Geojson - temperature_conversion.ipynb

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        "## Converting .NC File to GEOJSON\n",
        "\n",
        "A .NC file, also known as a NetCDF file, is a common data format used for storing multidimensional scientific data. \"NC\" stands for \"Network Common Data Form.\" NetCDF files are widely used in the geosciences, atmospheric sciences, oceanography, climate research, and other scientific disciplines for storing and sharing data.\n"
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        "Libraries required for this notebook.\n",
        "1. Geopandas - It provides data structures and operations for working with geospatial data, such as points, lines, polygons, and multi-geometry types.\n",
        "2. Fiona - It is used for reading and writing geospatial data formats.\n",
        "3. Xarray - It is designed for working with labeled multidimensional arrays, particularly those with dimensions representing time, space, and other coordinates. It is widely used in the geosciences and atmospheric sciences for handling climate, weather, and other environmental data. It is used to read NETCDF files."
      ],
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        "id": "OczKW1jHv7JW"
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        "id": "_11p1sVmTvbm",
        "outputId": "9c6112d3-c560-4232-f7c8-1649783ce20e"
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          "text": [
            "Requirement already satisfied: geopandas in /usr/local/lib/python3.10/dist-packages (0.13.2)\n",
            "Requirement already satisfied: fiona>=1.8.19 in /usr/local/lib/python3.10/dist-packages (from geopandas) (1.9.6)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from geopandas) (24.0)\n",
            "Requirement already satisfied: pandas>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from geopandas) (1.5.3)\n",
            "Requirement already satisfied: pyproj>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from geopandas) (3.6.1)\n",
            "Requirement already satisfied: shapely>=1.7.1 in /usr/local/lib/python3.10/dist-packages (from geopandas) (2.0.3)\n",
            "Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.19->geopandas) (23.2.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.19->geopandas) (2024.2.2)\n",
            "Requirement already satisfied: click~=8.0 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.19->geopandas) (8.1.7)\n",
            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.19->geopandas) (1.1.1)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.19->geopandas) (0.7.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.19->geopandas) (1.16.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.0->geopandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.0->geopandas) (2023.4)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.0->geopandas) (1.25.2)\n",
            "Requirement already satisfied: geojson in /usr/local/lib/python3.10/dist-packages (3.1.0)\n"
          ]
        }
      ],
      "source": [
        "#installing libraries as these are not available as default for Collab\n",
        "!pip install geopandas\n",
        "!pip install geojson"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importing python libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import xarray as xr\n",
        "import json\n",
        "import os\n",
        "import geojson\n",
        "from geojson import Point, MultiPoint, Feature, FeatureCollection, dump\n",
        "import geopandas as gpd"
      ],
      "metadata": {
        "id": "K7cauJwLUIWS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# mounting google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "qCBwJn0BWtnd",
        "outputId": "62bf28ce-bca4-4854-932f-84d7b399e492"
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      "execution_count": null,
      "outputs": [
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          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "#reading netcdf file using xarray library\n",
        "ds=xr.open_dataset(\"/content/drive/MyDrive/DS Project/Omdena X Valutus/temp_yearly_85.nc\")"
      ],
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        "id": "kZkFxlHfXKOX"
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              "  * time       (time) datetime64[ns] 2020-12-31 2021-12-31 ... 2050-12-31\n",
              "    height     float64 ...\n",
              "Dimensions without coordinates: y, x\n",
              "Data variables:\n",
              "    tasAdjust  (time, y, x) float32 ...\n",
              "Attributes: (12/30)\n",
              "    CDI:                      Climate Data Interface version 1.9.6 (http://mp...\n",
              "    Conventions:              CF-1.6\n",
              "    CDO:                      Climate Data Operators version 1.9.6 (http://mp...\n",
              "    creation_date:            2020-02-13T23:47:09ZCET+0100\n",
              "    NCO:                      netCDF Operators version 4.7.7 (Homepage = http...\n",
              "    acknowledgements:         This work was performed within Copernicus Clima...\n",
              "    ...                       ...\n",
              "    invar_rcm_model_driver:   ICHEC-EC-EARTH\n",
              "    invar_experiment_id:      rcp85\n",
              "    invar_realisation_id:     r12i1p1\n",
              "    invar_rcm_model_id:       CLMcom-CCLM4-8-17\n",
              "    time_period:              2018\n",
              "    history:                  </pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f166abdb-449d-44ee-9d10-856a44abd7b2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f166abdb-449d-44ee-9d10-856a44abd7b2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>y</span>: 950</li><li><span>x</span>: 1000</li><li><span class='xr-has-index'>time</span>: 31</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1790657e-d2d0-488e-bea1-c4e348bfe7ee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1790657e-d2d0-488e-bea1-c4e348bfe7ee' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84f2217b-1b5d-4897-a08c-922ffee6e473' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84f2217b-1b5d-4897-a08c-922ffee6e473' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caebc7c8-824b-4fb5-b428-e34f486380e7' class='xr-var-data-in' type='checkbox'><label for='data-caebc7c8-824b-4fb5-b428-e34f486380e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>[950000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0ae32724-6c18-47f6-8d80-b7baab8875d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ae32724-6c18-47f6-8d80-b7baab8875d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2f3f7ae-a35c-4f64-a801-778929a644a5' class='xr-var-data-in' type='checkbox'><label for='data-d2f3f7ae-a35c-4f64-a801-778929a644a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>[950000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-12-31 ... 