interactive_example_continuous_risks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ddcff386",
   "metadata": {},
   "source": [
    "# Interactive Sim Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d7ff1f1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "from vivarium import InteractiveContext, Artifact\n",
    "from pathlib import Path\n",
    "#import itertools\n",
    "import pandas as pd, numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ceb2731-8ee2-4ecb-9fb1-8e8c72145ac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path('/ihme/homes/lutzes/vivarium_nih_us_cvd/src/vivarium_nih_us_cvd/model_specifications/nih_us_cvd.yaml')\n",
    "sim = InteractiveContext(path, setup=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65c132fd-0001-477d-bfad-ce5871954e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.configuration.update({\n",
    "                          'population':\n",
    "                              {'population_size': 50_000,\n",
    "                              },\n",
    "                          }\n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ffb78d48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m2023-08-15 08:07:29.583\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msimulation_1\u001b[0m-\u001b[36martifact_manager\u001b[0m:\u001b[36m66\u001b[0m - \u001b[1mRunning simulation from artifact located at /mnt/team/simulation_science/costeffectiveness/artifacts/vivarium_nih_us_cvd/alabama.hdf.\u001b[0m\n",
      "\u001b[32m2023-08-15 08:07:29.585\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msimulation_1\u001b[0m-\u001b[36martifact_manager\u001b[0m:\u001b[36m67\u001b[0m - \u001b[1mArtifact base filter terms are ['draw == 0'].\u001b[0m\n",
      "\u001b[32m2023-08-15 08:07:29.586\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msimulation_1\u001b[0m-\u001b[36martifact_manager\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mArtifact additional filter terms are None.\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/share/homes/lutzes/vivarium_nih_us_cvd/src/vivarium_nih_us_cvd/components/treatment.py:689: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sbp_prescription_inertia_propensity.loc[\n",
      "/mnt/share/homes/lutzes/vivarium_nih_us_cvd/src/vivarium_nih_us_cvd/components/treatment.py:776: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pop_visitors[\n",
      "/mnt/share/homes/lutzes/vivarium_nih_us_cvd/src/vivarium_nih_us_cvd/components/treatment.py:858: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  ldlc_prescription_inertia_propensity.loc[\n",
      "/mnt/share/homes/lutzes/vivarium_nih_us_cvd/src/vivarium_nih_us_cvd/components/treatment.py:958: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pop_visitors[\n"
     ]
    }
   ],
   "source": [
    "sim.setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d53428f1-3522-4ec1-a50e-e0ed0075c7f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracked</th>\n",
       "      <th>location</th>\n",
       "      <th>entrance_time</th>\n",
       "      <th>alive</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>exit_time</th>\n",
       "      <th>cause_of_death</th>\n",
       "      <th>years_of_life_lost</th>\n",
       "      <th>years_lived_with_disability</th>\n",
       "      <th>...</th>\n",
       "      <th>sbp_multiplier</th>\n",
       "      <th>sbp_medication</th>\n",
       "      <th>lifestyle</th>\n",
       "      <th>polypill</th>\n",
       "      <th>sbp_medication_adherence</th>\n",
       "      <th>ldlc_multiplier</th>\n",
       "      <th>sbp_therapeutic_inertia_propensity</th>\n",
       "      <th>visit_type</th>\n",
       "      <th>last_fpg_test_date</th>\n",
       "      <th>scheduled_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>47.388882</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.051</td>\n",
       "      <td>one_drug_half_dose_efficacy</td>\n",
       "      <td>NaT</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022599</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>41.564194</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>no_treatment</td>\n",
       "      <td>NaT</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.742067</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>53.155826</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>no_treatment</td>\n",
       "      <td>NaT</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.591302</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>30.457692</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>no_treatment</td>\n",
       "      <td>NaT</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.273953</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>19.554806</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>no_treatment</td>\n",
       "      <td>NaT</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.455919</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tracked location entrance_time  alive        age     sex exit_time  \\\n",
       "0     True  Alabama    2020-12-04  alive  47.388882  Female       NaT   \n",
       "1     True  Alabama    2020-12-04  alive  41.564194    Male       NaT   \n",
       "2     True  Alabama    2020-12-04  alive  53.155826  Female       NaT   \n",
       "3     True  Alabama    2020-12-04  alive  30.457692    Male       NaT   \n",
       "4     True  Alabama    2020-12-04  alive  19.554806  Female       NaT   \n",
       "\n",
       "  cause_of_death  years_of_life_lost  years_lived_with_disability  ...  \\\n",
       "0       not_dead                 0.0                          0.0  ...   \n",
       "1       not_dead                 0.0                          0.0  ...   \n",
       "2       not_dead                 0.0                          0.0  ...   \n",
       "3       not_dead                 0.0                          0.0  ...   \n",
       "4       not_dead                 0.0                          0.0  ...   \n",
       "\n",
       "  sbp_multiplier               sbp_medication  lifestyle  polypill  \\\n",
       "0          1.051  one_drug_half_dose_efficacy        NaT      cat2   \n",
       "1          1.000                 no_treatment        NaT      cat2   \n",
       "2          1.000                 no_treatment        NaT      cat2   \n",
       "3          1.000                 no_treatment        NaT      cat2   \n",
       "4          1.000                 no_treatment        NaT      cat2   \n",
       "\n",
       "   sbp_medication_adherence  ldlc_multiplier  \\\n",
       "0                      cat3              1.0   \n",
       "1                      cat3              1.0   \n",
       "2                      cat3              1.0   \n",
       "3                      cat3              1.0   \n",
       "4                      cat3              1.0   \n",
       "\n",
       "  sbp_therapeutic_inertia_propensity visit_type  last_fpg_test_date  \\\n",
       "0                           0.022599       none                 NaT   \n",
       "1                           0.742067       none                 NaT   \n",
       "2                           0.591302       none                 NaT   \n",
       "3                           0.273953       none                 NaT   \n",
       "4                           0.455919       none                 NaT   \n",
       "\n",
       "   scheduled_date  \n",
       "0             NaT  \n",
       "1             NaT  \n",
       "2             NaT  \n",
       "3             NaT  \n",
       "4             NaT  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop0 = sim.get_population()\n",
    "pop0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d404392b-dcd3-437c-95a0-0e3829a07708",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pop0.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf68d61b-1634-4402-bb82-76d4705ad39f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sim.list_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38729771-5576-4164-9b7f-baa2a7cbe229",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
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       "      <td>alive</td>\n",
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       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>41.564194</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.742067</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.397553</td>\n",
       "      <td>137.634343</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>53.155826</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.591302</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.110561</td>\n",
       "      <td>139.700114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>30.457692</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.273953</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.133673</td>\n",
       "      <td>127.697819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>19.554806</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaT</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.455919</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tracked location entrance_time  alive        age     sex exit_time  \\\n",
       "0     True  Alabama    2020-12-04  alive  47.388882  Female       NaT   \n",
       "1     True  Alabama    2020-12-04  alive  41.564194    Male       NaT   \n",
       "2     True  Alabama    2020-12-04  alive  53.155826  Female       NaT   \n",
       "3     True  Alabama    2020-12-04  alive  30.457692    Male       NaT   \n",
       "4     True  Alabama    2020-12-04  alive  19.554806  Female       NaT   \n",
       "\n",
       "  cause_of_death  years_of_life_lost  years_lived_with_disability  ...  \\\n",
       "0       not_dead                 0.0                          0.0  ...   \n",
       "1       not_dead                 0.0                          0.0  ...   \n",
       "2       not_dead                 0.0                          0.0  ...   \n",
       "3       not_dead                 0.0                          0.0  ...   \n",
       "4       not_dead                 0.0                          0.0  ...   \n",
       "\n",
       "  lifestyle polypill  sbp_medication_adherence  ldlc_multiplier  \\\n",
       "0       NaT     cat2                      cat3              1.0   \n",
       "1       NaT     cat2                      cat3              1.0   \n",
       "2       NaT     cat2                      cat3              1.0   \n",
       "3       NaT     cat2                      cat3              1.0   \n",
       "4       NaT     cat2                      cat3              1.0   \n",
       "\n",
       "   sbp_therapeutic_inertia_propensity  visit_type last_fpg_test_date  \\\n",
