wsf_v1.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2ZcIHg8e6Fb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install landsatxplore\n",
        "!pip install gdal\n",
        "!pip install rasterio"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KqDp6XZIolru",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ERrjpJvLoo_7",
        "colab_type": "text"
      },
      "source": [
        "**IMPORT LIBRARIES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XomWAZrCZNBy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import rasterio\n",
        "from rasterio.plot import show\n",
        "import gdal\n",
        "from gdalconst import GA_ReadOnly \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "import subprocess\n",
        "IMG=2200"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-KYfg4ntowEv",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n26TQ8aDkE4Z",
        "colab_type": "text"
      },
      "source": [
        "**DOWNLOAD REFERENCE DATA SET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZiQhdmXkCWc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget https://land.copernicus.eu/land-files/7ac95361f9ac3cecdf37785bc183ff02dd765a16.zip\n",
        "!unzip /content/7ac95361f9ac3cecdf37785bc183ff02dd765a16.zip\n",
        "!unzip /content/clc2018_clc2018_v2018_20_raster100m.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y4pqwphspfCJ",
        "colab_type": "text"
      },
      "source": [
        "**TRANSFORM REFERENCE DATA**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqiLhKtxwXpJ",
        "colab_type": "code",
        "outputId": "1e5b575a-722e-4fa5-86fc-5ce359eac2bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "!gdalwarp -co \"COMPRESS=DEFLATE\" -t_srs EPSG:32618  '/content/clc2018_clc2018_v2018_20_raster100m/CLC2018_CLC2018_V2018_20.tif' europe-clc.tif"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating output file that is 54709P x 82959L.\n",
            "Processing input file /content/clc2018_clc2018_v2018_20_raster100m/CLC2018_CLC2018_V2018_20.tif.\n",
            "Using internal nodata values (e.g. -32768) for image /content/clc2018_clc2018_v2018_20_raster100m/CLC2018_CLC2018_V2018_20.tif.\n",
            "Copying nodata values from source /content/clc2018_clc2018_v2018_20_raster100m/CLC2018_CLC2018_V2018_20.tif to destination europe-clc.tif.\n",
            "0...10...20...30...40...50...60...70...80...90...100 - done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncy5hH9Ap6HL",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXs_tIo8p7Cb",
        "colab_type": "text"
      },
      "source": [
        "**DOWNLOAD TEMPORAL SCENES OF WEST EUROPE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2wiPXemiz2E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from landsatxplore.earthexplorer import EarthExplorer\n",
        "ee = EarthExplorer('username', 'password')\n",
        "ee.download(scene_id='LC08_L1TP_201024_20180901_20180912_01_T1', output_dir='./data/scene1')\n",
        "ee.download(scene_id='LC08_L1TP_201024_20180629_20180716_01_T1', output_dir='./data/scene2')\n",
        "ee.download(scene_id='LC08_L1TP_201024_20170610_20170627_01_T1', output_dir='./data/scene3')\n",
        "ee.logout()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7C9cp7paqXlt",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BsrQ_i1NqYeY",
        "colab_type": "text"
      },
      "source": [
        "**PREPROCESS SCENES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4mZ8rbR2fuC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!tar xvzf '/content/data/scene1/LC08_L1TP_201024_20180901_20180912_01_T1.tar.gz' -C './data/scene1'\n",
        "!tar xvzf '/content/data/scene2/LC08_L1TP_201024_20180629_20180716_01_T1.tar.gz' -C './data/scene2'\n",
