create_features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "BAG_Panden = '/home/data/citycentre/BAG_Panden.shp'\n",
    "CIR = '/home/data/processing/CIR_2015_10.tif'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import pairwise_distances_argmin\n",
    "from sklearn.datasets import load_sample_image\n",
    "from sklearn.utils import shuffle\n",
    "from time import time\n",
    "from PIL import Image\n",
    "import os, glob\n",
    "import pandas as pd\n",
    "from shutil import copyfile\n",
    "import matplotlib.image as mpimg\n",
    "import numpy\n",
    "import geopandas as gpd\n",
    "import fiona\n",
    "import rasterio\n",
    "import rasterio.mask\n",
    "import os\n",
    "from shapely.geometry import shape\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "shapefile=gpd.read_file(BAG_Panden)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We now ave all shapefile IDs\n"
     ]
    }
   ],
   "source": [
    "### all_ids= []\n",
    "[all_ids.append(i) for i in shapefile['Identifica']]\n",
    "print('We now ave all shapefile IDs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Old\n",
    "nonveg_percent_list=[]\n",
    "nonveg_abs_list=[]\n",
    "inten_percent_list=[]\n",
    "inten_abs_list=[]\n",
    "exten_percent_list=[]\n",
    "exten_abs_list=[]\n",
    "trees_percent_list=[]\n",
    "trees_abs_list=[]\n",
    "total_abs_list = []\n",
    "area_per_roof_list = []\n",
    "mean = []\n",
    "max_value = []\n",
    "min_value = []\n",
    "nasa_classification_list = []\n",
    "average_vegetation = []\n",
    "ID_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Statistical information\n",
    "\n",
    "pct_05 = []\n",
    "pct_10 = []\n",
    "pct_15 = []\n",
    "pct_20 = []\n",
    "pct_25 = []\n",
    "pct_30 = []\n",
    "pct_35 = []\n",
    "pct_40 = []\n",
    "pct_45 = []\n",
    "pct_50 = []\n",
    "pct_55 = []\n",
    "pct_60 = []\n",
    "pct_65 = []\n",
    "pct_70 = []\n",
    "pct_75 = []\n",
    "pct_80 = []\n",
    "pct_85 = []\n",
    "pct_90 = []\n",
    "pct_95 = []\n",
    "\n",
    "pc_1_08 = []\n",
    "pc_08_06 = []\n",
    "pc_06_04 = []\n",
    "pc_04_02 = []\n",
    "pc_02_00 = []\n",
    "pc_00_02 = []\n",
    "pc_02_04 = []\n",
    "pc_04_06 = []\n",
    "pc_06_08 = []\n",
    "pc_08_1 = []\n",
    "\n",
    "var = []\n",
    "std = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Clusters\n",
    "\n",
    "k2_01 = []\n",
    "k2_02 = []\n",
    "k4_01 = []\n",
    "k4_02 = []\n",
    "k4_03 = []\n",
    "k4_04 = []\n",
    "\n",
    "pc_2_01 = []\n",
    "pc_2_02 = []\n",
    "pc_4_01 = []\n",
    "pc_4_02 = []\n",
    "pc_4_03 = []\n",
    "pc_4_04 = []\n",
    "\n",
    "area_2_01 = []\n",
    "area_2_02 = []\n",
    "area_4_01 = []\n",
    "area_4_02 = []\n",
    "area_4_03 = []\n",
    "area_4_04 = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def recreate_image(codebook, labels, w, h):\n",
    "    \"\"\"Recreate the (compressed) image from the code book & labels\"\"\"\n",
    "    d = codebook.shape[1]\n",
    "    image = np.zeros((w, h))\n",
    "    label_idx = 0\n",
    "    for i in range(w):\n",
    "        for j in range(h):\n",
    "            image[i][j] = codebook[labels[label_idx]]\n",
    "            label_idx += 1\n",
    "    return(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cluster_per_k(imarray, n_colors):\n",
    "        \n",
    "    # Load Image and transform to a 2D numpy array.\n",
    "    w, h= original_shape = tuple(imarray.shape)\n",
    "    \n",
    "    d = 1\n",
    "    image_array = np.reshape(imarray, (w * h, d))\n",
    "    \n",
    "    \n",
    "    \n",
    "    #print(\"Fitting model on a small sub-sample of the data\")\n",
    "    t0 = time()\n",
    "    image_array_sample = shuffle(image_array, random_state=0)[:1000]\n",
    "    kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(image_array_sample)\n",
    "    \n",
    "    # Get labels for all points\n",
    "    #print(\"Predicting color indices on the full image (k-means)\")\n",
    "    t0 = time()\n",
    "    labels = kmeans.predict(image_array)\n",
    "    \n",
    "    \n",
    "    codebook_random = shuffle(image_array, random_state=0)[:n_colors + 1]\n",
    "    #print(\"Predicting color indices on the full image (random)\")\n",
    "    t0 = time()\n",
    "    labels_random = pairwise_distances_argmin(codebook_random, image_array, axis=0)\n",
    "    \n",
    "    \n",
    "    \n",
    "    #plt.title('Quantized image (2 colors, K-Means)')\n",
    "    return(recreate_image(kmeans.cluster_centers_, labels, w, h))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_table_of_clusters_from_id(roof_id, shp_file = \"/home/data/citycentre/BAG_Panden.shp\", raster_file = \"/home/data/citycentre/lufo_2014.tif\"):\n",
    "    try:\n",
    "        ID = roof_id\n",
    "        shapes = fiona.open(shp_file, \"r\")\n",
    "        roof_idx = [str(feat['properties']['Identifica']) for feat in shapes].index(roof_id)\n",
    "        area_per_roof = shapes[roof_idx]['properties']['SHAPE_Area']\n",
    "        \n",
    "\n",
    "        feat1 = [shapes[roof_idx]['geometry'] ]\n",
    "        with rasterio.open('/home/data/processing/CIR_2015_10.tif') as src:\n",
    "                out_image, out_transform = rasterio.mask.mask(src, feat1,crop=True, nodata=10, all_touched=True)\n",
    "                out_meta = src.meta.copy()  \n",
    "            \n",
    "         \n",
    "        out_meta.update({\"driver\": \"GTiff\",\n",
    "                         \"height\": out_image.shape[1],\n",
    "                         \"width\": out_image.shape[2],\n",
    "                         \"transform\": out_transform})\n",
    "        \n",
    "        NIR = np.matrix(out_image[0, :, :]).astype(float)\n",
    "        NIR[NIR==10] = 90\n",
    "        RED = np.matrix(out_image[1, :, :]).astype(float)\n",
    "        \n",
    "        ndvi = (NIR-RED)/(NIR+RED)\n",
    "\n",
    "        ndvi_not_nan = ndvi[ndvi != .8]\n",
    "        ndvi[ndvi == .8] = np.nan \n",
    "        \n",
    "        k4_ndvi = cluster_per_k(np.array(ndvi_not_nan), 5)\n",
    "        k2_ndvi = cluster_per_k(np.array(ndvi_not_nan), 3)\n",
    "              \n",
    "        list_of_4_cluster_values = (np.unique(k4_ndvi.flatten())).tolist()[:4]\n",
    "        k4_01.append(list_of_4_cluster_values[0])\n",
    "        k4_02.append(list_of_4_cluster_values[1])\n",
    "        k4_03.append(list_of_4_cluster_values[2])\n",
    "        k4_04.append(list_of_4_cluster_values[3])\n",
    "        \n",
    "        list_of_2_cluster_values = (np.unique(k2_ndvi.flatten())).tolist()[:2]\n",
    "        k2_01.append(list_of_2_cluster_values[0])\n",
    "        k2_02.append(list_of_2_cluster_values[1])\n",
    "      \n",
    "        \n",
    "        total_abs = (((ndvi_not_nan < 0.088)).sum() + ((0.088 < ndvi_not_nan) & (ndvi_not_nan < 0.21)).sum()\\\n",
    "                     + ((0.21 < ndvi_not_nan) & (ndvi_not_nan < 0.276)).sum() + ((0.276 < ndvi_not_nan) & \\\n",
    "                     (ndvi_not_nan < 1)).sum())\n",
    "        \n",
    "        nonveg_percent = (((ndvi_not_nan < 0.088)).sum())/total_abs\n",
    "        exten_percent = (((0.088 < ndvi_not_nan) & (ndvi_not_nan < 0.21)).sum())/total_abs\n",
    "        inten_percent = (((0.21 < ndvi_not_nan) & (ndvi_not_nan < 0.276)).sum())/total_abs     \n",
    "        trees_percent = (((0.276 < ndvi_not_nan) & (ndvi_not_nan < 1)).sum())/total_abs    \n",
    "        nonveg_abs = area_per_roof*nonveg_percent\n",
    "        exten_abs = area_per_roof*exten_percent\n",
    "        inten_abs = area_per_roof*inten_percent\n",
    "        trees_abs = area_per_roof*trees_percent\n",
    "        mean_roof = np.nanmean(ndvi_not_nan)\n",
    "        max_roof = np.nanmax(ndvi_not_nan)\n",
    "        min_roof = np.nanmin(ndvi)\n",
    "        \n",
