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test(backtest): add back tests
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‎notebooks/generate_exp_results.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Generate expected results for back tests\n",
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"\n",
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"They save results to `.csv` and use these to verify that the model produces consistent results."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# pylint: disable=missing-module-docstring\n",
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"%load_ext autoreload\n",
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"%autoreload 1\n",
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"%aimport simulation\n",
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"\n",
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"# pylint: disable=wrong-import-position\n",
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"import os\n",
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"import pandas as pd\n",
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"\n",
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"from simulation.parameters import Param\n",
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"from simulation.model import Model\n",
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"from simulation.runner import Runner"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define path to folder for expected results for tests\n",
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"TESTS = \"../tests/exp_results/\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The raw `audit_list` from a single run of the model (converted to a dataframe)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>time</th>\n",
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" <th>asu_occupancy</th>\n",
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" <th>rehab_occupancy</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1095</td>\n",
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" <td>7</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1096</td>\n",
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" <td>5</td>\n",
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" <td>11</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1097</td>\n",
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" <td>5</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1098</td>\n",
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" <td>4</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1099</td>\n",
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" <td>5</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" time asu_occupancy rehab_occupancy\n",
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"0 1095 7 12\n",
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"1 1096 5 11\n",
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"2 1097 5 10\n",
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"3 1098 4 10\n",
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"4 1099 5 10"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Run model\n",
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"model = Model(param=Param(), run_number=0)\n",
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"model.run()\n",
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"\n",
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"# Convert audit list to dataframe and preview\n",
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"audit_list = pd.DataFrame(model.audit_list)\n",
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"display(audit_list.head())\n",
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"\n",
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"# Save to csv\n",
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"audit_list.to_csv(os.path.join(TESTS, \"audit_list.csv\"), index=False)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The dataframes returned by `run_single()` from `Runner`, from a single replication."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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"\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>beds</th>\n",
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" <th>freq</th>\n",
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" <th>pct</th>\n",
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" <th>c_pct</th>\n",
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" <th>prob_delay</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>0.000548</td>\n",
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" <td>0.000548</td>\n",
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" <td>1.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>12</td>\n",
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" <td>0.006575</td>\n",
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" <td>0.007123</td>\n",
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" <td>0.923077</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>3</td>\n",
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" <td>39</td>\n",
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" <td>0.021370</td>\n",
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" <td>0.028493</td>\n",
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" <td>0.750000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
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" <td>76</td>\n",
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" <td>0.041644</td>\n",
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" <td>0.070137</td>\n",
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" <td>0.593750</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>134</td>\n",
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" <td>0.073425</td>\n",
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" <td>0.143562</td>\n",
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" <td>0.511450</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" beds freq pct c_pct prob_delay\n",
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"0 1 1 0.000548 0.000548 1.000000\n",
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"1 2 12 0.006575 0.007123 0.923077\n",
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"2 3 39 0.021370 0.028493 0.750000\n",
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"3 4 76 0.041644 0.070137 0.593750\n",
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"4 5 134 0.073425 0.143562 0.511450"
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]
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},
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"metadata": {},
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>beds</th>\n",
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" <th>freq</th>\n",
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" <th>pct</th>\n",
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" <th>c_pct</th>\n",
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" <th>prob_delay</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>3</td>\n",
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" <td>9</td>\n",
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" <td>0.004932</td>\n",
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" <td>0.004932</td>\n",
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" <td>1.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>4</td>\n",
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" <td>25</td>\n",
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" <td>0.013699</td>\n",
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" <td>0.018630</td>\n",
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" <td>0.735294</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>5</td>\n",
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" <td>74</td>\n",
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" <td>0.040548</td>\n",
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" <td>0.059178</td>\n",
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" <td>0.685185</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>6</td>\n",
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" <td>147</td>\n",
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" <td>0.080548</td>\n",
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" <td>0.139726</td>\n",
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" <td>0.576471</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>7</td>\n",
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" <td>176</td>\n",
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" <td>0.096438</td>\n",
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" <td>0.236164</td>\n",
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" <td>0.408353</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" beds freq pct c_pct prob_delay\n",
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"0 3 9 0.004932 0.004932 1.000000\n",
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"1 4 25 0.013699 0.018630 0.735294\n",
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"2 5 74 0.040548 0.059178 0.685185\n",
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"3 6 147 0.080548 0.139726 0.576471\n",
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"4 7 176 0.096438 0.236164 0.408353"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Run model\n",
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"runner = Runner(param=Param())\n",
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"runner_df = runner.run_single(run=0)\n",
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"\n",
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"# Preview the dataframes\n",
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"display(runner_df[\"asu\"].head())\n",
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"display(runner_df[\"rehab\"].head())\n",
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"\n",
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"# Save to csv\n",
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"runner_df[\"asu\"].to_csv(os.path.join(TESTS, \"asu_occupancy.csv\"), index=False)\n",
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"runner_df[\"rehab\"].to_csv(os.path.join(TESTS, \"rehab_occupancy.csv\"),\n",
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" index=False)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "stroke-rap-python",
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"language": "python",
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"name": "python3"
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎tests/exp_results/asu_occupancy.csv‎

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beds,freq,pct,c_pct,prob_delay
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1,1,0.000547945205479452,0.000547945205479452,1.0
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2,12,0.006575342465753425,0.007123287671232877,0.923076923076923
4+
3,39,0.021369863013698632,0.02849315068493151,0.75
5+
4,76,0.041643835616438356,0.07013698630136986,0.59375
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5,134,0.07342465753424658,0.14356164383561643,0.5114503816793894
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6,215,0.1178082191780822,0.26136986301369863,0.45073375262054505
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7,226,0.12383561643835617,0.3852054794520548,0.32147937411095306
9+
8,266,0.14575342465753424,0.530958904109589,0.27450980392156865
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9,245,0.13424657534246576,0.6652054794520548,0.20181219110378915
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10,163,0.08931506849315068,0.7545205479452055,0.11837327523602033
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11,165,0.09041095890410959,0.8449315068493151,0.10700389105058367
13+
12,108,0.05917808219178082,0.9041095890410958,0.06545454545454546
14+
13,89,0.04876712328767123,0.952876712328767,0.05117883841288097
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14,34,0.01863013698630137,0.9715068493150684,0.019176536943034405
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15,15,0.00821917808219178,0.9797260273972601,0.008389261744966443
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16,19,0.010410958904109589,0.9901369863013697,0.010514665190924186
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17,6,0.0032876712328767125,0.9934246575342465,0.003309431880860453
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18,5,0.0027397260273972603,0.9961643835616437,0.0027502750275027505
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19,2,0.001095890410958904,0.9972602739726026,0.001098901098901099
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20,1,0.000547945205479452,0.997808219178082,0.0005491488193300384
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21,1,0.000547945205479452,0.9983561643835615,0.0005488474204171241
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22,1,0.000547945205479452,0.9989041095890409,0.0005485463521667582
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23,0,0.0,0.9989041095890409,0.0
25+
24,1,0.000547945205479452,0.9994520547945203,0.0005482456140350878
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25,0,0.0,0.9994520547945203,0.0
27+
26,0,0.0,0.9994520547945203,0.0
28+
27,1,0.000547945205479452,0.9999999999999997,0.0005479452054794522

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