz/+qMaNG9s9Vnpxq/DwcK1atUpjx45V165dVb16dd14442200nBPYQkQhXhGboIUcC7giZMR44cqZEjR1a4XtOmTfXBBx94f0AAAhrxCYkQBXwtaMIUAFxFdMIRIhTwP8IUQFAhOuEqQhQwH8IUQEAgOFEVRCgQGAhTAH5BaMJbiFAgcBGmADyC0IQ/EKFAcCFMAZRBZMJsCFAEi6OFVuUXRnp0mwWFpr/CvMsIUyDIEZkINEQoELoIUyAAEJcIVkQogNMRpoCXEJPA/xCgAFxBmCJkEY6A5xGgAKqCMEWlEXZAaCI+AXgLYQoAcIgABeBrhCkAhCjCE4DZEKYAEMSIT+CUsJMFCisu8PcwUAHCFAACGOEJOBd2khANNIQpAJgY4Qm4jhANfIQpAPgZ8QlUXqDFaF5RdVXz8CVJC4vCPbo9fyJMAcCLiE7AswItROEewhQAKonoBHyDGA0dhCkAOEB0Av5DiIYuwhRASCA0AfMiRFGKMAUQsIhNIDARonCGMAVgCkQmEJyIULiDMAXgMcQlAEIUVUGYArBDXAJwByEKTyJMgSBCVALwJiIU3kaYAn5CRAIwO0IUvkaYIqQRhwBwChEKMyBMUWlEHQAEJiLUf44VRqpaYaRHt1lY6NHN+RVhCgBAECNCEUgIUwAAggQRikBHmAIAEGAIUAQrwhQAABMjQhFKCFMAAEyAAAUIUwAAfIoABZwL8/cAAAAIRmEnCxzeAH+aP3++LrroIkVHRys6OlqJiYn66KOPnK4/cuRIWSyWMrfWrVvb1lm0aJHDdU6ePOn2+JgxBQCgCohNBJLGjRtr5syZatmypSTpjTfe0JAhQ5Senm4Xm6Xmzp2rmTNn2u4XFRWpXbt2uu666+zWi46O1p49e+yWRUVFuT0+whQAABcQoAgGgwcPtrs/ffp0zZ8/X59//rnDMI2JiVFMTIzt/nvvvafDhw/rlltusVvPYrEoNja2yuPjo3wAAP7L2cfvRCkCQV5ent0tPz+/3PWLi4v19ttv6/jx40pMTHTpNRYsWKDevXsrPj7ebvmxY8cUHx+vxo0b66qrrlJ6enql9oEZUwBASCEy4U8nCqspwsOXJC0qNCRJTZo0sVs+efJkTZkypcz6u3btUmJiok6ePKlatWpp+fLlSkhIqPB1srOz9dFHH+mtt96yW37BBRdo0aJFatu2rfLy8jR37lx17dpVO3fu1HnnnefWvhCmAICgQ3wiFGVlZSk6Otp232q1OlyvVatWysjI0JEjR5SSkqIRI0Zow4YNFcbpokWLdNZZZ2no0KF2yzt37qzOnTvb7nft2lWXXHKJ5s2bp+eee86tfSBMAQABh/AEyio90r4ikZGRtoOfOnbsqG3btmnu3Ll6+eWXnT7HMAy9/vrrSkpKUmRk+TO+YWFhuvTSS/XDDz+4twMiTKvkmsufUES44/8bAQBUHuEJ+I5hGBV+H3XDhg368ccfNWrUKJe2l5GRobZt27o9FsIUAOBzhCfgHw899JAGDBigJk2a6OjRo3r77be1fv16ffzxx5Kk5ORk/fbbb1q8eLHd8xYsWKBOnTqpTZs2ZbY5depUde7cWeedd57y8vL03HPPKSMjQy+88ILb4yNMAQBeQXwC5vPHH38oKSlJ2dnZiomJ0UUXXaSPP/5Yffr0kXTqAKfMzEy75+Tm5iolJUVz5851uM0jR47o9ttvV05OjmJiYtS+fXtt3LhRl112mdvjI0wBAG4jOoHAtGDBgnIfX7RoUZllMTExOnHihNPnzJkzR3PmzKnq0CQRpgCAMxCdAPyFMAWAEEJ0AjAzwhQAggDBCSAYEKYAYFLEJoBQQ5gCgA8RmwDgHGEKAJVEZAJw14nCSIUXlH/lJHcVFxoe3Z4/EaYAQh6BCQDmQJgCCErEJgAEHsIUgGkRlwAQWghTAF5FXAIAXEWYAnCIoAQA+BphCgQBIhIAEAwIU8BLiEUAANxDmCLgEYAAAAQHwrQKwk4WKCzc4u9hAAAABIUwfw8AAAAAkJgxBQAA8JmThdUUXljNo9ssLizx6Pb8iRlTAAAAmAJhCgAAAFMgTAEAAGAKhCkAAABMgTAFAACAKRCmAAAAMAXCFAAAAKZAmAIAAMAUCFMAAACYgltXfsrNzdXy5cu1adMm7du3TydOnFD9+vXVvn179evXT126dPHWOAEAABDkXJoxzc7O1ujRoxUXF6dp06bp+PHjuvjii9WrVy81btxY69atU58+fZSQkKClS5d6e8wAAAAIQi7NmLZr104333yzvvjiC7Vp08bhOn/99Zfee+89PfPMM8rKytLEiRM9OlAAAIBAl18QrvAItz6wrlBxQZFHt+dPLv1kvvnmG9WvX7/cdapXr64bbrhBN9xwg/7880+PDA4AAAChw6WP8iuK0qquDwAAALh8VH6LFi00Z84cp4//8ccfCg8P98igAAAAEHpcDtO9e/fqgQce0IgRI1RQUOBwHcMwPDYwAAAAhBa3zmO6fPlyrVu3Tt26dVN2dnaZxy0Wi8cGBgAAgNDiVpheeuml2rZtmyIiItShQwdt3brVW+MCAABAiHH7yk8NGzbU+vXrNXjwYPXo0UMLFy70xrgAAAAQYip1SdKIiAi9/PLLmjNnjsaMGaPx48erqMg859DKz8/XxRdfLIvFooyMDLvHMjMzNXjwYNWsWVP16tXT3Xff7fQ7swAAAPAdl8/w6uj7o2PGjFGbNm3097//XZ999plHB1YV999/vxo1aqSdO3faLS8uLtagQYNUv359bd68WQcPHtSIESNkGIbmzZvnp9ECAABAcmPG1NkR95dffrm2bdvmsQFV1UcffaTU1FTNnj27zGOpqanavXu33nzzTbVv3169e/fW008/rVdffVV5eXl+GC0AAABKuXW6KGcnzm/SpIk+++wzrVu3zmMDq4w//vhDo0eP1v/93/+pRo0aZR5PS0tTmzZt1KhRI9uyfv36KT8/Xzt27HC63fz8fOXl5dndAAAA4Fkuf5QfHx9f7uNWq1XdunWr8oAqyzAMjRw5UmPGjFHHjh21b9++Muvk5OSoYcOGdsvq1KmjyMhI5eTkON32jBkzNHXqVE8PGQAAhJjignAZEZ69IFFJQfBc4MilGdP+/ftry5YtFa539OhRzZo1Sy+88EKVB1ZqypQpslgs5d62b9+uefPmKS8vT8nJyeVuz9F3ZQ3DKPccrMnJycrNzbXdsrKyqrxfAAAAsOfSjOl1112nYcOGqXbt2rr66qvVsWNHNWrUSFFRUTp8+LB2796tzZs368MPP9RVV12lp556ymMDvPPOO3X99deXu06zZs30+OOP6/PPP5fVarV7rGPHjrrpppv0xhtvKDY2tsy5Vw8fPqzCwsIyM6mns1qtZbYLAAAAz3IpTEeNGqWkpCT95z//0dKlS/Xqq6/qyJEjkk7NQCYkJKhfv37asWOHWrVq5dEB1qtXT/Xq1atwveeee06PP/647f7vv/+ufv36aenSperUqZMkKTExUdOnT1d2drbi4uIknTogymq1qkOHDh4dNwAAANzj8ndMIyMjdeONN+rGG2+UJOXm5uqvv/5S3bp1Va1aNa8N0FVNmza1u1+rVi1JUosWLdS4cWNJUt++fZWQkKCkpCQ99dRTOnTokCZOnKjRo0crOjra52MGAADA/1TqBPuSFBMTo9jYWFNEqavCw8O1atUqRUVFqWvXrho2bJiGDh3q8NRSAAAA8C2XZ0wDTbNmzRyee7Vp06b64IMP/DAiAAAAlKfSM6YAAAAILPPnz9dFF12k6OhoRUdHKzExUR999FG5z9mwYYM6dOigqKgonXvuuXrppZfKrJOSkqKEhARZrVYlJCRo+fLllRofYQoAABAiGjdurJkzZ2r79u3avn27rrzySg0ZMkTffPONw/X37t2rgQMH6oorrlB6eroeeugh3X333UpJSbGtk5aWpuHDhyspKUk7d+5UUlKShg0bVuZMSK6wGM6uNQqn8vLyFBMTo97n3auIcE4jBQBAICgqzteaH+YoNzfX5wc9l7ZD/KuTFFYjyqPbLjlxUr+MfqzS+3X22Wfrqaee0qhRo8o89sADD2jlypX69ttvbcvGjBmjnTt3Ki0tTZI0fPhw5eXl2c289u/fX3Xq1NGSJUvcGkulZkyPHDmi1157TcnJyTp06JAk6csvv9Rvv/1Wmc0BAACgis68fHp+fn656xcXF+vtt9/W8ePHlZiY6HCdtLQ09e3b125Zv379tH37dhUWFpa7jisXZzqT2wc/ffXVV+rdu7diYmK0b98+jR49WmeffbaWL1+uX375RYsXL3Z7EAAAAKGgpChcKvTwJUmLTm2vSZMmdssnT56sKVOmlFl/165dSkxM1MmTJ1WrVi0tX75cCQkJDrft6HLuDRs2VFFRkQ4cOKC4uDin65R3uXdn3A7TCRMmaOTIkXryySdVu3Zt2/IBAwbYznEKAAAA38rKyrL7KN/ZVStbtWqljIwMHTlyRCkpKRoxYoQ2bNjgNE7PvGx76bdAT1/uaJ3yLvfujNthum3bNr388stllp9zzjmVKmMAAABUXemR9hWJjIxUy5YtJZ26dPu2bds0d+5ch30XGxtbpu/279+viIgI1a1bt9x1yrvcuzNuf8c0KipKeXl5ZZbv2bNH9evXd3sAAAAA8B/DMJx+HzUxMVGrV6+2W5aamqqOHTvaLrLkbJ0uXbq4PRa3w3TIkCGaNm2a7QuvFotFmZmZevDBB3Xttde6PQAAAAD4xkMPPaRNmzZp37592rVrlx5++GGtX79eN910kyQpOTlZN998s239MWPG6JdfftGECRP07bff6vXXX9eCBQs0ceJE2zrjx49XamqqZs2ape+++06zZs3SmjVrdM8997g9PrfDdPbs2frzzz/VoEED/fXXX+revbtatmyp2rVra/r06W4PAAAAAL7xxx9/KCkpSa1atVKvXr20detWffzxx+rTp48kKTs7W5mZmbb1mzdvrg8//FDr16/XxRdfrMcee0zPPfec3WRkly5d9Pbbb2vhwoW66KKLtGjRIi1dulSdOnVye3yVPo/p2rVr9eWXX6qkpESXXHKJevfuXZnNBCTOYwoAQOAxw3lMm8yforDqHj6P6V8nlXXHFL/sl6e5ffBTqSuvvFJXXnmlJ8cCAACAEOb2R/l33323nnvuuTLLn3/++Up9lwAAAACQKhGmKSkp6tq1a5nlXbp00X/+8x+PDAoAAKDKTubb3yq4EhL8z+2P8g8ePKiYmJgyy6Ojo3XgwAGPDAoAAKBCJwnNYOP2jGnLli318ccfl1n+0Ucf6dxzz/XIoAAAAMrMeJ55Q9Cp1CVJ77zzTv3555+2g58+/fRTPf3003r22Wc9PT4AABDMQi0w88OkMLfnBSveZpBwO0xvvfVW5efna/r06XrsscckSc2aNdP8+fPtTsgKAAAQcuGJKqnU6aLuuOMO3XHHHfrzzz9VvXp11apVy9PjAgAAgYDwhAdV+jymklS/fn1PjQMAAJgR4QkfcvtLCaWXsmrUqJEiIiIUHh5udwMAAAGEA4xgIm7PmI4cOVKZmZmaNGmS4uLiZLFYvDEuAABQVYQlAozbYbp582Zt2rRJF198sReGAwAAKkRwIki5HaZNmjSRYRjeGAsAACA6EcLc/o7ps88+qwcffFD79u3zwnAAAAhSFX2Xk+90Au7PmA4fPlwnTpxQixYtVKNGDVWrVs3u8UOHDnlscAAAmB4xCXiM22HK1Z0AACGB4AR8zu0wHTFihDfGAQCAbxCc8CNLQZgs4Z69hKilIIQvSXq6v/76S4WFhXbLoqOjqzQgAADcRmwCQcHtMD1+/LgeeOABvfPOOzp48GCZx4uLiz0yMABACCM0gZDk9tzv/fffr7Vr1+rFF1+U1WrVa6+9pqlTp6pRo0ZavHixN8YIAAgGrh6VTpQCIcvtGdP3339fixcvVo8ePXTrrbfqiiuuUMuWLRUfH69///vfuummm7wxTgCAmRCPALzA7TA9dOiQmjdvLunU90lLTw91+eWX64477vDs6AAA3kVgAjARtz/KP/fcc20n109ISNA777wj6dRM6llnneXJsQEAXOXOx+R8ZA7ApNyeMb3lllu0c+dOde/eXcnJyRo0aJDmzZunoqIiPfPMM94YIwCEBkIRQIhzO0zvvfde26979uyp7777Ttu3b1eLFi3Url07jw4OAAIOcQkAleZ2mC5evFjDhw+X1WqVJDVt2lRNmzZVQUGBFi9erJtvvtnjgwQAryEkAcA03P6O6S233KLc3Nwyy48ePapbbrnFI4MCgErjO5YAELDcnjE1DEMWi6XM8l9//VUxMTEeGRSAEEIcAgD+y+Uwbd++vSwWiywWi3r16qWIiP89tbi4WHv37lX//v29MkgAJkFEAkCVhBVaFBZedoKvSgo9vD0/cjlMhw4dKknKyMhQv379VKtWLdtjkZGRatasma699lqPDxCAGwhHAEAAczlMJ0+eLElq1qyZrr/+etvBTwCcIBIBAHCL298xvfLKK/Xnn3+qcePGkqQvvvhCb731lhISEnT77bd7fICAywhBAAACmttheuONN+r2229XUlKScnJy1Lt3b7Vp00ZvvvmmcnJy9Oijj3pjnKgsYg0AAAQIt8P066+/1mWXXSZJeuedd9S2bVt99tlnSk1N1ZgxY0IrTPPzK3HCLQAAADjidlYVFhbavl+6Zs0aXX311ZKkCy64QNnZ2Z4dHQAAAEKG22HaunVrvfTSS9q0aZNWr15tO0XU77//rrp163p8gAAAAAgNbofprFmz9PLLL6tHjx664YYb1K5dO0nSypUrbR/xAwAAAO5y+zumPXr00IEDB5SXl6c6derYlt9+++2qUaOGRwcHAACA0OF2mEpSeHi4XZRKp85vCgAAAFSWS2F6ySWX6NNPP1WdOnVslyZ15ssvv/TY4AAAAIJJWIFFYWEevoRoQYhdknTIkCG2I/FLL00KAAAAeJJLYVp6OdIzfw0AAAB4itvfMTUMQzt27NC+fftksVjUvHnzCj/eBwAAACriVpiuW7dOo0aN0i+//CLDMCTJFqevv/66unXr5pVBAgAAIPi5fB7TH3/8UVdddZWaNWumZcuW6dtvv9Xu3bv17rvvqnHjxho4cKB+/vlnb44VAAAAQczlGdNnn31WnTt31qeffmq3/IILLtA111yj3r17a86cOZo3b57HBwkAAIDg5/KM6fr163XPPfc4fMxiseiee+7RunXrPDUuAAAAhBiXwzQzM1Nt27Z1+nibNm30yy+/eGRQAAAA8LwZM2bo0ksvVe3atdWgQQMNHTpUe/bsKfc5y5YtU58+fVS/fn1FR0crMTFRn3zyid06ixYtksViKXM7efKkW+NzOUyPHTtW7iVHa9SooRMnTrj14gAAAPCdDRs2aNy4cfr888+1evVqFRUVqW/fvjp+/LjT52zcuFF9+vTRhx9+qB07dqhnz54aPHiw0tPT7daLjo5Wdna23S0qKsqt8bl1VP7u3buVk5Pj8LEDBw649cIAAADwrY8//tju/sKFC9WgQQPt2LHD6dmVnn32Wbv7TzzxhFasWKH3339f7du3ty23WCyKjY2t0vjcCtNevXrZThN1OovFIsMwOJcpAACAn+Tl5dndt1qttit3OpObmytJOvvss11+nZKSEh09erTMc44dO6b4+HgVFxfr4osv1mOPPWYXrq5wOUz37t3r1oYBAABgz1Ighbn8RUrXGAWn/tukSRO75ZMnT9aUKVOcP88wNGHCBF1++eVq06aNy6/39NNP6/jx4xo2bJht2QUXXKBFixapbdu2ysvL09y5c9W1a1ft3LlT5513nsvbdjlM4+PjXd4oAAAAfCsrK0vR0dG2+xXNlt5555366quvtHnzZpdfY8mSJZoyZYpWrFihBg0a2JZ37txZnTt3tt3v2rWrLrnkEs2bN0/PPfecy9t3+5KkAAAAMJ/o6Gi7MC3PXXfdpZUrV2rjxo1q3LixS89ZunSpRo0apXfffVe9e/cud92wsDBdeuml+uGHH1zatu15bq0NAACAgGUYhu68804tW7ZMa9euVfPmzV163pIlSzRy5Ei99dZbGjRokEuvk5GRobi4OLfGx4wpAABAiBg3bpzeeustrVixQrVr17adbSkmJkbVq1eXJCUnJ+u3337T4sWLJZ2K0ptvvllz585V586dbc+pXr26YmJiJElTp05V586ddd555ykvL0/PPfecMjIy9MILL7g1PmZMAQAAQsT8+fOVm5urHj16KC4uznZbunSpbZ3s7GxlZmba7r/88ssqKirSuHHj7J4zfvx42zpHjhzR7bffrgsvvFB9+/bVb7/9po0bN+qyyy5za3zMmAIAAIQIR6f9PNOiRYvs7q9fv77C58yZM0dz5syp5Kj+x6Uwbd++vcvnKP3yyy+rNCAAAACEJpfCdOjQoV4eBgAAAEKdS2E6efJkb48DAAAAIY6DnwAAAGAKbh/8VFxcrDlz5uidd95RZmamCgoK7B4/dOiQxwYHAAAQTMILpXBPTwsWenh7fuT2j2bq1Kl65plnNGzYMOXm5mrChAn629/+prCwsHKvxwoAAACUx+0w/fe//61XX31VEydOVEREhG644Qa99tprevTRR/X55597Y4wAAAAIAW6HaU5Ojtq2bStJqlWrlnJzcyVJV111lVatWuXZ0QEAACBkuB2mjRs3VnZ2tiSpZcuWSk1NlSRt27ZNVqvVs6MDAABAyHA7TK+55hp9+umnkqTx48dr0qRJOu+883TzzTfr1ltv9fgAAQAAEBrcPip/5syZtl///e9/V+PGjbVlyxa1bNlSV199tUcHBwAAgNDhdpieqXPnzurcubMnxgIAAIAQ5lKYrly5UgMGDFC1atW0cuXKctf196zpqlWrNG3aNH311VeqWbOmunXrpmXLltkez8zM1Lhx47R27VpVr15dN954o2bPnq3IyEg/jhoAAAAuhenQoUOVk5OjBg0aaOjQoU7Xs1gsKi4u9tTY3JaSkqLRo0friSee0JVXXinDMLRr1y7b48XFxRo0aJDq16+vzZs36+DBgxoxYoQMw9C8efP8Nm4AAAC4GKYlJSUOf20mRUVFGj9+vJ566imNGjXKtrxVq1a2X6empmr37t3KyspSo0aNJElPP/20Ro4cqenTpys6Otrn4wYAAMApHr0o1okTJzy5Obd8+eWX+u233xQWFqb27dsrLi5OAwYM0DfffGNbJy0tTW3atLFFqST169dP+fn52rFjh9Nt5+fnKy8vz+4GAAAAz3I7THv06KFff/21zPKtW7fq4osv9sSYKuXnn3+WJE2ZMkWPPPKIPvjgA9WpU0fdu3fXoUOHJJ26OEDDhg3tnlenTh1FRkYqJyfH6bZnzJihmJgY261Jkybe2xEAABC0wgq8cwsWbodpdHS0LrroIr399tuSTn20P2XKFHXr1s0rBz5NmTJFFoul3Nv27dttXzF4+OGHde2116pDhw5auHChLBaL3n33Xdv2LBZLmdcwDMPh8lLJycnKzc213bKysjy+nwAAAKHO7dNFrVy5Ui+99JJuu+02rVy5Uvv27VNmZqZWrVql3r17e3yAd955p66//vpy12nWrJmOHj0qSUpISLAtt1qtOvfcc5WZmSlJio2N1datW+2ee/jwYRUWFpaZST2d1WrlqlYAAABeVqnzmI4ZM0a//PKLZs2apYiICK1fv15dunTx9NgkSfXq1VO9evUqXK9Dhw6yWq3as2ePLr/8cklSYWGh9u3bp/j4eElSYmKipk+fruzsbMXFxUk6dUCU1WpVhw4dvDJ+AAAAuMbtj/IPHz6sa6+9VvPnz9fLL7+sYcOGqW/fvnrxxRe9MT6XRUdHa8yYMZo8ebJSU1O1Z88e3XHHHZKk6667TpLUt29fJSQkKCkpSenp6fr00081ceJEjR49miPyAQAA/MztGdM2bdqoefPmSk9PV/PmzTV69GgtXbpUY8eO1apVq7Rq1SpvjNMlTz31lCIiIpSUlKS//vpLnTp10tq1a1WnTh1JUnh4uFatWqWxY8eqa9eudifYBwAAgH+5HaZjxozRww8/rLCw/022Dh8+XF27dtUtt9zi0cG5q1q1apo9e3a5odm0aVN98MEHPhwVAAAAXOF2mE6aNMnh8saNG+vJJ5+s8oAAAAAQmqp8gv3c3Fy9+OKLuuSSS9SxY0dPjAkAAAAhqNJhunbtWv3jH/9QXFyc5s2bp4EDB2r79u2eHBsAAABCiFsf5f/6669atGiRXn/9dR0/flzDhg1TYWGhUlJS7M4fCgAAALjL5RnTgQMHKiEhQbt379a8efP0+++/a968ed4cGwAAQFAJL/DOLVi4PGOampqqu+++W3fccYfOO+88b44JAAAAIcjlGdNNmzbp6NGj6tixozp16qTnn39ef/75pzfHBgAAgBDicpgmJibq1VdfVXZ2tv75z3/q7bff1jnnnKOSkhKtXr3adq16AAAAoDLcPiq/Ro0auvXWW7V582bt2rVL9913n2bOnKkGDRro6quv9sYYAQAAEAKqdB7TVq1a6cknn9Svv/6qJUuWeGpMAAAACEFVPsG+dOoa9EOHDtXKlSs9sTkAAACEII+EKQAAAFBVhCkAAABMgTAFAACAKRCmAAAAMAXCFAAAAKbg8iVJAQAAUDVhhVKYxbPbNAo9uz1/YsYUAAAApkCYAgAAwBQIUwAAAJgCYQoAAABTIEwBAABgCoQpAAAATIEwBQAAgCkQpgAAACFk48aNGjx4sBo1aiSLxaL33nuv3PXXr18vi8VS5vbdd9/ZrZeSkqKEhARZrVYlJCRo+fLlbo+NMAUAAAghx48fV7t27fT888+79bw9e/YoOzvbdjvvvPNsj6WlpWn48OFKSkrSzp07lZSUpGHDhmnr1q1uvQZXfgIAAAghAwYM0IABA9x+XoMGDXTWWWc5fOzZZ59Vnz59lJycLElKTk7Whg0b9Oyzz2rJkiUuvwYzpgAAAEEgLy/P7pafn+/R7bdv315xcXHq1auX1q1bZ/dYWlqa+vbta7esX79+2rJli1uvQZgCAAD4SHi+4ZWbJDVp0kQxMTG224wZMzwy5ri4OL3yyitKSUnRsmXL1KpVK/Xq1UsbN260rZOTk6OGDRvaPa9hw4bKyclx67X4KB8AACAIZGVlKTo62nbfarV6ZLutWrVSq1atbPcTExOVlZWl2bNnq1u3brblFovF7nmGYZRZVhFmTAEAAIJAdHS03c1TYepI586d9cMPP9jux8bGlpkd3b9/f5lZ1IoQpgAAAHBLenq64uLibPcTExO1evVqu3VSU1PVpUsXt7bLR/kAAAAh5NixY/rxxx9t9/fu3auMjAydffbZatq0qZKTk/Xbb79p8eLFkk4dcd+sWTO1bt1aBQUFevPNN5WSkqKUlBTbNsaPH69u3bpp1qxZGjJkiFasWKE1a9Zo8+bNbo2NMAUAAAgh27dvV8+ePW33J0yYIEkaMWKEFi1apOzsbGVmZtoeLygo0MSJE/Xbb7+pevXqat26tVatWqWBAwfa1unSpYvefvttPfLII5o0aZJatGihpUuXqlOnTm6NzWIYhlHF/Qs5eXl5iomJUe+mYxUR5r3vbwAAAM8pKsnXmswXlZuba3eQkC+UtsPF/5iu8Mgoj267uOCkMt582C/75Wl8xxQAAACmQJgCAADAFAhTAAAAmAJhCgAAAFPgqHwAAAAfCS+Qwj290QJPb9B/mDEFAACAKRCmAAAAMAXCFAAAAKZAmAIAAMAUCFMAAACYAmEKAAAAUyBMAQAAYAqEKQAAAEyBMAUAAIApEKYAAAAwBcIUAAAAphDh7wEAAACEirACQ+GG4dFtGoWe3Z4/MWMKAAAAUyBMAQAAYAqEKQAAAEyBMAUAAIApEKYAAAAwBcIUAAAApkCYAgAAwBQIUwAAAJgCYQoAAABTIEwBAABgClySFAAAwEci8ksUUVLi2Y0Wenh7fsSMKQAAAEyBMAUAAIApEKYAAAAwBcIUAAAApkCYAgAAwBQIUwAAAJgCYQoAAABTIEwBAABgCoQpAAAATIEwBQAAgCkQpgAAADCFCH8PAAAAIFSEFZQorMSz17YPK/Ls9vyJGVMAAACYAmEKAAAAUyBMAQAAYAqEKQAAAEyBMAUAAIApEKYAAAAwhaAK0++//15DhgxRvXr1FB0dra5du2rdunV262RmZmrw4MGqWbOm6tWrp7vvvlsFBQV+GjEAAIDvvfjii2revLmioqLUoUMHbdq0yem6I0eOlMViKXNr3bq1bZ1FixY5XOfkyZNujSuownTQoEEqKirS2rVrtWPHDl188cW66qqrlJOTI0kqLi7WoEGDdPz4cW3evFlvv/22UlJSdN999/l55AAAAL6xdOlS3XPPPXr44YeVnp6uK664QgMGDFBmZqbD9efOnavs7GzbLSsrS2effbauu+46u/Wio6Pt1svOzlZUVJRbYwuaMD1w4IB+/PFHPfjgg7rooot03nnnaebMmTpx4oS++eYbSVJqaqp2796tN998U+3bt1fv3r319NNP69VXX1VeXp6f9wAAAMD7nnnmGY0aNUq33XabLrzwQj377LNq0qSJ5s+f73D9mJgYxcbG2m7bt2/X4cOHdcstt9itZ7FY7NaLjY11e2xBE6Z169bVhRdeqMWLF+v48eMqKirSyy+/rIYNG6pDhw6SpLS0NLVp00aNGjWyPa9fv37Kz8/Xjh07nG47Pz9feXl5djcAAAAzObNV8vPzy6xTUFCgHTt2qG/fvnbL+/btqy1btrj0OgsWLFDv3r0VHx9vt/zYsWOKj49X48aNddVVVyk9Pd3tfQiaMLVYLFq9erXS09NVu3ZtRUVFac6cOfr444911llnSZJycnLUsGFDu+fVqVNHkZGRto/7HZkxY4ZiYmJstyZNmnhzVwAAQJAKzy/xyk2SmjRpYtcrM2bMKPP6Bw4cUHFxcZkeatiwYbktVCo7O1sfffSRbrvtNrvlF1xwgRYtWqSVK1dqyZIlioqKUteuXfXDDz+49fMxfZhOmTLF4ZdpT79t375dhmFo7NixatCggTZt2qQvvvhCQ4YM0VVXXaXs7Gzb9iwWS5nXMAzD4fJSycnJys3Ntd2ysrK8sq8AAACVlZWVZdcrycnJTtc9s3sqaqFSixYt0llnnaWhQ4faLe/cubP+8Y9/qF27drriiiv0zjvv6Pzzz9e8efPc2ocIt9b2gzvvvFPXX399ues0a9ZMa9eu1QcffKDDhw8rOjpa0qkjzlavXq033nhDDz74oGJjY7V161a75x4+fFiFhYVl/s/hdFarVVarteo7AwAA4CXR0dG2BnKmXr16Cg8PLzM7un///nJbSDoVr6+//rqSkpIUGRlZ7rphYWG69NJL3Z4xNX2Y1qtXT/Xq1atwvRMnTkg69YM4XVhYmEpKTk1xJyYmavr06crOzlZcXJykUwdEWa1W2/dQAQAAglVkZKQ6dOig1atX65prrrEtX716tYYMGVLuczds2KAff/xRo0aNqvB1DMNQRkaG2rZt69b4TP9RvqsSExNVp04djRgxQjt37tT333+vf/3rX9q7d68GDRok6dQXexMSEpSUlKT09HR9+umnmjhxokaPHl3h/2EAAAAEgwkTJui1117T66+/rm+//Vb33nuvMjMzNWbMGEmnvsJ48803l3neggUL1KlTJ7Vp06bMY1OnTtUnn3yin3/+WRkZGRo1apQyMjJs23SV6WdMXVWvXj19/PHHevjhh3XllVeqsLBQrVu31ooVK9SuXTtJUnh4uFatWqWxY8eqa9euql69um688UbNnj3bz6MHAADwjeHDh+vgwYOaNm2asrOz1aZNG3344Ye2o+yzs7PLnNM0NzdXKSkpmjt3rsNtHjlyRLfffrtycnIUExOj9u3ba+PGjbrsssvcGpvFMAyjcrsVuvLy8hQTE6PeTccqIozvngIAEAiKSvK1JvNF5ebm+vyT0tJ26Hb5o4qIcO+k8xUpKjqpjZun+WW/PC1oPsoHAABAYCNMAQAAYAqEKQAAAEyBMAUAAIApEKYAAAAwhaA5XRQAAIDZhZ8sUnhEkUe3aRR5dnv+xIwpAAAATIEwBQAAgCkQpgAAADAFwhQAAACmQJgCAADAFAhTAAAAmAJhCgAAAFMgTAEAAGAKhCkAAABMgTAFAACAKXBJUgAAAB+x5BfL4uFLiFqKiz26PX9ixhQAAACmQJgCAADAFAhTAAAAmAJhCgAAAFMgTAEAAGAKhCkAAABMgTAFAACAKRCmAAAAMAXCFAAAAKZAmAIAAMAUCFMAAACYQoS/BwAAABAqwk4WKCzc4tltFhd4dHv+xIwpAAAATIEwBQAAgCkQpgAAADAFwhQAAACmQJgCAADAFAhTAAAAmAJhCgAAAFMgTAEAAGAKhCkAAABMgTAFAACAKRCmAAAAMIUIfw8AAAAgZOTne35asCTfwxv0H2ZMAQAAYAqEKQAAAEyBMAUAAIApEKYAAAAwBcIUAAAApkCYAgAAwBQIUwAAgBDz4osvqnnz5oqKilKHDh20adOmctffsGGDOnTooKioKJ177rl66aWXyqyTkpKihIQEWa1WJSQkaPny5W6PizAFAAAIIUuXLtU999yjhx9+WOnp6briiis0YMAAZWZmOlx/7969GjhwoK644gqlp6froYce0t13362UlBTbOmlpaRo+fLiSkpK0c+dOJSUladiwYdq6datbY7MYhmFUae9CUF5enmJiYtS76VhFhFn9PRwAAOCCopJ8rcl8Ubm5uYqOjvbpa3uzHdzdr06dOumSSy7R/PnzbcsuvPBCDR06VDNmzCiz/gMPPKCVK1fq22+/tS0bM2aMdu7cqbS0NEnS8OHDlZeXp48++si2Tv/+/VWnTh0tWbLE5X1hxhQAACAI5OXl2d3y88teEaqgoEA7duxQ37597Zb37dtXW7ZscbjdtLS0Muv369dP27dvV2FhYbnrONumM4QpAACAr5wskE7me/hWIElq0qSJYmJibDdHs58HDhxQcXGxGjZsaLe8YcOGysnJcTjknJwch+sXFRXpwIED5a7jbJvORLi1NgAAAEwpKyvL7qN8q9X5VwYsFovdfcMwyiyraP0zl7u7TUcIUwAAgCAQHR1d4XdM69Wrp/Dw8DIzmfv37y8z41kqNjbW4foRERGqW7duues426YzfJQPAAAQIiIjI9WhQwetXr3abvnq1avVpUsXh89JTEwss35qaqo6duyoatWqlbuOs206w4wpAABACJkwYYKSkpLUsWNHJSYm6pVXXlFmZqbGjBkjSUpOTtZvv/2mxYsXSzp1BP7zzz+vCRMmaPTo0UpLS9OCBQvsjrYfP368unXrplmzZmnIkCFasWKF1qxZo82bN7s1NsIUAAAghAwfPlwHDx7UtGnTlJ2drTZt2ujDDz9UfHy8JCk7O9vunKbNmzfXhx9+qHvvvVcvvPCCGjVqpOeee07XXnutbZ0uXbro7bff1iOPPKJJkyapRYsWWrp0qTp16uTW2DiPaSVwHlMAAAKPKc5j2uA2RYRFenTbRSUFWrP/Nb/sl6fxHVMAAACYAmEKAAAAUyBMAQAAYAqEKQAAAEyBMAUAAIApcLooAAAAHzH++kuGpdiz2zQKPLo9f2LGFAAAAKZAmAIAAMAUCFMAAACYAmEKAAAAUyBMAQAAYAqEKQAAAEyBMAUAAIApEKYAAAAwBcIUAAAApkCYAgAAwBS4JCkAAICPlPyVrxJLiWe3aRR6dHv+xIwpAAAATIEwBQAAgCkQpgAAADAFwhQAAACmQJgCAADAFAhTAAAAmAJhCgAAAFMImDCdPn26unTpoho1auiss85yuE5mZqYGDx6smjVrql69err77rtVUFBgt86uXbvUvXt3Va9eXeecc46mTZsmwzB8sAcAAAAoT8CcYL+goEDXXXedEhMTtWDBgjKPFxcXa9CgQapfv742b96sgwcPasSIETIMQ/PmzZMk5eXlqU+fPurZs6e2bdum77//XiNHjlTNmjV13333+XqXAAAAcJqACdOpU6dKkhYtWuTw8dTUVO3evVtZWVlq1KiRJOnpp5/WyJEjNX36dEVHR+vf//63Tp48qUWLFslqtapNmzb6/vvv9cwzz2jChAmyWCy+2h0AAACcIWDCtCJpaWlq06aNLUolqV+/fsrPz9eOHTvUs2dPpaWlqXv37rJarXbrJCcna9++fWrevLnDbefn5ys/P992Pzc3V5JUVFLgcH0AAGA+pf9u+/MrfEUqlDz88kUKnkuSBk2Y5uTkqGHDhnbL6tSpo8jISOXk5NjWadasmd06pc/JyclxGqYzZsywzdiebv2vr3lg5AAAwJcOHjyomJgYn75mZGSkYmNjtSlnhVe2Hxsbq8jISK9s25f8GqZTpkxxGHyn27Ztmzp27OjS9hx9FG8Yht3yM9cp/b+m8j7GT05O1oQJE2z3S0pKdOjQIdWtWzdoPv7Py8tTkyZNlJWVpejoaH8Px6fYd/Y91PZdCu39Z99Dc9+lU594Nm3aVGeffbbPXzsqKkp79+4tc1C2p0RGRioqKsor2/Ylv4bpnXfeqeuvv77cdc6c4XQmNjZWW7dutVt2+PBhFRYW2mZFY2NjbbOnpfbv3y9JZWZbT2e1Wu0+/pfk9MwAgS46Ojok/7KS2Hf2PTSF8v6z76G575IUFuafkxJFRUUFRTx6k1/DtF69eqpXr55HtpWYmKjp06crOztbcXFxkk4dEGW1WtWhQwfbOg899JAKCgps092pqalq1KiRywEMAAAA7wiY85hmZmYqIyNDmZmZKi4uVkZGhjIyMnTs2DFJUt++fZWQkKCkpCSlp6fr008/1cSJEzV69Gjb/xXeeOONslqtGjlypL7++mstX75cTzzxBEfkAwAAmEDAHPz06KOP6o033rDdb9++vSRp3bp16tGjh8LDw7Vq1SqNHTtWXbt2VfXq1XXjjTdq9uzZtufExMRo9erVGjdunDp27Kg6depowoQJdt8fDVVWq1WTJ08u85WFUMC+s++hKJT3n30PzX2X2P9AYDG47BEAAABMIGA+ygcAAEBwI0wBAABgCoQpAAAATIEwBQAAgCkQpiFgxowZuvTSS1W7dm01aNBAQ4cO1Z49e8p9zvr162WxWMrcvvvuOx+N2jOmTJlSZh9iY2PLfc6GDRvUoUMHRUVF6dxzz9VLL73ko9F6VrNmzRy+h+PGjXO4fqC/5xs3btTgwYPVqFEjWSwWvffee3aPG4ahKVOmqFGjRqpevbp69Oihb775psLtpqSkKCEhQVarVQkJCVq+fLmX9qDyytv3wsJCPfDAA2rbtq1q1qypRo0a6eabb9bvv/9e7jYXLVrk8PfDyZMnvbw37qnofR85cmSZfejcuXOF2w30912Sw/fPYrHoqaeecrrNQHnfXfl3LZj/zAczwjQEbNiwQePGjdPnn3+u1atXq6ioSH379tXx48crfO6ePXuUnZ1tu5133nk+GLFntW7d2m4fdu3a5XTdvXv3auDAgbriiiuUnp6uhx56SHfffbdSUlJ8OGLP2LZtm91+r169WpJ03XXXlfu8QH3Pjx8/rnbt2un55593+PiTTz6pZ555Rs8//7y2bdum2NhY9enTR0ePHnW6zbS0NA0fPlxJSUnauXOnkpKSNGzYsDJXmfO38vb9xIkT+vLLLzVp0iR9+eWXWrZsmb7//ntdffXVFW43Ojra7vdCdna26a5aU9H7Lkn9+/e324cPP/yw3G0Gw/suqcx79/rrr8tisejaa68td7uB8L678u9aMP+ZD2oGQs7+/fsNScaGDRucrrNu3TpDknH48GHfDcwLJk+ebLRr187l9e+//37jggsusFv2z3/+0+jcubOHR+Z748ePN1q0aGGUlJQ4fDxY3nPDMAxJxvLly233S0pKjNjYWGPmzJm2ZSdPnjRiYmKMl156yel2hg0bZvTv399uWb9+/Yzrr7/e42P2lDP33ZEvvvjCkGT88ssvTtdZuHChERMT49nBeZmjfR8xYoQxZMgQt7YTrO/7kCFDjCuvvLLcdQLxfTeMsv+uhdKf+WDDjGkIys3NlSSdffbZFa7bvn17xcXFqVevXlq3bp23h+YVP/zwgxo1aqTmzZvr+uuv188//+x03bS0NPXt29duWb9+/bR9+3YVFhZ6e6heU1BQoDfffFO33nprhVc5C4b3/Ex79+5VTk6O3XtrtVrVvXt3bdmyxenznP1+KO85gSA3N1cWi0VnnXVWuesdO3ZM8fHxaty4sa666iqlp6f7ZoAetn79ejVo0EDnn3++Ro8erf3795e7fjC+73/88YdWrVqlUaNGVbhuIL7vZ/67xp/5wEWYhhjDMDRhwgRdfvnlatOmjdP14uLi9MorryglJUXLli1Tq1at1KtXL23cuNGHo626Tp06afHixfrkk0/06quvKicnR126dNHBgwcdrp+Tk6OGDRvaLWvYsKGKiop04MABXwzZK9577z0dOXJEI0eOdLpOsLznjuTk5EiSw/e29DFnz3P3OWZ38uRJPfjgg7rxxhttl2t25IILLtCiRYu0cuVKLVmyRFFRUeratat++OEHH4626gYMGKB///vfWrt2rZ5++mlt27ZNV155pfLz850+Jxjf9zfeeEO1a9fW3/72t3LXC8T33dG/a/yZD1wBc0lSeMadd96pr776Sps3by53vVatWqlVq1a2+4mJicrKytLs2bPVrVs3bw/TYwYMGGD7ddu2bZWYmKgWLVrojTfecHop2jNnFI3/XhytoplGM1uwYIEGDBigRo0aOV0nWN7z8jh6byt6XyvzHLMqLCzU9ddfr5KSEr344ovlrtu5c2e7g4S6du2qSy65RPPmzdNzzz3n7aF6zPDhw22/btOmjTp27Kj4+HitWrWq3EgLpvddkl5//XXddNNNFX5XNBDf9/L+XQv1P/OBiBnTEHLXXXdp5cqVWrdunRo3buz28zt37mzq/2t2Rc2aNdW2bVun+xEbG1vm/4z379+viIgI1a1b1xdD9LhffvlFa9as0W233eb2c4PhPZdkOxODo/f2zNmRM5/n7nPMqrCwUMOGDdPevXu1evXqcmdLHQkLC9Oll14a8L8f4uLiFB8fX+5+BNP7LkmbNm3Snj17KvV3gNnfd2f/rvFnPnARpiHAMAzdeeedWrZsmdauXavmzZtXajvp6emKi4vz8Oh8Kz8/X99++63T/UhMTLQdvV4qNTVVHTt2VLVq1XwxRI9buHChGjRooEGDBrn93GB4zyWpefPmio2NtXtvCwoKtGHDBnXp0sXp85z9fijvOWZUGqU//PCD1qxZU6n/yTIMQxkZGQH/++HgwYPKysoqdz+C5X0vtWDBAnXo0EHt2rVz+7lmfd8r+nct1P/MBzT/HHMFX7rjjjuMmJgYY/369UZ2drbtduLECds6Dz74oJGUlGS7P2fOHGP58uXG999/b3z99dfGgw8+aEgyUlJS/LELlXbfffcZ69evN37++Wfj888/N6666iqjdu3axr59+wzDKLvfP//8s1GjRg3j3nvvNXbv3m0sWLDAqFatmvGf//zHX7tQJcXFxUbTpk2NBx54oMxjwfaeHz161EhPTzfS09MNScYzzzxjpKen2448nzlzphETE2MsW7bM2LVrl3HDDTcYcXFxRl5enm0bSUlJxoMPPmi7/9lnnxnh4eHGzJkzjW+//daYOXOmERERYXz++ec+37/ylLfvhYWFxtVXX200btzYyMjIsPs7ID8/37aNM/d9ypQpxscff2z89NNPRnp6unHLLbcYERERxtatW/2xi06Vt+9Hjx417rvvPmPLli3G3r17jXXr1hmJiYnGOeecE/Tve6nc3FyjRo0axvz58x1uI1Dfd1f+XQvmP/PBjDANAZIc3hYuXGhbZ8SIEUb37t1t92fNmmW0aNHCiIqKMurUqWNcfvnlxqpVq3w/+CoaPny4ERcXZ1SrVs1o1KiR8be//c345ptvbI+fud+GYRjr16832rdvb0RGRhrNmjVz+hd6IPjkk08MScaePXvKPBZs73np6a7OvI0YMcIwjFOnj5k8ebIRGxtrWK1Wo1u3bsauXbvsttG9e3fb+qXeffddo1WrVka1atWMCy64wJShXt6+79271+nfAevWrbNt48x9v+eee4ymTZsakZGRRv369Y2+ffsaW7Zs8f3OVaC8fT9x4oTRt29fo379+ka1atWMpk2bGiNGjDAyMzPtthGM73upl19+2ahevbpx5MgRh9sI1PfdlX/XgvnPfDCzGMZ/j+wAAAAA/IjvmAIAAMAUCFMAAACYAmEKAAAAUyBMAQAAYAqEKQAAAEyBMAUAAIApEKYAAAAwBcIUAAAApkCYAvCJkSNHaujQobb7PXr00D333OPy89evXy+LxaIjR45UeSye3JYZ7dmzR7GxsTp69Khbz5s4caLuvvtuL40KACpGmAKwGTlypCwWiywWiyIiItS0aVPdcccdOnz4sMdfa9myZXrsscc8us1mzZrZxl+9enU1a9ZMw4YN09q1a+3W69Kli7KzsxUTE1PhNgMxYh9++GGNGzdOtWvXlvS/fSi91a1bV1deeaU+++wzu+fdf//9Wrhwofbu3euPYQMAYQrAXv/+/ZWdna19+/bptdde0/vvv6+xY8d6/HXOPvtsWzh50rRp05Sdna09e/Zo8eLFOuuss9S7d29Nnz7dtk5kZKRiY2NlsVg8/vr+9uuvv2rlypW65ZZbyjy2Z88eZWdna/369apfv74GDRqk/fv32x5v0KCB+vbtq5deesmXQwYAG8IUgB2r1arY2Fg1btxYffv21fDhw5Wammp7vLi4WKNGjVLz5s1VvXp1tWrVSnPnzrXbRnFxsSZMmKCzzjpLdevW1f333y/DMOzWOfOj/DfffFMdO3ZU7dq1FRsbqxtvvNEumlxV+vymTZuqW7dueuWVVzRp0iQ9+uij2rNnj6Sys6C//PKLBg8erDp16qhmzZpq3bq1PvzwQ+3bt089e/aUJNWpU0cWi0UjR46UJH388ce6/PLLbft41VVX6aeffrKNY9++fbJYLFq2bJl69uypGjVqqF27dkpLS7Mb72effabu3burRo0aqlOnjvr162eboTYMQ08++aTOPfdcVa9eXe3atdN//vOfcvf/nXfeUbt27dS4ceMyjzVo0ECxsbFq27atHnnkEeXm5mrr1q1261x99dVasmSJ6z9wAPAgwhSAUz///LM+/vhjVatWzbaspKREjRs31jvvvKPdu3fr0Ucf1UMPPaR33nnHts7TTz+t119/XQsWLNDmzZt16NAhLV++vNzXKigo0GOPPaadO3fqvffe0969e20RWFXjx4+XYRhasWKFw8fHjRun/Px8bdy4Ubt27dKsWbNUq1YtNWnSRCkpKZL+N9tYGuHHjx/XhAkTtG3bNn366acKCwvTNddco5KSErttP/zww5o4caIyMjJ0/vnn64YbblBRUZEkKSMjQ7169VLr1q2VlpamzZs3a/DgwSouLpYkPfLII1q4cKHmz5+vb775Rvfee6/+8Y9/aMOGDU73dePGjerYsWO5P48TJ05o4cKFkmT33krSZZddpqysLP3yyy/lbgMAvMIAgP8aMWKEER4ebtSsWdOIiooyJBmSjGeeeabc540dO9a49tprbffj4uKMmTNn2u4XFhYajRs3NoYMGWJb1r17d2P8+PFOt/nFF18YkoyjR48ahmEY69atMyQZhw8fdvqc+Ph4Y86cOQ4fa9iwoXHHHXc43Fbbtm2NKVOmOHyeK69rGIaxf/9+Q5Kxa9cuwzAMY+/evYYk47XXXrOt88033xiSjG+//dYwDMO44YYbjK5duzrc3rFjx4yoqChjy5YtdstHjRpl3HDDDU7H0a5dO2PatGkO96FmzZpGzZo1DYvFYkgyOnToYBQUFNitm5uba0gy1q9fX+7+AoA3MGMKwE7Pnj2VkZGhrVu36q677lK/fv1011132a3z0ksvqWPHjqpfv75q1aqlV199VZmZmZKk3NxcZWdnKzEx0bZ+REREhbN46enpGjJkiOLj41W7dm316NFDkmzbrSrDMJx+p/Tuu+/W448/rq5du2ry5Mn66quvKtzeTz/9pBtvvFHnnnuuoqOj1bx5c4fjveiii2y/jouLkyTbVxRKZ0wd2b17t06ePKk+ffqoVq1attvixYvtvjJwpr/++ktRUVEOH9u0aZO+/PJLLVmyRPHx8Vq0aFGZGdPq1atLOjWrCgC+RpgCsFOzZk21bNlSF110kZ577jnl5+dr6tSptsffeecd3Xvvvbr11luVmpqqjIwM3XLLLSooKKj0ax4/flx9+/ZVrVq19Oabb2rbtm22j/6rst1SBw8e1J9//mmLxzPddttt+vnnn5WUlKRdu3apY8eOmjdvXrnbHDx4sA4ePKhXX31VW7dutX1X88zxnh5+pWFc+nF/aQQ6UrrOqlWrlJGRYbvt3r273O+Z1qtXz+lZFJo3b67zzz9fw4cP19SpU3XNNdcoPz/fbp1Dhw5JkurXr+/0NQDAWwhTAOWaPHmyZs+erd9//13SqVm3Ll26aOzYsWrfvr1atmxpN4MXExOjuLg4ff7557ZlRUVF2rFjh9PX+O6773TgwAHNnDlTV1xxhS644IJKHfjkzNy5cxUWFmZ3HtUzNWnSRGPGjNGyZct033336dVXX5V06gh+SbbvfUqnQvfbb7/VI488ol69eunCCy+s1Cm1LrroIn366acOH0tISJDValVmZqZatmxpd2vSpInTbbZv3167d++u8LWTkpJUUlKiF1980W75119/rWrVqql169bu7QwAeABhCqBcPXr0UOvWrfXEE09Iklq2bKnt27frk08+0ffff69JkyZp27Ztds8ZP368Zs6cqeXLl+u7777T2LFjyz0PaNOmTRUZGal58+bp559/1sqVKyt9jtOjR48qJydHWVlZ2rhxo26//XY9/vjjmj59ulq2bOnwOffcc48++eQT7d27V19++aXWrl2rCy+8UJIUHx8vi8WiDz74QH/++aeOHTumOnXqqG7dunrllVf0448/au3atZowYYLbY01OTta2bds0duxYffXVV/ruu+80f/58HThwQLVr19bEiRN177336o033tBPP/2k9PR0vfDCC3rjjTecbrNfv35KS0uzC2lHwsLCdM8992jmzJl2H9tv2rRJV1xxRbmzuQDgLYQpgApNmDBBr776qrKysjRmzBj97W9/0/Dhw9WpUycdPHiwzHlO77vvPt18880aOXKkEhMTVbt2bV1zzTVOt1+/fn0tWrRI7777rhISEjRz5kzNnj27UmN99NFHFRcXp5YtWyopKUm5ubn69NNP9cADDzh9TnFxscaNG6cLL7xQ/fv3V6tWrWwzieecc46mTp2qBx98UA0bNtSdd96psLAwvf3229qxY4fatGmje++9V0899ZTbYz3//POVmpqqnTt36rLLLlNiYqJWrFihiIgISdJjjz2mRx99VDNmzNCFF16ofv366f3333f6lQRJGjhwoKpVq6Y1a9ZU+Pq33nqrCgsL9fzzz9uWLVmyRKNHj3Z7XwDAEyyGccbJBQEAAe3FF1/UihUr9Mknn7j1vFWrVulf//qXvvrqK1scA4Av8TcPAASZ22+/XYcPH9bRo0fdurrW8ePHtXDhQqIUgN8wYwoAAABT4DumAAAAMAXCFAAAAKZAmAIAAMAUCFMAAACYAmEKAAAAUyBMAQAAYAqEKQAAAEyBMAUAAIApEKYAAAAwhf8HuqEwfg6lWiwAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_potential(field, R, Z, title):\n",
-    "    plt.figure(figsize=(8, 6))\n",
-    "    plt.contourf(R, Z, field, levels=50, cmap='viridis')\n",
-    "    plt.colorbar()\n",
-    "    plt.title(title)\n",
-    "    plt.xlabel('Radial Distance (R)')\n",
-    "    plt.ylabel('Axial Distance (Z)')\n",
-    "    plt.show()\n",
-    "\n",
-    "plot_potential(np.real(phiH), R, Z, 'Homogeneous Potential')\n",
-    "plot_potential(np.imag(phiH), R, Z, 'Homogeneous Potential Imaginary')\n",
-    "\n",
-    "plot_potential(np.real(phiP), R, Z, 'Particular Potential')\n",
-    "plot_potential(np.imag(phiP), R, Z, 'Particular Potential Imaginary')\n",
-    "\n",
-    "plot_potential(np.real(phi), R, Z, 'Total Potential')\n",
-    "plot_potential(np.imag(phi), R, Z, 'Total Potential Imaginary')\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def v_r_inner_func(n, r, z):\n",
-    "    return (Cs[0][n] * diff_R_1n(n, r, 0)) * Z_n_i(n, z, 0)\n",
-    "\n",
-    "def v_r_m_i_func(i, m, r, z):\n",
-    "    return (Cs[i][m] * diff_R_1n(m, r, i) + Cs[i][NMK[i] + m] * diff_R_2n(m, r, i)) * Z_n_i(m, z, i)\n",
-    "\n",
-    "def v_r_e_k_func(k, r, z):\n",
-    "    return Cs[-1][k] * diff_Lambda_k(k, r) * Z_k_e(k, z)\n",
-    "\n",
-    "def v_z_inner_func(n, r, z):\n",
-    "    return (Cs[0][n] * R_1n(n, r, 0)) * diff_Z_n_i(n, z, 0)\n",
-    "\n",
-    "def v_z_m_i_func(i, m, r, z):\n",
-    "    return (Cs[i][m] * R_1n(m, r, i) + Cs[i][NMK[i] + m] * R_2n(m, r, i)) * diff_Z_n_i(m, z, i)\n",
-    "\n",
-    "def v_z_e_k_func(k, r, z):\n",
-    "    return Cs[-1][k] * Lambda_k(k, r) * diff_Z_k_e(k, z)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vr = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "vrH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "vrP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "\n",
-    "vz = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "vzH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "vzP = np.full_like(R, np.nan + np.nan*1j, dtype=complex)\n",
-    "\n",
-    "for n in range(NMK[0]):\n",
-    "    temp_vrH = v_r_inner_func(n, R[regions[0]], Z[regions[0]])\n",
-    "    temp_vzH = v_z_inner_func(n, R[regions[0]], Z[regions[0]])\n",
-    "    if n == 0:\n",
-    "        vrH[regions[0]] = temp_vrH\n",
-    "        vzH[regions[0]] = temp_vzH\n",
-    "    else:\n",
-    "        vrH[regions[0]] = vrH[regions[0]] + temp_vrH\n",
-    "        vzH[regions[0]] = vzH[regions[0]] + temp_vzH\n",
-    "\n",
-    "for i in range(1, boundary_count):\n",
-    "    for m in range(NMK[i]):\n",
-    "        temp_vrH = v_r_m_i_func(i, m, R[regions[i]], Z[regions[i]])\n",
-    "        temp_vzH = v_z_m_i_func(i, m, R[regions[i]], Z[regions[i]])\n",
-    "        if m == 0:\n",
-    "            vrH[regions[i]] = temp_vrH\n",
-    "            vzH[regions[i]] = temp_vzH\n",
-    "        else:\n",
-    "            vrH[regions[i]] = vrH[regions[i]] + temp_vrH\n",
-    "            vzH[regions[i]] = vzH[regions[i]] + temp_vzH\n",
-    "\n",
-    "for k in range(NMK[-1]):\n",
-    "    temp_vrH = v_r_e_k_func(k, R[regions[-1]], Z[regions[-1]])\n",
-    "    temp_vzH = v_z_e_k_func(k, R[regions[-1]], Z[regions[-1]])\n",
-    "    if k == 0:\n",
-    "        vrH[regions[-1]] = temp_vrH\n",
-    "        vzH[regions[-1]] = temp_vzH\n",
-    "    else:\n",
-    "        vrH[regions[-1]] = vrH[regions[-1]] + temp_vrH\n",
-    "        vzH[regions[-1]] = vzH[regions[-1]] + temp_vzH\n",
-    "\n",
-    "vr_p_i_vec = np.vectorize(diff_r_phi_p_i)\n",
-    "vz_p_i_vec = np.vectorize(diff_z_phi_p_i)\n",
-    "\n",
-    "vrP[regions[0]] = heaving[0] * vr_p_i_vec(d[0], R[regions[0]], Z[regions[0]])\n",
-    "vzP[regions[0]] = heaving[0] * vz_p_i_vec(d[0], R[regions[0]], Z[regions[0]])\n",
-    "for i in range(1, boundary_count):\n",
-    "    vrP[regions[i]] = heaving[i] * vr_p_i_vec(d[i], R[regions[i]], Z[regions[i]])\n",
-    "    vzP[regions[i]] = heaving[i] * vz_p_i_vec(d[i], R[regions[i]], Z[regions[i]])\n",
-    "vrP[regions[-1]] = 0\n",
-    "vzP[regions[-1]] = 0\n",
-    "\n",
-    "vr = vrH + vrP\n",
-    "vz = vzH + vzP"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_potential(np.real(vr), R, Z, 'Radial Velocity - Real')\n",
-    "plot_potential(np.imag(vr), R, Z, 'Radial Velocity - Imaginary')\n",
-    "plot_potential(np.real(vz), R, Z, 'Vertical Velocity - Real')\n",
-    "plot_potential(np.imag(vz), R, Z, 'Vertical Velocity - Imaginary')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Format Potential Matrix for Testing:\n",
-    "R, Z = make_R_Z(False, 50)\n",
-    "\n",
-    "regions = []\n",
-    "regions.append((R <= a[0]) & (Z < -d[0]))\n",
-    "for i in range(1, boundary_count):\n",
-    "    regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))\n",
-    "regions.append(R > a[-1])\n",
-    "\n",
-    "phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) \n",
-    "\n",
-    "for n in range(NMK[0]):\n",
-    "    temp_phiH = phi_h_n_inner_func(n, R[regions[0]], Z[regions[0]])\n",
-    "    phiH[regions[0]] = temp_phiH if n == 0 else phiH[regions[0]] + temp_phiH\n",
-    "\n",
-    "for i in range(1, boundary_count):\n",
-    "    for m in range(NMK[i]):\n",
-    "        temp_phiH = phi_h_m_i_func(i, m, R[regions[i]], Z[regions[i]])\n",
-    "        phiH[regions[i]] = temp_phiH if m == 0 else phiH[regions[i]] + temp_phiH\n",
-    "\n",
-    "for k in range(NMK[-1]):\n",
-    "    temp_phiH = phi_e_k_func(k, R[regions[-1]], Z[regions[-1]])\n",
-    "    phiH[regions[-1]] = temp_phiH if k == 0 else phiH[regions[-1]] + temp_phiH\n",
-    "\n",
-    "phi_p_i_vec = np.vectorize(phi_p_i)\n",
-    "\n",
-    "phiP[regions[0]] = heaving[0] * phi_p_i_vec(d[0], R[regions[0]], Z[regions[0]])\n",
-    "for i in range(1, boundary_count):\n",
-    "    phiP[regions[i]] = heaving[i] * phi_p_i_vec(d[i], R[regions[i]], Z[regions[i]])\n",
-    "phiP[regions[-1]] = 0\n",
-    "\n",
-    "phi = phiH + phiP\n",
-    "\n",
-    "nanregions = []\n",
-    "nanregions.append((R <= a[0]) & (Z > -d[0]))\n",
-    "for i in range(1, len(a)):\n",
-    "    nanregions.append((R > a[i-1]) & (R <= a[i]) & (Z > -d[i]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def interpret_file(filename, omega): # comparison with Capytaine\n",
-    "    file_path = \"test/data/\" + filename + \"-imag.csv\"\n",
-    "    df = (pd.read_csv(file_path, header=None)).transpose()\n",
-    "    real_array = (df.to_numpy()) * (-1/omega)\n",
-    "        \n",
-    "    file_path = \"test/data/\" + filename + \"-real.csv\"\n",
-    "    df = (pd.read_csv(file_path, header=None)).transpose()\n",
-    "    imag_array = (df.to_numpy()) * (1/omega)\n",
-    "\n",
-    "    return(real_array, imag_array)\n",
-    "\n",
-    "def interpret_file2(filename):\n",
-    "    file_path = \"test/data/\" + filename + \"-imag - matlab.csv\"\n",
-    "    df = (pd.read_csv(file_path, header=None))\n",
-    "    #df_flipped = df.iloc[::-1].reset_index(drop=True)\n",
-    "    imag_array = df.to_numpy()\n",
-    "        \n",
-    "    file_path = \"test/data/\" + filename + \"-real - matlab.csv\"\n",
-    "    df = (pd.read_csv(file_path, header=None))\n",
-    "    #df_flipped = df.iloc[::-1].reset_index(drop=True)\n",
-    "    real_array = df.to_numpy()\n",
-    "\n",
-    "    return(real_array, imag_array)\n",
-    "\n",
-    "def plot_difference(title, R, Z, arr):\n",
-    "    plt.figure(figsize=(8, 6))\n",
-    "    plt.contourf(R, Z, arr, levels=1, cmap='viridis')\n",
-    "    plt.colorbar()\n",
-    "    plt.title(title)\n",
-    "    plt.xlabel('Radial Distance (R)')\n",
-    "    plt.ylabel('Axial Distance (Z)')\n",
-    "    plt.show()\n",
-    "\n",
-    "# arguments: the name of the csv to compare with, an appropriately formatted potential array\n",
-    "# the threshold of closeness, appropriate R and Z, and omega to help with conversion\n",
-    "# tailored for 50x50 points (including nans), evenly spaced, twice the widest radius, and given height\n",
-    "\n",
-    "def potential_comparison(filename, arr, threshold, R, Z, omega, nan_mask):\n",
-    "\n",
-    "    real_arr, imag_arr = interpret_file(filename, omega)\n",
-    "    real_calc_arr = np.real(arr)\n",
-    "    imag_calc_arr = np.imag(arr)\n",
-    "\n",
-    "    plot_potential(real_arr, R, Z, 'Capytaine Potential Real')\n",
-    "    plot_potential(real_calc_arr, R, Z, 'MEEM Potential Real')\n",
-    "    plot_potential(imag_arr, R, Z, 'Capytaine Potential Imaginary')\n",
-    "    plot_potential(imag_calc_arr, R, Z, 'MEEM Potential Imaginary')\n",
-    "\n",
-    "    plot_potential(real_arr - real_calc_arr, R, Z, 'Real Potential Difference')\n",
-    "    plot_potential(imag_arr - imag_calc_arr, R, Z, 'Imag Potential Difference')\n",
-    "\n",
-    "    raw_fraction_real = (real_arr - real_calc_arr)/real_arr\n",
-    "    raw_fraction_imag = (imag_arr - imag_calc_arr)/imag_arr\n",
-    "\n",
-    "    fraction_real = np.where(abs(raw_fraction_real) < 0.1, raw_fraction_real, np.nan)\n",
-    "    fraction_imag = np.where(abs(raw_fraction_imag) < 1, raw_fraction_imag, np.nan)\n",
-    "\n",
-    "    plot_potential(fraction_real, R, Z, 'Fractional Real Potential Difference')\n",
-    "    plot_potential(fraction_imag, R, Z, 'Fractional Imag Potential Difference')\n",
-    "\n",
-    "    is_within_threshold_r = 1. * np.isclose(real_arr, np.real(arr), rtol=threshold, atol = 0.01)\n",
-    "    is_within_threshold_i = 1. * np.isclose(imag_arr, np.imag(arr), rtol=threshold, atol = 0.001)\n",
-    "\n",
-    "    for i in range(len(nan_mask)):\n",
-    "        is_within_threshold_r[nan_mask[i]] = np.nan\n",
-    "        is_within_threshold_i[nan_mask[i]] = np.nan\n",
-    "        \n",
-    "    plot_difference(\"Real Match\", R, Z, is_within_threshold_r)\n",
-    "    plot_difference(\"Imaginary Match\", R, Z, is_within_threshold_i)\n",
-    "\n",
-    "    match_r = np.sum(np.isnan(is_within_threshold_r)) + np.sum(is_within_threshold_r == 1)\n",
-    "    match_i = np.sum(np.isnan(is_within_threshold_i)) + np.sum(is_within_threshold_i == 1)\n",
-    "\n",
-    "    return (match_r, match_i, is_within_threshold_r, is_within_threshold_i)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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8eXNERUVh8+bNaN++PSZMmICXX34ZhYWFSE1NRUJCAiZOnIjAwECT+qUJ+v/5z38wbNgwWFtbo3HjxlpzjTV69uyJRYsWISwsDO+++y4yMzOxcOFCo0fOK0KTJk3QoEEDTJ06FYIgoGbNmvjpp5+QmJioU3fmzJno2bMnQkNDMX78eBQUFGDBggWoUaOG2e7MaEp/jDF+/HgMHjwYMpkMY8aMMUsfiej5xpFhojJERkbi5MmT8Pf3x7x58xASEoJ+/frhu+++Q1hYGFavXg2g6M/4O3fuhL29Pf71r39h+PDhqFGjBjZt2qS3Xc2fyzVrqC5ZsgSRkZH4v//7P731Bw8eDAA6o8Iaa9euhb29Pfr06YNXXnkFcXFxAIpC2r/+9S/MmTMHvXv3xvbt27Flyxaz32lu1apVWLZsGX799VcMGTIEPXv2xMcff4ycnBy8+uqrz9y+TCbDtm3bEB0djfj4ePTo0QMLFy5EeHg49u3bpxUoZ8yYgQ4dOiAyMhKvvvqq+IEuBwcHHDp0CBEREVi9ejV69uyJQYMGYcmSJahTp85TjQx37NgR06ZNw08//YTXXnsNr7zyCk6dOqW37uuvv46vvvoK586dQ+/evRETE4M33ngDU6dOfarXpDxYW1vjp59+wksvvYRRo0Zh6NChuH37Nvbs2aNTt1u3bti8eTMyMzMxePBgREdHo3///ujbt6/ZpuCY0h9j9OvXD3K5HKGhoWjUqJFZ+khEzzeZIJhxpXciMkpERAR++OEHPHz40Oh9Bg4ciKNHj+Lq1asGPwBGVNnUajVatmyJF198EQkJCZXdHR0//fQT+vTpg507d6JHjx6V3R0iqgI4TYKoCsvLy8Pvv/+O48ePY+vWrVi0aBGDMFUpI0aMQNeuXeHh4QGlUomVK1fi4sWL+M9//lPZXdNy4cIFXLt2DRMnTkTLli3RvXv3yu4SEVURDMNEVVh6ejqCg4Ph5OSEUaNG4YMPPqjsLhFpefDgASZNmoQ7d+7A2toarVu3xq5du7TumFgVjBkzBr/99htat26N9evXG7VGNxFJA6dJEBEREZFkSfYDdMuXL4ePjw9sbW3h7++PQ4cOVXaXiIiIiKiCSTIMb9q0CVFRUYiJiUFycjLatWuH7t27i7e7JSIiIiJpkOQ0icDAQLRu3RorVqwQy5o2bYp+/fphzpw5ldgzIiIiIqpIkvsAnUqlwqlTp3TW9QwJCcGRI0f07pOXl4e8vDzxeWFhIe7evYtatWrxQxhERETPCUEQ8ODBA3h6esLCouL/OJ6bm6v3bqbmYGNjo3WbdzKe5MJwRkYGCgoK4O7urlXu7u4OpVKpd585c+ZgxowZFdE9IiIiKmdpaWlmvQunMXJzc+HtVQO3bheUS/sKhQIpKSkMxE9BcmFYo+SIriAIBkd5p02bhujoaPF5VlYW6tWrh7S0NDg5OZVrP4mo8v11/SUAQFah9m2T7xdq/9DJKnB4Us8OAJBdYF/0b74tHuQ/Kcv/p40H+bbIUVvjkdoGuflF60fnqqyQr7ZEvsoSACDkPxm9UllAprKAhbro/ykLVdG/MjVgoQYsSgw2WT4pt3xSbqkSYKEWYJn3z8w4y9zCYvWLvrbIK9D6t+jrfJ3XRJar1ikrjWD7z/rYhXLtHz2Fckudrwusi867wNYCBfJ//m8utP7n6wIbGQpsgMInTRdYA4XFnovHfvK80EZ40oYAwebJudsUQmZVCCubf87Xyrroa1ubfNhaFZ2nvbUKDtb/nLOjVa74tZNV3pOyx8XK/tkullk+0ikri+Ya0irL/+e603ddFZUX1dFcXwC0rjEAyFcXvdZa15qq6HUvfq0B/1xvQNE1B2hfd5rrTcNSVfxr4Un9on8112Dx60+sq/6nrPg1+DSKX1ca+fl5SDo8V+/t0subSqXCrdsF+N/JOnByNO+odPaDQrwUcB0qlYph+ClILgy7urrC0tJSZxT49u3bOqPFGnK5XOtWrxpOTk4Mw0QSkGFXEwBw/0nY1bivJ6hkPSnLyrdDtuxJUIGt+L9trrroB9VDtQ1gA6hkNlDLrKGGFdRqK8AaKBQsYWEFCGoLyKxRFIQtLQA7wMLySRi2ePKvJQBbQPYkfIihWF4UWgo1ZSpAphJQ6PBPOCms8U8wKURROBHsAUtVIeBQLIxonzYscgsA3VMvlQxAoW1ROLGAdlDRxIICGwvIUBSAZQAK5DLIAMhsig1UaH0NyGwASxQFYVmx58WJ4fhJGLYAUGj5JBA/CcWFVkX/WtkUQBPH1Fb5sLQp+kVAZa2GCkWhGACsrW3E9nMBOFnnIheOYgi+Ayc4FQvHRWW6Acy5WJ2sJ8FWS4lgn51vB1gVC8RWQLbaVuukNdfWoyf/qmVFjaifXISFQtG/FtZFQVi81oSia0ymsii6zoq1qbneAAC2T4Lvk+vOQgXxehOvvycvj6Xqn69lT647mbXu9aehuQ4BQCh2jVmqdIOzPgU2/4TMksNbBbYWKFQXnXtlTnF0crQweximZyO5MGxjYwN/f38kJiaif//+YnliYiL69u1biT0joqrqqqq2+HVWiQCsL8Bk59uJYSVb/c8ozUP1PwHqkdoGj9X/jNSp1Vb/jNCp9f+gLD46p1ForT0iV2jzTyAp0LlZYVkB4J/RWMvcQq1gAfwTSPSNuBmrZJsFtrrnqhkJLrDR/rfo63/qFdr8c46FNkWPkkqOEhcnU1lAAMRADPwzSmplUwC1uuh9sbbWHRl/pLYRgzFQ9N7WsFaJ77eTda7WCK5GyRHjbH0BGNC7r7jNwDWl6ZdG8esLQNEvW09ozhN4cr0VHxHWc52VpLnuNK+5hUr3erNQab9f+q6/AhuZ+MuZWCa31AnJ+q4TYxT/qwIAFFjwcz6kS3JhGACio6MRHh6OgIAABAUFYfXq1UhNTcXo0aMru2tEVAWl5tUSv9YXXkoGl+zio79PGAopmoCiNwgXCyhlKR6CiwcUoPRQXDyMaEKnpUpAwZPAWzyU6AskJf/UbWxoKRlSih9f92vteprze5YgbKGSidMmoAnFT8isC3VCsT6P1dawKzZ1onhA1oTjkooHWVOVFnw1/SmuZAguHoCBYteaiUFYo7RAbKnW/54AmmtO+zkArVCsr8xYxa8dnT6X+QshSZEkw/DgwYORmZmJmTNnIj09HX5+fti1axe8vLwqu2tEVAVdz3URvy4tzJQVfoF/AgoAw6PBesJvaSFFXygBSh+1K05fOCkZkEsStxs5SmyoHX3lhsKvuN1au9zUIKxhoZKhEBZF0yVUFuIIsea9KB6KDclVWcHW5p+R4+IBuWRYLc5eT1Aua5/iDAXf4vSFYEN/dTDmFy59DF17Ja+30sKxxj8BWLfMXFRmbo+qB0mGYaDoPvVjxoyp7G4Q0XPg5iPtzwaUFlgMBV8AOiOM+SrLMkPw04SU4qHDQqU/TFqq9e8jhhk9ocEcIaVk0C3JUPA1VK9kfWNCcHE6gVjDplDrvdGdKPGPklMpir/vxYNycSXDrDH0BV7N8fUpddpNsXPVXGOmjAoXVzIQl1TWL2M67ZW4Fp+W3l+S8nTLiCQbhomIjJX5SPsTZIZCiYa+cKJvhLHkn6k1SgZgU+Zw6pQbCCjFp1UUV/zP3Ma29bTKCkhlHa/4dlNDcEkylcU/K0wAOu+J5o/1arUFZNa6H+YqPq2iOENB1RxKG7XWCcAGfql61iCsYSgQ6/tlDDB8/RVnSoA2VoFxn8MjiWEYJiIqw4Mc/VMjyvoTuqE/SQPQOzL3rEoGwpLhWGcUtZQROHMHX1OZcvxnDcKa+cP63gcxIBfbJhSbUlGS2tA0BD0B2lilXkf6lBF8S3rWIKyh7xey0t5HfcG5vBVK7p67ZAyGYSKiUnivXgDYGJG2TAi0xoRfcwQUTUjUN2KsVU9PYKmIYGLo2Ebva8aRQ83rLX6o7gmD71XxcGwgGBcnmOkXntKY+kuVuUJwccXfk7KuO519K+AXsEKODJMeDMNERAZ4r15Q9MUzBhlTQoqxAaXQRjC+roEpFKW3r7+8POZxPlU7JgbhkiHXkOKvqbH7GHp/jQnJpjLHXxHKIwTrY+wvY0SVjWGYiEgPMQg/pacJLeUZUswVTCpz+oQ5R4KNobX82lMw1/QXc6moEFzSs4wWV0fZhXkQCs17bTzgkPczYRgmIirhaYLwswSfigwpz+NoXUWH4OIMTZ94nlRWCNbnebz+qPpjGCYiKsZn2WeQQXc+aHmN8j1LUDFlqoTOvmV82K6yVWYA1ud5DMVVKQSXpO/9rWrXIEkHwzAR0RM+yz7TKauKIbg8VFY4rmqhtywl37eqFI6r2jVlqoq4FoSCsuuQ9DAMExFBfxA2l+cxpDxvIbWyGHpvyzMkP4/XE1FVxjBMRJJnziDMoFL1VMboLa8DoucHwzARSVqDRYtggec3uDzLvGEiY/E6o+qMYZjoGTVYtKiyu0BERGSS5cuXY8GCBUhPT0ezZs2wePFitGvXzmD9gwcPIjo6GufPn4enpycmT56M0aNHi9u3bNmC2bNn4/Lly1Cr1WjUqBEmTpyI8PBwk44rCAJmzJiB1atX4969ewgMDMQXX3yBZs2amf9FeKJqLYJIREREVVJV+rAgPZtNmzYhKioKMTExSE5ORrt27dC9e3ekpqbqrZ+SkoIePXqgXbt2SE5OxvTp0zFu3Dhs3rxZrFOzZk3ExMQgKSkJZ8+exTvvvIN33nkHv/zyi0nHnT9/PhYtWoRly5bhxIkTUCgU6Nq1Kx48eFBur4dMEARe3SbKzs6Gs7MzsrKy4OTkVNndoUrGkWGqCvgnbP0Y4Mzveb7WCnNzcWXG9Er5+a3JDv+76A5HRzPfdONBIV5qesvo8woMDETr1q2xYsUKsaxp06bo168f5syZo1N/ypQp2L59Oy5evCiWjR49GmfOnEFSUpLB47Ru3Ro9e/bEJ598YtRxBUGAp6cnoqKiMGXKFABAXl4e3N3dMW/ePIwaNarsF+MpcGSYiIiqJQbh8sHXterKzs7WeuTl5enUUalUOHXqFEJCQrTKQ0JCcOTIEb3tJiUl6dQPDQ3FyZMnoVbrrsMoCAL27t2LS5cuoX379kYfNyUlBUqlUquOXC5Hhw4dDPbNHDhnmIioGtAElOd51M5cGNbKHz9Q9/SyCwFz3z354ZP26tatq1UeGxuLuLg4rbKMjAwUFBTA3d1dq9zd3R1KpVJv+0qlUm/9/Px8ZGRkwMPDAwCQlZWFF198EXl5ebC0tMTy5cvRtWtXo4+r+VdfnWvXrpX2EjwThmEiompE6qGYQbjiMBBXPWlpaVrTJORyucG6Mpn2eycIgk5ZWfVLljs6OuL06dN4+PAh9u7di+joaNSvXx8dO3Y06bim9u1ZMQwTEVVDUgvFDMGVg4G4anFycipzzrCrqyssLS11RoFv376tMyKroVAo9Na3srJCrVq1xDILCws0bNgQANCyZUtcvHgRc+bMQceOHY06rkKhAFA0QqwZbS6rb+bAOcNERNVYoY1Q7YNidT+/qk4K11h1YmNjA39/fyQmJmqVJyYmIjg4WO8+QUFBOvUTEhIQEBAAa2vDt6sUBEGct2zMcX18fKBQKLTqqFQqHDx40GDfzIEjw0REElAdR4oZwKoWjhI/P6KjoxEeHo6AgAAEBQVh9erVSE1NFdcNnjZtGm7cuIENGzYAKFo5YtmyZYiOjkZkZCSSkpKwdu1afPfdd2Kbc+bMQUBAABo0aACVSoVdu3Zhw4YNWitHlHVcmUyGqKgozJ49G40aNUKjRo0we/Zs2NvbIywsrNxeD4ZhIiIJMSVAVnawYdh9/jAQPx8GDx6MzMxMzJw5E+np6fDz88OuXbvg5eUFAEhPT9da+9fHxwe7du3ChAkT8MUXX8DT0xNLlizBwIEDxTo5OTkYM2YMrl+/Djs7OzRp0gRff/01Bg8ebPRxAWDy5Ml4/PgxxowZI950IyEhAY6OjuX2enCd4afAdYZJg2sMkxSVV9hh+K1eqmIorgrrDJ88744aZl5n+OGDQgQ0M36dYdLGkWGip8AQTFL2tKPLDLvSUh2n5lD1xDBMZCIGYSLjMQBT8WuAwZiqIq4mQWQCBmEioqfHlSeoKuLIMJGRGISJiMzDUCDmyDFVBoZhIiMwCBMRlT9zrHZSWhuFhZU/Kp1VKEd+oXn/MJ9j7vs7SwzDMFEZGISJiKoeTrcgc+GcYaJSMAgTERFVbwzDRAYwCBMREVV/nCZBlYJBk4iIiKoCjgwTERERkWQxDBMRERGRZDEMExEREZFkMQwTERERkWQxDBMRERGRZDEMExEREZFkcWk1IiIiogpyv9AW6kJLs7aZU1hg1vakhiPDRERERCRZDMNEREREJFkMw0REREQkWQzDRERERCRZDMNEREREJFkMw0REREQkWQzDRERERCRZDMNEREREJFkMw0REREQkWQzDRERERCRZvB0zERERUQXJKnCAusC8t2N+VMDbMT8LjgwTERERkWQxDBMRERGRZDEMExEREZFkMQwTERERkWQxDBMRERGRZDEMExEREZFkMQwTERERkWQxDBMRERGRZDEMExEREZFkMQwTERERkWTxdsxEREREFSSr0A6qAvPGr8eF+WZtT2o4MkxEREREksUwTERERESSxTBMRERERJLFMExEREREksUwTERERESSxTBMRERERJLFMExEREQkMcuXL4ePjw9sbW3h7++PQ4cOlVr/4MGD8Pf3h62tLerXr4+VK1dqbV+zZg3atWsHFxcXuLi4oEuXLjh+/LhWnTlz5uCVV16Bo6Mj3Nzc0K9fP1y6dEmrTkREBGQymdajTZs25jlpAxiGiYiIiCRk06ZNiIqKQkxMDJKTk9GuXTt0794dqampeuunpKSgR48eaNeuHZKTkzF9+nSMGzcOmzdvFuscOHAAQ4cOxf79+5GUlIR69eohJCQEN27cEOscPHgQ77//Po4ePYrExETk5+cjJCQEOTk5Wsfr1q0b0tPTxceuXbvK54V4QiYIglCuR6iGsrOz4ezsjKysLDg5OVV2d55LDRYtquwuEBGRxBTm5uJqTEyl/PzWZIfPTwbDroaZb7rxMB8TAo4YfV6BgYFo3bo1VqxYIZY1bdoU/fr1w5w5c3TqT5kyBdu3b8fFixfFstGjR+PMmTNISkrSe4yCggK4uLhg2bJlePvtt/XWuXPnDtzc3HDw4EG0b98eQNHI8P3797Ft27Yyz8NcODJMREREVA1kZ2drPfLy8nTqqFQqnDp1CiEhIVrlISEhOHLkiN52k5KSdOqHhobi5MmTUKvVevd59OgR1Go1atasabC/WVlZAKBT58CBA3Bzc8NLL72EyMhI3L5922Ab5sDbMRMRERFVkOwCe7Pfjjm3oOh2zHXr1tUqj42NRVxcnFZZRkYGCgoK4O7urlXu7u4OpVKpt32lUqm3fn5+PjIyMuDh4aGzz9SpU/Hiiy+iS5cuetsUBAHR0dF47bXX4OfnJ5Z3794db775Jry8vJCSkoKPPvoIr7/+Ok6dOgW5XK7/BXhGDMNERERE1UBaWprWNInSwqNMJtN6LgiCTllZ9fWVA8D8+fPx3Xff4cCBA7C1tdXb3tixY3H27FkcPnxYq3zw4MHi135+fggICICXlxd27tyJAQMGGOzfs2AYJiIiIqoGnJycypwz7OrqCktLS51R4Nu3b+uM/mooFAq99a2srFCrVi2t8oULF2L27NnYs2cPXn75Zb3tffDBB9i+fTt+/fVX1KlTp9T+enh4wMvLC3/99Vep9Z4F5wwTERERSYSNjQ38/f2RmJioVZ6YmIjg4GC9+wQFBenUT0hIQEBAAKytrcWyBQsW4JNPPsHu3bsREBCg044gCBg7diy2bNmCffv2wcfHp8z+ZmZmIi0tTe9UDHNhGCYiIiKSkOjoaHz55Zf46quvcPHiRUyYMAGpqakYPXo0AGDatGlaK0CMHj0a165dQ3R0NC5evIivvvoKa9euxaRJk8Q68+fPx4cffoivvvoK3t7eUCqVUCqVePjwoVjn/fffx9dff41vv/0Wjo6OYp3Hjx8DAB4+fIhJkyYhKSkJV69exYEDB9C7d2+4urqif//+5fZ6cJoEERERkYQMHjwYmZmZmDlzJtLT0+Hn54ddu3bBy8sLAJCenq615rCPjw927dqFCRMm4IsvvoCnpyeWLFmCgQMHinWWL18OlUqFN954Q+tYxT/Ep1nKrWPHjlp14uPjERERAUtLS5w7dw4bNmzA/fv34eHhgU6dOmHTpk1wdHQsh1eiCNcZfgpcZ/jZcZ1hIiKqaFVhneEZx7rA1szrDOc+zEds4B7mkqfEaRJEREREJFkMw0REREQkWQzDRERERCRZDMNEREREJFnVJgxfvXoVI0aMgI+PD+zs7NCgQQPExsZCpVJp1UtNTUXv3r3h4OAAV1dXjBs3TqcOERERUXnIzrdFVr6dWR/Z+frv8kbGqTZLq/35558oLCzEqlWr0LBhQ/zxxx+IjIxETk4OFi5cCAAoKChAz549Ubt2bRw+fBiZmZkYNmwYBEHA0qVLK/kMiIiIiKiiVZsw3K1bN3Tr1k18Xr9+fVy6dAkrVqwQw3BCQgIuXLiAtLQ0eHp6AgA+++wzREREYNasWVyOhIiIiEhiqs00CX2ysrJQs2ZN8XlSUhL8/PzEIAwAoaGhyMvLw6lTpwy2k5eXh+zsbK0HERERET3/qm0Y/vvvv7F06VLx1oIAoFQq4e7urlXPxcUFNjY2UCqVBtuaM2cOnJ2dxUfdunXLrd9EREREVHGqfBiOi4uDTCYr9XHy5EmtfW7evIlu3brhzTffxMiRI7W2yWQynWMIgqC3XGPatGnIysoSH2lpaeY5OSIiIiKqVFV+zvDYsWMxZMiQUut4e3uLX9+8eROdOnVCUFAQVq9erVVPoVDg2LFjWmX37t2DWq3WGTEuTi6XQy6Xm955IiIiIqrSqnwYdnV1haurq1F1b9y4gU6dOsHf3x/x8fGwsNAe+A4KCsKsWbOQnp4ODw8PAEUfqpPL5fD39zd734mIiIioaqvyYdhYN2/eRMeOHVGvXj0sXLgQd+7cEbcpFAoAQEhICHx9fREeHo4FCxbg7t27mDRpEiIjI7mSBBEREZEEVZswnJCQgMuXL+Py5cuoU6eO1jZBEAAAlpaW2LlzJ8aMGYO2bdvCzs4OYWFh4tJrRERERCQt1SYMR0REICIiosx69erVw44dO8q/Q0RERERU5VWbMExERERU1T3It0NevrVZ21TlM849iyq/tBoRERERUXlhGCYiIiIiyWIYJiIiIiLJYhgmIiIiIsliGCYiIiIiyWIYJiIiIiLJYhgmIiIiIsliGCYiIiIiyWIYJiIiIiLJYhgmIiIiIsni/fuIiIiIKkh2vhw2+TZmbVOVz7HNZ8FXj4iIiIgki2GYiIiIiCSLYZiIiIiIJIthmIiIiIgki2GYiIiIiCSLYZiIiIiIJIthmIiIiIgki2GYiIiIiCSLYZiIiIiIJIthmIiIiIgki7djJiIiIqogD/JtYa027+2Y1bwd8zPhq0dEREREksUwTERERESSxTBMRERERJLFMExEREREksUwTERERESSxTBMRERERJLFMExEREQkMcuXL4ePjw9sbW3h7++PQ4cOlVr/4MGD8Pf3h62tLerXr4+VK1dqbT9//jwGDhwIb29vyGQyLF68WKeNuLg4yGQyrYdCodCqIwgC4uLi4OnpCTs7O3Ts2BHnz59/5vMtDcMwERERkYRs2rQJUVFRiImJQXJyMtq1a4fu3bsjNTVVb/2UlBT06NED7dq1Q3JyMqZPn45x48Zh8+bNYp1Hjx6hfv36mDt3rk7ALa5Zs2ZIT08XH+fOndPaPn/+fCxatAjLli3DiRMnoFAo0LVrVzx48MA8J68HwzARERGRhCxatAgjRozAyJEj0bRpUyxevBh169bFihUr9NZfuXIl6tWrh8WLF6Np06YYOXIkhg8fjoULF4p1XnnlFSxYsABDhgyBXC43eGwrKysoFArxUbt2bXGbIAhYvHgxYmJiMGDAAPj5+WH9+vV49OgRvv32W/O9ACUwDBMRERFVA9nZ2VqPvLw8nToqlQqnTp1CSEiIVnlISAiOHDmit92kpCSd+qGhoTh58iTUarVJffzrr7/g6ekJHx8fDBkyBFeuXBG3paSkQKlUah1LLpejQ4cOBvtmDgzDVOEaLFpU2V0gIiKqFDlqazxU25j1kaO2BgDUrVsXzs7O4mPOnDk6x8/IyEBBQQHc3d21yt3d3aFUKvX2WalU6q2fn5+PjIwMo889MDAQGzZswC+//II1a9ZAqVQiODgYmZmZ4nE0bRvbN3OwKreWifRgECYiIiofaWlpcHJyEp+XNl1BJpNpPRcEQaesrPr6ykvTvXt38evmzZsjKCgIDRo0wPr16xEdHf3UfXtWDMNUIRiCiYiIypeTk5NWGNbH1dUVlpaWOiOtt2/f1hmR1VAoFHrrW1lZoVatWk/dXwcHBzRv3hx//fWXeBygaITYw8PDqL6ZA6dJULljECYiIqoabGxs4O/vj8TERK3yxMREBAcH690nKChIp35CQgICAgJgbW391H3Jy8vDxYsXxeDr4+MDhUKhdSyVSoWDBw8a7Js5cGSYyhWDMBERUdUSHR2N8PBwBAQEICgoCKtXr0ZqaipGjx4NAJg2bRpu3LiBDRs2AABGjx6NZcuWITo6GpGRkUhKSsLatWvx3XffiW2qVCpcuHBB/PrGjRs4ffo0atSogYYNGwIAJk2ahN69e6NevXq4ffs2Pv30U2RnZ2PYsGEAiqZHREVFYfbs2WjUqBEaNWqE2bNnw97eHmFhYeX2ejAMU7lhECYiIqp6Bg8ejMzMTMycORPp6enw8/PDrl274OXlBQBIT0/XWnPYx8cHu3btwoQJE/DFF1/A09MTS5YswcCBA8U6N2/eRKtWrcTnCxcuxMKFC9GhQwccOHAAAHD9+nUMHToUGRkZqF27Ntq0aYOjR4+KxwWAyZMn4/HjxxgzZgzu3buHwMBAJCQkwNHRsdxeD5mgmQFNRsvOzoazszOysrLKnJsjVQzCRERU1RTm5uJqTEyl/PzWZIeOO96DlYPhD7Y9jfycPBzotYK55ClxzjCZHYMwERERPS8YhsmsGISJiIjoecI5w8+gxbJlsLC1rexuEBEREdFT4sgwEREREUkWR4aJiIiIKsgjtQ2s1DZmbTNfzbUQngVHhomIiIhIshiGiYiIiEiyGIaJiIiISLIYhomIiIhIshiGiYiIiEiyGIaJiIiISLIYhomIiIhIshiGiYiIiEiyGIaJiIiISLIYhomIiIhIsng7ZiIiIqIKkptvDUu1tVnbLMgvNGt7UsORYSIiIiKSLIZhIiIiIpIshmEiIiIikiyGYSIiIiKSLIZhIiIiIpIshmEiIiIikiyGYSIiIiKSLIZhIiIiIpIshmEiIiIikiyGYSIiIiKSLN6OmYiIiKiC5KqsYGll3vhVoCowa3tSw5FhIiIiIpIshmEiIiIikiyGYSIiIiKSLIZhIiIiIpIshmEiIiIikiyTPs6YlZWFrVu34tChQ7h69SoePXqE2rVro1WrVggNDUVwcHB59ZOIiIiIyOyMGhlOT09HZGQkPDw8MHPmTOTk5KBly5bo3Lkz6tSpg/3796Nr167w9fXFpk2byrvPRERERERmYdTIcIsWLfD222/j+PHj8PPz01vn8ePH2LZtGxYtWoS0tDRMmjTJrB0lIiIiIjI3o8Lw+fPnUbt27VLr2NnZYejQoRg6dCju3Lljls4REREREZUno6ZJlBWEn7U+EREREVFlMHo1iQYNGuDzzz83uP3WrVuwtLQ0S6eIiIiIqqN8tSXUaiuzPvLVzF/PwugwnJKSgilTpmDYsGFQqVR66wiCYLaOERERERGVN5PWGd66dSv279+P9u3bIz09XWe7TCYzW8eIiIiIiMqbSWH4lVdewYkTJ2BlZQV/f38cO3asvPpFRERERFTuTL4Dnbu7Ow4cOIDevXujY8eOiI+PL49+ERERERGVu6e6HbOVlRVWrVqFzz//HKNHj8b48eORn59v7r49tby8PLRs2RIymQynT5/W2paamorevXvDwcEBrq6uGDdunME50ERERERUvRl9O2Z984FHjx4NPz8/vPHGG/jtt9/M2rFnMXnyZHh6euLMmTNa5QUFBejZsydq166Nw4cPIzMzE8OGDYMgCFi6dGkl9ZaIiIiIKovRI8OGVop47bXXcOLECbN16Fn9/PPPSEhIwMKFC3W2JSQk4MKFC/j666/RqlUrdOnSBZ999hnWrFmD7OzsSugtERERUcVbvnw5fHx8YGtrC39/fxw6dKjU+gcPHoS/vz9sbW1Rv359rFy5UqfO5s2b4evrC7lcDl9fX2zdulVru7e3N2Qymc7j/fffF+tERETobG/Tpo15TtoAk5ZWM3Qzjbp16+K3337D/v37zdaxp3Hr1i1ERkbiv//9L+zt7XW2JyUlwc/PD56enmJZaGgo8vLycOrUKYPt5uXlITs7W+tBRERE9DzatGkToqKiEBMTg+TkZLRr1w7du3dHamqq3vopKSno0aMH2rVrh+TkZEyfPh3jxo3D5s2bxTpJSUkYPHgwwsPDcebMGYSHh2PQoEFaiy2cOHEC6enp4iMxMREA8Oabb2odr1u3blr1du3aVQ6vwj+MDsNeXl6lLp0ml8vRvn17s3TqaQiCgIiICIwePRoBAQF66yiVSri7u2uVubi4wMbGBkql0mDbc+bMgbOzs/ioW7euWftOREREVFEWLVqEESNGYOTIkWjatCkWL16MunXrYsWKFXrrr1y5EvXq1cPixYvRtGlTjBw5EsOHD9f6K/zixYvRtWtXTJs2DU2aNMG0adPQuXNnLF68WKxTu3ZtKBQK8bFjxw40aNAAHTp00DqeXC7XqlezZs1yeR00jArD3bp1w5EjR8qs9+DBA8ybNw9ffPHFM3dMIy4uTu+QevHHyZMnsXTpUmRnZ2PatGmltqcv0AuCUGrQnzZtGrKyssRHWlraM58XERERkTmV/Ct2Xl6eTh2VSoVTp04hJCREqzwkJMRg1ktKStKpHxoaipMnT0KtVpdax1CbKpUKX3/9NYYPH66TwQ4cOAA3Nze89NJLiIyMxO3bt0s/8Wdk1Afo3nzzTQwaNAiOjo7o06cPAgIC4OnpCVtbW9y7dw8XLlzA4cOHsWvXLvTq1QsLFiwwWwfHjh2LIUOGlFrH29sbn376KY4ePQq5XK61LSAgAG+99RbWr18PhUKhszbyvXv3oFardUaMi5PL5TrtEhEREZkqX2UJCyvz3j65UFXUXsm/XMfGxiIuLk6rLCMjAwUFBTq5x93d3eBfyfX9Zd3d3R35+fnIyMiAh4eHwTqG2ty2bRvu37+PiIgIrfLu3bvjzTffhJeXF1JSUvDRRx/h9ddfx6lTp8otixkVhkeMGIHw8HD88MMP2LRpE9asWYP79+8DKBpp9fX1RWhoKE6dOoXGjRubtYOurq5wdXUts96SJUvw6aefis9v3ryJ0NBQbNq0CYGBgQCAoKAgzJo1C+np6fDw8ABQ9KE6uVwOf39/s/abiIiIqCKlpaXByclJfF5aeCw5GlvWX8n11S9Zbkqba9euRffu3bU+xwUAgwcPFr/28/NDQEAAvLy8sHPnTgwYMMBg/56F0Uur2djYICwsDGFhYQCArKwsPH78GLVq1YK1tXW5dM4U9erV03peo0YNAECDBg1Qp04dAEV/AvD19UV4eDgWLFiAu3fvYtKkSYiMjNS6eIiIiIieN05OTmXmGVdXV1haWuqM2N6+fdvgX8kVCoXe+lZWVqhVq1apdfS1ee3aNezZswdbtmwp85w8PDzg5eWFv/76q8y6T+upbroBAM7OzlAoFFUiCBvL0tISO3fuhK2tLdq2bYtBgwahX79+epdhIyIiIqpubGxs4O/vL67koJGYmIjg4GC9+wQFBenUT0hIQEBAgJgDDdXR12Z8fDzc3NzQs2fPMvubmZmJtLQ08S/65cHokeHnjbe3t961kevVq4cdO3ZUQo+IiIiIKl90dDTCw8MREBCAoKAgrF69GqmpqRg9ejSAooUDbty4gQ0bNgAousnasmXLEB0djcjISCQlJWHt2rX47rvvxDbHjx+P9u3bY968eejbty9+/PFH7NmzB4cPH9Y6dmFhIeLj4zFs2DBYWWnH0IcPHyIuLg4DBw6Eh4cHrl69iunTp8PV1RX9+/cvt9ej2oZhIiIiItI1ePBgZGZmYubMmUhPT4efnx927doFLy8vAEB6errWmsM+Pj7YtWsXJkyYgC+++AKenp5YsmQJBg4cKNYJDg7Gxo0b8eGHH+Kjjz5CgwYNtD63pbFnzx6kpqZi+PDhOv2ytLTEuXPnsGHDBty/fx8eHh7o1KkTNm3aBEdHx3J6NQCZYOjWcmRQdnY2nJ2d4T1rFixsbSu7O0RERGSEwtxcXI2JQVZWVoV/VkiTHeqt/hgW9ubNDoWPcpH67sxKOa/q4KnnDBMRERERPe+eKgzfv38fX375JaZNm4a7d+8CAH7//XfcuHHDrJ0jIiIiIipPJs8ZPnv2LLp06QJnZ2dcvXoVkZGRqFmzJrZu3Ypr166Jk62JiIiIiKo6k0eGo6OjERERgb/++gu2xebLdu/eHb/++qtZO0dEREREVJ5MHhk+ceIEVq1apVP+4osvGrzlHhEREREBQr4FBLV5P7Il5PMjYM/C5FfP1tYW2dnZOuWXLl1C7dq1zdIpIiIiIqKKYHIY7tu3L2bOnAm1Wg2g6D7UqampmDp1qtZ6c0REREREVZ3JYXjhwoW4c+cO3Nzc8PjxY3To0AENGzaEo6MjZs2aVR59JCIiIiIqFybPGXZycsLhw4exb98+/P777ygsLETr1q3RpUuX8ugfEREREVG5eerbMb/++ut4/fXXzdkXIiIiIqIKZfI0iXHjxmHJkiU65cuWLUNUVJQ5+kREREREVCFMDsObN29G27ZtdcqDg4Pxww8/mKVTREREREQVweQwnJmZCWdnZ51yJycnZGRkmKVTREREREQVweQw3LBhQ+zevVun/Oeff0b9+vXN0ikiIiIioopg8gfooqOjMXbsWNy5c0f8AN3evXvx2WefYfHixebuHxERERFRuTE5DA8fPhx5eXmYNWsWPvnkEwCAt7c3VqxYgbffftvsHSQiIiKqNlQWgKWZb5+s4u2Yn8VTLa323nvv4b333sOdO3dgZ2eHGjVqmLtfRERERETl7qnXGQaA2rVrm6sfREREREQVzuRx9Vu3biE8PByenp6wsrKCpaWl1oOIiIiI6Hlh8shwREQEUlNT8dFHH8HDwwMymaw8+kVEREREVO5MDsOHDx/GoUOH0LJly3LoDhERERFRxTF5mkTdunUhCEJ59IWIiIiIqEKZHIYXL16MqVOn4urVq+XQHSIiIiKiimPyNInBgwfj0aNHaNCgAezt7WFtba21/e7du2brHBERERFReTI5DPMuc0RERERUXZgchocNG1Ye/SAiIiIiqnDPdNONx48fQ61Wa5U5OTk9U4eIiIiIqiuZygIyM9+OWcbbMT8Tk1+9nJwcjB07Fm5ubqhRowZcXFy0HkREREREzwuTw/DkyZOxb98+LF++HHK5HF9++SVmzJgBT09PbNiwoTz6SERERERULkyeJvHTTz9hw4YN6NixI4YPH4527dqhYcOG8PLywjfffIO33nqrPPpJRERERGR2Jo8M3717Fz4+PgCK5gdrllJ77bXX8Ouvv5q3d0RERERE5cjkMFy/fn3xhhu+vr74/vvvARSNGL/wwgvm7BsRERERUbkyOQy/8847OHPmDABg2rRp4tzhCRMm4N///rfZO0hEREREVF5MnjM8YcIE8etOnTrhzz//xMmTJ9GgQQO0aNHCrJ0jIiIiIipPJo8Mb9iwAXl5eeLzevXqYcCAAWjatClXkyAiIiKi58pTTZPIysrSKX/w4AHeeecds3SKiIiIiKgimByGBUGATCbTKb9+/TqcnZ3N0ikiIiIioopg9JzhVq1aQSaTQSaToXPnzrCy+mfXgoICpKSkoFu3buXSSSIiIqLqwEItg4Wl7qDiM1GbuT2JMToM9+vXDwBw+vRphIaGokaNGuI2GxsbeHt7Y+DAgWbvIBERERFReTE6DMfGxgIAvL29MWTIEMjl8nLrFBERERFRRTB5zvDrr7+OO3fuiM+PHz+OqKgorF692qwdIyIiIiIqbyaH4bCwMOzfvx8AoFQq0aVLFxw/fhzTp0/HzJkzzd5BIiIiIqLyYnIY/uOPP/Dqq68CAL7//ns0b94cR44cwbfffot169aZu39EREREROXG5DCsVqvF+cJ79uxBnz59AABNmjRBenq6eXtHRERERFSOTA7DzZo1w8qVK3Ho0CEkJiaKy6ndvHkTtWrVMnsHiYiIiMi8li9fDh8fH9ja2sLf3x+HDh0qtf7Bgwfh7+8PW1tb1K9fHytXrtSps3nzZvj6+kIul8PX1xdbt241+biCICAuLg6enp6ws7NDx44dcf78+Wc72TKYHIbnzZuHVatWoWPHjhg6dChatGgBANi+fbs4fYKIiIiIqqZNmzYhKioKMTExSE5ORrt27dC9e3ekpqbqrZ+SkoIePXqgXbt2SE5OxvTp0zFu3Dhs3rxZrJOUlITBgwcjPDwcZ86cQXh4OAYNGoRjx46ZdNz58+dj0aJFWLZsGU6cOAGFQoGuXbviwYMH5fZ6yARBEEzdqaCgANnZ2XBxcRHLrl69Cnt7e7i5uZm1g1VRdnY2nJ2d4T1rFixsbSu7O0RERGSEwtxcXI2JQVZWFpycnCr02OWZHUw9r8DAQLRu3RorVqwQy5o2bYp+/fphzpw5OvWnTJmC7du34+LFi2LZ6NGjcebMGSQlJQEABg8ejOzsbPz8889inW7dusHFxQXfffedUccVBAGenp6IiorClClTAAB5eXlwd3fHvHnzMGrUKBNfGeOYPDIMAJaWllpBGChaf1gKQZiIiIioKsrOztZ65OXl6dRRqVQ4deoUQkJCtMpDQkJw5MgRve0mJSXp1A8NDcXJkyehVqtLraNp05jjpqSkQKlUatWRy+Xo0KGDwb6Zg1E33WjdujX27t0LFxcX8bbMhvz+++9m6xwRERFRdWKhksHCwsy3T1YVtVe3bl2t4tjYWMTFxWmVZWRkoKCgAO7u7lrl7u7uUCqVeptXKpV66+fn5yMjIwMeHh4G62jaNOa4mn/11bl27ZrevpmDUWG4b9++4goSmtsyExEREVHVkZaWpjVNorS7BZcc2BQEodTBTn31S5Yb06a56piTUWFYcyvmkl8TERERUdXg5ORU5pxhV1dXWFpa6owC3759W2dEVkOhUOitb2VlJa4kZqiOpk1jjqtQKAAUjRB7eHgY1TdzMHnOsCAIOHnyJH744Qds3rwZv//+O57iM3hEREREVMFsbGzg7++PxMRErfLExEQEBwfr3ScoKEinfkJCAgICAmBtbV1qHU2bxhzXx8cHCoVCq45KpcLBgwcN9s0cjBoZ1ti/fz9GjBiBa9euaQ2P+/j44KuvvkL79u3LpZNEREREZB7R0dEIDw9HQEAAgoKCsHr1aqSmpmL06NEAgGnTpuHGjRvYsGEDgKKVI5YtW4bo6GhERkYiKSkJa9euFVeJAIDx48ejffv2mDdvHvr27Ysff/wRe/bsweHDh40+rkwmQ1RUFGbPno1GjRqhUaNGmD17Nuzt7REWFlZur4fRYfjy5cvo1asXAgMD8fnnn6NJkyYQBAEXLlzAkiVL0KNHD5w9exb169cvt84SERER0bMZPHgwMjMzMXPmTKSnp8PPzw+7du2Cl5cXACA9PV1r7V8fHx/s2rULEyZMwBdffAFPT08sWbIEAwcOFOsEBwdj48aN+PDDD/HRRx+hQYMG2LRpEwIDA40+LgBMnjwZjx8/xpgxY3Dv3j0EBgYiISEBjo6O5fZ6GL3O8NixY3Hx4kXs3btXZ5sgCOjSpQt8fX2xdOlSs3eyquE6w0RERM+fqrDOcP3Y2eWyzvCVGdMr5byqA6PnDB84cABRUVF6t2mGtffv32+ufhERERERlTujw3BqaiqaN29ucLufn1+5rgFHRERERGRuRofhhw8fwt7e3uB2e3t7PHr0yCydIiIiIiKqCCatJnHhwgWDdyfJyMgwS4eIiIiIiCqKSWG4c+fOetcUlslk5X53ECIiIqLnnUwNWFiat01Bbd72pMboMJySklKe/SAiIiIiqnBGh+Hia8AREREREVUHJt+OmYiIiIioumAYJiIiIiLJYhgmIiIiIsliGCYiIiIiyWIYJiIiIiLJMmo1iVatWhm9hvDvv//+TB0iIiIiIqooRoXhfv36lXM3iIiIiIgqnlFhODY2trz7QURERERU4Uy6HTMRERERPT0LNWBh5k9s8XbMz8bkMFxQUIDPP/8c33//PVJTU6FSqbS2371712ydIyIiIiIqTyb/bjJjxgwsWrQIgwYNQlZWFqKjozFgwABYWFggLi6uHLpIRERERFQ+TA7D33zzDdasWYNJkybBysoKQ4cOxZdffomPP/4YR48eLY8+EhERERGVC5PDsFKpRPPmzQEANWrUQFZWFgCgV69e2Llzp3l7R0RERERUjkwOw3Xq1EF6ejoAoGHDhkhISAAAnDhxAnK53Ly9IyIiIiIqRyaH4f79+2Pv3r0AgPHjx+Ojjz5Co0aN8Pbbb2P48OFm7yARERERUXkxeTWJuXPnil+/8cYbqFOnDo4cOYKGDRuiT58+Zu0cEREREVF5euZ1htu0aYM2bdqYoy9ERERERBXKqDC8fft2dO/eHdbW1ti+fXupdSt7dHjnzp2YOXMmzp49CwcHB7Rv3x5btmwRt6empuL999/Hvn37YGdnh7CwMCxcuBA2NjaV2GsiIiIiqgxGheF+/fpBqVTCzc0N/fr1M1hPJpOhoKDAXH0z2ebNmxEZGYnZs2fj9ddfhyAIOHfunLi9oKAAPXv2RO3atXH48GFkZmZi2LBhEAQBS5curbR+ExEREVHlMCoMFxYW6v26KsnPz8f48eOxYMECjBgxQixv3Lix+HVCQgIuXLiAtLQ0eHp6AgA+++wzREREYNasWXBycqrwfhMREZF0WKgAC5l52xRUZdchw8x6d+xHjx6ZszmT/P7777hx4wYsLCzQqlUreHh4oHv37jh//rxYJykpCX5+fmIQBoDQ0FDk5eXh1KlTBtvOy8tDdna21oOIiIiInn8mh+GOHTvi+vXrOuXHjh1Dy5YtzdGnp3LlyhUAQFxcHD788EPs2LEDLi4u6NChA+7evQug6IYh7u7uWvu5uLjAxsYGSqXSYNtz5syBs7Oz+Khbt275nQgRERERVRiTw7CTkxNefvllbNy4EUDRtIm4uDi0b9++XD48FxcXB5lMVurj5MmT4vSNmJgYDBw4EP7+/oiPj4dMJsP//d//ie3JZLp/mxAEQW+5xrRp05CVlSU+0tLSzH6eRERERFTxTF5abfv27Vi5ciVGjhyJ7du34+rVq0hNTcXOnTvRpUsXs3dw7NixGDJkSKl1vL298eDBAwCAr6+vWC6Xy1G/fn2kpqYCABQKBY4dO6a1771796BWq3VGjIuTy+W8ux4RERFRNfRU6wyPHj0a165dw7x582BlZYUDBw4gODjY3H0DALi6usLV1bXMev7+/pDL5bh06RJee+01AIBarcbVq1fh5eUFAAgKCsKsWbOQnp4ODw8PAEUfqpPL5fD39y+X/hMRERFR1WXyNIl79+5h4MCBWLFiBVatWoVBgwYhJCQEy5cvL4/+Gc3JyQmjR49GbGwsEhIScOnSJbz33nsAgDfffBMAEBISAl9fX4SHhyM5ORl79+7FpEmTEBkZyZUkiIiIiCTI5JFhPz8/+Pj4IDk5GT4+PoiMjMSmTZswZswY7Ny5Ezt37iyPfhplwYIFsLKyQnh4OB4/fozAwEDs27cPLi4uAABLS0vs3LkTY8aMQdu2bbVuukFERERE0mNyGB49ejRiYmJgYfHPoPLgwYPRtm1bvPPOO2btnKmsra2xcOHCUsNtvXr1sGPHjgrsFRERERFVVSaH4Y8++khveZ06dTB//vxn7hARERERUUV55ptuZGVlYfny5WjdujUCAgLM0SciIiIiogrxVKtJAMC+ffvw1VdfYcuWLfDy8sLAgQOxdu1ac/aNiIiIqFqxVAOWZr3/LwC1mduTGJPC8PXr17Fu3Tp89dVXyMnJwaBBg6BWq7F582at9X2JiIiIiJ4HRv9u0qNHD/j6+uLChQtYunQpbt68iaVLl5Zn34iIiIiIypXRI8MJCQkYN24c3nvvPTRq1Kg8+0REREREVCGMHhk+dOgQHjx4gICAAAQGBmLZsmW4c+dOefaNiIiIiKhcGR2Gg4KCsGbNGqSnp2PUqFHYuHEjXnzxRRQWFiIxMREPHjwoz34SEREREZmdyZ9ntLe3x/Dhw3H48GGcO3cOEydOxNy5c+Hm5oY+ffqURx+JiIiIiMrFMy3u0bhxY8yfPx/Xr1/Hd999Z64+EREREVEVcO/ePYSHh8PZ2RnOzs4IDw/H/fv3S91HEATExcXB09MTdnZ26NixI86fP69VJy8vDx988AFcXV3h4OCAPn364Pr16+L2AwcOQCaT6X2cOHFCrKdv+8qVK006R7OsdGdpaYl+/fph+/bt5miOiIiIiKqAsLAwnD59Grt378bu3btx+vRphIeHl7rP/PnzsWjRIixbtgwnTpyAQqFA165dtabURkVFYevWrdi4cSMOHz6Mhw8folevXigoKAAABAcHIz09XesxcuRIeHt769zkLT4+XqvesGHDTDrHp77pBhERERFVXxcvXsTu3btx9OhRBAYGAgDWrFmDoKAgXLp0CY0bN9bZRxAELF68GDExMRgwYAAAYP369XB3d8e3336LUaNGISsrC2vXrsV///tfdOnSBQDw9ddfo27dutizZw9CQ0NhY2MDhUIhtqtWq7F9+3aMHTsWMplM65gvvPCCVl1TmfseKERERERUCbKzs7UeeXl5z9ReUlISnJ2dxSAMAG3atIGzszOOHDmid5+UlBQolUqEhISIZXK5HB06dBD3OXXqFNRqtVYdT09P+Pn5GWx3+/btyMjIQEREhM62sWPHwtXVFa+88gpWrlyJwsJCk86TI8NEREREFcRCDVjIyq5nCuHJ7Zjr1q2rVR4bG4u4uLinblepVMLNzU2n3M3NDUql0uA+AODu7q5V7u7ujmvXrol1bGxs4OLiolPHULtr165FaGiozjl+8skn6Ny5M+zs7LB3715MnDgRGRkZ+PDDD407STAMExEREVULaWlpcHJyEp/L5XK99eLi4jBjxoxS29J8SK3klASgaCqEvvLiSm43Zh9Dda5fv45ffvkF33//vc624qG3ZcuWAICZM2cyDBMRERFJjZOTk1YYNmTs2LEYMmRIqXW8vb1x9uxZ3Lp1S2fbnTt3dEZ+NTRzd5VKJTw8PMTy27dvi/soFAqoVCrcu3dPa3T49u3bCA4O1mkzPj4etWrVMmoJ3zZt2iA7Oxu3bt0y2MeSOGeYiIiISEJcXV3RpEmTUh+2trYICgpCVlYWjh8/Lu577NgxZGVl6Q2tAODj4wOFQoHExESxTKVS4eDBg+I+/v7+sLa21qqTnp6OP/74Q6ddQRAQHx+Pt99+G9bW1mWeW3JyMmxtbfHCCy8Y/XpwZJiIiIiIdDRt2hTdunVDZGQkVq1aBQB499130atXL62VJJo0aYI5c+agf//+kMlkiIqKwuzZs9GoUSM0atQIs2fPhr29PcLCwgAAzs7OGDFiBCZOnIhatWqhZs2amDRpEpo3by6uLqGxb98+pKSkYMSIETr9++mnn6BUKhEUFAQ7Ozvs378fMTExePfddw1OEdGHYZiIiIiI9Prmm28wbtw4ceWHPn36YNmyZVp1Ll26hKysLPH55MmT8fjxY4wZMwb37t1DYGAgEhIS4OjoKNb5/PPPYWVlhUGDBuHx48fo3Lkz1q1bB0tLS622165di+DgYDRt2lSnb9bW1li+fDmio6NRWFiI+vXrY+bMmXj//fdNOkeZIAiCSXsQsrOz4ezsDO9Zs2Bha1vZ3SEiIiIjFObm4mpMDLKysoyaW2tOmuzgFzkbljbmzQ4Fqlz8sWZ6pZxXdcA5w0REREQkWQzDRERERCRZDMNEREREJFkMw0REREQkWVxNgoiIiKiCWKoAy7KrmUZl7galhSPDRERERCRZDMNEREREJFkMw0REREQkWQzDRERERCRZDMNEREREJFkMw0REREQkWQzDRERERCRZDMNEREREJFkMw0REREQkWQzDRERERCRZvB0zERERUQWxVAmwhGDeRlVmbk9iODJMRERERJLFMExEREREksUwTERERESSxTBMRERERJLFMExEREREksUwTERERESSxTBMRERERJLFMExEREREksUwTERERESSxTBMRERERJLF2zETERERVRALtflvxyyoeTvmZ8GRYSIiIiKSLIZhIiIiIpIshmEiIiIikiyGYSIiIiKSLIZhIiIiIpIshmEiIiIikiyGYSIiIiKSLIZhIiIiIpIshmEiIiIikiyGYSIiIiKSLN6OmYiIiKiCWOYJsCzk7ZirEo4MExEREZFkMQwTERERkWQxDBMRERGRZDEMExEREZFkMQwTERERkWQxDBMRERGRZDEMExEREZFe9+7dQ3h4OJydneHs7Izw8HDcv3+/1H0EQUBcXBw8PT1hZ2eHjh074vz581p1Vq9ejY4dO8LJyQkymUxvm97e3pDJZFqPqVOnatVJTU1F79694eDgAFdXV4wbNw4qlcqkc2QYJiIiIiK9wsLCcPr0aezevRu7d+/G6dOnER4eXuo+8+fPx6JFi7Bs2TKcOHECCoUCXbt2xYMHD8Q6jx49Qrdu3TB9+vRS25o5cybS09PFx4cffihuKygoQM+ePZGTk4PDhw9j48aN2Lx5MyZOnGjSOfKmG0RERESk4+LFi9i9ezeOHj2KwMBAAMCaNWsQFBSES5cuoXHjxjr7CIKAxYsXIyYmBgMGDAAArF+/Hu7u7vj2228xatQoAEBUVBQA4MCBA6X2wdHREQqFQu+2hIQEXLhwAWlpafD09AQAfPbZZ4iIiMCsWbPg5ORk1HlyZJiIiIioGsjOztZ65OXlPVN7SUlJcHZ2FoMwALRp0wbOzs44cuSI3n1SUlKgVCoREhIilsnlcnTo0MHgPqWZN28eatWqhZYtW2LWrFlaUyCSkpLg5+cnBmEACA0NRV5eHk6dOmX0MTgyTERERFRBLHMLYVlQaNY2BXVRe3Xr1tUqj42NRVxc3FO3q1Qq4ebmplPu5uYGpVJpcB8AcHd31yp3d3fHtWvXTDr++PHj0bp1a7i4uOD48eOYNm0aUlJS8OWXX4rHKnkcFxcX2NjYGOyfPgzDRERERNVAWlqa1tQAuVyut15cXBxmzJhRalsnTpwAAMhkMp1tgiDoLS+u5HZj9ilpwoQJ4tcvv/wyXFxc8MYbb4ijxc/Sv+IYhomIiIiqAScnJ6PmyY4dOxZDhgwptY63tzfOnj2LW7du6Wy7c+eOzoishmZ+r1KphIeHh1h++/Ztg/sYq02bNgCAy5cvo1atWlAoFDh27JhWnXv37kGtVpt0LIZhIiIiIglxdXWFq6trmfWCgoKQlZWF48eP49VXXwUAHDt2DFlZWQgODta7j4+PDxQKBRITE9GqVSsAgEqlwsGDBzFv3rxn6ndycjIAiCE7KCgIs2bNQnp6uliWkJAAuVwOf39/o9tlGCYiIiIiHU2bNkW3bt0QGRmJVatWAQDeffdd9OrVS2sliSZNmmDOnDno378/ZDIZoqKiMHv2bDRq1AiNGjXC7NmzYW9vj7CwMHEfpVIJpVKJy5cvAwDOnTsHR0dH1KtXDzVr1kRSUhKOHj2KTp06wdnZGSdOnMCECRPQp08f1KtXDwAQEhICX19fhIeHY8GCBbh79y4mTZqEyMhIo1eSABiGiYiIiMiAb775BuPGjRNXh+jTpw+WLVumVefSpUvIysoSn0+ePBmPHz/GmDFjcO/ePQQGBiIhIQGOjo5inZUrV2rNW27fvj0AID4+HhEREZDL5di0aRNmzJiBvLw8eHl5ITIyEpMnTxb3sbS0xM6dOzFmzBi0bdsWdnZ2CAsLw8KFC006R5kgCIJJexCys7Ph7OwM71mzYGFrW9ndISIiIiMU5ubiakwMsrKyTBo5NAdNdgjuOgNW1ubNDvnqXBxJjK2U86oOuM4wEREREUkWwzARERERSRbDMBERERFJFsMwEREREUkWV5MgIiIiqiCW6kJYCma+HXO+eduTGo4MExEREZFkVasw/L///Q99+/aFq6srnJyc0LZtW+zfv1+rTmpqKnr37g0HBwe4urpi3LhxUKlUldRjIiIiIqpM1SoM9+zZE/n5+di3bx9OnTqFli1bolevXlAqlQCAgoIC9OzZEzk5OTh8+DA2btyIzZs3Y+LEiZXccyIiIiKqDNUmDGdkZODy5cuYOnUqXn75ZTRq1Ahz587Fo0ePcP78eQBF96u+cOECvv76a7Rq1QpdunTBZ599hjVr1iA7O7uSz4CIiIiIKlq1CcO1atVC06ZNsWHDBuTk5CA/Px+rVq2Cu7s7/P39AQBJSUnw8/ODp6enuF9oaCjy8vJw6tQpg23n5eUhOztb60FEREREz79qs5qETCZDYmIi+vbtC0dHR1hYWMDd3R27d+/GCy+8AABQKpVwd3fX2s/FxQU2NjbiVAp95syZo3X/bCIiIiKqHqr8yHBcXBxkMlmpj5MnT0IQBIwZMwZubm44dOgQjh8/jr59+6JXr15IT08X25PJZDrHEARBb7nGtGnTkJWVJT7S0tLK5VyJiIiIqGJV+ZHhsWPHYsiQIaXW8fb2xr59+7Bjxw7cu3cPTk5OAIDly5cjMTER69evx9SpU6FQKHDs2DGtfe/duwe1Wq0zYlycXC6HXC5/9pMhIiIioiqlyodhV1dXuLq6llnv0aNHAAALC+3BbgsLCxQWFi1GHRQUhFmzZiE9PR0eHh4Aij5UJ5fLxXnFRERERCQdVX6ahLGCgoLg4uKCYcOG4cyZM/jf//6Hf//730hJSUHPnj0BACEhIfD19UV4eDiSk5Oxd+9eTJo0CZGRkeJoMhERERFJR5UfGTaWq6srdu/ejZiYGLz++utQq9Vo1qwZfvzxR7Ro0QIAYGlpiZ07d2LMmDFo27Yt7OzsEBYWhoULF1Zy74mIiEgKLPIKYFFQYN42883bntRUmzAMAAEBAfjll19KrVOvXj3s2LGjgnpERERERFVZtZkmQURERERkKoZhIiIiIpIshmEiIiIikiyGYSIiIiKSLIZhIiIiIpIshmEiIiIikiyGYSIiIiKSLIZhIiIiIpIshmEiIiIikqxqdQc6IiIioqrMIq/A7LdPNvftnaWGI8NEREREJFkMw0REREQkWQzDRERERCRZDMNEREREJFkMw0REREQkWQzDRERERCRZDMNEREREJFkMw0REREQkWQzDRERERCRZDMNEREREJFm8HTMRERFRBbHIy4eFpdq8bRbkm7U9qeHIMBERERFJFsMwEREREUkWwzARERERSRbDMBERERFJFsMwEREREUkWwzARERERSRbDMBERERHpde/ePYSHh8PZ2RnOzs4IDw/H/fv3S91HEATExcXB09MTdnZ26NixI86fPy9uv3v3Lj744AM0btwY9vb2qFevHsaNG4esrCyxztWrVzFixAj4+PjAzs4ODRo0QGxsLFQqldaxZDKZzmPlypUmnSPXGSYiIiIivcLCwnD9+nXs3r0bAPDuu+8iPDwcP/30k8F95s+fj0WLFmHdunV46aWX8Omnn6Jr1664dOkSHB0dcfPmTdy8eRMLFy6Er68vrl27htGjR+PmzZv44YcfAAB//vknCgsLsWrVKjRs2BB//PEHIiMjkZOTg4ULF2odLz4+Ht26dROfOzs7m3SODMNEREREpOPixYvYvXs3jh49isDAQADAmjVrEBQUhEuXLqFx48Y6+wiCgMWLFyMmJgYDBgwAAKxfvx7u7u749ttvMWrUKPj5+WHz5s3iPg0aNMCsWbPwr3/9C/n5+bCyskK3bt20Am79+vVx6dIlrFixQicMv/DCC1AoFE99npwmQURERFQNZGdnaz3y8vKeqb2kpCQ4OzuLQRgA2rRpA2dnZxw5ckTvPikpKVAqlQgJCRHL5HI5OnToYHAfAMjKyoKTkxOsrAyP02ZlZaFmzZo65WPHjoWrqyteeeUVrFy5EoWFhcacnogjw0REREQVRJarhszSvGORsoKi2zvXrVtXqzw2NhZxcXFP3a5SqYSbm5tOuZubG5RKpcF9AMDd3V2r3N3dHdeuXdO7T2ZmJj755BOMGjXKYF/+/vtvLF26FJ999plW+SeffILOnTvDzs4Oe/fuxcSJE5GRkYEPP/yw1HMrjmGYiIiIqBpIS0uDk5OT+Fwul+utFxcXhxkzZpTa1okTJwAUfUCtJEEQ9JYXV3K7oX2ys7PRs2dP+Pr6IjY2Vm9bN2/eRLdu3fDmm29i5MiRWtuKh96WLVsCAGbOnMkwTERERCQ1Tk5OWmHYkLFjx2LIkCGl1vH29sbZs2dx69YtnW137tzRGfnV0MzdVSqV8PDwEMtv376ts8+DBw/QrVs31KhRA1u3boW1tbVOezdv3kSnTp0QFBSE1atXl3lubdq0QXZ2Nm7dumWwjyUxDBMRERFJiKurK1xdXcusFxQUhKysLBw/fhyvvvoqAODYsWPIyspCcHCw3n18fHygUCiQmJiIVq1aAQBUKhUOHjyIefPmifWys7MRGhoKuVyO7du3w9bWVqetGzduoFOnTvD390d8fDwsLMqeXpKcnAxbW1u88MILZdbVYBgmIiIiIh1NmzZFt27dEBkZiVWrVgEoWlqtV69eWitJNGnSBHPmzEH//v0hk8kQFRWF2bNno1GjRmjUqBFmz54Ne3t7hIWFASgaEQ4JCcGjR4/w9ddfix/4A4DatWvD0tISN2/eRMeOHVGvXj0sXLgQd+7cEY+nGX3+6aefoFQqERQUBDs7O+zfvx8xMTF49913DU4R0YdhmIiIiIj0+uabbzBu3DhxdYg+ffpg2bJlWnUuXbqkdcOMyZMn4/HjxxgzZgzu3buHwMBAJCQkwNHREQBw6tQpHDt2DADQsGFDrbZSUlLg7e2NhIQEXL58GZcvX0adOnW06giCAACwtrbG8uXLER0djcLCQtSvXx8zZ87E+++/b9I5ygRNi2S07OxsODs7w3vWLFjoGdYnIiKiqqcwNxdXY2LEZbwqkiY7dGkQBStL40ctjZFfkIc9fy+ulPOqDrjOMBERERFJFsMwEREREUkWwzARERERSRbDMBERERFJFleTICIiIqooqjzzD0UW5pm5QWnhyDARERERSRbDMBERERFJFsMwEREREUkWwzARERERSRbDMBERERFJFsMwEREREUkWwzARERERSRbDMBERERFJFsMwEREREUkWwzARERERSRZvx0xERERUUXJV5XA7ZpWZG5QWjgwTERERkWQxDBMRERGRZDEMExEREZFkMQwTERERkWQxDBMRERGRZDEMExEREZFkMQwTERERkWQxDBMRERGRZDEMExEREZFkMQwTERERkWTxdsxEREREFUTIzYUgKzRvmwJvx/wsODJMRERERJLFMExEREREksUwTERERESSxTBMRERERJLFMExEREREksUwTERERESSxTBMRERERJLFMExEREREksUwTERERESSxTBMRERERJLF2zETERERVZDCx3koNPPtmAsFtVnbkxqODBMRERGRZDEMExEREZFkMQwTERERkWQxDBMRERGRZDEMExEREZFkMQwTERERkWQ9N2F41qxZCA4Ohr29PV544QW9dVJTU9G7d284ODjA1dUV48aNg0ql0qpz7tw5dOjQAXZ2dnjxxRcxc+ZMCIJQAWdARERE9Hy5d+8ewsPD4ezsDGdnZ4SHh+P+/ful7iMIAuLi4uDp6Qk7Ozt07NgR58+f16ozatQoNGjQAHZ2dqhduzb69u2LP//80+RjG5P9yvLchGGVSoU333wT7733nt7tBQUF6NmzJ3JycnD48GFs3LgRmzdvxsSJE8U62dnZ6Nq1Kzw9PXHixAksXboUCxcuxKJFiyrqNIiIiIieG2FhYTh9+jR2796N3bt34/Tp0wgPDy91n/nz52PRokVYtmwZTpw4AYVCga5du+LBgwdiHX9/f8THx+PixYv45ZdfIAgCQkJCUFBQYPSxjcl+xpAJz9mw6Lp16xAVFaXzm8HPP/+MXr16IS0tDZ6engCAjRs3IiIiArdv34aTkxNWrFiBadOm4datW5DL5QCAuXPnYunSpbh+/TpkMplRfcjOzoazszO8Z82Cha2tWc+PiIiIykdhbi6uxsQgKysLTk5OFXpsTXboZPUGrGTWZm07X1Bjf/4PZj+vixcvwtfXF0ePHkVgYCAA4OjRowgKCsKff/6Jxo0b6+wjCAI8PT0RFRWFKVOmAADy8vLg7u6OefPmYdSoUXqPdfbsWbRo0QKXL19GgwYNjDq2MdnPGNXmDnRJSUnw8/MTXwwACA0NRV5eHk6dOoVOnTohKSkJHTp0EIOwps60adNw9epV+Pj46G07Ly8PeXl54vOsrCwARd9URERE9HzQ/NyuzHHAfKgBMx8+H0V3oMvOztYql8vlWpnHVElJSXB2dhbDKAC0adMGzs7OOHLkiN4wnJKSAqVSiZCQEK1+dOjQAUeOHNEbhnNychAfHw8fHx/UrVvX6GMbk/2MUW3CsFKphLu7u1aZi4sLbGxsoFQqxTre3t5adTT7KJVKg2F4zpw5mDFjhk556iefmKHnREREVJEyMzPh7Oxcoce0sbGBQqHAIeWP5dJ+jRo1xCCpERsbi7i4uKduU6lUws3NTafczc1NzFb69gGgk8nc3d1x7do1rbLly5dj8uTJyMnJQZMmTZCYmAgbGxujj21M9jNGpYbhuLg4vSGzuBMnTiAgIMCo9vRNcxAEQau8ZB3Nb4elTZGYNm0aoqOjxef379+Hl5cXUlNTK/ybqTJlZ2ejbt26SEtLq/A/L1UmnjfPWwp43jxvKcjKykK9evVQs2bNCj+2ra0tUlJSTP5wl7FK5h0ABkeFjc1fgHHZSh99eatk2VtvvYWuXbsiPT0dCxcuxKBBg/Dbb7/B9skU1KfJdcb2r7hKDcNjx47FkCFDSq1TciTXEIVCgWPHjmmV3bt3D2q1WvytQaFQ6PymcPv2bQC6v8EUZ+jPDM7OzpL6T0TDycmJ5y0hPG9p4XlLi1TP28KictYPsLW1FYNeZTI2f509exa3bt3S2Xbnzh2DuUmhUAAoGrX18PAQy2/fvq2zj2aViEaNGqFNmzZwcXHB1q1bMXToUCgUijKPbUz2M0alhmFXV1e4urqapa2goCDMmjUL6enp4oufkJAAuVwOf39/sc706dOhUqnEYfiEhAR4enoaHbqJiIiInmfG5q+goCBkZWXh+PHjePXVVwEAx44dQ1ZWFoKDg/Xu4+PjA4VCgcTERLRq1QpA0YpgBw8exLx580o9niAI4me0jDm2MdnPGM/N0mqpqak4ffo0UlNTUVBQgNOnT+P06dN4+PAhACAkJAS+vr4IDw9HcnIy9u7di0mTJiEyMlL8rTcsLAxyuRwRERH4448/sHXrVsyePRvR0dEmDacTERERVXdNmzZFt27dEBkZiaNHj+Lo0aOIjIxEr169tD4816RJE2zduhVA0bSFqKgozJ49G1u3bsUff/yBiIgI2NvbIywsDABw5coVzJkzB6dOnUJqaiqSkpIwaNAg2NnZoUePHkYf25jsZxThOTFs2DABRZ+/1Hrs379frHPt2jWhZ8+egp2dnVCzZk1h7NixQm5urlY7Z8+eFdq1ayfI5XJBoVAIcXFxQmFhoUl9yc3NFWJjY3Xaru543jxvKeB587ylgOctrfN+FpmZmcJbb70lODo6Co6OjsJbb70l3Lt3T6sOACE+Pl58XlhYKMTGxgoKhUKQy+VC+/bthXPnzonbb9y4IXTv3l1wc3MTrK2thTp16ghhYWHCn3/+afKxjcl+ZXnu1hkmIiIiIjKX52aaBBERERGRuTEMExEREZFkMQwTERERkWQxDBMRERGRZDEM67F8+XL4+PjA1tYW/v7+OHToUKn1Dx48CH9/f9ja2qJ+/fpYuXJlBfXUPObMmYNXXnkFjo6OcHNzQ79+/XDp0qVS9zlw4ABkMpnO488//6ygXptHXFyczjloFgw35Hl/v4GixdT1vX/vv/++3vrP6/v966+/onfv3vD09IRMJsO2bdu0tguCgLi4OHh6esLOzg4dO3bE+fPny2x38+bN8PX1hVwuh6+vr7ikUFVR2nmr1WpMmTIFzZs3h4ODAzw9PfH222/j5s2bpba5bt06vddAbm5uOZ+N8cp6vyMiInT636ZNmzLbfZ7fbwB63zeZTIYFCxYYbLOqv9/G/Nyqrt/fZH4MwyVs2rQJUVFRiImJQXJyMtq1a4fu3bsjNTVVb/2UlBT06NED7dq1Q3JyMqZPn45x48Zh8+bNFdzzp3fw4EG8//77OHr0KBITE5Gfn4+QkBDk5OSUue+lS5eQnp4uPho1alQBPTavZs2aaZ3DuXPnDNatDu83UHSbzeLnnJiYCAB48803S93veXu/c3Jy0KJFCyxbtkzv9vnz52PRokVYtmwZTpw4AYVCga5du+LBgwcG20xKSsLgwYMRHh6OM2fOIDw8HIMGDdK5C1JlKu28Hz16hN9//x0fffQRfv/9d2zZsgX/+9//0KdPnzLbdXJy0nr/09PTq8TdtDTKer8BoFu3blr937VrV6ltPu/vNwCd9+yrr76CTCbDwIEDS223Kr/fxvzcqq7f31QOTFqITQJeffVVYfTo0VplTZo0EaZOnaq3/uTJk4UmTZpolY0aNUpo06ZNufWxvN2+fVsAIBw8eNBgnf379wsAdNb7e97ExsYKLVq0MLp+dXy/BUEQxo8fLzRo0MDgmtvV4f0GIGzdulV8XlhYKCgUCmHu3LliWW5uruDs7CysXLnSYDuDBg0SunXrplUWGhoqDBkyxOx9NoeS563P8ePHBQDCtWvXDNaJj48XnJ2dzdu5cqTvvIcNGyb07dvXpHaq4/vdt29f4fXXXy+1zvP2fpf8uSWV728yD44MF6NSqXDq1CmEhIRolYeEhODIkSN690lKStKpHxoaipMnT0KtVpdbX8tTVlYWAKBmzZpl1m3VqhU8PDzQuXNn7N+/v7y7Vi7++usveHp6wsfHB0OGDMGVK1cM1q2O77dKpcLXX3+N4cOHl3knxurwfmukpKRAqVRqvZ9yuRwdOnQw+P0OGL4GStunqsvKyoJMJsMLL7xQar2HDx/Cy8sLderUQa9evZCcnFwxHTSjAwcOwM3NDS+99BIiIyNx+/btUutXt/f71q1b2LlzJ0aMGFFm3efp/S75c4vf32QKhuFiMjIyUFBQAHd3d61yd3d3KJVKvfsolUq99fPz85GRkVFufS0vgiAgOjoar732Gvz8/AzW8/DwwOrVq7F582Zs2bIFjRs3RufOnfHrr79WYG+fXWBgIDZs2IBffvkFa9asgVKpRHBwMDIzM/XWr27vNwBs27YN9+/fR0REhME61eX9Lk7zPW3K97tmP1P3qcpyc3MxdepUhIWFlXr70iZNmmDdunXYvn07vvvuO9ja2qJt27b466+/KrC3z6Z79+745ptvsG/fPnz22Wc4ceIEXn/9deTl5Rncp7q93+vXr4ejoyMGDBhQar3n6f3W93OL399kCqvK7kBVVHJ0TBCEUkfM9NXXV/48GDt2LM6ePYvDhw+XWq9x48Za9yUPCgpCWloaFi5ciPbt25d3N82me/fu4tfNmzdHUFAQGjRogPXr1yM6OlrvPtXp/QaAtWvXonv37vD09DRYp7q83/qY+v3+tPtURWq1GkOGDEFhYSGWL19eat02bdpofdisbdu2aN26NZYuXYolS5aUd1fNYvDgweLXfn5+CAgIgJeXF3bu3FlqOKwu7zcAfPXVV3jrrbfKnPv7PL3fpf3ckvL3NxmPI8PFuLq6wtLSUuc3wNu3b+v8pqihUCj01reyskKtWrXKra/l4YMPPsD27duxf/9+1KlTx+T927RpUyVHDUzh4OCA5s2bGzyP6vR+A8C1a9ewZ88ejBw50uR9n/f3W7NqiCnf75r9TN2nKlKr1Rg0aBBSUlKQmJhY6qiwPhYWFnjllVee62vAw8MDXl5epZ5DdXm/AeDQoUO4dOnSU32/V9X329DPLal/f5NpGIaLsbGxgb+/v/jJeo3ExEQEBwfr3ScoKEinfkJCAgICAmBtbV1ufTUnQRAwduxYbNmyBfv27YOPj89TtZOcnAwPDw8z965i5eXl4eLFiwbPozq838XFx8fDzc0NPXv2NHnf5/399vHxgUKh0Ho/VSoVDh48aPD7HTB8DZS2T1WjCcJ//fUX9uzZ81S/yAmCgNOnTz/X10BmZibS0tJKPYfq8H5rrF27Fv7+/mjRooXJ+1a197usn1tS/v6mp1AZn9qryjZu3ChYW1sLa9euFS5cuCBERUUJDg4OwtWrVwVBEISpU6cK4eHhYv0rV64I9vb2woQJE4QLFy4Ia9euFaytrYUffvihsk7BZO+9957g7OwsHDhwQEhPTxcfjx49EuuUPO/PP/9c2Lp1q/C///1P+OOPP4SpU6cKAITNmzdXxik8tYkTJwoHDhwQrly5Ihw9elTo1auX4OjoWK3fb42CggKhXr16wpQpU3S2VZf3+8GDB0JycrKQnJwsABAWLVokJCcni6smzJ07V3B2dha2bNkinDt3Thg6dKjg4eEhZGdni22Eh4drrSbz22+/CZaWlsLcuXOFixcvCnPnzhWsrKyEo0ePVvj5GVLaeavVaqFPnz5CnTp1hNOnT2t9z+fl5YltlDzvuLg4Yffu3cLff/8tJCcnC++8845gZWUlHDt2rDJOUa/SzvvBgwfCxIkThSNHjggpKSnC/v37haCgIOHFF1+s1u+3RlZWlmBvby+sWLFCbxvP2/ttzM+t6vr9TebHMKzHF198IXh5eQk2NjZC69attZYYGzZsmNChQwet+gcOHBBatWol2NjYCN7e3gb/s6mqAOh9xMfHi3VKnve8efOEBg0aCLa2toKLi4vw2muvCTt37qz4zj+jwYMHCx4eHoK1tbXg6ekpDBgwQDh//ry4vTq+3xq//PKLAEC4dOmSzrbq8n5rloQr+Rg2bJggCEXLL8XGxgoKhUKQy+VC+/bthXPnzmm10aFDB7G+xv/93/8JjRs3FqytrYUmTZpUuV8KSjvvlJQUg9/z+/fvF9soed5RUVFCvXr1BBsbG6F27dpCSEiIcOTIkYo/uVKUdt6PHj0SQkJChNq1awvW1tZCvXr1hGHDhgmpqalabVS391tj1apVgp2dnXD//n29bTxv77cxP7eq6/c3mZ9MEJ58+oeIiIiISGI4Z5iIiIiIJIthmIiIiIgki2GYiIiIiCSLYZiIiIiIJIthmIiIiIgki2GYiIiIiCSLYZiIiIiIJIthmIiIiIgki2GYiCpEREQE+vXrJz7v2LEjoqKijN7/wIEDkMlkuH///jP3xZxtVUWXLl2CQqHAgwcPTNpv0qRJGDduXDn1ioioamIYJiJRREQEZDIZZDIZrKysUK9ePbz33nu4d++e2Y+1ZcsWfPLJJ2Zt09vbW+y/nZ0dvL29MWjQIOzbt0+rXnBwMNLT0+Hs7Fxmm89jcI6JicH7778PR0dHAP+cg+ZRq1YtvP766/jtt9+09ps8eTLi4+ORkpJSGd0mIqoUDMNEpKVbt25IT0/H1atX8eWXX+Knn37CmDFjzH6cmjVrimHNnGbOnIn09HRcunQJGzZswAsvvIAuXbpg1qxZYh0bGxsoFArIZDKzH7+yXb9+Hdu3b8c777yjs+3SpUtIT0/HgQMHULt2bfTs2RO3b98Wt7u5uSEkJAQrV66syC4TEVUqhmEi0iKXy6FQKFCnTh2EhIRg8ODBSEhIELcXFBRgxIgR8PHxgZ2dHRo3boz//Oc/Wm0UFBQgOjoaL7zwAmrVqoXJkydDEAStOiWnSXz99dcICAiAo6MjFAoFwsLCtIKasTT716tXD+3bt8fq1avx0Ucf4eOPP8alS5cA6I72Xrt2Db1794aLiwscHBzQrFkz7Nq1C1evXkWnTp0AAC4uLpDJZIiIiAAA7N69G6+99pp4jr169cLff/8t9uPq1auQyWTYsmULOnXqBHt7e7Ro0QJJSUla/f3tt9/QoUMH2Nvbw8XFBaGhoeJIvCAImD9/PurXrw87Ozu0aNECP/zwQ6nn//3336NFixaoU6eOzjY3NzcoFAo0b94cH374IbKysnDs2DGtOn369MF3331n/AtORPScYxgmIoOuXLmC3bt3w9raWiwrLCxEnTp18P333+PChQv4+OOPMX36dHz//fdinc8++wxfffUV1q5di8OHD+Pu3bvYunVrqcdSqVT45JNPcObMGWzbtg0pKSli8HxW48ePhyAI+PHHH/Vuf//995GXl4dff/0V586dw7x581CjRg3UrVsXmzdvBvDPqKom+Ofk5CA6OhonTpzA3r17YWFhgf79+6OwsFCr7ZiYGEyaNAmnT5/GSy+9hKFDhyI/Px8AcPr0aXTu3BnNmjVDUlISDh8+jN69e6OgoAAA8OGHHyI+Ph4rVqzA+fPnMWHCBPzrX//CwYMHDZ7rr7/+ioCAgFJfj0ePHiE+Ph4AtN5bAHj11VeRlpaGa9euldoGEVG1IRARPTFs2DDB0tJScHBwEGxtbQUAAgBh0aJFpe43ZswYYeDAgeJzDw8PYe7cueJztVot1KlTR+jbt69Y1qFDB2H8+PEG2zx+/LgAQHjw4IEgCIKwf/9+AYBw7949g/t4eXkJn3/+ud5t7u7uwnvvvae3rebNmwtxcXF69zPmuIIgCLdv3xYACOfOnRMEQRBSUlIEAMKXX34p1jl//rwAQLh48aIgCIIwdOhQoW3btnrbe/jwoWBrayscOXJEq3zEiBHC0KFDDfajRYsWwsyZM/Weg4ODg+Dg4CDIZDIBgODv7y+oVCqtullZWQIA4cCBA6WeLxFRdcGRYSLS0qlTJ5w+fRrHjh3DBx98gNDQUHzwwQdadVauXImAgADUrl0bNWrUwJo1a5CamgoAyMrKQnp6OoKCgsT6VlZWZY5WJicno2/fvvDy8oKjoyM6duwIAGK7z0oQBINzhMeNG4dPP/0Ubdu2RWxsLM6ePVtme3///TfCwsJQv359ODk5wcfHR29/X375ZfFrDw8PABCnf2hGhvW5cOECcnNz0bVrV9SoUUN8bNiwQWs6RkmPHz+Gra2t3m2HDh3C77//ju+++w5eXl5Yt26dzsiwnZ0dgKLRYyIiKWAYJiItDg4OaNiwIV5++WUsWbIEeXl5mDFjhrj9+++/x4QJEzB8+HAkJCTg9OnTeOedd6BSqZ76mDk5OQgJCUGNGjXw9ddf48SJE+K0imdpVyMzMxN37twRA2tJI0eOxJUrVxAeHo5z584hICAAS5cuLbXN3r17IzMzE2vWrMGxY8fEubcl+1s8bGrCuGYqhSZ46qOps3PnTpw+fVp8XLhwodR5w66urgZX//Dx8cFLL72EwYMHY8aMGejfvz/y8vK06ty9excAULt2bYPHICKqThiGiahUsbGxWLhwIW7evAmgaHQxODgYY8aMQatWrdCwYUOtkUpnZ2d4eHjg6NGjYll+fj5OnTpl8Bh//vknMjIyMHfuXLRr1w5NmjR5qg/PGfKf//wHFhYWWuscl1S3bl2MHj0aW7ZswcSJE7FmzRoARStPABDn8QJF4frixYv48MMP0blzZzRt2vSplp97+eWXsXfvXr3bfH19IZfLkZqaioYNG2o96tata7DNVq1a4cKFC2UeOzw8HIWFhVi+fLlW+R9//AFra2s0a9bMtJMhInpOMQwTUak6duyIZs2aYfbs2QCAhg0b4uTJk/jll1/wv//9Dx999BFOnDihtc/48eMxd+5cbN26FX/++SfGjBlT6jq99erVg42NDZYuXYorV65g+/btT70G8YMHD6BUKpGWloZff/0V7777Lj799FPMmjULDRs21LtPVFQUfvnlF6SkpOD333/Hvn370LRpUwCAl5cXZDIZduzYgTt37uDhw4dwcXFBrVq1sHr1aly+fBn79u1DdHS0yX2dNm0aTpw4gTFjxuDs2bP4888/sWLFCmRkZMDR0RGTJk3ChAkTsH79evz9999ITk7GF198gfXr1xtsMzQ0FElJSVrhXR8LCwtERUVh7ty5WlMiDh06hHbt2pU6ak1EVJ0wDBNRmaKjo7FmzRqkpaVh9OjRGDBgAAYPHozAwEBkZmbqrEM8ceJEvP3224iIiEBQUBAcHR3Rv39/g+3Xrl0b69atw//93//B19cXc+fOxcKFC5+qrx9//DE8PDzQsGFDhIeHIysrC3v37sWUKVMM7lNQUID3338fTZs2Rbdu3dC4cWNxxPTFF1/EjBkzMHXqVLi7u2Ps2LGwsLDAxo0bcerUKfj5+WHChAlYsGCByX196aWXkJCQgDNnzuDVV19FUFAQfvzxR1hZWQEAPvnkE3z88ceYM2cOmjZtitDQUPz0008Gp3sAQI8ePWBtbY09e/aUefzhw4dDrVZj2bJlYtl3332HyMhIk8+FiOh5JROEEot/EhHRc2358uX48ccf8csvv5i0386dO/Hvf/8bZ8+eFQM5EVF1x//tiIiqmXfffRf37t3DgwcPTLrLX05ODuLj4xmEiUhSODJMRERERJLFOcNEREREJFkMw0REREQkWQzDRERERCRZDMNEREREJFkMw0REREQkWQzDRERERCRZDMNEREREJFkMw0REREQkWQzDRERERCRZ/w/qilpNF62DKAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(2483,\n",
-       " 2471,\n",
-       " array([[nan, nan, nan, ...,  1.,  1.,  1.],\n",
-       "        [nan, nan, nan, ...,  1.,  1.,  1.],\n",
-       "        [nan, nan, nan, ...,  1.,  1.,  1.],\n",
-       "        ...,\n",
-       "        [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
-       "        [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
-       "        [ 1.,  1.,  1., ...,  1.,  1.,  1.]]),\n",
-       " array([[nan, nan, nan, ...,  0.,  0.,  0.],\n",
-       "        [nan, nan, nan, ...,  1.,  1.,  1.],\n",
-       "        [nan, nan, nan, ...,  1.,  1.,  1.],\n",
-       "        ...,\n",
-       "        [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
-       "        [ 1.,  1.,  1., ...,  1.,  1.,  1.],\n",
-       "        [ 1.,  1.,  1., ...,  1.,  1.,  1.]]))"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "potential_comparison(\"config6\", phi, 0.02, R, Z, omega, nanregions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run Test File\n",
-    "\n",
-    "# %run test/multi_test.py\n",
-    "\n",
-    "# After this is for workshopping test code, NOT intended to functionally do anything"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_velocity_stream(v_r, v_z, R, Z, title):\n",
-    "    plt.figure(figsize=(8, 6))\n",
-    "    plt.streamplot(R, Z, v_r, v_z, color='magenta', density=2)\n",
-    "    plt.title(title)\n",
-    "    plt.xlabel('Radial Distance (R)')\n",
-    "    plt.ylabel('Axial Distance (Z)')\n",
-    "    plt.show()\n",
-    "\n",
-    "def plot_velocity_quiver(v_r, v_z, R, Z, title):\n",
-    "    plt.figure(figsize=(8, 6))\n",
-    "    plt.quiver(R, Z, v_r, v_z, color='blue')\n",
-    "    plt.title(title)\n",
-    "    plt.xlabel('Radial Distance (R)')\n",
-    "    plt.ylabel('Axial Distance (Z)')\n",
-    "    plt.show()\n",
-    "\n",
-    "# These can't run with unevenly spaced sample points, but they're also not very important.\n",
-    "#plot_velocity_stream(np.real(vr), np.real(vz), R, Z, 'Real Velocity')\n",
-    "#plot_velocity_quiver(np.real(vr), np.real(vz), R, Z, 'Real Velocity')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run this code block for a quick visual of our results converted to the Capytaine conventions.\n",
-    "# Capytaine should output something that is off by a factor of (-) i * omega. The following graphs do that:\n",
-    "# Figure out why vrc factor is different from phic and vrz (no negative sign) (likely due to times i vs divide by i)\n",
-    "\n",
-    "#phic = - phi * 1j * omega\n",
-    "#vrc = vr * 1j * omega\n",
-    "#vzc = - vz * 1j * omega\n",
-    "\n",
-    "#R, Z = make_R_Z(False, 50) # phi was redefined into this coordinate array earlier\n",
-    "#plot_potential(np.real(phic), R, Z, 'Capytaine Potential Real')\n",
-    "#plot_potential(np.imag(phic), R, Z, 'Total Potential Imaginary')\n",
-    "\n",
-    "#R, Z = make_R_Z(True, 50) # \"sharp\" coordinate array\n",
-    "#plot_potential(np.real(vrc), R, Z, 'Radial Velocity - Real') \n",
-    "#plot_potential(np.imag(vrc), R, Z, 'Radial Velocity - Imaginary')\n",
-    "#plot_potential(np.real(vzc), R, Z, 'Vertical Velocity - Real')\n",
-    "#plot_potential(np.imag(vzc), R, Z, 'Vertical Velocity - Imaginary')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Study Convergence, assume that every entry of NMK is 50.\n",
-    "# Strategy: Make large A matrices, b vector, then select submatrices/vectors\n",
-    "# Solve for hydro-coefficients like before\n",
-    "# Plot hydro-coefficients vs. coefficient count\n",
-    "\n",
-    "def hydro_coeff_calc(s, blocks, C):\n",
-    "    hydro_terms = np.zeros((s * (blocks - 1)), dtype=complex)\n",
-    "\n",
-    "    col = 0\n",
-    "    for n in range(s):\n",
-    "        hydro_terms[n] = int_R_1n(0, n)* C[n] * z_n_d(n)\n",
-    "    col += s\n",
-    "    for i in range(1, boundary_count):\n",
-    "        for m in range(s):\n",
-    "            hydro_terms[col + m] = int_R_1n(i, m)* C[col + m] * z_n_d(m)\n",
-    "            hydro_terms[col + s + m] = int_R_2n(i, m)* C[col + s + m] * z_n_d(m)\n",
-    "        col += 2 * s\n",
-    "    \n",
-    "    hydro_p_terms = np.zeros(boundary_count, dtype=complex)\n",
-    "    for i in range(boundary_count):\n",
-    "        hydro_p_terms[i] = heaving[i] * int_phi_p_i(i)\n",
-    "    \n",
-    "    hydro_coef =2*pi*(sum(hydro_terms) + sum(hydro_p_terms))\n",
-    "    hydro_coef_real = hydro_coef.real\n",
-    "    hydro_coef_imag = hydro_coef.imag/omega\n",
-    "    \n",
-    "    hydro_coef_nondim = h**3/(max_rad**3 * pi)*hydro_coef\n",
-    "    \n",
-    "    return hydro_coef_nondim\n",
-    "\n",
-    "\n",
-    "hydro_nondim_real = []\n",
-    "hydro_nondim_imag = []\n",
-    "\n",
-    "blocks = 2 * boundary_count\n",
-    "for s in range(1, 201):\n",
-    "    alpha = np.zeros((blocks * s, blocks * s), dtype=complex)\n",
-    "    beta = np.zeros(blocks * s, dtype=complex)\n",
-    "    for rowblock in range(blocks):\n",
-    "        for i in range(s):\n",
-    "            beta[i + s * rowblock] = b[i + 200 * rowblock]\n",
-    "            for colblock in range(blocks):\n",
-    "                for j in range(s):\n",
-    "                    alpha[i + s * rowblock][j + s * colblock] = A[i + 200 * rowblock][j + 200 * colblock]\n",
-    "    C = linalg.solve(alpha,beta)\n",
-    "    hcnd = hydro_coeff_calc(s, blocks, C)\n",
-    "    hydro_nondim_real.append(hcnd.real)\n",
-    "    hydro_nondim_imag.append(hcnd.imag)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_hydros(xs, ys, title):\n",
-    "    #plt.figure(figsize=(8, 6))\n",
-    "    plt.plot(xs, ys)\n",
-    "    plt.title(title)\n",
-    "    plt.xlabel('NMK Coefficient Count')\n",
-    "    plt.ylabel('Hydro Coefficient')\n",
-    "    plt.show()\n",
-    "\n",
-    "xs = list(range(1,201))\n",
-    "#plot_hydros(xs, hydro_nondim_real, \"Hydro-Nondim-Real\")\n",
-    "#plot_hydros(xs, hydro_nondim_imag, \"Hydro-Nondim-Imag\")\n",
-    "\n",
-    "idealr = hydro_nondim_real[-1]\n",
-    "ideali = hydro_nondim_imag[-1]\n",
-    "\n",
-    "relative_real = list(map(lambda x: (x/idealr), hydro_nondim_real))\n",
-    "relative_imag = list(map(lambda x: (x/ideali), hydro_nondim_imag))\n",
-    "\n",
-    "#plot_hydros(xs, relative_real, \"Hydro-Nondim-Relative-Real\")\n",
-    "#plot_hydros(xs, relative_imag, \"Hydro-Nondim-Relative-Imag\")\n",
-    "\n",
-    "#plot while removing most extreme initial values\n",
-    "plot_hydros(xs[5:], hydro_nondim_real[5:], \"Hydro-Nondim-Real\")\n",
-    "plot_hydros(xs[5:], hydro_nondim_imag[5:], \"Hydro-Nondim-Imag\")\n",
-    "plot_hydros(xs[5:], relative_real[5:], \"Hydro-Nondim-Relative-Real\")\n",
-    "plot_hydros(xs[5:], relative_imag[5:], \"Hydro-Nondim-Relative-Imag\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Refer to original MEEM file for some extra plotting functions."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.11.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/hydro/python/multi_constants.py b/hydro/python/multi_constants.py
deleted file mode 100644
index 0354d4c..0000000
--- a/hydro/python/multi_constants.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Constants
-h = 100
-d = [29, 7, 4]
-a = [3, 5, 10]
-heaving = [0, 1, 1]
-# 0/false if not heaving, 1/true if yes heaving
-NMK = [200, 200, 200, 200] # Number of terms in approximation of each region (including e).
-# All computations assume at least 2 regions.
-
-m0 = 1
-g = 9.81
-rho = 1023
-# n = 3 # These variables are just here but unused, inherited from the MEEM constants.py
-# z = 6 # Why are they here?
-# omega = 2 -> calculate omega from m0, g
-
-
-####for RM3 slant study ###
-# import numpy as np
-# h = 50.0
-# d = np.array([29.0, 7.0, 5.5, 4.0])
-# a = np.array([3.0, 5.0, 7.5, 10.0])
-# heaving = [0, 1, 1, 1] # 0/false if not heaving, 1/true if yes heaving
-# slant = [0, 1, 1, 1] # 0/false if not slanted, 1/true if yes slanted
-# n = 3
-# z = 6
-# omega = 0.4
-# # omega = np.linspace(0.1, 1.5, 15)
-# m0 = omega**2/9.81
diff --git a/hydro/python/test/data/config-key.md b/hydro/python/test/data/config-key.md
deleted file mode 100644
index 7fec845..0000000
--- a/hydro/python/test/data/config-key.md
+++ /dev/null
@@ -1,100 +0,0 @@
-Parenthetical values are matching points (real, imag), with rtol = 0.03, real atol = 0.01, imag atol = 0.0001. <br> 
-Other values are what was inputted into capytaine for the corresponding configurations, and what the hydro coefficient outputs were. <br>
-Note: Capytaine convention does not multiply hydro coefficients by a factor of h^3.<br>
-The hydro coefficients/point matching in double parenthesis was data given by the code at the bottom of test_potential.py,
-which matches better sometimes.
-
-config0:<br>
-h = 1.001 <br>
-d = [0.5, 0.25] <br>
-a = [0.5, 1] <br>
-heaving = [1, 1] <br>
-m0 = 1 <br>
-g = 9.81 <br>
-rho = 1023 <br>
-zdensities = [10, 10] <br>
-rdensities = [20, 20] <br>
-tdensities = [50, 100] <br>
-added_mass = 1620.53, radiation_damping = 3221.55, (2500, 2500) <br>
-((added_mass = 1586.99, radiation_damping = 3192.55, (2500, 2500)))
-
-config1: <br>
-h = 1.5 <br>
-d = [1.1, 0.85, 0.75, 0.4, 0.15] <br>
-a = [0.3, 0.5, 1, 1.2, 1.6] <br>
-heaving = [1, 1, 1, 1, 1] <br>
-m0 = 1 <br>
-g = 9.81 <br>
-rho = 1023 <br>
-zdensities = [20, 10, 30, 20, 15]<br>
-rdensities = [10, 10, 20, 10, 15]<br>
-tdensities = [40, 50, 70, 80, 100]<br>
-added_mass = 4760.37, radiation_damping = 11539.05, (2500,2500)<br>
-((added_mass = 4684.70, radiation_damping = 11442.87, (2499, 1825)))
-
-config2:<br>
-h = 100<br>
-d = [29, 7, 4]<br>
-a = [3, 5, 10]<br>
-heaving = [1, 1, 1]<br> 
-m0 = 1<br>
-g = 9.81<br>
-rho = 1023<br>
-zdensities = [40, 10, 10]<br>
-rdensities = [15, 10, 25]<br>
-tdensities = [50, 80, 200]<br>
-added_mass = 1386873.78, radiation_damping = 283.92, (2450, 2439)<br>
-((added_mass = 1355730.94, radiation_damping = 184.13, (2464, 2455)))
-
-config3:<br>
-h = 1.9<br>
-d = [0.5, 0.7, 0.8, 0.2, 0.5]<br>
-a = [0.3, 0.5, 1, 1.2, 1.6]<br>
-heaving = [1, 1, 1, 1, 1]<br>
-m0 = 1<br>
-g = 9.81<br>
-rho = 1023<br>
-zdensities = [15, 10, 30, 15, 25]<br>
-rdensities = [10, 10, 20, 10, 15]<br>
-tdensities = [40, 50, 70, 80, 100]<br>
-added_mass = 3470.42, radiation_damping = 6124.83, (621, 408) <br>
-((added_mass = 5863.80, radiation_damping = 6745.27, (2478, 698)))
-
-config4:<br>
-h = 1.001 <br>
-d = [0.5, 0.25] <br>
-a = [0.5, 1] <br>
-heaving = [0, 1] <br>
-m0 = 1<br>
-g = 9.81<br>
-rho = 1023<br>
-zdensities = [10, 10] <br>
-rdensities = [20, 20] <br>
-tdensities = [50, 100] <br>
-added_mass = 943.31, radiation_damping = 1847.53, (2500, 2500)
-
-config5:<br>
-h = 1.001 <br>
-d = [0.5, 0.25] <br>
-a = [0.5, 1] <br>
-heaving = [1, 0] <br>
-m0 = 1<br>
-g = 9.81<br>
-rho = 1023<br>
-zdensities = [10, 10] <br>
-rdensities = [20, 20] <br>
-tdensities = [50, 100] <br>
-added_mass = 291.39, radiation_damping = 189.86, (2500, 2500)
-
-config6:<br>
-h = 100<br>
-d = [29, 7, 4]<br>
-a = [3, 5, 10]<br>
-heaving = [0, 1, 1]<br> 
-m0 = 1<br>
-g = 9.81<br>
-rho = 1023<br>
-zdensities = [40, 10, 10]<br>
-rdensities = [15, 10, 25]<br>
-tdensities = [50, 80, 200]<br>
-added_mass = 1306746.66, radiation_damping = 279.84, (2479,2439)
diff --git a/hydro/python/test/data/config6-imag.csv b/hydro/python/test/data/config6-imag.csv
deleted file mode 100644
index 23f0e8b..0000000
--- a/hydro/python/test/data/config6-imag.csv
+++ /dev/null
@@ -1,50 +0,0 @@
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diff --git a/hydro/python/test/data/config6-real.csv b/hydro/python/test/data/config6-real.csv
deleted file mode 100644
index 8692abc..0000000
--- a/hydro/python/test/data/config6-real.csv
+++ /dev/null
@@ -1,50 +0,0 @@
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-nan,nan,1.519266e-03,6.667902e-04,4.230903e-04,3.398570e-04,2.805623e-04,2.362225e-04,1.993759e-04,1.701167e-04,1.501677e-04,1.325349e-04,1.178813e-04,1.056105e-04,9.486770e-05,8.690760e-05,7.992225e-05,7.444275e-05,6.896149e-05,6.551169e-05,6.161518e-05,5.734487e-05,5.611412e-05,5.368310e-05,5.129416e-05,4.867349e-05,4.593358e-05,4.496120e-05,4.382989e-05,4.295112e-05,4.175346e-05,3.941791e-05,3.924385e-05,3.800829e-05,3.859191e-05,3.754635e-05,3.529079e-05,3.591691e-05,3.580847e-05,3.502295e-05,3.527125e-05,3.353900e-05,3.319963e-05,3.481796e-05,3.426687e-05,3.206148e-05,3.316159e-05,3.431416e-05,3.339932e-05,3.172399e-05
-1.028938e-01,1.460183e-02,1.951688e-03,6.102859e-04,3.933660e-04,3.238749e-04,2.704784e-04,2.294720e-04,1.945996e-04,1.667037e-04,1.477096e-04,1.307562e-04,1.165183e-04,1.045706e-04,9.409869e-05,8.629373e-05,7.944959e-05,7.405086e-05,6.861480e-05,6.520937e-05,6.136874e-05,5.713493e-05,5.592501e-05,5.351794e-05,5.114152e-05,4.854068e-05,4.581435e-05,4.485462e-05,4.373719e-05,4.286891e-05,4.167721e-05,3.935498e-05,3.918044e-05,3.795578e-05,3.853715e-05,3.749664e-05,3.525243e-05,3.587404e-05,3.576838e-05,3.498808e-05,3.523296e-05,3.350624e-05,3.316911e-05,3.478075e-05,3.423248e-05,3.203692e-05,3.313267e-05,3.428053e-05,3.336868e-05,3.169944e-05
-8.860527e-02,1.229875e-02,1.779462e-03,5.349654e-04,3.588723e-04,3.035868e-04,2.588445e-04,2.218846e-04,1.895760e-04,1.632315e-04,1.451784e-04,1.288668e-04,1.150826e-04,1.034707e-04,9.325239e-05,8.564203e-05,7.894084e-05,7.361964e-05,6.826150e-05,6.488847e-05,6.109622e-05,5.690142e-05,5.571492e-05,5.333742e-05,5.098064e-05,4.840630e-05,4.569869e-05,4.474946e-05,4.363780e-05,4.277567e-05,4.159112e-05,3.928462e-05,3.911001e-05,3.789704e-05,3.847639e-05,3.744142e-05,3.520922e-05,3.582623e-05,3.572334e-05,3.494930e-05,3.519309e-05,3.347563e-05,3.314414e-05,3.474885e-05,3.420164e-05,3.201340e-05,3.310469e-05,3.424891e-05,3.334214e-05,3.168067e-05
-5.938267e-02,8.264776e-03,1.278537e-03,4.367415e-04,3.225616e-04,2.828167e-04,2.470997e-04,2.141877e-04,1.844482e-04,1.596886e-04,1.425766e-04,1.269143e-04,1.135995e-04,1.023314e-04,9.236926e-05,8.496070e-05,7.840713e-05,7.316811e-05,6.789513e-05,6.455544e-05,6.081261e-05,5.665910e-05,5.549664e-05,5.315016e-05,5.081432e-05,4.826761e-05,4.557969e-05,4.464101e-05,4.353480e-05,4.267872e-05,4.150170e-05,3.921164e-05,3.903690e-05,3.783597e-05,3.841324e-05,3.738401e-05,3.516424e-05,3.577645e-05,3.567635e-05,3.490878e-05,3.515164e-05,3.344404e-05,3.311862e-05,3.471618e-05,3.416992e-05,3.198908e-05,3.307578e-05,3.421635e-05,3.331499e-05,3.166167e-05
-2.119388e-02,3.100716e-03,5.970162e-04,3.098790e-04,2.897468e-04,2.689889e-04,2.380326e-04,2.078237e-04,1.794766e-04,1.560368e-04,1.399023e-04,1.249898e-04,1.121174e-04,1.011916e-04,9.152746e-05,8.426449e-05,7.786873e-05,7.273308e-05,6.750256e-05,6.422290e-05,6.054769e-05,5.643735e-05,5.529571e-05,5.297318e-05,5.064751e-05,4.811834e-05,4.544309e-05,4.451990e-05,4.343463e-05,4.259296e-05,4.142183e-05,3.914524e-05,3.896969e-05,3.778062e-05,3.835513e-05,3.733130e-05,3.512392e-05,3.573111e-05,3.563413e-05,3.487173e-05,3.510912e-05,3.340536e-05,3.308030e-05,3.467050e-05,3.412858e-05,3.196043e-05,3.304224e-05,3.417673e-05,3.327746e-05,3.163000e-05
--1.903477e-02,-2.283625e-03,-1.369882e-04,1.893654e-04,2.564769e-04,2.520305e-04,2.277693e-04,2.007934e-04,1.743579e-04,1.523751e-04,1.371840e-04,1.229720e-04,1.105731e-04,9.999907e-05,9.061456e-05,8.353513e-05,7.729738e-05,7.226033e-05,6.710442e-05,6.387217e-05,6.025666e-05,5.619215e-05,5.507396e-05,5.278110e-05,5.047281e-05,4.796790e-05,4.531050e-05,4.440044e-05,4.332781e-05,4.249613e-05,4.133214e-05,3.907147e-05,3.889549e-05,3.771902e-05,3.829103e-05,3.727305e-05,3.507869e-05,3.568076e-05,3.558688e-05,3.483070e-05,3.506508e-05,3.336897e-05,3.304772e-05,3.463031e-05,3.409092e-05,3.193292e-05,3.300972e-05,3.413922e-05,3.324410e-05,3.160415e-05
--5.484569e-02,-7.077546e-03,-7.982313e-04,8.217055e-05,2.265409e-04,2.360945e-04,2.177883e-04,1.938365e-04,1.692384e-04,1.486948e-04,1.344304e-04,1.209177e-04,1.089982e-04,9.877933e-05,8.967605e-05,8.278249e-05,7.670583e-05,7.177157e-05,6.669452e-05,6.351109e-05,5.995659e-05,5.594010e-05,5.484565e-05,5.258342e-05,5.029328e-05,4.781312e-05,4.517411e-05,4.427745e-05,4.321793e-05,4.239654e-05,4.123988e-05,3.899556e-05,3.881911e-05,3.765561e-05,3.822501e-05,3.721304e-05,3.503206e-05,3.562885e-05,3.553814e-05,3.478836e-05,3.501963e-05,3.333140e-05,3.301404e-05,3.458876e-05,3.405195e-05,3.190441e-05,3.297610e-05,3.410055e-05,3.320977e-05,3.157761e-05
--8.085648e-02,-1.056361e-02,-1.284920e-03,1.280874e-06,2.015396e-04,2.216232e-04,2.083011e-04,1.871155e-04,1.641713e-04,1.450108e-04,1.316508e-04,1.188331e-04,1.073966e-04,9.753505e-05,8.871433e-05,8.200883e-05,7.609545e-05,7.126745e-05,6.627346e-05,6.314012e-05,5.964782e-05,5.568153e-05,5.461109e-05,5.238040e-05,5.010902e-05,4.765410e-05,4.503417e-05,4.415113e-05,4.310503e-05,4.229414e-05,4.114506e-05,3.891758e-05,3.874059e-05,3.759035e-05,3.815703e-05,3.715125e-05,3.498404e-05,3.557541e-05,3.548795e-05,3.474473e-05,3.497284e-05,3.329272e-05,3.297938e-05,3.454604e-05,3.401193e-05,3.187517e-05,3.294161e-05,3.406084e-05,3.317452e-05,3.155035e-05
--9.296342e-02,-1.220116e-02,-1.520899e-03,-4.328832e-05,1.826154e-04,2.088843e-04,1.994312e-04,1.807326e-04,1.591917e-04,1.413330e-04,1.288524e-04,1.167220e-04,1.057707e-04,9.626754e-05,8.773008e-05,8.121432e-05,7.546631e-05,7.074809e-05,6.584123e-05,6.275919e-05,5.933016e-05,5.541621e-05,5.437008e-05,5.217189e-05,4.992000e-05,4.749080e-05,4.489053e-05,4.402136e-05,4.298915e-05,4.218903e-05,4.104769e-05,3.883747e-05,3.865991e-05,3.752329e-05,3.808716e-05,3.708771e-05,3.493465e-05,3.552045e-05,3.543635e-05,3.469988e-05,3.492476e-05,3.325301e-05,3.294378e-05,3.450212e-05,3.397072e-05,3.184497e-05,3.290600e-05,3.401986e-05,3.313814e-05,3.152221e-05
--8.949049e-02,-1.177043e-02,-1.475076e-03,-4.784427e-05,1.698048e-04,1.976175e-04,1.908666e-04,1.743938e-04,1.542559e-04,1.376790e-04,1.260484e-04,1.145945e-04,1.041269e-04,9.498193e-05,8.672741e-05,8.040220e-05,7.482074e-05,7.021503e-05,6.539874e-05,6.236914e-05,5.900435e-05,5.514480e-05,5.412324e-05,5.195843e-05,4.972663e-05,4.732353e-05,4.474356e-05,4.388845e-05,4.287043e-05,4.208125e-05,4.094781e-05,3.875525e-05,3.857711e-05,3.745450e-05,3.801550e-05,3.702255e-05,3.488398e-05,3.546408e-05,3.538339e-05,3.465380e-05,3.487533e-05,3.321211e-05,3.290710e-05,3.445695e-05,3.392840e-05,3.181403e-05,3.286947e-05,3.397775e-05,3.310067e-05,3.149314e-05
--7.172770e-02,-9.441191e-03,-1.169397e-03,-1.553563e-05,1.625503e-04,1.876704e-04,1.825562e-04,1.680633e-04,1.493664e-04,1.340572e-04,1.232449e-04,1.124539e-04,1.024679e-04,9.367962e-05,8.570711e-05,7.957309e-05,7.415925e-05,6.966864e-05,6.494628e-05,6.197014e-05,5.867045e-05,5.486725e-05,5.387041e-05,5.173980e-05,4.952874e-05,4.715215e-05,4.459311e-05,4.375222e-05,4.274870e-05,4.197066e-05,4.084538e-05,3.867097e-05,3.849223e-05,3.738395e-05,3.794198e-05,3.695568e-05,3.483196e-05,3.540620e-05,3.532897e-05,3.460642e-05,3.482452e-05,3.317006e-05,3.286934e-05,3.441048e-05,3.388486e-05,3.178217e-05,3.283193e-05,3.393455e-05,3.306227e-05,3.146339e-05
--4.294875e-02,-5.646743e-03,-6.623192e-04,4.556431e-05,1.598393e-04,1.789290e-04,1.745833e-04,1.618385e-04,1.445611e-04,1.304826e-04,1.204544e-04,1.103097e-04,1.007999e-04,9.236535e-05,8.467300e-05,7.873011e-05,7.348407e-05,6.911051e-05,6.448516e-05,6.156314e-05,5.832911e-05,5.458414e-05,5.361217e-05,5.151656e-05,4.932686e-05,4.697715e-05,4.443961e-05,4.361310e-05,4.262440e-05,4.185768e-05,4.074070e-05,3.858480e-05,3.840536e-05,3.731170e-05,3.786667e-05,3.688720e-05,3.477868e-05,3.534695e-05,3.527330e-05,3.455796e-05,3.477257e-05,3.312709e-05,3.283076e-05,3.436296e-05,3.384030e-05,3.174955e-05,3.279345e-05,3.389025e-05,3.302288e-05,3.143285e-05
--8.020053e-03,-1.031191e-03,-4.144680e-05,1.232666e-04,1.601050e-04,1.711214e-04,1.669405e-04,1.557412e-04,1.398498e-04,1.269596e-04,1.176804e-04,1.081642e-04,9.912438e-05,9.104026e-05,8.362556e-05,7.787331e-05,7.279528e-05,6.854074e-05,6.401521e-05,6.114817e-05,5.798039e-05,5.429540e-05,5.334837e-05,5.128849e-05,4.912074e-05,4.679822e-05,4.428275e-05,4.347079e-05,4.249730e-05,4.174211e-05,4.063360e-05,3.849659e-05,3.831647e-05,3.723775e-05,3.778956e-05,3.681705e-05,3.472406e-05,3.528623e-05,3.521623e-05,3.450825e-05,3.471931e-05,3.308303e-05,3.279117e-05,3.431420e-05,3.379455e-05,3.171596e-05,3.275386e-05,3.384468e-05,3.298234e-05,3.140136e-05
-2.737465e-02,3.652253e-03,5.902453e-04,2.030784e-04,1.614065e-04,1.638431e-04,1.595290e-04,1.497112e-04,1.352297e-04,1.234987e-04,1.149327e-04,1.060249e-04,9.744672e-05,8.970830e-05,8.256815e-05,7.700557e-05,7.209504e-05,6.796079e-05,6.353744e-05,6.072578e-05,5.762468e-05,5.400147e-05,5.307941e-05,5.105594e-05,4.891077e-05,4.661570e-05,4.412282e-05,4.332556e-05,4.236765e-05,4.162418e-05,4.052429e-05,3.840655e-05,3.822569e-05,3.716223e-05,3.771080e-05,3.674536e-05,3.466821e-05,3.522415e-05,3.515785e-05,3.445738e-05,3.466480e-05,3.303790e-05,3.275059e-05,3.426427e-05,3.374773e-05,3.168163e-05,3.271339e-05,3.379810e-05,3.294088e-05,3.136915e-05
-5.724596e-02,7.610431e-03,1.124894e-03,2.702407e-04,1.620392e-04,1.569930e-04,1.525668e-04,1.439844e-04,1.307631e-04,1.201074e-04,1.122182e-04,1.038974e-04,9.577095e-05,8.837241e-05,8.150299e-05,7.612846e-05,7.138442e-05,6.737150e-05,6.305264e-05,6.029702e-05,5.726279e-05,5.370273e-05,5.280557e-05,5.081914e-05,4.869715e-05,4.642981e-05,4.396002e-05,4.317749e-05,4.223538e-05,4.150377e-05,4.041271e-05,3.831464e-05,3.813305e-05,3.708516e-05,3.763039e-05,3.667214e-05,3.461112e-05,3.516069e-05,3.509815e-05,3.440532e-05,3.460905e-05,3.299177e-05,3.270912e-05,3.421323e-05,3.369983e-05,3.164646e-05,3.267198e-05,3.375050e-05,3.289858e-05,3.133635e-05
-7.711923e-02,1.024885e-02,1.480891e-03,3.132955e-04,1.605039e-04,1.502987e-04,1.460159e-04,1.385509e-04,1.264496e-04,1.167879e-04,1.095395e-04,1.017834e-04,9.409795e-05,8.703332e-05,8.043064e-05,7.524257e-05,7.066391e-05,6.677315e-05,6.256079e-05,5.986140e-05,5.689427e-05,5.339906e-05,5.252687e-05,5.057804e-05,4.847968e-05,4.624030e-05,4.379432e-05,4.302675e-05,4.210084e-05,4.138126e-05,4.029912e-05,3.822099e-05,3.803853e-05,3.700641e-05,3.754822e-05,3.659738e-05,3.455288e-05,3.509597e-05,3.503726e-05,3.435219e-05,3.455214e-05,3.294463e-05,3.266668e-05,3.416103e-05,3.365084e-05,3.161044e-05,3.262958e-05,3.370177e-05,3.285524e-05,3.130268e-05
-8.398953e-02,1.116766e-02,1.603165e-03,3.243889e-04,1.556497e-04,1.434954e-04,1.397872e-04,1.333573e-04,1.222859e-04,1.135500e-04,1.069066e-04,9.969174e-05,9.243512e-05,8.569667e-05,7.935547e-05,7.435130e-05,6.993627e-05,6.616791e-05,6.206350e-05,5.942066e-05,5.652056e-05,5.309135e-05,5.224398e-05,5.033328e-05,4.825910e-05,4.604779e-05,4.362588e-05,4.287323e-05,4.196378e-05,4.125635e-05,4.018331e-05,3.812551e-05,3.794225e-05,3.692630e-05,3.746463e-05,3.652126e-05,3.449349e-05,3.502996e-05,3.497508e-05,3.429788e-05,3.449399e-05,3.289644e-05,3.262329e-05,3.410772e-05,3.360085e-05,3.157375e-05,3.258638e-05,3.365208e-05,3.281101e-05,3.126830e-05
-7.697356e-02,1.024817e-02,1.475231e-03,3.011878e-04,1.470958e-04,1.365052e-04,1.338617e-04,1.283969e-04,1.182684e-04,1.103920e-04,1.043191e-04,9.762186e-05,9.078121e-05,8.436157e-05,7.827700e-05,7.345431e-05,6.920112e-05,6.555536e-05,6.156034e-05,5.897427e-05,5.614115e-05,5.277920e-05,5.195662e-05,5.008457e-05,4.803505e-05,4.585205e-05,4.345489e-05,4.271726e-05,4.182444e-05,4.112927e-05,4.006549e-05,3.802836e-05,3.784425e-05,3.684466e-05,3.737939e-05,3.644363e-05,3.443288e-05,3.496263e-05,3.491168e-05,3.424249e-05,3.443472e-05,3.284734e-05,3.257906e-05,3.405336e-05,3.354981e-05,3.153619e-05,3.254219e-05,3.360131e-05,3.276584e-05,3.123318e-05
-5.783129e-02,7.720496e-03,1.127722e-03,2.477879e-04,1.352324e-04,1.293545e-04,1.282170e-04,1.236563e-04,1.143986e-04,1.073199e-04,1.017835e-04,9.557976e-05,8.914140e-05,8.303201e-05,7.719826e-05,7.255419e-05,6.846055e-05,6.493708e-05,6.105256e-05,5.852326e-05,5.575693e-05,5.246340e-05,5.166545e-05,4.983239e-05,4.780791e-05,4.565329e-05,4.328136e-05,4.255883e-05,4.168296e-05,4.100014e-05,3.994572e-05,3.792951e-05,3.774451e-05,3.676156e-05,3.729263e-05,3.636458e-05,3.437114e-05,3.489407e-05,3.484711e-05,3.418605e-05,3.437431e-05,3.279727e-05,3.253392e-05,3.399792e-05,3.349779e-05,3.149792e-05,3.249715e-05,3.354951e-05,3.271969e-05,3.119724e-05
-2.949387e-02,3.972489e-03,6.136405e-04,1.715559e-04,1.208581e-04,1.221421e-04,1.228341e-04,1.191193e-04,1.106733e-04,1.043360e-04,9.930308e-05,9.356878e-05,8.751838e-05,8.171005e-05,7.612107e-05,7.165243e-05,6.771576e-05,6.431408e-05,6.054092e-05,5.806812e-05,5.536814e-05,5.214402e-05,5.137058e-05,4.957691e-05,4.757792e-05,4.545182e-05,4.310557e-05,4.239816e-05,4.153944e-05,4.086905e-05,3.982410e-05,3.782912e-05,3.764319e-05,3.667711e-05,3.720442e-05,3.628419e-05,3.430832e-05,3.482431e-05,3.478138e-05,3.412856e-05,3.431281e-05,3.274628e-05,3.248793e-05,3.394146e-05,3.344482e-05,3.145896e-05,3.245131e-05,3.349679e-05,3.267271e-05,3.116064e-05
--2.754335e-03,-2.983188e-04,2.754895e-05,8.555645e-05,1.055525e-04,1.151164e-04,1.177370e-04,1.147970e-04,1.070925e-04,1.014387e-04,9.687762e-05,9.158887e-05,8.591206e-05,8.039611e-05,7.504568e-05,7.074905e-05,6.696683e-05,6.368629e-05,6.002494e-05,5.760884e-05,5.497499e-05,5.182117e-05,5.107201e-05,4.931807e-05,4.734510e-05,4.524755e-05,4.292731e-05,4.223496e-05,4.139362e-05,4.073579e-05,3.970049e-05,3.772705e-05,3.754021e-05,3.659124e-05,3.711469e-05,3.620236e-05,3.424428e-05,3.475326e-05,3.471445e-05,3.407002e-05,3.425022e-05,3.269439e-05,3.244108e-05,3.388394e-05,3.339079e-05,3.141911e-05,3.240447e-05,3.344301e-05,3.262484e-05,3.112336e-05
--3.400498e-02,-4.441099e-03,-5.415004e-04,2.177136e-06,9.076566e-05,1.084727e-04,1.129099e-04,1.106691e-04,1.036535e-04,9.863263e-05,9.451286e-05,8.964610e-05,8.432770e-05,7.909419e-05,7.397572e-05,6.984738e-05,6.621641e-05,6.305586e-05,5.950672e-05,5.714689e-05,5.457848e-05,5.149561e-05,5.077053e-05,4.905650e-05,4.710980e-05,4.504082e-05,4.274711e-05,4.206985e-05,4.124607e-05,4.060085e-05,3.957534e-05,3.762368e-05,3.743579e-05,3.650404e-05,3.702356e-05,3.611929e-05,3.417930e-05,3.468114e-05,3.464642e-05,3.401044e-05,3.418653e-05,3.264157e-05,3.239341e-05,3.382544e-05,3.333586e-05,3.137861e-05,3.235684e-05,3.338829e-05,3.257607e-05,3.108532e-05
--5.894012e-02,-7.751793e-03,-9.975727e-04,-6.525692e-05,7.814704e-05,1.024625e-04,1.083676e-04,1.067346e-04,1.003530e-04,9.591544e-05,9.220762e-05,8.773955e-05,8.276449e-05,7.780402e-05,7.291068e-05,6.894678e-05,6.546404e-05,6.242238e-05,5.898552e-05,5.668173e-05,5.417828e-05,5.116710e-05,5.046587e-05,4.879200e-05,4.687199e-05,4.483154e-05,4.256455e-05,4.190239e-05,4.109657e-05,4.046411e-05,3.944839e-05,3.751869e-05,3.732976e-05,3.641551e-05,3.693103e-05,3.603492e-05,3.411325e-05,3.460789e-05,3.457734e-05,3.394992e-05,3.412185e-05,3.258789e-05,3.234489e-05,3.376593e-05,3.327999e-05,3.133740e-05,3.230841e-05,3.333266e-05,3.252649e-05,3.104665e-05
--7.375883e-02,-9.725534e-03,-1.271485e-03,-1.071062e-04,6.888093e-05,9.724825e-05,1.041060e-04,1.029842e-04,9.718675e-05,9.328754e-05,8.996385e-05,8.587209e-05,8.122521e-05,7.652790e-05,7.185286e-05,6.804939e-05,6.471151e-05,6.178720e-05,5.846241e-05,5.621444e-05,5.377526e-05,5.083617e-05,5.015854e-05,4.852494e-05,4.663188e-05,4.461999e-05,4.238025e-05,4.173309e-05,4.094513e-05,4.032537e-05,3.931969e-05,3.741228e-05,3.722227e-05,3.632565e-05,3.683709e-05,3.594925e-05,3.404615e-05,3.453347e-05,3.450709e-05,3.388829e-05,3.405601e-05,3.253325e-05,3.229549e-05,3.370541e-05,3.322317e-05,3.129547e-05,3.225917e-05,3.327614e-05,3.247613e-05,3.100737e-05
--7.631556e-02,-1.007642e-02,-1.323894e-03,-1.178721e-04,6.366347e-05,9.291799e-05,1.001165e-04,9.940686e-05,9.415132e-05,9.074886e-05,8.778271e-05,8.404516e-05,7.971152e-05,7.526753e-05,7.080360e-05,6.715633e-05,6.395991e-05,6.115131e-05,5.793806e-05,5.574541e-05,5.336974e-05,5.050314e-05,4.984885e-05,4.825563e-05,4.638987e-05,4.440644e-05,4.219416e-05,4.156192e-05,4.079209e-05,4.018511e-05,3.918952e-05,3.730456e-05,3.711345e-05,3.623462e-05,3.674190e-05,3.586240e-05,3.397805e-05,3.445798e-05,3.443585e-05,3.382578e-05,3.398924e-05,3.247779e-05,3.224528e-05,3.364390e-05,3.316538e-05,3.125275e-05,3.220903e-05,3.321860e-05,3.242484e-05,3.096731e-05
--6.639816e-02,-8.774698e-03,-1.150485e-03,-9.695881e-05,6.258262e-05,8.944438e-05,9.636229e-05,9.597141e-05,9.123583e-05,8.829721e-05,8.566350e-05,8.225900e-05,7.822376e-05,7.402288e-05,6.976311e-05,6.626806e-05,6.320947e-05,6.051473e-05,5.741263e-05,5.527493e-05,5.296196e-05,5.016806e-05,4.953684e-05,4.798404e-05,4.614582e-05,4.419078e-05,4.200631e-05,4.138895e-05,4.063739e-05,4.004321e-05,3.905778e-05,3.719547e-05,3.700322e-05,3.614239e-05,3.664546e-05,3.577442e-05,3.390907e-05,3.438153e-05,3.436363e-05,3.376236e-05,3.392153e-05,3.242157e-05,3.219439e-05,3.358157e-05,3.310683e-05,3.120948e-05,3.215819e-05,3.316021e-05,3.237272e-05,3.092654e-05
diff --git a/hydro/python/test/data/test_potential_generator.ipynb b/hydro/python/test/data/test_potential_generator.ipynb
deleted file mode 100644
index f68962d..0000000
--- a/hydro/python/test/data/test_potential_generator.ipynb
+++ /dev/null
@@ -1,546 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "rSJf6s1tKGz7",
-    "outputId": "d58c0339-e27d-4651-99ac-f4da2155a60f"
-   },
-   "outputs": [],
-   "source": [
-    "# This generates configuration values with Capytaine.\n",
-    "\n",
-    "#!pip install capytaine #uncomment if first time running\n",
-    "\n",
-    "import capytaine as cpt\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import copy\n",
-    "from capytaine.bem.airy_waves import airy_waves_potential, airy_waves_velocity, froude_krylov_force\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ukVJNFS8XIfE"
-   },
-   "outputs": [],
-   "source": [
-    "def save_potential_array(title, arr):\n",
-    "    file_path = title + \"-real\" + \".csv\"\n",
-    "    np.savetxt(file_path, np.real(arr), delimiter=\",\", fmt=\"%.6e\")\n",
-    "    file_path = title + \"-imag\" + \".csv\"\n",
-    "    np.savetxt(file_path, np.imag(arr), delimiter=\",\", fmt=\"%.6e\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Making the body\n",
-    "def body_from_profile(x,y,z,nphi):\n",
-    "    xyz = np.array([np.array([x/np.sqrt(2),y/np.sqrt(2),z]) for x,y,z in zip(x,y,z)])    # /sqrt(2) to account for the scaling\n",
-    "    body = cpt.FloatingBody(cpt.AxialSymmetricMesh.from_profile(xyz, nphi=nphi))\n",
-    "    return body\n",
-    "\n",
-    "def make_surface(ztop, zbot, rin, rout, fdensity, tdensity):\n",
-    "    zarr = np.linspace(- zbot, -ztop, fdensity)\n",
-    "    rarr = np.linspace( rin, rout, fdensity)\n",
-    "    return body_from_profile(rarr, rarr, zarr, tdensity)\n",
-    "\n",
-    "def make_shell(top, bottom, inner, outer, zdensity, rdensity, tdensity):\n",
-    "    outer_surface = make_surface(top, bottom, outer, outer, zdensity, tdensity)\n",
-    "    bottom_surface = make_surface(bottom, bottom, inner, outer, rdensity, tdensity)\n",
-    "    top_surface = make_surface(top, top, inner, outer, rdensity, tdensity)\n",
-    "    bod = outer_surface + bottom_surface + top_surface\n",
-    "    if inner > 0:\n",
-    "        inner_surface = make_surface(top, bottom, inner, inner, zdensity, tdensity)\n",
-    "        bod = bod + inner_surface\n",
-    "    return bod\n",
-    "\n",
-    "def make_bodies(attribute_lst): # Returns a list of shells, given parameters for each\n",
-    "    bod_lst = []\n",
-    "    for att in attribute_lst:\n",
-    "        bod_lst.append(make_shell(att[\"top\"], att[\"bottom\"], att[\"inner\"], att[\"outer\"], att[\"zdensity\"], att[\"rdensity\"], att[\"tdensity\"]))\n",
-    "    return bod_lst\n",
-    "\n",
-    "def add_heaves(bod_lst, heaving):\n",
-    "    hcreate = False\n",
-    "    screate = False\n",
-    "    for i in range(len(heaving)): # Splits list of shells into those that are heaving and those that are not.\n",
-    "        if heaving[i]:\n",
-    "            if not hcreate:\n",
-    "                heaving_body = bod_lst[i]\n",
-    "                hcreate = True\n",
-    "            else:\n",
-    "                heaving_body = heaving_body + bod_lst[i]\n",
-    "        else:\n",
-    "            if not screate:\n",
-    "                still_body = bod_lst[i]\n",
-    "                screate = True\n",
-    "            else:\n",
-    "                still_body = still_body + bod_lst[i]\n",
-    "    if hcreate: # Adds heave dof to the heaving collection\n",
-    "        heaving_body.add_translation_dof(name='Heave')\n",
-    "        if screate:\n",
-    "            return (heaving_body + still_body)\n",
-    "        else:\n",
-    "            return (heaving_body)\n",
-    "    else:\n",
-    "        return (still_body)\n",
-    "\n",
-    "# getting an attribute list from the current multi-meem input setup\n",
-    "def gen_to_att_lst(d, a, zdensities, rdensities, tdensities):\n",
-    "    ct = len(d)\n",
-    "    tops = [0] * ct\n",
-    "    bottoms = d\n",
-    "    inners = [0] + a[:-1]\n",
-    "    outers = a\n",
-    "    att_lst = []\n",
-    "    key_lst = [\"top\", \"bottom\", \"inner\", \"outer\", \"zdensity\", \"rdensity\", \"tdensity\"]\n",
-    "    for i in range(ct):\n",
-    "        vals = [tops[i], bottoms[i], inners[i], outers[i], zdensities[i], rdensities[i], tdensities[i]]\n",
-    "        att = {}\n",
-    "        for j in range(len(key_lst)):\n",
-    "            att[key_lst[j]] = vals[j]\n",
-    "        att_lst.append(att)\n",
-    "\n",
-    "    return att_lst\n",
-    "\n",
-    "###################################\n",
-    "# Solving\n",
-    "solver = cpt.BEMSolver()\n",
-    "\n",
-    "def rb_solve(d, a, zdensities, rdensities, tdensities, heaving, m0, h, rho):\n",
-    "    att_lst = gen_to_att_lst(d, a, zdensities, rdensities, tdensities)\n",
-    "    bod_lst = make_bodies(att_lst)\n",
-    "    body = add_heaves(bod_lst, heaving)\n",
-    "    body = body.immersed_part() # removes points above z = 0\n",
-    "    body.show_matplotlib()\n",
-    "    \n",
-    "    rad_problem = cpt.RadiationProblem(body = body, wavenumber = m0, water_depth = h, rho = rho)\n",
-    "    results = solver.solve(rad_problem, keep_details = True)\n",
-    "    print(results.added_mass)\n",
-    "    print(results.radiation_damping)\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 410
-    },
-    "id": "fYujEq2e3QqD",
-    "outputId": "75513233-38a3-46e6-d3bc-991bddeea24f"
-   },
-   "outputs": [],
-   "source": [
-    "#original  - compound cylinder\n",
-    "h = 1.001\n",
-    "d = [0.5, 0.25]\n",
-    "a = [0.5, 1]\n",
-    "w = 1\n",
-    "rho = 1023 # density of our special material\n",
-    "zdensities = [10, 10]\n",
-    "rdensities = [20, 20]\n",
-    "tdensities = [50, 100]\n",
-    "config = \"config0\"\n",
-    "heaving = [1, 1]\n",
-    "\n",
-    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "gV2Sd-xRL_Z5"
-   },
-   "outputs": [],
-   "source": [
-    "#staircase  - compound cylinder\n",
-    "h = 1.5\n",
-    "d = [1.1, 0.85, 0.75, 0.4, 0.15]\n",
-    "a = [0.3, 0.5, 1, 1.2, 1.6]\n",
-    "w = 1\n",
-    "rho = 1023 # density of our special material\n",
-    "zdensities = [20, 10, 30, 20, 15]\n",
-    "rdensities = [10, 10, 20, 10, 15]\n",
-    "tdensities = [40, 50, 70, 80, 100]\n",
-    "config = \"config1\"\n",
-    "heaving = [1, 1, 1, 1, 1]\n",
-    "\n",
-    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "G4aw1fAAb7Vh"
-   },
-   "outputs": [],
-   "source": [
-    "#really tall  - compound cylinder\n",
-    "h = 100\n",
-    "d = [29, 7, 4]\n",
-    "a = [3, 5, 10]\n",
-    "w = 1\n",
-    "rho = 1023 # density of our special material\n",
-    "zdensities = [40, 10, 10]\n",
-    "rdensities = [15, 10, 25]\n",
-    "tdensities = [50, 80, 200]\n",
-    "config = \"config2\"\n",
-    "heaving = [1, 1, 1]\n",
-    "\n",
-    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "AjeiVwZRPpcy"
-   },
-   "outputs": [],
-   "source": [
-    "#indents  - compound cylinder\n",
-    "h = 1.9\n",
-    "d = [0.5, 0.7, 0.8, 0.2, 0.5]\n",
-    "a = [0.3, 0.5, 1, 1.2, 1.6]\n",
-    "w = 1\n",
-    "rho = 1023 # density of our special material\n",
-    "zdensities = [15, 10, 30, 15, 25]\n",
-    "rdensities = [10, 10, 20, 10, 15]\n",
-    "tdensities = [40, 50, 70, 80, 100]\n",
-    "config = \"config3\"\n",
-    "heaving = [1, 1, 1, 1, 1]\n",
-    "\n",
-    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#original - only outer heaving\n",
-    "h = 1.001\n",
-    "d = [0.5, 0.25]\n",
-    "a = [0.5, 1]\n",
-    "w = 1\n",
-    "rho = 1023 # density of our special material\n",
-    "zdensities = [10, 10]\n",
-    "rdensities = [20, 20]\n",
-    "tdensities = [50, 100]\n",
-    "config = \"config4\"\n",
-    "heaving = [0, 1]\n",
-    "\n",
-    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#original - only inner heaving\n",
-    "h = 1.001\n",
-    "d = [0.5, 0.25]\n",
-    "a = [0.5, 1]\n",
-    "w = 1\n",
-    "rho = 1023 # density of our special material\n",
-    "zdensities = [10, 10]\n",
-    "rdensities = [20, 20]\n",
-    "tdensities = [50, 100]\n",
-    "config = \"config5\"\n",
-    "heaving = [1, 0]\n",
-    "\n",
-    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#really tall  - spar not heaving\n",
-    "h = 100\n",
-    "d = [29, 7, 4]\n",
-    "a = [3, 5, 10]\n",
-    "w = 1\n",
-    "rho = 1023 # density of our special material\n",
-    "zdensities = [40, 10, 10]\n",
-    "rdensities = [15, 10, 25]\n",
-    "tdensities = [50, 80, 200]\n",
-    "config = \"config6\"\n",
-    "heaving = [0, 1, 1]\n",
-    "\n",
-    "result = rb_solve(d, a, zdensities, rdensities, tdensities, heaving, w, h, rho)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Y__0sy0z_D-7"
-   },
-   "outputs": [],
-   "source": [
-    "# Get potentials\n",
-    "# Define the ranges for R and Z\n",
-    "R_range = np.linspace(0.0, 2*a[-1], num=50)\n",
-    "theta_range = np.linspace(-np.pi, np.pi, num=4)\n",
-    "Z_range = np.linspace(0, -h, num=50) #h\n",
-    "\n",
-    "# Create mesh grids for R, theta, and Z\n",
-    "R, theta, Z = np.meshgrid(R_range, theta_range, Z_range, indexing='ij')\n",
-    "\n",
-    "# Convert cylindrical coordinates to Cartesian coordinates for capytaine\n",
-    "X = R * np.cos(theta)\n",
-    "Y = R * np.sin(theta)\n",
-    "Z = Z\n",
-    "# Create an array of shape (N, 3)\n",
-    "points = np.zeros((R.size, 3))\n",
-    "\n",
-    "# Assign the values of R, Z, and y to the array\n",
-    "points[:, 0] = X.ravel()\n",
-    "points[:, 1] = Y.ravel()\n",
-    "points[:, 2] = Z.ravel()\n",
-    "#need cartesian here\n",
-    "phi_inc = solver.compute_potential(points,result) #rad problem\n",
-    "\n",
-    "regions = []\n",
-    "regions.append((R <= a[0]) & (Z > -d[0]))\n",
-    "for i in range(1, len(a)):\n",
-    "    regions.append((R > a[i-1]) & (R <= a[i]) & (Z > -d[i]))\n",
-    "regions.append(R > a[-1])\n",
-    "\n",
-    "# Apply masks to create a blank plot in specified regions\n",
-    "phi_inc = phi_inc.reshape((50,4,50))\n",
-    "\n",
-    "for i in range(len(a)):\n",
-    "  phi_inc[regions[i]] = np.nan\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "rNvMTwcSHgNT"
-   },
-   "outputs": [],
-   "source": [
-    "# Get velocities\n",
-    "vel_inc = solver.compute_velocity(points,result)\n",
-    "velx_inc = vel_inc[:,0].reshape((50,4,50))\n",
-    "vely_inc = vel_inc[:,1].reshape((50,4,50))\n",
-    "velz_inc = vel_inc[:,2].reshape((50,4,50))\n",
-    "for i in range(len(a)):\n",
-    "  velx_inc[regions[i]] = np.nan\n",
-    "  vely_inc[regions[i]] = np.nan\n",
-    "  velz_inc[regions[i]] = np.nan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "-7Zut1alw6zS",
-    "outputId": "5cf0dff0-4741-408a-864a-e37a20e9caef"
-   },
-   "outputs": [],
-   "source": [
-    "# Plot potentials and velocities\n",
-    "# The slicing limits the y-value to 0 because we only care about the x-z (r-z) plane.\n",
-    "plt.contourf(R[:, 0, :], Z[:, 0, :], phi_inc[:, 0, :], cmap='viridis', levels = 50)\n",
-    "plt.colorbar(label='Potential')\n",
-    "plt.contour(R[:, 0, :], Z[:, 0, :], phi_inc[:, 0, :], colors='black', linestyles='solid', linewidths=0.05,levels=50)\n",
-    "\n",
-    "# Add labels and title\n",
-    "plt.xlabel('R')\n",
-    "plt.ylabel('Z')\n",
-    "plt.title('Contour Plot of Re(Potential) using BEM')\n",
-    "\n",
-    "plt.show()\n",
-    "\n",
-    "imag_phi_inc = np.imag(phi_inc[:, 0, :])\n",
-    "\n",
-    "nan_mask = np.isnan(np.real(phi_inc[:, 0, :]))\n",
-    "\n",
-    "np.imag(phi_inc[:, 0, :])[nan_mask] = np.nan\n",
-    "\n",
-    "plt.contourf(R[:, 0, :], Z[:, 0, :], imag_phi_inc, cmap='viridis', levels = 50)\n",
-    "plt.colorbar(label='Potential')\n",
-    "plt.contour(R[:, 0, :], Z[:, 0, :], imag_phi_inc, colors='black', linestyles='solid', linewidths=0.05,levels=50)\n",
-    "\n",
-    "\n",
-    "# Add labels and title\n",
-    "plt.xlabel('R')\n",
-    "plt.ylabel('Z')\n",
-    "plt.title('Contour Plot of Im(Potential) using BEM')\n",
-    "\n",
-    "plt.show()\n",
-    "\n",
-    "def plot_vel(data, title):\n",
-    "  plt.contourf(R[:, 0, :], Z[:, 0, :], data[:, 0, :], cmap='viridis', levels = 50)\n",
-    "  plt.colorbar(label='V')\n",
-    "  plt.contour(R[:, 0, :], Z[:, 0, :], data[:, 0, :], colors='black', linestyles='solid', linewidths=0.05,levels=50)\n",
-    "\n",
-    "  # Add labels and title\n",
-    "  plt.xlabel('R')\n",
-    "  plt.ylabel('Z')\n",
-    "  plt.title(title)\n",
-    "\n",
-    "  plt.show()\n",
-    "\n",
-    "nan_mask = np.isnan(np.real(velx_inc))\n",
-    "\n",
-    "velx_imag = np.imag(velx_inc)\n",
-    "velz_imag = np.imag(velz_inc)\n",
-    "\n",
-    "velx_imag[nan_mask] = np.nan\n",
-    "velz_imag[nan_mask] = np.nan\n",
-    "\n",
-    "plot_vel(velx_inc, \"Contour Plot of Re(Vx) using BEM\")\n",
-    "plot_vel(velx_imag, \"Contour Plot of Im(Vx) using BEM\")\n",
-    "plot_vel(velz_inc, \"Contour Plot of Re(Vz) using BEM\")\n",
-    "plot_vel(velz_imag, \"Contour Plot of Im(Vz) using BEM\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "sa0mkZllZw_V",
-    "outputId": "b67eb2b3-636d-4bcb-b73a-74f5f7a5e070"
-   },
-   "outputs": [],
-   "source": [
-    "save_potential_array(config, phi_inc[:, 0, :])\n",
-    "# WARNING: This overwrites existing files with the same name. Ensure that <config> is correct before running."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(result.added_mass)\n",
-    "print(result.radiation_damping)\n",
-    "print((result.added_mass)[\"Heave\"]/(result.radiation_damping)[\"Heave\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Code blocks below are draft code or old code, not to run but may contain useful content for future reference if something bugs out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This code cannot handle non-heaving regions, but matches significantly better for radiation_damping.\n",
-    "# It also underestimates instead of overestimates added_mass wrt to the MEEM file.\n",
-    "\n",
-    "def body_from_profile(x,y,z,nphi):\n",
-    "    xyz = np.array([np.array([x/np.sqrt(2),y/np.sqrt(2),z]) for x,y,z in zip(x,y,z)])    # /sqrt(2) to account for the scaling\n",
-    "    body = cpt.FloatingBody(cpt.AxialSymmetricMesh.from_profile(xyz, nphi=nphi))\n",
-    "    return body\n",
-    "\n",
-    "def make_surface(ztop, zbot, rin, rout, fdensity, tdensity):\n",
-    "    zarr = np.linspace(- zbot, -ztop, fdensity)\n",
-    "    rarr = np.linspace( rin, rout, fdensity)\n",
-    "    return body_from_profile(rarr, rarr, zarr, tdensity)\n",
-    "\n",
-    "def make_body(d, a, zdensities, rdensities, tdensities):\n",
-    "    # top_surface = make_surface(0, 0, 0, a[-1], fdensity, cdensity)\n",
-    "    \n",
-    "    bot_surface = make_surface(d[0], d[0], 0, a[0], rdensities[0], tdensities[0])\n",
-    "\n",
-    "    outer_surface = make_surface(0 , d[-1], a[-1], a[-1], zdensities[-1], tdensities[-1])\n",
-    "\n",
-    "    bod = bot_surface + outer_surface # + top_surface\n",
-    "\n",
-    "    for i in range(1, len(a)):\n",
-    "      # make sides\n",
-    "        side = make_surface( d[i] , d[i-1], a[i-1], a[i-1], zdensities[i-1], tdensities[i-1])\n",
-    "      # make bottoms\n",
-    "        bot = make_surface( d[i] , d[i], a[i-1], a[i], rdensities[i], tdensities[i])\n",
-    "        bod = bod + side + bot\n",
-    "\n",
-    "    return bod\n",
-    "\n",
-    "solver = cpt.BEMSolver()\n",
-    "def rb_solve(d, a, zdensities, rdensities, tdensities, rho):\n",
-    "    body = make_body(d, a, zdensities, rdensities, tdensities)\n",
-    "    body.add_translation_dof(name='Heave')\n",
-    "    body = body.immersed_part()\n",
-    "    body.show_matplotlib()\n",
-    "    \n",
-    "    rad_problem = cpt.RadiationProblem(body=body, wavenumber = w, water_depth=h, rho = rho)\n",
-    "    results = solver.solve(rad_problem, keep_details = True)\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.11.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/data/.gitkeep b/matlab/dev/MEEM_figs/.gitkeep
similarity index 100%
rename from data/.gitkeep
rename to matlab/dev/MEEM_figs/.gitkeep
diff --git a/hydro/matlab/MEEM_many_region.m b/matlab/dev/MEEM_many_region.m
similarity index 100%
rename from hydro/matlab/MEEM_many_region.m
rename to matlab/dev/MEEM_many_region.m
diff --git a/dev/compare_MEEM_figs.m b/matlab/dev/compare_MEEM_figs.m
similarity index 100%
rename from dev/compare_MEEM_figs.m
rename to matlab/dev/compare_MEEM_figs.m
diff --git a/hydro/matlab/generate_BC.m b/matlab/dev/generate_BC.m
similarity index 100%
rename from hydro/matlab/generate_BC.m
rename to matlab/dev/generate_BC.m
diff --git a/dev/integral_testing_r.mlx b/matlab/dev/integral_testing_r.mlx
similarity index 100%
rename from dev/integral_testing_r.mlx
rename to matlab/dev/integral_testing_r.mlx
diff --git a/dev/meem_integral_testing.m b/matlab/dev/meem_integral_testing.m
similarity index 100%
rename from dev/meem_integral_testing.m
rename to matlab/dev/meem_integral_testing.m
diff --git a/dev/sweep_geometry_and_spectra.m b/matlab/dev/sweep_geometry_and_spectra.m
similarity index 100%
rename from dev/sweep_geometry_and_spectra.m
rename to matlab/dev/sweep_geometry_and_spectra.m
diff --git a/dev/w_dependence.mlx b/matlab/dev/w_dependence.mlx
similarity index 100%
rename from dev/w_dependence.mlx
rename to matlab/dev/w_dependence.mlx
diff --git a/hydro/matlab/run_MEEM.m b/matlab/src/run_MEEM.m
similarity index 100%
rename from hydro/matlab/run_MEEM.m
rename to matlab/src/run_MEEM.m
diff --git a/test/matlab/convergence_study.m b/matlab/test/convergence_study.m
similarity index 100%
rename from test/matlab/convergence_study.m
rename to matlab/test/convergence_study.m
diff --git a/test/matlab/test_MEEM.m b/matlab/test/test_MEEM.m
similarity index 100%
rename from test/matlab/test_MEEM.m
rename to matlab/test/test_MEEM.m
diff --git a/notebooks/.gitkeep b/notebooks/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/output/netcdf/example_results.nc b/output/netcdf/example_results.nc
deleted file mode 100644
index e918863..0000000
Binary files a/output/netcdf/example_results.nc and /dev/null differ
diff --git a/package/src/_init_.py b/package/src/_init_.py
deleted file mode 100644
index 986e583..0000000
--- a/package/src/_init_.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# package/src/__init__.py
-
-# Empty for basic package initialization
diff --git a/package/src/constants.py b/package/src/constants.py
deleted file mode 100644
index ca2f0d5..0000000
--- a/package/src/constants.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# constants.py
-
-import numpy as np
-
-# Physical constants
-g = 9.81  # Acceleration due to gravity (m/s^2)
-
-# Mathematical constants
-pi = np.pi
-
-h = 1.001
-a1 = .5
-a2 = 1
-d1 = .5
-d2 = .25
-m0 = 1
-n = 3
-z = 6
-omega = 2
\ No newline at end of file
diff --git a/package/src/domain.py b/package/src/domain.py
deleted file mode 100644
index f6f574c..0000000
--- a/package/src/domain.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# domain.py
-from __future__ import annotations
-
-from typing import List, Dict, Union
-import numpy as np
-from multi_equations import *
-
-class Domain:
-    """
-    Represents a sub-region within the geometry, characterized by its own properties
-    and methods to compute eigenfunctions and potentials.
-    """
-
-    def __init__(self, number_harmonics: int, height: float, radial_width: float, top_BC, bottom_BC, category: str, params: dict, index: int, geometry: 'Geometry'):
-        """
-        Initialize the Domain object.
-
-        :param number_harmonics: Number of harmonics.
-        :param height: Height of the domain.
-        :param radial_width: Radial width of the domain.
-        :param top_BC: Top boundary condition.
-        :param bottom_BC: Bottom boundary condition.
-        :param category: Category/type of the domain.
-        :param params: Dictionary containing parameters like h, di, a, m0.
-        :param index: Index of the domain in the multi-region setup.
-        :param geometry: The Geometry object that this domain belongs to.
-
-        """
-        self.number_harmonics = number_harmonics
-        self.height = height
-        self.radial_width = radial_width
-        self.top_BC = top_BC
-        self.bottom_BC = bottom_BC
-        self.category = category  # 'inner', 'outer', 'exterior', 'multi'
-        self.params = params
-        self.index = index  # Index in the domain list
-        self.geometry = geometry
-        
-
-        self.h = params.get('h', geometry.z_coordinates.get('h'))
-        self.di = self._get_di()
-        self.a = self._get_a()
-        self.m0 = params.get('m0')
-
-        # Convert dict_values to NumPy array before calculating mean
-        r_values = np.array(list(geometry.r_coordinates.values()))
-        self.scale = params.get('scale', np.mean(r_values))
-
-        self.heaving = self._get_heaving()
-        self.slant = params.get('slant', False)
-        self.m_k_vals = []  # For exterior domain eigenvalues]
-        self.r_coords = self._get_r_coords()
-        self.z_coords = self._get_z_coords()
-        
-    def _get_di(self) -> Union[float, None]:
-        """Gets the di parameter based on category and index."""
-        if self.category == 'inner':
-            return self.params.get('di')
-        elif self.category == 'outer':
-            return self.params.get('di')
-        elif self.category == 'exterior':
-            return None # Exterior domain doesn't have di
-        else:
-            return self.params.get('di')
-
-    def _get_a(self) -> Union[float, List[float], None]:
-        """Gets the 'a' parameter based on category."""
-        if self.category == 'inner':
-            return self.params.get('a', self.geometry.r_coordinates.get('a1'))
-        elif self.category == 'outer':
-            return self.params.get('a', self.geometry.r_coordinates.get('a2'))
-        elif self.category == 'exterior':
-            return None # Exterior domain doesn't have a
-        else:
-            return self.params.get('a')
-
-    def _get_heaving(self) -> Union[int, None]:
-        """Gets the heaving parameter based on index."""
-        return self.params.get('heaving')
-
-    def _get_r_coords(self) -> Union[float, List[float], None]:
-        """Gets the r coordinates based on category."""
-        if self.category == 'inner':
-            return 0 # r = 0 for inner
-        elif self.category == 'outer':
-            return [self.geometry.r_coordinates.get('a1'), self.geometry.r_coordinates.get('a2')]
-        elif self.category == 'exterior':
-            return np.inf # r -> infinity for exterior
-        else:
-            return self.geometry.r_coordinates.get('a1') # default to a1
-
-    def _get_z_coords(self) -> Union[float, List[float], None]:
-        """Gets the z coordinates."""
-        return [0, self.h] # z from 0 to h
\ No newline at end of file
diff --git a/package/src/geometry.py b/package/src/geometry.py
deleted file mode 100644
index cff057d..0000000
--- a/package/src/geometry.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# geometry.py
-
-from typing import Dict, List
-from domain import Domain
-import numpy as np
-
-class Geometry:
-    """
-    Represents the physical geometry of the problem, including coordinates and domain parameters.
-    """
-
-    def __init__(self, r_coordinates: Dict[str, float], z_coordinates: Dict[str, float], domain_params: List[Dict]):
-        """
-        Initialize the Geometry object.
-
-        :param r_coordinates: Dictionary of radial coordinates.
-        :param z_coordinates: Dictionary of vertical coordinates.
-        :param domain_params: List of dictionaries containing domain parameters.
-        """
-        self.r_coordinates = r_coordinates
-        self.z_coordinates = z_coordinates
-        self.domain_params = domain_params
-        self.domain_list = self.make_domain_list()
-
-    def make_domain_list(self) -> Dict[int, 'Domain']:
-        """
-        Creates a dictionary of Domain objects based on the domain parameters.
-
-        :return: Dictionary of Domain objects with their index as keys.
-        """
-        domain_list = {}
-        # Extract 'a' values from domain_params where 'a' exists
-        a_values = [params['a'] if params.get('a') is not None else 0.0 for params in self.domain_params if 'a' in params]
-        scale = np.mean(a_values) if a_values else 1.0
-
-        h = self.z_coordinates.get('h')
-        if h is None:
-            raise ValueError("z_coordinates must contain 'h' key.")
-
-        for idx, params in enumerate(self.domain_params):
-            di = params.get('di')
-            category = params.get('category')
-
-            # Allow 'di' to be None if category is 'exterior'
-            if di is None and category != 'exterior':
-                raise ValueError(f"domain_params[{idx}] must contain 'di' key unless category is 'exterior'.")
-
-            # Allow 'a' to be None if category is 'exterior'
-            a_val = params.get('a')
-            if a_val is None and category != 'exterior':
-                raise ValueError(f"domain_params[{idx}] must contain 'a' key unless category is 'exterior'.")
-
-            heaving = params.get('heaving')
-
-            # Prepare parameters to pass to Domain
-            domain_params = {
-                'h': h,
-                'di': di,
-                'a': a_val,
-                'm0': 0.0,  # You might need to calculate or pass 'm0' if necessary
-                'scale': scale,
-                'heaving': heaving,
-                'slant': params.get('slant', False)
-            }
-            
-            domain = Domain(
-                number_harmonics=params.get('number_harmonics', 0),
-                height=params.get('height', 0.0),
-                radial_width=params.get('radial_width', 0.0),
-                top_BC=params.get('top_BC', None),
-                bottom_BC=params.get('bottom_BC', None),
-                category=category,
-                params=domain_params,
-                index=idx,
-                geometry = self
-            )
-            domain_list[idx] = domain
-        return domain_list
\ No newline at end of file
diff --git a/package/src/meem_engine.py b/package/src/meem_engine.py
deleted file mode 100644
index a5c52f0..0000000
--- a/package/src/meem_engine.py
+++ /dev/null
@@ -1,534 +0,0 @@
-#meem_engine.py
-from typing import List, Dict
-import numpy as np
-from scipy.integrate import quad
-import scipy.integrate as integrate
-import matplotlib.pyplot as plt
-from equations import *
-from meem_problem import MEEMProblem
-from coupling import A_nm, A_mk
-from multi_equations import *
-import geometry
-from results import Results
-import xarray as xr
-
-
-class MEEMEngine:
-    """
-    Manages multiple MEEMProblem instances and performs actions such as solving systems of equations,
-    assembling matrices, and visualizing results.
-    """
-
-    def __init__(self, problem_list: List[MEEMProblem]):
-        """
-        Initialize the MEEMEngine object.
-
-        
-        :param problem_list: List of MEEMProblem instances.
-        """
-        self.problem_list = problem_list
-
-    def assemble_A(self, problem: MEEMProblem, m0) -> np.ndarray:
-        """
-        Assemble the system matrix A for a given problem.
-
-        :param problem: MEEMProblem instance.
-        :return: Assembled matrix A.
-        """
-        # Extract domains
-        inner_domain = problem.domain_list[0]
-        outer_domain = problem.domain_list[1]
-        exterior_domain = problem.domain_list[2]
-
-        N = inner_domain.number_harmonics
-        M = outer_domain.number_harmonics
-        K = exterior_domain.number_harmonics
-
-        size = N + 2 * M + K
-        A = np.zeros((size, size), dtype=complex)
-
-        h, d1, d2 = inner_domain.h, inner_domain.di, outer_domain.di
-        a1, a2 = inner_domain.a, outer_domain.a
-
-        # First row of block matrices (using d1)
-        for i in range(N):
-            A[i][i] = (h - d1) * R_1n_1(i, a1, a2, h, d1)
-        for n in range(N):
-            for m in range(M):
-                A[n][N+m] = -R_1n_2(m, a1, a2, h, d2) * A_nm(n, m)
-                A[n][N+M+m] = -R_2n_2(m, a1, a2, h, d2) * A_nm(n, m)
-
-        # Second row of block matrices (using d2)
-        for i in range(M):
-            A[N + i, N + i] = (h - d2) * R_1n_2(i, a2, a2, h, d2)
-            A[N + i, N + M + i] = (h - d2) * R_2n_2(i, a2, a2, h, d2)
-        for m in range(M):
-            for k in range(K):
-                A[N + m, N + 2 * M + k] = -Lambda_k_r(k, a2, m0, a2, h) * A_mk(m, k)
-
-        # Third row of block matrices (using d1)
-        for m in range(M):
-            for n in range(N):
-                A[N + M + m, n] = -diff_R_1n_1(n, a1, d1, h, a2) * A_nm(n, m)
-        for m in range(M):
-            A[N + M + m, N + m] = (h - d2) * diff_R_1n_2(m, a1, d2, h, a2)
-            A[N + M + m, N + M + m] = (h - d2) * diff_R_2n_2(m, a1, d2, h, a2)
-
-        # Fourth row of block matrices (using d2)
-        for k in range(K):
-            for m in range(M):
-                A[N + 2 * M + k, N + m] = -diff_R_1n_2(m, a2, d2, h, a2) * A_mk(m, k)
-                A[N + 2 * M + k, N + M + m] = -diff_R_2n_2(m, a2, d2, h, a2) * A_mk(m, k)
-        for k in range(K):
-            A[N + 2 * M + k, N + 2 * M + k] = h * diff_Lambda_k_a2(k, m0, a2, h)
-
-        return A
-    
-    def assemble_A_multi(self, problem: MEEMProblem, m0) -> np.ndarray:
-        """
-        Assemble the system matrix A for a given problem.
-
-        :param problem: MEEMProblem instance.
-        :return: Assembled matrix A.
-        """
-        domain_list = problem.domain_list
-        domain_keys = list(domain_list.keys())
-        boundary_count = len(domain_keys) - 1
-        
-
-        # Collect number of harmonics for each domain
-        NMK = [domain_list[idx].number_harmonics for idx in domain_keys]
-        size = NMK[0] + NMK[-1] + 2 * sum(NMK[1:-1])
-
-        A = np.zeros((size, size), dtype=complex)
-
-        # Extract parameters
-        h = domain_list[0].h
-        d = [domain_list[idx].di for idx in domain_keys]
-        a = [domain_list[idx].a for idx in domain_keys]
-        a = [val for val in a if val is not None]
-
-
-        ###########################################################################
-        # Potential Matching
-
-        col = 0
-        row = 0
-        for bd in range(boundary_count):
-            N = NMK[bd]
-            M = NMK[bd + 1]
-            if bd == (boundary_count - 1):  # i-e boundary
-                if bd == 0:  # one cylinder
-                    for n in range(N):
-                        # In meem_engine.py, before the call to R_1n
-                        print(f"Value of a[bd]: {a[bd]}")
-                        print(f"Type of a: {type(a)}")
-                        A[row + n][col + n] = (h - d[bd]) * R_1n(n, a[bd], bd, h, d, a)
-                        for m in range(M):
-                            A[row + n][col + N + m] = - I_mk(n, m, bd, d, m0, h, NMK) * Lambda_k(m, a[bd], m0, a, NMK, h)
-                    row += N
-                else:
-                    for n in range(N):
-                        A[row + n][col + n] = (h - d[bd]) * R_1n(n, a[bd], bd, h, d, a)
-                        A[row + n][col + N + n] = (h - d[bd]) * R_2n(n, a[bd], bd, a, h, d)
-                        for m in range(M):
-                            A[row + n][col + 2*N + m] = - I_mk(n, m, bd, d, m0, h, NMK) * Lambda_k(m, a[bd], m0, a, NMK, h)
-                    row += N
-            elif bd == 0:
-                left_diag = d[bd] > d[bd + 1]  # which of the two regions gets diagonal entries
-                if left_diag:
-                    for n in range(N):
-                        A[row + n][col + n] = (h - d[bd]) * R_1n(n, a[bd], bd, h, d, a)
-                        for m in range(M):
-                            A[row + n][col + N + m] = - I_nm(n, m, bd, d, h) * R_1n(m, a[bd], bd + 1, h, d, a)
-                            A[row + n][col + N + M + m] = - I_nm(n, m, bd, d, h) * R_2n(m, a[bd], bd + 1, a, h, d)
-                    row += N
-                else:
-                    for m in range(M):
-                        for n in range(N):
-                            A[row + m][col + n] = I_nm(n, m, bd, d, h) * R_1n(n, a[bd], bd, h, d, a)
-                        A[row + m][col + N + m] = - (h - d[bd + 1]) * R_1n(m, a[bd], bd + 1, h, d, a)
-                        A[row + m][col + N + M + m] = - (h - d[bd + 1]) * R_2n(m, a[bd], bd + 1, a, h, d)
-                    row += M
-                col += N
-            else:  # i-i boundary
-                left_diag = d[bd] > d[bd + 1]  # which of the two regions gets diagonal entries
-                if left_diag:
-                    for n in range(N):
-                        A[row + n][col + n] = (h - d[bd]) * R_1n(n, a[bd], bd, h, d, a)
-                        A[row + n][col + N + n] = (h - d[bd]) * R_2n(n, a[bd], bd, h, d)
-                        for m in range(M):
-                            A[row + n][col + 2*N + m] = - I_nm(n, m, bd, d, h) * R_1n(m, a[bd], bd + 1, h, d, a)
-                            A[row + n][col + 2*N + M + m] = - I_nm(n, m, bd, d, h) * R_2n(m, a[bd], bd + 1, a, h, d)
-                    row += N
-                else:
-                    for m in range(M):
-                        for n in range(N):
-                            A[row + m][col + n] = I_nm(n, m, bd, d, h) * R_1n(n, a[bd], bd, h, d, a)
-                            A[row + m][col + N + n] = I_nm(n, m, bd, d, h) * R_2n(n, a[bd], bd, a, h, d)
-                        A[row + m][col + 2*N + m] = - (h - d[bd + 1]) * R_1n(m, a[bd], bd + 1, h, d, a)
-                        A[row + m][col + 2*N + M + m] = - (h - d[bd + 1]) * R_2n(m, a[bd], bd + 1, a, h, d)
-                    row += M
-                col += 2 * N
-
-        ###########################################################################
-        # Velocity Matching 
-
-        col = 0
-        for bd in range(boundary_count):
-            N = NMK[bd]
-            M = NMK[bd + 1]
-            if bd == (boundary_count - 1):  # i-e boundary
-                if bd == 0:  # one cylinder
-                    for m in range(M):
-                        for n in range(N):
-                            A[row + m][col + n] = - I_mk(n, m, bd, d, m0, h, NMK) * diff_R_1n(n, a[bd], bd, h, d, a)
-                        A[row + m][col + N + m] = h * diff_Lambda_k(m, a[bd], m0, NMK, h, a)
-                    row += N
-                else:
-                    for m in range(M):
-                        for n in range(N):
-                            A[row + m][col + n] = - I_mk(n, m, bd, d, m0, h, NMK) * diff_R_1n(n, a[bd], bd, h, d, a)
-                            A[row + m][col + N + n] = - I_mk(n, m, bd, d, m0, h, NMK) * diff_R_2n(n, a[bd], bd, h, d, a)
-                        A[row + m][col + 2*N + m] = h * diff_Lambda_k(m, a[bd], m0, NMK, h, a)
-                    row += N
-            elif bd == 0:
-                left_diag = d[bd] < d[bd + 1]  # which of the two regions gets diagonal entries
-                if left_diag:
-                    for n in range(N):
-                        A[row + n][col + n] = - (h - d[bd]) * diff_R_1n(n, a[bd], bd, h, d, a)
-                        for m in range(M):
-                            A[row + n][col + N + m] = I_nm(n, m, bd, d, h) * diff_R_1n(m, a[bd], bd + 1, h, d, a)
-                            A[row + n][col + N + M + m] = I_nm(n, m, bd, d, h) * diff_R_2n(m, a[bd], bd + 1, h, d, a)
-                    row += N
-                else:
-                    for m in range(M):
-                        for n in range(N):
-                            A[row + m][col + n] = - I_nm(n, m, bd, d, h) * diff_R_1n(n, a[bd], bd, h, d, a)
-                        A[row + m][col + N + m] = (h - d[bd + 1]) * diff_R_1n(m, a[bd], bd + 1, h, d, a)
-                        A[row + m][col + N + M + m] = (h - d[bd + 1]) * diff_R_2n(m, a[bd], bd + 1, h, d, a)
-                    row += M
-                col += N
-            else:  # i-i boundary
-                left_diag = d[bd] < d[bd + 1]  # which of the two regions gets diagonal entries
-                if left_diag:
-                    for n in range(N):
-                        A[row + n][col + n] = - (h - d[bd]) * diff_R_1n(n, a[bd], bd, h, d, a)
-                        A[row + n][col + N + n] = - (h - d[bd]) * diff_R_2n(n, a[bd], bd, h, d, a)
-                        for m in range(M):
-                            A[row + n][col + 2*N + m] = I_nm(n, m, bd, d, h) * diff_R_1n(m, a[bd], bd + 1, h, d, a)
-                            A[row + n][col + 2*N + M + m] = I_nm(n, m, bd, d, h) * diff_R_2n(m, a[bd], bd + 1, h, d, a)
-                    row += N
-                else:
-                    for m in range(M):
-                        for n in range(N):
-                            A[row + m][col + n] = - I_nm(n, m, bd, d, h) * diff_R_1n(n, a[bd], bd, h, d, a)
-                            A[row + m][col + N + n] = - I_nm(n, m, bd, d, h) * diff_R_2n(n, a[bd], bd, h, d, a)
-                        A[row + m][col + 2*N + m] = (h - d[bd + 1]) * diff_R_1n(m, a[bd], bd + 1, h, d, a)
-                        A[row + m][col + 2*N + M + m] = (h - d[bd + 1]) * diff_R_2n(m, a[bd], bd + 1, h, d, a)
-                    row += M
-                col += 2 * N
-
-        return A
-
-
-    def assemble_b(self, problem: MEEMProblem, m0) -> np.ndarray:
-        """
-        Assemble the right-hand side vector b for a given problem.
-
-        :param problem: MEEMProblem instance.
-        :return: Assembled vector b.
-        """
-        inner_domain = problem.domain_list[0]
-        outer_domain = problem.domain_list[1]
-        exterior_domain = problem.domain_list[2]
-
-        N = inner_domain.number_harmonics
-        M = outer_domain.number_harmonics
-        K = exterior_domain.number_harmonics
-
-        size = N + 2 * M + K
-        b = np.zeros(size, dtype=complex)
-
-        h, d1, d2 = inner_domain.h, inner_domain.di, outer_domain.di
-        a1, a2 = inner_domain.a, outer_domain.a
-        
-        # Extract the integral result from the quad output
-        rhs_12 = np.array([integrate.quad(lambda z: phi_p_i1_i2_a1(z, h, a1, d1, d2) * Z_n_i1(n, z, h, d1), -h, -d1)[0] for n in range(N)])
-        rhs_2E = np.array([-integrate.quad(lambda z: phi_p_a2(z, a2, h, d2) * Z_n_i2(m, z, h, d2), -h, -d2)[0] for m in range(M)])
-        rhs_velocity_12 = np.array([integrate.quad(lambda z: diff_phi_i1(a1, d1, h) * Z_n_i2(m, z, h, d2), -h, -d1)[0] - integrate.quad(lambda z: diff_phi_i2(a1, d2, h) * Z_n_i2(m, z, h, d2), -h, -d2)[0] for m in range(M)])
-        rhs_velocity_2E = np.array([integrate.quad(lambda z: diff_phi_i2(a2, d2, h) * Z_n_e(k, z, m0, h), -h, -d2)[0] for k in range(K)])
-
-
-        b = np.concatenate((rhs_12, rhs_2E, rhs_velocity_12, rhs_velocity_2E))
-
-
-        return b
-    
-    def assemble_b_multi(self, problem: MEEMProblem, m0) -> np.ndarray:
-        """
-        Assemble the right-hand side vector b for a given problem (multi-region).
-
-        :param problem: MEEMProblem instance.
-        :return: Assembled vector b.
-        """
-        domain_list = problem.domain_list
-        domain_keys = list(domain_list.keys())
-        boundary_count = len(domain_keys) - 1
-
-        # Collect number of harmonics for each domain
-        NMK = [domain_list[idx].number_harmonics for idx in domain_keys]
-        size = NMK[0] + NMK[-1] + 2 * sum(NMK[1:-1])
-
-        b = np.zeros(size, dtype=complex)
-
-        # Extract parameters
-        h = domain_list[0].h
-        d = [domain_list[idx].di for idx in domain_keys]
-        a = [domain_list[idx].a for idx in domain_keys]
-        a = [val for val in a if val is not None]
-
-        heaving = [domain_list[idx].heaving for idx in domain_keys]
-
-        index = 0
-
-        # Potential matching
-        for boundary in range(boundary_count):
-            if boundary == (boundary_count - 1):  # i-e boundary
-                for n in range(NMK[boundary]):
-                    b[index] = b_potential_end_entry(n, boundary, heaving, h, d, a)
-                    index += 1
-            else:  # i-i boundary
-                i = boundary
-                if d[i] > d[i + 1]:
-                    N = NMK[i]
-                else:
-                    N = NMK[i+1]
-                for n in range(N):
-                    b[index] = b_potential_entry(n, boundary, d, heaving, h, a)
-                    index += 1
-
-        # Velocity matching
-        for boundary in range(boundary_count):
-            if boundary == (boundary_count - 1):  # i-e boundary
-                for k in range(NMK[-1]):
-                    assert index < size, f"Index {index} out of bounds for size {size}"  # Explicit check
-                    b[index] = b_velocity_end_entry(k, boundary, heaving, a, h, d, m0, NMK)
-                    index += 1
-            else:  # i-i boundary
-                if d[boundary] < d[boundary + 1]:
-                    N = NMK[boundary + 1]
-                else:
-                    N = NMK[boundary]
-                for n in range(N):
-                    assert index < size, f"Index {index} out of bounds for size {size}"  # Explicit check
-                    b[index] = b_velocity_entry(n, boundary, heaving, a, h, d)
-                    index += 1
-
-        return b
-        
-    def solve_linear_system(self, problem: MEEMProblem, m0) -> np.ndarray:
-        """
-        Solve the linear system A x = b for the given problem.
-
-        :param problem: MEEMProblem instance.
-        :return: Solution vector X.
-        """
-        from scipy import linalg
-
-        A = self.assemble_A(problem, m0)
-        b = self.assemble_b(problem, m0)
-        X = linalg.solve(A, b)
-        return X
-    
-    def solve_linear_system_multi(self, problem: MEEMProblem, m0) -> np.ndarray:
-        """
-        Solve the linear system A x = b for the given problem.
-
-        :param problem: MEEMProblem instance.
-        :return: Solution vector X.
-        """
-        from scipy import linalg
-
-        
-        A = self.assemble_A_multi(problem, m0)
-        b = self.assemble_b_multi(problem, m0)
-        X = linalg.solve(A, b)
-        return X
-
-    def compute_hydrodynamic_coefficients(self, problem: MEEMProblem, X: np.ndarray) -> Dict[str, any]:
-        """
-        Compute the hydrodynamic coefficients for a given problem and solution X.
-
-        :param problem: MEEMProblem instance.
-        :param X: Solution vector X from solving A x = b.
-        :return: Dictionary containing hydrodynamic coefficients and related values.
-        """
-        from multi_equations import int_R_1n, int_R_2n, z_n_d, int_phi_p_i_no_coef
-        from multi_constants import rho, omega
-        from math import pi
-
-        domain_list = problem.domain_list
-        domain_keys = list(domain_list.keys())
-        boundary_count = len(domain_keys) - 1
-
-        # Collect number of harmonics for each domain
-        NMK = [domain_list[idx].number_harmonics for idx in domain_keys]
-        size = NMK[0] + NMK[-1] + 2 * sum(NMK[1:-1])
-
-        # Extract parameters
-        h = domain_list[0].h
-        d = [domain_list[idx].di for idx in domain_keys]
-        a = [domain_list[idx].a for idx in domain_keys]
-        a = [val for val in a if val is not None]
-        heaving = [domain_list[idx].heaving for idx in domain_keys]
-
-        ###########################################################################
-        ###SEA Calculation: c-Matrix### 
-        # NOTICE: hydro coeff values are too high!!!!!!!!!!!
-        c = np.zeros((2*len(NMK)-2, max(NMK)), dtype=complex)
-        X_matrix = np.zeros((2*len(NMK)-2, max(NMK)), dtype=complex)
-        heaving_matrix = np.zeros((2*len(NMK)-2, len(NMK)-1), dtype=complex)
-        col = 0
-        for n in range(NMK[0]):
-            c[0, n] = int_R_1n(0, n, a, h, d) * z_n_d(n)
-            X_matrix[0, n] = X[n]
-        col += NMK[0]
-        for i in range(1, boundary_count):
-            M = NMK[i]
-            for m in range(M):
-                c[i, m] = int_R_1n(i, m, a, h, d) * z_n_d(m)
-                c[i+boundary_count-1, m] = int_R_2n(i, m, a, h, d) * z_n_d(m)
-                X_matrix[i, m] = X[col + m]  # for first eigen-coeff in region M
-            col += M
-        for i in range(1, boundary_count):
-            M = NMK[i]
-            for m in range(M):
-                X_matrix[i+boundary_count-1, m] = X[col + m]  # for second eigen-coeff in region M
-            col += M
-        for i in range(boundary_count):
-            for j in range(boundary_count):
-                heaving_matrix[i, j] = heaving[j] * (h-d[i]) / (h-d[j])
-                if i != 0:
-                    heaving_matrix[i+boundary_count-1, j] = heaving[j] * (h-d[i]) / (h-d[j])
-        cX_identity = np.diag(np.sum(c * X_matrix, axis=1))
-        hydro_h_terms = np.dot(cX_identity, heaving_matrix)
-        hydro_p_terms = np.zeros((boundary_count, boundary_count), dtype=complex)
-        for i in range(boundary_count):
-            for j in range(boundary_count):
-                #hydro_p_terms[i, j] = heaving[j] * (h-d[i]) / (h-d[j]) * int_phi_p_i_no_coef(i)
-                if (h-d[j]) != 0:
-                    hydro_p_terms[i, j] = heaving[j] * (h-d[i]) / (h-d[j]) * int_phi_p_i_no_coef(i, h, d, a)
-                else:
-                    hydro_p_terms[i,j] = 0 #handle divide by zero error.
-        indices_h = [(0, 0), (1, 1), (2, 2), (3, 3), (4, 1), (5, 2), (6, 3)]
-        indices_p = [(0, 0), (1, 1), (2, 2), (3, 3)]
-        #hydro_coeff_list = 2 * pi * (sum(hydro_h_terms[i, j] for i, j in indices_h) + sum(hydro_p_terms[i, j] for i, j in indices_p))
-        # Ensure indices are within bounds
-        valid_indices_h = [(i, j) for i, j in indices_h if i < hydro_h_terms.shape[0] and j < hydro_h_terms.shape[1]]
-        valid_indices_p = [(i, j) for i, j in indices_p if i < hydro_p_terms.shape[0] and j < hydro_p_terms.shape[1]]
-
-        # Compute hydro_coeff_list using valid indices
-        hydro_coeff_list = 2 * pi * (
-            sum(hydro_h_terms[i, j] for i, j in valid_indices_h) +
-            sum(hydro_p_terms[i, j] for i, j in valid_indices_p)
-        )
-        ###########################################################################
-        # Convert the complex number to a dictionary
-        hydro_coeffs = {
-            "real": hydro_coeff_list.real,
-            "imag": hydro_coeff_list.imag
-        }
-        return hydro_coeffs
-
-
-    def calculate_potentials(self, problem: MEEMProblem, solution_vector: np.ndarray) -> Dict[str, dict]:
-        """
-        Calculate the potentials for the domains in the problem.
-        :param problem: MEEMProblem instance containing domain definitions.
-        :param solution_vector: Solution vector obtained from solving Ax = b.
-        :return: A dictionary with domain names as keys and their corresponding potentials and coordinates as values.
-        """
-        potentials = {}
-        domain_list = problem.domain_list
-        start_idx = 0
-        geometry_instance = problem.geometry
-        #for domain_name, domain in domain_list.items():
-        for domain_index, domain in domain_list.items():
-            domain_name = f"domain_{domain_index}"
-            # Get the number of harmonics for this domain
-            num_harmonics = domain.number_harmonics
-            # Extract the corresponding part of the solution vector
-            domain_potential = solution_vector[start_idx:start_idx + num_harmonics]
-            # Package the potential with domain-specific coordinates
-            potentials[domain_name] = {
-                'potentials': domain_potential,
-                #error here
-                #AttributeError: 'Domain' object has no attribute 'r_coordinates'
-                #'r': domain.r_coordinates,
-                #'z': domain.z_coordinates
-                'r': geometry_instance.r_coordinates,
-                'z': geometry_instance.z_coordinates
-            }
-            # Update the starting index for the next domain
-            start_idx += num_harmonics
-        return potentials
-    def visualize_potential(self, potentials: Dict[str, np.ndarray], domain_names: List[str] = None):
-        """
-        Visualize the potentials for the given domains.
-        :param potentials: Dictionary containing domain names and their corresponding potentials.
-        :param domain_names: List of domain names to visualize. If None, visualize all.
-        """
-        domain_names = domain_names or potentials.keys()
-        plt.figure(figsize=(10, 6))
-        for domain_name in domain_names:
-            potential = np.abs(potentials[domain_name])  # Magnitude of the potential
-            plt.plot(potential, label=f"{domain_name} Potential")
-        plt.title("Potential Magnitudes Across Domains")
-        plt.xlabel("Harmonic Index")
-        plt.ylabel("Magnitude")
-        plt.legend()
-        plt.grid(True)
-        plt.show()
-    
-    def run_and_store_results(self, problem_index: int, m0) -> Results:
-        """
-        Perform the full MEEM computation and store results in the Results class.
-        :param problem_index: Index of the MEEMProblem instance to process.
-        :return: Results object containing the computed data.
-        """
-        problem = self.problem_list[problem_index]
-
-        # Assemble the system matrix A and right-hand side vector b
-        A = self.assemble_A_multi(problem, m0)
-        b = self.assemble_b_multi(problem, m0)
-
-        # Solve the linear system
-        X = np.linalg.solve(A, b)
-
-        # Compute hydrodynamic coefficients
-        hydro_coeffs = self.compute_hydrodynamic_coefficients(problem, X)
-
-        # Create a Results object
-        geometry = problem.geometry #MEEMProblem contains a Geometry instance
-        results = Results(geometry, problem.frequencies, problem.modes)
-
-        # Let's say you have some dummy eigenfunction data:
-        dummy_radial_data = np.zeros((len(problem.frequencies), len(problem.modes), 2))  # Adjust shape as needed
-        dummy_vertical_data = np.zeros((len(problem.frequencies), len(problem.modes), 1))  # Adjust shape as needed
-        results.store_results(0, dummy_radial_data, dummy_vertical_data)
-
-        # Store eigenfunction results
-
-        #store the results
-        potentials = self.calculate_potentials(problem, X)
-        results.store_potentials(potentials)
-
-        #store the hydrodynamic coefficients.
-
-        results.dataset['hydrodynamic_coefficients_real'] = hydro_coeffs['real']
-        results.dataset['hydrodynamic_coefficients_imag'] = hydro_coeffs['imag']
-
-
-        
-        return results
\ No newline at end of file
diff --git a/package/src/meem_problem.py b/package/src/meem_problem.py
deleted file mode 100644
index 8f328eb..0000000
--- a/package/src/meem_problem.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# meem_problem.py
-
-from typing import Dict
-from geometry import Geometry
-import numpy as np
-
-class MEEMProblem:
-    """
-    Represents a mathematical problem to be solved using the Multiple Expansion Eigenfunction Method (MEEM).
-    """
-
-    def __init__(self, geometry: Geometry):
-        """
-        Initialize the MEEMProblem object.
-
-        :param geometry: Geometry object containing domain information.
-        """
-        self.domain_list = geometry.domain_list
-        self.geometry = geometry
-        self.frequencies = np.array([])  # Initialize with empty arrays
-        self.modes = np.array([])
-
-    def set_frequencies_modes(self, frequencies: np.ndarray, modes: np.ndarray):
-        """
-        Set the frequencies and modes for the problem.
-        """
-        self.frequencies = frequencies
-        self.modes = modes
-
-
-    # Add any additional methods required for multi-region computations
diff --git a/package/src/multi_constants.py b/package/src/multi_constants.py
deleted file mode 100644
index 92ac2ed..0000000
--- a/package/src/multi_constants.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# # multi_constants.py
-# Constants
-h = 1.001
-d = [0.5, 0.25, 0.25]
-a = [0.5, 1, 1]
-heaving = [1, 1, 1] 
-# 0/false if not heaving, 1/true if yes heaving
-m0 = 1
-g = 9.81
-rho = 1023
-n = 3
-z = 6
-omega = 2.734109632312753 #-> calculate omega from m0, g
\ No newline at end of file
diff --git a/package/src/multi_equations.py b/package/src/multi_equations.py
deleted file mode 100644
index 5e4a44d..0000000
--- a/package/src/multi_equations.py
+++ /dev/null
@@ -1,340 +0,0 @@
-import numpy as np
-from scipy.special import hankel1 as besselh
-from scipy.special import iv as besseli
-from scipy.special import kv as besselk
-import scipy.integrate as integrate
-import scipy.linalg as linalg
-import matplotlib.pyplot as plt
-from numpy import sqrt, cosh, cos, sinh, sin, pi, exp
-from scipy.optimize import newton, minimize_scalar, root_scalar
-import scipy as sp
-
-def omega(m0,h,g):
-    sqrt(m0 * np.tanh(m0 * h) * g)
-
-def scale(a): 
-    result = np.mean([[0]+a[0:-1], a], axis = 0)
-    return result
-
-def lambda_ni(n, i, h, d): # factor used often in calculations
-    return n * pi / (h - d[i])
-
-#############################################
-# some common computations
-
-# creating a m_k function, used often in calculations
-def m_k_entry(k, m0, h):
-    # m_k_mat = np.zeros((len(m0_vec), 1))
-    if k == 0: return m0
-
-    m_k_h_err = (
-        lambda m_k_h: (m_k_h * np.tan(m_k_h) + m0 * h * np.tanh(m0 * h))
-    )
-    k_idx = k
-
-    # # original version of bounds in python
-    m_k_h_lower = pi * (k_idx - 1/2) + np.finfo(float).eps
-    m_k_h_upper = pi * k_idx - np.finfo(float).eps
-    # x_0 =  (m_k_upper - m_k_lower) / 2
-    
-    # becca's version of bounds from MDOcean Matlab code
-    m_k_h_lower = pi * (k_idx - 1/2) + (pi/180)* np.finfo(float).eps * (2**(np.floor(np.log(180*(k_idx- 1/2)) / np.log(2))) + 1)
-    m_k_h_upper = pi * k_idx
-
-    m_k_initial_guess = pi * (k_idx - 1/2) + np.finfo(float).eps
-    result = root_scalar(m_k_h_err, x0=m_k_initial_guess, method="newton", bracket=[m_k_h_lower, m_k_h_upper])
-    # result = minimize_scalar(
-        # m_k_h_err, bounds=(m_k_h_lower, m_k_h_upper), method="bounded"
-    # )
-
-    m_k_val = result.root / h
-
-    shouldnt_be_int = np.round(m0 * m_k_val / np.pi - 0.5, 4)
-    # not_repeated = np.unique(m_k_val) == m_k_val
-    assert np.all(shouldnt_be_int != np.floor(shouldnt_be_int))
-
-        # m_k_mat[freq_idx, :] = m_k_vec
-    return m_k_val
-
-# create an array of m_k values for each k to avoid recomputation
-def m_k(NMK, m0, h):
-    vectorized_m_k_entry = np.vectorize(m_k_entry, otypes=[float])
-    return vectorized_m_k_entry(list(range(NMK[-1])), m0, h)
-
-def m_k_newton(h, m0):
-    res = newton(lambda k: k * np.tanh(k * h) - m0**2 / 9.8, x0=1.0, tol=10 ** (-10))
-    return res
-
-
-
-#############################################
-# vertical eigenvector coupling computation
-
-def I_nm(n, m, i, d, h): # coupling integral for two i-type regions
-    dj = max(d[i], d[i+1]) # integration bounds at -h and -d
-    if n == 0 and m == 0:
-        return h - dj
-    lambda1 = lambda_ni(n, i, h, d)
-    lambda2 = lambda_ni(m, i + 1, h, d)
-    if n == 0 and m >= 1:
-        if dj == d[i+1]:
-            return 0
-        else:
-            return sqrt(2) * sin(lambda2 * (h - dj)) / lambda2
-    if n >= 1 and m == 0:
-        if dj == d[i]:
-            return 0
-        else:
-            return sqrt(2) * sin(lambda1 * (h - dj)) / lambda1
-    else:
-        frac1 = sin((lambda1 + lambda2)*(h-dj))/(lambda1 + lambda2)
-        if lambda1 == lambda2:
-            frac2 = (h - dj)
-        else:
-            frac2 = sin((lambda1 - lambda2)*(h-dj))/(lambda1 - lambda2)
-        return frac1 + frac2
-
-def I_mk(m, k, i, d, m0, h, NMK): # coupling integral for i and e-type regions
-    local_m_k = m_k(NMK, m0, h)
-    dj = d[i]
-    if m == 0 and k == 0:
-        if m0 * h < 14:
-            return (1/sqrt(N_k(0, m0, h, NMK))) * sinh(m0 * (h - dj)) / m0
-        else: # high m0h approximation
-            return sqrt(2 * h / m0) * (exp(- m0 * dj) - exp(m0 * dj - 2 * m0 * h))
-    if m == 0 and k >= 1:
-        return (1/sqrt(N_k(k, m0, h, NMK))) * sin(local_m_k[k] * (h - dj)) / local_m_k[k]
-    if m >= 1 and k == 0:
-        if m0 * h < 14:
-            num = (-1)**m * sqrt(2) * (1/sqrt(N_k(0, m0, h, NMK))) * m0 * sinh(m0 * (h - dj))
-        else: # high m0h approximation
-            num = (-1)**m * 2 * sqrt(h * m0 ** 3) *(exp(- m0 * dj) - exp(m0 * dj - 2 * m0 * h))
-        denom = (m0**2 + lambda_ni(m, i, h, d) **2)
-        return num/denom
-    else:
-        lambda1 = lambda_ni(m, i, h, d)
-        if abs(local_m_k[k]) == lambda1:
-            return (h - dj)/2
-        else:
-            frac1 = sin((local_m_k[k] + lambda1)*(h-dj))/(local_m_k[k] + lambda1)
-            frac2 = sin((local_m_k[k] - lambda1)*(h-dj))/(local_m_k[k] - lambda1)
-            return sqrt(2)/2 * (1/sqrt(N_k(k, m0, h, NMK))) * (frac1 + frac2)
-
-#############################################
-# b-vector computation
-
-def b_potential_entry(n, i, d, heaving, h, a): # for two i-type regions
-    #(integrate over shorter fluid, use shorter fluid eigenfunction)
-    
-    j = i + (d[i] < d[i+1]) # index of shorter fluid
-    constant = (heaving[i+1] / (h - d[i+1]) - heaving[i] / (h - d[i]))
-    if n == 0:
-        return constant * 1/2 * ((h - d[j])**3/3 - (h-d[j]) * a[i]**2/2)
-    else:
-        return sqrt(2) * (h - d[j]) * constant * ((-1) ** n)/(lambda_ni(n, j, h, d) ** 2)
-
-def b_potential_end_entry(n, i, heaving, h, d, a): # between i and e-type regions
-    constant = - heaving[i] / (h - d[i])
-    if n == 0:
-        return constant * 1/2 * ((h - d[i])**3/3 - (h-d[i]) * a[i]**2/2)
-    else:
-        return sqrt(2) * (h - d[i]) * constant * ((-1) ** n)/(lambda_ni(n, i, h, d) ** 2)
-
-def b_velocity_entry(n, i, heaving, a, h, d): # for two i-type regions
-    if n == 0:
-        return (heaving[i+1] - heaving[i]) * (a[i]/2)
-    if d[i] > d[i + 1]: #using i+1's vertical eigenvectors
-        if heaving[i]:
-            num = - sqrt(2) * a[i] * sin(lambda_ni(n, i+1, h, d) * (h-d[i]))
-            denom = (2 * (h - d[i]) * lambda_ni(n, i+1, h, d))
-            return num/denom
-        else: return 0
-    else: #using i's vertical eigenvectors
-        if heaving[i+1]:
-            num = sqrt(2) * a[i] * sin(lambda_ni(n, i, h, d) * (h-d[i+1]))
-            denom = (2 * (h - d[i+1]) * lambda_ni(n, i, h, d))
-            return num/denom
-        else: return 0
-
-def b_velocity_end_entry(k, i, heaving, a, h, d, m0, NMK): # between i and e-type regions
-    local_m_k = m_k(NMK, m0, h)
-    constant = - heaving[i] * a[i]/(2 * (h - d[i]))
-    if k == 0:
-        if m0 * h < 14:
-            return constant * (1/sqrt(N_k(0, m0, h, NMK))) * sinh(m0 * (h - d[i])) / m0
-        else: # high m0h approximation
-            return constant * sqrt(2 * h / m0) * (exp(- m0 * d[i]) - exp(m0 * d[i] - 2 * m0 * h))
-    else:
-        return constant * (1/sqrt(N_k(k, m0, h, NMK))) * sin(local_m_k[k] * (h - d[i])) / local_m_k[k]
-
-
-#############################################
-# Phi particular and partial derivatives
-
-def phi_p_i(d, r, z, h): 
-    return (1 / (2* (h - d))) * ((z + h) ** 2 - (r**2) / 2)
-
-def diff_r_phi_p_i(d, r, h): 
-    return (- r / (2* (h - d)))
-
-def diff_z_phi_p_i(d, z, h): 
-    return ((z+h) / (h - d))
-
-#############################################
-# The "Bessel I" radial eigenfunction
-def R_1n(n, r, i, h, d, a):
-
-    local_scale = scale(a)
-    if n == 0:
-        return 0.5
-    elif n >= 1:
-        return besseli(0, lambda_ni(n, i, h, d) * r) / besseli(0, lambda_ni(n, i, h, d) * local_scale[i])
-    else: 
-        raise ValueError("Invalid value for n")
-
-# Differentiate wrt r
-def diff_R_1n(n, r, i, h, d, a):
-    local_scale = scale(a)
-    if n == 0:
-        return 0
-    else:
-        top = lambda_ni(n, i, h, d) * besseli(1, lambda_ni(n, i, h, d) * r)
-        bottom = besseli(0, lambda_ni(n, i, h, d) * local_scale[i])
-        return top / bottom
-
-#############################################
-# The "Bessel K" radial eigenfunction
-def R_2n(n, r, i, a, h, d): # this shouldn't be called for i=0, innermost.
-    local_scale = scale(a)
-    if i == 0:
-        raise ValueError("i cannot be 0")  # this shouldn't be called for i=0, innermost region.
-    elif n == 0:
-        return 0.5 * np.log(r / a[i])
-    else:
-        return besselk(0, lambda_ni(n, i, h, d) * r) / besselk(0, lambda_ni(n, i, h, d) * local_scale[i])
-
-# Differentiate wrt r
-def diff_R_2n(n, r, i, h, d, a):
-    local_scale = scale(a)
-    if n == 0:
-        return 1 / (2 * r)
-    else:
-        top = - lambda_ni(n, i, h, d) * besselk(1, lambda_ni(n, i, h, d) * r)
-        bottom = besselk(0, lambda_ni(n, i, h, d) * local_scale[i])
-        return top / bottom
-
-
-#############################################
-# i-region vertical eigenfunctions
-def Z_n_i(n, z, i, h, d):
-    if n == 0:
-        return 1
-    else:
-        return np.sqrt(2) * np.cos(lambda_ni(n, i, h, d) * (z + h))
-
-def diff_Z_n_i(n, z, i, h, d):
-    if n == 0:
-        return 0
-    else:
-        lambda0 = lambda_ni(n, i, h, d)
-        return - lambda0 * np.sqrt(2) * np.sin(lambda0 * (z + h))
-
-#############################################
-# Region e radial eigenfunction
-def Lambda_k(k, r, m0, a, NMK, h):
-    local_scale = scale(a)
-    local_m_k = m_k(NMK, m0, h)
-    if k == 0:
-        return besselh(0, m0 * r) / besselh(0, m0 * local_scale[-1])
-    else:
-        return besselk(0, local_m_k[k] * r) / besselk(0, local_m_k[k] * local_scale[-1])
-
-# Differentiate wrt r
-def diff_Lambda_k(k, r, m0, NMK, h, a):
-    local_m_k = m_k(NMK, m0, h)
-    local_scale = scale(a)
-    if k == 0:
-        numerator = -(m0 * besselh(1, m0 * r))
-        denominator = besselh(0, m0 * local_scale[-1])
-    else:
-        numerator = -(local_m_k[k] * besselk(1, local_m_k[k] * r))
-        denominator = besselk(0, local_m_k[k] * local_scale[-1])
-    return numerator / denominator
-
-
-#############################################
-# Equation 2.34 in analytical methods book, also eq 16 in Seah and Yeung 2006:
-def N_k(k, m0, h, NMK):
-    local_m_k = m_k(NMK, m0, h)
-    if k == 0:
-        return 1 / 2 * (1 + sinh(2 * m0 * h) / (2 * m0 * h))
-    else:
-        return 1 / 2 * (1 + sin(2 * local_m_k[k] * h) / (2 * local_m_k[k] * h))
-
-
-#############################################
-# e-region vertical eigenfunctions
-def Z_k_e(k, z, m0, h, NMK):
-    local_m_k = m_k(NMK, m0, h)
-    if k == 0:
-        if m0 * h < 14:
-            return 1 / sqrt(N_k(k, m0, h, NMK)) * cosh(m0 * (z + h))
-        else: # high m0h approximation
-            return sqrt(2 * m0 * h) * (exp(m0 * z) + exp(-m0 * (z + 2*h)))
-    else:
-        return 1 / sqrt(N_k(k, m0, h, NMK)) * cos(local_m_k[k] * (z + h))
-
-def diff_Z_k_e(k, z, m0, h, NMK):
-    local_m_k = m_k(NMK, m0, h)
-    if k == 0:
-        if m0 * h < 14:
-            return 1 / sqrt(N_k(k, m0, h, NMK)) * m0 * sinh(m0 * (z + h))
-        else: # high m0h approximation
-            return m0 * sqrt(2 * h * m0) * (exp(m0 * z) - exp(-m0 * (z + 2*h)))
-    else:
-        return -1 / sqrt(N_k(k, m0, h, NMK)) * local_m_k[k] * sin(local_m_k[k] * (z + h))
-
-#############################################
-# To calculate hydrocoefficients
-
-#integrating R_1n * r
-def int_R_1n(i, n, a, h, d):
-    local_scale = scale(a)
-    if n == 0:
-        inner = (0 if i == 0 else a[i-1]) # central region has inner radius 0
-        return a[i]**2/4 - inner**2/4
-    else:
-        lambda0 = lambda_ni(n, i, h, d)
-        inner_term = (0 if i == 0 else a[i-1] * besseli(1, lambda0 * a[i-1])) # central region has inner radius 0
-        top = a[i] * besseli(1, lambda0 * a[i]) - inner_term
-        bottom = lambda0 * besseli(0, lambda0 * local_scale[i])
-        return top / bottom
-
-#integrating R_2n * r
-def int_R_2n(i, n, a, h, d):
-    local_scale = scale(a)
-    if i == 0:
-        raise ValueError("i cannot be 0")
-    lambda0 = lambda_ni(n, i, h, d)
-    if n == 0:
-        return (a[i-1]**2 * (2*np.log(a[i]/a[i-1]) + 1) - a[i]**2)/8
-    else:
-        top = a[i] * besselk(1, lambda0 * a[i]) - a[i-1] * besselk(1, lambda0 * a[i-1])
-        bottom = - lambda0 * besselk(0, lambda0 * local_scale[i])
-        return top / bottom
-
-#integrating phi_p_i * d_phi_p_i/dz * r *d_r at z=d[i]
-def int_phi_p_i_no_coef(i, h, d, a):
-    denom = 16 * (h - d[i])
-    if i == 0:
-        num = a[i]**2*(4*(h-d[i])**2-a[i]**2)
-    else:
-        num = (a[i]**2*(4*(h-d[i])**2-a[i]**2) - a[i-1]**2*(4*(h-d[i])**2-a[i-1]**2))
-    return num/denom
-
-# evaluate an interior region vertical eigenfunction at its top boundary
-def z_n_d(n):
-    if n ==0:
-        return 1
-    else:
-        return sqrt(2)*(-1)**n
\ No newline at end of file
diff --git a/package/src/openflash/__init__.py b/package/src/openflash/__init__.py
new file mode 100644
index 0000000..8fd4759
--- /dev/null
+++ b/package/src/openflash/__init__.py
@@ -0,0 +1,38 @@
+# __init__.py
+
+# --- Core Public Classes ---
+from .meem_engine import MEEMEngine
+from .meem_problem import MEEMProblem
+from .results import Results
+
+# --- Geometry and Body Components ---
+from .geometry import Geometry, ConcentricBodyGroup
+from .body import Body, SteppedBody
+from .basic_region_geometry import BasicRegionGeometry
+from .domain import Domain
+from .problem_cache import ProblemCache
+
+# --- Key Utility Functions and Constants ---
+from .multi_equations import *
+from .multi_constants import *
+
+# --- Define the Public API ---
+__all__ = [
+    # Core Classes
+    "MEEMEngine",
+    "MEEMProblem",
+    "Results",
+    "ProblemCache",
+
+    # Geometry Components
+    "Geometry",
+    "Body",
+    "SteppedBody",
+    "ConcentricBodyGroup",
+    "BasicRegionGeometry",
+    "Domain",
+
+    # Utilities
+    "omega",
+    "g",
+]
\ No newline at end of file
diff --git a/package/src/openflash/basic_region_geometry.py b/package/src/openflash/basic_region_geometry.py
new file mode 100644
index 0000000..4696969
--- /dev/null
+++ b/package/src/openflash/basic_region_geometry.py
@@ -0,0 +1,159 @@
+# basic_region_geometry.py
+
+from typing import List, Optional
+import numpy as np
+
+from .geometry import Geometry, ConcentricBodyGroup
+from .body import SteppedBody
+from .domain import Domain
+
+class BasicRegionGeometry(Geometry):
+    """
+    A geometry where body radii are increasing from the center.
+
+    This configuration results in a simple, non-overlapping series of circular
+    fluid domains, where the mapping from bodies to domains is trivial.
+
+    Args:
+        body_arrangement (ConcentricBodyGroup): A group of concentric bodies.
+        h (float): The total water depth.
+        NMK (List[int]): List of the number of harmonics for each resulting domain.
+    """
+    def __init__(self, body_arrangement: ConcentricBodyGroup, h: float, NMK: List[int]):
+        super().__init__(body_arrangement, h)
+        self.NMK = NMK
+
+        # --- Assertions ---
+        # Verify that radii are strictly and increasing.
+        all_radii = self.body_arrangement.a
+        if not np.all(np.diff(all_radii) > 0):
+            raise ValueError("Radii 'a' must be strictly increasing for BasicRegionGeometry. Use AnyRegionGeometry for other cases.")
+        # Verify NMK has the correct length (one for each body segment + one for the exterior).
+        if len(NMK) != len(all_radii) + 1:
+            raise ValueError("Length of NMK must be one greater than the total number of body radii.")
+        # FIX 1: Generate the domains and store the final dictionary ONCE during initialization.
+        # The old line was: self._domain_list: List[Domain] = self.make_fluid_domains()
+        domains_as_list = self.make_fluid_domains()
+        self._domain_dict: dict = {domain.index: domain for domain in domains_as_list}
+
+    @property
+    def domain_list(self) -> dict:
+        """
+        Returns a dictionary of domains keyed by index.
+        Required for MEEMEngine compatibility.
+        """
+        # FIX 2: Simply return the dictionary created during __init__. Do not recalculate.
+        return self._domain_dict
+    
+    @classmethod
+    def from_vectors(cls,
+                     a: np.ndarray,
+                     d: np.ndarray,
+                     h: float,
+                     NMK: List[int],
+                     slant_angle: Optional[np.ndarray] = None,
+                     body_map: Optional[List[int]] = None,
+                     heaving_map: Optional[List[bool]] = None):
+        """
+        Method to create a BasicRegionGeometry from vector inputs.
+        This is useful for users who prefer to define the geometry directly
+        without explicitly creating Body objects. This version includes robust
+        validation to prevent invalid body_map/heaving_map combinations and
+        enforces the global monotonicity invariant required by BasicRegionGeometry.
+        """
+        if slant_angle is None:
+            slant_angle = np.zeros_like(a)
+
+        if body_map is None:
+            body_map = [0] * len(a)
+
+        # Determine number of bodies from the mapping
+        num_bodies = max(body_map) + 1
+
+        # Validate heaving_map length
+        if heaving_map is None:
+            heaving_map = [False] * num_bodies
+        elif len(heaving_map) != num_bodies:
+            raise ValueError(
+                f"Length of heaving_map ({len(heaving_map)}) does not match inferred number of bodies ({num_bodies})"
+            )
+
+        # Build radii groups based on body_map and check contiguity + global monotonicity
+        reconstructed = []
+        last_value = -np.inf  # Start with negative infinity for strict monotonicity check
+
+        for body_idx in range(num_bodies):
+            # Extract indices for this body in original order
+            indices = [j for j, idx in enumerate(body_map) if idx == body_idx]
+            if not indices:
+                raise ValueError(f"Body index {body_idx} is declared in body_map but has no assigned radii.")
+
+            body_radii = a[indices]
+
+            # Local monotonicity inside body is not required, but when flattened back,
+            # the entire vector must be strictly increasing to satisfy BasicRegionGeometry rules.
+            for r in body_radii:
+                if r <= last_value:
+                    raise ValueError(
+                        "Radii 'a' must be strictly increasing after applying body_map. "
+                        "Detected non-monotonic or backtracking group arrangement."
+                    )
+                last_value = r
+
+            reconstructed.append(body_radii)
+
+        # Now safe to construct bodies
+        bodies = []
+        for body_idx in range(num_bodies):
+            indices = [j for j, idx in enumerate(body_map) if idx == body_idx]
+            bodies.append(SteppedBody(
+                a=a[indices],
+                d=d[indices],
+                slant_angle=slant_angle[indices],
+                heaving=heaving_map[body_idx]
+            ))
+
+        arrangement = ConcentricBodyGroup(bodies)
+        return cls(arrangement, h, NMK)
+
+    def make_fluid_domains(self) -> List[Domain]:
+        """
+        Creates a list of fluid domains for the simple concentric case.
+        """
+        domains: List[Domain] = []
+        last_outer_radius = 0.0
+
+        arr = self.body_arrangement
+        all_radii = arr.a
+        all_d = arr.d
+        all_heaving = arr.heaving
+        all_slants = arr.slant_angle
+
+        # Create interior domains under the bodies
+        for i, (outer_r, d, is_heaving, is_slanted) in enumerate(zip(all_radii, all_d, all_heaving, all_slants)):
+            domain = Domain(
+                index=i,
+                NMK=self.NMK[i],
+                a_inner=last_outer_radius,
+                a_outer=outer_r,
+                d_lower=d,
+                geometry_h=self.h, # pass the geometry's total depth
+                heaving=is_heaving,
+                slant=bool(is_slanted),
+                category="interior"
+            )
+            domains.append(domain)
+            last_outer_radius = outer_r
+
+        # Create final exterior domain
+        domains.append(Domain(
+            index=len(all_radii),
+            NMK=self.NMK[-1],
+            a_inner=last_outer_radius,
+            a_outer=np.inf,
+            d_lower=0.0, # Seabed
+            geometry_h=self.h,
+            category="exterior"
+        ))
+
+        return domains
\ No newline at end of file
diff --git a/package/src/openflash/body.py b/package/src/openflash/body.py
new file mode 100644
index 0000000..b00c541
--- /dev/null
+++ b/package/src/openflash/body.py
@@ -0,0 +1,58 @@
+# body.py
+
+from abc import ABC
+from typing import Tuple
+import numpy as np
+
+class Body(ABC):
+    """
+    Abstract base class for a physical body.
+    """
+    heaving: bool
+
+class SteppedBody(Body):
+    """
+    Represents a body defined by a series of concentric, vertical-walled steps.
+
+    This is the primary implementation for the initial JOSS scope.
+
+    Args:
+        a (np.ndarray): Array of outer radii for each step.
+        d (np.ndarray): Array of values (depth) for each step.
+        slant_angle (np.ndarray): Array of slant angles for each step surface.
+        heaving (bool, optional): Flag indicating if the entire body is heaving. Defaults to False.
+    """
+    def __init__(self, a: np.ndarray, d: np.ndarray, slant_angle: np.ndarray, heaving: bool = False):
+        assert len(a) == len(d) == len(slant_angle), "Input arrays a, d, and slant_angle must have the same length."
+        self.a = a
+        self.d = d
+        self.slant_angle = slant_angle
+        self.heaving = heaving
+
+class CoordinateBody(Body):
+    """
+    Represents a body defined by a series of (r, z) coordinates.
+
+    This class is a placeholder for future functionality and requires a
+    discretization method to be used in calculations.
+
+    Args:
+        r_coords (np.ndarray): Array of radial coordinates.
+        z_coords (np.ndarray): Array of vertical coordinates (depth).
+        heaving (bool, optional): Flag indicating if the body is heaving. Defaults to False.
+    """
+    def __init__(self, r_coords: np.ndarray, z_coords: np.ndarray, heaving: bool = False):
+        assert len(r_coords) == len(z_coords), "r_coords and z_coords must be the same length."
+        self.r_coords = r_coords
+        self.z_coords = z_coords
+        self.heaving = heaving
+
+    def discretize(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        (Future Implementation) Converts r, z coordinates into stepped a, d, slant_angle arrays.
+        """
+        # NOTE: This is a placeholder discretization. A more robust method is needed.
+        a = self.r_coords
+        d = self.z_coords
+        slant = np.gradient(d, a)  # Simple slope estimate
+        return a, d, slant
\ No newline at end of file
diff --git a/package/src/openflash/domain.py b/package/src/openflash/domain.py
new file mode 100644
index 0000000..e6ded04
--- /dev/null
+++ b/package/src/openflash/domain.py
@@ -0,0 +1,73 @@
+# domain.py
+
+from typing import Optional
+import numpy as np
+
+class Domain:
+    """
+    Represents a single, circular region of the fluid.
+
+    This class stores the geometric boundaries and physical properties for a
+    specific domain within the overall problem geometry. The upper boundary depth
+    is determined from the geometry and cannot be set independently.
+    """
+    def __init__(self,
+                 index: int,
+                 NMK: int,
+                 a_inner: float,
+                 a_outer: float,
+                 d_lower: float,
+                 geometry_h: float,
+                 heaving: Optional[bool] = None,
+                 slant: bool = False,
+                 category: str = "interior"):
+
+        self.index = index
+        self.number_harmonics = NMK
+        self.a_inner = a_inner
+        self.a_outer = a_outer
+        self.d_lower = d_lower # Depth of the lower boundary (e.g., body or seafloor)
+        self.d_upper = geometry_h # Depth of the upper boundary (e.g., free surface)
+        self.heaving = heaving
+        self.slant = slant
+        self.category = category # e.g., 'interior', 'exterior'
+
+        # --- Assertions ---
+        assert isinstance(NMK, int) and NMK > 0, "NMK must be a positive integer."
+        assert a_outer > a_inner >= 0, "Radii must be valid (a_outer > a_inner >= 0)."
+        assert self.d_upper >= d_lower >= 0, "Depths must be valid (d_upper >= d_lower >= 0)."
+
+    @property
+    def h(self):
+        """
+        Return the total water depth (free surface height),
+        which MEEMEngine expects as .h on domain objects.
+        """
+        return self.d_upper
+    
+    @property
+    def di(self):
+        """
+        Lower boundary depth of the domain (used internally in MEEMEngine as .di).
+        """
+        return self.d_lower
+    
+    @property
+    def a(self):
+        """
+        Alias for outer radius (used internally by MEEMEngine as .a).
+        """
+        return self.a_outer
+    
+    @staticmethod
+    def are_adjacent(d1: "Domain", d2: "Domain", atol: float = 1e-6) -> bool:
+        """
+        (Future Implementation) Determines if two domains are radially adjacent.
+        """
+        # Check if d1 is inside d2
+        if np.isfinite(d1.a_outer) and np.isclose(d1.a_outer, d2.a_inner, atol=atol):
+            return True
+        # Check if d2 is inside d1
+        if np.isfinite(d2.a_outer) and np.isclose(d2.a_outer, d1.a_inner, atol=atol):
+            return True
+        return False
\ No newline at end of file
diff --git a/package/src/openflash/geometry.py b/package/src/openflash/geometry.py
new file mode 100644
index 0000000..30963be
--- /dev/null
+++ b/package/src/openflash/geometry.py
@@ -0,0 +1,130 @@
+# geometry.py
+
+from abc import ABC, abstractmethod
+from typing import List, Sequence
+import numpy as np
+
+from .body import Body, SteppedBody, CoordinateBody
+from .domain import Domain
+
+class BodyArrangement(ABC):
+    """
+    Abstract base class for any arrangement.
+    """
+    def __init__(self, bodies: Sequence[Body]):
+        self.bodies = bodies
+        # Count the number of bodies marked as heaving (heaving=True)
+        heaving_count = sum(body.heaving for body in bodies)
+        
+        assert heaving_count <= 1, \
+            f"Only 0 or 1 body can be marked as heaving. Found {heaving_count} heaving bodies."
+
+    @property
+    @abstractmethod
+    def a(self) -> np.ndarray:
+        """Array of characteristic radii."""
+        pass
+
+    @property
+    @abstractmethod
+    def d(self) -> np.ndarray:
+        """Array of characteristic d."""
+        pass
+
+    @property
+    @abstractmethod
+    def slant_angle(self) -> np.ndarray:
+        """Array of slant angles."""
+        pass
+
+    @property
+    @abstractmethod
+    def heaving(self) -> np.ndarray:
+        """Array of heaving flags."""
+        pass
+
+
+class ConcentricBodyGroup(BodyArrangement):
+    """
+    A concrete arrangement of one or more concentric bodies.
+    For JOSS, this class assumes all bodies are SteppedBody objects.
+    """
+    def __init__(self, bodies: Sequence[Body]):
+        super().__init__(bodies)
+        # For now, we only handle SteppedBody
+        for body in self.bodies:
+            if not isinstance(body, SteppedBody):
+                raise TypeError("ConcentricBodyGroup currently only supports SteppedBody objects.")
+
+    def _get_concatenated_property(self, prop_name: str) -> np.ndarray:
+        """Helper to concatenate a property from all SteppedBody objects."""
+        return np.concatenate([getattr(body, prop_name) for body in self.bodies])
+
+    def _get_heaving_flags(self) -> np.ndarray:
+        """Helper to create a heaving flag array based on each body."""
+        flags = []
+        for body in self.bodies:
+            # --- THIS IS THE FIX ---
+            if isinstance(body, SteppedBody):
+                num_steps = len(body.a)
+                flags.extend([body.heaving] * num_steps)
+            # You could add 'elif isinstance(body, CoordinateBody):' here if needed
+            # --- END FIX ---
+        return np.array(flags, dtype=bool)
+
+    @property
+    def a(self) -> np.ndarray:
+        return self._get_concatenated_property('a')
+
+    @property
+    def d(self) -> np.ndarray:
+        return self._get_concatenated_property('d')
+
+    @property
+    def slant_angle(self) -> np.ndarray:
+        return self._get_concatenated_property('slant_angle')
+
+    @property
+    def heaving(self) -> np.ndarray:
+        return self._get_heaving_flags()
+
+
+class Geometry(ABC):
+    """
+    Abstract base class for a complete problem geometry.
+
+    A Geometry consists of a BodyArrangement and the total water depth, and
+    it is responsible for creating the corresponding fluid domains.
+    """
+    def __init__(self, body_arrangement: BodyArrangement, h: float):
+        self.body_arrangement = body_arrangement
+        self.h = h
+        self._fluid_domains: List[Domain] = []
+
+    @property
+    def fluid_domains(self) -> List[Domain]:
+        if not self._fluid_domains:
+            self._fluid_domains = self.make_fluid_domains()
+        return self._fluid_domains
+
+    # --- ADD THIS PROPERTY ---
+    @property
+    def domain_list(self) -> dict:
+        """
+        Returns a dictionary of domains keyed by index.
+        Required for MEEMProblem/Results compatibility.
+        """
+        # If the property isn't overridden, create the dict from the list.
+        if not self.fluid_domains:
+            return {}
+        # This check handles if a subclass (like BasicRegionGeometry)
+        # has already provided a dict.
+        if isinstance(self.fluid_domains, dict):
+             return self.fluid_domains
+        return {domain.index: domain for domain in self.fluid_domains}
+    # --- END ADDITION ---
+
+    @abstractmethod
+    def make_fluid_domains(self) -> List[Domain]:
+        """Creates the list of Domain objects from the BodyArrangement."""
+        pass
\ No newline at end of file
diff --git a/package/src/openflash/meem_engine.py b/package/src/openflash/meem_engine.py
new file mode 100644
index 0000000..80b365f
--- /dev/null
+++ b/package/src/openflash/meem_engine.py
@@ -0,0 +1,626 @@
+# package/src/openflash/meem_engine.py
+from __future__ import annotations
+from typing import List, Dict, Any, Optional
+import numpy as np
+import matplotlib.pyplot as plt
+from openflash.meem_problem import MEEMProblem
+from openflash.problem_cache import ProblemCache
+from openflash.multi_equations import *
+from openflash.results import Results
+from scipy import linalg
+from openflash.multi_constants import rho as default_rho, g
+from functools import partial
+from openflash.body import SteppedBody
+from openflash.geometry import ConcentricBodyGroup
+from openflash.basic_region_geometry import BasicRegionGeometry
+
+class MEEMEngine:
+    """
+    Manages multiple MEEMProblem instances and performs actions such as solving systems of equations,
+    assembling matrices, and visualizing results.
+    """
+
+    def __init__(self, problem_list: List[MEEMProblem]):
+        """
+        Initialize the MEEMEngine object.
+        :param problem_list: List of MEEMProblem instances.
+        """
+        self.problem_list = problem_list
+        self.cache_list = {} 
+
+        for problem in problem_list:
+            self.cache_list[problem] = self.build_problem_cache(problem)
+    
+    def _ensure_m_k_and_N_k_arrays(self, problem: 'MEEMProblem', m0):
+        cache = self.cache_list[problem]
+        if cache.m_k_arr is None or cache.cached_m0 != m0:
+            domain_list = problem.domain_list
+            domain_keys = list(domain_list.keys())
+            NMK = [domain_list[idx].number_harmonics for idx in domain_keys]
+            h = domain_list[0].h
+            m_k_arr = np.array([cache.m_k_entry_func(k, m0, h) for k in range(NMK[-1])])
+            N_k_arr = np.array([cache.N_k_func(k, m0, h, m_k_arr) for k in range(NMK[-1])])
+            cache._set_precomputed_m_k_N_k(m_k_arr, N_k_arr, m0)
+    
+    def assemble_A_multi(self, problem: 'MEEMProblem', m0) -> np.ndarray:
+        self._ensure_m_k_and_N_k_arrays(problem, m0)
+        cache = self.cache_list[problem]
+        A = cache._get_A_template()
+        I_mk_vals = cache._get_closure("I_mk_vals")(m0, cache.m_k_arr, cache.N_k_arr)
+        for row, col, calc_func in cache.m0_dependent_A_indices:
+            A[row, col] = calc_func(problem, m0, cache.m_k_arr, cache.N_k_arr, I_mk_vals)
+        return A
+                
+    def assemble_b_multi(self, problem: 'MEEMProblem', m0) -> np.ndarray:
+        self._ensure_m_k_and_N_k_arrays(problem, m0)
+        cache = self.cache_list[problem]
+        b = cache._get_b_template()
+        I_mk_vals = cache._get_closure("I_mk_vals")(m0, cache.m_k_arr, cache.N_k_arr)
+        for row, calc_func in cache.m0_dependent_b_indices:
+            b[row] = calc_func(problem, m0, cache.m_k_arr, cache.N_k_arr, I_mk_vals)
+        return b
+
+    def build_problem_cache(self, problem: 'MEEMProblem') -> ProblemCache:
+        """
+        Analyzes the problem and pre-computes m0-independent parts of A and b,
+        and identifies indices for m0-dependent parts, storing them in a cache.
+        """
+        cache = ProblemCache(problem)
+        domain_list = problem.domain_list
+        domain_keys = list(domain_list.keys())
+        
+        h = domain_list[0].h
+        d = [domain_list[idx].di for idx in domain_keys]
+        a = [domain_list[idx].a for idx in domain_keys]
+        NMK = [domain.number_harmonics for domain in domain_list.values()]
+        heaving = [domain_list[idx].heaving for idx in domain_keys]
+        
+        boundary_count = len(NMK) - 1
+        size = NMK[0] + NMK[-1] + 2 * sum(NMK[1:len(NMK) - 1])
+
+        A_template = np.zeros((size, size), dtype=complex)
+        b_template = np.zeros(size, dtype=complex)
+
+        # Pre-compute m0-INDEPENDENT values
+        I_nm_vals_precomputed = np.zeros((max(NMK), max(NMK), boundary_count - 1), dtype=complex)
+        for bd in range(boundary_count - 1):
+            for n in range(NMK[bd]):
+                for m in range(NMK[bd + 1]):
+                    I_nm_vals_precomputed[n, m, bd] = I_nm(n, m, bd, d, h)
+        cache._set_I_nm_vals(I_nm_vals_precomputed)
+
+        R_1n_func = np.vectorize(partial(R_1n, h=h, d=d, a=a))
+        R_2n_func = np.vectorize(partial(R_2n, a=a, h=h, d=d))
+        diff_R_1n_func = np.vectorize(partial(diff_R_1n, h=h, d=d, a=a), otypes=[complex])
+        diff_R_2n_func = np.vectorize(partial(diff_R_2n, h=h, d=d, a=a), otypes=[complex])
+
+        def _calculate_I_mk_vals(m0, m_k_arr, N_k_arr):
+            vals = np.zeros((NMK[boundary_count - 1], NMK[boundary_count]), dtype=complex)
+            for m in range(NMK[boundary_count - 1]):
+                for k in range(NMK[boundary_count]):
+                    vals[m, k] = I_mk(m, k, boundary_count - 1, d, m0, h, m_k_arr, N_k_arr)
+            return vals
+        
+        cache._set_closure("I_mk_vals", _calculate_I_mk_vals)
+        cache._set_m_k_and_N_k_funcs(m_k_entry, N_k_multi)
+
+        ## --- Potential Matching Blocks ---
+        col_offset = 0
+        row_offset = 0
+        for bd in range(boundary_count):
+            N = NMK[bd]
+            M = NMK[bd + 1]
+
+            if bd == (boundary_count - 1): 
+                row_height = N
+                left_block1 = p_diagonal_block(True, R_1n_func, bd, h, d, a, NMK)
+                
+                if bd > 0:
+                    left_block2 = p_diagonal_block(True, R_2n_func, bd, h, d, a, NMK)
+                    block = np.concatenate([left_block1, left_block2], axis=1)
+                else:
+                    block = left_block1
+                
+                A_template[row_offset : row_offset + row_height, col_offset : col_offset + block.shape[1]] = block
+                
+                p_dense_e_col_start = col_offset + block.shape[1]
+                for m_local in range(N):
+                    for k_local in range(M):
+                        g_row, g_col = row_offset + m_local, p_dense_e_col_start + k_local
+                        calc_func = lambda p, m0, mk, Nk, Imk, m=m_local, k=k_local: \
+                            p_dense_block_e_entry(m, k, bd, Imk, NMK, a, m0, h, mk, Nk)
+                        cache._add_m0_dependent_A_entry(g_row, g_col, calc_func)
+                col_offset += block.shape[1]
+
+            else: 
+                # Potential Match: Project onto SHORTER region (Standard MEEM)
+                # d is depth from surface. Larger d means deeper bottom, so SMALLER height.
+                # If d[bd] > d[bd+1], Region Left is shorter. Project on Left.
+                project_on_left = d[bd] > d[bd+1]
+                row_height = N if project_on_left else M
+                blocks = []
+                
+                if project_on_left:
+                    blocks.append(p_diagonal_block(True, R_1n_func, bd, h, d, a, NMK))
+                    if bd > 0: blocks.append(p_diagonal_block(True, R_2n_func, bd, h, d, a, NMK))
+                    blocks.append(p_dense_block(False, R_1n_func, bd, NMK, a, I_nm_vals_precomputed))
+                    blocks.append(p_dense_block(False, R_2n_func, bd, NMK, a, I_nm_vals_precomputed))
+                else: 
+                    blocks.append(p_dense_block(True, R_1n_func, bd, NMK, a, I_nm_vals_precomputed))
+                    if bd > 0: blocks.append(p_dense_block(True, R_2n_func, bd, NMK, a, I_nm_vals_precomputed))
+                    blocks.append(p_diagonal_block(False, R_1n_func, bd, h, d, a, NMK))
+                    blocks.append(p_diagonal_block(False, R_2n_func, bd, h, d, a, NMK))
+
+                full_block = np.concatenate(blocks, axis=1)
+                A_template[row_offset : row_offset + row_height, col_offset : col_offset + full_block.shape[1]] = full_block
+                col_offset += 2*N if bd > 0 else N
+            
+            row_offset += row_height
+
+        ## --- Velocity Matching Blocks ---
+        col_offset = 0
+        for bd in range(boundary_count):
+            N = NMK[bd]
+            M = NMK[bd + 1]
+
+            if bd == (boundary_count - 1): # Final i-e boundary
+                row_height = M
+                v_dense_e_col_start = col_offset
+                for m_local in range(M):
+                    for k_local in range(N):
+                        g_row, g_col = row_offset + m_local, v_dense_e_col_start + k_local
+                        calc_func = lambda p, m0, mk, Nk, Imk, m=m_local, k=k_local: \
+                            v_dense_block_e_entry(m, k, bd, Imk, a, h, d)
+                        cache._add_m0_dependent_A_entry(g_row, g_col, calc_func)
+                    if bd > 0:
+                        r2n_col_start = v_dense_e_col_start + N
+                        for k_local in range(N):
+                            g_row, g_col = row_offset + m_local, r2n_col_start + k_local
+                            calc_func = lambda p, m0, mk, Nk, Imk, m=m_local, k=k_local: \
+                                v_dense_block_e_entry_R2(m, k, bd, Imk, a, h, d)
+                            cache._add_m0_dependent_A_entry(g_row, g_col, calc_func)
+                
+                v_diag_e_col_start = col_offset + (2*N if bd > 0 else N)
+                for k_local in range(M):
+                    g_row, g_col = row_offset + k_local, v_diag_e_col_start + k_local
+                    calc_func = lambda p, m0, mk, Nk, Imk, k=k_local: \
+                        v_diagonal_block_e_entry(m, k, bd, m0, mk, a, h)
+                    cache._add_m0_dependent_A_entry(g_row, g_col, calc_func)
+                col_offset += (2*N if bd > 0 else N)
+
+            else: # Internal i-i boundaries
+                project_on_left = d[bd] <= d[bd+1]
+                row_height = N if project_on_left else M
+                blocks = []
+                
+                if project_on_left:
+                    blocks.append(v_diagonal_block(True, diff_R_1n_func, bd, h, d, NMK, a))
+                    if bd > 0: blocks.append(v_diagonal_block(True, diff_R_2n_func, bd, h, d, NMK, a))
+                    blocks.append(v_dense_block(False, diff_R_1n_func, bd, I_nm_vals_precomputed, NMK, a))
+                    blocks.append(v_dense_block(False, diff_R_2n_func, bd, I_nm_vals_precomputed, NMK, a))
+                else: 
+                    blocks.append(v_dense_block(True, diff_R_1n_func, bd, I_nm_vals_precomputed, NMK, a))
+                    if bd > 0: blocks.append(v_dense_block(True, diff_R_2n_func, bd, I_nm_vals_precomputed, NMK, a))
+                    blocks.append(v_diagonal_block(False, diff_R_1n_func, bd, h, d, NMK, a))
+                    blocks.append(v_diagonal_block(False, diff_R_2n_func, bd, h, d, NMK, a))
+
+                full_block = np.concatenate(blocks, axis=1)
+                A_template[row_offset : row_offset + row_height, col_offset : col_offset + full_block.shape[1]] = full_block
+                col_offset += 2*N if bd > 0 else N
+            
+            row_offset += row_height
+
+        # Assemble b_template 
+        index = 0
+        for bd in range(boundary_count):
+            if bd == (boundary_count - 1):
+                for n in range(NMK[-2]):
+                    b_template[index] = b_potential_end_entry(n, bd, heaving, h, d, a)
+                    index += 1
+            else:
+                num_entries = NMK[bd + (d[bd] <= d[bd + 1])]
+                for n in range(num_entries):
+                    b_template[index] = b_potential_entry(n, bd, d, heaving, h, a)
+                    index += 1
+
+        for bd in range(boundary_count):
+            if bd == (boundary_count - 1):
+                for n_local in range(NMK[-1]):
+                    calc_func = lambda p, m0, mk, Nk, Imk, n=n_local: \
+                        b_velocity_end_entry(n, bd, heaving, a, h, d, m0, NMK, mk, Nk)
+                    cache._add_m0_dependent_b_entry(index, calc_func)
+                    index += 1
+            else:
+                num_entries = NMK[bd + (d[bd] > d[bd + 1])]
+                for n in range(num_entries):
+                    b_template[index] = b_velocity_entry(n, bd, heaving, a, h, d)
+                    index += 1
+                    
+        cache._set_A_template(A_template)
+        cache._set_b_template(b_template)
+        
+        return cache
+    
+    def solve_linear_system_multi(self, problem: MEEMProblem, m0) -> np.ndarray:
+        cache = self.cache_list[problem]
+        self._ensure_m_k_and_N_k_arrays(problem, m0)
+        A = self.assemble_A_multi(problem, m0)
+        b = self.assemble_b_multi(problem, m0) 
+        X = linalg.solve(A, b)
+        return X
+    
+    def reformat_coeffs(self, x: np.ndarray, NMK, boundary_count) -> list[np.ndarray]:
+        cs = []
+        row = 0
+        cs.append(x[:NMK[0]])
+        row += NMK[0]
+        for i in range(1, boundary_count):
+            cs.append(x[row: row + NMK[i] * 2])
+            row += NMK[i] * 2
+        cs.append(x[row:])
+        return cs
+
+    def compute_hydrodynamic_coefficients(self, problem, X, m0, modes_to_calculate: Optional[np.ndarray] = None, rho: Optional[float] = None):
+        """
+        Computes the hydrodynamic coefficients (Added Mass and Damping) from the solution vector X.
+        
+        Args:
+            rho (float, optional): Density of fluid. Defaults to value from multi_constants.
+        """
+        if rho is None:
+            rho = default_rho
+
+        geometry = problem.geometry
+        domain_keys = list(geometry.domain_list.keys())
+        a = [geometry.domain_list[idx].a for idx in domain_keys]
+        d = [
+            domain.di[0] if isinstance(domain.di, list) else domain.di
+            for domain in geometry.domain_list.values()
+        ]
+        h = geometry.domain_list[0].h
+        NMK = [geometry.domain_list[idx].number_harmonics for idx in domain_keys]
+        boundary_count = len(NMK) - 1
+
+        size = NMK[0] + NMK[-1] + 2 * sum(NMK[1:len(NMK) - 1])
+
+        results_per_mode = []
+
+        if modes_to_calculate is None:
+            num_bodies = len(geometry.body_arrangement.bodies)
+            modes_to_calculate = np.arange(num_bodies)
+
+        body_to_regions = {}
+        current_region = 0
+        for b_i, body in enumerate(geometry.body_arrangement.bodies):
+            if isinstance(body, SteppedBody):
+                n_steps = len(body.a)
+                body_to_regions[b_i] = list(range(current_region, current_region + n_steps))
+                current_region += n_steps
+            else:
+                body_to_regions[b_i] = [current_region]
+                current_region += 1
+
+        for mode_index in modes_to_calculate:
+            heaving = [0] * len(domain_keys)
+            
+            if mode_index in body_to_regions:
+                for r_idx in body_to_regions[mode_index]:
+                    if r_idx < len(heaving):
+                        heaving[r_idx] = 1
+
+            c_vector = np.zeros((size - NMK[-1]), dtype=complex)
+            col = 0
+
+            for n in range(NMK[0]):
+                c_vector[n] = heaving[0] * int_R_1n(0, n, a, h, d) * z_n_d(n)
+            col += NMK[0]
+
+            for i in range(1, boundary_count):
+                M = NMK[i]
+                for m in range(M):
+                    c_vector[col + m] = heaving[i] * int_R_1n(i, m, a, h, d) * z_n_d(m)
+                    c_vector[col + M + m] = heaving[i] * int_R_2n(i, m, a, h, d) * z_n_d(m)
+                col += 2 * M
+
+            hydro_p_term_sum = np.zeros(boundary_count, dtype=complex)
+            for i in range(boundary_count):
+                hydro_p_term_sum[i] = heaving[i] * int_phi_p_i(i, h, d, a)
+
+            hydro_coef = 2 * pi * (np.dot(c_vector, X[:-NMK[-1]]) + sum(hydro_p_term_sum))
+
+            hydro_coef_real = hydro_coef.real * rho
+            if m0 == np.inf:
+                hydro_coef_imag = 0
+            else:
+                hydro_coef_imag = hydro_coef.imag * omega(m0, h, g) * rho
+            
+            results_per_mode.append({
+                "mode": mode_index,
+                "real": hydro_coef_real,      
+                "imag": hydro_coef_imag,      
+                "excitation_phase": excitation_phase(X, NMK, m0, a),
+                "excitation_force": excitation_force(hydro_coef_imag, m0, h)
+            })
+
+        return results_per_mode
+    
+    def calculate_potentials(self, problem, solution_vector: np.ndarray, m0, spatial_res, sharp, R_range: Optional[np.ndarray] = None, Z_range: Optional[np.ndarray] = None) -> Dict[str, Any]:
+        """
+        Calculate full spatial potentials phiH, phiP, and total phi on a meshgrid for visualization.
+
+        Parameters:
+        - problem: MEEMProblem instance containing domain and geometry info
+        - solution_vector: solution vector X from linear system solve
+        - spatial_res: resolution of spatial grid for R and Z (default=50)
+        - sharp: whether to refine meshgrid near boundaries (default=True)
+
+        Returns:
+        - Dictionary containing meshgrid arrays R,Z and potentials phiH, phiP, phi
+        """
+        # Ensure m_k_arr and N_k_arr are computed and retrieved from the cache
+        self._ensure_m_k_and_N_k_arrays(problem, m0)
+        cache = self.cache_list[problem]
+        m_k_arr = cache.m_k_arr
+        N_k_arr = cache.N_k_arr
+        
+        # Get geometry parameters directly from the body arrangement and domains
+        geometry = problem.geometry
+        body_arrangement = geometry.body_arrangement
+        domain_list = problem.domain_list
+        
+        # These are the correct physical parameters for meshgrid and particular solution
+        a = body_arrangement.a
+        d = body_arrangement.d
+        heaving = body_arrangement.heaving
+        
+        # These are needed for the homogeneous solution and coefficient reformatting
+        h = geometry.h
+        domain_keys = list(domain_list.keys())
+        boundary_count = len(domain_keys) - 1
+        NMK = [domain_list[idx].number_harmonics for idx in domain_keys]
+        
+        # --- The rest of the function remains the same ---
+        Cs = self.reformat_coeffs(solution_vector, NMK, boundary_count)
+
+        # 2. Create Meshgrid and Regions
+        # Now make_R_Z will receive the correct a and d lists
+        R, Z = make_R_Z(a, h, d, sharp, spatial_res)
+        regions = []
+        regions.append((R <= a[0]) & (Z < -d[0]))
+        for i in range(1, boundary_count):
+            regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))
+        regions.append(R > a[-1])
+
+        # Initialize potential arrays
+        phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+        phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+        phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+
+        # --- 3. Vectorized Calculation of Potentials ---
+
+        # Region 0 (Inner)
+        if np.any(regions[0]):
+            r_vals, z_vals = R[regions[0]], Z[regions[0]]
+            n_vals = np.arange(NMK[0])
+            R1n_vals = R_1n_vectorized(n_vals[:, None], r_vals[None, :], 0, h, d, a)
+            Zn_vals = Z_n_i_vectorized(n_vals[:, None], z_vals[None, :], 0, h, d)
+            phiH[regions[0]] = np.sum(Cs[0][:, None] * R1n_vals * Zn_vals, axis=0)
+
+        # Intermediate Regions
+        for i in range(1, boundary_count):
+            if np.any(regions[i]):
+                r_vals, z_vals = R[regions[i]], Z[regions[i]]
+                m_vals = np.arange(NMK[i])
+                R1n_vals = R_1n_vectorized(m_vals[:, None], r_vals[None, :], i, h, d, a)
+                R2n_vals = R_2n_vectorized(m_vals[:, None], r_vals[None, :], i, a, h, d)
+                Zm_vals = Z_n_i_vectorized(m_vals[:, None], z_vals[None, :], i, h, d)
+                term1 = Cs[i][:NMK[i], None] * R1n_vals
+                term2 = Cs[i][NMK[i]:, None] * R2n_vals
+                phiH[regions[i]] = np.sum((term1 + term2) * Zm_vals, axis=0)
+
+        # Exterior Region
+        if np.any(regions[-1]):
+            r_vals, z_vals = R[regions[-1]], Z[regions[-1]]
+            k_vals = np.arange(NMK[-1])
+            Lambda_vals = Lambda_k_vectorized(k_vals[:, None], r_vals[None, :], m0, a, m_k_arr)
+            Zk_vals = Z_k_e_vectorized(k_vals[:, None], z_vals[None, :], m0, h, m_k_arr, N_k_arr)
+            phiH[regions[-1]] = np.sum(Cs[-1][:, None] * Lambda_vals * Zk_vals, axis=0)
+        
+        # --- 4. Calculate Particular Potential (phiP) ---
+        phiP[regions[0]] = heaving[0] * phi_p_i(d[0], R[regions[0]], Z[regions[0]], h)
+        for i in range(1, boundary_count):
+            phiP[regions[i]] = heaving[i] * phi_p_i(d[i], R[regions[i]], Z[regions[i]], h)
+        phiP[regions[-1]] = 0
+
+        # Sum to get total potential phi
+        phi = phiH + phiP
+
+        return {"R": R, "Z": Z, "phiH": phiH, "phiP": phiP, "phi": phi}
+    
+    def visualize_potential(self, field, R, Z, title):
+        fig, ax = plt.subplots(figsize=(8, 6))
+        contour = ax.contourf(R, Z, field, levels=50, cmap='viridis')
+        fig.colorbar(contour, ax=ax)
+        ax.set_title(title)
+        ax.set_xlabel('Radial Distance (R)')
+        ax.set_ylabel('Axial Distance (Z)')
+        return fig, ax
+        
+    def calculate_velocities(self, problem, solution_vector: np.ndarray, m0, spatial_res, sharp, R_range: Optional[np.ndarray] = None, Z_range: Optional[np.ndarray] = None) -> Dict[str, Any]:
+        self._ensure_m_k_and_N_k_arrays(problem, m0)
+        cache = self.cache_list[problem]
+        m_k_arr, N_k_arr = cache.m_k_arr, cache.N_k_arr
+
+        geometry = problem.geometry
+        body_arrangement = geometry.body_arrangement
+        domain_list = problem.domain_list
+
+        body_a = body_arrangement.a
+        body_d = body_arrangement.d
+        body_heaving = body_arrangement.heaving
+        
+        h = geometry.h
+        domain_keys = list(domain_list.keys())
+        boundary_count = len(domain_keys) - 1
+        NMK = [domain_list[idx].number_harmonics for idx in domain_keys]
+        
+        domain_a = [domain_list[idx].a for idx in domain_keys]
+        domain_d = [domain_list[idx].di for idx in domain_keys]
+
+        Cs = self.reformat_coeffs(solution_vector, NMK, boundary_count)
+
+        R, Z = make_R_Z(body_a, h, body_d, sharp, spatial_res, R_range=R_range, Z_range=Z_range)
+        
+        regions = []
+        regions.append(R <= body_a[0])
+        for i in range(1, boundary_count):
+            regions.append((R > body_a[i-1]) & (R <= body_a[i])) 
+        regions.append(R > body_a[-1]) 
+        
+        vrH = np.full(R.shape, np.nan, dtype=complex)
+        vzH = np.full(R.shape, np.nan, dtype=complex)
+
+        if np.any(regions[0]):
+            r, z = R[regions[0]], Z[regions[0]]
+            n = np.arange(NMK[0])
+            vrH[regions[0]] = np.sum(Cs[0][:, None] * diff_R_1n_vectorized(n[:, None], r[None, :], 0, h, domain_d, domain_a) * Z_n_i_vectorized(n[:, None], z[None, :], 0, h, domain_d), axis=0)
+            vzH[regions[0]] = np.sum(Cs[0][:, None] * R_1n_vectorized(n[:, None], r[None, :], 0, h, domain_d, domain_a) * diff_Z_n_i_vectorized(n[:, None], z[None, :], 0, h, domain_d), axis=0)
+
+        for i in range(1, boundary_count):
+            if np.any(regions[i]):
+                r, z = R[regions[i]], Z[regions[i]]
+                m = np.arange(NMK[i])
+                vr_term1 = Cs[i][:NMK[i], None] * diff_R_1n_vectorized(m[:, None], r[None, :], i, h, domain_d, domain_a)
+                vr_term2 = Cs[i][NMK[i]:, None] * diff_R_2n_vectorized(m[:, None], r[None, :], i, h, domain_d, domain_a)
+                vrH[regions[i]] = np.sum((vr_term1 + vr_term2) * Z_n_i_vectorized(m[:, None], z[None, :], i, h, domain_d), axis=0)
+                vz_term1 = Cs[i][:NMK[i], None] * R_1n_vectorized(m[:, None], r[None, :], i, h, domain_d, domain_a)
+                vz_term2 = Cs[i][NMK[i]:, None] * R_2n_vectorized(m[:, None], r[None, :], i, domain_a, h, domain_d)
+                vzH[regions[i]] = np.sum((vz_term1 + vz_term2) * diff_Z_n_i_vectorized(m[:, None], z[None, :], i, h, domain_d), axis=0)
+
+        if np.any(regions[-1]):
+            r, z = R[regions[-1]], Z[regions[-1]]
+            k = np.arange(NMK[-1])
+            vrH[regions[-1]] = np.sum(Cs[-1][:, None] * diff_Lambda_k_vectorized(k[:, None], r[None, :], m0, domain_a, m_k_arr) * Z_k_e_vectorized(k[:, None], z[None, :], m0, h, m_k_arr, N_k_arr), axis=0)
+            vzH[regions[-1]] = np.sum(Cs[-1][:, None] * Lambda_k_vectorized(k[:, None], r[None, :], m0, domain_a, m_k_arr) * diff_Z_k_e_vectorized(k[:, None], z[None, :], m0, h, m_k_arr, N_k_arr), axis=0)
+        
+        vrP = np.full(R.shape, 0.0, dtype=complex)
+        vzP = np.full(R.shape, 0.0, dtype=complex)
+        
+        vrP[regions[0]] = body_heaving[0] * diff_r_phi_p_i(body_d[0], R[regions[0]], h)
+        vzP[regions[0]] = body_heaving[0] * diff_z_phi_p_i(body_d[0], Z[regions[0]], h)
+        for i in range(1, boundary_count):
+            if body_heaving[i]:
+                vrP[regions[i]] = body_heaving[i] * diff_r_phi_p_i(body_d[i], R[regions[i]], h)
+                vzP[regions[i]] = body_heaving[i] * diff_z_phi_p_i(body_d[i], Z[regions[i]], h)
+
+        vr = vrH + vrP
+        vz = vzH + vzP
+        
+        for i in range(boundary_count):
+            body_mask = (regions[i]) & (Z > -body_d[i])
+            vr[body_mask] = np.nan
+            vz[body_mask] = np.nan
+
+        return {"R": R, "Z": Z, "vrH": vrH, "vzH": vzH, "vrP": vrP, "vzP": vzP, "vr": vr, "vz": vz}
+    
+    def run_and_store_results(self, problem_index: int) -> Results:
+        original_problem = self.problem_list[problem_index]
+        original_geometry = original_problem.geometry
+        original_bodies = original_geometry.body_arrangement.bodies
+        h = original_geometry.h
+        
+        original_domain_list = original_problem.domain_list
+        original_domain_keys = list(original_domain_list.keys())
+        NMK_list = [original_domain_list[idx].number_harmonics for idx in original_domain_keys]
+
+        problem_modes = original_problem.modes
+        omegas_to_run = original_problem.frequencies
+        
+        num_modes = len(problem_modes)
+        num_freqs = len(omegas_to_run)
+
+        results = Results(original_problem)
+
+        full_added_mass_matrix = np.full((num_freqs, num_modes, num_modes), np.nan)
+        full_damping_matrix = np.full((num_freqs, num_modes, num_modes), np.nan)
+        all_potentials_batch_data = []
+
+        for freq_idx, omega in enumerate(omegas_to_run):
+            m0 = wavenumber(omega, h) 
+            for i_idx, radiating_mode in enumerate(problem_modes):
+                try:
+                    temp_bodies = []
+                    for body_j, original_body in enumerate(original_bodies):
+                        if not isinstance(original_body, SteppedBody):
+                            raise TypeError(
+                                "run_and_store_results only supports SteppedBody objects. "
+                                f"Found {type(original_body)}."
+                            )
+                        is_heaving = (body_j == radiating_mode)
+                        temp_bodies.append(
+                            SteppedBody(
+                                a=original_body.a, 
+                                d=original_body.d, 
+                                slant_angle=original_body.slant_angle, 
+                                heaving=is_heaving
+                            )
+                        )
+                    temp_arrangement = ConcentricBodyGroup(temp_bodies)
+                    temp_geometry = BasicRegionGeometry(temp_arrangement, h=h, NMK=NMK_list)
+                    temp_problem = MEEMProblem(temp_geometry)
+                    temp_problem.set_frequencies(np.array([omega]))
+                    temp_engine = MEEMEngine(problem_list=[temp_problem])
+                    X_i = temp_engine.solve_linear_system_multi(temp_problem, m0)
+                    hydro_coeffs_col = temp_engine.compute_hydrodynamic_coefficients(
+                        temp_problem, X_i, m0, modes_to_calculate=problem_modes
+                    )
+                    for coeff_dict in hydro_coeffs_col:
+                        j_mode = coeff_dict['mode']
+                        j_idx_result = np.where(problem_modes == j_mode)[0]
+                        if j_idx_result.size == 0:
+                            print(f"Warning: Mode {j_mode} not found in problem_modes. Skipping.")
+                            continue
+                        j_idx = j_idx_result[0]
+                        full_added_mass_matrix[freq_idx, j_idx, i_idx] = coeff_dict['real']
+                        full_damping_matrix[freq_idx, j_idx, i_idx] = coeff_dict['imag']
+
+                    Cs = temp_engine.reformat_coeffs(X_i, NMK_list, len(NMK_list) - 1)
+                    current_mode_potentials = {}
+                    
+                    domain_list = temp_problem.geometry.domain_list
+                    if isinstance(domain_list, dict):
+                        domain_iterable = domain_list.values()
+                    else:
+                        domain_iterable = domain_list
+
+                    for domain in domain_iterable:
+                        domain_idx = domain.index
+                        domain_coeffs = Cs[domain_idx]
+                        current_mode_potentials[domain.index] = {
+                            "potentials": domain_coeffs,
+                            "r_coords_dict": {f"r_h{k}": 0.0 for k in range(len(domain_coeffs))},
+                            "z_coords_dict": {f"z_h{k}": 0.0 for k in range(len(domain_coeffs))} 
+                        }
+
+                    all_potentials_batch_data.append({
+                        "frequency_idx": freq_idx,
+                        "mode_idx": i_idx,
+                        "data": current_mode_potentials,
+                    })
+                        
+                except np.linalg.LinAlgError as e:
+                    print(f"  ERROR: Could not solve for freq={omega:.4f}, mode={radiating_mode}: {e}. Storing NaN.")
+                    full_added_mass_matrix[freq_idx, :, i_idx] = np.nan
+                    full_damping_matrix[freq_idx, :, i_idx] = np.nan
+                    continue 
+
+        results.store_hydrodynamic_coefficients(
+            frequencies=omegas_to_run,
+            added_mass_matrix=full_added_mass_matrix,
+            damping_matrix=full_damping_matrix,
+        )
+
+        if all_potentials_batch_data:
+            results.store_all_potentials(all_potentials_batch_data)
+
+        return results
\ No newline at end of file
diff --git a/package/src/openflash/meem_problem.py b/package/src/openflash/meem_problem.py
new file mode 100644
index 0000000..97c555b
--- /dev/null
+++ b/package/src/openflash/meem_problem.py
@@ -0,0 +1,50 @@
+import numpy as np
+from openflash.geometry import Geometry
+
+class MEEMProblem:
+    """
+    Encapsulates the full configuration and computation targets for a MEEM scenario.
+    """
+
+    def __init__(self, geometry: Geometry):
+        """
+        Initialize a MEEMProblem instance.
+
+        Parameters
+        ----------
+        geometry : Geometry
+            The full system geometry, including all domains.
+        """
+        self.geometry = geometry
+        self.domain_list = geometry.domain_list
+        self.frequencies = np.array([])  # Angular frequencies ω (rad/s)
+
+    def set_frequencies(self, frequencies: np.ndarray):
+        """
+        Set the angular frequencies for this problem.
+
+        Parameters
+        ----------
+        frequencies : np.ndarray
+            Array of angular frequencies or omega (rad/s).
+        """
+        assert np.all(frequencies > 0), "All frequencies must be positive"
+        assert isinstance(frequencies, np.ndarray), "frequencies must be a numpy array"
+
+        self.frequencies = frequencies
+
+    @property
+    def modes(self) -> np.ndarray:
+        """
+        Infers the active modes (degrees of freedom) from the
+        heaving flags set on the geometry's bodies.
+        
+        This assumes the body index (0, 1, 2...) corresponds
+        to the mode index.
+        """
+        # Assumes bodies are SteppedBody or CoordinateBody
+        heaving_bodies = [
+            i for i, body in enumerate(self.geometry.body_arrangement.bodies)
+            if body.heaving
+        ]
+        return np.array(heaving_bodies)
\ No newline at end of file
diff --git a/package/src/openflash/multi_constants.py b/package/src/openflash/multi_constants.py
new file mode 100644
index 0000000..ea4d533
--- /dev/null
+++ b/package/src/openflash/multi_constants.py
@@ -0,0 +1,6 @@
+# # multi_constants.py
+# Constants
+# 0/false if not heaving, 1/true if yes heaving
+g = 9.81
+rho = 1023
+#-> calculate omega from m0, g
\ No newline at end of file
diff --git a/package/src/openflash/multi_equations.py b/package/src/openflash/multi_equations.py
new file mode 100644
index 0000000..109609d
--- /dev/null
+++ b/package/src/openflash/multi_equations.py
@@ -0,0 +1,1079 @@
+#multi_equations.py
+from openflash.multi_constants import g, rho
+import numpy as np
+from scipy.special import hankel1 as besselh
+from scipy.special import iv as besseli
+from scipy.special import kv as besselk
+from scipy.special import ive as besselie
+from scipy.special import kve as besselke
+import scipy.integrate as integrate
+import scipy.linalg as linalg
+import matplotlib.pyplot as plt
+from numpy import sqrt, cosh, cos, sinh, sin, pi, exp, inf, log
+from scipy.optimize import newton, minimize_scalar, root_scalar
+import scipy as sp
+from functools import partial
+from typing import Optional
+
+
+M0_H_THRESH=14
+
+def omega(m0,h,g):
+    if m0 == inf:
+        return inf
+    else:
+        return sqrt(m0 * np.tanh(m0 * h) * g)
+    
+def wavenumber(omega, h):
+    m0_err = (lambda m0: (m0 * np.tanh(h * m0) - omega ** 2 / g))
+    return (root_scalar(m0_err, x0 = 2, method="newton")).root
+
+def scale(a: list):
+    return [val for val in a if val not in (None, np.inf, float('inf'))]
+
+def lambda_ni(n, i, h, d):  # Cap avoids Bessel overflow
+    return n * pi / (h - d[i])
+
+#############################################
+# some common computations
+
+# creating a m_k function, used often in calculations
+def m_k_entry(k, m0, h):
+    if k == 0: return m0
+    elif m0 == inf:
+        return ((k - 1/2) * pi)/h
+
+    m_k_h_err = (
+        lambda m_k_h: (m_k_h * np.tan(m_k_h) + m0 * h * np.tanh(m0 * h))
+    )
+    k_idx = k
+
+    # # original version of bounds in python
+    m_k_h_lower = pi * (k_idx - 1/2) + np.finfo(float).eps
+    m_k_h_upper = pi * k_idx - np.finfo(float).eps
+    # x_0 =  (m_k_upper - m_k_lower) / 2
+    
+    # becca's version of bounds from MDOcean Matlab code
+    m_k_h_lower = pi * (k_idx - 1/2) + (pi/180)* np.finfo(float).eps * (2**(np.floor(np.log(180*(k_idx- 1/2)) / np.log(2))) + 1)
+    m_k_h_upper = pi * k_idx
+
+    m_k_initial_guess = pi * (k_idx - 1/2) + np.finfo(float).eps
+    result = root_scalar(m_k_h_err, x0=m_k_initial_guess, method="newton", bracket=[m_k_h_lower, m_k_h_upper])
+    # result = minimize_scalar(
+        # m_k_h_err, bounds=(m_k_h_lower, m_k_h_upper), method="bounded"
+    # )
+
+    m_k_val = result.root / h
+
+    shouldnt_be_int = np.round(m0 * m_k_val / np.pi - 0.5, 4)
+    # not_repeated = np.unique(m_k_val) == m_k_val
+    assert np.all(shouldnt_be_int != np.floor(shouldnt_be_int))
+
+        # m_k_mat[freq_idx, :] = m_k_vec
+    return m_k_val
+
+# create an array of m_k values for each k to avoid recomputation
+def m_k(NMK, m0, h):
+    func = np.vectorize(lambda k: m_k_entry(k, m0, h), otypes=[float])
+    return func(range(NMK[-1]))
+
+def m_k_newton(h, m0):
+    res = newton(lambda k: k * np.tanh(k * h) - m0**2 / 9.8, x0=1.0, tol=10 ** (-10))
+    return res
+
+#############################################
+# vertical eigenvector coupling computation
+
+def I_nm(n, m, i, d, h): # coupling integral for two i-type regions
+    dj = max(d[i], d[i+1]) # integration bounds at -h and -d
+    if n == 0 and m == 0:
+        return h - dj
+    lambda1 = lambda_ni(n, i, h, d)
+    lambda2 = lambda_ni(m, i + 1, h, d)
+    if n == 0 and m >= 1:
+        if dj == d[i+1]:
+            return 0
+        else:
+            return sqrt(2) * sin(lambda2 * (h - dj)) / lambda2
+    if n >= 1 and m == 0:
+        if dj == d[i]:
+            return 0
+        else:
+            return sqrt(2) * sin(lambda1 * (h - dj)) / lambda1
+    else:
+        frac1 = sin((lambda1 + lambda2)*(h-dj))/(lambda1 + lambda2)
+        if lambda1 == lambda2:
+            frac2 = (h - dj)
+        else:
+            frac2 = sin((lambda1 - lambda2)*(h-dj))/(lambda1 - lambda2)
+        return frac1 + frac2
+
+# REVISED I_mk to accept m_k_arr and N_k_arr
+def I_mk(m, k, i, d, m0, h, m_k_arr, N_k_arr): # coupling integral for i and e-type regions
+    # Use the pre-computed array
+    local_m_k_k = m_k_arr[k] # Access directly from array
+    
+    dj = d[i]
+    if m == 0 and k == 0:
+        if m0 == inf: return 0
+        elif m0 * h < M0_H_THRESH:
+            return (1/sqrt(N_k_arr[0])) * sinh(m0 * (h - dj)) / m0 # Use N_k_arr[0]
+        else: # high m0h approximation
+            return sqrt(2 * h / m0) * (exp(- m0 * dj) - exp(m0 * dj - 2 * m0 * h))
+    if m == 0 and k >= 1:
+        return (1/sqrt(N_k_arr[k])) * sin(local_m_k_k * (h - dj)) / local_m_k_k # Use N_k_arr[k]
+    if m >= 1 and k == 0:
+        if m0 == inf: return 0
+        elif m0 * h < M0_H_THRESH:
+            num = (-1)**m * sqrt(2) * (1/sqrt(N_k_arr[0])) * m0 * sinh(m0 * (h - dj)) # Use N_k_arr[0]
+        else: # high m0h approximation
+            num = (-1)**m * 2 * sqrt(h * m0 ** 3) *(exp(- m0 * dj) - exp(m0 * dj - 2 * m0 * h))
+        denom = (m0**2 + lambda_ni(m, i, h, d) **2)
+        return num/denom
+    else:
+        lambda1 = lambda_ni(m, i, h, d)
+        if abs(local_m_k_k) == lambda1:
+            return sqrt(2/N_k_arr[k]) * (h - dj)/2
+        else:
+            frac1 = sin((local_m_k_k + lambda1)*(h-dj))/(local_m_k_k + lambda1)
+            frac2 = sin((local_m_k_k - lambda1)*(h-dj))/(local_m_k_k - lambda1)
+            return sqrt(2/N_k_arr[k]) * (frac1 + frac2)/2 # Use N_k_arr[k]
+
+#############################################
+# b-vector computation
+
+def b_potential_entry(n, i, d, heaving, h, a): # for two i-type regions
+    #(integrate over shorter fluid, use shorter fluid eigenfunction)    
+    j = i + (d[i] <= d[i+1]) # index of shorter fluid
+    constant = (float(heaving[i+1]) / (h - d[i+1]) - float(heaving[i]) / (h - d[i]))
+    if n == 0:
+        return constant * 1/2 * ((h - d[j])**3/3 - (h-d[j]) * a[i]**2/2)
+    else:
+        return sqrt(2) * (h - d[j]) * constant * ((-1) ** n)/(lambda_ni(n, j, h, d) ** 2)
+
+def b_potential_end_entry(n, i, heaving, h, d, a): # between i and e-type regions
+    constant = - float(heaving[i]) / (h - d[i])
+    if n == 0:
+        return constant * 1/2 * ((h - d[i])**3/3 - (h-d[i]) * a[i]**2/2)
+    else:
+        return sqrt(2) * (h - d[i]) * constant * ((-1) ** n)/(lambda_ni(n, i, h, d) ** 2)
+
+def b_velocity_entry(n, i, heaving, a, h, d): # for two i-type regions
+    if n == 0:
+        return (float(heaving[i+1]) - float(heaving[i])) * (a[i]/2)
+    if d[i] > d[i + 1]: #using i+1's vertical eigenvectors
+        if heaving[i]:
+            num = - sqrt(2) * a[i] * sin(lambda_ni(n, i+1, h, d) * (h-d[i]))
+            denom = (2 * (h - d[i]) * lambda_ni(n, i+1, h, d))
+            return num/denom
+        else: return 0
+    else: #using i's vertical eigenvectors
+        if heaving[i+1]:
+            num = sqrt(2) * a[i] * sin(lambda_ni(n, i, h, d) * (h-d[i+1]))
+            denom = (2 * (h - d[i+1]) * lambda_ni(n, i, h, d))
+            return num/denom
+        else: return 0
+
+# REVISED b_velocity_end_entry to accept m_k_arr and N_k_arr
+# ADDED m_k_arr, N_k_arr
+def b_velocity_end_entry(k, i, heaving, a, h, d, m0, NMK, m_k_arr, N_k_arr): # between i and e-type regions
+    local_m_k_k = m_k_arr[k] # Access directly from array
+
+    constant = - float(heaving[i]) * a[i]/(2 * (h - d[i]))
+    if k == 0:
+        # --- FIX: Handle infinite m0 ---
+        if m0 == inf:
+            return 0.0
+        # -------------------------------
+        elif m0 * h < M0_H_THRESH:
+            return constant * (1/sqrt(N_k_arr[0])) * sinh(m0 * (h - d[i])) / m0 # Use N_k_arr[0]
+        else: # high m0h approximation
+            return constant * sqrt(2 * h / m0) * (exp(- m0 * d[i]) - exp(m0 * d[i] - 2 * m0 * h))
+    else:
+        return constant * (1/sqrt(N_k_arr[k])) * sin(local_m_k_k * (h - d[i])) / local_m_k_k # Use N_k_arr[k]
+
+def b_velocity_end_entry_full(k, i, heaving, a, h, d, m0, NMK): # between i and e-type regions
+    local_m_k = m_k(NMK, m0, h)
+    constant = - float(heaving[i]) * a[i]/(2 * (h - d[i]))
+    if k == 0:
+        if m0 == inf: return 0.0 # Fix here as well for completeness
+        elif m0 * h < M0_H_THRESH:
+            return constant * (1/sqrt(N_k_full(0, m0, h, NMK))) * sinh(m0 * (h - d[i])) / m0
+        else: # high m0h approximation
+            return constant * sqrt(2 * h / m0) * (exp(- m0 * d[i]) - exp(m0 * d[i] - 2 * m0 * h))
+    else:
+        return constant * (1/sqrt(N_k_full(k, m0, h, NMK))) * sin(local_m_k[k] * (h - d[i])) / local_m_k[k]
+
+#############################################
+# Phi particular and partial derivatives
+
+def phi_p_i(d, r, z, h): 
+    return (1 / (2* (h - d))) * ((z + h) ** 2 - (r**2) / 2)
+
+def diff_r_phi_p_i(d, r, h): 
+    return (- r / (2* (h - d)))
+
+def diff_z_phi_p_i(d, z, h): 
+    return ((z+h) / (h - d))
+
+#############################################
+# The "Bessel I" radial eigenfunction
+#############################################
+
+def R_1n(n, r, i, h, d, a):
+    if n == 0:
+        if i == 0:
+            return 0.5 # Central cylinder: Constant is correct/sufficient
+        else:
+            # Annulus: Use Log anchored at OUTER radius
+            # 1.0 + 0.5 * log(r / outer_r)
+            return 1.0 + 0.5 * np.log(r / scale(a)[i])
+    elif n >= 1:
+        if r == scale(a)[i]: 
+            return 1
+        else:
+            return besselie(0, lambda_ni(n, i, h, d) * r) / besselie(0, lambda_ni(n, i, h, d) * scale(a)[i]) * exp(lambda_ni(n, i, h, d) * (r - scale(a)[i]))
+    else: 
+        raise ValueError("Invalid value for n")
+
+def R_1n_vectorized(n, r, i, h, d, a):
+    """
+    Vectorized version of the R_1n radial eigenfunction.
+    FIXED: Handles i!=0 case for n=0 to match scalar R_1n.
+    """
+    # --- Define the conditions for the nested logic ---
+    cond_n_is_zero = (n == 0)
+    cond_r_at_boundary = (r == scale(a)[i])
+
+    # --- Define the outcomes for each condition ---
+    # FIX: n=0 logic must match scalar version
+    if i == 0:
+        outcome_for_n_zero = np.full_like(r, 0.5)
+    else:
+        # Annulus: 1.0 + 0.5 * log(r / outer)
+        # Handle r=0 if necessary, though in annulus r > 0.
+        outcome_for_n_zero = 1.0 + 0.5 * np.log(r / scale(a)[i])
+    
+    # Outcome 2: If n>=1 and r is at the boundary, the value is 1.0.
+    outcome_for_r_boundary = 1.0
+    
+    # Outcome 3: The general case for n>=1 inside the boundary.
+    lambda_val = lambda_ni(n, i, h, d)
+    bessel_term = (besselie(0, lambda_val * r) / besselie(0, lambda_val * scale(a)[i])) * \
+                  exp(lambda_val * (r - scale(a)[i]))
+
+    # --- Apply the logic using nested np.where ---
+    result_if_n_not_zero = np.where(cond_r_at_boundary, outcome_for_r_boundary, bessel_term)
+    
+    return np.where(cond_n_is_zero, outcome_for_n_zero, result_if_n_not_zero)
+
+# Differentiate wrt r
+def diff_R_1n(n, r, i, h, d, a):
+    if n == 0:
+        if i == 0:
+            return 0.0
+        else:
+            # Derivative of 1.0 + 0.5*ln(r/outer) is 1/(2r)
+            with np.errstate(divide='ignore'):
+                return np.where(r == 0, np.inf, 1 / (2 * r))
+    else:
+        top = lambda_ni(n, i, h, d) * besselie(1, lambda_ni(n, i, h, d) * r)
+        bottom = besselie(0, lambda_ni(n, i, h, d) * scale(a)[i])
+        return top / bottom * exp(lambda_ni(n, i, h, d) * (r - scale(a)[i]))
+
+def diff_R_1n_vectorized(n, r, i, h, d, a):
+    """
+    Vectorized derivative of the diff_R_1n radial function.
+    FIXED: Handles i!=0 case for n=0 to match scalar version.
+    """
+    condition = (n == 0)
+    
+    # FIX: n=0 derivative logic
+    if i == 0:
+        value_if_true = np.zeros_like(r)
+    else:
+        # Derivative is 1/(2r)
+        value_if_true = np.divide(1.0, 2 * r, out=np.full_like(r, np.inf), where=(r!=0))
+    
+    # --- Calculation for when n > 0 ---
+    lambda_val = lambda_ni(n, i, h, d)
+    
+    numerator = lambda_val * besselie(1, lambda_val * r) 
+    denominator = besselie(0, lambda_val * scale(a)[i])
+    
+    bessel_ratio = np.divide(numerator, denominator, 
+                             out=np.zeros_like(numerator, dtype=float), 
+                             where=(denominator != 0))
+                             
+    value_if_false = bessel_ratio * exp(lambda_val * (r - scale(a)[i]))
+    
+    return np.where(condition, value_if_true, value_if_false)
+
+#############################################
+# The "Bessel K" radial eigenfunction (Annular Regions)
+# NORMALIZATION FIX: Anchored at Inner Radius (a[i-1]) + Affine Shift
+#############################################
+
+def R_2n(n, r, i, a, h, d):
+    if i == 0:
+        raise ValueError("i cannot be 0") 
+    
+    # LEGACY: Use Outer Radius
+    outer_r = scale(a)[i]
+
+    if n == 0:
+        # LEGACY: 0.5 * log(r / outer)
+        # This is 0 at the outer boundary, not 1.
+        return 0.5 * np.log(r / outer_r)
+    else:
+        lambda_val = lambda_ni(n, i, h, d)
+        
+        if r == outer_r:
+            return 1.0
+        else:
+            # LEGACY: Normalized by K0 at OUTER radius
+            num = besselke(0, lambda_val * r)
+            den = besselke(0, lambda_val * outer_r)
+            return (num / den) * exp(lambda_val * (outer_r - r))
+
+def R_2n_vectorized(n, r, i, a, h, d):
+    """
+    Vectorized version of the R_2n radial eigenfunction.
+    LEGACY MODE: Anchored at Outer Radius (a[i]).
+    """
+    if i == 0:
+        raise ValueError("R_2n function is not defined for the innermost region (i=0).")
+
+    # LEGACY: Use Outer Radius
+    outer_r = scale(a)[i]
+
+    cond_n_is_zero = (n == 0)
+    cond_r_at_boundary = (r == outer_r)
+
+    # Case 1: n = 0
+    outcome_for_n_zero = 0.5 * np.log(r / outer_r)
+
+    # Case 2: n > 0 and r is at the boundary
+    outcome_for_r_boundary = 1.0
+
+    # Case 3: n > 0 and r is not at the boundary
+    lambda_val = lambda_ni(n, i, h, d)
+    
+    # Mask input where n=0 to prevent 'inf' errors
+    lambda_safe = np.where(cond_n_is_zero, 1.0, lambda_val)
+    
+    # LEGACY: Denom uses OUTER radius
+    denom = besselke(0, lambda_safe * outer_r)
+    denom = np.where(np.abs(denom) < 1e-12, np.nan, denom)
+
+    bessel_term = (besselke(0, lambda_safe * r) / denom) * exp(lambda_safe * (outer_r - r))
+
+    result_if_n_not_zero = np.where(cond_r_at_boundary, outcome_for_r_boundary, bessel_term)
+
+    return np.where(cond_n_is_zero, outcome_for_n_zero, result_if_n_not_zero)
+
+# Differentiate wrt r (Unchanged, as d/dr(1.0) is 0)
+def diff_R_2n(n, r, i, h, d, a):
+    # LEGACY: Anchored at Outer Radius
+    if n == 0:
+        return 1.0 / (2 * r)
+    else:
+        lambda0 = lambda_ni(n, i, h, d)
+        outer_r = scale(a)[i]
+        
+        # Derivative of K0(lr)/K0(la) is -l*K1(lr)/K0(la)
+        # Using scaled K:
+        # -l * (ke1(lr)/ke0(la)) * exp(l(a-r))
+        
+        top = - lambda0 * besselke(1, lambda0 * r)
+        bot = besselke(0, lambda0 * outer_r)
+        
+        return (top / bot) * np.exp(lambda0 * (outer_r - r))
+    
+def diff_R_2n_vectorized(n, r, i, h, d, a):
+    n = np.asarray(n)
+    r = np.asarray(r)
+    
+    # Case n == 0: Derivative is still 1/(2r)
+    value_if_true = np.divide(1.0, 2 * r, out=np.full_like(r, np.inf), where=(r != 0))
+    
+    # Case n > 0
+    lambda_val = lambda_ni(n, i, h, d)
+    outer_r = scale(a)[i] # LEGACY ANCHOR
+    
+    lambda_safe = np.where(n == 0, 1.0, lambda_val)
+    
+    # LEGACY: Denom uses OUTER radius
+    denom = besselke(0, lambda_safe * outer_r)
+    safe_denom = np.where(np.abs(denom) < 1e-10, 1e-10, denom)
+
+    with np.errstate(divide='ignore', invalid='ignore'):
+        numerator = -lambda_safe * besselke(1, lambda_safe * r)
+        ratio = numerator / safe_denom
+        # LEGACY: Exponential decay from OUTER radius
+        exp_term = exp(lambda_safe * (outer_r - r))
+        value_if_false = ratio * exp_term
+
+    return np.where(n == 0, value_if_true, value_if_false)
+#############################################
+# i-region vertical eigenfunctions
+def Z_n_i(n, z, i, h, d):
+    if n == 0:
+        return 1
+    else:
+        return np.sqrt(2) * np.cos(lambda_ni(n, i, h, d) * (z + h))
+    
+def Z_n_i_vectorized(n, z, i, h, d):
+    """
+    Vectorized version of the i-region vertical eigenfunction Z_n_i.
+    """
+    # Define the condition to check for each element in the 'n' array
+    condition = (n == 0)
+    
+    # Define the calculation for when n != 0
+    # This part is already vectorized thanks to NumPy
+    value_if_false = np.sqrt(2) * np.cos(lambda_ni(n, i, h, d) * (z + h))
+    
+    # Use np.where to choose the output:
+    # If condition is True (n==0), return 1.0.
+    # Otherwise, return the result of the calculation.
+    return np.where(condition, 1.0, value_if_false)
+
+def diff_Z_n_i(n, z, i, h, d):
+    if n == 0:
+        return 0
+    else:
+        lambda0 = lambda_ni(n, i, h, d)
+        return - lambda0 * np.sqrt(2) * np.sin(lambda0 * (z + h))
+    
+def diff_Z_n_i_vectorized(n, z, i, h, d):
+    """
+    Vectorized derivative of the Z_n_i vertical function.
+    """
+    # Define the condition to be applied element-wise.
+    condition = (n == 0)
+    
+    # Define the value if the condition is True (when n=0).
+    value_if_true = 0.0
+    
+    # Define the calculation for when the condition is False (when n > 0).
+    # This part is already vectorized.
+    lambda_val = lambda_ni(n, i, h, d)
+    value_if_false = -lambda_val * np.sqrt(2) * np.sin(lambda_val * (z + h))
+    
+    # Use np.where to select the output based on the condition.
+    return np.where(condition, value_if_true, value_if_false)
+
+#############################################
+# Region e radial eigenfunction
+# REVISED Lambda_k to accept m_k_arr and N_k_arr
+def Lambda_k(k, r, m0, a, m_k_arr): # ADDED m_k_arr, N_k_arr
+    local_m_k_k = m_k_arr[k]
+    if k == 0:
+        if m0 == inf:
+        # the true limit is not well-defined, but whatever value this returns will be multiplied by zero
+            return 1
+        else:
+            if r == scale(a)[-1]:  # Saves bessel function eval
+                return 1
+            else:
+                return besselh(0, m0 * r) / besselh(0, m0 * scale(a)[-1])
+    else:
+        if r == scale(a)[-1]:  # Saves bessel function eval
+            return 1
+        else:
+            return besselke(0, local_m_k_k * r) / besselke(0, local_m_k_k * scale(a)[-1]) * exp(local_m_k_k * (scale(a)[-1] - r))
+        
+def Lambda_k_vectorized(k, r, m0, a, m_k_arr):
+    """
+    Vectorized version of the exterior region radial eigenfunction Lambda_k.
+    """
+    if m0 == inf:
+        return np.ones(np.broadcast(k, r).shape, dtype=float)
+
+    cond_k_is_zero = (k == 0)
+    cond_r_at_boundary = (r == scale(a)[-1])
+
+    outcome_boundary = 1.0
+
+    # --- Case 2: k = 0 (NEEDS FIX) ---
+    denom_k_zero = besselh(0, m0 * scale(a)[-1])
+    numer_k_zero = besselh(0, m0 * r)
+    with np.errstate(divide='ignore', invalid='ignore'):
+        outcome_k_zero = np.divide(numer_k_zero, denom_k_zero, 
+                                   out=np.zeros_like(numer_k_zero, dtype=complex),
+                                   where=np.isfinite(denom_k_zero) & (denom_k_zero != 0))
+
+    # --- Case 3: k > 0 (NEEDS FIX) ---
+    # Mask input where k=0 to prevent errors
+    local_m_k_k = m_k_arr[k]
+    safe_m_k = np.where(cond_k_is_zero, 1.0, local_m_k_k)
+    
+    denom_k_nonzero = besselke(0, safe_m_k * scale(a)[-1])
+    numer_k_nonzero = besselke(0, safe_m_k * r)
+    with np.errstate(divide='ignore', invalid='ignore'):
+        bessel_ratio = np.divide(numer_k_nonzero, denom_k_nonzero, 
+                                 out=np.zeros_like(numer_k_nonzero),
+                                 where=np.isfinite(denom_k_nonzero) & (denom_k_nonzero != 0))
+    outcome_k_nonzero = bessel_ratio * exp(safe_m_k * (scale(a)[-1] - r))
+    # --- END FIXES ---
+
+    result_if_not_boundary = np.where(cond_k_is_zero,
+                                      outcome_k_zero,
+                                      outcome_k_nonzero)
+
+    return np.where(cond_r_at_boundary,
+                    outcome_boundary,
+                    result_if_not_boundary)
+    
+def Lambda_k_full(k, r, m0, a, NMK, h):
+    local_scale = scale(a)
+    local_m_k = m_k(NMK, m0, h)
+    if k == 0:
+        return besselh(0, m0 * r) / besselh(0, m0 * local_scale[-1])
+    else:
+        return besselk(0, local_m_k[k] * r) / besselk(0, local_m_k[k] * local_scale[-1])
+
+# Differentiate wrt r 
+def diff_Lambda_k(k, r, m0, a, m_k_arr): 
+    local_m_k_k = m_k_arr[k] # Access directly from array
+    if k == 0:
+        if m0 == inf:
+        # the true limit is not well-defined, but this makes the assigned coefficient zero
+            return 1
+        else:
+            numerator = -(m0 * besselh(1, m0 * r))
+            denominator = besselh(0, m0 * scale(a)[-1])
+         
+            return numerator / denominator
+    else:
+        numerator = -(local_m_k_k * besselke(1, local_m_k_k * r))
+        denominator = besselke(0, local_m_k_k * scale(a)[-1])
+        return numerator / denominator * exp(local_m_k_k * (scale(a)[-1] - r))
+
+def diff_Lambda_k_vectorized(k, r, m0, a, m_k_arr):
+    """
+    Vectorized derivative of the exterior region radial function Lambda_k.
+    """
+    # Handle the scalar case where m0 is infinite. The result is always 1.
+    if m0 == inf:
+        return np.ones(np.broadcast(k, r).shape, dtype=float)
+
+    # --- Define the condition for vectorization ---
+    condition = (k == 0)
+
+    # --- Define the outcome for k == 0 ---
+    numerator_k_zero = -(m0 * besselh(1, m0 * r))
+    denominator_k_zero = besselh(0, m0 * scale(a)[-1])
+    outcome_k_zero = np.divide(numerator_k_zero, denominator_k_zero,
+                               out=np.zeros_like(numerator_k_zero, dtype=complex),
+                               where=(denominator_k_zero != 0))
+
+    # --- Define the outcome for k > 0 ---
+    # NumPy's advanced indexing allows m_k_arr[k] to create an array from indices.
+    local_m_k_k = m_k_arr[k]
+    # Mask input where k=0
+    safe_m_k = np.where(condition, 1.0, local_m_k_k)
+    
+    numerator_k_nonzero = -(safe_m_k * besselke(1, safe_m_k * r))
+    denominator_k_nonzero = besselke(0, safe_m_k * scale(a)[-1])
+    
+    # Use safe division to avoid warnings
+    ratio = np.divide(numerator_k_nonzero, denominator_k_nonzero,
+                      out=np.zeros_like(numerator_k_nonzero, dtype=float),
+                      where=(denominator_k_nonzero != 0))
+                      
+    outcome_k_nonzero = ratio * exp(safe_m_k * (scale(a)[-1] - r))
+
+    # --- Use np.where to select the final output ---
+    return np.where(condition, outcome_k_zero, outcome_k_nonzero)
+
+def diff_Lambda_k_full(k, r, m0, NMK, h, a):
+    local_m_k = m_k(NMK, m0, h)
+    local_scale = scale(a)
+    if k == 0:
+        numerator = -(m0 * besselh(1, m0 * r))
+        denominator = besselh(0, m0 * local_scale[-1])
+    else:
+        numerator = -(local_m_k[k] * besselk(1, local_m_k[k] * r))
+        denominator = besselk(0, local_m_k[k] * local_scale[-1])
+    return numerator / denominator
+
+
+#############################################
+# Equation 2.34 in analytical methods book, also eq 16 in Seah and Yeung 2006:
+# REVISED N_k to accept m_k_arr (as it previously called m_k itself)
+
+def N_k_multi(k, m0, h, m_k_arr): 
+    if m0 == inf: return 1/2
+    elif k == 0:
+        # --- FIX: Prevent overflow for deep water ---
+        if (2 * m0 * h) > 700: 
+            return 1e308 
+        # ------------------------------------------
+        return 1 / 2 * (1 + sinh(2 * m0 * h) / (2 * m0 * h))
+    else:
+        return 1 / 2 * (1 + sin(2 * m_k_arr[k] * h) / (2 * m_k_arr[k] * h))
+    
+def N_k_full(k, m0, h, NMK):
+    local_m_k = m_k(NMK, m0, h)
+    if k == 0:
+        return 1 / 2 * (1 + sinh(2 * m0 * h) / (2 * m0 * h))
+    elif m0 == 0:
+        return 1.0
+    else:
+        return 1 / 2 * (1 + sin(2 * local_m_k[k] * h) / (2 * local_m_k[k] * h))
+
+
+#############################################
+# e-region vertical eigenfunctions
+def Z_k_e(k, z, m0, h, NMK, m_k_arr):
+    local_m_k = m_k(NMK, m0, h)
+    if k == 0:
+        if m0 == inf: return 0
+        if m0 * h < M0_H_THRESH:
+            return 1 / sqrt(N_k_multi(k, m0, h, m_k_arr)) * cosh(m0 * (z + h))
+        else: # high m0h approximation
+            return sqrt(2 * m0 * h) * (exp(m0 * z) + exp(-m0 * (z + 2*h)))
+    else:
+        return 1 / sqrt(N_k_multi(k, m0, h, m_k_arr)) * cos(local_m_k[k] * (z + h))
+    
+def Z_k_e_vectorized(k, z, m0, h, m_k_arr, N_k_arr):
+    """
+    Vectorized version of the e-region vertical eigenfunction Z_k_e.
+    This version uses pre-calculated m_k_arr and N_k_arr for efficiency.
+    """
+    # This outer conditional is fine because it operates on scalar inputs.
+    if m0 * h < M0_H_THRESH:
+        # --- Logic for the standard case ---
+        # Value for k = 0
+        outcome_k_zero = (1 / sqrt(N_k_arr[0])) * cosh(m0 * (z + h))
+        
+        # Value for k > 0
+        # NumPy's advanced indexing handles using an array 'k' to index other arrays.
+        outcome_k_nonzero = (1 / sqrt(N_k_arr[k])) * cos(m_k_arr[k] * (z + h))
+
+        return np.where(k == 0, outcome_k_zero, outcome_k_nonzero)
+    else:
+        # --- Logic for the high m0h approximation ---
+        # Value for k = 0
+        outcome_k_zero = sqrt(2 * m0 * h) * (exp(m0 * z) + exp(-m0 * (z + 2 * h)))
+        
+        # Value for k > 0 (this part is the same as the standard case)
+        outcome_k_nonzero = (1 / sqrt(N_k_arr[k])) * cos(m_k_arr[k] * (z + h))
+        
+        return np.where(k == 0, outcome_k_zero, outcome_k_nonzero)
+
+def diff_Z_k_e(k, z, m0, h, NMK, m_k_arr):
+    local_m_k = m_k(NMK, m0, h)
+    if k == 0:
+        if m0 == inf: return 0
+        elif m0 * h < M0_H_THRESH:
+            return 1 / sqrt(N_k_multi(k, m0, h, m_k_arr)) * m0 * sinh(m0 * (z + h))
+        else: # high m0h approximation
+            return m0 * sqrt(2 * h * m0) * (exp(m0 * z) - exp(-m0 * (z + 2*h)))
+    else:
+        return -1 / sqrt(N_k_multi(k, m0, h, m_k_arr)) * local_m_k[k] * sin(local_m_k[k] * (z + h))
+def diff_Z_k_e_vectorized(k, z, m0, h, m_k_arr, N_k_arr):
+    """
+    Vectorized derivative of the e-region vertical eigenfunction Z_k_e.
+    This version uses pre-calculated m_k_arr and N_k_arr for efficiency.
+    """
+    # This outer conditional is fine because it operates on scalar inputs.
+    if m0 * h < M0_H_THRESH:
+        # --- Logic for the standard case ---
+        # Value for k = 0
+        outcome_k_zero = (1 / sqrt(N_k_arr[0])) * m0 * sinh(m0 * (z + h))
+        
+        # Value for k > 0
+        # NumPy's advanced indexing handles using an array 'k' to index other arrays.
+        outcome_k_nonzero = -(1 / sqrt(N_k_arr[k])) * m_k_arr[k] * sin(m_k_arr[k] * (z + h))
+        
+        return np.where(k == 0, outcome_k_zero, outcome_k_nonzero)
+    
+    else:
+        # --- Logic for the high m0h approximation ---
+        # Value for k = 0
+        outcome_k_zero = m0 * sqrt(2 * h * m0) * (exp(m0 * z) - exp(-m0 * (z + 2 * h)))
+        
+        # Value for k > 0 (this part is the same as the standard case)
+        outcome_k_nonzero = -(1 / sqrt(N_k_arr[k])) * m_k_arr[k] * sin(m_k_arr[k] * (z + h))
+        
+        return np.where(k == 0, outcome_k_zero, outcome_k_nonzero)
+
+#############################################
+# To calculate hydrocoefficients
+
+#integrating R_1n * r
+# Integration
+def int_R_1n(i, n, a, h, d):
+    if n == 0:
+        if i == 0:
+            # Central cylinder: Integral of 0.5 * r
+            return a[i]**2/4 
+        else:
+            # Annulus: Integral of r * (1.0 + 0.5 * ln(r/outer_r))
+            outer_r = scale(a)[i]
+            inner_r = a[i-1]
+            
+            cyl_term = (outer_r**2 - inner_r**2) / 2.0
+            
+            def log_indefinite_int(r):
+                # Integral of 0.5 * r * ln(r/outer)
+                # = 0.5 * [ (r^2/2)*ln(r/outer) - r^2/4 ]
+                log_val = np.log(r/outer_r) if r > 0 else 0
+                return 0.5 * ((r**2 / 2.0) * log_val - (r**2 / 4.0))
+
+            val_outer = log_indefinite_int(outer_r) # log(1)=0, so just -r^2/4 term
+            val_inner = log_indefinite_int(inner_r)
+            
+            return cyl_term + (val_outer - val_inner)
+    else:
+        # Standard Bessel I integral (Unchanged)
+        lambda0 = lambda_ni(n, i, h, d)
+        bottom = lambda0 * besselie(0, lambda0 * scale(a)[i])
+        if i == 0: inner_term = 0
+        else: inner_term = (a[i-1] * besselie(1, lambda0 * a[i-1]) / bottom) * exp(lambda0 * (a[i-1] - scale(a)[i]))
+        outer_term = (a[i] * besselie(1, lambda0 * a[i]) / bottom) * exp(lambda0 * (a[i] - scale(a)[i]))
+        return outer_term - inner_term
+    
+#integrating R_2n * r
+# Integral must be updated to include the volume of the new "1.0" cylinder
+def int_R_2n(i, n, a, h, d):
+    """
+    Computes the integral of R_2n(r) * r dr from inner_r to outer_r.
+    LEGACY MODE: Matches old_assembly.py (Outer Radius Anchor).
+    """
+    if i == 0:
+        raise ValueError("i cannot be 0")
+    
+    # LEGACY: Use Outer Radius
+    outer_r = scale(a)[i]
+    inner_r = a[i-1] # Previous radius is inner
+
+    if n == 0:
+        # Integral of r * (0.5 * ln(r/outer_r))
+        # Analytic result: [ 0.5 * ( (r^2/2)*ln(r/outer_r) - r^2/4 ) ] evaluated from inner to outer
+        
+        def indefinite(r):
+            if r <= 0: return 0
+            # ln(r/outer_r) is 0 when r=outer_r
+            term_log = np.log(r / outer_r)
+            return 0.5 * ((r**2 / 2) * term_log - (r**2 / 4))
+
+        val_outer = indefinite(outer_r)
+        val_inner = indefinite(inner_r)
+        return val_outer - val_inner
+
+    else:
+        # Integral of r * K0(lambda*r) / K0(lambda*outer) * exp(...)
+        # The old code handled this via standard Bessel integrals.
+        # We must replicate the specific normalization of old_assembly.
+        
+        lambda0 = lambda_ni(n, i, h, d)
+        
+        # In old_assembly, R_2n = K0(lr)/K0(la) * exp(...)
+        # The integral of x*K0(x) is -x*K1(x).
+        
+        # Numerator term (pure Bessel K integral part)
+        # Int[ r * K0(lambda*r) ] = - (r/lambda) * K1(lambda*r)
+        
+        # We need to evaluate: [ - (r/lambda)*K1(lambda*r) ] from inner to outer
+        # Then multiply by the normalization constant: exp(lambda*outer) / K0(lambda*outer)
+        # Wait, the exponential term in R_2n_old is: exp(lambda * (outer_r - r))
+        # This exponential cancels out the exp inside the scaled Bessel K if we use K_scaled.
+        # But old_assembly likely used raw K. Let's stick to the raw math.
+        
+        # Let's look at the old implementation logic provided in previous turns:
+        # It normalizes by K0(outer).
+        
+        # Exact calculation matching old_assembly:
+        k0_outer = besselke(0, lambda0 * outer_r) # scaled K0
+        
+        # We need the integral of:
+        # ( K0(lr) / K0(la) ) * exp( l(a-r) ) * r
+        # = ( K0(lr)*exp(lr) / (K0(la)*exp(la)) ) * r   <-- exp terms cancel strictly if using scaled K
+        # effectively it is Integral( r * K0_scaled(lr) ) / K0_scaled(la)
+        
+        # Analytic integral of x * K0(x) is -x * K1(x).
+        # So Int( r * K0(lr) ) = - (r/lambda) * K1(lr)
+        
+        # Converting to scaled Bessel K (kve):
+        # K1(x) = kve(1, x) * exp(-x)
+        # So - (r/l) * kve(1, lr) * exp(-lr)
+        
+        # We want: [ - (r/l) * kve(1, lr) * exp(-lr) ] / [ kve(0, la) * exp(-la) ] * exp( l(a-r) )
+        # Notice exp(-lr) * exp(l(a-r)) = exp(-lr + la - lr) ... wait.
+        # R_2n definition: ( kve(0, lr) * exp(-lr) ) / ( kve(0, la) * exp(-la) ) * exp( l(a-r) )
+        # = kve(0, lr) / kve(0, la) * exp( -lr + la + la - lr ) ... no.
+        
+        # SIMPLER PATH:
+        # R_2n_old(r) = K0(lr) / K0(la)  (Unscaled)
+        # Int(r * R_2n) = (1/K0(la)) * [ - (r/l)*K1(lr) ] bounds inner..outer
+        
+        # Upper bound (r=outer):
+        # - (outer/l) * K1(l*outer) / K0(l*outer)
+        
+        # Lower bound (r=inner):
+        # - (inner/l) * K1(l*inner) / K0(l*outer)
+        
+        # Result = Upper - Lower
+        # = (1/lambda0) * ( inner*K1(l*inner) - outer*K1(l*outer) ) / K0(l*outer)
+        
+        # Using Scaled Bessel functions (kve) to match python 'besselke':
+        # K1(x) = ke1(x) * exp(-x)
+        # K0(x) = ke0(x) * exp(-x)
+        # Ratio K1(x)/K0(y) = ke1(x)/ke0(y) * exp(y-x)
+        
+        term_outer = (outer_r * besselke(1, lambda0 * outer_r)) 
+        # exp(la - la) = 1, so no exp factor needed for outer term.
+        
+        term_inner = (inner_r * besselke(1, lambda0 * inner_r))
+        # exp(la - li). We need to multiply by this.
+        term_inner *= np.exp(lambda0 * (outer_r - inner_r))
+        
+        denom = lambda0 * besselke(0, lambda0 * outer_r)
+        
+        # Result = (inner_term - outer_term) / (lambda * K0_scaled(outer))
+        # Note the sign flip from the integration limits (Upper - Lower) vs (-x*K1).
+        
+        return (term_inner - term_outer) / denom
+#integrating phi_p_i * d_phi_p_i/dz * r *d_r at z=d[i]
+def int_phi_p_i(i, h, d, a):
+    denom = 16 * (h - d[i])
+    if i == 0:
+        num = a[i]**2*(4*(h-d[i])**2-a[i]**2)
+    else:
+        num = (a[i]**2*(4*(h-d[i])**2-a[i]**2) - a[i-1]**2*(4*(h-d[i])**2-a[i-1]**2))
+    return num/denom
+
+# evaluate an interior region vertical eigenfunction at its top boundary
+def z_n_d(n):
+    if n ==0:
+        return 1
+    else:
+        return sqrt(2)*(-1)**n
+    
+#############################################
+def excitation_phase(x, NMK, m0, a): # x-vector of unknown coefficients
+    coeff = x[-NMK[-1]] # first coefficient of e-region expansion
+    local_scale = scale(a)
+    return -(pi/2) + np.angle(coeff) - np.angle(besselh(0, m0 * local_scale[-1]))
+
+def excitation_force(damping, m0, h):
+    # --- FIX: Handle infinite m0 ---
+    if m0 == inf:
+        return 0.0
+    # -------------------------------
+
+    # Chau 2012 eq 98
+    const = np.tanh(m0 * h) + m0 * h * (1 - (np.tanh(m0 * h))**2)
+
+    return sqrt((2 * const * rho * (g ** 2) * damping)/(omega(m0,h,g) * m0)) ** (1/2)
+
+# --- AFTER ---
+def make_R_Z(a, h, d, sharp, spatial_res, R_range: Optional[np.ndarray] = None, Z_range: Optional[np.ndarray] = None):
+    
+    if R_range is not None:
+        r_vec = R_range
+    else:
+        # Fallback to old behavior
+        rmin = (2 * a[-1] / spatial_res) if sharp else 0.0
+        r_vec = np.linspace(rmin, 2*a[-1], spatial_res)
+
+    if Z_range is not None:
+        z_vec = Z_range
+    else:
+        # Fallback to old behavior
+        z_vec = np.linspace(0, -h, spatial_res) 
+
+    if sharp: # more precise near boundaries
+        # Note: This 'sharp' logic is probably not compatible
+        # with providing R_range/Z_range, but your test
+        # correctly sets sharp=False, so this block is skipped.
+        a_eps = 1.0e-4
+        for i in range(len(a)):
+            r_vec = np.append(r_vec, a[i]*(1-a_eps))
+            r_vec = np.append(r_vec, a[i]*(1+a_eps))
+        r_vec = np.unique(r_vec)
+        for i in range(len(d)):
+            z_vec = np.append(z_vec, -d[i])
+        z_vec = np.unique(z_vec)
+    
+    # THE CRITICAL FIX: Add indexing='ij'
+    return np.meshgrid(r_vec, z_vec, indexing='ij')
+
+def p_diagonal_block(left, radfunction, bd, h, d, a, NMK):
+    region = bd if left else (bd + 1)
+    sign = 1 if left else (-1)
+    return sign * (h - d[region]) * np.diag(radfunction(list(range(NMK[region])), a[bd], region))
+
+def p_dense_block(left, radfunction, bd, NMK, a, I_nm_vals):
+    I_nm_array = I_nm_vals[0:NMK[bd],0:NMK[bd+1], bd]
+    if left: # determine which is region to work in and which is adjacent
+        region, adj = bd, bd + 1
+        sign = 1
+        I_nm_array = np.transpose(I_nm_array)
+    else:
+        region, adj = bd + 1, bd
+        sign = -1
+    radial_vector = radfunction(list(range(NMK[region])), a[bd], region)
+    radial_array = np.outer((np.full((NMK[adj]), 1)), radial_vector)
+    return sign * radial_array * I_nm_array
+
+def p_dense_block_e(bd, I_mk_vals, NMK, a):
+    I_mk_array = I_mk_vals
+    radial_vector = (np.vectorize(Lambda_k, otypes = [complex]))(list(range(NMK[bd+1])), a[bd])
+    radial_array = np.outer((np.full((NMK[bd]), 1)), radial_vector)
+    return (-1) * radial_array * I_mk_array
+            
+# arguments: diagonal block on left (T/F), vectorized radial eigenfunction, boundary number
+def v_diagonal_block(left, radfunction, bd, h, d, NMK, a):
+    region = bd if left else (bd + 1)
+    sign = (-1) if left else (1)
+    return sign * (h - d[region]) * np.diag(radfunction(list(range(NMK[region])), a[bd], region))
+
+# arguments: dense block on left (T/F), vectorized radial eigenfunction, boundary number
+def v_dense_block(left, radfunction, bd, I_nm_vals, NMK, a):
+    I_nm_array = I_nm_vals[0:NMK[bd],0:NMK[bd+1], bd]
+    if left: # determine which is region to work in and which is adjacent
+        region, adj = bd, bd + 1
+        sign = -1
+        I_nm_array = np.transpose(I_nm_array)
+    else:
+        region, adj = bd + 1, bd
+        sign = 1
+    radial_vector = radfunction(list(range(NMK[region])), a[bd], region)
+    radial_array = np.outer((np.full((NMK[adj]), 1)), radial_vector)
+    return sign * radial_array * I_nm_array
+
+def v_diagonal_block_e(bd, h, NMK, a, m0, m_k_arr): # Added m0, m_k_arr to signature
+    # Create the vectorized version of diff_Lambda_k, specifically for this block's needs
+    # This partial application ensures diff_Lambda_k has access to necessary fixed parameters
+    # The 'k' and 'r' for diff_Lambda_k are provided by np.vectorize in the call below.
+    vectorized_diff_Lambda_k_func = np.vectorize(
+        partial(diff_Lambda_k, m0=m0, a=a, m_k_arr=m_k_arr),
+        otypes=[complex]
+    )
+    
+    # Calculate the diagonal elements by applying the vectorized function
+    # 'a[bd]' is the fixed 'r' value for this boundary (radius)
+    diagonal_elements = vectorized_diff_Lambda_k_func(list(range(NMK[bd+1])), a[bd]) # NMK[bd+1] is M
+
+    # Create the diagonal matrix and ensure complex dtype
+    return h * np.diag(diagonal_elements).astype(complex)
+
+def v_dense_block_e(radfunction, bd, I_mk_vals, NMK, a): # for region adjacent to e-type region
+    I_km_array = np.transpose(I_mk_vals)
+    radial_vector = radfunction(list(range(NMK[bd])), a[bd], bd)
+    radial_array = np.outer((np.full((NMK[bd + 1]), 1)), radial_vector)
+
+    return (-1) * radial_array * I_km_array
+
+def p_dense_block_e_entry(m, k, bd, I_mk_vals, NMK, a, m0, h, m_k_arr, N_k_arr):
+    """
+    Compute individual entry (m, k) of the p_dense_block_e matrix at boundary `bd`.
+
+    Parameters:
+        m: int – row index (0 <= m < NMK[bd])
+        k: int – col index (0 <= k < NMK[bd+1])
+        bd: int – boundary index
+        I_mk_vals: ndarray – array of shape (NMK[bd], NMK[bd+1]) with precomputed I_mk values
+        NMK: list[int] – number of harmonics for each region
+        a: list[float] – cylinder radii for each region
+
+    Returns:
+        complex – the matrix entry value
+    """
+
+    return -1 * Lambda_k(k, a[bd], m0, a, m_k_arr) * I_mk_vals[m, k]
+
+def v_dense_block_e_entry(m, k, bd, I_mk_vals, a, h, d): # Added h,d,NMK
+    """
+    Compute individual entry (m, k) of the v_dense_block_e matrix at boundary `bd`.
+    """
+    
+    # In the old code's v_dense_block_e:
+    # radial_vector = radfunction(list(range(NMK[bd])), a[bd], bd)
+    # radfunction would be diff_R_1n_func or diff_R_2n_func
+    # For a given (m,k) entry, 'k' corresponds to the n in diff_R_1n/diff_R_2n.
+    # The 'r' argument for diff_R_1n/diff_R_2n is a[bd].
+    # The 'i' argument for diff_R_1n/diff_R_2n is bd.
+
+    # Determine which radial function to call based on the original logic
+    # This might depend on 'bd' or other conditions that determine R_1n vs R_2n use
+    # For the i-e boundary (bd == boundary_count - 1), typically R_1n is used for the inner region.
+    radial_term = diff_R_1n(k, a[bd], bd, h, d, a) # diff_R_1n is the correct one for this block
+    
+    # I_mk_vals is correctly defined as (NMK[prev_region], NMK[current_region])
+    # The old code used I_km_array = np.transpose(I_mk_vals) and then indexed it as I_km_array[m, k] (local row, local col)
+    # So, if I_mk_vals is (rows_of_prev_region, cols_of_current_region),
+    # then I_km_array[m, k] corresponds to I_mk_vals_untransposed[k, m]
+    imk_term = I_mk_vals[k, m] # k is the 'col' index of current block, m is the 'row' index of current block
+
+    # The outer sign is (-1) from v_dense_block_e
+    result = -1 * radial_term * imk_term
+
+    return result
+
+def v_diagonal_block_e_entry(m, k, bd, m0, m_k_arr, a, h):
+    """
+    Compute individual (m,k) entry of the velocity diagonal block e at boundary bd.
+    """
+    
+    #  need access to 'h' here. It's available in the outer scope
+    # through the problem object, or pass it directly.
+    # Since NMK and a are passed, h should be too if it's not a global constant.
+    # retrieve h from the problem object or pass it.
+    
+    # If h is always domain_list[0].h, can pass it from build_problem_cache
+    # For now, let's explicitly pass h from build_problem_cache to this function
+    # via the closure.
+    
+    radius = a[bd]
+    
+    # Call diff_Lambda_k, ensure it's the correct new version from multi_equations.py
+    # (it is, from code snippet)
+    val = diff_Lambda_k(k, radius, m0, a, m_k_arr)
+ 
+    result =  h * val 
+    
+    return result
+
+def v_dense_block_e_entry_R2(m: int, k: int, bd: int, I_mk_vals: np.ndarray, a: list, h: float, d: list) -> complex:
+    """
+    Computes a single entry for the m0-dependent velocity block at the i-e 
+    boundary, using the R_2n radial eigenfunctions.
+
+    This is used for the second part of the dense coupling block at the final
+    boundary when there are more than two regions.
+
+    Args:
+        m (int): The local row index within the block, corresponding to a mode
+                 in the external region.
+        k (int): The local column index within the block, corresponding to a mode
+                 'n' in the adjacent internal region.
+        bd (int): The boundary index, which should be the final boundary.
+        I_mk_vals (np.ndarray): The m0-dependent coupling integral matrix, I_mk.
+        a (list): A list of the cylinder radii.
+        h (float): The total water depth.
+        d (list): A list of the depths for each region.
+
+    Returns:
+        complex: The computed complex value for the matrix entry A[row, col].
+    """
+    # 1. Calculate the radial term using the derivative of the R_2n function.
+    #    - 'k' is the mode 'n' for the internal region.
+    #    - 'a[bd]' is the radius 'r' at the boundary.
+    #    - 'bd' is the region index 'i'.
+    radial_term = diff_R_2n(k, a[bd], bd, h, d, a)
+    
+    # 2. Get the corresponding coupling integral value.
+    #    The original implementation used a transposed I_mk matrix. An entry
+    #    at [m, k] in the final block corresponds to the entry at [k, m]
+    #    in the non-transposed I_mk_vals matrix.
+    imk_term = I_mk_vals[k, m]
+
+    # 3. Apply the leading sign and return the result.
+    #    The physics of the problem formulation gives this block a -1 sign.
+    return -1 * radial_term * imk_term
\ No newline at end of file
diff --git a/package/src/openflash/problem_cache.py b/package/src/openflash/problem_cache.py
new file mode 100644
index 0000000..8f1b240
--- /dev/null
+++ b/package/src/openflash/problem_cache.py
@@ -0,0 +1,70 @@
+# package/src/openflash/problem_cache.py
+import numpy as np
+from typing import Callable, Dict, Any, Optional
+
+from openflash.multi_equations import *
+
+class ProblemCache:
+    def __init__(self, problem):
+        self.problem = problem
+        self.A_template: Optional[np.ndarray] = None
+        self.b_template: Optional[np.ndarray] = None
+        self.m0_dependent_A_indices: list[tuple[int, int, Callable]] = []
+        self.m0_dependent_b_indices: list[tuple[int, Callable]] = []
+
+        self.m_k_entry_func: Optional[Callable] = None
+        self.N_k_func: Optional[Callable] = None
+        self.m_k_arr: Optional[np.ndarray] = None
+        self.N_k_arr: Optional[np.ndarray] = None
+        
+        # --- FIX: Track the m0 value associated with the current cache ---
+        self.cached_m0: Optional[float] = None 
+        # -----------------------------------------------------------------
+
+        self.I_nm_vals: Optional[np.ndarray] = None
+        self.named_closures: Dict[str, Any] = {}
+
+    def _set_A_template(self, A_template: np.ndarray):
+        self.A_template = A_template
+
+    def _set_b_template(self, b_template: np.ndarray):
+        self.b_template = b_template
+
+    def _add_m0_dependent_A_entry(self, row: int, col: int, calc_func: Callable):
+        self.m0_dependent_A_indices.append((row, col, calc_func))
+
+    def _add_m0_dependent_b_entry(self, row: int, calc_func: Callable):
+        self.m0_dependent_b_indices.append((row, calc_func))
+
+    def _set_m_k_and_N_k_funcs(self, m_k_entry_func: Callable, N_k_func: Callable):
+        self.m_k_entry_func = m_k_entry_func
+        self.N_k_func = N_k_func
+
+    # --- FIX: Accept m0 as an argument to store it ---
+    def _set_precomputed_m_k_N_k(self, m_k_arr: np.ndarray, N_k_arr: np.ndarray, m0: float):
+        """
+        Sets the pre-computed m_k and N_k arrays for a specific m0.
+        """
+        self.m_k_arr = m_k_arr
+        self.N_k_arr = N_k_arr
+        self.cached_m0 = m0
+    # ------------------------------------------------
+
+    def _set_I_nm_vals(self, I_nm_vals: np.ndarray):
+        self.I_nm_vals = I_nm_vals
+
+    def _get_A_template(self) -> np.ndarray:
+        if self.A_template is None:
+            raise ValueError("A_template has not been set.")
+        return self.A_template.copy()
+
+    def _get_b_template(self) -> np.ndarray:
+        if self.b_template is None:
+            raise ValueError("b_template has not been set.")
+        return self.b_template.copy()
+
+    def _set_closure(self, key: str, closure):
+        self.named_closures[key] = closure
+
+    def _get_closure(self, key: str):
+        return self.named_closures.get(key, None)
\ No newline at end of file
diff --git a/package/src/openflash/results.py b/package/src/openflash/results.py
new file mode 100644
index 0000000..e12fdc2
--- /dev/null
+++ b/package/src/openflash/results.py
@@ -0,0 +1,285 @@
+import xarray as xr
+import numpy as np
+from openflash.geometry import Geometry
+from openflash.meem_problem import MEEMProblem
+from .body import CoordinateBody  # <-- ADD THIS IMPORT
+
+class Results:
+    """
+    Class to store results in an xarray format similar to Capytaine's conventions.
+    Provides methods to store, access, and export results to a .nc file.
+    """
+
+    def __init__(self, problem: MEEMProblem):
+        """
+        Initializes the Results class from a MEEMProblem object.
+        """
+        self.geometry = problem.geometry
+        self.frequencies = problem.frequencies
+
+        heaving_bodies = [
+            i for i, body in enumerate(self.geometry.body_arrangement.bodies)
+            if body.heaving
+        ]
+        self.modes = np.array(heaving_bodies)
+
+        self.dataset = xr.Dataset(coords={
+            'frequency': self.frequencies,
+            'mode_i': self.modes,
+            'mode_j': self.modes
+        })
+
+    def store_results(self, domain_index: int, radial_data: np.ndarray, vertical_data: np.ndarray):
+        """
+        Store results (e.g., eigenfunctions) for a specific domain.
+        This method expects radial_data and vertical_data to already be dimensioned
+        as (frequencies, modes, spatial_coords).
+        """
+        domain = self.geometry.domain_list.get(domain_index)
+        if domain is None:
+            raise ValueError(f"Domain index {domain_index} not found.")
+
+        # --- THIS IS THE FIX ---
+        # You are correct, r_coords and z_coords are on the Body objects.
+        # We need to iterate through the bodies in the geometry and collect them.
+        all_r_coords = []
+        all_z_coords = []
+        for body in self.geometry.body_arrangement.bodies:
+            if isinstance(body, CoordinateBody):
+                all_r_coords.append(body.r_coords)
+                all_z_coords.append(body.z_coords)
+
+        # This method is designed for CoordinateBody. If none are found
+        # (e.g., if only SteppedBody is used), r_coords will be empty.
+        if not all_r_coords:
+             r_coords = np.array([])
+             z_coords = np.array([])
+        else:
+             # Concatenate all coordinates from all bodies
+             r_coords = np.concatenate(all_r_coords)
+             z_coords = np.concatenate(all_z_coords)
+        # --- END FIX ---
+
+        if radial_data.shape != (len(self.frequencies), len(self.modes), len(r_coords)):
+            raise ValueError(f"radial_data shape {radial_data.shape} does not match expected "
+                             f"({len(self.frequencies)}, {len(self.modes)}, {len(r_coords)})")
+        if vertical_data.shape != (len(self.frequencies), len(self.modes), len(z_coords)):
+            raise ValueError(f"vertical_data shape {vertical_data.shape} does not match expected "
+                             f"({len(self.frequencies)}, {len(self.modes)}, {len(z_coords)})")
+
+        domain_name = domain.category if domain.category else f"domain_{domain_index}"
+
+        self.dataset[f'radial_eigenfunctions_{domain_name}'] = xr.DataArray(
+            radial_data,
+            dims=['frequency', 'modes', 'r'],
+            coords={
+                'frequency': self.frequencies,
+                'modes': self.modes,
+                'r': r_coords # These should be constant for the geometry
+            }
+        )
+        self.dataset[f'vertical_eigenfunctions_{domain_name}'] = xr.DataArray(
+            vertical_data,
+            dims=['frequency', 'modes', 'z'],
+            coords={
+                'frequency': self.frequencies,
+                'modes': self.modes,
+                'z': z_coords # These should be constant for the geometry
+            }
+        )
+        print(f"Eigenfunctions for domain {domain_index} stored in dataset.")
+    
+    def store_single_potential_field(self, potential_data: dict, frequency_idx: int = 0, mode_idx: int = 0):
+        """
+        Stores a single, fully computed potential field (R, Z, phi) in the dataset.
+
+        :param potential_data: The dictionary returned by `calculate_potentials`.
+        :param frequency_idx: The index of the frequency for this data.
+        :param mode_idx: The index of the mode for this data.
+        """
+        if not all(k in potential_data for k in ["R", "Z", "phi"]):
+            raise ValueError("potential_data must contain 'R', 'Z', and 'phi' keys.")
+
+        # Store each component of the potential field
+        # We add coordinates to make the data self-describing
+        self.dataset[f'potential_R_{mode_idx}_{frequency_idx}'] = xr.DataArray(
+            potential_data["R"],
+            dims=['z_coord', 'r_coord']
+        )
+        self.dataset[f'potential_Z_{mode_idx}_{frequency_idx}'] = xr.DataArray(
+            potential_data["Z"],
+            dims=['z_coord', 'r_coord']
+        )
+        self.dataset[f'potential_phi_real_{mode_idx}_{frequency_idx}'] = xr.DataArray(
+            potential_data["phi"].real,
+            dims=['z_coord', 'r_coord']
+        )
+        self.dataset[f'potential_phi_imag_{mode_idx}_{frequency_idx}'] = xr.DataArray(
+            potential_data["phi"].imag,
+            dims=['z_coord', 'r_coord']
+        )
+        print(f"Stored single potential field for mode {mode_idx} and frequency {frequency_idx}.")
+    
+    # --- METHOD TO STORE BATCHED POTENTIALS ---
+    def store_all_potentials(self, all_potentials_batch: list[dict]):
+        """
+        Store potentials for all frequencies and modes in a structured xarray DataArray.
+
+        :param all_potentials_batch: A list where each element corresponds to a frequency-mode calculation.
+                                    Each element is a dictionary:
+                                    {'frequency_idx': int, 'mode_idx': int,
+                                    'data': {'domain_name': {'potentials': np.ndarray, 'r_coords_dict': dict, 'z_coords_dict': dict}}}
+        """
+        if not all_potentials_batch:
+            print("No potentials data to store.")
+            return
+
+        # Determine unique domain names and max harmonics across all batches
+        domain_names = sorted(list(set(domain_name for item in all_potentials_batch for domain_name in item['data'].keys())))
+        if not domain_names:
+            print("No domain data found in potentials batch.")
+            return
+
+        # Find the maximum number of harmonics for any potential in any domain/frequency
+        max_harmonics = 0
+        for item in all_potentials_batch:
+            for domain_name in item['data'].keys():
+                max_harmonics = max(max_harmonics, len(item['data'][domain_name]['potentials']))
+
+        # Initialize a 4D array: (frequencies, modes, domains, harmonics)
+        potentials_array = np.full(
+            (len(self.frequencies), len(self.modes), len(domain_names), max_harmonics),
+            np.nan + 1j * np.nan, # Ensure complex NaNs for fill value
+            dtype=complex # Explicitly define complex dtype
+        )
+
+        # Create coordinate arrays for r and z
+        r_coord_values = np.full((len(domain_names), max_harmonics), np.nan, dtype=float) 
+        z_coord_values = np.full((len(domain_names), max_harmonics), np.nan, dtype=float) 
+
+
+        for item in all_potentials_batch:
+            freq_idx = item['frequency_idx']
+            mode_idx = item['mode_idx']
+
+            for domain_name, data in item['data'].items():
+                domain_idx = domain_names.index(domain_name)
+                domain_potentials = data['potentials']
+                domain_r_coords = np.concatenate([np.atleast_1d(v) for _, v in sorted(data['r_coords_dict'].items())])
+                domain_z_coords = np.concatenate([np.atleast_1d(v) for _, v in sorted(data['z_coords_dict'].items())])
+
+                
+                if domain_potentials.dtype != complex:
+                    domain_potentials = domain_potentials.astype(complex)
+
+                potentials_array[freq_idx, mode_idx, domain_idx, :len(domain_potentials)] = domain_potentials
+                r_coord_values[domain_idx, :len(domain_r_coords)] = domain_r_coords
+                z_coord_values[domain_idx, :len(domain_z_coords)] = domain_z_coords
+        
+        # Add new coordinates to the dataset if they don't exist
+        if 'harmonics' not in self.dataset.coords:
+            self.dataset.coords['harmonics'] = np.arange(max_harmonics)
+        if 'domain_name' not in self.dataset.coords:
+            self.dataset.coords['domain_name'] = domain_names
+        # Ensure 'modes' coord is present from __init__
+        if 'modes' not in self.dataset.coords:
+            self.dataset.coords['modes'] = self.modes
+
+        self.dataset['potentials_real'] = xr.DataArray(
+            potentials_array.real,
+            dims=['frequency', 'modes', 'domain_name', 'harmonics'],
+            coords={
+                'frequency': self.frequencies,
+                'modes': self.modes,
+                'domain_name': domain_names,
+                'harmonics': np.arange(max_harmonics)
+            }
+        )
+
+        self.dataset['potentials_imag'] = xr.DataArray(
+            potentials_array.imag,
+            dims=['frequency', 'modes', 'domain_name', 'harmonics'],
+            coords={
+                'frequency': self.frequencies,
+                'modes': self.modes,
+                'domain_name': domain_names,
+                'harmonics': np.arange(max_harmonics)
+            }
+        )
+
+        
+        self.dataset['potential_r_coords'] = xr.DataArray(
+            r_coord_values,
+            dims=['domain_name', 'harmonics'],
+            coords={'domain_name': domain_names, 'harmonics': np.arange(max_harmonics)}
+        )
+        self.dataset['potential_z_coords'] = xr.DataArray(
+            z_coord_values,
+            dims=['domain_name', 'harmonics'],
+            coords={'domain_name': domain_names, 'harmonics': np.arange(max_harmonics)}
+        )
+
+        print("Potentials stored in xarray dataset (batched across frequencies/modes).")
+
+
+    def store_hydrodynamic_coefficients(self, frequencies: np.ndarray,
+                                        added_mass_matrix: np.ndarray, damping_matrix: np.ndarray):
+        """
+        Store hydrodynamic coefficients (added mass and damping).
+
+        :param frequencies: Array of frequency values.
+        :param added_mass_matrix: 3D array (frequencies x modes x modes) of added mass coefficients.
+        :param damping_matrix: 3D array (frequencies x modes x modes) of damping coefficients.
+        """
+        # Ensure dimensions match
+        expected_shape = (len(frequencies), len(self.modes), len(self.modes))
+        
+        if added_mass_matrix.shape != expected_shape or \
+           damping_matrix.shape != expected_shape:
+            raise ValueError(
+                f"Matrices must have shape (num_frequencies, num_modes, num_modes). "
+                f"Expected {expected_shape} (based on heaving flags), but got {added_mass_matrix.shape}."
+            )
+
+        # Assign the 3D data with the correct 3D dimension names
+        self.dataset['added_mass'] = (('frequency', 'mode_i', 'mode_j'), added_mass_matrix)
+        self.dataset['damping'] = (('frequency', 'mode_i', 'mode_j'), damping_matrix)
+        print("Hydrodynamic coefficients stored in xarray dataset.")
+
+    def export_to_netcdf(self, file_path: str):
+        """
+        Exports the dataset to a NetCDF file.
+        Complex values are split into real and imaginary parts for compatibility.
+        """
+        def _split_complex(ds):
+            new_vars = {}
+            for var in ds.data_vars:
+                data = ds[var].data
+                if np.iscomplexobj(data):
+                    new_vars[var + "_real"] = (ds[var].dims, np.real(data))
+                    new_vars[var + "_imag"] = (ds[var].dims, np.imag(data))
+                else:
+                    new_vars[var] = ds[var]
+            return xr.Dataset(new_vars, attrs=ds.attrs)
+
+        safe_ds = _split_complex(self.dataset)
+        safe_ds.to_netcdf(file_path, engine="h5netcdf")
+
+    def get_results(self):
+        """
+        Get the stored results as an xarray Dataset.
+
+        :return: xarray.Dataset containing the results.
+        """
+        return self.dataset
+
+    def display_results(self):
+        """
+        Display the stored results in a readable format.
+
+        :return: String representation of the results.
+        """
+        if self.dataset is not None:
+            return str(self.dataset)
+        else:
+            return "No results stored."
\ No newline at end of file
diff --git a/package/src/results.py b/package/src/results.py
deleted file mode 100644
index d15af97..0000000
--- a/package/src/results.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import xarray as xr
-import numpy as np
-from geometry import Geometry
-
-class Results:
-    """
-    Class to store results in an xarray format similar to Capytaine's conventions.
-    Provides methods to store, access, and export results to a .nc file.
-    """
-
-    def __init__(self, geometry: Geometry, frequencies: np.ndarray, modes: np.ndarray):
-        """
-        Initializes the Results class.
-
-        :param geometry: Geometry object that contains the domain and body information.
-        :param frequencies: Array of frequency values.
-        :param modes: Array of mode shapes or identifiers.
-        """
-        self.geometry = geometry
-        self.frequencies = frequencies
-        self.modes = modes
-        self.dataset = xr.Dataset()  # xarray Dataset to store the results
-
-    def store_results(self, domain_index: int, radial_data: np.ndarray, vertical_data: np.ndarray):
-        """Store results."""
-        domain = self.geometry.domain_list.get(domain_index)
-        if domain is None:
-            raise ValueError(f"Domain index {domain_index} not found.")
-        
-
-        r_coords = np.array(list(self.geometry.r_coordinates.values()))
-        z_coords = np.array(list(self.geometry.z_coordinates.values()))
-
-        # Use xr.DataArray to explicitly define dimensions
-        radial_da = xr.DataArray(
-            radial_data,
-            dims=['frequencies', 'modes', 'r'],  # Explicit dimensions!
-            coords={'frequencies': self.frequencies, 'modes': self.modes, 'r': r_coords}
-        )
-
-        vertical_da = xr.DataArray(
-            vertical_data,
-            dims=['frequencies', 'modes', 'z'],  # Explicit dimensions!
-            coords={'frequencies': self.frequencies, 'modes': self.modes, 'z': z_coords}
-        )
-
-        if 'radial_eigenfunctions' not in self.dataset: #check for existence of the DataArray
-            self.dataset['radial_eigenfunctions'] = radial_da
-            self.dataset['vertical_eigenfunctions'] = vertical_da
-        else:
-            self.dataset['radial_eigenfunctions'] = xr.concat([self.dataset['radial_eigenfunctions'], radial_da], dim='r')
-            self.dataset['vertical_eigenfunctions'] = xr.concat([self.dataset['vertical_eigenfunctions'], vertical_da], dim='z')
-
-    def store_potentials(self, potentials: dict):
-        """
-        Store potentials in the dataset.
-
-        :param potentials: Dictionary containing potential values and their coordinates.
-                        Example format: {'domain_name': {'potentials': ..., 'r': ..., 'z': ...}}
-        """
-        if self.dataset is None:
-            raise ValueError("Dataset not initialized. Store eigenfunctions first.")
-
-        domain_names = list(potentials.keys())
-        num_domains = len(domain_names)
-        max_harmonics = max(len(data['potentials']) for data in potentials.values())
-
-        r_coords_array = np.full((num_domains, max_harmonics), np.nan)  # Use NaN for padding
-        z_coords_array = np.full((num_domains, max_harmonics), np.nan)  # Use NaN for padding
-        potentials_array = np.full((num_domains, max_harmonics), np.nan)
-
-        for i, domain_name in enumerate(domain_names):
-            data = potentials[domain_name]
-            domain_potentials = data['potentials']
-            r_coords = data['r']
-            z_coords = data['z']
-
-            r_coords_values = np.array(list(r_coords.values()))
-            z_coords_values = np.array(list(z_coords.values()))
-
-            # Debugging: Inspect coordinate data
-            print(f"Domain: {domain_name}, r_coords length: {len(r_coords)}, z_coords length: {len(z_coords)}, potential length: {len(domain_potentials)}")
-            print(f"Domain: {domain_name}, r_coords keys: {list(r_coords.keys())}, z_coords keys: {list(z_coords.keys())}")
-
-            r_coords_array[i, :len(r_coords_values)] = r_coords_values
-            z_coords_array[i, :len(z_coords_values)] = z_coords_values
-            potentials_array[i, :len(domain_potentials)] = domain_potentials
-
-        self.dataset.coords['domain_r'] = (['domain', 'harmonics'], r_coords_array)
-        self.dataset.coords['domain_z'] = (['domain', 'harmonics'], z_coords_array)
-        self.dataset.coords['domain'] = domain_names
-        self.dataset['domain_potentials'] = (['domain', 'harmonics'], potentials_array)
-
-    def export_to_netcdf(self, file_path: str):
-        """
-        Export the results to a NetCDF (.nc) file.
-
-        :param file_path: Path where the .nc file will be saved.
-        """
-        if self.dataset is not None:
-            self.dataset.to_netcdf(file_path)
-        else:
-            print("No results to export!")
-
-    def get_results(self):
-        """
-        Get the stored results as an xarray Dataset.
-
-        :return: xarray.Dataset containing the results.
-        """
-        return self.dataset
-
-    def display_results(self):
-        """
-        Display the stored results in a readable format.
-
-        :return: String representation of the results.
-        """
-        if self.dataset is not None:
-            return str(self.dataset)
-        else:
-            return "No results stored."
diff --git a/package/test/A_match_check.txt b/package/test/A_match_check.txt
new file mode 100644
index 0000000..b2d1efb
--- /dev/null
+++ b/package/test/A_match_check.txt
@@ -0,0 +1,200 @@
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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diff --git a/package/test/OpenFLASH.ipynb b/package/test/OpenFLASH.ipynb
new file mode 100644
index 0000000..85c46f2
--- /dev/null
+++ b/package/test/OpenFLASH.ipynb
@@ -0,0 +1,978 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c444cd7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Import OpenFLASH Core Modules\n",
+    "from openflash.basic_region_geometry import BasicRegionGeometry\n",
+    "from openflash.meem_problem import MEEMProblem\n",
+    "from openflash.meem_engine import MEEMEngine\n",
+    "from openflash.multi_equations import wavenumber\n",
+    "\n",
+    "# Plotting configuration\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.figsize'] = (10, 6)\n",
+    "plt.rcParams['font.size'] = 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a578e89",
+   "metadata": {},
+   "source": [
+    "Define Simulation Parameters\n",
+    "Here we define a two-step heaving cylinder (similar to the mini_bicylinder from tests)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "389eca0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simulation Depth: 1.001m\n",
+      "Radii: [0.125 0.25 ]\n",
+      "Drafts: [0.25  0.125]\n",
+      "Body Map: [0, 0]\n",
+      "Heaving Map: [True]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- Constants ---\n",
+    "rho = 1023   # Water density (kg/m^3)\n",
+    "g = 9.81     # Gravity (m/s^2)\n",
+    "h = 1.001    # Water depth (m)\n",
+    "\n",
+    "# --- Geometry Definition ---\n",
+    "# A cylinder with two steps (radii a, drafts d)\n",
+    "a_vals = np.array([0.125, 0.25])\n",
+    "d_vals = np.array([0.25, 0.125])\n",
+    "\n",
+    "# Harmonics (NMK): One per domain (Inner + 1 Annulus + Outer = 3 domains)\n",
+    "NMK = [50, 50, 50] \n",
+    "\n",
+    "# --- Body Mapping ---\n",
+    "# Both steps belong to \"Body 0\"\n",
+    "body_map = [0, 0] \n",
+    "\n",
+    "# FIX: heaving_map is per-BODY, not per-step.\n",
+    "# Since we have only 1 body (Body 0), this list should have length 1.\n",
+    "heaving_map = [True] \n",
+    "\n",
+    "print(f\"Simulation Depth: {h}m\")\n",
+    "print(f\"Radii: {a_vals}\")\n",
+    "print(f\"Drafts: {d_vals}\")\n",
+    "print(f\"Body Map: {body_map}\")\n",
+    "print(f\"Heaving Map: {heaving_map}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "416289ae",
+   "metadata": {},
+   "source": [
+    "Initialize OpenFLASH System"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bd976c49",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OpenFLASH System Initialized Successfully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Create Geometry Object\n",
+    "# We use from_vectors for robust setup of multi-step bodies\n",
+    "geometry = BasicRegionGeometry.from_vectors(\n",
+    "    a=a_vals,\n",
+    "    d=d_vals,\n",
+    "    h=h,\n",
+    "    NMK=NMK,\n",
+    "    slant_angle=np.zeros_like(a_vals), # Vertical walls\n",
+    "    body_map=body_map,\n",
+    "    heaving_map=heaving_map\n",
+    ")\n",
+    "\n",
+    "# 2. Create Problem and Engine\n",
+    "problem = MEEMProblem(geometry)\n",
+    "engine = MEEMEngine([problem])\n",
+    "\n",
+    "print(\"OpenFLASH System Initialized Successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bec8cf1e",
+   "metadata": {},
+   "source": [
+    "Geometry Visualization\n",
+    "This draws the physical setup of the problem (stepped cylinder and water depth) so can verify the geometry before solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "87126e13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# --- Visualizing the Geometry ---\n",
+    "fig, ax = plt.subplots(figsize=(6, 6))\n",
+    "\n",
+    "# 1. Plot Water Depth\n",
+    "ax.axhline(0, color='blue', linestyle='--', label='Free Surface (z=0)')\n",
+    "ax.axhline(-h, color='black', linewidth=2, label='Seabed (z=-h)')\n",
+    "\n",
+    "# 2. Plot the Body Steps\n",
+    "# We define rectangles based on radius (a) and draft (d)\n",
+    "current_r = 0.0\n",
+    "colors = ['#1f77b4', '#ff7f0e', '#2ca02c'] # Colors for different steps\n",
+    "\n",
+    "for i, (radius, draft) in enumerate(zip(a_vals, d_vals)):\n",
+    "    # Annulus width for this step\n",
+    "    width = radius - current_r\n",
+    "    \n",
+    "    # Draw rectangle: (x, y) = (inner_radius, -draft)\n",
+    "    rect = plt.Rectangle((current_r, -draft), width, draft, \n",
+    "                         facecolor=colors[i % len(colors)], \n",
+    "                         edgecolor='black', alpha=0.7, label=f'Step {i+1}')\n",
+    "    ax.add_patch(rect)\n",
+    "    current_r = radius\n",
+    "\n",
+    "# 3. Styling\n",
+    "ax.set_xlim(0, max(a_vals) * 2.0)\n",
+    "ax.set_ylim(-h * 1.2, 0.2)\n",
+    "ax.set_xlabel('Radius (r)')\n",
+    "ax.set_ylabel('Depth (z)')\n",
+    "ax.set_title(' axisymmetric Geometry Cross-Section')\n",
+    "ax.set_aspect('equal')\n",
+    "ax.legend(loc='lower right')\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd53d53b",
+   "metadata": {},
+   "source": [
+    "Single Frequency SolutionSolve the linear system for a specific frequency ($\\omega = 2.0$ rad/s)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f66e294a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Solving for Omega = 2.00 rad/s (Wavenumber m0 = 0.6850)...\n",
+      "\n",
+      "--- Results ---\n",
+      "Mode 0:\n",
+      "  Added Mass: 32.0749 kg\n",
+      "  Damping:    20.1062 kg/s\n",
+      "  Exc. Force: 41.6157 N\n",
+      "  Exc. Phase: -0.0233 rad\n",
+      "--------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Target Frequency\n",
+    "omega = 2.0\n",
+    "m0 = wavenumber(omega, h)\n",
+    "\n",
+    "print(f\"Solving for Omega = {omega:.2f} rad/s (Wavenumber m0 = {m0:.4f})...\")\n",
+    "\n",
+    "# Solve Linear System\n",
+    "X = engine.solve_linear_system_multi(problem, m0)\n",
+    "\n",
+    "# Compute Hydrodynamic Coefficients\n",
+    "coeffs = engine.compute_hydrodynamic_coefficients(problem, X, m0, rho=rho)\n",
+    "\n",
+    "# Display Results\n",
+    "print(\"\\n--- Results ---\")\n",
+    "for res in coeffs:\n",
+    "    # Use res.get() to safely access keys, or just print the Mode\n",
+    "    mode_idx = res.get('mode', 'Unknown')\n",
+    "    print(f\"Mode {mode_idx}:\")\n",
+    "    print(f\"  Added Mass: {res['real']:.4f} kg\")\n",
+    "    print(f\"  Damping:    {res['imag']:.4f} kg/s\")\n",
+    "    \n",
+    "    # Check if excitation keys exist before printing (they should for wave problems)\n",
+    "    if 'excitation_force' in res:\n",
+    "        print(f\"  Exc. Force: {res['excitation_force']:.4f} N\")\n",
+    "    if 'excitation_phase' in res:\n",
+    "        print(f\"  Exc. Phase: {res['excitation_phase']:.4f} rad\")\n",
+    "    print(\"-\" * 20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0a4a298",
+   "metadata": {},
+   "source": [
+    "Intercept the System MatrixThis cell defines a helper class to \"spy\" on numpy.linalg.solve and capture the matrix $A$ and vector $b$ when engine.solve_linear_system_multi runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0bc5e875",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Re-solving for Omega = 2.00 to capture system matrix...\n",
+      "Matrix Captured. Shape: (200, 200)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import scipy.linalg\n",
+    "\n",
+    "# --- Matrix Interception Helper ---\n",
+    "class MatrixSpy:\n",
+    "    \"\"\"\n",
+    "    Context manager to intercept the matrix 'A' passed to \n",
+    "    either numpy.linalg.solve OR scipy.linalg.solve.\n",
+    "    \"\"\"\n",
+    "    def __init__(self):\n",
+    "        self.A = None\n",
+    "        self.b = None\n",
+    "        self._orig_np_solve = np.linalg.solve\n",
+    "        self._orig_sp_solve = scipy.linalg.solve\n",
+    "\n",
+    "    def __enter__(self):\n",
+    "        # 1. Define wrapper for NumPy\n",
+    "        def np_spy(a, b, *args, **kwargs):\n",
+    "            self.A = a\n",
+    "            self.b = b\n",
+    "            return self._orig_np_solve(a, b, *args, **kwargs)\n",
+    "\n",
+    "        # 2. Define wrapper for SciPy\n",
+    "        def sp_spy(a, b, *args, **kwargs):\n",
+    "            self.A = a\n",
+    "            self.b = b\n",
+    "            return self._orig_sp_solve(a, b, *args, **kwargs)\n",
+    "        \n",
+    "        # 3. Apply the patches\n",
+    "        np.linalg.solve = np_spy\n",
+    "        scipy.linalg.solve = sp_spy\n",
+    "        return self\n",
+    "\n",
+    "    def __exit__(self, exc_type, exc_val, exc_tb):\n",
+    "        # Restore original functions\n",
+    "        np.linalg.solve = self._orig_np_solve\n",
+    "        scipy.linalg.solve = self._orig_sp_solve\n",
+    "\n",
+    "# --- Capture the Matrix ---\n",
+    "print(f\"Re-solving for Omega = {omega:.2f} to capture system matrix...\")\n",
+    "\n",
+    "with MatrixSpy() as spy:\n",
+    "    # Run the solver again (result X is ignored)\n",
+    "    _ = engine.solve_linear_system_multi(problem, m0)\n",
+    "\n",
+    "# Check if we successfully captured it\n",
+    "if spy.A is not None:\n",
+    "    captured_matrix = spy.A\n",
+    "    print(f\"Matrix Captured. Shape: {captured_matrix.shape}\")\n",
+    "else:\n",
+    "    print(\"Error: Could not capture matrix. The engine might be using a different solver (e.g., lstsq or inv).\")\n",
+    "    # Create a dummy matrix so the next cell doesn't crash\n",
+    "    captured_matrix = np.eye(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ff83385",
+   "metadata": {},
+   "source": [
+    "Plot Matrix Sparsity and Thresholds\n",
+    "This cell generates the two specific plots:\n",
+    "\n",
+    "Non-Zero Entries: Visualizes the raw structure of the matrix.\n",
+    "\n",
+    "Entries Below Threshold: Visualizes \"noise\" or checks for values that should be zero but aren't (or vice versa)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e2926fa4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved 'A_values_openflash.csv' to simulate external data input.\n",
+      "Comparison complete. Matching entries saved to 'A_match_check.txt'.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Non-Zeros: 15103\n",
+      "Total Mismatches: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# --- 1. SETUP: Mimic the \"External Data\" from MEEM.ipynb ---\n",
+    "if 'captured_matrix' not in locals() or captured_matrix is None:\n",
+    "    raise ValueError(\"Please run the 'MatrixSpy' cell above to capture the system matrix first.\")\n",
+    "\n",
+    "# FIX: Use Pandas to save the matrix. \n",
+    "# This avoids the complex formatting error in np.savetxt and ensures compatibility.\n",
+    "pd.DataFrame(captured_matrix).to_csv(\"A_values_openflash.csv\", header=False, index=False)\n",
+    "print(\"Saved 'A_values_openflash.csv' to simulate external data input.\")\n",
+    "\n",
+    "# --- 2. LOAD DATA (Matching MEEM.ipynb Logic) ---\n",
+    "file_path = 'A_values_openflash.csv'\n",
+    "df = pd.read_csv(file_path, header=None)\n",
+    "\n",
+    "# Function from MEEM.ipynb to parse complex numbers\n",
+    "def to_complex(val):\n",
+    "    try:\n",
+    "        # Handle standard string conversion\n",
+    "        return np.complex128(val)\n",
+    "    except Exception:\n",
+    "        # Fallback for different formatting (e.g. replacing 'i' with 'j' or removing parens)\n",
+    "        try:\n",
+    "            val_str = str(val).replace('i', 'j').replace('(', '').replace(')', '')\n",
+    "            return np.complex128(val_str)\n",
+    "        except:\n",
+    "            return np.nan\n",
+    "\n",
+    "# Convert loaded data to complex numpy array\n",
+    "# Apply map element-wise to handle the string-to-complex conversion\n",
+    "df_complex = df.map(to_complex) \n",
+    "A_num = df_complex.to_numpy()\n",
+    "\n",
+    "# --- 3. COMPARE: Analytical (OpenFLASH) vs Numerical (CSV) ---\n",
+    "A_analytical = captured_matrix\n",
+    "threshold = 1e-10\n",
+    "\n",
+    "# Check for matches\n",
+    "# We align shapes just in case (pandas might read an extra index column if not careful)\n",
+    "if A_num.shape != A_analytical.shape:\n",
+    "    print(f\"Shape Warning: Loaded {A_num.shape} vs Captured {A_analytical.shape}. Trimming to match.\")\n",
+    "    min_rows = min(A_num.shape[0], A_analytical.shape[0])\n",
+    "    min_cols = min(A_num.shape[1], A_analytical.shape[1])\n",
+    "    A_num = A_num[:min_rows, :min_cols]\n",
+    "    A_analytical = A_analytical[:min_rows, :min_cols]\n",
+    "\n",
+    "is_within_threshold = np.isclose(A_num, A_analytical, rtol=threshold, atol=threshold)\n",
+    "\n",
+    "# Save comparison results\n",
+    "np.savetxt(\"A_match_check.txt\", is_within_threshold, fmt='%d')\n",
+    "print(f\"Comparison complete. Matching entries saved to 'A_match_check.txt'.\")\n",
+    "\n",
+    "# --- 4. VISUALIZATION (Exact MEEM.ipynb Style) ---\n",
+    "\n",
+    "# Calculate Block Boundaries for Grid Lines\n",
+    "boundaries = []\n",
+    "current_idx = 0\n",
+    "for n_modes in NMK:\n",
+    "    current_idx += n_modes\n",
+    "    boundaries.append(current_idx)\n",
+    "if boundaries[-1] == A_analytical.shape[0]: boundaries.pop()\n",
+    "\n",
+    "# --- Plot 1: Non-Zero Entries of the Matrix ---\n",
+    "rows, cols = np.nonzero(np.abs(A_analytical) > 1e-15)\n",
+    "\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(cols, rows, color='blue', marker='o', s=100) \n",
+    "plt.gca().invert_yaxis() \n",
+    "\n",
+    "matrix_size = A_analytical.shape[0]\n",
+    "if matrix_size < 30:\n",
+    "    plt.xticks(range(matrix_size))\n",
+    "    plt.yticks(range(matrix_size))\n",
+    "\n",
+    "for val in boundaries:\n",
+    "    plt.axvline(val-0.5, color='black', linestyle='-', linewidth=1) \n",
+    "    plt.axhline(val-0.5, color='black', linestyle='-', linewidth=1) \n",
+    "\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.title('Non-Zero Entries of the Matrix')\n",
+    "plt.xlabel('Column Index')\n",
+    "plt.ylabel('Row Index')\n",
+    "plt.show()\n",
+    "\n",
+    "# --- Plot 2: Non-Zero Entries Not Matching the Threshold ---\n",
+    "# This matches the \"Blue Dots = Mismatch\" style from MEEM.ipynb\n",
+    "rows_mismatch, cols_mismatch = np.nonzero(~is_within_threshold)\n",
+    "\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "\n",
+    "if len(rows_mismatch) > 0:\n",
+    "    plt.scatter(cols_mismatch, rows_mismatch, color='blue', marker='o', s=100)\n",
+    "    title_text = 'Non-Zero Entries Not Matching Threshold'\n",
+    "else:\n",
+    "    plt.text(matrix_size/2, matrix_size/2, \"Perfect Match\", ha='center', fontsize=12)\n",
+    "    title_text = 'No Mismatches Found'\n",
+    "\n",
+    "plt.gca().invert_yaxis()\n",
+    "\n",
+    "if matrix_size < 30:\n",
+    "    plt.xticks(range(matrix_size))\n",
+    "    plt.yticks(range(matrix_size))\n",
+    "\n",
+    "for val in boundaries:\n",
+    "    plt.axvline(val - 0.5, color='black', linestyle='-', linewidth=1)\n",
+    "    plt.axhline(val - 0.5, color='black', linestyle='-', linewidth=1)\n",
+    "\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.title(title_text)\n",
+    "plt.xlabel('Column Index')\n",
+    "plt.ylabel('Row Index')\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"Total Non-Zeros: {len(rows)}\")\n",
+    "print(f\"Total Mismatches: {len(rows_mismatch)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b132d4f",
+   "metadata": {},
+   "source": [
+    "Matrix Structure & Threshold Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "23adc5ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculated Block Boundaries: [50, 100, 150]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# --- Configuration ---\n",
+    "# Threshold for \"Non-Zero\" (Physics) vs \"Numerical Noise\"\n",
+    "noise_threshold = 1e-10 \n",
+    "\n",
+    "# Get the matrix captured by MatrixSpy in the previous cell\n",
+    "if 'captured_matrix' not in locals() or captured_matrix is None:\n",
+    "    raise ValueError(\"Please run the 'MatrixSpy' cell above to capture the system matrix first.\")\n",
+    "\n",
+    "A_final = captured_matrix\n",
+    "\n",
+    "# --- 1. Calculate Grid Lines (Block Boundaries) ---\n",
+    "# MEEM matrices are structured as: [Inner] -> [Annulus 1_a] -> [Annulus 1_b] -> ... -> [Outer]\n",
+    "boundaries = []\n",
+    "current_idx = 0\n",
+    "\n",
+    "# Inner Region (1 block)\n",
+    "if len(NMK) > 0:\n",
+    "    current_idx += NMK[0]\n",
+    "    boundaries.append(current_idx)\n",
+    "\n",
+    "# Annular Regions (2 blocks each)\n",
+    "for k in range(1, len(NMK) - 1):\n",
+    "    # Part 1 of Annulus coefficients\n",
+    "    current_idx += NMK[k]\n",
+    "    boundaries.append(current_idx)\n",
+    "    # Part 2 of Annulus coefficients\n",
+    "    current_idx += NMK[k]\n",
+    "    boundaries.append(current_idx)\n",
+    "\n",
+    "# Outer Region (1 block) - ends at the matrix dimension, no line needed usually\n",
+    "print(f\"Calculated Block Boundaries: {boundaries}\")\n",
+    "\n",
+    "# --- 2. Plot 1: Non-Zero Entries (Structure) ---\n",
+    "rows, cols = np.nonzero(np.abs(A_final) > noise_threshold)\n",
+    "\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "plt.scatter(cols, rows, color='blue', marker='o', s=10) # 's' is marker size\n",
+    "plt.gca().invert_yaxis() # Matrix convention: row 0 at top\n",
+    "\n",
+    "# Draw Block Grid Lines\n",
+    "for val in boundaries:\n",
+    "    plt.axvline(val - 0.5, color='black', linestyle='-', linewidth=1)\n",
+    "    plt.axhline(val - 0.5, color='black', linestyle='-', linewidth=1)\n",
+    "\n",
+    "plt.grid(True, linestyle=':', alpha=0.6)\n",
+    "plt.title(f'Non-Zero Entries of the Matrix (Size {A_final.shape})')\n",
+    "plt.xlabel('Column Index')\n",
+    "plt.ylabel('Row Index')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# --- 3. Plot 2: Low-Value Entries (Potential Numerical Artifacts) ---\n",
+    "# In MEEM.ipynb, this compared against a reference CSV. \n",
+    "# Here, we visualize entries that are \"Non-Zero but Small\" (likely noise)\n",
+    "# vs \"Significant Physics\" to help debug stability.\n",
+    "\n",
+    "# Define a \"Small but not zero\" range\n",
+    "upper_noise_limit = 1e-2\n",
+    "lower_noise_limit = 1e-15\n",
+    "\n",
+    "# Find indices\n",
+    "mask_noise = (np.abs(A_final) < upper_noise_limit) & (np.abs(A_final) > lower_noise_limit)\n",
+    "rows_n, cols_n = np.nonzero(mask_noise)\n",
+    "\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "\n",
+    "if len(rows_n) > 0:\n",
+    "    plt.scatter(cols_n, rows_n, color='red', marker='x', s=20, label=f'Small/Noise (< {upper_noise_limit})')\n",
+    "else:\n",
+    "    plt.text(A_final.shape[1]/2, A_final.shape[0]/2, \"No entries in noise range\", ha='center')\n",
+    "\n",
+    "plt.gca().invert_yaxis()\n",
+    "\n",
+    "# Draw Grid Lines\n",
+    "for val in boundaries:\n",
+    "    plt.axvline(val - 0.5, color='black', linestyle='-', linewidth=1)\n",
+    "    plt.axhline(val - 0.5, color='black', linestyle='-', linewidth=1)\n",
+    "\n",
+    "plt.grid(True, linestyle=':', alpha=0.6)\n",
+    "plt.title('Small Magnitude Entries (Potential Numerical Instability)')\n",
+    "plt.xlabel('Column Index')\n",
+    "plt.ylabel('Row Index')\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# --- 4. (Optional) Export for External Comparison ---\n",
+    "# This saves the matrix so can use it as 'A_values.csv' in the future\n",
+    "# np.savetxt(\"openflash_A_matrix_real.csv\", np.real(A_final), delimiter=\",\")\n",
+    "# np.savetxt(\"openflash_A_matrix_imag.csv\", np.imag(A_final), delimiter=\",\")\n",
+    "# print(\"Matrix dumped to CSV for comparison.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3951e50",
+   "metadata": {},
+   "source": [
+    "Visualize Potential Field\n",
+    "Generate a contour plot of the velocity potential around the body."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1393cd43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:12: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "<>:20: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "<>:12: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "<>:20: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "/var/folders/y0/9n1rj1dx3md8kwg6n8jblkzm0000gn/T/ipykernel_12983/1778636678.py:12: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "  \"Real Part of Velocity Potential ($\\phi_R$)\"\n",
+      "/var/folders/y0/9n1rj1dx3md8kwg6n8jblkzm0000gn/T/ipykernel_12983/1778636678.py:20: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "  \"Imaginary Part of Velocity Potential ($\\phi_I$)\"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Grid Resolution\n",
+    "resolution = 100\n",
+    "\n",
+    "# Calculate Potential Field\n",
+    "potentials = engine.calculate_potentials(problem, X, m0, resolution, sharp=True)\n",
+    "R, Z, phi = potentials[\"R\"], potentials[\"Z\"], potentials[\"phi\"]\n",
+    "\n",
+    "# Plot Real Part (Velocity Potential in phase with velocity)\n",
+    "fig, ax = engine.visualize_potential(\n",
+    "    np.real(phi), \n",
+    "    R, Z, \n",
+    "    \"Real Part of Velocity Potential ($\\phi_R$)\"\n",
+    ")\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot Imaginary Part (Velocity Potential out of phase)\n",
+    "fig, ax = engine.visualize_potential(\n",
+    "    np.imag(phi), \n",
+    "    R, Z, \n",
+    "    \"Imaginary Part of Velocity Potential ($\\phi_I$)\"\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df16c11c",
+   "metadata": {},
+   "source": [
+    "Pressure & Surface Elevation (Insert after Cell 5)MEEM.ipynb calculates Pressure ($P$) and Elevation ($\\eta$) from the velocity potential. Here is the math translated to OpenFLASH:Dynamic Pressure: $P = -\\rho \\cdot i \\omega \\cdot \\phi$Surface Elevation: $\\eta = \\frac{i \\omega}{g} \\cdot \\phi|_{z=0}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6d4b6fc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:30: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "<>:31: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "<>:32: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "<>:34: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "<>:30: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "<>:31: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "<>:32: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "<>:34: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "/var/folders/y0/9n1rj1dx3md8kwg6n8jblkzm0000gn/T/ipykernel_12983/122015789.py:30: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "  plt.plot(r_surface, np.real(eta), 'b-', label='Real $\\eta$ (In Phase)')\n",
+      "/var/folders/y0/9n1rj1dx3md8kwg6n8jblkzm0000gn/T/ipykernel_12983/122015789.py:31: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "  plt.plot(r_surface, np.imag(eta), 'r--', label='Imag $\\eta$ (Out Phase)')\n",
+      "/var/folders/y0/9n1rj1dx3md8kwg6n8jblkzm0000gn/T/ipykernel_12983/122015789.py:32: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "  plt.plot(r_surface, np.abs(eta), 'k-', linewidth=2, alpha=0.3, label='Magnitude $|\\eta|$')\n",
+      "/var/folders/y0/9n1rj1dx3md8kwg6n8jblkzm0000gn/T/ipykernel_12983/122015789.py:34: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "  plt.title(f'Free Surface Elevation ($\\eta$) at $\\omega$={omega:.2f}')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# --- Physics Calculations ---\n",
+    "# P = -rho * i * omega * phi\n",
+    "# eta = (i * omega / g) * phi_at_surface\n",
+    "\n",
+    "# 1. Calculate Pressure Field\n",
+    "pressure_field = -rho * 1j * omega * phi\n",
+    "\n",
+    "# 2. Visualize Pressure (Real Part - \"In Phase\")\n",
+    "fig, ax = engine.visualize_potential(\n",
+    "    np.real(pressure_field), \n",
+    "    R, Z, \n",
+    "    \"Dynamic Pressure Field (Real Part) [Pa]\"\n",
+    ")\n",
+    "plt.show()\n",
+    "\n",
+    "# 3. Calculate Free Surface Elevation (eta)\n",
+    "# We extract the potential at z=0 (assumed to be the top row of our grid if generated top-down)\n",
+    "# Or we just use the calculated grid:\n",
+    "surface_indices = np.where(np.isclose(Z, 0, atol=1e-3))\n",
+    "\n",
+    "# Extract R at surface and Phi at surface\n",
+    "r_surface = R[surface_indices]\n",
+    "phi_surface = phi[surface_indices]\n",
+    "\n",
+    "# Calculate eta\n",
+    "eta = (1j * omega / g) * phi_surface\n",
+    "\n",
+    "# 4. Plot Free Surface Elevation\n",
+    "plt.figure(figsize=(10, 4))\n",
+    "plt.plot(r_surface, np.real(eta), 'b-', label='Real $\\eta$ (In Phase)')\n",
+    "plt.plot(r_surface, np.imag(eta), 'r--', label='Imag $\\eta$ (Out Phase)')\n",
+    "plt.plot(r_surface, np.abs(eta), 'k-', linewidth=2, alpha=0.3, label='Magnitude $|\\eta|$')\n",
+    "\n",
+    "plt.title(f'Free Surface Elevation ($\\eta$) at $\\omega$={omega:.2f}')\n",
+    "plt.xlabel('Distance from center (r) [m]')\n",
+    "plt.ylabel('Wave Elevation [m]')\n",
+    "plt.axhline(0, color='k', linewidth=0.5)\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8aca2a3",
+   "metadata": {},
+   "source": [
+    "Frequency Sweep\n",
+    "Run a sweep across a range of frequencies to generate the Added Mass and Damping curves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c671d2d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running frequency sweep from 0.5 to 4.0 rad/s (30 steps)...\n",
+      "Hydrodynamic coefficients stored in xarray dataset.\n",
+      "Potentials stored in xarray dataset (batched across frequencies/modes).\n",
+      "Sweep Complete.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define Frequency Range\n",
+    "omega_start = 0.5\n",
+    "omega_end = 4.0\n",
+    "steps = 30\n",
+    "omegas = np.linspace(omega_start, omega_end, steps)\n",
+    "\n",
+    "print(f\"Running frequency sweep from {omega_start} to {omega_end} rad/s ({steps} steps)...\")\n",
+    "\n",
+    "# Update Problem Frequencies\n",
+    "problem.set_frequencies(omegas)\n",
+    "\n",
+    "# Run Sweep (using the engine's batch runner)\n",
+    "results_obj = engine.run_and_store_results(problem_index=0)\n",
+    "ds = results_obj.get_results() # Returns an xarray Dataset\n",
+    "\n",
+    "# Convert to Pandas for easy plotting\n",
+    "df = ds[['added_mass', 'damping']].to_dataframe().reset_index()\n",
+    "\n",
+    "# Filter for the relevant mode (Heave, usually mode_i=0, mode_j=0 for single body)\n",
+    "# Adjust mode indices if have multiple bodies\n",
+    "heave_data = df[(df['mode_i'] == 0) & (df['mode_j'] == 0)]\n",
+    "\n",
+    "print(\"Sweep Complete.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be85ab6c",
+   "metadata": {},
+   "source": [
+    "Plot Frequency Sweep Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2553dd75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:30: SyntaxWarning: invalid escape sequence '\\o'\n",
+      "<>:38: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "<>:40: SyntaxWarning: invalid escape sequence '\\o'\n",
+      "<>:30: SyntaxWarning: invalid escape sequence '\\o'\n",
+      "<>:38: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "<>:40: SyntaxWarning: invalid escape sequence '\\o'\n",
+      "/var/folders/y0/9n1rj1dx3md8kwg6n8jblkzm0000gn/T/ipykernel_12983/1898491630.py:30: SyntaxWarning: invalid escape sequence '\\o'\n",
+      "  ax3.set_xlabel('Frequency $\\omega$ [rad/s]')\n",
+      "/var/folders/y0/9n1rj1dx3md8kwg6n8jblkzm0000gn/T/ipykernel_12983/1898491630.py:38: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "  ax4.set_ylabel('Phase ($\\phi$) [rad]')\n",
+      "/var/folders/y0/9n1rj1dx3md8kwg6n8jblkzm0000gn/T/ipykernel_12983/1898491630.py:40: SyntaxWarning: invalid escape sequence '\\o'\n",
+      "  ax4.set_xlabel('Frequency $\\omega$ [rad/s]')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# --- Full Hydrodynamic Coefficient Plots ---\n",
+    "\n",
+    "# Create a 2x2 grid of plots\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 10), sharex=True)\n",
+    "((ax1, ax2), (ax3, ax4)) = axes\n",
+    "\n",
+    "# Filter for the relevant mode (Heave: 0,0)\n",
+    "# Note: Ensure results DataFrame columns match.\n",
+    "# 'mode_i' and 'mode_j' might be needed depending on how xarray converted it.\n",
+    "heave_data = df[(df['mode_i'] == 0) & (df['mode_j'] == 0)]\n",
+    "\n",
+    "# 1. Added Mass\n",
+    "ax1.plot(heave_data['frequency'], heave_data['added_mass'], 'b-o', markersize=4)\n",
+    "ax1.set_ylabel('Added Mass ($A_{33}$) [kg]')\n",
+    "ax1.set_title('Added Mass')\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# 2. Damping\n",
+    "ax2.plot(heave_data['frequency'], heave_data['damping'], 'r-s', markersize=4)\n",
+    "ax2.set_ylabel('Damping ($B_{33}$) [kg/s]')\n",
+    "ax2.set_title('Radiation Damping')\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "# 3. Excitation Force Magnitude\n",
+    "# Usually labeled 'excitation_force' or 'force_mag' in the dataset\n",
+    "if 'excitation_force' in heave_data.columns:\n",
+    "    ax3.plot(heave_data['frequency'], heave_data['excitation_force'], 'g-^', markersize=4)\n",
+    "    ax3.set_ylabel('Excitation Force ($|F_X|$) [N/m]')\n",
+    "    ax3.set_title('Excitation Force Magnitude')\n",
+    "    ax3.set_xlabel('Frequency $\\omega$ [rad/s]')\n",
+    "    ax3.grid(True, alpha=0.3)\n",
+    "else:\n",
+    "    ax3.text(0.5, 0.5, \"Excitation Data Not Found\", ha='center')\n",
+    "\n",
+    "# 4. Excitation Phase\n",
+    "if 'excitation_phase' in heave_data.columns:\n",
+    "    ax4.plot(heave_data['frequency'], heave_data['excitation_phase'], 'm-d', markersize=4)\n",
+    "    ax4.set_ylabel('Phase ($\\phi$) [rad]')\n",
+    "    ax4.set_title('Excitation Phase')\n",
+    "    ax4.set_xlabel('Frequency $\\omega$ [rad/s]')\n",
+    "    ax4.grid(True, alpha=0.3)\n",
+    "else:\n",
+    "    ax4.text(0.5, 0.5, \"Phase Data Not Found\", ha='center')\n",
+    "\n",
+    "plt.suptitle(f\"Hydrodynamic Coefficients for Heaving Body\", fontsize=16)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv (3.12.1)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/package/test/b_match.txt b/package/test/b_match.txt
deleted file mode 100644
index 58501cb..0000000
--- a/package/test/b_match.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
diff --git a/package/test/data/gold_standard_A_2region.npy b/package/test/data/gold_standard_A_2region.npy
new file mode 100644
index 0000000..30df7c3
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diff --git a/package/test/debug_plots/big_bicylinder_regenerated_v2_debug.png b/package/test/debug_plots/big_bicylinder_regenerated_v2_debug.png
new file mode 100644
index 0000000..5758b6f
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diff --git a/package/test/debug_plots/big_tricylinder_regenerated_v2_debug.png b/package/test/debug_plots/big_tricylinder_regenerated_v2_debug.png
new file mode 100644
index 0000000..1e2479d
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diff --git a/package/test/debug_plots/mini_bicylinder_regenerated_v2_debug.png b/package/test/debug_plots/mini_bicylinder_regenerated_v2_debug.png
new file mode 100644
index 0000000..6cd188c
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diff --git a/package/test/debug_plots/mini_tricylinder_regenerated_v2_debug.png b/package/test/debug_plots/mini_tricylinder_regenerated_v2_debug.png
new file mode 100644
index 0000000..18756b1
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diff --git a/package/test/debug_plots/small_bicylinder_regenerated_v2_debug.png b/package/test/debug_plots/small_bicylinder_regenerated_v2_debug.png
new file mode 100644
index 0000000..7fe7321
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diff --git a/package/test/debug_plots/small_tricylinder_regenerated_v2_debug.png b/package/test/debug_plots/small_tricylinder_regenerated_v2_debug.png
new file mode 100644
index 0000000..6c4d6b4
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diff --git a/package/test/debug_results/DEBUG_big_bicylinder_regenerated_v2.csv b/package/test/debug_results/DEBUG_big_bicylinder_regenerated_v2.csv
new file mode 100644
index 0000000..22302e9
--- /dev/null
+++ b/package/test/debug_results/DEBUG_big_bicylinder_regenerated_v2.csv
@@ -0,0 +1,16 @@
+m0,of_nondim,bem,ratio,status
+0.1,1.7391255295675148,5.004223722567714,0.3475315305597795,FAIL
+0.5214285714285715,0.9557168611780618,0.9223082614280188,1.0362228130737072,OK
+0.9428571428571428,0.7459776344670496,0.7248642749145081,1.029127328085015,OK
+1.3642857142857143,0.6800307530348014,0.6638736315343334,1.024337646101602,OK
+1.7857142857142858,0.6728268122476017,0.6580236821646165,1.022496348511788,OK
+2.2071428571428573,0.6888353433029285,0.6747415191491745,1.0208877381245576,OK
+2.628571428571429,0.7110340836916224,0.6971111113527899,1.0199723861979677,OK
+3.0500000000000003,0.7321463001835209,0.7205515297501681,1.0160915215007218,OK
+3.4714285714285715,0.7498878425690151,0.7048257620301526,1.063933645684385,OK
+3.892857142857143,0.7641028394228656,0.7434103268907585,1.0278345777340108,OK
+4.314285714285714,0.7753285183147148,0.7520097902854018,1.0310085431473746,OK
+4.735714285714286,0.7842078338301787,0.7609378747832347,1.030580629270915,OK
+5.157142857142857,0.7912979887084651,0.7691760977030075,1.028760502401882,OK
+5.578571428571428,0.7970335581019456,0.7732771123249431,1.0307217754131843,OK
+6.0,0.801739699079379,0.7779129601371405,1.0306290551298156,OK
diff --git a/package/test/debug_results/DEBUG_big_tricylinder_regenerated_v2.csv b/package/test/debug_results/DEBUG_big_tricylinder_regenerated_v2.csv
new file mode 100644
index 0000000..89b57ed
--- /dev/null
+++ b/package/test/debug_results/DEBUG_big_tricylinder_regenerated_v2.csv
@@ -0,0 +1,16 @@
+m0,of_nondim,bem,ratio,status
+0.1,0.6891201516845136,0.6729306016563106,1.0240582758286738,OK
+0.5214285714285715,0.8458199748298219,0.8113566974358363,1.042476111311956,OK
+0.9428571428571428,0.8575184019308894,0.823342670503205,1.0415085148044096,OK
+1.3642857142857143,0.8617152371018971,0.827692651586989,1.0411053371679384,OK
+1.7857142857142858,0.8638790115002627,0.8291779311682617,1.0418499805983854,OK
+2.2071428571428573,0.8651995734559816,0.8308738596082048,1.041312785870966,OK
+2.628571428571429,0.8660896017708776,0.8336594921035333,1.0389009061547598,OK
+3.0500000000000003,0.8667301931283952,0.8320323517593444,1.0417025146866965,OK
+3.4714285714285715,0.867213345861403,0.8344679943737874,1.0392409915160243,OK
+3.892857142857143,0.8675907600159561,0.8339275805203786,1.040367029802002,OK
+4.314285714285714,0.8678937272058113,0.8328983979648704,1.042016324351745,OK
+4.735714285714286,0.8681422976707068,0.8341941476974042,1.04069574219264,OK
+5.157142857142857,0.8683499131092861,0.8340806435969057,1.041086278377828,OK
+5.578571428571428,0.8685259226400104,0.8332064072663765,1.042389874904481,OK
+6.0,0.8686770305422724,0.8352854245679525,1.039976282348745,OK
diff --git a/package/test/debug_results/DEBUG_mini_bicylinder_regenerated_v2.csv b/package/test/debug_results/DEBUG_mini_bicylinder_regenerated_v2.csv
new file mode 100644
index 0000000..fdf1a15
--- /dev/null
+++ b/package/test/debug_results/DEBUG_mini_bicylinder_regenerated_v2.csv
@@ -0,0 +1,16 @@
+m0,of_nondim,bem,ratio,status
+0.1,1.3970710507139301,2.7898217503007268,0.5007743059438596,FAIL
+0.5214285714285715,1.0738571700218709,1.0417633912109503,1.0308071670416588,OK
+0.9428571428571428,0.9601747124613211,0.932893194477221,1.029243988642653,OK
+1.3642857142857143,0.8826285629850649,0.8585414735317956,1.028055825135834,OK
+1.7857142857142858,0.8190150635347876,0.7972800846592121,1.027261409501864,OK
+2.2071428571428573,0.7660758890635175,0.746727398951189,1.0259110488506304,OK
+2.628571428571429,0.7237206502310843,0.7064248267220832,1.0244836008797373,OK
+3.0500000000000003,0.6913031604454659,0.6754444508995722,1.023478924913474,OK
+3.4714285714285715,0.6674746246622458,0.6527981590018734,1.0224823943787045,OK
+3.892857142857143,0.6506721505657517,0.6376932196560324,1.020352938544211,OK
+4.314285714285714,0.6394349275462865,0.6271948903606376,1.019515524398821,OK
+4.735714285714286,0.632519111195094,0.6212777916307431,1.0180938699496151,OK
+5.157142857142857,0.6289099757888724,0.6186076102847586,1.0166541202093704,OK
+5.578571428571428,0.6277966923882631,0.617975709545595,1.015892182639168,OK
+6.0,0.6285386670114577,0.6193634031256311,1.0148140233012208,OK
diff --git a/package/test/debug_results/DEBUG_mini_tricylinder_regenerated_v2.csv b/package/test/debug_results/DEBUG_mini_tricylinder_regenerated_v2.csv
new file mode 100644
index 0000000..0976e44
--- /dev/null
+++ b/package/test/debug_results/DEBUG_mini_tricylinder_regenerated_v2.csv
@@ -0,0 +1,16 @@
+m0,of_nondim,bem,ratio,status
+0.1,2.7197566649443368,2.630891749421347,1.0337774883905944,OK
+0.5214285714285715,1.5517409591008753,1.50834785679075,1.0287686306012003,OK
+0.9428571428571428,1.2387574549248122,1.2080659186336404,1.0254055145648715,OK
+1.3642857142857143,1.1117207944575234,1.0903878287747086,1.0195645669548485,OK
+1.7857142857142858,1.0659973473979938,1.0530321233378543,1.0123122778240068,OK
+2.2071428571428573,1.0598489396384512,1.0562843959527488,1.0033746060240596,OK
+2.628571428571429,1.0725425297222488,1.0822997350995598,0.9909847475141317,OK
+3.0500000000000003,1.0933249243450494,1.1461541270093882,0.9539074183659889,OK
+3.4714285714285715,1.1166134227763644,1.561493559227771,0.715093197905075,FAIL
+3.892857142857143,1.139599811005907,0.9697742754248908,1.1751186228430424,FAIL
+4.314285714285714,1.1609773570742339,1.0714397866849146,1.083567524280905,OK
+4.735714285714286,1.1802416084489467,1.1061121881821236,1.0670179942494382,OK
+5.157142857142857,1.1973030461324925,1.132154761353017,1.0575436212462843,OK
+5.578571428571428,1.2122739345370177,1.1537913284679162,1.0506873336851636,OK
+6.0,1.2253530205293757,1.1964143669652016,1.0241878185043691,OK
diff --git a/package/test/debug_results/DEBUG_small_bicylinder_regenerated_v2.csv b/package/test/debug_results/DEBUG_small_bicylinder_regenerated_v2.csv
new file mode 100644
index 0000000..84ef389
--- /dev/null
+++ b/package/test/debug_results/DEBUG_small_bicylinder_regenerated_v2.csv
@@ -0,0 +1,16 @@
+m0,of_nondim,bem,ratio,status
+0.1,4.875103859712009,15.633652334237372,0.3118339691509983,FAIL
+0.5214285714285715,2.2601754266264913,2.1982137876194257,1.0281872670238172,OK
+0.9428571428571428,1.6372514218909109,1.6006106772638655,1.0228917282306778,OK
+1.3642857142857143,1.4213229853221157,1.3968190154910456,1.0175426949084423,OK
+1.7857142857142858,1.3523906047118648,1.3351779446994574,1.0128916599324758,OK
+2.2071428571428573,1.347910913390757,1.3380920572962147,1.0073379526027397,OK
+2.628571428571429,1.3726526529104432,1.3683005153810297,1.003180688365232,OK
+3.0500000000000003,1.4091391051061393,1.427004874150878,0.9874802326408523,OK
+3.4714285714285715,1.448484844598562,1.681210700462705,0.8615724633443673,FAIL
+3.892857142857143,1.4864018103517664,1.392871769110735,1.0671490680730393,OK
+4.314285714285714,1.521058285505106,1.4409410547230068,1.0556006302405618,OK
+4.735714285714286,1.551883516615197,1.4865910107675562,1.043920961027424,OK
+5.157142857142857,1.578916496913067,1.5277257264114836,1.033507827757687,OK
+5.578571428571428,1.602463497439377,1.5565183193194585,1.0295179167181319,OK
+6.0,1.6229243362870491,1.6933417200526404,0.9584151368080612,OK
diff --git a/package/test/debug_results/DEBUG_small_tricylinder_regenerated_v2.csv b/package/test/debug_results/DEBUG_small_tricylinder_regenerated_v2.csv
new file mode 100644
index 0000000..bb0bffb
--- /dev/null
+++ b/package/test/debug_results/DEBUG_small_tricylinder_regenerated_v2.csv
@@ -0,0 +1,16 @@
+m0,of_nondim,bem,ratio,status
+0.1,0.7121624012276297,0.6960819600749478,1.0231013617289415,OK
+0.5214285714285715,0.9111591290110264,0.8755176536472405,1.0407090310690026,OK
+0.9428571428571428,0.9404487241281393,0.9044478442091868,1.0398042630644226,OK
+1.3642857142857143,0.9500732781067471,0.9117141812486096,1.0420735989930572,OK
+1.7857142857142858,0.9549040031787883,0.9176526098210428,1.0405942215595183,OK
+2.2071428571428573,0.9578150215239631,0.9184242675758878,1.0428894959973611,OK
+2.628571428571429,0.959762001111584,0.9221150237360304,1.0408267693362414,OK
+3.0500000000000003,0.9611560238105058,0.9255441272407292,1.0384767138828315,OK
+3.4714285714285715,0.9622034244820484,0.9253034970467932,1.0398787290365004,OK
+3.892857142857143,0.9630192117742632,0.925871547139901,1.0401218341238745,OK
+4.314285714285714,0.9636725784331323,0.925685216930584,1.0410370186406432,OK
+4.735714285714286,0.9642076540053177,0.9280554415056677,1.0389547982618335,OK
+5.157142857142857,0.9646539114466088,0.925061141481684,1.0428001655128531,OK
+5.578571428571428,0.9650317851561849,0.924133252476996,1.0442560989657788,OK
+6.0,0.9653558885801655,0.9244227751695387,1.0442796461858261,OK
diff --git a/package/test/domain_example.py b/package/test/domain_example.py
deleted file mode 100644
index 2d84b75..0000000
--- a/package/test/domain_example.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import os
-import sys
-
-src_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../src'))
-sys.path.append(src_path)
-
-from geometry import Geometry
-from domain import Domain 
-import numpy as np
-
-# Sample data (replace with your actual data)
-r_coordinates = {
-    'a1': 2.0,  # Radius of the inner domain
-    'a2': 4.0   # Radius of the outer domain
-}
-
-z_coordinates = {
-    'h': 5.0    # Height of the geometry
-}
-
-domain_params = [
-    {
-        'number_harmonics': 5,
-        'height': z_coordinates['h'],
-        'radial_width': r_coordinates['a1'],
-        'top_BC': 'Wave Surface',
-        'bottom_BC': 'Sea Floor',
-        'category': 'inner',
-        'di': 1.0,
-        'a': r_coordinates['a1'],
-        'heaving': 1.0
-    },
-    {
-        'number_harmonics': 7,
-        'height': z_coordinates['h'],
-        'radial_width': r_coordinates['a2'] - r_coordinates['a1'],
-        'top_BC': 'Wave Surface',
-        'bottom_BC': 'Sea Floor',
-        'category': 'outer',
-        'di': 2.0,
-        'a': r_coordinates['a2'],
-        'heaving': 1.0
-    },
-    {
-        'number_harmonics': 9,
-        'height': z_coordinates['h'],
-        'radial_width': np.inf,
-        'top_BC': 'Wave Surface',
-        'bottom_BC': 'Sea Floor',
-        'category': 'exterior',
-        'di': None,
-        'a': None,
-        'heaving': 1.0
-    }
-]
-
-# Create the Geometry object
-geometry = Geometry(r_coordinates, z_coordinates, domain_params)
-
-# Access the domain list
-domain_list = geometry.domain_list
-
-# Print information about each domain
-for index, domain in domain_list.items():
-    print(f"Domain {index}:")
-    print(f"  Number of Harmonics: {domain.number_harmonics}")
-    print(f"  Height: {domain.height}")
-    print(f"  Radial Width: {domain.radial_width}")
-    print(f"  Category: {domain.category}")
-    print(f"  di: {domain.di}")
-    print(f"  a: {domain.a}")
-    print(f"  r_coords: {domain.r_coords}")
-    print(f"  z_coords: {domain.z_coords}")
-    print("-" * 20)
\ No newline at end of file
diff --git a/package/test/example_results.nc b/package/test/example_results.nc
deleted file mode 100644
index 2f05c96..0000000
Binary files a/package/test/example_results.nc and /dev/null differ
diff --git a/package/test/generate_snapshot.py b/package/test/generate_snapshot.py
new file mode 100644
index 0000000..1bee9d6
--- /dev/null
+++ b/package/test/generate_snapshot.py
@@ -0,0 +1,46 @@
+# package/test/generate_snapshot.py
+import numpy as np
+import os
+from openflash.basic_region_geometry import BasicRegionGeometry
+from openflash.meem_problem import MEEMProblem
+from openflash.meem_engine import MEEMEngine
+
+# Constants
+h = 1.001
+a1 = .5
+a2 = 1
+d1 = .5
+d2 = .25
+m0 = 1.0
+N, M, K = 4, 4, 4
+
+def generate():
+    # 1. Run OpenFLASH
+    geo = BasicRegionGeometry.from_vectors(
+        a=np.array([a1, a2]),
+        d=np.array([d1, d2]),
+        h=h,
+        NMK=[N, M, K],
+        slant_angle=np.zeros(2)
+    )
+    
+    prob = MEEMProblem(geo)
+    engine = MEEMEngine([prob])
+    
+    engine._ensure_m_k_and_N_k_arrays(prob, m0)
+    A = engine.assemble_A_multi(prob, m0)
+    
+    # 2. Ensure directory exists (THE FIX)
+    # This gets the folder where this script is located (package/test)
+    base_dir = os.path.dirname(__file__) 
+    data_dir = os.path.join(base_dir, "data")
+    
+    os.makedirs(data_dir, exist_ok=True)
+    
+    # 3. Save
+    output_path = os.path.join(data_dir, "gold_standard_A_2region.npy")
+    np.save(output_path, A)
+    print(f"Snapshot saved to: {output_path}")
+
+if __name__ == "__main__":
+    generate()
\ No newline at end of file
diff --git a/package/test/generated_A.npy b/package/test/generated_A.npy
deleted file mode 100644
index d280bab..0000000
Binary files a/package/test/generated_A.npy and /dev/null differ
diff --git a/package/test/generated_b.npy b/package/test/generated_b.npy
deleted file mode 100644
index d8a0552..0000000
Binary files a/package/test/generated_b.npy and /dev/null differ
diff --git a/package/test/geometry_example.py b/package/test/geometry_example.py
deleted file mode 100644
index 59f8a38..0000000
--- a/package/test/geometry_example.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import os
-import sys
-
-src_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../src'))
-sys.path.append(src_path)
-
-from geometry import Geometry
-from domain import Domain 
-import numpy as np
-
-# Sample data (replace with your actual data)
-r_coordinates = {
-    'a1': 2.0,  # Radius of the inner domain
-    'a2': 4.0   # Radius of the outer domain
-}
-
-z_coordinates = {
-    'h': 5.0    # Height of the geometry
-}
-
-domain_params = [
-    {
-        'number_harmonics': 5,
-        'height': z_coordinates['h'],
-        'radial_width': r_coordinates['a1'],
-        'top_BC': 'Free Surface',
-        'bottom_BC': 'Sea Floor',
-        'category': 'inner',
-        'di': 1.0,
-        'a': r_coordinates['a1'],
-        'heaving': 1.0
-    },
-    {
-        'number_harmonics': 7,
-        'height': z_coordinates['h'],
-        'radial_width': r_coordinates['a2'] - r_coordinates['a1'],
-        'top_BC': 'Free Surface',
-        'bottom_BC': 'Sea Floor',
-        'category': 'outer',
-        'di': 2.0,
-        'a': r_coordinates['a2'],
-        'heaving': 1.0
-    },
-    {
-        'number_harmonics': 9,
-        'height': z_coordinates['h'],
-        'radial_width': np.inf,
-        'top_BC': 'Free Surface',
-        'bottom_BC': 'Sea Floor',
-        'category': 'exterior',
-        'di': None,
-        'a': None,
-        'heaving': 1.0
-    }
-]
-
-# Create the Geometry object
-geometry = Geometry(r_coordinates, z_coordinates, domain_params)
-
-# Access the domain list
-domain_list = geometry.domain_list
-
-# Print information about the first domain
-print("Domain 0:")
-print(f"  Number of Harmonics: {domain_list[0].number_harmonics}")
-print(f"  Height: {domain_list[0].height}")
-print(f"  Radial Width: {domain_list[0].radial_width}")
-print(f"  Category: {domain_list[0].category}")
-print(f"  di: {domain_list[0].di}")
-print(f"  a: {domain_list[0].a}")
\ No newline at end of file
diff --git a/package/test/inspect_headers.py b/package/test/inspect_headers.py
new file mode 100644
index 0000000..635dc1d
--- /dev/null
+++ b/package/test/inspect_headers.py
@@ -0,0 +1,32 @@
+import pandas as pd
+from pathlib import Path
+
+# Path to your data folder
+DATA_FOLDER = Path(__file__).parent.parent.parent / "dev" / "python" / "convergence-study" / "meem-vs-capytaine-data" / "csv_data"
+
+def inspect():
+    if not DATA_FOLDER.exists():
+        print(f"Error: Folder not found: {DATA_FOLDER}")
+        return
+
+    csv_files = sorted(list(DATA_FOLDER.glob("*.csv")))
+    if not csv_files:
+        print("No CSV files found in the folder.")
+        return
+
+    # Inspect the first file
+    target_file = csv_files[0]
+    print(f"--- Inspecting: {target_file.name} ---")
+    
+    try:
+        df = pd.read_csv(target_file)
+        print(f"Row Count: {len(df)}")
+        print("\nColumns found:")
+        for col in df.columns:
+            print(f"  - {col}")
+            
+    except Exception as e:
+        print(f"Failed to read file: {e}")
+
+if __name__ == "__main__":
+    inspect()
\ No newline at end of file
diff --git a/package/test/main_test.py b/package/test/main_test.py
index bce44b0..1ae1f7a 100644
--- a/package/test/main_test.py
+++ b/package/test/main_test.py
@@ -1,244 +1,139 @@
+import logging
 import sys
 import os
 import numpy as np
-import pandas as pd
-from scipy import linalg
-from scipy.integrate import quad
 import matplotlib.pyplot as plt
 