2050-12-31</div><input id='attrs-4d0ae32a-aca9-4b3b-b337-ddc085238ab6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d0ae32a-aca9-4b3b-b337-ddc085238ab6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f3b445a-0038-4363-b5d5-fa39dc2f776e' class='xr-var-data-in' type='checkbox'><label for='data-1f3b445a-0038-4363-b5d5-fa39dc2f776e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-12-31T00:00:00.000000000&#x27;, &#x27;2021-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2022-12-31T00:00:00.000000000&#x27;, &#x27;2023-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2024-12-31T00:00:00.000000000&#x27;, &#x27;2025-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2026-12-31T00:00:00.000000000&#x27;, &#x27;2027-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2028-12-31T00:00:00.000000000&#x27;, &#x27;2029-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2030-12-31T00:00:00.000000000&#x27;, &#x27;2031-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2032-12-31T00:00:00.000000000&#x27;, &#x27;2033-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2034-12-31T00:00:00.000000000&#x27;, &#x27;2035-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2036-12-31T00:00:00.000000000&#x27;, &#x27;2037-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2038-12-31T00:00:00.000000000&#x27;, &#x27;2039-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2040-12-31T00:00:00.000000000&#x27;, &#x27;2041-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2042-12-31T00:00:00.000000000&#x27;, &#x27;2043-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2044-12-31T00:00:00.000000000&#x27;, &#x27;2045-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2046-12-31T00:00:00.000000000&#x27;, &#x27;2047-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2048-12-31T00:00:00.000000000&#x27;, &#x27;2049-12-31T00:00:00.000000000&#x27;,\n",
              "       &#x27;2050-12-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5ab4dc0-f7f3-42f2-97d5-47fe2eaf05c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5ab4dc0-f7f3-42f2-97d5-47fe2eaf05c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e063f793-b986-4b10-b604-d4f7e6697f0a' class='xr-var-data-in' type='checkbox'><label for='data-e063f793-b986-4b10-b604-d4f7e6697f0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de6078ca-5d73-44d3-aea7-f55afa29ae45' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de6078ca-5d73-44d3-aea7-f55afa29ae45' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tasAdjust</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dae06de5-e1b3-4589-a265-b49b2cbf3202' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dae06de5-e1b3-4589-a265-b49b2cbf3202' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-886c00e6-b594-4101-b148-d75d97278fe5' class='xr-var-data-in' type='checkbox'><label for='data-886c00e6-b594-4101-b148-d75d97278fe5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area:areacella</dd><dt><span>cell_methods :</span></dt><dd>time:mean</dd><dt><span>long_name :</span></dt><dd>mean air temperature</dd><dt><span>standard_name :</span></dt><dd>mean_temperature_index_per_time_period</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>variable :</span></dt><dd>tasAdjust</dd></dl></div><div class='xr-var-data'><pre>[29450000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-684f5d07-7fb8-4706-8d83-b7b4b18b186c' class='xr-section-summary-in' type='checkbox'  ><label for='section-684f5d07-7fb8-4706-8d83-b7b4b18b186c' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c911549d-28aa-44dd-ab12-84475a663ab4' class='xr-index-data-in' type='checkbox'/><label for='index-c911549d-28aa-44dd-ab12-84475a663ab4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-12-31&#x27;, &#x27;2021-12-31&#x27;, &#x27;2022-12-31&#x27;, &#x27;2023-12-31&#x27;,\n",
              "               &#x27;2024-12-31&#x27;, &#x27;2025-12-31&#x27;, &#x27;2026-12-31&#x27;, &#x27;2027-12-31&#x27;,\n",
              "               &#x27;2028-12-31&#x27;, &#x27;2029-12-31&#x27;, &#x27;2030-12-31&#x27;, &#x27;2031-12-31&#x27;,\n",
              "               &#x27;2032-12-31&#x27;, &#x27;2033-12-31&#x27;, &#x27;2034-12-31&#x27;, &#x27;2035-12-31&#x27;,\n",
              "               &#x27;2036-12-31&#x27;, &#x27;2037-12-31&#x27;, &#x27;2038-12-31&#x27;, &#x27;2039-12-31&#x27;,\n",
              "               &#x27;2040-12-31&#x27;, &#x27;2041-12-31&#x27;, &#x27;2042-12-31&#x27;, &#x27;2043-12-31&#x27;,\n",
              "               &#x27;2044-12-31&#x27;, &#x27;2045-12-31&#x27;, &#x27;2046-12-31&#x27;, &#x27;2047-12-31&#x27;,\n",
              "               &#x27;2048-12-31&#x27;, &#x27;2049-12-31&#x27;, &#x27;2050-12-31&#x27;],\n",
              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3bd7199-a424-4238-b639-8eaaebad02e9' class='xr-section-summary-in' type='checkbox'  ><label for='section-a3bd7199-a424-4238-b639-8eaaebad02e9' class='xr-section-summary' >Attributes: <span>(30)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 1.9.6 (http://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 1.9.6 (http://mpimet.mpg.de/cdo)</dd><dt><span>creation_date :</span></dt><dd>2020-02-13T23:47:09ZCET+0100</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.7 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>acknowledgements :</span></dt><dd>This work was performed within Copernicus Climate Change Service - C3S_424_SMHI, https://climate.copernicus.eu/operational-service-water-sector, on behalf of ECMWF and EU.</dd><dt><span>contact :</span></dt><dd>Hydro.fou@smhi.se</dd><dt><span>data_quality :</span></dt><dd>Testing of EURO-CORDEX data performed by ESGF nodes. In the contract C3S_424_SMHI additional tests were performed during the bias adjustment and the prodcution of the CII.</dd><dt><span>domain :</span></dt><dd>EUR-11</dd><dt><span>institution :</span></dt><dd>SMHI, www.smhi.se</dd><dt><span>invar_bc_institution :</span></dt><dd>Swedish Meteorological and Hydrological Institute</dd><dt><span>invar_bc_method :</span></dt><dd>TimescaleBC, description in deliverable C3S_D424.SMHI.1.3b</dd><dt><span>invar_bc_method_id :</span></dt><dd>TimescaleBC v1.02</dd><dt><span>invar_bc_observation :</span></dt><dd>EFAS-Meteo, https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/efas-meteo-european-daily-high-resolution-gridded-meteorological-data-set-1990-2011</dd><dt><span>invar_bc_observation_id :</span></dt><dd>EFAS-Meteo</dd><dt><span>invar_bc_period :</span></dt><dd>1990-2018</dd><dt><span>keywords :</span></dt><dd>temperature, air</dd><dt><span>license :</span></dt><dd>Copernicus License V1.2</dd><dt><span>output_frequency :</span></dt><dd>daily</dd><dt><span>project_id :</span></dt><dd>C3S_424_SMHI</dd><dt><span>references :</span></dt><dd>Ntegeka et al., 2013 (http://dx.doi.org/10.2788/51262)</dd><dt><span>source :</span></dt><dd>The RCM data originate from EURO-CORDEX (Coordinated Downscaling Experiment - European Domain, EUR-11) https://euro-cordex.net/.</dd><dt><span>summary :</span></dt><dd>Daily mean air temperature at 2m height from an ensmeble of EURO-CORDEX EUR11. The variable is bias adjusted with theTimescaleBC method and using the EFAS-Meteo reference dataset. </dd><dt><span>tittle :</span></dt><dd>Daily mean air Temperature at 2m height</dd><dt><span>invar_rcm_model_driver :</span></dt><dd>ICHEC-EC-EARTH</dd><dt><span>invar_experiment_id :</span></dt><dd>rcp85</dd><dt><span>invar_realisation_id :</span></dt><dd>r12i1p1</dd><dt><span>invar_rcm_model_id :</span></dt><dd>CLMcom-CCLM4-8-17</dd><dt><span>time_period :</span></dt><dd>2018</dd><dt><span>history :</span></dt><dd></dd></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### High Level info:\n",
        "1. There are three dimensions (y, x, time).\n",
        "2. (y,X) are used for longitude and latitude.\n",
        "3. time has 31 points. From our previous knowledge we know that the range covered is a from 2020-2050. We have a time series and the data is yearly.\n",
        "4. Variable is `tasAdjust` which is a mean air temperature and has values in Kelvin.\n",
        "5. height is same for each variable as temperature has been measured at 2m height."