        "!tar xvzf '/content/data/scene3/LC08_L1TP_201024_20170610_20170627_01_T1.tar.gz' -C './data/scene3'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkRc5zcARc-W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!gdalwarp -co \"COMPRESS=DEFLATE\" -t_srs EPSG:32631  /content/clc2018_clc2018_v2018_20_raster100m/CLC2018_CLC2018_V2018_20.tif europe.tif"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RXb8rEuiRdNm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!gdalinfo europe.tif"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHpq2CgJRdKe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=gdal.Open('/content/data/scene1/LC08_L1TP_201024_20180901_20180912_01_T1_B1.TIF',GA_ReadOnly) \n",
        "geoTransform=data.GetGeoTransform() \n",
        "minx=geoTransform[0] \n",
        "maxy=geoTransform[3] \n",
        "maxx = minx + geoTransform[1] * data.RasterXSize \n",
        "miny = maxy + geoTransform[5] * data.RasterYSize \n",
        "subprocess.call('gdal_translate -projwin ' + ' '.join([str(x) for x in [minx, maxy, maxx, miny]]) + ' -of GTiff europe.tif img_out.tif', shell=True) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EGspQK55RdFj",
        "colab_type": "code",
        "outputId": "194bc3a1-7694-4099-f2ea-0d6811ecd8f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/img_out.tif' 'Y.tif'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input file size is 2262, 2287\n",
            "Warning 6: driver GTiff does not support creation option QUALITY\n",
            "Warning 6: driver GTiff does not support creation option PROGRESSIVE\n",
            "0...10...20...30...40...50...60...70...80...90...100 - done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o1n9tLoWYwRt",
        "colab_type": "text"
      },
      "source": [
        "SCENE 1 PREPROCESSING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yn9chtqDWWrO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene1/LC08_L1TP_201024_20180901_20180912_01_T1_B2.TIF' 's1b2.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene1/LC08_L1TP_201024_20180901_20180912_01_T1_B3.TIF' 's1b3.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene1/LC08_L1TP_201024_20180901_20180912_01_T1_B4.TIF' 's1b4.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene1/LC08_L1TP_201024_20180901_20180912_01_T1_B5.TIF' 's1b5.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene1/LC08_L1TP_201024_20180901_20180912_01_T1_B6.TIF' 's1b6.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene1/LC08_L1TP_201024_20180901_20180912_01_T1_B7.TIF' 's1b7.tif'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fkz6I-M5X_0l",
        "colab_type": "text"
      },
      "source": [
        "SCENE 2 PREPROCESSING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V9z8XrvsXIdn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene2/LC08_L1TP_201024_20180629_20180716_01_T1_B2.TIF' 's2b2.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene2/LC08_L1TP_201024_20180629_20180716_01_T1_B3.TIF' 's2b3.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene2/LC08_L1TP_201024_20180629_20180716_01_T1_B4.TIF' 's2b4.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene2/LC08_L1TP_201024_20180629_20180716_01_T1_B5.TIF' 's2b5.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene2/LC08_L1TP_201024_20180629_20180716_01_T1_B6.TIF' 's2b6.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene2/LC08_L1TP_201024_20180629_20180716_01_T1_B7.TIF' 's2b7.tif'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TB-9A3F5YEFb",
        "colab_type": "text"
      },
      "source": [
        "SCENE 3 PREPROCESSING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v1Ki05QaYGKO",
        "colab_type": "code",
        "outputId": "4d7baf46-4a18-4d1f-c724-b9f65e118210",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "source": [