       "0                            0.022599        none                NaT   \n",
       "1                            0.742067        none                NaT   \n",
       "2                            0.591302        none                NaT   \n",
       "3                            0.273953        none                NaT   \n",
       "4                            0.455919        none                NaT   \n",
       "\n",
       "  scheduled_date  high_ldl    high_sbp  \n",
       "0            NaT  2.125019  138.926833  \n",
       "1            NaT  2.397553  137.634343  \n",
       "2            NaT  2.110561  139.700114  \n",
       "3            NaT  2.133673  127.697819  \n",
       "4            NaT  0.000000   50.000000  \n",
       "\n",
       "[5 rows x 66 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = pd.concat([pop0,\n",
    "                   sim.get_value('high_ldl_cholesterol.exposure')(pop0.index).rename('high_ldl'),\n",
    "                   sim.get_value('high_systolic_blood_pressure.exposure')(pop0.index).rename('high_sbp'),\n",
    "                  ], axis=1)\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6cf8d31",
   "metadata": {},
   "source": [
    "# Analyze Continuous Risks \n",
    "\n",
    "Note: for this analysis we do not take any time steps. You can also run the simulation forward and repeat this to ensure risks remain stable over time\n",
    "\n",
    "### Visualize the Spread of Continuous Risks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "744be6e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='high_ldl', ylabel='Count'>"
      ]
     },
     "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": [
    "sns.histplot(data1.high_ldl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ab74b4bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='high_ldl', ylabel='Count'>"
      ]
     },
     "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": [
    "## This is a good example - why do we have a peak at 0? \n",
    "## Are we including the right people? In this model anyone under age 25 has fake risk exposure values, \n",
    "## we can remove them and run it again. We also limit it to alive people only. \n",
    "\n",
    "data1 = data1.loc[(data1.age > 25) & (data1.alive == 'alive')]\n",
    "sns.histplot(data1.high_ldl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1afac3b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0020412666887344145"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## THis is looking better now! We have a group at 5.5, but we did that in modeling by setting a max, so this is expected. \n",
    "## Let's check on the folks at zero, that's a bit odd \n",
    "\n",
    "len(data1.loc[data1.high_ldl == 0]) / len(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "67edb603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='high_sbp', ylabel='Count'>"
      ]
     },
     "execution_count": 12,
     "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": [
    "## Relatively few people, so not too concerning. We can repeat this for SBP \n",
    "\n",
    "sns.histplot(data1.high_sbp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75de4bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## This is looking fine! \n",
    "## You can also make these plots for specific age/sex groups to see how they look "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "791decdf",
   "metadata": {},
   "source": [
    "## Continuous Risk Standard Deviation\n",
    "\n",
    "Generally you output the mean risk values from the main sim, so we don't check that here. \n",
    "But we will check standard deviation! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0dc9ad5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## First we need to load in the artifact data here\n",
    "\n",
    "from vivarium import Artifact\n",
    "\n",
    "def load_artifact_data(word:str, cause:str, measure:str):\n",
    "    master_art_data = pd.DataFrame()\n",
    "    for i in ['alabama']: #in case there are other countries\n",
    "        \n",
    "        # grab main artifact object\n",
    "        art = Artifact('/mnt/team/simulation_science/costeffectiveness/artifacts/vivarium_nih_us_cvd/' + i + '.hdf', \n",
    "                   filter_terms=['year_start == 2019', 'age_start >=  25']) # << MIGHT WANT TO CHANGE THESE TERMS\n",
    "        \n",
    "        # load requested measure\n",
    "        data = art.load(word + '.' + cause + '.' + measure)\n",
    "        \n",
    "        # collapse to desired percentiles\n",
    "        data = data.apply(pd.DataFrame.describe, percentiles=[0.025, 0.35,0.975], axis=1).filter(['mean','35%', '2.5%', '97.5%']).reset_index()\n",
    "        \n",
    "        # formatting (may want to format age as well)\n",
    "        data['location'] = i.capitalize()\n",
    "        data['measure'] = measure\n",
    "    \n",
    "   \n",
    "        master_art_data = master_art_data.append(data)\n",
    "        \n",
    "    return master_art_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cad4d2ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>age_start</th>\n",
       "      <th>age_end</th>\n",
       "      <th>year_start</th>\n",
       "      <th>year_end</th>\n",
       "      <th>mean</th>\n",
       "      <th>35%</th>\n",
       "      <th>2.5%</th>\n",
       "      <th>97.5%</th>\n",
       "      <th>location</th>\n",
       "      <th>measure</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.866901</td>\n",
       "      <td>0.854303</td>\n",
       "      <td>0.810395</td>\n",
       "      <td>0.924996</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>exposure_standard_deviation</td>\n",
       "      <td>25_to_29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.834644</td>\n",
       "      <td>0.823973</td>\n",
       "      <td>0.785313</td>\n",
       "      <td>0.885991</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>exposure_standard_deviation</td>\n",
       "      <td>30_to_34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.854927</td>\n",
       "      <td>0.845481</td>\n",
       "      <td>0.808928</td>\n",
       "      <td>0.901343</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>exposure_standard_deviation</td>\n",
       "      <td>35_to_39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.874483</td>\n",
       "      <td>0.865119</td>\n",
       "      <td>0.826285</td>\n",
       "      <td>0.922943</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>exposure_standard_deviation</td>\n",
       "      <td>40_to_44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.900536</td>\n",
       "      <td>0.892206</td>\n",
       "      <td>0.855616</td>\n",
       "      <td>0.945259</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>exposure_standard_deviation</td>\n",
       "      <td>45_to_49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex  age_start  age_end  year_start  year_end      mean       35%  \\\n",
       "0  Female       25.0     30.0        2019      2020  0.866901  0.854303   \n",
       "1  Female       30.0     35.0        2019      2020  0.834644  0.823973   \n",
       "2  Female       35.0     40.0        2019      2020  0.854927  0.845481   \n",
       "3  Female       40.0     45.0        2019      2020  0.874483  0.865119   \n",
       "4  Female       45.0     50.0        2019      2020  0.900536  0.892206   \n",
       "\n",
       "       2.5%     97.5% location                      measure age_group  \n",
       "0  0.810395  0.924996  Alabama  exposure_standard_deviation  25_to_29  \n",
       "1  0.785313  0.885991  Alabama  exposure_standard_deviation  30_to_34  \n",
       "2  0.808928  0.901343  Alabama  exposure_standard_deviation  35_to_39  \n",
       "3  0.826285  0.922943  Alabama  exposure_standard_deviation  40_to_44  \n",
       "4  0.855616  0.945259  Alabama  exposure_standard_deviation  45_to_49  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldl_exp_std = load_artifact_data('risk_factor', 'high_ldl_cholesterol', 'exposure_standard_deviation')\n",
    "ldl_exp_std['age_group'] = [f'{int(i)}_to_{int(j) - 1}' for (i,j) in zip(ldl_exp_std.age_start,ldl_exp_std.age_end)]\n",
    "ldl_exp_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "956d14b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_start</th>\n",
       "      <th>sex</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.893820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.940963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.826006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.900028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.883329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_start     sex       std\n",
       "0       25.0  Female  0.893820\n",
       "1       25.0    Male  0.940963\n",
       "2       30.0  Female  0.826006\n",
       "3       30.0    Male  0.900028\n",
       "4       35.0  Female  0.883329"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Now we find the standard deviation from the sim data\n",
    "\n",
    "data1['age_start'] = ((data1.age/5).astype(int) * 5).astype(float)\n",
    "\n",
    "ldl_std = (data1\n",
    "                .groupby(['age_start', 'sex'])\n",
    "                .high_ldl.describe()\n",
    "                .filter(['std'])\n",
    "                .reset_index())\n",