    "        nonveg_percent_list.append(nonveg_percent)\n",
    "        nonveg_abs_list.append(nonveg_abs)\n",
    "        inten_percent_list.append(inten_percent)\n",
    "        inten_abs_list.append(inten_abs)\n",
    "        exten_percent_list.append(exten_percent)\n",
    "        exten_abs_list.append(exten_abs)\n",
    "        trees_percent_list.append(trees_percent)\n",
    "        trees_abs_list.append(trees_abs)        \n",
    "        total_abs_list.append(total_abs)\n",
    "        area_per_roof_list.append(area_per_roof)\n",
    "        ID_list.append(ID)\n",
    "        \n",
    "        min_value.append(min_roof)\n",
    "        pct_05.append(np.percentile(ndvi_not_nan, 5))\n",
    "        pct_10.append(np.percentile(ndvi_not_nan, 10))\n",
    "        pct_15.append(np.percentile(ndvi_not_nan, 15))\n",
    "        pct_20.append(np.percentile(ndvi_not_nan, 20))        \n",
    "        pct_25.append(np.percentile(ndvi_not_nan, 25))\n",
    "        pct_30.append(np.percentile(ndvi_not_nan, 30))\n",
    "        pct_35.append(np.percentile(ndvi_not_nan, 35))\n",
    "        pct_40.append(np.percentile(ndvi_not_nan, 40))\n",
    "        pct_45.append(np.percentile(ndvi_not_nan, 45))\n",
    "        pct_50.append(np.percentile(ndvi_not_nan, 50))\n",
    "        pct_55.append(np.percentile(ndvi_not_nan, 55))\n",
    "        pct_60.append(np.percentile(ndvi_not_nan, 60))        \n",
    "        pct_65.append(np.percentile(ndvi_not_nan, 65))\n",
    "        pct_70.append(np.percentile(ndvi_not_nan, 70))\n",
    "        pct_75.append(np.percentile(ndvi_not_nan, 75))\n",
    "        pct_80.append(np.percentile(ndvi_not_nan, 80))\n",
    "        pct_85.append(np.percentile(ndvi_not_nan, 85))\n",
    "        pct_90.append(np.percentile(ndvi_not_nan, 90))\n",
    "        pct_95.append(np.percentile(ndvi_not_nan, 95))\n",
    "        max_value.append(max_roof)\n",
    "        \n",
    "        pc_1_08.append((((-1 < ndvi_not_nan) & (ndvi_not_nan < -0.8)).sum())/total_abs) \n",
    "        pc_08_06.append((((-0.8 < ndvi_not_nan) & (ndvi_not_nan < -0.6)).sum())/total_abs)\n",
    "        pc_06_04.append((((-0.6 < ndvi_not_nan) & (ndvi_not_nan < -0.4)).sum())/total_abs)\n",
    "        pc_04_02.append((((-0.4 < ndvi_not_nan) & (ndvi_not_nan < -0.2)).sum())/total_abs)\n",
    "        pc_02_00.append((((-0.2 < ndvi_not_nan) & (ndvi_not_nan < 0)).sum())/total_abs)\n",
    "        pc_00_02.append((((0.0 < ndvi_not_nan) & (ndvi_not_nan < 0.2)).sum())/total_abs)\n",
    "        pc_02_04.append((((0.2 < ndvi_not_nan) & (ndvi_not_nan < 0.4)).sum())/total_abs)\n",
    "        pc_04_06.append((((0.4 < ndvi_not_nan) & (ndvi_not_nan < 0.6)).sum())/total_abs)\n",
    "        pc_06_08.append((((0.6 < ndvi_not_nan) & (ndvi_not_nan < 0.8)).sum())/total_abs)\n",
    "        pc_08_1.append((((0.8 < ndvi_not_nan) & (ndvi_not_nan < 1)).sum())/total_abs)\n",
    "        \n",
    "        pc_2_01.append(((k2_ndvi == list_of_2_cluster_values[0]).sum())/total_abs)\n",
    "        pc_2_02.append(((k2_ndvi == list_of_2_cluster_values[1]).sum())/total_abs)\n",
    "        pc_4_01.append(((k4_ndvi == list_of_4_cluster_values[0]).sum())/total_abs)\n",
    "        pc_4_02.append(((k4_ndvi == list_of_4_cluster_values[1]).sum())/total_abs)\n",
    "        pc_4_03.append(((k4_ndvi == list_of_4_cluster_values[2]).sum())/total_abs)\n",
    "        pc_4_04.append(((k4_ndvi == list_of_4_cluster_values[3]).sum())/total_abs)\n",
    "     \n",
    "        area_2_01.append((((k2_ndvi == list_of_2_cluster_values[0]).sum())/total_abs)*area_per_roof)\n",
    "        area_2_02.append((((k2_ndvi == list_of_2_cluster_values[1]).sum())/total_abs)*area_per_roof)\n",
    "        area_4_01.append((((k4_ndvi == list_of_4_cluster_values[0]).sum())/total_abs)*area_per_roof)\n",
    "        area_4_02.append((((k4_ndvi == list_of_4_cluster_values[1]).sum())/total_abs)*area_per_roof)\n",
    "        area_4_03.append((((k4_ndvi == list_of_4_cluster_values[2]).sum())/total_abs)*area_per_roof)\n",
    "        area_4_04.append((((k4_ndvi == list_of_4_cluster_values[3]).sum())/total_abs)*area_per_roof)\n",
    "  \n",
    "    \n",
    "        std.append(np.std(ndvi_not_nan))\n",
    "        var.append(np.var(ndvi_not_nan))\n",
    "        mean.append(np.nanmean(ndvi_not_nan))\n",
    "    \n",
    "    except ValueError:\n",
    "        print('A building partly outside the AoI was just detected.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "A building partly outside the AoI was just detected.\n",
      "The wait is over.\n"
     ]
    }
   ],
   "source": [
    "[get_table_of_clusters_from_id(x) for x in all_ids]\n",
    "print('The wait is over.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>nasa_nonveg_area</th>\n",
       "      <th>nasa_nonveg_pc</th>\n",
       "      <th>nasa_ext_area</th>\n",
       "      <th>nasa_ext_pc</th>\n",
       "      <th>nasa_int_area</th>\n",
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       "      <th>nasa_tree_pc</th>\n",
       "      <th>total_area</th>\n",
       "      <th>naive_label</th>\n",
       "      <th>...</th>\n",
       "      <th>pc_4_02</th>\n",
       "      <th>pc_4_03</th>\n",
       "      <th>pc_4_04</th>\n",
       "      <th>area_2_01</th>\n",
       "      <th>area_2_02</th>\n",
       "      <th>area_4_01</th>\n",
       "      <th>area_4_02</th>\n",
       "      <th>area_4_03</th>\n",
       "      <th>area_4_04</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
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       "      <th>0599100000082215</th>\n",
       "      <td>29.105542</td>\n",
       "      <td>0.407407</td>\n",
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       "      <td>1.763972</td>\n",
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       "      <td>71.440875</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.123457</td>\n",
       "      <td>0.228395</td>\n",
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       "      <td>14.993764</td>\n",
       "      <td>0.327586</td>\n",
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       "    <tr>\n",
       "      <th>0599100000111188</th>\n",
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       "    <tr>\n",
       "      <th>0599100000138014</th>\n",
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       "    <tr>\n",
       "      <th>0599100000215405</th>\n",
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       "    <tr>\n",
       "      <th>0599100000289238</th>\n",
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       "    <tr>\n",
       "      <th>0599100000289242</th>\n",
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       "    <tr>\n",
       "      <th>0599100000617607</th>\n",
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       "      <td>13.303490</td>\n",
       "      <td>9.312443</td>\n",
       "      <td>11.086242</td>\n",
       "      <td>0.214286</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617608</th>\n",
       "      <td>121.983551</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.492163</td>\n",
       "      <td>0.225705</td>\n",
       "      <td>0.094044</td>\n",
       "      <td>94.282107</td>\n",
       "      <td>41.795161</td>\n",
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       "      <td>76.300468</td>\n",
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       "      <td>14.579707</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617609</th>\n",
       "      <td>304.233711</td>\n",
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       "      <td>5.191702</td>\n",
       "      <td>0.007728</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0.222566</td>\n",
       "      <td>0.180062</td>\n",
       "      <td>232.069060</td>\n",
       "      <td>239.337442</td>\n",
       "      <td>229.992379</td>\n",
       "      <td>157.308557</td>\n",
