diff --git a/.dockerignore b/.dockerignore
index b4b8e35..8b4fc71 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -2,6 +2,7 @@
 data/*
 code/results/*
 code/figures/*
+_build
 
 
 ### Python ###
diff --git a/.flake8 b/.flake8
deleted file mode 100644
index 0b791a0..0000000
--- a/.flake8
+++ /dev/null
@@ -1,5 +0,0 @@
-# flake8 does not support pyproject.toml, see:
-# https://github.com/PyCQA/flake8/issues/234
-[flake8]
-exclude = docs,venv
-max-line-length = 100
diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index 4f648ed..e8b6e0e 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -1,6 +1,9 @@
-name: Github Pages
+# Simple workflow for deploying static content to GitHub Pages
+name: Deploy static content to Pages
 
 on:
+  push:
+    branches: ["main"]
   pull_request:
     branches: ["main"]
 
@@ -16,22 +19,54 @@ jobs:
       PUBLISH_DIR: ./_build/html
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - name: Setup Python
         uses: actions/setup-python@v4
         with:
           python-version: "3.10"
 
+      - name: Cache
+        id: cache
+        uses: actions/cache@v3
+        with:
+          path: |
+            ~/.cache/pip
+            ~/_build
+          key: cache_v1
+          restore-keys: |
+            cache_v1
+
       - name: Install dependencies
         run: python3 -m pip install -r requirements-docs.txt
 
+
       - name: Build docs
         run: jupyter book build .
-  
-      - name: Upload documentation as artifact
-        uses: actions/upload-artifact@v3
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v2
         with:
-          name: documentation
           path: ${{ env.PUBLISH_DIR }}
-          if-no-files-found: error
\ No newline at end of file
+
+  # Single deploy job since we're just deploying
+  deploy:
+    if: github.ref == 'refs/heads/main'
+    needs: build
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Pages
+        uses: actions/configure-pages@v3
+
+
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v2
diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
deleted file mode 100644
index 1382032..0000000
--- a/.github/workflows/deploy.yml
+++ /dev/null
@@ -1,54 +0,0 @@
-name: Deploy to Github pages
-
-on:
-  push:
-    branches: [main]
-
-# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
-permissions:
-  contents: read
-  pages: write
-  id-token: write
-
-# Allow one concurrent deployment
-concurrency:
-  group: "pages"
-  cancel-in-progress: true
-
-jobs:  
-
-
-  # Build documentation/website. Will be downloaded in first step
-  build-docs:
-    uses: ./.github/workflows/build_docs.yml
-
-  deploy:
-    needs: [build-docs]
-
-    environment:
-      name: github-pages
-      url: ${{ steps.deployment.outputs.page_url }}
-
-    runs-on: ubuntu-latest
-    steps:
-      - name: Download docs artifact
-        # docs artifact is uploaded by build-docs job
-        uses: actions/download-artifact@v3
-        with:
-          name: documentation
-          path: "./public"
-  
-      - name: Upload artifact
-        uses: actions/upload-pages-artifact@v1
-        with:
-          path: "./public"
-
-      - name: Checkout repository
-        uses: actions/checkout@v3
-
-      - name: Setup Pages
-        uses: actions/configure-pages@v3
-
-      - name: Deploy to GitHub Pages
-        id: deployment
-        uses: actions/deploy-pages@v2
diff --git a/.github/workflows/docker-image.yml b/.github/workflows/docker-image.yml
index d8b34da..3862ba0 100644
--- a/.github/workflows/docker-image.yml
+++ b/.github/workflows/docker-image.yml
@@ -25,16 +25,16 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v2
+        uses: docker/setup-qemu-action@v3
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
 
       - name: Log in to the Container registry
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
         with:
           registry: ${{ env.REGISTRY }}
           username: ${{ github.actor }}
@@ -42,12 +42,12 @@ jobs:
 
       - name: Extract metadata (tags, labels) for Docker
         id: meta
-        uses: docker/metadata-action@v4
+        uses: docker/metadata-action@v5
         with:
           images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
 
       - name: Build and push Docker image
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v5
         with:
           context: .
           push: true
diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
new file mode 100644
index 0000000..83e94e7
--- /dev/null
+++ b/.github/workflows/pre-commit.yml
@@ -0,0 +1,21 @@
+name: Pre-commit
+
+on:
+  push:
+    branches: ["main"]
+  pull_request:
+    branches: ["main"]
+
+
+jobs:
+  check-code:
+    runs-on: ubuntu-22.04
+    steps:
+      # This action sets the current path to the root of your github repo
+      - uses: actions/checkout@v4
+
+      - name: Install pre-commit
+        run: python3 -m pip install pre-commit
+
+      - name: Run hooks
+        run: pre-commit run --all
diff --git a/.github/workflows/reproduce_results.yml b/.github/workflows/reproduce_results.yml
new file mode 100644
index 0000000..dc82691
--- /dev/null
+++ b/.github/workflows/reproduce_results.yml
@@ -0,0 +1,59 @@
+# Simple workflow for deploying static content to GitHub Pages
+name: Reproduce results
+
+on:
+  push:
+    branches: ["main"]
+  pull_request:
+    branches: ["main"]
+
+    # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+  workflow_call:
+
+
+jobs:
+  run:
+    runs-on: ubuntu-22.04
+    env:
+      # Directory that will be published on github pages
+      DATAPATH: ./artifacts/data/data.json
+      FIGDIR: ./artifacts/figures
+      RESULTDIR: ./artifacts/results
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+
+      - name: Cache
+        id: cache
+        uses: actions/cache@v3
+        with:
+          path: |
+            ~/.cache/pip
+          key: cache_v1
+          restore-keys: |
+            cache_v1
+
+      - name: Install dependencies
+        run: python3 -m pip install -r requirements.txt
+
+      - name: Run pre-processing
+        run: python3 code/pre_processing.py --datapath=${{ env.DATAPATH }}
+
+      - name: Run simulation
+        run: python3 code/run_all.py --resultdir=${{ env.RESULTDIR }}
+
+      - name: Postprocess
+        run: python3 code/postprocess.py --resultdir=${{ env.RESULTDIR }} --datapath=${{ env.DATAPATH }} --figdir=${{ env.FIGDIR }}
+
+      - name: Upload artifact
+        if: always()
+        uses: actions/upload-artifact@v3
+        with:
+          path: ./artifacts
+          if-no-files-found: error
diff --git a/.gitignore b/.gitignore
index 8490f15..1f077c5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,8 @@
+_build
 code/results
-data/data.txt
+data/data.json
 code/figures
+.DS_Store
 # Created by https://www.toptal.com/developers/gitignore/api/python,visualstudiocode
 # Edit at https://www.toptal.com/developers/gitignore?templates=python,visualstudiocode
 
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 925332f..0d22950 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v4.5.0
     hooks:
       - id: check-yaml
       - id: end-of-file-fixer
@@ -8,34 +8,33 @@ repos:
       - id: check-docstring-first
       - id: debug-statements
       - id: requirements-txt-fixer
+      - id: check-added-large-files
+      - id: check-toml
 
-  - repo: https://github.com/asottile/reorder_python_imports
-    rev: v3.8.3
+  - repo: https://github.com/asottile/add-trailing-comma
+    rev: v3.1.0
     hooks:
-      - id: reorder-python-imports
+      - id: add-trailing-comma
 
   - repo: https://github.com/psf/black
-    rev: 22.10.0
+    rev: 23.10.1
     hooks:
       - id: black
 
-  - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v2.0.0
+  - repo: https://github.com/charliermarsh/ruff-pre-commit
+    # Ruff version.
+    rev: 'v0.1.4'
     hooks:
-      - id: flake8
+      - id: ruff
 
-  - repo: https://github.com/asottile/add-trailing-comma
-    rev: v2.3.0
-    hooks:
-      - id: add-trailing-comma
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.982
+    rev: v1.6.1
     hooks:
       - id: mypy
 
-  - repo: https://github.com/asottile/pyupgrade
-    rev: v3.1.0
+  - repo: https://github.com/streetsidesoftware/cspell-cli
+    rev: v7.3.2
     hooks:
-      - id: pyupgrade
-        args: [--py38-plus]
+      - id: cspell
+        files: docs/(.+).md|README.md
diff --git a/README.md b/README.md
index 5cc3554..6f0f83d 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@
 
 This repository contains supplementary code for the paper
 > Finsberg, H., Dokken, J. 2022.
-> Title of paper, Journal of blabla, volume, page, url
+> Title of paper, Journal of ..., volume, page, url
 
 
 ## Abstract
@@ -11,7 +11,7 @@ Provide the abstract of the paper
 
 ## Getting started
 
-We provide a pre-build Docker image which can be used to run the the code in this repository. First thing you need to do is in ensure that you have [docker installed](https://docs.docker.com/get-docker/).
+We provide a pre-build Docker image which can be used to run the the code in this repository. First thing you need to do is to ensure that you have [docker installed](https://docs.docker.com/get-docker/).
 
 To start an interactive docker container you can execute the following command
 
diff --git a/_config.yml b/_config.yml
index e1bd2a4..0afdee5 100644
--- a/_config.yml
+++ b/_config.yml
@@ -2,9 +2,10 @@
 # Learn more at https://jupyterbook.org/customize/config.html
 
 title: Example paper
-author: Henrik Finsberg
+author: Henrik Finsberg and Jørgen Dokken
 logo: "docs/logo.png"
-copyright: "2022"
+copyright: "2023"
+only_build_toc_files: true
 
 # Force re-execution of notebooks on each build.
 # See https://jupyterbook.org/content/execute.html
@@ -30,13 +31,24 @@ parse:
     - dollarmath
     - linkify
 
+
 sphinx:
   config:
     bibtex_bibfiles: ["docs/refs.bib"]
+    nb_execution_show_tb: True
+    html_theme_options:
+      navigation_with_keys: false
     html_last_updated_fmt: "%b %d, %Y"
+    nb_custom_formats:  # https://jupyterbook.org/en/stable/file-types/jupytext.html#file-types-custom
+        .py:
+            - jupytext.reads
+            - fmt: py
 
   extra_extensions:
   - 'sphinx.ext.autodoc'
   - 'sphinx.ext.napoleon'
   - 'sphinx.ext.viewcode'
   - "sphinxcontrib.bibtex"
+
+
+exclude_patterns: [".pytest_cache/*", ".github/*"]
diff --git a/_toc.yml b/_toc.yml
index 9183ca1..4b8090c 100644
--- a/_toc.yml
+++ b/_toc.yml
@@ -1,5 +1,5 @@
 format: jb-book
-root: index
+root: README
 
