AgriField_DataPreparation.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dzjXjwBrCas7"
      },
      "outputs": [],
      "source": [
        "!pip install rasterio\n",
        "\n",
        "!pip install radiant_mlhub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VfjSdkQWCiSz",
        "outputId": "63752614-f580-4adc-b31d-e9953c158907"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.9.1\n"
          ]
        }
      ],
      "source": [
        "from IPython.display import clear_output\n",
        "\n",
        "!python --version\n",
        "\n",
        "!wget -O mini.sh https://repo.anaconda.com/miniconda/Miniconda3-py39_4.9.2-Linux-x86_64.sh\n",
        "\n",
        "!chmod +x mini.sh\n",
        "\n",
        "!bash ./mini.sh -b -f -p /usr/local\n",
        "\n",
        "!conda install -q -y jupyter\n",
        "\n",
        "!conda install -q -y google-colab -c conda-forge\n",
        "\n",
        "!python -m ipykernel install --name \"py39\" --user\n",
        "\n",
        "clear_output()\n",
        "\n",
        "!python --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZPciFbDRCkW8"
      },
      "outputs": [],
      "source": [
        "!pip install rasterio==1.3.0\n",
        "\n",
        "!pip install radiant_mlhub==0.5.2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kPZ_osoaCuin"
      },
      "source": [
        "##Downloading Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9xtqpngCCm__",
        "outputId": "84e1693c-1531-4501-cbaa-2de4b2e0534a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting my_downloader.sh\n"
          ]
        }
      ],
      "source": [
        "%%writefile my_downloader.sh\n",
        "\n",
        "import rasterio\n",
        "\n",
        "from radiant_mlhub import Dataset\n",
        "\n",
        "import radiant_mlhub\n",
        "\n",
        "print(rasterio.__version__, radiant_mlhub.__version__)\n",
        "\n",
        "import os\n",
        "\n",
        "import glob\n",
        "\n",
        "Full_bands = [ 'B01','B02', 'B03', 'B04','B05', 'B06', 'B07', 'B08','B8A', 'B09', 'B11', 'B12']\n",
        "\n",
        "selected_bands = Full_bands \n",
        "\n",
        "#define dataset collection_id , assets and necessary paths to collections\n",
        "\n",
        "main = 'ref_agrifieldnet_competition_v1'\n",
        "\n",
        "assets = ['field_ids','raster_labels']\n",
        "\n",
        "source_collection = f'{main}_source'\n",
        "\n",
        "train_label_collection = f'{main}_labels_train'\n",
        "\n",
        "test_label_collection = f'{main}_labels_test'\n",
        "\n",
        "api_key = \"58eba2df5be9b75483d17bf844d250b36d641d94c4a6584c025f9bd2953dfd59\"\n",
        "\n",
        "os.environ['MLHUB_API_KEY'] = api_key # getpass.getpass(prompt=\"MLHub API Key: \")\n",
        "\n",
        "dataset = Dataset.fetch(main)\n",
        "\n",
        "my_filter = dict(\n",
        "\n",
        "ref_agrifieldnet_competition_v1_labels_train=assets,\n",
        "\n",
        "ref_agrifieldnet_competition_v1_labels_test=[assets[0]],\n",
        "\n",
        "ref_agrifieldnet_competition_v1_source=selected_bands\n",
        "\n",
        ")\n",
        "\n",
        "dataset.download(collection_filter=my_filter)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B0oKO-WmCrJb",
        "outputId": "6eefe5e9-b921-4574-dd53-4a59d9985284"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.3.0 0.5.2\n",
            "unarchive ref_agrifieldnet_competition_v1.tar.gz: 100% 6186/6186 [00:00<00:00, 62955.01it/s]\n",
            "filter by collection ids and asset keys: 231716it [00:00, 1292546.05it/s]\n",
            "download assets: 100% 17641/17641 [21:46<00:00, 13.50it/s]\n"
          ]
        }
      ],
      "source": [
        "!python my_downloader.sh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WYb60XLPCtFj"
      },
      "outputs": [],
      "source": [
        "from radiant_mlhub import Dataset\n",
        "import radiant_mlhub\n",
        "import rasterio\n",
        "#import libraries\n",
        "import os\n",
        "import glob\n",
        "import json\n",
        "import getpass\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VkpK39FdCzBg"
      },
      "outputs": [],
      "source": [
        "\n",
        "Full_bands = ['B01', 'B02', 'B03', 'B04','B05', 'B06', 'B07', 'B08','B8A', 'B09', 'B11', 'B12']\n",
        "\n",
        "selected_bands =Full_bands \n",
        "selected_bands"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ziJnfSlmDBgi"
      },
      "outputs": [],
      "source": [
        "#define dataset collection_id , assets and necessary paths to collections\n",
        "\n",
        "main = 'ref_agrifieldnet_competition_v1'\n",
        "\n",
        "assets = ['field_ids','raster_labels']\n",
        "\n",
        "source_collection = f'{main}_source'\n",
        "train_label_collection = f'{main}_labels_train'\n",
        "test_label_collection = f'{main}_labels_test'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N3MxO8odDERo"
      },
      "outputs": [],
      "source": [
        "#load collection json and retrieve all unique folder ids \n",
        "#use all unique folder ids to create a list of field and label paths for all tiles\n",
        "\n",
        "with open (f'{main}/{train_label_collection}/collection.json') as f:\n",
        "    train_json = json.load(f)\n",
        "    \n",
        "train_folder_ids = [i['href'].split('_')[-1].split('.')[0] for i in train_json['links'][4:]]\n",
        "\n",
        "train_field_paths = [f'{main}/{train_label_collection}/{train_label_collection}_{i}/field_ids.tif' for i in train_folder_ids]\n",
        "train_label_paths = [f'{main}/{train_label_collection}/{train_label_collection}_{i}/raster_labels.tif' for i in train_folder_ids]"
      ]
    },
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              "     unique_folder_id                                        field_paths\n",
              "0               28852  ref_agrifieldnet_competition_v1/ref_agrifieldn...\n",
              "1               d987c  ref_agrifieldnet_competition_v1/ref_agrifieldn...\n",
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              "4               7575d  ref_agrifieldnet_competition_v1/ref_agrifieldn...\n",
              "...               ...                                                ...\n",
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              "1162            e3ad4  ref_agrifieldnet_competition_v1/ref_agrifieldn...\n",
              "1163            3ab12  ref_agrifieldnet_competition_v1/ref_agrifieldn...\n",
              "1164            83a33  ref_agrifieldnet_competition_v1/ref_agrifieldn...\n",
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          },
          "metadata": {},
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      ],
      "source": [
        "#create dataset for folder_ids and field_paths\n",
        "\n",
        "competition_train_data = pd.DataFrame(train_folder_ids, columns=['unique_folder_id'])\n",
        "competition_train_data['field_paths'] = train_field_paths\n",
        "competition_train_data"
      ]
    },
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      "execution_count": null,
      "metadata": {
        "id": "AmU_tjBFDJNU"
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        "with open (f'{main}/{test_label_collection}/collection.json') as f:\n",
        "    test_json = json.load(f)\n",
        "    \n",
        "test_folder_ids = [i['href'].split('_')[-1].split('.')[0] for i in test_json['links'][4:]]\n",
        "\n",
        "test_field_paths = [f'{main}/{test_label_collection}/{test_label_collection}_{i}/field_ids.tif' for i in test_folder_ids]"
      ]
    },
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              "    unique_folder_id                                        field_paths\n",
              "0              6199c  ref_agrifieldnet_competition_v1/ref_agrifieldn...\n",
              "1              6c81d  ref_agrifieldnet_competition_v1/ref_agrifieldn...\n",
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              "4              65812  ref_agrifieldnet_competition_v1/ref_agrifieldn...\n",
              "..               ...                                                ...\n",
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              "705            f87d2  ref_agrifieldnet_competition_v1/ref_agrifieldn...\n",
              "706            b13db  ref_agrifieldnet_competition_v1/ref_agrifieldn...\n",
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              "      <th>704</th>\n",
              "      <td>30f20</td>\n",
              "      <td>ref_agrifieldnet_competition_v1/ref_agrifieldn...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>705</th>\n",
              "      <td>f87d2</td>\n",
              "      <td>ref_agrifieldnet_competition_v1/ref_agrifieldn...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>706</th>\n",
              "      <td>b13db</td>\n",
              "      <td>ref_agrifieldnet_competition_v1/ref_agrifieldn...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>707 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4fc6bfa2-ec3b-4fca-9439-5ab225c88aa0')\"\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4fc6bfa2-ec3b-4fca-9439-5ab225c88aa0');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "competition_test_data = pd.DataFrame(test_folder_ids , columns=['unique_folder_id'])\n",
        "competition_test_data['field_paths'] = test_field_paths\n",
        "competition_test_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2DGXKR1DQTE"
      },
      "source": [
        "##Data Preparation-Feature Engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KEe8BWJtD0hs"
