Untitled.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "f94e0d22-b1a3-4618-87a8-05eb561fe244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "import datetime as dt\n",
    "import sqlite3 as sql\n",
    "import os\n",
    "from pathlib import Path\n",
    "import pickle\n",
    "import re\n",
    "\n",
    "from distfit import distfit\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import logging, sys\n",
    "logging.disable(sys.maxsize)\n",
    "\n",
    "print(os.getenv('GRADDNODI_OUTPUT'))\n",
    "\n",
    "def results_comp(study):\n",
    "    results = list()\n",
    "    comparisons = [d for d in Path(f'/var/mnt/Little Guy/Graddnodi/Output/{study}/Matched Measurements/').glob('**/*.db')]\n",
    "    results_db_path = Path(f'/var/mnt/Little Guy/Graddnodi/Output/{study}/Results/Results.db')\n",
    "\n",
    "    results_con = sql.connect(results_db_path)\n",
    "    for db in comparisons:\n",
    "        dfit = distfit(distr='norm')\n",
    "        comp_name = db.parts[-2]\n",
    "        pollutant = db.stem\n",
    "        if pollutant in [\"NO\", \"PM4\", \"PM1\"]:\n",
    "            continue\n",
    "        cg = re.search(r\"(?P<calibrated>.*) vs (?P<reference>.*)\", comp_name)\n",
    "        x_name = cg.group('calibrated')\n",
    "        y_name = cg.group('reference')\n",
    "        con = sql.connect(db)\n",
    "        x_df = pd.read_sql('SELECT * FROM x', con=con)[pollutant]\n",
    "        y_df = pd.read_sql('SELECT * FROM y', con=con)[pollutant]\n",
    "        print(comp_name, pollutant, x_name, y_name)\n",
    "\n",
    "        dfit.fit_transform(x_df)\n",
    "        dfit.plot()\n",
    "        plt.savefig(f'img/{comp_name} {pollutant} - x.png')\n",
    "        plt.close()\n",
    "        x_dict = dict(dfit.model)\n",
    "        x_dict['Comparison'] = comp_name\n",
    "        x_dict['Pollutant'] = pollutant\n",
    "        x_dict['Device'] = 'x'\n",
    "        dfit.fit_transform(y_df)\n",
    "        dfit.plot()\n",
    "        plt.savefig(f'img/{comp_name} {pollutant} - y.png')\n",
    "        plt.close()\n",
    "        \n",
    "        results_db = pd.read_sql(f\"SELECT [Root Mean Squared Error], [Reference Mean] FROM Results WHERE Field = '{pollutant}' AND Reference = '{y_name}' AND Calibrated = '{x_name}';\", con=results_con)\n",
    "        normalised = results_db['Root Mean Squared Error'].div(results_db['Reference Mean']).median()\n",
    "        x_dict['Median Error'] = normalised\n",
    "        \"\"\"\n",
    "        y_dict = dict(dfit.model)\n",
    "        y_dict['Comparison'] = comp_name\n",
    "        y_dict['Pollutant'] = pollutant\n",
    "        y_dict['Device'] = 'y'\n",
    "        con.close()\n",
    "        \"\"\"\n",
    "        results.extend([pd.Series(x_dict)])#, pd.Series(y_dict)])\n",
    "        \n",
    "    results_con.close()\n",
    "    results_df = pd.DataFrame(results)\n",
    "    results_df = results_df.rename(columns={'score': 'Residual Sum of Squares'})\n",
    "    display(results_df)\n",
    "    if study != \"Bushy Park\":\n",
    "        results_df.plot.scatter('Residual Sum of Squares', 'Median Error', ylim=(0, 1), logx=True, logy=True)\n",
    "    else:\n",
    "        results_df[\n",
    "            results_df['Comparison'].isin(['NPL AQMesh A vs AirView Reference A', 'NPL AQMesh B vs AirView Reference B'])\n",
    "        ].plot.scatter('Residual Sum of Squares', 'Median Error', ylim=(0, 1), logx=True, logy=True)\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    with open(f'{study}.pkl', 'wb') as pkl:\n",
    "        pickle.dump(results_df, pkl)\n",
    "\n",
    "    for pol, data in results_df.groupby('Pollutant'):\n",
    "        print(pol)\n",
    "        plt.close()\n",
    "        data[['loc', 'scale']].boxplot()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5fc6c57-12b0-41fe-8119-deaca72272ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AirView Reference A vs Bushy Park FDMS PM2.5 AirView Reference A Bushy Park FDMS\n",
      "AirView Reference B vs Bushy Park FDMS PM2.5 AirView Reference B Bushy Park FDMS\n",
      "Nova Sensor A vs Bushy Park FDMS PM2.5 Nova Sensor A Bushy Park FDMS\n",
      "Nova Sensor B vs Bushy Park FDMS PM2.5 Nova Sensor B Bushy Park FDMS\n",
      "AirView Low-Cost A vs Bushy Park FDMS PM2.5 AirView Low-Cost A Bushy Park FDMS\n",
      "AirView Low-Cost B vs Bushy Park FDMS PM2.5 AirView Low-Cost B Bushy Park FDMS\n",
      "NPL AQMesh A vs Bushy Park FDMS PM2.5 NPL AQMesh A Bushy Park FDMS\n",
      "NPL AQMesh B vs Bushy Park FDMS PM2.5 NPL AQMesh B Bushy Park FDMS\n",
      "Breathe London AQMesh vs Bushy Park FDMS PM2.5 Breathe London AQMesh Bushy Park FDMS\n",
      "NPL AQMesh C vs Bushy Park FDMS PM2.5 NPL AQMesh C Bushy Park FDMS\n",
      "Nova Sensor A vs Bushy Park OPC PM2.5 Nova Sensor A Bushy Park OPC\n",
      "Nova Sensor A vs Bushy Park OPC PM10 Nova Sensor A Bushy Park OPC\n",
      "Nova Sensor B vs Bushy Park OPC PM2.5 Nova Sensor B Bushy Park OPC\n",
      "Nova Sensor B vs Bushy Park OPC PM10 Nova Sensor B Bushy Park OPC\n",
      "NPL AQMesh A vs Bushy Park OPC PM2.5 NPL AQMesh A Bushy Park OPC\n",
      "NPL AQMesh A vs Bushy Park OPC PM10 NPL AQMesh A Bushy Park OPC\n",
      "NPL AQMesh B vs Bushy Park OPC PM2.5 NPL AQMesh B Bushy Park OPC\n",
      "NPL AQMesh B vs Bushy Park OPC PM10 NPL AQMesh B Bushy Park OPC\n",
      "Breathe London AQMesh vs Bushy Park OPC PM2.5 Breathe London AQMesh Bushy Park OPC\n",
      "Breathe London AQMesh vs Bushy Park OPC PM10 Breathe London AQMesh Bushy Park OPC\n",
      "NPL AQMesh C vs Bushy Park OPC PM2.5 NPL AQMesh C Bushy Park OPC\n",
      "NPL AQMesh C vs Bushy Park OPC PM10 NPL AQMesh C Bushy Park OPC\n",
      "AirView Reference B vs AirView Reference A PM2.5 AirView Reference B AirView Reference A\n",
      "AirView Reference B vs AirView Reference A PM10 AirView Reference B AirView Reference A\n",
      "AirView Reference B vs AirView Reference A NO2 AirView Reference B AirView Reference A\n",
      "AirView Reference B vs AirView Reference A O3 AirView Reference B AirView Reference A\n",
      "Nova Sensor A vs AirView Reference A PM2.5 Nova Sensor A AirView Reference A\n",
      "Nova Sensor A vs AirView Reference A PM10 Nova Sensor A AirView Reference A\n",
      "Nova Sensor B vs AirView Reference A PM2.5 Nova Sensor B AirView Reference A\n",
      "Nova Sensor B vs AirView Reference A PM10 Nova Sensor B AirView Reference A\n",
      "AirView Low-Cost A vs AirView Reference A PM2.5 AirView Low-Cost A AirView Reference A\n",
      "AirView Low-Cost B vs AirView Reference A PM2.5 AirView Low-Cost B AirView Reference A\n",
      "NPL AQMesh A vs AirView Reference A PM2.5 NPL AQMesh A AirView Reference A\n",
      "NPL AQMesh A vs AirView Reference A PM10 NPL AQMesh A AirView Reference A\n",
      "NPL AQMesh A vs AirView Reference A NO2 NPL AQMesh A AirView Reference A\n",
      "NPL AQMesh A vs AirView Reference A O3 NPL AQMesh A AirView Reference A\n",
      "NPL AQMesh B vs AirView Reference A PM2.5 NPL AQMesh B AirView Reference A\n",
      "NPL AQMesh B vs AirView Reference A PM10 NPL AQMesh B AirView Reference A\n",
      "NPL AQMesh B vs AirView Reference A NO2 NPL AQMesh B AirView Reference A\n",
      "NPL AQMesh B vs AirView Reference A O3 NPL AQMesh B AirView Reference A\n",
      "Breathe London AQMesh vs AirView Reference A PM2.5 Breathe London AQMesh AirView Reference A\n",
      "Breathe London AQMesh vs AirView Reference A PM10 Breathe London AQMesh AirView Reference A\n",
      "Breathe London AQMesh vs AirView Reference A NO2 Breathe London AQMesh AirView Reference A\n",
      "Breathe London AQMesh vs AirView Reference A O3 Breathe London AQMesh AirView Reference A\n",
      "NPL AQMesh C vs AirView Reference A PM2.5 NPL AQMesh C AirView Reference A\n",
      "NPL AQMesh C vs AirView Reference A PM10 NPL AQMesh C AirView Reference A\n",
      "NPL AQMesh C vs AirView Reference A NO2 NPL AQMesh C AirView Reference A\n",
      "NPL AQMesh C vs AirView Reference A O3 NPL AQMesh C AirView Reference A\n",
      "Nova Sensor A vs AirView Reference B PM2.5 Nova Sensor A AirView Reference B\n",
      "Nova Sensor A vs AirView Reference B PM10 Nova Sensor A AirView Reference B\n",
      "Nova Sensor B vs AirView Reference B PM2.5 Nova Sensor B AirView Reference B\n",
      "Nova Sensor B vs AirView Reference B PM10 Nova Sensor B AirView Reference B\n",
      "AirView Low-Cost A vs AirView Reference B PM2.5 AirView Low-Cost A AirView Reference B\n",
      "AirView Low-Cost B vs AirView Reference B PM2.5 AirView Low-Cost B AirView Reference B\n",
      "NPL AQMesh A vs AirView Reference B PM2.5 NPL AQMesh A AirView Reference B\n",
      "NPL AQMesh A vs AirView Reference B PM10 NPL AQMesh A AirView Reference B\n",
      "NPL AQMesh A vs AirView Reference B NO2 NPL AQMesh A AirView Reference B\n",
      "NPL AQMesh A vs AirView Reference B O3 NPL AQMesh A AirView Reference B\n",
      "NPL AQMesh B vs AirView Reference B PM2.5 NPL AQMesh B AirView Reference B\n",
      "NPL AQMesh B vs AirView Reference B PM10 NPL AQMesh B AirView Reference B\n",
      "NPL AQMesh B vs AirView Reference B NO2 NPL AQMesh B AirView Reference B\n",
      "NPL AQMesh B vs AirView Reference B O3 NPL AQMesh B AirView Reference B\n",
      "Breathe London AQMesh vs AirView Reference B PM2.5 Breathe London AQMesh AirView Reference B\n",
      "Breathe London AQMesh vs AirView Reference B PM10 Breathe London AQMesh AirView Reference B\n",
      "Breathe London AQMesh vs AirView Reference B NO2 Breathe London AQMesh AirView Reference B\n",
      "Breathe London AQMesh vs AirView Reference B O3 Breathe London AQMesh AirView Reference B\n",
      "NPL AQMesh C vs AirView Reference B PM2.5 NPL AQMesh C AirView Reference B\n",
      "NPL AQMesh C vs AirView Reference B PM10 NPL AQMesh C AirView Reference B\n",
      "NPL AQMesh C vs AirView Reference B NO2 NPL AQMesh C AirView Reference B\n",
      "NPL AQMesh C vs AirView Reference B O3 NPL AQMesh C AirView Reference B\n",
      "Nova Sensor B vs Nova Sensor A PM2.5 Nova Sensor B Nova Sensor A\n",
      "Nova Sensor B vs Nova Sensor A PM10 Nova Sensor B Nova Sensor A\n",
      "AirView Low-Cost A vs Nova Sensor A PM2.5 AirView Low-Cost A Nova Sensor A\n",
      "AirView Low-Cost B vs Nova Sensor A PM2.5 AirView Low-Cost B Nova Sensor A\n",
      "NPL AQMesh A vs Nova Sensor A PM2.5 NPL AQMesh A Nova Sensor A\n",
      "NPL AQMesh A vs Nova Sensor A PM10 NPL AQMesh A Nova Sensor A\n",
      "NPL AQMesh B vs Nova Sensor A PM2.5 NPL AQMesh B Nova Sensor A\n",
      "NPL AQMesh B vs Nova Sensor A PM10 NPL AQMesh B Nova Sensor A\n",
      "Breathe London AQMesh vs Nova Sensor A PM2.5 Breathe London AQMesh Nova Sensor A\n",
      "Breathe London AQMesh vs Nova Sensor A PM10 Breathe London AQMesh Nova Sensor A\n",
      "NPL AQMesh C vs Nova Sensor A PM2.5 NPL AQMesh C Nova Sensor A\n",
      "NPL AQMesh C vs Nova Sensor A PM10 NPL AQMesh C Nova Sensor A\n",
      "AirView Low-Cost A vs Nova Sensor B PM2.5 AirView Low-Cost A Nova Sensor B\n",
      "AirView Low-Cost B vs Nova Sensor B PM2.5 AirView Low-Cost B Nova Sensor B\n",
      "NPL AQMesh A vs Nova Sensor B PM2.5 NPL AQMesh A Nova Sensor B\n",
      "NPL AQMesh A vs Nova Sensor B PM10 NPL AQMesh A Nova Sensor B\n",
      "NPL AQMesh B vs Nova Sensor B PM2.5 NPL AQMesh B Nova Sensor B\n",
      "NPL AQMesh B vs Nova Sensor B PM10 NPL AQMesh B Nova Sensor B\n",
      "Breathe London AQMesh vs Nova Sensor B PM2.5 Breathe London AQMesh Nova Sensor B\n",
      "Breathe London AQMesh vs Nova Sensor B PM10 Breathe London AQMesh Nova Sensor B\n",
      "NPL AQMesh C vs Nova Sensor B PM2.5 NPL AQMesh C Nova Sensor B\n",
      "NPL AQMesh C vs Nova Sensor B PM10 NPL AQMesh C Nova Sensor B\n",
      "AirView Low-Cost B vs AirView Low-Cost A PM2.5 AirView Low-Cost B AirView Low-Cost A\n",
      "NPL AQMesh A vs AirView Low-Cost A PM2.5 NPL AQMesh A AirView Low-Cost A\n",
      "NPL AQMesh B vs AirView Low-Cost A PM2.5 NPL AQMesh B AirView Low-Cost A\n",
      "Breathe London AQMesh vs AirView Low-Cost A PM2.5 Breathe London AQMesh AirView Low-Cost A\n",
      "NPL AQMesh C vs AirView Low-Cost A PM2.5 NPL AQMesh C AirView Low-Cost A\n",
      "NPL AQMesh A vs AirView Low-Cost B PM2.5 NPL AQMesh A AirView Low-Cost B\n",
      "NPL AQMesh B vs AirView Low-Cost B PM2.5 NPL AQMesh B AirView Low-Cost B\n",
      "Breathe London AQMesh vs AirView Low-Cost B PM2.5 Breathe London AQMesh AirView Low-Cost B\n",
      "NPL AQMesh C vs AirView Low-Cost B PM2.5 NPL AQMesh C AirView Low-Cost B\n",
      "NPL AQMesh B vs NPL AQMesh A PM2.5 NPL AQMesh B NPL AQMesh A\n",
      "NPL AQMesh B vs NPL AQMesh A PM10 NPL AQMesh B NPL AQMesh A\n",
      "NPL AQMesh B vs NPL AQMesh A NO2 NPL AQMesh B NPL AQMesh A\n",
      "NPL AQMesh B vs NPL AQMesh A O3 NPL AQMesh B NPL AQMesh A\n",
      "Breathe London AQMesh vs NPL AQMesh A PM2.5 Breathe London AQMesh NPL AQMesh A\n",
      "Breathe London AQMesh vs NPL AQMesh A PM10 Breathe London AQMesh NPL AQMesh A\n",
      "Breathe London AQMesh vs NPL AQMesh A NO2 Breathe London AQMesh NPL AQMesh A\n",
      "Breathe London AQMesh vs NPL AQMesh A O3 Breathe London AQMesh NPL AQMesh A\n",
      "NPL AQMesh C vs NPL AQMesh A PM2.5 NPL AQMesh C NPL AQMesh A\n",
      "NPL AQMesh C vs NPL AQMesh A PM10 NPL AQMesh C NPL AQMesh A\n",
      "NPL AQMesh C vs NPL AQMesh A NO2 NPL AQMesh C NPL AQMesh A\n",
      "NPL AQMesh C vs NPL AQMesh A O3 NPL AQMesh C NPL AQMesh A\n",
      "Breathe London AQMesh vs NPL AQMesh B PM2.5 Breathe London AQMesh NPL AQMesh B\n",
      "Breathe London AQMesh vs NPL AQMesh B PM10 Breathe London AQMesh NPL AQMesh B\n",
      "Breathe London AQMesh vs NPL AQMesh B NO2 Breathe London AQMesh NPL AQMesh B\n",
      "Breathe London AQMesh vs NPL AQMesh B O3 Breathe London AQMesh NPL AQMesh B\n",
      "NPL AQMesh C vs NPL AQMesh B PM2.5 NPL AQMesh C NPL AQMesh B\n",
      "NPL AQMesh C vs NPL AQMesh B PM10 NPL AQMesh C NPL AQMesh B\n",
      "NPL AQMesh C vs NPL AQMesh B NO2 NPL AQMesh C NPL AQMesh B\n",
      "NPL AQMesh C vs NPL AQMesh B O3 NPL AQMesh C NPL AQMesh B\n",
      "NPL AQMesh C vs Breathe London AQMesh PM2.5 NPL AQMesh C Breathe London AQMesh\n",
      "NPL AQMesh C vs Breathe London AQMesh PM10 NPL AQMesh C Breathe London AQMesh\n",
      "NPL AQMesh C vs Breathe London AQMesh NO2 NPL AQMesh C Breathe London AQMesh\n",
      "NPL AQMesh C vs Breathe London AQMesh O3 NPL AQMesh C Breathe London AQMesh\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>Residual Sum of Squares</th>\n",
       "      <th>loc</th>\n",
       "      <th>scale</th>\n",
       "      <th>arg</th>\n",
       "      <th>params</th>\n",
       "      <th>model</th>\n",
       "      <th>bootstrap_score</th>\n",
       "      <th>bootstrap_pass</th>\n",