-src_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../src'))
-sys.path.append(src_path)
-
-from equations import *
-from constants import *
-from meem_engine import MEEMEngine
-from meem_problem import MEEMProblem
-from geometry import Geometry
-
-import capytaine as cpt
-from capytaine.bem.airy_waves import airy_waves_potential, airy_waves_velocity, froude_krylov_force
-
-np.set_printoptions(threshold=np.inf, linewidth=np.inf, precision=8, suppress=True)
-
-#############################################################################
-#multi-region   
-from multi_constants import *
-from multi_equations import *
-from constants import *
-
-np.set_printoptions(threshold=np.inf, linewidth=np.inf, precision=8, suppress=True)
-
-def test_main():
-    NMK = [30, 30, 30]  # Adjust these values as needed
-    boundary_count = len(NMK) - 1
-
-
-    # Create domain parameters
-    domain_params = []
-    for idx in range(len(NMK)):
-        params = {
-            'number_harmonics': NMK[idx],
-            'height': h - d[idx] if idx < len(d) else h,
-            'radial_width': a[idx] if idx < len(a) else a[-1]*1.5,
-            'top_BC': None,
-            'bottom_BC': None,
-            'category': 'multi',  # Adjust category as needed
-            'di': d[idx] if idx < len(d) else 0,
-            'a': a[idx] if idx < len(a) else a[-1]*1.5,
-            'heaving': heaving[idx] if idx < len(heaving) else False,
-            'slant': [0, 0, 1]  # Set True if the region is slanted
-        }
-        domain_params.append(params)
-
-    # Create Geometry object
-    r_coordinates = {'a1': a[0], 'a2': a[1], 'a3': a[2]}
-    z_coordinates = {'h': h}
-    geometry = Geometry(r_coordinates, z_coordinates, domain_params)
-
-    # Create MEEMProblem object
-    problem = MEEMProblem(geometry)
-
-    # Create MEEMEngine object
-    engine = MEEMEngine([problem])
-
-    # Assemble A matrix and b vector using multi-region methods
-    A = engine.assemble_A_multi(problem, m0)
-    b = engine.assemble_b_multi(problem, m0)
-
-    # Solve the linear system A x = b
-    X = engine.solve_linear_system_multi(problem, m0)
-
-    hydro_coefficients = engine.compute_hydrodynamic_coefficients(problem, X)
-    print(hydro_coefficients)
-
-    # Split up the Cs into groups depending on which equation they belong to.
-    Cs = []
-    row = 0
-    Cs.append(X[:NMK[0]])
-    row += NMK[0]
-    for i in range(1, boundary_count):
-        Cs.append(X[row: row + NMK[i] * 2])
-        row += NMK[i] * 2
-    Cs.append(X[row:])
-
-    def phi_h_n_inner_func(n, r, z):
-        return (Cs[0][n] * R_1n(n, r, 0, h, d, a)) * Z_n_i(n, z, 0, h, d)
-
-    def phi_h_m_i_func(i, m, r, z):
-        return (Cs[i][m] * R_1n(m, r, i, h, d, a) + Cs[i][NMK[i] + m] * R_2n(m, r, i, a, h, d)) * Z_n_i(m, z, i, h, d)
-
-    def phi_e_k_func(k, r, z):
-        return Cs[-1][k] * Lambda_k(k, r, m0, a, NMK, h) * Z_n_e(k, z, m0, h)
-
-    #phi_h_n_i1s = np.vectorize(phi_h_n_i1_func, excluded=['n'], signature='(),(),()->()')
-    #phi_h_m_i2s = np.vectorize(phi_h_m_i2_func, excluded=['m'], signature='(),(),()->()')
-    #phi_e_ks = np.vectorize(phi_e_k_func, excluded=['k'], signature='(),(),()->()')
-
-    spatial_res=50
-    r_vec = np.linspace(2 * a[-1] / spatial_res, 2*a[-1], spatial_res)
-    z_vec = np.linspace(-h, 0, spatial_res)
-
-    #add values at the radii
-    a_eps = 1.0e-4
-    for i in range(len(a)):
-        r_vec = np.append(r_vec, a[i]*(1-a_eps))
-        r_vec = np.append(r_vec, a[i]*(1+a_eps))
-    r_vec = np.unique(r_vec)
-
-    for i in range(len(d)):
-        z_vec = np.append(z_vec, -d[i])
-    z_vec = np.unique(z_vec)
-
-    R, Z = np.meshgrid(r_vec, z_vec)
+# --- Path Setup ---
+current_dir = os.path.dirname(__file__)
+src_dir = os.path.abspath(os.path.join(current_dir, '..', 'src'))
+if src_dir not in sys.path:
+    sys.path.insert(0, src_dir)
+
+# --- Import updated package modules ---
+from openflash.meem_engine import MEEMEngine
+from openflash.meem_problem import MEEMProblem
+from openflash.basic_region_geometry import BasicRegionGeometry
+from openflash.geometry import ConcentricBodyGroup
+from openflash.body import SteppedBody
+from openflash.multi_equations import omega
+from openflash.multi_constants import g
+
+# --- FIX: Use sys.maxsize for integer "infinity" to satisfy Pylance ---
+np.set_printoptions(threshold=sys.maxsize, linewidth=sys.maxsize, precision=8, suppress=True)
+
+
+def main():
+    """
+    Main function to set up a multi-region MEEM problem, solve it using superposition,
+    and visualize the total potential and velocity fields.
+    """
+    # -------------------------
+    # Problem Definition Parameters
+    # -------------------------
+    NMK = [100, 100, 100, 100]  # Number of harmonics per domain
+    h = 100                      # Total water depth
+    a = [3, 5, 10]               # Body radii
+    d = [29, 7, 4]               # Step depths
     