      },
      "source": [
        "###Field pixel size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "utnMFfI5D_7A"
      },
      "outputs": [],
      "source": [
        "train_label_collection = f'{main}_labels_train'\n",
        "test_label_collection = f'{main}_labels_test'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hT86mT4FDR6i"
      },
      "outputs": [],
      "source": [
        "#We will use the number of pixel per field as an indicator of the size of the field\n",
        "def get_pixels_perField(collection, folder_ids):\n",
        "\n",
        "    D = {}\n",
        "    pbar = tqdm(folder_ids)\n",
        "    pbar.set_description(f'Extracting {collection}',)\n",
        "\n",
        "    for idx in pbar:\n",
        "        with rasterio.open(f'./{main}/{collection}/{collection}_{idx}/field_ids.tif') as field:\n",
        "            arr = field.read()\n",
        "            \n",
        "            L=list(np.unique(arr))\n",
        "            L.remove(0)\n",
        "            for f in L:\n",
        "              for i in range(256):\n",
        "                for j in range(256):\n",
        "                  if(arr[0][i][j]==f):\n",
        "                    try:\n",
        "                      D[f]+=1\n",
        "                    except KeyError:\n",
        "                      D[f]=1\n",
        "\n",
        "\n",
        "    return D"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rnsTOLRQDmX6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc9c6e43-5130-4977-cd73-31518160f306"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting ref_agrifieldnet_competition_v1_labels_train: 100%|██████████| 1165/1165 [03:22<00:00,  5.77it/s]\n"
          ]
        }
      ],
      "source": [
        "D=get_pixels_perField(train_label_collection, train_folder_ids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BlaVqKLBDoII",
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          "height": 424
        },
        "outputId": "73f10b2f-7ee0-462f-c7d4-ffb3e0cc631a"
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        {
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          "data": {
            "text/plain": [
              "      field_id  nbr_pixels\n",
              "0          756          14\n",
              "1          757          21\n",
              "2         1372          59\n",
              "3         1374          36\n",
              "4         1986          14\n",
              "...        ...         ...\n",
              "5546      1057          34\n",
              "5547      1058          12\n",
              "5548      1382          34\n",
              "5549      1383           9\n",
              "5550      1385          19\n",
              "\n",
              "[5551 rows x 2 columns]"
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              "<p>5551 rows × 2 columns</p>\n",
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              "          const element = document.querySelector('#df-3b50faea-e1cb-43cb-b7ed-55eb8a596d37');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "train_fields_pixels=pd.DataFrame()\n",
        "train_fields_pixels['field_id']=list(D.keys())\n",
        "train_fields_pixels['nbr_pixels']=list(D.values())\n",
        "train_fields_pixels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RT8z8EsMDqA2"
      },
      "outputs": [],
      "source": [
        "train_fields_pixels.to_csv('train_pixels.csv',index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BSTkAFWADsGC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8dbc5e2-4931-4561-8a9a-e5b9c9203114"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting ref_agrifieldnet_competition_v1_labels_test: 100%|██████████| 707/707 [00:54<00:00, 12.90it/s]\n"
          ]
        }
      ],
      "source": [
        "D=get_pixels_perField(test_label_collection, test_folder_ids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3B2vYQWGDt3t",
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          "height": 424
        },
        "outputId": "d7cf8571-2302-4f03-a4f2-d0c352b5a056"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      field_id  nbr_pixels\n",
              "0         5407         131\n",
              "1         6309          21\n",
              "2         6310          17\n",
              "3         6311          18\n",
              "4         6314          15\n",
              "...        ...         ...\n",
              "1525      2127          40\n",
              "1526      3286          13\n",
              "1527      3595          33\n",
              "1528       626          16\n",
              "1529       971          60\n",
              "\n",
              "[1530 rows x 2 columns]"
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              "\n",
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "test_fields_pixels=pd.DataFrame()\n",
        "test_fields_pixels['field_id']=list(D.keys())\n",
        "test_fields_pixels['nbr_pixels']=list(D.values())\n",
        "test_fields_pixels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8DeoFaa8Dvyi"
      },
      "outputs": [],
      "source": [
        "test_fields_pixels.to_csv('test_pixels.csv',index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j02oOsvTD3y9"
      },
      "source": [
        "###Tile correspending to each field"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xGgeWpWzD8c1"
      },
      "outputs": [],
      "source": [
        "def get_field_ids_per_chips(collection, folder_ids):\n",
        "\n",
        "    chips = {}\n",
        "\n",
        "    pbar = tqdm(folder_ids)\n",
        "    pbar.set_description(f'Extracting {collection}',)\n",
        "\n",
        "    for idx in pbar:\n",
        "        with rasterio.open(f'./{main}/{collection}/{collection}_{idx}/field_ids.tif') as src:\n",
        "            field_data = src.read()\n",
        "            field_data = list(np.unique(field_data))\n",
        "            field_data.remove(0)\n",
        "            chips[idx] = field_data\n",
        "\n",
        "    return chips"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LJ7BuUKRECQd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f39118a-bc0e-47e7-8467-3a04745e1b38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting ref_agrifieldnet_competition_v1_labels_train: 100%|██████████| 1165/1165 [00:04<00:00, 281.69it/s]\n",
            "Extracting ref_agrifieldnet_competition_v1_labels_test: 100%|██████████| 707/707 [00:02<00:00, 281.59it/s]\n"
          ]
        }
      ],
      "source": [
        "train_field_ids_per_chip = get_field_ids_per_chips(train_label_collection, train_folder_ids)\n",
        "test_field_ids_per_chip = get_field_ids_per_chips(test_label_collection, test_folder_ids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7VRR5DT0EFgZ"
      },
      "outputs": [],
      "source": [
        "#this function is used to verify is the field already exists in a chip \n",
        "#in order to capture fields corresponding to more than one chip\n",
        "def checkKey(dic, key):\n",
        "    if key in dic.keys():\n",
        "        return True\n",
        "    return False    \n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "96vRR9QREFX7"
      },
      "outputs": [],
      "source": [
        "D=dict()\n",
        "for key,value in train_field_ids_per_chip.items():\n",
        "  for v in value:\n",
        "    if(checkKey(D, v)):\n",
        "      D[v]=str(D[v])+'+'+key\n",
        "    else:  \n",
        "      D[v]=key"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pGWsNfbnEcSG"
      },
      "outputs": [],
      "source": [
        "chips_mapping_train=pd.DataFrame()\n",
        "chips_mapping_train['chip']=list(D.values())\n",
        "chips_mapping_train['field_id']=list(D.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mgMG72-iEegE"
      },
      "outputs": [],
      "source": [
        "D=dict()\n",
        "for key,value in test_field_ids_per_chip.items():\n",
        "  for v in value:\n",
        "    if(checkKey(D, v)):\n",
        "      D[v]=str(D[v])+'+'+key\n",
        "    else:  \n",
        "      D[v]=key"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3x6w22_2Egfl",
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          "height": 424
        },
        "outputId": "a0e97599-001a-48e3-8c50-2cc03aee83b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       chip  field_id\n",
              "0     6199c      5407\n",
              "1     6c81d      6309\n",
              "2     6c81d      6310\n",
              "3     6c81d      6311\n",
              "4     6c81d      6314\n",
              "...     ...       ...\n",
              "1525  2f048      2127\n",
              "1526  30f20      3286\n",
              "1527  30f20      3595\n",
              "1528  f87d2       626\n",
              "1529  f87d2       971\n",
              "\n",
              "[1530 rows x 2 columns]"
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              "      <td>971</td>\n",
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              "<p>1530 rows × 2 columns</p>\n",
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              "          const element = document.querySelector('#df-75a4e70c-806f-4406-9eaf-a2fc83f60ae8');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "chips_mapping_test=pd.DataFrame()\n",
        "chips_mapping_test['chip']=list(D.values())\n",
        "chips_mapping_test['field_id']=list(D.keys())\n",
        "chips_mapping_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u4ogPhaVEkIj"
      },
      "outputs": [],
      "source": [
        "chips_mapping_train.to_csv('chips_mapping_train.csv',index=False)\n",
