5_1_ParaHotspotAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identify School Deficit Hotspots in Florianopolis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data and geospatial libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "import urbanpy as up\n",
    "import h3\n",
    "import libpysal\n",
    "import contextily as ctx\n",
    "from esda.getisord import G_Local"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load hexagon data for Florianopolis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "hex_df = gpd.read_parquet(\n",
    "    \"outputs/20240129_para_hexs_with_accessibility_capacity_vars.parquet\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "hex_gdf = gpd.read_parquet(\n",
    "    \"outputs/20240129_para_hexs_with_accessibility_capacity_vars.parquet\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate Hotspots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove disconnected hexagons\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select disconnected hexagons\n",
    "# disconnected_hexs = [0, 1, 2, 3, 4, 985, 986, 987, 988]\n",
    "\n",
    "# ax = hex_gdf.plot(facecolor=\"white\", edgecolor=\"black\")\n",
    "# hex_gdf.loc[disconnected_hexs, \"geometry\"].plot(color=\"red\", ax=ax)\n",
    "# ax.set_axis_off()\n",
    "# Add index text in red text\n",
    "# for idx, row in hex_gdf.reset_index().iterrows():\n",
    "# plt.text(row.geometry.centroid.x, row.geometry.centroid.y, row['index'], color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hex_gdf = hex_gdf.drop(disconnected_hexs, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define variables to be used in the analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['hex', 'territory type', 'population_2020', 'pop_6_14_years_adj',\n",
      "       'income_pc', 'ensino_fundamental', 'duration_to_school_min_by_foot',\n",
      "       'duration_to_school_min_by_foot_cat', 'duration_to_school_min_by_car',\n",
      "       'duration_to_school_min_by_car_cat',\n",
      "       'schools_within_15min_travel_time_foot',\n",
      "       'schools_within_30min_travel_time_foot',\n",
      "       'schools_within_15min_travel_time_car',\n",
      "       'schools_within_30min_travel_time_car', 'geometry',\n",
      "       'students_per_professor_FUND', 'students_per_class_FUND', 'MAT_FUND',\n",
      "       'IED_NIV_1_FUND', 'IED_NIV_2_FUND', 'IED_NIV_3_FUND', 'IED_NIV_4_FUND',\n",
      "       'IED_NIV_5_FUND', 'IED_NIV_6_FUND'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(hex_gdf.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_var_labels = {\n",
    "    \"hex\": \"H3 Hexagon Index\",\n",
    "    \"territory type\": \"Territory Type\",\n",
    "    \"population_2020\": \"Population All Ages\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "access_var_labels = {\n",
    "    \"pop_6_14_years_adj\": \"Population Ages 6-14\",\n",
    "    \"income_pc\": \"Avg Income Per Cápita (R$)\",\n",
    "    \"ensino_fundamental\": \"# of schools - Ensino Fundamental\",\n",
    "    \"duration_to_school_min_by_foot\": \"Travel time to the nearest school by foot\",\n",
    "    \"duration_to_school_min_by_foot_cat\": \"Travel time to the nearest school by foot categories\",\n",
    "    \"duration_to_school_min_by_car\": \"Travel time to the nearest school by car\",\n",
    "    \"duration_to_school_min_by_car_cat\": \"Travel time to the nearest school by car categories\",\n",
    "    \"schools_within_15min_travel_time_foot\": \"# of schools at <15 minutes by foot\",\n",
    "    \"schools_within_30min_travel_time_foot\": \"# of schools at <30 minutes by foot\",\n",
    "    \"schools_within_15min_travel_time_car\": \"# of schools at <15 minutes by car\",\n",
    "    \"schools_within_30min_travel_time_car\": \"# of schools at <30 minutes by car\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "capacity_var_labels = {\n",
    "    \"students_per_professor_FUND\": \"Average # Students per Teacher\",\n",
    "    \"students_per_class_FUND\": \"Average # Students per Class (calculated)\",\n",
    "    \"MAT_FUND\": \"Average # Students per Class (given)\",\n",
    "    \"IED_NIV_1_FUND\": \"Perc. Teachers with effort indicator - Level 1\",\n",
    "    \"IED_NIV_2_FUND\": \"Perc. Teachers with effort indicator - Level 2\",\n",
    "    \"IED_NIV_3_FUND\": \"Perc. Teachers with effort indicator - Level 3\",\n",
    "    \"IED_NIV_4_FUND\": \"Perc. Teachers with effort indicator - Level 4\",\n",
    "    \"IED_NIV_5_FUND\": \"Perc. Teachers with effort indicator - Level 5\",\n",
    "    \"IED_NIV_6_FUND\": \"Perc. Teachers with effort indicator - Level 6\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "access_names = [\n",
    "    \"pop_6_14_years_adj\",\n",
    "    \"income_pc\",\n",
    "    \"ensino_fundamental\",\n",
    "    \"duration_to_school_min_by_foot\",\n",
    "    \"schools_within_15min_travel_time_foot\",\n",
    "    \"schools_within_30min_travel_time_foot\",\n",
    "    \"duration_to_school_min_by_car\",\n",
    "    \"schools_within_15min_travel_time_car\",\n",
    "    \"schools_within_30min_travel_time_car\",\n",
    "]\n",
    "\n",
    "access_features_weights = [0.2, 0.2, 0.2, 0.066, 0.066, 0.068, 0.066, 0.066, 0.068]\n",
    "\n",
    "kring = 3  # Number of neighbors to consider\n",
    "\n",
    "significance = 0.05  # Statistical significance level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    assert np.array(access_features_weights).sum() == 1.0\n",
    "except AssertionError as e:\n",
    "    e.args += (\"Features weights must sum to 1.0\",)\n",
    "    raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pop_6_14_years_adj</th>\n",
       "      <td>131841.0</td>\n",
       "      <td>8.78</td>\n",
       "      <td>53.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.05</td>\n",
       "      <td>4.71</td>\n",
       "      <td>2832.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_pc</th>\n",
       "      <td>119556.0</td>\n",
       "      <td>202.47</td>\n",
       "      <td>137.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119.79</td>\n",
       "      <td>168.37</td>\n",
       "      <td>244.41</td>\n",
       "      <td>2829.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ensino_fundamental</th>\n",
       "      <td>6160.0</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>68.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_to_school_min_by_foot</th>\n",
       "      <td>132303.0</td>\n",
       "      <td>42.06</td>\n",
       "      <td>146.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.03</td>\n",
       "      <td>11.76</td>\n",
       "      <td>34.14</td>\n",
       "      <td>6898.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>schools_within_15min_travel_time_foot</th>\n",
       "      <td>132361.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>schools_within_30min_travel_time_foot</th>\n",
       "      <td>132361.0</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_to_school_min_by_car</th>\n",
       "      <td>132291.0</td>\n",
       "      <td>41.97</td>\n",