       "      <th>color</th>\n",
       "      <th>CII_min_alpha</th>\n",
       "      <th>CII_max_alpha</th>\n",
       "      <th>Comparison</th>\n",
       "      <th>Pollutant</th>\n",
       "      <th>Device</th>\n",
       "      <th>Median Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.040105</td>\n",
       "      <td>9.414187</td>\n",
       "      <td>9.745698</td>\n",
       "      <td>()</td>\n",
       "      <td>(9.414186984744818, 9.745698017324264)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-6.616060</td>\n",
       "      <td>25.444434</td>\n",
       "      <td>AirView Reference A vs Bushy Park FDMS</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>x</td>\n",
       "      <td>0.281867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.031989</td>\n",
       "      <td>9.707076</td>\n",
       "      <td>9.649229</td>\n",
       "      <td>()</td>\n",
       "      <td>(9.707075819119504, 9.649229308523884)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-6.164494</td>\n",
       "      <td>25.578646</td>\n",
       "      <td>AirView Reference B vs Bushy Park FDMS</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>x</td>\n",
       "      <td>0.275835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.050495</td>\n",
       "      <td>9.425315</td>\n",
       "      <td>10.547691</td>\n",
       "      <td>()</td>\n",
       "      <td>(9.42531462332499, 10.547691073251004)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-7.924093</td>\n",
       "      <td>26.774723</td>\n",
       "      <td>Nova Sensor A vs Bushy Park FDMS</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>x</td>\n",
       "      <td>0.309659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.050495</td>\n",
       "      <td>9.425315</td>\n",
       "      <td>10.547691</td>\n",
       "      <td>()</td>\n",
       "      <td>(9.42531462332499, 10.547691073251004)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-7.924093</td>\n",
       "      <td>26.774723</td>\n",
       "      <td>Nova Sensor B vs Bushy Park FDMS</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>x</td>\n",
       "      <td>0.309659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.020211</td>\n",
       "      <td>11.834954</td>\n",
       "      <td>14.057724</td>\n",
       "      <td>()</td>\n",
       "      <td>(11.834954258046961, 14.05772387722451)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-11.287944</td>\n",
       "      <td>34.957852</td>\n",
       "      <td>AirView Low-Cost A vs Bushy Park FDMS</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>x</td>\n",
       "      <td>0.384674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>72.881635</td>\n",
       "      <td>35.948993</td>\n",
       "      <td>()</td>\n",
       "      <td>(72.88163538100544, 35.94899261729049)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>13.750804</td>\n",
       "      <td>132.012466</td>\n",
       "      <td>NPL AQMesh C vs NPL AQMesh B</td>\n",
       "      <td>O3</td>\n",
       "      <td>x</td>\n",
       "      <td>0.477824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.024448</td>\n",
       "      <td>19.934395</td>\n",
       "      <td>38.050864</td>\n",
       "      <td>()</td>\n",
       "      <td>(19.934394693244666, 38.050864304641514)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-42.653707</td>\n",
       "      <td>82.522497</td>\n",
       "      <td>NPL AQMesh C vs Breathe London AQMesh</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>x</td>\n",
       "      <td>0.706701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.006389</td>\n",
       "      <td>55.901694</td>\n",
       "      <td>104.696401</td>\n",
       "      <td>()</td>\n",
       "      <td>(55.90169355937922, 104.69640076978692)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-116.308561</td>\n",
       "      <td>228.111948</td>\n",
       "      <td>NPL AQMesh C vs Breathe London AQMesh</td>\n",
       "      <td>PM10</td>\n",
       "      <td>x</td>\n",
       "      <td>0.742423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.008290</td>\n",
       "      <td>18.088153</td>\n",
       "      <td>7.126109</td>\n",
       "      <td>()</td>\n",
       "      <td>(18.088152785354417, 7.126109279175785)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>6.366746</td>\n",
       "      <td>29.809559</td>\n",
       "      <td>NPL AQMesh C vs Breathe London AQMesh</td>\n",
       "      <td>NO2</td>\n",
       "      <td>x</td>\n",
       "      <td>0.354703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>74.799043</td>\n",
       "      <td>35.859345</td>\n",
       "      <td>()</td>\n",
       "      <td>(74.79904346265151, 35.85934495409567)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>15.815670</td>\n",
       "      <td>133.782417</td>\n",
       "      <td>NPL AQMesh C vs Breathe London AQMesh</td>\n",
       "      <td>O3</td>\n",
       "      <td>x</td>\n",
       "      <td>0.422529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  Residual Sum of Squares        loc       scale arg  \\\n",
       "0    norm                 0.040105   9.414187    9.745698  ()   \n",
       "1    norm                 0.031989   9.707076    9.649229  ()   \n",
       "2    norm                 0.050495   9.425315   10.547691  ()   \n",
       "3    norm                 0.050495   9.425315   10.547691  ()   \n",
       "4    norm                 0.020211  11.834954   14.057724  ()   \n",
       "..    ...                      ...        ...         ...  ..   \n",
       "120  norm                 0.000150  72.881635   35.948993  ()   \n",
       "121  norm                 0.024448  19.934395   38.050864  ()   \n",
       "122  norm                 0.006389  55.901694  104.696401  ()   \n",
       "123  norm                 0.008290  18.088153    7.126109  ()   \n",
       "124  norm                 0.000206  74.799043   35.859345  ()   \n",
       "\n",
       "                                       params  \\\n",
       "0      (9.414186984744818, 9.745698017324264)   \n",
       "1      (9.707075819119504, 9.649229308523884)   \n",
       "2      (9.42531462332499, 10.547691073251004)   \n",
       "3      (9.42531462332499, 10.547691073251004)   \n",
       "4     (11.834954258046961, 14.05772387722451)   \n",
       "..                                        ...   \n",
       "120    (72.88163538100544, 35.94899261729049)   \n",
       "121  (19.934394693244666, 38.050864304641514)   \n",
       "122   (55.90169355937922, 104.69640076978692)   \n",
       "123   (18.088152785354417, 7.126109279175785)   \n",
       "124    (74.79904346265151, 35.85934495409567)   \n",
       "\n",
       "                                                 model  bootstrap_score  \\\n",
       "0    <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "1    <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "2    <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "3    <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "4    <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "..                                                 ...              ...   \n",
       "120  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "121  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "122  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "123  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "124  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "\n",