      ],
      "metadata": {
        "id": "jvvAxIKyxlBk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# reading high-level metadata (file level information)\n",
        "ds.attrs"
      ],
      "metadata": {
        "id": "LwY1XPO5YU47",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "964c4255-b682-4763-bfc5-6610393f5fd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CDI': 'Climate Data Interface version 1.9.6 (http://mpimet.mpg.de/cdi)',\n",
              " 'Conventions': 'CF-1.6',\n",
              " 'CDO': 'Climate Data Operators version 1.9.6 (http://mpimet.mpg.de/cdo)',\n",
              " 'creation_date': '2020-02-13T23:47:09ZCET+0100',\n",
              " 'NCO': 'netCDF Operators version 4.7.7 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)',\n",
              " 'acknowledgements': 'This work was performed within Copernicus Climate Change Service - C3S_424_SMHI, https://climate.copernicus.eu/operational-service-water-sector, on behalf of ECMWF and EU.',\n",
              " 'contact': 'Hydro.fou@smhi.se',\n",
              " 'data_quality': 'Testing of EURO-CORDEX data performed by ESGF nodes. In the contract C3S_424_SMHI additional tests were performed during the bias adjustment and the prodcution of the CII.',\n",
              " 'domain': 'EUR-11',\n",
              " 'institution': 'SMHI, www.smhi.se',\n",
              " 'invar_bc_institution': 'Swedish Meteorological and Hydrological Institute',\n",
              " 'invar_bc_method': 'TimescaleBC, description in deliverable C3S_D424.SMHI.1.3b',\n",
              " 'invar_bc_method_id': 'TimescaleBC v1.02',\n",
              " 'invar_bc_observation': 'EFAS-Meteo, https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/efas-meteo-european-daily-high-resolution-gridded-meteorological-data-set-1990-2011',\n",
              " 'invar_bc_observation_id': 'EFAS-Meteo',\n",
              " 'invar_bc_period': '1990-2018',\n",
              " 'keywords': 'temperature, air',\n",
              " 'license': 'Copernicus License V1.2',\n",
              " 'output_frequency': 'daily',\n",
              " 'project_id': 'C3S_424_SMHI',\n",
              " 'references': 'Ntegeka et al., 2013 (http://dx.doi.org/10.2788/51262)',\n",
              " 'source': 'The RCM data originate from EURO-CORDEX (Coordinated Downscaling Experiment - European Domain, EUR-11) https://euro-cordex.net/.',\n",
              " 'summary': 'Daily mean air temperature at 2m height from an ensmeble of EURO-CORDEX EUR11. The variable is bias adjusted with theTimescaleBC method and using the EFAS-Meteo reference dataset. ',\n",
              " 'tittle': 'Daily mean air Temperature at 2m height',\n",
              " 'invar_rcm_model_driver': 'ICHEC-EC-EARTH',\n",
              " 'invar_experiment_id': 'rcp85',\n",
              " 'invar_realisation_id': 'r12i1p1',\n",
              " 'invar_rcm_model_id': 'CLMcom-CCLM4-8-17',\n",
              " 'time_period': '2018',\n",
              " 'history': ''}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# variable level metadata\n",
        "ds['height'].attrs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7kEXgrIYYeH",
        "outputId": "b369661f-22b2-4fbe-80fe-84cea414ac41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'units': 'm',\n",
              " 'axis': 'Z',\n",
              " 'positive': 'up',\n",
              " 'long_name': 'height',\n",
              " 'standard_name': 'height'}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# variable level metadata\n",
        "ds['tasAdjust'].attrs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mqHT_atYbff",
        "outputId": "c9973763-b094-4d32-fa1d-fc61285d89c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'cell_measures': 'area:areacella',\n",
              " 'cell_methods': 'time:mean',\n",
              " 'long_name': 'mean air temperature',\n",
              " 'standard_name': 'mean_temperature_index_per_time_period',\n",
              " 'units': 'K',\n",
              " 'variable': 'tasAdjust'}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save File level and variable level metadata"
      ],
      "metadata": {
        "id": "m0-nfpH8zMCE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For conversion of Netcdf file to GeoJSON, I'm planning to save just the\n",
        "\n",
        "*   Geometry - Point - Longitude and Latitude\n",
        "*   Feature - properties - Varaible (`tasAdjust`) and time.\n",
        "\n",
        "In order to preserve the file level info saving the ds.attrs as metadata.json since it is originally saved as dictionary with key value pair."