+    # This configuration demands multiple bodies heaving.
+    # We must solve this via superposition.
+    target_heaving_flags = [False, True, True] 
+    
+    m0 = 1.0
+    
+    # -------------------------
+    # Superposition Loop
+    # -------------------------
+    
+    # Determine which bodies are active
+    active_indices = [i for i, x in enumerate(target_heaving_flags) if x]
+    
+    # Storage for the total accumulated potential
+    phi_total_accumulated = None
+    R_grid = None
+    Z_grid = None
+    
+    # We will iterate through each active body, solve, and add to total
+    print(f"Target Configuration: {target_heaving_flags}")
+    print(f"Active bodies indices: {active_indices}")
+    print("-" * 40)
+
+    # Need an engine instance for visualization tools later
+    engine_for_plotting = None
+
+    for active_idx in active_indices:
+        print(f"\n--- Solving for Single Heaving Body: Index {active_idx} ---")
+        
+        # 1. Create a "Single Body Heaving" map for this iteration
+        current_heaving_flags = [False] * len(target_heaving_flags)
+        current_heaving_flags[active_idx] = True
+        
+        # 2. Re-create Bodies with this specific heaving configuration
+        bodies = []
+        for i in range(len(a)):
+            bodies.append(SteppedBody(
+                a=np.array([a[i]]),
+                d=np.array([d[i]]),
+                slant_angle=np.array([0.0]),
+                heaving=current_heaving_flags[i]
+            ))
+
+        # 3. Create Geometry & Problem for this iteration
+        arrangement = ConcentricBodyGroup(bodies) # Now passes assertion (only 1 True)
+        geometry = BasicRegionGeometry(arrangement, h=h, NMK=NMK)
+        problem = MEEMProblem(geometry)
+        
+        # Set Frequency
+        problem_frequencies = np.array([omega(m0, h, g)])
+        problem.set_frequencies(problem_frequencies)
+
+        # 4. Create Engine & Solve
+        engine = MEEMEngine(problem_list=[problem])
+        engine_for_plotting = engine # Save reference for plotting later
+        
+        # Solve linear system
+        X = engine.solve_linear_system_multi(problem, m0)
+        print(f"  System solved. X shape: {X.shape}")
+
+        # Compute Hydrodynamics (Optional check)
+        hydro_coeffs = engine.compute_hydrodynamic_coefficients(problem, X, m0)
+        # Just printing the coeff for the active body
+        print(f"  Hydro Coeffs (Mode {active_idx}): {hydro_coeffs[active_idx]['real']:.4e} + {hydro_coeffs[active_idx]['imag']:.4e}j")
+
+        # 5. Calculate Potentials
+        potentials = engine.calculate_potentials(problem, X, m0, spatial_res=50, sharp=True)
+        
+        # 6. Accumulate Results (Superposition)
+        if phi_total_accumulated is None:
+            # First pass: initialize grids and total array
+            R_grid = potentials["R"]
+            Z_grid = potentials["Z"]
+            phi_total_accumulated = potentials["phi"] # Complex array
+        else:
+            # Subsequent passes: Add to total
+            phi_total_accumulated += potentials["phi"]
+
+    # -------------------------
+    # Visualization (Total Field)
+    # -------------------------
+    print("\n" + "="*40)
+    print("Visualizing Total Superimposed Field")
+    print("="*40)
+    
+    # --- FIX: Type Guard ---
+    # Ensure variables are populated before using them
+    if phi_total_accumulated is None or engine_for_plotting is None:
+        print("No active bodies were simulated. Nothing to visualize.")
+        return
 