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene3/LC08_L1TP_201024_20170610_20170627_01_T1_B2.TIF' 's3b2.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene3/LC08_L1TP_201024_20170610_20170627_01_T1_B3.TIF' 's3b3.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene3/LC08_L1TP_201024_20170610_20170627_01_T1_B4.TIF' 's3b4.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene3/LC08_L1TP_201024_20170610_20170627_01_T1_B5.TIF' 's3b5.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene3/LC08_L1TP_201024_20170610_20170627_01_T1_B6.TIF' 's3b6.tif'\n",
        "!gdal_translate -co QUALITY=100 -co PROGRESSIVE=ON -outsize 2200 2200 -r bilinear '/content/data/scene3/LC08_L1TP_201024_20170610_20170627_01_T1_B7.TIF' 's3b7.tif'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input file size is 8091, 8171\n",
            "Warning 6: driver GTiff does not support creation option QUALITY\n",
            "Warning 6: driver GTiff does not support creation option PROGRESSIVE\n",
            "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
            "Input file size is 8091, 8171\n",
            "Warning 6: driver GTiff does not support creation option QUALITY\n",
            "Warning 6: driver GTiff does not support creation option PROGRESSIVE\n",
            "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
            "Input file size is 8091, 8171\n",
            "Warning 6: driver GTiff does not support creation option QUALITY\n",
            "Warning 6: driver GTiff does not support creation option PROGRESSIVE\n",
            "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
            "Input file size is 8091, 8171\n",
            "Warning 6: driver GTiff does not support creation option QUALITY\n",
            "Warning 6: driver GTiff does not support creation option PROGRESSIVE\n",
            "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
            "Input file size is 8091, 8171\n",
            "Warning 6: driver GTiff does not support creation option QUALITY\n",
            "Warning 6: driver GTiff does not support creation option PROGRESSIVE\n",
            "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
            "Input file size is 8091, 8171\n",
            "Warning 6: driver GTiff does not support creation option QUALITY\n",
            "Warning 6: driver GTiff does not support creation option PROGRESSIVE\n",
            "0...10...20...30...40...50...60...70...80...90...100 - done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7trWUiYUrnF5",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzenEZ8bZGHq",
        "colab_type": "text"
      },
      "source": [
        "**HELPER** **FUNCTIONS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMUtteXFXIl3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#CALCULATE SPECTRAL INDICES LIKE NDVI, NDBI etc..\n",
        "def calculate_spectral_index(band1,band2):\n",
        "  ndbi=np.where((band1+band2)==0.,0,(band1-band2)/(band1+band2))\n",
        "  return ndbi\n",
        "\n",
        "def read_band_data(fileName):\n",
        "        a=gdal.Open(fileName)\n",
        "        a1=a.GetRasterBand(1).ReadAsArray().astype(dtype='float')\n",
        "        return a1\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HXBMV0wXIhf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#CALCULATE SPECTRAL INDICES FOR A SINGLE SCENE\n",
        "def calculate_all_indices_for_a_scene(root_name):\n",
        "        temp_list=[]\n",
        "        b2=read_band_data((root_name+'b'+str(2)+'.tif'))\n",
        "        b3=read_band_data((root_name+'b'+str(3)+'.tif'))\n",
        "        b4=read_band_data((root_name+'b'+str(4)+'.tif'))\n",
        "        b5=read_band_data((root_name+'b'+str(5)+'.tif'))\n",
        "        b6=read_band_data((root_name+'b'+str(6)+'.tif'))\n",
        "        b7=read_band_data((root_name+'b'+str(7)+'.tif'))\n",
        "\n",
        "        temp_list.append(calculate_spectral_index(b6,b5))\n",
        "        temp_list.append(calculate_spectral_index(b3,b5))\n",