      ],
      "metadata": {
        "id": "wy85cU8oxVWw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# function to save the metadata.\n",
        "def save_metadata(ds, path):\n",
        "  metadata=ds.attrs\n",
        "  metadata['height']= ds['height'].attrs\n",
        "  metadata['height']['value']= ds['height'].values.tolist()\n",
        "  metadata['temperature']=ds['tasAdjust'].attrs\n",
        "  with open(path, \"w\") as json_file:\n",
        "    json.dump(metadata, json_file, indent=4)\n",
        "\n",
        "  print(\"[...] Metadata saved as a JSON file\")\n",
        "  return metadata"
      ],
      "metadata": {
        "id": "4-nQQekxXKGD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# saving the metadata\n",
        "meta_path= \"/content/drive/MyDrive/DS Project/Omdena X Valutus/metadata_rcp85.json\"\n",
        "save_metadata(ds, meta_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pxCKFG78EmP9",
        "outputId": "5c07f33a-f6c3-4c22-a731-f6de2ae012f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[...] Metadata saved as a JSON file\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CDI': 'Climate Data Interface version 1.9.6 (http://mpimet.mpg.de/cdi)',\n",
              " 'Conventions': 'CF-1.6',\n",
              " 'CDO': 'Climate Data Operators version 1.9.6 (http://mpimet.mpg.de/cdo)',\n",
              " 'creation_date': '2020-02-13T23:47:09ZCET+0100',\n",
              " 'NCO': 'netCDF Operators version 4.7.7 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)',\n",
              " 'acknowledgements': 'This work was performed within Copernicus Climate Change Service - C3S_424_SMHI, https://climate.copernicus.eu/operational-service-water-sector, on behalf of ECMWF and EU.',\n",
              " 'contact': 'Hydro.fou@smhi.se',\n",
              " 'data_quality': 'Testing of EURO-CORDEX data performed by ESGF nodes. In the contract C3S_424_SMHI additional tests were performed during the bias adjustment and the prodcution of the CII.',\n",
              " 'domain': 'EUR-11',\n",
              " 'institution': 'SMHI, www.smhi.se',\n",
              " 'invar_bc_institution': 'Swedish Meteorological and Hydrological Institute',\n",
              " 'invar_bc_method': 'TimescaleBC, description in deliverable C3S_D424.SMHI.1.3b',\n",
              " 'invar_bc_method_id': 'TimescaleBC v1.02',\n",
              " 'invar_bc_observation': 'EFAS-Meteo, https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/efas-meteo-european-daily-high-resolution-gridded-meteorological-data-set-1990-2011',\n",
              " 'invar_bc_observation_id': 'EFAS-Meteo',\n",
              " 'invar_bc_period': '1990-2018',\n",
              " 'keywords': 'temperature, air',\n",
              " 'license': 'Copernicus License V1.2',\n",
              " 'output_frequency': 'daily',\n",
              " 'project_id': 'C3S_424_SMHI',\n",
              " 'references': 'Ntegeka et al., 2013 (http://dx.doi.org/10.2788/51262)',\n",
              " 'source': 'The RCM data originate from EURO-CORDEX (Coordinated Downscaling Experiment - European Domain, EUR-11) https://euro-cordex.net/.',\n",
              " 'summary': 'Daily mean air temperature at 2m height from an ensmeble of EURO-CORDEX EUR11. The variable is bias adjusted with theTimescaleBC method and using the EFAS-Meteo reference dataset. ',\n",
              " 'tittle': 'Daily mean air Temperature at 2m height',\n",
              " 'invar_rcm_model_driver': 'ICHEC-EC-EARTH',\n",
              " 'invar_experiment_id': 'rcp85',\n",
              " 'invar_realisation_id': 'r12i1p1',\n",
              " 'invar_rcm_model_id': 'CLMcom-CCLM4-8-17',\n",
              " 'time_period': '2018',\n",
              " 'history': '',\n",
              " 'height': {'units': 'm',\n",
              "  'axis': 'Z',\n",
              "  'positive': 'up',\n",
              "  'long_name': 'height',\n",
              "  'standard_name': 'height',\n",
              "  'value': 2.0},\n",
              " 'temperature': {'cell_measures': 'area:areacella',\n",
              "  'cell_methods': 'time:mean',\n",
              "  'long_name': 'mean air temperature',\n",
              "  'standard_name': 'mean_temperature_index_per_time_period',\n",
              "  'units': 'K',\n",
              "  'variable': 'tasAdjust'}}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving the .NC file as CSV (Optional)"
      ],
      "metadata": {
        "id": "MHhWv4cyfBqO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# converting xarray dataset to dataframe\n",
        "df=ds.to_dataframe()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "JJFkFArkXJ1q",
        "outputId": "c7befde6-8a3f-4869-c097-99ae50cedd7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          lon        lat   tasAdjust  height\n",
              "y   x   time                                                \n",
              "0   0   2020-12-31  -8.229275  27.802845  295.029877     2.0\n",
              "        2021-12-31  -8.229275  27.802845  295.414185     2.0\n",
              "        2022-12-31  -8.229275  27.802845  295.056427     2.0\n",
              "        2023-12-31  -8.229275  27.802845  295.779083     2.0\n",
              "        2024-12-31  -8.229275  27.802845  295.092072     2.0\n",
              "...                       ...        ...         ...     ...\n",
              "949 999 2046-12-31  73.937675  58.246738         NaN     2.0\n",
              "        2047-12-31  73.937675  58.246738         NaN     2.0\n",
              "        2048-12-31  73.937675  58.246738         NaN     2.0\n",
              "        2049-12-31  73.937675  58.246738         NaN     2.0\n",
              "        2050-12-31  73.937675  58.246738         NaN     2.0\n",
              "\n",
              "[29450000 rows x 4 columns]"
            ],
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              "\n",
              "  <div id=\"df-b7fb71d1-81ee-4db1-81fe-46ccdf4d541a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>lon</th>\n",
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              "      <th>tasAdjust</th>\n",
              "      <th>height</th>\n",
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              "    <tr>\n",
              "      <th>y</th>\n",
              "      <th>x</th>\n",
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              "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>-8.229275</td>\n",
              "      <td>27.802845</td>\n",
              "      <td>295.029877</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-31</th>\n",
              "      <td>-8.229275</td>\n",
              "      <td>27.802845</td>\n",
              "      <td>295.414185</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-31</th>\n",
              "      <td>-8.229275</td>\n",
              "      <td>27.802845</td>\n",
              "      <td>295.056427</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-12-31</th>\n",
              "      <td>-8.229275</td>\n",
              "      <td>27.802845</td>\n",
              "      <td>295.779083</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>2024-12-31</th>\n",
              "      <td>-8.229275</td>\n",
              "      <td>27.802845</td>\n",
              "      <td>295.092072</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th rowspan=\"5\" valign=\"top\">949</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">999</th>\n",
              "      <th>2046-12-31</th>\n",
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              "      <td>58.246738</td>\n",
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              "      <th>2048-12-31</th>\n",
              "      <td>73.937675</td>\n",
              "      <td>58.246738</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2049-12-31</th>\n",