 chapters:
   - file: docs/abstract
diff --git a/code/demo.ipynb b/code/demo.ipynb
deleted file mode 100644
index 8bb2ae3..0000000
--- a/code/demo.ipynb
+++ /dev/null
@@ -1,227 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "997a012a",
-   "metadata": {},
-   "source": [
-    "# Demo\n",
-    "\n",
-    "This notebook contains a simple demo on how to work with the code."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "911ffb19",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from scipy.integrate import solve_ivp\n",
-    "import ap_features as apf\n",
-    "\n",
-    "from run_simulation import fitzhugh_nagumo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "82a69c6f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = -0.22\n",
-    "b = 1.17\n",
-    "time = np.arange(0, 1000.0, 1.0)\n",
-    "\n",
-    "res = solve_ivp(\n",
-    "    fitzhugh_nagumo,\n",
-    "    [0, 1000.0],\n",
-    "    [0, 0],\n",
-    "    args=(a, b),\n",
-    "    t_eval=time,\n",
-    ")\n",
-    "\n",
-    "v_all = apf.Beats(y=res.y[0, :], t=time)\n",
-    "w_all = apf.Beats(y=res.y[1, :], t=time)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f30930f3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 640x480 with 1 Axes>, <AxesSubplot: xlabel='Time (ms)'>)"
-      ]
-     },
-     "execution_count": 5,
-     "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": [
-    "v_all.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "fbe1d9c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 640x480 with 1 Axes>, <AxesSubplot: xlabel='Time (ms)'>)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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E3Pp74vEoMTo5HRFnNZ5KVRVLFUwRH0mws9ncunnmVipXImOuGq+/uwO1+FTXLZkCzea2eEF1btTVRsl0gCJmvZ15dNUCFDWpbiQTQBwEedTAGlmqBfiOfA6DteeSOujuaDI3TnVix8v1ygvARwTVfWs2Fsfchmsb2FWJjddswZMXQuxSC+lgk4BLvJACSXWie+Z4xIRCkYlVA93I1YxaSTJB3WU3Gm9wJjGjrjZS13FoYbx7WdBVJZ/lSkgf4COVJ57bAiYiqMYa7O2Irt+CzkL09+NFanVipvLSW7uW1ORl9+gUSpUQHfkg8l/lcgF6axsH6uek2dzUfaO+ji+NxemAlbX3OwgCLKy9c2NMyktybmos6rkdmirh5fEaoR6cSXJHiNfKtLlRPyOlciWKA0mypNJV1L1XVIoqOTc11oFx2rHCMIw2Cyv6uzM+zQ9PXgihGripKhwgoYQU+QJ8Mm0UKz20D26zXacaC6BfTJsHeBWUeAJ8cm7JAE8ddHc22QlyEbNf75+ZosrnAnR35FjGS3smuUjnyoGeusqHXmYimLxv6jnhIoFDC7vRVchHP1fmdT7SGc+tt5PpmayRicHeDiysEZbqeNW5TTDNTaXyAb7rODwyiXIlRGc+F3kCAaC3o3otueZ22OKZ9426Ad/IRCk6104p1LMJdvLy2c9+FmvXrkV3dzfOPvts3HfffamfP3DgAK688kqsXLkSXV1dOO6443DHHXdwf00SqHLooQQrVQw/DIFxwocp6aBvRiioF+7YXxOP1VXIoVALGpQBPgzDeLy6ufGkHxrNukBtB9/JtYOvpQOS6kRtrENcJLChJ0P0nAjs4FWAHyNfuOvTKvF4KjDxKy+9XXLPCJAgSwIEPiITxEGwmZ8NSBIKpnRfQqHuZSKdaqxVg7FCDfCRpWabvF5GYgZUSaeyRcwmWMnL7bffjmuuuQY33HADHnroIZx22mk4//zzsWfPnqafLxaLeMMb3oDnn38eX//61/H000/jlltuwerVqzm/Jhl2R+QlZtxdhVy0K6Rc4JR8192Rw6LeePeiUjnk+f4maZwgCKLxKOe2vyaZA/UMv0+lH4jnpmTexjzuAga/0thUKZLFVzVNG/HMTTXdU4h31XQLXKlciarcViYCxQKmAB/NbbB+F9jLRJbU+52UzLmUF6XiNu5wVRCkJp3qviXXLi7lZW80VuN94yFLarzl/UnywkOUVHWiSmM2jjdBPV5tbssS942LBO6qNWJsh5QRABSyP2KPT33qU7jiiitw+eWXAwBuvvlmfPe738Wtt96Kj3zkIzM+f+utt2L//v346U9/io6OakBeu3Zt6hhTU1OYmooNZSMjfAeJZUEtOMmXMggC9HUVcHBiGqOTJQwRdVRWL8lQf3fkoAeSAZc2baReksYHt6+rgAPj06RkSc1t8YLOOsl8AZNaEC0AfV11P+/rKmDv6BRpgFfmx56OfF3aTf039Y5ajbdsYf3cOFSl/eNFhCEQBHEVDsBHzFrNjSNNNTZVighKcjwuMhHNreGZVGSJMv0wXa5E5s7keFxpnFbvW08HPaEOw7DpteRSJ6K5NTyTXMpLs7n1dPCQQEXeV7ZByghgVF6KxSIefPBBbNy4MR4sl8PGjRuxdevWpr/z7W9/Gxs2bMCVV16JoaEhnHzyyfjkJz+Jcrn1Tdi8eTMGBgaiP2vWrCGfiy521xrDNbJujooj1cdiacMCsJApbaRekiVNAjxAPbfqQqrKhznHAuK5LW1YcDgUg3is+rlFaSOuubW4lpS7M1UBs7i3s86DwkU6VZ+axiDIkcpJks4FCdLJlcaJ3+/mzwnltVTEJZ8LopJlIH7+KdPdQPY7QPlMjk6VIsN9klD0JkggZTVhq/eNgyyVK2HUp6Z+btWxpsshpst0xQZK6WwHvwvASF727duHcrmMoaGhup8PDQ1heHi46e88++yz+PrXv45yuYw77rgD1113Hf7mb/4GH//4x1uOc+211+LgwYPRnx07dpDOwwR7RmZKr0BiEaBcTGsLjir5i8eqpY0YmiEBM19Kjl11RJQWtCBKxGmjVguOCkyUhGJvRMxaqAVMO/jG8ThMra3GWsCQogJaE3iOVE7LgNvFrLy0UMwo1RClFixe0Nng0+AhZntbqEocc1PPyMKuQp1PQykhpUqIImGAb6W8RGSJkAi+PF5EpaZ0Lk7EATU36vF2N/F0ziZY00amqFQqWL58OT7/+c8jn8/jzDPPxM6dO/FXf/VXuOGGG5r+TldXF7q6upr+nSQOTZWiILe84eaq8tspwpckWrgbXhI1VpGwvDcpvTYGis5ap9FimWH30jC37tpLOUncfKzVDn4Bhzox1pyYsaVWWpClBQw7+FYBfgFTqbS6ljOIGcPcWpHOJFEKw7Auhes0niDpbEUm1DNSLFcwXa6gI0+z142eyRYBnlLp2dtinexNBvhiuS497YJ4k9eYEqNXldTcFvV21t2bznzVZ1muhJgolqMSdFdE6+TC2Y+3AKPysnTpUuTzeezevbvu57t378aKFSua/s7KlStx3HHHIZ+PH6R169ZheHgYxSLPgV1UUJVGCzrrvQxAIsATEopo4W5QXtRYlHLhWLEcSa9LGtQJDrKkXpJGVSmaW4mOKBVLlaiJW2NKjIWYjaoFoH5uqvkYZU+ZMAzj56RFDp52l9t84VY73mKpQizRqyDYXA2kVAxaK1g8/YBaXctehiDYaiOU3MFTqlgtiRmHqtSCmHXkc9H7TTq3lsqLUt/pn8nGuQVBkCBLdOOp9OLi3s6MT8qAjbx0dnbizDPPxJYtW6KfVSoVbNmyBRs2bGj6O+eccw5++ctfolKJF4FnnnkGK1euRGdne1ywVni51tK6MQACiFgxJaFotXthIRO1F7K3Mx/tjjjHa5bHrRuL8Dom8/2DPfU7lI6CIkuUxKz5ws3xjIxMlDBdI14tiSDlM5kxt+p4NOSlVK5E79wMNbAgN7fuxI6dirwkSWfLIMhCJmY+I8q7xJHKaW1qpSdmzdQClvEyiBlHKjNtbpT3LSIvC9ojFrOWSl9zzTW45ZZb8E//9E/4+c9/jve9730YGxuLqo8uvfRSXHvttdHn3/e+92H//v246qqr8Mwzz+C73/0uPvnJT+LKK6/k/Jok2D9W3b0vanJjWQJ8i/QDp8rTqLrUj8exy60fLwrwDGRiSUO+H0gqL/xBMA64dMqECkoLuwsz+jJ0MKhKrXbUnXXkheZaVk/oBXIB6kymANCRr95HFi/DjGcyfmao5nZwYjomnS2UTo6NUGMQDIIgUgSpxpucLkd+vFaplYlpwvvWYi0BEDVqnCQary693pjyZqgA2tvC1A0ky87pjd3N4sBsgNXz8va3vx179+7F9ddfj+HhYZx++um48847IxPv9u3bkcvFC9uaNWvw/e9/Hx/84Adx6qmnYvXq1bjqqqvw4Q9/mPNrkkDtApM9VxQ4CEUrAy3H4tYq318/Hl0QfCkyIzcP8KTeoRYBtzpeNTCxkKUmu1ygmn4oV8K6ah3XsRplZYBJDWzxTHIE+LicvmvGteKZW/OgFAQBOvIBaWWHCkr93YUZXox4bvQes9bPSZmMCKq5dRZy6O+uDz8sSmcLYgYAhVrsKRF1Pk+STol1Oc2DEqWhiYjZdDlOrzduFmYL7IbdTZs2YdOmTU3/7p577pnxsw0bNuBnP/sZ87eix8sp+UCe3VLz0uVo1ymg8gBM6kSLHLya23S5QmaOjPw1TXYTHEHwpRaGPrVwq/HyOXcDYTox4wgUze9bIZ9DLgAqId1z0oooAUmFjsFf0yLAT5fLKBERir0tiBJQvZYADzFr/g7E7xzlWEsXdM54fztqJLRU4VCN0zZetM/kwhTSSfWMVMdrPTdFzKaJiJnanAcBMNgm5MWfbUSE/Up5SUkbUeXEk9Jr426Jp7Kpucm0bjzCfH+rMnBFlMKQbrf0UsquU43Ho/S0VieoAnyazNtJHJTqxmvyDlArBvs1UpmUc3s5Jd/fQUzg1YF6za4jx31T4zXbUVMTwQMTrdPrHKnMeG4zFXFqYnZwQqnv/M8IABxMmxvx5kS924saejjNJjx5IcKBsbSXhGdxy+cC9Pc0N9AqdYICcUqMX50YL5Yj1agxUHQ2qBMUUItps91EvADQXMdyJYyOBmgcryORPqVacNRzMpj6TBIGipTFu5PYr3RwPOW+MXheDk6kXUvqIFgda6Cn9X2jJC8H094BNR6RGjKSMrdC7TqWKIlZNN7MuUXqBNE7kPa+RXMjVJXS5katYsXkhabsmgKevBBBR3mhCoLJxa1ReuVQJ9IWbmozshqrIx/U9WKo/ix+XKnGUwtOs8WUegevFu5m4+VyQXTIJdViejBtcWMwYirjY3+zoEtcKRaRzpSxqOYWhmFiPAF1Yjwl4DJ4Xg7U1q5m11IFXQlCzaGYpRJ4YnVCay0hTGVG901gc7K/hQ9xNuHJCxFSPS/Rg0TjNFcPbdOXhEGdSNsJUpOX5ALQSMwKuQDqR1RBcGQWiNmCznzThl/UOXgd5YWamOWC+IiK+vFovVhpgYI6RXVoqoRybSOQHiioiFlaUKJVeYqlSnSAZTqhmHvELAzDKJXT9DkhVifSVNxoY0KovKSty9QqVruVSQOevJBhf8SC0wy79MpLI3jUiRSyJCiZVys7iMlS2uJGPLe0xa06Hm0QTFu4qe/bgcR9ayw5B/jIksQOXj2TnYXcjJJzIH5OqAJFlBITUAPV3IIAWNikEyt12kiSmI0Xy9GaK6FOHExRsJTKQ2XYDcNQS8WiGi8iL21SJg148kIG9SA1Y6bUpdKpjDupTpCPx9/DJm1uANDFtRNMK3EnDhTN0ioAyInZwbTUSjQWP6EGGHbwGmkcapWn2XVMjifhnSAPuDUy0d/d0dSI2UGcNkojZtQEVz0jnflc1EOmbryIUFBvTpqpPLRzGyuWI1tAs3egQOz7SlM6ZwuevBCgXAkjdWLRgtbpB6qKnCx1givopuVyqSpyDk60VrCAhHdC4FpSj5XmLQAY1BCN1Ar54tbivtGnxGpBV0AxS/N8AfRzi57JZukHYpUnjShVx2MinSnEjEotiBTj3pkpaCBOG4m8bwVaP5uaW1UNnBnGC3laYjYy6cnLvMTo5DSUNzbV0Ce1mBLuPKt54xR1ohCfW0MB/R383NsJplVaAPRG0wMp943erJhOzOh9IRpGTOI0bbN3G4j9DNRzkzCRZ6lK9OOpVCa/WpD2bgP0Kla654XvvjUjZtRK58hEtUqS6pBHCnjyQoDRWvlrd0euzjCrIJ1aoTwDaGI6kTeW8IVkyJNqB0OhYpXKlahfTrPxuoiDYNYut5MwlVNHOmfZqwTQpx8kPS/qvmWl++gIdbZ3gloJaaWY8fm++H0aaWMB9GmjtPvWSayEZG1gqQ3CSnlpbM0xm/DkhQCRl6EFK50t8kJRlqcW7kJuZulycqy5qLyonitAq/QDtTm4tRKSHI9ibpPTleh7p/bvEFpMKX0hSbOiSEosxWQKxO+ARNCNVB4hxYw6bXRQoxcK/UaoBTGjThul3bc8bdro5ei+pafXqUqzRzJi3GzAkxcCKOVlYXdzVtopbJ6KF2/30uy0njJArE5IkRdKQqHG6usqNC1dpq/+kUsbqYCbzwVYkEY6yVMrWWqg+zOZZVYk97xopx/cxyuWKtHJw2kpaKryXt0UNMW11O6XI0Q6Z8NoPV2haR6aVmgA0JeBqxjXSn2cDXjyQoDRyXRZmeMAMEBDoid4KbNeEq65tVpwKMlSWgk4wNh3pcVuqZOwF0qSTDQ1K85WaoVgJ1hfutyadFI3hWwdBOnIUn3p8szNEHXbhexKKrq5jRXLqf1yqH0aWaSTUumpds9OaQpZGysMEV0DF2RtFqiJWay8+LTRvMJIpLykvyRl4hbbrSpyIkJB8OBmEaU8cdvrrPEiFz3hApBFAqlUpUzDLuEOXledk+thQ6c+JlMdTc2KTKQz65mk8PMkS5eb9cuJvAzESme2n4diI5SskJmpBlK30Nefm/t4h6ZKUIJKVv8tirVrJGNulAd4lith5A30yss8Q6S8tGCl5GdoZAZ4ukVgJIvhExIlIHuXmyeUQ3VTVBQ7JSC9IV5yPIrnJGvhpj5rSJuYEapKmf1yiMtEM68lwX0bieT55msJ9dzitYs/CB5SAbDFOpl8/ilSKyrgtkznE3qV1HXsKjQv2lBrMkBz77LmRtk48VDCG9hqvNmAJy8EUGVkrZQXFXApgmAYhgnW3YosqQeXPwjGZELGGFYgvJZZgSJP7Ng/lBmY6BS6Qxk+rLgsla6Fftp4lEQwa27Uiln23OhUJTW3vq4MczDR+6bm1pfh16MMgn1Njo8AGg4nJXgus8ajLHHPfEYSc6NYlzOvJWEZuCLv3R05dBVmKmazBU9eCJClvEQsmGDBmSpVon8niyxRjJfF8OOUGA0xy1pM4+ZLlAtAuqpUJgrw6lq2WnDyhAcz6gZcqlSmbqCgeCYPZVxHasUs8zlhUCeanQ+VHKtcCUnmNyoYBEcz3m3VBgGgUVbVutxqnaRsipf1/OdyQeL9pptba8WMjnRmVdPOFjx5IUCWEzsiEyQPbXWsIAB6m+SNgTjAU+5yWy3cBcI0zlSpEgXu7CBIESjU4palvAgRsxzhfcskSrTpviyyRLmYZo2VJO+U6QcJshS9bxnXEaAlS1mbBQoVK5vgJpQXArO1LoEnIRMZ15F6vGzFjLKlRPt11wU8eSHByGR6ECRd3NRD21loaugDaAP8WLRwtyBKhEFQzQ0AFnTy7+DHpqolqS0XU0JVabxYjgx9C1sRQUKFblSbdLqPVa6EGmSJY27ZEr1EmopSMcskStRzy1R66BS60YzCho4kMSNMnWarSoRza/G+AcnzjfjfgQLhxivqruvJy/xDpLxkpHFIyg1ri82CFg8tQJxayVhMKQNucrFpScyiHTzhAtBSCaG/b/lc0LS8V/0dQKVO1HrYZCkhFAS3GJPOLFWJRjFLJ2b5fDL94PaclMoVTExnkNwcXYDXJUrV7+Y2tzAME+Pxt0KIlM4W1zEIAtJ3LksNYVGoU5QXSvUxq7cYZffgkQxbxGzBkxcCZCsvdDv4rIAL0Cov8QKQsYOnlELTiBnDYtqKCFKmcZI76mblvUCicotSLRBQQtRYnfnWhj5Sz4sm6aQYT6lzQMpzQthXKZN01s3N7Z2bnI79c5kkl9IXkrJ2xQTebbxKQg1smcok9WGlEzOAts2DrkGYpAJONfP0npf5h2zPC92DpBfgKQNF3IW26VgML+SCFikqgHiXmyGZc6lKrZCnDBRZ5sjaMxmG1YWecywgsculNCNnKCEU441OpZfAAvH7Tbk5aTW3XC6Amp7reGpuaf45DvO/ztrlOrfx6ew0LanSabKppFDMstRHwfT6bMGTFwKMZigvlOW9WSZTgNaroR7czN0L6c6sNcMnrcjRrZARUpU6CIlZVk48T+gvyBoLoCXUarxWSkgyteI6t6wdLpBsxS4V4Gk2DNHzr+Gfo0yJ6ZBcqrmlpWkpFfGRjDQOQJeqnZyuRN85SzGjuG8qLdzsmJHZhCcvBBjJCoKUO3gNFsyh9GQFCgnzIECbp9bNiVcI1AmddF903yiN3Rp9J1yvpZYaSOixyUqtJL0TzuqEkWJGGeCzCbzrhkFLMWMocW/lr6kfz5V0xopxqzRtnjQFna6EAHRpaLVZDoLWhIKjkCLNZzkb8OTFEdPl+PRekfLejF0nQL2Dz0obMfhrdHbwAk2z6syRggFeggjWqROuQVBQMgf0SC6VSV5nbh2CJe4A3TuXZQ4GgDyh+T8rJQbQETMtJYRFNdZ5Jl3Tfdn+Odq0UfYzORvw5MUR4wlDX2/L8l7KfH86mQDoXpJkbrV12ohOnRjT8k5w7AQ1vBPOMna6WpAcj6QPhIGp1V15yTYrku5ydQgFkUKnQ6gp+wHpEAoqVSlLMQbkU2JU903LY0baM0pDNSa6b1lmfIC2MaqyDnjlZZ5B5QM782mGPro281qLG9ECMDFdhvonspQXgDAHL+CdKFdCjBf1+rxUxyPyTggsbkB2oEgaP11VLJ2UGOlZKyaEgkhVSkt1kB7roDU3mrRwnDbKTlHReF6yCTyVYqDlVSKtAtX3IpKtJUL3LW7P4T0v8wrjNfLSm1YhQ/mSCKZW1EsSBEBvy9wqfYBPl8xpZGyT3iQU42ndNyKz4lSpHKUy05pmkRk/DciERJM6gHCXa6Aq0TQfyw7wVEFXkYl0j5mc0gkQrl0apJP0vhmQTue1REvFpUv3qbXSp43mGZSBtlVHWIC2N4lkaiVZjZCVW6UYz6SHjeuOQqc3ST4XQE2b7Fpq9bhwm1t9bxJ+Um1SReI61nS5gilFzCR2uUbPJH+aFqBLd5ioPJS+kFRCTfRMjmR49QDiKlCj/lsCJnIGo7VPG80zjBv0JglDocoOsl2nvlkRcPf0GPl5iOaWJYVSma11TG9Ui5taSHs68hFpaAYyU6tBSsx1l6uuI5DRZZpql2tkoKVL06YrZkRBUOv9pllLypUQY0XlnWj9zlF5o3R8GhyqksS6rLOWUKbEvGF3nkKHlZJ6J7RYN1FOXGOsXEKdcPX0ZPWUARKGPmfpNXvhBuiCoJZkTpQSG80oJVagNrWmkwkaxUzdt65CLvr+TccjVh/T5kalmKmxcgFa9iYB6J6TcY0AT+XXU0csZI1HtVkY1+hNQnXfKpUwmp8eWXJcJ2sksFUqH6A+ksYbducllOlTJ20EyOwEqQyEOsoLQFcqGgXBlGtJtZge0hgLoJd69QK8Y1CKnsl0VYnK1Kp33hbNddTNv1MFJp1r2UHkVRqPglLrNC1AuINXfr2UuVGlcZRCnQuqxDNrPGdipq6lhjrnOrfJUtzNN51Q0JDOiaIGUSKaWxiGvkndfIXJAgBQyKH6O0HX1Er80OoGCoEgSLRwTxSzxwLiBmSuRFDtzHSeE9fdkrqOrUr3FahSAuNaO0EitaA2Vk/GQkpFqCNDvgahpiJKadexbjzXZ1KDmFEpj2OJTV67EDMqX4hSJoIA6G7hnwPo3jc1t7R3gDKVqYiZV17mGXTygfnEyyqxwFGlVtRLmbWYkr0oGnOjaiylPTeyHHx2EKQjZppBME+jYumM10GVDtAwyAN0BD7ewae9b1QkMDvgAoTvQEQE5dI4WaST6r7pEDOq+6bG6unItzxmAaAjS/E7oLNO8leczhY8eXFEFARTFjfKw9QmEtJyK5DlqTWDINWLorN4x+XEjmNpKCEAXaDQI2a06YesQEElLY+ZqBNEAT5zbkSKmYmq5Jp+G9d4twG6kvoJE18IVSozY/ce+7BofCFpxIzKF6Lz/AN0npd47eJ/38am9BSz2YAnL44Y10ytUCw4xVJ8hH2qZEicDkhbAAC6CgGdoEudNtJfcBznZpA2ojMravp5BIgZlTlYJ/0G0M1NJ21E977pKS9U54mpwJSefqBKv8XqRBqoUtA6xIyKvOum+6hScONT2c8J1fvWrg3qAE9enHFI04lNsZiqIAFoplacA67eYkohv5YrYdS/Q8uwK+SdIFOVpgx2S2Q7eBnvhJ46QWUO1lQniAm13vtGcx21n0kiH5ZW9Q9RwNVtTSBDzGjVQF1CTUaWBM6IatceL4AnL84wzlM7LDjJowjSykTjw9Rk0kYU8ut4ouNt2oJDtaPQrcihUMxK5QqKtWsjmTZKS2UClFUyGuoEWRpH830jC0wmihmdqTUNZF2mp+J+QK1ATXCzVVwqr5K++V/aaO2azo/egZT7JtmfarbgyYsjdCpkAJogqF9pQV1Fwt8vRBGlrFJKqjx17J3gV8zGEz0udFJiEmQCoNmdTZcrkZqiRczIykR10w9UBJ6/Id6E8A5ep+SWbrOgV24bpakECAW1D0s3BrgWUuhsTtRYrgfm6vSUmS148uII3QY+FIHCXAmRzcG7jDeWCBJpxjC6yg651IpKGeVzATp1GqsRLdxZ/gKKfkDjdalMAQNhNDc9Qu0y3rSmYkaeftMlZg5zS/bvkAnwpikx/o0XnS/E0IclMDeqY1smNTewswFPXhyhu6OgKBXVrbSgTq3oLjgu4+kTJapKC7O0EdXc0ntcEJlaDdUJF9Kp5lbIBS1PVQfoTpXWVScoNgu6xIz6fctWOt3XkqlSJTqKQKQ1gWZKjIqYGXXYrYQIQ4pNpe7c+LsHJw/MpVi7sjZCswFPXhxxSLMhWJ4gL65b3itpxAQSzdUcxtMuyyZusa2bNnIL8LL9csa0U2IUxEyP4FLv4LPUCYrjCHSJmbzS6Z6mMlfMZEgnRXPBJDHTSdO6jqejYCXHI1MfNYhZdTyHdXm6kjnWbMGTF0fo7nIpXkqdihUgucsVanaWc8/l6hr66Mp75fwF2v07qA2EGbslCrI0obmjJm+hnzk3OY8ZWRWJYfqBYkfdWcjVBbpGkKVWdNUJgrXLlJgBNO9A9ubEfZ0slSsoalRlJgs63LyIXnmZt1Dlhrr9