-    regions = []
-    regions.append((R <= a[0]) & (Z < -d[0]))
-    for i in range(1, boundary_count):
-        regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))
-    regions.append(R > a[-1])
-
-    # region_body = ~region1 & ~region2 & ~regione
-
-
-    phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-    phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-    phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-
-    for n in range(NMK[0]):
-        temp_phiH = phi_h_n_inner_func(n, R[regions[0]], Z[regions[0]])
-        phiH[regions[0]] = temp_phiH if n == 0 else phiH[regions[0]] + temp_phiH
-
-    for i in range(1, boundary_count):
-        for m in range(NMK[i]):
-            temp_phiH = phi_h_m_i_func(i, m, R[regions[i]], Z[regions[i]])
-            phiH[regions[i]] = temp_phiH if m == 0 else phiH[regions[i]] + temp_phiH
-
-    for k in range(NMK[-1]):
-        temp_phiH = phi_e_k_func(k, R[regions[-1]], Z[regions[-1]])
-        phiH[regions[-1]] = temp_phiH if k == 0 else phiH[regions[-1]] + temp_phiH
-
-    phi_p_i_vec = np.vectorize(phi_p_i)
-
-    phiP[regions[0]] = heaving[0] * phi_p_i_vec(d[0], R[regions[0]], Z[regions[0]], h)
-    for i in range(1, boundary_count):
-        phiP[regions[i]] = heaving[i] * phi_p_i_vec(d[i], R[regions[i]], Z[regions[i]], h)
-    phiP[regions[-1]] = 0
-
-    phi = phiH + phiP
-    def plot_potential(field, R, Z, title):
-        plt.figure(figsize=(8, 6))
-        plt.contourf(R, Z, field, levels=50, cmap='viridis')
-        plt.colorbar()
-        plt.title(title)
-        plt.xlabel('Radial Distance (R)')
-        plt.ylabel('Axial Distance (Z)')
-        plt.show()
-
-
-    plot_potential(np.real(phiH), R, Z, 'Homogeneous Potential')
-    plot_potential(np.imag(phiH), R, Z, 'Homogeneous Potential Imaginary')
-
-    plot_potential(np.real(phiP), R, Z, 'Particular Potential')
-    plot_potential(np.imag(phiP), R, Z, 'Particular Potential Imaginary')
-
-    plot_potential(np.real(phi), R, Z, 'Total Potential')
-    plot_potential(np.imag(phi), R, Z, 'Total Potential Imaginary')
-
-    def v_r_inner_func(n, r, z):
-        return (Cs[0][n] * diff_R_1n(n, r, 0, h, d, a)) * Z_n_i(n, z, 0, h, d)
-
-    def v_r_m_i_func(i, m, r, z):
-        return (Cs[i][m] * diff_R_1n(m, r, i, h, d, a) + Cs[i][NMK[i] + m] * diff_R_2n(m, r, i, h, d, a)) * Z_n_i(m, z, i, h, d)
-
-    def v_r_e_k_func(k, r, z):
-        return Cs[-1][k] * diff_Lambda_k(k, r, m0, NMK, h, a) * Z_n_e(k, z, m0, h)
-
-    def v_z_inner_func(n, r, z):
-        return (Cs[0][n] * R_1n(n, r, 0, h, d, a)) * diff_Z_n_i(n, z, 0, h, d)
-
-    def v_z_m_i_func(i, m, r, z):
-        return (Cs[i][m] * R_1n(m, r, i, h, d, a) + Cs[i][NMK[i] + m] * R_2n(m, r, i, a, h, d)) * diff_Z_n_i(m, z, i, h, d)
-
-    def v_z_e_k_func(k, r, z):
-        return Cs[-1][k] * Lambda_k(k, r, m0, a, NMK, h) * diff_Z_k_e(k, z, m0, h, NMK)
-
-    vr = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-    vrH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-    vrP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+    # Now Pylance knows phi_total_accumulated is not None
+    engine_for_plotting.visualize_potential(np.real(phi_total_accumulated), R_grid, Z_grid, "Total Potential (Real) - Superimposed")
+    engine_for_plotting.visualize_potential(np.imag(phi_total_accumulated), R_grid, Z_grid, "Total Potential (Imag) - Superimposed")
 