        "chips_mapping_test.to_csv('chips_mapping_test.csv',index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HzBRFw4UEowo"
      },
      "source": [
        "###Extracting bands + Coordinates of each chip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FgGOZurKEq3-"
      },
      "outputs": [],
      "source": [
        "#Extract field_crop Pairs \n",
        "\n",
        "def field_crop_extractor(crop_field_files):\n",
        "    field_crops = {}\n",
        "\n",
        "    for label_field_file in tqdm(crop_field_files):\n",
        "        with rasterio.open(f'{main}/{train_label_collection}/{train_label_collection}_{label_field_file}/field_ids.tif') as src:\n",
        "            field_data = src.read()[0]\n",
        "        with rasterio.open(f'{main}/{train_label_collection}/{train_label_collection}_{label_field_file}/raster_labels.tif') as src:\n",
        "            crop_data = src.read()[0]\n",
        "    \n",
        "        for x in range(0, crop_data.shape[0]):\n",
        "            for y in range(0, crop_data.shape[1]):\n",
        "                field_id = str(field_data[x][y])\n",
        "                field_crop = crop_data[x][y]\n",
        "\n",
        "                if field_crops.get(field_id) is None:\n",
        "                    field_crops[field_id] = []\n",
        "\n",
        "                if field_crop not in field_crops[field_id]:\n",
        "                    field_crops[field_id].append(field_crop)\n",
        "    \n",
        "    field_crop_map  =[[k, v[0]]  for k, v in field_crops.items() ]\n",
        "    field_crop = pd.DataFrame(field_crop_map , columns=['field_id','crop_id'])\n",
        "\n",
        "    return field_crop[field_crop['field_id']!='0']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ot_IIgFVEs1N",
        "colab": {
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          "height": 442
        },
        "outputId": "c30583cd-6f09-4fa1-d835-4944945226ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1165/1165 [02:04<00:00,  9.36it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     field_id  crop_id\n",
              "1         757        6\n",
              "2         756        6\n",
              "3        1372        5\n",
              "4        1374        1\n",
              "5        1986        4\n",
              "...       ...      ...\n",
              "5547     1385        2\n",
              "5548      789        2\n",
              "5549      923        5\n",
              "5550      924        1\n",
              "5551      791        1\n",
              "\n",
              "[5551 rows x 2 columns]"
            ],
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              "      <th>5547</th>\n",
              "      <td>1385</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5548</th>\n",
              "      <td>789</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5549</th>\n",
              "      <td>923</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5550</th>\n",
              "      <td>924</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5551</th>\n",
              "      <td>791</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5551 rows × 2 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "field_crop_pair = field_crop_extractor(train_folder_ids)\n",
        "field_crop_pair"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mA0ErF4yEu5M"
      },
      "outputs": [],
      "source": [
        "# each row is a pixel and each column is one of the band observations mapped to its corresponding field. \n",
        "# I also extract the bbox of each tile. \n",
        "\n",
        "\n",
        "img_sh = 256\n",
        "n_selected_bands=5# len(selected_bands)\n",
        "\n",
        "n_obs = 1  #imagery per chip(no time series)\n",
        "\n",
        "def feature_extractor(data_ ,   path ):\n",
        "    '''\n",
        "        data_: Dataframe with 'field_paths' and 'unique_folder_id' columns\n",
        "        path: Path to source collections files\n",
        "\n",
        "        returns: pixel dataframe with corresponding field_ids\n",
        "        '''\n",
        "    \n",
        "    X = np.empty((0, n_selected_bands * n_obs))\n",
        "    X_tile = np.empty((img_sh * img_sh, 0))\n",
        "    X_arrays = []\n",
        "        \n",
        "    field_ids = np.empty((0, 1))\n",
        "    coordinates=[]\n",
        "    chips=[]\n",
        "    for idx, tile_id in tqdm(enumerate(data_['unique_folder_id'])):\n",
        "        \n",
        "        field_src =   rasterio.open( data_['field_paths'].values[idx])\n",
        "        field_array = field_src.read(1)\n",
        "        chips.append( tile_id )\n",
        "        field_ids = np.append(field_ids, field_array.flatten())\n",
        "  \n",
        "        \n",
        "        bands_src = [rasterio.open(f'{main}/{path}/{path}_{tile_id}/{band}.tif') for band in selected_bands]\n",
        "        bands_array = [np.expand_dims(band.read(1).flatten(), axis=1) for band in bands_src]\n",
        "        \n",
        "        X_tile = np.hstack(bands_array)\n",
        "\n",
        "        X_arrays.append(X_tile)\n",
        "        #/content/ref_agrifieldnet_competition_v1/ref_agrifieldnet_competition_v1_source/ref_agrifieldnet_competition_v1_source_001c1/ref_agrifieldnet_competition_v1_source_001c1.json\n",
        "        with open (f'{main}/{path}/{path}_{tile_id}/{path}_{tile_id}.json') as f:\n",
        "            _json = json.load(f)\n",
        "        coordinates.append(_json['bbox'])\n",
        "        \n",
        "        \n",
        "\n",
        "    X = np.concatenate(X_arrays)\n",
        "    \n",
        "    data = pd.DataFrame(X, columns=selected_bands)\n",
        "\n",
        "    data['field_id'] = field_ids\n",
        "\n",
        "    return data[data['field_id']!=0],chips,coordinates"
      ]
    },
    {
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          "name": "stderr",
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            "1165it [02:46,  7.00it/s]\n"
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        },
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "train_data,field_ids,coordinates = feature_extractor(competition_train_data, source_collection)\n",
        "train_data"
      ]
    },
    {
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              "       chip                                               bbox\n",
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          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "geo=pd.DataFrame(field_ids,columns=['chip'])\n",
        "geo['bbox']=coordinates\n",
        "geo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zRIwbtmdFGmE"
      },
      "outputs": [],
      "source": [
        "geo.to_csv('train_geo.csv',index=False)"
      ]
    },
    {
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      "metadata": {
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            "707it [01:08, 10.26it/s]\n"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "test_data,chips_test,coords = feature_extractor(competition_test_data,  source_collection)\n",
        "test_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8K2t2K8qFLL2",
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          "height": 424
        },
        "outputId": "1668a440-99e9-436a-dd45-fe46d6e91f91"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      chip                                               bbox\n",
              "0    6199c  [76.84606653839002, 24.617101431568184, 76.871...\n",
              "1    6c81d  [87.30587182973441, 25.57625793721506, 87.3314...\n",
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              "4    65812  [83.41235872444402, 19.339169627559386, 83.437...\n",
              "..     ...                                                ...\n",
              "702  b5766  [76.66972314175382, 24.66542007779033, 76.6953...\n",
              "703  2f048  [81.28622938826972, 27.93390555987311, 81.3123...\n",
              "704  30f20  [81.59459446124482, 27.216435666106115, 81.620...\n",
              "705  f87d2  [82.21596186186991, 27.30474312284826, 82.2420...\n",
              "706  b13db  [76.67126625560921, 24.780971480564144, 76.696...\n",
              "\n",
              "[707 rows x 2 columns]"
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              "    <tr>\n",
              "      <th>703</th>\n",
              "      <td>2f048</td>\n",
              "      <td>[81.28622938826972, 27.93390555987311, 81.3123...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>704</th>\n",
              "      <td>30f20</td>\n",
              "      <td>[81.59459446124482, 27.216435666106115, 81.620...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>705</th>\n",
              "      <td>f87d2</td>\n",
              "      <td>[82.21596186186991, 27.30474312284826, 82.2420...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>706</th>\n",
              "      <td>b13db</td>\n",
              "      <td>[76.67126625560921, 24.780971480564144, 76.696...</td>\n",
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              "<p>707 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "geo_test=pd.DataFrame(chips_test,columns=['chip'])\n",
        "geo_test['bbox']=coords\n",
        "geo_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6FXm33A_FSDL"
      },
      "outputs": [],
      "source": [
        "geo_test.to_csv('test_geo.csv',index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iY4j2JN4FVwq"
      },
      "outputs": [],
      "source": [