CygqcqRSK7rGzw6CHYWpv0YsB0+g9EjPTZdQU5ClMY2eEwBlC33N/jyEHrPs6h8qE7kZyXVROm3Ov3JJrej0JgFoFDP1THZpEjPA7Z3TblJHsU4mzP+pjVET03ZRBHX7Ks0GPHlxhLq53Zo7QScWbNqgSyhtRGJq1RyLrNmZbmAikOh1d2YdVCWw2ioWgToxrUcC6c4aMksbOQUl7YBL3alYs3swSSpTL+ACgMv09O9brVsxwTqpW0QBUF3L9jH/B0FAqhpnxbfZgCcvDqhUQkxq5gRJSm41Ky3IT2YVIBTGvRLIWnpL9Ocx63FB1fZd92wjFzVElwTG5ygJdTMlmFvkMcsKuIn0G43xk1+h01ZC6g6V5d+cUJxirdO/BqgnZhSEIq1pHEBTKalr/gdoVGOvvMxTqI6wgEHba8EAT5V+0G4sRaJOZKWoZCupKK5lRCY0S5elFDOKdF8UKMS6i6rAJKAqqaCUORZVZYdp92ABMpFLpFYIqtIkeg/ppk2DICA5amFM8/2mOANL974BtKqxN+zOM0wk8o9ZshpFT40oCGp2TnV5Ieu6wmruYJzy1IanzkqcYA0Qq0qZpcvETawEzqQyTWWWidQJ7SZ1Aq0JCkTeCfNu3RSVi/qpFYmNF0VLCV1zMEDT0dfUY0axlmQpnQDRxkvT0zkb8OTFAbqOfYC4kVvGTpBk4dbsCgvQVq1oqxMO6YDkAZe6izdNAz69xc3FiJnscdFelVRU5ki5hmCxd8Ik/cB/LfMUnhdDAy0g85xQEGrd5x9IvAME73emx4xgndTd5AFUaSqvvMxLTBrkAylb6Ov2SqCQC/O5ILVdP0AjT0qmxJLnKOnn4AkUM83FjarHhb4vRNg7QSJj87dijw73M0kbSfQDIkxBZwWlXC6IKlckKu4ozMi66yRA42kzb7vA3w8LoPEHmsQ4aXjy4oCJYs2sqyGpUZZKS6RWokqLDhNjGEVqhb/6R43VkQ9SD7isjifXpI7SF1UdT7d0k987QdE0K9muP7MzMqVXyWButqpZGIYGKTh3z4uJEZPUO6GZgnZqeKlZAQrQ9APSb5fh7nmZNJkbYRNKX200z2CSD6R8kEQqLQzkQooeHsY7M4pdpxbplLtvSjELQ/vx1DPZmc9OZZI0ctMtlSZommXiMaOstMiaG4Xxc7ocRr/bLZDumzRZuxzfuTAMMVnSq8qkMNCakBcKg7CqONV9JsXnRqL2+7ON5hWMGD6hMUwy36+VEiM0fkrszCY1zYoAjcdGN1DkCcpSJ6KdUvarTdGkTnduFE2z1FhBkH7yOEDTmkA3KNWNR0HMCrol7jI7eNcgWEfMMuZGsXZF7SsM7hsNodB8JqXmRtEKwRt25ydMyshikx2/HEop82otbgSLqe61VGpBGNof9a672AC0jv2uLDMyQVnqpKZaANCk+6Y0AzxF06zJWpq2u6CTyqQj1BJEcGo69ph15NPnRrlZMCJmlnObLCWIWWf6tcwTeJUmTe6b47lslUoYtevXbpdBsFkweSYlfJazAU9eHGAivVL01NAdj0KeNMutuhvRlKycueskqOwwmRuF0VpbeSGY21TJZEdNQHJtAoVjENQiZoKqEuBOlqLrWMhlEjMKQm2iKrlWAE0WY8UsrSssQFMqbXTfHNPCdcQss1LSnXSazM31vlUqoRHJlYYnLw7QzYkDNCWA2rlVigVAczdRHY9gbppKTwdBasVs4XY37JreN8A+6E4U9UhgcjyKxVTCQBipgRkpI0A+wLumqYzGoqiAMyCdriW3yVSHBDFTz0mW0lk3nuN9A7LvHYmfx0ARd216mWzC6pWXeQYT4ydFzb1uoFC7wIpDasVGenUKFCW98SjUCaOFm0JV0hwvWZZqHyhqYxmkjWjmZpKmsiWdFlUkwukHkbkRKGZTRjt4GsXMjJi5rCXmnhfX+6ZlkKdQA42UVTdilmwp4ZWXeQYzCY+u5l4iwNukjSQMhBTtys3Miu4HxVkFJkcZW0edIDl5WdDUGqUWBcYC7NIPttfSyl8j7HlxVcxMrqPLOmmiTrhuvMT9c0Xzd8C1ui/rdO7ZgicvDjAx7Lo+SHXlhgIHjlmRF6cdvN5Lmc8FUMqztRxqUyZKkDYyqxBwTK0IpftsdvDuczMxK7qnVozSD46bBaPWBFIpMUevxmR0HWVMplORN0qHULgZhMX9c2puWtfSbePVzucaAZ68OEGyGVKy3DBrMa0/CZY/B095Ro6Ei16yBBbQT4kB7kc7GEnmBL1QzKof3J4TEzMySSpTsBdKdB11vEokXZ9tKqncdvBGBJfCF6JxLV0Nwjak00l9t1CVbJ+TqF1GG6aMAE9enGDWpI6w3DCzn0CiIZjzjsIg3285t2S5oUTO307qdQ8UXTqLqSPJnbIhEyTVRvyLqWQapzqejWHXzUSup+ISdGot2RAKfoN8B4VXSZFcARUrmpuBQZ5EeTFqHuqodHrlZf5hsqgvT7ruBJMNurLKDZOpFdv+BZLqRNLVbmayc5SxBchEGIbR/EQXHDFfiLk50jbo2s1tbqhKEwbPJEXVim5/HiBxTpRrSqwNlRdXxcwkwJP0HjLYCLn75/SJ2WzAkxcHxKXSOseTu+0opgzKDQE6s5beAkCzuAGGKQGBXa5rY6kkMdPaeTqnjeQ6p1Z9WCZ+BteeGhXtsWh8IeZpI3ePmaxh16hnjkTlIkl1n8n77bYuSxvkzRQ6ok2exn2bDbTnt5ojMCqVJmofrluy5iqbm6UfHHsl1BaAjnyg5Wp3bppV0idmriXuaqdUHU8j6DordHL9QqZKFYS1r2kynm3O36RqxTX9kDTIS1TcGaWgCY+s0HkHnNVAA+MzhTphQpZcNwtGXdYp+yoZrCW2Xd1NnpHZgCcvDjDyTjirBfoPLUBRlmqwM3PcdZrIvAABWTIwK7qeBJskZoWMdB9A0MjNItVhO7epabt0n2s3U/HzeCRK3I3StG7PSPIE66x2/YB7szOT1CLNQYlyaSqT1EoHYV8lCeVlKiLv7UkT2vNbzREYldwKVsgAFGTJolOrY5dKnZ0ZQCDRW7SZd75vhsTMuiGYZIVM7TpWz+PRT+W4kiUTE7m1OdjAIA/MrVLpYtlMMXM9WV2ykrCOmEm8A0bKi/SZVFTmf6+8zDsoZipinjKQXinGi1ts66sT7iqPmaokQSioVCXd++ZqxjQyYjr6eUza9QPu3iibZmeu6pyOQR6g8GHpX0vndv3FhA9LYHNi1QvI8n2bLoeoGBAzV/XRyqsk1TOKaJ3MOsF9ttCe32qOwKj5kmsQjBYAoQBvING7n31imDbKuy04JsSMys+jK726ejXMDkqkIp2G6T4BM7J7dZ+dQV5ic0LVrl/nBOvkeBJ+Parnvzoev0FYUuVJqkpam0rHhpcmDfhmA568OGDKIN/pvBM0Ney6nuBrkad2TXXom5HliBmVYqZzHQG69IPkYYI6yiPgft6QVam0wHk8AIGqZLSjplGwdE6wBty9GmbHcTj6NGpj5TQVM7I+LwKqkmlLCVcz8pSBYX02IEJePvvZz2Lt2rXo7u7G2Wefjfvuu0/r92677TYEQYCLLrqI9wtaYsqgdNPZPGXoeXEN8Cb5TucSWAMvA5AgZhIGQteTWS3vm3P6QWAxNenSClCQTptAwZ+iAmbrvskoZq6mdZvzryi8gVqKGZHSY0I6bQ/MNW0p4ZzOT5xt1I5g/1a33347rrnmGtxwww146KGHcNppp+H888/Hnj17Un/v+eefx5/8yZ/g1a9+NfdXtIZVuaGzWmDmL5DM5dqeoWGqvLieR2JCzDoEu/kCFAfFmXQzpVGwTNNG9qTToARW8DwegO78Hx3fV4ejv8ak0RlAQSjMTeTW5fSGSqf7ptK8E7nteIqYFTQN8lSGfF2/njTYycunPvUpXHHFFbj88stx4okn4uabb0Zvby9uvfXWlr9TLpdx8cUX48/+7M9w1FFHcX9FaxgpL0QLjpTyItv23VKdcM5Ty1WRGPfnETh00nVHPWVA3gG683/MSm4dnxGp8n3JjZBBozNAukkdTcNL/Y0QjefFxEBbHc98fua9vmisA7+RpdLFYhEPPvggNm7cGA+Yy2Hjxo3YunVry9/78z//cyxfvhzvfve7M8eYmprCyMhI3R8JTJcrcR8IkWojw1Jp512unJ/H9qWU8KG4B1xDYkZmsuMvSzW9b849cywUM9f7JhXg474rAmlaYxWXJiVm2nclDF0CvGE/LMdn0sRoDdity9LeQJNq2tkAK3nZt28fyuUyhoaG6n4+NDSE4eHhpr/zk5/8BF/4whdwyy23aI2xefNmDAwMRH/WrFnj/L11kDRPmSgvzguOoTnSJrWSbPuu08SKTJ3QLrmVIxR0Kk/7KmYSB+4BsYHQXaHTr7SwTWVOiQd4m/J9x94kYiZy81QmANgMZ17YQHMmlYk3ELBbl002JgCd5+U3UnkxxejoKC655BLccsstWLp0qdbvXHvttTh48GD0Z8eOHczfsoqkeUrH0ERn6OPvsGvaxMrVOxGlOrR3ufY7eNNyQ6oqMf0S99rcCEp8dceSWtxcm9QZldy6kkDDrs9UBmGzfiFulVT6nheaVI7OOlmnTljML1YLZEzkccWp3tyUh9jmOTGeG1nvofZUXrJPFHTA0qVLkc/nsXv37rqf7969GytWrJjx+V/96ld4/vnnceGFF0Y/q9R2aYVCAU8//TSOPvrout/p6upCV1cXw7dPh3qQOjXLDakMfRK+ENMmVu59J2wrqewXN93xXHe5pl0qXc4jMT8o0U0yN1aViA7wNKnIKdfSDzrvaBKmZaKuAd5kPDU3VbWS0zgPLImiZYB36egLmG0WALtraZrqcK24KybigA4KuQDT5dBqPNO50R0P0J7khVV56ezsxJlnnoktW7ZEP6tUKtiyZQs2bNgw4/MnnHACHn/8cTzyyCPRnze/+c347d/+bTzyyCNiKSEdWJ815NhTQ9tF76CGqACYC6DVxKqDqHOqadrIZgefPI/HrLuoLDFzVcx0FjjnvhOmJe5Ei6me0hl/xm6Xa1Ym6lpJVayNpxMEk2dklS18ISaFBoD7USNTBv2AXCtyTMlEh6PHzJosWYxnOjf3bt3tXSrNqrwAwDXXXIPLLrsM69evx1lnnYVPf/rTGBsbw+WXXw4AuPTSS7F69Wps3rwZ3d3dOPnkk+t+f3BwEABm/Hy2Eb2QxnljqbSRfWolucPV2bE6n1ljWJLnssudShAznUWAqmmWfhC0V+iKSR+WVpt5N9KpdtSdeVMzsp2qVDQ5jiMZBMshTDeP8fttePSBcxDUV8wAu7lFQVCj3BZItAtw9A9ppY2C5Nxs1Amz983VF2JMlnI5TKLitHYZK2aOG+Z2VV7Yycvb3/527N27F9dffz2Gh4dx+umn484774xMvNu3b0cu157MLg2mD5KzL8RSMnRJrUikcZLjGedyBWRl13blU4aBwkXpmTIkL0nSaZVaMQ3wDjvBYuJ6mJBOQG0YzBbgmJjxq0phGMbjCczNVi2wJrkGAT6XC5ALqikxq+fEIo0DuKzLhmTJwSQfkXdjM7KbN6pdDbvs5AUANm3ahE2bNjX9u3vuuSf1d7/0pS/RfyECmJcuO/bUMPAyVMdzCPCGB3K5+nmsVSyXnZnhdXRduPV38A5powRRMmn7DlSDhUaGsH68cjkaTwcu74ApMUs28XLyTgicdF4lj9X/1iEUVHMzDfCubeZNjpEolioimxNXv56kf8h2I+R6yOtvZKn0fIathOf+IOkqPfaKQRSUhORJazlU0rFvu3Bb7uDdzIpmxAywrOwwVF5czshJpsR0rmXSw2pDlkxTKy7vQFJV0rl3rnOzVgssxipXwug9lVhP7J9JKUXcfl2eMqiSBNzT+e2uvLTnt5oDsG4+ZmvoM5CVAWnlxVGdKJuO57AAGO86q5+zPY/EVFVyOdbB3KzotoM3Ta3EjRPddp061TVBEDgFJtPnxMU7MZVou6BzLYMgcCO5pgHewWNm6sMC3PyBtmqgq4lcwkQbrZO6XiXB6r7ZgCcvljBXC4hSK4at2F28E8bSq2PVirmK5aAWGF5HwL4CCLBRXiTMivXGT+PxDIlZh4Op1ZSYAW4pAUlPm3pGOvKBdtmzW4A3M1q7kM46xUxApbb1Ydmsk6VEl3UJJdd0bi7PSBiGiVPj25MmtOe3mgOwPy14/qVW1M4stFUnLKVXux283XUEbBccsxSci9Rra1YE3IKg7k7QRQ009RYANIqB+X3jJ9SA267avJLKnVDngvrnLX08h4o7w2fSyWOWTPcJHEdgvxFynZtXXuYVbHe5rs2QTExvgG1qxTDgJtQJG/XFmFC47HIdfCFOi6lhSkwi4AZB4CRjmz4nLsZP07EAt8oOG5Mp4Jh+s1KVJIIgTZpWt5qNxvPCX0ll6sMCXNs8yKWgJ5P9sLznZX7B+FwXx/yjbYWAS6m0jTohWd4o4XnpcDyPxHpuAgEXoEkJSKiBNmkjyfEKDmTCNG2aHM9OeTEzftKouObPpFtKTI6Y5XNBXePANMzG++byjASB/rWURnt+qzkAW+VF9dQwH89OMXCT6M3SOLbj2be95g/wucR5JHaqkq1XiZ+YAYkzgATGy0dqID+hBlwDk201oYPJ1GRuLv1CLI3WUqTTKSVm2ArB6ToaVqQBbhV3kfoocN+S75tp/ycpePJiCWNfiONpqab9Qjoc8sa2Kg/gFnT1jWguczNveS3pnXArlTafm4vZ2pTkuhyW6GLYFVFeXBonWikvLiW3pukHORM54JYSszf/8xMlwO14AEkzcnzf2tPvAnjyYg3TMjKXnhqVSmi/WxLYdaqumIBj50iB01JNu1QCstfSxYwsvcu1N7W6XEcTU6tc6jTvQiYcPC8yFXD2c3NJ90n4hyjUCRPlxaXq1LhnlJOCZX7fpNG+36zNYe4LSXgnDB8mG+d3h0tqxZDhAzQSvbkvRGjBIeh6a25GlkmtuJUTm6qPBIupwX2TrNwied8MiJmLaV20W7eF54WknFiiaZyhYgwQKXTG5+nxN2mcDbTvN2tzTEamN/5+Iaat0QG31IppHwjAfjEtV8IouIhUUlkRM/eeObKl0gb3jaCjr/ku10Uxk/JOmAVBSSUEcPN9RddSu9mZrOeFRMUyLpW2J0o2hNrOG2VXKu2kUHvlZf7BpSLH9GFSO6XAqFeCgxHTIsDb7uBtOnDK+0IciKCxL4Qi4JrsBN39Q/odfQnMyFbKC//iLZl+AxwDvLHHTFYNdFJWLSupnDreWmwW7Pw8tmZkmfJ9abTvN2tzmEp4Lj01kiY0014JEn1XkuMZp8RsOnBSVAgY+ULsVSXTc10oAq5VObGD0iOiBlp4lSRJLo2fh/+ZBJIpAf7UiqqkkioDtz6ywoFM2JBOCbLk0lPGZi2RRvt+szaHaSdHwL7G36VXgtSDa7uDt+rASdDnxaoXioOqZErM7BY3s1LK5HimRDAMQ9HTie28Su5kyfy+yZBOEh+KwA7eqpKKpIU+f/8t0waU1fHcr6XkRqjDe17mH6x28JY9NVyqSKQCvC2hSI6lqyqJl0pbLnB2KTFZP49t+iG5U5XoYWPjeXGrEpsF46fV+yZQ3SdY/QMQpcQED7C1Ip1z5Lwtb9idh4gPU7NRXuw8LzZVJC6t2CV2SzaO/Q6CtJFE3wkXr5LEWUOA/QKn5gYIBQrDBl2A/XNik+6jqEiTU15sz9uSqaRy6wckd2SFi+dlzpSB+7TR/INLasU8UDjIkwJeBsC+f4FNUKLZUfPn4G26VNKcqCucEtOuWnHPwVsZrS2VTpPxaBqC8auBFZvqPgKfhtX7LWBaj9ZkAQNtcjyJFJxLTxnveZnHkDRHmlasAK6pFRc51DLAW/SUkSiBBeJdtali5rJwSxy4BzgYrSPlMUBOU1VyysHb+AsETeTSJ2Zbk86y+dzcPC82Ad7eh2WqTpCc2yTwvgHm70DS92h6JI0nL/MYkr0ZrM6scWpiJSeH2hCzuJJKqqW33e4szombdIV1IWYWgcJWMbPoceFUBu7gL7BN05qYyGl6yvCXE08lTgsWUZUsSKeLD0vFaPP+PA4paIGWEoB5Ci55JI3pczltkYKWRvt+szbHdMTw9RY3wL0iR6pzqs1LaatOuHS8danssNnBm943G7WA4qwhiWqjaG42xyxI9dSw9vPEREnfRC5b4h4b8g3ft7KND8vdRG5T2GD7TALmHa1tDsy1WbtcCilslRfAYu3yysv8hWmvBIAgbWTU44JgB29xZoe5OuGgFgidvGy7O4vmJq2YWXUP5ldeXM6kMjWZAvbPiY3ySNNc0ISYVcczNpGL94xySNNariWAgQ+L4NgWCQJfKlei76efEos/Z5te9ORlHsLOsOuYNrJIUdmlVhyCoG21kVSlhU31g22p9CyViZoQaltvVNR8zOoZkTKRW+7gbQhu3v59c2m7YKtOmKwl6ntJnBEF2Cs9yXVS14fldGzLtE0FnLqWduskYN6aALBP5/tS6XmIuImPftrINjDZqBN5ktSKzWFq/J6XDoIdvEiptE2DLoIcvI03SsLzIq2Y2T6TVtU/wkdWdLgqL5adis1TK+Zz64zS6/ykM9nqwladsKsCdajusziSpmhJlrzyMg9hs6t2rewwW0yFA4WjRC/V48KFLJkSQatnhMRobVMlZuqdsPdFSaVWXEulbZ5JuwBv/5xYpwMsOhVXx+O/lmqdtA24NiQQsCeCNmTJtst6IRfUeVnSEASBdcsMnzaap7ApyQMcdoIWO3i31IpLUzyBXS5BZYdV1YpAibvLwW2mDbqS49met2WXymxzVcnqvsXfS2I8W+OnjYrb6aBOuPh5pEinsv5YE3gb0lkyJJ2WSojtc+INu/MUpUqiJM/KsMu/AEiXSlurE05nDUmVpdqRJReTabufKm1H3qufDcNqszQTSKpKLn42wMFfYEHMZJSXhC9EIAh2WqZWbDZCgIMaYtMKwbGZp+ncbFUs73mZp0jmHztMSqUFH1zb0k2b1uj14/FL5rZEqTqeBaFw9vPw37fkeHaNE20rqcxJJ2BzLQVVJYf7BrioE+ZEUNLzAsgcbaLmVhRQOoHYr2SqhricBi6hYFXHc/MPmZIlSbTvN2tjJB88kZ4aDkY0ie6igMvRBzbEzE4KDcNka3QBX4jgfUuOJ2K0dvB8Vcfjv5b2qpJ9KhOweefMiZlrtZHJ3JLeCYmSW9uSeluTacE1wFscD2BLXsTSRt7zMj+hHtpcUG9my4JtC33JNvM257oASa+G2Uupvp+VWmDcNTL+fIeN+dm22ZnluS62Lb2tzqSy3uVaph8EVCx7Vcl8rKSH0lZ9tKs2kkmtWBvyLa5lp/DcrCu3rJrUWfZVsr1vece0kScv8wvWDN/Sz1AsVT9vEnBjydxup5T8N3SQt5ToZ6MrrOl4eVvHvkO/HEBGxbJuM+9QkQa4NI6zIGYCqlJSnZDwvNimjWzTAa7VTVYbIYFnEnDwD1k0D+2IUmJzxLBr4OmUhicvFlAPeYehmcn2pZy2MtnZLdzJsXQ7cAL26oTNtbStbErmtE3GsyWCNk3jbNWJUrkC9XGb5mq2plZTz0tU2SFg7C7Y7nItGhkC9n4lF4O8VGql09VjY1VtxO8dqo5n69czb1Ln2oDPXFVya4XglZd5BpvdBJDoc2GcWqkFeCvJ3JK8GM7NVZ0wMT4romRataLmljfolQDEO3hT0mmnvCTUCYPxbDpwVseTK3GvG882UAioSjZp2up4bj4z0SZ1hsTMua+MVdpIprzX9pyo+HgAAa+So9rv00YeAOzLyKwX02g8g4BrKWHHSoj+WECyw6gdMbNJ4wBm1Q9Fy7nZH/BnszNLpI0MFm9ro7VjwJVInSZVJbveQ5aVVAZBCXA5/sOmIkeuVBpwOX1cbm7WFTk5NyJoczyAaSWV9dwKtmkj87VLGu37zdoY1gu3Y08Nm9SK7SnPpimxvKU6oRYMk/FsD1MrWs7N9UBN24ocG2JmrirZBlxL9dEitVLnVZLw81gHePPxbNN91iZyS2IWn28kUW0kZyKvjue2VnZbdJm2T9MaqkqWqUxfbTRPYUMmAPvqBxtfiH1qxZxMVD/vdvSBTU8ZwGy3FFU2WQcl/gAfBIGV+dmmqgMg6GZqSnItnhM1N9PxovJ9614o/BU5tuk+lWY1VicciZnJeJVKsjWB/tw6rZUX89QiIGtqte6MbDm3mJjJEEFJtO83a2PYKi/WRlOLcmLb1IpaMEwfWluzoo0aYluRY+vnsVbMLBcAm2upepOYp3EcW+jbBngTxSyhKpm0JrA+VbpsHpQAu/fbNd1nHOBtiZlF0LVXzOxUXPtGbm5kycjz4tjnxbpJnfe8eADyZWtW1UbJhmAm6oRtasVxbiY+lFwuiPpqmAQmW8XMus182W7BsekMO2mtvLj6sPhTcM6qku1ZYoYB3qbzs82Be8mx7HtGmT2TnRZ+nqRiZmRGLsgGXBsiWJfus/C0maaoXGOO7abSe17mGWxNbx2OD65R2ijxWZvdkkn1D2DvorchZoBdn4v4Ohoadh0rcmzVEKO52apK1t2D1a7TrmrFjHQ6qkoCTSEBu8aQ0kHJVXlRPae0xqrdtyAw6xll69OwblJnYWqtS/dZHHIp1qTOtlmp97zMT9imH6IFx2ABAOyqZKyNn5bKi1oATBY3IHbdG49nEXSt+/MIl27atCu3701iqU4IVtxNWlR1AC7HcdiZWm2OdrDvnGr5TDqbkW2eSbOeUZKnSgN2ZKku3WejvEgZdi1UrOQxKp68zDNYS+aWuyWbPi+255HYG3bNAy4Qp6mMFxyL6gdbP4/9KbeWC45FSsA6KLlWP1ju4G2qjcz9NbY9ZVyVF/O0kW3AlSoD77RQJ2zTpq4KtcSp0uq+mR4RU7Asy3YnZvxeJWm07zdrY9g+SLYueqXUmKepzJUe1wBvW5JnS5bMJHo3YibdNMsoCNoGJVtfiGWAt/F92ftr7IyfNm3fq+NZ+Hkc+3fYKi+2lZImzc6sVVxLhdpWLbAh8Lbqu+25TUqhto4BFuq7zXiSaN9v1saw7YViGwSd01QCplabnDiQnJudx8ZmB29/32x3gnYVAlaeF0vlJblgaY0nSODdG3TZKWbWxMyiAs7Wh2Wr4kqU1Md+NrumkLZVYhLvt/066db53ER9r45nv1kAPHmZd7Bn+G5B0Lb3ik0u17jDrmPayNZjY5Q2KtkuAG4dP20PijMJuuLVCI5l5zbPpNSBe7aE2uY5sfVhWVcuWqqPnRbvt23AjbrQGhJq+2tpnlopWSohyWojk1Pj3edmQ8wC5AyM1tLw5MUC0mkj27b2LmkjiZcEcDDsWqgTtpVNtlKv6w7eRKK3T/fZpY1sx1PPiUl7dNv75pruk9jBO6uqpsSs4qYYmBAK6zSt8yGvpqTTXlWyJYFhaNejyjoGCKRppdHe365NEZVuSqeNBPKdzmNZp43scvB2gYJ/AXAZz6oM3JEEGu9yHQO8VZWY5X0znZsKmraByUxVshvLVg20T1OZE4qSI1GSUpWcUtCW7zZg2sxQ7jmZCw3qAE9erOBaAmvyIJUrodXZJ9Xxag+uweJtmxKzIUqAPcu3OWvF1qvkHij4FTp1j00qH6qfd1PMkqdg68AmwLseWWHbe0hCDYwDvKnny1KdsE0bFRxMrZZqQbFsllpx9ryYbPIcz0kDhNKL0ZEV5uukJy/zELZn5NiVLseftd8J8h9e6OovkEkbye2ogXjnKJl+kJCVk+PZ+kJs0kYSzz+QDEyG19KhfN/c8yX7vimSanLf7Kv77I7/sCWCdmmj2twMyXvyWtikvK3fb4vjODx5mYewZaY23RWTnRwldhTOuxeDtFGlEiYkev5qC/tAYX7fwjAULQNX19HWz2NakVNyfk74iVnBYizAvXGimWJmafxM9AuxUycsK4AE3jfb1Ip9SwmHtcSQvOeTR5sYjOdsEDbyKtndN2m097drU7imjWyMmID9gmMUKEp2L6WVWpAgVaYVCTZpI3uTqfl9Sy665rsllyoxux28yY4acFexjI4HcFQDTe4bIKsGqu9WsHxGAFvjp6VXyWIsl7nZrJW21U02980mwLuoIfbvmzfsekC2Iie56zRpsV39HZvUiq1h17wrpks/ARcXvYTKk1RpJE2tpoHC5lC62VKVJBZuIL7uxs+kRSrHlUxU/w2LEl/bKjELxcz4OiZSMTYdfU3OUQLsVCXblHDyd6zUR4E2D7adiqXR3t+uTWFfKm2RW7WUQuvGM0obSXbzdQnwck3qYpXHrsW2dQVQm5aBu6hKNiW3rpVN5UpoqE7UApOE+mh535IkVaQJZc5+LTEdK5c4YVsiTdVhQeBt7xtg14TPOXVqsU56z8s8RHRmh2Uu164kz/4lsdkt2Uuv5mPlE4uVLmz8DO7pAJl0n914lj6NqIrEnChZjWdBqF07tSb/jSwkVSXTSipJE7mtOhEpdIbvm815WyWXtcviOAL7SkmlGvN7h6q/U9ucWGz0bCsXrY7j8ORl/sG146dVHtdFnmzTaiPbNA5glxKIVCzBnjKFnE26z6bE3Y2YmZSluihmdipWrSzbKbWidy2Tz5Nt6tRus2D2jCTVCRuSa9sKwW7tsg/wZiq1m0FYQuUBYHWopu26bHO+l/e8zGO45vslDqVL/o7ELtfFX2NHzCwOirNNrVgEXJKcuI0R0/iMqMQOXnM8N1XJIcA7kBfdd67OqySYNrJ5TkxJdTJ9Zh/gzdUCm7XLpkePfSdy8+MBbI8aAeyaUNqnjcw3QlM+bTR/IXmOjMvuxSVtJDG3KP3msgAYSK+uxyyUKyEqmgGeQla2ShuZpjoSAVp3PBdVycYgXLIk1HkLdYLCq2TVL8SB5OqOR0E6JdQCwO6gV9t2AW7vm837bVEA4NoKweK+dRoehCoNT14sIJk2cjFP2ZUAunYz1e87YdvtE7Ds1Gq5W0ou9LrmSFsSCCSqVkx6MzgSM0D/OXG6bxY9c2yfScDcO5F8nkx9IXbHA7iQXLMgWG+0tkwbWZyTZloBB5inVsRVJcujD5K/I5kSM2su6NNG8xa26YfZ25nxV3YULIKgW68E89Js13Obqv+GWYA3NX0CsGr9bmuOtGlXPm3ZyTT5O1b+ISeSa6ZO2LQmKFhsTmzP/6mOZzg3h4aXBQufhktFjmm34jrSaXzUgn1zQZdCCqsqMYET6l02XpJo72/XprBtjW4lTzpIr6LlxBbmSLeUmHnayNWsWB3PMMAbPiPJ8WwqLUzvWxAExs+Jk6okbfw0DYIOqpLd++0wniGhUN8rF8C4uk964xX7eWxSYpZpIwuPmZ2fR61d/O+A3X2zS9NKw5MXC8SymllO0Ca1YqvyVMczlwztiVn8ed0XxSW3auWxsSSCyZbe2mkjF9JpkTaiqOzQJYIuAdfO+GlPlkxNrS7pAKs+ToLqBEVXWOlKSX0/j31KzO3oA5uUmJmyWnc4r6ViZuPnsVE6JdHe365NYe15SXzeWMZ22MGbvJS2xCy5kzP1F9gw/E4bA6FLZYdx+sGh0sLC1Opijox3nmZB0MbLYHViNkXllnHayJ502njabK6luTph/0w6BXiHtctUDbTpGWXlVSJ4TuxSYnYxxya9bjM3SbT3t2tTWJOXXJK8aAYKB/OU63EEJgiCwHgRcCoTdTAQ2uzgo7nppo1czIoWTbNo+gFpGj/n0A7e9AysWFWyIRMWaSNRdcLON1EdK1aNdUFBlkzXSZcUtI1iZpcSMyPU9RVwdq0QrJoL+rTR/INrPwHAJMC77zqNnOYUC5z2SynsL3BKrdj5C+x2ZuY5cdtSSiBBlnTTRk5BaXbum+61JDlwT6wXimHayKmySW0WLFQlm3Jiw9SK20bIwvzvQHJNTbR1RmvLVghmqpL9uiyJ9v52bYhk+3DTHXw+F0AVMJinjVx2ZuYvpUtg0k4bOTR6skobOalYZkoPTapDNm1kmu6z84VYHEfg4rEx7DDqQpQ6XQ5mtEitmKoTtgdcAsmzjWQauZkGXRfyHquqsqXSpnMr5ALkTI9RMVR5gDhe2KjGkvDkxRDlSgjltTV9UaqVHXa7Jfm0kcBO0MGsaJc2kiMUbtUINhK9e5pKl+SKN+BzCRSGKhZJZ2SpLtOC6oRLya1IaqXk4B2yOijR3bRuOjep++ZCBCXR3t+uDeHiagdg3FOAJlCYp43sfCFmu2rbyibAUp1wKvG1u292ZMJcopc0tar7a1ON4FIBZ3cGlnwZuJgZ2dT4SZA2MtrBO6UyzVRjCqJkddK5zXNiWeIuHQNs0n2S8OTFEC7tw4HETlCi+kHYX2CaNiLZUQi56G0DvNR9i1vot7eqZJrqANxSmaYpMSefhqBBHrA3fjr5QsRSmaak0z1tZKN02lRKmlfA2fWnAur9PLrtOeK0UXvTg/b+dm2IUh15kSsBdHpwrUql5RYcpwBvcraRw9xMjaYuAd60KyyQMNlZqVhmJb4uamCn4Y4aSKbE7O+bcUWO0zMpG+D11QmCbt1WFXAOTSgF7lvBUDEGZIlg9Pw7dOsOQ0THJ2SP59NG8xJJ85Rp+3DAPG1EsaPQXQCSzZAkSjclTaaAmznSNv3gcqCmzQ7eaoGbjQBvFSjmSNrI5FgHBxOtrA/LnJhFZEkwTetifC6W9dUJyRJ3l7klr72pN8obdgF89rOfxdq1a9Hd3Y2zzz4b9913X8vP3nLLLXj1q1+NRYsWYdGiRdi4cWPq56XhYgwDzNNGJM3HDBc3wK26yXQ8m1OlTdNGYRgSlaWaLTguvhAb8mJFco17ociSTskUnHSVGEWvI4mGl1FDPJs0rcD5Xi7PZPKd0VUn4meSP73oMjers8scnklJsH+722+/Hddccw1uuOEGPPTQQzjttNNw/vnnY8+ePU0/f8899+Ad73gH7r77bmzduhVr1qzBeeedh507d3J/VS1EOyWLFxKwr8iRkCeTAcUlbWRecmuffjDtTQLIELNZq1pxCEy69y1WC/grmwAaNUTXICzZUyb5WbtOzKbGT6Ua86sFgJtiJloGnvgdmb4ylp4Xh7XEZDwXYiYJdvLyqU99CldccQUuv/xynHjiibj55pvR29uLW2+9tennv/zlL+OP/uiPcPrpp+OEE07AP/7jP6JSqWDLli3cX1ULLn1XAIuzVkr2Qcn4RN26U2ddjiOQMBDakUBAxmjqZugzC/BJVUmyxF06bWRjojVtaz8dKatSc3PxKkma/+PyfePUiqCKa2d8jn9H3/dF4R/SnJtDq4DkuWza74ADEZQE67crFot48MEHsXHjxnjAXA4bN27E1q1btf6N8fFxTE9PY/HixU3/fmpqCiMjI3V/ODHtsJACFhK9YBVJcqdk4+exnZtTNYLhQYm245kefUBzCJ7mTimxW3SpgNP2vKimcVbNxxy8SgL3jSS1aFDZ4eJVsibUVr4Q+x28U68jwfS6yXhu/XnkStyBxEbPMAX3G11ttG/fPpTLZQwNDdX9fGhoCMPDw1r/xoc//GGsWrWqjgAlsXnzZgwMDER/1qxZ4/y90+Cyw03+nmiZnPZL4jY30/SD7RlRgHm1kboGNge31Y0nEeBdvEouKTgRQm1jRrZ/B4zTRhX7QKGuh1llh/vmRCbAx9dD4ogM29SKmDpBQAQl5gaYn8tWcngHJNHW1Oqmm27Cbbfdhm9+85vo7u5u+plrr70WBw8ejP7s2LGD9Tu5HlplmjaaIjHsmqVxbIJEcjzTY+ylVSUbdBiqStECYEGUjMlEyU15MSWdKsC7nFmjG+ArlTD6nIhh14HAJ1M/xv4ChxJ3STNy8t/JAsVZShKEGnA42kSAwLt4vgDzDsKuZEkKBc5/fOnSpcjn89i9e3fdz3fv3o0VK1ak/u5f//Vf46abbsJ//Md/4NRTT235ua6uLnR1dZF8Xx04s2DB1IppTw1Xl7lpgHfrHmyn8ljPzbjE3d1orU0CE4uSFaGIWujLpY2A6jXK5/LpYyXmJnIInst9S6R+iuUKepA+t+Q5aaLVRo6+EBGyJNiFFqiulcVSxeCdI/C8mFYuWloVTJsZulojpMBKrTo7O3HmmWfWmW2V+XbDhg0tf+8v//IvceONN+LOO+/E+vXrOb+iMVwaZgEuJjuX/gX8EjbgkMt1OnRSpvGSsYpFkDbS7TuRvG9WXiVTAyGBVwnQI57Ox3EIvm+mp8Y7e5UMA3zJ4b4FQRAFMwlfiPH5P44bL3N1wn1zIlW6bN+j6jdYeQGAa665BpdddhnWr1+Ps846C5/+9KcxNjaGyy+/HABw6aWXYvXq1di8eTMA4C/+4i9w/fXX4ytf+QrWrl0beWP6+vrQ19fH/XUz4ZoPNK42qn3OrheKoRLiaAyzbcUu2qDLNiVmeo4MgWIGVANc1v1QSoh17yHLEnerSqqEOqETBF2N1pLPZPXg1QDT5VBvbo5eJfPUqbtfr1Qpa41XqYQkJfWm6T7XTaX2qfGSKTGHTR5gTgRd2jxIgp28vP3tb8fevXtx/fXXY3h4GKeffjruvPPOyMS7fft25BIL2uc+9zkUi0X8/u//ft2/c8MNN+BjH/sY99fNRNHR1Gp6yq3LgmOaNnLdvRhXrRAYP1XpZpbi4Lwzy5ktOC4kN+l/KJVDdKRnHwh2nabmZ/tnMlczTJcroda1VGmjXAA7o7UinQKnSqvfmy7rBXhnr5JgqXR1vACY1nsH6tJ9Asqqet9sCDVgk6ZySRvFa5cOXE95tu5W3OaGXXbyAgCbNm3Cpk2bmv7dPffcU/f/zz//PP8XckDJwawImB8C5tRdMXpo+SubAJu0kUMVSZ13Isx0/buqSrFXyTBtJOCdcPYqWapY9rvcKnnROcXXubrPtFOrqznSwPyc/IzLQZDGFTKOyqrOtUySDllDPr864exVMtwIORcbmBLBOaK8tPe3a0NILm7Jz7lK5lreCVdTq2TaKJckLzpB0HVucmkjY+9E5OexJdR2+X7XIKinTjj6sAQPnQTMDPmxWmDnVZJsUgeYpbzrU2ICvhAHjxlg9n4nyZvEUSPOLSwMe2KVHJriSaK9v10bIm6x7Vhya9ifxKafgOmZHVGptHDaSMIc6eIdqo4nlzZS3gnd8eKD24Ty/URKj84OPlZ5XN83s3SATdM4wGyXGwcl2+ZjhukAIl+ITtBVz6R1us8wtUL2TJp6lRxK3MWqQPP670CyW7c/mHGewVVSM67siHaeGcaHJijUBXj9l9LmhQTM00ZFh111PhdAbVa1FlNXr5KpL8S54Z/5YmpdSilu/KypIRqLqbMPy1CidzVHmqiPzqTTMCXmWmxgEnTjAOhKqGXUQBN1YrYq4OzT+QbpvmQFnCWBl0J7f7s2BJX0anyGhsPZJ7rjOfdCsfRO2CzeQRAYBSbnHby16Y0/3SFdSllyvpYGqRVXolQwNUe6psT0DcIuRwMAsK6As292ph90XdN9BUNi5v6+6asTyevtct6W6VpivzmxTPdZbmKl4MmLIehkbNOGQW7eCZPdknuTOpm212Z5aqLFzVjGFvBOuBqtbXeCzuqjQQ8b55SYlKpkEOCJvEPac6NSH40INb/KUx3PUekxIEvJubl4laRTYnrPZPwZW1Ithfb+dm0IqrONzM2RdupEbLLTkQxdd0t2O0Fn/5DBS+mcWjEsuXVtiqeTNqKrtJAJ8AUDdcJ112nbU8PdF2KwWRDyYVEpdCJVYlH6TbdCxpEIGqhYrilh45SYQ1VmdTyTdF/SaO2Vl3kF17ONTH0hip3nJSo7aguFjcEOMD/6QJmIbVUsk5LDkuPOzLZLpe14BYu0kX2Al+uMXB1P3xdCp86Zqkr819KZTBie0E2lBoqkMtVYpbLW592JWfX3piR8WKbvm7NXyWKdzNmpSpLw5MUQzhJe9FKa7ShsDvgDDFMrZF4G/pQYYEYoyo79eUzTRlS9GUxy8LY7M9ODIN2Nn+beibmiTtikjWzH6rI0tboSChNi5pzuE1IDY7LET8y6jEknzdx0iJnr3CTR/t+wzaCCl3MppUbaqFIJoWKlRHljqeKYWrFUldxPejZIGwkY+qrj0ewE9QK8nE8j+TnnijujXihCaSMyz4tBgHdVQgzLwGXVCbu5dZnOzZHkdhby2uO591XSHwsAWf8tE0Ld7mXSgCcvxoiUENtyYstmSLapHCvJUGBnBiRSYgJzUykqCQULIDTR6pRuup63ZdwvxNVjY9KFVpqY0aQE9Kr7aN43c+WFnyy59suxnZtruk+ixN1+bvzjxRvK9qcG7f8N2wzR7sVRedFJGyXVGXt1wqIix5lMGKbEBAzCKsBLECWAsLxRJ1A4kgnTtJF7JYl52sjlKILqWLJzM+nPI3UQqnsQ1FcMnJUQYdJpovRQpd9KlRAVg+ahtmqIyfvteoyKJDx5MYR7vxD9tBFF2VrcC0U/bZS3HMuETNSlxBwNwjqBwlVVkt/Bm6fEpErc3Q+Kk0utiKeNLI4HkPAyVL+TW3VTpwFZkk6JFV1VY4tn0nXdAoTNzwZqf7uXSQOevBiDSlY2SXUAMqWizpVURgs3ATEzWExd/TXGZ604po1MOjGTmYNNq42sF1OD0k1HGVuadBoFwZIbCYzIhHafF6IgqKNOON63ZMDVOpdNNCVGY5AHdE20NF4liblJov2/YZsh7vjpWkWioxZUH6RcAOSse6GYlG66lS7HRkyzlJh9qbR5JZV1GbhBUKpUwthj4+qdMDAQSqTfAHcZW5FVnR4es5USszlLDLA7Vdo11SHVV8kotUJElADNzQmRL0TLjOxskI+vv9777fZMmrUmcHtGJOHJiyHI2lBr7KgjCc/BPFUwSBuVidJGJkoI4ECWLJQekTQOBTETLLntNEwbuXon7Dq1upmDxdNGGpsT1xS0um+V0KwBmWuzM5kjKxLkRUSdqPl5jOZmd9+CIDBKwUmWgbtuhCTR/t+wzeDqnTBZTJMNg2xhsvN0NuxatLQH3NNGWn4e19PAjQJu/H1cUwIyrdhVwNVLZSreKZI2IjQH66QfYlWJP91HVUUC6KoT8r1QbNUCU/Liuqk0q8hxG8t0PLqGf/zvmyQ8eTGEa9+JKCjp7MwcG6sBtmkjx4Vbi0xUv08Q2KdyjNJGzk3qTNqHx5+R8EaRdfw0CLiAvfHTJG3kvOtM/J7puTU2MFMn6OZmFuAd+5MYqAW2G5NcLrBSeqzJklFKzM2rZDyes4olFwMk0f7fsM3g+lKapI1cfROAXdpI4vTSKI3j4GqPJXr+l9ImbZRzIWaCaSObJlaAtIncNo0Tf8es8cqVEEqcsT7fy2RzEvULsbuO+VwA1cHdSA0R3cE7vN8mRlNBM7LrZgEwNXbPxn3zysu8g3sLfX0jpnqQbANgdTzzB9e1DFzH9KbUGZe5mZxOXHbt82LUGt1992LTXNC1IV45YTRuhSTBcSUUJkcf2Ab45CYji1DUqUpzwM+T9E5kvXM06T4T74SbEmI6nus7Fx21YHLfHCpyjAzCroUURqXS7sRMCu3/DdsMot0OCcxTJhUJZUeDsNFuouJGlICYCJqQJdcDNbUWN8edUvV39X0h7qXS+upEklC7Vm7ppXFcm0ImKjsy5lYkIC/RfTPo40SSftC8bwBBEDQIuBTqRNb7HYZh/A44VmUaqRMuGy9BNcRMwfKG3XkLZ4Zv0zDIIcDbtfTmL6WkSInZ9J1wbVcealR2UEivkYplovQ4pjKr/5bg3AQk+iCIvRNZhKLeq+QaKDT8PFH3YPtrqfvOURAzG3XCpdmZbjVhkgM7VxsZqTzuqrHOtXRdK42aCxL4LKXgyYshnJshGbwkrr1JquOZS4aunSN1SjcpUmImBsKyo9JjUtnhasRMjifZih3I9r1QzM3Ky+CQftAdL0neg8CRvEgFeE21s0SQ7rNJG7mQXN3xknPPO77fOpsFV4U6OZ6EfcBK7fdN6uYfJMsNKUytJrlVZ4ZvEOCj6ygmvToqLwaVHRRmRZvSTdsKuFwuiHv0SM7NQH2UqOygOFHXxETu6lVK/m7W3JKqk+0rZ7N2SagTSZ+W7VrZaXTf3NUJk2tJpbwY+bC88jL/ME10HonOoVyupy4nxxNh+IlrMjWdtZjW5iaUEnM1WhfyuWjR1w/wMnNzrYAzGS9KdZA8k+XMz7oecAnoeycoNgsmqRXJ9zvp+XJVlXTUidiQ7+7X051bdTy3tUvq/J/YPpD+DoRh6Pyc2GzyvOdlHoKqKyago07IBkFXU2tdgNedm8DiBiQXHPdrmRUEXRudAYaLKYFiEM8tfTGl8CoZ3TeKa6mp9LimFuvGMlDMnPo4aRKKsvBGKCaCAsQs2dFaIrVCoCrppjIpVCWbVgi+z8s8RMmx+sHkUC6Kktt415m9y3U9VRrQXwTKgrIy4N4ZOTmeTlkqIBng5So7SgQB3sS07tp7CDBJG8k9/wAtodZWAwX8NUDCr0dBBLM2QrXr6HIGnM37RqEGZhOzhKrk6ucR8ipJwZMXQ7j2nTA5lEt+t0Sw89QMgtOCRAlwP/qgOp6e2ZpkcTNZcBx72ABAV4dmoBBcuAFaAq/7vjkpnSabBUqFTnctEVKVyhR+PcN0n4Q6Vx2PLnWavYGlVJV00rQ+bTRv4Wr8DILAYEfhzoKNSrMdVSXAJMDTpcT0Arw7EdS9ltIl7iXCoKu7E5Tw1wA0yktXR/WZ1A0UFCTQxCAvqSqR+DQMWhNINNgkMdBKE2rNw1CTaSNr8mKQNqJIQUvBkxdDuB69DiRMfQILjk0QFA3wUmXgguXLNIupSUk9hYqlSToJiZlOgKeoANJ930h6DxmVSrsHQd33jZQoGbQmkPDrSabfAPdu3SbjTVOYkYWr+6TQ/t+wzUDSoEjbFyK7o5A0CJdIiVm2HEoSBI0XU/egpFXZQZISm4VdrrTnJaOyI/a8yKZWRJQXwpSwiTpBkhbWTWUSpY2yTh93LWwAku+AnkHepfeQGkvn+I+4uq/9qUH7f8M2QhiGNIuAqdQrYHqrH48/B0/ir7HoYUMSmDIWHMq5iSlmmoSC0l+QVU4PuHdGTo6n/UwKtyagIBQ6h04Ccr2HKAz50dEmuv15CFJUgP47IFEGTtl7KPnvtUK0gXVoCikFT14MQHHqLGAQBAnLRKVy8LHxM2tusl0qS5QpAUHDro6qJHktSQ7UtEg/SCgGFCkx6Uoq3aMWSDpaG6gTFITCtEmd07qVbGGhSXIlFOoyBXm3qHB18T1Kof2/YRshWbYmUXJL6gsRrjaS2OXqehmoxtMlgqQl50Znn/D3eSE1kRuVpfITeJKxal6ldlOVKIkSYEAoJAh1tG7RBHgRAq+tdBIoLwYVrhRKjxQ8eTFA/eFmkkY0GXWC0iCs3cNGaAdP67HRu28S1T8A1TlRpsqLi2RuYEYWVEMoPGa6JefJ8UR6oRCkoI3UCcF2ARRzSx6RkVWVI3lsC8UGNggC7fQixfEfUmj/b9hGqDvcTHC3REKU2qwhmPip0oInwVIcqCl9QrduiW/cv0OKdBIqZhlqCOUzomOOpGlNILgRMlEnSIoN9A5epQjwgPm1lCkDd79vgHla2Depm2dIBiXbTo6AfhAk6TthsMulNBC229wkSzcpiZlJObGEjD1NUSZqEuAFG5CR+AuMvBOERmuBKjEbdULCtE5BzOrG0/YiCs7NkUyodS9LeSl65WV+guKQOEB/J0jRm8Tm/B8JNYTkeAAL7wSFD0U3JSZdciurBgoFeFLzM3++32xucoSaKghKNsVT10WbmLnOzdiLyE+oKUhncjz9FFz7U4P2/4ZtBAoyAQg3ctM0YoZhSFtOrOsLoSATOmXgFH4Gw14oFJ4XndPHo26mguX7FM+IzngknhfDJnUuz0i1H0f1v6cyS+rlVKU4jSOjTpD4hwSb1AEWvY4ke0Y5xhzT56TTp43mF6YJghJg/pI4BSWLA8AkTnqeJp5bVukmafdUgXSAyenjpD4UgYXbLMDTeV6yG7m576iT5khdZVWinJjC1JocT2IHb2xGFvKFUJ6Bld13hYaYRSdLZ8ytSHQtJdD+37CNQNHEDTAPgiTpAM0ACMh4XigDLpCegw/DkDaVI7DLVX4eID1QUClmXcaSOU31Q1aAL5OcyaNn/CwTEOrqeIbKKkEDsrZTJwgJfGaAJygnBkz8eoStEASaeQLmxQa+VHqeQb1Eruc+mAZBmgUgPf2QXCAkz+ygONcFSL+WSWLm1p/E8Pwfp2oEvd4MZL2HTI91EHoHSAzC2j1s3Al1dTzd54SiImeWTK0iqVO9AzUp+q4AFsqLYBm4C1ECzBVxf7bRPAMZCzZ8cCkMfUB6oKgrAxeokqE8IwfQD/A0QZA/wOuePp68b27+Ak11giDgVseTNwjrqwUygYJCVZJMQQMmzdXk/HMUzwigTwQpK+50r6M7oZbdnEig/b9hG2GayLCre2Iw5VlDQEYQrL0kQeD6Uhr2ZiBqLKVLXkQrqVwDvMZiqgg1IHTeFtEOXkfFIkv3afs0iD1tAqqS6anSYkFQkHRSXMe68dqwe7Dr3NT7pp0W9mmj+QWK8zoAkyAoqU7Izi1+Kfll7FJiMZIwR1K12NabW1J54VfMKErck+OlXctkllOynNg5CBpWN4kEeKL0g34nZjl1gsJAC1goq3NIVYpOqc9KnRLFOAl48mIAqlJp3XLDaYIdfC4XaPVLmLVOjmS7pdbXkjptlHVYIpXyokVeyBQzQ2ImEOCpfFjaBlri9zstUNCpSnoqLkWTRsDEryfXe4iamEkYhHWfSYpnBNDvoE3ljZJA+3/DNkKRSFY27RciUd00W9UIVHObTKlaSe6UgoB/t0RWlaYRBOkVs/YhZmXhdB+V8qLzfpOrSkJB0PhaCvRCiYiZtC/EqQGf4dycY47egaE+bTRPQWVmMjUrkgWKVOMnTaqjS9eIRp2CSxmPKo0jecAfoEk6hRUzemKmlxITaZxInO5LmxuVqqTbhZbc1Kr5frsQig5tNXB2iJmIx4xKeTFMG7mOJwFPXgwgXSpNHSjSJXo5tQBIpMQEfCEUZ9YA0O5NQpHuA/Q6CFMrZtmeFyJzZD77OakzIxOkjSR62ACzpCoJmIPrxktTlSphpCxJVhtJbSopKyWzOmhLz43iqBEptP83bCOQGTG1ezPQ5qnTFm/JdACQKBMVSYkRG2gzm50pyZx/wSkTzU3/gD9ac6ROKjPvmu7T9IWQtdDXUAyoVCXjsmzB+1YdTyDdR+3D0q6UdJ9b1nhUHrOuDr1qI4qjRqTgyYsBisTKi1jzJR1zJHUnR4EGXYDe4k1xSBxgXkklkYOnMisqQ59+/w5+9ZFcCdE+VVoiwFNVwJk1TnTdUesRapqmkEkykXb8h/Shk5RnzgG67wDNfdOuJvRpo/kF9VAnHzwbyB/1rlh3ikRfplnctIkZVS8UjeZqZEFJ8GwjQK9TK1mZqKE6IaK8UO0629HPQ6UqCXbrBvR8KNMJYkZ1vlfa8R8UPWWS4+kexyHRwoKsZ5RmBZwnL/MUVORFu2GQYHUTXTMk0/SDhJ+HKLWiKb2SzU0nUBAZ7EwrLSSeE/ITdaWaC2qQTmpVqVwJ6xSPGeNRpfs0CHw5QTQoDnkF9FJwEsSsLiVGdICnyNqlUW1ENTcptP83bCOoh6zLmbyYqRN0TnONqhVXn4ZubpUo6OrsKKgrLbLvG/HcpgXmZto5VSD9QHWiblKdSEs/kFelCahKdekHgfSiViVV7fkPgmqfKeuxDBtsSqRp65pCihYb8Pd5oTqcVwqevBiAyvOiX7ZGE5i6a4RiMiUIUqUDujv05kaV7tB5KekbPeneN/7SbPpSSilCnU1yqY3WWeOJtiYgUpV0D/CkblKX1siNqmJF9/gPye7BJaKUGKDb5oG22iht7aIq35eCJy8GoEobKTKR3TCINuevo7yQBSXtcmLHa1nQJ2au17FbN21EdcBfRDrlcuLFciW1dJOsH5BOAz4io7V6RqrjCQaKtGeSSlVKfNeplC7T02Q+LH11giIA6qkTkooZTYk7oJmCI4sB2WtXnfLiycv8whQRedHewRO56PWUF5rdklJeJnWbIVEpLxq5XGcFK7G4pQZ44sU0dbdENJZ6RqrjCVQ/aBAzque/Ix9AeWJ1+sqIpGmJ7lsQBAmylO1DcSdmOooZXZdWreM/iIigjvk/6Qtxje96zQxprqXOM0J1jIoUPHkxQERe8vmMT6YjVgv0zJESZXJUC45aAKbL6QZCKmlZjZdGlshUpUSA1zEQUik9qcoL8TMCZKkhtMQslVAT+aKCIIjeudQAT5TK1NosEN03wJAsOT+T+veNYvcePyf8KTidTWUyve5SJQaY+fXovEp6JNB1bhLw5MUAZNVG2soL7a46XcamCRRqcQP08qvuFUAaOwqiqq3uxH3XUbGoCIVESqyQz0X/hkSaSicFR9nts0sj6MYNwajum4w6YaKsUqkTkwJVYkDyOeF/B7o0NpWUKTEd+wCVGdnEYzYXVBfAkxcjRIZdV8+LpjohqrwQmYO7kv4CncXbNQjqKC9EZKKQz0Uvtpbxk4p0Ckn0eqY+msCks4On8mkA8XMiYdjt0gm4hGfIaAV4ovRDZMgX8PMAeio19dwk0uuArvpIrSrxxwApzI1v2SZQJ+5SKS+AjDphFAQdF5x8LogUDp1UDtlLqbF7Idnlaiw4VC299SR6uvSDVpqKeAefTiZoFDNAkyyRETP960gZBLXeASqDvICfB9BTzKhVJZ1nkqJ9vuS6rGUiJ9wsSMCTFwNEfV4I1QnJShKt9APJDl5fDqVTXjRMbxQ5eIN0h7tiNjvKS7oaIkfMqMrAAbNqC4lupqSqUoeG+khlatVRXoief0BPMaMaz4jgkmwWDIgZVZM6gW7dUvDkxQBUaaOkOqGzwFHtBLVc7ZQvpYCMrbOYUp3MCugqL4IyNqE50sSHIrmY0ngnDBQzMsMuvw+rOp6cqVVLLSDdLBi8AwL3jTLAd+m0eSA+IiM9BnjPy7wFlWEX0DWHEZm1NBYAql4JgFkrahEZm7L6QSvAy/VmoNzldglWyRiRCULlRScwOZvWCxrknVB5iZ8TgV5HBn2VSFUlgbVEJ7VCGeC1fCiClVRUirEU5sa3bBNE5IVwJ9jqYapUQigvr/tiaqK8CO+WBGRsKi8DIGuy0zNH0uXgTUpuqZoZarVGp7hvWmWwRKZ1nSoSwmdSS3khVjq1TK2E75uE+dmoAo6wkkpnA0v1vqUViVBaByTgyYsBqJrUAdnqRN0hWUSsWyLgArJeDR3lhfKkVK3AJGggjKsfZEytdEdWmJiRpXbwtO0CJJQQQFMxI1OV9NNGUsoLdX+eYrnSMsBT+rBMKiWpjhoBWm8YKO+bBDx5McB4sfqQLegqOP9bWYQieYaG+4Mrl+qojqcvh7rvcjWUF8K56aUEqHLw+rtc1yZWgF4Onu5ATYN+IaTPpAYxo2qcKKW8GJkx6Uhnq0Muefw8/MqqTqNG2o2QPoF3PmrEYG4UFXASmBvfsk0wMjkNABjoISAvGQsOZatmrQZdlBUCGeOFYUjnLzAxEJKkH+T8PCY+DcpAIVGRoJUSoyxxN/BOSCgv0kGQmnRWwpg0NILSz6OjTlA3TgRav99Ux3EAhqSToAllVo8qygo4CXjyookwDDEyUSUvC7s7nP+9rABfdwAYkak1PcDT7ZZMiJn7LtekdJNfeSlXQqgNKVU5sU4vIFqJnr/ENy7vlamAMzmOwL0Tc7a/gFYNlOsro9OjKm6hT+hVSpkb1bXU6VFFdRwHoNuokdAgnNEPiLJoQwKevGhirFiODLT9BOQlK7WiFtJcUD0a3mkso/QDv9SbJGZk5Y061UYkTbPSA0Uy3UdVTqyTfqA1R/KnH9RY5UoY3Z+ZY8mWgVO9A8kA3+odoCo5B3RJLtHc6o7I4PdOdGtUUlFey6x3jtSrpHOgJoP5udUhl5RFGxIQIS+f/exnsXbtWnR3d+Pss8/Gfffdl/r5r33tazjhhBPQ3d2NU045BXfccYfE10zFaC1l1JEP6s7vsUXWLle80ZOgYXc6EeDJTvAVqn7ozvBO1BEzonLiqVKKv4Ch2Vmra8mR7gNaE0+qfjnV8QTNyMkjMrLmRupVyva0uapKyVOsRdYurYocejWktapE+UxmEzOORo2trqUvlW7A7bffjmuuuQY33HADHnroIZx22mk4//zzsWfPnqaf/+lPf4p3vOMdePe7342HH34YF110ES666CJs27aN+6umYmSiBKCqulCcuJmtvBDuJrSUF5qeMkB2oCgnU2JUrdiFqh/ia6lTJea6g9fwF1CqSlnPJGG6L9luoBVZojpRFzBriuf6nORyQTS/1soLpaqkU1JPWZqtlxYmnZuGQV4iwMsfx0GpGuuRTu95qeFTn/oUrrjiClx++eU48cQTcfPNN6O3txe33npr08//7d/+Ld74xjfiQx/6ENatW4cbb7wRr3jFK/D3f//3TT8/NTWFkZGRuj8cUGbd/h73lBGgo7wIS+YqwAt4XpTyEgR0ykta+oGUmGXI2MnvQNUVM3U80kCR8UwmCJTrc5LLBVHLgVbEk6NqRasqTYAI0qpKcmfkVMfLSgvTN7wUS51qBnip4zgoyVJPVsqb8L5JgJW8FItFPPjgg9i4cWM8YC6HjRs3YuvWrU1/Z+vWrXWfB4Dzzz+/5ec3b96MgYGB6M+aNWvoJpBAbNZ1rzQC9He5lI3ViqUKKi0bFNGVyWUFCo7+HYBO+oHfQJjcvbsqdFr+AqID95Ljafl5SHP+/DvBroyqlUqd0ZquKi3L80KjKukrq6QdtNulnFjw9HGO4zjSKqlIq5tq401kpvs8ecG+fftQLpcxNDRU9/OhoSEMDw83/Z3h4WGjz1977bU4ePBg9GfHjh00X74BZx6xCLf94Stxw4Unkvx7cZO6VuYp+pcEiM9najmegPJCmVvVSz8QVuRkLNyU5YZJf4FEoMjKwdf7efjLl0m9Shmkc5rQaA1kEwpaVUm/2RmlIb/ltRRMUQGyfWVoj+PQMOySbvT0Ciko7psEaGSEWURXVxe6urrYxxns7cQrj1pC9u9l9dTgcJkD1Qc3SWYax5N4SSiJkko/FEuVlPQDZS+UDNNb1OiMZvfS3ZHHVKnScjxaVUmPTAC0pZutgi6pVylDeUmWNNN6NdKvpQSZSJbv0yirWdeSQZ0Qq5TMSp3WNkJCpJOy/1ZPhvJCed8kwEqxli5dinw+j927d9f9fPfu3VixYkXT31mxYoXR5+cqMn0hhAt3h06DIsFqI2p5Ujf9QLpbaqm80Kk8QHZnWFKJPquSKrG4UZjW4+omAa+SploAUJsx059Jms1JehBMpvsoz8DKJtRCvhCOs5Sy1EBK8q5RKk1xLXs69Z5Jb9gF0NnZiTPPPBNbtmyJflapVLBlyxZs2LCh6e9s2LCh7vMAcNddd7X8/FyFpOkNMGhQJJCnppYns9MP9F0xs8oNKRZSIHtulCpW5nUkDErV8TKUF0pfSKZaQKu8ZPqHJCvgCBteAtnpRcq0cNYzSVm+nxwvyyBP8UxmtSYAiO0DGcd/UBqfJcCeNrrmmmtw2WWXYf369TjrrLPw6U9/GmNjY7j88ssBAJdeeilWr16NzZs3AwCuuuoqvPa1r8Xf/M3f4IILLsBtt92GBx54AJ///Oe5v6oodH0hVOdMdHfkMV4sZwYKibbXlDtqIFsxIO2pkaW8EHa8BbJVJQ7lJfMZIXomMw8nZSgDz6q0oGgKCegEQUpVSS/gAjLlxKTdgzXTbwBxSizjOaH1KmkoLxRzqykvE0X+VKYE2MnL29/+duzduxfXX389hoeHcfrpp+POO++MTLnbt29HLnFjXvWqV+ErX/kK/uf//J/46Ec/imOPPRbf+ta3cPLJJ3N/VVGol3KiyG/8BLLl15jhy5UbUkjYgIYPRbADJ5fyIpHuy0rjULcP124IJhjgqYhZdkqM8L5l9iYhrhLT7ONEmoIWKN+vjifXC6WxCrQZaaZUO9VzMl88LyKG3U2bNmHTpk1N/+6ee+6Z8bO3ve1teNvb3sb8rWYXWWYtSoYPJMxaLcgS5QF/WcYwqtN7FbLSD5TSa9bCTelVArIXU8pKqizlhVLBqo6nJ2NTtguQSoll+ocIFbpMMlF7t8lUJUFPW3ZqkZaYZTbgI0yt1B0EWapEnpS68QgJRU+n3trl+7x4pKK3s8obx1uQCWoJT70YrQhFHHQJXO2d6USJ2s+TRcwog2BvpmOfdgHIum8lBtKZ1cOGameW1TSLowy85cJNbLTWLwPnD7jThCpPdbwsdYLeY9bqkEvq8n3JLrTdiSrQZiS3Ugmj8/Qo7l1Pxjvgjwfw0EIWC6Y2tWY9uJQLTqbyQmh6A4CeGhHMVJVIdi+165hFzIgDfJaKRUo6BUrcgeymWZTj9WZUWtCn+2ppYYH71p3RhJKyVwhg0kKftkdVswDP5ufJUMwonslCPhddo2bXkvKoEUDjfZtjaSNPXmYJ3VlqAdFBagoqMLVUeghTOZnKC/XcMgIF5a5akYlxgbJswOBaUs6tWGr699TKS2/GM0k5nprbdDmMSFES1Ebrno4aoc4kS3SEutV41EEpiwhydAcHmr8DybWEonw/VnFl2jykEQqulFiWV4nKi8gNT15mCXGg4N+ZJcfLVkP4lRdqX0hvhvJCmafWJRNU6kRv5niE960z3lE328FTmoPrx0s3rZM06MoI8JTqXHW8DEM+IVlKnmKdNjfq+9aK5FKqj7lckKpiUZacAzrEjK6wAUjfxNYpL6RN6mSIGTc8eZklqICb9ZLQLaZ6PhTKnWCmRE+9AAgSs6kWEj2lZA5ozI2SmNWdE5USBIlTYq2VHjoi2JnPQX3tZim/aeJdZxahpnwHcrkg1fdFaVgH5INg2twoz2RLjiWtPk5MzxyP7zgOGWLGjbnxLechdNUJMVMrYYDvrUnmWRI9+dwEGv6poNRqPOq0UVZqhYOYAS2CILGq1JPRd4KyuikIglS1k5pQZ6UXqVshpKWFqQl1fN9aKS+KCFK9A62LG6JqOyGDPLX62JvyDigyQVUlluWzpCZm3PDkZZbQ3RlLoWE4cwdP7fzOrFqhLAHsTOSpBaXeLGJGfU5UKnkhJmYSnZhzufggyKaBgi2Vmb7LpQvwrX0olKXL1bGyWhPIEXhKDwqgT6jJVeNmfh7i8v2stYTSqwSkp+BKxHPL8llSEzNuzI1vOQ+hdhNh2LzEUVydEJToqSs7dH0oFIGpLgffdJdLHQTTS+qpSW5azn8upzKr47UmZlwBvhUxow666UFQuAKOuOw8TTXmS2XKdNBOu5bUbReyD9T0youHBrozXfTEi5tm2ojiwQ2CIFXqpa5+kG6K15u2g6cmZrrljcQkNy3AUy/cUr2OVDozjZhRe5UySSexsbvZ3Nh6D2WqE/yqMfkmL0OhpjY/pyk9bERpnhwP4MnLLKGQz6Ez39pFT76j0EwbkUuUKWkj6gWn9W5Jzj8kXf1A3cMj7TmhPrgts7KDeLxuHV+IVJUYsRqSRpYoq7aApKoko06kpanY1smstURAWSUngZopaCqlkxuevMwi0oIu244iQw6lGy+lvJFJVcpqoU+9wKUZCKkWgKwdPHVgSntO5JUX4iCo4wsRS63I7eDpz6TSU5XojzaZmRIjN9B2ZKVpqVXj1uskdUuJ7GojWk8bN+bGt5ynSAu6XKw7Uw4VSFORHw+Q2XdCrlsx5TELgL6BkEwxSGmuRu9B0Qvw5OM184VINxek3sGnvN/UakHU5iFDnaDroK3hC2FQHpsVUlAb8tNK6sl7D9WekVKleRUotRrIDU9eZhE6LnqJipwwDOmrZFJeSr5dbquzXXg8Nuklt1Klm8TpB0nlRTNtJKIqSZ+3JaoGMpXvtwzwPI0aU/1zxAQXaNGyn81o3XpuVGXg6twmQKZSkhuevMwi0uRX6sU0vQ11vADR9bnI7opJLZln9i8QUAyoD/jTPw2cSjZv3Z8kfiapG4LJVq00nRt587H04wHod/AaTeqI15JyJUSx6Q5erkkdV/k+kN6yn37jNVMNpO491FXIQZ2gkFYF6g27HplIW3C4OkemyZMAYbOn1K6YtCW3MQmcuQCEYUhflppW3sjUQj8rJUauhkgoZhkSPTkxS5sbUxAsVUIUm7RCIN/Ba5TcUqu4ADDZtLkatTqRVt1Hu5bk63odpaQX52C1URAE6b4vYoM8N+bGt5ynSPNOkJsVUxaAZP5TYudJf6q0HjGj8k70pngnpplSYi1P8BU0Y3IZrVv3OpIzCFMbrbPOUiJXldLStMRBqSNxGvJ4imIwF8v3gaz0Ig+BT6+AowvTvV3V52Rsiv+Z5IYnL7OI9LQRtVkxuyy7Op5EqTRtoIjTRunHypO59lNaelNXdqi5FcuV6LopVCoh1PQkymC5mp0BGYZFiXQfMVHqyAfRv5U+N/4UnHSAp/ZhpSrUDGqBVlWaQPNQjqZxC9KaGfomdR66SA0UxPJkWmtotXADANVzm9bNlKvvSrMAX2IgZrF3Iq2lN60SAsx8TupOnRUoS6VeuJO9jpr5UPiadPGTiSyJnsuQn9oZmdCIqWNalzjahMNkmt4PiKeHjYRXqTpeTXkRUno4MTe+5TxFatqIfPdSfWinSpU6pQWoX9yCgHa8tDJw6rQRMHOBKyeIGfVOMNUXQrQAdBXioxZmkhf6dJ9OkzrKnZlOyo/a8yJhtAaSQTDFjEmeWmlNqCn7d6ReS6bNicTRB0BWzxzq7sHZTepIlZeu2rWcSkn3eeXFIwtpZkwu7wQwk1BQmweBdKWH2mSXPEupcTGdTgR46mZPEqmV5GnIjdeyTnmhPg1ZgJglx2ucW7J8n/q+pR06SalOpKkh5KpSmlpA/L5Vx0vrTyKXNqJu0gik9zqiJrmpzwjH3FKUFw4CzwlPXmYRqb0ZiI+VT56G3DgeR64zLQhSd8VMDfAJtYBOVZIztQKtqy1KnKqSADFLjjdDMWMo3081rXP4QjSIoMRRCyyel46UQy6ZKu6aV/fR9kIB9I6RoK4CTTORU6aNIuVFQA3khicvs4heQXUilwtayq/UPWUAvZeENv3QXH6lrsYB9M42Ih2vhX9IzS0XVO8vzVg6ygthoGihYtUZralN66lqASXpbH4twzAkL19OJUocVSspaWGuJpSpJJCSUGsYdukVs5SjD0g3C63TVBzpRU7MjW85T7GgVrZ2qEn+kVqdSI7XWCbHcabFgs60udETir4aWRqbaiRmDNKruo5Cjn11Lccb7xvD3NIDhZKV6b0TjTn4+pQYVRBUCze/0Rpone5gaQqpY2ql9PNoPCfULfTTVSV60tnUtC5YKUntDQQS1UZN1mXqBnzc8ORlFtEXkQkZdSIK8DOUF3q5MFZeZIyfrYggh/ISEyWZXgmt5kbdTwPQeyYpd7kt7xtD76EFKfeNY7PQ0+KQP46mkOk7ao6qleySWxlViaGSSkN9pJ5b00pJhrnFG68Ug7BPG3lkoZUSAvCoE60CBfX5IMmxmgVBDuNnlqpEqU4oJaTp3Bj6TrS6ltSmz7SxquMpHxY/WeLoz5NOzOgDxcJu9b5N1/2coymkImbNlE6eqpXm71u5EkI1S6ZSlZIbocqMSkl6P0/ac0L9fivSCcwkFBzrZG9K5RZHmooTnrzMItIWHJag2+KlpO7LkByr+dzo5cmWQVBQ5QF4lJdWihl12SaQPjfqs1aq46m58Rut+xLpvsbjCDgMu/H73Vp5oXou1dyKpcqME4M5VKVWxKyufJ+ICKpnEkhTjTlS3vxHLXQWcuisFVPMVB8ZUpkpG2aOOMCJufEt5ynUgiPV7bBVgKfuiJk2FsAjTyoZe4aqxOKv0dmZ0XteZs6NcddZnHneEKfSI5HuU2NVwtb9gGQUM3pVqS7Az9ic0L/frQJ8fQUcXaWk+u6t/Xr0pLOp+siShk5XH6U67HK835zw5GUW0ZvC8Dnz1K0WHIk0DpAkS3TjtVoAWCXzVBlbTjGj9aDko3+78bwhjlLKhYq8TAoYrTvz0am6rVKnpESwRXox+UxSqUod+XgHPzrZODcOZbWFQZ6h63MQBOhrpfQwbk4an5FKIiXGcS1b+b5IY4CO8uI9Lx5ZkMytJsdrdJpznqFRLFdmnKrLYxBubkTjkMz7Ervcxrb2HK3Y+1osOFHAZVB5gDSlh5+YcZRlB0GQ8Cs1T+VIpBc51EAgJoIz0osM5fut07T0fh4gRenhUKi7W9w3hpQYAPR1dVTHayTwLH695spLsimkTxt5ZEIx7onpcsuW/SwSfZF/MU3K2DP6ygiqExz+mu6OuKNvK4+NZLURpQcllwsiha5l+kHARM5VttlSMWDwF6ggKOExA1LSVBwes9rcRlvMLU+oKgFpqRV6FTdL6ayOJ9DmgSEGtOqwyzU3TnjyMotINaIxSPRZu1zKIJiUsWXLl4V28C2DLn1gajU3rhx19tw4PDb8aSNAx2NDr9BJPP9Acm4tVCUJPw+DB6U6XvUdaEyJ8bSUyPYqSRQ38MQAjb5KPm3kkYV6Ixr/4t2qPwmH6a06XovyZeIW20BavxCmINjCz8Bissvs80I7tyjotvKhsFQ3ST+TklVp/KkOQKdRI3+Aj4zP1HPr7mg6HmdauNX7Bgj1qGI5UFNHeZkbtGBufMt5iuQOfiahEKzsYMrBtzSicfZmaHH0AfV5HZImu5aKGVNfhgWtmhlyVK20CLgcfVeA1pVbHD01JLs+A4n3e4Z3gtPU2sKHxUXMWrQL4CBmrd5tyuM4gIRXqVVaWODYlhJDBRw3PHmZZWTtBDn6F0iMlT4efWDKMvRJqUocfoaWChZTdYCoOTJDoudKibXaLPA0cuMn70Ba0BVUXhh6DwFp7QL4NguT0/VdbzkIbnK8GUSQIS2slJfpcoipUjxeXX8eT148dJBVciiyg2fLU6cHJokcfFz9I2SOFOyMzHHKM6CRWhGYG1cQ7IsUs8aSW4a5tVJ5mNTA7H4h9O/bxHS5LsBz+bCiCqBWnhfStSQf/XcyvcKndDYvA+d4B5KVkslrqZ4RalWJE568zDJ6MxY4Fom+xenE9KmV9PyqRPqBq+V1pg9F4kwq4aoVHjNyPFayKR6/GZlfVVJdaKdKzXfwbHNrle5jSD8A9e83H+nM2AgRXsuuQh6d+ZnFBlwVcAtbqIEc6mM+F0TXcqQJeaFWlTgxd77pPEUrr8Y0Sw4+yzvBs8ttWd4ouIOnPuY9czEVVJX4qlYkCHXc9TZ5si63GVlSnaiON3MHz6UGNqoT0wzvdzLAJ68ld7pvZiWVXEk9f9qIvzMyEJPq0clY6SkzbfI44cnLLCPr/BPRk5epF4CMtvYSO3huw65ER9+WeWqmBacV6eQgFOqgOKD+OeFWzFr5sCgb/tV1vU2kBLhIZ9Z94ypfTo7H1aW1VWqFzffVZK1kN8i3KDvnIy+J943JaM0JT15mGc0W0zAM2fsX1Ev0vAuAZMv+xh08hxKSHG9m+oFBVepMSPTJHTyTqtTSQMiwwOVyQTS/pkFQON1HWb4PNDdbc5TAVseqdWqd0ZqANy08WhfgZVMrfKrxTBWLa5OXVd1EvXYtrJWd1ykvTOk+TsydbzpP0d/kQaprhsSQNqqE1ePlFbgk+v4mDB/gMbX2duSjrrfJXS5XEGxV3sjhnSjkc+jpaC1jSxk/48MLBXa5wuoEn8dmZkl93BRSqHuwYM8cbh+WlGqcOjeuvkoCmzwgVl6Snpe5digj4MnLrKO/p0peRiZm7joB6vRDPvr3RhJkiWu3pOZ2cILfRZ/LBdGOInktOVqjA4n7NtmiaoV48VYLTvJalpgWnGY5cYCve2qza8l3HdVmgb+nBpBUQ2YSM+lSaQkTLZ8PK71nlIQPhbvaSMI/BzR/B7iIEic8eZll9DcJSlwHgAVB0JRQcJw6CzQPSskDwOiD4MxrWWZaAJRi1kjMuALTQERy+aXegRakM04/8Ch0SdLJtaMeaEE6uYym8dz471tLQs30TC5sct+4vErNFGqA0ffVRJ3gImbNPCgAv/JSr/ZX56Y8WnMBc+ebzlM0W0yT3Q6pc/ADKUoP9UvZLMAnz9Bgm9tkM2LGEwRnBng5QsEWcKPrKJN+aEbMuIyYrUinJBHkum8tn0kmVanZeFyHTra6b9NMhHow9b7xPCPjxXI0HwAosm0WZiovxZL3vHgYopkSohh+wNAwqKnSw5Q2SlMLANrKDiB+KUeapFbIg6C4qjRzPK4W+pnEjGnxrk+J8QTB1gGeVzGrD/C8921kYhqVxHsmeS0jMlEgnltvdazJ6UpdxV2RyYzcbO2aLvGoEyqNAzS/lvTjzVQDuUggJ+bON52naB5weYIEkPTYNAkU5DK2IkozjWGALFmi31HX5jbenJhJEArpHXxUciswNy7lRY3VGAS50h3NnskiM5mohPWN6opMQbDZfSuqAE/t0+gqIKjdGokAn0bMuojnls8FTT1t00zErFkhRXQd58iJ0oAnL7OO+CVpYgxjeJCaKz3MC3erlJiAD4U7tTI6VYp2ucmUmMwOnterVCxVMDmdCPBKMRAx7PL5NGY9CDLt4Ls78tG/mSTVilBweWzqyAvTDj6XC5pu9KaZ0h1NiRmTqpQc78D4zLmRE0GVNpryyouHA5Q6UbdwMzYMak4oeFMrySCYDPDU01PScrMUHHnArS0AYRj3uajz8wiYWrl2Zn2dhejeqEBRKlegWgNRK3Rpu1zqhTuXC6Iy9+bXknhuTZ5JrgAPyF7LNGLWwWD8TJ2boKrEcd8Ge5sQszLPtWxmEC4yefU4MXe+6TzFQEqA53iQ+puUE8d9J4hLKZsFwUS+Pwh45NBm1Q/UJrvujjy6aotKMsArSJSdTzHt4HO5mVVpxcTcqMdruoNnmhswk1BUKmH0nIimVhgDfLMgyDW3A02UTurUSnI8CSKYlqaVIp1cc2tWKs1JOrkwd77pPMWCJgGey0ALpPsLqJWeZO+Vg1GA50l1AOmBoquDfzw1Vi6QUSciL0M+3/R3KMdTc6uOx5/um2JSC4CZAV6amHH6CxqV1SQxkzC1SqhKdakVLsNuE8WMs5w4XenhKnGfqVB7z4uHNprtcjkZfrM0lYTUq8bjUguA5t6JomAQVHPrKtCTCXF1IoW8iARBybmV+YjZbCkv0VqS6BlF/X43TYlFa4nMxovLIByNNT4dHaXClX6rG08g3beotxNAVTFTc+MykXNi7nzTeYwolTNZHygk1AIAUdVFl8BiyjlW8wBfHU8iUHASs2ZeJZH0QxPSSZ3ua2ZaFw3wjKpS8/dtdkgn59ziIMinBja+3+VKCGUz40obFcuV6Ky0KSYlBIjnVq8q8Wxilb+mXAmjXk4+beRhhcbGcXGAZ1gAmjj2Y8WAUempzY2TmDVNP7DOrXmgkPIycI7Xn9h5AnxlotWxYhk7CoKC17KYSNNS91VSY41OlqLqMM7KjhnELFHdx0VeypUQY7Wz0qYZlZfB3sa58alKfV2FKI3eOJ6454WhKk0dhvryWBEAX/qNE568tAEa29pPTc9OEOQgSy3VCYE0DiCtTsgoWI27XBZC0V2vhnD1CgGa73I5032NJJeVBCYakI1OCow3g7zwEbOejnwU7BrHkzDsJtN91EE3CIIZDT05PS+DPZ11Y4VhyJqmWrSgOt7+8XrywjEWF+bON53HUDnIl2osmFMtUGPtHy9GQZBzvEE13oy50ROlqNxwshRV/hQZ56YW05fH5ZSX6XIYnQg+G54XjrGa7XI571urAM8xt85CfCJ4Y9DlCBSDDSk4zvLeIAjqvCGAjGF3RCDdlxyvUcWS2HiVK2HUmoBjvMWKvByqrsu+VNrDCkv7ugAALx2aAgAUy3w7+CV91Yd2croSBUFOxSCa21h1blO1cnCOtNGi3s6ockuCLC2tXUt13zhVpd7OuDT7JbXgMBKKJQsUoZ5iHysIgohU7zvEP56aW+MzwrVwL6qR6v0NEj3LDr5XeSdUUOJNB8QVQOqZrAVBgc1CsoqK2ocFAAPK2Dpe/76xVlJNqGck0TOKIQW3uIXy4smLhxGiQFELSiptxBFwF3QVop3gvoagy0EoVIDfN1q/mHIE+HwuiF7KfRIBPiKd9WN1ddDftyAIIiK4TxFBxmu5dGELMsG0uEXPSUTg+e+b2NwWqvEankmG8dTc9o5W5xYTJfpnEkDimeRPPzSSTu6Au7RxLWEcb0lffQyoT4kxKC81YhZ5Xhi9Slzw5KUNoBa3SJ1gJBNA/KLsEyBLSxY0Ki98AR5ILKYRMeOrNmoci9ODAiSC4Ci/OhHNrUY6pxjJBAAsawjwsYrFp5g1kgkO5REAls14Tvgk+hljRdeRJyipZ3JvwzPJobwsaxiLWzGLn8kaEWQM8Op92z9eRKlcqasS4+j35T0vHiRQO4q90cJdS60wPUgzdp6MgWlpw46C018DJHYwDekOnpRYAwlkJEoAsGxG0OUbT5FOiTQO0CR1KkDM9h6aqhkjebrrNo4Xk05GQr0wVifKldj0yVUC20iWOBvwKaJ0aKqEiWKZX3lppWIxeVByQfW4kf1jxbqxOFJiSqFWyov3vHhYYUmLhZtLeVnWSCimOT02caAAeBduYKZiwEvM4