scratch_mi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VAX_WEEK</th>\n",
       "      <th>variable</th>\n",
       "      <th>percentages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-06</td>\n",
       "      <td>pctVaccineDose2</td>\n",
       "      <td>0.000031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-06</td>\n",
       "      <td>pctVaccineDose1</td>\n",
       "      <td>0.000068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>pctVaccineDose2</td>\n",
       "      <td>0.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>pctVaccineDose1</td>\n",
       "      <td>0.001182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>pctVaccineDose2</td>\n",
       "      <td>0.000037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    VAX_WEEK         variable  percentages\n",
       "1 2020-12-06  pctVaccineDose2     0.000031\n",
       "2 2020-12-06  pctVaccineDose1     0.000068\n",
       "4 2020-12-13  pctVaccineDose2     0.000033\n",
       "5 2020-12-13  pctVaccineDose1     0.001182\n",
       "7 2020-12-20  pctVaccineDose2     0.000037"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vax_path = \"/Users/jeandavidt/Library/CloudStorage/OneDrive-UniversitéLaval/Université/Doctorat/COVID/Latest Data/Input/MI/Cumulative vaccinations area.xlsx\"\n",
    "\n",
    "df = pd.read_excel(vax_path, header=1)\n",
    "df[\"VAX_WEEK\"] = df[\"VAX_WEEK\"].ffill()\n",
    "df[\"VAX_WEEK\"] = pd.to_datetime(df[\"VAX_WEEK\"])\n",
    "\n",
    "\n",
    "vals = []\n",
    "for idx, row in df.iterrows():\n",
    "    for col in df.columns[4:]:\n",
    "        if not np.isnan(row[col]):\n",
    "            vals.append(row[col])\n",
    "            continue\n",
    "df = df.drop(columns=df.columns[4:])\n",
    "df[\"percentages\"] = vals\n",
    "\n",
    "df = df.drop(columns=[\"Vax week whitespace buffer\", \"Age\"])\n",
    "\n",
    "df = df.rename(columns={\"Unnamed: 3\": \"variable\"})\n",
    "df = df[df[\"variable\"] != \"Up to date\"]\n",
    "df[\"variable\"] = df[\"variable\"].map(\n",
    "    {\n",
    "        \"At least one dose\": \"pctVaccineDose1\",\n",
    "        \"Completed vaccine series\": \"pctVaccineDose2\",\n",
    "    }\n",
    ")\n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = {\n",
    "    \"hosp\": \"/Users/jeandavidt/Library/CloudStorage/OneDrive-UniversitéLaval/Université/Doctorat/COVID/Latest Data/Input/MI/hcounty.csv\",\n",
    "    \"deaths\": \"/Users/jeandavidt/Library/CloudStorage/OneDrive-UniversitéLaval/Université/Doctorat/COVID/Latest Data/Input/MI/dcounty.csv\",\n",
    "    \"wwtp\": \"/Users/jeandavidt/Library/CloudStorage/OneDrive-UniversitéLaval/Université/Doctorat/COVID/Latest Data/Input/MI/Metro Plant data.xlsx\",\n",
    "    \"pct_pos\": \"/Users/jeandavidt/Library/CloudStorage/OneDrive-UniversitéLaval/Université/Doctorat/COVID/Latest Data/Input/MI/ww_MN metro percent positive.xlsx\"\n",
    "}\n",
    "COUNTIES = [\n",
    "    \"Anoka County\",\n",
    "    \"Washington County\",\n",
    "    \"Dakota County\",\n",
    "    \"Ramsey County\",\n",
    "    \"Hennepin County\",\n",
    "    \"Carver County\",\n",
    "    \"Scott County\"\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25174  rows\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adm_date_mmwr</th>\n",
       "      <th>county</th>\n",
       "      <th>case_count</th>\n",
       "      <th>mmwr_startdate</th>\n",
       "      <th>mmwr_enddate</th>\n",
       "      <th>pop_size</th>\n",
       "      <th>outcome</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202004</td>\n",
       "      <td>Hennepin County</td>\n",
       "      <td>1</td>\n",
       "      <td>01/19/2020</td>\n",
       "      <td>01/25/2020</td>\n",
       "      <td>1245837</td>\n",
       "      <td>hospitalization</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202004</td>\n",
       "      <td>Total</td>\n",
       "      <td>1</td>\n",
       "      <td>01/19/2020</td>\n",
       "      <td>01/25/2020</td>\n",
       "      <td>5563378</td>\n",
       "      <td>hospitalization</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202009</td>\n",
       "      <td>Hennepin County</td>\n",
       "      <td>1</td>\n",
       "      <td>02/23/2020</td>\n",
       "      <td>02/29/2020</td>\n",
       "      <td>1245837</td>\n",
       "      <td>hospitalization</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202009</td>\n",
