Change_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42b364f3",
   "metadata": {},
   "source": [
    "# Libraries\n",
    "\n",
    "1) rasters comparison\n",
    "2) differences mapping\n",
    "3) area analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7e586a8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#libraries\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import rasterio as rio\n",
    "from itertools import product\n",
    "from rasterio import windows\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "import seaborn as sns\n",
    "\n",
    "from osgeo import gdal\n",
    "from osgeo.gdalconst import GDT_Int16"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5089ccfa",
   "metadata": {},
   "source": [
    "# Helpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d73a742",
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_2d_array(x: np.ndarray)->np.ndarray: #misha grol functions\n",
    "    return x.reshape(x.shape[0], x.shape[1] * x.shape[2])\n",
    "\n",
    "def save_tif(raster_input:str, raster_output:str, values:np.array):\n",
    "    in_data, out_data = None, None\n",
    "    in_data = gdal.Open(raster_input)\n",
    "    if in_data is None:\n",
    "        print ('Unable to open %s' % raster_input)\n",
    "    band1 = in_data.GetRasterBand(1)\n",
    "    rows = in_data.RasterYSize\n",
    "    cols = in_data.RasterXSize\n",
    "    driver = in_data.GetDriver()\n",
    "    out_data = driver.Create(raster_output, cols, rows, 1, GDT_Int16)\n",
    "    dem_data = np.array(values)\n",
    "    out_band = out_data.GetRasterBand(1)\n",
    "    out_band.WriteArray(dem_data)\n",
    "    out_band.FlushCache()\n",
    "    out_band.SetNoDataValue(-1)\n",
    "\n",
    "    out_data.SetGeoTransform(in_data.GetGeoTransform())\n",
    "    out_data.SetProjection(in_data.GetProjection())\n",
    "    del out_data\n",
    "    return 'Map with changes is saved as GeoTIFF'\n",
    "\n",
    "#turn classified raster to dataframe\n",
    "def get_classified_data(raster:str):\n",
    "    \"\"\"\n",
    "    Function takes path to the GeoTIFF raster file and return dataframe\n",
    "    \"\"\"\n",
    "    with rio.open(raster, 'r+') as src:\n",
    "        x = src.read()\n",
    "        col_names = ['class']\n",
    "        params = x[:len(x), ...]\n",
    "        params = to_2d_array(x[:len(x), ...]) \n",
    "        df = pd.DataFrame(params.T, columns=col_names)\n",
    "    return df\n",
    "\n",
    "#get results of pixelwise comparasion of 2 rasters as df and as a map\n",
    "def get_changes(raster1:str, raster2:str, return_change_map:bool=True):\n",
    "    \"\"\"\n",
    "    Function takes two rasters and combines them. The outputs are dataframe with\n",
    "    values from raster 1, values from raster 2, bool variable showing are they the same \n",
    "    or not, and new variable 'changed', which takes 0, if value in the same pixels of two \n",
    "    rasters are the same, and 1 if they are not\n",
    "    \"\"\"\n",
    "    with rio.open(raster1, 'r+') as src:\n",
    "        x = src.read()\n",
    "    \n",
    "    df1 = get_classified_data(raster1)\n",
    "    df2 = get_classified_data(raster2)\n",
    "    \n",
    "    df = pd.DataFrame(df1['class'] == df2['class'])\n",
    "    df['old_class'] = df1['class']\n",
    "    df['new_class'] = df2['class']\n",
    "    df['changed'] = df['class'].apply(lambda x: 0 if x==True else 1).astype('int16')\n",
    "\n",
    "    if return_change_map is True:\n",
    "        changes = df['changed']\n",
    "        reclassified = changes.values\n",
    "        forest_tile = reclassified.reshape((x.shape[1], x.shape[2]))\n",
    "        output_mask=forest_tile\n",
    "        raster_output = 'change_map.tif'\n",
    "        status = save_tif(raster_input=fname, raster_output=raster_output, values=output_mask)\n",
    "        print(status)\n",
    "        return df\n",
    "    else: return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bff050a",
   "metadata": {},
   "source": [
    "# Results example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5959beed",
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = 'prediction_forest_rf.tif'\n",
    "fname2 = 'prediction_forest_knn_worst.tif'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c94f88e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>old_class</th>\n",
       "      <th>new_class</th>\n",