       "    bootstrap_pass    color  CII_min_alpha  CII_max_alpha  \\\n",
       "0             None  #e41a1c      -6.616060      25.444434   \n",
       "1             None  #e41a1c      -6.164494      25.578646   \n",
       "2             None  #e41a1c      -7.924093      26.774723   \n",
       "3             None  #e41a1c      -7.924093      26.774723   \n",
       "4             None  #e41a1c     -11.287944      34.957852   \n",
       "..             ...      ...            ...            ...   \n",
       "120           None  #e41a1c      13.750804     132.012466   \n",
       "121           None  #e41a1c     -42.653707      82.522497   \n",
       "122           None  #e41a1c    -116.308561     228.111948   \n",
       "123           None  #e41a1c       6.366746      29.809559   \n",
       "124           None  #e41a1c      15.815670     133.782417   \n",
       "\n",
       "                                 Comparison Pollutant Device  Median Error  \n",
       "0    AirView Reference A vs Bushy Park FDMS     PM2.5      x      0.281867  \n",
       "1    AirView Reference B vs Bushy Park FDMS     PM2.5      x      0.275835  \n",
       "2          Nova Sensor A vs Bushy Park FDMS     PM2.5      x      0.309659  \n",
       "3          Nova Sensor B vs Bushy Park FDMS     PM2.5      x      0.309659  \n",
       "4     AirView Low-Cost A vs Bushy Park FDMS     PM2.5      x      0.384674  \n",
       "..                                      ...       ...    ...           ...  \n",
       "120            NPL AQMesh C vs NPL AQMesh B        O3      x      0.477824  \n",
       "121   NPL AQMesh C vs Breathe London AQMesh     PM2.5      x      0.706701  \n",
       "122   NPL AQMesh C vs Breathe London AQMesh      PM10      x      0.742423  \n",
       "123   NPL AQMesh C vs Breathe London AQMesh       NO2      x      0.354703  \n",
       "124   NPL AQMesh C vs Breathe London AQMesh        O3      x      0.422529  \n",
       "\n",
       "[125 rows x 16 columns]"
      ]
     },
     "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NO2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM10\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_comp('Bushy Park')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "188552be-f129-4600-b6cf-d6da649962f3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zep_1107b vs Reference PM2.5 Zep_1107b Reference\n",
      "Zep_1107b vs Reference PM10 Zep_1107b Reference\n",
      "Zep_1107b vs Reference NO2 Zep_1107b Reference\n",
      "Zep_1107b vs Reference O3 Zep_1107b Reference\n",
      "Zep_753 vs Reference PM2.5 Zep_753 Reference\n",
      "Zep_753 vs Reference PM10 Zep_753 Reference\n",
      "Zep_753 vs Reference NO2 Zep_753 Reference\n",
      "Zep_753 vs Reference O3 Zep_753 Reference\n",
      "Zep_754 vs Reference PM2.5 Zep_754 Reference\n",
      "Zep_754 vs Reference PM10 Zep_754 Reference\n",
      "Zep_754 vs Reference NO2 Zep_754 Reference\n",
      "Zep_754 vs Reference O3 Zep_754 Reference\n",
      "Zep_757 vs Reference PM2.5 Zep_757 Reference\n",
      "Zep_757 vs Reference PM10 Zep_757 Reference\n",
      "Zep_757 vs Reference NO2 Zep_757 Reference\n",
      "Zep_757 vs Reference O3 Zep_757 Reference\n",
      "Zep_858 vs Reference PM2.5 Zep_858 Reference\n",
      "Zep_858 vs Reference PM10 Zep_858 Reference\n",
      "Zep_858 vs Reference NO2 Zep_858 Reference\n",
      "Zep_858 vs Reference O3 Zep_858 Reference\n"
     ]
    },
    {
     "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>name</th>\n",
       "      <th>Residual Sum of Squares</th>\n",
       "      <th>loc</th>\n",
       "      <th>scale</th>\n",
       "      <th>arg</th>\n",
       "      <th>params</th>\n",
       "      <th>model</th>\n",
       "      <th>bootstrap_score</th>\n",
       "      <th>bootstrap_pass</th>\n",
       "      <th>color</th>\n",
       "      <th>CII_min_alpha</th>\n",
       "      <th>CII_max_alpha</th>\n",
       "      <th>Comparison</th>\n",
       "      <th>Pollutant</th>\n",
       "      <th>Device</th>\n",
       "      <th>Median Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.144173</td>\n",
       "      <td>10.301664</td>\n",
       "      <td>7.447608</td>\n",
       "      <td>()</td>\n",
       "      <td>(10.30166439193916, 7.447608287235027)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-1.948561</td>\n",
       "      <td>22.551890</td>\n",
       "      <td>Zep_1107b vs Reference</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>x</td>\n",
       "      <td>0.617727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.147921</td>\n",
       "      <td>10.629047</td>\n",
       "      <td>8.411385</td>\n",
       "      <td>()</td>\n",
       "      <td>(10.629046998584261, 8.411384515344748)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-3.206449</td>\n",
       "      <td>24.464543</td>\n",
       "      <td>Zep_1107b vs Reference</td>\n",
       "      <td>PM10</td>\n",
       "      <td>x</td>\n",
       "      <td>0.697749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.021023</td>\n",
       "      <td>10.613616</td>\n",
       "      <td>10.960277</td>\n",
       "      <td>()</td>\n",
       "      <td>(10.613616443908235, 10.960276804092931)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-7.414435</td>\n",
       "      <td>28.641667</td>\n",
       "      <td>Zep_1107b vs Reference</td>\n",
       "      <td>NO2</td>\n",
       "      <td>x</td>\n",
       "      <td>0.476999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>51.773691</td>\n",
       "      <td>23.368996</td>\n",
       "      <td>()</td>\n",
       "      <td>(51.77369082771798, 23.36899612950345)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>13.335113</td>\n",
       "      <td>90.212269</td>\n",
       "      <td>Zep_1107b vs Reference</td>\n",
       "      <td>O3</td>\n",
       "      <td>x</td>\n",
       "      <td>0.181654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.018983</td>\n",
       "      <td>8.942813</td>\n",
       "      <td>8.895867</td>\n",
       "      <td>()</td>\n",
       "      <td>(8.942812702563106, 8.895867199134212)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-5.689587</td>\n",
       "      <td>23.575212</td>\n",
       "      <td>Zep_753 vs Reference</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>x</td>\n",
       "      <td>1.052561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.036600</td>\n",
       "      <td>10.573579</td>\n",
       "      <td>9.048104</td>\n",
       "      <td>()</td>\n",
       "      <td>(10.573579326322259, 9.048104283862232)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-4.309228</td>\n",
       "      <td>25.456386</td>\n",
       "      <td>Zep_753 vs Reference</td>\n",
       "      <td>PM10</td>\n",
       "      <td>x</td>\n",
       "      <td>0.722528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.003075</td>\n",
       "      <td>16.823164</td>\n",
       "      <td>11.572679</td>\n",
       "      <td>()</td>\n",
       "      <td>(16.823164131335552, 11.572679414053246)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-2.212200</td>\n",
       "      <td>35.858528</td>\n",
       "      <td>Zep_753 vs Reference</td>\n",
       "      <td>NO2</td>\n",