       "      <td>145.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.05</td>\n",
       "      <td>11.77</td>\n",
       "      <td>34.13</td>\n",
       "      <td>6896.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>schools_within_15min_travel_time_car</th>\n",
       "      <td>132361.0</td>\n",
       "      <td>0.88</td>\n",
       "      <td>3.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>schools_within_30min_travel_time_car</th>\n",
       "      <td>132361.0</td>\n",
       "      <td>2.44</td>\n",
       "      <td>7.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>156.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          count    mean     std  min     25%  \\\n",
       "pop_6_14_years_adj                     131841.0    8.78   53.68  0.0    0.14   \n",
       "income_pc                              119556.0  202.47  137.76  0.0  119.79   \n",
       "ensino_fundamental                       6160.0    1.17    1.45  0.0    1.00   \n",
       "duration_to_school_min_by_foot         132303.0   42.06  146.31  0.0    3.03   \n",
       "schools_within_15min_travel_time_foot  132361.0    0.19    0.93  0.0    0.00   \n",
       "schools_within_30min_travel_time_foot  132361.0    0.37    1.58  0.0    0.00   \n",
       "duration_to_school_min_by_car          132291.0   41.97  145.68  0.0    3.05   \n",
       "schools_within_15min_travel_time_car   132361.0    0.88    3.12  0.0    0.00   \n",
       "schools_within_30min_travel_time_car   132361.0    2.44    7.03  0.0    0.00   \n",
       "\n",
       "                                          50%     75%      max  \n",
       "pop_6_14_years_adj                       1.05    4.71  2832.00  \n",
       "income_pc                              168.37  244.41  2829.54  \n",
       "ensino_fundamental                       1.00    1.00    68.00  \n",
       "duration_to_school_min_by_foot          11.76   34.14  6898.78  \n",
       "schools_within_15min_travel_time_foot    0.00    0.00    76.00  \n",
       "schools_within_30min_travel_time_foot    0.00    0.00    76.00  \n",
       "duration_to_school_min_by_car           11.77   34.13  6896.92  \n",
       "schools_within_15min_travel_time_car     0.00    1.00    78.00  \n",
       "schools_within_30min_travel_time_car     1.00    2.00   156.00  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_gdf[access_names].describe().round(2).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def minmax_scaler(col, a=0, b=1):\n",
    "    \"\"\"\n",
    "    Min-max scaler\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    col : pandas.Series\n",
    "        Series to be scaled\n",
    "    a : float, optional\n",
    "        Min value of the scaled Series. Default is 0.\n",
    "    b : float, optional\n",
    "        Max value of the scaled Series. Default is 1.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    pandas.Series\n",
    "        Scaled Series\n",
    "    \"\"\"\n",
    "    return (col - col.min()) * (b - a) / (col.max() - col.min()) + a\n",
    "\n",
    "\n",
    "def linear_combination(x, w):\n",
    "    \"\"\"\n",
    "    Linear combination of features\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    row : pandas.Series\n",
    "        Series with features to be combined\n",
    "    weights : list\n",
    "        List of weights for each feature\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float\n",
    "        Linear combination of features\n",
    "    \"\"\"\n",
    "    return np.dot(x, w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handle missing values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "hex_gdf_clean = (\n",
    "    hex_gdf[access_names].fillna(0)\n",
    "    # .filllna(subset=[\"population_2020\"]) # Drop rows with missing values\n",
    "    # .apply(lambda col: col.fillna(col.mean()), axis=0) # Fill missing values with mean\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new column with the weighted sum of mix-max scaled features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "hex_gdf_clean[\"score\"] = hex_gdf_clean.apply(\n",
    "    minmax_scaler, axis=0\n",
    ").apply(  # Make columns values between 0 and 1\n",
    "    linear_combination, w=access_features_weights, axis=1\n",
    ")  # Row-wise linear combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert hex_gdf_clean[\"score\"].shape[0] == hex_gdf.shape[0]\n",
    "\n",
    "hex_gdf_clean[\"hex\"] = hex_gdf[\"hex\"]\n",
    "hex_gdf_clean = gpd.GeoDataFrame(\n",
    "    hex_gdf_clean, geometry=hex_gdf[\"geometry\"]\n",
    ")  # Add geometry column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate spatial weights matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://gist.github.com/darribas/c909209bc58fc0deddf46d8ec8fce6d0\n",
    "def w_from_hids(hids, kring=1):\n",
    "    \"\"\"\n",
    "    Create a spatial weights matrix from a list of hexagons and a k-ring\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    hids : list\n",
    "        List of hexagons ids\n",
    "    kring : int, optional\n",
    "        Number of rings to be considered. Default is 1.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    pysal.lib.weights.W\n",
    "        Spatial weights matrix\n",
    "\n",
    "    \"\"\"\n",
    "    shids = set(hids)\n",
    "    neis = {}\n",
    "    for hid in hids:\n",
    "        neis[hid] = list(h3.k_ring(hid, kring).intersection(shids))\n",
    "    w = libpysal.weights.W(neis, id_order=hids, ids=hids)\n",
    "    return w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate spatial weights matrix with kring neighbors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/claudio/Documents/edu-brazil/.env/lib/python3.11/site-packages/libpysal/weights/weights.py:224: UserWarning: The weights matrix is not fully connected: \n",
      " There are 4417 disconnected components.\n",
      "  warnings.warn(message)\n"
     ]
    }
   ],
   "source": [
    "w_h3_k3 = w_from_hids(hex_gdf_clean[\"hex\"].tolist(), kring=kring)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate Gi\\* statistic for our score variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/claudio/Documents/edu-brazil/.env/lib/python3.11/site-packages/libpysal/weights/weights.py:224: UserWarning: The weights matrix is not fully connected: \n",
      " There are 4417 disconnected components.\n",
