304_top_states_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import textwrap\n",
    "import helper_400\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "helper_400.set_sns_style()\n",
    "\n",
    "import plotting\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "hs_dict = plotting.get_core_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set times new roman\n",
    "plt.rcParams['font.family'] = 'Times New Roman'\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gpd.read_file('data/usastate/tl_2020_us_state.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gdf[gdf['STUSPS'].isin(['AZ', 'CA', 'CO', 'ID', 'MT', 'NV', 'NM', 'OR', 'UT', 'WA','WY'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>GEOID</th>\n",
       "      <th>wfday</th>\n",
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       "<p>5 rows × 25 columns</p>\n",
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      ],
      "text/plain": [
       "                  GEOID  wfday  heatday  smoke_pm_non_zero  smoke_pm_gt_five  \\\n",
       "time                                                                           \n",
       "2006-01-01  04001942600  False    False              False             False   \n",
       "2006-01-01  04001942700  False    False              False             False   \n",
       "2006-01-01  04001944000  False    False              False             False   \n",
       "2006-01-01  04001944100  False    False              False             False   \n",
       "2006-01-01  04001944201  False    False              False             False   \n",
       "\n",
       "               hw   _hws     hs    hws     ws  ...  wfday_2d  _hws_2d  hs_2d  \\\n",
       "time                                           ...                             \n",
       "2006-01-01  False  False  False  False  False  ...         0    False      0   \n",
       "2006-01-01  False  False  False  False  False  ...         0    False      0   \n",
       "2006-01-01  False  False  False  False  False  ...         0    False      0   \n",
       "2006-01-01  False  False  False  False  False  ...         0    False      0   \n",
       "2006-01-01  False  False  False  False  False  ...         0    False      0   \n",
       "\n",
       "            hws_2d  ws_2d  hw_2d  smoke_pm_gt_five_2d  hs_2d_5  ws_2d_5  \\\n",
       "time                                                                      \n",
       "2006-01-01       0      0      0                    0        0        0   \n",
       "2006-01-01       0      0      0                    0        0        0   \n",
       "2006-01-01       0      0      0                    0        0        0   \n",
       "2006-01-01       0      0      0                    0        0        0   \n",
       "2006-01-01       0      0      0                    0        0        0   \n",
       "\n",
       "            hws_2d_5  \n",
       "time                  \n",
       "2006-01-01         0  \n",
       "2006-01-01         0  \n",
       "2006-01-01         0  \n",
       "2006-01-01         0  \n",
       "2006-01-01         0  \n",
       "\n",
       "[5 rows x 25 columns]"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet(\"outputs/d2-events-2d5-230929.parquet\") \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th>120</th>\n",
       "      <td>04</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "      <td>04011</td>\n",
       "      <td>Greenlee</td>\n",
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       "      <td>Census Tract 9601, Greenlee County, Arizona</td>\n",
       "      <td>1277.334491</td>\n",
       "      <td>2643</td>\n",
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       "      <td>MULTIPOLYGON (((-109.49570 33.18048, -109.4955...</td>\n",
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       "      <td>04</td>\n",
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       "      <td>04005</td>\n",
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       "      <td>Census Tract 15, Coconino County, Arizona</td>\n",
       "      <td>2670.191015</td>\n",
       "      <td>2957</td>\n",
       "      <td>427</td>\n",
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       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>186</td>\n",
       "      <td>1532</td>\n",
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       "      <td>40</td>\n",
       "      <td>633</td>\n",
       "      <td>MULTIPOLYGON (((-111.74845 34.96031, -111.7486...</td>\n",
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       "      <th>110</th>\n",
       "      <td>04</td>\n",
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       "      <td>AZ</td>\n",