       "      <td>x</td>\n",
       "      <td>0.316619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>33.841677</td>\n",
       "      <td>19.670166</td>\n",
       "      <td>()</td>\n",
       "      <td>(33.84167671100332, 19.670165929590368)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>1.487133</td>\n",
       "      <td>66.196220</td>\n",
       "      <td>Zep_753 vs Reference</td>\n",
       "      <td>O3</td>\n",
       "      <td>x</td>\n",
       "      <td>0.159371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.000672</td>\n",
       "      <td>125.249451</td>\n",
       "      <td>289.778728</td>\n",
       "      <td>()</td>\n",
       "      <td>(125.24945093794304, 289.7787283711814)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-351.394141</td>\n",
       "      <td>601.893043</td>\n",
       "      <td>Zep_754 vs Reference</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>x</td>\n",
       "      <td>1.402333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.000681</td>\n",
       "      <td>129.599586</td>\n",
       "      <td>289.925500</td>\n",
       "      <td>()</td>\n",
       "      <td>(129.59958639373542, 289.9254997241142)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-347.285423</td>\n",
       "      <td>606.484596</td>\n",
       "      <td>Zep_754 vs Reference</td>\n",
       "      <td>PM10</td>\n",
       "      <td>x</td>\n",
       "      <td>0.993838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.011791</td>\n",
       "      <td>9.479755</td>\n",
       "      <td>8.593018</td>\n",
       "      <td>()</td>\n",
       "      <td>(9.47975526948623, 8.59301811087723)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-4.654502</td>\n",
       "      <td>23.614012</td>\n",
       "      <td>Zep_754 vs Reference</td>\n",
       "      <td>NO2</td>\n",
       "      <td>x</td>\n",
       "      <td>0.325285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>53.704398</td>\n",
       "      <td>19.478345</td>\n",
       "      <td>()</td>\n",
       "      <td>(53.704397913970624, 19.47834471535242)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>21.665372</td>\n",
       "      <td>85.743424</td>\n",
       "      <td>Zep_754 vs Reference</td>\n",
       "      <td>O3</td>\n",
       "      <td>x</td>\n",
       "      <td>0.149678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.010652</td>\n",
       "      <td>8.107618</td>\n",
       "      <td>5.981682</td>\n",
       "      <td>()</td>\n",
       "      <td>(8.10761751667522, 5.981681511923518)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-1.731373</td>\n",
       "      <td>17.946608</td>\n",
       "      <td>Zep_757 vs Reference</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>x</td>\n",
       "      <td>0.404008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.025654</td>\n",
       "      <td>9.433458</td>\n",
       "      <td>6.792218</td>\n",
       "      <td>()</td>\n",
       "      <td>(9.433458012269186, 6.792218136141556)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-1.738747</td>\n",
       "      <td>20.605663</td>\n",
       "      <td>Zep_757 vs Reference</td>\n",
       "      <td>PM10</td>\n",
       "      <td>x</td>\n",
       "      <td>0.376010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.000792</td>\n",
       "      <td>22.672793</td>\n",
       "      <td>7.980655</td>\n",
       "      <td>()</td>\n",
       "      <td>(22.672792819185343, 7.980654564286419)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>9.545784</td>\n",
       "      <td>35.799801</td>\n",
       "      <td>Zep_757 vs Reference</td>\n",
       "      <td>NO2</td>\n",
       "      <td>x</td>\n",
       "      <td>0.384857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>49.563971</td>\n",
       "      <td>14.320565</td>\n",
       "      <td>()</td>\n",
       "      <td>(49.563971464989336, 14.320565232716103)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>26.008738</td>\n",
       "      <td>73.119205</td>\n",
       "      <td>Zep_757 vs Reference</td>\n",
       "      <td>O3</td>\n",
       "      <td>x</td>\n",
       "      <td>0.156473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.016660</td>\n",
       "      <td>8.351932</td>\n",
       "      <td>8.341747</td>\n",
       "      <td>()</td>\n",
       "      <td>(8.351931807491372, 8.341747442855358)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-5.369022</td>\n",
       "      <td>22.072885</td>\n",
       "      <td>Zep_858 vs Reference</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>x</td>\n",
       "      <td>0.402615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.023544</td>\n",
       "      <td>11.341545</td>\n",
       "      <td>9.302002</td>\n",
       "      <td>()</td>\n",
       "      <td>(11.34154540555151, 9.302001557009843)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>-3.958886</td>\n",
       "      <td>26.641976</td>\n",
       "      <td>Zep_858 vs Reference</td>\n",
       "      <td>PM10</td>\n",
       "      <td>x</td>\n",
       "      <td>0.993766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>20.892248</td>\n",
       "      <td>10.153752</td>\n",
       "      <td>()</td>\n",
       "      <td>(20.89224819201948, 10.153751529601577)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>4.190813</td>\n",
       "      <td>37.593683</td>\n",
       "      <td>Zep_858 vs Reference</td>\n",
       "      <td>NO2</td>\n",
       "      <td>x</td>\n",
       "      <td>0.303948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>35.370266</td>\n",
       "      <td>17.759555</td>\n",
       "      <td>()</td>\n",
       "      <td>(35.37026641630309, 17.759555437966778)</td>\n",
       "      <td>&lt;scipy.stats._distn_infrastructure.rv_continuo...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>6.158397</td>\n",
       "      <td>64.582136</td>\n",
       "      <td>Zep_858 vs Reference</td>\n",
       "      <td>O3</td>\n",
       "      <td>x</td>\n",
       "      <td>0.108123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  Residual Sum of Squares         loc       scale arg  \\\n",
       "0   norm                 0.144173   10.301664    7.447608  ()   \n",
       "1   norm                 0.147921   10.629047    8.411385  ()   \n",
       "2   norm                 0.021023   10.613616   10.960277  ()   \n",
       "3   norm                 0.000094   51.773691   23.368996  ()   \n",
       "4   norm                 0.018983    8.942813    8.895867  ()   \n",
       "5   norm                 0.036600   10.573579    9.048104  ()   \n",
       "6   norm                 0.003075   16.823164   11.572679  ()   \n",
       "7   norm                 0.000468   33.841677   19.670166  ()   \n",
       "8   norm                 0.000672  125.249451  289.778728  ()   \n",
       "9   norm                 0.000681  129.599586  289.925500  ()   \n",
       "10  norm                 0.011791    9.479755    8.593018  ()   \n",
       "11  norm                 0.000246   53.704398   19.478345  ()   \n",
       "12  norm                 0.010652    8.107618    5.981682  ()   \n",
       "13  norm                 0.025654    9.433458    6.792218  ()   \n",
       "14  norm                 0.000792   22.672793    7.980655  ()   \n",
       "15  norm                 0.000050   49.563971   14.320565  ()   \n",
