Data_download.ipynb

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        "!pip -qq install radiant_mlhub\n",
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            "\u001b[?25l\r\u001b[K     |████▌                           | 10 kB 24.7 MB/s eta 0:00:01\r\u001b[K     |█████████                       | 20 kB 30.7 MB/s eta 0:00:01\r\u001b[K     |█████████████▌                  | 30 kB 22.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 40 kB 12.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▌         | 51 kB 14.9 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 61 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▋| 71 kB 6.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 72 kB 981 kB/s \n",
            "\u001b[?25h\u001b[?25l\r\u001b[K     |█████▍                          | 10 kB 33.0 MB/s eta 0:00:01\r\u001b[K     |██████████▊                     | 20 kB 37.3 MB/s eta 0:00:01\r\u001b[K     |████████████████                | 30 kB 46.5 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▍          | 40 kB 53.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▊     | 51 kB 58.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 61 kB 7.0 MB/s \n",
            "\u001b[?25h\u001b[?25l\r\u001b[K     |██▌                             | 10 kB 35.8 MB/s eta 0:00:01\r\u001b[K     |█████                           | 20 kB 45.2 MB/s eta 0:00:01\r\u001b[K     |███████▍                        | 30 kB 56.0 MB/s eta 0:00:01\r\u001b[K     |█████████▉                      | 40 kB 62.9 MB/s eta 0:00:01\r\u001b[K     |████████████▎                   | 51 kB 67.2 MB/s eta 0:00:01\r\u001b[K     |██████████████▊                 | 61 kB 72.8 MB/s eta 0:00:01\r\u001b[K     |█████████████████▏              | 71 kB 44.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████▋            | 81 kB 45.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 92 kB 37.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▌       | 102 kB 37.5 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 112 kB 37.5 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▌  | 122 kB 37.5 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 133 kB 37.5 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 133 kB 37.5 MB/s \n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.25.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[K     |████████████████████████████████| 19.3 MB 167 kB/s \n",
            "\u001b[?25h"
          ]
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5lkavxbCUnBS"
      },
      "source": [
        "# Required libraries\n",
        "import os\n",
        "import tarfile\n",
        "import json\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from pathlib import Path\n",
        "import shutil\n",
        "from radiant_mlhub.client import _download as download_file\n",
        "import rasterio\n",
        "from rasterio.plot import show\n",
        "import datetime\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import log_loss\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "from joblib import Parallel, delayed\n",
        "\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "os.environ['MLHUB_API_KEY'] = 'N/A'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1cp5WV9UixxP",
        "outputId": "8ee820e4-8d91-4fb5-a723-34c408df1f7f"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25jT7DI2AC8g",
        "outputId": "682b26db-85e9-4fa4-f09d-fd457d0ef282"
      },
      "source": [
        "!pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SesTuk23BC3U"
      },
      "source": [
        "os.mkdir('Radiant_Data')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kooy723UUnBT"
      },
      "source": [
        "## Download Options\n",
        "\n",
        "By editing the cell below, you can chose which bands of the Sentinel-2 imagery to download and whether or not to download the Sentinel-1 data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MA3v-a-4UnBU"
      },
      "source": [
        "# DOWNLOAD_S1 = True # If you set this to true then the Sentinel-1 data will be downloaded\n",
        "\n",
        "# Select which imagery bands you'd like to download here\n",
        "# Download data in batches\n",
        "DOWNLOAD_BANDS_1 = {'B01': True,'B02': True,'B03': True,'B04': False,'B05': False,'B06': False,'B07': False,'B08': False,'B8A': False,\\\n",
        "                  'B09': False,'B11': False,'B12': False,'CLM': False}\n",
        "\n",
        "DOWNLOAD_BANDS_2 = {'B01': False,'B02': False,'B03': False,'B04': True,'B05': True,'B06': True,'B07': False,'B08': False,'B8A': False,\\\n",
        "                  'B09': False,'B11': False,'B12': False,'CLM': False}\n",
        "\n",
        "DOWNLOAD_BANDS_3 = {'B01': False,'B02': False,'B03': False,'B04': False,'B05': False,'B06': False,'B07': True,'B08': True,'B8A': True,\\\n",