oU7DEP2HXwjoeDsFxIFwYadoFjaiHFuakddLFUwOlWKPGZsz2SLFBxXOiAIqidL7x8rRh4Urvu2rFENZHzfFnYVondr36Ep9pOQG5UXzrnlc0G0Vu4ZnWIvXY4KN8b4y8C5MHe+6TzGsgbvBKfJFEikcho9L4ym1tHJEqZKZX7lRQX4xjQVy9yqY02VKjg0VeJXJxbWG4Q5CYUKgmFYNUhKKS+N/gKOuXV35NFXO5xx7+gUK5kAZs4tOv+H4VoW8rlYyR2dYvVpAPHatVeAmAVBEBGKugDP/EzGKTFedSK5GeKe2+IFyvzs+7x4OEClOiamyxibKrEH+OROsFwJo7ONuMqJVc52/1iRnZhFcxPwavR0xs2e9h0qsiohQOsAzxUEFycqgOKxZLxK/GRJPSdTrFVi1bGa+0L4n5Mp9s6pjaqSVCpn36GpuB+WkOeFnQgmPD2TzHNrNHarytPezgLLeBzw5KUN0NuZR3dHXAbLmcYBkupEsb59OFMZ7JJExRFnWTZQX5qdTOVwLwIvHRIMgsI+lH2HpvhTYn2xWgDwm2iTRJDb89IYBNU7IOHVGK+tJT1MKehGYhYHQR6Smwzwh6aqvWz6unkCbqOJfEyN18U0t8T7PcY8txX93QCA4YOTqFRCHKodE7CAaW4c8OSlDdBYBsu9g19St+ssRz+XSOXwB6WZagHA5x+qG0+sSqyBmAnsqrnNyI2qkhTJ3XdoCuNF3oW7kXSqoLuQKTAln0nV1XdhotMvJVSAf+lQEZVKiNFJNTfe8fYdSpCXLt7rqAzC3NcyqRpHzwjT3FYMdCMIqmrSS2NF9meSA568tAmW117K4YOT7ORl+cIq6949Eo+VC6qpApbx+uO5TU7zkpdlfWpuU5goxsSsmzndsWc03i1xSa/LErvcielylO7rZ1pMFencOzoVnU3Vz7zL3T82hVK5Eh0Yp7rhUiNJzFRXU67rqO7b6GQJk9PlaG5cgSIZ4CMywRQE1TNSqoQ4ODEdBXg+QhE/k6PM17GvqxAp4vsOTUXqBNfcksqLmhuX8tKRz0Xj7To4kVCVeN4BDnjy0iZYs7gXALB9/zg7w1+zuAcAsPPARHSKKddYAHB4bW479o9Hrba5gpKa297RKQyPTAIAFnTm2YjgYYtmzm2AaW6rF1XnNjpZwvb94wCqVQpcEv3KgSoR3HVwMjq/iY1M9HWhM59DJQR+ufdQdIghF6FQsnl1brxEqb8nDoI79o9HihnXO6c2J7sOTibWEp4g2FnIRR2Edx2cjHbwXCRXbfJ2j/ArL0mD8PDIJEaF0lS7k2MxzQ0AVg5W15MXD8Tj+bSRhzGOSJAX1f5aLQrUWDnQg458gOlyiKeGRwDEbcU5cHhibgdqAV6deUSNgZ6OaKF+/NcHo59x4Ygl1bm98BI/eentLEQLXHJuHH0ggJgs/frlCfa55XMBDqsRz8dqc+vM56KgTw01t50vTySUF74guLoWKJ7cNVL7GZ8actiieHPCncapjld9B36xZxQ1zsk23mrhuan7tmP/OHtq5bDEM3lIYG6ros1JrLws9MqLhynWJNQJVb7GFeDzuSBacB7ZcYB1LKBeVVLnhAwyBcEgCCJCoYLgAOPcDl/SjJgxkqXatXx8Jz8xO6xZgGccb+2SBQBiYtbfU+AjZoPV6/jrA+ORqsQaBGvv2893jQIA+joL5Kc8K6h3uz4I8u3g1XOi5pbPBWykc40i1AmFmlOdUNfymd2HooMSuQL8mtpYuw5OROskq/IyEG9OlNHaKy8exqhTJyLlhTHoLq4P8FwqDxCrE3VzW8Cv9DxWC/BcRAmoV8zUybqchEKRpUejAM+/o97xciLdJ5BeVPeNd27VhXvXgcnorJz+Hv4A/8SLcnPbMzoVmYR5lRdFXkZqY/GTztGpEnYemIjG40Lj3DiJ2dK+LnQWqqnTZ3ZXiSDn3FYNVpWXX+45FP2MKyXGATbysn//flx88cXo7+/H4OAg3v3ud+PQoUOpn3//+9+P448/Hj09PTj88MPxgQ98AAcPHuT6im0FFZR27B+PFlOJVM5jvz4AgJcoqR3FgfHpyKsx0MNJzNQO/gAA3ut42KJe5IJqieiv9lafb9Y0VcPcWInS4urcRidL0QInkYJTc+MkSiv6u9HTkUepEmLbzhH28Y5aWr1vj9aUTs6gNNjbEd0nifHW1uamVFxOtaCnM4+hWgHAw9ur47HOraYGPrz95WgsLmKWywXRZuhhgfumNidqbl2FHFsfJw6wkZeLL74YTzzxBO666y585zvfwY9+9CP84R/+YcvPv/jii3jxxRfx13/919i2bRu+9KUv4c4778S73/1urq/YVhha2B2xbgUJ8qJ6XHCOtaCrEJUdKkjMTV1LzoDbWchF8quq/hlkJGYqwKu5cSpm3R356FoqUytrSkxwbrlcgGOW9wGIS5fVeS8cUGOp66iqZjgQBAGObRhvRc3fwIFjly8EgEidW1V7H7hw3FDDeIN84x07VH8d2ee2ojo3pVBzzu342lhqbkplmitgIS8///nPceedd+If//EfcfbZZ+Pcc8/FZz7zGdx222148cUXm/7OySefjP/7f/8vLrzwQhx99NF43eteh0984hP4t3/7N5RKJY6v2VbI5QIcs6wv+v8lCzpZWbB6SRRWMi5uQLzAAUBPRz7q3soBFSgUjqjtnrhwdGK8jnyAlYN81/KoZfVzOXIp79xUoFBQBIMDRy+rv29HNfw/NVRgkhiv8To23kfy8Rre77WM78BxQ43vG98zAgDHNz6Ti/nmdvSyPiStSdxzO255/dzU5oEDhy/urUuBrWEciwMs5GXr1q0YHBzE+vXro59t3LgRuVwO9957r/a/c/DgQfT396NQaC2dTU1NYWRkpO7PXMWZRyyK/nvdyn7WsU5fM4ik+nniygHW8ZJzO37FQjazIgCcethA3RkdJzQs5NRYn5jbkUsXsB5utm5lf11p9LHLeef2X9Yujv77sEU9rO3DD1/cW6dINJJQapyVmNvSvk5WhW7lQHdUuQLwk87kM9nXRPmkxGBvZ929Wss8t+Raks8FkXeDA90deZxy2GD0/9wboeTcAF5ils8FOGNNPJ5K788VsKyyw8PDWL58ed3PCoUCFi9ejOHhYa1/Y9++fbjxxhtTU00AsHnzZgwMDER/1qxZY/29ZxuvOyG+ZqccxksmBno68IrD4wf3xFW8ZOn16+K5nb5mkHWs7o48zj5ySfT/J63mndtvHb8sHmsV733ryOfwqqOXRv9/KvNz8prj4rlxE+ogCPCa4+K5nZYIGhxIzo37vgVBUPecNAYpapx7THwdT1k9wObTUPitxLVMEl4ObDg6frdPXtXP1lxT4bcT9+3so3jntn5t/FwM9XdhgDF1CtSvy+ccsyTlk+0Ho7v+kY98BEEQpP556qmnnL/UyMgILrjgApx44on42Mc+lvrZa6+9FgcPHoz+7Nixw3n82cJvHb8MF5yyEq88ajHec+6R7ON97MKTcMrqAdx40cms+X4AOOPwRXj3uUfiNcctw6bXHcM6FgDccOGJOHl1Pzb/7ilR0y4unHrYIC7bcATWrezHNW84jnUsALj+v56I09YM4rr/eiK71Hv8ioV4328djWOX9+Gjv7OOdSwA+JPzjsfJq/vxvt86mp1QrxrswY1vOQlnHD6IP3vzSaxjAcA1bzgOG45agv/3tUfhVGZitry/Gx+/6GQcs7wP1/3XE1nHAoD3v+5YnHvMUvw/Zx+Os47kDfCDvZ34y98/FUcs6RWZ2xWvPgob1w3hd89YXUfSONDdkcffveMMrBroxiffegrrWADwzlcegTeftgoXnLISG9cNsY9HiSAMwzD7Y1Xs3bsXL730UupnjjrqKPzzP/8z/viP/xgvv/xy9PNSqYTu7m587Wtfw1vf+taWvz86Oorzzz8fvb29+M53voPubrPAMzIygoGBgSjl5OHh4eHh4dH+MInfRgnsZcuWYdmybOa5YcMGHDhwAA8++CDOPPNMAMAPfvADVCoVnH322alf/Pzzz0dXVxe+/e1vGxMXDw8PDw8Pj/kPlmThunXr8MY3vhFXXHEF7rvvPvznf/4nNm3ahD/4gz/AqlWrAAA7d+7ECSecgPvuuw9Albicd955GBsbwxe+8AWMjIxgeHgYw8PDKJfLacN5eHh4eHh4/AaBrXTgy1/+MjZt2oTXv/71yOVy+L3f+z383d/9XfT309PTePrppzE+Xm1a9tBDD0WVSMccU++JeO6557B27Vqur+rh4eHh4eExh2DkeZkL8J4XDw8PDw+PuQeT+O3PNvLw8PDw8PCYU/DkxcPDw8PDw2NOwZMXDw8PDw8PjzkFT148PDw8PDw85hQ8efHw8PDw8PCYU/DkxcPDw8PDw2NOwZMXDw8PDw8PjzkFT148PDw8PDw85hQ8efHw8PDw8PCYU/DkxcPDw8PDw2NOge1so9mCOu1gZGRklr+Jh4eHh4eHhy5U3NY5tWjekZfR0VEAwJo1a2b5m3h4eHh4eHiYYnR0FAMDA6mfmXcHM1YqFbz44otYuHAhgiAg/bdHRkawZs0a7Nixwx/6yAh/nWXgr7Mc/LWWgb/OMuC6zmEYYnR0FKtWrUIul+5qmXfKSy6Xw2GHHcY6Rn9/v38xBOCvswz8dZaDv9Yy8NdZBhzXOUtxUfCGXQ8PDw8PD485BU9ePDw8PDw8POYUPHkxQFdXF2644QZ0dXXN9leZ1/DXWQb+OsvBX2sZ+Ossg3a4zvPOsOvh4eHh4eExv+GVFw8PDw8PD485BU9ePDw8PDw8POYUPHnx8PDw8PDwmFPw5MXDw8PDw8NjTsGTF0189rOfxdq1a9Hd3Y2zzz4b991332x/pTmFzZs347/8l/+ChQsXYvny5bjooovw9NNP131mcnISV155JZYsWYK+vj783u/9Hnbv3l33me3bt+OCCy5Ab28vli9fjg996EMolUqSU5lTuOmmmxAEAa6++uroZ/4602Dnzp145zvfiSVLlqCnpwennHIKHnjggejvwzDE9ddfj5UrV6KnpwcbN27EL37xi7p/Y//+/bj44ovR39+PwcFBvPvd78ahQ4ekp9LWKJfLuO6663DkkUeip6cHRx99NG688ca682/8tTbHj370I1x44YVYtWoVgiDAt771rbq/p7qmjz32GF796leju7sba9aswV/+5V/STCD0yMRtt90WdnZ2hrfeemv4xBNPhFdccUU4ODgY7t69e7a/2pzB+eefH37xi18Mt23bFj7yyCPh7/zO74SHH354eOjQoegz733ve8M1a9aEW7ZsCR944IHwla98ZfiqV70q+vtSqRSefPLJ4caNG8OHH344vOOOO8KlS5eG11577WxMqe1x3333hWvXrg1PPfXU8Kqrrop+7q+zO/bv3x8eccQR4bve9a7w3nvvDZ999tnw+9//fvjLX/4y+sxNN90UDgwMhN/61rfCRx99NHzzm98cHnnkkeHExET0mTe+8Y3haaedFv7sZz8Lf/zjH4fHHHNM+I53vGM2ptS2+MQnPhEuWbIk/M53vhM+99xz4de+9rWwr68v/Nu//dvoM/5am+OOO+4I//RP/zT8xje+EQIIv/nNb9b9PcU1PXjwYDg0NBRefPHF4bZt28KvfvWrYU9PT/i///f/dv7+nrxo4KyzzgqvvPLK6P/L5XK4atWqcPPmzbP4reY29uzZEwIIf/jDH4ZhGIYHDhwIOzo6wq997WvRZ37+85+HAMKtW7eGYVh92XK5XDg8PBx95nOf+1zY398fTk1NyU6gzTE6Ohoee+yx4V133RW+9rWvjciLv840+PCHPxyee+65Lf++UqmEK1asCP/qr/4q+tmBAwfCrq6u8Ktf/WoYhmH45JNPhgDC+++/P/rM9773vTAIgnDnzp18X36O4YILLgj/+3//73U/+93f/d3w4osvDsPQX2sKNJIXqmv6D//wD+GiRYvq1o0Pf/jD4fHHH+/8nX3aKAPFYhEPPvggNm7cGP0sl8th48aN2Lp16yx+s7mNgwcPAgAWL14MAHjwwQcxPT1dd51POOEEHH744dF13rp1K0455RQMDQ1Fnzn//PMxMjKCJ554QvDbtz+uvPJKXHDBBXXXE/DXmQrf/va3sX79erztbW/D8uXLccYZZ+CWW26J/v65557D8PBw3XUeGBjA2WefXXedBwcHsX79+ugzGzduRC6Xw7333is3mTbHq171KmzZsgXPPPMMAODRRx/FT37yE7zpTW8C4K81B6iu6datW/Ga17wGnZ2d0WfOP/98PP3003j55ZedvuO8O5iRGvv27UO5XK5byAFgaGgITz311Cx9q7mNSqWCq6++Gueccw5OPvlkAMDw8DA6OzsxODhY99mhoSEMDw9Hn2l2H9TfeVRx22234aGHHsL9998/4+/8dabBs88+i8997nO45ppr8NGPfhT3338/PvCBD6CzsxOXXXZZdJ2aXcfkdV6+fHnd3xcKBSxevNhf5wQ+8pGPYGRkBCeccALy+TzK5TI+8YlP4OKLLwYAf60ZQHVNh4eHceSRR874N9TfLVq0yPo7evLiIY4rr7wS27Ztw09+8pPZ/irzDjt27MBVV12Fu+66C93d3bP9deYtKpUK1q9fj09+8pMAgDPOOAPbtm3DzTffjMsuu2yWv938wr/8y7/gy1/+Mr7yla/gpJNOwiOPPIKrr74aq1at8tf6Nxg+bZSBpUuXIp/Pz6jG2L17N1asWDFL32ruYtOmTfjOd76Du+++G4cddlj08xUrVqBYLOLAgQN1n09e5xUrVjS9D+rvPKppoT179uAVr3gFCoUCCoUCfvjDH+Lv/u7vUCgUMDQ05K8zAVauXIkTTzyx7mfr1q3D9u3bAcTXKW3dWLFiBfbs2VP396VSCfv37/fXOYEPfehD+MhHPoI/+IM/wCmnnIJLLrkEH/zgB7F582YA/lpzgOqacq4lnrxkoLOzE2eeeSa2bNkS/axSqWDLli3YsGHDLH6zuYUwDLFp0yZ885vfxA9+8IMZUuKZZ56Jjo6Ouuv89NNPY/v27dF13rBhAx5//PG6F+auu+5Cf3//jEDym4rXv/71ePzxx/HII49Ef9avX4+LL744+m9/nd1xzjnnzCj1f+aZZ3DEEUcAAI488kisWLGi7jqPjIzg3nvvrbvOBw4cwIMPPhh95gc/+AEqlQrOPvtsgVnMDYyPjyOXqw9V+XwelUoFgL/WHKC6phs2bMCPfvQjTE9PR5+56667cPzxxzuljAD4Umkd3HbbbWFXV1f4pS99KXzyySfDP/zDPwwHBwfrqjE80vG+970vHBgYCO+5555w165d0Z/x8fHoM+9973vDww8/PPzBD34QPvDAA+GGDRvCDRs2RH+vSnjPO++88JFHHgnvvPPOcNmyZb6ENwPJaqMw9NeZAvfdd19YKBTCT3ziE+EvfvGL8Mtf/nLY29sb/vM//3P0mZtuuikcHBwM//Vf/zV87LHHwre85S1NS03POOOM8N577w1/8pOfhMcee+xvdPluM1x22WXh6tWro1Lpb3zjG+HSpUvD//E//kf0GX+tzTE6Oho+/PDD4cMPPxwCCD/1qU+FDz/8cPjCCy+EYUhzTQ8cOBAODQ2Fl1xySbht27bwtttuC3t7e32ptCQ+85nPhIcffnjY2dkZnnXWWeHPfvaz2f5KcwoAmv754he/GH1mYmIi/KM/+qNw0aJFYW9vb/jWt7413LVrV92/8/zzz4dvetObwp6ennDp0qXhH//xH4fT09PCs5lbaCQv/jrT4N/+7d/Ck08+Oezq6gpPOOGE8POf/3zd31cqlfC6664Lh4aGwq6urvD1r399+PTTT9d95qWXXgrf8Y53hH19fWF/f394+eWXh6Ojo5LTaHuMjIyEV111VXj44YeH3d3d4VFHHRX+6Z/+aV35rb/W5rj77rubrsmXXXZZGIZ01/TRRx8Nzz333LCrqytcvXp1eNNNN5F8/yAME20KPTw8PDw8PDzaHN7z4uHh4eHh4TGn4MmLh4eHh4eHx5yCJy8eHh4eHh4ecwqevHh4eHh4eHjMKXjy4uHh4eHh4TGn4MmLh4eHh4eHx5yCJy8eHh4eHh4ecwqevHh4eHh4eHjMKXjy4uHhQYJ3vetduOiii2Zt/EsuuSQ65ZkDTz75JA477DCMjY2xjeHh4aEH32HXw8MjE0EQpP79DTfcgA9+8IMIwxCDg4MyXyqBRx99FK973evwwgsvoK+vj22c3//938dpp52G6667jm0MDw+PbHjy4uHhkYnh4eHov2+//XZcf/31dacq9/X1sZKGLLznPe9BoVDAzTffzDrOd7/7XVxxxRXYvn07CoUC61geHh6t4dNGHh4emVixYkX0Z2BgAEEQ1P2sr69vRtrot37rt/D+978fV199NRYtWoShoSHccsstGBsbw+WXX46FCxfimGOOwfe+9726sbZt24Y3velN6Ovrw9DQEC655BLs27ev5Xcrl8v4+te/jgsvvLDu52vXrsXHP/5xXHrppejr68MRRxyBb3/729i7dy/e8pa3oK+vD6eeeioeeOCB6HdeeOEFXHjhhVi0aBEWLFiAk046CXfccUf09294wxuwf/9+/PCHP3S8oh4eHi7w5MXDw4MN//RP/4SlS5fivvvuw/vf/368733vw9ve9ja86lWvwkMPPYTzzjsPl1xyCcbHxwEABw4cwOte9zqcccYZeOCBB3DnnXdi9+7d+G//7b+1HOOxxx7DwYMHsX79+hl/97/+1//COeecg4cffhgXXHABLrnkElx66aV45zvfiYceeghHH300Lr30UigB+sorr8TU1BR+9KMf4fHHH8df/MVf1ClKnZ2dOP300/HjH/+Y+Ep5eHgYgeRsag8Pj98YfPGLXwwHBgZm/Pyyyy4L3/KWt0T//9rXvjY899xzo/8vlUrhggULwksuuST62a5du0IA4datW8MwDMMbb7wxPO+88+r+3R07doQAwqeffrrp9/nmN78Z5vP5sFKp1P38iCOOCN/5znfOGOu6666LfrZ169YQQLhr164wDMPwlFNOCT/2sY+lzv+tb31r+K53vSv1Mx4eHrzwyouHhwcbTj311Oi/8/k8lixZglNOOSX62dDQEABgz549AKrG27vvvjvy0PT19eGEE04AAPzqV79qOsbExAS6urqamoqT46ux0sb/wAc+gI9//OM455xzcMMNN+Cxxx6b8W/29PRESpGHh8fswJMXDw8PNnR0dNT9fxAEdT9ThKNSqQAADh06hAsvvBCPPPJI3Z9f/OIXeM1rXtN0jKVLl2J8fBzFYjF1fDVW2vjvec978Oyzz+KSSy7B448/jvXr1+Mzn/lM3b+5f/9+LFu2TO8CeHh4sMCTFw8Pj7bBK17xCjzxxBNYu3YtjjnmmLo/CxYsaPo7p59+OoBqHxYKrFmzBu9973vxjW98A3/8x3+MW265pe7vt23bhjPOOINkLA8PDzt48uLh4dE2uPLKK7F//3684x3vwP33349f/epX+P73v4/LL78c5XK56e8sW7YMr3jFK/CTn/zEefyrr74a3//+9/Hcc8/hoYcewt13341169ZFf//8889j586d2Lhxo/NYHh4e9vDkxcPDo22watUq/Od//ifK5TLOO+88nHLKKbj66qsxODiIXK71cvWe97wHX/7yl53HL5fLuPLKK7Fu3Tq88Y1vxHHHHYd/+Id/iP7+q1/9Ks477zwcccQRzmN5eHjYwzep8/DwmPOYmJjA8ccfj9tvvx0bNmxgGaNYLOLYY4/FV77yFZxzzjksY3h4eOjBKy8eHh5zHj09Pfg//+f/pDazc8X27dvx0Y9+1BMXD482gFdePDw8PDw8POYUvPLi4eHh4eHhMafgyYuHh4eHh4fHnIInLx4eHh4eHh5zCp68eHh4eHh4eMwpePLi4eHh4eHhMafgyYuHh4eHh4fHnIInLx4eHh4eHh5zCp68eHh4eHh4eMwpePLi4eHh4eHhMafw/wMvq6zLkPhEIAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "w_all.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "a6fc5339",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "33.752141484704055\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 640x480 with 1 Axes>, <AxesSubplot: xlabel='Time (ms)'>)"
-      ]
-     },
-     "execution_count": 9,
-     "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": [
-    "v = v_all.average_beat()\n",
-    "print(v.apd(50))\n",
-    "v.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ca413ded",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "32.828196670599496\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 640x480 with 1 Axes>, <AxesSubplot: xlabel='Time (ms)'>)"
-      ]
-     },
-     "execution_count": 10,
-     "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": [
-    "w = w_all.average_beat()\n",
-    "print(w.apd(50))\n",
-    "w.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1664494b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.6 64-bit",
-   "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": "3.10.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/code/demo.py b/code/demo.py
new file mode 100644
index 0000000..9d79d69
--- /dev/null
+++ b/code/demo.py
@@ -0,0 +1,41 @@
+# # Demo
+#
+# This notebook contains a simple demo on how to work with the code.
+#
+
+import sys
+import numpy as np
+from scipy.integrate import solve_ivp
+import ap_features as apf
+
+sys.path.insert(0, "../code")
+from run_simulation import fitzhugh_nagumo  # noqa: E402
+
+# +
+a = -0.22
+b = 1.17
+time = np.arange(0, 1000.0, 1.0)
+
+res = solve_ivp(
+    fitzhugh_nagumo,
+    [0, 1000.0],
+    [0, 0],
+    args=(a, b),
+    t_eval=time,
+)
+
+v_all = apf.Beats(y=res.y[0, :], t=time)
+w_all = apf.Beats(y=res.y[1, :], t=time)
+# -
+
+v_all.plot()
+
+w_all.plot()
+
+v = v_all.average_beat()
+print(v.apd(50))
+v.plot()
+
+w = w_all.average_beat()
+print(w.apd(50))
+w.plot()
diff --git a/code/postprocess.py b/code/postprocess.py
index 98db5b0..24a81e9 100644
--- a/code/postprocess.py
+++ b/code/postprocess.py
@@ -9,6 +9,8 @@
 """
 from __future__ import annotations
 