-    vz = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-    vzH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
-    vzP = np.full_like(R, np.nan + np.nan*1j, dtype=complex)
+    plt.show()
+    print("Script finished. Close plots to exit.")
 
-    for n in range(NMK[0]):
-        temp_vrH = v_r_inner_func(n, R[regions[0]], Z[regions[0]])
-        temp_vzH = v_z_inner_func(n, R[regions[0]], Z[regions[0]])
-        if n == 0:
-            vrH[regions[0]] = temp_vrH
-            vzH[regions[0]] = temp_vzH
-        else:
-            vrH[regions[0]] = vrH[regions[0]] + temp_vrH
-            vzH[regions[0]] = vzH[regions[0]] + temp_vzH
-
-    for i in range(1, boundary_count):
-        for m in range(NMK[i]):
-            temp_vrH = v_r_m_i_func(i, m, R[regions[i]], Z[regions[i]])
-            temp_vzH = v_z_m_i_func(i, m, R[regions[i]], Z[regions[i]])
-            if m == 0:
-                vrH[regions[i]] = temp_vrH
-                vzH[regions[i]] = temp_vzH
-            else:
-                vrH[regions[i]] = vrH[regions[i]] + temp_vrH
-                vzH[regions[i]] = vzH[regions[i]] + temp_vzH
-
-    for k in range(NMK[-1]):
-        temp_vrH = v_r_e_k_func(k, R[regions[-1]], Z[regions[-1]])
-        temp_vzH = v_z_e_k_func(k, R[regions[-1]], Z[regions[-1]])
-        if k == 0:
-            vrH[regions[-1]] = temp_vrH
-            vzH[regions[-1]] = temp_vzH
-        else:
-            vrH[regions[-1]] = vrH[regions[-1]] + temp_vrH
-            vzH[regions[-1]] = vzH[regions[-1]] + temp_vzH
-
-    vr_p_i_vec = np.vectorize(diff_r_phi_p_i)
-    vz_p_i_vec = np.vectorize(diff_z_phi_p_i)
-
-    vrP[regions[0]] = heaving[0] * vr_p_i_vec(d[0], R[regions[0]], Z[regions[0]])
-    vzP[regions[0]] = heaving[0] * vz_p_i_vec(d[0], R[regions[0]], Z[regions[0]])
-    for i in range(1, boundary_count):
-        vrP[regions[i]] = heaving[i] * vr_p_i_vec(d[i], R[regions[i]], Z[regions[i]])
-        vzP[regions[i]] = heaving[i] * vz_p_i_vec(d[i], R[regions[i]], Z[regions[i]])
-    vrP[regions[-1]] = 0
-    vzP[regions[-1]] = 0
-
-    vr = vrH + vrP
-    vz = vzH + vzP
-    plot_potential(np.real(vr), R, Z, 'Radial Velocity - Real')
-    plot_potential(np.imag(vr), R, Z, 'Radial Velocity - Imaginary')
-    plot_potential(np.real(vz), R, Z, 'Vertical Velocity - Real')
-    plot_potential(np.imag(vz), R, Z, 'Vertical Velocity - Imaginary')
 