      " There are 2269 islands with ids: 885f269a51fffff, 888b8a7955fffff, 888aa5768bfffff, 888a937933fffff, 88816b1831fffff, 88816a2a63fffff, 888a9a2f3bfffff, 888b9ab1a7fffff, 888ab57351fffff, 88814b7105fffff, 88806e0a4dfffff, 88814582cdfffff, 888074393bfffff, 88816ac719fffff, 88816a0e1dfffff, 888a9932abfffff, 888a905325fffff, 888069391bfffff, 888b986b49fffff, 88806e6cb9fffff, 885f254057fffff, 8880752203fffff, 88806cc4a9fffff, 888a837ab1fffff, 888045233dfffff, 888b9a36a3fffff, 888ab66d0dfffff, 888a9c8621fffff, 888a9d3ac7fffff, 888040d531fffff, 888a82aab1fffff, 888054ab49fffff, 888060b141fffff, 888a98a72dfffff, 888073d307fffff, 888a862165fffff, 888b9a25dbfffff, 888040b8d1fffff, 885f248a91fffff, 88815ba8a1fffff, 888aa031d7fffff, 888b9b41c5fffff, 888a936b1dfffff, 888a95080bfffff, 888ab659ddfffff, 88814d271bfffff, 888149190dfffff, 888040ccbdfffff, 88806eb655fffff, 888a802da5fffff, 888a834739fffff, 888064ab2bfffff, 8880453747fffff, 888148168dfffff, 885f2460d9fffff, 888a8b573bfffff, 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8880630e93fffff, 888a9e52e5fffff, 888a9c59d1fffff, 8880747613fffff, 888b9e2e59fffff, 88814d113bfffff, 88814b2999fffff, 888a842d9dfffff, 888ab05641fffff, 888a929531fffff, 888a846353fffff, 88814ea81bfffff, 888aa50d2dfffff, 8880401055fffff, 8880625599fffff, 8880408db1fffff, 888040364bfffff, 888a992029fffff, 888aa212a9fffff, 8880606827fffff, 888aa50da5fffff, 888aa14d2bfffff, 888a8a6ca3fffff, 888b9b25a1fffff, 888b9ab0dbfffff, 88815c2aa9fffff, 888aa57929fffff, 888041ca67fffff, 88814d0d37fffff, 88806da1bbfffff, 88816e386dfffff, 888141a339fffff, 8880704eb7fffff, 888a83aeb9fffff, 888a8411bdfffff, 88814e98ebfffff, 888ab518b3fffff, 888061b10dfffff, 888075d889fffff, 8880603b09fffff, 8881636649fffff, 888aa5284dfffff, 88806d3143fffff, 888145ce3dfffff, 888a840201fffff, 888aa22109fffff, 888073d521fffff, 8880746f15fffff, 888ab3c0ebfffff, 88806a95a5fffff, 88806ca6b5fffff, 8880654b1dfffff, 888ab4b269fffff, 888040119dfffff, 8880600459fffff, 885f252911fffff, 885f26d2e7fffff, 885f2469b5fffff, 88806adaa1fffff, 888b8a410dfffff, 88814db649fffff, 8881432907fffff, 888ab3a837fffff, 888aa0c181fffff, 888b9a2249fffff, 888ab0a6b7fffff, 888040c1e1fffff, 88814d498dfffff, 888143b6e1fffff, 888aae4b27fffff, 88806e8b55fffff, 88816bd697fffff, 888a96b70dfffff, 888065d325fffff, 88814d1059fffff, 888a99c8ddfffff, 888a81c0cdfffff, 888a935b3dfffff, 88806dd2d9fffff, 888065a159fffff, 888ab39b5bfffff, 888a910143fffff, 888aa32295fffff, 888b9b4ab9fffff, 888a92ac15fffff, 888070d689fffff, 88806004a5fffff, 888040a981fffff, 8880446d07fffff, 88816e384bfffff, 88805496a1fffff, 885f208081fffff, 88806e726dfffff, 888aa6c99bfffff, 885f34c0bbfffff, 88806ada13fffff, 8881454d69fffff, 88804644d1fffff, 88807431c9fffff, 888ab31639fffff, 88806e6b2dfffff, 888b9b6aabfffff, 888a9116a5fffff, 8880669697fffff, 885f243429fffff, 888a996707fffff, 88814a8283fffff, 888a910a95fffff, 88806cac93fffff, 8881439193fffff, 888ab64101fffff, 888b9a04d9fffff, 888aa25321fffff, 888a8005b1fffff, 88814d8d13fffff, 885f20326bfffff, 885f251981fffff, 8880601361fffff, 888aa0d84dfffff, 888149d9a5fffff, 8881414a8dfffff, 888071b701fffff, 888ab3da61fffff, 88806da1a9fffff, 888b8a6737fffff, 888a9a9801fffff, 888aa20101fffff, 885f31a4a7fffff, 8881453491fffff, 888ab4b72dfffff, 888a91c9e3fffff, 8881418e9dfffff, 888145c66bfffff, 88807221a7fffff, 885f207a01fffff, 8880641583fffff, 8880709403fffff, 88816c6b2bfffff, 888a9c92a9fffff, 888a985839fffff, 888aa17927fffff, 88814d9631fffff, 888a837ac9fffff, 888145c893fffff, 888141b29bfffff, 888ab249e3fffff, 888040b8cdfffff, 888a984857fffff, 888168498dfffff, 888ab56b35fffff, 888a9384a5fffff, 888ab2cabdfffff, 888041b85dfffff, 888aa10045fffff, 88806569edfffff, 888aa46de9fffff, 8880634201fffff, 888a86a019fffff, 888148b4b5fffff, 888b9a08b9fffff, 888070d66dfffff, 888b8a7691fffff, 888a9596a9fffff, 88814b8cddfffff, 888ab316a3fffff, 888040b939fffff, 88806162d1fffff, 8880442d87fffff, 88816ab119fffff, 888ad2d0c3fffff, 88814dd8a1fffff, 88816a55b1fffff, 888060a635fffff, 888a9530e9fffff, 888ab28a09fffff, 888b9b6e11fffff, 888b9b2531fffff, 888ab299a5fffff, 88806e80a3fffff, 885f252ac7fffff, 888ab24923fffff, 8880614445fffff, 8881685a35fffff, 888071d58dfffff, 88814bcc5dfffff, 888ab191d1fffff, 88806ab85bfffff, 888b984281fffff, 888aa20503fffff, 888045aaadfffff, 888aa28459fffff, 885f35cd83fffff, 888a944f57fffff, 88816e3069fffff, 88806eb491fffff, 888045a59bfffff, 888a96665dfffff, 888ab0adbbfffff, 88806dd2cdfffff, 888a838995fffff, 888040a701fffff, 885f25335dfffff, 888a919b55fffff, 8881636eb5fffff, 888145b753fffff, 888ab5b23dfffff, 888b9bcea7fffff, 885f35575bfffff, 888ab2d94dfffff, 888ab05461fffff, 888a9559a3fffff, 88806ad32bfffff, 8881413133fffff, 885f347357fffff, 88806e1a35fffff, 888b8a7459fffff, 885f260309fffff, 888aa55255fffff, 888a92ccabfffff, 888aa62699fffff, 888aa66b13fffff, 888aa22883fffff, 888b9bce51fffff, 8881408657fffff, 885f318327fffff, 8880740049fffff, 888a9150e5fffff, 888071b0b3fffff, 8881412603fffff, 888ab00b27fffff, 88814da747fffff, 88807045b1fffff, 88804520cdfffff, 888b9a80bbfffff, 888054a09bfffff, 888a9dca0dfffff, 888ab5a1dbfffff, 888ad64e11fffff, 888ab2161bfffff, 8880646439fffff, 888045a719fffff, 8881452109fffff, 8881480921fffff, 888a84cc09fffff, 888a955a6bfffff, 888a99cf63fffff, 88816bae51fffff, 888a9e2497fffff, 888a931b31fffff, 8880656141fffff, 8880453469fffff, 8881452081fffff, 888b9b6849fffff, 888b9b7963fffff, 888044a493fffff, 885f20a1ddfffff, 888b9b536dfffff, 88814cade3fffff, 888069c09bfffff, 888aa66411fffff, 888a919223fffff, 888a800c83fffff, 88806068cdfffff, 888aa11553fffff, 88806a70e9fffff, 888b9a2043fffff, 885f2259bbfffff, 88816ad811fffff, 888a84b525fffff, 88814bcc37fffff, 888061b0cbfffff, 88806ecca5fffff, 8880630f65fffff, 8880419121fffff, 888a8a5459fffff, 885f26d29bfffff, 888a85d4c3fffff, 888aa57669fffff, 888a9151a3fffff, 888a8a9337fffff, 88815b2d67fffff, 888ab239b7fffff, 888040d58dfffff, 888a85b001fffff, 888a918843fffff, 888a8310d5fffff, 8880708ce3fffff, 888060b761fffff, 8880419a03fffff, 888040c147fffff, 88806090d5fffff, 88804119c7fffff, 88814d0001fffff, 888040a1d3fffff, 888ab59045fffff, 888aa23b2bfffff, 888068993dfffff, 888041b405fffff, 885f049167fffff, 885f2431e3fffff, 888a934933fffff, 888aa2d017fffff, 88814596ddfffff, 8881408ed1fffff, 88804569a1fffff, 888a992487fffff, 888ab3ba11fffff, 8880758225fffff, 888041c951fffff, 888b9ab045fffff, 888ab291d9fffff, 888a834623fffff, 88816eb917fffff, 88814d6d5dfffff, 888aa05245fffff, 88806e52adfffff, 888ab07363fffff, 888a8a9563fffff, 888148975bfffff, 888069db03fffff, 888061918bfffff, 88806cb6e5fffff, 88814b48e3fffff, 888ad218b3fffff, 8880462499fffff, 888040cd97fffff, 888aa30105fffff, 888aa375ddfffff, 885f228a91fffff, 88806d71b5fffff, 888ab6710dfffff, 88806e9429fffff, 8880618b6bfffff, 8880448c09fffff, 88815db839fffff, 88816339a7fffff, 888aae582bfffff, 88806d0293fffff, 888a93a5a7fffff, 888ab23425fffff, 8881684f0dfffff, 888072b9ddfffff, 888a84562bfffff, 88814a0707fffff, 888aa50901fffff, 888a85d245fffff, 888ad24717fffff, 88816ac2ddfffff, 888ab5a693fffff, 8881411943fffff, 88816e1499fffff, 888a9cb009fffff, 8880625147fffff, 888a82ad4bfffff, 88816ad52dfffff, 888075801bfffff, 888aa32b65fffff, 888a9ad325fffff, 888aa5ce29fffff, 888ab52c23fffff, 888a91c9cbfffff, 888075330dfffff, 88816a8b63fffff, 88806ea23dfffff, 888040cc4dfffff, 888a86b769fffff, 88816a0ca3fffff, 888068a5abfffff, 888a91921dfffff, 888ab3999bfffff, 888ab3d159fffff, 8880608337fffff, 888a840b39fffff, 888a945085fffff, 888069da89fffff, 8881454ac1fffff, 88816a44d3fffff, 88814d0187fffff, 888149d453fffff, 8880456e49fffff, 88814d0563fffff, 888068d4ddfffff, 888a906f13fffff, 8880405b01fffff, 888159a463fffff, 888141b555fffff, 888148b413fffff, 888aa37061fffff, 888045069bfffff, 8880450ae3fffff, 888ad67511fffff, 888aa28161fffff, 8880601317fffff, 8880455a05fffff, 888a902b19fffff, 888ab06831fffff, 88804474b9fffff, 888ab62ec9fffff, 888b8a4b3bfffff, 888a992ce7fffff, 885f354f35fffff, 888aa3a9a9fffff, 888041cc33fffff, 888b9bae85fffff, 888ab568cbfffff, 885f309657fffff, 