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       "      <td>0.400000</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617610</th>\n",
       "      <td>68.101024</td>\n",
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       "      <td>0.082873</td>\n",
       "      <td>64.068726</td>\n",
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       "      <td>40.322974</td>\n",
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       "      <td>0.228346</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617611</th>\n",
       "      <td>49.359151</td>\n",
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       "      <td>0.489510</td>\n",
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       "      <td>8.736133</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617612</th>\n",
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       "      <td>0.507042</td>\n",
       "      <td>0.352113</td>\n",
       "      <td>7.502409</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617613</th>\n",
       "      <td>165.969289</td>\n",
       "      <td>0.894057</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.310078</td>\n",
       "      <td>0.183463</td>\n",
       "      <td>0.108527</td>\n",
       "      <td>79.147204</td>\n",
       "      <td>95.456325</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617614</th>\n",
       "      <td>47.679956</td>\n",
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       "      <td>64.302325</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.251701</td>\n",
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       "      <td>5.249169</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617615</th>\n",
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       "      <td>0.010526</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.126316</td>\n",
       "      <td>0.052632</td>\n",
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       "      <td>23.686119</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617616</th>\n",
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       "      <td>60.552870</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.347826</td>\n",
       "      <td>0.072464</td>\n",
       "      <td>12.286089</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617617</th>\n",
       "      <td>118.834271</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.336918</td>\n",
       "      <td>0.182796</td>\n",
       "      <td>0.039427</td>\n",
       "      <td>56.266227</td>\n",
       "      <td>63.468304</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617618</th>\n",
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       "      <td>107.918956</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.095436</td>\n",
       "      <td>0.323651</td>\n",
       "      <td>0.132780</td>\n",
       "      <td>49.705411</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617619</th>\n",
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       "      <td>3.164465</td>\n",
       "      <td>0.002770</td>\n",
       "      <td>1142.371966</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.310711</td>\n",
       "      <td>0.442752</td>\n",
       "      <td>0.090489</td>\n",
       "      <td>350.200824</td>\n",
       "      <td>650.825026</td>\n",
       "      <td>90.187261</td>\n",
       "      <td>354.947522</td>\n",
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       "      <td>103.372533</td>\n",
       "      <td>0.303030</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617620</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.271429</td>\n",
       "      <td>0.335714</td>\n",
       "      <td>0.271429</td>\n",
       "      <td>26.157505</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617621</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>100.221614</td>\n",
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       "      <td>0.366379</td>\n",
       "      <td>0.150862</td>\n",
       "      <td>0.077586</td>\n",
       "      <td>38.879074</td>\n",
       "      <td>49.678817</td>\n",
       "      <td>36.719126</td>\n",
       "      <td>36.719126</td>\n",
       "      <td>15.119640</td>\n",
       "      <td>7.775815</td>\n",
       "      <td>0.200000</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617622</th>\n",
       "      <td>84.077089</td>\n",
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       "      <td>48.044051</td>\n",
       "      <td>0.347826</td>\n",
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       "      <td>0.040134</td>\n",
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       "      <td>138.126647</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.244147</td>\n",
       "      <td>0.227425</td>\n",
       "      <td>0.147157</td>\n",
       "      <td>55.435444</td>\n",
       "      <td>52.201709</td>\n",
       "      <td>35.109114</td>\n",
       "      <td>33.723228</td>\n",
       "      <td>31.413418</td>\n",
       "      <td>20.326329</td>\n",
       "      <td>0.333333</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617623</th>\n",
       "      <td>38.219962</td>\n",
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       "      <td>0.142857</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>26.358594</td>\n",
       "      <td>22.404805</td>\n",
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       "      <td>8.346888</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617624</th>\n",
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       "      <td>10.544846</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617625</th>\n",
       "      <td>46.958895</td>\n",
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       "      <td>0.102941</td>\n",
       "      <td>0.080882</td>\n",
       "      <td>43.480458</td>\n",
       "      <td>9.565701</td>\n",
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       "      <td>16.522574</td>\n",
       "      <td>6.087264</td>\n",
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       "      <td>0.236994</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617626</th>\n",
       "      <td>44.779403</td>\n",
       "      <td>0.719424</td>\n",
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       "      <td>0.237410</td>\n",
       "      <td>0.179856</td>\n",
       "      <td>0.071942</td>\n",
       "      <td>37.614699</td>\n",
       "      <td>16.568379</td>\n",
       "      <td>27.763230</td>\n",
       "      <td>14.777203</td>\n",
       "      <td>11.194851</td>\n",
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       "      <td>0.250000</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617896</th>\n",
       "      <td>41.013435</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.321429</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.116071</td>\n",
       "      <td>11.224730</td>\n",
       "      <td>33.242468</td>\n",
       "      <td>10.361289</td>\n",
       "      <td>15.541933</td>\n",
       "      <td>15.110213</td>\n",
       "      <td>5.612365</td>\n",
       "      <td>0.205479</td>\n",
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       "    <tr>\n",
       "      <th>0599100000617897</th>\n",
       "      <td>90.864474</td>\n",
       "      <td>0.810924</td>\n",
       "      <td>20.244417</td>\n",
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       "      <td>0.941601</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>112.050492</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.361345</td>\n",
       "      <td>0.424370</td>\n",
       "      <td>0.088235</td>\n",
       "      <td>39.076432</td>\n",
       "      <td>62.616451</td>\n",
       "      <td>9.886808</td>\n",
       "      <td>40.488833</td>\n",
       "      <td>47.550839</td>\n",
       "      <td>9.886808</td>\n",
       "      <td>0.230769</td>\n",
