{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dca1d140-379d-4953-a3bf-32dfed6bbacd",
   "metadata": {},
   "source": [
    "# Analysis of stroke type and pre-stroke modified Rankin Scale by age\n",
    "\n",
    "The proportion of stroke types (haemorrhagic, nlvo ischaemic, lvo ischaemic) are shown by age.\n",
    "\n",
    "NIHSS of 0-10, and 11+, are taken as surrogates of nlvo and lvo respectively.\n",
    "\n",
    "The average mRS (modified Rankin Scale) is shown by age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4dcd024-f827-4fd3-a13f-79a5199e9407",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Change default colour scheme:\n",
    "plt.style.use('seaborn-colorblind')\n",
    "\n",
    "# import data\n",
    "data = pd.read_csv(\n",
    "    './../data/2019-11-04-HQIP303-Exeter_MA.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96b347b-f0d4-410a-b4c1-ce3c9c5b67b4",
   "metadata": {},
   "source": [
    "Get occurance of ischaemic and haemorrgahic by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60d6097b-5f8f-436f-90db-7d4d81dc2faf",
   "metadata": {},
   "outputs": [],
   "source": [
    "ages = []\n",
    "for group in data.S1AgeOnArrival.values:\n",
    "    minage, maxage = group.split(',')\n",
    "    \n",
    "    minage = int(''.join(list(minage)[1:]))\n",
    "    maxage = int(''.join(list(maxage)[:-1]))\n",
    "    \n",
    "    ages.append(np.median([minage,maxage]))\n",
    "    \n",
    "data['Age_midpoint'] = ages\n",
    "\n",
    "# Censor data to 40-100\n",
    "mask = (data['Age_midpoint'] > 50) & (data['Age_midpoint'] <100)\n",
    "data = data[mask]\n",
    "\n",
    "# Remove unknown stroke stype or NIHSS\n",
    "data.dropna(subset=['S2StrokeType'], inplace=True)\n",
    "data.dropna(subset=['S2NihssArrival'], inplace=True)\n",
    "\n",
    "# Add row for counting\n",
    "data['count'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "217296f8-886b-4d40-9c32-17331ab2d318",
   "metadata": {},
   "outputs": [
    {
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       "      <th>all</th>\n",
       "      <th>infarction</th>\n",
       "      <th>haemorrhage</th>\n",
       "      <th>prop_haemorrhage</th>\n",
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      ],
      "text/plain": [
       "                all  infarction  haemorrhage  prop_haemorrhage\n",
       "Age midpoint                                                  \n",
       "52.5           9334        8346          988          0.105850\n",
       "57.5          12394       11235         1159          0.093513\n",
       "62.5          15967       14359         1608          0.100708\n",
       "67.5          21406       19130         2276          0.106325\n",
       "72.5          28421       25049         3372          0.118645\n",
       "77.5          33288       29138         4150          0.124670\n",
       "82.5          37404       32638         4766          0.127420\n",
       "87.5          32585       28577         4008          0.123001\n",
       "92.5          18260       16298         1962          0.107448\n",
       "97.5           5489        5014          475          0.086537"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame()\n",
    "results.index.name='Age midpoint'\n",
    "\n",
    "# Count all by age \n",
    "results['all'] = data.groupby('Age_midpoint').count()['count']\n",
    "\n",
    "# Get ischaemic strokes\n",
    "mask = data['S2StrokeType'] == 'Infarction'\n",
    "results['infarction'] = data[mask].groupby('Age_midpoint').count()['count']\n",
    "\n",
    "# Get haemorrhagic stroke\n",
    "mask = data['S2StrokeType'] == 'Primary Intracerebral Haemorrhage'\n",