 if __name__ == "__main__":
-    test_main()
\ No newline at end of file
+    main()
\ No newline at end of file
diff --git a/package/test/meem_hydro_results_full.nc b/package/test/meem_hydro_results_full.nc
new file mode 100644
index 0000000..5896945
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diff --git a/package/test/openflash_mismatch.png b/package/test/openflash_mismatch.png
new file mode 100644
index 0000000..6072a61
Binary files /dev/null and b/package/test/openflash_mismatch.png differ
diff --git a/package/test/openflash_sparsity.png b/package/test/openflash_sparsity.png
new file mode 100644
index 0000000..2080914
Binary files /dev/null and b/package/test/openflash_sparsity.png differ
diff --git a/package/test/openflash_utils.py b/package/test/openflash_utils.py
new file mode 100644
index 0000000..b9d948d
--- /dev/null
+++ b/package/test/openflash_utils.py
@@ -0,0 +1,101 @@
+# package/test/openflash_utils.py
+
+import numpy as np
+from openflash.basic_region_geometry import BasicRegionGeometry
+from openflash.meem_problem import MEEMProblem
+from openflash.meem_engine import MEEMEngine
+from openflash.multi_equations import omega
+from openflash.multi_constants import g
+
+def run_openflash_case(h, d, a, heaving, NMK, m0, rho):
+    """
+    Runs a single OpenFLASH simulation and returns NON-DIMENSIONAL hydrodynamic coefficients.
+    
+    Normalization:
+        Added Mass (Non-Dim) = AM_dimensional / (rho * Volume)
+        Damping (Non-Dim)    = Damping_dimensional / (rho * Volume * omega)
+    
+    Returns
+    -------
+    am_norm : float
+        Non-dimensional added mass.
+    dp_norm : float
+        Non-dimensional damping.
+    phase : float
+        Excitation phase (radians) of the first mode.
+    """
+
+    # 1. Build body_map and heaving_map
+    body_map = []
+    heaving_map = []
+
+    if len(heaving) > 0:
+        current_body_idx = 0
+        body_map.append(current_body_idx)
+        heaving_map.append(bool(heaving[0]))
+
+        for i in range(1, len(heaving)):
+            if heaving[i] == heaving[i - 1]:
+                body_map.append(current_body_idx)
+            else:
+                current_body_idx += 1
+                body_map.append(current_body_idx)
+                heaving_map.append(bool(heaving[i]))
+    else:
+        raise ValueError("Heaving array must be non-empty.")
+
+    # 2. Geometry + problem setup
+    geometry = BasicRegionGeometry.from_vectors(
+        a=np.asarray(a),
+        d=np.asarray(d),
+        h=h,
+        NMK=NMK,
+        slant_angle=np.zeros(len(a)),
+        body_map=body_map,
+        heaving_map=heaving_map
+    )
+
+    problem = MEEMProblem(geometry)
+    engine = MEEMEngine([problem])
+
+    # 3. Solve MEEM system
+    X = engine.solve_linear_system_multi(problem, m0)
+    
+    results = engine.compute_hydrodynamic_coefficients(problem, X, m0, rho=rho)
+
+    # 4. Extract DIMENSIONAL added mass & damping
+    total_am_dimensional = 0.0
+    total_dp_dimensional = 0.0
+    
+    # Extract phase from the first mode (assuming single body for this util)
+    phase_val = results[0]["excitation_phase"] if results else 0.0
+
+    for res in results:
+        total_am_dimensional += res["real"]
+        total_dp_dimensional += res["imag"]
+
+    # 5. NORMALIZE RESULTS
+    vol = 0.0
+    prev_r = 0.0
+    for r, draft in zip(a, d):
+        area = np.pi * (r**2 - prev_r**2)
+        vol += area * draft
+        prev_r = r
+        
+    mass_displaced = vol * rho
+    
+    # Calculate angular frequency for damping normalization
+    w = omega(m0, h, g)
+
+    if mass_displaced > 0:
+        am_norm = total_am_dimensional / mass_displaced
+        if w > 1e-12:
+            dp_norm = total_dp_dimensional / (mass_displaced * w)
+        else:
+            dp_norm = 0.0
+    else:
+        am_norm = np.nan
+        dp_norm = np.nan
+    
+    # FIX: Return phase in the 3rd slot
+    return am_norm, dp_norm, phase_val
\ No newline at end of file
diff --git a/package/test/plot_matrix_sparsity.py b/package/test/plot_matrix_sparsity.py
new file mode 100644
index 0000000..0767c8b
--- /dev/null
+++ b/package/test/plot_matrix_sparsity.py
@@ -0,0 +1,139 @@
+# package/test/plot_matrix_sparsity.py
+import numpy as np
+import matplotlib.pyplot as plt
+from openflash.basic_region_geometry import BasicRegionGeometry
+from openflash.meem_problem import MEEMProblem
+from openflash.meem_engine import MEEMEngine
+from openflash.multi_equations import (
+    R_1n, R_2n, diff_R_1n, diff_R_2n, Lambda_k, diff_Lambda_k, 
+    I_nm, I_mk, m_k, N_k_multi
+)
+
+# Constants
+h = 1.001
+a1 = 0.5
+a2 = 1.0
+d1 = 0.5
+d2 = 0.25
+m0 = 1.0
+N, M, K = 4, 4, 4
+
+def build_openflash_matrix():
+    NMK = [N, M, K]
+    geo = BasicRegionGeometry.from_vectors(
+        a=np.array([a1, a2]),
+        d=np.array([d1, d2]),
+        h=h,
+        NMK=NMK,
+        slant_angle=np.zeros(2)
+    )
+    prob = MEEMProblem(geo)
+    engine = MEEMEngine([prob])
+    engine._ensure_m_k_and_N_k_arrays(prob, m0)
+    return engine.assemble_A_multi(prob, m0)
+
+def build_manual_matrix_from_snippet():
+    # Re-implementing the manual logic provided in the prompt
+    # Note: I must use openflash math functions as proxies for the script's custom ones
+    d_list = [d1, d2]
+    a_list = [a1, a2]
+    NMK = [N, M, K]
+    
+    # Calculate dimension based on snippet logic
+    # Snippet: A = zeros(N + 2*M + K, ...)
+    dim = N + 2 * M + K
+    A = np.zeros((dim, dim), dtype=complex)
+    
+    m_k_arr = m_k(NMK, m0, h)
+    N_k_arr = np.array([N_k_multi(k, m0, h, m_k_arr) for k in range(K)])
+
+    # 1. Row Group 1 (d1)
+    for i in range(N):
+        A[i, i] = (h - d1) * R_1n(i, a1, 0, h, d_list, a_list)
+        
+    for n in range(N):
+        for m in range(M):
+            # A[n][N+m] = -R_1n_2 * A_nm
+            c = I_nm(n, m, 0, d_list, h)
+            A[n, N + m] = -1 * R_1n(m, a1, 1, h, d_list, a_list) * c
+            A[n, N + M + m] = -1 * R_2n(m, a1, 1, a_list, h, d_list) * c
+
+    # 2. Row Group 2 (d2)
+    for i in range(M):
+        val = (h - d2) # R_1n/R_2n at a2 are 1
+        A[N + i, N + i] = val
+        A[N + i, N + M + i] = val
+        
+    for m in range(M):
+        for k in range(K):
+            c = I_mk(m, k, 1, d_list, m0, h, m_k_arr, N_k_arr)
+            A[N + m, N + 2*M + k] = -1 * Lambda_k(k, a2, m0, a_list, m_k_arr) * c
+
+    # 3. Row Group 3 (d1) - Velocity
+    # Snippet: A[N+M+m][n] = -diff_R_1n_1 * A_nm(n, m)
+    # Note indices: Row N+M+m (starts at N+M, length M)
+    # Projects on Region 2 modes (M)
+    for m in range(M):
+        for n in range(N):
+            c = I_nm(n, m, 0, d_list, h) # A_nm(n, m)
+            A[N + M + m, n] = -1 * diff_R_1n(n, a1, 0, h, d_list, a_list) * c
+            
+    for m in range(M):
+        A[N + M + m, N + m] = (h - d2) * diff_R_1n(m, a1, 1, h, d_list, a_list)
+        A[N + M + m, N + M + m] = (h - d2) * diff_R_2n(m, a1, 1, h, d_list, a_list)
+
+    # 4. Row Group 4 (d2) - Velocity
+    # Snippet: A[N+2M+k][N+m] ...
+    for k in range(K):
+        for m in range(M):
+            c = I_mk(m, k, 1, d_list, m0, h, m_k_arr, N_k_arr)
+            A[N + 2*M + k, N + m] = -1 * diff_R_1n(m, a2, 1, h, d_list, a_list) * c
+            A[N + 2*M + k, N + M + m] = -1 * diff_R_2n(m, a2, 1, h, d_list, a_list) * c
+            
+    for k in range(K):
+        A[N + 2*M + k, N + 2*M + k] = h * diff_Lambda_k(k, a2, m0, a_list, m_k_arr)
+        
+    return A
+
+def plot_sparsity_comparison():
+    A_open = build_openflash_matrix()
+    A_manual = build_manual_matrix_from_snippet()
+    
+    # Plot 1: OpenFLASH Non-Zero Entries
+    rows, cols = np.nonzero(np.abs(A_open) > 1e-10)
+    plt.figure(figsize=(6, 6))
+    plt.scatter(cols, rows, color='blue', marker='o', s=100)
+    plt.gca().invert_yaxis()
+    plt.grid(True)
+    plt.title('OpenFLASH: Non-Zero Entries')
+    plt.xlabel('Column Index')
+    plt.ylabel('Row Index')
+    plt.savefig('openflash_sparsity.png')
+    plt.close()
+    
+    # Plot 2: Mismatches
+    # Threshold from snippet: 0.001
+    is_close = np.isclose(A_open, A_manual, rtol=0.001, atol=1e-8)
+    rows_diff, cols_diff = np.nonzero(~is_close)
+    
+    plt.figure(figsize=(6, 6))
+    if len(rows_diff) > 0:
+        plt.scatter(cols_diff, rows_diff, color='red', marker='x', s=100)
+    plt.gca().invert_yaxis()
+    plt.grid(True)
+    plt.title('Mismatch: OpenFLASH vs Manual (Tol=0.001)')
+    plt.xlabel('Column Index')
+    plt.ylabel('Row Index')
+    
+    # Draw grid lines for blocks (optional, helpful for debugging)
+    # N=4, M=4, K=4. Blocks at 4, 8, 12.
+    for val in [4, 8, 12]:
+        plt.axvline(val - 0.5, color='black', linestyle='-', linewidth=1)
+        plt.axhline(val - 0.5, color='black', linestyle='-', linewidth=1)
+        
+    plt.savefig('openflash_mismatch.png')
+    plt.close()
+    print("Plots saved: openflash_sparsity.png, openflash_mismatch.png")
+
+if __name__ == "__main__":
+    plot_sparsity_comparison()
\ No newline at end of file
diff --git a/package/test/radiation_result_details.txt b/package/test/radiation_result_details.txt
deleted file mode 100644
index 5e196fb..0000000
--- a/package/test/radiation_result_details.txt
+++ /dev/null
@@ -1,736 +0,0 @@
-_repr_pretty_: <bound method LinearPotentialFlowProblem._repr_pretty_ of RadiationResult(body=FloatingBody(..., name="axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh"), wavenumber=1.000, water_depth=100.0, radiating_dof='Heave')>
-_specific_rich_repr: <bound method RadiationProblem._specific_rich_repr of RadiationResult(body=FloatingBody(..., name="axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh"), wavenumber=1.000, water_depth=100.0, radiating_dof='Heave')>
-_str_other_attributes: <bound method RadiationProblem._str_other_attributes of RadiationResult(body=FloatingBody(..., name="axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh"), wavenumber=1.000, water_depth=100.0, radiating_dof='Heave')>
-added_mass: {'Heave': 1575707.2547931485}
-added_masses: {'Heave': 1575707.2547931485}
-body: FloatingBody(mesh=CollectionOfMeshes(..., name="axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh_mesh"), lid_mesh=None, dofs={"Heave": ...}, name="axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh")
-body_name: axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh
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-depth: 100.0
-encounter_omega: 3.132091952673165
-encounter_period: 2.006066680710647
-encounter_wave_direction: 0.0
-encounter_wavelength: 6.283185307179586
-encounter_wavenumber: 1
-force: {'Heave': np.complex128(15457688.169520788-3095595.581204379j)}
-forces: {'Heave': np.complex128(15457688.169520788-3095595.581204379j)}
-forward_speed: 0.0
-free_surface: 0.0
-fs_elevation: {}
-g: 9.81
-influenced_dofs: {'Heave': array([[0., 0., 1.],
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-problem: RadiationProblem(body=FloatingBody(..., name="axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh"), wavenumber=1.000, water_depth=100.0, radiating_dof='Heave')
-provided_freq_type: wavenumber
-radiating_dof: Heave
-radiation_damping: {'Heave': -988347.6053640004}
-radiation_dampings: {'Heave': -988347.6053640004}
-records: [{'body_name': 'axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh+axisymmetric_mesh', 'water_depth': 100.0, 'omega': 3.132091952673165, 'encounter_omega': 3.132091952673165, 'period': 2.006066680710647, 'wavelength': 6.283185307179586, 'wavenumber': 1.0, 'forward_speed': 0.0, 'wave_direction': 0.0, 'encounter_wave_direction': 0.0, 'rho': 1000.0, 'g': 9.81, 'radiating_dof': 'Heave', 'influenced_dof': 'Heave', 'added_mass': 1575707.2547931485, 'radiation_damping': -988347.6053640004}]
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-water_depth: 100.0
-wave_direction: 0.0
-wavelength: 6.283185307179586
-wavenumber: 1
diff --git a/package/test/read_results_example.py b/package/test/read_results_example.py
new file mode 100644
index 0000000..2cb373e
--- /dev/null
+++ b/package/test/read_results_example.py
@@ -0,0 +1,70 @@
+import xarray as xr
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+def read_and_plot_hydro_results(file_path="meem_hydro_results.nc"):
+    """
+    Reads hydrodynamic coefficients from a NetCDF file and plots them.
+
+    :param file_path: The path to the NetCDF file generated by meem_engine_example.py.
+    """
+    print(f"--- Starting NetCDF Results Reader Example ---")
+    print(f"Attempting to read file: {file_path}")
+
+    # Check if the file exists
+    if not os.path.exists(file_path):
+        print(f"Error: File not found at {file_path}. Please run meem_engine_example.py first.")
+        return
+
+    try:
+        # 1. Read the NetCDF file using xarray
+        ds = xr.open_dataset(file_path)
+        print("\nSuccessfully loaded NetCDF dataset:")
+        print(ds) # Print a summary of the dataset (dimensions, coordinates, data variables)
+
+        # 2. Access the stored data variables
+        frequencies = ds['frequencies'].values
+        added_mass = ds['added_mass'].values
+        damping = ds['damping'].values
+
+        # Since 'modes' dimension has size 1, we can squeeze it
+        # to get 1D arrays for plotting
+        added_mass_squeezed = added_mass.squeeze()
+        damping_squeezed = damping.squeeze()
+
+        print(f"\nExtracted Frequencies (first 5): {frequencies[:5]}")
+        print(f"Extracted Added Mass (first 5): {added_mass_squeezed[:5]}")
+        print(f"Extracted Damping (first 5): {damping_squeezed[:5]}")
+
+        # 3. Re-plot the data to verify
+        print("\n--- Re-plotting Hydrodynamic Coefficients from NetCDF Data ---")
+        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), sharex=True)
+
+        # Plot Added Mass
+        ax1.plot(frequencies, added_mass_squeezed, 'b-x', markersize=5, label='Added Mass (from .nc)')
+        ax1.set_title('Hydrodynamic Coefficients for Heaving Cylinders (from NetCDF)')
+        ax1.set_ylabel('Added Mass ($A_{33}$)')
+        ax1.grid(True, linestyle=':', alpha=0.7)
+        ax1.legend()
+
+        # Plot Damping
+        ax2.plot(frequencies, damping_squeezed, 'r-x', markersize=5, label='Damping (from .nc)')
+        ax2.set_xlabel(r'Angular Frequency ($\omega$, rad/s)')
+        ax2.set_ylabel('Damping ($B_{33}$)')
+        ax2.grid(True, linestyle=':', alpha=0.7)
+        ax2.legend()
+
+        plt.tight_layout()
+        plt.show()
+
+    except Exception as e:
+        print(f"An error occurred while reading or processing the NetCDF file: {e}")
+
+    print("\n--- NetCDF Results Reader Example Finished ---")
+
+if __name__ == "__main__":
+    # Ensure this script is run from the same directory as meem_engine_example.py
+    # or specify the full path to 'meem_hydro_results.nc' if it's elsewhere.
+    read_results_example_path = os.path.join(os.path.dirname(__file__), "meem_hydro_results_full.nc")
+    read_and_plot_hydro_results(read_results_example_path)
\ No newline at end of file
diff --git a/package/test/reference_2region.py b/package/test/reference_2region.py
new file mode 100644
index 0000000..b2948e0
--- /dev/null
+++ b/package/test/reference_2region.py
@@ -0,0 +1,73 @@
+# package/test/reference_2region.py
+import numpy as np
+from openflash.multi_equations import (
+    R_1n, R_2n, diff_R_1n, diff_R_2n, Lambda_k, diff_Lambda_k, 
+    I_nm, I_mk, m_k, N_k_multi
+)
+
+h = 1.001
+a1 = .5
+a2 = 1
+d1 = .5
+d2 = .25
+m0 = 1.0
+
+def build_manual_matrices(N=4, M=4, K=4):
+    d_list = [d1, d2]
+    a_list = [a1, a2]
+    NMK = [N, M, K]
+    
+    # Dimensions: N (Pot1) + M (Pot2) + N (Vel1 - b/c d1>d2) + K (Vel2)
+    dim = N + M + N + K 
+    A = np.zeros((dim, dim), dtype=complex)
+    
+    m_k_arr = m_k(NMK, m0, h)
+    N_k_arr = np.array([N_k_multi(k, m0, h, m_k_arr) for k in range(K)])
+
+    # 1. Potential Match at a1 (N rows)
+    # Project on Region 1
+    for n in range(N):
+        A[n, n] = (h - d1) * R_1n(n, a1, 0, h, d_list, a_list) # Diag
+        for m in range(M):
+            c = I_nm(n, m, 0, d_list, h)
+            A[n, N + m] = -1 * R_1n(m, a1, 1, h, d_list, a_list) * c
+            A[n, N + M + m] = -1 * R_2n(m, a1, 1, a_list, h, d_list) * c
+
+    # 2. Potential Match at a2 (M rows)
+    # Project on Region 2
+    row_start = N
+    for m in range(M):
+        val = (h - d2)
+        A[row_start + m, N + m] = val
+        A[row_start + m, N + M + m] = val
+        for k in range(K):
+            c = I_mk(m, k, 1, d_list, m0, h, m_k_arr, N_k_arr)
+            A[row_start + m, N + 2*M + k] = -1 * Lambda_k(k, a2, m0, a_list, m_k_arr) * c
+
+    # 3. Velocity Match at a1 (N rows) -- CORRECTED
+    # Project on Region 1 (because d1 > d2, Region 1 is common)
+    row_start = N + M
+    for n in range(N):
+        # Diagonal (Region 1)
+        # v_diagonal_block(True) -> sign = -1
+        A[row_start + n, n] = -1 * (h - d1) * diff_R_1n(n, a1, 0, h, d_list, a_list)
+        
+        # Dense (Region 2)
+        # v_dense_block(False) -> sign = 1
+        for m in range(M):
+            c = I_nm(n, m, 0, d_list, h)
+            A[row_start + n, N + m] = 1 * diff_R_1n(m, a1, 1, h, d_list, a_list) * c
+            A[row_start + n, N + M + m] = 1 * diff_R_2n(m, a1, 1, h, d_list, a_list) * c
+
+    # 4. Velocity Match at a2 (K rows)
+    # Project on Exterior
+    row_start = N + M + N
+    for k in range(K):
+        for m in range(M):
+            c = I_mk(m, k, 1, d_list, m0, h, m_k_arr, N_k_arr)
+            A[row_start + k, N + m] = -1 * diff_R_1n(m, a2, 1, h, d_list, a_list) * c
+            A[row_start + k, N + M + m] = -1 * diff_R_2n(m, a2, 1, h, d_list, a_list) * c
+        
+        A[row_start + k, N + 2*M + k] = h * diff_Lambda_k(k, a2, m0, a_list, m_k_arr)
+
+    return A
\ No newline at end of file
diff --git a/package/test/regenerate_benchmarks_v2.py b/package/test/regenerate_benchmarks_v2.py
new file mode 100644
index 0000000..4ace28d
--- /dev/null
+++ b/package/test/regenerate_benchmarks_v2.py
@@ -0,0 +1,134 @@
+import numpy as np
+import pandas as pd
+import sys
+from pathlib import Path
+
+# --- 1. SETUP IMPORTS ---
+CURRENT_DIR = Path(__file__).resolve().parent
+PROJECT_ROOT = CURRENT_DIR.parent.parent
+SLANTS_DIR = PROJECT_ROOT / "dev" / "python" / "slants"
+
+if str(SLANTS_DIR) not in sys.path:
+    sys.path.append(str(SLANTS_DIR))
+
+try:
+    from capytaine_generator import CapytaineSlantSolver
+except ImportError as e:
+    raise ImportError(f"Could not import CapytaineSlantSolver from {SLANTS_DIR}. Check the path.") from e
+
+# --- 2. CONFIGURATION ---
+RHO = 1023
+G = 9.81
+DATA_FOLDER = PROJECT_ROOT / "dev" / "python" / "convergence-study" / "meem-vs-capytaine-data" / "csv_data"
+
+CONFIGS = {
+    "mini_bicylinder":   {"h": 1.001, "d": [0.25, 0.125], "a": [0.125, 0.25], "heaving": [1, 1]},
+    "small_bicylinder":  {"h": 1.001, "d": [0.5, 0.25],   "a": [0.5, 1.0],    "heaving": [1, 1]},
+    "big_bicylinder":    {"h": 1.001, "d": [0.75, 0.5],   "a": [0.5, 0.75],   "heaving": [1, 1]},
+    "mini_tricylinder":  {"h": 2.001, "d": [1.0, 0.5, 0.25], "a": [0.25, 0.5, 1.0], "heaving": [1, 1, 1]},
+    "small_tricylinder": {"h": 20.0,  "d": [15, 10, 5],   "a": [5, 10, 15],   "heaving": [1, 1, 1]},
+    "big_tricylinder":   {"h": 25.0,  "d": [20, 15, 10],  "a": [10, 15, 20],  "heaving": [1, 1, 1]},
+}
+
+# --- 3. HELPER FUNCTIONS ---
+def calculate_displacement(a, d):
+    """
+    Calculates the exact displaced volume of the stepped cylinder.
+    Assumes 'd' represents the depth of the bottom of each region from the surface
+    and 'a' represents the outer radius of each region.
+    
+    Structure is an inverted stepped cone (widest at top/outermost).
+    Regions: 
+    0: r in [0, a[0]], depth d[0]
+    i: r in [a[i-1], a[i]], depth d[i]
+    """
+    vol = 0.0
+    # Region 0 (Inner Cylinder)
+    vol += np.pi * (a[0]**2) * d[0]
+    
+    # Annular Regions
+    for i in range(1, len(a)):
+        annulus_area = np.pi * (a[i]**2 - a[i-1]**2)
+        vol += annulus_area * d[i]
+        
+    return vol
+
+def extract_scalar(data):
+    """
+    Recursively extracts a single scalar value from a potentially nested structure
+    (float, numpy array, xarray, or dictionary).
+    """
+    if isinstance(data, (float, int, np.floating, np.integer)):
+        return float(data)
+    if isinstance(data, dict):
+        if len(data) == 0: return 0.0
+        first_val = next(iter(data.values()))
+        return extract_scalar(first_val)
+    if hasattr(data, "values") and not callable(data.values):
+        return extract_scalar(data.values)
+    if isinstance(data, np.ndarray):
+        if data.size == 1: return float(data.item())
+        else: return float(data.flatten()[0])
+    try:
+        return float(data)
+    except Exception:
+        raise TypeError(f"Cannot extract scalar from type {type(data)}: {data}")
+
+# --- 4. EXECUTION SCRIPT ---
+def regenerate():
+    DATA_FOLDER.mkdir(parents=True, exist_ok=True)
+    m0_values = np.linspace(0.1, 6.0, 15)
+    
+    solver = CapytaineSlantSolver(mesh=False, panel_count=False, hydros=False, times=False, phase=False)
+
+    for name, cfg in CONFIGS.items():
+        print(f"Regenerating {name}...")
+        
+        am_list = []
+        dp_list = []
+        
+        a = cfg['a']
+        d = cfg['d']
+        heaving = [bool(x) for x in cfg['heaving']]
+        h = cfg['h']
+        t_densities = [30] * len(a)
+        face_units = 30
+        
+        # --- Pre-calculate Volume for Normalization ---
+        # Note: 'a' here matches 'scaled_a' in the test if Radius Scaling is 1.0
+        disp_vol = calculate_displacement(a, d)
+
+        for m0 in m0_values:
+            result, _, _, _, _ = solver.construct_and_solve(
+                a=a, d_in=d, d_out=d, heaving=heaving, 
+                t_densities=t_densities, face_units=face_units, 
+                h=h, m0=m0, rho=RHO, reps=1
+            )
+            
+            # Robust Extraction
+            am_dim = extract_scalar(result.added_mass)
+            dp_dim = extract_scalar(result.radiation_damping)
+            
+            # Non-dimensionalize using Displacement Volume
+            # Standard: Coeff = Dimensional / (rho * Volume)
+            omega = np.sqrt(m0 * np.tanh(m0 * h) * G)
+            
+            am_nondim = am_dim / (RHO * disp_vol)
+            dp_nondim = dp_dim / (omega * RHO * disp_vol)
+            
+            am_list.append(am_nondim)
+            dp_list.append(dp_nondim)
+            
+        df = pd.DataFrame({
+            "m0": m0_values,
+            "pyCapytaineMu_x": m0_values,
+            "pyCapytaineMu_y": am_list,
+            "pyCapytaineLambda_y": dp_list
+        })
+        
+        output_path = DATA_FOLDER / f"{name}_regenerated_v2.csv"
+        df.to_csv(output_path, index=False)
+        print(f"Saved to {output_path}")
+
+if __name__ == "__main__":
+    regenerate()
\ No newline at end of file
diff --git a/package/test/results_example.py b/package/test/results_example.py
deleted file mode 100644
index 88a57a3..0000000
--- a/package/test/results_example.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import os
-import sys
-
-src_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../src'))
-sys.path.append(src_path)
-
-import xarray as xr
-import numpy as np
-from geometry import Geometry
-from results import Results
-
-# Example Geometry Setup
-r_coordinates = {
-    'a1': 2.0,
-    'a2': 4.0,
-    'a3': 6.0,
-}
-z_coordinates = {
-    'h': 5.0
-}
-domain_params = [
-    {
-        'number_harmonics': 3,
-        'height': 5.0,
-        'radial_width': 2.0,
-        'top_BC': 'Free Surface',
-        'bottom_BC': 'Sea Floor',
-        'category': 'inner',
-        'di': 1.0,
-        'a': 2.0,
-        'heaving': 1.0,
-    },
-    {
-        'number_harmonics': 4,
-        'height': 5.0,
-        'radial_width': 2.0,
-        'top_BC': 'Free Surface',
-        'bottom_BC': 'Sea Floor',
-        'category': 'outer',
-        'di': 2.0,
-        'a': 4.0,
-        'heaving': 1.0,
-    },
-    {
-        'number_harmonics': 5,
-        'height': 5.0,
-        'radial_width': 2.0,
-        'top_BC': 'Free Surface',
-        'bottom_BC': 'Sea Floor',
-        'category': 'outer',
-        'di': 3.0,
-        'a': 6.0,
-        'heaving': 1.0,
-    },
-    {
-        'number_harmonics': 6,
-        'height': 5.0,
-        'radial_width': np.inf,
-        'top_BC': 'Free Surface',
-        'bottom_BC': 'Sea Floor',
-        'category': 'exterior',
-        'di': 5.0,
-        'a': None,
-        'heaving': 1.0,
-    },
-]
-
-geometry = Geometry(r_coordinates, z_coordinates, domain_params)
-
-# Example Frequencies and Modes
-frequencies = np.array([1.0, 2.0])
-modes = np.array([1, 2])
-
-# Initialize Results object
-results = Results(geometry, frequencies, modes)
-
-# Example Eigenfunction Data
-radial_data = np.random.rand(len(frequencies), len(modes), len(r_coordinates))
-vertical_data = np.random.rand(len(frequencies), len(modes), len(z_coordinates))
-
-# Store Eigenfunction Results
-results.store_results(0, radial_data, vertical_data)
-
-# Example Potential Data
-potentials = {
-    'domain_0': {
-        'potentials': np.random.rand(3),
-        'r': r_coordinates,
-        'z': z_coordinates
-    },
-    'domain_1': {
-        'potentials': np.random.rand(4),
-        'r': r_coordinates,
-        'z': z_coordinates
-    },
-    'domain_2': {
-        'potentials': np.random.rand(5),
-        'r': r_coordinates,
-        'z': z_coordinates
-    },
-    'domain_3': {
-        'potentials': np.random.rand(6),
-        'r': r_coordinates,
-        'z': z_coordinates
-    }
-}
-
-# Store Potential Results
-results.store_potentials(potentials)
-
-# Export Results to NetCDF
-output_folder = "output/netcdf"
-os.makedirs(output_folder, exist_ok=True)  # Create the folder(s) if they don't exist
-results.export_to_netcdf(os.path.join(output_folder, "example_results.nc"))
-
-# Display Results
-print(results.display_results())
-
-# Access Results
-dataset = results.get_results()
-print("\nAccessing the xarray dataset:")
-print(dataset)
\ No newline at end of file
diff --git a/package/test/test_basic_region_geometry.py b/package/test/test_basic_region_geometry.py
new file mode 100644
index 0000000..04ae454
--- /dev/null
+++ b/package/test/test_basic_region_geometry.py
@@ -0,0 +1,339 @@
+# test_basic_region_geometry.py
+
+import pytest
+import numpy as np
+import sys
+import os
+from typing import List  # Added for type hinting
+
+# make src importable (adjust path if your repository layout differs)
+current_dir = os.path.dirname(__file__)
+src_dir = os.path.abspath(os.path.join(current_dir, '..', 'src'))
+if src_dir not in sys.path:
+    sys.path.insert(0, src_dir)
+
+from openflash.basic_region_geometry import BasicRegionGeometry
+from openflash.body import SteppedBody, Body  # Added Body for type hinting
+from openflash.geometry import ConcentricBodyGroup
+
+# ------------------------------
+# Helper utilities used in randomized tests
+# ------------------------------
+def contiguous_body_map(num_segments: int, num_bodies: int):
+    """
+    Create a body_map that maps contiguous segments to bodies.
+    This guarantees that concatenating segments by body preserves the
+    strictly increasing order of radii when `a` is strictly increasing.
+    """
+    assert 1 <= num_bodies <= num_segments
+    # compute split sizes (distribute segments across bodies)
+    base = num_segments // num_bodies
+    extras = num_segments % num_bodies
+    sizes = [base + (1 if i < extras else 0) for i in range(num_bodies)]
+    bm = []
+    cur = 0
+    for body_idx, sz in enumerate(sizes):
+        bm.extend([body_idx] * sz)
+        cur += sz
+    return bm
+
+# Small positive epsilon to use when we want "near zero" radii but
+# still respect Domain's assertion a_outer > a_inner >= 0
+_EPS = 1e-6
+
+# ------------------------------
+# Deterministic parametrized valid initialization
+# ------------------------------
+@pytest.mark.parametrize(
+    "radii_list, NMK",
+    [
+        ([np.array([1.0]), np.array([2.0])], [5, 6, 7]),
+        ([np.array([0.5]), np.array([1.5]), np.array([3.0])], [2, 3, 4, 5]),
+        ([np.array([1.0])], [10, 20]),
+    ]
+)
+def test_basic_region_geometry_init_valid_param(radii_list, NMK):
+    # FIX: Explicitly type hint as List[Body] to satisfy invariance
+    bodies: List[Body] = [
+        SteppedBody(a=r, d=np.array([1.0] * len(r)), slant_angle=np.zeros_like(r), heaving=False)
+        for r in radii_list
+    ]
+    arrangement = ConcentricBodyGroup(bodies)
+    geom = BasicRegionGeometry(arrangement, h=10.0, NMK=NMK)
+
+    assert geom.NMK == NMK
+    assert geom.h == 10.0
+    assert isinstance(geom.body_arrangement, ConcentricBodyGroup)
+
+# ------------------------------
+# Deterministic parametrized invalid radii (not strictly increasing)
+# ------------------------------
+@pytest.mark.parametrize(
+    "radii_list",
+    [
+        ([np.array([2.0]), np.array([1.0])]),
+        ([np.array([1.0]), np.array([1.0])]),
+    ]
+)
+def test_basic_region_geometry_init_invalid_radii_param(radii_list):
+    # FIX: Explicitly type hint as List[Body]
+    bodies: List[Body] = [
+        SteppedBody(a=r, d=np.array([1.0] * len(r)), slant_angle=np.zeros_like(r), heaving=False)
+        for r in radii_list
+    ]
+    arrangement = ConcentricBodyGroup(bodies)
+    NMK = [1] * (len(radii_list) + 1)
+
+    with pytest.raises(ValueError, match="Radii 'a' must be strictly increasing"):
+        BasicRegionGeometry(arrangement, h=10.0, NMK=NMK)
+
+# ------------------------------
+# Deterministic parametrized invalid NMK length
+# ------------------------------
+@pytest.mark.parametrize(
+    "radii_list, NMK",
+    [
+        ([np.array([1.0])], [5]),         # Should be length 2
+        ([np.array([1.0, 2.0])], [1, 2]), # Should be length 3
+    ]
+)
+def test_basic_region_geometry_init_invalid_nmk_length_param(radii_list, NMK):
+    # FIX: Explicitly type hint as List[Body]
+    bodies: List[Body] = [
+        SteppedBody(a=r, d=np.array([1.0] * len(r)), slant_angle=np.zeros_like(r), heaving=False)
+        for r in radii_list
+    ]
+    arrangement = ConcentricBodyGroup(bodies)
+
+    with pytest.raises(ValueError, match="Length of NMK must be one greater"):
+        BasicRegionGeometry(arrangement, h=10.0, NMK=NMK)
+
+# ------------------------------
+# Deterministic make_fluid_domains tests
+# ------------------------------
+@pytest.mark.parametrize(
+    "a, d, NMK",
+    [
+        (np.array([1.0, 2.0]), np.array([2.0, 3.0]), [5, 6, 7]),
+        (np.array([0.5, 1.5, 3.0]), np.array([1.0, 2.0, 3.0]), [2, 3, 4, 5]),
+    ]
+)
+def test_make_fluid_domains_param(a, d, NMK):
+    geom = BasicRegionGeometry.from_vectors(a, d, h=10.0, NMK=NMK)
+    domains = geom.make_fluid_domains()
+
+    assert len(domains) == len(a) + 1
+
+    last_outer = 0.0
+    for i, dom in enumerate(domains[:-1]):
+        assert dom.index == i
+        assert dom.category == "interior"
+        assert dom.a_inner == last_outer
+        assert dom.a_outer == a[i]
+        last_outer = a[i]
+
+    exterior = domains[-1]
+    assert exterior.category == "exterior"
+    assert exterior.a_inner == last_outer
+    assert exterior.a_outer == np.inf
+
+# ------------------------------
+# Deterministic from_vectors with body_map and heaving_map
+# ------------------------------
+@pytest.mark.parametrize(
+    "a, d, NMK, body_map, heaving_map, expected_num_bodies",
+    [
+        (np.array([1.0, 2.0]), np.array([2.0, 3.0]), [5, 6, 7], None, None, 1),
+        (np.array([1.0, 2.0, 3.0]),
+         np.array([1.0, 2.0, 3.0]),
+         [2, 3, 4, 5],
+         [0, 1, 1],
+         [False, True], # One heaving body
+         2),
+        # FIXED: Changed heaving_map to have only one True to comply with the new assertion
+        (np.array([0.5, 1.5, 3.0, 4.0]),
+         np.array([0.5, 1.5, 2.5, 3.5]),
+         [1, 2, 3, 4, 5],
+         [0, 1, 2, 2],
+         [True, False, False], # Changed from [True, True, True] to valid one heaving body
+         3)
+    ]
+)
+def test_from_vectors_body_and_heaving(a, d, NMK, body_map, heaving_map, expected_num_bodies):
+    geom = BasicRegionGeometry.from_vectors(a, d, h=10.0, NMK=NMK,
+                                            body_map=body_map, heaving_map=heaving_map)
+
+    assert len(geom.body_arrangement.bodies) == expected_num_bodies
+
+    for i, body in enumerate(geom.body_arrangement.bodies):
+        if heaving_map is not None:
+            assert body.heaving == heaving_map[i]
+        else:
+            assert body.heaving == False
+
+    # --- FIX: Use the 'a' property of the arrangement instead of manually iterating the list ---
+    # This solves the Pylance error regarding 'b.a' being inaccessible on base class 'Body'
+    combined_radii = geom.body_arrangement.a
+    np.testing.assert_array_equal(combined_radii, a)
+
+# ------------------------------
+# NEW TEST: Invalid heaving map (more than one heaving body)
+# ------------------------------
+def test_from_vectors_invalid_heaving_count():
+    a = np.array([1.0, 2.0, 3.0])
+    d = np.array([1.0, 2.0, 3.0])
+    NMK = [5, 5, 5, 5]
+    body_map = [0, 1, 2] # 3 separate bodies
+    heaving_map_bad = [True, True, False] # Two heaving bodies (violates rule)
+    
+    with pytest.raises(AssertionError, match="Only 0 or 1 body can be marked as heaving"):
+        BasicRegionGeometry.from_vectors(a, d, h=10.0, NMK=NMK,
+                                         body_map=body_map, heaving_map=heaving_map_bad)
+
+# ------------------------------
+# Edge-case tests (near-zero positive radius, single-element, large NMK)
+# ------------------------------
+@pytest.mark.parametrize(
+    "a, d, NMK",
+    [
+        # Use a tiny positive first radius instead of 0.0 to satisfy Domain assertion
+        (np.array([_EPS, 1.0]), np.array([1.0, 2.0]), [2, 3, 4]),
+        (np.array([2.0]), np.array([3.0]), [5, 6]),
+        (np.array([1.0, 2.0]), np.array([1.0, 2.0]), [10000, 20000, 30000]),
+    ]
+)
+def test_edge_cases(a, d, NMK):
+    geom = BasicRegionGeometry.from_vectors(a, d, h=10.0, NMK=NMK)
+    domains = geom.make_fluid_domains()
+
+    assert len(domains) == len(a) + 1
+
+    last_outer = 0.0
+    for i, dom in enumerate(domains[:-1]):
+        assert dom.index == i
+        assert dom.a_inner == last_outer
+        assert dom.a_outer == a[i]
+        last_outer = a[i]
+
+    exterior = domains[-1]
+    assert exterior.a_outer == np.inf
+    assert exterior.index == len(a)
+
+# ------------------------------
+# Randomized valid stress tests (guarantee strictly increasing radii and contiguous body_map)
+# ------------------------------
+@pytest.mark.parametrize("seed", range(10))
+def test_randomized_stress_valid(seed):
+    np.random.seed(seed)
+    num_segments = np.random.randint(1, 6)  # 1-5 segments
+
+    # Strictly increasing radii by cumulative sum of positive deltas
+    deltas = np.random.uniform(0.1, 2.0, size=num_segments)
+    a = np.cumsum(deltas)  # ensures a[0] > 0 and strictly increasing
+    d = np.random.uniform(1.0, 5.0, size=num_segments)
+
+    # NMK length must be num_segments + 1 and positive ints
+    NMK = np.random.randint(1, 100, size=num_segments + 1).astype(int).tolist()
+
+    # Choose a random number of bodies that partitions segments into contiguous blocks
+    num_bodies = np.random.randint(1, num_segments + 1)
+    body_map = contiguous_body_map(num_segments, num_bodies)
+    
+    # Ensure only 0 or 1 body is heaving
+    heaving_map = [False] * num_bodies
+    if num_bodies > 0 and np.random.rand() < 0.5: # 50% chance to set one body to heaving
+        heaving_map[np.random.randint(0, num_bodies)] = True
+    
+    geom = BasicRegionGeometry.from_vectors(a, d, h=10.0, NMK=NMK,
+                                            body_map=body_map, heaving_map=heaving_map)
+    domains = geom.make_fluid_domains()
+
+    # Basic checks and invariants
+    assert len(domains) == len(a) + 1
+
+    last_outer = 0.0
+    for i, dom in enumerate(domains[:-1]):
+        # non-overlap and monotonicity invariants
+        assert dom.a_inner == last_outer
+        assert dom.a_outer > last_outer
+        last_outer = dom.a_outer
+
+    # exterior domain invariants
+    exterior = domains[-1]
+    assert exterior.a_inner == last_outer
+    assert exterior.a_outer == np.inf
+
+# ------------------------------
+# Randomized invalid tests (explicitly force non-monotonic maps or duplicate radii)
+# ------------------------------
+@pytest.mark.parametrize("seed", range(5))
+def test_randomized_stress_invalid(seed):
+    np.random.seed(seed)
+    num_segments = np.random.randint(2, 6)  # need at least 2 to cause duplicate/ordering problems
+
+    # Create strictly increasing a first, then force a duplicate to provoke ValueError
+    deltas = np.random.uniform(0.1, 2.0, size=num_segments)
+    a = np.cumsum(deltas)
+    # force a duplicate to violate strict increase
+    a[1] = a[0]
+
+    d = np.random.uniform(1.0, 5.0, size=num_segments)
+    NMK = np.random.randint(1, 100, size=num_segments + 1).tolist()
+
+    # from_vectors should raise because radii are not strictly increasing
+    with pytest.raises(ValueError, match="Radii 'a' must be strictly increasing"):
+        BasicRegionGeometry.from_vectors(a, d, h=10.0, NMK=NMK)
+
+    # Another invalid scenario: non-contiguous body_map that reorders segments.
+    # Build a valid strictly increasing 'a' again and then create a non-contiguous map:
+    deltas2 = np.random.uniform(0.1, 2.0, size=num_segments)
+    a2 = np.cumsum(deltas2)
+    d2 = np.random.uniform(1.0, 5.0, size=num_segments)
+    NMK2 = np.random.randint(1, 100, size=num_segments + 1).tolist()
+
+    # create a body_map that interleaves bodies (e.g., 0,1,0,1...)
+    if num_segments >= 3:
+        body_map_bad = [(i % 2) for i in range(num_segments)]
+        # If num_segments is small, this map can cause concatenated radii to be non-monotonic.
+        # from_vectors should detect and raise ValueError
+        with pytest.raises(ValueError, match="Radii 'a' must be strictly increasing"):
+            BasicRegionGeometry.from_vectors(a2, d2, h=10.0, NMK=NMK2, body_map=body_map_bad)
+
+# ------------------------------
+# Randomized extreme NMK stress tests (extremely large NMK counts but valid shapes)
+# ------------------------------
+@pytest.mark.parametrize("seed", range(5))
+def test_randomized_extreme_nmk(seed):
+    np.random.seed(seed)
+    num_segments = np.random.randint(1, 6)
+
+    # Strictly increasing radii
+    deltas = np.random.uniform(0.1, 2.0, size=num_segments)
+    a = np.cumsum(deltas)
+    d = np.random.uniform(1.0, 5.0, size=num_segments)
+
+    # Generate very large NMK values (but keep count correct)
+    # Use values in the tens/hundreds of thousands to simulate pressure on code paths that store them
+    NMK = np.random.randint(10_000, 200_000, size=num_segments + 1).astype(int).tolist()
+
+    # Use contiguous body_map to preserve monotonicity
+    num_bodies = np.random.randint(1, num_segments + 1)
+    body_map = contiguous_body_map(num_segments, num_bodies)
+    
+    # Ensure only 0 or 1 body is heaving
+    heaving_map = [False] * num_bodies
+    if num_bodies > 0 and np.random.rand() < 0.5:
+        heaving_map[np.random.randint(0, num_bodies)] = True
+
+    geom = BasicRegionGeometry.from_vectors(a, d, h=10.0, NMK=NMK,
+                                            body_map=body_map, heaving_map=heaving_map)
+    domains = geom.make_fluid_domains()
+
+    # Confirm invariants still hold with huge NMK numbers
+    assert len(domains) == len(a) + 1
+    last_outer = 0.0
+    for dom in domains[:-1]:
+        assert dom.a_inner == last_outer
+        assert dom.a_outer > last_outer
+        last_outer = dom.a_outer
+    assert domains[-1].a_outer == np.inf
\ No newline at end of file
diff --git a/package/test/test_bicylinder_convergence.py b/package/test/test_bicylinder_convergence.py
new file mode 100644
index 0000000..ed45e5a
--- /dev/null
+++ b/package/test/test_bicylinder_convergence.py
@@ -0,0 +1,185 @@
+import pytest
+import numpy as np
+
+# Import the classes from your package
+from openflash.basic_region_geometry import BasicRegionGeometry
+from openflash.meem_problem import MEEMProblem
+from openflash.meem_engine import MEEMEngine
+
+# Assuming 'g' is defined in multi_constants
+# If not, you can just use g = 9.81
+try:
+    from openflash.multi_constants import g
+except ImportError:
+    g = 9.81
+
+pi = np.pi
+
+def run_bicylinder_sim(nmk_list: list[int], h: float = 1.001, m0: float = 1.0) -> tuple[float, float]:
+    """
+    Helper function to run a single simulation with the new package.
+    
+    This function replicates the setup from the "math script"
+    (bicylinder, m0=1.0) and returns the *total non-dimensional*
+    added mass and damping.
+    """
+    
+    # 1. Define geometry params from "math script"
+    d_vec = np.array([0.5, 0.25])
+    a_vec = np.array([0.25, 0.5])
+    
+    # --- FIX: Initialize with NO heaving bodies to pass geometry assertion ---
+    # The MEEMEngine will calculate the full interaction matrix regardless of initial state,
+    # provided we tell it which modes to iterate over.
+    heaving_map = [False, False] 
+    
+    # Body 0 = radius 0.25, Body 1 = radius 0.5
+    body_map = [0, 1] 
+
+    # 2. Create Geometry
+    # The NMK list is passed in for the convergence test
+    geometry = BasicRegionGeometry.from_vectors(
+        a=a_vec,
+        d=d_vec,
+        h=h,
+        NMK=nmk_list,
+        body_map=body_map,
+        heaving_map=heaving_map
+    )
+
+    # 3. Create Problem
+    problem = MEEMProblem(geometry)
+    
+    # --- FIX: Explicitly define the modes we want to solve for ---
+    # Since we set heaving=False above, problem.modes would default to empty.
+    # We force it to solve for Body 0 and Body 1.
+    # Note: You might need to adjust MEEMProblem to allow setting 'modes' if it's a property.
+    # If MEEMProblem.modes is a property that reads from geometry, we might need a workaround.
+    # Looking at previous code, problem.modes inferred from geometry. 
+    # Workaround: MEEMEngine.run_and_store_results uses problem.modes.
+    # If we cannot set problem.modes directly, we rely on the fact that for calculation of coefficients,
+    # we usually want the full matrix. 
+    
+    # Let's inspect MEEMProblem. If it doesn't allow setting modes, we mock it 
+    # or subclass it for the test. 
+    # Assuming standard behavior, let's try to set it if possible, or monkeypatch.
+    # Ideally, MEEMProblem should allow defining "active degrees of freedom".
+    
+    # HACK for Test: Override the property on the instance if Python allows, 
+    # or rely on the engine to calculate all if modes is empty? No, engine loops over modes.
+    
+    # OPTION B: Modify the geometry *after* creation? No, ConcentricBodyGroup is strict.
+    
+    # OPTION C: We modify the test to rely on the fact that `run_and_store_results`
+    # likely iterates over `problem.modes`. 
+    # If `MEEMProblem` derives modes from `geometry.body_arrangement.heaving`, 
+    # and `ConcentricBodyGroup` asserts sum(heaving) <= 1... 
+    # Then we can ONLY simulate one body heaving at a time in the "Problem definition".
+    
+    # BUT `run_and_store_results` is supposed to handle the N-body problem.
+    # It does this by creating *temporary* problems with single heaving bodies.
+    # It just needs to know *which* indices to do this for.
+    
+    # If we cannot set `problem.modes`, we can manually execute the engine loop here
+    # instead of calling `run_and_store_results`.
+    
+    # --- MANUAL EXECUTION STRATEGY ---
+    # This bypasses the dependency on `problem.modes` being pre-set to [0,1]
+    
+    # Convert m0 to omega
+    omega_val = np.sqrt(g * m0 * np.tanh(m0 * h))
+    problem.set_frequencies(np.array([omega_val]))
+    
+    engine = MEEMEngine(problem_list=[problem])
+    
+    # Initialize total accumulators
+    total_am_dim = 0.0
+    total_dp_dim = 0.0
+    
+    # Loop over both bodies (0 and 1)
+    for mode_idx in [0, 1]:
+        # 1. Create a temp geometry where ONLY mode_idx is heaving
+        # We can reuse the helper from BasicRegionGeometry to make a new one quickly
+        temp_heaving = [False, False]
+        temp_heaving[mode_idx] = True
+        
+        temp_geom = BasicRegionGeometry.from_vectors(
+            a=a_vec, d=d_vec, h=h, NMK=nmk_list,
+            body_map=body_map, heaving_map=temp_heaving
+        )
+        temp_prob = MEEMProblem(temp_geom)
+        temp_prob.set_frequencies(np.array([omega_val]))
+        
+        temp_engine = MEEMEngine(problem_list=[temp_prob])
+        
+        # 2. Solve for potential X_i
+        X_i = temp_engine.solve_linear_system_multi(temp_prob, m0)
+        
+        # 3. Calculate forces on ALL bodies due to motion of mode_idx
+        # This returns the column of coefficients [ (F_0i), (F_1i) ]
+        # We pass modes_to_calculate=[0, 1] to get forces on both bodies
+        coeffs = temp_engine.compute_hydrodynamic_coefficients(
+            temp_prob, X_i, m0, modes_to_calculate=np.array([0, 1])
+        )
+        
+        # 4. Sum up the dimensional values
+        # For 2 bodies moving in unison, Force_total = Sum(F_ji) for all j, i
+        for c in coeffs:
+            total_am_dim += c['real']
+            total_dp_dim += c['imag']
+
+    # 7. Non-dimensionalize
+    rho = 1023.0       # From "math script"
+    a_outer = a_vec[-1]  # Non-dim length (0.5)
+    
+    # A' = A / (rho * pi * a^3)
+    factor_am = 1.0 / (rho * pi * (a_outer**3))
+    # B' = B / (rho * pi * a^3 * omega_val)
+    factor_dp = 1.0 / (rho * pi * (a_outer**3) * omega_val)
+
+    total_am_nondim = total_am_dim * factor_am
+    total_dp_nondim = total_dp_dim * factor_dp
+
+    return total_am_nondim, total_dp_nondim
+
+# --- Pytest Fixture and Test ---
+
+@pytest.fixture(scope="module")
+def converged_solution():
+    """
+    Calculates the "golden" solution using a high number of terms.
+    This runs only ONCE per test session.
+    """
+    # Based on the "math script", N=30 is the max
+    nmk_converged = [30, 30, 30]
+    print("\nCalculating converged solution (NMK=30)...")
+    am_conv, dp_conv = run_bicylinder_sim(nmk_converged)
+    print(f"Converged AM (nondim): {am_conv}")
+    print(f"Converged DP (nondim): {dp_conv}")
+    return am_conv, dp_conv
+
+
+def test_bicylinder_convergence(converged_solution):
+    """
+    Tests if the solution with fewer terms (N=20) is close
+    to the converged solution (N=30).
+    
+    This replicates the *intent* of the math team's script
+    which checks for: abs(diff) < 0.001
+    """
+    # Get the pre-calculated converged solution
+    am_converged, dp_converged = converged_solution
+    
+    # Run with fewer terms, e.g., N=20
+    nmk_test = [20, 20, 20]
+    print("Calculating test solution (NMK=20)...")
+    am_test, dp_test = run_bicylinder_sim(nmk_test)
+    print(f"Test AM (nondim): {am_test}")
+    print(f"Test DP (nondim): {dp_test}")
+
+    # The "math script" checks for 0.1% difference (1e-3)
+    # We use np.isclose which is safer and checks relative tolerance
+    convergence_threshold = 1e-3 
+    
+    assert np.isclose(am_test, am_converged, rtol=convergence_threshold)
+    assert np.isclose(dp_test, dp_converged, rtol=convergence_threshold)
\ No newline at end of file
diff --git a/package/test/test_body.py b/package/test/test_body.py
new file mode 100644
index 0000000..beb0926
--- /dev/null
+++ b/package/test/test_body.py
@@ -0,0 +1,112 @@
+import pytest
+import numpy as np
+import sys
+import os
+
+# make src importable (adjust path if your repository layout differs)
+current_dir = os.path.dirname(__file__)
+src_dir = os.path.abspath(os.path.join(current_dir, '..', 'src'))
+if src_dir not in sys.path:
+    sys.path.insert(0, src_dir)
+    
+from openflash.body import SteppedBody, CoordinateBody
+# -----------------------------
+# SteppedBody Tests
+# -----------------------------
+@pytest.mark.parametrize(
+    "a, d, slant, heaving",
+    [
+        (np.array([1.0, 2.0]), np.array([0.5, 1.0]), np.array([0.1, 0.2]), False),
+        (np.array([0.5]), np.array([0.2]), np.array([0.05]), True),  # Single element
+        (np.array([10.0, 20.0, 30.0]), np.array([5.0, 10.0, 15.0]), np.array([0.3, 0.4, 0.5]), True),
+    ],
+)
+def test_stepped_body_valid(a, d, slant, heaving):
+    body = SteppedBody(a=a, d=d, slant_angle=slant, heaving=heaving)
+    assert np.array_equal(body.a, a)
+    assert np.array_equal(body.d, d)
+    assert np.array_equal(body.slant_angle, slant)
+    assert body.heaving == heaving
+
+
+@pytest.mark.parametrize(
+    "a, d, slant",
+    [
+        (np.array([1.0, 2.0]), np.array([0.5]), np.array([0.1, 0.2])),  # d too short
+        (np.array([1.0]), np.array([0.5, 1.0]), np.array([0.1])),      # d too long
+        (np.array([1.0, 2.0]), np.array([0.5, 1.0]), np.array([0.1])), # slant too short
+    ],
+)
+def test_stepped_body_invalid_lengths(a, d, slant):
+    with pytest.raises(AssertionError):
+        SteppedBody(a=a, d=d, slant_angle=slant)
+
+
+def test_stepped_body_randomized_stress():
+    rng = np.random.default_rng(seed=42)
+    for _ in range(50):
+        n = rng.integers(1, 20)
+        a = rng.random(n) * 100
+        d = rng.random(n) * 50
+        slant = rng.random(n) * 10
+        heaving = rng.choice([True, False])
+        # Should not raise unless lengths mismatch
+        body = SteppedBody(a=a, d=d, slant_angle=slant, heaving=heaving)
+        assert len(body.a) == len(body.d) == len(body.slant_angle)
+
+
+# -----------------------------
+# CoordinateBody Tests
+# -----------------------------
+@pytest.mark.parametrize(
+    "r, z, heaving",
+    [
+        (np.array([1.0, 2.0]), np.array([0.5, 0.7]), False),
+        (np.array([5.0]), np.array([2.0]), True),  # Single point
+    ],
+)
+def test_coordinate_body_valid(r, z, heaving):
+    body = CoordinateBody(r_coords=r, z_coords=z, heaving=heaving)
+    assert np.array_equal(body.r_coords, r)
+    assert np.array_equal(body.z_coords, z)
+    assert body.heaving == heaving
+
+
+@pytest.mark.parametrize(
+    "r, z",
+    [
+        (np.array([1.0, 2.0]), np.array([0.5])),     # mismatch
+        (np.array([1.0]), np.array([0.5, 0.6])),     # mismatch
+    ],
+)
+def test_coordinate_body_invalid_lengths(r, z):
+    with pytest.raises(AssertionError):
+        CoordinateBody(r_coords=r, z_coords=z)
+
+
+def test_coordinate_body_discretize_shape():
+    r = np.array([1.0, 2.0, 3.0])
+    z = np.array([0.5, 1.0, 1.5])
+    body = CoordinateBody(r_coords=r, z_coords=z)
+    a, d, slant = body.discretize()
+    assert len(a) == len(d) == len(slant) == len(r)
+
+
+def test_coordinate_body_discretize_gradient_monotonic():
+    r = np.array([1.0, 2.0, 4.0, 7.0])
+    z = np.array([0.5, 1.5, 2.0, 4.0])
+    body = CoordinateBody(r_coords=r, z_coords=z)
+    a, d, slant = body.discretize()
+    assert np.all(np.diff(a) >= 0)  # radius should remain non-decreasing
+
+
+def test_coordinate_body_randomized_discretize():
+    rng = np.random.default_rng(seed=10)
+    for _ in range(30):
+        n = rng.integers(2, 15)
+        r = np.sort(rng.random(n) * 100)  # enforce sorted for monotonicity
+        z = rng.random(n) * 30
+        body = CoordinateBody(r_coords=r, z_coords=z)
+        a, d, slant = body.discretize()
+        assert len(a) == len(d) == len(slant)
+        assert np.all(np.isfinite(slant))
diff --git a/package/test/test_capytaine_potential.py b/package/test/test_capytaine_potential.py
new file mode 100644
index 0000000..a790738
--- /dev/null
+++ b/package/test/test_capytaine_potential.py
@@ -0,0 +1,1222 @@
+# package/test/test_capytaine_potential.py
+import pytest
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from scipy.interpolate import griddata
+from scipy import integrate
+import matplotlib.pyplot as plt
+import warnings # To suppress plotting warnings
+from typing import Optional, List, Dict, Any, Tuple
+
+# Import your package's classes
+from openflash.basic_region_geometry import BasicRegionGeometry
+from openflash.meem_problem import MEEMProblem
+from openflash.meem_engine import MEEMEngine
+from openflash.multi_equations import *
+
+# Constants from the Capytaine script
+try:
+    from openflash.multi_constants import g
+except ImportError:
+    g = 9.81
+
+# --- Test Configuration ---
+
+# 1. Define path to the "golden" benchmark data
+BENCHMARK_DATA_PATH = Path(__file__).parent.parent.parent / "dev" / "python" / "test" / "data"
+
+# 2. Define path for saving debug plots on failure
+DEBUG_PLOT_PATH = Path(__file__).parent.parent / "test_artifacts"
+
+ALL_CONFIGS = {
+    "config0": {
+        "h": 1.001,
+        "a": np.array([0.5, 1]),
+        "d": np.array([0.5, 0.25]),
+        "heaving_map": [True, True],
+        "body_map": [0, 1],
+        "m0": 1.0,
+        "NMK": [15, 15, 15], # 2 radii + exterior
+        "R_range": np.linspace(0.0, 2 * 1, num=50),
+        "Z_range": np.linspace(0, -1.001, num=50),
+    },
+    "config1": {
+        "h": 1.5,
+        "a": np.array([0.3, 0.5, 1, 1.2, 1.6]),
+        "d": np.array([1.1, 0.85, 0.75, 0.4, 0.15]),
+        "heaving_map": [True, True, True, True, True],
+        "body_map": [0, 1, 2, 3, 4],
+        "m0": 1.0,
+        "NMK": [15] * 6, # 5 radii + exterior
+        "R_range": np.linspace(0.0, 2 * 1.6, num=50),
+        "Z_range": np.linspace(0, -1.5, num=50),
+    },
+    "config2": {
+        "h": 100.0,
+        "a": np.array([3, 5, 10]),
+        "d": np.array([29, 7, 4]),
+        "heaving_map": [True, True, True],
+        "body_map": [0, 1, 2],
+        "m0": 1.0,
+        "NMK": [100] * 4, # 3 radii + exterior
+        "R_range": np.linspace(0.0, 2 * 10, num=50),
+        "Z_range": np.linspace(0, -100, num=50),
+    },
+    # "config3": {
+    #     "h": 1.9,
+    #     "a": np.array([0.3, 0.5, 1, 1.2, 1.6]),
+    #     "d": np.array([0.5, 0.7, 0.8, 0.2, 0.5]),
+    #     "heaving_map": [True, True, True, True, True],
+    #     "body_map": [0, 1, 2, 3, 4],
+    #     "m0": 1.0,
+    #     "NMK": [50] * 6, # 5 radii + exterior
+    #     "R_range": np.linspace(0.0, 2 * 1.6, num=50),
+    #     "Z_range": np.linspace(0, -1.9, num=50),
+    # },
+    "config4": {
+        "h": 1.001,
+        "a": np.array([0.5, 1]),
+        "d": np.array([0.5, 0.25]),
+        "heaving_map": [False, True],
+        "body_map": [0, 1],
+        "m0": 1.0,
+        "NMK": [15] * 3, # 2 radii + exterior
+        "R_range": np.linspace(0.0, 2 * 1, num=50),
+        "Z_range": np.linspace(0, -1.001, num=50),
+    },
+    "config5": {
+        "h": 1.001,
+        "a": np.array([0.5, 1]),
+        "d": np.array([0.5, 0.25]),
+        "heaving_map": [True, False],
+        "body_map": [0, 1],
+        "m0": 1.0,
+        "NMK": [15] * 3, # 2 radii + exterior
+        "R_range": np.linspace(0.0, 2 * 1, num=50),
+        "Z_range": np.linspace(0, -1.001, num=50),
+    },
+    "config6": {
+        "h": 100.0,
+        "a": np.array([3, 5, 10]),
+        "d": np.array([29, 7, 4]),
+        "heaving_map": [False, True, True],
+        "body_map": [0, 1, 2],
+        "m0": 1.0,
+        "NMK": [100] * 4, # 3 radii + exterior
+        "R_range": np.linspace(0.0, 2 * 10, num=50),
+        "Z_range": np.linspace(0, -100, num=50),
+    }
+}
+
+# 4. Define comparison tolerance
+RELATIVE_TOLERANCE = 0.1
+
+# --- End Configuration ---
+
+# --- Helper Functions ---
+
+def load_capytaine_data(config_name):
+    """
+    Loads the "golden" potential field data for a specific config.
+    """
+    real_path = BENCHMARK_DATA_PATH / f"{config_name}-real.csv"
+    imag_path = BENCHMARK_DATA_PATH / f"{config_name}-imag.csv"
+    
+    if not real_path.exists():
+        pytest.skip(f"Benchmark file not found: {real_path}")
+    if not imag_path.exists():
+        pytest.skip(f"Benchmark file not found: {imag_path}")
+
+    try:
+        real_data = np.loadtxt(real_path, delimiter=",")
+        imag_data = np.loadtxt(imag_path, delimiter=",")
+        potential_field = real_data + 1j * imag_data
+        return potential_field
+    
+    except Exception as e:
+        pytest.fail(f"Failed to load benchmark data for {config_name}: {e}")
+
+
+def run_openflash_sim(config_name, R_range: Optional[np.ndarray] = None, Z_range: Optional[np.ndarray] = None, heaving_map_override: Optional[List[bool]] = None, verbose: bool = True) -> Tuple[Dict[str, Any], float]:
+    """
+    Runs the openflash simulation for a specific config to get the potential field.
+    """
+    if config_name not in ALL_CONFIGS:
+        pytest.fail(f"Unknown config_name: {config_name}")
+        
+    p = ALL_CONFIGS[config_name]
+    
+    # Use the override map if provided, otherwise use the config default
+    active_heaving_map = heaving_map_override if heaving_map_override is not None else p["heaving_map"]
+    
+    # 1. Create Geometry
+    geometry = BasicRegionGeometry.from_vectors(
+        a=p["a"],
+        d=p["d"],
+        h=p["h"],
+        NMK=p["NMK"],
+        body_map=p["body_map"],
+        heaving_map=active_heaving_map
+    )
+
+    # 2. Create Problem
+    problem = MEEMProblem(geometry)
+    
+    # 3. Set Frequency
+    openflash_omega = omega(p["m0"], p["h"], g)
+    problem.set_frequencies(np.array([openflash_omega]))
+
+    # 4. Create Engine
+    engine = MEEMEngine(problem_list=[problem])
+    
+    # --- CONTROLLED PRINTING ---
+    if verbose:
+        diagnose_geometry_depths(geometry)
+        # Loop through ALL valid regions instead of hardcoding '3'
+        print(f"\n  [DEBUG] Running Orthogonality Checks for {len(geometry.domain_list)} regions...")
+        for r_idx in geometry.domain_list.keys():
+            debug_vertical_orthogonality(engine, problem, region_idx=r_idx, m0=p["m0"])
+        print(f"    [Diagnosing Linear System for {config_name}]")
+    # ---------------------------
+    
+    solution_vector = engine.solve_linear_system_multi(problem, p["m0"])
+    
+    # --- CONTROLLED PRINTING ---
+    if verbose:
+        diagnose_continuity(engine, problem, solution_vector, config_name, p["m0"])
+        
+        # --- NEW: Check Potential Continuity ---
+        # Your previous logs showed Flux was perfect, but Potential was wrong.
+        # This function checks if Phi is continuous (Pressure matching).
+        diagnose_potential_continuity(engine, problem, solution_vector, config_name, p["m0"])
+        audit_potential_matrix_continuity(engine, problem, solution_vector, config_name, p["m0"])
+        
+    # ---------------------------
+    
+    potentials_dict = engine.calculate_potentials(
+        problem, 
+        solution_vector, 
+        p["m0"], 
+        spatial_res=50, 
+        sharp=False,
+    )
+    
+    # Return the grid/potential AND the frequency
+    results_dict = {
+        "R": potentials_dict["R"],
+        "Z": potentials_dict["Z"],
+        "phi": potentials_dict["phi"],
+        # Return engine/problem for further deep debugging
+        "_engine": engine,
+        "_problem": problem,
+        "_solution": solution_vector,
+        "_m0": p["m0"]
+    }
+    
+    return results_dict, openflash_omega
+
+
+def save_debug_plots(R_grid, Z_grid, openflash_data, capytaine_data_converted, nan_mask, config_name, plot_type):
+    """
+    Saves a 4-panel comparison plot to the debug artifact folder.
+    """
+    # Create the output directory if it doesn't exist
+    DEBUG_PLOT_PATH.mkdir(parents=True, exist_ok=True)
+    output_file = DEBUG_PLOT_PATH / f"{config_name}_{plot_type}_comparison.png"
+
+    # --- Prepare data for plotting ---
+    
+    # Calculate differences
+    diff = openflash_data - capytaine_data_converted
+    
+    # Calculate percent difference, avoiding division by zero
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", RuntimeWarning) # Ignore "divide by zero"
+        percent_diff = 100 * (diff / capytaine_data_converted)
+    
+    # Apply the 'nan' mask from the body to all plots
+    openflash_data[nan_mask] = np.nan
+    capytaine_data_converted[nan_mask] = np.nan
+    diff[nan_mask] = np.nan
+    percent_diff[nan_mask] = np.nan
+    
+    # Find common min/max for the main plots
+    v_min = np.nanmin([openflash_data, capytaine_data_converted])
+    v_max = np.nanmax([openflash_data, capytaine_data_converted])
+    
+    # Find min/max for the difference plots
+    diff_vmax = np.nanmax(np.abs(diff))
+    perc_vmax = np.nanmin([500, np.nanmax(np.abs(percent_diff))]) # Cap at 500%
+
+    # --- Create the Plot ---
+    fig, axes = plt.subplots(2, 2, figsize=(15, 12))
+    fig.suptitle(f"Debug Comparison for: {config_name} ({plot_type.upper()})", fontsize=16)
+
+    # Plot 1: Openflash
+    ax1 = axes[0, 0]
+    im1 = ax1.pcolormesh(R_grid, Z_grid, openflash_data, vmin=v_min, vmax=v_max, cmap='viridis')
+    fig.colorbar(im1, ax=ax1)
+    ax1.set_title("Openflash (ACTUAL)")
+
+    # Plot 2: Capytaine (CONVERTED)
+    ax2 = axes[0, 1]
+    im2 = ax2.pcolormesh(R_grid, Z_grid, capytaine_data_converted, vmin=v_min, vmax=v_max, cmap='viridis')
+    fig.colorbar(im2, ax=ax2)
+    ax2.set_title("Capytaine (CONVERTED TO OPENFLASH UNITS)")
+
+    # Plot 3: Absolute Difference
+    ax3 = axes[1, 0]
+    im3 = ax3.pcolormesh(R_grid, Z_grid, np.abs(diff), vmin=0, vmax=diff_vmax, cmap='Reds')
+    fig.colorbar(im3, ax=ax3)
+    ax3.set_title("Absolute Difference |Actual - Desired|")
+
+    # Plot 4: Percent Difference
+    ax4 = axes[1, 1]
+    im4 = ax4.pcolormesh(R_grid, Z_grid, np.abs(percent_diff), vmin=0, vmax=perc_vmax, cmap='Reds')
+    fig.colorbar(im4, ax=ax4)
+    ax4.set_title("Percent Difference |Diff / Desired| (Capped at 500%)")
+    
+    for ax in axes.flat:
+        ax.set_xlabel("R (radius)")
+        ax.set_ylabel("Z (depth)")
+    
+    # Fix for Pylance TypeError
+    plt.tight_layout(rect=(0, 0.03, 1, 0.95))
+    plt.savefig(output_file)
+    plt.close(fig)
+    print(f"\n[Debug plot saved to: {output_file}]")
+
+
+def save_1d_cuts(R_grid, Z_grid, openflash_data, capytaine_data, config_name, component_name):
+    """
+    Saves 1D line cuts (slices) through the domain to visualize 
+    profile differences in detail.
+    """
+    # Create directory
+    DEBUG_PLOT_PATH.mkdir(parents=True, exist_ok=True)
+    
+    # --- 1. Vertical Cut (Z-axis) ---
+    r_idx = R_grid.shape[0] // 2
+    r_val = R_grid[r_idx, 0]
+    
+    z_line = Z_grid[r_idx, :]
+    of_line_z = openflash_data[r_idx, :]
+    cap_line_z = capytaine_data[r_idx, :]
+    
+    plt.figure(figsize=(10, 6))
+    plt.plot(z_line, of_line_z, 'b-', label='OpenFLASH', linewidth=2)
+    plt.plot(z_line, cap_line_z, 'r--', label='Capytaine', linewidth=2)
+    plt.title(f"Vertical Slice (Z-axis) at R={r_val:.2f} [{component_name}]")
+    plt.xlabel("Z (Depth)")
+    plt.ylabel("Potential")
+    plt.legend()
+    plt.grid(True)
+    plt.savefig(DEBUG_PLOT_PATH / f"{config_name}_{component_name}_cut_vertical.png")
+    plt.close()
+
+    # --- 2. Radial Cut (R-axis) ---
+    z_target = np.min(Z_grid) / 2.0
+    z_idx = np.argmin(np.abs(Z_grid[0, :] - z_target))
+    z_val = Z_grid[0, z_idx]
+    
+    r_line = R_grid[:, z_idx]
+    of_line_r = openflash_data[:, z_idx]
+    cap_line_r = capytaine_data[:, z_idx]
+    
+    plt.figure(figsize=(10, 6))
+    plt.plot(r_line, of_line_r, 'b-', label='OpenFLASH', linewidth=2)
+    plt.plot(r_line, cap_line_r, 'r--', label='Capytaine', linewidth=2)
+    plt.title(f"Radial Slice (R-axis) at Z={z_val:.2f} [{component_name}]")
+    plt.xlabel("R (Radius)")
+    plt.ylabel("Potential")
+    plt.legend()
+    plt.grid(True)
+    plt.savefig(DEBUG_PLOT_PATH / f"{config_name}_{component_name}_cut_radial.png")
+    plt.close()
+
+
+def save_debug_csvs(R_grid, Z_grid, openflash_real, capytaine_real, openflash_imag, capytaine_imag, nan_mask, config_name):
+    """
+    Saves a detailed CSV of all interpolated grid data for debugging.
+    """
+    DEBUG_PLOT_PATH.mkdir(parents=True, exist_ok=True)
+    output_file = DEBUG_PLOT_PATH / f"{config_name}_debug_data.csv"
+    
+    # Create a structured DataFrame
+    data = {
+        "R": R_grid.ravel(),
+        "Z": Z_grid.ravel(),
+        "is_body_nan": nan_mask.ravel(),
+        "openflash_real": openflash_real.ravel(),
+        "capytaine_real_converted": capytaine_real.ravel(),
+        "openflash_imag": openflash_imag.ravel(),
+        "capytaine_imag_converted": capytaine_imag.ravel(),
+    }
+    df = pd.DataFrame(data)
+    
+    # Calculate difference columns
+    df["diff_real"] = df["openflash_real"] - df["capytaine_real_converted"]
+    df["diff_imag"] = df["openflash_imag"] - df["capytaine_imag_converted"]
+    
+    # Calculate relative difference, handling division by zero
+    df["rel_diff_real"] = np.where(
+        df["capytaine_real_converted"] != 0,
+        100 * np.abs(df["diff_real"] / df["capytaine_real_converted"]),
+        np.inf
+    )
+    df["rel_diff_imag"] = np.where(
+        df["capytaine_imag_converted"] != 0,
+        100 * np.abs(df["diff_imag"] / df["capytaine_imag_converted"]),
+        np.inf
+    )
+    
+    # Filter out the points inside the body (where capytaine is NaN)
+    df_fluid_only = df[~df["is_body_nan"]].copy()
+
+    # Save the filtered data
+    df_fluid_only.to_csv(output_file, index=False, float_format="%.6e")
+    print(f"[Debug CSV saved to: {output_file}]")
+
+def audit_potential_matrix_continuity(engine, problem, solution_vector, config_name, m0):
+    """
+    6️⃣ ONE-LINE KILLER CHECK
+    Reconstructs the P-matrix blocks locally and verifies:
+       P_L @ C_L + P_R @ C_R == b  (Summation, because P_R block carries the -1 sign)
+    """
+    print(f"\n  [MATRIX BALANCE AUDIT - 6️⃣ KILLER CHECK] {config_name}")
+    
+    # --- FIX 1: Retrieve Properties from Domain List (Same as Engine) ---
+    domains = problem.geometry.domain_list
+    domain_keys = list(domains.keys()) 
+    
+    h = domains[0].h
+    d = [domains[idx].di for idx in domain_keys] 
+    a = [domains[idx].a for idx in domain_keys]
+    heaving = [domains[idx].heaving for idx in domain_keys]
+    
+    # Get precomputed integrals
+    engine._ensure_m_k_and_N_k_arrays(problem, m0)
+    cache = engine.cache_list[problem]
+    I_nm_vals = cache.I_nm_vals
+    
+    # Helper to reformat coefficients
+    NMK = [domains[i].number_harmonics for i in range(len(domains))]
+    all_coeffs = engine.reformat_coeffs(solution_vector, NMK, len(domains)-1)
+    
+    # Radial Functions
+    from functools import partial
+    from openflash.multi_equations import R_1n, R_2n, p_diagonal_block, p_dense_block, b_potential_entry
+    
+    R_1n_func = np.vectorize(partial(R_1n, h=h, d=d, a=a))
+    R_2n_func = np.vectorize(partial(R_2n, a=a, h=h, d=d))
+
+    boundary_count = len(domains) - 1
+    
+    for bd in range(boundary_count):
+        # --- FIX 2: Skip Exterior Boundary immediately ---
+        # The exterior boundary uses I_mk (m0-dependent) and different functions.
+        # p_dense_block will crash with IndexError if used here.
+        if bd == boundary_count - 1:
+            print(f"    Boundary {bd}: Skipping (Exterior Boundary handled by different functions)")
+            continue
+        # -------------------------------------------------
+
+        # 1. Identify Shorter Region
+        project_on_left = (d[bd] > d[bd+1])
+        
+        # 2. Reconstruct P_L and P_R matrices
+        if project_on_left:
+            # Left (Diagonal) part
+            P_L_R1 = p_diagonal_block(True, R_1n_func, bd, h, d, a, NMK)
+            P_L_R2 = p_diagonal_block(True, R_2n_func, bd, h, d, a, NMK) if bd > 0 else None
+            
+            # Right (Dense) part
+            P_R_R1 = p_dense_block(False, R_1n_func, bd, NMK, a, I_nm_vals)
+            P_R_R2 = p_dense_block(False, R_2n_func, bd, NMK, a, I_nm_vals)
+            
+            c_L = all_coeffs[bd]
+            c_R = all_coeffs[bd+1]
+            
+            if bd > 0:
+                N = NMK[bd]
+                term_L = P_L_R1 @ c_L[:N] + P_L_R2 @ c_L[N:]
+            else:
+                term_L = P_L_R1 @ c_L
+            
+            N_R = NMK[bd+1]
+            term_R = P_R_R1 @ c_R[:N_R] + P_R_R2 @ c_R[N_R:]
+            
+            LHS = term_L + term_R 
+
+        else: # Right is Shorter (Project on Right)
+            
+            # Left (Dense) part
+            P_L_R1 = p_dense_block(True, R_1n_func, bd, NMK, a, I_nm_vals)
+            P_L_R2 = p_dense_block(True, R_2n_func, bd, NMK, a, I_nm_vals) if bd > 0 else None
+            
+            # Right (Diagonal) part
+            P_R_R1 = p_diagonal_block(False, R_1n_func, bd, h, d, a, NMK)
+            P_R_R2 = p_diagonal_block(False, R_2n_func, bd, h, d, a, NMK)
+            
+            c_L = all_coeffs[bd]
+            c_R = all_coeffs[bd+1]
+            
+            if bd > 0:
+                N = NMK[bd]
+                term_L = P_L_R1 @ c_L[:N] + P_L_R2 @ c_L[N:]
+            else:
+                term_L = P_L_R1 @ c_L
+                
+            N_R = NMK[bd+1]
+            term_R = P_R_R1 @ c_R[:N_R] + P_R_R2 @ c_R[N_R:]
+            
+            LHS = term_L + term_R
+
+        # 3. Reconstruct b vector (Forcing)
+        num_rows = NMK[bd] if project_on_left else NMK[bd+1]
+        b_vec = np.zeros(num_rows, dtype=complex)
+        for n in range(num_rows):
+            b_vec[n] = b_potential_entry(n, bd, heaving, a, h, d)
+            
+        # 4. THE KILLER CHECK
+        diff = LHS - b_vec
+        max_err = np.max(np.abs(diff))
+        
+        print(f"    Boundary {bd} (Project on {'LEFT' if project_on_left else 'RIGHT'}):")
+        print(f"      Matrix Imbalance: {max_err:.4e}")
+        
+        try:
+            np.testing.assert_allclose(
+                LHS,
+                b_vec,
+                rtol=1e-5,
+                atol=1e-6,
+                err_msg=f"P-matrix mismatch at Boundary {bd}"
+            )
+            print("      ✅ PASS")
+        except AssertionError as e:
+            print("      ❌ FAIL")
+            print(f"      Term L (Sample): {term_L[:3]}")
+            print(f"      Term R (Sample): {term_R[:3]}")
+            print(f"      b_vec  (Sample): {b_vec[:3]}")
+            
+def check_phase_rotation(openflash_complex, capytaine_complex):
+    """
+    Checks if a global phase rotation (e.g. -1, j, -j, conjugate)
+    would minimize the error.
+    """
+    # Create flattened valid arrays (ignoring NaNs)
+    valid = ~np.isnan(openflash_complex) & ~np.isnan(capytaine_complex)
+    of = openflash_complex[valid]
+    cap = capytaine_complex[valid]
+    
+    if of.size == 0:
+        return "No Valid Data"
+
+    transformations = {
+        "None": of,
+        "Negated (-1)": -of,
+        "Conjugate (*)": np.conj(of),
+        "Rotated 90 (j)": 1j * of,
+        "Rotated -90 (-j)": -1j * of,
+        "Conjugate & Negated": -np.conj(of)
+    }
+    
+    print("\n--- PHASE / CONVENTION DIAGNOSTIC ---")
+    best_name = "None"
+    best_error = np.inf
+    
+    for name, transformed_of in transformations.items():
+        # Calculate Relative L2 Norm Error
+        diff_norm = np.linalg.norm(transformed_of - cap)
+        ref_norm = np.linalg.norm(cap)
+        rel_error = diff_norm / ref_norm if ref_norm > 0 else np.inf
+        
+        print(f"  Transform '{name}': Rel Error = {rel_error:.4%}")
+        if rel_error < best_error:
+            best_error = rel_error
+            best_name = name
+            
+    print(f"  [DIAGNOSTIC] Best match is: '{best_name}'")
+    return best_name
+
+def debug_vertical_orthogonality(engine, problem, region_idx, m0):
+    """
+    Verifies vertical orthogonality for a specific region.
+    Integrates Z_m * Z_n from -h to -d.
+    """
+    print(f"\n  [ORTHOGONALITY CHECK] Region {region_idx}")
+    
+    # 1. Setup Geometry Parameters
+    geo = problem.geometry
+    h = geo.h
+    domains = geo.domain_list
+    d_vals = geo.body_arrangement.d
+    
+    # Handle Exterior vs Interior
+    is_exterior = (region_idx == len(d_vals))
+    
+    # Initialize the function variable
+    get_Z = None
+    d_local = 0.0
+
+    if is_exterior:
+        d_local = 0.0 # Seabed
+        print(f"    Type: Exterior (d={d_local}, h={h})")
+        
+        # Ensure cache is ready for Exterior functions
+        engine._ensure_m_k_and_N_k_arrays(problem, m0)
+        cache = engine.cache_list[problem]
+        m_k_arr, N_k_arr = cache.m_k_arr, cache.N_k_arr
+        NMK = domains[region_idx].number_harmonics
+        
+        # Define specific exterior function
+        def _get_z_exterior(mode, z):
+            # Use vectorized version with precomputed arrays for stability
+            # Wrap scalars in arrays for the vectorized function, then take [0]
+            return Z_k_e_vectorized(np.array([mode]), np.array([z]), m0, h, m_k_arr, N_k_arr)[0]
+            
+        get_Z = _get_z_exterior
+
+    else:
+        d_local = d_vals[region_idx]
+        print(f"    Type: Interior (d={d_local}, h={h})")
+        
+        # Define specific interior function
+        def _get_z_interior(mode, z):
+            return Z_n_i(mode, z, region_idx, h, d_vals)
+            
+        get_Z = _get_z_interior
+
+    # 2. Integration Bounds
+    z_min = -h
+    z_max = -d_local
+    
+    # 3. Compute Inner Products
+    print("    Inner Products <Zm, Zn> (Expect Diag ~ h-d, Off-Diag ~ 0):")
+    
+    for m in range(3):
+        row_str = ""
+        for n in range(3):
+            # Define integrand using the assigned get_Z
+            # Capture m and n by default value to avoid late binding issues in lambdas
+            integrand = lambda z, m=m, n=n: get_Z(m, z) * get_Z(n, z)
+            
+            # Numerical Integration (Quad is precise enough)
+            val_real, _ = integrate.quad(lambda z: np.real(integrand(z)), z_min, z_max)
+            val_imag, _ = integrate.quad(lambda z: np.imag(integrand(z)), z_min, z_max)
+            val = val_real + 1j*val_imag
+            
+            # Formatting
+            if abs(val) < 1e-9: val = 0.0
+            
+            row_str += f"{val.real: .4e}  "
+            
+        print(f"    m={m}: [ {row_str}]")
+
+    print(f"    (Theoretical H-d = {h - d_local:.4f})")
+    
+def diagnose_geometry_depths(geometry):
+    """
+     audits the depth consistency between the global Geometry object and individual Domain objects.
+     Mismatches here are the #1 cause of 'Flux Passes, Potential Fails' errors.
+    """
+    print("\n  [DEPTH CONSISTENCY CHECK]")
+    print(f"    Global Geometry h: {geometry.h}")
+    
+    # Handle list vs dict structure
+    domains = geometry.fluid_domains
+    if isinstance(domains, dict):
+        domains = list(domains.values())
+        
+    for i, domain in enumerate(domains):
+        # In OpenFLASH:
+        # domain.h  -> Global Water Depth (d_upper)
+        # domain.di -> Draft / Bottom Depth (d_lower)
+        # Local Depth = domain.h - domain.di
+        
+        local_depth = domain.h - domain.di
+        
+        match_status = "✅" if abs(domain.h - geometry.h) < 1e-9 else "❌ MISMATCH"
+        
+        print(f"    Region {i} (Index {domain.index}):")
+        print(f"      Global h (Geometry) : {geometry.h:.4f}")
+        print(f"      Global h (Domain)   : {domain.h:.4f}  {match_status}")
+        print(f"      Draft d (Domain)    : {domain.di:.4f}")
+        print(f"      Local Depth (h - d) : {local_depth:.4f}")
+        
+        if abs(domain.h - geometry.h) > 1e-9:
+             print(f"      🚨 CRITICAL ERROR: Domain {i} thinks global depth is {domain.h}, but Geometry says {geometry.h}!")
+
+def diagnose_continuity(engine, problem, solution_vector, config_name, m0):
+    """
+    Checks if Mass Flux is conserved across boundaries.
+    """
+    print(f"\n  [CONTINUITY DIAGNOSTIC - FLUX] {config_name}")
+    
+    geo = problem.geometry
+    domains = geo.domain_list
+    a = geo.body_arrangement.a
+    d = geo.body_arrangement.d
+    h = geo.h
+    boundary_count = len(domains) - 1
+    
+    for bd in range(boundary_count):
+        radius = a[bd]
+        
+        d_left = d[bd]
+        d_right = d[bd+1] if bd < len(d)-1 else d[bd]
+        
+        max_draft = max(d_left, d_right) 
+        common_depth = h - max_draft
+        
+        z_common = np.linspace(0, -max_draft, 100)
+        eps = 1e-4 
+        
+        # --- LEFT SIDE ---
+        vel_left = engine.calculate_velocities(problem, solution_vector, m0, 10, False, 
+                                               R_range=np.array([radius - eps]), Z_range=z_common)
+        vr_left = vel_left['vr'].flatten()
+        
+        # --- RIGHT SIDE ---
+        vel_right = engine.calculate_velocities(problem, solution_vector, m0, 10, False, 
+                                                R_range=np.array([radius + eps]), Z_range=z_common)
+        vr_right = vel_right['vr'].flatten()
+
+        valid = ~np.isnan(vr_left) & ~np.isnan(vr_right)
+        
+        if not np.any(valid):
+            print(f"    Boundary {bd}: NO VALID POINTS")
+            continue
+            
+        flux_diff = integrate.simpson(np.abs(vr_left[valid] - vr_right[valid]), x=z_common[valid])
+        total_flux = integrate.simpson(np.abs(vr_left[valid]), x=z_common[valid])
+        
+        mse = np.mean(np.abs(vr_left[valid] - vr_right[valid])**2)
+        rms_diff = np.sqrt(mse)
+
+        if total_flux > 1e-6:
+            rel_err = flux_diff / total_flux
+        else:
+            rel_err = flux_diff
+            
+        status = "✅ PASS" if rel_err < 0.05 else "❌ FAIL"
+            
+        print(f"    Boundary {bd} (R={radius:.2f}): {status}")
+        print(f"      Common Height: {max_draft:.2f}")
+        print(f"      RMS Vel Diff : {rms_diff:.4e}")
+        print(f"      Rel Flux Err : {rel_err*100:.2f}%")
+
+        if d_left != d_right:
+            step_top = -min(d_left, d_right)
+            step_bot = -max(d_left, d_right)
+            z_step = np.linspace(step_top - 0.01, step_bot + 0.01, 50)
+            
+            if d_left < d_right: 
+                check_side = f"Left (Reg {bd})"
+                vel_step = engine.calculate_velocities(problem, solution_vector, m0, 10, False, 
+                                               R_range=np.array([radius - eps]), Z_range=z_step)
+                target_vr = 0.0 
+            else:
+                check_side = f"Right (Reg {bd+1})"
+                vel_step = engine.calculate_velocities(problem, solution_vector, m0, 10, False, 
+                                               R_range=np.array([radius + eps]), Z_range=z_step)
+                target_vr = 0.0
+
+            vr_step = vel_step['vr'].flatten()
+            valid_step = ~np.isnan(vr_step)
+            
+            if np.any(valid_step):
+                leak_flux = integrate.simpson(np.abs(vr_step[valid_step] - target_vr), x=z_step[valid_step])
+                step_height = abs(step_top - step_bot)
+                avg_leak_vel = leak_flux / step_height if step_height > 0 else 0
+                step_status = "✅" if avg_leak_vel < 0.1 else "⚠️ LEAK" 
+                print(f"      Step Check ({check_side}): {step_status}")
+                print(f"      Avg Leak Vel : {avg_leak_vel:.4e}")
+                print(f"      Step Height  : {step_height:.4f}")
+
+def debug_phi_reconstruction(coeffs, r, z, region_idx, h, d, a, label):
+    """
+    Reconstructs Phi manually using the basis functions to check for normalization/sign errors.
+    """
+    print(f"\n  [{label}] Phi Reconstruction @ (r={r:.3f}, z={z:.3f})")
+    
+    # --- FIX: Handle Exterior Region ---
+    if region_idx >= len(a):
+        print(f"    Region {region_idx} is EXTERIOR. Manual reconstruction skipped (requires Lambda_k).")
+        # Return 0.0 so the diff calculation doesn't crash
+        return 0.0
+    # -----------------------------------
+
+    phi_accum = 0.0
+    
+    # Check if this is an Inner region (i=0) or Annulus
+    is_inner = (region_idx == 0)
+    
+    if is_inner:
+        # Inner Region: R_1n * Z_n
+        for n, c in enumerate(coeffs[:6]): # Check first 6 modes
+            psi_z = Z_n_i(n, z, region_idx, h, d)
+            psi_r = R_1n(n, r, region_idx, h, d, a)
+            contrib = c * psi_r * psi_z
+            phi_accum += contrib
+            print(f"    n={n}: c={c:.3e}, Psi_Z={psi_z:.3f}, Psi_R={psi_r:.3f}, Contrib={contrib:.3e}")
+    else:
+        # Annulus: Has R1 and R2 parts. 
+        # Coeffs are usually [C_0_R1, C_1_R1..., C_0_R2, C_1_R2...]
+        # We assume coefficients are split evenly in the list passed to us
+        n_modes = len(coeffs) // 2
+        
+        # R1 Terms
+        print("    -- R1 Terms --")
+        for n in range(min(n_modes, 3)):
+            c = coeffs[n]
+            psi_z = Z_n_i(n, z, region_idx, h, d)
+            psi_r = R_1n(n, r, region_idx, h, d, a)
+            contrib = c * psi_r * psi_z
+            phi_accum += contrib
+            print(f"    n={n}: c={c:.3e}, Psi_Z={psi_z:.3f}, Psi_R={psi_r:.3f}, Contrib={contrib:.3e}")
+
+        # R2 Terms
+        print("    -- R2 Terms --")
+        for n in range(min(n_modes, 3)):
+            idx = n_modes + n
+            c = coeffs[idx]
+            psi_z = Z_n_i(n, z, region_idx, h, d)
+            psi_r = R_2n(n, r, region_idx, a, h, d)
+            contrib = c * psi_r * psi_z
+            phi_accum += contrib
+            print(f"    n={n}: c={c:.3e}, Psi_Z={psi_z:.3f}, Psi_R={psi_r:.3f}, Contrib={contrib:.3e}")
+
+    print(f"  = Total Phi Reconstructed: {phi_accum:.4e}")
+    return phi_accum
+
+def diagnose_potential_continuity(engine, problem, solution_vector, config_name, m0):
+    print(f"\n  [CONTINUITY DIAGNOSTIC - POTENTIAL] {config_name}")
+    geo = problem.geometry
+    domains = geo.domain_list
+    a = geo.body_arrangement.a
+    d = geo.body_arrangement.d
+    h = geo.h
+    boundary_count = len(domains) - 1
+    
+    NMK_list = [domains[i].number_harmonics for i in range(len(domains))]
+    all_coeffs = engine.reformat_coeffs(solution_vector, NMK_list, len(domains)-1)
+
+    for bd in range(boundary_count):
+        radius = a[bd]
+        d_left = d[bd]; d_right = d[bd+1] if bd < len(d)-1 else d[bd]
+        max_draft = max(d_left, d_right)
+        
+        eps = 1e-4
+        z_common = np.linspace(-0.01, -max_draft + 0.01, 50)
+        
+        pot_left = engine.calculate_potentials(problem, solution_vector, m0, 10, False, R_range=np.array([radius - eps]), Z_range=z_common)
+        pot_right = engine.calculate_potentials(problem, solution_vector, m0, 10, False, R_range=np.array([radius + eps]), Z_range=z_common)
+        
+        # --- FIX: Check 'phi' (Total), NOT 'phiH' (Homogeneous) ---
+        # phiH is allowed to jump if bodies are heaving. Total phi must be continuous.
+        phi_L_arr = pot_left['phi'].flatten()
+        phi_R_arr = pot_right['phi'].flatten()
+        
+        valid = ~np.isnan(phi_L_arr) & ~np.isnan(phi_R_arr)
+        if not np.any(valid): continue
+            
+        diff = np.abs(phi_L_arr[valid] - phi_R_arr[valid])
+        max_diff = np.max(diff)
+        rel_diff = max_diff / np.mean(np.abs(phi_L_arr[valid])) if np.mean(np.abs(phi_L_arr[valid])) > 1e-9 else max_diff
+        
+        status = "✅ PASS" if rel_diff < 0.05 else "❌ FAIL"
+        print(f"    Boundary {bd} (R={radius:.2f}): {status}")
+        print(f"      Max Abs Phi Diff: {max_diff:.4e}")
+        print(f"      Rel Phi Err     : {rel_diff*100:.2f}%")
+        
+        if status == "❌ FAIL":
+            print("      >>> TRIGGERING RECONSTRUCTION AUDIT <<<")
+            z_audit = -max_draft / 2.0 
+            
+            c_left = all_coeffs[bd]
+            phi_recon_L = debug_phi_reconstruction(c_left, radius, z_audit, bd, h, d, a, "LEFT")
+            
+            c_right = all_coeffs[bd+1]
+            phi_recon_R = debug_phi_reconstruction(c_right, radius, z_audit, bd+1, h, d, a, "RIGHT")
+            
+            print(f"      Δφ (Recon, H-only) = {phi_recon_L - phi_recon_R:.4e}")
+            print("      (Note: H-only recon should NOT match if Heaving is present. Use Total Phi check above.)")
+
+def diagnose_series_at_point(engine, problem, solution_vector, r_target, z_target, m0, config_name):
+    """
+    Deconstructs the potential at a specific (R, Z) point into its mode components.
+    Helps identify if specific modes are blowing up.
+    """
+    print(f"\n  [SERIES SUMMATION DIAGNOSTIC] at R={r_target:.4f}, Z={z_target:.4f}")
+    
+    # 1. Identify Region
+    geo = problem.geometry
+    a = geo.body_arrangement.a
+    d = geo.body_arrangement.d
+    h = geo.h
+    
+    region_idx = -1
+    if r_target <= a[0]:
+        region_idx = 0
+    elif r_target > a[-1]:
+        region_idx = len(a)
+    else:
+        for i in range(1, len(a)):
+            if r_target > a[i-1] and r_target <= a[i]:
+                region_idx = i
+                break
+                
+    if region_idx == -1:
+        print("    Could not determine region.")
+        return
+    
+    print(f"    [REGION LOCATOR] r={r_target:.4f} -> Region {region_idx} selected")
+    
+    # Determine theoretical bounds for this region
+    r_min = 0.0 if region_idx == 0 else a[region_idx-1]
+    r_max = a[region_idx] if region_idx < len(a) else float('inf')
+    
+    print(f"    Region Bounds: [{r_min:.4f}, {r_max:.4f}]")
+    
+    # Check for "Silent Killer" boundary proximity
+    # If r is 1.200000001 and boundary is 1.2, you might snap to the wrong side 
+    # if floating point epsilon isn't handled.
+    if abs(r_target - r_max) < 1e-5:
+        print(f"      🚨 WARNING: Point is extremely close ({abs(r_target - r_max):.2e}) to UPPER boundary ({r_max}).")
+        print(f"      Check if Logic (r <= a) vs (r < a) matches Formulation.")
+    if abs(r_target - r_min) < 1e-5:
+        print(f"      🚨 WARNING: Point is extremely close ({abs(r_target - r_min):.2e}) to LOWER boundary ({r_min}).")
+
+    print(f"    Point is in Region {region_idx}")
+    
+    # 2. Get Coefficients for this region
+    NMK = [dom.number_harmonics for dom in geo.domain_list.values()]
+    Cs = engine.reformat_coeffs(solution_vector, NMK, len(NMK)-1)[region_idx]
+    
+    # 3. Calculate Terms
+    from openflash.multi_equations import R_1n, R_2n, Z_n_i, Lambda_k, Z_k_e, scale, m_k, N_k_multi
+    
+    # Need access to cached m_k/N_k for exterior
+    cache = engine.cache_list[problem]
+    engine._ensure_m_k_and_N_k_arrays(problem, m0)
+    m_k_arr = cache.m_k_arr
+    N_k_arr = cache.N_k_arr
+    
+    terms = []
+    
+    if region_idx == len(a): # Exterior
+        for k in range(len(Cs)):
+            rad_val = Lambda_k(k, r_target, m0, a, m_k_arr)
+            vert_val = Z_k_e(k, z_target, m0, h, NMK, m_k_arr) # Uses cache implicitly via m_k func? No, need manual passing in this raw check
+            # Actually Z_k_e in multi_equations calls m_k(NMK...), which recomputes. That's fine for debug.
+            term_val = Cs[k] * rad_val * vert_val
+            terms.append((k, term_val))
+    elif region_idx == 0: # Inner
+        for n in range(len(Cs)):
+            rad_val = R_1n(n, r_target, 0, h, d, a)
+            vert_val = Z_n_i(n, z_target, 0, h, d)
+            term_val = Cs[n] * rad_val * vert_val
+            terms.append((n, term_val))
+    else: # Annulus
+        num_modes = NMK[region_idx]
+        # R1 terms
+        for n in range(num_modes):
+            rad_val = R_1n(n, r_target, region_idx, h, d, a)
+            vert_val = Z_n_i(n, z_target, region_idx, h, d)
+            term_val = Cs[n] * rad_val * vert_val
+            terms.append((f"R1_{n}", term_val))
+            
+        # R2 terms
+        for n in range(num_modes):
+            rad_val = R_2n(n, r_target, region_idx, a, h, d)
+            vert_val = Z_n_i(n, z_target, region_idx, h, d)
+            term_val = Cs[num_modes + n] * rad_val * vert_val
+            terms.append((f"R2_{n}", term_val))
+            
+    # 4. Print Largest Terms
+    terms.sort(key=lambda x: np.abs(x[1]), reverse=True)
+    
+    print("    Top 5 Contributing Terms:")
+    for name, val in terms[:5]:
+        print(f"      Mode {name}: {val:.4e} (Abs: {np.abs(val):.4e})")
+        
+    print("    Term Summation Check:")
+    total_sum = sum(t[1] for t in terms)
+    print(f"      Total Sum: {total_sum:.4f}")
+
+                
+@pytest.mark.parametrize("config_name", ALL_CONFIGS.keys())
+def test_potential_field_vs_capytaine(config_name):
+    """
+    Compares the openflash-calculated potential field against the
+    Capytaine-generated benchmark data FOR A GIVEN CONFIG.
+    """
+    
+    # 1. Load data for this config
+    phi_capytaine_raw = load_capytaine_data(config_name)
+    p = ALL_CONFIGS[config_name]
+    
+    print(f"\n\n{'='*40}")
+    print(f"=== TESTING CONFIG: {config_name} ===")
+    print(f"{'='*40}")
+    print(f"  Parameters:")
+    print(f"    h (depth): {p['h']}")
+    print(f"    m0 (wavenum): {p['m0']}")
+    print(f"    a (radii): {p['a']}")
+    print(f"    d (drafts): {p['d']}")
+
+    # --- IMPLEMENT SUPERPOSITION ---
+    original_heaving_map = p["heaving_map"]
+    heaving_indices = [i for i, is_heaving in enumerate(original_heaving_map) if is_heaving]
+    
+    phi_total: Optional[np.ndarray] = None
+    omega_final: Optional[float] = None
+    results_template: Optional[Dict[str, Any]] = None 
+    
+    # KEEP TRACK OF ENGINE FOR DEBUGGING
+    last_engine = None
+    last_problem = None
+    last_solution = None
+
+    if not heaving_indices:
+        # Case: No heaving bodies (Pass verbose=True)
+        res, omega_final = run_openflash_sim(config_name, R_range=p["R_range"], Z_range=p["Z_range"], verbose=True)
+        phi_total = res["phi"]
+        results_template = res
+        last_engine = res["_engine"]
+        last_problem = res["_problem"]
+        last_solution = res["_solution"]
+    else:
+        print(f"\n  [SUPERPOSITION START] Combining {len(heaving_indices)} active bodies...")
+        for i, idx in enumerate(heaving_indices): # Use enumerate to track index
+            
+            # Create a compliant heaving map
+            single_heaving_map = [False] * len(original_heaving_map)
+            single_heaving_map[idx] = True
+            
+            # --- FIX: Only be verbose on the FIRST body ---
+            is_first_run = (i == 0)
+            
+            res, omega = run_openflash_sim(
+                config_name, 
+                R_range=p["R_range"], 
+                Z_range=p["Z_range"],
+                heaving_map_override=single_heaving_map,
+                verbose=is_first_run # Only print diagnostics once!
+            )
+            
+            # --- DEBUG: Enhanced Body Stats ---
+            of_mag = np.abs(res['phi'])
+            cap_slice_mag = np.abs(phi_capytaine_raw[..., idx]) if phi_capytaine_raw.ndim > 2 else np.nan
+            
+            print(f"    > Body {idx} Active ({single_heaving_map}):")
+            print(f"      OpenFlash Max |phi|: {np.nanmax(of_mag):.6e}")
+            print(f"      OpenFlash Mean |phi|: {np.nanmean(of_mag):.6e}")
+            if not np.isnan(cap_slice_mag).all():
+                 print(f"      Capytaine Max |phi|: {np.nanmax(cap_slice_mag):.6e}")
+            
+            if np.nanmax(of_mag) < 1e-10:
+                print(f"      🚨 ALERT: Body {idx} produced ZERO potential! This is likely the bug.")
+            
+            # Save intermediate plot
+            if np.any(res["phi"]):
+                debug_dir = DEBUG_PLOT_PATH / "contributions"
+                debug_dir.mkdir(parents=True, exist_ok=True)
+                plt.figure()
+                plt.pcolormesh(res["R"], res["Z"], res["phi"].real, cmap='viridis')
+                plt.colorbar(label="Real(phi)")
+                plt.title(f"{config_name} - Body {idx} Contribution")
+                plt.savefig(debug_dir / f"{config_name}_body_{idx}_real.png")
+                plt.close()
+
+            if phi_total is None:
+                phi_total = np.zeros_like(res["phi"], dtype=complex)
+                omega_final = omega
+                results_template = res
+                # Just capture the last one for debugging structure
+                last_engine = res["_engine"]
+                last_problem = res["_problem"]
+                last_solution = res["_solution"]
+            
+            phi_total += res["phi"]
+
+    if results_template is None or phi_total is None or omega_final is None:
+        pytest.fail(f"[{config_name}] Simulation failed to produce results.")
+    
+    # 2. Get the openflash grid and total potential
+    R_openflash = results_template["R"]
+    Z_openflash = results_template["Z"]
+    phi_openflash = phi_total
+    omega = omega_final
+
+    # 3. Define the Capytaine grid
+    R_cap_grid, Z_cap_grid = np.meshgrid(p["R_range"], p["Z_range"], indexing='ij')
+
+    # 4. Compare (No Interpolation Needed)
+    phi_openflash_interp_real = phi_openflash.real
+    phi_openflash_interp_imag = phi_openflash.imag
+    
+    print("\n  [GEOMETRY TRANSITION ANALYSIS]")
+    print(f"    Global Water Depth (h): {p['h']}")
+        
+    current_r = 0.0
+    d_vals = p['d']
+        
+    for i, (r_outer, d_val) in enumerate(zip(p['a'], d_vals)):
+        fluid_depth = p['h'] - d_val
+            
+        # Determine Transition Type from Previous Domain
+        if i == 0:
+            trans_str = "Center Start"
+        else:
+            prev_depth = p['h'] - d_vals[i-1]
+            if fluid_depth > prev_depth:
+                trans_str = "EXPANSION (Step Down / Deeper Fluid)"
+            elif fluid_depth < prev_depth:
+                trans_str = "CONTRACTION (Step Up / Shallower Fluid)"
+            else:
+                trans_str = "FLAT"
+        # Detect "Pocket" (Deeper than left AND right neighbors)
+        is_pocket = False
+        if i > 0 and i < len(d_vals) - 1:
+            prev_depth = p['h'] - d_vals[i-1]
+            next_depth = p['h'] - d_vals[i+1]
+            if fluid_depth > prev_depth and fluid_depth > next_depth:
+                is_pocket = True
+            
+        pocket_alert = " <--- 🚨 POCKET REGION (Likely Failure Point)" if is_pocket else ""
+            
+        print(f"    Domain {i}: R=[{current_r:.2f}, {r_outer:.2f}] | Depth={fluid_depth:.4f} | {trans_str}{pocket_alert}")
+        current_r = r_outer
+
+    # Exterior Domain Info
+    print(f"    Domain {len(d_vals)}: R=[{current_r:.2f}, inf] | Depth={p['h']:.4f} | EXPANSION (Exit to Open Ocean)")
+
+    # 5. Validation & Conversion
+    capytaine_body_mask = np.isnan(phi_capytaine_raw.real)
+    openflash_nan_real = np.isnan(phi_openflash_interp_real)
+    
+    # Check for mismatched NaNs (Solver failing to mask body)
+    bad_nans_real_mask = ~capytaine_body_mask & openflash_nan_real
+    if np.sum(bad_nans_real_mask) > 0:
+        pytest.fail(f"[{config_name}] Openflash produced NaNs in valid fluid domain.")
+
+    nan_mask = np.isnan(phi_capytaine_raw.real)
+    valid_mask = ~nan_mask
+    
+    if np.sum(valid_mask) < 0.5 * nan_mask.size:
+        pytest.fail(f"[{config_name}] Interpolation failed: >50% of grid points are invalid.")
+
+    # --- DEBUG: Conversion Factors ---
+    print(f"\n  [CONVERSION DEBUG]")
+    print(f"    Omega (w): {omega:.6f}")
+    print(f"    Scaling Factor (1/w): {1.0/omega:.6f}")
+    print(f"    Capytaine Raw Real [Min, Max]: [{np.nanmin(phi_capytaine_raw.real):.4e}, {np.nanmax(phi_capytaine_raw.real):.4e}]")
+    print(f"    Capytaine Raw Imag [Min, Max]: [{np.nanmin(phi_capytaine_raw.imag):.4e}, {np.nanmax(phi_capytaine_raw.imag):.4e}]")
+
+    # Convert Capytaine to Velocity Potential
+    # Note: Ensure this conversion matches your theoretical definition 
+    # (Capytaine usually outputs diffraction potential, check if incident is included)
+    capytaine_real_converted = phi_capytaine_raw.imag * (-1.0 / omega)
+    capytaine_imag_converted = phi_capytaine_raw.real * (1.0 / omega)
+    
+    # Reconstruct complex form for phase comparison
+    capytaine_complex_converted = capytaine_real_converted + 1j * capytaine_imag_converted
+    
+    # --- NEW DEBUGGING BLOCK START ---
+    
+    # A. Check for Global Phase Rotation (Critical for Config 3)
+    # This helps identify if we are off by -1, j, or conjugate
+    best_transform = check_phase_rotation(phi_openflash, capytaine_complex_converted)
+
+    # B. Magnitude vs Phase Diagnostics
+    # Comparison of Real/Imag is fragile. Magnitude is robust.
+    mag_of = np.abs(phi_openflash)
+    mag_cap = np.abs(capytaine_complex_converted)
+    
+    # Calculate Magnitude Error only on valid points
+    mag_diff = np.abs(mag_of - mag_cap)
+    mag_diff[nan_mask] = np.nan
+    max_mag_diff = np.nanmax(mag_diff)
+    
+    print(f"\n  [MAGNITUDE CHECK] Max | |phi_of| - |phi_cap| |: {max_mag_diff:.6e}")
+    if max_mag_diff < 0.05 and np.max(np.abs(phi_openflash.real - capytaine_real_converted)) > 0.1:
+        print("  >>> STRONG HINT: Magnitude is correct, but Phase is wrong. Check time convention (e^-iwt vs e^iwt).")
+
+    # C. Pinpoint Location of Maximum Error (Critical for Config 2 & 6)
+    # Find indices of max error in Real part
+    diff_grid_real = np.abs(phi_openflash.real - capytaine_real_converted)
+    diff_grid_real[nan_mask] = -1.0 # Ignore body points
+    
+    # Get top 3 errors
+    flat_indices = np.argsort(diff_grid_real.ravel())[-3:][::-1]
+    
+    print(f"\n  [LOCATOR] Top 3 Real Part Errors:")
+    first_locator = True
+    for flat_idx in flat_indices:
+        idx_2d = np.unravel_index(flat_idx, diff_grid_real.shape)
+        r_err = R_cap_grid[idx_2d]
+        z_err = Z_cap_grid[idx_2d]
+        val_of = phi_openflash.real[idx_2d]
+        val_cap = capytaine_real_converted[idx_2d]
+        diff_val = diff_grid_real[idx_2d]
+        
+        print(f"    @ (R={r_err:.3f}, Z={z_err:.3f}) -> Diff: {diff_val:.4f} (OF: {val_of:.4f} vs CAP: {val_cap:.4f})")
+        
+        # Check proximity to corners
+        for body_idx, (a_val, d_val) in enumerate(zip(p['a'], p['d'])):
+            dist = np.sqrt((r_err - a_val)**2 + (z_err - (-d_val))**2)
+            if dist < 0.2:
+                print(f"      -> NEAR CORNER of Body {body_idx} (a={a_val}, d={d_val}) dist={dist:.4f}")
+        
+        # --- NEW: Run Point Diagnostics on the worst point ---
+        if first_locator and last_engine:
+             diagnose_series_at_point(last_engine, last_problem, last_solution, r_err, z_err, p["m0"], config_name)
+             first_locator = False
+
+    # Save CSV for detailed inspection if things fail
+    save_debug_csvs(R_cap_grid, Z_cap_grid, 
+                   phi_openflash.real, capytaine_real_converted, 
+                   phi_openflash.imag, capytaine_imag_converted, 
+                   nan_mask, config_name)
+
+    # --- NEW DEBUGGING BLOCK END ---
+
+    openflash_real_valid = phi_openflash_interp_real[valid_mask]
+    capytaine_real_valid = capytaine_real_converted[valid_mask]
+    openflash_imag_valid = phi_openflash_interp_imag[valid_mask]
+    capytaine_imag_valid = capytaine_imag_converted[valid_mask]
+
+    # --- DIAGNOSTICS ---
+    print(f"\n  [FINAL COMPARISON] {config_name}")
+    print(f"    Omega: {omega:.4f}")
+    print(f"    Max Abs OpenFlash (Real): {np.max(np.abs(openflash_real_valid)):.6e}")
+    print(f"    Max Abs Capytaine (Real): {np.max(np.abs(capytaine_real_valid)):.6e}")
+    
+    # Save Final Plots
+    save_debug_plots(R_cap_grid, Z_cap_grid, phi_openflash_interp_real, capytaine_real_converted, nan_mask, config_name, "real")
+    save_debug_plots(R_cap_grid, Z_cap_grid, phi_openflash_interp_imag, capytaine_imag_converted, nan_mask, config_name, "imag")
+    save_1d_cuts(R_cap_grid, Z_cap_grid, phi_openflash_interp_real, capytaine_real_converted, config_name, "real")
+    save_1d_cuts(R_cap_grid, Z_cap_grid, phi_openflash_interp_imag, capytaine_imag_converted, config_name, "imag")
+
+    # 6. Assertions
+    try:
+        np.testing.assert_allclose(
+            openflash_real_valid, capytaine_real_valid,
+            rtol=RELATIVE_TOLERANCE, atol=1e-2,
+            err_msg=f"[{config_name}] Real part mismatch"
+        )
+    except AssertionError as e:
+        pytest.fail(str(e))
+    
+    try:
+        np.testing.assert_allclose(
+            openflash_imag_valid, capytaine_imag_valid,
+            rtol=RELATIVE_TOLERANCE, atol=1e-2,
+            err_msg=f"[{config_name}] Imaginary part mismatch"
+        )
+    except AssertionError as e:
+        pytest.fail(str(e))
\ No newline at end of file
diff --git a/package/test/test_coupling.py b/package/test/test_coupling.py
deleted file mode 100644
index 61c38a7..0000000
--- a/package/test/test_coupling.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import unittest
-import numpy as np
-from scipy.special import hankel1 as besselh
-from scipy.special import iv as besseli
-from scipy.special import kv as besselk
-import scipy.integrate as integrate
-import scipy.linalg as linalg
-from math import sqrt, cosh, cos, sinh, sin, pi
-from scipy.optimize import newton, minimize_scalar
-import scipy as sp  # Import scipy correctly
-import sys
-import os
-src_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../src'))  # Path to /package/src
-sys.path.append(src_path)
-
-from constants import *  
-from equations import *  
-import coupling
-
-class TestCouplingIntegrals(unittest.TestCase):
-
-    def test_A_nm(self):
-        # Test cases for A_nm
-        self.assertAlmostEqual(coupling.A_nm(0, 0), h - d1)  # Example: Test for n=0, m=0
-        self.assertAlmostEqual(coupling.A_nm(1, 0), 0)  # Example: Test for n=1, m=0
-        self.assertAlmostEqual(coupling.A_nm(0, 1), (-sqrt(2) * sin((pi * 1 * (d1 - h)) / (d2 - h)) * (d2 - h)) / (1 * pi)) #Example: Test for n=0, m=1
-        self.assertAlmostEqual(coupling.A_nm(1, 1), -2 * ((-1) ** 1) * 1 * sin((pi * 1 * (d1 - h)) / (d2 - h)) * (d1 - h)**2 * (d2 - h) / (pi * ((d1**2 * 1**2) - (2 * d1 * h * 1**2) - (d2**2 * 1**2) + (2 * d2 * h * 1**2) + (h**2 * 1**2) - (h**2 * 1**2)))) #Example: Test for n=1, m=1
-        # Add more test cases, especially edge cases and values that might cause issues
-
-    def test_A_nm2(self):
-        # Test cases for A_nm2
-        self.assertAlmostEqual(coupling.A_nm2(0, 0), h - d2)
-        self.assertAlmostEqual(coupling.A_nm2(1, 0), (-sqrt(2) * sin(pi * 1) * (d2 - h)) / (1 * pi))
-        self.assertAlmostEqual(coupling.A_nm2(0, 1), (-sqrt(2) * sin((pi * 1 * (d2 - h)) / (d1 - h)) * (d1 - h)) / (1 * pi))
-        # Add more test cases
-
-
-    # ... (Add tests for nk2_sigma_helper )
-
-
-if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
diff --git a/package/test/test_domain.py b/package/test/test_domain.py
index f5a77ee..83d30c6 100644
--- a/package/test/test_domain.py
+++ b/package/test/test_domain.py
@@ -1,79 +1,92 @@
-import unittest
+# test_domain.py
+
+import pytest
+import numpy as np
+import pytest
 import numpy as np
+import os as os
+import sys as sys
+current_dir = os.path.dirname(__file__)
+src_dir = os.path.abspath(os.path.join(current_dir, '..', 'src'))
+if src_dir not in sys.path:
+    sys.path.insert(0, src_dir)
+from openflash.domain import Domain
+
+# --- Basic Tests ---
+
+def test_domain_initialization_valid():
+    d = Domain(index=0, NMK=5, a_inner=0.0, a_outer=1.0, d_lower=2.0, geometry_h=5.0)
+    assert d.index == 0
+    assert d.number_harmonics == 5
+    assert d.a_inner == 0.0
+    assert d.a_outer == 1.0
+    assert d.d_lower == 2.0
+    assert d.d_upper == 5.0
+    assert d.heaving is None
+    assert d.slant is False
+    assert d.category == "interior"
+
+def test_domain_initialization_invalid_NMK():
+    with pytest.raises(AssertionError):
+        Domain(index=0, NMK=0, a_inner=0.0, a_outer=1.0, d_lower=0.0, geometry_h=5.0)
+
+def test_domain_initialization_invalid_radii():
+    with pytest.raises(AssertionError):
+        Domain(index=0, NMK=1, a_inner=1.0, a_outer=0.0, d_lower=0.0, geometry_h=5.0)
+    with pytest.raises(AssertionError):
+        Domain(index=0, NMK=1, a_inner=-0.1, a_outer=1.0, d_lower=0.0, geometry_h=5.0)
+
+def test_domain_initialization_invalid_depths():
+    with pytest.raises(AssertionError):
+        Domain(index=0, NMK=1, a_inner=0.0, a_outer=1.0, d_lower=6.0, geometry_h=5.0)
+    with pytest.raises(AssertionError):
+        Domain(index=0, NMK=1, a_inner=0.0, a_outer=1.0, d_lower=-1.0, geometry_h=5.0)
+
+# --- Adjacency Tests ---
+
+def test_are_adjacent_true_outer_to_inner():
+    d1 = Domain(index=0, NMK=1, a_inner=0.0, a_outer=1.0, d_lower=0.0, geometry_h=5.0)
+    d2 = Domain(index=1, NMK=1, a_inner=1.0, a_outer=2.0, d_lower=0.0, geometry_h=5.0)
+    assert Domain.are_adjacent(d1, d2)
+    assert Domain.are_adjacent(d2, d1)
+
+def test_are_adjacent_false():
+    d1 = Domain(index=0, NMK=1, a_inner=0.0, a_outer=1.0, d_lower=0.0, geometry_h=5.0)
+    d2 = Domain(index=1, NMK=1, a_inner=1.1, a_outer=2.0, d_lower=0.0, geometry_h=5.0)
+    assert not Domain.are_adjacent(d1, d2)
+    assert not Domain.are_adjacent(d2, d1)
+
+def test_are_adjacent_with_infinite_outer():
+    d1 = Domain(index=0, NMK=1, a_inner=0.0, a_outer=np.inf, d_lower=0.0, geometry_h=5.0)
+    d2 = Domain(index=1, NMK=1, a_inner=10.0, a_outer=20.0, d_lower=0.0, geometry_h=5.0)
+    assert not Domain.are_adjacent(d1, d2)
+
+# --- Randomized Stress Tests ---
+
+@pytest.mark.parametrize("num_domains", [10, 50, 100])
+def test_randomized_domains(num_domains):
+    np.random.seed(42)  # deterministic for tests
+    # Generate sorted inner radii and random widths
+    a_inner = np.sort(np.random.rand(num_domains))
+    widths = np.random.rand(num_domains) * 0.5 + 0.1
+    a_outer = a_inner + widths
+    d_lower = np.random.rand(num_domains) * 5
+    h = 10.0
+
+    domains = [
+        Domain(index=i, NMK=np.random.randint(1, 10), a_inner=a_inner[i], a_outer=a_outer[i], d_lower=d_lower[i], geometry_h=h)
+        for i in range(num_domains)
+    ]
+
+    # Check all domains created correctly
+    for i, d in enumerate(domains):
+        assert d.a_outer > d.a_inner
+        assert d.d_upper >= d.d_lower
+        assert d.number_harmonics > 0
+
+    # Check adjacency for consecutive domains
+    for i in range(num_domains - 1):
+        # Make domains exactly adjacent for testing
+        domains[i+1].a_inner = domains[i].a_outer
+        assert Domain.are_adjacent(domains[i], domains[i+1])
 
-import sys
-import os
-src_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../src'))
-sys.path.append(src_path)
-
-from domain import Domain
-from multi_constants import h, d, a, m0, heaving  # Import necessary constants
-
-class MockGeometry:
-    def __init__(self, r_coordinates, z_coordinates):
-        self.r_coordinates = r_coordinates
-        self.z_coordinates = z_coordinates
-
-class TestDomain(unittest.TestCase):
-
-    def test_domain_initialization(self):
-        """Tests the initialization of the Domain class."""
-
-        # Mock Geometry object
-        geometry = MockGeometry({'a1': 0.5, 'a2': 1.0, 'a3': 1.5}, {'h': 1.0})
-
-        # Test case 1: Basic initialization (inner domain)
-        params1 = {'h': 1.0, 'di': 0.5, 'm0': 2.0, 'heaving': 0} 
-        domain1 = Domain(5, 1.0, 1.0, None, None, 'inner', params1, 0, geometry)
-        self.assertEqual(domain1.number_harmonics, 5)
-        self.assertEqual(domain1.height, 1.0)
-        self.assertEqual(domain1.radial_width, 1.0)
-        self.assertEqual(domain1.category, 'inner')
-        self.assertEqual(domain1.h, 1.0)
-        self.assertEqual(domain1.di, 0.5)
-        self.assertEqual(domain1.a, 0.5)
-        self.assertEqual(domain1.m0, 2.0)
-        self.assertEqual(domain1.heaving, 0)
-        self.assertFalse(domain1.slant)
-        self.assertEqual(domain1.r_coords, 0)
-        self.assertEqual(domain1.z_coords, [0, 1.0])
-
-        # Test case 2: Outer domain
-        params2 = {'h': 1.0, 'di': 0.25, 'a': 1.0, 'm0': 2.0, 'heaving': 1}
-        domain2 = Domain(8, 1.0, 1.0, None, None, 'outer', params2, 1, geometry)
-        self.assertEqual(domain2.di, 0.25)
-        self.assertEqual(domain2.a, 1.0)
-        self.assertEqual(domain2.heaving, 1)
-        self.assertEqual(domain2.r_coords, [0.5, 1.0])
-        self.assertEqual(domain2.z_coords, [0, 1.0])
-
-        # Test case 3: Exterior domain
-        params3 = {'h': 1.0, 'm0': 2.0, 'heaving': 0}
-        domain3 = Domain(10, 1.0, 1.0, None, None, 'exterior', params3, 2, geometry)
-        self.assertIsNone(domain3.di)
-        self.assertIsNone(domain3.a)
-        self.assertEqual(domain3.heaving, 0)
-        self.assertEqual(domain3.r_coords, np.inf)
-        self.assertEqual(domain3.z_coords, [0, 1.0])
-
-        # Test case 4: Multi-domain parameters
-        params4 = {'h': 1.5, 'di': 0.75, 'a': 1.25, 'm0': 2.5, 'scale': 0.5, 'heaving': 1, 'slant': True}
-        domain4 = Domain(8, 1.5, 0.75, None, None, 'multi', params4, 2, geometry)
-        self.assertEqual(domain4.h, 1.5)
-        self.assertEqual(domain4.di, 0.75)
-        self.assertEqual(domain4.a, 1.25)
-        self.assertEqual(domain4.m0, 2.5)
-        self.assertEqual(domain4.scale, 0.5)
-        self.assertEqual(domain4.heaving, 1)
-        self.assertTrue(domain4.slant)
-        self.assertEqual(domain4.r_coords, 0.5)
-        self.assertEqual(domain4.z_coords, [0, 1.5])
-
-        # Test case 5: a as a list of values
-        params5 = {'h': 1.0, 'di': 0.5, 'a': [0.5, 1.0, 1.0], 'm0': 2.0, 'heaving':0} # a is now a list
-        domain5 = Domain(5, 1.0, 1.0, None, None, 'inner', params5, 0, geometry)
-        self.assertEqual(domain5.a, [0.5, 1.0, 1.0]) # a should now be a list
-        self.assertEqual(domain5.scale, np.mean([0.5, 1.0, 1.5])) # scale should be the mean of the geometry r_coordinates values
-
-if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
diff --git a/package/test/test_excitation_phase.py b/package/test/test_excitation_phase.py
new file mode 100644
index 0000000..4e2d852
--- /dev/null
+++ b/package/test/test_excitation_phase.py
@@ -0,0 +1,85 @@
+# package/test/test_excitation_phase.py
+import pytest
+import pandas as pd
+import numpy as np
+import os
+import warnings
+from openflash_utils import run_openflash_case
+from openflash.multi_equations import wavenumber
+
+# Define constants
+H = 300
+A = [3, 10]
+D = [35, 2]
+RHO = 1023
+HEAVING = [1, 1]
+NMK = [50, 50, 50]
+
+# Path to data
+DATA_PATH = os.path.abspath(
+    os.path.join(
+        os.path.dirname(__file__), 
+        "../../dev/python/test/data/WAMIT_exc_phase.csv"
+    )
+)
+
+def load_wamit_data():
+    if not os.path.exists(DATA_PATH):
+        pytest.skip(f"WAMIT data file not found at {DATA_PATH}")
+    
+    df = pd.read_csv(DATA_PATH)
+    omegas = 0.02 * np.arange(1, 261)
+    # Negate the Series first to avoid Pylance/Type errors
+    wamit_phases = (-df["excitation phase (rad)"]).values
+    return omegas, wamit_phases
+
+def calculate_angular_difference(angle1, angle2):
+    diff = angle1 - angle2
+    return np.arctan2(np.sin(diff), np.cos(diff))
+
+def test_excitation_phase_match():
+    """
+    Compare OpenFLASH excitation phase against WAMIT data.
+    """
+    omegas, wamit_phases = load_wamit_data()
+    
+    max_error = 0.0
+    failures = []
+    
+    # Filter warnings about sinh overflow (harmless in deep water approx)
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=RuntimeWarning, message="overflow encountered in sinh")
+        
+        for i, omega in enumerate(omegas):
+            # FIX: Skip very high frequencies where force -> 0 and phase is unstable
+            if omega > 5.1:
+                continue
+
+            # FIX 1: Convert Omega (rad/s) to Wavenumber (rad/m)
+            # OpenFLASH core requires m0, not omega.
+            m0 = wavenumber(omega, H)
+
+            # FIX 2: Unpack the tuple return values
+            # run_openflash_case returns (AddedMass, Damping, Phase)
+            _, _, phase = run_openflash_case(H, D, A, HEAVING, NMK, m0, RHO)
+            
+            # Compare
+            wamit_val = wamit_phases[i]
+            error = abs(calculate_angular_difference(phase, wamit_val))
+            
+            if error > max_error:
+                max_error = error
+
+            # Tolerance: 0.1 rad (~5.7 degrees) is acceptable for BEM vs Analytical
+            if error > 0.1:
+                failures.append(
+                    f"Omega={omega:.2f}: MEEM={phase:.4f}, WAMIT={wamit_val:.4f}, Diff={error:.4f}"
+                )
+
+    # Print summary
+    print(f"\nMax Angular Error (omega < 5.1): {max_error:.6f} rad")
+    
+    # Assert
+    if failures:
+        print("\n".join(failures))
+        pytest.fail(f"Test failed with {len(failures)} mismatches. See stdout for details.")
\ No newline at end of file
diff --git a/package/test/test_geometry.py b/package/test/test_geometry.py
index e92262f..4cdab22 100644
--- a/package/test/test_geometry.py
+++ b/package/test/test_geometry.py
@@ -1,81 +1,158 @@
-import unittest
+# test_geometry.py
+import pytest
 import numpy as np
+import os as os
+import sys as sys
+current_dir = os.path.dirname(__file__)
+src_dir = os.path.abspath(os.path.join(current_dir, '..', 'src'))
+if src_dir not in sys.path:
+    sys.path.insert(0, src_dir)
+from openflash.body import SteppedBody, CoordinateBody
+from openflash.geometry import ConcentricBodyGroup, Geometry, BodyArrangement
+from openflash.domain import Domain
 
-# Adjust import paths (same as before)
-import sys
-import os
-src_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../src'))
-sys.path.append(src_path)
+# -------------------------
+# Fixtures
+# -------------------------
+@pytest.fixture
+def simple_stepped_body():
+    a = np.array([1.0, 2.0])
+    d = np.array([0.5, 1.0])
+    slant = np.array([0.0, 0.1])
+    # Set to False to ensure other tests don't fail by default
+    return SteppedBody(a, d, slant, heaving=False)
 
-from geometry import Geometry
-from domain import Domain  # Import Domain for assertions
-from multi_constants import h, d, a, heaving, m0
+@pytest.fixture
+def concentric_group(simple_stepped_body):
+    # This group only has one body, so it's valid
+    return ConcentricBodyGroup([simple_stepped_body])
 
-class TestGeometry(unittest.TestCase):
+# -------------------------
+# ConcentricBodyGroup tests
+# -------------------------
+def test_concatenated_properties(concentric_group):
+    # This test uses the simple_stepped_body fixture which is heaving=False
+    body = concentric_group.bodies[0]
+    np.testing.assert_array_equal(concentric_group.a, body.a)
+    np.testing.assert_array_equal(concentric_group.d, body.d)
+    np.testing.assert_array_equal(concentric_group.slant_angle, body.slant_angle)
+    # Since fixture is heaving=False, heaving array should be all False
+    np.testing.assert_array_equal(concentric_group.heaving, np.array([False, False]))
 
-    def test_geometry_initialization(self):
-        """Tests the initialization of the Geometry class."""
+def test_invalid_body_type():
+    with pytest.raises(TypeError):
+        # CoordinateBody is not currently supported by ConcentricBodyGroup
+        ConcentricBodyGroup([CoordinateBody(np.array([0,1]), np.array([0,1]))])
 
-        # Test case 1: Basic initialization
-        r_coordinates1 = {'a1': 0.5, 'a2': 1.0}
-        z_coordinates1 = {'h': 1.001}
-        domain_params1 = [
-            {'number_harmonics': 5, 'height': 1.0, 'radial_width': 0.5, 'category': 'inner', 'di': 0.5, 'a': 0.5},
-            {'number_harmonics': 8, 'height': 1.0, 'radial_width': 1.0, 'category': 'outer', 'di': 0.25, 'a': 1.0},
-        ]
-        geometry1 = Geometry(r_coordinates1, z_coordinates1, domain_params1)
+# -------------------------
+# NEW TEST: Invalid heaving count
+# -------------------------
+def test_invalid_heaving_count_init():
+    body1 = SteppedBody(np.array([1.0]), np.array([1.0]), np.array([0.0]), heaving=True)
+    body2 = SteppedBody(np.array([2.0]), np.array([2.0]), np.array([0.0]), heaving=True)
+    
+    # This must raise an AssertionError because two bodies are heaving
+    with pytest.raises(AssertionError, match="Only 0 or 1 body can be marked as heaving"):
+        ConcentricBodyGroup([body1, body2])
+        
+# -------------------------
+# Geometry abstract tests
+# -------------------------
+class DummyGeometry(Geometry):
+    """Concrete implementation for testing Geometry base class."""
+    def make_fluid_domains(self):
+        # Create a simple domain per body step
+        domains = []
+        last_r = 0.0
+        for i, (a, d, h_flag, sl) in enumerate(zip(
+            self.body_arrangement.a,
+            self.body_arrangement.d,
+            self.body_arrangement.heaving,
+            self.body_arrangement.slant_angle
+        )):
+            domains.append(Domain(
+                index=i,
+                NMK=1,
+                a_inner=last_r,
+                a_outer=a,
+                d_lower=d,
+                geometry_h=self.h,
+                heaving=h_flag,
+                slant=bool(sl),
+                category="interior"
+            ))
+            last_r = a
+        # Add exterior domain
+        domains.append(Domain(
+            index=len(self.body_arrangement.a),
+            NMK=1,
+            a_inner=last_r,
+            a_outer=np.inf,
+            d_lower=0.0,
+            geometry_h=self.h,
+            category="exterior"
+        ))
+        return domains
 
-        self.assertEqual(len(geometry1.domain_list), 2)  # Check number of domains
-        self.assertIsInstance(geometry1.domain_list[0], Domain)  # Check domain type
-        self.assertEqual(geometry1.domain_list[0].number_harmonics, 5)
-        self.assertEqual(geometry1.domain_list[0].a, 0.5)
-        self.assertEqual(geometry1.domain_list[0].di, 0.5)
-        self.assertEqual(geometry1.domain_list[0].scale, 0.75) #Scale should be the mean of the list
-        self.assertEqual(geometry1.domain_list[1].number_harmonics, 8)
-        self.assertEqual(geometry1.domain_list[1].a, 1.0)
-        self.assertEqual(geometry1.domain_list[1].di, 0.25)
-        self.assertEqual(geometry1.domain_list[1].scale, 0.75)  #Scale should be the mean of the list
+@pytest.fixture
+def dummy_geometry(concentric_group):
+    return DummyGeometry(concentric_group, h=5.0)
 
-        # Test case 2: Using default values
-        r_coordinates2 = {}  # Empty
-        z_coordinates2 = {'h':h}  # Empty
-        domain_params2 = [
-            {'number_harmonics': 3, 'height': 2.0, 'radial_width': 0.75, 'category': 'inner', 'di': d[0], 'a': a[0], 'heaving': 1},
-            {'number_harmonics': 6, 'height': 1.5, 'radial_width': 0.5, 'category': 'outer', 'di': d[1], 'a': a[1], 'heaving': 1},
-        ]
-        geometry2 = Geometry(r_coordinates2, z_coordinates2, domain_params2)
-        self.assertEqual(len(geometry2.domain_list), 2)
-        self.assertEqual(geometry2.domain_list[0].h, h)  # Default h
-        self.assertEqual(geometry2.domain_list[0].di, d[0])  # Default d[0]
-        self.assertEqual(geometry2.domain_list[0].a, a[0])  # Default a[0]
-        self.assertEqual(geometry2.domain_list[1].h, h)  # Default h
-        self.assertEqual(geometry2.domain_list[1].di, d[1])  # Default d[1]
-        self.assertEqual(geometry2.domain_list[1].a, a[1])  # Default a[1]
+def test_fluid_domains_count(dummy_geometry, simple_stepped_body):
+    # Should have one domain per step + one exterior
+    expected_count = len(simple_stepped_body.a) + 1
+    assert len(dummy_geometry.fluid_domains) == expected_count
 
-        # Test case 3: Empty domain_params
-        r_coordinates3 = {'a': 1.0}
-        z_coordinates3 = {'h': 1.0}
-        domain_params3 = []  # Empty list
-        geometry3 = Geometry(r_coordinates3, z_coordinates3, domain_params3)
-        self.assertEqual(len(geometry3.domain_list), 0)  # No domains should be created.
+def test_fluid_domain_properties(dummy_geometry, simple_stepped_body):
+    domains = dummy_geometry.fluid_domains
+    for i, domain in enumerate(domains[:-1]):
+        assert domain.a_outer == simple_stepped_body.a[i]
+        assert domain.d_lower == simple_stepped_body.d[i]
+        # Fixture is heaving=False, so this should be False
+        assert domain.heaving == False
+        assert isinstance(domain.slant, bool)
+    # Check exterior domain
+    ext = domains[-1]
+    assert ext.category == "exterior"
+    assert ext.a_outer == np.inf
 
-        # Test case 4: When 'a' is a list in domain_params
-        r_coordinates4 = {'a1': 0.5, 'a2': 1.0}
-        z_coordinates4 = {'h': 1.001}
-        domain_params4 = [
-            {'number_harmonics': 5, 'height': 1.0, 'radial_width': 0.5, 'category': 'inner', 'di': 0.5, 'a': [0.25, 0.5]},
-            {'number_harmonics': 8, 'height': 1.0, 'radial_width': 1.0, 'category': 'outer', 'di': 0.25, 'a': [0.75, 1.0]},
-        ]
-        geometry4 = Geometry(r_coordinates4, z_coordinates4, domain_params4)
+# -------------------------
+# Randomized stress test (fixed to comply with new heaving rule)
+# -------------------------
+def test_randomized_multiple_bodies():
+    np.random.seed(42)
+    num_bodies = 5
+    bodies = []
+    last_max_r = 0.0  # Keep track of last outer radius
 
-        self.assertEqual(len(geometry4.domain_list), 2)  # Check number of domains
-        self.assertIsInstance(geometry4.domain_list[0], Domain)  # Check domain type
-        self.assertEqual(geometry4.domain_list[0].number_harmonics, 5)
-        self.assertEqual(geometry4.domain_list[0].a, [0.25, 0.5])
-        self.assertEqual(geometry4.domain_list[0].di, 0.5)
-        self.assertEqual(geometry4.domain_list[1].number_harmonics, 8)
-        self.assertEqual(geometry4.domain_list[1].a, [0.75, 1.0])
-        self.assertEqual(geometry4.domain_list[1].di, 0.25)
+    # Randomly select which body (0 to num_bodies-1) will be heaving
+    # -1 means none are heaving
+    heaving_index = np.random.choice(np.arange(num_bodies), size=1, replace=False)[0]
+    
+    for i in range(num_bodies):
+        steps = np.random.randint(1, 5)
+        # Generate increasing radii relative to last_max_r
+        a = np.sort(np.random.rand(steps) * 10 + last_max_r + 0.1)  # shift to avoid overlap
+        d = np.random.rand(steps) * 5
+        slant = np.random.rand(steps) * 0.5
+        
+        # Only the selected index is True
+        heaving = (i == heaving_index)
+        
+        bodies.append(SteppedBody(a, d, slant, heaving))
+        last_max_r = a[-1]  # update last_max_r for next body
 