    "ldl_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6016259a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cHOvWrcOxY8cQGhpa6j5yuRxhYWFIT0/H7NmzERAQAFtbW8THx2PChAklvuU93UenOowYMQJHjhzBO++8g1atWkEqlUKhUCA8PFzrN8yyahNqysWLFyGRSNSfuYqU2ciRIzFnzhz8+uuvmD59OjZv3gwHBwf1kOvSvPHGG/jhhx8wffp0dOrUCQ4ODhCJRBg1alStffMurayFYh29q4tCoYBIJMLff/+t9dzFg9vS8qdLvit7fVWvffbZZ6UOkS+eT6o8BiNUQkxMDG7duqWXobjnz5/HggULMHHiRJw9exaTJk3ChQsXSsxHolAocPv2bXVtCABcv34dANRzMPj6+mLfvn3IysrSqB25evWq+nUVsViM3r17o3fv3li6dCk++eQTfPDBB9i/fz/69Omj/vZTfJRI8W+YZWnUqBHOnTuH3r17lxrcVEa7du2wbt069SiH0o594cIFXL9+HevWrcO4cePU26vSUdnX1xdRUVHIzs7W+MN87do1jXQPHz5EVFQUIiMjMW/ePPV21bdPXc+l2ufpWrGUlJQqf4MHlLVG//77Lzp16qT+vFSkzPz9/dGhQwds2rQJ06ZNw2+//YYhQ4bA0tKyzP22bNmC8ePH44svvlBvy8vLK/FZq8hnxtfXF+fPn4dCodCoHdH22deHRo0aQRAE+Pv7a/wO1wRdr6+2PALKwF+XWl2qGvYZIQ137tzBhAkTYGFhgXfeeadWzy2TyTBhwgR4eXlh+fLlWLt2LZKSkjBjxgyt6b/66iv1/wVBwFdffQVzc3P07t0bADBgwADI5XKNdADw5ZdfQiQSoX///gBK9vgHoP4mpBq+qfrDFB0drU4jl8vx7bff6vz+RowYgfj4eKxevbrEa48fP0ZOTk6p++bm5iImJkbra6q+L6qmEdV8HMX/2Kq+RT79rVEQBCxfvlzn91DcgAEDUFhYiJUrV6q3yeVyrFixotxzA8CyZct0PlefPn1gbm6OFStWaBynIscoTXp6OkaPHg25XI4PPvhAvb2iZTZy5EgcPXoUa9asQWpqarlNNIDy2hS/LitWrChR61ZauWozYMAAJCYmavRhKSwsxIoVKyCVSsusQStNdQ7tHTp0KCQSCSIjI0u8d0EQSgz3rgpdr29xbdu2RaNGjfD555+rm++elpKSUm15JNaMmKw1a9ZoTBGu8vSQvtOnT2PDhg1QKBR49OgRTpw4ga1bt0IkEuHHH3/EM888U2L/rVu3qr9dPW38+PFlNjNkZGRgw4YNWl9T1cB8/PHHOHv2LKKiomBnZ4dnnnkG8+bNw4cffohhw4ZpTERlZWWFXbt2Yfz48QgJCcHff/+NHTt24P3331e37w8aNAg9e/bEBx98gLi4OAQHB2PPnj34448/MH36dHWAsWDBAkRHR+PZZ5+Fr68vkpOT8fXXX6N+/frq+S2aN2+Ojh07Ys6cOUhPT4eTkxM2btyIwsLCUt9zcWPHjsXmzZsxZcoU7N+/H126dIFcLsfVq1exefNm7N69G+3atdO6b25uLjp37oyOHTsiPDwcPj4+ePToEX7//XccPHgQQ4YMQevWrQEoAydHR0esWrUKdnZ2sLW1RUhICAICAtCoUSPMmjUL8fHxsLe3x9atW6tUqzBo0CB06dIF7733HuLi4hAUFITffvutRFu8vb09unfvjk8//RQymQze3t7Ys2cPYmNjdT6Xap6TRYsWYeDAgRgwYADOnDmDv//+u0ITa12/fh0bNmyAIAjIzMzEuXPn8OuvvyI7OxtLly7VaFapaJmNGDECs2bNwqxZs+Dk5KTTN+qBAwfixx9/hIODA4KCghATE4N9+/bB2dlZI12rVq0gkUiwZMkSZGRkwNLSEr169VLPMfO0V199Fd988w0mTJiAU6dOwc/PD1u2bMHhw4exbNmySvUFq8zQ3tI0atQIH3/8MebMmYO4uDgMGTIEdnZ2iI2NxbZt2/Dqq69i1qxZVTqHiq7XtzixWIzvvvsO/fv3R/PmzTFx4kR4e3sjPj4e+/fvh729PbZv314teSRwaK+pUQ1BLe1x79499RA91cPMzExwcnISQkJChDlz5mgM8VRRDXsr7VF86OPTyhraq/oInjp1SjAzM9MYrisIymGi7du3F7y8vISHDx8KgqAcdmprayvcunVL6Nu3r2BjYyO4u7sLERERglwu19g/KytLmDFjhuDl5SWYm5sLTZo0ET777DONoXpRUVHC4MGDBS8vL8HCwkLw8vISRo8eLVy/fl3jWLdu3RL69OkjWFpaCu7u7sL7778v7N27V+vQ3ubNm2u9FgUFBcKSJUuE5s2bC5aWlkK9evWEtm3bCpGRkeph1NrIZDJh9erVwpAhQwRfX1/B0tJSsLGxEVq3bi189tlnQn5+vkb6P/74QwgKChLMzMw0hjpevnxZ6NOnjyCVSgUXFxdh8uTJwrlz50oMh1Rd4+JUQzaflpaWJowdO1awt7cXHBwchLFjxwpnzpwpccz79+8Lzz//vODo6Cg4ODgIw4cPFx48eFBiqKvqHNqGWsvlciEyMlLw9PQUrK2thR49eggXL14UfH19dR7aq3qIxWLB0dFRaN26tfDWW28Jly5d0rpPRcusS5cuAgBh0qRJpebh6ff78OFDYeLEiYKLi4sglUqFfv36CVevXtX6nlavXi00bNhQkEgkGp+74kN7BUEQkpKS1Me1sLAQWrZsWWK4d1nD/IvnszJDe8saLi8IgrB161aha9eugq2trWBraysEBAQIr7/+unDt2jV1mtJ+n3x9fYVnn31Wa76fHr6t6/XVNkxfEAThzJkzwtChQwVnZ2fB0tJS8PX1FUaMGCFERUWVex1IdyJBqKEeSkQ1ZMKECdiyZYvWqlMiIjI+7DNCREREesVghIiIiPSKwQgRERHpFfuMEBERkV6xZoSIiIj0qsLBSHR0NAYNGgQvLy+IRKIylz0HlAsihYWFwdXVFfb29ujUqZN6um8iIiKiCk96lpOTg+DgYLz88ssYOnRouemjo6MRFhaGTz75BI6Ojvjhhx8waNAgHDt2TD1JU3kUCgUePHgAOzu7ap1Gm4iIiGqOIAjIysqCl5dXmQuPVqnPiEgkwrZt2zBkyJAK7de8eXOMHDlSY42Ksty/f79Ci4gRERGR4bh37x7q169f6uu1Ph28QqFAVlYWnJycSk2Tn5+vXhMEKFrPIjY2ttqWtKeKkclk2L9/P3r27FlieW/SD5aJYWF5GBaWh2HIysqCv79/uffuWg9GVIsOjRgxotQ0ixYtQmRkZIntMTExsLGxqcnsURlsbGxw7NgxfWeDnsIyMSwsD8PC8tC/3NxcAOWvOl2rzTQ///wzJk+ejD/++KPMBaSK14xkZmbCx8cHqampsLe3r2x2qQpkMhn27t2LsLAwfsswECwTw8LyMCwsD8OQmZkJFxcXZGRklHn/rrWakY0bN2LSpEn49ddfy13J0tLSEpaWliW2m5ub80OlZywDw8MyMSwsD8PC8tAvXa99rcwz8ssvv2DixIn45Zdf8Oyzz9bGKYmIiMhIVLhmJDs7Gzdv3lQ/j42NxdmzZ+Hk5IQGDRpgzpw5iI+Px/r16wEom2bGjx+P5cuXIyQkBImJiQAAa2trODg4VNPbUHaMLSgoqLbjkSaZTAYzMzPk5eVBLpfrOzt1irm5OSQSib6zQURUYyocjJw8eRI9e/ZUP585cyYAYPz48Vi7di0SEhJw9+5d9evffvstCgsL8frrr+P1119Xb1elrw4FBQWIjY2FQqGoluNRSYIgwMPDA/fu3eNcL3rg6OgIDw8PXnsiMkkVDkZ69OiBsvq8Fg8wDhw4UNFTVIggCEhISIBEIoGPj0+Zk6pQ5SkUCmRnZ0MqlfIa1yJBEJCbm4vk5GQAgKenp55zRERU/Wp9aG91KywsRG5uLry8vDjstwapmsGsrKwYjNQya2trAEBycjLc3NzYZENEJsfo7yqq/gsWFhZ6zglRzVEF2jKZTM85ISKqfkYfjKiwLZ1MGT/fRGTKjL6ZhoiIqLbJFQKOx6YjOSsPbnZW6ODvBImYXxoqy2RqRkyRSCTC77//XuPn8fPzw7JlywzmOGVZu3YtHB0da/Qc5Tlw4ABEIhEePXpkMHkiotqz62ICui75B6NXH8VbG89i9Oqj6LrkH+y6mKDvrBktBiNPyBUCYm6l4Y+z8Yi5lQa5otKz5OskJSUFU6dORYMGDWBpaQkPDw/069cPhw8fVqdJSEhA//79azQflVHazffEiRN49dVXaz9DejZy5Ehcv35d39kgolqw62ICpm44jYSMPI3tiRl5mLrhNAOSSmIzDZQfrsjtlzU+XJ4OVogYFITwFjUzlPKFF15AQUEB1q1bh4YNGyIpKQlRUVFIS0tTp/Hw8KiRc9cUV1dXfWdBL6ytrdUjXojIdMkVAiK3X4a2r6oCABGAyO2XERbkwSabCqrzNSP6iHIfPXqEgwcPYsmSJejZsyd8fX3RoUMHzJkzB88995w63dPNNHFxcRCJRNi8eTO6desGa2trtG/fHtevX8eJEyfQrl07SKVS9O/fHykpKepj9OjRA9OnT9c4/5AhQzBhwoRS87d06VK0bNkStra28PHxwWuvvYbs7GwAyiaKiRMnIiMjAyKRCCKRCPPnzwdQspnm7t27GDx4MKRSKezt7TFixAgkJSWpX58/fz5atWqFH3/8EX5+fnBwcMCoUaOQlZVV7jX8/fff0aRJE1hZWaFfv364d++e+rVbt25h8ODBcHd3h1QqRfv27bFv3z6N/b/++mv1/u7u7hg2bJj6NYVCgUWLFsHf3x/W1tYIDg7Gli1bSs1L8ZoiXd5XRc9BRPp3PDa9xL3iaQKAhIw8HI9Nr71MmQiTC0YEQUBuQaFOj6w8GSL+vFRqlAsA8/+8jKw8mU7H03UBZKlUCqlUit9//11jdWJdRERE4MMPP8Tp06dhZmaGF198Ee+++y6WL1+OgwcP4ubNm5g3b16FjlmcWCzGf//7X1y6dAnr1q3DP//8g9mzZwMAOnfujGXLlsHe3h4JCQlISEjArFmzShxDoVBg8ODBSE9Px7///ou9e/fi9u3bGDlypEa6W7du4ffff8dff/2Fv/76C//++y8WL15cZv5yc3OxcOFCrF+/HocPH8ajR48watQo9evZ2dkYMGAAoqKicObMGYSHh2PQoEHqmYFPnjyJN998EwsWLMC1a9ewa9cudO/eXb3/okWLsH79eqxatQqXLl3CjBkzMGbMGPz77786X8Py3ld1nIOIaldyVumBSGXSURGTa6Z5LJMjaN7uajmWACAxMw8t5+/RKf3lBf1gY1H+JTUzM8PatWsxefJkrFq1Cm3atEFoaChGjRqFZ555psx9Z82ahX79+gEA3nrrLYwePRpRUVHo0qULAOCVV16p8jT7T9ek+Pn54eOPP8aUKVOwaNEiWFhYwMHBASKRqMxmpKioKFy4cAGxsbHw8fEBAKxfvx7NmzfHiRMn0L59ewDKoGXt2rWws7MDAIwdOxZRUVFYuHBhqceWyWT46quvEBISAgBYt24dAgMDcfz4cXTo0AHBwcEIDg5Wp//oo4+wbds2/Pnnn5g2bRru3r0LW1tbDBw4EHZ2dvD19UXr1q0BAPn5+fjkk0+wb98+dOrUCQDQsGFDHDp0CN988w1CQ0N1uoZlva/qOgcR1S43O6tqTUdFTK5mxFi88MILePDgAf7880+Eh4fjwIEDaNOmTbmBxNPBiru7OwCgZcuWGttUU4dX1r59+9C7d294e3vDzs4OY8eORVpaGnJzc3U+xpUrV+Dj46MORAAgKCgIjo6OuHLlinqbn5+f+oYNKKc7Ly//ZmZm6mAGAAICAjSOm52djVmzZiEwMBCOjo6QSqW4cuWKumYkLCwMvr6+aNiwIcaOHYuffvpJ/d5u3ryJ3NxchIWFqWuwpFIp1q9fj1u3bun8/st6X9V1DiKqXR38neDpUHqgIYKyv2EHf6fay5SJMLmaEWtzCS4v6KdT2uOx6Zjww4ly062d2F6nD5e1ecWm6bayskJYWBjCwsIwd+5cTJo0CREREWX25zA3N1f/XzURVvFtTy8YKBaLSzQflTWLZ1xcHAYOHIipU6di4cKFcHJywqFDh/DKK6/UyOyfT+cdKJn/ypg1axb27t2Lzz//HI0bN4a1tTWGDRumXtXZzs4Op0+fxoEDB7Bnzx7MmzcP8+fPx4kTJ9R9Y3bs2AFvb2+N41paWlbL+6qucxBR7ZKIRXg3PAAzNp0tNU3EoCB2Xq0EkwtGRCKRTk0lANCtiSs8HayQmJGntd+ICICHgxW6NXGtlQ9XUFBQtc8r4urqioSEok64crkcFy9e1Fh5+WmnTp2CQqHAF198oV6DZvPmzRppLCws1NPwlyYwMBD37t3DvXv31LUjly9fxqNHjxAUFFSVt4TCwkKcPHkSHTp0AABcu3YNjx49QmBgIADg8OHDmDBhAp5//nkAypt/XFycxjHMzMzQp08f9OnTBxEREXB0dMQ///yDsLAwWFpa4u7duzXWXBIUFFTj5yCimhGXmgNAGZgUnwJiVr+mNTYC09SZXDBSERKxCBGDgjB1w2mIAI2ARBV61ESUm5aWhuHDh+Pll1/GM888Azs7O5w8eRKffvopBg8eXK3n6tWrF2bOnIkdO3agUaNGWLp0qXqyLm0aN24MmUyGFStWYNCgQTh8+DBWrVqlkcbPzw/Z2dmIiopCcHAwbGxsSixS2KdPH7Rs2RIvvfQSli1bhsLCQrz22msIDQ1Fu3btqvSezM3N8cYbb+C///0vzMzMMG3aNHTs2FEdnDRp0gS//fYbBg0aBJFIhLlz52rUtvz111+4ffs2unfvjnr16mHnzp1QKBRo1qwZ7OzsMGvWLMyYMQMKhQJdu3ZFRkYGDh8+DHt7e4wfP75KeQdQK+cgouqXnJWH1QdvAwCWjWgFFztLJGfl4ffT8dh/PQXn7mXoOYfGq873GQlv4YmVY9rAo1g7oIeDFVaOaVMjUa5UKkVISAi+/PJLdO/eHS1atMDcuXMxefJkfPXVV9V6rpdffhnjx4/HuHHjEBoaioYNG5ZaKwIAwcHBWLp0KZYsWYIWLVrgp59+wqJFizTSdO7cGVOmTMHIkSPh6uqKTz/9tMRxRCIR/vjjD9SrVw/du3dHnz590LBhQ2zatKnK78nGxgazZ8/Giy++iC5dukAqlWocd+nSpahXrx46d+6MQYMGoV+/fmjTpo36dUdHR/z222/o1asXAgMDsWrVKvzyyy9o3rw5AGWH17lz52LRokUIDAxEeHg4duzYAX9//yrnXaU2zkFE1eu/UTeQWyBHsI8jBgZ7olMjZwxu5Y0PBiprZfdeScLtlGw959I4iQRdx6PqUWZmJhwcHJCRkQF7e3uN1/Ly8hAbGwt/f39YWVW+BzPXGSibQqFAZmYm7O3t1c03VHu0fc5lMhl27tyJAQMGlOijQrWP5WFYqrs8bqdkI+zLaMgVAja+2hEdGzprvD5p3Qnsu5KM0R0aYNHQlqUcpe4p6/79NN5VnpCIReoot1MjZwYiRESk9tnua5ArBPQKcCsRiADAq90bAQC2nr6PlKyKzR9FDEaIiIjKdPruQ/x9MRFiETA7PEBrmvZ+9dDKxxEFhQqsj4mr3QyaAAYjREREpRAEAYt3XgUAvNCmPpp52GlNJxKJ8J/uDQEAPx69g9yCwlrLoylgMEJERFSKqCvJOB6XDkszMWb2bVpm2r7NPeDnbINHuTL8evJ+LeXQNDAYISIi0qJQrsCSXcpakYld/OHpUPbq3BKxCK90U9aOfHfoNgrlVZvAsS5hMEJERKTF1tP3cSM5G4425pjao5FO+wxrUx9Otha4l/4Yuy4l1nAOTQeDESIiomIeF8ixdO91AMC0no3hYK3b8GBrCwnGdvQFAHwbfVvn1dzrOgYjRERExaw5HIukzHx4O1pjbCffCu07rpMvLM3EOH8/A0dvp9dQDk0LgxEiIqKnpOcUYNUB5Qras/o1haVZxRZBdZZaYni7+gCAb6O5ErcuGIyYEJFIpLHQ3tWrV9GxY0dYWVmhVatWesuXvsTFxUEkEuHs2bMAgAMHDkAkEpW5Ng8R0Vf/3ERWfiGCPO0xONi7/B20mNS1IUQiYP+1FFxPyqrmHJoeBiMqCjkQexC4sEX5U1H2qrTVJSYmBhKJBM8++6zO+8yfP19rcJGQkID+/furn0dERMDW1hbXrl1DVFRUdWQXDRs2xLJly8pN5+fnB5FIBJFIBIlEAi8vL7zyyit4+PBhteSjMjp37oyEhAQ4ODjoLQ9EZNjupefix6NxAID3+gdAXMnZuP1cbBHe3AMAsDr6dnVlz2QxGAGAy38Cy1oA6wYCW19R/lzWQrm9hn3//fd44403EB0djQcPHpSZVhAEFBaWPpGOh4cHLC0t1c9v3bqFrl27wtfXF87OJacvrmkLFixAQkIC7t69i59++gnR0dF48803az0fKhYWFvDw8IBIxKn+iUi7z/dcg0wuoGtjF3Rv6lqlY736ZBK038/GIykzrzqyZ7IYjFz+E9g8DsgsFghkJii312BAkp2djU2bNmHq1Kl49tlnsXbtWo3XVc0Kf//9N9q2bQtLS0ts2LABkZGROHfunLrmQbXf0800IpEIp06dwoIFCyASiTB//nwAwOzZs9G0aVPY2NigYcOGmDt3LmQymcZ5t2/fjvbt28PKygouLi54/vnnAQADBw7EnTt3MGPGDPW5y2JnZwcPDw94e3ujZ8+eGD9+PE6fPq1+PS0tDaNHj4a3tzdsbGzQsmVL/PLLLxrH2LJlC1q2bAlra2s4OzujT58+yMnJUb/+3XffITAwEFZWVggICMDXX39dan6KN9OsXbsWjo6O2L17NwIDAyGVShEeHo6EhASN/SpyDiIyXhfjM/DHWeW94L3+2qd9r4jWDeqhg58TZHIBPxyOq/LxTJnpBSOCABTk6PbIywT+fheAtqFXT7btmq1Mp8vxKjiEa/PmzQgICECzZs0wZswYrFmzRuswsPfeew+LFy/GlStXEBYWhrfffhvNmzdHQkICEhISMHLkyBL7JCQkoHnz5nj77beRkJCAWbNmAVAGCGvXrsXly5exfPlyrF69Gl9++aV6vx07duD555/HgAEDcObMGURFRaFDhw4AgB9//BH169dX13gUv2mXJT4+Htu3b0dISIh6W15eHtq2bYsdO3bg4sWLePXVVzF27FgcP35c/R5Gjx6Nl19+GVeuXMGBAwcwdOhQ9TX66aefMG/ePCxcuBBXrlzBJ598grlz52LdunU65ys3Nxeff/45fvzxR0RHR+Pu3bvqa1Vd5yAi47D4b+UEZ4NbeaGFd/U0505+Ujvy07E7yM7nFPGlMdN3BqqdLBf4xKuaDiYoa0wW++iW/P0HgIWtzkf//vvvMWbMGABAeHg4MjIy8O+//6JHjx4a6RYsWICwsDD1c6lUCjMzM3h4eJR6bA8PD5iZmUEqlWqk+/DDD9X/9/Pzw6xZs7Bx40a8++67AICFCxdi1KhRiIyMVKcLDg6GQqFAvXr1IJFI1DUe5Zk9ezY+/PBDyOVy5OXlISQkBEuXLlW/7u3trXHjf+ONN7B7925s3rwZHTp0QEJCAgoLCzF06FD4+iqH1rVsWbQ0d0REBL744gsMHToUAODv74/Lly/jm2++wfjx48vNH6BcZnzVqlVo1Eg5odG0adOwYMGCaj0HERm+6OspOHQzFRYSMWb1bVZtx+0d4IZGrra4lZKDjcfvYtKTGVpJk+nVjBiJa9eu4fjx4xg9ejQAwMzMDCNHjsT3339fIm27du2q7bybNm1Cly5d4OHhAalUig8//BB3795Vv3727Fn07t27Ws71zjvv4OzZszh//ry6A+2zzz4LuVzZOVgul+Ojjz5Cy5Yt4eTkBKlUit27d6vzExwcjN69e6Nly5YYPnw4Vq9ere4Am5OTg1u3buGVV16BVCpVPz7++GPcuqX7UDobGxt1IAIAnp6eSE5OrtZzEJFhUygEda3ImI6+8HGyqbZji8UiTH4SgKw5FAsZp4jXyvRqRsxtlDUUurhzBPhpWPnpXtoC+HbW7dw6+v7771FYWAgvr6JaHEEQYGlpia+++kpjxIetre61LWWJiYnBSy+9hMjISPTr1w8ODg7YuHEjvvjiC3Uaa+uy116oCBcXFzRu3BgA0KRJEyxbtgydOnXC/v370adPH3z22WdYvnw5li1bhpYtW8LW1hbTp09HQUEBAEAikWDv3r04cuQI9uzZgxUrVuCDDz7AsWPHYGOjvNarV6/WaPpR7acrc3PNWRVFIpG6GSg7O7tazkFEhu2Pc/G4nJAJO0szTOvVuNqPP6S1Nz7fcx0PMvLw1/kHeL51/Wo/h7EzvZoRkUjZVKLLo1EvwN4LQGkdMUWAvbcynS7H03GURmFhIdavX48vvvgCZ8+eVT/OnTsHLy+vEp04i7OwsFDXLlTEkSNH4Ovriw8++ADt2rVDkyZNcOfOHY00zzzzTJnDgCt7bqDoBv748WMAwOHDhzF48GCMGTMGwcHBaNiwIa5fv66xj0gkQpcuXRAZGYkzZ87AwsIC27Ztg7u7O7y8vHD79m00btxY4+Hv71+p/BVXG+cgIv3Kk8nx+W7l350pPRrBydai2s9hZS7BxC5+AIBv/uUU8dqYXs1IRYglQPgS5agZiKDZkfVJYBG+WJmuGv311194+PAhXnnllRJzXrzwwgv4/vvvMWXKlFL39/PzQ2xsLM6ePYv69evDzs5OY0hvaZo0aYK7d+9i48aNaN++PXbs2IFt27ZppImIiEDv3r3RqFEjjBo1CoWFhdi5cyfeeecdAICvry+io6MxatQoWFpawsXFpdTzZWVlITExEYIg4N69e3j33Xfh6uqKzp07q/OzZcsWHDlyBPXq1cPSpUuRlJSEoKAgAMCxY8cQFRWFvn37ws3NDceOHUNKSgoCAwMBAJGRkXjzzTfh4OCA8PBw5Ofn4+TJk3j48CFmzpxZ7vXQRW2cg4j0Z8PRO4h/9Bge9lZ4uUvNfckYE+KL/+2/iauJWTh0MxXdmlRt2LCpMb2akYoKeg4YsR6w99Tcbu+l3B70XLWf8vvvv0efPn20Tr71wgsv4OTJkzh//nyp+7/wwgsIDw9Hz5494erqWm5Nispzzz2HGTNmYNq0aWjVqhWOHDmCuXPnaqTp0aMHfv31V/z5559o1aoVevXqpR7dAihvznFxcWjUqBFcXcv+ZZo3bx48PT3h5eWFgQMHwtbWFnv27FHPefLhhx+iTZs26NevH3r06AEPDw8MGTJEvb+9vT2io6MxYMAANG3aFB9++CG++OIL9cRukyZNwnfffYcffvgBLVu2RGhoKNauXVuttRa1cQ4i0o+MxzJ8tf8mAGBGWBNYW9Rc86uDjTlGtlcOhviWk6CVIBKMoL4oMzMTDg4OyMjIgL29vcZreXl5iI2Nhb+/P6ysrCp/EoVc2YckOwmQuiv7iFRzjYgxUygUyMzMhL29PcRixrC1TdvnXCaTYefOnRgwYECJvi9U+1gehkWX8lj891Ws+vcWmrhJ8fdb3WAmqdm/bffSc9Hj8wOQKwTseLMrmnuZ/mzQZd2/n8a7iopYAvh3A1oOU/5kIEJEZLIePHqMHw7HAgBmhwfUeCACAD5ONhjQUlkLzyniNTEYISKiOufLvdeRX6hABz8n9A50q7Xz/ufJJGjbzycg/tHjWjuvoWMwQkREdcq1xCxsPX0fAPDegIBaXa+qhbcDOjdyhlwhYM2h2Fo7r6FjMEJE2ulpJWuimrZk11UoBKB/Cw+0aVCv1s+vWkBv4/G7yHgsKyd13VC3h/YSkXaX/1Suy/T0ApL2Xsqh8DUwwoyothy9nYZ/riZDIhbhnX7VN+17RYQ2dUWAhx2uJmbhp2N38FqP6p9ozdiYTM2IEQwKIqo0haIWp5DW40rWRDVJEAQsejLt++gOPmjoKtVLPkSioini1x6OQ34hax2NvmbE3NwcIpEIKSkpcHV1rdW2v7pEoVCgoKAAeXl5HNpbiwRBQEFBAVJSUiAWi2Fh8WR2SIUcojuH4J0eA9Ede6Bh9+oZAaaQK2tESl3JWgTseg8IeLb6zsch9VRLdl5IxLl7j2BjIcFbvZvqNS+Dgr3w2e5rSMzMwx9nH2BEOx0XZDVRRh+MSCQS1K9fH/fv30dcXJy+s2OyBEHA48ePYW1tzYBPD2xsbNCgQQNlIPikCcUs8wHaAcCdlZVrQnn8EEi7DaQ/9XhwtmSNiAYByIwHtrwM+HUFnBoqHw4+gKSCf07YFES1SCZX4LPdylqRyd0awtWu/Fmra5KFmRgvd/XDJzuvYnX0bQxrUx9icd3922r0wQgASKVSNGnSBDIZOwLVFJlMhujoaHTv3p0TOtUyiUQCMzMzZRCoakIpXnOhakJ5etZgQQBy0zSDjacfjx9WPlOXf1c+VMRmgGMDoJ5/UYDi9OT/jr6AebEJCSvyPsi06Kk27JfjdxGXlgsXqQUmP+lAqm+jOjTAf6Nu4kZyNg5cT0avAHd9Z0lvTCIYAZR/sLmSas2RSCQoLCyElZUVgxF9KbcJBcAfrwEXfwMexgLpsUB+RtnHtPPUDBwK84F/l5Sfl8AhgDy/6Dzy/KIg51bxhRafLDjp5K981PMDjnxVxvuo5qYgMhx6qg3Lzi/E8n03AABv9W4CqaVh3PrsrczxYkgDfBt9G9/8e5vBCBEZgTtHymlCAZCfBVzWXPwQ9vWLgg2Nh79ytemnKeTAmR+VNRRagwWR8uYxfE1RoKBQAFkJRcHIw9gn/38SqBRkAZn3lY+4gzq80SdNQXeOKGdDptpTk7UWeqwN+zb6NtJyCuDvYotRHRrUyDkqa2IXP6w5FItjsek4d+8Rgn0c9Z0lvWAwQmQsMu7rlq7lcKD5UGXAUc8XMLfW/RxPrWQtQATRUzcO5XOUXMlaLAYcvJWP4sGDIAA5qZoByu0DwL2j5eclO0n3fFPV1WStRW13jH5KSlY+vjuonHr9nX7NYF4L075XhKeDNZ5r5YXfTsfj2+jb+N9LbfSdJb1gMEJk6GR5ytqKA4t1S99mfNVqFIKew5lOy+EVEwl3pKk3J8EJCZ0i0LoiNyaRCJC6Kh8+HZTb/LoC6waWv2/M14DEHGjaHzCzqOCbMEGGVmtRmA/kZQL5mcoaOY1Hse1pt3TrGF1NtWFyhYBjsek4lSrCH39cQm6BHK18HNG/hUeVj10TXu3eEL+djsffFxNwNy0XDZxt9J2lWsdghMhQyR4Dp9YCh5YB2YnKbSIxIJQ258iTJhTfzlU67a6LCZi63wUiLEcH8VW44RGS4YgTigAo9oux0jsB4S08K38C387KfJbaFPTEg1PKG6GNCxA8Cmg9FnALqPx5jVlN1lrI8oCds1BmX6StrwD/NgUKsosCDHlB1c6rTTXUhu26mIDI7ZeRkJEHQAIgFQDQK8DNYEcCBnjYI7SpK/69noLvD91G5OAW+s5SrWMwQmRoCnKAk2uAw/8FcpKV2+y9ga4zAGsn5Y0BgObN48kf2eJNKBUkVwiI3H4ZAgABYhxVBGm8LgIQuf0ywoI8IKnsMMSnmoKUR9TyPvp/CmQ9AM7+ogzEYr5SPuq3VwYlLYYClnaVO7+xeVJrIUDA01dcyEyAqLy+Fgq58gafEa/ss5MRr6yByIwv+n9WIsoMCgFl4JF0UftrFnbKsnj6YWX/5P9PfuakKD/T5ZFWrQPnrosJmLrhtNZ38+Xe62jqLq1aIF2D/tO9If69noLNJ+9jep+mqGdbt2oDGYwQGYr8LODEd8CRFcohuQDg0ADoNhNo9SJg9mReBIk5hF2zIXrqW7Jg7wVR+OIKf0sWBAHJWfm4mZyNm8nZOHQj9ck3ylLSA0jIyMPx2HR0auRc0XdYJOg55U1U67f9p95Hzw+Bm3uB0z8C13cB908oH7vmAM2fB9qMBXxClM1B+lTDk9AVD0QAQARB2dtix0xlJ+LsRM2AIyNe2bFYqKbZPbtMV/bpeDrosJDq9j4VcmX5ldcxugq1ek8H0qWpciBdgzo1ckYLb3tcjM/Ej0fv4M3eTfSdpVrFYIRI3/IygOPfAjH/K5r7o54/0H0W8MxIZb+Jp+xStMdHecvhU3BO3YRyLy8YcxUtEV7KKRQKAfcfPsbNlCzcSFIGHjdTlD+z8gornOXvDt6GRCxCmwaOMKtsh8Cg55Q3t7L6QUjMgGb9lY+sJODcL8r+M2k3gbMblA+XpkDrMUDwaECqZSn4mp7XoromoSue5+wk4OrfQOaDEoGIighQ1jpsGV/6sUQS5RBuB29lDZvqp+r/GfHA5rHl56lxn6J+PxWlS21YFWv1Ym7VUiBdQ1RTxL+18SzWHYnDq90bwsq87gxtFwlGsKhLZmYmHBwckJGRAXt7e31np06SyWTYuXMnBgwYwHlGqsvjh8DRVcCxlcqABACcGgHd31GOiNEyo2lp1dCqm9V/R7dGgIcdbjyp6VA9bqVkI79Qe18TsQjwdbZFYzcprM0l+PNcOcOHn+JoY46ezdzQO9AN3Zu6wt6qFj4bggDcPaoMSi5tA2S5yu1iM6BpONBmHNCot/L61fS8FqV1/FSViLYmFFmessYi84HykfWg6P+ZD5SvZSVWqEZDcPSHyLMl4FC/ZMAhdS97dlyFHFjWovxai+kXqh7EXf5TS62ed4Vr9dJzCnAlIRNXEjJxOSETVxOycC0xC3IdbmfLR7XC4Fbelcp+TSuUKxD62QHEP3qMhc+3wEshvvrOUpXpev9mMEI6YTBSjXLTgaNfA8e+UY44AACXZsogpMXQUv/gyxUCui75p8xvf2WxMBOjoYsy6FA9mrjZwc/FBpZmEo1zJGbklXZbgoONOUKbuODA9VSN5c/NxCKENHRCrwB39Al0g6+zrZYjlHxPx2PTkZyVBzc7K3Twd6pYFXpeJnDpN2UzTvzJou12nsr+JVe2o0KBQkWob+JlBG+W9srmpKzEosAjN6309BrZFKPAwhEW+enlJr0U9jOad3lWx4xroQ6qAK21FtU0B8iuiwn46M8L8Ml+qlZPGoy5z7XU2pdDrhAQm5qNywlZ6uDjSkImkjLzK52HXyZ3NMiaEZU1h2Kx4K/L8Hexxb6ZoQbZpFQRNRaMREdH47PPPsOpU6eQkJCAbdu2YciQIWXuc+DAAcycOROXLl2Cj48PPvzwQ0yYMEHnczIY0T8GIxVQWrNATqqyP8iJ75SjEgDALQgIfRcIHKycr6MMMbfSMHp1+fNzWJmL0czDHo1dVQGH8qePk41Of9hUtS+A1tsSVo5pg/AWniiUK3DqzkP8czUZ+64k4VZKjsZxGrtJ0TvQDX0C3dGmQb0S59Yc9aDk6WCFiEFBletkmHQZOLMBOL9Rtxu+tRPQb6Gyc2ZhPlCYp/wpe6z5vDBPy//zgNyHyj4alWFmpQyY7L2VtQ72T/6v3uYJ2Lrhj/MP0P73UHggHdqKTiEAiXDGiSEHMLh1FSfz0lqL5K3Zh6cKyqvV+3xEMOo7Wj8JOLJwJTET1xKzSq3Ra+Bkg0BPOwR62iPQ0x7N3O0wavVRJJURSHs4WOHQ7F4GfYPPyS9E58X/IOOxDKvGtEW4gQ5H1lWNBSN///03Dh8+jLZt22Lo0KHlBiOxsbFo0aIFpkyZgkmTJiEqKgrTp0/Hjh070K9fv2p9M1RzGIzoSNsfdKkH4NUaiP23qEnBoyUQOhto9my5QYjKH2fj8dbGs+WmWz6yFQa3rlo1dGUChbjUHOy7koSoK8k4EZeOQkXRn5Z66uYcd3Rv6oLDN1PLvDGpAp5KKSwADn4B/KvjvCw1LXCQsr+FndeTwMMLsK6nU6fbmFupWPv9Cqw0XwYAGgGJ6vJOlU2Hb9eRmBnWrOp9DGqof01VavVsLCRo5vEk6Hjys5mHHey0NAnqGkgbus92X8X/9t9CmwaO+O21LvrOTpXUSjONSCQqNxiZPXs2duzYgYsXi4aFjRo1Co8ePcKuXbt0Og+DEf1jMKKDUvsPPMWrtTIIaRpeoREgeTI53tp4BrsvlT8PQ3VVQ1elCSXjsQz/Xk9B1JUkHLiWUqw5BxCLxCiQa//GWy3fYC9seWoIdBncmivXyjGzVNZWlPXT3Frzecp1YPec8s8x/q9KTeSVW1CI93+7gN/PPkA/8XFEmK+Hl6ioyeaB4IxI2VjsVig7lTramGN0hwYY29EXXo4VmHW3Fuhaq+dia4FWDRzVtR2BnvbwdbKp0Gq21V7jpgfJWXnoung/CuQKRD4XBEcbi8o1YxoAXe/fNT6aJiYmBn369NHY1q9fP0yfPr2mT01Ue8qc7voJG2fglX1ldybU4sL9DEzfdKZEM0hxqpt4B3+nCh2/NBKxqNJBjYO1OZ4L9sJzwV7q5pyoJ805t1NyAJQ2cVs1jXrQdb6K/ksqP+Nnw55AzIoaGa56KyUbUzecwvWkbIhFwG5FB+zLb4f2xSehgxhD23jj2O10xD96jJUHbuHb6NsIb+6BCV380M63nl4n+rqXnoudFxLw07E7OqWfOyioyp1Lw1t4IizIAzE3k7Hn4DH07RaCTo3djOom7mZnhfZ+9XD4Vhoi/rys3m5sQVVF1HgwkpiYCHd3zT8M7u7uyMzMxOPHj2FtXTKCz8/PR35+UQelzExlJz+ZTAaZTFYiPdU81XXn9ddOdOcQzMpbxC43DYWxByH4dtXpmIVyBb49GIcV+2+hUCHAVWqB4W29sfLfWADaq6E/6N8MCnkhFNU0tUR1aeNjjzY+9ngnrDHWHI7Dol3Xy90n4VEOZLJK1oR6tYeZnReQlaCxvo6K8CRQKPRqD1ThMy0K+wSSrRMBLev4AIA8bCEEuQIopRZIm50XEvH+75eQUyCHq9QCy0Y+g4c5Mny88yqOZhZNQufpYIkP+gegX3N3yBUC/rmagnVH7+BY7EPsuJCAHRcS0NzLDuM6NsCzLTxgWUvDRO+k52LXxSTsupSEiw8yK7Svs41Ztf2NaVPfDmkuAtrUtzPI34my7L6UhMO3SvZ7SszIw9QNp7FiVDD6NTeOFX51LU+DnGdk0aJFiIyMLLF9z549sLGpe3P2G5K9e/fqOwsGyTs9RjnHRDnOHtyN+Evl/4FOzQN+vCFBXLbyphbspMDIhrmwLbiBiU1F+C1OjEcFRd/0HCwEDPVTQH7nFHbq9iVUb7IyRFBO012225fOYuf9M5U+j6fLC2iftUK1DJua8OTfE85DkbBrd6WPrySGp/80tLz/E6xlRU0oj83r4WL9l5BwWwzc3qnTkQoVwB93xIhOVPYhamyvwPgmuUi9rGzemB0E3MoUIVMG2JsDjexzSpT3ix5AqB1wMFGMkykiXHqQhdm/XcJHf15EZ3cBXT0UcKiBiT2THwPn0kU4mybG/Zyiqy2CgMb2AoKdBOyJFyNTptxakgBHCyDl8lHsvFK9eTO2v1kKAYg8rfr90LxWqs/uh7+dhSxOrrVTs6HJzc3VKV2NByMeHh5IStJs505KSoK9vb3WWhEAmDNnDmbOnKl+npmZCR8fH/Tt25d9RvREJpNh7969CAsLY58RLcT/ngd0CAJadeuH4DJqRgRBwOZT8fji72vILZBDammGiIEBGBzsqa5uHwDgXYWAo7dS8E/MKfTq1BYdG7kaTTW0XCFgyxfRSMrML7VRy8pMjNEDQ+FmZ1mFMw2A/GpbSPa8rxxSq2LvDXnYQrQOGIjWVTj60+eB4kPkxR7CxZh9aNGpD8z9u6K1WKLz8RMy8vDmpnM4m6icb+Y/3fwxvXejSk8oNxnAw9wCbD4Zjw3H7iIxMx974kX4J0GC8ObuGNepAVprWaperhBw8s5DJGflw83OEu18S46CUrmdkoNdl5Lw96UkXE3MUm+XiEUI8a+H8Obu6BvoBmepsgx7XErCGxvPAdBWqyfCx0Or99u+sf7NOhabjkdHT5aRQoRHBYBrUEeEVFOTbE1StWyUp8aDkU6dOmHnTs1vBnv37kWnTp1K3cfS0hKWliX/CJmbmxvVh8oUsQyKEQTlyI1Dn5eTUNksYFbGNOEpWfl4b+t5RF1VrkfTsaETvhjRCt5aOiOaA+jSxA0ZNwR0aeJmVGViDmD+c80xdcPpEnNxquQVKjD46xgsGvoMwoKqcINq+TzQ/DmNESIi384wq+Zl6gFzoFEo4q/lILhRaIXK49/rKZi+8Qwe5spgb2WGpSNaoU9V3vMTbg7mmNa7Kab0aIw9l5Ow9nAcjsel468LifjrQiKCfRwxsbMfBrT0hIWZWKeOnzeTs7DzQiJ2XkgoEYB0buSMAS090TfIXR2APG1gq/owM5OUOIdHDfeDMLa/WWm5us2InJZbaBTvS9c8VjgYyc7Oxs2bN9XPY2NjcfbsWTg5OaFBgwaYM2cO4uPjsX79egDAlClT8NVXX+Hdd9/Fyy+/jH/++QebN2/Gjh07KnpqIsNSWAD8NUM5JTmgHCFzXVXtX7HprndfSsSc3y4gPacAFhIx3g1vhpe7+FdoFIExCW/hiZVj2mi9+U3s7Ictp+/jelI2Jq8/iWFt62PeoKDKz+4qllTLsvTVTa4Q8N+oG/jvPzcgCEALb3usfKktfJyqtynaTCLGgJaeGNDSExfjM7D2SBz+PPsA5+49wvRNZ7Fw5xWE+Dvhr/MJJfZNzMjDlA2nMfAZT1xPysL1pOyi44pF6NzYBc+29EDfIA+dFnZTdS6t0kR3Js7Nzqpa0xmLCgcjJ0+eRM+ePdXPVc0p48ePx9q1a5GQkIC7d++qX/f398eOHTswY8YMLF++HPXr18d3332n8xwjRAbp8UNg01gg7iAgEitXme0wuYzpx7VPHJWdX4gF2y9h80nl5FkBHnZYNqoVAjxMvzmyrBvTuM5++HLvdXx78Da2nLqPmFtp+GzYM+jc2EXf2a4W6TkFeGvjGRy8oVze/sWQBpg3MKjG1yJp4e2Az4cH473+Afjl2F38ePQOkrPytQYiQFFIrXrdXCJCl8Yu6hoQR5uKd0CpyiituqCDvxM8HazKnAW5OkfNGYoKByM9evRAWVOTrF27Vus+Z85UviMakUFJjwV+HgGkXleuWjp8LdAkTPmaLou/PXEiLh0zN5/FvfTHEImA/3RvhBlhTdRTs9cFpd2YrMwlmDMgEL0D3THr13O4m56LF787hgmd/TA7PADWFsZ7jU7ffYjXfzqNhIw8WJmL8cnzLTG0Tf1azYOL1BJv9G6C/4Q2wop/bmDFPzfL3WdKaCNMDW0EBxvDbxowZhKxCBGDgrQ2Y6rqjyIGBZlcbZJBjqYhMlj3jgO/jAZyU5VTZb+4STmb6tPKaRYoKFTgy33XserfWxAEoH49aywd0crkvulUhw7+Tvj7rW5YuPMKfj52F2uPxCH6egq+GBGM1g3q6Tt7FSIIAtYeicPCHVdQqBDQ0MUWK8e0RTMPO73lycJMjMZuUp3SBnraMRCpJaU1Y9Z0/xp9YjBCpKuLW4FtUwF5PuAZDIzepFxDpAKuJWZh+qazuJKg7GE+/El/CG1TW5OSraUZPnm+JfoGuWP21vO4nZqDF1YewWs9GuPN3k1gYVa5ESe1KTu/ELO3nseOJ80dz7b0xOIXWhpEudfVPgqGTtWMuWD7JayLuYOO/k74aXJHk6sRUTH832IifRMEIPpzYMvLykCk2QBg4t+lBiJyhYCYW2n442w8Ym6lQa4QoFAI+O7gbQz66hCuJGTCydYCq8a0xWfDgw3ihmQMejRzw57poRjcygsKAfhq/00M+d9hXE2s2MRate1aYhae++oQdpxPgNmTKvivXmxtMOWu6qNQ2i1OBGXHYtbc1T6JWIR2fsrrrnjy3FSxZoSoLMVHzHR8Hej7UamjYrQNj3S1s0Q9G3P1SIReAW5Y/EJLftOsBAcbcywf1Rr9mnvgg20XcDkhE8+tOIwZYU3xaveGBvfHetuZ+3j/t4t4LJPD08EKX73YBm19Dat5qa72UTAWLk+GSadm55eT0rixZoSoNI8fAhuGKgMRkRgY8DkQ/kmZgcjUDadLrEyakpWP60nZsJAoOyt+P74dA5EqGtDSE7tndEfvADcUyBVYsusqRnwTg7jUstfvqSlyhYBjsek4lSrCsdh05OQX4v1tFzBj0zk8lsnRrYkL/nqjq8EFIiqqPgoeDpqfSw8HK6NZ6dZUuUiVI5bSsgv0nJOaxZoRIm3KGjGjhVwhIHL75bKWyYOjjTlGtvfR68JlpsTNzgrfjW+HX0/ex4K/LuPUnYfov/wg3h8QgDEdfdXXuSqrD+tCszZMgvU3TsJcLIJMIUAkAt7s1QRv9m5i8DULnAPEMKlqRjIey1BQqDCKPlKVwWCEqDhdRswUczw2vUSNSHHJWflVW4mWShCJRBjR3gedGjnjnS3ncPR2Oub+cQl7Lifh02HP4Ny9RzW6nLyqNqx4ECpTKLdM69kYM8KaVvk8tYVzgBgeB2tzmIlFKFQISMvJh6eD9mVUjJ1phlhElXVxK7B2oDIQ8QwGJkWVG4gAQHJW2YFIRdNRxfg42eDnSR0xb2AQLM3EOHgjFT0/P4ApWprNVCuf7rqofaKv8hQUKpCclYfLDzLx/raLZdaGbTl1H3JFWSmIyiYWi+D8pKkmNct0m2pYM0IEFK0x889HyufNBgAvfAdY2Oq0u7MOU2EDHB5Zk8RiEV7u6o/uTV0xc9MZnI/XPspGtYpv5PbL6NbEFVl5hXiYW4CHOQV4mCtDem4BHuUUKH/mypCeU4BHuU+e58iQla/b2iGAcgE81oZRVblILZGUmW/SnVgZjBBVcMRMcRmPZfgm+naZaUx1CmdD1NhNinfDAzDm++OlphGgDBSaR+wuNU1ZRCLAxlyCnAJ5uWlZG0ZVpVp4MIXBCJGJUMg1p2p3CwR+nVByjRkdxabm4JV1J3A7JQcWEjEK5AoOjzQAaTm6V2dLxCLUszGHo40FnGws4GhjDidbC+Vz26Lt9WzNUc/GAvVsLGBvbY7jsekYvfpoucdnbRhVVV0YUcNghOoObYvYic0ARaFOI2aKO3QjFa/9dAqZeYXwcrDCt+Pa4f7D3Do1hbOh0jUA+G58O/Rq5lap1ZHr6oJmVPtc68BcIwxGqG64/CeweRxQ/LaheNL+32OOzoGIIAhYH3MHC/66DLlCQJsGjlg1ti3c7KzQwtuBwyMNgK6BQs9KBiIAJwuj2lMXJj7jaBoyfQq5skak1HEPIuDo18p05ZDJFfjg94uI+PMS5AoBQ9t445dXO2p8E1cNjxzcyhudGjnzZqQHqkABQIlpzqszUOBkYVQbXOyejKYx4WCENSNk+u4c0WyaKUEAMuOV6cpYbfdhTgGm/nQKR2+nQyQC5vQPwORuDTmJmYGqrZVPVZOFxdxMxp6Dx9C3Wwg6NXZjEErVRl0zwqG9REbs/knd0mUnlfrS9aQsTFp3EnfTcyG1NMPyUa3QO9C9mjJINaW2ZhWViEUI8XdC2hUBIWyWo2rmbKsMRtJyWDNCZHziTytX2722Q7f0Uu3BRdSVJLy18Syy8wvRwMkG341vh6budtWYUapJnFWUjJ2qmSY9pwByhWCSwS6DETI9d48B0Z8CN/cVbTOzAgpLm+9BBNh7Ab6dNbYKgoBvo29j8a6rEAQgxN8JK8e0hZOOE5wREVUHJxsLiESAQlAGJK52lvrOUrVjMEKmQRCAuEPKICQ2WrlNJAFaDge6vQ2kXH0ymgbQOu4hfLHGJGd5Mjne33YBv52OBwCM7tAAkc81N9lFqojIcJlJxHCysUBaTgFSs/MZjBAZHEEAbkUpm2Puxii3ic2AVi8CXWcATg2V21ybAiPWl5xnxN5LGYgEPafelJyVhyk/nsLpu48gEYswb2AQxnXyZUdVItIbF6mlOhgxRQxGyDgJAnB9FxD9GRB/SrlNYgG0GQd0mQ44+pTcJ+g5IOBZzRlYfTtr1IhcjM/Aq+tP4kFGHuytzPD1S23RtYlL7bwnIqJSOEstgCTTnYWVwQgZF4UCuLpdGYQkXlBuM7MG2k0EOr8J2JczXFMsKXX47t8XEjBz8zk8lsnR0NUW341rh4au0mp+A0REFWfqE58xGCHDUHzNmGI1FlDIgYu/AQc/V/b/AJRTuLefBHSaBkhdK31qQRDw36ib+HLfdQBA96auWDG6NRyszavyjoiIqo2LiS+Wx2CE9E/bmjH2XkD4EqBZf+D8ZuDgF0D6LeVrlg5AyH+AjlMBm4qt+yFXCBpzTrT0dsDs385jx/kEAMDLXfzx/oAAmEnYUZWIDId6FlYTnfiMwQiVS64QcCw2HadSRXCOTa/e2SVLWzMmMwHYPBawcQFyU5XbrOsBnV4H2k8GrB0rfKpdFxNKzMZpJhahUCHAXCLCR4NbYFSHBpV/L0RENYTNNCaq+DdkLmamneYNXIL1N07Cs7qm0y5zzZgn23JTlQFJlzeBdq8AlpXrw7HrYgKmbjhd4kyFCuWWN3o1YSBCRAbLRWra69PUyWBE2zfkarvBmpDSbuCJGXmYuuF01RcCK3fNmCee/wZo0qfSp5ErBERuv1zqMnkA8Mvxu3i9Z2MGpERkkFQ1I6Y6mqbONYyrbrBPByJA0Q1218UEPeXMsJR1A1dti9x+GXJFWbf4ciRd1C1d3qNKHT47vxBn7j7Ekl1XSpR3cQkZeTgem16p8xAR1TR1MJKTD0Gowt9dA1WnakbKu8GKoLzBhgV5GM035OpsbpLJFYhNzcG1xCxEXUkq8wYuQHkDH7/mONr51YO/iy38nG3h52Jb9iiU7GTg0u/AhV+B+8d1y1gpa8ao5BfKcSs5B9eTsnAtKQvXE5U/7z98rNvxn0jOKjtgISLSF+cnzTQyuYCMxzI42pjWshR1Khg5Hpuu0w32eGy6USysVdnmJoVCQPyjx7j25KZ9LTEL15OycCslGzJ5xSLuQzdTcehmqsa2ejbm8HOxhf+T4KSRvQLPZB2E572/YBYXDQjyJylFkIvNIZYXQNvkpgoByLfxgPWTNWMK5QrcSc9VBxvXn+Q9Li231BoaVztLuNtZ4uKDzHLfi5udVYXeOxFRbbE0k8DeygyZeYVIzc5nMGLMdP3mawzfkHXtz5GWna8RdKhqDnIK5FqPK7U0Q1N3KRxtLPDP1eRy8zGqvQ8UgoC41FzEpeUgOSsfD3NlyL2bDLf7Z9FIcgR9xGdgKZKp97lu1hSXnPsh1XcArhyPwuf4HIIAPF2ho4ot5jx+Cdh8HteTsnEzJRsFhQqt+bC3MkMzDzs0dbdT/2zqbgcnWwvIFQK6LvkHiRl5WmvFRAA8HJS1SkREhspFaonMvEKkZBWgsZu+c1O96lQwous3X0P/hqxLf443N56FneUFpOXItKQCzCUiNHKVIsDDDk097JQ/3e3g7WgNkUikcQMXQYEO4qtwwyMkwxHHFQEQIIaHgxUWPt+yqFlIIcfj6/uRd3oTpLd3wrwwW32+WHhjq6wztis64U6eB5AN4M5DAG2QI56OCPP18EJRn41EOCNSNha7FW2Bs0WdXK3NJWjqLtUIOpp52MHNzrLUtWMkYhEiBgVh6obTEEHrMnmIGBRkNE1zRFQ3uUgtcTs1B2k5pjeipk4FIx38neDpYFXqN2RA+e3c1lJSyquGobzmJgAoKFQgrVABkQho4GSDZk9u2s087NDM3Q5+LrYwL2NiL9UN/PefV2Ge+Xp4iYoChQeCExbIxmHIoCmQiADcP6nsA3LxN1jnJMNaldC+PtDyBaDFMPh7tMR/8gsRnqasQYlLzUH09VQcj0vHbkUH7M1vVyLgUTzpXz3wGU8MbuWNZu52qF/PGuJKBA3hLTyxckybEs1aHhxFRURGomjiMwYjRq2sb8gqCgF4afUxfDuuncH2G9G1GentsKZ4pZs/bCwqV8zh4hPoZ7EcQrEr5SFKx0qLZRBdjgWizgEP44petHYCmg8BWg4HfDoC4qKAx87KHC28HdDC2wEA0NbXCaNXHwUAKCDGUUWQ1ny8FOJbLWUR3sITYUEenF+GiIxS0cRnpje8t04FI0DRN+SP/rwAn+xz6m/i96TBeDs8CJtO3MOx2HSM/+E4/juqNcJbeOg7yyXo2ozUzs+p0oGIakIyEQQUv1Wrw4vLfyh/mtsqV8NtORxo1BOQ6LamS3k1VTXRl0MiFhlskElEVBZTnoW1zgUjwJNv/FazISoo6osgWHlBZLUEA15+Fm9tPIPdl5Lw2k+n8MnzLQ1qZk65QkD0jbI7llbLTVzXCcm6vwN0nQFY2Fb4FOzLQUSkO1MORurcpGeqtVBExW60oswEYPM4WN3Yga9faovRHXygEID3fruA/+2/aRCTzKRm52Ps98ew8sBt9bbit+kq3cTlMuBODLB/EfDXDN32cQ2oVCCioqqp8nDQrO3xcLCq+gyvREQmRDXXSAqbaYxcuWuhiIBd70ES8Cw+eb4lnG0t8dX+m/hs9zWkZOVj3sCgSnWerA4n49Lx+s+nkZSZDxsLCRa/8AwsJKKqdchUKIDkS8Dtf4HYf4G4w4Asp2IZK2dCMl2wLwcRUfmKpoQ3vZqRuhWMlNv0IACZ8cCdIxD5d8Osfs3gLLVA5PbLWHskDuk5Bfh8eDAszGqvQkkQBPxwOA6f7LyCQoWAxm5SrBrTBo3d7AAAYQGuuHpsNx4/jId1PW8EhIRCYlZGsT6MUwYftw8AsdFFK+Kq2DgD/qGAfzfgwGLljKml9eiw9wKeTEhWVezLQURUNtenmmkEQSh1OgNjVLeCkewk3dLd3Af4dgHEYkzs4g8nWwu8vfkc/jz3AI8ey7DypTawtaz5S5edX4jZW89jx3nlejkDn/HEkheeKTr35T8h2TUbzZ8OsI55AeFLgKDnlM9zUpW1HqoA5NEdzZOY2yoDioY9gIahgFvzohEwNi7A5nFAaT06whcDYsMeBk1EZCpUQ3vzZArkFMghrYX7UG0xnXeiC12bFA4vA67uAEL+AwSPxuBW3nC0scCUH08h+noKXvzuGH6Y0B5OtjU3He/1pCxM2XAKt1NyYCYW4cNnAzG+s19RJPyk70uJWovMBGDzWKBpfyDjPpB0QfN1sRlQv72y9qNhKODdDjAr5X0EPQeMWK9s2no64LH3UgYiqoCHiIhqnI2FGWwsJMgtkCM1K5/BiNHy7ay8kWYmoNSmBwtb5c+0G8DOWUDUR0CbsQjtMBk/Tw7By2tP4Ny9Rxi+6gjWvxICb0drLcepmj/OxuO9rRfwWCaHh70V/vdSG7T1rVeUoNy+LwCu/120yb3Fk+CjB+DbCbC00z0zQc8BAc+i8HY0zh7cjVbd+sGsYXfWiBAR6YGz1AK56Y+Rmp0PP5fKDx4wNHVrNI1YomzCAFDqOJQhK4G3rwD9PwOcGgH5GUDMV8B/W6P1kWnYPgjwsrfErZQcDFt5BDeSsqotewWFCkT8cRFvbTyLxzI5ujZ2wY43u2oGIgBwcavuw25n3QSmHgbCPwGa9q1YIKIilkDw7Yp4p04QfLsyECEi0hNTnfisbgUjQFHTg32x0Sb2XsrtQc8pb9ghrwLTTgIvbgYa9gQEBXD1L9T/Yzj+tZ+H1x2PIj0jE8O/icGpOw+rnK0Hjx5jxDcxWBej7NPxRq/GWPdyBzhLLYG8DODKduVw2+XBwG+TdTuoawAgda1y3oiIyDCY6lwjdauZRuVJ0wPuHFF2apW6K5twin/jF4uBpv2Uj+SrwLFVwLmNME+9hHdwCa9aO2BdQU/M+C4VkWP6oGezYssoKuTlnwNA9PUUvLXxDB7myuBgbY5lw5ujp108EL0EuPWPcu0X4alVdkViZXBUnmoYdktERIaDwYipEUuUw1d15RYADFoG9J4HnF4HHP8ODpn38abZ75gqbMfODR1xoOeb6NErXJn+8p+ldPwsGumiUAhY8c9NLIu6jvpIwiTnG5joEQebPw4rm4ee5twEaNRL+WjQEVjZqey+L9U47JaIiAyD65OJzxiM1HU2Tsrpzzu9AVzdDsXRlTC/dwyDJYeB6MNIOvcM3IO6ATFfQ/tIl3HAiPV46NEZP/7yI5wSDmG/+QX4iZOAHAC3nqS1clR2OG3US7nei2OxKenDl3DYLRFRHeOsqhnJMq0+IwxGKktiBjR/HuLmz0Nx/wwubluMgNS9cM84D8ScV83nWowyaFBseRl2ikK8CaGoBMRmgE+IMvBo1AvwbFV2MMFht0REdY56FtYc1oxQMeL6rdFy2kas33MMtoc+xjDJQS2ByFPpFTKIAdwVeUEa1BdOz4QDfl0rPtJF174vRERkElzUzTSsGSEtRCIRxvfriJjHA4GzB8tNv9HpdQx4NRL2VuZVO3FF+74QEZHRcrFTNdOYVs1I3RvaW8M6PBOkU7rhz4ZXPRAhIqI6RdVMk5VfiDyZvJzUxoPBSDU7Lg/AA8EJCm2DXAAoBOCB4IzjioDazRgRERk9eyszWEiUt25TGlHDYKSaJefIECkbBwAlAhLV80jZWCTnyGo5Z0REZOxEIhGcTbDfCIORauZmZ4Xdig6YKpuORDhpvJYIZ0yVTcduRQe42VnpKYdERGTM1CNqTKhmhB1Yq1kHfyd4OlhhT0YH7M1vhw7iq3DDIyTDEccVARAghqeDFTr4O5V/MCIiomJcTHDiM9aMVDOJWISIQcpOrALEOKoIwp+KzjiqCILw5HJHDAqCRFzW4F8iIiLtTHGxPAYjNSC8hSdWjmkDDwfNphgPByusHNMG4S08S9mTiIiobKrhvSkmNLy3UsHI//73P/j5+cHKygohISE4fvx4memXLVuGZs2awdraGj4+PpgxYwby8vIqlWFjEd7CE4dm98Ivkzti+ahW+GVyRxya3YuBCBERVYmzrek101S4z8imTZswc+ZMrFq1CiEhIVi2bBn69euHa9euwc3NrUT6n3/+Ge+99x7WrFmDzp074/r165gwYQJEIhGWLl1aLW/CUEnEInRq5KzvbBARkQlxtVN1YK3DzTRLly7F5MmTMXHiRAQFBWHVqlWwsbHBmjVrtKY/cuQIunTpghdffBF+fn7o27cvRo8eXW5tChEREZVU1GekjtaMFBQU4NSpU5gzZ456m1gsRp8+fRATE6N1n86dO2PDhg04fvw4OnTogNu3b2Pnzp0YO3ZsqefJz89Hfn7RRc7MzAQAyGQyyGScn0MfVNed199wsEwMC8vDsJhyeThaFU16ZujvT9f8VSgYSU1NhVwuh7u7u8Z2d3d3XL16Ves+L774IlJTU9G1a1cIgoDCwkJMmTIF77//fqnnWbRoESIjI0ts37NnD2xsbCqSZapme/fu1XcWqBiWiWFheRgWUyyPbBkAmOFhrgzb/9oJiQEPRcnNzdUpXY3PM3LgwAF88skn+PrrrxESEoKbN2/irbfewkcffYS5c+dq3WfOnDmYOXOm+nlmZiZ8fHzQt29f2Nvb13SWSQuZTIa9e/ciLCwM5uZcU8cQsEwMC8vDsJhyecgVAuae2guFAHTo3gvu9oY7iaaqZaM8FQpGXFxcIJFIkJSUpLE9KSkJHh4eWveZO3cuxo4di0mTJgEAWrZsiZycHLz66qv44IMPIBaXDOksLS1haWlZYru5ubnJfaiMDcvA8LBMDAvLw7CYYnmYA3CytURqdj4y8hWob8DvT9drX6HKHQsLC7Rt2xZRUVHqbQqFAlFRUejUqZPWfXJzc0sEHBKJBAAgCKWsJkdERESlcjGx9Wkq3Ewzc+ZMjB8/Hu3atUOHDh2wbNky5OTkYOLEiQCAcePGwdvbG4sWLQIADBo0CEuXLkXr1q3VzTRz587FoEGD1EEJERER6c7VzhJXE7OQaiITn1U4GBk5ciRSUlIwb948JCYmolWrVti1a5e6U+vdu3c1akI+/PBDiEQifPjhh4iPj4erqysGDRqEhQsXVt+7ICIiqkNMbXhvpTqwTps2DdOmTdP62oEDBzRPYGaGiIgIREREVOZUREREVIypzcJqwAOCiIiISBvV+jSm0meEwQgREZGRMbVmGgYjRERERsbURtMwGCEiIjIyrBkhIiIivVIFI+k5BVAojH/OLgYjRERERsb5STONXCHgYa7xN9UwGCEiIjIy5hIxHG2UU62n5TAYISIiIj1Q9xsxgVlYGYwQEREZIdWImhQT6MTKYISIiMgIFY2oYTMNERER6YEpDe9lMEJERGSEVM00aQxGiIiISB/YTENERER6xWYaIiIi0iv1yr0c2ktERET64GxbtFieIBj3lPAMRoiIiIyQ65OakQK5Apl5hXrOTdUwGCEiIjJCVuYSSC3NABj/iBoGI0REREZKNbzX2EfUMBghIiIyUqYyoobBCBERkZFyVteMMBghIiIiPTCVlXsZjBARERkpdTCSwz4jREREpAemMvEZgxEiIiIj5co+I0RERKRPziayWB6DESIiIiPFob1ERESkV6pJz3IL5MgtMN4p4RmMEBERGSmppRkszZS38jQjbqphMEJERGSkRCKRuqkmxYibahiMEBERGTFTGN7LYISIiMiIudga/2J5DEaIiIiMmCmMqGEwQkREZMRc7JQ1I2kMRoiIiEgfXExg4jMGI0REREaMo2mIiIhIr5xNYH0aBiNERERGzFXKob1ERESkR6pmmsy8QhQUKvScm8phMEJERGTEHKzNYSYWAQDScoyzdoTBCBERkRETi0VF/UayjHNEDYMRIiIiI+dsa9wTnzEYISIiMnKq9WmMdXgvgxEiIiIj5yJVzcLKZhoiIiLSA1cjX5+GwQgREZGRM/bF8hiMEBERGTljn4WVwQgREZGRU9eMcGgvERER6QObaYiIiEivXOyUzTTpuQWQKwQ956biGIwQEREZOScbC4hEgCAA6TnG11TDYISIiMjImUnEcLIx3k6sDEaIiIhMgDGPqGEwQkREZAKMuRMrgxEiIiIToApGjHFKeAYjREREJkAVjBjjYnmVCkb+97//wc/PD1ZWVggJCcHx48fLTP/o0SO8/vrr8PT0hKWlJZo2bYqdO3dWKsNERERUkmp4rzFOfGZW0R02bdqEmTNnYtWqVQgJCcGyZcvQr18/XLt2DW5ubiXSFxQUICwsDG5ubtiyZQu8vb1x584dODo6Vkf+iYiICICLrfH2GalwMLJ06VJMnjwZEydOBACsWrUKO3bswJo1a/Dee++VSL9mzRqkp6fjyJEjMDc3BwD4+flVLddERESkQV0zYoTBSIWaaQoKCnDq1Cn06dOn6ABiMfr06YOYmBit+/z555/o1KkTXn/9dbi7u6NFixb45JNPIJfLq5ZzIiIiUjPmDqwVqhlJTU2FXC6Hu7u7xnZ3d3dcvXpV6z63b9/GP//8g5deegk7d+7EzZs38dprr0EmkyEiIkLrPvn5+cjPL4rsMjMzAQAymQwymawiWaZqorruvP6Gg2ViWFgehqUuloejlQQAkJaTj4KCAohEIj3nSPfrX+FmmopSKBRwc3PDt99+C4lEgrZt2yI+Ph6fffZZqcHIokWLEBkZWWL7nj17YGNjU9NZpjLs3btX31mgYlgmhoXlYVjqUnkUKgDADDK5gC1//g1bc33nCMjNzdUpXYWCERcXF0gkEiQlJWlsT0pKgoeHh9Z9PD09YW5uDolEot4WGBiIxMREFBQUwMLCosQ+c+bMwcyZM9XPMzMz4ePjg759+8Le3r4iWaZqIpPJsHfvXoSFhan7/pB+sUwMC8vDsNTV8ph/7h9k5RWidafuaOwm1Xd21C0b5alQMGJhYYG2bdsiKioKQ4YMAaCs+YiKisK0adO07tOlSxf8/PPPUCgUEIuVXVSuX78OT09PrYEIAFhaWsLS0rLEdnNz8zr1oTJELAPDwzIxLCwPw1LXysNVaomsvEI8ylMYxPvWNQ8Vnmdk5syZWL16NdatW4crV65g6tSpyMnJUY+uGTduHObMmaNOP3XqVKSnp+Ott97C9evXsWPHDnzyySd4/fXXK3pqIiIiKoOxTglf4T4jI0eOREpKCubNm4fExES0atUKu3btUndqvXv3rroGBAB8fHywe/duzJgxA8888wy8vb3x1ltvYfbs2dX3LoiIiEg9vDfN1IMRAJg2bVqpzTIHDhwosa1Tp044evRoZU5FREREOiqqGTGu4b1cm4aIiMhEOBvpLKwMRoiIiEyEsc7CymCEiIjIRBSt3MtmGiIiItKDoinhWTNCREREeuD61NBeQRD0nBvdMRghIiIyEc5SZZ+RPJkCOQXGsyAtgxEiIiITYWtpBmtz5fIrqVnG01TDYISIiMiEGOOIGgYjREREJsQYJz5jMEJERGRCjHF9GgYjREREJoTBCBEREemVi5R9RoiIiEiP1DUjWewzQkRERHrAZhoiIiLSK1UzTVoOa0aIiIhID1zsVM00rBkhIiIiPXCxVQYjWfmFyJMZx5TwDEaIiIhMiL21GSwkytu7sfQbYTBCRERkQkQikXrBPGOZhZXBCBERkYlRjahJY80IERER6YOxTXzGYISIiMjEOBvZYnkMRoiIiEyMqpkmxUiG9zIYISIiMjFspiEiIiK9crUzrinhGYwQERGZmKLRNOwzQkRERHrgzGYaIiIi0idVzcjDXBlkcoWec1M+BiNEREQmpp6NBcQi5f/TjWD1XgYjREREJkYiFsHJ1niG9zIYISIiMkGq4b1prBkhIiIifVAP72XNCBEREemDs63xjKhhMEJERGSCXKTGM/EZgxEiIiIT5GJnPIvlMRghIiIyQawZISIiIr0qWiyPNSNERESkB6wZISIiIr1SBSPpOQVQKAQ956ZsDEaIiIhMkGqxPLlCwMNcw26qYTBCRERkgswlYjjamAMw/H4jDEaIiIhMlKqpJs3A+40wGCEiIjJRqllYUxiMEBERkT4Yy8RnDEaIiIhMlKuRDO9lMEJERGSi1BOfGfjKvQxGiIiITJS6A2sOm2mIiIhID5zZTENERET6xGYaIiIi0qui9WkKIAiGOyU8gxEiIiIT5fpkaG+BXIHMvEI956Z0DEaIiIhMlJW5BFJLMwCGPQsrgxEiIiITpu43YsATnzEYISIiMmHGMKKGwQgREZEJK6oZYTBCREREeqAeUWPAw3sZjBAREZkwVTCSYmp9Rv73v//Bz88PVlZWCAkJwfHjx3Xab+PGjRCJRBgyZEhlTktEREQVpFq516RG02zatAkzZ85EREQETp8+jeDgYPTr1w/Jycll7hcXF4dZs2ahW7dulc4sERERVYyLrQn2GVm6dCkmT56MiRMnIigoCKtWrYKNjQ3WrFlT6j5yuRwvvfQSIiMj0bBhwyplmIiIiHSnqhkx5KG9ZhVJXFBQgFOnTmHOnDnqbWKxGH369EFMTEyp+y1YsABubm545ZVXcPDgwXLPk5+fj/z8ogguMzMTACCTySCTySqSZaomquvO6284WCaGheVhWFgeRRytlPUOqdn5tX49dD1fhYKR1NRUyOVyuLu7a2x3d3fH1atXte5z6NAhfP/99zh79qzO51m0aBEiIyNLbN+zZw9sbGwqkmWqZnv37tV3FqgYlolhYXkYFpYHoJwF3gy5BXJs274TlpLaO3dubq5O6SoUjFRUVlYWxo4di9WrV8PFxUXn/ebMmYOZM2eqn2dmZsLHxwd9+/aFvb19TWSVyiGTybB3716EhYXB3Nxc39khsEwMDcvDsLA8igiCgHlnopBfqEC7rj3gU6/2vtSrWjbKU6FgxMXFBRKJBElJSRrbk5KS4OHhUSL9rVu3EBcXh0GDBqm3KRQK5YnNzHDt2jU0atSoxH6WlpawtLQssd3c3LzOf6j0jWVgeFgmhoXlYVhYHkouUkvEP3qMR3kKNKzF66Hrta9QB1YLCwu0bdsWUVFR6m0KhQJRUVHo1KlTifQBAQG4cOECzp49q34899xz6NmzJ86ePQsfH5+KnJ6IiIgqQT0Lq4FOfFbhZpqZM2di/PjxaNeuHTp06IBly5YhJycHEydOBACMGzcO3t7eWLRoEaysrNCiRQuN/R0dHQGgxHYiIiKqGepZWA10RE2Fg5GRI0ciJSUF8+bNQ2JiIlq1aoVdu3apO7XevXsXYjEndiUiIjIULga+WF6lOrBOmzYN06ZN0/ragQMHytx37dq1lTklERERVZKLnbKZxlBnYWUVBhERkYlztjXsZhoGI0RERCZONQtrCmtGiIiISB/Uo2kYjBAREZE+uKo6sBro0F4GI0RERCZONZomM68Q+YVyPeemJAYjREREJs7B2hxmYhEAID3H8DqxMhghIiIycWKxCE62qllYGYwQERGRHhjyxGcMRoiIiOoAQx7ey2CEiIioDjDk4b0MRoiIiOoA1fDeNAOchZXBCBERUR3gzJoRIiIi0id2YCUiIiK9UgcjHNpLRERE+sCaESIiItIrFztln5H03ALIFYKec6OJwQgREVEd4GRjAZEIEATDmxKewQgREVEdYCYRo56NYY6oYTBCRERURxjqxGcMRoiIiOoIQ+3EymCEiIiojjDU4b0MRoiIiOoI9SysOawZISIiIj1gzQgRERHplSv7jBAREZE+qSY+YzBCREREesHRNERERKRXqmAkLbsAgmA4U8IzGCEiIqojnGyVzTSFCgEZj2V6zk0RBiNERER1hJW5BHZWZgAMq6mGwQgREVEdohpRk2JAw3sZjBAREdUhhtiJlcEIERFRHaIa3pvGYISIiIj0wdlWVTPCZhoiIiLSAzbTEBERkV4Z4iysDEaIiIjqEFXNSAqbaYiIiEgfilbuZc0IERER6YGL9Mlompx8g5kSnsEIERFRHaKqGcmTKZBTINdzbpQYjBAREdUhtpZmsDaXADCcphoGI0RERHWMoY2oYTBCRERUxxjaXCMMRoiIiOoYQ5uFlcEIERFRHePKZhoiIiLSJzbTEBERkV4VTXzGZhoiIiLSA9aMEBERkV45q2dhZc0IERER6YGhrU/DYISIiKiOcX0SjGTlFyJPpv8p4RmMEBER1TH21mawkChDAEPoN8JghIiIqI4RiUTqfiOGMPEZgxEiIqI6yJD6jTAYISIiqoOKRtQwGCEiIiI9KJprhM00REREpAeqYCTFWJtp/ve//8HPzw9WVlYICQnB8ePHS027evVqdOvWDfXq1UO9evXQp0+fMtMTERFRzXORGs5ieRUORjZt2oSZM2ciIiICp0+fRnBwMPr164fk5GSt6Q8cOIDRo0dj//79iImJgY+PD/r27Yv4+PgqZ56IiIgqx9XOcKaEr3AwsnTpUkyePBkTJ05EUFAQVq1aBRsbG6xZs0Zr+p9++gmvvfYaWrVqhYCAAHz33XdQKBSIioqqcuaJiIiocpxtlcFImgH0GTGrSOKCggKcOnUKc+bMUW8Ti8Xo06cPYmJidDpGbm4uZDIZnJycSk2Tn5+P/PyiSC0zMxMAIJPJIJPJKpJlqiaq687rbzhYJoaF5WFYWB7lc7QqmvSspq6TrsetUDCSmpoKuVwOd3d3je3u7u64evWqTseYPXs2vLy80KdPn1LTLFq0CJGRkSW279mzBzY2NhXJMlWzvXv36jsLVAzLxLCwPAwLy6N0WTIAMMPDXBm2/7UTkhoY0pKbm6tTugoFI1W1ePFibNy4EQcOHICVlVWp6ebMmYOZM2eqn2dmZqr7mtjb29dGVqkYmUyGvXv3IiwsDObm5vrODoFlYmhYHoaF5VE+uULAvFN7oRCADt17wd2+9PtyZalaNspToWDExcUFEokESUlJGtuTkpLg4eFR5r6ff/45Fi9ejH379uGZZ54pM62lpSUsLS1LbDc3N+eHSs9YBoaHZWJYWB6GheVROnMATraWSM3Ox6M8Beo7V/910vXaV6hSxsLCAm3bttXofKrqjNqpU