        "                  'B09': False,'B11': False,'B12': False,'CLM': False}    \n",
        "\n",
        "DOWNLOAD_BANDS_4 = {'B01': False,'B02': False,'B03': False,'B04': False,'B05': False,'B06': False,'B07': False,'B08': False,'B8A': False,\\\n",
        "                  'B09': True,'B11': True,'B12': True,'CLM': True}    \n",
        "            "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3BIioR8fUnBU"
      },
      "source": [
        "Downloading Datasets and Loading Asset File Paths into a Pandas Dataframe\n",
        "===\n",
        "\n",
        "The cells in this notebook will show you how to download all of the datasets for this competition and read the STAC metadata into a pandas dataframe. There will be two dataframes, one for train and one for test, which contain all of the information you will need to filter based off datetime, satellite platform, and asset type. Contained in each row of the dataframe is also the file path for that asset being described. Assets which have a `None` value for the  `datetime` and `satellite_platform` columns are assets which are related to the label item."
      ]
    },
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        },
        "id": "GOysbC69UnBV",
        "outputId": "c775711a-0a7f-4e15-a21c-5c896089cf1a"
      },
      "source": [
        "%%time\n",
        "# Download the data in batches to avoid using all RAM\n",
        "FOLDER_BASE = 'ref_south_africa_crops_competition_v1'\n",
        "for down_bands, down_s1, file_no in zip([DOWNLOAD_BANDS_1, DOWNLOAD_BANDS_2, DOWNLOAD_BANDS_3, DOWNLOAD_BANDS_4], [True, False, False, False], [1, 2, 3, 4]):\n",
        "  DOWNLOAD_BANDS = down_bands.copy()\n",
        "  DOWNLOAD_S1 = down_s1\n",
        "  def download_archive(archive_name):\n",
        "      if os.path.exists(archive_name.replace('.tar.gz', '')):\n",
        "          return\n",
        "      \n",
        "      print(f'Downloading {archive_name} ...')\n",
        "      download_url = f'https://radiant-mlhub.s3.us-west-2.amazonaws.com/archives/{archive_name}'\n",
        "      download_file(download_url, '.')\n",
        "      print(f'Extracting {archive_name} ...')\n",
        "      with tarfile.open(archive_name) as tfile:\n",
        "          tfile.extractall()\n",
        "      os.remove(archive_name)\n",
        "\n",
        "  for split in ['train', 'test']:\n",
        "      # Download the labels\n",
        "      labels_archive = f'{FOLDER_BASE}_{split}_labels.tar.gz'\n",
        "      download_archive(labels_archive)\n",
        "      \n",
        "      # Download Sentinel-1 data\n",
        "      if DOWNLOAD_S1:\n",
        "          s1_archive = f'{FOLDER_BASE}_{split}_source_s1.tar.gz'\n",
        "          download_archive(s1_archive)\n",
        "          \n",
        "\n",
        "      for band, download in DOWNLOAD_BANDS.items():\n",
        "          if not download:\n",
        "              continue\n",
        "          s2_archive = f'{FOLDER_BASE}_{split}_source_s2_{band}.tar.gz'\n",
        "          download_archive(s2_archive)\n",
        "          \n",
        "  def resolve_path(base, path):\n",
        "      return Path(os.path.join(base, path)).resolve()\n",
        "          \n",
        "  def load_df(collection_id):\n",
        "      split = collection_id.split('_')[-2]\n",
        "      collection = json.load(open(f'{collection_id}/collection.json', 'r'))\n",
        "      rows = []\n",
        "      item_links = []\n",
        "      for link in collection['links']:\n",
        "          if link['rel'] != 'item':\n",
        "              continue\n",
        "          item_links.append(link['href'])\n",
        "          \n",
        "      for item_link in item_links:\n",
        "          item_path = f'{collection_id}/{item_link}'\n",
        "          current_path = os.path.dirname(item_path)\n",
        "          item = json.load(open(item_path, 'r'))\n",
        "          tile_id = item['id'].split('_')[-1]\n",
        "          for asset_key, asset in item['assets'].items():\n",
        "              rows.append([\n",
        "                  tile_id,\n",
        "                  None,\n",
        "                  None,\n",
        "                  asset_key,\n",
        "                  str(resolve_path(current_path, asset['href']))\n",
        "              ])   \n",
        "          for link in item['links']:\n",
        "              if link['rel'] != 'source':\n",
        "                  continue\n",
        "              source_item_id = link['href'].split('/')[-2]\n",
        "              \n",
        "              if source_item_id.find('_s1_') > 0 and not DOWNLOAD_S1:\n",
        "                  continue\n",
        "              elif source_item_id.find('_s1_') > 0:\n",