        "        temp_list.append(calculate_spectral_index(b5,b4))\n",
        "        temp_list.append(calculate_spectral_index(b6,b7))\n",
        "        temp_list.append(calculate_spectral_index(b4,b2))\n",
        "        temp_list.append(calculate_spectral_index(b3,b2))\n",
        "\n",
        "        return temp_list"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMAFPZnw3bGf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scene1_indices=calculate_all_indices_for_a_scene('s1')\n",
        "scene2_indices=calculate_all_indices_for_a_scene('s2')\n",
        "scene3_indices=calculate_all_indices_for_a_scene('s3')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zczynFCZEc0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#CALCULATE TEMPORAL STATISTICS LIKE MEAN, MIN, MAX etc..\n",
        "def calc_temporal_statistics(scene1,scene2,scene3):\n",
        "  temp_min = np.zeros(shape=(IMG,IMG))\n",
        "  temp_max = np.zeros(shape=(IMG,IMG))\n",
        "  temp_mean = np.zeros(shape=(IMG,IMG))\n",
        "  temp_std = np.zeros(shape=(IMG,IMG))\n",
        "  temp_mean_slope = np.zeros(shape=(IMG,IMG))\n",
        "\n",
        "  for i in tqdm(range(0,IMG)):\n",
        "    for j in range(0,IMG):\n",
        "      x=scene1[i][j]\n",
        "      y=scene2[i][j]\n",
        "      z=scene3[i][j]\n",
        "\n",
        "      temp_min[i][j]=np.amin([x,y,z])\n",
        "      temp_max[i][j]=np.amax([x,y,z])\n",
        "      temp_mean[i][j]=np.mean([x,y,z])\n",
        "      temp_std[i][j]=np.std([x,y,z])  \n",
        "      temp_mean_slope[i][j]=np.average(np.diff([x,y,z]))\n",
        "      \n",
        "  return (temp_min,temp_max,temp_mean,temp_std,temp_mean_slope)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWPgK4qcsnKc",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ir0L4dxqsoGp",
        "colab_type": "text"
      },
      "source": [
        "**COMPUTE FEATURES BASED ON TEMPORAL CHARACTERSITCS OF DIFFERENT SCENES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhY9YsGKZEig",
        "colab_type": "code",
        "outputId": "982126dd-aa5b-4b39-b80e-b494897f3fbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "b7786fa167e84a9b928f4c3da3e67f23",
            "cfa34636a980489c88c9805d9f4419fb",
            "10f2dff24b174faa9ac1f65962759132",
            "00f849d538a544a7907a9be1e2870cd1",
            "e6112c617f6247cc8788d15ee9a4cb1f",
            "c82e2e2693a147c0b85b108adf29237d",
            "368250ed918841d58e16fb9767404b17",
            "085a1aaa97e545c185a3a0950183a86d"
          ]
        }
      },
      "source": [
        "ndbi_statistics=calc_temporal_statistics(scene1_indices[0],scene2_indices[0],scene3_indices[0])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b7786fa167e84a9b928f4c3da3e67f23",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2200), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2yYnqwIojt7",
        "colab_type": "code",
        "outputId": "3a90474e-1f29-487f-d731-0e23731a6d30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "047b8b39bcf144cc9c5b711251fd26da",
            "5d6bf9f5dc7349ec9b9c0c55d0525cb0",
            "59c68bb904024e3e86273b614bdaaf7a",
            "faf70f41eac942edb70f02e0eadc411d",
            "25f7d8c53cd8473a93f444f20f440e7b",
            "833fed91ef734891adbcb7235b2c2917",
            "f688a3157695450b9d46e25ac9348fe4",
            "cb43253c1ce54a1a88679d5d840d7db4"
          ]
        }
      },
      "source": [
        "mndwi_statistics=calc_temporal_statistics(scene1_indices[1],scene2_indices[1],scene3_indices[1])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "047b8b39bcf144cc9c5b711251fd26da",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2200), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7pHJiyEGoj8y",
        "colab_type": "code",