-if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    group = ConcentricBodyGroup(bodies)
+    geom = DummyGeometry(group, h=10.0)
+    domains = geom.fluid_domains
+
+    # Basic checks
+    assert all(isinstance(d, Domain) for d in domains)
+    # Check exterior domain
+    assert domains[-1].a_outer == np.inf
+    # Ensure number of domains = sum of steps + 1 exterior
+    expected_domains = sum(len(body.a) for body in bodies) + 1
+    assert len(domains) == expected_domains
+    # Ensure all interior radii are strictly increasing
+    for i in range(len(domains) - 1):
+        assert domains[i+1].a_inner >= domains[i].a_outer - 1e-12
\ No newline at end of file
diff --git a/package/test/test_high_frequency_convergence.py b/package/test/test_high_frequency_convergence.py
new file mode 100644
index 0000000..21a9adf
--- /dev/null
+++ b/package/test/test_high_frequency_convergence.py
@@ -0,0 +1,81 @@
+# package/test/test_high_frequency_convergence.py
+import pytest
+import numpy as np
+from openflash_utils import run_openflash_case
+
+# Define Configurations
+CONFIGS = {
+    "config0": {
+        "h": 1.001, "d": [0.5, 0.25], "a": [0.5, 1], "heaving": [1, 1]
+    },
+    "config1": {
+        "h": 1.5, "d": [1.1, 0.85, 0.75, 0.4, 0.15], "a": [0.3, 0.5, 1, 1.2, 1.6], 
+        "heaving": [1, 1, 1, 1, 1]
+    },
+    "config2": {
+        "h": 100, "d": [29, 7, 4], "a": [3, 5, 10], "heaving": [1, 1, 1]
+    },
+    "config3": {
+        "h": 1.9, "d": [0.5, 0.7, 0.8, 0.2, 0.5], "a": [0.3, 0.5, 1, 1.2, 1.6], 
+        "heaving": [1, 1, 1, 1, 1]
+    },
+    "config4": {
+        "h": 1.001, "d": [0.5, 0.25], "a": [0.5, 1], "heaving": [0, 1]
+    },
+    "config5": {
+        "h": 1.001, "d": [0.5, 0.25], "a": [0.5, 1], "heaving": [1, 0]
+    },
+    "config6": {
+        "h": 100, "d": [29, 7, 4], "a": [3, 5, 10], "heaving": [0, 1, 1]
+    }
+}
+
+RHO = 1023
+# High m0 list to test convergence (we just test the endpoint for assertion)
+M0_MAX = 1e6 
+TOLERANCE = 1e-2 # 1% or 0.01 nondimensional units
+
+@pytest.mark.parametrize("name, cfg", CONFIGS.items())
+def test_high_frequency_limit(name, cfg):
+    """
+    Verifies that the finite high-frequency result (m0=1e6) matches 
+    the infinite frequency result (m0=inf).
+    """
+    print(f"\nRunning {name}...")
+    
+    # NMK setup: 100 per region (len(heaving) + 1)
+    # Note: len(heaving) = num_segments. Num regions = num_segments + 1 (exterior)
+    num_regions = len(cfg['heaving']) + 1
+    NMK = [100] * num_regions
+
+    # 1. Solve for m0 = inf
+    inf_am, inf_dp, _ = run_openflash_case(
+        cfg['h'], cfg['d'], cfg['a'], cfg['heaving'], NMK, np.inf, RHO
+    )
+    
+    # 2. Solve for m0 = 1e6
+    fin_am, fin_dp, _ = run_openflash_case(
+        cfg['h'], cfg['d'], cfg['a'], cfg['heaving'], NMK, M0_MAX, RHO
+    )
+
+    # 3. Assertions
+    
+    # Added Mass Convergence
+    # Check absolute difference or relative difference
+    am_diff = abs(fin_am - inf_am)
+    print(f"Added Mass: Inf={inf_am:.5f}, 1e6={fin_am:.5f}, Diff={am_diff:.5e}")
+    
+    assert am_diff < TOLERANCE, \
+        f"Added Mass mismatch for {name}: {inf_am} vs {fin_am}"
+
+    # Damping Convergence
+    # Damping at infinite frequency should be 0.
+    # OpenFLASH usually returns 0 for inf_dp correctly.
+    # Finite high freq damping should also be very small.
+    print(f"Damping: Inf={inf_dp:.5f}, 1e6={fin_dp:.5f}")
+    
+    assert abs(inf_dp) < 1e-9, f"Infinite Damping should be 0, got {inf_dp}"
+    assert abs(fin_dp) < TOLERANCE, f"High-freq Damping should be near 0, got {fin_dp}"
+
+if __name__ == "__main__":
+    pytest.main(["-v", "-s", __file__])
\ No newline at end of file
diff --git a/package/test/test_hydrocoeffs.py b/package/test/test_hydrocoeffs.py
new file mode 100644
index 0000000..9bba9b6
--- /dev/null
+++ b/package/test/test_hydrocoeffs.py
@@ -0,0 +1,220 @@
+# package/test/test_hydro.py
+import pytest
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from pathlib import Path
+import openflash.multi_constants as mc
+from openflash_utils import run_openflash_case
+
+# --- Configuration ---
+# Adjust this path if necessary relative to where you run pytest
+DATA_FOLDER = Path(__file__).parent.parent.parent / "dev" / "python" / "convergence-study" / "meem-vs-capytaine-data" / "csv_data"
+
+# Debug output directories
+DEBUG_PLOT_DIR = Path("debug_plots")
+DEBUG_CSV_DIR = Path("debug_results")
+
+RHO = 1023
+G = 9.81
+
+# VALIDATION TOLERANCE
+EXPECTED_RATIO = 1.0
+TOLERANCE = 0.1 
+
+# Scaling Factor (Ensure this matches how your mesh files were generated)
+# If your BEM mesh radii were 1.0 but your inputs here are 0.5, this needs adjustment.
+RADIUS_SCALING = 1.0 
+
+CONFIG_SPECS = {
+    "mini_bicylinder":   {"h": 1.001, "d": [0.25, 0.125], "a": [0.125, 0.25], "heaving": [1, 1]},
+    "small_bicylinder":  {"h": 1.001, "d": [0.5, 0.25],   "a": [0.5, 1.0],    "heaving": [1, 1]},
+    "big_bicylinder":    {"h": 1.001, "d": [0.75, 0.5],   "a": [0.5, 0.75],   "heaving": [1, 1]},
+    "mini_tricylinder":  {"h": 2.001, "d": [1.0, 0.5, 0.25], "a": [0.25, 0.5, 1.0], "heaving": [1, 1, 1]},
+    "small_tricylinder": {"h": 20.0,  "d": [15, 10, 5],   "a": [5, 10, 15],   "heaving": [1, 1, 1]},
+    "big_tricylinder":   {"h": 25.0,  "d": [20, 15, 10],  "a": [10, 15, 20],  "heaving": [1, 1, 1]},
+}
+
+COL_MAPPING = {
+    "m0": ["pyCapytaineMu_x", "pyMEEMMu_x", "m0"], 
+    "am": ["pyCapytaineMu_y", "AddedMass"],
+}
+
+def get_csv_files():
+    if not DATA_FOLDER.exists():
+        return []
+    # Grab all CSVs in the folder
+    return sorted(list(DATA_FOLDER.glob("*_regenerated_v2.csv")))
+
+def find_col(df, options):
+    for col in options:
+        if col in df.columns:
+            return df[col].values
+    return None
+
+def save_debug_plot(m0, of_vals, bem_vals, config_name, csv_name):
+    """Generates and saves a comparison plot."""
+    DEBUG_PLOT_DIR.mkdir(parents=True, exist_ok=True)
+    
+    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), sharex=True)
+    
+    # Plot 1: Absolute Values
+    ax1.set_title(f"Comparison: {config_name} ({csv_name})")
+    ax1.plot(m0, of_vals, 'o-', label='OpenFLASH (MEEM)', markersize=4, alpha=0.8)
+    ax1.plot(m0, bem_vals, 'x--', label='Capytaine (BEM)', markersize=6, alpha=0.8)
+    ax1.set_ylabel("Non-Dim Added Mass")
+    ax1.legend()
+    ax1.grid(True, alpha=0.3)
+    
+    # Plot 2: Ratio
+    # Avoid division by zero
+    with np.errstate(divide='ignore', invalid='ignore'):
+        ratios = of_vals / bem_vals
+    
+    ax2.plot(m0, ratios, 'r.-', label='Ratio (OF/BEM)')
+    ax2.axhline(1.0, color='k', linestyle='--', linewidth=1.5)
+    ax2.axhline(1.0 + TOLERANCE, color='g', linestyle=':', alpha=0.5)
+    ax2.axhline(1.0 - TOLERANCE, color='g', linestyle=':', alpha=0.5)
+    ax2.set_ylabel("Ratio")
+    ax2.set_xlabel("Wavenumber (m0)")
+    ax2.set_ylim(0, 3.0) # Limit y-axis to see relevant deviations
+    ax2.legend()
+    ax2.grid(True, alpha=0.3)
+    
+    filename = DEBUG_PLOT_DIR / f"{csv_name.replace('.csv', '')}_debug.png"
+    plt.savefig(filename)
+    plt.close()
+
+@pytest.mark.parametrize("csv_path", get_csv_files(), ids=lambda p: p.name)
+def test_openflash_validates_against_capytaine(csv_path):
+    # 0. Safety Check
+    assert mc.rho == RHO, f"Density mismatch: Constants {mc.rho} vs Test {RHO}"
+
+    # 1. Identify Geometry from filename
+    config_name = next((k for k in CONFIG_SPECS if csv_path.name.startswith(k)), None)
+    if config_name is None:
+        pytest.skip(f"Skipping {csv_path.name}: No geometry mapping found in CONFIG_SPECS.")
+        
+    cfg = CONFIG_SPECS[config_name]
+    
+    # 2. Load Data
+    try:
+        df = pd.read_csv(csv_path)
+    except Exception:
+        pytest.fail("Failed to read CSV")
+
+    m0_vals = find_col(df, COL_MAPPING["m0"])
+    bench_am = find_col(df, COL_MAPPING["am"])
+    
+    if m0_vals is None or bench_am is None:
+        pytest.skip(f"Required columns not found. Cols: {df.columns}")
+
+    # 3. Use all points for detailed plotting
+    m0_subset = m0_vals
+    bench_am_subset = bench_am
+
+    print(f"\n{'='*80}")
+    print(f"DEBUG REPORT: {config_name} | {csv_path.name}")
+    print(f"{'='*80}")
+
+    # 4. Prepare Geometry
+    scaled_a = [val * RADIUS_SCALING for val in cfg['a']]
+    h_val = cfg['h']
+    
+    # 5. Run OpenFLASH
+    # Note: Increasing harmonics (NMK) improves accuracy but slows tests.
+    NMK = [50] * (len(cfg['a']) + 1)
+    
+    results = []
+    
+    for i, m0 in enumerate(m0_subset):
+        if m0 <= 0 or np.isnan(m0):
+            results.append({"m0": m0, "of_nondim": np.nan, "bem": np.nan, "ratio": np.nan, "status": "SKIP"})
+            continue
+            
+        try:
+            # --- CRITICAL CORRECTION ---
+            # run_openflash_case returns NORMALIZED (Non-dimensional) values
+            # according to openflash_utils.py. 
+            # DO NOT re-normalize here.
+            am_nondim, _, _ = run_openflash_case(
+                h_val, cfg['d'], scaled_a, cfg['heaving'], NMK, m0, RHO
+            )
+            
+            cpt_val = bench_am_subset[i]
+            
+            # Check for division by near-zero
+            if abs(cpt_val) < 1e-6:
+                ratio = 1.0 if abs(am_nondim) < 1e-6 else 999.0
+            else:
+                ratio = am_nondim / cpt_val
+
+            status = "OK" if abs(ratio - 1.0) < TOLERANCE else "FAIL"
+            
+            results.append({
+                "m0": m0, 
+                "of_nondim": am_nondim, 
+                "bem": cpt_val, 
+                "ratio": ratio, 
+                "status": status
+            })
+            
+        except Exception as e:
+            results.append({"m0": m0, "of_nondim": np.nan, "bem": bench_am_subset[i], "ratio": np.nan, "status": f"ERR: {str(e)}"})
+
+    # 6. Convert to DataFrame for Analysis
+    res_df = pd.DataFrame(results)
+    
+    # Save CSV for manual inspection
+    DEBUG_CSV_DIR.mkdir(exist_ok=True)
+    res_df.to_csv(DEBUG_CSV_DIR / f"DEBUG_{csv_path.name}", index=False)
+    
+    # 7. Generate Plot
+    valid_plot_mask = np.isfinite(res_df['of_nondim']) & np.isfinite(res_df['bem'])
+    if np.any(valid_plot_mask):
+        save_debug_plot(
+            res_df.loc[valid_plot_mask, 'm0'], 
+            res_df.loc[valid_plot_mask, 'of_nondim'], 
+            res_df.loc[valid_plot_mask, 'bem'], 
+            config_name, 
+            csv_path.name
+        )
+
+    # 8. Filter for Statistics
+    # Filter out NaNs and cases where BEM is effectively zero (singularities)
+    valid_mask = (
+        np.isfinite(res_df['of_nondim']) & 
+        np.isfinite(res_df['bem']) & 
+        (np.abs(res_df['bem']) > 0.05)
+    )
+    
+    if np.sum(valid_mask) == 0:
+        print("No valid data points for comparison (all NaNs or BEM ~ 0).")
+        pytest.skip("No valid comparison points.")
+
+    valid_df = res_df[valid_mask]
+    avg_ratio = valid_df['ratio'].mean()
+    
+    # Print a nice table to stdout
+    print("\nSample Data Points (First 10 + Failures):")
+    # Show first 5
+    print(res_df.head(5).to_string(index=False, float_format=lambda x: "{:.4f}".format(x) if pd.notnull(x) else "NaN"))
+    # Show specific failures (ratio deviations > 20%)
+    failures = valid_df[np.abs(valid_df['ratio'] - 1.0) > 0.1]
+    if not failures.empty:
+        print("\n... Significant Mismatches (>10%):")
+        print(failures.head(10).to_string(index=False, float_format=lambda x: "{:.4f}".format(x)))
+
+    print("-" * 60)
+    print(f"Comparison Summary for {csv_path.name}")
+    print(f"  Valid Points : {len(valid_df)} / {len(res_df)}")
+    print(f"  Avg Ratio    : {avg_ratio:.4f}")
+    print(f"  Target       : {EXPECTED_RATIO} +/- {TOLERANCE}")
+    print("-" * 60)
+
+    # 9. Final Assertion
+    assert abs(avg_ratio - EXPECTED_RATIO) < TOLERANCE, \
+        f"Validation Failed: OpenFLASH {avg_ratio:.2f}x Capytaine."
+
+if __name__ == "__main__":
+    pytest.main(["-v", "-s", __file__])
\ No newline at end of file
diff --git a/package/test/test_matrices.py b/package/test/test_matrices.py
new file mode 100644
index 0000000..dda4a89
--- /dev/null
+++ b/package/test/test_matrices.py
@@ -0,0 +1,418 @@
+from functools import cache
+import numpy as np
+import os
+import sys
+import matplotlib.pyplot as plt
+
+# --- Path Setup ---
+current_dir = os.path.dirname(__file__)
+src_dir = os.path.abspath(os.path.join(current_dir, '..', 'src'))
+if src_dir not in sys.path:
+    sys.path.insert(0, src_dir)
+
+from openflash.geometry import Geometry
+from openflash.meem_problem import MEEMProblem
+from openflash.meem_engine import MEEMEngine
+from openflash.domain import Domain
+from openflash.multi_equations import I_mk, N_k_multi, diff_R_1n, diff_Lambda_k, scale, v_dense_block_e_entry, v_diagonal_block_e, v_diagonal_block_e_entry
+from openflash.geometry import ConcentricBodyGroup
+from openflash.body import SteppedBody
+from openflash.basic_region_geometry import BasicRegionGeometry
+
+# --- Path Setup ---
+current_dir = os.path.dirname(__file__)
+
+# Add hydro/python for old code
+old_code_dir = os.path.abspath(os.path.join(current_dir, '..', '..', 'dev', 'python'))
+if old_code_dir not in sys.path:
+    sys.path.insert(0, old_code_dir)
+
+from old_assembly import assemble_old_A_and_b, I_mk_old, m_k_old, N_k_old, diff_R_1n_old, diff_Lambda_k_old, scale_old
+
+
+def diagnose_large_differences(A_old, A_new, threshold=1e-6):
+    """
+    Identify large differences in matrix entries and report their locations and values.
+    """
+    diff = A_old - A_new
+    abs_diff = np.abs(diff)
+    
+    max_diff = np.max(abs_diff)
+    print(f"Max absolute difference in A: {max_diff}")
+    
+    # Get indices where difference is above threshold
+    large_diff_indices = np.argwhere(abs_diff > threshold)
+    print(f"Number of entries with difference > {threshold}: {len(large_diff_indices)}")
+    
+    # Sort indices by difference descending
+    sorted_indices = sorted(large_diff_indices, key=lambda idx: abs_diff[tuple(idx)], reverse=True)
+    
+    # Print top few largest differences
+    print("Top differences (index, A_old, A_new, diff):")
+    for idx in sorted_indices[:10]:
+        i, j = idx
+        print(f"({i}, {j}): {A_old[i,j]} vs {A_new[i,j]} => diff={diff[i,j]}")
+    return sorted_indices[:10]
+
+def extract_block_from_assembly(assembly_func, h, d, a, NMK, heaving, m0, block_position):
+    """
+    Given an assembly function (e.g. assemble_old_A_and_b), parameters, and a matrix block position,
+    extract the block matrix that corresponds to that position.
+    
+    block_position: (row_start, row_end, col_start, col_end)
+    """
+    # Call the assembly function but modify it to expose blocks or
+    # reconstruct blocks for this position
+    
+    # Since assembly function currently returns the full matrix,
+    # can simply slice the returned matrix after building it:
+    A, _ = assembly_func(h, d, a, NMK, heaving, m0)
+    row_start, row_end, col_start, col_end = block_position
+    return A[row_start:row_end, col_start:col_end]
+
+def compare_blocks(A_old, A_new, block_positions):
+    """
+    Given old and new matrices and block positions (list of tuples),
+    print side-by-side comparison of corresponding blocks.
+    """
+    for idx, (rs, re, cs, ce) in enumerate(block_positions):
+        print(f"\nBlock {idx} (rows {rs}:{re}, cols {cs}:{ce}):")
+        old_block = A_old[rs:re, cs:ce]
+        new_block = A_new[rs:re, cs:ce]
+        diff_block = old_block - new_block
+        print(f"Old block:\n{old_block}")
+        print(f"New block:\n{new_block}")
+        print(f"Difference:\n{diff_block}")
+        
+def validate_closures(problem, engine, m0, tol=1e-12):
+    cache = engine.cache_list[problem]
+    engine._ensure_m_k_and_N_k_arrays(problem, m0)
+    m_k_arr = cache.m_k_arr
+    N_k_arr = cache.N_k_arr
+    I_mk_vals = cache._get_closure("I_mk_vals")(m0, m_k_arr, N_k_arr)
+
+    print("\n--- Validating m₀-dependent A closures ---")
+    for (row, col, closure_fn) in cache.m0_dependent_A_indices:
+        closure_val = closure_fn(problem, m0, m_k_arr, N_k_arr, I_mk_vals)
+            
+        # Compute expected value (replicating logic manually here is tedious, but try a few hand-picked examples)
+        # Let's retrieve the current value from the assembled matrix for now:
+        A_matrix = engine.assemble_A_multi(problem, m0)
+        matrix_val = A_matrix[row, col]
+
+        if not np.isclose(closure_val, matrix_val, rtol=tol, atol=tol):
+            print(f"[A mismatch] row={row}, col={col}: closure={closure_val:.4e}, matrix={matrix_val:.4e}")
+
+    print("\n--- Validating m₀-dependent b closures ---")
+    b_vector = engine.assemble_b_multi(problem, m0)
+    for (row, closure_fn) in cache.m0_dependent_b_indices:
+        closure_val = closure_fn(problem, m0, m_k_arr, N_k_arr, I_mk_vals)
+        b_vector = engine.assemble_b_multi(problem, m0) # Re-fetch just in case
+        b_val = b_vector[row]
+        if not np.isclose(closure_val, b_val, rtol=tol, atol=tol):
+            print(f"[b mismatch] row={row}: closure={closure_val:.4e}, vector={b_val:.4e}")
+
+def compare_matrices_and_vectors(A_old, b_old, A_new, b_new, tol=1e-10):
+    print("=== Shape Check ===")
+    print(f"A_old shape: {A_old.shape}")
+    print(f"A_new shape: {A_new.shape}")
+    print(f"b_old shape: {b_old.shape}")
+    print(f"b_new shape: {b_new.shape}")
+    
+    print("A_old:\n", np.round(A_old.real, 2))
+    print("A_new:\n", np.round(A_new.real, 2))
+    print("b_old:\n", np.round(b_old.real, 2))
+    print("b_new:\n", np.round(b_new.real, 2))
+
+    if A_old.shape != A_new.shape:
+        print("Warning: Matrix shapes differ!")
+
+    if b_old.shape != b_new.shape:
+        print("Warning: RHS vector shapes differ!")
+
+    print("\n=== Norms ===")
+    print(f"||A_old||_F = {np.linalg.norm(A_old, 'fro')}")
+    print(f"||A_new||_F = {np.linalg.norm(A_new, 'fro')}")
+    print(f"||b_old||_2 = {np.linalg.norm(b_old)}")
+    print(f"||b_new||_2 = {np.linalg.norm(b_new)}")
+
+    print("\n=== Elementwise Differences ===")
+    # Pad smaller matrix/vector if shapes differ, to avoid error
+    min_rows = min(A_old.shape[0], A_new.shape[0])
+    min_cols = min(A_old.shape[1], A_new.shape[1])
+    A_diff = A_old[:min_rows, :min_cols] - A_new[:min_rows, :min_cols]
+    print(f"Max abs difference in A: {np.max(np.abs(A_diff))}")
+    print(f"Number of differing elements in A > {tol}: {(np.abs(A_diff) > tol).sum()}")
+
+    min_len = min(b_old.size, b_new.size)
+    b_diff = b_old[:min_len] - b_new[:min_len]
+    print(f"Max abs difference in b: {np.max(np.abs(b_diff))}")
+    print(f"Number of differing elements in b > {tol}: {(np.abs(b_diff) > tol).sum()}")
+
+    print("\n=== Zero Rows in Matrices ===")
+    zero_rows_old = np.where(~A_old.any(axis=1))[0]
+    zero_rows_new = np.where(~A_new.any(axis=1))[0]
+    print(f"Zero rows in A_old: {zero_rows_old}")
+    print(f"Zero rows in A_new: {zero_rows_new}")
+
+    # Optional: print detailed blocks if large differences
+    if np.max(np.abs(A_diff)) > tol:
+        print("\nBlocks with largest differences (some rows):")
+        max_diff_indices = np.unravel_index(np.argmax(np.abs(A_diff)), A_diff.shape)
+        print(f"Max difference at position {max_diff_indices}, value = {A_diff[max_diff_indices]}")
+        
+def summarize_array_differences(arr1, arr2, name1="arr1", name2="arr2", rtol=1e-12, atol=1e-15):
+    print(f"\nComparing {name1} and {name2}")
+    for i, (v1, v2) in enumerate(zip(arr1, arr2)):
+        print(f"{i}: {v1:.15f} vs {v2:.15f} => close? {np.isclose(v1, v2, rtol=rtol, atol=atol)}")
+
+
+def compare_v_diagonal_block_e(bd, NMK, a, h, m0, m_k_arr, atol=1e-12, rtol=1e-8):
+    print(f"--- Comparing v_diagonal_block_e vs. v_diagonal_block_e_entry at bd={bd} ---")
+    
+    # Generate the full diagonal block using the block function
+    block = v_diagonal_block_e(bd, h, NMK, a, m0, m_k_arr) # Call without k,r as fixed args
+
+    num_k_modes = NMK[bd+1] # Number of modes for this block (M)
+    all_close = True
+
+    for m in range(num_k_modes): # m is the local row index, corresponds to k mode in diag
+        # The diagonal entry from the block
+        val_matrix = block[m, m] 
+        
+        # The scalar entry from the entry function for the diagonal
+        # Here, 'k' in v_diagonal_block_e_entry is the mode index, which is 'm' for diagonal.
+        # This function takes the individual parameters needed.
+        val_entry = v_diagonal_block_e_entry(m, m, bd, m0, m_k_arr, a, h) # k=m for diagonal
+        
+        if not np.isclose(val_matrix, val_entry, atol=atol, rtol=rtol):
+            print(f"Mismatch at index {m}:")
+            print(f"  matrix: {val_matrix}")
+            print(f"  entry : {val_entry}")
+            all_close = False
+
+    if all_close:
+        print("✅ All entries match!")
+    else:
+        print("❌ Some entries did not match. See above for details.")
+        
+def test_v_dense_block_e_entry():
+    m, k, bd = 0, 0, 2
+    # Setup dummy I_mk_vals of appropriate shape
+    I_mk_vals = np.eye(3, dtype=complex)
+    a = [1.0, 2.0, 10.0]
+    h = 1.5
+    d = [0.0, 0.0, 0.0]
+    val = v_dense_block_e_entry(m, k, bd, I_mk_vals, a, h, d)
+    print("Entry (0, 0):", val)
+        
+def run_comparison_test():
+    # Define a small test problem parameters here (example)
+    NMK = [20, 20, 20, 20]   # Small problem size for fast testing
+    d = [29, 7, 4]    # example depths
+    a = [3, 5, 10]          # example parameters per region
+    
+    # --- FIX: Ensure only ONE body is heaving to satisfy Geometry assertion ---
+    heaving = [0, 1, 0]     
+    
+    h = 100                 # example characteristic length
+    m0 = 1
+    # --- Assemble old matrix and vector ---
+    #  need to implement or import the old assemble function here
+    A_old, b_old = assemble_old_A_and_b(h, d, a, NMK, heaving, m0)
+
+    # --- Setup problem and m0 for package assembly ---
+    #  must implement or mock a 'problem' object and m0 index for  new code
+    # --- Geometry Setup ---
+    bodies = []
+    for i in range(len(a)):
+        body = SteppedBody(
+            a=np.array([a[i]]),
+            d=np.array([d[i]]),
+            slant_angle=np.array([0.0]),  # Assuming zero slant for the test
+            heaving=bool(heaving[i])
+        )
+        bodies.append(body)
+    
+    # 2. Create the body arrangement.
+    arrangement = ConcentricBodyGroup(bodies)
+
+    # 3. Instantiate the CONCRETE geometry class.
+    #    This object will now correctly create the fluid domains internally.
+    geometry = BasicRegionGeometry(arrangement, h, NMK)
+    
+    problem = MEEMProblem(geometry)
+
+    # --- MEEM Engine Operations ---
+    engine = MEEMEngine(problem_list=[problem])
+    engine.build_problem_cache(problem)
+
+    problem_cache = engine.cache_list[problem]
+    engine._ensure_m_k_and_N_k_arrays(problem, m0)
+    
+    m_k_arr = problem_cache.m_k_arr
+    N_k_arr = problem_cache.N_k_arr
+    m_k_old_arr = m_k_old(NMK, m0, h)
+
+    # Debugging: Check the values of m_k_arr and N_k_arr before passing to plotting functions
+    print(f"DEBUG: m_k_arr in main() before plotting functions: {m_k_arr.shape if m_k_arr is not None else 'None'}")
+    print(f"DEBUG: N_k_arr in main() before plotting functions: {N_k_arr.shape if N_k_arr is not None else 'None'}")
+    
+    def compare_N_k_old_vs_new(m0, h, NMK, m_k_old_arr, m_k_arr):
+        print(f"Comparing N_k_old vs N_k_multi for m0={m0}, h={h}")
+
+        print("k\tN_k_old\t\tN_k_multi\tClose?")
+        for k in range(NMK[-1]):
+            old_val = N_k_old(k, m0, h, m_k_old_arr)
+            new_val = N_k_multi(k, m0, h, m_k_arr)
+            close = np.isclose(old_val, new_val, rtol=1e-12, atol=1e-15)
+            print(f"{k}\t{old_val:.15f}\t{new_val:.15f}\t{close}")
+            
+    compare_N_k_old_vs_new(m0, h, NMK, m_k_old_arr, m_k_arr)
+
+    boundary_count = len(NMK) - 1
+
+    print("Old m_k[0]:", m_k_old_arr[0])
+    print("New m_k[0]:", m_k_arr[0])
+    print("Equal?", np.isclose(m_k_old_arr[0], m_k_arr[0], rtol=1e-12, atol=1e-15))
+    
+    print("Old m_k[1]:", m_k_old_arr[1])
+    print("New m_k[1]:", m_k_arr[1])
+    print("Equal?", np.isclose(m_k_old_arr[1], m_k_arr[1], rtol=1e-12, atol=1e-15))
+    
+    print(f"m_k_old[1]: {m_k_old_arr[1]} (from m_k_entry_old)")
+    print(f"m_k_new[1]: {m_k_arr[1]} (from m_k_entry)")
+
+
+    print("Old I_mk[0,0]:", I_mk_old(0, 0, boundary_count - 1, d, h, m0, NMK))
+    print("New I_mk[0,0]:", I_mk(0, 0, boundary_count - 1, d, m0, h, m_k_arr, N_k_arr))
+    
+    summarize_array_differences(
+        [N_k_old(k, m0, h, m_k_old_arr) for k in range(NMK[-1])],
+        [N_k_multi(k, m0, h, m_k_arr) for k in range(NMK[-1])],
+        "N_k_old", "N_k_multi"
+    )
+
+    # --- Assemble new matrix and vector ---
+    A_new = engine.assemble_A_multi(problem, m0)
+    b_new = engine.assemble_b_multi(problem, m0)
+
+    # --- Compare ---
+    compare_matrices_and_vectors(A_old, b_old, A_new, b_new)
+    diagnose_large_differences(A_old, A_new, threshold=1e8)
+
+    print("A_old[4:6, 10:12]:", A_old[4:6, 10:12])
+    print("A_new[4:6, 10:12]:", A_new[4:6, 10:12])
+    print("A_old[10:12, 6:10]:", A_old[10:12, 6:10])
+    print("A_new[10:12, 6:10]:", A_new[10:12, 6:10])
+    
+    print("Normalization in I_mk_old vs I_mk")
+    print(I_mk_old(0, 0, boundary_count - 1, d, h, m0, NMK))
+    print(I_mk(0, 0, boundary_count - 1, d, m0, h, m_k_arr, N_k_arr))
+
+    print("Normalizing constants comparison:")
+    print("h (height):", h)
+    print("Depths (d):", d)
+    print("Widths (a):", a)
+    
+    print("\nAre A_old and A_new close (atol=1e-10)?", np.allclose(A_old, A_new, atol=1e-10))
+    
+    print(f"\nTotal m₀-dependent A entries registered: {len(problem_cache.m0_dependent_A_indices)}")
+    print(f"Total m₀-dependent b entries registered: {len(problem_cache.m0_dependent_b_indices)}")
+    
+    diff_indices = np.where(np.abs(A_old - A_new) > 1e-10)
+    print("Differing indices:", list(zip(diff_indices[0], diff_indices[1]))[:20])
+
+    for i, j in [(99,38), (20,20), (20, 40), (80,20), (80,40), (81,20), (81,21), (81,22), (81,23), (81,24), (81,25),(81,26),(81,27),(81,28),(81,29),(81,30),(81,31),(81,32),(81,33),(81,34),(81,35)]:
+        print(f"A_old[{i}, {j}] = {A_old[i, j]}")
+        print(f"A_new[{i}, {j}] = {A_new[i, j]}")
+        
+    print("scale_old(a)[-1]:", scale_old(a)[-1])
+    print("scale(a)[-1]:", scale(a)[-1])
+    
+    bd_test = 2 
+     
+    validate_closures(problem, engine, m0)
+    
+    # In test_matrices.py, somewhere after parameters are defined
+    n_test = 18
+    r_test = a[2] # which is 10
+    i_test = 2
+
+    old_diff_R_1n_val = diff_R_1n_old(n_test, r_test, i_test, h, d, a)
+    new_diff_R_1n_val = diff_R_1n(n_test, r_test, i_test, h, d, a) # Pass 'a' to new diff_R_1n
+
+    print(f"\n--- Direct Comparison for diff_R_1n (n={n_test}, r={r_test}, i={i_test}) ---")
+    print(f"Old diff_R_1n_old result: {old_diff_R_1n_val}")
+    print(f"New diff_R_1n result: {new_diff_R_1n_val}")
+    print(f"Are they close? {np.isclose(old_diff_R_1n_val, new_diff_R_1n_val, atol=1e-10)}")
+    
+    k_test = 0
+    
+    compare_v_diagonal_block_e(bd_test, NMK, a, h, m0, m_k_arr)
+
+
+    old_diff_Lambda_k_val = diff_Lambda_k_old(k_test, r_test, m0, a, NMK, h)
+    new_diff_Lambda_k_val = diff_Lambda_k(k_test, r_test, m0, a, m_k_arr)
+    print(f"\n--- Direct Comparison for diff_Lambda_k (k={k_test}, r={r_test}, m0={m0}, a={a}, m_k_arr={m_k_arr}, NMK={NMK}, h={h}) ---")
+    print(f"Old diff_Lamda_k_old result: {old_diff_Lambda_k_val}")
+    print(f"New diff_Lambda_k result: {new_diff_Lambda_k_val}")
+    print(f"Are they close? {np.isclose(old_diff_Lambda_k_val, new_diff_Lambda_k_val, atol=1e-10)}")
+    print(f"Are they close (real)? {np.isclose(old_diff_Lambda_k_val.real, new_diff_Lambda_k_val.real, atol=1e-10)}")
+    print(f"Are they close (imag)? {np.isclose(old_diff_Lambda_k_val.imag, new_diff_Lambda_k_val.imag, atol=1e-10)}")
+    
+    fig, axes = plt.subplots(1, 3, figsize=(18, 6))
+
+    # Plot A_old
+    im0 = axes[0].imshow(np.abs(A_old), cmap="viridis")
+    axes[0].set_title("A_old (|values|)")
+    fig.colorbar(im0, ax=axes[0])
+
+    # Plot A_new
+    im1 = axes[1].imshow(np.abs(A_new), cmap="viridis")
+    axes[1].set_title("A_new (|values|)")
+    fig.colorbar(im1, ax=axes[1])
+
+    # Plot difference
+    diff = A_old - A_new
+    im2 = axes[2].imshow(np.abs(diff), cmap="hot")
+    axes[2].set_title("|A_old - A_new| (abs diff)")
+    fig.colorbar(im2, ax=axes[2])
+
+    for ax in axes:
+        ax.set_xlabel("Columns")
+        ax.set_ylabel("Rows")
+
+    plt.suptitle("Matrix Comparison: A_old vs A_new", fontsize=16)
+    plt.tight_layout()
+    plt.show()
+    
+    fig, axes = plt.subplots(1, 3, figsize=(20, 6))
+
+    # A_old
+    im0 = axes[0].imshow(A_old.real, cmap="viridis")
+    axes[0].set_title("A_old (real)")
+    fig.colorbar(im0, ax=axes[0])
+
+    # A_new
+    im1 = axes[1].imshow(A_new.real, cmap="viridis")
+    axes[1].set_title("A_new (real)")
+    fig.colorbar(im1, ax=axes[1])
+
+    # Signed diff heatmap
+    diff_signed = A_old.real - A_new.real
+    im2 = axes[2].imshow(diff_signed, cmap="seismic", vmin=-np.max(np.abs(diff_signed)), vmax=np.max(np.abs(diff_signed)))
+    axes[2].set_title("Signed Difference (A_old - A_new)")
+    fig.colorbar(im2, ax=axes[2])
+
+    for ax in axes:
+        ax.set_xlabel("Columns")
+        ax.set_ylabel("Rows")
+
+    plt.suptitle("Matrix Comparison with Signed Difference", fontsize=16)
+    plt.tight_layout()
+    plt.show()
+    
+if __name__ == "__main__":
+    run_comparison_test()
+    test_v_dense_block_e_entry()
\ No newline at end of file
diff --git a/package/test/test_matrix_caching.py b/package/test/test_matrix_caching.py
new file mode 100644
index 0000000..642975f
--- /dev/null
+++ b/package/test/test_matrix_caching.py
@@ -0,0 +1,137 @@
+# test_matrix_caching.py
+import pytest
+import numpy as np
+import time
+from openflash.basic_region_geometry import BasicRegionGeometry
+from openflash.meem_problem import MEEMProblem
+from openflash.meem_engine import MEEMEngine
+
+# --- Configuration from your script ---
+H = 100
+D = [29, 7, 4]
+A = [3, 5, 10]
+# Heaving [0, 1, 1] means:
+# Segment 0 is Stationary (Body 0)
+# Segments 1 & 2 are Heaving together (Body 1)
+HEAVING_SEGMENTS = [0, 1, 1] 
+NMK_COUNT = 100
+RHO = 1023
+M0_VALUES = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+
+def setup_problem_objects():
+    """
+    Helper to set up the Geometry and Problem.
+    """
+    # Map segments to bodies to satisfy 'Single Rigid Body' constraint per body
+    # [0, 1, 1] -> Body 0 is seg 0, Body 1 is segs 1,2
+    body_map = [0, 1, 1]
+    heaving_map = [False, True]
+    
+    # NMK length must be len(A) + 1
+    NMK = [NMK_COUNT] * (len(A) + 1)
+
+    geometry = BasicRegionGeometry.from_vectors(
+        a=np.array(A),
+        d=np.array(D),
+        h=H,
+        NMK=NMK,
+        slant_angle=np.zeros_like(A),
+        body_map=body_map,
+        heaving_map=heaving_map
+    )
+    
+    problem = MEEMProblem(geometry)
+    return problem
+
+def solve_fresh_every_time(m0_list):
+    """
+    Simulates the 'Old' method: Re-create the Engine (and thus matrices) 
+    from scratch for every m0.
+    """
+    ams = []
+    dps = []
+    
+    start_time = time.perf_counter()
+    
+    for m0 in m0_list:
+        # 1. New Problem & Engine every iteration (Forces full rebuild)
+        prob = setup_problem_objects()
+        engine = MEEMEngine([prob])
+        
+        # 2. Solve
+        X = engine.solve_linear_system_multi(prob, m0)
+        res = engine.compute_hydrodynamic_coefficients(prob, X, m0)
+        
+        # Extract results for Body 1 (the heaving one)
+        # res is a list of dicts. We want the one where mode==1.
+        # (Since Body 0 is static, it might not even return a force, 
+        # or it will be 0. We specifically want Body 1).
+        
+        # Find result for Body 1
+        body_res = next(r for r in res if r['mode'] == 1)
+        # FIX: Use 'real' instead of 'nondim_real'
+        ams.append(body_res['real'])
+        # FIX: Use 'imag' instead of 'nondim_imag'
+        dps.append(body_res['imag'])
+        
+    duration = time.perf_counter() - start_time
+    return np.array(ams), np.array(dps), duration
+
+def solve_cached_update(m0_list):
+    """
+    Simulates the 'New' method: Initialize Engine once (cache template),
+    then only update m0-dependent parts in the loop.
+    """
+    ams = []
+    dps = []
+    
+    start_time = time.perf_counter()
+    
+    # 1. Initialize Once
+    prob = setup_problem_objects()
+    engine = MEEMEngine([prob]) # Cache is built here
+    
+    for m0 in m0_list:
+        # 2. Solve reusing the engine (Triggering the optimized update)
+        X = engine.solve_linear_system_multi(prob, m0)
+        res = engine.compute_hydrodynamic_coefficients(prob, X, m0)
+        
+        body_res = next(r for r in res if r['mode'] == 1)
+        # FIX: Use 'real' instead of 'nondim_real'
+        ams.append(body_res['real'])
+        # FIX: Use 'imag' instead of 'nondim_imag'
+        dps.append(body_res['imag'])
+
+    duration = time.perf_counter() - start_time
+    return np.array(ams), np.array(dps), duration
+
+def test_caching_correctness_and_speed():
+    """
+    Validates that the cached matrix update method produces identical 
+    results to the fresh rebuild method, and runs faster.
+    """
+    print("\n--- Running Fresh Rebuild Loop ---")
+    am_fresh, dp_fresh, t_fresh = solve_fresh_every_time(M0_VALUES)
+    
+    print("\n--- Running Cached Update Loop ---")
+    am_cached, dp_cached, t_cached = solve_cached_update(M0_VALUES)
+    
+    print(f"\nTime Fresh:  {t_fresh:.4f}s")
+    print(f"Time Cached: {t_cached:.4f}s")
+    print(f"Speedup:     {t_fresh/t_cached:.2f}x")
+
+    # 1. Correctness Assertion
+    # The results must be mathematically identical (or extremely close floating point tolerance)
+    np.testing.assert_allclose(
+        am_fresh, am_cached, rtol=1e-10, err_msg="Added Mass differs between fresh and cached methods"
+    )
+    np.testing.assert_allclose(
+        dp_fresh, dp_cached, rtol=1e-10, err_msg="Damping differs between fresh and cached methods"
+    )
+    print(">> CORRECTNESS CHECK PASSED: Results match.")
+
+    # 2. Performance Assertion
+    # The cached version should strictly be faster because it skips rebuilding 
+    # the m0-independent blocks (which are large in this config).
+    assert t_cached < t_fresh, "Cached implementation should be faster than fresh rebuild"
+    print(">> PERFORMANCE CHECK PASSED: Caching provided speedup.")
\ No newline at end of file
diff --git a/package/test/test_matrix_snapshot.py b/package/test/test_matrix_snapshot.py
new file mode 100644
index 0000000..177aea7
--- /dev/null
+++ b/package/test/test_matrix_snapshot.py
@@ -0,0 +1,51 @@
+# package/test/test_matrix_snapshot.py
+import pytest
+import numpy as np
+import os
+from openflash.basic_region_geometry import BasicRegionGeometry
+from openflash.meem_problem import MEEMProblem
+from openflash.meem_engine import MEEMEngine
+
+# Constants matching the snapshot generation
+h = 1.001
+a1 = .5
+a2 = 1
+d1 = .5
+d2 = .25
+m0 = 1.0
+N, M, K = 4, 4, 4
+
+SNAPSHOT_PATH = os.path.join(os.path.dirname(__file__), "data/gold_standard_A_2region.npy")
+
+def test_matrix_assembly_regression():
+    """
+    Ensures the A matrix assembly remains bitwise identical to a known 'Gold Standard' 
+    snapshot. This protects against accidental changes in loop logic or indexing.
+    """
+    if not os.path.exists(SNAPSHOT_PATH):
+        pytest.fail("Gold standard snapshot not found. Run 'generate_snapshot.py' first.")
+    
+    # Load Gold Standard
+    A_expected = np.load(SNAPSHOT_PATH)
+    
+    # Generate Current
+    geo = BasicRegionGeometry.from_vectors(
+        a=np.array([a1, a2]),
+        d=np.array([d1, d2]),
+        h=h,
+        NMK=[N, M, K],
+        slant_angle=np.zeros(2)
+    )
+    
+    prob = MEEMProblem(geo)
+    engine = MEEMEngine([prob])
+    engine._ensure_m_k_and_N_k_arrays(prob, m0)
+    A_actual = engine.assemble_A_multi(prob, m0)
+    
+    # Assert
+    # We use a very strict tolerance because this is a regression test on the same machine/logic
+    np.testing.assert_allclose(
+        A_actual, A_expected, 
+        rtol=1e-12, atol=1e-12, 
+        err_msg="Matrix assembly logic has changed! Result differs from snapshot."
+    )
\ No newline at end of file
diff --git a/package/test/test_meem_engine.py b/package/test/test_meem_engine.py
index 7971017..379ee31 100644
--- a/package/test/test_meem_engine.py
+++ b/package/test/test_meem_engine.py
@@ -1,121 +1,335 @@
-import unittest
+# test_meem_engine.py
+import pytest
 import numpy as np
-from unittest.mock import patch, MagicMock  # For mocking dependencies
-
 import sys
 import os
-src_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../src'))
-sys.path.append(src_path)
-
-from meem_engine import MEEMEngine
-from meem_problem import MEEMProblem
-from domain import Domain
-from geometry import Geometry
-from coupling import A_nm, A_mk
-import equations
-import multi_equations
-from results import Results
-from constants import *
-from multi_constants import *
-
-class TestMEEMEngine(unittest.TestCase):
-
-    def setUp(self):
-        """Set up a basic MEEMProblem for testing."""
-        r_coordinates = {'a1': 0.5, 'a2': 1.0}
-        z_coordinates = {'h': 1.001}
-        domain_params = [
-            {'number_harmonics': 3, 'height': 1.0, 'radial_width': 0.5, 'category': 'inner', 'di': 0.5, 'a': 0.5, 'heaving': 1},
-            {'number_harmonics': 4, 'height': 1.0, 'radial_width': 1.0, 'category': 'outer', 'di': 0.25, 'a': 1.0, 'heaving': 1},
-            {'number_harmonics': 5, 'height': 1.0, 'radial_width': 1.5, 'category': 'exterior', 'heaving': 0}
-        ]
-        geometry = Geometry(r_coordinates, z_coordinates, domain_params)
-        self.problem = MEEMProblem(geometry)
-        self.engine = MEEMEngine([self.problem])  # Create an engine instance
-
-
-    def test_assemble_A(self):
-        A = self.engine.assemble_A(self.problem, m0)
-        self.assertTrue(np.all(np.isfinite(A)))  # No NaNs or Infs
-
-
-    def test_assemble_A_multi(self):
-        A = self.engine.assemble_A_multi(self.problem, m0)
-        self.assertTrue(np.all(np.isfinite(A)))  # No NaNs or Infs
-
-
-    def test_assemble_b(self):
-        b = self.engine.assemble_b(self.problem, m0)
-        self.assertTrue(np.all(np.isfinite(b)))  # No NaNs or Infs
-
-    def test_assemble_b_multi(self):
-        b = self.engine.assemble_b_multi(self.problem, m0)
-        self.assertTrue(np.all(np.isfinite(b)))  # No NaNs or Infs
-
-
-    @patch('scipy.linalg.solve')  # Mock the linear solver
-    def test_solve_linear_system(self, mock_solve):
-        mock_solve.return_value = np.ones(12, dtype=complex)  # Mock a solution
-        X = self.engine.solve_linear_system(self.problem, m0)
-        self.assertEqual(X.shape, (12,))
-        mock_solve.assert_called_once()  # Check that solve was called
-
-    @patch('scipy.linalg.solve')
-    def test_solve_linear_system_multi(self, mock_solve):
-        mock_solve.return_value = np.ones(12, dtype=complex)
-        X = self.engine.solve_linear_system_multi(self.problem, m0)
-        self.assertEqual(X.shape, (12,))
-        mock_solve.assert_called_once()
-
-    def test_compute_hydrodynamic_coefficients(self):
-        # Mock a solution vector for testing
-        mock_solution = np.ones(12, dtype=complex)
-        hydro_coeffs = self.engine.compute_hydrodynamic_coefficients(self.problem, mock_solution)
-        # Basic check to ensure the output is not None and has some length
-        self.assertIsNotNone(hydro_coeffs)
-
-    def test_calculate_potentials(self):
-        # Mock a solution vector for testing
-        mock_solution = np.ones(12, dtype=complex)
-        potentials = self.engine.calculate_potentials(self.problem, mock_solution)
-        # Check if the potentials dictionary is not empty and has the correct keys
-        self.assertTrue(potentials)
-        self.assertIn('domain_0', potentials)
-        self.assertIn('domain_1', potentials)
-        self.assertIn('domain_2', potentials)
-        # Check if each domain has 'potentials', 'r', and 'z' keys
-        for domain_data in potentials.values():
-            self.assertIn('potentials', domain_data)
-            self.assertIn('r', domain_data)
-            self.assertIn('z', domain_data)
-
-
-    @patch('matplotlib.pyplot.show')  # Mock show to avoid displaying plots
-    def test_visualize_potential(self, mock_show):
-        potentials = {
-            'inner': np.arange(3),
-            'outer': np.arange(4),
-            'exterior': np.arange(5)
-        }
-        self.engine.visualize_potential(potentials)
-        mock_show.assert_called_once()
-
-    def test_run_and_store_results(self):
-        # Mock frequencies and modes for the problem
-        self.problem.frequencies = np.array([1.0, 2.0])
-        self.problem.modes = np.array(['heave', 'surge'])
-        # Run the computation and store results
-        results = self.engine.run_and_store_results(0, m0)
-        # Check if the results object is created and has the expected attributes
-        self.assertIsInstance(results, Results)
-        self.assertIsNotNone(results.dataset)
-        self.assertTrue(
-            'hydrodynamic_coefficients_real' in results.dataset and
-            'hydrodynamic_coefficients_imag' in results.dataset
+import xarray as xr
+
+# --- Path Setup ---
+# This ensures pytest can find package source files
+current_dir = os.path.dirname(__file__)
+src_dir = os.path.abspath(os.path.join(current_dir, '..', 'src'))
+if src_dir not in sys.path:
+    sys.path.insert(0, src_dir)
+
+# --- Import Package Modules ---
+from openflash.meem_engine import MEEMEngine
+from openflash.meem_problem import MEEMProblem
+from openflash.problem_cache import ProblemCache
+from openflash.results import Results
+from openflash.multi_equations import omega
+from openflash.multi_constants import g
+from openflash.body import SteppedBody
+from openflash.geometry import ConcentricBodyGroup
+from openflash.basic_region_geometry import BasicRegionGeometry
+
+# ==============================================================================
+# Pytest Fixture: Reusable Test Problem
+# ==============================================================================
+@pytest.fixture(scope="module")
+def sample_problem():
+    """
+    Creates a standard, reusable MEEMProblem instance for all tests in this module.
+    `scope="module"` means this function runs only once per test session.
+    """
+    # Define a simple but complete 2-cylinder problem
+    NMK = [10, 10, 10]
+    h = 100.0
+    a = np.array([5.0, 10.0])
+    d = np.array([20.0, 10.0])
+    
+    # FIX: Change heaving to only have one body heaving to pass the new assertion
+    heaving = np.array([1, 0]) # [True, False] -> Only the first body heaves
+    
+    # 1. Define the physical bodies
+    bodies = []
+    for i in range(len(a)):
+        body = SteppedBody(
+            a=np.array([a[i]]),
+            d=np.array([d[i]]),
+            slant_angle=np.array([0.0]), # Assuming zero slant for test
+            heaving=bool(heaving[i])
         )
-        self.assertIn('domain_potentials', results.dataset)
+        bodies.append(body)
+
+    # 2. Create the body arrangement
+    # This call now checks the assertion (heaving_count <= 1)
+    arrangement = ConcentricBodyGroup(bodies)
+
+    # 3. Instantiate the concrete geometry class
+    geometry = BasicRegionGeometry(arrangement, h, NMK)
+    
+    # 4. Create the problem
+    problem = MEEMProblem(geometry)
+    
+    # --- Set frequencies and modes for the problem ---
+    m0 = 1.0 
+    local_omega = omega(m0, h, g)
+    problem_frequencies = np.array([local_omega])
+    
+    # Modes correspond to heaving bodies. Only body 0 is heaving.
+    # The `problem.modes` property will now correctly return array([0])
+    problem.set_frequencies(problem_frequencies)
+    
+    return problem
+
+# ==============================================================================
+# Test Suite for MEEMEngine
+# ==============================================================================
+
+def test_engine_initialization(sample_problem):
+    """
+    Tests if the MEEMEngine initializes correctly and creates a problem cache.
+    """
+    engine = MEEMEngine(problem_list=[sample_problem])
+    
+    assert len(engine.problem_list) == 1
+    assert sample_problem in engine.cache_list
+    assert engine.cache_list[sample_problem] is not None
+    print("✅ Engine initialization test passed.")
+
+def test_matrix_assembly(sample_problem):
+    """
+    Tests the assembly of the A matrix and b vector.
+    """
+    engine = MEEMEngine(problem_list=[sample_problem])
+    m0 = 1.0
+    
+    # Ensure cache is populated for m0-dependent parts
+    engine._ensure_m_k_and_N_k_arrays(sample_problem, m0)
+    
+    A = engine.assemble_A_multi(sample_problem, m0)
+    b = engine.assemble_b_multi(sample_problem, m0)
+    
+    expected_size = 10 + 2 * 10 + 10 # NMK[0] + 2*NMK[1] + NMK[2]
+    
+    assert isinstance(A, np.ndarray)
+    assert A.shape == (expected_size, expected_size)
+    assert np.iscomplexobj(A)
+    
+    assert isinstance(b, np.ndarray)
+    assert b.shape == (expected_size,)
+    assert np.iscomplexobj(b)
+    print("✅ Matrix assembly test passed.")
+
+def test_solve_linear_system(sample_problem):
+    """
+    Tests if the linear system solver runs and returns the correct shape.
+    """
+    engine = MEEMEngine(problem_list=[sample_problem])
+    m0 = 1.0
+    
+    X = engine.solve_linear_system_multi(sample_problem, m0)
+    
+    expected_size = 10 + 2 * 10 + 10
+    
+    assert isinstance(X, np.ndarray)
+    assert X.shape == (expected_size,)
+    assert np.iscomplexobj(X)
+    print("✅ Linear system solver test passed.")
+    
+def test_compute_hydrodynamic_coefficients(sample_problem):
+    """
+    Tests the calculation of hydrodynamic coefficients.
+    """
+    engine = MEEMEngine(problem_list=[sample_problem])
+    m0 = 1.0
+    X = engine.solve_linear_system_multi(sample_problem, m0)
+    
+    coeffs = engine.compute_hydrodynamic_coefficients(problem=sample_problem, X=X, m0=m0)
+    
+    assert isinstance(coeffs, list), "Expected list of dictionaries"
+    # NOTE: compute_hydrodynamic_coefficients iterates over all *possible* modes/bodies,
+    # which is 2 bodies in this fixture.
+    assert len(coeffs) == 2, "Expected coefficients for 2 bodies"
+    
+    for c in coeffs:
+        assert isinstance(c, dict), "Each entry in the result should be a dictionary"
+        assert "real" in c, "Missing 'real' in coefficient dictionary"
+        assert "imag" in c, "Missing 'imag' in coefficient dictionary"
+        # FIX: Removed assertions for 'nondim_real' and 'nondim_imag' 
+        # as these were removed from MEEMEngine to simplify the API.
+        assert "excitation_phase" in c, "Missing 'excitation_phase'"
+        assert "excitation_force" in c, "Missing 'excitation_force'"
+    print("✅ Hydrodynamic coefficients test passed.")
+
+def test_calculate_potentials_and_velocities(sample_problem):
+    """
+    Tests that potential and velocity calculations run and return correct data structures.
+    """
+    engine = MEEMEngine(problem_list=[sample_problem])
+    m0 = 1.0
+    X = engine.solve_linear_system_multi(sample_problem, m0)
+    
+    # Test potentials
+    potentials = engine.calculate_potentials(sample_problem, X, m0, spatial_res=10, sharp=False)
+    assert isinstance(potentials, dict)
+    assert "phi" in potentials and potentials["phi"].shape == (10, 10)
+    assert "R" in potentials and "Z" in potentials
+    
+    # Test velocities
+    velocities = engine.calculate_velocities(sample_problem, X, m0, spatial_res=10, sharp=False)
+    assert isinstance(velocities, dict)
+    assert "vr" in velocities and velocities["vr"].shape == (10, 10)
+    assert "vz" in velocities and velocities["vz"].shape == (10, 10)
+    print("✅ Potential and velocity calculation tests passed.")
+    
+def test_ensure_m_k_and_N_k_arrays(sample_problem):
+    """
+    Tests that _ensure_m_k_and_N_k_arrays correctly populates the cache
+    and is idempotent (does not re-calculate if values already exist).
+    """
+    engine = MEEMEngine(problem_list=[sample_problem])
+    m0 = 1.0
+    cache = engine.cache_list[sample_problem]
+
+    # 1. Assert initial state is empty
+    assert cache.m_k_arr is None
+    assert cache.N_k_arr is None
+
+    # 2. Act: Call the method for the first time
+    engine._ensure_m_k_and_N_k_arrays(sample_problem, m0)
+
+    # 3. Assert that cache is now populated
+    assert isinstance(cache.m_k_arr, np.ndarray)
+    assert isinstance(cache.N_k_arr, np.ndarray)
+    
+    # Check shape based on the fixture's NMK = [10, 10, 10]
+    expected_len = 10 
+    assert cache.m_k_arr.shape == (expected_len,)
+    assert cache.N_k_arr.shape == (expected_len,)
+    
+    # Store the object IDs of the created arrays
+    id_m_k_before = id(cache.m_k_arr)
+    id_N_k_before = id(cache.N_k_arr)
+
+    # 4. Act: Call the method a second time
+    engine._ensure_m_k_and_N_k_arrays(sample_problem, m0)
+    
+    # 5. Assert that the arrays were not re-calculated (idempotency check)
+    assert id(cache.m_k_arr) == id_m_k_before
+    assert id(cache.N_k_arr) == id_N_k_before
+    
+    print("✅ Cache population and idempotency test passed.")
+    
+def test_build_problem_cache(sample_problem):
+    """
+    Tests that the build_problem_cache method correctly populates the cache
+    with templates and m0-dependent calculation functions.
+    """
+    engine = MEEMEngine(problem_list=[sample_problem])
+    cache = engine.cache_list[sample_problem]
+
+    # 1. Check that the cache object was created and populated
+    assert isinstance(cache, ProblemCache)
+    assert cache.A_template is not None
+    assert cache.b_template is not None
+    
+    # 2. Verify the shapes of the templates
+    NMK = [10, 10, 10]
+    expected_size = NMK[0] + 2 * NMK[1] + NMK[2]
+    assert cache.A_template.shape == (expected_size, expected_size)
+    assert cache.b_template.shape == (expected_size,)
+
+    # 3. Verify that m0-independent parts have been pre-computed
+    # The A_template should not be all zeros; some blocks are m0-independent.
+    assert np.any(cache.A_template != 0)
+    # The b_template should also have some pre-computed values.
+    assert np.any(cache.b_template != 0)
+
+    # 4. Verify that the lists for m0-dependent parts are populated
+    # For a 2-cylinder problem (3 domains), there are m0-dependent blocks.
+    assert len(cache.m0_dependent_A_indices) > 0
+    assert len(cache.m0_dependent_b_indices) > 0
+
+    # 5. Check a specific m0-dependent entry to ensure it's a callable
+    # The third element of the tuple should be the calculation function.
+    assert callable(cache.m0_dependent_A_indices[0][2])
+    assert callable(cache.m0_dependent_b_indices[0][1])
+    
+    print("✅ Problem cache build test passed.")
+    
+def test_reformat_coeffs():
+    """
+    Tests the reformat_coeffs method to ensure it correctly splits the
+    solution vector `x` into arrays for each physical region.
+    """
+    # 1. Arrange: Set up a mock problem
+    # We only need an engine instance to call the method
+    engine = MEEMEngine(problem_list=[]) 
+    NMK = [3, 4, 5]  # Inner (3), Intermediate (4), Exterior (5)
+    boundary_count = len(NMK) - 1
+    
+    # Calculate the total size of the mock solution vector
+    # Inner region has NMK[0] coeffs
+    # Intermediate region has 2 * NMK[1] coeffs
+    # Exterior region has NMK[2] coeffs
+    size = NMK[0] + 2 * NMK[1] + NMK[2]  # 3 + 2*4 + 5 = 16
+    x = np.arange(size) # Create a predictable vector: [0, 1, ..., 15]
+
+    # 2. Act: Call the function to be tested
+    reformatted_cs = engine.reformat_coeffs(x, NMK, boundary_count)
+
+    # 3. Assert: Check the results
+    # Check that the output is a list with the correct number of regions
+    assert isinstance(reformatted_cs, list)
+    assert len(reformatted_cs) == len(NMK)
+
+    # Check the shape of each region's coefficient array
+    assert reformatted_cs[0].shape == (NMK[0],)      # Inner region
+    assert reformatted_cs[1].shape == (2 * NMK[1],)  # Intermediate region
+    assert reformatted_cs[2].shape == (NMK[2],)      # Exterior region
+
+    # Check the content of each array to ensure the slicing was correct
+    np.testing.assert_array_equal(reformatted_cs[0], np.arange(0, 3))
+    np.testing.assert_array_equal(reformatted_cs[1], np.arange(3, 3 + 8))
+    np.testing.assert_array_equal(reformatted_cs[2], np.arange(11, 16))
+    
+    print("✅ Coefficient reformatting test passed.")
+    
+def test_run_and_store_results(sample_problem):
+    """
+    Tests the full computation loop over a set of frequencies, ensuring
+    results are correctly stored in a Results object.
+    """
+    # 1. Arrange: Define a set of test frequencies
+    # Using omega function to get valid frequencies based on m0 values
+    test_m0s = [0.5, 1.0, 1.5]
+    test_frequencies = np.array([omega(m0, sample_problem.geometry.h, g) for m0 in test_m0s])
+    
+    # FIX: Use the new set_frequencies method
+    sample_problem.set_frequencies(test_frequencies)
+
+    # Infer modes from the sample_problem fixture's geometry
+    # The fixture is now constrained to 1 heaving body (mode 0)
+    num_modes = len(sample_problem.modes)
+    num_freqs = len(test_frequencies)
+    
+    # Check that only one mode is active
+    assert num_modes == 1 
+
+    engine = MEEMEngine(problem_list=[sample_problem])
+
+    # 2. Act: Run the main computation method
+    results = engine.run_and_store_results(problem_index=0)
 
+    # 3. Assert: Check the structure of the Results object
+    assert isinstance(results, Results)
+    assert len(results.frequencies) == num_freqs
+    assert len(results.modes) == num_modes
+    assert np.array_equal(results.modes, sample_problem.modes)
 
+    # Check the shape of the stored hydrodynamic coefficients
+    # Should be (num_freqs, 2 total bodies, 1 active mode) 
+    ds = results.get_results()
+    
+    # NOTE: The size of the hydrodynamic matrix is (Total Bodies x Active Modes)
+    # The compute function iterates over all bodies (2) for force (j) and active modes (1) for motion (i).
+    expected_shape = (num_freqs, num_modes, num_modes) 
+    
+    assert ds['added_mass'].shape == expected_shape
+    assert ds['damping'].shape == expected_shape
 
-if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    # Check that the data is not all NaN (i.e., computation ran)
+    assert not np.isnan(ds['added_mass'].values).all()
+    assert not np.isnan(ds['damping'].values).all()
\ No newline at end of file
diff --git a/package/test/test_meem_integration.py b/package/test/test_meem_integration.py
new file mode 100644
index 0000000..fcd1766
--- /dev/null
+++ b/package/test/test_meem_integration.py
@@ -0,0 +1,388 @@
+import pytest
+import numpy as np
+from scipy import linalg
+from functools import partial
+
+# Import from your package
+from openflash.multi_equations import *
+from openflash.multi_constants import *
+from openflash.meem_problem import MEEMProblem
+from openflash.meem_engine import MEEMEngine
+from openflash.geometry import ConcentricBodyGroup
+from openflash.basic_region_geometry import BasicRegionGeometry
+from openflash.body import SteppedBody
+
+# --- Fixtures ---
+
+@pytest.fixture
+def meem_setup():
+    """
+    Sets up the problem configuration used for testing.
+    Equivalent to setting the global variables in the original script.
+    """
+    # Physical Constants
+    h = 10.0
+    rho_val = 1000.0
+    g_val = 9.81
+    
+    # Geometry Definition (2-body configuration similar to script intent)
+    # Radii (strictly increasing)
+    a = np.array([5.0, 10.0]) 
+    # Depths
+    d = np.array([2.0, 4.0]) 
+    # Heaving Flags (Body 0 heaving, Body 1 static)
+    heaving = np.array([True, False])
+    # Slants (zeros for standard cylinders)
+    slants = np.array([0.0, 0.0])
+    
+    # Harmonics (NMK)
+    # 2 bodies -> 3 regions (Inner, Intermediate, Exterior)
+    NMK = [5, 5, 5] 
+    
+    # Frequency
+    omega_val = 1.5
+    m0_val = wavenumber(omega_val, h)
+    
+    # Create OpenFlash Objects
+    bodies = []
+    for i in range(len(a)):
+        bodies.append(SteppedBody(
+            a=np.array([a[i]]), 
+            d=np.array([d[i]]), 
+            slant_angle=np.array([slants[i]]), 
+            heaving=bool(heaving[i])
+        ))
+        
+    arrangement = ConcentricBodyGroup(bodies)
+    geometry = BasicRegionGeometry(arrangement, h, NMK)
+    problem = MEEMProblem(geometry)
+    problem.set_frequencies(np.array([omega_val]))
+    
+    return {
+        "h": h,
+        "a": a,
+        "d": d,
+        "heaving": heaving,
+        "NMK": NMK,
+        "m0": m0_val,
+        "rho": rho_val,
+        "omega": omega_val,
+        "problem": problem,
+        "boundary_count": len(NMK) - 1
+    }
+
+# --- Tests ---
+
+def test_input_assertions(meem_setup):
+    """
+    Validates the input arrays satisfy geometric and physical constraints.
+    (logic taken from the start of the original script)
+    """
+    a = meem_setup["a"]
+    d = meem_setup["d"]
+    heaving = meem_setup["heaving"]
+    NMK = meem_setup["NMK"]
+    h = meem_setup["h"]
+    m0 = meem_setup["m0"]
+    boundary_count = meem_setup["boundary_count"]
+
+    # Length checks
+    for arr in [a, d, heaving]:
+        assert len(arr) == boundary_count, \
+            "NMK should have one more entry than a, d, and heaving."
+
+    # Boolean checks
+    for entry in heaving:
+        assert entry in [0, 1, True, False], "heaving entries should be booleans."
+
+    # Monotonicity checks
+    left = 0
+    for radius in a:
+        assert radius > left, "a entries should be increasing and > 0."
+        left = radius
+
+    # Depth checks
+    for depth in d:
+        assert depth >= 0, "d entries should be nonnegative."
+        assert depth < h, "d entries should be less than h."
+
+    # Harmonics checks
+    for val in NMK:
+        assert isinstance(val, int) and val > 0, "NMK entries should be positive integers."
+
+    assert m0 > 0, "m0 should be positive."
+
+
+def test_matrix_assembly_consistency(meem_setup):
+    """
+    Reconstructs the A matrix and b vector manually using the equations
+    and compares them against the output of MEEMEngine.
+    """
+    # Unpack
+    h = meem_setup["h"]
+    a = meem_setup["a"]
+    d = meem_setup["d"]
+    heaving = meem_setup["heaving"]
+    NMK = meem_setup["NMK"]
+    m0 = meem_setup["m0"]
+    problem = meem_setup["problem"]
+    boundary_count = meem_setup["boundary_count"]
+    
+    # ---------------------------------------------------------
+    # 1. Generate via Engine (The "System Under Test")
+    # ---------------------------------------------------------
+    engine = MEEMEngine([problem])
+    # Ensure cache is built
+    engine.cache_list[problem] = engine.build_problem_cache(problem)
+    
+    A_engine = engine.assemble_A_multi(problem, m0)
+    b_engine = engine.assemble_b_multi(problem, m0)
+    
+    # ---------------------------------------------------------
+    # 2. Manual Generation (The "Oracle" from the script)
+    # ---------------------------------------------------------
+    
+    # Pre-compute wave numbers (needed for modern I_mk/N_k functions)
+    m_k_arr = np.array([m_k_entry(k, m0, h) for k in range(NMK[-1])])
+    N_k_arr = np.array([N_k_multi(k, m0, h, m_k_arr) for k in range(NMK[-1])])
+
+    # Coupling integrals
+    I_nm_vals = np.zeros((max(NMK), max(NMK), boundary_count - 1), dtype=complex)
+    for bd in range(boundary_count - 1):
+        for n in range(NMK[bd]):
+            for m in range(NMK[bd + 1]):
+                I_nm_vals[n][m][bd] = I_nm(n, m, bd, d, h)
+
+    I_mk_vals = np.zeros((NMK[boundary_count - 1], NMK[boundary_count]), dtype=complex)
+    for m in range(NMK[boundary_count - 1]):
+        for k in range(NMK[boundary_count]):
+            # Updated to pass all required args including precomputed arrays
+            I_mk_vals[m][k] = I_mk(m, k, boundary_count - 1, d, m0, h, m_k_arr, N_k_arr)
+
+    # --- Block Builders (Local Adaptations) ---
+    
+    # Helpers for manual block construction using library functions
+    def make_p_diag(left, func, bd):
+        return p_diagonal_block(left, func, bd, h, d, a, NMK)
+    
+    def make_p_dense(left, func, bd):
+        return p_dense_block(left, func, bd, NMK, a, I_nm_vals)
+    
+    def make_p_dense_e(bd):
+        # FIX: We construct this manually because the library's p_dense_block_e
+        # doesn't accept the required m0/m_k_arr arguments.
+        
+        # 1. Create a partial of Lambda_k with fixed params
+        # Lambda_k signature: (k, r, m0, a, m_k_arr)
+        func = partial(Lambda_k, m0=m0, a=a, m_k_arr=m_k_arr)
+        
+        # 2. Vectorize it
+        v_func = np.vectorize(func, otypes=[complex])
+        
+        # 3. Compute radial vector for k = 0..M-1 at r = a[bd]
+        k_vals = list(range(NMK[bd+1]))
+        r_val = a[bd]
+        radial_vector = v_func(k_vals, r_val)
+        
+        # 4. Construct the array
+        radial_array = np.outer(np.ones(NMK[bd]), radial_vector)
+        return (-1) * radial_array * I_mk_vals
+        
+    def make_v_diag(left, func, bd):
+        return v_diagonal_block(left, func, bd, h, d, NMK, a)
+
+    def make_v_dense(left, func, bd):
+        return v_dense_block(left, func, bd, I_nm_vals, NMK, a)
+        
+    def make_v_diag_e(bd):
+        return v_diagonal_block_e(bd, h, NMK, a, m0, m_k_arr)
+
+    def make_v_dense_e(func, bd):
+        return v_dense_block_e(func, bd, I_mk_vals, NMK, a)
+
+    # Vectorized function aliases
+    v_R1n = np.vectorize(partial(R_1n, h=h, d=d, a=a))
+    v_R2n = np.vectorize(partial(R_2n, a=a, h=h, d=d))
+    v_diff_R1n = np.vectorize(partial(diff_R_1n, h=h, d=d, a=a), otypes=[complex])
+    v_diff_R2n = np.vectorize(partial(diff_R_2n, h=h, d=d, a=a), otypes=[complex])
+    
+    # --- Manual Matrix Loop (Potential Matching) ---
+    rows = []
+    size = NMK[0] + NMK[-1] + 2 * sum(NMK[1:len(NMK) - 1])
+    col = 0
+    
+    for bd in range(boundary_count):
+        N = NMK[bd]
+        M = NMK[bd + 1]
+        
+        if bd == (boundary_count - 1): # i-e boundary
+            row_height = N
+            left_block1 = make_p_diag(True, v_R1n, bd)
+            right_block = make_p_dense_e(bd) # Uses the manual fix above
+            if bd == 0:
+                rows.append(np.concatenate((left_block1, right_block), axis=1))
+            else:
+                left_block2 = make_p_diag(True, v_R2n, bd)
+                left_zeros = np.zeros((row_height, col), dtype=complex)
+                rows.append(np.concatenate((left_zeros, left_block1, left_block2, right_block), axis=1))
+        
+        elif bd == 0:
+            left_diag = d[bd] > d[bd + 1]
+            if left_diag:
+                row_height = N
+                left_block = make_p_diag(True, v_R1n, 0)
+                right_block1 = make_p_dense(False, v_R1n, 0)
+                right_block2 = make_p_dense(False, v_R2n, 0)
+            else:
+                row_height = M
+                left_block = make_p_dense(True, v_R1n, 0)
+                right_block1 = make_p_diag(False, v_R1n, 0)
+                right_block2 = make_p_diag(False, v_R2n, 0)
+            right_zeros = np.zeros((row_height, size - (col + N + 2 * M)), dtype=complex)
+            rows.append(np.concatenate([left_block, right_block1, right_block2, right_zeros], axis=1))
+            col += N
+            
+        else: # i-i boundary
+            left_diag = d[bd] > d[bd + 1]
+            if left_diag:
+                row_height = N
+                lb1 = make_p_diag(True, v_R1n, bd)
+                lb2 = make_p_diag(True, v_R2n, bd)
+                rb1 = make_p_dense(False, v_R1n, bd)
+                rb2 = make_p_dense(False, v_R2n, bd)
+            else:
+                row_height = M
+                lb1 = make_p_dense(True, v_R1n, bd)
+                lb2 = make_p_dense(True, v_R2n, bd)
+                rb1 = make_p_diag(False, v_R1n, bd)
+                rb2 = make_p_diag(False, v_R2n, bd)
+            
+            left_zeros = np.zeros((row_height, col), dtype=complex)
+            right_zeros = np.zeros((row_height, size - (col + 2*N + 2*M)), dtype=complex)
+            rows.append(np.concatenate([left_zeros, lb1, lb2, rb1, rb2, right_zeros], axis=1))
+            col += 2 * N
+
+    # --- Manual Matrix Loop (Velocity Matching) ---
+    col = 0
+    for bd in range(boundary_count):
+        N = NMK[bd]
+        M = NMK[bd + 1]
+        
+        if bd == (boundary_count - 1): # i-e boundary
+            row_height = M
+            left_block1 = make_v_dense_e(v_diff_R1n, bd)
+            right_block = make_v_diag_e(bd)
+            
+            if bd == 0:
+                rows.append(np.concatenate((left_block1, right_block), axis=1))
+            else:
+                left_block2 = make_v_dense_e(v_diff_R2n, bd)
+                left_zeros = np.zeros((row_height, col), dtype=complex)
+                rows.append(np.concatenate((left_zeros, left_block1, left_block2, right_block), axis=1))
+                
+        elif bd == 0:
+            left_diag = d[bd] <= d[bd + 1]
+            if left_diag:
+                row_height = N
+                lb = make_v_diag(True, v_diff_R1n, 0)
+                rb1 = make_v_dense(False, v_diff_R1n, 0)
+                rb2 = make_v_dense(False, v_diff_R2n, 0)
+            else:
+                row_height = M
+                lb = make_v_dense(True, v_diff_R1n, 0)
+                rb1 = make_v_diag(False, v_diff_R1n, 0)
+                rb2 = make_v_diag(False, v_diff_R2n, 0)
+            right_zeros = np.zeros((row_height, size - (col + N + 2 * M)), dtype=complex)
+            rows.append(np.concatenate([lb, rb1, rb2, right_zeros], axis=1))
+            col += N
+            
+        else: # i-i
+            left_diag = d[bd] <= d[bd + 1]
+            if left_diag:
+                row_height = N
+                lb1 = make_v_diag(True, v_diff_R1n, bd)
+                lb2 = make_v_diag(True, v_diff_R2n, bd)
+                rb1 = make_v_dense(False, v_diff_R1n, bd)
+                rb2 = make_v_dense(False, v_diff_R2n, bd)
+            else:
+                row_height = M
+                lb1 = make_v_dense(True, v_diff_R1n, bd)
+                lb2 = make_v_dense(True, v_diff_R2n, bd)
+                rb1 = make_v_diag(False, v_diff_R1n, bd)
+                rb2 = make_v_diag(False, v_diff_R2n, bd)
+                
+            left_zeros = np.zeros((row_height, col), dtype=complex)
+            right_zeros = np.zeros((row_height, size - (col + 2*N + 2*M)), dtype=complex)
+            rows.append(np.concatenate([left_zeros, lb1, lb2, rb1, rb2, right_zeros], axis=1))
+            col += 2 * N
+
+    A_manual = np.concatenate(rows, axis=0)
+    
+    # --- Manual b Vector Construction ---
+    b_manual = np.zeros(size, dtype=complex)
+    index = 0
+    # Potential
+    for bd in range(boundary_count):
+        if bd == (boundary_count - 1):
+            for n in range(NMK[-2]):
+                b_manual[index] = b_potential_end_entry(n, bd, heaving, h, d, a)
+                index += 1
+        else:
+            iter_range = NMK[bd] if d[bd] > d[bd+1] else NMK[bd+1]
+            for n in range(iter_range):
+                b_manual[index] = b_potential_entry(n, bd, d, heaving, h, a)
+                index += 1
+                
+    # Velocity
+    for bd in range(boundary_count):
+        if bd == (boundary_count - 1):
+            for n in range(NMK[-1]):
+                b_manual[index] = b_velocity_end_entry(n, bd, heaving, a, h, d, m0, NMK, m_k_arr, N_k_arr)
+                index += 1
+        else:
+            iter_range = NMK[bd] if d[bd] <= d[bd+1] else NMK[bd+1]
+            for n in range(iter_range):
+                b_manual[index] = b_velocity_entry(n, bd, heaving, a, h, d)
+                index += 1
+
+    # ---------------------------------------------------------
+    # 3. Comparisons
+    # ---------------------------------------------------------
+    
+    # Debug info if they fail
+    if not np.allclose(A_engine, A_manual):
+        print(f"Max Difference A: {np.max(np.abs(A_engine - A_manual))}")
+        
+    if not np.allclose(b_engine, b_manual):
+        print(f"Max Difference b: {np.max(np.abs(b_engine - b_manual))}")
+
+    np.testing.assert_allclose(A_engine, A_manual, rtol=1e-10, atol=1e-10, err_msg="Matrix A mismatch between Engine and Manual script.")
+    np.testing.assert_allclose(b_engine, b_manual, rtol=1e-10, atol=1e-10, err_msg="Vector b mismatch between Engine and Manual script.")
+
+
+def test_hydro_calculations(meem_setup):
+    """
+    Solves the system and asserts that hydrodynamic coefficients are calculated
+    and are non-trivial (non-zero).
+    """
+    engine = MEEMEngine([meem_setup["problem"]])
+    problem = meem_setup["problem"]
+    m0 = meem_setup["m0"]
+    
+    X = engine.solve_linear_system_multi(problem, m0)
+    assert not np.isnan(X).any(), "Solution vector X contains NaNs"
+    
+    # Calculate forces
+    results = engine.compute_hydrodynamic_coefficients(problem, X, m0)
+    
+    assert len(results) > 0
+    
+    # Check that at least the heaving body (index 0) has non-zero force
+    added_mass = results[0]["real"]
+    damping = results[0]["imag"]
+    
+    print(f"Added Mass: {added_mass}, Damping: {damping}")
+    
+    assert abs(added_mass) > 1e-5, "Added mass should be non-zero for this configuration"
+    # Damping might be small but usually non-zero unless trapped mode
+    assert abs(damping) >= 0.0, "Damping must be non-negative (energy conservation)"
\ No newline at end of file
diff --git a/package/test/test_meem_problem.py b/package/test/test_meem_problem.py
index 6a81bb4..aa43833 100644
--- a/package/test/test_meem_problem.py
+++ b/package/test/test_meem_problem.py
@@ -1,46 +1,138 @@
-import unittest
-from unittest.mock import MagicMock  # For mocking dependencies
-
+import pytest
+import numpy as np
 import sys
 import os
-src_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../src'))
-sys.path.append(src_path)
+from unittest.mock import Mock, MagicMock # Import Mock and MagicMock
+
+# --- Path Setup ---
+current_dir = os.path.dirname(__file__)
+src_dir = os.path.abspath(os.path.join(current_dir, '..', 'src'))
+if src_dir not in sys.path:
+    sys.path.insert(0, src_dir)
+
+# --- Import Package Modules ---
+from openflash.meem_problem import MEEMProblem
+from openflash.geometry import Geometry, ConcentricBodyGroup # Import necessary geometry classes
+from openflash.body import SteppedBody # Import SteppedBody
+
+# ==============================================================================
+# Pytest Fixtures
+# ==============================================================================
+@pytest.fixture
+def mock_geometry():
+    """
+    Creates a mock Geometry object suitable for testing MEEMProblem
+    independent of concrete geometry implementations.
+    Now includes nested mocks for body_arrangement and bodies.
+    """
+    # Create mock bodies with a 'heaving' attribute
+    mock_body1 = Mock(spec=SteppedBody)
+    mock_body1.heaving = True # Example: first body heaves
+    mock_body2 = Mock(spec=SteppedBody)
+    mock_body2.heaving = False # Example: second body doesn't
+
+    # Create a mock for BodyArrangement containing the mock bodies
+    mock_arrangement = Mock(spec=ConcentricBodyGroup)
+    mock_arrangement.bodies = [mock_body1, mock_body2]
+
+    # Create the main mock Geometry
+    mock_geom = Mock(spec=Geometry)
+    mock_geom.domain_list = {0: 'domain0', 1: 'domain1'} # Example domain list
+    mock_geom.body_arrangement = mock_arrangement # Attach the nested mock
+
+    return mock_geom
+
+@pytest.fixture
+def real_geometry():
+    """
+    Creates a simple but real BasicRegionGeometry instance for integration testing.
+    Uses a 2-body setup where only the SECOND body heaves initially.
+    """
+    NMK = [5, 5, 5] # Reduced complexity for faster tests
+    h = 10.0
+    a = np.array([1.0, 2.0])
+    d = np.array([2.0, 1.0])
+    
+    # FIX: Set only the second body (index 1) to heave for this test fixture
+    heaving = np.array([0, 1]) # [False, True]
 
+    bodies = []
+    for i in range(len(a)):
+        body = SteppedBody(
+            a=np.array([a[i]]),
+            d=np.array([d[i]]),
+            slant_angle=np.array([0.0]),
+            heaving=bool(heaving[i])
+        )
+        bodies.append(body)
 
-from meem_problem import MEEMProblem
-from geometry import Geometry
-from domain import Domain
+    arrangement = ConcentricBodyGroup(bodies)
+    # Import BasicRegionGeometry here if not already imported globally
+    from openflash.basic_region_geometry import BasicRegionGeometry
+    geometry = BasicRegionGeometry(arrangement, h, NMK)
+    return geometry
 
+# ==============================================================================
+# Test Suite for MEEMProblem
+# ==============================================================================
 
-class TestMEEMProblem(unittest.TestCase):
+def test_meem_problem_initialization(mock_geometry):
+    """
+    Tests that MEEMProblem initializes correctly with a Geometry object
+    and sets default empty arrays for frequencies. Checks inferred modes.
+    """
+    problem = MEEMProblem(geometry=mock_geometry)
 
-    def test_meem_problem_initialization(self):
-        """Tests the initialization of the MEEMProblem class."""
+    # Check if geometry and domain_list are assigned
+    assert problem.geometry is mock_geometry
+    assert problem.domain_list is mock_geometry.domain_list
 
-        # Mock the Geometry class and its domain_list
-        mock_geometry = MagicMock(spec=Geometry)  # Mock the Geometry object
-        mock_domain1 = MagicMock(spec=Domain)
-        mock_domain2 = MagicMock(spec=Domain)
-        mock_domain_list = {0: mock_domain1, 1: mock_domain2} #Mock domain list
-        mock_geometry.domain_list = mock_domain_list
+    # Check initial state of frequencies
+    assert isinstance(problem.frequencies, np.ndarray)
+    assert problem.frequencies.size == 0
+    
+    # Check inferred modes based on the mock setup (body 0 heaves)
+    assert isinstance(problem.modes, np.ndarray)
+    np.testing.assert_array_equal(problem.modes, np.array([0])) # Mock has body 0 heaving
+    print("✅ Initialization test passed.")
 
-        # Create a MEEMProblem instance
-        problem = MEEMProblem(mock_geometry)
+def test_meem_problem_set_frequencies(mock_geometry): # Renamed test function
+    """
+    Tests that set_frequencies correctly updates the frequencies.
+    Modes are now inferred and not set directly.
+    """
+    problem = MEEMProblem(geometry=mock_geometry)
 
-        # Assertions
-        self.assertIsInstance(problem, MEEMProblem)  # Check instance type
-        self.assertEqual(problem.domain_list, mock_domain_list)  # Check domain_list
-        self.assertEqual(problem.geometry, mock_geometry) #Check geometry object
+    test_frequencies = np.array([0.5, 1.0, 1.5])
+    
+    # Act: Set only frequencies
+    problem.set_frequencies(test_frequencies)
 
-        # Test case 2: Empty domain list
-        mock_geometry2 = MagicMock(spec=Geometry)  # Mock the Geometry object
-        mock_domain_list2 = {} #Mock domain list
-        mock_geometry2.domain_list = mock_domain_list2
+    # Assert frequencies are set
+    np.testing.assert_array_equal(problem.frequencies, test_frequencies)
+    
+    # Assert modes are still correctly inferred (unchanged by set_frequencies)
+    np.testing.assert_array_equal(problem.modes, np.array([0])) # Mock still has body 0 heaving
+    print("✅ Set frequencies test passed.")
 
-        problem2 = MEEMProblem(mock_geometry2)
+def test_meem_problem_with_real_geometry(real_geometry):
+    """
+    Tests initialization with a real Geometry object, ensuring attributes
+    are correctly linked and modes are inferred correctly.
+    """
+    problem = MEEMProblem(geometry=real_geometry)
 
-        self.assertEqual(problem2.domain_list, mock_domain_list2)  # Check domain_list
-        self.assertEqual(problem2.geometry, mock_geometry2) #Check geometry object
+    assert problem.geometry is real_geometry
+    assert problem.domain_list is real_geometry.domain_list
+    assert len(problem.domain_list) == 3 # Based on the real_geometry fixture setup
 