        "                  for band in ['VV', 'VH']:\n",
        "                      asset_path = Path(f'{FOLDER_BASE}_{split}_source_s1/{source_item_id}/{band}.tif').resolve()\n",
        "                      date = '-'.join(source_item_id.split('_')[10:13])\n",
        "                      \n",
        "                      rows.append([\n",
        "                          tile_id,\n",
        "                          f'{date}T00:00:00Z',\n",
        "                          's1',\n",
        "                          band,\n",
        "                          asset_path\n",
        "                      ])\n",
        "                  \n",
        "              if source_item_id.find('_s2_') > 0:\n",
        "                  for band, download in DOWNLOAD_BANDS.items():\n",
        "                      if not download:\n",
        "                          continue\n",
        "                      \n",
        "                      asset_path = Path(f'{FOLDER_BASE}_{split}_source_s2_{band}/{source_item_id}_{band}.tif').resolve()\n",
        "                      date = '-'.join(source_item_id.split('_')[10:13])\n",
        "                      rows.append([\n",
        "                          tile_id,\n",
        "                          f'{date}T00:00:00Z',\n",
        "                          's2',\n",
        "                          band,\n",
        "                          asset_path\n",
        "                      ])\n",
        "              \n",
        "      return pd.DataFrame(rows, columns=['tile_id', 'datetime', 'satellite_platform', 'asset', 'file_path'])\n",
        "\n",
        "  train_df = load_df(f'{FOLDER_BASE}_train_labels')\n",
        "  test_df = load_df(f'{FOLDER_BASE}_test_labels')\n",
        "  train_df.to_csv(f'train{file_no}.csv', index = False)\n",
        "  test_df.to_csv(f'test{file_no}.csv', index = False)\n",
        "\n",
        "# "
      ],
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading ref_south_africa_crops_competition_v1_train_labels.tar.gz ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6f12aa9686334b9bb2783362d9ded68d",
              "version_major": 2,
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ref_south_africa_crops_competition_v1_train_labels.tar.gz ...\n",
            "Downloading ref_south_africa_crops_competition_v1_train_source_s1.tar.gz ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ref_south_africa_crops_competition_v1_train_source_s1.tar.gz ...\n",
            "Downloading ref_south_africa_crops_competition_v1_train_source_s2_B01.tar.gz ...\n"
          ]
        },
        {
          "data": {
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        },
        {
          "metadata": {
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          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ref_south_africa_crops_competition_v1_train_source_s2_B01.tar.gz ...\n",
            "Downloading ref_south_africa_crops_competition_v1_train_source_s2_B02.tar.gz ...\n"
          ]
        },
        {
          "data": {
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        },
        {
          "metadata": {
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          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ref_south_africa_crops_competition_v1_train_source_s2_B02.tar.gz ...\n",
            "Downloading ref_south_africa_crops_competition_v1_train_source_s2_B03.tar.gz ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
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          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ref_south_africa_crops_competition_v1_train_source_s2_B03.tar.gz ...\n",
            "Downloading ref_south_africa_crops_competition_v1_test_labels.tar.gz ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
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          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ref_south_africa_crops_competition_v1_test_labels.tar.gz ...\n",
            "Downloading ref_south_africa_crops_competition_v1_test_source_s1.tar.gz ...\n"
          ]
        },
        {
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ref_south_africa_crops_competition_v1_test_source_s1.tar.gz ...\n",
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          ]
        },
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          "output_type": "stream",
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          ]
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          ]
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          ]
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          ]
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          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ref_south_africa_crops_competition_v1_test_source_s2_B8A.tar.gz ...\n",