        "outputId": "52176965-a0fd-4db0-fe9c-1ab344dcbbd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "60b6e4a4d1024e36a2455052aec96804",
            "9bafa91e8f2a42b58a216755e3028241",
            "924993aa5aee4e4aadc3ee4d6a6ac0ea",
            "e6187482de444ea1ad21df112acb62e5",
            "5ed9836b40ba426aa7d9b9bf3317c768",
            "9fe99f865c9c4185bec51c7a6c347662",
            "518c54ddf55147428a14c90e148b8f15",
            "cd7c1f75cf38411dad6ea3c6202aa1b5"
          ]
        }
      },
      "source": [
        "ndvi_statistics=calc_temporal_statistics(scene1_indices[2],scene2_indices[2],scene3_indices[2])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "60b6e4a4d1024e36a2455052aec96804",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2200), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzaccrvJokF3",
        "colab_type": "code",
        "outputId": "9365fbc3-d121-4e62-890e-61e5c6c223f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "ee89b38e89a7467c84c6a5e1d25f4cc3",
            "4aa2fda81faa4c1bab065c0d050ce97b",
            "0f61c1c9d64143c39e4bd4a938f238c2",
            "beaddd95021b41d48bb2b9eca40a9cd1",
            "7d2f83d9927c41c18ade1f9e398cbd0a",
            "30349fa2130343389953a99fd914e2b4",
            "3ca26894fc704af58863f1c07860b23c",
            "55836585277544ab90e81914bcfac7fe"
          ]
        }
      },
      "source": [
        "ndmir_statistics=calc_temporal_statistics(scene1_indices[3],scene2_indices[3],scene3_indices[3])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ee89b38e89a7467c84c6a5e1d25f4cc3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2200), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anhKY7_PokOX",
        "colab_type": "code",
        "outputId": "ab1370dd-57d9-441a-c823-c0839a52a08f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "6fe24b63ce9e4b62aa59b0df1b07b1cf",
            "8b8552571dc44d7da25195c61151b083",
            "a38fadf23ca740f0b38699e516fc1506",
            "6d8882a528914c90b70d9976d971c2cc",
            "6195bbdd34ea4ee68d567d58517bcbdc",
            "92929aada58f452da2fbd9e42dc5b2b6",
            "38b0503e4c564b09a65b230c7a15d01d",
            "fdbf3ce2b8704fee9b2d6b29d8755e40"
          ]
        }
      },
      "source": [
        "ndrb_statistics=calc_temporal_statistics(scene1_indices[4],scene2_indices[4],scene3_indices[4])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6fe24b63ce9e4b62aa59b0df1b07b1cf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2200), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHlPygWCo0b8",
        "colab_type": "code",
        "outputId": "9b8e230d-f60f-4ffc-9973-ac450c7698a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "e6b762234ed54e20a79f07923a40fe0f",
            "818c5f7f2a7445588ab012e7f6997e1c",
            "a5954299456b43f3a8151e001d7fba36",
            "68ac582f189541f2887232a829eb9f0f",
            "48cac27dd5654eb287187e91b38904ac",
            "dde5ec525a1e4191846723e405c0f956",
            "c20d3e40169a492f8b06f5f8c3daaecc",
            "56c74ec4ed92411f88de5a087a33f7cb"
          ]
        }
      },
      "source": [
        "ndgb_statistics=calc_temporal_statistics(scene1_indices[5],scene2_indices[5],scene3_indices[5])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e6b762234ed54e20a79f07923a40fe0f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=2200), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rujfAHZzo3CQ",
        "colab_type": "text"
      },
      "source": [
        "**EXAMPLE: VISUALIZING TEMPORAL MAX OF NDBI**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwkHbgKtZEn6",
        "colab_type": "code",
        "outputId": "2e7890cc-88ce-44b9-e051-1e6dc68b2840",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.imshow(ndbi_statistics[2]) "