        "train_data.field_id = [str(int(i)) for i in train_data.field_id.values]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VDuJNvl2FYCY",
        "colab": {
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          "height": 206
        },
        "outputId": "01f6bed0-9ae0-43a2-d68a-3e35764261de"
      },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   B01  B02  B03  B04  B05  B06  B07  B08  B8A  B09  B11  B12 field_id  \\\n",
              "0   43   39   38   38   41   54   63   61   64   12   57   37      757   \n",
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              "4   43   39   38   38   42   57   67   64   72   12   63   42      757   \n",
              "\n",
              "   crop_id  \n",
              "0        6  \n",
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              "2        6  \n",
              "3        6  \n",
              "4        6  "
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "train_df = pd.merge(train_data, field_crop_pair , on='field_id' )\n",
        "train_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QL89lyWEFses"
      },
      "outputs": [],
      "source": [
        "train_data.field_id = [str(int(i)) for i in train_data.field_id.values]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNwPPWJvGNTK"
      },
      "source": [
        "###Vegetation indices"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7O0e6eSxGQSA"
      },
      "source": [
        "for feature enginnering i'm creating around 90 vegetation index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DkGQZ9CBGPim"
      },
      "outputs": [],
      "source": [
        "def FE(df) : \n",
        "  #SOIL\n",
        "  df['SARVI2']=2.5 * (df['B08'] - df['B04']) / (1.0 + df['B08'] + 6.0 * df['B04'] - 7.5 * df['B02'])\n",
        "  df['SAVI3']=(1.0 + 0.5) * (df['B08'] - df['B04']) / (df['B08'] + df['B04'] + 0.5)\n",
        "  df['SAVI'] = ((df['B08'] - df['B04'])/(df['B08'] + df['B04'] + 0.33)) * (1 + 0.33)\n",
        "  df['SBL']=df['B09'] - 2.4 * df['B04']\n",
        "  df['SC']=(df['B11'] - df['B08']) / (df['B11'] + df['B08'])\n",
        "  df['SLAVI']=df['B08'] / (df['B04'] + df['B12'])\n",
        "  df['ATSAVI']=1.22 * (df['B08'] - 1.22 * df['B04'] - 0.03) / (1.22 * df['B08'] + df['B04'] - 1.22 * 0.03 + 0.08 * (1.0 + pow(1.22, 2.0)))\n",
        "  df['AFRI1600']=df['B08'] - 0.66 * df['B11'] / (df['B08'] + 0.66 * df['B11'])\n",
        "  df['AFRI1200']= df['B08'] - 0.5 * df['B12'] / (df['B08'] + 0.56 * df['B12'])\n",
        "  df['Alteration']=df['B11'] / df['B12']\n",
        "  df['ARI']=1.0 / df['B03'] - 1.0 / df['B05']\n",
        "  df['AVI']=2.0 * df['B09'] - df['B04']\n",
        "  df['ARVI']=(df['B8A'] - df['B04'] - 2 * (df['B04'] - df['B02'])) / (df['B8A'] + df['B04'] - 2 * (df['B04'] - df['B02']))\n",
        "  df['ARVI2']=-0.18 + 1.17 * ((df['B08'] - df['B04']) / (df['B08'] + df['B04']))\n",
        "  df['BDWDRVI']=(0.1 * df['B08'] - df['B02']) / (0.1 * df['B08'] + df['B02'])\n",
        "  df['BRI']=(1.0 / df['B03'] - 1.0 / df['B05']) / df['B08']\n",
        "  \n",
        "  #Chlorophyl\n",
        "  df['CCCI']=((df['B08'] - df['B05']) / (df['B08'] + df['B05'])) / ((df['B08'] - df['B04']) / (df['B08'] + df['B04']))\n",
        "  df['CARI']=(df['B05'] / df['B04']) * (np.sqrt(np.power(((df['B05'] - df['B03']) / 150.0 * 670.0 + df['B04'] + (df['B03'] - ((df['B05'] - df['B03']) / 150.0 * 550.0))), 2.0))) / (np.power(((df['B05'] - df['B03']) / np.power(150.0, 2.0) + 1.0), 0.5))\n",
        "  a = 0.496\n",
        "  df['CARI2']=(np.abs(((df['B05'] - df['B03']) / 150.0 * df['B04'] + df['B04'] + df['B03'] - (a * df['B03']))) / pow((pow(a, 2.0) + 1.0), 0.5)) * (df['B05'] / df['B04'])\n",
        "  df['Chlgreen']=np.power((df['B07'] / df['B03']), (-1.0))\n",
        "  df['Chgreen']=df['B08'] / df['B03'] - 1.0\n",
        "  df['Clrededge']=df['B08'] / df['B05'] - 1.0\n",
        "  df['Chlrededge']=np.power((df['B07'] / df['B05']), (-1.0))\n",
        "  df['CVI']=df['B08'] * df['B04'] / np.power(df['B03'], 2.0)\n",
        "  df['CI']=(df['B04'] - df['B02']) / df['B04']\n",
        "  df['CTI']=(((df['B04'] - df['B03']) / (df['B04'] + df['B03'])) + 0.5) / np.abs(((df['B04'] - df['B03']) / (df['B04'] + df['B03'])) + 0.5) * np.sqrt(np.abs((((df['B04'] - df['B03']) / (df['B04'] + df['B03']))) + 0.5))\n",
        "  df['Datt1']=(df['B08'] - df['B05']) / (df['B08'] - df['B04'])\n",
        "  df['Datt4']=df['B04'] / (df['B03'] * df['B05'])\n",
        "  df['Datt6']=df['B8A'] / (df['B03'] * df['B05'])\n",
        "  df['GDVI']=df['B08'] - df['B03']\n",
        "  df['DVIMSS']=2.4 * df['B09'] - df['B04']\n",
        "  df['EVI']=2.5 * (df['B08'] - df['B04']) / ((df['B08'] + 6.0 * df['B04'] - 7.5 * df['B02']) + 1.0)\n",
        "  df['EVI2']=2.4 * (df['B08'] - df['B04']) / (df['B08'] + df['B04'] + 1.0)\n",
        "  df['EVI22']=2.5 * (df['B08'] - df['B04']) / (df['B08'] + 2.4 * df['B04'] + 1.0)\n",
        "  df['Fe2+']=df['B12'] / df['B08'] + df['B03'] / df['B04']\n",
        "  df['Fe3+']=df['B04'] / df['B03']\n",
        "  df['FO']=df['B11']/ df['B08']\n",
        "  df['FI']=df['B12'] / df['B08'] + df['B03'] / df['B04']\n",
        "  df['FS']=df['B12'] / df['B11']\n",
        "  df['GEMI']=((2.0 * (np.power(df['B08'], 2.0) - np.power(df['B04'], 2.0)) + 1.5 * df['B08'] + 0.5 * df['B04']) / (df['B08'] + df['B04'] + 0.5) * (1.0 - 0.25 * (2.0 * (np.power(df['B08'], 2.0) - np.power(df['B04'], 2.0)) + 1.5 * df['B08'] + 0.5 * df['B04']) / (df['B08'] + df['B04'] + 0.5)) - (df['B04'] - 0.125) / (1.0 - df['B04']))\n",
        "  df['GVMI']= ((df['B08'] + 0.1) - (df['B12'] + 0.02)) / ((df['B08'] + 0.1) + (df['B12'] + 0.02))\n",
        "  df['GARI']=(df['B08'] - (df['B03'] - (df['B02'] - df['B04']))) / (df['B08'] - (df['B03'] + (df['B02']- df['B04'])))\n",
        "  df['GLI']=(2.0 * df['B03'] - df['B04'] - df['B02']) / (2.0 * df['B03'] + df['B04'] + df['B02'])\n",
        "  df['GBNDVI']=(df['B08'] - (df['B03'] + df['B02'])) / (df['B08']+ (df['B03'] + df['B02']))\n",
        "  df['GRNDVI']=(df['B08'] - (df['B03'] + df['B04'])) / (df['B08'] + (df['B03'] + df['B04']))\n",
        "  df['Hue']=np.arctan((2.0 * df['B04'] - df['B03'] - df['B02']) / 30.5 * (df['B03'] - df['B02']))\n",
        "  df['I']=(1.0 / (30.5)) * (df['B04']+ df['B03'] + df['B02'])\n",
        "  df['Latirite']=df['B11'] / df['B12']\n",
        "  df['LCI']=(df['B08'] - df['B05']) / (df['B08'] + df['B04'])\n",
        "  df['logR']=np.log(df['B08'] / df['B04'])\n",
        "  df['r']=df['B08'] / df['B04']\n",
        "  df['Maccioni']=(df['B07'] - df['B05']) / (df['B07'] - df['B04'])\n",
        "  df['McRIG']=(np.power(df['B02'], (-1.0)) - np.power(df['B03'], (-1.0))) * df['B08']\n",
        "  df['MVI']=df['B09'] / df['B11']\n",
        "  df['MGVI']=-0.386 * df['B03'] - 0.53 * df['B04'] + 0.535 * df['B06'] + 0.532 * df['B09']\n",
        "  df['MNSI']=0.404 * df['B03'] - 0.039 * df['B04'] - 0.505 * df['B06'] + 0.762 * df['B09']\n",
        "  df['MSBI']= 0.406 * df['B03'] + 0.6 * df['B04'] + 0.645 * df['B06'] + 0.243 * df['B09']\n",
        "  df['MYVI']=0.723 * df['B03'] - 0.597 * df['B04'] + 0.206 * df['B06'] - 0.278 * df['B09']\n",
        "  df['mARI']=(np.power(df['B03'], (-1.0)) - np.power(df['B05'], (-1.0))) * df['B08']\n",
        "  df['mCARI']=((df['B05'] - df['B04']) - 0.2 * (df['B05']- df['B03'])) * (df['B05'] / df['B04'])\n",
        "  df['mNDVI']=(df['B08'] - df['B04']) / (df['B08']+ df['B04'] - 2.0 * df['B01'])\n",
        "  df['NLI']=(np.power(df['B08'], 2.0) - df['B04']) / (np.power(df['B08'], 2.0) + df['B04'])\n",
        "  df['NORM_G']=df['B03'] / (df['B08'] + df['B04'] + df['B03'])\n",
        "  df['NORM_NIR']=df['B08'] / (df['B08'] + df['B04'] + df['B03'])\n",
        "  df['NORM_R']=df['B04'] / (df['B08'] + df['B04'] + df['B03']) \n",
        "  df['PPR']=(df['B03'] - df['B01']) / (df['B03']+ df['B01'])\n",
        "  df['PVR']=(df['B03'] - df['B04']) / (df['B03'] + df['B04'])\n",
        "  df['SIWSI']=(df['B8A'] - df['B11']) / (df['B8A'] + df['B11'])\n",
        "  df['NDVI']=(df['B12'] - df['B08']) / (df['B12'] + df['B08'])\n",
        "  df['BNDVI']= (df['B08']- df['B02']) / (df['B08'] + df['B02'])\n",
        "  df['REP']=700.0 + 40.0 * (((df['B04'] + df['B07']) / 2.0) - df['B05']) / (df['B06'] - df['B05'])\n",
        "  df['Rre']=(df['B04'] + df['B07']) / 2.0;\n",
        "  df['IF']=(2.0 * df['B04'] -df['B03'] - df['B02']) / (df['B03']- df['B02'])\n",
        "  df['BGI']=df['B01'] / df['B03']\n",
        "  df['RENDVI'] = (df['B06'] - df['B05']) / (df['B06'] + df['B05']) \n",
        "  df['FIDET'] = df['B12'] / (df['B8A'] * df['B09'])\n",
        "  df['RATIO2'] = df['B01'] / df['B05']\n",
        "  df['Ratio3']=df['B01']*df['B09']\n",
        "  df['Ratio4']=df['B01']/df['B09']\n",
        "  df['Ratio5']=df['B01']*df['B09']*df['B8A']\n",
        "  df['Ratio6']=df['B07']/df['B04']\n",
        "  df['Ratio7']=df['B01']/df['B03']\n",
        "  df['Ratio8']=df['B8A']/df['B09']\n",
        "  df['Ratio9']=df['B07']/df['B06']\n",
        "  df['S2REP']=705 + 35 * ((df['B04'] + df['B07'])/2) - df['B05'] / (df['B06'] - df['B05'])\n",
        "  eps=0.5\n",
        "  df['RESR']=(df['B06']-df['B01'])/(df['B06']+df['B01']+eps)\n",
        "  df['MI']=(df['B8A'] - df['B11']) / (df['B8A'] + df['B11'])\n",
        "  return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CImVpiiLGwqF"
      },
      "outputs": [],
      "source": [
        "train_data=FE(train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PUGkRBkRGz7G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "d743730f-cce6-4380-e906-f34d11bb12e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  field_id        B01        B02        B03        B04        B05        B06  \\\n",
              "0        1  45.000000  42.444444  42.722222  48.000000  49.666667  58.000000   \n",