       "      <td>Total</td>\n",
       "      <td>1</td>\n",
       "      <td>02/23/2020</td>\n",
       "      <td>02/29/2020</td>\n",
       "      <td>5563378</td>\n",
       "      <td>hospitalization</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202010</td>\n",
       "      <td>Aitkin County</td>\n",
       "      <td>0</td>\n",
       "      <td>03/01/2020</td>\n",
       "      <td>03/07/2020</td>\n",
       "      <td>15834</td>\n",
       "      <td>hospitalization</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   adm_date_mmwr           county  case_count mmwr_startdate mmwr_enddate  \\\n",
       "0         202004  Hennepin County           1     01/19/2020   01/25/2020   \n",
       "1         202004            Total           1     01/19/2020   01/25/2020   \n",
       "2         202009  Hennepin County           1     02/23/2020   02/29/2020   \n",
       "3         202009            Total           1     02/23/2020   02/29/2020   \n",
       "4         202010    Aitkin County           0     03/01/2020   03/07/2020   \n",
       "\n",
       "   pop_size          outcome  rate  \n",
       "0   1245837  hospitalization   0.1  \n",
       "1   5563378  hospitalization   0.0  \n",
       "2   1245837  hospitalization   0.1  \n",
       "3   5563378  hospitalization   0.0  \n",
       "4     15834  hospitalization   0.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(files[\"hosp\"])\n",
    "print(len(df), \" rows\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"mmwr_startdate\"] = pd.to_datetime(df[\"mmwr_startdate\"], format=\"%m/%d/%Y\")\n",
    "df[\"mmwr_enddate\"] = pd.to_datetime(df[\"mmwr_enddate\"], format=\"%m/%d/%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.groupby(\"mmwr_startdate\").sum(numeric_only=True)\n",
    "\n",
    "df2 = df2.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
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   "source": [
    "ser1 = df2[\"case_count\"]\n",
    "\n",
    "ser2 = ser1.asfreq(\"24H\").interpolate() / 7\n",
    "ser2.name = \"CPHD_hospcen_report_value\"\n",
    "\n",
    "px.scatter(ser2)\n"
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  },
  {
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   "metadata": {},
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    original  rolling_sum\n",
       "0        1.0          1.0\n",
       "1        1.0          2.0\n",
       "2        1.0          3.0\n",
       "3        1.0          4.0\n",
       "4        1.0          4.0\n",
       "..       ...          ...\n",
       "95       1.0          4.0\n",
       "96       1.0          4.0\n",
       "97       1.0          4.0\n",
       "98       1.0          4.0\n",
       "99       1.0          4.0\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"original\":pd.Series(np.ones(100))})\n",
    "df[\"rolling_sum\"] = df[\"original\"].rolling(4, center=False, min_periods=1).sum()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "config": {
        "plotlyServerURL": "https://plot.ly"
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         "mode": "markers",
         "name": "new cases",
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         "type": "scattergl",
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   "source": [
    "# building the active cases time series\n",
    "ser3 = ser2.rolling(14, center=False, min_periods=1).sum()\n",
    "px.scatter(pd.DataFrame({\"new cases\": ser2, \"active cases\": ser3}))\n"
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    "resampled = ser2.resample(\"1W-SUN\", convention=\"start\").sum()\n",
    "resampled.name = \"CPHD\"\n",
    "\n",
    "px.scatter(pd.DataFrame({\"resampled\": resampled, \"original\": df2[\"case_count\"]}))"
   ]
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  {
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   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39mloc[df[\u001b[39m\"\u001b[39m\u001b[39mcounty\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39misin(COUNTIES)]\n\u001b[1;32m      2\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mlen\u001b[39m(df), \u001b[39m\"\u001b[39m\u001b[39m rows\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
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   ],
   "source": [
    "\n",
    "df = df.loc[df[\"county\"].isin(COUNTIES)]\n",