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       "    <tr>\n",
       "      <th>0599100000631878</th>\n",
       "      <td>27.736625</td>\n",
       "      <td>0.322581</td>\n",
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       "      <td>8.320987</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>13.868312</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>85.983536</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.322581</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>52.699587</td>\n",
       "      <td>27.736625</td>\n",
       "      <td>24.962962</td>\n",
       "      <td>27.736625</td>\n",
       "      <td>19.415637</td>\n",
       "      <td>8.320987</td>\n",
       "      <td>0.492063</td>\n",
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       "    <tr>\n",
       "      <th>0599100000631880</th>\n",
       "      <td>44.839619</td>\n",
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       "      <td>29.013871</td>\n",
       "      <td>0.320388</td>\n",
       "      <td>6.154458</td>\n",
       "      <td>0.067961</td>\n",
       "      <td>10.550499</td>\n",
       "      <td>0.116505</td>\n",
       "      <td>90.558447</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.262136</td>\n",
       "      <td>0.097087</td>\n",
       "      <td>0.067961</td>\n",
       "      <td>43.081203</td>\n",
       "      <td>29.893080</td>\n",
       "      <td>40.443578</td>\n",
       "      <td>23.738622</td>\n",
       "      <td>8.792082</td>\n",
       "      <td>6.154458</td>\n",
       "      <td>0.380000</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>0599100000622970</th>\n",
       "      <td>97.573253</td>\n",
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       "      <td>117.370725</td>\n",
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       "      <td>0.246988</td>\n",
       "      <td>0.343373</td>\n",
       "      <td>0.259036</td>\n",
       "      <td>31.110313</td>\n",
       "      <td>63.634730</td>\n",
       "      <td>0.707053</td>\n",
       "      <td>28.989155</td>\n",
       "      <td>40.301996</td>\n",
       "      <td>30.403260</td>\n",
       "      <td>0.162791</td>\n",
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       "    <tr>\n",
       "      <th>0599100000622972</th>\n",
       "      <td>126.406307</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.095890</td>\n",
       "      <td>0.191781</td>\n",
       "      <td>0.178082</td>\n",
       "      <td>65.672027</td>\n",
       "      <td>46.908591</td>\n",
       "      <td>62.215604</td>\n",
       "      <td>13.825690</td>\n",
       "      <td>27.651380</td>\n",
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       "      <td>0.129630</td>\n",
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       "    <tr>\n",
       "      <th>0599100000622975</th>\n",
       "      <td>132.760588</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>145.795264</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.453642</td>\n",
       "      <td>0.225166</td>\n",
       "      <td>0.208609</td>\n",
       "      <td>72.414866</td>\n",
       "      <td>58.897425</td>\n",
       "      <td>3.379360</td>\n",
       "      <td>66.138911</td>\n",
       "      <td>32.828073</td>\n",
       "      <td>30.414244</td>\n",
       "      <td>0.141304</td>\n",
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       "    <tr>\n",
       "      <th>0599100000658187</th>\n",
       "      <td>177.501961</td>\n",
       "      <td>0.934959</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>189.849924</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.382114</td>\n",
       "      <td>0.252033</td>\n",
       "      <td>0.105691</td>\n",
       "      <td>106.501177</td>\n",
       "      <td>64.826803</td>\n",
       "      <td>37.043888</td>\n",
       "      <td>72.544280</td>\n",
       "      <td>47.848355</td>\n",
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       "      <td>0.137931</td>\n",
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       "    <tr>\n",
       "      <th>0599100000686574</th>\n",
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       "      <td>3.012953</td>\n",
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       "      <td>0.466667</td>\n",
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       "      <td>0.133333</td>\n",
       "      <td>16.571243</td>\n",
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       "      <td>10.545337</td>\n",
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       "      <td>0.319588</td>\n",
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       "    <tr>\n",
       "      <th>0599100000686576</th>\n",
       "      <td>11.512626</td>\n",
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       "      <td>1.644661</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.076923</td>\n",
       "      <td>12.334957</td>\n",
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       "      <td>11.512626</td>\n",
       "      <td>4.111652</td>\n",
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       "      <td>0.282609</td>\n",
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       "    <tr>\n",
       "      <th>0599100000686577</th>\n",
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       "      <td>1.177543</td>\n",
       "      <td>0.057143</td>\n",
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       "      <td>0.171429</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>9.420341</td>\n",
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       "      <td>9.420341</td>\n",
       "      <td>2.355085</td>\n",
       "      <td>3.532628</td>\n",
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       "      <td>0.300971</td>\n",
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       "    <tr>\n",
       "      <th>0599100000686578</th>\n",
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       "      <td>17.927324</td>\n",
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       "      <td>0.919350</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0.413043</td>\n",
       "      <td>0.369565</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>8.733824</td>\n",
       "      <td>11.491874</td>\n",
       "      <td>1.838700</td>\n",
       "      <td>8.733824</td>\n",
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       "    <tr>\n",
       "      <th>0599100000756944</th>\n",
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       "      <td>0.129944</td>\n",
       "      <td>0.042373</td>\n",
       "      <td>154.597206</td>\n",
       "      <td>31.684233</td>\n",
       "      <td>71.562664</td>\n",
       "      <td>87.404781</td>\n",
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       "      <th>0599100000756971</th>\n",
       "      <td>180.969564</td>\n",
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       "      <td>185.860633</td>\n",
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       "      <td>0.134211</td>\n",
       "      <td>0.042105</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>160.916180</td>\n",
       "      <td>21.520705</td>\n",
       "      <td>149.666720</td>\n",
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       "      <th>0599100000757176</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11724 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  nasa_nonveg_area  nasa_nonveg_pc  nasa_ext_area  \\\n",
       "ID                                                                  \n",
       "0599100000082215         29.105542        0.407407      32.192493   \n",
       "0599100000111188         49.865211        0.654762      24.479285   \n",
       "0599100000138014         46.591342        0.634146      26.431627   \n",
       "0599100000215405         41.459618        0.859649       5.076688   \n",