              "1       10  43.421053  38.578947  37.473684  36.894737  41.157895  59.052632   \n",
              "2      100  44.071429  39.750000  39.642857  39.964286  43.821429  60.964286   \n",
              "3     1000  43.000000  39.454545  39.454545  41.636364  44.181818  59.000000   \n",
              "4     1001  44.156863  41.441176  41.372549  43.990196  46.637255  60.362745   \n",
              "\n",
              "         B07        B08        B8A  ...     Ratio3    Ratio4      Ratio5  \\\n",
              "0  65.222222  60.277778  71.944444  ...  28.000000  3.750000  179.777778   \n",
              "1  68.578947  65.157895  76.052632  ...  13.578947  3.589406  119.578947   \n",
              "2  70.500000  66.142857  77.571429  ...  16.857143  3.672619  209.571429   \n",
              "3  68.454545  63.727273  74.727273  ...  47.000000  3.307692  184.181818   \n",
              "4  69.147059  65.049020  76.441176  ...  50.392157  3.471342  123.009804   \n",
              "\n",
              "     Ratio6    Ratio7    Ratio8    Ratio9        S2REP      RESR        MI  \n",
              "0  1.359298  1.053559  5.995370  1.124509  2680.361772  0.125559  1.627160  \n",
              "1  1.867879  1.161427  6.289136  1.161661  2548.474252  0.151681  0.064573  \n",
              "2  1.783027  1.114645  6.464286  1.156620  2635.475313  0.160002  0.331894  \n",
              "3  1.650936  1.091675  5.748252  1.160226  2628.514596  0.155952  0.484867  \n",
              "4  1.580935  1.069863  6.008547  1.145666  2681.448795  0.154012  1.254113  \n",
              "\n",
              "[5 rows x 100 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-531bd070-3cb2-4396-a979-cc45d073d781\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>field_id</th>\n",
              "      <th>B01</th>\n",
              "      <th>B02</th>\n",
              "      <th>B03</th>\n",
              "      <th>B04</th>\n",
              "      <th>B05</th>\n",
              "      <th>B06</th>\n",
              "      <th>B07</th>\n",
              "      <th>B08</th>\n",
              "      <th>B8A</th>\n",
              "      <th>...</th>\n",
              "      <th>Ratio3</th>\n",
              "      <th>Ratio4</th>\n",
              "      <th>Ratio5</th>\n",
              "      <th>Ratio6</th>\n",
              "      <th>Ratio7</th>\n",
              "      <th>Ratio8</th>\n",
              "      <th>Ratio9</th>\n",
              "      <th>S2REP</th>\n",
              "      <th>RESR</th>\n",
              "      <th>MI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>42.444444</td>\n",
              "      <td>42.722222</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>49.666667</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>65.222222</td>\n",
              "      <td>60.277778</td>\n",
              "      <td>71.944444</td>\n",
              "      <td>...</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>179.777778</td>\n",
              "      <td>1.359298</td>\n",
              "      <td>1.053559</td>\n",
              "      <td>5.995370</td>\n",
              "      <td>1.124509</td>\n",
              "      <td>2680.361772</td>\n",
              "      <td>0.125559</td>\n",
              "      <td>1.627160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10</td>\n",
              "      <td>43.421053</td>\n",
              "      <td>38.578947</td>\n",
              "      <td>37.473684</td>\n",
              "      <td>36.894737</td>\n",
              "      <td>41.157895</td>\n",
              "      <td>59.052632</td>\n",
              "      <td>68.578947</td>\n",
              "      <td>65.157895</td>\n",
              "      <td>76.052632</td>\n",
              "      <td>...</td>\n",
              "      <td>13.578947</td>\n",
              "      <td>3.589406</td>\n",
              "      <td>119.578947</td>\n",
              "      <td>1.867879</td>\n",
              "      <td>1.161427</td>\n",
              "      <td>6.289136</td>\n",
              "      <td>1.161661</td>\n",
              "      <td>2548.474252</td>\n",
              "      <td>0.151681</td>\n",
              "      <td>0.064573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100</td>\n",
              "      <td>44.071429</td>\n",
              "      <td>39.750000</td>\n",
              "      <td>39.642857</td>\n",
              "      <td>39.964286</td>\n",
              "      <td>43.821429</td>\n",
              "      <td>60.964286</td>\n",
              "      <td>70.500000</td>\n",
              "      <td>66.142857</td>\n",
              "      <td>77.571429</td>\n",
              "      <td>...</td>\n",
              "      <td>16.857143</td>\n",
              "      <td>3.672619</td>\n",
              "      <td>209.571429</td>\n",
              "      <td>1.783027</td>\n",
              "      <td>1.114645</td>\n",
              "      <td>6.464286</td>\n",
              "      <td>1.156620</td>\n",
              "      <td>2635.475313</td>\n",
              "      <td>0.160002</td>\n",
              "      <td>0.331894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>39.454545</td>\n",
              "      <td>39.454545</td>\n",
              "      <td>41.636364</td>\n",
              "      <td>44.181818</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>68.454545</td>\n",
              "      <td>63.727273</td>\n",
              "      <td>74.727273</td>\n",
              "      <td>...</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>3.307692</td>\n",
              "      <td>184.181818</td>\n",
              "      <td>1.650936</td>\n",
              "      <td>1.091675</td>\n",
              "      <td>5.748252</td>\n",
              "      <td>1.160226</td>\n",
              "      <td>2628.514596</td>\n",
              "      <td>0.155952</td>\n",
              "      <td>0.484867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1001</td>\n",
              "      <td>44.156863</td>\n",
              "      <td>41.441176</td>\n",
              "      <td>41.372549</td>\n",
              "      <td>43.990196</td>\n",
              "      <td>46.637255</td>\n",
              "      <td>60.362745</td>\n",
              "      <td>69.147059</td>\n",
              "      <td>65.049020</td>\n",
              "      <td>76.441176</td>\n",
              "      <td>...</td>\n",
              "      <td>50.392157</td>\n",
              "      <td>3.471342</td>\n",
              "      <td>123.009804</td>\n",
              "      <td>1.580935</td>\n",
              "      <td>1.069863</td>\n",
              "      <td>6.008547</td>\n",
              "      <td>1.145666</td>\n",
              "      <td>2681.448795</td>\n",
              "      <td>0.154012</td>\n",
              "      <td>1.254113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 100 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-531bd070-3cb2-4396-a979-cc45d073d781')\"\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-531bd070-3cb2-4396-a979-cc45d073d781 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-531bd070-3cb2-4396-a979-cc45d073d781');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "train_data_grouped = train_data.groupby(['field_id']).mean().reset_index()\n",
        "train_data_grouped.field_id = [str(int(i)) for i in train_data_grouped.field_id.values]\n",
        "train_data_grouped.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f0_TAHtOG8t-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "7df98da7-0961-4d89-9d11-58c9247ff599"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  field_id        B01        B02        B03        B04        B05        B06  \\\n",
              "0        1  45.000000  42.444444  42.722222  48.000000  49.666667  58.000000   \n",
              "1       10  43.421053  38.578947  37.473684  36.894737  41.157895  59.052632   \n",
              "2      100  44.071429  39.750000  39.642857  39.964286  43.821429  60.964286   \n",
              "3     1000  43.000000  39.454545  39.454545  41.636364  44.181818  59.000000   \n",
              "4     1001  44.156863  41.441176  41.372549  43.990196  46.637255  60.362745   \n",
              "\n",
              "         B07        B08        B8A  ...    Ratio4      Ratio5    Ratio6  \\\n",
              "0  65.222222  60.277778  71.944444  ...  3.750000  179.777778  1.359298   \n",
              "1  68.578947  65.157895  76.052632  ...  3.589406  119.578947  1.867879   \n",
              "2  70.500000  66.142857  77.571429  ...  3.672619  209.571429  1.783027   \n",
              "3  68.454545  63.727273  74.727273  ...  3.307692  184.181818  1.650936   \n",
              "4  69.147059  65.049020  76.441176  ...  3.471342  123.009804  1.580935   \n",
              "\n",
              "     Ratio7    Ratio8    Ratio9        S2REP      RESR        MI  crop_id  \n",
              "0  1.053559  5.995370  1.124509  2680.361772  0.125559  1.627160        1  \n",
              "1  1.161427  6.289136  1.161661  2548.474252  0.151681  0.064573        1  \n",
              "2  1.114645  6.464286  1.156620  2635.475313  0.160002  0.331894        2  \n",
              "3  1.091675  5.748252  1.160226  2628.514596  0.155952  0.484867        1  \n",
              "4  1.069863  6.008547  1.145666  2681.448795  0.154012  1.254113        2  \n",
              "\n",
              "[5 rows x 101 columns]"
            ],
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              "      <th></th>\n",
              "      <th>field_id</th>\n",
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              "      <th>Ratio6</th>\n",
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              "      <td>1.254113</td>\n",