              "      <td>73.937675</td>\n",
              "      <td>58.246738</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>2050-12-31</th>\n",
              "      <td>73.937675</td>\n",
              "      <td>58.246738</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>29450000 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7fb71d1-81ee-4db1-81fe-46ccdf4d541a')\"\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 -960 960 960\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      padding: 0 0 0 0;\n",
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              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-b7fb71d1-81ee-4db1-81fe-46ccdf4d541a 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-b7fb71d1-81ee-4db1-81fe-46ccdf4d541a');\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",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-24665fb5-1a46-41a5-966f-f8817568ccfb')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-24665fb5-1a46-41a5-966f-f8817568ccfb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_de1757b5-6e53-4f41-b2df-365bb122fc1b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
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              "\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# resetting the index as original index is y, x and time\n",
        "df.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "_YRMq6FiHkyz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "AK-1dlPDHmnr",
        "outputId": "c8534a61-205f-4e37-d2df-ce0607f1052c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "            y    x       time        lon        lat   tasAdjust  height\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "    (() => {\n",
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              "\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        document.querySelector('#id_018dd781-81e3-4d03-90e7-a976cf580b24 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for duplicates\n",
        "df.loc[df.duplicated()== True]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "l-3PKaJPGWlP",
        "outputId": "bc3996bc-7d0b-4839-81f5-3a8a37d2b81f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [y, x, time, lon, lat, tasAdjust, height]\n",
              "Index: []"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>y</th>\n",
              "      <th>x</th>\n",
              "      <th>time</th>\n",
              "      <th>lon</th>\n",
              "      <th>lat</th>\n",
              "      <th>tasAdjust</th>\n",
              "      <th>height</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    .colab-df-container {\n",
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              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-c42546ca-51c8-4fd9-8c85-5439de4e9327 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-c42546ca-51c8-4fd9-8c85-5439de4e9327');\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",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 0,\n  \"fields\": [\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"NaT\",\n        \"max\": \"NaT\",\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tasAdjust\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F-wbV8-IFPHD",
        "outputId": "9480636a-145f-4293-c0ce-ed2519a6f5ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data has no duplicates as confirmed form code above."
      ],
      "metadata": {
        "id": "UQTnqwuwzuaR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cleaning the time as year value\n",
        "Since the time is saved as time stamp with format `yyyy-mm-dd` extracting the year form the data and saving it."
      ],
      "metadata": {
        "id": "Eq-TpnbxfKff"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['time']=df['time'].dt.year"
      ],
      "metadata": {
        "id": "CIKz3p92eMeJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "sM5gP2j1eUzk",
        "outputId": "9e832cb2-1c7a-4ac9-a3f6-362623b0636d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "            y    x  time        lon        lat   tasAdjust  height\n",
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              "                                                    [key], {});\n",
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              "\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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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2412a729-30ea-4328-b017-9a7251ee52f4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5d025fd1-90ab-4205-9417-b31b02fb999a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5d025fd1-90ab-4205-9417-b31b02fb999a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 30 million rows that is a huge number and processing this data for conversion to GeoJSON quickly exhausts system resources and leads to performance issues such as system hang or excessive memory usage.\n",
        "\n",
        "The conversion of a .nc file to GeoJSON can involve both I/O-bound and CPU-bound aspects. The I/O-bound aspect is prominent during the reading of data from the huge .nc file, while the CPU-bound aspect arises during the processing and conversion of the data into GeoJSON format.\n",
        "\n",
        "To address this issue, several strategies can be employed:\n",
        "\n",
        "**Batch Processing**: Instead of processing all 30 million rows at once, breaking down the dataset into smaller batches like breaking into chunks with ds.isel(time =1, chunk_size) and processing them iteratively. This approach reduces memory consumption and minimizes the risk of system hang.\n",
        "\n",
        "**Sampling**: Rather than processing the entire dataset, we can work on one subset that is the representation of the complete data. *But in our case our aim is to convert complete data, analysis has been done already.*\n",
        "\n",
        "**Using data streaming libraries** : Libraries like Apache Kafka, Apache Flink, or Apache Storm, are designed for processing continuous streams of data in real-time. For my current use case I just need to store the GeoJSON file to a location, *I don't need any real-time data processing capabilities so setting up clusters and using streaming libararies for this is unnecessary.*\n",
        "\n",
        "**Parallel Processing**: Parallel processing can significantly improve processing speed and scalability, especially for computationally intensive tasks.\n",
        "\n",
        "This involves -\n",
        "Multi-threading and multi-processing techniques involve executing multiple tasks concurrently on different CPU cores or processes. In Python, libraries such as concurrent.futures and multiprocessing provide high-level interfaces for parallelizing tasks across CPU cores. By distributing the workload across multiple cores, parallel processing can significantly improve processing speed and scalability, especially for tasks that can be executed independently in parallel.\n",
        "\n",
        "[Setting up Spark on Docker](https://medium.com/@MarinAgli1/setting-up-a-spark-standalone-cluster-on-docker-in-layman-terms-8cbdc9fdd14b)"
      ],
      "metadata": {
        "id": "VJ0KCelD0Cmt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving the Data as CSV file"
      ],
      "metadata": {
        "id": "d0-cV-jpI5kl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('/content/drive/MyDrive/DS Project/Omdena X Valutus/temp_rcp85.csv', index=False)"
      ],
      "metadata": {
        "id": "7rh8hp9Neomq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Converting to GeoJSON\n",
        "\n",
        "### Implementing Batch Processing to avoid system hangs beacuse of huge RAM requirement and longer processing of files 1H 20 Min appox."