       "16  norm                 0.016660    8.351932    8.341747  ()   \n",
       "17  norm                 0.023544   11.341545    9.302002  ()   \n",
       "18  norm                 0.002154   20.892248   10.153752  ()   \n",
       "19  norm                 0.000322   35.370266   17.759555  ()   \n",
       "\n",
       "                                      params  \\\n",
       "0     (10.30166439193916, 7.447608287235027)   \n",
       "1    (10.629046998584261, 8.411384515344748)   \n",
       "2   (10.613616443908235, 10.960276804092931)   \n",
       "3     (51.77369082771798, 23.36899612950345)   \n",
       "4     (8.942812702563106, 8.895867199134212)   \n",
       "5    (10.573579326322259, 9.048104283862232)   \n",
       "6   (16.823164131335552, 11.572679414053246)   \n",
       "7    (33.84167671100332, 19.670165929590368)   \n",
       "8    (125.24945093794304, 289.7787283711814)   \n",
       "9    (129.59958639373542, 289.9254997241142)   \n",
       "10      (9.47975526948623, 8.59301811087723)   \n",
       "11   (53.704397913970624, 19.47834471535242)   \n",
       "12     (8.10761751667522, 5.981681511923518)   \n",
       "13    (9.433458012269186, 6.792218136141556)   \n",
       "14   (22.672792819185343, 7.980654564286419)   \n",
       "15  (49.563971464989336, 14.320565232716103)   \n",
       "16    (8.351931807491372, 8.341747442855358)   \n",
       "17    (11.34154540555151, 9.302001557009843)   \n",
       "18   (20.89224819201948, 10.153751529601577)   \n",
       "19   (35.37026641630309, 17.759555437966778)   \n",
       "\n",
       "                                                model  bootstrap_score  \\\n",
       "0   <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "1   <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "2   <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "3   <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "4   <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "5   <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "6   <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "7   <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "8   <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "9   <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "10  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "11  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "12  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "13  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "14  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "15  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "16  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "17  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "18  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "19  <scipy.stats._distn_infrastructure.rv_continuo...                0   \n",
       "\n",
       "   bootstrap_pass    color  CII_min_alpha  CII_max_alpha  \\\n",
       "0            None  #e41a1c      -1.948561      22.551890   \n",
       "1            None  #e41a1c      -3.206449      24.464543   \n",
       "2            None  #e41a1c      -7.414435      28.641667   \n",
       "3            None  #e41a1c      13.335113      90.212269   \n",
       "4            None  #e41a1c      -5.689587      23.575212   \n",
       "5            None  #e41a1c      -4.309228      25.456386   \n",
       "6            None  #e41a1c      -2.212200      35.858528   \n",
       "7            None  #e41a1c       1.487133      66.196220   \n",
       "8            None  #e41a1c    -351.394141     601.893043   \n",
       "9            None  #e41a1c    -347.285423     606.484596   \n",
       "10           None  #e41a1c      -4.654502      23.614012   \n",
       "11           None  #e41a1c      21.665372      85.743424   \n",
       "12           None  #e41a1c      -1.731373      17.946608   \n",
       "13           None  #e41a1c      -1.738747      20.605663   \n",
       "14           None  #e41a1c       9.545784      35.799801   \n",
       "15           None  #e41a1c      26.008738      73.119205   \n",
       "16           None  #e41a1c      -5.369022      22.072885   \n",
       "17           None  #e41a1c      -3.958886      26.641976   \n",
       "18           None  #e41a1c       4.190813      37.593683   \n",
       "19           None  #e41a1c       6.158397      64.582136   \n",
       "\n",
       "                Comparison Pollutant Device  Median Error  \n",
       "0   Zep_1107b vs Reference     PM2.5      x      0.617727  \n",
       "1   Zep_1107b vs Reference      PM10      x      0.697749  \n",
       "2   Zep_1107b vs Reference       NO2      x      0.476999  \n",
       "3   Zep_1107b vs Reference        O3      x      0.181654  \n",
       "4     Zep_753 vs Reference     PM2.5      x      1.052561  \n",
       "5     Zep_753 vs Reference      PM10      x      0.722528  \n",
       "6     Zep_753 vs Reference       NO2      x      0.316619  \n",
       "7     Zep_753 vs Reference        O3      x      0.159371  \n",
       "8     Zep_754 vs Reference     PM2.5      x      1.402333  \n",
       "9     Zep_754 vs Reference      PM10      x      0.993838  \n",
       "10    Zep_754 vs Reference       NO2      x      0.325285  \n",
       "11    Zep_754 vs Reference        O3      x      0.149678  \n",
       "12    Zep_757 vs Reference     PM2.5      x      0.404008  \n",
       "13    Zep_757 vs Reference      PM10      x      0.376010  \n",
       "14    Zep_757 vs Reference       NO2      x      0.384857  \n",
       "15    Zep_757 vs Reference        O3      x      0.156473  \n",
       "16    Zep_858 vs Reference     PM2.5      x      0.402615  \n",
       "17    Zep_858 vs Reference      PM10      x      0.993766  \n",
       "18    Zep_858 vs Reference       NO2      x      0.303948  \n",
       "19    Zep_858 vs Reference        O3      x      0.108123  "
      ]
     },
     "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NO2\n"
     ]
    },
    {
     "data": {
      "image/png": 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ffFBFRUV68803NXr0aK1YsUJz587tcJ9gMKhgMBhe9vv9kiTLsmRZlpPlwUGhUCj8Gss4te7rxFg7VRMAZzh5fiP+RTLOjoaP48ePq6mpSf/+7/+uhx9+WI888ogqKip02223aevWrbrhhhva7VNWVqbS0tJ26ysrK5WcnOxkeXDQkSZJcqmqqkqHB8fen8/ni7kPp2sC4Awnzm/Ev0Ag0OW2CbZt29EeKCEhQZs2bQrPajQ2NurSSy/V3XffrWeffTbc7pZbblFKSop+85vftOujo5mPrKwsnThxQl6vN9rS0M3ebvRr7oZqbf7GdZqQGf04WZYln8+ngoICud3uuKgJgDOcPL8R//x+v4YNG6bTp09f8Pvb0ZmPYcOGyeVyafz48W3Wjxs3TlVVVR3u4/F45PF42q13u928WeOYy+UKvzoxTk6Mt9M1AXAGn+f9QyRj7Oh9PgYOHKipU6eqvr6+zfoDBw5o1KhRTh4KAAD0UhHPfDQ1NengwYPh5YaGBtXV1Sk9PV3Z2dlavny5vvSlL+n666/XzJkzVVFRoT/84Q/atm2bk3UDAIBeKuLwUVNTo5kzZ4aXS0pKJEnz589XeXm5vvCFL+iJJ55QWVmZli5dqpycHL3wwgvKy8tzrmoAANBrRRw+8vPzdaFrVBcsWKAFCxZEXRQAAOi7eLYLAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAoyIOH9u3b9ecOXOUmZmphIQEbd68+bxt77vvPiUkJGjt2rUxlAgAAPqSiMNHc3OzJk+erPXr13fabtOmTaqurlZmZmbUxQEAgL7HFekOxcXFKi4u7rTNBx98oCVLlujPf/6zbrrppqiLAwAAfU/E4eNCWlpaNG/ePC1fvlwTJky4YPtgMKhgMBhe9vv9kiTLsmRZltPlwSGhUCj8Gss4te7rxFg7VRMAZzh5fiP+RTLOjoePRx55RC6XS0uXLu1S+7KyMpWWlrZbX1lZqeTkZKfLg0OONEmSS1VVVTo8OPb+fD5fzH04XRMAZzhxfiP+BQKBLrd1NHzU1tbqZz/7mXbv3q2EhIQu7bNixQqVlJSEl/1+v7KyslRYWCiv1+tkeXDQ241+rd5Trby8PE3IjH6cLMuSz+dTQUGB3G53XNQEwBlOnt+If62/XHSFo+Hjtdde0/Hjx5WdnR1ed+7cOT3wwANau3at3nvvvXb7eDweeTyeduvdbjdv1jjmcrnCr06MkxPj7XRNAJzB53n/EMkYOxo+5s2bp9mzZ7dZV1RUpHnz5umrX