    "print(len(df), \" rows\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>N1 value (copies/L)</th>\n",
       "      <th>N1 Stnd error (copies/L)</th>\n",
       "      <th>N2 value (copies/L)</th>\n",
       "      <th>N2 Stnd error (copies/L)</th>\n",
       "      <th>Flow (million gallons/d)</th>\n",
       "      <th>Influent Suspended Solids (mg/l)</th>\n",
       "      <th>Influent Temperature (C)</th>\n",
       "      <th>Influent CBOD-5day Liquids (mg/l)</th>\n",
       "      <th>Influent COD (mg/l)</th>\n",
       "      <th>Influent Ammonia Nitrogen (mgN/l)</th>\n",
       "      <th>Influent Total Kjeldahl Nitrogen (mgTKN/l)</th>\n",
       "      <th>Influent Total Phosphorus (mgP/l</th>\n",
       "      <th>Influent Maximum pH (su)</th>\n",
       "      <th>Influent Minimum pH (su)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>504561.2625</td>\n",
       "      <td>nd</td>\n",
       "      <td>686687.625</td>\n",
       "      <td>nd</td>\n",
       "      <td>156.820770</td>\n",
       "      <td>146</td>\n",
       "      <td>18.0</td>\n",
       "      <td>260</td>\n",
       "      <td>438</td>\n",
       "      <td>28.900000</td>\n",
       "      <td>46</td>\n",
       "      <td>4.76</td>\n",
       "      <td>7.15</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-11-02</td>\n",
       "      <td>583589.240625</td>\n",
       "      <td>nd</td>\n",
       "      <td>853229.98125</td>\n",
       "      <td>nd</td>\n",
       "      <td>163.118896</td>\n",
       "      <td>230</td>\n",
       "      <td>18.0</td>\n",
       "      <td>300</td>\n",
       "      <td>572</td>\n",
       "      <td>29.799999</td>\n",
       "      <td>48</td>\n",
       "      <td>5.46</td>\n",
       "      <td>7.13</td>\n",
       "      <td>6.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>428719.425</td>\n",
       "      <td>nd</td>\n",
       "      <td>987253.6125</td>\n",
       "      <td>nd</td>\n",
       "      <td>160.892029</td>\n",
       "      <td>188</td>\n",
       "      <td>18.0</td>\n",
       "      <td>270</td>\n",
       "      <td>518</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>49</td>\n",
       "      <td>5.53</td>\n",
       "      <td>7.28</td>\n",
       "      <td>6.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-11-04</td>\n",
       "      <td>353631.014192</td>\n",
       "      <td>nd</td>\n",
       "      <td>725334.025614</td>\n",
       "      <td>nd</td>\n",
       "      <td>159.936325</td>\n",
       "      <td>196</td>\n",
       "      <td>18.0</td>\n",
       "      <td>280</td>\n",
       "      <td>542</td>\n",
       "      <td>29.299999</td>\n",
       "      <td>53</td>\n",
       "      <td>6.90</td>\n",
       "      <td>7.08</td>\n",
       "      <td>6.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-11-05</td>\n",
       "      <td>511944.444444</td>\n",
       "      <td>nd</td>\n",
       "      <td>906111.111111</td>\n",
       "      <td>nd</td>\n",
       "      <td>158.947968</td>\n",
       "      <td>328</td>\n",
       "      <td>18.0</td>\n",
       "      <td>250</td>\n",
       "      <td>571</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>51</td>\n",
       "      <td>5.79</td>\n",
       "      <td>7.22</td>\n",
       "      <td>6.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date N1 value (copies/L) N1 Stnd error (copies/L) N2 value (copies/L)  \\\n",
       "0 2020-11-01         504561.2625                       nd          686687.625   \n",
       "1 2020-11-02       583589.240625                       nd        853229.98125   \n",
       "2 2020-11-03          428719.425                       nd         987253.6125   \n",
       "3 2020-11-04       353631.014192                       nd       725334.025614   \n",
       "4 2020-11-05       511944.444444                       nd       906111.111111   \n",
       "\n",
       "  N2 Stnd error (copies/L)  Flow (million gallons/d)  \\\n",
       "0                       nd                156.820770   \n",
       "1                       nd                163.118896   \n",
       "2                       nd                160.892029   \n",
       "3                       nd                159.936325   \n",
       "4                       nd                158.947968   \n",
       "\n",
       "   Influent Suspended Solids (mg/l)  Influent Temperature (C)  \\\n",