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc7593f47b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LihHodruUSiVisRiNRkPXn5ubm2QyGfL5vK4v3W63Mki/9a1vsbOzQz6fZ2Fh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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQgdZocAtfco",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__BiV1LotgV-",
        "colab_type": "text"
      },
      "source": [
        "**PREPARING TRAINING DATA**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APNfZTH_AkXJ",
        "colab_type": "code",
        "outputId": "6d4074e2-1168-43a9-ff5b-cb77fc6f7930",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "tif2=gdal.Open('Y.tif')\n",
        "Y_raster = tif2.GetRasterBand(1).ReadAsArray()\n",
        "Y_raster[Y_raster <= 150] = 0\n",
        "Y_raster[Y_raster > 150] = 255\n",
        "print(\"UNIQUE CLASSES: \", np.unique(Y_raster))\n",
        "Y_list=Y_raster.flatten().tolist()\n",
        "y=np.reshape(Y_list,(IMG*IMG,1))\n",
        "print(y.shape)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "UNIQUE CLASSES:  [  0 255]\n",
            "(4840000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EdYjfkpkZEt4",
        "colab_type": "code",
        "outputId": "58a41db4-c8b8-41cf-d8cd-e5f5e93bffdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "7703fc27565e43e9ac59acceba056dde",
            "c1ff71f54e5b4cdfbdccaf98e22203aa",
            "7fd9a127e1ab4de09e4bbb141abfbbec",
            "23105cf89979461581cb405d53cbef30",
            "620f8407bb7443d48a205042be55c9e0",
            "aedeac5d5bb449088e2dae10ed0ef39c",
            "cc1f646088bb4b6e8734e4b801949f7e",
            "e5508cc43ff64e77bb6c1d1c000a5ba1"
          ]
        }
      },
      "source": [
        "records=[]\n",
        "for i in tqdm(range(0,5)):\n",
        "          records.append(ndbi_statistics[i].flatten().tolist())\n",
        "          records.append(mndwi_statistics[i].flatten().tolist())\n",
        "          records.append(ndvi_statistics[i].flatten().tolist())\n",
        "          records.append(ndmir_statistics[i].flatten().tolist())\n",
        "          records.append(ndrb_statistics[i].flatten().tolist())\n",
        "          records.append(ndgb_statistics[i].flatten().tolist())\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7703fc27565e43e9ac59acceba056dde",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCRBqxTs6-69",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "record=np.column_stack(records)\n",
        "X=np.append(record, y, axis=1)\n",
        "print('FULL TRAINING DATA SHAPE: ')\n",
        "print(X.shape)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TT2Fp7NR8GF9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#np.savetxt('X.csv',record[np.random.choice(np.shape(X)[0], 1000, replace=False), :],delimiter=\",\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZ7h2HAlTHWU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_small=X[np.random.choice(np.shape(X)[0], 10000, replace=False), :]\n",
        "X_train=X_small[:,0:30]\n",
        "Y_train=X_small[:,30]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yg-Z1goXuRJG",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0shhi6ehR_RF",
        "colab_type": "text"
      },
      "source": [
        "**CLASSIFICATION :  SUPPORT VECTOR MACHINE - RBF KERNEL**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VuSXgMogupRP",
        "colab_type": "text"
      },
      "source": [
        "TRAINING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Kf6auIsZFFH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "c06e4bc9-e3f2-4771-a1bf-d94c8bdcf66f"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "svclassifier = SVC(kernel='rbf',C=500,gamma=100)\n",
        "svclassifier.fit(X_train, Y_train)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=500, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma=100, kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ai8LA7PsuzZ0",
        "colab_type": "text"
      },
      "source": [