888045556bfffff, 888aa64341fffff, 888a93173bfffff, 888aa04c39fffff, 888ab234b7fffff, 88816e4b03fffff, 888ab235b1fffff, 888a830d05fffff, 88814d4cbbfffff, 88814868c7fffff, 888b9b5453fffff, 888aa168a7fffff, 88814daeadfffff, 888a9118edfffff, 888a918d13fffff, 88806d693bfffff, 888a837259fffff, 8880456401fffff, 888aa298a1fffff, 888a98a183fffff, 888060c53dfffff, 888041d1ddfffff, 888068aed9fffff, 888a8a21dbfffff, 888ab53915fffff, 888a996e69fffff, 88815dbae1fffff, 8880401333fffff, 88804051c5fffff, 888a966833fffff, 888b9b643dfffff, 88804500ebfffff, 888a92da59fffff, 888141321dfffff, 8880665ac3fffff, 88806c6c3dfffff, 888ad2112dfffff, 885f204e9dfffff, 88814184e9fffff, 8880619369fffff, 888a99d647fffff, 888a91b5a1fffff, 888aa09487fffff, 888065a409fffff, 8880704945fffff, 88816a7a03fffff, 888b9b0c61fffff, 888aaec897fffff, 88806a1aa5fffff, 888a98425dfffff, 888a91a15bfffff, 88816eb751fffff, 888b9b1443fffff, 88814180bdfffff, 888a8a39adfffff, 8880400059fffff, 888148d50dfffff, 888a83014bfffff, 88816e7435fffff, 888064b09dfffff, 8881432f3bfffff, 888040b00dfffff, 888a955083fffff, 8880411149fffff, 888ab2176dfffff, 888ab58321fffff, 888046263bfffff, 888ab22851fffff, 885f227489fffff, 888ab5d533fffff, 88816e6d5bfffff, 8880646523fffff, 888aa18cbdfffff, 888149555bfffff, 88816a4c15fffff, 888aa01457fffff, 888148c907fffff, 88814192e5fffff, 888a8b31e5fffff, 885f245889fffff, 888a8311b3fffff, 888068d6e1fffff, 888b8a51e5fffff, 888b9bba45fffff, 888ab59443fffff, 88806d6665fffff, 888041932dfffff, 88806a8281fffff, 888a945521fffff, 888aa354e5fffff, 88806ca69dfffff, 888aa6425dfffff, 88805412cdfffff, 885f24d419fffff, 88814da28bfffff, 88804626c7fffff, 888a906f35fffff, 8881482dbdfffff, 88806170b5fffff, 88816b2ccbfffff, 88814e9633fffff, 888ab24eb1fffff, 888aa44947fffff, 888a99680dfffff, 88814b78e7fffff, 888141331bfffff, 888aa23a89fffff, 888a99330bfffff, 885f34c547fffff, 88816e2625fffff, 888a91874dfffff, 88816ac63bfffff, 8880662597fffff, 888075105bfffff, 888a842a97fffff, 888aaec99dfffff, 888a95ce11fffff, 8880752347fffff, 888045d36bfffff, 888a99150bfffff, 888aa11707fffff, 888054158dfffff, 888054a103fffff, 8880402627fffff, 8880415041fffff.\n",
      "  warnings.warn(message)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('WARNING: ', '885f269a51fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b8a7955fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa5768bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a937933fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816b1831fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816a2a63fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a9a2f3bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b9ab1a7fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab57351fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814b7105fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88806e0a4dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814582cdfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888074393bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816ac719fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816a0e1dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a9932abfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a905325fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888069391bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b986b49fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88806e6cb9fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '885f254057fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880752203fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88806cc4a9fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a837ab1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888045233dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b9a36a3fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab66d0dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a9c8621fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a9d3ac7fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888040d531fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a82aab1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888054ab49fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888060b141fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a98a72dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888073d307fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a862165fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b9a25dbfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888040b8d1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '885f248a91fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88815ba8a1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa031d7fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b9b41c5fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a936b1dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a95080bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab659ddfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814d271bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888149190dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888040ccbdfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88806eb655fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a802da5fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a834739fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888064ab2bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880453747fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888148168dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '885f2460d9fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a8b573bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88804464b9fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab56d19fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816a94c9fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa3ac45fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880665a3bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880401035fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8881482401fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888066c59bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa28f1dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b8a4e3dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816a10d7fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888040551dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b9b1163fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '885f31c361fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a8aad97fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a955b69fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814a6761fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880462443fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8881498207fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab02c49fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888145cf29fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814891a5fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888040a04dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a994c83fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88806131c3fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a9b662bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880638605fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a8a7207fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8881412c27fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88807224d1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a9540d9fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816e6541fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a842ee1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888145981bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816e7525fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88815990c7fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888040c8b1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa25107fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816e1461fffff', ' is an island (no neighbors)')\n",