       "0599100000289238         38.475543        0.775862       7.267603   \n",
       "0599100000289242         54.328643        0.748466      18.257987   \n",
       "0599100000617607         37.249773        0.604317      23.946283   \n",
       "0599100000617608        121.983551        0.786834      32.075356   \n",
       "0599100000617609        304.233711        0.452859     346.286494   \n",
       "0599100000617610         68.101024        0.839779      12.096892   \n",
       "0599100000617611         49.359151        0.790210      10.920166   \n",
       "0599100000617612         55.606094        0.887324       7.061091   \n",
       "0599100000617613        165.969289        0.894057      19.187201   \n",
       "0599100000617614         47.679956        0.741497      16.184939   \n",
       "0599100000617615         23.686119        0.621053      12.043789   \n",
       "0599100000617616         45.634047        0.753623      14.918823   \n",
       "0599100000617617        118.834271        0.946237       6.301817   \n",
       "0599100000617618        105.232177        0.975104       2.686779   \n",
       "0599100000617619        719.388441        0.629732     374.461725   \n",
       "0599100000617620         39.236258        0.642857      21.797921   \n",
       "0599100000617621         89.421871        0.892241      10.799743   \n",
       "0599100000617622         84.077089        0.608696      48.044051   \n",
       "0599100000617623         38.219962        0.654135      17.133086   \n",
       "0599100000617624         39.543173        0.703125      16.696007   \n",
       "0599100000617625         46.958895        0.794118      11.739724   \n",
       "0599100000617626         44.779403        0.719424      14.777203   \n",
       "0599100000617896         41.013435        0.848214       7.339246   \n",
       "0599100000617897         90.864474        0.810924      20.244417   \n",
       "0599100000631878         27.736625        0.322581      36.057612   \n",
       "0599100000631880         44.839619        0.495146      29.013871   \n",
       "...                            ...             ...            ...   \n",
       "0599100000622970         97.573253        0.831325      19.797472   \n",
       "0599100000622972        126.406307        0.876712      17.775887   \n",
       "0599100000622975        132.760588        0.910596      13.034676   \n",
       "0599100000658187        177.501961        0.934959      12.347963   \n",
       "0599100000686574          3.012953        0.133333      13.558290   \n",
       "0599100000686576         11.512626        0.538462       6.578644   \n",
       "0599100000686577          9.420341        0.457143       6.476484   \n",
       "0599100000686578          1.379025        0.065217      17.927324   \n",
       "0599100000756944        190.105399        0.983051       3.277679   \n",
       "0599100000756971        180.969564        0.973684       4.401962   \n",
       "0599100000757176         66.552323        0.687805      21.240103   \n",
       "0599100000757177         49.297715        0.512077      37.205823   \n",
       "0599100000757178         45.690954        0.933333       2.797405   \n",
       "0599100010034672        301.690792        0.705686     125.823648   \n",
       "0599100010045360         74.390113        0.362903     125.636636   \n",
       "0599100000627120        109.873887        0.902439      11.878258   \n",
       "0599100000627124        116.558428        0.955556       5.421322   \n",
       "0599100000627128        119.951233        0.970000       3.709832   \n",
       "0599100000627132         13.414210        0.215385      42.638024   \n",
       "0599100000627138        133.070037        0.820755      26.002191   \n",
       "0599100000627140        140.444111        0.919094      12.363038   \n",
       "0599100000690226       1157.569652        0.925177      90.454896   \n",
       "0599100000757111         58.697210        0.619289      31.273104   \n",
       "0599100000757172         57.012802        0.581006      29.054601   \n",
       "0599100000757174         82.276669        0.829787      12.130535   \n",
       "0599100000767076         35.045116        0.217666     119.864456   \n",
       "0599100000767077         23.150969        0.141975     136.389403   \n",
       "0599100000755931       2765.547872        1.000000       0.000000   \n",
       "0599100000700214       3391.266252        0.964143     126.121472   \n",
       "0599100000701988       8202.746109        1.000000       0.000000   \n",
       "\n",
       "                  nasa_ext_pc  nasa_int_area  nasa_int_pc  nasa_tree_area  \\\n",
       "ID                                                                          \n",
       "0599100000082215     0.450617       8.378868     0.117284        1.763972   \n",
       "0599100000111188     0.321429       1.359960     0.017857        0.453320   \n",
       "0599100000138014     0.359756       0.447994     0.006098        0.000000   \n",
       "0599100000215405     0.105263       1.269172     0.026316        0.423057   \n",
       "0599100000289238     0.146552       2.137530     0.043103        1.710024   \n",
       "0599100000289242     0.251534       0.000000     0.000000        0.000000   \n",
       "0599100000617607     0.388489       0.443450     0.007194        0.000000   \n",
       "0599100000617608     0.206897       0.485990     0.003135        0.485990   \n",
       "0599100000617609     0.515456      16.094275     0.023957        5.191702   \n",
       "0599100000617610     0.149171       0.896066     0.011050        0.000000   \n",
       "0599100000617611     0.174825       2.184033     0.034965        0.000000   \n",
       "0599100000617612     0.112676       0.000000     0.000000        0.000000   \n",
       "0599100000617613     0.103359       0.479680     0.002584        0.000000   \n",
       "0599100000617614     0.251701       0.437431     0.006803        0.000000   \n",
       "0599100000617615     0.315789       2.007298     0.052632        0.401460   \n",
       "0599100000617616     0.246377       0.000000     0.000000        0.000000   \n",
       "0599100000617617     0.050179       0.450130     0.003584        0.000000   \n",
       "0599100000617618     0.024896       0.000000     0.000000        0.000000   \n",
       "0599100000617619     0.327793      45.357336     0.039705        3.164465   \n",
       "0599100000617620     0.357143       0.000000     0.000000        0.000000   \n",
       "0599100000617621     0.107759       0.000000     0.000000        0.000000   \n",
       "0599100000617622     0.347826       5.543544     0.040134        0.461962   \n",
       "0599100000617623     0.293233       3.075169     0.052632        0.000000   \n",
       "0599100000617624     0.296875       0.000000     0.000000        0.000000   \n",
       "0599100000617625     0.198529       0.434805     0.007353        0.000000   \n",
       "0599100000617626     0.237410       2.686764     0.043165        0.000000   \n",
       "0599100000617896     0.151786       0.000000     0.000000        0.000000   \n",
       "0599100000617897     0.180672       0.941601     0.008403        0.000000   \n",
       "0599100000631878     0.419355       8.320987     0.096774       13.868312   \n",
       "0599100000631880     0.320388       6.154458     0.067961       10.550499   \n",
       "...                       ...            ...          ...             ...   \n",
       "0599100000622970     0.168675       0.000000     0.000000        0.000000   \n",
       "0599100000622972     0.123288       0.000000     0.000000        0.000000   \n",
       "0599100000622975     0.089404       0.000000     0.000000        0.000000   \n",
       "0599100000658187     0.065041       0.000000     0.000000        0.000000   \n",