              "      <td>2</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 101 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e41e1922-4b6c-4e74-af00-aa8f62026dd3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "    }\n",
              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e41e1922-4b6c-4e74-af00-aa8f62026dd3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "# merge pixel dataframe to field_crop_pair dataframe\n",
        "\n",
        "train_df = pd.merge(train_data_grouped, field_crop_pair , on='field_id' )\n",
        "train_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S4ruUHnTG_cX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46c00f00-1b90-4898-8e8c-598e179377aa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5551, 101)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "train_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9PUvp2GMHBST"
      },
      "outputs": [],
      "source": [
        "train_df.to_csv('train_mean_fe.csv',index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1BwMG5ohHEHs"
      },
      "outputs": [],
      "source": [
        "test_data=FE(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QHZMEgRxHFuu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "3c8c4f1a-0710-4528-d7d3-b11c58feaf73"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     field_id        B01        B02        B03        B04        B05  \\\n",
              "0          11  43.086957  39.057971  38.420290  38.405797  42.507246   \n",
              "1          13  44.000000  40.692308  41.307692  46.769231  49.461538   \n",
              "2          19  45.469388  41.918367  42.530612  44.367347  47.122449   \n",
              "3          21  44.000000  40.250000  40.750000  40.000000  44.250000   \n",
              "4          25  47.000000  44.300000  47.200000  52.900000  54.900000   \n",
              "...       ...        ...        ...        ...        ...        ...   \n",
              "1525     7319  45.000000  36.333333  31.222222  24.722222  28.166667   \n",
              "1526     7325  45.705882  37.735294  32.205882  26.205882  28.500000   \n",
              "1527     7329  47.000000  40.588235  35.823529  30.823529  32.941176   \n",
              "1528     7330  46.562500  40.000000  34.750000  29.125000  31.562500   \n",
              "1529     7333  47.000000  40.750000  36.125000  33.125000  34.500000   \n",
              "\n",
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              "          RESR        MI  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "test_data_grouped = test_data.groupby(['field_id']).mean().reset_index()\n",
        "test_data_grouped.field_id = [str(int(i)) for i in test_data_grouped.field_id.values]\n",
        "test_data_grouped"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rA19uYxdHH6p"
      },
      "outputs": [],
      "source": [
        "test_data_grouped.to_csv('test_mean_fe.csv',index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MGZtAR1jHXPF"
      },
      "source": [
        "###Extracting Field coordinates"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mURIX5eJHbBL"
      },
      "source": [
        "Given that each chip is mapped to 256x256 grid we can easily extract the field id coordinates .For example let ($x_{1}$,$y_{1}$) (top leftmost point) and ($x_{2},y_{2}$) (down right) the bbox the chip ,when iterating the chip array (arr)\n",
        "`if arr[i][j]=field_id` ,this coordinate (x,y) is:\n",
        "\n",
        "\n",
        "```\n",
        "x=x1+j*(x2-x1)/256 \n",
        "y=y1-i*(y1-y2)/256\n",
        "```\n",
        "\n",
        ", then we only take the **leftmost point** and the **down right most** point"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XjczcnnhHajx"
      },
      "outputs": [],
      "source": [
        "train_box=pd.read_csv('/content/train_geo.csv')"
      ]
    },
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              "       chip                                               bbox         x1  \\\n",
              "0     28852  [82.29382899483097, 27.32713829135441, 82.3199...  82.293829   \n",
              "1     d987c  [81.54278059765775, 27.193543671621423, 81.568...  81.542781   \n",
              "2     ca1d4  [81.5432292600233, 27.28598875637792, 81.56921...  81.543229   \n",
              "3     2ec18  [87.71232081886878, 25.343603898226164, 87.737...  87.712321   \n",
              "4     7575d  [83.67644488209265, 19.104289532145508, 83.701...  83.676445   \n",
              "...     ...                                                ...        ...   \n",
              "1160  516f8  [83.85019591601247, 19.309674021821294, 83.874...  83.850196   \n",
              "1161  b8afb  [76.9226069295929, 24.662353415107034, 76.9482...  76.922607   \n",
              "1162  e3ad4  [81.13052370114117, 28.28081255432777, 81.1566...  81.130524   \n",
              "1163  3ab12  [81.49251813933866, 27.517298977274603, 81.518...  81.492518   \n",
              "1164  83a33  [82.44905041066741, 27.325585951347183, 82.475...  82.449050   \n",
              "\n",
              "             y1         x2         y2  \n",
              "0     27.327138  82.319974  27.350486  \n",
              "1     27.193544  81.568744  27.216758  \n",
              "2     27.285989  81.569214  27.309203  \n",
              "3     25.343604  87.737900  25.366846  \n",
              "4     19.104290  83.701132  19.127756  \n",
              "...         ...        ...        ...  \n",
              "1160  19.309674  83.874938  19.333163  \n",
              "1161  24.662353  76.948252  24.685787  \n",
              "1162  28.280813  81.156662  28.303949  \n",
              "1163  27.517299  81.518548  27.540504  \n",
              "1164  27.325586  82.475225  27.348961  \n",
              "\n",
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              "      <th>1</th>\n",
              "      <td>d987c</td>\n",
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              "      <th>4</th>\n",
              "      <td>7575d</td>\n",
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              "      <td>83.676445</td>\n",
              "      <td>19.104290</td>\n",
              "      <td>83.701132</td>\n",
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              "      <td>83.850196</td>\n",
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              "      <td>83.874938</td>\n",
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              "      <th>1161</th>\n",
              "      <td>b8afb</td>\n",
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              "      <td>76.922607</td>\n",
              "      <td>24.662353</td>\n",
              "      <td>76.948252</td>\n",
              "      <td>24.685787</td>\n",
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              "      <th>1162</th>\n",
              "      <td>e3ad4</td>\n",
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              "      <td>81.130524</td>\n",
              "      <td>28.280813</td>\n",
              "      <td>81.156662</td>\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "train_box['x1']=(train_box['bbox'].apply(lambda x:x if (isinstance(x,int) or isinstance(x,float)) else x.split(',')[0][1:])).astype(float)\n",
        "train_box['y1']=(train_box['bbox'].apply(lambda x:x if (isinstance(x,int) or isinstance(x,float)) else x.split(',')[1])).astype(float)\n",
        "train_box['x2']=(train_box['bbox'].apply(lambda x:x if (isinstance(x,int) or isinstance(x,float)) else x.split(',')[2])).astype(float)\n",
        "train_box['y2']=(train_box['bbox'].apply(lambda x:x if (isinstance(x,int) or isinstance(x,float)) else x.split(',')[-1][:-1])).astype(float)\n",
        "train_box"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dG_EBUmKIXcv"
      },
      "outputs": [],
      "source": [
        "test_box=pd.read_csv('/content/test_geo.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4BmDzSjnIacL"
      },
      "outputs": [],
      "source": [
        "test_box['x1']=(test_box['bbox'].apply(lambda x:x if (isinstance(x,int) or isinstance(x,float)) else x.split(',')[0][1:])).astype(float)\n",
        "test_box['y1']=(test_box['bbox'].apply(lambda x:x if (isinstance(x,int) or isinstance(x,float)) else x.split(',')[1])).astype(float)\n",
        "test_box['x2']=(test_box['bbox'].apply(lambda x:x if (isinstance(x,int) or isinstance(x,float)) else x.split(',')[2])).astype(float)\n",
        "test_box['y2']=(test_box['bbox'].apply(lambda x:x if (isinstance(x,int) or isinstance(x,float)) else x.split(',')[-1][:-1])).astype(float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KkT1u2M5IcVS"
      },
      "outputs": [],
      "source": [
        "def get_field_coordinates(collection, folder_ids,df):\n",
        "\n",
        "    D = {}\n",
        "    d=dict(zip(df.chip.values,list(x for x in zip(df.x2.values,df.y1.values,df.x1.values,df.y2.values))))\n",
        "    pbar = tqdm(folder_ids)\n",
        "    pbar.set_description(f'Extracting {collection}',)\n",
        "\n",
        "    for idx in pbar:\n",
        "        with rasterio.open(f'./{main}/{collection}/{collection}_{idx}/field_ids.tif') as field:\n",
        "            arr = field.read()\n",
        "            top,left,bottom,right=d[idx]\n",
        "            d_step,r_step=(top-bottom)/256,(right-left)/256\n",
        "            L=list(np.unique(arr))\n",
        "            L.remove(0)\n",
        "            for f in L:\n",
        "              start=False\n",
        "              mx=0\n",
        "              for i in range(256):\n",
        "                for j in range(256):\n",
        "                  if(arr[0][i][j]==f):\n",