       "      <th>changed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313231</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313232</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313233</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313234</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313235</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>313236 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        class  old_class  new_class  changed\n",
       "0        True          0          0        0\n",
       "1        True          0          0        0\n",
       "2        True          0          0        0\n",
       "3        True          0          0        0\n",
       "4        True          0          0        0\n",
       "...       ...        ...        ...      ...\n",
       "313231   True          0          0        0\n",
       "313232   True          0          0        0\n",
       "313233   True          0          0        0\n",
       "313234   True          0          0        0\n",
       "313235   True          0          0        0\n",
       "\n",
       "[313236 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "change_data=get_changes(fname, fname2, return_change_map=False) \n",
    "change_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971569f0",
   "metadata": {},
   "source": [
    "# Analysis and interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "65db9606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RkSGYGPKPb968We9+aGgo7O3tceTIEXh7e0NEMGPGDIwbNw5t2rQBAISFhcHBwQFLlixBv379DFE2ERERERlIjurzGhsbCwAoUKAAAODKlSuIioqCn5+fuo25uTl8fHywb9++Zz5HSkoK4uLi9G5ERERElDvkmPAqIhg2bBhq1aqFMmXKAACioqIAAA4ODnrbOjg4qOueFhISAjs7O/VWtGjRd1s4EREREWWbHBNeAwMDceLECSxdujTLOkVR9O6LSJZlOmPGjEFsbKx6i4yMfCf1EhEREVH2M2ifV51BgwZh3bp12L17N4oUKaIud3R0BJDRAuvk5KQuv3v3bpbWWB1zc3OYm5u/24KJiIiIyCAM2vIqIggMDMTq1avx559/ws3NTW+9m5sbHB0dsW3bNnXZ48ePsWvXLnh5eWV3uURERERkYAZteQ0ICMCSJUuwdu1a2NjYqP1Y7ezsYGlpCUVRMGTIEAQHB8Pd3R3u7u4IDg6GlZUVunTpYsjSiYiIiMgADBpe58yZAwCoU6eO3vLQ0FD07NkTADBq1CgkJydj4MCBePDgATw9PbF161bY2Nhkc7VEREREZGgGDa8i8tJtFEVBUFAQgoKC3n1BRERERJSj5ZjRBoiIiIiIXobhlYiIiIg0g+GViIiIiDSD4ZWIiIiINIPhlYiIiIg0g+GViIiIiDSD4ZWIiIiINIPhlYiIiIg0w6CTFBARvczsg9cNXYJBDPR0MXQJREQ5ElteiYiIiEgz3ii8bt68GXv27FHvz5o1CxUqVECXLl3w4MGDt1YcEREREVFmbxReR44cibi4OADAyZMnMXz4cDRp0gSXL1/GsGHD3mqBREREREQ6b9Tn9cqVKyhVqhQAYNWqVWjWrBmCg4Nx9OhRNGnS5K0WSERERESk80Ytr2ZmZkhKSgIA/PHHH/Dz8wMAFChQQG2RJSIiIiJ6296o5bVWrVoYNmwYatasiUOHDuG3334DAPzzzz8oUqTIWy2QiIiIiEjnjVpeZ86cCRMTE6xcuRJz5sxB4cKFAQCbNm1Co0aN3mqBREREREQ6b9Ty6uLigg0bNmRZ/u233/7ngoiIiIiInueVw+vr9GW1tbV9o2KIiIiIiF7klcNr/vz5oSjKC7cRESiKgrS0tP9cGBERERHR0145vO7YseNd1kFERERE9FKvHF59fHzeZR1ERERERC/1RqMNAMBff/2Fbt26wcvLCzdv3gQA/PLLL3rTxhIRERERvU1vFF5XrVqFhg0bwtLSEkePHkVKSgoAID4+HsHBwW+1QCIiIiIinTcKr1999RV+/PFHzJ07F6ampupyLy8vHD169K0VR0RERESU2RuF1/Pnz8Pb2zvLcltbWzx8+PC/1kRERERE9ExvFF6dnJxw8eLFLMv37NmDDz744D8XRURERET0LG8UXvv164fBgwfj4MGDUBQFt27dwq+//ooRI0Zg4MCBr/w8u3fvRvPmzeHs7AxFURAeHq63vmfPnlAURe9WvXr1NymZiIiIiHKBN5oedtSoUYiNjUXdunXx6NEjeHt7w9zcHCNGjEBgYOArP09iYiLKly+PXr16oW3bts/cplGjRggNDVXvm5mZvUnJRERERJQLvFF4BYCvv/4a48aNw5kzZ5Ceno5SpUrB2tr6tZ6jcePGaNy48Qu3MTc3h6Oj45uWSURERES5yBuF1/nz56N3796wsrJClSpV1OWpqamYMGECQkJC3lqBO3fuhL29PfLnzw8fHx98/fXXsLe3f+72KSkp6tBdABAXF/fWaiEiotxr+/bthi7BIOrVq2foEoheyxv1eR0+fDjatm2L+/fvq8vOnTuHatWqYfny5W+tuMaNG+PXX3/Fn3/+iWnTpuHw4cPw9fXVC6dPCwkJgZ2dnXorWrToW6uHiIiIiAzrjcLrsWPHcOfOHZQtWxbbtm3DrFmzUKlSJZQpUwbHjx9/a8V17NgRTZs2RZkyZdC8eXNs2rQJ//zzDzZu3Pjcx4wZMwaxsbHqLTIy8q3VQ0RERESG9UbdBtzc3LB7924MHToUjRo1grGxMRYtWoROnTq97fr0ODk5wdXVFRcuXHjuNubm5jA3N3+ndRARERGRYbxRyysAbNiwAUuXLoWXlxfy58+PuXPn4tatW2+ztixiYmIQGRkJJyend/p3iIiIiChneuNxXjt06IBRo0Zh9+7dOHHiBMzNzVG2bNnX6vOakJCA48ePq10Nrly5guPHj+P69etISEjAiBEjsH//fly9ehU7d+5E8+bNUahQIbRu3fpNyiYiIiIijXujbgN79+7FwYMHUb58eQCAo6Mjfv/9d8yaNQuffPIJOnTo8ErPExERgbp166r3hw0bBgDo0aMH5syZg5MnT2LRokV4+PAhnJycULduXfz222+wsbF5k7KJiIiISOPeKLweOXLkmf1KAwICUL9+/Vd+njp16kBEnrt+y5Ytb1IeEREREeVSb9Rt4EUXRHl4eLxxMUREREREL/LKLa+VKlXC9u3b8d5776FixYpQFOW52x49evStFEdERERElNkrh9eWLVuqLa6tWrWCoigvPOVPRERERPS2vXJ4nThxIpKSkhAQEIDw8HA8efIE9erVww8//IBChQq9yxqJiIiIiAC8Zp/XiRMnYuHChWjatCk6d+6MP/74AwMGDHhXtRERERER6Xmt0QZWr16N+fPnqzNpde3aFTVr1kRaWhqMjY3fSYFERERERDqv1fIaGRmJ2rVrq/erVasGExOTdz6zFhERERER8JrhNS0tDWZmZnrLTExMkJqa+laLIiIiIiJ6ltfqNiAi6Nmzp944r48ePUL//v2RL18+ddnq1avfXoVERERERP/vtcJrjx49sizr1q3bWyuGiIiIiOhFXiu8hoaGvqs6iIiIiIhe6o2mhyUiIiIiMgSGVyIiIiLSDIZXIiIiItIMhlciIiIi0gyGVyIiIiLSDIZXIiIiItIMhlciIiIi0gyGVyIiIiLSDIZXIiIiItIMhlciIiIi0gyGVyIiIiLSDIZXIiIiItIMhlciIiIi0gyGVyIiIiLSDIZXIiIiItIMg4bX3bt3o3nz5nB2doaiKAgPD9dbLyIICgqCs7MzLC0tUadOHZw+fdowxRIRERGRwRk0vCYmJqJ8+fKYOXPmM9dPnjwZ06dPx8yZM3H48GE4OjqiQYMGiI+Pz+ZKiYiIiCgnMDHkH2/cuDEaN278zHUighkzZmDcuHFo06YNACAsLAwODg5YsmQJ+vXrl52lEhEREVEOkGP7vF65cgVRUVHw8/NTl5mbm8PHxwf79u177uNSUlIQFxendyMiIiKi3CHHhteoqCgAgIODg95yBwcHdd2zhISEwM7OTr0VLVr0ndZJRERERNknx4ZXHUVR9O6LSJZlmY0ZMwaxsbHqLTIy8l2XSERERETZxKB9Xl/E0dERQEYLrJOTk7r87t27WVpjMzM3N4e5ufk7r4+IiIiIsl+ObXl1c3ODo6Mjtm3bpi57/Pgxdu3aBS8vLwNWRkRERESGYtCW14SEBFy8eFG9f+XKFRw/fhwFChSAi4sLhgwZguDgYLi7u8Pd3R3BwcGwsrJCly5dDFg1ERERERmKQcNrREQE6tatq94fNmwYAKBHjx5YuHAhRo0aheTkZAwcOBAPHjyAp6cntm7dChsbG0OVTEREREQGZNDwWqdOHYjIc9crioKgoCAEBQVlX1FERERElGPl2D6vRERERERPY3glIiIiIs1geCUiIiIizWB4JSIiIiLNYHglIiIiIs1geCUiIiIizWB4JSIiIiLNYHglIiIiIs1geCUiIiIizWB4JSIiIiLNYHglIiIiIs1geCUiIiIizWB4JSIiIiLNYHglIiIiIs0wMXQBRET09p34YoahSzCIcp8PMXQJRPSOseWViIiIiDSD4ZWIiIiINIPhlYiIiIg0g+GViIiIiDSD4ZWIiIiINIPhlYiIiIg0g+GViIiIiDSD4ZWIiIiINIPhlYiIiIg0g+GViIiIiDSD4ZWIiIiINCNHh9egoCAoiqJ3c3R0NHRZRERERGQgJoYu4GVKly6NP/74Q71vbGxswGqIiIiIyJByfHg1MTFhaysRERERAcjh3QYA4MKFC3B2doabmxs6deqEy5cvv3D7lJQUxMXF6d2IiIiIKHfI0eHV09MTixYtwpYtWzB37lxERUXBy8sLMTExz31MSEgI7Ozs1FvRokWzsWIiIiIiepdydHht3Lgx2rZti7Jly6J+/frYuHEjACAsLOy5jxkzZgxiY2PVW2RkZHaVS0RERETvWI7v85pZvnz5ULZsWVy4cOG525ibm8Pc3DwbqyIiIiKi7JKjW16flpKSgrNnz8LJycnQpRARERGRAeTo8DpixAjs2rULV65cwcGDB9GuXTvExcWhR48ehi6NiIiIiAwgR3cbuHHjBjp37ozo6Gi8//77qF69Og4cOABXV1dDl0ZEREREBpCjw+uyZcsMXQIRERER5SA5utsAEREREVFmDK9EREREpBkMr0RERESkGQyvRERERKQZDK9EREREpBkMr0RERESkGQyvRERERKQZDK9EREREpBkMr0RERESkGQyvRERERKQZDK9EREREpBkMr0RERESkGQyvRERERKQZDK9EREREpBkMr0RERESkGQyvRERERKQZDK9EREREpBkMr0RERESkGQyvRERERKQZJoYuICdQFMXQJRiEiBi6BCIiIqLXwvBKRERElI3GLOtt6BIMIqTT/LfyPOw2QERERESawfBKRERERJrB8EpEREREmsHwSkRERESawfBKRERERJqhifA6e/ZsuLm5wcLCApUrV8Zff/1l6JKIiIiIyAByfHj97bffMGTIEIwbNw7Hjh1D7dq10bhxY1y/ft3QpRERERFRNsvx47xOnz4dvXv3Rp8+fQAAM2bMwJYtWzBnzhyEhIRk2T4lJQUpKSnq/djYWABAXFxc9hSsIf9ln6Q/Tn2LlWjHf30fJT959JYq0Zb/st+SE+PfYiXa8V/fawmP+F57XYmJiW+xEu3g92P2S0l6bOgSDOJF7zXduleaQElysJSUFDE2NpbVq1frLf/000/F29v7mY+ZOHGiAOCNN95444033njjTWO3yMjIl+bDHN3yGh0djbS0NDg4OOgtd3BwQFRU1DMfM2bMGAwbNky9n56ejvv376NgwYI5bhrYuLg4FC1aFJGRkbC1tTV0OZrAffZmuN9eH/fZm+F+e33cZ2+G++315eR9JiKIj4+Hs7PzS7fN0eFV5+nQKSLPDaLm5uYwNzfXW5Y/f/53VdpbYWtrm+PeRDkd99mb4X57fdxnb4b77fVxn70Z7rfXl1P3mZ2d3Sttl6Mv2CpUqBCMjY2ztLLevXs3S2ssEREREeV+OTq8mpmZoXLlyti2bZve8m3btsHLy8tAVRERERGRoeT4bgPDhg3Dxx9/jCpVqqBGjRr4+eefcf36dfTv39/Qpf1n5ubmmDhxYpZuDvR83Gdvhvvt9XGfvRnut9fHffZmuN9eX27ZZ4rIq4xJYFizZ8/G5MmTcfv2bZQpUwbffvstvL29DV0WEREREWUzTYRXIiIiIiIgh/d5JSIiIiLKjOGViIiIiDSD4ZWIiIiINIPhlYiISKN42QrlRQyvRETZjIGD3pZdu3YZugSibMfwSkSUjdLT09XprW/dumXgakjLIiIi4Ovri9GjRxu6FKJsxfBKRAaVnp5u6BKyjYjAyCjjsDtq1Cj07NkTsbGxBq5KO3Qt1snJyWy9BuDq6opvv/0WoaGhGDt2rKHL0bznHYv4Xst5cvwMW/QvEYGiKIiPj8fjx49RsGBBQ5eUY+j2zaFDh2BqaoqKFSsauiR6Benp6WqYW7VqFR4+fIhChQqhZcuWBq7s7dO9RwFg37592LdvH6ZNmwY7OzsDV6YNuv23efNm/P777/D390fp0qXV909e8uWXX8Lf3x+Ojo7o0aMHjIyMMHHiRIgIQkJCDF2eJmU+Fu3YsQM3b95EgQIFULp0abi6uuqtzwt0n7e///4bRkZG8PDwgJmZmaHLUuWd/xMap3sjrV+/Hi1btkSVKlXQoUMHrFu3Lk+1XD2Lbt+sWbMGLVq0QFhYGKKjow1dFr1E5lbIzz77DD179sTMmTPRunVrBAYG4v79+wau8O3SBdfffvsNs2fPxgcffABPT0+kpqYauDJtUBQFq1evRseOHWFjYwNLS0u9MJFXWsdiYmKwe/duxMTEAADy58+Pzp07Y9KkSfjpp58wZswYA1eoTZnPiPj7+2PatGmYMWMG6tSpowa4vCLzd2qjRo2wdevWnHc8FtKM9evXi7W1tUyYMEH+/PNP8fHxkSpVqshPP/0kqamphi7PoDZv3ixWVlayYMECefDggaHLoddw+fJl8fLykqNHj8r9+/dl27ZtYmlpKT169JDo6GhDl/dWpaenS5cuXcTW1lbKly8vaWlpIiLqf+n5jh8/Lvb29rJgwQK95bdv35akpCQRyTv7MSUlRURE/vzzT7l586aIiNy7d0++//57ee+99+Szzz4zZHmaNW/ePLG3t5f9+/eLiMjUqVNFURRZuXKlgSvLfps2bZJ8+fLJTz/9JDExMVnWp6enG6CqfzG8asSlS5ekcuXKMmPGDBERSUpKksKFC4ubm5uUL19e5s2bl2cO3E978uSJ9O3bV4YOHSoiIvHx8fL333/LiBEj5IcffpC///7bwBXS8wQHB0ubNm2ka9eu8ujRI3X5rl27xNLSUnr27KnpAPusz+Tjx49l+PDhUrhwYQkKCpL4+HgRMfyXQU6UeZ9s2rRJvLy8JDY2VuLj42X+/PlSv359KVOmjPTs2VPu3btnwEqzX1JSktSoUUMKFiwot27dEhEG2Nf19OdzyJAhMnbsWBERCQ8PF2tra/n5559FJON75e7du9leoyE8fvxYOnXqJIGBgSKS8V47f/68TJo0SX766Se5dOmSgSsUYZ/XHOZ5/Wp0p4Y6d+6M27dvo3bt2mjVqhWCg4NRs2ZNfPfdd4iLi8Onn34KY2NjA1RuOEZGRrh+/TrS09Nx8+ZNTJgwAVevXkV0dDRiYmJw8uRJzJ49O8/tFy1wdHTE2rVr4e7ujoSEBJibmyM9PR3e3t7YsmULmjZtigcPHiAsLExzfUMzf5YjIiKQnp6O1NRUeHl54ZtvvsGjR4+wYcMGWFlZISAgAFZWVnmuX93T5P9PV8bGxsLExAT58uXDpk2b4OLiAktLSxw8eBAhISHYuHEjihUrhtKlS6NBgwb46aefcPz4cdSvX9/QLyHbWFpaIjQ0FH379kXNmjWxZ88eODs7o3PnzgCAr776ComJifj+++8NXGnOJJm6Le3fvx9lypRBYmIiChcujA0bNqBbt26YMmUK/P39kZ6ejlWrViE+Ph59+/bNUX0/34X09HTEx8cjf/78OHbsGObOnYsLFy7g3LlzKFy4MPbv34/Zs2fDwsJC7Q6V7QydnimrW7duyfXr10VEZOXKlTJlyhQRyTg9JiIydOhQ6dSpk8TGxoqISN++faVQoULSsmVLuX//vmGKzkbPaqHavXu3ODg4iLW1tbRt21aWL18uIiKzZs2SSpUqSUJCQnaXSU953pmB5cuXi4mJiYwePVqePHkiIv/+P962bZv4+vpq7qxC5vfo2LFj5cMPP5QyZcpI/vz5xd/fXx4+fChPnjyR/v37S7Vq1WTKlClqC2xelp6eLnfu3BFnZ2dZsWKF/PLLL6IoiqxZs0ZERL799ltp1aqVDB8+XE6dOiUiIqmpqVKxYkXZtGmTASt/93TvqfT0dLXbgIjI1atXpXr16uLm5qZ2IYiOjpb//e9/4ubmJnfu3DFIvTlZ5uPJyJEjpWzZsnL58mUZO3asODk5iY2NjcyZM0fdJiYmRvz8/OTLL780RLnvTOb31NPmzJkjBQoUEFtbW2nfvr0sWbJERETGjBkjjRo1ytY6n4XhNQdJT09XuwN06NBBZs+eLYqiyKJFi/S2a9++vfTq1Uu9P2jQIAkLC1NPHeVmug/Z7t27ZeLEiRIQECDLli0TkYwDzIEDB/S2GzJkiLRs2VISExMNUzCJiP6XxT///COHDh2ShIQESU5OFhGRsLAwMTY2lnHjxmUJsM96Dq2YNm2aFCpUSO1DFxQUJIqiqPefPHkiAwYMkGLFismvv/5qyFJzlKFDh0q+fPnEyMhIPW2r8/QP0XHjxknx4sUlMjIyO0vMVrrPwubNm6VXr15Sq1Yt+eKLL2TPnj0iInLt2jU1wOq+B2JiYvJEY8Z/ERUVJW3atJHt27ery+rVqycFChSQQ4cOyc2bN+XKlSvSqFEjqVq1qnpsyk10P4QOHDggYWFhMmvWLPUHz9mzZ2Xfvn0i8u/xd/DgwdK6dWtJSkoyaFcnhtcc6NKlS2JlZSXGxsYyffp0dXlqaqo8fvxYPvnkE/H19ZXg4GD59NNPJX/+/GpLbV6watUqee+996Rt27YSGBgoiqJIYGCgXkA9cuSIfPbZZ2JnZ8c+rwaW+QA3ZswYcXd3F2tra/noo4+kf//+amtRWFiYmJiYyIQJE3LNl8THH38sM2fOFBGRFStWSP78+WX27NkiIur7NSUlRSZPnpznL7oU+fcL8u+//xZFUcTU1FSWL1+u99nWvZ8WLFggvXv3lkKFCsnRo0cNUm92Cg8PF3Nzc+nTp4/0799fSpQoIb6+vuqPnitXroi3t7fY2dmpZ+no+X744QcpWrSoeHp6yuXLl9Xl9+7dU38IFChQQKpXry7Vq1eXx48fi4jkis/p7NmzpWbNmur9pUuXipWVlXz00UdSuHBhKViwoISFhemdDTpz5oyMGTNGbG1t5cSJE4YoWw/Daw6TkpIi0dHRYmZmJqamptK9e3e5evWq3jbXrl2Txo0bS+XKlaVixYpy7NgxwxRrABcvXpQPPvhAPaWTkJAgVlZWMnLkSHWbkydPyieffCLlypWT48ePG6pUesr06dOlYMGCsmHDBjlz5ox89dVX4uPjI61atZKoqCgREVm8eLEoiiI//fSTgav97+Lj46V48eKyatUq2bt3r1hbW6vv28ePH8vIkSPljz/+0HtMbvhifBsePnwohw4dkhEjRoi5ubmEhoZmOXsSHh4uLVu2lNOnTxuoyuyjC1RTp05Vl50+fVq6du0q9erVk8OHD4uIyLlz56RRo0Zy4cIFQ5WaYz195ub8+fNSrlw5MTMzU8+EZP6hvXXrVvntt9/kr7/+Uj+XueFHdVpamvz222/i6uoqLVq0kEePHknbtm0lNDRUHj58KKmpqdKvXz9xcnJSuwocOXJEfH19pWzZsjnmO5XhNYe6efOmnDp1SiwsLKRz585ZAmxaWpqkpKTIw4cPDVShYZw4cUL9xXjp0iUpXLiw9O3bV12vO2j//fffaoseZT/dl4FOUlKSNG/ePEufscWLF0uVKlX0zjBs2bJFc18Sx44dU0/XTpgwQX39Y8eOFU9PT7GwsNAb4un+/ftSv359+fbbbw1Rbo6jCw03btyQixcv6gWNgIAAMTc3l0WLFqkBdtasWXLx4kW1NSy3S0hIEA8PD/XHj25/nT59WlxcXPQ+P3lln7ypgwcPqqMGXLp0SUqUKCE1atRQP7/P+wGpxW5Lz/Po0SNZu3atlChRQmrWrCn169eXc+fO6W3Tp08fcXJyUjPGvn37ctQZXobXHEB3IIqNjc0yFMf+/fvFwsJCunbtKleuXBERka+++kqmTZuW3WXmCLt37xZXV1fZsWOHuLm5Sd++fdWDzYEDB6Rhw4Y5YhiPvGzSpEnSpk0bSU9P17sgwM/PT++Hhk7Hjh2lTp06WZZrJcCePn1aSpUqJWPHjpV+/fqJoihy8uRJEcloHfzoo4/E29tbPTUZFRUlTZo0ES8vL7a0ZrJy5UopVaqUFCxYUDp16qQ3tmZgYKDky5dPbx/rLtjKjZ6+kCYqKkrKly8vn3/+uYhkfDZ06zp27CitW7fOVeHqXUhPT5fdu3eLoigyefJkdQi+ixcvSrFixcTHxyfXd7fIfEx+9OiRrFmzRqpUqSIWFhZqA5muT3lcXJzY29vn2L74DK8GpnsjrV+/XmrWrCklS5YUHx8f2b59u8TFxYlIRoC1sbGROnXqSKtWrcTKykqOHDliyLKzxbM6g8fHx0vjxo0lX7580qFDB711Y8aMEW9vb15da2CnTp1Sg+fFixdFJKPVon///lKhQgW9/mUiIjNmzBBfX1/14i0tmjp1qtjb24uFhYVs3bpVb92PP/4oNWrUkMKFC0u1atWkUqVKUqVKlVzVh+6/On36tLi6usq3334rYWFh4uvrK3Xr1pV58+ap24wdO1Z8fHzEy8srV3eV0h33/vjjDxkzZow6/vGcOXNEURRZtWqV3vYtWrSQYcOGZXudWvX5559LwYIFZerUqerYwLruaHXr1pUbN24YuMJ3TxfSk5KSZM2aNVK0aFHx8fHR2yYqKko++OADdaSPnIbhNZs9a2iK9evXi42NjUyYMEEOHTok3t7eUqFCBQkLC1MD7JEjR6RXr17Sq1evHNFZ+l3LPKrA5MmTZdCgQbJu3TpJSUmRVatWyYcffigdO3aU/fv3y969e2X48OFiZ2eXJ/ZNTvX0j41Vq1aJk5OTrF+/XkQyziy4urpKnTp15MSJExIXFydJSUni4+Mj3bp1M0TJ/5kueK5fv15cXFzEw8NDxo0bJ9euXdPbLiIiQsLCwuTrr7+WpUuX5qo+dP/VmTNnZNKkSTJq1Ch12T///CPt27cXHx8fmT9/vrr87t27uXpIMd1naOXKlVKgQAH59NNPJSIiQl0/YsQIURRFhg8fLiEhITJ48GCxsbHJE/1+X1fm49HTPxAnTZokdnZ2MmXKFLUF9tKlS2JpaSkDBw7M1jqz27lz58TIyEiWLl0qIhktsOHh4VK4cGHx8fGRU6dOybFjx2TChAlSoECBLI0NOQXDazb7559/9O5fvXpVqlWrpnYDiI2NlWLFikmRIkXE1dVVFi1apAbYlJSUPPVlt2rVKrGxsZE+ffqoF6i1b99eRDJasxo1aiQmJiZSrlw5qVatWo7pSJ5XZe5rFxcXJ+fPn5dOnTpJ+fLlZd26dSKS0afR3d1dPDw8xN3dXapVqyZlypRRH6uVWaaervPevXty//59mTx5slSoUEFGjBjx0v5hbHHNuDCrVq1a6liSmZ07d07atWsn9erVU0doyAsOHjwo+fPnzzJEmO4zsmjRIqlVq5ZUrlxZGjZsyOPeS3zzzTeyYMGCLGd2goKCxMzMTKZOnaqerbt582au/1ympKTIgAEDxMrKSu2aowuwLi4uYm5uLq1bt5YuXbrk6DO8DK/ZaP369eLo6KjXDH/t2jX57rvvJCYmRm7fvi0lSpRQf/lVrVpVypYtKz/++GOeG2T/4sWL4u7uLj/++KOIZIR8a2trGT58uLrNkydP5NixY3L79m2OZ2hgq1evVk9nDho0SOrWrSsiIocOHZJu3bpJ6dKl1RbY5ORkCQsLkylTpsiPP/6o/iDTyg+zzH0L7927Jzdv3tQLs1999ZVUqFBBRo8erY492rVrV70WtLwu8/7at2+f1K1bVzw8PNT3iM758+fFz89PmjVrlmcuTp09e7Y0btxYRDIu7Fu9erW0atVKypYtK6GhoSIi6iQXee174U307t1bTE1NZenSpVkCbLt27cTJyUm++OILefDggbo8NwXYzJ813b9TUlJk6NChYmpqmiXAurm5SbNmzfQmwsiJGF6z0YEDB6Rbt25StmxZvQCr6yg9bNgwadu2rd7MWZaWllK7du1cf+B+uiUrIiJCSpcuLSIily9fFhcXF/H391fX79u3T+0LRobXoUMHMTExkRYtWsh7772nN7Zu5gCra4F9mla+LDIH1y+++EJ8fHwkf/78MmjQIL3g9fXXX0vlypXF19dXateuLQ4ODpoJ5+9S5otTk5OT1S/IiIgI8fb2lubNm2eZKevChQu5egKCpy1btkwURZFZs2ZJvXr1pGnTptKjRw/p16+fmJiYZBl5hv61Y8cO9d9ffPGFhIWFiYhI//79xcrKSpYsWaIXYIcMGSIfffSRNGrUSDNnfd7Etm3b5NChQyKSNcCamZmpeSQxMVE2bNigXquQkzG8ZrNTp05Jv379pHTp0rJx40a9dZ07d5Z+/fqpX3JDhw6VLVu25Ikhn3QfqD///FP+/vtv2b59u3h7e8vZs2elaNGi4u/vrwaciIgIGTp0aJYuGGRYJUuWFBMTE71he3QOHTokH3/8sZQrV05Wr15tgOrervHjx8v7778vS5YskTVr1qgDmevGRRQRmT9/vowYMUL69u2rfqa1EtLfBd1nfMOGDeLr6yuenp5SsWJF9QK3w4cPi7e3tzRr1kw2b95syFINKiYmRsaOHSvFihWTvn37qjMcxcXFSYUKFTjpynNERkaKm5ub1KlTR4YMGSLm5uZ6XSr69u0rVlZW8uuvv6rDYnXs2FEiIiJeOE2qVuleS0xMjPTu3VsURVHP/ujWxcfHS7NmzaRAgQKyYsUKg9X6Jhhes4nuS+vkyZMSFBQkzs7O8sEHH6itDOnp6dKlSxcpW7asTJkyRfr37y82NjZZLvzIzXbs2CGKosjGjRvl7t274ujoqM6eldmwYcPEx8dHvVKUDCslJUUSExPFz89PGjduLHZ2drJy5cosp50OHTokjRo1kq5duxqo0rdj69atUqpUKTVU7N69W8zMzKRq1apSrVq1534JsOU1I7haWlpKcHCw7Nu3T9q1aycWFhbql+rBgwfV1upt27YZuFrDeror1GeffSalSpXKMpxiXqfrl5mSkiK7d+8WW1tbsbKyUkN+5sktAgICxMHBQSpWrChly5aVjz76SP1c5sahxtasWSM9e/aUU6dOSdeuXcXGxkZtgdUZPny45M+fX95//331+hotYHjNRitWrJACBQrI0KFDpUePHvLhhx9KyZIl1Sb7x48fS7169dThdHLzcDBPu3z5soSHh8s333yjLtu+fbs4ODioHcc5qkDO8aIDfefOncXW1jZLgI2Pj5eoqCjNfUk83Rpz/vx5CQ4OFhGRTZs2ScGCBWXBggVy8uRJcXJyksqVK+tdIU8ZHj16JC1btpSgoCARybh4r0SJElnG/t21a5c0bdo0Rw2Ibkh//PGH+Pv7S4ECBfLUd8KrmDt3riiKonbZ2bt3rzg7O4uLi4v4+fmpn93MXQUWLVokX375pd401LnpjIjuNZ89e1aKFy8uCxculNTUVLlz54507NhRbG1t9QLsiBEjJDw8XGJiYgxV8htheM0md+/elUqVKqlfeiIif/31l3Tt2lU8PDzUvoBpaWly//59Tf0C+q+uXr0qZmZmYmFhIV999ZW6PCUlRbZt2yYuLi5StGhR8fDwEE9PTx7ADSxz+Ny1a5esW7dO9u7dq9cHuXPnzvLee+/JkiVLJDIyUpo0aSJt2rR55nNoxYoVK9QhiR4+fCiPHj2Spk2byqRJk9TXU79+ffHw8JBPP/3UkKXmSImJiVK6dGmJiIiQBw8eiLOzs15wnTt3rvoFquUxf19GFy5SU1OzhKanfyjduHFDZsyYIc2aNVMnvqB/3b9/XwYNGiTm5uZqN7zbt2/L9u3bpWTJklKvXj11W91n9OkzILkpuOocOXJEpkyZonZZyjzZRdeuXcXS0lJ69eolbdu2lYIFC2qyCx7Daza5d++eODs7q9P76ezevVuKFSsmH374od6MMrnZ9evX1VOrS5culS5dusiPP/4o77///jPH+4yLi5Njx47JhQsXOKqAgWX+ch0zZow4OztLmTJlxMzMTIYOHarXx6x79+5SsGBB8fDwkHLlyml62sp//vlHqlWrptefNz4+Xj766COZMmWKiGS8T7t06SLLli3TZDjPDi1bthR/f39xcXGRAQMGqO+JuLg4adKkiTokVm7qe5iZ7nVt2rRJ+vXrJ9WrV5fg4GDZuXPncx8TExOjXsRLWT148EACAgLE1NRUb0STDRs2SMmSJaVBgwbqtv369ZNffvnFUKVmi/T0dKlbt64oiiLVqlXLEs6fPHkiISEh0qhRI2nTpo1m+1AzvGaTpKQkadasmQwdOjRLq2qHDh3E0dFRvLy8JC4uLtceuEUyukZ06tRJvLy8ZOjQoaIoioSGhkp6erosWLBATE1NZdy4cer27CeYM33zzTdSuHBh2bNnj4hkzFpjamoqvXv31guw69evl7Vr12puUP5nhc+RI0eKm5ubOibknTt3pE2bNtKsWTMJDg4WPz8/8fT0VB+blwOs7hiWlJQkSUlJ6rJp06aJvb291KxZU2/7sWPHioeHhzoFdm4WHh4ulpaWMn78ePnyyy+lYcOGUqxYMblw4YKhS9OMpz9bcXFxMnDgQDExMdELsBs3bpSSJUuqF3K5uLho5hj0XyQlJUnbtm3F3t5eFi5cqJ4Vy5wtUlJSNN2gwPCajb755huxt7fXm3ggPT1d/P39ZerUqXmmI/6DBw/E09NTFEWRAQMGqMuTkpJk3rx5YmJiIuPHjzdghfS0zF8WkZGR0r59e3XO69WrV0v+/Pmlf//+Ym1tLd26dZOjR49meQ4tnp5bsWKF3pXvXl5e0qhRI/X+pk2bpFWrVlKpUiVp1qyZ+mWQl4Orztq1a6Vhw4ZSo0YNdcD9hIQE6d27t5QvX17atGkjkyZNks6dO0v+/PnzRHegu3fvipeXl/zwww8iknEsLFiwoAwdOtTAlWnTkiVL1IuaY2Nj1QCr64aXkpIiJ06ckOHDh8uoUaNyXR/XtLQ09Vhz584dSUxMVLNFUlKSNGjQQCpXriyrVq3KddNRM7xmg8y/dgYNGiQODg7Sr18/+d///if9+/cXJyenPDV23+PHj8XX11cqVKggDRo0kMWLF6vrdAHW0tKSB/Qc4ungKpJx1fjDhw/l8OHD4uLiIt99952IZIytaG1tLR06dNBkP6rMDhw4IIqiiKOjo4wYMUJEMsJq/fr19WY/iouLk4SEBPVznhdadl5m//798t5770lAQID06dNHjIyMZODAgfLo0SNJSEiQ2bNnS9OmTaVOnTrSt2/fXDu9aXp6ut7xPzo6WkqWLCmXLl2Sq1evSpEiRfTGr960aRMvVHtF9+/fFxMTE/H19ZUbN26IyLNbYJ+WG8LbihUr9Ga/Wr16tVSuXFk8PDxk0KBB6hmxxMREqVevnlSuXFnWrFmj6ZbWpzG8vmXPO+Wf+QMzY8YM6dChg3h4eIiPj88zW6lyu0ePHsnt27eladOmUrdu3Sz9kKZPny4ODg55pjU6p1q5cqXaF3vYsGHi5+cnqamp6vAzQUFB0qJFC/V+cHCw1K1bV9q1a6e51kddvbrP8IMHD6R79+7SoUMHKVasmHTp0kVmzJghTZs2lT59+qgXF2X+bGvtNb8tT4e0HTt2yKRJk9T769evFyMjI+nXr5/Ex8c/93G5VXh4uISFhcmpU6ekUqVKsm7dOnFzc5M+ffqo75kLFy5I79699Qbap389631y/vx5KVy4sPj5+ak/rGNjYyUgIEAsLCxy5XUkZ86cUc/0nDt3Tq5duyb58+eXyZMny+jRo8XPz09vqLnExERp2LChFC9e/LmBXosYXt+SzFeQZpb5fuZ/P3nyROLi4vQO5HnRpUuXpGnTplKvXj1ZtGiRiGT0n+zRo4fmhu7IbdLT0yUwMFAURZEWLVqIjY2N2rlf94U7cOBAadSokURFRYlIxgU5mWeP02KY27t3r/rvX375Rdq1aydnz56VKVOmyMCBA6VYsWKiKMozJ2PIq3THv4MHD0poaKj06NFDvv76a71tNmzYIEZGRjJo0CC1pSw30+2TU6dOiaIosnDhQhHJGIlDUZQs4x1/9tlnUr58+Tyxb/4L3ZkN3f79559/xNHRUS/AxsXFSdeuXcXb29tgdb5LS5YskQYNGki7du0kODhYJkyYoK7bvn27tG7dWry8vNQAm5CQIC1btpTLly8bquS3juH1LdB9iP744w8JCAiQrl27SkhIiERHR4uIfmjNC60Mr+vy5cvSunVrKVOmjFSpUkXs7OzkwIEDhi6L/p+7u7uYmprKtGnTsqxbvXq1WFhYSPXq1cXDw0NKlSqV5ctFSyIiIkRRFGnVqpXaB7NLly7SpEkTEcl4r37xxReiKIr4+vqKiDZf57uwbt06URRFKleuLIqiiJeXl97FeyIiGzduFEVRZOTIkbni9O3LHDhwQDZt2qQXLnRnnBwdHWXhwoXy448/SmBgoNjY2GTZX6Rv8uTJ0q5dO/VMj+6zd/78eXn//felVatWahe8xMRETf54fpHMXZKWL18uDRo0EBcXFxk8eLDedroA6+3tnWUmz9yC4fUtWbNmjVhYWEifPn2kQYMGUqVKFSlWrJj6KzovHKj/ixs3bsj8+fNl0qRJcu7cOUOXQ/Lve7Zp06bqTEjLly9X1+u+ONasWSMTJkyQzz//XPMXRDx69EhOnTolNWrUEG9vbxkzZozcuHFDmjdvLrNmzVK3+/PPPzX7Gt8m3Xvg+vXr0r17d5k7d646TJGzs7P4+/vLqVOn9B6zZcsWOXPmjCHKzVYJCQlSrlw5URRFOnTooLcuOjpaevXqJeXKlZOyZctK69atOfHKMzwdPn///XcxMzOT3r17qwFWt833338viqKIt7e3OiLIs55Dy3Sft9OnT8v9+/dl3bp1UrlyZfnwww+zvH927Nghvr6+0rBhQ0lMTMx1P7IZXt/A02+Ce/fuSYUKFWTy5MnqspMnT0qDBg2kePHiPP1NmpH5QP/0hUcDBw4Uc3PzLNOfPn2aU8sXLOk+23FxcfLdd99JrVq1xMHBQZo2bSodOnRQ50TXYYDN6CrQpUsX8fHxkYsXL6rL165dK0WLFpXevXvn2guyXubo0aPSoEEDcXV1lZs3b4qI/mcsKipKEhMT1eHE6F+Z99PWrVvVU95//vmnWFtbS69evSQhIUHdJjQ0VHr37i0tWrTIVYFVJ3NjgYODgwQFBUlqaqqsWrVKfHx8pFWrVlla7nfv3q12pchtGF5fg+7No/vA6D4gV65cEQcHB9m6dau6bWpqqhw7dkwqVaqkttjktl8+lLtkPuD//PPP0q9fP+nevbt8++236vJPP/1UrKysZPHixXLz5k1p1aqV2qqUW97fmcdpjYmJkeHDh4utra0oipLrBzh/E+vXrxcPDw+xsrKStWvXZln3wQcfSMeOHeXs2bMGqjB7POv9/+TJEzlx4oSUL19eypUrpw5jpLvqO7d8Zt62zPtl9OjRUqpUKQkJCVFbW7dv3y42NjbSvXt3OXnypERHR0urVq1k7ty56uNyY4DdsGGDWFpayty5c/VC6Zo1a6R+/frSsmVLzU468LoYXl/TnTt3pFChQvLbb7+py5KTk6VKlSp6g+uLZHwAq1WrJoGBgdldJtEbGzlypDg4OMj48eNl2LBhYm9vL126dNFbryiKlC5dWkqXLq254VfeJDCsX79ePvvsM023Kr9L27dvl8qVK0uzZs3kr7/+0lu3cuVKKVOmTJZW69xE957at2+fTJ06VYKCguTw4cPq+lOnTknZsmWlfPnyaoDNjeHqv3p6n4SEhEjBggVl//798vDhQ711+/fvl0KFComzs7MUKVJEKlSooLlj0etITk6W9u3by9ixY0Uko0/vP//8I5MnT5YtW7ZISEiING/eXOrWrZulq05uxPD6mu7duyeffPKJmJmZSXh4uIhkDIQcEBAgNWrUkFWrVult37p1axk/fnyeGRKGtG3Pnj1SokQJ2b9/v4iIrFq1SvLly6c3rqmIyLZt22TdunWanjnr2rVrWbo8PP0ZfVbA0MprfRd0++fYsWOydu1a+fnnn9WpS7dt2yaenp7Svn17dZxJnbwwqsqqVavE3t5evL29pVGjRmJkZKTXyHHq1CmpWLGiuLi45In98bqe7oLz8OFDadiwofz4448i8u97L/Nn8ubNm7J06VJZunSp5o5FryspKUmqVKkigwYNkpiYGAkMDBQfHx9xcnKSIkWKyLRp02ThwoXSrFmzXNtVIDOG15d4VuC8c+eOfPrpp6IoiqxevVpEMjrg+/n5SfXq1WXIkCGyfPlyCQwMFFtb21x/uoxyjxUrVkjlypVFJGMkARsbG/XLIz4+Xp25JjMt9vscM2aMlC1bVuzs7GTEiBF6szvxR+aLrVy5UooUKSLVqlWTSpUqSaFChdT3xaZNm8TT01M6d+6sN15pbt+n+/btE3t7e/W09fXr18XIyEgsLCzUz4+IyPHjx6VmzZpy6dIlQ5WaI/Xq1UtGjx6tt+z+/ftSuHBhmTFjRpbtk5KSnjmkmBaPRa8jLCxMLC0txdbWVlq3bi1hYWEiktGdy8/PT0Tyxg9FEYbXF9L9wktISFBbF3Ru3bqljoGpGwg5OjpaRowYITVq1BB3d3epXbt2npjykLTpWYFi3bp10rJlS1m8eLFYW1vrffFu27ZN+vfvr8nZ4DK31ixevFiKFi0qS5YskenTp4ubm5t06tRJb3zX3B623tShQ4ekYMGCsmDBAhHJOA4qiiJTpkxRt/n999/Fw8NDevXqlScuRHr8+LHMnj1bHQ7r+vXr4uLiIgMHDpRx48aJiYmJXl/plJQUQ5WaI6WkpMjKlSuz9AO+e/eu1KhRQwYPHizJycl6jzl48KAMGDAgT05ic/r0afX6Gt1xTTdEZ156bzG8vsQ///wjFSpUkFq1aklYWJhs2bJFXffo0SMJCAgQRVHUIYSePHkiqampcvfuXb0rIYlykszhbNGiRWpL0Pnz56VAgQKiKIp8//336jbJycnSqFEj6datm6aD3d69e2XkyJFqi4VIxpAy5cuXlw4dOugF2LzuWRd+LF++XNq3by8iGcdGV1dX6du3r7pe1/K1ZcsWuXLlSrbUaSiZPwenT5+WQ4cOSWJiotSpU0f8/f0lLS1NLl68KNbW1qIoihr46fnmz58vTZo0UYPs7NmzxcjISGbNmqX2FY6NjZVmzZpJ69at83y/4bNnz8rYsWPFzs5OTp48aehyspUJ6LnS09OxcOFC/P3337CwsMDDhw+RlJSEAgUKoFq1aujVqxd69eqFggULomPHjrC1tUXDhg0BAO+//76Bqyd6tvT0dBgZGQEAjh8/junTpyM0NBRhYWH48MMPsXjxYrRq1QrHjx/HsmXLkC9fPnz//fe4c+cO1q9fD0VRICJQFMXAr+TViQjOnj2L+vXr48mTJwgJCVHX1alTBzNmzMCQIUMwc+ZMPH78GHXq1DFcsTnAX3/9BR8fH8ydOxe9e/dWl588eRLR0dGIiYlB/fr10ahRI8yZMwcAsHTpUhw6dAjTp0+Hn5+foUp/p1JTU2FsbAxFUZCeng5jY2MAQKlSpQAAFy9eRFxcHHr27AkjIyOYmZmhffv2cHd3R/Xq1Q1Zeo6U+Vh0/fp1xMXF4datW+jZsycWLlyIAQMG4OHDhxg8eLB67ImNjUVcXByOHj0KIyMjzR2L3pYjR45g2rRpOH78OHbt2oUyZcoYuqTsZeDwnOPdvn1bhgwZIs2bN5ehQ4fKxYsXZdKkSVK/fn2xt7eXkiVLSpMmTcTe3l4URZFdu3YZumSi58rcWhQcHCwdO3aUUqVKiYmJidStW1dtLfv999+lYsWKUrRoUalRo4a0a9dObQ3RSr8y3WvN/JrXrFkjTk5O0qJFiyxjj+7cuVMcHR31ZkPKy8aPHy8WFhZ6LYaHDx8WT09PsbW1lT59+ojIv6cuhw0bJm3bts3SxSo3ePq9sm3bNunXr58EBATIzJkz1ffY7t27RVEU2bRpk8THx8uECROkZs2aeaL7xOvK/LkcOHCgdO/eXa5fvy5z586VypUrS6dOndRjTnh4uEyYMEF69+4tISEh6kVZufXirFeRlJQku3fvluvXrxu6FINgeH0FN2/elICAAKlatarMnj1bXX7gwAFZvXq1NGrUSJ1JJa8Oxk3aMnXqVLG2tpatW7fK2bNnZdq0aVK9enXx8fFRA2xMTIzcunVLoqOj1S8arXxZZD6d+ODBA4mLi1ND9/Lly6Vw4cIycODALBdTHj16VDPh/F3JHCqCgoLE2NhYDbAxMTHSo0cP+eCDD9Txf69fvy5jx46VQoUK5crj32+//SYVK1ZU+63++eefoiiKdOrUSWrWrCklS5aUatWqqUGrd+/eoiiKlClTRuzs7OTo0aOGLD/Hu3nzplStWlV27twpIhnd8X7++WepXLmydOjQQe3H+XQXgbz+Oc3rGF5fke4CrapVq8rXX3+tt+7x48eSkpKiNyUdUU6VnJwsLVu2zHJ177Jly+Sjjz6SevXqPXOoFa30dc1c5+TJk8XX11c8PT2lfv366uvKHGCfNR1xXvti1AWDzD9OoqKiROTfADtv3jwREYmMjJSPP/5Y3N3dpUCBAlK1alUpXrx4rg1pFy5ckIYNG4qvr6+EhYVJ//791eD++PFj2bt3r5QtW1Y8PT3Vx6xevVqWLFmizgpFzxYcHCwtW7aUrl276rVOJycny88//yxVqlSRzp0756kLkejVMLy+htu3b0tgYKB4enpKSEiIulwrrVFEOu3atZOWLVtmWT5w4EBRFEUaNGigno7SSmh92tixY+X999+X0NBQ2bFjhxQuXFjKli0r0dHRIpIRYF1dXaVz585y7do1A1dreNeuXZNPP/1URDLGLPXw8FCnNJ04caIYGxurQ0E9ePBAzpw5Iz/++KPs2rXrmcMW5SZXrlyRJk2aSNOmTaV8+fLy+++/q+tSU1Nl9+7dUrJkSQkNDTVckRqQufU0LS1NZsyYIVZWVlK6dGl1RAHdD8fk5GSZN2+euLi4yMSJEw1RLuVgDK+vSRdga9asKZ9//rmhyyF6oeddjTt58mQpU6aM/Pnnn3qz0vz000/SsmVLadq0qQQGBmq2xePatWtSpUoVdXSQ9evXi52dnV63H5GM+dBbtWqV569aFhGZNWuWVKpUSfz8/MTU1FQWLVqkt14XYHUtsHnNpUuXpEWLFqIoinz22Wd66xITE6VcuXIyZswYA1WnLffv3xeRjH6bCxYsEFNTU70ZKnUBNikpSW8yFCIdhtc3cPv2benZs6fUr19fbcUhymkyB7JNmzbJr7/+Kr/88oskJCRIamqq+Pj4SKVKlWTdunUSExMj8fHx0qpVK5k6daqMHz9eihcvLvfu3TPgK3hzx44dEycnJxER2bhxo96YtfHx8fL9999nOWOSVwNs5pZ13djVderUUX+4ZA4OEydOFEtLyyw/AvKKyMhIadGihVSsWFEWLlyot87Pz09GjRrF2RSfIfNn65dffhEXFxc5f/68iGSM8zpnzhwxNjaWL7/8Ut3u6cDKAEuZMby+oaioKLVPGFFONmrUKClcuLA0aNBAihYtKjVr1pTdu3dLSkqK+Pr6StmyZcXZ2VnKlCkj7u7uIpIx9mmJEiXU08Y5WeagoPv3vXv3xNfXV4YPHy7W1tZ609uePn1aGjZsqF4gkheDxrP6uB47dkyCgoKkb9++4u3tLZ988onajz/zdqNHj5aCBQtmmWs+r7hy5Yo0bdpUypYtK0OGDJGlS5fKqFGjxNzcXM6cOWPo8nKczMF11apVMnPmTFEURWrXri0XLlwQkX8nejAxMZGvvvrKUKWShjC8EuViCxYsEGdnZ4mIiBARkblz54qRkZGsX79eRDJCyfbt2+X777+X0NBQtXWjX79+Urt2bXVg8Jwq8xdjenq62gUiISFBOnXqJIqiyKBBg9RtkpKS1L6LebWlVefixYvqhAO//fabuLi4yMGDB0VEZPr06eLl5SWffPKJ3ixGFy9eFBHRbIv823LlyhVp2bKlGBsbS8WKFWXYsGG5cqSFt2n06NHi7Ows06dPl4CAAClRooSULl1aL8D++OOPoigK+w7TSykiIoYea5aI3o3PPvsMiYmJ+OGHH/Dbb7+hX79+CAkJwYABAxAfH4/U1FS899576vYHDx7EL7/8giVLlmDnzp0oV66cAat/dZMnT8b+/fsRFRWFjz/+GB07dgQANG/eHGlpaShXrhyKFSuGrVu34sGDBzhy5AhMTU31BknPa86cOQNPT0+UKlUKhw8fRmhoKHr06KGu//bbb7Fq1Sp8+OGHCAoKwvz587F69Wrs27cPNjY2Bqw8Z7hx4wa6du2KIkWKYObMmXqfI9J35swZ1KtXDz///DOaN28OALhy5QratGmDtLQ0rFq1Cu7u7nj8+DF+//13NGvWDCYmnEOJni9vHrWJcqGnf4eKCM6fPw8XFxccPXoUffr0wTfffIMBAwaos8eFh4cjLS1Nfcz169dx/PjxHB9c09PT1X8HBQXhf//7H1xdXVG6dGlMmDABAQEBePjwIcLDw9GwYUOcO3cOERERqFixIo4ePQpTU1Okpqbm2eAKZMwK9fnnn+Pw4cMoX748unTpAuDffTt06FB06tQJp0+fhpeXF+bPn4958+YxuP6/IkWKICwsDN988w2D60skJycjJSUF7u7uADLeY25ubli0aBFu3LiBvn374vLlyzAzM0Pz5s1hYmKC1NRUA1dNORlbXolymZCQEHh6esLX1xcLFixAYGAgUlJSsHjxYnTu3BkAkJCQgDZt2qBq1ar4+uuv9R4fHx+vmYBy/fp1zJkzB35+fqhbty4AYOvWrRg7dixKly6NBQsWQFEU9aaTlpamTu2Zl61cuRJnzpzBzz//jDJlymDx4sUoVKiQ3v45f/48zp8/j/Lly8PV1dXAFVNO96yzGampqfjggw/QqVMnTJ48WV3+8OFDNGzYEOfPn4ebmxuOHTsGAHl2yld6dXm32YEol9q3bx++/PJLJCcno0mTJujQoQMcHBxQqFAhJCcn48KFC2jfvj3u37+PSZMmZXm8VoLrunXrUKxYMSxYsECv1dnPzw9ffvklli1bhr/++gtGRkZZvgjzanDV7aekpCQ8efIE7dq1w+eff45Nmzbh77//Rrdu3XD//n11/+zatQseHh5o0aIFgyu9VObg+scffyA8PBxr1qyBiYkJBg4ciF27dmH69Onq9ubm5vjwww+xYcMGREdHY+zYsQDA4EovxU4lRLnMxx9/jG+++Qbnz59HhQoV4O/vD2NjYzRp0gSFCxeGnZ0d7OzssH//fpiYmGi2FbJq1aoYOHAgZs+ejcjISAAZLTwmJiZo3Lgx3N3dERERgTp16hi20BxC15r1+++/Y/Hixbhw4QKqVauGJk2aoGnTpti2bRsaNWqEzp0746uvvsLatWuxYMECREREwNnZ2dDlUw4nImpwHTt2LBYtWgR7e3ucPXsWvXv3RuvWrREVFYX58+djz5498PLywtq1a5GSkoIqVaqgVKlSiI6ONvCrIK1gtwEijXrRxUaVKlVC8eLFsWLFCgAZfc5OnDiBGzduwN7eHjVr1oSRkZEa9nK6573W6OhoDBs2DCtXrsSGDRvg6+sLAIiLi0PFihUxcuRI9O/fP7vLzbHWrl2LTp06Yfz48ShYsCB27tyJFStW4PTp0yhZsiQuXLiAevXqwdLSEomJiVi7di0qV65s6LJJQyZPnowZM2YgPDwc1apVww8//IDBgwejV69e6N69O27fvo1Zs2bByMgI77//PpYsWQIzMzM0a9YMZcuWRUhICLsN0EsxvBJpXFhYGMzMzNCiRQvky5cPQEa/zyFDhmDGjBnw8/N7ZvjTypX2metcu3YtoqKiAGR0D3Bzc0NiYiL69u2L1atXo3///nBycsJff/2Fq1ev4tixY5oI59khNjYW7du3R5MmTTBkyBDcu3cPFSpUQOvWrTFz5kx1u0ePHuHYsWNwc3ODo6OjASsmrbl16xbGjh2Lxo0bo2PHjli9ejX69OmDgIAAfP/992jWrBmCg4OzdEEZOXIkfvnlF/z111/qRV1EL5T9o3MR0duSkpIi1atXlypVqkjJkiVlw4YNcu3aNUlJSZEaNWqo01XmhjFNhw8fLoUKFZJatWqJtbW1VK1aVWbMmCEiGbNm9e7dWxRFkXbt2sny5cvl0aNHIiJZZtLKq+7duyclSpSQo0ePys2bN6Vw4cLi7++vrl+5cqWcPXvWgBWS1iUnJ8vq1avlwYMHcvjwYSlWrJh89913IiIydepUURRFfHx85Pr16yIicvz4cRk0aJC4ubnJ0aNHDVk6aUzOb3YhIlXmYa1EBGZmZti1axcWLlyIWrVqYfDgwejUqRPWrl2L7t2747vvvsPp06c10cL6IitXrsSSJUuwefNm7N69G5GRkahQoQJWrlyJ+fPnw9raGl9++SX69u2LTZs2oXDhwjA3N0dKSkqebXmV/z+pdvz4cURGRsLCwgLFihXDkSNHULNmTTRp0gRz5swBkDFm6e+//45//vkny5BrRK/KwsICzZo1Q/78+bF9+3aUKlVKHTvY3Nwc3bp1g6WlJQoXLgwAKF++PFq0aIFdu3ahYsWKhiydNEbb32hEecSyZcsAZFwlrxuHU1EUpKenw8zMDKVLl8bcuXOxYMECtGjRAj169MCiRYuQnJyMbdu2GbL0t+Ly5ctwcXFBuXLlICLInz8/vvzyS9jb22Pp0qUAACcnJ3z55Zdo3bo1mjZtip07d8Lc3NzAlRuG/H+fwfDwcDRt2hQ///wzrK2tUbx4cfTt2xcVK1bEjz/+qF6oN2vWLBw4cAAVK1ZkX0P6T3Q/Fi9evIi4uDgoioJHjx5hy5YtaNq0KTZt2gQjIyM8efIEAFC/fn0ULVrUkCWTBrHPK1EOt3z5cgwYMACDBg1CUFAQgKz9VZ++f+nSJYSFheHq1atYsGCBZlsfdSFs+vTpWLRoEfbu3Yt8+fKpF5pFRESgWrVqOHLkiNpyEx0djT59+iAiIgIXLlyApaWlgV+FYWzcuBHt27fH999/j0aNGqFIkSIAMkaj2LJlC4YOHQojIyNcvnwZS5cuxV9//YXy5csbuGrKLQ4ePIjatWvDw8MDKSkpsLCwwNGjRzV7LKKcheGVKIeLjIxEaGgoli1bhjZt2uCrr74C8PwLrnTLnzx5AlNTUwDQzKgCz3PmzBmUK1cOEyZMwMSJE9XlBw8ehL+/P8LDw/HBBx+oy2NiYpCSkpJnh3h69OgRunfvDnd3d3z99ddISkrCzZs3ER4ejpIlS2L+/PlISUnBnTt3UKZMGYwaNQplypQxdNmUyxw9ehSrV6+Gra0thg0bps6cpeVjEeUMfAcR5WCpqakoWrQoBg8eDHNzcyxatAg2NjYYPXo0jIyMnhlgdfd1wRWA5r8sSpUqhfnz58Pf3x/x8fFo06YN3nvvPUyaNAn58+dHsWLF9LYvWLCgYQrNIUQEV65cgaOjI+7fv4+JEyfixIkTuHDhAszMzPDpp5+ib9++MDIygomJCczMzAxdMuVClSpVQqVKldT7DK70trDllSiHkkxjHYaFhWHfvn1Ys2YNkpKSMGbMGIwbNw6Adoa8ehtWrVqFQYMGQVEUWFlZwd7eHjt37oSpqWme2g+vYtGiRejfvz9MTU1Rr149tGrVCt27d8fgwYNx8uRJbN26lUGCiDSJ4ZUoh5swYQLmzJmDqVOnQkQQHh6OM2fOoGvXrs/tA5ubRUVF4c6dO3j8+DEqV66sqckWstuZM2dw8+ZNNGjQQH2PBAYGIj4+Hj///HOevaCNiLSN4ZUoB4uKikLz5s0RGBioDjkTGRmJOXPmYPHixQgMDMSoUaMA5K0Am1lefd2v69y5c/jll18wa9Ys7Nmzh31ciUizeMQnysGsra1x7949REZGqsuKFi2KwMBAFChQAF999RVGjhwJAHk2wOXV1/06jhw5gi+++AJr1qzBrl27GFyJSNN4no0oh3hWC6KiKKhZsybOnDmDyMhIdTxEZ2dneHp6wsrKCvfv3+dc4PRCpUqVwoABA1CsWDGOqUlEmsfwSpQDZA6uZ86cQUJCAlxdXeHg4IB+/fqhefPmKFy4MPr164cSJUogKSkJMTEx+OSTT9C7d28oisIAS89laWmJ2rVrG7oMIqK3gn1eiQwsc+gcP348VqxYgdTUVKSnp6NNmzYIDg7G77//joEDB8Ld3V1tbU1OTsbx48dhbGzM4EpERHkGO4sRGVDm0Dl9+nTMmzcPc+bMwaVLl1C7dm0sXLgQR48eRevWrbFmzRq0bt0aDg4O8PX1xdGjR2FsbIy0tDQGVyIiyjPY8kpkANeuXYOrqysAIC0tDQDQrl071KtXD4GBgVi/fj26deuGyZMno1+/fkhJSXnmsEYcIoqIiPIatrwSZbN+/fqpA8UDgLGxMR49eoRr166hTp062L17N7p06