       "0599100000686574     0.600000       3.012953     0.133333        3.012953   \n",
       "0599100000686576     0.307692       1.644661     0.076923        1.644661   \n",
       "0599100000686577     0.314286       3.532628     0.171429        1.177543   \n",
       "0599100000686578     0.847826       0.919350     0.043478        0.919350   \n",
       "0599100000756944     0.016949       0.000000     0.000000        0.000000   \n",
       "0599100000756971     0.023684       0.489107     0.002632        0.000000   \n",
       "0599100000757176     0.219512       8.496041     0.087805        0.472002   \n",
       "0599100000757177     0.386473       5.580873     0.057971        4.185655   \n",
       "0599100000757178     0.057143       0.000000     0.000000        0.466234   \n",
       "0599100010034672     0.294314       0.000000     0.000000        0.000000   \n",
       "0599100010045360     0.612903       4.959341     0.024194        0.000000   \n",
       "0599100000627120     0.097561       0.000000     0.000000        0.000000   \n",
       "0599100000627124     0.044444       0.000000     0.000000        0.000000   \n",
       "0599100000627128     0.030000       0.000000     0.000000        0.000000   \n",
       "0599100000627132     0.684615       6.228026     0.100000        0.000000   \n",
       "0599100000627138     0.160377       3.059081     0.018868        0.000000   \n",
       "0599100000627140     0.080906       0.000000     0.000000        0.000000   \n",
       "0599100000690226     0.072295       3.162759     0.002528        0.000000   \n",
       "0599100000757111     0.329949       3.848997     0.040609        0.962249   \n",
       "0599100000757172     0.296089       7.674800     0.078212        4.385600   \n",
       "0599100000757174     0.122340       3.691902     0.037234        1.054829   \n",
       "0599100000767076     0.744479       5.586903     0.034700        0.507900   \n",
       "0599100000767077     0.836420       3.522974     0.021605        0.000000   \n",
       "0599100000755931     0.000000       0.000000     0.000000        0.000000   \n",
       "0599100000700214     0.035857       0.000000     0.000000        0.000000   \n",
       "0599100000701988     0.000000       0.000000     0.000000        0.000000   \n",
       "\n",
       "                  nasa_tree_pc   total_area naive_label    ...      pc_4_02  \\\n",
       "ID                                                         ...                \n",
       "0599100000082215      0.024691    71.440875           2    ...     0.123457   \n",
       "0599100000111188      0.005952    76.157777           1    ...     0.392857   \n",
       "0599100000138014      0.000000    73.470963           1    ...     0.280488   \n",
       "0599100000215405      0.008772    48.228535           1    ...     0.385965   \n",
       "0599100000289238      0.034483    49.590700           1    ...     0.250000   \n",
       "0599100000289242      0.000000    72.586629           1    ...     0.319018   \n",
       "0599100000617607      0.000000    61.639505           1    ...     0.215827   \n",
       "0599100000617608      0.003135   155.030887           1    ...     0.492163   \n",
       "0599100000617609      0.007728   671.806181           2    ...     0.234158   \n",
       "0599100000617610      0.000000    81.093982           1    ...     0.497238   \n",
       "0599100000617611      0.000000    62.463350           1    ...     0.279720   \n",
       "0599100000617612      0.000000    62.667185           1    ...     0.063380   \n",
       "0599100000617613      0.000000   185.636170           1    ...     0.310078   \n",
       "0599100000617614      0.000000    64.302325           1    ...     0.476190   \n",
       "0599100000617615      0.010526    38.138666           1    ...     0.189474   \n",
       "0599100000617616      0.000000    60.552870           1    ...     0.413043   \n",
       "0599100000617617      0.000000   125.586218           1    ...     0.336918   \n",
       "0599100000617618      0.000000   107.918956           1    ...     0.095436   \n",
       "0599100000617619      0.002770  1142.371966           1    ...     0.310711   \n",
       "0599100000617620      0.000000    61.034179           1    ...     0.271429   \n",
       "0599100000617621      0.000000   100.221614           1    ...     0.366379   \n",
       "0599100000617622      0.003344   138.126647           1    ...     0.244147   \n",
       "0599100000617623      0.000000    58.428218           1    ...     0.285714   \n",
       "0599100000617624      0.000000    56.239180           1    ...     0.187500   \n",
       "0599100000617625      0.000000    59.133423           1    ...     0.279412   \n",
       "0599100000617626      0.000000    62.243371           1    ...     0.237410   \n",
       "0599100000617896      0.000000    48.352681           1    ...     0.321429   \n",
       "0599100000617897      0.000000   112.050492           1    ...     0.361345   \n",
       "0599100000631878      0.161290    85.983536           2    ...     0.322581   \n",
       "0599100000631880      0.116505    90.558447           1    ...     0.262136   \n",
       "...                        ...          ...         ...    ...          ...   \n",
       "0599100000622970      0.000000   117.370725           1    ...     0.246988   \n",
       "0599100000622972      0.000000   144.182194           1    ...     0.095890   \n",
       "0599100000622975      0.000000   145.795264           1    ...     0.453642   \n",
       "0599100000658187      0.000000   189.849924           1    ...     0.382114   \n",
       "0599100000686574      0.133333    22.597150           2    ...     0.466667   \n",
       "0599100000686576      0.076923    21.380591           1    ...     0.192308   \n",
       "0599100000686577      0.057143    20.606996           1    ...     0.114286   \n",
       "0599100000686578      0.043478    21.145048           2    ...     0.413043   \n",
       "0599100000756944      0.000000   193.383078           1    ...     0.451977   \n",
       "0599100000756971      0.000000   185.860633           1    ...     0.134211   \n",
       "0599100000757176      0.004878    96.760470           1    ...     0.229268   \n",
       "0599100000757177      0.043478    96.270066           1    ...     0.140097   \n",
       "0599100000757178      0.009524    48.954593           1    ...     0.361905   \n",
       "0599100010034672      0.000000   427.514440           1    ...     0.257525   \n",
       "0599100010045360      0.000000   204.986090           2    ...     0.137097   \n",
       "0599100000627120      0.000000   121.752145           1    ...     0.323171   \n",
       "0599100000627124      0.000000   121.979750           1    ...     0.444444   \n",
       "0599100000627128      0.000000   123.661065           1    ...     0.360000   \n",
       "0599100000627132      0.000000    62.280260           2    ...     0.276923   \n",
       "0599100000627138      0.000000   162.131310           1    ...     0.317610   \n",
       "0599100000627140      0.000000   152.807149           1    ...     0.333333   \n",
       "0599100000690226      0.000000  1251.187306           1    ...     0.505561   \n",
       "0599100000757111      0.010152    94.781560           1    ...     0.187817   \n",
       "0599100000757172      0.044693    98.127803           1    ...     0.178771   \n",
       "0599100000757174      0.010638    99.153935           1    ...     0.260638   \n",
       "0599100000767076      0.003155   161.004375           2    ...     0.479495   \n",
       "0599100000767077      0.000000   163.063345           2    ...     0.425926   \n",
       "0599100000755931      0.000000  2765.547872           1    ...     0.344111   \n",
       "0599100000700214      0.000000  3517.387724           1    ...     0.262948   \n",