        "                    if(start):\n",
        "                      if(j>mx):\n",
        "                        mx=j\n",
        "                        D[f].append((top-d_step*i,left+r_step*j))\n",
        "                \n",
        "                    else:\n",
        "                      start=True\n",
        "                      D[f]=[(top-d_step*i,left+r_step*j)] \n",
        "                   \n",
        "\n",
        "\n",
        "\n",
        "            \n",
        "    for x in D.keys():\n",
        "      D[x]=[D[x][0],D[x][-1]]\n",
        "\n",
        "    return D"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-cCEQvGjIg8v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "116c0d8c-aa5e-4f44-e46d-911d90de884b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting ref_agrifieldnet_competition_v1_labels_train: 100%|██████████| 1165/1165 [03:16<00:00,  5.93it/s]\n"
          ]
        }
      ],
      "source": [
        "D=get_field_coordinates(train_label_collection, train_folder_ids,train_box)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DYowapWoIjEP"
      },
      "outputs": [],
      "source": [
        "train_fields_geo=pd.DataFrame()\n",
        "train_fields_geo['field_id']=list(D.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "wuYqk1uTIlV2",
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        "outputId": "7fc8c876-f331-4f5a-d6c4-7444e43161cb"
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              "      field_id  top_left_x  top_left_y  down_right_x  down_right_y\n",
              "0          756   82.315378   27.330878     82.315378     27.331060\n",
              "1          757   82.315582   27.329236     82.315174     27.329874\n",
              "2         1372   82.319700   27.313791     82.319700     27.314065\n",
              "3         1374   82.319700   27.312879     82.319394     27.313517\n",
              "4         1986   81.566614   27.194632     81.566614     27.194813\n",
              "...        ...         ...         ...           ...           ...\n",
              "5546      1057   82.458866   27.337730     82.458866     27.337821\n",
              "5547      1058   82.455492   27.338369     82.455390     27.338552\n",
              "5548      1382   82.461831   27.342844     82.461831     27.343026\n",
              "5549      1383   82.454878   27.336361     82.454878     27.336543\n",
              "5550      1385   82.454367   27.344305     82.454163     27.344852\n",
              "\n",
              "[5551 rows x 5 columns]"
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>757</td>\n",
              "      <td>82.315582</td>\n",
              "      <td>27.329236</td>\n",
              "      <td>82.315174</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>82.319394</td>\n",
              "      <td>27.313517</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1986</td>\n",
              "      <td>81.566614</td>\n",
              "      <td>27.194632</td>\n",
              "      <td>81.566614</td>\n",
              "      <td>27.194813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5546</th>\n",
              "      <td>1057</td>\n",
              "      <td>82.458866</td>\n",
              "      <td>27.337730</td>\n",
              "      <td>82.458866</td>\n",
              "      <td>27.337821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5547</th>\n",
              "      <td>1058</td>\n",
              "      <td>82.455492</td>\n",
              "      <td>27.338369</td>\n",
              "      <td>82.455390</td>\n",
              "      <td>27.338552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5548</th>\n",
              "      <td>1382</td>\n",
              "      <td>82.461831</td>\n",
              "      <td>27.342844</td>\n",
              "      <td>82.461831</td>\n",
              "      <td>27.343026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5549</th>\n",
              "      <td>1383</td>\n",
              "      <td>82.454878</td>\n",
              "      <td>27.336361</td>\n",
              "      <td>82.454878</td>\n",
              "      <td>27.336543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5550</th>\n",
              "      <td>1385</td>\n",
              "      <td>82.454367</td>\n",
              "      <td>27.344305</td>\n",
              "      <td>82.454163</td>\n",
              "      <td>27.344852</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5551 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91b35d66-8405-4de8-8f12-4634e7e8af09')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-91b35d66-8405-4de8-8f12-4634e7e8af09 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-91b35d66-8405-4de8-8f12-4634e7e8af09');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "train_fields_geo['top_left_x']=[x[0][0] for x in list(D.values())]\n",
        "train_fields_geo['top_left_y']=[x[0][1] for x in list(D.values())]\n",
        "\n",
        "train_fields_geo['down_right_x']=[x[1][0] for x in list(D.values())]\n",
        "train_fields_geo['down_right_y']=[x[1][1] for x in list(D.values())]\n",
        "train_fields_geo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xVfbo-GPIpOQ"
      },
      "outputs": [],
      "source": [
        "train_fields_geo.to_csv('fields_geo_train.csv',index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2AemowoxIrNV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e81a9d4-7264-4c8e-94da-a8c8caa72a1d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting ref_agrifieldnet_competition_v1_labels_test: 100%|██████████| 707/707 [01:02<00:00, 11.39it/s]\n"
          ]
        }
      ],
      "source": [
        "D = get_field_coordinates(test_label_collection, test_folder_ids,test_box)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7bU1cOZ4ItIy"
      },
      "outputs": [],
      "source": [
        "test_fields_geo=pd.DataFrame()\n",
        "test_fields_geo['field_id']=list(D.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b8POOsNUIvvO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "042a051e-abd8-4759-b280-ee20a13fbd7f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      field_id  top_left_x  top_left_y  down_right_x  down_right_y\n",
              "0         5407   76.857977   24.636406     76.857877     24.636863\n",
              "1         6309   87.308766   25.598435     87.308766     25.598616\n",
              "2         6310   87.307269   25.598254     87.307169     25.598797\n",
              "3         6311   87.308367   25.598707     87.308367     25.598978\n",
              "4         6314   87.307569   25.598254     87.307469     25.598616\n",
              "...        ...         ...         ...           ...           ...\n",
              "1525      2127   81.307731   27.953362     81.307527     27.954086\n",
              "1526      3286   81.605554   27.238207     81.605554     27.238480\n",
              "1527      3595   81.599465   27.228773     81.599161     27.229227\n",
              "1528       626   82.221574   27.316775     82.221574     27.316957\n",
              "1529       971   82.231983   27.312035     82.231065     27.314223\n",
              "\n",
              "[1530 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b2487b54-8de5-44e9-9240-0ee93e56ed47\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>field_id</th>\n",
              "      <th>top_left_x</th>\n",
              "      <th>top_left_y</th>\n",
              "      <th>down_right_x</th>\n",
              "      <th>down_right_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5407</td>\n",
              "      <td>76.857977</td>\n",
              "      <td>24.636406</td>\n",
              "      <td>76.857877</td>\n",
              "      <td>24.636863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6309</td>\n",
              "      <td>87.308766</td>\n",
              "      <td>25.598435</td>\n",
              "      <td>87.308766</td>\n",
              "      <td>25.598616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6310</td>\n",
              "      <td>87.307269</td>\n",
              "      <td>25.598254</td>\n",
              "      <td>87.307169</td>\n",
              "      <td>25.598797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6311</td>\n",
              "      <td>87.308367</td>\n",
              "      <td>25.598707</td>\n",
              "      <td>87.308367</td>\n",
              "      <td>25.598978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6314</td>\n",
              "      <td>87.307569</td>\n",
              "      <td>25.598254</td>\n",
              "      <td>87.307469</td>\n",
              "      <td>25.598616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1525</th>\n",
              "      <td>2127</td>\n",
              "      <td>81.307731</td>\n",
              "      <td>27.953362</td>\n",
              "      <td>81.307527</td>\n",
              "      <td>27.954086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1526</th>\n",
              "      <td>3286</td>\n",
              "      <td>81.605554</td>\n",
              "      <td>27.238207</td>\n",
              "      <td>81.605554</td>\n",
              "      <td>27.238480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1527</th>\n",
              "      <td>3595</td>\n",
              "      <td>81.599465</td>\n",
              "      <td>27.228773</td>\n",
              "      <td>81.599161</td>\n",
              "      <td>27.229227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1528</th>\n",
              "      <td>626</td>\n",
              "      <td>82.221574</td>\n",
              "      <td>27.316775</td>\n",
              "      <td>82.221574</td>\n",