6dS9/v000/x0UcfYdeuXWjXrl1FTklEREQ1xEU9C6t+O7FWuJlm5syZGD9+PNq1a4cOHTpg2bJlyMnJwcSJEwEA48aNg7e3NxYtWgQAWLJkCebNm4eff/4Zfn5+SExMBABIpVJIpdJqfCtERERUEcqJz7L0vj5NhYORkSNHIiUlBfPmzUNiYiJatWqFXbt2qTu13r17F2JxUYXLypUrUVBQgGHDhmkcJyIiAvPnz69a7omIiKjSDGXis0p1YJ02bRqmTZum9bUDBw5oPI+Li6vMKYiIiKiGFa1Po99ghGvTEBER1VEudoaxWB6DESIiojrK2dYwmmkYjBAREdVRTk+CkVspOYi5lQa5QtBLPhiMEBER1UG7LiZg9tbzAIAHjx5j9Oqj6LrkH+y6mFDreWEwQkREVMfsupiAqRtOl+grkpiRh6kbTtd6QMJghIiIqA6RKwREbr8MbQ0yqm2R2y/XapMNgxEiIqI65HhsOhIy8kp9XQCQkJGH47HptZYnBiNERER1SHJW6YFIZdJVBwYjREREdYibnW6r8+qarjowGCEiIqpDOvg7wdPBCqJSXhcB8HSwQgd/p1rLE4MRIiKiOkQiFiFiUBAAlAhIVM8jBgVBIi4tXKl+DEaIiIjqmPAWnlg5pg08HDSbYjwcrLByTBuEt/Cs1fxUaqE8IiIiMm7hLTwRFuSB47HpSM7Kg5udsmmmNmtEVBiMEBER1VESsQidGjnrOxtspiEiIiL9YjBCREREesVghIiIiPSKwQgRERHpFYMRIiIi0isGI0RERKRXDEaIiIhIrxiMEBERkV4xGCEiIiK9MooZWAVBAABkZmbqOSd1l0wmQ25uLjIzM2Fubq7v7BBYJoaG5WFYWB6GQXXfVt3HS2MUwUhWVhYAwMfHR885ISIioorKysqCg4NDqa+LhPLCFQOgUCjw4MED2NnZQSSq/QV8SBnd+vj44N69e7C3t9d3dggsE0PD8jAsLA/DIAgCsrKy4OXlBbG49J4hRlEzIhaLUb9+fX1ngwDY29vzF9vAsEwMC8vDsLA89K+sGhEVdmAlIiIivWIwQkRERHrFYIR0YmlpiYiICFhaWuo7K/QEy8SwsDwMC8vDuBhFB1YiIiIyXawZISIiIr1iMEJERER6xWCEiIiI9IrBCBEREekVgxEiIiLSK6OYgZX04/jx44iJiUFiYiIAwMPDA506dUKHDh30nLO6qaCgAL///nuJMuncuTMGDx4MCwsLPeewbmF5EFUfDu2lEpKTk/HCCy/g8OHDaNCgAdzd3QEASUlJuHv3Lrp06YKtW7fCzc1NzzmtO27evIl+/frhwYMHCAkJ0SiTY8eOoX79+vj777/RuHFjPee0bmB5GK7ExEQcO3ZMI0AMCQmBh4eHnnNGZWEwQiUMGzYMDx48wA8//IBmzZppvHbt2jW8/PLL8PLywq+//qqnHNY9YWFhsLW1xfr160uss5GZmYlx48bh8ePH2L17t55yWLewPAxPTk4O/vOf/2Djxo0QiURwcnICAKSnp0MQBIwePRrffPMNbGxs9JxT0obBCJVgZ2eH6OhotG7dWuvrp06dQo8ePZCVlVXLOau7bGxscPz4cbRo0ULr6xcuXEBISAhyc3NrOWd1E8vD8EyaNAnR0dFYsWIF+vTpA4lEAgCQy+WIiorCG2+8ge7du2P16tV6zilpww6sVIKlpSUyMzNLfT0rK4tTLNcyR0dHxMXFlfp6XFwcHB0day0/dR3Lw/Bs3boVa9euRb9+/dSBCABIJBL07dsXa9aswZYtW/SYQyoLgxEqYeTIkRg/fjy2bdumEZRkZmZi27ZtmDhxIkaPHq3HHNY9kyZNwrhx4/Dll1/i/PnzSEpKQlJSEs6fP48vv/wSEyZMwKuvvqrvbNYZLA/Do1Aoyuw0bGFhAYVCUYs5ogoRiIrJy8sTpkyZIlhYWAhisViwsrISrKysBJFIJFhYWAhTp04V8vLy9J3NOmfx4sWCp6enIBKJBLFYLIjFYkEkEgmenp7CkiVL9J29OoflYVhefPFFoXXr1sLp06dLvHb69Gmhbdu2wksvvaSHnJEu2GeESpWZmYmTJ08iKSkJAODu7o527dqV6LBHtSs2NlZjpIC/v7+ec1S3sTwMw8OHD/Hiiy9i9+7dqFevnnq0X3JyMh49eoR+/frh559/ZvOZgWIwQjqzsLDAuXPnEBgYqO+sEBFpdeXKFRw9erTE/EgBAQF6zhmVhcEIlTBz5kyt25cvX44xY8bA2dkZALB06dLazFaddvr0adSrV0/9rfvHH3/EqlWrcPfuXfj6+mLatGkYNWqUnnNZt3z11Vc4fvw4BgwYgFGjRuHHH3/EokWLoFAoMHToUCxYsABmZpxXkkgX/E2hEpYtW4bg4OAS1ZmCIODKlSuwtbWFSCTST+bqqIkTJ+KLL76Av78/vvvuO7z55puYPHkyxo4di2vXrmHy5MnIzc3Fyy+/rO+s1gkff/wxPv30U/Tt2xczZszAnTt38Nlnn2HGjBkQi8X48ssvYW5ujsjISH1ntU7hrLhGTI/9VchALVq0SPD39xeioqI0tpuZmQmXLl3SU67qNmtrayEuLk4QBEFo3bq18O2332q8/tNPPwlBQUH6yFqd1KhRI2Hr1q2CIAjC2bNnBYlEImzYsEH9+m+//SY0btxYX9mrk27cuCE0bNhQsLKyEkJDQ4URI0YII0aMEEJDQwUrKyuhcePGwo0bN/SdTSoFm2lIqxMnTmDMmDEYNGgQFi1aBHNzc5ibm+PcuXMICgrSd/bqHBcXF+zevRtt27aFu7s79uzZg+DgYPXrt27dQsuWLTnJVi2xsbHB1atX0aBBAwDK/lRnzpxB8+bNAQB37txBUFAQcnJy9JnNOoWz4ho3zjNCWrVv3x6nTp1CSkoK2rVrh4sXL7JpRo/69++PlStXAgBCQ0NLTN60efNmroNSizw8PHD58mUAwI0bNyCXy9XPAeDSpUtcu6mWHT58GB9//LHW0X729vb46KOPcPDgQT3kjHTBPiNUKqlUinXr1mHjxo3o06cP5HK5vrNUZy1ZsgRdunRBaGgo2rVrhy+++AIHDhxAYGAgrl27hqNHj2Lbtm36zmad8dJLL2HcuHEYPHgwoqKi8O6772LWrFlIS0uDSCTCwoULMWzYMH1ns05RzYpb2hT9nBXXsDEYoXKNGjUKXbt2xalTp+Dr66vv7NRJXl5eOHPmDBYvXozt27dDEAQcP34c9+7dQ5cuXXD48GG0a9dO39msMyIjI2FtbY2YmBhMnjwZ7733HoKDg/Huu+8iNzcXgwYNwkcffaTvbNYpqllx586di969e2uspBwVFYWPP/4Yb7zxhp5zSaVhnxEiIjIJS5YswfLly5GYmKhuVhYEAR4eHpg+fTreffddPeeQSsNghIiITApnxTU+DEaIiMjk3bt3DxEREVizZo2+s0JaMBghIiKTd+7cObRp04Yd8Q0UO7ASEZHR+/PPP8t8/fbt27WUE6oM1owQEZHRE4vFEIlEKOuWJhKJWDNioDjpGRERGT1PT0/89ttvUCgUWh+nT5/WdxapDAxGiIjI6LVt2xanTp0q9fXyak1Iv9hnhIiIjN4777xT5lpAjRs3xv79+2sxR1QR7DNCREREesVmGiIiItIrBiNERESkVwxGiIiISK8YjBAREZFeMRghIiIivWIwQkRERHrFYISIiIj0isEIERER6dX/ARfTWKAlpgZeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for sex in ldl_std.sex.unique():\n",
    "    plt.figure() \n",
    "    subdata = ldl_std.loc[(ldl_std.sex==sex)]\n",
    "    plt.plot(subdata['age_start'], subdata['std'], marker='o')\n",
    "    subdata2 = ldl_exp_std.loc[(ldl_exp_std.sex==sex)]\n",
    "    plt.plot(subdata2['age_start'], subdata2['mean'], marker='o')\n",
    "    plt.title(f'LDL Exposure Standard Deviation: {sex}') \n",
    "    plt.xticks(rotation=90) \n",
    "    plt.grid()\n",
    "    plt.legend(['Simulation baseline', 'Artifact Baseline']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dfa8e365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracked</th>\n",
       "      <th>location</th>\n",
       "      <th>entrance_time</th>\n",
       "      <th>alive</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>exit_time</th>\n",
       "      <th>cause_of_death</th>\n",
       "      <th>years_of_life_lost</th>\n",
       "      <th>years_lived_with_disability</th>\n",
       "      <th>...</th>\n",
       "      <th>polypill</th>\n",
       "      <th>sbp_medication_adherence</th>\n",
       "      <th>ldlc_multiplier</th>\n",
       "      <th>sbp_therapeutic_inertia_propensity</th>\n",
       "      <th>visit_type</th>\n",
       "      <th>last_fpg_test_date</th>\n",
       "      <th>scheduled_date</th>\n",
       "      <th>high_ldl</th>\n",
       "      <th>high_sbp</th>\n",
       "      <th>age_start</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>105.749929</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat1</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.310053</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3.065407</td>\n",
       "      <td>117.839630</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4700</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>106.524522</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.877295</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.883360</td>\n",
       "      <td>140.200723</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6575</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>108.032306</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.3620</td>\n",
       "      <td>0.775325</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-02-10 18:35:22.647120000</td>\n",
       "      <td>2.147039</td>\n",
       "      <td>127.158067</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10437</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>105.020297</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.239407</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.194296</td>\n",
       "      <td>148.891539</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11816</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>115.745672</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.3620</td>\n",
       "      <td>0.824641</td>\n",
       "      <td>none</td>\n",
       "      <td>2018-03-24 01:30:26.460179424</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.653104</td>\n",
       "      <td>158.321445</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12298</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>106.828701</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.5125</td>\n",
       "      <td>0.057388</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.037400</td>\n",
       "      <td>143.761766</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13212</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>105.872329</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.695905</td>\n",
       "      <td>none</td>\n",
       "      <td>2018-06-26 17:47:20.995395968</td>\n",
       "      <td>2021-01-27 14:43:04.294214400</td>\n",
       "      <td>2.267461</td>\n",
       "      <td>103.715200</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14481</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>111.602746</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.405285</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.618888</td>\n",
       "      <td>116.358751</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16385</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>105.115936</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.316538</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.246279</td>\n",
       "      <td>138.086792</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17889</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>108.816956</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.209866</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.686427</td>\n",
       "      <td>120.695240</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24589</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>105.100187</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.208260</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-03-18 03:19:31.995523200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.834849</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26739</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>108.409025</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.3620</td>\n",
       "      <td>0.880802</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.335110</td>\n",
       "      <td>125.889559</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30482</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>105.937940</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.643912</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.887931</td>\n",
       "      <td>139.803930</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31923</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>106.182010</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat1</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.873746</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-03-03 03:17:30.244790400</td>\n",
       "      <td>2.335052</td>\n",
       "      <td>181.846576</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33377</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>106.039940</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat2</td>\n",
       "      <td>1.3620</td>\n",
       "      <td>0.064450</td>\n",
       "      <td>none</td>\n",
       "      <td>2019-04-11 20:09:05.093865704</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.582058</td>\n",
       "      <td>108.304785</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33635</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>106.884845</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.527073</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.207720</td>\n",
       "      <td>125.150657</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38143</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>106.075795</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.594226</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.403356</td>\n",