       "0                               146                      18.0   \n",
       "1                               230                      18.0   \n",
       "2                               188                      18.0   \n",
       "3                               196                      18.0   \n",
       "4                               328                      18.0   \n",
       "\n",
       "   Influent CBOD-5day Liquids (mg/l)  Influent COD (mg/l)  \\\n",
       "0                                260                  438   \n",
       "1                                300                  572   \n",
       "2                                270                  518   \n",
       "3                                280                  542   \n",
       "4                                250                  571   \n",
       "\n",
       "   Influent Ammonia Nitrogen (mgN/l)  \\\n",
       "0                          28.900000   \n",
       "1                          29.799999   \n",
       "2                          30.000000   \n",
       "3                          29.299999   \n",
       "4                          28.500000   \n",
       "\n",
       "   Influent Total Kjeldahl Nitrogen (mgTKN/l)  \\\n",
       "0                                          46   \n",
       "1                                          48   \n",
       "2                                          49   \n",
       "3                                          53   \n",
       "4                                          51   \n",
       "\n",
       "   Influent Total Phosphorus (mgP/l  Influent Maximum pH (su)  \\\n",
       "0                              4.76                      7.15   \n",
       "1                              5.46                      7.13   \n",
       "2                              5.53                      7.28   \n",
       "3                              6.90                      7.08   \n",
       "4                              5.79                      7.22   \n",
       "\n",
       "   Influent Minimum pH (su)  \n",
       "0                      6.69  \n",
       "1                      6.63  \n",
       "2                      6.65  \n",
       "3                      6.57  \n",
       "4                      6.29  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(files[\"wwtp\"])\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.replace(\"nd\", np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date datetime64[ns]\n",
      "N1 value (copies/L) float64\n",
      "N1 Stnd error (copies/L) float64\n",
      "N2 value (copies/L) float64\n",
      "N2 Stnd error (copies/L) float64\n",
      "Flow (million gallons/d) float64\n",
      "Influent Suspended Solids (mg/l) int64\n",
      "Influent Temperature (C) float64\n",
      "Influent CBOD-5day Liquids (mg/l) int64\n",
      "Influent COD (mg/l) int64\n",
      "Influent Ammonia Nitrogen (mgN/l) float64\n",
      "Influent Total Kjeldahl Nitrogen (mgTKN/l) int64\n",
      "Influent Total Phosphorus (mgP/l float64\n",
      "Influent Maximum pH (su) float64\n",
      "Influent Minimum pH (su) float64\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(col, df[col].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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           "bgcolor": "#E5ECF6",
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          },
          "title": {
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "def gcl_2_gcml(vals):\n",
    "    return vals * 0.001\n",
    "\n",
    "\n",
    "def mgd_2_m3d(vals):\n",
    "    return vals * 0.0037854118 * 1e6\n",
    "\n",
    "variables = {\n",
    "    \"N1 value (copies/L)\": {\n",
    "        \"table\": \"WWMeasure\",\n",
    "        \"type\": \"covN1\",\n",
    "        \"unit\": \"gcMl\",\n",
    "        \"aggregation\": \"single\",\n",
    "        \"qualityFlag\": False    ,\n",
    "        \"transform\": gcl_2_gcml,\n",
    "    },\n",
    "    \"N1 Stnd error (copies/L)\": {\n",
    "        \"table\": \"WWMeasure\",\n",
    "        \"type\": \"covN1\",\n",
    "        \"unit\": \"gcMl\",\n",
    "        \"aggregation\": \"std\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": gcl_2_gcml,\n",
    "    },\n",
    "    \"N2 value (copies/L)\": {\n",
    "        \"table\": \"WWMeasure\",\n",
    "        \"type\": \"covN2\",\n",
    "        \"unit\": \"gcMl\",\n",