       "      <td>04007</td>\n",
       "      <td>Gila</td>\n",
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       "      <td>1008.779499</td>\n",
       "      <td>5873</td>\n",
       "      <td>494</td>\n",
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       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>04</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "      <td>04009</td>\n",
       "      <td>Graham</td>\n",
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       "      <td>Census Tract 9616, Graham County, Arizona</td>\n",
       "      <td>1277.688975</td>\n",
       "      <td>2962</td>\n",
       "      <td>424</td>\n",
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       "      <td>14</td>\n",
       "      <td>20</td>\n",
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       "      <td>29</td>\n",
       "      <td>98</td>\n",
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       "      <td>04009</td>\n",
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       "             ST    STATE ST_ABBR STCNTY    COUNTY         FIPS  \\\n",
       "ST_ABBR                                                          \n",
       "AZ      120  04  Arizona      AZ  04011  Greenlee  04011960100   \n",
       "        75   04  Arizona      AZ  04005  Coconino  04005001500   \n",
       "        110  04  Arizona      AZ  04007      Gila  04007940400   \n",
       "        118  04  Arizona      AZ  04009    Graham  04009961600   \n",
       "        111  04  Arizona      AZ  04009    Graham  04009940500   \n",
       "\n",
       "                                                LOCATION    AREA_SQMI  \\\n",
       "ST_ABBR                                                                 \n",
       "AZ      120  Census Tract 9601, Greenlee County, Arizona  1277.334491   \n",
       "        75     Census Tract 15, Coconino County, Arizona  2670.191015   \n",
       "        110      Census Tract 9404, Gila County, Arizona  1008.779499   \n",
       "        118    Census Tract 9616, Graham County, Arizona  1277.688975   \n",
       "        111    Census Tract 9405, Graham County, Arizona  1684.663596   \n",
       "\n",
       "             E_TOTPOP  M_TOTPOP  ...  hws5  ws5  heatday_2d  \\\n",
       "ST_ABBR                          ...                          \n",
       "AZ      120      2643       346  ...     9   36         296   \n",
       "        75       2957       427  ...     3   19         186   \n",
       "        110      5873       494  ...     9   18         244   \n",
       "        118      2962       424  ...    14   20         259   \n",
       "        111      4698       334  ...     4   12         252   \n",
       "\n",
       "             smoke_pm_non_zero_2d  wfday_2d  _hws_2d  hs_2d  hws_2d  ws_2d  \\\n",
       "ST_ABBR                                                                      \n",
       "AZ      120                  1039       351     1261    159      35    264   \n",
       "        75                   1532       927     1887    118      40    633   \n",
       "        110                   686       168      867    115      32    111   \n",
       "        118                   569       238      843    118      29     98   \n",
       "        111                  1025       283     1204    132      29    220   \n",
       "\n",
       "                                                      geometry  \n",
       "ST_ABBR                                                         \n",
       "AZ      120  MULTIPOLYGON (((-109.49570 33.18048, -109.4955...  \n",
       "        75   MULTIPOLYGON (((-111.74845 34.96031, -111.7486...  \n",
       "        110  MULTIPOLYGON (((-110.75277 33.17056, -110.7422...  \n",
       "        118  MULTIPOLYGON (((-110.45152 32.51400, -110.4492...  \n",
       "        111  MULTIPOLYGON (((-110.45797 33.29433, -110.4571...  \n",
       "\n",
       "[5 rows x 181 columns]"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = gpd.read_file(\"outputs/d2-events-2d-230929_SVI_shapefile.geojson\")\n",
    "# Step 1: Filter Top 50 geoid by Exposure in Each State\n",
    "top_geoid_per_state = data.groupby('ST_ABBR').apply(lambda x: x.nlargest(100, 'hws'))\n",
    "top_geoid_per_state.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CA', 'AZ', 'OR', 'ID', 'NM'], dtype='object', name='ST_ABBR')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 2: Identify the 5 States for Plotting (assuming the top 5 states by total exposure)\n",
    "top_states = data.groupby('ST_ABBR')['hws'].sum().nlargest(5).index\n",