    "results['haemorrhage'] = data[mask].groupby('Age_midpoint').count()['count']\n",
    "\n",
    "# Calculate proportion haemorrhagic\n",
    "results['prop_haemorrhage'] = results['haemorrhage'] / results['all']\n",
    "\n",
    "# Show results\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bca7d33a-c47c-4520-aa8f-079cbfe1718e",
   "metadata": {},
   "source": [
    "Add breakdown of ischaemic strokes by nLVO (NIHSS 0-10) and LVO (NIHSS 11+)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e48edc11-4b09-4bc5-90ec-142301dbacab",
   "metadata": {},
   "outputs": [
    {
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       "      <th>infarction</th>\n",
       "      <th>haemorrhage</th>\n",
       "      <th>prop_haemorrhage</th>\n",
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       "      <th>lvo</th>\n",
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       "      <th>prop_lvo</th>\n",
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       "      <th>Age midpoint</th>\n",
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       "      <td>0.189618</td>\n",
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       "    <tr>\n",
       "      <th>82.5</th>\n",
       "      <td>37404</td>\n",
       "      <td>32638</td>\n",
       "      <td>4766</td>\n",
       "      <td>0.127420</td>\n",
       "      <td>24391</td>\n",
       "      <td>8247</td>\n",
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       "      <td>0.220484</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "                all  infarction  haemorrhage  prop_haemorrhage   nlvo   lvo  \\\n",
       "Age midpoint                                                                  \n",
       "52.5           9334        8346          988          0.105850   7188  1158   \n",
       "57.5          12394       11235         1159          0.093513   9668  1567   \n",
       "62.5          15967       14359         1608          0.100708  12197  2162   \n",
       "67.5          21406       19130         2276          0.106325  16022  3108   \n",
       "72.5          28421       25049         3372          0.118645  20452  4597   \n",
       "77.5          33288       29138         4150          0.124670  22826  6312   \n",
       "82.5          37404       32638         4766          0.127420  24391  8247   \n",
       "87.5          32585       28577         4008          0.123001  19777  8800   \n",
       "92.5          18260       16298         1962          0.107448  10305  5993   \n",
       "97.5           5489        5014          475          0.086537   2873  2141   \n",
       "\n",
       "              prop_nlvo  prop_lvo  checksum  \n",
       "Age midpoint                                 \n",
       "52.5           0.770088  0.124063       1.0  \n",
       "57.5           0.780055  0.126432       1.0  \n",
       "62.5           0.763888  0.135404       1.0  \n",
       "67.5           0.748482  0.145193       1.0  \n",
       "72.5           0.719609  0.161747       1.0  \n",
       "77.5           0.685713  0.189618       1.0  \n",
       "82.5           0.652096  0.220484       1.0  \n",
       "87.5           0.606936  0.270063       1.0  \n",
       "92.5           0.564348  0.328204       1.0  \n",
       "97.5           0.523410  0.390053       1.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NIHSS 0-10 is a surrogate for non large vessel occlusions\n",
    "mask = (data['S2StrokeType'] == 'Infarction') & (data['S2NihssArrival'] < 11)\n",
    "results['nlvo'] = data[mask].groupby('Age_midpoint').count()['count']\n",
    "\n",
    "# NIHSS 11+ is a surrogate for non large vessel occlusions\n",
    "mask = (data['S2StrokeType'] == 'Infarction') & (data['S2NihssArrival'] >10)\n",