       "0599100000701988      0.000000  8202.746109           1    ...     0.166667   \n",
       "\n",
       "                   pc_4_03   pc_4_04    area_2_01    area_2_02    area_4_01  \\\n",
       "ID                                                                            \n",
       "0599100000082215  0.228395  0.209877    25.577597    23.372632    23.813625   \n",
       "0599100000111188  0.255952  0.184524    43.518729    32.185727    12.239643   \n",
       "0599100000138014  0.115854  0.298780    28.671595    18.815734    18.367741   \n",
       "0599100000215405  0.087719  0.043860    36.805987     6.345860    20.306752   \n",
       "0599100000289238  0.155172  0.086207    34.200483    10.260145    22.230314   \n",
       "0599100000289242  0.184049  0.159509    37.851923    23.601788    14.695453   \n",
       "0599100000617607  0.151079  0.179856    34.145625    13.746940    22.172484   \n",
       "0599100000617608  0.225705  0.094044    94.282107    41.795161    16.037678   \n",
       "0599100000617609  0.222566  0.180062   232.069060   239.337442   229.992379   \n",
       "0599100000617610  0.270718  0.082873    64.068726    12.544925     8.064595   \n",
       "0599100000617611  0.489510  0.076923     8.736133    46.738311     7.425713   \n",
       "0599100000617612  0.507042  0.352113     7.502409    53.840821     3.971864   \n",
       "0599100000617613  0.183463  0.108527    79.147204    95.456325    65.236484   \n",
       "0599100000617614  0.251701  0.115646     5.249169    46.805094     5.249169   \n",
       "0599100000617615  0.126316  0.052632    25.693417    10.036491    23.686119   \n",
       "0599100000617616  0.347826  0.072464    12.286089    42.562524     6.581834   \n",
       "0599100000617617  0.182796  0.039427    56.266227    63.468304    54.915837   \n",
       "0599100000617618  0.323651  0.132780    49.705411    41.645074    42.540667   \n",
       "0599100000617619  0.442752  0.090489   350.200824   650.825026    90.187261   \n",
       "0599100000617620  0.335714  0.271429    26.157505    30.081131     3.923626   \n",
       "0599100000617621  0.150862  0.077586    38.879074    49.678817    36.719126   \n",
       "0599100000617622  0.227425  0.147157    55.435444    52.201709    35.109114   \n",
       "0599100000617623  0.142857  0.105263    26.358594    22.404805    23.722735   \n",
       "0599100000617624  0.171875  0.187500    29.437696    13.620426    19.332218   \n",
       "0599100000617625  0.102941  0.080882    43.480458     9.565701    30.436321   \n",
       "0599100000617626  0.179856  0.071942    37.614699    16.568379    27.763230   \n",
       "0599100000617896  0.312500  0.116071    11.224730    33.242468    10.361289   \n",
       "0599100000617897  0.424370  0.088235    39.076432    62.616451     9.886808   \n",
       "0599100000631878  0.225806  0.096774    52.699587    27.736625    24.962962   \n",
       "0599100000631880  0.097087  0.067961    43.081203    29.893080    40.443578   \n",
       "...                    ...       ...          ...          ...          ...   \n",
       "0599100000622970  0.343373  0.259036    31.110313    63.634730     0.707053   \n",
       "0599100000622972  0.191781  0.178082    65.672027    46.908591    62.215604   \n",
       "0599100000622975  0.225166  0.208609    72.414866    58.897425     3.379360   \n",
       "0599100000658187  0.252033  0.105691   106.501177    64.826803    37.043888   \n",
       "0599100000686574  0.200000  0.133333    16.571243     3.012953     1.506477   \n",
       "0599100000686576  0.115385  0.076923    12.334957     5.756313    11.512626   \n",
       "0599100000686577  0.171429  0.200000     9.420341     5.887713     9.420341   \n",
       "0599100000686578  0.369565  0.086957     8.733824    11.491874     1.838700   \n",
       "0599100000756944  0.129944  0.042373   154.597206    31.684233    71.562664   \n",
       "0599100000756971  0.042105  0.010526   160.916180    21.520705   149.666720   \n",
       "0599100000757176  0.102439  0.102439    65.608318    15.104073    45.784222   \n",
       "0599100000757177  0.251208  0.086957    52.088152    32.555095    46.042205   \n",
       "0599100000757178  0.076190  0.019048    34.967567    13.054558    26.109117   \n",
       "0599100010034672  0.187291  0.137124   190.165286   151.560303   152.990117   \n",
       "0599100010045360  0.270161  0.254032    65.297988    84.308795    46.287182   \n",
       "0599100000627120  0.262195  0.341463     4.454347    64.588028     4.454347   \n",
       "0599100000627124  0.194444  0.033333    88.774151    28.461942    36.593925   \n",
       "0599100000627128  0.140000  0.025000    80.379692    38.953235    57.502395   \n",
       "0599100000627132  0.300000  0.200000     1.916316    36.889077     1.916316   \n",
       "0599100000627138  0.182390  0.062893    90.242899    53.533923    61.181626   \n",
       "0599100000627140  0.190939  0.058252    93.959088    46.979544    60.826147   \n",
       "0599100000690226  0.143579  0.054601   986.780687   204.946758   328.294344   \n",
       "0599100000757111  0.182741  0.162437    52.442589    24.056234    42.820096   \n",
       "0599100000757172  0.139665  0.089385    58.109202    27.410001    54.271802   \n",
       "0599100000757174  0.058511  0.085106    75.947695    11.075706    57.488186   \n",
       "0599100000767076  0.347003  0.085174    78.216636    65.011230     7.110603   \n",
       "0599100000767077  0.382716  0.080247    77.002135    74.989007    10.065639   \n",
       "0599100000755931  0.274827  0.191686  1028.298401  1066.620080   357.669009   \n",
       "0599100000700214  0.131474  0.023904  2354.267481  1051.012268  2017.943555   \n",
       "0599100000701988  0.300000  0.400000  1549.407598  3281.098444   546.849741   \n",
       "\n",
       "                    area_4_02    area_4_03    area_4_04       max  \n",
       "ID                                                                 \n",
       "0599100000082215     8.819861    16.316743    14.993764  0.327586  \n",
       "0599100000111188    29.919126    19.492764    14.052923  0.552448  \n",
       "0599100000138014    20.607709     8.511880    21.951690  0.217391  \n",
       "0599100000215405    18.614522     4.230573     2.115287  0.298701  \n",
       "0599100000289238    12.397675     7.695109     4.275060  0.311828  \n",
       "0599100000289242    23.156471    13.359502    11.578235  0.191489  \n",
       "0599100000617607    13.303490     9.312443    11.086242  0.214286  \n",
       "0599100000617608    76.300468    34.991297    14.579707  0.287129  \n",
       "0599100000617609   157.308557   149.521005   120.966646  0.400000  \n",
       "0599100000617610    40.322974    21.953619     6.720496  0.228346  \n",
       "0599100000617611    17.472266    30.576465     4.804873  0.243902  \n",
       "0599100000617612     3.971864    31.774911    22.065910  0.195652  \n",
       "0599100000617613    57.561603    34.057282    20.146561  0.217391  \n",
       "0599100000617614    30.620155    16.184939     7.436323  0.215190  \n",
       "0599100000617615     7.226274     4.817516     2.007298  0.384615  \n",
       "0599100000617616    25.010968    21.061868     4.387889  0.195122  \n",
       "0599100000617617    42.312202    22.956620     4.951428  0.259259  \n",
       "0599100000617618    10.299319    34.928127    14.329488  0.111111  \n",
       "0599100000617619   354.947522   505.787034   103.372533  0.303030  \n",
       "0599100000617620    16.566420    20.490046    16.566420  0.189189  \n",
       "0599100000617621    36.719126    15.119640     7.775815  0.200000  \n",
       "0599100000617622    33.723228    31.413418    20.326329  0.333333  \n",
       "0599100000617623    16.693776     8.346888     6.150339  0.253333  \n",
       "0599100000617624    10.544846     9.666109    10.544846  0.197080  \n",
       "0599100000617625    16.522574     6.087264     4.782850  0.236994  \n",