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      "('WARNING: ', '8880608337fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a840b39fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a945085fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888069da89fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8881454ac1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816a44d3fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814d0187fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888149d453fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880456e49fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814d0563fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888068d4ddfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a906f13fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880405b01fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888159a463fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888141b555fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888148b413fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa37061fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888045069bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880450ae3fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ad67511fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa28161fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880601317fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880455a05fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a902b19fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab06831fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88804474b9fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab62ec9fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b8a4b3bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a992ce7fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '885f354f35fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa3a9a9fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888041cc33fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b9bae85fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab568cbfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '885f309657fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888045556bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa64341fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a93173bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa04c39fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab234b7fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816e4b03fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab235b1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a830d05fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814d4cbbfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814868c7fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b9b5453fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa168a7fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814daeadfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a9118edfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a918d13fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88806d693bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a837259fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880456401fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa298a1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a98a183fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888060c53dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888041d1ddfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888068aed9fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a8a21dbfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab53915fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a996e69fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88815dbae1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880401333fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88804051c5fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a966833fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b9b643dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88804500ebfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a92da59fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888141321dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880665ac3fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88806c6c3dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ad2112dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '885f204e9dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814184e9fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880619369fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a99d647fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a91b5a1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa09487fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888065a409fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880704945fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816a7a03fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b9b0c61fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aaec897fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88806a1aa5fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a98425dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a91a15bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816eb751fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b9b1443fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814180bdfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a8a39adfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880400059fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888148d50dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a83014bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816e7435fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888064b09dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8881432f3bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888040b00dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a955083fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880411149fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab2176dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab58321fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888046263bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab22851fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '885f227489fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab5d533fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816e6d5bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880646523fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa18cbdfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888149555bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816a4c15fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa01457fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888148c907fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814192e5fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a8b31e5fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '885f245889fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a8311b3fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888068d6e1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b8a51e5fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888b9bba45fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab59443fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88806d6665fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888041932dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88806a8281fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a945521fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa354e5fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88806ca69dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa6425dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88805412cdfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '885f24d419fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814da28bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88804626c7fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a906f35fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8881482dbdfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88806170b5fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816b2ccbfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814e9633fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888ab24eb1fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa44947fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a99680dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88814b78e7fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888141331bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa23a89fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a99330bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '885f34c547fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816e2625fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a91874dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '88816ac63bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880662597fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888075105bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a842a97fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aaec99dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a95ce11fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880752347fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888045d36bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888a99150bfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888aa11707fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888054158dfffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '888054a103fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880402627fffff', ' is an island (no neighbors)')\n",
      "('WARNING: ', '8880415041fffff', ' is an island (no neighbors)')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/claudio/Documents/edu-brazil/.env/lib/python3.11/site-packages/esda/getisord.py:514: RuntimeWarning: invalid value encountered in divide\n",
      "  z_scores = (statistic - expected_value) / np.sqrt(expected_variance)\n",
      "/Users/claudio/Documents/edu-brazil/.env/lib/python3.11/site-packages/esda/getisord.py:443: RuntimeWarning: invalid value encountered in divide\n",
      "  self.z_sim = (self.Gs - self.EG_sim) / self.seG_sim\n"
     ]
    }
   ],
   "source": [
    "lg_h3_k3 = G_Local(hex_gdf_clean[\"score\"], w_h3_k3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the number of significant hexagons\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of significant hexagons: 58940 out of 132361 (44.53%)\n"
     ]
    }
   ],
   "source": [
    "n_sig = (lg_h3_k3.p_sim < significance).sum()\n",
    "n_total = lg_h3_k3.p_sim.shape[0]\n",
    "prop_sig = n_sig / n_total\n",
    "print(f\"Number of significant hexagons: {n_sig} out of {n_total} ({prop_sig:.2%})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assing Gi\\* and p-value to hexagons\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "hex_gdf_clean[\"Gi_h3_k3\"] = lg_h3_k3.Gs\n",
    "hex_gdf_clean[\"p_sim_h3_k3\"] = lg_h3_k3.p_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    132361.000000\n",
       "mean          0.000007\n",
       "std           0.000006\n",
       "min           0.000000\n",
       "25%           0.000005\n",
       "50%           0.000006\n",
       "75%           0.000009\n",
       "max           0.000139\n",
       "Name: Gi_h3_k3, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hex_gdf_clean[\"Gi_h3_k3\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the local G index after filtering not significant and unpopulated hexagons. Higher values indicate a hotspot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting numba\n",
      "  Using cached numba-0.58.1-cp311-cp311-macosx_11_0_arm64.whl.metadata (2.7 kB)\n",
      "Collecting llvmlite<0.42,>=0.41.0dev0 (from numba)\n",
      "  Using cached llvmlite-0.41.1-cp311-cp311-macosx_11_0_arm64.whl.metadata (4.8 kB)\n",
      "Requirement already satisfied: numpy<1.27,>=1.22 in ./.env/lib/python3.11/site-packages (from numba) (1.26.1)\n",
      "Using cached numba-0.58.1-cp311-cp311-macosx_11_0_arm64.whl (2.6 MB)\n",
      "Using cached llvmlite-0.41.1-cp311-cp311-macosx_11_0_arm64.whl (28.8 MB)\n",
      "Installing collected packages: llvmlite, numba\n",
      "Successfully installed llvmlite-0.41.1 numba-0.58.1\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/claudio/Documents/edu-brazil/.env/lib/python3.11/site-packages/geopandas/plotting.py:777: UserWarning: Numba not installed. Using slow pure python version.\n",
      "  binning = mapclassify.classify(\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[95], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m10\u001b[39m))\n\u001b[1;32m      2\u001b[0m (\n\u001b[1;32m      3\u001b[0m     \u001b[43mhex_gdf_clean\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mp_sim_h3_k3 < 0.05\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# .query(\"population_2020 > 0\")\u001b[39;49;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGi_h3_k3\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mOrRd_r\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheme\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mFisherJenks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.8\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m )\n\u001b[1;32m      8\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_axis_off()\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Add basemap\u001b[39;00m\n",