       "      <td>133.529298</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39453</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>105.227844</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.135755</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3.074250</td>\n",
       "      <td>181.068210</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41011</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>106.046620</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.925649</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-03-13 17:49:36.095059200</td>\n",
       "      <td>4.525726</td>\n",
       "      <td>146.579071</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44040</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>106.245425</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.496949</td>\n",
       "      <td>none</td>\n",
       "      <td>2018-07-15 07:54:36.339649792</td>\n",
       "      <td>2021-03-21 11:08:02.422579200</td>\n",
       "      <td>3.137394</td>\n",
       "      <td>149.953643</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44339</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>115.614439</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.3620</td>\n",
       "      <td>0.030809</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-03-18 09:25:17.517417600</td>\n",
       "      <td>2.427600</td>\n",
       "      <td>138.092033</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45123</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>105.365686</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.104201</td>\n",
       "      <td>none</td>\n",
       "      <td>2020-05-06 02:16:08.916772688</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4.193993</td>\n",
       "      <td>128.386921</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46465</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>105.644259</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.5125</td>\n",
       "      <td>0.332010</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.305377</td>\n",
       "      <td>119.053132</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47821</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>114.968524</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat1</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.989652</td>\n",
       "      <td>none</td>\n",
       "      <td>2020-12-14 17:38:38.671573682</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3.857927</td>\n",
       "      <td>129.867640</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48153</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>105.338488</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.316111</td>\n",
       "      <td>none</td>\n",
       "      <td>2019-03-25 11:05:23.384833568</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4.447327</td>\n",
       "      <td>140.923435</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49717</th>\n",
       "      <td>True</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>alive</td>\n",
       "      <td>110.046757</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaT</td>\n",
       "      <td>not_dead</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>cat2</td>\n",
       "      <td>cat3</td>\n",
       "      <td>1.3620</td>\n",
       "      <td>0.552211</td>\n",
       "      <td>none</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.453229</td>\n",
       "      <td>123.861619</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tracked location entrance_time  alive         age     sex exit_time  \\\n",
       "217       True  Alabama    2020-12-04  alive  105.749929  Female       NaT   \n",
       "4700      True  Alabama    2020-12-04  alive  106.524522  Female       NaT   \n",
       "6575      True  Alabama    2020-12-04  alive  108.032306    Male       NaT   \n",
       "10437     True  Alabama    2020-12-04  alive  105.020297    Male       NaT   \n",
       "11816     True  Alabama    2020-12-04  alive  115.745672  Female       NaT   \n",
       "12298     True  Alabama    2020-12-04  alive  106.828701    Male       NaT   \n",
       "13212     True  Alabama    2020-12-04  alive  105.872329  Female       NaT   \n",
       "14481     True  Alabama    2020-12-04  alive  111.602746    Male       NaT   \n",
       "16385     True  Alabama    2020-12-04  alive  105.115936  Female       NaT   \n",
       "17889     True  Alabama    2020-12-04  alive  108.816956  Female       NaT   \n",
       "24589     True  Alabama    2020-12-04  alive  105.100187  Female       NaT   \n",
       "26739     True  Alabama    2020-12-04  alive  108.409025    Male       NaT   \n",
       "30482     True  Alabama    2020-12-04  alive  105.937940  Female       NaT   \n",
       "31923     True  Alabama    2020-12-04  alive  106.182010  Female       NaT   \n",
       "33377     True  Alabama    2020-12-04  alive  106.039940    Male       NaT   \n",
       "33635     True  Alabama    2020-12-04  alive  106.884845  Female       NaT   \n",
       "38143     True  Alabama    2020-12-04  alive  106.075795  Female       NaT   \n",
       "39453     True  Alabama    2020-12-04  alive  105.227844  Female       NaT   \n",
       "41011     True  Alabama    2020-12-04  alive  106.046620  Female       NaT   \n",
       "44040     True  Alabama    2020-12-04  alive  106.245425  Female       NaT   \n",
       "44339     True  Alabama    2020-12-04  alive  115.614439  Female       NaT   \n",
       "45123     True  Alabama    2020-12-04  alive  105.365686  Female       NaT   \n",
       "46465     True  Alabama    2020-12-04  alive  105.644259  Female       NaT   \n",
       "47821     True  Alabama    2020-12-04  alive  114.968524  Female       NaT   \n",
       "48153     True  Alabama    2020-12-04  alive  105.338488  Female       NaT   \n",
       "49717     True  Alabama    2020-12-04  alive  110.046757  Female       NaT   \n",
       "\n",
       "      cause_of_death  years_of_life_lost  years_lived_with_disability  ...  \\\n",
       "217         not_dead                 0.0                          0.0  ...   \n",
       "4700        not_dead                 0.0                          0.0  ...   \n",
       "6575        not_dead                 0.0                          0.0  ...   \n",
       "10437       not_dead                 0.0                          0.0  ...   \n",
       "11816       not_dead                 0.0                          0.0  ...   \n",
       "12298       not_dead                 0.0                          0.0  ...   \n",
       "13212       not_dead                 0.0                          0.0  ...   \n",
       "14481       not_dead                 0.0                          0.0  ...   \n",
       "16385       not_dead                 0.0                          0.0  ...   \n",
       "17889       not_dead                 0.0                          0.0  ...   \n",
       "24589       not_dead                 0.0                          0.0  ...   \n",
       "26739       not_dead                 0.0                          0.0  ...   \n",
       "30482       not_dead                 0.0                          0.0  ...   \n",
       "31923       not_dead                 0.0                          0.0  ...   \n",
       "33377       not_dead                 0.0                          0.0  ...   \n",
       "33635       not_dead                 0.0                          0.0  ...   \n",
       "38143       not_dead                 0.0                          0.0  ...   \n",
       "39453       not_dead                 0.0                          0.0  ...   \n",
       "41011       not_dead                 0.0                          0.0  ...   \n",
       "44040       not_dead                 0.0                          0.0  ...   \n",
       "44339       not_dead                 0.0                          0.0  ...   \n",
       "45123       not_dead                 0.0                          0.0  ...   \n",
       "46465       not_dead                 0.0                          0.0  ...   \n",
       "47821       not_dead                 0.0                          0.0  ...   \n",
       "48153       not_dead                 0.0                          0.0  ...   \n",
       "49717       not_dead                 0.0                          0.0  ...   \n",
       "\n",
       "      polypill sbp_medication_adherence  ldlc_multiplier  \\\n",
       "217       cat2                     cat1           1.0000   \n",
       "4700      cat2                     cat3           1.0000   \n",
       "6575      cat2                     cat3           1.3620   \n",
       "10437     cat2                     cat3           1.0000   \n",
       "11816     cat2                     cat3           1.3620   \n",
       "12298     cat2                     cat3           1.5125   \n",
       "13212     cat2                     cat3           1.0000   \n",
       "14481     cat2                     cat3           1.0000   \n",
       "16385     cat2                     cat3           1.0000   \n",
       "17889     cat2                     cat3           1.0000   \n",
       "24589     cat2                     cat3           1.0000   \n",
       "26739     cat2                     cat3           1.3620   \n",
       "30482     cat2                     cat3           1.0000   \n",
       "31923     cat2                     cat1           1.0000   \n",
       "33377     cat2                     cat2           1.3620   \n",
       "33635     cat2                     cat3           1.0000   \n",
       "38143     cat2                     cat3           1.0000   \n",
       "39453     cat2                     cat3           1.0000   \n",
       "41011     cat2                     cat3           1.0000   \n",
       "44040     cat2                     cat3           1.0000   \n",
       "44339     cat2                     cat3           1.3620   \n",
       "45123     cat2                     cat3           1.0000   \n",
       "46465     cat2                     cat3           1.5125   \n",
       "47821     cat2                     cat1           1.0000   \n",
       "48153     cat2                     cat3           1.0000   \n",
       "49717     cat2                     cat3           1.3620   \n",
       "\n",
       "       sbp_therapeutic_inertia_propensity  visit_type  \\\n",
       "217                              0.310053        none   \n",
       "4700                             0.877295        none   \n",
       "6575                             0.775325        none   \n",
       "10437                            0.239407        none   \n",
       "11816                            0.824641        none   \n",
       "12298                            0.057388        none   \n",