+from typing import Sequence
+import argparse
 import json
 from dataclasses import dataclass
 from datetime import datetime
@@ -19,8 +21,8 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from pre_processing import datapath
-from run_all import result_directory
+
+here = Path(__file__).absolute().parent
 
 
 @dataclass
@@ -71,6 +73,8 @@ def beatrate_w(self) -> float:
 
 def load_result(path: Path) -> Result:
     """Load result from json file into a Result object"""
+    if not path.is_file():
+        raise FileNotFoundError(f"File {path} does not exist")
     data = json.loads(path.read_text())
     data["y"] = np.array(data["y"])
     data["time"] = np.array(data["time"])
@@ -79,8 +83,12 @@ def load_result(path: Path) -> Result:
     return Result(**data)
 
 
-def load_results() -> list[Result]:
+def load_results(result_directory: Path) -> list[Result]:
     """Load all results from the result directory"""
+
+    if not result_directory.is_dir():
+        raise FileNotFoundError(f"Directory {result_directory} does not exist")
+
     results: list[Result] = []
     for path in result_directory.iterdir():
         if not path.suffix == ".json":
@@ -89,7 +97,9 @@ def load_results() -> list[Result]:
     return results
 
 
-def load_data():
+def load_data(datapath: Path):
+    if not datapath.is_file():
+        raise FileNotFoundError(f"File {datapath} does not exist")
     return json.loads(datapath.read_text())
 
 
@@ -107,7 +117,11 @@ def align_at_peak(
     return new_t_v, new_t_w
 
 
-def figure1(results: list[Result], data: dict[str, list[float]], fname: str) -> None:
+def figure1(
+    results: list[Result],
+    data: dict[str, list[float]],
+    fname: str,
+) -> None:
     """Reproducing Figure 1 in the paper"""
     fig, ax = plt.subplots(2, 1, figsize=(12, 6))
     lines = []
@@ -153,9 +167,14 @@ def figure1(results: list[Result], data: dict[str, list[float]], fname: str) ->
         title="Parameters",
     )
     fig.savefig(fname, bbox_inches="tight", dpi=500)
+    print(f"Save figure 1 to {fname}")
+
+    r1 = next(filter(lambda d: np.allclose((d.a, d.b), (-0.3, 1.1)), results))
+    assert np.isclose(r1.v.max(), 1.1020528, rtol=1e-3), r1.v.max()
+    assert np.isclose(r1.w.max(), 0.6689413, rtol=1e-3), r1.w.max()
 
 
-def table1(results: list[Result]):
+def table1(results: list[Result], outfile: Path):
     """Reproduce table 1 in the paper by printing the Latex table"""
     data = []
     for result in results:
@@ -169,19 +188,63 @@ def table1(results: list[Result]):
                 "Beatrate (W)": result.beatrate_w,
             },
         )
+
+    assert len(data) == 6
+
+    r1 = next(filter(lambda d: np.allclose((d["a"], d["b"]), (-0.2, 1.1)), data))
+    assert np.isclose(r1["APD50 (V)"], 32.3730067, rtol=1e-3), r1["APD50 (V)"]
+    assert np.isclose(r1["APD50 (W)"], 31.8498996, rtol=1e-3), r1["APD50 (W)"]
+
+    r2 = next(filter(lambda d: np.allclose((d["a"], d["b"]), (-0.3, 1.2)), data))
+    assert np.isclose(r2["APD50 (V)"], 28.9285658187, rtol=1e-3), r2["APD50 (V)"]
+    assert np.isclose(r2["APD50 (W)"], 29.1713021926, rtol=1e-3), r2["APD50 (W)"]
+
     df = pd.DataFrame(data)
-    print(df.style.to_latex())
+    table = df.style.to_latex()
+    outfile.write_text(table)
+    print(f"Save table to {outfile}")
+    print(table)
 
 
-def main():
-    figdir = Path("figures")
-    figdir.mkdir(exist_ok=True)
-    results = load_results()
-    data = load_data()
+def main(argv: Sequence[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "-f",
+        "--figdir",
+        type=Path,
+        default=here / "figures",
+        help="Directory where to dump the figures",
+    )
+    parser.add_argument(
+        "-r",
+        "--resultdir",
+        type=Path,
+        default=here / "results",
+        help="Directory where the results are stored",
+    )
+    parser.add_argument(
+        "-d",
+        "--datapath",
+        type=Path,
+        default=here / ".." / "data" / "data.json",
+        help="Directory where the results are stored",
+    )
+    args = vars(parser.parse_args(argv))
+
+    figdir = args["figdir"]
+    figdir.mkdir(exist_ok=True, parents=True)
+
+    results = load_results(result_directory=args["resultdir"])
+    data = load_data(datapath=args["datapath"])
 
     figure1(results, data, figdir / "figure1.png")
-    table1(results)
+    table1(results, outfile=figdir / "table1.txt")
+
+    return 0
 
 
 if __name__ == "__main__":
-    main()
+    raise SystemExit(main())
diff --git a/code/pre_processing.py b/code/pre_processing.py
index 50a4541..9b77f0c 100644
--- a/code/pre_processing.py
+++ b/code/pre_processing.py
@@ -8,6 +8,8 @@
 """
 import json
 from pathlib import Path
+import argparse
+from typing import Sequence
 
 import ap_features as apf
 import numpy as np
@@ -16,11 +18,9 @@
 
 
 here = Path(__file__).absolute().parent
-datadir = here / ".." / "data"
-datapath = datadir / "data.json"
 
 
-def generate_syntetic_data():
+def generate_syntetic_data(datapath: Path) -> None:
     a = -0.22
     b = 1.17
     time = np.arange(0, 1000.0, 1.0)
@@ -38,7 +38,7 @@ def generate_syntetic_data():
 
     v = v_all.average_beat()
     w = w_all.average_beat()
-
+    datapath.parent.mkdir(exist_ok=True, parents=True)
     datapath.write_text(
         json.dumps(
             {
@@ -51,5 +51,21 @@ def generate_syntetic_data():
     )
 