    "top_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_geoid_per_state = top_geoid_per_state[top_geoid_per_state['ST_ABBR'].isin(top_states)]\n",
    "top_geoid_per_state = top_geoid_per_state.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
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       "    <tr>\n",
       "      <th>time</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>04001944000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                  GEOID  wfday  heatday  smoke_pm_non_zero  smoke_pm_gt_five  \\\n",
       "time                                                                           \n",
       "2006-01-01  04001942600  False    False              False             False   \n",
       "2006-01-01  04001942700  False    False              False             False   \n",
       "2006-01-01  04001944000  False    False              False             False   \n",
       "2006-01-01  04001944100  False    False              False             False   \n",
       "2006-01-01  04001944201  False    False              False             False   \n",
       "\n",
       "               hw   _hws     hs    hws     ws  ...  wfday_2d  _hws_2d  hs_2d  \\\n",
       "time                                           ...                             \n",
       "2006-01-01  False  False  False  False  False  ...         0    False      0   \n",
       "2006-01-01  False  False  False  False  False  ...         0    False      0   \n",
       "2006-01-01  False  False  False  False  False  ...         0    False      0   \n",
       "2006-01-01  False  False  False  False  False  ...         0    False      0   \n",
       "2006-01-01  False  False  False  False  False  ...         0    False      0   \n",
       "\n",
       "            hws_2d  ws_2d  hw_2d  smoke_pm_gt_five_2d  hs_2d_5  ws_2d_5  \\\n",
       "time                                                                      \n",
       "2006-01-01       0      0      0                    0        0        0   \n",
       "2006-01-01       0      0      0                    0        0        0   \n",
       "2006-01-01       0      0      0                    0        0        0   \n",
       "2006-01-01       0      0      0                    0        0        0   \n",
       "2006-01-01       0      0      0                    0        0        0   \n",
       "\n",
       "            hws_2d_5  \n",
       "time                  \n",
       "2006-01-01         0  \n",
       "2006-01-01         0  \n",
       "2006-01-01         0  \n",
       "2006-01-01         0  \n",
       "2006-01-01         0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_geo_ids = top_geoid_per_state['FIPS'].unique()\n",
    "df = df[df['GEOID'].isin(unique_geo_ids)]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index()\n",
    "df['time'] = pd.to_datetime(df['time'])\n",
    "# Extract the year from the time column\n",
    "df['year'] = df['time'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "         GEOID  year  days_of_exposure\n",
       "0  04001944901  2007                 1\n",
       "1  04001944901  2009                 1\n",
       "2  04003000101  2017                 4\n",
       "3  04003000102  2011                 1\n",
       "4  04003000201  2015                 1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exposure_counts = df[df['hws']].groupby(['GEOID', 'year']).size().reset_index(name='days_of_exposure')\n",
    "exposure_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04003000304</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "year         2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016  \\\n",
       "GEOID                                                                           \n",
       "04001944901   0.0   1.0   0.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "04003000101   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "04003000102   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   0.0   \n",
       "04003000201   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   \n",
       "04003000304   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "year         2017  2018  2019  2020  \n",
       "GEOID                                \n",
       "04001944901   0.0   0.0   0.0   0.0  \n",
       "04003000101   4.0   0.0   0.0   0.0  \n",
       "04003000102   0.0   0.0   0.0   0.0  \n",
       "04003000201   0.0   0.0   0.0   0.0  \n",
       "04003000304   0.0   0.0   0.0   0.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table = exposure_counts.pivot(index='GEOID', columns='year', values='days_of_exposure').fillna(0)\n",
    "pivot_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th>GEOID</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>ST_ABBR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04001944901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>04001944901</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04003000101</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>04003000101</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04003000102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>04003000102</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04003000201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>04003000201</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04003000304</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>04003000304</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         GEOID  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  \\\n",
       "0  04001944901   0.0   1.0   0.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1  04003000101   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2  04003000102   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   \n",
       "3  04003000201   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   \n",
       "4  04003000304   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   \n",
       "\n",
       "   2016  2017  2018  2019  2020         FIPS ST_ABBR  \n",
       "0   0.0   0.0   0.0   0.0   0.0  04001944901      AZ  \n",
       "1   0.0   4.0   0.0   0.0   0.0  04003000101      AZ  \n",
       "2   0.0   0.0   0.0   0.0   0.0  04003000102      AZ  \n",
       "3   0.0   0.0   0.0   0.0   0.0  04003000201      AZ  \n",
       "4   0.0   0.0   0.0   0.0   0.0  04003000304      AZ  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table = pivot_table.reset_index()\n",
    "pivot_table = pivot_table.merge(top_geoid_per_state[['FIPS', 'ST_ABBR']], left_on='GEOID', right_on='FIPS')\n",
    "pivot_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table = pivot_table.drop(columns=['FIPS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>2006</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AZ</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04003000304</td>\n",
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      "text/plain": [
       "         GEOID  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  \\\n",
       "0  04001944901   0.0   1.0   0.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "1  04003000101   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2  04003000102   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   \n",
       "3  04003000201   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   \n",
       "4  04003000304   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   \n",
       "\n",
       "   2016  2017  2018  2019  2020 ST_ABBR  \n",
       "0   0.0   0.0   0.0   0.0   0.0      AZ  \n",
       "1   0.0   4.0   0.0   0.0   0.0      AZ  \n",
       "2   0.0   0.0   0.0   0.0   0.0      AZ  \n",
       "3   0.0   0.0   0.0   0.0   0.0      AZ  \n",
       "4   0.0   0.0   0.0   0.0   0.0      AZ  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table[\"total_exposure\"] = pivot_table[pivot_table.columns[1:-2]].sum(axis=1)\n",
    "# sort by total exposure\n",
    "pivot_table = pivot_table.sort_values(by='total_exposure', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\") #white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def annotate_bar1(ax, text, bar_idx, offset_x=-0.4, offset_y=5, bar_hight=1):\n",
    "    # x coordinate for the bar, adjusting to position the text box\n",
    "    x = bar_idx + offset_x\n",
    "    # y coordinate for the bar, using the height of the bar plus a different offset\n",
    "    y = bar_hight + offset_y\n",
    "    \n",
    "    # Add annotation with a thin black line\n",
    "    ax.annotate(text, xy=(bar_idx, bar_hight), xytext=(x, y),\n",
    "                textcoords=\"offset points\",  # Specify text coordinates\n",
    "                arrowprops=dict(arrowstyle=\"->\", color='black', lw=0.5), # Use arrow with head\n",
    "                bbox=dict(boxstyle=\"round,pad=0.3\", ec=\"black\", fc=\"white\", lw=0.5),\n",
    "                ha='right', fontsize=7)  # Align text to the right for consistency\n",
    "\n",
    "# Annotate the first, second, and third bars with adjusted offsets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table['GEOID'] = pivot_table['GEOID'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</div>"
      ],
      "text/plain": [
       "           GEOID  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  \\\n",
       "258  35017964100   0.0   4.0   0.0   0.0   2.0   0.0   1.0   9.0   0.0   3.0   \n",