      "File \u001b[0;32m~/Documents/edu-brazil/.env/lib/python3.11/site-packages/geopandas/plotting.py:979\u001b[0m, in \u001b[0;36mGeoplotAccessor.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    977\u001b[0m kind \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkind\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgeo\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    978\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgeo\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 979\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    980\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pandas_kinds:\n\u001b[1;32m    981\u001b[0m     \u001b[38;5;66;03m# Access pandas plots\u001b[39;00m\n\u001b[1;32m    982\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m PlotAccessor(data)(kind\u001b[38;5;241m=\u001b[39mkind, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/Documents/edu-brazil/.env/lib/python3.11/site-packages/geopandas/plotting.py:777\u001b[0m, in \u001b[0;36mplot_dataframe\u001b[0;34m(df, column, cmap, color, ax, cax, categorical, legend, scheme, k, vmin, vmax, markersize, figsize, legend_kwds, categories, classification_kwds, missing_kwds, aspect, **style_kwds)\u001b[0m\n\u001b[1;32m    774\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m classification_kwds:\n\u001b[1;32m    775\u001b[0m     classification_kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m k\n\u001b[0;32m--> 777\u001b[0m binning \u001b[38;5;241m=\u001b[39m \u001b[43mmapclassify\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclassify\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    778\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m~\u001b[39;49m\u001b[43mnan_idx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheme\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mclassification_kwds\u001b[49m\n\u001b[1;32m    779\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    780\u001b[0m \u001b[38;5;66;03m# set categorical to True for creating the legend\u001b[39;00m\n\u001b[1;32m    781\u001b[0m categorical \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/Documents/edu-brazil/.env/lib/python3.11/site-packages/mapclassify/_classify_API.py:214\u001b[0m, in \u001b[0;36mclassify\u001b[0;34m(y, scheme, k, pct, pct_sampled, truncate, hinge, multiples, mindiff, initial, bins, lowest, anchor)\u001b[0m\n\u001b[1;32m    204\u001b[0m     classifier \u001b[38;5;241m=\u001b[39m _classifiers[scheme](y, bins, lowest)\n\u001b[1;32m    206\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m scheme \u001b[38;5;129;01min\u001b[39;00m [\n\u001b[1;32m    207\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mequalinterval\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    208\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfisherjenks\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprettybreaks\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    213\u001b[0m ]:\n\u001b[0;32m--> 214\u001b[0m     classifier \u001b[38;5;241m=\u001b[39m \u001b[43m_classifiers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mscheme\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m classifier\n",
      "File \u001b[0;32m~/Documents/edu-brazil/.env/lib/python3.11/site-packages/mapclassify/classifiers.py:1977\u001b[0m, in \u001b[0;36mFisherJenks.__init__\u001b[0;34m(self, y, k)\u001b[0m\n\u001b[1;32m   1973\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1974\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFewer unique values (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnu\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) than specified classes (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1975\u001b[0m     )\n\u001b[1;32m   1976\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mk \u001b[38;5;241m=\u001b[39m k\n\u001b[0;32m-> 1977\u001b[0m \u001b[43mMapClassifier\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1978\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFisherJenks\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32m~/Documents/edu-brazil/.env/lib/python3.11/site-packages/mapclassify/classifiers.py:686\u001b[0m, in \u001b[0;36mMapClassifier.__init__\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    684\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt \u001b[38;5;241m=\u001b[39m FMT\n\u001b[1;32m    685\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my \u001b[38;5;241m=\u001b[39m y\n\u001b[0;32m--> 686\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_classify\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    687\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_summary()\n",
      "File \u001b[0;32m~/Documents/edu-brazil/.env/lib/python3.11/site-packages/mapclassify/classifiers.py:705\u001b[0m, in \u001b[0;36mMapClassifier._classify\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    704\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_classify\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 705\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    706\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39myb, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcounts \u001b[38;5;241m=\u001b[39m bin1d(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbins)\n",
      "File \u001b[0;32m~/Documents/edu-brazil/.env/lib/python3.11/site-packages/mapclassify/classifiers.py:1982\u001b[0m, in \u001b[0;36mFisherJenks._set_bins\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1980\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_set_bins\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1981\u001b[0m     x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msort(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39my)\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mf8\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1982\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbins \u001b[38;5;241m=\u001b[39m \u001b[43m_fisher_jenks_means\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclasses\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/edu-brazil/.env/lib/python3.11/site-packages/mapclassify/classifiers.py:74\u001b[0m, in \u001b[0;36mnjit.<locals>.decorator_njit.