scripts/validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PopulationSim validation script\n",
    "\n",
    "This notebook helps visualize and summarize the results of a PopulationSim model run.\n",
    "\n",
    "## Inputs\n",
    "\n",
    "This script uses several core input/output files from PopulationSim. A `.yaml` configuration helper file allows the user of this notebook to pick and choose which summaries to calculate. This configuration file can be rewritten to handle different regions without the need to rewite the script itself. The example file provided here is for the Corvallis-Albany-Lebanon Modeling (CALM) region in Oregon.\n",
    "\n",
    "### config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "region_yaml = 'calm_verification.yaml'  # default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It contains several model-specific parameters:\n",
    "\n",
    "- `popsim_dir:` the path to the main model run, containing popsim's data/output files,\n",
    "- `scenario:` and `region:` for labeling output plots\n",
    "- `summaries:` the list of `summary_*.csv` output files to use for validation\n",
    "- `aggregate_summaries:` a list of desired control vs. results comparison columns to calculate from the summary files\n",
    "\n",
    "See the example `region_yaml` for more.\n",
    "\n",
    "### CSV file (optional)\n",
    "\n",
    "Additional inputs parameters may be given via CSV.\n",
    "\n",
    "E.x.\n",
    "- POPSIMDIR\n",
    "- VALID_DIR\n",
    "\n",
    "Both input files may either be given directly in the notebook below, or on the command line (download the script by choosing `File` > `Download as` > `Python (.py)`):\n",
    "\n",
    "```script\n",
    "python validation.py calm_verification.yaml [parameters.csv]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outputs\n",
    "\n",
    "- A CSV summary of statistics for each control group in `aggregate_summaries`\n",
    "- A plot of the frequency distribution for each control group\n",
    "- A plot of the standard deviation for each control group\n",
    "- A CSV summary of aggregated statistics for a given geography (e.g. \"PUMA\")\n",
    "- A plot of the household expansion factor distribution for the given geography\n",
    "\n",
    "Requests for further outputs options and configurations are welcome via GitHub!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages\n",
    "\n",
    "Required packages. The notebook requires `jupyter`, while the script needs only the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "try:\n",
    "    import yaml\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "except ImportError:\n",
    "    packages = 'pyyaml pandas numpy matplotlib'\n",
    "    ret = os.system(f'conda install {packages}')\n",
    "    if ret != 0:\n",
    "        os.system(f'{sys.executable} -m pip install {packages}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import cycler\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "plt.rcParams['axes.prop_cycle'] = cycler(color=['b', 'g', 'r', 'y'])\n",
    "plt.rcParams['savefig.dpi'] = 200\n",
    "plt.rcParams['figure.constrained_layout.use'] = True\n",
    "plt.rcParams['figure.constrained_layout.h_pad'] = 0.5\n",
    "plt.rcParams['figure.figsize'] = (8,15)\n",
    "plt.rcParams['hist.bins'] = 25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load input parameters\n",
    "\n",
    "Read in the input file(s). If you encounter errors at this stage, be sure that\n",
    "1. the paths to the files are correct\n",
    "2. the YAML is well-structured (fields can't contain special characters like ':')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {}\n",
    "\n",
    "# ignore sys.argv if running validation notebook\n",
    "is_script = 'ipykernel' not in sys.modules\n",
    "\n",
    "if is_script and len(sys.argv) > 1:\n",
    "    if os.path.isfile(sys.argv[1]):\n",
    "        region_yaml = sys.argv[1]\n",
    "\n",
    "with open(region_yaml) as f:\n",
    "    parameters.update(yaml.safe_load(f))\n",
    "\n",
    "if is_script and len(sys.argv) > 2:\n",
    "    if os.path.isfile(sys.argv[2]):\n",
    "        parameters_csv = sys.argv[2]\n",
    "        parameters.update(pd.read_csv(parameters_csv, header=None, index_col=0, comment='#').to_dict()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aggregate_summaries:\n",
      "- control: num_hh_control\n",
      "  geography: TAZ\n",
      "  name: Total Households\n",
      "  result: num_hh_result\n",
      "- control: hh_size_1_control\n",
      "  geography: TAZ\n",
      "  name: 'Household Size: 1 person HH'\n",
      "  result: hh_size_1_result\n",
      "- control: hh_size_2_control\n",
      "  geography: TAZ\n",
      "  name: 'Household Size: 2 person HH'\n",
      "  result: hh_size_2_result\n",
      "- control: hh_size_3_control\n",
      "  geography: TAZ\n",
      "  name: 'Household Size: 3 person HH'\n",
      "  result: hh_size_3_result\n",
      "- control: hh_size_4_plus_control\n",