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          "output_type": "stream",
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          "output_type": "stream",
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          "output_type": "stream",
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        },
        {
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          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ref_south_africa_crops_competition_v1_test_source_s2_B12.tar.gz ...\n",
            "Downloading ref_south_africa_crops_competition_v1_test_source_s2_CLM.tar.gz ...\n"
          ]
        },
        {
          "data": {
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          "metadata": {},
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ref_south_africa_crops_competition_v1_test_source_s2_CLM.tar.gz ...\n",
            "CPU times: user 40min 4s, sys: 16min 14s, total: 56min 19s\n",
            "Wall time: 45min 22s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0MrtiUMuxe9",
        "outputId": "f07646bc-686a-4e27-a550-cc6f4a05187d"
      },
      "source": [
        "# Check files in colab directory\n",
        "# Remove irrelevant files and retain only radiant data\n",
        "import glob\n",
        "all = glob.glob('/content/*')\n",
        "all.remove('/content/drive')\n",
        "all.remove('/content/sample_data')\n",
        "\n",
        "all"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/ref_south_africa_crops_competition_v1_train_labels',\n",
              " '/content/train4.csv',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s1',\n",
              " '/content/train1.csv',\n",
              " '/content/test1.csv',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_B12',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_B03',\n",
              " '/content/test3.csv',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_B02',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_B01',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_B05',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_B11',\n",
              " '/content/train2.csv',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_B8A',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_B08',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_B04',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_B01',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_labels',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_B09',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_CLM',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s1',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_B05',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_B06',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_B09',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_B06',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_B11',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_B07',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_B03',\n",
              " '/content/test2.csv',\n",
              " '/content/test4.csv',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_B02',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_B8A',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_B08',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_B04',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_B07',\n",
              " '/content/train3.csv',\n",
              " '/content/ref_south_africa_crops_competition_v1_train_source_s2_B12',\n",
              " '/content/ref_south_africa_crops_competition_v1_test_source_s2_CLM']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "W6WceHxat2lq",
        "outputId": "f9e2921e-4fc3-4150-cf29-b97cd5c64da7"
      },
      "source": [
        "# Zip all all data to one file\n",
        "%%time\n",
        "shutil.make_archive('/content/Radiant_Data', 'zip', '/content/Radiant_Data')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 55min 39s, sys: 6min 1s, total: 1h 1min 40s\n",
            "Wall time: 1h 33min 57s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/Radiant_Data.zip'"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PywF2RA8Mrpu"
      },
      "source": [
        "import gc\n",
        "gc.collect()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNTkX42Et2gY",
        "outputId": "05d8d93c-66f4-40ae-fcda-3e523b439318"
      },
      "source": [
        "# Move downloaded data to gdrive\n",
        "%%time\n",
        "!mv '/content/Radiant_Data.zip' '/content/drive/MyDrive/CompeData/Radiant'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 6.14 s, sys: 903 ms, total: 7.04 s\n",
            "Wall time: 16min 21s\n"
          ]
        }
      ]
    }
  ]
}