    "        \"aggregation\": \"single\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": gcl_2_gcml,\n",
    "    },\n",
    "    \"N2 Stnd error (copies/L)\": {\n",
    "        \"table\": \"WWMeasure\",\n",
    "        \"type\": \"covN2\",\n",
    "        \"unit\": \"gcMl\",\n",
    "        \"aggregation\": \"std\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": gcl_2_gcml,\n",
    "    },\n",
    "    \"Flow (million gallons/d)\": {\n",
    "        \"table\": \"SiteMeasure\",\n",
    "        \"type\": \"wwFlow\",\n",
    "        \"unit\": \"m3d\",\n",
    "        \"aggregation\": \"mean\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": mgd_2_m3d,\n",
    "    },\n",
    "    \"Influent Suspended Solids (mg/l)\": {\n",
    "        \"table\": \"SiteMeasure\",\n",
    "        \"type\": \"wwTSS\",\n",
    "        \"unit\": \"mgL\",\n",
    "        \"aggregation\": \"single\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": None,\n",
    "    },\n",
    "    \"Influent Temperature (C)\": {\n",
    "        \"table\": \"SiteMeasure\",\n",
    "        \"type\": \"wwTemp\",\n",
    "        \"unit\": \"degC\",\n",
    "        \"aggregation\": \"mean\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": None,\n",
    "    },\n",
    "    \"Influent CBOD-5day Liquids (mg/l)\": {\n",
    "        \"table\": \"SiteMeasure\",\n",
    "        \"type\": \"wwBOD5c\",\n",
    "        \"unit\": \"mgL\",\n",
    "        \"aggregation\": \"single\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": None,\n",
    "    },\n",
    "    \"Influent COD (mg/l)\": {\n",
    "        \"table\": \"SiteMeasure\",\n",
    "        \"type\": \"wwCOD\",\n",
    "        \"unit\": \"mgL\",\n",
    "        \"aggregation\": \"single\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": None,\n",
    "    },\n",
    "    \"Influent Ammonia Nitrogen (mgN/l)\": {\n",
    "        \"table\": \"SiteMeasure\",\n",
    "        \"type\": \"wwNH4N\",\n",
    "        \"unit\": \"mgL\",\n",
    "        \"aggregation\": \"single\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": None,\n",
    "    },\n",
    "    \"Influent Total Kjeldahl Nitrogen (mgTKN/l)\": {\n",
    "        \"table\": \"SiteMeasure\",\n",
    "        \"type\": \"wwTKN\",\n",
    "        \"unit\": \"mgL\",\n",
    "        \"aggregation\": \"single\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": None,\n",
    "    },\n",
    "    \"Influent Total Phosphorus (mgP/l\": {\n",
    "        \"table\": \"SiteMeasure\",\n",
    "        \"type\": \"wwPP\",\n",
    "        \"unit\": \"mgL\",\n",
    "        \"aggregation\": \"single\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": None,\n",
    "    },\n",
    "    \"Influent Maximum pH (su)\": {\n",
    "        \"table\": \"SiteMeasure\",\n",
    "        \"type\": \"wwPh\",\n",
    "        \"unit\": \"pH\",\n",
    "        \"aggregation\": \"max\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": None,\n",
    "    },\n",
    "    \"Influent Minimum pH (su)\": {\n",
    "        \"table\": \"SiteMeasure\",\n",
    "        \"type\": \"wwPh\",\n",
    "        \"unit\": \"pH\",\n",
    "        \"aggregation\": \"min\",\n",
    "        \"qualityFlag\": False,\n",
    "        \"transform\": None,\n",
    "    },\n",
    "}\n",
    "\n",
    "def build_prefix(info: dict) -> str:\n",
    "    return \"_\".join([\n",
    "        info[\"table\"],\n",
    "        info[\"type\"],\n",
    "        info[\"unit\"],\n",
    "        info[\"aggregation\"],\n",
    "        ])\n",
    "\n",
    "df2 = df.copy()\n",
    "for variable, info in variables.items():\n",
    "    prefix = build_prefix(info)\n",
    "    value_name = \"_\".join([prefix, \"value\"])\n",
    "    qf_name = \"_\".join([prefix, \"qualityFlag\"])\n",
    "    df2[variable] = info[\"transform\"](df2[variable]) if info[\"transform\"] is not None else df2[variable]\n",
    "    df2 = df2.rename(columns={variable: value_name})\n",
    "    df2[qf_name] = info[\"qualityFlag\"]\n",
    "df2 = df2.set_index(\"Date\", drop=True)\n",
    "df2.head()\n",
    "val_cols = [col for col in df2.columns if \"value\" in str(col)]\n",
    "df3 = df2[val_cols]\n",
    "px.line(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Dakota', 'Washington', 'Ramsey', 'Anoka')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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