/2qk4cCAAC9VMTho6mpSQcPHgwvNzQ0qK6uTunp6crOztbQoUPbtHe73Ro+fLhycnJirxYAAPR6EYePmpoazZw5M7zcer3G/PnzVV5e7lhhAACgb4o4fOTn58u27S637+g6DwAA0H/xbBcAAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYFXH42L59u+bMmaPMzEwlJCRo8+bN4W2WZelb3/qWJk2apJSUFGVmZuqee+5RY2OjkzUDAIBeLOLw0dzcrMmTJ2v9+vXttgUCAe3evVsrV67U7t279fvf/1719fW65ZZbHCkWAAD0fq5IdyguLlZxcXGH29LS0uTz+dqs+8UvfqFrrrlG77//vrKzs6OrEgAA9Bndfs3H6dOnlZCQoCFDhnT3oQAAQC8Q8cxHJD755BN961vf0t133y2v19thm2AwqGAwGF72+/2SPrt+xLKs7iwPMQiFQuHXWMapdV8nxtqpmgA4w8nzG/EvknHutvBhWZa++MUvyrZtbdiw4bztysrKVFpa2m59ZWWlkpOTu6s8xOhIkyS5VFVVpcODY+/v73+ui4bTNQFwhhPnN+JfIBDocttuCR+twePw4cN65ZVXzjvrIUkrVqxQSUlJeNnv9ysrK0uFhYWd7oee9XajX6v3VCsvL08TMqMfJ8uy5PP5VFBQILfbHRc1AXCGk+c34l/rLxdd4Xj4aA0e7777rrZu3aqhQ4d22t7j8cjj8bRb73a7ebPGMZfLFX51YpycGG+nawLgDD7P+4dIxjji8NHU1KSDBw+GlxsaGlRXV6f09HSNGDFCd9xxh3bv3q2XXnpJ586d09GjRyVJ6enpGjhwYKSHAwAAfUzE4aOmpkYzZ84ML7f+ZDJ//nz94Ac/0JYtWyRJV111VZv9tm7dqvz8/OgrBQAAfULE4SM/P1+2bZ93e2fbAAAAeLYLAAAwivABAACM6tabjAEA+qZAIKD9+/d32qbpbFCv7zmki4bVaHBS+79q/Hu5ubnc36mfIHwAACK2f/9+XX311V1q+2gX+6ytrdWUKVOiLwq9BuEDABCx3Nxc1dbWdtqm/sNTKnluj9bcOUk5I4Z0qU/0D4QPAEDEkpOTLzhLkXj4L/K8dlbjJk7WVaM6v+Ek+hcuOAUAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBR3F4dANBOw4lmNQdDMfVx6KPm8KvLFfvXTYrHpdHDUmLuBz2P8AEAaKPhRLNmrt7mWH8PPL/Hsb62/ls+AaQPIHwAANponfFY+6WrNDZjcPT9nA3qpW07dXP+NKUkeWKq6eDxJi3777qYZ2MQHwgfAIAOjc0YrImXpkW9v2VZOnqxNGXURXK73Q5Wht6OC04BAIBRhA8AAGAU4QMAABhF+AAAAEZxwSmictY6J0na+8HpmPppPhtUzUfS8MMfO3I1PAAg/hE+EJVDf/2if+j3Tvz9vku/Ovi/DvTzmRQPb2sAiGd8SiMqhROGS5LGZAxWkntA1P3Uf3haDzy/R4/dMUk5I6L/k75W3AERAOIf4QNRSU8ZqLuuyY65n1DosxsGjbk4Jab7CQAAeg8uOAUAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgVMT3+di+fbt+8pOfqLa2Vh9++KE2bdqkuXPnhrfbtq3vf//7euqpp3Tq1CnNmDFDGzZs0OWXX+5k3QCAbpTg8qvBX6/EQYOj7iMUCqkx1Kh9J/fJ5YrttlIN/iYluPwx9YH4EfG7obm5WZMnT9aCBQt02223tdv+6KOP6uc//7mefvppjR49WitXrlRRUZHeeecdDRo0yJGiAQDdyz1kl779xo8d6evxiscd6cc9ZJakGx3pCz0r4vBRXFys4uLiDrfZtq21a9fqu9/9rm699VZJ0jPPPKNLLrlEmzdv1l133RVbtQAAI6xT1+qxm76sMRmxzXzsqNqhGXkzYp75OHS8SUv/61BMfSB+OHp79YaGBh09elSzZ88Or0tLS9O1116rnTt3dhg+gsGggsFgeNnv/2xazbIsWZblZHmIQ623Vw+FQow3ECdCoZDskFdZyWN0udcbdT+WZanB1aCxqWPldrtjqunTJr/s0Ed8VsSxSMbF0fBx9OhRSdIll1zSZv0ll1wS3vb3ysrKVFpa2m59ZWWlkpOTnSwPcehIkyS5VF1drQ/29nQ1AKT/f15WVVXpcPQTH2E+ny/mPpyuCc4LBAJdbtvjD5ZbsWKFSkpKwst+v19ZWVkqLCyUN4bEjd7hrfdPSntqdN1112lydnpPlwNA0tuNfq3eU628vDxNyIxt5sPn86mgoCDmmQ+nakL3af3loiscDR/Dh3/2mPVjx45pxIgR4fXHjh3TVVdd1eE+Ho9HHo+n3Xq32x3zmxXxr/V3YJfLxXgDccLp89KJz3M+K+JfJOPi6H0+Ro8ereHDh+vll18Or/P7/dq1a5emTZvm5KEAAEAvFfHMR1NTkw4ePBhebmhoUF1dndLT05Wdna1ly5bp4Ycf1uWXXx7+U9vMzMw29wIBAAD9V8Tho6amRjNnzgwvt16vMX/+fJWXl+vBBx9Uc3OzFi5cqFOnTikvL08VFRXc4wMAAEiKInzk5+fLtu3zbk9ISNAPf/hD/fCHP4ypMAAA0DfxbBcAAGAU4QMAABhF+AAAAEYRPgAAgFE9fodT9F2BQED79+/vtE39h6cUPHpQ+/YmqeUvQy7YZ25uLrfdB4BejvCBbrN//35dffXVXWr75ae71mdtba2mTJkSQ1UAgJ5G+EC3yc3NVW1tbadtms4G9cetO3XTzGkanNT+Nvsd9QkA6N0IH+g2ycnJF5ylsCxLH584rmnX/APPawCAfoILTgEAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARrl6ugAAQHw5a52TJO394HRM/TSfDarmI2n44Y+VkuSJqa+Dx5ti2h/xhfABAGjj0F+/6B/6/R4HenPpVwf/14F+PpPi4WurL2AUAQBtFE4YLkkakzFYSe4BUfdT/+FpPfD8Hj12xyTljEiLua4Uj0ujh6XE3A96HuEDANBGespA3XVNdsz9hEIhSdKYi1M08dLYwwf6Di44BQAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGCU4+Hj3LlzWrlypUaPHq2kpCSNGTNGP/rRj2TbttOHAgAAvZDjNxl75JFHtGHDBj399NOaMGGCampq9NWvflVpaWlaunSp04cDAAC9jOPh4/XXX9ett96qm266SZJ02WWX6Te/+Y3eeOMNpw8FAAB6IcfDx/Tp0/XLX/5SBw4c0BVXXKG33npLVVVVWrNmTYftg8GggsFgeNnv90uSLMuSZVlOl4c40zrGjDXQuwQCAdXX13fa5sCHpxU8elB76wbq02MXvr16Tk6OkpOTnSoRhkXyOe54+HjooYfk9/uVm5urAQMG6Ny5c1q1apW+8pWvdNi+rKxMpaWl7dZXVlbyJuxHfD5fT5cAIAKHDh3SAw880KW2857uWp+PPfaYxowZE0NV6EmBQKDLbRNsh68E/e1vf6vly5frJz/5iSZMmKC6ujotW7ZMa9as0fz589u172jmIysrSydOnJDX63WyNMQhy7Lk8/lUUFAgt9vd0+UA6KKuzHw0nQ3qz6/9r4r+caoGJ3ku2CczH72b3+/XsGHDdPr06Qt+fzs+87F8+XI99NBDuuuuuyRJkyZN0uHDh1VWVtZh+PB4PPJ42r8p3W43X0b9COMN9C5paWm65pprOm1jWZbOnDqpf5x+Hed3PxDJGDv+p7aBQECJiW27HTBggFpaWpw+FAAA6IUcn/mYM2eOVq1apezsbE2YMEFvvvmm1qxZowULFjh9KAAA0As5Hj7WrVunlStX6v7779fx48eVmZmpf/mXf9H3vvc9pw8FAAB6IcfDR2pqqtauXau1a9c63TUAAOgDeLYLAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMMrxO5zGyrZtSZ89mhd9n2VZCgQC8vv9PPUS6GM4v/uX1u/t1u/xzsRd+Dhz5owkKSsrq4crAQAAkTpz5ozS0tI6bZNgdyWiGNTS0qLGxkalpqYqISGhp8tBN/P7/crKytKRI0fk9Xp7uhwADuL87l9s29aZM2eUmZmpxMTOr+qIu5mPxMREjRw5sqfLgGFer5cPJ6CP4vzuPy4049GKC04BAIBRhA8AAGAU4QM9yuPx6Pvf/748Hk9PlwLAYZzfOJ+4u+AUAAD0bcx8AAAAowgfAADAKMIHAAAwivCBbpefn69ly5b1dBkA4kh5ebmGDBnS02WghxA+AACAUYQPAABgFOEDRn388ce65557dNFFFyk5OVnFxcV6991327TZsWOH8vPzlZycrIsuukhFRUX6+OOPe6hioH97/vnnNWnSJCUlJWno0KGaPXu2mpubJUn/+Z//qQkTJsjj8WjEiBFavHhxeL81a9Zo0qRJSklJUVZWlu6//341NTV1eqwXX3xRU6ZM0aBBg/S5z31OpaWlCoVC3fr/Q88gfMCoe++9VzU1NdqyZYt27twp27Z14403yrIsSVJdXZ1mzZql8ePHa+fOnaqqqtKcOXN07ty5Hq4c6H8+/PBD3X333VqwYIH27dunbdu26bbbbpNt29qwYYMWLVqkhQsXas+ePdqyZYvGjh0b3jcxMVE///nP9fbbb+vpp5/WK6+8ogcffPC8x3rttdd0zz336Jvf/KbeeecdPfnkkyovL9eqVatM/Fdhmg10sxtuuMH+5je/aR84cMCWZO/YsSO87cSJE3ZSUpL9u9/9zrZt27777rvtGTNm9FSpAP5GbW2tLcl+77332m3LzMy0v/Od73S5r+eee84eOnRoeHnjxo12WlpaeHnWrFn2j3/84zb7/OpXv7JHjBgReeGIe3H3VFv0Xfv27ZPL5dK1114bXjd06FDl5ORo3759kj6b+bjzzjt7qkQAf2Py5MmaNWuWJk2apKKiIhUWFuqOO+6QZVlqbGzUrFmzzrvv//zP/6isrEz79++X3+9XKBTSJ598okAgoOTk5Hbt33rrLe3YsaPNTMe5c+c63Qe9Fz+7IK4kJSX1dAkA/mrAgAHy+Xz605/+pPHjx2vdunXKycnRsWPHOt3vvffe080336wrr7xSL7zwgmpra7V+/XpJ0qefftrhPk1NTSotLVVdXV343549e/Tuu+9q0KBBjv/f0LMIHzBm3LhxCoVC2rVrV3jdX/7yF9XX12v8+PGSpCuvvFIvv/xyT5UI4O8kJCRoxowZKi0t1ZtvvqmBAwfK5/PpsssuO++5Wltbq5aWFj322GO67rrrdMUVV6ixsbHT40yZMkX19fUaO3Zsu3+JiXxV9TX87AJjLr/8ct166636+te/rieffFKpqal66KGHdOmll+rWW2+VJK1YsUKTJk3S/fffr/vuu08DBw7U1q1bdeedd2rYsGE9/D8A+pddu3bp5ZdfVmFhoTIyMrRr1y599NFHGjdunH7wgx/ovvvuU0ZGhoqLi3XmzBnt2LFDS5Ys0dixY2VZltatW6c5c+Zox44deuKJJzo91ve+9z3dfPPNys7O1h133KHExES99dZb2rt3rx5++GFD/2MY09MXnaDva73g1LZt++TJk/a8efPstLQ0OykpyS4qKrIPHDjQpv22bdvs6dOn2x6Pxx4yZIhdVFRkf/zxx+YLB/q5d955xy4qKrIvvvhi2+Px2FdccYW9bt268PYnnnjCzsnJsd1utz1ixAh7yZIl4W1r1qyxR4wYET7Pn3nmGVtS+Fz++wtObdu2Kyoq7OnTp9tJSUm21+u1r7nmGvuXv/ylif8qDEuwbdvu6QAEAAD6D35IAwAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGPX/AKm1sf7VteE1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM10\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_comp('Cranfield')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b79d1cce-fcef-4b73-9e0b-f498432e5e30",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def dists_comp(study):\n",
    "    measurements_db_path = Path(f'/var/mnt/Little Guy/Graddnodi/Output/{study}/Measurements/Measurements.db')\n",
    "    con = sql.connect(measurements_db_path)\n",
    "    cursor = con.cursor()\n",
    "    cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n",
    "    tables_to_dl = cursor.fetchall()\n",
    "    cursor.close()\n",
    "\n",
    "    dist_models = {}\n",
    "\n",
    "    for table in tables_to_dl:\n",
    "        print(table)\n",
    "        dfit = distfit(distr=[\n",
    "            'norm', 'lognorm', 't', 'expon', 'pareto', 'beta', 'gamma', 'genextreme'\n",
    "        ])\n",
    "        df = pd.read_sql(f\"SELECT * from '{table[0]}'\", con=con, index_col='_time')\n",
    "        for col, data in df.items():\n",
    "\n",
    "            dfit.fit_transform(data.dropna())\n",
    "            dfit.plot()\n",
    "            plt.savefig(f'img/{table[0]} {col} dist.png')\n",
    "            plt.close()\n",
    "\n",
    "            if col not in dist_models:\n",
    "                dist_models[col] = {}\n",
    "\n",