      ],
      "metadata": {
        "id": "MqaqGrC-fT40"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Steps Involved :\n",
        "1. Slicing the Xarray dataset as chunks. Each chunk represent one period of the 'time' dimension of the data.\n",
        "For example, We have NetCDF file with three dimensions:\n",
        "![image.png](data:image/png;base64,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)\n"
      ],
      "metadata": {
        "id": "Jhejrqh-JFTO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I will be slicing the ds dataset for one time period at a time.\n",
        "\n",
        "2. I process the chunk.\n",
        "  - Convert it into dataframe.\n",
        "  - Transform the timestamp to just the Year.\n",
        "  - Replace NaNs with None so that it can be properly encoded as JSON Null while saving it as GeoJSON\n",
        "  - Iterate over each rows of chunk and save the Latitude, Longitude as 'Geometry Points' , time and Variable as 'Feature Properties'.\n",
        "  \n",
        "3. Used fiona library to dynamically write the GeoJSON data as it is processed for each chunk.\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "# Original Code that saves all the chunk level GeoJSON in a single file\n",
        "\n",
        "def geo_conversion(ds, geojson_path):\n",
        "    with fiona.open(geojson_path, 'w', driver='GeoJSON', schema={\n",
        "        'geometry': 'Point',\n",
        "        'properties': {'time': 'str', 'tasAdjust': 'float'}\n",
        "    }) as dst:\n",
        "        for item in range(len(ds['time'])):\n",
        "            chunk = slicing_func(ds, item, chunk_size=1)\n",
        "            chunk_df = chunk.to_dataframe().reset_index()\n",
        "            features = process_chunk(chunk_df)\n",
        "            # Write GeoJSON data to the output file using fiona\n",
        "            dst.writerecords(features)\n",
        "\n",
        "        print(\"[...] converted 1 file.\")\n",
        "\n",
        "    print(\"[...]. Files Converted to GeoJSON\")\n",
        "    ds.close()\n",
        "\n",
        "```\n",
        "\n",
        "Fiona allows you to specify a schema for the GeoJSON file, including the geometry type and properties. This ensures that the resulting GeoJSON file adheres to a specific structure and schema, which can be important for compatibility with other GIS software and applications.\n",
        "\n",
        "- It opens a GeoJSON file for writing using Fiona (fiona.open()).\n",
        "- It specifies the schema for the GeoJSON file, including the geometry type (Point) and properties (time as a string and tasAdjust as a float).\n",
        "It iterates over the items in the dataset (ds['time']) and processes each item in chunks.\n",
        "- For each chunk, it converts it to a DataFrame (chunk.to_dataframe()) and processes it further (process_chunk() function).\n",
        "- It writes the resulting features (geospatial data) to the GeoJSON file using Fiona (dst.writerecords()).\n",
        "\n",
        "I started with json.dumps but the format of the geojson was not saved properly for all the chunks."
      ],
      "metadata": {
        "id": "yZPezievKhev"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(ds['time'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QxfnaUWcPB-n",
        "outputId": "2cea213f-465e-4a84-80b3-9446448ec73c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds.isel(time=1).to_dataframe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "LOP6zsS_Ptoh",
        "outputId": "52682436-6567-4914-a2db-ffede6b2d4e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               lon        lat   tasAdjust       time  height\n",
              "y   x                                                       \n",
              "0   0    -8.229275  27.802845  295.414185 2021-12-31     2.0\n",
              "    1    -8.180322  27.813362  295.448212 2021-12-31     2.0\n",
              "    2    -8.131361  27.823853  295.486389 2021-12-31     2.0\n",
              "    3    -8.082390  27.834314  295.548737 2021-12-31     2.0\n",
              "    4    -8.033411  27.844748  295.604584 2021-12-31     2.0\n",
              "...            ...        ...         ...        ...     ...\n",
              "949 995  73.721550  58.389679         NaN 2021-12-31     2.0\n",
              "    996  73.775719  58.353977         NaN 2021-12-31     2.0\n",
              "    997  73.829788  58.318253         NaN 2021-12-31     2.0\n",
              "    998  73.883781  58.282505         NaN 2021-12-31     2.0\n",
              "    999  73.937675  58.246738         NaN 2021-12-31     2.0\n",
              "\n",
              "[950000 rows x 5 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>lon</th>\n",
              "      <th>lat</th>\n",
              "      <th>tasAdjust</th>\n",
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              "    <tr>\n",
              "      <th>y</th>\n",
              "      <th>x</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
              "      <th>0</th>\n",
              "      <td>-8.229275</td>\n",
              "      <td>27.802845</td>\n",
              "      <td>295.414185</td>\n",
              "      <td>2021-12-31</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-8.180322</td>\n",
              "      <td>27.813362</td>\n",
              "      <td>295.448212</td>\n",
              "      <td>2021-12-31</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-8.131361</td>\n",
              "      <td>27.823853</td>\n",
              "      <td>295.486389</td>\n",
              "      <td>2021-12-31</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-8.082390</td>\n",
              "      <td>27.834314</td>\n",
              "      <td>295.548737</td>\n",
              "      <td>2021-12-31</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-8.033411</td>\n",
              "      <td>27.844748</td>\n",
              "      <td>295.604584</td>\n",
              "      <td>2021-12-31</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">949</th>\n",
              "      <th>995</th>\n",
              "      <td>73.721550</td>\n",
              "      <td>58.389679</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021-12-31</td>\n",
              "      <td>2.0</td>\n",
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              "      <th>996</th>\n",
              "      <td>73.775719</td>\n",
              "      <td>58.353977</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021-12-31</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>73.829788</td>\n",
              "      <td>58.318253</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021-12-31</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>73.883781</td>\n",
              "      <td>58.282505</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021-12-31</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>73.937675</td>\n",
              "      <td>58.246738</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021-12-31</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>950000 rows × 5 columns</p>\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5041c4f2-000b-4987-a78e-e880cc6d0881 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-5041c4f2-000b-4987-a78e-e880cc6d0881');\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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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "    width: 32px;\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7e6d6a01-7598-4bb7-99a2-7dab5c740b59 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I'll try to save the GeoJSON futher as 5 files. Each File consisting of data for 5 years. For example file 1 has time period covered form 2020-2024."