      "  geography: TAZ\n",
      "  name: 'Household Size: 4+ person HH'\n",
      "  result: hh_size_4_plus_result\n",
      "- control: hh_age_15_24_control\n",
      "  geography: TAZ\n",
      "  name: 'Householder Age: 15-24 years'\n",
      "  result: hh_age_15_24_result\n",
      "- control: hh_age_25_54_control\n",
      "  geography: TAZ\n",
      "  name: 'Householder Age: 25-54 years'\n",
      "  result: hh_age_25_54_result\n",
      "- control: hh_age_55_64_control\n",
      "  geography: TAZ\n",
      "  name: 'Householder Age: 55-64 years'\n",
      "  result: hh_age_55_64_result\n",
      "- control: hh_age_65_plus_control\n",
      "  geography: TAZ\n",
      "  name: 'Householder Age: >65 years'\n",
      "  result: hh_age_65_plus_result\n",
      "- control: hh_inc_15_control\n",
      "  geography: TAZ\n",
      "  name: 'Household Income: (-Inf,21297)'\n",
      "  result: hh_inc_15_result\n",
      "- control: hh_inc_15_30_control\n",
      "  geography: TAZ\n",
      "  name: 'Household Income: [21297,42593)'\n",
      "  result: hh_inc_15_30_result\n",
      "- control: hh_inc_30_60_control\n",
      "  geography: TAZ\n",
      "  name: 'Household Income: [42593,85185)'\n",
      "  result: hh_inc_30_60_result\n",
      "- control: hh_inc_60_plus_control\n",
      "  geography: TAZ\n",
      "  name: 'Household Income: [85185,+Inf)'\n",
      "  result: hh_inc_60_plus_result\n",
      "- control: students_by_family_housing_type_control\n",
      "  geography: TAZ\n",
      "  name: Students by family\n",
      "  result: students_by_family_housing_type_result\n",
      "- control: students_by_nonfamily_housing_type_control\n",
      "  geography: TAZ\n",
      "  name: Students by non-family\n",
      "  result: students_by_nonfamily_housing_type_result\n",
      "- control: hh_by_type_sf_control\n",
      "  geography: TRACT\n",
      "  name: SF\n",
      "  result: hh_by_type_sf_result\n",
      "- control: hh_by_type_mf_control\n",
      "  geography: TRACT\n",
      "  name: MF\n",
      "  result: hh_by_type_mf_result\n",
      "- control: hh_by_type_mh_control\n",
      "  geography: TRACT\n",
      "  name: MH\n",
      "  result: hh_by_type_mh_result\n",
      "- control: hh_by_type_dup_control\n",
      "  geography: TRACT\n",
      "  name: Dup\n",
      "  result: hh_by_type_dup_result\n",
      "- control: hh_wrks_0_control\n",
      "  geography: TRACT\n",
      "  name: 'Household Workers: 0 worker HH'\n",
      "  result: hh_wrks_0_result\n",
      "- control: hh_wrks_1_control\n",
      "  geography: TRACT\n",
      "  name: 'Household Workers: 1 worker HH'\n",
      "  result: hh_wrks_1_result\n",
      "- control: hh_wrks_2_control\n",
      "  geography: TRACT\n",
      "  name: 'Household Workers: 2 worker HH'\n",
      "  result: hh_wrks_2_result\n",
      "- control: hh_wrks_3_plus_control\n",
      "  geography: TRACT\n",
      "  name: 'Household Workers: 3+ worker HH'\n",
      "  result: hh_wrks_3_plus_result\n",
      "- control: persons_occ_1_control\n",
      "  geography: REGION\n",
      "  name: Occupation Type 1\n",
      "  result: persons_occ_1_result\n",
      "- control: persons_occ_2_control\n",
      "  geography: REGION\n",
      "  name: Occupation Type 2\n",
      "  result: persons_occ_2_result\n",
      "- control: persons_occ_3_control\n",
      "  geography: REGION\n",
      "  name: Occupation Type 3\n",
      "  result: persons_occ_3_result\n",
      "- control: persons_occ_4_control\n",
      "  geography: REGION\n",
      "  name: Occupation Type 4\n",
      "  result: persons_occ_4_result\n",
      "- control: persons_occ_5_control\n",
      "  geography: REGION\n",
      "  name: Occupation Type 5\n",
      "  result: persons_occ_5_result\n",
      "- control: persons_occ_6_control\n",
      "  geography: REGION\n",
      "  name: Occupation Type 6\n",
      "  result: persons_occ_6_result\n",
      "- control: persons_occ_7_control\n",
      "  geography: REGION\n",
      "  name: Occupation Type 7\n",
      "  result: persons_occ_7_result\n",
      "- control: persons_occ_8_control\n",
      "  geography: REGION\n",
      "  name: Occupation Type 8\n",
      "  result: persons_occ_8_result\n",
      "expanded_hhid: output/final_expanded_household_ids.csv\n",
      "expanded_hhid_col: hh_id\n",
      "geographies: data/geo_cross_walk.csv\n",
      "group_geographies:\n",
      "- REGION\n",
      "- PUMA\n",
      "- TRACT\n",
      "- TAZ\n",
      "popsim_dir: ../example_calm\n",
      "region: CALM\n",
      "scenario: Base\n",
      "seed_cols:\n",
      "  geog: PUMA\n",
      "  geog_weight: WGTP\n",
      "  hh_id: hhnum\n",
      "seed_households: data/seed_households.csv\n",
      "summaries:\n",
      "- output/final_summary_TAZ_PUMA.csv\n",
      "- output/final_summary_TRACT.csv\n",
      "- output/final_summary_TAZ.csv\n",