    "            dist_models[col][table[0]] = dfit.model['model']\n",
    "\n",
    "    con.close()\n",
    "\n",
    "    return dist_models"
   ]
  },
  {
   "cell_type": "raw",
   "id": "33850444-2255-4c07-8a94-8bfb26cf55eb",
   "metadata": {},
   "source": [
    "bp_models = dists_comp('Bushy Park')\n",
    "bp_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f43cf0b1-fe24-456e-a482-e5f8636a2c37",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Reference',)\n",
      "('Zep_1107b',)\n",
      "('Zep_753',)\n",
      "('Zep_754',)\n",
      "('Zep_757',)\n",
      "('Zep_858',)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'NO2': {'Reference': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f394693f9d0>,\n",
       "  'Zep_1107b': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3945bbb510>,\n",
       "  'Zep_753': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f39456e4650>,\n",
       "  'Zep_754': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3946ad2dd0>,\n",
       "  'Zep_757': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3947b56710>,\n",
       "  'Zep_858': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f39489f4950>},\n",
       " 'O3': {'Reference': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f39466cc4d0>,\n",
       "  'Zep_1107b': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3946897490>,\n",
       "  'Zep_753': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3945e2f950>,\n",
       "  'Zep_754': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3946b3aa90>,\n",
       "  'Zep_757': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3947bd4310>,\n",
       "  'Zep_858': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f394845bf90>},\n",
       " 'PM10': {'Reference': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f39466f3910>,\n",
       "  'Zep_1107b': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3945ad7950>,\n",
       "  'Zep_753': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3944c204d0>,\n",
       "  'Zep_754': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3946d91910>,\n",
       "  'Zep_757': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3947d20f90>,\n",
       "  'Zep_858': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3948b0ca50>},\n",
       " 'PM2.5': {'Reference': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f39463404d0>,\n",
       "  'Zep_1107b': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f394562a150>,\n",
       "  'Zep_753': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3958cd70d0>,\n",
       "  'Zep_754': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3946b1e310>,\n",
       "  'Zep_757': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f394802ae10>,\n",
       "  'Zep_858': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3948433610>},\n",
       " 'RH': {'Zep_1107b': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3946285690>,\n",
       "  'Zep_753': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f39454404d0>,\n",
       "  'Zep_754': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3945cc0550>,\n",
       "  'Zep_757': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3944914a10>,\n",
       "  'Zep_858': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f39482ff510>},\n",
       " 'NO': {'Zep_1107b': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3945c99c90>,\n",
       "  'Zep_753': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f394689e110>,\n",
       "  'Zep_754': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3946aa3c90>,\n",
       "  'Zep_757': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3947961e50>,\n",
       "  'Zep_858': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f39486db3d0>},\n",
       " 'T': {'Zep_1107b': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3945468550>,\n",
       "  'Zep_753': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3944b83610>,\n",
       "  'Zep_754': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f3947632290>,\n",
       "  'Zep_757': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f394803a5d0>,\n",
       "  'Zep_858': <scipy.stats._distn_infrastructure.rv_continuous_frozen at 0x7f39492c0b10>}}"
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     },
     "execution_count": 5,
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   ],
   "source": [
    "cr_models = dists_comp('Cranfield')\n",
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",
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   ],
   "source": [
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "\n",
    "FIGURE_LAYOUT = {\n",
    "    \"margin\": {\"pad\": 0, \"b\": 0, \"t\": 0, \"l\": 0, \"r\": 0},\n",
    "    \"showlegend\": False,\n",
    "    \"font\": {\"size\": 16},\n",
    "    \"paper_bgcolor\": \"rgba(0,0,0,0)\",\n",
    "    \"plot_bgcolor\": \"rgba(0,0,0,0)\",\n",
    "    \"colorway\": [\"#1d2021\"],\n",
    "    \"scattermode\": \"group\",\n",
    "}\n",
    "\n",
    "def plot_dists(dists, pollutants):\n",
    "    for pol, sites in dists.items():\n",
    "        if pol not in pollutants:\n",
    "            continue\n",
    "        fig = go.Figure()\n",
    "        min_x = min([r.mean()-5*r.std() for r in sites.values()])\n",
    "        max_x = max([r.mean()+5*r.std() for r in sites.values()])\n",
    "        try:\n",
    "            x = np.arange(-400, 400, 0.01)\n",
    "        except ValueError:\n",
    "            x = np.arange(-400, 400, 0.01)\n",
    "        for site, model in sites.items():\n",
    "            y = model.pdf(x)\n",
    "            fig.add_trace(go.Scatter(x=x, y=y, mode='lines'))\n",
    "            print(pol, site)\n",
    "        fig.update_layout(\n",
    "            **FIGURE_LAYOUT,\n",
    "            width=800,\n",
    "            height=800,\n",
    "            title={\n",
    "                'text': pol\n",
    "            }\n",
    "        )\n",
    "        #fig.write_image(f'img/{pol} dist.pdf')\n",
    "        fig.show()\n",
    "\n",
    "\n",
    "def plot_results(study):\n",
    "    with Path(f'{study}.pkl').open('rb') as file:\n",
    "        df = pickle.load(file)\n",
    "    if study == \"Bushy Park\":\n",
    "        df = df[df['Comparison'].isin([\n",
    "            'NPL AQMesh A vs AirView Reference B', 'NPL AQMesh B vs AirView Reference B',\n",
    "            'NPL AQMesh A vs AirView Reference A', 'NPL AQMesh B vs AirView Reference A',\n",
    "            'NPL AQMesh A vs Bushy Park FDMS', 'NPL AQMesh B vs Bushy Park FDMS',\n",
    "            'NPL AQMesh A vs Bushy Park OPC', 'NPL AQMesh B vs Bushy Park OPC',\n",
    "        ])]\n",
    "    df = df[df['Pollutant'].isin(['PM2.5', 'PM10', 'NO2', 'O3'])]\n",
    "    fig = go.Figure()\n",
    "    x = df['Residual Sum of Squares']\n",
    "    y = df['Median Error']\n",
    "    fig.add_trace(go.Scatter(x=x, y=y, mode='markers'))\n",
    "\n",
    "    fig.update_layout(\n",
    "        **FIGURE_LAYOUT,\n",
    "        width=800,\n",
    "        height=800\n",
    "    )\n",
    "    fig.update_xaxes(type='log', title='Residual Sum of Squares')\n",
    "    fig.update_yaxes(type='log', title='Median nRMSE')\n",
    "    # fig.write_image(f'img/{pol} dist.pdf')\n",
    "    fig.show()\n",
    "\n",
    "plot_results('Bushy Park')\n",
    "plot_results('Cranfield')"
   ]
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