 
+def main(argv: Sequence[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "-d",
+        "--datapath",
+        type=Path,
+        default=here / ".." / "data" / "data.json",
+        help="Directory where to dump the data",
+    )
+    generate_syntetic_data(**vars(parser.parse_args(argv)))
+    return 0
+
+
 if __name__ == "__main__":
-    generate_syntetic_data()
+    raise SystemExit(main())
diff --git a/code/run_all.py b/code/run_all.py
index 6fa5443..aba1581 100644
--- a/code/run_all.py
+++ b/code/run_all.py
@@ -5,11 +5,13 @@
 and output results in the `results` folder.
 """
 from pathlib import Path
+from typing import Sequence
+import argparse
 
-from run_simulation import main
+import run_simulation
 
 here = Path(__file__).absolute().parent
-result_directory = here / "results"
+
 
 parameter_sets = [
     {"a": -0.2, "b": 1.1},
@@ -20,9 +22,39 @@
     {"a": -0.3, "b": 1.2},
 ]
 
-if __name__ == "__main__":
+
+def main(argv: Sequence[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    parser.add_argument(
+        "-r",
+        "--resultdir",
+        type=Path,
+        default=here / "results",
+        help="Directory where the results are stored",
+    )
+    args = vars(parser.parse_args(argv))
+    result_directory: Path = args["resultdir"]
 
     for p in parameter_sets:
-        # This would by the equivalent list of arguments passe from the command line
-        args = ["-o", result_directory.as_posix(), "-a", str(p["a"]), "-b", str(p["b"])]
-        main(args)
+        # This would by the equivalent list of
+        # arguments passe from the command line
+        run_simulation.main(
+            [
+                "-o",
+                str(result_directory.as_posix()),
+                "-a",
+                str(p["a"]),
+                "-b",
+                str(p["b"]),
+            ],
+        )
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/cspell.json b/cspell.json
new file mode 100644
index 0000000..8d72e79
--- /dev/null
+++ b/cspell.json
@@ -0,0 +1,19 @@
+{
+    "version": "0.2",
+    "ignorePaths": [],
+    "dictionaryDefinitions": [],
+    "dictionaries": [],
+    "words": [
+        "docname",
+        "docnames",
+        "Dokken",
+        "Finsberg",
+        "fitzhugh",
+        "nagumo",
+        "postprocess",
+        "scientificcomputing",
+        "zenodo"
+    ],
+    "ignoreWords": [],
+    "import": []
+}
diff --git a/data/README.md b/data/README.md
index 1c795a2..e833c24 100644
--- a/data/README.md
+++ b/data/README.md
@@ -1,6 +1,6 @@
 # Data
 
-You can add a README file to explain what the data is, where it is coming from and if the data is not in the repository you could also specify which files are expected to be in this folder
+You can add a README file to explain what the data is, and where it is coming from. If the data is not in the repository you could also specify which files are expected to be in this folder
 
 In the example repo we have a text file containing synthetic traces to match with the Fitzhugh-Nagumo neural model. These are presented with the [pre-processing script](../code/pre_processing.py)
 ¨¨
diff --git a/data/data.json b/data/data.json
deleted file mode 100644
index 519ddc2..0000000
--- a/data/data.json
+++ /dev/null
@@ -1 +0,0 @@
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0.6436168449108481, 0.6435205378662071, 0.6433123811285023, 0.6429893476057018, 0.6425473614889058, 0.6419799846307914, 0.6412797208924728, 0.6404388100106371, 0.6394475217791595, 0.638293405574324, 0.6369632463572786, 0.6354420320793384, 0.6337054997795402, 0.6317176724275999, 0.6294401314646825, 0.6268323604709297, 0.6238444597923476, 0.6204170476426734, 0.6164864422108078, 0.6119407356711555, 0.6065177129945976, 0.5998575466995171, 0.5915977573653382, 0.5814424123606623, 0.5693168787069861, 0.5552717756712904, 0.5393948181797587, 0.5219739269326812, 0.5035215574683275, 0.484594717249844, 0.4657071629226069, 0.4471441555934858, 0.42894064637656165, 0.41108429694923065, 0.3935615753728497, 0.37636481977079284, 0.3594991692617983, 0.3429717216498063, 0.32678754584181335, 0.310944474203385, 0.2954351720391409, 0.2802519905379939, 0.26538812128774164, 0.2508382668899978, 0.23659777306188212, 0.22266186668688334, 0.20902531002173275, 0.1956833997546324, 0.18263177646256284, 0.16986632517486758, 0.15738346167642062, 0.14517885465939706, 0.1332479245616172, 0.12158593875154655, 0.11018805660953507, 0.09905020950117524, 0.0881684267944838, 0.07753879763558288, 0.06715767813935096, 0.057021312219119526, 0.047126032333994185, 0.03746837829801376, 0.028044699362375847, 0.018851242338718977, 0.009884073983088314, 0.0011391168419896585, -0.0073870161514778265, -0.015697404218153476, -0.023794827163970595, -0.03168180211727412, -0.039361311104306615, -0.04683649464670328, -0.054110765044926935, -0.06118737583935702, -0.06806900633426224, -0.07475820751631079, -0.08125733207146, -0.08756887232231868, -0.09369546626374686, -0.09963978510338824, -0.10540435186460621, -0.110991396176155, -0.1164031430743727, -0.12164184387672143, -0.12670987716326945, -0.13160917618854537, -0.1363413854453109, -0.1409080222924963, -0.1453103687506771, -0.1495499198173103, -0.15362825970351088, -0.15754699618962031, -0.16130750988478976, -0.16491077892888323, -0.16835769940165143, -0.1716490124506519, -0.17478519402117051, -0.17776667760909576, -0.18059389670887455]}
diff --git a/index.md b/index.md
deleted file mode 100644
index 891ba84..0000000
--- a/index.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# Supplementary code for the paper: Title of paper
-
-This repository contains supplementary code for the paper
-> Finsberg, H., Dokken, J. 2022.
-> Title of paper, Journal of blabla, volume, page, url
-
-
-## Contents
-```{tableofcontents}
-```
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..9161841
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,73 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "my-paper"
+classifiers = ["Private :: Do Not Upload"]
+version = "0"
+dependencies = [
+    "ap_features",
+    "matplotlib",
+    "numpy",
+    "scipy",
+    "pandas[output-formatting]",
+]
+
+
+[project.optional-dependencies]
+dev = [
+    "pdbpp",
+    "pre-commit",
+]
+docs = [
+    "jupyter-book",
+    "jupytext",
+    "sphinxcontrib-bibtex",
+]
+
+[tool.ruff]
+# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
+select = ["E", "F"]
+ignore = ["E402", "E741", "E731", "E743"]
+
+# Allow autofix for all enabled rules (when `--fix`) is provided.
+fixable = ["A", "B", "C", "D", "E", "F"]
+unfixable = []
+
+# Exclude a variety of commonly ignored directories.
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+]
+
+# Same as Black.
+line-length = 100
+
+# Allow unused variables when underscore-prefixed.
+dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+
+# Assume Python 3.10.
+target-version = "py310"
+
+[tool.ruff.mccabe]
+# Unlike Flake8, default to a complexity level of 10.
+max-complexity = 10
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 23de719..218e400 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,2 +1,94 @@
-pdbpp
-pre-commit
+#
+# This file is autogenerated by pip-compile with Python 3.11
+# by the following command:
+#
+#    pip-compile --extra=dev --output-file=requirements-dev.txt pyproject.toml
+#
+ap-features==2023.7.4
+    # via my-paper (pyproject.toml)
+attrs==23.1.0
+    # via wmctrl
+cfgv==3.4.0
+    # via pre-commit
+contourpy==1.2.0
+    # via matplotlib
+cycler==0.12.1
+    # via matplotlib
+distlib==0.3.7
+    # via virtualenv
+fancycompleter==0.9.1
+    # via pdbpp
+filelock==3.13.1
+    # via virtualenv
+fonttools==4.44.0
+    # via matplotlib
+identify==2.5.31
+    # via pre-commit
+jinja2==3.1.2
+    # via pandas
+kiwisolver==1.4.5
+    # via matplotlib
+llvmlite==0.40.1
+    # via numba
+markupsafe==2.1.3
+    # via jinja2
+matplotlib==3.8.1
+    # via my-paper (pyproject.toml)
+nodeenv==1.8.0
+    # via pre-commit
+numba==0.57.1
+    # via ap-features
+numpy==1.24.4
+    # via
+    #   ap-features
+    #   contourpy
+    #   matplotlib
+    #   my-paper (pyproject.toml)
+    #   numba
+    #   pandas
+    #   scipy
+packaging==23.2
+    # via matplotlib
+pandas[output-formatting]==2.1.2
+    # via my-paper (pyproject.toml)
+pdbpp==0.10.3
+    # via my-paper (pyproject.toml)
+pillow==10.1.0
+    # via matplotlib
+platformdirs==3.11.0
+    # via virtualenv
+pre-commit==3.5.0
+    # via my-paper (pyproject.toml)
+pygments==2.16.1
+    # via pdbpp
+pyparsing==3.1.1
+    # via matplotlib
+pyrepl==0.9.0
+    # via fancycompleter
+python-dateutil==2.8.2
+    # via
+    #   matplotlib
+    #   pandas
+pytz==2023.3.post1
+    # via pandas
+pyyaml==6.0.1
+    # via pre-commit
+scipy==1.11.3
+    # via
+    #   ap-features
+    #   my-paper (pyproject.toml)
+six==1.16.0
+    # via python-dateutil
+tabulate==0.9.0
+    # via pandas
+tqdm==4.66.1
+    # via ap-features
+tzdata==2023.3
+    # via pandas
+virtualenv==20.24.6
+    # via pre-commit
+wmctrl==0.5
+    # via pdbpp
+
+# The following packages are considered to be unsafe in a requirements file:
+# setuptools
diff --git a/requirements-docs.txt b/requirements-docs.txt
index 601f1cb..3e27fc5 100644
--- a/requirements-docs.txt
+++ b/requirements-docs.txt
@@ -1,3 +1,347 @@
-jupyter-book
-jupytext
-sphinxcontrib-bibtex
+#
+# This file is autogenerated by pip-compile with Python 3.11
+# by the following command:
+#
+#    pip-compile --extra=docs --output-file=requirements-docs.txt pyproject.toml
+#
+accessible-pygments==0.0.4
+    # via pydata-sphinx-theme
+alabaster==0.7.13
+    # via sphinx
+ap-features==2023.7.4
+    # via my-paper (pyproject.toml)
+appnope==0.1.3
+    # via
+    #   ipykernel
+    #   ipython
+asttokens==2.4.1
+    # via stack-data
+attrs==23.1.0
+    # via
+    #   jsonschema
+    #   jupyter-cache
+    #   referencing
+babel==2.13.1
+    # via
+    #   pydata-sphinx-theme
+    #   sphinx
+beautifulsoup4==4.12.2
+    # via pydata-sphinx-theme
+certifi==2023.7.22
+    # via requests
+charset-normalizer==3.3.2
+    # via requests
+click==8.1.7
+    # via
+    #   jupyter-book
+    #   jupyter-cache
+    #   sphinx-external-toc
+comm==0.1.4
+    # via ipykernel
+contourpy==1.2.0
+    # via matplotlib
+cycler==0.12.1
+    # via matplotlib
+debugpy==1.8.0
+    # via ipykernel
+decorator==5.1.1
+    # via ipython
+docutils==0.18.1
+    # via
+    #   jupyter-book
+    #   myst-parser
+    #   pybtex-docutils
+    #   pydata-sphinx-theme
+    #   sphinx
+    #   sphinx-togglebutton
+    #   sphinxcontrib-bibtex
+executing==2.0.1
+    # via stack-data
+fastjsonschema==2.18.1
+    # via nbformat
+fonttools==4.44.0
+    # via matplotlib
+idna==3.4
+    # via requests
+imagesize==1.4.1
+    # via sphinx
+importlib-metadata==6.8.0
+    # via
+    #   jupyter-cache
+    #   myst-nb
+ipykernel==6.26.0
+    # via myst-nb
+ipython==8.17.2
+    # via
+    #   ipykernel
+    #   myst-nb
+jedi==0.19.1
+    # via ipython
+jinja2==3.1.2
+    # via
+    #   jupyter-book
+    #   myst-parser
+    #   pandas
+    #   sphinx
+jsonschema==4.19.2
+    # via
+    #   jupyter-book
+    #   nbformat
+jsonschema-specifications==2023.7.1
+    # via jsonschema
+jupyter-book==0.15.1
+    # via my-paper (pyproject.toml)
+jupyter-cache==0.6.1
+    # via myst-nb
+jupyter-client==8.5.0
+    # via
+    #   ipykernel
+    #   nbclient
+jupyter-core==5.5.0
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   nbclient
+    #   nbformat
+jupytext==1.15.2
+    # via my-paper (pyproject.toml)
+kiwisolver==1.4.5
+    # via matplotlib
+latexcodec==2.0.1
+    # via pybtex
+linkify-it-py==2.0.2
+    # via jupyter-book
+llvmlite==0.40.1
+    # via numba
+markdown-it-py==2.2.0
+    # via
+    #   jupytext
+    #   mdit-py-plugins
+    #   myst-parser
+markupsafe==2.1.3
+    # via jinja2
+matplotlib==3.8.1
+    # via my-paper (pyproject.toml)
+matplotlib-inline==0.1.6
+    # via
+    #   ipykernel
+    #   ipython
+mdit-py-plugins==0.3.5
+    # via
+    #   jupytext
+    #   myst-parser
+mdurl==0.1.2
+    # via markdown-it-py
+myst-nb==0.17.2
+    # via jupyter-book
+myst-parser==0.18.1
+    # via myst-nb
+nbclient==0.7.4
+    # via
+    #   jupyter-cache
+    #   myst-nb
+nbformat==5.9.2
+    # via
+    #   jupyter-cache
+    #   jupytext
+    #   myst-nb
+    #   nbclient
+nest-asyncio==1.5.8
+    # via ipykernel
+numba==0.57.1
+    # via ap-features
+numpy==1.24.4
+    # via
+    #   ap-features
+    #   contourpy
+    #   matplotlib
+    #   my-paper (pyproject.toml)
+    #   numba
+    #   pandas
+    #   scipy
+packaging==23.2
+    # via
+    #   ipykernel
+    #   matplotlib
+    #   pydata-sphinx-theme
+    #   sphinx
+pandas[output-formatting]==2.1.2
+    # via my-paper (pyproject.toml)
+parso==0.8.3
+    # via jedi
+pexpect==4.8.0
+    # via ipython
+pillow==10.1.0
+    # via matplotlib
+platformdirs==3.11.0
+    # via jupyter-core
+prompt-toolkit==3.0.39
+    # via ipython
+psutil==5.9.6
+    # via ipykernel
+ptyprocess==0.7.0
+    # via pexpect
+pure-eval==0.2.2
+    # via stack-data
+pybtex==0.24.0
+    # via
+    #   pybtex-docutils
+    #   sphinxcontrib-bibtex
+pybtex-docutils==1.0.3
+    # via sphinxcontrib-bibtex
+pydata-sphinx-theme==0.14.3
+    # via sphinx-book-theme
+pygments==2.16.1
+    # via
+    #   accessible-pygments
+    #   ipython
+    #   pydata-sphinx-theme
+    #   sphinx
+pyparsing==3.1.1
+    # via matplotlib
+python-dateutil==2.8.2
+    # via
+    #   jupyter-client
+    #   matplotlib
+    #   pandas
+pytz==2023.3.post1
+    # via pandas
+pyyaml==6.0.1
+    # via
+    #   jupyter-book
+    #   jupyter-cache
+    #   jupytext
+    #   myst-nb
+    #   myst-parser
+    #   pybtex
+    #   sphinx-external-toc
+pyzmq==25.1.1
+    # via
+    #   ipykernel
+    #   jupyter-client
+referencing==0.30.2
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
+requests==2.31.0
+    # via sphinx
+rpds-py==0.12.0
+    # via
+    #   jsonschema
+    #   referencing
+scipy==1.11.3
+    # via
+    #   ap-features
+    #   my-paper (pyproject.toml)
+six==1.16.0
+    # via
+    #   asttokens
+    #   latexcodec
+    #   pybtex
+    #   python-dateutil
+snowballstemmer==2.2.0
+    # via sphinx
+soupsieve==2.5
+    # via beautifulsoup4
+sphinx==5.0.2
+    # via
+    #   jupyter-book
+    #   myst-nb
+    #   myst-parser
+    #   pydata-sphinx-theme
+    #   sphinx-book-theme
+    #   sphinx-comments
+    #   sphinx-copybutton
+    #   sphinx-design
+    #   sphinx-external-toc
+    #   sphinx-jupyterbook-latex
+    #   sphinx-multitoc-numbering
+    #   sphinx-thebe
+    #   sphinx-togglebutton
+    #   sphinxcontrib-applehelp
+    #   sphinxcontrib-bibtex
+    #   sphinxcontrib-devhelp
+    #   sphinxcontrib-htmlhelp
+    #   sphinxcontrib-qthelp
+    #   sphinxcontrib-serializinghtml
+sphinx-book-theme==1.0.1
+    # via jupyter-book
+sphinx-comments==0.0.3
+    # via jupyter-book
+sphinx-copybutton==0.5.2
+    # via jupyter-book
+sphinx-design==0.3.0
+    # via jupyter-book
+sphinx-external-toc==0.3.1
+    # via jupyter-book
+sphinx-jupyterbook-latex==0.5.2
+    # via jupyter-book
+sphinx-multitoc-numbering==0.1.3
+    # via jupyter-book
+sphinx-thebe==0.2.1
+    # via jupyter-book
+sphinx-togglebutton==0.3.2
+    # via jupyter-book
+sphinxcontrib-applehelp==1.0.7
+    # via sphinx
+sphinxcontrib-bibtex==2.5.0
+    # via
+    #   jupyter-book
+    #   my-paper (pyproject.toml)
+sphinxcontrib-devhelp==1.0.5
+    # via sphinx
+sphinxcontrib-htmlhelp==2.0.4
+    # via sphinx
+sphinxcontrib-jsmath==1.0.1
+    # via sphinx
+sphinxcontrib-qthelp==1.0.6
+    # via sphinx
+sphinxcontrib-serializinghtml==1.1.9
+    # via sphinx
+sqlalchemy==2.0.23
+    # via jupyter-cache
+stack-data==0.6.3
+    # via ipython
+tabulate==0.9.0
+    # via
+    #   jupyter-cache
+    #   pandas
+toml==0.10.2
+    # via jupytext
+tornado==6.3.3
+    # via
+    #   ipykernel
+    #   jupyter-client
+tqdm==4.66.1
+    # via ap-features
+traitlets==5.13.0
+    # via
+    #   comm
+    #   ipykernel
+    #   ipython
+    #   jupyter-client
+    #   jupyter-core
+    #   matplotlib-inline
+    #   nbclient
+    #   nbformat
+typing-extensions==4.8.0
+    # via
+    #   myst-nb
+    #   myst-parser
+    #   pydata-sphinx-theme
+    #   sqlalchemy
+tzdata==2023.3
+    # via pandas
+uc-micro-py==1.0.2
+    # via linkify-it-py
+urllib3==2.0.7
+    # via requests
+wcwidth==0.2.9
+    # via prompt-toolkit
+wheel==0.41.3
+    # via sphinx-togglebutton
+zipp==3.17.0
+    # via importlib-metadata
+
+# The following packages are considered to be unsafe in a requirements file:
+# setuptools
diff --git a/requirements.txt b/requirements.txt
index 66856bb..83375aa 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,18 +1,61 @@
-ap-features==2022.4.2
-contourpy==1.0.6
-cycler==0.11.0
-fonttools==4.38.0
-Jinja2==3.1.2
-kiwisolver==1.4.4
-MarkupSafe==2.1.1
-matplotlib==3.6.2
-numpy==1.23.5
-packaging==21.3
-pandas==1.5.1
-Pillow==9.3.0
-pyparsing==3.0.9
+#
+# This file is autogenerated by pip-compile with Python 3.11
+# by the following command:
+#
+#    pip-compile --output-file=requirements.txt pyproject.toml
+#
+ap-features==2023.7.4
+    # via my-paper (pyproject.toml)
+contourpy==1.2.0
+    # via matplotlib
+cycler==0.12.1
+    # via matplotlib
+fonttools==4.44.0
+    # via matplotlib
+jinja2==3.1.2
+    # via pandas
+kiwisolver==1.4.5
+    # via matplotlib
+llvmlite==0.40.1
+    # via numba
+markupsafe==2.1.3
+    # via jinja2
+matplotlib==3.8.1
+    # via my-paper (pyproject.toml)
+numba==0.57.1
+    # via ap-features
+numpy==1.24.4
+    # via
+    #   ap-features
+    #   contourpy
+    #   matplotlib
+    #   my-paper (pyproject.toml)
+    #   numba
+    #   pandas
+    #   scipy
+packaging==23.2
+    # via matplotlib
+pandas[output-formatting]==2.1.2
+    # via my-paper (pyproject.toml)
+pillow==10.1.0
+    # via matplotlib
+pyparsing==3.1.1
+    # via matplotlib
 python-dateutil==2.8.2
-pytz==2022.6
-scipy==1.9.3
+    # via
+    #   matplotlib
+    #   pandas
+pytz==2023.3.post1
+    # via pandas
+scipy==1.11.3
+    # via
+    #   ap-features
+    #   my-paper (pyproject.toml)
 six==1.16.0
-tqdm==4.64.1
+    # via python-dateutil
+tabulate==0.9.0
+    # via pandas
+tqdm==4.66.1
+    # via ap-features
+tzdata==2023.3
+    # via pandas