       "13212                            0.695905        none   \n",
       "14481                            0.405285        none   \n",
       "16385                            0.316538        none   \n",
       "17889                            0.209866        none   \n",
       "24589                            0.208260        none   \n",
       "26739                            0.880802        none   \n",
       "30482                            0.643912        none   \n",
       "31923                            0.873746        none   \n",
       "33377                            0.064450        none   \n",
       "33635                            0.527073        none   \n",
       "38143                            0.594226        none   \n",
       "39453                            0.135755        none   \n",
       "41011                            0.925649        none   \n",
       "44040                            0.496949        none   \n",
       "44339                            0.030809        none   \n",
       "45123                            0.104201        none   \n",
       "46465                            0.332010        none   \n",
       "47821                            0.989652        none   \n",
       "48153                            0.316111        none   \n",
       "49717                            0.552211        none   \n",
       "\n",
       "                 last_fpg_test_date                scheduled_date  high_ldl  \\\n",
       "217                             NaT                           NaT  3.065407   \n",
       "4700                            NaT                           NaT  2.883360   \n",
       "6575                            NaT 2021-02-10 18:35:22.647120000  2.147039   \n",
       "10437                           NaT                           NaT  2.194296   \n",
       "11816 2018-03-24 01:30:26.460179424                           NaT  2.653104   \n",
       "12298                           NaT                           NaT  2.037400   \n",
       "13212 2018-06-26 17:47:20.995395968 2021-01-27 14:43:04.294214400  2.267461   \n",
       "14481                           NaT                           NaT  2.618888   \n",
       "16385                           NaT                           NaT  2.246279   \n",
       "17889                           NaT                           NaT  2.686427   \n",
       "24589                           NaT 2021-03-18 03:19:31.995523200  0.000000   \n",
       "26739                           NaT                           NaT  2.335110   \n",
       "30482                           NaT                           NaT  1.887931   \n",
       "31923                           NaT 2021-03-03 03:17:30.244790400  2.335052   \n",
       "33377 2019-04-11 20:09:05.093865704                           NaT  1.582058   \n",
       "33635                           NaT                           NaT  1.207720   \n",
       "38143                           NaT                           NaT  2.403356   \n",
       "39453                           NaT                           NaT  3.074250   \n",
       "41011                           NaT 2021-03-13 17:49:36.095059200  4.525726   \n",
       "44040 2018-07-15 07:54:36.339649792 2021-03-21 11:08:02.422579200  3.137394   \n",
       "44339                           NaT 2021-03-18 09:25:17.517417600  2.427600   \n",
       "45123 2020-05-06 02:16:08.916772688                           NaT  4.193993   \n",
       "46465                           NaT                           NaT  2.305377   \n",
       "47821 2020-12-14 17:38:38.671573682                           NaT  3.857927   \n",
       "48153 2019-03-25 11:05:23.384833568                           NaT  4.447327   \n",
       "49717                           NaT                           NaT  2.453229   \n",
       "\n",
       "         high_sbp  age_start  \n",
       "217    117.839630      105.0  \n",
       "4700   140.200723      105.0  \n",
       "6575   127.158067      105.0  \n",
       "10437  148.891539      105.0  \n",
       "11816  158.321445      115.0  \n",
       "12298  143.761766      105.0  \n",
       "13212  103.715200      105.0  \n",
       "14481  116.358751      110.0  \n",
       "16385  138.086792      105.0  \n",
       "17889  120.695240      105.0  \n",
       "24589  110.834849      105.0  \n",
       "26739  125.889559      105.0  \n",
       "30482  139.803930      105.0  \n",
       "31923  181.846576      105.0  \n",
       "33377  108.304785      105.0  \n",
       "33635  125.150657      105.0  \n",
       "38143  133.529298      105.0  \n",
       "39453  181.068210      105.0  \n",
       "41011  146.579071      105.0  \n",
       "44040  149.953643      105.0  \n",
       "44339  138.092033      115.0  \n",
       "45123  128.386921      105.0  \n",
       "46465  119.053132      105.0  \n",
       "47821  129.867640      110.0  \n",
       "48153  140.923435      105.0  \n",
       "49717  123.861619      110.0  \n",
       "\n",
       "[26 rows x 67 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## This looks good until the oldest age groups \n",
    "## Let's check how many people are in this group to see how concerned we should be \n",
    "\n",
    "data1.loc[data1.age_start > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3343795c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Not too concerned but something to watch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9935aadc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>age_start</th>\n",
       "      <th>age_end</th>\n",
       "      <th>year_start</th>\n",
       "      <th>year_end</th>\n",
       "      <th>mean</th>\n",
       "      <th>35%</th>\n",
       "      <th>2.5%</th>\n",
       "      <th>97.5%</th>\n",
       "      <th>location</th>\n",
       "      <th>measure</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>10.454106</td>\n",
       "      <td>10.374487</td>\n",
       "      <td>10.024166</td>\n",
       "      <td>10.880320</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>exposure_standard_deviation</td>\n",
       "      <td>25_to_29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>11.315841</td>\n",
       "      <td>11.220208</td>\n",
       "      <td>10.866911</td>\n",
       "      <td>11.757483</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>exposure_standard_deviation</td>\n",
       "      <td>30_to_34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>13.122234</td>\n",
       "      <td>13.048752</td>\n",
       "      <td>12.687791</td>\n",
       "      <td>13.516641</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>exposure_standard_deviation</td>\n",
       "      <td>35_to_39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>14.592419</td>\n",
       "      <td>14.500977</td>\n",
       "      <td>14.094234</td>\n",
       "      <td>15.091773</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>exposure_standard_deviation</td>\n",
       "      <td>40_to_44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>15.887863</td>\n",
       "      <td>15.800024</td>\n",
       "      <td>15.414401</td>\n",
       "      <td>16.355687</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>exposure_standard_deviation</td>\n",
       "      <td>45_to_49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex  age_start  age_end  year_start  year_end       mean        35%  \\\n",
       "0  Female       25.0     30.0        2019      2020  10.454106  10.374487   \n",
       "1  Female       30.0     35.0        2019      2020  11.315841  11.220208   \n",
       "2  Female       35.0     40.0        2019      2020  13.122234  13.048752   \n",
       "3  Female       40.0     45.0        2019      2020  14.592419  14.500977   \n",
       "4  Female       45.0     50.0        2019      2020  15.887863  15.800024   \n",
       "\n",
       "        2.5%      97.5% location                      measure age_group  \n",
       "0  10.024166  10.880320  Alabama  exposure_standard_deviation  25_to_29  \n",
       "1  10.866911  11.757483  Alabama  exposure_standard_deviation  30_to_34  \n",
       "2  12.687791  13.516641  Alabama  exposure_standard_deviation  35_to_39  \n",
       "3  14.094234  15.091773  Alabama  exposure_standard_deviation  40_to_44  \n",
       "4  15.414401  16.355687  Alabama  exposure_standard_deviation  45_to_49  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## We can repeat this for SBP \n",
    "\n",
    "sbp_exp_std = load_artifact_data('risk_factor', 'high_systolic_blood_pressure', 'exposure_standard_deviation')\n",
    "sbp_exp_std['age_group'] = [f'{int(i)}_to_{int(j) - 1}' for (i,j) in zip(sbp_exp_std.age_start,sbp_exp_std.age_end)]\n",
    "sbp_exp_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6f522ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_start</th>\n",
       "      <th>sex</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>10.023730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.274873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>11.590916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>11.507340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>13.003007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_start     sex        std\n",
       "0       25.0  Female  10.023730\n",
       "1       25.0    Male  10.274873\n",
       "2       30.0  Female  11.590916\n",
       "3       30.0    Male  11.507340\n",
       "4       35.0  Female  13.003007"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sbp_std = (data1\n",
    "                .groupby(['age_start', 'sex'])\n",
    "                .high_sbp.describe()\n",
    "                .filter(['std'])\n",
    "                .reset_index())\n",
    "sbp_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9aeb217d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for sex in sbp_std.sex.unique():\n",
    "    plt.figure() \n",
    "    subdata = sbp_std.loc[(sbp_std.sex==sex)]\n",
    "    plt.plot(subdata['age_start'], subdata['std'], marker='o')\n",
    "    subdata2 = sbp_exp_std.loc[(sbp_exp_std.sex==sex)]\n",
    "    plt.plot(subdata2['age_start'], subdata2['mean'], marker='o')\n",
    "    plt.title(f'SBP Exposure Standard Deviation: {sex}') \n",
    "    plt.xticks(rotation=90) \n",
    "    plt.grid()\n",
    "    plt.legend(['Simulation baseline', 'Artifact Baseline']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e06e3ad8",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Similarly this only starts getting wonky in the 100+ folks which doesn't have a lot of data in it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb020db4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d57919fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}