       "306  41019210000   0.0   0.0   0.0   1.0   0.0   0.0   0.0   2.0   0.0   4.0   \n",
       "169  06093001300   1.0   0.0   6.0   0.0   0.0   0.0   7.0   0.0   0.0   0.0   \n",
       "349  41045970900   2.0   4.0   0.0   0.0   0.0   0.0   9.0   1.0   0.0   3.0   \n",
       "246  35003976401   0.0   2.0   0.0   0.0   0.0   1.0   2.0   0.0   0.0   2.0   \n",
       "\n",
       "     2016  2017  2018  2019  2020 ST_ABBR  total_exposure  \n",
       "258   3.0   2.0   0.0   0.0   1.0      NM            25.0  \n",
       "306   0.0   8.0   6.0   0.0   1.0      OR            22.0  \n",
       "169   0.0   6.0   2.0   0.0   0.0      CA            22.0  \n",
       "349   0.0   0.0   0.0   0.0   2.0      OR            21.0  \n",
       "246   2.0   4.0   5.0   0.0   3.0      NM            21.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table[\"total_exposure\"] = pivot_table[pivot_table.columns[1:-2]].sum(axis=1)\n",
    "# sort by total exposure\n",
    "pivot_table = pivot_table.sort_values(by='total_exposure', ascending=False)\n",
    "pivot_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 725x300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 3: Create Subplots for Each of the 5 States\n",
    "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(7.25, 3), sharey=True)\n",
    "\n",
    "# times new roman\n",
    "plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "for i, state in enumerate(top_states):\n",
    "    state_data = pivot_table[pivot_table['ST_ABBR']==state]\n",
    "\n",
    "    # Assuming 'state_data' is your DataFrame and you've already sorted it and reset the index\n",
    "    sorted_state_data = state_data.sort_values(by='total_exposure', ascending=False).reset_index(drop=True).head(50).copy()\n",
    "\n",
    "    # Extract the 'GEOID' and relevant columns for plotting\n",
    "    plot_data = sorted_state_data[pivot_table.columns[0:-1]]\n",
    "\n",
    "    #plot_data.plot(x='GEOID', kind='bar', stacked=True, ax=axes[i], width=1)\n",
    "\n",
    "    # Now, when plotting, the bars should reflect the sorted order because the index has been realigned\n",
    "     \n",
    "    #plot_data.plot(x='GEOID', kind='bar', stacked=True, ax=axes[i], width=1)\n",
    "    #plot_data['GEOID'] = plot_data['GEOID'].astype(str)\n",
    "    plot_data.set_index('GEOID').plot(kind='bar', stacked=True, ax=axes[i], width=1, cmap='inferno_r')  # Set bar width to 1 for 100% width\n",
    "\n",
    "    \n",
    "    axes[i].set_xlabel(state)\n",
    "    axes[i].set_ylabel('Days of Exposure')\n",
    "    axes[i].tick_params(axis='x', labelbottom=False)  # Remove xticklabels\n",
    "    if i < len(top_states)-1:\n",
    "        axes[i].legend().set_visible(False)  # Hide the legend\n",
    "    sns.despine(ax=axes[i], left=True, bottom=True)  # Remove the left and bottom spines\n",
    "    # Access the current axis\n",
    "    ax = axes[i] # plt.gca()\n",
    "\n",
    "    # Disable vertical grid lines\n",
    "    ax.xaxis.grid(False)\n",
    "\n",
    "    # Optionally, ensure that the grid lines are behind the plot elements\n",
    "    ax.set_axisbelow(True)\n",
    "\n",
    "    annotate_bar1(ax, f'{sorted_state_data[\"GEOID\"].iloc[0]}', 0, \n",
    "                  offset_x=60, offset_y=-15, bar_hight=sorted_state_data['total_exposure'].iloc[0])\n",
    "    annotate_bar1(ax, f'{sorted_state_data[\"GEOID\"].iloc[1]}', 1, \n",
    "                  offset_x=70, offset_y=-25, bar_hight=sorted_state_data['total_exposure'].iloc[1])\n",
    "    annotate_bar1(ax, f'{sorted_state_data[\"GEOID\"].iloc[2]}', 2, \n",
    "                  offset_x=74, offset_y=-50, bar_hight=sorted_state_data['total_exposure'].iloc[2])\n",
    "\n",
    "#plt.legend(loc='upper right', bbox_to_anchor=(0, 0),  ncol=3, fontsize=8, title='Year')\n",
    "plt.legend(loc='center left', ncol=1, fontsize=7, title='Year', bbox_to_anchor=(1.05, 0.53))\n",
    "\n",
    "# Adjust layout to make room for the legend below the subplot\n",
    "#plt.subplots_adjust(bottom=0.2)\n",
    "plt.subplots_adjust(wspace=0.01)  # Adjust these values as needed\n",
    "\n",
    "plt.suptitle('Census tracts most exposed to the co-occurrence of extreme heat, wildfire burn zone and wildfire smoke \\nin the five most affected states California, Arizona, Oregon, Idaho and New Mexico', \n",
    "             fontsize=10, ha='center',  fontweight='bold', x=0.5, y=.92) \n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('figures/sorted_states_ct2.png', dpi=300, bbox_inches='tight')\n",
    "plt.savefig('figures/pdf/fig6.pdf', format=\"pdf\", dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
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