        "PREDICTIONS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvnV_cJBu0ww",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_pred = svclassifier.predict(X[:,0:30])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wx-li3Kju3hH",
        "colab_type": "text"
      },
      "source": [
        "METRICS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anBVKraKSsgJ",
        "colab_type": "code",
        "outputId": "b6fcb1d7-d880-440f-b972-673305d65b01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(X[:,30], Y_pred))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.73      0.37      0.49    880598\n",
            "       255.0       0.87      0.97      0.92   3959402\n",
            "\n",
            "    accuracy                           0.86   4840000\n",
            "   macro avg       0.80      0.67      0.70   4840000\n",
            "weighted avg       0.85      0.86      0.84   4840000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7GCaXyanvEny",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "86xmSFNlvGb3",
        "colab_type": "text"
      },
      "source": [
        "**VISUALIZATION OF RESULTS - HUMAN SETTLEMENT - WEST EUROPE AREA**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LMU13kbZ05-u",
        "colab_type": "text"
      },
      "source": [
        "ORIGINAL RGB IMAGE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93s76rq5ZFLv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "58556e35-797d-4b47-b43e-361f6bdb0834"
      },
      "source": [
        "s4=gdal.Open('s2b4.tif')\n",
        "red = s4.GetRasterBand(1).ReadAsArray()\n",
        "r=red.astype(dtype='int')\n",
        "\n",
        "s3 = gdal.Open('s2b3.tif')\n",
        "green = s3.GetRasterBand(1).ReadAsArray()\n",
        "g=green.astype(dtype='int')\n",
        "\n",
        "s2 = gdal.Open('s2b2.tif')\n",
        "blue = s2.GetRasterBand(1).ReadAsArray()\n",
        "b=blue.astype(dtype='int')\n",
        "\n",
        "def normalize(array):\n",
        "    array_min, array_max = array.min(), array.max()\n",
        "    return ((array - array_min)/(array_max - array_min))\n",
        "redn = normalize(r)\n",
        "greenn = normalize(g)\n",
        "bluen = normalize(b)\n",
        "rgb = np.dstack((redn, greenn, bluen))\n",
        "plt.imshow(rgb)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc758d1c400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GkZOFZ4yZuhaeOJ7ROkdbOV68Gsm5yLnNPnXvB+XUeQilebgZA/PK0i4sXYLdEKi9pW0N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y26QCmZ1llQhsM846zhumsYc+Y4BdpGY7WjNeBjRBsBQOcQuRx61q7w8d7z9HLFq+PMNAd+/Lrn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\niSWui2WAWGKJJa6LZYBYYoklrov/H7HL20nhBnRaAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwGfrvX20_XM",
        "colab_type": "text"
      },
      "source": [
        "CLASSIFICATION RESULTS : BLACK=BUILT UP, WHITE=OTHERS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULNtkFpCZFIf",
        "colab_type": "code",
        "outputId": "46248366-c5a6-476a-e1b5-0fb3bba50fb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.gray()\n",
        "plt.imshow(np.reshape(Y_pred,(2200,2200)))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc7595e47b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w88bmzZtzqigiNuuFVOsas5gUexYNDRZ7MIwxU/oGfsg/UOonyIlCuIRV7Brl\ndHL3xXiT8fUDAIYIx34Au4noLCLaim5fjG8CeBDANiLaSkRnonNk7s+v9jiYKr0vBOci5E8w05hp\n7eOxa1xe/lAbYvVNSe8rI2Q62fXN8RPUJFZW6L6O6fBsXTDl7ovxa0S0HQADOAbgowDAzIeI6C4A\nT6Dbku9aZn65z+c6APcBOAPAPmY+VLMhpmMv9KBjnUAqDGJpfdqIy/npqseQ1nZYSoip4zVMs5jj\nUlKPmrieS62yx2xD676Qqvti9OlvAnCT4/i9AO5Nql0Ckk6Res4ldCQe/dhsFKqra1YuMUlc5cbq\nlZO/K41EOxp7EE8ppFaRxa+kLFXRXE5Ju/O6nJC5jr8SlT416lCzXimhVqmwHsO5KwmB1sp7HVi8\ngMjt2Ob1Up9DyHdQEm6UIPE/+MrMKde+bgo1u8YAHFMgpGhzqyJMFi8gYtQIjUnyDXWekpm/lBSh\nMlYERNr2sZ5VLlLHamr4eUmstIDInTl9332fBwYh4Zp5fANubM0jhbE69diDpXb+qYK+VIttmZUW\nECUzks+OjjnXUmdeaWjN9JWM2eHGyH9pA2TMCNDSWLyAqKElmNRYZ1ADX4jUVb/UEGisXJ+5lCs8\npprhS9PGyGnH0qMoixcQOYPFZwpIr5MQUlNTSfGHxNolVYdd97WGXyJUP+kzqaGhpTJW9Kh1Fi8g\ngLyVgblhNkn+oVkjN8xpCif7vyttDvbKTTvfGoTuu6TuMT+QhBa0oKWwWAHhWnGYep15fY161MjP\nztv2O/j8IykaUWhNg2QNQy1SoivDn/msU+romkRqCJtVZ7ECwhVmHIip2JIVfmOou5L6hfKWREFy\n6jyVA7QGuTZ9LGRZusZlVVmkgCixs2MdbBAgUmeg9Ji0fmYeEu3E5cBMDZ3aWoNU2NZCer9S2pdb\nvsuMW1fzAliAgHjooYdedazEzpauRXDNqtKFM6WUdkrJAA8JmlC6Majh+Iydk04oc7S/ZZoXEDt2\n7Kian3RWtv+k+bkGd060xP6c2lFD0QjJdTWoHWKUCruQdpRr3q0rzQuI2vhCnLXCWKlahsTuLzEb\nUuoiOZ8T8aklKEImkDSSJbmXKhxOsVYCwpxVp1YlbTs3dTBLBm6L5kDNeoW0vznWRqwDayUgxrR1\nU8qWhhNrr88oyT83v5qDsdYirSVEa1ohd+Ocv6FTm+YcI6JH+uMXENG/Gef+0rhmBxE9Rt3GObfQ\nAp9Q6QKkqcqzy/WZVBJnZq6QyonuhCjtLusejchFsjfnFwD8LwC3DweY+beHz0R0M4DnjfRPMfN2\nRz63Avg9AN9A92apKwB8JdMjAdkAAAioSURBVL3KZZgdZS61vKTclGt9Woptt4ccsamLkVxOwZi2\nJGlTzj10tU+FRBpFG+f0WsAHAdwRyoO6l9y+gZkf4O4J3Q7g/enVLUdi17cQ+/dRqsWYazxKwsWS\nukmFWa2FT2a5QxoVCGWU+iDeC+AkMx8xjm0lon8ior8novf2x85Dt1nOQHDjHBLsizEmYzrVhvyn\n7rhTzKB2e8dY1CQpX4VCPUoFxFU4XXs4AeAXmfldAP47gL8mojekZsoT7YuRQw1beI56TDFQp1pI\nllK+UobEB+GEiF4D4LcA/GwlEzO/CODF/vNDRPQUgLeh2yTnzcblwY1zWsGlIpeG02pHUsZU4xWl\nRIP4bwC+xcw/Mx2IaDN1O3mDiN6CbuOcp5n5BIAXiOiS3m9xNYAvF5Q9CWNFLWp4+EsGfA1tYq4g\n1AKDX4tGEua8A8D/B/BLRPQMEX2kP7Ubr3ZOvg/Ao33Y8/8B+BgzDw7O3wfwf9Dt+P0UZohgTMUY\nHnnz2pQFVDZDWaUDbS6NRDWhacndOAfM/DuOY3cDuNuT/iCAdybWb3HkhDCnCHna6XWgKRLWaiXl\nFIwx8HwrAKVlSbz7dt45GsbU6n9rKyJbqkstVECMSO0fKdmfY2WagkHyAyUzXe21CWPQ2jqHlupS\nCxUQI1J7EVJqtML3OSQs5urkrc++rddvLFRANEjt3zHYtDbT5fhSpqa1ezYVKiAapOb6iSl+sVma\nV66jVRkfFRArTk4YtGZ+KXlJnI5SU0mpgwoI5TTm1FRSnY5TaBLrLoRUQKwgY/swXKyq2r+q7ZKi\nAmIFkfow1n12VOKogFgxct8ANVcdlLZRAaFkExIEsdfdKctABcSKMeWvPGOvqQu9MEdZBioglJ+R\nM3BjGkJry6FzWGeNJ/uFMcp6Ivm5+NIFgs2qtScFFRAKgPQ3U63zoFkn1MRQALx6S8J1VquVU0je\nKHU+EX2diJ4gokNE9PH++LlEdICIjvT/N/XHibqNcY4S0aNEdJGR154+/REi2jNes5QcdP8IxUai\nQbwE4A+Y+UIAlwC4loguBHA9gPuZeRuA+/vvAHAlundRbgNwDboNc0BE5wL4FIB3A7gYwKcGoaIo\nS2HdNCvJxjknmPnh/vOPATyJbk+LXQBu65PdhlMb4ewCcDt3PADgHOo2zrkcwAFmfo6ZfwTgALrd\ntRRlMUy918fcJPkgiOgCAO9Ct33elv5t1QDwfQBb+s/nAfiecdmwSY7vuKucWTfOUZQQ62R+iQUE\nEb0O3QtpP8HML5jnuLtj1e5ayxvnKMo6IRIQRPRadMLhi8z8pf7wyd50GPbefLY/fhzA+cblwyY5\nvuOKojSKJIpBAD4P4Elm/oxxaj+AIRKxB6c2wtkP4Oo+mnEJgOd7U+Q+AJcR0abeOXlZf0xRlEaR\nLJT6VQAfAvBYvyEOAPwxgD8FcFe/kc530e3yDQD3AtiJboOcnwD4MAAw83NE9CcAHuzTfdrYVEdZ\nA+Z+Xdzc5S8RycY5/wjA57a91JGeAVzryWsfgH0pFVRWB/t1cXO8Jn+uspeKrqRUZmHKd0vqCtF8\nVEAoszP2bG5uCqSaQxoqIJS1wNYgSrcZXBdUQCiLYYw9PlSrCKM/91YWQ+1X8qtgiKMahLJ4cjUL\nNS3iqIBQFk9IE1inN1+NgQoIZS1R4SBDfRDKSuPa/3P4r0IijgoIZa1QoZCGmhiKonhRAaEoBhrZ\nOB0VEIpioCbI6aiAUBQB66pZUOsSk4h+DODw3PUo5I0Afjh3JQpZhTYAq9GO2m34L8zsfLfjEqIY\nh5l5Y+5KlEBEB7UNbbAK7ZiyDWpiKIriRQWEoiheliAg9s5dgQpoG9phFdoxWRuad1IqijIfS9Ag\nFEWZCRUQiqJ4aVZAENEVRHSYiI4S0fXxK+aDiI4R0WNE9AgRHeyPnUtEB4joSP9/U3+ciOiWvl2P\nEtFFM9Z7HxE9S0SPG8eS601Ee/r0R4hoj6usidtwIxEd75/HI0S00zh3Q9+Gw0R0uXF8tv5GROcT\n0deJ6AkiOkREH++Pz/8shjf+tvQH4AwATwF4C4AzAfwzgAvnrlegvscAvNE69mcAru8/Xw/gf/Sf\ndwL4Crq9Ri4B8I0Z6/0+ABcBeDy33gDOBfB0/39T/3nTzG24EcAfOtJe2PelswBs7fvYGXP3NwBv\nAnBR//n1AL7d13X2Z9GqBnExgKPM/DQz/xTAnQB2zVynVHYBuK3/fBuA9xvHb+eOBwCcM+xxOjXM\n/A8A7N3NUut9OYADzPwcM/8IwAEAV4xf+w5PG3zsAnAnM7/IzN9Bt/vbxZi5vzHzCWZ+uP/8YwBP\notv5fvZn0aqAOA/A94zvz/THWoUBfJWIHiKia/pjW7jbkxQAvg9gS/+59bal1rvV9lzXq9/7BtUc\nC2gDEV0A4F0AvoEGnkWrAmJpvIeZLwJwJYBrieh95knu9L/FxZOXWm8AtwJ4K4DtAE4AuHne6sgg\notcBuBvAJ5j5BfPcXM+iVQFxHMD5xvc398eahJmP9/+fBXAPOpX15GA69P+f7ZO33rbUejfXHmY+\nycwvM/MrAD6H7nkADbeBiF6LTjh8kZm/1B+e/Vm0KiAeBLCNiLYS0ZkAdgPYP3OdnBDR2UT0+uEz\ngMsAPI6uvoMXeQ+AL/ef9wO4uvdEXwLgeUONbIHUet8H4DIi2tSr8pf1x2bD8ul8AN3zALo27Cai\ns4hoK4BtAL6Jmfsbdb8l/zyAJ5n5M8ap+Z/FVJ7aDM/uTnTe3KcAfHLu+gTq+RZ0Xu9/BnBoqCuA\nnwdwP4AjAL4G4Nz+OAH4i75djwHYmLHud6BTwf8dnb36kZx6A/hddA6/owA+3EAb/qqv46P9YHqT\nkf6TfRsOA7iyhf4G4D3ozIdHATzS/+1s4VnoUmtFUby0amIoitIAKiAURfGiAkJRFC8qIBRF8aIC\nQlEULyogFEXxogJCURQv/wGkFZ5glt79lwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}