      "validation_dir: calm_validation_results\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(yaml.dump(parameters))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `parameters` var contains all the input information needed for the validation script. Values may be updated either in the source files (recommended), or here in the notebook by updating keys of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "popsim_dir      = parameters.get('POPSIMDIR') or parameters.get('popsim_dir')\n",
    "validation_dir  = parameters.get('VALID_DIR') or parameters.get('validation_dir')\n",
    "geography_file  = parameters.get('geographies')\n",
    "use_geographies = parameters.get('group_geographies')\n",
    "summary_files   = parameters.get('summaries', [])\n",
    "aggregate_list  = parameters.get('aggregate_summaries', [])\n",
    "scenario        = parameters.get('scenario')\n",
    "region          = parameters.get('region')\n",
    "exp_hh_file     = parameters.get('expanded_hhid')\n",
    "exp_hh_id_col   = parameters.get('expanded_hhid_col')\n",
    "seed_hh_file    = parameters.get('seed_households')\n",
    "seed_hh_cols    = parameters.get('seed_cols')\n",
    "\n",
    "if not os.path.isdir(validation_dir):\n",
    "    os.mkdir(validation_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summaries\n",
    "Create the master summary DataFrame from `parameters['summaries']`, listed either in the input YAML or above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
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       "    .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>geography</th>\n",
       "      <th>id</th>\n",
       "      <th>num_hh_control</th>\n",
       "      <th>hh_size_1_control</th>\n",
       "      <th>hh_size_2_control</th>\n",
       "      <th>hh_size_3_control</th>\n",
       "      <th>hh_size_4_plus_control</th>\n",
       "      <th>hh_age_15_24_control</th>\n",
       "      <th>hh_age_25_54_control</th>\n",
       "      <th>hh_age_55_64_control</th>\n",
       "      <th>...</th>\n",
       "      <th>hh_by_type_mh_diff</th>\n",
       "      <th>hh_by_type_dup_diff</th>\n",
       "      <th>persons_occ_1_diff</th>\n",
       "      <th>persons_occ_2_diff</th>\n",
       "      <th>persons_occ_3_diff</th>\n",
       "      <th>persons_occ_4_diff</th>\n",
       "      <th>persons_occ_5_diff</th>\n",
       "      <th>persons_occ_6_diff</th>\n",
       "      <th>persons_occ_7_diff</th>\n",
       "      <th>persons_occ_8_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PUMA</td>\n",
       "      <td>600</td>\n",
       "      <td>62041</td>\n",
       "      <td>17156</td>\n",
       "      <td>22701</td>\n",
       "      <td>9524</td>\n",
       "      <td>12660</td>\n",
       "      <td>7258</td>\n",
       "      <td>30222</td>\n",
       "      <td>11049</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TRACT</td>\n",
       "      <td>100</td>\n",
       "      <td>2921</td>\n",
       "      <td>762</td>\n",
       "      <td>1086</td>\n",
       "      <td>528</td>\n",
       "      <td>545</td>\n",
       "      <td>453</td>\n",
       "      <td>1711</td>\n",
       "      <td>463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRACT</td>\n",
       "      <td>202</td>\n",
       "      <td>2302</td>\n",
       "      <td>644</td>\n",
       "      <td>909</td>\n",
       "      <td>331</td>\n",
       "      <td>418</td>\n",
       "      <td>257</td>\n",
       "      <td>1013</td>\n",
       "      <td>392</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRACT</td>\n",
       "      <td>400</td>\n",
       "      <td>3298</td>\n",
       "      <td>824</td>\n",
       "      <td>1328</td>\n",
       "      <td>534</td>\n",
       "      <td>612</td>\n",
       "      <td>208</td>\n",
       "      <td>1595</td>\n",
       "      <td>684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TRACT</td>\n",
       "      <td>500</td>\n",
       "      <td>1294</td>\n",
       "      <td>308</td>\n",
       "      <td>570</td>\n",
       "      <td>186</td>\n",
       "      <td>230</td>\n",
       "      <td>18</td>\n",
       "      <td>482</td>\n",
       "      <td>361</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  geography   id  num_hh_control  hh_size_1_control  hh_size_2_control  \\\n",
       "0      PUMA  600           62041              17156              22701   \n",
       "0     TRACT  100            2921                762               1086   \n",
       "1     TRACT  202            2302                644                909   \n",
       "2     TRACT  400            3298                824               1328   \n",
       "3     TRACT  500            1294                308                570   \n",
       "\n",
       "   hh_size_3_control  hh_size_4_plus_control  hh_age_15_24_control  \\\n",
       "0               9524                   12660                  7258   \n",
       "0                528                     545                   453   \n",
       "1                331                     418                   257   \n",