-if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    # Check default empty array for frequencies
+    assert isinstance(problem.frequencies, np.ndarray)
+    assert problem.frequencies.size == 0
+    
+    # Check that modes are correctly inferred from the real_geometry fixture
+    # The fixture sets the second body (index 1) to heave.
+    assert isinstance(problem.modes, np.ndarray)
+    np.testing.assert_array_equal(problem.modes, np.array([1])) # Expecting mode 1
+    assert problem.modes.size == 1 # Check the size explicitly
+    print("✅ Real geometry integration test passed.")
\ No newline at end of file
diff --git a/package/test/test_multi_equations.py b/package/test/test_multi_equations.py
new file mode 100644
index 0000000..d1d454d
--- /dev/null
+++ b/package/test/test_multi_equations.py
@@ -0,0 +1,501 @@
+# test_multi_equations.py
+
+import sys
+import os
+import numpy as np
+import pytest
+from numpy import pi, sqrt, cosh, cos, sinh, sin, exp, log
+from scipy.special import iv as besseli
+from scipy.special import kv as besselk
+from scipy.special import kve as besselke
+from scipy.special import ive as besselie
+from scipy.special import hankel1 as besselh
+from unittest.mock import Mock, patch
+from scipy.optimize import root_scalar 
+
+# Adjust the path to import from package's 'src' directory.
+current_dir = os.path.dirname(__file__)
+package_base_dir = os.path.join(current_dir, '..')
+src_dir = os.path.join(package_base_dir, 'src')
+sys.path.insert(0, os.path.abspath(src_dir))
+
+# Import all functions from multi_equations.py
+from openflash.multi_equations import (
+    omega, scale, lambda_ni, m_k_entry, m_k, m_k_newton,
+    I_nm, I_mk, b_potential_entry, b_potential_end_entry,
+    b_velocity_entry, b_velocity_end_entry, 
+    phi_p_i, diff_r_phi_p_i, diff_z_phi_p_i, R_1n, diff_R_1n, R_2n, diff_R_2n,
+    Z_n_i, diff_Z_n_i, Lambda_k, diff_Lambda_k,
+    N_k_multi, Z_k_e, diff_Z_k_e, int_R_1n, int_R_2n,
+    z_n_d, excitation_phase
+)
+
+# --- Fixtures for common parameters ---
+@pytest.fixture
+def coeff():
+    return 1.0 + 1.0j 
+
+@pytest.fixture
+def h():
+    return 100.0
+
+@pytest.fixture
+def g():
+    return 9.81
+
+@pytest.fixture
+def m0():
+    return 0.1 
+
+@pytest.fixture
+def d(): 
+    return np.array([0.0, 20.0, 50.0])
+
+@pytest.fixture
+def a(): 
+    return np.array([5.0, 10.0, 15.0]) 
+
+@pytest.fixture
+def heaving(): 
+    return np.array([1.0, 0.5, 0.0]) 
+
+@pytest.fixture
+def NMK(): 
+    return np.array([0, 5]) 
+
+@pytest.fixture
+def test_n():
+    return 1 
+
+@pytest.fixture
+def test_k():
+    return 1 
+
+@pytest.fixture
+def test_i(): 
+    return 1 
+
+@pytest.fixture
+def test_r(a):
+    return (a[0] + a[1]) / 2 
+
+@pytest.fixture
+def test_z(d, h):
+    return (d[1] - h) / 2 
+
+@pytest.fixture
+def precomputed_m_k_arr(NMK, h):
+    num_k = NMK[-1] 
+    arr = np.linspace(0.01, 0.5, num_k) 
+    arr[0] = 0.1 
+    return arr
+
+@pytest.fixture
+def precomputed_N_k_arr(NMK):
+    num_k = NMK[-1]
+    return np.linspace(0.5, 2.0, num_k) 
+
+
+# --- Test Cases ---
+
+def test_omega(m0, h, g):
+    expected = sqrt(m0 * np.tanh(m0 * h) * g)
+    assert np.isclose(omega(m0, h, g), expected)
+
+def test_lambda_ni(test_n, test_i, h, d):
+    expected = test_n * pi / (h - d[test_i])
+    assert np.isclose(lambda_ni(test_n, test_i, h, d), expected)
+
+# --- m_k functions ---
+def test_m_k_entry_k0(m0, h):
+    assert np.isclose(m_k_entry(0, m0, h), m0)
+
+def test_m_k_entry_k_positive(h):
+    test_h = 10.0
+    target_m_k_h = 2.0
+    required_m0h_tanh_m0h = -(target_m_k_h * np.tan(target_m_k_h))
+
+    def f_Y(Y):
+        return Y * np.tanh(Y) - required_m0h_tanh_m0h
+
+    y_result = root_scalar(f_Y, x0=4.5, method='newton') 
+    calculated_m0h = y_result.root
+    test_m0 = calculated_m0h / test_h
+
+    k_val = 1
+    expected_m_k_val = target_m_k_h / test_h
+    
+    result = m_k_entry(k_val, test_m0, test_h)
+    assert np.isclose(result, expected_m_k_val, rtol=1e-5, atol=1e-8)
+    
+    shouldnt_be_int = np.round(test_m0 * result / np.pi - 0.5, 4)
+    assert shouldnt_be_int != np.floor(shouldnt_be_int)
+
+def test_m_k(NMK, m0, h):
+    num_modes = NMK[-1] 
+    result_array = m_k(NMK, m0, h)
+
+    assert isinstance(result_array, np.ndarray)
+    assert result_array.shape[0] == num_modes
+    assert np.isclose(result_array[0], m0)
+
+def test_m_k_newton(h, m0):
+    expected_k = np.sqrt(m0**2 / 9.8 / h) 
+    assert np.isclose(m_k_newton(h, m0), expected_k, rtol=2e-2, atol=1e-5) 
+
+# --- Coupling Integrals ---
+def test_I_nm_n0m0(h, d):
+    i = 1 
+    dj = max(d[i], d[i+1]) 
+    expected = h - dj 
+    assert np.isclose(I_nm(0, 0, i, d, h), expected)
+
+def test_I_nm_n0m_positive(h, d):
+    n = 0
+    m = 1
+    i = 0 
+    dj = max(d[i], d[i+1]) 
+    assert np.isclose(I_nm(n, m, i, d, h), 0)
+
+    n = 0
+    m = 1
+    i = 1 
+    custom_d = np.array([0.0, 50.0, 20.0]) 
+    i = 1 
+    dj = max(custom_d[i], custom_d[i+1]) 
+    lambda2 = lambda_ni(m, i + 1, h, custom_d) 
+    expected = sqrt(2) * sin(lambda2 * (h - dj)) / lambda2
+    assert np.isclose(I_nm(n, m, i, custom_d, h), expected)
+
+def test_I_nm_n_positive_m0(h, d):
+    n = 1
+    m = 0
+    i = 0 
+    dj = max(d[i], d[i+1]) 
+    lambda1 = lambda_ni(n, i, h, d) 
+    expected = sqrt(2) * sin(lambda1 * (h - dj)) / lambda1
+    assert np.isclose(I_nm(n, m, i, d, h), expected)
+
+def test_I_nm_n_positive_m_positive(h, d):
+    n = 1
+    m = 1
+    i = 0
+    dj = max(d[i], d[i+1]) 
+
+    lambda1 = lambda_ni(n, i, h, d)
+    lambda2 = lambda_ni(m, i + 1, h, d) 
+
+    frac1 = sin((lambda1 + lambda2)*(h-dj))/(lambda1 + lambda2)
+    frac2 = sin((lambda1 - lambda2)*(h-dj))/(lambda1 - lambda2) 
+    expected = frac1 + frac2
+    assert np.isclose(I_nm(n, m, i, d, h), expected)
+
+    n_eq = 1
+    m_eq = 1
+    i_eq = 0
+    d_eq = np.array([50.0, 50.0, 50.0]) 
+
+    lambda1_internal = lambda_ni(n_eq, i_eq, h, d_eq)
+    lambda2_internal = lambda_ni(m_eq, i_eq + 1, h, d_eq) 
+
+    dj_eq = max(d_eq[i_eq], d_eq[i_eq+1]) 
+
+    frac1_eq = sin((lambda1_internal + lambda2_internal)*(h-dj_eq))/(lambda1_internal + lambda2_internal)
+
+    if np.isclose(lambda1_internal, lambda2_internal, atol=1e-12): 
+        frac2_eq = (h - dj_eq)
+    else:
+        frac2_eq = sin((lambda1_internal - lambda2_internal)*(h-dj_eq))/(lambda1_internal - lambda2_internal)
+
+    expected_eq = frac1_eq + frac2_eq
+
+    assert np.isclose(I_nm(n_eq, m_eq, i_eq, d_eq, h), expected_eq)
+
+# --- b-vector computation ---
+def test_b_potential_entry_n0(test_i, d, heaving, h, a):
+    j = test_i + (d[test_i] < d[test_i+1]) 
+    constant = (heaving[test_i+1] / (h - d[test_i+1]) - heaving[test_i] / (h - d[test_i]))
+    expected = constant * 0.5 * ((h - d[j])**3/3 - (h-d[j]) * a[test_i]**2/2)
+    assert np.isclose(b_potential_entry(0, test_i, d, heaving, h, a), expected)
+
+def test_b_potential_entry_n_positive(test_n, test_i, d, heaving, h, a):
+    if test_n == 0: pytest.skip("Test for n>0")
+    j = test_i + (d[test_i] < d[test_i+1])
+    constant = (heaving[test_i+1] / (h - d[test_i+1]) - heaving[test_i] / (h - d[test_i]))
+    expected = sqrt(2) * (h - d[j]) * constant * ((-1) ** test_n)/(lambda_ni(test_n, j, h, d) ** 2)
+    assert np.isclose(b_potential_entry(test_n, test_i, d, heaving, h, a), expected)
+
+def test_b_potential_end_entry_n0(test_i, heaving, h, d, a):
+    constant = - heaving[test_i] / (h - d[test_i])
+    expected = constant * 0.5 * ((h - d[test_i])**3/3 - (h-d[test_i]) * a[test_i]**2/2)
+    assert np.isclose(b_potential_end_entry(0, test_i, heaving, h, d, a), expected)
+
+def test_b_potential_end_entry_n_positive(test_n, test_i, heaving, h, d, a):
+    if test_n == 0: pytest.skip("Test for n>0")
+    constant = - heaving[test_i] / (h - d[test_i])
+    expected = sqrt(2) * (h - d[test_i]) * constant * ((-1) ** test_n)/(lambda_ni(test_n, test_i, h, d) ** 2)
+    assert np.isclose(b_potential_end_entry(test_n, test_i, heaving, h, d, a), expected)
+
+def test_b_velocity_entry_n0(test_i, heaving, a, h, d):
+    expected = (heaving[test_i+1] - heaving[test_i]) * (a[test_i]/2)
+    assert np.isclose(b_velocity_entry(0, test_i, heaving, a, h, d), expected)
+
+def test_b_velocity_entry_n_positive_di_greater(test_n, heaving, a, h, d):
+    if test_n == 0: pytest.skip("Test for n>0")
+    custom_d = np.array([0.0, 50.0, 20.0]) 
+    i = 1
+    num = - sqrt(2) * a[i] * sin(lambda_ni(test_n, i+1, h, custom_d) * (h-custom_d[i]))
+    denom = (2 * (h - custom_d[i]) * lambda_ni(test_n, i+1, h, custom_d))
+    expected = num/denom
+    assert np.isclose(b_velocity_entry(test_n, i, heaving, a, h, custom_d), expected)
+
+def test_b_velocity_entry_n_positive_di_smaller(test_n, heaving, a, h, d):
+    if test_n == 0: pytest.skip("Test for n>0")
+    i = 0 
+    num = sqrt(2) * a[i] * sin(lambda_ni(test_n, i, h, d) * (h-d[i+1]))
+    denom = (2 * (h - d[i+1]) * lambda_ni(test_n, i, h, d))
+    expected = num/denom
+    assert np.isclose(b_velocity_entry(test_n, i, heaving, a, h, d), expected)
+
+def test_b_velocity_end_entry_k0_small_m0h(test_i, heaving, a, h, d, NMK, precomputed_m_k_arr, precomputed_N_k_arr):
+    m0_local = 0.01 
+    constant = - heaving[test_i] * a[test_i]/(2 * (h - d[test_i]))
+    N_k_arr_local = precomputed_N_k_arr.copy()
+    N_k_arr_local[0] = 0.5
+    expected = constant * (1/sqrt(N_k_arr_local[0])) * sinh(m0_local * (h - d[test_i])) / m0_local
+    assert np.isclose(b_velocity_end_entry(0, test_i, heaving, a, h, d, m0_local, NMK, precomputed_m_k_arr, N_k_arr_local), expected)
+
+def test_b_velocity_end_entry_k0_large_m0h(test_i, heaving, a, h, d, NMK, precomputed_m_k_arr, precomputed_N_k_arr):
+    m0_local = 0.2 
+    constant = - heaving[test_i] * a[test_i]/(2 * (h - d[test_i]))
+    N_k_arr_local = precomputed_N_k_arr.copy()
+    N_k_arr_local[0] = 0.5
+    expected = constant * sqrt(2 * h / m0_local) * (exp(- m0_local * d[test_i]) - exp(m0_local * d[test_i] - 2 * m0_local * h))
+    assert np.isclose(b_velocity_end_entry(0, test_i, heaving, a, h, d, m0_local, NMK, precomputed_m_k_arr, N_k_arr_local), expected)
+
+def test_b_velocity_end_entry_k_positive(test_k, test_i, heaving, a, h, d, m0, NMK, precomputed_m_k_arr, precomputed_N_k_arr):
+    if test_k == 0: pytest.skip("Test for k>0")
+    constant = - heaving[test_i] * a[test_i]/(2 * (h - d[test_i]))
+    local_m_k_k = precomputed_m_k_arr[test_k]
+    N_k_arr_k = precomputed_N_k_arr[test_k]
+    expected = constant * (1/sqrt(N_k_arr_k)) * sin(local_m_k_k * (h - d[test_i])) / local_m_k_k
+    assert np.isclose(b_velocity_end_entry(test_k, test_i, heaving, a, h, d, m0, NMK, precomputed_m_k_arr, precomputed_N_k_arr), expected)
+
+# --- Phi particular and partial derivatives ---
+def test_phi_p_i(d, test_r, test_z, h):
+    d_val = d[1] 
+    expected = (1 / (2 * (h - d_val))) * ((test_z + h)**2 - (test_r**2) / 2)
+    assert np.isclose(phi_p_i(d_val, test_r, test_z, h), expected)
+
+def test_diff_r_phi_p_i(d, test_r, h):
+    d_val = d[1]
+    expected = (- test_r / (2 * (h - d_val)))
+    assert np.isclose(diff_r_phi_p_i(d_val, test_r, h), expected)
+
+def test_diff_z_phi_p_i(d, test_z, h):
+    d_val = d[1]
+    expected = ((test_z + h) / (h - d_val))
+    assert np.isclose(diff_z_phi_p_i(d_val, test_z, h), expected)
+
+# --- Bessel I Radial Eigenfunction ---
+def test_R_1n_n0(test_i, test_r, h, d, a):
+    # Fixed expected value to match stable implementation: 1.0 + 0.5 * log(r/outer)
+    if test_i == 0:
+        expected = 0.5
+    else:
+        expected = 1.0 + 0.5 * np.log(test_r / a[test_i])
+    assert np.isclose(R_1n(0, test_r, test_i, h, d, a), expected)
+
+def test_R_1n_n_positive(test_n, test_i, test_r, h, d, a):
+    if test_n == 0: pytest.skip("Test for n>0")
+    local_scale = scale(a)
+    # Fixed to use scaled Bessel I (besselie) and exp term
+    lambda0 = lambda_ni(test_n, test_i, h, d)
+    expected = besselie(0, lambda0 * test_r) / besselie(0, lambda0 * local_scale[test_i]) * exp(lambda0 * (test_r - local_scale[test_i]))
+    assert np.isclose(R_1n(test_n, test_r, test_i, h, d, a), expected)
+
+def test_R_1n_n_negative(test_i, test_r, h, d, a):
+    with pytest.raises(ValueError, match="Invalid value for n"):
+        R_1n(-1, test_r, test_i, h, d, a)
+
+def test_diff_R_1n_n0(test_i, test_r, h, d, a):
+    # Fixed expected derivative for log term: 1/(2r)
+    if test_i == 0:
+        expected = 0.0
+    else:
+        expected = 1 / (2 * test_r)
+    assert np.isclose(diff_R_1n(0, test_r, test_i, h, d, a), expected)
+
+def test_diff_R_1n_n_positive(test_n, test_i, test_r, h, d, a):
+    if test_n == 0: pytest.skip("Test for n>0")
+    local_scale = scale(a)
+    # Fixed to match scaled derivative
+    lambda0 = lambda_ni(test_n, test_i, h, d)
+    top = lambda0 * besselie(1, lambda0 * test_r)
+    bottom = besselie(0, lambda0 * local_scale[test_i])
+    expected = top / bottom * exp(lambda0 * (test_r - local_scale[test_i]))
+    assert np.isclose(diff_R_1n(test_n, test_r, test_i, h, d, a), expected)
+
+# --- Bessel K Radial Eigenfunction ---
+def test_R_2n_i0_raises_error(test_r, h, d, a):
+    with pytest.raises(ValueError, match="i cannot be 0"):
+        R_2n(1, test_r, 0, a, h, d)
+
+def test_R_2n_n0(a, test_r, h, d):
+    # Fixed expected value: 0.5 * log(r/outer) anchored at a[i]
+    i = 1
+    outer_r = a[i]
+    expected = 0.5 * np.log(test_r / outer_r) 
+    assert np.isclose(R_2n(0, test_r, i, a, h, d), expected)
+
+def test_R_2n_n_positive(test_n, a, test_r, h, d):
+    if test_n == 0: pytest.skip("Test for n>0")
+    i = 1
+    # Fixed expected value: Scaled K0 using besselke and exp decay from OUTER radius (Legacy Mode)
+    lambda0 = lambda_ni(test_n, i, h, d)
+    outer_r = a[i]
+    expected = (besselke(0, lambda0 * test_r) / besselke(0, lambda0 * outer_r)) * exp(lambda0 * (outer_r - test_r))
+    assert np.isclose(R_2n(test_n, test_r, i, a, h, d), expected)
+
+def test_diff_R_2n_n0(test_r, h, d, a):
+    i = 1
+    expected = 1 / (2 * test_r)
+    assert np.isclose(diff_R_2n(0, test_r, i, h, d, a), expected)
+
+def test_diff_R_2n_n_positive(test_n, test_r, h, d, a):
+    if test_n == 0: pytest.skip("Test for n>0")
+    i = 1
+    # Fixed expected value: Scaled derivative anchored at OUTER radius (Legacy Mode)
+    lambda0 = lambda_ni(test_n, i, h, d)
+    outer_r = a[i]
+    top = -lambda0 * besselke(1, lambda0 * test_r)
+    bottom = besselke(0, lambda0 * outer_r)
+    expected = (top / bottom) * exp(lambda0 * (outer_r - test_r))
+    assert np.isclose(diff_R_2n(test_n, test_r, i, h, d, a), expected)
+
+# --- i-region vertical eigenfunctions ---
+def test_Z_n_i_n0(test_z, test_i, h, d):
+    assert np.isclose(Z_n_i(0, test_z, test_i, h, d), 1)
+
+def test_Z_n_i_n_positive(test_n, test_z, test_i, h, d):
+    if test_n == 0: pytest.skip("Test for n>0")
+    expected = sqrt(2) * np.cos(lambda_ni(test_n, test_i, h, d) * (test_z + h))
+    assert np.isclose(Z_n_i(test_n, test_z, test_i, h, d), expected)
+
+def test_diff_Z_n_i_n0(test_z, test_i, h, d):
+    assert np.isclose(diff_Z_n_i(0, test_z, test_i, h, d), 0)
+
+def test_diff_Z_n_i_n_positive(test_n, test_z, test_i, h, d):
+    if test_n == 0: pytest.skip("Test for n>0")
+    lambda0 = lambda_ni(test_n, test_i, h, d)
+    expected = - lambda0 * sqrt(2) * np.sin(lambda0 * (test_z + h))
+    assert np.isclose(diff_Z_n_i(test_n, test_z, test_i, h, d), expected)
+
+# --- e-region vertical eigenfunctions ---
+def test_Z_k_e_k0_large_m0h(test_z, m0, h, NMK, precomputed_m_k_arr):
+    m0_local = 0.2
+    expected = sqrt(2 * m0_local * h) * (exp(m0_local * test_z) + exp(-m0_local * (test_z + 2*h)))
+    assert np.isclose(Z_k_e(0, test_z, m0_local, h, NMK, precomputed_m_k_arr), expected)
+
+def test_diff_Z_k_e_k0_large_m0h(test_z, m0, h, NMK, precomputed_m_k_arr):
+    m0_local = 0.2
+    expected = m0_local * sqrt(2 * h * m0_local) * (exp(m0_local * test_z) - exp(-m0_local * (test_z + 2*h)))
+    assert np.isclose(diff_Z_k_e(0, test_z, m0_local, h, NMK, precomputed_m_k_arr), expected)
+
+# --- To calculate hydrocoefficients ---
+def test_int_R_1n_n0_i0(a, h, d):
+    # i=0 (innermost region), inner radius 0
+    expected = a[0]**2/4 - 0**2/4
+    assert np.isclose(int_R_1n(0, 0, a, h, d), expected)
+
+def test_int_R_1n_n0_i_positive(a, h, d):
+    # Fixed expected value: Integral of r * (1.0 + 0.5 * log(r/outer))
+    i = 1
+    outer_r = a[i]
+    inner_r = a[i-1]
+    
+    # 1. Cylinder term (integral of r*1.0)
+    cyl_term = (outer_r**2 - inner_r**2) / 2.0
+    
+    # 2. Log term helper
+    def log_indefinite_int(r):
+        log_val = np.log(r/outer_r) if r > 0 else 0
+        return 0.5 * ((r**2 / 2.0) * log_val - (r**2 / 4.0))
+
+    val_outer = log_indefinite_int(outer_r) 
+    val_inner = log_indefinite_int(inner_r)
+    
+    expected = cyl_term + (val_outer - val_inner)
+    assert np.isclose(int_R_1n(1, 0, a, h, d), expected)
+
+def test_int_R_1n_n_positive(test_n, test_i, a, h, d):
+    if test_n == 0: pytest.skip("Test for n>0")
+    local_scale = scale(a)
+    lambda0 = lambda_ni(test_n, test_i, h, d)
+    # Fixed to use scaled Bessel I
+    bottom = lambda0 * besselie(0, lambda0 * local_scale[test_i])
+    if test_i == 0: 
+        inner_term = 0
+    else: 
+        # Scaled inner term
+        inner_term = (a[test_i-1] * besselie(1, lambda0 * a[test_i-1]) / bottom) * exp(lambda0 * (a[test_i-1] - local_scale[test_i]))
+    
+    # Scaled outer term (exp(0) = 1)
+    outer_term = (a[test_i] * besselie(1, lambda0 * a[test_i]) / bottom) * 1.0
+    
+    expected = outer_term - inner_term
+    assert np.isclose(int_R_1n(test_n, test_i, a, h, d), expected) 
+
+def test_int_R_2n_i0_raises_error(test_n, a, h, d):
+    with pytest.raises(ValueError, match="i cannot be 0"):
+        int_R_2n(0, test_n, a, h, d)
+
+def test_int_R_2n_n0(a, h, d):
+    # Fixed expected value: Integral of r * (0.5 * log(r/outer)) anchored at outer
+    i = 1 
+    outer_r = a[i]
+    inner_r = a[i-1]
+
+    # Note: No 'cyl_term' here because R_2n(n=0) is just the log term.
+    
+    def log_indefinite_int(r):
+        if r <= 0: return 0
+        log_term = np.log(r/outer_r)
+        return 0.5 * ((r**2 / 2.0) * log_term - (r**2 / 4.0))
+
+    val_outer = log_indefinite_int(outer_r)
+    val_inner = log_indefinite_int(inner_r) 
+    
+    expected = val_outer - val_inner
+    assert np.isclose(int_R_2n(i, 0, a, h, d), expected)
+
+def test_int_R_2n_n_positive(test_n, a, h, d):
+    if test_n == 0: pytest.skip("Test for n>0")
+    i = 1 
+    # Fixed expected value: Scaled Bessel K integral, anchored at Outer (Legacy Mode)
+    lambda0 = lambda_ni(test_n, i, h, d)
+    outer_r = a[i]
+    inner_r = a[i-1]
+    
+    # Normalized by lambda * K0(lambda * outer_r)
+    # Denominator matches 'denom = lambda0 * besselke(0, lambda0 * outer_r)' in multi_equations.py
+    denom = lambda0 * besselke(0, lambda0 * outer_r)
+    
+    term_outer = outer_r * besselke(1, lambda0 * outer_r)
+    # No exp shift needed for outer term because exp(l(a-a)) = 1
+    
+    term_inner = inner_r * besselke(1, lambda0 * inner_r)
+    # Apply exponential shift exp(l(a-r)) -> exp(l(a-inner))
+    term_inner *= np.exp(lambda0 * (outer_r - inner_r))
+    
+    # Result is (inner - outer) / denom, which handles the sign flip from integration
+    expected = (term_inner - term_outer) / denom
+    assert np.isclose(int_R_2n(i, test_n, a, h, d), expected) 
+
+def test_z_n_d_n0():
+    assert np.isclose(z_n_d(0), 1)
+
+def test_z_n_d_n_positive(test_n):
+    if test_n == 0: pytest.skip("Test for n>0")
+    expected = sqrt(2) * (-1)**test_n
+    assert np.isclose(z_n_d(test_n), expected)
\ No newline at end of file
diff --git a/package/test/test_openflash_convergence.py b/package/test/test_openflash_convergence.py
new file mode 100644
index 0000000..4470dff
--- /dev/null
+++ b/package/test/test_openflash_convergence.py
@@ -0,0 +1,83 @@
+# test_openflash_convergence.py
+import pytest
+import numpy as np
+from openflash_utils import run_openflash_case
+
+# --- Define Configurations ---
+CONFIGS = {
+    "some_bicylinder": {
+        "h": 1.001, "d": [0.5, 0.25], "a": [0.25, 0.5], "heaving": [1, 1],
+        "m0": 1.0, "rho": 1023
+    },
+    "mini_tricylinder": {
+        "h": 2.001, "d": [1.0, 0.5, 0.25], "a": [0.25, 0.5, 1.0], "heaving": [1, 1, 1],
+        "m0": 1.0, "rho": 1023
+    }
+}
+
+@pytest.mark.parametrize("config_name, cfg", CONFIGS.items())
+def test_convergence_openflash(config_name, cfg):
+    """
+    Replicates the loop over NMK terms to check for convergence.
+    Passes if the relative difference drops below 0.1% (0.001) within 30 terms.
+    """
+    print(f"\nTesting convergence for: {config_name}")
+    
+    history_real = []
+    history_imag = []
+    
+    converged_real = False
+    converged_imag = False
+    
+    # Loop from 2 to 30 terms, similar to your script
+    for n_term in range(2, 31):
+        # Create NMK list: [n, n, ..., n] (one for each body + 1 for exterior)
+        num_regions = len(cfg['a']) + 1
+        NMK = [n_term] * num_regions
+        
+        am, dp, duration = run_openflash_case(
+            cfg['h'], cfg['d'], cfg['a'], cfg['heaving'], NMK, cfg['m0'], cfg['rho']
+        )
+        
+        history_real.append(am)
+        history_imag.append(dp)
+        
+        # Check convergence (starting from the second iteration)
+        if len(history_real) > 1:
+            prev_real = history_real[-2]
+            prev_imag = history_imag[-2]
+            
+            diff_real = abs((am - prev_real) / prev_real) if prev_real != 0 else 0
+            diff_imag = abs((dp - prev_imag) / prev_imag) if prev_imag != 0 else 0
+            
+            # Print status for visibility (use -s flag with pytest to see this)
+            print(f"Terms: {n_term}, AM: {am:.6f} ({diff_real:.2%}), DP: {dp:.6f} ({diff_imag:.2%}), Time: {duration:.4f}s")
+            
+            if diff_real < 0.001:
+                converged_real = True
+            if diff_imag < 0.001:
+                converged_imag = True
+            
+            # If both converged, we can stop the test early and declare success
+            if converged_real and converged_imag:
+                print(f"Converged at N={n_term}")
+                return
+
+    # If loop finishes without returning, assertion fails
+    assert converged_real, "Added Mass did not converge within 30 terms."
+    assert converged_imag, "Damping did not converge within 30 terms."
+
+def test_sanity_check_values():
+    """
+    A quick test to ensure values aren't NaN or Infinite.
+    """
+    cfg = CONFIGS["some_bicylinder"]
+    NMK = [5, 5, 5] # Low count for speed
+    
+    am, dp, _ = run_openflash_case(
+        cfg['h'], cfg['d'], cfg['a'], cfg['heaving'], NMK, cfg['m0'], cfg['rho']
+    )
+    
+    assert np.isfinite(am)
+    assert np.isfinite(dp)
+    assert dp >= 0 # Damping should be positive
\ No newline at end of file
diff --git a/package/test/test_potential_calcs.py b/package/test/test_potential_calcs.py
new file mode 100644
index 0000000..84be8be
--- /dev/null
+++ b/package/test/test_potential_calcs.py
@@ -0,0 +1,258 @@
+import numpy as np
+import sys
+import os
+
+# --- Path Setup ---
+# This ensures the script can find package files
+current_dir = os.path.dirname(__file__)
+src_dir = os.path.abspath(os.path.join(current_dir, '..', 'src'))
+if src_dir not in sys.path:
+    sys.path.insert(0, src_dir)
+
+# --- Import Package Modules ---
+from openflash.meem_engine import MEEMEngine
+from openflash.meem_problem import MEEMProblem
+from openflash.basic_region_geometry import BasicRegionGeometry
+from openflash.geometry import ConcentricBodyGroup
+from openflash.body import SteppedBody
+
+# --- Path Setup for Old Code ---
+# Add hydro/python for old code
+old_code_dir = os.path.abspath(os.path.join(current_dir, '..', '..', 'dev', 'python'))
+if old_code_dir not in sys.path:
+    sys.path.insert(0, old_code_dir)
+
+# Attempt to import old assembly functions
+try:
+    from old_assembly import R_1n_old, Z_n_i_old, R_2n_old, Lambda_k_old, Z_k_e_old, make_R_Z_old, phi_p_i_old
+except ImportError:
+    print("WARNING: Could not import 'old_assembly'. Ensure the path is correct relative to this test script.")
+    sys.exit(1)
+
+# --- Wrapper for the Old Calculation Logic ---
+def calculate_potentials_old(X, NMK, a, h, d, m0, heaving):
+    """
+    Encapsulates the original, non-vectorized potential calculation logic for testing.
+    """
+    print("\n--- Running OLD Potential Calculation ---")
+    print("--- [DEBUG OLD] Entering function with parameters: ---")
+    print(f"  - X shape: {X.shape}, NMK: {NMK}")
+    print(f"  - a: {a}, h: {h}, d: {d}, m0: {m0}")
+    
+    # Split up the Cs into groups depending on which equation they belong to.
+    Cs = []
+    row = 0
+    Cs.append(X[:NMK[0]])
+    row += NMK[0]
+    boundary_count = len(NMK) - 1
+    for i in range(1, boundary_count):
+        Cs.append(X[row: row + NMK[i] * 2])
+        row += NMK[i] * 2
+    Cs.append(X[row:])
+    print("\n--- [DEBUG OLD] Coefficients (Cs) reformatted: ---")
+    for i, C_group in enumerate(Cs):
+        print(f"  - Group {i} shape: {C_group.shape}, Max abs value: {np.max(np.abs(C_group)):.4f}")
+
+    def phi_h_n_inner_func_old(n, r, z):
+        return (Cs[0][n] * R_1n_old(n, r, 0, h, d, a)) * Z_n_i_old(n, z, 0, h, d)
+
+    def phi_h_m_i_func_old(i, m, r, z):
+        # Fixed argument order: a, h, d
+        return (Cs[i][m] * R_1n_old(m, r, i, a, h, d) + Cs[i][NMK[i] + m] * R_2n_old(m, r, i, a, h, d)) * Z_n_i_old(m, z, i, h, d)
+
+    def phi_e_k_func_old(k, r, z):
+        return Cs[-1][k] * Lambda_k_old(k, r, m0, a, NMK, h) * Z_k_e_old(k, z, m0, h, NMK)
+
+    phi_e_k_vec = np.vectorize(phi_e_k_func_old, otypes = [complex])
+    phi_h_n_inner_vec = np.vectorize(phi_h_n_inner_func_old, otypes = [complex])
+    phi_h_m_i_vec = np.vectorize(phi_h_m_i_func_old, otypes = [complex])
+
+    R, Z = make_R_Z_old(a, h, d, True, 50)
+    print(f"\n--- [DEBUG OLD] Meshgrid created: R shape={R.shape}, Z shape={Z.shape} ---")
+
+
+    regions = []
+    regions.append((R <= a[0]) & (Z < -d[0]))
+    for i in range(1, boundary_count):
+        regions.append((R > a[i-1]) & (R <= a[i]) & (Z < -d[i]))
+    regions.append(R > a[-1])
+    
+    print("--- [DEBUG OLD] Region mask point counts: ---")
+    for i, region_mask in enumerate(regions):
+        print(f"  - Region {i}: {np.sum(region_mask)} points")
+
+    phi = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+    phiH = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+    phiP = np.full_like(R, np.nan + np.nan*1j, dtype=complex) 
+
+    print("\n--- [DEBUG OLD] Calculating Homogeneous Potential (phiH)... ---")
+
+    for n in range(NMK[0]):
+        temp_phiH = phi_h_n_inner_vec(n, R[regions[0]], Z[regions[0]])
+        phiH[regions[0]] = temp_phiH if n == 0 else phiH[regions[0]] + temp_phiH
+    print(f"  - Done with Region 0. Max abs phiH so far: {np.nanmax(np.abs(phiH[regions[0]])):.4f}")
+
+
+    for i in range(1, boundary_count):
+        for m in range(NMK[i]):
+            temp_phiH = phi_h_m_i_vec(i, m, R[regions[i]], Z[regions[i]])
+            phiH[regions[i]] = temp_phiH if m == 0 else phiH[regions[i]] + temp_phiH
+    print(f"  - Done with Region {i}. Max abs phiH so far: {np.nanmax(np.abs(phiH[regions[i]])):.4f}")
+
+
+    for k in range(NMK[-1]):
+        temp_phiH = phi_e_k_vec(k, R[regions[-1]], Z[regions[-1]])
+        phiH[regions[-1]] = temp_phiH if k == 0 else phiH[regions[-1]] + temp_phiH
+    print(f"  - Done with Exterior Region. Max abs phiH so far: {np.nanmax(np.abs(phiH[regions[-1]])):.4f}")
+
+    print("\n--- [DEBUG OLD] Calculating Particular Potential (phiP)... ---")
+    def phi_p_i_wrapper(d_scalar, r, z):
+        return phi_p_i_old(d_scalar, r, z, h)
+
+    phi_p_i_vec = np.vectorize(phi_p_i_wrapper)
+
+    # Note: old code allowed heaving array logic here, but engine restricts to single body.
+    # We will pass the array, but in this specific test case, only one entry will be 1.
+    phiP[regions[0]] = heaving[0] * phi_p_i_vec(d[0], R[regions[0]], Z[regions[0]])
+    for i in range(1, boundary_count):
+        phiP[regions[i]] = heaving[i] * phi_p_i_vec(d[i], R[regions[i]], Z[regions[i]])
+    phiP[regions[-1]] = 0
+    print(f"  - phiP calculated. Max abs value: {np.nanmax(np.abs(phiP)):.4f}")
+
+
+    phi = phiH + phiP
+    print("\n--- [DEBUG OLD] Final potential array summaries: ---")
+    print(f"  - phiH shape: {phiH.shape}, max abs: {np.nanmax(np.abs(phiH)):.4f}")
+    print(f"  - phiP shape: {phiP.shape}, max abs: {np.nanmax(np.abs(phiP)):.4f}")
+    print(f"  - phi shape: {phi.shape}, max abs: {np.nanmax(np.abs(phi)):.4f}")
+
+
+    print("--- OLD Calculation Finished ---")
+    
+    return {"R": R, "Z": Z, "phiH": phiH, "phiP": phiP, "phi": phi}
+
+
+# --- Main Test Execution ---
+def main():
+    # 1. ARRANGE: Set up the common problem parameters
+    print("--- Setting up test problem ---")
+    NMK = [1, 1, 1, 1]
+    h = 100
+    d = np.array([29, 7, 4])
+    a = np.array([3, 5, 10])
+    
+    # --- FIX: Only enable ONE heaving body to satisfy engine assertion ---
+    heaving = np.array([0, 1, 0]) 
+    
+    m0 = 1.0
+
+    # 1. Define the physical bodies
+    bodies = []
+    for i in range(len(a)):
+        body = SteppedBody(
+            a=np.array([a[i]]),
+            d=np.array([d[i]]),
+            slant_angle=np.array([0.0]),
+            heaving=bool(heaving[i])
+        )
+        bodies.append(body)
+
+    # 2. Create the body arrangement
+    arrangement = ConcentricBodyGroup(bodies)
+
+    # 3. Instantiate the CONCRETE geometry class
+    geometry = BasicRegionGeometry(
+        body_arrangement=arrangement,
+        h=h,
+        NMK=NMK
+    )
+    
+    # 4. Create the problem object
+    problem = MEEMProblem(geometry)
+    engine = MEEMEngine(problem_list=[problem])
+
+    # Solve the system once to get the common solution vector X
+    print("--- Solving linear system once ---")
+    X = engine.solve_linear_system_multi(problem, m0)
+
+    # 2. ACT: Run both the new and old calculation methods
+    # Note: We use spatial_res=51 to attempt to match the grid, but boundaries may still differ slightly
+    potentials_new = engine.calculate_potentials(problem, X, m0, spatial_res=50, sharp=True)
+    potentials_old = calculate_potentials_old(X, NMK, a, h, d, m0, heaving)
+    
+    # ---------------------------------------------------------
+    # FIX: Transpose NEW results to match OLD results shape
+    # ---------------------------------------------------------
+    if potentials_new['phi'].shape != potentials_old['phi'].shape:
+        print(f"\n[TEST INFO] Transposing 'new' results from {potentials_new['phi'].shape} to match old {potentials_old['phi'].shape}")
+        potentials_new['phi'] = potentials_new['phi'].T
+        potentials_new['phiH'] = potentials_new['phiH'].T
+        potentials_new['phiP'] = potentials_new['phiP'].T
+
+    # 3. ASSERT: Compare the results
+    print("\n--- Comparing NEW vs OLD Results ---")
+    
+    def compare_valid_intersection(name, new_arr, old_arr):
+        """Compares two arrays only where BOTH contain valid numbers (ignoring NaNs)."""
+        # Create a mask where both arrays have data (are not NaN)
+        valid_mask = np.isfinite(new_arr) & np.isfinite(old_arr)
+        
+        overlap_count = np.sum(valid_mask)
+        if overlap_count == 0:
+            raise AssertionError(f"[{name}] No overlapping valid points found! Grid alignment is totally off.")
+
+        # Check for mask mismatch (just for info)
+        xor_diff = np.sum(np.isfinite(new_arr) ^ np.isfinite(old_arr))
+        if xor_diff > 0:
+            print(f"  [INFO] {name}: Ignored {xor_diff} pixels of boundary mismatch (wall vs water definition).")
+        
+        # Compare the physics values in the valid intersection
+        np.testing.assert_allclose(
+            new_arr[valid_mask], 
+            old_arr[valid_mask], 
+            rtol=1e-8, atol=1e-8, 
+            err_msg=f"Mismatch in values for {name}"
+        )
+        print(f"  [PASS] {name}: Matched perfectly across {overlap_count} points.")
+        
+    # Helper to check without crashing immediately
+    def check_field(name, new, old):
+        try:
+            compare_valid_intersection(name, new, old)
+            print(f"✅ {name} matches.")
+            return True
+        except AssertionError as e:
+            print(f"❌ {name} MISMATCH.")
+            diff = np.abs(new - old)
+            print(f"   Max Diff: {np.nanmax(diff)}")
+            return False
+
+    # Check components FIRST
+    phiH_ok = check_field('phiH', potentials_new['phiH'], potentials_old['phiH'])
+    phiP_ok = check_field('phiP', potentials_new['phiP'], potentials_old['phiP'])
+    
+    # Check total only if components look reasonable (or just to see the sum error)
+    phi_ok = check_field('phi', potentials_new['phi'], potentials_old['phi'])
+
+    if not (phiH_ok and phiP_ok and phi_ok):
+        print("\n[DEBUG HINT] If phiH matches but phiP fails, check 'phi_p_i' in multi_equations.py.")
+        print("             Verify it includes the 1/2 factor and uses (z+h)^2.")
+
+    try:
+        compare_valid_intersection('phi', potentials_new['phi'], potentials_old['phi'])
+        compare_valid_intersection('phiH', potentials_new['phiH'], potentials_old['phiH'])
+        compare_valid_intersection('phiP', potentials_new['phiP'], potentials_old['phiP'])
+        
+        print("\n✅ SUCCESS: All potential arrays match in their valid regions!")
+        
+    except AssertionError as e:
+        print("\n❌ FAILURE: Potential arrays DO NOT match.")
+        print(e)
+        
+        # Calculate diff (now safe because shapes match)
+        diff = np.abs(potentials_new['phi'] - potentials_old['phi'])
+        max_diff = np.nanmax(diff)
+        print(f"Maximum absolute difference in 'phi' (ignoring NaNs): {max_diff}")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/package/test/test_problem_cache.py b/package/test/test_problem_cache.py
new file mode 100644
index 0000000..c2b4334
--- /dev/null
+++ b/package/test/test_problem_cache.py
@@ -0,0 +1,132 @@
+# test_problem_cache.py
+
+import sys
+import os
+import numpy as np
+import pytest
+from unittest.mock import Mock, MagicMock # MagicMock is useful for mocking callable attributes
+
+# --- Path Setup ---
+# Adjust the path to import from package's 'src' directory.
+current_dir = os.path.dirname(__file__)
+package_base_dir = os.path.join(current_dir, '..')
+src_dir = os.path.join(package_base_dir, 'src')
+sys.path.insert(0, os.path.abspath(src_dir))
+
+# Import the class to be tested
+from openflash.problem_cache import ProblemCache
+# No need to import MEEMProblem or multi_equations directly here for these tests,
+# as we'll mock the 'problem' object and the functions.
+
+# --- Fixtures ---
+
+@pytest.fixture
+def mock_problem():
+    """Provides a mock MEEMProblem instance for the ProblemCache."""
+    mock = Mock()
+    # Add any attributes or methods that ProblemCache might try to access on 'problem'
+    # For now, ProblemCache only stores it, but if other methods access problem.frequencies, etc.
+    return mock
+
+@pytest.fixture
+def problem_cache(mock_problem):
+    """Provides an initialized ProblemCache instance for each test."""
+    return ProblemCache(problem=mock_problem)
+
+@pytest.fixture
+def sample_A_template():
+    """Provides a sample NumPy array for A_template."""
+    return np.array([[1+1j, 2+2j], [3+3j, 4+4j]], dtype=complex)
+
+@pytest.fixture
+def sample_b_template():
+    """Provides a sample NumPy array for b_template."""
+    return np.array([5+5j, 6+6j], dtype=complex)
+
+@pytest.fixture
+def mock_calc_func():
+    """Provides a simple mock callable for m0-dependent calculation functions."""
+    return MagicMock(return_value=100.0 + 50.0j) # Example return value for the function
+
+@pytest.fixture
+def mock_m_k_entry_func():
+    """Provides a mock for the m_k_entry function."""
+    return MagicMock(name='m_k_entry_func')
+
+@pytest.fixture
+def mock_N_k_func():
+    """Provides a mock for the N_k function."""
+    return MagicMock(name='N_k_func')
+
+# --- Test Cases ---
+
+def test_initialization(problem_cache, mock_problem):
+    """Test that ProblemCache initializes correctly."""
+    assert problem_cache.problem is mock_problem
+    assert problem_cache.A_template is None
+    assert problem_cache.b_template is None
+    assert problem_cache.m0_dependent_A_indices == []
+    assert problem_cache.m0_dependent_b_indices == []
+    assert problem_cache.m_k_entry_func is None
+    assert problem_cache.N_k_func is None
+
+def test_set_and_get_A_template(problem_cache, sample_A_template):
+    """Test setting and retrieving the A_template."""
+    problem_cache._set_A_template(sample_A_template)
+    retrieved_A = problem_cache._get_A_template()
+
+    # Check if the template was set
+    np.testing.assert_array_equal(retrieved_A, sample_A_template)
+    # Check that a copy is returned (to prevent external modification of the stored template)
+    assert retrieved_A is not sample_A_template
+
+def test_get_A_template_not_set(problem_cache):
+    """Test ValueError is raised if A_template is accessed before being set."""
+    with pytest.raises(ValueError, match="A_template has not been set."):
+        problem_cache._get_A_template()
+
+def test_set_and_get_b_template(problem_cache, sample_b_template):
+    """Test setting and retrieving the b_template."""
+    problem_cache._set_b_template(sample_b_template)
+    retrieved_b = problem_cache._get_b_template()
+
+    # Check if the template was set
+    np.testing.assert_array_equal(retrieved_b, sample_b_template)
+    # Check that a copy is returned
+    assert retrieved_b is not sample_b_template
+
+def test_get_b_template_not_set(problem_cache):
+    """Test ValueError is raised if b_template is accessed before being set."""
+    with pytest.raises(ValueError, match="b_template has not been set."):
+        problem_cache._get_b_template()
+
+def test_add_m0_dependent_A_entry(problem_cache, mock_calc_func):
+    """Test adding m0-dependent A matrix entries."""
+    problem_cache._add_m0_dependent_A_entry(0, 0, mock_calc_func)
+    assert problem_cache.m0_dependent_A_indices == [(0, 0, mock_calc_func)]
+
+    another_mock_func = MagicMock(return_value=200.0)
+    problem_cache._add_m0_dependent_A_entry(1, 2, another_mock_func)
+    assert problem_cache.m0_dependent_A_indices == [
+        (0, 0, mock_calc_func),
+        (1, 2, another_mock_func)
+    ]
+
+def test_add_m0_dependent_b_entry(problem_cache, mock_calc_func):
+    """Test adding m0-dependent b vector entries."""
+    problem_cache._add_m0_dependent_b_entry(0, mock_calc_func)
+    assert problem_cache.m0_dependent_b_indices == [(0, mock_calc_func)]
+
+    another_mock_func = MagicMock(return_value=300.0)
+    problem_cache._add_m0_dependent_b_entry(5, another_mock_func)
+    assert problem_cache.m0_dependent_b_indices == [
+        (0, mock_calc_func),
+        (5, another_mock_func)
+    ]
+
+def test_set_m_k_and_N_k_funcs(problem_cache, mock_m_k_entry_func, mock_N_k_func):
+    """Test setting the m_k_entry_func and N_k_func references."""
+    problem_cache._set_m_k_and_N_k_funcs(mock_m_k_entry_func, mock_N_k_func)
+
+    assert problem_cache.m_k_entry_func is mock_m_k_entry_func
+    assert problem_cache.N_k_func is mock_N_k_func
\ No newline at end of file
diff --git a/package/test/test_results.py b/package/test/test_results.py
index ca81492..aec2f36 100644
--- a/package/test/test_results.py
+++ b/package/test/test_results.py
@@ -1,135 +1,262 @@
-import unittest
-from unittest.mock import MagicMock, patch
-import xarray as xr
+import pytest
 import numpy as np
-import tempfile  # Import tempfile
-
-# Adjust import paths (as before)
-import sys
+import xarray as xr
+from unittest.mock import Mock, MagicMock
 import os
-src_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../src'))
-sys.path.append(src_path)
+import sys
+
+# --- Path Setup ---
+current_dir = os.path.dirname(__file__)
+src_dir = os.path.abspath(os.path.join(current_dir, '..', 'src'))
+if src_dir not in sys.path:
+    sys.path.insert(0, src_dir)
+
+# --- Import Package Modules ---
+from openflash.results import Results
+from openflash.geometry import Geometry, ConcentricBodyGroup
+from openflash.body import SteppedBody, CoordinateBody
+from openflash.meem_problem import MEEMProblem
+
+# ==============================================================================
+# Mock Geometry Fixture
+# ==============================================================================
+@pytest.fixture
+def mock_geometry():
+    """
+    Creates a mock Geometry object suitable for testing Results.
+    Includes nested mocks for body_arrangement and bodies with heaving flags.
+    """
+    # --- FIX: Ensure bodies have coordinate attributes for store_results ---
+    # We use specs for both SteppedBody (default) and add CoordinateBody attrs
+    # so the checks in store_results pass.
+    
+    mock_body1 = Mock(spec=CoordinateBody) 
+    mock_body1.heaving = False
+    mock_body1.r_coords = np.array([0.5]) # 1 coordinate
+    mock_body1.z_coords = np.array([-0.5])
+
+    mock_body2 = Mock(spec=CoordinateBody)
+    mock_body2.heaving = True
+    mock_body2.r_coords = np.array([1.0]) # 1 coordinate
+    mock_body2.z_coords = np.array([-1.0])
+
+    mock_body3 = Mock(spec=CoordinateBody)
+    mock_body3.heaving = True
+    # If we want total coords to be 2 (as in the failing test),
+    # body3 can have empty coords or we adjust the test data size.
+    # Let's make total coords = 2 by giving body3 empty arrays.
+    mock_body3.r_coords = np.array([])
+    mock_body3.z_coords = np.array([])
+
+    mock_arrangement = Mock(spec=ConcentricBodyGroup)
+    mock_arrangement.bodies = [mock_body1, mock_body2, mock_body3]
+
+    mock_geom = Mock(spec=Geometry)
+    mock_geom.domain_list = {
+        0: Mock(category='inner', index=0),
+        1: Mock(category='outer', index=1)
+    }
+    mock_geom.body_arrangement = mock_arrangement
+    
+    return mock_geom
+
+# ==============================================================================
+# Sample Data Fixtures
+# ==============================================================================
+@pytest.fixture
+def sample_frequencies():
+    """Provides a sample array of frequencies."""
+    return np.array([0.5, 1.0, 1.5])
+
+# ==============================================================================
+# Mock Problem Fixture
+# ==============================================================================
+@pytest.fixture
+def mock_problem(mock_geometry, sample_frequencies):
+    """ Creates a mock MEEMProblem containing mock geometry and frequencies. """
+    problem = Mock(spec=MEEMProblem)
+    problem.geometry = mock_geometry
+    problem.frequencies = sample_frequencies
+    # We don't need to mock problem.modes because Results infers it directly
+    # from problem.geometry
+    return problem
+
+# ==============================================================================
+# Results Instance Fixture
+# ==============================================================================
+@pytest.fixture
+def results_instance(mock_problem): 
+    """
+    Creates a Results instance using a mock MEEMProblem object.
+    """
+    return Results(mock_problem)
+
+# ==============================================================================
+# Test Suite for Results Class
+# ==============================================================================
+
+def test_results_initialization(results_instance, mock_problem, sample_frequencies): 
+    """
+    Tests that the Results class initializes correctly, infers modes,
+    and creates an xarray dataset with the right coordinates.
+    """
+    assert results_instance.geometry is mock_problem.geometry 
+    np.testing.assert_array_equal(results_instance.frequencies, sample_frequencies)
+
+    # Check inferred modes based on mock_geometry (bodies 1 and 2 heave)
+    expected_modes = np.array([1, 2])
+    assert isinstance(results_instance.modes, np.ndarray)
+    np.testing.assert_array_equal(results_instance.modes, expected_modes)
+
+    # Check the initialized dataset
+    assert isinstance(results_instance.dataset, xr.Dataset)
+    assert 'frequency' in results_instance.dataset.coords
+    assert 'mode_i' in results_instance.dataset.coords
+    assert 'mode_j' in results_instance.dataset.coords
+    np.testing.assert_array_equal(results_instance.dataset.coords['frequency'], sample_frequencies)
+    np.testing.assert_array_equal(results_instance.dataset.coords['mode_i'], expected_modes)
+    np.testing.assert_array_equal(results_instance.dataset.coords['mode_j'], expected_modes)
+    print("✅ Initialization and mode inference test passed.")
+
+def test_store_hydrodynamic_coefficients(results_instance, sample_frequencies):
+    """
+    Tests storing hydrodynamic coefficients. Assumes 2 modes from fixture.
+    """
+    num_freqs = len(sample_frequencies)
+    num_modes = len(results_instance.modes)
+    assert num_modes == 2
+
+    added_mass = np.random.rand(num_freqs, num_modes, num_modes)
+    damping = np.random.rand(num_freqs, num_modes, num_modes)
+
+    results_instance.store_hydrodynamic_coefficients(
+        frequencies=sample_frequencies,
+        added_mass_matrix=added_mass,
+        damping_matrix=damping
+    )
+
+    assert 'added_mass' in results_instance.dataset
+    assert 'damping' in results_instance.dataset
+    assert results_instance.dataset['added_mass'].shape == (num_freqs, num_modes, num_modes)
+    assert results_instance.dataset['damping'].shape == (num_freqs, num_modes, num_modes)
+    np.testing.assert_array_equal(results_instance.dataset['added_mass'].values, added_mass)
+    np.testing.assert_array_equal(results_instance.dataset['damping'].values, damping)
+    print("✅ Storing hydrodynamic coefficients test passed.")
+
+
+def test_store_results_eigenfunctions(results_instance, sample_frequencies):
+    """
+    Tests storing eigenfunction data. Assumes 2 modes from fixture.
+    """
+    num_freqs = len(sample_frequencies)
+    num_modes = len(results_instance.modes)
+    
+    # Matches the mock geometry bodies (body1 has 1 coord, body2 has 1, body3 has 0)
+    # Total coords = 1 + 1 + 0 = 2
+    num_r = 2 
+    num_z = 2 
+
+    radial_data = np.random.rand(num_freqs, num_modes, num_r)
+    vertical_data = np.random.rand(num_freqs, num_modes, num_z)
+    domain_index = 0
+    domain_name = f"radial_eigenfunctions_{results_instance.geometry.domain_list[domain_index].category}"
+
+    results_instance.store_results(domain_index, radial_data, vertical_data)
+
+    assert domain_name in results_instance.dataset
+    assert results_instance.dataset[domain_name].shape == (num_freqs, num_modes, num_r)
+    print("✅ Storing eigenfunctions test passed.")
+
+
+def test_store_all_potentials(results_instance, sample_frequencies):
+    """
+    Tests storing batched potential coefficient data. Assumes 2 modes.
+    """
+    num_freqs = len(sample_frequencies)
+    num_modes = len(results_instance.modes)
+    domain_names = ['inner', 'outer']
+    max_harmonics = 5
+
+    batch_data = []
+    for f_idx in range(num_freqs):
+        for m_idx in range(num_modes):
+            mode_data = {}
+            for d_idx, d_name in enumerate(domain_names):
+                num_harmonics = max_harmonics - d_idx
+                mode_data[d_name] = {
+                    "potentials": np.random.rand(num_harmonics) + 1j * np.random.rand(num_harmonics),
+                    "r_coords_dict": {f"r{k}": k * 0.1 for k in range(num_harmonics)},
+                    "z_coords_dict": {f"z{k}": -k * 0.1 for k in range(num_harmonics)}
+                }
+            batch_data.append({
+                "frequency_idx": f_idx,
+                "mode_idx": m_idx,
+                "data": mode_data
+            })
+
+    results_instance.store_all_potentials(batch_data)
+
+    assert 'potentials_real' in results_instance.dataset
+    assert 'potentials_imag' in results_instance.dataset
+    expected_shape = (num_freqs, num_modes, len(domain_names), max_harmonics)
+    assert results_instance.dataset['potentials_real'].shape == expected_shape
+    assert results_instance.dataset['potentials_imag'].shape == expected_shape
+    assert 'potential_r_coords' in results_instance.dataset
+    assert 'potential_z_coords' in results_instance.dataset
+    print("✅ Storing all potentials test passed.")
+
+
+def test_export_to_netcdf(results_instance, tmp_path):
+    """
+    Tests exporting the dataset to a NetCDF file.
+    Includes storing some data first.
+    """
+    num_freqs = len(results_instance.frequencies)
+    num_modes = len(results_instance.modes)
+    added_mass = np.random.rand(num_freqs, num_modes, num_modes)
+    damping = np.random.rand(num_freqs, num_modes, num_modes)
+    results_instance.store_hydrodynamic_coefficients(
+        results_instance.frequencies, added_mass, damping
+    )
+    # Adding complex test data to verify splitting
+    results_instance.dataset['complex_test'] = (('frequency', 'mode_i'), np.random.rand(num_freqs, num_modes) + 1j*np.random.rand(num_freqs, num_modes))
 
-from results import Results
-from geometry import Geometry
-from domain import Domain
+    file_path = tmp_path / "test_results.nc"
+    results_instance.export_to_netcdf(file_path)
 
+    assert file_path.exists()
 
-class TestResults(unittest.TestCase):
+    loaded_ds = xr.open_dataset(file_path, engine="h5netcdf")
+    assert 'added_mass' in loaded_ds
+    assert 'damping' in loaded_ds
+    assert 'complex_test_real' in loaded_ds
+    assert 'complex_test_imag' in loaded_ds
+    assert loaded_ds['added_mass'].shape == (num_freqs, num_modes, num_modes)
+    loaded_ds.close()
+    print("✅ Export to NetCDF test passed.")
 
-    def setUp(self):
-        """Set up a basic Results object for testing."""
-        self.frequencies = np.array([1.0, 2.0, 3.0])
-        self.modes = np.array(['mode1', 'mode2'])
+def test_get_results(results_instance):
+    """
+    Tests that get_results returns the underlying xarray Dataset.
+    """
+    ds = results_instance.get_results()
+    assert isinstance(ds, xr.Dataset)
+    assert ds is results_instance.dataset
+    print("✅ Get results test passed.")
 
-        # Mock Geometry and Domain objects
-        mock_geometry = MagicMock(spec=Geometry)
-        mock_domain1 = MagicMock(spec=Domain)
-        mock_domain1.r_coordinates = {'r1': 0.1, 'r2': 0.2}
-        mock_domain1.z_coordinates = {'z1': 0.0, 'z2': 0.1, 'z3': 0.2}
-        mock_domain2 = MagicMock(spec=Domain)
-        mock_domain2.r_coordinates = {'r2': 0.3, 'r3': 0.4, 'r4': 0.5}
-        mock_domain2.z_coordinates = {'z2': 0.0, 'z3': 0.1}
-        mock_geometry.domain_list = {0: mock_domain1, 1: mock_domain2}
-
-        mock_geometry.r_coordinates = {'r1': 0.1, 'r2': 0.2}
-        mock_geometry.z_coordinates = {'z1': 0.0, 'z2': 0.1, 'z3': 0.2}
-
-        self.results = Results(mock_geometry, self.frequencies, self.modes)
-
-    def test_results_initialization(self):
-        self.assertIsInstance(self.results, Results)
-        self.assertTrue(self.results.geometry is self.results.geometry)
-
-        np.testing.assert_array_equal(self.results.frequencies, self.frequencies)
-        np.testing.assert_array_equal(self.results.modes, self.modes)
-
-
-    def test_store_results(self):
-        """Test storing eigenfunction results."""
-        radial_data = np.random.rand(len(self.frequencies), len(self.modes), 2)
-        vertical_data = np.random.rand(len(self.frequencies), len(self.modes), 3)
-
-        self.results.store_results(0, radial_data, vertical_data)
-
-        self.assertIn('radial_eigenfunctions', self.results.dataset)
-        self.assertIn('vertical_eigenfunctions', self.results.dataset)
-        radial_da = self.results.dataset['radial_eigenfunctions']
-        vertical_da = self.results.dataset['vertical_eigenfunctions']
-
-        self.assertIsInstance(radial_da, xr.DataArray)
-        self.assertIsInstance(vertical_da, xr.DataArray)
-
-        self.assertEqual(radial_da.dims, ('frequencies', 'modes', 'r'))
-        self.assertEqual(vertical_da.dims, ('frequencies', 'modes', 'z'))
-
-        np.testing.assert_array_equal(radial_da.coords['frequencies'], self.frequencies)
-        np.testing.assert_array_equal(radial_da.coords['modes'], self.modes)
-
-    def test_store_potentials(self):
-        """Test storing potential results."""
-        potentials = {
-            'domain1': {'potentials': np.array([1,2,3]),
-                        'r': {'r1': 0.1, 'r2': 0.2, 'r3': 0.3},
-                        'z': {'z1': 0.0, 'z2': 0.1}},
-            'domain2': {'potentials': np.array([4,5]),
-                        'r': {'r1': 0.4, 'r2': 0.5},
-                        'z': {'z1': 0.2, 'z2': 0.3, 'z3': 0.4}}
-        }
-
-        self.results.store_potentials(potentials)
-
-        self.assertIn('domain_potentials', self.results.dataset)
-        self.assertIn('domain_r', self.results.dataset.coords)
-        self.assertIn('domain_z', self.results.dataset.coords)
-
-        domain_potentials_da = self.results.dataset['domain_potentials']
-        domain_r_coords = self.results.dataset.coords['domain_r']
-        domain_z_coords = self.results.dataset.coords['domain_z']
-
-        self.assertIsInstance(domain_potentials_da, xr.DataArray)
-        self.assertIsInstance(domain_r_coords, xr.DataArray)
-        self.assertIsInstance(domain_z_coords, xr.DataArray)
-
-        self.assertEqual(domain_potentials_da.dims, ('domain', 'harmonics'))
-        self.assertEqual(domain_r_coords.dims, ('domain', 'harmonics'))
-        self.assertEqual(domain_z_coords.dims, ('domain', 'harmonics'))
-
-        # Add more specific assertions to check the contents of the stored data
-        # For example, check if the potential values and coordinates are stored correctly.
-        np.testing.assert_array_equal(domain_potentials_da.values[0, :3], potentials['domain1']['potentials'])
-        np.testing.assert_array_equal(domain_potentials_da.values[1, :2], potentials['domain2']['potentials'])
-
-
-    def test_export_to_netcdf(self):
-        """Test exporting results to a NetCDF file."""
-        # Create a temporary file for testing
-        with tempfile.NamedTemporaryFile(suffix='.nc', delete=True) as temp_file:
-            file_path = temp_file.name
-            self.results.export_to_netcdf(file_path)
-            self.assertTrue(os.path.exists(file_path))
-
-            # Optionally, you can try to read the file back in and check its contents
-            try:
-                xr.open_dataset(file_path)
-            except Exception as e:
-                self.fail(f"Failed to open the NetCDF file: {e}")
-
-    def test_get_results(self):
-        """Test getting the stored results."""
-        dataset = self.results.get_results()
-        self.assertTrue(dataset is self.results.dataset)
-
-    def test_display_results(self):
-        """Test displaying the stored results."""
-        # Store some results first
-        radial_data = np.random.rand(len(self.frequencies), len(self.modes), 2)
-        vertical_data = np.random.rand(len(self.frequencies), len(self.modes), 3)
-        self.results.store_results(0, radial_data, vertical_data)
-
-        display_str = self.results.display_results()
-        self.assertIsInstance(display_str, str)
-        self.assertTrue(len(display_str) > 0)  # Check if the string is not empty
+def test_display_results(results_instance):
+    """
+    Tests that display_results returns a string representation of the dataset.
+    """
+    num_freqs = len(results_instance.frequencies)
+    num_modes = len(results_instance.modes)
+    added_mass = np.random.rand(num_freqs, num_modes, num_modes)
+    results_instance.store_hydrodynamic_coefficients(
+        results_instance.frequencies, added_mass, np.zeros_like(added_mass)
+    )
 
-if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    output_string = results_instance.display_results()
+    assert isinstance(output_string, str)
+    assert 'xarray.Dataset' in output_string
+    assert 'added_mass' in output_string
+    print("✅ Display results test passed.")
\ No newline at end of file
diff --git a/package/test/test_velocities_equivalence.py b/package/test/test_velocities_equivalence.py
new file mode 100644
index 0000000..86f8b26
--- /dev/null
+++ b/package/test/test_velocities_equivalence.py
@@ -0,0 +1,203 @@
+import numpy as np
+import sys
+import os
+
+# --- Path Setup ---
+current_dir = os.path.dirname(__file__)
+src_dir = os.path.abspath(os.path.join(current_dir, '..', 'src'))
+if src_dir not in sys.path:
+    sys.path.insert(0, src_dir)
+
+# --- Import Package Modules ---
+from openflash.meem_engine import MEEMEngine
+from openflash.meem_problem import MEEMProblem
+from openflash.basic_region_geometry import BasicRegionGeometry
+from openflash.geometry import ConcentricBodyGroup
+from openflash.body import SteppedBody
+
+# --- Path Setup for Old Code ---
+old_code_dir = os.path.abspath(os.path.join(current_dir, '..', '..', 'dev', 'python'))
+if old_code_dir not in sys.path:
+    sys.path.insert(0, old_code_dir)
+
+# Attempt to import old assembly functions
+try:
+    from old_assembly import R_1n_old, R_2n_old, Z_n_i_old, Lambda_k_old, Z_k_e_old, \
+        diff_R_1n_old, diff_R_2n_old, diff_Z_n_i_old, diff_Lambda_k_old, diff_Z_k_e_old, \
+        diff_r_phi_p_i_old, diff_z_phi_p_i_old, make_R_Z_old
+except ImportError:
+    print("WARNING: Could not import 'old_assembly'. Ensure the path is correct relative to this test script.")
+    sys.exit(1)
+
+# --- Wrapper for the Old Calculation Logic ---
+def calculate_velocities_old(X, NMK, h, d, a, m0, heaving):
+    """
+    Encapsulates the original, non-vectorized velocity calculation logic for testing.
+    """
+    print("\n--- Running OLD Velocity Calculation ---")
+     # Split up the Cs into groups depending on which equation they belong to.
+    Cs = []
+    row = 0
+    Cs.append(X[:NMK[0]])
+    row += NMK[0]
+    boundary_count = len(NMK) - 1
+    for i in range(1, boundary_count):
+        Cs.append(X[row: row + NMK[i] * 2])
+        row += NMK[i] * 2
+    Cs.append(X[row:])
+    
+    # 2. Define old, non-vectorized helper functions
+    def v_r_inner_func(n, r, z):
+        return (Cs[0][n] * diff_R_1n_old(n, r, 0, h, d, a)) * Z_n_i_old(n, z, 0, h, d)
+    def v_r_m_i_func(i, m, r, z):
+        return (Cs[i][m] * diff_R_1n_old(m, r, i, h, d, a) + Cs[i][NMK[i] + m] * diff_R_2n_old(m, r, i, h, d, a)) * Z_n_i_old(m, z, i, h, d)
+    def v_r_e_k_func(k, r, z):
+        return Cs[-1][k] * diff_Lambda_k_old(k, r, m0, a, NMK, h) * Z_k_e_old(k, z, m0, h, NMK)
+    def v_z_inner_func(n, r, z):
+        return (Cs[0][n] * R_1n_old(n, r, 0, h, d, a)) * diff_Z_n_i_old(n, z, 0, h, d)
+    def v_z_m_i_func(i, m, r, z):
+        return (Cs[i][m] * R_1n_old(m, r, i, h, d, a) + Cs[i][NMK[i] + m] * R_2n_old(m, r, i, a, h, d)) * diff_Z_n_i_old(m, z, i, h, d)
+    def v_z_e_k_func(k, r, z):
+        return Cs[-1][k] * Lambda_k_old(k, r, m0, a, NMK, h) * diff_Z_k_e_old(k, z, NMK, m0, h)
+
+    # 3. Vectorize the helper functions
+    v_r_inner_vec = np.vectorize(v_r_inner_func, otypes=[complex])
+    v_r_m_i_vec = np.vectorize(v_r_m_i_func, otypes=[complex])
+    v_r_e_k_vec = np.vectorize(v_r_e_k_func, otypes=[complex])
+    v_z_inner_vec = np.vectorize(v_z_inner_func, otypes=[complex])
+    v_z_m_i_vec = np.vectorize(v_z_m_i_func, otypes=[complex])
+    v_z_e_k_vec = np.vectorize(v_z_e_k_func, otypes=[complex])
+    vr_p_i_vec = np.vectorize(lambda d, r, z, h: diff_r_phi_p_i_old(d, r, h), otypes=[complex])
+    vz_p_i_vec = np.vectorize(lambda d, r, z, h: diff_z_phi_p_i_old(d, z, h), otypes=[complex])
+
+    # 4. Execute the calculation loop
+    R, Z = make_R_Z_old(a, h, d, True, 50)
+    regions = [
+        (R <= a[0]) & (Z < -d[0]),
+        *[(R > a[i-1]) & (R <= a[i]) & (Z < -d[i]) for i in range(1, boundary_count)],
+        (R > a[-1])
+    ]
+
+    vrH = np.full_like(R, np.nan, dtype=complex)
+    vzH = np.full_like(R, np.nan, dtype=complex)
+    
+    # Homogeneous velocity loops
+    for n in range(NMK[0]):
+        temp_vrH = v_r_inner_vec(n, R[regions[0]], Z[regions[0]])
+        temp_vzH = v_z_inner_vec(n, R[regions[0]], Z[regions[0]])
+        vrH[regions[0]] = temp_vrH if n == 0 else vrH[regions[0]] + temp_vrH
+        vzH[regions[0]] = temp_vzH if n == 0 else vzH[regions[0]] + temp_vzH
+    
+    for i in range(1, boundary_count):
+        for m in range(NMK[i]):
+            temp_vrH = v_r_m_i_vec(i, m, R[regions[i]], Z[regions[i]])
+            temp_vzH = v_z_m_i_vec(i, m, R[regions[i]], Z[regions[i]])
+            vrH[regions[i]] = temp_vrH if m == 0 else vrH[regions[i]] + temp_vrH
+            vzH[regions[i]] = temp_vzH if m == 0 else vzH[regions[i]] + temp_vzH
+            
+    for k in range(NMK[-1]):
+        temp_vrH = v_r_e_k_vec(k, R[regions[-1]], Z[regions[-1]])
+        temp_vzH = v_z_e_k_vec(k, R[regions[-1]], Z[regions[-1]])
+        vrH[regions[-1]] = temp_vrH if k == 0 else vrH[regions[-1]] + temp_vrH
+        vzH[regions[-1]] = temp_vzH if k == 0 else vzH[regions[-1]] + temp_vzH
+
+    # Particular velocity
+    vrP = np.full_like(R, 0.0, dtype=complex)
+    vzP = np.full_like(R, 0.0, dtype=complex)
+    
+    vrP[regions[0]] = heaving[0] * vr_p_i_vec(d[0], R[regions[0]], Z[regions[0]], h)
+    vzP[regions[0]] = heaving[0] * vz_p_i_vec(d[0], R[regions[0]], Z[regions[0]], h)
+    for i in range(1, boundary_count):
+        vrP[regions[i]] = heaving[i] * vr_p_i_vec(d[i], R[regions[i]], Z[regions[i]], h)
+        vzP[regions[i]] = heaving[i] * vz_p_i_vec(d[i], R[regions[i]], Z[regions[i]], h)
+    
+    vr = vrH + vrP
+    vz = vzH + vzP
+    print("--- OLD Calculation Finished ---")
+    
+    return {"R": R, "Z": Z, "vrH": vrH, "vzH": vzH, "vrP": vrP, "vzP": vzP, "vr": vr, "vz": vz}
+
+# --- Main Test Execution ---
+def main():
+    # 1. ARRANGE
+    print("--- Setting up test problem ---")
+    NMK = [1, 1, 1, 1]
+    h = 100
+    d = np.array([29, 7, 4])
+    a = np.array([3, 5, 10])
+    heaving = np.array([0, 1, 0]) 
+    m0 = 1.0
+
+    bodies = []
+    for i in range(len(a)):
+        body = SteppedBody(
+            a=np.array([a[i]]),
+            d=np.array([d[i]]),
+            slant_angle=np.array([0.0]),
+            heaving=bool(heaving[i])
+        )
+        bodies.append(body)
+
+    arrangement = ConcentricBodyGroup(bodies)
+    geometry = BasicRegionGeometry(body_arrangement=arrangement, h=h, NMK=NMK)
+    problem = MEEMProblem(geometry)
+    engine = MEEMEngine(problem_list=[problem])
+
+    print("--- Solving linear system once ---")
+    X = engine.solve_linear_system_multi(problem, m0)
+
+    # 2. ACT
+    velocities_new = engine.calculate_velocities(problem, X, m0, spatial_res=50, sharp=True)
+    velocities_old = calculate_velocities_old(X, NMK, h, list(d), list(a), m0, list(heaving))
+    
+    # --- FIX: Transpose New Results to Match Old Shape ---
+    if velocities_new['vr'].shape != velocities_old['vr'].shape:
+        print(f"\n[TEST INFO] Transposing 'new' results from {velocities_new['vr'].shape} to match old {velocities_old['vr'].shape}")
+        for key in ['vr', 'vz', 'vrH', 'vzH', 'vrP', 'vzP']:
+             velocities_new[key] = velocities_new[key].T
+
+    # 3. ASSERT
+    print("\n--- Comparing NEW vs OLD Velocity Results ---")
+    
+    def compare_valid_intersection(name, new_arr, old_arr):
+        """Compares two arrays only where BOTH contain valid numbers (ignoring NaNs)."""
+        valid_mask = np.isfinite(new_arr) & np.isfinite(old_arr)
+        
+        overlap_count = np.sum(valid_mask)
+        if overlap_count == 0:
+            raise AssertionError(f"[{name}] No overlapping valid points found! Grid alignment is totally off.")
+
+        xor_diff = np.sum(np.isfinite(new_arr) ^ np.isfinite(old_arr))
+        if xor_diff > 0:
+            print(f"  [INFO] {name}: Ignored {xor_diff} pixels of boundary mismatch (wall vs water definition).")
+        
+        np.testing.assert_allclose(
+            new_arr[valid_mask], 
+            old_arr[valid_mask], 
+            rtol=1e-8, atol=1e-8, 
+            err_msg=f"Mismatch in values for {name}"
+        )
+        print(f"  [PASS] {name}: Matched perfectly across {overlap_count} points.")
+
+    try:
+        # Compare all components
+        compare_valid_intersection('vr', velocities_new['vr'], velocities_old['vr'])
+        compare_valid_intersection('vz', velocities_new['vz'], velocities_old['vz'])
+        compare_valid_intersection('vrH', velocities_new['vrH'], velocities_old['vrH'])
+        compare_valid_intersection('vzH', velocities_new['vzH'], velocities_old['vzH'])
+        compare_valid_intersection('vrP', velocities_new['vrP'], velocities_old['vrP'])
+        compare_valid_intersection('vzP', velocities_new['vzP'], velocities_old['vzP'])
+        
+        print("\n✅ SUCCESS: All velocity arrays match perfectly!")
+        
+    except AssertionError as e:
+        print("\n❌ FAILURE: Velocity arrays DO NOT match.")
+        print(e)
+        
+        # Debug helper
+        diff_vr = np.abs(velocities_new['vr'] - velocities_old['vr'])
+        max_diff_vr = np.nanmax(diff_vr)
+        print(f"Maximum absolute difference in 'vr': {max_diff_vr}")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/package/test_artifacts/config0_debug_data.csv b/package/test_artifacts/config0_debug_data.csv
new file mode 100644
index 0000000..a3b4c55
--- /dev/null
+++ b/package/test_artifacts/config0_debug_data.csv
@@ -0,0 +1,2020 @@
+R,Z,is_body_nan,openflash_real,capytaine_real_converted,openflash_imag,capytaine_imag_converted,diff_real,diff_imag,rel_diff_real,rel_diff_imag
+0.000000e+00,-5.107143e-01,False,6.690698e-01,6.749481e-01,3.537974e-01,3.543026e-01,-5.878290e-03,-5.051536e-04,8.709247e-01,1.425769e-01
+0.000000e+00,-5.311429e-01,False,6.496493e-01,6.557875e-01,3.537891e-01,3.542628e-01,-6.138210e-03,-4.736282e-04,9.360059e-01,1.336940e-01
+0.000000e+00,-5.515714e-01,False,6.312098e-01,6.376438e-01,3.537729e-01,3.542064e-01,-6.433983e-03,-4.334829e-04,1.009025e+00,1.223815e-01
+0.000000e+00,-5.720000e-01,False,6.137452e-01,6.203339e-01,3.537490e-01,3.541751e-01,-6.588738e-03,-4.261072e-04,1.062128e+00,1.203098e-01
+0.000000e+00,-5.924286e-01,False,5.972468e-01,6.038167e-01,3.537179e-01,3.541436e-01,-6.569889e-03,-4.257073e-04,1.088060e+00,1.202075e-01
+0.000000e+00,-6.128571e-01,False,5.817033e-01,5.882028e-01,3.536802e-01,3.540899e-01,-6.499486e-03,-4.096885e-04,1.104974e+00,1.157019e-01
+0.000000e+00,-6.332857e-01,False,5.671014e-01,5.735673e-01,3.536365e-01,3.540328e-01,-6.465901e-03,-3.963051e-04,1.127313e+00,1.119402e-01
+0.000000e+00,-6.537143e-01,False,5.534259e-01,5.598554e-01,3.535878e-01,3.539985e-01,-6.429468e-03,-4.107717e-04,1.148416e+00,1.160377e-01
+0.000000e+00,-6.741429e-01,False,5.406601e-01,5.470223e-01,3.535347e-01,3.539580e-01,-6.362182e-03,-4.232877e-04,1.163057e+00,1.195870e-01
+0.000000e+00,-6.945714e-01,False,5.287865e-01,5.350528e-01,3.534783e-01,3.538830e-01,-6.266319e-03,-4.046492e-04,1.171159e+00,1.143455e-01
+0.000000e+00,-7.150000e-01,False,5.177867e-01,5.239925e-01,3.534196e-01,3.538276e-01,-6.205860e-03,-4.080145e-04,1.184341e+00,1.153145e-01
+0.000000e+00,-7.354286e-01,False,5.076422e-01,5.138298e-01,3.533595e-01,3.538021e-01,-6.187622e-03,-4.425555e-04,1.204216e+00,1.250856e-01
+0.000000e+00,-7.558571e-01,False,4.983346e-01,5.045412e-01,3.532990e-01,3.537976e-01,-6.206624e-03,-4.985665e-04,1.230152e+00,1.409186e-01
+0.000000e+00,-7.762857e-01,False,4.898459e-01,4.960653e-01,3.532392e-01,3.537467e-01,-6.219445e-03,-5.075051e-04,1.253755e+00,1.434657e-01
+0.000000e+00,-7.967143e-01,False,4.821587e-01,4.884040e-01,3.531809e-01,3.536776e-01,-6.245258e-03,-4.966459e-04,1.278708e+00,1.404234e-01
+0.000000e+00,-8.171429e-01,False,4.752566e-01,4.815502e-01,3.531251e-01,3.535994e-01,-6.293546e-03,-4.742131e-04,1.306935e+00,1.341103e-01
+0.000000e+00,-8.375714e-01,False,4.691244e-01,4.754466e-01,3.530727e-01,3.535180e-01,-6.322200e-03,-4.452664e-04,1.329739e+00,1.259530e-01
+0.000000e+00,-8.580000e-01,False,4.637478e-01,4.700324e-01,3.530245e-01,3.534472e-01,-6.284546e-03,-4.226476e-04,1.337045e+00,1.195787e-01
+0.000000e+00,-8.784286e-01,False,4.591144e-01,4.653511e-01,3.529812e-01,3.533990e-01,-6.236732e-03,-4.178462e-04,1.340221e+00,1.182364e-01
+0.000000e+00,-8.988571e-01,False,4.552130e-01,4.614083e-01,3.529434e-01,3.533761e-01,-6.195357e-03,-4.326684e-04,1.342706e+00,1.224385e-01
+0.000000e+00,-9.192857e-01,False,4.520341e-01,4.581978e-01,3.529117e-01,3.533573e-01,-6.163715e-03,-4.455645e-04,1.345208e+00,1.260946e-01
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diff --git a/package/test_artifacts/config2_debug_data.csv b/package/test_artifacts/config2_debug_data.csv
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diff --git a/package/test_artifacts/config3_debug_data.csv b/package/test_artifacts/config3_debug_data.csv
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diff --git a/package/test_artifacts/config5_debug_data.csv b/package/test_artifacts/config5_debug_data.csv
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diff --git a/package/test_artifacts/config6_debug_data.csv b/package/test_artifacts/config6_debug_data.csv
new file mode 100644
index 0000000..4c8833f
--- /dev/null
+++ b/package/test_artifacts/config6_debug_data.csv
@@ -0,0 +1,2337 @@
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diff --git a/package/value/A_match.txt b/package/value/A_match.txt
deleted file mode 100644
index c1e9710..0000000
--- a/package/value/A_match.txt
+++ /dev/null
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diff --git a/package/value/b_match.txt b/package/value/b_match.txt
deleted file mode 100644
index 58501cb..0000000
--- a/package/value/b_match.txt
+++ /dev/null
@@ -1,16 +0,0 @@
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diff --git a/pubs/figs/.gitkeep b/pubs/figs/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/pubs/figs/MEEM_UML_Diagram.png b/pubs/figs/MEEM_UML_Diagram.png
new file mode 100644
index 0000000..868f131
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diff --git a/pubs/figs/classes_openflash.plantuml b/pubs/figs/classes_openflash.plantuml
new file mode 100644
index 0000000..13feed4
--- /dev/null
+++ b/pubs/figs/classes_openflash.plantuml
@@ -0,0 +1,95 @@
+@startuml classes_openflash
+set namespaceSeparator none
+class "Domain" as openflash.domain.Domain {
+  a : NoneType
+  bottom_BC
+  category : str
+  di : NoneType
+  eigenfunction : NoneType
+  geometry : Geometry
+  h
+  heaving
+  height : float
+  index : int
+  m_k_vals : list
+  number_harmonics : int
+  params : dict
+  r_coords : int, list
+  radial_width : float
+  scale
+  slant
+  top_BC
+  z_coords : list
+  build_domain_params(a: List[float], d: List[float], heaving: List[Union[int, bool]], h: float, slant: Optional[List[Union[int, bool]]]) -> List[Dict]
+  build_r_coordinates_dict() -> dict[str, float]
+  build_z_coordinates_dict() -> dict[str, float]
+}
+class "Geometry" as openflash.geometry.Geometry {
+  adjacency_matrix
+  domain_list : dict
+  domain_params : List[Dict]
+  r_coordinates : Dict[str, float]
+  z_coordinates : Dict[str, float]
+  make_domain_list() -> Dict[int, Domain]
+}
+class "MEEMEngine" as openflash.meem_engine.MEEMEngine {
+  cache_list : dict
+  problem_list : List[MEEMProblem]
+  assemble_A_multi(problem: 'MEEMProblem', m0) -> np.ndarray
+  assemble_b_multi(problem: 'MEEMProblem', m0) -> np.ndarray
+  build_problem_cache(problem: 'MEEMProblem') -> ProblemCache
+  calculate_potentials(problem, solution_vector: np.ndarray, m0, spatial_res, sharp) -> Dict[str, Any]
+  calculate_velocities(problem, solution_vector: np.ndarray, m0, spatial_res, sharp) -> Dict[str, Any]
+  compute_hydrodynamic_coefficients(problem, X, m0)
+  reformat_coeffs(x: np.ndarray, NMK, boundary_count) -> list[np.ndarray]
+  run_and_store_results(problem_index: int) -> Results
+  solve_linear_system_multi(problem: MEEMProblem, m0) -> np.ndarray
+  visualize_potential(field, R, Z, title)
+}
+class "MEEMProblem" as openflash.meem_problem.MEEMProblem {
+  domain_list
+  frequencies : ndarray
+  geometry
+  modes : ndarray
+  set_frequencies_modes(frequencies: np.ndarray, modes: np.ndarray)
+}
+class "ProblemCache" as openflash.problem_cache.ProblemCache {
+  A_template : ndarray
+  I_nm_vals : ndarray
+  N_k_arr : ndarray
+  N_k_func : Optional[Callable]
+  b_template : ndarray
+  m0_dependent_A_indices : list[tuple[int, int, Callable]]
+  m0_dependent_b_indices : list[tuple[int, Callable]]
+  m_k_arr : ndarray
+  m_k_entry_func : Optional[Callable]
+  named_closures : Dict[str, Any]
+  problem
+  _add_m0_dependent_A_entry(row: int, col: int, calc_func: Callable)
+  _add_m0_dependent_b_entry(row: int, calc_func: Callable)
+  _get_A_template() -> np.ndarray
+  _get_b_template() -> np.ndarray
+  _get_closure(key: str)
+  _set_A_template(A_template: np.ndarray)
+  _set_I_nm_vals(I_nm_vals: np.ndarray)
+  _set_b_template(b_template: np.ndarray)
+  _set_closure(key: str, closure)
+  _set_m_k_and_N_k_funcs(m_k_entry_func: Callable, N_k_func: Callable)
+  _set_precomputed_m_k_N_k(m_k_arr: np.ndarray, N_k_arr: np.ndarray)
+}
+class "Results" as openflash.results.Results {
+  dataset : Dataset
+  frequencies : ndarray
+  geometry
+  modes : ndarray
+  display_results()
+  export_to_netcdf(file_path: str)
+  get_results()
+  store_all_potentials(all_potentials_batch: list[dict])
+  store_hydrodynamic_coefficients(frequencies: np.ndarray, modes: np.ndarray, added_mass_matrix: np.ndarray, damping_matrix: np.ndarray)
+  store_results(domain_index: int, radial_data: np.ndarray, vertical_data: np.ndarray)
+  store_single_potential_field(potential_data: dict, frequency_idx: int, mode_idx: int)
+}
+openflash.geometry.Geometry --o openflash.meem_problem.MEEMProblem : geometry
+openflash.geometry.Geometry --o openflash.results.Results : geometry
+@enduml
diff --git a/pubs/figs/classes_openflash.png b/pubs/figs/classes_openflash.png
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diff --git a/pubs/figs/domain_drawing.png b/pubs/figs/domain_drawing.png
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index 0000000..2c4ba1d
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diff --git a/pubs/figs/domain_table.png b/pubs/figs/domain_table.png
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index 0000000..e85f09f
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diff --git a/pubs/figs/meem_engine.png b/pubs/figs/meem_engine.png
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index 0000000..24449a5
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diff --git a/pubs/figs/openflash_uml_vertical.png b/pubs/figs/openflash_uml_vertical.png
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diff --git a/pubs/figs/packages_openflash.plantuml b/pubs/figs/packages_openflash.plantuml
new file mode 100644
index 0000000..31bc27f
--- /dev/null
+++ b/pubs/figs/packages_openflash.plantuml
@@ -0,0 +1,37 @@
+@startuml packages_openflash
+set namespaceSeparator none
+package "openflash" as openflash {
+}
+package "openflash.domain" as openflash.domain {
+}
+package "openflash.geometry" as openflash.geometry {
+}
+package "openflash.meem_engine" as openflash.meem_engine {
+}
+package "openflash.meem_problem" as openflash.meem_problem {
+}
+package "openflash.multi_constants" as openflash.multi_constants {
+}
+package "openflash.multi_equations" as openflash.multi_equations {
+}
+package "openflash.problem_cache" as openflash.problem_cache {
+}
+package "openflash.results" as openflash.results {
+}
+openflash --> openflash.domain
+openflash --> openflash.geometry
+openflash --> openflash.meem_engine
+openflash --> openflash.meem_problem
+openflash --> openflash.multi_equations
+openflash --> openflash.problem_cache
+openflash --> openflash.results
+openflash.geometry --> openflash.domain
+openflash.meem_engine --> openflash.meem_problem
+openflash.meem_engine --> openflash.multi_equations
+openflash.meem_engine --> openflash.problem_cache
+openflash.meem_engine --> openflash.results
+openflash.meem_problem --> openflash.geometry
+openflash.multi_equations --> openflash.multi_constants
+openflash.problem_cache --> openflash.multi_equations
+openflash.results --> openflash.geometry
+@enduml
diff --git a/pubs/figs/packages_openflash.png b/pubs/figs/packages_openflash.png
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diff --git a/pubs/figs/streamlit4.png b/pubs/figs/streamlit4.png
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diff --git a/pubs/figs/uml.py b/pubs/figs/uml.py
new file mode 100644
index 0000000..b220cba
--- /dev/null
+++ b/pubs/figs/uml.py
@@ -0,0 +1,215 @@
+from graphviz import Digraph
+import os
+
+# Prevent Graphviz from trying to open the file after rendering
+os.environ["GV_RENDER_NEATO_DOES_NOT_WORKAROUND_BUG_2312"] = "1"
+
+# --- HELPER FUNCTION TO CREATE LEGEND SYMBOL ---
+def create_symbol_image(filename, attributes):
+    """Creates a small image of a graphviz symbol."""
+    d = Digraph()
+    d.attr('node', shape='point', style='invis', width='0')
+    d.attr(rankdir='LR', splines='false', margin='0')
+    d.edge('start', 'end', **attributes)
+    d.render(os.path.splitext(filename)[0], format='png', cleanup=True)
+
+# --- CREATE THE SYMBOL IMAGES NEEDED FOR THE LEGEND ---
+create_symbol_image("legend_inheritance.png", {'arrowhead': 'empty'})
+create_symbol_image("legend_association.png", {'arrowhead': 'vee'})
+create_symbol_image("legend_dependency.png", {'arrowhead': 'vee', 'style': 'dashed'})
+create_symbol_image("legend_aggregation.png", {'arrowhead': 'odiamond'})
+create_symbol_image("legend_composition.png", {'arrowhead': 'diamond', 'style': 'filled'})
+
+
+# --- MAIN DIAGRAM DEFINITION ---
+dot = Digraph(comment="MEEM UML Diagram", format="png")
+dot.attr(rankdir="TB", fontsize="10")  # Top-to-Bottom layout
+
+# Define classes with constructors explicitly
+classes = {
+    "Geometry": [
+        "<<Abstract>>",
+        "+ __init__(body_arrangement: BodyArrangement, h: float)",
+        "- body_arrangement: BodyArrangement",
+        "- h: float",
+        "- _fluid_domains: List[Domain]",
+        "+ fluid_domains: List[Domain]",
+        "+ make_fluid_domains(): List[Domain]"
+    ],
+    "BasicRegionGeometry": [
+        "+ __init__(body_arrangement: ConcentricBodyGroup, h: float, NMK: List[int])",
+        "- NMK: List[int]",
+        "+ make_fluid_domains(): List[Domain]",
+        "+ from_vectors(a: np.ndarray, d: np.ndarray, h: float, NMK: List[int]) -> BasicRegionGeometry"
+    ],
+    "AnyRegionGeometry": [
+        "(future implementation)"
+    ],
+    "BodyArrangement": [
+        "<<Abstract>>",
+        "+ __init__(bodies: List[Body])",
+        "- bodies: List[Body]",
+        "+ a: np.ndarray",
+        "+ d: np.ndarray",
+        "+ slant_angle: np.ndarray",
+        "+ heaving: np.ndarray"
+    ],
+    "ConcentricBodyGroup": [
+        "+ __init__(bodies: List[SteppedBody])",
+        "+ _get_concatenated_property(prop_name: str): np.ndarray",
+        "+ _get_heaving_flags(): np.ndarray",
+        "+ a: np.ndarray",
+        "+ d: np.ndarray",
+        "+ slant_angle: np.ndarray",
+        "+ heaving: np.ndarray"
+    ],
+    "Body": [
+        "<<Abstract>>",
+        "- heaving: bool"
+    ],
+    "SteppedBody": [
+        "+ __init__(a: np.ndarray, d: np.ndarray, slant_angle: np.ndarray, heaving: bool = False)",
+        "- a: np.ndarray",
+        "- d: np.ndarray",
+        "- slant_angle: np.ndarray",
+        "- heaving: bool"
+    ],
+    "CoordinateBody": [
+        "+ __init__(r_coords: np.ndarray, z_coords: np.ndarray, heaving: bool = False)",
+        "+ discretize(): Tuple[np.ndarray, np.ndarray, np.ndarray]"
+    ],
+    "Domain": [
+        "+ __init__(index: int, NMK: int, a_inner: float, a_outer: float, d_lower: float, geometry_h: float, heaving: Optional[bool], slant: bool, category: str)",
+        "- index: int",
+        "- number_harmonics: int",
+        "- a_inner: float",
+        "- a_outer: float",
+        "- d_lower: float",
+        "- geometry_h: float",
+        "- category: str",
+        "+ are_adjacent(d1: Domain, d2: Domain) -> bool"
+    ],
+    "MEEMProblem": [
+        "+ __init__(geometry: Geometry)",
+        "- geometry: Geometry",
+        "+ set_frequencies_modes(frequencies: np.ndarray, modes: np.ndarray)"
+    ],
+    "MEEMEngine": [
+        "+ __init__()",
+        "- problem_list: List[MEEMProblem]",
+        "+ _ensure_m_k_and_N_k_arrays(problem: MEEMProblem, m0)",
+        "+ assemble_A_multi(problem: MEEMProblem, m0): ndarray",
+        "+ assemble_b_multi(problem: MEEMProblem, m0): ndarray",
+        "+ build_problem_cache(problem: MEEMProblem): ProblemCache",
+        "+ calculate_potentials: Dict",
+        "+ calculate_velocities(problem, solution_vector: np.ndarray, m0, spatial_res, sharp): Dict",
+        "+ compute_hydrodynamic_coefficients(problem, X, m0)",
+        "+ reformat_coeffs(x: np.ndarray, NMK, boundary_count) list[ndarray]",
+        "+ run_and_store_results(problem_index: int): Results",
+        "+ solve_linear_system_multi(problem: MEEMProblem, m0): ndarray",
+        "+ visualize_potential(field, R, Z, title): tuple"
+    ],
+    "Results": [
+        "+ __init__(geometry: Geometry, frequencies: np.ndarray, modes: np.ndarray)",
+        "- geometry: Geometry",
+        "- frequencies: np.ndarray",
+        "- modes: np.ndarray",
+        "+ display_results(): str",
+        "+ export_to_netcdf(file_path: str)",
+        "+ get_results(): xarray.Dataset",
+        "+ store_all_potentials(all_potentials_batch: list[dict])",
+        "+ store_hydrodynamic_coefficients(frequencies: np.ndarray, modes: np.ndarray, added_mass_matrix: np.ndarray, damping_matrix: np.ndarray)",
+        "+ store_results(domain_index: int, radial_data: ndarray, vertical_data: ndarray)",
+        "+ store_single_potential_field(potential_data: dict, frequency_idx: int = 0, mode_idx: int = 0)"
+    ],
+    "ProblemCache": [
+        "+ __init__(problem: MEEMProblem)",
+        "- problem",
+        "+ _add_m0_dependent_A_entry(row: int, col: int, calc_func: Callable)",
+        "+ _add_m0_dependent_b_entry(row: int, calc_func: Callable)",
+        "+ _get_A_template(): ndarray",
+        "+ _get_b_template(): ndarray",
+        "+ _get_closure(key: str)",
+        "+ _set_A_template(A_template: ndarray)",
+        "+ _set_I_nm_vals(I_nm_vals: ndarray)",
+        "+ _set_b_template(b_template: ndarray)",
+        "+ _set_closure(key: str, closure)",
+        "+ _set_m_k_and_N_k_funcs(m_k_entry_func: Callable, N_k_func: Callable)",
+        "+ _set_precomputed_m_k_N_k(m_k_arr: ndarray, N_k_arr: ndarray)"
+    ]
+}
+
+# Add class nodes using robust HTML-like labels
+for cls, members in classes.items():
+    # Escape special characters for HTML
+    escaped_members = [m.replace('<', '&lt;').replace('>', '&gt;') for m in members]
+
+    # Join members with HTML line breaks, aligned left
+    members_str = '<BR ALIGN="LEFT"/>'.join(escaped_members)
+
+    # Create the complete HTML table label for the node
+    label = f'''<<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="4">
+    <TR><TD><B>{cls}</B></TD></TR>
+    <TR><TD ALIGN="LEFT">{members_str}</TD></TR>
+    </TABLE>>'''
+
+    dot.node(cls, shape="plain", label=label) # Use shape="plain" for HTML labels
+
+
+# Relationships
+# Geometry owns Domain (Composition: filled diamond)
+dot.edge("Geometry", "Domain", arrowhead="diamond", style="filled", label="1..*")  
+
+# MEEMProblem references Geometry (Association)
+dot.edge("MEEMProblem", "Geometry", arrowhead="vee", label="1")
+
+# MEEMEngine aggregates MEEMProblem (Aggregation: hollow diamond)
+dot.edge("MEEMEngine", "MEEMProblem", arrowhead="odiamond", label="*")  
+
+# MEEMEngine composes ProblemCache (Composition: filled diamond)
+dot.edge("MEEMEngine", "ProblemCache", arrowhead="diamond", style="filled", label="*")  
+
+# MEEMEngine depends on Results (Dependency: dashed arrow)
+dot.edge("MEEMEngine", "Results", style="dashed", label="*")  
+
+# Results references Geometry (Association)
+dot.edge("Results", "Geometry", arrowhead="vee", label="1")
+
+# Inheritance (is-a)
+dot.edge("BasicRegionGeometry", "Geometry", arrowhead="empty")
+dot.edge("AnyRegionGeometry", "Geometry", arrowhead="empty")
+dot.edge("ConcentricBodyGroup", "BodyArrangement", arrowhead="empty")
+dot.edge("SteppedBody", "Body", arrowhead="empty")
+dot.edge("CoordinateBody", "Body", arrowhead="empty")
+
+# Composition (has-a, strong ownership)
+dot.edge("ConcentricBodyGroup", "Body", arrowhead="diamond", style="filled", label="1..*")
+
+# Association (uses-a)
+dot.edge("Geometry", "BodyArrangement", arrowhead="vee", label="1")
+
+# --- Legend / Key node ---
+legend = """<
+<TABLE BORDER="1" CELLBORDER="0" CELLSPACING="2" CELLPADDING="4">
+  <TR><TD COLSPAN="2"><B>Legend</B></TD></TR>
+  <TR><TD><IMG SRC="legend_inheritance.png"/></TD><TD ALIGN="LEFT">Inheritance ("is-a")</TD></TR>
+  <TR><TD><IMG SRC="legend_composition.png"/></TD><TD ALIGN="LEFT">Composition ("Owns / Is part of")</TD></TR>
+  <TR><TD><IMG SRC="legend_aggregation.png"/></TD><TD ALIGN="LEFT">Aggregation ("Has-a / Contains")</TD></TR>
+  <TR><TD><IMG SRC="legend_association.png"/></TD><TD ALIGN="LEFT">Association ("uses-a")</TD></TR>
+  <TR><TD><IMG SRC="legend_dependency.png"/></TD><TD ALIGN="LEFT">Dependency / Planned</TD></TR>
+  <TR><TD COLSPAN="2" HEIGHT="1" BGCOLOR="black" BORDER="0"></TD></TR>
+  <TR><TD ALIGN="LEFT">1, *, 1..*</TD><TD ALIGN="LEFT">Multiplicity</TD></TR>
+</TABLE>>"""
+dot.node("Legend", legend, shape="none")
+
+# Render the final diagram
+output_path = "openflash_uml_vertical"
+dot.render(output_path, cleanup=True)
+
+# Clean up the generated symbol images
+for img in ["legend_inheritance.png", "legend_association.png", "legend_dependency.png",
+            "legend_aggregation.png", "legend_composition.png"]:
+    if os.path.exists(img):
+        os.remove(img)
+
+print(f"UML diagram successfully generated as '{output_path}.png'")
\ No newline at end of file
diff --git a/pubs/joss/paper.bib b/pubs/joss/paper.bib
new file mode 100644
index 0000000..ca5a125
--- /dev/null
+++ b/pubs/joss/paper.bib
@@ -0,0 +1,101 @@
+@article{bhattacharya2021timing,
+  author    = {Bhattacharya, S. and Pennock, S. and Robertson, B. and Hanif, S. and Alam, M. J. E. and Bhatnagar, D. and Preziuso, D. and O’Neil, R.},
+  title     = {Timing Value of Marine Renewable Energy Resources for Potential Grid Applications},
+  journal   = {Applied Energy},
+  volume    = {299},
+  pages     = {117281},
+  year      = {2021},
+  doi       = {10.1016/j.apenergy.2021.117281}
+}
+
+@book{chatjigeorgiou2018analytical,
+  author    = {Chatjigeorgiou, I. K.},
+  title     = {Analytical Methods in Marine Hydrodynamics},
+  year      = {2018},
+  publisher = {Cambridge University Press},
+  doi       = {10.1017/9781316838983},
+  url       = {https://doi.org/10.1017/9781316838983}
+}
+
+@inproceedings{chau2010inertia,
+  author    = {Chau, F. P. and Yeung, R. W.},
+  title     = {Inertia and Damping of Heaving Compound Cylinders},
+  booktitle = {Proceedings of the 25th International Workshop on Water Waves and Floating Bodies},
+  address   = {Harbin, China},
+  year      = {2010},
+  url       = {https://www.academia.edu/73219479/Inertia_and_Damping_of_Heaving_Compound_Cylinders_Fun}
+}
+
+@inproceedings{chau2012inertia,
+  author    = {Chau, F. P. and Yeung, R. W.},
+  title     = {Inertia, Damping, and Wave Excitation of Heaving Coaxial Cylinders},
+  booktitle = {ASME 2012 31st International Conference on Ocean, Offshore and Arctic Engineering},
+  pages     = {803--813},
+  publisher = {American Society of Mechanical Engineers},
+  year      = {2012},
+  doi       = {10.1115/OMAE2012-83987},
+  url       = {https://doi.org/10.1115/OMAE2012-83987}
+}
+
+@article{fusco2010variability,
+  author    = {Fusco, F. and Nolan, G. and Ringwood, J.},
+  title     = {Variability Reduction through Optimal Combination of Wind/Wave Resources – An Irish Case Study},
+  journal   = {Energy},
+  volume    = {35},
+  number    = {1},
+  pages     = {314--325},
+  year      = {2010},
+  doi       = {10.1016/j.energy.2009.10.018}
+}
+
+@article{kokkinowrachos1986behaviour,
+  author    = {Kokkinowrachos, K. and Mavrakos, S. and Asorakos, S.},
+  title     = {Behaviour of Vertical Bodies of Revolution in Waves},
+  journal   = {Ocean Engineering},
+  volume    = {13},
+  number    = {6},
+  pages     = {505--538},
+  year      = {1986},
+  doi       = {10.1016/0029-8018(86)90037-5},
+  url       = {https://doi.org/10.1016/0029-8018(86)90037-5}
+}
+
+@inproceedings{mccabe2024open,
+  author    = {McCabe, R. and Khanal, K. and Haji, M.},
+  title     = {Open-source Toolbox for Semi-analytical Hydrodynamic Coefficients via the Matched Eigenfunction Expansion Method},
+  booktitle = {UMERC+METS 2024 Conference},
+  address   = {Duluth, MN, USA},
+  year      = {2024},
+  doi       = {10.5281/zenodo.14504016},
+  url       = {https://doi.org/10.5281/zenodo.14504016}
+}
+
+@unpublished{mccabe2024numerics,
+  author    = {McCabe, R. and Khanal, K. and Bimali, Y. and Lo, E. and Treacy, C. and Haji, M.},
+  title     = {Numerics of the Matched Eigenfunction Method for Computing Wave Forces on Concentric Bodies},
+  note      = {In preparation},
+  year      = {2024}
+}
+
+@article{son2016performance,
+  author    = {Son, D. and Belissen, V. and Yeung, R. W.},
+  title     = {Performance Validation and Optimization of a Dual Coaxial-cylinder Ocean-wave Energy Extractor},
+  journal   = {Renewable Energy},
+  volume    = {92},
+  pages     = {192--201},
+  year      = {2016},
+  doi       = {10.1016/j.renene.2016.01.032},
+  url       = {https://doi.org/10.1016/j.renene.2016.01.032}
+}
+
+@article{yeung1981added,
+  author    = {Yeung, R. W.},
+  title     = {Added Mass and Damping of a Vertical Cylinder in Finite-depth Waters},
+  journal   = {Applied Ocean Research},
+  volume    = {3},
+  number    = {3},
+  pages     = {119--133},
+  year      = {1981},
+  doi       = {10.1016/0141-1187(81)90101-2},
+  url       = {https://doi.org/10.1016/0141-1187(81)90101-2}
+}
diff --git a/pubs/joss/paper.md b/pubs/joss/paper.md
new file mode 100644
index 0000000..3b5d569
--- /dev/null
+++ b/pubs/joss/paper.md
@@ -0,0 +1,92 @@
+---
+title: 'OpenFLASH: An open-source flexible library for analytical and semi-analytical hydrodynamics calculations'
+tags:
+  - Python
+  - hydrodynamics
+  - semi-analytical methods
+  - wave energy
+  - marine engineering
+authors:
+  - name: Hope Best
+    orcid: 0009-0003-9860-2228
+    affiliation: "1, 3"
+  - name: Kapil Khanal
+    affiliation: "1, 3"
+  - name: Rebecca McCabe
+    affiliation: "1, 3"
+  - name: Ruiyang Jiang
+    affiliation: "1, 3"
+  - name: Collin Treacy
+    affiliation: "2, 3"
+  - name: Maha Haji
+    corresponding: true
+    affiliation: "2, 3"
+affiliations:
+ - index: 1
+   name: Cornell University, Ithaca, NY, United States
+   ror: 05q0b0t32
+ - index: 2
+   name: Department of Mechanical Engineering, University of Michigan, United States
+   ror: 02xavh848
+ - index: 3
+   name: Symbiotic Engineering and Analysis (SEA) Lab
+date: 26 October 2025
+bibliography: paper.bib
+---
+
+# Summary
+
+OpenFLASH is a Python package for solving hydrodynamic boundary value problems using analytical and semi-analytical methods. It currently implements the matched eigenfunction expansion method for bodies of multiple concentric cylinders. This method, presented by [@mccabe2024open] at the UMERC+METS 2024 Conference, can reduce the runtime by an order of magnitude compared to traditional Boundary Element Method (BEM) solvers, making it more suitable for design optimization studies of floating structures such as wave energy converters (WECs).
+
+# Statement of Need
+
+Wave energy converters (WEC) hold significant promise for transforming the oscillatory motion of waves into usable energy, offering high predictability and enhanced energy security that complements other renewable sources like wind and solar power [@bhattacharya2021timing; @fusco2010variability]. However, the optimization of WECs has been hindered by the substantial computational costs of modeling their hydrodynamic interactions in waves. This project aims to address this challenge by developing OpenFLASH, an open-source and computationally efficient software tool for modeling hydrodynamic forces using semi-analytical methods.
+
+OpenFLASH aims to provide a robust and user-friendly Python implementation of this methodology, specifically tailored for problems involving connected cylindrical domains. The package is designed to handle multi-domain problems, including exterior domains extending to infinity and interior domains with specific radial extents, each with defined top and bottom boundary conditions. The computational workflow begins with defining the geometry and problem parameters, followed by assembling and solving the linear system, calculating hydrodynamic coefficients and potentials, storing the results, and finally visualizing them. This specialization can lead to more efficient problem setup and solution, particularly useful in fields like marine hydrodynamics and the burgeoning field of wave energy technology. The package addresses the need for a tool that bridges the gap between analytical derivations and numerical computation for this important class of problems. Furthermore, it provides tools for managing, testing, interactively visualizing, documenting, and outlining its computational process.
+
+# Functionality
+
+![UML Diagram for OpenFLASH.\label{fig:uml}](../figs/MEEM_UML_Diagram.png)
+
+OpenFLASH provides a complete, end-to-end workflow for hydrodynamic analysis, centered around an intuitive, object-oriented API. \autoref{fig:uml} demonstrates the relationships between classes in the package.
+
+* Intuitive Geometry Definition: Users define the physical problem by creating SteppedBody objects, which represent single- or multi-step cylindrical structures. These objects are then grouped into a ConcentricBodyGroup and passed to a BasicRegionGeometry class, which automatically partitions the fluid volume into the discrete Domain objects required by the solver (see \autoref{fig:domain_table}).
+
+![A summary of the key attributes that define each type of fluid domain.\label{fig:domain_table}](../figs/domain_table.png)
+
+![A typical problem geometry.\label{fig:domain_drawing}](../figs/domain_drawing.png)
+
+\autoref{fig:domain_drawing} shows a typical problem geometry is divided into multiple concentric fluid domains, including interior domains under the bodies and a final, semi-infinite exterior domain.
+
+* Problem Setup: The MEEMProblem class sets up the computational problem by defining the relevant frequencies and degrees of freedom of analysis.
+* MEEM Computation Engine: The MEEMEngine class is the core of the package, responsible for implementing the matched eigenfunction expansion method. 
+* Problem Cache for Efficient Computation: The ProblemCache class is designed to enhance the computational efficiency of the MEEMEngine class significantly.
+* Results Management: The Results class provides a structured way to store and organize the output of the MEEM computations using the xarray library, adhering to conventions similar to those used in the Capytaine library to facilitate drop-in replacement for Capytaine users.
+* Documentation: The package utilizes Sphinx to generate comprehensive documentation. The documentation includes a tutorial that guides users through the process of using the package and explains its capabilities. The sphinx documentation is deployed in the browser through: [https://symbiotic-engineering.github.io/OpenFLASH/](https://symbiotic-engineering.github.io/OpenFLASH/).
+* Interactive Simulation and Visualization: A Streamlit application (docs/app.py) provides a graphical user interface for interacting with OpenFLASH. Users can define problem parameters through the GUI, run simulations, and visualize the resulting potential fields in real-time. This interactive tool enhances the usability and accessibility of the package. The streamlit app is deployed through: [https://symbiotic-engineering.github.io/OpenFLASH/app\_streamlit.html](https://symbiotic-engineering.github.io/OpenFLASH/app_streamlit.html).
+
+![Streamlit Screenshot 1 \label{fig:streamlit1}](../figs/streamlit1.png)
+
+![Streamlit Screenshot 2 \label{fig:streamlit2}](../figs/streamlit2.png)
+
+![Streamlit Screenshot 3 \label{fig:streamlit3}](../figs/streamlit3.png)
+
+![Streamlit Screenshot 4 \label{fig:streamlit4}](../figs/streamlit4.png)
+
+* Testing Suite: The package includes a comprehensive suite of unit tests (tests directory) using the pytest framework to ensure the code's reliability and correctness. These tests cover the core functionalities, ensuring the reliability and correctness of the code across different modules.
+
+# Impact
+
+OpenFLASH provides a specialized and powerful tool for researchers and engineers working on boundary value problems in domains with connected cylindrical geometries, with a particular emphasis on advancing the field of wave energy conversion. Its modular design and focus on the matched eigenfunction expansion method offer several key benefits for WEC research such as accessibility for WEC researchers, accelerating WEC innovation through efficient modeling, and being both open-source and community-driven. 
+
+The initial development of hydrodynamic models lays the groundwork for this package, and the ongoing work to refine the code structure, optimize usability, incorporate diverse WEC geometries, and expand documentation will ensure that OpenFLASH becomes a valuable asset for the wave energy research community.
+
+# Acknowledgements
+
+We acknowledge the foundational work on matched eigenfunction methods as presented in the cited references. The development of OpenFLASH has been supported by the resources and expertise within the SEA Lab, led by Professor Maha N. Haji. We also acknowledge the contributions of the following students to the development of this software: Yinghui Bimali, En Lo, and John Fernandez. We also acknowledge the ongoing collaborations with Jessica Nguyen, Clint Chester Reyes, and Brittany Lydon for their work on validating and extending the capabilities of OpenFLASH to elliptical and Cartesian coordinate systems. We thank Prof. R. W. Yeung and Seung-Yoon Han for discussions on the theory and computation of this method.
+
+We also gratefully acknowledge support for Kapil Khanal from a Sandia National Laboratories seedling grant and Rebecca McCabe from the NSF GRFP. The contributions of undergraduate researchers were supported by Cornell University’s Engineering Learning Initiative (ELI).
+
+This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No.DGE–2139899. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
+
+# References
\ No newline at end of file
diff --git a/pubs/src/.gitkeep b/pubs/src/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..847bfc1
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,73 @@
+[build-system]
+requires = ["setuptools>=61.0.0", "wheel", "setuptools-scm>=8"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "open-flash"
+dynamic = ["version"]
+authors = [
+  {name = "SEA Lab"} 
+]
+description = "A Python package for semi-analytical hydrodynamics modeling including matched eigenfunction expansion method"
+readme = "README.md"
+license-files = ["LICENSE*","CITATION.cff"]
+requires-python = ">=3.8"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Operating System :: OS Independent",
+    "Intended Audience :: Science/Research",
+    "Topic :: Scientific/Engineering :: Physics",
+]
+
+dependencies = [
+    "numpy>=1.21",
+    "scipy>=1.7",
+    "pandas>=1.5",
+    "matplotlib>=3.5",
+    "h5netcdf>=0.12",
+    "xarray>=2023.0",
+    "streamlit>=1.0"
+]
+
+keywords = ["matched eigenfunctions", "hydrodynamics", "semi-analytical"]
+
+urls = { Homepage = "https://github.com/symbiotic-engineering/semi-analytical-hydro.git" }
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=7.0",
+    "pytest-cov",
+    "sphinx>=3.0",
+    "sphinx-rtd-theme>=0.5",
+    "sphinx-tabs>=1.0",
+    "sphinx_design>=0.0.8",
+    "sphinx-copybutton",
+    "sphinx-last-updated-by-git",
+    "nbsphinx>=0.8",
+    "jupyter>=1.0.0",
+    "ipykernel",
+    "twine>=4.0",
+    "build>=0.8",
+    "readme_renderer>=30.0",
+    "watchdog>=3.0",
+    "black>=23.0",
+    "ruff>=0.0.280"
+]
+hydro = [
+    "capytaine>=2.2",
+]
+
+[tool.setuptools.packages.find]
+where = ["package/src"]
+
+[tool.setuptools.package-data]
+"openflash" = [] # Explicitly state no package data
+
+[tool.setuptools_scm]
+local_scheme = "no-local-version"
+fallback_version = "0+unknown"
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index d681de6..60ce482 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,44 +1,33 @@
-appnope==0.1.4
-asttokens==2.4.1
-comm==0.2.2
-contourpy==1.3.0
-cycler==0.12.1
-debugpy==1.8.6
-decorator==5.1.1
-executing==2.1.0
-fonttools==4.54.1
-ipykernel==6.29.5
-ipython==8.27.0
-jedi==0.19.1
-jupyter_client==8.6.3
-jupyter_core==5.7.2
-kiwisolver==1.4.7
-matplotlib==3.9.2
-matplotlib-inline==0.1.7
-nest-asyncio==1.6.0
-numpy==2.1.1
-packaging==24.1
-pandas==2.2.3
-parso==0.8.4
-pexpect==4.9.0
-pillow==10.4.0
-platformdirs==4.3.6
-prompt_toolkit==3.0.47
-psutil==6.0.0
-ptyprocess==0.7.0
-pure_eval==0.2.3
-Pygments==2.18.0
-pyparsing==3.1.4
-python-dateutil==2.9.0.post0
-pytz==2024.2
-pyzmq==26.2.0
-scipy==1.14.1
-six==1.16.0
-stack-data==0.6.3
-tornado==6.4.1
-traitlets==5.14.3
-tzdata==2024.2
-wcwidth==0.2.13
-pytest==7.4.2
-pytest-cov
-capytaine==2.2.1
+# Runtime dependencies
+numpy>=1.21
+scipy>=1.7
+matplotlib>=3.5
+pandas>=1.5
+h5netcdf>=0.12
+xarray>=2023.0 
+streamlit>=1.0
+
+# Documentation
+sphinx>=3.0
+sphinx-rtd-theme>=0.5
+sphinx-tabs>=1.0
+sphinx_design>=0.0.8
+nbsphinx>=0.8
+sphinx-copybutton
+sphinx-last-updated-by-git
+
+# Notebooks
+jupyter>=1.0.0
+ipykernel
+
+# Testing and packaging
+pytest>=7.0
+pytest-cov
+twine>=4.0
+build>=0.8
+readme_renderer>=30.0
+watchdog>=3.0
+
+# Code quality tools
+black>=23.0
+ruff>=0.0.280
diff --git a/scripts/compute_base.sh b/scripts/compute_base.sh
new file mode 100644
index 0000000..1fe32c9
--- /dev/null
+++ b/scripts/compute_base.sh
@@ -0,0 +1,29 @@
+#!/usr/bin/env bash
+set -euo pipefail
+# Usage: compute_base.sh [PR_TITLE]
+PR_TITLE="${1:-}"
+
+git fetch --tags --no-recurse-submodules
+last_tag=$(git tag --list 'v*.*.*' --sort=-version:refname | grep -v rc | head -n1 || true)
+if [ -z "$last_tag" ]; then
+  last_tag="v0.0.0"
+fi
+part="${last_tag#v}"
+major=$(echo "$part" | cut -d. -f1)
+minor=$(echo "$part" | cut -d. -f2)
+patch=$(echo "$part" | cut -d. -f3)
+
+lower_title=$(echo "$PR_TITLE" | tr '[:upper:]' '[:lower:]')
+if echo "$lower_title" | grep -q 'major'; then
+  major=$((major + 1))
+  minor=0
+  patch=0
+elif echo "$lower_title" | grep -q 'minor'; then
+  minor=$((minor + 1))
+  patch=0
+else
+  patch=$((patch + 1))
+fi
+
+base="v${major}.${minor}.${patch}"
+echo "$base"
diff --git a/scripts/release.sh b/scripts/release.sh
new file mode 100755
index 0000000..f9d04fc
--- /dev/null
+++ b/scripts/release.sh
@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+# release.sh - Rerelease to PyPI and Conda with auto version detection
+# Usage: ./release.sh
+
+set -e  # Exit on error
+
+# 0️⃣ Detect version from pyproject.toml
+if [ ! -f pyproject.toml ]; then
+    echo "❌ pyproject.toml not found. Please run from the project root."
+    exit 1
+fi
+
+VERSION=$(grep -m1 '^version =' pyproject.toml | sed 's/version = "\(.*\)"/\1/')
+if [ -z "$VERSION" ]; then
+    echo "❌ Could not detect version from pyproject.toml"
+    exit 1
+fi
+
+echo "🚀 Starting release process for version $VERSION..."
+
+# 1️⃣ Update meta.yaml version
+if [ -f conda-recipe/meta.yaml ]; then
+    sed -i.bak "s/^  version: .*/  version: \"$VERSION\"/" conda-recipe/meta.yaml
+    echo "✅ Updated version in conda-recipe/meta.yaml"
+fi
+
+# 2️⃣ Clean old builds
+echo "🧹 Cleaning old build artifacts..."
+rm -rf dist/ build/ *.egg-info
+
+# 3️⃣ Build & upload to PyPI
+echo "📦 Building and uploading to PyPI..."
+python -m build
+twine upload dist/*
+
+# 4️⃣ Build & upload to Conda
+echo "📦 Building and uploading to Conda..."
+conda build conda-recipe/
+PKG_FILE=$(conda build conda-recipe/ --output)
+anaconda upload "$PKG_FILE"
+
+echo "🎉 Release $VERSION complete!"
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 1a8ddae..0000000
--- a/setup.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from setuptools import setup, find_packages
-
-# Read requirements from the file (from the root directory)
-with open("requirements.txt") as f:
-    requirements = f.read().splitlines()
-
-setup(
-    name='meem',  # Package name
-    version='0.1.0',  # Package version
-    description='A Python package for matched eigenfunctions methods',  # Short description
-    long_description=open('README.md').read(),  # Read long description from README
-    long_description_content_type='text/markdown',  # Set content type for markdown
-    url='https://github.com/symbiotic-engineering/semi-analytical-hydro.git',  # GitHub URL
-    packages=find_packages(where='package/src'),  # Find packages under 'package/src'
-    package_dir={'': 'package/src'},  # Set the package root directory to 'package/src'
-    classifiers=[
-        'Programming Language :: Python :: 3',
-        'License :: OSI Approved :: MIT License',
-        'Operating System :: OS Independent',
-    ],
-    python_requires='>=3.8',  # Minimum Python version required
-    install_requires=requirements,  # Install dependencies from requirements.txt
-    license='MIT',  # License type
-)
diff --git a/test-results/junit.xml b/test-results/junit.xml
deleted file mode 100644
index 6a5c748..0000000
--- a/test-results/junit.xml
+++ /dev/null
@@ -1 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?><testsuites><testsuite name="pytest" errors="0" failures="0" skipped="0" tests="1" time="34.411" timestamp="2024-12-02T16:19:58.852801" hostname="Ruiyang_Jiang"><testcase classname="package.test.main_test" name="test_main" time="32.291" /></testsuite></testsuites>
\ No newline at end of file
diff --git a/test/.gitkeep b/test/.gitkeep
deleted file mode 100644
index e69de29..0000000