       "0599100000617626    14.777203    11.194851     4.477940  0.250000  \n",
       "0599100000617896    15.541933    15.110213     5.612365  0.205479  \n",
       "0599100000617897    40.488833    47.550839     9.886808  0.230769  \n",
       "0599100000631878    27.736625    19.415637     8.320987  0.492063  \n",
       "0599100000631880    23.738622     8.792082     6.154458  0.380000  \n",
       "...                       ...          ...          ...       ...  \n",
       "0599100000622970    28.989155    40.301996    30.403260  0.162791  \n",
       "0599100000622972    13.825690    27.651380    25.676281  0.129630  \n",
       "0599100000622975    66.138911    32.828073    30.414244  0.141304  \n",
       "0599100000658187    72.544280    47.848355    20.065439  0.137931  \n",
       "0599100000686574    10.545337     4.519430     3.012953  0.319588  \n",
       "0599100000686576     4.111652     2.466991     1.644661  0.282609  \n",
       "0599100000686577     2.355085     3.532628     4.121399  0.300971  \n",
       "0599100000686578     8.733824     7.814474     1.838700  0.317073  \n",
       "0599100000756944    87.404781    25.128875     8.194198  0.163842  \n",
       "0599100000756971    24.944453     7.825711     1.956428  0.236641  \n",
       "0599100000757176    22.184108     9.912048     9.912048  0.347826  \n",
       "0599100000757177    13.487111    24.183785     8.371310  0.373134  \n",
       "0599100000757178    17.716900     3.729874     0.932468  0.304348  \n",
       "0599100010034672   110.095692    80.069594    58.622381  0.203390  \n",
       "0599100010045360    28.102932    55.379307    52.073079  0.275000  \n",
       "0599100000627120    39.346730    31.922819    41.573903  0.208791  \n",
       "0599100000627124    54.213222    23.718285     4.065992  0.163636  \n",
       "0599100000627128    44.517983    17.312549     3.091527  0.166667  \n",
       "0599100000627132    17.246841    18.684078    12.456052  0.275000  \n",
       "0599100000627138    51.494536    29.571119    10.196938  0.243902  \n",
       "0599100000627140    50.935716    29.176770     8.901387  0.205128  \n",
       "0599100000690226   632.551722   179.644689    68.315586  0.253731  \n",
       "0599100000757111    17.801613    17.320488    15.395990  0.368421  \n",
       "0599100000757172    17.542401    13.705000     8.771200  0.358025  \n",
       "0599100000757174    25.843313     5.801560     8.438633  0.304348  \n",
       "0599100000767076    77.200836    55.869026    13.713306  0.277778  \n",
       "0599100000767077    69.452906    62.406959    13.085330  0.263158  \n",
       "0599100000755931   951.655042   760.046644   530.116567  0.085714  \n",
       "0599100000700214   924.890796   462.445398    84.080981  0.180723  \n",
       "0599100000701988  1367.124352  2460.823833  3281.098444  0.057143  \n",
       "\n",
       "[11724 rows x 52 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df = pd.DataFrame({'ID': pd.Series(ID_list),\\\n",
    "                       'nasa_nonveg_area': pd.Series(nonveg_abs_list), \\\n",
    "                       'nasa_nonveg_pc': pd.Series(nonveg_percent_list), \\\n",
    "                       'nasa_ext_area': pd.Series(exten_abs_list),\\\n",
    "                       'nasa_ext_pc': pd.Series(exten_percent_list),\\\n",
    "                       'nasa_int_area': pd.Series(inten_abs_list),\\\n",
    "                       'nasa_int_pc': pd.Series(inten_percent_list),\\\n",
    "                       'nasa_tree_area': pd.Series(trees_abs_list),\\\n",
    "                       'nasa_tree_pc': pd.Series(trees_percent_list),\\\n",
    "                       'total_area': pd.Series(area_per_roof_list),\\\n",
    "                       'k2_01': pd.Series(k2_01),\\\n",
    "                       'k2_02': pd.Series(k2_02),\\\n",
    "                       'k4_01': pd.Series(k4_01),\\\n",
    "                       'k4_02': pd.Series(k4_02),\\\n",
    "                       'k4_03': pd.Series(k4_03),\\\n",
    "                       'k4_04': pd.Series(k4_04),\\\n",
    "                       'std': pd.Series(std),\\\n",
    "                       'var': pd.Series(var),\\\n",
    "                       'mean': pd.Series(mean),\\\n",
    "                       'min': pd.Series(min_value),\\\n",
    "                       'pct_05': pd.Series(pct_05),\\\n",
    "                       'pct_10': pd.Series(pct_10),\\\n",
    "                       'pct_15': pd.Series(pct_15),\\\n",
    "                       'pct_20': pd.Series(pct_20),\\\n",
    "                       'pct_25': pd.Series(pct_25),\\\n",
    "                       'pct_30': pd.Series(pct_30),\\\n",
    "                       'pct_35': pd.Series(pct_35),\\\n",
    "                       'pct_40': pd.Series(pct_40),\\\n",
    "                       'pct_45': pd.Series(pct_45),\\\n",
    "                       'pct_50': pd.Series(pct_50),\\\n",
    "                       'pct_55': pd.Series(pct_55),\\\n",
    "                       'pct_60': pd.Series(pct_60),\\\n",
    "                       'pct_65': pd.Series(pct_65),\\\n",
    "                       'pct_70': pd.Series(pct_70),\\\n",
    "                       'pct_75': pd.Series(pct_75),\\\n",
    "                       'pct_80': pd.Series(pct_80),\\\n",
    "                       'pct_85': pd.Series(pct_85),\\\n",
    "                       'pct_90': pd.Series(pct_90),\\\n",
    "                       'pct_95': pd.Series(pct_95),\\\n",
    "                       'pc_2_01': pd.Series(pc_2_01),\\\n",
    "                       'pc_2_02': pd.Series(pc_2_02),\\\n",
    "                       'pc_4_01': pd.Series(pc_4_01),\\\n",
    "                       'pc_4_02': pd.Series(pc_4_02),\\\n",
    "                       'pc_4_03': pd.Series(pc_4_03),\\\n",
    "                       'pc_4_04': pd.Series(pc_4_04),\\\n",
    "                       'area_2_01': pd.Series(area_2_01),\\\n",
    "                       'area_2_02': pd.Series(area_2_02),\\\n",
    "                       'area_4_01': pd.Series(area_4_01),\\\n",
    "                       'area_4_02': pd.Series(area_4_02),\\\n",
    "                       'area_4_03': pd.Series(area_4_03),\\\n",
    "                       'area_4_04': pd.Series(area_4_04),\\\n",
    "                       'max': pd.Series(max_value),})\n",
    "\n",
    "\n",
    "raw_df['nasa_max_pc'] = raw_df[['nasa_nonveg_pc','nasa_ext_pc','nasa_int_pc', 'nasa_tree_pc']].max(axis=1)\n",
    "raw_df.loc[(raw_df['nasa_nonveg_pc'] == raw_df['nasa_max_pc'], 'naive_label')] = '1'\n",
    "raw_df.loc[(raw_df['nasa_ext_pc'] == raw_df['nasa_max_pc'], 'naive_label')] = '2'\n",
    "raw_df.loc[(raw_df['nasa_int_pc'] == raw_df['nasa_max_pc'], 'naive_label')] = '3'\n",
    "raw_df.loc[(raw_df['nasa_tree_pc'] == raw_df['nasa_max_pc'], 'naive_label')] = '4'\n",
    "\n",
    "\n",
    "raw_df.set_index('ID', inplace=True)\n",
    "\n",
    "raw_df = raw_df[['nasa_nonveg_area', 'nasa_nonveg_pc', 'nasa_ext_area', 'nasa_ext_pc',\\\n",
    "        'nasa_int_area', 'nasa_int_pc', 'nasa_tree_area', 'nasa_tree_pc', 'total_area', 'naive_label', \\\n",
    "        'k2_01', 'k2_01', 'k4_01', 'k4_02', 'k4_03', 'k4_04', 'std', 'var', 'mean', \\\n",
    "        'min', 'pct_05', 'pct_10', 'pct_15', 'pct_20', 'pct_25', 'pct_30', 'pct_35', 'pct_40', 'pct_45', \\\n",
    "        'pct_50', 'pct_55', 'pct_60', 'pct_65', 'pct_70','pct_75', 'pct_80', 'pct_85', 'pct_90', 'pct_95',\\\n",
    "        'pc_2_01', 'pc_2_02', 'pc_4_01', 'pc_4_02', 'pc_4_03', 'pc_4_04', 'area_2_01', 'area_2_02',\\\n",
    "        'area_4_01', 'area_4_02', 'area_4_03', 'area_4_04', 'max']].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw_df.to_csv('processing/features_table.csv')"
   ]
  }
 ],
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   "file_extension": ".py",
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