       "2                534                     612                   208   \n",
       "3                186                     230                    18   \n",
       "\n",
       "   hh_age_25_54_control  hh_age_55_64_control  ...  hh_by_type_mh_diff  \\\n",
       "0                 30222                 11049  ...                 3.0   \n",
       "0                  1711                   463  ...                 0.0   \n",
       "1                  1013                   392  ...                 0.0   \n",
       "2                  1595                   684  ...                 0.0   \n",
       "3                   482                   361  ...                 0.0   \n",
       "\n",
       "   hh_by_type_dup_diff  persons_occ_1_diff  persons_occ_2_diff  \\\n",
       "0                -13.0                51.0                45.0   \n",
       "0                  0.0                 NaN                 NaN   \n",
       "1                  0.0                 NaN                 NaN   \n",
       "2                  0.0                 NaN                 NaN   \n",
       "3                  0.0                 NaN                 NaN   \n",
       "\n",
       "   persons_occ_3_diff  persons_occ_4_diff  persons_occ_5_diff  \\\n",
       "0                13.0                39.0                27.0   \n",
       "0                 NaN                 NaN                 NaN   \n",
       "1                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "\n",
       "   persons_occ_6_diff  persons_occ_7_diff  persons_occ_8_diff  \n",
       "0                13.0                 1.0                 5.0  \n",
       "0                 NaN                 NaN                 NaN  \n",
       "1                 NaN                 NaN                 NaN  \n",
       "2                 NaN                 NaN                 NaN  \n",
       "3                 NaN                 NaN                 NaN  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_df = pd.DataFrame()\n",
    "for summary_file in summary_files:\n",
    "    filepath = os.path.join(popsim_dir, summary_file)\n",
    "    df = pd.read_csv(filepath)\n",
    "    summary_df = summary_df.append(df)\n",
    "\n",
    "summary_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PUMA', 'TRACT', 'TAZ'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_df.geography.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>geography</th>\n",
       "      <th>id</th>\n",
       "      <th>num_hh_control</th>\n",
       "      <th>hh_size_1_control</th>\n",
       "      <th>hh_size_2_control</th>\n",
       "      <th>hh_size_3_control</th>\n",
       "      <th>hh_size_4_plus_control</th>\n",
       "      <th>hh_age_15_24_control</th>\n",
       "      <th>hh_age_25_54_control</th>\n",
       "      <th>hh_age_55_64_control</th>\n",
       "      <th>...</th>\n",
       "      <th>hh_by_type_mh_diff</th>\n",
       "      <th>hh_by_type_dup_diff</th>\n",
       "      <th>persons_occ_1_diff</th>\n",
       "      <th>persons_occ_2_diff</th>\n",
       "      <th>persons_occ_3_diff</th>\n",
       "      <th>persons_occ_4_diff</th>\n",
       "      <th>persons_occ_5_diff</th>\n",
       "      <th>persons_occ_6_diff</th>\n",
       "      <th>persons_occ_7_diff</th>\n",
       "      <th>persons_occ_8_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>TAZ</td>\n",
       "      <td>1290</td>\n",
       "      <td>61</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>TAZ</td>\n",
       "      <td>1291</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>TAZ</td>\n",
       "      <td>1292</td>\n",
       "      <td>196</td>\n",
       "      <td>43</td>\n",
       "      <td>67</td>\n",
       "      <td>34</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>103</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>TAZ</td>\n",
       "      <td>1293</td>\n",
       "      <td>788</td>\n",
       "      <td>114</td>\n",
       "      <td>273</td>\n",
       "      <td>149</td>\n",
       "      <td>252</td>\n",
       "      <td>38</td>\n",
       "      <td>481</td>\n",
       "      <td>136</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>REGION</td>\n",
       "      <td>1</td>\n",
       "      <td>62041</td>\n",
       "      <td>17156</td>\n",
       "      <td>22701</td>\n",
       "      <td>9524</td>\n",
       "      <td>12660</td>\n",
       "      <td>7258</td>\n",
       "      <td>30222</td>\n",
       "      <td>11049</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    geography    id  num_hh_control  hh_size_1_control  hh_size_2_control  \\\n",
       "777       TAZ  1290              61                  8                 24   \n",
       "778       TAZ  1291              40                  7                 13   \n",
       "779       TAZ  1292             196                 43                 67   \n",
       "780       TAZ  1293             788                114                273   \n",