    "results['lvo'] = data[mask].groupby('Age_midpoint').count()['count']\n",
    "\n",
    "# Calculate proportions\n",
    "results['prop_nlvo'] = results['nlvo'] / results['all']\n",
    "results['prop_lvo'] = results['lvo'] / results['all']\n",
    "\n",
    "# Check proportions add up\n",
    "results['checksum'] = \\\n",
    "    results['prop_haemorrhage'] + results['prop_nlvo'] + results['prop_lvo']\n",
    "\n",
    "# Show results\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2defd46-a5ac-420d-a07b-27c8de0848d1",
   "metadata": {},
   "source": [
    "Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc80b675-8db6-4062-91f5-c1bac4f1667c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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c0qMUDhPm5VfrVe+LaerUqVx77bXEx8fTo0ePKrUqK7vyyitJTEwkPj4eDw8PrrrqKl5++WWmT5/OXXfdhbe3N7/88ot1/fDwcF555RWGDx+O1pqrrrqKq6++us5Y6jpHaY/g4GC6du3Knj176Nu3LwC9evVi+vTp1umZM2dWOz8JcPXVV5OQkED79tXL3D3yyCO8++675x3PqVOnuOOOO6wPXOvbty/33XcfJ0+e5NFHH+X48eN4eXkRGhpaYx3Q22+/nVtuuYX27dsTEhLCggULatzPli1buOGGG8jOzubLL7/k73//O7t37yYkJIRnnnmGPn36APDss89aT5mcPHkSb29vwsPDz7td55J6lI2IK9ajPJer1DOsTWNuX1paGrfeeis//PBDvbfRmNt3rjfffJOAgABuv/32asvOtx6lHHoLcYkIDw/njjvusN5w7uqCgoKstx5dKDn0FuISctNNNzk7hIumvhenaiI9SiGEsEESpRBC2CCJUgghbJBEKYQQNkiiFE2OK5ZZS0lJISYmpkG29dtvvzFz5kzAHLvBYCApKcm6PCYmxjo8Mjo6moyMDOBssZEKc+bM4b777gPMY+KHDRvGoEGD6NKlC3feeSdgfnLk1KlTrYVBBg8eTF5e9Xtnn3rqKVq1alVtH3WVT7OlIcuo2SKJUjhc6ek0Ul4eiun0iQbZ3vmUWUtMTLRrCJvWmvLy8hqXnZsoP/nkE7p27Wp/wBfZyy+/bK0qBOZhfi+99NIFbfOBBx7g4YcfZuPGjezdu9e6/bfffpuwsDB27tzJrl27mD17Nu7u7tW+f+2117J58+Zq8yvKp/3pT3+qdd9z5sypcZx4RRm1i0ESpXC4jJUvULB/A+krX2iQ7blimbXK+vXrx+7du63Tw4YNY9u2bWRlZXH99dcTFxdH//79q/QSK5w5c4akpCS6d+9unXfNNdewe/dukpOTz+8HXUlaWhpRUWefzx4bG2udHxkZaZ3fqVOnKmPWK/Tv37/GETK1lU+zR0OWUbNF7qMU9XZi3kMUHUmscx1tKqbw4GbQ5WT/9CH5h7aS6VV7D8+rdQ9aTn3L7hhcqcxahcmTJ7No0SKef/550tLSOH78OL179+b++++nZ8+erFixgh9//JFbb7212ljorVu3VjuENxgMPP7447z88stVKoCfq7CwkB49elins7KyrD+nhx9+mBEjRtC3b1+uuuoqZsyYQVBQELfddhujR49myZIljBw5kmnTptGhQwd7/ukuWEOWUbO5L4duXVzySjIOQ8UwWa0xZR1p0O27Upm1CjfddBOLFy8GYNGiRdx4440AbNiwgVtuuQWAESNGkJmZSU5OTpXvpqWlERoaWm2bf/rTn9i0aZP1MQ018fb2thaiSExMrBLnjBkz2Lt3LzfccAMJCQn079+f4uJievTowaFDh3jsscfIysqiT58+7N27t8722SMzM5MePXrQo0cPnn32WT788EPr9M6dO63rNVQZNVukRynqzVbPr/R0GgcevQyoqCeg0YWnibp7Acaglg0SgyuVWasQGRlJs2bNSEpKYuHChdZiu/bUbvT29ra2rTKj0cgjjzzCq6++ancc54qIiOCWW27hnnvuISYmhl27dtG7d29rDczx48djMBhYtWrVBdUDAGjWrJm1tzxnzhxSUlJqPE/ZUGXUbJEepXCYjJUvoHXVCyS6vLzBzlXaqymWWZs8eTKvvfYaOTk51vOBl19+OfPmzQPM52ebN29uff5NhS5dunDgwIEatzl9+nRWr15d47NrbPn2228pLS0F4MSJE2RmZhIZGcnGjRvJzs4GoKSkhD179tRYTs1RGqqMmi2SKIXDFBz4BUwlVWeWlVBwwPZV64Y0depUtm7dSnx8PPPmzauzzNq4ceOs5dgqP4XwrrvuqnarUeUya927d6dXr152lVl79tlnbcY8ceJEFixYUGVs9nPPPcfWrVuJi4vjySefrPF8Y+fOncnJyakxsXt4ePDAAw9w6tQpm/s/1/fff09MTAwDBw5kzJgxvP7667Rs2ZKDBw8ydOhQYmNj6dmzJ/Hx8UyYMKHa9x9//HGioqIoKCggKirK2jvcsmULUVFRLF68mD//+c/nlfQasoyaLVJmrRGRMmtNX2No35tvvom/v7/1XsqG1BjaV6GuMmq2SJk1IS5xd999d4236LiahiyjZotczBHCxXh5eVmvjruyhiyjZotDe5RKqSuVUslKqQNKqSdrWB6olPpSKbVDKbVbKXXxWi7qramdrhGisvr8/josUSql3ID3gLFAV2CKUurccV/3Anu01t2BYcA/lVIejopJXDgvLy8yMzMlWYomSWtNZmYmXl5e5/U9Rx569wUOaK0PASilFgDXAZWrCWjAX5lvBvMDsgDHj0cS9RYVFUVqamq9bjEB831v5/tL2pRI+xo/Ly+vKsMx7eHIRBkJHK00nQr0O2edd4EvgOOAPzBJn3vjHaCUuhO4EyAsLKzKc3xdSV5ensu2rUJeXl61CjKuRNrXNNR2L21tHJkoaxricO7x2hggERgBtAN+UEqt11pXefqR1voj4CMw3x7U2G+hqa+mcHvQhXL1Nkr7XJMjL+akAq0qTUdh7jlWNgNYps0OAH8ANd8NLIQQTuLIHuUWoINSqi1wDJgMnFt07ggwElivlAoDOgGHHBiTw7Sc/xwni6oXLA3z8uPElOcufkBCiAbjsESptTYppe4DvgPcgE+11ruVUndZln8IvADMUUrtxHyo/oTWOsNRMTlSTUmyrvlCiKbDoTeca61XAavOmfdhpc/HgdGO2HdD9/C01pwpLSatMJcThWdIK8glrfAMJyzvdVl1dC+dg1rQxjcYt3oUKBVCOJfLjsyxt4dXVl5OelGeOekV5pJWcKZKMjxReIY0y+fCstJq2/MwuBHuE1BtfmVXr54NgKebkY4BoXQODKVzYAs6BbawvIfi5+76Q86EaKpcNlHW5arvP7EkwFxOFeVRXsPN00Ee3oR7+9PS25/+oa0J9w6gpbc/4T4B5vk+/oR7BxDk4Y1SCvXZo7Xub8NV97Iv5xT7ck6RnJNOYtZxlh7eWWW/UT6BhJW7M2BTNp0tCbRzYAsifAJqrJEo50SFuHguyUR5qiiPCJ8AejePMic/SwI0fw4gzNsfb2P1ByTV16CwtgwKa1tlXnGZiYNnMth3Ot2aRLccPcDcA1s5U1psXc/P6EnnoNAqybNTYKicExXiIrokE+XWcQ81+DbDvPxq7eHVxNPNSNeglnStVOk7ISGBoUOHklaYy77TpywJ1JxI1544xP8ObrcrlvOt1C2EqNslmSgdoaEOd5VSRPgEEuETyIiIqg9pyistZr8lcU5d93+1bsP/f0/RMSCUjoGhdAxobnk3v4I8HV82XwhX47KJ8nx7eE2Bn7snvZpH0at5VJ2J8vYOfdmfm86WjKMsTtlR5VxoqJdvpSQaSifLezv/ZnjVcbpBzomKS5nLJspL+T/v2/2vt34uLjNx6Ewm+3PS2Z+bzv7cDPbnpPNN6j4+K9xiXU+haOMXVCWJVvRIW/sGyzlRcUlz2UTp6uztMXu6GekSFEaXoLBq6+aWFPF7boY5gVoTaXq1C0oeBreGb4AQTYgkyiaqIXrMAR5e9G4eRe/mVUtOaa05VZR3NnnmpPParoRat/NN6l6GtmyHj1FKiQrXJIlSVKOUIszbnzBvf4a0vAygzkR51Q+z8XQzcnnYZYyJ7MiYyE50C2opV96Fy5BEKS7Yt6Pv4LtjyXx3LJlHt3zFo1u+ItInkDGRnRgT2ZErIjoS4unj7DCFqDdJlMIudZ0TNSfETgAczTvNd8fNSXPZ4Z18+vtmDErRt3lrxkR2JLSogCHl5TLmXTQpkiiFXew9J9rKL4iZHfsxs2M/TOVlbE4/ak2csxJXo9E8PX87oyI6WBNslG+QQ2MX4kJJohQOYzS4MTAsmoFh0TzfcwyZRfm8vXolqUFufHcsmcUpSQB0CwqzJs3Lwy6z3s8p926KxkISpbhomnn5MsIvgmGDh6G1Zlf2CfO5zePJvLt3I//avQ4vNyPDWrZjTGQnuXdTNBqSKIVTKKWIDQknNiScR2OHkV9azNoTh/juWDLfHkvm4c1fODtEIawkUYpGwdfdk6tadeGqVl0ASDmTRdslL9e6fqGptEErPAlRF7n0KBqlaP+QOpe3mP8ckxP+x5KUHeRXGkUkhCNIj1I0SX+6rCfLj+xk4R+JeLu5MzaqMxOj47g6qgsBHl7ODk+4GEmUotGq697N/wyayHsDbmD9yT9YmpLE0sM7WXZ4J55uRkZHdGRidBzjWnWTsnKiQUiiFI2WrVuAjAY3hoe3Z3h4e97pfz0/nzrMkpQklqYk8eXRPbgb3LgivAMTomO5rnUMzb18L07gwuVIohQuwaAMDA5ry+Cwtvyr77VsyTjK0pSdLElJYubGxfz556UMD2/HhDax3NAmljBvf2eHLJoQSZTC5RiUgX6hbegX2oZX46/mt8xjLD28k8UpO7j7l2Xc88tyLm/Zlglt4hjfJpZI30BAbnAXtbOZKJVSvkCh1rpcKdUR6Ax8o7Wu/uxWIRoZpZS1KvyLva5kV/YJlh5OYklKEg/8uoIHfl3BwBbRTIyOkxvcRa3s6VGuA4YopYKBNcBWYBIw1ZGBCdHQKt/k/lzPMew9fZKlKTtZejiJv8gN7qIO9txHqbTWBcB44N9a6xuAro4NSwjH6xIUxtM9ruC36/7C7xOedHY4ohGzK1EqpQZg7kF+bZkn5zaFS2kf0LzO5V2WvcbT277ht8xj6EoPaxOXBnsS3oPAX4HlWuvdSqnLgJ8cG5YQjUuETwCv7PyRl5LW0NYvhAnRsUxoE0ff0FYYlAxwc3X2JMowrfW4igmt9SGl1HoHxiSEU9R1g/uaK+8ioyiflUd2sTRlJ2/v2cAbu9YS6RPI+DaxTIiOZXCLtk6IWlwM9iTKvwKL7ZgnRJNm6xag5l6+3N6xH7d37Mfp4kK+St3D0pSdfLx/E//eu4EWXn70dQ+h5Fg4w8Pb4y5Pr3QZtSZKpdRY4CogUin1TqVFAYDJ0YEJ0ZgFeXpzc7ve3NyuN3mlxXyTuo+lh3fyRcpOvvr+Y4I9vBnXuhsT2sQyKqKjtRixaJrq6lEex3wr0DhgW6X5Z4CHHRmUEE2Jn7snN7btzo1tu/P9j2soat+SpSlJrDyym7kHtuJn9OSaVl2YEB3L2MjO+Lp7OjtkcZ5qTZRa6x3ADqXU/8nN5ULYx8PgxujW3RjXuhslZSZ+OnGQpSlJrDiyiwWWSkdXRnZiQnQcf9n8BadkJFCTYM85yr5KqeeANpb1FaC11pc5MjAhmjoPN6P1WUDvDxjPhpN/WKscLT+yq9bvyUigxseeRDkb86H2NqDMseEI4ZqMBjeGhbdnWHh73u53Hb+mH2Hg1+86OyxhJ3tuAMvRWn+jtT6ltc6seDk8MiFclEEZGNAius51Vh3dS7kuvzgBCZvs6VH+pJR6HVgGWGvua623OywqIS5xV6+eTceAUB7oOphp7ePxkwtATmVPouxneY+vNE8DIxo+HCEEwP8Nncpbu9dx36blPLX9G2Z26Md9XQbZfJaQcAybiVJrPfxiBCLEpaaukUBTLuvJlMt6sunUYd7as5639qznzT3ruL51DA91HcLgsLYopZwQ9aXJnnqUYcDLQITWeqxSqiswQGs92+HRCeHC7LkFqH+LNixo0Yajead5f9/P/Cf5F5Yd3knPkEge6jaESW174OkmNWoczZ6LOXOA74AIy/R+4CEHxSOEqEErvyBeib+K1EnP8J+BEykuNzFt/QLaLH6J53/7npOFZ5wdokuzJ1E211ovAsoBtNYm5DYhIZzCx+jBnZ36s+v6R/l+9J30bhbJc4nf03rRi0xfv4DfMo85O0SXZE+fPV8p1QzzBRyUUv2BHIdGJYSok1KKUZEdGRXZkeScU/x7zwbmHNjK3ANbuTzsMh7sOoTrWnfDzSAl4BqCPT/FvwBfAO2UUhuB/wL3OzQqIYTdOgW24N0B40m96Rne6HMNh/OzmfDTXNovfYV/7krgdHGhs0Ns8uy56r1dKTUU6IR5+GKyjP0WovEJ8vTmkZhhPNh1CF8c2cPbe9bz6Jav+Ptv3zOjQx8WHEokozi/2vdkbLltdZVZG6G1/lEpNf6cRR2VUmitl9nauFLqSuBtwA34RGv9jxrWGQa8BbgDGVrrofaHL4Q4l9HgxvjoWMZHx/Jb5jHe3rOej5I3UVJe86UFGVtuW109yqHAj8C1NSzTmEfq1Eop5Qa8B4wCUoEtSqkvtNZ7Kq0TBLwPXKm1PqKUanF+4Qsh6tKzWSRzhkzm1firabngeWeH02TVVWbt75aPs7TWf1ReppSyp+Z9X+CA1vqQ5TsLgOuAPZXW+ROwTGt9xLLPU+cRuxDCTmHe/nUu356RSq/mURcpmqbHnos5S2uYt8SO70UCRytNp1rmVdYRCFZKJSiltimlbrVju0KIBtb7y7fo/9U7zP19C4UmuQRxrrrOUXYGugGB55ynDAC87Nh2TeOrzn3OpxHoDYwEvIFflFKbtNb7z4nlTuBOgLCwMBISEuzYfdOTl5fnsm2r4OptbKrtuzekK1+ePsL0DQt54OdljPFrxbiA1kS5+1VZr6m270LVdY6yE3ANEETV85RngDvs2HYq0KrSdBTmx0ucu06G1jof8/2a64DumEf/WGmtPwI+AoiPj9fDhg2zY/dNT0JCAq7atgqu3sbG3L6wtIRax5a/e91t/FtrEk4c5IN9P7P88C4W5x7iiogO3NN5INe26orR4Nao2+dIdZ2jXAmsVEoN0Fr/Uo9tbwE6WM5nHgMmYz4nWdlK4F2llBHwwFyp6M167EsIYYOtW4CUUgwPb8/w8PakFeQye/9m/pP8C+N/nEukTyB3dOxHN9OlWSPTnpE5vyml7sV8GG495NZa31bXl7TWJqXUfZjHibsBn2qtdyul7rIs/1BrvVcp9S2QhHmI5Cda69pr5AshLopwnwCe7nEFT8YN5+uje/kg+ReeS/weA4rrfzzJ3Z0HMCK8PQZ1aYz8sSdRfg7sA8YAs4CpwF57Nq61XgWsOmfeh+dMvw68bs/2hBAXl9HgxnVtYriuTQwHczN4avUiVp84yLLDO+kQ0Jy7Ow9kWvt4Qjx9nB2qQ9nz56C91voZIF9rPRe4Goh1bFhCiMamXUBz7grpSupNz/D55VNo7unLXzZ/QeTCWcxYv4DN6UfQ+tzrta7Bnh5lxb0Cp5VSMcAJINphEQkhGjUvozs3t+vNze16syPrOB/s+5n/HdzOnANb6dUskrs7D2RK2x60W/JKrRePmtqQSXt6lB8ppYKBZzAXx9gDvOrQqIQQTUL3kAg+HDiR45Oe5b3+N1BSVsYdGxcTueiFWodGNsUhk/YUxfjE8nEtIM/yFkJUE+DhxT1dBnF354FsPJXC+/t+Zv6h35wdVoOx2aNUSjVTSv1bKbXdMnrmLUt9SiGEqEIpxeCwtvzf0KnODqVB2XPovQA4BUwAJgIZwEJHBiWEcG1DV73P/w5uazLDJe1JlCFa6xe01n9YXi9iHq0jhBD1cqwgh1vWzSdi4Swe2LSCnVlpzg6pTvYkyp+UUpOVUgbL6ybga0cHJoRo2sK8/Gqdv3/CE6wZ82eujOzEf5J/IW7lP+n/1TvM3v8reaXFFzlS2+y5PejPmB8H8bll2g3zuOy/AFprHeCo4IQQTZetW4BGRHRgREQHMory+fzgNj5O3sTMjYt5ePMXTGnbkzs69aN3s6hG8fxye656113ITgghLkBzL18e7nY5D3Udws+nUvh4/698fnAbH+3fRI+QCO7o2I+p7XoR6OHttBgvjYGaQohGTynFoLC2zBky2XpfJsC9m5YTvmAW09cvYOPJP5wy+seeQ28hhLiogjy9rfdlbstM5aPkTcw/lMjcA1vpGhTGzI79uLVdb5p5+V6UeGrtUdr5uAchhHAYpRTxzVvx0aAbSZv8LB8PuhE/owd/2fwFEQtn8ae18/gp7YDDe5l19SiXAL2VUmu01iMdGoUQQtjg5+7JzI79mNmxH0lZx/l4/6/87+B25h/6jfb+zZnZsS//3L2W9KKGfyRvXYnSoJT6O+bH0/7l3IVa63/Ve69CCHEB4kIi+Hf/G3gt/hqWpCTx8f5NPLltVa3rX+j48rou5kwGijAnU/8aXkII4VTeRnduad+bdVfdy94bHnfYfup6FEQy8KpSKklr/Y3DIhBCiAbQOaiFw7Ztz+1BPyul/qWU2mp5/VMpFeiwiIQQopGxJ1F+ivnJizdZXrnAZ44MSgghGhN77qNsp7WeUGn6eaVUooPiEUKIegvz8qu1qvqFsCdRFiqlBmutNwAopQYBhRe0VyGEcABHPWLCnkR5F/DfSucls4FpDolGCCEaIXuKYuwAuiulAizTuQ6PSgghGhG7x3pLghRCXKqkepAQQtggiVIIIWyw69BbKTUQiK68vtb6vw6KSQghGhWbiVIp9TnQDkgEyiyzNSCJUghxSbCnRxkPdNXOKCsshBCNgD3nKHcBLR0diBBCNFb29CibA3uUUpsB63MktdbjHBaVEEI0IvYkyuccHYQQQjRm9ozMWauUCgP6WGZt1lqfcmxYQgjReNg8R6mUugnYDNyIuczar0qpiY4OTAghGgt7Dr2fAvpU9CKVUqHAaswPHxNCCJdnz1VvwzmH2pl2fk8IIVyCPT3Kb5VS3wHzLdOTgNofdyaEEC7Gnos5jymlJgCDAAV8pLVe7vDIhBCikbBrrLfWeimw1MGxCCFEo1RrolRKbdBaD1ZKncE8ttu6CNBa6wCHRyeEEI1AXc/1Hmx597944QghRONjz32Un9szTwghXJU9t/l0qzyhlDICvR0TjhBCND61Jkql1F8t5yfjlFK5ltcZ4CSw8qJFKIQQTlZrotRavwIEAv/VWgdYXv5a62Za679evBCFEMK56jz01lqXA93ru3Gl1JVKqWSl1AGl1JN1rNdHKVUmY8iFEI2RPecoNyml+therSqllBvwHjAW6ApMUUp1rWW9V4HvzncfQghxMdiTKIcDvyilDiqlkpRSO5VSSXZ8ry9wQGt9SGtdAiwArqthvfsx38wupduEEI2SPSNzxtZz25HA0UrTqUC/yisopSKBG4ARnK13KYQQjYo9Y70PK6W6A0Mss9ZrrXfYsW1V0+bOmX4LeEJrXaZUTatbNqTUncCdAGFhYSQkJNix+6YnLy/PZdtWwdXbKO1rHAwFmQQkzCJ32N8p9wm54O3Z87jaB4E7gGWWWf9TSn2ktf63ja+mAq0qTUcBx89ZJx5YYEmSzYGrlFImrfWKyitprT8CPgKIj4/Xw4YNsxV2k5SQkICrtq2Cq7dR2tc4HPvkNnJO7aJD+hrCp713wduz5xzl7UA/rfWzWutngf6YE6ctW4AOSqm2SikPYDLwReUVtNZttdbRWutozIWA7zk3SQohhL3KS4o4seBxctZ/Brqc0+s/w3T6xAVv155EqYCyStNl1HxYXYXW2gTch/lq9l5gkdZ6t1LqLqXUXfUJVgghanMm8WsOPhVD1jevgzKnNq3LSF/5wgVv256LOZ9hfk7OcswJ8jpgtj0b11qv4pwiv1rrD2tZd7o92xRCiMpKTh3ixLwHyUv8CvcW7cHoAaYS80JTCafXf0bodc9gDGpZ733Y7FFqrf8FzACyMD8GYobW+q1671EIIRpAeXEBp5b9nYN/60rBvgRaTHod364jqq3XEL1Kuwr3WiigHDsOu4UQwlG01pzZvpKT//cQpRmHCeg/hbBJr+MeEsnBZ3qe7U1WMJVQcODnC9qnPVe9n8X8qNqlmJPkZ0qpxVrrFy9oz0IIcZ6KT+znxP8eJH/nt3hGdqPNkz/h22WYdXm7F35zyH7t6VFOAXpqrYsAlFL/ALYDkiiFEBdFeXE+6V+8RNa3/0S5exH2pzcJGXkvyuh+UfZvT6JMAbyAIsu0J3DQUQEJIUQFrTVnti7lxP/9BVPWUQIH3kLYpNcu6MJMfdiTKIuB3UqpHzCPrBkFbFBKvQOgtX7AgfEJIS5Rxcf3cuJ/D5C/ezWerbsTdff/4dNxsFNisSdRLre8KiQ4JhQhhICywjNkfPECmd+9icHTj5a3vEvw8D+j3M7n2nPDsmes91zLyJqOllnJWutSx4YlhLjUaK3J3bSAkwsexXT6OEGX30aLG1/BGNDC2aHZddV7GDAX87lKBbRSSk3TWq9zaGRCiEtGUeouTnx+PwX7EvBq04uo+5fi076/s8Oysqcv+09gtNY6GUAp1RGYjzxgTAhxgcoKckhf8TxZP7yDm3cg4dM/JGjoTJTBzdmhVWFPonSvSJIAWuv9SqmLc01eCOGStNbk/Pw/Ti58jLLcUwQPu5PQiS9h9Gvm7NBqZE+i3KaUmg1UPMt7KrDNcSEJIVxZ0ZEdpP33Xgp/34h3u360fPgrvNvGOzusOtmTKO8C7gUewHyOch3wviODEkK4jtLTaRx7fzLh0z8ia827ZK95Hze/EMJvn03Q4Okogz1FzJyrzkSplDIA27TWMcC/Lk5IQghXkr5iFgXJ6zn4bA8wlRA84m5aTHgBN99gZ4dmtzoTpda6XCm1QynVWmt95GIFJYRwDTlbl3M64T+ABlMJrR/9Dr+YK5wd1nmz59A7HPPInM1AfsVMrfU4h0UlhGjSik/8zqlFT3BmW6WxKgYjZ7Ytd9lE+bzDoxBCuISyvCzSV75A1pr3zAUrDEYoN1kWNkwRXWeoNVEqpbwwX8hpD+wEZlse7yCEEFVoUwlZa94nfeUsygtyCLr8drSphJxf55ur2FasZymi2xAP/LqY6upRzgVKgfWYn+3dFXjwYgQlhGgaKoronlr4GCUnD+DbbRRhU/6JV6tYhxXRdYa6EmVXrXUsgOU+ys0XJyQhRFNQ+Mc2Ts7/CwXJ6/CM6Eqrv6zCL+5KLI+fdlgRXWeoK1FaC19orU0VjRdCXNpKs1I5tfhv5Pz8OW7+obSc9gHBQ2c6tbqPo9XVsu5KqVzLZwV4W6YVoLXWAQ6PTgjRaJQX5eG7/VMOzFsCupxmVz9J82v/ipu366eCWhOl1rpxjUoXQjiFLi/j9LrPSF/2DL45J/DvP4UWE1/GIzTa2aFdNK7bVxZCXLC8XT9wcv4jFKfuxLv9QE4NeYauN97j7LAuOkmUQohqio/t4eTCx8jbsQr30LZE3bsI/z4TObx2rbNDcwpJlEIIK1PuKdKXP0d2wkcYvPxoMel1Qkbdj8Hd09mhOZUkSiEE5SVFZP3wNhlfvkx5cT7BI+4m9Pq/Y/Rv7uzQGgVJlEJcYirKnkXdsxC3wDByf13IqcVPUppxGL8e1xI26TU8Izo7O8xGRRKlEJeYjJUvULB/A8f/ey9lp49TeHATXq17EPHEp/h2HeHs8BolSZRCXEJKT6dxet2noMvJ27YMt4AWRNz+KYGDb210z6lpTCRRCnGJKDq6k6PvjEebis0zlBv+PccRdPkM5wbWBDT+GuxCiAtSeHAzR966jkNPx1F66sDZBbqMnJ/nYTp9wnnBNRGSKIVwQVpr8vf8yOFXr+CPWf0o2L8er+h4cPM4Zz1z2TNRN0mUQrgQrTVnfvuSlBcGcvjVkRQf202LSa/T4Z+H0eUmKHONsmcXm5yjFMIF6PIycjcvJuOrVyg+moR782ha3vo+QUNmYPDwAlyr7NnFJolSiCZMm0o4vfFzMr/+ByUnD+AR3pmIO+YS2H+K+VEMokFIohSiCSovLiB77SdkfvM6pqxUvNr0Iur+pfj3ur5JPCe7qZFEKUQTUlaQQ/aa98n87k3KzqTj03EIEbd9gm/MaKS4tuNIohSiCTDlppP1/dtkrXmX8oIcfGOvpPm1f8O30xBnh3ZJkEQpRCNWmpVK5jf/JDvhI3RpIf7xE2h+zV/xju7l7NAuKZIohWiESk4eIOPrVzm9YS7ocgIH3kzzq5/AM6KLs0O7JEmiFMLJKlfzMZ1JJ+OrV8j9dSHK6E7wsDtoNvaxS+qxC42RJEohnMxczWc9h2YNwJSZgsHLj2ZjHyFkzMO4B4U7OzyBJEohnEJrTXHqLnJ++T+yf/oQtMaUmUKzKx+h+bV/w80vxNkhikokUQpxkZSXFFGwL4EziV+Sl/gVpZlHLEsst/W4eVBeUihJshGSRCmEA5WeTiMv8WvydnxF3q4f0CUFKA8f/GJGETzqAdKXPoUutZQ9Kyvh9PrPCL3uGYxBLZ0buKjCoYlSKXUl8DbgBnyitf7HOcunAk9YJvOAu7XWOxwZkxAOpcsp/GMbeYlfcWbHVxT9sRUA92atCRoyA/8e1+DTeRgGDy/S5t6D1rrq1y3VfMKnveeM6EUtHJYolVJuwHvAKCAV2KKU+kJrvafSan8AQ7XW2UqpscBHQD9HxSSEI5QX55O/ezVnEr+i2ebl/FGYCUrh3W4ALSa+jF+Pa/CMiqk2cqbgwC9gkmo+TYEje5R9gQNa60MASqkFwHWANVFqrSv/RmwCohwYjxANpiTjMHk7viYv8Svy9/6ILi3G4B1AaVhPWo26Db+4sRgDQuvchlTzaTocmSgjgaOVplOpu7d4O/BNTQuUUncCdwKEhYWRkJDQQCE2Lnl5eS7btgpNoY2GgkwCEmaRO+zvlPtYLqyUl2HM2Ifn0V/wPPoLxuxDAJj8IynpeC3FUQMoDYslr7CYXJMfbN/txBY4TlP493MERybKmkbo6xrmoZQajjlRDq5pudb6I8yH5cTHx+thw4Y1UIiNS0JCAq7atgpNoY1pc+8h+9Qu2qWtwj92DGcSvyIvaRVlZzLA4IZPx8H4j7kHvx7X4NGyY5VD6qbQvgvh6u2rjSMTZSrQqtJ0FHD83JWUUnHAJ8BYrXWmA+MRok5aa/L3JpC99mPQ5eSsm03OutkYfIPxj7sKvx7X4Bc7BjffYGeHKi4yRybKLUAHpVRb4BgwGfhT5RWUUq2BZcAtWuv9DoxFiBqVZh8nf/dq8nf/QP7u1ZhyKj1oS7nh1/NaWt23GOUmd9Jdyhz2r6+1Niml7gO+w3x70Kda691Kqbssyz8EngWaAe9bDl9MWut4R8UkRHlRHvn71lqTY/Ex87lEN//meLcfSF7SN1BWal5Zl5G/8zvKzmTIfY2XOIf+mdRarwJWnTPvw0qfZwIzHRmDuLTp8jIK/9hK/q4fyN/9g/mWnLJSlLsnPh2HEDjoVnxjRuHVqjsnPr8PzrmFR+5rFCAjc4SL0VpTeuogeZbEmL/3J8oLTgPg1aYnzcY8jG/MKHw6DMLg4V3lu3Jfo6iNJErR5JnyMsnfvcZ6OF2akQKYR8MExE/At9sV+HYdKfc1inqTRCkavcr1Go1BLSkvLabw943WXmPR4e2gNQbvAHy7DKfZ2EfxjRmFR1gHeY6MaBCSKEWjl75iFgX713PknfG4eftTsH89uqQQ3Ix4t+tP6PXP4RszCu+2feTqtHAI+a0SjYbWGlP2MYqP76X4+F5Kju+l6GgihQc2AVB08BfcwzoQPPQOfLtdgU/nYbh5+zs5anEpkEQpLjpdZqLk1EFrMiw+vpfitL2UpO2jvCjPup7BJwhl9ARlAF0Obh74dRtFy5vfdmL04lIkiVKcl3PPF9alvDif4rTkSslwHyEHtrL3v8fP3qsIGIMj8YzoQtCQGXiEd8Yzogue4V0o1+UcfKydOUmC1GsUTiOJUpwX8/NdNlS5t9CUl3k2GVYcNqftpTTj8NkvGtzwaNGOMv9IwgZNtiZDj4jOuHkH1Lgvc73G8irz5L5G4QySKIVN5UV5mHJOUJS6i+y1n4AuJzvhPxSlbKMk/aC5WISF8vDGs2UnvNsPJOjy2/GM6IJHeBc8wtpjcPckISGBnnYWVZD7GkVjIYmyiTufQ+HKtKkUU+4pTDknan6dTrN+1sX51TdQXkZJ5mH8e11vTYaeEZ1xb9YGZTA0SNvkvkbRWEiibOIqHwq3vPVdygtOV0p2FYkvrdJn86ssLwN09ap3Bt9gjIEtMQa2xPuyvtbPyt2Lkwsfq9LDKy/IocX4F+R8oXB5kigbWH17eLXR5WWU5WViyjmJKfcUZbmnMJ0xv5ekp5D76wLzofCPH5C97pPqh6qAcve0JjyPFu3w6TDIPB1knudmWWYMCMPg4VVjHGlz76kem5wvvGDztqXy1Df7OJJdSOtgb14a25mpvaXQf2Pj8omyoROXLTVd7KhMa40uzjcnPkvCM1lefnu2k7rnw7MJMfdUrT0/3IwoN4+zV4SVwjOiK0GDbsUYFG5NjMbAlhh8Ai94hMqlcr7wYiauedtSuXNxEgWlZQAczi7kzsVJAA7dpyTm8+fyidJW4qqLLi9Hl5WiTSXoshLzu+VFxfxKy0ynT5C9dra5h7f2Y1AGdEm+NRGWnTG/65LCGvfn5eFLUUgkbgEt8AjvhE/HIbgFtMAY0AJjYBhu/pbPAS0oKynk4OPtKwdLSVoygf2nOOQPwqVwvtBW4tJaYyrXlJjKKS4rp9hkfpVU+rwzy4Q+kGGdPnd5sanMMk/zz7UHrfuqUFBaxv3Ld1FQWoavhxu+HkbL+9nPPpWm3Qz2/wF0RmJ2Fercx2U2dvHx8Xrr1q12rVt6Oo3fH24F5WWgDHh3GIRSqlqC0zUkPW0qMX/vgiiMIZEY/VucTXgBFZ/DKn1ugZt/KOs2/mJ3mf20ufeQvW521V6e0YPgy2c26kPh832UwIX0gExl5eQWm8gpNJFbXEpOoYmcolJyikzkFFreLdO5RSaW70yjyFRebTsK8DQaKC4rr7Fz70yeRkO1RHo2mVZNsh9vOkxOkanaNtoEe5Py9BV27c+VHwWhlNpWWz1cl+5RZqx8Acotv/haU3JiP54RXTB4B6CMHuaXmzvKzePstHV+peU1zbe8sCwvKzzD8Q8mmxOshXL34rK/b3FID+9SOBSet/UodyxJorDU/G94OLuQ2xYm8l3yKTqE+lmTXW6lhJdTdDYh5pfY/kPn4WYg0NtIoJd7jUkSzA96un9wWzyNBjyMBjzdDHgaz748Kk3v272Lvr17VJrnZllHWT9XfKfdy2s4nF396CIq0ItfHhhMfkkZ+SUmy/s5n4urLysoLbNOZ+SXcDi70LKueb3a2nc4u5ARH/xMx1A/OoX6mt9b+BEd7I3RrWHuYGjqXDZRlp5O4/T6zzj7PDNNeWEuUXfPd0jiSpt7T7UnpznyYocrHAqXlpVzLKeII9mFHM4u4MjpQsvnQo6cLmTfybxqP9OSMs3n244B4O1uINDLnUAvIwGW98hALwK9jNb5gd7u1umAivneZ5d7ubtZtx394uoaE1ebYG9eu7arXW3yT9/H0HbN7Vr3pbGdqxwKA/i4u/GPq7sQFeRdxzfrp80Lqzlyunr7fD3cKCwtZ1HicbILK42YMijaNfOhUws/axLNzzTRObeIMH/PS6oyk8smyoyVL1BWVk7lv4dlZY5LXJdED+88D4Nzi0qrJL7DWYVs2V/I0zs3cDi7kOO5RZSfkwlD/TxoHeRNlxZ+7D2ZV+N2FVD06tV4GBu2t1Nb4nppbOcG3U+Fip/dxbq48vJVNbfvPxPjrOdgM/NLSE7PZ396HvvT80m2vH+7L52SMnOP9KGffyDAy0jHUF86hZqTaMXnDqG++HmeTSuucvHIZRPlsaS1+JRXTVyG8hKOJSUQ7oD9OaOH5+wrtHcs3sHvGXl0buHPkYpkmF1o7SGeez7M3U0R6gkdww2M7NCc1sHetA7ypk2wD62DvWkV5IWPx9lfydp6eK2DvRs8ScLFT1wV+7xYicNW+5RSNPfzpLmfJ4PahlT5blm55kh2IYvX/IxX+GXmJHoqj/V/ZDFv+7Eq60YGetEx1BcFrP8ji9Iy81/DpnzxyGUT5cTgtzlM9f9kob4erD6eS+tgb4K83Z0QWcO4kCuYWmsKSsrILTaf3zO/Sq3TZyrPLy4lt8jEsqQ0Cs85x1VYWs7z3/9unQ7ydrckPm+GXBZCm4pEGOJD6yBvwvw9Wb9uLcOGDbSrjRe7hwcXN3E5Q33b52ZQtG3mQ98WRoYNuazKsoISEwcyCtifnmftge5Pz2fzkexqRwwFpWXcs3QnHkYDvaMCaRvi0yQO4V02UR6poScCkJ5fQvd/rgUgwMto/Y/dOtjSs6k0HR7gdd63Xzi6N1JerskpKuXxr/bUeGvJvct2si01p0qSM382J8MzxWXkFpVW+wWuiYebgQAvIwFexmpJsoICkh4dSutgbwK8GvYPjzN6eOL8+XgYiYsIIC6ianETwyNf1rh+brGJm/67DYBgb3fiWwXSOyrI+t4m2LvRJU+XTZStg71rPGxr6e/J29fHVLmAcDi7kJ9TsqucyAbzyeyoIC9rAjUnU+8qybTiUPF8e3jFpjKyCkrJLiglq6CErIJSfj5aym9rD5JVUGp5lZBdWPVzdmFpnbeo5BSZ+OTXI9YE5+9pJMDTSHiAJwGWCxoBnpWWWS6EBFg/m5f7exnxNNq+0NE62JuY8Jqr/zQEV+/hubLa/g+2DvJi6fQ+bEs9zdajOWxLPc0bCQcxWf56N/NxJ75VEL2jAolvFUR8VBBRQV5OTZ4umyhrO2x749qu3NQjosbvnCkyWa68FlS5AHHkdCEJBzM4llP94kNzXw9aB3uz9+QZ620sFQpKy/jzkiSW7kw7m+wKSskqLKWgtltXEvdgUObD2BAfD0J83Gnm606H5r4E+7gT4mOe/+IP+8ksKK329dZB3hx+xr574s6HMw6DRdNW2+/My1d1MSfAVkH8eYB5flFpGUlpuWxLzWHr0dNsS83h1Z8OUmb5Dxfq50F8pV5nfKtAIgJqTp6OOLJz2URZn8M2fy8j3Vr6061lzY8XMFXczlLpooX5cwHbU2s+NM0vKWN/ej4hPu5cFuJDSJRHlYQXYkmIwT7u7E/azlUjhhDgacRg45A/1Nejll9C17hCK5q+8/md8XJ3o2/rYPq2DrbOKywtY8fxXLYdPc1WSwL9LvmUtbMS5u9JvKXXWdH7/PH3DIeMPnLZRAkNf9hmdDPQJsSHNiE+DDlnWV334O16bJhd2887aLD7ApOrX6EVruFCfme83d3o3yaY/m3OJs+CEhOJxyw9z9TTbD16mlX7TllPR7kpKKvhAtJT3+yTRNkYyBVaIRzPx8PIwLYhDKx0+1JesYnEYzlsTc3h4ZW7a/xebRd37SXjkxrI1N5RfHRjnPmKHeae5Ec3xkkiE8LB/DyNDL6sGQ9dfhltgmse0dS6lvn2kh5lA5IenhDO5agjO+lRCiFchqOO7KRHKYRwKY44spMepRBC2CCJUgghbJBEKYQQNkiiFEIIGyRRCiGEDZIohRDCBkmUQghhgyRKIYSwQRKlEELYIIlSCCFskEQphBA2SKIUQggbJFEKIYQNkiiFEMIGSZRCCGGDQxOlUupKpVSyUuqAUurJGpYrpdQ7luVJSqlejoxHCCHqw2GJUinlBrwHjAW6AlOUUl3PWW0s0MHyuhP4wFHxCCFEfTmyR9kXOKC1PqS1LgEWANeds851wH+12SYgSCkV7sCYhBDivDkyUUYCRytNp1rmne86QgjhVI58Zo6qYZ6uxzoope7EfGgOkKeUSr7A2Bqr5kCGs4NwMFdvo7Sv6WpT2wJHJspUoFWl6SjgeD3WQWv9EfBRQwfY2Ciltmqt450dhyO5ehulfa7JkYfeW4AOSqm2SikPYDLwxTnrfAHcarn63R/I0VqnOTAmIYQ4bw7rUWqtTUqp+4DvADfgU631bqXUXZblHwKrgKuAA0ABMMNR8QghRH059LneWutVmJNh5XkfVvqsgXsdGUMT4/KnF3D9Nkr7XJAy5yohhBC1kSGMQghhgyRKJ1JKBSmlliil9iml9iqlBiilQpRSPyilfre8Bzs7zvpQSnVSSiVWeuUqpR5ylfYBKKUeVkrtVkrtUkrNV0p5uVj7HrS0bbdS6iHLPJdp3/mQROlcbwPfaq07A92BvcCTwBqtdQdgjWW6ydFaJ2ute2itewC9MV+sW46LtE8pFQk8AMRrrWMwX7CcjOu0Lwa4A/MIu+7ANUqpDrhI+86XJEonUUoFAJcDswG01iVa69OYh3XOtaw2F7jeGfE1sJHAQa31YVyrfUbAWyllBHww3wPsKu3rAmzSWhdorU3AWuAGXKd950USpfNcBqQDnymlflNKfaKU8gXCKu4ltby3cGaQDWQyMN/y2SXap7U+BrwBHAHSMN8D/D0u0j5gF3C5UqqZUsoH8218rXCd9p0XSZTOYwR6AR9orXsC+bjgYYxlsME4YLGzY2lIlnNz1wFtgQjAVyl1s3Ojajha673Aq8APwLfADsDk1KCcSBKl86QCqVrrXy3TSzAnzpMVFZQs76ecFF9DGQts11qftEy7SvuuAP7QWqdrrUuBZcBAXKd9aK1na617aa0vB7KA33Gh9p0PSZROorU+ARxVSnWyzBoJ7ME8rHOaZd40YKUTwmtIUzh72A2u074jQH+llI9SSmH+99uL67QPpVQLy3trYDzmf0eXad/5kBvOnUgp1QP4BPAADmEewmkAFgGtMf9nvFFrneWsGC+E5dzWUeAyrXWOZV4zXKd9zwOTMB+S/gbMBPxwnfatB5oBpcBftNZrXOnf73xIohRCCBvk0FsIIWyQRCmEEDZIohRCCBskUQohhA2SKIUQwgZJlKJJU0rdoJTSSqnOzo5FuC5JlKKpmwJswDyeXAiHkEQpmiyllB8wCLgdS6JUShmUUu9baih+pZRapZSaaFnWWym1Vim1TSn1XcVQPCFskUQpmrLrMdfz3A9kKaV6YR5qFw3EYh4pMwBAKeUO/BuYqLXuDXwKvOSEmEUT5NCHiwnhYFOAtyyfF1im3YHFWuty4IRS6ifL8k5ADPCDeWg2bpjLowlhkyRK0SRZxhyPAGKUUhpz4tOYq6jX+BVgt9Z6wEUKUbgQOfQWTdVE4L9a6zZa62itdSvgDyADmGA5VxkGDLOsnwyEKqWsh+JKqW7OCFw0PZIoRVM1heq9x6WYi+imYq7Q/R/gV8zVx0swJ9dXlVI7gETM9SOFsEmqBwmXo5Ty01rnWQ7PNwODLPU/hagXOUcpXNFXSqkgzHU+X5AkKS6U9CiFEMIGOUcphBA2SKIUQggbJFEKIYQNkiiFEMIGSZRCCGGDJEohhLDh/wFhap9kqzapbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = fig.add_subplot()\n",
    "ax.plot(results['prop_haemorrhage'], marker='o', label='haemorrhage')\n",
    "ax.plot(results['prop_nlvo'], marker='s', label='infarction: nlvo (NIHSS 0-10)')\n",
    "ax.plot(results['prop_lvo'], marker = '^', label='infarction: lvo (NIHSS 11+)')\n",
    "ax.set_ylabel('Proportion of patients')\n",
    "ax.set_xlabel('Age')\n",
    "ax.set_ylim(0, 1)\n",
    "ax.grid()\n",
    "plt.legend()\n",
    "plt.savefig('./output/stroke_type_by_age.jpg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df8f8aa-ad8a-464b-9e18-433a7e3634ab",
   "metadata": {},
   "source": [
    "## Show mRS by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "acbe520d-2ccd-410b-a4d8-d282b537a88e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mrs_results = pd.DataFrame()\n",
    "mrs_results.index.name='Age midpoint'\n",
    "# Get prestroke mRS for all patients\n",
    "mrs_results['all'] = data.groupby('Age_midpoint').mean()['S2RankinBeforeStroke']\n",
    "\n",
    "# Get prestroke mRS for haemorrgagic patients\n",
    "mask = data['S2StrokeType'] == 'Primary Intracerebral Haemorrhage'\n",
    "mrs_results['haemorrhage'] = \\\n",
    "    data[mask].groupby('Age_midpoint').mean()['S2RankinBeforeStroke']\n",
    "\n",
    "# Get prestroke mRS for nlvo patients\n",
    "mask = (data['S2StrokeType'] == 'Infarction') & (data['S2NihssArrival'] < 11)\n",
    "mrs_results['nlvo'] = \\\n",
    "    data[mask].groupby('Age_midpoint').mean()['S2RankinBeforeStroke']\n",
    "\n",
    "# Get prestroke mRS for lvo patients\n",
    "mask = (data['S2StrokeType'] == 'Infarction') & (data['S2NihssArrival'] >10)\n",
    "mrs_results['lvo'] = \\\n",
    "    data[mask].groupby('Age_midpoint').mean()['S2RankinBeforeStroke']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "818b537e-3adf-4d9d-86b3-43a78b922ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = fig.add_subplot()\n",
    "ax.plot(mrs_results['haemorrhage'], marker = 'o', label='haemorrhagic')\n",
    "ax.plot(mrs_results['nlvo'], marker = 's', label='infarction: nlvo (NIHSS 0-10)')\n",
    "ax.plot(mrs_results['lvo'], marker = '^', label='infarction: lvo (NIHSS 10+)')\n",
    "ax.set_ylabel('Mean mRS before stroke')\n",
    "ax.set_xlabel('Age')\n",
    "ax.set_ylim(0, 3)\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "plt.savefig('./output/mrs_by_age.jpg', dpi=300)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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