      ],
      "metadata": {
        "id": "fkfybjvzQGes"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import fiona\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "def slicing_func(ds, current_pos, chunk_size=1):\n",
        "    \"\"\"\n",
        "    Given that ds obj has time as dimension\n",
        "    \"\"\"\n",
        "    return ds.isel(time=slice(current_pos, current_pos+chunk_size))\n",
        "\n",
        "\n",
        "def process_chunk(chunk_df):\n",
        "    features = []\n",
        "    # preprocessing the data at chunk level\n",
        "\n",
        "    chunk_df['time'] = chunk_df['time'].dt.year\n",
        "    chunk_df.replace({np.nan: None}, inplace=True)\n",
        "\n",
        "    for index, row in chunk_df.iterrows():\n",
        "        # Extract relevant data\n",
        "        time = str(row['time'])  # Convert time to string if necessary\n",
        "        lat = row['lat']\n",
        "        lon = row['lon']\n",
        "        tasAdjust = row['tasAdjust']\n",
        "\n",
        "        # Construct GeoJSON feature\n",
        "        feature = {\n",
        "            \"type\": \"Feature\",\n",
        "            \"geometry\": {\n",
        "                \"type\": \"Point\",\n",
        "                \"coordinates\": [lon, lat]\n",
        "            },\n",
        "            \"properties\": {\n",
        "                \"time\": time,\n",
        "                \"tasAdjust\": tasAdjust\n",
        "            }\n",
        "        }\n",
        "        features.append(feature)\n",
        "\n",
        "    return features\n",
        "\n",
        "\n",
        "def geo_conversion(ds, geojson_path):\n",
        "\n",
        "    for item in range(0, len(ds['time']), 6):\n",
        "        chunk_ds = slicing_func(ds, item, chunk_size=6)\n",
        "        min_date=int(chunk_ds['time'].dt.year.min().values)\n",
        "        max_date=int(chunk_ds['time'].dt.year.max().values)\n",
        "        new_path= os.path.join(geojson_path, f\"temperature_rcp85_{min_date}_{max_date}.geojson\")\n",
        "        with fiona.open(new_path, 'w', driver='GeoJSON', schema={\n",
        "            'geometry': 'Point',\n",
        "            'properties': {'time': 'str', 'tasAdjust': 'float'}\n",
        "        }) as dst:\n",
        "          for item in range(len(chunk_ds['time'])):\n",
        "              chunk = slicing_func(chunk_ds, item, chunk_size=1)\n",
        "              chunk_df = chunk.to_dataframe().reset_index()\n",
        "              features = process_chunk(chunk_df)\n",
        "              # Write GeoJSON data to the output file using fiona\n",
        "              dst.writerecords(features)\n",
        "              chunk_ds.close()\n",
        "        print(f\"[...] converted {item+1} file.\")\n",
        "\n",
        "    print(\"[...]. Files Converted to GeoJSON\")\n",
        "    ds.close()"
      ],
      "metadata": {
        "id": "ZMT9DW_ie9AB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "int(ds['time'].dt.year.min().values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5OsWDcjQRotx",
        "outputId": "073a3fd3-e71a-4126-8ed1-e6646deff321"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "\n",
        "# Suppress future warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "count=0\n",
        "for item in range(0, len(ds['time']), 6):\n",
        "  chunk = slicing_func(ds, item, chunk_size=6)\n",
        "  min_date=int(chunk['time'].dt.year.min().values)\n",
        "  max_date=int(chunk['time'].dt.year.max().values)\n",
        "  count+=1\n",
        "  print(f\"Data chunked from {min_date} - {max_date} for chunk - {count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NqSazfwQ-pj",
        "outputId": "67686ad8-1769-40a7-f56d-233700575e4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data chunked from 2020 - 2025 for chunk - 1\n",
            "Data chunked from 2026 - 2031 for chunk - 2\n",
            "Data chunked from 2032 - 2037 for chunk - 3\n",
            "Data chunked from 2038 - 2043 for chunk - 4\n",
            "Data chunked from 2044 - 2049 for chunk - 5\n",
            "Data chunked from 2050 - 2050 for chunk - 6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "geojson_path= '/content/drive/MyDrive/DS Project/Omdena X Valutus/'\n",
        "geo_conversion(ds, geojson_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3vQzxVITko0d",
        "outputId": "0cc274f8-7587-4b16-bd80-5653b495ce78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[...] converted 6 file.\n",
            "[...] converted 6 file.\n",
            "[...] converted 6 file.\n",
            "[...] converted 6 file.\n",
            "[...] converted 6 file.\n",
            "[...] converted 1 file.\n",
            "[...]. Files Converted to GeoJSON\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# geojson_path= '/content/drive/MyDrive/DS Project/Omdena X Valutus/'\n",
        "# min_date=2033\n",
        "# max_dat=2044\n",
        "# os.path.join(geojson_path, f\"temperature_rcp85_{min_date}_{max_date}.geojson\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "t981CEEPaf7c",
        "outputId": "e99523a0-b660-448e-ea54-f1d0881cd6c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/MyDrive/DS Project/Omdena X Valutus/temperature_rcp85_2033_2050.geojson'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Basic testing of the converted GeoJSON"
      ],
      "metadata": {
        "id": "h_KDbP_EuzZP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import geopandas as gpd\n",
        "import matplotlib.pyplot as plt\n",
        "\n"
      ],
      "metadata": {
        "id": "ABZGAgJUtRgq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import files\n",
        "# files.download('/content/temperature_rcp45.geojson')"
      ],
      "metadata": {
        "id": "O5aVW_Y37bLw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reading GeoJSON file and plotting the variable for a particular time stamp."