       "0      REGION     1           62041              17156              22701   \n",
       "\n",
       "     hh_size_3_control  hh_size_4_plus_control  hh_age_15_24_control  \\\n",
       "777                 14                      15                     1   \n",
       "778                  7                      13                     1   \n",
       "779                 34                      52                     2   \n",
       "780                149                     252                    38   \n",
       "0                 9524                   12660                  7258   \n",
       "\n",
       "     hh_age_25_54_control  hh_age_55_64_control  ...  hh_by_type_mh_diff  \\\n",
       "777                    33                    16  ...                 NaN   \n",
       "778                    25                     6  ...                 NaN   \n",
       "779                   103                    46  ...                 NaN   \n",
       "780                   481                   136  ...                 NaN   \n",
       "0                   30222                 11049  ...                 3.0   \n",
       "\n",
       "     hh_by_type_dup_diff  persons_occ_1_diff  persons_occ_2_diff  \\\n",
       "777                  NaN                 NaN                 NaN   \n",
       "778                  NaN                 NaN                 NaN   \n",
       "779                  NaN                 NaN                 NaN   \n",
       "780                  NaN                 NaN                 NaN   \n",
       "0                  -13.0                51.0                45.0   \n",
       "\n",
       "     persons_occ_3_diff  persons_occ_4_diff  persons_occ_5_diff  \\\n",
       "777                 NaN                 NaN                 NaN   \n",
       "778                 NaN                 NaN                 NaN   \n",
       "779                 NaN                 NaN                 NaN   \n",
       "780                 NaN                 NaN                 NaN   \n",
       "0                  13.0                39.0                27.0   \n",
       "\n",
       "     persons_occ_6_diff  persons_occ_7_diff  persons_occ_8_diff  \n",
       "777                 NaN                 NaN                 NaN  \n",
       "778                 NaN                 NaN                 NaN  \n",
       "779                 NaN                 NaN                 NaN  \n",
       "780                 NaN                 NaN                 NaN  \n",
       "0                  13.0                 1.0                 5.0  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def meta_geog_df(summary_df, meta_geog):\n",
    "    geography_df = pd.read_csv(os.path.join(popsim_dir, geography_file))\n",
    "    geog = use_geographies[use_geographies.index(meta_geog) + 1]  # next geography in list\n",
    "    meta_df = geography_df[[meta_geog, geog]].drop_duplicates(ignore_index=True)\n",
    "    geog_df = summary_df[summary_df.geography == geog].copy()\n",
    "    geog_df['geography'] = meta_geog\n",
    "    geog_df['id'] = geog_df.id.map(meta_df.set_index(geog).to_dict()[meta_geog])\n",
    "    \n",
    "    return geog_df\n",
    "    \n",
    "for geog in use_geographies:\n",
    "    if not geog in summary_df.geography.unique():\n",
    "        summary_df = summary_df.append(meta_geog_df(summary_df, geog))\n",
    "\n",
    "summary_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handle each control vs. result comparison specified in `parameters['aggregate_summaries']`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_control(summary_df, name, geography, control, result):\n",
    "    \"\"\"\n",
    "    Global\n",
    "    ------\n",
    "    summary_df: pandas DataFrame\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    name: str, output plot title\n",
    "    geography: str, groupby geography\n",
    "    control: str, control column name in summary table\n",
    "    result: str, result column name in summary table\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    \n",
    "    stats: pandas Series of statistics, aggregated by geography/control/result\n",
    "    frequencies: pandas Series of control vs. results differences\n",
    "    \"\"\"\n",
    "\n",
    "    sub_df = summary_df[summary_df.geography == geography][[control, result]].dropna(axis=0, how='any')\n",
    "    \n",
    "    observed = sub_df[control]\n",
    "    non_zero_observed = observed[observed > 0]\n",
    "    predicted = sub_df[result]\n",
    "    difference = predicted - observed\n",
    "    pc_difference = (difference/non_zero_observed)*100\n",
    "    rmse = (difference ** 2).mean() ** 0.5\n",
    "    \n",
    "    frequencies = non_zero_observed.groupby(difference).count()\n",
    "\n",
    "    stats = pd.Series({\n",
    "        'name': name,\n",
    "        'geography': geography,\n",
    "        'observed': observed.sum(),\n",
    "        'predicted': predicted.sum(),\n",