aIXXOfOnYtDhw5leS4GVyIiymsYXomyWZMmTfD333/j22+/xYkTJwAA+fLlg6urK9q2bYsmTZpg5syZ6NevHwDg4cOHWLlyJf7++29Dlk1ERJQjsNsAUTbSneb/448/4O/vj/r166N///6oXLky9uzZg8DAQCiKgmPHjkFEEBsbiy5duiA+Ph47d+6EsbGxoV8CERGRQfGcI1E2SU9PV0/zu7i4oGnTpli8eDFSUlIwYcIEeHl54dNPP8XkyZPh6uoKNzc3PHr0CI8fP8bBgwfVi7MYYImIKC9jyytRNhs+fDjWrFmD5s2b4+bNmwgPD0eHDh3w5Zdfonjx4rh69SpCQ0NhamoKJycn9OzZE8bGxrw4i4iICAyvRNlq7969aNmyJdatWwcvLy8AwIYNG9CjRw80aNAAEydOxEcffZTlcWxxJSIiysBmHKJsZGJiAgsLC9jY2ADI6ErQrFkzzJs3D23btkX+/PnRq1cveHp66j2OwZWIiCgDRxsgekd0JzUyn9wwNjbGw4cPcf36dQDAkydPAAC+vr5wcXHBzz//jB07dmR/sURERBrB8Er0DqSnp6uzXqWmpqrLq1Spgq5du6JLly74+++/1YkH0tLS0LhxY6xcuRIjR440SM1ERERawD6vRG9Zeno6jIwyfhfOnDkTe/fuxZMnT1C6dGmMGzcOjx8/xscff4xNmzYhKCgI+fLlw7p165CUlIQ9e/ZAURT2cSUiInoOhleid+Szzz7DvHnz4O/vj4sXL+Ls2bMwNTXFvn37YGlpiYkTJ2LVqlUwMzODk5MTwsPDYWpqChFRW22JiIhIH8Mr0Ttw6tQptGzZEj/99BPq168PADh69Cj8/f1hZmaG3bt3w9TUFA8ePICFhQUsLCygKAqHwyIiInoJ9nklegfu37+P6OhoFC9eXF1Wvnx5TJs2DbGxsdiyZQsAwNbWFpaWllAURW8SAyIiIno2hlei/yjzyYv09HQAgLu7OxwdHbF582Z1nbGxMcqXL4/Y2FhERkaqy3R0/WSJiIjo+fhtSfQfZB5VQHcfAOzs7FCpUiWsXLkSmzZtUtebmJjA2dkZ1tbW2V4rERFRbsA+r0RvKPOoAj/88AOOHDmCy5cvo3PnzujYsSNSU1PRoUMHPH78GOXLl0fVqlWxePFiREdH49ixYxxNgIiI6A2w5ZXoNel+7+mC6+jRozFp0iRYW1ujePHiGD9+PAICApCQkIAVK1agXr16OHLkCObPn49ChQrhyJEjMDY2RlpamiFfBhERkSax5ZXoDehaXSMiItC+fXv8+uuv8PLyAgBs3boVY8aMQdmyZREaGqoOfZWQkKBOC8tRBYiIiN4Mvz2JXtH48ePh4OCAQYMG6V1c9eTJE+TLlw9ARqusn58f0tLS0LJlS/Tu3Ru1a9cGADW4igiDKxER0RviNyjRK7h48SL27duHtLQ0WFtbo1evXgAypnWNjY3F3bt3AQCPHz+Gubk5GjdujOLFi+PEiRNqeNXhBARERERvjn1eiV6idOnSWLduHX799VfY29tj3rx5mD9/PgDA09MT7du3R8eOHXHu3DmYm5sDAB48eAAAKFCggMHqJiIiyo3Y55XoBb744gusWrUKhw8fhpmZGWJjY9G/f3/cuHEDPXr0QJ8+fRAVFQV/f39s374dQUFBMDMzw5YtW3Dr1i0cPXqUowoQERG9RWx5JXqB2NhYmJiYwMzMDMOHD0d4eDi+//57ODg4ICwsDGFhYXB0dMSyZcswdOhQ/Prrr1i+fDlsbW0RERHBUQWIiIjeMra8Ej2DboSAPXv2oG/fvjA1NcXVq1dx6NAheHh44M6dOwgICEBUVBT69OmDnj17AgBiYmJga2sLExMTKIrCUQWIiIjeMra8Ej2D7qKqWrVqwcXFBSdPnkTt2rXh4eEBAHBwcMCsWbPg6OiIBQsWqH1gCxYsCFNTUyiKwlEFiIiI3gGGV6IXuH//PkxNTTFp0iRcuXIF3bp1U9dlDrBTp07Fxo0b9R7LUQWIiIjePnYbIHqJtLQ0GBkZITQ0FFOmTEHlypWxePFidf2tW7cwa9YsfPHFF7w4i4iI6B1jeCV6RYmJiVi+fDkmT56MKlWq4JdffsmyTVpaGgMsERHRO8TwSvQaEhMTsWLFCkydOhWurq5ZugoQERHRu8WrSYheQ758+dC+fXskJiZi7969SE9P15sqloiIiN4ttrwSvYFHjx7B3NwciqIwwBIREWUjhlei/0A3HiwRERFlDzYXEf0HDK5ERETZi+GViIiIiDSD4ZWIiIiINIPhlYiIiIg0g+GViIiIiDSD4ZWIiIiINIPhlYiIiIg0g+GViIiIiDSD4ZWIiIiINIPhlYiIiIg04/8APp9vNBP1e0UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dict_normal_names = {0: 'No data',\n",
    "                     1: 'trees', \n",
    "2: 'flooded vegetation',\n",
    "3: 'open water',\n",
    "4: 'settlements', \n",
    "5: 'bare soil',\n",
    "6: 'agriculture', \n",
    "7: 'shrubs',\n",
    "#8: 'grass'\n",
    "}\n",
    "data = change_data.groupby('old_class')['old_class'].agg({'count'}).reset_index()\n",
    "data['percent'] = data['count'].apply(lambda x: x/sum(data['count'])*100)\n",
    "data['names'] = data['old_class'].apply(lambda x: dict_normal_names[x])\n",
    "\n",
    "colors =['black','#117733','#AA4499',  '#88CCEE', '#DE7486',\n",
    "    '#bcbcbc',  '#F9CB9C', '#74b357' \n",
    "         \n",
    "]\n",
    "\n",
    "plt.figure(figsize=(8,3))\n",
    "sns.barplot(data=data, x='names', y='percent', palette=colors)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Pixels')\n",
    "plt.title('Old data')\n",
    "plt.xticks(rotation=45)\n",
    "#plt.savefig('Number_of_pixels.png', dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b03f5107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dict_normal_names = {0: 'No data',\n",
    "                     1: 'trees', \n",
    "2: 'flooded vegetation',\n",
    "3: 'open water',\n",
    "4: 'settlements', \n",
    "5: 'bare soil',\n",
    "6: 'agriculture', \n",
    "7: 'shrubs',\n",
    "#8: 'grass'\n",
    "}\n",
    "data = change_data.groupby('new_class')['new_class'].agg({'count'}).reset_index()\n",
    "data['percent'] = data['count'].apply(lambda x: x/sum(data['count'])*100)\n",
    "data['names'] = data['new_class'].apply(lambda x: dict_normal_names[x])\n",
    "\n",
    "colors =['black','#117733','#AA4499',  '#88CCEE', '#DE7486',\n",
    "    '#bcbcbc',  '#F9CB9C', '#74b357' \n",
    "         \n",
    "]\n",
    "\n",
    "plt.figure(figsize=(8,3))\n",
    "sns.barplot(data=data, x='names', y='percent', palette=colors)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Pixels')\n",
    "plt.title('New data')\n",
    "plt.xticks(rotation=45)\n",
    "#plt.savefig('Number_of_pixels.png', dpi = 300)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.16"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}