              "      <td>27.316957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1529</th>\n",
              "      <td>971</td>\n",
              "      <td>82.231983</td>\n",
              "      <td>27.312035</td>\n",
              "      <td>82.231065</td>\n",
              "      <td>27.314223</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1530 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2487b54-8de5-44e9-9240-0ee93e56ed47')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b2487b54-8de5-44e9-9240-0ee93e56ed47 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b2487b54-8de5-44e9-9240-0ee93e56ed47');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "test_fields_geo['top_left_x']=[x[0][0] for x in list(D.values())]\n",
        "test_fields_geo['top_left_y']=[x[0][1] for x in list(D.values())]\n",
        "\n",
        "test_fields_geo['down_right_x']=[x[1][0] for x in list(D.values())]\n",
        "test_fields_geo['down_right_y']=[x[1][1] for x in list(D.values())]\n",
        "test_fields_geo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X0tMJnPGIyfR"
      },
      "outputs": [],
      "source": [
        "test_fields_geo.to_csv('fields_geo_test.csv',index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wpWBpcihI1EP"
      },
      "source": [
        "###Connected component in each chip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r8mlNSq4I6dG"
      },
      "source": [
        "An other interesting feature is capturing the connected components in the chip ,this is a well-known problem in competitive programming and computer science.I used flood fill algorithm to do so with little adjustement\n",
        "\n",
        "Example: "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgPi2fncJXdU"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hYzSVmp8I55j"
      },
      "outputs": [],
      "source": [
        "def flood_fill_util(z,x ,y, new,vis):\n",
        "    # we need the x and y of the start position,\n",
        "    # and the new value\n",
        "    # the flood fill has 4 parts\n",
        "    # firstly, make sure the x and y are inbounds\n",
        "    if x < 0 or x >= z.shape[0] or y < 0 or y >= z.shape[1]:\n",
        "        return\n",
        "    # secondly, check if the current position equals to zero(no field) or we already visited it\n",
        "    if (z[x][y] == 0 or vis[x][y]):\n",
        "        return\n",
        "    # thirdly, set the current position to the new value\n",
        "    z[x][y] = new\n",
        "    vis[x][y]=1\n",
        "    # fourthly, attempt to fill the neighboring positions\n",
        "    flood_fill_util(z,x+1, y,  new,vis)\n",
        "    flood_fill_util(z,x-1, y,  new,vis)\n",
        "    flood_fill_util(z,x, y+1,  new,vis)\n",
        "    flood_fill_util(z,x, y-1,  new,vis)\n",
        "    flood_fill_util(z,x-1, y-1, new,vis)\n",
        "    flood_fill_util(z,x-1, y+1, new,vis)\n",
        "    flood_fill_util(z,x+1, y-1, new,vis)\n",
        "    flood_fill_util(z,x+1, y+1, new,vis)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ACdU80ABJ5zG"
      },
      "outputs": [],
      "source": [
        "def flood_fill(z,color):\n",
        "  field=z.copy()\n",
        "  n,m=field.shape\n",
        "  vis=np.zeros((n,m))\n",
        "  for i in range(n):\n",
        "    for j in range(m):\n",
        "      if(vis[i][j]==0 and field[i][j]!=0):\n",
        "        flood_fill_util(field,i,j,color,vis)\n",
        "        color+=1\n",
        "  return field,color      "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BUGWS0Q-J8o8"
      },
      "outputs": [],
      "source": [
        "def get_components_per_chip(collection, folder_ids):\n",
        "\n",
        "    D = {}\n",
        "    pbar = tqdm(folder_ids)\n",
        "    pbar.set_description(f'Extracting {collection}',)\n",
        "    color=7999#random number ,i choosed >max(field id) to avoid confusion\n",
        "    for idx in pbar:\n",
        "        \n",
        "        with rasterio.open(f'./{main}/{collection}/{collection}_{idx}/field_ids.tif') as field:\n",
        "            arr = field.read()[0]\n",
        "            \n",
        "            components,color=flood_fill(arr,color)\n",
        "            color+=1\n",
        "            for i in range(256):\n",
        "              for j in range(256):\n",
        "                if(arr[i][j]!=0):\n",
        "                  D[arr[i][j]]=components[i][j]\n",
        "    return D              "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ed9uGwArJ-qk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be96195d-8beb-41de-cbff-e24a8007aa6f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting ref_agrifieldnet_competition_v1_labels_train: 100%|██████████| 1165/1165 [06:39<00:00,  2.92it/s]\n"
          ]
        }
      ],
      "source": [
        "D=get_components_per_chip(train_label_collection, train_folder_ids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M3IolkbCKAYg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "4e66ebd6-a840-445a-d5df-36d625ce9e08"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      field_id  component\n",
              "0          757       7999\n",
              "1          756       8000\n",
              "2         1372       9617\n",
              "3         1374       9617\n",
              "4         1986       8004\n",
              "...        ...        ...\n",
              "5546      1385      13260\n",
              "5547       789      13260\n",
              "5548       923      13261\n",
              "5549       924      13262\n",
              "5550       791      13262\n",
              "\n",
              "[5551 rows x 2 columns]"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1c975a53-d66d-4001-816d-68977b2c9eeb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1c975a53-d66d-4001-816d-68977b2c9eeb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ],
      "source": [
        "train_components=pd.DataFrame()\n",
        "train_components['field_id']=list(D.keys())\n",
        "train_components['component']=list(D.values())\n",
        "train_components"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dj583H0NKCVA"
      },
      "outputs": [],
      "source": [
        "train_components.to_csv('train_components.csv',index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d6JNizyGKEkw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65a8d85c-c76d-46e6-84dd-c4f914d3a749"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting ref_agrifieldnet_competition_v1_labels_test: 100%|██████████| 707/707 [03:53<00:00,  3.03it/s]\n"
          ]
        }
      ],
      "source": [
        "D=get_components_per_chip(test_label_collection, test_folder_ids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "firUfFdzKF_2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "9a2bf640-5ed4-4145-c7f4-fb1d29f2a3d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      field_id  component\n",
              "0         5407       7999\n",
              "1         6309       8001\n",
              "2         6311       8001\n",
              "3         6314       8002\n",
              "4         6310       8002\n",
              "...        ...        ...\n",
              "1525      2127      10104\n",
              "1526      3286      10106\n",
              "1527      3595      10107\n",
              "1528       971      10109\n",
              "1529       626      10110\n",
              "\n",
              "[1530 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fd15faa3-a915-46c0-8671-99968aed1163\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>field_id</th>\n",
              "      <th>component</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5407</td>\n",
              "      <td>7999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6309</td>\n",
              "      <td>8001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6311</td>\n",
              "      <td>8001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6314</td>\n",
              "      <td>8002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6310</td>\n",
              "      <td>8002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1525</th>\n",
              "      <td>2127</td>\n",
              "      <td>10104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1526</th>\n",
              "      <td>3286</td>\n",
              "      <td>10106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1527</th>\n",
              "      <td>3595</td>\n",
              "      <td>10107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1528</th>\n",
              "      <td>971</td>\n",
              "      <td>10109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1529</th>\n",
              "      <td>626</td>\n",
              "      <td>10110</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1530 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd15faa3-a915-46c0-8671-99968aed1163')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fd15faa3-a915-46c0-8671-99968aed1163 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fd15faa3-a915-46c0-8671-99968aed1163');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "test_components=pd.DataFrame()\n",
        "test_components['field_id']=list(D.keys())\n",
        "test_components['component']=list(D.values())\n",
        "test_components"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nXx8mb5sKIfh"
      },
      "outputs": [],
      "source": [
        "test_components.to_csv('test_components.csv',index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "92KUZnS-KJGG"
      },
      "source": [
        "##We're done let's start modelling"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}