    "        'difference': difference.sum(),\n",
    "        'pc_difference': (difference.sum()/observed.sum())*100,\n",
    "        'mean_pc_difference': pc_difference.mean(),\n",
    "        'N': non_zero_observed.shape[0],\n",
    "        'rmse': rmse.sum(),\n",
    "        'std': pc_difference.std(),\n",
    "    })\n",
    "\n",
    "    return stats, frequencies, difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot each comparison and save outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2bdr4HcdaRQAILIfDoZKSEt92SUmJHA6Hb/v48ePat2+ffv/730uSSktL9cc//lFPP/10tZA6NTVVqampvm2Px1PP1QeXy+WiRwugR2tITEwMdgkBw7xdd+HwWaBHawiHHi9m3q73AESSbr31Vr388su666675HA4ZLPZfPsSEhLO+dwz1ypK8q1V/HEAcnqt4gcffHDpGwAAVJOUlKSioiIVFxfL4XAoPz9fo0eP9u2PiYlRdna2b3vy5Mm6//77OUMPAIKEeRtAuKvXAKS0tFTNmzfXq6++Kkn6+uuvqx2Tm5t7zteoaa3izp07/Y45c60iAQgABEZkZKRGjBihqVOnyjAM9evXT23btlVubq6SkpLkdruDXSIA4AzM2wDCXb0GIE8++aQWL17sCzlefPFF/e53v7ukY1hxraKV1m1ZqReJfkJdRESEYmJi6n0clyu63sdoKJKTk6tdKG/w4ME1Hjt58uQAVAQAOBfmbQDhrF4DENM0/ba3bdt2wa8RjmsVrbRuy0q9SPQT6gwjQeXl5fU+jsdTdsHPCac15gAAAEAoqtcA5MxrfdQVaxUBAAAAAMDFqtcApKqqSlu2bPFtG4bhty1JnTt3PudrsFYRAAAAAABcrHoNQOLj4/WnP/3Jtx0bG+u3bbPZNGfOnPO+DmsVAQAAAADAxajXAGTu3Ln1+fIAAAAAAAC1EhHsAgAAAAAAAOobAQgAAAAAALA8AhAAAAAAAGB5BCAAAAAAAMDyCEAAAAAAAIDlEYAAAAAAAADLIwABAAAAAACWRwACAAAAAAAsjwAEAAAAAABYHgEIAAAAAACwPAIQAAAAAABgeQQgAAAAAADA8ghAAAAAAACA5RGAAAAAAAAAyyMAAQAAAAAAlkcAAgAAAAAALI8ABAAAAAAAWB4BCAAAAAAAsDwCEAAAAAAAYHkEIAAAAAAAwPIIQAAAAAAAgOURgAAAAAAAAMsjAAEAAAAAAJZHAAIAAAAAACyPAAQAAAAAAFgeAQgAAAAAALA8AhAAAAAAAGB5BCAAAAAAAMDyCEAAAAAAAIDlEYAAAAAAAADLIwABAAAAAACWRwACAAAAAAAszx7sAgAADVdBQYFycnJkGIb69++vtLQ0v/0fffSRVqxYocjISDVr1kyPP/64WrZsGaRqAQDM2wDCGWeAAADqxDAMZWdnKyMjQy+//LLWrl2rwsJCv2PatWunzMxMvfTSS7rpppv0+uuvB6laAADzNoBwF/JngJBSA0Bo2rVrl1q3bq2EhARJUkpKitavX682bdr4juncubPv6w4dOmjNmjUBrxMAcArzNoBwF9JngJBSA0Do8nq9cjqdvm2n0ymv13vW41euXKlu3boFojQAQA2YtwGEu5A+A4SUGgCsYfXq1dqzZ48mT55c4/68vDzl5eVJkjIzM+VyuQJYXeDZ7XZ6tAB6hJUxb/sLh88CPVpDOPR4MUI6AKkppd65c+dZjyelBoDAcTgcKikp8W2XlJTI4XBUO+7rr7/WsmXLNHnyZDVq1KjG10pNTVVqaqpv2+PxXPqCQ4jL5aJHC6BHa0hMTAx2CQHDvF134fBZoEdrCIceL2beDukA5EKcL6WWGk5SbaXUzkq9SPQT6iIiIhQTE1Pv47hc0fU+RkOQlJSkoqIiFRcXy+FwKD8/X6NHj/Y7Zu/evVqwYIEyMjIUHx8fpEoBABLzNgCEdAByKVNqqeEk1VZK7azUi0Q/oc4wElReXl7v43g8ZRf8HCv+D2NkZKRGjBihqVOnyjAM9evXT23btlVubq6SkpLkdrv1+uuv6/jx45o+fbqkUz9z48aNC3LlABCemLcBhLuQDkBIqQEgtCUnJys5OdnvscGDB/u+njhxYqBLAgCcA/M2gHAW0gEIKTUAAAAAALgUQjoAkUipAQAAAADAxYsIdgEAAAAAAAD1jQAEAAAAAABYHgEIAAAAAACwPAIQAAAAAABgeQQgAAAAAADA8ghAAAAAAACA5RGAAAAAAAAAyyMAAQAAAAAAlkcAAgAAAAAALI8ABAAAAAAAWB4BCAAAAAAAsDwCEAAAAAAAYHkEIAAAAAAAwPIIQAAAAAAAgOURgAAAAAAAAMsjAAEAAAAAAJZHAAIAAAAAACyPAAQAAAAAAFgeAQgAAAAAALA8AhAAAAAAAGB5BCAAAAAAAMDyCEAAAAAAAIDlEYAAAAAAAADLIwABAAAAAACWRwACAAAAAAAsjwAEAAAAAABYHgEIAAAAAACwPAIQAAAAAABgeQQgAAAAAADA8ghAAAAAAACA5RGAAAAAAAAAyyMAAQAAAAAAlkcAAgAAAAAALI8ABAAAAAAAWB4BCAAAAAAAsDx7sAuojYKCAuXk5MgwDPXv319paWl++ysqKjRnzhzt2bNHcXFxGjNmjFq1ahWkagEgfDA/A0DDwrwNIJyF/BkghmEoOztbGRkZevnll7V27VoVFhb6HbNy5Uo1bdpUs2fP1m233aY33ngjSNUCQPhgfgaAhoV5G0C4C/kAZNeuXWrdurUSEhJkt9uVkpKi9evX+x2zYcMG9e3bV5J00003acuWLTJNMwjVAkD4YH4GgIaFeRtAuAv5AMTr9crpdPq2nU6nvF7vWY+JjIxUTEyMysrKAlonAIQb5mcAaFiYtwGEuwZxDZBLJS8vT3l5eZKkzMxMJSYmBrmiswvl2i6UlXqR6CeUTZkiSXEBGCkQY4SXhjQ/Xyr0aA30iHDFvG1N9GgN4dBjXYX8GSAOh0MlJSW+7ZKSEjkcjrMeU1VVpfLycsXFVf8HSmpqqjIzM5WZmVm/RV+k8ePHB7uES8ZKvUj0E+qs1k+oq6/5ORzeR3q0Bnq0hnDo8TTm7bqjR2ugR2u4mB5DPgBJSkpSUVGRiouLVVlZqfz8fLndbr9jevTooVWrVkmS1q1bp+uuu042my0I1QJA+GB+BoCGhXkbQLgL+SUwkZGRGjFihKZOnSrDMNSvXz+1bdtWubm5SkpKktvt1i233KI5c+Zo1KhRio2N1ZgxY4JdNgBYHvMzADQszNsAwl3IByCSlJycrOTkZL/HBg8e7Ps6KipKY8eODXRZ9SY1NTXYJVwyVupFop9QZ7V+GoL6mJ/D4X2kR2ugR2sIhx7PxLxdN/RoDfRoDRfTo83kvlYAAAAAAMDiQv4aIAAAAAAAABerQSyBCVcffvihXnvtNS1cuFDNmjULdjl19tprr2njxo2y2+1KSEhQenq6mjZtGuyyLlhBQYFycnJkGIb69++vtLS0YJdUJx6PR3PnzlVpaalsNptSU1N16623Brusi2YYhsaPHy+HwxEWV7+2gvN9pioqKjRnzhzt2bNHcXFxGjNmjFq1ahWkauvmfD1+9NFHWrFihSIjI9WsWTM9/vjjatmyZZCqrZvazo3r1q3T9OnTNW3aNCUlJQW4yotTmx7z8/O1dOlS2Ww2XXHFFXryySeDUGndna/H0393HD16VIZhaOjQodWWUYSyV155RZs2bVJ8fLyysrKq7TdNUzk5Odq8ebOio6OVnp6uq666KgiVhjbmbebthoJ5m3n7rEyEpIMHD5rPP/+8+fjjj5uHDx8OdjkXpaCgwKysrDRN0zRfe+0187XXXgtyRReuqqrKHDlypHngwAGzoqLCfOqpp8x9+/YFu6w68Xq95u7du03TNM3y8nJz9OjRDbaXM3344YfmjBkzzGnTpgW7FNRCbT5Ty5cvN+fPn2+apml+9tln5vTp04NRap3Vpsd//vOf5vHjx03TNM1PPvnEkj2a5qm55rnnnjMzMjLMXbt2BaHSuqtNj/v37zd/97vfmWVlZaZpmmZpaWkwSq2z2vQ4b94885NPPjFN0zT37dtnpqenB6PUOtu6dau5e/duc+zYsTXu37hxozl16lTTMAzzX//6lzlhwoQAVxj6mLdPYd4OfczbpzBv14wlMCFq8eLFuu+++yxx27GuXbsqMjJSktSxY0d5vd4gV3Thdu3apdatWyshIUF2u10pKSlav359sMuqkxYtWvjS0SZNmugnP/lJg3xPzlRSUqJNmzapf//+wS4FtVSbz9SGDRvUt29fSdJNN92kLVu2yGxAl62qTY+dO3dWdHS0JKlDhw4N7rNY27kxNzdXd955pxo1ahSEKi9ObXpcsWKFfv7znys2NlaSFB8fH4xS66w2PdpsNpWXl0uSysvL1aJFi2CUWmedOnXyvT812bBhg/r06SObzaaOHTvq6NGjOnToUAArDH3M26cwb4c+5u1TmLdrRgASgtavXy+Hw6F27doFu5RLbuXKlerWrVuwy7hgXq9XTqfTt+10OhvcX3g1KS4u1t69e9W+fftgl3JRFi1apGHDhlkiMAwXtflMnXlMZGSkYmJiVFZWFtA6L8aFzhsNcX6sTY979uyRx+NpUKfdnqk2Pe7fv19FRUWaOHGinnnmGRUUFAS6zItSmx7vvvturVmzRo899pimTZumESNGBLrMeuX1euVyuXzbVvl7/lJi3q6OeTs0MW+fwrxdM64BEiRTpkxRaWlptcfvvfdeLVu2TM8++2wQqqq7c/Vz/fXXS5L++te/KjIyUr179w50eajB8ePHlZWVpeHDhysmJibY5dTZxo0bFR8fr6uuukpbt24NdjlAnaxevVp79uzR5MmTg13KJWUYhpYsWaL09PRgl1KvDMNQUVGRJk2aJK/Xq0mTJumll15qkNe7Opu1a9eqb9++uuOOO7Rjxw7Nnj1bWVlZiojg/9IQnpi3Gzbm7fBFABIkEydOrPHx77//XsXFxfrd734n6dSp/ePGjdO0adPUvHnzQJZ4Qc7Wz2mrVq3Sxo0b9dxzzzXI/6V3OBwqKSnxbZeUlMjhcASxootTWVmprKws9e7dWzfeeGOwy7ko//rXv7RhwwZt3rxZJ0+e1LFjxzRr1iyNHj062KXhHGrzmTp9jNPpVFVVlcrLyxUXFxfoUuustvPG119/rWXLlmny5MkN7lTj8/V4/Phx7du3T7///e8lSaWlpfrjH/+op59+usFcUK+2P6sdOnSQ3W5Xq1atdNlll6moqKjBnF1Xmx5XrlypjIwMSaeWs1ZUVKisrKzBnTZ+Ng6HQx6Px7fd0P+erw/M2/8f83ZoY94+hXm7ZuEd/4Sgyy+/XAsXLtTcuXM1d+5cOZ1OvfDCCyEdfpxPQUGB3n//fY0bN863ZrKhSUpKUlFRkYqLi1VZWan8/Hy53e5gl1Unpmlq3rx5+slPfqLbb7892OVctKFDh2revHmaO3euxowZo86dOxN+NAC1+Uz16NFDq1atknTqSvTXXXddgwpQa9Pj3r17tWDBAj399NMN8heS8/UYExOj7Oxs399pHTp0aFC/REu1ex9vuOEG3xloR44cUVFRkRISEoJRbp3UpkeXy6UtW7ZIkgoLC1VRUdGg71D3Y263W6tXr5ZpmtqxY4diYmIa3Hr5+sa8fQrzduhj3j6FebtmNrMhXZkoDD3xxBOaNm1ag/5hHTVqlCorK30XsenQoYMeffTRIFd14TZt2qTFixfIIT4TAAALqElEQVTLMAz169dPAwcODHZJdbJ9+3Y999xzuvzyy32/lAwZMqTBrvM809atW/Xhhx9yG9wGoqbPVG5urpKSkuR2u3Xy5EnNmTNHe/fuVWxsrMaMGdOgfjmRzt/jlClT9P333/tCbpfLpXHjxgW56gtzvh7PNHnyZN1///0N6hdp6fw9mqapJUuWqKCgQBERERo4cKB69eoV7LIvyPl6LCws1Pz583X8+HFJ0rBhw9S1a9cgV117M2bM0LZt23z/+3nPPfeosrJSkjRgwACZpqns7Gx99dVXioqKUnp6eoP7OQ0E5m3m7YaCeZt5+2wIQAAAAAAAgOWxBAYAAAAAAFgeAQgAAAAAALA8AhAAAAAAAGB5BCAAAAAAAMDyCEAAAAAAAIDlEYBAf/7zn/XOO+/4tj/99FP96le/0v3336+ysjJt375do0eP1v33368vv/wyiJVa3+TJk7VixYpgl3HJbd26VY899liwywAAAAAQxuzBLgD164knnlBpaakiIyMVERGhNm3aqE+fPkpNTVVExKn869FHH/UdX1lZqcWLF2vq1Klq166dJOntt9/WL37xC916663BaKHBePvtt3XgwAGNHj062KWEvCeeeEK//vWv1aVLl2CXAgAAACBMEICEgXHjxqlLly4qLy/Xtm3blJOTo127dik9Pb3asYcPH1ZFRYXatm3re+zgwYNq06ZNncauqqpSZGRknWu3EtM0ZZqmL3gKRbxfAAAAAKyKACSMxMTEyO12q3nz5nrmmWd0++236/LLL9fcuXPldDrVp08fjRs3TpI0fPhwtW/fXh6PR8XFxXrhhRcUERGhV199VRUVFVq8eLE2b94sm82mfv366Z577lFERIRWrVqlFStWKCkpSatXr9aAAQP0X//1X/rLX/6izz//XJWVlbr++us1fPhwRUVFaevWrZo9e7Zuu+02vf/++4qIiNCQIUPUr18/SdLJkyf11ltvad26dTp69Kguv/xyTZw4UVFRUdqxY4eWLFmiwsJCtWzZUsOHD9d1111Xq++Fx+PRokWL9M0338g0TfXq1UsPP/ywDMPQsmXLtGLFCp08eVLdunXTiBEjFBMTo+LiYo0cOVLp6enKzc3VyZMnddttt2ngwIEqKCjQsmXLJEnr169X69at9eKLL2ry5Mm6+uqrtW3bNu3Zs0dZWVk6fPiwFi1apP379ysxMVHDhw/X1Vdffc56vV6vRo0apfnz5ys2NlaStHfvXj3//POaP3++PB6P/vSnP+nbb7+V3W5X586d9Zvf/Oa834cLfb+OHDmiV155Rdu3b5fNZlPbtm01efJkRURE6J577tGsWbPUunVrSfL9XN17771+Y86ePVsej8f3MzVo0CDdeeedtXrfAAAAAKCuCEDCUPv27eVwOLR9+3ZdfvnlvscTExOVlZWlkSNHatGiRb4zAX68XGHGjBmKj4/XrFmzdOLECWVmZsrpdOpnP/uZJGnnzp1KSUnRggULVFVVpTfeeEM//PCDXnzxRUVGRmrmzJl65513NHToUElSaWmpysvLNW/ePH399deaPn26rr/+esXGxvoCjueff17NmzfXzp07ZbPZ5PV6lZmZqZEjR6pbt27asmWLsrKyNGPGDDVr1kzvvfeetm/frvHjx1fr3zAMvfDCC7ruuus0d+5cRUREaM+ePZJOBQKrVq3SpEmTFB8frzlz5ig7O1ujRo3yPX/79u2aOXOm9u/fr4yMDN1www3q1q2b7rrrrhqXwKxevVoZGRlKTExUWVmZJkyYoIceeki9evXS559/rszMTM2aNUtxcXFnfc8cDoc6duyodevWKTU1VZL02Wef6cYbb5Tdbtdbb72lrl27atKkSaqsrPT1UxsX8n599NFHcjgcWrhwoe+5Nput1mNJ0qhRo7R9+3aWwAAAAAAIqNA9Fx/1yuFw6P/+7/8u+HmlpaXavHmzhg8frsaNGys+Pl633Xab8vPzfce0aNFC//mf/6nIyEg1atRIK1as0IMPPqjY2Fg1adJEAwcO1Nq1a33HR0ZGatCgQbLb7UpOTlbjxo21f/9+GYah//3f/9Xw4cPlcDgUERGhq6++Wo0aNdLq1avVvXt3JScnKyIiQl26dFFSUpI2bdokSUpLS6sx/JCkXbt2yev16v7771fjxo0VFRWla665RtKpUOH2229XQkKCGjdurKFDhyo/P19VVVW+5999992KiopSu3btdMUVV+i777475/esb9++atu2rSIjI/X111+rdevW6tOnjyIjI3XzzTcrMTFRGzduPO/3/uabb/Z930zTVH5+vm6++WZJkt1u18GDB3Xo0CG/fmrjQt6vyMhIlZaWyuPxyG6369prr73gAAQAAAAAgoEzQMKU1+v1LaW4EB6PR1VVVX4XTjVNU06n07ftcrl8Xx85ckQnTpzwCyNM05RhGL7tuLg4v+tOREdH6/jx4yorK1NFRYVvScWP61i3bp1fcFBVVVWrJTAej0ctW7as8VoXhw4dUsuWLf16qaqq0uHDh32PNW/evFqt53Lm98br9fq9viS1bNlSXq/3vHXfeOONevXVV3Xo0CEVFRXJZrPp2muvlSQNGzZMb731ljIyMtS0aVPdfvvtuuWWW877mqd7PO1879cvf/lLLV26VM8//7wkKTU1VWlpabUaBwAAAACCiQAkDJ0+A+JCzhI4zel0ym63Kzs7u1YXy4yLi1NUVJSmT58uh8NxQWPFxcWpUaNGOnDggO+ONGfW0bt37zrdWtXlcvmCnB/30KJFCx08eNC37fF4FBkZqfj4eJWUlJzzdc92JsSZjzscDn3xxRd++z0ej7p163beumNjY9W1a1fl5+fr3//+t1JSUnyv3bx5c9/3Yvv27ZoyZYo6depUY3h0Lud7v5o0aaIHHnhADzzwgL7//nv94Q9/UFJSkv7jP/5D0dHROnHihO/Y0tJSv/AHAAAAAIKJJTBhpLy8XBs3btTMmTPVu3dvv+t/1FaLFi3UtWtXLVmyROXl5TIMQwcOHNC2bdtqPD4iIkL9+/fXokWLfGdReL1eFRQUnHesiIgI9evXT0uWLJHX65VhGNqxY4cqKirUu3dvbdy4UQUFBTIMQydPntTWrVvPG1JIp66B0qJFC73xxhs6fvy4Tp48qe3bt0uSevXqpY8//ljFxcU6fvy4/vKXv6hnz561Cnvi4+N18OBBv7Nbfqx79+4qKirSZ599pqqqKuXn56uwsFDJycnnfX3p1DKY1atXa926db7lL5L0+eef+3pv2rSppLMHMudyvvdr48aNOnDggEzTVExMjCIiInzjtGvXTp999pkMw1BBQcFZfyakU4FNcXHxBdcHAAAAAHXFGSBh4IUXXlBkZKRsNpvatGmj2267TQMGDKjz640cOVJvvPGGxo4dq2PHjikhIeGcd/G477779M477+iZZ55RWVmZHA6Hfvazn9XqrIcHHnhAb775piZMmKDjx4+rXbt2euaZZ+RyufT000/r9ddf18yZMxUREaH27dvrV7/6lSTpr3/9q7Zv366MjIxqrxkREaFx48bp1VdfVXp6umw2m3r16qVrrrlG/fr106FDhzRp0iSdPHlSXbt21YgRI2r1fenZs6fWrFmjhx9+WK1atdILL7xQ7Zi4uDiNHz9eOTk5WrBggVq3bq3x48erWbNmtRrD7XZr3rx5crlcfmfF7N69W4sWLVJ5ebmaN2+uhx56SAkJCZKksWPH6q677lLv3r1rNca53q+ioiK9+uqrOnLkiJo2baoBAwaoc+fOkk7dOWju3Ln65JNPdP311+v6668/6xhpaWl69dVX9frrr2vgwIH65S9/WavaAAAAAKCubKZpmsEuAgAAAAAAoD6xBAYAAAAAAFgeAQgAAAAAALA8AhAAAAAAAGB5BCAAAAAAAMDyCEAAAAAAAIDlEYAAAAAAAADLIwABAAAAAACWRwACAAAAAAAsjwAEAAAAAABY3v8D6JTQlLHnjyMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x3960 with 33 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_w = 3\n",