      ],
      "metadata": {
        "id": "hb3APZlKNAHW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the GeoJSON file\n",
        "geojson_path = \"/content/drive/MyDrive/DS Project/Omdena X Valutus/temperature_rcp85_2032_2037.geojson\"  # Replace with your GeoJSON file path\n",
        "gdf = gpd.read_file(geojson_path)\n",
        "\n"
      ],
      "metadata": {
        "id": "WiYKgnHAtgru"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "et2xrg-3XlKi",
        "outputId": "4fbe0acd-fcbd-4d50-c2c7-44b8891b377c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         time   tasAdjust                   geometry\n",
              "0        2032  295.549408  POINT (-8.22927 27.80285)\n",
              "1        2032  295.589325  POINT (-8.18032 27.81336)\n",
              "2        2032  295.633972  POINT (-8.13136 27.82385)\n",
              "3        2032  295.700897  POINT (-8.08239 27.83431)\n",
              "4        2032  295.755219  POINT (-8.03341 27.84475)\n",
              "...       ...         ...                        ...\n",
              "5699995  2037         NaN  POINT (73.72155 58.38968)\n",
              "5699996  2037         NaN  POINT (73.77572 58.35398)\n",
              "5699997  2037         NaN  POINT (73.82979 58.31825)\n",
              "5699998  2037         NaN  POINT (73.88378 58.28251)\n",
              "5699999  2037         NaN  POINT (73.93768 58.24674)\n",
              "\n",
              "[5700000 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f988a201-3b33-454b-bfdd-84645a99c9b2\" 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>time</th>\n",
              "      <th>tasAdjust</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2032</td>\n",
              "      <td>295.549408</td>\n",
              "      <td>POINT (-8.22927 27.80285)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2032</td>\n",
              "      <td>295.589325</td>\n",
              "      <td>POINT (-8.18032 27.81336)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2032</td>\n",
              "      <td>295.633972</td>\n",
              "      <td>POINT (-8.13136 27.82385)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2032</td>\n",
              "      <td>295.700897</td>\n",
              "      <td>POINT (-8.08239 27.83431)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2032</td>\n",
              "      <td>295.755219</td>\n",
              "      <td>POINT (-8.03341 27.84475)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5699995</th>\n",
              "      <td>2037</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (73.72155 58.38968)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5699996</th>\n",
              "      <td>2037</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (73.77572 58.35398)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5699997</th>\n",
              "      <td>2037</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (73.82979 58.31825)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5699998</th>\n",
              "      <td>2037</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (73.88378 58.28251)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5699999</th>\n",
              "      <td>2037</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (73.93768 58.24674)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5700000 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f988a201-3b33-454b-bfdd-84645a99c9b2')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-f988a201-3b33-454b-bfdd-84645a99c9b2 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-f988a201-3b33-454b-bfdd-84645a99c9b2');\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",
              "\n",
              "\n",
              "<div id=\"df-145302eb-e708-4411-896c-4fb35f2a4435\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-145302eb-e708-4411-896c-4fb35f2a4435')\"\n",
              "            title=\"Suggest charts\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-145302eb-e708-4411-896c-4fb35f2a4435 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b64253ef-0ee6-4593-b227-f3af5fc6c9e4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('gdf')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b64253ef-0ee6-4593-b227-f3af5fc6c9e4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('gdf');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "gdf"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gdf.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TKZbhQ0Tt4Oq",
        "outputId": "b2ff9eb2-12db-433f-f7f1-392d44fe5658"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
            "RangeIndex: 5700000 entries, 0 to 5699999\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Dtype   \n",
            "---  ------     -----   \n",
            " 0   time       object  \n",
            " 1   tasAdjust  float64 \n",
            " 2   geometry   geometry\n",
            "dtypes: float64(1), geometry(1), object(1)\n",
            "memory usage: 130.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Plot the GeoDataFrame\n",
        "gdf.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "uoeHcSiPth8i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "e5ec460d-92fa-47b1-d59a-068429434088"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_0=gdf.loc[gdf['time']=='2035',:]"
      ],
      "metadata": {
        "id": "MfRRakkXqrd_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "QxI6hWX1gF2_",
        "outputId": "fea1a58e-6a40-43c0-db9f-9239c88bf61e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         time   tasAdjust                   geometry\n",
              "2850000  2035  295.542053  POINT (-8.22927 27.80285)\n",
              "2850001  2035  295.579437  POINT (-8.18032 27.81336)\n",
              "2850002  2035  295.624176  POINT (-8.13136 27.82385)\n",
              "2850003  2035  295.680878  POINT (-8.08239 27.83431)\n",
              "2850004  2035  295.739685  POINT (-8.03341 27.84475)\n",
              "...       ...         ...                        ...\n",
              "3799995  2035         NaN  POINT (73.72155 58.38968)\n",
              "3799996  2035         NaN  POINT (73.77572 58.35398)\n",
              "3799997  2035         NaN  POINT (73.82979 58.31825)\n",
              "3799998  2035         NaN  POINT (73.88378 58.28251)\n",
              "3799999  2035         NaN  POINT (73.93768 58.24674)\n",
              "\n",
              "[950000 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e606c5d6-07a5-4090-b528-c4795218067a\" 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>time</th>\n",
              "      <th>tasAdjust</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2850000</th>\n",
              "      <td>2035</td>\n",
              "      <td>295.542053</td>\n",
              "      <td>POINT (-8.22927 27.80285)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2850001</th>\n",
              "      <td>2035</td>\n",
              "      <td>295.579437</td>\n",
              "      <td>POINT (-8.18032 27.81336)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2850002</th>\n",
              "      <td>2035</td>\n",
              "      <td>295.624176</td>\n",
              "      <td>POINT (-8.13136 27.82385)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2850003</th>\n",
              "      <td>2035</td>\n",
              "      <td>295.680878</td>\n",
              "      <td>POINT (-8.08239 27.83431)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2850004</th>\n",
              "      <td>2035</td>\n",
              "      <td>295.739685</td>\n",
              "      <td>POINT (-8.03341 27.84475)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3799995</th>\n",
              "      <td>2035</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (73.72155 58.38968)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3799996</th>\n",
              "      <td>2035</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (73.77572 58.35398)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3799997</th>\n",
              "      <td>2035</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (73.82979 58.31825)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3799998</th>\n",
              "      <td>2035</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (73.88378 58.28251)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3799999</th>\n",
              "      <td>2035</td>\n",
              "      <td>NaN</td>\n",
              "      <td>POINT (73.93768 58.24674)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>950000 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e606c5d6-07a5-4090-b528-c4795218067a')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-e606c5d6-07a5-4090-b528-c4795218067a 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-e606c5d6-07a5-4090-b528-c4795218067a');\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",
              "\n",
              "\n",
              "<div id=\"df-c0c8ce85-d3dc-4fe2-98e4-0c3d9b147565\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c0c8ce85-d3dc-4fe2-98e4-0c3d9b147565')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c0c8ce85-d3dc-4fe2-98e4-0c3d9b147565 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_786d4721-84a0-4e61-a097-dc8e2248ff8e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_0')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_786d4721-84a0-4e61-a097-dc8e2248ff8e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_0');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_0"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Plot the GeoDataFrame\n",
        "# Plot a variable from the DataFrame\n",
        "df_0.plot(column='tasAdjust', cmap='coolwarm', legend=True)\n",
        "\n",
        "plt.title(f\"Mean Temperature {df_0['time'].tolist()[0]}\")\n",
        "plt.xlabel('Longitude')\n",
        "plt.ylabel('Latitude')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "RWLsY6ovdEqd",
        "outputId": "b579ca5b-0e25-4301-fdd3-d95915c4401e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}