<locals>.wrapper_decorator\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper_decorator\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 74\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/edu-brazil/.env/lib/python3.11/site-packages/mapclassify/classifiers.py:620\u001b[0m, in \u001b[0;36m_fisher_jenks_means\u001b[0;34m(values, classes)\u001b[0m\n\u001b[1;32m    618\u001b[0m i4 \u001b[38;5;241m=\u001b[39m i3 \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    619\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i4 \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 620\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m2\u001b[39m, classes \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m    621\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m mat2[_l, j] \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m (v \u001b[38;5;241m+\u001b[39m mat2[i4, j \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m]):\n\u001b[1;32m    622\u001b[0m             mat1[_l, j] \u001b[38;5;241m=\u001b[39m i3\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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aYv369fGDH/wgdu/eHV/+8pejt7c3li5dGhERixcvjhUrVgzO//KXvxy/+93v4u67747XX389Nm3aFKtWrYply5aduVcBAADwHkr+PT8LFiyIw4cPx8qVK6OzszNmzZoVW7ZsGfwShAMHDkR5+Z+aqr6+Pl588cW499574+qrr47p06fH3XffHffdd9+ZexUAAADvoeTf83M2+D0/AACQxznxe34AAADGK/EDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASGFE8bN27dqYMWNGVFdXR0NDQ2zbtu201m3YsCHKyspi/vz5I7ksAADAiJUcPxs3boyWlpZobW2NHTt2xMyZM6O5uTkOHTp0ynX79++Pr371q3HDDTeMeLMAAAAjVXL8PP744/GlL30pli5dGh//+Mdj3bp1ccEFF8T3v//9k67p7++PL37xi/Hwww/HpZde+r42DAAAMBIlxU9fX19s3749mpqa/vQE5eXR1NQUHR0dJ133jW98IyZPnhy33XbbaV3n2LFj0dPTM+QBAADwfpQUP0eOHIn+/v6oq6sbMl5XVxednZ3DrvnFL34RTz/9dKxfv/60r9PW1ha1tbWDj/r6+lK2CQAAcIJR/ba3o0ePxqJFi2L9+vUxadKk0163YsWK6O7uHnwcPHhwFHcJAABkMKGUyZMmTYqKioro6uoaMt7V1RVTpkw5Yf6vfvWr2L9/f8ybN29wbGBg4I8XnjAh9u7dG5dddtkJ66qqqqKqqqqUrQEAAJxSSXd+KisrY/bs2dHe3j44NjAwEO3t7dHY2HjC/CuuuCJeffXV2LVr1+Djs5/9bNx0002xa9cuH2cDAADGTEl3fiIiWlpaYsmSJTFnzpyYO3durFmzJnp7e2Pp0qUREbF48eKYPn16tLW1RXV1dVx55ZVD1l900UURESeMAwAAjKaS42fBggVx+PDhWLlyZXR2dsasWbNiy5Ytg1+CcODAgSgvH9UfJQIAAChZWVEUxdnexHvp6emJ2tra6O7ujpqamrO9HQAAYBSN1vt/t2gAAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAgBfEDAACkIH4AAIAUxA8AAJCC+AEAAFIQPwAAQAriBwAASEH8AAAAKYgfAAAghRHFz9q1a2PGjBlRXV0dDQ0NsW3btpPOXb9+fdxwww0xceLEmDhxYjQ1NZ1yPgAAwGgoOX42btwYLS0t0draGjt27IiZM2dGc3NzHDp0aNj5W7dujYULF8bLL78cHR0dUV9fHzfffHO8+eab73vzAAAAp6usKIqilAUNDQ1x7bXXxhNPPBEREQMDA1FfXx933XVXLF++/D3X9/f3x8SJE+OJJ56IxYsXn9Y1e3p6ora2Nrq7u6OmpqaU7QIAAOPMaL3/L+nOT19fX2zfvj2ampr+9ATl5dHU1BQdHR2n9Rxvv/12vPPOO3HxxRefdM6xY8eip6dnyAMAAOD9KCl+jhw5Ev39/VFXVzdkvK6uLjo7O0/rOe67776YNm3akID6c21tbVFbWzv4qK+vL2WbAAAAJxjTb3tbvXp1bNiwIZ5//vmorq4+6bwVK1ZEd3f34OPgwYNjuEsAAOCDaEIpkydNmhQVFRXR1dU1ZLyrqyumTJlyyrWPPvporF69On72s5/F1Vdffcq5VVVVUVVVVcrWAAAATqmkOz+VlZUxe/bsaG9vHxwbGBiI9vb2aGxsPOm6b3/72/HII4/Eli1bYs6cOSPfLQAAwAiVdOcnIqKlpSWWLFkSc+bMiblz58aaNWuit7c3li5dGhERixcvjunTp0dbW1tERPzzP/9zrFy5Mp599tmYMWPG4M8GfehDH4oPfehDZ/ClAAAAnFzJ8bNgwYI4fPhwrFy5Mjo7O2PWrFmxZcuWwS9BOHDgQJSX/+mG0ne/+93o6+uLz3/+80Oep7W1Nb7+9a+/v90DAACcppJ/z8/Z4Pf8AABAHufE7/kBAAAYr8QPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIAXxAwAApCB+AACAFMQPAACQgvgBAABSED8AAEAK4gcAAEhB/AAAACmIHwAAIIURxc/atWtjxowZUV1dHQ0NDbFt27ZTzv/Rj34UV1xxRVRXV8dVV10VmzdvHtFmAQAARqrk+Nm4cWO0tLREa2tr7NixI2bOnBnNzc1x6NChYee/8sorsXDhwrjtttti586dMX/+/Jg/f3689tpr73vzAAAAp6usKIqilAUNDQ1x7bXXxhNPPBEREQMDA1FfXx933XVXLF++/IT5CxYsiN7e3vjpT386OPa3f/u3MWvWrFi3bt1pXbOnpydqa2uju7s7ampqStkuAAAwzozW+/8JpUzu6+uL7du3x4oVKwbHysvLo6mpKTo6OoZd09HRES0tLUPGmpub44UXXjjpdY4dOxbHjh0b/HN3d3dE/PFfAgAA8MH27vv+Eu/TvKeS4ufIkSPR398fdXV1Q8br6upiz549w67p7Owcdn5nZ+dJr9PW1hYPP/zwCeP19fWlbBcAABjH/ud//idqa2vP2POVFD9jZcWKFUPuFr311lvx4Q9/OA4cOHBGXzy8q6enJ+rr6+PgwYM+Wsmocc4YC84Zo80ZYyx0d3fHJZdcEhdffPEZfd6S4mfSpElRUVERXV1dQ8a7urpiypQpw66ZMmVKSfMjIqqqqqKqquqE8draWv+RMapqamqcMUadc8ZYcM4Ybc4YY6G8/Mz+Zp6Snq2ysjJmz54d7e3tg2MDAwPR3t4ejY2Nw65pbGwcMj8i4qWXXjrpfAAAgNFQ8sfeWlpaYsmSJTFnzpyYO3durFmzJnp7e2Pp0qUREbF48eKYPn16tLW1RUTE3XffHTfeeGM89thjccstt8SGDRvil7/8ZTz11FNn9pUAAACcQsnxs2DBgjh8+HCsXLkyOjs7Y9asWbFly5bBLzU4cODAkNtT1113XTz77LPx4IMPxv333x9/8zd/Ey+88EJceeWVp33NqqqqaG1tHfajcHAmOGOMBeeMseCcMdqcMcbCaJ2zkn/PDwAAwHh0Zn+CCAAA4BwlfgAAgBTEDwAAkIL4AQAAUjhn4mft2rUxY8aMqK6ujoaGhti2bdsp5//oRz+KK664Iqqrq+Oqq66KzZs3j9FOGa9KOWPr16+PG264ISZOnBgTJ06Mpqam9zyTEFH632Xv2rBhQ5SVlcX8+fNHd4N8IJR6zt56661YtmxZTJ06NaqqquLyyy/3v5ucUqlnbM2aNfHRj340zj///Kivr4977703/vCHP4zRbhmPfv7zn8e8efNi2rRpUVZWFi+88MJ7rtm6dWt88pOfjKqqqvjIRz4SzzzzTMnXPSfiZ+PGjdHS0hKtra2xY8eOmDlzZjQ3N8ehQ4eGnf/KK6/EwoUL47bbboudO3fG/PnzY/78+fHaa6+N8c4ZL0o9Y1u3bo2FCxfGyy+/HB0dHVFfXx8333xzvPnmm2O8c8aTUs/Zu/bv3x9f/epX44YbbhijnTKelXrO+vr64tOf/nTs378/nnvuudi7d2+sX78+pk+fPsY7Z7wo9Yw9++yzsXz58mhtbY3du3fH008/HRs3boz7779/jHfOeNLb2xszZ86MtWvXntb8X//613HLLbfETTfdFLt27Yp77rknbr/99njxxRdLu3BxDpg7d26xbNmywT/39/cX06ZNK9ra2oad/4UvfKG45ZZbhow1NDQU//AP/zCq+2T8KvWM/bnjx48XF154YfGDH/xgtLbIB8BIztnx48eL6667rvje975XLFmypPjc5z43BjtlPCv1nH33u98tLr300qKvr2+stsg4V+oZW7ZsWfF3f/d3Q8ZaWlqK66+/flT3yQdHRBTPP//8Ked87WtfKz7xiU8MGVuwYEHR3Nxc0rXO+p2fvr6+2L59ezQ1NQ2OlZeXR1NTU3R0dAy7pqOjY8j8iIjm5uaTzie3kZyxP/f222/HO++8ExdffPFobZNxbqTn7Bvf+EZMnjw5brvttrHYJuPcSM7ZT37yk2hsbIxly5ZFXV1dXHnllbFq1aro7+8fq20zjozkjF133XWxffv2wY/G7du3LzZv3hyf+cxnxmTP5HCm3v9POJObGokjR45Ef39/1NXVDRmvq6uLPXv2DLums7Nz2PmdnZ2jtk/Gr5GcsT933333xbRp0074jw7eNZJz9otf/CKefvrp2LVr1xjskA+CkZyzffv2xX/8x3/EF7/4xdi8eXO88cYb8ZWvfCXeeeedaG1tHYttM46M5IzdeuutceTIkfjUpz4VRVHE8ePH48477/SxN86ok73/7+npid///vdx/vnnn9bznPU7P3CuW716dWzYsCGef/75qK6uPtvb4QPi6NGjsWjRoli/fn1MmjTpbG+HD7CBgYGYPHlyPPXUUzF79uxYsGBBPPDAA7Fu3bqzvTU+ILZu3RqrVq2KJ598Mnbs2BE//vGPY9OmTfHII4+c7a3BCc76nZ9JkyZFRUVFdHV1DRnv6uqKKVOmDLtmypQpJc0nt5GcsXc9+uijsXr16vjZz34WV1999Whuk3Gu1HP2q1/9Kvbv3x/z5s0bHBsYGIiIiAkTJsTevXvjsssuG91NM+6M5O+zqVOnxnnnnRcVFRWDYx/72Meis7Mz+vr6orKyclT3zPgykjP20EMPxaJFi+L222+PiIirrroqent744477ogHHnggysv9f+28fyd7/19TU3Pad30izoE7P5WVlTF79uxob28fHBsYGIj29vZobGwcdk1jY+OQ+RERL7300knnk9tIzlhExLe//e145JFHYsuWLTFnzpyx2CrjWKnn7IorrohXX301du3aNfj47Gc/O/gtNvX19WO5fcaJkfx9dv3118cbb7wxGNcREa+//npMnTpV+HCCkZyxt99++4TAeTe2//iz7PD+nbH3/6V9F8Po2LBhQ1FVVVU888wzxX//938Xd9xxR3HRRRcVnZ2dRVEUxaJFi4rly5cPzv/P//zPYsKECcWjjz5a7N69u2htbS3OO++84tVXXz1bL4FzXKlnbPXq1UVlZWXx3HPPFb/97W8HH0ePHj1bL4FxoNRz9ud82xuno9RzduDAgeLCCy8s/vEf/7HYu3dv8dOf/rSYPHly8c1vfvNsvQTOcaWesdbW1uLCCy8s/u3f/q3Yt29f8e///u/FZZddVnzhC184Wy+BceDo0aPFzp07i507dxYRUTz++OPFzp07i9/85jdFURTF8uXLi0WLFg3O37dvX3HBBRcU//RP/1Ts3r27WLt2bVFRUVFs2bKlpOueE/FTFEXxne98p7jkkkuKysrKYu7cucV//dd/Df6zG2+8sViyZMmQ+T/84Q+Lyy+/vKisrCw+8YlPFJs2bRrjHTPelHLGPvzhDxcRccKjtbV17DfOuFLq32X/P/HD6Sr1nL3yyitFQ0NDUVVVVVx66aXFt771reL48eNjvGvGk1LO2DvvvFN8/etfLy677LKiurq6qK+vL77yla8U//u//zv2G2fcePnll4d9r/Xu2VqyZElx4403nrBm1qxZRWVlZXHppZcW//qv/1rydcuKwv1IAADgg++s/8wPAADAWBA/AABACuIHAABIQfwAAAApiB8AACAF8QMAAKQgfgAAgBTEDwAAkIL4AQAAUhA/AABACuIHAABIQfwAAAAp/D9KOblcLMUwowAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "(\n",
    "    hex_gdf_clean.query(\"p_sim_h3_k3 < 0.05\")\n",
    "    # .query(\"population_2020 > 0\")\n",
    "    .plot(column=\"Gi_h3_k3\", cmap=\"OrRd_r\", scheme=\"FisherJenks\", k=4, ax=ax, alpha=0.8)\n",
    ")\n",
    "\n",
    "ax.set_axis_off()\n",
    "\n",
    "# Add basemap\n",
    "ctx.add_basemap(\n",
    "    ax,\n",
    "    source=ctx.providers.CartoDB.DarkMatterNoLabels,\n",
    "    crs=hex_gdf_clean.crs.to_string(),\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Remove figure white border\n",
    "plt.savefig(\n",
    "    \"outputs/maps/flor/11-para-hotspots.png\", bbox_inches=\"tight\", pad_inches=0, dpi=300\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "edu-brazil",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}