    "fig_l = int(len(aggregate_list) / fig_w) + 1\n",
    "\n",
    "summary_fig, axes = plt.subplots(fig_l, fig_w, figsize=(fig_w * 5,fig_l*5))\n",
    "\n",
    "stats = []\n",
    "for params, ax in zip(aggregate_list, axes.ravel()):\n",
    "    s, f, diff = process_control(summary_df, **params)\n",
    "    stats.append(s)\n",
    "    \n",
    "    ax.set_title(f\"{params['geography']} - {params['name']}\")\n",
    "    ax.set_ylabel('Frequency'); ax.set_xlabel('Difference: control vs. result')\n",
    "    ax.hist(diff, bins=10, range=(-5,5), alpha=0.5)\n",
    "\n",
    "summary_fig.savefig(os.path.join(validation_dir, 'frequencies.pdf'))\n",
    "stats_df = pd.DataFrame(stats)\n",
    "stats_df.to_csv(os.path.join(validation_dir, f'{scenario}_{region}_popsim_stats.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the standard deviation for each comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "std_fig = plt.figure()\n",
    "std_fig.suptitle('PopulationSim Controls Percentage Difference')\n",
    "plt.errorbar(stats_df['mean_pc_difference'],\n",
    "             stats_df['name'],\n",
    "             xerr=stats_df['std'],\n",
    "             linestyle='None',\n",
    "             marker='.')\n",
    "\n",
    "std_fig.savefig(os.path.join(validation_dir, f'{scenario}_{region}_popsim_convergence_stdev.pdf'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uniformity\n",
    "\n",
    "Examine the household weight distribution by geography using `expanded_hhid` and `seed_households`\n",
    "```yaml\n",
    "seed_cols:\n",
    "    geog: PUMA\n",
    "    geog_weight: WGTP\n",
    "    hh_id: hh_id\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed_cols = (seed_hh_cols.values())\n",
    "geog = seed_hh_cols.get('geog')\n",
    "geog_weight = seed_hh_cols.get('geog_weight')\n",
    "hh_id = seed_hh_cols.get('hh_id')\n",
    "\n",
    "expanded_hhids = pd.read_csv(os.path.join(popsim_dir, exp_hh_file), usecols=[exp_hh_id_col])\n",
    "seed_hh_df = pd.read_csv(os.path.join(popsim_dir, seed_hh_file), usecols=seed_cols, index_col=hh_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weight each household by its frequency in the household file, and by the given weight column. Group by geography and summarize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>W</th>\n",
       "      <th>Z</th>\n",
       "      <th>N</th>\n",
       "      <th>EXP</th>\n",
       "      <th>EXP_MIN</th>\n",
       "      <th>EXP_MAX</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUMA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>77536</td>\n",
       "      <td>62041.0</td>\n",
       "      <td>4841</td>\n",
       "      <td>0.800157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.09444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W        Z     N       EXP  EXP_MIN  EXP_MAX     RMSE\n",
       "PUMA                                                           \n",
       "600   77536  62041.0  4841  0.800157      0.0     12.0  1.09444"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_mask = seed_hh_df[geog_weight] > 0\n",
    "weight = expanded_hhids[exp_hh_id_col].value_counts()[weight_mask]\n",
    "expansion_factor = (weight/seed_hh_df[geog_weight]).fillna(0)\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    geog: seed_hh_df[geog],\n",
    "    geog_weight: seed_hh_df[geog_weight],\n",
    "    'weight': weight,\n",
    "    'ef': expansion_factor,\n",
    "})\n",
    "\n",
    "geog_group = df.groupby(geog)\n",
    "geog_final_weight = geog_group.sum()['weight']\n",
    "expansion = geog_final_weight/geog_group.sum()[geog_weight]\n",
    "\n",
    "expansion.name = 'avg_expansion'\n",
    "df = df.join(expansion, on=geog)\n",
    "df['diff_sq'] = (df['avg_expansion'] - df['ef']) ** 2\n",
    "rmse = df.groupby(geog).mean()['diff_sq'] ** 0.5\n",
    "\n",
    "uniformity_df = pd.DataFrame({\n",
    "    'W': geog_group.sum()[geog_weight],\n",
    "    'Z': geog_group.sum()['weight'],\n",
    "    'N': geog_group.count()[geog_weight],\n",
    "    'EXP': expansion,\n",
    "    'EXP_MIN': geog_group.min()['ef'],\n",
    "    'EXP_MAX': geog_group.max()['ef'],\n",
    "    'RMSE': rmse, \n",
    "})\n",
    "\n",
    "uniformity_df.to_csv(os.path.join(validation_dir, 'uniformity.csv'))\n",
    "uniformity_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geogs = df[geog].unique()\n",
    "geog_fig = plt.figure(figsize=(10*len(geogs), 10))\n",
    "\n",
    "for i, g in enumerate(geogs):\n",
    "    geog_df = df[df[geog] == g]\n",
    "    counts, bins = np.histogram(geog_df['ef'])\n",
    "    ax = geog_fig.add_subplot(1, len(geogs), i+1)\n",
    "    ax.set_title(f'{geog} {g}')\n",
    "    ax.set_ylabel('Percentage'); ax.set_xlabel('Expansion Factor Range')\n",
    "    ax.hist(bins[:-1], bins, weights=counts*100/len(geog_df), alpha=0.6)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}